[ { "path": ".gitignore", "content": "*.DS_Store\n*.pyc\nlarge_files\nlarge_files/*\nnlp_class2/chunking/*\n" }, { "path": "README.md", "content": "machine_learning_examples\n=========================\n\nA collection of machine learning examples and tutorials.\n\nFind associated tutorials at https://lazyprogrammer.me\n\nFind associated courses at https://deeplearningcourses.com\n\nPlease note that not all code from all courses will be found in this repository. Some newer code examples (e.g. most of Tensorflow 2.0) were done in Google Colab. Therefore, you should check the instructions given in the lectures for the course you are taking.\n\n\nHow to I find the code for a particular course?\n===============================================\n\nThe code for each course is separated by folder. You can determine which folder corresponds with which course by watching the \"Where to get the code\" lecture inside the course (usually Lecture 2 or 3).\n\nRemember: one folder = one course.\n\n\nWhy you should not fork this repo\n=================================\n\nI've noticed that many people have out-of-date forks. Thus, I recommend not forking this repository if you take one of my courses. I am constantly updating my courses, and your fork will soon become out-of-date. You should clone the repository instead to make it easy to get updates (i.e. just \"git pull\" randomly and frequently).\n\n\nWhere is the code for your latest courses?\n==========================================\n\nBeginning with Tensorflow 2, I started to use Google Colab. For those courses, unless otherwise noted, the code will be on Google Colab. Links to the notebooks are provided in the course. See the lecture \"Where to get the code\" for further details.\n\n\nVIP Course Links\n===================\n\n**Advanced AI: Deep Reinforcement Learning in PyTorch (v2)**\n\nhttps://deeplearningcourses.com/c/deep-reinforcement-learning-in-pytorch\n\n\n**Data Science: Transformers for Natural Language Processing**\n\nhttps://deeplearningcourses.com/c/data-science-transformers-nlp\n\n\n**Machine Learning: Natural Language Processing in Python (V2)**\n\nhttps://deeplearningcourses.com/c/natural-language-processing-in-python\n\n\n**Time Series Analysis, Forecasting, and Machine Learning**\n\nhttps://deeplearningcourses.com/c/time-series-analysis\n\n\n**Financial Engineering and Artificial Intelligence in Python**\n\nhttps://deeplearningcourses.com/c/ai-finance\n\n\n**PyTorch: Deep Learning and Artificial Intelligence**\n\nhttps://deeplearningcourses.com/c/pytorch-deep-learning\n\n\n**Tensorflow 2.0: Deep Learning and Artificial Intelligence** (VIP Version)\n\nhttps://deeplearningcourses.com/c/deep-learning-tensorflow-2\n\n\n**Math 0-1: Linear Algebra for Data Science & Machine Learning**\n\nhttps://deeplearningcourses.com/c/linear-algebra-data-science\n\n**Math 0-1: Probability for Data Science & Machine Learning**\n\nhttps://deeplearningcourses.com/c/probability-data-science-machine-learning\n\n\nDeep Learning Courses Exclusives\n================================\n\nData Science: Bayesian Linear Regression in Python\nhttps://deeplearningcourses.com/c/bayesian-linear-regression-in-python\n\nData Science: Bayesian Classification in Python\nhttps://deeplearningcourses.com/c/bayesian-classification-in-python\n\nClassical Statistical Inference and A/B Testing in Python\nhttps://deeplearningcourses.com/c/statistical-inference-in-python\n\nLinear Programming for Linear Regression in Python\nhttps://deeplearningcourses.com/c/linear-programming-python\n\nMATLAB for Students, Engineers, and Professionals in STEM\nhttps://deeplearningcourses.com/c/matlab\n\n\n\nOther Course Links\n==================\n\nGenerative AI: ChatGPT & OpenAI LLMs in Python\nhttps://deeplearningcourses.com/c/genai-openai-chatgpt\n\nMath 0-1: Matrix Calculus for Data Science & Machine Learning\nhttps://deeplearningcourses.com/c/matrix-calculus-machine-learning\n\nMachine Learning: Modern Computer Vision & Generative AI\nhttps://deeplearningcourses.com/c/computer-vision-kerascv\n\nDeepFakes & Voice Cloning: Machine Learning The Easy Way\nhttps://deeplearningcourses.com/c/deepfakes-voice-cloning\n\nFinancial Analysis: Build a ChatGPT Pairs Trading Bot\nhttps://deeplearningcourses.com/c/chatgpt-pairs-trading\n\nMath 0-1: Calculus for Data Science & Machine Learning\nhttps://deeplearningcourses.com/c/calculus-data-science\n\nData Science & Machine Learning: Naive Bayes in Python\nhttps://deeplearningcourses.com/c/data-science-machine-learning-naive-bayes-in-python\n\nCutting-Edge AI: Deep Reinforcement Learning in Python\nhttps://deeplearningcourses.com/c/cutting-edge-artificial-intelligence\n\nRecommender Systems and Deep Learning in Python\nhttps://deeplearningcourses.com/c/recommender-systems\n\nMachine Learning and AI: Support Vector Machines in Python\nhttps://deeplearningcourses.com/c/support-vector-machines-in-python\n\nDeep Learning: Advanced Computer Vision\nhttps://deeplearningcourses.com/c/advanced-computer-vision\n\nDeep Learning: Advanced NLP and RNNs\nhttps://deeplearningcourses.com/c/deep-learning-advanced-nlp\n\nDeep Learning: GANs and Variational Autoencoders\nhttps://deeplearningcourses.com/c/deep-learning-gans-and-variational-autoencoders\n\nAdvanced AI: Deep Reinforcement Learning in Python\nhttps://deeplearningcourses.com/c/deep-reinforcement-learning-in-python\n\nArtificial Intelligence: Reinforcement Learning in Python\nhttps://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n\nNatural Language Processing with Deep Learning in Python\nhttps://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n\nDeep Learning: Recurrent Neural Networks in Python\nhttps://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python\n\nUnsupervised Machine Learning: Hidden Markov Models in Python\nhttps://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python\n\nDeep Learning Prerequisites: The Numpy Stack in Python\nhttps://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python\n\nDeep Learning Prerequisites: Linear Regression in Python\nhttps://deeplearningcourses.com/c/data-science-linear-regression-in-python\n\nDeep Learning Prerequisites: Logistic Regression in Python\nhttps://deeplearningcourses.com/c/data-science-logistic-regression-in-python\n\nData Science: Deep Learning and Neural Networks in Python\nhttps://deeplearningcourses.com/c/data-science-deep-learning-in-python\n\nCluster Analysis and Unsupervised Machine Learning in Python\nhttps://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python\n\nData Science: Supervised Machine Learning in Python\nhttps://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python\n\nBayesian Machine Learning in Python: A/B Testing\nhttps://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing\n\nData Science: Natural Language Processing in Python\nhttps://deeplearningcourses.com/c/data-science-natural-language-processing-in-python\n\nModern Deep Learning in Python\nhttps://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n\nEnsemble Machine Learning in Python: Random Forest and AdaBoost\nhttps://deeplearningcourses.com/c/machine-learning-in-python-random-forest-adaboost\n\nDeep Learning: Convolutional Neural Networks in Python\nhttps://deeplearningcourses.com/c/deep-learning-convolutional-neural-networks-theano-tensorflow\n\nUnsupervised Deep Learning in Python\nhttps://deeplearningcourses.com/c/unsupervised-deep-learning-in-python\n" }, { "path": "ab_testing/advertisement_clicks.csv", "content": 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nB,0\nB,1\nA,0\nB,0\nB,0\nA,0\nA,0\nA,0\nB,1\nA,0\nB,1\nB,0\nB,1\nA,0\nB,0\nB,1\nB,1\nB,0\nB,0\nA,0\nB,0\nB,0\nB,0\nB,0\nA,0\nA,0\nB,1\nA,0\nB,0\nB,1\nA,1\nA,0\nA,0\nA,0\nA,1\nB,0\nA,0\nA,1\nA,1\nB,0\nA,0\nA,1\nA,0\nA,1\nB,0\nA,1\nB,1\nA,0\nA,0\nA,1\nB,1\nA,0\nA,0\nA,0\nB,0\nA,0\nB,0\nA,0\nA,0\nB,1\nA,1\nB,0\nA,0\nB,0\nB,1\nA,0\nB,1\nA,1\nB,1\nB,1\nA,0\nB,0\nB,0\nB,0\nA,1\nB,1\nA,0\nB,0\nA,1\nB,1\nA,0\nB,0\nB,1\nA,0\nB,0\nA,1\nB,0\nB,0\nB,0\nB,0\nA,0\nA,0\nA,0\nB,0\nA,1\nB,0\nB,1\nB,0\nA,0\nB,0\nB,0\nB,0\nB,0\nB,1\nB,0\nA,0\nA,0\nA,0\nB,1\nB,0\nB,1\nB,0\nA,1\nA,1\nA,1\nB,0\nB,0\nB,0\nB,0\nA,0\nA,0\nB,0\nB,1\nA,1\nA,0\nB,0\nB,1\nA,1\nB,1\nA,0\nB,1\nB,0\nA,0\nA,0\nA,1\nA,1\nA,0\nA,0\nB,1\nA,1\nB,0\nB,1\nA,0\nA,1\nA,0\nA,0\nA,0\nA,0\nB,0\nB,0\nB,0\nB,0\nA,0\nA,0\nA,1\nB,0\nB,1\nA,0\nA,0\nB,1\nB,1\nB,1\nA,0\nA,0\nA,0\nA,0\nA,0\nB,0\nB,0\nA,1\nB,0\nB,0\nA,0\nA,0\nB,0\nA,1\nA,0\nA,0\nA,1\nA,0\nB,0\nB,0\nA,0\nB,1\nA,0\nA,0\nA,0\nA,0\nB,0\nB,0\nA,1\nB,1\nB,1\nA,0\nA,0\nA,0\nB,0\nA,0\nA,1\nA,1\nB,0\nA,0\nA,1\nA,0\nA,0\nA,0\nA,0\nA,1\nA,0\nA,0\nA,0\nA,0\nA,0\nB,0\nA,0\nB,0\nA,0\nB,1\nA,0\nB,0\nA,0\nB,1\nB,1\nB,0\nB,0\nB,1\nA,1\nA,0\nB,0\nB,1\nA,0\nA,1\nB,0\nA,0\nB,1\nA,1\nB,0\nB,0\nB,0\nA,0\nA,0\nA,1\nB,0\nB,1\nB,0\nA,0\nA,1\nB,0\nA,1\nA,0\nA,1\nB,0\nB,0\nA,0\nB,0\nA,0\nB,1\nA,0\nA,0\nA,0\nA,1\nA,0\nB,1\nB,1\nB,0\nA,1\nB,1\nA,0\nA,0\nB,0\nA,0\nA,0\nA,0\nA,0\nA,0\nA,1\nB,0\nA,1\nA,0\nB,0\nB,1\nB,0\nB,0\nA,1\nB,0\nA,0\nB,0\nB,0\nA,1\nB,1\nB,1\nA,0\nA,1\nA,0\nA,0\nB,0\nA,1\nB,1\nA,0\nA,1\nA,0\nA,0\nA,1\nA,0\nB,0\nA,1\nA,1\nA,1\nA,0\nA,0\nA,0\nA,0\nA,0\nB,1\nB,0\nB,1\nB,0\nB,0\nB,1\nB,1\nA,0\nB,1\nB,1\nB,0\nA,0\nB,0\nA,0\nB,1\nB,1\nA,0\nB,0\nA,0\nB,0\nA,1\nB,1\nB,0\nB,1\nB,0\nB,0\nB,1\nA,0\nA,0\nB,1\nB,1\nB,1\nA,1\nB,1\nB,0\nB,1\nA,0\nA,0\nB,0\nB,0\nB,0\nA,0\nA,1\nA,0\nB,0\nA,0\nB,1\nB,0\nB,1\nB,1\nA,0\nB,0\nB,0\nA,1\nA,0\nB,0\nB,0\nA,1\nB,1\nA,0\nA,1\nB,1\nB,1\nA,0\nA,0\nB,1\nB,1\nA,1\nB,0\nB,1\nB,0\nA,0\nA,0\nB,0\nB,1\nB,1\nB,0\nB,0\nA,0\nA,0\nB,0\nA,1\nB,0\nA,1\nA,0\nB,1\nA,1\nA,0\nA,0\nA,0\nB,1\nB,1\nA,0\nB,1\nA,1\nA,0\nB,0\nA,1\nA,0\nA,1\nA,1\nA,1\nB,0\nB,0\nB,1\nB,0\nB,0\nB,0\nB,0\nB,1\nB,0\nA,1\nA,0\nB,1\nA,1\nB,0\nB,0\nB,0\nA,0\nB,0\nA,0\nB,1\nA,0\nB,0\nA,0\nA,1\nB,1\nB,1\nB,0\nB,0\nA,0\nB,1\nA,0\nA,1\nA,1\nB,0\nB,0\nB,0\nB,1\nA,1\nB,0\nB,0\nA,0\nB,0\nA,0\nA,1\nB,1\nB,1\nA,0\nB,0\nB,0\nB,1\nA,0\nA,0\nB,0\nA,0\nB,0\nA,0\nB,0\nA,1\nA,0\nA,0\nB,1\nA,0\nB,1\nB,1\nA,0\nB,0\nB,0\nB,0\nB,0\nB,0\nB,1\nB,0\nA,0\nA,0\nA,0\nA,0\nA,0\nA,1\nA,0\nA,0\nB,0\nB,0\nA,0\nA,0\nA,0\nB,1\nB,1\nA,0\nB,0\nB,1\nA,1\nB,0\nA,0\nB,0\nA,0\nB,0\nB,0\nB,0\nA,0\nB,0\nA,0\nB,0\nA,0\nA,0\nA,1\nA,1\nB,0\nA,0\nB,0\nA,1\nA,0\nB,1\nB,0\nB,0\nA,0\nB,0\nA,0\nA,0\nA,0\nA,0\nB,0\nA,0\nA,0\nA,1\nA,0\nA,0\nA,0\nB,0\nA,0\nB,0\nB,1\nA,0\nA,0\nB,0\nA,0\nA,1\nB,1\nA,1\nB,0\nB,0\nB,0\nB,1\nB,0\nB,1\nB,1\nB,1\nB,0\nA,0\nA,1\nA,0\nB,0\nA,0\nB,0\nB,1\nA,1\nB,0\nA,1\nB,1\nA,0\nA,1\nA,1\nA,0\nB,0\nA,0\nA,0\nA,0\nA,1\nA,0\nB,1\nB,1\nB,0\nB,0\nB,1\nB,0\nB,0\nB,0\nA,0\nA,0\nB,0\nA,0\nB,0\nA,0\nB,0\nB,1\nA,0\nB,0\nB,0\nB,0\nB,1\nB,0\nB,1\nB,1\nB,1\nB,1\nA,1\nB,1\nA,0\nB,0\nB,1\nB,0\nB,1\nA,0\nA,1\nA,0\nB,0\nA,0\nA,0\nB,0\nB,1\nA,0\nB,0\nB,1\nA,0\nA,0\nA,0\nB,0\nA,0\nB,0\nA,0\nB,0\nA,0\nA,0\nA,0\nA,0\nB,0\nB,1\nA,0\nB,0\nB,0\nA,0\nB,1\nB,0\nB,1\nA,0\nB,1\nB,1\nB,1\nB,1\nB,0\nB,0\nB,0\nB,0\nB,1\nB,0\nB,0\nB,0\nA,0\nA,0\nA,0\nA,0\nB,1\nB,0\nA,0\nB,0\nA,0\nB,1\nA,0\nA,1\nB,1\nB,0\nA,1\nB,0\nB,0\nB,0\nA,0\nA,1\nB,0\nB,0\nB,1\nA,0\nA,0\nB,1\nB,1\nA,0\nA,0\nA,0\nA,1\nA,0\nB,0\nB,0\nA,1\nB,1\nA,1\nB,0\nA,0\nB,0\nA,0\nA,1\nB,0\nA,0\nB,0\nB,1\nB,0\nA,1\nA,1\nB,1\nA,0\nB,1\nB,0\nA,0\nB,0\nA,0\nA,1\nA,1\nA,0\nB,0\nA,0\nA,0\nB,0\nA,1\nA,0\nA,0\nB,1\nB,0\nB,0\nB,0\nB,0\nB,1\nA,1\nB,0\nA,0\nB,0\nA,1\nB,0\nB,0\nB,1\nB,1\nB,1\nB,0\nA,0\nB,0\nB,0\nA,0\nB,0\nB,1\nB,0\nB,0\nA,0\nB,0\nA,0\nB,1\nB,0\nA,1\nA,0\nB,1\nB,0\nB,1\nA,0\nA,1\nB,0\nB,1\nB,0\nB,0\nA,0\nB,1\nA,0\nB,1\nA,0\nB,0\nB,0\nB,0\nB,0\nB,1\nA,0\nB,1\nB,0\nB,1\nB,0\nB,0\nA,0\nB,1\nA,0\nA,0\nB,1\nB,1\nA,0\nB,0\nB,0\nB,0\nB,1\nA,1\nB,0\nB,0\nB,1\nA,1\nB,0\nA,0\nA,1\nB,1\nB,0\nB,0\nB,0\nA,1\nA,0\nA,0\nA,1\nB,1\nA,0\nA,0\nA,1\nB,0\nA,0\nA,0\nB,1\nB,0\nA,0\nB,1\nA,0\nB,0\nB,0\nA,0\nB,1\nB,1\nA,0\nB,1\nB,1\nA,1\nA,0\nA,0\nB,0\nB,0\nB,0\nB,1\nB,1\nB,0\nA,0\nB,0\nA,0\nA,0\nA,0\nA,0\nA,1\nB,1\nA,1\nA,0\nB,1\nA,0\nB,1\nA,0\nA,0\nB,0\nB,1\nA,0\nA,1\nB,0\nA,0\nA,1\nB,0\nA,0\nB,0\nB,1\nB,0\nB,0\nA,0\nB,0\nA,0\nA,1\nB,1\nA,1\nB,0\nB,0\nB,0\nA,1\nB,0\nA,1\nB,1\nB,1\nB,1\nA,0\nA,1\nA,0\nB,1\nB,0\nB,1\nA,0\nB,0\nA,1\nA,1\nA,1\nA,0\nA,0\nA,0\nA,1\nB,1\nB,1\nA,0\nB,0\nB,0\nA,1\nA,1\nA,0\nB,0\nA,0\nA,1\nB,0\nA,1\nB,0\nA,0\nB,0\nA,0\nA,0\nB,0\nB,0\nA,0\nA,0\nB,0\nA,1\nA,0\nB,1\nB,0\nA,0\nA,1\nB,1\nB,1\nB,1\nA,0\nA,0\nB,0\nA,1\nA,0\nB,0\nA,0\nA,0\nB,0\nB,1\nB,0\nA,0\nB,1\nA,1\nB,1\nB,0\nA,1\nB,0\nA,0\nA,0\nB,0\nA,0\nB,0\nA,0\nA,0\nB,0\nA,0\nB,0\nB,0\nB,1\nB,0\nA,1\nA,0\nA,0\nB,1\nB,0\nB,0\nA,0\nB,1\nB,1\nB,1\nB,1\nA,1\nA,0\nA,1\nA,0\nA,0\nA,0\nA,0\nB,0\nB,0\nA,0\nA,0\nA,0\nB,1\nB,0\nB,1\nA,0\nA,1\nB,0\nA,0\nB,0\nB,1\nA,0\nA,1\nA,1\nA,0\nA,0\nA,0\nA,1\nA,0\nA,0\nA,0\nA,1\nA,0\n" }, { "path": "ab_testing/bayesian_bandit.py", "content": "# From the course: Bayesin Machine Learning in Python: A/B Testing\n# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing\n# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.stats import beta\n\n\n# np.random.seed(2)\nNUM_TRIALS = 2000\nBANDIT_PROBABILITIES = [0.2, 0.5, 0.75]\n\n\nclass Bandit:\n def __init__(self, p):\n self.p = p\n self.a = 1\n self.b = 1\n self.N = 0 # for information only\n\n def pull(self):\n return np.random.random() < self.p\n\n def sample(self):\n return np.random.beta(self.a, self.b)\n\n def update(self, x):\n self.a += x\n self.b += 1 - x\n self.N += 1\n\n\ndef plot(bandits, trial):\n x = np.linspace(0, 1, 200)\n for b in bandits:\n y = beta.pdf(x, b.a, b.b)\n plt.plot(x, y, label=f\"real p: {b.p:.4f}, win rate = {b.a - 1}/{b.N}\")\n plt.title(f\"Bandit distributions after {trial} trials\")\n plt.legend()\n plt.show()\n\n\ndef experiment():\n bandits = [Bandit(p) for p in BANDIT_PROBABILITIES]\n\n sample_points = [5,10,20,50,100,200,500,1000,1500,1999]\n rewards = np.zeros(NUM_TRIALS)\n for i in range(NUM_TRIALS):\n # Thompson sampling\n j = np.argmax([b.sample() for b in bandits])\n\n # plot the posteriors\n if i in sample_points:\n plot(bandits, i)\n\n # pull the arm for the bandit with the largest sample\n x = bandits[j].pull()\n\n # update rewards\n rewards[i] = x\n\n # update the distribution for the bandit whose arm we just pulled\n bandits[j].update(x)\n\n # print total reward\n print(\"total reward earned:\", rewards.sum())\n print(\"overall win rate:\", rewards.sum() / NUM_TRIALS)\n print(\"num times selected each bandit:\", [b.N for b in bandits])\n\n\nif __name__ == \"__main__\":\n experiment()\n" }, { "path": "ab_testing/bayesian_normal.py", "content": "# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.stats import norm\n\n\nnp.random.seed(1)\nNUM_TRIALS = 2000\nBANDIT_MEANS = [1, 2, 3]\n\n\nclass Bandit:\n def __init__(self, true_mean):\n self.true_mean = true_mean\n # parameters for mu - prior is N(0,1)\n self.m = 0\n self.lambda_ = 1\n self.tau = 1\n self.N = 0\n\n def pull(self):\n return np.random.randn() / np.sqrt(self.tau) + self.true_mean\n\n def sample(self):\n return np.random.randn() / np.sqrt(self.lambda_) + self.m\n\n def update(self, x):\n self.m = (self.tau * x + self.lambda_ * self.m) / (self.tau + self.lambda_)\n self.lambda_ += self.tau\n self.N += 1\n\n\ndef plot(bandits, trial):\n x = np.linspace(-3, 6, 200)\n for b in bandits:\n y = norm.pdf(x, b.m, np.sqrt(1. / b.lambda_))\n plt.plot(x, y, label=f\"real mean: {b.true_mean:.4f}, num plays: {b.N}\")\n plt.title(f\"Bandit distributions after {trial} trials\")\n plt.legend()\n plt.show()\n\n\ndef run_experiment():\n bandits = [Bandit(m) for m in BANDIT_MEANS]\n\n sample_points = [5,10,20,50,100,200,500,1000,1500,1999]\n rewards = np.empty(NUM_TRIALS)\n for i in range(NUM_TRIALS):\n # Thompson sampling\n j = np.argmax([b.sample() for b in bandits])\n\n # plot the posteriors\n if i in sample_points:\n plot(bandits, i)\n\n # pull the arm for the bandit with the largest sample\n x = bandits[j].pull()\n\n # update the distribution for the bandit whose arm we just pulled\n bandits[j].update(x)\n\n # update rewards\n rewards[i] = x\n\n cumulative_average = np.cumsum(rewards) / (np.arange(NUM_TRIALS) + 1)\n\n # plot moving average ctr\n plt.plot(cumulative_average)\n for m in BANDIT_MEANS:\n plt.plot(np.ones(NUM_TRIALS)*m)\n plt.show()\n\n return cumulative_average\n\nif __name__ == '__main__':\n run_experiment()\n\n\n" }, { "path": "ab_testing/bayesian_starter.py", "content": "# From the course: Bayesin Machine Learning in Python: A/B Testing\n# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing\n# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.stats import beta\n\n\n# np.random.seed(2)\nNUM_TRIALS = 2000\nBANDIT_PROBABILITIES = [0.2, 0.5, 0.75]\n\n\nclass Bandit:\n def __init__(self, p):\n self.p = p\n self.a = # TODO\n self.b = # TODO\n self.N = 0 # for information only\n\n def pull(self):\n return np.random.random() < self.p\n\n def sample(self):\n return # TODO - draw a sample from Beta(a, b)\n\n def update(self, x):\n self.a = # TODO\n self.b = # TODO\n self.N += 1\n\n\ndef plot(bandits, trial):\n x = np.linspace(0, 1, 200)\n for b in bandits:\n y = beta.pdf(x, b.a, b.b)\n plt.plot(x, y, label=f\"real p: {b.p:.4f}, win rate = {b.a - 1}/{b.N}\")\n plt.title(f\"Bandit distributions after {trial} trials\")\n plt.legend()\n plt.show()\n\n\ndef experiment():\n bandits = [Bandit(p) for p in BANDIT_PROBABILITIES]\n\n sample_points = [5,10,20,50,100,200,500,1000,1500,1999]\n rewards = np.zeros(NUM_TRIALS)\n for i in range(NUM_TRIALS):\n # Thompson sampling\n j = # TODO\n\n # plot the posteriors\n if i in sample_points:\n plot(bandits, i)\n\n # pull the arm for the bandit with the largest sample\n x = bandits[j].pull()\n\n # update rewards\n rewards[i] = x\n\n # update the distribution for the bandit whose arm we just pulled\n bandits[j].update(x)\n\n # print total reward\n print(\"total reward earned:\", rewards.sum())\n print(\"overall win rate:\", rewards.sum() / NUM_TRIALS)\n print(\"num times selected each bandit:\", [b.N for b in bandits])\n\n\nif __name__ == \"__main__\":\n experiment()\n" }, { "path": "ab_testing/cdfs_and_percentiles.py", "content": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.stats import norm\n\n\nmu = 170\nsd = 7\n\n\n# generate samples from our distribution\nx = norm.rvs(loc=mu, scale=sd, size=100)\n\n# maximum likelihood mean\nx.mean()\n\n# maximum likelihood variance\nx.var()\n\n# maximum likelihood std\nx.std()\n\n# unbiased variance\nx.var(ddof=1)\n\n# unbiased std\nx.std(ddof=1)\n\n# at what height are you in the 95th percentile?\nnorm.ppf(0.95, loc=mu, scale=sd)\n\n# you are 160 cm tall, what percentile are you in?\nnorm.cdf(160, loc=mu, scale=sd)\n\n# you are 180 cm tall, what is the probability that someone is taller than you?\n1 - norm.cdf(180, loc=mu, scale=sd)" }, { "path": "ab_testing/chisquare.py", "content": "# From the course: Bayesin Machine Learning in Python: A/B Testing\n# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing\n# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.stats import chi2, chi2_contingency\n\n# contingency table\n# click no click\n#------------------------------\n# ad A | a b\n# ad B | c d\n#\n# chi^2 = (ad - bc)^2 (a + b + c + d) / [ (a + b)(c + d)(a + c)(b + d)]\n# degrees of freedom = (#cols - 1) x (#rows - 1) = (2 - 1)(2 - 1) = 1\n\n# short example\n\n# T = np.array([[36, 14], [30, 25]])\n# c2 = np.linalg.det(T)**2 * T.sum() / ( T[0].sum()*T[1].sum()*T[:,0].sum()*T[:,1].sum() )\n# p_value = 1 - chi2.cdf(x=c2, df=1)\n\n# equivalent:\n# (36-31.429)**2/31.429+(14-18.571)**2/18.571 + (30-34.571)**2/34.571 + (25-20.429)**2/20.429\n\n\nclass DataGenerator:\n def __init__(self, p1, p2):\n self.p1 = p1\n self.p2 = p2\n\n def next(self):\n click1 = 1 if (np.random.random() < self.p1) else 0\n click2 = 1 if (np.random.random() < self.p2) else 0\n return click1, click2\n\n\ndef get_p_value(T):\n # same as scipy.stats.chi2_contingency(T, correction=False)\n det = T[0,0]*T[1,1] - T[0,1]*T[1,0]\n c2 = float(det) / T[0].sum() * det / T[1].sum() * T.sum() / T[:,0].sum() / T[:,1].sum()\n p = 1 - chi2.cdf(x=c2, df=1)\n return p\n\n\ndef run_experiment(p1, p2, N):\n data = DataGenerator(p1, p2)\n p_values = np.empty(N)\n T = np.zeros((2, 2)).astype(np.float32)\n for i in range(N):\n c1, c2 = data.next()\n T[0,c1] += 1\n T[1,c2] += 1\n # ignore the first 10 values\n if i < 10:\n p_values[i] = None\n else:\n p_values[i] = get_p_value(T)\n plt.plot(p_values)\n plt.plot(np.ones(N)*0.05)\n plt.show()\n\nrun_experiment(0.1, 0.11, 20000)\n" }, { "path": "ab_testing/ci_comparison.py", "content": "# From the course: Bayesin Machine Learning in Python: A/B Testing\n# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing\n# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.stats import beta, norm\n\nT = 501 # number of coin tosses\ntrue_ctr = 0.5\na, b = 1, 1 # beta priors\nplot_indices = (10, 20, 30, 50, 100, 200, 500)\ndata = np.empty(T)\nfor i in range(T):\n x = 1 if np.random.random() < true_ctr else 0\n data[i] = x\n\n # update a and b\n a += x\n b += 1 - x\n\n if i in plot_indices:\n # maximum likelihood estimate of ctr\n p = data[:i].mean()\n n = i + 1 # number of samples collected so far\n std = np.sqrt(p*(1-p)/n)\n \n # gaussian\n x = np.linspace(0, 1, 200)\n g = norm.pdf(x, loc=p, scale=std)\n plt.plot(x, g, label='Gaussian Approximation')\n\n # beta\n posterior = beta.pdf(x, a=a, b=b)\n plt.plot(x, posterior, label='Beta Posterior')\n plt.legend()\n plt.title(\"N = %s\" % n)\n plt.show()\n" }, { "path": "ab_testing/client.py", "content": "# From the course: Bayesin Machine Learning in Python: A/B Testing\n# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing\n# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport requests\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\n\n# get data\ndf = pd.read_csv('advertisement_clicks.csv')\na = df[df['advertisement_id'] == 'A']\nb = df[df['advertisement_id'] == 'B']\na = a['action'].values\nb = b['action'].values\n\nprint(\"a.mean:\", a.mean())\nprint(\"b.mean:\", b.mean())\n\n\ni = 0\nj = 0\ncount = 0\nwhile i < len(a) and j < len(b):\n # quit when there's no data left for either ad\n r = requests.get('http://localhost:8888/get_ad')\n # print(r.content)\n r = r.json()\n if r['advertisement_id'] == 'A':\n action = a[i]\n i += 1\n else:\n action = b[j]\n j += 1\n\n if action == 1:\n # only click the ad if our dataset determines that we should\n requests.post(\n 'http://localhost:8888/click_ad',\n data={'advertisement_id': r['advertisement_id']}\n )\n\n # log some stats\n count += 1\n if count % 50 == 0:\n print(\"Seen %s ads, A: %s, B: %s\" % (count, i, j))\n" }, { "path": "ab_testing/comparing_epsilons.py", "content": "# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nclass BanditArm:\n def __init__(self, m):\n self.m = m\n self.m_estimate = 0\n self.N = 0\n\n def pull(self):\n return np.random.randn() + self.m\n\n def update(self, x):\n self.N += 1\n self.m_estimate = (1 - 1.0/self.N)*self.m_estimate + 1.0/self.N*x\n\n\ndef run_experiment(m1, m2, m3, eps, N):\n bandits = [BanditArm(m1), BanditArm(m2), BanditArm(m3)]\n\n # count number of suboptimal choices\n means = np.array([m1, m2, m3])\n true_best = np.argmax(means)\n count_suboptimal = 0\n\n data = np.empty(N)\n \n for i in range(N):\n # epsilon greedy\n p = np.random.random()\n if p < eps:\n j = np.random.choice(len(bandits))\n else:\n j = np.argmax([b.m_estimate for b in bandits])\n x = bandits[j].pull()\n bandits[j].update(x)\n\n if j != true_best:\n count_suboptimal += 1\n\n # for the plot\n data[i] = x\n cumulative_average = np.cumsum(data) / (np.arange(N) + 1)\n\n # plot moving average ctr\n plt.plot(cumulative_average)\n plt.plot(np.ones(N)*m1)\n plt.plot(np.ones(N)*m2)\n plt.plot(np.ones(N)*m3)\n plt.xscale('log')\n plt.show()\n\n for b in bandits:\n print(b.m_estimate)\n\n print(\"percent suboptimal for epsilon = %s:\" % eps, float(count_suboptimal) / N)\n\n return cumulative_average\n\nif __name__ == '__main__':\n m1, m2, m3 = 1.5, 2.5, 3.5\n c_1 = run_experiment(m1, m2, m3, 0.1, 100000)\n c_05 = run_experiment(m1, m2, m3, 0.05, 100000)\n c_01 = run_experiment(m1, m2, m3, 0.01, 100000)\n\n # log scale plot\n plt.plot(c_1, label='eps = 0.1')\n plt.plot(c_05, label='eps = 0.05')\n plt.plot(c_01, label='eps = 0.01')\n plt.legend()\n plt.xscale('log')\n plt.show()\n\n\n # linear plot\n plt.plot(c_1, label='eps = 0.1')\n plt.plot(c_05, label='eps = 0.05')\n plt.plot(c_01, label='eps = 0.01')\n plt.legend()\n plt.show()\n\n" }, { "path": "ab_testing/convergence.py", "content": "# From the course: Bayesin Machine Learning in Python: A/B Testing\n# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing\n# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom bayesian_bandit import Bandit\n\n\ndef run_experiment(p1, p2, p3, N):\n bandits = [Bandit(p1), Bandit(p2), Bandit(p3)]\n\n data = np.empty(N)\n \n for i in range(N):\n # thompson sampling\n j = np.argmax([b.sample() for b in bandits])\n x = bandits[j].pull()\n bandits[j].update(x)\n\n # for the plot\n data[i] = x\n cumulative_average_ctr = np.cumsum(data) / (np.arange(N) + 1)\n\n # plot moving average ctr\n plt.plot(cumulative_average_ctr)\n plt.plot(np.ones(N)*p1)\n plt.plot(np.ones(N)*p2)\n plt.plot(np.ones(N)*p3)\n plt.ylim((0,1))\n plt.xscale('log')\n plt.show()\n\n\nrun_experiment(0.2, 0.25, 0.3, 100000)" }, { "path": "ab_testing/demo.py", "content": "# From the course: Bayesin Machine Learning in Python: A/B Testing\n# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing\n# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.stats import beta\n\ndef plot(a, b, trial, ctr):\n x = np.linspace(0, 1, 200)\n y = beta.pdf(x, a, b)\n mean = float(a) / (a + b)\n plt.plot(x, y)\n plt.title(\"Distributions after %s trials, true rate = %.1f, mean = %.2f\" % (trial, ctr, mean))\n plt.show()\n\ntrue_ctr = 0.3\na, b = 1, 1 # beta parameters\nshow = [0, 5, 10, 25, 50, 100, 200, 300, 500, 700, 1000, 1500]\nfor t in range(1501):\n coin_toss_result = (np.random.random() < true_ctr)\n if coin_toss_result:\n a += 1\n else:\n b += 1\n\n if t in show:\n plot(a, b, t+1, true_ctr)\n" }, { "path": "ab_testing/epsilon_greedy.py", "content": "# From the course: Bayesin Machine Learning in Python: A/B Testing\n# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing\n# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nNUM_TRIALS = 10000\nEPS = 0.1\nBANDIT_PROBABILITIES = [0.2, 0.5, 0.75]\n\n\nclass BanditArm:\n def __init__(self, p):\n # p: the win rate\n self.p = p\n self.p_estimate = 0.\n self.N = 0. # num samples collected so far\n\n def pull(self):\n # draw a 1 with probability p\n return np.random.random() < self.p\n\n def update(self, x):\n self.N += 1.\n self.p_estimate = ((self.N - 1)*self.p_estimate + x) / self.N\n\n\ndef choose_random_argmax(a):\n idx = np.argwhere(np.amax(a) == a).flatten()\n return np.random.choice(idx)\n\n\ndef experiment():\n bandits = [BanditArm(p) for p in BANDIT_PROBABILITIES]\n\n rewards = np.zeros(NUM_TRIALS)\n num_times_explored = 0\n num_times_exploited = 0\n num_optimal = 0\n optimal_j = np.argmax([b.p for b in bandits])\n print(\"optimal j:\", optimal_j)\n\n for i in range(NUM_TRIALS):\n\n # use epsilon-greedy to select the next bandit\n if np.random.random() < EPS:\n num_times_explored += 1\n j = np.random.randint(len(bandits))\n else:\n num_times_exploited += 1\n j = choose_random_argmax([b.p_estimate for b in bandits])\n\n if j == optimal_j:\n num_optimal += 1\n\n # pull the arm for the bandit with the largest sample\n x = bandits[j].pull()\n\n # update rewards log\n rewards[i] = x\n\n # update the distribution for the bandit whose arm we just pulled\n bandits[j].update(x)\n\n \n\n # print mean estimates for each bandit\n for b in bandits:\n print(\"mean estimate:\", b.p_estimate)\n\n # print total reward\n print(\"total reward earned:\", rewards.sum())\n print(\"overall win rate:\", rewards.sum() / NUM_TRIALS)\n print(\"num_times_explored:\", num_times_explored)\n print(\"num_times_exploited:\", num_times_exploited)\n print(\"num times selected optimal bandit:\", num_optimal)\n\n # plot the results\n cumulative_rewards = np.cumsum(rewards)\n win_rates = cumulative_rewards / (np.arange(NUM_TRIALS) + 1)\n plt.plot(win_rates)\n plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES))\n plt.show()\n\nif __name__ == \"__main__\":\n experiment()\n" }, { "path": "ab_testing/epsilon_greedy_starter.py", "content": "# From the course: Bayesin Machine Learning in Python: A/B Testing\n# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing\n# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nNUM_TRIALS = 10000\nEPS = 0.1\nBANDIT_PROBABILITIES = [0.2, 0.5, 0.75]\n\n\nclass BanditArm:\n def __init__(self, p):\n # p: the win rate\n self.p = p\n self.p_estimate = # TODO\n self.N = # TODO\n\n def pull(self):\n # draw a 1 with probability p\n return np.random.random() < self.p\n\n def update(self, x):\n self.N = # TODO\n self.p_estimate = # TODO\n\n\ndef experiment():\n bandits = [BanditArm(p) for p in BANDIT_PROBABILITIES]\n\n rewards = np.zeros(NUM_TRIALS)\n num_times_explored = 0\n num_times_exploited = 0\n num_optimal = 0\n optimal_j = np.argmax([b.p for b in bandits])\n print(\"optimal j:\", optimal_j)\n\n for i in range(NUM_TRIALS):\n\n # use epsilon-greedy to select the next bandit\n if np.random.random() < EPS:\n num_times_explored += 1\n j = # TODO\n else:\n num_times_exploited += 1\n j = # TODO\n\n if j == optimal_j:\n num_optimal += 1\n\n # pull the arm for the bandit with the largest sample\n x = bandits[j].pull()\n\n # update rewards log\n rewards[i] = x\n\n # update the distribution for the bandit whose arm we just pulled\n bandits[j].update(x)\n\n \n\n # print mean estimates for each bandit\n for b in bandits:\n print(\"mean estimate:\", b.p_estimate)\n\n # print total reward\n print(\"total reward earned:\", rewards.sum())\n print(\"overall win rate:\", rewards.sum() / NUM_TRIALS)\n print(\"num_times_explored:\", num_times_explored)\n print(\"num_times_exploited:\", num_times_exploited)\n print(\"num times selected optimal bandit:\", num_optimal)\n\n # plot the results\n cumulative_rewards = np.cumsum(rewards)\n win_rates = cumulative_rewards / (np.arange(NUM_TRIALS) + 1)\n plt.plot(win_rates)\n plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES))\n plt.show()\n\nif __name__ == \"__main__\":\n experiment()\n" }, { "path": "ab_testing/ex_chisq.py", "content": "# From the course: Bayesin Machine Learning in Python: A/B Testing\n# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing\n# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport pandas as pd\nfrom scipy.stats import chi2, chi2_contingency\n\n# contingency table\n# click no click\n#------------------------------\n# ad A | a b\n# ad B | c d\n\n\ndef get_p_value(T):\n # same as scipy.stats.chi2_contingency(T, correction=False)\n det = T[0,0]*T[1,1] - T[0,1]*T[1,0]\n c2 = float(det) / T[0].sum() * det / T[1].sum() * T.sum() / T[:,0].sum() / T[:,1].sum()\n p = 1 - chi2.cdf(x=c2, df=1)\n return p\n\n# get data\ndf = pd.read_csv('advertisement_clicks.csv')\na = df[df['advertisement_id'] == 'A']\nb = df[df['advertisement_id'] == 'B']\na = a['action']\nb = b['action']\n\nA_clk = a.sum()\nA_noclk = a.size - a.sum()\nB_clk = b.sum()\nB_noclk = b.size - b.sum()\n\nT = np.array([[A_clk, A_noclk], [B_clk, B_noclk]])\n\nprint(get_p_value(T))" }, { "path": "ab_testing/ex_ttest.py", "content": "# From the course: Bayesin Machine Learning in Python: A/B Testing\n# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing\n# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\n\n# get data\ndf = pd.read_csv('advertisement_clicks.csv')\na = df[df['advertisement_id'] == 'A']\nb = df[df['advertisement_id'] == 'B']\na = a['action']\nb = b['action']\n\nprint(\"a.mean:\", a.mean())\nprint(\"b.mean:\", b.mean())\n\n# built-in t-test:\nt, p = stats.ttest_ind(a, b)\nprint(\"t:\\t\", t, \"p:\\t\", p)\n\n# welch's t-test:\nt, p = stats.ttest_ind(a, b, equal_var=False)\nprint(\"Welch's t-test:\")\nprint(\"t:\\t\", t, \"p:\\t\", p)\n\n# welch's t-test manual:\nN1 = len(a)\ns1_sq = a.var()\nN2 = len(b)\ns2_sq = b.var()\nt = (a.mean() - b.mean()) / np.sqrt(s1_sq / N1 + s2_sq / N2)\n\nnu1 = N1 - 1\nnu2 = N2 - 1\ndf = (s1_sq / N1 + s2_sq / N2)**2 / ( (s1_sq*s1_sq) / (N1*N1 * nu1) + (s2_sq*s2_sq) / (N2*N2 * nu2) )\np = (1 - stats.t.cdf(np.abs(t), df=df))*2\nprint(\"Manual Welch t-test\")\nprint(\"t:\\t\", t, \"p:\\t\", p)" }, { "path": "ab_testing/extra_reading.txt", "content": "The Unbiased Estimate of the Covariance Matrix\nhttps://lazyprogrammer.me/covariance-matrix-divide-by-n-or-n-1/\n\nAlgorithms for the multi-armed bandit problem\nhttps://www.cs.mcgill.ca/~vkules/bandits.pdf\n\nUCB REVISITED: IMPROVED REGRET BOUNDS FOR THE STOCHASTIC MULTI-ARMED BANDIT PROBLEM\nhttp://personal.unileoben.ac.at/rortner/Pubs/UCBRev.pdf\n\nFinite-time Analysis of the Multiarmed Bandit Problem\nhttps://link.springer.com/article/10.1023/A:1013689704352\n\nA Tutorial on Thompson Sampling\nhttps://web.stanford.edu/~bvr/pubs/TS_Tutorial.pdf\n\nAn Empirical Evaluation of Thompson Sampling\nhttps://papers.nips.cc/paper/4321-an-empirical-evaluation-of-thompson-sampling.pdf\n\nAnalysis of Thompson Sampling for the Multi-armed Bandit Problem\nhttp://proceedings.mlr.press/v23/agrawal12/agrawal12.pdf" }, { "path": "ab_testing/optimistic.py", "content": "# From the course: Bayesin Machine Learning in Python: A/B Testing\n# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing\n# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nNUM_TRIALS = 10000\nEPS = 0.1\nBANDIT_PROBABILITIES = [0.2, 0.5, 0.75]\n\n\nclass Bandit:\n def __init__(self, p):\n # p: the win rate\n self.p = p\n self.p_estimate = 5.\n self.N = 1. # num samples collected so far\n\n def pull(self):\n # draw a 1 with probability p\n return np.random.random() < self.p\n\n def update(self, x):\n self.N += 1.\n self.p_estimate = ((self.N - 1)*self.p_estimate + x) / self.N\n\n\ndef experiment():\n bandits = [Bandit(p) for p in BANDIT_PROBABILITIES]\n\n rewards = np.zeros(NUM_TRIALS)\n for i in range(NUM_TRIALS):\n # use optimistic initial values to select the next bandit\n j = np.argmax([b.p_estimate for b in bandits])\n\n # pull the arm for the bandit with the largest sample\n x = bandits[j].pull()\n\n # update rewards log\n rewards[i] = x\n\n # update the distribution for the bandit whose arm we just pulled\n bandits[j].update(x)\n\n\n # print mean estimates for each bandit\n for b in bandits:\n print(\"mean estimate:\", b.p_estimate)\n\n # print total reward\n print(\"total reward earned:\", rewards.sum())\n print(\"overall win rate:\", rewards.sum() / NUM_TRIALS)\n print(\"num times selected each bandit:\", [b.N for b in bandits])\n\n # plot the results\n cumulative_rewards = np.cumsum(rewards)\n win_rates = cumulative_rewards / (np.arange(NUM_TRIALS) + 1)\n plt.ylim([0, 1])\n plt.plot(win_rates)\n plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES))\n plt.show()\n\nif __name__ == \"__main__\":\n experiment()\n" }, { "path": "ab_testing/optimistic_starter.py", "content": "# From the course: Bayesin Machine Learning in Python: A/B Testing\n# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing\n# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nNUM_TRIALS = 10000\nEPS = 0.1\nBANDIT_PROBABILITIES = [0.2, 0.5, 0.75]\n\n\nclass Bandit:\n def __init__(self, p):\n # p: the win rate\n self.p = p\n self.p_estimate = # TODO\n self.N = # TODO\n\n def pull(self):\n # draw a 1 with probability p\n return np.random.random() < self.p\n\n def update(self, x):\n # TODO\n self.p_estimate = # TODO\n\n\ndef experiment():\n bandits = [Bandit(p) for p in BANDIT_PROBABILITIES]\n\n rewards = np.zeros(NUM_TRIALS)\n for i in range(NUM_TRIALS):\n # use optimistic initial values to select the next bandit\n j = # TODO\n\n # pull the arm for the bandit with the largest sample\n x = bandits[j].pull()\n\n # update rewards log\n rewards[i] = x\n\n # update the distribution for the bandit whose arm we just pulled\n bandits[j].update(x)\n\n\n # print mean estimates for each bandit\n for b in bandits:\n print(\"mean estimate:\", b.p_estimate)\n\n # print total reward\n print(\"total reward earned:\", rewards.sum())\n print(\"overall win rate:\", rewards.sum() / NUM_TRIALS)\n print(\"num times selected each bandit:\", [b.N for b in bandits])\n\n # plot the results\n cumulative_rewards = np.cumsum(rewards)\n win_rates = cumulative_rewards / (np.arange(NUM_TRIALS) + 1)\n plt.ylim([0, 1])\n plt.plot(win_rates)\n plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES))\n plt.show()\n\nif __name__ == \"__main__\":\n experiment()\n" }, { "path": "ab_testing/server_solution.py", "content": "# From the course: Bayesin Machine Learning in Python: A/B Testing\n# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing\n# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nfrom flask import Flask, jsonify, request\nfrom scipy.stats import beta\n\n# create an app\napp = Flask(__name__)\n\n\n# define bandit\n# there's no \"pull arm\" here\n# since that's technically now the user/client\nclass Bandit:\n def __init__(self, name):\n self.clks = 0\n self.views = 0\n self.name = name\n\n def sample(self):\n # Beta(1, 1) is the prior\n a = 1 + self.clks\n b = 1 + self.views - self.clks\n return np.random.beta(a, b)\n\n def add_click(self):\n self.clks += 1\n\n def add_view(self):\n self.views += 1\n\n # print some helpful stats\n if self.views % 50 == 0:\n print(\"%s: clks=%s, views=%s\" % (self.name, self.clks, self.views))\n\n\n# initialize bandits\nbanditA = Bandit('A')\nbanditB = Bandit('B')\n\n\n\n@app.route('/get_ad')\ndef get_ad():\n if banditA.sample() > banditB.sample():\n ad = 'A'\n banditA.add_view()\n else:\n ad = 'B'\n banditB.add_view()\n return jsonify({'advertisement_id': ad})\n\n\n@app.route('/click_ad', methods=['POST'])\ndef click_ad():\n result = 'OK'\n if request.form['advertisement_id'] == 'A':\n banditA.add_click()\n elif request.form['advertisement_id'] == 'B':\n banditB.add_click()\n else:\n result = 'Invalid Input.'\n\n # nothing to return really\n return jsonify({'result': result})\n\n\nif __name__ == '__main__':\n app.run(host='127.0.0.1', port='8888')" }, { "path": "ab_testing/server_starter.py", "content": "# From the course: Bayesin Machine Learning in Python: A/B Testing\n# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing\n# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nfrom flask import Flask, jsonify, request\nfrom scipy.stats import beta\n\n# create an app\napp = Flask(__name__)\n\n\n# define bandits\n# there's no \"pull arm\" here\n# since that's technically now the user/client\nclass Bandit:\n def __init__(self, name):\n self.name = name\n\n def sample(self):\n # TODO\n return 1\n\n # TODO - what else does the Bandit need to do?\n\n\n# initialize bandits\nbanditA = Bandit('A')\nbanditB = Bandit('B')\n\n\n\n@app.route('/get_ad')\ndef get_ad():\n # TODO\n return jsonify({'advertisement_id': 'A'})\n\n\n@app.route('/click_ad', methods=['POST'])\ndef click_ad():\n result = 'OK'\n if request.form['advertisement_id'] == 'A':\n # TODO\n pass\n elif request.form['advertisement_id'] == 'B':\n # TODO\n pass\n else:\n result = 'Invalid Input.'\n\n # nothing to return really\n return jsonify({'result': result})\n\n\nif __name__ == '__main__':\n app.run(host='127.0.0.1', port='8888')" }, { "path": "ab_testing/ttest.py", "content": "# From the course: Bayesin Machine Learning in Python: A/B Testing\n# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing\n# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nfrom scipy import stats\n\n# generate data\nN = 10\na = np.random.randn(N) + 2 # mean 2, variance 1\nb = np.random.randn(N) # mean 0, variance 1\n\n# roll your own t-test:\nvar_a = a.var(ddof=1) # unbiased estimator, divide by N-1 instead of N\nvar_b = b.var(ddof=1)\ns = np.sqrt( (var_a + var_b) / 2 ) # balanced standard deviation\nt = (a.mean() - b.mean()) / (s * np.sqrt(2.0/N)) # t-statistic\ndf = 2*N - 2 # degrees of freedom\np = 1 - stats.t.cdf(np.abs(t), df=df) # one-sided test p-value\nprint(\"t:\\t\", t, \"p:\\t\", 2*p) # two-sided test p-value\n\n# built-in t-test:\nt2, p2 = stats.ttest_ind(a, b)\nprint(\"t2:\\t\", t2, \"p2:\\t\", p2)" }, { "path": "ab_testing/ucb1.py", "content": "# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python\n# https://books.google.ca/books?id=_ATpBwAAQBAJ&lpg=PA201&ots=rinZM8jQ6s&dq=hoeffding%20bound%20gives%20probability%20%22greater%20than%201%22&pg=PA201#v=onepage&q&f=false\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nNUM_TRIALS = 100000\nEPS = 0.1\nBANDIT_PROBABILITIES = [0.2, 0.5, 0.75]\n\n\nclass Bandit:\n def __init__(self, p):\n # p: the win rate\n self.p = p\n self.p_estimate = 0.\n self.N = 0. # num samples collected so far\n\n def pull(self):\n # draw a 1 with probability p\n return np.random.random() < self.p\n\n def update(self, x):\n self.N += 1.\n self.p_estimate = ((self.N - 1)*self.p_estimate + x) / self.N\n\n\ndef ucb(mean, n, nj):\n return mean + np.sqrt(2*np.log(n) / nj)\n\n\ndef run_experiment():\n bandits = [Bandit(p) for p in BANDIT_PROBABILITIES]\n rewards = np.empty(NUM_TRIALS)\n total_plays = 0\n\n # initialization: play each bandit once\n for j in range(len(bandits)):\n x = bandits[j].pull()\n total_plays += 1\n bandits[j].update(x)\n \n for i in range(NUM_TRIALS):\n j = np.argmax([ucb(b.p_estimate, total_plays, b.N) for b in bandits])\n x = bandits[j].pull()\n total_plays += 1\n bandits[j].update(x)\n\n # for the plot\n rewards[i] = x\n cumulative_average = np.cumsum(rewards) / (np.arange(NUM_TRIALS) + 1)\n\n # plot moving average ctr\n plt.plot(cumulative_average)\n plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES))\n plt.xscale('log')\n plt.show()\n\n # plot moving average ctr linear\n plt.plot(cumulative_average)\n plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES))\n plt.show()\n\n for b in bandits:\n print(b.p_estimate)\n\n print(\"total reward earned:\", rewards.sum())\n print(\"overall win rate:\", rewards.sum() / NUM_TRIALS)\n print(\"num times selected each bandit:\", [b.N for b in bandits])\n\n return cumulative_average\n\nif __name__ == '__main__':\n run_experiment()\n\n" }, { "path": "ab_testing/ucb1_starter.py", "content": "# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python\n# https://books.google.ca/books?id=_ATpBwAAQBAJ&lpg=PA201&ots=rinZM8jQ6s&dq=hoeffding%20bound%20gives%20probability%20%22greater%20than%201%22&pg=PA201#v=onepage&q&f=false\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nNUM_TRIALS = 100000\nEPS = 0.1\nBANDIT_PROBABILITIES = [0.2, 0.5, 0.75]\n\n\nclass Bandit:\n def __init__(self, p):\n # p: the win rate\n self.p = p\n self.p_estimate = 0.\n self.N = 0. # num samples collected so far\n\n def pull(self):\n # draw a 1 with probability p\n return np.random.random() < self.p\n\n def update(self, x):\n self.N += 1.\n self.p_estimate = ((self.N - 1)*self.p_estimate + x) / self.N\n\n\ndef ucb(mean, n, nj):\n return # TODO\n\n\ndef run_experiment():\n bandits = [Bandit(p) for p in BANDIT_PROBABILITIES]\n rewards = np.empty(NUM_TRIALS)\n total_plays = 0\n\n # initialization: play each bandit once\n for j in range(len(bandits)):\n x = bandits[j].pull()\n total_plays += 1\n bandits[j].update(x)\n \n for i in range(NUM_TRIALS):\n j = # TODO\n x = bandits[j].pull()\n total_plays += 1\n bandits[j].update(x)\n\n # for the plot\n rewards[i] = x\n cumulative_average = np.cumsum(rewards) / (np.arange(NUM_TRIALS) + 1)\n\n # plot moving average ctr\n plt.plot(cumulative_average)\n plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES))\n plt.xscale('log')\n plt.show()\n\n # plot moving average ctr linear\n plt.plot(cumulative_average)\n plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES))\n plt.show()\n\n for b in bandits:\n print(b.p_estimate)\n\n print(\"total reward earned:\", rewards.sum())\n print(\"overall win rate:\", rewards.sum() / NUM_TRIALS)\n print(\"num times selected each bandit:\", [b.N for b in bandits])\n\n return cumulative_average\n\nif __name__ == '__main__':\n run_experiment()\n\n" }, { "path": "airline/ann.py", "content": "# The corresponding tutorial for this code was released EXCLUSIVELY as a bonus\n# If you want to learn about future bonuses, please sign up for my newsletter at:\n# https://lazyprogrammer.me\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport theano\nimport theano.tensor as T\nfrom sklearn.utils import shuffle\n\ndef init_weight(M1, M2):\n return np.random.randn(M1, M2) / np.sqrt(M1 + M2)\n\ndef myr2(T, Y):\n Ym = T.mean()\n sse = (T - Y).dot(T - Y)\n sst = (T - Ym).dot(T - Ym)\n return 1 - sse / sst\n\nclass HiddenLayer(object):\n def __init__(self, M1, M2, f, an_id):\n self.id = an_id\n self.M1 = M1\n self.M2 = M2\n self.f = f\n W = init_weight(M1, M2)\n b = np.zeros(M2)\n self.W = theano.shared(W, 'W_%s' % self.id)\n self.b = theano.shared(b, 'b_%s' % self.id)\n self.params = [self.W, self.b]\n\n def forward(self, X):\n return self.f(X.dot(self.W) + self.b)\n\n\nclass ANN(object):\n def __init__(self, hidden_layer_sizes):\n self.hidden_layer_sizes = hidden_layer_sizes\n\n def fit(self, X, Y, activation=T.tanh, learning_rate=1e-3, mu=0.5, reg=0, epochs=5000, batch_sz=None, print_period=100, show_fig=True):\n X = X.astype(np.float32)\n Y = Y.astype(np.float32)\n\n # initialize hidden layers\n N, D = X.shape\n self.hidden_layers = []\n M1 = D\n count = 0\n for M2 in self.hidden_layer_sizes:\n h = HiddenLayer(M1, M2, activation, count)\n self.hidden_layers.append(h)\n M1 = M2\n count += 1\n W = np.random.randn(M1) / np.sqrt(M1)\n b = 0.0\n self.W = theano.shared(W, 'W_last')\n self.b = theano.shared(b, 'b_last')\n\n if batch_sz is None:\n batch_sz = N\n\n # collect params for later use\n self.params = [self.W, self.b]\n for h in self.hidden_layers:\n self.params += h.params\n\n # for momentum\n dparams = [theano.shared(np.zeros(p.get_value().shape)) for p in self.params]\n\n # set up theano functions and variables\n thX = T.matrix('X')\n thY = T.vector('Y')\n Yhat = self.forward(thX)\n\n rcost = reg*T.mean([(p*p).sum() for p in self.params])\n cost = T.mean((thY - Yhat).dot(thY - Yhat)) + rcost\n prediction = self.forward(thX)\n grads = T.grad(cost, self.params)\n\n # momentum only\n updates = [\n (p, p + mu*dp - learning_rate*g) for p, dp, g in zip(self.params, dparams, grads)\n ] + [\n (dp, mu*dp - learning_rate*g) for dp, g in zip(dparams, grads)\n ]\n\n train_op = theano.function(\n inputs=[thX, thY],\n outputs=[cost, prediction],\n updates=updates,\n )\n\n self.predict_op = theano.function(\n inputs=[thX],\n outputs=prediction,\n )\n\n n_batches = N / batch_sz\n # print \"N:\", N, \"batch_sz:\", batch_sz\n # print \"n_batches:\", n_batches\n costs = []\n for i in xrange(epochs):\n X, Y = shuffle(X, Y)\n for j in xrange(n_batches):\n Xbatch = X[j*batch_sz:(j*batch_sz+batch_sz)]\n Ybatch = Y[j*batch_sz:(j*batch_sz+batch_sz)]\n\n c, p = train_op(Xbatch, Ybatch)\n costs.append(c)\n if (j+1) % print_period == 0:\n print \"i:\", i, \"j:\", j, \"nb:\", n_batches, \"cost:\", c\n \n if show_fig:\n plt.plot(costs)\n plt.show()\n\n def forward(self, X):\n Z = X\n for h in self.hidden_layers:\n Z = h.forward(Z)\n return Z.dot(self.W) + self.b\n\n def score(self, X, Y):\n Yhat = self.predict_op(X)\n return myr2(Y, Yhat)\n\n def predict(self, X):\n return self.predict_op(X)\n\n# we need to skip the 3 footer rows\n# skipfooter does not work with the default engine, 'c'\n# so we need to explicitly set it to 'python'\ndf = pd.read_csv('international-airline-passengers.csv', engine='python', skipfooter=3)\n\n# rename the columns because they are ridiculous\ndf.columns = ['month', 'num_passengers']\n\n# plot the data so we know what it looks like\n# plt.plot(df.num_passengers)\n# plt.show()\n\n# let's try with only the time series itself\nseries = df.num_passengers.as_matrix()\n# series = (series - series.mean()) / series.std() # normalize the values so they have mean 0 and variance 1\nseries = series.astype(np.float32)\nseries = series - series.min()\nseries = series / series.max()\n\n# let's see if we can use D past values to predict the next value\nN = len(series)\nfor D in (2,3,4,5,6,7):\n n = N - D\n X = np.zeros((n, D))\n for d in xrange(D):\n X[:,d] = series[d:d+n]\n Y = series[D:D+n]\n\n print \"series length:\", n\n Xtrain = X[:n/2]\n Ytrain = Y[:n/2]\n Xtest = X[n/2:]\n Ytest = Y[n/2:]\n\n model = ANN([200])\n model.fit(Xtrain, Ytrain, activation=T.tanh)\n print \"train score:\", model.score(Xtrain, Ytrain)\n print \"test score:\", model.score(Xtest, Ytest)\n\n # plot the prediction with true values\n plt.plot(series)\n\n train_series = np.empty(n)\n train_series[:n/2] = model.predict(Xtrain) \n train_series[n/2:] = np.nan\n # prepend d nan's since the train series is only of size N - D\n plt.plot(np.concatenate([np.full(d, np.nan), train_series]))\n\n test_series = np.empty(n)\n test_series[:n/2] = np.nan\n test_series[n/2:] = model.predict(Xtest)\n plt.plot(np.concatenate([np.full(d, np.nan), test_series]))\n\n plt.show()\n" }, { "path": "airline/international-airline-passengers.csv", "content": "\"Month\",\"International airline passengers: monthly totals in thousands. Jan 49 ? Dec 60\"\r\n\"1949-01\",112\r\n\"1949-02\",118\r\n\"1949-03\",132\r\n\"1949-04\",129\r\n\"1949-05\",121\r\n\"1949-06\",135\r\n\"1949-07\",148\r\n\"1949-08\",148\r\n\"1949-09\",136\r\n\"1949-10\",119\r\n\"1949-11\",104\r\n\"1949-12\",118\r\n\"1950-01\",115\r\n\"1950-02\",126\r\n\"1950-03\",141\r\n\"1950-04\",135\r\n\"1950-05\",125\r\n\"1950-06\",149\r\n\"1950-07\",170\r\n\"1950-08\",170\r\n\"1950-09\",158\r\n\"1950-10\",133\r\n\"1950-11\",114\r\n\"1950-12\",140\r\n\"1951-01\",145\r\n\"1951-02\",150\r\n\"1951-03\",178\r\n\"1951-04\",163\r\n\"1951-05\",172\r\n\"1951-06\",178\r\n\"1951-07\",199\r\n\"1951-08\",199\r\n\"1951-09\",184\r\n\"1951-10\",162\r\n\"1951-11\",146\r\n\"1951-12\",166\r\n\"1952-01\",171\r\n\"1952-02\",180\r\n\"1952-03\",193\r\n\"1952-04\",181\r\n\"1952-05\",183\r\n\"1952-06\",218\r\n\"1952-07\",230\r\n\"1952-08\",242\r\n\"1952-09\",209\r\n\"1952-10\",191\r\n\"1952-11\",172\r\n\"1952-12\",194\r\n\"1953-01\",196\r\n\"1953-02\",196\r\n\"1953-03\",236\r\n\"1953-04\",235\r\n\"1953-05\",229\r\n\"1953-06\",243\r\n\"1953-07\",264\r\n\"1953-08\",272\r\n\"1953-09\",237\r\n\"1953-10\",211\r\n\"1953-11\",180\r\n\"1953-12\",201\r\n\"1954-01\",204\r\n\"1954-02\",188\r\n\"1954-03\",235\r\n\"1954-04\",227\r\n\"1954-05\",234\r\n\"1954-06\",264\r\n\"1954-07\",302\r\n\"1954-08\",293\r\n\"1954-09\",259\r\n\"1954-10\",229\r\n\"1954-11\",203\r\n\"1954-12\",229\r\n\"1955-01\",242\r\n\"1955-02\",233\r\n\"1955-03\",267\r\n\"1955-04\",269\r\n\"1955-05\",270\r\n\"1955-06\",315\r\n\"1955-07\",364\r\n\"1955-08\",347\r\n\"1955-09\",312\r\n\"1955-10\",274\r\n\"1955-11\",237\r\n\"1955-12\",278\r\n\"1956-01\",284\r\n\"1956-02\",277\r\n\"1956-03\",317\r\n\"1956-04\",313\r\n\"1956-05\",318\r\n\"1956-06\",374\r\n\"1956-07\",413\r\n\"1956-08\",405\r\n\"1956-09\",355\r\n\"1956-10\",306\r\n\"1956-11\",271\r\n\"1956-12\",306\r\n\"1957-01\",315\r\n\"1957-02\",301\r\n\"1957-03\",356\r\n\"1957-04\",348\r\n\"1957-05\",355\r\n\"1957-06\",422\r\n\"1957-07\",465\r\n\"1957-08\",467\r\n\"1957-09\",404\r\n\"1957-10\",347\r\n\"1957-11\",305\r\n\"1957-12\",336\r\n\"1958-01\",340\r\n\"1958-02\",318\r\n\"1958-03\",362\r\n\"1958-04\",348\r\n\"1958-05\",363\r\n\"1958-06\",435\r\n\"1958-07\",491\r\n\"1958-08\",505\r\n\"1958-09\",404\r\n\"1958-10\",359\r\n\"1958-11\",310\r\n\"1958-12\",337\r\n\"1959-01\",360\r\n\"1959-02\",342\r\n\"1959-03\",406\r\n\"1959-04\",396\r\n\"1959-05\",420\r\n\"1959-06\",472\r\n\"1959-07\",548\r\n\"1959-08\",559\r\n\"1959-09\",463\r\n\"1959-10\",407\r\n\"1959-11\",362\r\n\"1959-12\",405\r\n\"1960-01\",417\r\n\"1960-02\",391\r\n\"1960-03\",419\r\n\"1960-04\",461\r\n\"1960-05\",472\r\n\"1960-06\",535\r\n\"1960-07\",622\r\n\"1960-08\",606\r\n\"1960-09\",508\r\n\"1960-10\",461\r\n\"1960-11\",390\r\n\"1960-12\",432\r\n\r\nInternational airline passengers: monthly totals in thousands. Jan 49 ? Dec 60\r\n\r\n" }, { "path": "airline/lr.py", "content": "# The corresponding tutorial for this code was released EXCLUSIVELY as a bonus\n# If you want to learn about future bonuses, please sign up for my newsletter at:\n# https://lazyprogrammer.me\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.linear_model import LinearRegression\n\n# we need to skip the 3 footer rows\n# skipfooter does not work with the default engine, 'c'\n# so we need to explicitly set it to 'python'\ndf = pd.read_csv('international-airline-passengers.csv', engine='python', skipfooter=3)\n\n# rename the columns because they are ridiculous\ndf.columns = ['month', 'num_passengers']\n\n# plot the data so we know what it looks like\nplt.plot(df.num_passengers)\nplt.show()\n\n# let's try with only the time series itself\nseries = df.num_passengers.as_matrix()\n\n# let's see if we can use D past values to predict the next value\nN = len(series)\nfor D in (2,3,4,5,6,7):\n n = N - D\n X = np.zeros((n, D))\n for d in xrange(D):\n X[:,d] = series[d:d+n]\n Y = series[D:D+n]\n\n print \"series length:\", n\n Xtrain = X[:n/2]\n Ytrain = Y[:n/2]\n Xtest = X[n/2:]\n Ytest = Y[n/2:]\n\n model = LinearRegression()\n model.fit(Xtrain, Ytrain)\n print \"train score:\", model.score(Xtrain, Ytrain)\n print \"test score:\", model.score(Xtest, Ytest)\n\n # plot the prediction with true values\n plt.plot(series)\n\n train_series = np.empty(n)\n train_series[:n/2] = model.predict(Xtrain) \n train_series[n/2:] = np.nan\n # prepend d nan's since the train series is only of size N - D\n plt.plot(np.concatenate([np.full(d, np.nan), train_series]))\n\n test_series = np.empty(n)\n test_series[:n/2] = np.nan\n test_series[n/2:] = model.predict(Xtest)\n plt.plot(np.concatenate([np.full(d, np.nan), test_series]))\n\n plt.show()\n" }, { "path": "airline/rnn.py", "content": "# The corresponding tutorial for this code was released EXCLUSIVELY as a bonus\n# If you want to learn about future bonuses, please sign up for my newsletter at:\n# https://lazyprogrammer.me\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport theano\nimport theano.tensor as T\nfrom sklearn.utils import shuffle\nfrom datetime import datetime\n\nimport os\nimport sys\nsys.path.append(os.path.abspath('..'))\nfrom rnn_class.lstm import LSTM\nfrom rnn_class.gru import GRU\n\ndef init_weight(M1, M2):\n return np.random.randn(M1, M2) / np.sqrt(M1 + M2)\n\ndef myr2(T, Y):\n Ym = T.mean()\n sse = (T - Y).dot(T - Y)\n sst = (T - Ym).dot(T - Ym)\n return 1 - sse / sst\n\nclass RNN(object):\n def __init__(self, hidden_layer_sizes):\n self.hidden_layer_sizes = hidden_layer_sizes\n\n def fit(self, X, Y, activation=T.tanh, learning_rate=1e-1, mu=0.5, reg=0, epochs=2000, show_fig=False):\n N, t, D = X.shape\n\n self.hidden_layers = []\n Mi = D\n for Mo in self.hidden_layer_sizes:\n ru = GRU(Mi, Mo, activation)\n self.hidden_layers.append(ru)\n Mi = Mo\n\n Wo = np.random.randn(Mi) / np.sqrt(Mi)\n bo = 0.0\n self.Wo = theano.shared(Wo)\n self.bo = theano.shared(bo)\n self.params = [self.Wo, self.bo]\n for ru in self.hidden_layers:\n self.params += ru.params\n\n lr = T.scalar('lr')\n thX = T.matrix('X')\n thY = T.scalar('Y')\n Yhat = self.forward(thX)[-1]\n\n # let's return py_x too so we can draw a sample instead\n self.predict_op = theano.function(\n inputs=[thX],\n outputs=Yhat,\n allow_input_downcast=True,\n )\n \n cost = T.mean((thY - Yhat)*(thY - Yhat))\n grads = T.grad(cost, self.params)\n dparams = [theano.shared(p.get_value()*0) for p in self.params]\n\n updates = [\n (p, p + mu*dp - lr*g) for p, dp, g in zip(self.params, dparams, grads)\n ] + [\n (dp, mu*dp - lr*g) for dp, g in zip(dparams, grads)\n ]\n\n self.train_op = theano.function(\n inputs=[lr, thX, thY],\n outputs=cost,\n updates=updates\n )\n\n costs = []\n for i in xrange(epochs):\n t0 = datetime.now()\n X, Y = shuffle(X, Y)\n n_correct = 0\n n_total = 0\n cost = 0\n for j in xrange(N):\n \n c = self.train_op(learning_rate, X[j], Y[j])\n cost += c\n if i % 10 == 0:\n print \"i:\", i, \"cost:\", cost, \"time for epoch:\", (datetime.now() - t0)\n if (i+1) % 500 == 0:\n learning_rate /= 10\n costs.append(cost)\n\n if show_fig:\n plt.plot(costs)\n plt.show()\n\n def forward(self, X):\n Z = X\n for h in self.hidden_layers:\n Z = h.output(Z)\n return Z.dot(self.Wo) + self.bo\n\n def score(self, X, Y):\n Yhat = self.predict(X)\n return myr2(Y, Yhat)\n\n def predict(self, X):\n N = len(X)\n Yhat = np.empty(N)\n for i in xrange(N):\n Yhat[i] = self.predict_op(X[i])\n return Yhat\n\n# we need to skip the 3 footer rows\n# skipfooter does not work with the default engine, 'c'\n# so we need to explicitly set it to 'python'\ndf = pd.read_csv('international-airline-passengers.csv', engine='python', skipfooter=3)\n\n# rename the columns because they are ridiculous\ndf.columns = ['month', 'num_passengers']\n\n# plot the data so we know what it looks like\n# plt.plot(df.num_passengers)\n# plt.show()\n\n# let's try with only the time series itself\nseries = df.num_passengers.as_matrix()\n# series = (series - series.mean()) / series.std() # normalize the values so they have mean 0 and variance 1\nseries = series.astype(np.float32)\nseries = series - series.min()\nseries = series / series.max()\n\n# let's see if we can use D past values to predict the next value\nN = len(series)\nfor D in (2,3,4,5):\n n = N - D\n X = np.zeros((n, D))\n for d in xrange(D):\n X[:,d] = series[d:d+n]\n Y = series[D:D+n]\n\n print \"series length:\", n\n Xtrain = X[:n/2]\n Ytrain = Y[:n/2]\n Xtest = X[n/2:]\n Ytest = Y[n/2:]\n\n Ntrain = len(Xtrain)\n Xtrain = Xtrain.reshape(Ntrain, D, 1)\n Ntest = len(Xtest)\n Xtest = Xtest.reshape(Ntest, D, 1)\n\n model = RNN([50])\n model.fit(Xtrain, Ytrain, activation=T.tanh)\n print \"train score:\", model.score(Xtrain, Ytrain)\n print \"test score:\", model.score(Xtest, Ytest)\n\n # plot the prediction with true values\n plt.plot(series)\n\n train_series = np.empty(n)\n train_series[:n/2] = model.predict(Xtrain) \n train_series[n/2:] = np.nan\n # prepend d nan's since the train series is only of size N - D\n plt.plot(np.concatenate([np.full(d, np.nan), train_series]))\n\n test_series = np.empty(n)\n test_series[:n/2] = np.nan\n test_series[n/2:] = model.predict(Xtest)\n plt.plot(np.concatenate([np.full(d, np.nan), test_series]))\n\n plt.show()\n" }, { "path": "ann_class/backprop.py", "content": "# backpropagation example for deep learning in python class.\n# with sigmoid activation\n#\n# the notes for this class can be found at: \n# https://deeplearningcourses.com/c/data-science-deep-learning-in-python\n# https://www.udemy.com/data-science-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nnp.random.seed(1)\n\ndef forward(X, W1, b1, W2, b2):\n Z = 1 / (1 + np.exp(-X.dot(W1) - b1))\n A = Z.dot(W2) + b2\n expA = np.exp(A)\n Y = expA / expA.sum(axis=1, keepdims=True)\n return Y, Z\n\n\n# determine the classification rate\n# num correct / num total\ndef classification_rate(Y, P):\n n_correct = 0\n n_total = 0\n for i in range(len(Y)):\n n_total += 1\n if Y[i] == P[i]:\n n_correct += 1\n return float(n_correct) / n_total\n\n\ndef derivative_w2(Z, T, Y):\n N, K = T.shape\n M = Z.shape[1] # H is (N, M)\n\n # # slow\n # ret1 = np.zeros((M, K))\n # for n in xrange(N):\n # for m in xrange(M):\n # for k in xrange(K):\n # ret1[m,k] += (T[n,k] - Y[n,k])*Z[n,m]\n\n # # a bit faster - let's not loop over m\n # ret2 = np.zeros((M, K))\n # for n in xrange(N):\n # for k in xrange(K):\n # ret2[:,k] += (T[n,k]* - Y[n,k])*Z[n,:]\n\n # assert(np.abs(ret1 - ret2).sum() < 0.00001)\n\n # # even faster - let's not loop over k either\n # ret3 = np.zeros((M, K))\n # for n in xrange(N): # slow way first\n # ret3 += np.outer( Z[n], T[n] - Y[n] )\n\n # assert(np.abs(ret1 - ret3).sum() < 0.00001)\n\n # fastest - let's not loop over anything\n ret4 = Z.T.dot(T - Y)\n # assert(np.abs(ret1 - ret4).sum() < 0.00001)\n\n return ret4\n\n\ndef derivative_w1(X, Z, T, Y, W2):\n N, D = X.shape\n M, K = W2.shape\n\n # slow way first\n # ret1 = np.zeros((X.shape[1], M))\n # for n in xrange(N):\n # for k in xrange(K):\n # for m in xrange(M):\n # for d in xrange(D):\n # ret1[d,m] += (T[n,k] - Y[n,k])*W2[m,k]*Z[n,m]*(1 - Z[n,m])*X[n,d]\n\n # fastest\n dZ = (T - Y).dot(W2.T) * Z * (1 - Z)\n ret2 = X.T.dot(dZ)\n\n # assert(np.abs(ret1 - ret2).sum() < 0.00001)\n\n return ret2\n\n\ndef derivative_b2(T, Y):\n return (T - Y).sum(axis=0)\n\n\ndef derivative_b1(T, Y, W2, Z):\n return ((T - Y).dot(W2.T) * Z * (1 - Z)).sum(axis=0)\n\n\ndef cost(T, Y):\n tot = T * np.log(Y)\n return tot.sum()\n\n\ndef main():\n # create the data\n Nclass = 500\n D = 2 # dimensionality of input\n M = 3 # hidden layer size\n K = 3 # number of classes\n\n X1 = np.random.randn(Nclass, D) + np.array([0, -2])\n X2 = np.random.randn(Nclass, D) + np.array([2, 2])\n X3 = np.random.randn(Nclass, D) + np.array([-2, 2])\n X = np.vstack([X1, X2, X3])\n\n Y = np.array([0]*Nclass + [1]*Nclass + [2]*Nclass)\n N = len(Y)\n # turn Y into an indicator matrix for training\n T = np.zeros((N, K))\n for i in range(N):\n T[i, Y[i]] = 1\n\n # let's see what it looks like\n plt.scatter(X[:,0], X[:,1], c=Y, s=100, alpha=0.5)\n plt.show()\n\n # randomly initialize weights\n W1 = np.random.randn(D, M)\n b1 = np.random.randn(M)\n W2 = np.random.randn(M, K)\n b2 = np.random.randn(K)\n\n learning_rate = 1e-3\n costs = []\n for epoch in range(1000):\n output, hidden = forward(X, W1, b1, W2, b2)\n if epoch % 100 == 0:\n c = cost(T, output)\n P = np.argmax(output, axis=1)\n r = classification_rate(Y, P)\n print(\"cost:\", c, \"classification_rate:\", r)\n costs.append(c)\n\n # this is gradient ASCENT, not DESCENT\n # be comfortable with both!\n # oldW2 = W2.copy()\n\n gW2 = derivative_w2(hidden, T, output)\n gb2 = derivative_b2(T, output)\n gW1 = derivative_w1(X, hidden, T, output, W2)\n gb1 = derivative_b1(T, output, W2, hidden)\n\n W2 += learning_rate * gW2\n b2 += learning_rate * gb2\n W1 += learning_rate * gW1\n b1 += learning_rate * gb1\n\n plt.plot(costs)\n plt.show()\n\n\nif __name__ == '__main__':\n main()\n\n" }, { "path": "ann_class/extra_reading.txt", "content": "The Chain Rule of Calculus\nhttp://tutorial.math.lamar.edu/Classes/CalcI/ChainRule.aspx\n\nYes you should understand backprop by Andrej Karpathy\nhttps://medium.com/@karpathy/yes-you-should-understand-backprop-e2f06eab496b\n\nThe Matrix Cookbook\nhttps://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf\n\nRumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986) Learning representations by back-propagating errors.\nhttps://www.iro.umontreal.ca/~vincentp/ift3395/lectures/backprop_old.pdf" }, { "path": "ann_class/forwardprop.py", "content": "# forward propagation example for deep learning in python class.\n#\n# the notes for this class can be found at: \n# https://deeplearningcourses.com/c/data-science-deep-learning-in-python\n# https://www.udemy.com/data-science-deep-learning-in-python\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nNclass = 500\n\nX1 = np.random.randn(Nclass, 2) + np.array([0, -2])\nX2 = np.random.randn(Nclass, 2) + np.array([2, 2])\nX3 = np.random.randn(Nclass, 2) + np.array([-2, 2])\nX = np.vstack([X1, X2, X3])\n\nY = np.array([0]*Nclass + [1]*Nclass + [2]*Nclass)\n\n# let's see what it looks like\nplt.scatter(X[:,0], X[:,1], c=Y, s=100, alpha=0.5)\nplt.show()\n\n# randomly initialize weights\nD = 2 # dimensionality of input\nM = 3 # hidden layer size\nK = 3 # number of classes\nW1 = np.random.randn(D, M)\nb1 = np.random.randn(M)\nW2 = np.random.randn(M, K)\nb2 = np.random.randn(K)\n\ndef sigmoid(a):\n return 1 / (1 + np.exp(-a))\n\ndef forward(X, W1, b1, W2, b2):\n Z = sigmoid(X.dot(W1) + b1) # sigmoid\n # Z = np.tanh(X.dot(W1) + b1) # tanh\n # Z = np.maximum(X.dot(W1) + b1, 0) # relu\n A = Z.dot(W2) + b2\n expA = np.exp(A)\n Y = expA / expA.sum(axis=1, keepdims=True)\n return Y\n\n# determine the classification rate\n# num correct / num total\ndef classification_rate(Y, P):\n n_correct = 0\n n_total = 0\n for i in range(len(Y)):\n n_total += 1\n if Y[i] == P[i]:\n n_correct += 1\n return float(n_correct) / n_total\n\nP_Y_given_X = forward(X, W1, b1, W2, b2)\nP = np.argmax(P_Y_given_X, axis=1)\n\n# verify we chose the correct axis\nassert(len(P) == len(Y))\n\nprint(\"Classification rate for randomly chosen weights:\", classification_rate(Y, P))\n\n" }, { "path": "ann_class/regression.py", "content": "from __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\n# NOTE: some people using the default Python\n# installation on Mac have had trouble with Axes3D\n# Switching to Python 3 (brew install python3) or\n# using Linux are both viable work-arounds\n\n\n\n\n\n# generate and plot the data\nN = 500\nX = np.random.random((N, 2))*4 - 2 # in between (-2, +2)\nY = X[:,0]*X[:,1] # makes a saddle shape\n# note: in this script \"Y\" will be the target,\n# \"Yhat\" will be prediction\n\nfig = plt.figure()\nax = fig.add_subplot(111, projection='3d')\nax.scatter(X[:,0], X[:,1], Y)\nplt.show()\n\n\n\n\n\n# make a neural network and train it\nD = 2\nM = 100 # number of hidden units\n\n# layer 1\nW = np.random.randn(D, M) / np.sqrt(D)\nb = np.zeros(M)\n\n# layer 2\nV = np.random.randn(M) / np.sqrt(M)\nc = 0\n\n\n\n\n# how to get the output\n# consider the params global\ndef forward(X):\n Z = X.dot(W) + b\n Z = Z * (Z > 0) # relu\n # Z = np.tanh(Z)\n\n Yhat = Z.dot(V) + c\n return Z, Yhat\n\n\n\n\n# how to train the params\ndef derivative_V(Z, Y, Yhat):\n return (Y - Yhat).dot(Z)\n\ndef derivative_c(Y, Yhat):\n return (Y - Yhat).sum()\n\ndef derivative_W(X, Z, Y, Yhat, V):\n # dZ = np.outer(Y - Yhat, V) * (1 - Z * Z) # this is for tanh activation\n dZ = np.outer(Y - Yhat, V) * (Z > 0) # relu\n return X.T.dot(dZ)\n\ndef derivative_b(Z, Y, Yhat, V):\n # dZ = np.outer(Y - Yhat, V) * (1 - Z * Z) # this is for tanh activation\n dZ = np.outer(Y - Yhat, V) * (Z > 0) # this is for relu activation\n return dZ.sum(axis=0)\n\ndef update(X, Z, Y, Yhat, W, b, V, c, learning_rate=1e-4):\n gV = derivative_V(Z, Y, Yhat)\n gc = derivative_c(Y, Yhat)\n gW = derivative_W(X, Z, Y, Yhat, V)\n gb = derivative_b(Z, Y, Yhat, V)\n\n V += learning_rate*gV\n c += learning_rate*gc\n W += learning_rate*gW\n b += learning_rate*gb\n\n return W, b, V, c\n\n\n\n\n# so we can plot the costs later\ndef get_cost(Y, Yhat):\n return ((Y - Yhat)**2).mean()\n\n\n\n# run a training loop\n# plot the costs\n# and plot the final result\ncosts = []\nfor i in range(200):\n Z, Yhat = forward(X)\n W, b, V, c = update(X, Z, Y, Yhat, W, b, V, c)\n cost = get_cost(Y, Yhat)\n costs.append(cost)\n if i % 25 == 0:\n print(cost)\n\n# plot the costs\nplt.plot(costs)\nplt.show()\n\n# plot the prediction with the data\nfig = plt.figure()\nax = fig.add_subplot(111, projection='3d')\nax.scatter(X[:,0], X[:,1], Y)\n\n# surface plot\nline = np.linspace(-2, 2, 20)\nxx, yy = np.meshgrid(line, line)\nXgrid = np.vstack((xx.flatten(), yy.flatten())).T\n_, Yhat = forward(Xgrid)\nax.plot_trisurf(Xgrid[:,0], Xgrid[:,1], Yhat, linewidth=0.2, antialiased=True)\nplt.show()\n\n\n\n\n# plot magnitude of residuals\nYgrid = Xgrid[:,0]*Xgrid[:,1]\nR = np.abs(Ygrid - Yhat)\n\nplt.scatter(Xgrid[:,0], Xgrid[:,1], c=R)\nplt.show()\n\nfig = plt.figure()\nax = fig.add_subplot(111, projection='3d')\nax.plot_trisurf(Xgrid[:,0], Xgrid[:,1], R, linewidth=0.2, antialiased=True)\nplt.show()\n\n\n" }, { "path": "ann_class/sklearn_ann.py", "content": "# Train a neural network in just 3 lines of code!\n#\n# the notes for this class can be found at: \n# https://deeplearningcourses.com/c/data-science-deep-learning-in-python\n# https://www.udemy.com/data-science-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport sys\nsys.path.append('../ann_logistic_extra')\nfrom process import get_data\n\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.utils import shuffle\n\n# get the data\nXtrain, Ytrain, Xtest, Ytest = get_data()\n\n# create the neural network\nmodel = MLPClassifier(hidden_layer_sizes=(20, 20), max_iter=2000)\n\n# train the neural network\nmodel.fit(Xtrain, Ytrain)\n\n# print the train and test accuracy\ntrain_accuracy = model.score(Xtrain, Ytrain)\ntest_accuracy = model.score(Xtest, Ytest)\nprint(\"train accuracy:\", train_accuracy, \"test accuracy:\", test_accuracy)\n" }, { "path": "ann_class/tf_example.py", "content": "# neural network in TensorFlow very simple example.\n#\n# the notes for this class can be found at: \n# https://deeplearningcourses.com/c/data-science-deep-learning-in-python\n# https://www.udemy.com/data-science-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\n# create random training data again\nNclass = 500\nD = 2 # dimensionality of input\nM = 3 # hidden layer size\nK = 3 # number of classes\n\nX1 = np.random.randn(Nclass, D) + np.array([0, -2])\nX2 = np.random.randn(Nclass, D) + np.array([2, 2])\nX3 = np.random.randn(Nclass, D) + np.array([-2, 2])\nX = np.vstack([X1, X2, X3]).astype(np.float32)\n\nY = np.array([0]*Nclass + [1]*Nclass + [2]*Nclass)\n\n# let's see what it looks like\nplt.scatter(X[:,0], X[:,1], c=Y, s=100, alpha=0.5)\nplt.show()\n\nN = len(Y)\n# turn Y into an indicator matrix for training\nT = np.zeros((N, K))\nfor i in range(N):\n T[i, Y[i]] = 1\n\n\n# tensor flow variables are not the same as regular Python variables\ndef init_weights(shape):\n return tf.Variable(tf.random_normal(shape, stddev=0.01))\n\n\ndef forward(X, W1, b1, W2, b2):\n Z = tf.nn.sigmoid(tf.matmul(X, W1) + b1)\n return tf.matmul(Z, W2) + b2\n\n\ntfX = tf.placeholder(tf.float32, [None, D])\ntfY = tf.placeholder(tf.float32, [None, K])\n\nW1 = init_weights([D, M]) # create symbolic variables\nb1 = init_weights([M])\nW2 = init_weights([M, K])\nb2 = init_weights([K])\n\nlogits = forward(tfX, W1, b1, W2, b2)\n\ncost = tf.reduce_mean(\n tf.nn.softmax_cross_entropy_with_logits_v2(\n labels=tfY,\n logits=logits\n )\n) # compute costs\n# WARNING: This op expects unscaled logits,\n# since it performs a softmax on logits\n# internally for efficiency.\n# Do not call this op with the output of softmax,\n# as it will produce incorrect results.\n\ntrain_op = tf.train.GradientDescentOptimizer(0.05).minimize(cost) # construct an optimizer\n# input parameter is the learning rate\n\npredict_op = tf.argmax(logits, 1)\n# input parameter is the axis on which to choose the max\n\n# just stuff that has to be done\nsess = tf.Session()\ninit = tf.global_variables_initializer()\nsess.run(init)\n\nfor i in range(1000):\n sess.run(train_op, feed_dict={tfX: X, tfY: T})\n pred = sess.run(predict_op, feed_dict={tfX: X, tfY: T})\n if i % 100 == 0:\n print(\"Accuracy:\", np.mean(Y == pred))\n\n" }, { "path": "ann_class/xor_donut.py", "content": "# revisiting the XOR and donut problems to show how features\n# can be learned automatically using neural networks.\n#\n# the notes for this class can be found at: \n# https://deeplearningcourses.com/c/data-science-deep-learning-in-python\n# https://www.udemy.com/data-science-deep-learning-in-python\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# for binary classification! no softmax here\n\ndef forward(X, W1, b1, W2, b2):\n # sigmoid\n # Z = 1 / (1 + np.exp( -(X.dot(W1) + b1) ))\n\n # tanh\n # Z = np.tanh(X.dot(W1) + b1)\n\n # relu\n Z = X.dot(W1) + b1\n Z = Z * (Z > 0)\n\n activation = Z.dot(W2) + b2\n Y = 1 / (1 + np.exp(-activation))\n return Y, Z\n\n\ndef predict(X, W1, b1, W2, b2):\n Y, _ = forward(X, W1, b1, W2, b2)\n return np.round(Y)\n\n\ndef derivative_w2(Z, T, Y):\n # Z is (N, M)\n return (T - Y).dot(Z)\n\ndef derivative_b2(T, Y):\n return (T - Y).sum()\n\n\ndef derivative_w1(X, Z, T, Y, W2):\n # dZ = np.outer(T-Y, W2) * Z * (1 - Z) # this is for sigmoid activation\n # dZ = np.outer(T-Y, W2) * (1 - Z * Z) # this is for tanh activation\n dZ = np.outer(T-Y, W2) * (Z > 0) # this is for relu activation\n return X.T.dot(dZ)\n\n\ndef derivative_b1(Z, T, Y, W2):\n # dZ = np.outer(T-Y, W2) * Z * (1 - Z) # this is for sigmoid activation\n # dZ = np.outer(T-Y, W2) * (1 - Z * Z) # this is for tanh activation\n dZ = np.outer(T-Y, W2) * (Z > 0) # this is for relu activation\n return dZ.sum(axis=0)\n\n\ndef get_log_likelihood(T, Y):\n return np.sum(T*np.log(Y) + (1-T)*np.log(1-Y))\n\n\n\ndef test_xor():\n X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n Y = np.array([0, 1, 1, 0])\n W1 = np.random.randn(2, 5)\n b1 = np.zeros(5)\n W2 = np.random.randn(5)\n b2 = 0\n LL = [] # keep track of log-likelihoods\n learning_rate = 1e-2\n regularization = 0.\n last_error_rate = None\n for i in range(30000):\n pY, Z = forward(X, W1, b1, W2, b2)\n ll = get_log_likelihood(Y, pY)\n prediction = predict(X, W1, b1, W2, b2)\n er = np.mean(prediction != Y)\n\n LL.append(ll)\n\n # get gradients\n gW2 = derivative_w2(Z, Y, pY)\n gb2 = derivative_b2(Y, pY)\n gW1 = derivative_w1(X, Z, Y, pY, W2)\n gb1 = derivative_b1(Z, Y, pY, W2)\n\n W2 += learning_rate * (gW2 - regularization * W2)\n b2 += learning_rate * (gb2 - regularization * b2)\n W1 += learning_rate * (gW1 - regularization * W1)\n b1 += learning_rate * (gb1 - regularization * b1)\n if i % 1000 == 0:\n print(ll)\n\n print(\"final classification rate:\", np.mean(prediction == Y))\n plt.plot(LL)\n plt.show()\n\n\ndef test_donut():\n # donut example\n N = 1000\n R_inner = 5\n R_outer = 10\n\n # distance from origin is radius + random normal\n # angle theta is uniformly distributed between (0, 2pi)\n R1 = np.random.randn(N//2) + R_inner\n theta = 2*np.pi*np.random.random(N//2)\n X_inner = np.concatenate([[R1 * np.cos(theta)], [R1 * np.sin(theta)]]).T\n\n R2 = np.random.randn(N//2) + R_outer\n theta = 2*np.pi*np.random.random(N//2)\n X_outer = np.concatenate([[R2 * np.cos(theta)], [R2 * np.sin(theta)]]).T\n\n X = np.concatenate([ X_inner, X_outer ])\n Y = np.array([0]*(N//2) + [1]*(N//2))\n\n n_hidden = 8\n W1 = np.random.randn(2, n_hidden)\n b1 = np.random.randn(n_hidden)\n W2 = np.random.randn(n_hidden)\n b2 = np.random.randn(1)\n LL = [] # keep track of log-likelihoods\n learning_rate = 0.00005\n regularization = 0.2\n last_error_rate = None\n for i in range(3000):\n pY, Z = forward(X, W1, b1, W2, b2)\n ll = get_log_likelihood(Y, pY)\n prediction = predict(X, W1, b1, W2, b2)\n er = np.abs(prediction - Y).mean()\n LL.append(ll)\n\n # get gradients\n gW2 = derivative_w2(Z, Y, pY)\n gb2 = derivative_b2(Y, pY)\n gW1 = derivative_w1(X, Z, Y, pY, W2)\n gb1 = derivative_b1(Z, Y, pY, W2)\n\n W2 += learning_rate * (gW2 - regularization * W2)\n b2 += learning_rate * (gb2 - regularization * b2)\n W1 += learning_rate * (gW1 - regularization * W1)\n b1 += learning_rate * (gb1 - regularization * b1)\n if i % 300 == 0:\n print(\"i:\", i, \"ll:\", ll, \"classification rate:\", 1 - er)\n plt.plot(LL)\n plt.show()\n\n\nif __name__ == '__main__':\n test_xor()\n # test_donut()\n\n \n\n\n" }, { "path": "ann_class2/__init__.py", "content": "" }, { "path": "ann_class2/adam.py", "content": "# Compare RMSprop with momentum vs. Adam\n# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow\n# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nfrom sklearn.utils import shuffle\nimport matplotlib.pyplot as plt\n\nfrom util import get_normalized_data, error_rate, cost, y2indicator\nfrom mlp import forward, derivative_w2, derivative_w1, derivative_b2, derivative_b1\n\n\ndef main():\n max_iter = 10\n print_period = 10\n\n Xtrain, Xtest, Ytrain, Ytest = get_normalized_data()\n reg = 0.01\n\n Ytrain_ind = y2indicator(Ytrain)\n Ytest_ind = y2indicator(Ytest)\n\n N, D = Xtrain.shape\n batch_sz = 500\n n_batches = N // batch_sz\n\n M = 300\n K = 10\n W1_0 = np.random.randn(D, M) / np.sqrt(D)\n b1_0 = np.zeros(M)\n W2_0 = np.random.randn(M, K) / np.sqrt(M)\n b2_0 = np.zeros(K)\n\n W1 = W1_0.copy()\n b1 = b1_0.copy()\n W2 = W2_0.copy()\n b2 = b2_0.copy()\n\n # 1st moment\n mW1 = 0\n mb1 = 0\n mW2 = 0\n mb2 = 0\n\n # 2nd moment\n vW1 = 0\n vb1 = 0\n vW2 = 0\n vb2 = 0\n\n # hyperparams\n lr0 = 0.001\n beta1 = 0.9\n beta2 = 0.999\n eps = 1e-8\n\n # 1. Adam\n loss_adam = []\n err_adam = []\n t = 1\n for i in range(max_iter):\n for j in range(n_batches):\n Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),]\n Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),]\n pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)\n\n # updates\n # gradients\n gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg*W2\n gb2 = derivative_b2(Ybatch, pYbatch) + reg*b2\n gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1\n gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1\n\n # new m\n mW1 = beta1 * mW1 + (1 - beta1) * gW1\n mb1 = beta1 * mb1 + (1 - beta1) * gb1\n mW2 = beta1 * mW2 + (1 - beta1) * gW2\n mb2 = beta1 * mb2 + (1 - beta1) * gb2\n\n # new v\n vW1 = beta2 * vW1 + (1 - beta2) * gW1 * gW1\n vb1 = beta2 * vb1 + (1 - beta2) * gb1 * gb1\n vW2 = beta2 * vW2 + (1 - beta2) * gW2 * gW2\n vb2 = beta2 * vb2 + (1 - beta2) * gb2 * gb2\n\n # bias correction\n correction1 = 1 - beta1 ** t\n hat_mW1 = mW1 / correction1\n hat_mb1 = mb1 / correction1\n hat_mW2 = mW2 / correction1\n hat_mb2 = mb2 / correction1\n\n correction2 = 1 - beta2 ** t\n hat_vW1 = vW1 / correction2\n hat_vb1 = vb1 / correction2\n hat_vW2 = vW2 / correction2\n hat_vb2 = vb2 / correction2\n\n # update t\n t += 1\n\n # apply updates to the params\n W1 = W1 - lr0 * hat_mW1 / (np.sqrt(hat_vW1) + eps)\n b1 = b1 - lr0 * hat_mb1 / (np.sqrt(hat_vb1) + eps)\n W2 = W2 - lr0 * hat_mW2 / (np.sqrt(hat_vW2) + eps)\n b2 = b2 - lr0 * hat_mb2 / (np.sqrt(hat_vb2) + eps)\n\n\n if j % print_period == 0:\n pY, _ = forward(Xtest, W1, b1, W2, b2)\n l = cost(pY, Ytest_ind)\n loss_adam.append(l)\n print(\"Cost at iteration i=%d, j=%d: %.6f\" % (i, j, l))\n\n err = error_rate(pY, Ytest)\n err_adam.append(err)\n print(\"Error rate:\", err)\n\n pY, _ = forward(Xtest, W1, b1, W2, b2)\n print(\"Final error rate:\", error_rate(pY, Ytest))\n\n\n # 2. RMSprop with momentum\n W1 = W1_0.copy()\n b1 = b1_0.copy()\n W2 = W2_0.copy()\n b2 = b2_0.copy()\n loss_rms = []\n err_rms = []\n\n # comparable hyperparameters for fair comparison\n lr0 = 0.001\n mu = 0.9\n decay_rate = 0.999\n eps = 1e-8\n\n # rmsprop cache\n cache_W2 = 1\n cache_b2 = 1\n cache_W1 = 1\n cache_b1 = 1\n\n # momentum\n dW1 = 0\n db1 = 0\n dW2 = 0\n db2 = 0\n \n for i in range(max_iter):\n for j in range(n_batches):\n Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),]\n Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),]\n pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)\n\n # derivatives\n gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg*W2\n gb2 = derivative_b2(Ybatch, pYbatch) + reg*b2\n gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1\n gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1\n\n # caches\n cache_W2 = decay_rate*cache_W2 + (1 - decay_rate)*gW2*gW2\n cache_b2 = decay_rate*cache_b2 + (1 - decay_rate)*gb2*gb2\n cache_W1 = decay_rate*cache_W1 + (1 - decay_rate)*gW1*gW1\n cache_b1 = decay_rate*cache_b1 + (1 - decay_rate)*gb1*gb1\n\n # momentum\n dW2 = mu * dW2 + (1 - mu) * lr0 * gW2 / (np.sqrt(cache_W2) + eps)\n db2 = mu * db2 + (1 - mu) * lr0 * gb2 / (np.sqrt(cache_b2) + eps)\n dW1 = mu * dW1 + (1 - mu) * lr0 * gW1 / (np.sqrt(cache_W1) + eps)\n db1 = mu * db1 + (1 - mu) * lr0 * gb1 / (np.sqrt(cache_b1) + eps)\n\n # updates\n W2 -= dW2\n b2 -= db2\n W1 -= dW1\n b1 -= db1\n\n if j % print_period == 0:\n pY, _ = forward(Xtest, W1, b1, W2, b2)\n l = cost(pY, Ytest_ind)\n loss_rms.append(l)\n print(\"Cost at iteration i=%d, j=%d: %.6f\" % (i, j, l))\n\n err = error_rate(pY, Ytest)\n err_rms.append(err)\n print(\"Error rate:\", err)\n\n pY, _ = forward(Xtest, W1, b1, W2, b2)\n print(\"Final error rate:\", error_rate(pY, Ytest))\n\n plt.plot(loss_adam, label='adam')\n plt.plot(loss_rms, label='rmsprop')\n plt.legend()\n plt.show()\n\n\nif __name__ == '__main__':\n main()" }, { "path": "ann_class2/batch_norm_tf.py", "content": "from __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nfrom sklearn.utils import shuffle\nfrom sklearn.model_selection import train_test_split\nfrom util import get_normalized_data\n\n\ndef init_weight(M1, M2):\n return np.random.randn(M1, M2) * np.sqrt(2.0 / M1)\n\n\nclass HiddenLayerBatchNorm(object):\n def __init__(self, M1, M2, f):\n self.M1 = M1\n self.M2 = M2\n self.f = f\n\n W = init_weight(M1, M2).astype(np.float32)\n gamma = np.ones(M2).astype(np.float32)\n beta = np.zeros(M2).astype(np.float32)\n\n self.W = tf.Variable(W)\n self.gamma = tf.Variable(gamma)\n self.beta = tf.Variable(beta)\n\n # for test time\n self.running_mean = tf.Variable(np.zeros(M2).astype(np.float32), trainable=False)\n self.running_var = tf.Variable(np.zeros(M2).astype(np.float32), trainable=False)\n\n def forward(self, X, is_training, decay=0.9):\n activation = tf.matmul(X, self.W)\n if is_training:\n batch_mean, batch_var = tf.nn.moments(activation, [0])\n update_running_mean = tf.assign(\n self.running_mean,\n self.running_mean * decay + batch_mean * (1 - decay)\n )\n update_running_var = tf.assign(\n self.running_var,\n self.running_var * decay + batch_var * (1 - decay)\n )\n \n with tf.control_dependencies([update_running_mean, update_running_var]):\n out = tf.nn.batch_normalization(\n activation,\n batch_mean,\n batch_var,\n self.beta,\n self.gamma,\n 1e-4\n )\n else:\n out = tf.nn.batch_normalization(\n activation,\n self.running_mean,\n self.running_var,\n self.beta,\n self.gamma,\n 1e-4\n )\n return self.f(out)\n\n\nclass HiddenLayer(object):\n def __init__(self, M1, M2, f):\n self.M1 = M1\n self.M2 = M2\n self.f = f\n W = np.random.randn(M1, M2) * np.sqrt(2.0 / M1)\n b = np.zeros(M2)\n self.W = tf.Variable(W.astype(np.float32))\n self.b = tf.Variable(b.astype(np.float32))\n\n def forward(self, X):\n return self.f(tf.matmul(X, self.W) + self.b)\n\n\nclass ANN(object):\n def __init__(self, hidden_layer_sizes):\n self.hidden_layer_sizes = hidden_layer_sizes\n\n def set_session(self, session):\n self.session = session\n\n def fit(self, X, Y, Xtest, Ytest, activation=tf.nn.relu, learning_rate=1e-2, epochs=15, batch_sz=100, print_period=100, show_fig=True):\n X = X.astype(np.float32)\n Y = Y.astype(np.int32)\n\n # initialize hidden layers\n N, D = X.shape\n self.layers = []\n M1 = D\n for M2 in self.hidden_layer_sizes:\n h = HiddenLayerBatchNorm(M1, M2, activation)\n self.layers.append(h)\n M1 = M2\n \n # final layer\n K = len(set(Y))\n h = HiddenLayer(M1, K, lambda x: x)\n self.layers.append(h)\n\n if batch_sz is None:\n batch_sz = N\n\n\n # note! we will need to build the output differently\n # for train and test (prediction)\n\n # set up theano functions and variables\n tfX = tf.placeholder(tf.float32, shape=(None, D), name='X')\n tfY = tf.placeholder(tf.int32, shape=(None,), name='Y')\n\n # for later use\n self.tfX = tfX\n\n # for training\n logits = self.forward(tfX, is_training=True)\n cost = tf.reduce_mean(\n tf.nn.sparse_softmax_cross_entropy_with_logits(\n logits=logits,\n labels=tfY\n )\n )\n # train_op = tf.train.AdamOptimizer(learning_rate).minimize(cost)\n # train_op = tf.train.RMSPropOptimizer(learning_rate, decay=0.99, momentum=0.9).minimize(cost)\n train_op = tf.train.MomentumOptimizer(learning_rate, momentum=0.9, use_nesterov=True).minimize(cost)\n # train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\n\n # for testing\n test_logits = self.forward(tfX, is_training=False)\n self.predict_op = tf.argmax(test_logits, 1)\n\n # accuracy = tf.reduce_mean(1.0*(tfY == tf.argmax(logits, 1)))\n\n # init the variables\n self.session.run(tf.global_variables_initializer())\n\n n_batches = N // batch_sz\n costs = []\n for i in range(epochs):\n if n_batches > 1:\n X, Y = shuffle(X, Y)\n for j in range(n_batches):\n Xbatch = X[j*batch_sz:(j*batch_sz+batch_sz)]\n Ybatch = Y[j*batch_sz:(j*batch_sz+batch_sz)]\n\n c, _, lgts = self.session.run([cost, train_op, logits], feed_dict={tfX: Xbatch, tfY: Ybatch})\n costs.append(c)\n if (j+1) % print_period == 0:\n acc = np.mean(Ybatch == np.argmax(lgts, axis=1))\n print(\"epoch:\", i, \"batch:\", j, \"n_batches:\", n_batches, \"cost:\", c, \"acc: %.2f\" % acc)\n # print('dbg:', self.session.run(self.layers[0].running_mean).sum())\n\n print(\"Train acc:\", self.score(X, Y), \"Test acc:\", self.score(Xtest, Ytest))\n \n if show_fig:\n plt.plot(costs)\n plt.show()\n\n def forward(self, X, is_training):\n out = X\n for h in self.layers[:-1]:\n out = h.forward(out, is_training)\n out = self.layers[-1].forward(out)\n return out\n\n def score(self, X, Y):\n P = self.predict(X)\n return np.mean(Y == P)\n\n def predict(self, X):\n return self.session.run(self.predict_op, feed_dict={self.tfX: X})\n\n\n\ndef main():\n # step 1: get the data and define all the usual variables\n Xtrain, Xtest, Ytrain, Ytest = get_normalized_data()\n\n ann = ANN([500, 300])\n\n session = tf.InteractiveSession()\n ann.set_session(session)\n\n ann.fit(Xtrain, Ytrain, Xtest, Ytest, show_fig=True)\n\n print(\"Train accuracy:\", ann.score(Xtrain, Ytrain))\n print(\"Test accuracy:\", ann.score(Xtest, Ytest))\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "ann_class2/batch_norm_theano.py", "content": "from __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport theano\nimport theano.tensor as T\nfrom theano.tensor.nnet.bn import batch_normalization_train, batch_normalization_test\nfrom sklearn.utils import shuffle\nfrom sklearn.model_selection import train_test_split\nfrom util import get_normalized_data\n\n\ndef init_weight(M1, M2):\n return np.random.randn(M1, M2) * np.sqrt(2.0 / M1)\n\n\nclass HiddenLayerBatchNorm(object):\n def __init__(self, M1, M2, f):\n self.M1 = M1\n self.M2 = M2\n self.f = f\n\n W = init_weight(M1, M2)\n gamma = np.ones(M2)\n beta = np.zeros(M2)\n\n self.W = theano.shared(W)\n self.gamma = theano.shared(gamma)\n self.beta = theano.shared(beta)\n\n self.params = [self.W, self.gamma, self.beta]\n\n # for test time\n # self.running_mean = T.zeros(M2)\n # self.running_var = T.zeros(M2)\n self.running_mean = theano.shared(np.zeros(M2))\n self.running_var = theano.shared(np.zeros(M2))\n\n def forward(self, X, is_training):\n activation = X.dot(self.W)\n if is_training:\n # returns:\n # batch-normalized output\n # batch mean\n # batch variance\n # running mean (for later use as population mean estimate)\n # running var (for later use as population var estimate)\n out, batch_mean, batch_invstd, new_running_mean, new_running_var = batch_normalization_train(\n activation,\n self.gamma,\n self.beta,\n running_mean=self.running_mean,\n running_var=self.running_var,\n )\n\n self.running_update = [\n (self.running_mean, new_running_mean),\n (self.running_var, new_running_var),\n ]\n\n # if you don't trust the built-in bn function\n # batch_var = 1 / (batch_invstd * batch_invstd)\n # self.running_update = [\n # (self.running_mean, 0.9*self.running_mean + 0.1*batch_mean),\n # (self.running_var, 0.9*self.running_var + 0.1*batch_var),\n # ]\n\n else:\n out = batch_normalization_test(\n activation,\n self.gamma,\n self.beta,\n self.running_mean,\n self.running_var\n )\n return self.f(out)\n\n\nclass HiddenLayer(object):\n def __init__(self, M1, M2, f):\n self.M1 = M1\n self.M2 = M2\n self.f = f\n W = init_weight(M1, M2)\n b = np.zeros(M2)\n self.W = theano.shared(W)\n self.b = theano.shared(b)\n self.params = [self.W, self.b]\n\n def forward(self, X):\n return self.f(X.dot(self.W) + self.b)\n\n\ndef momentum_updates(cost, params, lr, mu):\n grads = T.grad(cost, params)\n updates = []\n\n for p, g in zip(params, grads):\n dp = theano.shared(p.get_value() * 0)\n new_dp = mu*dp - lr*g\n new_p = p + new_dp\n updates.append((dp, new_dp))\n updates.append((p, new_p))\n return updates\n\n\nclass ANN(object):\n def __init__(self, hidden_layer_sizes):\n self.hidden_layer_sizes = hidden_layer_sizes\n\n def fit(self, X, Y, Xtest, Ytest, activation=T.nnet.relu, learning_rate=1e-2, mu=0.9, epochs=15, batch_sz=100, print_period=100, show_fig=True):\n X = X.astype(np.float32)\n Y = Y.astype(np.int32)\n\n # initialize hidden layers\n N, D = X.shape\n self.layers = []\n M1 = D\n for M2 in self.hidden_layer_sizes:\n h = HiddenLayerBatchNorm(M1, M2, activation)\n self.layers.append(h)\n M1 = M2\n \n # final layer\n K = len(set(Y))\n h = HiddenLayer(M1, K, T.nnet.softmax)\n self.layers.append(h)\n\n if batch_sz is None:\n batch_sz = N\n\n # collect params for later use\n self.params = []\n for h in self.layers:\n self.params += h.params\n\n # note! we will need to build the output differently\n # for train and test (prediction)\n\n # set up theano functions and variables\n thX = T.matrix('X')\n thY = T.ivector('Y')\n\n # for training\n p_y_given_x = self.forward(thX, is_training=True)\n\n cost = -T.mean(T.log(p_y_given_x[T.arange(thY.shape[0]), thY]))\n prediction = T.argmax(p_y_given_x, axis=1)\n grads = T.grad(cost, self.params)\n\n # momentum only\n updates = momentum_updates(cost, self.params, learning_rate, mu)\n for layer in self.layers[:-1]:\n updates += layer.running_update\n\n train_op = theano.function(\n inputs=[thX, thY],\n outputs=[cost, prediction],\n updates=updates,\n )\n\n # for testing\n test_p_y_given_x = self.forward(thX, is_training=False)\n test_prediction = T.argmax(test_p_y_given_x, axis=1)\n\n self.predict = theano.function(\n inputs=[thX],\n outputs=test_prediction,\n )\n\n n_batches = N // batch_sz\n costs = []\n for i in range(epochs):\n if n_batches > 1:\n X, Y = shuffle(X, Y)\n for j in range(n_batches):\n Xbatch = X[j*batch_sz:(j*batch_sz+batch_sz)]\n Ybatch = Y[j*batch_sz:(j*batch_sz+batch_sz)]\n\n c, p = train_op(Xbatch, Ybatch)\n costs.append(c)\n if (j+1) % print_period == 0:\n accuracy = np.mean(p == Ybatch)\n print(\"epoch:\", i, \"batch:\", j, \"n_batches:\", n_batches, \"cost:\", c, \"accuracy:\", accuracy)\n\n print(\"Train acc:\", self.score(X, Y), \"Test acc:\", self.score(Xtest, Ytest))\n \n if show_fig:\n plt.plot(costs)\n plt.show()\n\n def forward(self, X, is_training):\n out = X\n for h in self.layers[:-1]:\n out = h.forward(out, is_training)\n out = self.layers[-1].forward(out)\n return out\n\n def score(self, X, Y):\n P = self.predict(X)\n return np.mean(Y == P)\n\n\n\ndef main():\n # step 1: get the data and define all the usual variables\n Xtrain, Xtest, Ytrain, Ytest = get_normalized_data()\n\n ann = ANN([500, 300])\n ann.fit(Xtrain, Ytrain, Xtest, Ytest, show_fig=True)\n\n print(\"Train accuracy:\", ann.score(Xtrain, Ytrain))\n print(\"Test accuracy:\", ann.score(Xtest, Ytest))\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "ann_class2/cntk_example.py", "content": "# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\n# simple installation with pip:\n#\n# just one line: pip install <....>.whl\n#\n# Linux\n# https://docs.microsoft.com/en-us/cognitive-toolkit/setup-linux-python\n#\n# Windows\n# https://docs.microsoft.com/en-us/cognitive-toolkit/setup-windows-python\n#\n# (No Mac)\n\n\n# Note: useful to understand keras_example.py first\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom util import get_normalized_data, y2indicator\n\n\nimport cntk as C\nfrom cntk.train import Trainer\nfrom cntk.learners import adam\nfrom cntk.ops import relu\nfrom cntk.layers import Dense, Sequential\nfrom cntk.losses import cross_entropy_with_softmax\nfrom cntk.metrics import classification_error\nfrom cntk.train.training_session import *\n\n\n# get the data, same as Theano + Tensorflow examples\nXtrain, Xtest, Ytrain, Ytest = get_normalized_data()\n\n# get shapes\nN, D = Xtrain.shape\nK = len(set(Ytrain))\n\n# we want one-hot encoded labels\nYtrain = y2indicator(Ytrain)\nYtest = y2indicator(Ytest)\n\n# split the data\nX = X.astype(np.float32)\nY = Y.astype(np.float32)\nXtest = Xtest.astype(np.float32)\nYtest = Ytest.astype(np.float32)\n\n\n# the model will be a sequence of layers\nmodel = Sequential(\n [\n Dense(500, activation=relu),\n Dense(300, activation=relu),\n Dense(K, activation=None),\n ]\n)\n\n\n# define the inputs and labels\ninputs = C.input_variable(D, np.float32, name='inputs')\nlabels = C.input_variable(K, np.float32, name='labels')\n\n\n# get the output\nlogits = model(inputs)\n\n\n# define loss / metrics\n# like Tensorflow the softmax is done\n# internally (if needed), so all we need are the logits\nce = cross_entropy_with_softmax(logits, labels)\npe = classification_error(logits, labels)\n\n\n\n# training config\nbatch_size = 32\nepochs = 15\nn_batches = len(Xtrain) // batch_size\n\n\n# do the training\n\n# specify the training algorithm\ntrainer = Trainer(logits, (ce, pe), adam(logits.parameters, lr=1e-2, momentum=0.9))\n\n\n# helper function\ndef get_output(node, X, Y):\n ret = node.forward(dict(inputs=X, labels=Y))\n return list(ret[1].values())[0].mean()\n\n\ncosts = []\nerrors = []\ntest_costs = []\ntest_errors = []\nfor i in range(epochs):\n cost = 0\n err = 0\n for j in range(n_batches):\n Xbatch = Xtrain[j*batch_size:(j+1)*batch_size]\n Ybatch = Ytrain[j*batch_size:(j+1)*batch_size]\n\n ret = trainer.train_minibatch(\n dict(inputs=Xbatch, labels=Ybatch),\n outputs=(ce, pe)\n )\n\n cost += ret[1][ce].mean()\n err += ret[1][pe].mean()\n costs.append(cost / n_batches)\n errors.append(err / n_batches)\n\n # test set calculations\n test_cost = get_output(ce, Xtest, Ytest)\n test_err = get_output(pe, Xtest, Ytest)\n\n test_costs.append(test_cost)\n test_errors.append(test_err)\n\n print(\"epoch i:\", i, \"cost:\", test_cost, \"err:\", test_err)\n\nplt.plot(costs, label='train cost')\nplt.plot(test_costs, label='test cost')\nplt.legend()\nplt.title(\"cost\")\nplt.show()\n\nplt.plot(errors, label='train error')\nplt.plot(test_errors, label='test error')\nplt.legend()\nplt.title(\"error\")\nplt.show()\n" }, { "path": "ann_class2/dropout_tensorflow.py", "content": "from __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow\n# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\n\nfrom util import get_normalized_data\nfrom sklearn.utils import shuffle\n\n\nclass HiddenLayer(object):\n def __init__(self, M1, M2):\n self.M1 = M1\n self.M2 = M2\n W = np.random.randn(M1, M2) * np.sqrt(2.0 / M1)\n b = np.zeros(M2)\n self.W = tf.Variable(W.astype(np.float32))\n self.b = tf.Variable(b.astype(np.float32))\n self.params = [self.W, self.b]\n\n def forward(self, X):\n return tf.nn.relu(tf.matmul(X, self.W) + self.b)\n\n\nclass ANN(object):\n def __init__(self, hidden_layer_sizes, p_keep):\n self.hidden_layer_sizes = hidden_layer_sizes\n self.dropout_rates = p_keep\n\n def fit(self, X, Y, Xvalid, Yvalid, lr=1e-4, mu=0.9, decay=0.9, epochs=15, batch_sz=100, print_every=50):\n X = X.astype(np.float32)\n Y = Y.astype(np.int64)\n Xvalid = Xvalid.astype(np.float32)\n Yvalid = Yvalid.astype(np.int64)\n\n # initialize hidden layers\n N, D = X.shape\n K = len(set(Y))\n self.hidden_layers = []\n M1 = D\n for M2 in self.hidden_layer_sizes:\n h = HiddenLayer(M1, M2)\n self.hidden_layers.append(h)\n M1 = M2\n W = np.random.randn(M1, K) * np.sqrt(2.0 / M1)\n b = np.zeros(K)\n self.W = tf.Variable(W.astype(np.float32))\n self.b = tf.Variable(b.astype(np.float32))\n\n # collect params for later use\n self.params = [self.W, self.b]\n for h in self.hidden_layers:\n self.params += h.params\n\n # set up theano functions and variables\n inputs = tf.placeholder(tf.float32, shape=(None, D), name='inputs')\n labels = tf.placeholder(tf.int64, shape=(None,), name='labels')\n logits = self.forward(inputs)\n\n cost = tf.reduce_mean(\n tf.nn.sparse_softmax_cross_entropy_with_logits(\n logits=logits,\n labels=labels\n )\n )\n train_op = tf.train.RMSPropOptimizer(lr, decay=decay, momentum=mu).minimize(cost)\n # train_op = tf.train.MomentumOptimizer(lr, momentum=mu).minimize(cost)\n # train_op = tf.train.AdamOptimizer(lr).minimize(cost)\n prediction = self.predict(inputs)\n\n # validation cost will be calculated separately since nothing will be dropped\n test_logits = self.forward_test(inputs)\n test_cost = tf.reduce_mean(\n tf.nn.sparse_softmax_cross_entropy_with_logits(\n logits=test_logits,\n labels=labels\n )\n )\n\n n_batches = N // batch_sz\n costs = []\n init = tf.global_variables_initializer()\n with tf.Session() as session:\n session.run(init)\n for i in range(epochs):\n print(\"epoch:\", i, \"n_batches:\", n_batches)\n X, Y = shuffle(X, Y)\n for j in range(n_batches):\n Xbatch = X[j*batch_sz:(j*batch_sz+batch_sz)]\n Ybatch = Y[j*batch_sz:(j*batch_sz+batch_sz)]\n\n session.run(train_op, feed_dict={inputs: Xbatch, labels: Ybatch})\n\n if j % print_every == 0:\n c = session.run(test_cost, feed_dict={inputs: Xvalid, labels: Yvalid})\n p = session.run(prediction, feed_dict={inputs: Xvalid})\n costs.append(c)\n e = error_rate(Yvalid, p)\n print(\"i:\", i, \"j:\", j, \"nb:\", n_batches, \"cost:\", c, \"error rate:\", e)\n \n plt.plot(costs)\n plt.show()\n\n def forward(self, X):\n # tf.nn.dropout scales inputs by 1/p_keep\n # therefore, during test time, we don't have to scale anything\n Z = X\n Z = tf.nn.dropout(Z, self.dropout_rates[0])\n for h, p in zip(self.hidden_layers, self.dropout_rates[1:]):\n Z = h.forward(Z)\n Z = tf.nn.dropout(Z, p)\n return tf.matmul(Z, self.W) + self.b\n\n def forward_test(self, X):\n Z = X\n for h in self.hidden_layers:\n Z = h.forward(Z)\n return tf.matmul(Z, self.W) + self.b\n\n def predict(self, X):\n pY = self.forward_test(X)\n return tf.argmax(pY, 1)\n\n\ndef error_rate(p, t):\n return np.mean(p != t)\n\n\ndef relu(a):\n return a * (a > 0)\n\n\ndef main():\n # step 1: get the data and define all the usual variables\n Xtrain, Xtest, Ytrain, Ytest = get_normalized_data()\n\n ann = ANN([500, 300], [0.8, 0.5, 0.5])\n ann.fit(Xtrain, Ytrain, Xtest, Ytest)\n\n\nif __name__ == '__main__':\n main()" }, { "path": "ann_class2/dropout_theano.py", "content": "# A 1-hidden-layer neural network in Theano.\n# This code is not optimized for speed.\n# It's just to get something working, using the principles we know.\n\n# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow\n# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport theano\nimport theano.tensor as T\nimport matplotlib.pyplot as plt\nfrom theano.tensor.shared_randomstreams import RandomStreams\nfrom util import get_normalized_data\nfrom sklearn.utils import shuffle\n\n\ndef momentum_updates(cost, params, lr, mu):\n grads = T.grad(cost, params)\n updates = []\n\n for p, g in zip(params, grads):\n dp = theano.shared(p.get_value() * 0)\n new_dp = mu*dp - lr*g\n new_p = p + new_dp\n updates.append((dp, new_dp))\n updates.append((p, new_p))\n return updates\n\n\nclass HiddenLayer(object):\n def __init__(self, M1, M2, an_id):\n self.id = an_id\n self.M1 = M1\n self.M2 = M2\n W = np.random.randn(M1, M2) * np.sqrt(2.0 / M1)\n b = np.zeros(M2)\n self.W = theano.shared(W, 'W_%s' % self.id)\n self.b = theano.shared(b, 'b_%s' % self.id)\n self.params = [self.W, self.b]\n\n def forward(self, X):\n return T.nnet.relu(X.dot(self.W) + self.b)\n\n\nclass ANN(object):\n def __init__(self, hidden_layer_sizes, p_keep):\n self.hidden_layer_sizes = hidden_layer_sizes\n self.dropout_rates = p_keep\n\n def fit(self, X, Y, Xvalid, Yvalid, learning_rate=1e-2, mu=0.9, decay=0.9, epochs=10, batch_sz=100, show_fig=False):\n X = X.astype(np.float32)\n Y = Y.astype(np.int32)\n Xvalid = Xvalid.astype(np.float32)\n Yvalid = Yvalid.astype(np.int32)\n\n self.rng = RandomStreams()\n\n # initialize hidden layers\n N, D = X.shape\n K = len(set(Y))\n self.hidden_layers = []\n M1 = D\n count = 0\n for M2 in self.hidden_layer_sizes:\n h = HiddenLayer(M1, M2, count)\n self.hidden_layers.append(h)\n M1 = M2\n count += 1\n W = np.random.randn(M1, K) * np.sqrt(2.0 / M1)\n b = np.zeros(K)\n self.W = theano.shared(W, 'W_logreg')\n self.b = theano.shared(b, 'b_logreg')\n\n # collect params for later use\n self.params = [self.W, self.b]\n for h in self.hidden_layers:\n self.params += h.params\n\n # set up theano functions and variables\n thX = T.matrix('X')\n thY = T.ivector('Y')\n pY_train = self.forward_train(thX)\n\n # this cost is for training\n cost = -T.mean(T.log(pY_train[T.arange(thY.shape[0]), thY]))\n updates = momentum_updates(cost, self.params, learning_rate, mu)\n\n train_op = theano.function(\n inputs=[thX, thY],\n updates=updates\n )\n\n # for evaluation and prediction\n pY_predict = self.forward_predict(thX)\n cost_predict = -T.mean(T.log(pY_predict[T.arange(thY.shape[0]), thY]))\n prediction = self.predict(thX)\n cost_predict_op = theano.function(inputs=[thX, thY], outputs=[cost_predict, prediction])\n\n n_batches = N // batch_sz\n costs = []\n for i in range(epochs):\n X, Y = shuffle(X, Y)\n for j in range(n_batches):\n Xbatch = X[j*batch_sz:(j*batch_sz+batch_sz)]\n Ybatch = Y[j*batch_sz:(j*batch_sz+batch_sz)]\n\n train_op(Xbatch, Ybatch)\n\n if j % 50 == 0:\n c, p = cost_predict_op(Xvalid, Yvalid)\n costs.append(c)\n e = error_rate(Yvalid, p)\n print(\"i:\", i, \"j:\", j, \"nb:\", n_batches, \"cost:\", c, \"error rate:\", e)\n \n if show_fig:\n plt.plot(costs)\n plt.show()\n\n def forward_train(self, X):\n Z = X\n for h, p in zip(self.hidden_layers, self.dropout_rates[:-1]):\n mask = self.rng.binomial(n=1, p=p, size=Z.shape)\n Z = mask * Z\n Z = h.forward(Z)\n mask = self.rng.binomial(n=1, p=self.dropout_rates[-1], size=Z.shape)\n Z = mask * Z\n return T.nnet.softmax(Z.dot(self.W) + self.b)\n\n def forward_predict(self, X):\n Z = X\n for h, p in zip(self.hidden_layers, self.dropout_rates[:-1]):\n Z = h.forward(p * Z)\n return T.nnet.softmax((self.dropout_rates[-1] * Z).dot(self.W) + self.b)\n\n def predict(self, X):\n pY = self.forward_predict(X)\n return T.argmax(pY, axis=1)\n\n\ndef error_rate(p, t):\n return np.mean(p != t)\n\n\ndef relu(a):\n return a * (a > 0)\n\n\ndef main():\n # step 1: get the data and define all the usual variables\n Xtrain, Xtest, Ytrain, Ytest = get_normalized_data()\n\n ann = ANN([500, 300], [0.8, 0.5, 0.5])\n ann.fit(Xtrain, Ytrain, Xtest, Ytest, show_fig=True)\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "ann_class2/extra_reading.txt", "content": "The Marginal Value of Adaptive Gradient Methods in Machine Learning\nhttps://arxiv.org/abs/1705.08292\n\nAsynchronous Stochastic Gradient Descent with Delay Compensation for Distributed Deep Learning\nhttps://arxiv.org/abs/1609.08326\n\nAsynchronous Stochastic Gradient Descent with Variance Reduction for Non-Convex Optimization\nhttps://arxiv.org/abs/1604.03584\n\nAdam: A Method for Stochastic Optimization\nhttps://arxiv.org/abs/1412.6980\n\nLarge Scale Distributed Deep Networks\nhttps://static.googleusercontent.com/media/research.google.com/en//archive/large_deep_networks_nips2012.pdf\n\nBatch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift\nSergey Ioffe, Christian Szegedy\nhttps://arxiv.org/abs/1502.03167\n\nXavier (Glorot) Normal Initializer\nhttp://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf\n\nHe Normal Initializer\nhttp://arxiv.org/abs/1502.01852\n\nFor understanding Nesterov Momentum:\nAdvances in optimizing Recurrent Networks by Yoshua Bengio, Section 3.5\nhttp://arxiv.org/pdf/1212.0901v2.pdf\n\nDropout: A Simple Way to Prevent Neural Networks from Overfitting\nhttps://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf\n\nThe Loss Surfaces of Multilayer Networks\nhttps://arxiv.org/pdf/1412.0233.pdf" }, { "path": "ann_class2/grid_search.py", "content": "# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow\n# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport theano.tensor as T\nfrom theano_ann import ANN\nfrom util import get_spiral, get_clouds\nfrom sklearn.utils import shuffle\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef grid_search():\n # get the data and split into train/test\n X, Y = get_spiral()\n # X, Y = get_clouds()\n X, Y = shuffle(X, Y)\n Ntrain = int(0.7*len(X))\n Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain]\n Xtest, Ytest = X[Ntrain:], Y[Ntrain:]\n\n # hyperparameters to try\n hidden_layer_sizes = [\n [300],\n [100,100],\n [50,50,50],\n ]\n learning_rates = [1e-4, 1e-3, 1e-2]\n l2_penalties = [0., 0.1, 1.0]\n\n # loop through all possible hyperparameter settings\n best_validation_rate = 0\n best_hls = None\n best_lr = None\n best_l2 = None\n for hls in hidden_layer_sizes:\n for lr in learning_rates:\n for l2 in l2_penalties:\n model = ANN(hls)\n model.fit(Xtrain, Ytrain, learning_rate=lr, reg=l2, mu=0.99, epochs=3000, show_fig=False)\n validation_accuracy = model.score(Xtest, Ytest)\n train_accuracy = model.score(Xtrain, Ytrain)\n print(\n \"validation_accuracy: %.3f, train_accuracy: %.3f, settings: %s, %s, %s\" %\n (validation_accuracy, train_accuracy, hls, lr, l2)\n )\n if validation_accuracy > best_validation_rate:\n best_validation_rate = validation_accuracy\n best_hls = hls\n best_lr = lr\n best_l2 = l2\n print(\"Best validation_accuracy:\", best_validation_rate)\n print(\"Best settings:\")\n print(\"hidden_layer_sizes:\", best_hls)\n print(\"learning_rate:\", best_lr)\n print(\"l2:\", best_l2)\n\n\n# def one():\n# X, Y = get_spiral()\n# X, Y = shuffle(X, Y)\n# Ntrain = int(0.7*len(X))\n# Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain]\n# # plt.scatter(Xtrain[:,0], Xtrain[:,1], c=Ytrain)\n# # plt.show()\n# Xtest, Ytest = X[Ntrain:], Y[Ntrain:]\n\n# model = ANN([100,100])\n# model.fit(Xtrain, Ytrain, activation=T.nnet.relu, learning_rate=1e-3, mu=0.99, reg=0., epochs=3000, show_fig=True)\n# validation_accuracy = model.score(Xtest, Ytest)\n# print(\"validation_accuracy:\", validation_accuracy)\n# print(\"train accuracy:\", model.score(Xtrain, Ytrain))\n\n\nif __name__ == '__main__':\n grid_search()\n # one()\n" }, { "path": "ann_class2/keras_example.py", "content": "# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Activation\nfrom util import get_normalized_data, y2indicator\n\nimport matplotlib.pyplot as plt\n\n# NOTE: do NOT name your file keras.py because it will conflict\n# with importing keras\n\n# installation is easy! just the usual \"sudo pip(3) install keras\"\n\n\n# get the data, same as Theano + Tensorflow examples\n# no need to split now, the fit() function will do it\nXtrain, Xtest, Ytrain, Ytest = get_normalized_data()\n\n# get shapes\nN, D = Xtrain.shape\nK = len(set(Ytrain))\n\n# by default Keras wants one-hot encoded labels\n# there's another cost function we can use\n# where we can just pass in the integer labels directly\n# just like Tensorflow / Theano\nYtrain = y2indicator(Ytrain)\nYtest = y2indicator(Ytest)\n\n\n# the model will be a sequence of layers\nmodel = Sequential()\n\n\n# ANN with layers [784] -> [500] -> [300] -> [10]\nmodel.add(Dense(units=500, input_dim=D))\nmodel.add(Activation('relu'))\nmodel.add(Dense(units=300)) # don't need to specify input_dim\nmodel.add(Activation('relu'))\nmodel.add(Dense(units=K))\nmodel.add(Activation('softmax'))\n\n\n# list of losses: https://keras.io/losses/\n# list of optimizers: https://keras.io/optimizers/\n# list of metrics: https://keras.io/metrics/\nmodel.compile(\n loss='categorical_crossentropy',\n optimizer='adam',\n metrics=['accuracy']\n)\n\n# note: multiple ways to choose a backend\n# either theano, tensorflow, or cntk\n# https://keras.io/backend/\n\n\n# gives us back a \nr = model.fit(Xtrain, Ytrain, validation_data=(Xtest, Ytest), epochs=15, batch_size=32)\nprint(\"Returned:\", r)\n\n# print the available keys\n# should see: dict_keys(['val_loss', 'acc', 'loss', 'val_acc'])\nprint(r.history.keys())\n\n# plot some data\nplt.plot(r.history['loss'], label='loss')\nplt.plot(r.history['val_loss'], label='val_loss')\nplt.legend()\nplt.show()\n\n# accuracies\nplt.plot(r.history['accuracy'], label='acc')\nplt.plot(r.history['val_accuracy'], label='val_acc')\nplt.legend()\nplt.show()\n\n\n" }, { "path": "ann_class2/keras_functional.py", "content": "# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nfrom keras.models import Model\nfrom keras.layers import Dense, Input\nfrom util import get_normalized_data, y2indicator\n\nimport matplotlib.pyplot as plt\n\n# NOTE: do NOT name your file keras.py because it will conflict\n# with importing keras\n\n# installation is easy! just the usual \"sudo pip(3) install keras\"\n\n\n# get the data, same as Theano + Tensorflow examples\n# no need to split now, the fit() function will do it\nXtrain, Xtest, Ytrain, Ytest = get_normalized_data()\n\n# get shapes\nN, D = Xtrain.shape\nK = len(set(Ytrain))\n\n# by default Keras wants one-hot encoded labels\n# there's another cost function we can use\n# where we can just pass in the integer labels directly\n# just like Tensorflow / Theano\nYtrain = y2indicator(Ytrain)\nYtest = y2indicator(Ytest)\n\n\n# ANN with layers [784] -> [500] -> [300] -> [10]\ni = Input(shape=(D,))\nx = Dense(500, activation='relu')(i)\nx = Dense(300, activation='relu')(x)\nx = Dense(K, activation='softmax')(x)\nmodel = Model(inputs=i, outputs=x)\n\n\n# list of losses: https://keras.io/losses/\n# list of optimizers: https://keras.io/optimizers/\n# list of metrics: https://keras.io/metrics/\nmodel.compile(\n loss='categorical_crossentropy',\n optimizer='adam',\n metrics=['accuracy']\n)\n\n# note: multiple ways to choose a backend\n# either theano, tensorflow, or cntk\n# https://keras.io/backend/\n\n\n# gives us back a \nr = model.fit(Xtrain, Ytrain, validation_data=(Xtest, Ytest), epochs=15, batch_size=32)\nprint(\"Returned:\", r)\n\n# print the available keys\n# should see: dict_keys(['val_loss', 'acc', 'loss', 'val_acc'])\nprint(r.history.keys())\n\n# plot some data\nplt.plot(r.history['loss'], label='loss')\nplt.plot(r.history['val_loss'], label='val_loss')\nplt.legend()\nplt.show()\n\n# accuracies\nplt.plot(r.history['accuracy'], label='acc')\nplt.plot(r.history['val_accuracy'], label='val_acc')\nplt.legend()\nplt.show()\n\n\n" }, { "path": "ann_class2/mlp.py", "content": "# Simple multi-layer preceptron / neural network in Python and Numpy\n# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow\n# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\n\ndef forward(X, W1, b1, W2, b2):\n # sigmoid\n # Z = 1 / (1 + np.exp(-( X.dot(W1) + b1 )))\n\n # relu\n Z = X.dot(W1) + b1\n Z[Z < 0] = 0\n\n A = Z.dot(W2) + b2\n expA = np.exp(A)\n Y = expA / expA.sum(axis=1, keepdims=True)\n return Y, Z\n\ndef derivative_w2(Z, T, Y):\n return Z.T.dot(Y - T)\n\ndef derivative_b2(T, Y):\n return (Y - T).sum(axis=0)\n\ndef derivative_w1(X, Z, T, Y, W2):\n # return X.T.dot( ( ( Y-T ).dot(W2.T) * ( Z*(1 - Z) ) ) ) # for sigmoid\n return X.T.dot( ( ( Y-T ).dot(W2.T) * (Z > 0) ) ) # for relu\n\ndef derivative_b1(Z, T, Y, W2):\n # return (( Y-T ).dot(W2.T) * ( Z*(1 - Z) )).sum(axis=0) # for sigmoid\n return (( Y-T ).dot(W2.T) * (Z > 0)).sum(axis=0) # for relu\n" }, { "path": "ann_class2/momentum.py", "content": "# Compare momentum with regular gradient descent\n# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow\n# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\n\n# NOTE: MUST restrict initial values of W by dividing by #\n# NOTE: sigmoid vs. rectifier for hiddens\n# We get 15% error rate with sigmoid, 3% error rate with ReLU\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nfrom sklearn.utils import shuffle\nimport matplotlib.pyplot as plt\n\nfrom util import get_normalized_data, error_rate, cost, y2indicator\nfrom mlp import forward, derivative_w2, derivative_w1, derivative_b2, derivative_b1\n\n\ndef main():\n # compare 3 scenarios:\n # 1. batch SGD\n # 2. batch SGD with momentum\n # 3. batch SGD with Nesterov momentum\n\n max_iter = 20 # make it 30 for sigmoid\n print_period = 50\n\n Xtrain, Xtest, Ytrain, Ytest = get_normalized_data()\n lr = 0.00004\n reg = 0.01\n\n Ytrain_ind = y2indicator(Ytrain)\n Ytest_ind = y2indicator(Ytest)\n\n N, D = Xtrain.shape\n batch_sz = 500\n n_batches = N // batch_sz\n\n M = 300\n K = 10\n W1 = np.random.randn(D, M) / np.sqrt(D)\n b1 = np.zeros(M)\n W2 = np.random.randn(M, K) / np.sqrt(M)\n b2 = np.zeros(K)\n\n # save initial weights\n W1_0 = W1.copy()\n b1_0 = b1.copy()\n W2_0 = W2.copy()\n b2_0 = b2.copy()\n\n # 1. batch\n losses_batch = []\n errors_batch = []\n for i in range(max_iter):\n Xtrain, Ytrain, Ytrain_ind = shuffle(Xtrain, Ytrain, Ytrain_ind)\n for j in range(n_batches):\n Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),]\n Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),]\n pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)\n # print \"first batch cost:\", cost(pYbatch, Ybatch)\n\n # gradients\n gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg*W2\n gb2 = derivative_b2(Ybatch, pYbatch) + reg*b2\n gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1\n gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1\n\n # updates\n W2 -= lr*gW2\n b2 -= lr*gb2\n W1 -= lr*gW1\n b1 -= lr*gb1\n\n if j % print_period == 0:\n pY, _ = forward(Xtest, W1, b1, W2, b2)\n l = cost(pY, Ytest_ind)\n losses_batch.append(l)\n print(\"Cost at iteration i=%d, j=%d: %.6f\" % (i, j, l))\n\n e = error_rate(pY, Ytest)\n errors_batch.append(e)\n print(\"Error rate:\", e)\n\n pY, _ = forward(Xtest, W1, b1, W2, b2)\n print(\"Final error rate:\", error_rate(pY, Ytest))\n\n # 2. batch with momentum\n W1 = W1_0.copy()\n b1 = b1_0.copy()\n W2 = W2_0.copy()\n b2 = b2_0.copy()\n losses_momentum = []\n errors_momentum = []\n mu = 0.9\n dW2 = 0\n db2 = 0\n dW1 = 0\n db1 = 0\n for i in range(max_iter):\n Xtrain, Ytrain, Ytrain_ind = shuffle(Xtrain, Ytrain, Ytrain_ind)\n for j in range(n_batches):\n Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),]\n Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),]\n pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)\n\n # gradients\n gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg*W2\n gb2 = derivative_b2(Ybatch, pYbatch) + reg*b2\n gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1\n gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1\n\n # update velocities\n dW2 = mu*dW2 - lr*gW2\n db2 = mu*db2 - lr*gb2\n dW1 = mu*dW1 - lr*gW1\n db1 = mu*db1 - lr*gb1\n\n # updates\n W2 += dW2\n b2 += db2\n W1 += dW1\n b1 += db1\n\n if j % print_period == 0:\n pY, _ = forward(Xtest, W1, b1, W2, b2)\n l = cost(pY, Ytest_ind)\n losses_momentum.append(l)\n print(\"Cost at iteration i=%d, j=%d: %.6f\" % (i, j, l))\n\n e = error_rate(pY, Ytest)\n errors_momentum.append(e)\n print(\"Error rate:\", e)\n pY, _ = forward(Xtest, W1, b1, W2, b2)\n print(\"Final error rate:\", error_rate(pY, Ytest))\n\n\n # 3. batch with Nesterov momentum\n W1 = W1_0.copy()\n b1 = b1_0.copy()\n W2 = W2_0.copy()\n b2 = b2_0.copy()\n\n losses_nesterov = []\n errors_nesterov = []\n\n mu = 0.9\n vW2 = 0\n vb2 = 0\n vW1 = 0\n vb1 = 0\n for i in range(max_iter):\n Xtrain, Ytrain, Ytrain_ind = shuffle(Xtrain, Ytrain, Ytrain_ind)\n for j in range(n_batches):\n Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),]\n Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),]\n pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)\n\n # updates\n gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg*W2\n gb2 = derivative_b2(Ybatch, pYbatch) + reg*b2\n gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1\n gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1\n\n # v update\n vW2 = mu*vW2 - lr*gW2\n vb2 = mu*vb2 - lr*gb2\n vW1 = mu*vW1 - lr*gW1\n vb1 = mu*vb1 - lr*gb1\n\n # param update\n W2 += mu*vW2 - lr*gW2\n b2 += mu*vb2 - lr*gb2\n W1 += mu*vW1 - lr*gW1\n b1 += mu*vb1 - lr*gb1\n\n if j % print_period == 0:\n pY, _ = forward(Xtest, W1, b1, W2, b2)\n l = cost(pY, Ytest_ind)\n losses_nesterov.append(l)\n print(\"Cost at iteration i=%d, j=%d: %.6f\" % (i, j, l))\n\n e = error_rate(pY, Ytest)\n errors_nesterov.append(e)\n print(\"Error rate:\", e)\n pY, _ = forward(Xtest, W1, b1, W2, b2)\n print(\"Final error rate:\", error_rate(pY, Ytest))\n\n\n\n plt.plot(losses_batch, label=\"batch\")\n plt.plot(losses_momentum, label=\"momentum\")\n plt.plot(losses_nesterov, label=\"nesterov\")\n plt.legend()\n plt.show()\n\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "ann_class2/mxnet_example.py", "content": "# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\n# installation is just one line:\n# https://mxnet.incubator.apache.org/get_started/install.html\n#\n# Mac:\n# pip install mxnet\n#\n# Linux (GPU):\n# pip install mxnet-cu80\n#\n# Windows (a little more involved):\n# https://mxnet.incubator.apache.org/get_started/windows_setup.html\n\nimport mxnet as mx\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom util import get_normalized_data, y2indicator\n\n\n\n# get the data, same as Theano + Tensorflow examples\n# no need to split now, the fit() function will do it\nXtrain, Xtest, Ytrain, Ytest = get_normalized_data()\n\n# get shapes\nN, D = Xtrain.shape\nK = len(set(Ytrain))\n\n# training config\nbatch_size = 32\nepochs = 15\n\n\n# convert the data into a format appropriate for input into mxnet\ntrain_iterator = mx.io.NDArrayIter(\n Xtrain,\n Ytrain,\n batch_size,\n shuffle=True\n)\ntest_iterator = mx.io.NDArrayIter(Xtest, Ytest, batch_size)\n\n\n\n# define a placeholder to represent the inputs\ndata = mx.sym.var('data')\n\n\n# define the model architecture\na1 = mx.sym.FullyConnected(data=data, num_hidden=500)\nz1 = mx.sym.Activation(data=a1, act_type=\"relu\")\na2 = mx.sym.FullyConnected(data=z1, num_hidden = 300)\nz2 = mx.sym.Activation(data=a2, act_type=\"relu\")\na3 = mx.sym.FullyConnected(data=z2, num_hidden=K)\ny = mx.sym.SoftmaxOutput(data=a3, name='softmax')\n\n\n\n\n# train it\n\n# required in order for progress to be printed\nimport logging\nlogging.getLogger().setLevel(logging.DEBUG)\n\n# use mx.gpu() if you have gpu\nmodel = mx.mod.Module(symbol=y, context=mx.cpu())\nmodel.fit(\n train_iterator, # train data\n eval_data=test_iterator, # validation data\n optimizer=mx.optimizer.Adam(),\n eval_metric='acc', # report accuracy during training\n batch_end_callback = mx.callback.Speedometer(batch_size, 100), # output progress for each 100 data batches\n num_epoch=epochs,\n)\n# no return value\n# list of optimizers: https://mxnet.incubator.apache.org/api/python/optimization.html\n\n\n# test it\n# predict accuracy of mlp\nacc = mx.metric.Accuracy()\nmodel.score(test_iterator, acc)\nprint(acc)\nprint(acc.get())\n\n\n# currently, there is no good way to plot the training loss / accuracy history\n# https://github.com/apache/incubator-mxnet/issues/2873\n#\n# some have suggested parsing the logs\n# https://github.com/apache/incubator-mxnet/blob/master/example/kaggle-ndsb1/training_curves.py\n" }, { "path": "ann_class2/pytorch_batchnorm.py", "content": "# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n# Note: is helpful to look at keras_example.py first\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom util import get_normalized_data\n\nimport torch\nfrom torch.autograd import Variable\nfrom torch import optim\n\n\n\n# get the data, same as Theano + Tensorflow examples\n# no need to split now, the fit() function will do it\nXtrain, Xtest, Ytrain, Ytest = get_normalized_data()\n\n# get shapes\n_, D = Xtrain.shape\nK = len(set(Ytrain))\n\n# Note: no need to convert Y to indicator matrix\n\n\n# the model will be a sequence of layers\nmodel = torch.nn.Sequential()\n\n\n# ANN with layers [784] -> [500] -> [300] -> [10]\nmodel.add_module(\"dense1\", torch.nn.Linear(D, 500))\nmodel.add_module(\"bn1\", torch.nn.BatchNorm1d(500))\nmodel.add_module(\"relu1\", torch.nn.ReLU())\nmodel.add_module(\"dense2\", torch.nn.Linear(500, 300))\nmodel.add_module(\"bn2\", torch.nn.BatchNorm1d(300))\nmodel.add_module(\"relu2\", torch.nn.ReLU())\nmodel.add_module(\"dense3\", torch.nn.Linear(300, K))\n# Note: no final softmax!\n# just like Tensorflow, it's included in cross-entropy function\n\n\n# define a loss function\n# other loss functions can be found here:\n# http://pytorch.org/docs/master/nn.html#loss-functions\nloss = torch.nn.CrossEntropyLoss(size_average=True)\n# Note: this returns a function!\n# e.g. use it like: loss(logits, labels)\n\n\n# define an optimizer\n# other optimizers can be found here:\n# http://pytorch.org/docs/master/optim.html\noptimizer = optim.Adam(model.parameters(), lr=1e-4)\n\n\n\n# define the training procedure\n# i.e. one step of gradient descent\n# there are lots of steps\n# so we encapsulate it in a function\n# Note: inputs and labels are torch tensors\ndef train(model, loss, optimizer, inputs, labels):\n # set the model to training mode\n # because batch norm has 2 different modes!\n model.train()\n\n inputs = Variable(inputs, requires_grad=False)\n labels = Variable(labels, requires_grad=False)\n\n # Reset gradient\n optimizer.zero_grad()\n\n # Forward\n logits = model.forward(inputs)\n output = loss.forward(logits, labels)\n\n # Backward\n output.backward()\n\n # Update parameters\n optimizer.step()\n\n # what's the difference between backward() and step()?\n\n return output.item()\n\n\n# similar to train() but not doing the backprop step\ndef get_cost(model, loss, inputs, labels):\n # set the model to testing mode\n # because batch norm has 2 different modes!\n model.eval()\n\n inputs = Variable(inputs, requires_grad=False)\n labels = Variable(labels, requires_grad=False)\n\n # Forward\n logits = model.forward(inputs)\n output = loss.forward(logits, labels)\n\n return output.item()\n\n\n# define the prediction procedure\n# also encapsulate these steps\n# Note: inputs is a torch tensor\ndef predict(model, inputs):\n # set the model to testing mode\n # because batch norm has 2 different modes!\n model.eval()\n\n inputs = Variable(inputs, requires_grad=False)\n logits = model.forward(inputs)\n return logits.data.numpy().argmax(axis=1)\n\n\n# return the accuracy\n# labels is a torch tensor\n# to get back the internal numpy data\n# use the instance method .numpy()\ndef score(model, inputs, labels):\n predictions = predict(model, inputs)\n return np.mean(labels.numpy() == predictions)\n\n\n### prepare for training loop ###\n\n# convert the data arrays into torch tensors\nXtrain = torch.from_numpy(Xtrain).float()\nYtrain = torch.from_numpy(Ytrain).long()\nXtest = torch.from_numpy(Xtest).float()\nYtest = torch.from_numpy(Ytest).long()\n\n# training parameters\nepochs = 15\nbatch_size = 32\nn_batches = Xtrain.size()[0] // batch_size\n\n# things to keep track of\ntrain_costs = []\ntest_costs = []\ntrain_accuracies = []\ntest_accuracies = []\n\n# main training loop\nfor i in range(epochs):\n cost = 0\n test_cost = 0\n for j in range(n_batches):\n Xbatch = Xtrain[j*batch_size:(j+1)*batch_size]\n Ybatch = Ytrain[j*batch_size:(j+1)*batch_size]\n cost += train(model, loss, optimizer, Xbatch, Ybatch)\n\n \n # we could have also calculated the train cost here\n # but I wanted to show you that we could also return it\n # from the train function itself\n train_acc = score(model, Xtrain, Ytrain)\n test_acc = score(model, Xtest, Ytest)\n test_cost = get_cost(model, loss, Xtest, Ytest)\n\n print(\"Epoch: %d, cost: %f, acc: %.2f\" % (i, test_cost, test_acc))\n\n # for plotting\n train_costs.append(cost / n_batches)\n train_accuracies.append(train_acc)\n test_costs.append(test_cost)\n test_accuracies.append(test_acc)\n\n\n\n# plot the results\nplt.plot(train_costs, label='Train cost')\nplt.plot(test_costs, label='Test cost')\nplt.title('Cost')\nplt.legend()\nplt.show()\n\nplt.plot(train_accuracies, label='Train accuracy')\nplt.plot(test_accuracies, label='Test accuracy')\nplt.title('Accuracy')\nplt.legend()\nplt.show()\n" }, { "path": "ann_class2/pytorch_dropout.py", "content": "# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n# Note: is helpful to look at keras_example.py first\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom util import get_normalized_data\n\nimport torch\nfrom torch.autograd import Variable\nfrom torch import optim\n\n\n\n# get the data, same as Theano + Tensorflow examples\n# no need to split now, the fit() function will do it\nXtrain, Xtest, Ytrain, Ytest = get_normalized_data()\n\n# get shapes\n_, D = Xtrain.shape\nK = len(set(Ytrain))\n\n# Note: no need to convert Y to indicator matrix\n\n\n# the model will be a sequence of layers\nmodel = torch.nn.Sequential()\n\n\n# ANN with layers [784] -> [500] -> [300] -> [10]\n# NOTE: the \"p\" is p_drop, not p_keep\nmodel.add_module(\"dropout1\", torch.nn.Dropout(p=0.2))\nmodel.add_module(\"dense1\", torch.nn.Linear(D, 500))\nmodel.add_module(\"relu1\", torch.nn.ReLU())\nmodel.add_module(\"dropout2\", torch.nn.Dropout(p=0.5))\nmodel.add_module(\"dense2\", torch.nn.Linear(500, 300))\nmodel.add_module(\"relu2\", torch.nn.ReLU())\nmodel.add_module(\"dropout3\", torch.nn.Dropout(p=0.5))\nmodel.add_module(\"dense3\", torch.nn.Linear(300, K))\n# Note: no final softmax!\n# just like Tensorflow, it's included in cross-entropy function\n\n\n# define a loss function\n# other loss functions can be found here:\n# http://pytorch.org/docs/master/nn.html#loss-functions\nloss = torch.nn.CrossEntropyLoss(size_average=True)\n# Note: this returns a function!\n# e.g. use it like: loss(logits, labels)\n\n\n# define an optimizer\n# other optimizers can be found here:\n# http://pytorch.org/docs/master/optim.html\noptimizer = optim.Adam(model.parameters(), lr=1e-4)\n\n\n# define the training procedure\n# i.e. one step of gradient descent\n# there are lots of steps\n# so we encapsulate it in a function\n# Note: inputs and labels are torch tensors\ndef train(model, loss, optimizer, inputs, labels):\n # set the model to training mode\n # because dropout has 2 different modes!\n model.train()\n\n inputs = Variable(inputs, requires_grad=False)\n labels = Variable(labels, requires_grad=False)\n\n # Reset gradient\n optimizer.zero_grad()\n\n # Forward\n logits = model.forward(inputs)\n output = loss.forward(logits, labels)\n\n # Backward\n output.backward()\n\n # Update parameters\n optimizer.step()\n\n # what's the difference between backward() and step()?\n\n return output.item()\n\n\n# similar to train() but not doing the backprop step\ndef get_cost(model, loss, inputs, labels):\n # set the model to testing mode\n # because dropout has 2 different modes!\n model.eval()\n\n inputs = Variable(inputs, requires_grad=False)\n labels = Variable(labels, requires_grad=False)\n\n # Forward\n logits = model.forward(inputs)\n output = loss.forward(logits, labels)\n\n return output.item()\n\n\n# define the prediction procedure\n# also encapsulate these steps\n# Note: inputs is a torch tensor\ndef predict(model, inputs):\n # set the model to testing mode\n # because dropout has 2 different modes!\n model.eval()\n\n inputs = Variable(inputs, requires_grad=False)\n logits = model.forward(inputs)\n return logits.data.numpy().argmax(axis=1)\n\n\n# return the accuracy\n# labels is a torch tensor\n# to get back the internal numpy data\n# use the instance method .numpy()\ndef score(model, inputs, labels):\n predictions = predict(model, inputs)\n return np.mean(labels.numpy() == predictions)\n\n\n### prepare for training loop ###\n\n# convert the data arrays into torch tensors\nXtrain = torch.from_numpy(Xtrain).float()\nYtrain = torch.from_numpy(Ytrain).long()\nXtest = torch.from_numpy(Xtest).float()\nYtest = torch.from_numpy(Ytest).long()\n\n# training parameters\nepochs = 15\nbatch_size = 32\nn_batches = Xtrain.size()[0] // batch_size\n\n# things to keep track of\ntrain_costs = []\ntest_costs = []\ntrain_accuracies = []\ntest_accuracies = []\n\n# main training loop\nfor i in range(epochs):\n cost = 0\n test_cost = 0\n for j in range(n_batches):\n Xbatch = Xtrain[j*batch_size:(j+1)*batch_size]\n Ybatch = Ytrain[j*batch_size:(j+1)*batch_size]\n cost += train(model, loss, optimizer, Xbatch, Ybatch)\n\n \n # we could have also calculated the train cost here\n # but I wanted to show you that we could also return it\n # from the train function itself\n train_acc = score(model, Xtrain, Ytrain)\n test_acc = score(model, Xtest, Ytest)\n test_cost = get_cost(model, loss, Xtest, Ytest)\n\n print(\"Epoch: %d, cost: %f, acc: %.2f\" % (i, test_cost, test_acc))\n\n # for plotting\n train_costs.append(cost / n_batches)\n train_accuracies.append(train_acc)\n test_costs.append(test_cost)\n test_accuracies.append(test_acc)\n\n\n\n# plot the results\nplt.plot(train_costs, label='Train cost')\nplt.plot(test_costs, label='Test cost')\nplt.title('Cost')\nplt.legend()\nplt.show()\n\nplt.plot(train_accuracies, label='Train accuracy')\nplt.plot(test_accuracies, label='Test accuracy')\nplt.title('Accuracy')\nplt.legend()\nplt.show()\n" }, { "path": "ann_class2/pytorch_example.py", "content": "# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n# Note: is helpful to look at keras_example.py first\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom util import get_normalized_data\n\nimport torch\nfrom torch.autograd import Variable\nfrom torch import optim\n\n\n\n# get the data, same as Theano + Tensorflow examples\n# no need to split now, the fit() function will do it\nXtrain, Xtest, Ytrain, Ytest = get_normalized_data()\n\n# get shapes\n_, D = Xtrain.shape\nK = len(set(Ytrain))\n\n# Note: no need to convert Y to indicator matrix\n\n\n# the model will be a sequence of layers\nmodel = torch.nn.Sequential()\n\n\n# ANN with layers [784] -> [500] -> [300] -> [10]\nmodel.add_module(\"dense1\", torch.nn.Linear(D, 500))\nmodel.add_module(\"relu1\", torch.nn.ReLU())\nmodel.add_module(\"dense2\", torch.nn.Linear(500, 300))\nmodel.add_module(\"relu2\", torch.nn.ReLU())\nmodel.add_module(\"dense3\", torch.nn.Linear(300, K))\n# Note: no final softmax!\n# just like Tensorflow, it's included in cross-entropy function\n\n\n\n# define a loss function\n# other loss functions can be found here:\n# http://pytorch.org/docs/master/nn.html#loss-functions\nloss = torch.nn.CrossEntropyLoss(size_average=True)\n\n\n\n# define an optimizer\n# other optimizers can be found here:\n# http://pytorch.org/docs/master/optim.html\noptimizer = optim.Adam(model.parameters())\n\n\n\n# define the training procedure\n# i.e. one step of gradient descent\n# there are lots of steps\n# so we encapsulate it in a function\n# Note: inputs and labels are torch tensors\ndef train(model, loss, optimizer, inputs, labels):\n # https://discuss.pytorch.org/t/why-is-it-recommended-to-wrap-your-data-with-variable-each-step-of-the-iterations-rather-than-before-training-starts/12683\n inputs = Variable(inputs, requires_grad=False)\n labels = Variable(labels, requires_grad=False)\n\n # Reset gradient\n # https://discuss.pytorch.org/t/why-do-we-need-to-set-the-gradients-manually-to-zero-in-pytorch/4903/7\n optimizer.zero_grad()\n\n # Forward\n logits = model.forward(inputs)\n output = loss.forward(logits, labels)\n\n # Backward\n output.backward()\n\n # Update parameters\n optimizer.step()\n\n # what's the difference between backward() and step()?\n # https://discuss.pytorch.org/t/what-does-the-backward-function-do/9944\n return output.item()\n\n\n# define the prediction procedure\n# also encapsulate these steps\n# Note: inputs is a torch tensor\ndef predict(model, inputs):\n inputs = Variable(inputs, requires_grad=False)\n logits = model.forward(inputs)\n return logits.data.numpy().argmax(axis=1)\n\n\n\n### prepare for training loop ###\n\n# convert the data arrays into torch tensors\nXtrain = torch.from_numpy(Xtrain).float()\nYtrain = torch.from_numpy(Ytrain).long()\nXtest = torch.from_numpy(Xtest).float()\n\n\n\nepochs = 15\nbatch_size = 32\nn_batches = Xtrain.size()[0] // batch_size\n\ncosts = []\ntest_accuracies = []\nfor i in range(epochs):\n cost = 0.\n for j in range(n_batches):\n Xbatch = Xtrain[j*batch_size:(j+1)*batch_size]\n Ybatch = Ytrain[j*batch_size:(j+1)*batch_size]\n cost += train(model, loss, optimizer, Xbatch, Ybatch)\n\n Ypred = predict(model, Xtest)\n acc = np.mean(Ytest == Ypred)\n print(\"Epoch: %d, cost: %f, acc: %.2f\" % (i, cost / n_batches, acc))\n\n # for plotting\n costs.append(cost / n_batches)\n test_accuracies.append(acc)\n\n\n# EXERCISE: plot test cost + training accuracy too\n\n# plot the results\nplt.plot(costs)\nplt.title('Training cost')\nplt.show()\n\nplt.plot(test_accuracies)\nplt.title('Test accuracies')\nplt.show()\n" }, { "path": "ann_class2/pytorch_example2.py", "content": "# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n# Note: is helpful to look at keras_example.py first\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom util import get_normalized_data\n\nimport torch\nfrom torch.autograd import Variable\nfrom torch import optim\n\n\n\n# get the data, same as Theano + Tensorflow examples\n# no need to split now, the fit() function will do it\nXtrain, Xtest, Ytrain, Ytest = get_normalized_data()\n\n# get shapes\n_, D = Xtrain.shape\nK = len(set(Ytrain))\n\n# Note: no need to convert Y to indicator matrix\n\n\n# the model will be a sequence of layers\nmodel = torch.nn.Sequential()\n\n\n# ANN with layers [784] -> [500] -> [300] -> [10]\nmodel.add_module(\"dense1\", torch.nn.Linear(D, 500))\nmodel.add_module(\"relu1\", torch.nn.ReLU())\nmodel.add_module(\"dense2\", torch.nn.Linear(500, 300))\nmodel.add_module(\"relu2\", torch.nn.ReLU())\nmodel.add_module(\"dense3\", torch.nn.Linear(300, K))\n# Note: no final softmax!\n# just like Tensorflow, it's included in cross-entropy function\n\n\n# define a loss function\n# other loss functions can be found here:\n# http://pytorch.org/docs/master/nn.html#loss-functions\nloss = torch.nn.CrossEntropyLoss(size_average=True)\n# Note: this returns a function!\n# e.g. use it like: loss(logits, labels)\n\n\n# define an optimizer\n# other optimizers can be found here:\n# http://pytorch.org/docs/master/optim.html\noptimizer = optim.Adam(model.parameters(), lr=1e-4)\n\n\n\n# define the training procedure\n# i.e. one step of gradient descent\n# there are lots of steps\n# so we encapsulate it in a function\n# Note: inputs and labels are torch tensors\ndef train(model, loss, optimizer, inputs, labels):\n inputs = Variable(inputs, requires_grad=False)\n labels = Variable(labels, requires_grad=False)\n\n # Reset gradient\n optimizer.zero_grad()\n\n # Forward\n logits = model.forward(inputs)\n output = loss.forward(logits, labels)\n\n # Backward\n output.backward()\n\n # Update parameters\n optimizer.step()\n\n # what's the difference between backward() and step()?\n\n return output.item()\n\n\n# similar to train() but not doing the backprop step\ndef get_cost(model, loss, inputs, labels):\n inputs = Variable(inputs, requires_grad=False)\n labels = Variable(labels, requires_grad=False)\n\n # Forward\n logits = model.forward(inputs)\n output = loss.forward(logits, labels)\n\n return output.item()\n\n\n# define the prediction procedure\n# also encapsulate these steps\n# Note: inputs is a torch tensor\ndef predict(model, inputs):\n inputs = Variable(inputs, requires_grad=False)\n logits = model.forward(inputs)\n return logits.data.numpy().argmax(axis=1)\n\n\n# return the accuracy\n# labels is a torch tensor\n# to get back the internal numpy data\n# use the instance method .numpy()\ndef score(model, inputs, labels):\n predictions = predict(model, inputs)\n return np.mean(labels.numpy() == predictions)\n\n\n### prepare for training loop ###\n\n# convert the data arrays into torch tensors\nXtrain = torch.from_numpy(Xtrain).float()\nYtrain = torch.from_numpy(Ytrain).long()\nXtest = torch.from_numpy(Xtest).float()\nYtest = torch.from_numpy(Ytest).long()\n\n# training parameters\nepochs = 15\nbatch_size = 32\nn_batches = Xtrain.size()[0] // batch_size\n\n# things to keep track of\ntrain_costs = []\ntest_costs = []\ntrain_accuracies = []\ntest_accuracies = []\n\n# main training loop\nfor i in range(epochs):\n cost = 0\n test_cost = 0\n for j in range(n_batches):\n Xbatch = Xtrain[j*batch_size:(j+1)*batch_size]\n Ybatch = Ytrain[j*batch_size:(j+1)*batch_size]\n cost += train(model, loss, optimizer, Xbatch, Ybatch)\n\n \n # we could have also calculated the train cost here\n # but I wanted to show you that we could also return it\n # from the train function itself\n train_acc = score(model, Xtrain, Ytrain)\n test_acc = score(model, Xtest, Ytest)\n test_cost = get_cost(model, loss, Xtest, Ytest)\n\n print(\"Epoch: %d, cost: %f, acc: %.2f\" % (i, test_cost, test_acc))\n\n # for plotting\n train_costs.append(cost / n_batches)\n train_accuracies.append(train_acc)\n test_costs.append(test_cost)\n test_accuracies.append(test_acc)\n\n\n\n# plot the results\nplt.plot(train_costs, label='Train cost')\nplt.plot(test_costs, label='Test cost')\nplt.title('Cost')\nplt.legend()\nplt.show()\n\nplt.plot(train_accuracies, label='Train accuracy')\nplt.plot(test_accuracies, label='Test accuracy')\nplt.title('Accuracy')\nplt.legend()\nplt.show()\n" }, { "path": "ann_class2/random_search.py", "content": "# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow\n# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\n# import theano.tensor as T\nfrom theano_ann import ANN\nfrom util import get_spiral, get_clouds\nfrom sklearn.utils import shuffle\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef random_search():\n # get the data and split into train/test\n X, Y = get_spiral()\n # X, Y = get_clouds()\n X, Y = shuffle(X, Y)\n Ntrain = int(0.7*len(X))\n Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain]\n Xtest, Ytest = X[Ntrain:], Y[Ntrain:]\n\n # starting hyperparameters\n M = 20\n nHidden = 2\n log_lr = -4\n log_l2 = -2 # since we always want it to be positive\n max_tries = 30\n \n\n # loop through all possible hyperparameter settings\n best_validation_rate = 0\n best_hls = None\n best_lr = None\n best_l2 = None\n for _ in range(max_tries):\n model = ANN([M]*nHidden)\n model.fit(\n Xtrain, Ytrain,\n learning_rate=10**log_lr, reg=10**log_l2,\n mu=0.99, epochs=3000, show_fig=False\n )\n validation_accuracy = model.score(Xtest, Ytest)\n train_accuracy = model.score(Xtrain, Ytrain)\n print(\n \"validation_accuracy: %.3f, train_accuracy: %.3f, settings: %s, %s, %s\" %\n (validation_accuracy, train_accuracy, [M]*nHidden, log_lr, log_l2)\n )\n if validation_accuracy > best_validation_rate:\n best_validation_rate = validation_accuracy\n best_M = M\n best_nHidden = nHidden\n best_lr = log_lr\n best_l2 = log_l2\n\n # select new hyperparams\n nHidden = best_nHidden + np.random.randint(-1, 2) # -1, 0, or 1\n nHidden = max(1, nHidden)\n M = best_M + np.random.randint(-1, 2)*10\n M = max(10, M)\n log_lr = best_lr + np.random.randint(-1, 2)\n log_l2 = best_l2 + np.random.randint(-1, 2)\n print(\"Best validation_accuracy:\", best_validation_rate)\n print(\"Best settings:\")\n print(\"best_M:\", best_M)\n print(\"best_nHidden:\", best_nHidden)\n print(\"learning_rate:\", best_lr)\n print(\"l2:\", best_l2)\n\n\nif __name__ == '__main__':\n random_search()\n" }, { "path": "ann_class2/rmsprop.py", "content": "# Compare RMSprop vs. constant learning rate\n# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow\n# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nfrom sklearn.utils import shuffle\nimport matplotlib.pyplot as plt\n\nfrom util import get_normalized_data, error_rate, cost, y2indicator\nfrom mlp import forward, derivative_w2, derivative_w1, derivative_b2, derivative_b1\n\n\ndef main():\n max_iter = 20 # make it 30 for sigmoid\n print_period = 10\n\n Xtrain, Xtest, Ytrain, Ytest = get_normalized_data()\n lr = 0.00004\n reg = 0.01\n\n Ytrain_ind = y2indicator(Ytrain)\n Ytest_ind = y2indicator(Ytest)\n\n N, D = Xtrain.shape\n batch_sz = 500\n n_batches = N // batch_sz\n\n M = 300\n K = 10\n W1 = np.random.randn(D, M) / np.sqrt(D)\n b1 = np.zeros(M)\n W2 = np.random.randn(M, K) / np.sqrt(M)\n b2 = np.zeros(K)\n\n # 1. const\n # cost = -16\n LL_batch = []\n CR_batch = []\n for i in range(max_iter):\n for j in range(n_batches):\n Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),]\n Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),]\n pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)\n # print \"first batch cost:\", cost(pYbatch, Ybatch)\n\n # gradients\n gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg*W2\n gb2 = derivative_b2(Ybatch, pYbatch) + reg*b2\n gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1\n gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1\n\n # updates\n W2 -= lr*gW2\n b2 -= lr*gb2\n W1 -= lr*gW1\n b1 -= lr*gb1\n\n if j % print_period == 0:\n # calculate just for LL\n pY, _ = forward(Xtest, W1, b1, W2, b2)\n # print \"pY:\", pY\n ll = cost(pY, Ytest_ind)\n LL_batch.append(ll)\n print(\"Cost at iteration i=%d, j=%d: %.6f\" % (i, j, ll))\n\n err = error_rate(pY, Ytest)\n CR_batch.append(err)\n print(\"Error rate:\", err)\n\n pY, _ = forward(Xtest, W1, b1, W2, b2)\n print(\"Final error rate:\", error_rate(pY, Ytest))\n\n\n # 2. RMSprop\n W1 = np.random.randn(D, M) / np.sqrt(D)\n b1 = np.zeros(M)\n W2 = np.random.randn(M, K) / np.sqrt(M)\n b2 = np.zeros(K)\n LL_rms = []\n CR_rms = []\n lr0 = 0.001 # if you set this too high you'll get NaN!\n cache_W2 = 1\n cache_b2 = 1\n cache_W1 = 1\n cache_b1 = 1\n decay_rate = 0.999\n eps = 1e-10\n for i in range(max_iter):\n for j in range(n_batches):\n Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),]\n Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),]\n pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)\n # print \"first batch cost:\", cost(pYbatch, Ybatch)\n\n # gradients\n gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg*W2\n gb2 = derivative_b2(Ybatch, pYbatch) + reg*b2\n gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1\n gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1\n\n # caches\n cache_W2 = decay_rate*cache_W2 + (1 - decay_rate)*gW2*gW2\n cache_b2 = decay_rate*cache_b2 + (1 - decay_rate)*gb2*gb2\n cache_W1 = decay_rate*cache_W1 + (1 - decay_rate)*gW1*gW1\n cache_b1 = decay_rate*cache_b1 + (1 - decay_rate)*gb1*gb1\n\n # updates\n W2 -= lr0 * gW2 / (np.sqrt(cache_W2) + eps)\n b2 -= lr0 * gb2 / (np.sqrt(cache_b2) + eps)\n W1 -= lr0 * gW1 / (np.sqrt(cache_W1) + eps)\n b1 -= lr0 * gb1 / (np.sqrt(cache_b1) + eps)\n\n if j % print_period == 0:\n # calculate just for LL\n pY, _ = forward(Xtest, W1, b1, W2, b2)\n # print \"pY:\", pY\n ll = cost(pY, Ytest_ind)\n LL_rms.append(ll)\n print(\"Cost at iteration i=%d, j=%d: %.6f\" % (i, j, ll))\n\n err = error_rate(pY, Ytest)\n CR_rms.append(err)\n print(\"Error rate:\", err)\n\n pY, _ = forward(Xtest, W1, b1, W2, b2)\n print(\"Final error rate:\", error_rate(pY, Ytest))\n\n plt.plot(LL_batch, label='const')\n plt.plot(LL_rms, label='rms')\n plt.legend()\n plt.show()\n\n\nif __name__ == '__main__':\n main()" }, { "path": "ann_class2/rmsprop_test.py", "content": "# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Activation\nfrom util import get_normalized_data, y2indicator\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport tensorflow as tf\n\nimport theano\nimport theano.tensor as T\n\n\n# RMSprop experiment\n# to compare TF / Keras / Theano\n\nN = 10\nD = 2\nX = np.random.randn(N, D).astype(np.float32)\nw = np.array([0.5, -0.5], dtype=np.float32)\nY = X.dot(w) + 1\nY = Y.reshape(-1, 1)\n\n\n\n# keras\n# the model will be a sequence of layers\nmodel = Sequential()\nmodel.add(Dense(units=1, input_dim=D))\n\n\n# copy the weights for later\nweights = model.layers[0].get_weights()\nw0 = weights[0].copy()\nb0 = weights[1].copy()\n\n\nmodel.compile(\n loss='mean_squared_error',\n optimizer='rmsprop',\n)\n\n\nr = model.fit(X, Y, epochs=15, batch_size=10)\n\n\n# print the available keys\nprint(r.history.keys())\n\n\n\n# tf\ninputs = tf.placeholder(tf.float32, shape=(None, 2))\ntargets = tf.placeholder(tf.float32, shape=(None, 1))\ntfw = tf.Variable(w0)\ntfb = tf.Variable(b0)\npred = tf.matmul(inputs, tfw) + tfb\n\nloss = tf.reduce_mean(tf.square(targets - pred))\ntrain_op = tf.train.RMSPropOptimizer(1e-3, epsilon=1e-8).minimize(loss)\n\ntflosses = []\ninit = tf.global_variables_initializer()\nwith tf.Session() as sess:\n sess.run(init)\n for e in range(15):\n _, l = sess.run([train_op, loss], feed_dict={inputs: X, targets: Y})\n tflosses.append(l)\n\n\n\n# theano\ndef rmsprop(cost, params, lr=1e-3, decay=0.9, eps=1e-8):\n # return updates\n lr = np.float32(lr)\n decay = np.float32(decay)\n eps = np.float32(eps)\n\n updates = []\n grads = T.grad(cost, params)\n\n # tf-like\n # caches = [theano.shared(np.ones_like(p.get_value(), dtype=np.float32)) for p in params]\n\n # keras-like\n caches = [theano.shared(np.zeros_like(p.get_value(), dtype=np.float32)) for p in params]\n \n new_caches = []\n for c, g in zip(caches, grads):\n new_c = decay*c + (np.float32(1) - decay)*g*g\n updates.append((c, new_c))\n new_caches.append(new_c)\n\n for p, new_c, g in zip(params, new_caches, grads):\n new_p = p - lr*g / T.sqrt(new_c + eps)\n updates.append((p, new_p))\n\n return updates\n\nthX = T.matrix('X')\nthY = T.matrix('Y')\nthw = theano.shared(w0)\nthb = theano.shared(b0)\nthP = thX.dot(thw) + thb\ncost = T.mean((thY - thP)**2)\nparams = [thw, thb]\nupdates = rmsprop(cost, params)\n\ntrain_op = theano.function(\n inputs=[thX, thY],\n outputs=cost,\n updates=updates,\n)\n\nthlosses = []\nfor e in range(15):\n c = train_op(X, Y)\n thlosses.append(c)\n\n\n# plot results\nplt.plot(r.history['loss'], label='keras loss')\nplt.plot(tflosses, label='tf loss')\nplt.plot(thlosses, label='theano loss')\nplt.legend()\nplt.show()\n\n\n" }, { "path": "ann_class2/sgd.py", "content": "# In this file we compare the progression of the cost function vs. iteration\n# for 3 cases:\n# 1) full gradient descent\n# 2) mini-batch gradient descent\n# 3) stochastic gradient descent\n#\n# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow\n# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.utils import shuffle\nfrom datetime import datetime\n\nfrom util import get_normalized_data, forward, error_rate, cost, gradW, gradb, y2indicator\n\n\ndef main():\n Xtrain, Xtest, Ytrain, Ytest = get_normalized_data()\n print(\"Performing logistic regression...\")\n\n N, D = Xtrain.shape\n Ytrain_ind = y2indicator(Ytrain)\n Ytest_ind = y2indicator(Ytest)\n\n # 1. full\n W = np.random.randn(D, 10) / np.sqrt(D)\n W0 = W.copy() # save for later\n b = np.zeros(10)\n test_losses_full = []\n lr = 0.9\n reg = 0.\n t0 = datetime.now()\n last_dt = 0\n intervals = []\n for i in range(50):\n p_y = forward(Xtrain, W, b)\n\n gW = gradW(Ytrain_ind, p_y, Xtrain) / N\n gb = gradb(Ytrain_ind, p_y) / N\n\n W += lr*(gW - reg*W)\n b += lr*(gb - reg*b)\n\n p_y_test = forward(Xtest, W, b)\n test_loss = cost(p_y_test, Ytest_ind)\n dt = (datetime.now() - t0).total_seconds()\n\n # save these\n dt2 = dt - last_dt\n last_dt = dt\n intervals.append(dt2)\n\n test_losses_full.append([dt, test_loss])\n if (i + 1) % 10 == 0:\n print(\"Cost at iteration %d: %.6f\" % (i + 1, test_loss))\n p_y = forward(Xtest, W, b)\n print(\"Final error rate:\", error_rate(p_y, Ytest))\n print(\"Elapsted time for full GD:\", datetime.now() - t0)\n\n # save the max time so we don't surpass it in subsequent iterations\n max_dt = dt\n avg_interval_dt = np.mean(intervals)\n\n\n # 2. stochastic\n W = W0.copy()\n b = np.zeros(10)\n test_losses_sgd = []\n lr = 0.001\n reg = 0.\n\n t0 = datetime.now()\n last_dt_calculated_loss = 0\n done = False\n for i in range(50): # takes very long since we're computing cost for 41k samples\n tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)\n for n in range(N):\n x = tmpX[n,:].reshape(1,D)\n y = tmpY[n,:].reshape(1,10)\n p_y = forward(x, W, b)\n\n gW = gradW(y, p_y, x)\n gb = gradb(y, p_y)\n\n W += lr*(gW - reg*W)\n b += lr*(gb - reg*b)\n\n dt = (datetime.now() - t0).total_seconds()\n dt2 = dt - last_dt_calculated_loss\n\n if dt2 > avg_interval_dt:\n last_dt_calculated_loss = dt\n p_y_test = forward(Xtest, W, b)\n test_loss = cost(p_y_test, Ytest_ind)\n test_losses_sgd.append([dt, test_loss])\n\n # time to quit\n if dt > max_dt:\n done = True\n break\n if done:\n break\n\n if (i + 1) % 1 == 0:\n print(\"Cost at iteration %d: %.6f\" % (i + 1, test_loss))\n p_y = forward(Xtest, W, b)\n print(\"Final error rate:\", error_rate(p_y, Ytest))\n print(\"Elapsted time for SGD:\", datetime.now() - t0)\n\n\n # 3. mini-batch\n W = W0.copy()\n b = np.zeros(10)\n test_losses_batch = []\n batch_sz = 500\n lr = 0.08\n reg = 0.\n n_batches = int(np.ceil(N / batch_sz))\n\n\n t0 = datetime.now()\n last_dt_calculated_loss = 0\n done = False\n for i in range(50):\n tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)\n for j in range(n_batches):\n x = tmpX[j*batch_sz:(j + 1)*batch_sz,:]\n y = tmpY[j*batch_sz:(j + 1)*batch_sz,:]\n p_y = forward(x, W, b)\n\n current_batch_sz = len(x)\n gW = gradW(y, p_y, x) / current_batch_sz\n gb = gradb(y, p_y) / current_batch_sz\n\n W += lr*(gW - reg*W)\n b += lr*(gb - reg*b)\n\n dt = (datetime.now() - t0).total_seconds()\n dt2 = dt - last_dt_calculated_loss\n\n if dt2 > avg_interval_dt:\n last_dt_calculated_loss = dt\n p_y_test = forward(Xtest, W, b)\n test_loss = cost(p_y_test, Ytest_ind)\n test_losses_batch.append([dt, test_loss])\n\n # time to quit\n if dt > max_dt:\n done = True\n break\n if done:\n break\n\n if (i + 1) % 10 == 0:\n print(\"Cost at iteration %d: %.6f\" % (i + 1, test_loss))\n p_y = forward(Xtest, W, b)\n print(\"Final error rate:\", error_rate(p_y, Ytest))\n print(\"Elapsted time for mini-batch GD:\", datetime.now() - t0)\n\n\n # convert to numpy arrays\n test_losses_full = np.array(test_losses_full)\n test_losses_sgd = np.array(test_losses_sgd)\n test_losses_batch = np.array(test_losses_batch)\n\n \n plt.plot(test_losses_full[:,0], test_losses_full[:,1], label=\"full\")\n plt.plot(test_losses_sgd[:,0], test_losses_sgd[:,1], label=\"sgd\")\n plt.plot(test_losses_batch[:,0], test_losses_batch[:,1], label=\"mini-batch\")\n plt.legend()\n plt.show()\n\n\n\nif __name__ == '__main__':\n main()" }, { "path": "ann_class2/tensorflow1.py", "content": "# This tutorial is analogous to theano1.py\n# It introduces basic variables and functions\n# and shows how you can optimize a function.\n# I compare this to theano1.py multiple times.\n# So you might want to check that out first.\n\n# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow\n# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport tensorflow as tf\n\n\n# you have to specify the type\nA = tf.placeholder(tf.float32, shape=(5, 5), name='A')\n\n\n# but shape and name are optional\nv = tf.placeholder(tf.float32)\n\n\n# I think this name is more appropriate than 'dot'\nw = tf.matmul(A, v)\n\n\n# similar to Theano, you need to \"feed\" the variables values.\n# In TensorFlow you do the \"actual work\" in a \"session\".\n\nwith tf.Session() as session:\n # the values are fed in via the appropriately named argument \"feed_dict\"\n # v needs to be of shape=(5, 1) not just shape=(5,)\n # it's more like \"real\" matrix multiplication\n output = session.run(w, feed_dict={A: np.random.randn(5, 5), v: np.random.randn(5, 1)})\n\n # what's this output that is returned by the session? let's print it\n print(output, type(output))\n\n # luckily, the output type is just a numpy array. back to safety!\n\n\n# TensorFlow variables are like Theano shared variables.\n# But Theano variables are like TensorFlow placeholders.\n# Are you confused yet?\n\n# A tf variable can be initialized with a numpy array or a tf array\n# or more correctly, anything that can be turned into a tf tensor\nshape = (2, 2)\nx = tf.Variable(tf.random_normal(shape))\n# x = tf.Variable(np.random.randn(2, 2))\nt = tf.Variable(0) # a scalar\n\n# you need to \"initialize\" the variables first\ninit = tf.global_variables_initializer()\n\nwith tf.Session() as session:\n out = session.run(init) # and then \"run\" the init operation\n print(out) # it's just None\n\n # eval() in tf is like get_value() in Theano\n print(x.eval()) # the initial value of x\n print(t.eval())\n\n\n# let's now try to find the minimum of a simple cost function like we did in Theano\nu = tf.Variable(20.0)\ncost = u*u + u + 1.0\n\n# One difference between Theano and TensorFlow is that you don't write the updates\n# yourself in TensorFlow. You choose an optimizer that implements the algorithm you want.\n# 0.3 is the learning rate. Documentation lists the params.\ntrain_op = tf.train.GradientDescentOptimizer(0.3).minimize(cost)\n\n# let's run a session again\ninit = tf.global_variables_initializer()\nwith tf.Session() as session:\n session.run(init)\n\n # Strangely, while the weight update is automated, the loop itself is not.\n # So we'll just call train_op until convergence.\n # This is useful for us anyway since we want to track the cost function.\n for i in range(12):\n session.run(train_op)\n print(\"i = %d, cost = %.3f, u = %.3f\" % (i, cost.eval(), u.eval()))\n\n" }, { "path": "ann_class2/tensorflow2.py", "content": "# 2-hidden layer NN in TensorFlow\n# This code is not optimized for speed.\n# It's just to get something working, using the principles we know.\n\n# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow\n# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport tensorflow as tf\n\nimport matplotlib.pyplot as plt\n\nfrom util import get_normalized_data, y2indicator\n\n\ndef error_rate(p, t):\n return np.mean(p != t)\n\n\n# copy this first part from theano2.py\ndef main():\n # step 1: get the data and define all the usual variables\n Xtrain, Xtest, Ytrain, Ytest = get_normalized_data()\n\n max_iter = 15\n print_period = 50\n\n lr = 0.00004\n reg = 0.01\n\n Ytrain_ind = y2indicator(Ytrain)\n Ytest_ind = y2indicator(Ytest)\n\n N, D = Xtrain.shape\n batch_sz = 500\n n_batches = N // batch_sz\n\n # add an extra layer just for fun\n M1 = 300\n M2 = 100\n K = 10\n W1_init = np.random.randn(D, M1) / np.sqrt(D)\n b1_init = np.zeros(M1)\n W2_init = np.random.randn(M1, M2) / np.sqrt(M1)\n b2_init = np.zeros(M2)\n W3_init = np.random.randn(M2, K) / np.sqrt(M2)\n b3_init = np.zeros(K)\n\n\n # define variables and expressions\n X = tf.placeholder(tf.float32, shape=(None, D), name='X')\n T = tf.placeholder(tf.float32, shape=(None, K), name='T')\n W1 = tf.Variable(W1_init.astype(np.float32))\n b1 = tf.Variable(b1_init.astype(np.float32))\n W2 = tf.Variable(W2_init.astype(np.float32))\n b2 = tf.Variable(b2_init.astype(np.float32))\n W3 = tf.Variable(W3_init.astype(np.float32))\n b3 = tf.Variable(b3_init.astype(np.float32))\n\n # define the model\n Z1 = tf.nn.relu( tf.matmul(X, W1) + b1 )\n Z2 = tf.nn.relu( tf.matmul(Z1, W2) + b2 )\n Yish = tf.matmul(Z2, W3) + b3 # remember, the cost function does the softmaxing! weird, right?\n\n # softmax_cross_entropy_with_logits take in the \"logits\"\n # if you wanted to know the actual output of the neural net,\n # you could pass \"Yish\" into tf.nn.softmax(logits)\n cost = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits_v2(logits=Yish, labels=T))\n\n # we choose the optimizer but don't implement the algorithm ourselves\n # let's go with RMSprop, since we just learned about it.\n # it includes momentum!\n train_op = tf.train.RMSPropOptimizer(lr, decay=0.99, momentum=0.9).minimize(cost)\n\n # we'll use this to calculate the error rate\n predict_op = tf.argmax(Yish, 1)\n\n costs = []\n init = tf.global_variables_initializer()\n with tf.Session() as session:\n session.run(init)\n\n for i in range(max_iter):\n for j in range(n_batches):\n Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),]\n Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),]\n\n session.run(train_op, feed_dict={X: Xbatch, T: Ybatch})\n if j % print_period == 0:\n test_cost = session.run(cost, feed_dict={X: Xtest, T: Ytest_ind})\n prediction = session.run(predict_op, feed_dict={X: Xtest})\n err = error_rate(prediction, Ytest)\n print(\"Cost / err at iteration i=%d, j=%d: %.3f / %.3f\" % (i, j, test_cost, err))\n costs.append(test_cost)\n\n plt.plot(costs)\n plt.show()\n # increase max_iter and notice how the test cost starts to increase.\n # are we overfitting by adding that extra layer?\n # how would you add regularization to this model?\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "ann_class2/tf_with_save.py", "content": "# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport os\nimport json\nimport numpy as np\nimport tensorflow as tf\n\nimport matplotlib.pyplot as plt\n\nfrom util import get_normalized_data, y2indicator\n\n\ndef error_rate(p, t):\n return np.mean(p != t)\n\n\nclass TFLogistic:\n def __init__(self, savefile, D=None, K=None):\n self.savefile = savefile\n if D and K:\n # we can define some parts in the model to be able to make predictions\n self.build(D, K)\n\n def build(self, D, K):\n W0 = np.random.randn(D, K) * np.sqrt(2.0 / D)\n b0 = np.zeros(K)\n\n # define variables and expressions\n self.inputs = tf.placeholder(tf.float32, shape=(None, D), name='inputs')\n self.targets = tf.placeholder(tf.int64, shape=(None,), name='targets')\n self.W = tf.Variable(W0.astype(np.float32), name='W')\n self.b = tf.Variable(b0.astype(np.float32), name='b')\n\n # variables must exist when calling this\n # try putting this line in the constructor and see what happens\n self.saver = tf.train.Saver({'W': self.W, 'b': self.b})\n\n logits = tf.matmul(self.inputs, self.W) + self.b\n cost = tf.reduce_mean(\n tf.nn.sparse_softmax_cross_entropy_with_logits(\n logits=logits,\n labels=self.targets\n )\n )\n self.predict_op = tf.argmax(logits, 1)\n return cost\n\n\n def fit(self, X, Y, Xtest, Ytest):\n N, D = X.shape\n K = len(set(Y))\n\n # hyperparams\n max_iter = 30\n lr = 1e-3\n mu = 0.9\n regularization = 1e-1\n batch_sz = 100\n n_batches = N // batch_sz\n\n cost = self.build(D, K)\n l2_penalty = regularization*tf.reduce_mean(self.W**2) / 2\n cost += l2_penalty\n train_op = tf.train.MomentumOptimizer(lr, momentum=mu).minimize(cost)\n\n costs = []\n init = tf.global_variables_initializer()\n with tf.Session() as session:\n session.run(init)\n\n for i in range(max_iter):\n for j in range(n_batches):\n Xbatch = X[j*batch_sz:(j*batch_sz + batch_sz),]\n Ybatch = Y[j*batch_sz:(j*batch_sz + batch_sz),]\n\n session.run(train_op, feed_dict={self.inputs: Xbatch, self.targets: Ybatch})\n if j % 200 == 0:\n test_cost = session.run(cost, feed_dict={self.inputs: Xtest, self.targets: Ytest})\n Ptest = session.run(self.predict_op, feed_dict={self.inputs: Xtest})\n err = error_rate(Ptest, Ytest)\n print(\"Cost / err at iteration i=%d, j=%d: %.3f / %.3f\" % (i, j, test_cost, err))\n costs.append(test_cost)\n\n # save the model\n self.saver.save(session, self.savefile)\n\n # save dimensions for later\n self.D = D\n self.K = K\n\n plt.plot(costs)\n plt.show()\n\n\n def predict(self, X):\n with tf.Session() as session:\n # restore the model\n self.saver.restore(session, self.savefile)\n P = session.run(self.predict_op, feed_dict={self.inputs: X})\n return P\n\n\n def score(self, X, Y):\n return 1 - error_rate(self.predict(X), Y)\n\n def save(self, filename):\n j = {\n 'D': self.D,\n 'K': self.K,\n 'model': self.savefile\n }\n with open(filename, 'w') as f:\n json.dump(j, f)\n\n @staticmethod\n def load(filename):\n with open(filename) as f:\n j = json.load(f)\n return TFLogistic(j['model'], j['D'], j['K'])\n\n\ndef main():\n Xtrain, Xtest, Ytrain, Ytest = get_normalized_data()\n\n model = TFLogistic(\"./tf.model\")\n model.fit(Xtrain, Ytrain, Xtest, Ytest)\n\n # test out restoring the model via the predict function\n print(\"final train accuracy:\", model.score(Xtrain, Ytrain))\n print(\"final test accuracy:\", model.score(Xtest, Ytest))\n\n # save the model\n model.save(\"my_trained_model.json\")\n\n # load and score again\n model = TFLogistic.load(\"my_trained_model.json\")\n print(\"final train accuracy (after reload):\", model.score(Xtrain, Ytrain))\n print(\"final test accuracy (after reload):\", model.score(Xtest, Ytest))\n\n\nif __name__ == '__main__':\n main()" }, { "path": "ann_class2/theano1.py", "content": "# Theano basics.\n# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow\n# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport theano.tensor as T\n\n# just some different types of variables\nc = T.scalar('c')\nv = T.vector('v')\nA = T.matrix('A')\n\n\n# we can define a matrix multiplication\nw = A.dot(v)\n\n# how do these variables actually take on values?\nimport theano\n\nmatrix_times_vector = theano.function(inputs=[A, v], outputs=w)\n\n# let's import numpy so we can create real arrays\nimport numpy as np\nA_val = np.array([[1,2], [3,4]])\nv_val = np.array([5,6])\n\nw_val = matrix_times_vector(A_val, v_val)\nprint(w_val)\n\n# let's create a shared variable to we can do gradient descent\n# this adds another layer of complexity to the theano function\n\nx = theano.shared(20.0, 'x')\n\n# the first argument is its initial value, the second is its name\n\n# a cost function that has a minimum value\ncost = x*x + x + 1\n\n# in theano, you don't have to compute gradients yourself!\nx_update = x - 0.3*T.grad(cost, x)\n\n# x is not an \"input\", it's a thing you update\n# in later examples, data and labels would go into the inputs\n# and model params would go in the updates\n# updates takes in a list of tuples, each tuple has 2 things in it:\n# 1) the shared variable to update, 2) the update expression\ntrain = theano.function(inputs=[], outputs=cost, updates=[(x, x_update)])\n\n# write your own loop to call the training function.\n# it has no arguments!\nfor i in range(25):\n cost_val = train()\n print(cost_val)\n\n# print the optimal value of x\nprint(x.get_value())\n" }, { "path": "ann_class2/theano2.py", "content": "# A 1-hidden-layer neural network in Theano.\n# This code is not optimized for speed.\n# It's just to get something working, using the principles we know.\n\n# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow\n# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport theano\nimport theano.tensor as T\nimport matplotlib.pyplot as plt\n\nfrom util import get_normalized_data, y2indicator\n\n\ndef error_rate(p, t):\n return np.mean(p != t)\n\n\ndef relu(a):\n return a * (a > 0)\n\n\ndef main():\n # step 1: get the data and define all the usual variables\n Xtrain, Xtest, Ytrain, Ytest = get_normalized_data()\n\n max_iter = 20\n print_period = 10\n\n lr = 0.0004\n reg = 0.01\n\n Xtrain = Xtrain.astype(np.float32)\n Ytrain = Ytrain.astype(np.float32)\n Xtest = Xtest.astype(np.float32)\n Ytest = Ytest.astype(np.float32)\n Ytrain_ind = y2indicator(Ytrain).astype(np.float32)\n Ytest_ind = y2indicator(Ytest).astype(np.float32)\n\n N, D = Xtrain.shape\n batch_sz = 500\n n_batches = N // batch_sz\n\n M = 300\n K = 10\n W1_init = np.random.randn(D, M) / np.sqrt(D)\n b1_init = np.zeros(M)\n W2_init = np.random.randn(M, K) / np.sqrt(M)\n b2_init = np.zeros(K)\n\n # step 2: define theano variables and expressions\n thX = T.matrix('X')\n thT = T.matrix('T')\n W1 = theano.shared(W1_init, 'W1')\n b1 = theano.shared(b1_init, 'b1')\n W2 = theano.shared(W2_init, 'W2')\n b2 = theano.shared(b2_init, 'b2')\n\n # we can use the built-in theano functions to do relu and softmax\n thZ = relu( thX.dot(W1) + b1 ) # relu is new in version 0.7.1 but just in case you don't have it\n thY = T.nnet.softmax( thZ.dot(W2) + b2 )\n\n # define the cost function and prediction\n cost = -(thT * T.log(thY)).sum() + reg*((W1*W1).sum() + (b1*b1).sum() + (W2*W2).sum() + (b2*b2).sum())\n prediction = T.argmax(thY, axis=1)\n\n # step 3: training expressions and functions\n # we can just include regularization as part of the cost because it is also automatically differentiated!\n update_W1 = W1 - lr*T.grad(cost, W1)\n update_b1 = b1 - lr*T.grad(cost, b1)\n update_W2 = W2 - lr*T.grad(cost, W2)\n update_b2 = b2 - lr*T.grad(cost, b2)\n\n train = theano.function(\n inputs=[thX, thT],\n updates=[(W1, update_W1), (b1, update_b1), (W2, update_W2), (b2, update_b2)],\n )\n\n # create another function for this because we want it over the whole dataset\n get_prediction = theano.function(\n inputs=[thX, thT],\n outputs=[cost, prediction],\n )\n\n costs = []\n for i in range(max_iter):\n for j in range(n_batches):\n Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),]\n Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),]\n\n train(Xbatch, Ybatch)\n if j % print_period == 0:\n cost_val, prediction_val = get_prediction(Xtest, Ytest_ind)\n err = error_rate(prediction_val, Ytest)\n print(\"Cost / err at iteration i=%d, j=%d: %.3f / %.3f\" % (i, j, cost_val, err))\n costs.append(cost_val)\n\n plt.plot(costs)\n plt.show()\n\n # how would you incorporate momentum into the gradient descent procedure?\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "ann_class2/theano_ann.py", "content": "# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow\n# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport theano\nimport theano.tensor as T\nfrom sklearn.utils import shuffle\n\n\ndef init_weight(M1, M2):\n return np.random.randn(M1, M2) * np.sqrt(2.0 / M1)\n\n\nclass HiddenLayer(object):\n def __init__(self, M1, M2, f):\n self.M1 = M1\n self.M2 = M2\n self.f = f\n W = init_weight(M1, M2)\n b = np.zeros(M2)\n self.W = theano.shared(W)\n self.b = theano.shared(b)\n self.params = [self.W, self.b]\n\n def forward(self, X):\n if self.f == T.nnet.relu:\n return self.f(X.dot(self.W) + self.b, alpha=0.1)\n return self.f(X.dot(self.W) + self.b)\n\n\nclass ANN(object):\n def __init__(self, hidden_layer_sizes):\n self.hidden_layer_sizes = hidden_layer_sizes\n\n def fit(self, X, Y, activation=T.nnet.relu, learning_rate=1e-3, mu=0.0, reg=0, epochs=100, batch_sz=None, print_period=100, show_fig=True):\n X = X.astype(np.float32)\n Y = Y.astype(np.int32)\n\n # initialize hidden layers\n N, D = X.shape\n self.layers = []\n M1 = D\n for M2 in self.hidden_layer_sizes:\n h = HiddenLayer(M1, M2, activation)\n self.layers.append(h)\n M1 = M2\n \n # final layer\n K = len(set(Y))\n # print(\"K:\", K)\n h = HiddenLayer(M1, K, T.nnet.softmax)\n self.layers.append(h)\n\n if batch_sz is None:\n batch_sz = N\n\n # collect params for later use\n self.params = []\n for h in self.layers:\n self.params += h.params\n\n # for momentum\n dparams = [theano.shared(np.zeros_like(p.get_value())) for p in self.params]\n\n # set up theano functions and variables\n thX = T.matrix('X')\n thY = T.ivector('Y')\n p_y_given_x = self.forward(thX)\n\n rcost = reg*T.mean([(p*p).sum() for p in self.params])\n cost = -T.mean(T.log(p_y_given_x[T.arange(thY.shape[0]), thY])) #+ rcost\n prediction = T.argmax(p_y_given_x, axis=1)\n grads = T.grad(cost, self.params)\n\n # momentum only\n updates = [\n (p, p + mu*dp - learning_rate*g) for p, dp, g in zip(self.params, dparams, grads)\n ] + [\n (dp, mu*dp - learning_rate*g) for dp, g in zip(dparams, grads)\n ]\n\n train_op = theano.function(\n inputs=[thX, thY],\n outputs=[cost, prediction],\n updates=updates,\n )\n\n self.predict_op = theano.function(\n inputs=[thX],\n outputs=prediction,\n )\n\n n_batches = N // batch_sz\n costs = []\n for i in range(epochs):\n if n_batches > 1:\n X, Y = shuffle(X, Y)\n for j in range(n_batches):\n Xbatch = X[j*batch_sz:(j*batch_sz+batch_sz)]\n Ybatch = Y[j*batch_sz:(j*batch_sz+batch_sz)]\n\n c, p = train_op(Xbatch, Ybatch)\n costs.append(c)\n if (j+1) % print_period == 0:\n print(\"i:\", i, \"j:\", j, \"nb:\", n_batches, \"cost:\", c)\n \n if show_fig:\n plt.plot(costs)\n plt.show()\n\n def forward(self, X):\n out = X\n for h in self.layers:\n out = h.forward(out)\n return out\n\n def score(self, X, Y):\n P = self.predict_op(X)\n return np.mean(Y == P)\n\n def predict(self, X):\n return self.predict_op(X)\n" }, { "path": "ann_class2/util.py", "content": "from __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n# Some utility functions we need for the class.\n# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow\n# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\n\n# Note: run this from the current folder it is in.\n\nimport os\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.decomposition import PCA\nfrom sklearn.linear_model import LogisticRegression\n\n\ndef get_clouds():\n Nclass = 500\n D = 2\n\n X1 = np.random.randn(Nclass, D) + np.array([0, -2])\n X2 = np.random.randn(Nclass, D) + np.array([2, 2])\n X3 = np.random.randn(Nclass, D) + np.array([-2, 2])\n X = np.vstack([X1, X2, X3])\n\n Y = np.array([0]*Nclass + [1]*Nclass + [2]*Nclass)\n return X, Y\n\n\ndef get_spiral():\n # Idea: radius -> low...high\n # (don't start at 0, otherwise points will be \"mushed\" at origin)\n # angle = low...high proportional to radius\n # [0, 2pi/6, 4pi/6, ..., 10pi/6] --> [pi/2, pi/3 + pi/2, ..., ]\n # x = rcos(theta), y = rsin(theta) as usual\n\n radius = np.linspace(1, 10, 100)\n thetas = np.empty((6, 100))\n for i in range(6):\n start_angle = np.pi*i / 3.0\n end_angle = start_angle + np.pi / 2\n points = np.linspace(start_angle, end_angle, 100)\n thetas[i] = points\n\n # convert into cartesian coordinates\n x1 = np.empty((6, 100))\n x2 = np.empty((6, 100))\n for i in range(6):\n x1[i] = radius * np.cos(thetas[i])\n x2[i] = radius * np.sin(thetas[i])\n\n # inputs\n X = np.empty((600, 2))\n X[:,0] = x1.flatten()\n X[:,1] = x2.flatten()\n\n # add noise\n X += np.random.randn(600, 2)*0.5\n\n # targets\n Y = np.array([0]*100 + [1]*100 + [0]*100 + [1]*100 + [0]*100 + [1]*100)\n return X, Y\n\n\n\ndef get_transformed_data():\n print(\"Reading in and transforming data...\")\n\n if not os.path.exists('../large_files/train.csv'):\n print('Looking for ../large_files/train.csv')\n print('You have not downloaded the data and/or not placed the files in the correct location.')\n print('Please get the data from: https://www.kaggle.com/c/digit-recognizer')\n print('Place train.csv in the folder large_files adjacent to the class folder')\n exit()\n\n df = pd.read_csv('../large_files/train.csv')\n data = df.values.astype(np.float32)\n np.random.shuffle(data)\n\n X = data[:, 1:]\n Y = data[:, 0].astype(np.int32)\n\n Xtrain = X[:-1000]\n Ytrain = Y[:-1000]\n Xtest = X[-1000:]\n Ytest = Y[-1000:]\n\n # center the data\n mu = Xtrain.mean(axis=0)\n Xtrain = Xtrain - mu\n Xtest = Xtest - mu\n\n # transform the data\n pca = PCA()\n Ztrain = pca.fit_transform(Xtrain)\n Ztest = pca.transform(Xtest)\n\n plot_cumulative_variance(pca)\n\n # take first 300 cols of Z\n Ztrain = Ztrain[:, :300]\n Ztest = Ztest[:, :300]\n\n # normalize Z\n mu = Ztrain.mean(axis=0)\n std = Ztrain.std(axis=0)\n Ztrain = (Ztrain - mu) / std\n Ztest = (Ztest - mu) / std\n\n return Ztrain, Ztest, Ytrain, Ytest\n\n\ndef get_normalized_data():\n print(\"Reading in and transforming data...\")\n\n if not os.path.exists('../large_files/train.csv'):\n print('Looking for ../large_files/train.csv')\n print('You have not downloaded the data and/or not placed the files in the correct location.')\n print('Please get the data from: https://www.kaggle.com/c/digit-recognizer')\n print('Place train.csv in the folder large_files adjacent to the class folder')\n exit()\n\n df = pd.read_csv('../large_files/train.csv')\n data = df.values.astype(np.float32)\n np.random.shuffle(data)\n X = data[:, 1:]\n Y = data[:, 0]\n\n Xtrain = X[:-1000]\n Ytrain = Y[:-1000]\n Xtest = X[-1000:]\n Ytest = Y[-1000:]\n\n # normalize the data\n mu = Xtrain.mean(axis=0)\n std = Xtrain.std(axis=0)\n np.place(std, std == 0, 1)\n Xtrain = (Xtrain - mu) / std\n Xtest = (Xtest - mu) / std\n \n return Xtrain, Xtest, Ytrain, Ytest\n\n\ndef plot_cumulative_variance(pca):\n P = []\n for p in pca.explained_variance_ratio_:\n if len(P) == 0:\n P.append(p)\n else:\n P.append(p + P[-1])\n plt.plot(P)\n plt.show()\n return P\n\n\ndef forward(X, W, b):\n # softmax\n a = X.dot(W) + b\n expa = np.exp(a)\n y = expa / expa.sum(axis=1, keepdims=True)\n return y\n\n\ndef predict(p_y):\n return np.argmax(p_y, axis=1)\n\n\ndef error_rate(p_y, t):\n prediction = predict(p_y)\n return np.mean(prediction != t)\n\n\ndef cost(p_y, t):\n tot = t * np.log(p_y)\n return -tot.sum()\n\n\ndef gradW(t, y, X):\n return X.T.dot(t - y)\n\n\ndef gradb(t, y):\n return (t - y).sum(axis=0)\n\n\ndef y2indicator(y):\n N = len(y)\n y = y.astype(np.int32)\n ind = np.zeros((N, 10))\n for i in range(N):\n ind[i, y[i]] = 1\n return ind\n\n\ndef benchmark_full():\n Xtrain, Xtest, Ytrain, Ytest = get_normalized_data()\n\n print(\"Performing logistic regression...\")\n # lr = LogisticRegression(solver='lbfgs')\n\n\n # convert Ytrain and Ytest to (N x K) matrices of indicator variables\n N, D = Xtrain.shape\n Ytrain_ind = y2indicator(Ytrain)\n Ytest_ind = y2indicator(Ytest)\n\n W = np.random.randn(D, 10) / np.sqrt(D)\n b = np.zeros(10)\n LL = []\n LLtest = []\n CRtest = []\n\n # reg = 1\n # learning rate 0.0001 is too high, 0.00005 is also too high\n # 0.00003 / 2000 iterations => 0.363 error, -7630 cost\n # 0.00004 / 1000 iterations => 0.295 error, -7902 cost\n # 0.00004 / 2000 iterations => 0.321 error, -7528 cost\n\n # reg = 0.1, still around 0.31 error\n # reg = 0.01, still around 0.31 error\n lr = 0.00004\n reg = 0.01\n for i in range(500):\n p_y = forward(Xtrain, W, b)\n # print \"p_y:\", p_y\n ll = cost(p_y, Ytrain_ind)\n LL.append(ll)\n\n p_y_test = forward(Xtest, W, b)\n lltest = cost(p_y_test, Ytest_ind)\n LLtest.append(lltest)\n \n err = error_rate(p_y_test, Ytest)\n CRtest.append(err)\n\n W += lr*(gradW(Ytrain_ind, p_y, Xtrain) - reg*W)\n b += lr*(gradb(Ytrain_ind, p_y) - reg*b)\n if i % 10 == 0:\n print(\"Cost at iteration %d: %.6f\" % (i, ll))\n print(\"Error rate:\", err)\n\n p_y = forward(Xtest, W, b)\n print(\"Final error rate:\", error_rate(p_y, Ytest))\n iters = range(len(LL))\n plt.plot(iters, LL, iters, LLtest)\n plt.show()\n plt.plot(CRtest)\n plt.show()\n\n\ndef benchmark_pca():\n Xtrain, Xtest, Ytrain, Ytest = get_transformed_data()\n print(\"Performing logistic regression...\")\n\n N, D = Xtrain.shape\n Ytrain_ind = np.zeros((N, 10))\n for i in range(N):\n Ytrain_ind[i, Ytrain[i]] = 1\n\n Ntest = len(Ytest)\n Ytest_ind = np.zeros((Ntest, 10))\n for i in range(Ntest):\n Ytest_ind[i, Ytest[i]] = 1\n\n W = np.random.randn(D, 10) / np.sqrt(D)\n b = np.zeros(10)\n LL = []\n LLtest = []\n CRtest = []\n\n # D = 300 -> error = 0.07\n lr = 0.0001\n reg = 0.01\n for i in range(200):\n p_y = forward(Xtrain, W, b)\n # print \"p_y:\", p_y\n ll = cost(p_y, Ytrain_ind)\n LL.append(ll)\n\n p_y_test = forward(Xtest, W, b)\n lltest = cost(p_y_test, Ytest_ind)\n LLtest.append(lltest)\n\n err = error_rate(p_y_test, Ytest)\n CRtest.append(err)\n\n W += lr*(gradW(Ytrain_ind, p_y, Xtrain) - reg*W)\n b += lr*(gradb(Ytrain_ind, p_y) - reg*b)\n if i % 10 == 0:\n print(\"Cost at iteration %d: %.6f\" % (i, ll))\n print(\"Error rate:\", err)\n\n p_y = forward(Xtest, W, b)\n print(\"Final error rate:\", error_rate(p_y, Ytest))\n iters = range(len(LL))\n plt.plot(iters, LL, iters, LLtest)\n plt.show()\n plt.plot(CRtest)\n plt.show()\n\n\nif __name__ == '__main__':\n # benchmark_pca()\n benchmark_full()\n\n" }, { "path": "ann_logistic_extra/__init__.py", "content": "" }, { "path": "ann_logistic_extra/ann_predict.py", "content": "from __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nfrom process import get_data\n\nX, Y, _, _ = get_data()\n\n# randomly initialize weights\nM = 5\nD = X.shape[1]\nK = len(set(Y))\nW1 = np.random.randn(D, M)\nb1 = np.zeros(M)\nW2 = np.random.randn(M, K)\nb2 = np.zeros(K)\n\n# make predictions\ndef softmax(a):\n expA = np.exp(a)\n return expA / expA.sum(axis=1, keepdims=True)\n\ndef forward(X, W1, b1, W2, b2):\n Z = np.tanh(X.dot(W1) + b1)\n return softmax(Z.dot(W2) + b2)\n\nP_Y_given_X = forward(X, W1, b1, W2, b2)\nprint(\"P_Y_given_X.shape:\", P_Y_given_X.shape)\npredictions = np.argmax(P_Y_given_X, axis=1)\n\n# calculate the accuracy\ndef classification_rate(Y, P):\n return np.mean(Y == P)\n\nprint(\"Score:\", classification_rate(Y, predictions))" }, { "path": "ann_logistic_extra/ann_train.py", "content": "from __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.utils import shuffle\nfrom process import get_data\n\ndef y2indicator(y, K):\n N = len(y)\n ind = np.zeros((N, K))\n for i in range(N):\n ind[i, y[i]] = 1\n return ind\n\nXtrain, Ytrain, Xtest, Ytest = get_data()\nD = Xtrain.shape[1]\nK = len(set(Ytrain) | set(Ytest))\nM = 5 # num hidden units\n\n# convert to indicator\nYtrain_ind = y2indicator(Ytrain, K)\nYtest_ind = y2indicator(Ytest, K)\n\n# randomly initialize weights\nW1 = np.random.randn(D, M)\nb1 = np.zeros(M)\nW2 = np.random.randn(M, K)\nb2 = np.zeros(K)\n\n# make predictions\ndef softmax(a):\n expA = np.exp(a)\n return expA / expA.sum(axis=1, keepdims=True)\n\ndef forward(X, W1, b1, W2, b2):\n Z = np.tanh(X.dot(W1) + b1)\n return softmax(Z.dot(W2) + b2), Z\n\ndef predict(P_Y_given_X):\n return np.argmax(P_Y_given_X, axis=1)\n\n# calculate the accuracy\ndef classification_rate(Y, P):\n return np.mean(Y == P)\n\ndef cross_entropy(Y, pY):\n return -np.sum(Y * np.log(pY)) / len(Y)\n\n\n# train loop\ntrain_costs = []\ntest_costs = []\nlearning_rate = 0.001\nfor i in range(10000):\n pYtrain, Ztrain = forward(Xtrain, W1, b1, W2, b2)\n pYtest, Ztest = forward(Xtest, W1, b1, W2, b2)\n\n ctrain = cross_entropy(Ytrain_ind, pYtrain)\n ctest = cross_entropy(Ytest_ind, pYtest)\n train_costs.append(ctrain)\n test_costs.append(ctest)\n\n # gradient descent\n gW2 = Ztrain.T.dot(pYtrain - Ytrain_ind)\n gb2 = (pYtrain - Ytrain_ind).sum(axis=0)\n dZ = (pYtrain - Ytrain_ind).dot(W2.T) * (1 - Ztrain * Ztrain)\n gW1 = Xtrain.T.dot(dZ)\n gb1 = dZ.sum(axis=0)\n W2 -= learning_rate * gW2\n b2 -= learning_rate * gb2\n W1 -= learning_rate * gW1\n b1 -= learning_rate * gb1\n if i % 1000 == 0:\n print(i, ctrain, ctest)\n\nprint(\"Final train classification_rate:\", classification_rate(Ytrain, predict(pYtrain)))\nprint(\"Final test classification_rate:\", classification_rate(Ytest, predict(pYtest)))\n\nplt.plot(train_costs, label='train cost')\nplt.plot(test_costs, label='test cost')\nplt.legend()\nplt.show()" }, { "path": "ann_logistic_extra/ecommerce_data.csv", "content": 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}, { "path": "ann_logistic_extra/logistic_predict.py", "content": "from __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nfrom process import get_binary_data\n\nX, Y, _, _ = get_binary_data()\n\n# randomly initialize weights\nD = X.shape[1]\nW = np.random.randn(D)\nb = 0 # bias term\n\n# make predictions\ndef sigmoid(a):\n return 1 / (1 + np.exp(-a))\n\ndef forward(X, W, b):\n return sigmoid(X.dot(W) + b)\n\nP_Y_given_X = forward(X, W, b)\npredictions = np.round(P_Y_given_X)\n\n# calculate the accuracy\ndef classification_rate(Y, P):\n return np.mean(Y == P)\n\nprint(\"Score:\", classification_rate(Y, predictions))" }, { "path": "ann_logistic_extra/logistic_softmax_train.py", "content": "from __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.utils import shuffle\nfrom process import get_data\n\ndef y2indicator(y, K):\n N = len(y)\n ind = np.zeros((N, K))\n for i in range(N):\n ind[i, y[i]] = 1\n return ind\n\nXtrain, Ytrain, Xtest, Ytest = get_data()\nD = Xtrain.shape[1]\nK = len(set(Ytrain) | set(Ytest))\n\n# convert to indicator\nYtrain_ind = y2indicator(Ytrain, K)\nYtest_ind = y2indicator(Ytest, K)\n\n# randomly initialize weights\nW = np.random.randn(D, K)\nb = np.zeros(K)\n\n# make predictions\ndef softmax(a):\n expA = np.exp(a)\n return expA / expA.sum(axis=1, keepdims=True)\n\ndef forward(X, W, b):\n return softmax(X.dot(W) + b)\n\ndef predict(P_Y_given_X):\n return np.argmax(P_Y_given_X, axis=1)\n\n# calculate the accuracy\ndef classification_rate(Y, P):\n return np.mean(Y == P)\n\ndef cross_entropy(Y, pY):\n return -np.sum(Y * np.log(pY)) / len(Y)\n\n\n# train loop\ntrain_costs = []\ntest_costs = []\nlearning_rate = 0.001\nfor i in range(10000):\n pYtrain = forward(Xtrain, W, b)\n pYtest = forward(Xtest, W, b)\n\n ctrain = cross_entropy(Ytrain_ind, pYtrain)\n ctest = cross_entropy(Ytest_ind, pYtest)\n train_costs.append(ctrain)\n test_costs.append(ctest)\n\n # gradient descent\n W -= learning_rate*Xtrain.T.dot(pYtrain - Ytrain_ind)\n b -= learning_rate*(pYtrain - Ytrain_ind).sum(axis=0)\n if i % 1000 == 0:\n print(i, ctrain, ctest)\n\nprint(\"Final train classification_rate:\", classification_rate(Ytrain, predict(pYtrain)))\nprint(\"Final test classification_rate:\", classification_rate(Ytest, predict(pYtest)))\n\nplt.plot(train_costs, label='train cost')\nplt.plot(test_costs, label='test cost')\nplt.legend()\nplt.show()" }, { "path": "ann_logistic_extra/logistic_train.py", "content": "from __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.utils import shuffle\nfrom process import get_binary_data\n\n# get the data\nXtrain, Ytrain, Xtest, Ytest = get_binary_data()\n\n# randomly initialize weights\nD = Xtrain.shape[1]\nW = np.random.randn(D)\nb = 0 # bias term\n\n# make predictions\ndef sigmoid(a):\n return 1 / (1 + np.exp(-a))\n\ndef forward(X, W, b):\n return sigmoid(X.dot(W) + b)\n\n# calculate the accuracy\ndef classification_rate(Y, P):\n return np.mean(Y == P)\n\n# cross entropy\ndef cross_entropy(T, pY):\n return -np.mean(T*np.log(pY) + (1 - T)*np.log(1 - pY))\n\n# train loop\ntrain_costs = []\ntest_costs = []\nlearning_rate = 0.001\nfor i in range(10000):\n pYtrain = forward(Xtrain, W, b)\n pYtest = forward(Xtest, W, b)\n\n ctrain = cross_entropy(Ytrain, pYtrain)\n ctest = cross_entropy(Ytest, pYtest)\n train_costs.append(ctrain)\n test_costs.append(ctest)\n\n # gradient descent\n W -= learning_rate*Xtrain.T.dot(pYtrain - Ytrain)\n b -= learning_rate*(pYtrain - Ytrain).sum()\n if i % 1000 == 0:\n print(i, ctrain, ctest)\n\nprint(\"Final train classification_rate:\", classification_rate(Ytrain, np.round(pYtrain)))\nprint(\"Final test classification_rate:\", classification_rate(Ytest, np.round(pYtest)))\n\nplt.plot(train_costs, label='train cost')\nplt.plot(test_costs, label='test cost')\nplt.legend()\nplt.show()\n\n\n" }, { "path": "ann_logistic_extra/process.py", "content": "from __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport pandas as pd\nimport os\n\n# so scripts from other folders can import this file\ndir_path = os.path.abspath(os.path.dirname(os.path.realpath(__file__)))\n\n# normalize numerical columns\n# one-hot categorical columns\n\ndef get_data():\n df = pd.read_csv(dir_path + '/ecommerce_data.csv')\n\n # just in case you're curious what's in it\n # df.head()\n\n # easier to work with numpy array\n data = df.to_numpy()\n\n # shuffle it\n np.random.shuffle(data)\n\n # split features and labels\n X = data[:,:-1]\n Y = data[:,-1].astype(np.int32)\n\n # one-hot encode the categorical data\n # create a new matrix X2 with the correct number of columns\n N, D = X.shape\n X2 = np.zeros((N, D+3))\n X2[:,0:(D-1)] = X[:,0:(D-1)] # non-categorical\n\n # one-hot\n for n in range(N):\n t = int(X[n,D-1])\n X2[n,t+D-1] = 1\n\n # method 2\n # Z = np.zeros((N, 4))\n # Z[np.arange(N), X[:,D-1].astype(np.int32)] = 1\n # # assign: X2[:,-4:] = Z\n # assert(np.abs(X2[:,-4:] - Z).sum() < 1e-10)\n\n # assign X2 back to X, since we don't need original anymore\n X = X2\n\n # split train and test\n Xtrain = X[:-100]\n Ytrain = Y[:-100]\n Xtest = X[-100:]\n Ytest = Y[-100:]\n\n # normalize columns 1 and 2\n for i in (1, 2):\n m = Xtrain[:,i].mean()\n s = Xtrain[:,i].std()\n Xtrain[:,i] = (Xtrain[:,i] - m) / s\n Xtest[:,i] = (Xtest[:,i] - m) / s\n\n return Xtrain, Ytrain, Xtest, Ytest\n\n\ndef get_binary_data():\n # return only the data from the first 2 classes\n Xtrain, Ytrain, Xtest, Ytest = get_data()\n X2train = Xtrain[Ytrain <= 1]\n Y2train = Ytrain[Ytrain <= 1]\n X2test = Xtest[Ytest <= 1]\n Y2test = Ytest[Ytest <= 1]\n return X2train, Y2train, X2test, Y2test\n" }, { "path": "bayesian_ml/1/Q.csv", "content": 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}, { "path": "bayesian_ml/1/README", "content": "This data set contains the following:\n\nXtrain: 11791 training examples of 4's and 9's, each 15 dimensions in length\nytrain: the corresponding labels of the training examples (0 = 4 digit, 1 = 9 digit)\n\nXtest: 1991 testing examples of 4's and 9's, each 15 dimensions in length used for prediction\nytest: the corresponding ground truth labels used for evaluating performance (0 = 4 digit, 1 = 9 digit)\n\nQ: A matrix of eigenvectors that is used to map the 15-dimensional feature vectors back into image space. For a 15-dimensional feature vector x, this can be done as follows in pseudocode\n\n\t1. Map x2 = Q*x\n\t2. Reshape x2 into a 28x28 matrix and show it as an image\n\nBecause the data has had the mean subtracted off, this image will not look exacly like a handwritten digit, but the contours of the 4 or 9 should be clear.\n" }, { "path": "bayesian_ml/1/Xtest.csv", "content": 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}, { "path": "bayesian_ml/1/nb.py", "content": "# Naive Bayes with prior on mean and precision of Gaussian\n# mean | precision ~ N(0, c / precision)\n# precision ~ Gamma(a, b)\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom scipy.stats import t\nfrom sortedcontainers import SortedList\n\n\nclass NB:\n def fit(self, X, Y):\n self.pyy = []\n self.tinfo = []\n N, D = X.shape\n for c in (0, 1):\n pyy_c = (1.0 + np.sum(Y == c)) / (N + 1.0 + 1.0)\n self.pyy.append(pyy_c)\n\n # for each dimension, we need to store the data we need to calculate\n # the posterior predictive distribution\n # t-distribution with 3 params: df, center, scale\n Xc = X[Y == c]\n tinfo_c = []\n for d in xrange(D):\n # first calculate the parameters of the normal gamma\n xbar = Xc[:,d].mean()\n mu = N*xbar / (1.0 + N)\n precision = 1.0 + N\n alpha = 1.0 + N/2.0\n beta = 1.0 + 0.5*Xc[:,d].var()*N + 0.5*N*(xbar*xbar)/precision\n\n tinfo_cd = {\n 'df': 2*alpha,\n 'center': mu,\n 'scale': np.sqrt( beta*(precision + 1)/(alpha * precision) ),\n }\n tinfo_c.append(tinfo_cd)\n self.tinfo.append(tinfo_c)\n\n def predict_proba(self, X):\n N, D = X.shape\n # P = np.zeros(N)\n # for n in xrange(N):\n # x = X[n]\n\n # pyx = []\n # for c in (0, 1):\n # pycx = self.pyy[c]\n # for d in xrange(D):\n # tinfo_cd = self.tinfo[c][d]\n # pdf_d = t.pdf(x[d], df=tinfo_cd['df'], loc=tinfo_cd['center'], scale=tinfo_cd['scale'])\n # pycx *= pdf_d\n # pyx.append(pycx)\n\n # py1x = pyx[1] / (pyx[0] + pyx[1])\n # # print \"p(y=1|x):\", py1x\n # P[n] = py1x\n\n posteriors = np.zeros((N, 2))\n for c in (0, 1):\n probability_matrix = np.zeros((N, D))\n for d in xrange(D):\n tinfo_cd = self.tinfo[c][d]\n pdf_d = t.pdf(X[:,d], df=tinfo_cd['df'], loc=tinfo_cd['center'], scale=tinfo_cd['scale'])\n probability_matrix[:,d] = pdf_d\n posteriors_c = np.prod(probability_matrix, axis=1)*self.pyy[c]\n posteriors[:,c] = posteriors_c\n P = posteriors[:,1] / np.sum(posteriors, axis=1)\n return P\n\n def predict(self, X):\n return np.round(self.predict_proba(X))\n\n def score(self, X, Y):\n return np.mean(self.predict(X) == Y)\n\n def confusion_matrix(self, X, Y):\n P = self.predict(X)\n M = np.zeros((2, 2))\n M[0,0] = np.sum(P[Y == 0] == Y[Y == 0])\n M[0,1] = np.sum(P[Y == 0] != Y[Y == 0])\n M[1,0] = np.sum(P[Y == 1] != Y[Y == 1])\n M[1,1] = np.sum(P[Y == 1] == Y[Y == 1])\n return M\n\n def get_3_misclassified(self, X, Y):\n P = self.predict(X)\n N = len(Y)\n samples = np.random.choice(N, 3, replace=False, p=(P != Y)/float(np.sum(P != Y)))\n return X[samples], Y[samples], P[samples]\n\n def get_3_most_ambiguous(self, X, Y):\n P = self.predict_proba(X)\n N = len(X)\n sl = SortedList(load=3) # stores (distance, sample index) tuples\n for n in xrange(N):\n p = P[n]\n dist = np.abs(p - 0.5)\n if len(sl) < 3:\n sl.add( (dist, n) )\n else:\n if dist < sl[-1][0]:\n del sl[-1]\n sl.add( (dist, n) )\n indexes = [v for k, v in sl]\n return X[indexes], Y[indexes]\n\ndef plot_image(x, Q, title):\n im = Q.dot(x)\n plt.imshow(im.reshape(28,28), cmap='gray')\n plt.title(title)\n plt.show()\n\nif __name__ == '__main__':\n Xtrain = pd.read_csv('Xtrain.csv', header=None).as_matrix()\n Xtest = pd.read_csv('Xtest.csv', header=None).as_matrix()\n Ytrain = pd.read_csv('ytrain.csv', header=None).as_matrix().flatten()\n # print \"Ytrain.shape:\", Ytrain.shape\n Ytest = pd.read_csv('ytest.csv', header=None).as_matrix().flatten()\n model = NB()\n model.fit(Xtrain, Ytrain)\n print \"train accuracy:\", model.score(Xtrain, Ytrain)\n print \"test accuracy:\", model.score(Xtest, Ytest)\n\n # confusion matrix\n M = model.confusion_matrix(Xtest, Ytest)\n print \"confusion matrix:\"\n print M\n print \"N:\", len(Ytest)\n print \"sum(M):\", M.sum()\n\n # plot 3 misclassified\n Q = pd.read_csv('Q.csv', header=None).as_matrix()\n misclassified, targets, predictions = model.get_3_misclassified(Xtrain, Ytrain)\n for x, y, p in zip(misclassified, targets, predictions):\n plot_image(x, Q, 'misclassified target=%s prediction=%s' % (y, int(p)))\n\n # ambiguous\n ambiguous, targets = model.get_3_most_ambiguous(Xtrain, Ytrain)\n for x, y in zip(ambiguous, targets):\n plot_image(x, Q, 'ambiguous target=%s' % y)\n" }, { "path": "bayesian_ml/1/ytest.csv", "content": 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}, { "path": "bayesian_ml/2/README", "content": "This data set contains the following:\n\nXtrain: 11791 training examples of 4's and 9's, each 15 dimensions in length\nytrain: the corresponding labels of the training examples (0 = 4 digit, 1 = 9 digit)\n\nXtest: 1991 testing examples of 4's and 9's, each 15 dimensions in length used for prediction\nytest: the corresponding ground truth labels used for evaluating performance (0 = 4 digit, 1 = 9 digit)\n\nQ: A matrix of eigenvectors that is used to map the 15-dimensional feature vectors back into image space. For a 15-dimensional feature vector x, this can be done as follows in pseudocode\n\n\t1. Map x2 = Q*x\n\t2. Reshape x2 into a 28x28 matrix and show it as an image\n\nBecause the data has had the mean subtracted off, this image will not look exacly like a handwritten digit, but the contours of the 4 or 9 should be clear.\n" }, { "path": "bayesian_ml/2/Xtest.csv", "content": 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92011,0.92918\n-4.3082,-0.29095,-1.098,-1.5904,0.84163,-0.63402,0.71184,1.0418,0.10144,-0.081221,0.1629,-0.7167,0.22019,-0.28333,0.072374\n-3.7304,0.98452,1.4461,-0.71581,0.099413,-0.52307,1.1656,0.36865,0.96444,-0.4254,0.91102,0.10761,0.68936,0.23966,0.30848\n-3.9019,-0.96718,-0.95242,-1.0812,0.70043,-1.7655,-0.097167,-0.36234,0.64107,0.73424,-0.37472,0.02413,0.88537,0.19002,-0.32446\n-3.6377,-0.11524,0.22216,-1.4145,1.4846,-1.4785,0.84057,-1.4927,1.0633,1.135,-0.10588,0.46041,0.77243,0.12377,-0.44409\n-3.4264,1.5059,1.139,-0.6112,0.5221,-1.1104,1.5495,1.3499,0.72282,-0.22459,0.42378,-0.12763,-0.1292,-1.1343,0.98985\n-1.695,-3.6906,0.58147,0.47004,2.454,-0.56253,1.1748,0.47573,0.44499,-0.49939,-0.57136,-1.8374,0.15731,-0.89349,0.20193\n-3.0507,-1.2956,-1.6691,-1.4194,0.5129,0.36311,-1.4334,-0.60235,0.41229,-1.9675,-0.29582,0.30957,0.26316,-0.03379,0.78652\n-3.1384,-1.9205,-1.8359,-1.5583,1.1638,1.0038,-1.4861,0.30843,0.47555,-1.4681,-1.4656,0.34139,-0.42305,0.44757,0.87923\n-2.6533,-1.6315,-2.5949,-0.68357,0.76346,2.1294,-1.1561,-0.55532,0.25017,-1.2454,-1.6974,0.36847,-0.18314,0.68786,0.23761\n-0.83746,-0.96006,-0.9514,-1.29,-0.64656,2.0835,-0.99411,-0.84631,0.022955,-0.55621,-0.86837,0.69627,1.367,1.5034,0.87398\n-2.8452,-0.57388,-2.6382,-0.19688,-0.13348,0.93657,0.040835,-0.39971,0.46478,-1.675,-0.29827,-1.0589,0.43039,-0.71133,-0.15874\n-2.3371,-0.58789,-2.3648,-0.026632,0.2275,2.34,-0.3747,-0.87236,0.71903,-1.3137,-0.1337,-0.19942,0.95104,0.80286,-0.32855\n-2.35,-0.76406,-0.47972,-1.0338,0.085147,1.6397,-0.83987,0.1465,0.79905,-0.95084,0.16913,-0.83299,1.9207,1.796,0.54669\n-2.7514,-1.028,0.21153,-1.3997,0.28374,1.6727,-0.25639,-0.44063,1.1628,-1.2411,0.42992,-1.607,1.5005,0.49365,0.73299\n-2.3512,-1.0148,-1.8745,-0.96903,0.43199,1.8583,-1.3756,-0.43813,0.056795,-1.0012,-0.76345,0.024682,1.1742,0.91184,1.5851\n-2.307,-0.97455,-2.5102,-0.35901,0.24727,1.7813,-0.99627,-1.243,0.34366,-1.7268,-0.61829,0.75832,0.54855,0.43315,0.77306\n" }, { "path": "bayesian_ml/2/em.py", "content": "# expectation-maximization for the model:\n# x(n) ~ N(Wz(n), sigma**2 I) (observed variables)\n# z(n) ~ N(0, I) (latent variables)\n# W ~ N(0, 1/lambda)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.stats import multivariate_normal as mvn\nfrom scipy.stats import norm\n\nN = 1000\nlam = 1.0\nsigma = 1.0\nD = 2\nK = 3\nsigmaI = sigma * sigma * np.eye(D)\n\n# generate the data\nZ = np.random.randn(N, K)\nW0 = np.random.randn(D, K)*lam\nX = np.zeros((N, D))\nfor i in xrange(N):\n X[i] = np.random.multivariate_normal(mean=W0.dot(Z[i]), cov=sigmaI)\n\ndef loglikelihood(X, Z, W):\n ZW = Z.dot(W.T)\n LL = 0\n for i in xrange(N):\n ll = mvn.logpdf(X[i], mean=ZW[i], cov=sigmaI)\n LL += ll\n LL += norm.logpdf(W.flatten(), scale=1/lam).sum()\n return LL\n\n# do EM\nW = np.random.randn(D, K) / np.sqrt(D + K)\ncosts = []\nfor t in xrange(50):\n # E-step\n R = np.linalg.solve(W.dot(W.T) + sigmaI, W).T\n # test = W.T.dot( np.linalg.inv(W.dot(W.T) + sigmaI) )\n # print \"R:\", R\n # print \"test:\", test\n # diff = np.abs(R - test).sum()\n # print \"diff:\", diff\n Ez = X.dot(R.T)\n\n # M-step\n xzT = X.T.dot(Ez)\n toinvert = N*(np.eye(K) - R.dot(W)) + Ez.T.dot(Ez) + sigma*sigma*lam*np.eye(K)\n W = np.linalg.solve(toinvert, xzT.T).T\n # test = xzT.dot( np.linalg.inv(toinvert) )\n # print \"W:\", W\n # print \"test:\", test\n # diff = np.abs(W - test).sum()\n # print \"diff:\", diff\n\n # likelihood\n cost = loglikelihood(X, Ez, W)\n costs.append(cost)\n\nplt.plot(costs)\nplt.show()\n\nprint \"actual W:\", W0\nprint \"predicted W:\", W\n\nprint \"log-likelihood given real W:\", loglikelihood(X, Z, W0)\n\nprint \"log-likelihood found:\", costs[-1]\n" }, { "path": "bayesian_ml/2/probit.py", "content": "# probit regression\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom scipy.stats import norm\n# from scipy.stats import t\nfrom sortedcontainers import SortedList\n\n\nclass ProbitRegression:\n def fit(self, X, Y, sigma=1.5, lam=1, show_w=set(), Q=None):\n\n # setup\n N, D = X.shape\n self.w = np.random.randn(D) / np.sqrt(D) # does not work if you don't scale first!\n Eq = np.zeros(N)\n idx1 = (Y == 1)\n idx0 = (Y == 0)\n A = lam*np.eye(D) + X.T.dot(X) / sigma**2\n costs = []\n\n for t in xrange(100):\n # calculate ln(p(Y, w | X))\n cdf = norm.cdf(X.dot(self.w) / sigma)\n cost = -lam/2 * self.w.dot(self.w) + Y.dot(np.log(cdf)) + (1 - Y).dot(np.log(1 - cdf))\n costs.append(cost)\n\n # E step\n Xw = X.dot(self.w)\n pdf = norm.pdf(-Xw / sigma)\n cdf = norm.cdf(-Xw / sigma)\n Eq[idx1] = Xw[idx1] + sigma*(pdf[idx1] / (1 - cdf[idx1]))\n Eq[idx0] = Xw[idx0] + sigma*(-pdf[idx0] / cdf[idx0])\n\n # M step\n b = X.T.dot(Eq) / sigma**2\n self.w = np.linalg.solve(A, b)\n\n if show_w and t in show_w:\n plot_image(self.w, Q, \"iteration: %s\" % (t+1))\n\n \n plt.plot(costs)\n plt.show()\n\n self.sigma = sigma\n self.lam = lam\n\n\n def predict_proba(self, X):\n N, D = X.shape\n return norm.cdf(X.dot(self.w) / self.sigma)\n\n def predict(self, X):\n return np.round(self.predict_proba(X))\n\n def score(self, X, Y):\n return np.mean(self.predict(X) == Y)\n\n def confusion_matrix(self, X, Y):\n P = self.predict(X)\n M = np.zeros((2, 2))\n M[0,0] = np.sum(P[Y == 0] == Y[Y == 0])\n M[0,1] = np.sum(P[Y == 0] != Y[Y == 0])\n M[1,0] = np.sum(P[Y == 1] != Y[Y == 1])\n M[1,1] = np.sum(P[Y == 1] == Y[Y == 1])\n return M\n\n def get_3_misclassified(self, X, Y):\n P = self.predict(X)\n N = len(Y)\n samples = np.random.choice(N, 3, replace=False, p=(P != Y)/float(np.sum(P != Y)))\n return X[samples], Y[samples], P[samples]\n\n def get_3_most_ambiguous(self, X, Y):\n P = self.predict_proba(X)\n N = len(X)\n sl = SortedList(load=3) # stores (distance, sample index) tuples\n for n in xrange(N):\n p = P[n]\n dist = np.abs(p - 0.5)\n if len(sl) < 3:\n sl.add( (dist, n) )\n else:\n if dist < sl[-1][0]:\n del sl[-1]\n sl.add( (dist, n) )\n indexes = [v for k, v in sl]\n return X[indexes], Y[indexes]\n\ndef plot_image(x, Q, title):\n im = Q.dot(x)\n plt.imshow(im.reshape(28,28), cmap='gray')\n plt.title(title)\n plt.show()\n\nif __name__ == '__main__':\n Xtrain = pd.read_csv('Xtrain.csv', header=None).as_matrix()\n Xtest = pd.read_csv('Xtest.csv', header=None).as_matrix()\n Ytrain = pd.read_csv('ytrain.csv', header=None).as_matrix().flatten()\n # print \"Ytrain.shape:\", Ytrain.shape\n Ytest = pd.read_csv('ytest.csv', header=None).as_matrix().flatten()\n model = ProbitRegression()\n model.fit(Xtrain, Ytrain)\n print \"train accuracy:\", model.score(Xtrain, Ytrain)\n print \"test accuracy:\", model.score(Xtest, Ytest)\n\n # confusion matrix\n M = model.confusion_matrix(Xtest, Ytest)\n print \"confusion matrix:\"\n print M\n print \"N:\", len(Ytest)\n print \"sum(M):\", M.sum()\n\n # plot 3 misclassified\n Q = pd.read_csv('Q.csv', header=None).as_matrix()\n misclassified, targets, predictions = model.get_3_misclassified(Xtrain, Ytrain)\n for x, y, p in zip(misclassified, targets, predictions):\n plot_image(x, Q, 'misclassified target=%s prediction=%s' % (y, int(p)))\n\n # ambiguous\n ambiguous, targets = model.get_3_most_ambiguous(Xtrain, Ytrain)\n for x, y in zip(ambiguous, targets):\n plot_image(x, Q, 'ambiguous target=%s' % y)\n\n # show w\n model.fit(Xtrain, Ytrain, show_w=set([0, 4, 9, 24, 49, 99]), Q=Q)\n" }, { "path": "bayesian_ml/2/ytest.csv", "content": 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1.2545e-07,1.4076e-07,7.8933e-07,8.0316e-07,1.124e-06,1.9518e-06,5.3112e-06,1.5839e-05,5.19e-05,0.00047723,0.00058627,0.00066404,0.0025221,0.0034094,0.0045047,0.013589,0.052374,0.13116,0.19555,0.21717,0.23133,0.27323,0.31515,0.36959,0.37352,0.43927,0.44323,0.46134,0.5086,0.54,0.61588,0.75271,0.82156,0.92252,0.94087,0.98911,0.99096,0.99447,1,0.99838,0.94836,0.89374,0.88805,0.80801,0.60894,0.60879,0.48589,0.30894,0.19899,0.14951,0.1446,0.091652,0.090231,0.079845,0.075208,0.068202,0.047046,0.036776,0.033149,0.031361,0.02989,0.024326,0.017228,0.016823,0.0136,0.012709,0.011731,0.0034573,0.002796,0.00022107,0.00010951,0.00010736,9.3094e-05,1.5911e-05,4.552e-06,1.6158e-06,7.2024e-07,5.9414e-07,7.6465e-08,5.5632e-08,4.6419e-09,3.3442e-09,5.6836e-10,1.0439e-10,2.7116e-12,1.1592e-12,8.2488e-13\n1,4.0901e-10,4.3914e-10,8.0742e-10,1.9455e-09,2.7886e-09,4.1009e-09,1.8328e-08,1.9734e-08,2.7414e-08,3.013e-08,3.103e-08,4.0169e-08,6.2058e-08,9.0898e-08,1.0211e-07,5.8296e-07,5.9329e-07,8.3327e-07,1.4559e-06,4.0066e-06,1.2104e-05,4.025e-05,0.00038146,0.00047003,0.00053337,0.0020684,0.0028102,0.0037307,0.011483,0.045544,0.11677,0.17617,0.19631,0.20954,0.24891,0.28859,0.34055,0.34431,0.40772,0.41155,0.42915,0.47529,0.50615,0.58141,0.71997,0.79151,0.90024,0.92088,0.97924,0.98181,0.98694,0.99838,1,0.96453,0.91668,0.91153,0.83722,0.64339,0.64323,0.51941,0.33653,0.22005,0.16677,0.16145,0.10362,0.10205,0.090588,0.085455,0.077683,0.05406,0.042495,0.03839,0.036364,0.034694,0.028359,0.020226,0.01976,0.016041,0.01501,0.013876,0.0041799,0.0033925,0.00027871,0.00013939,0.00013668,0.00011875,2.0753e-05,6.0253e-06,2.1639e-06,9.7301e-07,8.043e-07,1.0571e-07,7.7154e-08,6.5913e-09,4.7629e-09,8.2235e-10,1.5325e-10,4.0998e-12,1.7641e-12,1.2586e-12\n1,6.7382e-11,7.2557e-11,1.368e-10,3.4201e-10,4.978e-10,7.4433e-10,3.5538e-09,3.8391e-09,5.4134e-09,5.9753e-09,6.162e-09,8.0719e-09,1.2725e-08,1.8975e-08,2.1432e-08,1.332e-07,1.3568e-07,1.9386e-07,3.4854e-07,1.0119e-06,3.2493e-06,1.1586e-05,0.00012665,0.0001583,0.00018121,0.00077546,0.0010788,0.0014648,0.0049603,0.022522,0.064525,0.10298,0.11658,0.12567,0.15335,0.18221,0.22139,0.22429,0.27435,0.27746,0.2918,0.3303,0.35676,0.42387,0.55848,0.63525,0.76764,0.79639,0.89392,0.89945,0.91132,0.94836,0.96453,1,0.98905,0.98702,0.94783,0.79875,0.79861,0.68144,0.48262,0.3388,0.26752,0.26018,0.17713,0.17479,0.15747,0.14961,0.13756,0.099806,0.080541,0.07355,0.070066,0.067176,0.056053,0.041329,0.040468,0.033507,0.031546,0.029376,0.0098135,0.0080997,0.00079753,0.00041726,0.00040966,0.0003591,6.9736e-05,2.1707e-05,8.2362e-06,3.8602e-06,3.2219e-06,4.6777e-07,3.4651e-07,3.3094e-08,2.4255e-08,4.5121e-09,9.0043e-10,2.7692e-11,1.2291e-11,8.8781e-12\n1,2.4142e-11,2.6038e-11,4.9777e-11,1.2701e-10,1.8644e-10,2.8134e-10,1.3931e-09,1.5077e-09,2.1436e-09,2.3717e-09,2.4476e-09,3.2273e-09,5.1444e-09,7.7476e-09,8.7776e-09,5.717e-08,5.8263e-08,8.4037e-08,1.535e-07,4.5901e-07,1.5243e-06,5.6488e-06,6.6814e-05,8.4168e-05,9.681e-05,0.00043743,0.00061658,0.00084764,0.0030256,0.014803,0.045091,0.074228,0.084776,0.091876,0.1138,0.13706,0.16924,0.17164,0.21375,0.21639,0.22865,0.26195,0.28516,0.34517,0.47063,0.54549,0.68158,0.71264,0.82414,0.83087,0.84552,0.89374,0.91668,0.98905,1,0.99991,0.98411,0.87262,0.8725,0.7675,0.57096,0.41683,0.33667,0.32827,0.23089,0.22808,0.20716,0.19759,0.18284,0.13574,0.11114,0.1021,0.097566,0.093794,0.079158,0.059446,0.05828,0.048784,0.046087,0.043089,0.01524,0.012696,0.0013815,0.000741,0.00072801,0.00064131,0.000132,4.2698e-05,1.67e-05,8.0083e-06,6.7201e-06,1.0307e-06,7.6981e-07,7.8188e-08,5.7755e-08,1.1196e-08,2.3203e-09,7.7064e-11,3.4797e-11,2.5305e-11\n1,2.1997e-11,2.3727e-11,4.5416e-11,1.161e-10,1.7055e-10,2.5757e-10,1.2796e-09,1.3851e-09,1.9707e-09,2.1808e-09,2.2508e-09,2.9695e-09,4.7383e-09,7.1422e-09,8.094e-09,5.294e-08,5.3954e-08,7.7888e-08,1.4247e-07,4.2716e-07,1.4228e-06,5.2909e-06,6.3024e-05,7.945e-05,9.1423e-05,0.00041511,0.0005858,0.00080623,0.0028914,0.014242,0.043619,0.072005,0.082301,0.089237,0.11068,0.13346,0.16503,0.1674,0.2088,0.21139,0.22347,0.25629,0.27919,0.33851,0.46297,0.53752,0.67363,0.70482,0.81728,0.8241,0.83896,0.88805,0.91153,0.98702,0.99991,1,0.98637,0.87862,0.87851,0.77489,0.579,0.42419,0.34331,0.33482,0.23618,0.23333,0.21209,0.20236,0.18736,0.13938,0.11427,0.10503,0.1004,0.096539,0.081558,0.061349,0.060152,0.0504,0.047628,0.044546,0.015836,0.013203,0.0014497,0.0007793,0.00076568,0.0006748,0.00013962,4.5318e-05,1.7774e-05,8.5404e-06,7.17e-06,1.1051e-06,8.2601e-07,8.4361e-08,6.2358e-08,1.2133e-08,2.523e-09,8.4376e-11,3.8158e-11,2.7766e-11\n1,6.7969e-12,7.3448e-12,1.4278e-11,3.7338e-11,5.5372e-11,8.4484e-11,4.3717e-10,4.7418e-10,6.8087e-10,7.5549e-10,7.8038e-10,1.0371e-09,1.6755e-09,2.5537e-09,2.9038e-09,2.0013e-08,2.0407e-08,2.9773e-08,5.5428e-08,1.7176e-07,5.9405e-07,2.3061e-06,2.9997e-05,3.8149e-05,4.4134e-05,0.00021293,0.00030492,0.00042551,0.0016185,0.0086653,0.02842,0.048567,0.056058,0.061151,0.077113,0.094402,0.11886,0.12071,0.1536,0.1557,0.16546,0.19233,0.21135,0.26164,0.371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6e-08,1.6449e-08,1.203e-08,1.1582e-08,7.254e-09,7.063e-09,6.1542e-09,5.1983e-09,4.1026e-09,2.1526e-10,1.7231e-10,1.5522e-10,1.2821e-10,1.1919e-10,9.5493e-11,4.2287e-11,3.784e-11,3.1113e-11,2.4868e-11,2.1443e-11,1.0682e-11,1.0077e-11,5.3267e-12,2.5254e-12,1.3204e-12,1.2487e-12,6.0366e-13,3.1575e-13,3.0371e-13,2.2843e-13,1.2625e-13,1.2458e-13,1.2095e-13,5.8162e-14,5.4233e-14,3.5396e-14,2.8664e-14,1.7817e-14,1.5646e-14,8.34e-15,7.4454e-15,3.8738e-15,3.2495e-15\n1,7.086e-08,8.0175e-08,8.5843e-08,1.0852e-07,1.4581e-07,1.4682e-07,1.587e-07,2.1678e-07,4.1285e-07,4.6466e-07,4.8689e-07,4.877e-07,5.8521e-07,8.0587e-07,8.4018e-07,1.0432e-06,2.0063e-06,3.7276e-06,4.0425e-06,5.8823e-06,7.2729e-06,8.612e-06,9.1505e-06,1.1428e-05,1.1583e-05,1.4292e-05,1.946e-05,2.544e-05,2.8462e-05,4.6961e-05,4.791e-05,7.1334e-05,8.9164e-05,9.4492e-05,0.00012098,0.00013908,0.00013941,0.00015,0.0004286,0.0011475,0.0014355,0.0014453,0.0014464,0.0018383,0.0026971,0.0028509,0.0029593,0.0052821,0.005634,0.0058315,0.01011,0.015409,0.019787,0.023269,0.028178,0.054655,0.0679,0.069652,0.092263,0.12421,0.14117,0.14124,0.1683,0.18507,0.19727,0.21361,0.2281,0.24939,0.25881,0.28747,0.34213,0.36809,0.4113,0.50376,0.51341,0.55088,0.57322,0.63937,0.75159,0.78049,0.85454,0.90537,0.92921,0.96342,0.9672,0.97053,0.98476,0.99077,0.99088,0.9947,0.99691,0.99932,0.99944,0.99949,0.99973,1,0.99924,0.99825,0.98844,0.98841,0.96547,0.96509,0.95864,0.9542,0.95122,0.94094,0.92931,0.88663,0.87986,0.85703,0.74289,0.70468,0.6916,0.60946,0.59955,0.58855,0.50562,0.49405,0.46015,0.44663,0.44201,0.43316,0.37752,0.32408,0.31883,0.30546,0.28253,0.21978,0.21726,0.21326,0.13172,0.09208,0.07853,0.073295,0.060274,0.054666,0.052118,0.04342,0.030247,0.024688,0.023663,0.022655,0.022505,0.020419,0.0084872,0.007208,0.0069509,0.0063689,0.004966,0.0043847,0.0029725,0.0028263,0.0026364,0.00255,0.0022933,0.0019436,0.001799,0.0016903,0.0013648,0.0012616,0.00098949,0.00092788,0.0007586,0.00072118,0.00066867,0.00066619,0.00062974,0.00053615,0.0003812,0.00037859,0.00028339,0.00024239,0.00021134,9.8449e-05,7.6742e-05,7.4864e-05,3.9714e-05,3.4204e-05,3.365e-05,2.4095e-05,2.3893e-05,1.8015e-05,1.2259e-05,7.1803e-06,6.5732e-06,5.3169e-06,3.4674e-06,2.8767e-06,2.4306e-06,2.0264e-06,1.1789e-06,8.8695e-07,8.8442e-07,7.6277e-07,6.0935e-07,5.8792e-07,4.297e-07,3.8006e-07,3.4039e-07,2.5195e-07,2.3239e-07,1.9804e-07,1.6624e-07,1.1911e-07,8.8452e-08,3.0801e-08,2.7308e-08,2.3889e-08,1.8922e-08,1.3855e-08,1.3342e-08,8.371e-09,8.1515e-09,7.1063e-09,6.0063e-09,4.7446e-09,2.5163e-10,2.0157e-10,1.8166e-10,1.5014e-10,1.3961e-10,1.1194e-10,4.971e-11,4.45e-11,3.6613e-11,2.9286e-11,2.5264e-11,1.2615e-11,1.1902e-11,6.3049e-12,2.9964e-12,1.5699e-12,1.485e-12,7.1951e-13,3.771e-13,3.6277e-13,2.7309e-13,1.512e-13,1.4921e-13,1.4487e-13,6.9821e-14,6.5119e-14,4.2555e-14,3.4483e-14,2.1465e-14,1.8856e-14,1.007e-14,8.9923e-15,4.6875e-15,3.9341e-15\n1,5.6641e-08,6.414e-08,6.8707e-08,8.6995e-08,1.1713e-07,1.1794e-07,1.2756e-07,1.7462e-07,3.3409e-07,3.7633e-07,3.9447e-07,3.9513e-07,4.7475e-07,6.553e-07,6.834e-07,8.4987e-07,1.6426e-06,3.0664e-06,3.3275e-06,4.8562e-06,6.0145e-06,7.1315e-06,7.5811e-06,9.4851e-06,9.6146e-06,1.1884e-05,1.6223e-05,2.1256e-05,2.3804e-05,3.9443e-05,4.0248e-05,6.0137e-05,7.5318e-05,7.986e-05,0.00010248,0.00011796,0.00011824,0.00012731,0.00036744,0.00099373,0.001246,0.0012547,0.0012556,0.0016,0.0023576,0.0024935,0.0025894,0.0046534,0.0049672,0.0051435,0.0089784,0.013761,0.017731,0.020898,0.025377,0.049724,0.061995,0.063622,0.084687,0.11463,0.13062,0.13068,0.15627,0.17218,0.18377,0.19935,0.21319,0.23355,0.24259,0.27014,0.32293,0.34812,0.39022,0.48096,0.49049,0.52757,0.54976,0.61581,0.72926,0.75883,0.83547,0.88913,0.91477,0.95252,0.95681,0.96062,0.97733,0.98479,0.98493,0.98997,0.99312,0.99713,0.99738,0.99749,0.99806,0.99924,1,0.99979,0.99357,0.99355,0.97474,0.97441,0.96881,0.9649,0.96227,0.95308,0.94255,0.90303,0.89667,0.87509,0.76493,0.72744,0.71456,0.63304,0.62314,0.61214,0.52872,0.51702,0.48264,0.46889,0.46419,0.45517,0.39829,0.34331,0.3379,0.32407,0.30033,0.235,0.23236,0.22818,0.14234,0.10017,0.085668,0.080052,0.066046,0.059996,0.057244,0.047828,0.0335,0.027423,0.026301,0.025196,0.025032,0.022742,0.0095635,0.0081386,0.0078518,0.0072021,0.0056326,0.0049807,0.0033918,0.0032268,0.0030123,0.0029147,0.0026244,0.0022283,0.0020643,0.0019409,0.0015707,0.0014532,0.0011427,0.0010723,0.00087847,0.00083557,0.00077533,0.00077248,0.00073064,0.00062306,0.00044449,0.00044148,0.00033139,0.00028388,0.00024783,0.00011627,9.0835e-05,8.8631e-05,4.728e-05,4.0772e-05,4.0118e-05,2.8808e-05,2.8569e-05,2.1592e-05,1.474e-05,8.6705e-06,7.943e-06,6.4357e-06,4.2111e-06,3.4988e-06,2.96e-06,2.4711e-06,1.4435e-06,1.0883e-06,1.0853e-06,9.3701e-07,7.4977e-07,7.2358e-07,5.3006e-07,4.6923e-07,4.2059e-07,3.1197e-07,2.8792e-07,2.4564e-07,2.0644e-07,1.4826e-07,1.1032e-07,3.869e-08,3.433e-08,3.0058e-08,2.3844e-08,1.7495e-08,1.6851e-08,1.0604e-08,1.0328e-08,9.0118e-09,7.625e-09,6.0321e-09,3.2566e-10,2.6122e-10,2.3555e-10,1.949e-10,1.8131e-10,1.4556e-10,6.4937e-11,5.8167e-11,4.791e-11,3.837e-11,3.3129e-11,1.6606e-11,1.5673e-11,8.3309e-12,3.975e-12,2.0899e-12,1.9773e-12,9.6171e-13,5.0573e-13,4.8661e-13,3.6684e-13,2.0372e-13,2.0105e-13,1.9524e-13,9.4442e-14,8.8112e-14,5.7702e-14,4.6807e-14,2.9204e-14,2.5671e-14,1.3751e-14,1.2286e-14,6.4247e-15,5.3966e-15\n1,5.0373e-08,5.7068e-08,6.1146e-08,7.7487e-08,1.0444e-07,1.0517e-07,1.1377e-07,1.5593e-07,2.9903e-07,3.3698e-07,3.5329e-07,3.5388e-07,4.2549e-07,5.8802e-07,6.1333e-07,7.6336e-07,1.4792e-06,2.7682e-06,3.0049e-06,4.3922e-06,5.4445e-06,6.4602e-06,6.8693e-06,8.6026e-06,8.7205e-06,1.0789e-05,1.4747e-05,1.9345e-05,2.1674e-05,3.5996e-05,3.6733e-05,5.4986e-05,6.8939e-05,7.3116e-05,9.3934e-05,0.0001082,0.00010846,0.00011682,0.00033892,0.00092141,0.0011568,0.0011649,0.0011657,0.0014875,0.0021967,0.0023241,0.002414,0.0043535,0.004649,0.0048151,0.0084351,0.012966,0.016736,0.019749,0.024016,0.047306,0.05909,0.060654,0.080942,0.10988,0.12536,0.12542,0.15025,0.16572,0.17701,0.19218,0.20568,0.22557,0.23441,0.26137,0.31318,0.33796,0.37944,0.46922,0.47867,0.51551,0.5376,0.60352,0.71747,0.74734,0.82521,0.88026,0.90679,0.94632,0.95087,0.95492,0.9729,0.9811,0.98126,0.98692,0.99055,0.99539,0.99572,0.99586,0.99659,0.99825,0.99979,1,0.99565,0.99564,0.97902,0.97872,0.97356,0.96994,0.96749,0.95888,0.94894,0.91115,0.90501,0.88411,0.7762,0.73914,0.72638,0.64529,0.63541,0.62443,0.54085,0.52909,0.49449,0.48063,0.47589,0.4668,0.40931,0.35357,0.34806,0.33401,0.30986,0.2432,0.2405,0.23622,0.14813,0.1046,0.089586,0.083764,0.069226,0.062938,0.060074,0.050268,0.03531,0.028949,0.027773,0.026615,0.026443,0.024041,0.010171,0.0086648,0.0083614,0.0076739,0.006011,0.0053194,0.0036309,0.0034553,0.003227,0.003123,0.0028138,0.0023913,0.0022163,0.0020846,0.001689,0.0015633,0.0012309,0.0011555,0.00094766,0.00090162,0.00083696,0.0008339,0.00078896,0.00067337,0.00048123,0.00047799,0.00035932,0.00030804,0.00026911,0.00012671,9.9113e-05,9.6719e-05,5.1744e-05,4.4652e-05,4.3938e-05,3.1599e-05,3.1337e-05,2.3713e-05,1.6215e-05,9.5597e-06,8.7608e-06,7.1045e-06,4.6569e-06,3.872e-06,3.278e-06,2.7386e-06,1.6032e-06,1.21e-06,1.2066e-06,1.0424e-06,8.3479e-07,8.0574e-07,5.9094e-07,5.2337e-07,4.693e-07,3.4849e-07,3.2172e-07,2.7464e-07,2.3096e-07,1.6607e-07,1.2371e-07,4.3544e-08,3.8652e-08,3.3859e-08,2.688e-08,1.9744e-08,1.9019e-08,1.1988e-08,1.1676e-08,1.0193e-08,8.6289e-09,6.8316e-09,3.7226e-10,2.9879e-10,2.6952e-10,2.2313e-10,2.0762e-10,1.6679e-10,7.459e-11,6.6835e-11,5.5082e-11,4.4142e-11,3.8129e-11,1.915e-11,1.8077e-11,9.6263e-12,4.6027e-12,2.4242e-12,2.294e-12,1.1179e-12,5.8891e-13,5.667e-13,4.2755e-13,2.3781e-13,2.347e-13,2.2793e-13,1.1047e-13,1.0308e-13,6.758e-14,5.4849e-14,3.4263e-14,3.0128e-14,1.6164e-14,1.4447e-14,7.5668e-15,6.3587e-15\n1,2.9198e-08,3.3145e-08,3.5553e-08,4.5228e-08,6.1259e-08,6.1692e-08,6.6827e-08,9.2067e-08,1.7851e-07,2.0158e-07,2.115e-07,2.1186e-07,2.5554e-07,3.5514e-07,3.707e-07,4.6315e-07,9.0807e-07,1.7189e-06,1.8688e-06,2.7509e-06,3.4238e-06,4.0758e-06,4.339e-06,5.4574e-06,5.5336e-06,6.8744e-06,9.4545e-06,1.2469e-05,1.4003e-05,2.3497e-05,2.3989e-05,3.6212e-05,4.5619e-05,4.8444e-05,6.2574e-05,7.2298e-05,7.2475e-05,7.8187e-05,0.00023238,0.00064718,0.00081718,0.00082302,0.00082365,0.0010576,0.0015778,0.0016719,0.0017384,0.0031864,0.003409,0.0035343,0.0062943,0.0098044,0.01276,0.015139,0.018532,0.037403,0.047121,0.048419,0.065373,0.089915,0.1032,0.10325,0.12474,0.13824,0.14814,0.16152,0.17347,0.19118,0.19909,0.22334,0.27048,0.29327,0.33176,0.41651,0.42554,0.46099,0.4824,0.5471,0.66204,0.69294,0.77544,0.83608,0.86634,0.91345,0.9191,0.9242,0.94771,0.95918,0.95941,0.96787,0.97365,0.98221,0.98285,0.98313,0.98464,0.98844,0.99357,0.99565,1,1,0.99368,0.99351,0.9905,0.98825,0.98668,0.98089,0.9738,0.94446,0.93944,0.922,0.82591,0.79132,0.77926,0.70113,0.69145,0.68065,0.5972,0.58529,0.55,0.53578,0.5309,0.52151,0.46167,0.40274,0.39687,0.38185,0.35589,0.28329,0.28031,0.2756,0.17699,0.12699,0.10949,0.10267,0.085515,0.078044,0.07463,0.062881,0.044752,0.03695,0.0355,0.034071,0.033858,0.030884,0.013435,0.011502,0.011111,0.010224,0.0080663,0.0071636,0.0049429,0.0047104,0.0044074,0.0042693,0.0038575,0.0032928,0.003058,0.002881,0.0023472,0.002177,0.0017247,0.0016215,0.0013366,0.0012732,0.0011841,0.0011799,0.0011178,0.00095777,0.00069005,0.00068551,0.00051881,0.00044637,0.00039118,0.00018734,0.00014732,0.00014384,7.799e-05,6.7508e-05,6.6452e-05,4.8116e-05,4.7725e-05,3.632e-05,2.5025e-05,1.4908e-05,1.3685e-05,1.1143e-05,7.3627e-06,6.1431e-06,5.2168e-06,4.3729e-06,2.585e-06,1.961e-06,1.9555e-06,1.6938e-06,1.3618e-06,1.3153e-06,9.6987e-07,8.6079e-07,7.7333e-07,5.772e-07,5.3359e-07,4.5672e-07,3.8522e-07,2.7853e-07,2.0849e-07,7.4647e-08,6.6388e-08,5.8277e-08,4.6436e-08,3.4274e-08,3.3034e-08,2.0971e-08,2.0435e-08,1.7876e-08,1.5172e-08,1.2055e-08,6.8554e-10,5.5196e-10,4.986e-10,4.1388e-10,3.855e-10,3.1064e-10,1.4047e-10,1.2605e-10,1.0416e-10,8.3723e-11,7.2461e-11,3.673e-11,3.4698e-11,1.8632e-11,8.9945e-12,4.7764e-12,4.5232e-12,2.2244e-12,1.1812e-12,1.1372e-12,8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761e-08,1.1141e-08,8.004e-09,6.7951e-09,4.6952e-09,4.2434e-09,2.598e-09,2.3776e-09,1.4261e-09,1.2426e-09\n1,6.8059e-13,7.9962e-13,8.7424e-13,1.1878e-12,1.749e-12,1.7648e-12,1.9545e-12,2.9444e-12,6.8858e-12,8.0514e-12,8.5653e-12,8.5842e-12,1.0929e-11,1.6716e-11,1.7669e-11,2.357e-11,5.6509e-11,1.3003e-10,1.4507e-10,2.4101e-10,3.2148e-10,4.0458e-10,4.3943e-10,5.9512e-10,6.0615e-10,8.0813e-10,1.2344e-09,1.7855e-09,2.0849e-09,4.1747e-09,4.2926e-09,7.482e-09,1.023e-08,1.1099e-08,1.5721e-08,1.9144e-08,1.9208e-08,2.1306e-08,9.5577e-08,4.0204e-07,5.5984e-07,5.6554e-07,5.6616e-07,8.0884e-07,1.4375e-06,1.5629e-06,1.6536e-06,4.0019e-06,4.4201e-06,4.6616e-06,1.0999e-05,2.15e-05,3.2192e-05,4.1929e-05,5.7454e-05,0.00017574,0.00025628,0.00026801,0.00044181,0.00076056,0.00096622,0.00096708,0.0013502,0.0016231,0.0018395,0.0021538,0.0024569,0.0029451,0.0031785,0.0039561,0.0057501,0.0067609,0.0087004,0.014143,0.01483,0.017755,0.019711,0.026605,0.043595,0.049552,0.069694,0.090245,0.10334,0.1299,0.13381,0.13752,0.15751,0.16966,0.16993,0.18048,0.18878,0.20352,0.20479,0.20535,0.20848,0.21726,0.23236,0.2405,0.28031,0.28039,0.33334,0.33407,0.34609,0.35399,0.35913,0.37613,0.3942,0.45393,0.46274,0.49149,0.62084,0.66054,0.6738,0.75358,0.7628,0.77293,0.84543,0.85494,0.88186,0.89216,0.89562,0.90214,0.94006,0.97014,0.97268,0.97874,0.98768,0.99998,1,0.99994,0.96521,0.90906,0.87877,0.8649,0.82372,0.80235,0.79177,0.75068,0.66833,0.62264,0.61322,0.6036,0.60214,0.58086,0.4069,0.37874,0.37267,0.35835,0.31982,0.30177,0.25052,0.24441,0.2362,0.23234,0.22042,0.20285,0.19505,0.18895,0.16924,0.16246,0.14297,0.13817,0.12403,0.12069,0.11585,0.11561,0.11213,0.10268,0.084977,0.084651,0.071897,0.065773,0.0608,0.038905,0.033539,0.033045,0.022509,0.020539,0.020334,0.016541,0.016455,0.013798,0.010829,0.0077026,0.0072781,0.006349,0.0048118,0.0042595,0.0038142,0.0033842,0.0023642,0.0019554,0.0019516,0.0017674,0.0015198,0.0014836,0.0012005,0.0011046,0.0010249,0.00083491,0.00079005,0.0007081,0.00062793,0.00049909,0.0004062,0.00019447,0.00017868,0.00016261,0.00013794,0.00011062,0.00010769,7.7288e-05,7.5836e-05,6.8757e-05,6.0963e-05,5.1481e-05,6.0724e-06,5.1556e-06,4.7742e-06,4.1466e-06,3.9293e-06,3.3359e-06,1.8233e-06,1.6787e-06,1.4509e-06,1.2276e-06,1.0989e-06,6.5203e-07,6.2407e-07,3.8623e-07,2.1969e-07,1.3428e-07,1.287e-07,7.3946e-08,4.5032e-08,4.371e-08,3.5128e-08,2.2263e-08,2.2036e-08,2.154e-08,1.2242e-08,1.1597e-08,8.3346e-09,7.077e-09,4.8919e-09,4.4217e-09,2.7085e-09,2.479e-09,1.4877e-09,1.2965e-09\n1,6.2866e-13,7.3878e-13,8.0783e-13,1.098e-12,1.6177e-12,1.6324e-12,1.8081e-12,2.7254e-12,6.3818e-12,7.4637e-12,7.9409e-12,7.9584e-12,1.0136e-11,1.5513e-11,1.6398e-11,2.1885e-11,5.2539e-11,1.2105e-10,1.3508e-10,2.2459e-10,2.9971e-10,3.7733e-10,4.0989e-10,5.5538e-10,5.6569e-10,7.5454e-10,1.1534e-09,1.6693e-09,1.9497e-09,3.9086e-09,4.0191e-09,7.012e-09,9.5927e-09,1.0409e-08,1.4753e-08,1.7972e-08,1.8032e-08,2.0004e-08,8.9984e-08,3.7956e-07,5.2887e-07,5.3426e-07,5.3485e-07,7.6466e-07,1.3605e-06,1.4795e-06,1.5655e-06,3.7957e-06,4.1934e-06,4.423e-06,1.0455e-05,2.0469e-05,3.0676e-05,3.9979e-05,5.4822e-05,0.00016815,0.00024546,0.00025672,0.00042377,0.00073058,0.00092876,0.00092959,0.0012991,0.0015625,0.0017715,0.0020752,0.0023681,0.0028403,0.0030661,0.0038189,0.0055574,0.0065378,0.0084205,0.013711,0.01438,0.017228,0.019133,0.025856,0.042452,0.048279,0.068006,0.088167,0.10103,0.12714,0.13099,0.13464,0.15433,0.1663,0.16656,0.17697,0.18516,0.1997,0.20095,0.2015,0.20459,0.21326,0.22818,0.23622,0.2756,0.27568,0.32813,0.32885,0.34077,0.34861,0.35371,0.37057,0.38852,0.4479,0.45667,0.4853,0.61442,0.65416,0.66745,0.74756,0.75684,0.76703,0.8402,0.84984,0.87714,0.8876,0.89112,0.89777,0.93652,0.96756,0.9702,0.97654,0.98598,0.99985,0.99994,1,0.96788,0.91322,0.88347,0.86981,0.82912,0.80797,0.79748,0.75668,0.67468,0.62906,0.61964,0.61002,0.60856,0.58728,0.4127,0.38435,0.37824,0.36381,0.32496,0.30675,0.25496,0.24879,0.24048,0.23658,0.22451,0.20671,0.19881,0.19263,0.17264,0.16576,0.14598,0.1411,0.12674,0.12334,0.11842,0.11818,0.11464,0.10502,0.086995,0.086663,0.073662,0.067416,0.06234,0.039968,0.034475,0.03397,0.023175,0.021153,0.020943,0.01705,0.016961,0.014232,0.011179,0.007961,0.0075238,0.0065663,0.004981,0.004411,0.0039513,0.0035072,0.0024528,0.0020298,0.0020259,0.0018353,0.0015789,0.0015414,0.0012479,0.0011486,0.0010659,0.00086884,0.00082229,0.00073722,0.00065397,0.00052011,0.00042354,0.00020316,0.00018671,0.00016996,0.00014423,0.00011573,0.00011267,8.093e-05,7.9414e-05,7.2017e-05,6.3873e-05,5.396e-05,6.3957e-06,5.4321e-06,5.031e-06,4.3709e-06,4.1424e-06,3.518e-06,1.9253e-06,1.7728e-06,1.5328e-06,1.2973e-06,1.1616e-06,6.8992e-07,6.604e-07,4.0909e-07,2.3295e-07,1.4252e-07,1.3661e-07,7.857e-08,4.7892e-08,4.6488e-08,3.7376e-08,2.3707e-08,2.3466e-08,2.2938e-08,1.305e-08,1.2364e-08,8.8905e-09,7.5512e-09,5.223e-09,4.7218e-09,2.8948e-09,2.6499e-09,1.5916e-09,1.3873e-09\n1,8.9625e-14,1.0591e-13,1.1616e-13,1.5956e-13,2.3825e-13,2.4048e-13,2.6732e-13,4.0875e-13,9.8628e-13,1.16e-12,1.2368e-12,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5,6.7884e-16,6.542e-16,6.3452e-16,5.7472e-16,5.4389e-16,3.2156e-16,2.2695e-16,1.7594e-16,1.5251e-16,1.1306e-16,8.7577e-17,8.3506e-17,5.8234e-17,4.7449e-17,3.6923e-17,2.4632e-17,1.4406e-17,1.3919e-17,1.1598e-17,1.0216e-17,9.6493e-18,5.2362e-18,4.426e-18,2.0512e-18,1.6552e-18,1.1329e-18,9.2415e-19,8.6506e-19,6.0298e-19,5.7544e-19,5.1944e-19,4.1518e-19,2.7035e-19\n1,7.5582e-06,9.5923e-06,9.7069e-06,1.0425e-05,1.1013e-05,1.1833e-05,1.3595e-05,1.885e-05,2.3146e-05,2.3884e-05,2.8078e-05,2.8826e-05,3.1991e-05,4.1135e-05,4.4483e-05,4.6961e-05,5.9294e-05,6.1701e-05,6.4478e-05,7.4701e-05,7.5887e-05,7.6577e-05,8.5594e-05,9.1381e-05,9.1576e-05,0.00010376,0.00010494,0.000111,0.00011183,0.00011263,0.00016414,0.00023349,0.00024096,0.0002669,0.00029226,0.00031349,0.00034055,0.00037477,0.00044293,0.00048524,0.00056219,0.00067519,0.00068661,0.00071154,0.0007353,0.00087144,0.00087167,0.0012969,0.0013351,0.0013567,0.0015244,0.0015374,0.0019264,0.0021027,0.0021804,0.0023601,0.0024813,0.0027026,0.0027749,0.0033922,0.0038333,0.0055806,0.0061723,0.0063824,0.0067748,0.0079668,0.0082333,0.0084765,0.0097067,0.011003,0.01152,0.01196,0.013681,0.016551,0.020136,0.022918,0.023542,0.029793,0.032265,0.033476,0.034026,0.036335,0.047291,0.047686,0.049872,0.053513,0.054253,0.059895,0.077982,0.078248,0.084094,0.086277,0.11448,0.12259,0.1232,0.13084,0.14174,0.14197,0.14288,0.14982,0.14988,0.15639,0.15896,0.19911,0.21878,0.21947,0.24337,0.25004,0.2606,0.26584,0.26896,0.27244,0.27357,0.27663,0.28914,0.29062,0.29368,0.2962,0.31563,0.33843,0.369,0.38086,0.40651,0.43602,0.44868,0.45526,0.53004,0.53887,0.54538,0.55832,0.57484,0.58918,0.60084,0.60292,0.60422,0.61891,0.6574,0.66021,0.67523,0.69832,0.71093,0.71297,0.78255,0.78326,0.79636,0.82123,0.82564,0.84174,0.84546,0.87055,0.88598,0.91797,0.91868,0.94936,0.95243,0.9534,0.96901,0.9699,0.97471,0.98043,0.98207,0.98326,0.98758,0.98823,0.98865,0.99148,0.99238,0.9926,0.9945,0.99512,0.99786,0.99995,1,1,0.99962,0.99929,0.99685,0.98842,0.98476,0.97797,0.97518,0.96658,0.95685,0.95601,0.95535,0.95527,0.95213,0.95186,0.94137,0.93625,0.9261,0.92564,0.92278,0.90334,0.90275,0.89284,0.88111,0.85899,0.85598,0.84725,0.84527,0.84023,0.83165,0.81822,0.81785,0.7862,0.7485,0.74441,0.73986,0.73844,0.71489,0.7002,0.64974,0.64708,0.6417,0.63262,0.59887,0.5638,0.52582,0.51452,0.50781,0.49088,0.4573,0.44281,0.42431,0.39657,0.37334,0.36922,0.32674,0.32136,0.31941,0.31002,0.30634,0.27784,0.26615,0.26282,0.26249,0.25561,0.24652,0.24238,0.24055,0.21002,0.20998,0.18487,0.1589,0.1374,0.11963,0.11252,0.11071,0.10658,0.10307,0.095152,0.094311,0.080541,0.077335,0.071228,0.064201,0.060229,0.059793,0.056736,0.05476,0.05015,0.043905,0.043354,0.040854,0.037161,0.034376,0.03425,0.026007,0.02484,0.024295,0.022947,0.022274,0.021247,0.019699,0.019103,0.018299,0.017068,0.015585,0.014278,0.014152,0.012702,0.011821,0.011092,0.0095814,0.0089554,0.0087373,0.0080016,0.0079589,0.0076558,0.0075255,0.0059799,0.0056332,0.0055035,0.0049454,0.0046458,0.0046018,0.0039713,0.0029777,0.0027298,0.0026095,0.0023483,0.002327,0.0020645,0.0020375,0.0017431,0.0015891,0.0013173,0.001295,0.0012744,0.001263,0.0012264,0.0011973,0.0011799,0.0010101,0.00097755,0.00071434,0.00064221,0.00059822,0.0005816,0.00045949,0.00045758,0.00042305,0.00040777,0.00033853,0.00033653,0.00033185,0.00028851,0.0002775,0.00027368,0.00026237,0.00024841,0.00023586,0.00022958,0.00017569,0.00017403,0.00015105,0.00012186,9.9353e-05,7.4531e-05,7.4225e-05,6.6956e-05,5.8766e-05,4.9404e-05,4.9309e-05,4.6418e-05,3.486e-05,3.26e-05,3.236e-05,2.295e-05,2.1268e-05,2.0353e-05,1.7767e-05,1.5516e-05,1.2066e-05,1.1598e-05,9.5717e-06,6.1954e-06,6.1591e-06,5.9416e-06,5.5199e-06,5.2094e-06,4.8497e-06,4.456e-06,2.0019e-06,1.6168e-06,1.1618e-06,9.0661e-07,8.9555e-07,8.7057e-07,5.9917e-07,5.488e-07,5.4481e-07,3.793e-07,2.0389e-07,1.9987e-07,1.6609e-07,1.5984e-07,1.0403e-07,9.2162e-08,7.0652e-08,6.1459e-08,5.2652e-08,5.1086e-08,4.2476e-08,4.1113e-08,4.048e-08,2.652e-08,2.3349e-08,2.2128e-08,1.8575e-08,1.7576e-08,1.3628e-08,1.2857e-08,1.1432e-08,8.5188e-09,8.4951e-09,7.3698e-09,7.0239e-09,7.0234e-09,5.1242e-09,5.0732e-09,4.4519e-09,3.8767e-09,2.8628e-09,2.1744e-09,1.7829e-09,1.2977e-09,9.6536e-10,6.5579e-10,6.4551e-10,4.7833e-10,3.579e-10,3.014e-10,1.8968e-10,1.6545e-10,1.2522e-10,1.0611e-10,7.4963e-11,7.2249e-11,6.6956e-11,5.6132e-11,3.8733e-11,2.638e-11,2.1499e-11,1.7513e-11,1.5732e-11,1.5495e-11,1.2747e-11,1.0164e-11,5.0599e-12,4.1962e-12,3.7841e-12,3.01e-12,3.0052e-12,2.3967e-12,2.33e-12,1.9974e-12,1.7644e-12,1.1598e-12,1.0912e-12,7.8447e-13,5.7608e-13,2.0784e-13,1.7128e-13,1.5597e-13,1.2195e-13,9.6722e-14,8.8549e-14,7.4206e-14,6.534e-14,5.578e-14,4.2916e-14,4.2856e-14,2.6674e-14,2.3504e-14,2.2891e-14,2.2571e-14,1.3881e-14,1.3727e-14,1.3068e-14,1.2592e-14,1.1438e-14,9.7504e-15,8.4475e-15,4.4539e-15,1.1181e-15,7.3732e-16,7.106e-16,6.8924e-16,6.2435e-16,5.9091e-16,3.4957e-16,2.4682e-16,1.914e-16,1.6594e-16,1.2306e-16,9.5349e-17,9.0922e-17,6.3431e-17,5.1696e-17,4.024e-17,2.6857e-17,1.5716e-17,1.5186e-17,1.2656e-17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}, { "path": "bayesian_ml/3/run.py", "content": "# variational-inference for linear regression\n# y(i) ~ N( x(i).dot(w), 1/lambda )\n# w ~ N( 0, diag(alpha_1, alpha_2, ..., alpha_D)^-1 )\n# alpha_i ~ Gamma(a, b)\n# lambda ~ Gamma(e, f)\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom scipy.stats import multivariate_normal as mvn\nfrom scipy.stats import gamma as gamma_dist\nfrom scipy.special import gamma, digamma\n\ndef e_ln_q_gamma(a, b):\n return np.log(b) - a - np.log(np.abs(gamma(a))) + (a - 1)*digamma(a)\n\ndef objective(X, Y, C, mu, a, b, e, f, a0, b0, e0, f0):\n log2pi = np.log(2*np.pi)\n N, D = X.shape\n\n # E(lnX) = digamma(a) - ln(b) for X ~ Gamma(a,b)\n E_ln_lambda = digamma(e) - np.log(f)\n E_ln_alpha = digamma(a) - np.log(b)\n\n # model likelihood\n total = (N/2.0)*(E_ln_lambda - log2pi)\n data_total = 0\n for i in xrange(N):\n delta = Y[i] - X[i].dot(mu)\n data_total += delta*delta + X[i].dot(C).dot(X[i])\n total -= (float(e)/f)/2.0 * data_total\n\n # print \"total after model likelihood:\", total\n\n # w likelihood\n total -= (D/2.0)*log2pi\n for k in xrange(D):\n total += 0.5*(E_ln_alpha[k] - (float(a[k])/b[k])*(C[k,k] + mu[k]*mu[k]))\n\n # print \"total after w likelihood:\", total\n\n # lambda likelihood\n total += e0*np.log(f0) - np.log(gamma(e0)) + (e0 - 1)*E_ln_lambda - f0*(float(e)/f)\n\n # print \"total after lambda likelihood:\", total\n\n # alpha likelihood\n for k in xrange(D):\n total += a0*np.log(b0) - np.log(gamma(a0)) + (a0 - 1)*E_ln_alpha[k] - b0*(float(a[k])/b[k])\n\n # print \"total after alpha likelihood:\", total\n\n # entropy\n # TODO: calculate this manually\n # total -= mvn.entropy(mean=mu, cov=C)\n # e1 = mvn.entropy(cov=C)\n # e2 = 0.5*np.log( np.linalg.det(2*np.pi*np.e*C) )\n # print \"e1:\", e1, \"e2:\", e2\n # total += 0.5*np.log( np.linalg.det(2*np.pi*np.e*C) )\n\n total += mvn.entropy(cov=C)\n # print \"det(C):\", np.linalg.det(C)\n # print \"total after lnq(w):\", total\n\n # total -= gamma_dist.entropy(e, scale=1.0/f)\n # e3 = gamma_dist.entropy(e, scale=1.0/f)\n # e4 = -e_ln_q_gamma(e, f)\n # print \"e3:\", e3, \"e4:\", e4\n # assert(np.abs(e3 - e4) < 1e-8)\n total += gamma_dist.entropy(e, scale=1.0/f)\n # total -= e_ln_q_gamma(e, f)\n # print \"total after lnq(lambda):\", total\n for k in xrange(D):\n # total -= e_ln_q_gamma(a[k], b[k])\n total += gamma_dist.entropy(a[k], scale=1.0/b[k])\n return total\n\n\ndef run(num=1, T=500):\n X = pd.read_csv('X_set%s.csv' % num, header=None).as_matrix()\n Y = pd.read_csv('y_set%s.csv' % num, header=None).as_matrix().flatten()\n Z = pd.read_csv('z_set%s.csv' % num, header=None).as_matrix().flatten()\n N, D = X.shape\n print X.shape, Y.shape, Z.shape\n\n a0 = 1e-16\n b0 = 1e-16\n e0 = 1\n f0 = 1\n\n # params for q(w) - doesn't matter what we set it to, we'll update this first\n C = np.eye(D)\n mu = np.zeros(D)\n\n # params for q(lambda)\n e = e0\n f = f0\n\n # params for q(alpha)\n a = np.ones(D)*a0\n b = np.ones(D)*b0\n a0ones = np.ones(D)*a0\n\n # objective\n L = np.empty(T)\n\n for t in xrange(T):\n # update q(w)\n C = np.linalg.inv(np.diag(1.0*a/b) + (1.0*e/f)*X.T.dot(X))\n mu = C.dot((1.0*e/f)*X.T.dot(Y))\n\n # update q(alpha)\n a = a0ones + 0.5\n b = b0 + 0.5*(np.diag(C) + mu*mu)\n # for k in xrange(D):\n # a[k] = a0 + 0.5\n # b[k] = b0 + 0.5*(C[k,k] + mu[k]*mu[k])\n\n # update q(lambda)\n e = e0 + N/2.0\n sum_for_f = 0\n # for i in xrange(N):\n # delta = Y[i] - X[i].dot(mu)\n # sum_for_f += delta*delta + X[i].dot(C).dot(X[i])\n delta = Y - X.dot(mu)\n sum_for_f = delta.dot(delta) + np.trace(X.dot(C).dot(X.T))\n f = f0 + 0.5*sum_for_f\n\n # update L\n L[t] = objective(X, Y, C, mu, a, b, e, f, a0, b0, e0, f0)\n if t % 20 == 0:\n print \"t:\", t\n if num == 3:\n print \"L:\", L[t]\n\n # plot 1/E[alpha]\n plt.plot(b/a)\n plt.show()\n\n # 1/E[lambda]\n print \"1/E[lambda]:\", f/e\n\n # plot L\n plt.plot(L)\n plt.show()\n\n Yhat = X.dot(mu)\n plt.plot(Z, Yhat)\n plt.scatter(Z, Y)\n plt.plot(Z, 10*np.sinc(Z))\n plt.show()\n\n\nrun(1)\nrun(2)\nrun(3)" }, { "path": "bayesian_ml/3/y_set1.csv", "content": "-1.9807\n1.3977\n0.39627\n0.11422\n0.59669\n1.6194\n1.9997\n0.27002\n-1.2381\n0.71259\n1.1677\n-0.56528\n-0.16157\n1.4075\n1.3544\n-0.0078934\n1.1667\n0.83691\n-0.75035\n-0.76558\n0.26084\n-1.4914\n-0.11861\n-0.77599\n0.65591\n1.753\n0.53411\n0.2034\n3.2292\n-1.9822\n-1.7943\n-1.167\n0.49746\n-1.9873\n-2.7712\n-2.2993\n-3.1986\n-1.6758\n-1.3253\n-2.7287\n0.26081\n-2.1141\n-1.7219\n-1.0972\n0.39853\n1.278\n5.6629\n4.4801\n7.797\n8.4042\n6.9353\n8.2126\n5.4825\n9.6335\n11.187\n11.906\n9.3574\n7.6739\n8.2142\n6.3477\n5.4952\n2.8962\n0.48351\n-0.30745\n-1.6697\n-1.567\n-3.1646\n-1.103\n-1.578\n-2.5523\n-2.1199\n-2.1978\n-1.8076\n-2.9934\n-1.1843\n-0.94178\n-0.97797\n-1.0436\n-2.7953\n-0.74118\n0.62131\n0.41625\n1.3788\n1.4919\n0.11158\n0.51488\n-0.95601\n-1.0738\n0.069906\n-2.1404\n-0.90422\n-0.31789\n-0.34168\n1.0552\n-1.2299\n0.55841\n-1.0027\n-0.038141\n-1.3129\n-0.47181\n" }, { "path": "bayesian_ml/3/y_set2.csv", "content": "1.0719\n0.11583\n0.93244\n0.57572\n0.31694\n0.83109\n-1.0948\n-0.96628\n-0.70567\n-0.42234\n0.50279\n-0.62063\n1.4233\n0.15329\n0.29718\n1.9027\n-0.29558\n0.70807\n-1.7488\n-0.055268\n-0.11002\n0.29835\n-1.2306\n0.12409\n0.87917\n0.11703\n2.1908\n1.9126\n1.3058\n1.2737\n1.3056\n0.041982\n-0.11861\n-0.0028592\n0.91428\n-0.88784\n-0.14079\n-0.47711\n-1.017\n-0.47484\n-2.1037\n-1.8068\n-1.1703\n-0.66441\n0.27772\n-1.43\n-0.38931\n-0.21783\n-0.23204\n-1.7272\n-1.5208\n1.7792\n-1.4753\n-1.1052\n0.11862\n1.7058\n2.9352\n1.5628\n1.3946\n0.11424\n0.4934\n1.2871\n2.5238\n2.0262\n1.0801\n-0.9816\n-0.36913\n2.1336\n0.61729\n0.2564\n0.84102\n0.23374\n-0.26162\n-2.0896\n-1.1948\n-1.937\n-1.435\n-0.81724\n-1.2405\n-2.9074\n-4.0468\n-2.4879\n-2.7022\n0.64254\n-1.4789\n0.081845\n-1.3779\n-0.23709\n0.28329\n0.17127\n-0.091373\n0.21315\n-0.054294\n0.47168\n1.0184\n0.50646\n0.34773\n0.63896\n1.9126\n1.882\n3.6891\n2.5319\n1.5174\n2.2353\n3.4245\n5.2983\n3.8079\n5.8691\n4.2966\n6.1311\n6.8197\n10.243\n8.5904\n8.5367\n10.189\n10.478\n10.912\n9.3415\n9.3864\n11.178\n8.9284\n10.957\n9.5685\n9.5365\n10.038\n8.5009\n9.8221\n10.111\n8.9823\n7.4138\n6.0708\n5.0509\n6.1225\n3.1199\n3.2787\n4.1355\n2.0782\n2.6844\n0.59051\n0.67348\n0.9691\n-0.35254\n-0.82645\n-0.22224\n-0.65941\n-0.89189\n-2.1968\n-1.6648\n-2.2095\n-2.7901\n-0.91662\n-2.7168\n-2.1209\n-2.3851\n-0.82882\n-1.7077\n-2.1833\n-2.2892\n-0.88709\n-0.97589\n-2.6419\n0.95409\n-0.46452\n-1.5276\n-0.84018\n-1.9559\n-0.81199\n0.48569\n-1.2974\n-1.3495\n1.2276\n-1.3513\n-0.69919\n-0.57888\n-0.02456\n1.7478\n0.1268\n0.74479\n1.5368\n0.28655\n1.1954\n-1.1805\n-0.15926\n1.946\n2.1353\n1.6037\n1.8437\n1.1915\n0.19297\n2.1556\n0.26826\n-0.65444\n-0.7975\n1.0501\n0.0019153\n0.47655\n-0.50907\n0.96367\n-0.329\n0.48126\n2.3427\n0.14366\n-0.34113\n0.023812\n0.85333\n0.81691\n-2.7054\n-1.062\n-1.2612\n-1.1259\n-2.5775\n-1.2307\n-0.9116\n-2.7058\n-1.7947\n0.032767\n1.361\n-1.4488\n0.78974\n1.0157\n-0.79251\n-0.46814\n0.52801\n1.801\n-0.39036\n1.387\n-0.51833\n1.3714\n-0.031317\n0.35643\n-0.36886\n1.4649\n0.63804\n0.27513\n-1.7868\n0.15545\n1.8684\n0.32499\n2.4063\n1.9406\n0.75147\n0.65898\n0.72895\n0.28186\n2.2324\n-0.53944\n0.54644\n0.117\n0.41722\n1.3173\n" }, { "path": "bayesian_ml/3/y_set3.csv", "content": 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"4.1018,2.7755\n-2.7843,-2.5328\n4.9085,1.7415\n-0.98785,-0.096165\n5.0936,1.4871\n-3.2698,-2.0138\n4.322,1.7522\n1.1916,1.0932\n2.8191,2.5074\n4.6657,1.7354\n-2.5031,3.9679\n2.4564,2.9697\n-6.737,-8.7944\n-3.3788,-4.7085\n2.9108,2.7162\n-3.4432,-1.5452\n2.2044,2.2752\n3.1178,2.0867\n2.0801,4.5122\n-0.4534,-0.40019\n-3.9462,-3.4262\n-2.7851,-2.8649\n-6.1236,-6.1929\n5.7535,0.34864\n-3.3615,3.2837\n-4.2186,5.1414\n2.9101,4.2141\n0.84418,-2.1573\n1.578,0.24561\n-3.4763,4.7377\n3.0744,2.1029\n3.4406,1.6932\n2.9462,3.1246\n5.3141,1.5204\n3.2627,1.7641\n1.3692,0.019343\n0.93969,4.4613\n0.84051,3.5441\n-4.4498,4.2623\n0.24152,-0.96248\n2.1622,3.4838\n-3.4546,4.983\n0.16953,0.58335\n-0.027432,-1.4383\n3.6449,3.4297\n-0.079127,-1.7929\n2.046,3.7616\n-5.4619,-4.3469\n0.66641,-0.53428\n-3.7058,-6.0215\n-3.8574,-5.3334\n2.2173,-0.91211\n-4.588,4.9909\n-0.29331,-0.6333\n-0.79856,-1.3326\n-2.1379,-3.148\n-3.3361,4.1204\n3.3964,2.8128\n-6.1827,-9.194\n-1.9853,-3.7974\n-0.12541,-0.92221\n4.3319,2.9815\n-2.9299,-5.7147\n-6.2677,-6.8165\n3.3636,1.8345\n-2.246,-2.6986\n2.4218,3.3688\n1.2219,-0.50176\n-3.9583,2.6322\n-5.1256,-6.3729\n0.54738,4.0857\n1.7943,-1.1886\n-0.69529,-0.70864\n3.4995,2.9259\n-3.7161,5.5869\n-5.675,-4.773\n-3.1841,-1.647\n1.3342,-1.3613\n2.0226,3.3061\n1.5212,3.6252\n-2.9016,-4.4155\n1.3775,-0.95756\n-2.9503,-3.7117\n5.0034,1.932\n0.76731,-0.74763\n0.032336,-2.7383\n-6.3487,-6.5736\n-0.2545,-0.75344\n-4.169,-4.1116\n2.3763,3.0075\n2.7965,3.1516\n-5.6324,-8.3578\n-1.5831,-2.5032\n3.8007,2.7588\n6.4521,1.3254\n-1.6098,-3.3926\n4.0821,2.7777\n1.845,-1.9623\n-1.3677,-0.55576\n2.9786,2.0606\n1.5702,3.6537\n-0.11889,-0.39907\n2.2032,4.5764\n3.0725,2.9404\n4.8477,2.3742\n3.051,3.4929\n2.0157,3.0993\n1.6125,5.1709\n4.9322,2.9016\n3.2214,1.1894\n3.9556,2.2825\n-0.57541,0.88392\n3.0282,2.5043\n3.3044,2.5408\n6.084,0.41893\n5.2028,1.8809\n1.1464,-1.4103\n-0.99623,-1.873\n3.1967,3.157\n-0.81208,0.49219\n5.6915,1.7463\n1.3163,-1.5315\n-4.9834,4.4425\n-5.0667,-5.3556\n2.7229,1.9391\n0.94494,-1.2182\n3.8887,1.3523\n2.3424,4.1084\n4.3535,2.5696\n0.079367,0.25433\n4.4885,2.1952\n-3.3392,4.5667\n-0.83181,-2.5322\n1.0295,-1.5603\n-2.6943,-3.7788\n1.1898,-1.0653\n-0.25662,-0.43771\n-3.8077,-5.9955\n0.45538,3.307\n2.7107,4.3586\n-4.4701,-6.4745\n2.4627,3.404\n2.9282,2.7614\n3.3275,2.9878\n4.4574,1.255\n-2.769,-3.073\n-3.1109,-3.9208\n4.0042,2.8929\n-8.463,-8.9882\n-3.1489,-2.696\n-3.9129,-3.7642\n4.243,2.8379\n-5.6065,-6.5221\n-1.189,-1.1011\n-1.5842,-1.6023\n-0.41358,-0.63273\n2.4407,3.3573\n3.9408,1.7018\n5.9457,1.1287\n1.0341,-0.52669\n-2.8595,4.4397\n0.63557,-0.30127\n-3.7764,-6.9249\n0.91297,-1.954\n5.0938,3.5936\n3.9196,2.5586\n-0.46777,-1.0298\n3.6313,1.6239\n-0.031369,-0.10111\n-4.3877,-4.8732\n1.2262,0.65494\n-2.6655,-2.2333\n3.9422,2.4165\n1.2014,-0.098558\n4.501,2.944\n-5.6844,-5.7906\n-4.0166,2.947\n0.28418,-1.8048\n4.6023,0.8213\n0.24267,-3.878\n-2.2338,-5.1638\n2.9347,1.7524\n-1.3805,-2.5762\n-0.90795,1.2333\n4.2075,3.1063\n-2.1278,4.3067\n-4.0478,-5.1684\n1.9329,2.1893\n-7.6256,-8.1264\n-4.7174,-7.1461\n-0.13106,0.014465\n-5.8557,-5.1366\n-5.9314,-4.6967\n4.2642,2.0679\n-6.727,-6.2622\n3.2245,3.4363\n-5.1829,5.1906\n-5.5965,-6.3422\n2.9379,3.3463\n5.2847,1.5374\n2.8052,2.5514\n-4.7574,5.5592\n-2.9844,-5.2619\n-3.31,4.0064\n2.8413,2.407\n3.8481,2.4584\n0.65418,-1.8281\n3.6013,2.1847\n2.4326,0.29105\n-4.5797,-3.4686\n-3.3286,3.1836\n-0.34821,-1.0012\n-5.3663,-9.3532\n-2.3033,-4.401\n5.0091,1.5007\n-3.7659,4.7209\n-4.4302,4.4777\n-4.3974,3.0623\n-6.6131,-5.6686\n0.25319,-1.2064\n3.6856,1.0241\n-5.6857,-6.4256\n3.0639,2.8238\n3.7332,1.5943\n-0.14387,-2.4385\n0.88598,-0.56042\n5.3827,1.2769\n-3.0166,-4.7408\n-2.4587,4.1896\n-0.62788,-1.3862\n2.4177,3.9261\n-0.85756,1.1946\n-6.3911,-5.8408\n4.5023,3.372\n-4.4713,-4.049\n2.1595,2.8096\n1.2035,4.5017\n3.5571,3.5699\n-3.4531,4.6434\n-3.3611,-5.4515\n4.5575,1.9208\n-3.9329,-5.3362\n2.9503,2.3361\n1.4231,-0.14189\n3.2051,2.5119\n-4.5733,4.2999\n-2.5123,-3.5152\n-4.4529,-5.1957\n-3.235,-3.6716\n0.57698,-2.2389\n" }, { "path": "bayesian_ml/4/emgmm.py", "content": "# GMM using Expectation-Maximization\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom scipy.stats import multivariate_normal\n\n\ndef gmm(X, K, max_iter=20, smoothing=1e-2):\n N, D = X.shape\n M = np.zeros((K, D))\n R = np.zeros((N, K))\n C = np.zeros((K, D, D))\n pi = np.ones(K) / K # uniform\n\n # initialize M to random, initialize C to spherical with variance 1\n for k in xrange(K):\n M[k] = X[np.random.choice(N)]\n C[k] = np.eye(D)\n\n costs = np.zeros(max_iter)\n weighted_pdfs = np.zeros((N, K)) # we'll use these to store the PDF value of sample n and Gaussian k\n for i in xrange(max_iter):\n # step 1: determine assignments / resposibilities\n for k in xrange(K):\n weighted_pdfs[:,k] = pi[k]*multivariate_normal.pdf(X, M[k], C[k])\n\n R = weighted_pdfs / weighted_pdfs.sum(axis=1, keepdims=True)\n # for k in xrange(K):\n # for n in xrange(N):\n # R[n,k] = weighted_pdfs[n,k] / weighted_pdfs[n,:].sum()\n\n # step 2: recalculate params\n for k in xrange(K):\n Nk = R[:,k].sum()\n pi[k] = Nk / N\n M[k] = R[:,k].dot(X) / Nk\n C[k] = np.sum(R[n,k]*np.outer(X[n] - M[k], X[n] - M[k]) for n in xrange(N)) / Nk + np.eye(D)*smoothing\n\n\n costs[i] = np.log(weighted_pdfs.sum(axis=1)).sum()\n if i > 0:\n if np.abs(costs[i] - costs[i-1]) < 0.1:\n break\n\n plt.plot(costs)\n plt.title(\"Costs\")\n plt.show()\n\n random_colors = np.random.random((K, 3))\n # colors = R.dot(random_colors)\n # plt.scatter(X[:,0], X[:,1], c=colors)\n plt.scatter(X[:,0], X[:,1], c=R.argmax(axis=1))\n plt.show()\n\n print \"pi:\", pi\n print \"means:\", M\n print \"covariances:\", C\n return R\n\n\ndef main():\n X = pd.read_csv('data.txt', header=None).as_matrix()\n\n # what does it look like without clustering?\n plt.scatter(X[:,0], X[:,1])\n plt.show()\n\n for K in (2,4,8,10):\n gmm(X, K, max_iter=100, smoothing=0)\n\n\n\nif __name__ == '__main__':\n main()" }, { "path": "bayesian_ml/4/npbgmm.py", "content": "# GMM using Bayesian Nonparametric Clustering\n# Gaussian Mixture Model\n# Dirichlet Process\n# Gibbs Sampling\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom scipy import stats\nfrom scipy.stats import multivariate_normal as mvn, dirichlet, wishart\nfrom scipy.special import digamma, gamma\n\n# scipy wishart!!!\n# parameters are df, sigma=scale\n# for us, df = a, inv(sigma) = B\n\n\ndef marginal(x, c, m, a, B):\n D = len(x)\n k0 = ( c / (np.pi * (1 + c)) )**(D/2.0)\n k1top = np.linalg.det(B + (c/(1+c)*np.outer(x - m, x - m)))**(-(a + 1.0)/2.0)\n k1bot = np.linalg.det(B)**(-a/2.0)\n k1 = k1top/k1bot\n k2log = 0\n for d in xrange(D):\n k2log += np.log(gamma( (a+1.0)/2.0 + (1.0-d)/2.0 )) - np.log(gamma( a/2.0 + (1.0-d)/2.0 ))\n k2 = np.exp(k2log)\n return k0*k1*k2\n\n\ndef normalize_phi_hat(phi_hat):\n # phi_hat is a dictionary: cluster index -> non-normalized probability of that cluster\n # normalization done in place so no need to return anything\n total = np.sum(phi_hat.values())\n\n for j, p_hat in phi_hat.iteritems():\n phi_hat[j] = p_hat / total\n\n\ndef sample_cluster_identity(phi):\n # phi is a dictionary: cluster index -> probability of that cluster\n # print \"dictionary sample from:\", phi\n p = np.random.random()\n cumulative = 0\n for j, q in phi.iteritems():\n cumulative += q\n if p < cumulative:\n return j\n # print \"cumulative:\", cumulative\n assert(False) # should never get here because cumulative = 1 by now\n\n\ndef sample_from_prior(c0, m0, a0, B0):\n precision0 = wishart.rvs(df=a0, scale=np.linalg.inv(B0))\n cov = np.linalg.inv(precision0)\n mean = mvn.rvs(mean=m0, cov=cov/c0)\n return mean, cov\n\n\n# samples mu, sigma from P(mu, sigma | X)\ndef sample_from_X(X, m0, c0, a0, B0):\n N = len(X)\n s = float(N)\n m = (c0 / (s + c0))*m0 + (1 / (s + c0))*X.sum(axis=0)\n c = s + c0\n a = s + a0\n meanX = X.mean(axis=0)\n B = (s / (a0*s + 1)) * np.outer(meanX - m0, meanX - m0) + B0\n for i in xrange(N):\n B += np.outer(X[i] - meanX, X[i] - meanX)\n return sample_from_prior(c, m, a, B)\n\n\ndef gmm(X, T=500):\n N, D = X.shape\n\n m0 = X.mean(axis=0)\n c0 = 0.1\n a0 = float(D)\n B0 = c0*D*np.cov(X.T)\n alpha0 = 1.0\n\n # cluster assignments - originally everything is assigned to cluster 0\n C = np.zeros(N)\n\n # keep as many as we need for each gaussian\n # originally we sample from the prior\n # TODO: just use the function above\n precision0 = wishart.rvs(df=a0, scale=np.linalg.inv(B0))\n covariances = [np.linalg.inv(precision0)]\n means = [mvn.rvs(mean=m0, cov=covariances[0]/c0)]\n\n cluster_counts = [1]\n K = 1\n observations_per_cluster = np.zeros((T, 6))\n for t in xrange(T):\n if t % 20 == 0:\n print t\n # 1) calculate phi[i,j]\n # Notes:\n # MANY new clusters can be made each iteration\n # A cluster can be DESTROYED if a x[i] is the only pt in cluster j and gets assigned to a new cluster\n # phi = np.empty((N, K))\n list_of_cluster_indices = range(K)\n next_cluster_index = K\n # phi = [] # TODO: do we need this at all?\n for i in xrange(N):\n phi_i = {}\n for j in list_of_cluster_indices:\n # don't loop through xrange(K) because clusters can be created or destroyed as we loop through i\n nj_noti = np.sum(C[:i] == j) + np.sum(C[i+1:] == j)\n if nj_noti > 0:\n # existing cluster\n # phi[i,j] = N(x[i] | mu[j], cov[j]) * nj_noti / (alpha0 + N - 1)\n # using the sampled mu / covs\n phi_i[j] = mvn.pdf(X[i], mean=means[j], cov=covariances[j]) * nj_noti / (alpha0 + N - 1.0)\n\n # new cluster\n # create a possible new cluster for every sample i\n # but only keep it if sample i occupies this new cluster j'\n # i.e. if C[i] = j' when we sample C[i]\n # phi[i,j'] = alpha0 / (alpha0 + N - 1) * p(x[i])\n # p(x[i]) is a marginal integrated over mu and precision\n phi_i[next_cluster_index] = alpha0 / (alpha0 + N - 1.0) * marginal(X[i], c0, m0, a0, B0)\n\n # normalize phi[i] and assign C[i] to its new cluster by sampling from phi[i]\n normalize_phi_hat(phi_i)\n\n # if C[i] = j' (new cluster), generate mu[j'] and cov[j']\n C[i] = sample_cluster_identity(phi_i)\n if C[i] == next_cluster_index:\n list_of_cluster_indices.append(next_cluster_index)\n next_cluster_index += 1\n new_mean, new_cov = sample_from_prior(c0, m0, a0, B0)\n means.append(new_mean)\n covariances.append(new_cov)\n\n # destroy any cluster with no points in it\n clusters_to_remove = []\n tot = 0\n for j in list_of_cluster_indices:\n nj = np.sum(C == j)\n # print \"number of pts in cluster %d:\" % j, nj\n tot += nj\n if nj == 0:\n clusters_to_remove.append(j)\n # print \"tot:\", tot\n assert(tot == N)\n for j in clusters_to_remove:\n list_of_cluster_indices.remove(j)\n\n # DEBUG - make sure no clusters are empty\n # counts = [np.sum(C == j) for j in list_of_cluster_indices]\n # for c in counts:\n # assert(c > 0)\n\n # re-order the cluster indexes so they range from 0..new K - 1\n new_C = np.zeros(N)\n for new_j in xrange(len(list_of_cluster_indices)):\n old_j = list_of_cluster_indices[new_j]\n new_C[C == old_j] = new_j\n C = new_C\n K = len(list_of_cluster_indices)\n list_of_cluster_indices = range(K) # redundant but if removed will break counts\n\n cluster_counts.append(K)\n # 2) calculate the new mu, covariance for every currently non-empty cluster\n # i.e. SAMPLE mu, cov from the new cluster assignments\n means = []\n covariances = []\n for j in xrange(K):\n # first calculate m', c', a', B'\n # then call the function that samples a mean and covariance using these\n mean, cov = sample_from_X(X[C == j], m0, c0, a0, B0)\n means.append(mean)\n covariances.append(cov)\n\n # plot number of observations per cluster for 6 most probable clusters per iteration\n counts = sorted([np.sum(C == j) for j in list_of_cluster_indices], reverse=True)\n # print \"counts:\", counts\n if len(counts) < 6:\n observations_per_cluster[t,:len(counts)] = counts\n else:\n observations_per_cluster[t] = counts[:6]\n\n # plot number of clusters per iteration\n plt.plot(cluster_counts)\n plt.show()\n\n # plot number of observations per cluster for 6 most probable clusters per iteration\n plt.plot(observations_per_cluster)\n plt.show()\n\n\ndef main():\n X = pd.read_csv('data.txt', header=None).as_matrix()\n gmm(X)\n\nif __name__ == '__main__':\n main()\n" }, { "path": "bayesian_ml/4/vigmm.py", "content": "# GMM using Variational Inference\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom scipy import stats\nfrom scipy.stats import multivariate_normal as mvn, dirichlet, wishart\nfrom scipy.special import digamma, gamma\n\ndef get_cost(X, K, cluster_assignments, phi, alphas, mu_means, mu_covs, a, B, orig_alphas, orig_c, orig_a, orig_B):\n N, D = X.shape\n total = 0\n ln2pi = np.log(2*np.pi)\n\n # calculate B inverse since we will need it\n Binv = np.empty((K, D, D))\n for j in xrange(K):\n Binv[j] = np.linalg.inv(B[j])\n\n # calculate expectations first \n Elnpi = digamma(alphas) - digamma(alphas.sum()) # E[ln(pi)]\n Elambda = np.empty((K, D, D))\n Elnlambda = np.empty(K)\n for j in xrange(K):\n Elambda[j] = a[j]*Binv[j]\n Elnlambda[j] = D*np.log(2) - np.log(np.linalg.det(B[j]))\n for d in xrange(D):\n Elnlambda[j] += digamma(a[j]/2.0 + (1 - d)/2.0)\n\n # now calculate the log joint likelihood\n # Gaussian part\n # total -= N*D*ln2pi\n # total += 0.5*Elnlambda.sum()\n # for j in xrange(K):\n # # total += 0.5*Elnlambda[j] # vectorized\n # for i in xrange(N):\n # if cluster_assignments[i] == j:\n # diff_ij = X[i] - mu_means[j]\n # total -= 0.5*( diff_ij.dot(Elambda[j]).dot(diff_ij) + np.trace(Elambda[j].dot(mu_covs[j])) )\n\n # mixture coefficient part\n # total += Elnpi.sum()\n\n # use phi instead\n for j in xrange(K):\n for i in xrange(N):\n diff_ij = X[i] - mu_means[j]\n inside = Elnlambda[j] - D*ln2pi\n inside += -diff_ij.dot(Elambda[j]).dot(diff_ij) - np.trace(Elambda[j].dot(mu_covs[j]))\n # inside += Elnpi[j]\n total += phi[i,j]*(0.5*inside + Elnpi[j])\n \n\n # E{lnp(mu)} - based on original prior\n for j in xrange(K):\n E_mu_dot_mu = np.trace(mu_covs[j]) + mu_means[j].dot(mu_means[j])\n total += -0.5*D*np.log(2*np.pi*orig_c) - 0.5*E_mu_dot_mu/orig_c\n\n # print \"total:\", total\n\n # E{lnp(lambda)} - based on original prior\n for j in xrange(K):\n total += (orig_a[j] - D - 1)/2.0*Elnlambda[j] - 0.5*np.trace(orig_B[j].dot(Elambda[j]))\n # print \"total 1:\", total\n total += -orig_a[j]*D/2.0*np.log(2) + 0.5*orig_a[j]*np.log(np.linalg.det(orig_B[j]))\n # print \"total 2:\", total\n total -= D*(D-1)/4.0*np.log(np.pi)\n # print \"total 3:\", total\n for d in xrange(D):\n total -= np.log(gamma(orig_a[j]/2.0 + (1 - d)/2.0))\n\n # E{lnp(pi)} - based on original prior\n # - lnB(orig_alpha) + sum[j]{ orig_alpha[j] - 1}*E[lnpi_j]\n total += np.log(gamma(orig_alphas.sum())) - np.log(gamma(orig_alphas)).sum()\n total += ((orig_alphas - 1)*Elnpi).sum() # should be 0 since orig_alpha = 1\n\n # calculate entropies of the q distributions\n # q(c)\n for i in xrange(N):\n total += stats.entropy(phi[i]) # categorical entropy\n\n # q(pi)\n total += dirichlet.entropy(alphas)\n\n # q(mu)\n for j in xrange(K):\n total += mvn.entropy(cov=mu_covs[j])\n\n # q(lambda)\n for j in xrange(K):\n total += wishart.entropy(df=a[j], scale=Binv[j])\n\n return total\n\n\n\ndef gmm(X, K, max_iter=100):\n N, D = X.shape\n\n # parameters for pi, mu, and precision\n alphas = np.ones(K, dtype=np.float32) # prior parameter for pi (dirichlet)\n orig_alphas = np.ones(K, dtype=np.float32) # prior parameter for pi (dirichlet)\n # mu_means = np.zeros((K, D), dtype=np.float32) # prior mean for mu (normal) ### No!\n # mu_covs = np.empty((K, D, D), dtype=np.float32) # prior covariance for mu (normal)\n \n orig_c = 10.0\n # for k in xrange(K):\n # mu_covs[k] = np.eye(D)*orig_c\n \n\n orig_a = np.ones(K, dtype=np.float32)*D\n a = np.ones(K, dtype=np.float32)*D # prior for precision (wishart)\n orig_B = np.empty((K, D, D))\n B = np.empty((K, D, D)) # precision (wishart)\n empirical_cov = np.cov(X.T)\n for k in xrange(K):\n B[k] = (D/10.0)*empirical_cov\n orig_B[k] = (D/10.0)*empirical_cov\n\n\n # try random init instead\n # mu_means = np.random.randn(K, D)*orig_c\n mu_means = np.empty((K, D))\n for j in xrange(K):\n mu_means[j] = X[np.random.choice(N)]\n mu_covs = wishart.rvs(df=orig_a[0], scale=np.linalg.inv(B[0]), size=K)\n\n costs = np.zeros(max_iter)\n for iter_idx in xrange(max_iter):\n # calculate q(c[i])\n # phi = np.empty((N,K)) # index i = sample, index j = cluster\n t1 = np.empty(K)\n t2 = np.empty((N,K))\n t3 = np.empty(K)\n t4 = np.empty(K)\n\n # calculate this first because we will use it multiple times\n Binv = np.empty((K, D, D))\n for j in range(K):\n Binv[j] = np.linalg.inv(B[j])\n\n for j in xrange(K):\n # calculate t1\n t1[j] = -np.log(np.linalg.det(B[j]))\n for d in xrange(D):\n t1[j] += digamma( (1 - d + a[j])/2.0 )\n\n # calculate t2\n for i in xrange(N):\n diff_ij = X[i] - mu_means[j]\n t2[i,j] = diff_ij.dot( (a[j]*Binv[j] ).dot(diff_ij) )\n\n # calculate t3\n t3[j] = np.trace( a[j]*Binv[j].dot(mu_covs[j]) )\n \n # calculate t4\n t4[j] = digamma(alphas[j]) - digamma(alphas.sum())\n\n # calculate phi from t's\n # MAKE SURE 1-d array gets added to 2-d array correctly\n phi = np.exp(0.5*t1 - 0.5*t2 - 0.5*t3 + t4)\n # print \"phi before normalize:\", phi\n phi = phi / phi.sum(axis=1, keepdims=True)\n\n # print \"phi:\", phi\n\n cluster_assignments = phi.argmax(axis=1)\n\n n = phi.sum(axis=0) # there should be K of these\n # print \"n[j]:\", n\n\n # update q(pi)\n alphas = orig_alphas + n\n # print \"alphas:\", alphas\n\n # update q(mu)\n for j in xrange(K):\n mu_covs[j] = np.linalg.inv( (1.0/orig_c)*np.eye(D) + n[j]*a[j]*Binv[j] )\n mu_means[j] = mu_covs[j].dot( a[j]*Binv[j] ).dot(phi[:,j].dot(X))\n\n # print \"means:\", mu_means\n # print \"mu_covs:\", mu_covs\n\n # update q(lambda)\n a = orig_a + n\n for j in xrange(K):\n B[j] = orig_B[j].copy()\n for i in xrange(N):\n diff_ij = X[i] - mu_means[j]\n B[j] += phi[i,j]*(np.outer(diff_ij, diff_ij) + mu_covs[j])\n\n # print \"a[j]:\", a\n # print \"B[j]:\", B\n\n costs[iter_idx] = get_cost(X, K, cluster_assignments, phi, alphas, mu_means, mu_covs, a, B, orig_alphas, orig_c, orig_a, orig_B)\n\n plt.plot(costs)\n plt.title(\"Costs\")\n plt.show()\n\n print \"cluster assignments:\\n\", cluster_assignments\n plt.scatter(X[:,0], X[:,1], c=cluster_assignments, s=100, alpha=0.7)\n plt.show()\n\n\ndef main():\n X = pd.read_csv('data.txt', header=None).as_matrix()\n\n # for K in (2,4):\n for K in (2,4,10,25):\n gmm(X, K)\n\nif __name__ == '__main__':\n main()" }, { "path": "best_fit_line.py", "content": "from pulp import *\n\n### remove variable b because it is unconstrained\n### it's just a linear combination of the others\n### you can get the result:\n# status: Optimal\n# values:\n# a: 20000.0\n# b: -10000.0\n# c: 0.0\n# z: 10000.0\n### or any other multiple thereof\n### ax + by - c = 0\n### is the same as y = (-a/b)x + (c/b)\n\n\nprob = LpProblem(\"best_fit_line\", LpMinimize)\nz = LpVariable('z',0)\na = LpVariable('a',0)\n# b = LpVariable('b')\nc = LpVariable('c',0)\n\n# objective function\nprob += z\n\npoints = [\n (1,3),\n (2,5),\n (3,7),\n (5,11),\n (7,14),\n (8,15),\n (10,19),\n]\n\nprob += (a != 0)\nfor x,y in points:\n prob += (a*x - y - c <= z)\n prob += (a*x - y - c >= -z)\n\nstatus = prob.solve(GLPK(msg = 0))\nprint \"status:\", LpStatus[status]\nprint \"values:\"\nprint \"\\ta:\", value(a)\n# print \"\\tb:\", value(b)\nprint \"\\tc:\", value(c)\nprint \"\\tz:\", value(z)\n\n\n# extra part to plot everything\nimport numpy as np\nimport matplotlib.pyplot as plt\ndata = np.array(points)\nplt.scatter(data[:,0], data[:,1])\nx = np.linspace(0, 11, 100)\ny = value(a)*x - value(c)\nplt.plot(x, y)\nplt.show()\n" }, { "path": "calculus/WHERE ARE THE NOTEBOOKS.txt", "content": "As stated in the \"where to get the code\" / \"where to get the notebooks\" lecture, the notebooks are NOT on Github.\n\nIf you missed this, please review the lecture for the actual location of the notebooks.\n\nIf, after reviewing it, you still need assistance, please contact info@deeplearningcourses.com." }, { "path": "calculus/extra_reading.txt", "content": "Calculus: Early Transcendentals\nhttps://amzn.to/3Kwmabe" }, { "path": "chatgpt_trading/WHERE ARE THE NOTEBOOKS.txt", "content": "As stated in the \"where to get the code\" / \"where to get the notebooks\" lecture, the notebooks are NOT on Github.\n\nIf you missed this, please review the lecture for the actual location of the notebooks.\n\nIf, after reviewing it, you still need assistance, please contact info@deeplearningcourses.com." }, { "path": "chatgpt_trading/extra_reading.txt", "content": "ARIMA (for mean reversion)\nhttps://deeplearningcourses.com/c/time-series-analysis\n\nFinancial Engineering\nhttps://deeplearningcourses.com/c/ai-finance" }, { "path": "cnn_class/WHERE ARE THE NOTEBOOKS.txt", "content": "As stated in the \"where to get the code\" / \"where to get the notebooks\" lecture, the notebooks are NOT on Github.\n\nIf you missed this, please review the lecture for the actual location of the notebooks.\n\nIf, after reviewing it, you still need assistance, please contact info@deeplearningcourses.com." }, { "path": "cnn_class/benchmark.py", "content": "# Vanilla deep network\n# https://deeplearningcourses.com/c/deep-learning-convolutional-neural-networks-theano-tensorflow\n# https://udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow\n# get the data: http://ufldl.stanford.edu/housenumbers/\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport os\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\n\nfrom scipy.io import loadmat\nfrom sklearn.utils import shuffle\nfrom datetime import datetime\n\n\ndef error_rate(p, t):\n return np.mean(p != t)\n\n\ndef flatten(X):\n # input will be (32, 32, 3, N)\n # output will be (N, 3072)\n N = X.shape[-1]\n flat = np.zeros((N, 3072))\n for i in range(N):\n flat[i] = X[:,:,:,i].reshape(3072)\n return flat\n\n# In [6]: train['X'].shape\n# Out[6]: (32, 32, 3, 73257)\n\n# In [7]: train['y'].shape\n# Out[7]: (73257, 1)\n\n# In [8]: set(train['y'].flatten().tolist())\n# Out[8]: {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}\n# We will change these to 0..9 to be 0-indexed\n\n# In [12]: test['X'].shape\n# Out[12]: (32, 32, 3, 26032)\n\n# In [13]: test['y'].shape\n# Out[13]: (26032, 1)\n\n\ndef get_data():\n if not os.path.exists('../large_files/train_32x32.mat'):\n print('Looking for ../large_files/train_32x32.mat')\n print('You have not downloaded the data and/or not placed the files in the correct location.')\n print('Please get the data from: http://ufldl.stanford.edu/housenumbers')\n print('Place train_32x32.mat and test_32x32.mat in the folder large_files adjacent to the class folder')\n exit()\n\n train = loadmat('../large_files/train_32x32.mat')\n test = loadmat('../large_files/test_32x32.mat')\n return train, test\n\n\ndef main():\n train, test = get_data()\n \n\n # Need to scale! don't leave as 0..255\n # Y is a N x 1 matrix with values 1..10 (MATLAB indexes by 1)\n # So flatten it and make it 0..9\n # Also need indicator matrix for cost calculation\n Xtrain = flatten(train['X'].astype(np.float32) / 255.)\n Ytrain = train['y'].flatten() - 1\n Xtrain, Ytrain = shuffle(Xtrain, Ytrain)\n\n Xtest = flatten(test['X'].astype(np.float32) / 255.)\n Ytest = test['y'].flatten() - 1\n\n # gradient descent params\n max_iter = 20\n print_period = 10\n N, D = Xtrain.shape\n batch_sz = 500\n n_batches = N // batch_sz\n\n # initial weights\n M1 = 1000 # hidden layer size\n M2 = 500\n K = 10\n W1_init = np.random.randn(D, M1) / np.sqrt(D + M1)\n b1_init = np.zeros(M1)\n W2_init = np.random.randn(M1, M2) / np.sqrt(M1 + M2)\n b2_init = np.zeros(M2)\n W3_init = np.random.randn(M2, K) / np.sqrt(M2 + K)\n b3_init = np.zeros(K)\n\n # define variables and expressions\n X = tf.placeholder(tf.float32, shape=(None, D), name='X')\n T = tf.placeholder(tf.int32, shape=(None,), name='T')\n W1 = tf.Variable(W1_init.astype(np.float32))\n b1 = tf.Variable(b1_init.astype(np.float32))\n W2 = tf.Variable(W2_init.astype(np.float32))\n b2 = tf.Variable(b2_init.astype(np.float32))\n W3 = tf.Variable(W3_init.astype(np.float32))\n b3 = tf.Variable(b3_init.astype(np.float32))\n\n Z1 = tf.nn.relu( tf.matmul(X, W1) + b1 )\n Z2 = tf.nn.relu( tf.matmul(Z1, W2) + b2 )\n logits = tf.matmul(Z2, W3) + b3\n\n cost = tf.reduce_sum(\n tf.nn.sparse_softmax_cross_entropy_with_logits(\n logits=logits,\n labels=T\n )\n )\n\n train_op = tf.train.RMSPropOptimizer(0.0001, decay=0.99, momentum=0.9).minimize(cost)\n\n # we'll use this to calculate the error rate\n predict_op = tf.argmax(logits, 1)\n\n t0 = datetime.now()\n LL = []\n init = tf.global_variables_initializer()\n with tf.Session() as session:\n session.run(init)\n\n for i in range(max_iter):\n for j in range(n_batches):\n Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),]\n Ybatch = Ytrain[j*batch_sz:(j*batch_sz + batch_sz),]\n\n session.run(train_op, feed_dict={X: Xbatch, T: Ybatch})\n if j % print_period == 0:\n test_cost = session.run(cost, feed_dict={X: Xtest, T: Ytest})\n prediction = session.run(predict_op, feed_dict={X: Xtest})\n err = error_rate(prediction, Ytest)\n print(\"Cost / err at iteration i=%d, j=%d: %.3f / %.3f\" % (i, j, test_cost, err))\n LL.append(test_cost)\n print(\"Elapsed time:\", (datetime.now() - t0))\n plt.plot(LL)\n plt.show()\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "cnn_class/blur.py", "content": "# https://deeplearningcourses.com/c/deep-learning-convolutional-neural-networks-theano-tensorflow\n# https://udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nfrom scipy.signal import convolve2d\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n\n# load the famous Lena image\nimg = mpimg.imread('lena.png')\n\n# what does it look like?\nplt.imshow(img)\nplt.show()\n\n# make it B&W\nbw = img.mean(axis=2)\nplt.imshow(bw, cmap='gray')\nplt.show()\n\n# create a Gaussian filter\nW = np.zeros((20, 20))\nfor i in range(20):\n for j in range(20):\n dist = (i - 9.5)**2 + (j - 9.5)**2\n W[i, j] = np.exp(-dist / 50.)\nW /= W.sum() # normalize the kernel\n\n# let's see what the filter looks like\nplt.imshow(W, cmap='gray')\nplt.show()\n\n# now the convolution\nout = convolve2d(bw, W)\nplt.imshow(out, cmap='gray')\nplt.show()\n\n# what's that weird black stuff on the edges? let's check the size of output\nprint(out.shape)\n# after convolution, the output signal is N1 + N2 - 1\n\n\n# we can also just make the output the same size as the input\nout = convolve2d(bw, W, mode='same')\nplt.imshow(out, cmap='gray')\nplt.show()\nprint(out.shape)\n\n\n# in color\nout3 = np.zeros(img.shape)\nprint(out3.shape)\nfor i in range(3):\n out3[:,:,i] = convolve2d(img[:,:,i], W, mode='same')\n# out3 /= out3.max() # can also do this if you didn't normalize the kernel\nplt.imshow(out3)\nplt.show() # does not look like anything\n\n" }, { "path": "cnn_class/cifar.py", "content": "# https://deeplearningcourses.com/c/deep-learning-convolutional-neural-networks-theano-tensorflow\nimport os\nimport numpy as np\nimport pandas as pd\nimport theano\nimport theano.tensor as T\nimport matplotlib.pyplot as plt\n\nfrom PIL import Image\nfrom sklearn.utils import shuffle\nfrom theano.tensor.nnet import conv2d\nfrom theano.tensor.signal import downsample\n\n# from util import getImageData, error_rate, init_weight_and_bias, init_filter\n# from ann_theano import HiddenLayer\n\ndef init_weight_and_bias(M1, M2):\n W = np.random.randn(M1, M2) / np.sqrt(M1 + M2)\n b = np.zeros(M2)\n return W.astype(np.float32), b.astype(np.float32)\n\n\ndef init_filter(shape, poolsz):\n w = np.random.randn(*shape) / np.sqrt(np.prod(shape[1:]) + shape[0]*np.prod(shape[2:] / np.prod(poolsz)))\n return w.astype(np.float32)\n\n\ndef error_rate(targets, predictions):\n return np.mean(targets != predictions)\n\n\ndef image2array(im):\n arr = np.array(im) # will be (H, W, 3)\n return arr.transpose((2, 1, 0))\n\n\ndef getImageData():\n N = 50000\n savedXpath = '../large_files/cifar10/train_all.npy'\n if not os.path.exists(savedXpath):\n X = np.zeros((N, 3, 32, 32))\n for i in xrange(N):\n im = Image.open(\"../large_files/cifar10/train/%s.png\" % (i + 1))\n X[i] = image2array(im)\n if i % 1000 == 0:\n print i\n np.save(savedXpath, X.astype(np.uint8))\n else:\n X = np.load(savedXpath)\n X = X.astype(np.float32) / 255.0\n\n # load labels\n Y = np.zeros(N)\n df = pd.read_csv('../large_files/cifar10/trainLabels.csv')\n S = df['label'].tolist()\n idx = 0\n label2idx = {}\n i = 0\n for s in S:\n if s not in label2idx:\n label2idx[s] = idx\n idx += 1\n Y[i] = label2idx[s]\n i += 1\n print \"done loading data\"\n X, Y = shuffle(X, Y)\n return X[:30000], Y[:30000]\n\n\nclass HiddenLayer(object):\n def __init__(self, M1, M2, an_id):\n self.id = an_id\n self.M1 = M1\n self.M2 = M2\n W, b = init_weight_and_bias(M1, M2)\n self.W = theano.shared(W, 'W_%s' % self.id)\n self.b = theano.shared(b, 'b_%s' % self.id)\n self.params = [self.W, self.b]\n\n def forward(self, X):\n return T.nnet.relu(X.dot(self.W) + self.b)\n\n\nclass ConvPoolLayer(object):\n def __init__(self, mi, mo, fw=5, fh=5, poolsz=(2, 2)):\n # mi = input feature map size\n # mo = output feature map size\n sz = (mo, mi, fw, fh)\n W0 = init_filter(sz, poolsz)\n self.W = theano.shared(W0)\n b0 = np.zeros(mo, dtype=np.float32)\n self.b = theano.shared(b0)\n self.poolsz = poolsz\n self.params = [self.W, self.b]\n\n def forward(self, X):\n conv_out = conv2d(input=X, filters=self.W)\n pooled_out = downsample.max_pool_2d(\n input=conv_out,\n ds=self.poolsz,\n ignore_border=True\n )\n return T.nnet.relu(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))\n\n\nclass CNN(object):\n def __init__(self, convpool_layer_sizes, hidden_layer_sizes):\n self.convpool_layer_sizes = convpool_layer_sizes\n self.hidden_layer_sizes = hidden_layer_sizes\n\n def fit(self, X, Y, lr=1e-4, mu=0.99, reg=1e-6, decay=0.99999, eps=1e-2, batch_sz=30, epochs=100, show_fig=True):\n lr = np.float32(lr)\n mu = np.float32(mu)\n reg = np.float32(reg)\n decay = np.float32(decay)\n eps = np.float32(eps)\n\n # make a validation set\n X, Y = shuffle(X, Y)\n X = X.astype(np.float32)\n Y = Y.astype(np.int32)\n Xvalid, Yvalid = X[-1000:], Y[-1000:]\n X, Y = X[:-1000], Y[:-1000]\n\n # initialize convpool layers\n N, c, width, height = X.shape\n mi = c\n outw = width\n outh = height\n self.convpool_layers = []\n for mo, fw, fh in self.convpool_layer_sizes:\n layer = ConvPoolLayer(mi, mo, fw, fh)\n self.convpool_layers.append(layer)\n outw = (outw - fw + 1) / 2\n outh = (outh - fh + 1) / 2\n mi = mo\n\n # initialize mlp layers\n K = len(set(Y))\n self.hidden_layers = []\n M1 = self.convpool_layer_sizes[-1][0]*outw*outh # size must be same as output of last convpool layer\n count = 0\n for M2 in self.hidden_layer_sizes:\n h = HiddenLayer(M1, M2, count)\n self.hidden_layers.append(h)\n M1 = M2\n count += 1\n\n # logistic regression layer\n W, b = init_weight_and_bias(M1, K)\n self.W = theano.shared(W, 'W_logreg')\n self.b = theano.shared(b, 'b_logreg')\n\n # collect params for later use\n self.params = [self.W, self.b]\n for c in self.convpool_layers:\n self.params += c.params\n for h in self.hidden_layers:\n self.params += h.params\n\n # for momentum\n dparams = [theano.shared(np.zeros(p.get_value().shape, dtype=np.float32)) for p in self.params]\n\n # for rmsprop\n cache = [theano.shared(np.zeros(p.get_value().shape, dtype=np.float32)) for p in self.params]\n\n # set up theano functions and variables\n thX = T.tensor4('X', dtype='float32')\n thY = T.ivector('Y')\n pY = self.forward(thX)\n\n rcost = reg*T.sum([(p*p).sum() for p in self.params])\n cost = -T.mean(T.log(pY[T.arange(thY.shape[0]), thY])) + rcost\n prediction = self.predict(thX)\n\n cost_predict_op = theano.function(inputs=[thX, thY], outputs=[cost, prediction])\n\n\n # momentum only\n updates = [\n (p, p + mu*dp - lr*T.grad(cost, p)) for p, dp in zip(self.params, dparams)\n ] + [\n (dp, mu*dp - lr*T.grad(cost, p)) for p, dp in zip(self.params, dparams)\n ]\n\n train_op = theano.function(\n inputs=[thX, thY],\n updates=updates\n )\n\n n_batches = N / batch_sz\n costs = []\n for i in xrange(epochs):\n X, Y = shuffle(X, Y)\n for j in xrange(n_batches):\n Xbatch = X[j*batch_sz:(j*batch_sz+batch_sz)]\n Ybatch = Y[j*batch_sz:(j*batch_sz+batch_sz)]\n\n train_op(Xbatch, Ybatch)\n\n if j % 20 == 0:\n c, p = cost_predict_op(Xvalid, Yvalid)\n costs.append(c)\n e = error_rate(Yvalid, p)\n print \"i:\", i, \"j:\", j, \"nb:\", n_batches, \"cost:\", c, \"error rate:\", e\n\n if show_fig:\n plt.plot(costs)\n plt.show()\n\n def forward(self, X):\n Z = X\n for c in self.convpool_layers:\n Z = c.forward(Z)\n Z = Z.flatten(ndim=2)\n for h in self.hidden_layers:\n Z = h.forward(Z)\n return T.nnet.softmax(Z.dot(self.W) + self.b)\n\n def predict(self, X):\n pY = self.forward(X)\n return T.argmax(pY, axis=1)\n\n\ndef main():\n X, Y = getImageData()\n model = CNN(\n convpool_layer_sizes=[(20, 5, 5), (20, 5, 5)],\n hidden_layer_sizes=[500, 300],\n )\n model.fit(X, Y)\n\nif __name__ == '__main__':\n main()\n" }, { "path": "cnn_class/cnn_tf.py", "content": "# New concepts and differences from Theano:\n# - stride is the interval at which to apply the convolution\n# - unlike previous course, we use constant-size input to the network\n# since not doing that caused us to start swapping\n# - the output after convpool is a different size (8,8) here, (5,5) in Theano\n\n# https://deeplearningcourses.com/c/deep-learning-convolutional-neural-networks-theano-tensorflow\n# https://udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\n\nfrom datetime import datetime\nfrom scipy.signal import convolve2d\nfrom scipy.io import loadmat\nfrom sklearn.utils import shuffle\n\nfrom benchmark import get_data, error_rate\n\n\ndef convpool(X, W, b):\n # just assume pool size is (2,2) because we need to augment it with 1s\n conv_out = tf.nn.conv2d(X, W, strides=[1, 1, 1, 1], padding='SAME')\n conv_out = tf.nn.bias_add(conv_out, b)\n pool_out = tf.nn.max_pool(conv_out, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n return tf.nn.relu(pool_out)\n\n\ndef init_filter(shape, poolsz):\n w = np.random.randn(*shape) * np.sqrt(2.0 / np.prod(shape[:-1]))\n return w.astype(np.float32)\n\n\ndef rearrange(X):\n # input is (32, 32, 3, N)\n # output is (N, 32, 32, 3)\n # N = X.shape[-1]\n # out = np.zeros((N, 32, 32, 3), dtype=np.float32)\n # for i in xrange(N):\n # for j in xrange(3):\n # out[i, :, :, j] = X[:, :, j, i]\n # return out / 255\n return (X.transpose(3, 0, 1, 2) / 255).astype(np.float32)\n\n\ndef main():\n train, test = get_data()\n\n # Need to scale! don't leave as 0..255\n # Y is a N x 1 matrix with values 1..10 (MATLAB indexes by 1)\n # So flatten it and make it 0..9\n # Also need indicator matrix for cost calculation\n Xtrain = rearrange(train['X'])\n Ytrain = train['y'].flatten() - 1\n # print len(Ytrain)\n del train\n Xtrain, Ytrain = shuffle(Xtrain, Ytrain)\n\n Xtest = rearrange(test['X'])\n Ytest = test['y'].flatten() - 1\n del test\n\n # gradient descent params\n max_iter = 6\n print_period = 10\n N = Xtrain.shape[0]\n batch_sz = 500\n n_batches = N // batch_sz\n\n # limit samples since input will always have to be same size\n # you could also just do N = N / batch_sz * batch_sz\n Xtrain = Xtrain[:73000,]\n Ytrain = Ytrain[:73000]\n Xtest = Xtest[:26000,]\n Ytest = Ytest[:26000]\n # print \"Xtest.shape:\", Xtest.shape\n # print \"Ytest.shape:\", Ytest.shape\n\n # initial weights\n M = 500\n K = 10\n poolsz = (2, 2)\n\n W1_shape = (5, 5, 3, 20) # (filter_width, filter_height, num_color_channels, num_feature_maps)\n W1_init = init_filter(W1_shape, poolsz)\n b1_init = np.zeros(W1_shape[-1], dtype=np.float32) # one bias per output feature map\n\n W2_shape = (5, 5, 20, 50) # (filter_width, filter_height, old_num_feature_maps, num_feature_maps)\n W2_init = init_filter(W2_shape, poolsz)\n b2_init = np.zeros(W2_shape[-1], dtype=np.float32)\n\n # vanilla ANN weights\n W3_init = np.random.randn(W2_shape[-1]*8*8, M) / np.sqrt(W2_shape[-1]*8*8 + M)\n b3_init = np.zeros(M, dtype=np.float32)\n W4_init = np.random.randn(M, K) / np.sqrt(M + K)\n b4_init = np.zeros(K, dtype=np.float32)\n\n\n # define variables and expressions\n # using None as the first shape element takes up too much RAM unfortunately\n X = tf.placeholder(tf.float32, shape=(batch_sz, 32, 32, 3), name='X')\n T = tf.placeholder(tf.int32, shape=(batch_sz,), name='T')\n W1 = tf.Variable(W1_init.astype(np.float32))\n b1 = tf.Variable(b1_init.astype(np.float32))\n W2 = tf.Variable(W2_init.astype(np.float32))\n b2 = tf.Variable(b2_init.astype(np.float32))\n W3 = tf.Variable(W3_init.astype(np.float32))\n b3 = tf.Variable(b3_init.astype(np.float32))\n W4 = tf.Variable(W4_init.astype(np.float32))\n b4 = tf.Variable(b4_init.astype(np.float32))\n\n Z1 = convpool(X, W1, b1)\n Z2 = convpool(Z1, W2, b2)\n Z2_shape = Z2.get_shape().as_list()\n Z2r = tf.reshape(Z2, [Z2_shape[0], np.prod(Z2_shape[1:])])\n Z3 = tf.nn.relu( tf.matmul(Z2r, W3) + b3 )\n Yish = tf.matmul(Z3, W4) + b4\n\n cost = tf.reduce_sum(\n tf.nn.sparse_softmax_cross_entropy_with_logits(\n logits=Yish,\n labels=T\n )\n )\n\n train_op = tf.train.RMSPropOptimizer(0.0001, decay=0.99, momentum=0.9).minimize(cost)\n\n # we'll use this to calculate the error rate\n predict_op = tf.argmax(Yish, 1)\n\n t0 = datetime.now()\n LL = []\n W1_val = None\n W2_val = None\n init = tf.global_variables_initializer()\n with tf.Session() as session:\n session.run(init)\n\n for i in range(max_iter):\n for j in range(n_batches):\n Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),]\n Ybatch = Ytrain[j*batch_sz:(j*batch_sz + batch_sz),]\n\n if len(Xbatch) == batch_sz:\n session.run(train_op, feed_dict={X: Xbatch, T: Ybatch})\n if j % print_period == 0:\n # due to RAM limitations we need to have a fixed size input\n # so as a result, we have this ugly total cost and prediction computation\n test_cost = 0\n prediction = np.zeros(len(Xtest))\n for k in range(len(Xtest) // batch_sz):\n Xtestbatch = Xtest[k*batch_sz:(k*batch_sz + batch_sz),]\n Ytestbatch = Ytest[k*batch_sz:(k*batch_sz + batch_sz),]\n test_cost += session.run(cost, feed_dict={X: Xtestbatch, T: Ytestbatch})\n prediction[k*batch_sz:(k*batch_sz + batch_sz)] = session.run(\n predict_op, feed_dict={X: Xtestbatch})\n err = error_rate(prediction, Ytest)\n print(\"Cost / err at iteration i=%d, j=%d: %.3f / %.3f\" % (i, j, test_cost, err))\n LL.append(test_cost)\n\n W1_val = W1.eval()\n W2_val = W2.eval()\n print(\"Elapsed time:\", (datetime.now() - t0))\n plt.plot(LL)\n plt.show()\n\n\n W1_val = W1_val.transpose(3, 2, 0, 1)\n W2_val = W2_val.transpose(3, 2, 0, 1)\n\n\n # visualize W1 (20, 3, 5, 5)\n # W1_val = W1.get_value()\n grid = np.zeros((8*5, 8*5))\n m = 0\n n = 0\n for i in range(20):\n for j in range(3):\n filt = W1_val[i,j]\n grid[m*5:(m+1)*5,n*5:(n+1)*5] = filt\n m += 1\n if m >= 8:\n m = 0\n n += 1\n plt.imshow(grid, cmap='gray')\n plt.title(\"W1\")\n plt.show()\n\n # visualize W2 (50, 20, 5, 5)\n # W2_val = W2.get_value()\n grid = np.zeros((32*5, 32*5))\n m = 0\n n = 0\n for i in range(50):\n for j in range(20):\n filt = W2_val[i,j]\n grid[m*5:(m+1)*5,n*5:(n+1)*5] = filt\n m += 1\n if m >= 32:\n m = 0\n n += 1\n plt.imshow(grid, cmap='gray')\n plt.title(\"W2\")\n plt.show()\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "cnn_class/cnn_tf_plot_filters.py", "content": "# New concepts and differences from Theano:\n# - stride is the interval at which to apply the convolution\n# - unlike previous course, we use constant-size input to the network\n# since not doing that caused us to start swapping\n# - the output after convpool is a different size (8,8) here, (5,5) in Theano\n\n# https://deeplearningcourses.com/c/deep-learning-convolutional-neural-networks-theano-tensorflow\n# https://udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\n\nfrom datetime import datetime\nfrom scipy.signal import convolve2d\nfrom scipy.io import loadmat\nfrom sklearn.utils import shuffle\n\nfrom benchmark import get_data, error_rate\n\n\ndef convpool(X, W, b):\n # just assume pool size is (2,2) because we need to augment it with 1s\n conv_out = tf.nn.conv2d(X, W, strides=[1, 1, 1, 1], padding='SAME')\n conv_out = tf.nn.bias_add(conv_out, b)\n pool_out = tf.nn.max_pool(conv_out, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n return tf.nn.relu(pool_out)\n\n\ndef init_filter(shape, poolsz):\n w = np.random.randn(*shape) * np.sqrt(2.0 / np.prod(shape[:-1]))\n return w.astype(np.float32)\n\n\ndef rearrange(X):\n # input is (32, 32, 3, N)\n # output is (N, 32, 32, 3)\n # N = X.shape[-1]\n # out = np.zeros((N, 32, 32, 3), dtype=np.float32)\n # for i in xrange(N):\n # for j in xrange(3):\n # out[i, :, :, j] = X[:, :, j, i]\n # return out / 255\n return (X.transpose(3, 0, 1, 2) / 255).astype(np.float32)\n\n\n\ntrain, test = get_data()\n\n# Need to scale! don't leave as 0..255\n# Y is a N x 1 matrix with values 1..10 (MATLAB indexes by 1)\n# So flatten it and make it 0..9\n# Also need indicator matrix for cost calculation\nXtrain = rearrange(train['X'])\nYtrain = train['y'].flatten() - 1\n# print len(Ytrain)\ndel train\nXtrain, Ytrain = shuffle(Xtrain, Ytrain)\n\nXtest = rearrange(test['X'])\nYtest = test['y'].flatten() - 1\ndel test\n\n# gradient descent params\nmax_iter = 6\nprint_period = 10\nN = Xtrain.shape[0]\nbatch_sz = 500\nn_batches = N // batch_sz\n\n# limit samples since input will always have to be same size\n# you could also just do N = N / batch_sz * batch_sz\nXtrain = Xtrain[:73000,]\nYtrain = Ytrain[:73000]\nXtest = Xtest[:26000,]\nYtest = Ytest[:26000]\n# print \"Xtest.shape:\", Xtest.shape\n# print \"Ytest.shape:\", Ytest.shape\n\n# initial weights\nM = 500\nK = 10\npoolsz = (2, 2)\n\nW1_shape = (5, 5, 3, 20) # (filter_width, filter_height, num_color_channels, num_feature_maps)\nW1_init = init_filter(W1_shape, poolsz)\nb1_init = np.zeros(W1_shape[-1], dtype=np.float32) # one bias per output feature map\n\nW2_shape = (5, 5, 20, 50) # (filter_width, filter_height, old_num_feature_maps, num_feature_maps)\nW2_init = init_filter(W2_shape, poolsz)\nb2_init = np.zeros(W2_shape[-1], dtype=np.float32)\n\n# vanilla ANN weights\nW3_init = np.random.randn(W2_shape[-1]*8*8, M) / np.sqrt(W2_shape[-1]*8*8 + M)\nb3_init = np.zeros(M, dtype=np.float32)\nW4_init = np.random.randn(M, K) / np.sqrt(M + K)\nb4_init = np.zeros(K, dtype=np.float32)\n\n\n# define variables and expressions\n# using None as the first shape element takes up too much RAM unfortunately\nX = tf.placeholder(tf.float32, shape=(batch_sz, 32, 32, 3), name='X')\nT = tf.placeholder(tf.int32, shape=(batch_sz,), name='T')\nW1 = tf.Variable(W1_init.astype(np.float32))\nb1 = tf.Variable(b1_init.astype(np.float32))\nW2 = tf.Variable(W2_init.astype(np.float32))\nb2 = tf.Variable(b2_init.astype(np.float32))\nW3 = tf.Variable(W3_init.astype(np.float32))\nb3 = tf.Variable(b3_init.astype(np.float32))\nW4 = tf.Variable(W4_init.astype(np.float32))\nb4 = tf.Variable(b4_init.astype(np.float32))\nparams = [W1, b1, W2, b2, W3, b3, W4, b4]\n\nZ1 = convpool(X, W1, b1)\nZ2 = convpool(Z1, W2, b2)\nZ2_shape = Z2.get_shape().as_list()\nZ2r = tf.reshape(Z2, [Z2_shape[0], np.prod(Z2_shape[1:])])\nZ3 = tf.nn.relu( tf.matmul(Z2r, W3) + b3 )\nYish = tf.matmul(Z3, W4) + b4\n\ncost = tf.reduce_sum(\n tf.nn.sparse_softmax_cross_entropy_with_logits(\n logits=Yish,\n labels=T\n )\n) + 0.01*sum(tf.reduce_sum(p*p) for p in params)\n\ntrain_op = tf.train.RMSPropOptimizer(0.0001, decay=0.99, momentum=0.9).minimize(cost)\n# train_op = tf.train.MomentumOptimizer(0.00001, momentum=0.99).minimize(cost)\n\n# we'll use this to calculate the error rate\npredict_op = tf.argmax(Yish, 1)\n\nt0 = datetime.now()\nLL = []\nW1_val = None\nW2_val = None\ninit = tf.global_variables_initializer()\nwith tf.Session() as session:\n session.run(init)\n\n for i in range(max_iter):\n for j in range(n_batches):\n Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),]\n Ybatch = Ytrain[j*batch_sz:(j*batch_sz + batch_sz),]\n\n if len(Xbatch) == batch_sz:\n session.run(train_op, feed_dict={X: Xbatch, T: Ybatch})\n if j % print_period == 0:\n # due to RAM limitations we need to have a fixed size input\n # so as a result, we have this ugly total cost and prediction computation\n test_cost = 0\n prediction = np.zeros(len(Xtest))\n for k in range(len(Xtest) // batch_sz):\n Xtestbatch = Xtest[k*batch_sz:(k*batch_sz + batch_sz),]\n Ytestbatch = Ytest[k*batch_sz:(k*batch_sz + batch_sz),]\n test_cost += session.run(cost, feed_dict={X: Xtestbatch, T: Ytestbatch})\n prediction[k*batch_sz:(k*batch_sz + batch_sz)] = session.run(\n predict_op, feed_dict={X: Xtestbatch})\n err = error_rate(prediction, Ytest)\n print(\"Cost / err at iteration i=%d, j=%d: %.3f / %.3f\" % (i, j, test_cost, err))\n LL.append(test_cost)\n\n W1_val = W1.eval()\n W2_val = W2.eval()\nprint(\"Elapsed time:\", (datetime.now() - t0))\nplt.plot(LL)\nplt.show()\n\n\nW1_val = W1_val.transpose(3, 2, 0, 1)\nW2_val = W2_val.transpose(3, 2, 0, 1)\n\n\n# visualize W1 (20, 3, 5, 5)\n# W1_val = W1.get_value()\ngrid = np.zeros((8*5, 8*5))\nm = 0\nn = 0\nfor i in range(20):\n for j in range(3):\n filt = W1_val[i,j]\n grid[m*5:(m+1)*5,n*5:(n+1)*5] = filt\n m += 1\n if m >= 8:\n m = 0\n n += 1\nplt.imshow(grid, cmap='gray')\nplt.title(\"W1\")\nplt.show()\n\n# visualize W2 (50, 20, 5, 5)\n# W2_val = W2.get_value()\ngrid = np.zeros((32*5, 32*5))\nm = 0\nn = 0\nfor i in range(50):\n for j in range(20):\n filt = W2_val[i,j]\n grid[m*5:(m+1)*5,n*5:(n+1)*5] = filt\n m += 1\n if m >= 32:\n m = 0\n n += 1\nplt.imshow(grid, cmap='gray')\nplt.title(\"W2\")\nplt.show()\n\n\n" }, { "path": "cnn_class/cnn_theano.py", "content": "# https://deeplearningcourses.com/c/deep-learning-convolutional-neural-networks-theano-tensorflow\n# https://udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport theano\nimport theano.tensor as T\nimport matplotlib.pyplot as plt\n\nfrom theano.tensor.nnet import conv2d\nfrom theano.tensor.signal import pool\n\nfrom scipy.io import loadmat\nfrom sklearn.utils import shuffle\n\nfrom datetime import datetime\n\nfrom benchmark import get_data, error_rate\n\n\ndef relu(a):\n return a * (a > 0)\n\n\ndef convpool(X, W, b, poolsize=(2, 2)):\n conv_out = conv2d(input=X, filters=W)\n\n # downsample each feature map individually, using maxpooling\n pooled_out = pool.pool_2d(\n input=conv_out,\n ws=poolsize,\n ignore_border=True\n )\n\n # add the bias term. Since the bias is a vector (1D array), we first\n # reshape it to a tensor of shape (1, n_filters, 1, 1). Each bias will\n # thus be broadcasted across mini-batches and feature map\n # width & height\n # return T.tanh(pooled_out + b.dimshuffle('x', 0, 'x', 'x'))\n return relu(pooled_out + b.dimshuffle('x', 0, 'x', 'x'))\n\n\ndef init_filter(shape, poolsz):\n w = np.random.randn(*shape) * np.sqrt(2.0 / np.prod(shape[1:]))\n return w.astype(np.float32)\n\n\ndef rearrange(X):\n # input is (32, 32, 3, N)\n # output is (N, 3, 32, 32)\n # N = X.shape[-1]\n # out = np.zeros((N, 3, 32, 32), dtype=np.float32)\n # for i in range(N):\n # for j in range(3):\n # out[i, j, :, :] = X[:, :, j, i]\n # return out / 255\n return (X.transpose(3, 2, 0, 1) / 255).astype(np.float32)\n\n\ndef main():\n # step 1: load the data, transform as needed\n train, test = get_data()\n\n # Need to scale! don't leave as 0..255\n # Y is a N x 1 matrix with values 1..10 (MATLAB indexes by 1)\n # So flatten it and make it 0..9\n # Also need indicator matrix for cost calculation\n Xtrain = rearrange(train['X'])\n Ytrain = train['y'].flatten() - 1\n del train\n Xtrain, Ytrain = shuffle(Xtrain, Ytrain)\n\n Xtest = rearrange(test['X'])\n Ytest = test['y'].flatten() - 1\n del test\n\n\n max_iter = 6\n print_period = 10\n\n lr = np.float32(1e-3)\n mu = np.float32(0.9)\n\n N = Xtrain.shape[0]\n batch_sz = 500\n n_batches = N // batch_sz\n\n M = 500\n K = 10\n poolsz = (2, 2)\n\n # after conv will be of dimension 32 - 5 + 1 = 28\n # after downsample 28 / 2 = 14\n W1_shape = (20, 3, 5, 5) # (num_feature_maps, num_color_channels, filter_width, filter_height)\n W1_init = init_filter(W1_shape, poolsz)\n b1_init = np.zeros(W1_shape[0], dtype=np.float32) # one bias per output feature map\n\n # after conv will be of dimension 14 - 5 + 1 = 10\n # after downsample 10 / 2 = 5\n W2_shape = (50, 20, 5, 5) # (num_feature_maps, old_num_feature_maps, filter_width, filter_height)\n W2_init = init_filter(W2_shape, poolsz)\n b2_init = np.zeros(W2_shape[0], dtype=np.float32)\n\n # vanilla ANN weights\n W3_init = np.random.randn(W2_shape[0]*5*5, M) / np.sqrt(W2_shape[0]*5*5 + M)\n b3_init = np.zeros(M, dtype=np.float32)\n W4_init = np.random.randn(M, K) / np.sqrt(M + K)\n b4_init = np.zeros(K, dtype=np.float32)\n\n\n # step 2: define theano variables and expressions\n X = T.tensor4('X', dtype='float32')\n Y = T.ivector('T')\n W1 = theano.shared(W1_init, 'W1')\n b1 = theano.shared(b1_init, 'b1')\n W2 = theano.shared(W2_init, 'W2')\n b2 = theano.shared(b2_init, 'b2')\n W3 = theano.shared(W3_init.astype(np.float32), 'W3')\n b3 = theano.shared(b3_init, 'b3')\n W4 = theano.shared(W4_init.astype(np.float32), 'W4')\n b4 = theano.shared(b4_init, 'b4')\n\n # forward pass\n Z1 = convpool(X, W1, b1)\n Z2 = convpool(Z1, W2, b2)\n Z3 = relu(Z2.flatten(ndim=2).dot(W3) + b3)\n pY = T.nnet.softmax( Z3.dot(W4) + b4)\n\n # define the cost function and prediction\n cost = -(T.log(pY[T.arange(Y.shape[0]), Y])).mean()\n prediction = T.argmax(pY, axis=1)\n\n # step 3: training expressions and functions\n params = [W1, b1, W2, b2, W3, b3, W4, b4]\n\n # momentum changes\n dparams = [\n theano.shared(\n np.zeros_like(\n p.get_value(),\n dtype=np.float32\n )\n ) for p in params\n ]\n\n updates = []\n grads = T.grad(cost, params)\n for p, dp, g in zip(params, dparams, grads):\n dp_update = mu*dp - lr*g\n p_update = p + dp_update\n\n updates.append((dp, dp_update))\n updates.append((p, p_update))\n\n train = theano.function(\n inputs=[X, Y],\n updates=updates,\n )\n\n # create another function for this because we want it over the whole dataset\n get_prediction = theano.function(\n inputs=[X, Y],\n outputs=[cost, prediction],\n )\n\n t0 = datetime.now()\n costs = []\n for i in range(max_iter):\n Xtrain, Ytrain = shuffle(Xtrain, Ytrain)\n for j in range(n_batches):\n Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),]\n Ybatch = Ytrain[j*batch_sz:(j*batch_sz + batch_sz),]\n\n train(Xbatch, Ybatch)\n if j % print_period == 0:\n cost_val, prediction_val = get_prediction(Xtest, Ytest)\n err = error_rate(prediction_val, Ytest)\n print(\"Cost / err at iteration i=%d, j=%d: %.3f / %.3f\" % (i, j, cost_val, err))\n costs.append(cost_val)\n print(\"Elapsed time:\", (datetime.now() - t0))\n plt.plot(costs)\n plt.show()\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "cnn_class/cnn_theano_plot_filters.py", "content": "# https://deeplearningcourses.com/c/deep-learning-convolutional-neural-networks-theano-tensorflow\n# https://udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport theano\nimport theano.tensor as T\nimport matplotlib.pyplot as plt\n\nfrom scipy.io import loadmat\nfrom sklearn.utils import shuffle\n\nfrom datetime import datetime\n\nfrom benchmark import get_data, error_rate, y2indicator\nfrom cnn_theano import convpool, relu, init_filter, rearrange\n\n\ndef main():\n # step 1: load the data, transform as needed\n train, test = get_data()\n\n # Need to scale! don't leave as 0..255\n # Y is a N x 1 matrix with values 1..10 (MATLAB indexes by 1)\n # So flatten it and make it 0..9\n # Also need indicator matrix for cost calculation\n Xtrain = rearrange(train['X'])\n Ytrain = train['y'].flatten() - 1\n del train\n Xtrain, Ytrain = shuffle(Xtrain, Ytrain)\n Ytrain_ind = y2indicator(Ytrain)\n\n Xtest = rearrange(test['X'])\n Ytest = test['y'].flatten() - 1\n del test\n Ytest_ind = y2indicator(Ytest)\n\n\n max_iter = 8\n print_period = 10\n\n lr = np.float32(0.00001)\n reg = np.float32(0.01)\n mu = np.float32(0.99)\n\n N = Xtrain.shape[0]\n batch_sz = 500\n n_batches = N // batch_sz\n\n M = 500\n K = 10\n poolsz = (2, 2)\n\n # after conv will be of dimension 32 - 5 + 1 = 28\n # after downsample 28 / 2 = 14\n W1_shape = (20, 3, 5, 5) # (num_feature_maps, num_color_channels, filter_width, filter_height)\n W1_init = init_filter(W1_shape, poolsz)\n b1_init = np.zeros(W1_shape[0], dtype=np.float32) # one bias per output feature map\n\n # after conv will be of dimension 14 - 5 + 1 = 10\n # after downsample 10 / 2 = 5\n W2_shape = (50, 20, 5, 5) # (num_feature_maps, old_num_feature_maps, filter_width, filter_height)\n W2_init = init_filter(W2_shape, poolsz)\n b2_init = np.zeros(W2_shape[0], dtype=np.float32)\n\n # vanilla ANN weights\n W3_init = np.random.randn(W2_shape[0]*5*5, M) / np.sqrt(W2_shape[0]*5*5 + M)\n b3_init = np.zeros(M, dtype=np.float32)\n W4_init = np.random.randn(M, K) / np.sqrt(M + K)\n b4_init = np.zeros(K, dtype=np.float32)\n\n\n # step 2: define theano variables and expressions\n X = T.tensor4('X', dtype='float32')\n Y = T.matrix('T')\n W1 = theano.shared(W1_init, 'W1')\n b1 = theano.shared(b1_init, 'b1')\n W2 = theano.shared(W2_init, 'W2')\n b2 = theano.shared(b2_init, 'b2')\n W3 = theano.shared(W3_init.astype(np.float32), 'W3')\n b3 = theano.shared(b3_init, 'b3')\n W4 = theano.shared(W4_init.astype(np.float32), 'W4')\n b4 = theano.shared(b4_init, 'b4')\n\n # momentum changes\n dW1 = theano.shared(np.zeros(W1_init.shape, dtype=np.float32), 'dW1')\n db1 = theano.shared(np.zeros(b1_init.shape, dtype=np.float32), 'db1')\n dW2 = theano.shared(np.zeros(W2_init.shape, dtype=np.float32), 'dW2')\n db2 = theano.shared(np.zeros(b2_init.shape, dtype=np.float32), 'db2')\n dW3 = theano.shared(np.zeros(W3_init.shape, dtype=np.float32), 'dW3')\n db3 = theano.shared(np.zeros(b3_init.shape, dtype=np.float32), 'db3')\n dW4 = theano.shared(np.zeros(W4_init.shape, dtype=np.float32), 'dW4')\n db4 = theano.shared(np.zeros(b4_init.shape, dtype=np.float32), 'db4')\n\n # forward pass\n Z1 = convpool(X, W1, b1)\n Z2 = convpool(Z1, W2, b2)\n Z3 = relu(Z2.flatten(ndim=2).dot(W3) + b3)\n pY = T.nnet.softmax( Z3.dot(W4) + b4)\n\n # define the cost function and prediction\n params = (W1, b1, W2, b2, W3, b3, W4, b4)\n reg_cost = reg*sum((param*param).sum() for param in params)\n cost = -(Y * T.log(pY)).sum() + reg_cost\n prediction = T.argmax(pY, axis=1)\n\n # step 3: training expressions and functions\n update_W1 = W1 + mu*dW1 - lr*T.grad(cost, W1)\n update_b1 = b1 + mu*db1 - lr*T.grad(cost, b1)\n update_W2 = W2 + mu*dW2 - lr*T.grad(cost, W2)\n update_b2 = b2 + mu*db2 - lr*T.grad(cost, b2)\n update_W3 = W3 + mu*dW3 - lr*T.grad(cost, W3)\n update_b3 = b3 + mu*db3 - lr*T.grad(cost, b3)\n update_W4 = W4 + mu*dW4 - lr*T.grad(cost, W4)\n update_b4 = b4 + mu*db4 - lr*T.grad(cost, b4)\n\n # update weight changes\n update_dW1 = mu*dW1 - lr*T.grad(cost, W1)\n update_db1 = mu*db1 - lr*T.grad(cost, b1)\n update_dW2 = mu*dW2 - lr*T.grad(cost, W2)\n update_db2 = mu*db2 - lr*T.grad(cost, b2)\n update_dW3 = mu*dW3 - lr*T.grad(cost, W3)\n update_db3 = mu*db3 - lr*T.grad(cost, b3)\n update_dW4 = mu*dW4 - lr*T.grad(cost, W4)\n update_db4 = mu*db4 - lr*T.grad(cost, b4)\n\n train = theano.function(\n inputs=[X, Y],\n updates=[\n (W1, update_W1),\n (b1, update_b1),\n (W2, update_W2),\n (b2, update_b2),\n (W3, update_W3),\n (b3, update_b3),\n (W4, update_W4),\n (b4, update_b4),\n (dW1, update_dW1),\n (db1, update_db1),\n (dW2, update_dW2),\n (db2, update_db2),\n (dW3, update_dW3),\n (db3, update_db3),\n (dW4, update_dW4),\n (db4, update_db4),\n ],\n )\n\n # create another function for this because we want it over the whole dataset\n get_prediction = theano.function(\n inputs=[X, Y],\n outputs=[cost, prediction],\n )\n\n t0 = datetime.now()\n LL = []\n for i in range(max_iter):\n for j in range(n_batches):\n Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),]\n Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),]\n\n train(Xbatch, Ybatch)\n if j % print_period == 0:\n cost_val, prediction_val = get_prediction(Xtest, Ytest_ind)\n err = error_rate(prediction_val, Ytest)\n print(\"Cost / err at iteration i=%d, j=%d: %.3f / %.3f\" % (i, j, cost_val, err))\n LL.append(cost_val)\n print(\"Elapsed time:\", (datetime.now() - t0))\n plt.plot(LL)\n plt.show()\n\n # visualize W1 (20, 3, 5, 5)\n W1_val = W1.get_value()\n grid = np.zeros((8*5, 8*5))\n m = 0\n n = 0\n for i in range(20):\n for j in range(3):\n filt = W1_val[i,j]\n grid[m*5:(m+1)*5,n*5:(n+1)*5] = filt\n m += 1\n if m >= 8:\n m = 0\n n += 1\n plt.imshow(grid, cmap='gray')\n plt.title(\"W1\")\n plt.show()\n\n # visualize W2 (50, 20, 5, 5)\n W2_val = W2.get_value()\n grid = np.zeros((32*5, 32*5))\n m = 0\n n = 0\n for i in range(50):\n for j in range(20):\n filt = W2_val[i,j]\n grid[m*5:(m+1)*5,n*5:(n+1)*5] = filt\n m += 1\n if m >= 32:\n m = 0\n n += 1\n plt.imshow(grid, cmap='gray')\n plt.title(\"W2\")\n plt.show()\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "cnn_class/custom_blur.py", "content": "# https://deeplearningcourses.com/c/deep-learning-convolutional-neural-networks-theano-tensorflow\n# https://udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\n# from scipy.signal import convolve2d\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nfrom datetime import datetime\n\n# def convolve2d(X, W):\n# t0 = datetime.now()\n# n1, n2 = X.shape\n# m1, m2 = W.shape\n# Y = np.zeros((n1 + m1 - 1, n2 + m2 - 1))\n# for i in xrange(n1 + m1 - 1):\n# for ii in xrange(m1):\n# for j in xrange(n2 + m2 - 1):\n# for jj in xrange(m2):\n# if i >= ii and j >= jj and i - ii < n1 and j - jj < n2:\n# Y[i,j] += W[ii,jj]*X[i - ii,j - jj]\n# print \"elapsed time:\", (datetime.now() - t0)\n# return Y\n\n# def convolve2d(X, W):\n# t0 = datetime.now()\n# n1, n2 = X.shape\n# m1, m2 = W.shape\n# Y = np.zeros((n1 + m1 - 1, n2 + m2 - 1))\n# for i in xrange(n1):\n# for j in xrange(n2):\n# Y[i:i+m1,j:j+m2] += X[i,j]*W\n# print \"elapsed time:\", (datetime.now() - t0)\n# return Y\n\n# same size as input\ndef convolve2d(X, W):\n n1, n2 = X.shape\n m1, m2 = W.shape\n Y = np.zeros((n1 + m1 - 1, n2 + m2 - 1))\n for i in range(n1):\n for j in range(n2):\n Y[i:i+m1,j:j+m2] += X[i,j]*W\n ret = Y[m1//2:-m1//2+1,m2//2:-m2//2+1]\n assert(ret.shape == X.shape)\n return ret\n\n# smaller than input\n# def convolve2d(X, W):\n# n1, n2 = X.shape\n# m1, m2 = W.shape\n# Y = np.zeros((n1 + m1 - 1, n2 + m2 - 1))\n# for i in xrange(n1):\n# for j in xrange(n2):\n# Y[i:i+m1,j:j+m2] += X[i,j]*W\n# ret = Y[m1-1:-m1+1,m2-1:-m2+1]\n# return ret\n\n# load the famous Lena image\nimg = mpimg.imread('lena.png')\n\n# what does it look like?\nplt.imshow(img)\nplt.show()\n\n# make it B&W\nbw = img.mean(axis=2)\nplt.imshow(bw, cmap='gray')\nplt.show()\n\n# create a Gaussian filter\nW = np.zeros((20, 20))\nfor i in range(20):\n for j in range(20):\n dist = (i - 9.5)**2 + (j - 9.5)**2\n W[i, j] = np.exp(-dist / 50.)\n\n# let's see what the filter looks like\nplt.imshow(W, cmap='gray')\nplt.show()\n\n# now the convolution\nout = convolve2d(bw, W)\nplt.imshow(out, cmap='gray')\nplt.show()\n\n# what's that weird black stuff on the edges? let's check the size of output\nprint(out.shape)\n# after convolution, the output signal is N1 + N2 - 1\n\n# try it in color\nout = np.zeros(img.shape)\nW /= W.sum()\nfor i in range(3):\n out[:,:,i] = convolve2d(img[:,:,i], W)\nplt.imshow(out)\nplt.show()\n\n\n\n" }, { "path": "cnn_class/echo.py", "content": "# https://deeplearningcourses.com/c/deep-learning-convolutional-neural-networks-theano-tensorflow\n# https://udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport wave\nimport sys\n\nfrom scipy.io.wavfile import write\n\n# If you right-click on the file and go to \"Get Info\", you can see:\n# sampling rate = 16000 Hz\n# bits per sample = 16\n# The first is quantization in time\n# The second is quantization in amplitude\n# We also do this for images!\n# 2^16 = 65536 is how many different sound levels we have\n# 2^8 * 2^8 * 2^8 = 2^24 is how many different colors we can represent\n\nspf = wave.open('helloworld.wav', 'r')\n\n#Extract Raw Audio from Wav File\nsignal = spf.readframes(-1)\nsignal = np.fromstring(signal, 'Int16')\nprint(\"numpy signal:\", signal.shape)\n\nplt.plot(signal)\nplt.title(\"Hello world without echo\")\nplt.show()\n\ndelta = np.array([1., 0., 0.])\nnoecho = np.convolve(signal, delta)\nprint(\"noecho signal:\", noecho.shape)\nassert(np.abs(noecho[:len(signal)] - signal).sum() < 0.000001)\n\nnoecho = noecho.astype(np.int16) # make sure you do this, otherwise, you will get VERY LOUD NOISE\nwrite('noecho.wav', 16000, noecho)\n\nfilt = np.zeros(16000)\nfilt[0] = 1\nfilt[4000] = 0.6\nfilt[8000] = 0.3\nfilt[12000] = 0.2\nfilt[15999] = 0.1\nout = np.convolve(signal, filt)\n\nout = out.astype(np.int16) # make sure you do this, otherwise, you will get VERY LOUD NOISE\nwrite('out.wav', 16000, out)\n\n# plt.plot(out)\n# plt.title(\"Hello world with small echo\")\n# plt.show()\n\n\n" }, { "path": "cnn_class/edge.py", "content": "# https://deeplearningcourses.com/c/deep-learning-convolutional-neural-networks-theano-tensorflow\n# https://udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nfrom scipy.signal import convolve2d\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n\n# load the famous Lena image\nimg = mpimg.imread('lena.png')\n\n# make it B&W\nbw = img.mean(axis=2)\n\n# Sobel operator - approximate gradient in X dir\nHx = np.array([\n [-1, 0, 1],\n [-2, 0, 2],\n [-1, 0, 1],\n], dtype=np.float32)\n\n# Sobel operator - approximate gradient in Y dir\nHy = np.array([\n [-1, -2, -1],\n [0, 0, 0],\n [1, 2, 1],\n], dtype=np.float32)\n\nGx = convolve2d(bw, Hx)\nplt.imshow(Gx, cmap='gray')\nplt.show()\n\nGy = convolve2d(bw, Hy)\nplt.imshow(Gy, cmap='gray')\nplt.show()\n\n# Gradient magnitude\nG = np.sqrt(Gx*Gx + Gy*Gy)\nplt.imshow(G, cmap='gray')\nplt.show()\n\n# The gradient's direction\ntheta = np.arctan2(Gy, Gx)\nplt.imshow(theta, cmap='gray')\nplt.show()" }, { "path": "cnn_class/edge_benchmark.py", "content": "# Vanilla deep network\n# https://deeplearningcourses.com/c/deep-learning-convolutional-neural-networks-theano-tensorflow\n# https://udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\n\nfrom scipy.signal import convolve2d\nfrom scipy.io import loadmat\nfrom sklearn.utils import shuffle\n\nfrom benchmark import error_rate\n\n\nHx = np.array([\n [-1, 0, 1],\n [-2, 0, 2],\n [-1, 0, 1],\n], dtype=np.float32)\n\n# Sobel operator - approximate gradient in Y dir\nHy = np.array([\n [-1, -2, -1],\n [0, 0, 0],\n [1, 2, 1],\n], dtype=np.float32)\n\n\ndef convolve_flatten(X):\n # input will be (32, 32, 3, N)\n # output will be (N, 32*32)\n N = X.shape[-1]\n flat = np.zeros((N, 32*32))\n for i in range(N):\n #flat[i] = X[:,:,:,i].reshape(3072)\n bw = X[:,:,:,i].mean(axis=2) # make it grayscale\n Gx = convolve2d(bw, Hx, mode='same')\n Gy = convolve2d(bw, Hy, mode='same')\n G = np.sqrt(Gx*Gx + Gy*Gy)\n G /= G.max() # normalize it\n flat[i] = G.reshape(32*32)\n return flat\n\n\ndef main():\n train = loadmat('../large_files/train_32x32.mat')\n test = loadmat('../large_files/test_32x32.mat')\n\n # Need to scale! don't leave as 0..255\n # Y is a N x 1 matrix with values 1..10 (MATLAB indexes by 1)\n # So flatten it and make it 0..9\n # Also need indicator matrix for cost calculation\n Xtrain = convolve_flatten(train['X'].astype(np.float32))\n Ytrain = train['y'].flatten() - 1\n Xtrain, Ytrain = shuffle(Xtrain, Ytrain)\n\n Xtest = convolve_flatten(test['X'].astype(np.float32))\n Ytest = test['y'].flatten() - 1\n\n # gradient descent params\n max_iter = 15\n print_period = 10\n N, D = Xtrain.shape\n batch_sz = 500\n n_batches = N // batch_sz\n\n # initial weights\n M1 = 1000 # hidden layer size\n M2 = 500\n K = 10\n W1_init = np.random.randn(D, M1) / np.sqrt(D + M1)\n b1_init = np.zeros(M1)\n W2_init = np.random.randn(M1, M2) / np.sqrt(M1 + M2)\n b2_init = np.zeros(M2)\n W3_init = np.random.randn(M2, K) / np.sqrt(M2 + K)\n b3_init = np.zeros(K)\n\n # define variables and expressions\n X = tf.placeholder(tf.float32, shape=(None, D), name='X')\n T = tf.placeholder(tf.int32, shape=(None,), name='T')\n W1 = tf.Variable(W1_init.astype(np.float32))\n b1 = tf.Variable(b1_init.astype(np.float32))\n W2 = tf.Variable(W2_init.astype(np.float32))\n b2 = tf.Variable(b2_init.astype(np.float32))\n W3 = tf.Variable(W3_init.astype(np.float32))\n b3 = tf.Variable(b3_init.astype(np.float32))\n\n Z1 = tf.nn.relu( tf.matmul(X, W1) + b1 )\n Z2 = tf.nn.relu( tf.matmul(Z1, W2) + b2 )\n Yish = tf.matmul(Z2, W3) + b3\n\n cost = tf.reduce_sum(\n tf.nn.sparse_softmax_cross_entropy_with_logits(\n logits=Yish,\n labels=T\n )\n )\n\n train_op = tf.train.RMSPropOptimizer(0.0001, decay=0.99, momentum=0.9).minimize(cost)\n\n # we'll use this to calculate the error rate\n predict_op = tf.argmax(Yish, 1)\n\n LL = []\n init = tf.global_variables_initializer()\n with tf.Session() as session:\n session.run(init)\n\n for i in range(max_iter):\n for j in range(n_batches):\n Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),]\n Ybatch = Ytrain[j*batch_sz:(j*batch_sz + batch_sz),]\n\n session.run(train_op, feed_dict={X: Xbatch, T: Ybatch})\n if j % print_period == 0:\n test_cost = session.run(cost, feed_dict={X: Xtest, T: Ytest})\n prediction = session.run(predict_op, feed_dict={X: Xtest})\n err = error_rate(prediction, Ytest)\n print(\"Cost / err at iteration i=%d, j=%d: %.3f / %.3f\" % (i, j, test_cost, err))\n LL.append(test_cost)\n\n plt.plot(LL)\n plt.show()\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "cnn_class/exercises.txt", "content": "Logistic Regression\nhttps://www.kaggle.com/uciml/pima-indians-diabetes-database\nhttps://lazyprogrammer.me/course_files/exercises/diabetes.csv\n\nLinear Regression\nhttps://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html\nhttps://lazyprogrammer.me/course_files/exercises/boston.txt\n\nANN\nhttps://archive.ics.uci.edu/ml/datasets/ecoli (orig)\nhttps://www.kaggle.com/elikplim/ecoli-data-set (alt)\nhttps://lazyprogrammer.me/course_files/exercises/ecoli.csv\n\nCNN\nhttps://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge\nhttps://archive.org/download/fer2013_202311/fer2013.csv\n\nNLP\nhttps://www.kaggle.com/crowdflower/twitter-airline-sentiment\nhttps://lazyprogrammer.me/course_files/exercises/AirlineSentimentTweets.csv" }, { "path": "cnn_class/extra_reading.txt", "content": "Gradient-Based Learning Applied to Document Recognition\nhttp://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf\n\nImageNet Classification with Deep Convolutional Neural Networks\nhttps://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf\n\nConvolution arithmetic tutorial\nhttps://theano-pymc.readthedocs.io/en/latest/tutorial/conv_arithmetic.html\n\nVery Deep Convolutional Networks for Large-Scale Visual Recognition\nhttp://www.robots.ox.ac.uk/~vgg/research/very_deep/\n\nImageNet Classification with Deep Convolutional Neural Networks\nhttp://image-net.org/challenges/LSVRC/2012/supervision.pdf\n\nGoing deeper with convolutions\nhttps://arxiv.org/pdf/1409.4842.pdf" }, { "path": "cnn_class/keras_example.py", "content": "# https://deeplearningcourses.com/c/advanced-computer-vision\n# https://www.udemy.com/advanced-computer-vision\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nfrom keras.models import Sequential, Model\nfrom keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Flatten, Dropout, BatchNormalization, Input\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\n\nfrom datetime import datetime\nfrom scipy.io import loadmat\nfrom sklearn.utils import shuffle\n\nfrom benchmark import get_data, error_rate\n\n\n# helper\n# def y2indicator(Y):\n# N = len(Y)\n# K = len(set(Y))\n# I = np.zeros((N, K))\n# I[np.arange(N), Y] = 1\n# return I\n\ndef rearrange(X):\n # input is (32, 32, 3, N)\n # output is (N, 32, 32, 3)\n # N = X.shape[-1]\n # out = np.zeros((N, 32, 32, 3), dtype=np.float32)\n # for i in xrange(N):\n # for j in xrange(3):\n # out[i, :, :, j] = X[:, :, j, i]\n # return out / 255\n return (X.transpose(3, 0, 1, 2) / 255.).astype(np.float32)\n\n\n# get the data\ntrain, test = get_data()\n\n# Need to scale! don't leave as 0..255\n# Y is a N x 1 matrix with values 1..10 (MATLAB indexes by 1)\n# So flatten it and make it 0..9\n# Also need indicator matrix for cost calculation\nXtrain = rearrange(train['X'])\nYtrain = train['y'].flatten() - 1\ndel train\n\nXtest = rearrange(test['X'])\nYtest = test['y'].flatten() - 1\ndel test\n\n\n\n# get shapes\nK = len(set(Ytrain))\n\n\n\n# make the CNN\ni = Input(shape=Xtrain.shape[1:])\nx = Conv2D(filters=20, kernel_size=(5, 5))(i)\nx = BatchNormalization()(x)\nx = Activation('relu')(x)\nx = MaxPooling2D()(x)\n\nx = Conv2D(filters=50, kernel_size=(5, 5))(x)\nx = BatchNormalization()(x)\nx = Activation('relu')(x)\nx = MaxPooling2D()(x)\n\nx = Flatten()(x)\nx = Dense(units=500)(x)\nx = Activation('relu')(x)\nx = Dropout(0.3)(x)\nx = Dense(units=K)(x)\nx = Activation('softmax')(x)\n\nmodel = Model(inputs=i, outputs=x)\n\n\n# list of losses: https://keras.io/losses/\n# list of optimizers: https://keras.io/optimizers/\n# list of metrics: https://keras.io/metrics/\nmodel.compile(\n loss='sparse_categorical_crossentropy',\n optimizer='adam',\n metrics=['accuracy']\n)\n\n# note: multiple ways to choose a backend\n# either theano, tensorflow, or cntk\n# https://keras.io/backend/\n\n\n# gives us back a \nr = model.fit(Xtrain, Ytrain, validation_data=(Xtest, Ytest), epochs=10, batch_size=32)\nprint(\"Returned:\", r)\n\n# print the available keys\n# should see: dict_keys(['val_loss', 'acc', 'loss', 'val_acc'])\nprint(r.history.keys())\n\n# plot some data\nplt.plot(r.history['loss'], label='loss')\nplt.plot(r.history['val_loss'], label='val_loss')\nplt.legend()\nplt.show()\n\n# accuracies\nplt.plot(r.history['accuracy'], label='acc')\nplt.plot(r.history['val_accuracy'], label='val_acc')\nplt.legend()\nplt.show()\n\n\n" }, { "path": "cnn_class2/WHERE ARE THE NOTEBOOKS.txt", "content": "As stated in the \"where to get the code\" / \"where to get the notebooks\" lecture, the notebooks are NOT on Github.\n\nIf you missed this, please review the lecture for the actual location of the notebooks.\n\nIf, after reviewing it, you still need assistance, please contact info@deeplearningcourses.com." }, { "path": "cnn_class2/class_activation_maps.py", "content": "# https://deeplearningcourses.com/c/advanced-computer-vision\n# https://www.udemy.com/advanced-computer-vision\n\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nfrom keras.models import Model\nfrom keras.applications.resnet50 import ResNet50, preprocess_input, decode_predictions\nfrom keras.preprocessing import image\n\nimport numpy as np\nimport scipy as sp\nimport matplotlib.pyplot as plt\n\nfrom glob import glob\n\n\n\n# get the image files\n# http://www.vision.caltech.edu/Image_Datasets/Caltech101/\n# http://www.vision.caltech.edu/Image_Datasets/Caltech256/\nimage_files = glob('../large_files/256_ObjectCategories/*/*.jp*g')\nimage_files += glob('../large_files/101_ObjectCategories/*/*.jp*g')\n\n\n\n# look at an image for fun\nplt.imshow(image.load_img(np.random.choice(image_files)))\nplt.show()\n\n\n# add preprocessing layer to the front of VGG\nresnet = ResNet50(input_shape=(224, 224, 3), weights='imagenet', include_top=True)\n\n# view the structure of the model\n# if you want to confirm we need activation_49\nresnet.summary()\n\n# make a model to get output before flatten\nactivation_layer = resnet.get_layer('activation_49')\n\n# create a model object\nmodel = Model(inputs=resnet.input, outputs=activation_layer.output)\n\n# get the feature map weights\nfinal_dense = resnet.get_layer('fc1000')\nW = final_dense.get_weights()[0]\n\n\nwhile True:\n img = image.load_img(np.random.choice(image_files), target_size=(224, 224))\n x = preprocess_input(np.expand_dims(img, 0))\n fmaps = model.predict(x)[0] # 7 x 7 x 2048\n\n # get predicted class\n probs = resnet.predict(x)\n classnames = decode_predictions(probs)[0]\n print(classnames)\n classname = classnames[0][1]\n pred = np.argmax(probs[0])\n\n # get the 2048 weights for the relevant class\n w = W[:, pred]\n\n # \"dot\" w with fmaps\n cam = fmaps.dot(w)\n\n # upsample to 224 x 224\n # 7 x 32 = 224\n cam = sp.ndimage.zoom(cam, (32, 32), order=1)\n\n plt.subplot(1,2,1)\n plt.imshow(img, alpha=0.8)\n plt.imshow(cam, cmap='jet', alpha=0.5)\n plt.subplot(1,2,2)\n plt.imshow(img)\n plt.title(classname)\n plt.show()\n\n ans = input(\"Continue? (Y/n)\")\n if ans and ans[0].lower() == 'n':\n break\n\n\n\n# def slowversion(A, w):\n# N = len(w)\n# result = np.zeros(A.shape[:-1])\n# for i in range(N):\n# result += A[:,:,i]*w[i]\n# return result\n\n" }, { "path": "cnn_class2/extra_reading.txt", "content": "A Neural Algorithm of Artistic Style\nhttps://arxiv.org/abs/1508.06576\n\nSSD: Single Shot MultiBox Detector\nhttps://arxiv.org/abs/1512.02325\n\nVery Deep Convolutional Networks for Large-Scale Image Recognition (VGG)\nhttps://arxiv.org/abs/1409.1556\n\nDeep Residual Learning for Image Recognition\nhttps://arxiv.org/abs/1512.03385\n\nGoing Deeper with Convolutions (Inception)\nhttps://arxiv.org/abs/1409.4842\n\nBatch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift\nhttps://arxiv.org/abs/1502.03167\n\nDeep learning improved by biological activation functions\nhttps://arxiv.org/pdf/1804.11237.pdf" }, { "path": "cnn_class2/fashion.py", "content": "# https://deeplearningcourses.com/c/advanced-computer-vision\n# https://www.udemy.com/advanced-computer-vision\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Flatten, Dropout, BatchNormalization\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\n\n\n# helper\ndef y2indicator(Y):\n N = len(Y)\n K = len(set(Y))\n I = np.zeros((N, K))\n I[np.arange(N), Y] = 1\n return I\n\n\n# get the data\n# https://www.kaggle.com/zalando-research/fashionmnist\ndata = pd.read_csv('../large_files/fashionmnist/fashion-mnist_train.csv')\ndata = data.values\nnp.random.shuffle(data)\n\nX = data[:, 1:].reshape(-1, 28, 28, 1) / 255.0\nY = data[:, 0].astype(np.int32)\n\n# get shapes\n# N = len(Y)\nK = len(set(Y))\n\n# by default Keras wants one-hot encoded labels\n# there's another cost function we can use\n# where we can just pass in the integer labels directly\n# just like Tensorflow / Theano\nY = y2indicator(Y)\n\n\n# the model will be a sequence of layers\nmodel = Sequential()\n\n\n# make the CNN\n# model.add(Input(shape=(28, 28, 1)))\nmodel.add(Conv2D(input_shape=(28, 28, 1), filters=32, kernel_size=(3, 3)))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D())\n\nmodel.add(Conv2D(filters=64, kernel_size=(3, 3)))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D())\n\nmodel.add(Conv2D(filters=128, kernel_size=(3, 3)))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D())\n\nmodel.add(Flatten())\nmodel.add(Dense(units=300))\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(units=K))\nmodel.add(Activation('softmax'))\n\n\n# list of losses: https://keras.io/losses/\n# list of optimizers: https://keras.io/optimizers/\n# list of metrics: https://keras.io/metrics/\nmodel.compile(\n loss='categorical_crossentropy',\n optimizer='adam',\n metrics=['accuracy']\n)\n\n# note: multiple ways to choose a backend\n# either theano, tensorflow, or cntk\n# https://keras.io/backend/\n\n\n# gives us back a \nr = model.fit(X, Y, validation_split=0.33, epochs=15, batch_size=32)\nprint(\"Returned:\", r)\n\n# print the available keys\n# should see: dict_keys(['val_loss', 'acc', 'loss', 'val_acc'])\nprint(r.history.keys())\n\n# plot some data\nplt.plot(r.history['loss'], label='loss')\nplt.plot(r.history['val_loss'], label='val_loss')\nplt.legend()\nplt.show()\n\n# accuracies\nplt.plot(r.history['accuracy'], label='acc')\nplt.plot(r.history['val_accuracy'], label='val_acc')\nplt.legend()\nplt.show()\n\n\n" }, { "path": "cnn_class2/fashion2.py", "content": "# https://deeplearningcourses.com/c/advanced-computer-vision\n# https://www.udemy.com/advanced-computer-vision\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nfrom keras.models import Sequential, Model\nfrom keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Flatten, Dropout, BatchNormalization, Input\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\n\n\n# helper\ndef y2indicator(Y):\n N = len(Y)\n K = len(set(Y))\n I = np.zeros((N, K))\n I[np.arange(N), Y] = 1\n return I\n\n\n# get the data\n# https://www.kaggle.com/zalando-research/fashionmnist\ndata = pd.read_csv('../large_files/fashionmnist/fashion-mnist_train.csv')\ndata = data.values\nnp.random.shuffle(data)\n\nX = data[:, 1:].reshape(-1, 28, 28, 1) / 255.0\nY = data[:, 0].astype(np.int32)\n\n# get shapes\n# N = len(Y)\nK = len(set(Y))\n\n# by default Keras wants one-hot encoded labels\n# there's another cost function we can use\n# where we can just pass in the integer labels directly\n# just like Tensorflow / Theano\nY = y2indicator(Y)\n\n\n\n\n# make the CNN\ni = Input(shape=(28, 28, 1))\nx = Conv2D(filters=32, kernel_size=(3, 3))(i)\nx = BatchNormalization()(x)\nx = Activation('relu')(x)\nx = MaxPooling2D()(x)\n\nx = Conv2D(filters=64, kernel_size=(3, 3))(x)\nx = BatchNormalization()(x)\nx = Activation('relu')(x)\nx = MaxPooling2D()(x)\n\nx = Flatten()(x)\nx = Dense(units=100)(x)\nx = Activation('relu')(x)\nx = Dropout(0.3)(x)\nx = Dense(units=K)(x)\nx = Activation('softmax')(x)\n\nmodel = Model(inputs=i, outputs=x)\n\n\n# list of losses: https://keras.io/losses/\n# list of optimizers: https://keras.io/optimizers/\n# list of metrics: https://keras.io/metrics/\nmodel.compile(\n loss='categorical_crossentropy',\n optimizer='adam',\n metrics=['accuracy']\n)\n\n# note: multiple ways to choose a backend\n# either theano, tensorflow, or cntk\n# https://keras.io/backend/\n\n\n# gives us back a \nr = model.fit(X, Y, validation_split=0.33, epochs=15, batch_size=32)\nprint(\"Returned:\", r)\n\n# print the available keys\n# should see: dict_keys(['val_loss', 'acc', 'loss', 'val_acc'])\nprint(r.history.keys())\n\n# plot some data\nplt.plot(r.history['loss'], label='loss')\nplt.plot(r.history['val_loss'], label='val_loss')\nplt.legend()\nplt.show()\n\n# accuracies\nplt.plot(r.history['accuracy'], label='acc')\nplt.plot(r.history['val_accuracy'], label='val_acc')\nplt.legend()\nplt.show()\n\n\n" }, { "path": "cnn_class2/make_limited_datasets.py", "content": "# https://deeplearningcourses.com/c/advanced-computer-vision\n# https://www.udemy.com/advanced-computer-vision\nimport os\n\ndef mkdir(p):\n if not os.path.exists(p):\n os.mkdir(p)\n\ndef link(src, dst):\n if not os.path.exists(dst):\n os.symlink(src, dst, target_is_directory=True)\n\nmkdir('../large_files/fruits-360-small')\n\n\nclasses = [\n 'Apple Golden 1',\n 'Avocado',\n 'Lemon',\n 'Mango',\n 'Kiwi',\n 'Banana',\n 'Strawberry',\n 'Raspberry'\n]\n\ntrain_path_from = os.path.abspath('../large_files/fruits-360/Training')\nvalid_path_from = os.path.abspath('../large_files/fruits-360/Validation')\n\ntrain_path_to = os.path.abspath('../large_files/fruits-360-small/Training')\nvalid_path_to = os.path.abspath('../large_files/fruits-360-small/Validation')\n\nmkdir(train_path_to)\nmkdir(valid_path_to)\n\n\nfor c in classes:\n link(train_path_from + '/' + c, train_path_to + '/' + c)\n link(valid_path_from + '/' + c, valid_path_to + '/' + c)" }, { "path": "cnn_class2/siamese.py", "content": "# https://deeplearningcourses.com/c/advanced-computer-vision\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nfrom keras.layers import Input, Lambda, Dense, Flatten, Conv2D, BatchNormalization, Activation, MaxPooling2D\nfrom keras.models import Model\nfrom keras.preprocessing import image\n\nimport keras.backend as K\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom glob import glob\nfrom collections import Counter\n\n\n# get the data from: http://vision.ucsd.edu/content/yale-face-database\nfiles = glob('../large_files/yalefaces/subject*')\n\n# easier to randomize later\nnp.random.shuffle(files)\n\n# number of samples\nN = len(files)\n\n\ndef load_img(filepath):\n # load image and downsample\n img = image.img_to_array(image.load_img(filepath, target_size=[60, 80])).astype('uint8')\n return img\n\n\n\n# look at an image for fun\nimg = load_img(np.random.choice(files))\nplt.imshow(img)\nplt.show()\n\n\n# try load images as arrays\n# yes, I cheated and checked beforehand that all the images were the same shape!\nshape = [N] + list(img.shape)\nimages = np.zeros(shape)\nfor i, f in enumerate(files):\n # img = image.img_to_array(image.load_img(f)).astype('uint8')\n img = load_img(f)\n images[i] = img\n\n\n# make the labels\n# all the filenames are something like 'subject13.happy'\nlabels = np.zeros(N)\nfor i, f in enumerate(files):\n filename = f.rsplit('/', 1)[-1]\n subject_num = filename.split('.', 1)[0]\n\n # subtract 1 since the filenames start from 1\n idx = int(subject_num.replace('subject', '')) - 1\n labels[i] = idx\n\n\n# how many of each subject do we have?\nlabel_count = Counter(labels)\n\n# set of unique labels\nunique_labels = set(label_count.keys())\n\n# get the number of subjects\nn_subjects = len(label_count)\n\n# let's make it so 3 images for each subject are test data\n# number of test points is then\nn_test = 3 * n_subjects\nn_train = N - n_test\n\n\n# initialize arrays to hold train and test images\ntrain_images = np.zeros([n_train] + list(img.shape))\ntrain_labels = np.zeros(n_train)\ntest_images = np.zeros([n_test] + list(img.shape))\ntest_labels = np.zeros(n_test)\n\n\ncount_so_far = {}\ntrain_idx = 0\ntest_idx = 0\nfor img, label in zip(images, labels):\n # increment the count\n count_so_far[label] = count_so_far.get(label, 0) + 1\n\n if count_so_far[label] > 3:\n # we have already added 3 test images for this subject\n # so add the rest to train\n train_images[train_idx] = img\n train_labels[train_idx] = label\n train_idx += 1\n\n else:\n # add the first 3 images to test\n test_images[test_idx] = img\n test_labels[test_idx] = label\n test_idx += 1\n\n\n# create label2idx mapping for easy access\ntrain_label2idx = {}\ntest_label2idx = {}\n\nfor i, label in enumerate(train_labels):\n if label not in train_label2idx:\n train_label2idx[label] = [i]\n else:\n train_label2idx[label].append(i)\n\nfor i, label in enumerate(test_labels):\n if label not in test_label2idx:\n test_label2idx[label] = [i]\n else:\n test_label2idx[label].append(i)\n\n\n# come up with all possible training sample indices\ntrain_positives = []\ntrain_negatives = []\ntest_positives = []\ntest_negatives = []\n\nfor label, indices in train_label2idx.items():\n # all indices that do NOT belong to this subject\n other_indices = set(range(n_train)) - set(indices)\n\n for i, idx1 in enumerate(indices):\n for idx2 in indices[i+1:]:\n train_positives.append((idx1, idx2))\n\n for idx2 in other_indices:\n train_negatives.append((idx1, idx2))\n\nfor label, indices in test_label2idx.items():\n # all indices that do NOT belong to this subject\n other_indices = set(range(n_test)) - set(indices)\n\n for i, idx1 in enumerate(indices):\n for idx2 in indices[i+1:]:\n test_positives.append((idx1, idx2))\n\n for idx2 in other_indices:\n test_negatives.append((idx1, idx2))\n\n\nbatch_size = 64\ndef train_generator():\n # for each batch, we will send 1 pair of each subject\n # and the same number of non-matching pairs\n n_batches = int(np.ceil(len(train_positives) / batch_size))\n \n while True:\n np.random.shuffle(train_positives)\n\n n_samples = batch_size * 2\n shape = [n_samples] + list(img.shape)\n x_batch_1 = np.zeros(shape)\n x_batch_2 = np.zeros(shape)\n y_batch = np.zeros(n_samples)\n\n for i in range(n_batches):\n pos_batch_indices = train_positives[i * batch_size: (i + 1) * batch_size]\n\n # fill up x_batch and y_batch\n j = 0\n for idx1, idx2 in pos_batch_indices:\n x_batch_1[j] = train_images[idx1]\n x_batch_2[j] = train_images[idx2]\n y_batch[j] = 1 # match\n j += 1\n\n # get negative samples\n neg_indices = np.random.choice(len(train_negatives), size=len(pos_batch_indices), replace=False)\n for neg in neg_indices:\n idx1, idx2 = train_negatives[neg]\n x_batch_1[j] = train_images[idx1]\n x_batch_2[j] = train_images[idx2]\n y_batch[j] = 0 # non-match\n j += 1\n\n x1 = x_batch_1[:j]\n x2 = x_batch_2[:j]\n y = y_batch[:j]\n yield [x1, x2], y\n\n\n# same thing as the train generator except no shuffling and it uses the test set\ndef test_generator():\n n_batches = int(np.ceil(len(test_positives) / batch_size))\n\n while True:\n n_samples = batch_size * 2\n shape = [n_samples] + list(img.shape)\n x_batch_1 = np.zeros(shape)\n x_batch_2 = np.zeros(shape)\n y_batch = np.zeros(n_samples)\n\n for i in range(n_batches):\n pos_batch_indices = test_positives[i * batch_size: (i + 1) * batch_size]\n\n # fill up x_batch and y_batch\n j = 0\n for idx1, idx2 in pos_batch_indices:\n x_batch_1[j] = test_images[idx1]\n x_batch_2[j] = test_images[idx2]\n y_batch[j] = 1 # match\n j += 1\n\n # get negative samples\n neg_indices = np.random.choice(len(test_negatives), size=len(pos_batch_indices), replace=False)\n for neg in neg_indices:\n idx1, idx2 = test_negatives[neg]\n x_batch_1[j] = test_images[idx1]\n x_batch_2[j] = test_images[idx2]\n y_batch[j] = 0 # non-match\n j += 1\n\n x1 = x_batch_1[:j]\n x2 = x_batch_2[:j]\n y = y_batch[:j]\n yield [x1, x2], y\n\n\n\n\n# build the base neural network\ni = Input(shape=img.shape)\nx = Conv2D(filters=32, kernel_size=(3, 3))(i)\nx = BatchNormalization()(x)\nx = Activation('relu')(x)\nx = MaxPooling2D()(x)\n\nx = Conv2D(filters=64, kernel_size=(3, 3))(x)\nx = BatchNormalization()(x)\nx = Activation('relu')(x)\nx = MaxPooling2D()(x)\n\nx = Flatten()(x)\nx = Dense(units=128, activation='relu')(x)\nx = Dense(units=50)(x) # feature vector\n\ncnn = Model(inputs=i, outputs=x)\n\n\n# feed both images into the same CNN\nimg_placeholder1 = Input(shape=img.shape)\nimg_placeholder2 = Input(shape=img.shape)\n\n# get image features\nfeat1 = cnn(img_placeholder1)\nfeat2 = cnn(img_placeholder2)\n\n\n# calculate the Euclidean distance between feature 1 and feature 2\ndef euclidean_distance(features):\n x, y = features\n return K.sqrt(K.sum(K.square(x - y), axis=1, keepdims=True))\n\n\n# lambda layer to output distance between feat1 and feat2\ndist_layer = Lambda(euclidean_distance)([feat1, feat2])\n\n\n# the model we will actually train\nmodel = Model(inputs=[img_placeholder1, img_placeholder2], outputs=dist_layer)\n\n\n# loss function for siamese network\ndef contrastive_loss(y_true, y_pred):\n margin = 1\n return K.mean(y_true * K.square(y_pred) + (1 - y_true) * K.square(K.maximum(margin - y_pred, 0)))\n\n\n# compile the model\nmodel.compile(\n loss=contrastive_loss,\n optimizer='adam',\n)\n\n\n# calculate accuracy before training\n# since the dataset is imbalanced, we'll report tp, tn, fp, fn\ndef get_train_accuracy(threshold=0.85):\n positive_distances = []\n negative_distances = []\n\n tp = 0\n tn = 0\n fp = 0\n fn = 0\n\n batch_size = 64\n x_batch_1 = np.zeros([batch_size] + list(img.shape))\n x_batch_2 = np.zeros([batch_size] + list(img.shape))\n n_batches = int(np.ceil(len(train_positives) / batch_size))\n for i in range(n_batches):\n print(f\"pos batch: {i+1}/{n_batches}\")\n pos_batch_indices = train_positives[i * batch_size: (i + 1) * batch_size]\n\n # fill up x_batch and y_batch\n j = 0\n for idx1, idx2 in pos_batch_indices:\n x_batch_1[j] = train_images[idx1]\n x_batch_2[j] = train_images[idx2]\n j += 1\n\n x1 = x_batch_1[:j]\n x2 = x_batch_2[:j]\n distances = model.predict([x1, x2]).flatten()\n positive_distances += distances.tolist()\n\n # update tp, tn, fp, fn\n tp += (distances < threshold).sum()\n fn += (distances > threshold).sum()\n\n n_batches = int(np.ceil(len(train_negatives) / batch_size))\n for i in range(n_batches):\n print(f\"neg batch: {i+1}/{n_batches}\")\n neg_batch_indices = train_negatives[i * batch_size: (i + 1) * batch_size]\n\n # fill up x_batch and y_batch\n j = 0\n for idx1, idx2 in neg_batch_indices:\n x_batch_1[j] = train_images[idx1]\n x_batch_2[j] = train_images[idx2]\n j += 1\n\n x1 = x_batch_1[:j]\n x2 = x_batch_2[:j]\n distances = model.predict([x1, x2]).flatten()\n negative_distances += distances.tolist()\n\n # update tp, tn, fp, fn\n fp += (distances < threshold).sum()\n tn += (distances > threshold).sum()\n\n tpr = tp / (tp + fn)\n tnr = tn / (tn + fp)\n print(f\"sensitivity (tpr): {tpr}, specificity (tnr): {tnr}\")\n\n plt.hist(negative_distances, bins=20, density=True, label='negative_distances')\n plt.hist(positive_distances, bins=20, density=True, label='positive_distances')\n plt.legend()\n plt.show()\n\n\n\ndef get_test_accuracy(threshold=0.85):\n positive_distances = []\n negative_distances = []\n\n tp = 0\n tn = 0\n fp = 0\n fn = 0\n\n batch_size = 64\n x_batch_1 = np.zeros([batch_size] + list(img.shape))\n x_batch_2 = np.zeros([batch_size] + list(img.shape))\n n_batches = int(np.ceil(len(test_positives) / batch_size))\n for i in range(n_batches):\n print(f\"pos batch: {i+1}/{n_batches}\")\n pos_batch_indices = test_positives[i * batch_size: (i + 1) * batch_size]\n\n # fill up x_batch and y_batch\n j = 0\n for idx1, idx2 in pos_batch_indices:\n x_batch_1[j] = test_images[idx1]\n x_batch_2[j] = test_images[idx2]\n j += 1\n\n x1 = x_batch_1[:j]\n x2 = x_batch_2[:j]\n distances = model.predict([x1, x2]).flatten()\n positive_distances += distances.tolist()\n\n # update tp, tn, fp, fn\n tp += (distances < threshold).sum()\n fn += (distances > threshold).sum()\n\n n_batches = int(np.ceil(len(test_negatives) / batch_size))\n for i in range(n_batches):\n print(f\"neg batch: {i+1}/{n_batches}\")\n neg_batch_indices = test_negatives[i * batch_size: (i + 1) * batch_size]\n\n # fill up x_batch and y_batch\n j = 0\n for idx1, idx2 in neg_batch_indices:\n x_batch_1[j] = test_images[idx1]\n x_batch_2[j] = test_images[idx2]\n j += 1\n\n x1 = x_batch_1[:j]\n x2 = x_batch_2[:j]\n distances = model.predict([x1, x2]).flatten()\n negative_distances += distances.tolist()\n\n # update tp, tn, fp, fn\n fp += (distances < threshold).sum()\n tn += (distances > threshold).sum()\n\n\n tpr = tp / (tp + fn)\n tnr = tn / (tn + fp)\n print(f\"sensitivity (tpr): {tpr}, specificity (tnr): {tnr}\")\n\n plt.hist(negative_distances, bins=20, density=True, label='negative_distances')\n plt.hist(positive_distances, bins=20, density=True, label='positive_distances')\n plt.legend()\n plt.show()\n\n\n\n\n# params for training\ntrain_steps = int(np.ceil(len(train_positives) * 2 / batch_size))\nvalid_steps = int(np.ceil(len(test_positives) * 2 / batch_size))\n\n# fit the model\nr = model.fit(\n train_generator(),\n steps_per_epoch=train_steps,\n epochs=20,\n validation_data=test_generator(),\n validation_steps=valid_steps,\n)\n\n# plot the loss\nplt.plot(r.history['loss'], label='train loss')\nplt.plot(r.history['val_loss'], label='val loss')\nplt.legend()\nplt.show()\n\nget_train_accuracy()\nget_test_accuracy()\n" }, { "path": "cnn_class2/ssd.py", "content": "# https://deeplearningcourses.com/c/advanced-computer-vision\n# https://www.udemy.com/advanced-computer-vision\n\n# simple script to adapt object detection notebook from\n# https://github.com/tensorflow/models\n# to work on videos\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport os, sys\nfrom datetime import datetime\n\nimport numpy as np\nimport tensorflow as tf\nfrom matplotlib import pyplot as plt\nfrom PIL import Image\nimport imageio\n\nif tf.__version__ < '1.4.0':\n raise ImportError(\n 'Please upgrade your tensorflow installation to v1.4.* or later!'\n )\n\n\n# change this to wherever you cloned the tensorflow models repo\n# which I assume you've already downloaded from:\n# https://github.com/tensorflow/models\nRESEARCH_PATH = '../../tf-models/research'\nMODELS_PATH = '../../tf-models/research/object_detection'\nsys.path.append(RESEARCH_PATH)\nsys.path.append(MODELS_PATH)\n\n# import local modules\nimport object_detection\nfrom utils import label_map_util\nfrom utils import visualization_utils as vis_util\n\n\n# I've assumed you already ran the notebook and downloaded the model\nMODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'\nPATH_TO_CKPT = '%s/%s/frozen_inference_graph.pb' % (MODELS_PATH, MODEL_NAME)\nPATH_TO_LABELS = '%s/data/mscoco_label_map.pbtxt' % MODELS_PATH\nNUM_CLASSES = 90\n\n\n# load the model into memory\ndetection_graph = tf.Graph()\nwith detection_graph.as_default():\n od_graph_def = tf.GraphDef()\n with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:\n serialized_graph = fid.read()\n od_graph_def.ParseFromString(serialized_graph)\n tf.import_graph_def(od_graph_def, name='')\n\n\n# load label map\nlabel_map = label_map_util.load_labelmap(PATH_TO_LABELS)\ncategories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)\ncategory_index = label_map_util.create_category_index(categories)\nprint(\"categories:\")\nprint(categories)\n\n\n# convert image -> numpy array\ndef load_image_into_numpy_array(image):\n (im_width, im_height) = image.size\n return np.array(image.getdata()).reshape(\n (im_height, im_width, 3)).astype(np.uint8)\n\n\n# do some object detection\nwith detection_graph.as_default():\n with tf.Session(graph=detection_graph) as sess:\n # Definite input and output Tensors for detection_graph\n image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')\n # Each box represents a part of the image where a particular object was detected.\n detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')\n # Each score represent how level of confidence for each of the objects.\n # Score is shown on the result image, together with the class label.\n detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')\n detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')\n num_detections = detection_graph.get_tensor_by_name('num_detections:0')\n\n # instead of looping through test images, we'll now loop\n # through our video!\n\n # get the videos from:\n # https://lazyprogrammer.me/cnn_class2_videos.zip\n # and put them into the same folder as this file\n\n # open the video\n # input_video = 'catdog'\n # input_video = 'safari'\n input_video = 'traffic'\n video_reader = imageio.get_reader('%s.mp4' % input_video)\n video_writer = imageio.get_writer('%s_annotated.mp4' % input_video, fps=10)\n\n # loop through and process each frame\n t0 = datetime.now()\n n_frames = 0\n for frame in video_reader:\n # rename for convenience\n image_np = frame\n n_frames += 1\n\n # Expand dimensions since the model expects images to have shape: [1, None, None, 3]\n image_np_expanded = np.expand_dims(image_np, axis=0)\n\n # Actual detection.\n (boxes, scores, classes, num) = sess.run(\n [detection_boxes, detection_scores, detection_classes, num_detections],\n feed_dict={image_tensor: image_np_expanded})\n\n # Visualization of the results of a detection.\n vis_util.visualize_boxes_and_labels_on_image_array(\n image_np,\n np.squeeze(boxes),\n np.squeeze(classes).astype(np.int32),\n np.squeeze(scores),\n category_index,\n use_normalized_coordinates=True,\n line_thickness=8)\n \n # instead of plotting image, we write the frame to video\n video_writer.append_data(image_np)\n\n fps = n_frames / (datetime.now() - t0).total_seconds()\n print(\"Frames processed: %s, Speed: %s fps\" % (n_frames, fps))\n\n # clean up\n video_writer.close()\n" }, { "path": "cnn_class2/style_transfer1.py", "content": "# https://deeplearningcourses.com/c/advanced-computer-vision\n# https://www.udemy.com/advanced-computer-vision\n\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n# In this script, we will focus on generating the content\n# E.g. given an image, can we recreate the same image\n\nfrom keras.layers import Input, Lambda, Dense, Flatten\nfrom keras.layers import AveragePooling2D, MaxPooling2D\nfrom keras.layers.convolutional import Conv2D\nfrom keras.models import Model, Sequential\nfrom keras.applications.vgg16 import VGG16\nfrom keras.applications.vgg16 import preprocess_input\nfrom keras.preprocessing import image\n\nimport keras.backend as K\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom scipy.optimize import fmin_l_bfgs_b\n\n\nimport tensorflow as tf\nif tf.__version__.startswith('2'):\n tf.compat.v1.disable_eager_execution()\n\n\ndef VGG16_AvgPool(shape):\n # we want to account for features across the entire image\n # so get rid of the maxpool which throws away information\n vgg = VGG16(input_shape=shape, weights='imagenet', include_top=False)\n\n # new_model = Sequential()\n # for layer in vgg.layers:\n # if layer.__class__ == MaxPooling2D:\n # # replace it with average pooling\n # new_model.add(AveragePooling2D())\n # else:\n # new_model.add(layer)\n\n i = vgg.input\n x = i\n for layer in vgg.layers:\n if layer.__class__ == MaxPooling2D:\n # replace it with average pooling\n x = AveragePooling2D()(x)\n else:\n x = layer(x)\n\n return Model(i, x)\n\ndef VGG16_AvgPool_CutOff(shape, num_convs):\n # there are 13 convolutions in total\n # we can pick any of them as the \"output\"\n # of our content model\n\n if num_convs < 1 or num_convs > 13:\n print(\"num_convs must be in the range [1, 13]\")\n return None\n\n model = VGG16_AvgPool(shape)\n # new_model = Sequential()\n # n = 0\n # for layer in model.layers:\n # if layer.__class__ == Conv2D:\n # n += 1\n # new_model.add(layer)\n # if n >= num_convs:\n # break\n\n n = 0\n output = None\n for layer in model.layers:\n if layer.__class__ == Conv2D:\n n += 1\n if n >= num_convs:\n output = layer.output\n break\n\n return Model(model.input, output)\n\n\ndef unpreprocess(img):\n img[..., 0] += 103.939\n img[..., 1] += 116.779\n img[..., 2] += 126.68\n img = img[..., ::-1]\n return img\n\n\ndef scale_img(x):\n x = x - x.min()\n x = x / x.max()\n return x\n\n\nif __name__ == '__main__':\n\n # open an image\n # feel free to try your own\n # path = '../large_files/caltech101/101_ObjectCategories/elephant/image_0002.jpg'\n path = 'content/elephant.jpg'\n img = image.load_img(path)\n\n # convert image to array and preprocess for vgg\n x = image.img_to_array(img)\n x = np.expand_dims(x, axis=0)\n x = preprocess_input(x)\n\n # we'll use this throughout the rest of the script\n batch_shape = x.shape\n shape = x.shape[1:]\n\n # see the image\n # plt.imshow(img)\n # plt.show()\n\n\n # make a content model\n # try different cutoffs to see the images that result\n content_model = VGG16_AvgPool_CutOff(shape, 11)\n\n # make the target\n target = K.variable(content_model.predict(x))\n\n\n # try to match the image\n\n # define our loss in keras\n loss = K.mean(K.square(target - content_model.output))\n\n # gradients which are needed by the optimizer\n grads = K.gradients(loss, content_model.input)\n\n # just like theano.function\n get_loss_and_grads = K.function(\n inputs=[content_model.input],\n outputs=[loss] + grads\n )\n\n\n def get_loss_and_grads_wrapper(x_vec):\n # scipy's minimizer allows us to pass back\n # function value f(x) and its gradient f'(x)\n # simultaneously, rather than using the fprime arg\n #\n # we cannot use get_loss_and_grads() directly\n # input to minimizer func must be a 1-D array\n # input to get_loss_and_grads must be [batch_of_images]\n #\n # gradient must also be a 1-D array\n # and both loss and gradient must be np.float64\n # will get an error otherwise\n\n l, g = get_loss_and_grads([x_vec.reshape(*batch_shape)])\n return l.astype(np.float64), g.flatten().astype(np.float64)\n\n\n\n from datetime import datetime\n t0 = datetime.now()\n losses = []\n x = np.random.randn(np.prod(batch_shape))\n for i in range(10):\n x, l, _ = fmin_l_bfgs_b(\n func=get_loss_and_grads_wrapper,\n x0=x,\n # bounds=[[-127, 127]]*len(x.flatten()),\n maxfun=20\n )\n x = np.clip(x, -127, 127)\n # print(\"min:\", x.min(), \"max:\", x.max())\n print(\"iter=%s, loss=%s\" % (i, l))\n losses.append(l)\n\n print(\"duration:\", datetime.now() - t0)\n plt.plot(losses)\n plt.show()\n\n newimg = x.reshape(*batch_shape)\n final_img = unpreprocess(newimg)\n\n\n plt.imshow(scale_img(final_img[0]))\n plt.show()\n" }, { "path": "cnn_class2/style_transfer2.py", "content": "# https://deeplearningcourses.com/c/advanced-computer-vision\n# https://www.udemy.com/advanced-computer-vision\n\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n# In this script, we will focus on generating an image\n# with the same style as the input image.\n# But NOT the same content.\n# It should capture only the essence of the style.\n\nfrom keras.models import Model, Sequential\nfrom keras.applications.vgg16 import preprocess_input\nfrom keras.preprocessing import image\nfrom keras.applications.vgg16 import VGG16\n\nfrom style_transfer1 import VGG16_AvgPool, unpreprocess, scale_img\n# from skimage.transform import resize\nfrom scipy.optimize import fmin_l_bfgs_b\nfrom datetime import datetime\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport keras.backend as K\n\n\n\ndef gram_matrix(img):\n # input is (H, W, C) (C = # feature maps)\n # we first need to convert it to (C, H*W)\n X = K.batch_flatten(K.permute_dimensions(img, (2, 0, 1)))\n \n # now, calculate the gram matrix\n # gram = XX^T / N\n # the constant is not important since we'll be weighting these\n G = K.dot(X, K.transpose(X)) / img.get_shape().num_elements()\n return G\n\n\ndef style_loss(y, t):\n return K.mean(K.square(gram_matrix(y) - gram_matrix(t)))\n\n\n# let's generalize this and put it into a function\ndef minimize(fn, epochs, batch_shape):\n t0 = datetime.now()\n losses = []\n x = np.random.randn(np.prod(batch_shape))\n for i in range(epochs):\n x, l, _ = fmin_l_bfgs_b(\n func=fn,\n x0=x,\n maxfun=20\n )\n x = np.clip(x, -127, 127)\n print(\"iter=%s, loss=%s\" % (i, l))\n losses.append(l)\n\n print(\"duration:\", datetime.now() - t0)\n plt.plot(losses)\n plt.show()\n\n newimg = x.reshape(*batch_shape)\n final_img = unpreprocess(newimg)\n return final_img[0]\n\n\nif __name__ == '__main__':\n # try these, or pick your own!\n path = 'styles/starrynight.jpg'\n # path = 'styles/flowercarrier.jpg'\n # path = 'styles/monalisa.jpg'\n # path = 'styles/lesdemoisellesdavignon.jpg'\n\n\n # load the data\n img = image.load_img(path)\n\n # convert image to array and preprocess for vgg\n x = image.img_to_array(img)\n\n # look at the image\n # plt.imshow(x)\n # plt.show()\n\n # make it (1, H, W, C)\n x = np.expand_dims(x, axis=0)\n\n # preprocess into VGG expected format\n x = preprocess_input(x)\n\n # we'll use this throughout the rest of the script\n batch_shape = x.shape\n shape = x.shape[1:]\n\n # let's take the first convolution at each block of convolutions\n # to be our target outputs\n # remember that you can print out the model summary if you want\n vgg = VGG16_AvgPool(shape)\n\n # Note: need to select output at index 1, since outputs at\n # index 0 correspond to the original vgg with maxpool\n symbolic_conv_outputs = [\n layer.get_output_at(1) for layer in vgg.layers \\\n if layer.name.endswith('conv1')\n ]\n\n # pick the earlier layers for\n # a more \"localized\" representation\n # this is opposed to the content model\n # where the later layers represent a more \"global\" structure\n # symbolic_conv_outputs = symbolic_conv_outputs[:2]\n\n # make a big model that outputs multiple layers' outputs\n multi_output_model = Model(vgg.input, symbolic_conv_outputs)\n\n # calculate the targets that are output at each layer\n style_layers_outputs = [K.variable(y) for y in multi_output_model.predict(x)]\n\n # calculate the total style loss\n loss = 0\n for symbolic, actual in zip(symbolic_conv_outputs, style_layers_outputs):\n # gram_matrix() expects a (H, W, C) as input\n loss += style_loss(symbolic[0], actual[0])\n\n grads = K.gradients(loss, multi_output_model.input)\n\n # just like theano.function\n get_loss_and_grads = K.function(\n inputs=[multi_output_model.input],\n outputs=[loss] + grads\n )\n\n\n def get_loss_and_grads_wrapper(x_vec):\n l, g = get_loss_and_grads([x_vec.reshape(*batch_shape)])\n return l.astype(np.float64), g.flatten().astype(np.float64)\n\n\n final_img = minimize(get_loss_and_grads_wrapper, 10, batch_shape)\n plt.imshow(scale_img(final_img))\n plt.show()\n" }, { "path": "cnn_class2/style_transfer3.py", "content": "# https://deeplearningcourses.com/c/advanced-computer-vision\n# https://www.udemy.com/advanced-computer-vision\n\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n# In this script, we will focus on generating an image\n# that attempts to match the content of one input image\n# and the style of another input image.\n#\n# We accomplish this by balancing the content loss\n# and style loss simultaneously.\n\nfrom keras.layers import Input, Lambda, Dense, Flatten\nfrom keras.layers import AveragePooling2D, MaxPooling2D\nfrom keras.layers.convolutional import Conv2D\nfrom keras.models import Model, Sequential\nfrom keras.applications.vgg16 import VGG16\nfrom keras.applications.vgg16 import preprocess_input\nfrom keras.preprocessing import image\nfrom skimage.transform import resize\n\nimport keras.backend as K\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom style_transfer1 import VGG16_AvgPool, VGG16_AvgPool_CutOff, unpreprocess, scale_img\nfrom style_transfer2 import gram_matrix, style_loss, minimize\nfrom scipy.optimize import fmin_l_bfgs_b\n\n\n# load the content image\ndef load_img_and_preprocess(path, shape=None):\n img = image.load_img(path, target_size=shape)\n\n # convert image to array and preprocess for vgg\n x = image.img_to_array(img)\n x = np.expand_dims(x, axis=0)\n x = preprocess_input(x)\n\n return x\n\n\n\ncontent_img = load_img_and_preprocess(\n # '../large_files/caltech101/101_ObjectCategories/elephant/image_0002.jpg',\n # 'batman.jpg',\n 'content/sydney.jpg',\n # (225, 300),\n)\n\n# resize the style image\n# since we don't care too much about warping it\nh, w = content_img.shape[1:3]\nstyle_img = load_img_and_preprocess(\n # 'styles/starrynight.jpg',\n # 'styles/flowercarrier.jpg',\n # 'styles/monalisa.jpg',\n 'styles/lesdemoisellesdavignon.jpg',\n (h, w)\n)\n\n\n# we'll use this throughout the rest of the script\nbatch_shape = content_img.shape\nshape = content_img.shape[1:]\n\n\n# we want to make only 1 VGG here\n# as you'll see later, the final model needs\n# to have a common input\nvgg = VGG16_AvgPool(shape)\n\n\n# create the content model\n# we only want 1 output\n# remember you can call vgg.summary() to see a list of layers\n# 1,2,4,5,7-9,11-13,15-17\ncontent_model = Model(vgg.input, vgg.layers[13].get_output_at(0))\ncontent_target = K.variable(content_model.predict(content_img))\n\n\n# create the style model\n# we want multiple outputs\n# we will take the same approach as in style_transfer2.py\nsymbolic_conv_outputs = [\n layer.get_output_at(1) for layer in vgg.layers \\\n if layer.name.endswith('conv1')\n]\n\n# make a big model that outputs multiple layers' outputs\nstyle_model = Model(vgg.input, symbolic_conv_outputs)\n\n# calculate the targets that are output at each layer\nstyle_layers_outputs = [K.variable(y) for y in style_model.predict(style_img)]\n\n# we will assume the weight of the content loss is 1\n# and only weight the style losses\nstyle_weights = [0.2,0.4,0.3,0.5,0.2]\n\n\n\n# create the total loss which is the sum of content + style loss\nloss = K.mean(K.square(content_model.output - content_target))\n\nfor w, symbolic, actual in zip(style_weights, symbolic_conv_outputs, style_layers_outputs):\n # gram_matrix() expects a (H, W, C) as input\n loss += w * style_loss(symbolic[0], actual[0])\n\n\n# once again, create the gradients and loss + grads function\n# note: it doesn't matter which model's input you use\n# they are both pointing to the same keras Input layer in memory\ngrads = K.gradients(loss, vgg.input)\n\n# just like theano.function\nget_loss_and_grads = K.function(\n inputs=[vgg.input],\n outputs=[loss] + grads\n)\n\n\ndef get_loss_and_grads_wrapper(x_vec):\n l, g = get_loss_and_grads([x_vec.reshape(*batch_shape)])\n return l.astype(np.float64), g.flatten().astype(np.float64)\n\n\nfinal_img = minimize(get_loss_and_grads_wrapper, 10, batch_shape)\nplt.imshow(scale_img(final_img))\nplt.show()\n" }, { "path": "cnn_class2/test_softmax.py", "content": "# https://deeplearningcourses.com/c/advanced-computer-vision\n# https://www.udemy.com/advanced-computer-vision\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport tensorflow as tf\nimport numpy as np\nimport keras\nimport keras.backend as K\n\ndef custom_softmax(x):\n m = tf.reduce_max(x, 1)\n x = x - m\n e = tf.exp(x)\n return e / tf.reduce_sum(e, -1)\n\n\na = np.random.randn(1, 1000)\n\ntfy = tf.nn.softmax(a)\nky = keras.activations.softmax(K.variable(a))\ntfc = custom_softmax(a)\n\nsession = K.get_session()\n\ntfy_ = session.run(tfy)\nky_ = session.run(ky)\ntfc_ = session.run(tfc)\n\nprint(\"tf vs k\", np.abs(tfy_ - ky_).sum())\nprint(\"tf vs custom\", np.abs(tfy_ - tfc_).sum())\nprint(\"custom vs k\", np.abs(tfc_ - ky_).sum())" }, { "path": "cnn_class2/tf_resnet.py", "content": "# https://deeplearningcourses.com/c/advanced-computer-vision\n# https://www.udemy.com/advanced-computer-vision\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\n# Let's go up to the end of the first conv block\n# to make sure everything has been loaded correctly\n# compared to keras\nimport tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport keras\n\nfrom keras.applications.resnet50 import ResNet50\nfrom keras.models import Model\nfrom keras.preprocessing import image\nfrom keras.layers import Dense\nfrom keras.applications.resnet50 import preprocess_input, decode_predictions\n\nfrom tf_resnet_convblock import ConvLayer, BatchNormLayer, ConvBlock\nfrom tf_resnet_identity_block import IdentityBlock\nfrom tf_resnet_first_layers import ReLULayer, MaxPoolLayer\n\n\n# NOTE: dependent on your Keras version\n# this script used 2.1.1\n# [,\n# ,\n# ,\n# ,\n# ,\n#\n# ConvBlock\n# IdentityBlock x 2\n#\n# ConvBlock\n# IdentityBlock x 3\n#\n# ConvBlock\n# IdentityBlock x 5\n#\n# ConvBlock\n# IdentityBlock x 2\n#\n# AveragePooling2D\n# Flatten\n# Dense (Softmax)\n# ]\n\n\n# define some additional layers so they have a forward function\nclass AvgPool:\n def __init__(self, ksize):\n self.ksize = ksize\n\n def forward(self, X):\n return tf.nn.avg_pool(\n X,\n ksize=[1, self.ksize, self.ksize, 1],\n strides=[1, 1, 1, 1],\n padding='VALID'\n )\n\n def get_params(self):\n return []\n\nclass Flatten:\n def forward(self, X):\n return tf.contrib.layers.flatten(X)\n\n def get_params(self):\n return []\n\n\ndef custom_softmax(x):\n m = tf.reduce_max(x, 1)\n x = x - m\n e = tf.exp(x)\n return e / tf.reduce_sum(e, -1)\n\n\nclass DenseLayer:\n def __init__(self, mi, mo):\n self.W = tf.Variable((np.random.randn(mi, mo) * np.sqrt(2.0 / mi)).astype(np.float32))\n self.b = tf.Variable(np.zeros(mo, dtype=np.float32))\n\n def forward(self, X):\n # unfortunately these all yield slightly different answers\n # return tf.nn.softmax(tf.matmul(X, self.W) + self.b)\n # return custom_softmax(tf.matmul(X, self.W) + self.b)\n # return keras.activations.softmax(tf.matmul(X, self.W) + self.b)\n return tf.matmul(X, self.W) + self.b\n\n def copyFromKerasLayers(self, layer):\n W, b = layer.get_weights()\n op1 = self.W.assign(W)\n op2 = self.b.assign(b)\n self.session.run((op1, op2))\n\n def get_params(self):\n return [self.W, self.b]\n\n\nclass TFResNet:\n def __init__(self):\n self.layers = [\n # before conv block\n ConvLayer(d=7, mi=3, mo=64, stride=2, padding='SAME'),\n BatchNormLayer(64),\n ReLULayer(),\n MaxPoolLayer(dim=3),\n # conv block\n ConvBlock(mi=64, fm_sizes=[64, 64, 256], stride=1),\n # identity block x 2\n IdentityBlock(mi=256, fm_sizes=[64, 64, 256]),\n IdentityBlock(mi=256, fm_sizes=[64, 64, 256]),\n # conv block\n ConvBlock(mi=256, fm_sizes=[128, 128, 512], stride=2),\n # identity block x 3\n IdentityBlock(mi=512, fm_sizes=[128, 128, 512]),\n IdentityBlock(mi=512, fm_sizes=[128, 128, 512]),\n IdentityBlock(mi=512, fm_sizes=[128, 128, 512]),\n # conv block\n ConvBlock(mi=512, fm_sizes=[256, 256, 1024], stride=2),\n # identity block x 5\n IdentityBlock(mi=1024, fm_sizes=[256, 256, 1024]),\n IdentityBlock(mi=1024, fm_sizes=[256, 256, 1024]),\n IdentityBlock(mi=1024, fm_sizes=[256, 256, 1024]),\n IdentityBlock(mi=1024, fm_sizes=[256, 256, 1024]),\n IdentityBlock(mi=1024, fm_sizes=[256, 256, 1024]),\n # conv block\n ConvBlock(mi=1024, fm_sizes=[512, 512, 2048], stride=2),\n # identity block x 2\n IdentityBlock(mi=2048, fm_sizes=[512, 512, 2048]),\n IdentityBlock(mi=2048, fm_sizes=[512, 512, 2048]),\n # pool / flatten / dense\n AvgPool(ksize=7),\n Flatten(),\n DenseLayer(mi=2048, mo=1000)\n ]\n self.input_ = tf.placeholder(tf.float32, shape=(None, 224, 224, 3))\n self.output = self.forward(self.input_)\n\n def copyFromKerasLayers(self, layers):\n # conv\n self.layers[0].copyFromKerasLayers(layers[1])\n # bn\n self.layers[1].copyFromKerasLayers(layers[2])\n # cb\n self.layers[4].copyFromKerasLayers(layers[5:17]) # size=12\n # ib x 2\n self.layers[5].copyFromKerasLayers(layers[17:27]) # size=10\n self.layers[6].copyFromKerasLayers(layers[27:37])\n # cb\n self.layers[7].copyFromKerasLayers(layers[37:49])\n # ib x 3\n self.layers[8].copyFromKerasLayers(layers[49:59])\n self.layers[9].copyFromKerasLayers(layers[59:69])\n self.layers[10].copyFromKerasLayers(layers[69:79])\n # cb\n self.layers[11].copyFromKerasLayers(layers[79:91])\n # ib x 5\n self.layers[12].copyFromKerasLayers(layers[91:101])\n self.layers[13].copyFromKerasLayers(layers[101:111])\n self.layers[14].copyFromKerasLayers(layers[111:121])\n self.layers[15].copyFromKerasLayers(layers[121:131])\n self.layers[16].copyFromKerasLayers(layers[131:141])\n # cb\n self.layers[17].copyFromKerasLayers(layers[141:153])\n # ib x 2\n self.layers[18].copyFromKerasLayers(layers[153:163])\n self.layers[19].copyFromKerasLayers(layers[163:173])\n # dense\n self.layers[22].copyFromKerasLayers(layers[175])\n\n\n def forward(self, X):\n for layer in self.layers:\n X = layer.forward(X)\n return X\n\n def predict(self, X):\n assert(self.session is not None)\n return self.session.run(\n self.output,\n feed_dict={self.input_: X}\n )\n\n def set_session(self, session):\n self.session = session\n for layer in self.layers:\n if isinstance(layer, ConvBlock) or isinstance(layer, IdentityBlock):\n layer.set_session(session)\n else:\n layer.session = session\n\n def get_params(self):\n params = []\n for layer in self.layers:\n params += layer.get_params()\n\n\nif __name__ == '__main__':\n # you can also set weights to None, it doesn't matter\n resnet_ = ResNet50(weights='imagenet')\n\n # make a new resnet without the softmax\n x = resnet_.layers[-2].output\n W, b = resnet_.layers[-1].get_weights()\n y = Dense(1000)(x)\n resnet = Model(resnet_.input, y)\n resnet.layers[-1].set_weights([W, b])\n\n # you can determine the correct layer\n # by looking at resnet.layers in the console\n partial_model = Model(\n inputs=resnet.input,\n outputs=resnet.layers[175].output\n )\n\n # maybe useful when building your model\n # to look at the layers you're trying to copy\n print(partial_model.summary())\n\n # create an instance of our own model\n my_partial_resnet = TFResNet()\n\n # make a fake image\n X = np.random.random((1, 224, 224, 3))\n\n # get keras output\n keras_output = partial_model.predict(X)\n\n ### get my model output ###\n\n # init only the variables in our net\n init = tf.variables_initializer(my_partial_resnet.get_params())\n\n # note: starting a new session messes up the Keras model\n session = keras.backend.get_session()\n my_partial_resnet.set_session(session)\n session.run(init)\n\n # first, just make sure we can get any output\n first_output = my_partial_resnet.predict(X)\n print(\"first_output.shape:\", first_output.shape)\n\n # copy params from Keras model\n my_partial_resnet.copyFromKerasLayers(partial_model.layers)\n\n # compare the 2 models\n output = my_partial_resnet.predict(X)\n diff = np.abs(output - keras_output).sum()\n if diff < 1e-10:\n print(\"Everything's great!\")\n else:\n print(\"diff = %s\" % diff)\n" }, { "path": "cnn_class2/tf_resnet_convblock.py", "content": "# https://deeplearningcourses.com/c/advanced-computer-vision\n# https://www.udemy.com/advanced-computer-vision\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef init_filter(d, mi, mo, stride):\n return (np.random.randn(d, d, mi, mo) * np.sqrt(2.0 / (d * d * mi))).astype(np.float32)\n\n\nclass ConvLayer:\n def __init__(self, d, mi, mo, stride=2, padding='VALID'):\n self.W = tf.Variable(init_filter(d, mi, mo, stride))\n self.b = tf.Variable(np.zeros(mo, dtype=np.float32))\n self.stride = stride\n self.padding = padding\n\n def forward(self, X):\n X = tf.nn.conv2d(\n X,\n self.W,\n strides=[1, self.stride, self.stride, 1],\n padding=self.padding\n )\n X = X + self.b\n return X\n\n def copyFromKerasLayers(self, layer):\n # only 1 layer to copy from\n W, b = layer.get_weights()\n op1 = self.W.assign(W)\n op2 = self.b.assign(b)\n self.session.run((op1, op2))\n\n # def copyFromWeights(self, W, b):\n # op1 = self.W.assign(W)\n # op2 = self.b.assign(b)\n # self.session.run((op1, op2))\n\n def get_params(self):\n return [self.W, self.b]\n\n\nclass BatchNormLayer:\n def __init__(self, D):\n self.running_mean = tf.Variable(np.zeros(D, dtype=np.float32), trainable=False)\n self.running_var = tf.Variable(np.ones(D, dtype=np.float32), trainable=False)\n self.gamma = tf.Variable(np.ones(D, dtype=np.float32))\n self.beta = tf.Variable(np.zeros(D, dtype=np.float32))\n\n def forward(self, X):\n return tf.nn.batch_normalization(\n X,\n self.running_mean,\n self.running_var,\n self.beta,\n self.gamma,\n 1e-3\n )\n\n def copyFromKerasLayers(self, layer):\n # only 1 layer to copy from\n # order:\n # gamma, beta, moving mean, moving variance\n gamma, beta, running_mean, running_var = layer.get_weights()\n op1 = self.running_mean.assign(running_mean)\n op2 = self.running_var.assign(running_var)\n op3 = self.gamma.assign(gamma)\n op4 = self.beta.assign(beta)\n self.session.run((op1, op2, op3, op4))\n\n def get_params(self):\n return [self.running_mean, self.running_var, self.gamma, self.beta]\n\n\nclass ConvBlock:\n def __init__(self, mi, fm_sizes, stride=2, activation=tf.nn.relu):\n # conv1, conv2, conv3\n # note: # feature maps shortcut = # feauture maps conv 3\n assert(len(fm_sizes) == 3)\n\n # note: kernel size in 2nd conv is always 3\n # so we won't bother including it as an arg\n\n # note: stride only applies to conv 1 in main branch\n # and conv in shortcut, otherwise stride is 1\n\n self.session = None\n self.f = tf.nn.relu\n \n # init main branch\n # Conv -> BN -> F() ---> Conv -> BN -> F() ---> Conv -> BN\n self.conv1 = ConvLayer(1, mi, fm_sizes[0], stride)\n self.bn1 = BatchNormLayer(fm_sizes[0])\n self.conv2 = ConvLayer(3, fm_sizes[0], fm_sizes[1], 1, 'SAME')\n self.bn2 = BatchNormLayer(fm_sizes[1])\n self.conv3 = ConvLayer(1, fm_sizes[1], fm_sizes[2], 1)\n self.bn3 = BatchNormLayer(fm_sizes[2])\n\n # init shortcut branch\n # Conv -> BN\n self.convs = ConvLayer(1, mi, fm_sizes[2], stride)\n self.bns = BatchNormLayer(fm_sizes[2])\n\n # in case needed later\n self.layers = [\n self.conv1, self.bn1,\n self.conv2, self.bn2,\n self.conv3, self.bn3,\n self.convs, self.bns\n ]\n\n # this will not be used when input passed in from\n # a previous layer\n self.input_ = tf.placeholder(tf.float32, shape=(1, 224, 224, mi))\n self.output = self.forward(self.input_)\n\n def forward(self, X):\n # main branch\n FX = self.conv1.forward(X)\n FX = self.bn1.forward(FX)\n FX = self.f(FX)\n FX = self.conv2.forward(FX)\n FX = self.bn2.forward(FX)\n FX = self.f(FX)\n FX = self.conv3.forward(FX)\n FX = self.bn3.forward(FX)\n\n # shortcut branch\n SX = self.convs.forward(X)\n SX = self.bns.forward(SX)\n\n return self.f(FX + SX)\n\n def predict(self, X):\n assert(self.session is not None)\n return self.session.run(\n self.output,\n feed_dict={self.input_: X}\n )\n\n def set_session(self, session):\n # need to make this a session\n # so assignment happens on sublayers too\n self.session = session\n self.conv1.session = session\n self.bn1.session = session\n self.conv2.session = session\n self.bn2.session = session\n self.conv3.session = session\n self.bn3.session = session\n self.convs.session = session\n self.bns.session = session\n\n def copyFromKerasLayers(self, layers):\n # [,\n # ,\n # ,\n # ,\n # ,\n # ,\n # ,\n # ,\n # ,\n # ,\n # ,\n # ]\n self.conv1.copyFromKerasLayers(layers[0])\n self.bn1.copyFromKerasLayers(layers[1])\n self.conv2.copyFromKerasLayers(layers[3])\n self.bn2.copyFromKerasLayers(layers[4])\n self.conv3.copyFromKerasLayers(layers[6])\n self.bn3.copyFromKerasLayers(layers[8])\n self.convs.copyFromKerasLayers(layers[7])\n self.bns.copyFromKerasLayers(layers[9])\n\n def get_params(self):\n params = []\n for layer in self.layers:\n params += layer.get_params()\n return params\n\n\nif __name__ == '__main__':\n conv_block = ConvBlock(mi=3, fm_sizes=[64, 64, 256], stride=1)\n\n # make a fake image\n X = np.random.random((1, 224, 224, 3))\n\n init = tf.global_variables_initializer()\n with tf.Session() as session:\n conv_block.set_session(session)\n session.run(init)\n\n output = conv_block.predict(X)\n print(\"output.shape:\", output.shape)\n" }, { "path": "cnn_class2/tf_resnet_convblock_starter.py", "content": "# https://deeplearningcourses.com/c/advanced-computer-vision\n# https://www.udemy.com/advanced-computer-vision\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nclass ConvBlock:\n def __init__(self):\n pass\n\n def predict(self, X):\n pass\n\n\nif __name__ == '__main__':\n conv_block = ConvBlock()\n\n\n # make a fake image\n X = np.random.random((1, 224, 224, 3))\n\n init = tf.global_variables_initializer()\n with tf.Session() as session:\n conv_block.session = session\n session.run(init)\n\n output = conv_block.predict(X):\n print(\"output.shape:\", output.shape)" }, { "path": "cnn_class2/tf_resnet_first_layers.py", "content": "# https://deeplearningcourses.com/c/advanced-computer-vision\n# https://www.udemy.com/advanced-computer-vision\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\n# Let's go up to the end of the first conv block\n# to make sure everything has been loaded correctly\n# compared to keras\nimport tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport keras\n\nfrom keras.applications.resnet50 import ResNet50\nfrom keras.models import Model\nfrom keras.preprocessing import image\nfrom keras.applications.resnet50 import preprocess_input, decode_predictions\n\nfrom tf_resnet_convblock import ConvLayer, BatchNormLayer, ConvBlock\n\n\n# NOTE: dependent on your Keras version\n# this script used 2.1.1\n# [,\n# ,\n# ,\n# ,\n# ,\n# ,\n# ,\n# ,\n# ,\n# ,\n# ,\n# ,\n# ,\n# ,\n# ,\n# ,\n# ]\n\n\n# define some additional layers so they have a forward function\nclass ReLULayer:\n def forward(self, X):\n return tf.nn.relu(X)\n\n def get_params(self):\n return []\n\nclass MaxPoolLayer:\n def __init__(self, dim):\n self.dim = dim\n\n def forward(self, X):\n return tf.nn.max_pool(\n X,\n ksize=[1, self.dim, self.dim, 1],\n strides=[1, 2, 2, 1],\n padding='VALID'\n )\n\n def get_params(self):\n return []\n\nclass PartialResNet:\n def __init__(self):\n self.layers = [\n # before conv block\n ConvLayer(d=7, mi=3, mo=64, stride=2, padding='SAME'),\n BatchNormLayer(64),\n ReLULayer(),\n MaxPoolLayer(dim=3),\n # conv block\n ConvBlock(mi=64, fm_sizes=[64, 64, 256], stride=1),\n ]\n self.input_ = tf.placeholder(tf.float32, shape=(None, 224, 224, 3))\n self.output = self.forward(self.input_)\n\n def copyFromKerasLayers(self, layers):\n self.layers[0].copyFromKerasLayers(layers[1])\n self.layers[1].copyFromKerasLayers(layers[2])\n self.layers[4].copyFromKerasLayers(layers[5:])\n\n def forward(self, X):\n for layer in self.layers:\n X = layer.forward(X)\n return X\n\n def predict(self, X):\n assert(self.session is not None)\n return self.session.run(\n self.output,\n feed_dict={self.input_: X}\n )\n\n def set_session(self, session):\n self.session = session\n self.layers[0].session = session\n self.layers[1].session = session\n self.layers[4].set_session(session)\n\n def get_params(self):\n params = []\n for layer in self.layers:\n params += layer.get_params()\n\n\nif __name__ == '__main__':\n # you can also set weights to None, it doesn't matter\n resnet = ResNet50(weights='imagenet')\n\n # you can determine the correct layer\n # by looking at resnet.layers in the console\n partial_model = Model(\n inputs=resnet.input,\n outputs=resnet.layers[16].output\n )\n print(partial_model.summary())\n # for layer in partial_model.layers:\n # layer.trainable = False\n\n my_partial_resnet = PartialResNet()\n\n # make a fake image\n X = np.random.random((1, 224, 224, 3))\n\n # get keras output\n keras_output = partial_model.predict(X)\n\n # get my model output\n init = tf.variables_initializer(my_partial_resnet.get_params())\n\n # note: starting a new session messes up the Keras model\n session = keras.backend.get_session()\n my_partial_resnet.set_session(session)\n session.run(init)\n\n # first, just make sure we can get any output\n first_output = my_partial_resnet.predict(X)\n print(\"first_output.shape:\", first_output.shape)\n\n # copy params from Keras model\n my_partial_resnet.copyFromKerasLayers(partial_model.layers)\n\n # compare the 2 models\n output = my_partial_resnet.predict(X)\n diff = np.abs(output - keras_output).sum()\n if diff < 1e-10:\n print(\"Everything's great!\")\n else:\n print(\"diff = %s\" % diff)\n" }, { "path": "cnn_class2/tf_resnet_first_layers_starter.py", "content": "# https://deeplearningcourses.com/c/advanced-computer-vision\n# https://www.udemy.com/advanced-computer-vision\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\n# Let's go up to the end of the first conv block\n# to make sure everything has been loaded correctly\n# compared to keras\nimport tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport keras\n\nfrom keras.applications.resnet50 import ResNet50\nfrom keras.models import Model\nfrom keras.preprocessing import image\nfrom keras.applications.resnet50 import preprocess_input, decode_predictions\n\nfrom tf_resnet_convblock import ConvLayer, BatchNormLayer, ConvBlock\n\n\n\nclass PartialResNet:\n def __init__(self):\n # TODO\n pass\n\n def copyFromKerasLayers(self, layers):\n # TODO\n pass\n\n def predict(self, X):\n # TODO\n pass\n\n def set_session(self, session):\n self.session = session\n # TODO: finish this\n\n def get_params(self):\n params = []\n # TODO: finish this\n\n\nif __name__ == '__main__':\n # you can also set weights to None, it doesn't matter\n resnet = ResNet50(weights='imagenet')\n\n # you can determine the correct layer\n # by looking at resnet.layers in the console\n partial_model = Model(\n inputs=resnet.input,\n outputs=resnet.layers[16].output\n )\n print(partial_model.summary())\n # for layer in partial_model.layers:\n # layer.trainable = False\n\n my_partial_resnet = PartialResNet()\n\n # make a fake image\n X = np.random.random((1, 224, 224, 3))\n\n # get keras output\n keras_output = partial_model.predict(X)\n\n # get my model output\n init = tf.variables_initializer(my_partial_resnet.get_params())\n\n # note: starting a new session messes up the Keras model\n session = keras.backend.get_session()\n my_partial_resnet.set_session(session)\n session.run(init)\n\n # first, just make sure we can get any output\n first_output = my_partial_resnet.predict(X)\n print(\"first_output.shape:\", first_output.shape)\n\n # copy params from Keras model\n my_partial_resnet.copyFromKerasLayers(partial_model.layers)\n\n # compare the 2 models\n output = my_partial_resnet.predict(X)\n diff = np.abs(output - keras_output).sum()\n if diff < 1e-10:\n print(\"Everything's great!\")\n else:\n print(\"diff = %s\" % diff)\n" }, { "path": "cnn_class2/tf_resnet_identity_block.py", "content": "# https://deeplearningcourses.com/c/advanced-computer-vision\n# https://www.udemy.com/advanced-computer-vision\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom tf_resnet_convblock import ConvLayer, BatchNormLayer\n\n\nclass IdentityBlock:\n def __init__(self, mi, fm_sizes, activation=tf.nn.relu):\n # conv1, conv2, conv3\n # note: # feature maps shortcut = # feauture maps conv 3\n assert(len(fm_sizes) == 3)\n\n # note: kernel size in 2nd conv is always 3\n # so we won't bother including it as an arg\n\n self.session = None\n self.f = tf.nn.relu\n \n # init main branch\n # Conv -> BN -> F() ---> Conv -> BN -> F() ---> Conv -> BN\n self.conv1 = ConvLayer(1, mi, fm_sizes[0], 1)\n self.bn1 = BatchNormLayer(fm_sizes[0])\n self.conv2 = ConvLayer(3, fm_sizes[0], fm_sizes[1], 1, 'SAME')\n self.bn2 = BatchNormLayer(fm_sizes[1])\n self.conv3 = ConvLayer(1, fm_sizes[1], fm_sizes[2], 1)\n self.bn3 = BatchNormLayer(fm_sizes[2])\n\n # in case needed later\n self.layers = [\n self.conv1, self.bn1,\n self.conv2, self.bn2,\n self.conv3, self.bn3,\n ]\n\n # this will not be used when input passed in from\n # a previous layer\n self.input_ = tf.placeholder(tf.float32, shape=(1, 224, 224, mi))\n self.output = self.forward(self.input_)\n\n def forward(self, X):\n # main branch\n FX = self.conv1.forward(X)\n FX = self.bn1.forward(FX)\n FX = self.f(FX)\n FX = self.conv2.forward(FX)\n FX = self.bn2.forward(FX)\n FX = self.f(FX)\n FX = self.conv3.forward(FX)\n FX = self.bn3.forward(FX)\n\n return self.f(FX + X)\n\n def predict(self, X):\n assert(self.session is not None)\n return self.session.run(\n self.output,\n feed_dict={self.input_: X}\n )\n\n def set_session(self, session):\n # need to make this a session\n # so assignment happens on sublayers too\n self.session = session\n self.conv1.session = session\n self.bn1.session = session\n self.conv2.session = session\n self.bn2.session = session\n self.conv3.session = session\n self.bn3.session = session\n\n def copyFromKerasLayers(self, layers):\n assert(len(layers) == 10)\n # ,\n # ,\n # ,\n # ,\n # ,\n # ,\n # ,\n # ,\n # ,\n # \n self.conv1.copyFromKerasLayers(layers[0])\n self.bn1.copyFromKerasLayers(layers[1])\n self.conv2.copyFromKerasLayers(layers[3])\n self.bn2.copyFromKerasLayers(layers[4])\n self.conv3.copyFromKerasLayers(layers[6])\n self.bn3.copyFromKerasLayers(layers[7])\n\n def get_params(self):\n params = []\n for layer in self.layers:\n params += layer.get_params()\n return params\n\n\nif __name__ == '__main__':\n identity_block = IdentityBlock(mi=256, fm_sizes=[64, 64, 256])\n\n # make a fake image\n X = np.random.random((1, 224, 224, 256))\n\n init = tf.global_variables_initializer()\n with tf.Session() as session:\n identity_block.set_session(session)\n session.run(init)\n\n output = identity_block.predict(X)\n print(\"output.shape:\", output.shape)\n" }, { "path": "cnn_class2/tf_resnet_identity_block_starter.py", "content": "# https://deeplearningcourses.com/c/advanced-computer-vision\n# https://www.udemy.com/advanced-computer-vision\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom tf_resnet_convblock import ConvLayer, BatchNormLayer\n\n\nclass IdentityBlock:\n def __init__(self):\n # TODO\n pass\n\n\n def predict(self, X):\n # TODO\n pass\n\n\n\nif __name__ == '__main__':\n identity_block = IdentityBlock()\n\n # make a fake image\n X = np.random.random((1, 224, 224, 256))\n\n init = tf.global_variables_initializer()\n with tf.Session() as session:\n identity_block.set_session(session)\n session.run(init)\n\n output = identity_block.predict(X)\n print(\"output.shape:\", output.shape)\n" }, { "path": "cnn_class2/use_pretrained_weights_resnet.py", "content": "# https://deeplearningcourses.com/c/advanced-computer-vision\n# https://www.udemy.com/advanced-computer-vision\n\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nfrom keras.layers import Input, Lambda, Dense, Flatten\nfrom keras.models import Model\nfrom keras.applications.resnet import ResNet50, preprocess_input\n# from keras.applications.inception_v3 import InceptionV3, preprocess_input\nfrom keras.preprocessing import image\nfrom keras.preprocessing.image import ImageDataGenerator\n\nfrom sklearn.metrics import confusion_matrix\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom glob import glob\n\n\n# re-size all the images to this\nIMAGE_SIZE = [100, 100] # feel free to change depending on dataset\n\n# training config:\nepochs = 16\nbatch_size = 32\n\n# https://www.kaggle.com/paultimothymooney/blood-cells\n# train_path = '../large_files/blood_cell_images/TRAIN'\n# valid_path = '../large_files/blood_cell_images/TEST'\n\n# https://www.kaggle.com/moltean/fruits\n# train_path = '../large_files/fruits-360/Training'\n# valid_path = '../large_files/fruits-360/Validation'\ntrain_path = '../large_files/fruits-360-small/Training'\nvalid_path = '../large_files/fruits-360-small/Validation'\n\n# useful for getting number of files\nimage_files = glob(train_path + '/*/*.jp*g')\nvalid_image_files = glob(valid_path + '/*/*.jp*g')\n\n# useful for getting number of classes\nfolders = glob(train_path + '/*')\n\n\n# look at an image for fun\nplt.imshow(image.load_img(np.random.choice(image_files)))\nplt.show()\n\n\n# add preprocessing layer to the front of VGG\nres = ResNet50(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)\n\n# don't train existing weights\nfor layer in res.layers:\n layer.trainable = False\n\n# our layers - you can add more if you want\nx = Flatten()(res.output)\n# x = Dense(1000, activation='relu')(x)\nprediction = Dense(len(folders), activation='softmax')(x)\n\n\n# create a model object\nmodel = Model(inputs=res.input, outputs=prediction)\n\n# view the structure of the model\nmodel.summary()\n\n# tell the model what cost and optimization method to use\nmodel.compile(\n loss='categorical_crossentropy',\n optimizer='rmsprop',\n metrics=['accuracy']\n)\n\n\n\n# create an instance of ImageDataGenerator\ngen = ImageDataGenerator(\n rotation_range=20,\n width_shift_range=0.1,\n height_shift_range=0.1,\n shear_range=0.1,\n zoom_range=0.2,\n horizontal_flip=True,\n vertical_flip=True,\n preprocessing_function=preprocess_input\n)\n\n\n# test generator to see how it works and some other useful things\n\n# get label mapping for confusion matrix plot later\ntest_gen = gen.flow_from_directory(valid_path, target_size=IMAGE_SIZE)\nprint(test_gen.class_indices)\nlabels = [None] * len(test_gen.class_indices)\nfor k, v in test_gen.class_indices.items():\n labels[v] = k\n\n# should be a strangely colored image (due to VGG weights being BGR)\nfor x, y in test_gen:\n print(\"min:\", x[0].min(), \"max:\", x[0].max())\n plt.title(labels[np.argmax(y[0])])\n plt.imshow(x[0])\n plt.show()\n break\n\n\n# create generators\ntrain_generator = gen.flow_from_directory(\n train_path,\n target_size=IMAGE_SIZE,\n shuffle=True,\n batch_size=batch_size,\n)\nvalid_generator = gen.flow_from_directory(\n valid_path,\n target_size=IMAGE_SIZE,\n shuffle=True,\n batch_size=batch_size,\n)\n\n\n# fit the model\nr = model.fit(\n train_generator,\n validation_data=valid_generator,\n epochs=epochs,\n steps_per_epoch=len(image_files) // batch_size,\n validation_steps=len(valid_image_files) // batch_size,\n)\n\n\n\ndef get_confusion_matrix(data_path, N):\n # we need to see the data in the same order\n # for both predictions and targets\n print(\"Generating confusion matrix\", N)\n predictions = []\n targets = []\n i = 0\n for x, y in gen.flow_from_directory(data_path, target_size=IMAGE_SIZE, shuffle=False, batch_size=batch_size * 2):\n i += 1\n if i % 50 == 0:\n print(i)\n p = model.predict(x)\n p = np.argmax(p, axis=1)\n y = np.argmax(y, axis=1)\n predictions = np.concatenate((predictions, p))\n targets = np.concatenate((targets, y))\n if len(targets) >= N:\n break\n\n cm = confusion_matrix(targets, predictions)\n return cm\n\n\ncm = get_confusion_matrix(train_path, len(image_files))\nprint(cm)\nvalid_cm = get_confusion_matrix(valid_path, len(valid_image_files))\nprint(valid_cm)\n\n\n# plot some data\n\n# loss\nplt.plot(r.history['loss'], label='train loss')\nplt.plot(r.history['val_loss'], label='val loss')\nplt.legend()\nplt.show()\n\n# accuracies\nplt.plot(r.history['accuracy'], label='train acc')\nplt.plot(r.history['val_accuracy'], label='val acc')\nplt.legend()\nplt.show()\n\nfrom util import plot_confusion_matrix\nplot_confusion_matrix(cm, labels, title='Train confusion matrix')\nplot_confusion_matrix(valid_cm, labels, title='Validation confusion matrix')" }, { "path": "cnn_class2/use_pretrained_weights_vgg.py", "content": "# https://deeplearningcourses.com/c/advanced-computer-vision\n# https://www.udemy.com/advanced-computer-vision\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nfrom keras.layers import Input, Lambda, Dense, Flatten\nfrom keras.models import Model\nfrom keras.applications.vgg16 import VGG16\nfrom keras.applications.vgg16 import preprocess_input\nfrom keras.preprocessing import image\nfrom keras.preprocessing.image import ImageDataGenerator\n\nfrom sklearn.metrics import confusion_matrix\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom glob import glob\n\n\n# re-size all the images to this\nIMAGE_SIZE = [100, 100] # feel free to change depending on dataset\n\n# training config:\nepochs = 5\nbatch_size = 32\n\n# https://www.kaggle.com/paultimothymooney/blood-cells\n# train_path = '../large_files/blood_cell_images/TRAIN'\n# valid_path = '../large_files/blood_cell_images/TEST'\n\n# https://www.kaggle.com/moltean/fruits\ntrain_path = '../large_files/fruits-360/Training'\nvalid_path = '../large_files/fruits-360/Validation'\n# train_path = '../large_files/fruits-360-small/Training'\n# valid_path = '../large_files/fruits-360-small/Validation'\n\n# useful for getting number of files\nimage_files = glob(train_path + '/*/*.jp*g')\nvalid_image_files = glob(valid_path + '/*/*.jp*g')\n\n# useful for getting number of classes\nfolders = glob(train_path + '/*')\n\n\n# look at an image for fun\nplt.imshow(image.img_to_array(image.load_img(np.random.choice(image_files))).astype('uint8'))\nplt.show()\n\n\n# add preprocessing layer to the front of VGG\nvgg = VGG16(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)\n\n# don't train existing weights\nfor layer in vgg.layers:\n layer.trainable = False\n\n# our layers - you can add more if you want\nx = Flatten()(vgg.output)\n# x = Dense(1000, activation='relu')(x)\nprediction = Dense(len(folders), activation='softmax')(x)\n\n\n# create a model object\nmodel = Model(inputs=vgg.input, outputs=prediction)\n\n# view the structure of the model\nmodel.summary()\n\n# tell the model what cost and optimization method to use\nmodel.compile(\n loss='categorical_crossentropy',\n optimizer='rmsprop',\n metrics=['accuracy']\n)\n\n\n# create an instance of ImageDataGenerator\ngen = ImageDataGenerator(\n rotation_range=20,\n width_shift_range=0.1,\n height_shift_range=0.1,\n shear_range=0.1,\n zoom_range=0.2,\n horizontal_flip=True,\n vertical_flip=True,\n preprocessing_function=preprocess_input\n)\n\n\n# test generator to see how it works and some other useful things\n\n# get label mapping for confusion matrix plot later\ntest_gen = gen.flow_from_directory(valid_path, target_size=IMAGE_SIZE)\nprint(test_gen.class_indices)\nlabels = [None] * len(test_gen.class_indices)\nfor k, v in test_gen.class_indices.items():\n labels[v] = k\n\n# should be a strangely colored image (due to VGG weights being BGR)\nfor x, y in test_gen:\n print(\"min:\", x[0].min(), \"max:\", x[0].max())\n plt.title(labels[np.argmax(y[0])])\n plt.imshow(x[0])\n plt.show()\n break\n\n\n# create generators\ntrain_generator = gen.flow_from_directory(\n train_path,\n target_size=IMAGE_SIZE,\n shuffle=True,\n batch_size=batch_size,\n)\nvalid_generator = gen.flow_from_directory(\n valid_path,\n target_size=IMAGE_SIZE,\n shuffle=True,\n batch_size=batch_size,\n)\n\n\n# fit the model\nr = model.fit(\n train_generator,\n validation_data=valid_generator,\n epochs=epochs,\n steps_per_epoch=len(image_files) // batch_size,\n validation_steps=len(valid_image_files) // batch_size,\n)\n\n\n\ndef get_confusion_matrix(data_path, N):\n # we need to see the data in the same order\n # for both predictions and targets\n print(\"Generating confusion matrix\", N)\n predictions = []\n targets = []\n i = 0\n for x, y in gen.flow_from_directory(data_path, target_size=IMAGE_SIZE, shuffle=False, batch_size=batch_size * 2):\n i += 1\n if i % 50 == 0:\n print(i)\n p = model.predict(x)\n p = np.argmax(p, axis=1)\n y = np.argmax(y, axis=1)\n predictions = np.concatenate((predictions, p))\n targets = np.concatenate((targets, y))\n if len(targets) >= N:\n break\n\n cm = confusion_matrix(targets, predictions)\n return cm\n\n\ncm = get_confusion_matrix(train_path, len(image_files))\nprint(cm)\nvalid_cm = get_confusion_matrix(valid_path, len(valid_image_files))\nprint(valid_cm)\n\n\n# plot some data\n\n# loss\nplt.plot(r.history['loss'], label='train loss')\nplt.plot(r.history['val_loss'], label='val loss')\nplt.legend()\nplt.show()\n\n# accuracies\nplt.plot(r.history['accuracy'], label='train acc')\nplt.plot(r.history['val_accuracy'], label='val acc')\nplt.legend()\nplt.show()\n\nfrom util import plot_confusion_matrix\nplot_confusion_matrix(cm, labels, title='Train confusion matrix')\nplot_confusion_matrix(valid_cm, labels, title='Validation confusion matrix')" }, { "path": "cnn_class2/util.py", "content": "# https://deeplearningcourses.com/advanced-computer-vision\n# https://www.udemy.com/advanced-computer-vision\n\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport itertools\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n \"\"\"\n This function prints and plots the confusion matrix.\n Normalization can be applied by setting `normalize=True`.\n \"\"\"\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.show()\n\n\ndef y2indicator(Y):\n K = len(set(Y))\n N = len(Y)\n I = np.empty((N, K))\n I[np.arange(N), Y] = 1\n return I" }, { "path": "data_csv/X.txt", "content": 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1200\nvolkswagen model 111\nplymouth cricket\ntoyota corona hardtop\ndodge colt hardtop\nvolkswagen type 3\nchevrolet vega\nford pinto runabout\nchevrolet impala\npontiac catalina\nplymouth fury iii\nford galaxie 500\namc ambassador sst\nmercury marquis\nbuick lesabre custom\noldsmobile delta 88 royale\nchrysler newport royal\nmazda rx2 coupe\namc matador (sw)\nchevrolet chevelle concours (sw)\nford gran torino (sw)\nplymouth satellite custom (sw)\nvolvo 145e (sw)\nvolkswagen 411 (sw)\npeugeot 504 (sw)\nrenault 12 (sw)\nford pinto (sw)\ndatsun 510 (sw)\ntoyouta corona mark ii (sw)\ndodge colt (sw)\ntoyota corolla 1600 (sw)\nbuick century 350\namc matador\nchevrolet malibu\nford gran torino\ndodge coronet custom\nmercury marquis brougham\nchevrolet caprice classic\nford ltd\nplymouth fury gran sedan\nchrysler new yorker brougham\nbuick electra 225 custom\namc ambassador brougham\nplymouth valiant\nchevrolet nova custom\namc hornet\nford maverick\nplymouth duster\nvolkswagen super beetle\nchevrolet impala\nford country\nplymouth custom suburb\noldsmobile vista cruiser\namc gremlin\ntoyota carina\nchevrolet vega\ndatsun 610\nmaxda rx3\nford pinto\nmercury capri v6\nfiat 124 sport coupe\nchevrolet monte carlo s\npontiac grand prix\nfiat 128\nopel manta\naudi 100ls\nvolvo 144ea\ndodge dart custom\nsaab 99le\ntoyota mark ii\noldsmobile omega\nplymouth duster\namc hornet\nchevrolet nova\ndatsun b210\nford pinto\ntoyota corolla 1200\nchevrolet vega\nchevrolet chevelle malibu classic\namc matador\nplymouth satellite sebring\nford gran torino\nbuick century luxus (sw)\ndodge coronet custom (sw)\nford gran torino (sw)\namc matador (sw)\naudi fox\nvolkswagen dasher\nopel manta\ntoyota corona\ndatsun 710\ndodge colt\nfiat 128\nfiat 124 tc\nhonda civic\nsubaru\nfiat x1.9\nplymouth valiant custom\nchevrolet nova\nmercury monarch\nford maverick\npontiac catalina\nchevrolet bel air\nplymouth grand fury\nford ltd\nbuick century\nchevroelt chevelle malibu\namc matador\nplymouth fury\nbuick skyhawk\nchevrolet monza 2+2\nford mustang ii\ntoyota corolla\nford pinto\namc gremlin\npontiac astro\ntoyota corona\nvolkswagen dasher\ndatsun 710\nford pinto\nvolkswagen rabbit\namc pacer\naudi 100ls\npeugeot 504\nvolvo 244dl\nsaab 99le\nhonda civic cvcc\nfiat 131\nopel 1900\ncapri ii\ndodge colt\nrenault 12tl\nchevrolet chevelle malibu classic\ndodge coronet brougham\namc matador\nford gran torino\nplymouth valiant\nchevrolet nova\nford maverick\namc hornet\nchevrolet chevette\nchevrolet woody\nvw rabbit\nhonda civic\ndodge aspen se\nford granada ghia\npontiac ventura sj\namc pacer d/l\nvolkswagen rabbit\ndatsun b-210\ntoyota corolla\nford pinto\nvolvo 245\nplymouth volare premier v8\npeugeot 504\ntoyota mark ii\nmercedes-benz 280s\ncadillac seville\nchevy c10\nford f108\ndodge d100\nhonda accord cvcc\nbuick opel isuzu deluxe\nrenault 5 gtl\nplymouth arrow gs\ndatsun f-10 hatchback\nchevrolet caprice classic\noldsmobile cutlass supreme\ndodge monaco brougham\nmercury cougar brougham\nchevrolet concours\nbuick skylark\nplymouth volare custom\nford granada\npontiac grand prix lj\nchevrolet monte carlo landau\nchrysler cordoba\nford thunderbird\nvolkswagen rabbit custom\npontiac sunbird coupe\ntoyota corolla liftback\nford mustang ii 2+2\nchevrolet chevette\ndodge colt m/m\nsubaru dl\nvolkswagen dasher\ndatsun 810\nbmw 320i\nmazda rx-4\nvolkswagen rabbit custom diesel\nford fiesta\nmazda glc deluxe\ndatsun b210 gx\nhonda civic cvcc\noldsmobile cutlass salon brougham\ndodge diplomat\nmercury monarch ghia\npontiac phoenix lj\nchevrolet malibu\nford fairmont (auto)\nford fairmont (man)\nplymouth volare\namc concord\nbuick century special\nmercury zephyr\ndodge aspen\namc concord d/l\nchevrolet monte carlo landau\nbuick regal sport coupe (turbo)\nford futura\ndodge magnum xe\nchevrolet chevette\ntoyota corona\ndatsun 510\ndodge omni\ntoyota celica gt liftback\nplymouth sapporo\noldsmobile starfire sx\ndatsun 200-sx\naudi 5000\nvolvo 264gl\nsaab 99gle\npeugeot 604sl\nvolkswagen scirocco\nhonda accord lx\npontiac lemans v6\nmercury zephyr 6\nford fairmont 4\namc concord dl 6\ndodge aspen 6\nchevrolet caprice classic\nford ltd landau\nmercury grand marquis\ndodge st. regis\nbuick estate wagon (sw)\nford country squire (sw)\nchevrolet malibu classic (sw)\nchrysler lebaron town @ country (sw)\nvw rabbit custom\nmaxda glc deluxe\ndodge colt hatchback custom\namc spirit dl\nmercedes benz 300d\ncadillac eldorado\npeugeot 504\noldsmobile cutlass salon brougham\nplymouth horizon\nplymouth horizon tc3\ndatsun 210\nfiat strada custom\nbuick skylark limited\nchevrolet citation\noldsmobile omega brougham\npontiac phoenix\nvw rabbit\ntoyota corolla tercel\nchevrolet chevette\ndatsun 310\nchevrolet citation\nford fairmont\namc concord\ndodge aspen\naudi 4000\ntoyota corona liftback\nmazda 626\ndatsun 510 hatchback\ntoyota corolla\nmazda glc\ndodge colt\ndatsun 210\nvw rabbit c (diesel)\nvw dasher (diesel)\naudi 5000s (diesel)\nmercedes-benz 240d\nhonda civic 1500 gl\nsubaru dl\nvokswagen rabbit\ndatsun 280-zx\nmazda rx-7 gs\ntriumph tr7 coupe\nhonda accord\nplymouth reliant\nbuick skylark\ndodge aries wagon (sw)\nchevrolet citation\nplymouth reliant\ntoyota starlet\nplymouth champ\nhonda civic 1300\nsubaru\ndatsun 210 mpg\ntoyota tercel\nmazda glc 4\nplymouth horizon 4\nford escort 4w\nford escort 2h\nvolkswagen jetta\nhonda prelude\ntoyota corolla\ndatsun 200sx\nmazda 626\npeugeot 505s turbo diesel\nvolvo diesel\ntoyota cressida\ndatsun 810 maxima\nbuick century\noldsmobile cutlass ls\nford granada gl\nchrysler lebaron salon\nchevrolet cavalier\nchevrolet cavalier wagon\nchevrolet cavalier 2-door\npontiac j2000 se hatchback\ndodge aries se\npontiac phoenix\nford fairmont futura\nvolkswagen rabbit l\nmazda glc custom l\nmazda glc custom\nplymouth horizon miser\nmercury lynx l\nnissan stanza xe\nhonda accord\ntoyota corolla\nhonda civic\nhonda civic (auto)\ndatsun 310 gx\nbuick century limited\noldsmobile cutlass ciera (diesel)\nchrysler lebaron medallion\nford granada l\ntoyota celica gt\ndodge charger 2.2\nchevrolet camaro\nford mustang gl\nvw pickup\ndodge rampage\nford ranger\nchevy s-10\n\n" }, { "path": "data_csv/readme.txt", "content": "___Data information___\n\n- The output we wish to predict is in \"y\":\n\ty: miles per gallon\n\n- Each row in \"X\" contains seven numbers. These numbers are scaled and should be used for your experiments. The original numbers are in \"X_orig\". The 7 columns of \"X\" correspond to:\n\tx1: intercept term\n\tx2: number of cylinders\n\tx3: displacement\n\tx4: horsepower\n\tx5: weight\n\tx6: acceleration\n\tx7: model year\n\n- \"legend\" contains car name for each entry of y and row of X\n- \"X_orig\" contains the original, unscaled numbers for those interested. This matrix is not necessary for completing the homework.\n" }, { "path": "data_csv/y.txt", "content": "18\n15\n18\n16\n17\n15\n14\n14\n14\n15\n15\n14\n15\n14\n24\n22\n18\n21\n27\n26\n25\n24\n25\n26\n21\n10\n10\n11\n9\n27\n28\n25\n19\n16\n17\n19\n18\n14\n14\n14\n14\n12\n13\n13\n18\n22\n19\n18\n23\n28\n30\n30\n31\n35\n27\n26\n24\n25\n23\n20\n21\n13\n14\n15\n14\n17\n11\n13\n12\n13\n19\n15\n13\n13\n14\n18\n22\n21\n26\n22\n28\n23\n28\n27\n13\n14\n13\n14\n15\n12\n13\n13\n14\n13\n12\n13\n18\n16\n18\n18\n23\n26\n11\n12\n13\n12\n18\n20\n21\n22\n18\n19\n21\n26\n15\n16\n29\n24\n20\n19\n15\n24\n20\n11\n20\n19\n15\n31\n26\n32\n25\n16\n16\n18\n16\n13\n14\n14\n14\n29\n26\n26\n31\n32\n28\n24\n26\n24\n26\n31\n19\n18\n15\n15\n16\n15\n16\n14\n17\n16\n15\n18\n21\n20\n13\n29\n23\n20\n23\n24\n25\n24\n18\n29\n19\n23\n23\n22\n25\n33\n28\n25\n25\n26\n27\n17.5\n16\n15.5\n14.5\n22\n22\n24\n22.5\n29\n24.5\n29\n33\n20\n18\n18.5\n17.5\n29.5\n32\n28\n26.5\n20\n13\n19\n19\n16.5\n16.5\n13\n13\n13\n31.5\n30\n36\n25.5\n33.5\n17.5\n17\n15.5\n15\n17.5\n20.5\n19\n18.5\n16\n15.5\n15.5\n16\n29\n24.5\n26\n25.5\n30.5\n33.5\n30\n30.5\n22\n21.5\n21.5\n43.1\n36.1\n32.8\n39.4\n36.1\n19.9\n19.4\n20.2\n19.2\n20.5\n20.2\n25.1\n20.5\n19.4\n20.6\n20.8\n18.6\n18.1\n19.2\n17.7\n18.1\n17.5\n30\n27.5\n27.2\n30.9\n21.1\n23.2\n23.8\n23.9\n20.3\n17\n21.6\n16.2\n31.5\n29.5\n21.5\n19.8\n22.3\n20.2\n20.6\n17\n17.6\n16.5\n18.2\n16.9\n15.5\n19.2\n18.5\n31.9\n34.1\n35.7\n27.4\n25.4\n23\n27.2\n23.9\n34.2\n34.5\n31.8\n37.3\n28.4\n28.8\n26.8\n33.5\n41.5\n38.1\n32.1\n37.2\n28\n26.4\n24.3\n19.1\n34.3\n29.8\n31.3\n37\n32.2\n46.6\n27.9\n40.8\n44.3\n43.4\n36.4\n30\n44.6\n33.8\n29.8\n32.7\n23.7\n35\n32.4\n27.2\n26.6\n25.8\n23.5\n30\n39.1\n39\n35.1\n32.3\n37\n37.7\n34.1\n34.7\n34.4\n29.9\n33\n33.7\n32.4\n32.9\n31.6\n28.1\n30.7\n25.4\n24.2\n22.4\n26.6\n20.2\n17.6\n28\n27\n34\n31\n29\n27\n24\n36\n37\n31\n38\n36\n36\n36\n34\n38\n32\n38\n25\n38\n26\n22\n32\n36\n27\n27\n44\n32\n28\n31\n" }, { "path": "financial_engineering/go_here_instead.txt", "content": "https://github.com/lazyprogrammer/financial_engineering\n" }, { "path": "hmm_class/__init__.py", "content": "" }, { "path": "hmm_class/coin_data.txt", "content": "THTHTHHHTTTTTHTHTHTHTTHHHHTTTH\nHHHHHHHHHTTHHTHHTTTTTHTTTTHTHT\nTTHHTTHTTTHTTTHHTTTTHTHHTHTHTT\nTHHHTHHHHTHHHTTHTTTTHHTHHTHTHH\nHTTHHTTTTTHTHTHTHTHTTTTHHTTHHH\nTHHTTHHHTHHHHHTTHTHTTHHHHHTHTT\nTHTTHTTHTHHHHTHHTHTTTHTTTTHHTT\nHTTHTHHTTHTTTHHHTHTHHHHHTHTHHH\nTTHTTTTTTHTHTHHTTHTHTHTHTTHTTT\nTHHTHHHHTHTHTTHHTTTHTHHHTHHTHT\nTTHHTTHTTHHTHTHTHTHTHTTHTHHTTH\nHTHTHTHTHHTTHTTHTHTHHTHTHTHTTT\nTTTHHTTTHHHTTHTTHTHTHTHTHHTHHT\nTTHHHTHHHHHHTHHTHHHHHHTHTHTTTT\nTTHTTTTTTTTTTHTHHHTHHHHHHHTHHH\nHHHTTHTTTTTHTTTHTHHTTHTTHTTHTH\nHHTHTHHHHHTTHTTHTTHHHHTTTHHHTT\nTHTTTHHTHTTHTHTHHTTHHTHHTHTHTH\nTHTTTHTHHTTTTHTTHTHHHHTTHHTTTT\nTTTTTTHTTTTHTTHTTHHTHHTHHTHTHT\nHHHTTTTTTHTHTHHHHTTTHHTTTHHTHT\nHTHTTTTTTHTTTHHHTTTTTHHTHTHHTT\nTTTHTHHHTTTHTHHHHHTHTTHTHHTTTH\nHHHHTTHHTTTTTTTTHHTHTHTTHTTTHH\nHTHHHTTHTTHTHTHTTTHTTHHTTTHTHT\nTTTTTTHTHTHTHHTTHTHTTHHTTHTHHT\nHTTTHTHHTHHHHTHTHTHHHTHHHTTTHH\nTHHTTTTHTHTHTTTHTTTHTTHTTTTHTH\nTTTTTTTHTTTHHTHTTHHTHHTTHTTTHT\nHTHTHTTTTTHHTTHTHHHTHTTHHTHHHT\nHTTTHHTTHTTTTTTTTTTHHHHHTTHTHH\nHTHTTHTHTTHTTHHTTHTTTTTTTTHTHH\nTHTTHTHTTHTTHHTTTHHHTHHHTTHTHT\nTTHTTHTTTHTHHTTHHHHTHTTHTHTTTT\nTHTTTTTHTTHHHTHHHHHTTHTHTHTHTH\nTTHTHTHHHHTTTHHTHHHHHHTTHTHHHT\nTTHTHTTTTTTTHTHTTHHTHTHHHTHHTT\nHHTHTTTTHTHTTTHHHTHHHTHTHTTTHT\nHTTTTTTHTTHTTTHHTTHHTTTTHTHHTH\nHHTHHHTHHTHHHHTTTHHHTHHTHTTTHT\nHTTTHTHHHTTHTHHHTHHTTHTTTTHHTT\nTTHHTHTTTTHTTTHTTHTHTTHTHTTHTT\nHHHHTTTHHTTTTHTTTTTHHHHTHHHTTT\nTHTTTTHTHHHTHTHTHTHTHTHTTTHTHT\nHHTTHHTHTHTTHTHTTTTTTHHTHTTTTT\nHTTTTTHHTHTTHHTTHTHTTTHHHHTHTH\nHTHTHTHHTHHTHHHTTTTHHTHTHTHHTT\nHTTHTHHHHTTHHHHTHTTHTTHHTTTHHT\nTTTHTHTTHTTHTTTHHTTTTHTHHTTHTH\nTHTHTHTTHTHTTHHTHTTTHHTTTTTTHT\n" }, { "path": "hmm_class/edgar_allan_poe.txt", "content": "LO! Death hath rear'd himself a throne\nIn a strange city, all alone,\nFar down within the dim west\nWhere the good, and the bad, and the worst, and the best,\nHave gone to their eternal rest.\n \nThere shrines, and palaces, and towers\nAre not like any thing of ours\nOh no! O no! ours never loom\nTo heaven with that ungodly gloom!\nTime-eaten towers that tremble not!\nResemble nothing that is ours.\nAround, by lifting winds forgot,\nResignedly beneath the sky\nThe melancholy waters lie.\n \nNo holy rays from heaven come down\nOn the long night-time of that town,\nBut light from out the lurid sea\nStreams up the turrets silently\nUp thrones up long-forgotten bowers\nOf scultur'd ivy and stone flowers\nUp domes up spires up kingly halls\nUp fanes up Babylon-like walls\nUp many a melancholy shrine\nWhose entablatures intertwine\nThe mask the viol and the vine.\n \nThere open temples open graves\nAre on a level with the waves\nBut not the riches there that lie\nIn each idol's diamond eye,\nNot the gaily-jewell'd dead\nTempt the waters from their bed:\nFor no ripples curl, alas!\nAlong that wilderness of glass\nNo swellings hint that winds may be\nUpon a far-off happier sea:\nSo blend the turrets and shadows there\nThat all seem pendulous in air,\nWhile from the high towers of the town\nDeath looks gigantically down.\n \nBut lo! a stir is in the air!\nThe wave there is a ripple there!\nAs if the towers had thrown aside,\nIn slightly sinking, the dull tide\nAs if the turret-tops had given\nA vacuum in the filmy heaven.\nThe waves have now a redder glow\nThe very hours are breathing low\nAnd when, amid no earthly moans,\nDown, down, that town shall settle hence,\nAll Hades, from a thousand thrones,\nShall do it reverence,\nAnd Death to some more happy clime\nShall give his undivided time.\n\nThe skies they were ashen and sober;\nThe leaves they were crisped and sere -\nThe leaves they were withering and sere;\nIt was night in the lonesome October\nOf my most immemorial year;\nIt was hard by the dim lake of Auber,\nIn the misty mid region of Weir -\nIt was down by the dank tarn of Auber,\nIn the ghoul-haunted woodland of Weir.\n\nHere once, through an alley Titanic,\nOf cypress, I roamed with my Soul -\nOf cypress, with Psyche, my Soul.\nThese were days when my heart was volcanic\nAs the scoriac rivers that roll -\nAs the lavas that restlessly roll\nTheir sulphurous currents down Yaanek\nIn the ultimate climes of the pole -\nThat groan as they roll down Mount Yaanek\nIn the realms of the boreal pole.\n\nOur talk had been serious and sober,\nBut our thoughts they were palsied and sere -\nOur memories were treacherous and sere, -\nFor we knew not the month was October,\nAnd we marked not the night of the year -\n(Ah, night of all nights in the year!)\nWe noted not the dim lake of Auber -\n(Though once we had journey down here),\nRemembered not the dank tarn of Auber,\nNor the ghoul-haunted woodland of Weir.\n\nAnd now, as the night was senescent,\nAnd star-dials pointed to morn -\nAs the star-dials hinted of morn -\nAt the end of our path a liquescent\nAnd nebulous lustre was born,\nOut of which a miraculous crescent\nArose with a duplicate horn -\nAstarte's bediamonded crescent\nDistinct with its duplicate horn.\n\nAnd I said - \"She is warmer than Dian:\nShe rolls through an ether of sighs -\nShe revels in a region of sighs:\nShe has seen that the tears are not dry on\nThese cheeks, where the worm never dies,\nAnd has come past the stars of the Lion\nTo point us the path to the skies -\nTo the Lethean peace of the skies -\nCome up, in despite of the Lion,\nTo shine on us with her bright eyes -\nCome up through the lair of the Lion,\nWith love in her luminous eyes.\"\n\nBut Psyche, uplifting her finger,\nSaid - \"Sadly this star I mistrust -\nHer pallor I strangely mistrust: -\nOh, hasten! - oh, let us not linger!\nOh, fly! - let us fly! - for we must.\"\nIn terror she spoke, letting sink her\nWings until they trailed in the dust -\nIn agony sobbed, letting sink her\nPlumes till they trailed in the dust -\nTill they sorrowfully trailed in the dust.\n\nI replied - \"This is nothing but dreaming:\nLet us on by this tremulous light!\nLet us bathe in this crystalline light!\nIts Sybilic splendor is beaming\nWith Hope and in Beauty to-night! -\nSee! - it flickers up the sky through the night!\nAh, we safely may trust to its gleaming,\nAnd be sure it will lead us aright -\nWe safely may trust to a gleaming,\nThat cannot but guide us aright,\nSince it flickers up to Heaven through the night.\"\n\nThus I pacified Psyche and kissed her,\nAnd tempted her out of her gloom -\nAnd conquered her scruples and gloom;\nAnd we passed to the end of the vista,\nBut were stopped by the door of a tomb -\nBy the door of a legended tomb;\nAnd I said - \"What is written, sweet sister,\nOn the door of this legended tomb?\"\nShe replied - \"Ulalume - Ulalume -\n‘Tis the vault of thy lost Ulalume!\"\n\nThen my heart it grew ashen and sober\nAs the leaves that were crisped and sere -\nAs the leaves that were withering and sere,\nAnd I cried - \"It was surely October\nOn this very night of last year\nThat I journeyed - I journeyed down here -\nThat I brought a dread burden down here!\nOn this night of all nights in the year,\nAh, what demon has tempted me here?\nWell I know, now, this dim lake of Auber -\nThis misty mid region of Weir -\nWell I know, now, this dank tarn of Auber, -\nThis ghoul-haunted woodland of Weir.\"\n\nWreathed in myrtle, my sword I'll conceal\nLike those champions devoted and brave,\nWhen they plunged in the tyrant their steel,\nAnd to Athens deliverance gave. \n\nBeloved heroes! your deathless souls roam\nIn the joy breathing isles of the blest;\nWhere the mighty of old have their home -\nWhere Achilles and Diomed rest. \n\nIn fresh myrtle my blade I'll entwine,\nLike Harmodious, the gallant and good,\nWhen he made at the tutelar shrine\nA libation of Tyranny's blood. \n\nYe deliverers of Athens from shame!\nYe avengers of Liberty's wrongs!\nEndless ages shall cherish your fame\nEmbalmed in their echoing songs!\n\nOf all who hail thy presence as the morning-\nOf all to whom thine absence is the night-\nThe blotting utterly from out high heaven\nThe sacred sun- of all who, weeping, bless thee\nHourly for hope- for life- ah! above all,\nFor the resurrection of deep-buried faith\nIn Truth- in Virtue- in Humanity-\nOf all who, on Despair's unhallowed bed\nLying down to die, have suddenly arisen\nAt thy soft-murmured words, 'Let there be light!'\nAt the soft-murmured words that were fulfilled\nIn the seraphic glancing of thine eyes-\nOf all who owe thee most- whose gratitude\nNearest resembles worship- oh, remember\nThe truest- the most fervently devoted,\nAnd think that these weak lines are written by him-\nBy him who, as he pens them, thrills to think\nHis spirit is communing with an angel's. \n\nThou wouldst be loved?- then let thy heart\nFrom its present pathway part not!\nBeing everything which now thou art,\nBe nothing which thou art not.\nSo with the world thy gentle ways,\nThy grace, thy more than beauty,\nShall be an endless theme of praise,\nAnd love- a simple duty. \n\nWhen from your gems of thought I turn \nTo those pure orbs, your heart to learn, \nI scarce know which to prize most high \nThe bright i-dea, or the bright dear-eye.\n\nOf all who hail thy presence as the morning-\nOf all to whom thine absence is the night-\nThe blotting utterly from out high heaven\nThe sacred sun- of all who, weeping, bless thee\nHourly for hope- for life- ah! above all,\nFor the resurrection of deep-buried faith\nIn Truth- in Virtue- in Humanity-\nOf all who, on Despair's unhallowed bed\nLying down to die, have suddenly arisen\nAt thy soft-murmured words, \"Let there be light!\"\nAt the soft-murmured words that were fulfilled\nIn the seraphic glancing of thine eyes-\nOf all who owe thee most- whose gratitude\nNearest resembles worship- oh, remember\nThe truest- the most fervently devoted,\nAnd think that these weak lines are written by him-\nBy him who, as he pens them, thrills to think\nHis spirit is communing with an angel's.\n\nFair isle, that from the fairest of all flowers,\nThy gentlest of all gentle names dost take!\nHow many memories of what radiant hours\nAt sight of thee and thine at once awake!\nHow many scenes of what departed bliss!\nHow many thoughts of what entombed hopes!\nHow many visions of a maiden that is\nNo more- no more upon thy verdant slopes!\nNo more! alas, that magical sad sound\nTransforming all! Thy charms shall please no more-\nThy memory no more! Accursed ground\nHenceforth I hold thy flower-enameled shore,\nO hyacinthine isle! O purple Zante!\n\"Isola d'oro! Fior di Levante!\"\n\nO! I care not that my earthly lot\nHath little of Earth in it,\nThat years of love have been forgot\nIn the fever of a minute:\n\nI heed not that the desolate\nAre happier, sweet, than I,\nBut that you meddle with my fate\nWho am a passer by.\n\nIt is not that my founts of bliss\nAre gushing- strange! with tears-\nOr that the thrill of a single kiss\nHath palsied many years-\n\n'Tis not that the flowers of twenty springs\nWhich have wither'd as they rose\nLie dead on my heart-strings\nWith the weight of an age of snows.\n\nNot that the grass- O! may it thrive!\nOn my grave is growing or grown-\nBut that, while I am dead yet alive\nI cannot be, lady, alone.\n\nSancta Maria! turn thine eyes -\nUpon the sinner's sacrifice,\nOf fervent prayer and humble love,\nFrom thy holy throne above. \nAt morn - at noon - at twilight dim -\nMaria! thou hast heard my hymn!\nIn joy and wo - in good and ill -\nMother of God, be with me still! \n\nWhen the Hours flew brightly by,\nAnd not a cloud obscured the sky,\nMy soul, lest it should truant be,\nThy grace did guide to thine and thee; \n\nNow, when storms of Fate o'ercast\nDarkly my Present and my Past,\nLet my Future radiant shine\nWith sweet hopes of thee and thine!\n\nThe bells! ah, the bells! \nThe little silver bells! \nHow fairy-like a melody there floats \nFrom their throats. \nFrom their merry little throats \nFrom the silver, tinkling throats \nOf the bells, bells, bells \nOf the bells! \n\nThe bells! ah, the bells! \nThe heavy iron bells! \nHow horrible a monody there floats \nFrom their throats \nFrom their deep-toned throats \nFrom their melancholy throats! \nHow I shudder at the notes \nOf the bells, bells, bells \nOf the bells!\n\nHow often we forget all time, when lone\nAdmiring Nature's universal throne;\nHer woods- her wilds- her mountains- the intense\nReply of HERS to OUR intelligence!\n\nIn youth have I known one with whom the Earth\nIn secret communing held- as he with it,\nIn daylight, and in beauty from his birth:\nWhose fervid, flickering torch of life was lit\nFrom the sun and stars, whence he had drawn forth\nA passionate light- such for his spirit was fit-\nAnd yet that spirit knew not, in the hour\nOf its own fervor what had o'er it power.\n\nPerhaps it may be that my mind is wrought\nTo a fever by the moonbeam that hangs o'er,\nBut I will half believe that wild light fraught\nWith more of sovereignty than ancient lore\nHath ever told- or is it of a thought\nThe unembodied essence, and no more,\nThat with a quickening spell doth o'er us pass\nAs dew of the night-time o'er the summer grass?\n\nDoth o'er us pass, when, as th' expanding eye\nTo the loved object- so the tear to the lid\nWill start, which lately slept in apathy?\nAnd yet it need not be- (that object) hid\nFrom us in life- but common- which doth lie\nEach hour before us- but then only, bid\nWith a strange sound, as of a harp-string broken,\nTo awake us- 'Tis a symbol and a token\n\nOf what in other worlds shall be- and given\nIn beauty by our God, to those alone\nWho otherwise would fall from life and Heaven\nDrawn by their heart's passion, and that tone,\nThat high tone of the spirit which hath striven,\nTho' not with Faith- with godliness- whose throne\nWith desperate energy 't hath beaten down;\nWearing its own deep feeling as a crown.\n\nNot long ago, the writer of these lines,\nIn the mad pride of intellectuality,\nMaintained \"the power of words\"- denied that ever\nA thought arose within the human brain\nBeyond the utterance of the human tongue:\nAnd now, as if in mockery of that boast,\nTwo words- two foreign soft dissyllables-\nItalian tones, made only to be murmured\nBy angels dreaming in the moonlit \"dew\nThat hangs like chains of pearl on Hermon hill,\"\nHave stirred from out the abysses of his heart,\nUnthought-like thoughts that are the souls of thought,\nRicher, far wilder, far diviner visions\nThan even seraph harper, Israfel,\n(Who has \"the sweetest voice of all God's creatures,\")\nCould hope to utter. And I! my spells are broken.\nThe pen falls powerless from my shivering hand.\nWith thy dear name as text, though bidden by thee,\nI cannot write- I cannot speak or think-\nAlas, I cannot feel; for 'tis not feeling,\nThis standing motionless upon the golden\nThreshold of the wide-open gate of dreams.\nGazing, entranced, adown the gorgeous vista,\nAnd thrilling as I see, upon the right,\nUpon the left, and all the way along,\nAmid empurpled vapors, far away\nTo where the prospect terminates- thee only.\n\nBeloved! amid the earnest woes\nThat crowd around my earthly path-\n(Drear path, alas! where grows\nNot even one lonely rose)-\nMy soul at least a solace hath\nIn dreams of thee, and therein knows\nAn Eden of bland repose.\n\nAnd thus thy memory is to me\nLike some enchanted far-off isle\nIn some tumultuous sea-\nSome ocean throbbing far and free\nWith storms- but where meanwhile\nSerenest skies continually\nJust o'er that one bright island smile.\n\nI saw thee once- once only- years ago: \nI must not say how many- but not many. \nIt was a July midnight; and from out \nA full-orbed moon, that, like thine own soul, soaring, \nSought a precipitate pathway up through heaven, \nThere fell a silvery-silken veil of light, \nWith quietude, and sultriness, and slumber, \nUpon the upturned faces of a thousand \nRoses that grew in an enchanted garden, \nWhere no wind dared to stir, unless on tiptoe- \nFell on the upturn'd faces of these roses \nThat gave out, in return for the love-light, \nTheir odorous souls in an ecstatic death- \nFell on the upturn'd faces of these roses \nThat smiled and died in this parterre, enchanted \nBy thee, and by the poetry of thy presence. \nClad all in white, upon a violet bank \nI saw thee half reclining; while the moon \nFell on the upturn'd faces of the roses, \nAnd on thine own, upturn'd- alas, in sorrow! \n\nWas it not Fate, that, on this July midnight- \nWas it not Fate, (whose name is also Sorrow,) \nThat bade me pause before that garden-gate, \nTo breathe the incense of those slumbering roses? \nNo footstep stirred: the hated world an slept, \nSave only thee and me. (Oh, Heaven!- oh, God! \nHow my heart beats in coupling those two words!) \nSave only thee and me. I paused- I looked- \nAnd in an instant all things disappeared. \n(Ah, bear in mind this garden was enchanted!) \n\nThe pearly lustre of the moon went out: \nThe mossy banks and the meandering paths, \nThe happy flowers and the repining trees, \nWere seen no more: the very roses' odors \nDied in the arms of the adoring airs. \nAll- all expired save thee- save less than thou: \nSave only the divine light in thine eyes- \nSave but the soul in thine uplifted eyes. \nI saw but them- they were the world to me! \nI saw but them- saw only them for hours, \nSaw only them until the moon went down. \nWhat wild heart-histories seemed to he enwritten \nUpon those crystalline, celestial spheres! \nHow dark a woe, yet how sublime a hope! \nHow silently serene a sea of pride! \nHow daring an ambition; yet how deep- \nHow fathomless a capacity for love! \n\nBut now, at length, dear Dian sank from sight, \nInto a western couch of thunder-cloud; \nAnd thou, a ghost, amid the entombing trees \nDidst glide away. Only thine eyes remained; \nThey would not go- they never yet have gone; \nLighting my lonely pathway home that night, \nThey have not left me (as my hopes have) since; \nThey follow me- they lead me through the years. \nThey are my ministers- yet I their slave. \nTheir office is to illumine and enkindle- \nMy duty, to be saved by their bright light, \nAnd purified in their electric fire, \nAnd sanctified in their elysian fire. \nThey fill my soul with Beauty (which is Hope), \nAnd are far up in Heaven- the stars I kneel to \nIn the sad, silent watches of my night; \nWhile even in the meridian glare of day \nI see them still- two sweetly scintillant \nVenuses, unextinguished by the sun!\n\nThe bowers whereat, in dreams, I see\nThe wantonest singing birds,\nAre lips- and all thy melody\nOf lip-begotten words-\n\nThine eyes, in Heaven of heart enshrined,\nThen desolately fall,\nO God! on my funereal mind\nLike starlight on a pall-\n\nThy heart- thy heart!- I wake and sigh,\nAnd sleep to dream till day\nOf the truth that gold can never buy-\nOf the baubles that it may.\n\nIn Heaven a spirit doth dwell\n\"Whose heart-strings are a lute\";\nNone sing so wildly well\nAs the angel Israfel,\nAnd the giddy stars (so legends tell),\nCeasing their hymns, attend the spell\nOf his voice, all mute.\n\nTottering above\nIn her highest noon,\nThe enamored moon\nBlushes with love,\nWhile, to listen, the red levin\n(With the rapid Pleiads, even,\nWhich were seven,)\nPauses in Heaven.\n\nAnd they say (the starry choir\nAnd the other listening things)\nThat Israfeli's fire\nIs owing to that lyre\nBy which he sits and sings-\nThe trembling living wire\nOf those unusual strings.\n\nBut the skies that angel trod,\nWhere deep thoughts are a duty-\nWhere Love's a grown-up God-\nWhere the Houri glances are\nImbued with all the beauty\nWhich we worship in a star.\n\nTherefore thou art not wrong,\nIsrafeli, who despisest\nAn unimpassioned song;\nTo thee the laurels belong,\nBest bard, because the wisest!\nMerrily live, and long!\n\nThe ecstasies above\nWith thy burning measures suit-\nThy grief, thy joy, thy hate, thy love,\nWith the fervor of thy lute-\nWell may the stars be mute!\n\nYes, Heaven is thine; but this\nIs a world of sweets and sours;\nOur flowers are merely- flowers,\nAnd the shadow of thy perfect bliss\nIs the sunshine of ours.\n\nIf I could dwell\nWhere Israfel\nHath dwelt, and he where I,\nHe might not sing so wildly well\nA mortal melody,\nWhile a bolder note than this might swell\nFrom my lyre within the sky.\n\nScience! true daughter of Old Time thou art!\nWho alterest all things with thy peering eyes.\nWhy preyest thou thus upon the poet's heart,\nVulture, whose wings are dull realities?\nHow should he love thee? or how deem thee wise,\nWho wouldst not leave him in his wandering\nTo seek for treasure in the jewelled skies,\nAlbeit he soared with an undaunted wing?\nHast thou not dragged Diana from her car?\nAnd driven the Hamadryad from the wood\nTo seek a shelter in some happier star?\nHast thou not torn the Naiad from her flood,\nThe Elfin from the green grass, and from me\nThe summer dream beneath the tamarind tree?\n\nI'll tell you a plan for gaining wealth,\nBetter than banking, trade or leases \nTake a bank note and fold it up, \nAnd then you will find your money in creases! \nThis wonderful plan, without danger or loss, \nKeeps your cash in your hands, where nothing can trouble it; \nAnd every time that you fold it across, \n'Tis as plain as the light of the day that you double it!\n\nThe skies they were ashen and sober;\nThe leaves they were crisped and sere-\nThe leaves they were withering and sere;\nIt was night in the lonesome October\nOf my most immemorial year;\nIt was hard by the dim lake of Auber,\nIn the misty mid region of Weir-\nIt was down by the dank tarn of Auber,\nIn the ghoul-haunted woodland of Weir.\n\nHere once, through an alley Titanic,\nOf cypress, I roamed with my Soul-\nOf cypress, with Psyche, my Soul.\nThere were days when my heart was volcanic\nAs the scoriac rivers that roll-\nAs the lavas that restlessly roll\nTheir sulphurous currents down Yaanek\nIn the ultimate climes of the pole-\nThat groan as they roll down Mount Yaanek\nIn the realms of the boreal pole.\n\nOur talk had been serious and sober,\nBut our thoughts they were palsied and sere-\nOur memories were treacherous and sere-\nFor we knew not the month was October,\nAnd we marked not the night of the year-\n(Ah, night of all nights in the year!)\nWe noted not the dim lake of Auber-\n(Though once we had journeyed down here),\nRemembered not the dank tarn of Auber,\nNor the ghoul-haunted woodland of Weir.\n\nAnd now, as the night was senescent,\nAnd star-dials pointed to morn-\nAs the star-dials hinted of morn-\nAt the end of our path a liquescent\nAnd nebulous lustre was born,\nOut of which a miraculous crescent\nArose with a duplicate horn-\nAstarte's bediamonded crescent\nDistinct with its duplicate horn.\n\nAnd I said- 'She is warmer than Dian:\nShe rolls through an ether of sighs-\nShe revels in a region of sighs:\nShe has seen that the tears are not dry on\nThese cheeks, where the worm never dies,\nAnd has come past the stars of the Lion,\nTo point us the path to the skies-\nTo the Lethean peace of the skies-\nCome up, in despite of the Lion,\nTo shine on us with her bright eyes-\nCome up through the lair of the Lion,\nWith love in her luminous eyes.'\n\nBut Psyche, uplifting her finger,\nSaid- 'Sadly this star I mistrust-\nHer pallor I strangely mistrust:-\nOh, hasten!- oh, let us not linger!\nOh, fly!- let us fly!- for we must.'\nIn terror she spoke, letting sink her\nWings until they trailed in the dust-\nIn agony sobbed, letting sink her\nPlumes till they trailed in the dust-\nTill they sorrowfully trailed in the dust.\n\nI replied- 'This is nothing but dreaming:\nLet us on by this tremulous light!\nLet us bathe in this crystalline light!\nIts Sybilic splendor is beaming\nWith Hope and in Beauty to-night:-\nSee!- it flickers up the sky through the night!\nAh, we safely may trust to its gleaming,\nAnd be sure it will lead us aright-\nWe safely may trust to a gleaming\nThat cannot but guide us aright,\nSince it flickers up to Heaven through the night.'\n\nThus I pacified Psyche and kissed her,\nAnd tempted her out of her gloom-\nAnd conquered her scruples and gloom;\nAnd we passed to the end of the vista,\nBut were stopped by the door of a tomb-\nBy the door of a legended tomb;\nAnd I said- 'What is written, sweet sister,\nOn the door of this legended tomb?'\nShe replied- 'Ulalume- Ulalume-\n'Tis the vault of thy lost Ulalume!'\n\nThen my heart it grew ashen and sober\nAs the leaves that were crisped and sere-\nAs the leaves that were withering and sere-\nAnd I cried- 'It was surely October\nOn this very night of last year\nThat I journeyed- I journeyed down here-\nThat I brought a dread burden down here-\nOn this night of all nights in the year,\nAh, what demon has tempted me here?\nWell I know, now, this dim lake of Auber-\nThis misty mid region of Weir-\nWell I know, now, this dank tarn of Auber,\nThis ghoul-haunted woodland of Weir.' \n\nHow often we forget all time, when lone \nAdmiring Nature's universal throne; \nHer woods - her winds - her mountains - the intense \nReply of Hers to Our intelligence! \n\nIn youth I have known one with whom the Earth \nIn secret communing held - as he with it, \nIn daylight, and in beauty, from his birth: \nWhose fervid, flickering torch of life was lit \nFrom the sun and stars, whence he had drawn forth \nA passionate light - such for his spirit was fit - \nAnd yet that spirit knew - not in the hour \nOf its own fervour - what had o'er it power. \n\nPerhaps it may be that my mind is wrought \nTo a fever by the moonbeam that hangs o'er, \nBut I will half believe that wild light fraught \nWith more of sovereignty than ancient lore \nHath ever told - or is it of a thought \nThe unembodied essence, and no more \nThat with a quickening spell doth o'er us pass \nAs dew of the night time, o'er the summer grass? \n\nDoth o'er us pass, when as th' expanding eye \nTo the loved object - so the tear to the lid \nWill start, which lately slept in apathy? \nAnd yet it need not be - (that object) hid \nFrom us in life - but common - which doth lie \nEach hour before us - but then only bid \nWith a strange sound, as of a harpstring broken \nT' awake us - 'Tis a symbol and a token - \n\nOf what in other worlds shall be - and given \nIn beauty by our God, to those alone \nWho otherwise would fall from life and Heaven \nDrawn by their heart's passion, and that tone, \nThat high tone of the spirit which hath striven \nThough not with Faith - with godliness - whose throne \nWith desperate energy 't hath beaten down; \nWearing its own deep feeling as a crown.\n\n'Tis said that when \nThe hands of men \nTamed this primeval wood, \nAnd hoary trees with groans of woe, \nLike warriors by an unknown foe, \nWere in their strength subdued, \nThe virgin Earth Gave instant birth \nTo springs that ne'er did flow \nThat in the sun Did rivulets run, \nAnd all around rare flowers did blow \nThe wild rose pale Perfumed the gale \nAnd the queenly lily adown the dale \n(Whom the sun and the dew \nAnd the winds did woo), \nWith the gourd and the grape luxuriant grew. \n\nSo when in tears \nThe love of years \nIs wasted like the snow, \nAnd the fine fibrils of its life \nBy the rude wrong of instant strife \nAre broken at a blow \nWithin the heart \nDo springs upstart \nOf which it doth now know, \nAnd strange, sweet dreams, \nLike silent streams \nThat from new fountains overflow, \nWith the earlier tide \nOf rivers glide \nDeep in the heart whose hope has died-- \nQuenching the fires its ashes hide,-- \nIts ashes, whence will spring and grow \nSweet flowers, ere long, \nThe rare and radiant flowers of song!\n\nIn the greenest of our valleys,\nBy good angels tenanted,\nOnce fair and stately palace --\nRadiant palace --reared its head.\nIn the monarch Thought's dominion --\nIt stood there!\nNever seraph spread a pinion\nOver fabric half so fair.\n\nBanners yellow, glorious, golden,\nOn its roof did float and flow;\n(This --all this --was in the olden\nTime long ago)\nAnd every gentle air that dallied,\nIn that sweet day,\nAlong the ramparts plumed and pallid,\nA winged odour went away.\n\nWanderers in that happy valley\nThrough two luminous windows saw\nSpirits moving musically\nTo a lute's well-tuned law,\nRound about a throne, where sitting\n(Porphyrogene!)\nIn state his glory well befitting,\nThe ruler of the realm was seen.\n\nAnd all with pearl and ruby glowing\nWas the fair palace door,\nThrough which came flowing, flowing, flowing\nAnd sparkling evermore,\nA troop of Echoes whose sweet duty\nWas but to sing,\nIn voices of surpassing beauty,\nThe wit and wisdom of their king.\n\nBut evil things, in robes of sorrow,\nAssailed the monarch's high estate;\n(Ah, let us mourn, for never morrow\nShall dawn upon him, desolate!)\nAnd, round about his home, the glory\nThat blushed and bloomed\nIs but a dim-remembered story\nOf the old time entombed.\n\nAnd travellers now within that valley,\nThrough the red-litten windows, see\nVast forms that move fantastically\nTo a discordant melody;\nWhile, like a rapid ghastly river,\nThrough the pale door,\nA hideous throng rush out forever,\nAnd laugh --but smile no more." }, { "path": "hmm_class/extra_reading.txt", "content": "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition\nhttps://www.ece.ucsb.edu/Faculty/Rabiner/ece259/Reprints/tutorial%20on%20hmm%20and%20applications.pdf\n\nSome Mathematics for HMM by Dawei Shen\nhttps://pdfs.semanticscholar.org/4ce1/9ab0e07da9aa10be1c336400c8e4d8fc36c5.pdf\n\nA Revealing Introduction to Hidden Markov Models\nhttps://www.cs.sjsu.edu/~stamp/RUA/HMM.pdf" }, { "path": "hmm_class/frost.py", "content": "# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python\n# http://lazyprogrammer.me\n# Model and generate Robert Frost poems.\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\n\nimport numpy as np\nimport string\nimport sys\n\n\ninitial = {} # start of a phrase\nsecond_word = {}\ntransitions = {}\n\n# unfortunately these work different ways\ndef remove_punctuation_2(s):\n return s.translate(None, string.punctuation)\n\ndef remove_punctuation_3(s):\n return s.translate(str.maketrans('','',string.punctuation))\n\nif sys.version.startswith('2'):\n remove_punctuation = remove_punctuation_2\nelse:\n remove_punctuation = remove_punctuation_3\n\n\ndef add2dict(d, k, v):\n if k not in d:\n d[k] = []\n d[k].append(v)\n\nfor line in open('robert_frost.txt'):\n tokens = remove_punctuation(line.rstrip().lower()).split()\n\n T = len(tokens)\n for i in range(T):\n t = tokens[i]\n if i == 0:\n # measure the distribution of the first word\n initial[t] = initial.get(t, 0.) + 1\n else:\n t_1 = tokens[i-1]\n if i == T - 1:\n # measure probability of ending the line\n add2dict(transitions, (t_1, t), 'END')\n if i == 1:\n # measure distribution of second word\n # given only first word\n add2dict(second_word, t_1, t)\n else:\n t_2 = tokens[i-2]\n add2dict(transitions, (t_2, t_1), t)\n\n\n# normalize the distributions\ninitial_total = sum(initial.values())\nfor t, c in iteritems(initial):\n initial[t] = c / initial_total\n\ndef list2pdict(ts):\n # turn each list of possibilities into a dictionary of probabilities\n d = {}\n n = len(ts)\n for t in ts:\n d[t] = d.get(t, 0.) + 1\n for t, c in iteritems(d):\n d[t] = c / n\n return d\n\nfor t_1, ts in iteritems(second_word):\n # replace list with dictionary of probabilities\n second_word[t_1] = list2pdict(ts)\n\nfor k, ts in iteritems(transitions):\n transitions[k] = list2pdict(ts)\n\n# generate 4 lines\ndef sample_word(d):\n # print \"d:\", d\n p0 = np.random.random()\n # print \"p0:\", p0\n cumulative = 0\n for t, p in iteritems(d):\n cumulative += p\n if p0 < cumulative:\n return t\n assert(False) # should never get here\n\ndef generate():\n for i in range(4):\n sentence =[]\n\n # initial word\n w0 = sample_word(initial)\n sentence.append(w0)\n\n # sample second word\n w1 = sample_word(second_word[w0])\n sentence.append(w1)\n\n # second-order transitions until END\n while True:\n w2 = sample_word(transitions[(w0, w1)])\n if w2 == 'END':\n break\n sentence.append(w2)\n w0 = w1\n w1 = w2\n print(' '.join(sentence))\n\ngenerate()\n\n# exercise: make them rhyme!\n" }, { "path": "hmm_class/generate_c.py", "content": "# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python\n# http://lazyprogrammer.me\n# Generate continuous data from an HMM.\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef simple_init():\n M = 1\n K = 1\n D = 1\n pi = np.array([1])\n A = np.array([[1]])\n R = np.array([[1]])\n mu = np.array([[[0]]])\n sigma = np.array([[[[1]]]])\n return M, K, D, pi, A, R, mu, sigma\n\ndef big_init():\n M = 5\n K = 3\n D = 2\n\n pi = np.array([1, 0, 0, 0, 0]) # initial state distribution\n\n A = np.array([\n [0.9, 0.025, 0.025, 0.025, 0.025],\n [0.025, 0.9, 0.025, 0.025, 0.025],\n [0.025, 0.025, 0.9, 0.025, 0.025],\n [0.025, 0.025, 0.025, 0.9, 0.025],\n [0.025, 0.025, 0.025, 0.025, 0.9],\n ]) # state transition matrix - likes to stay where it is\n\n R = np.ones((M, K)) / K # mixture proportions\n\n mu = np.array([\n [[0, 0], [1, 1], [2, 2]],\n [[5, 5], [6, 6], [7, 7]],\n [[10, 10], [11, 11], [12, 12]],\n [[15, 15], [16, 16], [17, 17]],\n [[20, 20], [21, 21], [22, 22]],\n ]) # M x K x D\n\n sigma = np.zeros((M, K, D, D))\n for m in range(M):\n for k in range(K):\n sigma[m,k] = np.eye(D)\n return M, K, D, pi, A, R, mu, sigma\n\ndef get_signals(N=20, T=100, init=big_init):\n M, K, D, pi, A, R, mu, sigma = init()\n X = []\n for n in range(N):\n x = np.zeros((T, D))\n s = 0 # initial state is 0 since pi[0] = 1\n r = np.random.choice(K, p=R[s]) # choose mixture\n x[0] = np.random.multivariate_normal(mu[s][r], sigma[s][r])\n for t in range(1, T):\n s = np.random.choice(M, p=A[s]) # choose state\n r = np.random.choice(K, p=R[s]) # choose mixture\n x[t] = np.random.multivariate_normal(mu[s][r], sigma[s][r])\n X.append(x)\n return X\n\nif __name__ == '__main__':\n T = 500\n x = get_signals(1, T)[0]\n axis = range(T)\n plt.plot(axis, x[:, 0], axis, x[:, 1])\n plt.show()\n\n" }, { "path": "hmm_class/generate_ht.py", "content": "# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python\n# http://lazyprogrammer.me\n# Generate discrete data from an HMM.\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\n\n\nsymbol_map = ['H', 'T']\npi = np.array([0.5, 0.5])\nA = np.array([[0.1, 0.9], [0.8, 0.2]])\nB = np.array([[0.6, 0.4], [0.3, 0.7]])\nM, V = B.shape\n\n\ndef generate_sequence(N):\n s = np.random.choice(xrange(M), p=pi) # initial state\n x = np.random.choice(xrange(V), p=B[s]) # initial observation\n sequence = [x]\n for n in range(N-1):\n s = np.random.choice(xrange(M), p=A[s]) # next state\n x = np.random.choice(xrange(V), p=B[s]) # next observation\n sequence.append(x)\n return sequence\n\n\ndef main():\n with open('coin_data.txt', 'w') as f:\n for n in range(50):\n sequence = generate_sequence(30)\n sequence = ''.join(symbol_map[s] for s in sequence)\n print sequence\n f.write(\"%s\\n\" % sequence)\n\n\nif __name__ == '__main__':\n main()" }, { "path": "hmm_class/hmm_classifier.py", "content": "# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python\n# http://lazyprogrammer.me\n# Demonstrate how HMMs can be used for classification.\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\n\nimport string\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom hmmd_theano2 import HMM\nfrom sklearn.utils import shuffle\nfrom nltk import pos_tag, word_tokenize\n\nclass HMMClassifier:\n def __init__(self):\n pass\n\n def fit(self, X, Y, V):\n K = len(set(Y)) # number of classes - assume 0..K-1\n N = len(Y)\n self.models = []\n self.priors = []\n for k in range(K):\n # gather all the training data for this class\n thisX = [x for x, y in zip(X, Y) if y == k]\n C = len(thisX)\n self.priors.append(np.log(C) - np.log(N))\n\n hmm = HMM(5)\n hmm.fit(thisX, V=V, print_period=1, learning_rate=1e-2, max_iter=80)\n self.models.append(hmm)\n\n def score(self, X, Y):\n N = len(Y)\n correct = 0\n for x, y in zip(X, Y):\n lls = [hmm.log_likelihood(x) + prior for hmm, prior in zip(self.models, self.priors)]\n p = np.argmax(lls)\n if p == y:\n correct += 1\n return float(correct) / N\n\n\n# def remove_punctuation(s):\n# return s.translate(None, string.punctuation)\n\ndef get_tags(s):\n tuples = pos_tag(word_tokenize(s))\n return [y for x, y in tuples]\n\ndef get_data():\n word2idx = {}\n current_idx = 0\n X = []\n Y = []\n for fn, label in zip(('robert_frost.txt', 'edgar_allan_poe.txt'), (0, 1)):\n count = 0\n for line in open(fn):\n line = line.rstrip()\n if line:\n print(line)\n # tokens = remove_punctuation(line.lower()).split()\n tokens = get_tags(line)\n if len(tokens) > 1:\n # scan doesn't work nice here, technically could fix...\n for token in tokens:\n if token not in word2idx:\n word2idx[token] = current_idx\n current_idx += 1\n sequence = np.array([word2idx[w] for w in tokens])\n X.append(sequence)\n Y.append(label)\n count += 1\n print(count)\n if count >= 50:\n break\n print(\"Vocabulary:\", word2idx.keys())\n return X, Y, current_idx\n \n\ndef main():\n X, Y, V = get_data()\n print(\"len(X):\", len(X))\n print(\"Vocabulary size:\", V)\n X, Y = shuffle(X, Y)\n N = 20 # number to test\n Xtrain, Ytrain = X[:-N], Y[:-N]\n Xtest, Ytest = X[-N:], Y[-N:]\n\n model = HMMClassifier()\n model.fit(Xtrain, Ytrain, V)\n print(\"Score:\", model.score(Xtest, Ytest))\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "hmm_class/hmmc.py", "content": "# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python\n# http://lazyprogrammer.me\n# Continuous-observation HMM with no scaling, but allowing multiple observations\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport wave\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom generate_c import get_signals, big_init, simple_init\nfrom scipy.stats import multivariate_normal as mvn\n\ndef random_normalized(d1, d2):\n x = np.random.random((d1, d2))\n return x / x.sum(axis=1, keepdims=True)\n\n\nclass HMM:\n def __init__(self, M, K):\n self.M = M # number of hidden states\n self.K = K # number of Gaussians\n \n def fit(self, X, max_iter=30, eps=1e0):\n # train the HMM model using the Baum-Welch algorithm\n # a specific instance of the expectation-maximization algorithm\n\n N = len(X)\n D = X[0].shape[1] # assume each x is organized (T, D)\n\n self.pi = np.ones(self.M) / self.M # initial state distribution\n self.A = random_normalized(self.M, self.M) # state transition matrix\n self.R = np.ones((self.M, self.K)) / self.K # mixture proportions\n print(\"initial A:\", self.A)\n print(\"initial R:\", self.R)\n self.mu = np.zeros((self.M, self.K, D))\n for i in range(self.M):\n for k in range(self.K):\n random_idx = np.random.choice(N)\n x = X[random_idx]\n random_time_idx = np.random.choice(len(x))\n self.mu[i,k] = x[random_time_idx]\n self.sigma = np.zeros((self.M, self.K, D, D))\n for j in range(self.M):\n for k in range(self.K):\n self.sigma[j,k] = np.eye(D)\n\n costs = []\n for it in range(max_iter):\n if it % 1 == 0:\n print(\"it:\", it)\n alphas = []\n betas = []\n gammas = []\n Bs = []\n # components = []\n P = np.zeros(N)\n\n for n in range(N):\n x = X[n]\n T = len(x)\n\n # calculate B so we can lookup when updating alpha and beta\n B = np.zeros((self.M, T))\n component = np.zeros((self.M, self.K, T)) # we'll need these later\n for j in range(self.M):\n for t in range(T):\n for k in range(self.K):\n p = self.R[j,k] * mvn.pdf(x[t], self.mu[j,k], self.sigma[j,k])\n component[j,k,t] = p\n B[j,t] += p\n Bs.append(B)\n\n alpha = np.zeros((T, self.M))\n alpha[0] = self.pi*B[:,0]\n for t in range(1, T):\n alpha[t] = alpha[t-1].dot(self.A) * B[:,t]\n P[n] = alpha[-1].sum()\n assert(P[n] <= 1)\n alphas.append(alpha)\n\n beta = np.zeros((T, self.M))\n beta[-1] = 1\n for t in range(T - 2, -1, -1):\n beta[t] = self.A.dot(B[:,t+1] * beta[t+1])\n betas.append(beta)\n\n # update for Gaussians\n gamma = np.zeros((T, self.M, self.K))\n for t in range(T):\n alphabeta = (alphas[n][t,:] * betas[n][t,:]).sum()\n for j in range(self.M):\n factor = alphas[n][t,j] * betas[n][t,j] / alphabeta\n # mixture_j = component[j,:,t].sum()\n for k in range(self.K):\n gamma[t,j,k] = factor * component[j,k,t] / B[j,t]\n gammas.append(gamma)\n\n cost = np.log(P).sum()\n costs.append(cost)\n\n # now re-estimate pi, A, R, mu, sigma\n self.pi = np.sum((alphas[n][0] * betas[n][0])/P[n] for n in range(N)) / N\n\n a_den = np.zeros((self.M, 1))\n a_num = 0\n r_num = np.zeros((self.M, self.K))\n r_den = np.zeros(self.M)\n mu_num = np.zeros((self.M, self.K, D))\n sigma_num = np.zeros((self.M, self.K, D, D))\n for n in range(N):\n x = X[n]\n T = len(x)\n B = Bs[n]\n gamma = gammas[n]\n\n # denominator for A\n a_den += (alphas[n][:-1] * betas[n][:-1]).sum(axis=0, keepdims=True).T / P[n]\n\n # numerator for A\n a_num_n = np.zeros((self.M, self.M))\n for i in range(self.M):\n for j in range(self.M):\n for t in range(T-1):\n a_num_n[i,j] += alphas[n][t,i] * self.A[i,j] * B[j,t+1] * betas[n][t+1,j]\n a_num += a_num_n / P[n]\n\n\n # update mixture components\n r_num_n = np.zeros((self.M, self.K))\n r_den_n = np.zeros(self.M)\n for j in range(self.M):\n for k in range(self.K):\n for t in range(T):\n r_num_n[j,k] += gamma[t,j,k]\n r_den_n[j] += gamma[t,j,k]\n r_num += r_num_n / P[n]\n r_den += r_den_n / P[n]\n\n mu_num_n = np.zeros((self.M, self.K, D))\n sigma_num_n = np.zeros((self.M, self.K, D, D))\n for j in range(self.M):\n for k in range(self.K):\n for t in range(T):\n # update means\n mu_num_n[j,k] += gamma[t,j,k] * x[t]\n\n # update covariances\n sigma_num_n[j,k] += gamma[t,j,k] * np.outer(x[t] - self.mu[j,k], x[t] - self.mu[j,k])\n mu_num += mu_num_n / P[n]\n sigma_num += sigma_num_n / P[n]\n\n self.A = a_num / a_den\n assert(np.all(self.A <= 1))\n\n # update R, mu, sigma\n for j in range(self.M):\n for k in range(self.K):\n self.R[j,k] = r_num[j,k] / r_den[j]\n self.mu[j,k] = mu_num[j,k] / r_num[j,k]\n self.sigma[j,k] = sigma_num[j,k] / r_num[j,k]\n\n print(\"A:\", self.A)\n print(\"mu:\", self.mu)\n print(\"sigma:\", self.sigma)\n print(\"R:\", self.R)\n print(\"pi:\", self.pi)\n\n plt.plot(costs)\n plt.show()\n\n def likelihood(self, x):\n # returns log P(x | model)\n # using the forward part of the forward-backward algorithm\n T = len(x)\n alpha = np.zeros((T, self.M))\n\n B = np.zeros((self.M, T))\n for j in range(self.M):\n for t in range(T):\n for k in range(self.K):\n p = self.R[j,k] * mvn.pdf(x[t], self.mu[j,k], self.sigma[j,k])\n B[j,t] += p\n\n alpha[0] = self.pi*B[:,0]\n for t in range(1, T):\n alpha[t] = alpha[t-1].dot(self.A) * B[:,t]\n return alpha[-1].sum()\n\n def get_state_sequence(self, x):\n # returns the most likely state sequence given observed sequence x\n # using the Viterbi algorithm\n T = len(x)\n\n # make the emission matrix B\n B = np.zeros((self.M, T))\n for j in range(self.M):\n for t in range(T):\n for k in range(self.K):\n p = self.R[j,k] * mvn.pdf(x[t], self.mu[j,k], self.sigma[j,k])\n B[j,t] += p\n\n # perform Viterbi as usual\n delta = np.zeros((T, self.M))\n psi = np.zeros((T, self.M))\n delta[0] = self.pi*B[:,0]\n for t in range(1, T):\n for j in range(self.M):\n delta[t,j] = np.max(delta[t-1]*self.A[:,j]) * B[j,t]\n psi[t,j] = np.argmax(delta[t-1]*self.A[:,j])\n\n # backtrack\n states = np.zeros(T, dtype=np.int32)\n states[T-1] = np.argmax(delta[T-1])\n for t in range(T-2, -1, -1):\n states[t] = psi[t+1, states[t+1]]\n return states\n\n def likelihood_multi(self, X):\n return np.array([self.likelihood(x) for x in X])\n\n def log_likelihood_multi(self, X):\n return np.log(self.likelihood_multi(X))\n\n def set(self, pi, A, R, mu, sigma):\n self.pi = pi\n self.A = A\n self.R = R\n self.mu = mu\n self.sigma = sigma\n M, K = R.shape\n self.M = M\n self.K = K\n\n\ndef real_signal():\n spf = wave.open('helloworld.wav', 'r')\n\n #Extract Raw Audio from Wav File\n # If you right-click on the file and go to \"Get Info\", you can see:\n # sampling rate = 16000 Hz\n # bits per sample = 16\n # The first is quantization in time\n # The second is quantization in amplitude\n # We also do this for images!\n # 2^16 = 65536 is how many different sound levels we have\n signal = spf.readframes(-1)\n signal = np.fromstring(signal, 'Int16')\n T = len(signal)\n\n hmm = HMM(10)\n hmm.fit(signal.reshape(1, T))\n\n\ndef fake_signal(init=simple_init):\n signals = get_signals(N=10, T=10, init=init)\n # for signal in signals:\n # for d in xrange(signal.shape[1]):\n # plt.plot(signal[:,d])\n # plt.show()\n\n hmm = HMM(2, 2)\n hmm.fit(signals)\n L = hmm.log_likelihood_multi(signals).sum()\n print(\"LL for fitted params:\", L)\n\n # test in actual params\n _, _, _, pi, A, R, mu, sigma = init()\n hmm.set(pi, A, R, mu, sigma)\n L = hmm.log_likelihood_multi(signals).sum()\n print(\"LL for actual params:\", L)\n\n # print most likely state sequence\n print(\"Most likely state sequence for initial observation:\")\n print(hmm.get_state_sequence(signals[0]))\n\nif __name__ == '__main__':\n # real_signal() # will break\n fake_signal(init=simple_init)\n # fake_signal(init=big_init) # will break\n\n" }, { "path": "hmm_class/hmmc_concat.py", "content": "# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python\n# http://lazyprogrammer.me\n# Continuous-observation HMM with no scaling, but treats multiple observations\n# as a concatenated single observation\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport wave\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom generate_c import get_signals, big_init, simple_init\nfrom scipy.stats import multivariate_normal as mvn\n\ndef random_normalized(d1, d2):\n x = np.random.random((d1, d2))\n return x / x.sum(axis=1, keepdims=True)\n\n\nclass HMM:\n def __init__(self, M, K):\n self.M = M # number of hidden states\n self.K = K # number of Gaussians\n \n def fit(self, X, max_iter=30, eps=1e0):\n # train the HMM model using the Baum-Welch algorithm\n # a specific instance of the expectation-maximization algorithm\n\n N = len(X)\n D = X[0].shape[1] # assume each x is organized (T, D)\n\n self.pi = np.ones(self.M) / self.M # initial state distribution\n self.A = random_normalized(self.M, self.M) # state transition matrix\n self.R = np.ones((self.M, self.K)) / self.K # mixture proportions\n print(\"initial A:\", self.A)\n print(\"initial R:\", self.R)\n self.mu = np.zeros((self.M, self.K, D))\n for i in range(self.M):\n for k in range(self.K):\n random_idx = np.random.choice(N)\n x = X[random_idx]\n random_time_idx = np.random.choice(len(x))\n self.mu[i,k] = x[random_time_idx]\n self.sigma = np.zeros((self.M, self.K, D, D))\n for j in range(self.M):\n for k in range(self.K):\n self.sigma[j,k] = np.eye(D)\n\n costs = []\n for it in range(max_iter):\n if it % 1 == 0:\n print(\"it:\", it)\n alphas = []\n betas = []\n gammas = []\n Bs = []\n # components = []\n P = np.zeros(N)\n\n for n in range(N):\n x = X[n]\n T = len(x)\n\n # calculate B so we can lookup when updating alpha and beta\n B = np.zeros((self.M, T))\n component = np.zeros((self.M, self.K, T)) # we'll need these later\n for j in range(self.M):\n for t in range(T):\n for k in range(self.K):\n p = self.R[j,k] * mvn.pdf(x[t], self.mu[j,k], self.sigma[j,k])\n component[j,k,t] = p\n B[j,t] += p\n Bs.append(B)\n\n alpha = np.zeros((T, self.M))\n alpha[0] = self.pi*B[:,0]\n for t in range(1, T):\n alpha[t] = alpha[t-1].dot(self.A) * B[:,t]\n P[n] = alpha[-1].sum()\n assert(P[n] <= 1)\n alphas.append(alpha)\n\n beta = np.zeros((T, self.M))\n beta[-1] = 1\n for t in range(T - 2, -1, -1):\n beta[t] = self.A.dot(B[:,t+1] * beta[t+1])\n betas.append(beta)\n\n # update for Gaussians\n gamma = np.zeros((T, self.M, self.K))\n for t in range(T):\n alphabeta = (alphas[n][t,:] * betas[n][t,:]).sum()\n for j in range(self.M):\n factor = alphas[n][t,j] * betas[n][t,j] / alphabeta\n # mixture_j = component[j,:,t].sum()\n for k in range(self.K):\n gamma[t,j,k] = factor * component[j,k,t] / B[j,t]\n gammas.append(gamma)\n\n cost = np.log(P).sum()\n costs.append(cost)\n\n # now re-estimate pi, A, R, mu, sigma\n self.pi = np.sum((alphas[n][0] * betas[n][0])/P[n] for n in range(N)) / N\n\n a_den = np.zeros((self.M, 1))\n a_num = 0\n r_num = np.zeros((self.M, self.K))\n r_den = np.zeros(self.M)\n mu_num = np.zeros((self.M, self.K, D))\n sigma_num = np.zeros((self.M, self.K, D, D))\n for n in range(N):\n x = X[n]\n T = len(x)\n B = Bs[n]\n gamma = gammas[n]\n\n # denominator for A\n a_den += (alphas[n][:-1] * betas[n][:-1]).sum(axis=0, keepdims=True).T / P[n]\n\n # numerator for A\n a_num_n = np.zeros((self.M, self.M))\n for i in range(self.M):\n for j in range(self.M):\n for t in range(T-1):\n a_num_n[i,j] += alphas[n][t,i] * self.A[i,j] * B[j,t+1] * betas[n][t+1,j]\n a_num += a_num_n / P[n]\n\n\n # update mixture components\n r_num_n = np.zeros((self.M, self.K))\n r_den_n = np.zeros(self.M)\n for j in range(self.M):\n for k in range(self.K):\n for t in range(T):\n r_num_n[j,k] += gamma[t,j,k]\n r_den_n[j] += gamma[t,j,k]\n r_num += r_num_n / P[n]\n r_den += r_den_n / P[n]\n\n mu_num_n = np.zeros((self.M, self.K, D))\n sigma_num_n = np.zeros((self.M, self.K, D, D))\n for j in range(self.M):\n for k in range(self.K):\n for t in range(T):\n # update means\n mu_num_n[j,k] += gamma[t,j,k] * x[t]\n\n # update covariances\n sigma_num_n[j,k] += gamma[t,j,k] * np.outer(x[t] - self.mu[j,k], x[t] - self.mu[j,k])\n mu_num += mu_num_n / P[n]\n sigma_num += sigma_num_n / P[n]\n\n self.A = a_num / a_den\n assert(np.all(self.A <= 1))\n\n # update R, mu, sigma\n for j in range(self.M):\n for k in range(self.K):\n self.R[j,k] = r_num[j,k] / r_den[j]\n self.mu[j,k] = mu_num[j,k] / r_num[j,k]\n self.sigma[j,k] = sigma_num[j,k] / r_num[j,k]\n\n print(\"A:\", self.A)\n print(\"mu:\", self.mu)\n print(\"sigma:\", self.sigma)\n print(\"R:\", self.R)\n print(\"pi:\", self.pi)\n\n plt.plot(costs)\n plt.show()\n\n def likelihood(self, x):\n # returns log P(x | model)\n # using the forward part of the forward-backward algorithm\n T = len(x)\n alpha = np.zeros((T, self.M))\n\n B = np.zeros((self.M, T))\n for j in range(self.M):\n for t in range(T):\n for k in range(self.K):\n p = self.R[j,k] * mvn.pdf(x[t], self.mu[j,k], self.sigma[j,k])\n B[j,t] += p\n\n alpha[0] = self.pi*B[:,0]\n for t in range(1, T):\n alpha[t] = alpha[t-1].dot(self.A) * B[:,t]\n return alpha[-1].sum()\n\n def likelihood_multi(self, X):\n return np.array([self.likelihood(x) for x in X])\n\n def log_likelihood_multi(self, X):\n return np.log(self.likelihood_multi(X))\n\n def set(self, pi, A, R, mu, sigma):\n self.pi = pi\n self.A = A\n self.R = R\n self.mu = mu\n self.sigma = sigma\n M, K = R.shape\n self.M = M\n self.K = K\n\n\ndef real_signal():\n spf = wave.open('helloworld.wav', 'r')\n\n #Extract Raw Audio from Wav File\n # If you right-click on the file and go to \"Get Info\", you can see:\n # sampling rate = 16000 Hz\n # bits per sample = 16\n # The first is quantization in time\n # The second is quantization in amplitude\n # We also do this for images!\n # 2^16 = 65536 is how many different sound levels we have\n signal = spf.readframes(-1)\n signal = np.fromstring(signal, 'Int16')\n T = len(signal)\n\n hmm = HMM(10)\n hmm.fit(signal.reshape(1, T))\n\n\ndef fake_signal(init=simple_init):\n signals = get_signals(N=10, T=10, init=init)\n # for signal in signals:\n # for d in xrange(signal.shape[1]):\n # plt.plot(signal[:,d])\n # plt.show()\n\n hmm = HMM(2, 2)\n hmm.fit(signals)\n L = hmm.log_likelihood_multi(signals).sum()\n print(\"LL for fitted params:\", L)\n\n # test in actual params\n _, _, _, pi, A, R, mu, sigma = init()\n hmm.set(pi, A, R, mu, sigma)\n L = hmm.log_likelihood_multi(signals).sum()\n print(\"LL for actual params:\", L)\n\nif __name__ == '__main__':\n # real_signal() # will break\n fake_signal(init=simple_init)\n # fake_signal(init=big_init) # will break\n\n" }, { "path": "hmm_class/hmmc_scaled_concat.py", "content": "# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://lazyprogrammer.me\n# Continuous-observation HMM with scaling and multiple observations (treated as concatenated sequence)\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport wave\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom generate_c import get_signals, big_init, simple_init\nfrom scipy.stats import multivariate_normal as mvn\n\ndef random_normalized(d1, d2):\n x = np.random.random((d1, d2))\n return x / x.sum(axis=1, keepdims=True)\n\nclass HMM:\n def __init__(self, M, K):\n self.M = M # number of hidden states\n self.K = K # number of Gaussians\n \n def fit(self, X, max_iter=25, eps=1e-1):\n # train the HMM model using the Baum-Welch algorithm\n # a specific instance of the expectation-maximization algorithm\n\n # concatenate sequences in X and determine start/end positions\n sequenceLengths = []\n for x in X:\n sequenceLengths.append(len(x))\n Xc = np.concatenate(X)\n T = len(Xc)\n startPositions = np.zeros(len(Xc), dtype=np.bool)\n endPositions = np.zeros(len(Xc), dtype=np.bool)\n startPositionValues = []\n last = 0\n for length in sequenceLengths:\n startPositionValues.append(last)\n startPositions[last] = 1\n if last > 0:\n endPositions[last - 1] = 1\n last += length\n\n D = X[0].shape[1] # assume each x is organized (T, D)\n\n # randomly initialize all parameters\n self.pi = np.ones(self.M) / self.M # initial state distribution\n self.A = random_normalized(self.M, self.M) # state transition matrix\n self.R = np.ones((self.M, self.K)) / self.K # mixture proportions\n self.mu = np.zeros((self.M, self.K, D))\n for i in range(self.M):\n for k in range(self.K):\n random_idx = np.random.choice(T)\n self.mu[i,k] = Xc[random_idx]\n self.sigma = np.zeros((self.M, self.K, D, D))\n for j in range(self.M):\n for k in range(self.K):\n self.sigma[j,k] = np.eye(D)\n\n # main EM loop\n costs = []\n for it in range(max_iter):\n if it % 1 == 0:\n print(\"it:\", it)\n \n scale = np.zeros(T)\n\n # calculate B so we can lookup when updating alpha and beta\n B = np.zeros((self.M, T))\n component = np.zeros((self.M, self.K, T)) # we'll need these later\n for j in range(self.M):\n for k in range(self.K):\n p = self.R[j,k] * mvn.pdf(Xc, self.mu[j,k], self.sigma[j,k])\n component[j,k,:] = p\n B[j,:] += p\n\n\n alpha = np.zeros((T, self.M))\n alpha[0] = self.pi*B[:,0]\n scale[0] = alpha[0].sum()\n alpha[0] /= scale[0]\n for t in range(1, T):\n if startPositions[t] == 0:\n alpha_t_prime = alpha[t-1].dot(self.A) * B[:,t]\n else:\n alpha_t_prime = self.pi * B[:,t]\n scale[t] = alpha_t_prime.sum()\n alpha[t] = alpha_t_prime / scale[t]\n logP = np.log(scale).sum()\n\n beta = np.zeros((T, self.M))\n beta[-1] = 1\n for t in range(T - 2, -1, -1):\n if startPositions[t + 1] == 1:\n beta[t] = 1\n else:\n beta[t] = self.A.dot(B[:,t+1] * beta[t+1]) / scale[t+1]\n\n # update for Gaussians\n gamma = np.zeros((T, self.M, self.K))\n for t in range(T):\n alphabeta = alpha[t,:].dot(beta[t,:])\n for j in range(self.M):\n factor = alpha[t,j] * beta[t,j] / alphabeta\n for k in range(self.K):\n gamma[t,j,k] = factor * component[j,k,t] / B[j,t]\n\n costs.append(logP)\n\n # now re-estimate pi, A, R, mu, sigma\n self.pi = np.sum((alpha[t] * beta[t]) for t in startPositionValues) / len(startPositionValues)\n\n a_den = np.zeros((self.M, 1)) # prob don't need this\n a_num = np.zeros((self.M, self.M))\n r_num = np.zeros((self.M, self.K))\n r_den = np.zeros(self.M)\n mu_num = np.zeros((self.M, self.K, D))\n sigma_num = np.zeros((self.M, self.K, D, D))\n\n\n\n nonEndPositions = (1 - endPositions).astype(np.bool)\n a_den += (alpha[nonEndPositions] * beta[nonEndPositions]).sum(axis=0, keepdims=True).T\n\n # numerator for A\n for i in range(self.M):\n for j in range(self.M):\n for t in range(T-1):\n if endPositions[t] != 1:\n a_num[i,j] += alpha[t,i] * beta[t+1,j] * self.A[i,j] * B[j,t+1] / scale[t+1]\n self.A = a_num / a_den\n\n\n # update mixture components\n r_num_n = np.zeros((self.M, self.K))\n r_den_n = np.zeros(self.M)\n for j in range(self.M):\n for k in range(self.K):\n for t in range(T):\n r_num_n[j,k] += gamma[t,j,k]\n r_den_n[j] += gamma[t,j,k]\n r_num = r_num_n\n r_den = r_den_n\n\n mu_num_n = np.zeros((self.M, self.K, D))\n sigma_num_n = np.zeros((self.M, self.K, D, D))\n for j in range(self.M):\n for k in range(self.K):\n for t in range(T):\n # update means\n mu_num_n[j,k] += gamma[t,j,k] * Xc[t]\n\n # update covariances\n sigma_num_n[j,k] += gamma[t,j,k] * np.outer(Xc[t] - self.mu[j,k], Xc[t] - self.mu[j,k])\n mu_num = mu_num_n\n sigma_num = sigma_num_n\n\n\n # update R, mu, sigma\n for j in range(self.M):\n for k in range(self.K):\n self.R[j,k] = r_num[j,k] / r_den[j]\n self.mu[j,k] = mu_num[j,k] / r_num[j,k]\n self.sigma[j,k] = sigma_num[j,k] / r_num[j,k] + np.eye(D)*eps\n assert(np.all(self.R <= 1))\n assert(np.all(self.A <= 1))\n print(\"A:\", self.A)\n print(\"mu:\", self.mu)\n print(\"sigma:\", self.sigma)\n print(\"R:\", self.R)\n print(\"pi:\", self.pi)\n\n plt.plot(costs)\n plt.show()\n\n def log_likelihood(self, x):\n # returns log P(x | model)\n # using the forward part of the forward-backward algorithm\n T = len(x)\n scale = np.zeros(T)\n B = np.zeros((self.M, T))\n for j in range(self.M):\n for k in range(self.K):\n p = self.R[j,k] * mvn.pdf(x, self.mu[j,k], self.sigma[j,k])\n B[j,:] += p\n\n alpha = np.zeros((T, self.M))\n alpha[0] = self.pi*B[:,0]\n scale[0] = alpha[0].sum()\n alpha[0] /= scale[0]\n for t in range(1, T):\n alpha_t_prime = alpha[t-1].dot(self.A) * B[:,t]\n scale[t] = alpha_t_prime.sum()\n alpha[t] = alpha_t_prime / scale[t]\n return np.log(scale).sum()\n\n def log_likelihood_multi(self, X):\n return np.array([self.log_likelihood(x) for x in X])\n\n def set(self, pi, A, R, mu, sigma):\n self.pi = pi\n self.A = A\n self.R = R\n self.mu = mu\n self.sigma = sigma\n M, K = R.shape\n self.M = M\n self.K = K\n\n\ndef real_signal():\n spf = wave.open('helloworld.wav', 'r')\n\n #Extract Raw Audio from Wav File\n # If you right-click on the file and go to \"Get Info\", you can see:\n # sampling rate = 16000 Hz\n # bits per sample = 16\n # The first is quantization in time\n # The second is quantization in amplitude\n # We also do this for images!\n # 2^16 = 65536 is how many different sound levels we have\n signal = spf.readframes(-1)\n signal = np.fromstring(signal, 'Int16')\n T = len(signal)\n signal = (signal - signal.mean()) / signal.std()\n hmm = HMM(5, 3)\n hmm.fit(signal.reshape(1, T, 1))\n print(\"LL for fitted params:\", hmm.log_likelihood(signal.reshape(T, 1)))\n\n\ndef fake_signal(init=big_init):\n signals = get_signals(init=init)\n # for signal in signals:\n # for d in xrange(signal.shape[1]):\n # plt.plot(signal[:,d])\n # plt.show()\n\n hmm = HMM(5, 3)\n hmm.fit(signals)\n L = hmm.log_likelihood_multi(signals).sum()\n print(\"LL for fitted params:\", L)\n\n # test in actual params\n _, _, _, pi, A, R, mu, sigma = init()\n hmm.set(pi, A, R, mu, sigma)\n L = hmm.log_likelihood_multi(signals).sum()\n print(\"LL for actual params:\", L)\n\nif __name__ == '__main__':\n # real_signal()\n fake_signal()\n\n" }, { "path": "hmm_class/hmmc_scaled_concat_diag.py", "content": "# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://lazyprogrammer.me\n# Continuous-observation HMM with scaling and multiple observations (treated as concatenated sequence)\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport wave\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom generate_c import get_signals, big_init, simple_init\nfrom scipy.stats import multivariate_normal as mvn\n\ndef random_normalized(d1, d2):\n x = np.random.random((d1, d2))\n return x / x.sum(axis=1, keepdims=True)\n\nclass HMM:\n def __init__(self, M, K):\n self.M = M # number of hidden states\n self.K = K # number of Gaussians\n \n def fit(self, X, max_iter=25, eps=1e-1):\n # train the HMM model using the Baum-Welch algorithm\n # a specific instance of the expectation-maximization algorithm\n\n # concatenate sequences in X and determine start/end positions\n sequenceLengths = []\n for x in X:\n sequenceLengths.append(len(x))\n Xc = np.concatenate(X)\n T = len(Xc)\n startPositions = np.zeros(len(Xc), dtype=np.bool)\n endPositions = np.zeros(len(Xc), dtype=np.bool)\n startPositionValues = []\n last = 0\n for length in sequenceLengths:\n startPositionValues.append(last)\n startPositions[last] = 1\n if last > 0:\n endPositions[last - 1] = 1\n last += length\n\n D = X[0].shape[1] # assume each x is organized (T, D)\n\n # randomly initialize all parameters\n self.pi = np.ones(self.M) / self.M # initial state distribution\n self.A = random_normalized(self.M, self.M) # state transition matrix\n self.R = np.ones((self.M, self.K)) / self.K # mixture proportions\n self.mu = np.zeros((self.M, self.K, D))\n for i in range(self.M):\n for k in range(self.K):\n random_idx = np.random.choice(T)\n self.mu[i,k] = Xc[random_idx]\n self.sigma = np.ones((self.M, self.K, D))\n\n # main EM loop\n costs = []\n for it in range(max_iter):\n if it % 1 == 0:\n print(\"it:\", it)\n \n scale = np.zeros(T)\n\n # calculate B so we can lookup when updating alpha and beta\n B = np.zeros((self.M, T))\n component = np.zeros((self.M, self.K, T)) # we'll need these later\n for j in range(self.M):\n for k in range(self.K):\n p = self.R[j,k] * mvn.pdf(Xc, self.mu[j,k], self.sigma[j,k])\n component[j,k,:] = p\n B[j,:] += p\n\n\n alpha = np.zeros((T, self.M))\n alpha[0] = self.pi*B[:,0]\n scale[0] = alpha[0].sum()\n alpha[0] /= scale[0]\n for t in range(1, T):\n if startPositions[t] == 0:\n alpha_t_prime = alpha[t-1].dot(self.A) * B[:,t]\n else:\n alpha_t_prime = self.pi * B[:,t]\n scale[t] = alpha_t_prime.sum()\n alpha[t] = alpha_t_prime / scale[t]\n logP = np.log(scale).sum()\n\n beta = np.zeros((T, self.M))\n beta[-1] = 1\n for t in range(T - 2, -1, -1):\n if startPositions[t + 1] == 1:\n beta[t] = 1\n else:\n beta[t] = self.A.dot(B[:,t+1] * beta[t+1]) / scale[t+1]\n\n # update for Gaussians\n gamma = np.zeros((T, self.M, self.K))\n for t in range(T):\n alphabeta = alpha[t,:].dot(beta[t,:])\n for j in range(self.M):\n factor = alpha[t,j] * beta[t,j] / alphabeta\n for k in range(self.K):\n gamma[t,j,k] = factor * component[j,k,t] / B[j,t]\n\n costs.append(logP)\n\n # now re-estimate pi, A, R, mu, sigma\n self.pi = np.sum((alpha[t] * beta[t]) for t in startPositionValues) / len(startPositionValues)\n\n a_den = np.zeros((self.M, 1)) # prob don't need this\n a_num = np.zeros((self.M, self.M))\n r_num = np.zeros((self.M, self.K))\n r_den = np.zeros(self.M)\n mu_num = np.zeros((self.M, self.K, D))\n sigma_num = np.zeros((self.M, self.K, D))\n\n\n\n nonEndPositions = (1 - endPositions).astype(np.bool)\n a_den += (alpha[nonEndPositions] * beta[nonEndPositions]).sum(axis=0, keepdims=True).T\n\n # numerator for A\n for i in range(self.M):\n for j in range(self.M):\n for t in range(T-1):\n if endPositions[t] != 1:\n a_num[i,j] += alpha[t,i] * beta[t+1,j] * self.A[i,j] * B[j,t+1] / scale[t+1]\n self.A = a_num / a_den\n\n\n # update mixture components\n r_num_n = np.zeros((self.M, self.K))\n r_den_n = np.zeros(self.M)\n for j in range(self.M):\n for k in range(self.K):\n for t in range(T):\n r_num_n[j,k] += gamma[t,j,k]\n r_den_n[j] += gamma[t,j,k]\n r_num = r_num_n\n r_den = r_den_n\n\n mu_num_n = np.zeros((self.M, self.K, D))\n sigma_num_n = np.zeros((self.M, self.K, D))\n for j in range(self.M):\n for k in range(self.K):\n for t in range(T):\n # update means\n mu_num_n[j,k] += gamma[t,j,k] * Xc[t]\n\n # update covariances\n sigma_num_n[j,k] += gamma[t,j,k] * (Xc[t] - self.mu[j,k])**2\n mu_num = mu_num_n\n sigma_num = sigma_num_n\n\n\n # update R, mu, sigma\n for j in range(self.M):\n for k in range(self.K):\n self.R[j,k] = r_num[j,k] / r_den[j]\n self.mu[j,k] = mu_num[j,k] / r_num[j,k]\n self.sigma[j,k] = sigma_num[j,k] / r_num[j,k] + np.ones(D)*eps\n assert(np.all(self.R <= 1))\n assert(np.all(self.A <= 1))\n print(\"A:\", self.A)\n print(\"mu:\", self.mu)\n print(\"sigma:\", self.sigma)\n print(\"R:\", self.R)\n print(\"pi:\", self.pi)\n\n plt.plot(costs)\n plt.show()\n\n def log_likelihood(self, x):\n # returns log P(x | model)\n # using the forward part of the forward-backward algorithm\n T = len(x)\n scale = np.zeros(T)\n B = np.zeros((self.M, T))\n for j in range(self.M):\n for k in range(self.K):\n p = self.R[j,k] * mvn.pdf(x, self.mu[j,k], self.sigma[j,k])\n B[j,:] += p\n\n alpha = np.zeros((T, self.M))\n alpha[0] = self.pi*B[:,0]\n scale[0] = alpha[0].sum()\n alpha[0] /= scale[0]\n for t in range(1, T):\n alpha_t_prime = alpha[t-1].dot(self.A) * B[:,t]\n scale[t] = alpha_t_prime.sum()\n alpha[t] = alpha_t_prime / scale[t]\n return np.log(scale).sum()\n\n def get_state_sequence(self, x):\n # returns the most likely state sequence given observed sequence x\n # using the Viterbi algorithm\n T = len(x)\n\n # make the emission matrix B\n logB = np.zeros((self.M, T))\n for j in range(self.M):\n for t in range(T):\n for k in range(self.K):\n p = np.log(self.R[j,k]) + mvn.logpdf(x[t], self.mu[j,k], self.sigma[j,k])\n logB[j,t] += p\n print(\"logB:\", logB)\n\n # perform Viterbi as usual\n delta = np.zeros((T, self.M))\n psi = np.zeros((T, self.M))\n\n # smooth pi in case it is 0\n pi = self.pi + 1e-10\n pi /= pi.sum()\n\n delta[0] = np.log(pi) + logB[:,0]\n for t in range(1, T):\n for j in range(self.M):\n next_delta = delta[t-1] + np.log(self.A[:,j])\n delta[t,j] = np.max(next_delta) + logB[j,t]\n psi[t,j] = np.argmax(next_delta)\n\n # backtrack\n states = np.zeros(T, dtype=np.int32)\n states[T-1] = np.argmax(delta[T-1])\n for t in range(T-2, -1, -1):\n states[t] = psi[t+1, states[t+1]]\n return states\n\n def log_likelihood_multi(self, X):\n return np.array([self.log_likelihood(x) for x in X])\n\n def set(self, pi, A, R, mu, sigma):\n self.pi = pi\n self.A = A\n self.R = R\n self.mu = mu\n self.sigma = sigma\n M, K = R.shape\n self.M = M\n self.K = K\n\n\ndef real_signal():\n spf = wave.open('helloworld.wav', 'r')\n\n #Extract Raw Audio from Wav File\n # If you right-click on the file and go to \"Get Info\", you can see:\n # sampling rate = 16000 Hz\n # bits per sample = 16\n # The first is quantization in time\n # The second is quantization in amplitude\n # We also do this for images!\n # 2^16 = 65536 is how many different sound levels we have\n signal = spf.readframes(-1)\n signal = np.fromstring(signal, 'Int16')\n T = len(signal)\n signal = (signal - signal.mean()) / signal.std()\n hmm = HMM(5, 3)\n hmm.fit(signal.reshape(1, T, 1), max_iter=35)\n print(\"LL for fitted params:\", hmm.log_likelihood(signal.reshape(T, 1)))\n\n\ndef fake_signal(init=big_init):\n signals = get_signals(init=init)\n # for signal in signals:\n # for d in xrange(signal.shape[1]):\n # plt.plot(signal[:,d])\n # plt.show()\n\n hmm = HMM(5, 3)\n hmm.fit(signals)\n L = hmm.log_likelihood_multi(signals).sum()\n print(\"LL for fitted params:\", L)\n\n # test in actual params\n _, _, _, pi, A, R, mu, sigma = init()\n hmm.set(pi, A, R, mu, sigma)\n L = hmm.log_likelihood_multi(signals).sum()\n print(\"LL for actual params:\", L)\n\n # print most likely state sequence\n print(\"Most likely state sequence for initial observation:\")\n print(hmm.get_state_sequence(signals[0]))\n\nif __name__ == '__main__':\n # real_signal()\n fake_signal()\n\n" }, { "path": "hmm_class/hmmc_tf.py", "content": "# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python\n# http://lazyprogrammer.me\n# Continuous-observation HMM in Theano using gradient descent.\n# TODO: seems to be broken on TF v1.2, but fine with TF v1.0\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport wave\nimport tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\nMVN = tf.contrib.distributions.MultivariateNormalDiag\nfrom generate_c import get_signals, big_init\n\n\nclass HMM:\n def __init__(self, M, K, D):\n self.M = M # number of hidden states\n self.K = K # number of Gaussians\n self.D = D # Gaussian dimensionality\n\n def set_session(self, session):\n self.session = session\n\n def init_random(self, X):\n pi0 = np.ones(self.M).astype(np.float32) # initial state distribution\n A0 = np.random.randn(self.M, self.M).astype(np.float32) # state transition matrix\n R0 = np.ones((self.M, self.K)).astype(np.float32) # mixture proportions\n # mu0 = np.random.randn(self.M, self.K, self.D).astype(np.float32)\n\n mu0 = np.zeros((self.M, self.K, self.D))\n for j in range(self.M):\n for k in range(self.K):\n n = np.random.randint(X.shape[0])\n t = np.random.randint(X.shape[1])\n mu0[j,k] = X[n,t]\n mu0 = mu0.astype(np.float32)\n\n sigma0 = np.random.randn(self.M, self.K, self.D).astype(np.float32)\n self.build(pi0, A0, R0, mu0, sigma0)\n\n def build(self, preSoftmaxPi, preSoftmaxA, preSoftmaxR, mu, logSigma):\n self.preSoftmaxPi = tf.Variable(preSoftmaxPi)\n self.preSoftmaxA = tf.Variable(preSoftmaxA)\n self.preSoftmaxR = tf.Variable(preSoftmaxR)\n self.mu = tf.Variable(mu)\n self.logSigma = tf.Variable(logSigma)\n\n\n pi = tf.nn.softmax(self.preSoftmaxPi)\n A = tf.nn.softmax(self.preSoftmaxA)\n R = tf.nn.softmax(self.preSoftmaxR)\n sigma = tf.exp(self.logSigma)\n\n\n # X will be TxD\n self.tfx = tf.placeholder(tf.float32, shape=(None, self.D), name='X')\n\n\n # first we need to calculate B\n # B[j,t] = probability of X being in state j at time t\n # = Gaussian mixture P( x(t) | mu(j), sigma(j) )\n # idea: first calculate components and sum\n # note: we can use a for loop because M and K are not TF variables\n self.mvns = []\n for j in range(self.M):\n self.mvns.append([])\n for k in range(self.K):\n self.mvns[j].append(\n MVN(self.mu[j,k], sigma[j,k])\n )\n\n # note: we can use a for loop because M and K are not TF variables\n B = []\n for j in range(self.M):\n components = []\n for k in range(self.K):\n components.append(\n self.mvns[j][k].prob(self.tfx)\n )\n\n # why?\n # because we can stack a list of tensors\n # but not a list of lists of tensors\n\n # components[j] will be K x T\n # we now want to multiply by the mixture probability (R)\n # result is M x T\n # which gives us P( X(t) | state j )\n components = tf.stack(components)\n R_j = tf.reshape(R[j], [1, self.K])\n p_x_t_j = tf.matmul(R_j, components)\n\n # now components[j] is just 1 x T --> T\n components = tf.reshape(p_x_t_j, [-1])\n\n # now append it to B\n B.append(components)\n \n\n # should now be M x T\n B = tf.stack(B)\n\n # we should make it T x M since scan will loop through first index\n B = tf.transpose(B, [1, 0])\n\n\n\n # now perform the forward algorithm\n def recurrence(old_a_old_s, B_t):\n old_a = tf.reshape(old_a_old_s[0], (1, self.M))\n a = tf.matmul(old_a, A) * B_t\n a = tf.reshape(a, (self.M,))\n s = tf.reduce_sum(a)\n return (a / s), s\n\n alpha, scale = tf.scan(\n fn=recurrence,\n elems=B[1:],\n initializer=(pi*B[0], np.float32(1.0)),\n )\n # note: tensorflow is very strict about what types you pass in to initializer!\n # - cannot be list, must be tuple\n # - cannot be 1 (int), must be float32\n\n self.cost_op = -tf.reduce_sum(tf.log(scale))\n self.train_op = tf.train.AdamOptimizer(1e-2).minimize(self.cost_op)\n\n def set(self, preSoftmaxPi, preSoftmaxA, preSoftmaxR, mu, logSigma):\n # assume build has already been called\n # we just assign these new variables\n op1 = self.preSoftmaxPi.assign(preSoftmaxPi)\n op2 = self.preSoftmaxA.assign(preSoftmaxA)\n op3 = self.preSoftmaxR.assign(preSoftmaxR)\n op4 = self.mu.assign(mu)\n op5 = self.logSigma.assign(logSigma)\n self.session.run([op1, op2, op3, op4, op5])\n \n def fit(self, X, max_iter=10):\n # train the HMM model using stochastic gradient descent\n\n N = len(X)\n print(\"number of train samples:\", N)\n\n costs = []\n for it in range(max_iter):\n if it % 1 == 0:\n print(\"it:\", it)\n \n for n in range(N):\n # this would of course be much faster if we didn't do this on\n # every iteration of the loop\n c = self.get_cost_multi(X).sum()\n costs.append(c)\n self.session.run(self.train_op, feed_dict={self.tfx: X[n]})\n\n plt.plot(costs)\n plt.show()\n\n def get_cost(self, x):\n return self.session.run(self.cost_op, feed_dict={self.tfx: x})\n\n def get_cost_multi(self, X):\n return np.array([self.get_cost(x) for x in X])\n\n\ndef real_signal():\n spf = wave.open('helloworld.wav', 'r')\n\n #Extract Raw Audio from Wav File\n # If you right-click on the file and go to \"Get Info\", you can see:\n # sampling rate = 16000 Hz\n # bits per sample = 16\n # The first is quantization in time\n # The second is quantization in amplitude\n # We also do this for images!\n # 2^16 = 65536 is how many different sound levels we have\n signal = spf.readframes(-1)\n signal = np.fromstring(signal, 'Int16')\n T = len(signal)\n signal = (signal - signal.mean()) / signal.std()\n\n # signal needs to be of shape N x T(n) x D\n signals = signal.reshape(1, T, 1)\n\n hmm = HMM(3, 3, 1)\n hmm.init_random(signals)\n\n init = tf.global_variables_initializer()\n session = tf.InteractiveSession()\n session.run(init)\n hmm.set_session(session)\n\n hmm.fit(signals, max_iter=30)\n\n\ndef fake_signal():\n signals = get_signals()\n # assume all signals are same length\n signals = np.array(signals)\n hmm = HMM(5, 3, signals[0].shape[1]) # M, K, D\n hmm.init_random(signals)\n\n init = tf.global_variables_initializer()\n session = tf.InteractiveSession()\n session.run(init)\n hmm.set_session(session)\n\n hmm.fit(signals, max_iter=30)\n L = hmm.get_cost_multi(signals).sum()\n print(\"LL for fitted params:\", L)\n\n # test in actual params\n _, _, _, pi, A, R, mu, sigma = big_init()\n\n # turn these into their \"pre-softmax\" forms\n pi = np.log(pi)\n A = np.log(A)\n R = np.log(R)\n M, K, D, _ = sigma.shape # need to convert full cov into diag cov\n logSigma = np.zeros((M, K, D))\n for j in range(M):\n for k in range(D):\n logSigma[j,k] = np.log(np.diag(sigma[j,k]))\n\n hmm.set(pi, A, R, mu, logSigma)\n L = hmm.get_cost_multi(signals).sum()\n print(\"LL for actual params:\", L)\n\nif __name__ == '__main__':\n # real_signal()\n fake_signal()\n\n" }, { "path": "hmm_class/hmmc_theano.py", "content": "# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python\n# http://lazyprogrammer.me\n# Continuous-observation HMM in Theano using gradient descent.\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport wave\nimport theano\nimport theano.tensor as T\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# from theano.sandbox import solve # does not have gradient functionality\nfrom generate_c import get_signals, big_init\n\ndef random_normalized(d1, d2):\n x = np.random.random((d1, d2))\n return x / x.sum(axis=1, keepdims=True)\n\n\nclass HMM:\n def __init__(self, M, K):\n self.M = M # number of hidden states\n self.K = K # number of Gaussians\n \n def fit(self, X, learning_rate=1e-2, max_iter=10):\n # train the HMM model using gradient descent\n\n N = len(X)\n D = X[0].shape[1] # assume each x is organized (T, D)\n\n pi0 = np.ones(self.M) / self.M # initial state distribution\n A0 = random_normalized(self.M, self.M) # state transition matrix\n R0 = np.ones((self.M, self.K)) / self.K # mixture proportions\n mu0 = np.zeros((self.M, self.K, D))\n for i in range(self.M):\n for k in range(self.K):\n random_idx = np.random.choice(N)\n x = X[random_idx]\n random_time_idx = np.random.choice(len(x))\n mu0[i,k] = x[random_time_idx]\n sigma0 = np.zeros((self.M, self.K, D, D))\n for j in range(self.M):\n for k in range(self.K):\n sigma0[j,k] = np.eye(D)\n\n thx, cost = self.set(pi0, A0, R0, mu0, sigma0)\n\n pi_update = self.pi - learning_rate*T.grad(cost, self.pi)\n pi_update = pi_update / pi_update.sum()\n\n A_update = self.A - learning_rate*T.grad(cost, self.A)\n A_update = A_update / A_update.sum(axis=1).dimshuffle(0, 'x')\n\n R_update = self.R - learning_rate*T.grad(cost, self.R)\n R_update = R_update / R_update.sum(axis=1).dimshuffle(0, 'x')\n\n updates = [\n (self.pi, pi_update),\n (self.A, A_update),\n (self.R, R_update),\n (self.mu, self.mu - learning_rate*T.grad(cost, self.mu)),\n (self.sigma, self.sigma - learning_rate*T.grad(cost, self.sigma)),\n ]\n\n train_op = theano.function(\n inputs=[thx],\n updates=updates,\n )\n\n costs = []\n for it in range(max_iter):\n print(\"it:\", it)\n \n for n in range(N):\n c = self.log_likelihood_multi(X).sum()\n print(\"c:\", c)\n costs.append(c)\n train_op(X[n])\n\n print(\"A:\", self.A.get_value())\n print(\"mu:\", self.mu.get_value())\n print(\"sigma:\", self.sigma.get_value())\n print(\"R:\", self.R.get_value())\n print(\"pi:\", self.pi.get_value())\n\n plt.plot(costs)\n plt.show()\n\n def set(self, pi, A, R, mu, sigma):\n self.pi = theano.shared(pi)\n self.A = theano.shared(A)\n self.R = theano.shared(R)\n self.mu = theano.shared(mu)\n self.sigma = theano.shared(sigma)\n M, K = R.shape\n self.M = M\n self.K = K\n\n D = self.mu.shape[2]\n twopiD = (2*np.pi)**D\n\n # set up theano variables and functions\n thx = T.matrix('X') # represents a TxD matrix of sequential observations\n def mvn_pdf(x, mu, sigma):\n k = 1 / T.sqrt(twopiD * T.nlinalg.det(sigma))\n e = T.exp(-0.5*(x - mu).T.dot(T.nlinalg.matrix_inverse(sigma).dot(x - mu)))\n return k*e\n\n def gmm_pdf(x):\n def state_pdfs(xt):\n def component_pdf(j, xt):\n Bj_t = 0\n for k in range(self.K):\n Bj_t += self.R[j,k] * mvn_pdf(xt, self.mu[j,k], self.sigma[j,k])\n return Bj_t\n\n Bt, _ = theano.scan(\n fn=component_pdf,\n sequences=T.arange(self.M),\n n_steps=self.M,\n outputs_info=None,\n non_sequences=[xt],\n )\n return Bt\n\n B, _ = theano.scan(\n fn=state_pdfs,\n sequences=x,\n n_steps=x.shape[0],\n outputs_info=None,\n )\n return B.T\n \n B = gmm_pdf(thx)\n # scale = T.zeros((thx.shape[0], 1), dtype=theano.config.floatX)\n # scale[0] = (self.pi*B[:,0]).sum()\n\n def recurrence(t, old_a, B):\n a = old_a.dot(self.A) * B[:, t]\n s = a.sum()\n return (a / s), s\n\n [alpha, scale], _ = theano.scan(\n fn=recurrence,\n sequences=T.arange(1, thx.shape[0]),\n outputs_info=[self.pi*B[:,0], None],\n n_steps=thx.shape[0]-1,\n non_sequences=[B],\n )\n\n cost = -T.log(scale).sum()\n self.cost_op = theano.function(\n inputs=[thx],\n outputs=cost,\n )\n return thx, cost\n\n def log_likelihood_multi(self, X):\n return np.array([self.cost_op(x) for x in X])\n\n\ndef real_signal():\n spf = wave.open('helloworld.wav', 'r')\n\n #Extract Raw Audio from Wav File\n # If you right-click on the file and go to \"Get Info\", you can see:\n # sampling rate = 16000 Hz\n # bits per sample = 16\n # The first is quantization in time\n # The second is quantization in amplitude\n # We also do this for images!\n # 2^16 = 65536 is how many different sound levels we have\n signal = spf.readframes(-1)\n signal = np.fromstring(signal, 'Int16')\n T = len(signal)\n signal = (signal - signal.mean()) / signal.std()\n\n hmm = HMM(5, 3)\n # signal needs to be of shape N x T(n) x D\n hmm.fit(signal.reshape(1, T, 1), learning_rate=1e-5, max_iter=20)\n\n\ndef fake_signal():\n signals = get_signals()\n hmm = HMM(5, 3)\n hmm.fit(signals)\n L = hmm.log_likelihood_multi(signals).sum()\n print(\"LL for fitted params:\", L)\n\n # test in actual params\n _, _, _, pi, A, R, mu, sigma = big_init()\n hmm.set(pi, A, R, mu, sigma)\n L = hmm.log_likelihood_multi(signals).sum()\n print(\"LL for actual params:\", L)\n\nif __name__ == '__main__':\n # real_signal()\n fake_signal()\n\n" }, { "path": "hmm_class/hmmc_theano2.py", "content": "# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python\n# http://lazyprogrammer.me\n# Continuous-observation HMM in Theano using gradient descent.\n\n# This script differs from hmmc_theano.py in the following way:\n# Instead of re-normalizing the parameters at each iteration,\n# we instead make the parameters free to vary between -inf to +inf.\n# We then use softmax to ensure the probabilities are positive and sum to 1.\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport wave\nimport theano\nimport theano.tensor as T\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# from theano.sandbox import solve # does not have gradient functionality\nfrom generate_c import get_signals, big_init\n\n\nclass HMM:\n def __init__(self, M, K):\n self.M = M # number of hidden states\n self.K = K # number of Gaussians\n \n def fit(self, X, learning_rate=1e-2, max_iter=10):\n # train the HMM model using gradient descent\n\n N = len(X)\n D = X[0].shape[1] # assume each x is organized (T, D)\n\n pi0 = np.ones(self.M) # initial state distribution\n A0 = np.random.randn(self.M, self.M) # state transition matrix\n R0 = np.ones((self.M, self.K)) # mixture proportions\n mu0 = np.zeros((self.M, self.K, D))\n for i in range(self.M):\n for k in range(self.K):\n random_idx = np.random.choice(N)\n x = X[random_idx]\n random_time_idx = np.random.choice(len(x))\n mu0[i,k] = x[random_time_idx]\n sigma0 = np.random.randn(self.M, self.K, D, D)\n\n thx, cost = self.set(pi0, A0, R0, mu0, sigma0)\n\n pi_update = self.preSoftmaxPi - learning_rate*T.grad(cost, self.preSoftmaxPi)\n A_update = self.preSoftmaxA - learning_rate*T.grad(cost, self.preSoftmaxA)\n R_update = self.preSoftmaxR - learning_rate*T.grad(cost, self.preSoftmaxR)\n mu_update = self.mu - learning_rate*T.grad(cost, self.mu)\n sigma_update = self.sigmaFactor - learning_rate*T.grad(cost, self.sigmaFactor)\n\n updates = [\n (self.preSoftmaxPi, pi_update),\n (self.preSoftmaxA, A_update),\n (self.preSoftmaxR, R_update),\n (self.mu, mu_update),\n (self.sigmaFactor, sigma_update),\n ]\n\n train_op = theano.function(\n inputs=[thx],\n updates=updates,\n )\n\n costs = []\n for it in range(max_iter):\n print(\"it:\", it)\n \n for n in range(N):\n c = self.log_likelihood_multi(X).sum()\n print(\"c:\", c)\n costs.append(c)\n train_op(X[n])\n\n plt.plot(costs)\n plt.show()\n\n def set(self, preSoftmaxPi, preSoftmaxA, preSoftmaxR, mu, sigmaFactor):\n self.preSoftmaxPi = theano.shared(preSoftmaxPi)\n self.preSoftmaxA = theano.shared(preSoftmaxA)\n self.preSoftmaxR = theano.shared(preSoftmaxR)\n self.mu = theano.shared(mu)\n self.sigmaFactor = theano.shared(sigmaFactor)\n M, K = preSoftmaxR.shape\n self.M = M\n self.K = K\n\n pi = T.nnet.softmax(self.preSoftmaxPi).flatten()\n A = T.nnet.softmax(self.preSoftmaxA)\n R = T.nnet.softmax(self.preSoftmaxR)\n\n\n D = self.mu.shape[2]\n twopiD = (2*np.pi)**D\n\n # set up theano variables and functions\n thx = T.matrix('X') # represents a TxD matrix of sequential observations\n def mvn_pdf(x, m, S):\n k = 1 / T.sqrt(twopiD * T.nlinalg.det(S))\n e = T.exp(-0.5*(x - m).T.dot(T.nlinalg.matrix_inverse(S).dot(x - m)))\n return k*e\n\n def gmm_pdf(x):\n def state_pdfs(xt):\n def component_pdf(j, xt):\n Bj_t = 0\n # j = T.cast(j, 'int32')\n for k in range(self.K):\n # k = int(k)\n # a = R[j,k]\n # b = mu[j,k]\n # c = sigma[j,k]\n L = self.sigmaFactor[j,k]\n S = L.dot(L.T)\n Bj_t += R[j,k] * mvn_pdf(xt, self.mu[j,k], S)\n return Bj_t\n\n Bt, _ = theano.scan(\n fn=component_pdf,\n sequences=T.arange(self.M),\n n_steps=self.M,\n outputs_info=None,\n non_sequences=[xt],\n )\n return Bt\n\n B, _ = theano.scan(\n fn=state_pdfs,\n sequences=x,\n n_steps=x.shape[0],\n outputs_info=None,\n )\n return B.T\n \n B = gmm_pdf(thx)\n # scale = T.zeros((thx.shape[0], 1), dtype=theano.config.floatX)\n # scale[0] = (self.pi*B[:,0]).sum()\n\n def recurrence(t, old_a, B):\n a = old_a.dot(A) * B[:, t]\n s = a.sum()\n return (a / s), s\n\n [alpha, scale], _ = theano.scan(\n fn=recurrence,\n sequences=T.arange(1, thx.shape[0]),\n outputs_info=[pi*B[:,0], None],\n n_steps=thx.shape[0]-1,\n non_sequences=[B],\n )\n\n cost = -T.log(scale).sum()\n self.cost_op = theano.function(\n inputs=[thx],\n outputs=cost,\n )\n return thx, cost\n\n def log_likelihood_multi(self, X):\n return np.array([self.cost_op(x) for x in X])\n\n\ndef real_signal():\n spf = wave.open('helloworld.wav', 'r')\n\n #Extract Raw Audio from Wav File\n # If you right-click on the file and go to \"Get Info\", you can see:\n # sampling rate = 16000 Hz\n # bits per sample = 16\n # The first is quantization in time\n # The second is quantization in amplitude\n # We also do this for images!\n # 2^16 = 65536 is how many different sound levels we have\n signal = spf.readframes(-1)\n signal = np.fromstring(signal, 'Int16')\n T = len(signal)\n signal = (signal - signal.mean()) / signal.std()\n\n hmm = HMM(3, 3)\n # signal needs to be of shape N x T(n) x D\n hmm.fit(signal.reshape(1, T, 1), learning_rate=2e-7, max_iter=20)\n\n\ndef fake_signal():\n signals = get_signals()\n hmm = HMM(5, 3)\n hmm.fit(signals, max_iter=3)\n L = hmm.log_likelihood_multi(signals).sum()\n print(\"LL for fitted params:\", L)\n\n # test in actual params\n _, _, _, pi, A, R, mu, sigma = big_init()\n\n # turn these into their \"pre-softmax\" forms\n pi = np.log(pi)\n A = np.log(A)\n R = np.log(R)\n\n # decompose sigma using cholesky factorization\n sigma = np.linalg.cholesky(sigma)\n\n hmm.set(pi, A, R, mu, sigma)\n L = hmm.log_likelihood_multi(signals).sum()\n print(\"LL for actual params:\", L)\n\nif __name__ == '__main__':\n # real_signal()\n fake_signal()\n\n" }, { "path": "hmm_class/hmmd.py", "content": "# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python\n# http://lazyprogrammer.me\n# Discrete Hidden Markov Model (HMM)\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom datetime import datetime\n\n\ndef random_normalized(d1, d2):\n x = np.random.random((d1, d2))\n return x / x.sum(axis=1, keepdims=True)\n\n\nclass HMM:\n def __init__(self, M):\n self.M = M # number of hidden states\n \n def fit(self, X, max_iter=30):\n t0 = datetime.now()\n np.random.seed(123)\n # train the HMM model using the Baum-Welch algorithm\n # a specific instance of the expectation-maximization algorithm\n\n # determine V, the vocabulary size\n # assume observables are already integers from 0..V-1\n # X is a jagged array of observed sequences\n V = max(max(x) for x in X) + 1\n N = len(X)\n\n self.pi = np.ones(self.M) / self.M # initial state distribution\n self.A = random_normalized(self.M, self.M) # state transition matrix\n self.B = random_normalized(self.M, V) # output distribution\n\n print(\"initial A:\", self.A)\n print(\"initial B:\", self.B)\n\n costs = []\n for it in range(max_iter):\n if it % 10 == 0:\n print(\"it:\", it)\n alphas = []\n betas = []\n P = np.zeros(N)\n for n in range(N):\n x = X[n]\n T = len(x)\n alpha = np.zeros((T, self.M))\n alpha[0] = self.pi*self.B[:,x[0]]\n for t in range(1, T):\n tmp1 = alpha[t-1].dot(self.A) * self.B[:, x[t]]\n # tmp2 = np.zeros(self.M)\n # for i in range(self.M):\n # for j in range(self.M):\n # tmp2[j] += alpha[t-1,i] * self.A[i,j] * self.B[j, x[t]]\n # print \"diff:\", np.abs(tmp1 - tmp2).sum()\n alpha[t] = tmp1\n P[n] = alpha[-1].sum()\n alphas.append(alpha)\n\n beta = np.zeros((T, self.M))\n beta[-1] = 1\n for t in range(T - 2, -1, -1):\n beta[t] = self.A.dot(self.B[:, x[t+1]] * beta[t+1])\n betas.append(beta)\n\n # print \"P:\", P\n # break\n assert(np.all(P > 0))\n cost = np.sum(np.log(P))\n costs.append(cost)\n\n # now re-estimate pi, A, B\n self.pi = np.sum((alphas[n][0] * betas[n][0])/P[n] for n in range(N)) / N\n # print \"self.pi:\", self.pi\n # break\n\n den1 = np.zeros((self.M, 1))\n den2 = np.zeros((self.M, 1))\n a_num = 0\n b_num = 0\n for n in range(N):\n x = X[n]\n T = len(x)\n # print \"den shape:\", den.shape\n # test = (alphas[n][:-1] * betas[n][:-1]).sum(axis=0, keepdims=True).T\n # print \"shape (alphas[n][:-1] * betas[n][:-1]).sum(axis=0): \", test.shape\n den1 += (alphas[n][:-1] * betas[n][:-1]).sum(axis=0, keepdims=True).T / P[n]\n den2 += (alphas[n] * betas[n]).sum(axis=0, keepdims=True).T / P[n]\n\n # tmp2 = np.zeros((self.M, 1))\n # for i in range(self.M):\n # for t in range(T-1):\n # tmp2[i] += alphas[n][t,i] * betas[n][t,i]\n # tmp2 /= P[n]\n # # print \"diff:\", np.abs(tmp1 - tmp2).sum()\n # den += tmp1\n\n # numerator for A\n a_num_n = np.zeros((self.M, self.M))\n for i in range(self.M):\n for j in range(self.M):\n for t in range(T-1):\n a_num_n[i,j] += alphas[n][t,i] * self.A[i,j] * self.B[j, x[t+1]] * betas[n][t+1,j]\n a_num += a_num_n / P[n]\n\n # numerator for B\n # b_num_n = np.zeros((self.M, V))\n # for i in range(self.M):\n # for j in range(V):\n # for t in range(T):\n # if x[t] == j:\n # b_num_n[i,j] += alphas[n][t][i] * betas[n][t][i]\n b_num_n2 = np.zeros((self.M, V))\n for i in range(self.M):\n for t in range(T):\n b_num_n2[i,x[t]] += alphas[n][t,i] * betas[n][t,i]\n b_num += b_num_n2 / P[n]\n # tmp1 = a_num / den1\n # tmp2 = np.zeros(a_num.shape)\n # for i in range(self.M):\n # for j in range(self.M):\n # tmp2[i,j] = a_num[i,j] / den1[i]\n # print \"diff:\", np.abs(tmp1 - tmp2).sum()\n # print \"tmp1:\", tmp1\n # print \"tmp2:\", tmp2\n self.A = a_num / den1\n self.B = b_num / den2\n # print \"P:\", P\n # break\n print(\"A:\", self.A)\n print(\"B:\", self.B)\n print(\"pi:\", self.pi)\n\n print(\"Fit duration:\", (datetime.now() - t0))\n\n plt.plot(costs)\n plt.show()\n\n def likelihood(self, x):\n # returns log P(x | model)\n # using the forward part of the forward-backward algorithm\n T = len(x)\n alpha = np.zeros((T, self.M))\n alpha[0] = self.pi*self.B[:,x[0]]\n for t in range(1, T):\n alpha[t] = alpha[t-1].dot(self.A) * self.B[:, x[t]]\n return alpha[-1].sum()\n\n def likelihood_multi(self, X):\n return np.array([self.likelihood(x) for x in X])\n\n def log_likelihood_multi(self, X):\n return np.log(self.likelihood_multi(X))\n\n def get_state_sequence(self, x):\n # returns the most likely state sequence given observed sequence x\n # using the Viterbi algorithm\n T = len(x)\n delta = np.zeros((T, self.M))\n psi = np.zeros((T, self.M))\n delta[0] = self.pi*self.B[:,x[0]]\n for t in range(1, T):\n for j in range(self.M):\n delta[t,j] = np.max(delta[t-1]*self.A[:,j]) * self.B[j, x[t]]\n psi[t,j] = np.argmax(delta[t-1]*self.A[:,j])\n\n # backtrack\n states = np.zeros(T, dtype=np.int32)\n states[T-1] = np.argmax(delta[T-1])\n for t in range(T-2, -1, -1):\n states[t] = psi[t+1, states[t+1]]\n return states\n\ndef fit_coin():\n X = []\n for line in open('coin_data.txt'):\n # 1 for H, 0 for T\n x = [1 if e == 'H' else 0 for e in line.rstrip()]\n X.append(x)\n\n hmm = HMM(2)\n hmm.fit(X)\n L = hmm.log_likelihood_multi(X).sum()\n print(\"LL with fitted params:\", L)\n\n # try true values\n hmm.pi = np.array([0.5, 0.5])\n hmm.A = np.array([[0.1, 0.9], [0.8, 0.2]])\n hmm.B = np.array([[0.6, 0.4], [0.3, 0.7]])\n L = hmm.log_likelihood_multi(X).sum()\n print(\"LL with true params:\", L)\n\n # try viterbi\n print(\"Best state sequence for:\", X[0])\n print(hmm.get_state_sequence(X[0]))\n\n\nif __name__ == '__main__':\n fit_coin()\n" }, { "path": "hmm_class/hmmd_scaled.py", "content": "# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python\n# http://lazyprogrammer.me\n# Discrete Hidden Markov Model (HMM) with scaling\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef random_normalized(d1, d2):\n x = np.random.random((d1, d2))\n return x / x.sum(axis=1, keepdims=True)\n\n\nclass HMM:\n def __init__(self, M):\n self.M = M # number of hidden states\n \n def fit(self, X, max_iter=30):\n np.random.seed(123)\n # train the HMM model using the Baum-Welch algorithm\n # a specific instance of the expectation-maximization algorithm\n\n # determine V, the vocabulary size\n # assume observables are already integers from 0..V-1\n # X is a jagged array of observed sequences\n V = max(max(x) for x in X) + 1\n N = len(X)\n\n self.pi = np.ones(self.M) / self.M # initial state distribution\n self.A = random_normalized(self.M, self.M) # state transition matrix\n self.B = random_normalized(self.M, V) # output distribution\n\n print(\"initial A:\", self.A)\n print(\"initial B:\", self.B)\n\n costs = []\n for it in range(max_iter):\n if it % 10 == 0:\n print(\"it:\", it)\n # alpha1 = np.zeros((N, self.M))\n alphas = []\n betas = []\n scales = []\n logP = np.zeros(N)\n for n in range(N):\n x = X[n]\n T = len(x)\n scale = np.zeros(T)\n # alpha1[n] = self.pi*self.B[:,x[0]]\n alpha = np.zeros((T, self.M))\n alpha[0] = self.pi*self.B[:,x[0]]\n scale[0] = alpha[0].sum()\n alpha[0] /= scale[0]\n for t in range(1, T):\n alpha_t_prime = alpha[t-1].dot(self.A) * self.B[:, x[t]]\n scale[t] = alpha_t_prime.sum()\n alpha[t] = alpha_t_prime / scale[t]\n logP[n] = np.log(scale).sum()\n alphas.append(alpha)\n scales.append(scale)\n\n beta = np.zeros((T, self.M))\n beta[-1] = 1\n for t in range(T - 2, -1, -1):\n beta[t] = self.A.dot(self.B[:, x[t+1]] * beta[t+1]) / scale[t+1]\n betas.append(beta)\n\n\n cost = np.sum(logP)\n costs.append(cost)\n\n # now re-estimate pi, A, B\n self.pi = np.sum((alphas[n][0] * betas[n][0]) for n in range(N)) / N\n\n den1 = np.zeros((self.M, 1))\n den2 = np.zeros((self.M, 1))\n a_num = np.zeros((self.M, self.M))\n b_num = np.zeros((self.M, V))\n for n in range(N):\n x = X[n]\n T = len(x)\n den1 += (alphas[n][:-1] * betas[n][:-1]).sum(axis=0, keepdims=True).T\n den2 += (alphas[n] * betas[n]).sum(axis=0, keepdims=True).T\n\n # numerator for A\n # a_num_n = np.zeros((self.M, self.M))\n for i in range(self.M):\n for j in range(self.M):\n for t in range(T-1):\n a_num[i,j] += alphas[n][t,i] * betas[n][t+1,j] * self.A[i,j] * self.B[j, x[t+1]] / scales[n][t+1]\n # a_num += a_num_n\n\n # numerator for B\n # for i in range(self.M):\n # for j in range(V):\n # for t in range(T):\n # if x[t] == j:\n # b_num[i,j] += alphas[n][t][i] * betas[n][t][i]\n for i in range(self.M):\n for t in range(T):\n b_num[i,x[t]] += alphas[n][t,i] * betas[n][t,i]\n self.A = a_num / den1\n self.B = b_num / den2\n print(\"A:\", self.A)\n print(\"B:\", self.B)\n print(\"pi:\", self.pi)\n\n plt.plot(costs)\n plt.show()\n\n def log_likelihood(self, x):\n # returns log P(x | model)\n # using the forward part of the forward-backward algorithm\n T = len(x)\n scale = np.zeros(T)\n alpha = np.zeros((T, self.M))\n alpha[0] = self.pi*self.B[:,x[0]]\n scale[0] = alpha[0].sum()\n alpha[0] /= scale[0]\n for t in range(1, T):\n alpha_t_prime = alpha[t-1].dot(self.A) * self.B[:, x[t]]\n scale[t] = alpha_t_prime.sum()\n alpha[t] = alpha_t_prime / scale[t]\n return np.log(scale).sum()\n\n def log_likelihood_multi(self, X):\n return np.array([self.log_likelihood(x) for x in X])\n\n def get_state_sequence(self, x):\n # returns the most likely state sequence given observed sequence x\n # using the Viterbi algorithm\n T = len(x)\n delta = np.zeros((T, self.M))\n psi = np.zeros((T, self.M))\n delta[0] = np.log(self.pi) + np.log(self.B[:,x[0]])\n for t in range(1, T):\n for j in range(self.M):\n delta[t,j] = np.max(delta[t-1] + np.log(self.A[:,j])) + np.log(self.B[j, x[t]])\n psi[t,j] = np.argmax(delta[t-1] + np.log(self.A[:,j]))\n\n # backtrack\n states = np.zeros(T, dtype=np.int32)\n states[T-1] = np.argmax(delta[T-1])\n for t in range(T-2, -1, -1):\n states[t] = psi[t+1, states[t+1]]\n return states\n\n\ndef fit_coin():\n X = []\n for line in open('coin_data.txt'):\n # 1 for H, 0 for T\n x = [1 if e == 'H' else 0 for e in line.rstrip()]\n X.append(x)\n\n hmm = HMM(2)\n hmm.fit(X)\n L = hmm.log_likelihood_multi(X).sum()\n print(\"LL with fitted params:\", L)\n\n # try true values\n hmm.pi = np.array([0.5, 0.5])\n hmm.A = np.array([[0.1, 0.9], [0.8, 0.2]])\n hmm.B = np.array([[0.6, 0.4], [0.3, 0.7]])\n L = hmm.log_likelihood_multi(X).sum()\n print(\"LL with true params:\", L)\n\n # try viterbi\n print(\"Best state sequence for:\", X[0])\n print(hmm.get_state_sequence(X[0]))\n\n\nif __name__ == '__main__':\n fit_coin()\n" }, { "path": "hmm_class/hmmd_tf.py", "content": "# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python\n# http://lazyprogrammer.me\n# Discrete Hidden Markov Model (HMM) in Tensorflow using gradient descent.\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\n\n\nclass HMM:\n def __init__(self, M):\n self.M = M # number of hidden states\n\n def set_session(self, session):\n self.session = session\n \n def fit(self, X, max_iter=10, print_period=1):\n # train the HMM model using stochastic gradient descent\n\n N = len(X)\n print(\"number of train samples:\", N)\n\n costs = []\n for it in range(max_iter):\n if it % print_period == 0:\n print(\"it:\", it)\n \n for n in range(N):\n # this would of course be much faster if we didn't do this on\n # every iteration of the loop\n c = self.get_cost_multi(X).sum()\n costs.append(c)\n self.session.run(self.train_op, feed_dict={self.tfx: X[n]})\n\n plt.plot(costs)\n plt.show()\n\n def get_cost(self, x):\n # returns log P(x | model)\n # using the forward part of the forward-backward algorithm\n # print \"getting cost for:\", x\n return self.session.run(self.cost, feed_dict={self.tfx: x})\n\n def log_likelihood(self, x):\n return -self.session.run(self.cost, feed_dict={self.tfx: x})\n\n def get_cost_multi(self, X):\n return np.array([self.get_cost(x) for x in X])\n\n def build(self, preSoftmaxPi, preSoftmaxA, preSoftmaxB):\n M, V = preSoftmaxB.shape\n\n self.preSoftmaxPi = tf.Variable(preSoftmaxPi)\n self.preSoftmaxA = tf.Variable(preSoftmaxA)\n self.preSoftmaxB = tf.Variable(preSoftmaxB)\n\n pi = tf.nn.softmax(self.preSoftmaxPi)\n A = tf.nn.softmax(self.preSoftmaxA)\n B = tf.nn.softmax(self.preSoftmaxB)\n\n # define cost\n self.tfx = tf.placeholder(tf.int32, shape=(None,), name='x')\n def recurrence(old_a_old_s, x_t):\n old_a = tf.reshape(old_a_old_s[0], (1, M))\n a = tf.matmul(old_a, A) * B[:, x_t]\n a = tf.reshape(a, (M,))\n s = tf.reduce_sum(a)\n return (a / s), s\n\n # remember, tensorflow scan is going to loop through\n # all the values!\n # we treat the first value differently than the rest\n # so we only want to loop through tfx[1:]\n # the first scale being 1 doesn't affect the log-likelihood\n # because log(1) = 0\n alpha, scale = tf.scan(\n fn=recurrence,\n elems=self.tfx[1:],\n initializer=(pi*B[:,self.tfx[0]], np.float32(1.0)),\n )\n\n self.cost = -tf.reduce_sum(tf.log(scale))\n self.train_op = tf.train.AdamOptimizer(1e-2).minimize(self.cost)\n\n def init_random(self, V):\n preSoftmaxPi0 = np.zeros(self.M).astype(np.float32) # initial state distribution\n preSoftmaxA0 = np.random.randn(self.M, self.M).astype(np.float32) # state transition matrix\n preSoftmaxB0 = np.random.randn(self.M, V).astype(np.float32) # output distribution\n\n self.build(preSoftmaxPi0, preSoftmaxA0, preSoftmaxB0)\n\n def set(self, preSoftmaxPi, preSoftmaxA, preSoftmaxB):\n op1 = self.preSoftmaxPi.assign(preSoftmaxPi)\n op2 = self.preSoftmaxA.assign(preSoftmaxA)\n op3 = self.preSoftmaxB.assign(preSoftmaxB)\n self.session.run([op1, op2, op3])\n\n\ndef fit_coin():\n X = []\n for line in open('coin_data.txt'):\n # 1 for H, 0 for T\n x = [1 if e == 'H' else 0 for e in line.rstrip()]\n X.append(x)\n\n # X = np.array(X).astype(np.int32)\n\n hmm = HMM(2)\n # the entire graph (including optimizer's variables) must be built\n # before calling global variables initializer!\n hmm.init_random(2)\n init = tf.global_variables_initializer()\n with tf.Session() as session:\n session.run(init)\n hmm.set_session(session)\n hmm.fit(X, max_iter=5)\n L = hmm.get_cost_multi(X).sum()\n print(\"LL with fitted params:\", L)\n\n # try true values\n # remember these must be in their \"pre-softmax\" forms\n pi = np.log( np.array([0.5, 0.5]) ).astype(np.float32)\n A = np.log( np.array([[0.1, 0.9], [0.8, 0.2]]) ).astype(np.float32)\n B = np.log( np.array([[0.6, 0.4], [0.3, 0.7]]) ).astype(np.float32)\n hmm.set(pi, A, B)\n L = hmm.get_cost_multi(X).sum()\n print(\"LL with true params:\", L)\n\n\nif __name__ == '__main__':\n fit_coin()\n" }, { "path": "hmm_class/hmmd_theano.py", "content": "# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python\n# http://lazyprogrammer.me\n# Discrete Hidden Markov Model (HMM) in Theano using gradient descent.\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport theano\nimport theano.tensor as T\nimport matplotlib.pyplot as plt\n\n\ndef random_normalized(d1, d2):\n x = np.random.random((d1, d2))\n return x / x.sum(axis=1, keepdims=True)\n\n\nclass HMM:\n def __init__(self, M):\n self.M = M # number of hidden states\n \n def fit(self, X, learning_rate=0.001, max_iter=10, V=None, p_cost=1.0, print_period=10):\n # train the HMM model using stochastic gradient descent\n\n # determine V, the vocabulary size\n # assume observables are already integers from 0..V-1\n # X is a jagged array of observed sequences\n if V is None:\n V = max(max(x) for x in X) + 1\n N = len(X)\n print(\"number of train samples:\", N)\n\n pi0 = np.ones(self.M) / self.M # initial state distribution\n A0 = random_normalized(self.M, self.M) # state transition matrix\n B0 = random_normalized(self.M, V) # output distribution\n\n thx, cost = self.set(pi0, A0, B0)\n\n pi_update = self.pi - learning_rate*T.grad(cost, self.pi)\n pi_update = pi_update / pi_update.sum()\n\n A_update = self.A - learning_rate*T.grad(cost, self.A)\n A_update = A_update / A_update.sum(axis=1).dimshuffle(0, 'x')\n\n B_update = self.B - learning_rate*T.grad(cost, self.B)\n B_update = B_update / B_update.sum(axis=1).dimshuffle(0, 'x')\n\n updates = [\n (self.pi, pi_update),\n (self.A, A_update),\n (self.B, B_update),\n ]\n\n train_op = theano.function(\n inputs=[thx],\n updates=updates,\n allow_input_downcast=True,\n )\n\n # self.cost_op = theano.function(\n # inputs=[thx],\n # outputs=cost,\n # allow_input_downcast=True,\n # )\n\n costs = []\n for it in range(max_iter):\n if it % print_period == 0:\n print(\"it:\", it)\n \n for n in range(N):\n # print \"about to get the cost\"\n # this would of course be much faster if we didn't do this on\n # every iteration of the loop\n c = self.get_cost_multi(X, p_cost).sum()\n costs.append(c)\n train_op(X[n])\n\n print(\"A:\", self.A.get_value())\n print(\"B:\", self.B.get_value())\n print(\"pi:\", self.pi.get_value())\n print(\"len(costs):\", len(costs))\n plt.plot(costs)\n plt.show()\n\n def get_cost(self, x):\n # returns log P(x | model)\n # using the forward part of the forward-backward algorithm\n return self.cost_op(x)\n\n def log_likelihood(self, x):\n return -self.cost_op(x)\n\n def get_cost_multi(self, X, p_cost=1.0):\n P = np.random.random(len(X))\n return np.array([self.get_cost(x) for x, p in zip(X, P) if p < p_cost])\n\n def set(self, pi, A, B):\n self.pi = theano.shared(pi)\n self.A = theano.shared(A)\n self.B = theano.shared(B)\n\n # define cost\n thx = T.ivector('thx')\n def recurrence(t, old_a, x):\n a = old_a.dot(self.A) * self.B[:, x[t]]\n s = a.sum()\n return (a / s), s\n\n [alpha, scale], _ = theano.scan(\n fn=recurrence,\n sequences=T.arange(1, thx.shape[0]),\n outputs_info=[self.pi*self.B[:,thx[0]], None],\n n_steps=thx.shape[0]-1,\n non_sequences=thx\n )\n\n cost = -T.log(scale).sum()\n self.cost_op = theano.function(\n inputs=[thx],\n outputs=cost,\n allow_input_downcast=True,\n )\n return thx, cost\n\n\ndef fit_coin():\n X = []\n for line in open('coin_data.txt'):\n # 1 for H, 0 for T\n x = [1 if e == 'H' else 0 for e in line.rstrip()]\n X.append(x)\n\n hmm = HMM(2)\n hmm.fit(X)\n L = hmm.get_cost_multi(X).sum()\n print(\"LL with fitted params:\", L)\n\n # try true values\n pi = np.array([0.5, 0.5])\n A = np.array([[0.1, 0.9], [0.8, 0.2]])\n B = np.array([[0.6, 0.4], [0.3, 0.7]])\n hmm.set(pi, A, B)\n L = hmm.get_cost_multi(X).sum()\n print(\"LL with true params:\", L)\n\n\nif __name__ == '__main__':\n fit_coin()\n" }, { "path": "hmm_class/hmmd_theano2.py", "content": "# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python\n# http://lazyprogrammer.me\n# Discrete Hidden Markov Model (HMM) in Theano using gradient descent.\n\n# This script differs from hmmd_theano.py in the following way:\n# Instead of re-normalizing the parameters at each iteration,\n# we instead make the parameters free to vary between -inf to +inf.\n# We then use softmax to ensure the probabilities are positive and sum to 1.\n\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport theano\nimport theano.tensor as T\nimport matplotlib.pyplot as plt\n\n\nclass HMM:\n def __init__(self, M):\n self.M = M # number of hidden states\n \n def fit(self, X, learning_rate=0.001, max_iter=10, V=None, print_period=1):\n # train the HMM model using stochastic gradient descent\n # print \"X to train:\", X\n\n # determine V, the vocabulary size\n # assume observables are already integers from 0..V-1\n # X is a jagged array of observed sequences\n if V is None:\n V = max(max(x) for x in X) + 1\n N = len(X)\n print(\"number of train samples:\", N)\n\n preSoftmaxPi0 = np.zeros(self.M) # initial state distribution\n preSoftmaxA0 = np.random.randn(self.M, self.M) # state transition matrix\n preSoftmaxB0 = np.random.randn(self.M, V) # output distribution\n\n thx, cost = self.set(preSoftmaxPi0, preSoftmaxA0, preSoftmaxB0)\n\n pi_update = self.preSoftmaxPi - learning_rate*T.grad(cost, self.preSoftmaxPi)\n A_update = self.preSoftmaxA - learning_rate*T.grad(cost, self.preSoftmaxA)\n B_update = self.preSoftmaxB - learning_rate*T.grad(cost, self.preSoftmaxB)\n\n updates = [\n (self.preSoftmaxPi, pi_update),\n (self.preSoftmaxA, A_update),\n (self.preSoftmaxB, B_update),\n ]\n\n train_op = theano.function(\n inputs=[thx],\n updates=updates,\n allow_input_downcast=True,\n )\n\n costs = []\n for it in range(max_iter):\n if it % print_period == 0:\n print(\"it:\", it)\n \n for n in range(N):\n # this would of course be much faster if we didn't do this on\n # every iteration of the loop\n c = self.get_cost_multi(X).sum()\n costs.append(c)\n train_op(X[n])\n\n # print \"A:\", self.A.get_value()\n # print \"B:\", self.B.get_value()\n # print \"pi:\", self.pi.get_value()\n plt.plot(costs)\n plt.show()\n\n def get_cost(self, x):\n # returns log P(x | model)\n # using the forward part of the forward-backward algorithm\n # print \"getting cost for:\", x\n return self.cost_op(x)\n\n def log_likelihood(self, x):\n return -self.cost_op(x)\n\n def get_cost_multi(self, X):\n return np.array([self.get_cost(x) for x in X])\n\n def set(self, preSoftmaxPi, preSoftmaxA, preSoftmaxB):\n self.preSoftmaxPi = theano.shared(preSoftmaxPi)\n self.preSoftmaxA = theano.shared(preSoftmaxA)\n self.preSoftmaxB = theano.shared(preSoftmaxB)\n\n pi = T.nnet.softmax(self.preSoftmaxPi).flatten()\n # softmax returns 1xD if input is a 1-D array of size D\n A = T.nnet.softmax(self.preSoftmaxA)\n B = T.nnet.softmax(self.preSoftmaxB)\n\n # define cost\n thx = T.ivector('thx')\n def recurrence(t, old_a, x):\n a = old_a.dot(A) * B[:, x[t]]\n s = a.sum()\n return (a / s), s\n\n [alpha, scale], _ = theano.scan(\n fn=recurrence,\n sequences=T.arange(1, thx.shape[0]),\n outputs_info=[pi*B[:,thx[0]], None],\n n_steps=thx.shape[0]-1,\n non_sequences=thx\n )\n\n cost = -T.log(scale).sum()\n self.cost_op = theano.function(\n inputs=[thx],\n outputs=cost,\n allow_input_downcast=True,\n )\n return thx, cost\n\n\ndef fit_coin():\n X = []\n for line in open('coin_data.txt'):\n # 1 for H, 0 for T\n x = [1 if e == 'H' else 0 for e in line.rstrip()]\n X.append(x)\n\n hmm = HMM(2)\n hmm.fit(X)\n L = hmm.get_cost_multi(X).sum()\n print(\"LL with fitted params:\", L)\n\n # try true values\n # remember these must be in their \"pre-softmax\" forms\n pi = np.log( np.array([0.5, 0.5]) )\n A = np.log( np.array([[0.1, 0.9], [0.8, 0.2]]) )\n B = np.log( np.array([[0.6, 0.4], [0.3, 0.7]]) )\n hmm.set(pi, A, B)\n L = hmm.get_cost_multi(X).sum()\n print(\"LL with true params:\", L)\n\n\nif __name__ == '__main__':\n fit_coin()\n" }, { "path": "hmm_class/robert_frost.txt", "content": "Two roads diverged in a yellow wood,\nAnd sorry I could not travel both\nAnd be one traveler, long I stood\nAnd looked down one as far as I could\nTo where it bent in the undergrowth; \n\nThen took the other, as just as fair,\nAnd having perhaps the better claim\nBecause it was grassy and wanted wear,\nThough as for that the passing there\nHad worn them really about the same,\n\nAnd both that morning equally lay\nIn leaves no step had trodden black.\nOh, I kept the first for another day! \nYet knowing how way leads on to way\nI doubted if I should ever come back.\n\nI shall be telling this with a sigh\nSomewhere ages and ages hence:\nTwo roads diverged in a wood, and I,\nI took the one less traveled by,\nAnd that has made all the difference.\n\nWhose woods these are I think I know.\nHis house is in the village, though; \nHe will not see me stopping here\nTo watch his woods fill up with snow.\n\nMy little horse must think it queer\nTo stop without a farmhouse near\nBetween the woods and frozen lake\nThe darkest evening of the year.\n\nHe gives his harness bells a shake\nTo ask if there is some mistake.\nThe only other sound's the sweep\nOf easy wind and downy flake.\n\nThe woods are lovely, dark and deep,\nBut I have promises to keep,\nAnd miles to go before I sleep,\nAnd miles to go before I sleep. \n\nSome say the world will end in fire,\nSome say in ice.\nFrom what I've tasted of desire\nI hold with those who favor fire.\nBut if it had to perish twice,\nI think I know enough of hate\nTo say that for destruction ice\nIs also great\nAnd would suffice.\n\nNature's first green is gold,\nHer hardest hue to hold.\nHer early leaf's a flower; \nBut only so an hour.\nThen leaf subsides to leaf,\nSo Eden sank to grief,\nSo dawn goes down to day\nNothing gold can stay.\n\nI have been one acquainted with the night.\nI have walked out in rain - and back in rain.\nI have outwalked the furthest city light.\n\nI have looked down the saddest city lane.\nI have passed by the watchman on his beat\nAnd dropped my eyes, unwilling to explain.\n\nI have stood still and stopped the sound of feet\nWhen far away an interrupted cry\nCame over houses from another street,\n\nBut not to call me back or say good-bye; \nAnd further still at an unearthly height,\nOne luminary clock against the sky\n\nProclaimed the time was neither wrong nor right.\nI have been one acquainted with the night.\n\nWhen I go up through the mowing field,\nThe headless aftermath,\nSmooth-laid like thatch with the heavy dew,\nHalf closes the garden path.\n\nAnd when I come to the garden ground,\nThe whir of sober birds\nUp from the tangle of withered weeds\nIs sadder than any words\n\nA tree beside the wall stands bare,\nBut a leaf that lingered brown,\nDisturbed, I doubt not, by my thought,\nComes softly rattling down.\n\nI end not far from my going forth\nBy picking the faded blue\nOf the last remaining aster flower\nTo carry again to you.\n\nA voice said, Look me in the stars\nAnd tell me truly, men of earth,\nIf all the soul-and-body scars\nWere not too much to pay for birth.\n\nTo think to know the country and now know\nThe hillside on the day the sun lets go\nTen million silver lizards out of snow!\nAs often as I've seen it done before\nI can't pretend to tell the way it's done.\nIt looks as if some magic of the sun\nLifted the rug that bred them on the floor\nAnd the light breaking on them made them run.\nBut if I though to stop the wet stampede,\nAnd caught one silver lizard by the tail,\nAnd put my foot on one without avail,\nAnd threw myself wet-elbowed and wet-kneed\nIn front of twenty others' wriggling speed,- \nIn the confusion of them all aglitter,\nAnd birds that joined in the excited fun\nBy doubling and redoubling song and twitter,\nI have no doubt I'd end by holding none.\n\nIt takes the moon for this. The sun's a wizard\nBy all I tell; but so's the moon a witch.\nFrom the high west she makes a gentle cast\nAnd suddenly, without a jerk or twitch,\nShe has her speel on every single lizard.\nI fancied when I looked at six o'clock\nThe swarm still ran and scuttled just as fast.\nThe moon was waiting for her chill effect.\nI looked at nine: the swarm was turned to rock\nIn every lifelike posture of the swarm,\nTransfixed on mountain slopes almost erect.\nAcross each other and side by side they lay.\nThe spell that so could hold them as they were\nWas wrought through trees without a breath of storm\nTo make a leaf, if there had been one, stir.\nOne lizard at the end of every ray.\nThe thought of my attempting such a stray! \n\nONCE on the kind of day called \"weather breeder,\"\nWhen the heat slowly hazes and the sun\nBy its own power seems to be undone,\nI was half boring through, half climbing through\nA swamp of cedar. Choked with oil of cedar\nAnd scurf of plants, and weary and over-heated,\nAnd sorry I ever left the road I knew,\nI paused and rested on a sort of hook\nThat had me by the coat as good as seated,\nAnd since there was no other way to look,\nLooked up toward heaven, and there against the blue,\nStood over me a resurrected tree,\nA tree that had been down and raised again—\nA barkless spectre. He had halted too,\nAs if for fear of treading upon me.\nI saw the strange position of his hands—\nUp at his shoulders, dragging yellow strands\nOf wire with something in it from men to men.\n\"You here?\" I said. \"Where aren't you nowadays\nAnd what's the news you carry—if you know?\nAnd tell me where you're off for—Montreal?\nMe? I'm not off for anywhere at all.\nSometimes I wander out of beaten ways\nHalf looking for the orchid Calypso.\"\n\nBrown lived at such a lofty farm\nThat everyone for miles could see\nHis lantern when he did his chores\nIn winter after half-past three.\n\nAnd many must have seen him make\nHis wild descent from there one night,\n'Cross lots, 'cross walls, 'cross everything,\nDescribing rings of lantern light.\n\nBetween the house and barn the gale\nGot him by something he had on\nAnd blew him out on the icy crust\nThat cased the world, and he was gone!\n\nWalls were all buried, trees were few:\nHe saw no stay unless he stove\nA hole in somewhere with his heel.\nBut though repeatedly he strove\n\nAnd stamped and said things to himself,\nAnd sometimes something seemed to yield,\nHe gained no foothold, but pursued\nHis journey down from field to field.\n\nSometimes he came with arms outspread\nLike wings, revolving in the scene\nUpon his longer axis, and\nWith no small dignity of mien.\n\nFaster or slower as he chanced,\nSitting or standing as he chose,\nAccording as he feared to risk\nHis neck, or thought to spare his clothes,\n\nHe never let the lantern drop.\nAnd some exclaimed who saw afar\nThe figures he described with it,\n\"I wonder what those signals are\n\nBrown makes at such an hour of night!\nHe's celebrating something strange.\nI wonder if he's sold his farm,\nOr been made Master of the Grange.\"\n\nHe reeled, he lurched, he bobbed, he checked;\nHe fell and made the lantern rattle\n(But saved the light from going out.)\nSo half-way down he fought the battle\n\nIncredulous of his own bad luck.\nAnd then becoming reconciled\nTo everything, he gave it up\nAnd came down like a coasting child.\n\n\"Well—I—be—\" that was all he said,\nAs standing in the river road,\nHe looked back up the slippery slope\n(Two miles it was) to his abode.\n\nSometimes as an authority\nOn motor-cars, I'm asked if I\nShould say our stock was petered out,\nAnd this is my sincere reply:\n\nYankees are what they always were.\nDon't think Brown ever gave up hope\nOf getting home again because\nHe couldn't climb that slippery slope;\n\nOr even thought of standing there\nUntil the January thaw\nShould take the polish off the crust.\nHe bowed with grace to natural law,\n\nAnd then went round it on his feet,\nAfter the manner of our stock;\nNot much concerned for those to whom,\nAt that particular time o'clock,\n\nIt must have looked as if the course\nHe steered was really straight away\nFrom that which he was headed for—\nNot much concerned for them, I say:\n\nNo more so than became a man—\nAnd politician at odd seasons.\nI've kept Brown standing in the cold\nWhile I invested him with reasons;\n\nBut now he snapped his eyes three times;\nThen shook his lantern, saying, \"Ile's\n'Bout out!\" and took the long way home\nBy road, a matter of several miles.\n\nNOW that they've got it settled whose I be,\nI'm going to tell them something they won't like:\nThey've got it settled wrong, and I can prove it.\nFlattered I must be to have two towns fighting\nTo make a present of me to each other.\nThey don't dispose me, either one of them,\nTo spare them any trouble. Double trouble's\nAlways the witch's motto anyway.\nI'll double theirs for both of them- you watch me.\nThey'll find they've got the whole thing to do over,\nThat is, if facts is what they want to go by.\nThey set a lot (now don't they?) by a record\nOf Arthur Amy's having once been up\nFor Hog Reeve in March Meeting here in Warren.\nI could have told them any time this twelvemonth\nThe Arthur Amy I was married to\nCouldn't have been the one they say was up\nIn Warren at March Meeting for the reason\nHe wa'n't but fifteen at the time they say.\nThe Arthur Amy I was married to\nvoted the only times he ever voted,\nWhich wasn't many, in the town of Wentworth.\nOne of the times was when 'twas in the warrant\nTo see if the town wanted to take over\nThe tote road to our clearing where we lived.\nI'll tell you who'd remember- Heman Lapish.\nTheir Arthur Amy was the father of mine.\nSo now they've dragged it through the law courts once\nI guess they'd better drag it through again.\nWentworth and Warren's both good towns to live in,\nOnly I happen to prefer to live\nIn Wentworth from now on; and when all's said,\nRight's right, and the temptation to do right\nWhen I can hurt someone by doing it\nHas always been too much for me, it has.\nI know of some folks that'd be set up\nAt having in their town a noted witch:\nBut most would have to think of the expense\nThat even I would be. They ought to know\nThat as a witch I'd often milk a bat\nAnd that'd be enough to last for days.\nIt'd make my position stronger, I think,\nIf I was to consent to give some sign\nTo make it surer that I was a witch?\nIt wa'n't no sign, I s'pose, when Mallice Huse\nSaid that I took him out in his old age\nAnd rode all over everything on him\nUntil I'd had him worn to skin and bones,\nAnd if I'd left him hitched unblanketed\nIn front of one Town Hall, I'd left him hitched\nIn front of every one in Grafton County.\nSome cried shame on me not to blanket him,\nThe poor old man. It would have been all right\nIf some one hadn't said to gnaw the posts\nHe stood beside and leave his trade mark on them,\nSo they could recognize them. Not a post\nThat they could hear tell of was scarified.\nThey made him keep on gnawing till he whined.\nThen that same smarty someone said to look- \nHe'd bet Huse was a cribber and had gnawed\nThe crib he slept in- and as sure's you're born\nThey found he'd gnawed the four posts of his bed,\nAll four of them to splinters. What did that prove?\nNot that he hadn't gnawed the hitching posts\nHe said he had besides. Because a horse\nGnaws in the stable ain't no proof to me\nHe don't gnaw trees and posts and fences too.\nBut everybody took it for proof.\nI was a strapping girl of twenty then.\nThe smarty someone who spoiled everything\nWas Arthur Amy. You know who he was.\nThat was the way he started courting me.\nHe never said much after we were married,\nBut I mistrusted he was none too proud\nOf having interfered in the Huse business.\nI guess he found he got more out of me\nBy having me a witch. Or something happened\nTo turn him round. He got to saying things\nTo undo what he'd done and make it right,\nLike, 'No, she ain't come back from kiting yet.\nLast night was one of her nights out. She's kiting.\nShe thinks when the wind makes a night of it\nShe might as well herself.' But he liked best\nTo let on he was plagued to death with me:\nIf anyone had seen me coming home\nOver the ridgepole, 'stride of a broomstick,\nAs often as he had in the tail of the night,\nHe guessed they'd know what he had to put up with.\nWell, I showed Arthur Amy signs enough\nOff from the house as far as we could keep\nAnd from barn smells you can't wash out of ploughed ground\nWith all the rain and snow of seven years;\nAnd I don't mean just skulls of Roger's Rangers\nOn Moosilauke, but woman signs to man,\nOnly bewitched so I would last him longer.\nUp where the trees grow short, the mosses tall,\nI made him gather me wet snow berries\nOn slippery rocks beside a waterfall.\nI made him do it for me in the dark.\nAnd he liked everything I made him do.\nI hope if he is where he sees me now\nHe's so far off he can't see what I've come to.\nYou _can_ come down from everything to nothing.\nAll is, if I'd a-known when I was young\nAnd full of it, that this would be the end,\nIt doesn't seem as if I'd had the courage\nTo make so free and kick up in folks' faces.\nI might have, but it doesn't seem as if.\n\nOld Davis owned a solid mica mountain\nIn Dalton that would someday make his fortune.\nThere'd been some Boston people out to see it:\nAnd experts said that deep down in the mountain\nThe mica sheets were big as plate-glass windows.\nHe'd like to take me there and show it to me.\n\n'I'll tell you what you show me. You remember\nYou said you knew the place where once, on Kinsman,\nThe early Mormons made a settlement\nAnd built a stone baptismal font outdoors-\nBut Smith, or someone, called them off the mountain\nTo go West to a worse fight with the desert.\nYou said you'd seen the stone baptismal font.\nWell, take me there.'\n\nSomeday I will.'\n\n'Today.'\n\n'Huh, that old bathtub, what is that to see?\nLet's talk about it.'\n\n'Let's go see the place.'\n\n'To shut you up I'll tell you what I'll do:\nI'll find that fountain if it takes all summer,\nAnd both of our united strengths, to do it.'\n\n'You've lost it, then?'\n\n'Not so but I can find it.\nNo doubt it's grown up some to woods around it.\nThe mountain may have shifted since I saw it\nIn eighty-five.'\n\n'As long ago as that?'\n\n'If I remember rightly, it had sprung\nA leak and emptied then. And forty years\nCan do a good deal to bad masonry.\nYou won't see any Mormon swimming in it.\nBut you have said it, and we're off to find it.\nOld as I am, I'm going to let myself\nBe dragged by you all over everywhere- '\n'I thought you were a guide.'\n\n'I am a guide,\nAnd that's why I can't decently refuse you.'\n\nWe made a day of it out of the world,\nAscending to descend to reascend.\nThe old man seriously took his bearings,\nAnd spoke his doubts in every open place.\n\nWe came out on a look-off where we faced\nA cliff, and on the cliff a bottle painted,\nOr stained by vegetation from above,\nA likeness to surprise the thrilly tourist.\n\n'Well, if I haven't brought you to the fountain,\nAt least I've brought you to the famous Bottle.'\n\n'I won't accept the substitute. It's empty.'\n\n'So's everything.'\n\n'I want my fountain.'\n\n'I guess you'd find the fountain just as empty.\nAnd anyway this tells me where I am.'\n\n'Hadn't you long suspected where you were?'\n\n'You mean miles from that Mormon settlement?\nLook here, you treat your guide with due respect\nIf you don't want to spend the night outdoors.\nI vow we must be near the place from where\nThe two converging slides, the avalanches,\nOn Marshall, look like donkey's ears.\nWe may as well see that and save the day.'\n\n'Don't donkey's ears suggest we shake our own?'\n\n'For God's sake, aren't you fond of viewing nature?\nYou don't like nature. All you like is books.\nWhat signify a donkey's cars and bottle,\nHowever natural? Give you your books!\nWell then, right here is where I show you books.\nCome straight down off this mountain just as fast\nAs we can fall and keep a-bouncing on our feet.\nIt's hell for knees unless done hell-for-leather.'\n\nBe ready, I thought, for almost anything.\n\nWe struck a road I didn't recognize,\nBut welcomed for the chance to lave my shoes\nIn dust once more. We followed this a mile,\nPerhaps, to where it ended at a house\nI didn't know was there. It was the kind\nTo bring me to for broad-board paneling.\nI never saw so good a house deserted.\n\n'Excuse me if I ask you in a window\nThat happens to be broken, Davis said.\n'The outside doors as yet have held against us.\nI want to introduce you to the people\nWho used to live here. They were Robinsons.\nYou must have heard of Clara Robinson,\nThe poetess who wrote the book of verses\nAnd had it published. It was all about\nThe posies on her inner windowsill,\nAnd the birds on her outer windowsill,\nAnd how she tended both, or had them tended:\nShe never tended anything herself.\nShe was 'shut in' for life. She lived her whole\nLife long in bed, and wrote her things in bed.\nI'll show You how she had her sills extended\nTo entertain the birds and hold the flowers.\nOur business first's up attic with her books.'\n\nWe trod uncomfortably on crunching glass\nThrough a house stripped of everything\nExcept, it seemed, the poetess's poems.\nBooks, I should say!- if books are what is needed.\nA whole edition in a packing case\nThat, overflowing like a horn of plenty,\nOr like the poetess's heart of love,\nHad spilled them near the window, toward the light\nWhere driven rain had wet and swollen them.\nEnough to stock a village library-\nUnfortunately all of one kind, though.\nThey bad been brought home from some publisher\nAnd taken thus into the family.\nBoys and bad hunters had known what to do\nWith stone and lead to unprotected glass:\nShatter it inward on the unswept floors.\nHow had the tender verse escaped their outrage?\nBy being invisible for what it was,\nOr else by some remoteness that defied them\nTo find out what to do to hurt a poem.\nYet oh! the tempting flatness of a book,\nTo send it sailing out the attic window\nTill it caught wind and, opening out its covers,\nTried to improve on sailing like a tile\nBy flying like a bird (silent in flight,\nBut all the burden of its body song),\nOnly to tumble like a stricken bird,\nAnd lie in stones and bushes unretrieved.\nBooks were not thrown irreverently about.\nThey simply lay where someone now and then,\nHaving tried one, had dropped it at his feet\nAnd left it lying where it fell rejected.\nHere were all those the poetess's life\nHad been too short to sell or give away.\n\n'Take one,' Old Davis bade me graciously.\n\n'Why not take two or three?'\n\n'Take all you want.'\nGood-looking books like that.' He picked one fresh\nIn virgin wrapper from deep in the box,\nAnd stroked it with a horny-handed kindness.\nHe read in one and I read in another,\nBoth either looking for or finding something.\n\nThe attic wasps went missing by like bullets.\n\nI was soon satisfied for the time being.\n\nAll the way home I kept remembering\nThe small book in my pocket. It was there.\nThe poetess had sighed, I knew, in heaven\nAt having eased her heart of one more copy-\nLegitimately. My demand upon her,\nThough slight, was a demand. She felt the tug.\nIn time she would be rid of all her books.\n\nI stay;\nBut it isn't as if\nThere wasn't always Hudson's Bay\nAnd the fur trade,\nA small skiff\nAnd a paddle blade.\n\nI can just see my tent pegged,\nAnd me on the floor,\nCross-legged,\nAnd a trapper looking in at the door\nWith furs to sell.\n\nHis name's Joe,\nAlias John,\nAnd between what he doesn't know\nAnd won't tell\nAbout where Henry Hudson's gone,\nI can't say he's much help;\nBut we get on.\n\nThe seal yelp\nOn an ice cake.\nIt's not men by some mistake?\nNo,\nThere's not a soul\nFor a windbreak\nBetween me and the North Pole—\n\nExcept always John-Joe,\nMy French Indian Esquimaux,\nAnd he's off setting traps\nIn one himself perhaps.\n\nGive a headshake\nOver so much bay\nThrown away\nIn snow and mist\nThat doesn't exist,\n\nI was going to say,\nFor God, man, or beast's sake,\nYet does perhaps for all three.\n\nDon't ask Joe\nWhat it is to him.\nIt's sometimes dim\nWhat it is to me,\nUnless it be\nIt's the old captain's dark fate\nWho failed to find or force a strait\nIn its two-thousand-mile coast;\nAnd his crew left him where be failed,\nAnd nothing came of all be sailed.\n\nIt's to say, 'You and I—'\nTo such a ghost—\nYou and I\nOff here\nWith the dead race of the Great Auk!'\nAnd, 'Better defeat almost,\nIf seen clear,\nThan life's victories of doubt\nThat need endless talk-talk\nTo make them out.' \n\nI staid the night for shelter at a farm\nBehind the mountains, with a mother and son,\nTwo old-believers. They did all the talking.\n\nMOTHER Folks think a witch who has familiar spirits\nShe could call up to pass a winter evening,\nBut won't, should be burned at the stake or something.\nSummoning spirits isn't 'Button, button,\nWho's got the button,' I would have them know.\n\nSON: Mother can make a common table rear\nAnd kick with two legs like an army mule.\n\nMOTHER: And when I've done it, what good have I\ndone?\nRather than tip a table for you, let me\nTell you what Ralle the Sioux Control once told me.\nHe said the dead had souls, but when I asked him\nHow could that be - I thought the dead were souls,\nHe broke my trance. Don't that make you suspicious\nThat there's something the dead are keeping back?\nYes, there's something the dead are keeping back.\n\nSON: You wouldn't want to tell him what we have\nUp attic, mother?\n\nMOTHER: Bones - a skeleton.\n\nSON: But the headboard of mother's bed is pushed\nAgainst the' attic door: the door is nailed.\nIt's harmless. Mother hears it in the night\nHalting perplexed behind the barrier\nOf door and headboard. Where it wants to get\nIs back into the cellar where it came from.\n\nMOTHER: We'll never let them, will we, son! We'll\nnever !\n\nSON: It left the cellar forty years ago\nAnd carried itself like a pile of dishes\nUp one flight from the cellar to the kitchen,\nAnother from the kitchen to the bedroom,\nAnother from the bedroom to the attic,\nRight past both father and mother, and neither stopped\nit.\nFather had gone upstairs; mother was downstairs.\nI was a baby: I don't know where I was.\n\nMOTHER: The only fault my husband found with me - \nI went to sleep before I went to bed,\nEspecially in winter when the bed\nMight just as well be ice and the clothes snow.\nThe night the bones came up the cellar-stairs\nToffile had gone to bed alone and left me,\nBut left an open door to cool the room off\nSo as to sort of turn me out of it.\nI was just coming to myself enough\nTo wonder where the cold was coming from,\nWhen I heard Toffile upstairs in the bedroom\nAnd thought I heard him downstairs in the cellar.\nThe board we had laid down to walk dry-shod on\nWhen there was water in the cellar in spring\nStruck the hard cellar bottom. And then someone\nBegan the stairs, two footsteps for each step,\nThe way a man with one leg and a crutch,\nOr a little child, comes up. It wasn't Toffile:\nIt wasn't anyone who could be there.\nThe bulkhead double-doors were double-locked\nAnd swollen tight and buried under snow.\nThe cellar windows were banked up with sawdust\nAnd swollen tight and buried under snow.\nIt was the bones. I knew them - and good reason.\nMy first impulse was to get to the knob\nAnd hold the door. But the bones didn't try\nThe door; they halted helpless on the landing,\nWaiting for things to happen in their favour.'\nThe faintest restless rustling ran all through them.\nI never could have done the thing I did\nIf the wish hadn't been too strong in me\nTo see how they were mounted for this walk.\nI had a vision of them put together\nNot like a man, but like a chandelier.\nSo suddenly I flung the door wide on him.\nA moment he stood balancing with emotion,\nAnd all but lost himself. (A tongue of fire\nFlashed out and licked along his upper teeth.\nSmoke rolled inside the sockets of his eyes.)\nThen he came at me with one hand outstretched,\nThe way he did in life once; but this time\nI struck the hand off brittle on the floor,\nAnd fell back from him on the floor myself.\nThe finger-pieces slid in all directions.\n(Where did I see one of those pieces lately?\nHand me my button-box- it must be there.)\nI sat up on the floor and shouted, 'Toffile,\nIt's coming up to you.' It had its choice\nOf the door to the cellar or the hall.\nIt took the hall door for the novelty,\nAnd set off briskly for so slow a thing,\nStillgoing every which way in the joints, though,\nSo that it looked like lightning or a scribble,\n>From the slap I had just now given its hand.\nI listened till it almost climbed the stairs\n>From the hall to the only finished bedroom,\nBefore I got up to do anything;\nThen ran and shouted, 'Shut the bedroom door,\nToffile, for my sake!' 'Company?' he said,\n'Don't make me get up; I'm too warm in bed.'\nSo lying forward weakly on the handrail\nI pushed myself upstairs, and in the light\n(The kitchen had been dark) I had to own\nI could see nothing. 'Toffile, I don't see it.\nIt's with us in the room though. It's the bones.'\n'What bones?' 'The cellar bones- out of the grave.'\nThat made him throw his bare legs out of bed\nAnd sit up by me and take hold of me.\nI wanted to put out the light and see\nIf I could see it, or else mow the room,\nWith our arms at the level of our knees,\nAnd bring the chalk-pile down. 'I'll tell you what-\nIt's looking for another door to try.\nThe uncommonly deep snow has made him think\nOf his old song, The Wild Colonial Boy,\nHe always used to sing along the tote-road.\nHe's after an open door to get out-doors.\nLet's trap him with an open door up attic.'\nToffile agreed to that, and sure enough,\nAlmost the moment he was given an opening,\nThe steps began to climb the attic stairs.\nI heard them. Toffile didn't seem to hear them.\n'Quick !' I slammed to the door and held the knob.\n'Toffile, get nails.' I made him nail the door shut,\nAnd push the headboard of the bed against it.\nThen we asked was there anything\nUp attic that we'd ever want again.\nThe attic was less to us than the cellar.\nIf the bones liked the attic, let them have it.\nLet them stay in the attic. When they sometimes\nCome down the stairs at night and stand perplexed\nBehind the door and headboard of the bed,\nBrushing their chalky skull with chalky fingers,\nWith sounds like the dry rattling of a shutter,\nThat's what I sit up in the dark to say-\nTo no one any more since Toffile died.\n2o3 Let them stay in the attic since they went there.\nI promised Toffile to be cruel to them\nFor helping them be cruel once to him.\n\nSON: We think they had a grave down in the cellar.\n\nMOTHER: We know they had a grave down in the cellar.\n\nSON: We never could find out whose bones they were.\n\nMOTHER: Yes, we could too, son. Tell the truth for once.\nThey were a man's his father killed for me.\nI mean a man he killed instead of me.\nThe least I could do was to help dig their grave.\nWe were about it one night in the cellar.\nSon knows the story: but 'twas not for him\nTo tell the truth, suppose the time had come.\nSon looks surprised to see me end a lie\nWe'd kept all these years between ourselves\nSo as to have it ready for outsiders.\nBut to-night I don't care enough to lie-\nI don't remember why I ever cared.\nToffile, if he were here, I don't believe\nCould tell you why he ever cared himself-\n\nShe hadn't found the finger-bone she wanted\nAmong the buttons poured out in her lap.\nI verified the name next morning: Toffile.\nThe rural letter-box said Toffile Lajway.\n\nThe west was getting out of gold,\nThe breath of air had died of cold,\nWhen shoeing home across the white,\nI thought I saw a bird alight.\n\nIn summer when I passed the place\nI had to stop and lift my face;\nA bird with an angelic gift\nWas singing in it sweet and swift.\n\nNo bird was singing in it now.\nA single leaf was on a bough,\nAnd that was all there was to see\nIn going twice around the tree.\n\nFrom my advantage on a hill\nI judged that such a crystal chill\nWas only adding frost to snow\nAs gilt to gold that wouldn't show.\n\nA brush had left a crooked stroke\nOf what was either cloud or smoke\nFrom north to south across the blue;\nA piercing little star was through.\n\nA governor it was proclaimed this time, \nWhen all who would come seeking in New Hampshire \nAncestral memories might come together. \nAnd those of the name Stark gathered in Bow, \nA rock-strewn town where farming has fallen off, \nAnd sprout-lands flourish where the axe has gone. \nSomeone had literally run to earth \nIn an old cellar hole in a by-road \nThe origin of all the family there. \nThence they were sprung, so numerous a tribe \nThat now not all the houses left in town \nMade shift to shelter them without the help \nOf here and there a tent in grove and orchard. \nThey were at Bow, but that was not enough: \nNothing would do but they must fix a day \nTo stand together on the crater's verge \nThat turned them on the world, and try to fathom \nThe past and get some strangeness out of it. \nBut rain spoiled all. The day began uncertain, \nWith clouds low trailing and moments of rain that misted. \nThe young folk held some hope out to each other \nTill well toward noon when the storm settled down \nWith a swish in the grass. 'What if the others \nAre there,' they said. 'It isn't going to rain.' \nOnly one from a farm not far away \nStrolled thither, not expecting he would find \nAnyone else, but out of idleness. \nOne, and one other, yes, for there were two. \nThe second round the curving hillside road \nWas a girl; and she halted some way off \nTo reconnoitre, and then made up her mind \nAt least to pass by and see who he was, \nAnd perhaps hear some word about the weather. \nThis was some Stark she didn't know. He nodded. \n'No fête to-day,' he said. \n'It looks that way.' \nShe swept the heavens, turning on her heel. \n'I only idled down.' \n'I idled down.' \nProvision there had been for just such meeting \nOf stranger cousins, in a family tree \nDrawn on a sort of passport with the branch \nOf the one bearing it done in detail- \nSome zealous one's laborious device. \nShe made a sudden movement toward her bodice, \nAs one who clasps her heart. They laughed together. \n'Stark?' he inquired. 'No matter for the proof.' \n'Yes, Stark. And you?' \n'I'm Stark.' He drew his passport. \n'You know we might not be and still be cousins: \nThe town is full of Chases, Lowes, and Baileys, \nAll claiming some priority in Starkness. \nMy mother was a Lane, yet might have married \nAnyone upon earth and still her children \nWould have been Starks, and doubtless here to-day.' \n'You riddle with your genealogy \nLike a Viola. I don't follow you.' \n'I only mean my mother was a Stark \nSeveral times over, and by marrying father \nNo more than brought us back into the name.' \n'One ought not to be thrown into confusion \nBy a plain statement of relationship, \nBut I own what you say makes my head spin. \nYou take my card- you seem so good at such things- \nAnd see if you can reckon our cousinship. \nWhy not take seats here on the cellar wall \nAnd dangle feet among the raspberry vines?' \n'Under the shelter of the family tree.' \n'Just so- that ought to be enough protection.' \n'Not from the rain. I think it's going to rain.' \n'It's raining.' \n'No, it's misting; let's be fair. \nDoes the rain seem to you to cool the eyes?' \nThe situation was like this: the road \nBowed outward on the mountain half-way up, \nAnd disappeared and ended not far off. \nNo one went home that way. The only house \nBeyond where they were was a shattered seedpod. \nAnd below roared a brook hidden in trees, \nThe sound of which was silence for the place. \nThis he sat listening to till she gave judgment. \n'On father's side, it seems, we're- let me see- - ' \n'Don't be too technical.- You have three cards.' \n'Four cards, one yours, three mine, one for each branch \nOf the Stark family I'm a member of.' \n'D'you know a person so related to herself \nIs supposed to be mad.' \n'I may be mad.' \n'You look so, sitting out here in the rain \nStudying genealogy with me \nYou never saw before. What will we come to \nWith all this pride of ancestry, we Yankees? \nI think we're all mad. Tell me why we're here \nDrawn into town about this cellar hole \nLike wild geese on a lake before a storm? \nWhat do we see in such a hole, I wonder.' \n'The Indians had a myth of Chicamoztoc, \nWhich means The Seven Caves that We Came out of. \nThis is the pit from which we Starks were digged.' \n'You must be learned. That's what you see in it?' \n'And what do you see?' \n'Yes, what do I see? \nFirst let me look. I see raspberry vines- - ' \n'Oh, if you're going to use your eyes, just hear \nWhat I see. It's a little, little boy, \nAs pale and dim as a match flame in the sun; \nHe's groping in the cellar after jam, \nHe thinks it's dark and it's flooded with daylight.' \n'He's nothing. Listen. When I lean like this \nI can make out old Grandsir Stark distinctly,- \nWith his pipe in his mouth and his brown jug- \nBless you, it isn't Grandsir Stark, it's Granny, \nBut the pipe's there and smoking and the jug. \nShe's after cider, the old girl, she's thirsty; \nHere's hoping she gets her drink and gets out safely.' \n'Tell me about her. Does she look like me?' \n'She should, shouldn't she, you're so many times \nOver descended from her. I believe \nShe does look like you. Stay the way you are. \nThe nose is just the same, and so's the chin- \nMaking allowance, making due allowance.' \n'You poor, dear, great, great, great, great Granny!' \n'See that you get her greatness right. Don't stint her.' \n'Yes, it's important, though you think it isn't. \nI won't be teased. But see how wet I am.' \n'Yes, you must go; we can't stay here for ever. \nBut wait until I give you a hand up. \nA bead of silver water more or less \nStrung on your hair won't hurt your summer looks. \nI wanted to try something with the noise \nThat the brook raises in the empty valley. \nWe have seen visions- now consult the voices. \nSomething I must have learned riding in trains \nWhen I was young. I used the roar \nTo set the voices speaking out of it, \nSpeaking or singing, and the band-music playing. \nPerhaps you have the art of what I mean. \nI've never listened in among the sounds \nThat a brook makes in such a wild descent. \nIt ought to give a purer oracle.' \n'It's as you throw a picture on a screen: \nThe meaning of it all is out of you; \nThe voices give you what you wish to hear.' \n'Strangely, it's anything they wish to give.' \n'Then I don't know. It must be strange enough. \nI wonder if it's not your make-believe. \nWhat do you think you're like to hear to-day?' \n'From the sense of our having been together- \nBut why take time for what I'm like to hear? \nI'll tell you what the voices really say. \nYou will do very well right where you are \nA little longer. I mustn't feel too hurried, \nOr I can't give myself to hear the voices.' \n'Is this some trance you are withdrawing into?' \n'You must be very still; you mustn't talk.' \n'I'll hardly breathe.' \n'The voices seem to say- - ' \n'I'm waiting.' \n'Don't! The voices seem to say: \nCall her Nausicaa, the unafraid \nOf an acquaintance made adventurously.' \n'I let you say that- on consideration.' \n'I don't see very well how you can help it. \nYou want the truth. I speak but by the voices. \nYou see they know I haven't had your name, \nThough what a name should matter between us- - ' \n'I shall suspect- - ' \n'Be good. The voices say: \nCall her Nausicaa, and take a timber \nThat you shall find lies in the cellar charred \nAmong the raspberries, and hew and shape it \nFor a door-sill or other corner piece \nIn a new cottage on the ancient spot. \nThe life is not yet all gone out of it. \nAnd come and make your summer dwelling here, \nAnd perhaps she will come, still unafraid, \nAnd sit before you in the open door \nWith flowers in her lap until they fade, \nBut not come in across the sacred sill- - ' \n'I wonder where your oracle is tending. \nYou can see that there's something wrong with it, \nOr it would speak in dialect. Whose voice \nDoes it purport to speak in? Not old Grandsir's \nNor Granny's, surely. Call up one of them. \nThey have best right to be heard in this place.' \n'You seem so partial to our great-grandmother \n(Nine times removed. Correct me if I err.) \nYou will be likely to regard as sacred \nAnything she may say. But let me warn you, \nFolks in her day were given to plain speaking. \nYou think you'd best tempt her at such a time?' \n'It rests with us always to cut her off.' \n'Well then, it's Granny speaking: 'I dunnow! \nMebbe I'm wrong to take it as I do. \nThere ain't no names quite like the old ones though, \nNor never will be to my way of thinking. \nOne mustn't bear too hard on the new comers, \nBut there's a dite too many of them for comfort. \nI should feel easier if I could see \nMore of the salt wherewith they're to be salted. \nSon, you do as you're told! You take the timber- \nIt's as sound as the day when it was cut- \nAnd begin over- - ' There, she'd better stop. \nYou can see what is troubling Granny, though. \nBut don't you think we sometimes make too much \nOf the old stock? What counts is the ideals, \nAnd those will bear some keeping still about.' \n'I can see we are going to be good friends.' \n'I like your 'going to be.' You said just now \nIt's going to rain.' \n'I know, and it was raining. \nI let you say all that. But I must go now.' \n'You let me say it? on consideration? \nHow shall we say good-bye in such a case?' \n'How shall we?' \n'Will you leave the way to me?' \n'No, I don't trust your eyes. You've said enough. \nNow give me your hand up.- Pick me that flower.' \n'Where shall we meet again?' \n'Nowhere but here \nOnce more before we meet elsewhere.' \n'In rain?' \n'It ought to be in rain. Sometime in rain. \nIn rain to-morrow, shall we, if it rains? \nBut if we must, in sunshine.' So she went.\n\nBuilder, in building the little house,\nIn every way you may please yourself;\nBut please please me in the kitchen chimney:\nDon't build me a chimney upon a shelf.\n\nHowever far you must go for bricks,\nWhatever they cost a-piece or a pound,\nBut me enough for a full-length chimney,\nAnd build the chimney clear from the ground.\n\nIt's not that I'm greatly afraid of fire,\nBut I never heard of a house that throve\n(And I know of one that didn't thrive)\nWhere the chimney started above the stove.\n\nAnd I dread the ominous stain of tar\nThat there always is on the papered walls,\nAnd the smell of fire drowned in rain\nThat there always is when the chimney's false.\n\nA shelf's for a clock or vase or picture,\nBut I don't see why it should have to bear\nA chimney that only would serve to remind me\nOf castles I used to build in air.\n\nI had for my winter evening walk- \nNo one at all with whom to talk,\nBut I had the cottages in a row\nUp to their shining eyes in snow.\n\nAnd I thought I had the folk within:\nI had the sound of a violin;\nI had a glimpse through curtain laces\nOf youthful forms and youthful faces.\n\nI had such company outward bound.\nI went till there were no cottages found.\nI turned and repented, but coming back\nI saw no window but that was black.\n\nOver the snow my creaking feet\nDisturbed the slumbering village street\nLike profanation, by your leave,\nAt ten o'clock of a winter eve.\n\nInscription for a Garden Wall\n\nWinds blow the open grassy places bleak;\nBut where this old wall burns a sunny cheek,\nThey eddy over it too toppling weak\nTo blow the earth or anything self-clear;\nMoisture and color and odor thicken here.\nThe hours of daylight gather atmosphere. \n\nTo Ridgely Torrence\nOn Last Looking into His 'Hesperides'\n\nI often see flowers from a passing car\nThat are gone before I can tell what they are.\n\nI want to get out of the train and go back\nTo see what they were beside the track.\n\nI name all the flowers I am sure they weren't;\nNot fireweed loving where woods have burnt- \n\nNot bluebells gracing a tunnel mouth- \nNot lupine living on sand and drouth.\n\nWas something brushed across my mind\nThat no one on earth will ever find?\n\nHeaven gives it glimpses only to those\nNot in position to look too close.\n\nBack out of all this now too much for us,\nBack in a time made simple by the loss\nOf detail, burned, dissolved, and broken off\nLike graveyard marble sculpture in the weather,\nThere is a house that is no more a house\nUpon a farm that is no more a farm\nAnd in a town that is no more a town.\nThe road there, if you'll let a guide direct you\nWho only has at heart your getting lost,\nMay seem as if it should have been a quarry -\nGreat monolithic knees the former town\nLong since gave up pretense of keeping covered.\nAnd there's a story in a book about it:\nBesides the wear of iron wagon wheels\nThe ledges show lines ruled southeast-northwest,\nThe chisel work of an enormous Glacier\nThat braced his feet against the Arctic Pole.\nYou must not mind a certain coolness from him\nStill said to haunt this side of Panther Mountain.\nNor need you mind the serial ordeal\nOf being watched from forty cellar holes\nAs if by eye pairs out of forty firkins.\nAs for the woods' excitement over you\nThat sends light rustle rushes to their leaves,\nCharge that to upstart inexperience.\nWhere were they all not twenty years ago?\nThey think too much of having shaded out\nA few old pecker-fretted apple trees.\nMake yourself up a cheering song of how\nSomeone's road home from work this once was,\nWho may be just ahead of you on foot\nOr creaking with a buggy load of grain.\nThe height of the adventure is the height\nOf country where two village cultures faded\nInto each other. Both of them are lost.\nAnd if you're lost enough to find yourself\nBy now, pull in your ladder road behind you\nAnd put a sign up CLOSED to all but me.\nThen make yourself at home. The only field\nNow left's no bigger than a harness gall.\nFirst there's the children's house of make-believe,\nSome shattered dishes underneath a pine,\nThe playthings in the playhouse of the children.\nWeep for what little things could make them glad.\nThen for the house that is no more a house,\nBut only a belilaced cellar hole,\nNow slowly closing like a dent in dough.\nThis was no playhouse but a house in earnest.\nYour destination and your destiny's\nA brook that was the water of the house,\nCold as a spring as yet so near its source,\nToo lofty and original to rage.\n(We know the valley streams that when aroused\nWill leave their tatters hung on barb and thorn.)\nI have kept hidden in the instep arch\nOf an old cedar at the waterside\nA broken drinking goblet like the Grail\nUnder a spell so the wrong ones can't find it,\nSo can't get saved, as Saint Mark says they mustn't.\n(I stole the goblet from the children's playhouse.)\nHere are your waters and your watering place.\nDrink and be whole again beyond confusion.\n\nI WALKED down alone Sunday after church\nTo the place where John has been cutting trees\nTo see for myself about the birch\nHe said I could have to bush my peas.\n\nThe sun in the new-cut narrow gap\nWas hot enough for the first of May,\nAnd stifling hot with the odor of sap\nFrom stumps still bleeding their life away.\n\nThe frogs that were peeping a thousand shrill\nWherever the ground was low and wet,\nThe minute they heard my step went still\nTo watch me and see what I came to get.\n\nBirch boughs enough piled everywhere!—\nAll fresh and sound from the recent axe.\nTime someone came with cart and pair\nAnd got them off the wild flower's backs.\n\nThey might be good for garden things\nTo curl a little finger round,\nThe same as you seize cat's-cradle strings,\nAnd lift themselves up off the ground.\n\nSmall good to anything growing wild,\nThey were crooking many a trillium\nThat had budded before the boughs were piled\nAnd since it was coming up had to come.\n\nOver back where they speak of life as staying\n('You couldn't call it living, for it ain't'),\nThere was an old, old house renewed with paint,\nAnd in it a piano loudly playing.\n\nOut in the plowed ground in the cold a digger,\nAmong unearthed potatoes standing still,\nWas counting winter dinners, one a hill,\nWith half an ear to the piano's vigor.\n\nAll that piano and new paint back there,\nWas it some money suddenly come into?\nOr some extravagance young love had been to?\nOr old love on an impulse not to care- \n\nNot to sink under being man and wife,\nBut get some color and music out of life?\n\nIt was long I lay\nAwake that night\nWishing that night\nWould name the hour\nAnd tell me whether\nTo call it day\n(Though not yet light)\nAnd give up sleep.\nThe snow fell deep\nWith the hiss of spray;\nTwo winds would meet,\nOne down one street,\nOne down another,\nAnd fight in a smother\nOf dust and feather.\nI could not say,\nBut feared the cold\nHad checked the pace\nOf the tower clock\nBy tying together\nIts hands of gold\nBefore its face.\n\nThen cane one knock!\nA note unruffled\nOf earthly weather,\nThough strange and muffled.\nThe tower said, 'One!'\nAnd then a steeple.\nThey spoke to themselves\nAnd such few people\nAs winds might rouse\nFrom sleeping warm\n(But not unhouse).\nThey left the storm\nThat struck en masse\nMy window glass\nLike a beaded fur.\nIn that grave One\nThey spoke of the sun\nAnd moon and stars,\nSaturn and Mars\nAnd Jupiter.\nStill more unfettered,\nThey left the named\nAnd spoke of the lettered,\nThe sigmas and taus\nOf constellations.\nThey filled their throats\nWith the furthest bodies\nTo which man sends his\nSpeculation,\nBeyond which God is;\nThe cosmic motes\nOf yawning lenses.\nTheir solemn peals\nWere not their own:\nThey spoke for the clock\nWith whose vast wheels\nTheirs interlock.\nIn that grave word\nUttered alone\nThe utmost star\nTrembled and stirred,\nThough set so far\nIts whirling frenzies\nAppear like standing\nin one self station.\nIt has not ranged,\nAnd save for the wonder \nOf once expanding\nTo be a nova,\nIt has not changed\nTo the eye of man\nOn planets over\nAround and under\nIt in creation\nSince man began\nTo drag down man\nAnd nation nation.\n\nThe surest thing there is is we are riders,\nAnd though none too successful at it, guiders,\nThrough everything presented, land and tide\nAnd now the very air, of what we ride.\n\nWhat is this talked-of mystery of birth\nBut being mounted bareback on the earth?\nWe can just see the infant up astride,\nHis small fist buried in the bushy hide.\n\nThere is our wildest mount- a headless horse.\nBut though it runs unbridled off its course,\nAnd all our blandishments would seem defied,\nWe have ideas yet that we haven't tried.\n\nI let myself in at the kitchen door.\n'It's you,' she said. 'I can't get up. Forgive me \nNot answering your knock. I can no more \nLet people in than I can keep them out. \nI'm getting too old for my size, I tell them. \nMy fingers are about all I've the use of \nSo's to take any comfort. I can sew: \nI help out with this beadwork what I can.' \n'That's a smart pair of pumps you're beading there. \nWho are they for?' \n'You mean?- oh, for some miss. \nI can't keep track of other people's daughters. \nLord, if I were to dream of everyone \nWhose shoes I primped to dance in!' \n'And where's John?' \n'Haven't you seen him? Strange what set you off \nTo come to his house when he's gone to yours. \nYou can't have passed each other. I know what: \nHe must have changed his mind and gone to Garlands. \nHe won't be long in that case. You can wait. \nThough what good you can be, or anyone- \nIt's gone so far. You've heard? Estelle's run off.' \n'Yes, what's it all about? When did she go?' \n'Two weeks since.' \n'She's in earnest, it appears.' \n'I'm sure she won't come back. She's hiding somewhere. \nI don't know where myself. John thinks I do. \nHe thinks I only have to say the word, \nAnd she'll come back. But, bless you, I'm her mother- \nI can't talk to her, and, Lord, if I could!' \n'It will go hard with John. What will he do? \nHe can't find anyone to take her place.' \n'Oh, if you ask me that, what will he do? \nHe gets some sort of bakeshop meals together, \nWith me to sit and tell him everything, \nWhat's wanted and how much and where it is. \nBut when I'm gone- of course I can't stay here: \nEstelle's to take me when she's settled down. \nHe and I only hinder one another. \nI tell them they can't get me through the door, though: \nI've been built in here like a big church organ. \nWe've been here fifteen years.' \n'That's a long time \nTo live together and then pull apart. \nHow do you see him living when you're gone? \nTwo of you out will leave an empty house.' \n'I don't just see him living many years, \nLeft here with nothing but the furniture. \nI hate to think of the old place when we're gone, \nWith the brook going by below the yard, \nAnd no one here but hens blowing about. \nIf he could sell the place, but then, he can't: \nNo one will ever live on it again. \nIt's too run down. This is the last of it. \nWhat I think he will do, is let things smash. \nHe'll sort of swear the time away. He's awful! \nI never saw a man let family troubles \nMake so much difference in his man's affairs. \nHe's just dropped everything. He's like a child. \nI blame his being brought up by his mother. \nHe's got hay down that's been rained on three times. \nHe hoed a little yesterday for me: \nI thought the growing things would do him good. \nSomething went wrong. I saw him throw the hoe \nSky-high with both hands. I can see it now- \nCome here- I'll show you- in that apple tree. \nThat's no way for a man to do at his age: \nHe's fifty-five, you know, if he's a day.' \n'Aren't you afraid of him? What's that gun for?' \n'Oh, that's been there for hawks since chicken-time. \nJohn Hall touch me! Not if he knows his friends. \nI'll say that for him, John's no threatener \nLike some men folk. No one's afraid of him; \nAll is, he's made up his mind not to stand \nWhat he has got to stand.' \n'Where is Estelle? \nCouldn't one talk to her? What does she say? \nYou say you don't know where she is.' \n'Nor want to! \nShe thinks if it was bad to live with him, \nIt must be right to leave him.' \n'Which is wrong!' \n'Yes, but he should have married her.' \n'I know.' \n'The strain's been too much for her all these years: \nI can't explain it any other way. \nIt's different with a man, at least with John: \nHe knows he's kinder than the run of men. \nBetter than married ought to be as good \nAs married- that's what he has always said. \nI know the way he's felt- but all the same!' \n'I wonder why he doesn't marry her \nAnd end it.' \n'Too late now: she wouldn't have him. \nHe's given her time to think of something else. \nThat's his mistake. The dear knows my interest \nHas been to keep the thing from breaking up. \nThis is a good home: I don't ask for better. \nBut when I've said, 'Why shouldn't they be married,' \nHe'd say, 'Why should they?' no more words than that.' \n'And after all why should they? John's been fair \nI take it. What was his was always hers. \nThere was no quarrel about property.' \n'Reason enough, there was no property. \nA friend or two as good as own the farm, \nSuch as it is. It isn't worth the mortgage.' \n'I mean Estelle has always held the purse.' \n'The rights of that are harder to get at. \nI guess Estelle and I have filled the purse. \n'Twas we let him have money, not he us. \nJohn's a bad farmer. I'm not blaming him. \nTake it year in, year out, he doesn't make much. \nWe came here for a home for me, you know, \nEstelle to do the housework for the board \nOf both of us. But look how it turns out: \nShe seems to have the housework, and besides, \nHalf of the outdoor work, though as for that, \nHe'd say she does it more because she likes it. \nYou see our pretty things are all outdoors. \nOur hens and cows and pigs are always better \nThan folks like us have any business with. \nFarmers around twice as well off as we \nHaven't as good. They don't go with the farm. \nOne thing you can't help liking about John, \nHe's fond of nice things- too fond, some would say. \nBut Estelle don't complain: she's like him there. \nShe wants our hens to be the best there are. \nYou never saw this room before a show, \nFull of lank, shivery, half-drowned birds \nIn separate coops, having their plumage done. \nThe smell of the wet feathers in the heat! \nYou spoke of John's not being safe to stay with. \nYou don't know what a gentle lot we are: \nWe wouldn't hurt a hen! You ought to see us \nMoving a flock of hens from place to place. \nWe're not allowed to take them upside down, \nAll we can hold together by the legs. \nTwo at a time's the rule, one on each arm, \nNo matter how far and how many times \nWe have to go.' \n'You mean that's John's idea.' \n'And we live up to it; or I don't know \nWhat childishness he wouldn't give way to. \nHe manages to keep the upper hand \nOn his own farm. He's boss. But as to hens: \nWe fence our flowers in and the hens range. \nNothing's too good for them. We say it pays. \nJohn likes to tell the offers he has had, \nTwenty for this cock, twenty-five for that. \nHe never takes the money. If they're worth \nThat much to sell, they're worth as much to keep. \nBless you, it's all expense, though. Reach me down \nThe little tin box on the cupboard shelf, \nThe upper shelf, the tin box. That's the one. \nI'll show you. Here you are.' \n'What's this?' \n'A bill- \nFor fifty dollars for one Langshang cock- \nReceipted. And the cock is in the yard.' \n'Not in a glass case, then?' \n'He'd need a tall one: \nHe can eat off a barrel from the ground. \nHe's been in a glass case, as you may say, \nThe Crystal Palace, London. He's imported. \nJohn bought him, and we paid the bill with beads- \nWampum, I call it. Mind, we don't complain. \nBut you see, don't you, we take care of him.' \n'And like it, too. It makes it all the worse.' \n'It seems as if. And that's not all: he's helpless \nIn ways that I can hardly tell you of. \nSometimes he gets possessed to keep accounts \nTo see where all the money goes so fast. \nYou know how men will be ridiculous. \nBut it's just fun the way he gets bedeviled- \nIf he's untidy now, what will he be- - ? \n'It makes it all the worse. You must be blind.' \n'Estelle's the one. You needn't talk to me.' \n'Can't you and I get to the root of it? \nWhat's the real trouble? What will satisfy her?' \n'It's as I say: she's turned from him, that's all.' \n'But why, when she's well off? Is it the neighbours, \nBeing cut off from friends?' \n'We have our friends. \nThat isn't it. Folks aren't afraid of us.' \n'She's let it worry her. You stood the strain, \nAnd you're her mother.' \n'But I didn't always. \nI didn't relish it along at first. \nBut I got wonted to it. And besides- \nJohn said I was too old to have grandchildren. \nBut what's the use of talking when it's done? \nShe won't come back- it's worse than that- she can't.' \n'Why do you speak like that? What do you know? \nWhat do you mean?- she's done harm to herself?' \n'I mean she's married- married someone else.' \n'Oho, oho!' \n'You don't believe me.' \n'Yes, I do, \nOnly too well. I knew there must be something! \nSo that was what was back. She's bad, that's all!' \n'Bad to get married when she had the chance?' \n'Nonsense! See what's she done! But who, who- - ' \n'Who'd marry her straight out of such a mess? \nSay it right out- no matter for her mother. \nThe man was found. I'd better name no names. \nJohn himself won't imagine who he is.' \n'Then it's all up. I think I'll get away. \nYou'll be expecting John. I pity Estelle; \nI suppose she deserves some pity, too. \nYou ought to have the kitchen to yourself \nTo break it to him. You may have the job.' \n'You needn't think you're going to get away. \nJohn's almost here. I've had my eye on someone \nComing down Ryan's Hill. I thought 'twas him. \nHere he is now. This box! Put it away. \nAnd this bill.' \n'What's the hurry? He'll unhitch.' \n'No, he won't, either. He'll just drop the reins \nAnd turn Doll out to pasture, rig and all. \nShe won't get far before the wheels hang up \nOn something- there's no harm. See, there he is! \nMy, but he looks as if he must have heard!' \nJohn threw the door wide but he didn't enter.\n'How are you, neighbour? Just the man I'm after. \nIsn't it Hell,' he said. 'I want to know. \nCome out here if you want to hear me talk. \nI'll talk to you, old woman, afterward. \nI've got some news that maybe isn't news. \nWhat are they trying to do to me, these two?' \n'Do go along with him and stop his shouting.' \nShe raised her voice against the closing door:\n'Who wants to hear your news, you- dreadful fool?'\n\nA winter garden in an alder swamp,\nWhere conies now come out to sun and romp,\nAs near a paradise as it can be\nAnd not melt snow or start a dormant tree.\n\nIt lifts existence on a plane of snow\nOne level higher than the earth below,\nOne level nearer heaven overhead,\nAnd last year's berries shining scarlet red.\n\nIt lifts a gaunt luxuriating beast\nWhere he can stretch and hold his highest feat\nOn some wild apple tree's young tender bark,\nWhat well may prove the year's high girdle mark.\n\nSo near to paradise all pairing ends:\nHere loveless birds now flock as winter friends,\nContent with bud-inspecting. They presume\nTo say which buds are leaf and which are bloom.\n\nA feather-hammer gives a double knock.\nThis Eden day is done at two o'clock.\nAn hour of winter day might seem too short\nTo make it worth life's while to wake and sport." }, { "path": "hmm_class/scan1.py", "content": "# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python\n# http://lazyprogrammer.me\n# theano scan example: calculate x^2\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport theano\nimport theano.tensor as T\n\n\nx = T.vector('x')\n\ndef square(x):\n return x*x\n\noutputs, updates = theano.scan(\n fn=square,\n sequences=x,\n n_steps=x.shape[0],\n)\n\nsquare_op = theano.function(\n inputs=[x],\n outputs=[outputs],\n)\n\no_val = square_op(np.array([1, 2, 3, 4, 5]))\n\nprint(\"output:\", o_val)" }, { "path": "hmm_class/scan2.py", "content": "# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python\n# http://lazyprogrammer.me\n# theano scan example: calculate fibonacci\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport theano\nimport theano.tensor as T\n\n\nN = T.iscalar('N')\n\ndef recurrence(n, fn_1, fn_2):\n return fn_1 + fn_2, fn_1\n\noutputs, updates = theano.scan(\n fn=recurrence,\n sequences=T.arange(N),\n n_steps=N,\n outputs_info=[1., 1.]\n)\n\nfibonacci = theano.function(\n inputs=[N],\n outputs=outputs,\n)\n\no_val = fibonacci(8)\n\nprint(\"output:\", o_val)\n" }, { "path": "hmm_class/scan3.py", "content": "# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python\n# http://lazyprogrammer.me\n# theano scan example - low pass filter\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport theano\nimport theano.tensor as T\n\n\nX = 2*np.random.randn(300) + np.sin(np.linspace(0, 3*np.pi, 300))\nplt.plot(X)\nplt.title(\"original\")\nplt.show()\n\ndecay = T.scalar('decay')\nsequence = T.vector('sequence')\n\ndef recurrence(x, last, decay):\n return (1-decay)*x + decay*last\n\noutputs, _ = theano.scan(\n fn=recurrence,\n sequences=sequence,\n n_steps=sequence.shape[0],\n outputs_info=[np.float64(0)],\n non_sequences=[decay]\n)\n\nlpf = theano.function(\n inputs=[sequence, decay],\n outputs=outputs,\n)\n\nY = lpf(X, 0.99)\nplt.plot(Y)\nplt.title(\"filtered\")\nplt.show()" }, { "path": "hmm_class/site_data.csv", "content": 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}, { "path": "hmm_class/sites.py", "content": "# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python\n# http://lazyprogrammer.me\n# Create a Markov model for site data.\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nimport numpy as np\n\ntransitions = {}\nrow_sums = {}\n\n# collect counts\nfor line in open('site_data.csv'):\n s, e = line.rstrip().split(',')\n transitions[(s, e)] = transitions.get((s, e), 0.) + 1\n row_sums[s] = row_sums.get(s, 0.) + 1\n\n# normalize\nfor k, v in iteritems(transitions):\n s, e = k\n transitions[k] = v / row_sums[s]\n\n# initial state distribution\nprint(\"initial state distribution:\")\nfor k, v in iteritems(transitions):\n s, e = k\n if s == '-1':\n print(e, v)\n\n# which page has the highest bounce?\nfor k, v in iteritems(transitions):\n s, e = k\n if e == 'B':\n print(\"bounce rate for %s: %s\" % (s, v))\n" }, { "path": "hmm_class/tf_scan1.py", "content": "# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://lazyprogrammer.me\n# tensorflow scan example: calculate x^2\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport tensorflow as tf\n\n# sequence of elements we want to square\nx = tf.placeholder(tf.int32, shape=(None,), name='x')\n\n# thing to do to every element of the sequence\n# notice how it always ignores the last output\ndef square(last, current):\n return current*current\n\n# this is a \"fancy for loop\"\n# it says: apply square to every element of x\nsquare_op = tf.scan(\n fn=square,\n elems=x,\n)\n\n# run it!\nwith tf.Session() as session:\n o_val = session.run(square_op, feed_dict={x: [1, 2, 3, 4, 5]})\n print(\"output:\", o_val)\n" }, { "path": "hmm_class/tf_scan2.py", "content": "# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://lazyprogrammer.me\n# tensorflow scan example: calculate fibonacci\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport tensorflow as tf\n\n\n# N = number of fibonacci numbers we want\n# shape=() means scalar\nN = tf.placeholder(tf.int32, shape=(), name='N')\n\n# recurrence and loop\n# notice how we don't use current_input at all!\ndef recurrence(last_output, current_input):\n return (last_output[1], last_output[0] + last_output[1])\n\nfibonacci = tf.scan(\n fn=recurrence,\n elems=tf.range(N),\n initializer=(0, 1),\n)\n\n# run it!\nwith tf.Session() as session:\n o_val = session.run(fibonacci, feed_dict={N: 8})\n print(\"output:\", o_val)\n" }, { "path": "hmm_class/tf_scan3.py", "content": "# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://lazyprogrammer.me\n# tensorflow scan example - low pass filter\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\n\n# original sequence is a noisy sine wave\noriginal = np.sin(np.linspace(0, 3*np.pi, 300))\nX = 2*np.random.randn(300) + original\nplt.plot(X)\nplt.title(\"original\")\nplt.show()\n\n# set up placeholders\ndecay = tf.placeholder(tf.float32, shape=(), name='decay')\nsequence = tf.placeholder(tf.float32, shape=(None,), name='sequence')\n\n# the recurrence function and loop\ndef recurrence(last, x):\n return (1.0-decay)*x + decay*last\n\nlpf = tf.scan(\n fn=recurrence,\n elems=sequence,\n initializer=0.0, # sequence[0] to use the first value of the sequence\n)\n\n# run it!\nwith tf.Session() as session:\n Y = session.run(lpf, feed_dict={sequence: X, decay: 0.97})\n\n plt.plot(Y)\n plt.plot(original)\n plt.title(\"filtered\")\n plt.show()\n" }, { "path": "keras_examples/ann.py", "content": "from __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom util import getKaggleMNIST\nfrom keras.models import Model\nfrom keras.layers import Dense, Activation, Input\n\n\n# get the data\nXtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()\n\n# get shapes\nN, D = Xtrain.shape\nK = len(set(Ytrain))\n\n\n# ANN with layers [784] -> [500] -> [300] -> [10]\ni = Input(shape=(D,))\nx = Dense(500, activation='relu')(i)\nx = Dense(300, activation='relu')(x)\nx = Dense(K, activation='softmax')(x)\n\n# instantiate the model object\nmodel = Model(inputs=i, outputs=x)\n\n\n# list of losses: https://keras.io/losses/\n# list of optimizers: https://keras.io/optimizers/\n# list of metrics: https://keras.io/metrics/\nmodel.compile(\n loss='sparse_categorical_crossentropy',\n optimizer='adam',\n metrics=['accuracy']\n)\n\n# note: multiple ways to choose a backend\n# either theano, tensorflow, or cntk\n# https://keras.io/backend/\n\n\n# gives us back a \nr = model.fit(Xtrain, Ytrain, validation_data=(Xtest, Ytest), epochs=15, batch_size=32)\nprint(\"Returned:\", r)\n\n# print the available keys\n# should see: dict_keys(['val_loss', 'acc', 'loss', 'val_acc'])\nprint(r.history.keys())\n\n# plot some data\nplt.plot(r.history['loss'], label='loss')\nplt.plot(r.history['val_loss'], label='val_loss')\nplt.legend()\nplt.show()\n\n# accuracies\nplt.plot(r.history['acc'], label='acc')\nplt.plot(r.history['val_acc'], label='val_acc')\nplt.legend()\nplt.show()\n\n\n# make predictions and evaluate\nprobs = model.predict(Xtest) # N x K matrix of probabilities\nPtest = np.argmax(probs, axis=1)\nprint(\"Validation acc:\", np.mean(Ptest == Ytest))\n\n" }, { "path": "keras_examples/basic_mlp.py", "content": "from __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom util import getKaggleMNIST, getKaggleFashionMNIST\nfrom sklearn.neural_network import MLPClassifier\n\n\n# get the data\nXtrain, Ytrain, Xtest, Ytest = getKaggleFashionMNIST()\n\n# inspect your data\nprint(Xtrain.shape)\nprint(Ytrain.shape)\n\n# look at an example\ni = np.random.choice(Xtrain.shape[0])\nplt.imshow(Xtrain[i].reshape(28, 28))\nplt.title(Ytrain[i])\nplt.show()\n\n# instantiate the model\nmodel = MLPClassifier()\n\n# train the model\nmodel.fit(Xtrain, Ytrain)\n\n# evaluate the model\nprint(model.score(Xtrain, Ytrain))\nprint(model.score(Xtest, Ytest))\n\n# for completion's sake, this is how you make predictions\nPtest = model.predict(Xtest)\n\n# an alternate way to calculate accuracy\nprint(np.mean(Ptest == Ytest))\n\n# get output probabilities\nprobs = model.predict_proba(Xtest)\nprint(\"np.argmax(probs, axis=1) == Ptest?\", np.all(np.argmax(probs, axis=1) == Ptest))" }, { "path": "keras_examples/batchnorm.py", "content": "from __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom util import getKaggleMNIST\nfrom keras.models import Model\nfrom keras.layers import Dense, Activation, Input, BatchNormalization\n\n\n# get the data\nXtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()\n\n# get shapes\nN, D = Xtrain.shape\nK = len(set(Ytrain))\n\n\n# ANN with layers [784] -> [500] -> [300] -> [10]\ni = Input(shape=(D,))\nx = Dense(500)(i)\nx = BatchNormalization()(x)\nx = Activation('relu')(x)\nx = Dense(300)(x)\nx = BatchNormalization()(x)\nx = Activation('relu')(x)\nx = Dense(K, activation='softmax')(x)\n\n# instantiate the model object\nmodel = Model(inputs=i, outputs=x)\n\n\n# list of losses: https://keras.io/losses/\n# list of optimizers: https://keras.io/optimizers/\n# list of metrics: https://keras.io/metrics/\nmodel.compile(\n loss='sparse_categorical_crossentropy',\n optimizer='adam',\n metrics=['accuracy']\n)\n\n# note: multiple ways to choose a backend\n# either theano, tensorflow, or cntk\n# https://keras.io/backend/\n\n\n# gives us back a \nr = model.fit(Xtrain, Ytrain, validation_data=(Xtest, Ytest), epochs=15, batch_size=32)\nprint(\"Returned:\", r)\n\n# print the available keys\n# should see: dict_keys(['val_loss', 'acc', 'loss', 'val_acc'])\nprint(r.history.keys())\n\n# plot some data\nplt.plot(r.history['loss'], label='loss')\nplt.plot(r.history['val_loss'], label='val_loss')\nplt.legend()\nplt.show()\n\n# accuracies\nplt.plot(r.history['acc'], label='acc')\nplt.plot(r.history['val_acc'], label='val_acc')\nplt.legend()\nplt.show()\n\n\n# make predictions and evaluate\nprobs = model.predict(Xtest) # N x K matrix of probabilities\nPtest = np.argmax(probs, axis=1)\nprint(\"Validation acc:\", np.mean(Ptest == Ytest))\n\n" }, { "path": "keras_examples/cnn.py", "content": "# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nfrom keras.models import Model\nfrom keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Flatten, Input\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\n\nfrom util import getKaggleMNIST3D, getKaggleFashionMNIST3D, getCIFAR10\n\n\n# get the data\nXtrain, Ytrain, Xtest, Ytest = getKaggleFashionMNIST3D()\n\n# get shapes\nN, H, W, C = Xtrain.shape\nK = len(set(Ytrain))\n\n\n\n\n# make the CNN\ni = Input(shape=(H, W, C))\nx = Conv2D(filters=32, kernel_size=(3, 3))(i)\nx = Activation('relu')(x)\nx = MaxPooling2D()(x)\n\nx = Conv2D(filters=64, kernel_size=(3, 3))(x)\nx = Activation('relu')(x)\nx = MaxPooling2D()(x)\n\nx = Flatten()(x)\nx = Dense(units=100)(x)\nx = Activation('relu')(x)\nx = Dense(units=K)(x)\nx = Activation('softmax')(x)\n\nmodel = Model(inputs=i, outputs=x)\n\n\n# list of losses: https://keras.io/losses/\n# list of optimizers: https://keras.io/optimizers/\n# list of metrics: https://keras.io/metrics/\nmodel.compile(\n loss='sparse_categorical_crossentropy',\n optimizer='adam',\n metrics=['accuracy']\n)\n\n# note: multiple ways to choose a backend\n# either theano, tensorflow, or cntk\n# https://keras.io/backend/\n\n\n# gives us back a \nr = model.fit(Xtrain, Ytrain, validation_data=(Xtest, Ytest), epochs=15, batch_size=32)\nprint(\"Returned:\", r)\n\n# print the available keys\n# should see: dict_keys(['val_loss', 'acc', 'loss', 'val_acc'])\nprint(r.history.keys())\n\n# plot some data\nplt.plot(r.history['loss'], label='loss')\nplt.plot(r.history['val_loss'], label='val_loss')\nplt.legend()\nplt.show()\n\n# accuracies\nplt.plot(r.history['acc'], label='acc')\nplt.plot(r.history['val_acc'], label='val_acc')\nplt.legend()\nplt.show()\n\n\n" }, { "path": "keras_examples/cnn_cifar.py", "content": "# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nfrom keras.models import Sequential, Model\nfrom keras.layers import Dense, Activation, Lambda, Conv2D, MaxPooling2D, Flatten, Dropout, BatchNormalization, Input\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\n\nfrom util import getCIFAR10\n\n\n# get the data\nXtrain, Ytrain, Xtest, Ytest = getCIFAR10()\n\n# get shapes\nN, H, W, C = Xtrain.shape\nK = len(set(Ytrain))\n\n\n\n\n# make the CNN\ni = Input(shape=(H, W, C))\nx = Lambda(lambda x: (x - 127.5) / 127.5)(i)\nx = Conv2D(filters=32, kernel_size=(3, 3))(x)\nx = BatchNormalization()(x)\nx = Activation('relu')(x)\nx = MaxPooling2D()(x)\n\nx = Conv2D(filters=64, kernel_size=(3, 3))(x)\nx = BatchNormalization()(x)\nx = Activation('relu')(x)\nx = MaxPooling2D()(x)\n\nx = Conv2D(filters=128, kernel_size=(3, 3))(x)\nx = BatchNormalization()(x)\nx = Activation('relu')(x)\nx = MaxPooling2D()(x)\n\nx = Flatten()(x)\nx = Dropout(0.5)(x)\nx = Dense(units=300)(x)\nx = Activation('relu')(x)\nx = Dropout(0.2)(x)\nx = Dense(units=K)(x)\nx = Activation('softmax')(x)\n\nmodel = Model(inputs=i, outputs=x)\n\n\n# list of losses: https://keras.io/losses/\n# list of optimizers: https://keras.io/optimizers/\n# list of metrics: https://keras.io/metrics/\nmodel.compile(\n loss='sparse_categorical_crossentropy',\n optimizer='adam',\n metrics=['accuracy']\n)\n\n# note: multiple ways to choose a backend\n# either theano, tensorflow, or cntk\n# https://keras.io/backend/\n\n\n# gives us back a \nr = model.fit(Xtrain, Ytrain, validation_data=(Xtest, Ytest), epochs=80, batch_size=32)\nprint(\"Returned:\", r)\n\n# print the available keys\n# should see: dict_keys(['val_loss', 'acc', 'loss', 'val_acc'])\nprint(r.history.keys())\n\n# plot some data\nplt.plot(r.history['loss'], label='loss')\nplt.plot(r.history['val_loss'], label='val_loss')\nplt.legend()\nplt.show()\n\n# accuracies\nplt.plot(r.history['acc'], label='acc')\nplt.plot(r.history['val_acc'], label='val_acc')\nplt.legend()\nplt.show()\n\n\n" }, { "path": "keras_examples/cnn_dropout_batchnorm.py", "content": "# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nfrom keras.models import Sequential, Model\nfrom keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Flatten, Dropout, BatchNormalization, Input\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\n\nfrom util import getKaggleMNIST3D, getKaggleFashionMNIST3D, getCIFAR10\n\n\n# get the data\nXtrain, Ytrain, Xtest, Ytest = getKaggleFashionMNIST3D()\n\n# get shapes\nN, H, W, C = Xtrain.shape\nK = len(set(Ytrain))\n\n\n\n\n# make the CNN\ni = Input(shape=(H, W, C))\nx = Conv2D(filters=32, kernel_size=(3, 3))(i)\nx = BatchNormalization()(x)\nx = Activation('relu')(x)\nx = MaxPooling2D()(x)\n\nx = Conv2D(filters=64, kernel_size=(3, 3))(x)\nx = BatchNormalization()(x)\nx = Activation('relu')(x)\nx = MaxPooling2D()(x)\n\nx = Flatten()(x)\nx = Dense(units=100)(x)\nx = Activation('relu')(x)\nx = Dropout(0.2)(x)\nx = Dense(units=K)(x)\nx = Activation('softmax')(x)\n\nmodel = Model(inputs=i, outputs=x)\n\n\n# list of losses: https://keras.io/losses/\n# list of optimizers: https://keras.io/optimizers/\n# list of metrics: https://keras.io/metrics/\nmodel.compile(\n loss='sparse_categorical_crossentropy',\n optimizer='adam',\n metrics=['accuracy']\n)\n\n# note: multiple ways to choose a backend\n# either theano, tensorflow, or cntk\n# https://keras.io/backend/\n\n\n# gives us back a \nr = model.fit(Xtrain, Ytrain, validation_data=(Xtest, Ytest), epochs=15, batch_size=32)\nprint(\"Returned:\", r)\n\n# print the available keys\n# should see: dict_keys(['val_loss', 'acc', 'loss', 'val_acc'])\nprint(r.history.keys())\n\n# plot some data\nplt.plot(r.history['loss'], label='loss')\nplt.plot(r.history['val_loss'], label='val_loss')\nplt.legend()\nplt.show()\n\n# accuracies\nplt.plot(r.history['acc'], label='acc')\nplt.plot(r.history['val_acc'], label='val_acc')\nplt.legend()\nplt.show()\n\n\n" }, { "path": "keras_examples/dropout.py", "content": "from __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom util import getKaggleMNIST\nfrom keras.models import Model\nfrom keras.layers import Dense, Activation, Input, Dropout\n\n\n# get the data\nXtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()\n\n# get shapes\nN, D = Xtrain.shape\nK = len(set(Ytrain))\n\n\n# ANN with layers [784] -> [500] -> [300] -> [10]\ni = Input(shape=(D,))\nx = Dropout(0.2)(i)\nx = Dense(500, activation='relu')(x)\nx = Dropout(0.5)(x)\nx = Dense(300, activation='relu')(x)\nx = Dropout(0.5)(x)\nx = Dense(K, activation='softmax')(x)\n\n# instantiate the model object\nmodel = Model(inputs=i, outputs=x)\n\n\n# list of losses: https://keras.io/losses/\n# list of optimizers: https://keras.io/optimizers/\n# list of metrics: https://keras.io/metrics/\nmodel.compile(\n loss='sparse_categorical_crossentropy',\n optimizer='adam',\n metrics=['accuracy']\n)\n\n# note: multiple ways to choose a backend\n# either theano, tensorflow, or cntk\n# https://keras.io/backend/\n\n\n# gives us back a \nr = model.fit(Xtrain, Ytrain, validation_data=(Xtest, Ytest), epochs=15, batch_size=32)\nprint(\"Returned:\", r)\n\n# print the available keys\n# should see: dict_keys(['val_loss', 'acc', 'loss', 'val_acc'])\nprint(r.history.keys())\n\n# plot some data\nplt.plot(r.history['loss'], label='loss')\nplt.plot(r.history['val_loss'], label='val_loss')\nplt.legend()\nplt.show()\n\n# accuracies\nplt.plot(r.history['acc'], label='acc')\nplt.plot(r.history['val_acc'], label='val_acc')\nplt.legend()\nplt.show()\n\n\n# make predictions and evaluate\nprobs = model.predict(Xtest) # N x K matrix of probabilities\nPtest = np.argmax(probs, axis=1)\nprint(\"Validation acc:\", np.mean(Ptest == Ytest))\n\n" }, { "path": "keras_examples/sentiment_analysis.py", "content": "# https://deeplearningcourses.com/c/deep-learning-advanced-nlp\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport os\nimport sys\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom keras.models import Model\nfrom keras.layers import Dense, Embedding, Input\nfrom keras.layers import LSTM, GRU\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\n\nimport keras.backend as K\nif len(K.tensorflow_backend._get_available_gpus()) > 0:\n from keras.layers import CuDNNLSTM as LSTM\n from keras.layers import CuDNNGRU as GRU\n\n\n\n# some configuration\nMAX_SEQUENCE_LENGTH = 50\nMAX_VOCAB_SIZE = 20000\nEMBEDDING_DIM = 10\nVALIDATION_SPLIT = 0.2\nBATCH_SIZE = 128\nEPOCHS = 5\n\n\n\n# get the data at: https://www.kaggle.com/c/sentiment-analysis-on-movie-reviews\n# prepare text samples and their labels\nprint('Loading in data...')\ntrain = pd.read_csv(\"../large_files/kaggle-sentiment-analysis/train.tsv\", sep='\\t')\nsentences = train[\"Phrase\"].values\ntargets = (train['Sentiment'].values > 3)\nK = len(set(targets))\n\n\n\n\n# convert the sentences (strings) into integers\ntokenizer = Tokenizer(num_words=MAX_VOCAB_SIZE)\ntokenizer.fit_on_texts(sentences)\nsequences = tokenizer.texts_to_sequences(sentences)\n\nprint(\"max sequence length:\", max(len(s) for s in sequences))\nprint(\"min sequence length:\", min(len(s) for s in sequences))\ns = sorted(len(s) for s in sequences)\nprint(\"median sequence length:\", s[len(s) // 2])\n\nmaxlen = min(max(len(s) for s in sequences), MAX_SEQUENCE_LENGTH)\n\n\n\n# get word -> integer mapping\nword2idx = tokenizer.word_index\nprint('Found %s unique tokens.' % len(word2idx))\n\n\n# pad sequences so that we get a N x T matrix\ndata = pad_sequences(sequences, maxlen=maxlen)\nprint('Shape of data tensor:', data.shape)\n\n\nprint('Building model...')\n\n# create an LSTM network with a single LSTM\ninput_ = Input(shape=(maxlen,))\nx = Embedding(len(word2idx) + 1, EMBEDDING_DIM)(input_)\nx = LSTM(5)(x)\noutput = Dense(K, activation='softmax')(x)\n\nmodel = Model(input_, output)\nmodel.compile(\n loss='sparse_categorical_crossentropy',\n optimizer='adam',\n metrics=['accuracy']\n)\n\n\nprint('Training model...')\nr = model.fit(\n data,\n targets,\n batch_size=BATCH_SIZE,\n epochs=EPOCHS,\n validation_split=VALIDATION_SPLIT\n)\n\n# plot some data\nplt.plot(r.history['loss'], label='loss')\nplt.plot(r.history['val_loss'], label='val_loss')\nplt.legend()\nplt.show()\n\n# accuracies\nplt.plot(r.history['acc'], label='acc')\nplt.plot(r.history['val_acc'], label='val_acc')\nplt.legend()\nplt.show()\n" }, { "path": "keras_examples/sine.py", "content": "# https://lazyprogrammer.me\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom keras.models import Model\nfrom keras.layers import Input, LSTM, GRU, SimpleRNN, Dense\nimport keras.backend as K\n\nfrom keras.optimizers import SGD, Adam\n\n\n# make the original data\nseries = np.sin(0.1*np.arange(200)) + np.random.randn(200)*0.1\n\n# plot it\nplt.plot(series)\nplt.show()\n\n\n### build the dataset\n# let's see if we can use T past values to predict the next value\nT = 10\nD = 1\nX = []\nY = []\nfor t in range(len(series) - T - 1):\n x = series[t:t+T]\n # print(\"x[-1]:\", x[-1])\n X.append(x)\n y = series[t+T]\n # print(\"y:\", y)\n Y.append(y)\n\nX = np.array(X)\nY = np.array(Y)\nN = len(X)\n\n\n\n### many-to-one RNN\ninputs = np.expand_dims(X, -1)\n\n# make the RNN\ni = Input(shape=(T, D))\nx = SimpleRNN(5)(i)\nx = Dense(1)(x)\nmodel = Model(i, x)\nmodel.compile(\n loss='mse',\n optimizer=Adam(lr=0.1),\n)\n\n# train the RNN\nr = model.fit(\n inputs[:-N//2], Y[:-N//2],\n batch_size=32,\n epochs=80,\n validation_data=(inputs[-N//2:], Y[-N//2:]),\n)\n\n# plot some data\nplt.plot(r.history['loss'], label='loss')\nplt.plot(r.history['val_loss'], label='val_loss')\nplt.legend()\nplt.show()\n\n\n# plot predictions vs targets\noutputs = model.predict(inputs)\nprint(outputs.shape)\npredictions = outputs[:,0]\n\nplt.plot(Y, label='targets')\nplt.plot(predictions, label='predictions')\nplt.title(\"many-to-one RNN\")\nplt.legend()\nplt.show()\n\n" }, { "path": "keras_examples/sine2.py", "content": "# https://lazyprogrammer.me\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom keras.models import Model\nfrom keras.layers import Input, SimpleRNN, Dense\nfrom keras.optimizers import SGD, Adam\n\n\n# make the original data\nseries1 = np.sin(0.1*np.arange(200))\nseries2 = np.sin(0.2*np.arange(200))\n\n# plot it\nplt.plot(series1)\nplt.plot(series2)\nplt.show()\n\n\n### build the dataset\n# let's see if we can use T past values to predict the next value\nT = 10\nD = 2\nX = []\nY = []\nfor t in range(len(series1) - T - 1):\n x = [series1[t:t+T], series2[t:t+T]]\n # print(\"x[-1]:\", x[-1])\n X.append(x)\n y = series1[t+T] + series2[t+T]\n # print(\"y:\", y)\n Y.append(y)\n\nX = np.array(X)\nprint(\"X.shape:\", X.shape)\nX = np.transpose(X, (0, 2, 1))\nY = np.array(Y)\nN = len(X)\n\n\n\n### many-to-one RNN\n\n# make the RNN\ni = Input(shape=(T, D))\nx = SimpleRNN(5)(i)\nx = Dense(1)(x)\nmodel = Model(i, x)\nmodel.compile(\n loss='mse',\n optimizer=Adam(lr=0.1),\n)\n\n# train the RNN\nr = model.fit(\n X[:-N//2], Y[:-N//2],\n batch_size=32,\n epochs=80,\n validation_data=(X[-N//2:], Y[-N//2:]),\n)\n\n# plot some data\nplt.plot(r.history['loss'], label='loss')\nplt.plot(r.history['val_loss'], label='val_loss')\nplt.legend()\nplt.show()\n\n\n# plot predictions vs targets\noutputs = model.predict(X)\nprint(outputs.shape)\npredictions = outputs[:,0]\n\nplt.plot(Y, label='targets')\nplt.plot(predictions, label='predictions')\nplt.title(\"many-to-one RNN\")\nplt.legend()\nplt.show()\n\n" }, { "path": "keras_examples/translation.py", "content": "# https://deeplearningcourses.com/c/deep-learning-advanced-nlp\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport os\nimport sys\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom keras.models import Model\nfrom keras.layers import Dense, Embedding, Input\nfrom keras.layers import LSTM, Bidirectional\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.optimizers import Adam\n\nimport keras.backend as K\nif len(K.tensorflow_backend._get_available_gpus()) > 0:\n from keras.layers import CuDNNLSTM as LSTM\n from keras.layers import CuDNNGRU as GRU\n\n\n\n\n# some configuration\nMAX_VOCAB_SIZE = 20000\nEMBEDDING_DIM = 20\nVALIDATION_SPLIT = 0.2\nBATCH_SIZE = 128\nEPOCHS = 30\nNUM_SAMPLES = 10000\n\n\n\n# Where we will store the data\ninput_texts = [] # sentence in original language\ntarget_texts = [] # sentence in target language\n\n\n# load in the data\n# download the data at: http://www.manythings.org/anki/\nt = 0\nfor line in open('../large_files/translation/spa.txt'):\n # only keep a limited number of samples\n t += 1\n if t > NUM_SAMPLES:\n break\n\n # input and target are separated by tab\n if '\\t' not in line:\n continue\n\n # split up the input and translation\n input_text, translation = line.rstrip().split('\\t')\n\n input_texts.append(input_text)\n target_texts.append(translation)\nprint(\"num samples:\", len(input_texts))\n\n\n\n# tokenize the inputs\ntokenizer_inputs = Tokenizer(num_words=MAX_VOCAB_SIZE)\ntokenizer_inputs.fit_on_texts(input_texts)\ninput_sequences = tokenizer_inputs.texts_to_sequences(input_texts)\n\n# get the word to index mapping for input language\nword2idx_inputs = tokenizer_inputs.word_index\nprint('Found %s unique input tokens.' % len(word2idx_inputs))\nnum_words_input = len(word2idx_inputs) + 1\n\n# determine maximum length input sequence\nmax_len_input = max(len(s) for s in input_sequences)\n\n# tokenize the outputs\n# don't filter out special characters\n# otherwise and won't appear\ntokenizer_outputs = Tokenizer(num_words=MAX_VOCAB_SIZE, filters='')\ntokenizer_outputs.fit_on_texts(target_texts)\ntarget_sequences = tokenizer_outputs.texts_to_sequences(target_texts)\n\n# get the word to index mapping for output language\nword2idx_outputs = tokenizer_outputs.word_index\nprint('Found %s unique output tokens.' % len(word2idx_outputs))\n\n# store number of output words for later\n# remember to add 1 since indexing starts at 1\nnum_words_output = len(word2idx_outputs) + 1\n\n# determine maximum length output sequence\nmax_len_target = max(len(s) for s in target_sequences)\n\n\nmax_len_both = max(max_len_input, max_len_target)\n\n\n\n# pad the sequences\ninputs_padded = pad_sequences(input_sequences, maxlen=max_len_both)\ntargets_padded = pad_sequences(target_sequences, maxlen=max_len_both)\n\n\n\n# create targets, since we cannot use sparse\n# categorical cross entropy when we have sequences\ntargets_padded_one_hot = np.zeros(\n (\n len(targets_padded),\n max_len_both,\n num_words_output\n ),\n dtype='float32'\n)\n\n# assign the values\nfor i, d in enumerate(targets_padded):\n for t, word in enumerate(d):\n targets_padded_one_hot[i, t, word] = 1\n\n\n\n\nprint('Building model...')\n\n# create an LSTM network with a single LSTM\ninput_ = Input(shape=(max_len_both,))\nx = Embedding(num_words_input, EMBEDDING_DIM)(input_)\nx = Bidirectional(LSTM(15, return_sequences=True))(x)\noutput = Dense(num_words_output, activation='softmax')(x)\n\nmodel = Model(input_, output)\nmodel.compile(\n loss='categorical_crossentropy',\n optimizer=Adam(lr=0.1),\n metrics=['accuracy']\n)\n\n\nprint('Training model...')\nr = model.fit(\n inputs_padded,\n targets_padded_one_hot,\n batch_size=BATCH_SIZE,\n epochs=EPOCHS,\n validation_split=VALIDATION_SPLIT\n)\n\n# plot some data\nplt.plot(r.history['loss'], label='loss')\nplt.plot(r.history['val_loss'], label='val_loss')\nplt.legend()\nplt.show()\n\n# accuracies\nplt.plot(r.history['acc'], label='acc')\nplt.plot(r.history['val_acc'], label='val_acc')\nplt.legend()\nplt.show()\n" }, { "path": "keras_examples/util.py", "content": "from __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn.utils import shuffle\nfrom scipy.io import loadmat\n\n\ndef getKaggleMNIST():\n # https://www.kaggle.com/c/digit-recognizer\n return getMNISTFormat('../large_files/train.csv')\n\n\ndef getKaggleFashionMNIST():\n # https://www.kaggle.com/zalando-research/fashionmnist\n return getMNISTFormat('../large_files/fashionmnist/fashion-mnist_train.csv')\n\ndef getMNISTFormat(path):\n # MNIST data:\n # column 0 is labels\n # column 1-785 is data, with values 0 .. 255\n # total size of CSV: (42000, 1, 28, 28)\n train = pd.read_csv(path).values.astype(np.float32)\n train = shuffle(train)\n\n Xtrain = train[:-1000,1:] / 255.0\n Ytrain = train[:-1000,0].astype(np.int32)\n\n Xtest = train[-1000:,1:] / 255.0\n Ytest = train[-1000:,0].astype(np.int32)\n return Xtrain, Ytrain, Xtest, Ytest\n\ndef getKaggleMNIST3D():\n Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()\n Xtrain = Xtrain.reshape(-1, 28, 28, 1)\n Xtest = Xtest.reshape(-1, 28, 28, 1)\n return Xtrain, Ytrain, Xtest, Ytest\n\ndef getKaggleFashionMNIST3D():\n Xtrain, Ytrain, Xtest, Ytest = getKaggleFashionMNIST()\n Xtrain = Xtrain.reshape(-1, 28, 28, 1)\n Xtest = Xtest.reshape(-1, 28, 28, 1)\n return Xtrain, Ytrain, Xtest, Ytest\n\ndef getCIFAR10():\n Xtrain = np.zeros((50000, 32, 32, 3), dtype=np.uint8)\n Ytrain = np.zeros(50000, dtype=np.uint8)\n\n # train data\n for i in range(5):\n fn = 'data_batch_%s.mat' % (i+1)\n d = loadmat('../large_files/cifar-10-batches-mat/' + fn)\n x = d['data']\n y = d['labels'].flatten()\n x = x.reshape(10000, 3, 32, 32)\n x = np.transpose(x, (0, 2, 3, 1))\n Xtrain[i*10000:(i+1)*10000] = x\n Ytrain[i*10000:(i+1)*10000] = y\n\n # test data\n d = loadmat('../large_files/cifar-10-batches-mat/test_batch.mat')\n x = d['data']\n y = d['labels'].flatten()\n x = x.reshape(10000, 3, 32, 32)\n x = np.transpose(x, (0, 2, 3, 1))\n Xtest = x\n Ytest = y\n\n return Xtrain, Ytrain, Xtest, Ytest\n\n" }, { "path": "kerascv/extra_reading.txt", "content": "KerasCV List of Models\nhttps://keras.io/api/keras_cv/models/\n\nFast R-CNN (Ross Girshick)\nhttps://arxiv.org/pdf/1504.08083.pdf\n\nFocal Loss for Dense Object Detection (Lin et al.)\nhttps://arxiv.org/abs/1708.02002" }, { "path": "kerascv/imagenet_label_names.json", "content": "[\"tench\",\n\"goldfish\",\n\"great white shark\",\n\"tiger shark\",\n\"hammerhead shark\",\n\"electric ray\",\n\"stingray\",\n\"cock\",\n\"hen\",\n\"ostrich\",\n\"brambling\",\n\"goldfinch\",\n\"house finch\",\n\"junco\",\n\"indigo bunting\",\n\"American robin\",\n\"bulbul\",\n\"jay\",\n\"magpie\",\n\"chickadee\",\n\"American dipper\",\n\"kite\",\n\"bald eagle\",\n\"vulture\",\n\"great grey owl\",\n\"fire salamander\",\n\"smooth newt\",\n\"newt\",\n\"spotted salamander\",\n\"axolotl\",\n\"American bullfrog\",\n\"tree frog\",\n\"tailed frog\",\n\"loggerhead sea turtle\",\n\"leatherback sea turtle\",\n\"mud turtle\",\n\"terrapin\",\n\"box turtle\",\n\"banded gecko\",\n\"green iguana\",\n\"Carolina anole\",\n\"desert grassland whiptail lizard\",\n\"agama\",\n\"frilled-necked lizard\",\n\"alligator lizard\",\n\"Gila monster\",\n\"European green lizard\",\n\"chameleon\",\n\"Komodo dragon\",\n\"Nile crocodile\",\n\"American alligator\",\n\"triceratops\",\n\"worm snake\",\n\"ring-necked snake\",\n\"eastern hog-nosed snake\",\n\"smooth green snake\",\n\"kingsnake\",\n\"garter snake\",\n\"water snake\",\n\"vine snake\",\n\"night snake\",\n\"boa constrictor\",\n\"African rock python\",\n\"Indian cobra\",\n\"green mamba\",\n\"sea snake\",\n\"Saharan horned viper\",\n\"eastern diamondback rattlesnake\",\n\"sidewinder\",\n\"trilobite\",\n\"harvestman\",\n\"scorpion\",\n\"yellow garden spider\",\n\"barn spider\",\n\"European garden spider\",\n\"southern black widow\",\n\"tarantula\",\n\"wolf spider\",\n\"tick\",\n\"centipede\",\n\"black grouse\",\n\"ptarmigan\",\n\"ruffed grouse\",\n\"prairie grouse\",\n\"peacock\",\n\"quail\",\n\"partridge\",\n\"grey parrot\",\n\"macaw\",\n\"sulphur-crested cockatoo\",\n\"lorikeet\",\n\"coucal\",\n\"bee eater\",\n\"hornbill\",\n\"hummingbird\",\n\"jacamar\",\n\"toucan\",\n\"duck\",\n\"red-breasted merganser\",\n\"goose\",\n\"black swan\",\n\"tusker\",\n\"echidna\",\n\"platypus\",\n\"wallaby\",\n\"koala\",\n\"wombat\",\n\"jellyfish\",\n\"sea anemone\",\n\"brain coral\",\n\"flatworm\",\n\"nematode\",\n\"conch\",\n\"snail\",\n\"slug\",\n\"sea slug\",\n\"chiton\",\n\"chambered nautilus\",\n\"Dungeness crab\",\n\"rock crab\",\n\"fiddler crab\",\n\"red king crab\",\n\"American lobster\",\n\"spiny lobster\",\n\"crayfish\",\n\"hermit crab\",\n\"isopod\",\n\"white stork\",\n\"black stork\",\n\"spoonbill\",\n\"flamingo\",\n\"little blue heron\",\n\"great egret\",\n\"bittern\",\n\"crane (bird)\",\n\"limpkin\",\n\"common gallinule\",\n\"American coot\",\n\"bustard\",\n\"ruddy turnstone\",\n\"dunlin\",\n\"common redshank\",\n\"dowitcher\",\n\"oystercatcher\",\n\"pelican\",\n\"king penguin\",\n\"albatross\",\n\"grey whale\",\n\"killer whale\",\n\"dugong\",\n\"sea lion\",\n\"Chihuahua\",\n\"Japanese Chin\",\n\"Maltese\",\n\"Pekingese\",\n\"Shih Tzu\",\n\"King Charles Spaniel\",\n\"Papillon\",\n\"toy terrier\",\n\"Rhodesian Ridgeback\",\n\"Afghan Hound\",\n\"Basset Hound\",\n\"Beagle\",\n\"Bloodhound\",\n\"Bluetick Coonhound\",\n\"Black and Tan Coonhound\",\n\"Treeing Walker Coonhound\",\n\"English foxhound\",\n\"Redbone Coonhound\",\n\"borzoi\",\n\"Irish Wolfhound\",\n\"Italian Greyhound\",\n\"Whippet\",\n\"Ibizan Hound\",\n\"Norwegian Elkhound\",\n\"Otterhound\",\n\"Saluki\",\n\"Scottish Deerhound\",\n\"Weimaraner\",\n\"Staffordshire Bull Terrier\",\n\"American Staffordshire Terrier\",\n\"Bedlington Terrier\",\n\"Border Terrier\",\n\"Kerry Blue Terrier\",\n\"Irish Terrier\",\n\"Norfolk Terrier\",\n\"Norwich Terrier\",\n\"Yorkshire Terrier\",\n\"Wire Fox Terrier\",\n\"Lakeland Terrier\",\n\"Sealyham Terrier\",\n\"Airedale Terrier\",\n\"Cairn Terrier\",\n\"Australian Terrier\",\n\"Dandie Dinmont Terrier\",\n\"Boston Terrier\",\n\"Miniature Schnauzer\",\n\"Giant Schnauzer\",\n\"Standard Schnauzer\",\n\"Scottish Terrier\",\n\"Tibetan Terrier\",\n\"Australian Silky Terrier\",\n\"Soft-coated Wheaten Terrier\",\n\"West Highland White Terrier\",\n\"Lhasa Apso\",\n\"Flat-Coated Retriever\",\n\"Curly-coated Retriever\",\n\"Golden Retriever\",\n\"Labrador Retriever\",\n\"Chesapeake Bay Retriever\",\n\"German Shorthaired Pointer\",\n\"Vizsla\",\n\"English Setter\",\n\"Irish Setter\",\n\"Gordon Setter\",\n\"Brittany\",\n\"Clumber Spaniel\",\n\"English Springer Spaniel\",\n\"Welsh Springer Spaniel\",\n\"Cocker Spaniels\",\n\"Sussex Spaniel\",\n\"Irish Water Spaniel\",\n\"Kuvasz\",\n\"Schipperke\",\n\"Groenendael\",\n\"Malinois\",\n\"Briard\",\n\"Australian Kelpie\",\n\"Komondor\",\n\"Old English Sheepdog\",\n\"Shetland Sheepdog\",\n\"collie\",\n\"Border Collie\",\n\"Bouvier des Flandres\",\n\"Rottweiler\",\n\"German Shepherd Dog\",\n\"Dobermann\",\n\"Miniature Pinscher\",\n\"Greater Swiss Mountain Dog\",\n\"Bernese Mountain Dog\",\n\"Appenzeller Sennenhund\",\n\"Entlebucher Sennenhund\",\n\"Boxer\",\n\"Bullmastiff\",\n\"Tibetan Mastiff\",\n\"French Bulldog\",\n\"Great Dane\",\n\"St. Bernard\",\n\"husky\",\n\"Alaskan Malamute\",\n\"Siberian Husky\",\n\"Dalmatian\",\n\"Affenpinscher\",\n\"Basenji\",\n\"pug\",\n\"Leonberger\",\n\"Newfoundland\",\n\"Pyrenean Mountain Dog\",\n\"Samoyed\",\n\"Pomeranian\",\n\"Chow Chow\",\n\"Keeshond\",\n\"Griffon Bruxellois\",\n\"Pembroke Welsh Corgi\",\n\"Cardigan Welsh Corgi\",\n\"Toy Poodle\",\n\"Miniature Poodle\",\n\"Standard Poodle\",\n\"Mexican hairless dog\",\n\"grey wolf\",\n\"Alaskan tundra wolf\",\n\"red wolf\",\n\"coyote\",\n\"dingo\",\n\"dhole\",\n\"African wild dog\",\n\"hyena\",\n\"red fox\",\n\"kit fox\",\n\"Arctic fox\",\n\"grey fox\",\n\"tabby cat\",\n\"tiger cat\",\n\"Persian cat\",\n\"Siamese cat\",\n\"Egyptian Mau\",\n\"cougar\",\n\"lynx\",\n\"leopard\",\n\"snow leopard\",\n\"jaguar\",\n\"lion\",\n\"tiger\",\n\"cheetah\",\n\"brown bear\",\n\"American black bear\",\n\"polar bear\",\n\"sloth bear\",\n\"mongoose\",\n\"meerkat\",\n\"tiger beetle\",\n\"ladybug\",\n\"ground beetle\",\n\"longhorn beetle\",\n\"leaf beetle\",\n\"dung beetle\",\n\"rhinoceros beetle\",\n\"weevil\",\n\"fly\",\n\"bee\",\n\"ant\",\n\"grasshopper\",\n\"cricket\",\n\"stick insect\",\n\"cockroach\",\n\"mantis\",\n\"cicada\",\n\"leafhopper\",\n\"lacewing\",\n\"dragonfly\",\n\"damselfly\",\n\"red admiral\",\n\"ringlet\",\n\"monarch butterfly\",\n\"small white\",\n\"sulphur butterfly\",\n\"gossamer-winged butterfly\",\n\"starfish\",\n\"sea urchin\",\n\"sea cucumber\",\n\"cottontail rabbit\",\n\"hare\",\n\"Angora rabbit\",\n\"hamster\",\n\"porcupine\",\n\"fox squirrel\",\n\"marmot\",\n\"beaver\",\n\"guinea pig\",\n\"common sorrel\",\n\"zebra\",\n\"pig\",\n\"wild boar\",\n\"warthog\",\n\"hippopotamus\",\n\"ox\",\n\"water buffalo\",\n\"bison\",\n\"ram\",\n\"bighorn sheep\",\n\"Alpine ibex\",\n\"hartebeest\",\n\"impala\",\n\"gazelle\",\n\"dromedary\",\n\"llama\",\n\"weasel\",\n\"mink\",\n\"European polecat\",\n\"black-footed ferret\",\n\"otter\",\n\"skunk\",\n\"badger\",\n\"armadillo\",\n\"three-toed sloth\",\n\"orangutan\",\n\"gorilla\",\n\"chimpanzee\",\n\"gibbon\",\n\"siamang\",\n\"guenon\",\n\"patas monkey\",\n\"baboon\",\n\"macaque\",\n\"langur\",\n\"black-and-white colobus\",\n\"proboscis monkey\",\n\"marmoset\",\n\"white-headed capuchin\",\n\"howler monkey\",\n\"titi\",\n\"Geoffroy's spider monkey\",\n\"common squirrel monkey\",\n\"ring-tailed lemur\",\n\"indri\",\n\"Asian elephant\",\n\"African bush elephant\",\n\"red panda\",\n\"giant panda\",\n\"snoek\",\n\"eel\",\n\"coho salmon\",\n\"rock beauty\",\n\"clownfish\",\n\"sturgeon\",\n\"garfish\",\n\"lionfish\",\n\"pufferfish\",\n\"abacus\",\n\"abaya\",\n\"academic gown\",\n\"accordion\",\n\"acoustic guitar\",\n\"aircraft carrier\",\n\"airliner\",\n\"airship\",\n\"altar\",\n\"ambulance\",\n\"amphibious vehicle\",\n\"analog clock\",\n\"apiary\",\n\"apron\",\n\"waste container\",\n\"assault rifle\",\n\"backpack\",\n\"bakery\",\n\"balance beam\",\n\"balloon\",\n\"ballpoint pen\",\n\"Band-Aid\",\n\"banjo\",\n\"baluster\",\n\"barbell\",\n\"barber chair\",\n\"barbershop\",\n\"barn\",\n\"barometer\",\n\"barrel\",\n\"wheelbarrow\",\n\"baseball\",\n\"basketball\",\n\"bassinet\",\n\"bassoon\",\n\"swimming cap\",\n\"bath towel\",\n\"bathtub\",\n\"station wagon\",\n\"lighthouse\",\n\"beaker\",\n\"military cap\",\n\"beer bottle\",\n\"beer glass\",\n\"bell-cot\",\n\"bib\",\n\"tandem bicycle\",\n\"bikini\",\n\"ring binder\",\n\"binoculars\",\n\"birdhouse\",\n\"boathouse\",\n\"bobsleigh\",\n\"bolo tie\",\n\"poke bonnet\",\n\"bookcase\",\n\"bookstore\",\n\"bottle cap\",\n\"bow\",\n\"bow tie\",\n\"brass\",\n\"bra\",\n\"breakwater\",\n\"breastplate\",\n\"broom\",\n\"bucket\",\n\"buckle\",\n\"bulletproof vest\",\n\"high-speed train\",\n\"butcher shop\",\n\"taxicab\",\n\"cauldron\",\n\"candle\",\n\"cannon\",\n\"canoe\",\n\"can opener\",\n\"cardigan\",\n\"car mirror\",\n\"carousel\",\n\"tool kit\",\n\"carton\",\n\"car wheel\",\n\"automated teller machine\",\n\"cassette\",\n\"cassette player\",\n\"castle\",\n\"catamaran\",\n\"CD player\",\n\"cello\",\n\"mobile phone\",\n\"chain\",\n\"chain-link fence\",\n\"chain mail\",\n\"chainsaw\",\n\"chest\",\n\"chiffonier\",\n\"chime\",\n\"china cabinet\",\n\"Christmas stocking\",\n\"church\",\n\"movie theater\",\n\"cleaver\",\n\"cliff dwelling\",\n\"cloak\",\n\"clogs\",\n\"cocktail shaker\",\n\"coffee mug\",\n\"coffeemaker\",\n\"coil\",\n\"combination lock\",\n\"computer keyboard\",\n\"confectionery store\",\n\"container ship\",\n\"convertible\",\n\"corkscrew\",\n\"cornet\",\n\"cowboy boot\",\n\"cowboy hat\",\n\"cradle\",\n\"crane (machine)\",\n\"crash helmet\",\n\"crate\",\n\"infant bed\",\n\"Crock Pot\",\n\"croquet ball\",\n\"crutch\",\n\"cuirass\",\n\"dam\",\n\"desk\",\n\"desktop computer\",\n\"rotary dial telephone\",\n\"diaper\",\n\"digital clock\",\n\"digital watch\",\n\"dining table\",\n\"dishcloth\",\n\"dishwasher\",\n\"disc brake\",\n\"dock\",\n\"dog sled\",\n\"dome\",\n\"doormat\",\n\"drilling rig\",\n\"drum\",\n\"drumstick\",\n\"dumbbell\",\n\"Dutch oven\",\n\"electric fan\",\n\"electric guitar\",\n\"electric locomotive\",\n\"entertainment center\",\n\"envelope\",\n\"espresso machine\",\n\"face powder\",\n\"feather boa\",\n\"filing cabinet\",\n\"fireboat\",\n\"fire engine\",\n\"fire screen sheet\",\n\"flagpole\",\n\"flute\",\n\"folding chair\",\n\"football helmet\",\n\"forklift\",\n\"fountain\",\n\"fountain pen\",\n\"four-poster bed\",\n\"freight car\",\n\"French horn\",\n\"frying pan\",\n\"fur coat\",\n\"garbage truck\",\n\"gas mask\",\n\"gas pump\",\n\"goblet\",\n\"go-kart\",\n\"golf ball\",\n\"golf cart\",\n\"gondola\",\n\"gong\",\n\"gown\",\n\"grand piano\",\n\"greenhouse\",\n\"grille\",\n\"grocery store\",\n\"guillotine\",\n\"barrette\",\n\"hair spray\",\n\"half-track\",\n\"hammer\",\n\"hamper\",\n\"hair dryer\",\n\"hand-held computer\",\n\"handkerchief\",\n\"hard disk drive\",\n\"harmonica\",\n\"harp\",\n\"harvester\",\n\"hatchet\",\n\"holster\",\n\"home theater\",\n\"honeycomb\",\n\"hook\",\n\"hoop skirt\",\n\"horizontal bar\",\n\"horse-drawn vehicle\",\n\"hourglass\",\n\"iPod\",\n\"clothes iron\",\n\"jack-o'-lantern\",\n\"jeans\",\n\"jeep\",\n\"T-shirt\",\n\"jigsaw puzzle\",\n\"pulled rickshaw\",\n\"joystick\",\n\"kimono\",\n\"knee pad\",\n\"knot\",\n\"lab coat\",\n\"ladle\",\n\"lampshade\",\n\"laptop computer\",\n\"lawn mower\",\n\"lens cap\",\n\"paper knife\",\n\"library\",\n\"lifeboat\",\n\"lighter\",\n\"limousine\",\n\"ocean liner\",\n\"lipstick\",\n\"slip-on shoe\",\n\"lotion\",\n\"speaker\",\n\"loupe\",\n\"sawmill\",\n\"magnetic compass\",\n\"mail bag\",\n\"mailbox\",\n\"tights\",\n\"tank suit\",\n\"manhole cover\",\n\"maraca\",\n\"marimba\",\n\"mask\",\n\"match\",\n\"maypole\",\n\"maze\",\n\"measuring cup\",\n\"medicine chest\",\n\"megalith\",\n\"microphone\",\n\"microwave oven\",\n\"military uniform\",\n\"milk can\",\n\"minibus\",\n\"miniskirt\",\n\"minivan\",\n\"missile\",\n\"mitten\",\n\"mixing bowl\",\n\"mobile home\",\n\"Model T\",\n\"modem\",\n\"monastery\",\n\"monitor\",\n\"moped\",\n\"mortar\",\n\"square academic cap\",\n\"mosque\",\n\"mosquito net\",\n\"scooter\",\n\"mountain bike\",\n\"tent\",\n\"computer mouse\",\n\"mousetrap\",\n\"moving van\",\n\"muzzle\",\n\"nail\",\n\"neck brace\",\n\"necklace\",\n\"nipple\",\n\"notebook computer\",\n\"obelisk\",\n\"oboe\",\n\"ocarina\",\n\"odometer\",\n\"oil filter\",\n\"organ\",\n\"oscilloscope\",\n\"overskirt\",\n\"bullock cart\",\n\"oxygen mask\",\n\"packet\",\n\"paddle\",\n\"paddle wheel\",\n\"padlock\",\n\"paintbrush\",\n\"pajamas\",\n\"palace\",\n\"pan flute\",\n\"paper towel\",\n\"parachute\",\n\"parallel bars\",\n\"park bench\",\n\"parking meter\",\n\"passenger car\",\n\"patio\",\n\"payphone\",\n\"pedestal\",\n\"pencil case\",\n\"pencil sharpener\",\n\"perfume\",\n\"Petri dish\",\n\"photocopier\",\n\"plectrum\",\n\"Pickelhaube\",\n\"picket fence\",\n\"pickup truck\",\n\"pier\",\n\"piggy bank\",\n\"pill bottle\",\n\"pillow\",\n\"ping-pong ball\",\n\"pinwheel\",\n\"pirate ship\",\n\"pitcher\",\n\"hand plane\",\n\"planetarium\",\n\"plastic bag\",\n\"plate rack\",\n\"plow\",\n\"plunger\",\n\"Polaroid camera\",\n\"pole\",\n\"police van\",\n\"poncho\",\n\"billiard table\",\n\"soda bottle\",\n\"pot\",\n\"potter's wheel\",\n\"power drill\",\n\"prayer rug\",\n\"printer\",\n\"prison\",\n\"projectile\",\n\"projector\",\n\"hockey puck\",\n\"punching bag\",\n\"purse\",\n\"quill\",\n\"quilt\",\n\"race car\",\n\"racket\",\n\"radiator\",\n\"radio\",\n\"radio telescope\",\n\"rain barrel\",\n\"recreational vehicle\",\n\"reel\",\n\"reflex camera\",\n\"refrigerator\",\n\"remote control\",\n\"restaurant\",\n\"revolver\",\n\"rifle\",\n\"rocking chair\",\n\"rotisserie\",\n\"eraser\",\n\"rugby ball\",\n\"ruler\",\n\"running shoe\",\n\"safe\",\n\"safety pin\",\n\"salt shaker\",\n\"sandal\",\n\"sarong\",\n\"saxophone\",\n\"scabbard\",\n\"weighing scale\",\n\"school bus\",\n\"schooner\",\n\"scoreboard\",\n\"CRT screen\",\n\"screw\",\n\"screwdriver\",\n\"seat belt\",\n\"sewing machine\",\n\"shield\",\n\"shoe store\",\n\"shoji\",\n\"shopping basket\",\n\"shopping cart\",\n\"shovel\",\n\"shower cap\",\n\"shower curtain\",\n\"ski\",\n\"ski mask\",\n\"sleeping bag\",\n\"slide rule\",\n\"sliding door\",\n\"slot machine\",\n\"snorkel\",\n\"snowmobile\",\n\"snowplow\",\n\"soap dispenser\",\n\"soccer ball\",\n\"sock\",\n\"solar thermal collector\",\n\"sombrero\",\n\"soup bowl\",\n\"space bar\",\n\"space heater\",\n\"space shuttle\",\n\"spatula\",\n\"motorboat\",\n\"spider web\",\n\"spindle\",\n\"sports car\",\n\"spotlight\",\n\"stage\",\n\"steam locomotive\",\n\"through arch bridge\",\n\"steel drum\",\n\"stethoscope\",\n\"scarf\",\n\"stone wall\",\n\"stopwatch\",\n\"stove\",\n\"strainer\",\n\"tram\",\n\"stretcher\",\n\"couch\",\n\"stupa\",\n\"submarine\",\n\"suit\",\n\"sundial\",\n\"sunglass\",\n\"sunglasses\",\n\"sunscreen\",\n\"suspension bridge\",\n\"mop\",\n\"sweatshirt\",\n\"swimsuit\",\n\"swing\",\n\"switch\",\n\"syringe\",\n\"table lamp\",\n\"tank\",\n\"tape player\",\n\"teapot\",\n\"teddy bear\",\n\"television\",\n\"tennis ball\",\n\"thatched roof\",\n\"front curtain\",\n\"thimble\",\n\"threshing machine\",\n\"throne\",\n\"tile roof\",\n\"toaster\",\n\"tobacco shop\",\n\"toilet seat\",\n\"torch\",\n\"totem pole\",\n\"tow truck\",\n\"toy store\",\n\"tractor\",\n\"semi-trailer truck\",\n\"tray\",\n\"trench coat\",\n\"tricycle\",\n\"trimaran\",\n\"tripod\",\n\"triumphal arch\",\n\"trolleybus\",\n\"trombone\",\n\"tub\",\n\"turnstile\",\n\"typewriter keyboard\",\n\"umbrella\",\n\"unicycle\",\n\"upright piano\",\n\"vacuum cleaner\",\n\"vase\",\n\"vault\",\n\"velvet\",\n\"vending machine\",\n\"vestment\",\n\"viaduct\",\n\"violin\",\n\"volleyball\",\n\"waffle iron\",\n\"wall clock\",\n\"wallet\",\n\"wardrobe\",\n\"military aircraft\",\n\"sink\",\n\"washing machine\",\n\"water bottle\",\n\"water jug\",\n\"water tower\",\n\"whiskey jug\",\n\"whistle\",\n\"wig\",\n\"window screen\",\n\"window shade\",\n\"Windsor tie\",\n\"wine bottle\",\n\"wing\",\n\"wok\",\n\"wooden spoon\",\n\"wool\",\n\"split-rail fence\",\n\"shipwreck\",\n\"yawl\",\n\"yurt\",\n\"website\",\n\"comic book\",\n\"crossword\",\n\"traffic sign\",\n\"traffic light\",\n\"dust jacket\",\n\"menu\",\n\"plate\",\n\"guacamole\",\n\"consomme\",\n\"hot pot\",\n\"trifle\",\n\"ice cream\",\n\"ice pop\",\n\"baguette\",\n\"bagel\",\n\"pretzel\",\n\"cheeseburger\",\n\"hot dog\",\n\"mashed potato\",\n\"cabbage\",\n\"broccoli\",\n\"cauliflower\",\n\"zucchini\",\n\"spaghetti squash\",\n\"acorn squash\",\n\"butternut squash\",\n\"cucumber\",\n\"artichoke\",\n\"bell pepper\",\n\"cardoon\",\n\"mushroom\",\n\"Granny Smith\",\n\"strawberry\",\n\"orange\",\n\"lemon\",\n\"fig\",\n\"pineapple\",\n\"banana\",\n\"jackfruit\",\n\"custard apple\",\n\"pomegranate\",\n\"hay\",\n\"carbonara\",\n\"chocolate syrup\",\n\"dough\",\n\"meatloaf\",\n\"pizza\",\n\"pot pie\",\n\"burrito\",\n\"red wine\",\n\"espresso\",\n\"cup\",\n\"eggnog\",\n\"alp\",\n\"bubble\",\n\"cliff\",\n\"coral reef\",\n\"geyser\",\n\"lakeshore\",\n\"promontory\",\n\"shoal\",\n\"seashore\",\n\"valley\",\n\"volcano\",\n\"baseball player\",\n\"bridegroom\",\n\"scuba diver\",\n\"rapeseed\",\n\"daisy\",\n\"yellow lady's slipper\",\n\"corn\",\n\"acorn\",\n\"rose hip\",\n\"horse chestnut seed\",\n\"coral fungus\",\n\"agaric\",\n\"gyromitra\",\n\"stinkhorn mushroom\",\n\"earth star\",\n\"hen-of-the-woods\",\n\"bolete\",\n\"ear\",\n\"toilet paper\"]\n" }, { "path": "kerascv/makelist.py", "content": "'''\nUse this script to generate a list of all XML files in a folder.\n'''\n\nfrom glob import glob\n\nfiles = glob('*.xml')\nwith open('xml_list.txt', 'w') as f:\n for fn in files:\n f.write(\"%s\\n\" % fn)" }, { "path": "kerascv/pascal2coco.py", "content": "# adapted from https://blog.roboflow.com/how-to-convert-annotations-from-voc-xml-to-coco-json/\n\nimport os\nimport argparse\nimport json\nimport xml.etree.ElementTree as ET\nfrom typing import Dict, List\nfrom tqdm import tqdm\nimport re\n\n\ndef get_label2id(labels_path: str) -> Dict[str, int]:\n \"\"\"id is 1 start\"\"\"\n with open(labels_path, 'r') as f:\n labels_str = f.read().split()\n labels_ids = list(range(0, len(labels_str)))\n return dict(zip(labels_str, labels_ids))\n\n\ndef get_annpaths(ann_dir_path: str = None,\n ann_ids_path: str = None,\n ext: str = '',\n annpaths_list_path: str = None) -> List[str]:\n # If use annotation paths list\n if annpaths_list_path is not None:\n with open(annpaths_list_path, 'r') as f:\n ann_paths = f.read().split()\n return ann_paths\n\n # If use annotaion ids list\n ext_with_dot = '.' + ext if ext != '' else ''\n with open(ann_ids_path, 'r') as f:\n ann_ids = f.read().split()\n ann_paths = [os.path.join(ann_dir_path, aid+ext_with_dot) for aid in ann_ids]\n return ann_paths\n\n\ndef get_image_info(annotation_root, extract_num_from_imgid=True):\n path = annotation_root.findtext('path')\n if path is None:\n filename = annotation_root.findtext('filename')\n else:\n filename = os.path.basename(path)\n img_name = os.path.basename(filename)\n img_id = os.path.splitext(img_name)[0]\n if extract_num_from_imgid and isinstance(img_id, str):\n img_id = int(re.findall(r'\\d+', img_id)[0])\n\n size = annotation_root.find('size')\n width = int(size.findtext('width'))\n height = int(size.findtext('height'))\n\n image_info = {\n 'file_name': filename,\n 'height': height,\n 'width': width,\n 'id': img_id\n }\n return image_info\n\n\ndef get_coco_annotation_from_obj(obj, label2id):\n label = obj.findtext('name')\n assert label in label2id, f\"Error: {label} is not in label2id !\"\n category_id = label2id[label]\n bndbox = obj.find('bndbox')\n xmin = int(bndbox.findtext('xmin')) - 1\n ymin = int(bndbox.findtext('ymin')) - 1\n xmax = int(bndbox.findtext('xmax'))\n ymax = int(bndbox.findtext('ymax'))\n assert xmax > xmin and ymax > ymin, f\"Box size error !: (xmin, ymin, xmax, ymax): {xmin, ymin, xmax, ymax}\"\n o_width = xmax - xmin\n o_height = ymax - ymin\n ann = {\n 'area': o_width * o_height,\n 'iscrowd': 0,\n 'bbox': [xmin, ymin, o_width, o_height],\n 'category_id': category_id,\n 'ignore': 0,\n 'segmentation': [] # This script is not for segmentation\n }\n return ann\n\n\ndef convert_xmls_to_cocojson(annotation_paths: List[str],\n label2id: Dict[str, int],\n output_jsonpath: str,\n extract_num_from_imgid: bool = True):\n output_json_dict = {\n \"images\": [],\n \"type\": \"instances\",\n \"annotations\": [],\n \"categories\": []\n }\n bnd_id = 1 # START_BOUNDING_BOX_ID, TODO input as args ?\n print('Start converting !')\n for a_path in tqdm(annotation_paths):\n # Read annotation xml\n ann_tree = ET.parse(a_path)\n ann_root = ann_tree.getroot()\n\n img_info = get_image_info(annotation_root=ann_root,\n extract_num_from_imgid=extract_num_from_imgid)\n img_id = img_info['id']\n output_json_dict['images'].append(img_info)\n\n for obj in ann_root.findall('object'):\n ann = get_coco_annotation_from_obj(obj=obj, label2id=label2id)\n ann.update({'image_id': img_id, 'id': bnd_id})\n output_json_dict['annotations'].append(ann)\n bnd_id = bnd_id + 1\n\n for label, label_id in label2id.items():\n category_info = {'supercategory': 'none', 'id': label_id, 'name': label}\n output_json_dict['categories'].append(category_info)\n\n with open(output_jsonpath, 'w') as f:\n output_json = json.dumps(output_json_dict)\n f.write(output_json)\n\n\ndef main():\n parser = argparse.ArgumentParser(\n description='This script support converting voc format xmls to coco format json')\n parser.add_argument('--ann_dir', type=str, default=None,\n help='path to annotation files directory. It is not need when use --ann_paths_list')\n parser.add_argument('--ann_ids', type=str, default=None,\n help='path to annotation files ids list. It is not need when use --ann_paths_list')\n parser.add_argument('--ann_paths_list', type=str, default=None,\n help='path of annotation paths list. It is not need when use --ann_dir and --ann_ids')\n parser.add_argument('--labels', type=str, default=None,\n help='path to label list.')\n parser.add_argument('--output', type=str, default='output.json', help='path to output json file')\n parser.add_argument('--ext', type=str, default='', help='additional extension of annotation file')\n args = parser.parse_args()\n label2id = get_label2id(labels_path=args.labels)\n ann_paths = get_annpaths(\n ann_dir_path=args.ann_dir,\n ann_ids_path=args.ann_ids,\n ext=args.ext,\n annpaths_list_path=args.ann_paths_list\n )\n convert_xmls_to_cocojson(\n annotation_paths=ann_paths,\n label2id=label2id,\n output_jsonpath=args.output,\n extract_num_from_imgid=True\n )\n\n\nif __name__ == '__main__':\n main()" }, { "path": "linear_algebra/WHERE ARE THE NOTEBOOKS.txt", "content": "As stated in the \"where to get the code\" / \"where to get the notebooks\" lecture, the notebooks are NOT on Github.\n\nIf you missed this, please review the lecture for the actual location of the notebooks.\n\nIf, after reviewing it, you still need assistance, please contact info@deeplearningcourses.com." }, { "path": "linear_algebra/extra_reading.txt", "content": "Introduction to Linear Algebra by Gilbert Strang\nhttps://amzn.to/2G3bvW1\n\nStill Don't Understand Gravity? This Will Help\n- this is included not because it's about calculus, but because it's yet another educator explaining why practice is important\nhttps://www.youtube.com/watch?v=cP2uVarXi1A" }, { "path": "linear_regression_class/data_1d.csv", "content": "95.724162408,197.179636092\n35.7576189281,67.5906695414\n28.8168474238,60.8541328206\n99.9584813087,196.907396981\n66.8097483121,125.311128524\n58.2156926413,115.785784589\n53.8210763379,110.762772705\n81.2960821704,157.98528569\n80.6486970595,159.61941373\n78.2528136925,149.003865539\n16.8634836868,31.4675908827\n90.799147557,184.185219661\n53.312737012,103.220610158\n71.6187890238,143.271818357\n18.4105917769,46.7364180117\n57.124346762,107.123794204\n81.1346839164,168.309374007\n82.0152554217,166.82994267\n36.924908251,70.5014555898\n44.5871260438,96.8693702549\n26.662354456,50.3732358383\n74.065057115,145.519170708\n17.7405767273,46.5518746077\n6.8197438189,13.2437863094\n26.3173600923,62.5006665037\n82.8041190512,159.916649576\n39.446536962,77.9842041485\n76.7281205911,147.269183138\n42.2158561432,83.6452954011\n94.3585758425,191.956072692\n42.141785126,91.7327341584\n3.21124860748,8.2514167727\n6.8856734002,16.0963894188\n13.8757721557,33.3544421438\n63.2974036434,131.505745533\n60.4925193638,128.698843062\n79.6072852308,160.310380503\n83.4443630603,160.603245737\n54.8920701084,104.400293158\n62.0459441682,126.969456046\n66.6537579409,124.519167614\n61.3198131584,125.217414427\n47.3195529929,98.7194843794\n81.1509876011,166.432455477\n91.0215136709,179.236872614\n16.8434620066,33.7772337402\n76.5064365659,161.372965132\n71.8657046789,138.575401004\n9.84808948357,17.9033282786\n35.7897124122,74.1085917146\n35.8886919974,74.8213229656\n90.8294487116,179.241547046\n7.26207283678,6.43437237688\n35.359288168,78.086356791\n79.0792537554,154.591789763\n21.5534583299,50.0307538993\n79.4764277802,165.441966192\n48.7679666405,110.897287971\n54.7938620084,114.691885107\n87.5035255175,186.744206306\n38.9491304682,70.6972229828\n9.39561282267,19.1790300473\n11.1211868171,27.022959917\n46.1355582629,87.2924492169\n85.6975735819,167.924614121\n50.1102964028,96.0205484441\n11.0736667277,24.9463246457\n22.2281308507,54.0041458755\n67.5539548043,133.165254434\n12.7871856602,28.0062232213\n46.9654908412,100.605589468\n4.84854064839,13.8572726025\n40.5373319344,82.5906629412\n26.3304612748,50.2318039097\n38.8847263526,79.4817724245\n93.8382665331,191.324317836\n60.0816599704,120.495115436\n50.1139652204,111.352322885\n17.7186852835,44.9376064869\n85.0730311421,172.133203076\n41.3215097262,84.889236\n35.45154709,69.9189912445\n74.0210255434,149.778646331\n90.5197211027,187.593909745\n34.5405970412,66.326858951\n94.5616621258,189.144964109\n52.9562678505,103.44683588\n80.1064364142,158.529430067\n49.822757583,98.6988396336\n61.7817253103,122.647395062\n60.2861083596,124.763346322\n77.3206901868,155.67749312\n15.6463587542,35.4106082545\n71.5406183401,151.323458712\n62.4523981129,129.43395879\n79.0598323714,163.096902614\n72.1976934443,142.898522638\n71.4177534183,148.09420854\n31.0088200801,63.7968762483\n40.3252822158,86.7236853299\n" }, { "path": "linear_regression_class/data_2d.csv", "content": "17.9302012052,94.5205919533,320.259529602\n97.1446971852,69.5932819844,404.634471526\n81.7759007845,5.73764809688,181.485107741\n55.8543424175,70.3259016768,321.773638018\n49.3665499855,75.1140401571,322.465485583\n3.19270246506,29.2562988631,94.6188108954\n49.2007840582,86.1444385075,356.348092747\n21.882803904,46.8415051959,181.653769226\n79.5098627219,87.397355535,423.557743194\n88.1538874975,65.2056419279,369.229245443\n60.7438543399,99.9576339008,427.605803661\n67.4155819451,50.3683096094,292.471821553\n48.3181157719,99.1289531425,395.529811407\n28.8299719729,87.1849488537,319.031348455\n43.853742664,64.4736390798,287.4281441\n25.3136940868,83.5452942552,292.768908839\n10.807726675,45.6955685904,159.663307674\n98.365745882,82.6973935253,438.798963866\n29.1469099692,66.3651067611,250.986309034\n65.1003018959,33.3538834975,231.711507921\n24.6441134909,39.5400527406,163.398160832\n37.5598048804,1.34572784158,83.4801551365\n88.1645062351,95.1536625708,466.265805791\n13.8346208354,25.494048204,100.886430268\n64.4108437467,77.2598381277,365.641048062\n68.925991798,97.4536007955,426.140015493\n39.4884422375,50.8561281903,235.532389457\n52.4631776831,59.7765096878,283.291640318\n48.4847869797,66.9703542227,298.581440375\n8.06208781377,98.2426001448,309.234108782\n32.7318877149,18.8535355289,129.610139015\n11.6523787962,66.2645117418,224.150542091\n13.7303535349,70.4725091314,235.305665631\n8.18555176536,41.8519894198,153.484189455\n53.6098761481,94.560121637,394.939444253\n95.3686098922,47.2955069599,336.126738879\n87.3336092062,93.8039343271,449.363351987\n66.3576110919,81.8475512843,387.014816485\n19.7547175302,65.5233009197,240.389441595\n21.1334404504,47.4371819938,177.14828058\n22.37386481,25.9556275364,119.61125795\n93.9904040537,0.127890520058,196.716166657\n86.7201980995,18.413766785,236.260808358\n98.9983729904,60.2312656882,384.381344695\n3.59396564372,96.2522173231,293.237183045\n15.1023633732,92.5569035696,304.890882807\n97.8341407695,2.02390810433,201.293598424\n19.9382196923,46.7782734632,170.610093035\n30.3735111369,58.7775251616,242.373484227\n73.2928831485,67.6696277593,353.082991304\n52.2309008804,81.9024482531,348.725688557\n86.4295761137,66.5402275975,365.959970954\n93.4008021441,18.0752459352,235.472381633\n13.213460059,91.4888587787,300.606878255\n4.59346270394,46.3359315158,145.818745251\n15.6692915825,35.5437439971,138.880334695\n52.9593597724,68.7202096086,317.163707741\n56.8175212312,47.5727319202,254.903631301\n51.1335430813,78.0421674552,334.5843335\n7.86216471526,17.7290817759,69.3555888141\n54.6986037014,92.7445841407,386.859937239\n86.3990630133,41.8886945857,294.871713576\n11.9475060186,42.9613867358,156.754219545\n70.3584010648,83.7062345104,391.806135291\n29.0223663255,84.327783082,319.310462851\n42.7594799067,97.4933260814,376.291589478\n96.2156564389,25.8342825832,280.617043873\n53.2277276552,27.9055085669,194.430465068\n30.3609896715,0.939644215313,69.6488631767\n83.2775653897,73.1793485695,384.597184993\n30.1876924786,7.1465385989,89.5390083986\n11.7884184617,51.6977608445,181.550682802\n18.2924240117,61.9779760484,224.773382884\n96.7126676927,9.0291015125,219.567093701\n31.0127386947,78.2833824603,298.490216481\n11.3972607536,61.728693243,199.944044715\n17.3925557906,4.24114086314,43.9156923531\n72.1826937267,34.5390721676,256.068378234\n73.980020785,3.71649343826,159.372580639\n94.4930583509,88.41719702,447.132703621\n84.5628207335,20.2411621935,233.078829579\n51.7424739723,11.009747964,131.070179635\n53.7485903953,60.0251022958,298.814332704\n85.0508347563,95.7369969538,451.80352299\n46.7772504514,90.2022062395,368.366435952\n49.7584341737,52.834494361,254.706774007\n24.1192565017,42.1028107802,168.308432759\n27.2015764537,29.9787492892,146.34226\n7.00959616769,55.876058392,176.810148976\n97.6469496688,8.14762512696,219.16028041\n1.38298250945,84.9440869198,252.905652954\n22.3235303534,27.5150750402,127.570478844\n45.0454062325,93.5204022248,375.822340339\n40.1639914677,0.161699234822,80.3890193252\n53.182739793,8.17031616228,142.718183077\n46.4567791559,82.000170914,336.876154384\n77.1303006946,95.1887594549,438.460586127\n68.600607572,72.5711807231,355.900286918\n41.6938871165,69.241125973,284.834636711\n4.1426693978,52.2547263792,168.034400947\n" }, { "path": "linear_regression_class/data_poly.csv", "content": "76.7007086033,663.797275569\n95.2735441552,1014.3622816\n73.0957232493,618.938826916\n46.9516354572,288.012877367\n33.3137480056,144.977555864\n58.8001284334,412.327812022\n86.4775958313,844.415014024\n26.1438291437,89.351909563\n97.6793058523,1053.20525097\n43.7453159626,240.908778383\n27.3960599569,115.936712225\n97.7709380144,1034.55054041\n0.477748646463,3.44508498982\n45.8230852085,254.058885722\n54.8058554165,353.273447055\n90.1526491199,926.945048811\n7.25055401942,30.7577367238\n45.32559552,241.735203861\n78.0299288822,692.797188617\n56.9992954919,382.995647161\n94.0058295504,978.718285869\n26.9031773559,98.2290062278\n23.0527128171,84.9785703587\n72.1620607036,584.521550996\n99.03856132,1082.25149734\n3.8567282135,5.37807424483\n29.1370660814,122.013630198\n86.1406581482,841.297543579\n30.6637414672,131.606512241\n45.7038082016,267.357505947\n20.8365297577,52.1846807158\n57.1870229314,388.795855138\n35.6458427285,155.657694842\n19.3869531878,87.1260781016\n21.9669393722,83.7232155716\n68.2754384479,530.711261039\n0.0499320826943,14.4928408734\n88.0160403117,843.24850174\n52.302680128,340.811945309\n30.8083679391,128.066959237\n36.7450495784,172.783502591\n32.497674786,147.228458255\n69.2984401147,538.180640028\n8.50775977301,18.1584427007\n70.340432818,559.410466793\n57.5800635973,384.884606047\n88.4806455787,875.860995014\n18.4605500858,58.9483005575\n55.8041981373,376.843030927\n16.4395710474,24.9459291843\n11.5900497701,32.6626606312\n46.8705089511,262.885902286\n84.33492545,798.336107587\n47.0879172063,278.115426472\n25.3595168427,104.171319469\n57.5279893076,411.579592637\n64.1988259571,471.645130755\n4.21211019461,-4.60107066464\n54.0943345308,363.848079875\n74.1390699605,634.232884203\n73.6494046203,614.61698055\n36.128161214,165.31378043\n52.0563603735,326.302161959\n47.1946296354,282.326194483\n38.4175210129,199.427772234\n54.6207713889,349.23680114\n64.7683580294,489.500982377\n84.4005467356,805.013871913\n21.4404800201,67.0955374565\n73.2615461079,611.057504623\n20.1698478817,69.446653099\n31.4337548498,138.906777891\n12.5179786376,48.9516001981\n3.61761195371,5.32627355217\n81.2515736807,731.3845794\n56.8424497509,378.289439943\n82.2665238066,748.343138324\n37.4722110019,175.748739041\n84.6946340015,800.047623475\n39.7685020685,203.056324686\n2.05616850915,6.23518914756\n97.1247091279,1042.6919584\n31.1291991206,130.113663552\n84.6126409599,790.864163252\n85.9561287143,827.455642687\n80.9652012247,738.33866824\n88.8979965529,893.81365673\n58.3641047494,402.882690162\n50.3024480011,314.117036852\n12.7419734701,12.9681174938\n77.320666256,687.654958048\n98.9646405094,1089.94766197\n56.3266253207,372.340305154\n79.8360680928,722.043849621\n84.2993047538,798.10497093\n45.3458596295,251.755172506\n3.13987798949,4.26261828882\n70.1062835334,550.923455067\n80.3106828908,728.06984768\n72.0680436903,581.130210853\n" }, { "path": "linear_regression_class/gd.py", "content": "import matplotlib.pyplot as plt\n\nlr = 1e-2\nx1 = 5\nx2 = -5\n\ndef J(x1, x2):\n return x1**2 + x2**4\n\ndef g1(x1):\n return 2*x1\n\ndef g2(x2):\n return 4*x2**3\n\nvalues = []\nfor i in range(1000):\n values.append(J(x1, x2))\n x1 -= lr * g1(x1)\n x2 -= lr * g2(x2)\nvalues.append(J(x1, x2))\n\nprint(x1, x2)\nplt.plot(values)\nplt.show()" }, { "path": "linear_regression_class/generate_1d.py", "content": "# generates 1-dimensional data for linear regression analysis\n#\n# notes for this course can be found at:\n# https://deeplearningcourses.com/c/data-science-linear-regression-in-python\n# https://www.udemy.com/data-science-linear-regression-in-python\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\n\nimport numpy as np\n\nN = 100\nwith open('data_1d.csv', 'w') as f:\n X = np.random.uniform(low=0, high=100, size=N)\n Y = 2*X + 1 + np.random.normal(scale=5, size=N)\n for i in range(N):\n f.write(\"%s,%s\\n\" % (X[i], Y[i]))\n\n" }, { "path": "linear_regression_class/generate_2d.py", "content": "# generates 2-dimensional data for linear regression analysis\n#\n# notes for this course can be found at:\n# https://deeplearningcourses.com/c/data-science-linear-regression-in-python\n# https://www.udemy.com/data-science-linear-regression-in-python\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\n\nimport numpy as np\n\nN = 100\nw = np.array([2, 3])\nwith open('data_2d.csv', 'w') as f:\n X = np.random.uniform(low=0, high=100, size=(N,2))\n Y = np.dot(X, w) + 1 + np.random.normal(scale=5, size=N)\n for i in range(N):\n f.write(\"%s,%s,%s\\n\" % (X[i,0], X[i,1], Y[i]))\n" }, { "path": "linear_regression_class/generate_poly.py", "content": "# generates 1-dimensional polynomial data for linear regression analysis\n#\n# notes for this course can be found at:\n# https://deeplearningcourses.com/c/data-science-linear-regression-in-python\n# https://www.udemy.com/data-science-linear-regression-in-python\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\n\nN = 100\nwith open('data_poly.csv', 'w') as f:\n X = np.random.uniform(low=0, high=100, size=N)\n X2 = X*X\n Y = 0.1*X2 + X + 3 + np.random.normal(scale=10, size=N)\n for i in range(N):\n f.write(\"%s,%s\\n\" % (X[i], Y[i]))\n\n" }, { "path": "linear_regression_class/gradient_descent.py", "content": "# notes for this course can be found at:\n# https://deeplearningcourses.com/c/data-science-linear-regression-in-python\n# https://www.udemy.com/data-science-linear-regression-in-python\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nN = 10\nD = 3\nX = np.zeros((N, D))\nX[:,0] = 1 # bias term\nX[:5,1] = 1\nX[5:,2] = 1\nY = np.array([0]*5 + [1]*5)\n\n# print X so you know what it looks like\nprint(\"X:\", X)\n\n# won't work!\n# w = np.linalg.solve(X.T.dot(X), X.T.dot(Y))\n\n# let's try gradient descent\ncosts = [] # keep track of squared error cost\nw = np.random.randn(D) / np.sqrt(D) # randomly initialize w\nlearning_rate = 0.001\nfor t in range(1000):\n # update w\n Yhat = X.dot(w)\n delta = Yhat - Y\n w = w - learning_rate*X.T.dot(delta)\n\n # find and store the cost\n mse = delta.dot(delta) / N\n costs.append(mse)\n\n# plot the costs\nplt.plot(costs)\nplt.show()\n\nprint(\"final w:\", w)\n\n# plot prediction vs target\nplt.plot(Yhat, label='prediction')\nplt.plot(Y, label='target')\nplt.legend()\nplt.show()" }, { "path": "linear_regression_class/l1_regularization.py", "content": "# notes for this course can be found at:\n# https://deeplearningcourses.com/c/data-science-linear-regression-in-python\n# https://www.udemy.com/data-science-linear-regression-in-python\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nN = 50\nD = 50\n\n# uniformly distributed numbers between -5, +5\nX = (np.random.random((N, D)) - 0.5)*10\n\n# true weights - only the first 3 dimensions of X affect Y\ntrue_w = np.array([1, 0.5, -0.5] + [0]*(D - 3))\n\n# generate Y - add noise\nY = X.dot(true_w) + np.random.randn(N)*0.5\n\n# perform gradient descent to find w\ncosts = [] # keep track of squared error cost\nw = np.random.randn(D) / np.sqrt(D) # randomly initialize w\nlearning_rate = 0.001\nl1 = 10.0 # Also try 5.0, 2.0, 1.0, 0.1 - what effect does it have on w?\nfor t in range(500):\n # update w\n Yhat = X.dot(w)\n delta = Yhat - Y\n w = w - learning_rate*(X.T.dot(delta) + l1*np.sign(w))\n\n # find and store the cost\n mse = delta.dot(delta) / N\n costs.append(mse)\n\n# plot the costs\nplt.plot(costs)\nplt.show()\n\nprint(\"final w:\", w)\n\n# plot our w vs true w\nplt.plot(true_w, label='true w')\nplt.plot(w, label='w_map')\nplt.legend()\nplt.show()" }, { "path": "linear_regression_class/l2_regularization.py", "content": "# demonstration of L2 regularization\n#\n# notes for this course can be found at:\n# https://deeplearningcourses.com/c/data-science-linear-regression-in-python\n# https://www.udemy.com/data-science-linear-regression-in-python\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nN = 50\n\n# generate the data\nX = np.linspace(0,10,N)\nY = 0.5*X + np.random.randn(N)\n\n# make outliers\nY[-1] += 30\nY[-2] += 30\n\n# plot the data\nplt.scatter(X, Y)\nplt.show()\n\n# add bias term\nX = np.vstack([np.ones(N), X]).T\n\n# plot the maximum likelihood solution\nw_ml = np.linalg.solve(X.T.dot(X), X.T.dot(Y))\nYhat_ml = X.dot(w_ml)\nplt.scatter(X[:,1], Y)\nplt.plot(X[:,1], Yhat_ml)\nplt.show()\n\n# plot the regularized solution\n# probably don't need an L2 regularization this high in many problems\n# everything in this example is exaggerated for visualization purposes\nl2 = 1000.0\nw_map = np.linalg.solve(l2*np.eye(2) + X.T.dot(X), X.T.dot(Y))\nYhat_map = X.dot(w_map)\nplt.scatter(X[:,1], Y)\nplt.plot(X[:,1], Yhat_ml, label='maximum likelihood')\nplt.plot(X[:,1], Yhat_map, label='map')\nplt.legend()\nplt.show()" }, { "path": "linear_regression_class/lr_1d.py", "content": "# shows how linear regression analysis can be applied to 1-dimensional data\n#\n# notes for this course can be found at:\n# https://deeplearningcourses.com/c/data-science-linear-regression-in-python\n# https://www.udemy.com/data-science-linear-regression-in-python\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# load the data\nX = []\nY = []\nfor line in open('data_1d.csv'):\n x, y = line.split(',')\n X.append(float(x))\n Y.append(float(y))\n\n# let's turn X and Y into numpy arrays since that will be useful later\nX = np.array(X)\nY = np.array(Y)\n\n\n# let's plot the data to see what it looks like\nplt.scatter(X, Y)\nplt.show()\n\n\n# apply the equations we learned to calculate a and b\n\n# denominator is common\n# note: this could be more efficient if\n# we only computed the sums and means once\ndenominator = X.dot(X) - X.mean() * X.sum()\na = ( X.dot(Y) - Y.mean()*X.sum() ) / denominator\nb = ( Y.mean() * X.dot(X) - X.mean() * X.dot(Y) ) / denominator\n\n# let's calculate the predicted Y\nYhat = a*X + b\n\n# let's plot everything together to make sure it worked\nplt.scatter(X, Y)\nplt.plot(X, Yhat)\nplt.show()\n\n# determine how good the model is by computing the r-squared\nd1 = Y - Yhat\nd2 = Y - Y.mean()\nr2 = 1 - d1.dot(d1) / d2.dot(d2)\nprint(\"the r-squared is:\", r2)\n" }, { "path": "linear_regression_class/lr_2d.py", "content": "# shows how linear regression analysis can be applied to 2-dimensional data\n#\n# notes for this course can be found at:\n# https://deeplearningcourses.com/c/data-science-linear-regression-in-python\n# https://www.udemy.com/data-science-linear-regression-in-python\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nfrom mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.pyplot as plt\n\n\n# load the data\nX = []\nY = []\nfor line in open('data_2d.csv'):\n x1, x2, y = line.split(',')\n X.append([float(x1), float(x2), 1]) # add the bias term\n Y.append(float(y))\n\n# let's turn X and Y into numpy arrays since that will be useful later\nX = np.array(X)\nY = np.array(Y)\n\n\n# let's plot the data to see what it looks like\nfig = plt.figure()\nax = fig.add_subplot(111, projection='3d')\nax.scatter(X[:,0], X[:,1], Y)\nplt.show()\n\n\n# apply the equations we learned to calculate a and b\n# numpy has a special method for solving Ax = b\n# so we don't use x = inv(A)*b\n# note: the * operator does element-by-element multiplication in numpy\n# np.dot() does what we expect for matrix multiplication\nw = np.linalg.solve(np.dot(X.T, X), np.dot(X.T, Y))\nYhat = np.dot(X, w)\n\n\n# determine how good the model is by computing the r-squared\nd1 = Y - Yhat\nd2 = Y - Y.mean()\nr2 = 1 - d1.dot(d1) / d2.dot(d2)\nprint(\"the r-squared is:\", r2)\n" }, { "path": "linear_regression_class/lr_poly.py", "content": "# shows how linear regression analysis can be applied to polynomial data\n#\n# notes for this course can be found at:\n# https://deeplearningcourses.com/c/data-science-linear-regression-in-python\n# https://www.udemy.com/data-science-linear-regression-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\n# load the data\nX = []\nY = []\nfor line in open('data_poly.csv'):\n x, y = line.split(',')\n x = float(x)\n X.append([1, x, x*x]) # add the bias term x0 = 1\n # our model is therefore y_hat = w0 + w1 * x + w2 * x**2\n Y.append(float(y))\n\n# let's turn X and Y into numpy arrays since that will be useful later\nX = np.array(X)\nY = np.array(Y)\n\n# let's plot the data to see what it looks like\nplt.scatter(X[:,1], Y)\nplt.title(\"The data we're trying to fit\")\nplt.show()\n\n\n# apply the equations we learned to calculate a and b\n# numpy has a special method for solving Ax = b\n# so we don't use x = inv(A)*b\n# note: the * operator does element-by-element multiplication in numpy\n# np.dot() does what we expect for matrix multiplication\nw = np.linalg.solve(np.dot(X.T, X), np.dot(X.T, Y))\n\n\n# let's plot everything together to make sure it worked\nplt.scatter(X[:,1], Y)\n\n# to plot our quadratic model predictions, let's\n# create a line of x's and calculate the predicted y's\nx_line = np.linspace(X[:,1].min(), X[:,1].max())\ny_line = w[0] + w[1] * x_line + w[2] * x_line * x_line\nplt.plot(x_line, y_line)\nplt.title(\"Our fitted quadratic\")\nplt.show()\n\n\n# determine how good the model is by computing the r-squared\nYhat = X.dot(w)\nd1 = Y - Yhat\nd2 = Y - Y.mean()\nr2 = 1 - d1.dot(d1) / d2.dot(d2)\nprint(\"the r-squared is:\", r2)\n" }, { "path": "linear_regression_class/moore.csv", "content": "Intel 4004\t2,300\t1971\tIntel\t10,000 nm\t12 mm²\nIntel 8008\t3,500\t1972\tIntel\t10,000 nm\t14 mm²\nIntel 8080\t4,500\t1974\tIntel\t6,000 nm\t20 mm²\nMotorola 6800\t4,100\t1974\tMotorola\t6,000 nm\t16 mm²\nRCA 1802\t5,000\t1974\tRCA\t5,000 nm\t27 mm²\nTMS 1000\t8,000\t1974[7]\tTexas Instruments\t8,000 nm\t\nMOS Technology 6502\t3,510[8]\t1975\tMOS Technology\t8,000 nm\t21 mm²\nIntel 8085\t6,500\t1976\tIntel\t3,000 nm\t20 mm²\nZilog Z80\t8,500\t1976\tZilog\t4,000 nm\t18 mm²\nIntel 8086\t29,000\t1978\tIntel\t3,000 nm\t33 mm²\nMotorola 6809\t9,000\t1978\tMotorola\t5,000 nm\t21 mm²\nIntel 8088\t29,000\t1979\tIntel\t3,000 nm\t33 mm²\nMotorola 68000\t68,000\t1979\tMotorola\t3,500 nm\t44 mm²\nWDC 65C02\t11,500[9]\t1981\tWDC\t3,000 nm\t6 mm²\nIntel 80186\t55,000\t1982\tIntel\t3,000 nm\t60 mm²\nIntel 80286\t134,000\t1982\tIntel\t1,500 nm\t49 mm²\nWDC 65C816\t22,000[10]\t1983\tWDC\t\t9 mm²\nMotorola 68020\t190,000[11]\t1984\tMotorola\t2,000 nm\t85 mm²\nARM 1\t25,000[11]\t1985\tAcorn\t3,000 nm\t50 mm²\nIntel 80386\t275,000\t1985\tIntel\t1,500 nm\t104 mm²\nNovix NC4016\t16,000[12]\t1985[13]\tHarris Corporation\t3,000 nm[14]\t\nARM 2\t30,000[11]\t1986\tAcorn\t2,000 nm\t30 mm²\nTI Explorer's 32-bit Lisp machine chip\t553,000[15]\t1987\tTexas Instruments\t\t\nDEC WRL MultiTitan\t180,000[16]\t1988\tDEC WRL\t1,500 nm\t61 mm²\nIntel i960\t250,000[17]\t1988\tIntel\t600 nm\t\nARM 3\t300,000\t1989\tAcorn\t\t\nIntel 80486\t1,180,235\t1989\tIntel\t1000 nm\t173 mm²\nARM 6\t35,000\t1991\tARM\t\t\nR4000\t1,350,000\t1991\tMIPS\t1,000 nm\t213 mm²\nPentium\t3,100,000\t1993\tIntel\t800 nm\t294 mm²\nARM700\t578,977[18]\t1994\tARM\t\t68.51 mm²\nPentium Pro\t5,500,000[19]\t1995\tIntel\t500 nm\t307 mm²\nSA-110\t2,500,000[11]\t1995\tAcorn/DEC/Apple\t350 nm\t50 mm²\nAMD K5\t4,300,000\t1996\tAMD\t500 nm\t251 mm²\nAMD K6\t8,800,000\t1997\tAMD\t350 nm\t162 mm²\nPentium II Klamath\t7,500,000\t1997\tIntel\t350 nm\t195 mm²\nPentium II Deschutes\t7,500,000\t1998\tIntel\t250 nm\t113 mm²\nAMD K6-III\t21,300,000\t1999\tAMD\t250 nm\t118 mm²\nAMD K7\t22,000,000\t1999\tAMD\t250 nm\t184 mm²\nARM 9TDMI\t111,000[11]\t1999\tAcorn\t350 nm\t4.8 mm²\nPentium II Mobile Dixon\t27,400,000\t1999\tIntel\t180 nm\t180 mm²\nPentium III Katmai\t9,500,000\t1999\tIntel\t250 nm\t128 mm²\nPentium 4 Willamette\t42,000,000\t2000\tIntel\t180 nm\t217 mm²\nPentium III Coppermine\t21,000,000\t2000\tIntel\t180 nm\t80 mm²\nPentium III Tualatin\t45,000,000\t2001\tIntel\t130 nm\t81 mm²\nItanium 2 McKinley\t220,000,000\t2002\tIntel\t180 nm\t421 mm²\nPentium 4 Northwood\t55,000,000\t2002\tIntel\t130 nm\t145 mm²\nAMD K8\t105,900,000\t2003\tAMD\t130 nm\t193 mm²\nBarton\t54,300,000\t2003\tAMD\t130 nm\t101 mm²\nItanium 2 Madison 6M\t410,000,000\t2003\tIntel\t130 nm\t374 mm²\nItanium 2 with 9 MB cache\t592,000,000\t2004\tIntel\t130 nm\t432 mm²\nPentium 4 Prescott\t112,000,000\t2004\tIntel\t90 nm\t110 mm²\nPentium 4 Prescott-2M\t169,000,000\t2005\tIntel\t90 nm\t143 mm²\nPentium D Smithfield\t228,000,000\t2005\tIntel\t90 nm\t206 mm²\nCell\t241,000,000\t2006\tSony/IBM/Toshiba\t90 nm\t221 mm²\nCore 2 Duo Conroe\t291,000,000\t2006\tIntel\t65 nm\t143 mm²\nDual-core Itanium 2\t1,700,000,000[26]\t2006\tIntel\t90 nm\t596 mm²\nPentium 4 Cedar Mill\t184,000,000\t2006\tIntel\t65 nm\t90 mm²\nPentium D Presler\t362,000,000\t2006\tIntel\t65 nm\t162 mm²\nAMD K10 quad-core 2M L3\t463,000,000[20]\t2007\tAMD\t65 nm\t283 mm²\nARM Cortex-A9\t26,000,000[21]\t2007\tARM\t45 nm\t31 mm²\nCore 2 Duo Allendale\t169,000,000\t2007\tIntel\t65 nm\t111 mm²\nCore 2 Duo Wolfdale\t411,000,000\t2007\tIntel\t45 nm\t107 mm²\nPOWER6\t789,000,000\t2007\tIBM\t65 nm\t341 mm²\nAMD K10 quad-core 6M L3\t758,000,000[20]\t2008\tAMD\t45 nm\t258 mm²\nAtom\t47,000,000\t2008\tIntel\t45 nm\t24 mm²\nCore 2 Duo Wolfdale 3M\t230,000,000\t2008\tIntel\t45 nm\t83 mm²\nCore i7 (Quad)\t731,000,000\t2008\tIntel\t45 nm\t263 mm²\nSix-core Xeon 7400\t1,900,000,000\t2008\tIntel\t45 nm\t503 mm²\nSix-core Opteron 2400\t904,000,000\t2009\tAMD\t45 nm\t346 mm²\n16-core SPARC T3\t1,000,000,000[22]\t2010\tSun/Oracle\t40 nm\t377 mm²\n8-core POWER7 32M L3\t1,200,000,000\t2010\tIBM\t45 nm\t567 mm²\n8-core Xeon Nehalem-EX\t2,300,000,000[30]\t2010\tIntel\t45 nm\t684 mm²\nQuad-core Itanium Tukwila\t2,000,000,000[28]\t2010\tIntel\t65 nm\t699 mm²\nQuad-core z196[24]\t1,400,000,000\t2010\tIBM\t45 nm\t512 mm²\nSix-core Core i7 (Gulftown)\t1,170,000,000\t2010\tIntel\t32 nm\t240 mm²\n10-core Xeon Westmere-EX\t2,600,000,000\t2011\tIntel\t32 nm\t512 mm²\nQuad-core + GPU Core i7\t1,160,000,000\t2011\tIntel\t32 nm\t216 mm²\nSix-core Core i7/8-core Xeon E5 (Sandy Bridge-E/EP)\t2,270,000,000[29]\t2011\tIntel\t32 nm\t434 mm²\n61-core Xeon Phi\t5,000,000,000[34]\t2012\tIntel\t22 nm\t350 mm²\n8-core AMD Bulldozer\t1,200,000,000[23]\t2012\tAMD\t32 nm\t315 mm²\n8-core Itanium Poulson\t3,100,000,000\t2012\tIntel\t32 nm\t544 mm²\n8-core POWER7+ 80 MB L3 cache\t2,100,000,000\t2012\tIBM\t32 nm\t567 mm²\nQuad-core + GPU AMD Trinity\t1,303,000,000\t2012\tAMD\t32 nm\t246 mm²\nQuad-core + GPU Core i7 Ivy Bridge\t1,400,000,000\t2012\tIntel\t22 nm\t160 mm²\nSix-core zEC12\t2,750,000,000\t2012\tIBM\t32 nm\t597 mm²\n12-core POWER8\t4,200,000,000\t2013\tIBM\t22 nm\t650 mm²\nApple A7 (dual-core ARM64 \"mobile SoC\")\t1,000,000,000\t2013\tApple\t28 nm\t102 mm²\nSix-core Core i7 Ivy Bridge E\t1,860,000,000\t2013\tIntel\t22 nm\t256 mm²\nXbox One main SoC\t5,000,000,000\t2013\tMicrosoft/AMD\t28 nm\t363 mm²\n15-core Xeon Ivy Bridge-EX\t4,310,000,000[33]\t2014\tIntel\t22 nm\t541 mm²\n18-core Xeon Haswell-E5\t5,560,000,000[35]\t2014\tIntel\t22 nm\t661 mm²\n8-core Core i7 Haswell-E\t2,600,000,000[31]\t2014\tIntel\t22 nm\t355 mm²\nApple A8 (dual-core ARM64 \"mobile SoC\")\t2,000,000,000\t2014\tApple\t20 nm\t89 mm²\nApple A8X (tri-core ARM64 \"mobile SoC\")\t3,000,000,000[32]\t2014\tApple\t20 nm\t128 mm²\nQuad-core + GPU Core i7 Haswell\t1,400,000,000[25]\t2014\tIntel\t22 nm\t177 mm²\nDuo-core + GPU Iris Core i7 Broadwell-U\t1,900,000,000[27]\t2015\tIntel\t14 nm\t133 mm²\nIBM z13\t3,990,000,000\t2015\tIBM\t22 nm\t678 mm²\nIBM z13 Storage Controller\t7,100,000,000\t2015\tIBM\t22 nm\t678 mm²\nQuad-core + GPU GT2 Core i7 Skylake K\tcca 1,750,000,000\t2015\tIntel\t14 nm\t122 mm²\nSPARC M7\t10,000,000,000[37]\t2015\tOracle\t20 nm\t\n22-core Xeon Broadwell-E5\t~7,200,000,000[36]\t2016\tIntel\t14 nm\t456 mm²" }, { "path": "linear_regression_class/moore.py", "content": "# shows how linear regression analysis can be applied to moore's law\n#\n# notes for this course can be found at:\n# https://deeplearningcourses.com/c/data-science-linear-regression-in-python\n# https://www.udemy.com/data-science-linear-regression-in-python\n# transistor count from: https://en.wikipedia.org/wiki/Transistor_count\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport re\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nX = []\nY = []\n\n# some numbers show up as 1,170,000,000 (commas)\n# some numbers have references in square brackets after them\nnon_decimal = re.compile(r'[^\\d]+')\n\nfor line in open('moore.csv'):\n r = line.split('\\t')\n\n x = int(non_decimal.sub('', r[2].split('[')[0]))\n y = int(non_decimal.sub('', r[1].split('[')[0]))\n X.append(x)\n Y.append(y)\n\n\nX = np.array(X)\nY = np.array(Y)\n\nplt.scatter(X, Y)\nplt.show()\n\nY = np.log(Y)\nplt.scatter(X, Y)\nplt.show()\n\n# copied from lr_1d.py\ndenominator = X.dot(X) - X.mean() * X.sum()\na = ( X.dot(Y) - Y.mean()*X.sum() ) / denominator\nb = ( Y.mean() * X.dot(X) - X.mean() * X.dot(Y) ) / denominator\n\n# let's calculate the predicted Y\nYhat = a*X + b\n\nplt.scatter(X, Y)\nplt.plot(X, Yhat)\nplt.show()\n\n# determine how good the model is by computing the r-squared\nd1 = Y - Yhat\nd2 = Y - Y.mean()\nr2 = 1 - d1.dot(d1) / d2.dot(d2)\nprint(\"a:\", a, \"b:\", b)\nprint(\"the r-squared is:\", r2)\n\n# how long does it take to double?\n# log(transistorcount) = a*year + b\n# transistorcount = exp(b) * exp(a*year)\n# 2*transistorcount = 2 * exp(b) * exp(a*year) = exp(ln(2)) * exp(b) * exp(a * year) = exp(b) * exp(a * year + ln(2))\n# a*year2 = a*year1 + ln2\n# year2 = year1 + ln2/a\nprint(\"time to double:\", np.log(2)/a, \"years\")\n" }, { "path": "linear_regression_class/overfitting.py", "content": "# notes for this course can be found at:\n# https://deeplearningcourses.com/c/data-science-linear-regression-in-python\n# https://www.udemy.com/data-science-linear-regression-in-python\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef make_poly(X, deg):\n n = len(X)\n data = [np.ones(n)]\n for d in range(deg):\n data.append(X**(d+1))\n return np.vstack(data).T\n\n\ndef fit(X, Y):\n return np.linalg.solve(X.T.dot(X), X.T.dot(Y))\n\n\ndef fit_and_display(X, Y, sample, deg):\n N = len(X)\n train_idx = np.random.choice(N, sample)\n Xtrain = X[train_idx]\n Ytrain = Y[train_idx]\n\n plt.scatter(Xtrain, Ytrain)\n plt.show()\n\n # fit polynomial\n Xtrain_poly = make_poly(Xtrain, deg)\n w = fit(Xtrain_poly, Ytrain)\n\n # display the polynomial\n X_poly = make_poly(X, deg)\n Y_hat = X_poly.dot(w)\n plt.plot(X, Y)\n plt.plot(X, Y_hat)\n plt.scatter(Xtrain, Ytrain)\n plt.title(\"deg = %d\" % deg)\n plt.show()\n\n\ndef get_mse(Y, Yhat):\n d = Y - Yhat\n return d.dot(d) / len(d)\n\n\ndef plot_train_vs_test_curves(X, Y, sample=20, max_deg=20):\n N = len(X)\n train_idx = np.random.choice(N, sample)\n Xtrain = X[train_idx]\n Ytrain = Y[train_idx]\n\n test_idx = [idx for idx in range(N) if idx not in train_idx]\n # test_idx = np.random.choice(N, sample)\n Xtest = X[test_idx]\n Ytest = Y[test_idx]\n\n mse_trains = []\n mse_tests = []\n for deg in range(max_deg+1):\n Xtrain_poly = make_poly(Xtrain, deg)\n w = fit(Xtrain_poly, Ytrain)\n Yhat_train = Xtrain_poly.dot(w)\n mse_train = get_mse(Ytrain, Yhat_train)\n\n Xtest_poly = make_poly(Xtest, deg)\n Yhat_test = Xtest_poly.dot(w)\n mse_test = get_mse(Ytest, Yhat_test)\n\n mse_trains.append(mse_train)\n mse_tests.append(mse_test)\n\n plt.plot(mse_trains, label=\"train mse\")\n plt.plot(mse_tests, label=\"test mse\")\n plt.legend()\n plt.show()\n\n plt.plot(mse_trains, label=\"train mse\")\n plt.legend()\n plt.show()\n\nif __name__ == \"__main__\":\n # make up some data and plot it\n N = 100\n X = np.linspace(0, 6*np.pi, N)\n Y = np.sin(X)\n\n plt.plot(X, Y)\n plt.show()\n\n for deg in (5, 6, 7, 8, 9):\n fit_and_display(X, Y, 10, deg)\n plot_train_vs_test_curves(X, Y)\n" }, { "path": "linear_regression_class/systolic.py", "content": "# https://deeplearningcourses.com/c/data-science-linear-regression-in-python\n# need to sudo pip install xlrd to use pd.read_excel\n# data is from:\n# http://college.cengage.com/mathematics/brase/understandable_statistics/7e/students/datasets/mlr/frames/mlr02.html\n\n# The data (X1, X2, X3) are for each patient.\n# X1 = systolic blood pressure\n# X2 = age in years\n# X3 = weight in pounds\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\ndf = pd.read_excel('mlr02.xls', engine='xlrd')\nX = df.values\n\n# using age to predict systolic blood pressure\nplt.scatter(X[:,1], X[:,0])\nplt.show()\n# looks pretty linear!\n\n# using weight to predict systolic blood pressure\nplt.scatter(X[:,2], X[:,0])\nplt.show()\n# looks pretty linear!\n\ndf['ones'] = 1\nY = df['X1']\nX = df[['X2', 'X3', 'ones']]\nX2only = df[['X2', 'ones']]\nX3only = df[['X3', 'ones']]\n\ndef get_r2(X, Y):\n w = np.linalg.solve( X.T.dot(X), X.T.dot(Y) )\n Yhat = X.dot(w)\n\n # determine how good the model is by computing the r-squared\n d1 = Y - Yhat\n d2 = Y - Y.mean()\n r2 = 1 - d1.dot(d1) / d2.dot(d2)\n return r2\n\nprint(\"r2 for x2 only:\", get_r2(X2only, Y))\nprint(\"r2 for x3 only:\", get_r2(X3only, Y))\nprint(\"r2 for both:\", get_r2(X, Y))\n\n\n\n\n\n\n" }, { "path": "logistic_regression_class/bad_xor.py", "content": "# logisitc regression classifier for the XOR problem.\n#\n# the notes for this class can be found at: \n# https://deeplearningcourses.com/c/data-science-logistic-regression-in-python\n# https://www.udemy.com/data-science-logistic-regression-in-python\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nN = 4\nD = 2\n\n# XOR\nX = np.array([\n [0, 0],\n [0, 1],\n [1, 0],\n [1, 1],\n])\nT = np.array([0, 1, 1, 0])\n\n# add a column of ones\nones = np.ones((N, 1))\n\n# add a column of xy = x*y\nXb = np.concatenate((ones, X), axis=1)\n\n# randomly initialize the weights\nw = np.random.randn(D + 1)\n\n# calculate the model output\nz = Xb.dot(w)\n\ndef sigmoid(z):\n return 1/(1 + np.exp(-z))\n\n\nY = sigmoid(z)\n\n# calculate the cross-entropy error\ndef cross_entropy(T, Y):\n return -(T*np.log(Y) + (1-T)*np.log(1-Y)).sum()\n\n\n# let's do gradient descent 100 times\nlearning_rate = 0.001\nerror = []\nw_mags = []\nfor i in range(100000):\n e = cross_entropy(T, Y)\n error.append(e)\n if i % 1000 == 0:\n print(e)\n\n # gradient descent weight udpate with regularization\n w += learning_rate * Xb.T.dot(T - Y)\n\n w_mags.append(w.dot(w))\n\n # recalculate Y\n Y = sigmoid(Xb.dot(w))\n\nplt.plot(error)\nplt.title(\"Cross-entropy per iteration\")\nplt.show()\n\nplt.plot(w_mags)\nplt.title(\"w^2 magnitudes\")\nplt.show()\n\nprint(\"Final w:\", w)\nprint(\"Final classification rate:\", 1 - np.abs(T - np.round(Y)).sum() / N)\n" }, { "path": "logistic_regression_class/l1_regularization.py", "content": "# notes for this course can be found at:\n# https://deeplearningcourses.com/c/data-science-logistic-regression-in-python\n# https://www.udemy.com/data-science-logistic-regression-in-python\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nfrom mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.pyplot as plt\n\ndef sigmoid(z):\n return 1/(1 + np.exp(-z))\n\nN = 50\nD = 50\n\n# uniformly distributed numbers between -5, +5\nX = (np.random.random((N, D)) - 0.5)*10\n# X = (np.random.randn(N, D) - 0.5)*10\n\n# true weights - only the first 3 dimensions of X affect Y\ntrue_w = np.array([1, 0.5, -0.5] + [0]*(D - 3))\n\n# generate Y - add noise with variance 0.5\nY = np.round(sigmoid(X.dot(true_w) + np.random.randn(N)*0.5))\n\n\n\n\n# let's plot the data to see what it looks like\nfig = plt.figure()\nax = fig.add_subplot(111, projection='3d')\nax.scatter(X[:,0], X[:,1], X[:,2], c=Y)\nplt.show()\n\n# perform gradient descent to find w\ncosts = [] # keep track of squared error cost\nw = np.random.randn(D) / np.sqrt(D) # randomly initialize w\nlearning_rate = 0.001\nl1 = 3.0 # try different values - what effect does it have on w?\nfor t in range(5000):\n # update w\n Yhat = sigmoid(X.dot(w))\n delta = Yhat - Y\n w = w - learning_rate*(X.T.dot(delta) + l1*np.sign(w))\n\n # find and store the cost\n cost = -(Y*np.log(Yhat) + (1-Y)*np.log(1 - Yhat)).mean() + l1*np.abs(w).mean()\n costs.append(cost)\n\n# plot the costs\nplt.plot(costs)\nplt.show()\n\nprint(\"final w:\", w)\n\n# plot our w vs true w\nplt.plot(true_w, label='true w')\nplt.plot(w, label='w_map')\nplt.legend()\nplt.show()" }, { "path": "logistic_regression_class/logistic1.py", "content": "# demonstrates how to calculate the output of a logistic unit using numpy.\n# the data X and weight matrix w are randomly generated from a\n# standard normal distribution.\n#\n# the notes for this class can be found at: \n# https://deeplearningcourses.com/c/data-science-logistic-regression-in-python\n# https://www.udemy.com/data-science-logistic-regression-in-python\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\n\nN = 100\nD = 2\n\n\nX = np.random.randn(N,D)\n# ones = np.array([[1]*N]).T # old\nones = np.ones((N, 1))\nXb = np.concatenate((ones, X), axis=1)\n\nw = np.random.randn(D + 1)\n\nz = Xb.dot(w)\n\ndef sigmoid(z):\n return 1/(1 + np.exp(-z))\n\nprint(sigmoid(z))\n" }, { "path": "logistic_regression_class/logistic2.py", "content": "# demonstrates how to calculate the cross-entropy error function\n# in numpy.\n#\n# the notes for this class can be found at: \n# https://deeplearningcourses.com/c/data-science-logistic-regression-in-python\n# https://www.udemy.com/data-science-logistic-regression-in-python\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\n\nimport numpy as np\n\nN = 100\nD = 2\n\n\nX = np.random.randn(N,D)\n\n# center the first 50 points at (-2,-2)\nX[:50,:] = X[:50,:] - 2*np.ones((50,D))\n\n# center the last 50 points at (2, 2)\nX[50:,:] = X[50:,:] + 2*np.ones((50,D))\n\n# labels: first 50 are 0, last 50 are 1\nT = np.array([0]*50 + [1]*50)\n\n# add a column of ones\n# ones = np.array([[1]*N]).T # old\nones = np.ones((N, 1))\nXb = np.concatenate((ones, X), axis=1)\n\n# randomly initialize the weights\nw = np.random.randn(D + 1)\n\n# calculate the model output\nz = Xb.dot(w)\n\ndef sigmoid(z):\n return 1/(1 + np.exp(-z))\n\nY = sigmoid(z)\n\n# calculate the cross-entropy error\ndef cross_entropy(T, Y):\n E = 0\n for i in range(len(T)):\n if T[i] == 1:\n E -= np.log(Y[i])\n else:\n E -= np.log(1 - Y[i])\n return E\n\nprint(cross_entropy(T, Y))\n\n# try it with our closed-form solution\nw = np.array([0, 4, 4])\n\n# calculate the model output\nz = Xb.dot(w)\nY = sigmoid(z)\n\n# calculate the cross-entropy error\nprint(cross_entropy(T, Y))\n\n" }, { "path": "logistic_regression_class/logistic3.py", "content": "# demonstrates how to do gradient descent with numpy matrices.\n#\n# the notes for this class can be found at: \n# https://deeplearningcourses.com/c/data-science-logistic-regression-in-python\n# https://www.udemy.com/data-science-logistic-regression-in-python\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nN = 100\nD = 2\n\nN_per_class = N//2\n\n\nX = np.random.randn(N,D)\n\n# center the first 50 points at (-2,-2)\nX[:N_per_class,:] = X[:N_per_class,:] - 2*np.ones((N_per_class,D))\n\n# center the last 50 points at (2, 2)\nX[N_per_class:,:] = X[N_per_class:,:] + 2*np.ones((N_per_class,D))\n\n# labels: first N_per_class are 0, last N_per_class are 1\nT = np.array([0]*N_per_class + [1]*N_per_class)\n\n# add a column of ones\n# ones = np.array([[1]*N]).T # old\nones = np.ones((N, 1))\nXb = np.concatenate((ones, X), axis=1)\n\n# randomly initialize the weights\nw = np.random.randn(D + 1)\n\n# calculate the model output\nz = Xb.dot(w)\n\ndef sigmoid(z):\n return 1/(1 + np.exp(-z))\n\n\nY = sigmoid(z)\n\n# calculate the cross-entropy error\ndef cross_entropy(T, Y):\n E = 0\n for i in range(len(T)):\n if T[i] == 1:\n E -= np.log(Y[i])\n else:\n E -= np.log(1 - Y[i])\n return E\n\n\n# let's do gradient descent 100 times\nlearning_rate = 0.1\nfor i in range(100):\n if i % 10 == 0:\n print(cross_entropy(T, Y))\n\n # gradient descent weight udpate\n w += learning_rate * Xb.T.dot(T - Y)\n\n # recalculate Y\n Y = sigmoid(Xb.dot(w))\n\n\nprint(\"Final w:\", w)\n\n# plot the data and separating line\nplt.scatter(X[:,0], X[:,1], c=T, s=100, alpha=0.5)\nx_axis = np.linspace(-6, 6, 100)\ny_axis = -(w[0] + x_axis*w[1]) / w[2]\nplt.plot(x_axis, y_axis)\nplt.show()\n\n" }, { "path": "logistic_regression_class/logistic4.py", "content": "# demonstrates how to do gradient descent with numpy matrices,\n# with regularization.\n#\n# the notes for this class can be found at: \n# https://deeplearningcourses.com/c/data-science-logistic-regression-in-python\n# https://www.udemy.com/data-science-logistic-regression-in-python\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\n\nimport numpy as np\n\nN = 100\nD = 2\n\n\nX = np.random.randn(N,D)\n\n# center the first 50 points at (-2,-2)\nX[:50,:] = X[:50,:] - 2*np.ones((50,D))\n\n# center the last 50 points at (2, 2)\nX[50:,:] = X[50:,:] + 2*np.ones((50,D))\n\n# labels: first 50 are 0, last 50 are 1\nT = np.array([0]*50 + [1]*50)\n\n# add a column of ones\n# ones = np.array([[1]*N]).T\nones = np.ones((N, 1))\nXb = np.concatenate((ones, X), axis=1)\n\n# randomly initialize the weights\nw = np.random.randn(D + 1)\n\n# calculate the model output\nz = Xb.dot(w)\n\ndef sigmoid(z):\n return 1/(1 + np.exp(-z))\n\n\nY = sigmoid(z)\n\n# calculate the cross-entropy error\ndef cross_entropy(T, Y):\n E = 0\n for i in range(len(T)):\n if T[i] == 1:\n E -= np.log(Y[i])\n else:\n E -= np.log(1 - Y[i])\n return E\n\n\n# let's do gradient descent 100 times\nlearning_rate = 0.1\nfor i in range(100):\n if i % 10 == 0:\n print(cross_entropy(T, Y))\n\n # gradient descent weight udpate with regularization\n w += learning_rate * ( Xb.T.dot(T - Y) - 0.1*w )\n\n # recalculate Y\n Y = sigmoid(Xb.dot(w))\n\n\nprint(\"Final w:\", w)\n\n\n" }, { "path": "logistic_regression_class/logistic_donut.py", "content": "# logisitc regression classifier for the donut problem.\n#\n# the notes for this class can be found at: \n# https://deeplearningcourses.com/c/data-science-logistic-regression-in-python\n# https://www.udemy.com/data-science-logistic-regression-in-python\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nN = 1000\nD = 2\n\nR_inner = 5\nR_outer = 10\n\n# distance from origin is radius + random normal\n# angle theta is uniformly distributed between (0, 2pi)\nR1 = np.random.randn(N//2) + R_inner\ntheta = 2*np.pi*np.random.random(N//2)\nX_inner = np.concatenate([[R1 * np.cos(theta)], [R1 * np.sin(theta)]]).T\n\nR2 = np.random.randn(N//2) + R_outer\ntheta = 2*np.pi*np.random.random(N//2)\nX_outer = np.concatenate([[R2 * np.cos(theta)], [R2 * np.sin(theta)]]).T\n\nX = np.concatenate([ X_inner, X_outer ])\nT = np.array([0]*(N//2) + [1]*(N//2)) # labels: first 50 are 0, last 50 are 1\n\nplt.scatter(X[:,0], X[:,1], c=T)\nplt.show()\n\n\n\n# add a column of ones\n# ones = np.array([[1]*N]).T # old\nones = np.ones((N, 1))\n\n# add a column of r = sqrt(x^2 + y^2)\nr = np.sqrt( (X * X).sum(axis=1) ).reshape(-1, 1)\nXb = np.concatenate((ones, r, X), axis=1)\n\n# randomly initialize the weights\nw = np.random.randn(D + 2)\n\n# calculate the model output\nz = Xb.dot(w)\n\ndef sigmoid(z):\n return 1/(1 + np.exp(-z))\n\n\nY = sigmoid(z)\n\n# calculate the cross-entropy error\ndef cross_entropy(T, Y):\n return -(T*np.log(Y) + (1-T)*np.log(1-Y)).sum()\n\n\n# let's do gradient descent 100 times\nlearning_rate = 0.0001\nerror = []\nfor i in range(5000):\n e = cross_entropy(T, Y)\n error.append(e)\n if i % 500 == 0:\n print(e)\n\n # gradient descent weight udpate with regularization\n w += learning_rate * ( Xb.T.dot(T - Y) - 0.1*w )\n\n # recalculate Y\n Y = sigmoid(Xb.dot(w))\n\nplt.plot(error)\nplt.title(\"Cross-entropy per iteration\")\nplt.show()\n\nprint(\"Final w:\", w)\nprint(\"Final classification rate:\", 1 - np.abs(T - np.round(Y)).sum() / N)\n" }, { "path": "logistic_regression_class/logistic_visualize.py", "content": "# visualizes the Bayes solution\n#\n# the notes for this class can be found at: \n# https://deeplearningcourses.com/c/data-science-logistic-regression-in-python\n# https://www.udemy.com/data-science-logistic-regression-in-python\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nN = 100\nD = 2\n\n\nX = np.random.randn(N,D)\n\n# center the first 50 points at (-2,-2)\nX[:50,:] = X[:50,:] - 2*np.ones((50,D))\n\n# center the last 50 points at (2, 2)\nX[50:,:] = X[50:,:] + 2*np.ones((50,D))\n\n# labels: first 50 are 0, last 50 are 1\nT = np.array([0]*50 + [1]*50)\n\n# add a column of ones\n# ones = np.array([[1]*N]).T\nones = np.ones((N, 1))\nXb = np.concatenate((ones, X), axis=1)\n\ndef sigmoid(z):\n return 1/(1 + np.exp(-z))\n\n# get the closed-form solution\nw = np.array([0, 4, 4])\n\n# calculate the model output\nz = Xb.dot(w)\nY = sigmoid(z)\n\nplt.scatter(X[:,0], X[:,1], c=T, s=100, alpha=0.5)\n\nx_axis = np.linspace(-6, 6, 100)\ny_axis = -x_axis\nplt.plot(x_axis, y_axis)\nplt.show()\n" }, { "path": "logistic_regression_class/logistic_xor.py", "content": "# logisitc regression classifier for the XOR problem.\n#\n# the notes for this class can be found at: \n# https://deeplearningcourses.com/c/data-science-logistic-regression-in-python\n# https://www.udemy.com/data-science-logistic-regression-in-python\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nN = 4\nD = 2\n\n# XOR\nX = np.array([\n [0, 0],\n [0, 1],\n [1, 0],\n [1, 1],\n])\nT = np.array([0, 1, 1, 0])\n\n# add a column of ones\n# ones = np.array([[1]*N]).T\nones = np.ones((N, 1))\n\n# add a column of xy = x*y\nxy = (X[:,0] * X[:,1]).reshape(N, 1)\nXb = np.concatenate((ones, xy, X), axis=1)\n\n# randomly initialize the weights\nw = np.random.randn(D + 2)\n\n# calculate the model output\nz = Xb.dot(w)\n\ndef sigmoid(z):\n return 1/(1 + np.exp(-z))\n\n\nY = sigmoid(z)\n\n# calculate the cross-entropy error\ndef cross_entropy(T, Y):\n E = 0\n for i in range(len(T)):\n if T[i] == 1:\n E -= np.log(Y[i])\n else:\n E -= np.log(1 - Y[i])\n return E\n\n\n# let's do gradient descent 100 times\nlearning_rate = 0.01\nerror = []\nfor i in range(10000):\n e = cross_entropy(T, Y)\n error.append(e)\n if i % 1000 == 0:\n print(e)\n\n # gradient descent weight udpate with regularization\n w += learning_rate * ( Xb.T.dot(T - Y) - 0.01*w )\n\n # recalculate Y\n Y = sigmoid(Xb.dot(w))\n\nplt.plot(error)\nplt.title(\"Cross-entropy per iteration\")\nplt.show()\n\nprint(\"Final w:\", w)\nprint(\"Final classification rate:\", 1 - np.abs(T - np.round(Y)).sum() / N)\n" }, { "path": "matrix_calculus/extra_reading.txt", "content": "The Matrix Cookbook\nhttps://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf" }, { "path": "mnist_csv/Q.txt", "content": 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6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n6\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n7\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n8\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n9\n" }, { "path": "naive_bayes/WHERE ARE THE NOTEBOOKS.txt", "content": "As stated in the \"where to get the code\" / \"where to get the notebooks\" lecture, the notebooks are NOT on Github.\n\nIf you missed this, please review the lecture for the actual location of the notebooks.\n\nIf, after reviewing it, you still need assistance, please contact info@deeplearningcourses.com." }, { "path": "naive_bayes/extra_reading.txt", "content": "Complement Naive Bayes\nhttps://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf\n\nSemi-Supervised Learning with Naive Bayes\nhttp://www.kamalnigam.com/papers/emcat-aaai98.pdf\n\nAn empirical study of the naive Bayes classifier\nhttps://faculty.cc.gatech.edu/~isbell/reading/papers/Rish.pdf" }, { "path": "nlp_class/all_book_titles.txt", "content": "Philosophy of Sex and Love A Reader\nReadings in Judaism, Christianity, and Islam\nMicroprocessors Principles and Applications\nBernhard Edouard Fernow: Story of North American Forestry\nEncyclopedia of Buddhism\nMotorola Microprocessor Family: 68000, 68008, 68010, 68020, 68030, and 68040, Programming and Interfacing with Applications\nAmerican Anthem: Student Edition Modern Era 2007\nHow to Read Literature Like a Professor A Lively and Entertaining Guide to Reading Between the Lines\nMen Are from Mars, Women Are from Venus Secrets of Great Sex, Improving Communication, Lasting Intimacy and Fulfillment, Giving and Receiving Love, Secrets of Passion, Understanding Martian\nReligious Traditions of the World A Journey Through Africa, Mesoamerica, North America, Judaism, Christianity, Islam, Hinduism, Buddhism, China, an\nWorld's Wisdom Sacred Texts of the World's Religions\nIllustrated World's Religions A Guide to Our Wisdom Traditions\nSoul of Sex Cultivating Life As an Act of Love\nThriving on Chaos Handbook for a Management Revolution\nBlood Relatives \nWheelock's Latin\nGreat by Choice : Uncertainty, Chaos and Luck--Why Some Thrive Despite Them All\nExpanding Discourse Feminism and Art History\nIntroduction to General Relativity (Pure & Applied Physics)\nExperiments in Microprocessors and Interfacing: Programming and Hardware\nPathophysiology Concepts and Applications for Health Care Professionals\nPower System Operation\nMachine Learning\nE-Commerce: Strategy, Technologies And Applications\nReal and Complex Analysis\nSchaum's Outline of Microprocessor Fundamentals\nSchaum's Microprocessor Fundamentals - Roger L. Tokheim - Paperback - 2nd ed\nProbability, Random Variables and Stochastic Processes\nMedical Microbiology & Immunology The Big Picture\nSecurity Analysis Sixth Edition, Foreword by Warren E. Buffett\nGoodman and Gilman's The Pharmacological Basis of Therapeutics, Twelfth Edition (Goodman and Gilman\"S the Pharmacological Basis of Therapeutics)\nOCA/OCP Oracle Database 11g All-in-One Exam Guide with CD-ROM: Exams 1Z0-051, 1Z0-052, 1Z0-053 (Oracle Press)\nClinical Ethics: A Practical Approach to Ethical Decisions in Clinical Medicine, Seventh Edition (LANGE Clinical Science)\nUnderstand Linguistics: A Teach Yourself Guide\nHarrison's Principles of Internal Medicine\nBasic and Clinical Pharmacology 12/E (LANGE Basic Science)\nHarrisons Principles of Internal Medicine Self-Assessment and Board Review 18th Edition\nStrategic Database Marketing 4e: The Masterplan for Starting and Managing a Profitable, Customer-Based Marketing Program\nReview of Medical Microbiology and Immunology, Twelfth Edition (Lange Medical Books)\nTintinalli's Emergency Medicine Manual 7/E (Emergency Medicine (Tintinalli))\nPharmacotherapy a Pathophysiologic Approach 9/e\nReview of Medical Microbiology and Immunology\nComputer Organization\nRisk Management and Insurance\nDesign of Analog Cmos Integrated Circuits\nPlant Design and Economics for Chemical Engineers\nFluid Mechanics With Engineering Applications\nOperating Systems: A Spiral Approach\nEssentials of Anatomy & Physiology\nDatabase Management Systems\nIntroduction to Probability and Statistics Principles and Applications for Engineering and the Computing Sciences\nHealth Program Planning An Educational and Ecological Approach\nGeographic Information Systems Applications in Forestry and Natural Resources Management\nFluid Mechanics for Chemical Engineers\nAdaptation Studying Film And Literature\nAmerican Art History and Culture\nEvolution of the Earth\nIntroduction To Women's Studies Gender In A Transnational World\nFundamentals of Aerodynamics \nIntroduction to Forensics & Criminalistics \nIntroduction to Biological Evolution \nAnatomy & Physiology: An Integrative Approach\nIntroduction To Chemical Engineering Thermodynamics Chemical Engineering Thermodynamics\nHuman Geography\nTheater Experience \nBioInformatics: A Computing Perspective\nExperience Sociology\nElectronic Principles \nGood Earth Introduction to Earth Sciences\nEssentials of World Regional Geography\nPhysical Geology\nStern's Introductory Plant Biology\nBusiness Statistics: Communicating with Numbers\nDigital Electronics: Principles and Applications\nMedical Insurance for Pharmacy Technicians\nInsurance in the Medical Office: From Patient to Payment\nLaw & Ethics for the Health Professions\nFoundations in Microbiology\nMicrobiology: A Human Perspective\nIntroduction to Programming with Java: A Problem Solving Approach\nModern Computer Networks: An Open Source Approach\nBusiness Driven Information Systems\nReal Estate Finance & Investments (Real Estate Finance and Investments)\nHuman Anatomy\nHole's Essentials of Human Anatomy & Physiology\nSeeley's Principles of Anatomy and Physiology\nAnatomy & Physiology: The Unity of Form and Function\nSeeley's Essentials of Anatomy and Physiology\nHole's Human Anatomy & Physiology\nHuman Anatomy\nVander's Human Physiology\nPower & Choice: An Introduction to Political Science\nA History of Western Art\nReconstructing Gender\nMechanics of Materials\nFluid Mechanics Fundamentals and Applications\nMicroelectronic Circuit Design\nFundamentals of Electric Circuits\nCMOS Digital Integrated Circuits Analysis & Design\nMicroelectronics Circuit Analysis and Design\nComputer Organization\nPower Electronics\nThe Theater Experience\nFinance: Applications and Theory\nEssentials of Investments (The Mcgraw-Hill/Irwin Series in Finance, Insurance, and Real Estate)\nEssentials of Corporate Finance (The Mcgraw-Hill/Irwin Series in Finance, Insurance, and Real Estate)\nUnderstanding Human Sexuality\nBiology\nEcology: Concepts and Applications\nEnvironmental Science\nUS: A Narrative History Volume 1: To 1877\nDifferential Equations for Engineers and Scientists\nElementary Statistics\nFilm History: An Introduction\nFilm Art: An Introduction\nThe Art of Watching Films\nStudying Religion\nFundamentals of Aerodynamics\nVector Mechanics for Engineers: Statics and Dynamics\nThermodynamics: An Engineering Approach\nSimulation Modeling and Analysis\nStatistical Techniques in Business and Economics\nEssentials of Business Statistics\nMicrobiology Fundamentals: A Clinical Approach\nPrescott's Microbiology\nMicrobiology: A Systems Approach\nContemporary Nutrition\nChemistry: The Molecular Nature of Matter and Change\nIntroduction to Chemistry: Third Edition\nChemistry\nPrinciples of General Chemistry\nChemistry\nOrganic Chemistry\nGeneral Chemistry: The Essential Concepts\nGeneral, Organic and Biochemistry\nOrganic Chemistry\nGeneral, Organic, & Biological Chemistry\nBusiness Communication: Developing Leaders for a Networked World\nLesikar's Business Communication: Connecting in a Digital World\nBusiness and Administrative Communication\nBusiness Communication: Building Critical Skills\nConcepts in Biology\nBiology : Concepts and Investigations\nHuman Physiology\nSeeley's Anatomy & Physiology\nMader's Understanding Human Anatomy & Physiology\nHole's Essentials of Human Anatomy & Physiology\nAdvertising and Promotion: An Integrated Marketing Communications Perspective\nSex & Gender \nThe Unfinished Nation: A Concise History of the American People\nExperiencing the World's Religions\nChemistry : Atoms First\nChemistry: Atoms First\nEssentials of Economics\nEconomics\nEssentials of Economics, 3rd Edition (The McGraw-Hill Series in Economics)\nUrban Economics\nEnvironmental Economics: An Introduction (The Mcgraw-Hill)\nBroadcasting Cable the Internet and Beyond: An Introduction to Modern Electronic Media\nPathways to Astronomy\nCollege Physics:With an Integrated Approach to Forces and Kinematics, Fourth Edition\nAstronomy: Journey to the Cosmic Frontier\nPhysics of Everyday Phenomena\nExplorations: Introduction to Astronomy\nPathways to Astronomy\nMedical Insurance: An Integrated Claims Process Approach\nUniversity Physics with Modern Physics\nPhysical Science\nPhysics of Everyday Phenomena\nTheater The Lively Art\nAnthology of Living Theater \nSurvey of Operating Systems\nSurvey of Operating Systems\nBasic Statistics for Business and Economics\nApplied Statistics in Business and Economics\nBusiness Statistics in Practice (McGraw-Hill/Irwin Series in Operations and Decision Sciences)\nBusiness Research Methods\nMicrobiology: A Systems Approach\nFoundations in Microbiology\nWardlaw's Perspectives in Nutrition\nContemporary World Regional Geography\nIntroduction to Geography\nIntroduction to Geography\nChemistry in Context\nInternational Economics\nEconomics: The Basics (Mcgraw-Hill/Irwin Series in Economics)\nLabor Economics\nManagerial Economics & Business Strategy\nComputer Networks : A Top down Approach\nAn Introduction to Object-Oriented Programming with Java\nDatabase System Concepts\nJava Programming: From the Ground Up\nAlgorithms\nThe Good Earth: Introduction to Earth Science\nEnvironmental Geology\nExploring Geology\nLaboratory Manual for Physical Geology\nMarine Biology\nVertebrates : Comparative Anatomy, Function, Evolution\nUnderstanding Business, 10th Edition\nEmployment Law for Business\nBusiness Law\nMatching Supply with Demand: An Introduction to Operations Management\nMolecular Biology\nHuman Biology\nBiology\nEssentials of Biology\nConcepts of Biology\nBiology: Concepts and Investigations\nHuman Anatomy\nElectronic Media: An Introduction\nMedia Ethics: Issues and Cases\nPower & Choice: An Introduction to Political Science\nMusic An Appreciation\nWorld Music: Traditions and Transformations\nSociology: A Brief Introduction, 9th Edition\nFoundations of Materials Science and Engineering\nShigley's Mechanical Engineering Design (Mcgraw-Hill Series in Mechanical Engineering)\nThermodynamics : An Engineering Approach, 7th Edition\nEngineering Circuit Analysis\nSemiconductor Physics and Devices\nInternational Marketing\nConsumer Behavior: Building Marketing Strategy, 12th Edition\nPersonal Finance (Mcgraw-Hill/Irwin Series in Finance, Insurance and Real Estate)\nCorporate Finance: Core Principles and Applications (McGraw-Hill/Irwin Series in Finance, Insurance and Real Estate)\nPersonal Finance\nSexuality Today\nTransformations: Women, Gender, and Psychology\nHuman Sexuality: Self, Society, and Culture\nBiology\nBiology Laboratory Manual\nCalculus : Early Transcendental Functions\nApplied Calculus for Business, Economics, and the Social and Life Sciences, Expanded Edition\nCalculus for Business, Economics, and the Social and Life Sciences, Brief Version\nEcology : Global Insights and Investigations\nEcology: Concepts and Applications\nPrinciples of Environmental Science: Inquiry and Applications\nFilm Art: An Introduction\nScriptures of the World's Religions\nEthical Issues in Modern Medicine: Contemporary Readings in Bioethics\nProblems from Philosophy\nIntroduction to Thermodynamics and Heat Transfer + Ees Software\nThe Good Earth: Introduction to Earth Science\nThe Unfinished Nation: A Concise History of the American People, Volume 1\nFoundations in Microbiology: Basic Principles\nExplorations: Introduction to Astronomy\nOrganic Chemistry\nThermodynamics: An Engineering Approach with Student Resources DVD\nAmerican History: A Survey Volume 2\nAmerican History: A Survey Volume 1\nVector Mechanics for Engineers: Statics\nVector Mechanics for Engineers: Dynamics\nThe Unfinished Nation: A Concise History of the American People Volume 1\nFundamentals of Thermal-Fluid Sciences with Student Resource DVD\nFluid Mechanics with Student DVD\nMusic: An Appreciation Brief Edition with 5-CD\nLoose-leaf for Fundamentals of Financial Accounting\nFoundations of Financial Management with Time Value of Money card (The Mcgraw-Hill/Irwin Series in Finance, Insurance, and Real Estate)\nElementary Statistics with CD : A Step by Step Approach with Formula Card and Data 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Methods\nTakaful Islamic Insurance: Concepts and Regulatory Issues (Wiley Finance)\nEncyclopedia Of Actuarial Science\nPhysics, 9th Edition\nPhysics 9e (Volume One) Chapters 1-17\nCalculus: Single and Multivariable\nCalculus: Single Variable\nIntroductory Statistics\nRegression Analysis by Example\nEnvironmental Science : Earth as a Living Planet, Eighth Edition Binder Ready Version\nBasic Concepts of Chemistry\nCalculus, Vol. 2: Multi-Variable Calculus and Linear Algebra with Applications to Differential Equations and Probability (Volume 2)\nElements of Real Analysis\nForestry Handbook\nStochastic Processes (Wiley Series in Probability and Statistics)\nFoundations of Aerodynamics: Bases of Aerodynamic Design\nStatistics and Data Analysis in Geology\nMathematical Methods in the Physical Sciences\nIntroduction to Thermal Systems Engineering Thermodynamics, Fluid Mechanics, and Heat Transfer\nPower Electronics Converters, Applications and Design\nBlue Planet: An Introduction to Earth 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Local Values and Forest Resource Management\nThe Forgotten Diaspora: Jewish Communities in West Africa and the Making of the Atlantic World\nGame Theory in Wireless and Communication Networks: Theory, Models, and Applications\nCambridge History of Judaism Introduction, the Persian Period\nThe Art of Electronics\nThe Art of Electronics Student Manual\nStudent's Latin Grammar/North American\nScience and Civilisation in China Volume 6: Biology and Biological Technology, Part 3, Agro-Industries and Forestry\nAn Introduction to Hinduism (Introduction to Religion)\nIntroduction to Hinduism\nSpeakable and Unspeakable in Quantum Mechanics\nChinese A Linguistic Introduction\nJet Propulsion A Simple Guide to the Aerodynamics and Thermodynamic Design and Performance of Jet Engines\nLogic in Computer Science Modelling and Reasoning About Systems\nAlgorithms on Strings, Trees, and Sequences Computer Science and Computational Biology\nFormation of Islam Religion and Society in the Near East, 600-1800\nEcological Census Techniques A Handbook\nIntroduction to Swaminarayan Hinduism\nLow-Speed Aerodynamics\nAn Introduction to Judaism (Introduction to Religion)\nRegression Modeling with Actuarial and Financial Applications (International Series on Actuarial Science)\nMicroprocessor Architecture: From Simple Pipelines to Chip Multiprocessors\nCambridge History of Judaism The Late Roman-Rabbinic Period\nIntroduction to Support Vector Machines And Other Kernel-Based Learning Methods\nBasic Aerodynamics: Incompressible Flow (Cambridge Aerospace Series)\nGeneral Relativity An Introduction for Physicists\nPolitical Game Theory An Introduction\nThermodynamics Concepts And Applications\nObservational Astronomy \nFallen Angels And The History Of Judaism And Christianity The Reception Of Enochic Literature\nPrinciples of Helicopter Aerodynamics \nProbability and Random Processes for Electrical and Computer Engineers\nQuantum Computer Science \nParallel Computer Organization and Design\nA 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An Introduction\nGame Theory for Applied Economics\nMathematical Foundations of Quantum Mechanics\nHarmonic Analysis: Real-Variable Methods, Orthogonality, and Oscillatory Integrals\nGame Theory for Political Scientists\nBehavioral Game Theory Experiments in Strategic Interaction\nReal Analysis Measure Theory, Integration, And Hilbert Spaces\nReal Analysis with Economic Applications\nCamphor Flame Popular Hinduism And Society In India\nBeyond Liberal Democracy Political Thinking for an East Asian Context\nEcological Models and Data in R\nPolitics of Piety - the Islamic Revival and the Feminist Subject\nA Trip to China: An Intermediate Reader of Modern Chinese (Princeton Language Program: Modern Chinese)\nField and Laboratory Methods for General Ecology\nSelfless Mind Personality, Consciousness and Nirvana in Early Buddhism\nBuddhism and Deconstruction Towards a Comparative Semiotics\nIntegrating East Asian Medicine into Contemporary Healthcare\nRang & Dale's Pharmacology: with STUDENT CONSULT Online Access\nKumar and Clark's Clinical Medicine, 8e (Kumar, Kumar and Clark's Clinical Medicine)\nWashington Conference, 1921-22 Naval Rivalry, East Asian Stability and the Road to Pearl Harbor\nSilent Theater The Art of Edward Hopper\nSpacetime Physics Introduction to Special Relativity\nMathematical Structures for Computer Science A Modern Approach to Discrete Mathematics\nPrinciples of Biochemistry\nMolecular Cell Biology \nMolecular Biology: Principles and Practice\nThe Law of Torts: Examples & Explanations, 4th Edition\nProblems in Contract Law: Cases and Materials, Seventh Edition (Aspen Casebook)\nConstitutional Law: Principles & Policies 4e\nHistory of Dance An Interactive Arts Approach\nAcsms exercise management for persons with chronic diseases and Disabilities\nFoundations of Sport and Exercise Psychology w/Web Study Guide-5th Edition\nPhysiology of Sport and Exercise w/Web Study Guide-5th Edition\nMaking the Connections 2: A How-To Guide for Organic Chemistry Lab 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Construction of Illness\nEssentials of Computer Organization And Architecture \nEssentials Of Public Health Biology: A Guide For The Study Of Pathophysiology (Essential Public Health)\nCriminology: Theory, Research, And Policy\nElements Of Real Analysis (International Series in Mathematics)\nFoundations of Algorithms, Fourth Edition\nComputer Networking Illuminated\nGerman Daggers Of World War II - A Photographic Reference Dlv/nsfk - Diplomats - Red Cross - Police And Fire - Rlb - Teno - Customs - Reichsbahn - Postal - Hunting And Forestry - Etc.\nBuddhism Plain and Simple\nAerodynamics of Road Vehicles: From Fluid Mechanics to Vehicle Engineering ([Proceedings] / SAE)\nDesign of High-Performance Microprocessor Circuits\n100 Case Studies in Pathophysiology \nPathophysiology\nEssentials of Research Methods in Health, Physical Education, Exercise Science, and Recreation\nHistology: A Text and Atlas: With Correlated Cell and Molecular Biology, North American Edition\nPrimary Care Medicine: Office Evaluation and Management of the Adult Patient\nEssentials of Pathophysiology : Concepts of Altered Health States\nBates' Nursing Guide to Physical Examination and History Taking (Guide to Physical Exam & History Taking (Bates))\nLippincott's Illustrated Reviews: Cell and Molecular Biology (North American Edition) (Lippincott's Illustrated Reviews Series)\nMedical Terminology, a Programmed Learning Approach to the Language of Health Care \nFundamentals of Nursing: The Art and Science of Nursing Care\nReligion of Reason Out of the Sources of Judaism\nLife And Health Insurance License Exam Cram\nMutual Causality in Buddhism and General Systems Theory The Dharma of Natural System\nCentrifugal Compressors A Strategy for Aerodynamic Design and Analysis\nTurbine Aerodynamics Axial-flow And Radial-flow Turbine Design And Analysis\nIntegration of Natural Language and Vision Processing Computational Models and Systems\nIntegration of Natural Language and Vision Processing Intelligent Multimedia\nIntegration of Natural Language and Vision Processing Theory and Grounding Representations\nIntegration of Natural Language and Vision Processing Recent Advances\nOur Vanishing Relative The Status of Wild Orang-Utans at the Close of the Twentieth Century\nLearning to Classify Text Using Support Vector Machines Methods, Theory, and Algorithms\nDiscrete Mathematics and Game Theory\nGame Theory Stochastics, Information, Strategies and Cooperation\nNatural Language Processing: The PLNLP Approach (The Springer International Series in Engineering and Computer Science)\nReversible Grammar in Natural Language Processing\nSuspect Relations Sex, Race, and Resistance in Colonial North Carolina\nMammalogy: Adaptation, Diversity, Ecology\nEerdmans Dictionary of Early Judaism\nClinical Immunology and Serology: A Laboratory Perspective\nNutrition & Diet Therapy\nMedical Terminology Simplified Text, Audio CD & TermPlus 3.0: A Programmed Learning Approach by Body Systems\nNew 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Nelson\nFundamental Concepts of Bioinformatics\nDiscovering Genomics, Proteomics and Bioinformatics (2nd Edition)\nModern Quantum Mechanics\nGravity: An Introduction to Einstein's General Relativity\nIntroductory Quantum Mechanics (4th Edition)\nQuantum Mechanics An Accessible Introduction\nSpacetime and Geometry An Introduction to General Relativity\nHuman Anatomy and Physiology\nMolecular Biology of the Gene\nTheoretical Issues in Natural Language Processing\nGlobal Linguistic Flows: Hip Hop Cultures, Youth Identities, and the Politics of Language\nA Dragon's Head and a Serpent's Tail: Ming China and the First Great East Asian War, 1592-1598 (The Campaigns and Commanders Series)\nForensic and Investigative Accounting (6th Edition)\nTheater Games for the Classroom A Teacher's Handbook\nTheater Games for the Lone Actor\nGeoffrey Holder A Life in Theater, Dance, and Art\nNonviolence and Peace Building in Islam: Theory and Practice\nSociety and the Environment: Pragmatic Solutions to Ecological Issues\nLanguage Files: Materials for an Introduction to Language and Linguistics, 11th Edition\nEast Main Street: Asian American Popular Culture\nUnderstanding Bioinformatics \nMolecular Biology of the Cell\nMolecular Biology of the Cell: Reference Edition\nPhylogenomics: A Primer\nMolecular Driving Forces: Statistical Thermodynamics in Biology, Chemistry, Physics, and Nanoscience\nCase Studies in Immunology: A Clinical Companion (Geha, Case Studies in Immunology: A Clinical Companion)\nPrinciples of Mucosal Immunology\nEssential Cell Biology\nReal-Time Software Design: A Guide for Microprocessor Systems - Philip Heller - Hardcover\nNeural Networks and Analog Computation Beyond the Turing Limit\nWorld Religions: Beliefs Behind Today's Headlines: Buddhism, Christianity, Confucianism, Hinduism, Islam, Shintoism, Taoism\nProbability: The Science of Uncertainty with Applications to Investments, Insurance, and Engineering\nGlobal Divas Filipino Gay Men in the Diaspora\nImpossible Desires Queer Diasporas and South Asian Public Cultures\nImage Matters: Archive, Photography, and the African Diaspora in Europe\nWars in the Woods The Rise of Ecological Forestry in America\nDifferential Geometry and Relativity Theory: An Introduction (Chapman & Hall/CRC Pure and Applied Mathematics)\nAsian Culture and Psychotherapy Implications for East and West\nHokkeji and the Reemergence of Female Monastic Orders in Premodern Japan (Studies in East Asian Buddhism)\nHandbook of Forensic Mental Health With Victims and Offenders Assessment, Treatment, and Research\nUnderstanding Microprocessors\nLA Civilisation Francaise En Evolution I Institutions Et Culture Avant LA Ve Republique\nEthics in Counseling & Psychotherapy\nBrooks/Cole Empowerment Series: Social Welfare Policy and Social Programs\nPopular Music in America: The Beat Goes On\nCengage Advantage Books: Evolution and Prehistory: The Human Challenge\nManagement of Human Service Programs\nChemistry and Chemical Reactivity\nUnderstandable Statistics: Concepts and Methods\nStudent Solutions Manual, (Chapters 1-11) for Stewart's Single Variable Calculus: Early Transcendentals, 7th\nStudent Solutions Manual (Chapters 1-11) for Stewart's Single Variable Calculus, 7th\nOrganic Chemistry\nStudy Guide with Student Solutions Manual for McMurry's Organic Chemistry, 8th\nCollege Physics\nChanging Earth : Exploring Geology and Evolution\nQuantitative Methods for Business\nStudent Solutions Manual for Devore's Probability and Statistics for Engineering and Science, 8th\nPrecalculus: Mathematics for Calculus\nBiochemistry\nStudent Solutions Manual for Stewart/Redlin/Watson's Precalculus: Mathematics for Calculus, 6th\nForensic Pathology\nActuarial Models for Disability Insurance\nAgroecology The Ecology of Sustainable Food Systems\nPractical Homicide Investigation Tactics, Procedures And Forensic Techniques\nRelative Permeability of Petroleum Reservoirs\nWildlife Habitat Management Concepts And Applications in Forestry\nInsect Pests in Tropical Forestry\nAn Introduction to Actuarial Studies, Second Edition\nDeities of Tibetan Buddhism The Zurich Paintings of the Icons Worthwhile to See Bris Sku Mthon Ba Donldan\nNyingma School of Tibetan Buddhism Its Fundamentals and History\nLatin for the New Millenium\nMicroprocessor Interfacing\nMeditations on First Philosophy In Which the Existence of God and the Distinction of the Soul from the Body Are Demonstrated\nThe Greek Particles\nIntroductory Readings in Ancient Greek and Roman Philosophy\nMoral Philosophy: A Reader\nPerformed Culture: An Approach to East Asian Language Pedagogy\nDiasporas in the New Media Age : Identity, Politics, and Community\nReal Estate Market Analysis: A Case Study Approach\nScholars' Guide to Washington, D. C. for East Asian Studies China, Japan, Korea, and Mongolia\nCalligraphy and the East Asian Book\nEncyclopedia of Eastern Philosophy and Religion Buddhism, Hinduism, Taoism, Zen\nPrimer of Ecology\nEcology, Second Edition\nAnimal Physiology, Third Edition\nHuman Sexuality, Fourth Edition\nDiscovering Human Sexuality, Second Edition\nEcology, Third Edition\nBioinformatics Sequence and Genome Analysis\nCatechism of the Catholic Church Revised in Accordance With the Official Latin Text Promulgated by Pope John Paul II\nChristianity: A Biblical, Historical, and Theological Guide for Students\nDiagnostic and Statistical Manual of Mental Disorders, 5th Edition: DSM-5\nAfricana Studies: A Survey of Africa And the African Diaspora\nMicrobiology: Laboratory Theory and Application, Third Edition\nA Photographic Atlas for the Microbiology Laboratory\nA Photographic Atlas for the Anatomy and Physiology Laboratory Seventh Edition\nMicrobiology Laboratory Theory & Application, Brief, 2nd Edition\nAin't I A Woman Black Women and Feminism\nFeminism Is for Everybody Passionate Politics\nDynamic Noncooperative Game Theory\nA First Course in Numerical Methods (Computational Science and Engineering)\nPrinciples of Ideal-Fluid Aerodynamics\nRe-Entry Aerodynamics (AIAA Education)\nPhysical Chemistry A Molecular Approach\nTranslating Buddhism from Tibetan\nTransactions of the Society of Actuaries, Vol. 47\nActuarial Mathematics\nIntroduction to East Asian and Tibetan Linguistics and Culture\nBiochemistry & Molecular Biology of Plants\nUnderstanding Variation: The Key to Managing Chaos\nHinduism and Ecology The Intersection of Earth, Sky, and Water\nHinduism and Ecology The Intersection of Earth, Sky, and Water\nLetters from Chittagong An American Forestry Couple's Letters Home, 1952-54\nStreet Smarts: High Probability Short-Term Trading Strategies\nJudaism Discovered: A Study of the Anti-Biblical Religion of Racism, Self-Worship, Superstition and Deceit\nFinancial Mathematics: A Practical Guide For Actuaries And Other Business Professionals\nFinancial Mathematics : A Practical Guide for Actuaries and Other Business Professionals\nGetting Started with Latin: Beginning Latin for Homeschooled and Self-Taught Students of Any Age\nComprehensive Math Review for Actuarial Exams\nOrganic Chemistry\nThe Tangled Bank: An Introduction to Evolution\nuC/OS-III, The Real-Time Kernel, or a High Performance, Scalable, ROMable, Preemptive, Multitasking Kernel for Microprocessors, Microcontrollers & DSPs (Book & Board Included)\nGame Theory\nActuarial Mathematics for Life Contingent Risks\nMachine Learning: The Art and Science of Algorithms that Make Sense of Data\nMachine Learning : The Art and Science of Algorithms That Make Sense of Data\nSolutions Manual for Actuarial Mathematics for Life Contingent Risks (International Series on Actuarial Science)\nSolutions Manual for Actuarial Mathematics for Life Contingent Risks\nThe Fundamentals of Political Science Research\nBioinformatics for Biologists\nIntroduction to Electronics\nPrinciples of Pharmacology for Medical Assisting\nPrinciples of Information Security\nPhilosophy: An Introduction to the Art of Wondering\nHealth, Safety, and Nutrition for the Young Child\nGuide to Operating Systems\nIntroduction to Forestry Science\nIntroduction to Law\nAbnormal Psychology: An Integrated Approach\nManagement of Electronic and Digital Media\nLinguistics for Everyone: An Introduction\nMedia Programming: Strategies and Practices\nA History of Modern Psychology\nSoap, Sex, and Cigarettes: A Cultural History of American Advertising\nGendered Lives\nResearch Methods and Statistics: A Critical Thinking Approach\nBasics of Research Methods for Criminal Justice and Criminology\nPsychology: Themes and Variations, 9th Edition\nBiology the Unity & Diversity of Life\nCalculus : Early Transcendentals, Hybrid Edition (with Enhanced WebAssign with eBook Printed Access Card for Multi Term Math and Science)\nPhysical Geography\nProbability for Engineering, Mathematics, and Sciences\nMicroeconomic Theory : Basic Principles and Extensions (with Economic Applications, InfoTrac� Printed Access Card)\nBusiness Law and the Legal Environment, Standard Edition\nThe Legal Environment Today: Business In Its Ethical, Regulatory, E-Commerce, and Global Setting\nPractical Management Science (with Decision Sciences CourseMate Printed Access Card)\nEssentials of Statistics for Business and Economics, Revised (with Printed Access Card)\nEthics in Information Technology\nSystems Analysis and Design in a Changing World, 6th Edition\nGuide to Parallel Operating Systems with Windows 7 and Linux (Networking)\nPersonal Nutrition\nHuman Physiology : From Cells to Systems\nMechanics of Materials\nFoundations of Marketing\nAdvertising Promotion and Other Aspects of Integrated Marketing Communications\nFundamentals of Chemical Engineering Thermodynamics\nSecurity+ Guide to Network Security Fundamentals\nSecurity Awareness: Applying Practical Security in Your World\nGardner's Art through the Ages: A Global History, Volume II (with CourseMate Printed Access Card)\nGardner's Art through the Ages: A Global History, Volume II (Book Only)\nGardner's Art through the Ages Vol. 1 : A Global History\nGardner's Art through the Ages: A Global History, Volume I (Book Only)\nEssentials of Business Communication\nMarketing\nEconomics and Contemporary Issues (with Economic Applications and InfoTrac 2-Semester Printed Access Card)\nOrganizational Behavior : Science, the Real World, and You\nConcepts of Database Management\nUnderstanding Basic Statistics\nProbability and Statistics for Engineers and Scientists\nA First Course in Differential Equations with Modeling Applications\nDifferential Equations with Boundary-Value Problems (Textbooks Available with Cengage Youbook)\nThe Anthropology of Language: An Introduction to Linguistic Anthropology\nThe Sociology of Health, Illness, and Health Care: A Critical Approach\nThe Anthropology of Language: An Introduction to Linguistic Anthropology Workbook/Reader\nCengage Advantage Books: Sociology\nRace, Class, & Gender: An Anthology\nStatistics for the Behavioral Sciences (Psy 200 (300) Quantitative Methods in Psychology)\nBiological Psychology\nSociology: The Essentials\nSociology in Our Times\nWorld History\nWorld History since 1500\nCengage Advantage Books: Understanding Humans: An Introduction to Physical Anthropology and Archaeology\nSafety, Nutrition and Health in Early Education\nDeveloping and Administering a Child Care and Education Program\nThe Essence of Anthropology\nIntroduction to Psychology: Gateways to Mind and Behavior, 13th Edition\nTen Questions: A Sociological Perspective\nSociology: Pop Culture to Social Structure\nSociology : A Global Perspective\nCengage Advantage Books: Cultural Anthropology: A Problem-Based Approach\nVoices of Wisdom: A Multicultural Philosophy Reader\nWorld Religions\nSexuality Now: Embracing Diversity\nEssentials of Physical Anthropology\nUnderstanding Statistics in the Behavioral Sciences (Psy 200 (300) Quantitative Methods in Psychology)\nArchetypes of Wisdom: An Introduction to Philosophy\nCompact Literature: Reading, Reacting, Writing\nMusic Listening Today (with 2 CD Set)\nIndustrial/Organizational Psychology : An Applied Approach\nGardner's Art Through the Ages: A Concise Global History (with Arts CourseMate Printed Access Card)\nGardner's Art Through the Ages: A Concise Global History (Book Only)\n21St Century Handbook Edition: Human Sexuality\nDatabase Systems Design Implementation Management\nEconomics Private and Public Choice 14e\nComputer Organization & Architecture: Themes and Variations\nHistorical Geology\nA History of Modern Psychology\nVisualizing Nutrition: Everyday Choices\nPsychology in Action, 10th Edition\nFundamental Molecular Biology\nOperating System Concepts\nThe Analysis and Design of Linear Circuits\nTourism: Principles, Practices, Philosophies\nIntroduction to Organic Chemistry\nEssential Biochemistry\nBusiness Data Communications and Networking\nBig Java Late Objects\nManagerial Accounting: Tools for Business Decision Making\nFundamentals of Engineering Thermodynamics, Appendices\nOperating System Concepts\nFundamentals of Fluid Mechanics\nProbability and Measure\nOperating System Concepts\nAccounting Principles\nFundamentals of Thermodynamics\nOrganic Chemistry\nIntermediate Accounting\nOrganic Chemistry\nElementary Differential Equations and Boundary Value Problems\nFinancial Accounting: Tools for Business Decision Making\nEngineering Fluid Mechanics\nVisualizing Human Biology\nApplied Calculus, Fifth Edition\nInternational Economics\nCell and Molecular Biology: Concepts and Experiments\nGame Theory: An Introduction\nEconomics of Strategy\nNutrition: Science and Applications\nExam 98-349 MTA Windows Operating System Fundamentals\nCell and Molecular Biology\nLoss Models: From Data to Decisions\nProbability and Stochastic Processes` : A Friendly Introduction for Electrical and Computer Engineers\nThe Handbook of Computational Linguistics and Natural Language Processing\nIntroducing Physical Geography\nThe World Today: Concepts and Regions in Geography\nEnvironmental Science: Earth as a Living Planet\nBig Java: Early Objects\nJava Concepts: Early Objects\nOrganic Chemistry\nIntroduction to Electric Circuits\nApplied Statistics and Probability for Engineers\nPhysical Geology : The Science of Earth\nAbnormal Psychology Twelfth Edition DSM-V Update\nGeography: Realms, Regions, and Concepts\nData Structures and Algorithms in Java\nOperating System Concepts Essentials, Second Edition\nResearch Methods for Business: A Skill-Building Approach\nTheory of Lift: Introductory Computational Aerodynamics in MATLAB/Octave\nBattery Systems Engineering\nStructural Health Monitoring: A Machine Learning Perspective\nPractical Lessons In Actuarial Science V2: Tables (1905)\nLogic and Philosophy: A Modern Introduction\nA History of Latin America\nIntroduction to Probability and Statistics\nFoundations of Astronomy\nGeneral, Organic, and Biological Chemistry\nPrinciples of Physics: A Calculus-Based Text\nIntroduction to General, Organic and Biochemistry\nLaboratory Experiments for Introduction to General, Organic and Biochemistry\nBiochemistry\nGEOL (with Earth Science CourseMate with eBook Printed Access Card)\nDifferential Equations (with DE Tools Printed Access Card)\nAn Introduction to Physical Science\nBrief Calculus: An Applied Approach\nIntroduction to Organic and Biochemistry (William H. Brown and Lawrence S. Brown)\nEssential Calculus: Early Transcendentals\nThermodynamics for Engineers\nFundamentals of World Regional Geography\nStudy Guide for Miller/Cross' The Legal Environment Today: Business In Its Ethical, Regulatory, E-Commerce, and Global Setting, 7th\nEssentials of Marketing Research (with Qualtrics Printed Access Card)\nInvitation to Computer Science\nVBA for Modelers: Developing Decision Support Systems (with Microsoft Office Excel Printed Access Card)\nCengage Advantage Books: Business Law Today : The Essentials\nStatistics for Business and Economics (with Printed Access Card)\nCircuit Analysis: Theory and Practice\nUnderstanding Health Insurance: A Guide to Billing and Reimbursement\nResearch Methods in Political Science (with MicroCase Printed Access Card)\nWhy Politics Matters: An Introduction to Political Science (book only)\nSoap, Sex, and Cigarettes : A Cultural History of American Advertising\nA History of Modern Psychology\nUnderstanding Health Insurance: A Guide to Billing and Reimbursement\nFinancial Markets and Institutions, Abridged Edition (with Stock-Trak Coupon)\nStudent Solutions Manual for Stewart's Essential Calculus: Early Transcendentals, 2nd\nC++ Programming: Program Design Including Data Structures, 6th Edition\nElectronic Commerce\nUnderstanding Nutrition\nBiology: The Dynamic Science\nCengage Advantage Books: Introduction to Sociology\nBusiness\nNutrition for Health and Healthcare\nNutrition Through the Life Cycle\nChemistry for Today: General, Organic, and Biochemistry\nNutrition : Concepts and Controversies\nEssentials of Cultural Anthropology\nGeology and the Environment\nFundamentals of Physical Geography\nAccounting\nApplied Calculus for the Managerial, Life, and Social Sciences\nData Structures and Algorithms in C++\nReason and Responsibility: Readings in Some Basic Problems of Philosophy\nThe Big Questions: A Short Introduction to Philosophy\nChemistry\nPhilosophy: A Text with Readings\nInternet Marketing: Integrating Online and Offline Strategies\nC++ Programming: From Problem Analysis to Program Design\nData Communications and Computer Networks\nContemporary Marketing\nEssentials of Sociology\nNutrition Now\nPsychology: Themes and Variations, Briefer Version\nMarketing 2014\nOur Sexuality\nOur Sexuality\nFinancial Markets and Institutions (with Stock Trak Coupon)\nModern Diesel Technology : Electricity and Electronics\nPushing Electrons\nFinancial & Managerial Accounting\nPhysics for Scientists and Engineers with Modern Physics\nListening to Music (with Introduction to Listening CD)\nGardner's Art through the Ages: A Concise History of Western Art (with CourseMate Printed Access Card)\nGOVT (with Political Science CourseMate with EBook Printed Access Card)\nFundamentals of Economics\nEssentials of Statistics for the Behavioral Sciences\nSociology in Our Times: The Essentials\nCultural Anthropology: The Human Challenge\nNutrition and Diet Therapy with Premium Website Printed Access Card and Studyware\nTX. GOV (with Political Science CourseMate with EBook Printed Access Card)\nEast Asian Ecocriticisms: A Critical Reader (Literatures, Cultures, and the Environment)\nIslamic Feminism in Kuwait: The Politics and Paradoxes\nMichigan Manual of Forestry, Volume 2\nPractical Forestry for Beginners in Forestry, Agricultural Students, Woodland Owners, and Others Desiring a General Knowledge of the Nature of\nSociology: A Down-to-Earth Approach CORE Concepts (5th Edition)\nExploring Bioinformatics: A Project-Based Approach\nPhilosophies and Theories for Advanced Practice Nursing\nPathophysiology: a Practical Approach\nThe Essentials of Computer Organization and Architecture\nGeneral Chemistry\nCalculus\nEvolution and Prehistory: The Human Challenge\nIntroduction to Programming with C++\nCriminal Law\nTerrorism and Homeland Security\nIntroduction to Physical Anthropology, 2013-2014 Edition\nEthical Dilemmas and Decisions in Criminal Justice\nFundamentals of Financial Management, Concise Edition (with Thomson ONE - Business School Edition 6-Month Printed Access Card)\nResearch Methods for Criminal Justice and Criminology\nCriminology : The Core\nGendered Lives\nJava Programming\nPFIN3 (with Finance CourseMate and EBook Printed Access Card)\nMKTG (with Marketing CourseMate with EBook and Career Transitions Printed Access Card)\nPrinciples of Economics\nBusiness Law: Text and Cases : Legal, Ethical, Global, and Corporate Environment\nDatabase Systems : Design, Implementation, and Management\nBusiness Ethics: Ethical Decision Making & Cases\nElectronic Commerce\nHealth, Safety, and Nutrition for the Young Child\nGeneral, Organic, and Biochemistry : An Applied Approach\nSociology: The Essentials\nJuvenile Delinquency: Theory, Practice, and Law\nSociology in Our Times\nApplied Calculus for the Managerial, Life, and Social Sciences: A Brief Approach\nChanging Earth : Exploring Geology and Evolution\nBusiness and Society: Ethics, Sustainability, and Stakeholder Management\nUnderstanding Health Insurance : A Guide to Billing and Reimbursement (with Cengage EncoderPro. com Demo Printed Access Card)\nUnderstanding Health Insurance: A Guide to Billing and Reimbursement (Book Only)\nPsychology : Modules for Active Learning\nSociology: A Global Perspective\nAbnormal Psychology: An Integrative Approach\nCalculus: Early Transcendental Functions\nC++ Programming : From Problem Analysis to Program Design\nFlapper: A Madcap Story of Sex, Style, Celebrity, and the Women Who Made America Modern\nRelativity in Rotating Frames: Relativistic Physics in Rotating Reference Frames (Fundamental Theories of Physics)\nStream Ecology Structure and Function of Running Waters\nBioinformatics And Molecular Evolution \nRetrieving the Ancients: An Introduction to Greek Philosophy\nEcology From Individuals To Ecosystems\nISLAM AND THE WEST: Understanding Islamic Fundamentalism\nLiving Language: An Introduction to Linguistic Anthropology (Primers in Anthropology)\nWestern Philosophy\nRisk Management and Insurance: Perspectives in a Global Economy\nIntroduction to Chinese Philosophy From Ancient Philosophy to Chinese Buddhism\nThe Blackwell Companion to Hinduism\nGames and Information An Introduction to Game Theory\nHinduism: A Reader\nAllah Made Us: Sexual Outlaws in an Islamic African City\nThe Handbook of Computational Linguistics and Natural Language Processing (Blackwell Handbooks in Linguistics)\nAmerica on Film: Representing Race, Class, Gender, and Sexuality at the Movies\nIntroducing Philosophy Through Film: Key Texts, Discussion, and Film Selections\nAn Introduction to Islam in the 21st Century\nRoitt's Essential Immunology\nBusiness Database Systems\nCulturally and Linguistically Diverse Exceptional Students: Strategies for Teaching and Assessment\nSex Crimes Patterns and Behavior\nData Analysis Using SAS\nGender, Race, and Class in Media: A Critical Reader\nCriminological Theory: Context and Consequences\nSociology: Exploring the Architecture of Everyday Life\nRace, Ethnicity, Gender, and Class : The Sociology of Group Conflict and Change\nAn Invitation to Environmental Sociology\nIntroduction to Criminology: Why Do They Do It?\nIntroduction to Forensic Psychology: Research and Application\nDiversity and Society: Race, Ethnicity, and Gender\nOur Social World: Introduction to Sociology\nDiscover Sociology\nGuyton and Hall Textbook of Medical Physiology: With STUDENT CONSULT Online Access\nBasic Immunology Updated Edition: Functions and Disorders of the Immune System With STUDENT CONSULT Online Access, 3e (Basic Immunology: Functions and Disorders of the Immune System)\nAtlas of Human Anatomy: with Student Consult Access (Netter Basic Science)\nAssessment & Intervention for Communication Disorders in Culturally & Linguistically Diverse Populations \nR Programming for Bioinformatics\nForensic Science an Introduction to Scientific and Investigative Techniques, Third Edition\nMachine Learning: An Algorithmic Perspective\nAlgorithms in Bioinformatics: A Practical Introduction (Chapman & Hall/Crc Mathematical and Computational Biology)\nHandbook of Natural Language Processing, Second Edition (Chapman & Hall/Crc: Machine Learning & Pattern Recognition)\nTort Law and Practice\nFundamentals of Python: From First Programs through Data Structures\nElectronic Commerce\nComputer Networking for LANs to WANs: Hardware, Software and Security\nInvestment Analysis for Real Estate Decisions\nGendered Lives\nAnatomy & Physiology for Speech, Language, and Hearing\nOutlines & Highlights for Introduction to Quantum Mechanics by David J. Griffiths, ISBN: 9780131118928\nKuby Immunology \nForensic and Legal Psychology\nCalculus: Early Transcendentals\nLehninger Principles of Biochemistry and Absolute Ultimate Guide\nPhysical Chemistry\nBiology: How Life Works\nKuby Immunology (Kindt, Kuby Immunology)\nTechniques in Organic Chemistry\nRaven Biology of Plants\nProbability and Statistics: The Science of Uncertainty\nBiochemistry (Biochemistry (Berg))\nEssentials of General, Organic, and Biochemistry\nMolecular Cell Biology\nLehninger Principles of Biochemistry\nRace, Class, and Gender in the United States: An Integrated Study\nEconomics\nInvestigating Chemistry: Introductory Chemistry From A Forensic Science Perspective\nPsychology, 10th Edition\nAbnormal Psychology\nBiochemistry: A Short Course, 2nd Edition\nAbsolute Ultimate Guide for Lehninger Principles of Biochemistry\nFundamentals of Abnormal Psychology\nLife: The Science of Biology\nExpert Oracle Database 11g Administration (Expert's Voice in Oracle)\nPublication Manual of the American Psychological Association, Sixth Edition\nPublication Manual of the American Psychological Association Spiral\nEssentials of Pharmacology for Health Occupations\nComputer Forensics: Hard Disk and Operating Systems (Ec-Council Press Series : Computer Forensics)\nManagement of Information Security\nGuide to Computer Forensics and Investigations\nPhysical Examination and Health Assessment\nThe Language of Medicine\nPathophysiology for the Health Professions\nPharmacology for the Surgical Technologist\nPocket Companion for Physical Examination and Health Assessment\nStudent Laboratory Manual for Physical Examination & Health Assessment\nCellular and Molecular Immunology: with STUDENT CONSULT Online Access, 7e (Abbas, Cellular and Molecular Immunology)\nPharmacology : A Nursing Process Approach\nIllustrated Dental Embryology, Histology, and Anatomy\nKrause's Food & the Nutrition Care Process (Krause's Food & Nutrition Therapy)\nPharmacology : Principles and Applications\nStudy Guide for Pharmacology for Nursing Care, 8e\nPharmacology for Nursing Care\nTwenty Questions: An Introduction to Philosophy\nCost Accounting: Foundations and Evolutions\nNutrition Therapy and Pathophysiology\nFoundations of Astronomy\nUnderstanding Operating Systems\nConnecting with Computer Science\nProbability, Statistics, and Reliability for Engineers and Scientists\nComputer Network Time Synchronization : The Network Time Protocol on Earth and in Space\nPension Fund Risk Management: Financial and Actuarial Modeling (Chapman & Hall/Crc Finance Series)\nHandbook of Solvency for Actuaries and Risk Managers\nFirst Course in Machine Learning\nForensic Science : An Introduction to Scientific and Investigative Techniques, Fourth Edition\nMachine Learning for Business Analytics\nMeditation in Judaism, Christianity and Islam : Cultural Histories\nNew Dynamics in East Asian Politics: Security, Political Economy, and Society\nStatistical Methods in Bioinformatics : An Introduction\nAdvanced Quantum Mechanics: Materials and Photons (Graduate Texts in Physics)\nPrinciples of Distributed Database Systems\nThe Philosophy of Sex\nFeminisms Matter: Debates, Theories, Activism\nDiscovering Statistics using IBM SPSS Statistics\nGuide to Computer Network Security (Computer Communications and Networks)\nThe Essentials of Computer Organization and Architecture\nMolecular Biology: Genes to Proteins\nObject-Oriented Data Structures Using Java\nEssentials of Computer Organization and Architecture\nPrinciples Of Modern Operating Systems\nJava Illuminated: An Active Learning Approach\nC++ Plus Data Structures\nElementary Information Security\nEssentials Of Health Policy And Law (Essential Public Health)\nDiscovering Nutrition\nIntroduction to Human Disease: Pathology and Pathophysiology Correlations\nComputer Science Illuminated\nChristian Paths to Health and Wellness-2nd Edition\nImmunology (Lippincott Illustrated Reviews Series)\nLippincott's Illustrated Reviews: Pharmacology (Lippincott's Illustrated Reviews Series)\nThe Washington Manual of Allergy, Asthma, and Immunology Subspecialty Consult (The Washington Manual Subspecialty Consult Series)\nEssentials of Pathophysiology: Text and Study Guide Package\nFocus on Nursing Pharmacology Pb\nPorth's Pathophysiology: Concepts of Altered Health States\nPrimary Care Medicine\nMicrobiology and Immunology (Board Review Series)\nPractical Essentials of Intensity Modulated Radiation Therapy\nAcsm Introduction Exercise Science\nPocket Medicine : The Massachusetts General Hospital Handbook of Internal Medicine\nWashington Manual of Medical Therapeutics, 34e Print + Online\nPocket Medicine: The Massachusetts General Hospital Handbook of Internal Medicine (Pocket Notebook)\nThe Relativity of Deviance\nRace, Ethnicity, Gender, and Class: The Sociology of Group Conflict and Change\nRacism, Sexism, and the Media: Multicultural Issues Into the New Communications Age\nA Novel Approach to Politics: Introducing Political Science through Books, Movies and Popular Culture, 3rd Edition\nPreparing Educators to Engage Families : Case Studies Using an Ecological Systems Framework\nIntroduction to Criminology: Theories, Methods, and Criminal Behavior\nThe Practice of Research in Criminology and Criminal Justice\nIntroduction to Criminology: A Text/Reader\nSociology: Exploring the Architecture of Everyday Life\nStatistics for People Who (Think They) Hate Statistics\nEthical Problems in the Practice of Law, 3rd Edition\nSecured Credit: A Systems Approach, Seventh Edition (Aspen Casebook)\nConstitutional Law\nCriminal Law and Its Processes: Cases and Materials (Aspen Casebook Series), 9th Edition\nConstitutional Law, Seventh Edition (Aspen Casebook Series)\nRules of Contract Law 2012-2013 Statutory Supplement\nVeterinary Immunology : An Introduction\nAtlas of Human Anatomy : Including Student Consult Interactive Ancillaries and Guides\nBasic Immunology: Functions and Disorders of the Immune System, 4e\nHealth Insurance Today - Text and Workbook Package: A Practical Approach, 4e\nPathophysiology, 5e\nLanguage of Medicine\nInsurance Handbook for the Medical Office\nPharmacology: A Patient-Centered Nursing Process Approach, 8e (Kee, Pharmacology)\nGould's Pathophysiology for the Health Professions\nMedical Terminology Online for The Language of Medicine (Access Code and Textbook Package), 10e\nStudy Guide for Pharmacology: A Patient-Centered Nursing Process Approach, 8e\nLiterature: the Human Experience : Reading and Writing\nLiterature: A Portable Anthology\nThe Bedford Introduction to Literature: Reading, Thinking, Writing\nBioinformatics for High Throughput Sequencing\nNew Perspectives on Industrial Organization: With Contributions from Behavioral Economics and Game Theory (Springer Texts in Business and Economics)\nDiscovering Psychology\nThe Basic Practice of Statistics: w/Student CD\nPsychology in Modules\nWhat Is Life? A Guide to Biology & Prep-U\nWhat Is Life? A Guide to Biology with Physiology & Prep-U\nWorld Regional Geography Without Subregions\nWorld Regional Geography\nExploring Psychology (Paper)\nExploring Psychology in Modules (Loose-Leaf) [Book Only] [2012] Ninth Edition Ed. David G. Myers\nAbnormal Psychology--DSM-5 Update\nInvestigating Astronomy\nIntroduction to the Practice of Statistics: w/CrunchIt/EESEE Access Card\nExploring Psychology with Dsm5 Update\nEmbedded Systems: Real-Time Operating Systems for Arm Cortex M Microcontrollers\nLinear Algebra and Probability for Computer Science Applications\nHomeland Security: An Introduction to Principles and Practice, Second Edition\nIntroduction to Computer Networks and Cybersecurity\nNetwork Anomaly Detection : A Machine Learning Perspective\nFundamentals of Research in Criminology and Criminal Justice\nEssentials of Sociology\nForestry A-Z\nTheater Tips and Strategies for Jury Trials\nClassical Tragedy Greek and Roman 8 Plays in Authoritative Modern Translations Accompanied by Critical Essays\nGuide to the Code of Ethics for Nurses\nC4.5 Programs for Machine Learning\nComputer Organization and Design: The Hardware/Software Interface: Student Edition\nTaking on the Big Boys Or Why Feminism Is Good for Families, Business, and the Nation\nAfrican Diaspora in the Mediterranean Lands of Islam\nPositive Impact Forestry A Sustainability Approach to Managing Woodlands\nTime for bed: The secret of shadows\nSkin Talking About Sex, Class & Literature\nIntake Aerodynamics (Aiaa Education Series)\nLinguistics of American Sign Language, 5th Ed.: An Introduction\nMaya Diaspora Guatemalan Roots, New American Lives\nUnited States History Preparing for the Advanced Placement Examination\nSacred East: An Illustrated Guide to Buddhism, Hinduism, Confucianism, Taoism and Shinto\nCompressor Aerodynamics\nIslam and the Muslim Community\nBuddhism: The Path to NIRVana (Religious Traditions of the World)\nUrban Forestry: Planning and Managing Urban Greenspaces\nClassics of Criminology, 4th Edition\nColonize This! Young Women of Color on Today's Feminism\nFull Frontal Feminism A Young Womans Guide to Why Feminism Matters\nThe History of U.S. Feminism\nFeminism and Pop Culture: Seal Studies\nEssentials of Pathophysiology: Concepts of Altered Health States\nWalking with God in the Classroom: Christian Approaches to Teaching and Learning\nClinical Management of Sex Addiction\nMicroprocessors From Assembly Language To C Using The Pic18fxx2\nModern Actuarial Theory and Practice \nStatistical and Probabilistic Methods in Actuarial Science \nApplied Game Theory and Strategic Behavior \nIntroduction to Latin\nAerodynamic Design of Transport Aircraft\nThe New Asian Hemisphere: The Irresistible Shift of Global Power to the East\nWho Is My Enemy?: Questions American Christians Must Face about Islam--and Themselves\nRadin, Rothchild, Reese and Silverman's Internet Commerce: The Emerging Legal Framework, 2d (University Casebook Series) (English and English Edition)\nStudy Guide and Solutions Manual for Exam P of the Society of Actuaries (2nd Edition)\nAlif Baa, Third Edition: Alif Baa: Introduction to Arabic Letters and Sounds (Al-Kitaab Arabic Language Program) (Arabic Edition)\nPython Programming : An Introduction to Computer Science\nThe Art of Buddhism: An Introduction to Its History and Meaning\nIntroduction to the Thermodynamics of Materials\nInside The Machine An Illustrated Introduction to Microprocessors and Computer Architecture\nPrinciples and Practice of Sex Therapy\nFeminism Seduced: How Global Elites Use Women's Labor and Ideas to Exploit the World\nSecurities Regulation: Cases and Analysis (University Casebook Series)\nChristian Morality (student Book) : Our Response to God's Love\nJudaism and the Gentiles\nReadings in Ancient Greek Philosophy : From Thales to Aristotle\nAtlas of Anatomy\nMahamudra for the Modern World: An Unprecedented Training Course in the Pinnacle Teachings of Tibetan Buddhism\nLatin American Identity and the African Diaspora : Ethnogenesis in Context\nEvolution, Third Edition\nACC Atlas of Pathophysiology\nPathophysiology : A Clinical Approach\nClinical Anesthesia Procedures of the Massachusetts General Hospital: Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School\nBurton's Microbiology for the Health Sciences, North American Edition\nRadiation Oncology: Management Decisions\nHandbook Of Research On Machine Learning Applications and Trends: Algorithms, Methods and Techniques (2 Volumes)\nPrinciples of Pharmacology\nMarks' Basic Medical Biochemistry (Lieberman, Marks's Basic Medical Biochemistry)\nPharmacology for Health Professionals\nPocket Medicine: The Massachusetts General Hospital Handbook of Internal Medicine (Pocket Notebook Series)\nPolitical Science Research Methods\nEncyclopedia of Hinduism\nOn Diaspora: Christianity, Religion, and Secularity\nMedical Physiology: Principles for Clinical Medicine, North American Edition (MEDICAL PHYSIOLOGY (RHOADES))\nExploring Anatomy & Physiology in the Laboratory\nThe Algorithm Design Manual\nCompressible Aerodynamics\nIntroducing Hinduism: A Graphic Guide (Introducing...)\nEssays on Game Theory\nConnectionist Natural Language Processing: Readings from Connection Science\nHinduism: Frequently Asked Questions\nGuide to Getting It On! A Book About the Wonders of Sex\nIntroduction to Probability, 2nd Edition\nMolecular Thermodynamics\nSpecial Relativity\nA Modern Approach to Quantum Mechanics\nDevelopmentally Appropriate Practice in Early Childhood Programs Serving Children from Birth Through Age 8\nAfricans in the Americas A History of Black Diaspora\nDiction for Singers\nCross-Border Commerce\nEvolution: Making Sense of Life\nEvolution: Making Sense of Life\nThe Tools & Techniques of Life Insurance Planning, 5th Edition\nGreen Processes : Green Synthesis\nNatural Language Processing And Information Systems 10th International Conference on Applicatioins of Natural Language to Information Systems, Nldb 2005, Alicante, Spain, June 15-17, Proceedings\nNatural Language Processing-IJCNLP 2005 Second International Joint Conference, Jeju Island, Korea, October 11-13, 2005, Proceedings\nCritical Phenomena in Natural Sciences Chaos, Fractals, Selforganization And Disorder Concepts And Tools\nNatural Language Processing And Information Systems 11th International Conference on Applications of Natural Language to Information Systems, Nldb 2006, Klagenfurt, Austria, May 31-june 2, 2006, Proceed\nAdvances in Natural Language Processing 5th International Conference on NLP, FinTAL 2006 Turku, Finland, August 23-25, 2006 Proceedings\nAerodynamic Drag Reduction Technologies: Proceedings of the CEAS/DragNet European Drag Reduction Conference, 19-21 June 2000, Potsdam, Germany (Notes ... Fluid Mechanics and Multidisciplinary Design)\nAdvances in Natural Language Processing Third International Conference, Portal 2002, Faro, Portugal, June 23-26, 2002 Proceedings\nNatural Language Processing\nEvaluating Natural Language Processing Systems An Analysis and Review\nNatural Language Processing - Nlp 2000 Second International Conference Patras, Greece, June 2-4, 2000 Proceedings\nComputer Network Security: Theory and Practice\nModern Actuarial Risk Theory: Using R\nAerodynamic Drag Reduction Technologies: Proceedings of the CEAS/DragNet European Drag Reduction Conference, 19-21 June 2000, Potsdam, Germany (Notes on ... Mechanics and Multidisciplinary Design)\nOptimization and Chaos (Studies in Economic Theory)\nChaos in Structural Mechanics (Understanding Complex Systems)\nLandscape Ecology in Forest Management and Conservation : Challenges and Solutions for Global Change\nTotal Artwork in Expressionism : Art, Film, Literature, Theater, Dance, and Architecture 1905-1925\nTheater of Manners: Photographs by Tina Barney - Tina Barney\nMathematical Foundations of Computer Science\nAn Introduction to Hinduism\nTribute to Hinduism: Thoughts and Wisdom Spanning Continents and Time About India an\nRelativity and Field Theory : Introduction to Special Relativity and to Classical and Quantum Field Theory\nMediating the Divine Prophecy and Revelation in the Dead Sea Scrolls and Second Temple Judaism\nA Wandering Galilean: Essays in Honour of Sean Freyne (Supplements to the Journal for the Study of Judaism)\nConstructing Irregular Theology: Bamboo and Minjung in East Asian Perspective (Studies in Systematic Theology)\nThe New Testament and Rabbinic Literature (Supplements to the Journal for the Study of Judaism)\nEsoteric Buddhism and the Tantras in East Asia (Handbook of Oriental Studies)\nAerodynamics of Large Bridges Proceedings of the First International Symposium, Copenhagen, Denmark, 19-21 February 1992\nJudaism and the Origins of Christianity \nSouth-East Asian Ceramics: Thai, Vietnamese, and Khmer From the Collection of the Art Gallery of South Australia, Adelaide (Asia Collection)\nProblems and Solutions on Quantum Mechanics Major American Universities Ph. D. Qualifying Questions and Solutions\nIntroduction to Compiler Construction in a Java World\nData Structures and Algorithms in Java\nGame Theory: A Nontechnical Introduction to the Analysis of Strategy\nSociety of Actuaries' Textbook on Life Contingencies - Chester Wallace Jordan - Paperback - 2ND\n" }, { "path": "nlp_class/article_spinner.py", "content": "# Very basic article spinner for NLP class, which can be found at:\n# https://deeplearningcourses.com/c/data-science-natural-language-processing-in-python\n# https://www.udemy.com/data-science-natural-language-processing-in-python\n\n# Author: http://lazyprogrammer.me\n\n# A very bad article spinner using trigrams.\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport nltk\nimport random\nimport numpy as np\n\nfrom bs4 import BeautifulSoup\n\n\n# load the reviews\n# data courtesy of http://www.cs.jhu.edu/~mdredze/datasets/sentiment/index2.html\npositive_reviews = BeautifulSoup(open('electronics/positive.review').read())\npositive_reviews = positive_reviews.findAll('review_text')\n\n\n# extract trigrams and insert into dictionary\n# (w1, w3) is the key, [ w2 ] are the values\ntrigrams = {}\nfor review in positive_reviews:\n s = review.text.lower()\n tokens = nltk.tokenize.word_tokenize(s)\n for i in range(len(tokens) - 2):\n k = (tokens[i], tokens[i+2])\n if k not in trigrams:\n trigrams[k] = []\n trigrams[k].append(tokens[i+1])\n\n# turn each array of middle-words into a probability vector\ntrigram_probabilities = {}\nfor k, words in iteritems(trigrams):\n # create a dictionary of word -> count\n if len(set(words)) > 1:\n # only do this when there are different possibilities for a middle word\n d = {}\n n = 0\n for w in words:\n if w not in d:\n d[w] = 0\n d[w] += 1\n n += 1\n for w, c in iteritems(d):\n d[w] = float(c) / n\n trigram_probabilities[k] = d\n\n\ndef random_sample(d):\n # choose a random sample from dictionary where values are the probabilities\n r = random.random()\n cumulative = 0\n for w, p in iteritems(d):\n cumulative += p\n if r < cumulative:\n return w\n\n\ndef test_spinner():\n review = random.choice(positive_reviews)\n s = review.text.lower()\n print(\"Original:\", s)\n tokens = nltk.tokenize.word_tokenize(s)\n for i in range(len(tokens) - 2):\n if random.random() < 0.2: # 20% chance of replacement\n k = (tokens[i], tokens[i+2])\n if k in trigram_probabilities:\n w = random_sample(trigram_probabilities[k])\n tokens[i+1] = w\n print(\"Spun:\")\n print(\" \".join(tokens).replace(\" .\", \".\").replace(\" '\", \"'\").replace(\" ,\", \",\").replace(\"$ \", \"$\").replace(\" !\", \"!\"))\n\n\nif __name__ == '__main__':\n test_spinner()\n" }, { "path": "nlp_class/cipher_placeholder.py", "content": "# https://deeplearningcourses.com/c/data-science-natural-language-processing-in-python\n# https://www.udemy.com/data-science-natural-language-processing-in-python\n\n# Author: http://lazyprogrammer.me\n\n# Get the data from here:\n# https://lazyprogrammer.me/course_files/moby_dick.txt\n\n### encode a message\n\n# this is a random excerpt from Project Gutenberg's\n# The Adventures of Sherlock Holmes, by Arthur Conan Doyle\n# https://www.gutenberg.org/ebooks/1661\n\noriginal_message = '''I then lounged down the street and found,\nas I expected, that there was a mews in a lane which runs down\nby one wall of the garden. I lent the ostlers a hand in rubbing\ndown their horses, and received in exchange twopence, a glass of\nhalf-and-half, two fills of shag tobacco, and as much information\nas I could desire about Miss Adler, to say nothing of half a dozen\nother people in the neighbourhood in whom I was not in the least\ninterested, but whose biographies I was compelled to listen to.\n'''" }, { "path": "nlp_class/electronics/negative.review", "content": "\n\nB00005UKBG:bad:j._brodeur_\"disgusted_consumer\"\n\n\nB00005UKBG\n\n\nAtlantic 1316 CD Storage Case (110-Capacity, Wave): Electronics\n\n\nelectronics\n\n\n15 of 16\n\n\n2.0\n\n\nbad\n\n\nMay 4, 2005\n\n\nJ. Brodeur \"disgusted consumer\"\n\n\n\n\n\ncons\ntips extremely easy on carpet and if you have a lot of cds stacked at the top\n\npoorly designed, it is a vertical cd rack that doesnt have individual slots for cds, so if you want a cd from the bottom of a stack you have basically pull the whole stack to get to it\n\nputting it together was a pain, the one i bought i had to break a piece of metal just to fit it in its guide holes.\n\nagain..poorly designed... doesnt even fit cds that well, there are gaps, and the cd casses are loose fitting\n\npros\n..........\ni guess it can hold a lot of cds....\n\n\n\n\nB00005UKBG:save_your_money!:heidi_myers\n\n\nB00005UKBG\n\n\nAtlantic 1316 CD Storage Case (110-Capacity, Wave): Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n1.0\n\n\nSave your money!\n\n\nMarch 18, 2004\n\n\nHeidi Myers\n\n\nTampa, FL\n\n\nIt's a nice look, but it tips over very easily. It is not steady on a rug surface dispite what the picture on the box shows. My advice is if you need a CD rack that holds a lot of CD's? Save your money and invest in something nicer and more sturdy\n\n\n\n\nB0009O5MWI:horrible_dvd_player:m._martinez\n\n\nB0009O5MWI\n\n\nPhilips HTS3400D/37 Home Theater System with DVD Player: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nHorrible DVD player\n\n\nJune 23, 2006\n\n\nM. Martinez\n\n\n\n\n\nI have bought and returned three of these units now. Each one has been defective, and finally I just gave up on returning the system. The DVD player constantly gives \"Bad Disc\" errors and skips if there is even the slightest smudge on a disc. The sound quality is very nice for the price, but since the player doesn't work, it's essentially useless. This is a complete rip-off at any price point\n\n\n\n\nB000067VBM:cases_break_instantly!:f._mcpartland_\"fmcp\"\n\n\nB000067VBM\n\n\n50-pack Slim CD/DVD Jewel Cases (5mm) Clear: Electronics\n\n\nelectronics\n\n\n12 of 13\n\n\n1.0\n\n\nCases break instantly!\n\n\nOctober 30, 2005\n\n\nF. MCPARTLAND \"FMCP\"\n\n\nVirginia\n\n\nI knew these were inexpensive CD cases, but I can't even open one without it breaking into two pieces..\n\n\n\n\nB000093IRC:these_dvd-rs_suck:suzette_truesdell\n\n\nB000093IRC\n\n\nMemorex 4.7GB 4x DVD+R Media (25-Pack Spindle): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n2.0\n\n\nThese DVD-Rs suck\n\n\nFebruary 11, 2006\n\n\nSuzette Truesdell\n\n\nKey Largo, FL\n\n\nI used a 25 pack of these doing DVD backups, and the last 5 or so failed. I thought it was my software, so I got new software. Guess what? My first disc out of the new package of 25 that I bought failed, too. It's not my burner (as another reviewer suggested, implying that people should learn to read), as it's brand new and doesn't require the firmware upgrade they mention. They just suck and have a high failure rate. So the consensus seems to be that TDK or Sony are best. I'm buying some now and save myself the frustration\n\n\n\n\nB000093IRC:waste_of_money:c._ambrow_\"renegade\"\n\n\nB000093IRC\n\n\nMemorex 4.7GB 4x DVD+R Media (25-Pack Spindle): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nWaste Of Money\n\n\nJanuary 21, 2006\n\n\nC. Ambrow \"Renegade\"\n\n\nTennessee\n\n\nI bought these discs at CompUSA because I needed a few before I got the ones I ordered online. Well, I just wated a good 25 bucks because out of 5 discs, I got one that burned good. Not much bang for the buck. I have burned many discs but never Memorex, usually Verbatim or Ridata or MAM. Very disgruntled with these discs making coasters. Maybe my burners don't like them but whatever the case, I won't buy them ever again. I thought Memorex made good products\n\n\n\n\nB000093IRC:good_when_it_works:stefanie_clay_\"stef1223\"\n\n\nB000093IRC\n\n\nMemorex 4.7GB 4x DVD+R Media (25-Pack Spindle): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\ngood when it works\n\n\nNovember 3, 2005\n\n\nStefanie Clay \"stef1223\"\n\n\nMorristown, NJ\n\n\nThe DVDs I burned successfully showed the movies in excellent quality. The only problem is that for every 3 good burns I get one bad one. I'm using the Plextor DVD burner (love it!!), but I'm going to try my luck with a different DVD brand. The failure rate for Memorex is unacceptable. It's less the cost that bothers me, but more the time wasted on burning coasters\n\n\n\n\nB000093IRC:is_it_live_or_is_it_memorex?:k._fournier\n\n\nB000093IRC\n\n\nMemorex 4.7GB 4x DVD+R Media (25-Pack Spindle): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nis it live or is it memorex?\n\n\nJanuary 16, 2005\n\n\nK. Fournier\n\n\nSitka AK\n\n\nI hope it's live because if it's memorex, it's crap! that's my latest opinion after using this product. I have a LiteOn SOHW-802S and it hates Memorex. I rarely get a decent burn. More often than not I'll discover while watching my movies just how horrid the quality it. My movies will skip, stutter, or stop all together. So disappointing! Unfortunately I found out too late that Memorex uses CMC media, which only has a 0-50% success rate! [...\n\n\n\n\nB00008RW8B:dead_on_arrival...:william_b._zimmerly_\"a_nobody\"\n\n\nB00008RW8B\n\n\nLexar Media USB 2.0 6-in-1 High Speed Reader: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n1.0\n\n\nDead On Arrival...\n\n\nApril 28, 2006\n\n\nWilliam B. Zimmerly \"A Nobody\"\n\n\nSt. Louis, Missouri, USA\n\n\nWhile I read the reviews on this machine, it is clear to me that Lexar has some serious quality-control problems. For some people it worked, for others it was a miserable failure. I am in the latter group. Sigh.\n\nMine was dead on arrival. I tried the device with *BOTH* of my Windows-XP machines and in both cases, it was unrecognized. Lexar tech support subsequently proved to be a contradiction in terms as they would not return my email requests for help\n\n\n\n\nB0007P4G76:beware,_re-_bate_not_honored!:roving_reviewer\n\n\nB0007P4G76\n\n\nXtreme TB264DKO 264-Disc CD/DVD Case: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nbeware, re- bate not honored!\n\n\nJuly 24, 2006\n\n\nRoving Reviewer\n\n\ni kept copies\n\n\ni knew the cash back offer sounded too good to be true...and sure enough they rejected my upcs (i kept copies) only have hounding them and contacting the better business bureau did they say 'whoops a mistake' i have a fleeting suspicion this was intentional\n\n\n\n\nB0007P4G76:no_cash_back_here!:mr._b_\"1st_gradeteacher\"\n\n\nB0007P4G76\n\n\nXtreme TB264DKO 264-Disc CD/DVD Case: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n1.0\n\n\nNo Cash Back Here!\n\n\nMay 15, 2006\n\n\nMr. B \"1st GradeTeacher\"\n\n\nWashington, D.C.\n\n\nPlease don't expect to get the cash back from this purchase as promised! To date, I've been denied twice. The second time they said the U P C number was invalid. Only, it's the original U P C barcode! I was meticulous filing the paperwork, yet they state, \"All required products were not submitted.\" Meanwhile, I have phoned AMAZ0N to lodge a formal complaint.\n\nThe product itself is average. I've used better and worse. That said, it's definitely not worth the full asking price\n\n\n\n\nB0007P4G76:tinstaafl:j._davis_\"jimidee\"\n\n\nB0007P4G76\n\n\nXtreme TB264DKO 264-Disc CD/DVD Case: Electronics\n\n\nelectronics\n\n\n6 of 10\n\n\n2.0\n\n\nTINSTAAFL\n\n\nMarch 23, 2006\n\n\nJ. Davis \"jimidee\"\n\n\nColumbia, SC USA/Tokyo, Japan\n\n\nNo free lunch & you get what you pay for...is that the verdict on this product?\n\nWell, the poor rating given by those who have purchased this product has discouraged me from buying it. I need disc binders and the Case Logic brand, while being the original, is pricey. Low cost alternatives are needed, but not at the expense of quality and TCO (total cost of ownership). But the flap about poor customer service (rebates) and poor quality (zippers and sleeve binding) is enough to give me pause..... So, if someone from Xtreme's management is paying attention....fix it or be content to lose business. Price isn't everything to everyone in the markt for your product..\n\n\n\n\nB0007P4G76:poor_quality_-_avoid:william_barham\n\n\nB0007P4G76\n\n\nXtreme TB264DKO 264-Disc CD/DVD Case: Electronics\n\n\nelectronics\n\n\n9 of 9\n\n\n1.0\n\n\nPoor quality - avoid\n\n\nFebruary 15, 2006\n\n\nWilliam Barham\n\n\nLos Gatos, CA\n\n\nI have a collection of over 1500 disks so I have bought many cd/dvd cases over the years and this brand is the worst quality on any that I have purchased. I would avoid this brand.\n\n[...\n\n\n\n\nB00026KJ26:worst_choice_of_my_life:yeppy_fan_\"alan\"\n\n\nB00026KJ26\n\n\nLexar Media 32X 1 GB Secure Digital Card (SD1GB-32-231): Electronics\n\n\nelectronics\n\n\n10 of 21\n\n\n1.0\n\n\nworst choice of my life\n\n\nMarch 6, 2005\n\n\nyeppy Fan \"alan\"\n\n\nNY\n\n\nBought this a month ago for use with my DX4330 kodak 3MP digital camera. It worked for 2 days.After that, everytime I on the camera, it wanted me to reformat the card,WTH is that?\nNow I can't use it at all....\nI'm always so confidence with my previous purchases from Amazon.One mistake I made,I threw away the shipping and product packages once I tested out the card, thought It worked and It would be OK.I can no longer return it now.\nNeedless to say,I'm too over confident with LEXAR's products.\nBuyers beware...\n\n\n\n\nB00006RZ1G:well_built,_poorly_suited.:cyber_kid\n\n\nB00006RZ1G\n\n\nSaitek P880 Dual Analog PC Game Pad: Electronics\n\n\nelectronics\n\n\n1 of 5\n\n\n2.0\n\n\nWell built, poorly suited.\n\n\nNovember 5, 2005\n\n\ncyber kid\n\n\n\n\n\nI purchased this game pad due to good reviews.\nFound it online for $15 with free shipping so had to try myself.\nIts true its built well and pretty comfortable in the hand.\nThe programming software is alright as well, not as nice as\nthe Nostromo n50/52 software for example.\n\nBut what i didnt like about it was much more relavant.\nFirstly there is a toggle lock shift button.\nThis button is easy to press by accident and since it is not\na momentary button you have to go back and press it off agian.\nInterupting game play.\n\nAlso buttons 7 and 8 are not paddles but rather thumb, and quite far from normal thumb use. I had a hard time using those buttons quickly.\n\nAlso the toggle and digital buttons cant be mapped for other uses so your limited to 8 total buttons rather than 10.\n\nAnd finally, for some reason there was no way to invoke fight in NHL06, i couldnt live with that so i had to ship it back, and ill soon go with the logitech\n\n\n\n\nB00009YUPS:i_would_never_recommend_this_product_to_anyone:rono_wubker\n\n\nB00009YUPS\n\n\nADS Tech DVD Express 2.0 (USBAV-701): Electronics\n\n\nelectronics\n\n\n0 of 2\n\n\n1.0\n\n\nI Would Never Recommend this Product to Anyone\n\n\nAugust 19, 2006\n\n\nRono Wubker\n\n\nCalifornia\n\n\nI've only got this thing to work for about 3 hours and that is not in a row. I've spent hours with this product on several machines and I can now say the hardware sucks. \n\n\n\n\nB00009YUPS:freezes_and_tech_help_not_good:jud\n\n\nB00009YUPS\n\n\nADS Tech DVD Express 2.0 (USBAV-701): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nFreezes and tech help not good\n\n\nJune 21, 2006\n\n\nJUD\n\n\nIndiana, Richmond\n\n\nI purchased dvd express and it never worked. I spent endless time with tech support. The old story about, \"It must be your computer.\" did not fly either, my machine is a media type and it far exceeds the system requirements advertised by ADS. I exchanged the item and it was supposedly checked out before I got it. Same problems. Tech support told me I was the only person having trouble with this product. They need to check out a few websites that review products there are hundreds of bad reviews very few good ones. My only mistake was not reading them before I wasted my time and money. ADS also told me it would not work with Ulead's Movie Factory 3, some of the software it came bundled with, but that was Uleads problem. I should use Cap Wiz ( Their program ) DUH!!! Guess what, it did not work with their program either. Ulead said they had several complaints with ADS's product. Their final solution hold on to the product and maybe they will have a new version of cap wiz and that might work. Save yourself time money and aggravation buy another product! P.S. Since ADS doesn't consider the product defective they will not refund your money or allow you trade it in for an upgrade. I don't think they want a product like that either. The only reason I gave one star is.....it was packed well.\nJU\n\n\n\n\nB0000DK3I4:kingston_2_gb_sd_memory_card:bernard_kliks_\"printmaker\"\n\n\nB0000DK3I4\n\n\nKingston 512 MB Secure Digital Card (SD/512): Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nKingston 2 GB SD Memory Card\n\n\nNovember 9, 2006\n\n\nBernard Kliks \"printmaker\"\n\n\nPortland, OR USA\n\n\nThis SD would not work in my Nikon Coolpix S6 camera (but the Fuji and Sandisk product did\n\n\n\n\nB00006JPV8:sad_compared_to_older_models:james_c._titus\n\n\nB00006JPV8\n\n\nNOVI WI-4C 4-Channel FM Wireless Intercom: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nSad compared to older Models\n\n\nOctober 22, 2006\n\n\nJames C. Titus\n\n\n\n\n\n Just purchased 3 units , thought they would work as well as the Novi units they were replacing however they have absolutely no range . My original Novi 2 channel units worked well from a distance of approx 110 ft from house to barn , these only work in the same room... I am attempting to return these currently.\n\n\n\n\nB0007N5LPE:look_for_other_alternatives...:barbara_a._blackburn_\"jollyrb\"\n\n\nB0007N5LPE\n\n\nAdditional Units for Reporter� Wireless Portable Intercom: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nLook for other alternatives...\n\n\nOctober 29, 2006\n\n\nBarbara A. Blackburn \"JollyRB\"\n\n\nChicago\n\n\nI bought four of these and returned them immediately. Two were dead out of the box. A third had horrible sound.\n\nAfter searching the internet and reading a variety of reviews for similar products I finally settled on the 900 Megahertz Wireless Intercom from Radio Shack. (43-3102)\n\nBeautiful sound. Well made and about half the price of the Reporter. Pretty much the same featues but it only claims a range of 300 feet as opposed to 1000 feet. So if you need more than 300 feet for range it's something to consider. Better warranty than the Reporter as well and for 8 bucks you can extend that warrarnty for another year.\n\nAnyway I couldn't be happier with the RS intercom. I just can't recommend the Reporters given that 3 out of 4 units that arrived were faulty\n\n\n\n\nB000092WZ1:worked_great_at_first......:scott_a._urbaniak\n\n\nB000092WZ1\n\n\nMonster Cable Ultra-High Capacity 2100mAh AA Rechargeable Powercells (4 pack): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nworked great at first......\n\n\nMarch 10, 2006\n\n\nScott A. Urbaniak\n\n\nmilwaukee, WI\n\n\nWhen I purchased these batteries, I was very pleased, but that wore off after a couple of months. I don't use my camera all that often, but when I do, I expect ther batteries to have some charge. I recharge them per instructions, and they do not hold a charge any longer.I know Monster makes good products, but I am a little disappointed in this one\n\n\n\n\nB0006ZSW1S:kodak_card_reader:digial_rebel_\"card_reader\"\n\n\nB0006ZSW1S\n\n\nKodak 8 - in - 1 Multi-card Reader, KRW8IN1SCS: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nKODAK CARD READER\n\n\nNovember 2, 2006\n\n\ndigial REBEL \"card reader\"\n\n\nny\n\n\nI got this from JnR . This unit powers up but the computer fails to detect it. I tried this on multiple computers. MAy be this was just a lemon. I will exchange it to a better brand\n\n\n\n\nB00004Z5QI:not_suitable_for_heavy_books:conal_ho\n\n\nB00004Z5QI\n\n\nFellowes Book Lift Copyholder - For Use with Textbooks and Large Manuals: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nNot suitable for heavy books\n\n\nOctober 10, 2006\n\n\nConal Ho\n\n\n\n\n\nThis bookstand, despite the description, is not suitable for heavy books. If you put heavy books on this, the stand that props up the whole unit is likely to snap off.\n\n\n\n\nB00004Z5QI:flimsy_product.:r._al_sarraj_\"anyothermonday\"\n\n\nB00004Z5QI\n\n\nFellowes Book Lift Copyholder - For Use with Textbooks and Large Manuals: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nFlimsy product.\n\n\nAugust 25, 2006\n\n\nR. Al Sarraj \"anyothermonday\"\n\n\nOberlin, OH\n\n\nIts made of flimsy plastic, and all the parts snap on. I'm afraid to put a heavy book on there, because I'm sure the parts will just snap off\n\n\n\n\nB00004Z5QI:total_rip-off:m._warren\n\n\nB00004Z5QI\n\n\nFellowes Book Lift Copyholder - For Use with Textbooks and Large Manuals: Electronics\n\n\nelectronics\n\n\n32 of 33\n\n\n1.0\n\n\nTotal rip-off\n\n\nAugust 8, 2005\n\n\nM. Warren\n\n\nUSA\n\n\nIn the ten minutes since I've had it out of the box two pieces have fallen off. More importantly, It can't handle large manuals as advertised. (The reason I purchased this particular book holder) My 1000 page manual only fits if you happen to only want to read the middle chapter. Based on that I estimate that this is a totally worthless product to anyone needing to go through a manual or training book more than five or six hundred pages. If you don't anticipate needing it for a book that big then just glue the rubber feet on, keep an eye on the page retainers (these are the parts that fell off) and you should be ok. As for me, I'm off to read Amazon's return policy and see if I can get rid of this useless hunk of plastic\n\n\n\n\nB0007XH72E:sent_it_back:s._anderson\n\n\nB0007XH72E\n\n\nJVC FSG5 180-Watt Micro Audio System with 5-Disc CD Changer and Digital AM/FM Tuner: Electronics\n\n\nelectronics\n\n\n16 of 20\n\n\n1.0\n\n\nSent it back\n\n\nOctober 2, 2005\n\n\nS. Anderson\n\n\nNew Hampshire USA\n\n\nI had to send this item back because it stopped playing cds. The sound quality was good, but the dependability of this item is questionable.\n\n\n\n\nB0000CD08M:poor_customer_service:j._l._knepp\n\n\nB0000CD08M\n\n\nSanDisk SDCFH-512-901 512 MB Ultra II CompactFlash Card (Retail Package): Electronics\n\n\nelectronics\n\n\n0 of 3\n\n\n1.0\n\n\npoor customer service\n\n\nAugust 10, 2006\n\n\nJ. L. Knepp\n\n\nSomerset, PA United States\n\n\nI purchased this memory card for a camera I was also purchasing. It was listed as an accessory to the camera on the same page. When I received the card first and then the camera, I was disappointed that the card was the wrong shape and size for the camera and would not fit. Because I had opened the plastic package to the card, I was unable to return it. In fact, when I emailed the company who sent it, I never even received a response.\n\n\n\n\nB000CO77FA:told_by_company_that_it_would_take_up_to_four_weeks!:jason_e._luba\n\n\nB000CO77FA\n\n\n2-Year Replacement Plan for Electronics Products $50 to $74.99: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nTold by company that it would take up to four weeks!\n\n\nNovember 1, 2006\n\n\nJason E. Luba\n\n\n\n\n\nI am the type of person that ALWAYS buys the extra warranty on whatever I purchase. However, I started reading the reviews on this company and many people reported that it was four weeks and counting and they still hadn't received their refund check. I was astonished. I contacted N.E.W. myself and asked them about this. With no shame or embarrassment on the part of the customer service reps, I was informed that if I did everything by email, it would take AT LEAST 15 business days (that's three weeks!) to receive my refund check AFTER I sent them the warrantied item. If I had them send me a pre-paid label for the defective item instead of using my own printer, it would take a week for me to receive and at least a week for them to receive the covered item. In other words, it would take no less than four weeks to receive the replacement check and possibly up to eight weeks! I cancelled the contract immediately. It is just not worth it when you really do the math to purchase this contract. I would recommend buying a product from a company that first makes a quality product and then back it up with a good warranty and gives good customer service such as Grado, Sennheiser or Koss. But stay away from these people; that's my feeling and my advice. \n\n\n\n\nB0007Y79AI:i've_finally_learned_a_very_expensive_lesson:jane_cox_\"linksys_fan\"\n\n\nB0007Y79AI\n\n\nApple 2 GB iPod Nano Black: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n1.0\n\n\nI've finally learned a VERY expensive lesson\n\n\nNovember 15, 2006\n\n\nJane Cox \"linksys fan\"\n\n\nBoca Raton, FL\n\n\nDON'T BUY THIS PRODUCT. There, simple advice I wish someone had given me before I bought two of these. With the first one the battery died 13 months after purchase. Since it was 30 days outside the warrantly, apple support told me \"sorry, tough luck.\" I made the mistake of buying another one figuring I just got a bad one and there was no way I could have the same problem again. WRONG!\n\nSecond ipod just died (same battery problem) - this one died within the warranty period but becuase it took me two weeks to get into the ipod store to see what was wrong and, of course, by the time I got in I was past the 12 month warranty period (who keeps track of the purchase date? I sure didn't).\n\nOther than the fact that your substnatial investment will be worthless in about a year - it is a fantastic product. I'm just sorry that it didn't last (twice!) cause otherwise I'd be a fan\n\n\n\n\nB0002MSTW2:had_pix_loss_w/lexar_cards:frederick_carr_\"flrhcarr\"\n\n\nB0002MSTW2\n\n\nLexar Media 1 GB 80x Pro Series Compact Flash Card (CF1GB-80-380): Electronics\n\n\nelectronics\n\n\n7 of 25\n\n\n1.0\n\n\nHad pix loss w/Lexar cards\n\n\nMarch 24, 2006\n\n\nFrederick Carr \"flrhcarr\"\n\n\nKent, WA\n\n\nAbout three years ago, maybe four, I bought two Lexar CFII cards. One never worked, the other worked, but lost all data. I wrote to Lexar, that I had two bad cards, they sent me one new card of the same size. I gave that to my girlfriend & told her to only use it on pix she didn't care about losing. I still have my card, in a ziplock. None of the file removal programs will get the images off, & I've used all of them, & had their (file removal) tech help give up.\n\nIn this day & age of \"just make the item\" I am upset. But you take your chances. Do you want the only picture of your great grand-mother lost because you used a Lexar? I wouldn't, I use an actual sandisk card, myself.\n\nIf they ever (Lexar) made it right, I may have written a better review, but they haven't. So that tells me that they don't care.\n\nHow important are your pictures to you?\n\n\nFrederic\n\n\n\n\nB00006HYUC:back-ups_es_drops_power_after_power_is_restored!:owen_ward\n\n\nB00006HYUC\n\n\nAPC BE350U 350VA Back-ups Es: Electronics\n\n\nelectronics\n\n\n8 of 11\n\n\n1.0\n\n\nBack-UPS ES drops power AFTER power is restored!\n\n\nNovember 17, 2005\n\n\nOwen Ward\n\n\n\n\n\nMost people buy a UPS, plug it in and never test it to make sure it works. I tested this device a number of times to make sure the software would at least shutdown the computer after an outage. I set it to shutdown Windows after 1 minute. Only the computer and a few small phone accessories are attached to it. \n\nAfter power was cut, it held the computer up and allowed it to shut down. Then I restored power to the UPS. AFter about 30 seconds, it beeped, clicked, and temporarily DROPPED power to the computer while it was rebooting! So when it switches back from battery power it causes power to be interrupted. That is unacceptable. A UPS should never drop power to the attached devices after power is restored to it.\n\n\n\n\nB00006HYUC:beware!:a._bernay-roman\n\n\nB00006HYUC\n\n\nAPC BE350U 350VA Back-ups Es: Electronics\n\n\nelectronics\n\n\n49 of 54\n\n\n2.0\n\n\nBeware!\n\n\nNovember 23, 2003\n\n\nA. BERNAY-ROMAN\n\n\nJupiter, Florida USA\n\n\nIf you have a cable modem for broadband connection to the internet, your unit will not be covered by the APS warranty on this device. Lightning hit near our house and the motherboard plus router got fried. The cable modem stayed intact and we filed a claim with APS. They denied it saying backend surges through the cable may have caused the damage (even though the modem wasn't hurt?)\n\n\n\n\nB000A5TBM8:unresolved_problem:leland_brun_\"penguin_pictures\"\n\n\nB000A5TBM8\n\n\nSony DRU-810A Internal Double/Dual Layer and Dual Format DVD Burner: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nUnresolved problem\n\n\nNovember 10, 2006\n\n\nLeland Brun \"Penguin Pictures\"\n\n\nHoneoye Falls, NY United States\n\n\nI am still awaiting resolution of a problem I have with these drives. In writing DVDs I get verification errors. This proboem arises if the drives are used in an external firewire enclosure. The drive works fine if internal in your computer attached to the IDE bus, but there is an issue if attached to your computer by firewire\n\n\n\n\nB000A5TBM8:wont_work_with_itunes_for_windows_:clifton_lowrey_\"clifton\"\n\n\nB000A5TBM8\n\n\nSony DRU-810A Internal Double/Dual Layer and Dual Format DVD Burner: Electronics\n\n\nelectronics\n\n\n0 of 5\n\n\n1.0\n\n\nwont work with itunes for windows \n\n\nSeptember 25, 2006\n\n\nClifton Lowrey \"clifton\"\n\n\noregon\n\n\nalso import a cd , money down the drai\n\n\n\n\nB000A5TBM8:worked_ok_for_about_230_days,_then_stopped.:paul_curtis\n\n\nB000A5TBM8\n\n\nSony DRU-810A Internal Double/Dual Layer and Dual Format DVD Burner: Electronics\n\n\nelectronics\n\n\n4 of 6\n\n\n2.0\n\n\nWorked OK for about 230 days, then stopped.\n\n\nAugust 23, 2006\n\n\nPaul Curtis\n\n\nJersey City, NJ United States\n\n\nThe drive arrived at the end of December, was quick to get up and running, and worked fine until yesterday. Then it got the machine equivalent of \"lockjaw\" and I've been unable to get the tray to release.\n\nSo, it's a good drive while it operates, but I'll probably make sure I pay for at least a 1-year warranty on my next purchase.\n\nAs far as my recommendation goes, it depends very much on how inconvenient it is when one's drive fails. Yesterday, I just went to a backup burner and kept working; if I had NEEDED to get the job done, and I had not previously developed a strategy of always keeping a backup device handy, I'd have been in a seething, hair-pulling rage. Computer hardware is never 100% reliable, but is it worth the risk to purchase a device that is known to fail in less than a year\n\n\n\n\nB0000B3AKR:garbage-stopped_working_after_2_months_of_extremely_gentle_use:optimist_\"optimist2\"\n\n\nB0000B3AKR\n\n\nViking USB00512L2 512 MB USB 2.0 Portable Mini Flash Memory Device: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n1.0\n\n\nGarbage-stopped working after 2 months of extremely gentle use\n\n\nMay 10, 2006\n\n\noptimist \"optimist2\"\n\n\nHouston, TX United States\n\n\nI never even traveled with it. It just stopped working completely making me lose data.\n\nNEVER AGAIN I'll buy anything from this company. This drive is a piece of rubbish!\n\n\n\n\nB00008EM7V:dont_buy_not_worth_a_cent:a._cunningham\n\n\nB00008EM7V\n\n\nMemorex Optifix Motorized CD/DVD Cleaner and Scratch Repair Kit: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n1.0\n\n\ndont buy not worth a cent\n\n\nAugust 12, 2006\n\n\nA. Cunningham\n\n\n\n\n\ngot it at walmart can't even remove a scuff. i give it -100 good thing i could return it\n\n\n\n\nB00008EM7U:sorry_-_didn't_do_the_job_for_me:caris_\"caris\"\n\n\nB00008EM7U\n\n\nMemorex CD/DVD-Player Laser-Lens Cleaner: Electronics\n\n\nelectronics\n\n\n1 of 4\n\n\n2.0\n\n\nsorry - didn't do the job for me\n\n\nJuly 8, 2006\n\n\ncaris \"caris\"\n\n\nLos Angeles, CA\n\n\ni've given it 2 stars instead of 0 because after i used this CD, the sound from my system did seem to be a little better (the CD's were not skipping as much). but the bottom line is it didn't fix the problem as the CDs are still skipping noticeably, although not as bad as before. ... not sure if this has anything to do with the fact that my set has a 6-disk CD changer\n\n\n\n\nB0000AKVHF:this_junk_died_and_erased_100_images:poet/naturalist\n\n\nB0000AKVHF\n\n\nSanDisk SDDR-88-A15 8-in-1 USB 2.0 Hi-speed Reader (Retail Package): Electronics\n\n\nelectronics\n\n\n0 of 7\n\n\n1.0\n\n\nThis junk died and erased 100 images\n\n\nAugust 5, 2005\n\n\nPoet/Naturalist\n\n\nMN USA\n\n\nThe is perhaps the lease durable item I have purchased in 40 years. It lights up just fine, and it is a reassuring led gold, but it refuses to do anything with my xd card. It worked just fine for 3-4 days, then erased about 100 photos, all of them irreplaceable. Cheap yes, but Cheap Junk.\n\n\n\n\nB000AY0HTU:doesn't_pass_the_test_of_time:i._oconnor_\"philosopher\"\n\n\nB000AY0HTU\n\n\nLogitech G15 Gaming Keyboard: Electronics\n\n\nelectronics\n\n\n2 of 5\n\n\n1.0\n\n\nDoesn't pass the test of time\n\n\nOctober 3, 2006\n\n\nI. OConnor \"Philosopher\"\n\n\nUSA\n\n\nGreat looking keyboard. Unfortunately all it takes is one minor spill and the keyboard is destroyed. That includes cleaning. And don't think you are ever going to take it apart and dry it. The keyboard self destructs after you have the, count them, 10s and 10s of screws.\n\nI called their customer support. They weren't like DELL, nobody is that bad, but their resolution was to buy a new one from them for 50% off their retail price. shipping was extra. So you save maybe $10 off street prices.\n\nAll-in-all they are no longer the good company they were in the past. They need competition. So I'm pulling out my old logitech from retirement. Too bad they don't make those old keyboards anylonger\n\n\n\n\nB0009V9I80:broken_within_hours._junk!:chris_b._wallace_\"cbw\"\n\n\nB0009V9I80\n\n\nSony DAV-FX100W Wireless Home Theater Dream System: Electronics\n\n\nelectronics\n\n\n2 of 5\n\n\n1.0\n\n\nBroken within hours. JUNK!\n\n\nJune 27, 2006\n\n\nChris B. Wallace \"CBW\"\n\n\nAtlanta, GA\n\n\nI ordered this item after reading the positive reviews here. BIG MISTAKE. Upon setting everything up, it all worked great for about two hours... then the dreaded \"UNPLUG/PROTECT\" screen came up. Now it won't turn on but for a few moments before returning to protect mode. (and no, it's not a short in the wires or overheating... it protects even when stone cold, and with no speakers plugged in) I called sony and they say it is a short in the box. So now my two options are to unhook everything, repack EXACTLY the way it came to me and reship back to the online store I bought it from (with enormous hastle and expense) or take the receiver in to a sony authorized service center (which happens to be across town, and only open monday-friday, 9-5pm. So much for being convenient). This thing is brand new and already needs repairs!!\n\nOn top of that, even out of the box, NONE of the video inputs worked. Audio was fine, but video was dead as a doornail, right from the start.\n\nAnd even on top of the fact that this machine CAME broken... it sucks even when it works right! The DVDs take forever to load, and it comes with HDMI out, but NO HDMI IN! So if you're an HDMI user, I hope you have multiple HDMI inputs on your television... one for your cable box, one for this crappy system.\n\nThis is a piece of junk. For the love of god, don't waste your money\n\n\n\n\nB0002ZAIM8:get_a_2g!:videogamer\n\n\nB0002ZAIM8\n\n\nApple 1 GB iPod Shuffle: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nGet a 2G!\n\n\nNovember 18, 2006\n\n\nvideogamer\n\n\nLockport,NY\n\n\nWhy would you want this item? Amazon is charging $50 MORE on this item than the 2nd Generation Shuffle! And, according to what I have read, THIS Shuffle isn't very derable. It doesn't even last a year! I don't know about you, but I'LL get a 2nd Generation Shuffle\n\n\n\n\nB0002ZAIM8:died_suddenly..._repair_impractical,_lost_my_stuff:mike_warot_\"--mike--\"\n\n\nB0002ZAIM8\n\n\nApple 1 GB iPod Shuffle: Electronics\n\n\nelectronics\n\n\n1 of 3\n\n\n1.0\n\n\nDied suddenly... repair impractical, lost my stuff\n\n\nOctober 27, 2006\n\n\nMike Warot \"--Mike--\"\n\n\nHammond, Indiana\n\n\nOne day it just stopped working. I got the dreaded \"orange and green flashing lights of death\"... the contents of the File space were un-recoverable... and I ended up just pitching it after extensive attempts to revive it.\n\nApparently this is yet another one of the bad products that Apple likes to keep hush-hush\n\n\n\n\nB0002ZAIM8:unreliable.__cute_design.__bad_engineering.__poor_quality_control:d._austin\n\n\nB0002ZAIM8\n\n\nApple 1 GB iPod Shuffle: Electronics\n\n\nelectronics\n\n\n5 of 7\n\n\n1.0\n\n\nUnreliable. Cute Design. Bad Engineering. Poor Quality Control\n\n\nSeptember 18, 2006\n\n\nD. Austin\n\n\nDC Area\n\n\nMy shuffle suffered the blinking green-orange lights so I took it into the Apple store. A salesman took 15 minutes to figure out that he couldn't do anything, and said I would have to sign up for a appointment at the \"Genius\" bar. At that appointment, the \"genius\" said he couldn't fix it, and because he said he didn't know anything about engineering he couldn't say whether the shuffle was not working because of a software or a hardware problem or both. Apparently being an Apple \"genius\" doesn't involve understanding how things work, though it does appear to require having a trendy haircut. The 'genius\" then offered a $10 discount on the purchase of a new shuffle - but couldn't answer the question of why anyone would want to waste money on such a poorly designed thing.\n\nPerhaps it should be retitled the \"idiot bar.\n\n\n\n\nB000BNMDC4:broke_very_easily:teresa_e._glodek_\"tglodek\"\n\n\nB000BNMDC4\n\n\nDLO 009-3032 Transpod All-in-One Car Solution for iPod (Silver): Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nBroke very easily\n\n\nNovember 3, 2006\n\n\nTeresa E. Glodek \"tglodek\"\n\n\nAnn Arbor, MI\n\n\nThe extendable arm breaks very easily. I had two of them and they both broke within the first month. The company does have a 3 month warranty but you have to pay for shipping to send it back to them and I have been waiting over a month for my replacement. Very poor product. Do not buy\n\n\n\n\nB0009FTMK0:better_not_to_buy:rajagopal_alagarsamy_\"-_raj\"\n\n\nB0009FTMK0\n\n\nSanDisk 2 GB Cruzer Micro USB Flash Drive (SDCZ4-2048-A10, Retail Package): Electronics\n\n\nelectronics\n\n\n11 of 13\n\n\n2.0\n\n\nBetter Not to Buy\n\n\nMay 17, 2006\n\n\nRajagopal Alagarsamy \"- Raj\"\n\n\nConcord, CA USA\n\n\nI'm the consumer of SanDisk USB flash drives. I have used couple of sandisk flash drives (i.e. 512MB Mini, 256MB Mini) before I bought this product. \n\nPerformance wise this particular product is not doing good. It's taking long time to copy files from my computers. Even it's taking long time to delete files from this disk. So I returned to Amazon and looking for Mini products from SanDisk.\n\nI doubt all the Micro products which is having capacity more than 512MB. Now I'm planning to buy 512MB or 1GB SanDisk Mini USB flash drive. \n\nBetter buy some good one than wasting your time on this product\n\n\n\n\nB0007PGADE:be_careful:k._young\n\n\nB0007PGADE\n\n\nMemorex DVD+R 16x 4.7GB 50 Pack Spindle: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nBe Careful\n\n\nNovember 2, 2006\n\n\nK. Young\n\n\nSan Diego, Ca\n\n\nThese disks would not work with my Sony brand DVD writer. I have tried several other brands and so far everything works but Memorex.\n\n\n\n\nB0007PGADE:garbage!:j._radell_\"joecritic\"\n\n\nB0007PGADE\n\n\nMemorex DVD+R 16x 4.7GB 50 Pack Spindle: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nGarbage!\n\n\nSeptember 18, 2006\n\n\nJ. Radell \"JoeCritic\"\n\n\nVA\n\n\nTo back up my personal DVD collection I burned 200 discs. 100 on these Memorex DVD+R's, 100 on Philips DVD+R's. EVERY Memorex had flaws during playback. EVERY Philips was flawless. I've been buying Memorex for years but I'll never buy Memorex again\n\n\n\n\nB0007WKB14:broke_after_1+_year:pedro_marmol\n\n\nB0007WKB14\n\n\nPanasonic DMR-ES10S DIGA Series DVD Recorder (Silver): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n2.0\n\n\nbroke after 1+ year\n\n\nJuly 19, 2006\n\n\nPedro Marmol\n\n\nAustin, Texas United States\n\n\nOverall the recorder worked OK. A moderate inconvenience was that once in a while the recorded DVDs could not be finalized for unexplained reasons.\nHowever, that is irrelevant now. A few weeks ago, the unit stop working. I does not turn on at all. What a waist! It is out of warranty now\n\n\n\n\nB0007WKB14:short-lived_machine--avoid_this_lemon:california_dreamer\n\n\nB0007WKB14\n\n\nPanasonic DMR-ES10S DIGA Series DVD Recorder (Silver): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nShort-lived machine--avoid this lemon\n\n\nJune 17, 2006\n\n\nCalifornia Dreamer\n\n\nSouthern California\n\n\nHere is the original review of this machine I wrote on June 17, 2006:\n\nWe bought this machine just over a year ago (a year and two weeks, to be specific). We had another very similar Panasonic DVD recorder (DMR-E55) in a different room and wanted to be able to record and watch DVD-RAMs on both. For a long time, the ES10S recorder worked fine. Recently, though, it has begun making noises when starting up and shutting down, and now it occasionally has problems reading the disks, ejecting them, and even playing them. At first we thought this might be because we'd recorded over the DVD-RAMs so many times. But today we tested one of them in the other machine, which has never made any similar noises, and the E55 had no problems with it. Needless to say, we're not happy that the ES10S seems to be dying, especially since the 1-year warranty just ended. If you buy or own this machine, follow up on any strange noises as soon as you hear them if you're within the warranty period, because the noises and problems just get worse. Panasonic is usually a reliable brand, one we've used for many years, but this machine seems to be a lemon. We're actually going to write to Panasonic about it--I'll try to post again with the results.\n\nHere is my update, which I wrote November 3:\n\nThis is a follow-up review to the one above, which I wrote on June 17. I wrote to Panasonic after my machine had stopped working just after the warranty ended. They sent me a nice letter authorizing a free repair, including labor, of the machine at one of three places they listed. The places were all within a half-hour drive of my home. After doing some research, I chose one and brought in the machine. They fixed it by replacing \"the entire internal mechanism\" (I don't know what that means). We got the machine back from the repair shop near the end of July; it's now the beginning of November. The fixed machine worked fine for a while, but in the past few weeks, we've been hearing the same old noises that were the beginning of the end for it before. I'm going to try writing to Panasonic again and asking for a refund, because this machine really seems to have something fundamentally wrong with it. It's still working, but I know from the last time with these noises and efforts to read the disks that soon it won't. Avoid this machine. Again, the previous Panasonic model (DMR-E55) we bought hasn't had any of these difficulties and continues to work great. We're still big Panasonic fans, but this particular model is a lemon.\n\n\n\n\n\nB00080FO4O:temporary_device:sooner4x4\n\n\nB00080FO4O\n\n\niHome iH5 Clock Radio for iPod White: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n1.0\n\n\nTemporary device\n\n\nNovember 12, 2006\n\n\nsooner4x4\n\n\nOklahoma\n\n\nI would only recommend this product if you need something that only lasts six months. There is a reason the warranty is only 90 days. The performance (sound quality) steadily declines over time. The ihome worked great when it was new, but it won't play songs from the ipod anymore, and the radio is a static box\n\n\n\n\nB00080FO4O:ihome_is_a_disappointment:manda_\"mnda\"\n\n\nB00080FO4O\n\n\niHome iH5 Clock Radio for iPod White: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n1.0\n\n\niHome is a disappointment\n\n\nNovember 10, 2006\n\n\nManda \"Mnda\"\n\n\nVirginia, USA\n\n\nSo when I first got the iHome for Christmas, I loved it. Well, I loved it in spite of the glaring bright light, that even at its lowest setting has to be blocked for me to get to sleep. However, after awhile the iHome will not register my iPods existence at all. Today I was able to enjoy thirty seconds of a song before it fell into disgrace again. I would not recommend this product if you want something that lasts. This device has become nothing more than a nuisance that won't even pick up radio stations. So, the alarm can only be set to a vaguely annoying beep when these functions are disabled. Useless\n\n\n\n\nB00080FO4O:too_bright!__not_for_light_sleepers!:kmoore\n\n\nB00080FO4O\n\n\niHome iH5 Clock Radio for iPod White: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n2.0\n\n\nToo bright! Not for light sleepers!\n\n\nOctober 24, 2006\n\n\nKMoore\n\n\nPhiladelphia, PA United States\n\n\nWe have had ours for almost a year - it still works great, but there is one big problem - the display is a white/violet bright light. Great for visibility of the clock when you need it, but unfortunately, even when it's on the lowest setting, it lights up our whole room! My husband sleeps through it fine, but I need a dark room to fall asleep and I always have to throw a sweater or towel over it. We finally gave it up and switched back to our old clock radio\n\n\n\n\nB00080FO4O:great_features,_unreliable_connections:k._n._gunn\n\n\nB00080FO4O\n\n\niHome iH5 Clock Radio for iPod White: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n2.0\n\n\nGreat Features, Unreliable Connections\n\n\nSeptember 21, 2006\n\n\nK. N. Gunn\n\n\nCarrboro, NC United States\n\n\nI like the features. I'm satisfied with the sound. The user interface could be more intuitive.\n\nHOWEVER, my overriding complaint is that the docking port (as many other reviewers have mentioned) is not reliable. The connection is poorly constructed, so just walking by the table on which it sits is often enough to make it think the iPod isn't there any more. If they could fix this quality problem, it would be well worth the $100, but since it is very hard to get it to recognize the connection reliably you just can't justify it at any price\n\n\n\n\nB000BFYPM8:kills_computer_/_un-stoppable_shriek_alarm__:flagday1960\n\n\nB000BFYPM8\n\n\nAPC BE350R Back-UPS 350VA for Home/Office Computers: Electronics\n\n\nelectronics\n\n\n2 of 21\n\n\n1.0\n\n\nKILLS COMPUTER / UN-STOPPABLE SHRIEK ALARM \n\n\nAugust 3, 2006\n\n\nflagday1960\n\n\nSt.Louis\n\n\nI mistakenly believed the hype that this thing would PROTECT my computer from BEING KILLED, but instead, this thing has KILLED my computer FOR THE SECOND TIME IN 4 DAYS, EMITTING AN UN-STOPPABLE(i.e., CAN'T TURN THE FEATURE OFF) SHRIEK ALARM. How do smart, talented people idiotically convince themselves that a home product should have an ear-piercing shriek-alarm that can't be defeated / silenced? \nIn addition to doing far, far more damage than it can ever prevent, ask yourself -- do you want this un-stoppable ear-piercing shriek alarm going off in your house at 2a.m.? I BOUGHT ONE APC PRODUCT, AND SO FAR, THEY'RE 100% DEFECTIVE AND 100% DELITERIOUS TO THE USE OF MY COMPUTER & IT'S A MATTER OF TIME BEFORE IT'S DELITERIOUS TO MY SLEEP.\n\n\n\n\nB00005BC0H:no_support:j._kim\n\n\nB00005BC0H\n\n\nC Pen 800C Handheld Scanner: Electronics\n\n\nelectronics\n\n\n9 of 9\n\n\n1.0\n\n\nNO SUPPORT\n\n\nJune 10, 2006\n\n\nJ. Kim\n\n\n\n\n\nLet's be honest.\nThis product ain't cheap. It's not something you can just throw away if it decides to stop working. \nI bought mine at cpenusa.com. Delivery was great, only took them a couple of days. However, my c-pen cable was damaged. The connector to the c-pen was, for some reason, cut in half. Without it, you can't recharge or connect the c-pen to the pc.\nI called their main office all the way in Sweden during the office hours. NO RESPONSE. I sent three e-mails to cpenusa. NO RESPONSE.\nI don't care if the product itself is great(I'll never know now, will I..). If you're unable to receive support when you need 'em, you're screwed. \nIf you're just interested in electronic equipments, just want to try things out and don't really care if you're on your own, go ahead and try it. Let me know how this thing really is. Otherwise, buy a product that you can trust on with respect to after-sales support\n\n\n\n\nB000COB58K:did_not_get_it_in_the_mail_yet:adolfo_reyes\n\n\nB000COB58K\n\n\n3-Year Service Plan for Personal Electronics $200 to $599.99: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n1.0\n\n\ndid not get it in the mail yet\n\n\nNovember 5, 2006\n\n\nAdolfo Reyes\n\n\n\n\n\nI have been charged however I did not get a copy of the policy to date.\n\n\n\n\nB0002CPBWS:bad_quality:harry_potter_tun_\"tun\"\n\n\nB0002CPBWS\n\n\nMicrosoft Wireless Notebook Optical Mouse 3000- Winter Blue: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n1.0\n\n\nbad quality\n\n\nNovember 7, 2006\n\n\nHarry Potter Tun \"Tun\"\n\n\nCA, USA\n\n\nI bought this item like 2 months ago, and now it's broken. I'm sooo frustrated. \n\nI 'd not recommend buying this item\n\n\n\n\nB0002CPBWS:great_for_a_while,_and_then_not_so_good:weary_road_warrior\n\n\nB0002CPBWS\n\n\nMicrosoft Wireless Notebook Optical Mouse 3000- Winter Blue: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nGreat for a while, and then not so good\n\n\nOctober 20, 2006\n\n\nWeary Road Warrior\n\n\nChicago, IL\n\n\nThis was a great mouse for about the first year I owned. After one year is when all of the problems started. The mouse now loses connection sproadically and sometimes it doesn't want to connect at all. I have changed the battery quite a few times and that doesn't seem to help. It is very tempremental, and I have now given up and am shopping for a replacement.\n\nSo, a word to the wise - in my experience the mouse will work great for a while, just be prepared to replace it in a year or so\n\n\n\n\nB00017IX10:good_system_-_one_huge_flaw:kevin\n\n\nB00017IX10\n\n\nAltec Lansing inMotion Portable Audio System for iPod: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nGood system - one huge flaw\n\n\nNovember 8, 2006\n\n\nKevin\n\n\nSLC, UT\n\n\nThis is a good product - sounds great, gets good battery life, everything you'd hope for in an iPod dock. But it has one major flaw. The iPod docking port is really flimsy, and has no structural support aside from the actual electronics themselves. After a little bit of use, the connection between the iPod and the dock deteriorated to the point that the volume fluctuated up and down, and sometimes the sound cuts out all together.\n\nIf they added some kind of simple support so that bumping the iPod wouldn't stress the electronics, I'd give this thing 5 stars. But without that, it simply breaks quickly, and no longer performs its only job - to sound good.\n\n\n\n\nB000B6KXM8:sony_rm-ax4000:joey\n\n\nB000B6KXM8\n\n\nSony RM-AX4000 Home Theater Remote Control: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nSony RM-AX4000\n\n\nNovember 3, 2006\n\n\nJoey\n\n\nNorth Wales, PA\n\n\nThis is a poorly designed remote. I have six devices connected to my HD television set. The software depends on the user assigning positions to the various inputs to the TV and, in my case, routinely activated the wrong device when I used the remote. I purchased a Logistics Harmony remote and it works perfectly. At this point the only thing that I use the remote for is to remotely turn on my satellite receiver at a certain time during the day to record to Replay TV (which cannot turn the TV on and off).\n\n\n\n\nB000078XAK:does_not_work!!:m._castellanos\n\n\nB000078XAK\n\n\nPOWER 2000 ACD-674 Rechargeable Battery ( CANON BP511 Equivalent ): Electronics\n\n\nelectronics\n\n\n10 of 10\n\n\n1.0\n\n\ndoes not work!!\n\n\nNovember 21, 2004\n\n\nM. Castellanos\n\n\nAR, USA\n\n\nWith a full charge it does not last more than 10min, could not take more than more 15 pics in my camera Canon Pro1. The original battery works well more than 250 pics on each charge. Initially I thought this problem was because it was new battery but after several cycles it does not work at all. I'll try to get a refund and check their customer service too\n\n\n\n\nB0002WPSBC:disappointed:epiphane_flavius\n\n\nB0002WPSBC\n\n\nLogitech Z-5500 Digital 5.1 5-Piece Speaker System with Subwoofer: Electronics\n\n\nelectronics\n\n\n0 of 2\n\n\n1.0\n\n\nDisappointed\n\n\nNovember 9, 2006\n\n\nEpiphane Flavius\n\n\nWichita Falls, TX USA\n\n\nI am disappointed in this product.\nI was suppose to write to you from the weekend.\nMy two rear speakers just stop working, and also my\nremote control. I have replace the batteries, but still\ndoes not work.\nI don't have any other rear speakers to try on the system.\nPleasy me what to do.\n\nThank you\nEpiphane Flaviu\n\n\n\n\nB0000AMRTM:check_compatibility_with_writer_before_buying_media!:\"suavedog\"\n\n\nB0000AMRTM\n\n\nMemorex 4.7GB 4x DVD-R (25-Pack Spindle): Electronics\n\n\nelectronics\n\n\n2 of 5\n\n\n2.0\n\n\nCheck compatibility with writer BEFORE buying media!\n\n\nJuly 17, 2004\n\n\n\"suavedog\"\n\n\nBoston, MA USA\n\n\nOne out of every 4 discs would fail on me when I'd burn to this media at 4x. Slowing down the burn to 2x didn't help either! I own a Sony DRU500A with the latest firmware. Memorex isn't on their compatibility list. Lesson learned: check the compatibility list BEFORE buying\n\n\n\n\nB0009MZ9UA:the_worst_brand_i've_tried.:r._m._aarons\n\n\nB0009MZ9UA\n\n\nMemorex 4.7Gb/16x DVD-R (50-Pack Spindle): Electronics\n\n\nelectronics\n\n\n2 of 5\n\n\n2.0\n\n\nThe worst brand I've tried.\n\n\nJune 28, 2006\n\n\nR. M. Aarons\n\n\nSan Francisco Bay Area, CA USA\n\n\nI've got a LITE-ON DVDRW SOHW-1673S burner (Revision JS02) in my fast G4 Mac running OS X Tiger and burning with Toast Titanium 7. Although my burner is nominally 16X-capable, it only burns at about 6X in my setup, probably due to the slow (ATA 33) bus it's on.\n I've used a number of different brands of 8X and 16X disks, both +R and -R, and have had a verification failure rate of less than 2% -- so few, in fact, that I often don't bother verifying video DVD's anymore when burning TDK or Maxell discs. (I always verify data DVD's, since errors in those are not ignorable.) But with Memorex, I've gotten about 20% failures, including a couple of aborted burns! In one case, the disc had a blank band between two burned ones!\n The Memorex discs I'm referring to have Manufacturer ID \"CMC MAG, AM3\". According to videohelp.com, these are sold under at least 7 different brand names. The reviews there are rather mixed, which is not IMHO good enough!\n I have not, BTW, have had any problems with the Memorex DVD+R discs I've tried. But I stopped using them because one of my players won't recognize the +R format\n\n\n\n\nB000C54E96:great_product_if_i_could_use_it:nancy_mooney\n\n\nB000C54E96\n\n\nNEC ND3550A / 16x8x16x DVD+RW / 16x6x16x DVD-RW / 8x DVD+R DL / 6x DVD-R DL / 48x32x48x CD-RW / Black / Dual Layer / Retail Box DVD Burner with Software: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n2.0\n\n\ngreat product if I could use it\n\n\nFebruary 22, 2006\n\n\nNancy Mooney\n\n\n\n\n\nI did the research on this product and it was one of the best. I don't know that to be true due to the fact that the software that they sent me was a demo version that was outdated for use. I have to buy another software package to opperate the product that had stated in the description that the software was included. One if not the only reason I had purchased it. I e-mailed tiger.com and received no reply. If your going to sell something false advertisement is not the way\n\n\n\n\nB000EH4Y56:unacceptable:dabigpaybackski\n\n\nB000EH4Y56\n\n\nSony MHCEC70 Mini Shelf System with 3-Disc CD Changer, Cassette Deck, and AM/FM Tuner: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n2.0\n\n\nUnacceptable\n\n\nOctober 5, 2006\n\n\ndabigpaybackski\n\n\nOR\n\n\nThis model was intended as a replacement for my not-very-old and yet quite functional Panasonic PM-series stereo. This is the kind of thing that happens when you browse Amazon while drunk late at night. \n\nUnfortunately, with the exception of a very slight advantage in audio quality, the Sony is in all respects inferior; ergonomics, appearance, and above all, FM reception, which is laughably poor. Do not buy this stereo if you expect a quality receiver. Hiss abounds, several local stations I heard previously are now unreachable, and to top it off, Sony has stuck with their time-honored habit of using proprietary connectors, so when I went to plug in an after-market antenna to get better reception, I found a weird little plug that I hadn't noticed when I put the damned thing together. Grrr. So now I'm going to sell this thing and buy a nice new Panasonic with the proceeds. Death to Sony\n\n\n\n\nB00000J506:not_very_durable._will_need_battery_changes.:d._amini_\"zbestdoc\"\n\n\nB00000J506\n\n\nAPC BP500UC Back-UPS Pro 500: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n2.0\n\n\nNot Very Durable. Will need battery changes.\n\n\nFebruary 5, 2006\n\n\nD. Amini \"zbestdoc\"\n\n\nWashington DC\n\n\nI have owned five of these UPS units over the past four years. I purchased three for my Mac and PC at home. Then I had to purchase two replacements for units that simply quit. Currently only ONE still works! I have very infrequent power outages, perhaps three in the past four years. These units work well to allow you to save your work and shut down. My G5 mac was so power hungry that I had to use one for the computer and another for the 23 inch LCD. I can't comment on other UPS brands such as Belkin, but I have seen many competing brands on the web and at the computer stores. There seen to be quite a few dealers selling refurbished APC products but this may be a reflection of APC's popularity rather than the poor reliability of their brand. My experience over the past four years has been mixed. These are not inexpensive units and you have to decide how precious your data becomes. If you really fear the loss of data during a blackout then the answer is clear. I chose to switch to high quality surge suppressors and never work during potential storms. Many users do not have that flexibility so a UPS is essential insurance. The units I puchased were identical and only one unit is still operational after four years. I can accept the need to replace the battery every one to two years; three of the batteries failed and alarms beeped until I bought new batteries. Prices vary from $15 to $50 depending on the vendor. Battery life depends on many factors so I don't begrudge APC for wear and tear on the battery but to have 4 out of 5 units simply stop working is unacceptable. I bought APC branded batteries and can't explain why the recurrent failures. I'm frustated with my APC products but it may be a problem with this model. Just beware of the hidden costs of battery replacement when purchasing an UPS\n\n\n\n\nB000DLB91A:okay,_i_guess...:c._w._loy\n\n\nB000DLB91A\n\n\nBelkin F8Z060 iPod Nano Silicon Sleeve (Black): Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nOkay, I guess...\n\n\nNovember 6, 2006\n\n\nC. W. Loy\n\n\nSan Diego, CA\n\n\nWhile the silicon used in the construction of the sleeve is very nice (feels grippy but not sticky if you know what I mean), some of the features are just wrong. For instance, the opening for the hold switch on top was apparently cut wrong and does not line up with the switch at all - requiring you to have to push the cover around so you can flip the hold on or off. Also, for whatever reason, the edge of the sleeve is right at the headphone jack which causes it to be somewhat difficult to keep some headphones seated during use (the sleeve can work its way in and pop the jack out). \nThe armband is satisfactory at best - it's just a standard, no frills, velcro armband\n\n\n\n\nB00006JPMF:chinese_made_junk_-_save_your_money!:b._roy_\"roytech\"\n\n\nB00006JPMF\n\n\nLenmar PRO-415 AA NiMH Rechargeable Batteries (4-Pack): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nChinese made Junk - save your money!\n\n\nFebruary 27, 2006\n\n\nB. Roy \"RoyTech\"\n\n\nSunnyvale, CA, USA\n\n\nI was tempted by the cheap price and got myself a 10 pack. They are rated at 2000 mAh, but run out of juice after the digital camera has shot maybe 5 frames. My 2 year old Panasonic 1600 mAh in contrast can run the same camera for over a 100 shots. I tried these batteries in my MP3 player - and even there, it won't supply enough juice after an hr worth of playback. It is not that the battery does not hold any charge - that it has. But it just does not supply the peak current required by most digital devices. It seems to be all right for use in a flashlight. A set of batteries that will not power a camera will power a flashlight (a bit dimly) for hours (yeah - I tested it). For the test, I had charged the panasonics and the lenmars for the same duration (it is not a charger issue).\nI plan to complain to the state consumer affairs department regarding this brand and hope action is taken against shady products which are nothing but rip-offs.\nOne last tip - nowadays, the best NiMH batteries are made by Sanyo. It is better to pay 2 - 4 times for them than for a Lenmar. Trust me\n\n\n\n\nB000652LUG:about_this_thing:c._housen\n\n\nB000652LUG\n\n\nSanDisk SDDR-103 MobileMate SD+ 5-in-1 Mobile Reader (SD/MiniSD/MMC/RS-MMC/TransFlash, Retail Package): Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nAbout This Thing\n\n\nNovember 9, 2006\n\n\nC. Housen\n\n\n\n\n\nThis product is not for the heavy handed \nafter 2 weeks of use one of the slots was non functional because spring leaf to hold the card against the contacts some how managed to get bent into the path of the card, and seeing that the slot is for a flash card it is almost impossible to get any tool to fit to do any 'repairs'.\non the bright side it did work with the stated flash types\n\n\n\n\nB0008G1RSU:don't_buy_if_near_us/canada_border:k._kelly\n\n\nB0008G1RSU\n\n\nTomTom GO 300 Portable GPS Navigation System with Maps of US Pre-loaded on SD Card: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n2.0\n\n\nDon't Buy if near US/Canada Border\n\n\nSeptember 22, 2006\n\n\nK. Kelly\n\n\n\n\n\nThis GPS comes with US map on the SD card and Canada map on the included CD but never the twain shall meet.\n\nBy car I'm a few hours from Canada and several days from the eastern US but I can't swap out the faraway eastern US part of the map on the TomTom Go 300 for a nearby Canadian map. It's either all Canada or all contiguous US. This is a major functional flaw if you live near the US/Canada border or live further away and like to take cross-border trips.\n\nOh, and if you do decide to swap out the US map, be sure to save it on your computer first - TomTom doesn't bother to provide a backup on the CD so if you don't first make your own backup before changing to the Canadian map you can't later get the US map back\n\n\n\n\nB0008G1RSU:good_potential,_but_nasty:frederick_p._barnes_\"fredpb\"\n\n\nB0008G1RSU\n\n\nTomTom GO 300 Portable GPS Navigation System with Maps of US Pre-loaded on SD Card: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n1.0\n\n\nGood potential, but nasty\n\n\nAugust 30, 2006\n\n\nFrederick P. Barnes \"fredpb\"\n\n\nDelray Beach, Florida USA\n\n\nThe Tomtom has great features. But I returned mine. First, the maps are very dated. This makes it iffy at best. It has terrific potential, if it worked. You can use a PDA stylus on the map sitting at home outside on batteries to plan a trip. It's amazing. But I wanted to plan a 600 mile trip myself using it's internal maps and waypoints. But I tried a local test first. I used roads as waypoints, main roads. This way I can force the Tomtom to use MY routes. I did not like it's choices, since it is a dated map database. I set up a local route, and started to follow it. It took me to the first road/waypoint no problem. But it would not go past the first waypoint. I would go down the road (first waypoint) then it would tell me to take a u turn!. I did. Then it said take another u turn. It had me going in circles. Perhaps it's not made to use roads as waypoints, but all indications it gave were that it would. The manual was no help. Support was no help. So it went back. I have ordered a Garmin i2. Hope this is ok. I have taken the same trip from SE FL USA to SC USA with a Garmin C330. The Garmin has maps that you cannot update, but got me to my destination no problem.\n\nI don't recommend this TomTom. Do research. Go to manufactures websites and look at unit manuals.\n\nHope this helps\n\n\n\n\n0899336795:stayaway_from_it:sheikhaq\n\n\n0899336795\n\n\nThe Blue Logger & Street Atlas USA 2006 or The Blue Logger & Street Atlas USA 2006 Handheld: Electronics\n\n\nelectronics\n\n\n3 of 5\n\n\n1.0\n\n\nstayaway from it\n\n\nNovember 30, 2005\n\n\nsheikhaq\n\n\nusa\n\n\ni have so for used inav iguidance,mapopolis,navigon navigator 5 and The Blue Logger & Street Atlas USA .out of all these The Blue Logger & Street Atlas USA is the worst and inav iguidance 2.1.1 is the best .blue logger gps hardware is good but v impractical to use bcuz of its strange onoff button....\ni will not recommend it to anyone....simply waste of time and mone\n\n\n\n\nB0000658CH:waste_of_money:m._laad\n\n\nB0000658CH\n\n\nLinksys USBHUB4C ProConnect Compact USB 4-Port Hub: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n1.0\n\n\nWaste of money\n\n\nSeptember 29, 2006\n\n\nM. Laad\n\n\n\n\n\nThis is a complete waste of money for a number of reasons.\n\n1. This hub will not work with any USB powered devices. So forget being able to charge your phone or running an unpowered external disk through this hub.\n\n2. Extremely slow USB 1.1. This device is NOT USB 2.0 compliant\n\n3. It is clunky\n\n4. Does not comke with a power adapte\n\n\n\n\nB0000658CH:love_affair_gone_sour:amanooensis\n\n\nB0000658CH\n\n\nLinksys USBHUB4C ProConnect Compact USB 4-Port Hub: Electronics\n\n\nelectronics\n\n\n8 of 8\n\n\n2.0\n\n\nLove affair gone sour\n\n\nApril 7, 2006\n\n\namanooensis\n\n\nFlyover Country\n\n\nI have owned this little hub for at least a year. I have an older PC which came without USB, so I added a PCI adapter. I soon realized that I needed more ports, and more convenience, than the two now located in the backplane of the PC on the floor.\n\nI shopped around, and this item looked appropriate. As for the short cord, I solved that issue by getting a USB extension cord at the same time. Besides solving the immediate problem, this approach gave me the option of moving the hub to my laptop at some point, and using the extension cord for some other purpose.\n\nFor a long time it did exactly what it was supposed to do. I attached an external hard drive through it. I used it for a compact flash card reader, and a digital camera. I connected an inkjet printer. Everything worked, once the drivers were installed and everything was set up correctly. (This was all with Windows 98 SE.)\n\nBut after a while, things started getting less reliable. The system would tell me that various \"drives\" (on the flash card reader, or the external hard drive) were not available. Or the digital camera would not stay connected to its software. And the printer even started disappearing.\n\nPlaying with the cables and connectors would sometimes bring devices back to life, and eventually I deduced that there was something wrong either with the connectors themselves being too loose, or with wiring inside the device. Either way, it has finally become a big problem, and I am shopping for a replacement.\n\nMost of the devices I connected were self-powered, that is, they did not need to draw power from the hub or the PCI channel at all. The hard drive, camera, and printer were all powered by batteries or AC adapters of their own. And the flash card reader, which did draw power from the hub, used to work fine. It does not matter what combination of devices I try, sooner or later the connection goes bad.\n\nIt is of course possible that there's something wrong in the PC -- but that would not explain why things are improved by messing with the connectors or the hub itself. So I have to recommend against buying it. I suspect it is discontinued and that this advice won't be necessary for much longer, but I want to prevent somebody from the bother I've been through, just in case\n\n\n\n\nB0001DBEM4:be_forewarned,_if_your_thinking_of_using_this_with_airport_express:jacqui\n\n\nB0001DBEM4\n\n\nHarman Kardon Soundsticks II 3-Piece Plug and Play Multimedia Speaker System: Electronics\n\n\nelectronics\n\n\n1 of 4\n\n\n1.0\n\n\nbe forewarned, if your thinking of using this with Airport Express\n\n\nNovember 13, 2006\n\n\nJacqui\n\n\nSF Bay Area\n\n\nThis Amazon listing also suggests buying the Soundsticks along with Airport Express.\nIf you're thinking of connecting them to the USB port of Express, Apple says it won't work.\n(Apple Airport Express FAQs\n\n\n\n\nB00007LTB6:worked_like_a_charm_for_24_hours:r._sewell\n\n\nB00007LTB6\n\n\nD-Link DWL-G650 Wireless Cardbus Adapter, 802.11g, 108Mbps: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nWorked like a charm for 24 hours\n\n\nSeptember 3, 2006\n\n\nR. Sewell\n\n\n\n\n\nInstall went smooth on my W2K/dell inspiron notebook. Everything worked fine for 24 hours then it just stopped. Worked with customer support for about an hour getting nowhere. This thing is junk. I'm sending it back\n\n\n\n\nB00007LTB6:unable_to_install,_despite_reaching_d-link_technical_support:boolaboola_\"nutmegger\"\n\n\nB00007LTB6\n\n\nD-Link DWL-G650 Wireless Cardbus Adapter, 802.11g, 108Mbps: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n2.0\n\n\nUnable to install, despite reaching D-Link technical support\n\n\nAugust 9, 2006\n\n\nBoolaBoola \"Nutmegger\"\n\n\nWhittier\n\n\nFrom looking at the D-Link website and talking to five tech support staff, it appears that there are countless versions of this card, each with different driver software. The one I got came with a CD with the wrong drivers. With tech support, I finally found the 8 mb driver file I was supposed to download. However, they did not provide any instructions on how to install the new driver. The normal \"automatic\" approach did not work. And their instructions did not match the screens I was getting on Windows XP SP 2.\nSo I called back again. And again. And again. \nDespite being given a case file, each time I was asked the same questions, including what, exactly, is wrong. And each time I was told to do the same thing: Install from the CD. Reboot. Remove the program. Reboot. Install from the new driver. Reboot. Remove the program. Talk to a senior tech support person.\nInstall from CD. Reboot. etc.\nIt would have been easier to send the card with the right files on the CD. A clearly designed website would be second best. And tech support that knows what they're doing and speaks intelligble English would be a nice last resort. \nI spent three hours calling D-Link technical support and speaking to five people, and they could not tell me how to install the software for this card!\nI am not alone in this experience; see cnet's web site user reviews of the DWL-G650. \nAnother reviewer reports that this software damaged his operating system. I believe he is right. It appears to have damaged my screen font for Windows message boxes. I have been unable to repair it. The DWL-G650 is bad news.\nI give them two stars cause they actually have tech support, though in this case it was utterly worthless. \nBack it goes\n\n\n\n\nB00007LTAU:rip-off:_doesn't_advertise_that_you_need_an_ac_adaptor:teacher_\"teacher\"\n\n\nB00007LTAU\n\n\nTargus PA050U USB 2.0 High-Speed Hub (4-port): Electronics\n\n\nelectronics\n\n\n8 of 15\n\n\n1.0\n\n\nRIP-OFF: Doesn't advertise that you need an AC adaptor\n\n\nFebruary 20, 2006\n\n\nteacher \"teacher\"\n\n\nNew York, New York USA\n\n\nI would like to believe that this hub works well- but I wouldn't know. I bought it to reduce clutter on my desk from all my devices hooking into my powermac. I hooked up my printer, ipod and digital camera and nothing happened. I called Targus and they said I needed an a/c adaptor. It doesn't come with one and doesn't say anything about one in the manual or instructions, or their website. They said I could get it at radio shack and gave me the info (5 volt, 2.1 amp, center positive). I've been to two radio shacks and online- they don't carry it. I'm upset. Any advice\n\n\n\n\nB000BONJXU:are_there_two_m230s_?:joseph_e._archie_\"jarchie\"\n\n\nB000BONJXU\n\n\nSanDisk Sansa M230 512 MB MP3 Player (Blue): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nAre there two m230s ?\n\n\nNovember 12, 2006\n\n\nJoseph E. Archie \"jarchie\"\n\n\nChicago, IL\n\n\nI have read all 181 reviews and decided that half the reviewers did not get the model that I did.\n\nOr else they wrote their review too soon.\n\nI bought mine in June 2006. It failed before the store warranty ran out. One down. The replacement lasted a month or two before it hung and wouldn't boot. Two down. Sandisk honored their 1 year warranty with a prompt free RMA. That one also failed. Three down. They replaced it with both a m230 AND a 2GB c150. The c150 hung after about 2 weeks. Four down. I have been using the m230 for two weeks now and hope it lasts until the c150 replacement arrives. I have gotten quite good at the RMA game and intend to keep playing it either until my warranty runs out or I get one of those that other people rave about.\n\nI don't think I'll buy another SanDisk - unless it is free and has a lifetime warranty.\n\nI suggest that everyone with a failed m230 to exercise thier warranty rights. Sandisk needs to know the true reliability of their products.\n\n\n\n\n\nB00006LELP:might_be_great_if_it_worked:lally123\n\n\nB00006LELP\n\n\nNintendo e-Reader - Game console trading card reader: Computer & Video Games\n\n\ncomputer & video games\n\n\n0 of 2\n\n\n2.0\n\n\nMight be great if it worked\n\n\nDecember 15, 2005\n\n\nlally123\n\n\n\n\n\nWe have been unsuccessful with this e-reader attachment since we purchased it. Cards read once and never again. Doesn't matter if we use the GB SP or Advance. Still doesn't do anything. Don't recommend you waste your money\n\n\n\n\nB0000DD7PE:sandisk_2gb:carlos_carbonell_\"ccg922\"\n\n\nB0000DD7PE\n\n\nSandisk 2GB Compactflash Card Type I (SDCFB-2048-A10, Retail Package): Electronics\n\n\nelectronics\n\n\n7 of 28\n\n\n1.0\n\n\nSandisk 2GB\n\n\nSeptember 6, 2005\n\n\nCarlos Carbonell \"ccg922\"\n\n\nMiami, FL USA\n\n\nI just cam from Las Vegas and the sandisk has a disk error that doesn't allow for more photos after only using 40mb. I have tried to format the disk using the camera and through Windows XP. It doesn't allow it to be formatted. Bad investment!!! Would not recommend the product unless there is a way I can get a refund or an exchange\n\n\n\n\nB0000A98AC:quality_control?:ruthanne_davis_\"the_bookworm\"\n\n\nB0000A98AC\n\n\nMemorex 4x DVD+RW 25-Pack Spindle: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nQuality control?\n\n\nAugust 20, 2006\n\n\nRuthanne Davis \"The Bookworm\"\n\n\nAbilene, Texas USA\n\n\nThe second disc in this package was faulty, which I did not discover until I spent two hours recording a movie. There was what appeared to be a half inch bare spot on the outer edge of the dvd+r. Not what I expected from Memorex which purports to be the best.\nI have switched to Maxell\n\n\n\n\nB0000A98AC:junk!:wahrani\n\n\nB0000A98AC\n\n\nMemorex 4x DVD+RW 25-Pack Spindle: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nJunk!\n\n\nJuly 8, 2006\n\n\nWahrani\n\n\nSan Francisco, CA United States\n\n\nReplaced first spindle and still junk. Will always stick with Verbatim from now on\n\n\n\n\nB0000A98AC:very_unhappy_with_this_purchase:gardenia_queen\n\n\nB0000A98AC\n\n\nMemorex 4x DVD+RW 25-Pack Spindle: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n1.0\n\n\nVery unhappy with this purchase\n\n\nJune 7, 2006\n\n\nGardenia Queen\n\n\n\n\n\nI bought this same brand of discs sometime last year on this very site. I was very pleased with the purchase then. I didn't have one problem with the discs. \n\nI bought another pack recently and I've been using the pack I just bought. All of the discs are messing up for me. I can understand having a few problematic discs but this is insane to have a whole pack of discs and every single one of them messes up for you. I wish I could send these discs back and get a refund. This purchase has scared me away from anything else related to Memorex. I thought Memorex was a very reliable brand and they would make sure they were sending out top notch items to consumers. I guess I was wrong. And apparently you get what you pay for. Don't be fooled by the good price. You'll just be out of your money and stuck with discs that wouldn't even be worth using as a frisbee. Find another brand to use and avoid this brand\n\n\n\n\nB00009ZY33:dwt108_double_wide_108_disc_dvd_tower:marjoire_j-b_simpson\n\n\nB00009ZY33\n\n\nCase Logic DWT108 Double-Wide 108-Disc DVD Tower: Electronics\n\n\nelectronics\n\n\n11 of 15\n\n\n2.0\n\n\ndwt108 double wide 108 disc dvd tower\n\n\nFebruary 25, 2006\n\n\nMarjoire J-b Simpson\n\n\n\n\n\nWas a little hard putting it together. You have to be very careful taking the cds out or it would fall apart. I am not happy with this product\n\n\n\n\nB00008EM7W:bad_compatibility:scott_baret\n\n\nB00008EM7W\n\n\nMemorex CD/DVD Compact LabelMaker System: Electronics\n\n\nelectronics\n\n\n6 of 9\n\n\n1.0\n\n\nBad Compatibility\n\n\nOctober 14, 2004\n\n\nScott Baret\n\n\nPittsburgh, PA USA\n\n\nI have the older, larger version of this product. However, it uses the same software. The labels are great and the spring device that is used to apply the labels is great, but the software is awful. There is no direct Macintosh program; rather there are templates. They were written for old versions of software and really don't work right. The Windows side includes an actual Print Shop-style program which works awfully. If you do get this, make your own template on AppleWorks or PowerPoint\n\n\n\n\nB00008EM7W:it_does_not_work_with_macs:\"cporro\"\n\n\nB00008EM7W\n\n\nMemorex CD/DVD Compact LabelMaker System: Electronics\n\n\nelectronics\n\n\n6 of 9\n\n\n1.0\n\n\nIt Does not Work with Macs\n\n\nJanuary 10, 2004\n\n\n\"cporro\"\n\n\nNew York, NY USA\n\n\nThey totally misrepresent this product. It does not work with Macs. I feel cheated and lied to by Memorex, shame on you\n\n\n\n\nB00005MIS8:card_error:ty_n.\n\n\nB00005MIS8\n\n\nViking CF512M 512 MB CompactFlash Card: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nCard Error\n\n\nNovember 21, 2005\n\n\nTy N.\n\n\ni.e. 3 shots/sec.\n\n\nAfter serveral months of use, \"Card Error\" was shown on my Canon Digital Rebel for my 512MB Viking card, which was half full with pictures. Fortunately, I just lost a few of pictures. I tried to format the file; \"Card Error\" kept occuring on my Rebel. This 512MB Viking card is now used for my data files. \n\nProbably, 512MB Viking cards are not designed for high Mega Pixel camera and taking many shots in a short period of time (i.e. 3 shots/sec.). My Canon A40 has no problems when used with the 512MB Viking card so far. Probably, I am going to buy a 512MB card from Sandisk or Simple Tech\n\n\n\n\nB0002OD0N8:specs__aren't_100%_correct:richmond_sweet\n\n\nB0002OD0N8\n\n\nApple M9339LL/C iPod Stereo Connectivity Kit: Electronics\n\n\nelectronics\n\n\n8 of 9\n\n\n2.0\n\n\nSpecs aren't 100% correct\n\n\nSeptember 12, 2005\n\n\nRichmond Sweet\n\n\nNorthern Virginia, USA\n\n\nI purchased this and am not impressed. Note that \"works with all click wheel iPods\" isn't true. It does NOT work with photo iPods (at least the 60 GB model). I'm on vacation and had to remove the cover to get it to fit. I'm ordering a 'photo dock' since I can't see any way to get a cover that is made for the 60GB/photo model.\n\nIf I'd known this, I'd purchased only the stereo cable and the photo dock separately, since I already have a charger (came with it...). Grr. :-\n\n\n\n\nB00006IW1X:has_anyone_else_had_this_problem??:perry_brubaker_\"confused!\"\n\n\nB00006IW1X\n\n\nMemorex 700MB/80-Minute 52x CD-R Media (100-Pack Spindle): Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n1.0\n\n\nHAs anyone else had this problem??\n\n\nOctober 30, 2006\n\n\nPerry Brubaker \"confused!\"\n\n\n\n\n\nHeres a new one....I have recently bought two spindles of these discs, and my computer (Imac w/ Intel chip-hardly outdated) simply doesnt recognize them-like I never put in a disc at all. The kicker is that this spring, I also ordered two spindles of these discs (exactly the same as far as I can tell), from which I burned one hundred discs (not a single faulty one!) for a freind overseas. I have no idea what the deal is, but have a feeling that the issue is not the quality of the discs. Anyone got ideas?\n\n\n\n\nB0006TI9WG:beware_of_mail-in_rebate_hoax:sanjeevp_\"bottom_line\"\n\n\nB0006TI9WG\n\n\nSanDisk 1 GB Memory Stick Pro Duo (SDMSPD-1024-A10): Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nBeware of Mail-in Rebate Hoax\n\n\nNovember 21, 2006\n\n\nSanjeevP \"Bottom Line\"\n\n\nUSA\n\n\nThe card works as advertised but SanDisk does not send in Mail-in-Rebate. Even though I mailed it in with the UPC Barcode, SanDisk sent me a post card stating that I needed to resubmit it with the barcode. How can I resubmit when they already have the barcode?\n\nIn my experience with SanDisk, I got rebate back on one of two cards that I submitted - so you get mail-in rebate 50% of the time with SanDisk. I think mail-in-rebate is a big hoax perpetrated by these companies. Don't trust it!\n\n\n\n\nB00008VF63:painful:t._fischetti_\"t.f.\"\n\n\nB00008VF63\n\n\nSony MDR-E828LP Fontopia Earbuds with Winding Case: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nPAINFUL\n\n\nNovember 9, 2006\n\n\nT. Fischetti \"T.F.\"\n\n\nNew Jersey\n\n\nI purchased 2 sets of these earphones for a 2 hour flight. My husband and I both used them, and we both took them out of our ears after 20 minutes. They were great at first, but after 20 minutes they really started to hurt our ears. The inside of my ears were so sore I couldn't even adjust the earphones when they started coming out, that's how bad my ears hurt. I had the foam pad over them too, and it made no difference. I would not recommend these\n\n\n\n\nB0002CPBWI:cheap_hunk_of_junk:d._j._mayard\n\n\nB0002CPBWI\n\n\nMicrosoft Wireless Notebook Optical Mouse 3000 -Slate: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nCheap Hunk of Junk\n\n\nNovember 9, 2006\n\n\nD. J. Mayard\n\n\nBaton Rouge, LA United States\n\n\nThis was the worst purchase of any type of computer hardware that I have ever made. It felt very light and flimsy, the definition of cheap plastic. Not just the construction but the performance was terrible. It would work perfectly for about a half hour and then go on a break and do NOTHING for about 10 minutes. This was very very frustrating. Maybe I was just unlucky and received a defective one, but I wouldn't take that chance again.\n\nI wish I could give this device a worse rating, somewhere around -4 would be much more appropriate.\n\n\n\n\nB0007SL4IW:memorex_blank_dvd's:coffeecorn\n\n\nB0007SL4IW\n\n\nMemorex DVD+R 16x 4.7GB 100 Pack Spindle: Electronics\n\n\nelectronics\n\n\n0 of 3\n\n\n1.0\n\n\nmemorex blank dvd's\n\n\nJuly 26, 2006\n\n\ncoffeecoRN\n\n\ndouglas,ga\n\n\nIn each pack there have been several dvd's that aren't any good. Too many bad discs in my opinio\n\n\n\n\nB00009XSYZ:worthless!!!:brien_penn\n\n\nB00009XSYZ\n\n\nADS Technologies HDUP1500 HDTV Upconverter: Electronics\n\n\nelectronics\n\n\n18 of 20\n\n\n1.0\n\n\nWorthless!!!\n\n\nFebruary 10, 2004\n\n\nBrien Penn\n\n\nBaltimore, MD\n\n\nI bought the ADS upconverter because I bought a HDTV only to find out that my cable co. didn't offer HDTV and after 3 failed attempts to switch to a satellite provider that did offer HDTV (too many trees around my house). I wasn't expecting the upconverter to magically turn my standard signals into an amazing HDTV picture, but for $550+ I was expecting the upconverter to do something that was actually noticable. When I first turned in on I asked my wife to look at the picture and tell me what she thought. She looked at the TV and said okay I'm ready turn it on. I said "it IS ON!!" This is pretty much the reaction I had also when I looked at it. The image I got for my digital cable was certianly different, but that's all it was, different. It was NOT noticably better. In fact, in some instances I though the picture was worse. I called ADS tech support and they kept insisting I try it with my DVD because that is what the product is best for (even though that's not what I got it for). So I did and to be honest I really preferred the picture I got directly from my DVD over the picture I got after running it thru the upconverter. It didn't matter what display format I put it in it just didn't do anything to improve the picture. Save your money. If you can't get HDTV don't expect this box to give you anything close to the next best thing\n\n\n\n\nB00006B85B:charger_stopped_charging:jennifer_\"jen\"\n\n\nB00006B85B\n\n\nKodak Rapid Charger with 4 AA NiMh Batteries: Electronics\n\n\nelectronics\n\n\n12 of 12\n\n\n1.0\n\n\nCharger stopped charging\n\n\nApril 25, 2005\n\n\nJennifer \"Jen\"\n\n\nNJ\n\n\nI was impressed with the charger for about 1 month---than it stopped charging the NiMh batteries it came with. I took it back to Best Buy only to find that ALL of the Rapid Chargers on thier shelf did not work either. A sales person and I tested them all, hoping I would be able to exchange my broken one with one that worked.\n\nIt was more disappointing after calling Kodak ---they were unable to replace it or get my money back. I thought purchasing a Kodak was a smart decision, however, they did not stand behind their product which was very disappointing. \n\n\n\n\nB000050AVT:viruses_on_mfg_software_caution_!!!:b_drake\n\n\nB000050AVT\n\n\nKB Gear Jam Studio: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n1.0\n\n\nVIRUSES ON MFG SOFTWARE CAUTION !!!\n\n\nJanuary 21, 2004\n\n\nb drake\n\n\nNJ\n\n\nI did not read the prior reviews, I bought this item & loaded the software that came with it in the sealed box from Target.com. The MFG SOFTWARE disc contains TWO VIRUSES one in Quicktime & the other in Totally Hip files. Target has been notified that the software is CORUPT with viruses & that the MFG has gone out of business. This item is only useful as a drawing tablet thru other software programs like paint shop pro as there is no WEB SITE ACCESS or tech support available for this item\n\n\n\n\nB000050AVT:install_cd_has_viruses:nicole\n\n\nB000050AVT\n\n\nKB Gear Jam Studio: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n1.0\n\n\nInstall CD has viruses\n\n\nDecember 2, 2003\n\n\nNicole\n\n\nHouston, TX\n\n\nI just recently bought this and when I installed it Norton caught 2 viruses on the install CD.\n\n\n\n\nB000093IRD:coasters!:valentina\n\n\nB000093IRD\n\n\nMemorex 4.7GB 4x DVD+R 4.7GB (50-Pack Spindle): Electronics\n\n\nelectronics\n\n\n0 of 2\n\n\n1.0\n\n\nCOASTERS!\n\n\nFebruary 1, 2005\n\n\nValentina\n\n\nFair Lawn, NJ United States\n\n\nI purchased this item and was excited to get them, but burning my first DVDs.. they're all coasters! ARG.. pass Memorex and go for a more expensive media, like Verbatim\n\n\n\n\nB0002A9SJ2:useless:c._nixdorff_\"cd_lover\"\n\n\nB0002A9SJ2\n\n\nAleratec DVD/CD Disc Repair Plus: Electronics\n\n\nelectronics\n\n\n7 of 15\n\n\n1.0\n\n\nUseless\n\n\nJune 23, 2006\n\n\nC. Nixdorff \"cd lover\"\n\n\nDestrehan, Louisiana United States\n\n\nAs far as I can tell this thing doesn't work at all. I have now tried it on 3 cds and it did not repair any of them. I should have just replaced the cds\n\n\n\n\nB0002A9SJ2:mine_was_doa_too:cara_thrace\n\n\nB0002A9SJ2\n\n\nAleratec DVD/CD Disc Repair Plus: Electronics\n\n\nelectronics\n\n\n9 of 14\n\n\n2.0\n\n\nMine was DOA too\n\n\nMay 26, 2006\n\n\nCara Thrace\n\n\n\n\n\nHow do I assign a rating? As several other people have experienced, my unit is also DOA. I received it, plugged it in, pressed 'clean', and the motor started up. I popped open the cover, put in the disc I wanted cleaned, and pressed 'clean' again. Nothing. Terrible quality control.\n\nWhat's worse is the unit case is glued together -- no way to open it up to try to fix the electronics inside without mutilating it and voiding warranty\n\n\n\n\nB0001WV6ZU:hard_to_rate;__didn't_arrive_in_working_order.:bridget_blackwood\n\n\nB0001WV6ZU\n\n\nPanasonic DVD-F87K 5-Disc HD-Compatible DVD/CD Player (Black): Electronics\n\n\nelectronics\n\n\n11 of 12\n\n\n1.0\n\n\nHard to rate; didn't arrive in working order.\n\n\nMarch 29, 2006\n\n\nBridget Blackwood\n\n\n\n\n\nIt didn't work properly from the time it came out of the box. The carousel wouldn't make a complete rotation, so CDs and DVDs would get 'trapped' inside the unit. And occasionally, it would simply refuse to play a DVD altogether. Panasonic has directed us to a local repair shop, so maybe they can get it to function as it should. If you do purchase one, please read the warranty carefully; a defective unit will either have to be repaired or replaced with a refurbished one. Panasonic informed me that they do not replace malfunctioning units with new ones, under any circumstances. \n\n\n\n\nB0000A0AJH:bad_disc_error_-_possible_fix:walter_c_\"walt\"\n\n\nB0000A0AJH\n\n\nCyberHome CH-DVD 300S Progressive-Scan DVD Player (Silver): Electronics\n\n\nelectronics\n\n\n8 of 8\n\n\n2.0\n\n\nBad Disc Error - possible fix\n\n\nAugust 11, 2006\n\n\nWalter C \"Walt\"\n\n\nOmaha, NE USA\n\n\nBAD DISC ERROR - possible fix\n\nAfter about 8 months just like the other reviewer it stopped working.\n\nI kept getting bad disc error on a new DVD I bought. I tried good discs that worked before but now got the same Bad Disc error.\n\nThis is not the first time I had problems loading. Previously, unplugging and plugging it in again sometimes corrected the problem.\n\nThis time after two weeks of Bad Disc errors, I was about to toss it when I decided what the heck, I'll check inside to see if it's dirty or something.\n\nFIRST I UNPLUGGED THE UNIT.\n\nI removed the 6 screws holding the cover. You may have to pull the sides out a bit if it catches to lift it off.\n\nWith my expertise knowledge. *eyeroll* I nudged the lense mechanism. It apparently slides to read the discs.\n\nI plugged the DVD in and loaded a disc again to see what happens. The lense slid under the tray and started reading the disc. I'll take the elation of dumb luck over the satisfaction calculated knowledge any day! lol\n\nSo hopefully this will last a little longer. But at least I can use it again for now ...\n\nI am downrating it to 2 stars solely on the bad disc error. The viewing quality and performance is decent and covered by other reviewers\n\n\n\n\nB0000A0AJH:what_happened_to_the_region_code_free_setting???_help!:valerie_c._feurich-sponsel_\"val\"\n\n\nB0000A0AJH\n\n\nCyberHome CH-DVD 300S Progressive-Scan DVD Player (Silver): Electronics\n\n\nelectronics\n\n\n2 of 4\n\n\n1.0\n\n\nWhat happened to the region code free setting??? HELP!\n\n\nJuly 25, 2006\n\n\nValerie C. Feurich-sponsel \"Val\"\n\n\nUSA\n\n\nHi! I bought my Cyberhome 300 in April 2006, and was able to set the region code to \"0\" to watch all DVD's. \nAfter not using the DVD Player for a few weeks (and unplugging it), the region code has automatically set back to \"1\" and I cannot change it anymore! What happened?? When I try to change it now I can only choose between the settings \"1\" and \"-\". Anyone who has the same problem? Thank yo\n\n\n\n\nB00004XRDM:not_a_quality_item:hugo_calendar\n\n\nB00004XRDM\n\n\nLinksys L03040 Power 2GO Inverter - Car Cigarette Lighter Power Supply: Electronics\n\n\nelectronics\n\n\n8 of 9\n\n\n1.0\n\n\nNot a quality item\n\n\nJuly 8, 2005\n\n\nHugo Calendar\n\n\nMonument, CO\n\n\nThis item worked for a while, but required fiddling (unplugging and replugging repeatedly) with the lighter socket each time I restarted my car. Eventually, however, it blew out the fuses in both my lighter sockets. Don't waste your money on this piece of crap\n\n\n\n\nB00004XRDM:the_linksys_converter_was_pure_junk!:gene_r._barnes_\"randy\"\n\n\nB00004XRDM\n\n\nLinksys L03040 Power 2GO Inverter - Car Cigarette Lighter Power Supply: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n1.0\n\n\nThe Linksys converter was pure junk!\n\n\nJuly 6, 2005\n\n\nGene R. Barnes \"Randy\"\n\n\nPerryton, Tx\n\n\nThe product not only did not work but blew out fuses in my car. The mechanic who checked my car out said the device had a dead short in it. I have written to your customer service department and asked for money back but to no avail. I will not purchase another thing from Amazon until my money is refunded\n\n\n\n\nB00004XRDM:did_not_perform_as_advertised:nicholas_wilson\n\n\nB00004XRDM\n\n\nLinksys L03040 Power 2GO Inverter - Car Cigarette Lighter Power Supply: Electronics\n\n\nelectronics\n\n\n16 of 20\n\n\n2.0\n\n\nDid not perform as advertised\n\n\nOctober 15, 2004\n\n\nNicholas Wilson\n\n\nMendocino CA\n\n\nThis unit failed to provide reliable power to my new Toshiba Satellite A60 laptop. It cycled on and off every 3 seconds. It powered a 100 watt lamp okay, but wouldn't start a 250 watt heat lamp at all. Product spec sheet says it can supply 400 watt surge and 200 watts for 2 min., and is rated at 140 watts continuous. My laptop power supply is rated at 2A input at 100-240 VAC, and if it drew 2A constantly I agree that it would need 200 to 230 watts, which exceeds the continuous rating of this inverter. However, even at that power demand the inverter should run it for 1-2 min., but only manages 3 sec. In addition, I doubt that the laptop power supply actually draws a full 2.0 amps continuously. I talked to Linksys support, and they suggested returning the unit for replacement or refund, so I did. It did not work for me. Maybe it would work for a laptop that only needs 100 watts, but I wouldn't recommend it for more than that\n\n\n\n\nB00000J1QK:allsop_lens_cleaner:leon_m._olszewski_\"wood_works\"\n\n\nB00000J1QK\n\n\nAllsop CD Laser-Lens Cleaner: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nAllsop Lens Cleaner\n\n\nNovember 9, 2006\n\n\nLeon M. Olszewski \"Wood works\"\n\n\nMountain View, MO\n\n\nI didn't notice any change in the operation of my cd writer after cleaning. The cleaner was supposed to play music while it cleaned (30 minutes) It didn't\n\n\n\n\nB00006JPFU:wanted_to_like_it,_but...:michael_feinstein\n\n\nB00006JPFU\n\n\nCoby CA-747 Dual Position CD/MD/MP3 Cassette Adapter: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nWanted to like it, but...\n\n\nNovember 10, 2006\n\n\nMichael Feinstein\n\n\nBedford, MA United States\n\n\nWhen I first started using this, I loved it. It worked fine in my car cassette deck and had decent sound. In fact, I bought a second one for my wife. Then, after about two weeks, it stopped working. It would no longer play in my cassette deck, which ejected it as soon as I put it in every time. I tried contacting Coby's support on the Web, but they were unresponsive. Luckily, Amazon took it back for a refund. Note that they no longer sell these directly. Bet they had too many returns..\n\n\n\n\nB00006JPFU:great_product!_for_two_days!:grant_d._green_\"contrabass.com\"\n\n\nB00006JPFU\n\n\nCoby CA-747 Dual Position CD/MD/MP3 Cassette Adapter: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nGreat product! For two days!\n\n\nOctober 26, 2006\n\n\nGrant D. Green \"Contrabass.com\"\n\n\nLos Altos, CA USA\n\n\nWhen this adapter first arrived, I thought it was great. The sound quality was excellent, and free of all the distortion and interference I get from using an FM transmitter-style adapter (there are few open frequencies in the SF Bay Area). The first day, I used it for about 20 minutes total (didn't have far to drive), and it worked great. The second day I had a bit farther to drive. After about 30 minutes, it quit. Hasn't worked again since.\n\nI didn't expect it to last forever, but less than an hour?\n\nYes, it has a 1 year warranty: just send the unit back with a money order ($6) to cover the return postage. Of course, then you're spending more in postage than you originally spent for the unit..\n\n\n\n\nB000204SWE:stops_working_after_3_months:ms._lingo\n\n\nB000204SWE\n\n\nPhilips DVP642 DivX-Certified Progressive-Scan DVD Player: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nStops working after 3 months\n\n\nNovember 15, 2006\n\n\nMs. Lingo\n\n\nNew York, NY\n\n\nThis DVD player does play all types of DVDs no matter how you burn it. But like other reviews, it stopped working after 3-6 months. In my case, it was 3 months. It just doesn't turn on anymore. I unplugged everything and replugged it but it just went dead. I don't watch DVDs much, so it's not like I overused it. Go with another model. This one has problems. I thought mines would be the exception, but I guess not. And now I can't return it because it's been over 30 days. \n\n\n\n\nB000F38IQK:panasonic_cd_player_is_not_continuous:john_e._abate_\"dr_abate\"\n\n\nB000F38IQK\n\n\nPanasonic SC-PM53 180-Watt 5-CD Executive Micro System: Electronics\n\n\nelectronics\n\n\n16 of 18\n\n\n2.0\n\n\nPanasonic CD player is not continuous\n\n\nOctober 21, 2006\n\n\nJohn E. Abate \"Dr Abate\"\n\n\nManasquan, NJ United States\n\n\nThe Panasonic CD player has a good sound quality, but does not automatically change from one CD to the next. It requires manual switching via the remote control to change from one CD to another. This feature was not explicitly stated in the specifications, and is misleading to a buyer who is seeking continuous playing of all five CD's\n\n\n\n\nB00020SEYM:short_bulb_life:john_l\n\n\nB00020SEYM\n\n\nInFocus X2 Multimedia DLP Projector: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nSHORT BULB LIFE\n\n\nNovember 24, 2006\n\n\nJohn L\n\n\nMI\n\n\nI've had the X2 for roughly 14 months. The X2 was used for watching movies (about once every 2 weeks) and big sports event (NCAA/NFL finals.. etc). The unit worked up to about 1200 hrs, then the lamp blew. I was hoping to get to at least 1500 hrs before the lamp went out. \n\nGood picture quality for the price (799) but short lamp life (get extended warranty). Check other units first\n\n\n\n\nB000093BD4:started_good_but_ended_bad:john_doe_\"go_mavericks=p\"\n\n\nB000093BD4\n\n\nPNY 256 MB SD Secure Digital Flash Memory Card: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nStarted good but ended bad\n\n\nJune 7, 2006\n\n\nJohn Doe \"Go Mavericks=p\"\n\n\nDallas ,Texas\n\n\nI had this card for more than 6 months, and It worked super in my Canon Powershot A510. Transfers went well too. Then all of a sudden for no reason I kept getting \"card error\" on my computer and digital camera a week ago. My SD/USB converter couldn't find the card either. I had really good photos I wanted to transfer to my computer and now they are probably lost for good. What a joke\n\n\n\n\nB0002ER3B8:poor_quality:m._andrews\n\n\nB0002ER3B8\n\n\nMemorex 4.7GB/8x DVD-R (50-Pack Spindle): Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n1.0\n\n\nPoor quality\n\n\nApril 19, 2006\n\n\nM. Andrews\n\n\nChicago, IL\n\n\nI found that these DVD-Rs did not work well in my system, were unreliable and slow. I cannot recommend them\n\n\n\n\nB0002ER3B8:these_do_not_work_with_the_burner_i_bought:younetta_hield\n\n\nB0002ER3B8\n\n\nMemorex 4.7GB/8x DVD-R (50-Pack Spindle): Electronics\n\n\nelectronics\n\n\n0 of 3\n\n\n1.0\n\n\nThese do not work with the burner i bought\n\n\nMarch 10, 2006\n\n\nYounetta Hield\n\n\n\n\n\nI have tried these dvdrs with the burner i bought from amazon and they do not work, i will try them again and send an email to you. I bought a few imation to try in the burner to ensure it wasnt faulty and they worked!!! what is up with these dvds?\n\n\n\n\nB000A2LSPO:don't_buy_this:richard_harrold\n\n\nB000A2LSPO\n\n\nBy HandHelditems Travel/ Wall Charger w/ IC Chip for Apple iPod 3G / 4G / Video / iPod Mini / iPod Nano , White: Electronics\n\n\nelectronics\n\n\n4 of 5\n\n\n1.0\n\n\nDon't Buy This\n\n\nJuly 31, 2006\n\n\nRichard Harrold\n\n\nSun Lakes, AZ\n\n\nI plugged my Nano into this charger for about 10 minutes and picked it up to check it out - it almost burnt my hand. My Nano is now Toast! I hope it just toasted the battery and not the whole device\n\n\n\n\nB000A2LSPO:works_good_for_a_while,_then_dies..._:marms79\n\n\nB000A2LSPO\n\n\nBy HandHelditems Travel/ Wall Charger w/ IC Chip for Apple iPod 3G / 4G / Video / iPod Mini / iPod Nano , White: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n2.0\n\n\nWorks good for a while, then dies... \n\n\nMay 2, 2006\n\n\nmarms79\n\n\nSeattle, WA United States\n\n\nI liked this when I first got it... very cheap, handy to travel with or leave at work, etc. After a couple months, it died completely... the little power light wouldn't even come on. I'm really disappointed all I have now is a charger that doesn't work.\n\n\n\n\nB0001DHHIY:you_get_what_you_pay_for_._._._:m._conway\n\n\nB0001DHHIY\n\n\nTRENDnet 54Mbps 802.11G Wireless PCI Adapter: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nYou get what you pay for . . . \n\n\nNovember 3, 2006\n\n\nM. Conway\n\n\nVirginia\n\n\nI purchased this wireless card based on the price, figuring that even if it didn't live up to the good reviews here, I would only be out ~$20. The set up was easy and smooth, but the card won't stay connected to my wireless network. I've got a Belkin wireless router. The Trendnet signal meter generally shows ~95% signal strength, but the connection only lasts for a few minutes, and I have to keep cycling the connection to regain network access--very annoying. I know there's nothing wrong with the network because my laptop's connection is stable. I purchased a D-Link card, and it has worked flawlessly. Unfortunately, I threw away the packaging when the Trendnet card arrived; if I hadn't, then this card would be on its way back to Amazon now. Like the title of my review says: you get what you pay for . . . \n\n\n\n\nB0001FSCZO:piece_of_crap.,...stay_away.:jacob_w._clark\n\n\nB0001FSCZO\n\n\nLinksys Storage Link for USB 2.0 Disk Drives NSLU2: Electronics\n\n\nelectronics\n\n\n1 of 7\n\n\n1.0\n\n\nPiece of crap.,...stay away.\n\n\nSeptember 29, 2006\n\n\nJacob W. Clark\n\n\n\n\n\nI have many Linksys devices and this is by far the worst one. I love Linksys stuff, which is why eveything that i own involving networking is linksis. But this thing is a piece of crap and now i cant recover the 60 gigs of personal pictures that were on my hardrive because of this things format. Spend a little more money and get a dedicated Network Attached Storage hard drive\n\n\n\n\nB0000CEPC8:unreliable_-_voice_unit_keeps_breaking:j._a._chen_\"loss4words\"\n\n\nB0000CEPC8\n\n\nPlantronics CS50 900 MHz Wireless Office Headset System: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n1.0\n\n\nUnreliable - voice unit keeps breaking\n\n\nSeptember 20, 2006\n\n\nJ. A. Chen \"Loss4words\"\n\n\nNYC\n\n\nMy company supplies these cordless headsets to its employees so I have alot of experience using headsets. This is the latest upgrade and it is the worst unit I have ever used. It works great for about 3 or 4 months on average. After that, the voice unit stops working. I can hear others speaking to me but no one can hear me. I have gone through 4 of these units in 13 months and the longest lived unit lasted < 5 months. One broke after < 2 months. Other colleagues have complained about the same thing happening with their units. After the experiences I have had with this piece of junk, I would never buy anything from Plantronics with my own money. Avoid this rubbish\n\n\n\n\nB000068IG0:hp_has_lost_me_as_a_customer..:andy_white_\"http://www.cartoonservices.com\"\n\n\nB000068IG0\n\n\nHP Color LaserJet 4600 Printer: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nHP has lost me as a customer..\n\n\nSeptember 28, 2006\n\n\nAndy White \"http://www.cartoonservices.com\"\n\n\nFort Worth TX USA\n\n\nI'm a cartoonist. I need to be able to print color images for clients. I bought this printer two years ago from Staples, for $2500! Worked great for a couple months, after which the toner cartridges started leaking and the print quality rapidly dimished.\n\nDO NOT BUY THIS PRINTER!\n\nI'm reluctant to buy anything else from HP, knowing that how it performs on the sales floor can be markedly different from how it will perform after a couple months of light use\n\n\n\n\nB000068IG0:total_garbage:d._mullins_\"ddmj\"\n\n\nB000068IG0\n\n\nHP Color LaserJet 4600 Printer: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nTotal Garbage\n\n\nApril 14, 2005\n\n\nD. Mullins \"ddmj\"\n\n\nNiceville, FL USA\n\n\nWe've had this printer at my office for about 2 1/2 years. It's a giant waste of money. Magenta cartridges spew toner everywhere. Cyan cartridges streak. It's even worse when you use the manual feed. HP has no answers because they deny all the issues. HP's support is lackluster at best anyway. The first time when this machine was under warranty it took me three weeks worth of phone calls to get anything accomplished with them. Stay away from this printer at all costs. If you own one it makes a nice doorstop or paperweight. This is totally unacceptable considering toner and printer costs. I expect more from a product with the HP brand stamped on the box. To bad you can't give 0 stars\n\n\n\n\nB000068IG0:magenta_toner_leaks_on_this_model:joseph_c._ross\n\n\nB000068IG0\n\n\nHP Color LaserJet 4600 Printer: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n1.0\n\n\nMagenta toner leaks on this model\n\n\nMarch 17, 2005\n\n\nJoseph C. Ross\n\n\n\n\n\nObviously HP has a design flaw on the magenta cartridges. I have also experienced leakage from only the magenta (why only this one?\n\n\n\n\nB000068IG0:same_as_the_others:michael_n._singer_\"sms8634\"\n\n\nB000068IG0\n\n\nHP Color LaserJet 4600 Printer: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nSame as the others\n\n\nOctober 20, 2004\n\n\nMichael N. Singer \"sms8634\"\n\n\nFair Lawn, NJ United States\n\n\nPowder from the toner cartridges get all over the inside of the printer and then ruins every page that is printed. Every page that is printed from my printer has a greyish color due to the powder dirt inside. It is impossible to clean it since the powder is everywhere. The only way to get it clean would be to bring it to a car wash (obviously this would leave the printer with bigger problems than page color). What a waste of money!!! Good thing my office bought it and not me or I would be furious\n\n\n\n\nB0002CWPV8:terrible..._spend_the_money_and_get_the_d-link_dwl-g730ap_:julie_fey_\"american_expat\"\n\n\nB0002CWPV8\n\n\nNetgear WGR101 Cable/DSL Wireless Travel Router: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nTerrible... spend the money and get the D-Link DWL-G730AP \n\n\nApril 29, 2006\n\n\nJulie Fey \"American Expat\"\n\n\nSongkhla, Thailand\n\n\nWe travel quite a bit, both business and pleasure, domestic and international... so the idea of a pocket wireless router was a God send. We bought this one at the beginning of our annual leave (aka tour the US in a month). We'd heard good things about the D-Link but with the rebates, this one was practically free.... there was a reason it's always on sale. \nIt took forever to set up. The old firmware is buggy, the new firmware is difficult and touchy to install. The router itself does not seem to be compatible with most personal cable/DSL modems. Out of the eight houses we were in, it would only work with one, although it appeared to connect with several others. It did work with the Ethernet connection of about 70% of the hotels... but that's not a great average. It also gets very very hot and than will quit working until it cools down again. Frustrating.\nAfter the first week we went out and got the pocket D-Link.. excellent- plug it in and it works EVERYWHERE: personal homes, questionable modems, hotels, etc... we have used it from Alabama to Bangkok and everywhere in between. It's also about half the size of the Netgear. Save the hassle and get it first.\n... also a tip, pick up a little usb wi-fi adaptor with your router. You will find that many people without a wireless router already, will only have a desktop, and you will need to make it \"wireless\" as you will be connecting their modem to your router.\n\n\n\n\nB0002CWPV8:not__acceptable_for_multi-user_or_wep_access:drjulians\n\n\nB0002CWPV8\n\n\nNetgear WGR101 Cable/DSL Wireless Travel Router: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nNot Acceptable for Multi-user or WEP Access\n\n\nJune 15, 2005\n\n\ndrjulians\n\n\nHouston, TX USA\n\n\nI hoping to use the WGR101 to provide wireless, encrypted access to two laptops from a wired ethernet connection in a hotel. It worked fine in single-user, no encryption mode but that is not what I needed. I experienced severe problems trying to use it in single user mode with WEP or in multi-user mode with and without WEP. One or both laptops would lose the ability to connect to the internet at seemingly random intervals. Sometimes cycling the laptop wireless radio would restablish the connection, sometimes I had to reboot the laptops, sometimes I had to reboot the WGR101, and sometimes I had to reboot the laptops and the WGR101. Stay away from this product unless single user, unencrypted access is what you need\n\n\n\n\nB00006HOKR:great_while_it_lasted,_but:rocket_scientist\n\n\nB00006HOKR\n\n\nSangean DT200V AM/FM/TV Portable Pocket-Size Radio: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n2.0\n\n\nGreat While It Lasted, but\n\n\nJune 7, 2006\n\n\nRocket Scientist\n\n\nHuntsville, Alabama\n\n\nMy Sangean DT200V pocket radio failed after 2.5 years of infrequent use. I kept it in a padded pocket of my briefcase where it experienced no significant shocks or temperature extremes. It worked perfectly on the few occasions when I used it over the 2.5 year period, until yesterday. The buttons, switches, and display still work perfectly, but only very faint white noise can be heard, whether via speaker or via earphone. The volumn of the faint white noise is not significantly affected by the volumn control. The batteries are new, and there is no corrosion from the previous batteries. \n\n\n\n\nB00006HOKR:serious_problems:csl\n\n\nB00006HOKR\n\n\nSangean DT200V AM/FM/TV Portable Pocket-Size Radio: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n2.0\n\n\nSerious problems\n\n\nApril 16, 2006\n\n\nCSL\n\n\nNW USA\n\n\n\n\n\nPerhaps I got a lemon, but I feel wary of buying another DT200V for 2 reasons:\n\n\n1. From the day I got it, the power button was so sensitive that a tiny waft of air (no kidding) would turn it off (or back on) at whim. Right in the middle of a song, or talk, this shortly became maddening.\n\nSince this is unacceptable for a $50-range radio; and since I didn't want to spend as much money mailing it to and fro as it was worth, instead of sending it back -(OH looking back, how I should have!)- I opened it up myself, and had to physically shave down the plastic underside of the on/off button where it touched the internal power switch. As I couldn't conversely move the guts further from the button, this was the only solution.\n\nThankfully this worked, and the on/off button did not give me further problems after this.\n\n\n\n\nHowever,\n\n2. Not many weeks or months of use in, the radio developed this habit of shorting out or something to where my pre-set stations just scanned as blank 'fuzz' (no station, not even 'intelligent' fuzz/crackle like it knew a station existed anywhere on the planet) and I could get zero stations of anything at all--not FM, not AM, and no TV.\n\nThis would happen right after turning the radio on, and I should note that I always used headphones (not the ones supplied; as with most, throw them away, they're not worth it). It seemed to me through trial and error that just turning the radio on was fine, but the act of plugging in some headphones joggled some connection or shorted something out, and did this.\n\nI tried several different pair and types of headphones (some with noise cancelling switches, some without, and with these On and Off), and used the 'lock' switch on the radio both in the on and off position while plugging in my headphones (as the company I bought them from recommended turning the lock switch on when changing batteries, to prevent shorts), but nothing I have done has prevented this from eventually happening again...repeatedly.\n\nIt is really annoying when it does, as removing the batteries (which always will lose all your presets, by the way) and waiting several minutes rarely clears the short.\n\nWeirdly, the thing I've found which clears the short or re-makes whatever connection is messed up correct itself, is opening up the back (screwdriver required) and kind of pushing the guts (board) one direction or another, kind of scooting it (there's minimal movement available, though), and saying a little prayer and putting the whole thing back together again. Most of the time, that has worked. Occasionally, I've had to repeat the process 3-5 times before it will.\n\nSometimes it lasts several weeks before this'll happen again, other times, it happens multiple times a day. The day I can't fix it (I don't even know what I'm doing that's fixing it!), I'll throw it across the room, against a wall, and I probably _won't_ buy another one.\n(If I do, I'm keeping the receipt, NEVER opening up the back as it voids any warranty, and returning it (not for repairs--for a refund!) the split second it gives me any trouble of any kind.)\n\n\n\n\n\nFor all this, I grudgingly say that it does have very good reception quality of FM (I almost exclusively listen to this), pulls the stations in nicely, and has very low amount of static. When one particular station does, it's usually more the fault of distance, or we're getting out of range, or the batteries are very low- that's at fault.\n\nIndoors in a computer-saturated office-cubby environment and not near a window, though, I'd have to say, it has had some significant problems pulling in any more than 3-4 strong-signal stations without major static. But your office environs may vary. (Some are more electrical field saturated than most.)\n\nThe FM (musical) sound quality itself is good/decent, and I might say better than many...but my 1 criticism on it is a certain 'canniness' of sound quality (across various headphones I've plugged in). It's lacking some fullness of the midrange, and warmth. Also, the bass is not what it could be. I'm not sure given its overall sound quality signature if the addition of a bass boost button would have helped it or not.\n\nFor me, clarity and volume have helped me ignore some of the lesser aspects of the DT200V's sound signature. However, shopping for my next purchase, that will move up higher in importance\n\n\n\n\nB00022TN9A:very_cheap_and_\"plasticy\":dave\n\n\nB00022TN9A\n\n\nLogitech Cordless Desktop - Black: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n1.0\n\n\nVery Cheap and \"Plasticy\"\n\n\nJanuary 28, 2006\n\n\nDave\n\n\n\n\n\nThis has to be the worst keyboard I've purchased. It's my first wireless. I've used and sold several keyboards and none have fealt as cheap as this.\n\nWith the added snap-on plastic palm rest it sets the base up too high. Without it there is a 'hump' where your palms go that puts your hands an an uncomfortable angle.\n\nThe plastic is really bad. It feels really cheap and slick. \n\nBefore I purchased this I had been trying to get in tough with Logitech about their products and compatability but they never bothered to respond. \n\nSo, bad product....company on the slide.....doesn't add up to a good value. \n\nLogitech used to be the best company out there...or at least one of the best...\n\n\n\n\nB00022TN9A:disappointed_with_the_mouse:stephen_matthews\n\n\nB00022TN9A\n\n\nLogitech Cordless Desktop - Black: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\ndisappointed with the mouse\n\n\nJuly 14, 2005\n\n\nStephen Matthews\n\n\nCrisfield, MD USA\n\n\nThough I like the keyboard, I've found two serious problems with the mouse. First, it is very sensitive to movement. I've tried making some adjustments but it is still difficult to control. Finally and more importantly, the mouse burns a set of AA batteries about every 8 days. I only use the mouse about 6 hours a week so I should be getting a lot more battery life. I've bought other Logitech products and have been pleased with them but in this case I wish I had bought another brand. \n\n\n\n\nB00005UEA6:slower_than_molasses--until_i_formatted_it.:fred_s._hann\n\n\nB00005UEA6\n\n\nViking 128 MB Secure Digital Card (SD128M): Electronics\n\n\nelectronics\n\n\n20 of 27\n\n\n1.0\n\n\nSlower than molasses--until I formatted it.\n\n\nOctober 16, 2004\n\n\nFred S. Hann\n\n\nDestin, FL USA\n\n\nNOTE TO EVERYONE WHO IS HAVING PROBLEMS WITH THIS CARD--I reformatted it in my camera and it works fine now!!\nInitially I wrote:\nI was very disappointed in the shot-to-shot speed with this card (using a Minolta Xg). The Panasonic card that came with the camera, which I thought would be cheap, is THREE TIMES FASTER than this \"upgrade\". This card is useless to me as it cripples the camera I bought it to work in. It takes FOURTEEN SECONDS from one shot to the next with this card. With the original card, it's less than four seconds. The claim that this is a \"high speed\" card is dishonest. This is the slowest card I've ever used. I will throw this card away rather than curse someone else with it. \n\n\n\n\nB00005UEA6:doa:d._n._weaver_\"orhuskerfan\"\n\n\nB00005UEA6\n\n\nViking 128 MB Secure Digital Card (SD128M): Electronics\n\n\nelectronics\n\n\n16 of 18\n\n\n1.0\n\n\nDOA\n\n\nAugust 7, 2004\n\n\nD. N. WEAVER \"orhuskerfan\"\n\n\nSalem, OR United States\n\n\nThe first card I received was defective. Even after formatting it in a camera, the image files could not be transferred to any of 3 computers with 2 different O/Ses via USB cable to the camera or card reader. I'm waiting on a replacement. Hope it works\n\n\n\n\nB0006OCZHG:all_setup_as_described_here,_unit_not_stable.:fabian_lamaestra\n\n\nB0006OCZHG\n\n\nLinksys WGA54AG Game Adapter 802.11A/G 54 Mbps: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nAll setup as described here, UNIT NOT STABLE.\n\n\nNovember 21, 2006\n\n\nFabian Lamaestra\n\n\nReston, VA\n\n\n \nAfter reading the detailed reports here for how to setup this Linksys gaming adapter unit, everything worked great for several days. I setup the fixed IPs, and all the rest of the settings through the web interface, all seemed well. \n\nNote: I'm a network engineer, so the dual-fixed-ip recommendation [one for each segment] is actually pretty normal in other network bridging appliances. I suspect the MS game adapter simply performs the DHCP handshake better than this unit and that is why it is so easy to setup. \n\n\nThen the weird stuff started. \n\nAfter having had this unit turned on for several days, I notice the connection would become slow and not quite as fast as when the Linksys is first turned on. Yet, the XBOX still shows the signal between 48 and 54 Mbps. Strange. \n\nI have reproduced this several times, booting the linksys device, then playing all kinds of games online for hours, running XBMC and playing media through the network perfectly. All is good for several hours, but come back the next day after leaving the Linksys on all day/night, turn on the XBOX, and the network connection is spotty again, but the link details show a full speed connection in the XBOX wireless interface details. \n\nA swift power cycle of the Linksys unit solves the problem immediately, but why should I need to do this? I suspect some NIC buffer is being filled on the Linksys and the power cycle clears out the unit's network buffers. \n\nIf anyone has figured out what settings to use to keep this thing stable, I am ready to listen, maybe I should select a G-only network and set the speed at 54 or 48 fixed, maybe that will work? \n\nAnyway, I am getting tired of power cycling this thing every day. \n\nGood Luck, \nF. \n\n\n\n\nB0006OCZHG:incompatible,_no_support:guy_terry\n\n\nB0006OCZHG\n\n\nLinksys WGA54AG Game Adapter 802.11A/G 54 Mbps: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nIncompatible, no support\n\n\nAugust 31, 2006\n\n\nGuy Terry\n\n\nLittle Elm, TX\n\n\nI purchased this device to add to my wireless network, which uses a Belkin wireless router. I had to connect the router directly to the game adapter since it could not be detected by my wireless router. After configuring it, I could never find it once it was disconnected from the ethernet cable from my wireless router. I made three attempts at customer service - my first call was disconnected after 20 minutes, the second call was working with a woman in India, who's headset had a ton of static to add to her accent. I had to end that session because we couldn't hear each other. I then used online chat, and after 90 minutes I still had not solved my problem, and that session was disconnected for unknown reasons. The lady from India did mention that mixing their product with any other wireless router would be a problem, and now I believe her\n\n\n\n\nB0000632G0:koss:\n\n\nB0000632G0\n\n\nKoss KD250 DVD Player: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n2.0\n\n\nKOSS\n\n\nMay 6, 2004\n\n\n\n\n\n\n\n\nDON'T BUY IT! I bought a KOSS DVD player and it broke in 6 months. I was told I was out of luck because the warranty is only 90 days. They do not do repairs. My only option is to buy a new one and wasn't even offered a discounted price for it. I have seen several complaints like mine about KOSS on the web. They make JUNK and they make it to last just to get past the warranty period and then it breaks. Oh, by the way I loved it until it broke\n\n\n\n\nB0007U0INM:caveat_emptor:mark_n._conry\n\n\nB0007U0INM\n\n\nToshiba Gigabeat MEG-F40S 40 GB Digital Audio Player Silver: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nCaveat Emptor\n\n\nOctober 23, 2006\n\n\nMark N. Conry\n\n\nCentennial, CO United States\n\n\nI recently purchased a refurbished Toshiba Gigabeat F40 for about $150. Quite a bargain for a 40-gig MP3 player. However, upon reflection, I regret the purchase.\n\nThe Gigabeat hardware seems to be OK, with good sound quality etc. The major failing is with the included software. As other reviews have pointed out, the Gigabeat will only play encrypted tracks. The Gigabeat is supplied with Gigabeat Room application software to provide the necessary encryption. Gigabeat Room starts automatically when connected to the PC via the USB port. From the time the Gigabeat is connected till the Gigabeat Room application is displayed on the screen is 30 minutes. If you switch from viewing the Gigabeat file directory to the PC file directory, the application hangs. If you try to update your track library, the application hangs. I have yet to get through a single operation from beginning to end without the program hanging. Every time the Gigabeat Room hangs, you have to restart the application, with yet another 30-minute wait to resume. The file directory on the Gigabeat is usually out of sync with what is actually present on the hard drive. Microsoft Media Player version 10 may also be used to synchronize the Gigabeat. I have a 35-gig library. It took Media Player 18 hours to encrypt and copy all 8100 tracks to the Gigabeat. In short, be prepared for a maddening and time consuming experience with this player. Software that gives error messages such as \"The Program Is Quitted\" and with such obvious fragility may have been expected in 1986 but is absolutely unreasonable in 2006.\n\nIn my estimation you would be much better off purchasing an MP3 player, even at greater cost, that can play MP3 files directly, with file maintenance provided by Windows Explorer and or Media Player.\n\n\n\n\nB00006JPFT:not_satisfied.:d._arnoczky_\"deba\"\n\n\nB00006JPFT\n\n\nCoby CA-706 CD/MD/MP3 Car Kit Adapter: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nNot satisfied.\n\n\nJuly 31, 2006\n\n\nD. Arnoczky \"DebA\"\n\n\nNorth Olmsted, OH USA\n\n\nEven the slightest bump cause the casette to pop out and stop the CD. I will be returning this to Amazon for a refund\n\n\n\n\nB00006JPFT:coby_universal_portable_cd/md_car_kit:m._a._vodjansky_\"mav\"\n\n\nB00006JPFT\n\n\nCoby CA-706 CD/MD/MP3 Car Kit Adapter: Electronics\n\n\nelectronics\n\n\n23 of 36\n\n\n1.0\n\n\nCoby Universal Portable CD/MD car kit\n\n\nJanuary 3, 2004\n\n\nM. A. Vodjansky \"mav\"\n\n\nWinlock, Wa USA\n\n\nThe picture shows the cassette feature along with the car kit. The cassette did not come with the kit. Very disappointed\n\n\n\n\nB0006ZACJ8:not_a_good_product:drew_ragan_\"drew\"\n\n\nB0006ZACJ8\n\n\nDLO iBoom Boombox for iPod White: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n2.0\n\n\nNot a good product\n\n\nMarch 15, 2006\n\n\nDrew Ragan \"Drew\"\n\n\nWoodstock, GA United States\n\n\nI purchased this to have a stereo for my iPod for primary use in my office, but to take with me other places. I have the 30 gig Video ipod - it fits loosly. The entire system seems cheaply made. As I mentioned, I wanted to use it in my office - well, at the lowest volume control, it is not very quite, so I would annoy my co-workers if I turned it on. Bottom line - Save your money!!\n\n\n\n\nB0008JFJRC:needs_significant_improvement:gerryc\n\n\nB0008JFJRC\n\n\nMotorola MD7081 5.8GHz Digital 2-line Phone: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n2.0\n\n\nNeeds Significant Improvement\n\n\nSeptember 21, 2006\n\n\nGerryC\n\n\nNorthport, NY\n\n\nI replaced a Siemens 8825 with the Motorola 7081 and was, unfortunately, really disappointed. The 8825 while rich in function, is quite lacking in quality. So what's good about the 7081 is:\n\n1.\tGreat design and feel, especially the handset.\n2.\tNice look to the unit.\n3.\tWell-lit displays.\n4.\tEasy to understand voice prompts when accessing remotely.\n5.\tReasonable range.\n6.\tNice feel to the buttons.\n7.\tFairly easy to go through the setup.\n8.\tAbility to sort the phonebook alphabetically.\n9.\tBase has phonebook that can be shared by the handsets.\n10.\tMotorola customer support (excellent).\n\nNow what's not good:\n\n1.\tEven at maximum volume, you can barely hear the speakerphone on \n the base.\n2.\tSound on the handset is distorted, fuzzy. The person on the other\n end hears a hiss. The volume is adequate, but barely.\n3.\tCannot search the phonebook. If you sorted it alphabetically and\n need to access a name starting with \"Z\" you have to scroll through \n the entire phonebook.\n4.\tCan't record an outgoing message remotely.\n5.\tBattery level indicator on handset is useless. When the handset is \n in the charging cradle, it shows a charging indication at all\n times. When it is out of the cradle, it doesn't show anything.\n\nI really wanted to like this unit because it has such a nice look and feel to it but the poor sound quality is a showstopper.\n\n\n\n\n\nB0008JFJRC:unacceptable:j._moore\n\n\nB0008JFJRC\n\n\nMotorola MD7081 5.8GHz Digital 2-line Phone: Electronics\n\n\nelectronics\n\n\n16 of 24\n\n\n1.0\n\n\nUnacceptable\n\n\nOctober 26, 2005\n\n\nJ. Moore\n\n\n\n\n\n\nI have had this phone for 20 hours - and am returning it.\n\nThere is static noise on the line when people talk on the other end. The echo of my voice from the earpiece is very distracting - I find it very disconcerting.\n\nThe wall mount bracket causes the base to stick straight out from the wall - and as a previous reviewer mentioned, is usually up to your nose given the location of most wall mounts. This strikes me as an unexcusable oversight on Motorola's part. Either make it desk only - or do the wall mount right.\n\nThe speaker for the speakerphone feature is in the back of the phone - while the microphone is in the front of the phone. Dumb design - I feel like I'm either not listening or not being picked up when talking.\n\nThe volume on the base speakerphone is too low at highest setting.\n\nI'm very disappointed in this phone. Much like a previous reviewer, the 1 star is only because I couldn't give it a 0 star rating\n\n\n\n\nB0002OZXHO:ipod_is_great_-_itunes_makes_it_suck!:rachel_alice_hunter_\"rachelsfx\"\n\n\nB0002OZXHO\n\n\nApple 20 GB iPod M9282LL/A�(4th Generation): Electronics\n\n\nelectronics\n\n\n5 of 7\n\n\n1.0\n\n\niPod is Great - iTunes makes it suck!\n\n\nAugust 28, 2006\n\n\nRachel Alice Hunter \"rachelsfx\"\n\n\nFlorida USA\n\n\nAfter using RCA and Creative (both suck), I decided to try an iPod--this one since only a moron watches video on a 3-4 inch screen.\n\niTunes doesn't like M$ too much I guess. iTunes is really crappy software. If you have a big WMA audio collection, go elsewhere. Conversion causes sound quality loss especially if you ripped at 64kbps. Protected files can't be converted so you have to burn to a CD then let iTunes rip it.\n\nIf you don't use iTunes to buy music, 5 stars. If you do, 1 star.\n\nThe iPod, which I returned after discovering it would take months to convert to iPod compatibility, is flawlessly designed and takes a licking.\n\nGoogle \"itunes problems\" and you'll find a lot of hate for iTunes.\n\nMy problem: why NOT support WMA files? iPod would grab 99% of the market if they did and kill the upcoming M$ MP3 player that will even play iTunes stuff\n\n\n\n\nB00065XSP8:the_volumen_is_not_enough:hernan_espinoza_ch\n\n\nB00065XSP8\n\n\nMacally Podwave Portable Stereo Speakers for iPod: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nthe volumen is not enough\n\n\nNovember 2, 2006\n\n\nHernan Espinoza Ch\n\n\n\n\n\nWhen I put this device in my mp3 player, I espect listen the music but I need a room completly silent and obviusly that no was my intention.\n\n\n\n\nB0001J3Q9W:killed_my_ipod_:thomas\n\n\nB0001J3Q9W\n\n\nBelkin F8V7067-APL Mobile Power Cord for iPod: Electronics\n\n\nelectronics\n\n\n2 of 4\n\n\n1.0\n\n\nkilled my ipod \n\n\nMay 17, 2006\n\n\nThomas\n\n\nArizona\n\n\nI was using this power cord in conjunction w/ the belkin fm transmitter during a recent 4 hour car ride. when I unplugged the ipod, it was incredibly hot, and now it does not work (I get the folder w/ the exclamation point on the display). I tried to restore the ipod, but it is toast\n\n\n\n\nB00064L1AI:didn't_work!:q._nguyen\n\n\nB00064L1AI\n\n\nSamsung SIR-T451 High Definition Terrestrial Tuner: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n1.0\n\n\nDidn't work!\n\n\nNovember 6, 2006\n\n\nQ. Nguyen\n\n\nMinneapolis\n\n\nI was extremely excited when I received this product and have had good success with Samsung products. However, when I made all the connections, the unit didn't work. I was disapointed and frustrated. All the connections and settings were correct, but it didn't work. I have an older DVI connection instead of the newer HDMI digital connection.\n\nI returned the product and bought the Humax brand HDTV tuner. I bought a DVI to HDMI cable and connected the unit. It works like a charm! I don't know what was wrong with the Samsung SIR-T451, but I wouldn't recommend this product to anyone. I'm also having second thoughts about Samsung products now. It seems like the reliability/quality factor of Samsung products has declined now that they are more mainstream\n\n\n\n\nB000261N6M:not_worth:viswaprasad_karunakaram\n\n\nB000261N6M\n\n\nCoby DVD-224 Compact DVD Player: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nNot worth\n\n\nNovember 21, 2006\n\n\nViswaprasad Karunakaram\n\n\nPhoenix, AZ\n\n\nThis player worked for 2 months. Then all of sudden it started failing with \"no disk\" message. Please do not go for cheap players\n\n\n\n\nB0006ZKX7Y:warning!_batteries_die_quickly!!:silent_watcher\n\n\nB0006ZKX7Y\n\n\nThink Outside Bluetooth Stowaway Travel Mouse for Pocket PC PDA: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nWarning! Batteries die quickly!!\n\n\nJanuary 19, 2006\n\n\nSilent Watcher\n\n\nNY, USA\n\n\nI've had it for one week only and I'm using 3rd pair of Duracell batteries right now! I was using it for 3-4 hours per day (I have another mouse at work). Stayed switched off for the rest of the day in order to save batteries. Nice mouse when it works.. Mine is piece of junk, maybe the latest batch they've got was not up to standard. So I totally agree with Mr. Loftin.. And it's silver one in my case, not a black one I've ordered..\n\n\n\n\nB0006ZKX7Y:stay_away!!!__eats_batteries!!!:scott_m._loftin\n\n\nB0006ZKX7Y\n\n\nThink Outside Bluetooth Stowaway Travel Mouse for Pocket PC PDA: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n1.0\n\n\nStay Away!!! Eats Batteries!!!\n\n\nJanuary 16, 2006\n\n\nScott M. Loftin\n\n\n\n\n\nI can use it for about 5 working days and Ka-Put! (Even though I switch it off when I'm not using it.) There is no intelligible low battery indication or warning either. Also when I changed batteries, the battery contacts fell out. Poor quality\n\n\n\n\nB00009KO0D:not_by_apple!!!!!!:b._sheehan\n\n\nB00009KO0D\n\n\nApple - Data cable: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nNOT BY APPLE!!!!!!\n\n\nJune 24, 2006\n\n\nB. Sheehan\n\n\nPhilly\n\n\nI wanted an original apple usb cord for my iPod, and the description said this is, but it isn't. So if you're like me and you want the original, don't buy this one\n\n\n\n\nB000E5XHGK:not_what_i_thought:treble\n\n\nB000E5XHGK\n\n\nApple MA242LL/A iPod AV Connection Kit: Electronics\n\n\nelectronics\n\n\n8 of 20\n\n\n1.0\n\n\nNot what I thought\n\n\nFebruary 22, 2006\n\n\nTreble\n\n\nArizona\n\n\n This product is not very good. There is no display on the TV, just a black screen. The remote, which is just a pause button anyways with no menu functions, does not make a difference because you are unable to see your small Ipod screen from far away. The overall video quality is quite low, even with an s-video cable\n\n\n\n\nB000CSP8IO:stay_away_from_this_product:leduberto_zepulturero\n\n\nB000CSP8IO\n\n\nGriffin Technology 9781-TRP30BK iTrip FM Transmitter with LCD and Dock Connector for iPod: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nstay away from this product\n\n\nNovember 9, 2006\n\n\nLeduberto Zepulturero\n\n\nNYC, NY\n\n\nAvoid this product at all costs!!! I should've listened to the other reviews but NOOOO, I just had to see it for myself. This product worked (barely) for 2 weeks then just totally died. In the short time that it worked, the sound quality was just horrible. No matter what free channel you play it on. I have a good 40 mile drive one way (not city or metropolitan driving) and in that span, I'll have to switch channels between 3 to 5 times. \nKudos to Amazon's hassle free return policy, I was able to return it and get the Belkin TuneBase FM which I highly recommend\n\n\n\n\nB0009U6YQK:the_worst:t._b._clavel_\"jb_clavel\"\n\n\nB0009U6YQK\n\n\nCTA DIGITAL iPod Fold-Up Amplified Aluminum Speakers ( White ): Electronics\n\n\nelectronics\n\n\n21 of 23\n\n\n1.0\n\n\nthe WORST\n\n\nNovember 20, 2005\n\n\nT. B. Clavel \"JB Clavel\"\n\n\nNew York\n\n\nNot at all durable due to very, very cheap construction. I had to massage my batteries to get the speakers to play. A total waste of time to purchase, try to use and return. Couldn't even give these away\n\n\n\n\nB0002YY1HW:phone_stops_ringing:dave_\"jungleman_techy\"\n\n\nB0002YY1HW\n\n\nAT&T 974 Small Business System Speakerphone with Intercom and Caller ID/Call Waiting (Titanium Blue): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nPhone stops ringing\n\n\nNovember 9, 2006\n\n\nDave \"jungleman techy\"\n\n\nMiami, FL\n\n\nI have a VOIP setup with voicepulse. This phone works great as long as I don't assign phone numbers to the presets. When I do it stops ringing after a few days. The lights flash indicating an incoming call and I can answer the phone if I notice the lights flashing but it doesn't ring. If I reset the phone to defaults, erasing all presets, then it works OK. I got a new one from AT&T and it does the same thing. I love the headset button to switch between speaker and headset. Let me know if you know a workaround\n\n\n\n\nB0000A8Y5K:barely_works_-_low_audio_quality:andrew_trieger\n\n\nB0000A8Y5K\n\n\nPanasonic KX-TCA92 Hands-free Headset with Comfort Fit and Fold Design: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nbarely works - low audio quality\n\n\nJune 14, 2006\n\n\nAndrew Trieger\n\n\n\n\n\ncallers can barely hear me, even with the mic right in my mouth (volume adjustment on cord doesnt affect it).\nand even worse, callers hear an echo of themselves, the mic picks up the caller in the earpiece enough to resend it! (echo!). Must be because its all on the same arm or something crappy. This makes it basically unusable, so waste of money, throw away, going to buy a better one\n\n\n\n\nB00005UK9X:incorrectly_described:j._ramirez_\"jose\"\n\n\nB00005UK9X\n\n\nGarmin Portable Friction Mount for GPS 010-10306-00: Electronics\n\n\nelectronics\n\n\n9 of 9\n\n\n1.0\n\n\nIncorrectly described\n\n\nSeptember 22, 2006\n\n\nJ. Ramirez \"Jose\"\n\n\nPiscataway, NJ\n\n\nThis Item is not described correctly. This ITEM is NOT for Garmin c550 or Any of the C line. I had to returned it at my OWN EXPENSE. AMAZON please correct the description of the ITEM\n\n\n\n\nB00004Z0BN:metal_stablized_system...:b._roy_\"roytech\"\n\n\nB00004Z0BN\n\n\nSennheiser MX-400 In-Ear Headphones: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nMetal stablized system...\n\n\nOctober 10, 2006\n\n\nB. Roy \"RoyTech\"\n\n\nSunnyvale, CA, USA\n\n\nOn the back of the package it says - \n 1) Transparent sound and powerful bass. - The \"Transparent sound\" part is true and \"powerful bass\" part is totally untrue.\n 2) Metal stabilized system for high levels. - What was that again? Metal stabilized system?? It has antiroll crossbars I guess!!\n\nWhen companies start making vague claims about their product - thats when you know that there is a problem.\n\nThese sounded terrible at first - no bass - period. \n\nThe sound changed for the better once I put on the foam covers. I could hear some bass at that point but even after equalization, it was clear that this product is designed to deliver a lot of high frequency, decent midrange but little by way of bass - even though it claims to have frequency response going from 18Hz to 20kHz. It fits my ears properly, so that is not a problem. \n\nThis product sounds far inferior to a pair of \"Aiwa\" ear-buds that came with a \"Made in Japan\" walkman purchased in 1990!\n\nI wouldn't even compare the sound of these MX400 to the excellent Koss over the ear headphones I purchased recently.\n\nMaybe they are better than the IPOD headphones (which suck even worse), and the reason people give this 5 stars. Really - no more than a 2 star product.\n\nExpected much better from Sennheiser. With this experience, I've decided not to go for their noise cancelling headphone and instead go with the one from Panasonic. Reputable companies should think twice before selling junk in the marketplace\n\n\n\n\nB000067SVG:useless_-_does_not_retract:james_murphy\n\n\nB000067SVG\n\n\nTargus PA220U Retractable USB Cord: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n1.0\n\n\nUseless - does not retract\n\n\nApril 6, 2004\n\n\nJames Murphy\n\n\nPelham, MA USA\n\n\nI agree with the previous review. This phone cord worked twice. Now, it only retracts about half way, and keeps getting worse. I'm returning it for a refund. Don't waste your time with this\n\n\n\n\nB00009YUOF:the_worst_technical_support:chas\n\n\nB00009YUOF\n\n\nPNY P-FD256U20-RF 256MB USB 2.0 Flash Drive: Electronics\n\n\nelectronics\n\n\n7 of 9\n\n\n1.0\n\n\nThe worst Technical support\n\n\nJuly 5, 2004\n\n\nCHas\n\n\nWisconsin\n\n\nIf you have problems with your flash drive ( like I did) don't count on any technical support. Lines are all automated and wait is always over 20 minutes for live support. Not to mention that it disconects you when it is done telling you this. PNY tech support is a joke\n\n\n\n\nB0002GWP46:brand_new_and_its_broken!!!:hotwhipt/a_\"hotwhip\"\n\n\nB0002GWP46\n\n\nHP DeskJet 3845 Color Inkjet Printer: Electronics\n\n\nelectronics\n\n\n2 of 8\n\n\n1.0\n\n\nBrand new and its BROKEN!!!\n\n\nMarch 27, 2006\n\n\nHotWhipT/A \"HotWhip\"\n\n\nTx\n\n\nI'm mad. I bought this one to replace my old one. This one and the other one are doing the samething. I had a chat with an HP service helper and they determined that it was broke. Their customer service is top notch thought! They are sending me a new one for free and apearently I get to keep the ink cartidges for free because of my troubles\n\n\n\n\nB0009T8HNE:i've_had_dish_networks_for_two_years_now.:jt\n\n\nB0009T8HNE\n\n\nDish Network Dish 500 Satellite Dish Assembly w/o LNBF: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nI've had Dish Networks for two years now.\n\n\nNovember 18, 2006\n\n\nJT\n\n\nSan Jose, CA United States\n\n\nI like the Dish Networks technology and the potential that it could be. One DVR which covers two rooms is great. The picture is clear when it works. The menus/programming are intuitive. The pricing is reasonable.\n\nBut, my complaint is RELIABILITY and SUPPORT...for the two years I have had Dish my TV picture seems to go out every 6 months or so. Everytime I talk to Dish customer service I get a different reason why it could be out. Several times I have gotten new DVR boxes mailed to me and there is always a dispute about who should pay. Several times I have had Dish Net come out to my house and they have always tried to make me pay until I told them I'm tired of the hassel and just cancel me. Now my dish is out again this weekend and I am missing the Ohio State / Michigan game because my TV says no Dish Signal (reason tbd).\n\nAnyway, if it worked consistently as advertised I would give it 4 or 5 stars. But due to reliability I give it 2 stars. And with Murphy's Law it always goes down when you want to watch that big event (prompting me to write this review instead of watching the game today)!! I may have to switch back to cable.\n\n - J.Clark / San Jose, CA\n\n\n\n\nB000EWD4TS:no_technical_support_available:data_guru\n\n\nB000EWD4TS\n\n\nSanDisk 2 GB Cruzer Micro with U3 ( SDCZ6-2048-A10, Retail Package): Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n1.0\n\n\nno technical support available\n\n\nOctober 7, 2006\n\n\nData Guru\n\n\nNew Jersey\n\n\nThis product stopped working a few days after purchase. 5 phone calls and 2 emails were not returned by SanDisk. This product did not work as advertised and no technical support of any kind is available. I would not buy this brand again\n\n\n\n\nB000BJ3BRO:olympus_ws-320m_:haim_perlstein\n\n\nB000BJ3BRO\n\n\nOlympus WS-320M 1 GB Digital Voice Recorder and Music Player: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n2.0\n\n\nOlympus WS-320M \n\n\nNovember 16, 2006\n\n\nHaim Perlstein\n\n\n\n\n\nThe Voice recorder meets all my expectations and more\nEasy to use, easy to transfer great results\n\n\n\n\nB00003006K:buyer_beware:g._price_\"techo_hobbyist\"\n\n\nB00003006K\n\n\nLinksys EZXS88W EtherFast 10/100 8-Port Workgroup Switch: Electronics\n\n\nelectronics\n\n\n1 of 4\n\n\n1.0\n\n\nBuyer Beware\n\n\nJanuary 21, 2006\n\n\nG. Price \"techo hobbyist\"\n\n\nDetroit, MI USA\n\n\nAll ports began running slow after just 6 months of use. Followed LinkSys RMA process and they sent me a hub in return by mistake. I followed yet another LinkSys RMA process and had to wait TWO MONTHS for the replacement switch covering their mistake. The replacement switch died after 1 week of use. The latest RMA unit ran excessively Hot until the smell of burning plastic left the air and then it would not power up. This is what I have learned about Linksys: #1) This should be a simple network pass-through device and it should not fail unless manufactured poorly - Linksys apparently cannot make a good one. #2) Do not even think about turning a broken unit into Linksys under their RMA process, they will simply send you a refurbished unit and it cannot be trusted. If these cheap switches fail they cannot be fixed and the original manufacture was flawed in the first place. #3) Choose a competitors product - I have had other brand switches running for years with no issues\n\n\n\n\nB0007OWNC2:customer_service_not_helpul:jul9876\n\n\nB0007OWNC2\n\n\nNetgear RangeMax Wireless PC Card (WPN511): Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n1.0\n\n\nCustomer service not helpul\n\n\nJune 26, 2006\n\n\njul9876\n\n\nNew York, NY\n\n\nPurchased this for my laptop (it was supposed to match the Rangemax modem). The adapter was not being recognized by my machine, and I contacted customer service. The customer service rep was obviously from India (suprize!). Unfortunately my inquiry was not resolved and I returned the product back. Probably would explore other brands next time around.\n\n\n\n\nB000EHUR3O:low_power:richard_p._stephens\n\n\nB000EHUR3O\n\n\nMicrosoft Wireless Laser Mouse 5000- Metallic Black ( 63A-00001 ): Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nlow power\n\n\nNovember 9, 2006\n\n\nRichard P. Stephens\n\n\n\n\n\nI don't like it because it is always saying \"low power\" and I do every thing it said to trouble shoot the problem with out and luck fixing the problem\n\n\n\n\nB000EHUR3O:microsoft_5000_laser_mouse:donald_a._sanders\n\n\nB000EHUR3O\n\n\nMicrosoft Wireless Laser Mouse 5000- Metallic Black ( 63A-00001 ): Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n2.0\n\n\nMicrosoft 5000 laser mouse\n\n\nOctober 5, 2006\n\n\nDonald A. Sanders\n\n\n\n\n\nI just purchased this mouse. It has a good feel. It does not scroll as smoothly as I would like. Contrary to what Microsoft says, the buttons are not programmable -- you have to select from a predefined list. I chose to use the small silver buttons on the side as \"copy\" and \"paste\". Neither one works.Another irritation, I am already getting a popup that says the \"signal\" is weak -- about 18\" away, yet the menu says it is fine I got a great buy on it at OfficeDepot (half price), otherwise I would return it\n\n\n\n\nB0001Z5KHC:poor_sound_quality_-_interference_from_other_electronics:k._strong_\"kudzuken\"\n\n\nB0001Z5KHC\n\n\nPanasonic KX-TG5200M GigaRange Supreme 5.8 GHz DSS Expandable Cordless Phone: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nPoor sound quality - interference from other electronics\n\n\nMarch 18, 2006\n\n\nK. Strong \"Kudzuken\"\n\n\nLas Vegas, NV\n\n\nInterference from other electronics is a severe problem - I had to return this item for a refund. If you can locate it several feet from any other electronics, it might work for you, but who wants a phone that you cannot place on your desktop, near a computer\n\n\n\n\nB0000ZKFKA:not_for_hardcore_gamers:damien_brooks\n\n\nB0000ZKFKA\n\n\nGateway 17\" Widescreen LCD Flat-Panel TV: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nNot for Hardcore Gamers\n\n\nAugust 11, 2005\n\n\nDamien Brooks\n\n\nOrlando, FL\n\n\nI am a LONG TIME pc user and pc gamer.\n\nI wanted this for a \n1. tv\n2. pc gaming monitor\n3. xbox gaming monitor\n4. dvd veiwing device. \n\nHere is how I rate this monitor on these four levels on a scale of 1 to 5:\n1. tv = 2\n2. pc gaming monitor = 2\n3. xbox = 1\n4. dvd = 3\n\nThe ghosting for normal operations is almost too much to live with. I returned this item just so you know. The rca inputs gave me only black and white images. This could have been because my monitor was defective. I am not sure.\n\nGhosting during gaming was very bad. Worse than that was it's lack of good brightness control.\n\nThis is not for gamers\n\n\n\n\nB00081QDDO:good_sound..._sucks_for_ipod!!!:darnley_r._hodge_jr.\n\n\nB00081QDDO\n\n\nAlpine KCA-420i iPod control interface: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n1.0\n\n\nGood sound... SUCKS for iPOD!!!\n\n\nMarch 13, 2006\n\n\nDarnley R. Hodge Jr.\n\n\n\n\n\nThe ONLY reason i bought this unit when I saw it in the store was for the iPod controller feature. I really looked forward to being able to control my iPod from the deck and hiding my ipod in my dash completely out of sight. Boy was I disappointed! The iPod adpater works and all... but barely. It takes FOREVER to scroll through the songs, artists or albums. Why the hell would Alpine put out a product that has an inferior scrolling mechanism and then lock you out of the iPod so you can't even select songs from the iPod itself? I have over 3000 songs on my iPod... by the time I scroll to the song I want I've already reached my destination... and that's if I even find the song at all! So now what I have to do is unplug my iPod, scroll to the song I want, then plug it back in. (Luckily the reciever continues to play whatever song was already playing when you plug your iPod in.) But again... that defeats the whole point of having the controls built into the deck. Bottom line, the iPod control feature flat SUCKS. The deck features a \"quick search\" function... but it is uselss... it isn't used to search through artsts or albums... only individual songs and there's no way to skip 10, 20, 30 or 100 songs at a time... only 1 by 1... What's \"quick\" about that? I have 3000 songs and hundreds of artists and albums and Alpine thinks I'm supposed to go through them 1 by 1???? Another annoying thing about this unit is that when you skip through songs while the iPod is playing, the display just says \"song up\" or \"song down\" instead of just showing the name of the damn song! Who thought of that? What possible help could \"song up\" be to anyone? Just show the name of the song while I'm skipping through... that makes more sense right? There are a bunch of other problems I have with the practicalness of this unit when used with the iPod adapter and iPod... but the sound quality is great which is the most important thing... but then again... who cares if the sound is great when it takes you all day to find the song you want to listen to\n\n\n\n\nB00081QDDO:not_good:steven_t._limbert_\"pismo10\"\n\n\nB00081QDDO\n\n\nAlpine KCA-420i iPod control interface: Electronics\n\n\nelectronics\n\n\n13 of 19\n\n\n1.0\n\n\nNot Good\n\n\nJune 8, 2005\n\n\nSteven T. Limbert \"pismo10\"\n\n\nMarshfield, MA United States\n\n\nI bought an Alpine 9847 and the Ipod interface. I works but it totally hijacks your Ipod. The beautiful intuitve, fast controls on the Ipod itself are locked out and you can ONLY control the Ipod from the car stereo. It is therefore very hard to deal with especially if you have a large music catalog on you Ipod. It is very slow, not at all intuitive. Even after getting used to it it is no good. The info on artist, album or song title imports from the IPod very slowly. Turning the volume knob is equivalent to circling on the Ipod but one turn eqauls about 3 spots on the list unlike one circle on the Ipod which equals dozens of spots on the list. Therefore if you have a large catalog you have to spin the volume knob 100 times to get thru a long catalog list which takes forever........Very annoying, very slow, very cumbersome. If only it did not deactivate the normal controls on the Ipod itself. A better and much cheaper solution is to find a car stereo with some form of line in, either stereo mini plug or RCA jacks. The just connect directly and use the IPods own controls which are infinitely better. I replace an old stereo to get the Ipod option and am very sorry I did so\n\n\n\n\nB000B5UINS:junk:john_m._goeke\n\n\nB000B5UINS\n\n\nInsten - Black Gooseneck FM Transmitter with Car Charger for iPod Nano: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nJunk\n\n\nNovember 6, 2006\n\n\nJohn M. Goeke\n\n\n\n\n\nThe only thing this item is good for is charging your ipod. The transmitter is so weak all you hear is static buzz with muffled music in the background. Don't waste your money. And, don't expect any help from the manufacturer who refuses to answer emails or accept a warranty return. \n\n\n\n\nB000B5UINS:insten_-_gooseneck_fm_trans._w/_car_charger_for_ipod_nano_,_:carol_burnett_\"cb\"\n\n\nB000B5UINS\n\n\nInsten - Black Gooseneck FM Transmitter with Car Charger for iPod Nano: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nInsten - Gooseneck FM Trans. w/ Car Charger for iPod Nano , \n\n\nOctober 11, 2006\n\n\nCarol Burnett \"CB\"\n\n\nTokyo, Japan\n\n\nTerrible Design did not fit my car's electical outlet, it does not work with my Jeep Liberty. The jack construction is defective does not even power on\n\n\n\n\nB00005K4PO:font_size_stuck_ridiculously_small:philip_greenspun\n\n\nB00005K4PO\n\n\nBrother PT-1750 Electronic Labeling System: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nfont size stuck ridiculously small\n\n\nOctober 1, 2006\n\n\nPhilip Greenspun\n\n\nCambridge, MA USA\n\n\nMine worked pretty well for about the first 50 labels that I made. Then the font size got stuck ridiculously small so that the message only covers about 10 percent of the tape surface. If you want really small labels with a hard-to-read font size, you could use my PT-1750 to print them out and then cut down with scissors to eliminate all of the wasted label area\n\n\n\n\nB00005K4PO:a_few_problems:\n\n\nB00005K4PO\n\n\nBrother PT-1750 Electronic Labeling System: Electronics\n\n\nelectronics\n\n\n16 of 26\n\n\n1.0\n\n\nA few problems\n\n\nJune 15, 2004\n\n\n\n\n\n\n\n\nI just purchased this product. In the instruction manual, it states that only the Brother AC adapter should be used and any other will invalidate the warranty. Why is it that only a Brother adapter can be used when AC adapters are supposed to be standard? A tactic to increase sales? The adapter itself costs an additional 15 to 20 dollars. In addition, I agree with some of the other reviewers that a significant amount of label is wasted with each printing due to the empty space on either side that must be cut off. The 2 line printing does look very nice. I have not used many of the other features and so cannot comment on those\n\n\n\n\nB000FJLBTU:don't_buy_this_ink_hog:david_h._reed\n\n\nB000FJLBTU\n\n\nHP Deskjet F380 All-in-One Printer/Scanner/Copier: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n2.0\n\n\nDon't Buy this Ink Hog\n\n\nNovember 17, 2006\n\n\nDavid H. Reed\n\n\nKirkland, WA United States\n\n\nDon't even accept it as a freebie (as I did) unless you are prepared to pay $1.15 per sheet for color ink. That was my experience with the first color cartridge, which was fully consumed on a total of 20 sheets, 12 of which were heavy color, the others being mixed color and text or very light color. At $17.99 per color cartridge and $14.99 for black I calculate the outrageous, obscene $1.15 per sheet figure for ink. This is about six times greater than the average ink cost for my old HP720c, which unfortunately does not work with my new HP Pavilion PC (no printer port on this model, among other issues). I have vowed to not purchase any HP product in the future if I can possibly avoid it. I just ordered a Canon Pixima to replace this ink hog. On the positive side, the print quality is good, speed is excellent, and color fidelity is fair. The software installed without incident, and the machine is easy to operate, but the HP software is overbearing and constantly trying to sell you something--worse than daytime television\n\n\n\n\nB000FJLBTU:hp_-_bad_printers,_bad_service,_and_a_bad_name.:jj_bugs_\"jj_bugs\"\n\n\nB000FJLBTU\n\n\nHP Deskjet F380 All-in-One Printer/Scanner/Copier: Electronics\n\n\nelectronics\n\n\n9 of 9\n\n\n1.0\n\n\nHP - Bad printers, bad service, and a bad name.\n\n\nOctober 5, 2006\n\n\nJJ Bugs \"JJ Bugs\"\n\n\nMinneapolis, MN U.S.A.\n\n\nBought this all-in-one printer and the install disc was bad. Prints test page every time I turn it on. Won't print. Jobs get stuck and cannot cancel. Spoke with customer service for 1.5 hours! Yes, that long. They would not refund me and told me that I should have did a better job researching their products. They got that right. All I could get was a refurbished replacement that also performs like crap. HP has lost a customer forever, and I am making it my mission to share with everyone how terrible they are with products and customer service. Canon, meet your next customer\n\n\n\n\nB0001RM3WU:doesn't_fit_etrex_vista_cx:richard_tabbutt\n\n\nB0001RM3WU\n\n\nGarmin Etrex Windshield Mounting Bracket: Electronics\n\n\nelectronics\n\n\n2 of 5\n\n\n1.0\n\n\nDoesn't Fit eTrex Vista CX\n\n\nAugust 29, 2006\n\n\nRichard Tabbutt\n\n\nWoodinville, WA USA\n\n\nThis bracket will not fit the eTrex Vista CX. This is because the battery cover supplied with the bracket (replaces the original battery cover to provide attachement lugs) will not clear the microSD card socket. This is very frustrating because the Garmin website even shows this bracket, part number 010-10361-00, in the eTrex Vista CX accessories list. So if you have a CX, don't purchase this item\n\n\n\n\nB0007ZFM38:not_too_good!:f._contreras_\"chama\"\n\n\nB0007ZFM38\n\n\nLogitech 980356-0403 Stereo USB Headset 250: Electronics\n\n\nelectronics\n\n\n0 of 2\n\n\n2.0\n\n\nNot too Good!\n\n\nNovember 3, 2006\n\n\nF. Contreras \"Chama\"\n\n\nLas Vegas, NV\n\n\nThe first few times it worked great, after that i haven't been able to use it, for some reason incoming audio is good but the mic does not work, i have to use my computer's which is anoying so i stopped using altogether. Waste of money\n\n\n\n\nB0001MR140:don't_buy_vtech_-_battery_issues:lg_\"lakeviewmom\"\n\n\nB0001MR140\n\n\nVTech ip5825 5.8 GHz DSS Cordless Speakerphone with Dual Handsets and Caller ID (Pewter): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nDon't Buy Vtech - BATTERY ISSUES\n\n\nAugust 24, 2006\n\n\nlg \"lakeviewmom\"\n\n\nChicago, IL\n\n\nWe have bought 3 of these VTech Phones for our house. Since the begining we have had battery problems. It is really annoying when you have to make a phone call and your phone doesn't work because of the battery.\n\nAt any point in time, at least one of the phones constantly reports a \"low charge\" or \"no battery\" message, which means you can't use that phone. Of course, you can hear the ring from the phone that is never at the same room that you are, so you have to run to pick up the other phone, since the one in your location has the no battery problem.\n\nAs other reviewers have noted, this in spite of the fact that we make minimal use of the phones and leave them in the charger most of the time. \n\nOn the beginning we used to take the battery out of phone and put it back in. That doesn't quite work anymore. I tried to get support but you can't get a live person at the VTech number. On their website they suggest replacing the batteries, but they are rechargeable, so how many times are we really supposed to buy new rechargeable baterries (I have already replaced it once)?\n\nI know we wont buy VTech products again.\n\n\n\n\nB0001MR140:very_disappointing_in_numerous_ways:ken_januski\n\n\nB0001MR140\n\n\nVTech ip5825 5.8 GHz DSS Cordless Speakerphone with Dual Handsets and Caller ID (Pewter): Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n1.0\n\n\nVery disappointing in numerous ways\n\n\nAugust 16, 2006\n\n\nKen Januski\n\n\nPhiladelphia, PA USA\n\n\nIt's not often I find a product that's as disappointing as this. The interference problem is very serious and makes most conversations a pain. Moving around the house and outside of the house, standing on your head holding the phone at a 45 angle, etc., are part of the daily ritual in trying to hold a legibible conversation.\n\nBut that's not the bad part. The bad part, as at least one other reviewer has noted, is that each of the phones constantly reports a \"low charge\" message or something similar. This in spite of the fact that we make minimal use of the phones and leave them in the charger most of the time. The only solution I've found is to take the battery out of phone and put it back in. Then it will work for a day or two. I suppose it could be just the batteries. But if so then we were shipped batteries that were already on life-support.\n\nI read a few good reviews of this phone, and of course there are a number right here. But out experience has been the opposite. The phones have been nothing other than a pain\n\n\n\n\nB00008DSR5:beware:j._turner\n\n\nB00008DSR5\n\n\nGN Netcom 4170 Headset Telephone: Electronics\n\n\nelectronics\n\n\n6 of 8\n\n\n1.0\n\n\nBeware\n\n\nJanuary 14, 2005\n\n\nJ. Turner\n\n\nHouston, Texas United States\n\n\nI picked up this headset telephone because I am spending more and more time on the phone. \n\nAlmost immediately after plugging it in, everyone I called asked me to speak up, repeat what I said, and that they could not hear me very well. \n\nWhen I attempted to setup a new voicemail system, I found out why. The headphone is amplified, but the microphone is not. No matter how loud I spoke (or shouted) the audio is severely suppressed and barely audible.\n\nSimply stated, this product is not usable. \n\n\n\n\nB00006B903:targus_pa400u_defcon_1_ultra_notebook_computer_security_system:saliya_ratnayaka\n\n\nB00006B903\n\n\nTargus PA450U DEFCON KL Notebook Computer Key Lock: Electronics\n\n\nelectronics\n\n\n1 of 5\n\n\n1.0\n\n\nTargus PA400U DEFCON 1 Ultra Notebook Computer Security System\n\n\nSeptember 7, 2006\n\n\nSaliya Ratnayaka\n\n\nTucson, AZ, USA\n\n\nI had this for approximately 2 years. It worked fine for couple of months but suddenely started to activate the alarm without any disturbances. One day I left my laptop attached the alarm in my office for about half an hour. During this period, the alarm was activated and I was embarrased to face my office workers. This is without increasing the sensitivity of the alarm. I sent it back to Targus and they were nice enough to give me a new alarm. But after several months, it started the same problem.\n So, now I am afraid to use this security system with my laptop. But I did not face problems like changing the combination by its own. You can use it without the alarm activation (i.e: without batteries) but the cable is so thin, it is not recommended to protect any valubles\n\n\n\n\nB0008ESGAY:poor_macintosh_support:ben_crane\n\n\nB0008ESGAY\n\n\nOlympus DS-2 Digital Voice Recorder: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n1.0\n\n\nPoor Macintosh Support\n\n\nNovember 20, 2006\n\n\nBen Crane\n\n\n\n\n\nI bought this item after seeing the almost uniformly good reviews including the one that says there is good Macintosh support. Unfortunately the player does not work well with the Macintosh. The included software often crashes and does not seem to be able to import DSS files. The software can move the files between the recorder and the computer but the files do not appear in the software window. The use interface is also fairly crude and looks as if it was ported directly from Windows 3.1. The DS-2 appears to the finder as a standard USB disk and I have been able to copy the DSS files from the player to the compuer using the finder. It is then possible to play the DSS files by double clicking them, but it will only play one file. If you want to play another file it is necessary to completely quit the DSS player and relanch it. Exporting the files to a format usable by other software such as AIFF is also nearly impossible. There is an export function in the menu, when it is selected it asks were the new file should be saved but no file ever appears. Unfortunately there is no other Macintosh software available to read or convert DSS files as Olympus refuses to allow others use the the format on the Macintosh. Search of the internet reveals that others have found extremely painful ways of converting the DSS files such as playing the entire file and re-recording it with another program\n\n\n\n\nB0009Q4PHO:poor_build_quality:c._castille_\"ccastille\"\n\n\nB0009Q4PHO\n\n\nUltimate Ears Super.fi 5 Pro Earphones (Black): Electronics\n\n\nelectronics\n\n\n3 of 7\n\n\n2.0\n\n\nPoor build quality\n\n\nOctober 18, 2006\n\n\nC. Castille \"ccastille\"\n\n\nLos Angeles, CA\n\n\nGreat sound, but the build quality is just not there. On a recent trip to Europe, the left bud completely fell apart and the leads became disconnected from the bass driver. They are only two months old and I always keep them in the case! I had to solder it back together and then super glue the bud back together. Since then I've also had to superglue the right bud as well. Also, the cable sheath is fraying over one bud lead, revealing the fine speaker wire underneath. I don't expect these phones to last much longer which is too bad. They are great sounding and comfortable, but unfortunately they seem to have cut some important corners. Be warned\n\n\n\n\nB000779RZA:aweful_headphones!:richard_g._trezevant\n\n\nB000779RZA\n\n\nSony MDR-EX51LP Fontopia Headphones, Blue: Electronics\n\n\nelectronics\n\n\n1 of 5\n\n\n2.0\n\n\nAweful Headphones!\n\n\nOctober 6, 2006\n\n\nRichard G. Trezevant\n\n\nSF, CA\n\n\n These headphones are so expensive and they are aweful! I dont remember if it was the left or the right but one side of the cord is longer than the other; its very annoying! I tried to loosen it and the wire was showing, what a piece of s***. Its very annoying because the ear bud fell out when I listened to music and it doesnt feel comfortable. Also the rubber on the ear bud falls off very easily. They are too expensive for a piece of junk! The only reason I rate it two stars is because the sound is great on the headphones. Instead purchase the Sony SRF-HM01V S2 Sports Walkman Street Style Headphone Radio. I purchased those after these fell apart and they are much better :)\n\n\n\n\nB000H72MMU:just_not_good_enough:s._walton_\"bibliophile42\"\n\n\nB000H72MMU\n\n\nMonster iCarPlay Plus Wireless FM Transmitter/Charger for iPod: Electronics\n\n\nelectronics\n\n\n10 of 11\n\n\n2.0\n\n\nJust not good enough\n\n\nOctober 22, 2006\n\n\nS. Walton \"bibliophile42\"\n\n\nJanesville, WI USA\n\n\nI SO wanted to love this thing. I have an iPod that I am seldom without, and my car isn't iPod compatible. I bought a Monster Wireless FM Transmitter/Charger so I could listen to my music on the way to my class reunion. Unfortunately, the thing just didn't work for me. \n\nThere are 8 preset stations (low on the dial) and if all of them are in use, you're out of luck. I don't live in a huge city (maybe 60,000 with farmland all around), but only two of these stations were available. Although I was able to tune in to my iPod, it wasn't crystal clear. In fact, it wasn't even plastic clear. The static sometimes overrode the music, which drove me nuts. This is like a fax machine - the original may be spotless, but the fax will be a little fuzzy. \n\nIf you have a cassette player in your car, there are adapters that go through the cassette player and are much better. If your car isn't iPod compatible and you don't have a cassette player, you're better off just burning a bunch of CD's\n\n\n\n\nB000BI7NGU:poor_item_choice:aimee_louise\n\n\nB000BI7NGU\n\n\nPalm Z22 Air Case: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n1.0\n\n\nPoor item choice\n\n\nAugust 19, 2006\n\n\nAimee Louise\n\n\nFlorida\n\n\n If all you want is a carrying case that is transparent and UNABLE to use the stylus with the case closed, then this is the perfect item for you(sarcasm).\n\n The Palm Z22 Air case states that you are able to use the sylus with the case closed, this was the selling and purchasing point for this item. Problem is, this feature does not work. Affortable, but what is the point if you can not utilize the item for it's selling feature... \n\n\n\n\n\nB00008UVAC:do_not_buy_this:spends_too_much\n\n\nB00008UVAC\n\n\nRCA RCU410MS Universal 4 Device Remote Control (Platinum): Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nDo Not Buy This\n\n\nApril 10, 2006\n\n\nSpends Too Much\n\n\nInternet\n\n\nI bought this even with the bad reviews. I have one of it's predecessors, a black one and it worked fantastically for 5 or 6 years. It still does but the on/off button finally died. I figured... how bad could it be? The one I have worked great until now.\n\nIt's BAD. It's AWFUL. It's WORTHLESS. I've had to enter the codes for my tv and vcr 3 times today. The batteries are brand new... that's the first thing I tried. DO NOT BUY THIS. I only gave it a 1 because you can't give it a zero\n\n\n\n\nB0009KF2PY:light_duty:michael_f._agunzo_\"toolengineer\"\n\n\nB0009KF2PY\n\n\nVelocity Nylon 600D CD Wallet (224 capacity, Black): Electronics\n\n\nelectronics\n\n\n5 of 6\n\n\n2.0\n\n\nLight Duty\n\n\nJune 26, 2006\n\n\nMichael F. Agunzo \"Toolengineer\"\n\n\nNew York\n\n\nProduct is light duty, over time it will not hold up to being flipped and opened often, If longevity is your goal purchase elsewher\n\n\n\n\nB000A3IAKY:disappointed_in_video_quality:j._wooding\n\n\nB000A3IAKY\n\n\nCreative Webcam Live! Motion- Titanium: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n2.0\n\n\nDisappointed in video quality\n\n\nMarch 8, 2006\n\n\nJ. Wooding\n\n\nCalifornia USA\n\n\nFrom the photos and specs I thought this was \"the camera\" to have (both my wife and I loved the look of the camera from the photos we had seen). There's always exceptions to rules, and the rule, \"the more you pay, the more you get\" is the exception to the rule for this web camera. After getting it, I had difficulty in getting the software to load but eventually had success. I was very disappointed in the video quality. There seemed to be a lot of noise in the picture, and tweaking gave no improvement. I returned it to Amazon and took a lose in the return shipping cost, but considered it worth it just to get the price of the camera back. Amazon lived up to their part and refunded the price paid (less return shipment) with no hassle\n\n\n\n\nB000A3IAKY:not_compatible_with_some_notebooks:carlos_escobar\n\n\nB000A3IAKY\n\n\nCreative Webcam Live! Motion- Titanium: Electronics\n\n\nelectronics\n\n\n2 of 9\n\n\n1.0\n\n\nnot compatible with some notebooks\n\n\nFebruary 18, 2006\n\n\nCarlos Escobar\n\n\n\n\n\nthis webcam is not compatible with acer ferrari, with windows xp. I don't know why. I just returned it\n\n\n\n\nB0006I5I7G:way_overpriced:gadgester\n\n\nB0006I5I7G\n\n\nMonster Ca M1000DV HDMI to DVI Cable ( 123165 ): Electronics\n\n\nelectronics\n\n\n8 of 18\n\n\n1.0\n\n\nWay overpriced\n\n\nJanuary 12, 2006\n\n\nGadgester\n\n\nNew York\n\n\nIf you got a crappy brand TV like the Samsung the East Meadow guy got, you might need a super-expensive cable like this to make the image quality acceptable, but if you have a namebrand TV like Sony or Sharp or Philips or Toshiba or Panasonic, a $15 HDMI-DVI cable totally suffices. This Monster cable is no better than the $15 guy (also available on Amazon, just search for \"hdmi dvi cable\"). Don't waste your money\n\n\n\n\nB0002DUQCM:doesn't_last_as_advertised:f._farahani\n\n\nB0002DUQCM\n\n\nRechargeable Aa Nimh Batteries: Electronics\n\n\nelectronics\n\n\n1 of 5\n\n\n2.0\n\n\nDoesn't last as advertised\n\n\nFebruary 10, 2006\n\n\nF. Farahani\n\n\n\n\n\nI have a Canon S2 and these batteries last almost as long as the cheap costco non-rechargable ones. They do take upto 8 hours to charge and Amazon is correct about that. Does anyone know of good long lasting 2500 Mah rechargable batteries?\n\n\n\n\nB00008RS97:i_hate_dese:ernest_jackson_\"e.b.j.\"\n\n\nB00008RS97\n\n\nKoss UR29 Collapsible Portable Headphones: Electronics\n\n\nelectronics\n\n\n0 of 16\n\n\n1.0\n\n\nI HATE DESE\n\n\nJuly 16, 2006\n\n\nernest jackson \"E.B.J.\"\n\n\nhouston texas\n\n\nI HATE DESE KIND OF PHONES. DAY HAVE EM AT MI SKOOL N DA R TURRBLE. THEY HAV OK SOUND BUT DAY FEEL TURRBLE LIK PUTTIN TOO MUCH PRESSURE ON UR EARS. THEY F--KING HURT DOG. DON'T GET DESE, GET SONY H.EAR DOSE R ALWAYZ DA BEST\n\n\n\n\nB00008RS97:portable?:dunckel_\"dunck\"\n\n\nB00008RS97\n\n\nKoss UR29 Collapsible Portable Headphones: Electronics\n\n\nelectronics\n\n\n0 of 7\n\n\n2.0\n\n\nPortable?\n\n\nJune 26, 2005\n\n\nDunckel \"Dunck\"\n\n\n\n\n\nThese seemed like good headphones at first - but problems with portablity were huge! The sound was good but 3 months into ownership one earpeice died so the phones are useless!! NOT worth spending time and money on these phones\n\n\n\n\nB00006B8NQ:no_security_&_difficult_to_use:dave\n\n\nB00006B8NQ\n\n\nKensington 64050 ComboSaver Notebook Lock and Security Cable (PC/Mac): Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n1.0\n\n\nNo security & difficult to use\n\n\nJune 24, 2006\n\n\ndave\n\n\nHockessin, DE United States\n\n\nNot a particularly easy lock to use (combo part rotates freely when not in computer, making it difficult to set the combo). Also changing combo is very easy to do accidently. However this is usually not a problem because it is incredibly easy to figure out the combo. I am not a professional lock picker, and I am able to break the combo without any difficulty (which is a problem with many low quality combo locks, but this design try to avoid it at all). \nI've noticed a lot of people have a problem with the lock \"spontaneously\" changing combinations. It's probably not because the lock is defective. It's probably because they inadvertently changed the combination because the mechanism to change it is so easy to engage.\nI would definitely never suggest this product to anyone (unless I really wanted their computer)\n\n\n\n\nB00006B8NQ:horrible_lock:christopher_r._gault\n\n\nB00006B8NQ\n\n\nKensington 64050 ComboSaver Notebook Lock and Security Cable (PC/Mac): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n1.0\n\n\nHorrible lock\n\n\nMarch 6, 2006\n\n\nChristopher R. Gault\n\n\n\n\n\nLike other people here my lock spontaneously changed its combination on me....Surprise! My laptop is currently locked up at my desk at work which ruined my day and is ruining my night. Another stressor I do not need to deal with...do not get this lock if you value the mobility of your laptop over a theft deterrant\n\n\n\n\nB0007SXHP0:stopped_working_after__a_couple_of_weeks_use.:tam_cerr_\"tamc\"\n\n\nB0007SXHP0\n\n\nLogitech Premium USB Headset 350 ( 980374-0403 ): Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nStopped Working After a couple of weeks use.\n\n\nNovember 4, 2006\n\n\nTam Cerr \"TamC\"\n\n\nEllicott City, MD USA\n\n\nWiring broke at interface box - poor design\n\n\n\n\nB00030CHRQ:disparate_cord_length-_more_than_irritating.:fieryfilly\n\n\nB00030CHRQ\n\n\nSony MDR-EX71SL/WK Fontopia Headphones (White): Electronics\n\n\nelectronics\n\n\n5 of 8\n\n\n2.0\n\n\nDisparate cord length- more than irritating.\n\n\nNovember 15, 2006\n\n\nFieryFilly\n\n\nUSA\n\n\nI read all of the reviews here and thought these would be the best option for easy transport AND low ambient sound i-pod headpohones without breaking the bank (I'm on a plane frequently). I wanted something that I wouldn't cry over breaking or losing during my many travels. I found that the uneven cord lengths are very irritating- especially if you like to use only one plug (like maybe at your desk at work). The weight of the shorter cord pulls on the longer cord and it is quite uncomfortable- even when sitting still. So then I thought these will be great workout headphones (won't be upset if I break or lose them), HOWEVER, the cords produce noise in the ear pieces everytime they rub on your clothing or your hand. The effect is very loud and disruptive to your music listening experience (even when you are sitting stationary at your desk). It is similar to wearing a stethescope, which defeats the point of wearing in-the-ear headphones (which are supposed to reduce the ambient noise). I do not recommend these headphones, because of this stethescope effect- I could get over the uneven cord lengths for certain uses (they are after all designed to wear behind the head). I'm on the hunt for better quality headphones\n\n\n\n\nB00005NND2:not_working_as_well_as_i'd_like:m._williams\n\n\nB00005NND2\n\n\nSony SRF-HM33 Walkman FM/AM Stereo Headphone Radio with 20 Preset Stations: Electronics\n\n\nelectronics\n\n\n9 of 11\n\n\n2.0\n\n\nNot working as well as I'd like\n\n\nAugust 10, 2005\n\n\nM. Williams\n\n\nAustin, TX\n\n\nI like this style of headphone radio (especially when I jog), and haven't found any similar models that I like. This is my third purchase of this radio. Each of the three have had problems. First died completely (although it was used a lot). Second still works, but last number doesn't show up on digital display. I wanted a backup in case second died, so I ordered another one. This third works least well of all. At times, entire display is off, radio doesn't work at all, or it won't change channels. Then other times it worked. Seems like might be affected by heat outside. However, I know have another persistent problem. That being, it won't let me program the preset stations. Again, I'm limited in my choices, and I use this type headphone a lot. So I suffer through poor quality\n\n\n\n\nB00004Z62V:does_not_retract:r._abramson\n\n\nB00004Z62V\n\n\nHP/Compaq CQ200U HP Retractable Phone Cord: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n1.0\n\n\nDoes not retract\n\n\nOctober 11, 2005\n\n\nR. Abramson\n\n\n\n\n\nAgree with the prior reviewers. I can not get it to retract any longer and that was only after 3 uses! \n\n\n\n\nB00004Z62V:useless_-_does_not_retract:james_murphy\n\n\nB00004Z62V\n\n\nHP/Compaq CQ200U HP Retractable Phone Cord: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n1.0\n\n\nUseless - does not retract\n\n\nApril 6, 2004\n\n\nJames Murphy\n\n\nPelham, MA USA\n\n\nI agree with the previous review. This phone cord worked twice. Now, it only retracts about half way, and keeps getting worse. I'm returning it for a refund. Don't waste your time with this\n\n\n\n\nB00006HMQZ:didn't_work_well_for_me_-_running_mac_os_10.4.8:c._dennington\n\n\nB00006HMQZ\n\n\nMacally iceMouse USB Optical Internet Mouse (White): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nDidn't work well for me - running Mac OS 10.4.8\n\n\nOctober 29, 2006\n\n\nC. Dennington\n\n\n\n\n\nThis mouse was barely functional. It did move the arrow across the screen, but it was in a very random fashion. I went to the Macally website and downloaded the latest drivers. Still no luck. I'm sending this back. Guess I'll have to stick with my reliable Kensington. Too bad it's so ugly and doesn't match my ice keyboard. Oh well... function over form\n\n\n\n\nB0002XJAKG:not_good_for_dvd_or_game_console:csd_north_carolina\n\n\nB0002XJAKG\n\n\nLogitech X-530 70-Watt 5.1 Surround Sound Speaker System: Electronics\n\n\nelectronics\n\n\n3 of 14\n\n\n1.0\n\n\nNot good for DVD or Game Console\n\n\nDecember 29, 2005\n\n\nCSD North Carolina\n\n\n\n\n\nThe product description for these speakers is misleading. They do not plug directly into a DVD player or game console. Then, after buying the adapters and doing the initial setup, three of the five speakers did not work. Perhaps these speakers are a good solution for PCs, but that's not why we bought them, and we immediately sent them back\n\n\n\n\nB000BKBZGC:great_sound.__but_lacking_in_other_areas.:dartnok\n\n\nB000BKBZGC\n\n\nAltec Lansing inMotion iM616 Headphones: Electronics\n\n\nelectronics\n\n\n13 of 14\n\n\n2.0\n\n\nGreat Sound. But lacking in other areas.\n\n\nJanuary 11, 2006\n\n\ndartnok\n\n\nSan Francisco, CA United States\n\n\nIsolation:\nI had to use the foam inserts because the plastic ones were a little too large for my ears. With the foam, it completely sealed my ear canal and blocked out most noise. My commute home on the J train (light-rail) was quite silent. I was listening to classical music for the first time on the train. With earbuds, I had to put the volume level around 50%. With the Sony MDR-EX71 model, I could to keep it around 40%. The noise attenuation wasn't that good with the Sony model, but definitely better than earbuds (but the sound quality was quite terrible though.)\n\n\nSound Quality:\nSuperb sound. The partnership with Etymotic Research is evident. Everything sounded great: rock, grunge, classical. The Sonys sounded really muddy while these had a really bright sound with good bass (when the seal was good). Very balanced sound.\n\nCord/Construction:\nI string my earphones through an REI One jacket, so this was more or less perfect length. The cord is symmetric so it's not weirdly strung behind your neck like the Sonys, and the connection to the ear pieces is solid, not fragile-looking like the ER6. The earphones connect to the replaceable eartips very tightly. The included case is ridiculous though; it's way too bulky for portability.\n\nComfort/Fit:\nSadly, this is primary reason I'm returning these. Both the flanged rubber tips and the included foam tips were too big. The foam models got the seal, but they were too big, so I felt an uncomfortable amount of pressure in my ear canals. Those were my only two options with the included package. The other problem is that I cannot find replacements that are smaller, anywhere on the web, including the Altec Lansing site.\n\nDocumentation:\nAnother reason I am returning these is the documentation. The manual says that I can get replacement ear tips and accessories at alteclansing.com/inmotionearphones, and I get a 404 error when I visit the link. That is pretty pathetic.\n\nUpdate: (1/19/2006)I emailed Altec Lansing regarding this issue on 1/10/2006, and I have yet to get a response from them.\n\nBottom Line:\nIf you have large ears, these may suit you. But if you know that you have smaller ears, then stay away from these, and go for something else\n\n\n\n\nB000B622DG:poor_reproduction:christopher_r._silvia\n\n\nB000B622DG\n\n\nSennheiser PX 200W Collapsable High Performance Closed Headphones: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n2.0\n\n\nPoor reproduction\n\n\nMarch 14, 2006\n\n\nChristopher R. Silvia\n\n\n\n\n\nI bought these to replace the standard iPod and Sony ear buds. I was under the impression that I would get a much deeper bass response. I was wrong. The bass reproduction is simply horrid. I read somewhere there is a \"break-in\" period. I have found this not to be true. They sound just as bad after 50 hours of listening as they did brand new. This is my first pair of Sennheiser and am very disappointed. I know they are low-end headphones - but I expected more. My recommendation is to keep researching\n\n\n\n\nB00004TDN2:1_shortcoming_only:craig_duncan\n\n\nB00004TDN2\n\n\nHandspring Visor Deluxe (Graphite): Electronics\n\n\nelectronics\n\n\n0 of 2\n\n\n2.0\n\n\n1 shortcoming only\n\n\nSeptember 9, 2005\n\n\nCraig Duncan\n\n\nSilver Spring, MD USA\n\n\nCons:\n Defect: the backlight does not work.\n Shortcoming: has Palm OS 3.1, an early version for Handspring Visors.\nNeutral:\n (expedited) Shipping time: 5 days\nPros:\n Price\n\nPS: Vendor customer support contacted me and provided a replacement unit with a working backlight, free of additional charge :)\n\n\n\n\nB000069LIG:item_was_the_wrong_color:natalie_t._goldberg\n\n\nB000069LIG\n\n\nPanasonic KX-TS105W Corded Speakerphone (White): Electronics\n\n\nelectronics\n\n\n1 of 10\n\n\n1.0\n\n\nItem was the wrong color\n\n\nAugust 25, 2006\n\n\nNatalie T. Goldberg\n\n\n\n\n\nItem was advertised as white, but it was really grey. As a result we had to return item and assume the cost of mailing it back\n\n\n\n\nB000069LIG:disappointing_speakerphone:j._b._oneill\n\n\nB000069LIG\n\n\nPanasonic KX-TS105W Corded Speakerphone (White): Electronics\n\n\nelectronics\n\n\n6 of 8\n\n\n2.0\n\n\nDisappointing speakerphone\n\n\nMarch 16, 2006\n\n\nJ. B. ONeill\n\n\n\n\n\nI bought this phone for use in my home office based on the rave reviews on this site for its speakerphone. Unfortunately, I have had nothing but repeated complaints that the voice is distorted or goes in and out, with the beginnings and ends of sentences frequently cut off. I am in the market for another phone after just 2 months\n\n\n\n\nB0007OWNBS:my_pc_is_now_dead,_and_netgear_doesn't_care:young_j._putt\n\n\nB0007OWNBS\n\n\nNetgear RangeMax Wireless PCI Adapter (WPN311): Electronics\n\n\nelectronics\n\n\n1 of 3\n\n\n1.0\n\n\nMy PC is now dead, and Netgear doesn't care\n\n\nOctober 8, 2005\n\n\nYoung J. Putt\n\n\n\n\n\nDuring the installation of the software on my PC running Windows 2000 Server, the installation program froze my PC completely. I had to turn off the PC, and now it does nothing when I turn it on. The lights on the front light up, but nothing happens at all. I called Netgear, waited on hold for 15 minutes, and then was connected to a gentleman whose English was not very good. I explained my problem about 5 times before he seemed to get it. He told me that the problem is that the product is not compatible with Win 2000 Server. I told him that I had never before encountered a piece of software that ran on 2000 Pro, but not 2000 Server (perhaps there are some), but he insisted that this was the case. I explained that, typically, installation programs that encounter and incompatible operating system would display a message to the user, and exit, rather than freezing and disabling the computer, but he held firm, and advised me that there was nothing he could do to help, and that he recommended that I contact my computer vendor so that they could restore my initial configuration. \n\nI went around and around with this fellow, then finally asked for his supervisor. After another 15 minutes on hold, another guy came on the line, who was pretty fluent in English. He explained his understanding of the situation, which was still wrong, and I explained the whole thing again to him. He had me turn the computer on and off, which of course I had already done several times, and check the monitor connection. After this analysis, he determined that the situation could not possibly have been the result of the frozen software installation that occurred just prior to it, and must be some other problem with my PC that had previously been working perfectly, that, coincidently, occurred at the exact same time as the software freeze. I expressed skepticism towards this explanation, and asked him if he would believe that if it was his PC. He said that absolutely he would, and reassured me that there was NO POSSIBLE WAY, (emphasis his) that his software could have caused these symptoms. \n\nSince I understood that it would be difficult for him to actually fix a computer in this state over the phone, I suggested that I could take it to a local technician, and send Netgear the bill for repairing my system. He declined that offer.\n\nSo, at the end of the day, I'm left with a dead PC, but, according to Netgear, a perfectly good wireless card and installation CD. \n\nNo more Netgear for me, thank you!\n\n\n\n\nB0007YPOAA:why,_oh_why,_didn't_i_check_amazon_reviews_first?:b._richards_\"frustrated_audiophile\"\n\n\nB0007YPOAA\n\n\nPIONEER CD-IB100 iPod Adapter for Pioneer Car Stereo Receiver: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nWhy, oh why, didn't I check Amazon reviews first?\n\n\nNovember 24, 2006\n\n\nB. Richards \"frustrated audiophile\"\n\n\nPhoenix, AZ\n\n\nI am totally stunned that this could be sold as iPOD compatible. Nothing, absolutely nothing, works as you would intuitively think it would. Pause does not pause, forward arrow does not forward to the next track (just starts the same track over and over again), and heaven help you if you actually want to find a specific song or artist. There is no way to actually speed through a menu - you have to advance one artist at a time. This is fine if you love Aaron Neville but chances are you will be in a limb severing accident if you are trying to get to ZZ Top. I am taking this back tomorrow and demanding something that will allow me to control my iPod again from my iPod\n\n\n\n\nB00003CWDQ:it_is_what_it_is:jeff\n\n\nB00003CWDQ\n\n\nMonster Cable MP HTS800 Home Theater PowerCenter HTS800: Electronics\n\n\nelectronics\n\n\n1 of 5\n\n\n2.0\n\n\nIt is what it is\n\n\nOctober 3, 2006\n\n\nJeff\n\n\nHouston, TX\n\n\nI guess for $50, this is an ok product. I am usually very against monster products, but I'd seen this for almost double at big box retailers, needed a surge protector for my new DLP tv, so figured what the heck. It arrived without incident, I hooked it up and so far so good. It basically a power strip with labels that also happens to be purple... for the \"neato\" effect I guess. Anyways, no idea if its worth more than your run of the mill surge protector. But the claims of independent circuitry to minimize cross-interference is what I was really interested in, as I have my reciever, cable box, DVD player, and subwoofer all connected... All in all a good buy I guess, at least you paid a lot less than retail... :\n\n\n\n\nB0007W1K8M:six_months_and_no_thinkpad_tip?_completely_unsatisfactory!:olin_sibert\n\n\nB0007W1K8M\n\n\nKensington 33197 120 Watt AC/DC Power Adapter for Notebook Computers, Cell Phone, DVD Players, PDAs: Electronics\n\n\nelectronics\n\n\n6 of 9\n\n\n1.0\n\n\nSix months and no Thinkpad tip? Completely unsatisfactory!\n\n\nSeptember 27, 2006\n\n\nOlin Sibert\n\n\nBoston, MA, USA\n\n\nI bought one of these a few months ago. It worked perfectly with my old Thinkpad, but not long thereafter I got a Thinkpad T60, thinking, \"Hey, universal means universal, of course it will work\". But no: SIX MONTHS after the T60 was introduced, Kensington STILL hasn't gotten their act together to produce a \"SmartTip\" for it. They haven't even added Lenovo to the list of manufacturers on their web site! All Customer Service can say is that they don't have one, they don't know when or if they ever will, and they suggest that I check back on the website someday if I still hold out any hope. So, I have a $100 brick on my hands. Very disappointing, Kensington.\n\nIt's really too bad: the unit is lightweight, sleek, and functional, and (unlike some reviewrs) I think the 5-inch cord was brilliant (and it doesn't get tangled up in my briefcase). The cables are much nicer than on my 70-watt iGo supply, and the connectors don't break like they did on the iGo. And it's not like there's some technical problem: the T60 needs 20 volts, 90 watts, well within the 24-volt, 120-watt capability of this unit. However, six months delay in supporting a major business computer brand is just unforgivable, so I can no longer recommend it\n\n\n\n\nB000E1FYQA:very_disappointed_and_i_returned_them:musicfan\n\n\nB000E1FYQA\n\n\nSennheiser CX300-B Earbuds (Black): Electronics\n\n\nelectronics\n\n\n1 of 5\n\n\n1.0\n\n\nVery disappointed and I returned them\n\n\nNovember 4, 2006\n\n\nMusicfan\n\n\nCalifornia\n\n\nI wanted to get something better than the standard earphones for my Ipod.\nI saw some good reviews and this brand has been known always for great headphones. Bought these and was shocked at the terrible sound quality. Yes, the sound is clean, but TOO clean. The highs are too high leading to tinny and over-brassy sound. Voices have too much hiss. There is virtually no low end bass of any type. Very disappointing\n\n\n\n\nB0001YFX7U:amazon_is_sleeping:baljit_singh\n\n\nB0001YFX7U\n\n\nRidata 8x 4.7 GB DVD+R Spindle (100 Discs): Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\namazon is sleeping\n\n\nSeptember 24, 2006\n\n\nBaljit Singh\n\n\nny usa\n\n\ndvds are less than 30 dollars now with 16x speed\nand look at amazon ,it is still selling then above 40 (only 8x speed\n\n\n\n\nB0001YFX7U:want_to_donate_your_money_to_ridata_plus_pay_tax_and_shipping?:ganesh_usha_\"ganush\"\n\n\nB0001YFX7U\n\n\nRidata 8x 4.7 GB DVD+R Spindle (100 Discs): Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n1.0\n\n\nWant to donate your money to Ridata plus pay tax and shipping?\n\n\nAugust 9, 2006\n\n\nganesh usha \"ganush\"\n\n\nCA\n\n\nThen buy these Ridata Disc\n\n\n\n\nB0001YFX7U:terrible_discs:big_leo\n\n\nB0001YFX7U\n\n\nRidata 8x 4.7 GB DVD+R Spindle (100 Discs): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n1.0\n\n\nTerrible discs\n\n\nJanuary 1, 2006\n\n\nBig Leo\n\n\nFt Lauderdale, FL\n\n\nBought 100 DVD blanks - so far every one a coaster. Never had this kind of problems with any type of discs. Tried different software and burn speeds but nothing good. Very disappointed - at least they were cheap (...). \n\n\n\n\nB000F8VIRQ:save_your_money:gerryc\n\n\nB000F8VIRQ\n\n\nPanasonic KX-TG6700B 5.8 Ghz Cordless Phone Two-line Expandable Phone System: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n2.0\n\n\nSave your Money\n\n\nSeptember 23, 2006\n\n\nGerryC\n\n\nNorthport, NY\n\n\nI am looking for a system to replace a Siemens 8825.\nThe 8825 has, in my opinion, the best set of functions of anything\ncurrently available but I have some reliability issues with it. I first tried the Motorola 7081 and found that unsatisfactory. \nMy next attempt was the Panasonic KX-TG6700B. This phone has decent volume and clarity at the base. The handset has good clarity and range but the volume could use a little help. The base layout is ok but the way you turn on the answering machine at the base is silly. The handset feels light and chintzy. The clarity of the outgoing messages on the answering machine is poor. The base is ugly; what were they thinking? \nAnyway, it ok, not wonderful. But since I couldn't get the answering machine to save messages on either line (it's defective) I sent it back. Back to the drawing board\n\n\n\n\nB0007UQ6LK:scan_disk_mini_sd:atf\n\n\nB0007UQ6LK\n\n\nSanDisk 1GB miniSD Card (SDSDM-1024-A10M, Retail Package): Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nScan disk mini Sd\n\n\nNovember 3, 2006\n\n\nATF\n\n\nPhoenix, AZ USA\n\n\nit did not work at all and Amazon would not take it back saying it was my responsibility that it did not work. I would never buy this or any other product from this seller\n\n\n\n\nB000CS1TLE:a_big_disappointment:naushad_a._virji_\"naushad_virji\"\n\n\nB000CS1TLE\n\n\nLogitech Harmony 890 Advanced Universal Remote Control: Electronics\n\n\nelectronics\n\n\n1 of 4\n\n\n1.0\n\n\nA Big Disappointment\n\n\nNovember 3, 2006\n\n\nNaushad A. Virji \"Naushad Virji\"\n\n\nOrlando, FL - USA\n\n\nI was excited about consolidating 3 remote controls I regularly use plus my fan and light controls. However, the Harmony 890 has proved to be a big disappointment. I was much better off with the 3 remotes I was using before. It was much simpler than fooling with the Harmony remote which requires so many key presses for basic functions. I never have been able to get the Harmony to control the fan and lights.\n\nSome reviewers have said that you just need to take time to do the programming properly. Even if you do, in my case it's just easier to juggle my 3 remotes and press ONE button to do what it would take with SEVERAL key presses on the Harmony.\n\nMy Harmony is now going to sit with the other remotes I never use and I'll just stick to the 3 that work for me\n\n\n\n\nB0006ZBHWY:buyer_beware!!!!:k._walls\n\n\nB0006ZBHWY\n\n\nWestern Digital 80 GB Passport 2.5-inch External Hard Drive: Electronics\n\n\nelectronics\n\n\n6 of 7\n\n\n1.0\n\n\nBuyer Beware!!!!\n\n\nOctober 6, 2006\n\n\nK. Walls\n\n\nMetro St Louis United States\n\n\nBefore you buy this drive, be sure to Google \"Cypress AT2LP RC42\" and \"western digital\".\n\nUntil yesterday, I recommended this drive to everyone. Then my drive failed, prompting me for new hardware and to install a driver for Cypress AT2LP RC42. It appears that there is a chip in the drive housing manufactured by Cypress that is failing in drives left and right. (Class action suit anyone?)\n\nThe Western Digital support staff is no help. Act like they have never heard of this error. Offer no option to just switch out the housing. Will replace drive- but you lose all the data. There are $30 replacement housings available - but you void your warranty. \n\nBottom line - if you buy this drive, be prepared for it to fail. Keep everything backed up, because there will come a day that this drive will fail\n\n\n\n\nB0006DPT5M:a_lot_lacking_for_this_kind_of_money:headset_horseman\n\n\nB0006DPT5M\n\n\nPlantronics CS50-USB Wireless Headset for VoIP Softphones: Electronics\n\n\nelectronics\n\n\n23 of 28\n\n\n2.0\n\n\nA lot lacking for this kind of money\n\n\nMay 2, 2005\n\n\nHeadset Horseman\n\n\nCalifornia\n\n\nWhile convenient and a nice, lightweight unit, the voice pickup is very poor, regardless of how you adjust the set. This is not only with the set I have used, but with others I have talked to - you can tell if the person on the other end of the phone is using one of these units because they are so faint. I've gotten so I just ask if they're using a Plantronics CS50 when they are faint and the answer is invariably \"yes\". Also, if you are in a building with a lot of exposed metal (window/door frames, high cubicles with lots of metal), the range deteriorates significantly\n\n\n\n\nB00003CWEZ:again,_monster_has_to_be_kidding_(and_they_are):great_movie_addict\n\n\nB00003CWEZ\n\n\nMonster Cable MVSV3-4M Monster Super Video 3 Double Shielded S-Video Cable. (13.12 ft.): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nAgain, Monster has to be kidding (and they are)\n\n\nApril 29, 2006\n\n\nGreat Movie Addict\n\n\nNew York City\n\n\nFor some years I tried this and other Monster products, to the tune of over $1000 in Monster a/v gear. The more you learn about video and audio, the more you'll avoid anything with the Monster label. Specifically, this s-video meets no NTSC, DVD or IEEE standard; it is common 30-ohm hookup wire with ineffective shielding and a poorly designed, low-conductance connect plug that can wreck your input jacks and gives poor grounding. Has a sharp image, but includes a catalog of chromatic aberrations from off-color skin tones to color blotching, dirty grays, burned highlights, murky shadows, noisy reds, and poor color balance due to impedance mismatching that results in cyan errors and bleeding reds. Makes video test patterns look great, but who watches test patterns all day? Amazon offers many other brands at good prices, why waste your $$ on this stuff? I removed every piece of Monster from my system and replaced them with AR, Belden, Viewsonics and others from Amazon. Result: vast improvement in every respect.\n\n\n\n\nB0008F69KM:poor_quality_video._not_worth_purchasing.:funboy3_\"funboy3\"\n\n\nB0008F69KM\n\n\nWhat Really Happened While You Were Away! Secret Security Camcorder Hidden in a Clock (SI401): Electronics\n\n\nelectronics\n\n\n46 of 48\n\n\n1.0\n\n\nPoor Quality Video. Not worth purchasing.\n\n\nOctober 12, 2005\n\n\nFunboy3 \"Funboy3\"\n\n\nPhoenix AZ\n\n\nI recently purchased this item from Sharper Image and was very disappointed. \n\nThe video is faint, grainy (even in strong lighting), and out of focus once you are more than 2 feet away from the camera. Colors all but disappear. It's practically black-and-white video.\n\nThe power supply weighs more than the device itself (one pound, including cord) and is HUGE. It also gets very warm after an hour or so. \n\nI've tested this product in every type of lighting situation and the results have been unsatisfactory in all cases. The only thing the device does well is detect motion.\n\n\n\n\nB000CRFI8A:lousy_customer_support_and_windows_xp_surprise_changes...:linksys_sufferer\n\n\nB000CRFI8A\n\n\nLinksys WUSB54GC Compact Wireless-G USB Adapter: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nLousy Customer Support and Windows XP surprise changes...\n\n\nOctober 22, 2006\n\n\nLinksys sufferer\n\n\nThousand Oaks, CA\n\n\nI bought this adapter along with the Linksys WRT54GL Broadband Router. I managed to set them up and install them properly. (The best advice I got from the \"Home Networking for Dummies\" book was to get a 100 foot Cat5e patch cable so that I could put my router in the place I where I wanted it, and hook it up with a wired connection first. I actually only needed a 50 foot cable, and got it for $20 from a local electronics supply store.)\n\nSo I got everything up and running (remember to install your router first, and then your network adapter), surfed the internet for awhile, and then shut down my computer. Here's where the trouble started....\n\nAs other reviewers have noted, the Linksys installation makes changes to your Windows XP configuration, de-activating the Welcome screen and the feature Microsoft calls \"Fast User Switching.\" This happens with no warning.\n\nWhen I went to turn on my computer again, I got a new start-up screen for Windows, demanding a password. I freaked out, because I don't remember ever setting a password for Windows. I called Linksys, and was extremely unimpressed with their customer support. The young woman seemed to be amused by my problem, and was actually laughing at me. She began to start me through the process of completely re-setting the router to the factory settings (this would NOT have helped).\n\nWhat finally helped was figuring out (with no help from Linksys) that if you never set a password for your Windows XP logon, all you have to do is hit \"Enter\" at the password prompt. That's all there is. Linksys could have told me this, or better yet, warned before installation that these changes to Windows XP would take place and suggest setting up a User Password in advance.\n\nAnother gripe about this network adapter and the Linksys router--neither came with instruction manuals. There is a 60-page instruction manual on the CD rom that comes with the product, but you'll have to print it out yourself (Do it before you install or try installing the product). In the case of the router, I also had to do deep searching to actually find the instruction manual on the CD-ROM--it wanted to start up the Setup Wizard automatically and the instruction manual was not easy to access from the CD-ROM. I guess this may be the norm now with computer hardware, but it still stinks.\n\nI do not recommend the Linksys WUSB54GC, primarily because of the unannounced changes it makes to the Windows XP system, and because of Linksys' non-existent or non-functional and rude customer support\n\n\n\n\nB0009PD3PA:not_worth_it:m._voges\n\n\nB0009PD3PA\n\n\nTomTom Navigator 5 (Software Only, US): Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nNot worth it\n\n\nSeptember 19, 2006\n\n\nM. Voges\n\n\nSan Clemente, CA USA\n\n\nI have owned the TomTom Navigator 5 for almost a year now. While the program is well thought out and provides a good function, it does not work well with my Tungsten T5. I have contacted customer service 4 times now and they have always been helpful in trying to fix my issues (listed below), but to no avail. \n\nSome of the issues I have had:\n\nAny time the software has to redirect or think, it locks up. If I stay on the original planned route, it sometimes works properly...\n\nIn a 30 minute trip from Orange County to Long Beach, I had to pull over 7 times to reset my device after the software locked up.\n\nVoices lock up mid sentence giving a stuttering sound until you are able to pull your car over to reset the device.\n\nIn a 20 minute trip from San Francisco to Oakland the device locked up 5 times. I finally gave up and called a friend to guide me.\n\nBear in mind, I have had no problems with my T5 in the 1.5 years of ownership...it is only with this software. Customer service has acknowledged the problems and given solutions that have solved little to nothing. \n\nIn summary, it's not worth spending the money to have to print out Mapquest directions to have when the device crashes...stick with Mapquest\n\n\n\n\nB0009PD3PA:okay_product._contemptuous_product_support.:jerry_saperstein\n\n\nB0009PD3PA\n\n\nTomTom Navigator 5 (Software Only, US): Electronics\n\n\nelectronics\n\n\n15 of 15\n\n\n2.0\n\n\nOkay product. Contemptuous product support.\n\n\nApril 1, 2006\n\n\nJerry Saperstein\n\n\nEvanston, IL USA\n\n\n Essentially once TomTom has your money they're finished with you . . . until you want to spend more money with them.\n\n The product is okay. The map data is from TeleAtlas, a vendor to many of the map software producers. What makes TomTom different is its GUI (Graphic User Interface) that the user interacts with. It really is a work of art most of the time. The map presentations are rich and may be viewed as 3D or 2D. The planning interface is icon driven and easy-to-use. Unfortunately TomTom really didn't think it all the way through.\n\n There are several irritants, all of which vie for first place. One is that TomTom doesn't give you the option of using its maps without a GPS. There are many instances where I know where I am begining from and going to and I don't need a GPS fix. TomTom ignores such desires and annoyingly keeps prompting for you to start your Bluetooth connection. There is one mode that you can use to plan a route without a GPS and it simply doesn't work very well, often informing you that TomTom can't route you from a point beginning on an interstate to another point further along the same highway. That's just plain dumb. This is a mapping application. If I want to use these expensive maps for walking directions without a GPS, I should be able to. TomTom certainly doesn't think so.\n\n Another irritant is the documentation. Probably in an effort to make TomTom appealing to the casual user, the documentation is dog simple . . . and often wrong. It appears that they didn't update the documentation to the newest version.\n\n Although you can load maps by region, TomTom makes no allowance for travelling from one region to another. You have to calculate your route to the ending point within one region and then begin it anew in the next region. Again, this strikes me as a silly omission. If you are in Wisconsin, which is one region, and drive to Minnesota, which is in another, you have to create two routes. Not bright.\n\n There are a number of idiosyncracies, anomalies and bugs that drive me up a wall. However, overall, TomTom is still better than the competition. It computes routes quickly (when it is capable of figuring a route at all). Annoying, however, it tends to prefer expressways, regardless of your preferences. In a major metro area laced with expressways going in all directions, this can create some incredibly convoluted - and ridiculous - routes. Creating alternative and more logical routes is possible, but tedious.\n\n The worst part of the TomTom experience is its customer support. Well, perhaps support is the wrong word for it. I encountered two problems installing the software. It was obviously that the tech support person was clueless and was simply guessing and grasping at straws. (I had resolved the issues before the rep called and already knew what the problem was.) The TomTom rep didn't listen, kept interrupting and when it was pointed out that he was contradicting the manual, he just kept moving. (The manual in at least one instance was correct and the rep wrong.)\n\n The TomTom packaging itself arrived in broken condition. The rep denied it was their packaging, claiming that I must have bought a third-party repackaged product. Talk about insulting: I have the package in front of me with the TomTom labels, the whole works and this guy is telling me that my lying eyes are deceiving me?\n\n A fax to TomTom management about my experiences has gone unanswered more than a week later.\n\n TomTom is riding high right now but I suspect in a couple of years as competitors introduce better products with better support that TomTom will be history. It won't be the first company that offered a good-enough product without support and possesing contempt for its customers to disappear and it won't be the last.\n\n I know that if I find a marginally better product with adequate support, I'd abandon TomTom in a flash.\n\nJerr\n\n\n\n\nB0009PD3PA:map_activation?_for_a_product_i_purchased?_:j._bougiamas_\"booyia\"\n\n\nB0009PD3PA\n\n\nTomTom Navigator 5 (Software Only, US): Electronics\n\n\nelectronics\n\n\n15 of 17\n\n\n1.0\n\n\nMap Activation? For a product I purchased? \n\n\nJanuary 5, 2006\n\n\nJ. Bougiamas \"booyia\"\n\n\nSyosset, NY USA\n\n\nThe TomTom Navigator is a nice enough device, until you get to the Maps activation. The Maps activation? Why should I have to activate my maps if I have paid for the product? Good question, well, I activated these maps on one PDA, and within 2 weeks my PDA died and I had to obtain warranty service. Well, I decided that I would upgrade to a new device and then sell my old device when I got it back from the manufacturers (I tend to switch my PDA every 3-4 months anyway).\n\nSo I installed the maps on the new device and it asked me to activate. OK, I figure...no problem, I will be able to activate it 1...2...3 because after all, only I have the activation card/code. No such luck, TomTom wants PROOF that I have brought a new device? what kind of nonsense is that? What if I lost my PDA in 2-3 months? What if I'm a nut who likes to change PDA's every 3-4 months? What, you mean I have to show you proof that I switched? Isn't the fact that I have the activation card proof enough? I don't know too many people who pay $269 for a product and let other people use the activation codes.\n\nAvoid them at all costs. You will regret your purchase if you change devices frequently.\n\nOne other problem with this product...out of the box it is not 100% compatible with Windows Mobile 5 devices (most of the new Axims are WM5. You really have to break your back to install on these devices.\n\nAvoid\n\n\n\n\nB000196C2A:why_is_this_so_cheap?:nedthedog\n\n\nB000196C2A\n\n\nGarmin 010-10454-00 Handlebar Mounting Unit for GPS 60 Series: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nWhy is this so cheap?\n\n\nOctober 30, 2006\n\n\nNedthedog\n\n\nOhio\n\n\nI agree, this is a flimsy piece of plastic that will not hold the GPS unit over a few little bumps. I expected more from Garmin. Consider this: Touratech Mount for Garmin 60C series. Its a solid unit. It costs a little more but you won't be replacing your GPS unit due to it falling to the ground.\n\n\n\n\nB0001DB6LS:barely_gets_the_job_done_...:michael_w._dini_\"mike_(la_jolla)\"\n\n\nB0001DB6LS\n\n\nGarmin iQue 3600 GPS Auto Bundle: Electronics\n\n\nelectronics\n\n\n6 of 8\n\n\n2.0\n\n\nBarely gets the job done ...\n\n\nMay 9, 2006\n\n\nMichael W. Dini \"Mike (La Jolla)\"\n\n\nSouthern California\n\n\n My company car has a built-in GPS unit and I have gotten to like it, sort-of. I have gotten lost in a rental car trolling around Silicon Valley enough to get a portable GPS unit. After some research, the Garmin iQue 3600 is what I ended up with and I paid about $700. The unique feature of this portable GPS unit is that a PalmPilot comes integrated into the package. I use a PalmPilot, so this GPS unit replaces my PalmV. Installation was quite awkward, and getting it up and running was a chore. I got the thing synced up and some maps loaded and have been using it for several months now. It does do the job, fitfully, and has several glaring issues. Most irritating, the Palm function and the GPS function are not seamlessly integrated. If an address is entered in the Palm part and then route to this address is selected, the iQue 3600 makes you find the location. But the address from the Palm section doesn't transfer cleanly into the find section and you must retype the address. I'm left-handed and graffiti has NEVER worked for me all my letters are drawn backward. The GPS can be engaged as soon as it recognizes where you want to go, but my unit takes a good 5-10 minutes to find the satellites when first exposed to sky. This means that I must have a good idea what to do for the first 5-10 minutes. Sometimes I just drive randomly until the GPS locks in and starts to issue navigation orders. Also, the antenna must be deployed to get any GPS reception and must be forward enough in the windshield to see sky. This is OK, except that the iQue 3600 doesn't tell you that the antenna has not been deployed. \n The iQue 3600 does not work propped up on the passenger seat so a 5 pound holder is necessary to hold the unit on the dashboard. If the antenna is not deployed, the iQue 3600 simply doesn't navigate and doesn't tell you why. Worse, it acts stupid by going into a dangerous and unhelpful simulate mode. If I ever figure out what that means, Ill tell you in a future post. I figured out the antenna problem the hard way in an ice storm outside of Ottawa. I'm a fan of GUI design, and I must say that the UI here is very, very bad. Not the worse I have ever seen, but very bad: non-intuitive, inconsistent, and arbitrary. The FIND function/button has an entirely different function in the GPS section from the Palm section. Of course, the same icon is used. I cannot imagine grandma using this thing to navigate. What little there is of the very pretty documentation is atrocious. About 10 times more information is needed here. It shuts down randomly on occasion for no apparent purpose (Battery savings maybe?).\n The secure digital card is too small (max of 256MB), so you can't put more than a few California counties in the memory. Maps storage is managed with a rather hostile GUI. This is not fatal, but is irritating. I would rather put a 1GB SD card in the iQue 3600 and be done with it.\n The iQue 3600 burns too much power to be used as a portable, so it must always be tethered to the car or resting in the cradle. I don't find the power issue to be a problem. I understand that to get clear, bright screens it is necessary to use power. I agree with the tradeoff here.\n\n\n\n\nB0001WW3F2:good_idea,_poor_design:w._d._henderson_\"radio_guy\"\n\n\nB0001WW3F2\n\n\nGriffin Technology RadioShark AM / FM Desktop Radio with Time-Shift Recording: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n1.0\n\n\nGood idea, poor design\n\n\nMay 27, 2006\n\n\nW. D. Henderson \"Radio guy\"\n\n\nHouston, TX\n\n\nSummary........good idea, very poor implementation. Good software and easy to use, major problem is reception of radio stations. I live in a major metropolitan area and have to practically stand on my head to get the radio shark to get clear reception (even with a plug in antennae)\n\n\n\n\nB0001XGQBS:very_cheap_cases...and_i_don't_mean_only_the_price:scott_k._mccormick\n\n\nB0001XGQBS\n\n\nFellowes 100PK SLIM JEWEL CASE ( 98335 ): Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n1.0\n\n\nVery cheap cases...and I don't mean only the price\n\n\nAugust 21, 2005\n\n\nScott K. Mccormick\n\n\n\n\n\nThe price on the cases is cheap but you get what you pay for. I will never buy these cases again. Quite a few of them were broke when I got them. Even the ones that weren't broke are made of VERY flimsy plastic and I have a feeling they will break if I open them very often. They also don't open and shut very easy. Look elsewhere for good cases\n\n\n\n\nB000DZS3UQ:cheap_costs_you_more_in_the_long_run:real_name_here_\"quilt_artist\"\n\n\nB000DZS3UQ\n\n\nSentry SP400 MP3 Folding Speaker System: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n1.0\n\n\nCheap costs you more in the long run\n\n\nMarch 26, 2006\n\n\nReal Name Here \"Quilt Artist\"\n\n\nRytchere\n\n\nI bought this and I'm sorry. It sounds horrible. Don't waste your money because you'll just have to buy another one anyway for any true enjoyment. This crap is barely good enough for my bathroom\n\n\n\n\nB00008ZLHG:got_mine_at_the_dollar_store:dan303\n\n\nB00008ZLHG\n\n\nMonster Cable AICYP iSplitter Mini Y-Adapter for iPod and Mac: Electronics\n\n\nelectronics\n\n\n8 of 20\n\n\n2.0\n\n\nGot mine at the dollar store\n\n\nJanuary 16, 2005\n\n\ndan303\n\n\n\n\n\nThey sell these at almost every dollar store, sure the sound quality might not be quite as good, but you're going to have signal loss whatever you hook it up to. These things sell for $1.50 CAN at dollar stores and are worth every penny of THAT pric\n\n\n\n\nB0000BZO6E:the_worst_wireless_card_ever:g-unit\n\n\nB0000BZO6E\n\n\nMicrosoft MN-720 Wireless 802.11g Notebook Adapter: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n1.0\n\n\nThe worst wireless card ever\n\n\nSeptember 6, 2005\n\n\nG-Unit\n\n\n\n\n\nAlright before I begin let me just say I know quite a bit about wireless networking. I had already connected 2 wireless pc's to my 128 bit WEP encrypted router. I had initially set it up as WPA but opted to change it as this hunk of junk doesn't support WPA. This card will detect your network no problem, just try connecting to it. Apparently I am typing in the wrong WEP pass.... I can assure you I am not. Perhaps there is an issue between Micro$oft and D-link I am unaware of...go figure. I do not have the time nor patience to deal with Micro$oft's customer support regarding this issue. Back to the store this goes. Do yourself a favour and do not buy this\n\n\n\n\nB0000BZO6E:an_interesting-looking,_but_useless,_blinking_bit_of_plastic:asdf\n\n\nB0000BZO6E\n\n\nMicrosoft MN-720 Wireless 802.11g Notebook Adapter: Electronics\n\n\nelectronics\n\n\n5 of 10\n\n\n1.0\n\n\nAn interesting-looking, but useless, blinking bit of plastic\n\n\nAugust 21, 2004\n\n\nasdf\n\n\nMN\n\n\nMicrosoft has never failed to disappoint me, (I suppose there is something to be said for consistency) and this carries on that somewhat dubious tradition. I bought this with a the MN-700 wireless router, and both worked fine for a couple weeks. However, they both conspiratively proceeded to stop functioning. This card was the most maddening of the pair, because it would find the wireless network every once in a while, yet refuse to connect. I've tried every channel, and made sure its on infrastructure mode, but it still refuses to work. I suppose I deserve it, though, expecting that a microsoft wireless card would connect to a microsoft wireless router with a microsoft operating system. Do yourself a favor and buy your wireless networking from a decent company that has some expertise in networking, like Linksys or Belkin. Whatever you do, give as little money as possible to this shoddy company\n\n\n\n\nB0009ORXEI:not_worth_the_money:stacey_mitchell\n\n\nB0009ORXEI\n\n\niRiver T10 512 MB MP3 Player with FM Tuner: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n1.0\n\n\nNot worth the money\n\n\nNovember 9, 2006\n\n\nStacey Mitchell\n\n\nPhiladelphia, PA\n\n\nI returned this product after a week - it was difficult to manage, the screen way too small to read (and I have 20/10 vision!) and just a very disappointing product overall. Do not waste your money. I ended up buying a Samsung and I couldn't be more happy\n\n\n\n\nB000FJLBTK:terrible!_do_not_purchase!:lenny\n\n\nB000FJLBTK\n\n\nHP Photosmart C4180 All in One Printer/Scanner/Copier: Electronics\n\n\nelectronics\n\n\n5 of 25\n\n\n1.0\n\n\nTerrible! Do not purchase!\n\n\nOctober 2, 2006\n\n\nLenny\n\n\nNorth Carolina, USA\n\n\nI just purchased this printer last night at BestBuy and took it home. When I removed the contents of the box, I noticed that the USB cord was not in there. I read the directions and said that you had to buy the cord separately. I couldn't believe it. So, I went to the store and bought one. This cost me an additional $11. I went home, installed the software and plugged everything in. I printed off a few things and it looked great. Today, I went to use the printer again and the power woudn't turn on! I disconnected then reconnected everything and still no power! I tried several more things and still couldn't turn the power on. Now I have to return it to BestBuy. This was the first and last time I will ever buy anything from HP!!\n\n\n\n\nB0002SQ0AE:fire_hazard:c._hadden\n\n\nB0002SQ0AE\n\n\nLogitech Z-5300e 5.1 5-Piece Surround Sound PC and Gaming Speaker System: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n1.0\n\n\nfire hazard\n\n\nSeptember 20, 2006\n\n\nC. Hadden\n\n\n\n\n\nWe purchased this system less than two years ago. It has seen light to moderate use. Several days ago, the speakers started intermittently cutting out. This afternoon, the subwoofer popped several times--then caught fire! Fortunately, my son was able to extinguish the flames before much damage was done. Beware!!!!\n\n\n\n\nB000FJEYZS:five_stars_for_the_buds,_zero_stars_for_the_cord:r._smith\n\n\nB000FJEYZS\n\n\nSennheiser MX75 Twist-To-Fit In-Ear Stereo Sport Headphones: Electronics\n\n\nelectronics\n\n\n1 of 4\n\n\n1.0\n\n\nFive Stars for the Buds, Zero Stars for the Cord\n\n\nNovember 11, 2006\n\n\nR. Smith\n\n\nNew Orleans, LA USA\n\n\nI do not recommend Sennheiser's MX75 headphones.\n\nFirst, the good features:\n1. As with all Sennheiser products, these earphones deliver first rate sound for its price range.\n2. The earphones are very comfortable and easy to fit properly using the included pads.\n3. Once inserted in the ear, these earphones will not fall out due to vibration, movement or sweating.\n\nHowever, the cord has ugly problems:\n1. The cord is unusually stiff and consequently tends to be tough to manage. This is especially true when the phones are first removed from the package, as the cord maintains a sort of corkscrew shape for quite some time.\n2. The cord running to the right earphone is substantially longer than the one connecting to the left earphone. The result is annoying slack around the face and neck.\n3. The cord is the ugliest possible shade of green.\n\nAll in all, should Sennheiser offer a new product using the same earphones, but with conventional wiring, I'll be a buyer. Until then, I've given up on using these lime green annoyances\n\n\n\n\nB000DIM9AS:wouldn't_work_for_me_-_soooo_disappointed:okcpa\n\n\nB000DIM9AS\n\n\nCoby TF-TV505 5\" LCD Color Television: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nWouldn't work for me - soooo disappointed\n\n\nNovember 7, 2006\n\n\nOKCPA\n\n\nTulsa, OK\n\n\nI bought this TV for my husband to take hunting but we can't get very many channels and the ones we do get have a terrible picture. The TV is an awesome size and the design is really nice with a good size screen. I didn't hook to cable so that may improve the picture but we don't have cable. I tried programming the channels from two different locations 20 miles apart from each other. I tried programming with the antenna on the TV and with separate rabbit ears. It would only pick up one of the three local network stations. It picked up maybe 8 other misc stations which aren't popular. I couldn't get a good picture on any of the stations and I tried adjusting all the menu items, brightness, hue, etc. It didn't even have a good black and white picture .... it was like a white screen with blurry images in the back. Sooooo, sooo disappointed because I wanted to be able to use this at work too. \n\n\n\n\nB0007TJ7TE:an_o.k._keyboard_with_cheesy_backlighting_and_no_fn_key:peter_frishauf\n\n\nB0007TJ7TE\n\n\nSaitek Eclipse Keyboard ( PZ30AU ): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nAn O.K. Keyboard with Cheesy Backlighting and no Fn Key\n\n\nOctober 15, 2006\n\n\nPeter Frishauf\n\n\nNew York, NY USA\n\n\nI wanted a keyboard that I could use in low light with both my PC and Mac, and this one is reasonably o.k. Those users who enjoy the backlight of a Mac PowerBook or MacBook Pro will be disappointed by the cheap approach Saitek used to illuminate the keys, though. On the Mac, each key has its own embedded light under the key. On the Saitek light is splashed under the entire keyboard, reducing the contrast and making it much harder to see the keys. But it's better than nothing, and is helpful in low light. It would have also been nice if they had an Fn key, but such is life\n\n\n\n\nB000095SMR:this_card_adapter_doesn't_work_with_my_hp_printer:teresa_l._carpenter_\"travel_lore_crafter\"\n\n\nB000095SMR\n\n\nFuji CompactFlash xD Card Adapter for xD-Picture Card: Electronics\n\n\nelectronics\n\n\n7 of 7\n\n\n1.0\n\n\nThis card adapter doesn't work with my HP printer\n\n\nNovember 17, 2004\n\n\nTeresa L. Carpenter \"travel lore crafter\"\n\n\nKingsport, TN USA\n\n\nI purchased my Fuji CompactFlash xD Card Adapter elsewhere and it doesn't work in my HP officejet 7130 all-in-one. It won't load in as it's supposed to but will fit correctly backwards. Except then I get an amber light that says there is a card error. This is supposed to work with my all-in-one but it doesn't. For that reason, I'd give it a zero star rating if such a rating existed. I'm going to take this CP version back and hope the more updated CPF version works. I'm a travel writer planning to take my own photos to go with my articles, but I'm getting nowhere fast with this card adapter that refuses to work properly. This one is definitely a thumbs down. However, this is my first dud Fuji product I've bought and I love Fuji digital cameras\n\n\n\n\nB0002DQUHC:cheap_and_cheaply_made!:m._eder\n\n\nB0002DQUHC\n\n\nD-Link DWL-G122 Compact Wireless USB Adapter, 802.11g, 54Mbps: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nCHEAP and cheaply made!\n\n\nOctober 31, 2006\n\n\nM. Eder\n\n\nNYC\n\n\nMine worked fine for about a month before it stopped working. dealing with D-Link isn't a pleasure either. \nDon't get it. Get something better. Spend a little more, avoid the hassles\n\n\n\n\nB00006B9ZB:if_you_have_a_tekxon_digital_camera_do_not_get_this!:tyler_reed_\"tyler\"\n\n\nB00006B9ZB\n\n\nLexar Media 128 MB Secure Digital Card: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nIF YOU HAVE A TEKXON DIGITAL CAMERA DO NOT GET THIS!\n\n\nFebruary 4, 2006\n\n\nTyler Reed \"tyler\"\n\n\nKeller, TX\n\n\nIF YOU HAVE A TEKXON DIGITAL CAMERA DO NOT GET THIS because when you put the memory card in and you turn the camera on the screen goes black! \n\n\n\n\n\nB00006B9ZB:stressful_30_minutes:peter_tonev\n\n\nB00006B9ZB\n\n\nLexar Media 128 MB Secure Digital Card: Electronics\n\n\nelectronics\n\n\n2 of 10\n\n\n2.0\n\n\nStressful 30 minutes\n\n\nDecember 27, 2003\n\n\nPeter Tonev\n\n\nToronto, Ontario Canada\n\n\nMy brother got a Lexar for Christmas. He put it in his Tungsten C and for 30 stressful minutes the card did not work. The device recognized the card, but you could not copy, format, nothing... just got an error "This card is read only and cannot be <whatever>." Then all of a sudden it started working. 2 stars for the stress and for the fear that it will stop working again\n\n\n\n\nB000233YDK:not_reliable_and_customer_service_is_poor:mr._stargazer_\"clear_dark_skies\"\n\n\nB000233YDK\n\n\nSyntax Olevia LT27HV 27\" HDTV-Ready Flat-Panel LCD TV: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n1.0\n\n\nNot Reliable and Customer Service is Poor\n\n\nOctober 16, 2006\n\n\nMr. Stargazer \"Clear Dark Skies\"\n\n\nWisconsin\n\n\nEverything your read about the picture quality is true... however, this product just isn't reliable. Cost me another $600 to get it fixed and it isn't even 18 months old. I am sure I am not alone since you can see these on that famous auction site may with similar power issues. Customer service from this company is also very poor. If I had it to do over I would not have purchased this product and I probably should not have spent the money to get it fixed. I think it will be just a matter of time before some of these 5 star reviews begin to change their tune\n\n\n\n\nB0001IXUDU:your_money_is_better_spent_elsewhere:joshua_hodge_\"aylwyne\"\n\n\nB0001IXUDU\n\n\nMonster Cable HDMI 400 High Resolution A/V Cable - 4 Meters: Electronics\n\n\nelectronics\n\n\n23 of 24\n\n\n1.0\n\n\nYour money is better spent elsewhere\n\n\nOctober 20, 2006\n\n\nJoshua Hodge \"Aylwyne\"\n\n\nBoston, MA\n\n\nLike most have pointed out, it's a digital signal and there's no need for all the gas-injected, gold plated, shielded stuff monster cables hype. For analog signals this stuff absolutely makes a difference because analog cables are highly affected by outside EM interference.\n\nSome people have pointed out that monster cables are more durable. So yes, if you plan on swinging around on your cables it might matter. If, like most people, you plug the stuff in behind your equipment then leave it be for months and months, don't waste your money\n\n\n\n\nB0001DYTY4:if_i_could_give_this_a_zero,_i_would.:bri-man_\"the_video_dork\"\n\n\nB0001DYTY4\n\n\nSimpleTech 160 GB USB 2.0 External Hard Drive ( STI-USB235/160 ): Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nIf I could give this a zero, I would.\n\n\nNovember 25, 2006\n\n\nBri-Man \"the video dork\"\n\n\nMN\n\n\nBought this and plugged it in and backedup some files, went to transfer them to another computer and the drive was dead, I treid a few computers and no of them could regonize the drive. I guess I have one of the many \"bad ones\".. \n\n\n\n\nB00023CJKE:too_many_disappointments:s._sturch\n\n\nB00023CJKE\n\n\nAT&T E5865 5.8 GHz DSS Expandable Cordless Phone with Answering System and Dual Keypads: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nToo many disappointments\n\n\nMarch 6, 2006\n\n\nS. Sturch\n\n\nSan Antonio, TX\n\n\nOthers have written about this phone not holding the charge, and I agree! I can't tell you how many times a call has gone dead in the middle of a conversation, with no warning, no low battery message. I've only had the phone since November. There is not a quick, convenient way to redail numbers--you have to choose from a list of options, 1-plus number, or the area code without the 1, or no 1 and no area code. No easy way to speed dial. Range is disappointing. I'm looking for a new phone. Back to Uniden or Panasonic\n\n\n\n\nB000799LQS:just_didn't_work!:mark_twain\n\n\nB000799LQS\n\n\nMemorex 2.4x 8.5 GB Double Layer DVD+R Pack (3 Discs): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nJust didn't work!\n\n\nJanuary 21, 2006\n\n\nMark Twain\n\n\nWest Coast\n\n\nAfter burning one coaster, I thought, it's just a fluke. I'll burn my first DVD in no time. I was wrong. After updating the firmware on my new Plextor DVD burner and downloading the latest version of Nero 6, I burned two more coasters. Don't buy these unless you want pain and frustration\n\n\n\n\nB000799LQS:worst_media_ever:nickyct\n\n\nB000799LQS\n\n\nMemorex 2.4x 8.5 GB Double Layer DVD+R Pack (3 Discs): Electronics\n\n\nelectronics\n\n\n6 of 7\n\n\n1.0\n\n\nWorst Media ever\n\n\nNovember 8, 2005\n\n\nNickyct\n\n\n\n\n\nPlays fine on the first layer. But picture will freeze during layer switch. I either have to stop or skip pass the chapter but picture will be jumping and pixellated, pretty much unwatchable.\nyou have to skip at least couple chapter away from the layer switch to get a decent picture on your screen.\ndon't waste your money. Buy verbatim instead. I never had any problem with that media.\n\n\n\n\nB00004Y3UX:not_for_a_serious_audiophile_setup:david_v._bowen\n\n\nB00004Y3UX\n\n\nMonster Cable SS4 Multi-Speaker Selector: Electronics\n\n\nelectronics\n\n\n11 of 12\n\n\n2.0\n\n\nNot for a serious Audiophile setup\n\n\nOctober 15, 2005\n\n\nDavid V. Bowen\n\n\nNew Jersey\n\n\nDissapointing. The sping-loaded connectors at the back of the selector are not particularly strong, so thick or rigid cables under tension (e.g. twisted at odd angles) easily slip out. The depth of the connectors is also not great, so wires cannot be inserted very far to compensate for the lack of traction. Connections might be better using Monster Cables' own Angled Gold Pin connectors, but I didn't try those\n\n\n\n\nB000E39V9O:disappointed_with_dph-50u:m._harris\n\n\nB000E39V9O\n\n\nD-Link DPH-50U Skype USB Phone Adapter: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nDisappointed with DPH-50U\n\n\nOctober 18, 2006\n\n\nM. Harris\n\n\nUtah\n\n\nOn the good side, it was easy to set up, and worked reasonably well if the phone (tried both cordless and corded) was plugged directly into unit. However, I could not get it to work on other phones in the house when I used the jack to plug it into the wall per the instructions (so it would work like a land line on any phone in the house). I do not have a land line, so it may just need a land line to work since you are supposed to be able to switch back and forth. I even tried disconnecting the line outside the house but that didn't work either. \n\nI experience sporatic dropped calls that were not present before installing this device. I contacted tech support and was sent a driver upgrade v 1.20 (not available from their website). This seemed to improve the dropped call situation a bit, but I still have more dropped calls than before. \n\nThe dealbreaker for me was after I found out you had to be logged in as an administrator for it to work. With an \"always on\" internet connection, I was not willing to expose myself to that security risk\n\n\n\n\nB000E39V9O:unacceptable.__this_is_worse_than_beta.:c_campbell\n\n\nB000E39V9O\n\n\nD-Link DPH-50U Skype USB Phone Adapter: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n2.0\n\n\nUnacceptable. This is worse than Beta.\n\n\nOctober 9, 2006\n\n\nC Campbell\n\n\nLos Angeles, CA United States\n\n\nI bought this a few weeks ago so that I could connect a cordless phone to my office computer and use Skype for my calling. When it works, it works well, and the sound quality is fine. Unfortunately, much of the time, it doesn't work. It drops calls at random. Customer support is dismal, I emailed them several days ago and didn't even get a response acknowledging the email, let alone a solution to my problem. Just now I have tried reinstalling the drivers but that seems to be failing. \n\nWe have a VoSky Call Center at home that so far has been working much better. I bought the the DPH-50U because I was in a hurry and the store I was at only carried it, not the VoSky. I wish I had waited and bought a second VoSky\n\n\n\n\nB0000BVYTV:sucks!:d._mentzer_\"dm\"\n\n\nB0000BVYTV\n\n\nAntec USB-Powered Notebook Cooler: Electronics\n\n\nelectronics\n\n\n1 of 4\n\n\n1.0\n\n\nsucks!\n\n\nAugust 18, 2006\n\n\nD. Mentzer \"DM\"\n\n\nLA, CA\n\n\nthe connection keeps coming loose and now it doesnt work unless you hold it in place - useless and a waste of mone\n\n\n\n\nB000F1O0X2:scroll_wheel_just_stops_working!!!:a._traverso_\"subgenius\"\n\n\nB000F1O0X2\n\n\nLogitech MX 400 Performance Laser Mouse: Electronics\n\n\nelectronics\n\n\n1 of 5\n\n\n2.0\n\n\nScroll Wheel just stops working!!!\n\n\nJune 5, 2006\n\n\nA. Traverso \"SubGenius\"\n\n\nPetaluma, CA USA\n\n\nI really wanted to like this mouse but so far it has given me nothing but grief. If you plan on leaving your computer on for extended periods of time and then coming back to it to do some work you may find that your scroll wheel no longer scrolls pages. I have had to on several occasions unplug and replug in my mouse in order to get the scroll wheel working again. This is absolutely UNEXCEPTIBLE Logitech! What happened to Logitech? You used to be able to count on them for quality products now I am wondering if I should switch back to Micro$oft products. I may give the Micro$oft Laser Mouse 6000 a try and take the MX 400 back\n\n\n\n\nB000F1O0X2:bad_-_bad_-_bad:biil_seeb\n\n\nB000F1O0X2\n\n\nLogitech MX 400 Performance Laser Mouse: Electronics\n\n\nelectronics\n\n\n0 of 9\n\n\n1.0\n\n\nBAD - BAD - BAD\n\n\nMay 27, 2006\n\n\nBiil Seeb\n\n\nPittsburgh PA\n\n\nLike the forward/back buttons but they are located too far back on the body for comfort. Like the scroll wheel and side-to-side scroll ability. Pointer flickers a lot sometimes and often disappears over certain buttons. Logitech website gives no help.\nComes with an installation CD for a Logitech MX320 !!! Be prepared for a 50MB download to install the MX400. Almost makes Microsot look good\n\n\n\n\nB0009L8Q8I:description_and_picture_are_very_misleading.:greg_stratakes_\"amazon_fan\"\n\n\nB0009L8Q8I\n\n\nGN-Netcom GN Netcom GN 6210 - headset ( 85-00006 ): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n1.0\n\n\nDescription and Picture are Very Misleading.\n\n\nJune 9, 2006\n\n\nGreg Stratakes \"Amazon_Fan\"\n\n\nBradenton, Florida United States\n\n\nI have the same issue as \"Misleading Item Description\" below. The only reason I bought this is because I thought it was the complete set. What good is a headset without the base? \n\nWARNING! TigerDirect does not except returns on this headset; so now I must return the headset to TigerDirect, then buy the full unit from TigerDirect or I lose my $70. The headset with the base is about $35 more expensive than what I would have payed at BUY.COM.\n\nI expect a lot more from Amazon. Very, Very Disappointed\n\n\n\n\nB0002ZFVB6:very_bad_product:yi_chen_\"courga_boy\"\n\n\nB0002ZFVB6\n\n\nRikaline GPS-6010 USB GPS receiver for laptop notebook PC WAAS: Electronics\n\n\nelectronics\n\n\n5 of 21\n\n\n1.0\n\n\nvery bad product\n\n\nMarch 10, 2005\n\n\nYi Chen \"Courga boy\"\n\n\nUSA\n\n\nI bought this Rikaline GPS 6010 a month ago. I tried to make it work for hundreds times, but it only worked once. Evertime when I turned on the GPS, it was always telling me it was initializing and then it just stopped working.I hate this thing because it embarrassed me before my friends.\n\n\n\n\nB0001D3K3A:don't_buy_this_junk.__plenty_of_other_good_adapters,_why_risk_buying_this:j._tone\n\n\nB0001D3K3A\n\n\nLinksys WMP54GS Wireless-G PCI Card with SpeedBooster: Electronics\n\n\nelectronics\n\n\n7 of 7\n\n\n1.0\n\n\nDon't buy this junk. Plenty of other good adapters, why risk buying this\n\n\nAugust 24, 2006\n\n\nJ. Tone\n\n\nSan Jose, CA\n\n\nMine actually works and I still hate it.\n\nI haven't had all the problems others are complaining about.\n---I run Windows XP SP2 had no problems compatability.\n---Signal strength is fine.\n---I had no problem installing on one computer....except that I installed the card before running the CD, just like every other card or peripheral I have ever installed. I've never seen one that didn't allow you to install hardware before drivers. So I had to take it out, run CD and put back in. Just a minor annoyance that cost 10 minutes. But c'mon Linksys why the special order? Most people that are willing to open the computer and install a card don't need instructions to know \"take card and put in slot\" and \"insert disc\", so they don't look at them. At least they put a warning sticker about the installation order on the disk cover for us non-direction readers. Annoying but no stars deducted for this.\n---Told Mom to buy one (this was before I had installed mine). Installed easy and it worked fine at first. Now it connects to router but can't find internet (yes other computers connect to net through this router just fine). Would think maybe firewall setting issue??? but I used to work then just stopped. C'mon Linksys if your connected to the router how hard it is to find the internet. Deduct 1 star.\n---Stupid software only supports hexidecimal passwords. Of course my router also support standard alphabet and my password had some G-Z letters, so I had go change network password and update all devices attached to the network. C'mon Linksys how hard it is to support A-Z. Annoying but no stars deducted.\n---SLOW SLOW SLOW SLOW TO CONNECT!!!! On my other computers you hit connect and are online in 5-10 seconds. This thing takes 30 seconds to a minute. Impatient, yes. But still unacceptable. Deduct 1 star.\n---Can't hibernate or sleep computer or card disables. Only way to get it turned on again is to reboot. So it's either leave computer on all day long, or reboot everytime I want go online. Deduct 1 stars.\n--Overall bad product. Deduct one star\n\n\n\n\nB0001D3K3A:terrible_connections:a._chu\n\n\nB0001D3K3A\n\n\nLinksys WMP54GS Wireless-G PCI Card with SpeedBooster: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n1.0\n\n\nTerrible connections\n\n\nJune 19, 2006\n\n\nA. Chu\n\n\nPalo Alto, CA USA\n\n\nI bought two of these cards to go alongside my wrt54gs. I though that they should work great together since they are from the same manufacturer. Oh boy was I wrong.\n\nEven the though I get full bars with these cards, I get very flaky connections with them. I've tried many different settings and firmwares to try to rectify the problem. Many hours have been spent trying to get a stable connection. No dice, very flaky.\n\nOther computers with different cards work fine with the wrt54gs and never dropout. So there is something about this card that just isn't right, and I can't imagine both cards being defective. I haven't tested them with another router, but if it can't work with a wrt54gs v3, then what's the point\n\n\n\n\nB00008YBCJ:amazing_furreal!:\n\n\nB00008YBCJ\n\n\nD-Link - Antenna cable - N-Series connector (M) - N-Series connector (F) - 30 ft: Electronics\n\n\nelectronics\n\n\n0 of 2\n\n\n2.0\n\n\nAmazing furreal!\n\n\nJanuary 12, 2006\n\n\n\n\n\n\n\n\nI got this toy for Christmas. Ilove it. I abanded this toy,and it got some brudle hits. It was still alive! Know I keep safe becuse I don't want it to die. I don't think this toy is a wast of money.Mine is ginger. It came with a silver/grey newborn kittin\n\n\n\n\nB00023NDLS:overpriced:james_b._beckham_\"jim\"\n\n\nB00023NDLS\n\n\nAcoustic Research AW-871 Wireless Stereo Speakers: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\noverpriced\n\n\nNovember 23, 2006\n\n\nJames B. Beckham \"jim\"\n\n\ncolumbia\n\n\nThese speakers are o.k., but don't expect high sound quality (I bought these as refurbs, so maybe that is it, but when you turn up the volume, they don't sound good - I'd recommend going to a store and testing units before buying new). Also, probably the biggest failure in my opinion is that they appear to be line of sight - that is, they fade in and out when you walk around the room. This should not be a problem on this low bandwidth (but it is), perhaps the antenna needs a boost\n\n\n\n\nB00023NDLS:not_so_good.:h.f.mudd\n\n\nB00023NDLS\n\n\nAcoustic Research AW-871 Wireless Stereo Speakers: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nNot so good.\n\n\nNovember 17, 2006\n\n\nH.F.Mudd\n\n\nRedding, CA\n\n\nToo much static with snapping and popping - even after making all of the adjustments possible. The speakers are ONLY 10 feet away from the transmitter but still poor-to-marginal sound quality. Don't see how these could work 300 feet away(?) I do NOT recommend this product\n\n\n\n\nB000063SNE:cheap_junk:r._johnson_\"19th_century_artist\"\n\n\nB000063SNE\n\n\nRCA ANT1250 UHF/VHF Amplified indoor Antenna: Electronics\n\n\nelectronics\n\n\n4 of 9\n\n\n1.0\n\n\nCheap junk\n\n\nFebruary 8, 2006\n\n\nR. Johnson \"19th century artist\"\n\n\nSoCal, USA\n\n\nCheap piece of junk. Probably has 2 cents of electronic parts in it. I'm returning it to Wallmart. It might work OK if you live like right next door to the broadcast transmitter. Otherwise it's just too weak in its signal pulling ability. Also, when I turn the gain dials, they don't seem like they are really variable. I think they are really just on/off switches disguised as variable dials, because i don't see any difference between medium gain and high gain on any of the channels. I'm going to now go to a REAL, serious electronics store and look for some SERIOUS tv electronics hardware instead of Wallmart garbage. I'm going to work on improving my roof antenna and its cabling and I'm going to try using a variable gain preamp if I can find one (a high quality one, not junk). I tried a preamp from Wallmart (which wasn't adjustable at all) and it did help a lot with UHF channels, but the VHF channels were much worse because it made the signal too strong. So I need a variable gain preamp, assuming there is such a thing. I would advise everyone to go read the information available on the internet about tv antennas and how to get the best picture with them. I did yesterday and there's a lot of good stuff I never knew before.\n\n\n\n\nB000063SNE:indoor_tv_antenna:miles_h._kennedy\n\n\nB000063SNE\n\n\nRCA ANT1250 UHF/VHF Amplified indoor Antenna: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n2.0\n\n\nIndoor TV antenna\n\n\nOctober 3, 2005\n\n\nMiles H. Kennedy\n\n\nCleveland, Ohio\n\n\nI live in a particularly bad spot for TV reception and have been forced to use cable. This antenna brings in several local stations but the quality of the picture is unsatisfactory. So, unfortunately, for me it's either back to cable or try an amplified outside antenna.\nMiles Kenned\n\n\n\n\nB0000DBLMB:does_not_work_in_presentation_view.:randy_dodd\n\n\nB0000DBLMB\n\n\nKensington 33062 Wireless Presentation Remote Control with Laser Pointer (PC/Mac): Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n2.0\n\n\nDoes not work in Presentation View.\n\n\nSeptember 18, 2006\n\n\nRandy Dodd\n\n\nNovato, CA United States\n\n\nYeah, they claim this thing works with MS Powerpoint. But what they do not tell you is that it only works with PART of Powerpoint. It only works in Slide Mode. It does NOT work in Presentation View mode. Of course, most of my presentations are run in Presentation View mode.\n\nSO the remote is useless. And they have no fix for it. They cannot even document that this is the case.\n\nI am going to look for something else. Maybe the Microsoft Remote.\n\n***UPDATE***\n\nIt's true, this presenter has problems with PowerPoint's Presenter View because in that mode PgUp and PgDown keys are used to scroll the notes. However, here's a neat trick I discovered that makes it work again: \n- Start the presentation in Presenter View \n- Pres \"B\" on your keyboard to show a blank screen \n- Click the mouse (anywhere in the blank screen) once to resume the presentation and then AGAIN to advance to the next slide \n- Magically, from this point on the presenter buttons work flawlessly. \n\n\n\n\n\nB0002ZZTFE:confusing_description_of_item:a._coughenour\n\n\nB0002ZZTFE\n\n\nMotorola MD71 5.8 GHz Cordless Accessory Handset for MD700 Series Phones: Electronics\n\n\nelectronics\n\n\n1 of 9\n\n\n1.0\n\n\nConfusing description of item\n\n\nMay 14, 2006\n\n\nA. Coughenour\n\n\nCalifornia\n\n\nI wish the description had beem better. Somethimg like THIS PHONE NEEDS ANOTHER UNIT TO WORK! People who have never seen an expansion phone can be missled like I was. Returned item\n\n\n\n\nB00006FXR9:review_of_zenith_zhdtv1-hdtv-uhf_digital_indoor_antenna:david_j._krupp_\"djk\"\n\n\nB00006FXR9\n\n\nZenith ZHDTV1 HDTV-UHF Digital Indoor Antenna (Silver and Black): Electronics\n\n\nelectronics\n\n\n0 of 4\n\n\n1.0\n\n\nReview of Zenith ZHDTV1-HDTV-UHF Digital Indoor Antenna\n\n\nNovember 16, 2006\n\n\nDavid J. Krupp \"DJK\"\n\n\nNYC , USA\n\n\nThe Antenna did not work at all! Save your money!\n\n\n\n\nB000I5PGOM:dead_unit:j._maldonado\n\n\nB000I5PGOM\n\n\nCreative Labs Zen Vision: M 60 GB Portable Media Player Black: Electronics\n\n\nelectronics\n\n\n0 of 22\n\n\n1.0\n\n\nDead Unit\n\n\nNovember 1, 2006\n\n\nJ. Maldonado\n\n\nGeorgia\n\n\nI recieved this unit 3 bussiness days after I ordered it. Fast shipping.\nTo bad when i recieved it it only worked for 2 minutes!!! Sending back tomorrow, not sure if i want another one. Only comes with usb charger, no wall charger\n\n\n\n\nB00005MITU:at&t_210:a._ritchie\n\n\nB00005MITU\n\n\nAT&T 210 Trimline Phone with Memory Dialing (Black): Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nAT&T 210\n\n\nNovember 9, 2006\n\n\nA. Ritchie\n\n\nWakefield, MA United States\n\n\nPhone seemed nice but I bought it to hang on a wall and it wouldn't due to manufacturing.\n\n\n\n\nB000136P8W:terrible,_useless,_and_unecessarily_complicated.:walter_o'brien_\"concretewo\"\n\n\nB000136P8W\n\n\nPlantronics S12 Corded Telephone Headset System: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nTerrible, useless, and unecessarily complicated.\n\n\nNovember 16, 2006\n\n\nWalter O'Brien \"concretewo\"\n\n\nClinton, NJ USA\n\n\nAll I wanted was something to make the incoming audio louder while listening to a call and typing during an interview. I couldn't find just a headset amp so I purchased this \"system\" based on Plantronics' good name. \n\nInstead of just plugging it into the wall phone jack, you have to use a regular phone, route the receiver/handset cord to this, then back out to the receiver/handset again, which makes this light unit basically hamstrung between the headset and handset and doesn't want to sit on the desk without taping it down. Then you have to take the receiver/handset off the hook, place it on the desk (assuming you have desk space, which I don't) then push a button to start up the headset. \n\nMost importantly, listening sound quality was muffled and not very loud. If I turned up the incoming sound volume, it immediately put a loud annoying squeal of feedback into the microphone, making it totally unusable. \n\nI went back to my regular, unampflied Plantronics headset which just plugs into the wall phone and is always on with no extra cords, ac adapter, or buttons to push. I'm not very thrilled with that, but for $15 it sounds MUCH better and much louder, without the feedback or the hassles. So for now I guess I'll stick with that until I find something else that just improves the incoming sound\n\n\n\n\nB0007N55LE:they_don't_stay_on/in_your_ears:tina_mazzarino\n\n\nB0007N55LE\n\n\nSony MDR-EX81LP Bud-Style Stereo Earphones (White): Electronics\n\n\nelectronics\n\n\n1 of 3\n\n\n1.0\n\n\nThey don't stay on/in your ears\n\n\nNovember 19, 2006\n\n\nTina Mazzarino\n\n\nMA\n\n\nI never even got a chance to listen to music through these headphones. I couldn't get them to stay in/on my ears. The loops that go around your ear are huge and just make them more cumbersome. These may fit men with bigger ears better but I wouldn't recommend them for females\n\n\n\n\nB0007N55LE:no_need_of_ear_hanger:j._kim_\"http://clixlounge.com/\"\n\n\nB0007N55LE\n\n\nSony MDR-EX81LP Bud-Style Stereo Earphones (White): Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n2.0\n\n\nNo need of ear hanger\n\n\nNovember 9, 2006\n\n\nJ. Kim \"http://clixlounge.com/\"\n\n\nMD USA\n\n\nI like the color and sony's technology of the earphone itself.\nBut the hanger that should be placed on your ear is worthless.\nWhen I put the earphone in the ears, the hanger part is just hanging in the air, not supporting the earphone to be placed in and steady.\nBecause of that, the earphones easily pop out of your ear\n\n\n\n\nB00005UK9T:adhesive_problems:m._shanafelt_\"shanafme\"\n\n\nB00005UK9T\n\n\nGarmin Auto Mounting Bracket for eTrex: Electronics\n\n\nelectronics\n\n\n9 of 9\n\n\n2.0\n\n\nAdhesive Problems\n\n\nJanuary 1, 2006\n\n\nM. Shanafelt \"shanafme\"\n\n\nHollidaysburg, PA United States\n\n\nThe device is well built but has one major flaw. The adhesive comes in 2 types, \"removable\" and \"permanent\" types. Neither adhesive would stick to the dash of my Jeep Liberty. I'm not sure what I'm going to do with it now, it appears as though I'm going to either have to find a more permanent mounting solution or find another type of adhesive\n\n\n\n\nB000B6D39I:right_click_is_tricky:christian_oliver_\"coliv77\"\n\n\nB000B6D39I\n\n\nApple Bluetooth Wireless Mighty Mouse: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nRight click is tricky\n\n\nNovember 19, 2006\n\n\nChristian Oliver \"coliv77\"\n\n\nAtlanta, GA\n\n\nAfter two weeks of use, I returned the mouse because 3 out of 4 times I tried to make a right click, it would not work. I eventually got frustrated and decided to return it.\nI'm sure it has to do with the way I use the mouse but I'm not planning on changing my angle or whatever it is that makes it fail. I think a regular 2 button mouse is just perfect.\nI wish this mouse had a real mechanical right button because it would be perfect.\nI LOVED the scroll wheel and will miss it greatly\n\n\n\n\nB00081GASC:it_arrived_doa:barry_weber\n\n\nB00081GASC\n\n\nEtycom Headset Pillow Pak: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nIt arrived DOA\n\n\nNovember 11, 2006\n\n\nBarry Weber\n\n\nPhiladelphia, PA USA\n\n\nI had high hopes for this headset. I found that I could not hear anything when I attached it to my Treo 650. The Treo uses a standard plug. I verified that my Treo is working by attaching a stereo headset\n\n\n\n\nB0001NA2S6:pure_junk!:erick_perez\n\n\nB0001NA2S6\n\n\nD-Link DP-G310 Wireless Print Server, USB 2.0 802.11g, 54Mbps: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nPure junk!\n\n\nSeptember 27, 2006\n\n\nErick Perez\n\n\nMiami, Fl\n\n\nMy friend bought one and I tried to set it up for him. Let me tell you, this thing is pure garbage. I spent a good hour and a half trying to get it to work. I tried to set it up using 2 laptops which were set up exactly how the booklet outlined. The direct ethernet connection wouldn't work and connecting to it through wireless was spotty at best. I could sometimes get into the setup page through wireless, but all of a sudden the setup page would stop working.\n\nStay away from D-Link! All their products are cheaply made and difficult to configure\n\n\n\n\nB0001NA2S6:difficult_to_install_and_unreliable:j._lin\n\n\nB0001NA2S6\n\n\nD-Link DP-G310 Wireless Print Server, USB 2.0 802.11g, 54Mbps: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nDifficult to Install and unreliable\n\n\nMarch 4, 2006\n\n\nJ. Lin\n\n\nSan Jose, CA United States\n\n\nI got this print server about a month back. Initially it took me 4 hours to get this thing installed and running in the wireless mode! After I got it setup, it worked fine for a week. But, then the wireless connection with the print server got flaky, so I switched to the wired mode. All was well again, but then after a couple of weeks the wired connection got flaky. It stopped printing and didn't recognize my HP-1200. After fiddling around with it for a few hours I finally got the contraption working again.\n\nBut, now my latest problem is that the print server prints garbage to the printer! Plugging the printer directly in to a computer works fine, but the print service somehow corrupts the data going to the printer and I end up with pages of garbage and error messages.\n\nSUMMARY:\nThis thing was a beast to set-up and every time something goes wrong, it takes a phenomenal amount of time to debug and correct. That's in addition to the questionable reliability of this device. I say steer clear of this thing. I'm now shopping for something better and never buying D-Link again\n\n\n\n\nB0002XHFPS:pathetic:brandon_king_\"brandon\"\n\n\nB0002XHFPS\n\n\nKensington WiFi Finder Plus: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n1.0\n\n\nPathetic\n\n\nJune 14, 2006\n\n\nBrandon King \"Brandon\"\n\n\nProvidence, RI\n\n\nThis thing does a horrible job at detecting wifi networks. I have to be within 10 feet of my router to get a 50% signal (even though I can walk out tot he edge of my house and easily still get a good connection). If it does detect Bluetooth, which half the time it doesn't (regardless of it being active), it cancels out the wifi detection. So if you have a bluetooth headset, and you're talking, the thing will just let you know that yes, there is a bluetooth device around. Thanks. I was wondering\n\n\n\n\nB0008G1RWG:needs_help:joanna_owen\n\n\nB0008G1RWG\n\n\nTomTom GO 700 Portable GPS Navigation with Hands-Free Calling: Electronics\n\n\nelectronics\n\n\n24 of 24\n\n\n2.0\n\n\nNeeds help\n\n\nAugust 21, 2006\n\n\nJoanna Owen\n\n\nBirmingham, Al USA\n\n\nMy husband bought me this for Christmas because we were moving to a new city. He picked it out because unlike Garmin and Magellen it was created from the ground up for use on the road. The features are easy to use, the directions are great and when you need a new route because of roadblock or traffic - it really comes up with a great plan. However - my first one lasted 3 months before it died, I then replaced it with another one and it died 3 months later as well. I was very reluctant to buy a different brand - but had to for dependability. I still don't like my Garmin as much - but at least it works. Come on Tomtom - get the electronics working as well as the maps and directions\n\n\n\n\nB0009DG7KK:not_ready_for_primetime...:douglas_f._jones\n\n\nB0009DG7KK\n\n\nWacom Graphire Bluetooth 6x8 Tablet: Electronics\n\n\nelectronics\n\n\n39 of 41\n\n\n2.0\n\n\nNot ready for primetime...\n\n\nOctober 30, 2005\n\n\nDouglas F. Jones\n\n\nLos Angeles, CA\n\n\nI'm a longtime user of Wacom Intuos tablets and I was looking for something similar with wireless capabilities for my new dual 2.5ghz-G5. There were no wireless Intuos products available at the time of purchase, so I went with the Graphire Bluetooth. Unfortunately, the product leaves a lot to be desired and after a couple of weeks of use, I plan on returning it. The major problem lies with the pen--both in design and responsiveness. \n\nDesign: the Graphire series is cheaper than the Intuos, and there's a good reason why: the Graphire pen is thinner, lighter, and has no grip. If you have very small hands this might be acceptable; in my hand this feels like a toothpick. It's very difficult to manipulate a tool effectively when it doesn't feel like it has any durability. Additionally, the Graphire pen stand is little more than a piece of transparent molded plastic and slides around the desk whenever I put the pen back into it.\n\nResponsiveness: There is a half-second delay whenever using the pen to click in any finder-related options--opening windows, selecting files, etc. 1/2 second may sound acceptable, but if you, like me, use the pen for everything, not just drawing, believe me it is utterly maddening. On the other hand, the mouse behaves perfectly with no delay at all, so I know it's the pen and not my computer.\n\nThe tablet is attractive and the mouse works fine, but the bottom line is that it's more important for me to have a responsive, durable pen than a wireless tablet. If you've never used an Intuos you may not recognize the deficiencies, but for those who have, this is like going from a Mercedes to a Honda Civic. You'll get better gas mileage with the Civic but the Benz gives you the best overall driving experience. I need the driving experience, so I'm going back to the Intuos--wire and all.\n\n\n\n\nB0001A99LK:p.o.s.:caesalpina_\"casealpina\"\n\n\nB0001A99LK\n\n\nApple 4 GB iPod Mini Pink M9435LL/A: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n1.0\n\n\nP.O.S.\n\n\nJune 2, 2006\n\n\ncaesalpina \"casealpina\"\n\n\nsouthern california\n\n\nI know that everyone else seems to love this, so perhaps my complaint is more about Apple in general. My Ipod Mini (pink)is a lemon. The battery has never worked properly. It takes longer to charge than to discharge, and my current playing time per charge (no changing settings, not backlighting, nothing but hitting play and waiting till the battery dies) is about 2 hours. Apple will gladly sell me a new ($70) battery, but other than that tough. It is apparently my fault thet they're selling shoddy products. I'm not saying don't buy it. There aren't realistically any better choices out there than the Ipod, but buy it when you have a week to sit around and evaluate it and when they get around to opening a store near you. As for me, I think I'm going to dig out my old discman\n\n\n\n\nB000BNMDBU:total_crap.__sound_quality_is_awful.:scott.hunter_\"professional_stud\"\n\n\nB000BNMDBU\n\n\nDLO 009-3030 Transpod In-Car Cradle/FM Transmitter, White ( 009-3030 ): Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n1.0\n\n\nTotal crap. Sound quality is AWFUL.\n\n\nJune 2, 2006\n\n\nScott.Hunter \"professional stud\"\n\n\nbonerland\n\n\nI returned this thing the day after I bought it. \n\nI should of been clued in when I purchased it at the store, ALL the Transpod models had a little sticker on them advising them about the store's return policy. I thought it a little unusual at the time, but after listening to the thing I understand why now. I'm sure a lot of people had been returning this model.\n\nThe sound quality was really bad. You have to turn your stereo WAY up to hear the music, and even then it sounds almost as bad as AM radio. I'll add that the files I was playing were 192Kbps aac encoded files so they're decent quality too\n\n\n\n\nB00005B9AQ:sub_woofer_problems:m._bouline\n\n\nB00005B9AQ\n\n\nHarman Kardon SoundSticks Computer Speakers: Electronics\n\n\nelectronics\n\n\n2 of 4\n\n\n1.0\n\n\nSub Woofer Problems\n\n\nOctober 29, 2005\n\n\nM. Bouline\n\n\nTexas\n\n\nAfter only a couple of months of light use, the sub woofer started making loud thumping sounds and the system stoped working. As soon as power is connected to the sub woofer, the loud thumping starts. I exchanged the system and the second set had the same problem after 6 weeks!! A total piece of junk\n\n\n\n\nB000F3YSQ4:fancy_features,_but_bottom_line...poor_performance:michael_r._bell\n\n\nB000F3YSQ4\n\n\nFujitsu ScanSnap S500 Clr 18PPM/36IPM Dupl Scanner: Electronics\n\n\nelectronics\n\n\n3 of 5\n\n\n2.0\n\n\nFancy features, but bottom line...poor performance\n\n\nOctober 20, 2006\n\n\nMichael R. Bell\n\n\nTokyo, Japan\n\n\nWhile a nice product packed with great features when it works, the bottom line is that it does NOT support TWAIN drivers, meaning you can not scan through ANY application other than ScanSnap. BIG MINUS. \nSecond minus is JAMS, JAMS, JAMS. I have barely used this machine, but the double feeds are incredible (the rollers are new and the machine is clean). I even tried using high grade color laser copier paper, but STILL double feeds big time! Upon occasion, I get a good feed, and I am so thankful for that. \nIf you are planning to do some serious office organizing with various types of paper, sizes and paper weights, skip this product and go for a ADF flatbed scanner. This baby will JAM up your day and consume all of your time with fiddling, reorganizing the misfeeds and clearing jams.\nThink deeply before buying this one. Also, with an ADF flatbed, you can scan books, and unusual documents (thin paper, very heavy paper) that an ADF (and the ScanSnap) won't process by simply lifting the lid of the ADF and taking the scan directly on the glass. With ScanSnap, you are screwed\n\n\n\n\nB000065UTC:too_loud:dag_a._schield\n\n\nB000065UTC\n\n\nKensington 62648 FlyFan USB Notebook Fan (PC/Mac): Electronics\n\n\nelectronics\n\n\n1 of 4\n\n\n1.0\n\n\nToo loud\n\n\nSeptember 29, 2005\n\n\nDag A. Schield\n\n\n\n\n\nJust plugged it in and I'm ready to ship it back. Creates an annoying, high pitched buzz\n\n\n\n\nB000065UTC:err...:kuyo\n\n\nB000065UTC\n\n\nKensington 62648 FlyFan USB Notebook Fan (PC/Mac): Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n2.0\n\n\nErr...\n\n\nSeptember 23, 2004\n\n\nKuyo\n\n\nHonolulu, HI USA\n\n\nI was hoping that by getting this I'd get a decent flow of air and i'd be able to use it anywayer. But I was sorely dissappointed. It doesn't give much air and all the air you get you need to be close to feel... plus its too loud to use in most places...\n\nIf you really need it, you can get it... but I can't use mine often because like I said its too loud. Not worth it, not enough air flowing\n\n\n\n\nB00005Q3UD:terrible_and_disappointing_product:fred_pickering_\"consmer_advocate\"\n\n\nB00005Q3UD\n\n\nAT&T 2255 2.4 GHz Dual Handset Cordless Phone with Answering System and Caller ID (Champagne): Electronics\n\n\nelectronics\n\n\n1 of 3\n\n\n1.0\n\n\nTerrible and disappointing product\n\n\nMay 9, 2005\n\n\nFred Pickering \"Consmer Advocate\"\n\n\nPurcellville VA\n\n\nThe battery life for this phone after one short year is abysmal. After charging it all night, it dies after a few hours of nonuse.\nThe redial button simply has never worked. \nIt is not user friendly at all although I could live with this if the two issues above were resolved. \nI contacted AT&T and requested that they send me a new model that works since this phone is obviously defective. \nIf they refuse then I won't ever buy an AT&T product again and neither will anyone I come into contact with. \nIt's that simple. \n\nUpdate...AT&T refused to do anything about the phone despite the obvious flaws,. As a result, I will never buy another AT&T product again.\nAT&T, you are exhibiting the traits of a company that will go out of business. \n\n\n\n\nB00005Q3UD:worst_phones_i_have_ever_used:ross_mccord_\"dork\"\n\n\nB00005Q3UD\n\n\nAT&T 2255 2.4 GHz Dual Handset Cordless Phone with Answering System and Caller ID (Champagne): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nWorst Phones I have ever used\n\n\nFebruary 22, 2005\n\n\nRoss Mccord \"dork\"\n\n\nSanta Cruz, CA USA\n\n\nWe bought a set of these phones because they looked nice, and because AT&T is a reliable company. But these phones are the worst. After having them for about 3 weeks, we began having terrible results with the volume/speakers in the phones. I took them back, and got a new set. These lasted about twice as long, before the battery life just became worthless. We are unable to leave these phones off the hook now for any extended period of time - say, 1 hour. And you can't talk on the phone for more than 30 minutes or so before the battery dies out, and takes 6 hours or more to get back charged again. And yes, we followed all the directions for letting the batteries charge, and all that kind of stuff. Still...I have never used a more frustrating phone, nor will I ever purchase an AT&T phone again. I bought a new phone today, and once fully charged, I will take these AT&T phones out back and smash them with a hammer\n\n\n\n\nB00005Q3UD:poor_battery_life_:bruce_e._gunn_\"gunnbug\"\n\n\nB00005Q3UD\n\n\nAT&T 2255 2.4 GHz Dual Handset Cordless Phone with Answering System and Caller ID (Champagne): Electronics\n\n\nelectronics\n\n\n4 of 5\n\n\n2.0\n\n\nPoor battery life \n\n\nOctober 18, 2004\n\n\nBruce E. Gunn \"gunnbug\"\n\n\nBelvidere, IL USA\n\n\nI have to agree with many others about the poor quality of this phone. The battery life decreased rapidly and after several weeks I was forced to keeps both phones on their chargers. Instead of gambling on replacement cells I bought a Panasonic phone as suggested by other disappointed AT&T model 2255 buyers\n\n\n\n\nB0001MQURE:pathetic_sound.:c._champion\n\n\nB0001MQURE\n\n\nMonster Cable iCarPlay iPod Cassette Adapter (AI CAS-ADPT): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nPathetic sound.\n\n\nOctober 17, 2006\n\n\nC. Champion\n\n\n\n\n\nI also was suckered in by the Monster name. I have an older Sony adapter that I've used in the past with no problems but couldn't locate it right before a road-trip, so I bought this one also. This actually sounds substantially worse than my actual cassettes (even though they're all over 10 years old).\nThe sound is hiss-filled, very poor signal-to-noise ratio.\nStay well away\n\n\n\n\nB0001MQURE:zero_stars:eieio\n\n\nB0001MQURE\n\n\nMonster Cable iCarPlay iPod Cassette Adapter (AI CAS-ADPT): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nZero Stars\n\n\nJuly 7, 2006\n\n\neieio\n\n\nChicago\n\n\nI attempted to use this product in my 2002 Chevy Tahoe factory audio system. As others here noted, the deck would quickly error and eject the Monster Adapter. The Monster Adapter did work in my 2000 Harley Davidson FLTRI factory audio system. The difference is that the Harley unit does not have \"cut-tape\" detection. The Tahoe does. \n\nI returned this Monster unit and picked up the Sony Sony CPA-9C. \n\nThe Sony device is a bit different as it has small link of simulated audio tape within the tape path. This little bit of simulated audio tape effectively fools the tape unit not to think that there is an error and eject the Cassette Adapter. There are other reasons that these adapters will auto-eject (Google is your friend). But the Sony unit seemed to be the solution for me. \n\nThe big reason for the zero star review is that Monster has ZERO information on their web site or product packaging regarding device compatibility or troubleshooting. \n\n\n\n\n\nB0001MQURE:so_quiet,_it_didn't_work_at_all:p._brown_\"babybrownbooks\"\n\n\nB0001MQURE\n\n\nMonster Cable iCarPlay iPod Cassette Adapter (AI CAS-ADPT): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nSo quiet, it didn't work at all\n\n\nJune 19, 2006\n\n\nP. Brown \"babybrownbooks\"\n\n\n\n\n\nI lost my adapter that came from Walmart and was a generic brand. It worked perfect. This thing was expensive and it didn't work at all. I'd have to turn the volume on both the ipod and stereo all the way up and then I'd get some sign of life, not anything you'd want to listen to. It's worst than even the worst RF transmitter I've ever tried. Don't waste your money, believe the reviews\n\n\n\n\nB000058BCR:the_worst_unit_i_have_ever_used:douglas_d._gaj\n\n\nB000058BCR\n\n\nGarmin eTrex Vista 24 MB GPS with Compass, Altimeter, and PC Cable: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nThe worst unit I have ever used\n\n\nNovember 8, 2006\n\n\nDouglas D. Gaj\n\n\nSeattle, WA\n\n\nI bought this unit a couple of years ago based on its features, but soon discovered that it can't normally do the one thing that all GPSs should, tell you where you are. I used my e-Trex Vista for about a year and half and would estimate that I spent 10% to 15% of the time staring at some type of lost signal message. The unit is totally useless under any type of tree cover and often has trouble holding a signal anywhere on overcast days, which means about half the time in the Seattle area where I live. I would not recommend this unit to anyone but my worst enemy\n\n\n\n\nB000058BCR:lackluster_product_at_best:byngbong_\"byngbong\"\n\n\nB000058BCR\n\n\nGarmin eTrex Vista 24 MB GPS with Compass, Altimeter, and PC Cable: Electronics\n\n\nelectronics\n\n\n5 of 10\n\n\n2.0\n\n\nLackluster product at best\n\n\nJune 13, 2006\n\n\nByngBong \"ByngBong\"\n\n\nTacoma, WA USA\n\n\nI had this GPS device for only 3 days and found it to be no better than mediocre.\n\nAlthough at one point I was able to track 6 WAAS satellites and the display claimed accuracy to 17 feet, while driving along a straight road for 5.5 miles, it indicated that I was traveling approximately 350 feet to the side of the road. On two occasions on this straight road, it said I actually crossed the road and traveled on the opposite side.\n\nI conducted a test by placing the unit where it would have an unobstructed view of the sky for 1 hour and leave it motionless. The trip computer reported that it had moved nearly a quarter-mile during that hour. I checked the accuracy at 15-minute intervals and it was constantly reporting accuracy between 12-24 feet.\n\nThe base maps are crude and inaccurate - a major 4-lane road (Hwy 7) in Tacoma, WA stops at 38th street. Really?!? If I drive down Hwy 7, it goes a couple of miles farther than the unit shows.\n\nWhile using the compass you must hold the unit level in order to obtain accurate readings - understandable. There is even a little message that tells you to hold it level if you are off a little - nice. I tried and tried to find \"level\" according to the unit and after about 3 or 4 minutes was able to determine where \"level\" was. It was nowhere close to actual level. Now, if I am trying to navigate with the unit's compass while it is mounted to my bike, there is no way it is going to be level and accurate.\n\nAt 5.3 ounces, it doesn't even pass off as a good paperweight.\n\nFortunately I was able to return it to the seller with no problems for a full refund (Thank you REI!). I found out that Wal-Mart.com sells the unit for 50 bucks less than REI after I bought it though.\n\nRecommendation: If you need a paperweight, get a rock - if you need a GPS, get a different one.\n\n\n\n\nB000058BCR:good_device,_worst_service_and_software_imaginable:d._j._lidsky_\"jolly_editor\"\n\n\nB000058BCR\n\n\nGarmin eTrex Vista 24 MB GPS with Compass, Altimeter, and PC Cable: Electronics\n\n\nelectronics\n\n\n9 of 21\n\n\n1.0\n\n\nGood device, worst service and software imaginable\n\n\nApril 17, 2006\n\n\nD. J. Lidsky \"Jolly Editor\"\n\n\nNew York, NY\n\n\nGarmin's devices are pretty good. Somewhat tempermental, but mostly reliable. It's connecting their software and dealing with Garmin support that is a nightmare. And, you will need to call them because their software has all kinds of bugs and an archaic key system. Customer service hold time: 30-35 minues. Response time for email tech requests: a staggering 5-7 working days. Clearly Garmin does not care about its customers\n\n\n\n\nB00014B4OQ:great_product_-_horrible_durability:laura_a._huber\n\n\nB00014B4OQ\n\n\nAcoustic Research AW771 900 MHz Wireless Stereo Headphones: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nGreat Product - Horrible Durability\n\n\nOctober 18, 2006\n\n\nLaura A. Huber\n\n\nBoston, MA\n\n\nWhen these headphones are working, they are great. I never have trouble receiving sound. They are amazingly comfortable. Great battery life, and batteries are rechargeable. Long distance is great too, I can walk throughout the whole house on multiple floors without any trouble.\n\nBut... it's FAR to often that these are NOT working. As a matter of fact I've gone through FOUR pair in the last 2 years. The first pair didn't work out of the box. The second pair lasted two weeks and then the headset just would not turn on. The third pair lasted about eight months before the same thing happened, they just would not turn on. The forth pair lasted about a year and then yesterday the very same thing happened.\n\n\n\n\nB000EWARBG:software_defeats_purpose_of_flash_drives.:l._f._martins\n\n\nB000EWARBG\n\n\nSanDisk 1 GB Cruzer Micro with U3 ( SDCZ6-1024-A10, Retail Package): Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nSoftware defeats purpose of flash drives.\n\n\nNovember 22, 2006\n\n\nL. F. Martins\n\n\n\n\n\nAfter having an experience with slow startup speeds (and actually freezing a computer) with the 1GB Cruzer Micro with U3, I feel compelled to write this negative review. The mere existence of a software layer to access the flash drive defeats the main purpose of existence of flash drives: a fast and higlhly compatible interface. Any hidrance to this convenience makes the device a liability. I work at an educational institution wher I am responsibel for organizing presentation sessions in which speakers often bring their presentations in a flash drive. As a matter of policy, we will not allow speakers to plug in to our computers devices that run any kind of software in the background\n\n\n\n\nB000EWARBG:how_to_remove_the_u3_launch_pad_:picasso_\"sharko2005\"\n\n\nB000EWARBG\n\n\nSanDisk 1 GB Cruzer Micro with U3 ( SDCZ6-1024-A10, Retail Package): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n2.0\n\n\nHow to remove the U3 Launch Pad \n\n\nOctober 12, 2006\n\n\nPicasso \"sharko2005\"\n\n\nCalifornia\n\n\nI bought the Cruzer Micro not knowing that it came with the U3 Launch Pad software preloaded. As soon as I tried to use it on my computer at work, the company's anti-virus/firewall decided it was an intruder, preventing it from working at all. I was ticked... I had almost given up on it and was actually shopping around for another USB drive that had no software on it, when I ran into a website that lets you download an uninstaller for the U3 Launch Pad. I downloaded it, ran it and it took no more than 15 seconds to clean out the offending U3 garbage! Now I like my Micro Cruzer all over again! Oh yeah... the website to uninstall yours at is: [...\n\n\n\n\nB000EWARBG:i_hate_this_drive:g._johnson_\"glj52\"\n\n\nB000EWARBG\n\n\nSanDisk 1 GB Cruzer Micro with U3 ( SDCZ6-1024-A10, Retail Package): Electronics\n\n\nelectronics\n\n\n4 of 7\n\n\n1.0\n\n\nI hate this drive\n\n\nOctober 5, 2006\n\n\nG. Johnson \"glj52\"\n\n\nSanta Fe\n\n\nI wish I had read the reviews before buying this piece of junk. As another reviewer says, U3 ruins it. I have a Mac so that feature is useless, but even worse it cannot be disabled. Everytime I plug in the drive I get this cheesy CD image cluttering up my desktop and I have to eject it, along with the drive icon, before the unit can be removed. What clunky engineering. I'll be returning this and scratching Sandisk off my list of reliable companies. I also agree with the reviewer who complained about the packaging. It is impossible to open without tearing it apart, which is probably a strategy to reduce returns\n\n\n\n\nB0000E0ZM0:if_the_case_doesn't_fit_you_must_acquit:pbb\n\n\nB0000E0ZM0\n\n\nPalm Hard Case for Tungsten E and Tungsten E2: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n1.0\n\n\nIf the case doesn't fit you must acquit\n\n\nFebruary 3, 2006\n\n\nPBB\n\n\nBrea, CA United States\n\n\nSpare SD card slots molded into the lid of this case prevent it from closing completely around the Tungsten E. You will see a noticeable gap around the bottom edge of the closed case. Slight pressure on the latch side side will cause the case to pop open unexpectedly in purse or brief case. I returned mine to Amazon a few days after receipt; still looking for an alternative\n\n\n\n\nB0003QIXBY:a_bad_choice_for_16x_writing:thomas_b._talbot\n\n\nB0003QIXBY\n\n\nVerbatim 4.7 GB 16x DVD+R Recordable Media Spindle (25 Discs): Electronics\n\n\nelectronics\n\n\n7 of 17\n\n\n1.0\n\n\nA bad choice for 16x writing\n\n\nDecember 1, 2004\n\n\nThomas B. Talbot\n\n\nManhattan, KS USA\n\n\nWith a destructive toddler in the house I like to back up my DVD movies.\nHaving recently purchased a 25 unit spindle, I recently tried backing up several DVDs with my sony DRU-710A 16x DVD recorder. Although the recording process initially did seem to work smoothly, when I attemped to play back the disks recorded at 16x speed, I would have read errors and lockups on DVD players usually towards the end of the film (the outside of the physical disk, where maximum recording speed is obtained.)\n I did have better reliability recording at 4x speed and suspect that 8x writes will work fine as well. Given the fact that I paid a premium for this new high speed media, I am disappointed. I have had nearly 100% reliability with Memorex 8x DVD+R, recently making 25 successful recordings in a row with this brand (ASIN: B0001MKU4S\n\n\n\n\nB00004Z7XX:an_expensive_piece_of_garbage:joshua_j._callahan_\"jcallahan1\"\n\n\nB00004Z7XX\n\n\nPlantronics MX10 Universal Amplifier for Headsets: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n1.0\n\n\nAn expensive piece of garbage\n\n\nApril 10, 2006\n\n\nJoshua J. Callahan \"jcallahan1\"\n\n\njcallahan1\n\n\nI bought this thing becuase I use voice recognition with my computer and I'm on the phone quite a bit. it was supposed to eliminate the need to for two head sets. With the amplifier and the plantronics headset to go with it The total cost came to around $200. Yay! \n\nThe amplifier has a strong hum when used through the phone and when I use it witht eh computer it has such poor sound quality that it doesn't work with my voice rec. software. I called customer service and they said I should send the unit to them and they would send me a new one. Funny, I have read that in a few places online. \n\nI went out and bought the Labtec equivelent, the dialog-301 at a whopping $15 (headset included, the head set is cheap though) and it is chrystal clear for both the phone and my computer. Plus if you want to upgrade the head set you can buy any analog computer headset. \n\nSave yourself $185 and just buy the dialog-301 you will be happy. \n\n\n\n\nB000076WEX:one_year_later_the_card_no_longer_works:m._murray\n\n\nB000076WEX\n\n\nKingston CF/1GB 1024 MB CompactFlash Card (1 GB): Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n1.0\n\n\nOne year later the Card no longer works\n\n\nDecember 19, 2005\n\n\nM. Murray\n\n\nNorth Coventry, Pennsylvania\n\n\nJust a fair warning to all out there considering this card. After purchasing this card it worked great. Then in the middle of my friend's wedding the card no longer was readable by my camera. I could not believe it! When I got home I put the original card in the camera and it worked fine. I reformatted this card and still the camera could not read the card. Not worth the money for only one years use\n\n\n\n\nB000076WEX:john_:jp_in_jc\n\n\nB000076WEX\n\n\nKingston CF/1GB 1024 MB CompactFlash Card (1 GB): Electronics\n\n\nelectronics\n\n\n7 of 8\n\n\n1.0\n\n\nJohn \n\n\nJune 11, 2005\n\n\nJP in JC\n\n\nJersey City NJ\n\n\nI have the Canon Rebel. I had the same problem where files were corrupted. Also, when clearing the card, some pics stayed filed. I am not happy with this flashcard at all.\n\n\n\n\nB000CFSLXG:crappy:sylvia_jensen\n\n\nB000CFSLXG\n\n\nApple iPod Retractable USB 2.0 Data Cable: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n1.0\n\n\nCrappy\n\n\nMarch 23, 2006\n\n\nSylvia Jensen\n\n\n\n\n\nIt doesn't work properly. I can charge the Ipod but it doesn't let me sync. Sent an email to the seller but no answer so far!\n\nI am very disappointed with my purchase\n\n\n\n\nB0000AINEO:the_biggest_mistake_i_'ve_made!!:titurel\n\n\nB0000AINEO\n\n\nSony RDR-GX7 DVD Recorder: Electronics\n\n\nelectronics\n\n\n12 of 22\n\n\n1.0\n\n\nTHE BIGGEST MISTAKE I 'VE MADE!!\n\n\nNovember 27, 2004\n\n\nTiturel\n\n\nMonsalvat\n\n\nDO NOT BUY THIS CRAPPY PIECE OF JUNK!!\n\nI purchased this unit with faith in SONY. Well forget it!! after 4 months the unit failed and began reading \"disc dirty\" half way through each recording. I sent the unit back for repair. They claimed to have \"fixed it\" but when I received it back it STILL failed on each disc (note that these failures RUIN the disc!). They sent me a brand new unit. GUESS WHAT..?! It failed after 3 months with the SAME PROBLEM!! I sent it back for repairs. AGAIN they claimed to have \"fixed it\". Well, it malfunctioned IMMEDIATELY!! I sent it back AGAIN!! This time they gave me a refurbished unit. I know you wont believe this...but the unit is WORSE than the other two!! They are currently sending me ANOTHER new unit. But I have no doubt this one will STILL be a piece of crap. They simply don't have this technology down. SONY should NEVER have put this junk on the market. BEWARE!!\n\n - UPDATE -\nThey sent they new unit. Guess what? PIECE OF JUNK. Fails on discs writing and when reading written discs. After trying FOUR of these units I can assure you this is a LEMON. Please note these positive reviews are from buyer who had their units for two weeks or similar time periods. I have had four different units for ONE YEAR and can tell you THIS IS JUNK! Selling this crap on ebay and buying a REAL unit. NOT SONY!\n\n\n\n\nB00005KJUG:better_late_than_never_...:ttocsmij\n\n\nB00005KJUG\n\n\nPhilips DVD782CH 5-Disc DVD Player: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nBetter late than never ...\n\n\nAugust 11, 2006\n\n\nttocsmij\n\n\nUpstate NY USA\n\n\nWe needed to know if this unit (Philips DVD 782C) was compatible with DVD+RW disks so I Googled it and found this Amazon listing.\n\nWe were totally amazed to find the reviews we found ... and wish we had done this years ago INSTEAD OF SPENDING ALL THIS TIME STRUGGLING WITH THIS POOR EXCUSE OF A DVD PLAYER. We have spent countless hours MR. Clean'ing and SkipDr'ing disks just to get them to play (and sometimes even that didn't work).\n\nJust in case anyone else comes along, take the excellent advice given several times in this collection of reviews ... RUN THE OTHER DIRECTION WITHOUT QUESTION IF SOMEONE OFFERS YOU THIS DVD PLAYER.\n\nNow, we do NOT believe this is a fundamental problem with Philips. We have, and have had, many fine products sold by Philips. We are guessing this particular DVD player was the result of some very poor product planning that went very badly before, during and after the fact. Hopefully the person(s) responsible has (have), per usual corporate practice, been promoted farther up the ladder where they can no longer inflict this kind of pain on Customers.\n\n\nP.S. We tried to give this product \"0\" stars but Amazon requires at least \"1\" star. Probably a quid pro quo agreement of some sort we imagine\n\n\n\n\nB00005KJUG:late_review_but_in_full_agreement:tim\n\n\nB00005KJUG\n\n\nPhilips DVD782CH 5-Disc DVD Player: Electronics\n\n\nelectronics\n\n\n7 of 7\n\n\n1.0\n\n\nLate Review But In Full Agreement\n\n\nMay 25, 2004\n\n\ntim\n\n\nFresno, CA, USA\n\n\nUnfortunately my wife and I purchased this "product" without reading the posted reviews here. ALL are entirely accurate! I cannot calculate how many hours I have spent trying to figure out what is wrong. No loading, skipping, freezing, not OPENING when it freezes, all down the line, combined with 100 trips to and from the video stores to exchange their "defective" DVDs. My wife was firmly of the belief the DVDs were just "dirty" but I finally decided (this agrees with one of the reviewers) there is a balance problem in the drawer. When there is a no-load, jiggling/adjusting the drawer sometimes (not always) fixes it, but only until the next time, and will not stop the freezing problems. If my wife had a dime for every time I said "this thing is a piece of @&*!" she would be a millionaire. This is very late but if this product is still on the market in ANY form stay many miles away. And Philips' arrogant lack of response (as reported; I have no personal experience here) is virtually criminal in this day and age\n\n\n\n\nB00003CWEY:again,_monster_has_to_be_kidding_(and_they_are):great_movie_addict\n\n\nB00003CWEY\n\n\nMonster Cable MVSV3-2M Monster Super Video 3 Double-Shielded S-Video Cable (6.56 Feet): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nAgain, Monster has to be kidding (and they are)\n\n\nApril 29, 2006\n\n\nGreat Movie Addict\n\n\nNew York City\n\n\nFor some years I tried this and other Monster products, to the tune of over $1000 in Monster a/v gear. The more you learn about video and audio, the more you'll avoid anything with the Monster label. Specifically, this s-video meets no NTSC, DVD or IEEE standard; it is common 30-ohm hookup wire with ineffective shielding and a poorly designed, low-conductance connect plug that can wreck your input jacks and gives poor grounding. Has a sharp image, but includes a catalog of chromatic aberrations from off-color skin tones to color blotching, dirty grays, burned highlights, murky shadows, noisy reds, and poor color balance due to impedance mismatching that results in cyan errors and bleeding reds. Makes video test patterns look great, but who watches test patterns all day? Amazon offers many other brands at good prices, why waste your $$ on this stuff? I removed every piece of Monster from my system and replaced them with AR, Belden, Viewsonics and others from Amazon. Result: vast improvement in every respect.\n\n\n\n\nB00003CWEY:i_am_unable_to_verify_its_ultra_high_performance:runfast_\"marathon_runner\"\n\n\nB00003CWEY\n\n\nMonster Cable MVSV3-2M Monster Super Video 3 Double-Shielded S-Video Cable (6.56 Feet): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n2.0\n\n\nI am unable to verify its ultra high performance\n\n\nJuly 18, 2002\n\n\nrunfast \"marathon_runner\"\n\n\nMiami, Florida\n\n\n~I bought the MVSV3-1m cable and put it between my Pioneer V555 DVD player and SONY WEGA tv KV-27FS12, I was unable to see with my naked eyes any improvement regading the visual quality, color, sharpness. Same as the result as I used the composite cable came with the box. I really don't know why. I have to say, my TV is a good one and the picture quality is already satisfactory even I don't use DVD player. Unfortunately my DVD player does not have component jack to let me try the next level video~~ experience. I don't want to say this expensive cable is a poor one, I was just unable to verify its alledged ultra high performance. we just don't have a benchmark to evaluate it.\n\n\n\n\nB00001P4ZD:disappointed_and_feeling_ripped_off:lesley_symmonds\n\n\nB00001P4ZD\n\n\nKoss KTX-Pro Portable Stereophone: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n2.0\n\n\nDisappointed and Feeling Ripped Off\n\n\nApril 19, 2002\n\n\nLesley Symmonds\n\n\nJacksonville, FL United States\n\n\nI bought these headphones based on the first couple of reviews I read here. I should have read further, because about two weeks after I purchased the earphones, the left side went out completely. There was nothing obvious on the outside of the unit and I didn't hear any \"rattling\" as described by other users. The darn things just quit! I must say, however, that the sound quality for those first two weeks was excellent, but all in all, not worth the money and frustration\n\n\n\n\nB000COVZD0:worst_customer_service_ever:earlyadopter_\"earlyadopter\"\n\n\nB000COVZD0\n\n\niAudio U3 2 GB MP3 Player: Electronics\n\n\nelectronics\n\n\n9 of 26\n\n\n1.0\n\n\nWorst Customer Service Ever\n\n\nFebruary 23, 2006\n\n\nEarlyAdopter \"EarlyAdopter\"\n\n\nUSA\n\n\n Cowon aka iAudio have the worst customer service of any other major company I dealt with. No phone support, they have an 800 number but when you call it they direct you to their website. Their website support gives you the option to submit a ticket or Live Chat. I waited over an hour on their Live Chat before I get a message saying that Live Chat is not available at this time. I submitted a ticket & guess how long before I heard a response. & the response was \"ship the item back for a refund\". I also tried to download the latest firmware and the link was broken. I heard a lot about how bad their customer service was, but when I tried it myself it was even worse\n\n\n\n\nB0000Y3KS0:not_sold_by_amazon_-_beware_of_electrogalaxy:h._christopher_giacinto\n\n\nB0000Y3KS0\n\n\nMaxell AA Alkaline Batteries Value Pack - 40 Pack: Electronics\n\n\nelectronics\n\n\n9 of 23\n\n\n1.0\n\n\nNOT sold by Amazon - BEWARE of Electrogalaxy\n\n\nMarch 2, 2006\n\n\nH. Christopher Giacinto\n\n\nTampa, FL\n\n\nThese batteries are NOT sold by Amazon. They are sold by an Amazon re-seller called Electrogalaxy. Please read the reviews of this company before purchasing. I do not recommend this company due to their poor return policy and terrible customer service. You have been officially warned. DO NOT BUY ANYTHING FROM ELECTROGALAXY\n\n\n\n\nB0000642RX:keys_rearranged:k._pemberton_\"cherith\"\n\n\nB0000642RX\n\n\nMicrosoft Natural Keyboard Elite: Electronics\n\n\nelectronics\n\n\n9 of 13\n\n\n1.0\n\n\nKeys rearranged\n\n\nMay 11, 2006\n\n\nK. Pemberton \"cherith\"\n\n\nPortland\n\n\nI went and got this keyboard after the first one broke. I found the keys were re-arranged. Instead of hitting home - I wound up deleting my work.\n\nThis is a keyboard I cannot work with. Model Number 1 and 3's were better.\n\n\n\n\nB00009KH5Q:the_most_difficult_thing_in_the_world_to_configure:chip_gandy\n\n\nB00009KH5Q\n\n\nD-Link DP-311U Wireless Print Server, 1-USB Port, 802.11b, 11Mbps: Electronics\n\n\nelectronics\n\n\n4 of 5\n\n\n1.0\n\n\nThe most difficult thing in the world to configure\n\n\nJuly 29, 2006\n\n\nChip Gandy\n\n\n\n\n\nProblem #1 - out of hte box, these units are assigned 192.168.0.10 as an IP address. find if your network is on the .0 subnet. So I had to change my entire network over to this in order to get it configured.\nProblem #2 - once changed, now i have Ip address issues because of the existing clients picking up the .10 address.\nProblem #3 - If you incorrectly specificy the SSID/wep key or IP address, you have to put it back to the defaults and start over. there's no way to configure the unit \nProblem #4 - it just plain don't work as it should.\n\nEasily the most difficult unit i've ever had to configure. It's a shame that I'm swearing off linksys as their prices are good. Despite it's decent price, stay away from this one\n\n\n\n\nB00009KH5Q:terrible:p._mansour\n\n\nB00009KH5Q\n\n\nD-Link DP-311U Wireless Print Server, 1-USB Port, 802.11b, 11Mbps: Electronics\n\n\nelectronics\n\n\n2 of 4\n\n\n1.0\n\n\nTerrible\n\n\nMay 27, 2006\n\n\nP. Mansour\n\n\nkirkland, wa United States\n\n\nI have a D-link router, and I couldn't even get this thing to connect wirelessly. It works if connected with a ethernet wire, but that isn't really the point, is it?\n\nI called customer support, and the call dropped 4 TIMES!!!! Even when I was able to stay connected, they obviously weren't listening to me, and were of no use in the end. I'm off to go return. Save your time, money, and aggrevation. Avoid this like the plague\n\n\n\n\nB00009KH5Q:not_even_worth_a_star:a._garcia_\"aruba60\"\n\n\nB00009KH5Q\n\n\nD-Link DP-311U Wireless Print Server, 1-USB Port, 802.11b, 11Mbps: Electronics\n\n\nelectronics\n\n\n1 of 5\n\n\n1.0\n\n\nNot even worth a star\n\n\nDecember 10, 2005\n\n\nA. Garcia \"Aruba60\"\n\n\nHuntersville, NC USA\n\n\nI bought this unit over a year ago and still haven't figured out how to work it. I have used the D-link wireless router with no trouble, but no matter what I do, I can't get the print server to work properly. This is after hours with their customer service people and doing everytihing possible. Based on other people's reviews this unit actually does work. It just hasn't for me.\n\n\n\n\nB00009KH5Q:not_compatible_with_multifunction_printers:n._herriges\n\n\nB00009KH5Q\n\n\nD-Link DP-311U Wireless Print Server, 1-USB Port, 802.11b, 11Mbps: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n2.0\n\n\nNOT compatible with multifunction printers\n\n\nSeptember 8, 2005\n\n\nN. Herriges\n\n\n\n\n\nAfter 3 hours of banging my head against a wall I finally called Tech Support. They finally figured out that this is not compatible with multi-function computers. You can learn that on their support website but no-where in the standard documentation that comes with it\n\n\n\n\nB000EF3D64:garbage:nathan_e._garrett_\"blueshadababy\"\n\n\nB000EF3D64\n\n\nSound Blaster Audigy Se: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n1.0\n\n\nGarbage\n\n\nAugust 28, 2006\n\n\nNathan E. Garrett \"blueshadababy\"\n\n\nChehalis, WA USA\n\n\nBefore you buy anything from Creative, google the words incompatible + legacy drivers + sounblaster + windows xp\n\nHuge compatibiltiy issues that will have Creative Tech help telling you to flash and update your system bios, chipsets, leaving an entire PCI slot between Sounblaster card and any other card, update drivers, yada, yada, etc. Read all of the issues from the aforementioned google, and ignore the Creative employee reviews (they are easy to spot). Creative ignores the fact that its products have huge conflicts with Windows operating systems\n\n\n\n\nB000816CE4:jamming_piece_o'_junk!:gurlfriday\n\n\nB000816CE4\n\n\nBrother IntelliFAX 2820 Laser Fax Machine and Printer: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nJamming Piece o' Junk!\n\n\nSeptember 9, 2006\n\n\nGurlFriday\n\n\n\n\n\nI bought this machine based on the good reviews I found here. I have had it for one month and it does nothing but jam. Now the two week return policy at the store has expired and I am left with a jamming piece o' junk.\nMy advice - look for a different fax machine\n\n\n\n\nB000816CE4:awful.__just_awful.:bad_movie_buster_\"bmb\"\n\n\nB000816CE4\n\n\nBrother IntelliFAX 2820 Laser Fax Machine and Printer: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n1.0\n\n\nAWFUL. JUST AWFUL.\n\n\nAugust 4, 2006\n\n\nBad movie buster \"BMB\"\n\n\nNew York, NY\n\n\nI am shocked by the amount of positive reviews on this site. Let me tell you about my experience. I bought this machine in June of 2005. In one year, I have replaced this machine THREE TIMES. All three times, I got error messages that the toner cartridge was out (Toner Life End) even when new toner cartridges were put in. I do give high marks to Brother for its customer service, though. Each time I had a problem, I called and they sent out a replacement by FedEx along with materials for me to send back the broken one. But when the fourth replacement arrived last week and I turned it on and got an immediate paper jam message, I had enough. I had a similar issue with a Brother mutlifunction machine I purchased around the same time. I am done with Brother products\n\n\n\n\nB000E204LY:returned_item,_wasn't_blue_tooth:nick\n\n\nB000E204LY\n\n\nLogitech Cordless Desktop S 530 Laser for Mac ( 967664-0403 ): Electronics\n\n\nelectronics\n\n\n3 of 9\n\n\n2.0\n\n\nReturned item, wasn't blue tooth\n\n\nNovember 4, 2006\n\n\nNick\n\n\nNorth East, USA\n\n\nI ordered this keyboard/mouse combo and it turned out to not be blue tooth, so I returned it. I'm sure it would have been great if I had used it but my Mac has blue tooth and I thought I was getting a blue tooth kbd and mouse. \nI got an apple blue tooth keyboard instead and I found a logitech blue tooth mouse, which I also got. (Apple doesn't offer a decent blue tooth 2 button mouse in anything like an acceptably reasonable price range)\n\n\n\n\nB000E204LY:very_disappointing:codex57\n\n\nB000E204LY\n\n\nLogitech Cordless Desktop S 530 Laser for Mac ( 967664-0403 ): Electronics\n\n\nelectronics\n\n\n2 of 7\n\n\n2.0\n\n\nVery disappointing\n\n\nAugust 31, 2006\n\n\nCodex57\n\n\nCalifornia\n\n\nI purchased this for my Intel iMac mainly because I was so frustrated with the Mighty Mouse. I wanted something with two separate buttons. I had tried the Logitech MX 610 (which I purchased as well for another computer) and thought that the ergonomic shape felt wonderful. So, I purchased this desktop set because I wanted it to match my Mac and so I could have a proper 2 button mouse. I figured the Mac keyboard could use improvement as well so the keyboard could come in handy as well. In fact, I thought I would get added convenience since this was a keyboard designed for a Mac and had extra shortcut buttons.\n\nOther than having 2 separate buttons on the mouse, this keyboard/mouse combo has been an utter disappointment.\n\nFirst of all, the white doesn't quite match. It's close, but just a touch too cream. Also, there's just too much silver. Especially on the mouse.\n\nSecondly, it doesn't have all the buttons the Mighty Mouse does. There is no button to switch to the Widgets. Luckily, you can press F12 on the keyboard. Not quite as convenient, but still possible. However, I have yet to find a button that allows you to switch between various open programs. This is especially necessary if you have multiple web browsers open.\n\nThirdly, this mouse just doesn't work as well as other Logitechs. It is too light, lacking the \"feel\" and a bit of the comforting weight that the MX 610 has when you move it around. Also, the speed of the cursor is off. No matter how I try to adjust the speed, it doesn't quite match the speed of the original Mighty Mouse. It is either too fast or too slow. I think part of it is simply OSX's lack of adjustability compared to [gasp] Windows for cursor speed.\n\nFinally, the keyboard is simply atrocious. I don't even use it anymore. It's packed up in the box and I plugged the original Mac keyboard back in. The keys make a horrendous clatter when typing. The pressure is nonexistant making touch typing just a bit more difficult than normal. Also, the depth is quite shallow. This is a desktop keyboard. You can press the key down further on nearly all the laptop keyboards I've tried. Also, the texture of the keys is far too smooth and uniform. The raised dots to help you find the F or J buttons is too shallow. I can't find the asdf an jkl; rows by touch. I have to look. As a touch typist, this is an unforgiveable sin. It basically transforms you from a touch typist to a hunt and peck typer who has to visually find each key before you can hit it. I'm typing on a Thinkpad whose laptop keyboard is far superior to this Logitech.\n\nSo, in short, save your money and avoid this keyboard/mouse combo. I rated it a two because the mouse did have 2 separate buttons, unlike the Mighty Mouse, which is why I purchased this in the first place. It hit my most basic requirement. Otherwise, it really deserves a 1 star rating\n\n\n\n\nB00005N5Y2:caller_id_stopped_working_on_day_3:elizabeth_\"mom2three\"\n\n\nB00005N5Y2\n\n\nNorthwestern Bell 76510 Corded Speakerphone with Caller ID: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nCaller ID Stopped Working on Day 3\n\n\nOctober 17, 2006\n\n\nElizabeth \"mom2three\"\n\n\nOhio\n\n\nLove this phone.....if only the Caller ID would work! It worked fine on day 1, day 2 worked on and off.....day 3 Caller ID not working. Tried moving the phone to different outlets, removing the batteries & putting them back in, tried new wire etc.... The phone works great otherwise. Loved the Caller ID when it was working--much larger than most and easy to read. Oh well\n\n\n\n\nB00005N5Y2:do_not_buy_a_nwb_phone:nancy_c._hunter\n\n\nB00005N5Y2\n\n\nNorthwestern Bell 76510 Corded Speakerphone with Caller ID: Electronics\n\n\nelectronics\n\n\n22 of 29\n\n\n1.0\n\n\nDo not buy a NWB phone\n\n\nJuly 18, 2003\n\n\nnancy c. hunter\n\n\ntwinsburg, ohio United States\n\n\nAfter having my old phone for 40 years I bought a new NWB phone due to remodeling. It lasted 5 months. You cannot get through on their help line and they do not answer their e-mail. Buy a NWB phone at your own risk. I had to buy another phone--AT&T and it works fine\n\n\n\n\nB00004TS2H:these_transfers_blow!:paul_e._shelley_\"brit_iron_rebel\"\n\n\nB00004TS2H\n\n\nEpson S041153 Iron-on Cool Transfer Paper: Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n1.0\n\n\nThese transfers blow!\n\n\nFebruary 27, 2006\n\n\nPaul E. Shelley \"Brit Iron Rebel\"\n\n\nGoshen, IN USA\n\n\nI am comparing these transfers to the HP transfers for colored shirts. these require a reverse image and are just not user friendly. after three unsuccessful attempts I just went back to the more expensive HP's. The transfers did not adhere well and would flake off in little chunks even before you tried to wash them\n\n\n\n\nB0001NNLHK:worthless:j._head\n\n\nB0001NNLHK\n\n\nShure E3c Sound Isolating Earphones: Electronics\n\n\nelectronics\n\n\n0 of 10\n\n\n1.0\n\n\nworthless\n\n\nNovember 10, 2006\n\n\nJ. Head\n\n\nCT\n\n\nThese sound awful. I'm embarrassed to admit I spent 140.00 on these. The earphones from my 1989 sony walkman are far superior. If you like your music to sound as if it came from the bottom of a well then these are for you.\n\n\n\n\nB0009MYS9S:so_so_product_with_tons_of_potential._but_breaks.:marcos_kirsch\n\n\nB0009MYS9S\n\n\nLogitech Wireless Headphones for MP3: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n1.0\n\n\nSo so product with tons of potential. But breaks.\n\n\nOctober 26, 2006\n\n\nMarcos Kirsch\n\n\nAustin, TX USA\n\n\nBought these pretty cheap, from woot.com.\n\nThe good:\n- Great sound quality, all digital transmission so no hissing.\n- Look good\n- Decent battery life\n\nThe bad:\n- needs drivers (most USB headsets - granted, not wireless - don't)\n- Doesn't work on the Mac\n- If you are not playing music, they beep every 15 seconds or so. Then after a couple of minutes they turn themselves off. This drove me crazy and was not configurable.\n- Takes you ~15 seconds every time you turn them on and try to pair them.\n- Not terribly comfortable.\n\nAfter about a month of normal use, the headset cracked in the middle and stopped working. Bad quality o material. The Logitech people said they will refund whatever I paid for after I send them a copy of the receipt and the broken product. Too bad I get to pay for shipping and I'm not even sure they'll refund the ORIGINAL shipping. Lesson learned\n\n\n\n\nB0007RB1X6:piss_poor_vcr:tom_->_\"'the_sadhu'\"\n\n\nB0007RB1X6\n\n\nToshiba SDV393 Progressive DVD/VCR Combination: Electronics\n\n\nelectronics\n\n\n9 of 15\n\n\n1.0\n\n\npiss poor vcr\n\n\nOctober 13, 2005\n\n\ntom -> \"'the sadhu'\"\n\n\nsame world as you\n\n\nthe toshiba vcr combo arrived, and was quite light in weight. it hooked up ok, but the orange (nice color) display, constantly stayed on zeros, and the vcr played tapes so bad, there was always a white static streak across the screen bottom. i sent it back after trying a few tapes (all tested poor.) better to get a good vcr for good vhs picture display quality\n\n\n\n\nB00019EYVG:buyer_beware!:c._erickson\n\n\nB00019EYVG\n\n\nD-Link DWL-2100AP Wireless Access Point w/ SNMP, AES, 802.11g, 108Mbps: Electronics\n\n\nelectronics\n\n\n3 of 7\n\n\n1.0\n\n\nBuyer Beware!\n\n\nJanuary 4, 2005\n\n\nC. Erickson\n\n\nAlaska\n\n\nI bought this unit because a DLink Sales Rep said it would be perfect for my application. Hah! It doesn't work at all (I hope Amazon will let me return it). The first thing I had to do was upgrade the firmware - no instructions on how to do this, but for the uninitiated: you have to download the .ftp file from Dlink, move it to the Dlink Firmware folder on your computer, then get the AP Manager to see your unit (that's another story involving IP addresses - again, no help from the install documents), and then you can install the update. I finally got the DWL-2100AP to see my network, but never could get it to talk back - it just sent gibberish. The Net tech and I worked on it for most of two working days, to no avail. Spent a lot of time on the phone with the DLink tech staff too, where everyone involved said we got some bad dope from the sales staff, but that we might get it to work, provided it is compatible with the existing equipment blah, blah, blah... My assessment of Dlink and the DLINK DWL-2100AP? Poor pre-sales support, poor post-sales support, confusing tech support, and poor documentation/install instruction for a product that may be (but then again, may not be) compatible with your existing equipment. What's up DLink\n\n\n\n\nB0007ZK364:spoilt_adapter:vinod_t._mistry_\"vinod\"\n\n\nB0007ZK364\n\n\nAcer AL1913cb 19\" LCD Monitor: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n2.0\n\n\nspoilt adapter\n\n\nMarch 14, 2006\n\n\nVinod T. Mistry \"vinod\"\n\n\ntrinidad\n\n\nwhen i got the moniter i said it was cool and alright but whe i plugged it in it did nat work and i was angry.i tried again but it did not work so i tried to send it back but it was to much of a hassle.i decided t see what was the problem and send it to a electronics store and the told me the the ic was bad.the only thing i could have done is to buy a new adapter.so i paid 800 trinidadian dollars to get it to work.i was furious\n\n\n\n\nB000EVM5DU:software_and_installation_issues,_buyer_beware:s._wilson\n\n\nB000EVM5DU\n\n\nMicrosoft LifeCam VX-6000 ( 68C-00001 ): Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n1.0\n\n\nSoftware and installation issues, buyer beware\n\n\nOctober 22, 2006\n\n\nS. Wilson\n\n\nMontana, USA\n\n\nMy family and I just relocated 3 states away from our extended family and wanted to get a good webcam to keep intouch with everyone and let our 2 year old see his grandmother once and a while. When I saw the Microsoft LifeCam at Target stores and that it was compatible with Windows Live I thought it would be worth the $100 price tag to be able to keep in touch with family.\n\nI purchased the software and started the installation process on my computer. Just so you know,my system is running a pentium 4 processor and has 8 active USB 2.0 connections, only 3 of which are being used for the keyboard, mouse, and this webcam, along with 512MB of RAM which is more than necessary for this product to function.\n\nThe software took about 10 minutes to install which I thought was a little odd, then it askes for you to plug in the camera. My XP system recognized the camera but told me that the hardware had encountered an error during installation and may not function properly. I uninstalled the software and tried the installation again with the same result. I then tried a different USB port just in case there was a problem. Following a stepwise process I checked and updated all drivers for the USB hub, ports, etc. and I checked under the system manager tools to ensure that all were working properly. I then went to the microsoft website and downloaded the updates for the software (this took another 15 minutes and the site crashed twice).\n\nBy now I was getting very frustrated but hoped that the software update would allow appropriate conectivity with the camera. I went into the software and found that it was recognizing the mic, but it said that there was no camera attached. I again tried to find the camera through other means with no results.\n\nFor the price paid and the 3+ hours I spent trying to install the hardware I can say with all assurity that this product is not worth it. I will be returning it to Target for a full refund and will look into investing my money with another brand.\n\nAs someone else said earlier in the reviews, \"does microsoft ever send their products through Quality Assurance before they release them?\n\n\n\n\nB000EVM5DU:software_issues:a._t._maruzook_\"the_trick_man\"\n\n\nB000EVM5DU\n\n\nMicrosoft LifeCam VX-6000 ( 68C-00001 ): Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n1.0\n\n\nSoftware issues\n\n\nOctober 16, 2006\n\n\nA. T. Maruzook \"the trick man\"\n\n\ncolombo, sri lanka\n\n\nDont microsoft ever send their software thru Quality Assurance checking? They asked me to install a windows update (KB something or the other) saying that it prevented the software from running at 100% CPU usage. Lo and behold, it still gets stuck!! Task Manager dutifully reports that it is after all consuming all resources\n\n\n\n\nB00006JILM:good_looks,_mediocre_performance:jake_sanders\n\n\nB00006JILM\n\n\nSony MDR-NC11 Fontopia Noise Canceling Headphones with In-line Volume Control: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n2.0\n\n\nGood looks, mediocre performance\n\n\nJuly 16, 2006\n\n\nJake Sanders\n\n\nBoulder, CO United States\n\n\nI've found these headphones to be highly overrated. \n\nThe noise cancelling is primarily from the snug fit in the ear. When the noise circuit is turned on, the background \"hiss\" becomes incredibly annoying. To be fair though, this model does remove some background low frequency sound, even in addition to the the noise reduction from the ear plugs. \n\nThe frequency response during playback sounds like an old am radio\n\n\n\n\nB00006JILM:break_easily_-_even_when_you_take_care_of_them.:derick_bailey_\"computer_guru,_audiophile\"\n\n\nB00006JILM\n\n\nSony MDR-NC11 Fontopia Noise Canceling Headphones with In-line Volume Control: Electronics\n\n\nelectronics\n\n\n3 of 5\n\n\n1.0\n\n\nBreak easily - even when you take care of them.\n\n\nFebruary 13, 2006\n\n\nDerick Bailey \"computer guru, audiophile\"\n\n\nWaco, TX\n\n\nIn terms of sound quality, noise cancelling, and general comfort - thise headphones are top of the line. I absolutely love the way the sound and the ammount of noise cancelling that they provide. I used them regularly when mowing the lawn and when flying, and I always recieved the highest quality sound and noise cancelleation. I was even able to listen to drum-n-bass while mowing the lawn and hear all of the appropriate frequencies on the low end. \n\nHowever... inside of 6 months, I have gone through 2 pairs of these headphones because the right ear headphone has gone out on them. The worst part is - I can't even begin to imagine why it goes out. I keep my heaphones in a locked cabinet in my office every night. Last friday, I placed back into the nice little carry bag that they come with, and placed them into this cabinet. This morning when I pulled them out for use, the right ear bud is out again... no physical damage is apparent anywhere on the headphones, and there is not a broken wire anywhere. In fact, the wiring is perfectly fine and still functional. It is the physical earbud - the hard plastic peice - that has gone out. If i squeeze the earbud hard enough, it will turn on again. \n\nSomething inside of the earbud is obviously designed improperly and I would highly recommend that noone purchase these headphones because of this\n\n\n\n\nB00006JILM:really_bad:stoak_\"soffak\"\n\n\nB00006JILM\n\n\nSony MDR-NC11 Fontopia Noise Canceling Headphones with In-line Volume Control: Electronics\n\n\nelectronics\n\n\n5 of 8\n\n\n1.0\n\n\nReally bad\n\n\nDecember 21, 2005\n\n\nSTOAK \"soffak\"\n\n\nAnchorage, AK United States\n\n\nSomeone previously pointed out that these earphones did not filter out the highs well. It may be the highs all I know is the only thing it seems to filter well is my wife and kids voices and allow in every other ambiant noise around. \n\nNow I may have gotten a lemon but the sound is poor. Volume cranked to high on any device I put this on comes out low at best. Put in any other set of earphones and it is painfully loud. \n\nThe little inline triangle that has the on/off switch is cumbersome and is at a strange distance from the earphones. Just seems to far from the earphones or not far enough. Just doesn't seem to clip naturally anywhere. Either to much cord or not enough. \n\nI am not a music or sound expert by any means so it does not bode well that I think these earphones are poor at best\n\n\n\n\nB00005A9AU:not_for_laptop_users:w._reals\n\n\nB00005A9AU\n\n\nPlantronics DSP-400 Digitally-Enhanced USB Foldable Stereo Headset and Software: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n2.0\n\n\nNot for Laptop Users\n\n\nSeptember 15, 2006\n\n\nW. Reals\n\n\n\n\n\nI got Avaya RoadWarrior up and running on my computer to use when I work remotely. My co-worker told me to get a GN Netcom headset but I figured I'd save some money and get this one. Big mistake.\n\nThere is a major problem with some laptops when the power cord is plugged in, the listener on the other end has what sounds like you in a an airplane during flight. The humming goes away when you unplug the power cord but that doesn't help me, I need to be able to use it when the power cord is plugged in or not.\n\nAnyway, I chatted with Plantronics support and then informed me that it's a known issue with no work around except to unplug the power cord while using it.\n\nSo, it's getting returned.\n\n\n\n\nB00005A9AU:inadequate_for_voice_recognition:j._richardson\n\n\nB00005A9AU\n\n\nPlantronics DSP-400 Digitally-Enhanced USB Foldable Stereo Headset and Software: Electronics\n\n\nelectronics\n\n\n8 of 11\n\n\n1.0\n\n\nInadequate for Voice Recognition\n\n\nAugust 21, 2006\n\n\nJ. Richardson\n\n\nPrinceton, NJ\n\n\nIt failed Dragon Naturally Speaking's sound tests, tests easily passed by the cheapo mike included with the program. And just try to return it. Antonline stonewalls, Plantronics doesn't respond at all\n\n\n\n\nB0007Y79G2:died_after_two_days_of_use:veniero\n\n\nB0007Y79G2\n\n\nCreative Zen V 4 GB Portable Media Player (Black/Blue): Electronics\n\n\nelectronics\n\n\n1 of 10\n\n\n1.0\n\n\nDied after two days of use\n\n\nOctober 22, 2006\n\n\nVeniero\n\n\nRichmond, MA USA\n\n\nGave it one star because there was no option for zero. Died after two days of use, would not turn on or reset. Previous Zen Nano, same thing after six months of use. I guess that would be my last Creative product\n\n\n\n\nB0006ZSXJE:lousy_drivers,_lousy_customer_support__(but_nice_hardware):twinehead_\"twinehead\"\n\n\nB0006ZSXJE\n\n\nRazer RZ-1600 Diamondback Precision Gaming Mouse- Chameleon Green: Electronics\n\n\nelectronics\n\n\n2 of 8\n\n\n2.0\n\n\nLousy Drivers, Lousy Customer Support (but nice hardware)\n\n\nNovember 17, 2005\n\n\ntwinehead \"twinehead\"\n\n\nWashington, DC\n\n\nThe first warning sign is that the drivers are not Windows approved and unsigned. Their installation instructions advise you to ignore the warnings.\n\nNext, is the cheesy \"cool\" looking set-up program. Please forget about the groovy graphics and just provide a usable interface.\n\nThen, the drivers don't work. My cursor slides all around the screen after I stop moving the mouse. I tried calling customer support, but there is no telephone support. I then tried connecting with their online chat support. No one helped me after 20 minutes. Two days later, in response to an e-mail, they sent me 3 pages of instructions about how to adjust my system to accommodate their crappy software.\n\nToo bad, because the mouse itself is a nice piece of hardware. Solid-feeling, well-built, and comfortable.\n\nI'm sending mine back for a refund\n\n\n\n\nB0001D3JXG:router_card:delores_j._johnson\n\n\nB0001D3JXG\n\n\nLinksys WPC54GS Wireless-G Notebook Adapter with SpeedBooster: Electronics\n\n\nelectronics\n\n\n0 of 4\n\n\n1.0\n\n\nROUTER CARD\n\n\nMarch 21, 2006\n\n\nDelores J. Johnson\n\n\nTULSA, OKLAHOMA United States\n\n\nTHE ROUTER CARD DOES NOT WORK, MY OLD ONE WORKS WITH THE NEW ROUTER BUT THE NEW CARD DOES NOT WORK AT ALL, SO I NEED A REFUND FOR IT, OR A REPLACEMENT, THANKS\n\n\n\n\nB0001D3JXG:absolute_junk!:mahfuzul_alam_\"dew\"\n\n\nB0001D3JXG\n\n\nLinksys WPC54GS Wireless-G Notebook Adapter with SpeedBooster: Electronics\n\n\nelectronics\n\n\n3 of 6\n\n\n1.0\n\n\nAbsolute junk!\n\n\nAugust 31, 2005\n\n\nMahfuzul Alam \"Dew\"\n\n\nLondon, England\n\n\nI bought it with my Linksys WRT54GS Wireless-G Broadband Router with SpeedBooster. The software installation procedure is really poor. When I ran the software first few times it constantly gave me error messages (can't remember what they were) so I had to reinstall it 2-3 time till it settled down finally. Then I set up the network settings with my router and scanned for the access point (Infrastructure) but it couldn't find anything even though the router was only 5-7 feet away! At one point it managed to connect to the router but it was still searching for the internet connectivity.. After several minutes it couldn't find anything and disconnected from the router. I knew there was nothing wrong the router or internet because I inserted my old Belkin adapter and it connected straight away!\n\nDon't waste your money on this. If you wanna get a router, get the one I've got which is quite good. If you want a WiFi Adapter then get Belkin F5D7010 Wireless 54Mbps Notebook Adapter. I couldn't find any 108g adapters by them but this one is really good.\n\nhttp://www.amazon.com/exec/obidos/tg/detail/-/B000085BCX/qid=1125546516/sr=8-5/ref=pd_bbs_unbuck_5/002-4891207-7082407?v=glance&s=electronics&n=17259\n\n\n\n\nB000CEN0YC:totally_worthless:robert_s._balaban\n\n\nB000CEN0YC\n\n\nGlobal Pet Finder GPS Tracking Device for Pets: Electronics\n\n\nelectronics\n\n\n23 of 24\n\n\n1.0\n\n\nTotally worthless\n\n\nFebruary 13, 2006\n\n\nRobert S. Balaban\n\n\n\n\n\nThis product keeps changing home addresses; none of which are the address I typed in the initial field. It does not stay charged so that it is actually able to function as advertised if you are gone from home any length of time and you put your dog in a yard. Additionally, gravity takes it down to the lower neck area in common use so that when the dog is doing normal dog things, the device sinks downward so that the antenna is pointing toward the ground. The pictures are misleading. We tried all of the advise the company gave us. It's just not a device that works when and where you need it to work. It was a lot to invest to have this many problems. \n\n\n\n\nB000CEN0YC:there's_this_cool_new_technology...:meg\n\n\nB000CEN0YC\n\n\nGlobal Pet Finder GPS Tracking Device for Pets: Electronics\n\n\nelectronics\n\n\n12 of 40\n\n\n1.0\n\n\nThere's this cool new technology...\n\n\nJanuary 25, 2006\n\n\nMeg\n\n\nCincinnati, OH\n\n\n... it's called a leash. Or a fence. How about keeping your pet safe the responsible-- not to mention cheap-- way. \n\nI've been a volunteer at a no-kill animal shelter for 5 years, and I'm still amazed at the lack of guardianship people show for their pets. So your dog has this $350-dollar doodad on its neck. It's not going to keep it from running out into the street and getting hit by a car, or picked up by somebody who wants to use it for dog-fighting bait or sell it to a lab (yes, these things happen-- often). \n\nHave some sense, people. This is ridiculous. \n\n\n\n\nB0002WPSB2:does_not_encrypt_fingerprint!:l._uyeno\n\n\nB0002WPSB2\n\n\nMicrosoft Fingerprint Reader: Electronics\n\n\nelectronics\n\n\n4 of 6\n\n\n1.0\n\n\nDoes not encrypt fingerprint!\n\n\nAugust 7, 2006\n\n\nL. Uyeno\n\n\nLos Angeles\n\n\nA nice device, but Microsoft didn't include software that encrypts the fingerprint image. Although very difficult to do someone *could* steal your fingerprint. To be safe I would not use this fingerprint reader for password protected websites that contain sensitive information or for network admin or computer BIOS privledges\n\n\n\n\nB0006GDCD0:image_nice,_it_works_when_it_does,_hard_to_manage:mr._ken\n\n\nB0006GDCD0\n\n\nD-Link Securicam Network DCS-950G Wireless Internet Camera: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nImage nice, it works when it does, hard to Manage\n\n\nOctober 9, 2006\n\n\nMr. Ken\n\n\nWestern Maryland\n\n\nThis camera had some nice features like the ability to write to a network storage device, smart motion detection, etc. The recorded videos have their own codec so you can use it or d-vix. Some videos are unreadable and if you dig deep enough, d-link has a utility to make them readable.\n\nI upgraded the latest firmware 1.06, tried using the ability to write to a storage device and found it very hard to manage. Motion triggering is touchy but works once you set it up. The wireless caused 90% of my communication problems. Ran a wire and things were fine. Clock could not keep time for anything. Set USA EDT and it was fine and shortly after it feel back one hour. D-Link knows of this and the only solution was to use Atlantic time. It has all the right features and image is great but I have to vote thumbs down on this unit.\n\nOne more thing, D-Link hard codes an IP address so if you try and factory reset remotely or of you happen to be using a different network addressing scheme, you're done. Why for the life of me they don't use DHCP by default is beyond me\n\n\n\n\nB0006GDCD0:great_idea_but_big_issues...:jdub_\"jdub\"\n\n\nB0006GDCD0\n\n\nD-Link Securicam Network DCS-950G Wireless Internet Camera: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n2.0\n\n\nGreat Idea but big issues...\n\n\nApril 10, 2006\n\n\nJDUB \"JDUB\"\n\n\nCali\n\n\nCamera will not stay up on wireless network with Dlink router! Firmware has some serious issues. Dlink support was worthless. I returned the camera and bought a Panasonic and never looked back\n\n\n\n\nB00006GETD:does_not_work_with_a_nikon_coolpix_4300:girlonthebay\n\n\nB00006GETD\n\n\nKingston 512 MB Type I CompactFlash Card (CF/512): Electronics\n\n\nelectronics\n\n\n22 of 24\n\n\n1.0\n\n\nDoes not work with a Nikon Coolpix 4300\n\n\nDecember 8, 2003\n\n\nGirlOnTheBay\n\n\nBoston MA\n\n\nIt would be really great if Amazon and other resellers did a better job outlining which accessories were compatible with which products. This is stated as a CF Type I card yet this does not work with a Nikon Coolpix 4300--another, more expensive Kingston card, must be used\n\n\n\n\nB00009ZHS7:very_large_size_for_travel_and_mediocre_sound:k._schneider_\"disappointed_purchaser\"\n\n\nB00009ZHS7\n\n\nPanasonic Folding Travel Speakers (RPSPT70): Electronics\n\n\nelectronics\n\n\n4 of 5\n\n\n2.0\n\n\nVery large size for travel and mediocre sound\n\n\nJuly 20, 2006\n\n\nK. Schneider \"Disappointed Purchaser\"\n\n\nLos Angeles, CA USA\n\n\nI have several pairs of portable speakers for my laptop but forgot to bring them on a recent trip and bought these based on the glowing reviews posted here. However, when I received the speakers I found that they are much larger in size and much lower in both quality of build and quality of sound than for example the Creative speakers. They have a huge and unstable footprint when opened (about 9 inches long and one inch deep) and are not particularly compact when folded either. The sound is not rich and they eat batteries amazingly fast, then begin to buzz. I do not recommend these speakers\n\n\n\n\nB0002R5Y5M:don't_waste_your_money:ninthnight_\"gadget_junkie\"\n\n\nB0002R5Y5M\n\n\nMacally PodFM FM transmitter for iPod: Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n1.0\n\n\nDon't waste your money\n\n\nMarch 15, 2006\n\n\nNinthnight \"gadget junkie\"\n\n\nUpstate NY\n\n\nI bought this for my 5g Ipod. I drive a late model Subaru with the lighter switch inside of the ashtray console. Firstly the transmitter was completely dangerous to operate while driving. Its impossible to use in Park and Neutral in my car. The controls are bulky. The transmission cut out in mountains, on hills, and on the highway whenever I drove over 60mph resulting in my ipod starting and stopping because it shakes and wobbles in the console. In short, it hardly worked and I was afraid to touch it while driving, lest I'd knock my gear shift out of place. Its cheaply made and all in all a complete piece of junk.\n\nThis is hands down the worst IPOD FM transmitter I have ever used. I've previously used a basic Kensington all in one model (stolen by wife, worked well but awkward) and ended up replacing this with a far superior Griffin Road Trip. I reccommend spending the extra $15 on the Road Trip.\n\n\n\n\nB000CFLES0:worked_well_for_a_while,_it_was_always_\"krinklely\"_and_then_the_sound_went_really_low.:cookievenom_\"cookievenom\"\n\n\nB000CFLES0\n\n\nApple Ipod mp3 Car Audio Tape Cassette Adapter: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nWorked well for a while, it was always \"krinklely\" and then the sound went really low.\n\n\nOctober 6, 2006\n\n\ncookievenom \"cookievenom\"\n\n\nArizona\n\n\nWorked well for a while ( 2 weeks) , the sound always had static, later the sound went really low and then it just died\n\n\n\n\nB000CFLES0:frustrating_to_use:shannon_\"jfaction\"\n\n\nB000CFLES0\n\n\nApple Ipod mp3 Car Audio Tape Cassette Adapter: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nFrustrating to use\n\n\nAugust 10, 2006\n\n\nShannon \"jfaction\"\n\n\nLafayette, CA USA\n\n\nI have the same problem some other reviewers had: the audio was extremely low unless I manually adjust the cassette. It's not that easy - you have to stick your finger into the cassette slot while the adapter is in there, and wiggle it forward and backward until the audio adjusts. But that doesn't always work. If that doesn't work, you then have to turn your hand over and use your fingernail to catch a ridge on the adapter and lift it up a bit. I'd rather not have an accident on the highway by either (1) fiddling with the audio, or (2) smashing the adapter into little pieces while I drive.\n\nHundreds of songs on the ipod and no way to listen while you drive...\n\n\n\n\n\nB00030CHP8:bad_quality,_donot_buy_pny_sd_card:vince_wilson\n\n\nB00030CHP8\n\n\nPNY PSD1GRF3 1 GB Secure Digital Card: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nBad quality, donot buy pny sd card\n\n\nAugust 5, 2006\n\n\nvince wilson\n\n\nVA USA\n\n\nAfter using this sd card only twice, it can not be recognized by any of my card reader. Totally a waste of money and time. Do not buy pny brand\n\n\n\n\nB0000CEPC0:worst_customer_support_i_have_ever_seen:netizen_fair\n\n\nB0000CEPC0\n\n\nD-Link DCS-900 10/100TX Internet Camera, Home Security: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n1.0\n\n\nworst customer support I have ever seen\n\n\nSeptember 27, 2006\n\n\nnetizen fair\n\n\nNJ, USA\n\n\nI had DCS-900W from the same product line. It craped out after 9 months, and now I am on the fifth call to try to resolve the issue and get it shipped back. I don't know where their support is located, but the line quality is very bad, they ran the same drill over and over, but just don't want to issue you an order for you to ship back.\n\nThis is the first D-Link product I own and probably the last. The resolution looks crappy even when it's new, and now it's totally dead, even reset won't work on it\n\n\n\n\nB00006AE3K:my_experience_with_this_item:y._lu_\"wsurainer\"\n\n\nB00006AE3K\n\n\nSanDisk SDCFB-1024-A10 1GB CF Type 1 Card (Retail Package): Electronics\n\n\nelectronics\n\n\n3 of 5\n\n\n1.0\n\n\nmy experience with this item\n\n\nNovember 26, 2005\n\n\nY. Lu \"wsurainer\"\n\n\nPullman, WA United States\n\n\nYesterday was the annual Thanksgiving sale day. I got this 1G CF card from Staples by $40 after easy rebate. With this price, I think it is a great deal. However, it worked for my digital rebel only half an hour before it was DEAD. All my photos in the card were lost. I am a graduate student majoring in computer engineering, I know how to recover data if a hard drive fails. But this time, I find I simply can't help. Fortunately, Staples have a good return policy and I just returned it without hassle. Hopefully, this is only my case. My suggestion: make sure your dealer has a good return policy before you get this item. \n\n\n\n\nB00006AE3K:pricy:r._sutton\n\n\nB00006AE3K\n\n\nSanDisk SDCFB-1024-A10 1GB CF Type 1 Card (Retail Package): Electronics\n\n\nelectronics\n\n\n1 of 8\n\n\n1.0\n\n\nPricy\n\n\nOctober 11, 2005\n\n\nR. Sutton\n\n\nRio Vista, CA USA\n\n\nOf course as all things computer related, as soon as this was purchased the price dropped drastically. Unit works great but buy the 2 gig. More bang for the $\n\n\n\n\nB00006AE3K:1gb_compact_flash_card:richard_w._bock_\"rick_bock\"\n\n\nB00006AE3K\n\n\nSanDisk SDCFB-1024-A10 1GB CF Type 1 Card (Retail Package): Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n1.0\n\n\n1GB Compact Flash Card\n\n\nAugust 10, 2005\n\n\nRichard W. Bock \"Rick Bock\"\n\n\nMedina, Ohio\n\n\nThe card failed after 23 pictures. Amazon credited me with a full purchase price credit to my credit card account\n\n\n\n\nB000CO90L4:on_hold_50_minutes,_52_seconds:brian_bowman\n\n\nB000CO90L4\n\n\n1-Year Replacement Plan for Electronics Products $175 to $199.99: Electronics\n\n\nelectronics\n\n\n20 of 22\n\n\n1.0\n\n\nOn hold 50 minutes, 52 seconds\n\n\nJuly 19, 2006\n\n\nBrian Bowman\n\n\nNW Ohio\n\n\nI was on hold 50 minutes, 52 seconds before anybody answered. I heard \"your call is important to us\" at least 100 times, which wasn't very convincing\n\n\n\n\nB00006B9QM:it_works,_but_it_is_super_slow!:b._williams\n\n\nB00006B9QM\n\n\nSanDisk SDDR-64-768 Compact Flash to PC Card Adapter: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n2.0\n\n\nIt works, but it is SUPER SLOW!\n\n\nAugust 19, 2006\n\n\nB. Williams\n\n\n\n\n\nI bought this reader for my laptop since it didn't have USB 2.0.\nBut to tell you the truth, I think this device is a little slower than USB 1.0! I am going to buy the Lexar 32Bit card and see if that improves the download speed, because this reader is so slow.\nIt took me about 45min to download 2GB of photos from my Extreme III card. That is unacceptable\n\n\n\n\nB00006B9QM:slow,_affects_entire_system:arvin_tanag\n\n\nB00006B9QM\n\n\nSanDisk SDDR-64-768 Compact Flash to PC Card Adapter: Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n2.0\n\n\nSlow, affects entire system\n\n\nApril 1, 2006\n\n\nArvin Tanag\n\n\nUSA\n\n\nNote: I am fully aware that compact flash cards have a maximum sustained upload speed which caps off the potential speed of the medium it is being transferred on.\n\n\nI purchased this product to use in conjunction with my SanDisk Ultra II compact flash cards. I thought that PC cards would be faster than USB 1.1 and 2.0, but thats not the truth.\n\nWhenever I try to upload files from my CF cards thru the PC card, it slows down the ENTIRE system. I can't do other tasks while the card is uploading files since everything is acting sparatic and slow.\n\nI'll admit, the card is a bit faster in uploading files than USB 1.1, but I can't do anything else on my computer while the card is working. Honestly, I would rather have a little bit slower performance with my USB 1.1 card reader and be able to work on my computer at the same time instead of having the card slow down my entire system.\n\nI believe this card is a 16-bit pcmcia card. I've heard good praise for Lexar's 32-bit pcmcia cards, but I have yet to try it out. I'm sure it costs more money than this PC card or a USB card reader, though...\n\n\nAll in all, I suggest getting a USB 2.0 compact flash reader over this product if you want to work on your computer at the same time as you are uploading files.\n\nOtherwise, if your the kinda person who doesnt mind leaving the computer alone while the files are uploaded to your computer, this will work just fine for you\n\n\n\n\nB0002IG356:keyboard_dead_in_less_than_6_months:an_average_joe\n\n\nB0002IG356\n\n\nMicrosoft Optical Desktop Elite for Bluetooth: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n1.0\n\n\nKeyboard Dead in Less than 6 Months\n\n\nAugust 25, 2006\n\n\nAn Average Joe\n\n\nCedar Rapids, IA\n\n\nand the worst part is that I'm actually glad about it!\n\nI bought this set to replace a [...]Logitech wireless mouse/keyboard combo that was about 3 years old when the mouse scroll wheel stopped working.\n\nI figured I'd treat myself to what looked like the best wireless set out there. Here's what I found:\n\n1) The keyboard is HUGE. I have Dell 19\" flat panel and the keyboard looks ridiculous in front of it.\n\n2) Even though the USB dongle is plugged in the Dell monitor about 1 foot from the mouse/keyboard it still loses its connection and I get the message \"USB device has malfunctioned.\" I have to remove and replace the dongle and it starts again. This happens a couple of times a week, but of course at the most inconvenient times.\n\n3) The keyboard goes to sleep like the mouse after a few minutes to save battery life. The mouse will wake up after a slight delay and I'm used to that but I can't tell you how many times I pressed \"Ctrl-C\" to copy something into the buffer and then discovered that it didn't \"take\" because the keyboard was asleep. The keyboard should never go to sleep or this \"feature\" should have a way to be disabled!\n\n4) The keyboard died (stopped responding) at less than 6 months old. I still use the mouse but dug out an ancient Dell quietkey wired keyboard. Frankly I don't miss the behemoth MS monster at all, at least the old keyboard doesn't take up the whole desk and when I hit Ctrl-C it works every time.\n\nMicrosoft has made some good keyboards and mice, but this set is not one of them. Buyer beware. \n\n\n\n\nB0002IG356:extremely_laggy_mouse:james_lin\n\n\nB0002IG356\n\n\nMicrosoft Optical Desktop Elite for Bluetooth: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nExtremely laggy mouse\n\n\nMay 7, 2006\n\n\nJames Lin\n\n\nSimi Valley, CA United States\n\n\nI bought this keyboard/mouse set hoping to consolidate most of my wireless devices onto a single HUB and standard. \n\nSetting up the set was easy enough in Windows, but I immediately found that the mouse did not work well at all. The cursor would move in increments of around 10 pixels at a time. I even put the Bluetooth adapter on a USB extension cord and placed it within inches of the mouse, but that didn't help at all.\n\nThe keyboard seemed to work well, but I didn't use it for very long because the mouse was unbearable to use. I have several wireless networks in my area, which may be causing interference with the Bluetooh though\n\n\n\n\nB00006RVNW:don't_waste_your_money_on_this_overpriced_ripoff:cosymoon_\"davidjones\"\n\n\nB00006RVNW\n\n\nMonster Cable ULT V1000 CV-4 THX Ultra 1000 Component Video Cable: Electronics\n\n\nelectronics\n\n\n12 of 13\n\n\n1.0\n\n\nDon't waste your money on this overpriced ripoff\n\n\nOctober 13, 2005\n\n\ncosymoon \"DavidJones\"\n\n\nBoulder, CO United States\n\n\nSpeaking from real experience, don't waste your money buying this cable which makes absolutely no difference from a cabe you can find with half or less the price. I bought a few top of the line monster cables once I got the top of the line SONY XBR960 HDTV. Later on I switched cables around, with other brand, cheaper component cables such as Philips PTS1000 (the HDMI standard owner by the way), and also other HDMI cables (even no name brands coming with my DVD player). You know what? I see absolutely no difference even under close examination. The SONY XBR960 produces the finest pitch you can find on any TV. You think about what that means. Stop buying this ridiculously priced cables, and save your $$ and buy somethinfg else on your shopping list. Digital is digital. Don't let monster mislead you that their cables produce more 11010 ... than others.\n\n\n\n\nB00067KETY:two_big_problems:patrick\n\n\nB00067KETY\n\n\nAT&T 959 Speakerphone with Caller ID (White/Gray): Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n2.0\n\n\nTwo big problems\n\n\nJuly 18, 2006\n\n\nPatrick\n\n\nPhilmont\n\n\nBasically a nice phone, BUT:\nA: I've had three of these phones, and sooner or later the little repositionable hang-up tab snaps off, rendering it useless as a wall-mount, and\nB:I can hear well with the speaker phone, but have to shout to be heard.\n\n\n\n\nB0007XXHNW:beware_if_there_are_few_unused_frequencies_in_your_area:zowie\n\n\nB0007XXHNW\n\n\nKensington Digital FM Transmitter/Car Charger for iPod: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n2.0\n\n\nBeware if there are few unused frequencies in your area\n\n\nNovember 20, 2006\n\n\nZowie\n\n\nNorthwestern NJ, USA\n\n\nI purchased the Kensington after my casette adapter developed an annoying wheel noise. I heard and read a lot about the problems with FM transmitters, like static and audio quality but the Kensington was the best rated unit.\n\nIt arrived the day before I had to take a business trip and thought it would be a great test. The first day I used it, it seemed to work great. The next day on my return trip home, all I got was static and could barely hear the music from my iPod. I tried every station I could find with little or no transmission on it, but the audio quality remained extremely poor.\n\nI am not sure if there are just too many stations broadcasting in the NYC metro area or this particular unit is defective, but since my car has a casette player I will go with casette adapter for now.\n\nIf you have lots of unused frequencies, it might work in your area. Just be sure you can return it if it doesn't work out for you.\n\n\n\n\n\nB0007XXHNW:terribly_weak_transmitter:jonathan_r._pedley_\"book_lover\"\n\n\nB0007XXHNW\n\n\nKensington Digital FM Transmitter/Car Charger for iPod: Electronics\n\n\nelectronics\n\n\n0 of 3\n\n\n1.0\n\n\nTerribly weak transmitter\n\n\nNovember 14, 2006\n\n\nJonathan R. Pedley \"Book Lover\"\n\n\nSan Francisco. CA\n\n\nI had a Monster iCarPlay for some time and had givben it to a friend. So when I was looking for a new one i thought I'd try the Kensington.\nBig mistake - my car couldn't pick the signal up without manually tuning - very painful. Go for the Monster products over this in a heartbeat\n\n\n\n\nB0007XXHNW:device_is_barely_acceptable:private_dad_\"electronic_drudge\"\n\n\nB0007XXHNW\n\n\nKensington Digital FM Transmitter/Car Charger for iPod: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n2.0\n\n\nDevice is Barely Acceptable\n\n\nNovember 10, 2006\n\n\nPrivate Dad \"electronic drudge\"\n\n\nHouston, TX USA\n\n\nUsing this in my wife's Expedition is very annoying. For some reason the cord must be placed in exactly the right position or static will drown out the music coming from the car's speakers. Note that no other electrical devices in this car (and there are a lot of them; she got every option available with the car) misbehave at all. I would not repeat this purchase, not can I recommend anyone else buy one of these. I only gave it two stars because with continual minor effort it can be made to work acceptably - however, fiddling with the cord while driving is certainly NOT a safe practice\n\n\n\n\nB000CO90SM:new_=_worthless_thieves!:jesse_p._hapke_\"concerned_fellow_enthusiast\"\n\n\nB000CO90SM\n\n\n4-Year Service Plan for Camcorders $1,000 to $1,999.99: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n1.0\n\n\nNEW = worthless thieves!\n\n\nOctober 10, 2006\n\n\nJesse P. Hapke \"concerned fellow enthusiast\"\n\n\nOmaha, NE, USA\n\n\nNEW are notorious thieves in the consumer electronics repair industry. I personally submitted for repair both a $1,600 video camera (Sony DCR-PC5) and a car CD player (a Sony CDX-C90, a $1,200 in-dash top of the line unit. If you've heard of it, you know what I'm talking about). I was told that the warranty did not cover what was wrong with my video camera (some ambiguous statement about logic board, power unit, etc. failure followed), and would have to pay ~$780 to have it fixed. I naturally refused, and was forced to keep my dead 4 month-old handycam, an unfortunate testament to some of Sony's now widely publicized QC issues. The reason I did not send in the camera initially has to do with the fiasco involving my CD player. I was told, explicitly, that if NEW could not fix it, they would either return the unit or write me a check in the amount of $1,000 (not even enough to cover the cost of the player). As an added insult, when they indeed could not fix the unit, they KEPT it, and I NEVER received a check!!! Simply outrageous! Blatant theft, and I'm not the only one who will tell you a story about NEW and such an act. They are IMPOSSIBLE to get ahold of, and literally take your money and run/disappear. Don't expect to ever hear from them, should your camera stop working, and good luck getting ahold of them if you happen to send it in beforehand. Chances are, you will have to eat the loss, as I did. This product deserves a negative star and should come with an automatic refund. I seriously thought it was a gag when I saw NEW was in charge of the coverage. What a joke! Whoever runs your company, NEW, ought to be both ashamed of themselves and held liable for all the countless damages and losses they have caused. The whole lot of you belong in an unfriendly prison\n\n\n\n\nB000CO90SM:4_years_of_svc......_several_months_of_headaches:carlo_c._chiulli_jr._\"carlo\"\n\n\nB000CO90SM\n\n\n4-Year Service Plan for Camcorders $1,000 to $1,999.99: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n1.0\n\n\n4 years of svc...... several months of headaches\n\n\nSeptember 7, 2006\n\n\nCarlo C. Chiulli Jr. \"Carlo\"\n\n\nNew York\n\n\nI originally placed my order with my a digital camera and this four year service plan. Three days after, I had to cancel my digital camera order for personal reasons. However the $161.80 total I spent on this warrant did not refund automatically and nor have I received a confirmation that it will be refunded. After several phone calls and emails of providing information for the requested refund. They finally tell me that it will take 8 weeks for my refund to process. That is two whole months. Horrible service I can't imagine how long it would take for them to either repair or replace my camera should it have faltered. I would not recommend this four year service plan or any other plan by N.E.W. Customer Service Companies to anyone\n\n\n\n\nB000E5XCEW:could_be_better:oleg_ursu\n\n\nB000E5XCEW\n\n\nApple MA360G/A In-Ear Lanyard Headphones for iPod Nano 1st Generation: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nCould be better\n\n\nNovember 9, 2006\n\n\nOleg Ursu\n\n\nAlbuquerque, NM USA\n\n\nThe sound is really great, but there is one problem with this headphones they keep falling from my ears no matter what rubber caps (they come with 3 different sizes) I used. The design flaw makes them unusable for me\n\n\n\n\nB000E5XCEW:earplugs_fall_out:steve_mcbride\n\n\nB000E5XCEW\n\n\nApple MA360G/A In-Ear Lanyard Headphones for iPod Nano 1st Generation: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n1.0\n\n\nEarplugs fall out\n\n\nSeptember 1, 2006\n\n\nSteve McBride\n\n\nSeattle, WA\n\n\nThe absolutely worst headphones I have ever had. Trying the different sizes, the earplugs just don't want to stay in my ears (even standing still). The headphones that come with the I-Pod, and the earlier version of lanyard headphones were both much better. The sound was fine, but not any better than the standard headphones. A poor product. I was very disappointed, as I have had good experiences in general with Apple in the past\n\n\n\n\nB0001RM9GA:this_is_ridiculous:david_kagle_\"frikkinweirdo\"\n\n\nB0001RM9GA\n\n\nSony SRS-T33 Compact Portable Speakers (Blue): Electronics\n\n\nelectronics\n\n\n11 of 17\n\n\n1.0\n\n\nThis is ridiculous\n\n\nNovember 7, 2005\n\n\nDavid Kagle \"frikkinweirdo\"\n\n\nBoston\n\n\nOk, after reading the multiple good reviews on Amazon i persuaded myself to buy these speakers thinking some people were just pessimistic. Ok, i got these and immediately put a pair of batteries in. These things sound absolutely awful, it goes about the volume of someone quietly talking and the sound is distorted and fuzzy, forget about any bass. Absolutely do not buy these things. Oh yeah, whoever has been rating these things as good must be working for sony or something cu these things are terribl\n\n\n\n\nB0001RM9GA:say_what?:palealien\n\n\nB0001RM9GA\n\n\nSony SRS-T33 Compact Portable Speakers (Blue): Electronics\n\n\nelectronics\n\n\n1 of 8\n\n\n1.0\n\n\nSay what?\n\n\nJuly 19, 2005\n\n\npalealien\n\n\nButte Creek Canyon, Ca United States\n\n\nTo anyone who may like these, I am sorry to learn about your hearing disability. The T33 looked like a nice little package, but I thought, \"How could it possibly sound even acceptable?\" I hoped for a Sony surprise, but it didn't happen. When I returned them later that day, the clerk asked, \n\"Is there anything wrong with them?\" I said, \"Yeah, they suck\". \nShe smiled and handed me a refund.\n\n\n\n\nB000EEP9V2:you_need_a_really_small_head...:c._heza_\"sylver\"\n\n\nB000EEP9V2\n\n\nCreative SL3100 Wireless Headphones: Electronics\n\n\nelectronics\n\n\n0 of 2\n\n\n1.0\n\n\nYou need a really small head...\n\n\nAugust 29, 2006\n\n\nC. Heza \"Sylver\"\n\n\nPittsburgh, PA\n\n\nI was excited about these headphones because Creative usually has great products.\nI was sadly disappointed.\nThe connector is clunky and surely going to wear out it's flimsy wire on the jack.\nThe bluetooth signal is weak, take a walk down the street and it begins to distort like crazy. It also distorts at high volumes which you will need to use anyway because the sound is so low.\nIt runs out of power after only a few short hours.\nAnd worst of all, the headphones are impossibly tight, causing real discomfort. After only two days of using these headphones, I couldn't take it any more\n\n\n\n\nB0009PD3P0:great_hardware_and_interface_spoiled_by_poor_map_data:b._daniels\n\n\nB0009PD3P0\n\n\nTomTom Navigator 5 Bluetooth Wireless GPS System for Pocket PC, Palm, and Other PDAs: Electronics\n\n\nelectronics\n\n\n12 of 12\n\n\n2.0\n\n\nGreat hardware and interface spoiled by poor map data\n\n\nJuly 14, 2006\n\n\nB. Daniels\n\n\n\n\n\nI really wanted to like the TomTom Navigator 5. \n\nThe GPS itself is tiny, well-designed, and very sensitive. It worked fine from down in a underdash cupholder or in the glove compartment.\n\nThe interface is attractive and easy to navigate. The voice prompts are clear and loud.\n\nThe installation on a Mac was problematic. Search on TomTom OSX for details, but suffice it to say that the installer included on the CDs simply doesn't work and a new one must be downloaded. Once past that, it ran fine on my Treo 700p.\n\nUnfortunately there is one fatal flaw - the map database. In using the Navigator in both California and North Carolina, the maps regularly had misnamed streets, exits referred to by numbers where there were none, and directions that lacked critical information like heading (i.e. take the 101 instead of take 101N when approaching a complex interchange). \n\nWorse, the company seems to be actively hostile to updating these maps. If you go to the TomTom site, you can purchase maps on a city-by-city basis. Are they newer than the ones that came with your unit? Who knows? TomTom lists no version info and seems to expect you to buy and cross your fingers.\n\nThere were just too many errors that I saw while traveling in areas that I'm familiar with to trust the TomTom maps for any unfamiliar territory. Everything else is great, but the whole point of a GPS is accurate navigation. And sadly, that's where the TomTom Navigator fails\n\n\n\n\nB0007ZFLYS:it_doesn't_work_well:q._wang\n\n\nB0007ZFLYS\n\n\nLogitech Quickcam for Notebooks ( 961404-0403 ): Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nit doesn't work well\n\n\nNovember 24, 2006\n\n\nQ. Wang\n\n\nNY,USA\n\n\nIt is not as good as recommended. The image is really unclear when the light is not strong, and it's totally white under strong light. The contrast is low.\n\nIt's not a good product, useles\n\n\n\n\nB000AR95EA:buyer_beware:dr._james_boggs_\"nuru\"\n\n\nB000AR95EA\n\n\nHP iPAQ RX1955 Pocket PC: Electronics\n\n\nelectronics\n\n\n0 of 5\n\n\n1.0\n\n\nBuyer beware\n\n\nNovember 14, 2006\n\n\nDr. James Boggs \"nuru\"\n\n\nSeattle, WA\n\n\nAt the moment I am in the middle of a 3-day ordeal sorting out Outlook 2002, which comes with the ipaq. Outlook 2002 has many many many known bugs (which only now I find out) and I can't for the life of me figure out why HP would ship it with their hardware. DO NOT INSTALL OUTLOOK 2002 on your machine.\n\nThe ipaq itself may be OK, but if this is a foreshadowing of the fun to come..\n\n\n\n\nB000AR95EA:don't_waste_your_time_on_this_paperweight:ammoken\n\n\nB000AR95EA\n\n\nHP iPAQ RX1955 Pocket PC: Electronics\n\n\nelectronics\n\n\n4 of 14\n\n\n1.0\n\n\nDon't Waste Your Time On This Paperweight\n\n\nAugust 12, 2006\n\n\nAMMOKen\n\n\n\n\n\nI'll admit, I'm not the techno wizard some on this panel seem to be, but then again, I don't believe you should have to be to operate what should be something simple, like a handheld computer. \n\nIn essence, the title of this review says it all. Almost $300 and several months later, I am unable to get this hunk of junk to even turn on. Let alone connect to a server. All of these things were supposed to be fixed by a 'patch' that if someone can download from the HP website, I'd love to see it. All downloads came back with an Error message when I attempted to download. \n\nLong story short, you'd be better off buying a laptop. HP is not known for their handhelds, do not buy this product or anything similar to this from HP. You will be sorry. \n\n\n\n\nB000E62GYS:disappointed:rr7539_\"rr7539\"\n\n\nB000E62GYS\n\n\nUniden DCT756-3 Expandable Cordless System with Call Waiting/Caller ID and Extra Handset and Charger: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\ndisappointed\n\n\nNovember 3, 2006\n\n\nrr7539 \"RR7539\"\n\n\nAmherst, NY United States\n\n\nI like the light that goes on when the phone rings but that is about all I like about this phone. Friends tell me I sound like I am under water and I have had to terminate many conversations due to static and interference. One friend insists that I have a microwave running as we speak. I am actively looking for a new set to buy because these are worthless\n\n\n\n\nB0007U8Y9M:thrown_together_phone:r._breeden\n\n\nB0007U8Y9M\n\n\nPanasonic GigaRange KX-TG5576M 5.8 GHz DSS Expandable Cordless Phone with Answering System and Color LCD on Handset (Metallic Grey): Electronics\n\n\nelectronics\n\n\n27 of 42\n\n\n2.0\n\n\nThrown Together Phone\n\n\nNovember 22, 2005\n\n\nR. Breeden\n\n\n\n\n\nIt looked flashy and well-conceived on the web shopping sites that featured it; however, after setting up Panasonic's KX-TG5576M, I'm underwhelmed... First of all, even in this day and age, it's not Mac-compatible (though the Pansonic Call Center Representative had assured me, after checking with her Superior, that it indeed was.) So, I cannot comment on downloading ringtones, wallpapers, etc. (Meanwhile, these mobile phone-like features were the main reason I wanted this particular cordless phone).\n\nSecondly, the built in digital message recorder is of the lowest quality. I was looking for an alternative to the monthly charge for voice mail through the phone company. This isn't it. If you prefer to record your own message in lieu of the generic built-in message options, your Callers will here something sounding like a worn-out Edison recording. It struck me as odd that with the included audio cable, you can connect the handset to an audio device with a headphone jack, to record custom ring tones, with good quality results. Yet, to record a message for the digital answering machine, you need to speak into the dinky little microphone hole on the base unit, with the aforementioned low sound quality. \n\nFinally, the battery life of the handset is rather short. This is despite the (too) low backlighting on the handset keypad. Oh, why no backlighting on the base unit? \n\n\n\n\nB0009Q5BT0:regional_coding:m._venzky-stalling_\"mvs\"\n\n\nB0009Q5BT0\n\n\nHP Photosmart 385 Compact Photo Printer (Q6387A#ABA): Electronics\n\n\nelectronics\n\n\n20 of 64\n\n\n1.0\n\n\nRegional coding\n\n\nOctober 22, 2005\n\n\nM. Venzky-stalling \"MVS\"\n\n\nUK\n\n\nI cannot recommend HP to anybody anymore. The company introduced regional coding, which means printer cartridges from one region cannot be used in another. For most this is probably not an issue, for those busily traveling around it is. Also, it is an issue of principle, its bad enough for DVD, but printer cartridges? HP has introduced this rather secretely and if you don't read the box cover etc very carefully, you may not notice. The main issue is in Europe. I got caught out. I bought a 3 catridges overseas - I could not use them on my HP printer. I asked for a refund, HP Email Support (US) was nice but ineffective. HP Customer Support (Europe) was snotty and very rude. I think HP does not care about its customers and this cost me over US$75, almost 50% of my printer (my sixth and last HP). Don't buy HP\n\n\n\n\nB00081A2DQ:missing_ear_plugs_and_travel_kit:juan_a._rufin-velasco\n\n\nB00081A2DQ\n\n\nKOSS Stereo In Ear Ear Plugs - The Spark Plug: Electronics\n\n\nelectronics\n\n\n0 of 12\n\n\n1.0\n\n\nMissing ear plugs and travel kit\n\n\nNovember 9, 2006\n\n\nJuan A. Rufin-velasco\n\n\n\n\n\nI cannot review this item since I have not received it so far at the shipping address I gave Amazon when I did the purchase.\nI bought four items, and I only got two of them by mail.\nWhat happened to the other two items? \n-KOSS Stereo In Ear Ear Plugs - The Spark Plug (Purchased on 09/15/2006) \n-Apple iPod USB Travel Kit with Car Charger, Travel Adapter & Cable (Purchased on 09/15/2006) \nWHO KNOWS..\n\n\n\n\nB00008Y1A7:tinny_indeed_:shelby_h._swatek\n\n\nB00008Y1A7\n\n\nSony SRS-A202 Active Speakers with Built-in Mega Bass Sound: Electronics\n\n\nelectronics\n\n\n18 of 20\n\n\n2.0\n\n\nTinny indeed \n\n\nMarch 4, 2005\n\n\nShelby H. Swatek\n\n\nSimi Valley, CA USA\n\n\nI agree with the viewer preceding me. Very tinny. The ones I was using from my old computer sound 100% better. These are a complete waste. Two stars because I do hear the music but it sounds awful\n\n\n\n\nB00008Y1A7:very_unhappy_with_this_product:j._harris\n\n\nB00008Y1A7\n\n\nSony SRS-A202 Active Speakers with Built-in Mega Bass Sound: Electronics\n\n\nelectronics\n\n\n27 of 29\n\n\n2.0\n\n\nVery unhappy with this product\n\n\nFebruary 27, 2005\n\n\nJ. Harris\n\n\nCambridge, MA\n\n\nThese speakers were a major disappointment. I have speakers that are about 5 years old, and wanted to replace them. But these Sony speakers were AWFUL - the sound was tinny, the bass was almost non-existent, and the sound was probably worse than that coming from my built-in laptop speakers.\n\nAnd these weren't even the \"cheap\" speakers - there were many sets that cost less than this. If you care at all about the quality of the speakers, spend another $20-$30 and get a decent set... don't buy these..\n\n\n\n\nB0006Q7FFG:horrible,_painful_product:pam\n\n\nB0006Q7FFG\n\n\nGriffin Technology 4039-EJAM EarJam Earbud Sound Enhancing Covers: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nHorrible, Painful Product\n\n\nOctober 30, 2006\n\n\nPam\n\n\nGA\n\n\nThese Ear Buds are large and hard. They have sharp edges and hurt my ears. I have large ears and large ear canals and wear other earbud type headphones all the time which do not bother me. These were not only painful but did not enhance the sound, but actually reduced it. This product should be taken off the market\n\n\n\n\nB00007MMNS:short_battery_life:anaiselise\n\n\nB00007MMNS\n\n\nGarmin Geko 201 Handheld GPS Navigator (Lime Green): Electronics\n\n\nelectronics\n\n\n11 of 13\n\n\n2.0\n\n\nShort battery life\n\n\nJuly 7, 2005\n\n\nanaiselise\n\n\n\n\n\nUsing Rechargeable AAA batteries, with the Battery Saver feature turned on, about two hours. Sometimes I get more using alkalines, but not much more. Honestly, this unit is worthless, because I can't trust the batteries.\n\nOrder a Garmin Etrex Legend instead. It isn't much more expensive, larger or heavier, and it includes a base map of roads, towns, and shorelines. The Legend comes with a cable to transfer data from your computer. The Legend's screen is a bit larger, but shows much more detail. And - the batteries last hours and hours.\n\n\n\n\nB0007RA6NW:static,_static,_static:catlover888_\"catlover888\"\n\n\nB0007RA6NW\n\n\nSony SRF-M97 Radio Walkman: Electronics\n\n\nelectronics\n\n\n0 of 2\n\n\n2.0\n\n\nStatic, Static, Static\n\n\nSeptember 20, 2006\n\n\ncatlover888 \"catlover888\"\n\n\nSF, CA\n\n\nThis did not live up to its price or my expectations. I walk to work and there was static on both AM and FM -\n\n\n\n\nB00005NGRQ:not_the_greatest_caller_id_unit:j._reagan\n\n\nB00005NGRQ\n\n\nGE 29016S Caller ID Box: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n1.0\n\n\nNot the greatest caller ID unit\n\n\nOctober 31, 2004\n\n\nJ. Reagan\n\n\n\n\n\nTakes 3 AAA batteries that in our experience run out too quick. Other units we've owned have lower battery drain. Also the LCD display has poor contrast. All in all, I'd buy another one that costs a few bucks more\n\n\n\n\nB000CFL5LG:third_times_a_charm?__no_not_really.:p._l._reed\n\n\nB000CFL5LG\n\n\nBargaincell- Apple Ipod Rapid Car Charger for Ipod / Mini / Nano (1gb 2gb 4gb) / Video: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nThird Times a Charm? No not really.\n\n\nJuly 31, 2006\n\n\nP. L. Reed\n\n\nAlexandria, Virginia United States\n\n\nI ordered this charger a month ago. Since, they've sent me three chargers all the same, all wrong. They've sent me the shuffle charger three times in a row. Don't waste your money on this product, for a few bucks more you can get a company that will actually send you the right product the first time\n\n\n\n\nB000CFL5LG:avoid_this_company!:r._roybal_\"rroybal\"\n\n\nB000CFL5LG\n\n\nBargaincell- Apple Ipod Rapid Car Charger for Ipod / Mini / Nano (1gb 2gb 4gb) / Video: Electronics\n\n\nelectronics\n\n\n15 of 19\n\n\n1.0\n\n\nAvoid this company!\n\n\nMarch 30, 2006\n\n\nR. Roybal \"RRoybal\"\n\n\nUSA\n\n\nBought a cell phone battery that, of course, died one week past their return/refund deadline. Refused to refund or replace even though the product is CLEARLY defective. This company is a ripoff and will never do business with them again. Consider yourself warned\n\n\n\n\nB000CRUZ2O:defective_product,_mediocre_service:d._scholnick_\"gadget_freak\"\n\n\nB000CRUZ2O\n\n\nActiontec VoSKY Skype Call Center ( USB07051C-01 ): Electronics\n\n\nelectronics\n\n\n3 of 7\n\n\n1.0\n\n\nDefective product, mediocre service\n\n\nJune 18, 2006\n\n\nD. Scholnick \"gadget freak\"\n\n\nBoston, MA USA\n\n\nI purchased a unit recently and after a couple hours tinkering with it, I couldn't get it to work. I called Actiontec/Vosky support and spent almost an hour on the phone with them. They advised me to uninstall their software and Skype and to try reinstalling each product. When that didn't work, they told me the product was defective. Unfortunately, they refused to take it back themselves and made me deal with the Seller (ANTOnline.com) first (not a very good seller, btw, as they gave me a terrible time when I tried to return the defective product). \n\nHopefully you'll have better luck.\n\nAlso, be sure to check out whether Vosky is officially supporting Skype 2.5. When I made my purchase, they did not\n\n\n\n\nB000196ENC:not_a_universal_mount!:suleman_ishaque\n\n\nB000196ENC\n\n\nMagellan RoadMate GPS Dash Mount: Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n1.0\n\n\nNot a Universal mount!\n\n\nAugust 3, 2006\n\n\nSuleman Ishaque\n\n\nSoCal, United States\n\n\nIf your cars Dash is curved or sloping, this mount will not work. Did not work on a 1988 Mercedes 300E or a Daewoo Lanos. It might work if you have a perfectly FLAT dash. I had to return the item as it did not sit properly. Even the slightest touch from a finger would make it slide down. Those of you who are clean freaks like me, BEWARE. Products like armour all which leave a protective coating on your dash will further reduce the grip of this stand! I would not trust a $800 piece of equipment on an unreliable stand like this when even a slight touch of a finger can make it slide down. What about jolts and bumps while normal driving? I did not wait to find out using my $800 GPS as an experiment\n\n\n\n\nB0002ZH71S:powerline_ethernet_works_great,_wireless_no_so:a._metcalf\n\n\nB0002ZH71S\n\n\nNetgear WGXB102 54Mbps Wall-Plugged Wireless Range Extender Kit: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nPowerline ethernet works great, wireless no so\n\n\nAugust 25, 2006\n\n\nA. Metcalf\n\n\nSan Diego, CA\n\n\nI bought this to run ethernet into my daughter's room. The powerline etherernet works flawlessly, I've had it up and running for six months without restart or problem into three other rooms. The wireless module however tends to overheat and drops the network after 24-48 hours of operation, so she has to unplug it every time she is done with the network, and plugs it back in whenever she wants to connect. I am going to upgrade her to another wired powerline ethernet module\n\n\n\n\nB00000J3UJ:have_an_ibook?_don't_buy_this_lock!:karl_rahder\n\n\nB00000J3UJ\n\n\nTargus PA400U DEFCON 1 Ultra Notebook Computer Security System: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nHave an iBook? Don't buy this lock!\n\n\nOctober 18, 2006\n\n\nKarl Rahder\n\n\nBaku, Tbilisi, Chicago\n\n\nI posted a review two weeks ago, but it hasn't appeared, so I'm trying again...I bought this lock two years ago. It has always been difficult to remove. (Note: despite what some other reviewers seem to think, it does NOT have a motion detector - that's a different lock.) Recently, the lock decided to remain firmly inside my iBook, despite my efforts to remove it. After a fruitless call to Targus's customer support, I finally got it out. That took over 30 minutes of struggle. My next step was going to be purchasing a hack saw. Apparently, the Targus lock is not a good fit for iBooks. I'm going to send mine back and demand compensation. If you own an iBook, I strongly suggest you buy a lock made by someone other than Targus\n\n\n\n\nB0001WDFBS:great_mechanics_but_the_coffee_taste_is_lost...really_lost.:pio\n\n\nB0001WDFBS\n\n\nZojirushi EC-BD15BA Fresh Brew Thermal Carafe Coffee Maker: Electronics\n\n\nelectronics\n\n\n10 of 10\n\n\n2.0\n\n\nGreat mechanics but the coffee taste is lost...really lost.\n\n\nNovember 9, 2004\n\n\nPio\n\n\nOrange County, CA\n\n\nWe drink a lot of coffee all day, every day. So we decided to convert to a thermal carafe coffeemaker in order to keep our coffee hotter and fresher-tasting. We first bought the 10-cup Capresso MT-500 which we loved except for the carafe which leaked slightly. The coffee was perfect everytime and the taste great. Unfortunately the control panel burned out about a year and half after we bought it. So instead of another expensive Capresso replacement, we decided to try a Japanese model which we thought might be more reliable. We have been using the Zojirushi for 2 weeks now and it works perfectly. No leaks and it just feels more mechanically reliable than the Capresso. Unfortunately, there is no water filter system in this model compared to others (we had taken this for granted and not checked for it). And no reuseable metal mesh filter (that I could find on the market) instead of paper filters. The water also does not seem as hot when it goes into the coffee grounds. We are not sure why our coffee just doesn't taste very good anymore, but we definitely have noted a real sharp drop in taste quality. Today, we pulled out our very old Krups ProAroma model and have noted a definite taste improvement. We will use this and the Zojirushi thermal carafe until we get a new Capresso coffeemaker replacement. Don't waste your money on this Zojirushi model unless you don't care about taste. It was money thrown out the window as far as we are concerned. Even mediocre coffee beans are too expensive to use with this machine\n\n\n\n\nB0002XIX0E:<em>poor_fit,_poor_quality_sound,_decent_mic</em>:j._moe\n\n\nB0002XIX0E\n\n\nLogitech 980231-0403 Precision PC Gaming Headset: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\n<em>Poor fit, poor quality sound, decent mic</em>\n\n\nSeptember 27, 2006\n\n\nJ. Moe\n\n\nUSA\n\n\nEveryone has already covered the basics, so here's the highlights:\n\n\u001aThe audio listening quality is poor, especially for 40mm drivers (which normally sound good with strong bass response). This is due to the fact the speaker itself is far from the ear canal, since the outer padding contacts the side of the head instead of the actual speaker pad.\n\n\u001aThe headphones are heavy, which means a large amount of pressure is required to hold them in position. They slide very easily off the head, and apply a lot of downward force on the ears. I have a big head, and even made some slight modifications that increase the pressure, now they don't slide off, but are uncomfortable for more than an hour of use.\n\n\u001aAt first, the volume slider is stiff, but then becomes smooth.\n\n\u001aAfter 4 months of use, the mute switch is getting loose, I don't expect it to work 4 months from now.\n\n\u001aI really like the microphone portion of the headset, the angle is adjustable, and the volume is loud and clear.\n\n\u001aIn the end, these headphones have a good microphone matched with very poor quality headphones. Next time, I'd grab the cheaper Logitech headset, where they are nice and light and the speaker pads make contact with the ears, not some large outer pad\n\n\n\n\nB0002XIX0E:not_for_people_with_small_heads...:vanessa_lane_\"vanessa\"\n\n\nB0002XIX0E\n\n\nLogitech 980231-0403 Precision PC Gaming Headset: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n2.0\n\n\nNot for people with small heads...\n\n\nSeptember 2, 2006\n\n\nVanessa Lane \"Vanessa\"\n\n\nUpstate New York\n\n\nI purchased this unit not for gaming, but for making PC to PC calls. Here are my observations:\n\nPros: \n1. Sound quality of the microphone on the receiving end is reported by my friends and family as exceptionally clear. Certainly much better than what you'd get out of a webcam.\n2. Chord is robust and long. Easily fits over my long desk to my CPU that rests beneath.\n3. Controls are easy to use, although the mic on/off switch doesn't click into place like I'd expect it to. It just slides back and forth. I can see people accidentally turning it off.\n4. Headphones produce good sound, although nothing extraordinary.\n5. The mic itself freely rotates, so you can position it however you desire.\n\nCons:\n1. First and foremost, if you've got a small head DO NOT PURCHASE THIS!! My head is not big and these headphones ARE NOT ADJUSTABLE! These headphones are so large that they're actually uncomfortable. Their entire weight rests on my ears and I can't wear them for very long without my ears falling off.\n\n2. These headphones are very heavy, uncomfortably so. I've resorted to holding them up to my head and listening through one speaker to take the weight off my ears.\n\n3. Perhaps having the airspace between the speakers and the frame keeps your ears cooler, but it still feels like I'm wearing a set of insulated ear muffs. Chatting on hot summer days is limited to 10 minutes or less until I'm dying of heat exhaustion.\n\n4. You need to get a Y splitter if you wish to have these headphones and your computer speakers on at the same time. If you don't have front headphone plugs on your case this is an issue. Otherwise you have to crawl under your desk and manually disconnect the headset from your motherboard jacks and plug in your speakers again. Y splitters are only a few dollars but it's an accessory you will have to purchase, especially since front speaker ports on most computers can't be hooked up at the same time as your rear motherboard jack (and most people have their main sound systems plugged in with the rear one). Granted, this is more of a general microphone/headset combo issue than anything Logitek related, but if this is your first microphone/headset purchase then you should be aware of this problem. I use onboard audio instead of a sound card, so I don't have multiple output jacks.\n\n5. These headphones are designed to rest behind your neck, not over the top of your head. Unfortunately, the plastic beam is too heavy and pulls the speakers back, placing all the weight on your ears. The plastic beam is also too short (and just!) to go over the top of my head for a more comfortable fit.\n \n6. The design also doesn't make sense. The beam is so short that it should rest behind your neck, yet the angle of the speakers through that air gap is better for wearing the beam over your head. It's pretty uncomfortable. If the headset was adjustable it would be fine! The earpieces are just too huge!\n\nOverall I'm disappointed with these. If you're under 5'10\" I say go with another model. The hardware works great, but the design is terrible. I'm returning these and purchasing an uglier, but more functional, set of smaller headphones\n\n\n\n\nB0002XIX0E:the_mic_is_not_good._the_design_is_not_very_comfortable:first_last_\"softorama\"\n\n\nB0002XIX0E\n\n\nLogitech 980231-0403 Precision PC Gaming Headset: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nThe mic is not good. The design is not very comfortable\n\n\nAugust 29, 2006\n\n\nFirst Last \"softorama\"\n\n\nSunnyvale, CA United States\n\n\nThe headset sits on the back of head, which is ok, however due to large ear rests it feals awkward and uncomfortable. I was not able to use volume control, as the headphones were only audible at higher volume level.\n\nThe mic is poor. Whether you want it for gaming or just general purpose use, its better to skip this for something else\n\n\n\n\nB000A1IUFQ:cool_toy,_but_not_a_useful_tool:w._hoffmann_\"strategic_advice\"\n\n\nB000A1IUFQ\n\n\nPalm 3207NA GPS Adapter Kit for Select Palm PDAs: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n2.0\n\n\nCool toy, but not a useful tool\n\n\nJuly 2, 2006\n\n\nW. Hoffmann \"Strategic advice\"\n\n\nPhoenix, AZ\n\n\nI have been using the Palmone Navigator with my TX for about eight weeks. Most of my comments are based on a comparison with the built-in navigation system in my Lexus. \n\nSoftware installation is fairly easy. However, you need at least a 256 MB SD card to load more than a couple of states; I recommend a 1 or 2 GB card. \n\nWhile the concept of a TX doubling as a navigation device is intriguing, the realty of it is rather sobering. It is a cool toy, but if you have to plan routes for your job or any other meaningful purpose, this is NOT the right product. \n\nThe pros are:\n - Inexpensive in comparison to built-ins und stand-alone products\n - Unit can be used in different locations and cars and even while walking\n - One device serves multiple purposes\n\nThe cons are:\n - It can take up to several minutes after starting the unit to acquire a satellite signal. This is not the case with my car's OEM navigation system.\n - The maps are outdated and not as precise as the OEM nav system: I live in Phoenix, AZ and my neighborhood (built seven years ago) as well as most of the freeways I take to work and back don't exist on the software. More recent updates are not available (I checked the Tomtom website).\n - Navigation is often not precise enough (e.g. it tells me to make a left in 50 yards, when I have already passed the intersection where it wants me to make a left). I never have this problem with the built-in navigation.\n - Look-up function for addresses and phone numbers is clumsy and doesn't display actual address until you map it.\n - It tends to freeze at least a couple times a day, requiring a soft reset. \n - Lost connections to the satellite receiver or lost satellite signals also tend to disrupt the navigation on longer trips with stops.\n\nOverall, the unit is too unreliable to be used as your only navigational aid. If you need navigation to get to your travel destination, you are better off with a stand-alone unit. I truly wish I had better things to say about this device since I really like the concept, but the reality just doesn't match the marketing.\n\n\n\n\n\nB000A1IUFQ:great_when_it_works_-_but_far_too_flaky:honest_guy\n\n\nB000A1IUFQ\n\n\nPalm 3207NA GPS Adapter Kit for Select Palm PDAs: Electronics\n\n\nelectronics\n\n\n7 of 7\n\n\n2.0\n\n\nGreat when it works - but far too flaky\n\n\nFebruary 21, 2006\n\n\nhonest guy\n\n\nScotch Plains, NJ United States\n\n\nThe GOOD news is that this is a full featured GPS solution that is easy to mount in the car and can navigate to contacts in your address book. Maps are detailed and the POI (Point of Interest) database is fairly extensive.\n\nThe BAD news is that it all too often fails to connect via Bluetooth. Installation of software and maps is error prone (do not follow any of the 'advanced' hints in the manual - they scrambled my SD card). \n\nOnly buy this product if you like puzzle solving and have a great deal of patience. If you are expecting an out of the box solution that is 100% reliable - look elsewhere.\n\n\n\n\nB0007LC49M:the_disappearing_mp3_player:jean_winkelman_\"jmwinke\"\n\n\nB0007LC49M\n\n\niRiver H10 5 GB Digital MP3 Player Remix Blue: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n2.0\n\n\nThe disappearing MP3 player\n\n\nJuly 2, 2006\n\n\nJean Winkelman \"jmwinke\"\n\n\nPasadena, Texas USA\n\n\nI have a 5gb but a lot of the H10's are having the same problem. I can play all of the songs that I owned myself but everything through the subscription service has run out and can't be reinstalled. I've done a little research about the disappearing MP3 player and it appears to be a problem that is affecting a lot of the H10's and 1 other model I can't remember right now and iRiver can't figure it out; although I've been told they are trying desperately to find the bug....So buy with caution.... \n\n\n\n\nB0000DIH1D:should_have_check_first:d._k._boyd\n\n\nB0000DIH1D\n\n\nNational Geographic Back Roads Explorer: Electronics\n\n\nelectronics\n\n\n5 of 7\n\n\n2.0\n\n\nShould have check first\n\n\nApril 25, 2005\n\n\nD. K. Boyd\n\n\nAustin, TX USA\n\n\nI went looking at Fry's for a mapping software and bought this before looking online at reviews. I'm now stuck with a bazillion (ok, just 17) CDs and a product that takes minutes to load once you initiallize the program. I still can't figure out how to access any info on Hawaii or Alaska and I'm waiting each time an average of four days before \"tech support\" haha will write me back.\n\nDon't buy this. \n\n\n\n\nB00005T3N3:not_bose_quality:\"boseman69\"\n\n\nB00005T3N3\n\n\nBose UFS-20 Universal Floor Stands (pair) - Black: Electronics\n\n\nelectronics\n\n\n7 of 14\n\n\n2.0\n\n\nNot Bose quality\n\n\nJanuary 10, 2002\n\n\n\"boseman69\"\n\n\nSedona, AZ United States\n\n\nSorry, the speakers don't attach well, and the quality of these stands is not what I'm used to with a Bose system\n\n\n\n\nB00005JDDZ:doesn't_play.....:wakeboardr99\n\n\nB00005JDDZ\n\n\nMemorex 40x Music CD-R Media (50-Pack Spindle): Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n1.0\n\n\ndoesn't play.....\n\n\nNovember 15, 2005\n\n\nwakeboardr99\n\n\n\n\n\nthese cds burn fine BUT they won't play in most cd players. I've tried it in two car aftermarket cd players, brand new mazda, and a brand new F1500 - they all did not read the cd. Most computers can read them, but other than that they are not very usable\n\n\n\n\nB000E9BXDU:acer_1916ab:frederick_carr_\"flrhcarr\"\n\n\nB000E9BXDU\n\n\nAcer AL1916AB 19\" LCD Monitor: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n2.0\n\n\nAcer 1916AB\n\n\nNovember 21, 2006\n\n\nFrederick Carr \"flrhcarr\"\n\n\nKent, WA\n\n\nFor over eight years (nearly 9), I had an Acer 17\" CRT & loved it. I use a KVM to switch computers, & the CRT kept all of my adjustments for that computer, every time.\nWhen it started showing signs of age (about four months ago), I decided to try a flat screen, or LCD, though I've heard horror stories about them, my laptop is great.\n\nI don't mind the dark overlay associated w/LCDs, I have that on my laptop. I was amazed at how poorly this monitor responds to things. I still swap computers. I can't get the same clarity that I once did.\n\nI swap between ME & XP, which shouldn't matter.\n\nDon't believe there isn't any flicker, that's wrong. If you change the refresh rate, the monitor changes, not the computer. What happened to dumb monitors (what happened to dumb printers)? Best, should be a button (turn on) away. Some tweaking isn't bad.\n\nFade out: I have the monitor, at the \"proper\" height, & the upper & lower corners fade. As looking at any lcd from an angle.\nYou can't tilt this monitor very much at all\n\nDon't spend much time on shut down or re-start in ME, the lines & jaggies will kill you.\n\nI'm sorry that I bought this monitor. The refesh rate for games, is fair. Don't play any games prior to ME! Because ME era games fade badly at the ends, nearly blacking out. Post XP isn't anything to write home about really.\n\nI would suggest trying out an hdtv first. With a direct (vga) plug in. It's a few dollars more, but if you're considering an hdtv, as well as a monitor, I'd chance it. You can always buy a 20\" CRT for less.\n\nI hope this helps\n\n\n\n\nB0009V6TL4:keyboard_layout_sucks;_mouse_only_2_buttons_+_wheel:thomas_hundt\n\n\nB0009V6TL4\n\n\nLogitech Cordless Desktop EX110 ( 967561-0403 ): Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n2.0\n\n\nKeyboard Layout Sucks; Mouse Only 2 Buttons + Wheel\n\n\nAugust 15, 2006\n\n\nThomas Hundt\n\n\nSan Francisco, CA United States\n\n\nThe keyboard with this set is flimsy and cheap feeling. \n\nWorse, the layout is nonstandard: they've messed up the key cluster above the arrows (Insert/Delete/Home/End/etc.). And the right hand Ctrl is not below the right end of the Shift key, it is under the left end of the Shift key, where one of the Windows keys (the context menu one) normally is. They've taken out one of the Windows menu keys, too. \n\nThe wireless features worked fine (I've used it two days) and worked even without installing their driver software (something I'm always reluctant to do). I think some of the extra buttons (multimedia controls) on the keyboard don't work for me, because of this. I use the computer it's on for work, not screwing around, so this doesn't bother me. (I have it installed on a Thinkpad laptop so I can keep the screen farther from my eyes, and my employer bought the thing; it wouldn't have been my first choice.)\n\nI've given it two stars only because it worked great, out of the box, with no driver software. (This is on a Thinkpad running WinXP.) I'm already dreading the battery usage (based on another review here). Thankfully, it's not my main keyboard\n\n\n\n\nB0009NMQ2I:i_wouldnt_recommend_this_unit:john_f._weiland\n\n\nB0009NMQ2I\n\n\nDelphi SkyFi2 XM Satellite Radio Receiver and Car Kit: Electronics\n\n\nelectronics\n\n\n8 of 12\n\n\n1.0\n\n\nI wouldnt recommend this unit\n\n\nApril 6, 2006\n\n\nJohn F. Weiland\n\n\nVerona, PA United States\n\n\nIve had this unit for over a 1+ year and ran into problems with reception it would drop signal and it would eventual come back. I found out that this unit had a defect and the xm rep said I could get a replacement for $50!!! They didnt figure to tell anybody about this? If this is true I would not recommend XM Radio at all.\nThanks John \n\n\n\n\nB0009U4ZDE:this_item_is_horrible:a._temple\n\n\nB0009U4ZDE\n\n\nCTA Digital 3 in 1 Car Kit for iPod: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nThis item is horrible\n\n\nOctober 19, 2006\n\n\nA. Temple\n\n\n\n\n\nI have to admit, the price of this item got me. It's now 40 bucks down the drain as it has failed to work completely. It worked for about 8 months but was very fuzzy. The quaility was that of an AM radio station. Recently, it quit working altogether so now I am back in the market for one that will work\n\n\n\n\nB0009U4ZDE:item_didn't_work:l._mikkelsen_\"lm\"\n\n\nB0009U4ZDE\n\n\nCTA Digital 3 in 1 Car Kit for iPod: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n1.0\n\n\nitem didn't work\n\n\nAugust 15, 2006\n\n\nL. Mikkelsen \"LM\"\n\n\nSeattle, WA\n\n\nUnfortunately the item didn't work. The company offered to replace it but I just decided to pay more and buy locally\n\n\n\n\nB000A2BJ1W:costly_to_run.:sherry\n\n\nB000A2BJ1W\n\n\nCanon PIXMA iP1600 Photo Printer (White): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nCostly to run.\n\n\nOctober 21, 2006\n\n\nSherry\n\n\nMichigan\n\n\nIt is an okay printer (I haven't done photos yet) and pretty fast but I have to change the cartridges every 3-4 weeks. This is just home use I'm talking about, not business use.\nThe cartridges are small, run out real fast and cost up to $60 a month for one black and one color.\nI'm thinking of going back to a Lexmark\n\n\n\n\nB000A2BJ1W:you_get_what_you_pay_for...:m._c._kerr_\"j.j.'s_mommy\"\n\n\nB000A2BJ1W\n\n\nCanon PIXMA iP1600 Photo Printer (White): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n2.0\n\n\nYou get what you pay for...\n\n\nSeptember 14, 2006\n\n\nM. C. Kerr \"J.J.'s Mommy\"\n\n\nPhiladelphia, PA\n\n\nI bought this printer because I wanted high quality photo printing and I didn't need any of the bells and whistles that come with more expensive printers. It's a Canon, so I figured I could count on the photo quality. I was terribly disappointed though. I generally buy Staples brand photo paper for general photo printing, and it always worked fine with my Epson printer. But my first prints on this printer looked dull and muddy -- like I was looking at the images though a mild but annoying grey/brown haze. So I went out and bought Canon paper instead. The quality was somewhat improved, but still not what I had expected. So yes, I got a printer for under $30, but now to get even passably OK prints, I need to spend a fortune on Canon brand inks and paper. Not much of a bargain.\n\n\n\n\nB000094FRI:threw_it_out.:j._wurzelmann_\"plays_well_with_others\"\n\n\nB000094FRI\n\n\nCABLES UNLIMITED Zip-Linq RJ12 Retractable Phone & Modem Cable: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nThrew it out.\n\n\nMay 26, 2006\n\n\nJ. Wurzelmann \"Plays Well With Others\"\n\n\nChapel Hill, NC USA\n\n\nAs best as I can tell the wires were too thin, and it could not handle any kind of bandwidth.\n\n\n\n\nB000062STU:doesn't_work_with_my_pc:carmine_d'emilio\n\n\nB000062STU\n\n\nAdaptec 1890600 Fireconnect 4300 Kit AFW-4300: Electronics\n\n\nelectronics\n\n\n1 of 3\n\n\n1.0\n\n\nDoesn't work with my PC\n\n\nFebruary 21, 2004\n\n\nCarmine D'Emilio\n\n\nYonkers, NY USA\n\n\nI installed the board and my PC (running XP Pro) won't boot. I tested all the PCI slots with other cards (and mixed them up too) and the PC boots fine. I returned the card and got a new one. Same problem. I removed all of my other PCI cards and left this one in there alone ... same problem. I'm speechless. I guess it's not compatable with some systems. By the way, I know my set-up is fine. I have 3 PCI slots filled and 3 additional USB devices and all is well. I never had a problem like this before\n\n\n\n\nB0000DZET4:if_you_have_sp2,_this_is_not_for_you:d._nasluchacz_\"kaleldpn\"\n\n\nB0000DZET4\n\n\nLinksys USBBT100 Bluetooth USB Adapter: Electronics\n\n\nelectronics\n\n\n1 of 3\n\n\n2.0\n\n\nIf you have SP2, this is not for you\n\n\nSeptember 3, 2006\n\n\nD. Nasluchacz \"kaleldpn\"\n\n\nRiverside\n\n\nThis adapter will not work at all with Windows XP SP2 - despite any promises from the Linksys website, this cannot be setup - buy the Anycom adapter instead\n\n\n\n\nB0000DZET4:has_yet_to_work:roger_miller_\"roger\"\n\n\nB0000DZET4\n\n\nLinksys USBBT100 Bluetooth USB Adapter: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nHas yet to work\n\n\nAugust 1, 2006\n\n\nRoger Miller \"Roger\"\n\n\nIllinois\n\n\nI have spent time with lynksys and Dell, both of which say they have done all they can do, (from their manuals). Because I have had to finish phone calls without without solving the problem, I call back and get conflicting oppinions...this keeps me coming back. It still is not working, they have that in common.\n\n\n\n\nB0007DFXD4:some_of_them_doesnt_work._cheap_quality_cdrs:murali_tammineni\n\n\nB0007DFXD4\n\n\nVelocity 52X 80 Minute/700 MB CD-R (25-Pack Spindle): Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nSome of them doesnt work. CHeap quality CDRs\n\n\nMarch 9, 2006\n\n\nMurali Tammineni\n\n\nWebster, NY, USA\n\n\nTheir quality is cheap. so do their price. Out of the 25 CDs, atleast 8 of them failed. The rest, okay. I would suggest Verbatim. They are pretty good\n\n\n\n\nB000EWTBRW:don't_waste_your_money:mintz_\"-mintz\"\n\n\nB000EWTBRW\n\n\nSanDisk Sansa e200 Silicone Case White: Electronics\n\n\nelectronics\n\n\n32 of 34\n\n\n1.0\n\n\nDon't Waste Your Money\n\n\nJune 27, 2006\n\n\nMintz \"-Mintz\"\n\n\nHuntsville, AL\n\n\nPros\n* Fits like a glove (goes without saying)\n* Um... Peace of mind?\n\nCons\n* This case makes pushing the buttons harder than it already is.\n* There is nothing to protect the screen. \n* If you're left-handed (or like to operate gadgets with your left hand) note that it is impossible to push the Menu button. It would take a double-jointed freak of nature to comfortably push it.\n* The case is a dust and hair magnet. Every time you pull it out of your pocket, it's encrusted with lint you didn't even know you had. Dust manages to sneak under every surface, and hair magically sticks to it. This case is a DNA-sample paradise for forensic experts.\n\nOne of the features of a flash-based memory player is the fact that they won't break as easily when you drop them. Unless you're paranoid about getting scratches on your shiny new Sansa, don't buy this\n\n\n\n\nB00006JPFP:bad_product_from_kb_electronics:v._sharda_\"goodone\"\n\n\nB00006JPFP\n\n\nCoby CA-979 Universal AC Power Adapter: Electronics\n\n\nelectronics\n\n\n10 of 14\n\n\n1.0\n\n\nBad product from KB Electronics\n\n\nJanuary 20, 2006\n\n\nV. Sharda \"GoodOne\"\n\n\nNJ, USA\n\n\nI bought this product from KB Electronics.\n\nBecause of this product, my digital camera stopped working.\n\nOn giving a bad review to this product, KB Electronics offered me 10% discount on my next product. So, I told them that I would give a good review to them if they can repair my camera. At this they went silent. So, basically this means that they would try to make someone silent with a few dollars, but if they have to fix their problems, then they would forget all morals and turn dumb.\n\nI don't know about the current company, but beware of the same thing happening to you guys. It is possible that KB Electronics has started selling the same product by the name of its sister concern - businesses do this all the time\n\n\n\n\nB00005T3ND:great_until_it_broke:\n\n\nB00005T3ND\n\n\nApex AD-1500 DVD Player: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n2.0\n\n\nGreat until it broke\n\n\nApril 16, 2004\n\n\n\n\n\n\n\n\nI bought this player a little over a year ago, mainly for the ability to upgrade the firmware to region-free. Played everything I put in it until a few days ago, when it started having intermittent problems- graphics were staticy and blocky, sound would just buzz and it wouldn't eject discs until I shut it down and restarted it. That lasted for two days before the problem became permanent. Now it's a doorstop. The Apex web site appears to be down right now, but based on the other reviews here I doubt there's much point in contacting them\n\n\n\n\nB00074Z9OQ:ever_wished_you_hadn't_spent_your_money...:k._little\n\n\nB00074Z9OQ\n\n\nApple M9758G/A iPod Shuffle Sport Case: Electronics\n\n\nelectronics\n\n\n5 of 6\n\n\n1.0\n\n\nEver wished you hadn't spent your money...\n\n\nJune 19, 2006\n\n\nK. Little\n\n\n\n\n\nThe resaon I purchased this sport case is because it was an Apple product. My thoughts were that is I was going to dive into the ipod experience I should invest in a quality case to protect it. I'm sure that most people would think the way I do. I read the reviews and felt that even though it was a little pricey I would go ahead. This product is not a good one for the price. Mine broke, specifically, the plastic slider on the side that locks the shuffle in. I know what your thinking... she abused the product but I swear I did not. I loathe returning things but, darn it, this is ridiculous. Avoid this one and go for something less expensive.\n\n\n\n\nB0000TNJU4:not_as_fast_as_they_claim:m._easton_\"cme\"\n\n\nB0000TNJU4\n\n\nBelkin Media Reader and Dock Connector for iPod: Electronics\n\n\nelectronics\n\n\n21 of 21\n\n\n2.0\n\n\nNot as fast as they claim\n\n\nOctober 28, 2004\n\n\nM. Easton \"CME\"\n\n\nSan Francisco, CA\n\n\nThough the concept is great, and the controls are straight forward and easy to use, the Belkin Reader falls short of usability. \nWhile out on a location, I was away from a power source and thought the Belkin reader would be a great alternative to lugging a laptop around for file transfers. Trying to transfer 1000 pics off a 1GB CF card was \nimpossible. The reader would drain a fully charged ipod and still not finish the transfer. Though it claims firewire transfer speeds, I did some tests from home and found otherwise:\n\n100 compressed 2K pics transferred via:\n\nUSB1 card reader to computer 1 min 20 sec.\nPCMCIA card reader in laptop 45 seconds.\nBelkin Media reader 6 min. 45 sec\n\nThis was just a simple example. You can see how hundreds of files would take 30 minutes or more. For the price, I'd suggest buying a few more memory cards and not worry about the transfers until you get home, unless you're in no rush and can charge up the ipod regularly\n\n\n\n\nB000F5K3Y8:very_poor!:r._sousa\n\n\nB000F5K3Y8\n\n\nMustang MV-TILT3 Tilt Wall Mount Adjustable for TV's up to 60-inches: Electronics\n\n\nelectronics\n\n\n2 of 6\n\n\n2.0\n\n\nVery poor!\n\n\nNovember 7, 2006\n\n\nR. Sousa\n\n\n\n\n\nArrived today in a taped, ripped, and open box. It appears as though all the parts are there, but this is definitely not a 'factory sealed' product. Beware!\n\nMy suggestion would be to buy from someone else\n\n\n\n\nB000F5K3Y8:product_missing_user's_guide:bejan_afsharpour_\"bejanjoon\"\n\n\nB000F5K3Y8\n\n\nMustang MV-TILT3 Tilt Wall Mount Adjustable for TV's up to 60-inches: Electronics\n\n\nelectronics\n\n\n33 of 46\n\n\n1.0\n\n\nProduct missing user's guide\n\n\nAugust 25, 2006\n\n\nBejan Afsharpour \"bejanjoon\"\n\n\nCalifornia\n\n\nThis product came with an italian users guide. In addition the specification fails to state the thickness of this wall mount. It turns out its about 3.5 inches thick and with the thickness of the monitor itself it stands out very far from the wall.\n\nI RETURNED THIS BACK TO AMAZO\n\n\n\n\nB00067TTZE:not_happy:l._dagenais\n\n\nB00067TTZE\n\n\nCreative Zen Micro 5 GB MP3 Player White: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nNot happy\n\n\nNovember 24, 2006\n\n\nL. Dagenais\n\n\n\n\n\nHard to figure out and organize music... battery life not what is promised and needs to be changed often... don't recommend it. Very \"Not happy\n\n\n\n\nB000B9RXPA:horrible,_horrible,_horrible:a._bower\n\n\nB000B9RXPA\n\n\nApple MA186G/A Armband Red for iPod Nano 1st Generation: Electronics\n\n\nelectronics\n\n\n7 of 7\n\n\n1.0\n\n\nHorrible, horrible, horrible\n\n\nJune 9, 2006\n\n\nA. Bower\n\n\n\n\n\nI adore my nano, but this armband is one of the worst products I have ever bought. I work out quite a bit with the nano and the band comes off multiple times during my workout. The spacing of the velcro dots means I never get exactly the right fit and the band gets ripped off anytime I do weight lifting close to my body. It is also as other reviewers mentioned hugely overpriced for what it is. I am ordering a band from a different company and this will go straight to the garbage.\n\n\n\n\nB0001AU6XU:i_hate_it!:laura_moffat\n\n\nB0001AU6XU\n\n\nApple iPod Mini Arm Band M9445G/A: Electronics\n\n\nelectronics\n\n\n6 of 7\n\n\n1.0\n\n\nI hate it!\n\n\nJuly 7, 2005\n\n\nLaura Moffat\n\n\n\n\n\nEither I am idiot or this product is very poorly designed! It takes forever to get it just right, so that it won't slip off and still it feels uncomfortable and not quite right. Now they have them in sorts of colors, don't buy it, you'll just get annoyed everytime you want to use it, like me.\n\n\n\n\nB0001AU6XU:the_clip_is_better:katie\n\n\nB0001AU6XU\n\n\nApple iPod Mini Arm Band M9445G/A: Electronics\n\n\nelectronics\n\n\n14 of 14\n\n\n2.0\n\n\nThe clip is better\n\n\nMay 3, 2005\n\n\nKatie\n\n\nSt. Louis, MO\n\n\nI bought the armband when I got my mini ipod. However, the belt clip that comes with the iPod is perfect to clip to my shorts while jogging. You don't need this large, clunky armband\n\n\n\n\nB0001AU6XU:destined_for_the_back_of_a_drawer:talawren\n\n\nB0001AU6XU\n\n\nApple iPod Mini Arm Band M9445G/A: Electronics\n\n\nelectronics\n\n\n34 of 35\n\n\n2.0\n\n\nDestined for the back of a drawer\n\n\nMarch 4, 2005\n\n\ntalawren\n\n\nBurlington, VT USA\n\n\nI found the armband to be uncomfortable and inconvenient. I think the upper arm is just a bad place to carry an iPod. The added weight is distracting when I'm running. Even worse is any activity that requires me to flex my biceps at all, so lifting weights is out. Put it on too tight, and I constrict the muscle. Loosen it a little, and the band slips off. There is no happy medium. Also, the earphone cord gets in the way more when it's leading from my arm.\n\nThe belt clip that came with my iPod mini works much better for me. I clip it on the waistband of my sweatpants when I'm working out and it remains very secure, even when I'm running on the treadmill. And the cord stays close to my body, where it is far less likely to get caught on something.\n\nThe only advantage of this product is that it lets you advertise to the world that you own an iPod mini. Big deal. My armband is destined to be forgotten in the back of a drawer at home\n\n\n\n\nB00004U8K1:very_poor_quality:jennifer_fife\n\n\nB00004U8K1\n\n\nPlantronics T10 Corded Headset Phone: Electronics\n\n\nelectronics\n\n\n3 of 5\n\n\n1.0\n\n\nVery Poor Quality\n\n\nApril 1, 2006\n\n\nJennifer Fife\n\n\nUnited States\n\n\nDo not waste your money on this product!! Whilst Amazon has a very good price for it, the headset is junk! I tried two different sets and both had a terrible hum. The hum was audible at all jacks in the house and on two different phone lines. It could be heard by the other party and made my hearing the other party nearly impossible. I work for the phone company and have used headsets over the past 25 years with no problmes. This set is very poor quality and not worth the money.\n\n\n\n\nB00004U8K1:buzz_in_microphone:b._coleman\n\n\nB00004U8K1\n\n\nPlantronics T10 Corded Headset Phone: Electronics\n\n\nelectronics\n\n\n5 of 6\n\n\n1.0\n\n\nBuzz in microphone\n\n\nJanuary 6, 2006\n\n\nB. Coleman\n\n\n\n\n\nAmazon quickly shipped this item to me and I was ready to rock 'n' roll -- until I plugged it in. There was a distinct buzzing noise that both myself and the caller on the other end could hear. The buzzing noise stopped when I hit mute, so the Plantronics rep said it was a microphone problem. Amazon was very gracious about their return policy and I was quickly sent a new phone free of charge. Well, the second phone does the same thing and I'm returning it to get a refund. Maybe just my phone line? Possibly. But I've tried two phones now and have the same problem with both\n\n\n\n\nB000AP1RE8:sound_quality_ok,_but:julian_finn\n\n\nB000AP1RE8\n\n\nStudio Condenser Mic with USB Output - SAM C01U: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nSound quality ok, but\n\n\nNovember 23, 2006\n\n\nJulian Finn\n\n\nKarlsruhe, Germany\n\n\nBought C01U and was glad I could send it back after a testing period: \nTried it witih Ableton Live on OS X 10.4.7 and a MacBookPro. Sound Quality was okay but either the buffer was too small (which caused the sound to scratch every few seconds) or the buffer was too large which ended in a lag causing me to get distracted from my monitor-earphones. I tried for about two hours to find the right balance but it didn't work out. This Microphone might have a nice sound but I can'T recommend it for high standard voice recording.\n\n\n\n\nB0006TIA34:didn't_last_long:manuel_ferrer\n\n\nB0006TIA34\n\n\nD-Link DWL-G650M Wireless 108G MIMO CardBus Adapter: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nDidn't last long\n\n\nJuly 24, 2006\n\n\nManuel Ferrer\n\n\n\n\n\nThe card lasted about 3 months then just died. I'm disappointed considering the cost. When it was working it seemed to have better range than my builtin centrino web access card. I don't know if I got a lemon but now I have to check the warranty. \n\n\n\n\nB0006TIA34:can't_connect_at_108mbs,_max_is_54mbps:a._verdugo\n\n\nB0006TIA34\n\n\nD-Link DWL-G650M Wireless 108G MIMO CardBus Adapter: Electronics\n\n\nelectronics\n\n\n3 of 8\n\n\n1.0\n\n\nCan't connect at 108mbs, max is 54mbps\n\n\nFebruary 3, 2005\n\n\nA. Verdugo\n\n\nCT\n\n\nThe card worked really well except for one little thing. There is no way to enbale super G mode (you need it to connect at 108mbps). Without the most I could connect at was 54mbps. The quick install guide and the manual said it's available, but it wasn't. Tech support was able to reproduce my problem, and tried many, many things. Even though they were very helpful, they coudln't fix it.\n\nMy guess is that it's going to take a firmware upgrade to enable Super G. If you can wait for that, then you will be happy. I returned mine, and bout a Linksys WPC54GX\n\n\n\n\nB0001M3N7O:i_hope_sony_will_go_back_to_the_old_(srf-h3)_design:l._eskin\n\n\nB0001M3N7O\n\n\nSony SRFH4 Analog Tuning AM / FM Headphone Radio: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n2.0\n\n\nI hope Sony will go back to the old (SRF-H3) design\n\n\nAugust 17, 2006\n\n\nL. Eskin\n\n\n\n\n\nLike another reviewer, I had the SRF-H3 model, and loved them - comfortable, secure, decent sound, and *almost* indestructable.\n\nI was hoping these would be \"new and improved\". Well, they are new... I really do not like them. The electronics/sound seem to be at least as good as the SRF-H3, which is fine, but the new design does not work for me at all, for 3 reasons.\n\n1. The bigger band that goes over the top of the head is almost square - flat on top, instead of curved, so the weight all rests right on the crown of my head.\n\n2. The smaller band, instead of keeping it secure, just loosens up rather than hanging on.\n\n3. The earpads, which were on wobbly stalks on the SRF-H3, to match the angle of the ears, are fixed on this model. so they push more on the rear edges of my ears, and push the headphones forward.\n\nThe quality of the electronics and sound don't do much good if the things are a pain to wear. Maybe they will work well for some head shapes, but not for mine. Bummer... Maybe I can find the old model somewhere.\n\n\n\n\nB000EUGX70:greatly_disappointing:c._douglas_\"cmd1\"\n\n\nB000EUGX70\n\n\nLogitech Harmony 550 Universal Remote: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n1.0\n\n\nGreatly Disappointing\n\n\nNovember 23, 2006\n\n\nC. Douglas \"cmd1\"\n\n\nAustin, TX United States\n\n\nI can't say enough positive about Logitech mice. This device, however, was a dismal disappointment. The PC interface works well, and its knowledge of existing components seems impressive. Unfortunately, that in no way translates into a good remote interface. The ability of the unit to reliably turn on multiple components was poor, and, for those with video recorder boxes, very important functionality like the ability to access or schedule recording lists is not readily available (if its there at all, its hidden many keystrokes deeper than I care to know about). Even when reverting to manual operation of individual components, changing which device is being accessed involves two keystrokes (as opposed to just one on less complex universal remotes--a much more irritating trait than one might think). Overall, for day-to-day functionality I actually consider the barely-programmable remote that came with my Scientific Atlanta DVR vastly superior to the Harmony, which I've given up on after only a few days of forced use.\n\nConsidering the ridiculous pricetag, I strongly advise you save yourself a lot of money and your relatives a lot of frustration this year by steering clear of this sad study in user interface failure\n\n\n\n\nB000A6PPOK:used_previous_natural_keyboard...:todd\n\n\nB000A6PPOK\n\n\nMicrosoft Natural Ergo Keyboard 4000: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nUsed previous natural keyboard...\n\n\nNovember 23, 2006\n\n\nTodd\n\n\nSimi Valley, CA USA\n\n\nI have 2 10 year old natural keyboards with the old keyboard layout and when I saw this new keyboard with the same layout I bought one. Sadly, this keyboard is painful to use. The keys - especially the space bar - require far more force to use that my previous keyboard. I am a software engineer and used to typing but this keyboard has slowed my typing rate and wears me out after 20 minutes. On the shelf it goes for emergencies as I'll continue to maintain my old keyboard\n\n\n\n\nB0007PF7FQ:good_b/w_print/scans,_poor_software_and_color_scans:ivan_ivanoff\n\n\nB0007PF7FQ\n\n\nBrother MFC-7820N 5-in-1 Network Monochrome Laser Multifunction Center: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n2.0\n\n\nGood B/W print/scans, poor software and color scans\n\n\nOctober 9, 2006\n\n\nIvan Ivanoff\n\n\nAtlanta, GA USA\n\n\nUsed this unit for a few weeks and returned.\n\nAnnoyances:\n\nSofware: With ADF scanning, you have to enter scan parameters (paper size etc) again for every scan and it does not have an option to let you to continue scanning more pages then ADF can hold. You will have to use \"add to file\" function and select all scan parameters again. There is no option to scan double-sided pages as well with using 2 pass scanning. So 3rd party software is definitively needed to get a real value out of ADF scanning.\n\nColor Scanning: While B/W scans are fast, color scanning speed is very poor to me. Scanning stack of color pages could take ages. Slow color scanning speed was observed in both USB and network modes. \n\nFAX: Number of rings before FAX answers is limited to 4 and I found no way to increase that. No good for SOHO\n\n\n\n\nB000B61X1S:quit_working_after_printing_three_pages_successfully:s._stanley\n\n\nB000B61X1S\n\n\nHP Deskjet 460C Mobile Printer: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nQuit working after printing three pages successfully\n\n\nNovember 15, 2006\n\n\nS. Stanley\n\n\nDenver, CO USA\n\n\nConfusing light display (looking in the manual) told me there was a paper jam after printing three pages. There is no jam. There is nothing stuck in the printer. Sending it back now. I do not have time to troubleshoot a new printer that fails to work properly after three pages. (Maybe you won't have time either!) I am very tech savvy and have checked the manual. The device simply is defective. \n\nI also noted that the top lid that folds down for packing to travel seems very flimsy. It's hard to imagine it would not break in time, and since it's the paper feeder, you'd be out of luck. I'd recommend something else\n\n\n\n\nB0007XGORI:not_for_mac_user:donttellmama_\"neodante\"\n\n\nB0007XGORI\n\n\nSmartDisk FCD20 FotoChute 20 GB Portable Digital Storage Device: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nNot for Mac User\n\n\nSeptember 17, 2006\n\n\ndonttellmama \"neodante\"\n\n\nNYC, NY USA\n\n\nThe description indicates that it works on Mac platform. \nBut it does not. And the funny thing is the instruction.\nIt says \"if it does not work, try different computer.\" \nVery helpful, isn't it?\nSo I had to use my PC and set up a network w/ Mac and trasnfer. \nToo much trouble if you have a Mac.\nHowever, it's easy to use. Hook up and click, click. that's it.\n\n\n\n\nB00093DA2I:important_read_this_reveiw!!!:jorge_castaneda\n\n\nB00093DA2I\n\n\nSkullcandy SCS-SC Skullcrusher Subwoofer Stereo Headphones (Green): Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n1.0\n\n\nIMPORTANT READ THIS REVEIW!!!\n\n\nSeptember 16, 2006\n\n\nJorge Castaneda\n\n\nAnaheim, CA USA\n\n\nThese are some great headphones, BUY THEM FROM SOMEWHERE ELSE! I chose to buy them here because of the lower price, big mistake, what I recieved wasen't the item picutered. I don't know if there an older version or some knock-offs, the sound is ok... but there very cheaply made and theres something wrong with the hinges they don't fold properly. I have an average size head and even fully extended to put them on you have to warp the headband to the point that it looks like its about to break. Returned them and spent the extra 20 bucks to buy them from the actual site\n\n\n\n\nB00093DA2I:dead_in_4_months!!!:mr._ownage\n\n\nB00093DA2I\n\n\nSkullcandy SCS-SC Skullcrusher Subwoofer Stereo Headphones (Green): Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n2.0\n\n\nDead in 4 Months!!!\n\n\nApril 14, 2006\n\n\nMr. Ownage\n\n\nKingston, On Canada\n\n\nWorked well when it was working...massive bass response, comfortable cushioning for big ears and funky styling. Only complaint is the cumbersome battery case and cheap feeling plastic construction. However, none of this mattered when it just stopped working 4 months after purchase. Gave absolutely no signs before hand, just working one day, completely dead the next. There are other reviewers here that shared my same fate, so if you want to gamble with these, go ahead, but know that the risk is a pair of dead headphones. \n\n\n\n\nB0002VMDIY:terrible_-_i_am_returning_it_:michael_w._yacenda\n\n\nB0002VMDIY\n\n\nPanasonic KX-TG6500B 5.8 GHz DSS Expandable 2-Line Cordless Phone (Black): Electronics\n\n\nelectronics\n\n\n12 of 16\n\n\n1.0\n\n\nTerrible - i am returning it \n\n\nNovember 3, 2005\n\n\nMichael W. Yacenda\n\n\nStamford CT\n\n\nLow volume ... people can not hear me at the other end. I therefore have to return it. \n\nI liked my Siemens 8825 much much better. \nOther complaints:\n1. No central office voice mail indicator\n2. All handsets display the same missed call messages; so the same missed call has to be handled 4 times. A very stupid \ndesign. \n3. Large heavy handset\n4. This is not a phone system. It is a collection of phones.\n\n\n\n\n\n\n\n\nB00004Z5H3:not_so_good:\n\n\nB00004Z5H3\n\n\nBelkin A3L791-14-BLU-S RJ45M/RJ45M 10/100BT Cat5e Snagless Patch Cable (14 FT, Blue): Electronics\n\n\nelectronics\n\n\n3 of 14\n\n\n2.0\n\n\nNot so good\n\n\nFebruary 11, 2003\n\n\n\n\n\n\n\n\nOne of the two ends was broken\n\n\n\n\nB0001N44TO:the_latest_version_fails_to_meet_expectations:willy_loo_\"freiwill01\"\n\n\nB0001N44TO\n\n\nCase Logic DVB-40 48-Disc DVD Wallet: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n1.0\n\n\nThe latest version fails to meet expectations\n\n\nJanuary 19, 2006\n\n\nWilly Loo \"freiwill01\"\n\n\nGreenville, SC\n\n\nThis version released late 2005 is obviously a cost-cutting result. This release drops the zipper which is essential when travelling. Also the price have doubled compared to the previous style. \n\n\n\n\nB0006BKH3S:nifty_device_but_display_tough_to_read_in_daylight:chaz_cone\n\n\nB0006BKH3S\n\n\nMonster A-IP-FM-CH-PS Wireless FM Transmitter: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n1.0\n\n\nNifty device but display tough to read in daylight\n\n\nNovember 2, 2006\n\n\nChaz Cone\n\n\nAtlanta, GA USA\n\n\nThis is my second one of these (I pinched off the cable on my first one with a storage console door and had to get another!)\n\nThe display has two settings: very dim and off. THe one I had before was quite bright so they must have changed something.\n\nThat said, it works perfectly. You shouldn't be looking at the display while driving anyway\n\n\n\n\nB0000DK662:this_is_a_pile_of_junk.:w._shivers\n\n\nB0000DK662\n\n\nHewlett Packard 49G+ Graphing Calculator: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nThis is a pile of junk.\n\n\nSeptember 23, 2006\n\n\nW. Shivers\n\n\n\n\n\nA $150 calculator that I am going to trash. I had the same problem with the keyboard (terrible response); I struggled with it for about a year and then called HP. My warranty was out, so Hp suggested I install an update, which I did. Now the keys are firing off 3 or 4 digits with one press of the button. This is ridiculous. \n\n\n\n\nB0000DK662:not_impressed:hydroman\n\n\nB0000DK662\n\n\nHewlett Packard 49G+ Graphing Calculator: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n1.0\n\n\nNot impressed\n\n\nMay 24, 2006\n\n\nhydroman\n\n\nBlacksburg,VA\n\n\nI am an RPN devotee and have owned two other HPs going back to the original 11C. But this calculator is horribly unreliable because it seems to ignore key entries occasionally. (Although you hear and feel the click, nothing happens) Also, the keys themselves may \"bust loose\" and start to wobble even after little use. This is my third replacement in the last two years. Be sure and save your receipt because quality control is absent on this one.\n\n\n\n\nB00001P4XA:pay_a_little_more_and_get_something_better:edward_b_greene\n\n\nB00001P4XA\n\n\nKoss \"The Plug\" Portable Headphones: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nPay a little more and get something better\n\n\nNovember 12, 2006\n\n\nEdward B Greene\n\n\nPonte Vedra Beach, FL\n\n\nThis was my first set of in-ear headphones and I bought them because they are cheap. Well, you get what you pay for. Being disappointed with the fit of the Koss phones, I bought the slightly more expensive JVC HA-FX55V. At least for me, a much better fit, which means more comfort and better sound. And I am still shopping. The array of offerings is daunting: JVC and JPlug at about $20 all the way up to Shure's top of the line at $500. So shop around, but forget about the Koss\n\n\n\n\nB000CO77TQ:who_really_knows_until_it's_too_late...:oyo\n\n\nB000CO77TQ\n\n\n4-Year Service Plan for Televisions $1,500 to $1,999.99: Electronics\n\n\nelectronics\n\n\n2 of 4\n\n\n1.0\n\n\nWho really knows until it's too late...\n\n\nSeptember 6, 2006\n\n\nOyo\n\n\nAtlanta, GA\n\n\nThis warranty is purchased through Amazon, but it is actually distributed by another company. When you search for TV warranties on Amazon, you will see 4 warranties that appear to be the same, but are different prices. I called the company that sells these warranties and asked what the difference was. They told me that there is no difference between the $119 warranty and the $499 warranty; they cover the exact same thing. The girl I spoke with even asked her manager and he said there is no difference. Warranties are a tricky item to review, because it all looks good until you actually need it. So, on this one, I suppose only time will tell. The one difference between the Amazon warranty and the electronic store warranty is that the Amazon one will not replace the first bulb in the TV; but I figure if I need to replace the bulb within those four years I will still be saving $100 because I can get a new bulb for around $200.\n\n\n\n\nB0001DYUJ8:quality_control_problems:markbrent_\"markbrent\"\n\n\nB0001DYUJ8\n\n\nSimpleTech SimpleDrive 250 GB External USB 2.0 Hard Drive: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n1.0\n\n\nQuality control problems\n\n\nOctober 26, 2006\n\n\nmarkbrent \"markbrent\"\n\n\nTexas\n\n\nBought this as a back-up for the internal hard drive of the laptop. My experience was sadly similar to many others on this site. Worked fine for two backups, then the drive stopped being recognized by my own or any other computer. Little red \"power\" light is on, little disc whirls around, but no blue \"data transmit\" light appears anymore. These are clearly a dismally unreliable and poorly made product- just imagine the disaster if this occurred when you were trying to restore data to a corrupted hard drive on your primary machine. A free replacement would not be much of a compensation. Avoid!!\n\n\n\n\nB0001CFSXG:wait....better_deal:xboxplayer_\"xboxplayer\"\n\n\nB0001CFSXG\n\n\nLexar JumpGear MPC064-231 MP3 Player with 64 MB JumpDrive Sport: Electronics\n\n\nelectronics\n\n\n5 of 12\n\n\n1.0\n\n\nWAIT....BETTER DEAL\n\n\nSeptember 4, 2004\n\n\nxboxplayer \"xboxplayer\"\n\n\nUSA\n\n\nBuy the \"empty\" player for 37, then get a 128 mb card....its only 5 bucks mopre total.....\n\n\n\n\nB0007KX4TC:ipod_-_great_toy_-_horrible_customer_service/support:once_and_done_\"once_and_done\"\n\n\nB0007KX4TC\n\n\nApple 30 GB iPod Photo M9829LL/A�(4th Generation): Electronics\n\n\nelectronics\n\n\n6 of 8\n\n\n2.0\n\n\niPod - Great toy - Horrible customer service/support\n\n\nFebruary 14, 2006\n\n\nOnce and Done \"Once and Done\"\n\n\nUSA\n\n\nThe iPod is cool but ours ... as we've found has happened to many other owners ... went on the fritz and our nightmare began. Apple's customer service is the worst CS that we've ever experienced regardless of the product or service supported. I could write for days about the awful experience but I've wasted so much time with them already ... I can't bear to waste more. The most notable point is that the iPod CS folks repeatedly stated that iPods are more or less disposable, commodity items and \"breakdowns\" were common. Wow!! .. $400 Bucks of throw away!?!?!? We still haven't recieved the return box for replacement/repair after several days even though it was promised in 24-48 hours. We also own an MP3 player that we refer to as a \"Dell-Pod\" from Dell Computer and have had to use their customer service as well. Dell's CS support was awesome. Based on our experience with Apple/iPod CS support ... this is the first and LAST Apple product that will be owned in our household\n\n\n\n\nB0007KI6OU:if_you_need_this_in_a_hurry_don't_buy_from_amazon!:novelty_1_\"love_my_kids!\"\n\n\nB0007KI6OU\n\n\nBrother TN 350 Black Toner Cartridge (HL-2040 and HL-2070N Printers): Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nIf you need this in a hurry don't buy from Amazon!\n\n\nNovember 24, 2006\n\n\nNovelty_1 \"Love my kids!\"\n\n\nSoCal\n\n\nI purchased this item on Nov. 17, 2006. Amazon claimed it was in stock. I then upgraded from free shipping to Standard, which promises a 3-5 days delivery time. Amazon now informs me that it will arrived on Nov. 29th! This is the second recent order with a longer than anticipated/promised delivery time. I don't know if Amazon is going down hill or what, but it's discouraging. Next time, I'll just buy the item locally and pay a few extra dollars. And by the way, don't even try to call Amazon Customer Service at 800-201-7575. They can't do anything to resolve a problem and barely speak or understand English. Another great American company moving towards metiocracy; what a shame\n\n\n\n\nB000IF45QM:microphone_volume_too_low_and_can't_be_set:r._stockton\n\n\nB000IF45QM\n\n\nKensington Vo300 USB Internet Speakerphone for use with Skype software K33378US: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nMicrophone volume too low and can't be set\n\n\nOctober 21, 2006\n\n\nR. Stockton\n\n\nMichigan, USA\n\n\nShort answer: The microphone volume on this is set too low relative to the speaker volume, and cannot be adjusted independently--either through the device or at the O/S level.\n\nI spoke with tech support, and they said this was to keep the echo-cancellation software from having issues.\n\nSo as a speakerphone, this device is useless.\n\nI'd have given it one star, but the rest of the device is actually very well designed, easy to setup, and so on.\n\n\n\n\nB00006JPU2:its_just_a_standard_component_cable..:william_the_fighter..\n\n\nB00006JPU2\n\n\nMonster Cable 3-Foot Component Video (BSV1CV-1M): Electronics\n\n\nelectronics\n\n\n14 of 20\n\n\n1.0\n\n\nits just a standard component cable..\n\n\nNovember 12, 2004\n\n\nwilliam the fighter..\n\n\nNC USA\n\n\ncompared this cable to a philips performance component cable and i am not impressed it was the exact same in picture quality.\n\nall your paying for is the name..\n\nwhy spend 50$ when you can spend 20$ on the philps and get the same thing?\n\n\n\n\nB00009MVHF:lcd_display_broke_after_2_months:n._nacey\n\n\nB00009MVHF\n\n\nPanasonic KX-TS3282B 2-Line Expandable Speakerphone with Data Port (Black): Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nLCD display broke after 2 months\n\n\nOctober 30, 2006\n\n\nN. Nacey\n\n\n\n\n\nI was fairly satisfied with this phone until I had to re-arrange my office and move my phone. Once I moved it and then tried to reconnect it, I no longer had LCD functionality or speakerphone functionality.\n\nI looked on their website and this is what it said on their FAQ:\n\nProblem:\nNo caller ID information received\nDoes the LCD screen on the handset show anything?\nNo\nYour telephone is defective and needs to be sent in for service\n\nSince it is on their FAQ I would assume this is a common problem. In trying to exchange my phone they were insistent that I send my phone in first so they could look at it and then refurbish it / exchange it. Since I use this phone for my home office this was problematic for me. \n\nI would not recommend this product. I personally will not by Panasonic again due to the quality of customer support (or lack thereof) that I received\n\n\n\n\nB000117DF8:poor_support:l._morgan_\"programming_instructor\"\n\n\nB000117DF8\n\n\nLexar Media 512 MB JumpDrive Sport Portable USB 2.0 Drive (PC/Mac): Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n1.0\n\n\nPoor support\n\n\nDecember 29, 2005\n\n\nL. Morgan \"Programming Instructor\"\n\n\n\n\n\nI didn't have the same experience as the \"Great support from Lexar\" review below.\n\nThe rubber caps on the Sport Jumpdrives will quickly split open and you will probably loose your jumpdrive when it fall off your keychain.\n\nUnlike the person below, Lexar would not send me a free replacement. They said that I could go to their web site an purchase another rubber cap.\n\nPoor support for a poor product\n\n\n\n\nB00009UHYL:not_worth_the_money:time_traveler\n\n\nB00009UHYL\n\n\nTERK BMS-58 5 In/8 Out Integrated Multiswitch (TERK BMS58): Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n1.0\n\n\nNOT WORTH THE MONEY\n\n\nAugust 31, 2006\n\n\nTime Traveler\n\n\nUSA\n\n\nEveryone knows TERK MAKES JUNK, and this multiswitch is no exception. I needed a multiswitch quick, went to Best Buy and paid, get this, $154.00 for this Terk ms!! Yeah, I know, but I needed it asap. ONE WEEK after installation, one of the satellite inputs crapped out, dead. And I do have surge protection on the lines. Also, the unit runs very hot. I've had three other Terk products do the same thing. Avoid Terk at all costs and go with Monster or even Radio Shack in a pinch. Will never buy a Terk product again. Ever\n\n\n\n\nB00006B7DB:defective_bluetooth:john_olson_\"golfer\"\n\n\nB00006B7DB\n\n\nD-Link DBT-120 Bluetooth USB Adapter, PC/Mac: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nDEFECTIVE BLUETOOTH\n\n\nNovember 6, 2006\n\n\nJohn Olson \"golfer\"\n\n\nmarshalltown, ia\n\n\nThe item I bought had to be returned due to the fact it did not wor\n\n\n\n\nB00004SY4H:did_i_get_a_bad_sample?_totally_disappointed!:eric_j._anderson\n\n\nB00004SY4H\n\n\nSennheiser HD600 Traditional Over-Ear Open Dynamic Hi-Fi Professional Stereo Headphones (Black): Electronics\n\n\nelectronics\n\n\n18 of 30\n\n\n1.0\n\n\nDid I get a bad sample? Totally disappointed!\n\n\nFebruary 8, 2005\n\n\nEric J. Anderson\n\n\nAnkeny, Iowa\n\n\nYes, I burned them in for at least 25 hours. They sounded muddy. Smooth as caramel, but veiled. The bass was heavy and indistinct. The high frequencies were not clean, and not abundant. \n\nHaving listened to Stax headphones for years and years, I know I got used to a superclean, almost clinical sound. But still I cannot believe how completely different the HD600s were. \n\nI borrowed these from a friend, who bought them new and had hardly used them. Frankly, I get more pleasure listening with my Sony MDR-W20G sports headphones that I bought for less than $10. I am at a loss to understand the glowing reviews. I would love to hear a headphone that sounds as wonderful as people are describing. But the one I heard did not. Not even after break-in. Never would I consider one of these phones without auditioning them. Perhaps I truly heard a rare, defective unit. But if so, both drivers were defective, because they sounded the same. \n\n\n\n\nB0007Y6AF8:a_disgrace_to_sony,_here's_why_in_point_form:p._an_duong\n\n\nB0007Y6AF8\n\n\nSony ICD-MX20 Memory Stick Pro Duo Digital Voice Recorder: Electronics\n\n\nelectronics\n\n\n9 of 18\n\n\n1.0\n\n\nA disgrace to sony, here's why in point form\n\n\nMarch 18, 2006\n\n\nP. An Duong\n\n\n\n\n\n-small button size\n-recording time is crap when used with the best batteries\n-hold button, many bugs with\n-bugs; files often end corrupt in various ways, meaning you get f***ed over\n-software is crap, also it cant convert to mp3, o and did i mention you have to convert in general not true for all ic recorders, which others record directly in mp3 for example\n-mic is without noice reduction\n-device is the most fragile ic recorder ever\n-a consumer grade product, not a professional grade product\nGo with the following: ds-2200 made by olympus, it smokes this ic recorder by a mile, and its tough and goes for about the same price perhaps cheaper.\n\n\n\n\nB000167V50:will_not_charge_micro_zen:ron_a._wodehouse_\"roadmouse\"\n\n\nB000167V50\n\n\nZip-Linq 12V DC Auto to 5V USB Adapter (ZIPPWR-DC): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nWill not charge Micro Zen\n\n\nJune 21, 2006\n\n\nRon A. Wodehouse \"Roadmouse\"\n\n\nSouthern Oregon\n\n\nWill not charge the Micro Zen, but will power the unit. Will toss it in the car for those times when the battery is dead and I just need my music.\n\n\n\n\nB000167V50:another_sad_confirmation..no_go_for_shuffle:b._b._helsabeck\n\n\nB000167V50\n\n\nZip-Linq 12V DC Auto to 5V USB Adapter (ZIPPWR-DC): Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n1.0\n\n\nAnother sad confirmation..no go for Shuffle\n\n\nDecember 30, 2005\n\n\nB. B. Helsabeck\n\n\nPompano Beach, FL USA\n\n\nIt is rather ironic that this device was so cheap that despite the fact that IT WILL NOT CHARGE AN IPOD SHUFFLE...it cost more to ship it back than I would get in a refund.\nBought from an Amazon associated store (Tiger Direct) for $4.99. I also bought the matching car charger for $3.99. NEITHER WORKS WITH THE IPOD SHUFFLE. Oh well. \n\nBelkin makes a iPod dock connector to USB adapter that allows you to use your existing FW based stuff for CHARGING ONLY...., I think that is the ticket for charging the Shuffle on the road\n\n\n\n\nB000167V50:nice_idea,_but...:f._paul\n\n\nB000167V50\n\n\nZip-Linq 12V DC Auto to 5V USB Adapter (ZIPPWR-DC): Electronics\n\n\nelectronics\n\n\n7 of 8\n\n\n2.0\n\n\nNice idea, but...\n\n\nOctober 4, 2005\n\n\nF. Paul\n\n\nOH, USA\n\n\n...while this device does power your USB cable it seems to fail charging my Blackberry or my iPod. Maybe the 400 mA is not enough for that. I would not suggest to buy this item for these devices. For the iPod you better get the USB charger from the Apple store for 25$ that even comes with a standard iPod cable\n\n\n\n\nB0007OCY2G:decent_but_there_has_to_be_a_better_solution_somewhere_out_there!:jdiddy\n\n\nB0007OCY2G\n\n\nDLO TransPod All-in-One Car Solution for iPod Black: Electronics\n\n\nelectronics\n\n\n5 of 6\n\n\n2.0\n\n\nDecent but there has to be a better solution somewhere out there!\n\n\nAugust 27, 2005\n\n\nJDiddy\n\n\nColumbia, MD\n\n\nI recently received an iPOD Photo and after loading in tons of CD's I said . . . hey why not buy an all in 1 adapter for my car. My transpod arrived but I had difficulty inserting the adapter into my car's charger. After working that out and using it everyday for a week, it stopped working. I realized that the fuse had blown. It's small and relatively compact unlike the Griffin RoadTrip, but it's a whole lot of drama for the price. \n\nWith regard to the quality of the sound through my vehicle's audio system, I am extremely disappointed. What sounds like a million dollars through headphones does not translate to what you hear through fm radio frequencies. I have adjusted the iPOD's equalizer and played around with some options inside my car, but I am extremely disappointed. If someone has a better alternative . . . GOD BLESS YOU but don't waste your money on this product\n\n\n\n\nB0006J59YW:broken:r._jensen\n\n\nB0006J59YW\n\n\nIogear Wireless Bluetooth Mini Mouse: Electronics\n\n\nelectronics\n\n\n0 of 2\n\n\n1.0\n\n\nBroken\n\n\nSeptember 21, 2006\n\n\nR. Jensen\n\n\n\n\n\nMy first mouse broke after only a few uses. When I sent it back for repairs, they sent back a refurbished mouse that did not work at all. Avoid buying this mouse at all costs\n\n\n\n\nB0006J59YW:100_uses_for_a_dead_mouse:arcane_questions_\"mousetapha\"\n\n\nB0006J59YW\n\n\nIogear Wireless Bluetooth Mini Mouse: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n2.0\n\n\n100 uses for a dead mouse\n\n\nAugust 8, 2006\n\n\nArcane Questions \"Mousetapha\"\n\n\nSeattle\n\n\nWhen it works, this mouse is simply wonderful. Small, accurate, easy to recharge, easy to carry. Under those conditions, it's probably my favorite mouse. I've found that it's equally good for both Mac and Windows.\n\nOne complaint is that when it's tethered to the USB port for recharging, it works nominally, but becomes very slow and awkward. It's not at all the same behavior as seen when it's completely free to move.\n\nMy first incarnation of mouse lived a long life of about 10 months. My second incarnation of mouse, however, just died at the tender age of 4 months. I've attempted mouse-to-mouse resuscitation, but to no avail, having found that Iogear technical support exists by name only. I have sent multiple requests for help, and have received not a single answer. Both mice died identical deaths. Suddenly it stops being able to hold a charge, and freezes after about 5 minutes of use. Even when tethered to the USB port for a recharge it freezes. \n\nI'm sad that a technologically good product should have such terrible quality assurance in its manufacture. And such terrible customer support in dealing with its lack of QA\n\n\n\n\nB00011PYC2:tell_apple_to_go_#$&%_themselves:kilgore_trout_\"worldwidewebb01\"\n\n\nB00011PYC2\n\n\nAppleCare M9404LL/A Protection Plan for iPod: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n1.0\n\n\nTell Apple to go #$&% themselves\n\n\nOctober 7, 2006\n\n\nKilgore Trout \"worldwidewebb01\"\n\n\nColumbia, MO\n\n\nWhat a waste of money. For $300 you shouldn't HAVE to buy a protection plan for the 2nd year of a products life; unfortunately the poor quality of iPods makes it a necessity. BUT, the protection plan is riddled with clauses that exclude any number of reasonable issues. \n\nI've bought Apple for the last 15 years, but never again will I send them my money\n\n\n\n\nB00011PYC2:less_expensive_policy_available_for_29.95_from_crutchfield.com:peter_g._gammon_\"peteg\"\n\n\nB00011PYC2\n\n\nAppleCare M9404LL/A Protection Plan for iPod: Electronics\n\n\nelectronics\n\n\n112 of 119\n\n\n2.0\n\n\nLess expensive policy available for 29.95 from Crutchfield.com\n\n\nDecember 21, 2005\n\n\nPeter G. Gammon \"PeteG\"\n\n\nChatt., TN\n\n\nAlthough this policy is thru Apple, it is 2x the price of similar policies available from other companies that also have very good reputations. One is Crutchfield.com which has a 2 yr. plan for $29.99 which starts *after* the standard Apple 1 yr labor coverage (for a TOTAL OF 3 YEARS). Apple extended warranty is a total of 2 years for $59.99, rather than a total of 3 years for $29.99 thru Crutchfield\n\n\n\n\nB00000JCT8:model_#_53725:p._emmett_\"traveler\"\n\n\nB00000JCT8\n\n\nMotorola TalkAbout Headset with Boom Microphone for All Series: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n1.0\n\n\nMODEL # 53725\n\n\nNovember 30, 2004\n\n\nP. Emmett \"Traveler\"\n\n\n\n\n\nAfter receiving this boon mic, I found it did not work consitently. Losing contact with lost words, and the mic had to be held to the lips, therefore it was not truly hands free. I require a mic that I do not have to touch to work and can be used with VO\n\n\n\n\nB0002UE9WI:just_average:jason_sartain\n\n\nB0002UE9WI\n\n\nCreative Labs WebCam Live!: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n2.0\n\n\nJust Average\n\n\nNovember 9, 2006\n\n\nJason Sartain\n\n\n\n\n\nI received the camera and was not happy when I found a cheap mic that was attached to the cord of an ear peace instead of the the superior sound mic advertised in the ad. I called Amazon, they had no appolgies and said the most they could do was refund my money.\n\nOn another note the picture quality was ok but froze at least 2 times or better per use, as for sound I had to put the mic up to my mouth before the person on the other end could hear me. \n\nNot very happ\n\n\n\n\nB000AM6QHO:failure_to_please:daniel_a._nichols\n\n\nB000AM6QHO\n\n\nLogitech Z-5450 Digital 5.1 Speaker System: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nFailure to please\n\n\nNovember 15, 2006\n\n\nDaniel A. Nichols\n\n\nUSA\n\n\nThe z-5450 has good points, reasonable cost, ease of use and ease of setup. It has a major fault in a buzzing noise made when the unit is turned on. Give this item a pass, wait for the next generation and a guarantee that this problem has been corrected\n\n\n\n\nB0009RVG7U:track_problems_-_buy_the_cyber_home:l._d._dietsch_\"digidog\"\n\n\nB0009RVG7U\n\n\nCoby DVD237 Progressive Scan DVD Player: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nTrack problems - buy the cyber home\n\n\nNovember 3, 2006\n\n\nL. D. Dietsch \"Digidog\"\n\n\nChicago, Illinois\n\n\nThis thing looks great - but it still has tracking problems as these cheap units tend to. Do yourself a favor and buy the cyber home - it's the only cheaper unit that seems to have some quality to it\n\n\n\n\nB0009RVG7U:not_what_i_thought_i_was_purchasing:s._das\n\n\nB0009RVG7U\n\n\nCoby DVD237 Progressive Scan DVD Player: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nNot What I Thought I Was Purchasing\n\n\nOctober 4, 2006\n\n\nS. Das\n\n\n\n\n\nI searched Amazon.com for a multi-region DVD player so I could view DVDs that I have purchased overseas. This DVD player came up. There are no instructions within the packaging that state how to watch Non-region 1 DVDs. I called the manufacturer and they told me how to \"rigg\" it to play region 2 DVDs. Also, the remote control did not work from the very beginning and the manufacturer said I would need to pay money to replace it. They also let me know that the DVD player will not work without the remote control (not compatible with universal remotes). This is not a good product\n\n\n\n\nB000085BD8:antenna_fell_off_several_times:roy_littwin_\"royale6\"\n\n\nB000085BD8\n\n\nLinksys WMP54G Wireless-G PCI Adapter: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n2.0\n\n\nAntenna fell off several times\n\n\nMay 22, 2006\n\n\nRoy Littwin \"royale6\"\n\n\nSonoma County, USA\n\n\nMaybe 2 1/2 stars. \nSoftware and wireless setup for this card went fine, even great, and have continued to work for over 1 1/2 years now but . . .\nHere's the rub.\nSeems the housing on the card that receives the threads of the antenna was not properly attached to the board.\nThe antenna would not work properly - 0-50% signal strength from the Access Point in the next room when it wasn't falling off the back of my machine! Futzed with the thing getting half-*** performance with it for toooo long until . . .\nHad to remove board from computer, use plier to remove antenna from the recept and VERRY carefully place the card in a vice and use vice-grip/clamp tool to forcefully slide the 1/2\" housing in place onto the board.\nDid this once and it didn't seat far enough (antenna fell off) and a couple weeks later had to repeat the process.\nApparently it needed to be pressed on even further and it actually snapped (securely ?) the 2nd time after using more force that you might imagine on such a delicate device.\nIt has worked as advertised since then (about a year now).\nThere ARE other reviewers who have suffered from \"antenna drops off\" too. Be warned.\nWhen one pays extra \"for quality\" they should expect better than this.\nIt's time again to add to my network and will try a different board (for half the price).\n\n\n\n\nB0002UHUTM:multiple_problems:bekra\n\n\nB0002UHUTM\n\n\nKensington Auto Charger for iPod and iPod Mini: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nmultiple problems\n\n\nSeptember 4, 2005\n\n\nbekra\n\n\nHouston, TX\n\n\nThis charger is flimsy. 1) As other mention, the \"retractable\" unit quickly became tangled, and without significant attention every single time (not feasible while driving), doesn't retract. 2) The plastic covering the wire at the base of the jack pulled loose, exposing the wires. 3) After a few months of very gentle use, the white plastic cover of the jack cracked and fell off. As a result, I'm back in the market for a charger, just months after buying this one\n\n\n\n\nB0000996B9:these_tapes_didn't_worked_for_me_and_ruin_my_camera:meyer\n\n\nB0000996B9\n\n\nJVC MDV60ME10P Mini DV Tape (10 Pack): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n1.0\n\n\nthese tapes didn't worked for me and ruin my camera\n\n\nJune 7, 2006\n\n\nmeyer\n\n\n\n\n\ni tried them on a sony dcr-trv33, and they clogged my heads in about 10min. of recording.\n\nthe camera has worked fine for about two years now, and it's the first time i use other than sony. this camera went to the sony service center for a full cleaning/service about two months ago, and worked perfectly fine until i tried these tapes.\n\ni read somewhere that the tapes have a surface compound that lubricates the head, and that it is incompatible between brands, clogging the tape head when the different compounds mix in it. i didn't believe it then, and stil not.\n\naccording to that, maybe they are fine and you only have to stick with the brand. and i'll do, but another one\n\n\n\n\nB0000996B9:low_cost_equals_low_quality:dadd75_\"dadd75\"\n\n\nB0000996B9\n\n\nJVC MDV60ME10P Mini DV Tape (10 Pack): Electronics\n\n\nelectronics\n\n\n5 of 9\n\n\n1.0\n\n\nLow Cost Equals Low Quality\n\n\nDecember 13, 2004\n\n\ndadd75 \"dadd75\"\n\n\nTraverse City, MI\n\n\nAfter purchasing my Canon ZR-90 this summer, and using the four pack of TDK tapes ABC Warehouse gave me with the camcorder, I went online and found these tapes and bought two ten packs due to the great price and the JVC name.\nThey have only done one thing, clogged up my video head on the camcorder. I videotape my sons basketball games for their coaches and it was only after the first couple of games and bad recordings that I got onto Canon's website and realized my video head was clogged. I purchased a head cleaner and the first tape after the cleaning taped great, but the next tape clogged my head up, so to use these tapes, I highly recommend you clean your video head after EACH use, which is not good for your video head on your camcorder\n\n\n\n\nB0009H9PZU:don't_waste_your_money:a._kellerman\n\n\nB0009H9PZU\n\n\niRiver H10 20 GB MP3 Player/Recorder (Blue): Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n1.0\n\n\nDon't waste your money\n\n\nNovember 17, 2006\n\n\nA. Kellerman\n\n\n\n\n\nIt worked okay for about a month and then froze up one day when I was running. I popped out the battery so it would turn off and when I turned it back on it had deleted all of the songs. \n\n\n\n\nB0009H9PZU:iriver:j._de_cou\n\n\nB0009H9PZU\n\n\niRiver H10 20 GB MP3 Player/Recorder (Blue): Electronics\n\n\nelectronics\n\n\n2 of 4\n\n\n1.0\n\n\niRiver\n\n\nNovember 10, 2006\n\n\nJ. De Cou\n\n\nAlpine, WY\n\n\nDon't waste your money. What iRiver doesn't tell their customers is that once the battery goes dead, the system is useless. I will never buy one of their products again and I'm doing my best to inform everyone out there that they'll be tossing their money in the trash by purchasing any iRiver products. My system cost me $340.00 and it is now as useless as the customer service department at iRive\n\n\n\n\nB000ANQXKS:cheap_&_buggy:capo\n\n\nB000ANQXKS\n\n\nM-Audio MicroTrack 24/96 Professional 2-Channel Mobile Digital Recorder: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nCheap & Buggy\n\n\nNovember 4, 2006\n\n\nCapo\n\n\nMemphis, TN\n\n\nThe features, size, and design of this unit are what drew me in. But after just a few uses, I quickly came to realize that this was nothing more than cheap electronics wrapped up in an attractive, but completely unusable package. To start with, the unit is small - the smallest of its kind. But the bulky external mic makes the overall footprint just as large as some of the palmtop units that this was made to replace. Second, the unit has a built-in rechargeable battery that cannot be removed. So, when it crashes, which it does frequently, there is no way to \"cold-boot\" the unit by removing the battery. This is also a problem because it reduces the unit's true portability because you can easily get stuck with no remaining battery life and no AC power to recharge it. Then, what you have is a paperweight. Oh, and by the way, when you do have it plugged in to charge, the backlight is always on (even if the power is off). So forget about using this unit as a plugged in desktop recorder either.\n\nBut, all of this aside, the firmware simply does not work. It locks up incessantly and, with no way to reboot, you literally have to unplug it, wait for it to auto-power-off, and hope that you can start the machine up again. And, to add insult to injury, the body is a cheap Chinese-made piece of plastic with absolutely no meat on its bones.\n\nOverall, don't be taken in by the low price. Paying any amount of money is too much for a unit that doesn't work\n\n\n\n\nB000ANQXKS:look_for_something_else:craig_gonter\n\n\nB000ANQXKS\n\n\nM-Audio MicroTrack 24/96 Professional 2-Channel Mobile Digital Recorder: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n1.0\n\n\nLook for something else\n\n\nSeptember 7, 2006\n\n\nCraig Gonter\n\n\nNew England\n\n\nThe microtrack will not hold a charge by itself or even operate very long when hooked up to an external usb battery pack. It just drains away under charge. The concept for this a pretty good , but the implementation woefully lacking almost to the point where I may not reliably recommend to others.\n\n\n\n\nB0000C0XS0:junk_at_the_best!:linuxrules\n\n\nB0000C0XS0\n\n\nNetgear WGT624 Super G 108 Mbps Wireless Router: Electronics\n\n\nelectronics\n\n\n0 of 5\n\n\n1.0\n\n\nJunk at the best!\n\n\nOctober 21, 2006\n\n\nLinuxRules\n\n\nNY USA\n\n\nI bought this product twice. Now I have two of them and none works. This router is not able to get itself an IP from the cable modem let alone sending out a wireless signal. First time i thought the router was defective and I ordered a new one hoping to return the first one, received the second one before i could return the first one, and the new one was no better than the other anyways. Avoid this product at all costs, or else\n\n\n\n\nB0000C0XS0:waste_of_money:bag_man\n\n\nB0000C0XS0\n\n\nNetgear WGT624 Super G 108 Mbps Wireless Router: Electronics\n\n\nelectronics\n\n\n2 of 5\n\n\n2.0\n\n\nwaste of money\n\n\nOctober 15, 2006\n\n\nBag man\n\n\nMA\n\n\nRun WinXP, sp2 with everything updated. I've had this router for 6 months. It is the WGT624v3, firmware updated. I never realized how bad the wireless was on this router until this week - dropped connetion 3 times in 15 minutes today alone, while reading the Amazon.com reviews. \nMy desktop PC's graphics card went down this past week and I had to RMA it... so about 10 days to send out and get a return. I have always used a wired connection for my home desktops. The wireless access was a 'it's there if I need it' novelty. I started brining my IBM R51 laptop home from work so I could get online, VPN into work, etc.. This is the first time I have used my wireless consistently for more than a hour or so.\nThe last 10 days have been rediculous to say the least. I logged on my first day and lost connection 5-6 times browsing MSN and Yahoo. At first I thought it might be channel interference since 2 other routers in range were using channel 11. I went ahead and disabled the 108G feature since I had never come close to hitting that speed anyways, and changed the channel.. I used the IBM ThinkVantage software to scan my area for other wireless routers and picked a channel not being used. I changed channels and I verified my signal strength before and after I changed, 95% signal strength. The connection is still getting dropped randomly. Changed channel to another channel not being used in my area and verified signal strength. No difference. Scanned the networks again using the IBM ThinkVantage software.. theres my router, 97% signal strength @ 54Mbs. unplugged router, modem, repaired my WINSOCKS with WinsockXP, rebooted PC. I turned on my modem first until is was ready to go, then the router until it was ready, and then my PC (as per the netgear website)and yea, no difference in the connections stability. The conection is still getting dropped randomly. \nI might expect this from a router thats on all the time and is getting tired from constantly being hammered. But I hardly ever used the wireless access (yes, wireless radio has been OFF). After reading these reviews, I am pretty sure the problem wont go away. \nThe part that upsets me the most, I payed premium money for a product that I thought was premium. If this had been priced (at the time) at bargain bin prices, I wouldn't of cared.. but it wasn't and i do. The 'you get what you pay for' saying does not hold true for this product. \nBuyer Beware\n\n\n\n\nB000F2M16E:pos_zen_micro_creative_support_now_sucks:ashley_j._gibbon\n\n\nB000F2M16E\n\n\nCreative Zen Micro Photo 4 GB MP3 Player Black: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nPOS ZEN Micro Creative support now sucks\n\n\nNovember 20, 2006\n\n\nAshley J. Gibbon\n\n\nGreensboro, MD United States\n\n\nCaution. Creative used to have great support. Now, 60 days of phone support. I have a 9 month old ZEN Micro. It will not start, if I plug it in, it appears to charge but never completes, If I turn it on while plugged in i get to the Creative boot up screen, it goes blank and never completes the boot up. Useless junk now. I cnnot find my original receipt, (who can). I cannot be returned, and I cannot get support on it.\n\nI will be returning my perfectly good running Vision M with receipt, as i cannot risk this device breaking down too.\n\nI have always hated Apple, I am a PC guy, but an Ipod will be my next MP3 device\n\n\n\n\nB00004Z6HU:completely_useless_for_me:the_stampfers\n\n\nB00004Z6HU\n\n\nBelkin A7J304-250 10/100BT 24 Awg PVC Cat5 250FT Bulk Cable (Stranded): Electronics\n\n\nelectronics\n\n\n1 of 75\n\n\n2.0\n\n\nCompletely Useless For Me\n\n\nJuly 21, 2001\n\n\nThe Stampfers\n\n\nBrookline, MA USA\n\n\nI wanted to connect a computer to a router, and the computer was going to be pretty far away from the router. So I figured I needed a long cord, and decided to get this one, which is supposed to be cat 5 stranded patch (which was what I needed.) Well, it's long. That's the only good thing about this cord. But the bad thing is so bad that the cord is useless. There is nothing on the ends! It's just a giant wire! There aren't any connectors on the ends that I can use to plug the cord into my computer, or the router. So I have a cord, but no connectors. It's completely useless, unless I buy the connectors separately, and attach them myself. Do I know how to do that? Do I want to do that? No. So I'm returning this. Maybe, if all you need is the cord (it's grey, by the way), and not the connectors, you might be able to use this. Otherwise, it's completely useless. If you can't find any cords this long that have connectors, maybe you should consider trying a shorter cord. ..\n\n\n\n\nB000067SVF:useless_-_does_not_retract:james_murphy\n\n\nB000067SVF\n\n\nTargus PA205U Retractable Phone Cord (20 Feet): Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n1.0\n\n\nUseless - does not retract\n\n\nApril 6, 2004\n\n\nJames Murphy\n\n\nPelham, MA USA\n\n\nI agree with the previous review. This phone cord worked twice. Now, it only retracts about half way, and keeps getting worse. I'm returning it for a refund. Don't waste your time with this\n\n\n\n\nB0002ZAEY0:warning_about_the_ipod:keith_daudt\n\n\nB0002ZAEY0\n\n\nApple 60 GB iPod Photo (4th Generation): Electronics\n\n\nelectronics\n\n\n6 of 7\n\n\n1.0\n\n\nWarning about the Ipod\n\n\nMarch 11, 2006\n\n\nKeith Daudt\n\n\nSeattle, WA USA\n\n\nI have really enjoyed my I pod until I started to get errors in January 2006. I started to get an error that state that Itunes can't read to or write form the Disk meaning the Ipod itself. That means I cannot add music to or take music off of the Iopd. This was very upsetting to me as I have had this item for less than a year. I Finlay called tech support at Apple. They asked for the serial# for my Ipod. They told me it was out of warranty, that the warranty had expired in December of 2005 and there was NOTHING I could do, literally. I checked my purchase and found out that I had purchased this on April 7, 2005. I was so angry that they would tell me that it was out of warranty when it clearly was not. So, this is a warning to all Ipod owners of this potential problem\n\n\n\n\nB00005BIDT:waste_of_money!!:p._iyer_\"paddy_i\"\n\n\nB00005BIDT\n\n\nJensen JTH940 Telephone Headset: Electronics\n\n\nelectronics\n\n\n0 of 4\n\n\n1.0\n\n\nWaste of Money!!\n\n\nJuly 19, 2002\n\n\nP. Iyer \"paddy_i\"\n\n\nSunnyvale, CA\n\n\nDOnt buy and I had tough time in adjusting the ear piece as it falls out every time you hang it over the ear.\n\n\n\n\nB000FCTAP4:do_your_research:thomas_kenny\n\n\nB000FCTAP4\n\n\nD-Link VTA-VR Broadband Telephone Adapter with Vonage: Electronics\n\n\nelectronics\n\n\n1 of 4\n\n\n2.0\n\n\nDo your research\n\n\nOctober 2, 2006\n\n\nThomas Kenny\n\n\n\n\n\nWe signed up with Vonage a couple of months ago. Setting up the D-Link adapter was a snap and I had a dial tone quickly. After several days, my line was ported and I thought everthing was ok. \n\nThat was until I tried calling my dad several times and he just hung up on me. I heard his side clearly, but all he heard was static. Others complained about our connection too. Vonage support helped me change a few things on my account to take care of those problems.\n\nHowever, I still have not been able to use my computer modem to fax. I have tried multiple tips to get it to work, and still no luck. Calling Vonage's tech support is horrible. Long wait to talk with someone who doesn't speak good English nor has a clue about what to do with your problems.\n\nThe worst part is that Vonage makes you sign up for a contract. If it wasn't for that, people would flee in droves. I think it will get better, but it has a way to g\n\n\n\n\nB0009M96K4:these_are_horrible.....:j._nosek\n\n\nB0009M96K4\n\n\nMemorex DVD+R 8.5GB Double Layer 2.4x 15 Pack Spindle: Electronics\n\n\nelectronics\n\n\n6 of 9\n\n\n1.0\n\n\nThese are horrible.....\n\n\nMarch 25, 2006\n\n\nJ. Nosek\n\n\nNorridge, IL United States\n\n\nThese were expensive.....\nThese are a P. O. S.\nIf you dont know what POS is google it, because I cant say it on amazon. Basically, the layer has an OFFSET. The layers do not match in position. This will give you CRC errors if you have a file which is directly on a layer break, or at the end of the disk. DO NOT BUY, I REPEAT, DO NOT BUY.\nIM NOT SURE WHY MEMOREX HASNT RECALLED THEIR CRUMMY PRODUCT\n\n\n\n\nB0009M96K4:was_not_recognizeable:paul_ferguson_\"pferg\"\n\n\nB0009M96K4\n\n\nMemorex DVD+R 8.5GB Double Layer 2.4x 15 Pack Spindle: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nWas not recognizeable\n\n\nMarch 23, 2006\n\n\nPaul Ferguson \"pferg\"\n\n\n\n\n\nthese were not recognizable by my Sony Dual layer burner... maybe with more attempts and practice they will be\n\n\n\n\nB00009KAPX:one-third_the_features_for_three-quarters_of_the_price:d._powell\n\n\nB00009KAPX\n\n\nBelkin Auto Kit for iPod: Electronics\n\n\nelectronics\n\n\n12 of 26\n\n\n1.0\n\n\nOne-third the features for three-quarters of the price\n\n\nDecember 13, 2005\n\n\nD. Powell\n\n\nPennsylvania, USA\n\n\nMy wife bought this as a gift for me, thinking that it included an FM tuner. For ten dollars more, she could have purchased a product that does everything this one does (it is essentially a $35 car charger) plus the tuner. If you want to use this product to actually listen to your iPod in your car, you'll need to purchase yet another device: either the cassette adapter (old school! part F8V366-APL) or the FM transmitter (part F8V367-APL). Either one connects through a jack in the side of this unit. The FM transmitter is another $40 (you're now $20-30 over what the Monster iCarPlay, which does all of the above, would have cost you)...and, surprise! The transmitter takes AAA batteries! There's no way to power the transmitter through the Auto Kit. To power the FM transmitter without batteries, you must purchase yet a third accessory, the \"Mobile Power Cord for iPod\" (F8V7067-APL, just $15!)...except your car's power plug is occupied by the Auto Kit, so you'll need to buy a twin-plug adapter for that.\n\nAre we having fun yet? I'm not, and am wishing I'd skipped all this nonsense and just purchased the iCarPlay in the first place\n\n\n\n\nB00001RME1:just_did_not_work!:\n\n\nB00001RME1\n\n\nInvent It! Iron-On Transfers: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n1.0\n\n\nJust did not work!\n\n\nJune 12, 2001\n\n\n\n\n\n\n\n\nI am the owner of a home day care and I had all of the kids bring in T-shirts so that we could all create our own unique father's day gifts with these iron-on transfers. When I say that I followed the directions Precisely I am not fooling! I was so concerned about slip ups that I made sure I followed the directions to the letter, but every time I tried to pull off the cooled transfer paper off came half of the design and the rest styed on the paper! Very disappointing\n\n\n\n\nB000BK2ERQ:bad_luck_or_low_quality_product??:cristian\n\n\nB000BK2ERQ\n\n\nRCA Lyra RD2317FM 1 GB Digital Audio Player with FM Transmitter: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n2.0\n\n\nBad luck or low quality product??\n\n\nAugust 9, 2006\n\n\nCristian\n\n\nSierra Vista, AZ USA\n\n\nVery bad start, bad point for Amazon. I wanted to get a good quality device and that is the reason I chose buying from Amazon even it was more expensive than other sites. \nI just turned on the device and tried to search for broadcasts. It was like all the radios in US were shut down or at least in my region. By mistake I filliped the device and surprise: the device started receiving radio signal. After that everything seemed to be OK but some strange noise in headphones. \nCons: failure/malfunctioning while operating the radio.\nPros: \t- great sound (with other than manufacturer provided headphones);\n\t- long operating time using rechargeable battery;\n\t- excellent expandable via SD cards feature;\n\t- I love the flip display capability.\nI really love that device because of its features and I hope it was only my mischance getting a damaged one. However I requested Amazon the replacement of the device instead of reimbursement. I also already advised some friends to buy that device.\nAs a good point for Amazon, the very fast answer when I requested the replacement of the device. \nI will come back with updates after I receive the replacement. Hope a good one next time.\nThank you Amazon Team.\n\nComing back after receiving the replacement device.\nFirst of all it was a very good idea buying Amazon fulfilled products even it was a bit more expensive than other sellers. The replacement came faster than the product. Excellent Amazon Team and very professional. It's a pleasure doing business with you.\nThe device works perfectly. \nReally a device you must have. \nMore than that, my friends I recommended the mp3 player are very happy with their new acquisition. They were luckier than me. \n5 Stars for the product now.\n\n\n\n\nB000BB80TQ:wish_i_had_done_more_research:angela_markwalter_\"1abm\"\n\n\nB000BB80TQ\n\n\nApple MA072G/A Dock for iPod Nano 1st Generation: Electronics\n\n\nelectronics\n\n\n31 of 33\n\n\n2.0\n\n\nWish I had done more research\n\n\nJanuary 28, 2006\n\n\nAngela Markwalter \"1abm\"\n\n\nDacula, GA USA\n\n\nYeah, the Nano Dock is useful; however, be aware that it comes with NO cords whatsoever. This means that you have to purchase yet another accessory for it to be of any practical use. BOO Apple! There is absolutely no excuse for following in the footsteps of Microsoft\n\n\n\n\nB0001OTBUK:images_heavily_out_of_focus_despite_adjustment_-_returning:j._hammerschmidt\n\n\nB0001OTBUK\n\n\nLabtec WebCam Pro: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nimages heavily out of focus despite adjustment - returning\n\n\nOctober 7, 2006\n\n\nJ. Hammerschmidt\n\n\nMountain View, CA\n\n\nJust got this camera in the mail as a birthday present. Installing the software was easy, but unfortunately the camera is essentially broken. It is only (somewhat) in-focus for items about 1 inch away. Adjusting the focus ring doesn't help, so this camera's build seems to be defective. My brother bought the same camera and it doesn't seem to have the same problem. Anyway, returning mine.\n\n\n\n\nB0000CEPE8:too_many_problems:wireless-less\n\n\nB0000CEPE8\n\n\nD-Link DWL-G120 Wireless USB Adapter, 802.11g, 54Mbps: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n1.0\n\n\nToo many problems\n\n\nAugust 22, 2006\n\n\nWireless-less\n\n\nPhiladelphia, Pa United States\n\n\nI had a D-link USB adaptor before which was really good, but was old. I upgraded to a wireless G Linksys USB adaptor, but I was supplied with old goods (a Linksys sales manager confirmed that), so I sent that back. I thought I'd try D-Link again. Bad move. First, I tried to upgrade the drivers. My current version was 1.0.5.0. I downloaded the version 1.12 from D-Link's site. After uninstalling and reinstalling (several times) I still had 1.0.5.0. An hour and a half on the phone with D-Link tech support and I still had 1.0.5.0. Another hour on Google, and I'm not the only one with this issue. Seems to be a version-control issue with D-Link. Then, even more irritating, every time after I use my wired connection and go back to wireless I have to reinstall the drivers and reboot. And re-enter all my security info again. (I believe this has something to do with using a different USB port from the last time, but whatever the reason, it's unacceptable). This adaptor's going back too. Don't know what I'll try next. \n\n\n\n\nB00004Z0C7:long_cord_didn't_work:ontheroad\n\n\nB00004Z0C7\n\n\nMaxell HP20 Headphone Extension Cord with Adapters: Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n1.0\n\n\nLong cord didn't work\n\n\nMarch 19, 2005\n\n\nOnTheRoad\n\n\nDenver, CO\n\n\nThe long cord simply didn't work. I returned it to Maxell, using the address on the back of the package, and had the product returned to me because the facility has closed. I've spent almost $5 trying to return an $8 product.\n\n\n\n\nB0009RKL5S:tdk_branded,_not_made_by_tdk:wannabe_patent_lawyer\n\n\nB0009RKL5S\n\n\nTDK Electronics 4.7GB/16x DVD-R (100-Pack Spindle): Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n1.0\n\n\nTDK Branded, not made by TDK\n\n\nNovember 12, 2006\n\n\nwannabe_patent_lawyer\n\n\n\n\n\nThese discs are not really TDK - they are low-quality discs branded and priced as TDK. \nI have a Samsung S182M DVD Burner and it will not recognize these discs at all. It just spins them around and around forever. The one time I got it to recognize one of these, it reported that it was Media ID Daxon0016s. Avoid these.\n\n\n\n\nB00004Z6BJ:terrible_network_card_:stefan\n\n\nB00004Z6BJ\n\n\nNetgear FA311 TX 10/100 Ethernet Card: Electronics\n\n\nelectronics\n\n\n6 of 7\n\n\n1.0\n\n\nTerrible network card \n\n\nSeptember 5, 2005\n\n\nStefan\n\n\nPA\n\n\nSUCKS I had this right form the start with my compaq 7970 which is amd k7 processor and has the via chipset. The computer seemed to work for a while or at least had few errors when I first used it but I guess that was when I had windows 98 for 2 years. When I upgraded to windows XP it just got terrible I had frequent blue screens and errors. I thought it was my video card or my ram or Irq sharing tried all that didn't seem to be it. Eventually I was wrong it was this card as mentioned before by others apparently it does not like some amount of internet use and when the card gets stuck it shuts down and windows xp just says error or points to the video card or irq sharing ot page fault in non page area as the problem. The card was the problem buy another card other than fa310,fa311 the one I have and fa312 even if you have intel or something else I would not waist my money on this card it will probably give you problems down the road. I have tried updates from [...] and updating my ram and video card have checked my irqs and reformating hard drive none worked. I put in a 3 com card I bought a few years before this computer and wow no crashes that other computer is now wirelessly connected to my home network\n\n\n\n\nB00004Z6BJ:ahhh!_its_horrible!:joe_shmoe\n\n\nB00004Z6BJ\n\n\nNetgear FA311 TX 10/100 Ethernet Card: Electronics\n\n\nelectronics\n\n\n2 of 5\n\n\n1.0\n\n\nAHHH! its horrible!\n\n\nMay 5, 2005\n\n\nJoe Shmoe\n\n\nUSA\n\n\nPlease! for the love of god! do not buy this product! it is absolutly horrible. i'll admit that since i bought my computer and go this card back in 2002, i hadn't had problems until the past few months. i play an online game - a MMORPG - and after every time the game is updated and i have to install the updates i am unable to log onto the game! it freezes up, and either makes my computer crash or destroys my internet connections, forcing me to restart. as you can imagine, this gets rather frustrating, and if you play MMORPGs, you can see how it is even more frustrating. i am currently running XP home edition. i beg you, DO NOT BUY THIS ETHERNET CARD JUST BECAUSE IT IS THE CHEAPEST ONE OUT THERE! you get what you pay for! now i'm off to buy a new one\n\n\n\n\nB00003CWDT:product_application:bob\n\n\nB00003CWDT\n\n\nMonster PC700 Computer PowerCenter� with Surge Protection: Electronics\n\n\nelectronics\n\n\n10 of 13\n\n\n2.0\n\n\nProduct application\n\n\nMay 7, 2004\n\n\nBob\n\n\nUSA\n\n\nThe photo on the Target website shows a cable input/output for cable protection. The unit arrived with a phone line input/output-not much help for those expecting protection on their cable line\n\n\n\n\nB00004SYNX:mac_users:_d-link's_claims_about_compatibility_are_false._look_elsewhere:traveler\n\n\nB00004SYNX\n\n\nD-Link DFE-530TX+ 10/100TX PCI Adapter, WOL: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n1.0\n\n\nMac users: D-Link's claims about compatibility are false. Look elsewhere\n\n\nJuly 13, 2006\n\n\nTraveler\n\n\nNew England\n\n\nI have a G4 tower Mac that did have a built in Ethernet. Unfortunately, a lightening strike took out the port and I was forced to get a PCI card to replace the port. The first card I bought was the D-Link DFE-530TX+. I was excited because the box clearly said that the card would work with Macs and that the \"minimum requirement\" was OS 10.2.\n\nThey lied. OS 10.2 is the _maximum_ for this card. For most people, it won't work with 10.3 and up. D-Link has no driver for 10.3 or higher, only 10.2. A google search shows that some people have been able to get the card to work with 10.3. I couldn't. I tried several different drivers (some users recommended a driver from Realtek) and none worked.\n\nIt's my guess (and totally a guess) that this card is really only for older Macs that are limited to 10.2. D-Link doesn't seem to have any intention of supporting modern Macs.\n\nThis is the second D-Link product I've used (the first was a cable modem) and I've been disappointed both times. I can't speak for Windows users, but this company doesn't deserve Mac business.\n\n\n\n\nB000FGNM6I:not_very_dependable:kkrome25_\"kkrome25\"\n\n\nB000FGNM6I\n\n\nTranscend 2GB (SD) Secure Digital Card: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nNot very dependable\n\n\nOctober 19, 2006\n\n\nkkrome25 \"kkrome25\"\n\n\nCalifornia, USA\n\n\nI started out with 2 gigabytes. And then after I had filled this card with about 500 photos, I downloaded all the photos to my computer. After deleting all of the photos from this card, the capacity had suddenly decreased to only half! What happened to the other 1 gigabyte? I'm sure they pre-formatted this card to appear like it's 2 gigabytes, but what a surprise when you start using it. Not good\n\n\n\n\nB0007DFXEI:item_not_delivered_in_reasonable_time_frame:g._galperin_\"gosha\"\n\n\nB0007DFXEI\n\n\nVelocity 8X 4.7 GB DVD-R (10-Pack Spindle): Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n1.0\n\n\nItem not delivered in reasonable time frame\n\n\nJuly 27, 2005\n\n\nG. Galperin \"Gosha\"\n\n\nNJ\n\n\nI canceled the order of Velocity DVD-R disks since it took too long to deliver\n\n\n\n\nB0001YFW3K:not_so_great_as_they_say.just_ok:toytreker_\"wallytrek\"\n\n\nB0001YFW3K\n\n\nMotorola T7400R GMRS / FRS Rechargeable 12 Mile Two-Way Radio, 2 Pack, Yellow: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n2.0\n\n\nNOT SO GREAT AS THEY SAY.JUST OK\n\n\nJune 15, 2006\n\n\nTOYTREKER \"wallytrek\"\n\n\nDOWNERS GROVE, IL USA\n\n\nThese radios look nice but there are some problems here. First the batteries do not last long unless you do not talk. Second the biggest problem for me was uncompatibility with other radios and brands. The receive quality is very poor and hard to understand at times. I also hace a T7200 and the difference is like night and day. The 7200 is the best. The 7400 i believe is an has flaws and that is why you see them priced so low. I paid $24.00 on ebay and also have seen the set at home depot clearance for $44.00 today. What good is any radio if you cant understand what someone is saying? Good try motorola but this is a poor product.NOT RECOMMENDED TO PURCHASE\n\n\n\n\nB0001YFW3K:so_not_available_in_2-3_weeks:laura\n\n\nB0001YFW3K\n\n\nMotorola T7400R GMRS / FRS Rechargeable 12 Mile Two-Way Radio, 2 Pack, Yellow: Electronics\n\n\nelectronics\n\n\n30 of 89\n\n\n1.0\n\n\nSo not available in 2-3 weeks\n\n\nSeptember 1, 2004\n\n\nLaura\n\n\nMA\n\n\nI ordered this radio back in May and I still don't have them. It is now September. I have exchanged several emails with Amazon, the first one I received a very nice but untrue reply. The second response was borderline rude but at least honest, they told me that they don't have the product and don't know when they will. If you are looking for something to use now, don't waste your time with ordering this radio\n\n\n\n\nB000BD35SK:horribly_noisy:numb_d._plum\n\n\nB000BD35SK\n\n\nGriffin Powerjolt Car Charger for iPod: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n2.0\n\n\nHorribly noisy\n\n\nMay 20, 2006\n\n\nNumb D. Plum\n\n\nColumbia MO, USA\n\n\nThe box says you can either \"power\" or \"charge\" your iPod with the Powerjolt. To me, \"power\" means listening to the iPod while it is plugged in to the Powerjolt. However, there is an overwhelming amount of buzz and static when the iPod is playing while connected to the Powerjolt. Maybe this thing works ok to \"charge\" while the iPod is not playing, but it is not an acceptable way to \"power\" the iPod while it is playing. \n\n\n\n\nB00085ESA0:cheap_as_in_quality_not_price:chaos_rex\n\n\nB00085ESA0\n\n\nPhilips HN 110 Folding Noise Canceling Headphones: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nCheap as in Quality not Price\n\n\nOctober 2, 2006\n\n\nChaos Rex\n\n\nBay Area, CA USA\n\n\nI commute via public transportation, the majority of that on a noisy train. Earbuds and over ear headphones don't block out enough of the noise to enjoy the music. I received these Philips HN110 headphones as a gift. \n\nWhen I first got these I was pretty disappointed at how flimsy they appeared. I have some KOSS over ear head phones which are nice and beefy. These are scrawny but on the other hand take up less space. They use a single AAA batt. Using them on the train I was able to turn down the volume on my music player a tad but a lot of the noise still gets through. I'm not an expert on these but I guess this noise cancelling works mainly on lower frequencies. Alot of the noise on the train is higher in pitch and thus gets through. The next thing I noticed was that with a cord coming out of both the left and right earphone you are limited in how you can put these on and take them off. My KOSS and Sony headphones and earbuds use the single cord to one phone and the connecting cord from there (or it runs through the headphone band). I realize I like this single cord feature very much. Again these Philips HN 110 don't have this. \n\nI also noticed that when the Noise Cancelling is turned on and the wind is blowing you can hear the wind as if it were amplified. \n\nAfter about a month and a half the battery compartment door latch broke inside. Now I can't open the battery compartment without doing some damage to these headphones. I checked Philips website for info on these phones but I couldn't find any. I called their customer support and they told me I had to call a different #. I called the different number and got the Return Merchandise Authorization # but was told I need to send the receipt in. I got them as a gift so I don't have the receipt. \n\nIn summary, their noise cancelling ability is pretty week. Better than my hefty non-noise cancelling KOSS over ears or my stethascope like Sony ear buds but only by about 10% or so (just to put a number in their). Flimsy design. Cords from both earphones. Hear the wind amplified. Battery compartment latch broke after a month and a half and their warrenty sucks\n\n\n\n\nB0002E1HGK:low_quality:j._walters\n\n\nB0002E1HGK\n\n\nMusician's Friend Lo-Z Microphone Cable, 20 Foot: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nLow quality\n\n\nNovember 23, 2006\n\n\nJ. Walters\n\n\nCharlotte, NC USA\n\n\nAfter only having the cable for a week under very light usage, the cable stopped working intermittently. It makes crackling noises when the cable is moved. I am not talking about whipping or quick movement, just gentle movement of the cable causes crackling and loss of signal. I was about to scrap the cable altogether but I decided to resolder the connector to see if that would help. I cut off about half a foot of the cable and resoldered it, only to find that the same problem persisted. I got a bad cable, obviously..\n\n\n\n\nB0009I6K7K:did_not_work-avoid_this_product:oceanblue996_\"blue\"\n\n\nB0009I6K7K\n\n\nAltec Lansing inMotion IM3C Portable Audio System for iPod and iPod Photo: Electronics\n\n\nelectronics\n\n\n0 of 3\n\n\n1.0\n\n\nDid not work-avoid this product\n\n\nNovember 11, 2006\n\n\nOceanblue996 \"Blue\"\n\n\nCalifornia\n\n\nThe iM3c was a gift - looked like a great little portable stereo for my Pod. First used it on a trip. Started off playing ok and then the sound cut in and out. Frustrated me for the week as I didn't know if it would play or not on any given day. When it did play it was ok but not great. Finally, it didn't play anymore.\n\nReturned it to the Apple Store where purchased to find that there was only a 15 day return policy and I missed it by 2 days. So, I tried to contact Altec Lansing. The phone number in the owners book didn't work although I tried for several days. Then I emailed the Altec support email. No response after two emails.\n\nSometimes a product doesn't work and it's easy to return or get repaired. On this one, it looks like this product will be finding the trash can.\n\nDo not buy.\n\n\n\n\n\nB00084O8A6:do_not_do_business_with_bargaincell!:r._roybal_\"rroybal\"\n\n\nB00084O8A6\n\n\nDigicom iPod Car Charger (White): Electronics\n\n\nelectronics\n\n\n1 of 10\n\n\n1.0\n\n\nDo NOT do business with Bargaincell!\n\n\nMarch 31, 2006\n\n\nR. Roybal \"RRoybal\"\n\n\nUSA\n\n\nBought a cell phone battery that, of course, died one week past their return/refund deadline. Refused to refund or replace even though the product is CLEARLY defective. This company is a ripoff and will never do business with them again. Consider yourself warned\n\n\n\n\nB0000SVSNA:short_lived_performance:digitalbuff\n\n\nB0000SVSNA\n\n\nMojo MP3/FM Player 256MB USB with voice Recorder & Headphones: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n2.0\n\n\nShort lived performance\n\n\nNovember 3, 2005\n\n\nDigitalBuff\n\n\nChicago, IL\n\n\nI bought the TDK Mojo 256 about a year and 3 months ago. It worked wonderfully for the first year - did what it was supposed to and the radio quality was great. Then I upgraded to Windows XP and that's when everything started going wrong. This MP3 player needs propreitary software to copy files from your hard disk or CD to the mp3 player. Unfortunately the software doesn't work well at all with XP. Right now, my player is piece of junk. I have tried copying files which it shows as copied but then when you reconnect the player, they don't show up on there. What's more, formatting the internal memory will also not release the space and so I have the internal memory full but it shows up in the software as free. However, it won't copy over any new files and gives me a \"Storage Full\" error. It doesn't act reliably with XP either and I had to unplug and replug several times for it to even read the list of songs on the mp3. It lasted all of 15 months. While it sounds like a great deal ( good features for price ). DO NOT buy this if you will be using it with XP and if you do, please don't expect it to last more than 12-15 months\n\n\n\n\nB0000SVSNA:its_a_piece_of_junk:mr._g._chawla\n\n\nB0000SVSNA\n\n\nMojo MP3/FM Player 256MB USB with voice Recorder & Headphones: Electronics\n\n\nelectronics\n\n\n5 of 7\n\n\n1.0\n\n\nIts a piece of junk\n\n\nFebruary 6, 2005\n\n\nMr. G. Chawla\n\n\nAtlanta\n\n\nThis player is a piece of junk.\nFirst the USB really doesnt mean you can hot swap. You can access the files only if the software is installed on the computer which means that you cannot pick up MP3 from any computer.\nSecond, it gives errors with XP. It tries to install drivers every time.\nTHird, removing old and adding new MP3 is such a hassle. It takes minutes to delete old files and add new. Seems like you are working with a floppy.\nFourth, its Mojo software hangs all the time.\nFifth, it sucks battery life.\nSixth, if your MP3 is not in a directory, i.e. if copied directly, it wont show. Then it wont delete and will show 200 MB as not free space. Formatting is the only way out.\nSeventh, it came with a mail in rebate which never showed up.\nOverall, a bad experience\n\n\n\n\nB0000AUB1U:poor_quality_and_\"zero_customer_service\":the_fiddler\n\n\nB0000AUB1U\n\n\nSkyGolf SkyCaddie GPS rangefinder delivers distances on the golf course: Electronics\n\n\nelectronics\n\n\n8 of 12\n\n\n1.0\n\n\nPoor Quality and \"Zero Customer Service\"\n\n\nApril 14, 2006\n\n\nThe Fiddler\n\n\nNew Orleans\n\n\nDoes the unit work??,...Well yes to a degree. It takes 6 holes to lock on to the satellites and then does a fair job of giving distances. The display is poor and though they say \"up to 40 objects per hole\" there is normally 2-4 objects per hole. All this depends upon the Skycaddie course recorder. The user (you) can not modify the course or add any objects that the course recorder didn't think was important.\nNow the bad part. The unit attaches via button and belt clip (like a cell phone). My button (part that attaches to main unit) has died twice in the first 30 days. When it breaks again,...which it will often,..the unit will probally hit the dirt. This isn't good. But fear not. It will be packed away weeks trying to get in touch with Skygolf. And another week to get a replacement button (which has been modified even weaker than the original one). Now my replacement broke on the first time I clipped it to the belt. Now back to Skycaddie to get another. After being on hold for another hour, they need to transfer you to another tech that handles parts. This will take another hour. You then get a message to leave a message. Normally within a week someone will call you back. By normally I mean about 35% of the time you leave messages they return them. Would I buy another,... duh,.... NO\n\n\n\n\nB0001IXUDK:be_an_educated_consumer!__you_don't_have_to_spend_a_lot_for_*digital*_cables:navarro_l._parker_\"motion_graphics_dude\"\n\n\nB0001IXUDK\n\n\nMonster Cable HDMI 400 Cable (2 Meters): Electronics\n\n\nelectronics\n\n\n26 of 26\n\n\n1.0\n\n\nBe an educated consumer! You don't have to spend a lot for *digital* cables\n\n\nSeptember 20, 2006\n\n\nNavarro L. Parker \"Motion Graphics Dude\"\n\n\nSan Diego, CA United States\n\n\nYou don't have to spend a fortune on expensive cables when you are dealing with digital signals. Keep in mind that all digital interfaces have error correction built into them. Did you know the internal components of consumer electronics are connected with tin and nickel? There's no gold inside! So there's no need for oxygen-free, gold tipped, multi sheilded, super mega supreme cables. If you don't believe me, buy this cable and a no-name cheapie. Compare them for yourself -- you will see no difference.\n\nWith analog signals, a good quality cable may make a difference (although the difference is usually only visible on an oscilloscope).\n\n\n\n\nB0001IXUDK:great_product,_terrible_cost.:g._harper_\"why_does_this_site_do_this?\"\n\n\nB0001IXUDK\n\n\nMonster Cable HDMI 400 Cable (2 Meters): Electronics\n\n\nelectronics\n\n\n56 of 57\n\n\n2.0\n\n\nGreat product, terrible cost.\n\n\nJuly 5, 2006\n\n\nG. Harper \"why does this site do this?\"\n\n\nCollege in KY, USA\n\n\nHave you ever heard the saying that something would cost you \"an arm and a leg\"? These cables cost that, your first son, and bidding rights to your estate. Don't get me wrong- Monster makes great cables. Let there be no confusion on this point. But they're just incredibly expensive, and this is made much worse with these cables.\n\nSeveral people have pointed out that the digital signal on HDMI cables must adhere to error-correction and certain standards (even a $10 cable cable must meet some specifications to even reach production)- and this is largely true. Unless BluRay implements new features that require new cables at some point in the future, there's really very little difference between HDMI cables. Yes, the gold-plated pins and other features will improve your connection a bit, but the improvement will be far less than you'd see between any other \"cheap\" versus \"designer\" cables on the market.\n\nBut Monster cables really are made to a standard that makes some difference, and that's worth mentioning. This cable is built sturdy, so that none but the most harsh of treatment could ever damage it. And the shielding isn't just the usual electrical shielding- Monster cables are toughened to the point that I wouldn't doubt if they're certified against nuclear fallout. If you have the money to blow and you want the confidence of knowing that your entertainment center could fare well in an EMP strike, you're looking at the right cables.\n\nBut for nearly all consumers, these cables cost 4x as much as alternatives, with no real plusses. At the very least, you can pick up a mid-grade cable (Sony, etc) at a mid-grade price ($40) to get the increased sturdiness and the gold-plated connections. And over these products, the Monster cables basically have nothing at all except their branding. Please do look up a cheaper alternative this time, and spend some of the extra money on an extra set of whatever tools you use to keep your screen clean. You'll get much better results with these tools than you will worrying about $100 HDMI cables\n\n\n\n\nB0001IXUDK:dumb_dumb_dumb:hibernia\n\n\nB0001IXUDK\n\n\nMonster Cable HDMI 400 Cable (2 Meters): Electronics\n\n\nelectronics\n\n\n66 of 66\n\n\n1.0\n\n\nDumb dumb dumb\n\n\nJune 26, 2006\n\n\nHibernia\n\n\nCork, County Cork Ireland\n\n\nWhat do they say about fools and money?\n\nHDMI uses TMDS which is a digital transmission system with built-in error correction. TMDS has been designed to work over cheap cabling and a $10 HDMI cable will meet the specification. If you spend 9 times more on this cable than another one then you have just wasted $80. That's all there is to it. \n\nYou don't need expensive shielding or gold plated connectors for a cable. The picture or the sound are not richer or more vibrant with a $90 cable over a $10. In fact the signal is identical. Let me repeat - HDMI (& DVI-D) devices talk digitally with error correction. This cable and ones like it are for suckers only.\n\n\n\n\nB000EF1820:think_before_you_buy:m._newman\n\n\nB000EF1820\n\n\nBelkin F8Z049-BLK TuneBase FM for iPod: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nThink Before You Buy\n\n\nOctober 21, 2006\n\n\nM. Newman\n\n\nSeattle\n\n\nIf you can't abide an ever-present low hiss in the background, and louder interference in the presence of power lines, don't buy this product. I wish I'd read the reviews more carefully before I bought. A number of even the most favorable ones mentioned this annoyance.\n\nThe manufacturer replaced the first unit after I complained, but it was no improvement. This may be the best product of its type out there, but it still comes up short.\n\n\n\n\n\nB000EF1820:love_the_concept,_hate_the_product!:jennifer_a._johnson\n\n\nB000EF1820\n\n\nBelkin F8Z049-BLK TuneBase FM for iPod: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n2.0\n\n\nLove the concept, hate the product!\n\n\nSeptember 6, 2006\n\n\nJennifer A. Johnson\n\n\nst. louis, mo\n\n\nI think this product design concept is great. I love the flexibe arm size. It fits great in my car. The CRAPPY part. I live near metro st. louis and i could not hold a signal ANYWHERE! I always had some intereference. whether it was fuzz or the signal going in and out. This product did not meet my expectations, especially since the back of the package states I should have crystal clear sound. This is not true. I have returned this product after little success over the first week of purchase. I am now waiting to get my DLO transpod 2 in the mail to try it out. I want something to work!\n\n\n\n\nB0007WTHLY:listen_to_the_rest_of_us:j._bart\n\n\nB0007WTHLY\n\n\nEtymotic Research ER6 Isolator Earphones (Black): Electronics\n\n\nelectronics\n\n\n5 of 8\n\n\n2.0\n\n\nListen to the rest of us\n\n\nAugust 27, 2006\n\n\nJ. Bart\n\n\nFlorida\n\n\nI have to say, since I really started listening to music I have payed attention to a lot more detail of sound. With that said I am not a bass junky; but bass and compression of the overall mix is what makes a sound a good sound. I currently owned a pair of Sony MDR-EX81LP (that cost me 30-40 dollars a year ago) I am a medical student and I study all day and all night, but cannot have my music blasting. Conversely I needed to have a nice isolation of my sound. Comparing my old Sony to the new ER-6 that I just got, I am EXTREMELY DISSAPOINTED with their performance. I was expecting a great sound with minimal noise from the outside and get almost no response at the level that I studied with my old ear buds. To be fair remember that these are not noise cancelling, they are noise reducing. There is no component involved here, they are basically really good earplugs with speakers in them. My wife just ran the vacuum and I barely heard it. The earplug reduced noise feature is great and comfortable; however my other ones (Sony earbuds) sound better. A combination of the two would be great. Keep in mind that this is my opinion and make your own based on why you are buying earbuds. Environmental sound reduction is great, sound quality is not as good as some others.....choose your fault and pay attention to pricing. \n\n\n\n\nB0007WTHLY:not_worth_the_money:scott_knecht_\"wagon78\"\n\n\nB0007WTHLY\n\n\nEtymotic Research ER6 Isolator Earphones (Black): Electronics\n\n\nelectronics\n\n\n2 of 4\n\n\n2.0\n\n\nNot Worth the Money\n\n\nAugust 20, 2006\n\n\nScott Knecht \"wagon78\"\n\n\nConnecticut\n\n\nI was disappointed with these earphones. The sound level was low and higher frequency sound seemed attenuated. The $30 Sony MDR-EX51 earphones I already owned sounded much better\n\n\n\n\nB0002MK9WU:phantom_paper_jam_can_cost_extra:savy_mom\n\n\nB0002MK9WU\n\n\nHP PhotoSmart 325 Compact Photo Printer: Electronics\n\n\nelectronics\n\n\n9 of 9\n\n\n1.0\n\n\nPhantom Paper Jam can cost extra\n\n\nDecember 24, 2005\n\n\nsavy mom\n\n\nMinnesota\n\n\nI have had the 325 model for less than a year. Up until recently I was satisfied with its performance using it to print less than 5 pictures at a time. Recently, I needed to print a series of pictures (more than 10 in one sitting) and had an error message-print cartridge jam-I fixed the paper jam but continued to get the error message. I called HP and they stated this was a fatal error in the unit and encouraged me to purchase the updated model rather than fix my printer. I searched the internet for a solution only to find that others had encountered this and the solution was to purchase the \"new'model- A not very cost effective solution as this was not supposted to be a disposable model.\n\n\n\n\nB0007TC39A:file_13:joblessnusa\n\n\nB0007TC39A\n\n\nSanDisk 1GB Cruzer Micro USB Flash Drive (SDCZ4-1024-A10, Retail Package): Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n1.0\n\n\nFILE 13\n\n\nNovember 17, 2006\n\n\nJOBLESSNUSA\n\n\n\n\n\nDON'T BUY THESE THINGS ! THEY GO BAD AND SANDISK WON'T REPLACE THEM. BUY OCZ OR A-DATA OR SOMETHING. THESE THINGS ARE VERY UNRELIABLE..\n\n\n\n\nB00022TN36:who_knows_how_to_solve_the_c:13:01_error_?:francisca_estay_\"fran_estay\"\n\n\nB00022TN36\n\n\nSony 128 MB Memory Stick Media (MSH-128): Electronics\n\n\nelectronics\n\n\n28 of 30\n\n\n1.0\n\n\nWho knows how to solve the C:13:01 error ?\n\n\nMay 26, 2005\n\n\nFrancisca Estay \"Fran Estay\"\n\n\nSantiago, Chile\n\n\nI bought the Sony 128 MB Memory Stick (MSH-128) a few months ago, but I only used it once. When I was in my 20th picture, an error appeared in the screen of my Sony P100 camera:\n\nError C:13:01, Memory Stick failed\n\nFirst, I went to Sony Service Center, but they didn't know what was happening. Then, I went to different Sony Stores, but the only answer they gave me was that my Memory Stick is failed, that it's impossible to fix or format it and, the worst, there is no way to save the pictures !!!!\n\nFinally, I tried to solved it by myself, and I \"google\" the C:13:01 error. If you do it, you will find hundreds of people with the same problem, looking for a solution.\n\nThat's why I don't recommend this product. If all of us have the same problem, you can be the next\n\n\n\n\nB0002XZQHM:be_careful_of_this_one:fei_zhao\n\n\nB0002XZQHM\n\n\nLexar Media 1 GB Secure Digital Memory Card (SD1GB-231): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nbe careful of this one\n\n\nMarch 20, 2006\n\n\nFei Zhao\n\n\n\n\n\nA friend gave me this card for free, when I travled to Arizona. I use this card in my Dell X51V.\n\nFrom time to time, this card can't be recoginize by the PDA. I have to pull it out and re-insert it in, and reset my PDA.\nThen oneday, my GPS software showed \"Map data corrupted\". All the data in the card was wiped out, and the card's space reduced to around 300M, have no idea where the remaining space goes to.\n\nDon't put important data on this one!!! That's my lesson\n\n\n\n\nB0002XZQHM:never_work_properly:anthony_villalobos_\"video_game_crazy_lover\"\n\n\nB0002XZQHM\n\n\nLexar Media 1 GB Secure Digital Memory Card (SD1GB-231): Electronics\n\n\nelectronics\n\n\n8 of 11\n\n\n1.0\n\n\nNever work properly\n\n\nAugust 18, 2005\n\n\nAnthony Villalobos \"Video Game Crazy Lover\"\n\n\nMaracaibo, Zulia - Venezuela\n\n\nI bought this SD card on April 11. Unfortunately, the card doesn't work properly, in fact, my camera and my computer never detect it. \n\nAlso, I recived a different card, it didn't like the picture on this page. I think I was swindled. \n\nWell, I had to buy another SD card\n\n\n\n\nB0002XZQHM:a_lame_duck_card:kyle_witten\n\n\nB0002XZQHM\n\n\nLexar Media 1 GB Secure Digital Memory Card (SD1GB-231): Electronics\n\n\nelectronics\n\n\n6 of 9\n\n\n2.0\n\n\nA lame duck card\n\n\nFebruary 8, 2005\n\n\nKyle Witten\n\n\nBerea, KY\n\n\nI purchased the 1gb Lexar card, and 5 hours after I started using it, I've got corrupted data that no amount of formating or data deletion will remedy. Given that I had already disceted the box into small pieces to send in for mail in rebates, I contacted Lexar and sent it to them on a Warrenty claim [cheaper then driving back to the retail location I purchased it at anyway].\n\nI got it back yesterday, and it was suffering the same problem I sent it in for in the first place!\n\nTheir solution, they pay for the return shipping of card #2, and swap it out for two 512mb cards.\n\nNot happy. But at least they are somewhat responsive to fixing the problem\n\n\n\n\nB0007KXFH8:two_units,_both_failed:james_l._guelde_\"hubba_bubba\"\n\n\nB0007KXFH8\n\n\nGarmin GPS10 Bluetooth Enabled Wireless GPS with Mount and Software: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n1.0\n\n\nTwo Units, Both Failed\n\n\nJune 10, 2006\n\n\nJames L. Guelde \"Hubba Bubba\"\n\n\nFar West\n\n\nI bought a brand new unit directly from Garmin. It worked for about a week, then it would no longer charge. I returned it and got a REFURBISHED UNIT in exchange.\n\nThe refurbished unit did better.. it lasted 3 weeks before failing. To use it, I must use the charger plug constantly, as the unit will not charge.\n\nSending it back now to try a third unit\n\n\n\n\nB0002H4YUI:look_like_multiple_functional_but_not_working:space_traveller_\"s.t.\"\n\n\nB0002H4YUI\n\n\nKensington 33117 International All-in-One Travel Plug Adapter: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n1.0\n\n\nlook like multiple functional but not working\n\n\nAugust 18, 2006\n\n\nspace traveller \"S.T.\"\n\n\nCo, USA\n\n\nI had bought this item due to good review before I went to a trip to Bulgaria. However, it plugs in but doesn't work. Why? I don't know. how should I deal with it now? Is it a defect one? I have no idea, to me I just lost 17 dollars for an experience. (don't buy electronic items from a book store, even it's cheaper) Fortunate I also bought Targus travel adaptor set that save me\n\n\n\n\nB000CLCRVM:don't_buy!!!!!!!!!!:ilovemusic\n\n\nB000CLCRVM\n\n\nDLO Podfolio for 5G iPod: Electronics\n\n\nelectronics\n\n\n2 of 7\n\n\n1.0\n\n\nDON'T BUY!!!!!!!!!!\n\n\nFebruary 1, 2006\n\n\nilovemusic\n\n\n\n\n\nIf you have a 30 gig. video iPod, mabey. But for a 60 gig. it is too tight to it, got a mark on iPod after taking it out, covered up part of the screen on mine, and I'm taking it back right away.\n\nIt is a waste of money to buy this product and I made a mistake by getting it for myself. Get a different case than this one\n\n\n\n\nB000CLCRVM:terrible:gerant_rivera\n\n\nB000CLCRVM\n\n\nDLO Podfolio for 5G iPod: Electronics\n\n\nelectronics\n\n\n1 of 6\n\n\n1.0\n\n\nTerrible\n\n\nJanuary 12, 2006\n\n\nGerant Rivera\n\n\nSan Diego, CA United States\n\n\nPlease, do not buy or use this product. The \"hand-stitched, supple leather\" scratched my new 60G black iPod video. The description says it \"will fit all iPod videos\" but mine was very tight. So far, DLO has not answered my complaints. Neither has Apple. I bought the Speck ToughSkin (have not received it yet) and hope it helps me heal the pain. Bad product\n\n\n\n\nB00006B9WE:didn't_know_you_can_sell_trash_over_amazon.:hovik_akopyan\n\n\nB00006B9WE\n\n\nCyber Acoustics CA2016 2-Piece USB Powered Computer Speaker System (Black): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nDidn't know you can sell trash over amazon.\n\n\nNovember 15, 2006\n\n\nHovik Akopyan\n\n\n\n\n\nIt was horrible, soon as i plug the speakers in it was making a constant noise, really annoying, the speakers were junk, worst purchase i've ever made from amazon. I would've returned them but I didn't want to go through the trouble of sending it back. So i just smashed them and went to best buy and got new ones. I know it was a cheap speaker, but hell it should atleast work properly. Talk about trash\n\n\n\n\nB00006B9WE:horrible_feedback:jc_\"jclair\"\n\n\nB00006B9WE\n\n\nCyber Acoustics CA2016 2-Piece USB Powered Computer Speaker System (Black): Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nHorrible Feedback\n\n\nNovember 12, 2006\n\n\nJC \"jclair\"\n\n\nNew York, NY\n\n\nThese speakers are unusable. They give off horrible feedback even when there's not a cell phone or cordless phone in the room\n\n\n\n\nB0006DNMMO:great__features,_great_price,_horrible_speakerphone:phil_gigante\n\n\nB0006DNMMO\n\n\nHP 1050 Monochrome Inkjet Fax Machine: Electronics\n\n\nelectronics\n\n\n14 of 14\n\n\n2.0\n\n\nGreat features, great price, horrible speakerphone\n\n\nJanuary 11, 2006\n\n\nPhil Gigante\n\n\nYonkers NY\n\n\nThis machine is great looking, compact, and seems to be the perfect combination fax/answering machine/speakerphone. One major problem: The phone is well below average in sound quality, and the speaker is absolutely terrible. The tiny, poorly made speaker is placed in the back left side of the unit and and is totaly inaudible at loud volumes due to distortion. The fax is great, the print quality is great, the speed is great, but you have to lower the volume and lean over with your head against the speaker to hear messages. This problem also renders the speakerphone useless. I returned this unit twice thinking it was defective, but spoke to HP and was told that is just the way this unit works. Buy the Panasonic KX-FHD351 if you need a great sounding unit. The only problem with the Panasonic is that it doesn't come in black, but is a much better phone/answering/speakerphone. \n\n\n\n\nB00005B8M3:don't_buy_it__for_c340:n._stoianova\n\n\nB00005B8M3\n\n\nGarmin 010-10085-00 Cigarette Lighter Adapter: Electronics\n\n\nelectronics\n\n\n6 of 7\n\n\n1.0\n\n\nDon't buy it for C340\n\n\nAugust 23, 2006\n\n\nN. Stoianova\n\n\n\n\n\nI bought it for my c340 but it does not work with it. Maybe it was my fault for not reading the description in depth. I really thought it did say it is good for street pilots, though. There might not be anything wrong with the item but it needs a better technical description\n\n\n\n\nB0000BVUZ4:useless_-_does_not_retract:james_murphy\n\n\nB0000BVUZ4\n\n\nTargus PA225U Retractable Phone & Ethernet Cord (Plastic Casing): Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n1.0\n\n\nUseless - does not retract\n\n\nApril 6, 2004\n\n\nJames Murphy\n\n\nPelham, MA USA\n\n\nI agree with the previous review. This phone cord worked twice. Now, it only retracts about half way, and keeps getting worse. I'm returning it for a refund. Don't waste your time with this\n\n\n\n\nB0000BVUZ4:do_not_wast_your_time_with_this_product...period:alan\n\n\nB0000BVUZ4\n\n\nTargus PA225U Retractable Phone & Ethernet Cord (Plastic Casing): Electronics\n\n\nelectronics\n\n\n4 of 5\n\n\n1.0\n\n\nDO NOT Wast your time with this product...period\n\n\nAugust 8, 2003\n\n\nAlan\n\n\nCarol Stream, IL USA\n\n\nI am on my second one and I will be returning this one also. Both of them worked once and then would not retract fully after that. I was so mad I almost threw it against the wall. I thought the first one was a defect, but then when I got the second one I realized it was a design flaw... If you buy the Targus one keep your receipt because I guarantee you will be returning it. What I don't get is how Targus has the guts to release this product and also put on it: Satisfaction Guaranteed. What a joke..\n\n\n\n\nB00006B9QF:seriously_annoyed:customer\n\n\nB00006B9QF\n\n\nSanDisk 512 MB CompactFlash Card, SDCFB-512-A10 (Retail Package): Electronics\n\n\nelectronics\n\n\n8 of 18\n\n\n1.0\n\n\nseriously annoyed\n\n\nAugust 13, 2005\n\n\ncustomer\n\n\nNew York City\n\n\nThis card is on its way back to Amazon. I purchased it as it was one of the \"recommended items\" with the camera I also purchased, never mind that it doesn't work with the camera. I'm not hugely tech savvy--I (wrongly) assumed that any items recommended with the main item (a $500 camera) would be compatible. Check to make sure that any recommended accessories will actually achieve what you want them to. \n\n\n\n\nB000EWE3GQ:i'm_shocked,_amazon_cancelling_orders_withouth_any_notification.:mark_e._michel\n\n\nB000EWE3GQ\n\n\nSanDisk 4 GB Ultra II Memory Stick PRO Duo (SDMSPDH-4096-901, Retail Package): Electronics\n\n\nelectronics\n\n\n13 of 22\n\n\n1.0\n\n\nI'm shocked, amazon cancelling orders withouth any notification.\n\n\nMarch 31, 2006\n\n\nMark E. Michel\n\n\n\n\n\nThis isn't the type of behavior i would expect from such a valuable and big company as Amazon is. They cancelled my order same way as the other people's. They didn't even send any notification email and i found my order cancelled by looking at the site. Amazon MUST learn that this is illegal and they must ship all the orders placed, if they made a mistake pricing the item is THEIR problem not ours and more after many customers confirmed the price over telephone. If Amazon don't ship the orders already placed they will throw stones over their own roof and make lots of bad publicity. I'm one of many who won't be buying anything ever if Amazon don't send me my ordered items. Count with me for any legal actions that may be taken\n\n\n\n\nB00000J3LY:what_a_joke:sat\n\n\nB00000J3LY\n\n\nHP C6049A Iron-on Transfers: Electronics\n\n\nelectronics\n\n\n3 of 7\n\n\n1.0\n\n\nWhat a joke\n\n\nDecember 10, 2004\n\n\nSat\n\n\n\n\n\nI bought these because I have an HP printer and figured they would be best for the shirts I want to make. I couldn't get them to take for anything in the world. I put the pressure on it, ironed them longer, everything you can think of and they still wouldn't take. Then when I finally got one to take it completely cracked and crumbled and started peeling off before an hour was up. It never made it to the wash, so now I am out the money I paid for them and still have no shirts. Any other brands you suggest instead\n\n\n\n\nB00005RZP3:no_customer_support:\n\n\nB00005RZP3\n\n\nSocket Communications Low Power Wireless LAN CF Card: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nNo customer support\n\n\nFebruary 7, 2004\n\n\n\n\n\n\n\n\nIt's the most expensive CF card with absolutely no customer support. After a while the CF card stopped working - no power light as if the card "burned out". I really can't tell you exactly what went wrong because it's impossible to contact someone at Socket customer support. I followed their web-site instructions but was never contacted. I'll never buy another Socket product again\n\n\n\n\nB00005RZP3:not_palm_compatible:\n\n\nB00005RZP3\n\n\nSocket Communications Low Power Wireless LAN CF Card: Electronics\n\n\nelectronics\n\n\n0 of 8\n\n\n1.0\n\n\nNOT PALM COMPATIBLE\n\n\nNovember 18, 2003\n\n\n\n\n\n\n\n\nConnects to your Microsoft Pocket PC and will not work with a Palm. If you mistakenly order, AMAZON.COM will not refund your shipping. Have a nice day\n\n\n\n\nB0006BB6Z6:technical_details_misleading:krob\n\n\nB0006BB6Z6\n\n\nHP 97 Tri-Color Inkjet Print Cartridge with Vivera Inks (C9363WN#140): Electronics\n\n\nelectronics\n\n\n4 of 7\n\n\n1.0\n\n\nTechnical Details Misleading\n\n\nJuly 14, 2006\n\n\nKROB\n\n\nChatsworth, CA USA\n\n\nIt states in the Technical Details that you can get 450 pictures from this cartridge. This is not true. After much discussion with HP, they finally admitted that it's only rated for 150 4 x 6 pictures. My experience is that you actually get a little under 100 pictures from it. If you're buying this cartridge, you've probably already bought the printer, but you should know before you buy the printer that you're going to go through a lot of expensive ink\n\n\n\n\nB0006BB6Z6:watch_out_for_shipping:kevin_k._fosler\n\n\nB0006BB6Z6\n\n\nHP 97 Tri-Color Inkjet Print Cartridge with Vivera Inks (C9363WN#140): Electronics\n\n\nelectronics\n\n\n10 of 15\n\n\n1.0\n\n\nWatch out for shipping\n\n\nJune 15, 2006\n\n\nKevin K. Fosler\n\n\n\n\n\nI put this item in my cart -- it was one of the \"buy both now\" deals. The problem is, the first item was sold by Amazon, with free shipping, the second item was sold by another company with outrageous shipping costs.\n\nI caught this easily while checking out but just wanted to post a \"buyer beware\" to check the shipping costs\n\n\n\n\nB000BFM68I:love_it_but_it_keeps_breaking:jeffrey_rabe\n\n\nB000BFM68I\n\n\nSpeck Nano Armband: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nLove it but it keeps breaking\n\n\nJune 14, 2006\n\n\nJeffrey Rabe\n\n\nDallas, TX USA\n\n\nI love the functionality of this armband. The problem is that it keeps breaking. The place at which the band loops through the holder keeps breaking.. I'm on my second one and it is almost ready for another replacement..\n\n\n\n\nB00006I5FR:problems_at_radom:sterling_j._badon\n\n\nB00006I5FR\n\n\nLexar Media 512 MB Secure Digital Memory Card: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nProblems at radom\n\n\nJuly 8, 2006\n\n\nSterling J. Badon\n\n\nLongmont,CO. USA\n\n\nThis LEXAR brand has had issues with it's Memory Cards. I lost lots of pictures from a vacation to Disney World and a Convention. Their site had SOFTWARE that was to help recover some of those lost images!! I have never had Flash memory trouble until LEXAR! I now only buy SCANDISK items. Samsung invented this tecnology and SD is their product. The Store I bought the LEXAR chip was in a hurry to give me another chip I could trust. He gave me the higher costing SD chip. I wasn't the first to have issues from buying it in his store. He took a $50.00 loss to make me happy. People take lots of photos now that we went Digital. Imagine looking for some pictures you know you took and they are not there! Most think they messed up. WIth this chip you will have that happen so pay very close attention. Most of what is lost can not be recovered. Pay the extra money for SCANDISK.\n\n\n\n\nB00009KO14:earbuds_fall_out_if_you_move:john_busco\n\n\nB00009KO14\n\n\nApple M9128G/A iPod Remote and Earbuds: Electronics\n\n\nelectronics\n\n\n1 of 3\n\n\n1.0\n\n\nEarbuds fall out if you move\n\n\nMarch 3, 2006\n\n\nJohn Busco\n\n\nSilicon Valley, CA USA\n\n\nThe remote control is OK.\n\nBut the earbuds are horrible! They are very sensitive to your particular \"ear anatomy\". I suppose they fit for some people, but for me they are useless. They stay in for a few seconds, but don't dare do anything like running, walking, or moving your head and expect them to stay in.\n\nMuch better to get lightweight headphones with an over-the-head or behind-the-head band, or even the models with \"hooks\" that fit over the top of your ears.\n\n\n\n\nB00009KO14:wires_too_long,_connector_sleeve_disconnected:paul_arellano_\"big_$$$-_phils-ny-uk-va\"\n\n\nB00009KO14\n\n\nApple M9128G/A iPod Remote and Earbuds: Electronics\n\n\nelectronics\n\n\n4 of 6\n\n\n2.0\n\n\nwires too long, connector sleeve disconnected\n\n\nNovember 14, 2005\n\n\nPaul Arellano \"Big $$$- Phils-NY-UK-VA\"\n\n\nVA, USA\n\n\nA short review...\n\nDon't buy this product. The wire is too long and the remote control is too heavy for the jack to hold. After a month of the remote and wire hanging and swinging about, the wire where it's connected at the jack gave out making this remote unuseable; I have to jiggle and find the sweet spot now just to make the remote work.\n\nLong wires plus a remote adds considerable stress and weight on the jack...a serious design flaw. If you want to buy this, I suggest you tie the wire and the remote using a rubber band around your IPOD to support it's weight without pulling the wire off the connector\n\n\n\n\nB00029U13A:massive_problems_with_sony_ce595_:j._miller\n\n\nB00029U13A\n\n\nSony SCD-CE595 5-Disc CD/Super Audio CD Player: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nMassive problems with Sony CE595 \n\n\nNovember 24, 2006\n\n\nJ. Miller\n\n\nAtlanta, GA\n\n\nI am sorry to say that my experience with the CD player has been miserable. After a series of unhelpful conversations with SONY folks, I am going to return it. It often fails to read the CD or cuts in and out on new CDs that play perfectly in other CD players; the shuffle program simply spins and spins, and rarely settles down to actually play a CD. It occasionally generates a read error message (uh, yeah...).\n\nEven if on \"continuous\" play, it often fails to recognize the CDs loaded into it. Instead, it displays a \"No Disc\" message. Then, even if it does play a series of CDs on continuous play, at some pt, it just stops. It has been like this since I first brought it home and has not improved in the weeks since. \n \nThe experience with the tech support people alone convinced me that they do not care. It took a long time to even get through their tech support lines the first night I had it, and then a lot of persuading to even get them to agree that their CD player was not working. Then they tried to convince to me send the brand new machine back, **for a price**, to have them repair it. Uh, no, not likely to fall for that--paying them to repair a brand new machine straight out of the box, taking time out of my way-too-busy life to ship it to them?? I finally have a reference number with permission to return it to the store for a full refund; so back it goes.\n \nThen there are the limitations on the remote control that others here have noted. \n\nSince I had the same shuffle problems with my previous SONY carousel player, I am not inclined to try a 3rd machine. \n\nNeedless to say, no more SONYs are coming into my life.\n \n\n\n\n\nB0001LTT64:at_this_price_point_i_think_the_many_other_mice_feel_more_solid_than_this_logitech_mouse.:awesomefunn\n\n\nB0001LTT64\n\n\nLogitech Optical Mouse- Black: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n2.0\n\n\nAt this price point i think the many other mice feel more solid than this logitech mouse.\n\n\nAugust 28, 2006\n\n\nAwesomefunn\n\n\nSt. Louis USA\n\n\nI mainly chose this mouse because I needed one quickly and it was black and matched my set up. I had been using the standard greyish white optical mouse with a scroll wheel and a red light that shined out from under my wrist. This one feels smaller, but still comfortable. However a couple weeks of normal usage made the mouse much less solid feeling. The plastic now mushes as the seems flex slightly in my hand. I decided to have a friend repair the cable of my old mouse. :-/ I am going to donate this mouse to a friend who still has a tracking ball mouse. I like some of Logitechs products and higher end mice. But I would stay away from this mouse. Its not bad, its just not good enough to pick over other mice that cost the same amount.\nHope that helps.\nAwesomefun\n\n\n\n\nB0006698SI:do_not_buy_this_adaptor!:joshua_hazel_\"josh_hazel\"\n\n\nB0006698SI\n\n\nD-Link DWL-G132 Compact Wireless USB 2.0 Adapter, Cradle Included, 802.11g, 108Mbps: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nDo not buy this adaptor!\n\n\nSeptember 6, 2006\n\n\nJoshua Hazel \"Josh Hazel\"\n\n\nVisalia, CA\n\n\nI first isntalled this to WinXP 2nd ED and all of a sudden I am getting blue screens and the system keeps rebooting over and over. I uninstall and the problem goes away. Coincidence? I will let you decide. I reinstalled the adaptor and it gets very very hot.. reminds me of my notebook that overheats and shuts down. I cant get a connection to my router. I have my notebook im typing on now connected right next to the adaptor desktop and get a signal just fine with my notebook. I will be sending this back\n\n\n\n\nB0006698SI:it's_a_4-star_product:l._gong_\"geoff\"\n\n\nB0006698SI\n\n\nD-Link DWL-G132 Compact Wireless USB 2.0 Adapter, Cradle Included, 802.11g, 108Mbps: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nit's a 4-star product\n\n\nAugust 11, 2006\n\n\nL. Gong \"Geoff\"\n\n\nMichigan\n\n\nOK. I take my words back. I found a solution to the connection problem with windows xp. Download a patch (\tWindowsXP-KB884020-x86-enu.exe, solving WPA issues) from microsoft, then everything goes as it should be. \n\n-------------------------------------\n\ni bought this USB adapter along with DI-624 router for $99. The router works perfectly fine. The USB adapter gave me so much trouble. i used it to connect my desktop pc to wireless network. It works when the network is not encrpted. But it won't find an IP address under WPA security. It searches IP address endlessly. I have winxp sp2 which is supposed to support wireless flawlessly. I think either this so-called 108m bps or the driver causing the trouble. Having seen others having problem with other brands of USB adapter, i think maybe for desktop, a wireless pci card is better\n\n\n\n\nB0006698SI:flakey:curley_q_joe\n\n\nB0006698SI\n\n\nD-Link DWL-G132 Compact Wireless USB 2.0 Adapter, Cradle Included, 802.11g, 108Mbps: Electronics\n\n\nelectronics\n\n\n1 of 4\n\n\n1.0\n\n\nflakey\n\n\nDecember 27, 2005\n\n\nCurley Q Joe\n\n\n\n\n\nIt dies if you plug in any external USB disk drive or card reader. D-Link just doesn't know how to write device drivers that work well. I've had problems with every D-Link product I've ever bought\n\n\n\n\nB000CO90YG:warranty_problem:j._golden_\"giant_fan\"\n\n\nB000CO90YG\n\n\n4-Year Service Plan for Televisions $200 to $999.99: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nwarranty problem\n\n\nNovember 9, 2006\n\n\nJ. Golden \"giant fan\"\n\n\nnew york\n\n\nwhen i purchased this plan it was for a tv between $1000 and 1499.99.\nnew contract claims that my purchase had to be under $1000 so they will NOT honor it. this means i do not have an extended warranty.\ni either want this warranty for the price that was agreed upon or a full refund.\ni'm happy i found out about this problem before i needed the warranty.\n\n\n\n\nB000CO90YG:bad_customer_service........:christine_baran\n\n\nB000CO90YG\n\n\n4-Year Service Plan for Televisions $200 to $999.99: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nBad customer service........\n\n\nNovember 6, 2006\n\n\nChristine Baran\n\n\n\n\n\nI never received the 4-year plan, when I purchased the TVHD in August 2006. I have wrote to Amazon several times with no follow up by Amozon.\nMy next step is legal, since Amazon is acting like it dosen't care about the customer.\nThe reason I both the TVHD was because of 4-year service plan.\n\nTerrible customer service......\n\n\n\n\nB00006I56B:do_you_feel_lucky?:j._d._henry\n\n\nB00006I56B\n\n\nCoby CX-TV6 5\" Portable Black & White TV with AM/FM Radio: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n1.0\n\n\nDo you feel lucky?\n\n\nSeptember 6, 2006\n\n\nJ. D. Henry\n\n\nusa\n\n\nIf you do, go ahead and take a chance. I did and got burned. Tv didn't work right out of the box. Will return soon. \n\n\n\n\nB0001PFQ08:junk!_stay_away:the_tech_dude_\"thetechdude\"\n\n\nB0001PFQ08\n\n\nD-Link DI-524 Wireless 54 Mbps High Speed Router (802.11g): Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n2.0\n\n\nJUNK! STAY AWAY\n\n\nOctober 13, 2006\n\n\nThe Tech Dude \"thetechdude\"\n\n\nKing of Prussia, PA United States\n\n\nUnfortunately I bought this router (aka junk) from Amazon. Never worked smoothly. Did not like WPA. After I set the the protocol to WPA and restarted it, the router froze. Updated firmware, no use. Now its not working. Power is intermittent, will shut off whenever it wants. Freezes randomly. This garbage is going back to Dlink today. Dlink tech support also said that they are having a HUGE number of complaints with 524 especially revision D.\nI will never buy another Dlink. DO YOURSELF A FAVOUR AND AVOID DI-52\n\n\n\n\nB0001PFQ08:if_you_like_headaches_and_hassles,_buy_this_router:l._michelle_brockway_\"brockway\"\n\n\nB0001PFQ08\n\n\nD-Link DI-524 Wireless 54 Mbps High Speed Router (802.11g): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n1.0\n\n\nIf you like headaches and hassles, buy this router\n\n\nSeptember 17, 2006\n\n\nL. Michelle Brockway \"brockway\"\n\n\nHouston, TX USA\n\n\nThis may be one of the cheapest routers out there, but it's cheap in every way. I spent hours either on hold or talking with poorly trained overseas \"tech support\" whose expertise consisted of (barely) reading a script. Eventually these computer whizzes determined that my D-Link router didn't work. They told me to send it back (on my nickel, of course), and they sent me another one, which also did not work, at least not with my computer system. After more wasted hours, I was referred to the one guy at D-Link who--supposedly--has mastered this router's bugs. The problem? He never answered the phone. And despite all my calls to him and all my voicemails asking him either to call me at work or to set a time for us to talk, he repeatedly returned my calls at 3:00 in the afternoon, the time when most people who can afford routers are working. Finally I had an epiphany: Life was too short to %*&#@ with D-Link. I packed up the router and sent it back to the CEO, Stephen Joe, with a letter. Then I went out and bought a Netgear router, and I was up and running in minutes. Mr. Joe never responded to my letter, but what else do you expect from a company that so clearly doesn't value its customers\n\n\n\n\nB0001PFQ08:not_a_reliable_router,_do_not_upgrade_firmware!:james_harper\n\n\nB0001PFQ08\n\n\nD-Link DI-524 Wireless 54 Mbps High Speed Router (802.11g): Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n1.0\n\n\nNot a reliable router, do not upgrade firmware!\n\n\nSeptember 5, 2006\n\n\nJames Harper\n\n\nNYC\n\n\nHave had this router for over a year. During this time the router constantly lost the internet connection, even when it's wired directly to my desktop PC. The disconnect can last anywhere from a couple of seconds to minutes. A new firmware (1.23) was released a few days ago for revision A (the version of the router I have). D-Link's website said the firmware \"improved reliability\" so I decided to upgrade. After flashing to the new firmware, the router will not let me into the control panel or respond to pings. The lights blink, that's all. I've reset the router, but this does nothing to restore the old firmware. D-link's firmware upgrade appears to have put this router out of its misery--which is perhaps for the best as it now forces me to finally go back to a reliable router company\n\n\n\n\nB0001YFWCQ:i_bought_2_pairs_of_this_radio:carlos_a._puello_matos\n\n\nB0001YFWCQ\n\n\nMotorola T5500R GMRS / FRS Rechargeable 8-Mile Two-Way Radio (2 Pack, Blue): Electronics\n\n\nelectronics\n\n\n1 of 28\n\n\n2.0\n\n\nI bought 2 pairs of this radio\n\n\nMarch 10, 2006\n\n\nCARLOS A. PUELLO MATOS\n\n\n\n\n\ni bought 2 pairs of this radios and 1 of the pairs have a damage radio, the microphone dosen't work and i cant fix it!! :(\n\nim sand about it!!!\n\n\n\n\nB0001YFWCQ:not_happy_with_what_i_got:elliot_gonzalez\n\n\nB0001YFWCQ\n\n\nMotorola T5500R GMRS / FRS Rechargeable 8-Mile Two-Way Radio (2 Pack, Blue): Electronics\n\n\nelectronics\n\n\n3 of 18\n\n\n1.0\n\n\nNot happy with what i got\n\n\nFebruary 24, 2006\n\n\nElliot Gonzalez\n\n\nSan Jose, Costa Rica\n\n\nA while ago I purchased the same item, maybe a month, and I received what I paid for: 2 walkie talkies, 2 RECHARGABLE BATTERIES, and a CHARGING BASE for the walkies. \n\nIn this last purchase I thought I bought the same things, I even paid the same amount of money, but neither the batteries nor the charging came in the package.\n\nThank you\n\nHumberto Gonzalez\nSan Jose, Costa Ric\n\n\n\n\nB0002CSK8U:bummer:robin_jordan_\"upset\"\n\n\nB0002CSK8U\n\n\nRazor Electric Mini Chopper Bike (Black): Electronics\n\n\nelectronics\n\n\n1 of 4\n\n\n1.0\n\n\nBummer\n\n\nDecember 8, 2005\n\n\nRobin Jordan \"UPSET\"\n\n\nSOUTH CAROLINA\n\n\nBike did not run right from the beginning. THe battery did not stay charged. After awhile just quite running altogether. Company was not very usefull. Warranty only 90 days. Do not buy this peice of junk\n\n\n\n\nB00006B72I:kds_not_interested_in_long-term_customers:technet_guy\n\n\nB00006B72I\n\n\nKDS CD Organizer Electronic USB75-cd Capacity 1.8 Access Time: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n1.0\n\n\nKDS not interested in long-term customers\n\n\nApril 28, 2005\n\n\nTechNet Guy\n\n\n\n\n\nI spoke with customer service rep at 800-283-1311. Devices have a one-year warranty. I stated that I sent in the Registration card, and asked if she would look up the purchase date. She said no, \"Registration and Warrany are in different departments.\"\n\nShe asked me to hold, and came back on the line stating that since this model originally sold in 2003, my unit went off warranty in 2004 unless I have the invoice. I called back about an hour later, after finding the invoice, and got the same customer service rep. She recognized me and belittled me for calling back and said unless I have a question, there's nothing they can do. I stated that I found the invoice as requested and she asked me to fax it to her. I did that, so far no response. We'll see if they replace the unit or not.\n\nJust an FYI...after the one-year warranty, there are no options. The do not offer parts or service after the one-year warranty\n\n\n\n\nB00006B72I:kds_cdo_not_ready_for_prime_time:c._j._west\n\n\nB00006B72I\n\n\nKDS CD Organizer Electronic USB75-cd Capacity 1.8 Access Time: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nKDS CDO Not Ready for Prime Time\n\n\nMay 19, 2004\n\n\nC. J. West\n\n\nWashington, IL USA\n\n\nI purchased two CDOs only to find out that some of the lights on one of them did not work. After 6 weeks, KDS finally replaced it. It did not cost me anything but time and frustration. After 2 months, my second one has now failed. Most of the trays do not eject. It is no longer returnable and I have CDs stuck in it. Do NOT make the mistake of purchasing even one of these. Go to K-Mart and get one of the old fashioned manual ones. You will be much happier\n\n\n\n\nB000BHWV2M:won't_return_money:marie\n\n\nB000BHWV2M\n\n\nEpson PictureMate Print Pack (2 Ink Cartridges/270 sheets of Glossy Photo Paper) - T5570-270: Electronics\n\n\nelectronics\n\n\n3 of 5\n\n\n1.0\n\n\nWon't return money\n\n\nSeptember 6, 2006\n\n\nMarie\n\n\nRedwood Falls, MN USA\n\n\nAdorama is a terrible company to deal with. They sent me a pkg of 2 Epson ink cartridges and 270 papers. I have called several times as the ink cartridges were not the correct ones. I returned them as instructed, written 3 letters and called. This started in mid July. I called 2 weeks ago (Aug 28th and a different gal said she thought I should have my money back and would see to it. That was over 2 weeks ago and nothing since then. \nI would recommend that anyone should NOT order from this company\n\n\n\n\nB000AUQICO:problems:robert_s._pollock\n\n\nB000AUQICO\n\n\nDelphi XM Roady XT Satellite Radio Receiver: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n1.0\n\n\nproblems\n\n\nNovember 23, 2006\n\n\nRobert S. Pollock\n\n\nLos Angeles, CA USA\n\n\nI bought this unit to use with a boombox and it functioned well but after a few minutes it just went blank. I tried batteries in the boombox, AC power and two boomboxes because they thought the boombox was the problem \n\nBut after two boomboxes I guess the problem is the Roady.\n\nAnyone else have this problem?\n\nI use the XM only at home with the boombox. \n\nCurrently waiting for response from Crutchfield and Delphi.\n\nI will post again when (if) this is resolved.\n\n\n\n\nB000AUQICO:below_average_sound_using_fm_wireless_signal;_also,_cancelling_service_is_a_joke:rws_\"rwscpa\"\n\n\nB000AUQICO\n\n\nDelphi XM Roady XT Satellite Radio Receiver: Electronics\n\n\nelectronics\n\n\n1 of 6\n\n\n2.0\n\n\nBelow Average Sound Using FM Wireless Signal; Also, cancelling service is a joke\n\n\nOctober 13, 2006\n\n\nRWS \"RWSCPA\"\n\n\nRedondo Beach, CA USA\n\n\nThis review is more about my several month's experience with XM satellite radio and the not-so-good quality using the wireless FM frequencies through our car's radios. I live in Los Angeles.\n\nI got this little Roady XT for me, and the XM2GO radio receiver for my wife. I was excited about getting these new satellite receivers in our cars. The Idea of having all these \"no ads\" radio stations playing through my car's FM frequency seemed cool. \n Setting up the unit and getting it to work was easy. Changing FM frequencies for the wireless transmitter is easy as well. It really is a neat little receiver. \n\nHowever, after setting up this unit in my car and the XM2GO in my wife's car, I was really disappointed in the music sound quality. The music sounds flat and with a hissy quality, almost monaural at times, and the volume seems a lot lower than the car's radio at the same volume setting. I had to turn the volume dial up quite a bit when playing satellite radio. The car's regular radio sounds much better than satellite radio if using wireless reception with any of the radio receivers, IMO. Besides, there are plenty of annoying ads and interruptions on XM radio as someone mentioned in another review.\n \nI thought about having the units professionally installed so that my wife and I wouldn't have to use the poor sounding wireless FM technology, but realized that both family cars have very expensive navigation systems and I didn't want the vehicle's factory warranty voided if some installer got inside our car's dash and accidentally messed something up.\n\nSo about two month's ago, after several months of off-and-on use of XM radio service, I called to cancel my subscriptions. I had paid for a six month subscription for both receivers when I joined. As I expected would happen, when I called them to cancel, I was on the phone for allloooooonnnng time waiting for a customer service rep. Finally, I got to tell the rep all my relevant account information so I could cancel the service only to be put on hold and transferred to their \"special\" customer service department for cancellations where I was \"accidentally\" disconnected and had to call back and start all over again.\n\nFinally, after another long call to them, I was able to cancel my service.....or, so I thought. Yesterday, I was looking at my September credit card bill and saw that XM radio had charged my credit card for the next six months despite the fact that they assured me that my two accounts would be closed. \n\nI called my bank and had them reverse the charges and I may have the bank issue me a new credit card with different numbers in case XM tries to charge me again six months from now.\n\n\n\n\nB0001Y9UEW:disappointing:rick_w._kasper\n\n\nB0001Y9UEW\n\n\nMonster Cable RadioPlay Car Stereo Wireless FM Transmitter (MBL-FM XMTR): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\ndisappointing\n\n\nAugust 9, 2006\n\n\nRick W. Kasper\n\n\n\n\n\nPurchased the transmitter for my MP3 player. Reception is poor with constant static through my car radio. The instructions (which you don't see until you have already purchased the product) says antennas integrated into the window can have problems. Since that is the only purpose for this device, I don't understand why Monster is selling a marginal product\n\n\n\n\nB00000J47L:i've_just_replaced_my_set_of_these:charles\n\n\nB00000J47L\n\n\nEnergizer NH15BP-4 ACCU 2500mAh Rechargeable AA Batteries (Four-pack): Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nI've just replaced my set of these\n\n\nNovember 10, 2006\n\n\nCharles\n\n\nNC\n\n\nI've had a set of these batteries for a year or two now. The last few times I've taken them out for vacation use in camera, I've had the same problem: I charge two sets (one Kodak and one these Energizers) when I put the camera away. A few weeks later, I go off on vacation. The Kodak 1600mAh set will last me all day on the charge from a month or so before. The Energizer 2500mAh set might last me half a day, having lost most of its charge from sitting for the same number weeks before use.\n\nAfter missing out on some pics on my latest trip due to dead batteries, I'd had enough. The Energizers are going in the bin, and I just followed the good reviews to buy a set of Duracells to replace them.\n\n\n\n\nB00000J47L:great_for_a_few_months,_then_won't_hold_charge:e._shen\n\n\nB00000J47L\n\n\nEnergizer NH15BP-4 ACCU 2500mAh Rechargeable AA Batteries (Four-pack): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nGreat for a few months, then won't hold charge\n\n\nOctober 22, 2006\n\n\nE. Shen\n\n\nIowa\n\n\nI got these batteries just over a few months ago for my portable GPS unit that I use often on my travels. In fact, I bought eight of them (two packs of 4). I was upgrading from my previous set of 1600mAh Rayovac NiMH AA batteries that were still going strong after 2 years. When I saw the new energizer 2500mAh rechargeables I was excited to purchase them thinking they would last longer than my old 1600mAh Rayovacs. Boy was I wrong!\n\nFor 2-3 months, the 2500mAh energizers worked well. A charge lasted about 6-7 hours in my portable GPS unit. I was getting roughly 5.5-6 hours on my old 1600mAh Rayovac AA's. My GPS unit is spec'ed for 6 hours on a pair of AA's so the new 2500mAh energizers seemed to work flawlessly.\n\nNow they work okay and seem to be getting worse each time I charge them, appearing to hold less of a charge each time. For the last month or two (4th & 5th month after their initial purchase) I'll be lucky if I can get 4-5 hours of use out of them in my portable GPS unit. I now get roughly 3.5-4 hours of use out of them. I pulled out and tried my old 1600mAh Rayovac AA's and once again got approximately 5.5-6 hours of use out of them in my GPS unit - and my trusty Rayovacs are just over 2 years old!\n\nI thought I might have had some defective energizers but I can't seem to get any pair of combinations to hold a decent charge. Charges just don't seem to last - it's really disappointing. I am going to look at another brand for high capacity 2500+ mAh NiMH batteries to replace my 1600mAh Rayovacs. Would be great if the energizer could hold its charge. But because they don't, I do not recommend the energizers to anyone\n\n\n\n\nB00000J47L:junk_nimh_batteries:gary_w._cleveland\n\n\nB00000J47L\n\n\nEnergizer NH15BP-4 ACCU 2500mAh Rechargeable AA Batteries (Four-pack): Electronics\n\n\nelectronics\n\n\n5 of 8\n\n\n1.0\n\n\nJunk NiMH batteries\n\n\nSeptember 9, 2006\n\n\nGary W. Cleveland\n\n\n\n\n\nI bought several packs of these batteries 6 months ago. They were great at first, but now most of them won't hold a charge at all. They discharge in 2 weeks without even being in a device. DON'T buy them! They're JUNK\n\n\n\n\nB00006JILC:good_sound,_when_they_stay_on_my_ears.:l._decker\n\n\nB00006JILC\n\n\nKoss KSC22 Sportclip Headphones (Red/Silver): Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nGood sound, when they stay on my ears.\n\n\nNovember 9, 2006\n\n\nL. Decker\n\n\nClifton, NJ, USA\n\n\nI had such high hopes for these headphones. I wanted to hook them over my ears and go running whenever I pleased. But it was not to be. \n\nThe day after I received my headphones, my grandmother passed away. I packed my bags, put my idpod and headphones in my carry-on, and headed to the airport. Once settled in my seat, I tried to put on my headphones, but they kept falling off my ears. I tried to adjust them, bending the earclips all sorts of ways, but it was extremely difficult to do so, and my adjustments wouldn't hold.\n\nIf I couldn't get these headphones to stay on when I was stationary, running was out of the question. \n\nMaybe others have had better luck. Maybe my ears are just too small for them. But I am not impressed with these headphones\n\n\n\n\nB00009V2PN:one_word_-_lousy:\"jbohnen\"\n\n\nB00009V2PN\n\n\nPanasonic KX-TCA98 Headset with Dual Wear Options: Electronics\n\n\nelectronics\n\n\n24 of 25\n\n\n1.0\n\n\nOne word - LOUSY\n\n\nFebruary 23, 2004\n\n\n\"jbohnen\"\n\n\nLombard, IL United States\n\n\nYou would think that Panasonic Cordless phone and Panasonic Hands-Free Headset would work well together, being that they are sold by the same company. Well, that is clearly not the case. I bought this headset specifically for the reason that it is sold by Panasonic to work with their cordless phones. I've tried it a few times and each time I get the same response from the person I'm talking to. They say I sound too far away, that they can barely hear me. This product is clearly not worth it\n\n\n\n\nB0006BB6YW:hp_96_ink_cartridge:walter_l._johnson\n\n\nB0006BB6YW\n\n\nHP 96 Black Inkjet Print Cartridge with Vivera Inks (C8767WN#140): Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nHP 96 Ink Cartridge\n\n\nNovember 9, 2006\n\n\nWalter L. Johnson\n\n\n\n\n\nI like the HP 96 because it can be used in many HP printers that use the HP 98, but its a better deal than the HP 98 because it contains almost twice as much ink. \n\nHP's ink cartridges provide excellent performance, but I think they are over priced \n\n\n\n\nB000AY0IT4:make_sure_you_keep_your_old_mouse_and_keyboard:nicholas_s._mead\n\n\nB000AY0IT4\n\n\nLogitech diNovo Media Desktop Laser: Electronics\n\n\nelectronics\n\n\n1 of 3\n\n\n1.0\n\n\nmake sure you keep your old mouse and keyboard\n\n\nAugust 18, 2006\n\n\nNicholas S. Mead\n\n\n\n\n\nLooks beautifull......works about 40% of the time. Be ready to reinstall the software weekly.\nVery expensive for something that dosent work all the tim\n\n\n\n\nB00009YW8B:wg311v2:aj\n\n\nB00009YW8B\n\n\nNetgear WG311 54 Mbps Wireless PCI Adapter: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nwg311v2\n\n\nNovember 20, 2006\n\n\naj\n\n\nMansfield, Texas United States\n\n\nTerrible product. I have tried using the Windows XP wireless application with just the netgear drivers and tried using the Netgear wireless application with the netgear drivers. Using either one the card disconnects at random using WPA, then you have to repair the connection and sometimes reboot to get the connection back. I reinstalled windows both times I changed drivers just to make sure nothing was conflicting with it, but nothing helped. Latest drivers are 2_0_0_7 and were published in Nov 2004, so netgear has moved on to other cards and is not supporting this one any longer\n\n\n\n\nB000E1FYRO:extremely_uncomfortable,_but_good_sounds:a_buyer\n\n\nB000E1FYRO\n\n\nSennheiser HD 205 Studio Monitor DJ Headphones w/ Swivel Ear Cup: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nextremely uncomfortable, but good sounds\n\n\nOctober 21, 2006\n\n\nA Buyer\n\n\nLos Angeles, CA\n\n\nthese are extremely uncomfortable. It's too tight and will hurt the ears. However, if you're head is small enough, perhaps it might be okay. On the bright side, they sound extremely well\n\n\n\n\nB000E1FYRO:good_sound,_terribly_uncomfortable:chris_desalle\n\n\nB000E1FYRO\n\n\nSennheiser HD 205 Studio Monitor DJ Headphones w/ Swivel Ear Cup: Electronics\n\n\nelectronics\n\n\n9 of 11\n\n\n2.0\n\n\nGood sound, terribly uncomfortable\n\n\nMay 3, 2006\n\n\nChris Desalle\n\n\n\n\n\nDo not get these headphones, as they are way too tight. I have been using them for a month now, hoping they would break in more, but they have not. You can only have them on for 20 minutes or so before your ears hurt so bad you have to take them. Im not sure why they made them so tight, but they would probably best fit a 10 year old.\n\n\n\n\nB000EPYLJ2:why_the_zboard_isn't_worth_it:c._merryman_\"bukkake_boy\"\n\n\nB000EPYLJ2\n\n\nIdeazon MERC Gaming Keyboard: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n2.0\n\n\nWhy the Zboard isn't worth it\n\n\nSeptember 7, 2006\n\n\nC. Merryman \"Bukkake Boy\"\n\n\nBeverly Hills, CA\n\n\nI bought the Zboard and was sorely disappointed in it's playbility, efficiency and value for several reasons:\n\n-The left buttons, which are the feature of the keyboard, are based on a mirroring system. You experience keyboard lag at times because the board has to interpret the button presses based on the zboard program, then the game. I personally had to press some buttons twice, or even three times in order for that command to register.\n\n-The left buttons cannot be configured to your game, you must configure the game to it. The templates for different games cannot be modified, all it does is change the button configuration. There are no patches for configuring the keys to how you want it.\n\n-According to the website, the top buttons for media can only be used with windows media player. If you use winamp, itunes, or anything else it wont work. \n\n-You cannot program any macros into any of the buttons. If you are looking for a keyboard that can autofire or program keystrokes this isn't it. \n\n- The numpad is configured differently than a normal keyboard. This can actually be a good thing if you like having a compacted pad. I found it frustrating since I put attack commands on the numbers, and would press the wrong one sometimes because of the layout.\n\nI experienced a lot of frustration with this controller while pvping in world of warcraft. The top buttons on the left side of the board give you access to a bigger arsenal of spells and abilities when programmed right. The problem is remembering how zboard configured the button layout. I would constantly get wailed on because a button wasn't where I originally remembered it to be. This forced me to sit in the key bindings menu to configure the buttons to where I could remember. After completely revamping my bindings, I gave up, restored my defaults, and used a regular keyboard instead.\n\nThere is nothing gaming enhancing about this keyboard beyond having an accessory on the left side that looks cool because the buttons are grey and red and contoured to your hand. \n\nTo achieve the same effect as the zboard, turn your current keyboard slightly to the right and enjoy.\n\n\n\n\n\nB0000C20XD:hummmmmmmmmmmmmmm...:r._durkin_\"my_opinion_only\"\n\n\nB0000C20XD\n\n\nAltec Lansing VS4121 2.1 Computer Speaker System, 3 piece: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n2.0\n\n\nHummmmmmmmmmmmmmm...\n\n\nJanuary 15, 2006\n\n\nR. Durkin \"my opinion only\"\n\n\nBay Area, CA\n\n\nI picked these speakers up because the price was right and they sounded great in the store. But I get them home to my quiet house and lo and behold the subwoofer constantly hums. I'm not an audiophile and therefore not overly particular about sound... other than this annoying hum from the subwoofer. Since these are set up in my home office where I spend a lot of time reading, I find this humming far too distracting. Although the price is good, I'd prefer to pay a little more just to omit the subwoofer's constant hum.\n\nI'm returning these speakers today and will shop around for another set\n\n\n\n\nB000BNDEZY:firmware_buggy:jimb\n\n\nB000BNDEZY\n\n\nBuffalo Technology AirStation Turbo G High Power Wireless Ethernet Converter ( WLI-TX4-G54HP ): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nFirmware Buggy\n\n\nSeptember 10, 2006\n\n\nJimB\n\n\nGeorgia\n\n\nCan not get this thing to connect to my Linksys Wireless router. Problem is not that it doesn't see the LinkSys, the problem is that the v2.50 firmware in the WLI-TX4-G54HP is _buggy_. If I try to enter a 10-digit hex key for 64-bit WEP encryption-- it says I have to enter a 5-digit value. This is the 2nd one I bought (I returned the first). Piece of junk, and _no_ support from the manufacturer\n\n\n\n\nB00006I56A:please_don't_bother!:n._j._dellolio\n\n\nB00006I56A\n\n\nCoby CX-TV1 5\" Black-and-White TV with AM/FM Tuner: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n1.0\n\n\nPlease don't bother!\n\n\nOctober 9, 2006\n\n\nN. J. DELLOLIO\n\n\nSan Francisco, CA United States\n\n\nOne star is too many! (It should be negative one). This sad excuse for a television/radio has to be the worst piece of lame crud I have ever bought in my entire life. Picture fades into black, terrible reception which fades in and out as you move about the room, smells like burning plastic after a half-hour, and so on. Please, Amazon shoppers, don't waste any money on this! You might just as well throw the $15 out the window. Step up to something more reliable from a better manufacturer\n\n\n\n\nB000BWACX2:what_if_you_want_to_use_it_outside_of_your_car?:j._adams\n\n\nB000BWACX2\n\n\nGriffin 9501-TRIPCB iTrip Auto FM Transmitter and Auto Charger for iPod: Electronics\n\n\nelectronics\n\n\n1 of 13\n\n\n2.0\n\n\nWhat if you want to use it outside of your car?\n\n\nOctober 25, 2006\n\n\nJ. Adams\n\n\nBloomington, MN United States\n\n\nI love bringing my ipod to parties and broadcasting it through the stereo, what use is this thing then, NONE! Thanks for making us spend so much money Apple. This type of stuff should be built into the iPod for $400!!\n\n\n\n\nB000B9OEFM:looks_good......._but:greg_rees_\"greg\"\n\n\nB000B9OEFM\n\n\nApple MA094G/A iPod Nano Armband (Grey): Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n2.0\n\n\nLooks good....... but\n\n\nAugust 20, 2006\n\n\nGreg Rees \"Greg\"\n\n\nMidwest\n\n\nI purchased this armband to hold my Nano during workouts. The band barely fits my arm and tends to slide down the arm while running or cross training. Looking for a replacement right now\n\n\n\n\nB000B9OEFM:horrible,_horrible,_horrible:a._bower\n\n\nB000B9OEFM\n\n\nApple MA094G/A iPod Nano Armband (Grey): Electronics\n\n\nelectronics\n\n\n7 of 7\n\n\n1.0\n\n\nHorrible, horrible, horrible\n\n\nJune 9, 2006\n\n\nA. Bower\n\n\n\n\n\nI adore my nano, but this armband is one of the worst products I have ever bought. I work out quite a bit with the nano and the band comes off multiple times during my workout. The spacing of the velcro dots means I never get exactly the right fit and the band gets ripped off anytime I do weight lifting close to my body. It is also as other reviewers mentioned hugely overpriced for what it is. I am ordering a band from a different company and this will go straight to the garbage.\n\n\n\n\nB0000APCIV:pay_attention_to_technology:j._d._robinson\n\n\nB0000APCIV\n\n\nTerk TV44 Amplified Clip-on Outdoor TV Antenna for Dual LNB Satellite Dishes: Electronics\n\n\nelectronics\n\n\n8 of 8\n\n\n1.0\n\n\npay attention to technology\n\n\nJune 23, 2005\n\n\nJ. D. Robinson\n\n\n\n\n\nI have a dishnetwork 942 HDTV DVR system. This antenna is designed for a much older system. It doesn't fit the dish very easily and was difficult to secure. I can't tell if it works because so far it just seems to be incompatible with my system\n\n\n\n\nB00006HO37:does_not_support_19"_or_larger_crts:\n\n\nB00006HO37\n\n\nALLSOP MetalArt Jr. Printer, Monitor or Peripheral Stand: Electronics\n\n\nelectronics\n\n\n3 of 5\n\n\n2.0\n\n\nDoes not support 19" or larger CRTs\n\n\nJanuary 8, 2004\n\n\n\n\n\n\n\n\nDo not be fooled when the description says this stand holds up to 21" monitors - that is only true for *flat-panel* monitors, not traditional CRT monitors. Note that the weight rating says 50 pounds! Most standard 19" monitors weigh 60 pounds or more. I purchased this item at a Staples near me, and brought it home to use with my 19" CRT. After placing the monitor on the stand, the stand bobbed up and down and seemed as if it would completely bend at any moment\n\n\n\n\nB000EPJLGK:so_bad_screen:may�re_\"weblord\"\n\n\nB000EPJLGK\n\n\nApple 30 GB iPod video White (5.5 Generation): Electronics\n\n\nelectronics\n\n\n1 of 19\n\n\n1.0\n\n\nSo bad screen\n\n\nNovember 24, 2006\n\n\nMay�re \"weblord\"\n\n\n\n\n\nI can't understand how apple sell so much ipod video while they are so bad in front of an archos. Just look at the screen and you'll understand !\nArchos is an hundred times better \n\n\n\n\nB000AY649C:amazon_lies:s._lee_\"serious_buyer\"\n\n\nB000AY649C\n\n\nLogitech Cordless Desktop MX 3000 Laser (967553-0403): Electronics\n\n\nelectronics\n\n\n0 of 6\n\n\n1.0\n\n\nAmazon lies\n\n\nSeptember 30, 2006\n\n\nS. Lee \"Serious Buyer\"\n\n\nBoston, USA\n\n\nAmazon says that it is in stock.\nDo not believe it.\nI ordered it at 8/26 and they could not even ship it at 10/1.\nThey delayed it several times saying that it is in stock.\nOf course the website says that it is always \"IN STOCK\"\nand can ship it now.\nMaybe their now is like 3 months...\nMy current keyboard has broken keys, and I have serious inconvenience because of this\n\n\n\n\nB00000J4GE:be_wary.:kamisuri_astalis\n\n\nB00000J4GE\n\n\nMemorex 32020700 Jewel Case Inserts (50-Pack): Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n2.0\n\n\nBe wary.\n\n\nAugust 15, 2005\n\n\nKamisuri Astalis\n\n\nMO USA\n\n\nWhile certain labels with this software are much more easier to work with such as the CDs and the general labels, I found this product much less than satisfactory. \n\nThe inserts and the software don't align too well and are a few centimeters off...especially on the lower half of the page. If one wants to keep the insert regardless of whether or not the \"cut offs\" are present it would be best if the paper was inserted within the opposite position in the paper tray in the printer so one can cut to the size needed.\n\nOtherwise, I would suggest what another Amazon customer has done. Use cardstock paper....at least for the back inserts. And trim with either a scissors or paper cutter.\n\nA product should do what it promises it will do. Unfortunately, this one does not. At least, within my experiance this does not measure up to what was previewed within the Memorex beginner's package w/ CD label stamper. \n\nTwo stars for at least being able to print the front side. Although it would have been nice if they had said so without stating they could do both when they cannot\n\n\n\n\nB00000J4GE:will_do...:anders\n\n\nB00000J4GE\n\n\nMemorex 32020700 Jewel Case Inserts (50-Pack): Electronics\n\n\nelectronics\n\n\n11 of 12\n\n\n2.0\n\n\nWill do...\n\n\nNovember 11, 2000\n\n\nAnders\n\n\nMarina Del Rey, CA USA\n\n\nI have used these labels for a long time, mostly because they seem to be the only ones out there. The paperstock is good, but if you have a good printer the paper won't do it justice. The labels always end up looking kinda cheap. I would love to see Memorex make a package with glossy paper/high end paper. Maybe Epson will beat them to it\n\n\n\n\nB00004UF7T:little_to_offer_over_the_gps_12:craig_a._wilson\n\n\nB00004UF7T\n\n\nGarmin eTrex Summit Handheld GPS Navigator: Electronics\n\n\nelectronics\n\n\n9 of 17\n\n\n2.0\n\n\nLittle to offer over the GPS 12\n\n\nDecember 8, 2001\n\n\nCraig A. Wilson\n\n\nMESA, AZ USA\n\n\nI really love my Garim GPS 12. It is easy to use and move between screens and features. The Summit is not as easy to use. It is not as convenient to identify way points, does not work with mapping software and its display is to simple. It was not a step up but sideways. I use it in conjunction with my GPS 12 mostly just to keep the track log\n\n\n\n\nB000BXEUDY:was_nice_until_it_broke:l._morin\n\n\nB000BXEUDY\n\n\nViewSonic VA1912WB 19\" Widescreen LCD Monitor (Black): Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nWas nice until it broke\n\n\nNovember 24, 2006\n\n\nL. Morin\n\n\nUSA\n\n\nI bought this monitor in June 2006 and at first was quite pleased with the nice display. Less than 6 months later, in November 2006, I heard a strange noise while using the monitor and all of the sudden the screen went very bright -- like an overexposed photo. I'm currently working with Viewsonic to get it repaired (I must mail it to CA to be repaired, postage must be paid by me). All in all a disappointment and a hassle. Next time I'll buy a name brand\n\n\n\n\nB0000524PB:great_cables,_until_one_of_the_colors_goes_out!:wesley_gallagher_\"mac_enthusiast\"\n\n\nB0000524PB\n\n\nMonster Cable GameLink 400 Component-Video Cable for PlayStation 2 (10 feet): Electronics\n\n\nelectronics\n\n\n1 of 3\n\n\n2.0\n\n\nGreat cables, until one of the colors goes out!\n\n\nNovember 26, 2005\n\n\nWesley Gallagher \"Mac Enthusiast\"\n\n\nCharlottesville, VA\n\n\nI bought these cables, brought them home and immediatly hooked them up. I set the PS2 for component and set my TV for it as well. And it looked awesome....for about 2 weeks. Then the short in the cables caused only the blue and green colors to work making everything look creepy. It would just fade back and forth until it just stop and the TV would have all the colors washed out in green.\n\nI found that I could stick a PS2 case under the cables at the base and it went away. But sometimes someone would move the case and it would come back. \n\nI have the PStwo (Small version) and the PS2... the small version has the problem, but the large one doesnt. So dont get them if you have the small version... The large one works fine. \n\n\n\n\nB000BB5T2M:great_looking_maps,_can't_find_its_way_out_of_a_paper_bag.:r._iser_\"electronics_fan\"\n\n\nB000BB5T2M\n\n\nLowrance iWay 350C Portable GPS Navigation System with MP3 Player and Picture Viewer: Electronics\n\n\nelectronics\n\n\n5 of 6\n\n\n1.0\n\n\nGreat looking maps, can't find its way out of a paper bag.\n\n\nSeptember 16, 2006\n\n\nR. Iser \"Electronics fan\"\n\n\nMaryland\n\n\nI logged 870 miles with the iway 350. 270 miles around maryland it was / is a great item. I took it on our first trip to New York and Long Island and it was terrible. The first problem occurred on the New Jersey turnpike. I was supposed to use the turnpike from start to exit 18 and at exit 4 we stopped to eat. The unit said I was off course because the rest area was 100 feet from the highway. However when we returned to the highway the unit could not chart the course any more. It said I was doing 95 miles a hour when i was doing 70, and it would lose satellites and go searching for them in the middle of the highway no obstructions of any kind anywhere. I even pulled over at exit 6 and we waited for 20 minutes to see if we just needed to stop the car for it to do the job. It did not recalculate our course until after exit 18 some 100 + miles from when it acted up. We even tried a full restart and cancel route and retried again from the address book and from direct typing the information again. About 1 mile from our destination it worked for 2 blocks and was lost again. Then after 2 more blocks we found where we where going. We often get lost on this trip this is why we purchased the unit. Then on the trip from the Bronx to Long Island a 30 mile trip it stopped 5 blocks into the journey and was recalculating. I had to call the people we where visiting and they directed us by phone. However about 1 mile from their place it started working again. We used it on long island with no trouble at all. Then on trip back to the Bronx it worked pretty good only losing its position once on a major highway no trees or obstructions. Then on our way back from the Bronx to long Island it was lost half the way finding position and calculating . The same exact way we went in both directions but it was clearly not right leaving the Bronx. On our way back to Maryland it operated pretty good after a short stretch of lost signals and redirecting and we used exactly the same roads in both directions. I did update the software to the new 1.2.0 days before we took the trip. \n\nAlso charting the 212 mile course to the Bronx NY took close to 20 minutes, then the extra destination to Long Island a 30 mile addition took the Iway 350 30 or so more minutes. I figured with all that time the trip home calculations would be faster but I fell asleep after waiting over a hour for it to complete the task. I feel this is one issue the unit has. Longer trips take so long to calculate so when it thinks your off track it takes forever to chart a new course. On short runs under 20 miles it was very fast.\n\nI returned the unit and got a Garmin C530. The C530 is a very nice unit fast in every respect.\n\n\n\n\nB000BB5T2M:how_to_get_lost!_over..:r._riedel_\"tybee_bob\"\n\n\nB000BB5T2M\n\n\nLowrance iWay 350C Portable GPS Navigation System with MP3 Player and Picture Viewer: Electronics\n\n\nelectronics\n\n\n5 of 6\n\n\n2.0\n\n\nHow to get lost! Over..\n\n\nAugust 1, 2006\n\n\nR. Riedel \"Tybee Bob\"\n\n\nTybee Island, GA USA\n\n\nThe 350C is well packaged with almost current programming. It only got me lost eleven (11) times on a 865 mile trip:All cities in Georgia - Tybee Island to Pine Mountain to Newnan to Stone Mountain and return to Tybee Island. (I packed and returned the unit the day after getting home.) It may suit others well, but I can not rely on the unit. Four of the eleven lost episodes were because newly constructed highways were not recognized. Five were due to the unit reacting too slowly in rapid turning sequences. (One wrong turn took me sixty miles out of the way.)Two were for unknown reasons. The unit seems to take forever to acquire available satellites. Instructions are clear, but preferred views automatically switch to \"Map\" even when not wanted. I need a faster reacting unit that allows programming while enroute to a destination (by a passanger, of course.\n\n\n\n\nB000BB5T2M:three_units_in_three_weeks!:aaron_c._burt_\"a-train\"\n\n\nB000BB5T2M\n\n\nLowrance iWay 350C Portable GPS Navigation System with MP3 Player and Picture Viewer: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n2.0\n\n\nThree Units in Three Weeks!\n\n\nJuly 31, 2006\n\n\nAaron C. Burt \"A-Train\"\n\n\nFt. Lauderdale, Fl.\n\n\nI bought this unit on a wim. I was leaving for a business trip and needed a GPS. I really like it and it worked well for me. Unfortunalty the unit wouldn't hold a charge on the battery. So I returned it for another one. It worked for one week and then I had the same exact problem. Which to me is not a good sign of quality when the same problem happens on multiple units. When I tried to return this unit, the unit I was given didn't work at all, it gave me an error message when the GPS booted up. Unfortunaltey the vendor I purchased it from didn't have any more units that I could exchange it for. So I called customer service and got an RAN. I am hoping when I get the next unit back in 7 to 10 days that I don't have any more problems\n\n\n\n\nB000932AMO:so_many_problems!:a._harris\n\n\nB000932AMO\n\n\nApple 1 GB iPod Nano White: Electronics\n\n\nelectronics\n\n\n1 of 3\n\n\n1.0\n\n\nSo many problems!\n\n\nNovember 9, 2006\n\n\nA. Harris\n\n\nOhio\n\n\nI received the iPod quickly which was a plus. But it was horrible when it would only download 5 songs. I had friends who have the same iPod try to fix it and it never worked. I sent it back and requested a refund. It has been a month and a half and I have YET to get my $120 back.\n\n\n\n\nB00067ARYQ:completely_unreliable:ed\n\n\nB00067ARYQ\n\n\nNetgear WG111T 108 Mbps Wireless USB 2.0 Adapter: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nCompletely unreliable\n\n\nJuly 16, 2006\n\n\nEd\n\n\nSan Francisco, CA\n\n\nI bought this since it's economical, compact, didn't need cabling (although it has an extension if you need it), advertised a whopping 108 mbps. Well so much for that:\n\n1) Drivers are problematic and causes conflicts with XP SP2. My first ever blue screen of death in about 3 or more years running XP came this item\n\n2) It is EXTREMELY UNRELIABLE:\n\n- already mentioned repeatedly, it overheats\n\n- it drops connections randomly and yes, the only way to re-acquire the signal is to unplug and plug back in\n\n- It's utility is worthless, just install the driver (if you can). If you try using WPA, forget about using this utility, just use Windows to configure it. \n\n- I've also found out that it's better if you just remove this utility from the STARTUP (don't run it at all) so that Windows can do everything it needs to connect, which is faster and more reliable. Be careful not to UNINSTALL, since it will also remove the driver...just remove it from your Windows Startup folder list. Netgear has an FAQ about this problem, but I'm not going to tweak Windows and risk more stuff for this cheap piece of hardware.\n\n3) 108 MBPS? Yeah right. You'll need the RIGHT KIND OF access point/wireless router for this, if not, it's 54 mbps. This was one of the selling points, but it's definitely a \"gotcha\". You're limited to specfic equipment, specific channel (6), so if you have neighbors on the same channel, well, you get the picture\n\n\n\n\nB0002GRUJ6:great_picture_--_when_signal_doesn't_drop:d._hawks\n\n\nB0002GRUJ6\n\n\nStarTech.com 6FT HDMI TO HDMI DIGITAL VIDEO ( HDMIMM6 ): Electronics\n\n\nelectronics\n\n\n18 of 26\n\n\n2.0\n\n\nGreat picture -- when signal doesn't drop\n\n\nNovember 29, 2005\n\n\nD. Hawks\n\n\nRochester, NY USA\n\n\nAll HDMI cables are created equal...well, yes and no. While the signal is 100% digital (which means that $20 & $150 cables give the exact same picture quality), it doesn't mean that each HDMI cable type gives the same rejection of noise (signal to noise ratio) as other HDMI cables.\nI've had this cable a few weeks now, and am consistently getting digital \"dropouts\" every now and again (only that part of the image looks scrambled for a few seconds) -- very annoying while the action is in the scrambled section. Went out and bought the Monster HDMI cable (not exactly cheap, though...) -- the Monster cable gives the exact same picture quality as this cable, except the Monster cable did not experience the digital \"dropouts\".\nIf you don't mind seeing the dropouts every now and again, this is a great cable. If you want a picture that doesn't experience any dropouts, get the Monster cable. Regardless, the picture quality is the exact same between the cables (minus the dropouts)\n\n\n\n\nB0000DJEK7:not_happy:jayakrishnan_adukkadukam_\"jay\"\n\n\nB0000DJEK7\n\n\nMagellan RoadMate 700 Vehicle GPS with Windshield Mount: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n2.0\n\n\nNot happy\n\n\nNovember 5, 2006\n\n\nJayakrishnan Adukkadukam \"Jay\"\n\n\nCA, USA\n\n\nI bought Magellan RoadMate 700 Vehicle GPS 2 months back and its not working as expected. its very poor in locating the the place and sometimes don't even work. Many times this sytem got hung and power off and on again is the solution to get it work again.\n\nMight this product is very good in market, but I got the bad piece\n\n\n\n\nB000636FJG:does_not_work,_bad_service:mauro\n\n\nB000636FJG\n\n\nSony DRX710UL DVD+R Double Layer External DVD+-RW Drive (16x): Electronics\n\n\nelectronics\n\n\n29 of 36\n\n\n1.0\n\n\nDoes not work, bad service\n\n\nJune 11, 2005\n\n\nMauro\n\n\nNew Haven, CT, USA\n\n\nThe DVD burner connected very easily through USB, was immediately recognized by WinXP, the software installation was fine. I was beginning to feel good. However, whenever I burned a DVD (I tried 4 different brands, some R, some RW etc...), several files would contain errors, and would be completely unreadable. The burning process moreover does not detect this: one needs to verify the data written to check and find these errors. Moreover, the firmware update just does not work on my machine, pardon, machineS. Completely standard Dell machines, all latest WinXP and drivers, good CPUs (3Ghz and up), lots of memory, big hard-drives. I called customer service to try to solve the problem. First of all the average waiting time to get to a technician was about 30 minutes. The rudeness and the plain lies these guys give you are amazing. To make a long story short, I had to call 3 times. First guy: oh, \"of course it doesn't work, you have EasyCD creator installed!\". Ok, I buy it, I go through the (painful) uninstallation process of Roxio's software. No problems solved. Second guy: \"Oh, of course it does not work, it requires 10Gb of free hard-drive space! It's written on the box\". Plain lie: on the box it says you need 10Gb for NeroVision (for burning videos), but only 1Gb for Nero. Quarrel (they are tough!) with the guy who could not care less about the fact I got him lying, or that the product description is wrong. Ok, patiently I freed up 10Gb from hard drive, try again, exactly same problem. The only thing they could offer in the end was a replacement. I got perhaps relative lucky because the replacement will be a 720, not a 710, since 710 is not manufactured anymore (wonder why?). Notice I tried this on two different machines (one laptop and one desktop), with identical results.\nOther lie: I noticed, as somebody else here, that the Nero software provided writes only on Sony drives: so if Nero is really the only software that should be installed because of conflicts with Roxio (et al.?), you will never be able to use your own CD drive to burn CDs. How ridicolous is that???? The technician said this is because I had both software(s) installed, but that was another lie.\nThe incompetence of these guys is beyond belief. If the new drive will not work, I hopefully will get a refund through my credit card. You know, the technician even said that that is not possible, credit cards companies do not do that (LOL)\n\n\n\n\nB000636FJG:good_drive_when_it_works,_but_warranty_support_is_awful.:gman79\n\n\nB000636FJG\n\n\nSony DRX710UL DVD+R Double Layer External DVD+-RW Drive (16x): Electronics\n\n\nelectronics\n\n\n17 of 19\n\n\n2.0\n\n\nGood drive when it works, but warranty support is AWFUL.\n\n\nJune 8, 2005\n\n\ngman79\n\n\n\n\n\nI am on my third drive now, thanks to the first two breaking. Sony sent me the 720UL as a replacement for my 710UL, but it's the same drive with the exception of some faster burn times and the 720UL doesn't support MAC OS.\n\nThe Sony Service Center in Manning, IA gave me a huge runaround... first they agreed to pay for return shipping on the second defective drive. Then they called me back 2 hours later and refused to pay for the shipping. What a joke.\n\nGood drive when it works, but may god have mercy on you if it breaks after the return period.\n\n\n\n\nB0000D8LSW:no_way!:\n\n\nB0000D8LSW\n\n\nApple iPod FireWire Power Adapter: Electronics\n\n\nelectronics\n\n\n8 of 41\n\n\n1.0\n\n\nNo Way!\n\n\nMarch 20, 2004\n\n\n\n\n\n\n\n\nwho would even think about buying something that plugs into the wall for sixty bucks! maybe thats why the ipod is so expensive...............\n\n\n\n\nB00005NWV1:not_compatible_with_windows_2000/xp,_horrible_tech_support_-_avoid!!:honest_opinion\n\n\nB00005NWV1\n\n\nNETGEAR FA511 32-bit CarBus PC Card Mobile (10/100): Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nNot Compatible with Windows 2000/XP, HORRIBLE Tech Support - AVOID!!\n\n\nJuly 10, 2006\n\n\nHonest Opinion\n\n\nBoston, MA USA\n\n\nI had this adapter running in my the Cardbus slot of my Windows 98 computer and it ran fine. I installed it on my Windows 2000 laptop. Well, my first sign of trouble was a warning popup when I installed the drivers that the drivers were not certified by Microsoft. So, imagine my shock when my brand new computer constantly got Blue Screen of Death (BSOD) crashes. Big blue screen, loss of data, computer freezes tight, power button inoperative, and when you have to use a pin to press that tiny little laptop reset button on a brand new computer, well, we all know that's when you're in Computer Hell.\n\nI'm a Microsoft Certified Professional so I was able to narrow down the source of the problem. After definitely identifying the FA511 as the culprit, I contacted Netgear's online tech support. And that's when things really started to get ugly.\n\nIt took forever for Netgear to finally give me an answer. This was after days of inactivity, wildly inappropriate responses, and repeatedly requesting same information from me.\n\nFinally, Netgear escalated the problem to their next level of tech support and I was shocked to read their solution: \"This is a Windows issue regarding permissions and ***16 BIT*** compatibility mode\" While they were vague as to the exact nature of the problem, and contradicted themselves as to whether compatibility mode had to be enabled or disabled, their solution was straightforward: I was directed to a Microsoft Knowledge Base article which tells you how to enable Compatibility Mode - and for 16-bit compatibility mode I had to make my nice clean Windows 2000 computer compatible with Windows 95 (yes, ninety-five).\n\nSo, while the FA511 hardware is a 32-bit Cardbus adapter, apparently their drivers are 16-bit!!! No wonder the FA511 was never certified for Windows 2000. And I wouldn't trust it for Windows XP either.\n\nThere are other Ethernet adapters out there by 3COM, Belkin, Linksys, SMC, and Xircom that work flawlessly with Windows 2000/XP by companies that will respond to your questions. Go get them. Avoid the FA511\n\n\n\n\nB000IK5XWW:no_way:will_moore\n\n\nB000IK5XWW\n\n\nApple iPod USB Power Adapter: Electronics\n\n\nelectronics\n\n\n5 of 15\n\n\n1.0\n\n\nNo way\n\n\nNovember 9, 2006\n\n\nWill Moore\n\n\nMonroe, GA USA\n\n\nNo way am I purchasing this for my iPod. I would love to be able to charge my iPod away from my iMac, but I refuse to purchase this. This should of been included in the box. It is a tremendous ripoff to buy an AC adapter seprately (and at $30), but then again, I'm sure Apple is making a ton off of this gimic, but I refuse to contribute. I can see not including a dock, A/V cables, etc, as those aren't essential iPod accessories. But an AC adapter? Come on. Bad Apple. Bad!\n\nOn the plus side, this AC adapter is smaller and comes with an iPod to charger (or iPod to computer) cable, which the previous model did not include. It's still a ripoff, though\n\n\n\n\nB00029ZV0I:nice_features,_but_can't_compete_with_cox_dvr:simon_mall_\"chris\"\n\n\nB00029ZV0I\n\n\nTiVo TCD540080 Series2 80-Hour Digital Video Recorder: Electronics\n\n\nelectronics\n\n\n7 of 10\n\n\n2.0\n\n\nNice features, but can't compete with Cox DVR\n\n\nJuly 16, 2006\n\n\nSimon Mall \"Chris\"\n\n\nBroken Arrow, OK United States\n\n\nI have been a DVR user through Cox Cable for approx. 1 yr, and have always been disappointed. I have kept my eye on Tivo for some time, and when my DVR box went out for the 2nd time, I decided it was time to try Tivo. I got a refurbished Series2 DVR that was free for signing a 1 yr commitment for $12.95 per month. \n\nI received the Tivo only 2 days after I ordered, very impressive since I didn't pay to have it shipped that quickly. The setup was reasonably straight forward. I had heard so much about Tivo that I had very high expectations, and here are the problems I encountered that surprised me:\n\n1) My first mistake was not recognizing that you have to get a Dual tuner unit to be able to record 2 seperate programs (DVR does this automatically). So I ordered the wrong unit. To be honest, I don't see how this service is very useful at all with only a single tuner, my family relies on dual tuners A LOT. I am shocked that Tivo even offers a single tuner system, to me it's like a tricycle compared to a 10 speed, a dual tuner is so much more useful. \n\n2) I thought I would be able to hook up a wireless receiver to connect to my home network. No, you have to buy theirs for $40 to $50. Since I didn't know that, I had to rely on the phone line option. This really stinks, it is very very very slow. I spent 2 hrs trying to download one days programming, the system kept getting interuppted during download (yes I disabled call waiting). \n\n3) When you use the online program guide, it covers up the entire screen. My Cox DVR puts the TV in upper right corner (approx. 1/4) asnd the rest of the screen lets you see the guide. We use this all of the time to see if there is anything better on, but that won't work so well with Tivo. Plus, scrolling up and down is painfully slow, the screen takes for ever to refresh.\n\n4) Similarly to item 3 above, when you review recorded programs the show you are watching is turned off completely, you can only see the Tivo guide. Again, Cox DVR lets me look at my list and delete files etc.. all while my TV program stays visible in the upper right corner. \n\n5) Remote wouldn't work with my 42\" Akai plasma. I can't fault Tivo too much on this one, none of the universal remotes seem to work with this TV for some reason. \n\n\nI did love some of the Tivo features and wish my Cox DVR had them:\n1) Wishlist\n2) Tivo Suggestions\n3) Can access your Tivo through internet\n4) View photo's through Tivo.\n\nIn the end my items # 3 and 4 were things I just couldn't get past, so I returned my Tivo. They were friendly and helpful and I really wanted to like this product, but in my opinion it's not able to compete with the speed and power of the Cable companies DVR. \n\n\n\n\nB000F4C2AM:junk,_sorry_panasonic:tall_paul\n\n\nB000F4C2AM\n\n\nPanasonic DMR-ES15S DVD Recorder with DV Input: Electronics\n\n\nelectronics\n\n\n3 of 6\n\n\n1.0\n\n\nJunk, sorry Panasonic\n\n\nSeptember 24, 2006\n\n\nTall Paul\n\n\nSan Diego, CA United States\n\n\nI bought a lot of Panasonic stuff in the past without any problems but the picture quality on this thing is awful, even at the highest quality setting. All I saw were blocky artifacts that seemed to be blinking. I even had the DNR (digital noise reduction) enabled and it didn't help. Most recordings skipped and stuttered at the beginning. I took it back for refund and Best Buy told me this model is known for doing that. I don't see how Panasonic can put their name on this junk. Those cheap $99 recorders you find at Walmart have better quality than this. \n\n\n\n\nB000IK5YZ8:fm_remote_users_beware:william_e._hamilton\n\n\nB000IK5YZ8\n\n\nApple iPod nano 2nd Generation Armband (Gray): Electronics\n\n\nelectronics\n\n\n7 of 14\n\n\n1.0\n\n\nFM remote users beware\n\n\nOctober 2, 2006\n\n\nWilliam E. Hamilton\n\n\nRochester Hills, MI USA\n\n\nHeaven help you if you want to use this with the FM remote -- a very common usage at heath clubs which provide TV rebroadcasts over FM. The FM remote connects to the iPod via a large plug on the left hand side. The armband provides a small circular hole on the right hand side, but no means of connecting the large FM remote plug\n\n\n\n\nB0000DDAED:portable_tv:corabeth_\"my1k9\"\n\n\nB0000DDAED\n\n\nJensen J50-MCTV Portable 5\" Color TV with Radio and Mounting Bracket: Electronics\n\n\nelectronics\n\n\n4 of 16\n\n\n1.0\n\n\nportable tv\n\n\nJanuary 18, 2004\n\n\nCorabeth \"my1k9\"\n\n\nVermont\n\n\nThis portable color tv doesn't give me any channel that you can actually watch. I returned it and I was credited. However, much to my surprise I received the same five inch tv as I had returned. It already cost me to have it returned. Since I didn't ask for it, should I consider it a gift\n\n\n\n\nB0002V8KX6:fuzzy_sound_and_worst_customer_service_ever!!!!!!!:m._reed_\"reedmt\"\n\n\nB0002V8KX6\n\n\nLinksys PAP2 Phone Adapter for Vonage Internet Phone Service: Electronics\n\n\nelectronics\n\n\n1 of 5\n\n\n1.0\n\n\nFuzzy sound and WORST CUSTOMER SERVICE EVER!!!!!!!\n\n\nJuly 7, 2006\n\n\nM. Reed \"reedmt\"\n\n\nHinesville Georgia\n\n\nStay away from vonage! I have had it for several months, and have been trying to cancel it. I have been put on hold a MINUMUM of 45 minutes to and HOUR each time, and then transferred to someone ELSE who then does the bait and switch AGAIN. When you go to their web site, the only thing you can get is their custumer service number, which takes you RIGHT BACK to the same old nonsense. I need to cancel because of a military deployment, and all I'm getting is a run around...the same thing happened with AOL, but this is EVEN WORSE. There are tons of blogs out now about how VONAGE SUCKS...check them out. It's no wonder this company is tanking, their customer service is the absolute WORST!!!!!!!! I hope they read this.... they really need to change!!\n\n\n\n\nB00009EFQH:combination_mysteriously_changed:sheetal_patel\n\n\nB00009EFQH\n\n\nTargus PA496U DEFCON VPKL - Video Port Key Lock: Electronics\n\n\nelectronics\n\n\n10 of 11\n\n\n1.0\n\n\nCombination mysteriously changed\n\n\nApril 30, 2006\n\n\nSheetal Patel\n\n\nNJ\n\n\nI had been using the lock for my laptop for the past 4 months without any trouble. All of a sudden, today, as I went to pick up the lock, which had been left untouched in the open position overnight with my combination in it, it decided to change combinations by itself. So it was in the locked position but the combination is still the one I had set. I tried so many combinations 'around' mine but obviously there are so many different possibilites. The fact that I even have to try to think of what combination my lock decided to change itself to today makes this a lock I can't rely on\n\n\n\n\nB0001OKBKY:save_your_money:k._muller\n\n\nB0001OKBKY\n\n\nQuikFinish PL100 10\" Document Laminator: Electronics\n\n\nelectronics\n\n\n48 of 48\n\n\n2.0\n\n\nSave Your Money\n\n\nAugust 22, 2005\n\n\nK. Muller\n\n\n\n\n\nI am in the process of looking to return. The seal is not so good, so you have to run it through the machine a couple of times. In addition, the lamination has ridges in it. It isn't a smoothe finish (and it isn't air pockets??\n\n\n\n\nB00005ARK3:low_quality_networking_solution_for_low_budget.:m._r._simmons_\"techie\"\n\n\nB00005ARK3\n\n\nLinksys BEFW11S4 Wireless-B Cable/DSL Router: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nLow quality networking solution for low budget.\n\n\nNovember 24, 2006\n\n\nM. R. Simmons \"Techie\"\n\n\nOld South\n\n\nI initially chose this router because of its low price. Sometimes, you get what you pay for, and that is exactly what happened to me. As a wired router, this works well enough. It connects four computers to the internet. Just don't do anything to intense with it, and you should be fine. I noticed that when I view video media, the connection totally freezes. But for basic internet and downloading, it works.\n\nI generally find, however, that no one buys a \"wireless\" router not to use wireless, and that is where the problem is. The receiver is so painfully finicky, it does not keep connections. The configuration of this router is about as easy as brain surgery. The linksys support team tries to be helpful, but I can't seem to make this router reliable. It drops reception all the time. Many times my laptop won't even detect a signal. The wireless performance of this router is abysmal. \n\nI sincerely hope that if you have this router, it is working flawlessly for you. I can't get it to work well for me not matter what I do. The difficulty that I am having with this equipment is so bad that I am going to replace it as soon as I can.\n\n\n\n\nB00005ARK3:good_in_theory..._major_issues_in_practice:m._hockenheimer\n\n\nB00005ARK3\n\n\nLinksys BEFW11S4 Wireless-B Cable/DSL Router: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nGood in theory... major issues in practice\n\n\nAugust 18, 2006\n\n\nM. Hockenheimer\n\n\nDayton, Ohio, USA\n\n\nI've been using my BEFW11S4 for a few months now. The little thing has its good points - a nice, unobtrusive little box I can just plug in and hide on top of some piece of office furniture, allowing my rather over-laptop-equipped household some nice convenient net access, while still allowing the home office systems a wired connection.\n\nSounds nice, if it only worked. Howver, BEFW11S4 has a MAJOR problem, and a rather common one at that. Specifically, it locks up. It does so frequently. I have to unplug the router at least once a day, and frequently twice, to deal with it locking up. Can't even access the administrator screen, and even hitting the reset button isn't enough to fix it. Wireless isn't too useful when you have to physically walk to the unit multiple times a day, is it?\n\nThe problem seems to lie not in the hardware, but in the firmware (the software that runs on the router). If Linksys would release reliable firmware for this router, I'd give it 4-5 stars. But until they resolve the issue that wires down their wireless modem, I can't possibly justify giving the BEFW11S4 more then two stars\n\n\n\n\nB00005ARK3:don't_buy_a_linksys_router_if_you_have_a_mac_computer:mara_kurtz\n\n\nB00005ARK3\n\n\nLinksys BEFW11S4 Wireless-B Cable/DSL Router: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nDON'T BUY A LINKSYS ROUTER IF YOU HAVE A MAC COMPUTER\n\n\nJuly 25, 2006\n\n\nMara Kurtz\n\n\nNew York, NY USA\n\n\nI bought this router and spent almost two hours online with Linksys. After trying every sort of suggestion and speaking with three different tech support people who barely spoke English I finally understood that Linksys has \"limited support for Macs.\" When I asked what \"limited support\" means, I was told \"we are not trained to help people with a Mac. If you can borrow a windows PC we can help you\". What a waste of time. I am sending the router back. Beware if you have a Mac\n\n\n\n\nB000CSQNL0:frustrating_item:f.b.\n\n\nB000CSQNL0\n\n\nGarmin eTrex Vista Cx Handheld GPS Navigator: Electronics\n\n\nelectronics\n\n\n4 of 6\n\n\n2.0\n\n\nFrustrating Item\n\n\nSeptember 18, 2006\n\n\nF.B.\n\n\nFlorida\n\n\nThe first Vista Cx I purchased died after only 3 days of use. I returned it to Amazon who immediately and painlessly replaced it. The second unit lasted for 4 days until I fell waist deep into a stream with the unit in my pocket. Though it was only submersed for a matter of seconds it immediately stopped working and water was seen internally around the screen. Garmin's claim of waterproofness for 1m for 10 minutes is utter baloney. The poorly designed T-style lock on the battery case cannot possibly assure water integrity from anything beyond high humidity. I probably wouldn't buy buy another Garmin but I'm stuck with them as I have already invested $200 in proprietary MapSource software. I guess that was their plan.\nOn the plus side, the Vista Cx did seem to function as it should...for all of 7 days\n\n\n\n\nB0000UI304:unbelievable_piece_of_junk_without_real_support:audiophile_\"without_speakers\"\n\n\nB0000UI304\n\n\nKlipsch ProMedia Ultra 5.1 Multimedia Speaker System, (6 Speakers, Black): Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nUnbelievable Piece of Junk without real Support\n\n\nNovember 1, 2006\n\n\nAudiophile \"Without Speakers\"\n\n\nSan Francisco, CA USA\n\n\nThis is the best sounding PC speaker system available. Absolutley stellar sound quality--for how long is a serious problem. I too own 2 of these systems. After eleven months of bliss my first amp also went out. \n\nI call Klipsch technical support, and they explain I can send it in but the amp is backordered for 4-6 weeks. So I buy another one from Amazon and thier retailer Beach Audio, Inc., not having 4-6 weeks to wait for bliss to return. \n\nSo get this please, not $360 plus tax once, but twice, and add 2 occurances of overnight shipping at $125 per, for a total spend of $970 plus tax!!!\n\nWell blow me down but the replacement for amp #1 hasen't even showed up, and 6 working days of bliss later? You guessed it amp #2 fails. Klipsch, well they obviously picked quite a piece of crap for an amp. They stand behind the product though--I have another RMA to send to the fine people at the Klipsch return center.\n\nThe retailer this site uses, Beach Audio Inc., offered me my money back, but would offer no replacement of any sort, and no refund for the $125 shipping charge. Nothing they wanted to do would help me get a working product. They refered me to Klipsch without mercy, or even lube.\n\nNow Klipsch makes some fine home audio (my Klipschhorns are 15 years old, have been rocked to death, and still sound awesome), and this ProMedia Ultra unit sounds fabulous--so I may do business with them again (perhaps not the PC Multimedia division), but I vow to never give another red cent to Amazon's retailer Beach Audio, Inc.. They were uncooperative, unsympathetic, curt, short, unprofessional, and ultimately completely useless when contacted about the failure.\n\nPLEASE LOOK ELSWHERE FOR YOUR NEXT PC MULTIMEDIA PURCHASE!!!!!!! THE KLIPSCH PROMEDIA DIVISION SHOULDN'T EVER BE CONSIDERED. \n\n\n\n\n\nB0000UI304:both_klipsch_subwoofers_died_:music_man_\"gibson\"\n\n\nB0000UI304\n\n\nKlipsch ProMedia Ultra 5.1 Multimedia Speaker System, (6 Speakers, Black): Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n1.0\n\n\nBoth Klipsch Subwoofers Died \n\n\nAugust 19, 2006\n\n\nMusic Man \"Gibson\"\n\n\nusa\n\n\nThe first subwoofer died (no sound) after 6 days. Remember, the sub powers all the speakers in this system. I sent it back to the company I bought it from and they were nice enough to take care of \"Klipsch's faulty product\". They asked me if I would like a refund and like an idiot I said, \"No, just send me another one\". The replacement Klipsch system arrived and it played great for about 14 months and then NOTHING. No power from the sub! Same problem but now my warranty is up. Klipsh has a way of designing things to work just past the warranty. Shame on Klipsch for making such a shoddy product. Shame on me for asking for a replacement and getting burned twice. Heed all these warnings my friends. I would buy used speakers at a garage sale before I would buy another Klipsh product. YOU HAVE BEEN WARNED! \n\n\n\n\nB0000UI304:sound_great...._but_stop_working!:brian\n\n\nB0000UI304\n\n\nKlipsch ProMedia Ultra 5.1 Multimedia Speaker System, (6 Speakers, Black): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nSound great.... but stop working!\n\n\nJuly 18, 2006\n\n\nBrian\n\n\nMA United States\n\n\nThese speakers do sound great but just like many others who have reviewed these speakers, they eventually just stop working. It sounds like I was one of the lucky ones as mine lasted two years. But I really didn't use them all that much and I don't blast the volumes. When you spend this kind of many on a product it should at least last 5 years or so. I've never had any other speaker brand just stop working on me before. Worse of all is to hear that Klipsch is charging a lot of money to repair their faulty products and that people had problems with them even after they were \"repaired\". Well, I'm not wasting time and money on repairing them. I'm just switching to Logitech. No more Klipsch ever again for me.\n\n\n\n\nB000BMAL10:so_many_problems!:a._harris\n\n\nB000BMAL10\n\n\nApple 1 GB iPod Nano Black: Electronics\n\n\nelectronics\n\n\n1 of 3\n\n\n1.0\n\n\nSo many problems!\n\n\nNovember 9, 2006\n\n\nA. Harris\n\n\nOhio\n\n\nI received the iPod quickly which was a plus. But it was horrible when it would only download 5 songs. I had friends who have the same iPod try to fix it and it never worked. I sent it back and requested a refund. It has been a month and a half and I have YET to get my $120 back.\n\n\n\n\nB000A2AIHI:good_idea_but_not_ready_yet,_doesn't_work,_unreliable:anonymous_\"none\"\n\n\nB000A2AIHI\n\n\nLaCie 40GB SAFE Mobile Hard Drive (300809) Biometric: Electronics\n\n\nelectronics\n\n\n4 of 5\n\n\n1.0\n\n\nGood idea but not ready yet, doesn't work, unreliable\n\n\nNovember 19, 2005\n\n\nAnonymous \"None\"\n\n\nColumbia, SC\n\n\nI like other Lacie drives I've used very much, thought I'd take a chance on this, but it just does not work. It takes me about a hundred swipes on average to get the biometric sensor to work...That is keeping data a bit TOO secure to be helpful. Also there have been reports of complete data loss by others,but I've not had that experience - could be I just haven't been able to GET to my data enough to find out. Support from Lacie was completely unhelpful (wash your hands and the sensor before swiping which I have already done dozens of times was the only suggestion). If the drive could be partioned so you could have a secure partition and an easier to access partion, the failure of biometrics would not be so serious, but as is, I would definitely not purchase this again, and I would strongly advise against it unless there is some kind of firmware or other improvement\n\n\n\n\nB0009JR5HS:don't_stomp_your_feet,_sneeze,_or_slam_the_door.:ultimatepete\n\n\nB0009JR5HS\n\n\nD-Link DWL-G520M Wireless 108G PCI Adapter, 2XR, MIMO, Smart Antenna: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n2.0\n\n\nDon't stomp your feet, sneeze, or slam the door.\n\n\nMarch 28, 2006\n\n\nUltimatePete\n\n\n\n\n\nWe have 50+ of these cards at work and I have 4 at home. I have never seen a more \"fragile\" PCI card. If you slam a door or stomp your feet near the PC, this card may very well cease to function on the next reboot. God forbid you attempt to move the tower. Every other day, someone has a malfunctioning card which needs to be jiggled, removed and reinserted, or otherwise screwed with.\n\nWhen it does actually work, the connection is usually good\n\n\n\n\nB0008JFJR2:poor_reception_-_very_static:faithful_shopper_\"jzk\"\n\n\nB0008JFJR2\n\n\nMotorola MD7001 2 Line 5.8GHz Digital Expandable Cordless Handset & Charger Accessory for MD7000 series: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nPoor Reception - Very Static\n\n\nNovember 12, 2006\n\n\nFaithful Shopper \"JZK\"\n\n\nCt\n\n\nWe purchased this phone to go with the rest of the set and we were extremely disappointed!! We had to return them for a different brand because we could not hear!!! They were so static that you often could not hear the person speaking!! You would think at this price it would not be so static. We went for a brand at a much lower price and they are crystal clear!\n\n\n\n\nB0008JFJR2:very_misleading:carlos_bustamante\n\n\nB0008JFJR2\n\n\nMotorola MD7001 2 Line 5.8GHz Digital Expandable Cordless Handset & Charger Accessory for MD7000 series: Electronics\n\n\nelectronics\n\n\n5 of 24\n\n\n1.0\n\n\nVERY MISLEADING\n\n\nJanuary 2, 2006\n\n\nCarlos Bustamante\n\n\nCartagena, Colombia\n\n\nJan 2 2006\nI two ordered the \"md7001 2 line phone\" I thought I was getting a cordless 2 line phone. What I actually got was a 2 line handset that requires a base unit for it to work. This info should be prominently displayed in their description.\n\n\n\n\nB00062GJ8E:easily_scratched_w/very_limited_lifespan:l._henderson\n\n\nB00062GJ8E\n\n\nPalm Premium Screen Protectors Multi-pack (3177WW): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nEasily Scratched w/Very Limited Lifespan\n\n\nJuly 27, 2006\n\n\nL. Henderson\n\n\nElkins Park, PA United States\n\n\nYou'd think that a Palm screen protector from Palm would be best in class. However, these Palm \"premium\" screen protectors have a lifespan of about 3-4 days at best. I'm very careful with my new Palm TX, yet under normal use the stylus easily scratches the screen protector to such an extent that the device no longer accurately recognizes stylus input. I also dislike having to cut the screen protectors to size. I've tried an Invisible Shield screen protector, and it was tough as nails; the problem was that installation was difficult, and the tacky surface also produced input errors. Next I'm trying a Boxwave ClearTouch. I never had this sort of problem with the old Fellowes WriteRight protector, which lasted a year or more on my old Palm m130\n\n\n\n\nB00008VF91:more_monster_snake_oil...:eduardo_nietzsche\n\n\nB00008VF91\n\n\nMonster Cable THX SP 16-100 Standard THX-Certified Speaker Cable (100 Feet): Electronics\n\n\nelectronics\n\n\n30 of 39\n\n\n1.0\n\n\nmore Monster snake oil...\n\n\nMarch 14, 2005\n\n\nEduardo Nietzsche\n\n\nHouston\n\n\nMonster Cable and its similarly overpriced imitators are a triumph of hype over substance, and ruthlessly exploits the consumer's naivete and trust in shady audio/video salespeople. \n\nYes, for most things you DO \"get what you pay for\"---but when it comes to speaker cables, interconnects, and other a/v wiring however it's another story. \n\nAll you need for optimal sound is any 12 to 16 gauge generic cable which you can buy at Home Depot for a fraction of the cost. \n\nSave your money for things like speaker stands, which really do make a world of difference\n\n\n\n\nB00068UJHK:do_not_buy_this_card_if_you_use_windows_98se:ben_franklin\n\n\nB00068UJHK\n\n\nCrucial Hi-Speed USB SD/MMC Card Reader: Electronics\n\n\nelectronics\n\n\n1 of 7\n\n\n1.0\n\n\nDO NOT BUY THIS CARD IF YOU USE WINDOWS 98SE\n\n\nMay 1, 2006\n\n\nBen Franklin\n\n\nOregon\n\n\nI AM DISGUSTED TO REALIZE THAT ALTHOUGH CRUCIAL MARKETS THIS CARD AS COMING WITH A DRIVER FOR WIN98SE, IT DOES NOT. THE CD IT CAME WITH DID NOT HAVE A DRIVER (although it said it did) THE WEB SIGHT DOES NOT HAVE DRIVERS AND AFTER TWO PROMISES ON THE PHONE FROM CRUCIAL TECH SUPPORT PROMISING TO SEND ME ONE, I STILL DON'T HAVE IT. I DON'T BELIEVE THIS DRIVER EXISTS. SO MUCH FOR TRUTH IN ADVERTISING AND AMERICAN COMPANY INTEGRITY\n\n\n\n\nB000AM6QH4:worked_for_seven_minutes,_then_quit:s._mcclary\n\n\nB000AM6QH4\n\n\nLogitech� mm28 Portable Speakers: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nWorked for Seven Minutes, Then Quit\n\n\nOctober 13, 2006\n\n\nS. McClary\n\n\nOakland, CA USA\n\n\nWhen I plugged these in they worked fine for about seven minutes, then the speaker's internal power simply cut off. Happened on the AC adapter or batteries, whether I was using my computer or my iPod as the sound source. Same behavior every time. \n\nLogitech sent me a replacement from a new production run, same thing happened. Sent it back to Logitech on my nickel, got another replacement from same production run (Product ID: A612). Same problem.\n\nAfter two months and many long waits for Logitech tech support, I'm giving up. Maybe it was just a bad run, or maybe my environment is flukey, but I'm sorry I ever bought this thing\n\n\n\n\nB00008Z1QI:weak_sound_unless_you_have_a_headphone_amp:tom_slick43\n\n\nB00008Z1QI\n\n\nKoss Pro4AAT Titanium Pro Headphones: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nweak sound unless you have a headphone amp\n\n\nNovember 3, 2006\n\n\nTom Slick43\n\n\nKaneohe HI USA\n\n\nThe Koss Pro4AAT headphones are quality headphones, but sounds weak when driven through a regular headphone jack from your receiver or tv. To get the great sounds that everyone else seems to rave about, you really need to run these headphones through a headphone amp. Also, the ear pads are of a vinyl-like material which doesn't breathe well, making it uncomfortable to wear for extended periods. I've read that leatherette pads are available, but so far haven't been able to find them. The warranty alone is great, but I wouldn't recommend these headphones unless you have a headphone amp\n\n\n\n\nB0001FV3AK:unreliable...don't_get_lulled_w/false_sense_of_security:d._plante\n\n\nB0001FV3AK\n\n\nSanDisk 512 MB Cruzer Titanium (SDCZ3-512-A10, Retail Package): Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nUnreliable...don't get lulled w/false sense of security\n\n\nNovember 13, 2006\n\n\nD. Plante\n\n\n\n\n\nDevice unexpectedly stopped working after 9 months. Has not been thru a washing machine, fallen in the ocean or been run over by a truck! Just nestled in my briefcase. Bullet-proof case is great gimick, but this device is unreliable as a backup for \"road\" files. Might be better off using your PDA with a large SD card\n\n\n\n\nB00000J0D2:worked_for_30_days_-_microphone_broke:s._pritchard_\"suzisbooks\"\n\n\nB00000J0D2\n\n\nUniden HS910 Headset for Cordless Phones: Electronics\n\n\nelectronics\n\n\n8 of 8\n\n\n1.0\n\n\nworked for 30 days - microphone broke\n\n\nApril 10, 2004\n\n\nS. Pritchard \"suzisbooks\"\n\n\nCA USA\n\n\nI had one that worked for a year that came with the original phone. Then, I just ordered this one from Amazon and it worked for 30 days. Like other reviews you see here, people started not being able to hear me, but I could hear them. So the microphone broke. But, I called Uniden tech support and they recommended I replace the battery on the phone for $37. So, I just ordered a new phone and tested the headset -- sure enough it wasn't the phone -- it was the headset. A costly mistake. I would not buy this headset again. Also, Amazon has a 30 day return policy, so if it breaks, make sure you return it within that 30 days or you are stuck with it\n\n\n\n\nB000853QUS:gs_not_as_good_as_the_original:m._a._dawe\n\n\nB000853QUS\n\n\nBFG Technologies RatpadzGS Gaming Surface: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nGS not as good as the original\n\n\nSeptember 18, 2006\n\n\nM. A. Dawe\n\n\nVictoria, BC\n\n\nI just bought a new RatpadzGS to replace my four year old ratpadz. Compaired to the old Ratpad, the RatpadzGS is made with a lower density Cheap-O(tm) plastic and tracks poorly with my logitech G7 mouse. When using the mouse on the GS it causes the curser to sort of stutter along. Overall I would pass on this product, for now my desk actually works better than this mousepad.\n\n\n\n\nB000853QUS:so-so_at_best.:mitchell_bloch_\"bazald\"\n\n\nB000853QUS\n\n\nBFG Technologies RatpadzGS Gaming Surface: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nSo-so at best.\n\n\nJune 28, 2006\n\n\nMitchell Bloch \"bazald\"\n\n\nAnn Arbor, Michigan United States\n\n\nIt was an okay mousepad for approximately a year and a half. Every so often, my optical mouse jittered from one edge of the screen to the other when left stationary, but I encountered no serious problems.\n\nUnfortunately, it seems that might have been indicative of a greater problem with the mousepad. It is all one color. The grooves have all worn away on a large portion of the pad, and now my mouse won't budge. I'm in the process of looking for an alternative right now\n\n\n\n\nB000EPHPPE:it_gets_ugly_fast:g._rosario_\"gerardo\"\n\n\nB000EPHPPE\n\n\nApple 80 GB iPod video White (5.5 Generation): Electronics\n\n\nelectronics\n\n\n5 of 20\n\n\n1.0\n\n\nIt gets ugly fast\n\n\nNovember 17, 2006\n\n\nG. Rosario \"Gerardo\"\n\n\nNashville, TN USA\n\n\ni don't like how easy it get scratch, and if u get the black one even worst, i went shopping for cases but they get scratch inside the cases too, just by been in the case, i return the case, then i when for those clear stickers and i recomend to put those on on a really clean table and very dry and clean hands may be use glove cause a little touch and there goes your finger print in the sticker, and with a black ipod you can't miss it. and if u don't take your time u will have a lot of air bubbles showing in your ipod, so after a week looks like something that i have for a year, it sucks.\n\n\n\n\nB00008SCFL:can't_get_it_to_work.:albert_a._mason\n\n\nB00008SCFL\n\n\nNetgear WGR614 802.11g Wireless Router: Electronics\n\n\nelectronics\n\n\n2 of 4\n\n\n2.0\n\n\nCan't get it to work.\n\n\nNovember 9, 2006\n\n\nAlbert A. Mason\n\n\n\n\n\nThe Netgear wireless router product is apparently in working order because I mailed it to my son in Tennessee who got it to work just fine on his equipment which is the same as mine. Surely, I'm not so technically savvy as my son, but I can read and write and follow directions. The setting up of the equipment is quite complicated, in my view, far moreso than it should be. I've set up other peripherals with success, but this product is more than I can handle. I've followed the directions, over and over, and still the router doesn't connect. I've given up. Can't handle the frustration. By the way, I'm way over the age of 13. I have grandchildren over the age of 13.\n\n\n\n\n\nB0002TPFW2:wireless_printing_problems:j._barber\n\n\nB0002TPFW2\n\n\nD-Link DP-G321 Wireless-G Print Server USB 2.0/Parallel Port: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nWireless printing problems\n\n\nSeptember 4, 2006\n\n\nJ. Barber\n\n\nUK\n\n\nAfter a bit of sweat I've managed to get the printer working fine from both my XP and MAC OS Tiger computers ...... until I unplug the direct ethernet cable to the router that is needed for set-up. Then nothing happens i.e. no wireless printing.\n\nI suspect it's an IP addressing issue having read a helpful article here (take note D-link - Amazon is becoming a better support site than your own!). Changing the IP address messes up my internet connection during install; the MAC works quite hard to re-establish the internet connection & perhaps this breaks the connection to the printer? I dunno, but it's giving me a headache.\n\nDocumentation & troublshooting help are very, very weak & time will tell if D-link on-line support are any better (22 hours & no response from them yet....)\n\nI'm sure network wizards will have this little beasty up & running in no time, but for normal people it's, well, a bit of a pain\n\n\n\n\nB0002TPFW2:garbage!-_it_does_not_work_with_most_usb_printers:y._morgan_\"wireless_mavrick\"\n\n\nB0002TPFW2\n\n\nD-Link DP-G321 Wireless-G Print Server USB 2.0/Parallel Port: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n2.0\n\n\nGarbage!- It does not work with most USB printers\n\n\nJune 29, 2006\n\n\nY. Morgan \"wireless mavrick\"\n\n\nRye NY\n\n\nI contacted D-Link support and they verified that this product was tested and worked with a very minimum number of popular USB printers (I believe the number was around 30-40).\nThe D-Link DP-G321 Wireless Print server did not work with any of the 3 USB printers I owned (Lexmark Z320, HP PSC 1250, HP Photosmart 7310). It worked okay with my one parallel HP Laserjet 1100 and I was able to connect it to my network and install the print server, but it was not at all compatible with my printers which was key. It this product was more compatible, I would have rated it 4 stars, because the installation was very easy and almost worry-free, plus it was very sensitive to the wireless radio waves i.e. it sensed my secured wireless network when other print server could not in that same location.\nI returned the D-Link and bought instead a Linksys WPS54GU2 print server that worked with all of my printers, but it was not as sensitive as the D-Link DP-G321, so I had to move my printers and print server closer to my wireless access point\n\n\n\n\nB00003CWEI:whoa!!!__apples_and_oranges:dwpc\n\n\nB00003CWEI\n\n\nMonster Cable MV3CV-1M Monster Video 3 High-Resolution Component-Video Cable: Electronics\n\n\nelectronics\n\n\n13 of 18\n\n\n1.0\n\n\nWhoa!!! Apples and Oranges\n\n\nJanuary 12, 2005\n\n\nDWPC\n\n\nVentura, CA United States\n\n\nSeveral reviews mention the improvement when these cables to replace a composite (L/R audio + video) cable, or an S-video (multi-pinned plug). ALmost all of that improvement is due to the fact that these cable connection methods are completely different types in signal format and the TV uses different internal circuitry for each one. Virtually any S-video cable connection will give a better image than a composite type cable, and any component (individual R-G-B video) connection will be better than S-video. Cable brand has absolutely nothing to do with it. There may be some difference between one brand of component cable and another, but its almost always unnoticable\n\n\n\n\nB000GUO4L0:good_printer_but_drops_the_internet_connection:wds_\"wds\"\n\n\nB000GUO4L0\n\n\nCanon PIXMA MP530 Office All-In-One Photo Printer with Fax (0580B002): Electronics\n\n\nelectronics\n\n\n2 of 5\n\n\n2.0\n\n\nGood Printer but drops the internet connection\n\n\nNovember 9, 2006\n\n\nWdS \"WdS\"\n\n\nOverland Park, KS\n\n\nI was in the market to upgrade my printer and used Amazon's reviews to decide on a Canon MP530. It does a good job, but when I plug the USB cord into the printer, the connection to the Internet drops every time. I have to disconnect from the printer so I can get on-line. Recently I filled out a rather lengthy application on the Internet and plugged in the USB cable so I could print. That step caused the connection to drop and I got to reenter all the information. I will be returning the Canon MP530 and start my search for another all-in one printer\n\n\n\n\nB000BPH00M:this_is_a_piece_of_[...]:h._everett_\"mr._x\"\n\n\nB000BPH00M\n\n\nMemorex 2GB USB 2.0 Mini Travel Drive (32509373): Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n1.0\n\n\nThis is a piece of [...]\n\n\nMay 18, 2006\n\n\nH. Everett \"Mr. X\"\n\n\nNew York, NY United States\n\n\nThe Memorex U3 TravelDrive is simply a piece of crap. I bought it for easy data transfer when on travel, but it seldom works in any computer. The logo comes up when I plug it in and it simply hangs there. It's utterly worthless\n\n\n\n\nB00005B9W6:it_is__piece_of_junk!!!!!!!!:julie_a._ricker_\"camera_hound\"\n\n\nB00005B9W6\n\n\nSkipdoctor CD Repair Kit: Electronics\n\n\nelectronics\n\n\n6 of 11\n\n\n1.0\n\n\nIt is piece of junk!!!!!!!!\n\n\nMarch 17, 2006\n\n\nJulie A. Ricker \"Camera Hound\"\n\n\nAugusta, Maine USA\n\n\nIt was not worth the cost to ship it back\n\n\n\n\nB000A3WS84:love_it.._hate_it:a._pereyra\n\n\nB000A3WS84\n\n\nApple 30 GB iPod with Video Playback Black (5th Generation): Electronics\n\n\nelectronics\n\n\n2 of 6\n\n\n1.0\n\n\nlove it.. hate it\n\n\nNovember 13, 2006\n\n\nA. Pereyra\n\n\nMiami, FL United States\n\n\nI bought with lots of ilussion this this Ipod, i put all my music in it and use it!! after a 5 months it stop working, it didn't charge the battery. I took it to best buy where I bought it and they told me that it has \"external damage\" and didn' repair it. I send it to Apple service and return it without services because the same reason. My Ipod didn't have that damage, it has only a few scratches on the screen. After lots of calls and fight they told me that to fix it I would have to pay US$250.00 that is almost what it cost new. TERRIBLE SERVICE FROM APPLE.. SHORT LIVE.. DON'T RECOMMENDED. Now I'm looking for another option for MP3 player to buy.\nIf you know one please let me know it.\n\n\n\n\n\nB00005T6GZ:setup_difficult:ned_block_\"ned_block\"\n\n\nB00005T6GZ\n\n\nLinksys BEFCMU10 EtherFast Cable Modem: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nSetup difficult\n\n\nNovember 9, 2006\n\n\nNed Block \"Ned Block\"\n\n\nCambridge, MA United States\n\n\nThe modem works well but was very hard to set up. Much harder than an Apple Airport that I had setup previousl\n\n\n\n\nB00005T6GZ:buyer_beware_-_amazon_selling_old_version_2:rsd2\n\n\nB00005T6GZ\n\n\nLinksys BEFCMU10 EtherFast Cable Modem: Electronics\n\n\nelectronics\n\n\n14 of 18\n\n\n1.0\n\n\nBUYER BEWARE - AMAZON SELLING OLD VERSION 2\n\n\nAugust 17, 2006\n\n\nRSD2\n\n\nCA United States\n\n\nI recently purchased this item from Amazon. What I received was version 2 of this modem. The current version on the Linksys site is version 4. As a Cox customer, the results of my purchase were unsatisfactory. I will be returning this and purchasing the version 4 Linksys from another vendor\n\n\n\n\nB00063IT0Y:total_garbage.:ariel_meadow_stallings_\"author,_offbeat_bride\"\n\n\nB00063IT0Y\n\n\nUltra-Cord 59-inch Retractable Stereo Earphones: Electronics\n\n\nelectronics\n\n\n36 of 36\n\n\n1.0\n\n\nTotal garbage.\n\n\nMarch 11, 2005\n\n\nAriel Meadow Stallings \"Author, Offbeat Bride\"\n\n\nSeattle, WA\n\n\nA short list of grievances:\n\n1. The earbuds don't fit well in the ear\n2. The retraction wheel is in the middle of the cord, so that if you're walking while listening to music, it bounces around and threatens to pull the buds out of your ears.\n3. The retraction wheel broke after a month, making these headphones no longer retractable.\n\nNOT WORTH THE MONEY YOU'LL SPEND ON SHIPPING, let alone the cost of the headphones. These headphones are garbage\n\n\n\n\nB0001EMLTS:lost_power_less_than_a_year:m._kelly\n\n\nB0001EMLTS\n\n\nCoby CTV555 5\" COLOR TV: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nLost power less than a year\n\n\nSeptember 22, 2006\n\n\nM. Kelly\n\n\n\n\n\nIs there a less than 1 star. After 5month, it just stopped working, How do i get my money back. Not worth it for the money. I have a smaller tv that I have had for 6 years and still works. \n\n\n\n\nB0001EMLTS:would_not_buy:oil_can_\"oil_can\"\n\n\nB0001EMLTS\n\n\nCoby CTV555 5\" COLOR TV: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nWould not buy\n\n\nSeptember 7, 2006\n\n\nOil Can \"Oil Can\"\n\n\nTexas\n\n\nI was never happy with this product. The picture always had a green tint and would not adjust out. After less than a year the tube blew out.\n\n\n\n\nB0001EMLTS:this_was_a_mistake.:l._nathenson_\"lee_in_chicago\"\n\n\nB0001EMLTS\n\n\nCoby CTV555 5\" COLOR TV: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n1.0\n\n\nThis was a mistake.\n\n\nJune 11, 2006\n\n\nL. Nathenson \"Lee in Chicago\"\n\n\nChicago\n\n\nThis set lost color and picture quality over the few months that I owned it and then it just stopped working. I'm going to buy something else and so should you\n\n\n\n\nB000E65Y92:?hello_?hello:t._amukele\n\n\nB000E65Y92\n\n\nUniden TRU9465-2 Expandable Cordless System with Dual Keypad and Call Waiting/Caller ID and Exta Handset and Charger: Electronics\n\n\nelectronics\n\n\n8 of 10\n\n\n2.0\n\n\n?Hello ?Hello\n\n\nJune 19, 2006\n\n\nT. Amukele\n\n\nSeattle, WA United States\n\n\nThis phone is really cool but it has two major flaws. The first is the caller ID problem which the other posts have already described in som detail. The second is that although the incoming volume is fine, the people on the other end have trouble hearing me and say that i sound 'far away and very soft'. Troubleshooting and reseting doesn't seem to help. Will return ASAP\n\n\n\n\nB000J43HJ8:scroll_pad_doesn't_work:m._storms\n\n\nB000J43HJ8\n\n\nLogitech diNovo Edge ( 967685-0403 ): Electronics\n\n\nelectronics\n\n\n10 of 14\n\n\n1.0\n\n\nScroll pad doesn't work\n\n\nNovember 19, 2006\n\n\nM. Storms\n\n\nLos Angeles, CA USA\n\n\nIt looks sexy, but it just doesn't work. The scroll pad is so finicky I wanted to throw it out the window after the first hour. About half the time it moves the cursor when you want to scroll, or it scrolls when you want to move the cursor. The other half the time, it does nothing no matter what part of the pad you touch or how hard or lightly you press. Also beware that if you return this just because you're not satisfied with how it works, Amazon will charge you for both outgoing and return postage! (I've giving it one star out of pity, and because it does look sharp.\n\n\n\n\nB000AY2L3U:good_but_problems...:recursive\n\n\nB000AY2L3U\n\n\nLogitech Cordless Desktop S510: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n2.0\n\n\ngood but problems...\n\n\nSeptember 21, 2006\n\n\nRecursive\n\n\nCalifornia, USA\n\n\nyou may notice i am writing this in lowercase. i am using the s510 keyboard i bought over a year ago. the shift keys suddenly went out so the keyboard is now useless. i have to subtract for longevity. the mouse that comes with the keyboard is poor but i use it with another mouse anyway. and, as noted in other reviews this, in common with most other usb keyboards, only works with basic functionality when hooked up through a usb kvm switch. on the good side, the battery life is very good and i think it looks nice, but i'll be looking for something else for a replacement..\n\n\n\n\nB0006B55B2:pricey_for_the_length:yarlee\n\n\nB0006B55B2\n\n\nAcoustic Research PR-185 Pro II Series HDmi Digital Audio/video Interconnect: Electronics\n\n\nelectronics\n\n\n6 of 11\n\n\n2.0\n\n\nPricey for the length\n\n\nJuly 4, 2006\n\n\nYarlee\n\n\nIrving, TX\n\n\nAfter seeing a wide range in the prices for HDMI cables, I did some research into why is it. Found out that these cables carry digital signals (0's and 1's), so no matter what kind of fancy name you add before this cable, its basic purpose is simple like a cat5 cable. Then I went ahead and bought the cheaper ones which are working perfectly fine. So, dont be fooled by the high price cables. There is no special sheilding &%*p in them. Its all misleading marketing. For this length of cable, I would pay maybe $5\n\n\n\n\nB0002SAE56:confused:j._raines_\"crusty_old_cyclist\"\n\n\nB0002SAE56\n\n\nJBL OnTour Portable Music Box: Electronics\n\n\nelectronics\n\n\n1 of 13\n\n\n2.0\n\n\nconfused\n\n\nOctober 14, 2006\n\n\nJ. Raines \"crusty old cyclist\"\n\n\nMinneapolis\n\n\nFrom reading the reviews the product weighs 14 oz and is tiny. from reading amazon's specs:\n12.0 x 14.0 x 10.0 inches ; 4.0 pound\n\n\n\n\nB0002SAE56:too_small:timothy_mcnulty_\"metal_head\"\n\n\nB0002SAE56\n\n\nJBL OnTour Portable Music Box: Electronics\n\n\nelectronics\n\n\n2 of 4\n\n\n2.0\n\n\nToo Small\n\n\nOctober 12, 2006\n\n\nTimothy Mcnulty \"Metal head\"\n\n\nKalispell, Mt usa\n\n\n Do not be deceived by the photo this thing is tiny. If that is what you are looking very small low volume very portable I suppose it is ok but over price for what you get. I think $30.00 would have been about right. There is no bass response and for there size how could there be. I am keeping them but only because they are so small they might come in handy for hotel use only! The packaging is very cool but the fidelity is non existent\n\n\n\n\nB000BM4U2G:been_using_it_one_week_-_dead!:toddman\n\n\nB000BM4U2G\n\n\nCreative Zen MicroPhoto 8 GB MP3 Player (Titanium Gray): Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n1.0\n\n\nBeen using it one week - DEAD!\n\n\nNovember 6, 2006\n\n\ntoddman\n\n\nIthaca, NY USA\n\n\nI've never had a product [...] out after one week of use. The screen froze up and no matter what troubleshooting tip I tried from the creative website, nothing worked. Now I'm waiting to hear back from email support because they don't have 24 hour phone support. Not impressed. Would have gotten an iPod, but this thing was given as a gift\n\n\n\n\nB000BM4U2G:awesome_when_it_worked:a._may\n\n\nB000BM4U2G\n\n\nCreative Zen MicroPhoto 8 GB MP3 Player (Titanium Gray): Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n2.0\n\n\nAwesome when it worked\n\n\nNovember 5, 2006\n\n\nA. May\n\n\nNew Orleans, LA\n\n\nI really enjoyed my Zen, when it worked. The first one I ordered I had to return becuase it would only play every other song, and after it started acting up all the lights would start blinking and I would have to take out the battery to make it stop. So, I exchanged it for a new one, and just like the other after only one week the lights started going crazy again, it would freeze up, and would only play 5 seconds of every song. What I liked about it, for the small amount of time I had it was the sound quality, the choice in colors, and the fact that it was so much cheaper than other models with the same amount of storage space. I'm not sure I would recommend it becuase of the fact that they both only worked for a few days. Now I have an Iriver Clix and have had no problems. It has less storage but I don't care, it is easier to control and I haven't had problems with it\n\n\n\n\nB00006HVNI:there_are_better_wrist_pads_on_the_market:victoria_siegel\n\n\nB00006HVNI\n\n\nBelkin Waverest Gel Wrist Pad Rubber Backing/Fabric Top (F8E244-BLK): Electronics\n\n\nelectronics\n\n\n17 of 21\n\n\n1.0\n\n\nThere are better wrist pads on the market\n\n\nNovember 15, 2004\n\n\nVictoria Siegel\n\n\nSeattle, WA United States\n\n\nI bought this as an extra support for my wrist and within 2 weeks the gel started to ooze from the seems. I ended up having to staple all around the edges of the bean to stop the ooze which has worked but definately a design flaw.\n\nI wouldn't buy this again\n\n\n\n\nB000F50VJK:do_not_buy_this:grifbret_\"grifbret\"\n\n\nB000F50VJK\n\n\nUSB SIM Card Reader: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n1.0\n\n\nDo not buy this\n\n\nMay 29, 2006\n\n\nGrifbret \"Grifbret\"\n\n\nHouston, TX United States\n\n\nThere are too many files you must hunt for online that are not on the cd. If you can read Chinese you may fare better than reading the English instructions for the software. I'm returning mine and will try something a little more expensive but from a company with better quality control\n\n\n\n\nB00005O3VE:unreliable_w/high-cost_of_ownership_in_heavy-duty_environmnt:eyesk\n\n\nB00005O3VE\n\n\nBrother PPF-2800 Plain Paper Fax Machine: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n2.0\n\n\nunreliable w/high-cost of ownership in heavy-duty environmnt\n\n\nJanuary 10, 2005\n\n\nEyesk\n\n\nfrom the swamps of Jersey\n\n\nWe'll give the unit two stars instead of one, since when it does work, it works well, with nice print-outs.\nBut in a span of two+ years, its has needed its drum replaced three times. And has once needed the main system/circuit board replaced. We'd purchased this back when it cost about $125 more than it does now. And the parts have not been quickly available from some vendors. We should have bought the heavier-duty Brother 4750 which we use in another office, and has been flawless. \n\n\n\n\nB00005O3VE:one_page_at_a_time_works_great:\n\n\nB00005O3VE\n\n\nBrother PPF-2800 Plain Paper Fax Machine: Electronics\n\n\nelectronics\n\n\n7 of 8\n\n\n2.0\n\n\nOne page at a time works great\n\n\nOctober 21, 2003\n\n\n\n\n\n\n\n\nI've had many fax machines and this one, when it worked, certainly wasn't the worst of the bunch. Problem is, the auto document feed tray is cheesy. No matter how many times I smoothed, then fanned the paper, it would accept two to five sheets at a time. If I fed one at a time, this unit worked like a champ. Unfortunately, that can be said of most units. I've had good Brother machines in the past, and probably will buy another, better, Brother fax. This one got me so frustrated I threw it out the back door. It's still laying there, only there seems to be more pieces now\n\n\n\n\nB000B59JLU:does_not_work_with_6682:kanishk_\"kanjoy\"\n\n\nB000B59JLU\n\n\n1GB Rs-mmc 1024MB: Electronics\n\n\nelectronics\n\n\n6 of 8\n\n\n1.0\n\n\nDoes not work with 6682\n\n\nAugust 5, 2006\n\n\nKanishk \"kanjoy\"\n\n\nCincinnati, OH United States\n\n\nThis card DOES NOT work with NOKIA 6682. I bought this card from an other website and missed the earlier review here. \n\nThe product description does not show voltage details and compatibility with phones. It is very misleading as the description uses the word \"mobile phone ready\".\n\nSandisk should address this isse by providing proper product description\n\n\n\n\nB00076TK6M:have_yet_to_get_it_to_work:bustin_justin\n\n\nB00076TK6M\n\n\nQ-STOR - USB To Irda Wirless connection: Electronics\n\n\nelectronics\n\n\n6 of 7\n\n\n1.0\n\n\nHave yet to get it to work\n\n\nAugust 2, 2005\n\n\nBustin Justin\n\n\nSocal\n\n\nThe software it is sent with is different than the instructions given, I have yet to get it to work with my laptop w/xp and a Ir HP laser jet. It may work fine for some cell apps phones etc. \n\n\n\n\nB0002ER3BS:horrible_dvd+r!:edwin_jhun\n\n\nB0002ER3BS\n\n\nMemorex 4.7GB 8x DVD+R (50-Pack Spindle): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nHORRIBLE DVD+R!\n\n\nJune 18, 2006\n\n\nEdwin Jhun\n\n\nTacoma, WA USA\n\n\nThe Memorex DVD+R has got to be one of the worst dvd-r's out in the market. Not only is the rate of a successful DVD burn very low (movies, games, etc.), it also has a horrible shelf life that lasts for about four months. From what I hear from other members, Memorex uses a low-quality dye, and is explicitly evident in the red colored deterioration of the dye that can be seen on the top layer of the disk. All my DVD+R's are actually stained RED for some reason?!? Overall, go with verbatim or heck, even COMPUSA dvd's will be more suffice than this garbage\n\n\n\n\nB00017IX0Q:okay_at_first.....:louiseddmsfan\n\n\nB00017IX0Q\n\n\niRiver IFP-180T 128 MB Flash MP3 Player: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nOkay at first.....\n\n\nJuly 2, 2006\n\n\nlouiseddmsfan\n\n\nCary, NC\n\n\nI got this MP3 at a Black Friday sale and I was absolutly thrilled with it...until the end broke off. The end where the battery was broke off one day and wouldn't stay on. I now have to hold it in place with rubber bands. Now, the player will constantly shut off whenever it moves the slightest bit. The radio, however(before it broke)was great, the organization of the files was good and the mic worked okay if you talked straight into it. Overall, this was okay for the money I paid for it, but I wouldn't recommend this to anyone\n\n\n\n\nB0001FV3C8:be_aware_before_you_buy_this!:lynn_hoffman_\"author:_the_new_short_course_in_wine\"\n\n\nB0001FV3C8\n\n\nSanDisk SDCZ2-1024-A10 1 GB Cruzer Mini USB 2.0 Flash Drive (Retail Package): Electronics\n\n\nelectronics\n\n\n6 of 11\n\n\n2.0\n\n\nBe aware before you buy this!\n\n\nOctober 28, 2006\n\n\nLynn Hoffman \"author: The New Short Course in Wine\"\n\n\nPhila., PA USA\n\n\nBe aware before you buy this flash drive that it will not accept file names with many commonly used characters like: '?' and '/'. When it encounters\nthese characters, it stops transferring files completely and you have to \nbegin the copying process again after 'correcting' the file names.\n\nFor me, this was a serious inconvenience and an undisclosed liability of the device. For others, it may be less important\n\n\n\n\nB0002AHRVI:clunker_--_poorly_made,_didn't_work:paul_dean\n\n\nB0002AHRVI\n\n\nGrundig FR200 Emergency Radio (Red): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nClunker -- Poorly Made, Didn't Work\n\n\nNovember 9, 2006\n\n\nPaul Dean\n\n\nChicago, IL USA\n\n\nWe ordered this radio to have on hand in case of emergency. After an hour of cranking to start the power, the crank came off. Got it back on, cranked more, came off again. Small heartattack later from all the fruitless cranking, we sent it back the next day. Looking for another option for an emergency radio, haven't found one yet. \n\n\n\n\nB00007E7L3:doesn't_seem_to_work_properly.:matt_b.\n\n\nB00007E7L3\n\n\nCOBY CA989 AC Adapter for Portable Electronics: Electronics\n\n\nelectronics\n\n\n6 of 7\n\n\n2.0\n\n\nDoesn't seem to work properly.\n\n\nNovember 19, 2005\n\n\nMatt B.\n\n\nWashington, DC\n\n\nI bought one of these, brand new. When I use it with a portable CD Player/Radio on the proper settings for my unit, the radio works for about 15 seconds, then shuts down. Like the other reviewer, I'm wondering if the voltages are off. Will try a meter..\n\n\n\n\nB00004WFSC:does_not_work_properly:\"cherab6\"\n\n\nB00004WFSC\n\n\nBrother GX-6750 Daisy Wheel Electronic Typewriter: Electronics\n\n\nelectronics\n\n\n11 of 16\n\n\n1.0\n\n\nDoes not work properly\n\n\nJune 1, 2004\n\n\n\"cherab6\"\n\n\nLas Vegas, NV United States\n\n\nI bought this for my 80-year-old mother, who is disabled and can't cope with a computer. Every time the typewriter is turned on, we find that it either types the wrong letter (press a Q, it prints an S; press E and get P, etc.) or else it prints the correct letter but they are not spaced correctly (some overlapping, some huge spaces). There is some esoteric combination of keystrokes that, supposedly, will reset the programming. But this is just too much for Mom. Also, every time it's turned on, you have to re-program the line spacing, the tabs and everything else. I ended up buying a 40-year-old manual typewriter on e-Bay and Mom is much happier with it\n\n\n\n\nB00004Z78R:don't_buy_this_printer!:\n\n\nB00004Z78R\n\n\nFellowes P-400C Confetti-Cut Paper Shreddrer with Basket (Black): Electronics\n\n\nelectronics\n\n\n24 of 69\n\n\n1.0\n\n\nDon't buy this printer!\n\n\nJanuary 19, 2003\n\n\n\n\n\n\n\n\nThis printer binds up on two pieces of paper! If there is the least resistance during a jam, the flimsy plastic gears become stripped and the printer is trash. This is what happened to me after less than two months of light use...and I stayed within the manufacturer's guidelines. However, there is NO WARRANTEE on this printer, so BUYER BEWARE\n\n\n\n\nB0002INL4M:they_fit_pretty_well,_but_sound_quality_suffers:davey\n\n\nB0002INL4M\n\n\nShure PA755 Triple-Flange Sleeves for Shure Sound-Isolating Earphones (1 Pair): Electronics\n\n\nelectronics\n\n\n1 of 4\n\n\n2.0\n\n\nThey fit pretty well, but sound quality suffers\n\n\nMarch 12, 2006\n\n\ndavey\n\n\nSt. Louis, Missouri\n\n\nHappy with the three tips included, I decided to try these for the heck of it. While they go nicely and comfortably deep into the ear and don't dare come out until you intend for them to, for some reason, they diminish the quality of the sound greatly. I can most closely describe it as the sound of overcompressed MP3s. They took nearly all of the clarity out of the music\n\n\n\n\nB000BM6Z6A:awesome_when_it_worked:a._may\n\n\nB000BM6Z6A\n\n\nCreative Zen Micro Photo 8 GB MP3 Player (Light Blue): Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n2.0\n\n\nAwesome when it worked\n\n\nNovember 5, 2006\n\n\nA. May\n\n\nNew Orleans, LA\n\n\nI really enjoyed my Zen, when it worked. The first one I ordered I had to return becuase it would only play every other song, and after it started acting up all the lights would start blinking and I would have to take out the battery to make it stop. So, I exchanged it for a new one, and just like the other after only one week the lights started going crazy again, it would freeze up, and would only play 5 seconds of every song. What I liked about it, for the small amount of time I had it was the sound quality, the choice in colors, and the fact that it was so much cheaper than other models with the same amount of storage space. I'm not sure I would recommend it becuase of the fact that they both only worked for a few days. Now I have an Iriver Clix and have had no problems. It has less storage but I don't care, it is easier to control and I haven't had problems with it\n\n\n\n\nB0000C4DX6:not_for_programmers!:m._hannemann\n\n\nB0000C4DX6\n\n\nKensington 64338 Comfort Type USB/PS2 Keyboard (PC/Mac): Electronics\n\n\nelectronics\n\n\n23 of 26\n\n\n1.0\n\n\nnot for programmers!\n\n\nSeptember 7, 2005\n\n\nM. Hannemann\n\n\n\n\n\nI recently bought this keyboard to go along with a fancy ergonomic laptop stand. The stand puts the laptop screen at a reasonable height for use on a desktop -- that works great. The keyboard, however, is fairly awful, and I'm returning it.\n\nI realize I shouldn't expect much for $20, but the keyboard has an exceptionally soft, mushy feeling. The \"comfort type\" slanted keys didn't get in my way, but they didn't help anything, either. The worst part about this keyboard, however, was that it uses a non-standard layout. Look closely at the right side -- the \"enter\" key is oversized, and the |\\ key is moved below to accomodate it, with the consequence that the right shift key is miniaturized.\n\nThis, for a shell user or programmer, is awful. When you need to pipe something, or escape the next character, you really don't mean to be hitting return instead. And with the small right shift key, even typing properly with balanced use of the keyboard is difficult. This small design decision makes this keyboard terrible for heavy computer users like me\n\n\n\n\nB00062GJ8O:too_lightweight,_no_reset_pin:paul_mischler\n\n\nB00062GJ8O\n\n\nPalm Stylus 3-Pack (3178WW): Electronics\n\n\nelectronics\n\n\n8 of 12\n\n\n2.0\n\n\nToo lightweight, no reset pin\n\n\nDecember 19, 2005\n\n\nPaul Mischler\n\n\nNJ, USA\n\n\nAfter purchasing the stylus, I immideatley noticed that the styli were far significantly lighter in weight than the factory included stylus for the Tungsten T5. Even worse, after a fatal exception and hard lock on the T5, I noticed that these styli have no built-in pin to hit the reset button with\n\n\n\n\nB0006SD4PE:hate_it!:j._barnett_\"jenny@designaweb.com\"\n\n\nB0006SD4PE\n\n\nJBL On Stage Speakers for iPod: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n1.0\n\n\nHATE it!\n\n\nNovember 2, 2006\n\n\nJ. Barnett \"jenny@designaweb.com\"\n\n\nHouston, TX USA\n\n\nThere is nothing good to say about this speaker except the size. The sound quality sucks. It stops mid song on a regular basis. It is just a piece of crap. It is so crappy that I am throwing it away rather than give to someone\n\n\n\n\nB000069K8O:worked_at_first_but....:william_r._geary_\"crjuphd\"\n\n\nB000069K8O\n\n\nOne For All URC 9910 Premium Learning Line 8-Device IR/RF Upgradeable Universal Remote Control: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n2.0\n\n\nWorked at first but....\n\n\nOctober 4, 2006\n\n\nWilliam R. Geary \"CrJuPhD\"\n\n\nHorsham, PA USA\n\n\nI bought this item about two months ago and was happy as can be. No more equipment in the tv room! About two weeks ago though, I noticed that the unit was slower and slower and I had to hit buttons repeatedly to get things done. It has eventually gotten to the point that it does not work at all. At first I thought it was batteries in the remote, so I replaced those a few times. Then I thought the placement of the IR transmitter was off, so I moved it closer to my AV rack. That worked at first, but the problem persisted so I reprogrammed, but to no avail. Eventually I narrowed it down to the remote unit. Even with the remote RIGHT NEXT TO THE IR Transmitter, it now does not work most of the time. Of course the real problem now is that all my av stuff is in a different room, so standard IR remotes (why do they even make those still????) are out of the question. I will be moving onto something else and keep my fingers crossed. \nAlong the way I learned a few other things...other reviewers are right in that the memory gets eaten up quickly when you have to program the unit (which I do with a new LG DVD player). In theory, this is a great unit and the remote is very good when it comes to commands. In practice, however, all it did was make me hungry for an RF system that will work more consistently. \n\n\n\n\nB00000J061:do_not_buy_this_radio!:j._beaupre\n\n\nB00000J061\n\n\nGE 7-2887 Superadio III Portable AM/FM Radio: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nDo not buy this radio!\n\n\nNovember 23, 2006\n\n\nJ. Beaupre\n\n\nCambridge, MA USA\n\n\nThe GE Superadio III used to be a great radio. I bought my first one seven years ago and it worked so well that I bought another. I have recently purchased two more as gifts and they are not the same radio. Side-by-side they look the same (except for the numbers on the tuning dial). Inside they have dissimilar circuit boards. The real difference is the two new radios are unable to tune stations nearly as well as the older radios. I would recommend not purchasing this radio\n\n\n\n\nB00000J061:not_very_happy:dennis_kazmierczak\n\n\nB00000J061\n\n\nGE 7-2887 Superadio III Portable AM/FM Radio: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n1.0\n\n\nnot very happy\n\n\nNovember 5, 2006\n\n\nDennis Kazmierczak\n\n\nbuffalo n.y.\n\n\ni was very disappointed with the ge superadio 3 i owned a superadio2 10 years ago which was far superior my advice save your money i had trouble with stations less than 90 miles away this radio is very overated.piossibly poor quality control\n\n\n\n\nB000EA6WOE:not_worth_buying:p._caceres_\"p.c.\"\n\n\nB000EA6WOE\n\n\nCreative Zen Sleek Photo 20 GB Multimedia Player (Aluminum/White): Electronics\n\n\nelectronics\n\n\n0 of 14\n\n\n1.0\n\n\nNot worth buying\n\n\nOctober 9, 2006\n\n\nP. Caceres \"p.c.\"\n\n\nnew york, NY USA\n\n\ni order this item and it got lost in the mail. They resend it which was fine but, as soon as i recieve it i turn on the this item and the screen turned blank. I advised not to buy this item\n\n\n\n\nB00006686A:didn't_work_properly:old_guy\n\n\nB00006686A\n\n\nPhilips Magnavox PM435S 4-Device Universal Remote Control: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nDidn't work properly\n\n\nJuly 20, 2006\n\n\nOld Guy\n\n\nMilford, MA United States\n\n\nAs mentioned by others the actual product does not look like the photo and is a little on the flimsy side. That did not concern me too much. My problem is that it does not work properly with my Samsung 13-inch color TV. It turns the set on and off and steps up/down the channels but I cannot ehter a two digit channel number and have it go to the correct channel. For example, if I enter \"54\" it goes to \"55\". I tried all the listed setup codes given in the instructions\n\n\n\n\nB00006686A:good_-_and_then_bad.:swomprat\n\n\nB00006686A\n\n\nPhilips Magnavox PM435S 4-Device Universal Remote Control: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nGood - and then BAD.\n\n\n\n\n\nswomprat\n\n\ncalifornia\n\n\nAbout 1 year after having this remote to go along with my Phillips DVD player the remote conked out. The buttons, while almost all responsive, will not sync up with the systems anymore, and the learning feature on the remote control seems to be defunct. You get what you pay for, I suppose\n\n\n\n\nB0002XM01C:doesn't_work:chris_doubleday\n\n\nB0002XM01C\n\n\nKensington FM Transmitter/Auto Charger for iPod (White): Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n1.0\n\n\nDoesn't work\n\n\nJanuary 2, 2006\n\n\nChris Doubleday\n\n\nPhiladelphia, PA USA\n\n\nI had reservations when buying - according to online reviews many of these devices seem to be defective (the Belkin as well). I purchased anyway and my fears were confirmed. LEDs do not light, does not transmit on any frequency. Manufacturer representative (from India) confirmed that it is defective. Return will be time consuming and frustrating, but $50 is enough to make me endure even more pain. Don't buy this item\n\n\n\n\nB0009OAP0W:dead_after_2_minutes:j._eno\n\n\nB0009OAP0W\n\n\nPhilips DVP3050V/37 DVD/VCR Combo: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nDead after 2 minutes\n\n\nAugust 13, 2006\n\n\nJ. Eno\n\n\nWashington, DC\n\n\nCan't say much for the video or sound quality, menu options, or anything really, since this thing broke after 2 minutes of watching Monsters, Inc. on DVD. Playback just froze, the thing stopped recognizing that DVDs were in unless you turned the thing off and then back on again, and after another minute it wouldn't even start playing the thing at all, just whirred a lot.\n\nSo, dud\n\n\n\n\nB0009OAP0W:does_not_play_dvd's_from_other_regions:v._parikh\n\n\nB0009OAP0W\n\n\nPhilips DVP3050V/37 DVD/VCR Combo: Electronics\n\n\nelectronics\n\n\n11 of 17\n\n\n2.0\n\n\nDoes not play DVD's from other regions\n\n\nMarch 19, 2006\n\n\nV. PARIKH\n\n\n\n\n\nGreat DVD/VCR combo but does not play DVD's from other region. Does not have an eject button on the remote either. If u have DVD's, VCD from other countries that you would be playing I do not recommed this player\n\n\n\n\nB00067TTZO:good_player_but__very_poor_construction:bootsy98\n\n\nB00067TTZO\n\n\nCreative Zen Micro 5 GB MP3 Player Silver: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nGood player but very poor construction\n\n\nNovember 25, 2006\n\n\nBootsy98\n\n\nChicago, IL United States\n\n\nMy player had the same headphones jack problem that so many people here have had, which ended up rendering it useless (out of warranty). \n\nHowever, while it worked, I was quite pleased with the player and the included software. Everything was intutive and ran smoothly (except for an occational freeze with the player which never became frequent enough to be a major burden). I also liked the fact that this player wasn't bound by iTunes or file format restrictions.\n\nMy only other complaint was that occasionally the touchpad made moving small increments like up one track or down one track somewhat difficult (just give me buttons, I know they aren't sexy but they're easy and work well). \n\nI am giving the product 2 stars because, while I liked it, Creative has known about the headphones flaw for a long time and has not improved the build (being that this is STILL a problem with these players). For the amount of money that a player like this costs, it should work for at least year\n\n\n\n\nB00067TTZO:great_features,_but_huge_flaws:lloyd_christmas\n\n\nB00067TTZO\n\n\nCreative Zen Micro 5 GB MP3 Player Silver: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nGreat Features, But Huge Flaws\n\n\nSeptember 28, 2006\n\n\nLloyd Christmas\n\n\nCanada\n\n\nThis music player was decent: Nice look, controls, and sound. There are many nice features including decent software and using it as a data drive. There are really good things about this unit; however, the faults are just too great; thus, superseding the great features.\n\nHowever, the reaction to your key pushes, and file search are way too slow. The software is pretty nice, but the bad thing is that it likes to rename your files for you even when you didn't ask it too. That gets very annoying. For example, I just want the file to be named as is, with no tags, but the Zen software will add tags no matter what, even if it has to put in the word \"unknown\". It also has these strange crashes that makes it hard to have it recover. The worst part about this product is that the earphone jack gets damaged in a few weeks or months -- unprovoked!\n\nIt was so frustrating to think my headphones where damaged, but I switched it up and found out that it was the Zen. Then I read online, and what do you know -- it's consensus, the product is faulty because many other people have experienced the same problem. Some people even found some fixes, and I've tried them, but to no avail. But then again, why do I have to fix a fairly new product? \n\nIf you're going to have a fault when it comes to a music player, you DON'T want it to be a physical issue that renders the player useless!\n\nI'd give it at least three stars as I was quite satisfied with it, but the physical fault with the headphone jack is just too much of negligence to overlook\n\n\n\n\nB0007OWNBI:absolute_garbage:barton_t._loeser_\"snwoburnt\"\n\n\nB0007OWNBI\n\n\nNetgear RangeMax Wireless USB Adapter (WPN111): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nAbsolute Garbage\n\n\nMay 15, 2006\n\n\nBarton T. Loeser \"Snwoburnt\"\n\n\nDC\n\n\nI purchased this product and ran home to try it out. not 5 minutes after plugging it in to my computer, my computer freezes. I restart and it says my device is malfunctioning. I manage to get it working for about 5 minutes before it craps out on me again. I try upgrading the drivers and it refuses to connect to my network. I'm a network engineer and I am well versed in what I am doing, but this card has given me nothing but trouble. I had to disable the software that came with it and use the windows wireless software to connect. This only keeps me alive for about 30 minutes before freezing. I talked to netgear support and they tell me that my processor isn't supported, which is total BS and just an attempt to get out of supporting me, they wouldn't even let me return the product. My advice: go with linksys or d-link or SMC, ANYONE but netgear\n\n\n\n\nB00003CWEK:whoa!!!__apples_and_oranges:dwpc\n\n\nB00003CWEK\n\n\nMonster Cable MV3CV-4M Monster Video 3 High-Resolution Component Video Cable (4 Meters): Electronics\n\n\nelectronics\n\n\n13 of 18\n\n\n1.0\n\n\nWhoa!!! Apples and Oranges\n\n\nJanuary 12, 2005\n\n\nDWPC\n\n\nVentura, CA United States\n\n\nSeveral reviews mention the improvement when these cables to replace a composite (L/R audio + video) cable, or an S-video (multi-pinned plug). ALmost all of that improvement is due to the fact that these cable connection methods are completely different types in signal format and the TV uses different internal circuitry for each one. Virtually any S-video cable connection will give a better image than a composite type cable, and any component (individual R-G-B video) connection will be better than S-video. Cable brand has absolutely nothing to do with it. There may be some difference between one brand of component cable and another, but its almost always unnoticable\n\n\n\n\nB000031XCM:never_trust_this_product.:rebecca_cho\n\n\nB000031XCM\n\n\nTargus PA410U DEFCON CL Notebook Computer Cable Lock: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nNever trust this product.\n\n\nNovember 18, 2006\n\n\nRebecca Cho\n\n\n\n\n\nI bought this product less than a year ago. 2 days ago, I locked my laptop and tried to unlock it. But I couldn't. It was somehow messed up and the combination did not work. I HAD TO CUT IT!!! I don't know how it got messed up but I'm sure that it was not study enough to last long\n\n\n\n\nB000031XCM:lock_changes_combination_by_itself_-_do_not_buy:lgi\n\n\nB000031XCM\n\n\nTargus PA410U DEFCON CL Notebook Computer Cable Lock: Electronics\n\n\nelectronics\n\n\n3 of 5\n\n\n1.0\n\n\nLock changes combination by itself - Do not buy\n\n\nSeptember 2, 2006\n\n\nLGI\n\n\nDubai, UAE\n\n\nI have been using this lock for a few months without changing the combination. Suddenly I could no longer open it as it appears that the lock has changed its combination by itself. \n\nI called engineering in the hotel I was staying in to help me. It took them less than a minute to cut the cable with ordinary pliers.\n\n\n\n\nB000031XCM:once_it's_on_it_may_never_come_off.:mary_ann_niles\n\n\nB000031XCM\n\n\nTargus PA410U DEFCON CL Notebook Computer Cable Lock: Electronics\n\n\nelectronics\n\n\n4 of 5\n\n\n1.0\n\n\nOnce it's on it may never come off.\n\n\nAugust 25, 2006\n\n\nMary Ann Niles\n\n\n\n\n\nWhile I don't have any evidence to refute the package claim that it \"attaches to any notebook\", once attached to my son's computer it was almost impossible to remove. It took 10 minutes to get it off and we were pulling so hard I thought we would damage the computer. We will be returning it. Not recommended\n\n\n\n\nB00022RV9E:poor_fm_stereo:marty_fin\n\n\nB00022RV9E\n\n\nSony STRDE197 Stereo Receiver: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n2.0\n\n\nPoor FM stereo\n\n\nAugust 19, 2006\n\n\nMarty Fin\n\n\n\n\n\nThe first unit couldn't even find FM stereo stations. It had similar FM specs as the Sony ST_JX410 AM/FM Tuner it was replacing. It was returned as a faulty unit. The replacement STRDE 197 was able to find stereo FM stations which played fine in monoral but were noisy in stereo. My 25 year old JX410 tuner did an excellent job on FM stereo stations. The second unit was returned for a refund. Sony's quality isn't what it use to be. I bought an Onkyo TX-SR304. The FM works fine with more features & expandability. \n\n\n\n\nB00022RV9E:an_inadequate_receiver:joseph_c._o'hagan_\"joeyoh\"\n\n\nB00022RV9E\n\n\nSony STRDE197 Stereo Receiver: Electronics\n\n\nelectronics\n\n\n11 of 13\n\n\n1.0\n\n\nAn Inadequate Receiver\n\n\nMarch 14, 2006\n\n\nJoseph C. O'Hagan \"joeyoh\"\n\n\nMinneapolis MN USA\n\n\nI bought this receiver from Amazon, & received it about 10 days ago. Since then, I've tried to play my cassettes of classical music. When used for this purpose, this receiver sucks big time. If I turn the volume loud enough to hear the orchestra playing softly, when the orchestra gets loud the amp shuts down & PROTECT flashes on & off. I have to turn the receiver off, then turn it back on, & lower the volume. Then of course when the music gets soft again, I can't hear it\n\n\n\n\nB00022RV9E:horrible_crackling_noise:stu_\"former_delco_audio_engineer\"\n\n\nB00022RV9E\n\n\nSony STRDE197 Stereo Receiver: Electronics\n\n\nelectronics\n\n\n9 of 14\n\n\n1.0\n\n\nHorrible crackling noise\n\n\nOctober 11, 2005\n\n\nStu \"former Delco audio engineer\"\n\n\nCary,NC\n\n\nI bought one of these, it has a bad audio \"crackling\" noise on every mode except FM tuner (an obvious defect). Sounds like a capacitor going bad, not sure how it got through final test. \n\nPlus, the audio has noise when the display updates or the volume is changed. It also hangs on to stereo mode on weak signals with no blend or rolloff, you hear audio tones from the stereo decoder PLL losing lock from the station 19 KHz pilot tone. AM section is not bad, but the FM selectivity is not good enough to block alternate stations that most of my other radios deal with just fine. Volume steps are so small it takes forever to change, needs a flippin' crank on the knob. \n\nDon't buy this steaming turd.....buy a no-name China box, then you can throw it away when it claps out without feeling like you got scammed. \n\n\n\n\nB0000696Q1:panasonic_-_terrible_customer_service:a._goldstein\n\n\nB0000696Q1\n\n\nPanasonic KX-TS208W 2-Line Corded Speakerphone with LCD Clock (White): Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n1.0\n\n\nPanasonic - terrible customer service\n\n\nMay 24, 2006\n\n\nA. Goldstein\n\n\nLivingston, NJ\n\n\nAfter two months, this phone stopped working. It was returned to Panasonic Repair and we received a notice thirty days later saying that the phone was back ordered and delivery would be in 6-8 weeks. This is not customer service. Panasonic should have refunded the purchase price or offered a comparable or better phone. When called today to check on status of order, answer was basically \"tough luck.\" Never again, Panasonic. Amazon should discontinue this brand\n\n\n\n\nB00062FL42:broken_latch,_poor_hinge_design:eskimospy\n\n\nB00062FL42\n\n\nPalmOne 3175WW Hard Case for Palm Tungsten T5: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n1.0\n\n\nbroken latch, poor hinge design\n\n\nMay 7, 2006\n\n\neskimospy\n\n\nWest Michigan USA\n\n\nAs others have reported, my latch broke as well, but I broke it within three days of ownership. Within a month, I cracked the bit of plastic that is supposed to hold the palm in place when I was removing it from the case to sync it. At the time, the case had so many gashes and dings that it really looked unpresentable. Bought a Rhinoskin to replace this. Look elsewhere before you purchase this one\n\n\n\n\nB00062FL42:problems:jeffrey_c._vandegrift\n\n\nB00062FL42\n\n\nPalmOne 3175WW Hard Case for Palm Tungsten T5: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n1.0\n\n\nProblems\n\n\nApril 7, 2006\n\n\nJeffrey C. Vandegrift\n\n\nActon, MA United States\n\n\nOK, so it protects the TX on a fall, however the latching mechanism depends on a thin piece of plastic that eventually breaks, rendering the case without anyway of remaining shut on its own.\n\nReplacing this with something like the BoxWave.com hardcase, availab le from Amazon\n\n\n\n\nB00062FL42:the_little_plastic_catch_broke_in_less_than_a_month:s._sweeney_\"ordinary_whiz\"\n\n\nB00062FL42\n\n\nPalmOne 3175WW Hard Case for Palm Tungsten T5: Electronics\n\n\nelectronics\n\n\n7 of 7\n\n\n1.0\n\n\nThe little plastic catch broke in less than a month\n\n\nMarch 7, 2006\n\n\nS. Sweeney \"ordinary whiz\"\n\n\n\n\n\nI purchased the case along with my new Palm TX to protect it on trips or whenever carrying it. It's a metal shell with a molded plastic liner. The little plastic catch in the lid half, which keeps the case closed, never seemed to work quite correctly, requiring several sliding motions before it would click shut. On a trip on March 3rd, this very small lip of plastic molded into the case broke away. Now it has no catch to keep it closed. Since the case cost me about $24 new and I'd only get about half price for the return, I figure the broken little catch costs me too much to return. I'll use a rubber band around the case, reminding me how flimsy and insufficient the case closing mechanism is. Save your money and don't buy this case. I don't remember reading the reviews before I ordered it -- so there's a $24 lesson\n\n\n\n\nB0007O7DTK:not_so_hot:jimbis\n\n\nB0007O7DTK\n\n\nSpeck Products ToughSkin for iPod 4G & iPod Photo - Black: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nNot so hot\n\n\nMay 30, 2006\n\n\njimbis\n\n\nCT\n\n\nOf the various iPod cases I've tried I found this one to be the worst. The click wheel cover is impractical and annoying and the screen cover falls out all too easily. Using plastic in rubber hinges was a bad idea which simply does not work well. See my other reviews for better iPod protectors\n\n\n\n\nB0007O7DTK:great_idea_-_bad_design:mark_mcelroy_\"www-madebymark-com\"\n\n\nB0007O7DTK\n\n\nSpeck Products ToughSkin for iPod 4G & iPod Photo - Black: Electronics\n\n\nelectronics\n\n\n18 of 19\n\n\n2.0\n\n\nGreat Idea - Bad Design\n\n\nOctober 3, 2005\n\n\nMark McElroy \"www-madebymark-com\"\n\n\nJackson, MS United States\n\n\nI was delighted with my ToughSkin for all of two hours. That's how long it took for the belt clip to break, rendering my new purchase worthless to me.\n\nThe ToughSkin itself is rugged; I loved it. I should have waited before buying it, though ... on my last visit to an Apple dealership, they had a bin full of *just the ToughSkins,* for sale at a discount. When I asked about these, I was told, \"The belt clips break, and customers bring the ToughSkins back.\"\n\nI should note I'm very careful with my IPod and accessories. I'm not rough on them. The clip just fell apart while I was walking down the street. Given other comments about the flimsy clips, this is clearly a design issue that Speck Products needs to address\n\n\n\n\nB0001MHYK6:not_for_me_(or_gamers):rubix13\n\n\nB0001MHYK6\n\n\nSennheiser PC155 Traditional Binaural PC Headset with USB and Noise canceling: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n2.0\n\n\nNot for me (or gamers)\n\n\nJune 16, 2006\n\n\nRubix13\n\n\nDallas TX\n\n\nFirst off, I bought this headset for gaming, which anyone who enjoys the pastime will tell you how quickly the hours can melt away. Gamers are dependant on an ultra comfortable pair of headphones that we shouldn't be able to feel. I have been searching high and low for the Holy Grail of comfort and I was certain paying $90+ for a \"gaming\" headset would be the end of my search. Sorry, these guys are going back.\n\nI've tried a couple \"cheap\" Best-Buy headsets that I found painful to wear after an hour or so. Then I decided to spring for \"top of the line\". I wore these for 15 minutes before it became obvious this headset was not the Holy Grail I've been looking for.\n\nThe headset itself feels flimsy and cheap. The padding is made of an itchy fabric, and even on my small head, it feels tight. The earphones do not cover my ears and I can feel them slowly being crushed. I'm not impressed with the sound, but I could overlook that if they were comfortable to wear. I didn't even bother to try the mic.\n\nIf this is how Sennheiser makes their high end gaming equipment, I think it's safe to say I will avoid these guys in the future. The $30 pair of Aiwa's I wear at work are more comfortable than this and sound the same... if only that pair had a mic.\n\n*My ears still feel uncomfortable after taking the set off and then writing this review- I couldn't imagine wearing them for a few hours while gaming. If I find the perfect pair I'll let you all know\n\n\n\n\nB0006BBIZO:disappointment:jtb_\"atlheff\"\n\n\nB0006BBIZO\n\n\nPanasonic RP-HJE50 High-quality Earbuds with Replacement Earpads: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\ndisappointment\n\n\nNovember 4, 2006\n\n\nJTB \"atlheff\"\n\n\nAtlanta, Georgia United States\n\n\nTerrible. No bass. \"J\" type wiring is hard to adjust to. Substandard product for Panasonic. AVOI\n\n\n\n\nB0006BBIZO:bad_sounding_headphones!:metal_fan\n\n\nB0006BBIZO\n\n\nPanasonic RP-HJE50 High-quality Earbuds with Replacement Earpads: Electronics\n\n\nelectronics\n\n\n0 of 4\n\n\n1.0\n\n\nBad sounding headphones!\n\n\nDecember 23, 2005\n\n\nMetal fan\n\n\nNYC\n\n\nI've been a fan of Sony earbuds for many years. Due to the bad reviews of durability of the latest Sony earbuds, I decided on purchasing these Panasonic headphones. I bought these headphones because my Sony headphones died after 3 years and to upgrade my factory Creative Zen Micro headphones. I found a perfect fit between the 3 size earplugs. I've noticed that it does block out the sound in your surroundings. It's good for that commute to work when there is a lot of noise. I like the around the neck feature. As far as quality, the extention part of the wiring looks durable but not the around the neck part of it. As far as the performance of the headphones, I'm really disappointed in these headphones. My factory Creative headphones had out performed these. The bass level is too low even when I max out the bass in the player. The mids and treble sound like crap. Don't waste your money on these! Now I have to spend more money to replace these poor quality headphones! argh!\n\n\n\n\nB00070FHXS:this_product_ruined_our_car_battery_and_2_ipods:d._williams\n\n\nB00070FHXS\n\n\nBelkin TuneBase FM Auto Charger/FM Transmitter for iPod Mini: Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n1.0\n\n\nThis product ruined our car battery and 2 Ipods\n\n\nSeptember 15, 2006\n\n\nD. Williams\n\n\nNew England, USA\n\n\nWe tried the Belkin in one car. When we turned the Ipod on, the car battery immediately discharged (we had to buy a new car battery) and the Ipod died. We bought another Belkin and tried it in a different car and with a different Ipod (in case the first incident was a coincidence) and this Ipod died too. The Ipods were under warranty, the car battery was not. We tried this with 2 different Belkins, in 2 different cars with 2 different Ipods. I would not recommend this product to anyone.\n\n\n\n\nB00004Z64M:labels_not_for_seiko_printer.:bonnie_w._suerig\n\n\nB00004Z64M\n\n\nDYMO 30252 Address Labels for LabelWriters: Electronics\n\n\nelectronics\n\n\n6 of 18\n\n\n1.0\n\n\nLabels not for Seiko Printer.\n\n\nOctober 11, 2002\n\n\nBonnie W. Suerig\n\n\nSpringdale, UT United States\n\n\nThese labels were advertised with the Seiko Label Printer. They did not work with the printer because the printer requires thermal labels. I sent these labels back to Amazon and they promptly refunded my money\n\n\n\n\nB0002OCR5K:waste_of_money:s._walter_\"qwicksilfer\"\n\n\nB0002OCR5K\n\n\nApple M9603G/A iPod Carrying Case with Belt Clip: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n1.0\n\n\nWaste of money\n\n\nSeptember 15, 2005\n\n\nS. Walter \"qwicksilfer\"\n\n\nCollege Park, MD\n\n\nInstead of getting this just get the cheaper skins. I figured to get the nicer one since I already shelled out all my money on an iPod, but it is a lot of money for a carrying case. Definetly get the skins\n\n\n\n\nB0002OCR5K:waste_of_money_-_:mehryar_yousefi_\"galdes\"\n\n\nB0002OCR5K\n\n\nApple M9603G/A iPod Carrying Case with Belt Clip: Electronics\n\n\nelectronics\n\n\n8 of 8\n\n\n1.0\n\n\nwaste of money - \n\n\nSeptember 8, 2005\n\n\nMehryar Yousefi \"galdes\"\n\n\nSunnyvale, CA USA\n\n\nthe case looks nice, but not practical. Everytime you need to change the volume or track you have to pull your ipod out. My ipod had scratch marks on it the first week. Unfortunately, all the picture you see of this case is from the side view, so you cannot see that there is no opening for the wheel and the display. I recommend iskin product. \n\n\n\n\nB000629GES:don't_work:rr\n\n\nB000629GES\n\n\nSony MDR-NC6 Noise Canceling Headphones: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\ndon't work\n\n\nNovember 25, 2006\n\n\nrr\n\n\nalameda, ca United States\n\n\nMaybe I got a defective pair, but the noise cancel barely does anything, maybe cuts out 3%. Not worth it, taking back and trying something else\n\n\n\n\nB00006JQ06:sony_walkman_radio:laurie_a._hanas\n\n\nB00006JQ06\n\n\nSony SRF-59 FM/AM Radio Walkman with Sony MDR Headphones: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n2.0\n\n\nSony Walkman Radio\n\n\nNovember 4, 2006\n\n\nLaurie A. Hanas\n\n\n\n\n\nTHe radio works great....except the last 2 Sony walkman am/fm radios I have had you were able to CLIP onto your WAISTE band when exercising.....it was just a large CLIP thing....THIS new one has a different type that you HAVE to use a BELT with! and WHO uses a belt to work out ? I certainly dont.....so I am using a VERY LARGE safety pin thru the hole in the back and then I pin it onto my shorts....which has made a nice hole in the material of two pairs. Otherwise it is a great item\n\n\n\n\nB000F1YF9Q:sent_it_right_back:amighty_\"celebrity_poker\"\n\n\nB000F1YF9Q\n\n\nToshiba SD-P1700 Portable DVD Player with 7\" Widescreen LCD: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n2.0\n\n\nSent it right back\n\n\nNovember 15, 2006\n\n\nAmighty \"Celebrity Poker\"\n\n\nNew York City\n\n\nI actually meant to order the Philips 7-incher (brighter screen, $20 cheaper, bulkier), but somehow I got confused when I placed the order...this one looked beautiful so I was pretty psyched anyway and was definitely going to keep it. I was planning on using this for work, as I get a lot of DVD's from clients - so I popped one of those in and guess what? It came up as \"invalid disc\". It's supposed to play the majority of formats, and I triple checked the disc in my other dvd players, and it worked in my Panasonic DVD/VHS combo player. A store-bought DVD worked fine (although I didn't play it much more than the first menu) so I know it's not broken.\n\nThe first reviewer also noticed that it doesn't play a bunch of homemade DVD's, so I will confirm that here - if that's gonna be a deal-breaker for you then stay away. BTW, the DVD I was trying to play was made on a Philips burner. I didn't bother to test it with any others\n\n\n\n\nB000234WFE:not_to_spec:daniel_e._walters_\"herr_x\"\n\n\nB000234WFE\n\n\nGMP ELITE LCD ARM ( MV17DC ): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nNot to spec\n\n\nSeptember 10, 2006\n\n\nDaniel E. Walters \"Herr X\"\n\n\nOC, CA USA\n\n\nI ordered 2 of these, realizing they would have limited adjustment range - other reviews are correct in that they are very limited in their travel. What prompts my one star rating and subsequent return of my order is that they will not hold their rated weight. This model is supposed to hold a 25 lb. monitor; it failed miserably at attempting to hold my 23 lb. iMac. There is no tension adjustment (already as tight as it would go) and the computer just flopped forward. With a light monitor, I'm sure it would be fine, but don't expect even close to the stated 25 lb. rating. \n\n\n\n\nB00062VYR0:waste_of_money!:d._gordon\n\n\nB00062VYR0\n\n\nAudiovox D1812PK 8\" Portable LCD DVD Player with Car Kit: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n1.0\n\n\nWaste of money!\n\n\nJuly 3, 2006\n\n\nD. Gordon\n\n\n\n\n\nI spent $400 16 months ago on two very similar portable Audiovox DVD players for my kids, for long roadtrips. They were handled with care, never abused or exposed to severe temperatures. I think we got about 6 trips out of them, then the screens went blank while in use and both units died within 24 hours of each other. Circuit City informed me the product is no longer on their shelves and referred me to Audiovox, who will do nothing since the product is now out of warranty. Extended warranties are useless on this kind of product -- who can wait 6-8 weeks for repairs? Parents, find another brand\n\n\n\n\nB000189VQ0:two_strikes_i'm_out!:d._obrien\n\n\nB000189VQ0\n\n\nGarmin GPSMap 60C Handheld GPS Navigator (56 MB): Electronics\n\n\nelectronics\n\n\n2 of 8\n\n\n1.0\n\n\nTwo Strikes I'm Out!\n\n\nMay 12, 2006\n\n\nD. OBRIEN\n\n\nSouthern California\n\n\nBought one from Amazon, they sat on the order for a week before shipping a unit that arrived dead. Went through the return/replacement process and they sat on the order for over a week this time and the that one arrived dead with the exact same failure mode. \n\nThe second one looked as if it had been opened before, accessory bag not sealed, manuals out of place, interior packaging worn. I believe the second one they sent was a return that was then resent to me. Bad news!!!\n\nI still had both and in hope of fixing on, I contacted Garmin who said they were indeed broken and offered to fix one and send it back. This would mean I'd have paid full price for what would then be a refurbished unit. Both of them returned.\n\nI needed the unit right away for a desert trip. Amazon's refund process made it such that they'd not refund all of the funds but hold them on account (those that were originally given to me as a gift certificate from my employer). Therefore I could not get the equipment for a local vendor. \n\nI opened one month nearly to the day dealing with slow order processing, bad units, and more. \n\nSo, I'd not recommend the unit and I think Amazon may need to review their own practices to ensure what they have in inventory is new and likely operational, and that when a customer orders something, they put in the effort to ship it ASAP!!\n\nWithout that, if you have to buy one, go to a local dealer, put that batteries in at that shop and have them prove it works before you leave with it. Since you can't so that here, don't buy it here. \n\n\n\n\nB000189VQ0:ok,_but_not_perfect!:adonis_good\n\n\nB000189VQ0\n\n\nGarmin GPSMap 60C Handheld GPS Navigator (56 MB): Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n2.0\n\n\nOK, but not perfect!\n\n\nMarch 2, 2006\n\n\nAdonis Good\n\n\nOxford, England\n\n\nMy first GPS. It was pretty expensive and had lots of nice features (ability to save many tracks). The software is very US-centric.\n\nIt worked very well on the flat and in unwooded conditions. However don't use one of these things to navigate in the bush (use a compass). The technology is just not there to cope with either valleys or an overhead canopy. It was handy to confrm where I was when I found a clearing on top of a hill. Finally although the battery life is better than most it is still abysmal.\n\nIn summary these things are still a toy for a tramper rather than an essential tool\n\n\n\n\nB000189VQ0:garmin_support_for_gps_60:w._isenberger\n\n\nB000189VQ0\n\n\nGarmin GPSMap 60C Handheld GPS Navigator (56 MB): Electronics\n\n\nelectronics\n\n\n5 of 6\n\n\n1.0\n\n\nGarmin support for GPS 60\n\n\nFebruary 1, 2006\n\n\nW. Isenberger\n\n\nSpringfield, MO United States\n\n\nI purchased two of these units the first one worked OK. The second one has a problem. In a side by side comparison the signal qualities of the two units vary. I checked there accuracy and tracking with OziExplorer moving map software. The first unit tracks pretty good, but the second units position is off a large percentage of the time and sometimes as much as 400 meters. I sent the faulty unit to Garmin to be repaired. They sent it back with a description of the problem and that it had been fixed. I checked the unit out and it is the same as when I sent it to them. Now I'm stuck with a dud\n\n\n\n\nB00006B9QE:very_disappointing:erin\n\n\nB00006B9QE\n\n\nSanDisk SDCFB-256-A10 256 MB CompactFlash Card (Retail Package): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n1.0\n\n\nVery disappointing\n\n\nJuly 20, 2005\n\n\nErin\n\n\nChicago, IL\n\n\nMy brand-new card failed the first time I tried to download pictures from it; I lost all of the pictures from my two-week trip to Guatemala\n\n\n\n\nB0002DDZ5M:omg!!!:sunday_m._thompson\n\n\nB0002DDZ5M\n\n\nCredit Card Digital Camera: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nOMG!!!\n\n\nOctober 17, 2006\n\n\nSunday M. Thompson\n\n\nNew Haven, CT\n\n\nThis has got to be the worst camera out there. I don't even think it's suitable for my 13 year old son. The pictures come out horrible! Good thing it was purchase mainly with gift certificates. I'm still sorry I wasted them on this piece of junk\n\n\n\n\nB0002DDZ5M:awful_camera:l._watson_\"busy_mom\"\n\n\nB0002DDZ5M\n\n\nCredit Card Digital Camera: Electronics\n\n\nelectronics\n\n\n10 of 10\n\n\n1.0\n\n\nAwful Camera\n\n\nDecember 25, 2005\n\n\nL. Watson \"busy mom\"\n\n\n\n\n\nI purchased this camera for my daughter for Christmas. It was small,compact, and fairly inexpesive, perfect for a preteen. First, it took forever to charge (the instructions indicate that it takes four hours). After waiting for hours, my daughter attempted to take pictures, but the camera would not work. The manual shows several different symbols that you should see in the LCD display, but none of these symbols appeared on this camera. I am returning the camera immediately and I would not advise anyone to purchase one.\n\n\n\n\nB00008ZEKC:piece_'o'_crap!!!!!:elizabeth_moe\n\n\nB00008ZEKC\n\n\nCoby CX-CD375 Micro Stereo System with Top-Loading CD Player: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nPiece 'O' Crap!!!!!\n\n\nNovember 20, 2006\n\n\nElizabeth Moe\n\n\n\n\n\nI bought this so called \"stereo\" system at a Big Lot's Store for about $40.00. I figured since they were selling it there it must be a good deal, and it looked very cool too. It worked for about a month before it started skipping and remote was wigging out. It never sounded any better than one of those old plastic 45 record players. It looked good in my daughters bedroom but it lookes and sounds much better in the dumpster. I can't believe this company can get away with selling this worthless crap. I will never buy anything from them again\n\n\n\n\nB0002XGSS8:spacebar_sticks:k._hiles_\"wldangel\"\n\n\nB0002XGSS8\n\n\nLogitech diNovo Cordless Desktop: Electronics\n\n\nelectronics\n\n\n6 of 7\n\n\n1.0\n\n\nspacebar sticks\n\n\nMay 8, 2006\n\n\nK. Hiles \"wldangel\"\n\n\nPA USA\n\n\nI would love this keyboard except for the spacebar, which sticks very badly. It installed easily-is sleek and comfortable-but is not usable for very long because of this glitch. I'm starting to see more reviews on this spacebar problem-so it looks to be fairly common with this model.\n\n\n\n\nB00023MEL8:good_sound,_not_so_good_quality:t._gray\n\n\nB00023MEL8\n\n\nAcoustic Research AW721 900 MHz Wireless Stereo Headphones: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n2.0\n\n\nGood sound, not so good quality\n\n\nSeptember 25, 2006\n\n\nT. Gray\n\n\n\n\n\nI've loved these headphones for the year or two I've had them. I still love them now.... when they work. The problem is that the cord is shoddy. When I plug into my laptop I have a hard time keeping it working. I have to twist the cord just right to get the sound to work. If its not just right I'll get sound in only one ear, if at all. I don't think this happened because of any handling issues seeing as how the base just sits on my desk. I don't think it has ever left my office and I don't tend to put items on top of the wire that might crush it. Overall I like these things, but I'm just getting sick of the sound phasing in and out when my laptop moves the slightest bit\n\n\n\n\nB000A2EXI8:adequate_for_simple_recording_only:jon_norris\n\n\nB000A2EXI8\n\n\nOlympus VN-960PC 128 MB Digital Voice Recorder with PC Link: Electronics\n\n\nelectronics\n\n\n4 of 5\n\n\n2.0\n\n\nadequate for simple recording only\n\n\nOctober 28, 2006\n\n\nJon Norris\n\n\nOregon, USA\n\n\nI purchased this in the hope that I would be able to record verbal messages to myself, creative thoughts and ideas, etc. It does an adequate job of that. I had also hoped I could use it as a simple player, to download affirmations and such to listen to throughout the day. This it won't do, and it was not clear to me from the information here and on the Olympus site that it would not function this way.\n\n First, even though it records wav files, it does not let you handle them well at all. To transfer recordings to your computer, you have to install special software (included), but you cannot transfer wav files back to the recorder (according to the software guide). If you want a recorder/player to function easily like that, you should go for the more expensive ones that allow you to use them as a USB flash drive (like the Creative Zen series). I did not install the software, as it offered nothing other than the simple transfer of a recording to my computer. I can record directly to the PC with much higher quality, so why bother? Noise recorded in a high quality format is still noise. It makes more sense to simply repeat anything I record on the Olympus and record it directly to the PC.\n\nSecond, the sound quality is not great. Given that wav files are what are used on CDs, one would expect more than what this device delivers. It is noisy on either of the mic settings, and I would have to describe the sound quality as medium. Don't expect high fidelity. This is a voice recorder for recording quick notes and not much else. Even analog tape would be better for most things. I may be fussier than most about this, having some professional background in audio, so the sound quality may be okay for most people. People have gotten used to lower standards in just about everything.\n\nFinally, playback was very disappointing to me. It has only the simplest playback features. It plays, pauses, fast forwards, and reverses, period. You cannot repeat either a single recording or portion of one, and you cannot create any kind of list to specify only certain files to play. It plays (manually) one recording at a time and that is it. You cannot set markers to fast forward or reverse to (like chapters on a DVD), and searching through a recording of any length is a major pain. In fact, this recorder is defined more by what it cannot do than what it can do. It is well built physically, although it is definitely designed for small hands, and can be a bit uncomfortable to use if your hands aren't small.\n\nIf all you want is to record medium quality voice, this will work fine. Don't expect it to record music or anything like that - you will be disappointed. Think of it as the audio version of a $20 digital camera. Cheap, quick, grabs the image, but no frills at all. Don't expect much and you won't be disappointed. \n\nI am usually very happy with Olympus gear, but this is definitely below their usual standard. Unless your needs are very spartan and smple, I would recommend looking at more capable devices\n\n\n\n\nB0001DHHI4:was_unable_to_use:jeffrey_smith\n\n\nB0001DHHI4\n\n\nTRENDnet 54Mbps 802.11G Wireless PC Card: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nWas unable to use\n\n\nFebruary 12, 2004\n\n\nJeffrey Smith\n\n\nPenfield, NY USA\n\n\nI was not able to get this card working with my Win2K laptop. I'm no novice, have configured the rest of my wireless network without any problem, and have had a friend's Netgear card working in the same system. I called tech support and after working with me they said the card was defective and to send it back, which I did. However, I had the same problem with the replacement card. So, I can't recommend this product\n\n\n\n\nB000092TSX:caveat_emptor:charles_anderson\n\n\nB000092TSX\n\n\nTimex T309T Alarm Clock Radio with Nature Sounds (Titanium): Electronics\n\n\nelectronics\n\n\n1 of 3\n\n\n1.0\n\n\nCaveat Emptor\n\n\nNovember 14, 2006\n\n\nCharles Anderson\n\n\n\n\n\n\nWhile spending hours trying to figure out how to set the alarm, I sometimes wonder what hater of humanity actual designed this thing\n\n\n\n\nB000092TSX:poor_design_award_winner:kent_s._policky\n\n\nB000092TSX\n\n\nTimex T309T Alarm Clock Radio with Nature Sounds (Titanium): Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n1.0\n\n\nPoor Design Award Winner\n\n\nNovember 2, 2006\n\n\nKent S. Policky\n\n\n\n\n\n1. Buttons are too small, some of which are not spaced apart very well (very convenient for somebody waking up who can accidentally hit the wrong button).\n\n2. Buttons are labeled, organized, and layed out confusingly. For example, why not have an \"alarm interface\" which would include all of the alarm modes and features, and maybe a \"radio interface\" which would include all of the radio functions/settings. You would really understand how confusing this is if you were to actually go through the process of operating this alarm clock. But really, don't do it, make the smart choice.\n\n3. Too many features/modes. I understand some people prefer all of the different features, but I wanted a simple alarm clock with a buzzer/radio, and clock. Of course all of these special modes are set as the default, so it is a pain to try and configure all of the settings to a simple mode. So for those who want a simple alarm clock, definitely DO NOT GET THIS!!\n\n4. The button handling event is flawed. For example, sometimes when I push a button, it does not handle the event (the device doesn't think that you pressed the button). So I would have to press the button again and check to see if the radio handled the button input correctly.\n\n5. It is very slow when holding the button down and waiting for the alarm clock to increment up and down.\n\n6. Radio reception is very poor. I have another radio right next to it that gets much better reception than this clock.\n\n7. LED is not very bright and hard to read from 10 feet and farther out.\n\nI have nothing against TIMEX (they make great watches). This was written to protect anyone who who does not want to waste $30.\n\n \n\n\n\n\nB000092TSX:keep_looking:m._l._moore_\"roller_mommy\"\n\n\nB000092TSX\n\n\nTimex T309T Alarm Clock Radio with Nature Sounds (Titanium): Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n1.0\n\n\nkeep looking\n\n\nJune 4, 2006\n\n\nM. L. Moore \"Roller Mommy\"\n\n\nLas Vegas, NV\n\n\nI had this 6 months (which is how long it takes to figure out how to use it!) and the backlight is no longer working. So I can't see the time, without a flashlight or lamp\n\n\n\n\nB00005T3RC:uncomfortable:n._collum\n\n\nB00005T3RC\n\n\nSony MDR-E829V Fontopia Earbuds with In-line Volume Control: Electronics\n\n\nelectronics\n\n\n2 of 4\n\n\n1.0\n\n\nUncomfortable\n\n\nApril 14, 2006\n\n\nN. Collum\n\n\n\n\n\ni had these headphones for a few years then they got crushed in half in my bag. they hurt your ears after about ten minutes. they are durable though. i would recommend the kind that clip behind your ear\n\n\n\n\nB00005T3RC:for_small_size_buy_in_the_ear_canal_type_earphone:nirmal_\"ketan_nirmal\"\n\n\nB00005T3RC\n\n\nSony MDR-E829V Fontopia Earbuds with In-line Volume Control: Electronics\n\n\nelectronics\n\n\n1 of 4\n\n\n1.0\n\n\nfor small size buy IN THE EAR CANAL type earphone\n\n\nFebruary 5, 2006\n\n\nnirmal \"ketan nirmal\"\n\n\nbaroda gujarat india\n\n\nmy this earbud headphone is poor in bass response comapared to my sony mdr g-54lp(beware mdr-g54lp and mdr g54lp is two different models there is difference of - sign) actually in experts view this type small earbuds use to give poor sound this is true with any brand and model so never buy earbud type small headphone if you want small headphone buy IN THE EAR CANAL type headphone in experts view this type in ear earphone use to give good crystal clear sound example sony mdr ex 71 / 81, shure e2c etc. if bigger headphone is ok than buy mdr-g54lp \n\n\n\n\nB00009MVGK:don't_buy_this_product:craig\n\n\nB00009MVGK\n\n\nGE JasHO97936 10-in-1 Card Reader/Writer: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nDon't buy this product\n\n\nApril 13, 2004\n\n\ncraig\n\n\nnevada, USA\n\n\nWhile the price of the 8 in 1 card reader is reasonable, consider this. This card reader has a nasty habit of rendering smart media card useless. Evidently, if you cut and paste or delete data on the SM card using thee 8 in 1 card reader, the reader rewrites the signature file so that it cannot be read by the camera. So that $24 card has the potential to cost you over $80.00. In addition, customer service is slow and I have had problems with the drivers\n\n\n\n\nB00009MVGK:eight_what&gt;:roger_s._wall\n\n\nB00009MVGK\n\n\nGE JasHO97936 10-in-1 Card Reader/Writer: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nEight What&gt;\n\n\nMarch 16, 2004\n\n\nRoger S. Wall\n\n\n\n\n\nWouldn't it be nice to know what eight types of media the device will handle\n\n\n\n\nB00028D778:beware!:erik_v_\"design_engineer\"\n\n\nB00028D778\n\n\nEscort Passport 8500 X50 Radar and Laser Detector (Red Display): Electronics\n\n\nelectronics\n\n\n27 of 29\n\n\n1.0\n\n\nBeware!\n\n\nAugust 3, 2006\n\n\nErik V \"Design Engineer\"\n\n\nKinnelon, NJ\n\n\nI ordered the Passport X50 from Amazon to save the $18 shipping Escort Radar was charging. I was told by someone at Escort Radar that Amazon was an authorized reseller.\n\nAfter having my detector on for ten minutes (right out of the box), it did three \"SELF CAL\"s, then said \"SERVICE REQUIRED\". I called Escort Radar and was told \"If you let the car power up the unit when you start the car, the voltage can be all over the place and the unit gets confused. You must first start the car, and then use the thumbwheel to turn on the detector.\" It makes some sense, but if this is really the case, why don't they put those instructions in their manual? After trying this many times, the unit was \"still confused\", so I returned it for a replacement.\n\nMy second detector worked for maybe thirty minutes before the same thing happened. This time Escort Radar told me \"if you leave it plugged into the cigarette lighter over night, this has been found to cause problems.\" Well my lighter power goes off when I turn off the ignition, so this doesn't apply. Escort Radar also told me that \"excessive heat\" has been causing them problems. Now we have had a few days of 100� heat, and I'm sure the car gets hotter parked in the sun, but the design engineers should have accounted for these conditions plus some margin when you design something that is supposed to work in a car. It gets a lot hotter in other parts of the country. There are no warnings in the manual about not using it above 90� or leaving it off in a hot car. You wouldn't design a ski that doesn't work below 35�, would you???\n\nThe most troubling part of my conversation with Escort Radar service department was they told me Amazon is not an authorized reseller, and they have no idea where they are getting the units from. Furthermore, they will not honor any warranty nor will they ever repair the unit, not even for money.\n\n\n\n\nB0000D8IYC:if_you_are_going_to_use_coaxial_cable_protection,_don't_buy_this_one:tony\n\n\nB0000D8IYC\n\n\nAPC PF11VT3 11-outlet SurgeArrest with Tel2/Splitter and Coax Protection: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n1.0\n\n\nIf you are going to use Coaxial cable protection, don't buy this one\n\n\nAugust 1, 2006\n\n\nTony\n\n\nBlacksburg, VA\n\n\nI bought this one for its coaxial cable protection function, but the result is terrible. It greatly degrades the signal. When I use it with TV, the program on TV became unclear. When I use it with computer, the high-speed internet kept dropping. \n\nI intended to return it to Amazon, and I found Amazon's return policy is that only partial refund for opened products. How can I know it's dysfunctional before I destroyed the package and used it? In the future, I only buy stuff at online stores with local stores, such as bestbuy or circuit city, so that I can easily get refund.\n\n\n\n\nB00067LYFW:pretty_dissappointing:m._smith\n\n\nB00067LYFW\n\n\niRiver AFT 100 Mobile FM Transmitter: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n2.0\n\n\nPretty Dissappointing\n\n\nNovember 18, 2006\n\n\nM. Smith\n\n\n\n\n\nI bought the iriver mobile transmitter hoping that i would be able to listen to my mp3 player through my radio. I like the fact that it is easy to use and that you can choose your station, but i live in monroe and we get stations from toledo and detroit, which is a problem because most or all of the stations are occupied or have interference and the transmitter is not strong enough to block out the slight interference from far away stations. I didnt think to check to see how many stations i could use for this product, looking back, i should have. i would only recommend this transmitter if you live somewhere that there aren't many stations that you can listen to on the radi\n\n\n\n\nB00067LYFW:fm_transmitter:misty_graham\n\n\nB00067LYFW\n\n\niRiver AFT 100 Mobile FM Transmitter: Electronics\n\n\nelectronics\n\n\n1 of 3\n\n\n1.0\n\n\nFM transmitter\n\n\nNovember 12, 2006\n\n\nMisty Graham\n\n\n\n\n\nIt broke after two uses. It has an auto shutoff if no detection after 30 seconds and I plugged it in to everything and it would shut off every 30 seconds. I was so mad I threw it out the window. What a waste of thirty bucks\n\n\n\n\nB0002DD944:worked_for_about_1_month,_then_nothing_but_trouble_since.:explaced\n\n\nB0002DD944\n\n\nBeltronics Pro RX65 Radar detector: Electronics\n\n\nelectronics\n\n\n3 of 9\n\n\n1.0\n\n\nWorked for about 1 month, then nothing but trouble since.\n\n\nMay 19, 2006\n\n\nExplaced\n\n\nNC\n\n\nThe Bel RX65 worked great, for about 1 month. After that, it started flashing \"SelfCal\" and \"Service Requiered\" on the screen. Sent it in to Bel for them to fix the problem. After about 10 days I got the product back, only to have it repeat exactly the same problems again within 2 days of having it. I feel like I've been ripped off, because they either didn't even look at it or they don't know how to test something for a problem. I spent $300 for a detector that doesn't work as well as a $40 one (and I know, I've had both)!\n\nI would say I recommend Escort, but I've since learned that they are owned by the same company as Bel. Valentine 1 is too costly, so I guess that doesn't really leave many good detectors on the market\n\n\n\n\nB0002DD944:way_to_expensive.:jimmy\n\n\nB0002DD944\n\n\nBeltronics Pro RX65 Radar detector: Electronics\n\n\nelectronics\n\n\n1 of 18\n\n\n1.0\n\n\nWay to expensive.\n\n\nFebruary 25, 2006\n\n\nJimmy\n\n\n\n\n\nThese things are not worth the money .. get a cheap one! Look at http://www.radarbusters.com/ or www.007radardetectors.com or www.consumersearch.com\n\n\n\n\nB00006JKWX:the_\"grip\"_behind_the_connector_is_too_fat:sendtoscott_\"sendtoscott\"\n\n\nB00006JKWX\n\n\nMonster Cable THX I100 FO-8 Standard THX-Certified Fiber Optic Digital Interconnect Cable (8 Feet): Electronics\n\n\nelectronics\n\n\n15 of 19\n\n\n1.0\n\n\nthe \"grip\" behind the connector is too fat\n\n\nDecember 28, 2004\n\n\nsendtoscott \"sendtoscott\"\n\n\nHouston, TX\n\n\nYou can't plug two of these in side-by-side, the plastic surrounding the business ends are too fat\n\n\n\n\nB00006JKWX:do_the_research_yourself:brian_meyer\n\n\nB00006JKWX\n\n\nMonster Cable THX I100 FO-8 Standard THX-Certified Fiber Optic Digital Interconnect Cable (8 Feet): Electronics\n\n\nelectronics\n\n\n15 of 32\n\n\n2.0\n\n\nDo the research yourself\n\n\nJuly 7, 2004\n\n\nBrian Meyer\n\n\nChicago, Il\n\n\nDon't listen to any of us.You already have an internet connection because you are here. Go to a browser and ask the question you asked here. You will find that any review that is NOT a store will tell you that ALL speaker wire is the same. They just look cool. If that's what you need, then buy it. Otherwise, stay away from monster. It's just like bose speakers. \n BUZZ word, that's all. These companies can't make any money selling you a 10 dollar cable.\n\n\n\n\nB0001ZY6Z4:broke_after_a_year.......:kimmiemann\n\n\nB0001ZY6Z4\n\n\nAudiovox VBP700 7\" DVD Player in a Bag: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nBroke after a year.......\n\n\nOctober 2, 2006\n\n\nkimmiemann\n\n\nPennsylvania\n\n\nAt first it wasn't a bad unit. But it broke within a year. Now that I read the past reviews, I see I'm not alone....\n\n\n\n\nB0001ZY6Z4:two_down...._one_to_go-_in_the_garbage!!!!!:mom_of_4_\"vanessa\"\n\n\nB0001ZY6Z4\n\n\nAudiovox VBP700 7\" DVD Player in a Bag: Electronics\n\n\nelectronics\n\n\n3 of 6\n\n\n1.0\n\n\nTWO DOWN.... ONE TO GO- IN THE GARBAGE!!!!!\n\n\nJuly 20, 2005\n\n\nMOM OF 4 \"VANESSA\"\n\n\nFLORIDA\n\n\nGOT THIS UNIT ON MARCH 28TH 2005 AT CIRCUIT CITY HAD IT FOR EXACTLY 2 WEEKS AND THE DISK STOPPED SPINNING! TOOK IT BACK TO THE STORE AND THEY DIDNT HAVE ANOTHER ONE SO THEY TOLD ME I COULD PICK IT UP AT ANOTHER ONE OF THEIR STORE LOCATIONS AND IT WOULD BE \"READY FOR ME\" TO PICK UP! PICKED IT UP TOOK IT HOME AND COUPLE OF DAYS LATER WENT TO USE IT AND IT DIDNT SPIN!!! RIGHT OUT OF THE BOX!!! TOOK IT BACK TO CIRCUIT CITY AND THEY SAID THEY COULD ONLY FIND THE RECEIPT ON FILE FOR THE FIRST ONE(THAT GOT RETURNED) AND NOT FOR THIS ONE--- AFTER MUCH DEBATE NOTHING GOT DONE FOR ME AT CIRCUIT CITY BECAUSE THE RECEIPT THEY GAVE ME BACK WAS THE OLD ONE AND NOW I'M STUCK WITH THIS PEICE OF JUNK I PAID $369.00 FOR AND CIRCUIT CITY SUCKS TOO! CALLED AUDIOVOX TODAY 7/19/05 (WAITED A WHILE I WAS BUSY GIVING BIRTH AT THE END OF MAY)AND THEY TOLD ME THE 90 DAYS WERE UP (BY 10 DAYS!) AND COULD ONLY FIX IT FOR A FLAT RATE OF $93.00 PLUS SHIP!!! I AM PISSED OF\n\n\n\n\nB000A6LSVY:great,_but_tweaks_os:natasha_greer_\"crimson_diva\"\n\n\nB000A6LSVY\n\n\nMicrosoft Wireless Laser Mouse 6000: Electronics\n\n\nelectronics\n\n\n2 of 5\n\n\n2.0\n\n\nGreat, but tweaks OS\n\n\nJuly 11, 2006\n\n\nNatasha Greer \"crimson_diva\"\n\n\nSacramento, CA USA\n\n\nI love this mouse! It's compact and picks up the signal on just about EVERY surface. I gave it 2 stars because I figured out that it was preventing my laptop from going into idle mode (Windows XP, SP2). Sadly, I'm going to have to get another mouse because of that\n\n\n\n\nB0001F21IS:very_handy:g._j._zachotina_\"georgez.\"\n\n\nB0001F21IS\n\n\nSanDisk SDCZ4-256-A10 256 MB Cruzer Micro with Skins (Retail Package): Electronics\n\n\nelectronics\n\n\n0 of 17\n\n\n1.0\n\n\nVery handy\n\n\nJune 28, 2006\n\n\nG. J. Zachotina \"GeorgeZ.\"\n\n\nAntioch, IL\n\n\nIs very neet item especiatlly since it is usable with 1:1 speed as well as 2\n\n\n\n\nB00006HCCS:the_slide_lock_key_is_too_weak:felix_tello_junco_\"felix_tello\"\n\n\nB00006HCCS\n\n\nLexar Media 256 MB Secure Digital Card: Electronics\n\n\nelectronics\n\n\n3 of 7\n\n\n1.0\n\n\nThe slide lock key is too weak\n\n\nMay 15, 2004\n\n\nFelix Tello Junco \"Felix Tello\"\n\n\nMiami ,FL USA\n\n\nI bought it last april and i didnt realize that slide lock key was too weak, and now I try to write data but a message is about the data is secure (locked)\n\n\n\n\nB000F2P300:do_not_buy:david_mier\n\n\nB000F2P300\n\n\nSamsung HL-S6186W 61\" DLP HDTV: Electronics\n\n\nelectronics\n\n\n0 of 5\n\n\n1.0\n\n\nDO NOT BUY\n\n\nOctober 19, 2006\n\n\nDavid Mier\n\n\nAL United States\n\n\nSamsung is the WORST. We bought this TV in August and it was delivered in September to our new house. We opened it on a Sunday, plugged it in and watched it for a while, then on Monday it would not come on. They thought it was a lamp, but that didn't fix it so they came and got it to service it. We bought it for football season and football season is half over and we still have no TV. They decided to replace it since they couldn't fix it. That was three weeks ago. Now they are giving us the runaround blaming shipping/warehouse/etc for the delay in sending the new one. During a phone conversation with Samsung they basically said they were so far behind on shipping because they had so many to ship out (ie lots of them have problems and are being replaced is the way I took it.\n\n\n\n\nB000F76W78:buy_a_pocket_pc_smartphone_instead:lorenzo\n\n\nB000F76W78\n\n\nNetgear SPH101 Skype Wi-Fi Phone: Electronics\n\n\nelectronics\n\n\n2 of 4\n\n\n2.0\n\n\nbuy a Pocket PC smartphone instead\n\n\nNovember 17, 2006\n\n\nLorenzo\n\n\n\n\n\nAfter waiting almost 6 months for this Skypephone, and its competitor from Belkin, to come to market through numerous postponements, I just gave up and bought an Asus P525 smartphone. For just over double the price of the Netgear I got a Windows CE PDA with GSM capability and full Skype compatibility. When browsing through your phone contacts your are given the choice to call through GSM or Skype. Because you have built-in Wi-Fi, you can make Skype calls and chats from any covered area, plus you can go on the internet using IE and check your e-mails with Outlook. I have also read that the Skype software that runs on dedicated phones like the Netgear and the Belkin is very crude due to the lack of processing power, whereas with a Windows CE-based device you have much faster processors that allow a Skype software closer to the one used on PCs.\nFor those of you alergic to Windows, just wait for the Skype for Symbian release and buy a Nokia or SonyEricsson smartphone.\nBottom line: Skype is too good an application to be wasted on poorly designed hardware\n\n\n\n\nB000F76W78:problems,_and_more_problems_with_this_telephone:andrew_revai\n\n\nB000F76W78\n\n\nNetgear SPH101 Skype Wi-Fi Phone: Electronics\n\n\nelectronics\n\n\n4 of 5\n\n\n1.0\n\n\nProblems, and more problems with this Telephone\n\n\nNovember 5, 2006\n\n\nAndrew Revai\n\n\nThornhill, Ontario Canada\n\n\nThis phone is a total piece of garbage. Freezing, rebooting, not being \nable to connect to hot spots, not logging you out of skype if you are \nlogged on at your computer at the same time. Horrible tech support. Can \nI add any more negative feedback? Oh yeah the rediculous ring tones and \nspeaker quality. Thanks for nothing netgear and for making me wait 6 \nmonths on pre order for a real piece of plastic junk\n\n\n\n\nB000AY5YI4:mouse?_what_mouse:robert_day\n\n\nB000AY5YI4\n\n\nLogitech G5 Laser Mouse (931376-0403): Electronics\n\n\nelectronics\n\n\n1 of 6\n\n\n1.0\n\n\nMouse? What Mouse\n\n\nNovember 9, 2006\n\n\nRobert Day\n\n\nAlaska\n\n\nAmazon does not ship to Post Office Boxes. The mouse could ONLY be shipped to a post office box per Amazon shipping proticol. Therefore, after TWO attempts to purchace this product from Amazon I had to buy elsewhere. If you live where the United States Postal Service does not deliver the mail to your house, pass of this item if not Amazon. I have considerable expierance buying mail order and online and this is the first time I have found a company setting rules that actually prevent the buyer from recieving the product\n\n\n\n\nB000AZ0QC2:great_prints_-_poor_reliability:d._g._alcini_\"electro-nerd\"\n\n\nB000AZ0QC2\n\n\nCanon PIXMA MP800 All In One Photo Printer: Electronics\n\n\nelectronics\n\n\n2 of 4\n\n\n2.0\n\n\nGreat prints - poor reliability\n\n\nOctober 24, 2006\n\n\nD. G. Alcini \"electro-nerd\"\n\n\nShelby, Mi.\n\n\nI bought this printer about 4 months ago. The prints are beautiful, and the speed quite good as well. However, I felt compelled to write a review tonight because of the constant error messages that pop up saying that there are too many ink cartridges of \"X\" color installed. It happens now between every print job, forcing me to open the printer, pull out and re-install one to three of the tanks, and then print my document. There is something wrong with this thing, and I don't appear to be the first experiencing it. Figures... I wait eight years to replace my printer, and end up with one that malfunctions! I will attempt to return this item\n\n\n\n\nB000BK106C:strong_+'s_injured_by_not-infrequent_paper_jams:ronster\n\n\nB000BK106C\n\n\nFujitsu SCANSNAP FI-5110EOXM-15PPM FOR MAC USB 2.0 ( PA03360-B035 ): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n2.0\n\n\nStrong +'s injured by not-infrequent paper jams\n\n\nSeptember 26, 2006\n\n\nronster\n\n\ncollege station, tx\n\n\nI love the basic design, great hardware/software linkage, and software interface of this Mac-happy scanner. However, I value my time too much to babysit the feed mechanism of the unit I received last week. It inconsistently graps multiple pages as other reviewers have disclosed. So, if you're scanning a 10-page doc and it misses one somewhere along the way, have you really accomplished anything? No. You must either try the whole thing again (with likely the same result), or you must figure out what page(s) is missing, scan it separately, and go thru a simple yet not expedient Acrobat process to insert the omitted page. In the end, this machine made it +90% of the way to being a really great product, but the shortcoming fouls half the machine's overall utility. Swing a little bit harder next time Fujitsu\n\n\n\n\nB0006698SS:piece_of_junk:ga_computer_user_\"ga_computer_user\"\n\n\nB0006698SS\n\n\nD-Link DWL-G710 Wireless Range Extender, 802.11g, 54Mbps: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\npiece of junk\n\n\nNovember 11, 2006\n\n\nGA computer user \"GA computer user\"\n\n\nalpharetta, GA USA\n\n\nI've gone thru multiple revisions of firmware, and multiple calls to D-Link support, and this piece of junk still does not work with the D-Link DWL-G700AP. D-Link support calls are worthless. As a computer professional, I find it appalling that D-Link would introduce and continue to sell this product, which clearly does not operate correctly. \n\n\n\n\nB000069LIL:don't_buy_this_machine_-_it's_a_dud:adriana\n\n\nB000069LIL\n\n\nBrother 4750e Intellifax Fax Machine: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n1.0\n\n\nDon't buy this machine - it's a DUD\n\n\nSeptember 8, 2005\n\n\nAdriana\n\n\nAustin, TX\n\n\nI bought this machine on August 3, 2005 and within one week of using it (as an outgoing fax only), there was a machine error. I called customer service and after being on hold for 23 minutes I was able to speak with a live representative. I was told that the machine would need to be replaced. They sent me a REFURBISHED unit (the warranty indicates that any replacement units will be refurbished units). They sent the base unit only, so I had to remove the drum unit, paper tray, and handset from the original fax machine to use with the replacement unit. So, after only a couple of hours of using the replacement unit, I was back on hold to report another machine error. 12 minutes and I finally get a rep. Apparently, the drum unit (which is less than one month old) is bad. So now I'm waiting for the replacement drum unit. DON'T EVER BUY A BROTHER MACHINE. THEY'RE HORRIBLE MACHINES AND TRYING TO GET THROUGH TO CUSTOMER SERVICE IS VERY, VERY TIME CONSUMING\n\n\n\n\nB00000J0D5:wireless_modem_jack:matthew_e._maloof\n\n\nB00000J0D5\n\n\nRCA RC930 Caller-ID-Compatible Wireless Modem Jack: Electronics\n\n\nelectronics\n\n\n5 of 9\n\n\n1.0\n\n\nWireless modem jack\n\n\nJune 30, 2005\n\n\nMatthew E. Maloof\n\n\n\n\n\nWow this is a real piece of junk if you have any other wireless devices going on in the home. And who nowadays doesn't? There is so much noise you can't even hear yourself think. Oh well lesson learned. If I could have given this device a zero I would have.\n\n\n\n\n\nB00007KJR6:grainy_quality,_very_slow_drying_time._:m._buckley\n\n\nB00007KJR6\n\n\nHP Premium Plus Photo Paper, Glossy (Q1978A, 4x6, 60 Sheets): Electronics\n\n\nelectronics\n\n\n3 of 9\n\n\n2.0\n\n\ngrainy quality, very slow drying time. \n\n\nMay 4, 2005\n\n\nM. Buckley\n\n\n\n\n\nI was terribly disappointed in this product which I bought for use in my Canon Pixma iP6000D printer. The image is grainy and, if you touch the photo within 5 minutes, you will leave marks. I laid anothe picture on top of one done 5 minutes earlier, and they stuck together. I have had very good luck with Canon paper\n\n\n\n\nB00006B8IP:smells_terrible!:allen\n\n\nB00006B8IP\n\n\nFellowes Gel Flex Transparent Mini Wrist Rest (Blue Crystal): Electronics\n\n\nelectronics\n\n\n3 of 5\n\n\n2.0\n\n\nSmells Terrible!\n\n\nApril 26, 2006\n\n\nAllen\n\n\nFL USA\n\n\nThis would be a GREAT product is the chemical smell was not so bad and last so long. It has a horrible smell that just will not go away. I saw others mention the same issue but thought I would give it a try. It smells.\n\nOther than that, the comfort is great. But I gave it 2 weeks to air out and the smell will not go away. I would suggest you don't buy this is you have a sensitive nose. It kinda smells like those squishy foam beds and slippers you see at Brookstone\n\n\n\n\nB000BMXJR8:warning!_not_for_ipod_mini!:parker_daniels_\"munchiniwa\"\n\n\nB000BMXJR8\n\n\nGriffin Technology 4031-RDGC RoadTrip FM Transmitter & Auto Charger and Cradle for iPod (Charcoal): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nWarning! NOT for iPod Mini!\n\n\nOctober 26, 2006\n\n\nParker Daniels \"Munchiniwa\"\n\n\nOrlando\n\n\nYes, yes, I know. Griffin has iPod Mini compatability all over their RoadTrip web page. But the sad fact is, when you recieve it you will NOT find an adapter for the iPod Mini in the box although there are adapters for other iPods. When you read the instruction book you will find that you must purchase an adapter from Apple Computer to make it work. Calling Apple, I find that they no longer sell the adapter. When I called Griffin I was surprised to find that if you have any other iPod than Mini and the adapter is not in the box, they will send you one. They recommended that I return the RoadTrip, which is what I did. Save yourself the aggrevation -- if you have an iPod Mini stay away from Griffin Technology\n\n\n\n\nB00006B9HC:agree...__speed_stinks!:datajockeys_\"robert_johnston\"\n\n\nB00006B9HC\n\n\nNetgear FVS318NA VPN Firewall Router with 8-Port Switch: Electronics\n\n\nelectronics\n\n\n7 of 10\n\n\n1.0\n\n\nAgree... Speed STINKS!\n\n\nApril 2, 2006\n\n\nDatajockeys \"Robert Johnston\"\n\n\nHillsboro, OR\n\n\nI had four of theese units in place and they all got replaced by the FVS338. The main reason is the speed. I have a lab and we tested the max Internet throughput of the FVS318's at 1.2Mbps! This is unacceptable. Oh yes, this is with the latest firmware.\n\nSeveral calls to customer service left me infuriated at the lack of comprehension of the problem. I contacted their legal deptartment the same day. Several promises were made on Netgears part wich resulted in them having another level 1 tech call me again to insist I reinstall my OS!\n\nI have successfully sued to get my money back.\n\nWith all of my venting done, I would like to say that I am still a fan of the Prosafe line and will continue to use it. But Netgear be warned, I will be all over you until you get this product line properly supported\n\n\n\n\nB00006B9HC:but_the_big_flaw_is_low_throughput:david_jameson\n\n\nB00006B9HC\n\n\nNetgear FVS318NA VPN Firewall Router with 8-Port Switch: Electronics\n\n\nelectronics\n\n\n5 of 6\n\n\n2.0\n\n\nBut the big flaw is low throughput\n\n\nMarch 26, 2006\n\n\nDavid Jameson\n\n\nChappaqua, NY, USA\n\n\nMost reviews don't seem to mention a key spec - the throughput of a firewall. I just upgraded my cable connection to the new (up to) 30Mbs plan and bought an FSV318v3 firewall to use with it. Without the firewall in place, I measured about 25Mbs download speed. When I installed the firewall in between the modem and one computer, download speed went down to 6.5Mbs. I also discovered that if the \"enable keyword blocking\" checkbox is enabled, the throughput went down to 5Mbs, EVEN IF THERE WERE NO KEYWORDS!\n\nThis firewall is useless if you have a high-speed connection\n\n\n\n\nB0000DC3S5:lousy_phone:ladyeve99\n\n\nB0000DC3S5\n\n\nUniden WXI377 900 MHz Water-Resistant Cordless Phone with Caller ID: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n1.0\n\n\nlousy phone\n\n\nJuly 10, 2006\n\n\nladyeve99\n\n\nCharles Town, WV United States\n\n\nSurprisingly poor quality for a Uniden phone. Volume is very low -- hard to hear caller. Don't waste your money. \n\n\n\n\nB0000DC3S5:such_a_disappointment:julie_\"mominaz\"\n\n\nB0000DC3S5\n\n\nUniden WXI377 900 MHz Water-Resistant Cordless Phone with Caller ID: Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n1.0\n\n\nSuch a disappointment\n\n\nOctober 13, 2005\n\n\nJulie \"MomInAZ\"\n\n\nScottsdale, AZ United States\n\n\nWe had such high hopes for this product and sadly had to return it. Only 20 feet away from the base unit, the clarity was very, very poor. We've owned many regular cordless phones of varying quality and price-points, yet the least expensive of these were better than this... they just weren't waterproof. It's a great concept if they could just make them work well. Sure wish we could find a good waterproof phone!!\n\n\n\n\nB0006GR8CG:looks_cool_at_first,_but...:j._beck_\"br4v3_s1r_r0b1n\"\n\n\nB0006GR8CG\n\n\nGriffin iBeam Flashlight and Laser Pointer for iPod: Electronics\n\n\nelectronics\n\n\n5 of 8\n\n\n2.0\n\n\nLooks cool at first, but...\n\n\nJuly 31, 2005\n\n\nJ. Beck \"br4v3_s1r_r0b1n\"\n\n\nSeattle, WA USA\n\n\nDon't get me wrong, this is a pretty clever gadget. However, it's mising one thing in terms of usability: an on/off switch. This really limits the usability of both, since it's typical to want to turn the flashlight off for a moment, or the laser pointer on for a moment. I can't see me using this laser pointer in a real presentation, since it'd either be on all the time, or I'd be pulling the light in and out all the time (neither of which make me look cool).\n\nSo if you're going to buy this, keep this limitation in mind\n\n\n\n\nB0006GR8CG:close_the_patent_office:r._smith\n\n\nB0006GR8CG\n\n\nGriffin iBeam Flashlight and Laser Pointer for iPod: Electronics\n\n\nelectronics\n\n\n12 of 25\n\n\n1.0\n\n\nClose the patent office\n\n\nMay 17, 2005\n\n\nR. Smith\n\n\nKirkland, WA\n\n\nFinally. \n\nNext time I can't find my keys in the dark all I need to do is find this thingie and fumble it into my iPod's holes. What a lifesaver!\n\nAnd in the same box is the laser pointer so I don't have to stop listening to 50cent just because I'm doing some silly corporate presentation! \n\nWhile it is a little tough to dance and fry an airline pilot's eyes at the same time, it is the latest craze so I LOVE THIS PRODUCT!\n\n;-)\n\n\n\n\nB00001MXVV:bothersome_@_times.:jose_nunez_\"coolman\"\n\n\nB00001MXVV\n\n\nFellowes Easy Glide Wrist Rest and Mouse Pad: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n2.0\n\n\nBothersome @ times.\n\n\nJuly 3, 2001\n\n\nJose Nunez \"coolman\"\n\n\nNaples, FL United States\n\n\nThis mousepad may seem as a great product but at times the rest becomes a little bit tiresome, and I think it get's in the way of my hand and the mouse. It did take me a great deal of time to get used to this pad but anyone else who use's it has found it in the way and annoying. I have to agree with this, b/c it truly does get into the way of my hand and its a little bit confusing. Also it is much bigger than most mousepad's it takes up a great deal of space\n\n\n\n\nB00061IYJC:less_than_impressed:rc\n\n\nB00061IYJC\n\n\nPhilips HN060/37 Noise-Canceling Earbuds: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nLess than impressed\n\n\nNovember 15, 2006\n\n\nrc\n\n\nFremont, CA\n\n\nThe noise canceling is just barely discernible in comparison to when it is turned off and the music quality is not comparable to other earbuds in this price range ($60 list, $35 Amazon). The cloth wrapped cords seem like a nice feature for making them a bit more rugged, but I don't like the way the cords are arranged which create this loop that you put your head into (combined with the stronger cord, better not wear them around machinery since getting them caught will likely result in something a bit more painful than just having the earbuds yanked out of your ears)\n\n\n\n\nB00061IYJC:great_technology__terrible_customer_experience:jpc\n\n\nB00061IYJC\n\n\nPhilips HN060/37 Noise-Canceling Earbuds: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nGreat Technology Terrible Customer Experience\n\n\nOctober 28, 2006\n\n\nJPC\n\n\nMichigan, USA\n\n\nI had the same exact experience with the poor fit of these headphones and the rude customer service. Their 'surround-sound' HE-592 phones don't fit well either.\n\nOn the phone Philips told me to buy a part at RadioShack to resolve my problem. They couldn't give me an RS part number because \"it isn't their product\" but they recommend that over selling me or sending me replacement ear pieces. According to them this problem was all my fault, I should have realized it sooner, saved my receipts & packaging an returned them within 30 days.\n\nFrom a technology standpoint the electronics are outstanding for the size and price. I like the performance of the phones on planes, trains, etc. They really work well but I am amazed that Phillips would risk losing customers over pennies worth of silicone. It is a shame Philips can't do better with the customer touchpoints like the ear piece design, their phone support and part replacement policies.\n\nTake a look at Shure's products. Shure sells replacement foam and triple flange sleeve ear pieces for their products. If you are in the same boat with me and like these except for the fit I think the triple flange sleeves may work as a replacement for the stock Phillips ear pieces\n\n\n\n\nB00061IYJC:noise_canceling_decent;_music_sound_bad:j.j._\"4string\"\n\n\nB00061IYJC\n\n\nPhilips HN060/37 Noise-Canceling Earbuds: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nNoise Canceling decent; music sound bad\n\n\nSeptember 25, 2006\n\n\nJ.J. \"4String\"\n\n\nSan Francisco, CA United States\n\n\nIf you're not that picky about sound quality with music, then these may work for you. I think the frequency response range with these earbuds are pretty minimal. To me, the music is all mids. It's missing significant highs and lows for this price range.\n\nI gave these away to a friend who's not that picky at all, but she could tell a difference in sound quality right away. She normally uses the IPod earbuds which are uncomfortable to her. However, the noise canceling out weighs the sound quality so she prefers to use these earbuds over her Ipod buds riding a crowded train or bus.\n\nHopefully I can find something affordable that has good frequency response. Or maybe I need to educate myself. Is it the frequency response, the impedence, or something else that covers the sound spectrum? I'm just looking for a good balance of lows, mids, and highs\n\n\n\n\nB00061IYJC:noise-cancelling_newbie...:k._wilcox\n\n\nB00061IYJC\n\n\nPhilips HN060/37 Noise-Canceling Earbuds: Electronics\n\n\nelectronics\n\n\n0 of 3\n\n\n2.0\n\n\nNoise-cancelling newbie...\n\n\nAugust 25, 2006\n\n\nK. Wilcox\n\n\nLos Angeles, CA\n\n\n...So I was satisfied with the noise-cancelling qualities. The cloth cable is very nice and doesn't tangle as much as the usual plastic ones. I liked not having to spend so much time untangling thes in the morning. The headphones I purchased come with 3 sizes of rubber cap earbud covers and I found only one to be suitable for my ears. Of course, after less than a month one of those earbuds fell off and now I can't use these earphones until I find a replacement somewhere, which is proving to be troublesome. Super. I didn't buy these on clearance at Target so that hurts my pocketbooks and I am very ANNOYED right now. In the meantime, I've returned to some $10 non-earbud headphones and I really miss the noise-cancelling quality of these Philips earbuds. Life sucks and I want to die because now I can hear everything going on around me during the morning commute on public transportation.\n\n\n\n\nB000068IH6:poor_image_quality:mac_user\n\n\nB000068IH6\n\n\nMonster A ITV PB -10 iTV� Link� for 15\" and 17\" PowerBook� Computer, iPod� Photo and Video iPod with Universal Dock (10 ft.): Electronics\n\n\nelectronics\n\n\n0 of 13\n\n\n1.0\n\n\nPoor image quality\n\n\nJune 7, 2004\n\n\nmac user\n\n\nMA, USA\n\n\nI agree with the person who says there are no instructions and poor image quality. I wonder if they gave us a mini jack like games systems use if it would work better on TVs that have the 3 jacks on the front. Games systems get good image quality so why is this so bad... No info on this from Monster. Looks like a very good product but I will return it unfortunately. I did discover though that I can use a stereo jack with R-L on one end and a mini jack on the ibook end to give me audio speakers on any TV or Stereo for my iTunes, and audio for a DVD that I am playing but I don't need to spend this much money to get that. If the image were good I would keep it. I would rather show my slide shows from the computer. The image was very bad..\n\n\n\n\nB00005B60G:looks_good,_but_not_best_performance:inderjeet_singh\n\n\nB00005B60G\n\n\nPolycom Soundpoint Pro SE-225 2-Line Professional Conference Phone with Caller ID: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n2.0\n\n\nLooks good, but not best performance\n\n\nFebruary 5, 2006\n\n\nInderjeet Singh\n\n\nSanta Clara, CA, USA\n\n\nI have had this phone for about three years. I bought it primarily to use it as a good quality speaker-phone (with mute) for long conference calls. It was quite expensive (I paid $200 to get it) but here are what I have found to be its shortcomings: No caller-id display. This just baffles me. I get caller-Id on all other phones in the house except this one. I have tried every possible varient (resetted settings, disconnected other phones, etc.) but nothing works: I always get \"NO ID MESSAGE\" on the LCD. The second major shortcoming is that called-parties often complain about the feedback on the speakerphone. One way to reduce that is by lowering the volume significantly, but that sort-of defeats the purpose of having a good speakerphone. Another minor shortcoming is that the phone requires to be powered even though it is a corded-phone (hence will not work in case of emergencies). Finally, two connect two lines you have to use an ugly adapter. One good thing about the phone is that it looks quite good, has a professional appearance, and claims to have a duplex-speakerphone.\n\n\n\n\nB0009A2L9E:i_can't_hear_you!:texas_traveler_\"jeff\"\n\n\nB0009A2L9E\n\n\nSamsung DVD-L70 7\" Portable DVD Player: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n1.0\n\n\nI can't hear you!\n\n\nJune 27, 2006\n\n\nTexas Traveler \"Jeff\"\n\n\nBeaumont, Texas United States\n\n\nSound output is terrible, you can't hear it in a car or airplane(with high quality, noise cancelling earphones). When I called customer service they told me it was not intended for use in a car or airplane. Picture is very good, but I have heard better sound from much cheaper players. Don't waste your money\n\n\n\n\nB000ELDX2W:junk,_junk,_junk!:johns_junk\n\n\nB000ELDX2W\n\n\nDLO 009-9765 HomeDock Deluxe for iPod: Electronics\n\n\nelectronics\n\n\n5 of 6\n\n\n1.0\n\n\nJUNK, JUNK, JUNK!\n\n\nJuly 23, 2006\n\n\njohns junk\n\n\nGrand Rapids, MI\n\n\nI just purchased yesterday the Home Dock Deluxe for list price at a local retailer. I read all the reviews, (most of them bad but I got it anyway) the menu on TV part being my motivation. And so far, I am not feeling good about buying it...\n\n1.\tThe sound quality is very poor. Almost every song has some distortion and sounds \"muffled\" along with a good hiss. I thought I was listening to a 70's era cassette it was so bad! I have a high end Sony/Infinity system and the poor sound is very noticeable. In desperation (and checking to make sure my amp was working fine) I plugged in my Apple dock from my 4th Gen Photo 60gb and the standard Apple AV cable. The sound problem disappeared, and was crystal clear.\n2.\tThe menus to not work properly. My Nano 5gb seems to work OK, but my 60gb 4th Gen Photo does not. The menus are rather slow, and the artist, or album, or song titles disappear at random. Sometimes you can back up a level on the menu and you will see the data, but mostly it is just blank. The cursor will scroll down as though there is data there, but nothing shows on the screen. You can play the music from the blank screen, but it is pot luck as to what you will play as you cannot see any titles. I like the idea, but VERY, VERY frustrating.\n3.\tThere is NO support at all from the DLO website. It just lets me download a PDF of the manual or to buy more accessories. I did not even bother contacting them due to all the problems. I am not impressed. \n4.\tThere also all the other problems listed elsewhere, too light and build quality poor, annoying power LED on front, no artwork on TV menu etc...\n5.\tSo basically, it is WAY too expensive. Not worth it. Not even worth 10% of the price charged.\n\nI should have listened to all the comments above, and not believed some of the \"official\" reviews on the web. So it is back in the box and ready for a trip back to the store. I guess my lesson is that I should trust the reviews\n\n\n\n\nB00006B8IO:great_form_but_does_not_work_with_my_optical_mouse:kenneth_b._emmerling_\"oceanguy2821\"\n\n\nB00006B8IO\n\n\nFellowes 91141 Gel Crystal Mousepad/Wrist Rest (Blue): Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nGreat form but does not work with my optical mouse\n\n\nAugust 12, 2006\n\n\nKenneth B. Emmerling \"oceanguy2821\"\n\n\nBaltimore, MD United States\n\n\nThe mouse pad is a great idea with superb support for the wrist. Unlike some customers, I did not detect any odors or problems with the materials use in the gel pad. However, the mouse pad did not work with my Logitech optical mouse. Great idea but poorly implemented.\n\n\n\n\nB00024ENHK:the_strap_sucks,_and_the_bag_is_really_heavy:michael_a._behr_\"mabehr\"\n\n\nB00024ENHK\n\n\nTargus TCG200 17 CityGear Miami Messenger Notebook Case: Electronics\n\n\nelectronics\n\n\n1 of 4\n\n\n1.0\n\n\nThe strap sucks, and the bag is really heavy\n\n\nSeptember 28, 2006\n\n\nMichael A. Behr \"mabehr\"\n\n\nNew York, NY United States\n\n\nWow, I can't believe how much I hate this bag. And it was expensive, compared to some of the stuff I passed up. I thought I couldn't go wrong with Targus...but I could and I did. First, the strap is horrible. It keeps getting twisted and I have marks on my shoulder from where it cuts in to it. 2nd, it's REALLY heavy. Lastly, there's all these compartments that don't do anything and mostly get in the way. Like the phone add-on. My phone doesn't fit in there, and even if it did, would I carry my phone on my bag? \n\n\n\n\nB000E5FOYS:kingston_2_gb_sd_memory_card:bernard_kliks_\"printmaker\"\n\n\nB000E5FOYS\n\n\nKingston 2 GB Elite Pro Secure Digital Memory Card ( SD/2GB-S ): Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n1.0\n\n\nKingston 2 GB SD Memory Card\n\n\nNovember 9, 2006\n\n\nBernard Kliks \"printmaker\"\n\n\nPortland, OR USA\n\n\nThis SD would not work in my Nikon Coolpix S6 camera (but the Fuji and Sandisk product did\n\n\n\n\nB000ANLCZO:beware!:brent_a._bowgren_\"favrefan\"\n\n\nB000ANLCZO\n\n\nTRENDnet 108Mbps 802.11G Wireless PCI Adapter: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nBeware!\n\n\nNovember 9, 2006\n\n\nBrent A. Bowgren \"FavreFan\"\n\n\nWisconsin\n\n\nThis card crashed my system with the blue screen of death after installation. Please be really careful with this one\n\n\n\n\nB000BJBCFW:laptop_must_draw_current_before_other_device_charges:buyerlsd_\"buyerlsd\"\n\n\nB000BJBCFW\n\n\nKensington Dual Charging Cable for Kensington Power Adapters 38013: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n2.0\n\n\nlaptop must draw current before other device charges\n\n\nNovember 9, 2006\n\n\nBuyerLSD \"buyerlsd\"\n\n\nedinburgh\n\n\nNot so happy. Ordered one of these and plugged by PDA which did not charge.\n\nTurns out that it only charges when the larger item is drawing current. So it will only charge the phone/pda etc IF the laptop is drawing current. This is very annoying when the laptop is shut down with a full battery since it draws no current, thus the phone/pda do not charge\n\n\n\n\nB00008V6TI:i_hate_it:marilyn_fedel-harley_\"some-thing\"\n\n\nB00008V6TI\n\n\nGrundig Eton Ego 4000 Plus Portable CD / MP3 Player with Built-In Removable Speakers: Electronics\n\n\nelectronics\n\n\n8 of 8\n\n\n1.0\n\n\nI HATE IT\n\n\nMarch 11, 2005\n\n\nMarilyn Fedel-Harley \"Some-thing\"\n\n\nCleveland\n\n\nI hate this contraption and I stupidly let 30 days pass before I realized how much I hate it. Now I start every morning with a \"boy I hate this thing.\" Why? well here are few reasons why: You cannot read the numbers unless you are within 18\" and to get a backlight you must depress \"any button\"---not a bad idea but horribly executed. The backlight lasts four seconds--way too fast in the night and \"any button\" is a lie because you can't press \"stop\" nor the \"on\" for radio or cd without starting a whole other process which you then, in the dark, have to figure out how to terminate. The cd player spends more time \"reading\" your cd than it does playing it. The \"tuner/band\" button is very sensitive and lord knows what you will wake up to, the screech of a not tuned in AM station happens way too often. The worst of it all was I called Eton to tell them how much I hated it, just thought they might want to know. I had a friendly conversation with a very nice receptionist who assured me they wanted me to be happy and would do something, but first she wanted me to talk to the engineer/designer. So I waited a week for him to return my call and he was quite rude saying if there is nothing wrong with it (everything is wrong, but they don't count that) there is nothing they can do. He offered to trade mine for a different one just the same and I said \"why bother.\" It was a classic story of \"I made it so it is by definition without errors--now leave me alone,just buy it and be thrilled with my brilliant design\n\n\n\n\nB0006ZO2CG:worked_great_for_a_while:cheryl_from_boston\n\n\nB0006ZO2CG\n\n\nAltec Lansing inMotion iMmini Portable Audio System for iPod Mini: Electronics\n\n\nelectronics\n\n\n10 of 14\n\n\n2.0\n\n\nWorked great for a while\n\n\nNovember 16, 2005\n\n\nCheryl from Boston\n\n\n\n\n\nI loved this speaker system when I got it last year. But in the last few months it developed a loud humming noise that makes it so you can not hear your music. Very disapointed\n\n\n\n\nB0007Y79B2:waste_of_money:p._guidry\n\n\nB0007Y79B2\n\n\nApple 4 GB iPod Nano Black: Electronics\n\n\nelectronics\n\n\n7 of 15\n\n\n2.0\n\n\nWaste of money\n\n\nNovember 18, 2006\n\n\nP. Guidry\n\n\n\n\n\n1st the ipod is expensive. It should at least come with a protective cover. 2nd i rarely used it and only when im in the gym. Quite to often when i go to turn the ipod on it goes into something called \"disk mode\" which freezes the unit up and cant be played. Theres not hard reset button so i have to go without listening to music for the hour or so im in the gym then take it home plug it in the cpu to get it to unfreeze. Im so pissed at this thing because im all exicted to finnaly listen to some music and get amped in the gym and the dang thing freezes. For the money i spent on this thing it shouldnt happe\n\n\n\n\nB000F36ERA:can't_find_the_timer_for_the_radio:earthling\n\n\nB000F36ERA\n\n\nGPX CRCD-2806 Clock Radio and CD Player with AM/FM Radio and Alarm: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n2.0\n\n\nCan't find the timer for the radio\n\n\nOctober 26, 2006\n\n\nEarthling\n\n\nPlanet Earth\n\n\nI'd really like to know how I listen to the radio at night and have it shut off in an hour and then have the alarm go off when I need it. I thought that's what a radio was supposed to do\n\n\n\n\nB0006BKG98:do_not_buy!!!:james_\"james\"\n\n\nB0006BKG98\n\n\nContour Design iSee 20 Case for iPod: Electronics\n\n\nelectronics\n\n\n2 of 5\n\n\n1.0\n\n\nDO NOT BUY!!!\n\n\nNovember 1, 2005\n\n\nJAMES \"JAMES\"\n\n\nBREWER, ME\n\n\n DO NOT BELIEVE THE POSITIVE REVIEWS!!!!!!!Like most of the other reviews- THIS CASE SCRATCHED MY IPOD---BAD!!! \nToo bad I didn't read all of these reviews before I purchased the case. I wonder if Contour Designs is going to blow me off when I contact them TODAY(11-1-05).........\n\n\n\n\nB000167V46:does_not_work_with_my_creative_zen:jrb\n\n\nB000167V46\n\n\nZip-Linq AC Wall Plug to 5V USB Adapter: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nDoes NOT work with my Creative ZEN\n\n\nNovember 2, 2006\n\n\nJRB\n\n\nSan Francisco, CA\n\n\nDoes NOT work with my Creative ZEN. Can leave plugged in overnight and it does NOT charge at all\n\n\n\n\nB000167V46:does_not_work_with_creative_labs_zen_micro_photo:c._grindland\n\n\nB000167V46\n\n\nZip-Linq AC Wall Plug to 5V USB Adapter: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nDoes not work with Creative Labs Zen Micro Photo\n\n\nJuly 26, 2006\n\n\nC. Grindland\n\n\nNW Florida\n\n\nDo not waste your money, this was under recommendations, but I would NOT recommend this with your Creative Zen.\nMy daughter purchased this after saving up for an MP3 player, this just does not seem to give enough power, we plug it in and 12 hours later it is still charging. Plug the MP3 into the computer USB and it is done in less than 2 hours.\nIt may work fine for some other things but not the Creative Zen. It would cost more to return and they will only exchange it, so guess we are just eating the cost and S & H. I should have read the reviews before; would have seen that you get what you pay for, don't waste your money\n\n\n\n\nB000167V46:confirmation_-_it_does_not_charge_an_ipod_shuffle:don_\"don\"\n\n\nB000167V46\n\n\nZip-Linq AC Wall Plug to 5V USB Adapter: Electronics\n\n\nelectronics\n\n\n2 of 6\n\n\n1.0\n\n\nConfirmation - it does not charge an iPod shuffle\n\n\nDecember 26, 2005\n\n\nDon \"Don\"\n\n\nGlendale, AZ United States\n\n\nWhat a waste of money. I wish someone had posted before the Dec. 22 posting (which was excellent) pointing out that this device doesn't work on an iPod \"shuffle\". I spent more to ship this item than I did for the item itself, so I am also stuck with one.\n\nRats!!\n\n\n\n\nB00004Y2Z1:buy_the_cheap_ones!:xsiman\n\n\nB00004Y2Z1\n\n\nMonster Cable MV2CV-6M Monster Video 2 Component Video Cable 6 meter: Electronics\n\n\nelectronics\n\n\n20 of 24\n\n\n2.0\n\n\nBuy the Cheap ones!\n\n\nJanuary 26, 2004\n\n\nXSIman\n\n\nLaguna Hills, Ca United States\n\n\nUsing the component input on your TV can make a huge difference in picture quality, but the differentce between the lower priced monster cables and the most expensive monster cables in not noticeable. Save yourself $50 and buy the cheap ones\n\n\n\n\nB000AY8DEQ:do_your_research:t._helems\n\n\nB000AY8DEQ\n\n\nLaCie 300GB d2 External Hard Drive With Triple Interface (300791U): Electronics\n\n\nelectronics\n\n\n4 of 10\n\n\n1.0\n\n\nDo Your Research\n\n\nMay 3, 2006\n\n\nT. Helems\n\n\nTulsa, OK\n\n\nI bought this drive with the advice from my father who has bought several LaCie's for his company. He's always had good luck with them.\n\nThe first thing that I noticed when I opened the box was that there was a gap between the case and the faceplate. I called LaCie customer service and they sent me instructions on how to open the case and also noted my serial number so that I wouldn't void my warranty. The problem was that there were 2 screws that hold the faceplate onto the internal chassis who were not even tighted (as if they started the screws and didn't tighten them).\n\nBy the way... the actual HD inside the case is a Maxtor. If I had know that prior to purchase, I would not have bought the LaCie.\n\nI fixed the problem with the case and turned it on.. only to be hit with a high pitched winning sound coming from the HD. This noise will drive me crazy and is not \"Ultra Quiet\" as noted in the description. I'm sending it back to Amazon for a refund\n\n\n\n\nB000AY8DEQ:lacie_drive_quit_working_:amy_h._sparks\n\n\nB000AY8DEQ\n\n\nLaCie 300GB d2 External Hard Drive With Triple Interface (300791U): Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n1.0\n\n\nLaCie drive quit working \n\n\nApril 3, 2006\n\n\nAmy H. Sparks\n\n\nMinneapolis, MN\n\n\nWhat a disappointment that what I thought was a reputable company makes such an unreliable product. After about a years time, my LaCie drive started clicking. And then, no data. It completely failed. I too made the mistake of trusting this drive and didn't back it up. I am sick about what I've lost. I've emailed LaCie about the failure and have not received a reply. Beware of LaCie external drives\n\n\n\n\nB0000ALFCI:poor,_poor,_poor:j._san\n\n\nB0000ALFCI\n\n\nLogitech Dual Action Game Pad USB: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nPoor, poor, poor\n\n\nNovember 6, 2006\n\n\nJ. San\n\n\nRochester, MN\n\n\nI had one of these a couple years ago. It worked very well for a few months. Then, all of a sudden, the left analog stick became non-responsive. It wouldn't work on any game I tried. After reading other reviews on the gamepad, I figured I just got one that was poorly made and that the analog stick problem I had was just one of those several ones that was bad. Since it was only $20, I just left it alone. Well...after going months without a gamepad, I decided to get another Logitech gamepad, the same one I had, and guess what. After literally ONE DAY, the right analog stick stuck and is completely unresponsive. So, as far as I'm concerned...they're all terrible controllers. PERIOD\n\n\n\n\nB0001F1XNC:file_error:d._petitto\n\n\nB0001F1XNC\n\n\nSanDisk SDMSV-1024-A10 1.0 GB Memory Stick Pro (Retail Package): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n1.0\n\n\nFile Error\n\n\nAugust 3, 2006\n\n\nD. Petitto\n\n\nPeabody, Ma United States\n\n\nI own a Sony DSC-S40. I had lost my Sony Memory Stick so I thought I'd try this one. I purchased it the end of June and almost every time I take a video and try to veiw it, it will play and at any point durring a video it will stop and say file error. This also causes a problem while I download them onto my pc. Only once had it done this on a picture and I ended up having to delete the picture because it wouldn't download. Sometimes it just don't pay to save money. I will never purchase this kind of Memory Stick again.\n\n\n\n\nB00005QSRF:screwed_by_antonline:neil_w._kienzle\n\n\nB00005QSRF\n\n\nMagellan Meridian External Power Cable with Cigarette Lighter Adapter: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nScrewed by ANTonline\n\n\nNovember 9, 2006\n\n\nNeil W. Kienzle\n\n\n\n\n\nANTonline sent me the wrong item and then wanted nothing to do with me when I went to exchange it. They have no phone number, so they email everything. It takes them a day to get back to you and they always have an air about them as if you are bothering them for thier mistake. After days of persistance and me being as rude to them as they were to me, they agreed to take the product back, but I had to eat the first shipping charge, pay to ship it back to them and pay a 20% restocking fee (for thier error!!) The item was only like $10, so by the time I would have paid the shipping and restock fees, I would be in the whole, so I decided to just tell them to go F@#K themselves. I feel better now.\n\n\n\n\nB00007KDVI:not_worth_the_hassle.:brian\n\n\nB00007KDVI\n\n\nLinksys WRT54G Wireless-G Router: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nNot worth the hassle.\n\n\nNovember 17, 2006\n\n\nBrian\n\n\nBloomington, IN USA\n\n\nFrom day one, back in late August 2006, I've had problems with this router--which I bought only because my ISP wouldn't install an extra cable outlet in my apartment without written permission from my new landlord. Since I work from home, this wasn't an option at the time. I'm not a tech guy, I don't care much for gadgets, but I'm not stupid either, and I couldn't even get this thing set up without calling support due to lack of documentation and the assumption that everyone buying this router uses some version of Windows--I don't, I have an iMac. Once set up, the router worked fine for about a month, and then I started experiencing connectivity issues. I called support again and they figured out some issue with 'mac cloning' or some such, reset my static IP, and all was well for another week, when the same issue--losing connection with the router after 10-20 minutes online--arose again. So I called support AGAIN, got a guy after 7 minutes, but he directed me to their website to download a firmware update, and then basically hung up on me. Unacceptable. So I called back, sat on hold for 40+ minutes before being disconnected without ever speaking to anyone. Finally, after a third try, I got someone on the line and he got me to the firmware and helped me install it via their web interface, only for me to experience the same connectivity problem the very next day. My ISP is coming Tuesday to install another outlet. No documentation. The install CD is configured for Windows and wouldn't work in Mac OS X. Online help sucks. The 800 support line sucks. This product sucks. Do not buy it\n\n\n\n\nB00007KDVI:waste_of_money:a._buttolph\n\n\nB00007KDVI\n\n\nLinksys WRT54G Wireless-G Router: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nWaste of Money\n\n\nNovember 16, 2006\n\n\nA. Buttolph\n\n\n\n\n\nI purchased this router (not from Amazon) about 6 months ago when my 5-year-old, very reliable, router died. I purchased it locally and someone came out and installed it. I have had numerous problems with this router since then. It does not stay connected and there is actually no option for \"always on.\" After having some other problems, I took it back and the vendor upgraded the firmware. After that, it lost the setup information twice and the info had to be re-entered. Then today, after being instructed to reset the router, it got stuck in a mode where it only wanted a firmware upgrade (there was no apparent way out of this). So, I obtained the upgrade and according to the router message, it was updated successfully. After that, I could not get the router to work at all. I gave up and ordered an entirely different router (the same brand that I has used before with tremendous success). My summary - save yourself time and aggravation and don't buy the Linksys WRT54G\n\n\n\n\nB0000CDEZB:not_as_represented:l_brown\n\n\nB0000CDEZB\n\n\nHP FA118A#ABA iPAQ Foldable Keyboard for H2200, H3800, H3900, H4100, H5000, H6300, RX3000, and HX4700 Series: Electronics\n\n\nelectronics\n\n\n7 of 14\n\n\n1.0\n\n\nNot as represented\n\n\nOctober 27, 2004\n\n\nL Brown\n\n\nChardon, OH United States\n\n\nOrdered for my HP4705. This is not a bluetooth keyboard. they sent two of them both the same non-bluetooth item. Now I have to send back and hope I get credit\n\n\n\n\nB0000C9ZJY:hp5510_scan_button:whitetail\n\n\nB0000C9ZJY\n\n\nHP OfficeJet 5510 All-in-One Printer, Fax, Scanner, Copier: Electronics\n\n\nelectronics\n\n\n16 of 20\n\n\n2.0\n\n\nHP5510 Scan button\n\n\nDecember 11, 2005\n\n\nWhitetail\n\n\n\n\n\nI've had my HP5510 for 4 weeks. So far everything works to my expectations except--when I press the scan button an error comes up on the LED: Unable to scan documents from Printer to PC. No Scan Options. You need to install or run HP software for\nfeature. Press OK to continue.\" My system is 4 weeks old. I had a local reputable company put my system together. We uninstalled and installed HP software several times. Went to HP website-could not find a patch. Couldn't repair error. My last resort is to call HP which I haven't yet. \n\n\n\n\nB0000C9ZJY:was_ok_for_6_months:kristi_g.,_mom_of_sage\n\n\nB0000C9ZJY\n\n\nHP OfficeJet 5510 All-in-One Printer, Fax, Scanner, Copier: Electronics\n\n\nelectronics\n\n\n28 of 30\n\n\n2.0\n\n\nWas ok for 6 months\n\n\nNovember 26, 2005\n\n\nKristi G., mom of Sage\n\n\nRome, GA\n\n\nThis printer was fine until I changed cartridges and a contact was dirty. Now it won't quit printing alignment pages all the time. I have about a million now, and its cost me a fortune in ink. Every 3 or 4 print jobs and I get another one.\n\nI've spent hours with technical support, who keep leading me through the same old song and dance, disconnect the usb cable, hit some buttons, print another page I don't need, and scan. Voila it works! Case finished.\n\nNO IT IS NOT.\n\nThe tech support people don't speak English and it shows in their grammar and inability to understand that my problem seems to be a variant on what they have in their knowlege base.\n\nHP would do better to hire native English speakers. I'll never buy another HP printer.\n\nOh, they print fine, that is, if you want a million alignment pages\n\n\n\n\nB0002F4EO6:just_stops_working!:donna_m._shepherd\n\n\nB0002F4EO6\n\n\nGriffin iTrip Mini FM Transmitter for iPod Mini: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n1.0\n\n\nJust stops working!\n\n\nApril 16, 2006\n\n\nDonna M. Shepherd\n\n\nCarlsbad, CA United States\n\n\nOk. This is the 2nd ipod mini transmitter I have purchased that has broken within a month. All of a sudden it just stops working, for no apparent reason. Don't waste your money. It's a piece of junk\n\n\n\n\nB0002F4EO6:disappointing.:caitfish\n\n\nB0002F4EO6\n\n\nGriffin iTrip Mini FM Transmitter for iPod Mini: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nDisappointing.\n\n\nApril 15, 2006\n\n\ncaitfish\n\n\n\n\n\nI bought this so I could listen to my iPod in my car in the city. The first couple of times, it worked great, but after that, something happened. It just wouldn't work. All I heard was static, no matter what frequency I tried this on. I have yet to take a road trip with it, so maybe that will work better, but right now, I am not happy with the product. Maybe the fact that I live in a city with radio stations on virtually every frequency has something to do with it, but it used to work on some frequencies, and now it just doesn't anymore. I am very disappointed\n\n\n\n\nB0001MR13G:has_it's_problems:s._howard\n\n\nB0001MR13G\n\n\nAT&T E5965C 5.8 GHz DSS Expandable Cordless Phone with Answering System: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nHas it's problems\n\n\nSeptember 12, 2006\n\n\nS. Howard\n\n\nGrand Terrace, CA United States\n\n\nThis would be a great phone if it worked all the time. I had to return the phone within 1 day because the handset was defective. Amazon was very helpful and prompt with a response. However the handset would periodically quit working and required that the batteries be removed and reinserted to make it operational. After 2 months the handset died completely. AT&T will replace the handset under warranty, but it could take up to 3 weeks to get the replacement\n\n\n\n\nB000A1AQOO:what_a_pain_in_the_butt:gene_bopp\n\n\nB000A1AQOO\n\n\nLinksys WTR54GS Wireless G Travel Router with Speedbooster: Electronics\n\n\nelectronics\n\n\n0 of 3\n\n\n1.0\n\n\nwhat a pain in the butt\n\n\nOctober 23, 2006\n\n\nGene Bopp\n\n\n\n\n\nI bought this to use in hotels. This thing sucks. I barely got it to work in my house. Too slow to set up. Based on the size of this thing, just bring a longer eithernet cord. I am really disapointed. \n\n\n\n\nB000EEZ2R8:not_compatible_with_2nd_generation_nano's:ehnj\n\n\nB000EEZ2R8\n\n\nBelkin F8Z063-BLK TuneBase FM for iPod Nano (Black): Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n1.0\n\n\nNot compatible with 2nd generation Nano's\n\n\nOctober 17, 2006\n\n\nehnj\n\n\n\n\n\nBe aware, the Belkin F8Z063-BLK TuneBase FM for iPod Nano is not compatible with the 2nd generation Nano. I am returning mine to Amazon, and buy a compatible one from the Apple store\n\n\n\n\nB000EEZ2R8:non_compatable_:connor_r._fulkerson\n\n\nB000EEZ2R8\n\n\nBelkin F8Z063-BLK TuneBase FM for iPod Nano (Black): Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n1.0\n\n\nNon Compatable \n\n\nOctober 16, 2006\n\n\nConnor R. Fulkerson\n\n\n\n\n\nI have several friends that have had similar items in the last few years that have worked nicely. I'm sure this is a great product. Having said that... this product is NOT and I repeat IS NOT compatable with the new ipod nanos. The new Ipod Nanos are slightly wider than the original and will not connect to this product. I really hope amazon puts up a warning soon and I will do my best to try to get that done. Again this does not work with the 4GB and I assume the 8GB nano as well so do not buy it unless you have the older model Nano.\n\n\n\n\nB000EEZ2R8:not_a_good_transmitter_for_big_city:k._moon\n\n\nB000EEZ2R8\n\n\nBelkin F8Z063-BLK TuneBase FM for iPod Nano (Black): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nNot a good transmitter for big city\n\n\nAugust 21, 2006\n\n\nK. Moon\n\n\nAtlanta, Georgia USA\n\n\nI live in the Atlanta area and was never able to get a good sound quality coming from the Belkin FM transmitter. It seemed like I was listening to a FM station that was not coming in good\n\n\n\n\nB0002ZH6XW:inferior_quality_product:matthew_p._cashdollar_\"r00tbeer\"\n\n\nB0002ZH6XW\n\n\nCreativeLabs SB LIVE 24BIT PCI SC ( 70SB041000000 ): Electronics\n\n\nelectronics\n\n\n1 of 5\n\n\n1.0\n\n\ninferior quality product\n\n\nJanuary 27, 2006\n\n\nMatthew P. Cashdollar \"r00tbeer\"\n\n\nUSA\n\n\nI had this in my computer for a month, with a satellite radio on the audio-in and speakers on the line out. It started crackling and the audio was distorted after a few weeks. I took the SB out and went back to the motherboard onboard audio and it worked fine.\n\nConclusion = Sound Blaster suck\n\n\n\n\nB0009RTGFY:sent_back_-_do_not_purchase_in_denver_area:randy_stanton_\"randy_stanton\"\n\n\nB0009RTGFY\n\n\nCoby CA-739 Wireless FM Transmitter: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nSent Back - Do not purchase in Denver Area\n\n\nNovember 9, 2006\n\n\nRandy Stanton \"Randy Stanton\"\n\n\nDenver Colorado\n\n\nI purchased the Coby CA-739 Wireless FM Transmitter and was horrified by how each of its pre-programmed frequencies were major Denver Area Radio Stations or had high amounts of static. The transmitter was noisy and weak. I sent it back and took a restocking fee hit plus I had to pay for the return postage.\nI purchased the I-River which allows you to program your own frequencies. Low noise. High quality. Much better....\n\n\n\n\nB0002INUIY:need_a_bit_more_longevity:kevin_d._krotzer_\"krotzerk\"\n\n\nB0002INUIY\n\n\nDelphi SA10085 Roady2 XM Satellite Radio Receiver with Built-in Wireless FM Modulator: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nNeed a bit more longevity\n\n\nSeptember 14, 2006\n\n\nKevin D. Krotzer \"krotzerk\"\n\n\nSarajevo\n\n\nI originally purchased the unit during the winter because of a sale, but I didn't install it until the beginning of July for our cross country trip from Oregon to Wash. DC. During the trip, there was quite a bit of interuption from hillsides and tall trees in the Pacific Northwest. I kind of wondered how well the reception would work in the city. Once I got out of the NW, I had no problems picking up a signal unless I was in a long tunnel or deep valley. \nI did notice the unit did get hot during the trip and I think that is why it is acting the way it is now (two months later). \n\nIt works fine when first turned on, but after 15 minutes, the signal flutuates and at times, no signal is received. The tech support said that I needed to check the wires, but since it is 'recently' installed and it doesn't happen when first turned on, I'm ruling it a receiver problem.\n\nThe unit is placed just below the radio head and has been exposed to direct sunlight when parked. I'm sure it didn't help.\n\nI'm currently looking for a better unit and XM said they would transfer the remainder of the account to the new radio without inducing an activation charge.\n\n\n\n\nB000095SLZ:limited_reliability,_not_so_much_fun:inna\n\n\nB000095SLZ\n\n\nPalmOne P10914U Games Essentials Card: Electronics\n\n\nelectronics\n\n\n6 of 7\n\n\n2.0\n\n\nlimited reliability, not so much fun\n\n\nNovember 22, 2005\n\n\nInna\n\n\nIndiana\n\n\nI bought this card more then a year ago. Overall I'm not impressed, and wouldn't buy it again. First of all, in ~30% of cases it crashes my Tungsten E. Secondly, the games are not that much fun. SimCity is EXTREMELY limited and not enjoyable to play at all. Honestly, I have more fun playing games I've downloaded for free. Final annoyance - the card is slimmer then a regular SD so it slides off easily from the slots of the hardcase making it easy to lose \n\n\n\n\nB0000AQPTR:the_little_vacuum_that_can't:tongue_tied\n\n\nB0000AQPTR\n\n\nMetropolitan Vacuum(R) DataVac(R) 3-In-1 Cordless Computer Vacuum: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nThe little vacuum that can't\n\n\nAugust 6, 2006\n\n\nTongue Tied\n\n\nNew York, NY USA\n\n\nI was hoping to buy a small handheld vacuum for my office and computer hardware (keyboads, air inlets, and what have you). However, the DataVac has so little power, even after a full charge, that it's next to useless. I can't recommend it.\n\n\n\n\nB0000AQPTR:metropolitan_vacuum(r)_datavac(r)_3-in-1_cordless_computer_vacuum:my_phuong_t._nguyen_\"dr._duong_t._nguyen\"\n\n\nB0000AQPTR\n\n\nMetropolitan Vacuum(R) DataVac(R) 3-In-1 Cordless Computer Vacuum: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n1.0\n\n\nMetropolitan Vacuum(R) DataVac(R) 3-In-1 Cordless Computer Vacuum\n\n\nFebruary 25, 2006\n\n\nMy Phuong T. Nguyen \"Dr. Duong T. Nguyen\"\n\n\nJonesboro, GA\n\n\nThe charger is very convenient. Power related to AD/DC is extremely useful. High-tech design and features. Sucking and blowing power does not measure up to the price. It has an extremely disappointing performance. It feels like a child toy rather than a useful tool for a personal computer or laptop. Will not recommend anyone to purchase this item due to the lack of performance\n\n\n\n\nB000089GN2:bad_headphones:james_gordon_\"nascar_guy\"\n\n\nB000089GN2\n\n\nSennheiser PXC 250 Noise Canceling Headphones (Silver): Electronics\n\n\nelectronics\n\n\n0 of 5\n\n\n1.0\n\n\nBad Headphones\n\n\nNovember 9, 2006\n\n\nJames Gordon \"nascar guy\"\n\n\nPennsauken,NJ\n\n\nThese headphones are terrible, the sound is not good,and they hiss. Not worth the money. I have real cheap ones that sound alot better\n\n\n\n\nB000A2BLEC:beware...things_are_usually_cheap_for_a_reason...:cassiopea\n\n\nB000A2BLEC\n\n\niPod USB Travel Power Charger Adapter for Nano, Shuffle, 4G, Mini, Photo and U2- HHI Brand: Electronics\n\n\nelectronics\n\n\n7 of 7\n\n\n1.0\n\n\nBeware...things are usually cheap for a reason...\n\n\nAugust 18, 2006\n\n\nCassiopea\n\n\nSan Diego\n\n\nBroken after 3 uses. Extremely flimsy. Spend a few more buck and buy something more substantial. \n\n\n\n\nB000815CF4:cord_is_so_short,_uncomfotable_to_try_and_use.:james_d._mccormack\n\n\nB000815CF4\n\n\nMicrophone, ME-15: Electronics\n\n\nelectronics\n\n\n0 of 2\n\n\n1.0\n\n\ncord is so short, uncomfotable to try and use.\n\n\nNovember 2, 2006\n\n\nJames D. Mccormack\n\n\n\n\n\nI am not using this Microphone, the cord from the back of my computer barely reaches. Uncomfortable to lean so far forward\n\n\n\n\nB0000721Z3:poor_quality:ann_e._peterson_\"ocean_floor\"\n\n\nB0000721Z3\n\n\nCanon Matte Photo Paper (8.5x11, 50 Sheets): Electronics\n\n\nelectronics\n\n\n1 of 8\n\n\n1.0\n\n\nPoor Quality\n\n\nOctober 1, 2005\n\n\nAnn E. Peterson \"ocean floor\"\n\n\n\n\n\nThe matte photo paper that I received was really poor quality. No matter what setting my printer was set to, the ink ran together and created a really low quality photo. I do not recomend this item.\n\n\n\n\nB0009K9FZW:broke_in_less_than_30_days!:jack_norton_\"tennis_memiac\"\n\n\nB0009K9FZW\n\n\nSB5120 Surfboard Cable Modem Docsis 1.1 and 2.0 Certified USB: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n1.0\n\n\nBroke in less than 30 days!\n\n\nNovember 10, 2006\n\n\nJack Norton \"Tennis memiac\"\n\n\nArlington, VA USA\n\n\nWhen it works, it works very well. But one day it suddenly died on me. I had a Comcast technician come and test my modem and after using some complicated equipments to test it, he concluded that the modem is dead. After he gave me another modem, my network is up again. Very bad reliability. MOTOROLA SHOULD BE MORE RESPONSIBLE FOR THEIR QUALITY CONTROL!\n\n\n\n\nB000084FD5:insufficient_sensitivity:david_h._stokes\n\n\nB000084FD5\n\n\nCoby CX-9 Mini Digital AM/FM Stereo Pocket Radio: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n1.0\n\n\ninsufficient sensitivity\n\n\nAugust 8, 2006\n\n\nDavid H. Stokes\n\n\nGrass Valley, CA United States\n\n\nThis receiver doesn't work in rural areas. It doesn't work as well as a cheap battery powered fm radio\n\n\n\n\nB0001IXUDA:dont_waste_your_money:splitfish_\"splitfish\"\n\n\nB0001IXUDA\n\n\nMonster Cable HDMI 400 High Resolution A/V Cable - 1 Meter: Electronics\n\n\nelectronics\n\n\n42 of 44\n\n\n1.0\n\n\nDont waste your money\n\n\nJuly 26, 2006\n\n\nSplitfish \"Splitfish\"\n\n\nCharlotte, NC United States\n\n\nA $10 cable does the same thing. Do not believe the hype. Monster is overpriced and people are starting to realize it. Its digital, 1s and 0s. Either it reads it or it doesnt.\n\n\n\n\nB0001IXUDA:great_product,_terrible_cost.:g._harper_\"why_does_this_site_do_this?\"\n\n\nB0001IXUDA\n\n\nMonster Cable HDMI 400 High Resolution A/V Cable - 1 Meter: Electronics\n\n\nelectronics\n\n\n56 of 57\n\n\n2.0\n\n\nGreat product, terrible cost.\n\n\nJuly 5, 2006\n\n\nG. Harper \"why does this site do this?\"\n\n\nCollege in KY, USA\n\n\nHave you ever heard the saying that something would cost you \"an arm and a leg\"? These cables cost that, your first son, and bidding rights to your estate. Don't get me wrong- Monster makes great cables. Let there be no confusion on this point. But they're just incredibly expensive, and this is made much worse with these cables.\n\nSeveral people have pointed out that the digital signal on HDMI cables must adhere to error-correction and certain standards (even a $10 cable cable must meet some specifications to even reach production)- and this is largely true. Unless BluRay implements new features that require new cables at some point in the future, there's really very little difference between HDMI cables. Yes, the gold-plated pins and other features will improve your connection a bit, but the improvement will be far less than you'd see between any other \"cheap\" versus \"designer\" cables on the market.\n\nBut Monster cables really are made to a standard that makes some difference, and that's worth mentioning. This cable is built sturdy, so that none but the most harsh of treatment could ever damage it. And the shielding isn't just the usual electrical shielding- Monster cables are toughened to the point that I wouldn't doubt if they're certified against nuclear fallout. If you have the money to blow and you want the confidence of knowing that your entertainment center could fare well in an EMP strike, you're looking at the right cables.\n\nBut for nearly all consumers, these cables cost 4x as much as alternatives, with no real plusses. At the very least, you can pick up a mid-grade cable (Sony, etc) at a mid-grade price ($40) to get the increased sturdiness and the gold-plated connections. And over these products, the Monster cables basically have nothing at all except their branding. Please do look up a cheaper alternative this time, and spend some of the extra money on an extra set of whatever tools you use to keep your screen clean. You'll get much better results with these tools than you will worrying about $100 HDMI cables\n\n\n\n\nB000C7DQ66:unimpressed:scott_vaughn\n\n\nB000C7DQ66\n\n\niHome iH5 Clock Radio for iPod Black: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nunimpressed\n\n\nNovember 4, 2006\n\n\nScott Vaughn\n\n\nTennessee\n\n\nOk...it functions well as a clock radio. Just fine. Reliable. But that's not too much to expect. Yes, it will charge your iPod and it does have the nifty function of an alarm that gradually (though not gradual enough) gets louder. The adaptor for my Nano fit fine; and I didn't find the clock too bright (it has 3 settings). But the sound is really terrible. It sounds like its coming from a transistor radio. \n\nAs well, it advertises that you will be able to go to sleep and wake up with your iPod. But as we know, you don't need a clock radio to do that--your iPod will do that on its own if you set it. \n\nI took this back after 5 days and hooked up some cheap computer speakers with a volume control. They really sound better--I swear. \n\n\n\n\n\nB00006JHV4:can't_figure_out_how_it_will_hold_the_ipaq.:rahul_agarwal_\"turbunator\"\n\n\nB00006JHV4\n\n\nHP/Compaq CQP111 HP Leather Folio Case for the iPAQ Pocket PC - Black/Brown (Leather): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n1.0\n\n\ncan't figure out how it will hold the ipaq.\n\n\nJanuary 14, 2006\n\n\nRahul Agarwal \"turbunator\"\n\n\ntampa, fl, USA\n\n\nmay be i got a defective one. \nthere is nothing inside to hold the ipaq. its just a 3 fold cover. IPAQ comes with a much better case when you buy it new\n\n\n\n\nB0002ZAILY:horrible_software!:ivan_ivanov\n\n\nB0002ZAILY\n\n\nApple 512 MB iPod Shuffle: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n1.0\n\n\nHORRIBLE SOFTWARE!\n\n\nOctober 29, 2006\n\n\nIvan Ivanov\n\n\nCA USA\n\n\nI had several problems with the iPod, mostly because of its horrible software iTunes. It messed up the firmware on the device several times, it would complain constantly that it is not up to date and then try to update it which resuted in several crashes. \nThe most horrible thing however was that it would mess up your MP3 music collection in a way that can never be undone. I had orgaized my collection in the file system nicely by genre, author, album etc, and this thing messed up everything. I have another mp3 player - iRiver - which I reccommend as a lot better than the iPod anyway - and I wanted to be able to copy things on it from my library. Now i am not able to and have to go back and re-organize the mess manually!. \nI do not recommend the iPod and I do not recommend using their software either.\n\n\n\n\nB0009NZ6M0:a_disappointment:j._liu\n\n\nB0009NZ6M0\n\n\nVerbatim DVD+R 4.7GB LightScribe 30pk Spindle: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nA disappointment\n\n\nJune 30, 2006\n\n\nJ. Liu\n\n\nCA\n\n\nI just purchased the HP DVD740e from costco, and burned the CDR disc came with drive. The lightscribe worked really well, it was the first experience, so I came here for reviews on DVDR, I was told that the Verbatim is of top quality and the lightscribe picture is even better than the HP's, so I purchased the Verbatim 30pk, received today. I was so excited, that I immediately burn two discs with the best setting, but both come out fuzzy and presented the \"out of focus\" dull feel, look terrible with no detail compared to the HP bonus disc. Is this a joke? This is my first Amazon review, can't help to vent my disappointment. Thanks for the super fast shipping anyway.\n\n\n\n\nB0001R041M:poor_performance,_no_warranty:m._ford_\"topmug\"\n\n\nB0001R041M\n\n\nLaCie 1 TB Bigger Disk Triple Interface FireWire and USB 2.0 Hard Drive (300875): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n1.0\n\n\nPoor performance, no warranty\n\n\nDecember 9, 2005\n\n\nM. Ford \"topmug\"\n\n\nCamarillo, CA United States\n\n\nI have purchased 10 of these drives, all at once. What a mistake. They are very flakey and will 'disappear' while copying data to them by usb. You have to turn the drive off and back on for windows to find it again. I do not have this problem with any other usb drive only Lacie drives. You cannot use one as a backup for this very reason, copying large amounts of data will fail and you have to start over or copy in chunks. \n\nOne drive failed after 8 months, only at the 10 month mark did I realize it was still under warranty. It had a 1 year warranty so 2 months left, I contacted lacie and after asking for a copy of the original invoice which clearly listed the purchase date they began ignoring my email and would not honor their warranty, no RMA after requesting one 5 times, no more email responses since they could not come up with a reason to refuse service. Do not buy one of these, there are many drives with longer warranties that are more reliable from better companies.\n\n\n\n\nB0001R041M:undependable,_noisy_drive,_poor_support:o_man\n\n\nB0001R041M\n\n\nLaCie 1 TB Bigger Disk Triple Interface FireWire and USB 2.0 Hard Drive (300875): Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n1.0\n\n\nUndependable, noisy drive, poor support\n\n\nAugust 31, 2005\n\n\nO_man\n\n\n\n\n\nThis drive suffers from repeated Delayed Write File errors. Ultimately it means you WILL lose all the data on it.\nIt is extremely noisy to the point of damaging your hearing.\nLacie support is poor.\nAVOID\n\n\n\n\nB0001R041M:stay_away:ali_hasan_\"filmmaker_-_teacher_-_political_commentator/writer\"\n\n\nB0001R041M\n\n\nLaCie 1 TB Bigger Disk Triple Interface FireWire and USB 2.0 Hard Drive (300875): Electronics\n\n\nelectronics\n\n\n15 of 16\n\n\n1.0\n\n\nStay Away\n\n\nJuly 28, 2005\n\n\nAli Hasan \"Filmmaker - Teacher - Political Commentator/Writer\"\n\n\nSurf City, California & Eagle, Colorado\n\n\nAs of right now, I am an Apple Student Developer\n\nMost of my work comes from Filmmaking, as I'm an Independent Filmmaker, and all of my post production work relies heavily upon my Apple G5 Tower, as well as a TON of external harddrive storage\n\nWhile some of my LaCie HD's have worked great, many of them have simply failed me\n\nhere are the pro's - \n\n1. great interface - both FireWire 400 and 800, and they are easy to daisychain\n\n2. they are fast - 7200 RPM, and usually, if the drive is good, the performance will show\n\nCONS -\n\n1. very weak body ---- forget if you drop this thing even one inch - it'll be dead\n\n2. very tough to hookup to other drives -- when I say this, I mean it is impossible to stack this drive, or try and put it in a space where it'll stay put\n\nTHE BIGGEST PROBLEM ---\n\nI don't know what accounts for this - whether its the bridge or the drive itself - but LaCie's drives fail way too much\n\nI've probably purchased around 14 LaCie drives in the past 4 years, for varioius film projects and associate ones ----- of the 14, I have had 5 completely fail on me\n\nThis is simply unacceptable for any drive company\n\nwhen you consider it all, companies like MAXTOR offer drives with the same interface, same speeds, but at a lower price\n\nwith that said, take a look at MAXTOR, whose drives are housed in a very indestructive body (...)\nOn a sidenote, LaCie has honored the warranties on their drives, and when they're open, their customer service is very helpful --- but I've lost too much data, at this point, to recommend the\n\n\n\n\nB000062XQE:i_hate_it:\n\n\nB000062XQE\n\n\nJabra EarPhone Hands-Free Headset with EarGels: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n1.0\n\n\nI hate it\n\n\nJuly 9, 2004\n\n\n\n\n\n\n\n\nI had a previous good experience with a Jabra headset. This one was recommended for my Panasonic cordless phone by Jabra. However, the sound quality is terrible. It lacks any kind of high frequency response, and so sounds very boomy and full of bass, making it almost impossible to use. It also needs a foam cover, since any kind of slight wind movement makes it extremely noisy. I contacted Jabra about this product, but received no response. Maybe it would work better with a different phone, but definitely NOT with my Panasonic\n\n\n\n\nB00019OWU4:dies_after_a_year:s._ruhl\n\n\nB00019OWU4\n\n\nLaCie 250 GB USB 2.0 External Hard Drive ( 300728U ): Electronics\n\n\nelectronics\n\n\n4 of 5\n\n\n1.0\n\n\nDies after a year\n\n\nOctober 6, 2006\n\n\nS. Ruhl\n\n\nBainbridge Island, WA\n\n\nI was using it as a backup device and it failed after about 12 months. So I am ripping it apart and putting in a new UltraATA disk in. Just hope the box is not frying the disk.\nI noticed in another review they had a Western Digital. I had a Segate Baracuda 7200.8. Doubt that it makes any difference. I am sure it is just low bidder\n\n\n\n\nB000EPHP4U:would_be_great,_if_it_worked...:r._a._edington_\"amazonaholic\"\n\n\nB000EPHP4U\n\n\nApple 8 GB iPod Nano Black (2nd Generation): Electronics\n\n\nelectronics\n\n\n6 of 11\n\n\n1.0\n\n\nWould be great, IF it worked...\n\n\nNovember 12, 2006\n\n\nR. A. Edington \"Amazonaholic\"\n\n\nBoise, ID, United States\n\n\nCONSUMER BEWARE!!!!\n\nThis product does not work as advertised. If you put more than 1000 songs onto this device, On-The-Go playlists cease to function.\n\nThis is very, very sad for a player with 8GB of memory. It's supposed to hold over 2000 songs, but stops working correctly after 1000 songs are loaded? Wow. Unbelievable.\n\nThis is a well-documented bug on the Apple discussion forums, yet Apple has neither acknowledged nor fixed this bug yet. Will they ever? Who cares? Why should they, if consumers like you keep buying it and RAVING about it?\n\nI bought my 8GB Nano, but returned it two days later when I found out the problem was known, but not fixed and not fixable.\n\nApple should not be shipping products with known bugs in them! Don't they test these things?!?!?!\n\nBy the way, the same problem also occurs on the 4GB Nano if you manage to squeeze more than 1000 songs onto it, which isn't all that hard to do...\n\nIf you don't care about having more than 1000 songs, or you don't care about On-The-Go playlists actually working as advertised, what are you doing shopping for an 8GB player? Better stick with your 2GB models..\n\n\n\n\nB000085ZKV:this_item_is_prone_to_breaking...:ravingtennisfan\n\n\nB000085ZKV\n\n\nLinksys USB200M EtherFast USB 2.0 10/100 Network Adapter: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nThis item is prone to breaking...\n\n\nSeptember 5, 2006\n\n\nRavingTennisFan\n\n\nNew York, NY\n\n\nDo not buy this item, it is very liable to break within the first week of purchase. And I bought 2 of them, so statistically it's not jsut me. The first time the door broke, and without the door there's no way to hold the ethernet cable to the connection. The second time, the USB part broke off and got stuck in my computer and I could not put it back together. All in all, not the best product out there. Try instead the Belkin USB to Ethernet adaptor\n\n\n\n\nB0007Y7976:i've_finally_learned_a_very_expensive_lesson:jane_cox_\"linksys_fan\"\n\n\nB0007Y7976\n\n\nApple 2 GB iPod Nano White: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n1.0\n\n\nI've finally learned a VERY expensive lesson\n\n\nNovember 15, 2006\n\n\nJane Cox \"linksys fan\"\n\n\nBoca Raton, FL\n\n\nDON'T BUY THIS PRODUCT. There, simple advice I wish someone had given me before I bought two of these. With the first one the battery died 13 months after purchase. Since it was 30 days outside the warrantly, apple support told me \"sorry, tough luck.\" I made the mistake of buying another one figuring I just got a bad one and there was no way I could have the same problem again. WRONG!\n\nSecond ipod just died (same battery problem) - this one died within the warranty period but becuase it took me two weeks to get into the ipod store to see what was wrong and, of course, by the time I got in I was past the 12 month warranty period (who keeps track of the purchase date? I sure didn't).\n\nOther than the fact that your substnatial investment will be worthless in about a year - it is a fantastic product. I'm just sorry that it didn't last (twice!) cause otherwise I'd be a fan\n\n\n\n\nB00004SB92:not_worth_the_hassle_:m._boughter\n\n\nB00004SB92\n\n\nLinksys BEFSR41 EtherFast Cable/DSL Router with 4-Port 10/100 Switch: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n1.0\n\n\nNot worth the hassle \n\n\nSeptember 28, 2006\n\n\nM. Boughter\n\n\nNew Castle, PA United States\n\n\nPlease search for the thread on the linksys support forum titled \"BEFSR41 v4 intermittent drop of connection\" which goes on for 4 pages with no reply from linksys support, I'd post a URL but that is against the review guidelines here at amazon.\n\nThe only way to fix the problems that occur with this router is to buy a different model or better yet a different brand. It appears that Linksys doesn't really care about the issue. \n\nI returned this router after the second day of dealing with it's constant connection drops and bought a Belkin instead. \n\n\n\n\nB000246XZ0:works_well_in_a_single_room:aleksandar_totic\n\n\nB000246XZ0\n\n\nUniden EXI976 900 MHz Analog Cordless Phone with Caller ID (White): Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n2.0\n\n\nWorks well in a single room\n\n\nOctober 8, 2005\n\n\nAleksandar Totic\n\n\nSan Francisco, CA USA\n\n\nI like the phone's simplicity and ergonomics. It's fatal flaw is the lack of range. My office is about 50ft away from the base, and I get unacceptable interference. Both plain old AT&T & GE 900 handle this distance without any problems\n\n\n\n\nB000246XZ0:stops_working_after_6_months:amazon_freak\n\n\nB000246XZ0\n\n\nUniden EXI976 900 MHz Analog Cordless Phone with Caller ID (White): Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n1.0\n\n\nStops working after 6 months\n\n\nSeptember 26, 2005\n\n\nAmazon Freak\n\n\n\n\n\nI've been 'trying' to use this phone for the past 8 months. Worked fine in the beginning, now I can't even answer my calls. I hit the TALK button, but the phone continues to ring. Spend a little more money but buy a better phone. This phone is a total waste of money\n\n\n\n\nB00021Y9OA:on_time_and_on_the_money!:r._montz_\"the_dude\"\n\n\nB00021Y9OA\n\n\nBose QuietComfort 2 Acoustic Noise Cancelling Headphones: Electronics\n\n\nelectronics\n\n\n0 of 33\n\n\n1.0\n\n\nOn time and on the money!\n\n\nJuly 31, 2006\n\n\nR. Montz \"The Dude\"\n\n\nNOLA, USA\n\n\nNo tricks, just treats and delivered on time as promised.\n\n\n\n\nB0001YFW94:exchanged_them_for_motorola_sx700:brian_j_\"brian\"\n\n\nB0001YFW94\n\n\nMotorola T6500R GMRS / FRS Rechargeable Two-Way Radios, Khaki (2 Pack): Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nExchanged them for Motorola SX700\n\n\nNovember 10, 2006\n\n\nBrian J \"Brian\"\n\n\nAtlanta, GA\n\n\nI don't know if I just got a bad pair of radios, but they didn't work at all. If we were beyond shouting distance the radios did not receive any signal at all. Even at very close range there was too much static to understand anything. I'll post a review of the SX700s after we get a chance to try them out\n\n\n\n\nB0001YFW94:love_the_features,_hate_the_range_and_usage!:mr._jann_linder\n\n\nB0001YFW94\n\n\nMotorola T6500R GMRS / FRS Rechargeable Two-Way Radios, Khaki (2 Pack): Electronics\n\n\nelectronics\n\n\n14 of 15\n\n\n2.0\n\n\nLove the features, Hate the range and usage!\n\n\nMarch 7, 2006\n\n\nMr. Jann Linder\n\n\nSan Jose, CA USA\n\n\nFrom cellphone and also the pager era I began my love affair with Motorola. Don't need to read much farther into my review to get to the meat of it. Sometimes companies make a lousy product even though they have others that are great! Don't Buy These! They fall into the former category \n\nI needed them quickly (to give my in-laws time to practice using them before we went on a cruise) so when I went to their house for the weekend I bought them from the hated \"Best Buy\". I am returning all 4! (2 sets of 2 handsets). Here was my experience:\n\nI went in and saw the Midland and the Cobra along side the Motorola. Not knowing much of Midland, but recognizing Cobra from their history with low-end phones and electronics, I opted with the very stylish Motorola T6500R's. I know. Big Mistake! I am a computer programmer and very intelligent, but when confronted with products I do not undestand, I go for the brand name. Which is something that can be forgiven if, as now, you find out what the problem is and correct it.\n\nI took them home and recharged them. First things first: The feel in your hand. EXCELLENT. They feel as good as they look. I purchased these to go on a cruise to the Mexican Riviera and to maintain contact both on the boat and in the ports: Puerto Vallarta, Mazatlan and Cabo san Lucas. We wanted to make sure we did not \"lose\" my in-laws (really! I mean it! No, really ....) \n\nAnyway. Charged them up and turned them on. They come with a plastic-encased group of 3 AA rechargable batteries that fit nicely (and only one way--they are keyed) in the dual-purpose battery compartment. This is nice: if you are out and running around and your charge goes down, simply buy 3 AA alkaline batteries and go on your way. There is no problem if you forget you have the NORMAL NON-RECHARGABLE AA batteries in the case if you put it in the charging stand. Unlike other units, the physical metal conductors to charge the battery pack are actually built into the pack, not the handset. If you put it batteries, the charger makes no connection to the handset. If you put in the rechargable battery pack, 2 silver contacts from the battery pack contact the charging stand and VOILA the unit charges. Fitting the handset into the recharger is a nightmare, though! It must be generic and able to fit several different Motorola products (production outsourced to Giant Int'l Ltd--so if you thought this was a genuine Motorola built item, you are mistaken. The entire Talkabout line are outsourced for production) as the dual-compartment charger has 2 inserts in each compartment and no indication of which one to use for the T6500R's. I am a smart person though so I chose the one that looked like it best fit and inserted it. Then I attempted to insert the T6500R's. I say attempted because the bottom of the handset is rounded and thus does not seat well in any direction. After you get it to EXACTLY the right position (look on the charger to watch for the LED to light up) the the hard part (of charging, that is) is over.\n\nNext item: Bad red LCD backed screen. Hard to read in ANY light and impossible in the sunlight. Settings were easy enough. Channel, sub-channel, vibrate, NOAA broadcasts, etc. However, the advertised QT (ensures no one can talk through your handset unless they are using a compatible Motorola handset) does not work unless you hit the PTT (push to talk) button (or the CALL button), wait about 3 seconds and begin speaking. My in-laws are new to technology and are used to experiencing walkie-talkies when their kids were young. Therefore this is unusual. They automatically want to push the button and talk. So, the first part of what they say is immediately lost. \n\nI say \"first part\". I am assuming I actually caught a portion of what they said. This brings me to the second major flaw. This unit contains a HORRIBLE speaker. If the volume is too low then you can't hear it. Too high and it is garbled beyond your ability to understand it. We were really hard-pressed to find a happy medium. AND, in a noisy room, it would've been impossible to hear anything.\n\nSome merchants list these as 5 mile radios. The packaging says 10. The 1 watt GMRS suggests about 5-10. We got less than 1. Directly over a lake. In vehicles on the I-5, we drove less than 2 miles from their house (which is DIRECTLY ON the interstate) and got no signal. \n\nI got back to my house in San Jose (on top of Communications Hill...the largest \"hill\" in the area) and could not communicate to my spouse less than 1/2 mile away while he was coming up our road!\n\nSince the weekend, I have spent all my extra time on Amazon.com and Consumer Reports reviewing all the offerings. (the latter, Consumer Reports' reviews are from July in 2002!) So, I think I am going to return these and purchase the Midland 5 watt GXT500's from Amazon. Remember, doubling the wattage (in this case MORE than doubling) does not give double the signal. As another others have stated, increasing wattage increases only another portion of the distance--ie: 2 watts to 4 watts does NOT double the distance the signal can reach.)\n\nHope this reveiw helps you\n\n\n\n\nB0001V2N62:worthless:mc_\"it_manager\"\n\n\nB0001V2N62\n\n\nAPC BIOPOD Biometric Password Manager (USB): Electronics\n\n\nelectronics\n\n\n8 of 8\n\n\n1.0\n\n\nWorthless\n\n\nApril 3, 2006\n\n\nMC \"IT Manager\"\n\n\nOklahoma, USA\n\n\nI purchased a Biopod in March of 2006. Received brand new unit that included Omnipass software version 2.09. This software version released in 2004.\n\nContacted company that produced the software and they refused to upgrade to their latest version (v4) that had been released a month earlier unless they charged me the upgrade fee.\n\nThe version of software that comes with the Biopod (v2.09) pretty much only works with Internet Explorer and other Microsoft software. The new version (4) supposedly works with Firefox and several other non-Microsoft software programs. \n\nI recommend you avoid this product due to old software. I returned mine and am ordering the IBM Fingerprint Reader\n\n\n\n\nB00000J4GP:these_disks_really_do_stink!!!!!:daniel_a._\"danielthechskid\"\n\n\nB00000J4GP\n\n\nMemorex 3.5\" PC-Formatted High-Density Floppy Disks with File Box (Colors, 40-Pack): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nThese disks really do STINK!!!!!\n\n\nAugust 28, 2003\n\n\nDaniel A. \"danielthechskid\"\n\n\nIowa USA\n\n\nI bought 1 40 pack of these disks so that I could create a set of Windows 95 setup disks. They worked to install windows once and then they all started dying. I went to run Windows setup and got halfway through and got a \"cannot read from device\" error. After that I ran scandisk on all of them and only 1 disk didnt have bad sectors. A few of the disks had bad sectors in the system area rendering them unformattable. I advise anybody who reads this to steer clear of these cute little disks unless you want to use them as christmas ornaments\n\n\n\n\nB0001RM7P8:pouch_case_for_magellan:always_lost\n\n\nB0001RM7P8\n\n\nMagellan 980667 Protective Pouch Case: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nPouch Case for Magellan\n\n\nOctober 26, 2006\n\n\nAlways Lost\n\n\nConnecticut\n\n\nBe Creative!! Mostly what you're paying for is the embroidered 'Magellan' name. I like the Crown Royal idea of pouch or you can sew or obtain a fleece and make a square with pockets and tuck everything in there, works fine. Since the reviews are bad, I will not get this just to hide my GPS away in an eye-catching steal-me monogrammed bag. \n\n\n\n\nB0009WDM36:just_flat_doesn't_work!:michael_w._wukitsch\n\n\nB0009WDM36\n\n\nMemorex 2 GB 2nd Generation USB 2.0 Travel Drive 32509070: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nJust flat doesn't work!\n\n\nOctober 6, 2006\n\n\nMichael W. Wukitsch\n\n\nSan Diego, CA\n\n\nI bought a second Traveldrive because the first one purchased worked just fine. The second drive does not work on my system (Windows Millenium). Calling the service line I found out that there are two versions and the second is not compatible with the first. After trying all the recommended solutions, the tech support suggested I request a warranty replacement/trade for the original version or simply get my money back. Requests for a resolution of the matter have fallen on deaf ears. I would not waste any more money on products from this company\n\n\n\n\nB000BQ535K:lasted_4_months:s._c._bhatti_\"iotcusa_wholesale\"\n\n\nB000BQ535K\n\n\nBrother HL-5250DN Network Ready Laser Printer with Duplex: Electronics\n\n\nelectronics\n\n\n2 of 4\n\n\n1.0\n\n\nLasted 4 Months\n\n\nNovember 1, 2006\n\n\nS. C. Bhatti \"iotcusa wholesale\"\n\n\nLong Beach, CA\n\n\nWe purchased this printer to replace old Brother HL-1440 for our small business printing needs. At first everything was great. After 4 months of use, still on first toner cartridge, the manual feed can no longer grab labels or envelopes. There is now a huge gap and the envelopes can slide all the way inside with no resistance. Huge problem for us and I hope we can return this piece of junk\n\n\n\n\nB000ATU8M6:like_a_porsche..._without_wheels!:nitropress_\"tech_wrangler\"\n\n\nB000ATU8M6\n\n\nCobra Nav One 4500 Mobile GPS Navigation System with Traffic: Electronics\n\n\nelectronics\n\n\n28 of 33\n\n\n2.0\n\n\nLike a Porsche... without wheels!\n\n\nApril 20, 2006\n\n\nNitroPress \"tech wrangler\"\n\n\nCalifornia\n\n\nWhen realtime traffic monitoring came to my city, I immediately started looking at solutions to help my wife's daily business driving (2-500 miles weekly, crisscrossing a 50-mile area through major traffic routes). I quickly settled on an automotive GPS with realtime traffic monitoring. There are not yet many choices in this area, so the options boiled down to this unit and the Garmin 2720. (These use FM radio for the traffic info; the XM-radio units like the TomTom 700 and Garmin 2730 had unacceptably high monthly costs since I don't want XM radio.)\n\nI chose the Cobra for its large screen and positive reviews in areas that were important to me. Its flawed routing ability was not a major issue as it was to be used in a well-known region. Address lookup and traffic monitoring were the primary needs.\n\nIn short, I don't know much about how the Cobra performs because I was only able to use it for a few short test trips. This is because the mounting hardware is one of the most deficient pieces of engineering I have ever encountered in higher-end electronics gear:\n\n- The only mounting option is via suction cup, which in tests would adhere reliably only to nearly flat glass. A stick-on disc for attaching the suction cup is also provided, but I did not attempt to use it and would not trust it to be able to hold this heavy (1.25 pound) unit over the long run. If your vehicle does not have a flat section of windshield in a convenient location, you will have very few options for mounting this unit.\n\n- California law prohibits attaching things to the windshield, meaning that California users either need to risk a minor ticket or find an acceptable alternate mounting point. (Good luck to you: I was unable to find one in three different vehicles!) Compounding the problem is that the mount has a very limited positioning range and easily runs out of adjustment room for most potential mounting alternatives.\n\n- Cobra does not offer any other mounts or mounting accessories at all - no beanbag or friction mount, no clamp, no fixed-base mount, nothing. If there is any aftermarket option (RAM Mount, etc.) I was unable to find it. You are stuck with Cobra's proprietary mount unless you are willing to modify the unit or its mounting pieces and fabricate an adapter for a Garmin or RAM Mount base.\n\nI was *almost* willing to go the extra mile to make this unit work in my vehicle, even though the best solution would have been very awkward to dismount and remount, making it a potential theft target.\n\nThe final straw was when I found that the traffic receiver was faulty and I was unable to get a reply from the manufacturer in a timely manner (two days now and counting...) Enough's enough; something was telling me that this unit wasn't the right choice.\n\nSo this unit is being returned and I've already ordered a Garmin 2720 with GTM11 traffic receiver in replacement. The prices for the units through Amazon are nearly the same (especially when factoring in the costs for 15 months of traffic monitoring - the Cobra comes with three months and gets $60 per year after that, while the Garmin comes with 15 months and has the same annual cost after that - so you have to add $60 to the Cobra to get a truly equivalent price). Garmin also has excellent mounts with a range of attachment options, some of the best map and routing tools, and a long track record with GPS units. It does have a smaller screen and some complaints about the user interface, but at least I can mount it properly and be able to tuck it away and remount it easily to prevent theft.\n\nThis unit gets two stars only because my brief experience with its function appears to live up to all the positive reviews - but overall, it's like a high-performance sports car with no wheels! I 100% recommend against it for California users and suggest that others try to lay hands on a sample unit to see if it can be properly mounted in your vehicle before laying out $750\n\n\n\n\nB0000AFX1G:died_after_a_few_weeks:j._ter_horst\n\n\nB0000AFX1G\n\n\nPNY 128 MB Attache USB 2.0 Portable Flash Memory Drive: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nDied after a few weeks\n\n\nAugust 9, 2006\n\n\nJ. ter Horst\n\n\nUK\n\n\nWorked fine for a couple of weeks and then just died. The problem was that it was not recognized by XP or Linux anymore.\n\n\n\n\nB0000AFX1G:bad.:mak_blast\n\n\nB0000AFX1G\n\n\nPNY 128 MB Attache USB 2.0 Portable Flash Memory Drive: Electronics\n\n\nelectronics\n\n\n5 of 12\n\n\n1.0\n\n\nBad.\n\n\nMarch 23, 2005\n\n\nMak Blast\n\n\n\n\n\nThis flash drive does not work with Windows 98SE. The back of the package clearly states you can download drivers for Windows 98SE at the maufacturer's web site. First I tried downloading the driver. Turns out the driver is a .zip file so I had to download WinZip so I could unzip the driver. After downloading the driver and WinZip the flash drive is still not recognized by my PC. The FAQ at the manufacturer's web site states if the device is not recognized the BIOS on my PC must be enabled. But the FAQ doesn't tell you how to enable the BIOS\n\n\n\n\nB0002IQ1FS:velocity_bait_and_switch:r._hsu_\"coasters_form_velocity\"\n\n\nB0002IQ1FS\n\n\nVerbatim 8x 4.7 GB DVD-R Spindle (100 Discs): Electronics\n\n\nelectronics\n\n\n1 of 18\n\n\n1.0\n\n\nVelocity Bait and switch\n\n\nMarch 27, 2006\n\n\nR. HSU \"coasters form Velocity\"\n\n\nSF, CA\n\n\nIt doesn't matter what DVD+ writer I used, Velocity worked! Not one disk became a coaster. I loved the Velocity DVD-R until I order 2 more spindles. I ordered it directly for the manufacture. When I received it, the outside plastic covering was cracked, half the Disks had the jitters and the other half was coasters. It seems to me that they trick you into ordering one with a great offer including free shipping. If you order any more, the person shipping it to you send you the rejects from quality assurance. I will never again order this product again period!\n\n\n\n\nB0002IQ1FS:three_in_a_row_made_as_data_dvd:digital_barista\n\n\nB0002IQ1FS\n\n\nVerbatim 8x 4.7 GB DVD-R Spindle (100 Discs): Electronics\n\n\nelectronics\n\n\n1 of 9\n\n\n1.0\n\n\nThree in a row made as data dvd\n\n\nNovember 1, 2005\n\n\ndigital barista\n\n\nwashington\n\n\nWell, so far so bad... I burned the DVDs on my Panasonic DVD recorder. They play in my newer DVD players, but I cannot make a copy of them with Sonic DVD, Nero, or One click DVD. UNacceptable! I have used many types of DVDs, never had this happen. I guess I know why some DVDs I have purchased are playable, but not copiable.. it is the media! I will stick with Maxell,or TDK\n\n\n\n\nB0001FV2YM:hunk_of_junk:wisconsin_dad\n\n\nB0001FV2YM\n\n\nBelkin TuneCast II FM Transmitter: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nHunk of Junk\n\n\nAugust 1, 2006\n\n\nWisconsin Dad\n\n\nFond du Lac, Wisconsin United States\n\n\nWorked for 6 hours, then just stopped transmitting. What a waste of cash. Do not buy this\n\n\n\n\nB0001FV2YM:sound_was_really_bad:p._etue\n\n\nB0001FV2YM\n\n\nBelkin TuneCast II FM Transmitter: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nSound was really bad\n\n\nJuly 11, 2006\n\n\nP. Etue\n\n\nMichigan\n\n\nI had a terrible time getting a station to come in even close to FM quality. There was always a bunch of static and the stereo buzzed between songs because I had to keep the volume too high. The high band of frequencies seemed to work best, but then it led to crackling speakers everytime I turned something electrical on in the car. It also seemed to kill batteries at a surprising rate.\n\nOn a positive note, I loved the fact that it would also function for my son's DVD but in the end, it had way too much static so I hunted for anothe\n\n\n\n\nB0001FV2YM:worthless:t._latta\n\n\nB0001FV2YM\n\n\nBelkin TuneCast II FM Transmitter: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nWorthless\n\n\nJune 8, 2006\n\n\nT. Latta\n\n\nRound Lake, IL United States\n\n\nThis is a total waste of money and engineering. Plenty of free frequencies in my area and it would barely connect to anything. Sound quality horrible even when the device is right next to the antenna on a portable FM player. Wrapping it back up and sending it back\n\n\n\n\nB0001FV2YM:what_a_piece_of_poo!:jeremy_aker\n\n\nB0001FV2YM\n\n\nBelkin TuneCast II FM Transmitter: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nWhat a piece of poo!\n\n\nMay 4, 2006\n\n\nJeremy Aker\n\n\nRaleigh, NC\n\n\nThe transmission quality is very poor. I was unable to find a single frequency free of static. While operating the Belkin TuneCast II in my car I found that driving over or under bridges, under traffic lights, under power lines, or near large trucks increases the static to a level where the music can not be heard at all. The only way I could get audio clarity equal to that of a dirty and scratched record or a distant AM station was to hold the transmitter to the face of my radio. What a piece of poo! Can I give it zero stars? And don't even get me started on the excessive packaging that you'll need to use a diamond tipped jack-hammer to open\n\n\n\n\nB0002XGYJ6:false_advertising:abra_carroll_nardo\n\n\nB0002XGYJ6\n\n\nPretec SD GPS PPC 2002/ PPC 2003 or later, Palm OS 5.x or later: Electronics\n\n\nelectronics\n\n\n3 of 5\n\n\n1.0\n\n\nFalse Advertising\n\n\nDecember 25, 2004\n\n\nAbra Carroll Nardo\n\n\nMemphis, TN\n\n\nIf you have a Palm, read this before buying this device:\n\n<<<Although our SD GPS card should work with Palm OS without a driver as its NMEA-0183 compliant, currently we are not aware of any 3rd-party software that supports our SD GPS card. Based on this I regrettably have to say that at this moment it does not work with Palm OS device. We are currently in the process of developing a Palm OS driver that will allow the device to be used independent of 3rd-party software support. When this becomes available it will be posted on our website.>>>\n\nI bought this for my father for Christmas. It cost a lot of money. We all got really excited about it. It doesn't work. :\n\n\n\n\nB00004WFZR:brother_ml-300_not_worth_considering:paul_f._cotter_\"lovegrass\"\n\n\nB00004WFZR\n\n\nBrother ML-300 Electronic Display Typewriter: Electronics\n\n\nelectronics\n\n\n7 of 7\n\n\n1.0\n\n\nBrother ml-300 not worth considering\n\n\nFebruary 8, 2006\n\n\nPaul F. Cotter \"lovegrass\"\n\n\nlubbock, TX USA\n\n\nI have had two of these units. I would definitely not recommend anyone buying the ML-300. Save yourself some money and get something else. Both of the units lasted only 14 months. I did hardly any typing with them. Only multi-page forms and applications. The printwheel motor failed on both of them and so did the electronic board for the keys. Took in in to a repair center and was told it was not worth repairing. Contacted Brother International USA and was told that the warranty was only good for 12 months. Save yourself some money and buy a Big Chief table and a number 2 pencil. \n\n\n\n\nB0001AP8CE:not_the_right_fit:dutch_treat_\"dt\"\n\n\nB0001AP8CE\n\n\nApple M9394G/B iPod Earbud Headphones: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nNot the right fit\n\n\nNovember 9, 2006\n\n\nDutch Treat \"DT\"\n\n\nKauai, HI\n\n\nAfter reading some good and bad reviews I went ahead and bought them anyway. They will not stay in my ears, I tried the three different sized ear buds but non will do the job, very irritating.\nSo, I do not recommend them at all, too bad because if they stay in when you sit really still the sound quality is really good.\nAloh\n\n\n\n\nB0001AP8CE:this_apple_product_is_rotten_to_the_core!:p._duval_\"pdv\"\n\n\nB0001AP8CE\n\n\nApple M9394G/B iPod Earbud Headphones: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nThis Apple product is rotten to the core!\n\n\nSeptember 28, 2006\n\n\nP. DuVal \"PDV\"\n\n\nNewbury Park, CA\n\n\nBought these and used them once. Had to remove them when I found myself in dire need of some Excedrin Migraine. What do you get for your $39? Cheap, tinny, bass-less sound that's like being plugged into a busted Walkman. The buds themselves don't remain stable in the ear, so forget about using them while engaging in motion-involved activity (such as jogging, walking, head-bobbing, neck movement, etc). These are by far the worst earbuds on the market. How could Apple stoop so low? Don't be fooled! Do a Google search for these and you'll find that the vast majority out there considers this a horrible product. You've been warned.\n\nI used to use Koss Earbuds, which were pretty good. I sold these iPod duds in favor of the XtremeMac In-Ear Headphones, which are the best ones available and worth the extra money\n\n\n\n\nB0002V8KW2:won't_connect_over_wpa:james_pezzella\n\n\nB0002V8KW2\n\n\nLinksys Wireless-G Internet Video Camera: Electronics\n\n\nelectronics\n\n\n13 of 18\n\n\n2.0\n\n\nWon't connect over WPA\n\n\nJune 20, 2006\n\n\nJames Pezzella\n\n\nOceanside, NY\n\n\nI've had this camera replaced by Linksys 4 times. The first time, was due to the camera crashing on a weekly basis. The second replacement worked fine until I upgraded my router and switched from WEP to WPA. After the switch the camera would not connect to the router, switching back to WEP, of course worked. Linksys has replaced the camera 3 times for this issue, and with the last support call confirmed that there is a KNOWN problem with WPA encryption and this camera\n\n\n\n\nB0002V8KW2:stay_away_from_this!:marklin_man\n\n\nB0002V8KW2\n\n\nLinksys Wireless-G Internet Video Camera: Electronics\n\n\nelectronics\n\n\n3 of 5\n\n\n1.0\n\n\nStay Away From This!\n\n\nApril 29, 2006\n\n\nMarklin Man\n\n\nLandenberg, PA\n\n\nI have extensive experience with wireless system setup and operation. My whole system is all Linksys gear. After four hours on this camera, I give up.\n\nWhen you update the firmware, it erases everything you programmed into the camera so beware.\n\nI could get the camera to work in WiFi mode. Picture is decent, not great. The motion sensing function triggers quite well.\n\nThe frustration comes from an inability to get the email function to work. Kept getting an error message with the system parameters setup so it should have worked.\n\nThe FAQ section on Linksys website is almost non-existent. I did not bother to call because in my past experience that is of minimal help and very frustrating.\n\nFeatures sound great but could not get it to work right. I will find another unit.\n\n\n\n\nB0000AOWVP:keyboard_has_splotchy_finish_and_not_durable:william_e._fournier\n\n\nB0000AOWVP\n\n\nMicrosoft Wireless Optical Desktop Pro: Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n2.0\n\n\nKeyboard has Splotchy finish and not durable\n\n\nFebruary 9, 2006\n\n\nWilliam E. Fournier\n\n\nSteamboat Springs, Colorado\n\n\nThis is my first Amazon review of any type. I was quite happy to finally get a wireless keyboard and mouse. When this was pulled from the box the finish was splotchy. It looks like someone painted the keys in a garage. I expected better. Now I have used it for a couple of weeks in an office environment, and already one of the plastic keyboard legs has broken. I am not hard on the equipment and don't know how it happened.\n\nOnce in a while I press keys and nothing happens. I can live with that.\n\nOn the other hand, the mouse works as expected.\n\nBefore you purchase one, check the process for returning it! I would rather put up with the crappy keyboard.....\n\nI will continue to purchase many books from Amazon but my computer hardware will come from Dell.\n\n\n\n\n\nB0006ZM4XK:1_step_forward,_50_steps_back.__just_horrible.:james_g._magnano\n\n\nB0006ZM4XK\n\n\nLinksys WRT54GX Wireless-G Broadband Router with SRX: Electronics\n\n\nelectronics\n\n\n7 of 10\n\n\n1.0\n\n\n1 step forward, 50 steps back. Just horrible.\n\n\nFebruary 28, 2006\n\n\nJames G. Magnano\n\n\nLos Angeles, CA. USA\n\n\nThis router had 1/4th the range of my standard linksys G-router which suddenly burned out after less then a year. The speed (when I could get a signal) was 1 to 11 Mbps, my old router held a steady 54Mbps. I did need a range expander for my old one to cover my whole house, but I couldn't use it with this router. The expander only works with the basic G-router, so no way to even boost the signal on this new one. Support was a joke, couldn't get any help or explaination as to why the \"Expanded Range\" was so much less range the the previous \"unexpanded range\" Bought another older version, and everything's great again. Don't waste your money or time on this\n\n\n\n\nB0006ZM4XK:very_disappointing!:j._christopher_jones\n\n\nB0006ZM4XK\n\n\nLinksys WRT54GX Wireless-G Broadband Router with SRX: Electronics\n\n\nelectronics\n\n\n7 of 12\n\n\n2.0\n\n\nVery Disappointing!\n\n\nJanuary 11, 2006\n\n\nJ. Christopher Jones\n\n\n\n\n\nDespite the slick marketing effort (i.e. \"With SRX\"), this product falls short.\n\nThe installation disk is useless.\nCustomer support was good, when I could get through - which was not very often.\nI constantly lost the wireless connection.\nI constantly had to re-boot.\nDespite significant effort (utilizing the online help features) I could never obtain a secure, encrypted connection.\n\nNo more Linksys for me! \n\n\n\n\nB000GP4G1I:disappointed_is_all_i_can_say...:mark_r._wuscher\n\n\nB000GP4G1I\n\n\nLogitech QuickCam Ultra Vision ( 961471-0403 ): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nDisappointed is all I can say...\n\n\nNovember 24, 2006\n\n\nMark R. Wuscher\n\n\n\n\n\nI am a huge Logitech fan and buy their products in the truck load, unfortunately, this webcam is a dog with fleas. The picture is blurry, the lighting is abmismal in low light and you can just get better performance from a less expensive web cam. I am still a Logitech fan, but you can get better performance from QuickZoom\n\n\n\n\nB000GP4G1I:logitech_quickcam_ultra_vision:snowmanbob_\"bob\"\n\n\nB000GP4G1I\n\n\nLogitech QuickCam Ultra Vision ( 961471-0403 ): Electronics\n\n\nelectronics\n\n\n1 of 11\n\n\n1.0\n\n\nLogitech QuickCam Ultra Vision\n\n\nNovember 10, 2006\n\n\nsnowmanbob \"Bob\"\n\n\nMinnesota\n\n\n\n\nI ordered this web cam because I wanted one of the better web cams on the market. I was NOT able to get it working. I returned it. Very dissapointed\n\n\n\n\nB000068IH5:monster_cable_rcas_for_ipod:timothy_j._travelbee_\"trvlb\"\n\n\nB000068IH5\n\n\nMonster Cable 126130 iCable for iPod: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nmonster cable RCAs for iPOD\n\n\nNovember 6, 2006\n\n\nTimothy J. Travelbee \"trvlb\"\n\n\nchicago\n\n\nPaid $34 at Best Buy for a cable that DOESN'T EVEN WORK. \n\nHave always been skeptical of Monster Cable price points, don't buy them, took a chance. \n\nAs a matter of principle, this product sucks. (For $34, I'm waiving the \"bad apple in every bunch...\" rebuttal\n\n\n\n\nB0007LC4DS:iriver:j._de_cou\n\n\nB0007LC4DS\n\n\niRiver H10 5 GB Digital MP3 Player Trace Red: Electronics\n\n\nelectronics\n\n\n0 of 2\n\n\n1.0\n\n\niriver\n\n\nNovember 10, 2006\n\n\nJ. De Cou\n\n\nAlpine, WY\n\n\nDon't waste your money. What iRiver doesn't tell their customers is that once the battery goes dead, the system is useless. I will never buy one of their products again and I'm doing my best to inform everyone out there that they'll be tossing their money in the trash by purchasing any iRiver products. My system cost me $[...] and it is now as useless as the customer service department at iRive\n\n\n\n\nB0007N55LO:excellent_sound,_painful_electric_shocks:matt_stewart_\"mdsandco_at_gmail_dot_com\"\n\n\nB0007N55LO\n\n\nSony MDR-EX81LP Bud-Style Stereo Earphones (Black): Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n2.0\n\n\nExcellent Sound, Painful Electric Shocks\n\n\nNovember 6, 2006\n\n\nMatt Stewart \"mdsandco at gmail dot com\"\n\n\nPalo Alto, CA\n\n\nThese earphones provide excellent sound for the money. Rich bass, sharp treble and tight sound isolation with the comfortable and adjustable rubber earpieces. \n\nHowever I experienced painful electric shocks in both ears if I put pressure on my eardrums, such as getting up from a chair. And that was before I stood in the puddle of water. Just kididng. Shocks occured in first and Sony-provided replacement pair. They eventually replaced with an on-the-ear model. \n\nI have no problems with other Sony headphones or earphones, I think the speaker membrane was positioned too close to my eardrums. Since there are on other comments about this issue, I'll assume its isolated, but beware of shocks on perhaps a smaller scale.\n\n\n\n\nB00006JI6Y:this_does_not_fit_the_ipaq_hx2755:susan_cannon\n\n\nB00006JI6Y\n\n\nHP/Compaq CQP121 HP Nylon Folio Case - Black (High Density Nylon): Electronics\n\n\nelectronics\n\n\n2 of 4\n\n\n1.0\n\n\nThis does not fit the iPAQ hx2755\n\n\nAugust 8, 2005\n\n\nSusan Cannon\n\n\nHouston TX USA\n\n\nThis does not fit the iPAQ hx2755. Do not try! There are plenty of other cases out there that will work. I ended up with a 94 cent digital camera case from Wal-Mart that works better than this thing. \n\n\n\n\n\nB000BDH2Y8:do_not_buy_this_product:d._neufeld_\"programmer\"\n\n\nB000BDH2Y8\n\n\nLogitech V270 Cordless Optical Bluetooth Mouse- Charcoal: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nDo not buy this product\n\n\nNovember 25, 2006\n\n\nD. Neufeld \"Programmer\"\n\n\nSan Diego, CA\n\n\nThis product is terrible and I'm going to return mine or throw it away. It constantly wanders to places I don't want it to be or trails where I think it should be based on my usage of other mice. At this point I consider it unusable. I've tried it on several surfaces to see if it was a tracking problem, but noticed no real change in behavior. I think what's happening is it keeps trying to go to sleep while I'm moving it, because the tracking is OK as long as I'm moving it very quickly, but when I'm trying to precision target something like an icon or a piece of text it starts jumping erratically. I keep it about 6 inches from my laptop at all times, and the laptop isn't complaining about connection problems\n\n\n\n\nB0002ZAYZ4:not_as_advertised:carol_burnett_\"cb\"\n\n\nB0002ZAYZ4\n\n\nAT&T 984 Small Business System Speakerphone with Digital Answering System and Caller ID/Call Waiting: Electronics\n\n\nelectronics\n\n\n10 of 10\n\n\n2.0\n\n\nNot as advertised\n\n\nJune 2, 2005\n\n\nCarol Burnett \"CB\"\n\n\nTokyo, Japan\n\n\nThis is a nice 4 line phone, but the digital answering system does not work. We have 2 984's and several other brand 4-line phones. I could not find any way to make either 984 work as a four line answering system. Auto-Attendent is useless in our situation. These phones are certainly not worth the money. Unfortunately there aren't many 4-line phones on the market and apparently no 4-line digital answering systems\n\n\n\n\nB000246U1C:the_first_one_came_broken:kage\n\n\nB000246U1C\n\n\nAcoustic Research AW-811 Indoor/Outdoor Wireless Speaker (single): Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nThe first one came broken\n\n\nNovember 4, 2006\n\n\nKage\n\n\nTruckee, CA\n\n\nThe first speaker came broken, I first called the manufacture but they wanted me to ship it at my cost for repair, that was not acceptable. I then called Amazon and you took the item back promptly. \nWhen I tried to have replacement shipped I was informed the price had increased and I would have to pay the difference, since this is the speaker my wife wanted I was forced to pay the increased price. I am not happy with this tansaction.\n\n\n\n\nB000246U1C:not_as_advertised:d._brady\n\n\nB000246U1C\n\n\nAcoustic Research AW-811 Indoor/Outdoor Wireless Speaker (single): Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nNot as advertised\n\n\nNovember 3, 2006\n\n\nD. Brady\n\n\n\n\n\nI can't get good reception at any range from source. Intermittent when two rooms away from source. Have to move the speaker unit around to find an acceptable location. Doesn't automatically retune when being turned back on. Not at all what I expected from this piece of equipment.\n\n\n\n\nB0001YXWNM:terrible_purchase:g._gotimer\n\n\nB0001YXWNM\n\n\nMacally PodTape iPod Cassette Tape Car Adapter: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nTerrible Purchase\n\n\nOctober 26, 2006\n\n\nG. Gotimer\n\n\n\n\n\nI assumed all tape adapters were comparable. This one worked fine for 1 week. Then it sporadically ejected itself from the tap deck. A few days later it broke completely and is now jammed in my tape deck. The tape deck on my car stereo is now broken as is the Macally Podtape. I would buy another a different brand of adapter, but I no longer have a tape deck that works in my car. My worst purchase on Amazon to date. \n\n\n\n\nB0000649EZ:buyer_beware:_do_not_buy_this_one:c._huang\n\n\nB0000649EZ\n\n\nRecoton DVD901 Audio Digital Optical Cable (6 Feet): Electronics\n\n\nelectronics\n\n\n11 of 12\n\n\n1.0\n\n\nBuyer beware: DO NOT buy this one\n\n\nNovember 29, 2004\n\n\nC. Huang\n\n\n\n\n\nHave this optical cable for about a year before it was broken. According to the package, it has lifetime warranty. Sent back to the company and got a rejected mail saying, \"COMPANY OUT OF BUSINESS, RETURN TO SENDER\". The company address on the label is: \n\nRecoton Accessories, Inc.\n2950 Lake Emma Road\nLake Mary, FL 32746.\nMade in Chin\n\n\n\n\nB00008W9PW:ambicom_wl1100c-cf_compactflash_11mbps_adapter:sikh_lord_\"mgabasooa\"\n\n\nB00008W9PW\n\n\nAmbiCom WL1100C-CF Compactflash 11MBPS Adapter: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nAmbiCom WL1100C-CF Compactflash 11MBPS Adapter\n\n\nJuly 26, 2006\n\n\nSikh Lord \"mgabasooa\"\n\n\nHouston, Texas\n\n\nI tried to use it in my Dell X50 and it hardly worked, was too slow, too short range, you have to unplug and plug it bacl again and again.\n\nI other words, do not buy this if you have a Dell X50, you will just waste your money and time trying to make it work\n\n\n\n\nB000B9RXP0:looks_good......._but:greg_rees_\"greg\"\n\n\nB000B9RXP0\n\n\nApple MA185G/A Armband Green for iPod Nano 1st Generation: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n2.0\n\n\nLooks good....... but\n\n\nAugust 20, 2006\n\n\nGreg Rees \"Greg\"\n\n\nMidwest\n\n\nI purchased this armband to hold my Nano during workouts. The band barely fits my arm and tends to slide down the arm while running or cross training. Looking for a replacement right now\n\n\n\n\nB000CO77SC:warranty:j._perez_\"xavper7\"\n\n\nB000CO77SC\n\n\n2-Year Service Plan for Televisions $200 to $999.99: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nWarranty\n\n\nNovember 11, 2006\n\n\nJ. Perez \"xavper7\"\n\n\nphoenix, az\n\n\nI purchased the 2 yrs warranty for my Samsung LCD tv but I never received any information on it.\n\n\n\n\nB000196ENM:magellan_externa_antenna_-_bad:wayne_mcfarland\n\n\nB000196ENM\n\n\nMagellan 980637 External Antenna: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nMagellan Externa Antenna - Bad\n\n\nNovember 4, 2006\n\n\nWayne Mcfarland\n\n\n\n\n\nThe plug on the external antenna does bot fit the jack on the Magellan unit - avoid this until Magellan figures this out. Seems like they would use a standard plug/jack pair for all products\n\n\n\n\nB000196ENM:at-65_substitution:brian_e._monahan\n\n\nB000196ENM\n\n\nMagellan 980637 External Antenna: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nAT-65 Substitution\n\n\nOctober 26, 2006\n\n\nBrian E. Monahan\n\n\nSan Diego\n\n\nGot this antenna from my wife for my birthday; to go with my Roadmate 760 I just purchased.. The antenna we received was the AT-65 antenna. Instead of the large round antenna in the picture it was a small rectangular antenna (1 inch x 1 1/2 inch) with no indication that it was an actual Magellan antenna, on the unit itself. The package had a Magellan cardboard insert but this was definitely not the antenna pictured\n\n\n\n\nB000196ENM:another_at-65_substitution:s_saunders_\"s_saunders\"\n\n\nB000196ENM\n\n\nMagellan 980637 External Antenna: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n1.0\n\n\nAnother AT-65 substitution\n\n\nAugust 23, 2006\n\n\nS Saunders \"S Saunders\"\n\n\nNJ\n\n\nUnfortunately, I too received a switched item (at-65)in a sealed Magellan box today. Jennifer in Customer service assured me that they will refund the purchase and that they will investigate the stock they have. She was very apologetic and helpful. I will see what happens but am thankful that she was there to help\n\n\n\n\nB000F9NXW8:midland_gtx600vp4_radios_bogus_-_midland_bogus:frank_r._cutaia_\"fc\"\n\n\nB000F9NXW8\n\n\nMidland GXT600VP4 5-Watt 22-Channel GMRS with NOAA All-Hazard Weather Alert: Electronics\n\n\nelectronics\n\n\n5 of 8\n\n\n1.0\n\n\nMidland GTX600VP4 Radios Bogus - Midland Bogus\n\n\nAugust 19, 2006\n\n\nFrank R. Cutaia \"FC\"\n\n\nNew York\n\n\nUsing this product to communicate between motorcycles as promoted by the very fact that there are motorcycle headsets for your helmut available for these radios is clearly unsafe. The distractions created by the radios having no range after about 100 feet can prove fatal for a motorcycle ryder due to a momentary lapse in concentration, just as it would be between a bike and a car.\n\nI purchased these radios as the better of the Midland lot - with an 18 mile range. An outright lie. I bought them to communicate between two motorcycles- either myself and my brother (after also purchasing 2 of the motorcycle helmut headstets), or between me on the bike and my girlfriend driving my jeep following me on a long distance ride. We don't even get 100 feet out of them when the radios begin to breakup and are usless. The range increases as long as you are not moving. I haven't tested them while one user is moving on the road or on the highway and one user sitting stationary. I intend to contact Midland about a full refund, but if they don't work when you are moving then what good are they?. \n\nHow could they fabricate these claims of 18 miles? Even if they couldn't do 18 miles, how about a half mile? How about 1/4 mile or 1/8 mile? Not even that. This is a discrace. I'm just another customer handed a bogus deal. Do not buy any GMRS stuff from Midland\n\n\n\n\nB00005BC0F:you_get_what_you_pay_for:\n\n\nB00005BC0F\n\n\nCyber Acoustics CA-4100 4.1 Flat Panel Computer Speakers (5-Speaker, White): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n2.0\n\n\nyou get what you pay for\n\n\nJuly 31, 2003\n\n\n\n\n\n\n\n\nsubwoofer should not be called that: it's tiny, and distorts if the slightest bass is put through it. Not good for music, not good for gaming. Steer clear\n\n\n\n\nB000EIBVKG:eton__red_cross_emergency_radio:bruce_c._payne\n\n\nB000EIBVKG\n\n\nAmerican Red Cross FR400 Emergency Radio: Electronics\n\n\nelectronics\n\n\n11 of 11\n\n\n1.0\n\n\nEton Red Cross Emergency Radio\n\n\nSeptember 13, 2006\n\n\nBruce C. Payne\n\n\nFt. Lauderdale Florida\n\n\nThis radio is poorly built. When you set it down the little door in back opens and the batteries fall out. You put them back in, carefully set it down, then when you pick it back up the little battery door falls open and the batteries fall out again...Now, I put rubber bands around the whole radio to keep the battery door in place....stay away from this particular model unless you have lots of rubber bands...good luck and thanks for the opportunity to write the review...\n Bruce Payn\n\n\n\n\nB000EIBVKG:poor_design_for_an_emergency_necessity:cheryl_l._soehl\n\n\nB000EIBVKG\n\n\nAmerican Red Cross FR400 Emergency Radio: Electronics\n\n\nelectronics\n\n\n18 of 18\n\n\n1.0\n\n\nPoor Design for an Emergency Necessity\n\n\nAugust 31, 2006\n\n\nCheryl L. Soehl\n\n\nColumbia, SC\n\n\nI will not be sending this item to my frail 85-year old mother in law to help her survive in hurricane alley on the west coast of Florida. It is virtually impossible to open the battery case cover to assemble the rechargeable battery connection for this radio. Most folks would buy this and store it until needed and so would not know in advance that they cannot access the battery compartment. This is unacceptable design failure for something the Red Cross is touting as a household essential.\n\nWhen the company was contacted for assistance, they suggested inserting a butter knife in the hinge and forcing the cover open!! Folks have already reported being injured in trying to force the cover open.\n\nI will be returning this one as soon as I can\n\n\n\n\nB0002QWU3W:returned_two:felicia_burford\n\n\nB0002QWU3W\n\n\nCoby TF-DVD7100 Portable DVD Player: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nReturned Two\n\n\nOctober 1, 2006\n\n\nFelicia Burford\n\n\nFlagstaff, AZ\n\n\nI had to return two faulty DVD players. One would freeze frame the picture and the audio would keep on playing. The replacement DVD player kept saying \"Wrong Disc\" and was very very loud when it would actually play a disc. I ended up having to search and find a different DVD player all together because they wouldn't replace the 2nd faulty product. (Which was packaged inappropriately and possibly damaged in shipment)\n\n\n\n\nB0000AN4EG:bad_quality:koko_\"kokojie.blogspot.com\"\n\n\nB0000AN4EG\n\n\nGarmin StreetPilot 2610 In-Car GPS Receiver with 128 MB CompactFlash Card: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n1.0\n\n\nBad quality\n\n\nNovember 12, 2006\n\n\nKoKo \"kokojie.blogspot.com\"\n\n\nUSA\n\n\nThe cigarette lighter adapter (with speaker) is really bad quality, the original one worn out within 1 year, I purchased a new adapter and worn out within 6 month. Garmin need to do better\n\n\n\n\nB00009V2WP:don't_bother_:inkypinky\n\n\nB00009V2WP\n\n\nGE Slimline Phone with Call-Waiting Caller ID: Electronics\n\n\nelectronics\n\n\n7 of 7\n\n\n2.0\n\n\nDon't bother \n\n\nAugust 26, 2006\n\n\nInkyPinky\n\n\n\n\n\nSHORT cord cannot be replaced, so don't bother with this phone unless you can sit RIGHT by it to chat.\n\n\n\n\nB000AY7A4K:beware-_shipping_charges_sky_high:rj\n\n\nB000AY7A4K\n\n\nHP 57/58 Series with 100 Sheet: Electronics\n\n\nelectronics\n\n\n8 of 17\n\n\n1.0\n\n\nBEWARE- SHIPPING CHARGES SKY HIGH\n\n\nDecember 16, 2005\n\n\nRJ\n\n\nMA\n\n\nThis seller COMPUTER BRAIN is charging $20.18 to ship these ink cartridges...I knew the price was too good to be true...\nBeware\n\n\n\n\nB0002ZSCJY:beware!:erik_v_\"design_engineer\"\n\n\nB0002ZSCJY\n\n\nEscort Passport 8500 X50 Radar and Laser Detector (Blue Display): Electronics\n\n\nelectronics\n\n\n27 of 29\n\n\n1.0\n\n\nBeware!\n\n\nAugust 3, 2006\n\n\nErik V \"Design Engineer\"\n\n\nKinnelon, NJ\n\n\nI ordered the Passport X50 from Amazon to save the $18 shipping Escort Radar was charging. I was told by someone at Escort Radar that Amazon was an authorized reseller.\n\nAfter having my detector on for ten minutes (right out of the box), it did three \"SELF CAL\"s, then said \"SERVICE REQUIRED\". I called Escort Radar and was told \"If you let the car power up the unit when you start the car, the voltage can be all over the place and the unit gets confused. You must first start the car, and then use the thumbwheel to turn on the detector.\" It makes some sense, but if this is really the case, why don't they put those instructions in their manual? After trying this many times, the unit was \"still confused\", so I returned it for a replacement.\n\nMy second detector worked for maybe thirty minutes before the same thing happened. This time Escort Radar told me \"if you leave it plugged into the cigarette lighter over night, this has been found to cause problems.\" Well my lighter power goes off when I turn off the ignition, so this doesn't apply. Escort Radar also told me that \"excessive heat\" has been causing them problems. Now we have had a few days of 100� heat, and I'm sure the car gets hotter parked in the sun, but the design engineers should have accounted for these conditions plus some margin when you design something that is supposed to work in a car. It gets a lot hotter in other parts of the country. There are no warnings in the manual about not using it above 90� or leaving it off in a hot car. You wouldn't design a ski that doesn't work below 35�, would you???\n\nThe most troubling part of my conversation with Escort Radar service department was they told me Amazon is not an authorized reseller, and they have no idea where they are getting the units from. Furthermore, they will not honor any warranty nor will they ever repair the unit, not even for money.\n\n\n\n\nB000HZ9CCA:hope_you_went_to_college:scott_styles\n\n\nB000HZ9CCA\n\n\nSanDisk Sansa e280 8 GB MP3 Player (Black): Electronics\n\n\nelectronics\n\n\n2 of 28\n\n\n2.0\n\n\nHope you went to college\n\n\nNovember 15, 2006\n\n\nScott Styles\n\n\nBolingbrook, IL United States\n\n\nI've got an engineering degree in computer science. I've been supporting multi-million dollar web applications for years. It took me 4 hours to get this device to sync properly with the Rhapsody music subscription service that it is promoted for. There is no way a normal person is going to be able to get it to function in any capacity except as a straight mp3 player.\n\nIt's not ipod caliber quality, the only reason you'd get it is for the Rhapsody service. If you are not highly technical and patient, move on!\n\nPlayer itself works fine\n\n\n\n\nB000HZ9CCA:impossibly_difficult_to_use:al_f._havemann_\"afh\"\n\n\nB000HZ9CCA\n\n\nSanDisk Sansa e280 8 GB MP3 Player (Black): Electronics\n\n\nelectronics\n\n\n5 of 16\n\n\n1.0\n\n\nImpossibly difficult to use\n\n\nNovember 13, 2006\n\n\nAl F. Havemann \"AFH\"\n\n\nUSA\n\n\nI ordered this unit with high expectations, lots of memory, expandable, multiple modes, from a well known company and an excellent price; how could I go wrong?. Easily enough, as it turns out. \n\nI was unable to load it and control the play lists. Trying to use it in MTP mode always crashed the host (BSOD) so I reverted to MSD (Mass Storage Device) mode using drag and drop.\n\nDragging a folder to the device was pointless. Most of the time the files didn't even show up, and when they did they were scattered all over the place. I copied 82 files from the Lord of the Rings set into a single folder, only 52 could be found even during play back, but they could be seen from \"My Computer\" on the host system. After fooling around with it for a couple of hours I gave up and called SanDisk technical support certain they would be able to help me sort it out. \n\nI have to admit that I was pleased with their support even though in the end they were unable to resolve any of the problems other than by suggesting that the only way to use drag and drop was to dump everything into the MUSIC folder where it would play in whatever order the device wanted; a totally useless method. \n\nThis was totally unsatisfactory since there was no control over the play list and no way to even find all of the files that would fit into 8+gb. Imagine listening to the first movement of Swan Lake followed immediately by something from 9 Inch Nails, then maybe a Bach Cantata!. \n\nUsing Media Player to sync did no better. Creating a playlist (m3u) of 82 files and then syncing did exactly the same as drag and drop. A hopeless mess, no play order, missing files and so on. \n\nAfter spending 3 hours with SanDisk tech support we were both more than ready to quit. The tech had no more idea that I did about how to solve the problems that plagued the player and we both gave it up as a bad job.\n\nThis is little machine has plenty going for it in the specification, but it's hopelessly flawed in the implementation, and this is without complaining about the stiff control wheel or the overly small buttons. \n\nThroughly frustrated, it was in the mail being returned less than 8 hours after I received it. I just cannot recommend this player, it needs a through overhaul of the interface and firmware; and fixing the control wheel and buttons would be a plus as well. As it stands, it's without merit as a player.\n\n\n\n\t\t\t\t\t\t\t\t\t\t\n\n\n\n\nB000HZ9CCA:nice,_but_no_control:confederate\n\n\nB000HZ9CCA\n\n\nSanDisk Sansa e280 8 GB MP3 Player (Black): Electronics\n\n\nelectronics\n\n\n6 of 18\n\n\n2.0\n\n\nNice, but No Control\n\n\nNovember 6, 2006\n\n\nConfederate\n\n\nBethesda, MD\n\n\nI had high expectations for this product, having bought some of Sandisk's other products. The problem with the e280 is that Sandisk doesn't give users the control or the versatility over file management. For example, using it in the (mass storage) MSC USB mode, one is forced to create playlists in Windows Media Player. This works, but one of the drawbacks is that playlist creations can be plagued with files that have errors or are skipped. \n\nIt would be far better if one could just drag folders and music mp3 files to the right folder and play them, with multiple folder levels. This would allow catogorizing music the way the user wishes; instead, Sandisk adopts an almost Microsoft mentality in its approach. \"It's our way or the highway.\" Once one masters creating playlists, they have to settle for dumping them all into one directory.\n\nIt's too bad because reading the manual and reading the reviews, one can easily get the idea that the e200 series can be set up like just another USB storage device (including other, previous Sandisk products). Well, you can, but it won't make much difference if the device won't play.\n\n\n\n\n\n\nB000BREQN4:worthless:j._gerstein_\"jason_in_dc\"\n\n\nB000BREQN4\n\n\nGriffin Technology 9500-TRIPDA iTrip LCD/FM Transmitter with Dock Connector for iPod: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nWorthless\n\n\nNovember 24, 2006\n\n\nJ. Gerstein \"Jason in DC\"\n\n\n\n\n\nDO NOT BUY THIS ITEM. IT IS TERRIBLE.\nThe itrip has awful reception. It isnt worth the money. Wait for something better\n\n\n\n\nB000BREQN4:junk!:a._tobias_\"tobiasfam\"\n\n\nB000BREQN4\n\n\nGriffin Technology 9500-TRIPDA iTrip LCD/FM Transmitter with Dock Connector for iPod: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nJUNK!\n\n\nOctober 30, 2006\n\n\nA. Tobias \"Tobiasfam\"\n\n\nM�rfelden-Walldorf, Germay\n\n\nBelieve the opinions of most of the people who have made the mistake - like me - of buying this thing: It is pure junk! It has to be one of the worst hoaxes ever put over on consumers. It does not work, and you can forget getting any real help or service out of Griffin Technology\n\n\n\n\nB000BREQN4:don't_waste_your_precious_money:aloha_tahiti_\"ia_orana\"\n\n\nB000BREQN4\n\n\nGriffin Technology 9500-TRIPDA iTrip LCD/FM Transmitter with Dock Connector for iPod: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n1.0\n\n\nDon't waste your precious money\n\n\nSeptember 8, 2006\n\n\nAloha Tahiti \"Ia Orana\"\n\n\nBrazil\n\n\nIt's better if you spend your money in something else. It has a ridiculous transmitting power. So bad that even radio transmitters from miles away interferes with this device 1 ft from the radio receiver. The audio quality is so bad that totally destroys the pleasure of listening to your music. In fact this device should be forbidden to be sold or should be tagged as phony advertise\n\n\n\n\nB000BREQN4:one_star_too_many!_horrible!!!!:lisa_chatterton\n\n\nB000BREQN4\n\n\nGriffin Technology 9500-TRIPDA iTrip LCD/FM Transmitter with Dock Connector for iPod: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n1.0\n\n\nONE STAR TOO MANY! HORRIBLE!!!!\n\n\nAugust 8, 2006\n\n\nLisa Chatterton\n\n\nTexas\n\n\nThis is about the worse thing i've ever bought for an ipod accessory. It does NOT work. All I heard was static, and very little music. If you do want to hear music, you literally have to hold your ipod right next to the radio and stay perfectly still or all you'll get is static. I bought this online and received it and was so excited to use it. I was hesitant to buy it, seeing almost every review has said it doesn't work but I bought it anyway, don't buy it! Every review I've seen has negative feedback for this item, and I agree with everyone. I figured since Griffin made a new itrip, it would be better and work great. However, it is the total opposite from that. I do NOT recommend this product at all. Don't waste your time or money\n\n\n\n\nB00000JZKB:no_service_really:daniel_g._ramirez\n\n\nB00000JZKB\n\n\nTexas Instruments BA II Plus Business Analyst Calculator: Electronics\n\n\nelectronics\n\n\n2 of 7\n\n\n1.0\n\n\nno service really\n\n\nMarch 5, 2006\n\n\nDaniel G. Ramirez\n\n\nPhoenix, Arizona\n\n\nnever received the item. Have to go thru the process of filing a claim with UPS for lost/not delivered item. No way to complain to Amazon.com.\n\n\n\n\nB00021XIJW:what_a_pain!:mark\n\n\nB00021XIJW\n\n\nLinksys Wireless-G Range Expander WRE54G: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n1.0\n\n\nWhat a pain!\n\n\nNovember 9, 2006\n\n\nMark\n\n\nAnn Arbor, MI USA\n\n\nI purchased this item after carefully reading all the other reviews. I, like others, cound not get it to work with wireless security turned on. I even tired using the \"tricks\" described in another review. \n\nTo be fair, the product worked fine without wireless security, but I see little point in helping my neighbors tap my internet connection.\n\nI am very disappointed in the product\n\n\n\n\nB000BQQL24:perhaps_you_might_be_using_home:atilla_kucuk_\"cavamaster\"\n\n\nB000BQQL24\n\n\nApple MA045G/B iPod Universal Dock: Electronics\n\n\nelectronics\n\n\n18 of 37\n\n\n1.0\n\n\nPerhaps you might be using home\n\n\nJune 29, 2006\n\n\nAtilla Kucuk \"Cavamaster\"\n\n\nOrlando\n\n\nI won't recommend to any body. You have the same way of charging thru your computer. I think is waste\n\n\n\n\nB00003006R:can't_install_out_of_box:r._stark\n\n\nB00003006R\n\n\nLinksys LNE100TX EtherFast 10/100 LAN Card: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nCan't install out of box\n\n\nOctober 8, 2005\n\n\nR. Stark\n\n\n\n\n\nPut the card in my W2000 PC, followed instructions. Got to the part about loading the driver, the book says to go to the \\drivers\\WIN2000 driver on the CD included in the box- guess what, it's not on the disk included in the box. Since I use a connection that needs this stupid card to work, now I have to find another PC and down load the driver\n\n\n\n\nB00067TTZY:not_happy:l._dagenais\n\n\nB00067TTZY\n\n\nCreative Zen Micro 5 GB MP3 Player Black: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nNot happy\n\n\nNovember 24, 2006\n\n\nL. Dagenais\n\n\n\n\n\nHard to figure out and organize music... battery life not what is promised and needs to be changed often... don't recommend it. Very \"Not happy\n\n\n\n\nB00067TTZY:disappointed:someone007_\"someone007\"\n\n\nB00067TTZY\n\n\nCreative Zen Micro 5 GB MP3 Player Black: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\ndisappointed\n\n\nSeptember 3, 2006\n\n\nsomeone007 \"someone007\"\n\n\nBoston, MA USA\n\n\nA year later and I wish I had the money for an ipod. I have been having the audiojack issue for a long time and don't even take my player with me anywhere anymore. What's the point of having an mp3 player you can't walk around with? Ridiculous\n\n\n\n\nB00067TTZY:don't_buy_anything_from_creative!!!:rio_rita_\"rio_rita\"\n\n\nB00067TTZY\n\n\nCreative Zen Micro 5 GB MP3 Player Black: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n1.0\n\n\nDON'T BUY ANYTHING FROM CREATIVE!!!\n\n\nAugust 7, 2006\n\n\nRio Rita \"Rio Rita\"\n\n\nOakland , CA\n\n\nWe bought three Creative players. My husband and I have the Zen micro and my daughter the Zen 20GB. All three of ours just froze up and stopped working with one year. I didn't even use mine but maybe two hours per week. Good thing my daughter's was within her one year period by a couple of days. She still had to pay $25.00 Now my husband and I have to just junk ours and buy something else. Customer service is awful!! They won't talk to you unless you show your receipt at least three times. What a piece of garbage!!\n\n\n\n\nB0007L29LK:iriver:j._de_cou\n\n\nB0007L29LK\n\n\niRiver 5 GB H10 MP3 Player Lounge Grey: Electronics\n\n\nelectronics\n\n\n0 of 2\n\n\n1.0\n\n\niriver\n\n\nNovember 10, 2006\n\n\nJ. De Cou\n\n\nAlpine, WY\n\n\nDon't waste your money. What iRiver doesn't tell their customers is that once the battery goes dead, the system is useless. I will never buy one of their products again and I'm doing my best to inform everyone out there that they'll be tossing their money in the trash by purchasing any iRiver products. My system cost me $[...] and it is now as useless as the customer service department at iRive\n\n\n\n\nB0007L29LK:the_disappearing_mp3_player:jean_winkelman_\"jmwinke\"\n\n\nB0007L29LK\n\n\niRiver 5 GB H10 MP3 Player Lounge Grey: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n2.0\n\n\nThe disappearing MP3 player\n\n\nJuly 2, 2006\n\n\nJean Winkelman \"jmwinke\"\n\n\nPasadena, Texas USA\n\n\nI have a 5gb but a lot of the H10's are having the same problem. I can play all of the songs that I owned myself but everything through the subscription service has run out and can't be reinstalled. I've done a little research about the disappearing MP3 player and it appears to be a problem that is affecting a lot of the H10's and 1 other model I can't remember right now and iRiver can't figure it out; although I've been told they are trying desperately to find the bug....So buy with caution.... \n\n\n\n\nB0007L29LK:not_worth_the_headache:mike\n\n\nB0007L29LK\n\n\niRiver 5 GB H10 MP3 Player Lounge Grey: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n2.0\n\n\nNot worth the headache\n\n\nJune 22, 2006\n\n\nMike\n\n\nUSA\n\n\nI've had this MP3 player since it came out and, if anyone is still considering getting it, I must advise otherwise. The MP3 player itself looks and feels great, and the navigational features are very easy to use. The problem is that the player itself is extremely unreliable. I have used 3 versions of this player (expecting each time that the player was simply faulty), and all of them glitch up terribly. I constantly have to take my battery out to reset the player as it will freeze during normal play. It also freezes half of the time that I try to sync with WMP. In addition, sometimes it simply refuses to take deleted songs off of my H10's list of tracks (even though they no longer exist on the player).\n\nFinally, iRiver has terrible customer service and they do not even back their own products! As a owner of two iRivers (H10 and the 120), I have seen for a while how they function: they will offer updates for their faulty firmware for about a month, and then they leave that product to produce a new one. It seems like they have a new Mp3 player being introduced every few months. Their customer service is extremely hard to get a hold of, and if you do, they will simply say you need to send it to the factory (occasionally you will get a rep that will actually send you a brand new one for free, though).\n\nBe smart, stick to a Creative, the new Toshiba Gigabeat S, or (clench your fists) an iPod\n\n\n\n\nB0006G0II2:disappointing_producet:r._oshiro_\"raymondo\"\n\n\nB0006G0II2\n\n\nEton FR300 Emergency Crank Radio Metallic Silver: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\ndisappointing producet\n\n\nNovember 3, 2006\n\n\nR. Oshiro \"Raymondo\"\n\n\nHawaii\n\n\nDoes not live up to expectations. Sound is tinny. Hard to find stations, very narrow band of reception...plan to purchase a grundig\n\n\n\n\nB000F5IBCO:you_get_what_you_pay_for:b._gravel_\"bgrav\"\n\n\nB000F5IBCO\n\n\nMustang MT-ARM1 Cantilever/Arm Mount 13-24\": Electronics\n\n\nelectronics\n\n\n4 of 5\n\n\n2.0\n\n\nYou get what you pay for\n\n\nAugust 8, 2006\n\n\nB. GRAVEL \"bgrav\"\n\n\nCambridge, MA USA\n\n\nI ordered this mount for a Sharp 20 inch LCD and it's not that great. It was very easy to install, however, the arm when fully outstretched does not sit level. Thus when the TV is pulled away from the wall, the set is not level and very annoying to look at. I don't believe this is a fault in the actual part, but the design of the arm. This was relatively inexpensive for a wall mount - and you get what you pay for\n\n\n\n\nB00004Z6ON:poorly_made,_instructions_unclear,_may_have_damaged_my_laptop:michael_c._berch\n\n\nB00004Z6ON\n\n\nKensington 64032 Master Lock Universal Notebook Security Cable (Black): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nPoorly made, instructions unclear, may have damaged my laptop\n\n\nAugust 19, 2006\n\n\nMichael C. Berch\n\n\nSan Francisco Area\n\n\nI'd recommend selecting a different security cable product, especially if you have a PowerBook or MacBook. This one is poorly made, even more poorly documented, and may have permanently damaged my PowerBook G4. (Yes, the product web site claims this model is compatible with the PB G4.) \n\nIt's unclear from the cryptic instruction diagram on the back of the packaging which direction is locked and which is unlocked. (Hey, would it be so bad to actually use *words* to explain things? Even in 4 or 5 languages if you like. The diagrams make no sense.)\n\nIn the process of trying to get this to work, it appears that the locking teeth chewed up the security slot of my PowerBook. It may have permanently damaged the slot. Needless to say, I will attempt to return the product for a refund. \n\nThere are plenty of similar products on the market, including others avaiable through Amazon. I'd suggest trying one of those.\n\n\n\n\nB00005UPF6:this_is_a_bad_product,_waste_your_money_on_something_else.:adam_dluzniewski\n\n\nB00005UPF6\n\n\nKoss EQ50 3-Band Stereo Equalizer: Electronics\n\n\nelectronics\n\n\n2 of 4\n\n\n1.0\n\n\nThis is a bad product, waste your money on something else.\n\n\nJanuary 3, 2006\n\n\nAdam Dluzniewski\n\n\n\n\n\nTotal garbage. No only this thing is huge, bigger than most mp3 players and it doesn't have a belt clip or anything, it sounds awful, absolutely horrible. The hiss others complained about is very loud, it also picks up all sorts of interference resulting in beeping, buzzing and all kinds of high pitched noises. Besides the hiss and the interference the sound is horrible, low dynamics and distorts easily. This is a bad product that should be taken off the market. It's not even worth the price here. Heck, even $1.99 would be a waste of money. Avoid\n\n\n\n\nB0009N5MDI:wireless_headphones:m._garib\n\n\nB0009N5MDI\n\n\nLogitech 980397-0403 Wireless Headphones for iPod, Gray: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nWireless headphones\n\n\nNovember 18, 2006\n\n\nM. Garib\n\n\nNewark, New Jersey USA\n\n\nIt is a great idea, with a few qualifiers.\nI have found that the forward and reverse buttons don't function for my video ipod or for my niece's nano (maybe it would for a non-apple mp3 player?). I have to pair the device to the Ipod every time I turn it on. The bluetooth transponder will drain the ipod's battery even though it has it's own battery. Sound quality is decent. \n\n\n\n\nB0009N5MDI:a_certainty_for_breaking_into_pieces!:francis_yap\n\n\nB0009N5MDI\n\n\nLogitech 980397-0403 Wireless Headphones for iPod, Gray: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n2.0\n\n\nA Certainty for Breaking into Pieces!\n\n\nOctober 3, 2006\n\n\nFrancis Yap\n\n\n\n\n\nIt was great at first until the connector to iPod break by itself. The bluetooth signal wasn't the best, it loses signal constantly. I had to re-sync and paired it up several times over a month period. The high price iMuffs MB210 from WI-GEAR gives a better promise with the ability to stop your iPod music and take a cellphone call with noise canceling microphone. Its Bluetooth adapter fits cleaning on iPods with dock connectors, likely will not break as easily. I sure hope Logiteh will follow suit in this trend\n\n\n\n\nB000068BUI:bad_connectors:q._zuber_\"q\"\n\n\nB000068BUI\n\n\nLabtec AC10REGN 20-foot Headphone Extension Cord with Miniplug and 3.5mm Jack: Electronics\n\n\nelectronics\n\n\n14 of 17\n\n\n2.0\n\n\nBad connectors\n\n\nDecember 17, 2005\n\n\nQ. Zuber \"Q\"\n\n\nColorado\n\n\nThe sound crackles if the cord bends a bit at the connector. All around poor quality and construction. I'm thinking about returning it..\n\n\n\n\nB00077IGZ2:how_can_this_not_have_a_built_in_microphone:m.d.\n\n\nB00077IGZ2\n\n\nLogitech QuickCam Chat Web Camera (White): Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n1.0\n\n\nhow can this not have a built in Microphone\n\n\nAugust 10, 2006\n\n\nM.D.\n\n\nSan Francisco, CA\n\n\nIt's Logitech...it should have a built in Microphone. instead there's a small head set. don't buy this. spend a little more for a built in microphone\n\n\n\n\nB000F2PHDI:good_tv_but_..:m._turner\n\n\nB000F2PHDI\n\n\nSamsung HL-S6187W 61\" 1080p DLP HDTV: Electronics\n\n\nelectronics\n\n\n7 of 10\n\n\n1.0\n\n\nGood TV but ..\n\n\nNovember 22, 2006\n\n\nM. Turner\n\n\nohio\n\n\nI have had this TV for less than 90 days and bulb has burned out, I called Samsung support and the bulb is covered by warranty. They said they would send my phone number to service center and they would contact me in no more than 72 hours. 72 hours later still no call, I again called Samsung and they said they would contact the service center again and I would be called in 24 hours, still no call. This was repeated 5 times and each time I asked to be referred to another center but was told none existed in my area.\n\nFianlly after 6 calls, Samsung referred me to another center. I then called them and they sent up an appointment 3 days later. Day of appointment technician called and told me from the problem I described when I made the appointment I needed a new bulb (duh) and he had ordered one this morning and would call to make another appointment once they received the bulb. I specifically asked if they had the bulb in stock when I made the appointment. Why they didn't order the bulb when they first set up the appointment, I don't know. I called Samsung again and was told bulbs are usually shipped within a week of the order. Neither the words usually or week improved my impression of their support. My fear is that when the technician comes out to change the bulb he will find another part he has to order and I am back on the Samsung support merry-go-round again. \n\nNow I realize that even though the set does (did) have a good picture etc, if you can't see it, it doesn't matter. I now have a very expensive paperweight. For me Samsung support has been a black hole and I have yet to see any results after two weeks and no hope in sight for the near future. What happens after the warranty is over and I have to pay for a new bulb every three months? If this sounds like I am not very happy with the situation that is the truth. I am tired of Samsung and the service centers being sorry for the problems and delays but then telling me there is nothing they can do at this point. I agree, you solve the problem by doing the right thing the first time or the second time or third time etc I am hoping maybe the eighth time is the charm with my Samsung experience.\n\n\n\n\nB0007VMROE:bad_product__/_bad_company:j._hill\n\n\nB0007VMROE\n\n\nPalm Tungsten E2 Handheld: Electronics\n\n\nelectronics\n\n\n11 of 12\n\n\n1.0\n\n\nBAD PRODUCT / BAD COMPANY\n\n\nOctober 3, 2006\n\n\nJ. Hill\n\n\n\n\n\nThis is the third Palm PDA I have owned. I bought the E2 directly from Palm after my previous palm died. It simply quit shortly after the warranty expired. Telephone support is poor and expensive. After research this is a common problem. \n\nMy employer pays for PDA's for executives and since I approve of all of these products, I will hereafter not allow any purchase of any Palm product based on falty products, poor customer support and extremely poor customer support.\n\n\n\n\nB0001BXVA4:never_got_past_installation_setup-customer_service_bad:h._beachum\n\n\nB0001BXVA4\n\n\nNetgear USB Mini Print Server (PS121): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nNever got past installation setup-customer service bad\n\n\nNovember 1, 2006\n\n\nH. Beachum\n\n\nAtlanta, GA\n\n\nI am somewhat computer savvy - I understand the the IP address assigned in installation needs to match what my own router assigned for an ip address. They don't match and I can't figure out how to change it. I called Netgear 4 days ago for help and they said their \"senior tech\" would have to call me back. Still waiting for that call. Back to Staples it goes. The installation appeared successful, but whenever I try to print, it says \"error - printer is busy\" The so-called instruction manual for this is laughable. IF you're not an advance level IT person, don't try it\n\n\n\n\nB0001BXVA4:easy_installation,_but_stop_working_after_4_months:lansing-omaha_\"ht\"\n\n\nB0001BXVA4\n\n\nNetgear USB Mini Print Server (PS121): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n2.0\n\n\neasy installation, but stop working after 4 months\n\n\nJune 21, 2006\n\n\nLansing-Omaha \"ht\"\n\n\n\n\n\nI bought this print server back in December 2005. I used it with my All-in-One Brother printer MFC-4800. The installation was easy. It worked great......for 4 months. Then...then... it just stoped working after 4 months. I tried to disconnect and reconnect the power, Ethernet cable etc...nothing helped. \nMay be I just got a bad product\n\n\n\n\nB000BOK3AM:don't_buy_-_take_your_cash_elsewhere:s._shulman\n\n\nB000BOK3AM\n\n\nShower Radio/Alarm Clock (WY521): Electronics\n\n\nelectronics\n\n\n7 of 7\n\n\n1.0\n\n\nDon't Buy - Take Your Cash Elsewhere\n\n\nJuly 18, 2006\n\n\nS. SHULMAN\n\n\n\n\n\nI had the same problems as already stated. I am currently having the volume issue as well, and yes my batteries are weak (the light is on).\n\nThe first unit I bought lasted two weaks and then there was no sound at all. I called Sharper Image and they agreed to send a replacement. I waited two weeks and nothing. I called again and they had no record of my previous phone call. They did send a replacement and told me it would be sent overnight if \"deemed appropriate by management.\" It was not shipped overnight.\n\nBottom line - this product is awful. Do not buy - I regret it! It is worth it to spend more money on a different product. This only has a 90 day warranty and it doesn't last quite that long.\n\n\n\n\nB00004WFZP:beware_of_used_typewriters:donna_purvis\n\n\nB00004WFZP\n\n\nBrother ML-100 Daisy Wheel Electronic Typewriter: Electronics\n\n\nelectronics\n\n\n10 of 20\n\n\n1.0\n\n\nBeware of used typewriters\n\n\nAugust 29, 2003\n\n\nDonna Purvis\n\n\nBonita Springs, FL United States\n\n\nI waited 10 days for this to arrive. The first time I plugged it in it ran for 2 minutes. I typed Dear and the machine typed wcm\" When I tried to erase wcm\" the erase mechanism did not work. I turned the machine off and when I switched it back on the carriage went to the far left and will not move, ie I cannot type or use the machine in any way. When the machine came two of the keys were knocked loose and I had to stick them back on. Now when I look at the machine I can see it is assymetrical...there is a 1/4 gap in the base, so it must have been dropped or damaged somewhere. This is not acceptable but I have no way to contact whoever Mstasad is. I have a huge project to do over the labor day weekend but I don't have a typewriter that works. I feel cheated and am very upset..\n\n\n\n\nB0002F3G7M:didn't_work_for_me:jiwon_youm_\"sauron82\"\n\n\nB0002F3G7M\n\n\nLinksys High Gain Antenna Kit for TNC Connectors HGA7T: Electronics\n\n\nelectronics\n\n\n0 of 2\n\n\n2.0\n\n\ndidn't work for me\n\n\nSeptember 27, 2006\n\n\nJiwon Youm \"sauron82\"\n\n\nPhiladelphia, PA\n\n\nI got 0~1 bars on my wifi detector and hoped that this would help. But it didn't get any better. You'll have to get some decent signal beforehand in order to get this antenna to make things better for you, I suppose.\n\n\n\n\nB00005T3EJ:usefull_information:thomas_rushing_\"sunshine1012\"\n\n\nB00005T3EJ\n\n\nMonster Cable MB AA 1850-4 Ultra-High Capacity Rechargeable Monster PowerCells: Electronics\n\n\nelectronics\n\n\n4 of 5\n\n\n1.0\n\n\nUsefull Information\n\n\nMarch 5, 2004\n\n\nThomas Rushing \"sunshine1012\"\n\n\nWashington, D.C.\n\n\nI bought these batteries on what was writen on their packaging. It was not true to what was said. These batteries died after only 25 minutes in my digital camera. And a set of Duracell Ultras, that are not rechargeable, lasted longer. Do not always believe what you read. Make sure you have the ability to return items. And is with this case, they went back. I went with a set of energizers. And they last much longer, with the power being the smae as these 1800mah monster brand\n\n\n\n\nB0009F4ORQ:worked_for_a_while,_but_not_that_great:harpoon_\"harpoon\"\n\n\nB0009F4ORQ\n\n\nCreative Zen Nano Plus 1 GB MP3 Player Red: Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n2.0\n\n\nWorked for a while, but not that great\n\n\nAugust 6, 2006\n\n\nHarpoon \"Harpoon\"\n\n\nMukilteo, WA USA\n\n\nI purchased the Zen Nano four months ago. It was working then total system failure. At first I thought the battery was dead, but a new battery didn't help. The screen would flash on and then off. It will not boot up. Plugging it into the computer did no good, it could not be reconized or accessed. It goes in the trash. In addition when it was working the shuffle mode would only play some of the songs stored. Also, the output volumn was to low. I would use it while riding my bike and it needs to be loud enough to hear it over the road noise, it wasn't up to the task. Now it is landfill\n\n\n\n\nB0001GU8MI:worked_for_1_year,_but_i_am_trying_a_logitech_now:gilberto_misawa\n\n\nB0001GU8MI\n\n\nTargus PAWM001U Wireless Optical Mouse/Pointer/Presenter: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n2.0\n\n\nWorked for 1 year, but I am trying a Logitech now\n\n\nAugust 17, 2006\n\n\nGilberto Misawa\n\n\nS�o Paulo, Brazil\n\n\nI had to buy a new one, and I chose another brand (Logitech) this time. \nThere is no On/Off button, so the batteries run out if you don't take them out (I think the buttons are pressed during transportation). Now the battery contacts are not functioning properly. \nAnd I had the same problem described in a recent review: \"I'll click three or four times and nothing happens. Then, I'll click and the slides go in fast forward until the end. It's quite embarrassing to be giving a professional presentation and have this happen.\"\n\n\n\n\nB0001GU8MI:awful:e._patterson\n\n\nB0001GU8MI\n\n\nTargus PAWM001U Wireless Optical Mouse/Pointer/Presenter: Electronics\n\n\nelectronics\n\n\n5 of 7\n\n\n1.0\n\n\nAwful\n\n\nFebruary 6, 2006\n\n\nE. Patterson\n\n\n\n\n\nDo not buy. This may be the worst product I've ever bought. It has never worked properly. I've kept fresh batteries in it, but it's still no good. Within 10 feet of the laptop, I'll click three or four times and nothing happens. Then, I'll click and the slides go in fast forward until the end. It's quite embarrassing to be giving a professional presentation and have this happen.\n\nAwful product. Believe the bad reviews. It's worse than they describe.\n\nThe only positive thing I can say about the product is that it doubles nicely as a cat toy. My cat loves the laser pointer\n\n\n\n\nB0001GU8MI:quite_possibly_the_worst_mouse_in_the_history_of_all_mice!!!!!:daniel_park\n\n\nB0001GU8MI\n\n\nTargus PAWM001U Wireless Optical Mouse/Pointer/Presenter: Electronics\n\n\nelectronics\n\n\n4 of 6\n\n\n1.0\n\n\nQUITE POSSIBLY THE WORST MOUSE IN THE HISTORY OF ALL MICE!!!!!\n\n\nFebruary 1, 2006\n\n\nDaniel Park\n\n\n\n\n\nDO NOT BUY THIS MOUSE! \nThis mouse has given me nothing but frustration.\n\n1. The front of the mouse where the buttons are located is extremely low, causing a muscle strain in your hand if you use it for long periods of time.\n\n2. Battery life is horrible. Like 2 weeks...\n\n3. When your mouse is idle for 8 minutes, it goes into an automatic sleep mode. The manual says to touch the metal lines on the side to \"revive\" it but when I do it simply freeze the cursor on the screen. So I have to reboot my computer.\n\nAbsolutely the worst!!\n\n\n\n\nB0001GU8MI:worked_for_only_3_months:ross_m._rolirad\n\n\nB0001GU8MI\n\n\nTargus PAWM001U Wireless Optical Mouse/Pointer/Presenter: Electronics\n\n\nelectronics\n\n\n0 of 2\n\n\n1.0\n\n\nWorked for only 3 months\n\n\nDecember 9, 2005\n\n\nRoss M. Rolirad\n\n\nKapolei, HI USA\n\n\nI bought one of these. It worked OK about three months and then just quit. I do not recommend for anyone else\n\n\n\n\nB000BBGCJQ:was_not_able_to_record_longer_songs:guenter_weissenseel\n\n\nB000BBGCJQ\n\n\nGemini iKey Portable USB Recording Device: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nWas not able to record longer songs\n\n\nNovember 10, 2006\n\n\nGuenter Weissenseel\n\n\n\n\n\nI tried it with 3 differnt usb sticks and I could sometiems successfully record a 3 minute songs, but most of the time it failed. I tried to record with an ipod - no success and also with 2 different external usb harddrives. \n\nI'm very glad that amazon took this unit back.\n\nI would not recommend this unit.\n\n\n\n\n\nB0007WCA4K:took_it_back:david_c._martin\n\n\nB0007WCA4K\n\n\nToshiba 27AF45 27\" Flat Screen TV (Silver): Electronics\n\n\nelectronics\n\n\n4 of 6\n\n\n2.0\n\n\ntook it back\n\n\nJuly 20, 2006\n\n\nDavid C. Martin\n\n\nEast Tennessee\n\n\nGot it home and it had poor focus. The center was ok but the sides were definitely out of focus. the next morning the focus problem was gone. Go figure. The big problem for me was the time it took to change channels. A full 3 seconds. Went back to the store and found this is a common problem with tube tvs. Well I took it back and went to Walmart and got a Sanyo HT27546 for $270. Good set with quick channel change. It also has an optical digital outputwhich the Toshiba did not. The Sanyo also has a full one year warrenty. Sound on both TVs is fine. A small point. The Toshiba can not be set up to go to closed caption when you hit Mute\n\n\n\n\nB00009EFRB:have_an_ibook?_don't_buy_this_lock!:karl_rahder\n\n\nB00009EFRB\n\n\nTargus PA492U DEFCON VPCL - Video Port Combination Lock: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nHave an iBook? Don't buy this lock!\n\n\nOctober 18, 2006\n\n\nKarl Rahder\n\n\nBaku, Tbilisi, Chicago\n\n\nI posted a review two weeks ago, but it hasn't appeared, so I'm trying again...I bought this lock two years ago. It has always been difficult to remove. (Note: despite what some other reviewers seem to think, it does NOT have a motion detector - that's a different lock.) Recently, the lock decided to remain firmly inside my iBook, despite my efforts to remove it. After a fruitless call to Targus's customer support, I finally got it out. That took over 30 minutes of struggle. My next step was going to be purchasing a hack saw. Apparently, the Targus lock is not a good fit for iBooks. I'm going to send mine back and demand compensation. If you own an iBook, I strongly suggest you buy a lock made by someone other than Targus\n\n\n\n\nB000BBAKSA:piece_of_junk:loren_woirhaye_\"malibumentor.com\"\n\n\nB000BBAKSA\n\n\nLogitech QuickCam Orbit MP Webcam ( 961422-0403 ): Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nPiece of Junk\n\n\nNovember 1, 2006\n\n\nLoren Woirhaye \"MalibuMentor.com\"\n\n\nTopanga, CA United States\n\n\nMine didn't work out of the box. Just my experience. I am taking this thing back to the store tomorrow. I returned another crappy webcam today to buy this, so you can imagine how irritated I am with the lame quality of these things\n\n\n\n\nB0001WW398:good_idea_not_so_good_product:d._l._shaughnessy\n\n\nB0001WW398\n\n\nGriffin Technology 4020-TALK iTalk Voice Recorder for iPod: Electronics\n\n\nelectronics\n\n\n2 of 4\n\n\n2.0\n\n\ngood idea not so good product\n\n\nMarch 17, 2006\n\n\nD. L. Shaughnessy\n\n\nOklahoma\n\n\nas far as using the ipod as a recorder I think an actual digital recorder would get better sound. The italk picks up all ambient sound. I have even used it with an external mic and still not wowed with the clarity\n\n\n\n\nB00079V9XG:not_suitable_for_use_in_a_moving_vehicle:cosmic_renardo\n\n\nB00079V9XG\n\n\nGarmin iQue M5 Integrated Pocket PC and GPS: Electronics\n\n\nelectronics\n\n\n2 of 5\n\n\n2.0\n\n\nNot suitable for use in a moving vehicle\n\n\nAugust 19, 2006\n\n\nCosmic Renardo\n\n\nZurich, Switzerland\n\n\nIt's partly my fault, of course. I bought this to use on my motorcycle. I thought the small size, especially the flat body, would fit nicely into the top of a tank bag. I also bought a special handlebar mount for it. Unfortunately, I've had nothing but trouble with it. It's slow to acquire satellites and to recalculate routes. Whether you're driving a car or a motorcycle, it's not up to the task of keeping pace with changes in your route. Maybe it was intended for pedestrians only. The user interface for the GPS is not intuitive (at least, not for me), Worse still, it freezes often when trying to download maps and when deleting stored routes. The Garmin helpline in the UK could only tell me to reset the machine each time. Not very helpful. My guess is that the combination of palm PC and GPS was too much for Garmin to handle. They should probably just stick to GPS.\n\n\n\n\nB00008SB1D:hate_it...:paper_pieces\n\n\nB00008SB1D\n\n\nBelkin TuneCast Mobile FM Transmitter: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nHate it...\n\n\nNovember 12, 2006\n\n\nPaper Pieces\n\n\n\n\n\nI use it for my Zen MicroPhoto and it does not work at all. It has the lowest battery life, used it for twenty mintues and it started to get worse and worse. There is no possible way of getting rid of the static unless you are the MP3 player below the transmitter, which is obviously dangerous while driving. You can only tune it to four stations which are 88.1, 88.3, 88.5 and 88.7 which is absolutely pointless approach to an FM transmitter. I seriously regret every buying this transmitter\n\n\n\n\nB00008SB1D:it'll_do_in_a_pinch:bsxx\n\n\nB00008SB1D\n\n\nBelkin TuneCast Mobile FM Transmitter: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nIt'll do in a pinch\n\n\nSeptember 20, 2006\n\n\nBSXX\n\n\n\n\n\nIt'll do in a pinch....but I hope there are better options. \n\nAt times the sound is clear, however, most of the time the static is either 1) just annoying or 2) the static is so loud and piercing that you have to turn it off. \n\nI hope there are units that broadcast a stronger signal, or have another way to connect, in order to provide a constant tolerable sound quality. I would recommend spending some more money to get a reliable device.\n\n\n\n\n\nB0006A7X36:out_of_the_box_and_didnt_connect_to_the_internet:s._gale\n\n\nB0006A7X36\n\n\nBelkin F5D8230-4 Wireless 802.11x Pre-N Router: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nout of the box and didnt connect to the internet\n\n\nNovember 10, 2006\n\n\nS. Gale\n\n\nGermany\n\n\nI've spent over two hours tweaking this, and over an hour on the phone with customer support. The thing still wont connect to the internet. Don't get me wrong, the lights light up and my laptop can connect to the ROUTER, but the router wont connect to the internet.\nBelkin tech support told me to send it back. I will and I think I will try the Dlink Pre N.\n\n\n\n\n\nB000EXS1BS:disappointed:tim_tapio\n\n\nB000EXS1BS\n\n\nGarmin Nuvi 360 Pocket Vehicle GPS Navigator and Personal Travel Assistant: Electronics\n\n\nelectronics\n\n\n15 of 21\n\n\n2.0\n\n\nDisappointed\n\n\nNovember 15, 2006\n\n\nTim Tapio\n\n\nRaeford, North Carolina United States\n\n\nThinks my street is 50 feet before it actually is, therefore recalculates before the turn.\nIf I go a different route, it continually tries to get me to make a \"U\" turn.\nInstead of shortest or fastest route, it may pick one that is very roundabout (you should see how it wants me to drive home from work).\nMaps need to be updated more often. Street Atlas or Streets and Maps do a better job of being updated...I'd use them except I don't want to tie up a PC just to drive across town.\nIt might work well for cross country, for me, it's an expensive toy that I have yet to develop trust in.\n\n\n\n\n\nB0006A2SNG:second_one_just_quit_working:keith_white_\"visit_http://www.keithwhite.us\"\n\n\nB0006A2SNG\n\n\nFirst Alert MotionSense PIR720R Light Control: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nSecond one just quit working\n\n\nAugust 31, 2006\n\n\nKeith White \"Visit http://www.keithwhite.us\"\n\n\nLincoln, Ne\n\n\nThis thing works well until a bulb blows out. When I replace the bulb, it will not turn off after 4 minutes and I end up having to replace it\n\n\n\n\nB0002CZPPG:dont_buy_this_here_!!!!!!!!:e._apostolo_\"ed\"\n\n\nB0002CZPPG\n\n\niTunes $15 Prepaid Card: Electronics\n\n\nelectronics\n\n\n16 of 17\n\n\n1.0\n\n\nDont buy this here !!!!!!!!\n\n\nMay 20, 2006\n\n\nE. Apostolo \"Ed\"\n\n\nMiami, Florida United States\n\n\nI knew I had to pay shipping for this cards ($5) so I decided to buy 4 at once, what was my surprise that at checkout they wanted to charge me five dollars shipping for each card eventhough they were going to be ship together I decided not to buy them anymore. They lie telling you that you are saving a huge 1% but at the end you pay almost 20 dollars for each card that is more or less 35% more than anywhere else. Its a shame that they try to rip you off that way. BUY THEM AT THE APPLE STORE ON LINE, NO SHIPPING FEE !!!!!!!! YOU PAY ONLY FACE VALUE NOT A CENT MOR\n\n\n\n\nB0002CZPPG:why_oh_why???:robert_evans\n\n\nB0002CZPPG\n\n\niTunes $15 Prepaid Card: Electronics\n\n\nelectronics\n\n\n5 of 17\n\n\n1.0\n\n\nWHY OH WHY???\n\n\nDecember 20, 2005\n\n\nRobert Evans\n\n\nSanta Monica, CA USA\n\n\nWhy does a 15.00 gift card cost 24.00? I was going to get one but first someone explain this to me? Is the card itself made out of gold? Whaz up wit dat\n\n\n\n\nB00069ECVI:wonderful_drive,_if_it_only_lasted:gradstudent_\"john\"\n\n\nB00069ECVI\n\n\nSeagate 5.0 GB Pocket External Hard Drive ST650211U-RK: Electronics\n\n\nelectronics\n\n\n5 of 6\n\n\n2.0\n\n\nWonderful drive, if it only lasted\n\n\nApril 2, 2006\n\n\nGradStudent \"John\"\n\n\nOhio\n\n\nI loved my little Seagate 5.0GB drive, even after the first one crashed and burned. But the second time, I cannot forgive. I carried it everywhere in my pocket, and maybe that was part of the problem, but if it can't handle that then it is no good to me. Solid state memory, here I come\n\n\n\n\nB000ETTFRG:this_product_stinks:chris\n\n\nB000ETTFRG\n\n\nSanDisk SDMX4-6144 Sansa e270 6 GB MP3 Player with SD Expansion Slot: Electronics\n\n\nelectronics\n\n\n11 of 30\n\n\n1.0\n\n\nThis product stinks\n\n\nNovember 21, 2006\n\n\nChris\n\n\n\n\n\nI dislike ipods so i figure I try something else and hope for the best..Unfortunately I was wrong\nItunes and Ipods are definitely much better..\nBasically this device is a cheap piece of crap and it seems like Sandisk's approach is WYSIWYG..disappointing\n\nPROS:\n- long battery life\n- email tech support is quick and good but website support sucks bad\n\nCONS:\n- it's easy to setup but instruction manual is useless\n- buttons are rigid and miniature and difficult to press with a skin on\n- wheel is very stiff and offers very little functionality\n- software is buggy and terrible\n- freezes occasionally\n- navigating through menus stutters and lags\n- boot time is 10 seconds\n- video mode has no options\n- needs serious firmware updates\n- time is listed only in settings menu = useless\n- tons of issues with playlists getting erased or not working properly\n- every firmware update erases all your settings, including radio presets\nand the list of issues goes on and on...\n\nanyone who has reviewed this product positively obviously didnt do a thorough job because these issues exist and its definitely irritating\nI am extremely disappointed and will never buy a sandisk player ever again\n\nMy suggestion is to try it out thorougly before buying..so you dont make the same mistake I did and trust other people's lopsided reviews..\n\n\n\n\nB0007YG8JQ:rma_issued:bryan_\"pyle112\"\n\n\nB0007YG8JQ\n\n\nAnycom Bluetooth Mouse & USB Adapter Bundle: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nRMA issued\n\n\nMarch 23, 2006\n\n\nBryan \"pyle112\"\n\n\nNew York, NY United States\n\n\nThe mouse worked well with my chipset, but beaware that earlier bluetooth chipsets do not have the power to work with this mouse, although the dongle that is bundled with this is excellent.\nThe mouse was very difficult to use and unresponsive at times, requiring multiple clicks to choose items. It was sluggish across the screen and very insensitive, even after maxing out all the mouse settings in either direction. \nLooking through the forums at Anycom informed me that many people had a similar problem and RMA's were being issued. Considering that these problems arose earlier last year, I was upset that I was sold a defective product. I just send my mouse back to Newport, California for a replacement. \nThe features are solid, the size is acceptable but prone to cramping at first. For a travel mouse it is exceptional, being rechargeable via USB. Usuage with the USB cable attatched is OK but somewhat awkward as the cable comes from the \"head\" of the mouse, not the \"tail.\"\nWould I buy again? only if I didn't find another product that was rechargeable, bluetooth and optical.\n\n\n\n\nB00005B60E:disappointing:elg476\n\n\nB00005B60E\n\n\nPolycom Soundpoint Pro SE-220 2-Line Conference Phone with Caller ID: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n1.0\n\n\ndisappointing\n\n\nAugust 23, 2005\n\n\nelg476\n\n\nRaleigh, NC, USA\n\n\nI bought this phone for a work-at-home situation. I then discovered that the maximum number of characters I could enter into a speed dial entry (the number itself, not the name) is 24 characters. A telephone calling card number is 25 characters long, 26 if you program a pause (a space, for this phone) between the phone number and the card id/pin. When I called Polycom about it, I was told that not everyone uses calling cards. Funny. I've been issued a calling card at every company for which I've worked (going back 25 years). This is the only phone I've ever owned (well, since the 80's) into which I could not program my calling cards. Pretty basic stuff for product marketing and development people to miss.\n\nOn top of that, when I try and enter the calling card number and card id by hand, it never \"takes\" on the first try: the network doesn't recognize the card id and pin the first time I enter them. I have to do it twice (I've tried this a number of times, and it's consistent).\n\nFor that matter, the sound quality, especially through the headset (also purchased from polycom) is not that good. Of course, that could be because the headset jack is \"loose.\" You can easily move the jack from side to side, which makes the sound quality even worse. \n\nI regret having made this purchase. I'm going to scrap it and look for a new one from a different manufacturer. The only reason I gave it one star is because the site interface here won't let me give it no stars\n\n\n\n\nB0000AAAPF:wow,_i_should've_done_research.:j._malone\n\n\nB0000AAAPF\n\n\nGriffin White iTrip FM Transmitter for iPod: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nWow, I should've done research.\n\n\nOctober 18, 2006\n\n\nJ. malone\n\n\nArcata, CA\n\n\nThis thing sucks. I wasted my money. No more Griffin products for me\n\n\n\n\nB0000AAAPF:fm_transmitter:crystal_k._tran_\"crystal_k_tran\"\n\n\nB0000AAAPF\n\n\nGriffin White iTrip FM Transmitter for iPod: Electronics\n\n\nelectronics\n\n\n1 of 3\n\n\n1.0\n\n\nfm transmitter\n\n\nJuly 24, 2006\n\n\nCrystal K. Tran \"Crystal K Tran\"\n\n\nSan Diego, CA USA\n\n\nThe transmitter doesn't work. I tried everything I could think of to try and make it work but it doesnt\n\n\n\n\nB0000AAAPF:misleading_product_description:kimberly\n\n\nB0000AAAPF\n\n\nGriffin White iTrip FM Transmitter for iPod: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n1.0\n\n\nmisleading product description\n\n\nJuly 19, 2006\n\n\nKimberly\n\n\nSalt Lake City, UT USA\n\n\nThis item said that it is compatible with all ipods but it is not. It is only compatible with the \"old\" ipods. Evidently there is a difference in some of the more recent ipods and this itrip doesn't work with them. We had to return the product\n\n\n\n\nB0009B0JWE:simply_unacceptable!:the_office_guy\n\n\nB0009B0JWE\n\n\nAltec Lansing 2-PIECE Music & Gaming Speakers Black Stero Satellite Speakers: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n1.0\n\n\nSimply unacceptable!\n\n\nJune 11, 2005\n\n\nThe Office Guy\n\n\nFort Lauderdale, Florida, USA\n\n\nI just got these at a local store and they just really blow!!! I was swayed by the brand name and the great price (although higher than Amazon). It's true when they say you get what you pay for... rock-bottom price = rock-bottom performance. Terrible to say the least. Mine are going back right now.\n\n\n\n\nB0001LQG9C:warning_check_battery_and_bad_customer_service:4girls\n\n\nB0001LQG9C\n\n\nVTech ip5850 5.8 GHz DSS Cordless Phone with Dual Handsets and Digital Answering System: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n2.0\n\n\nWarning check battery and bad customer service\n\n\nApril 20, 2006\n\n\n4girls\n\n\nMD\n\n\nMine worked great for the first few months, then I started getting the \"warning, check battery\". If you take the batteries out and reinsert, it works for a few hours but then the same warning message comes up. It happened on both headsets. I called customer service and they told me this is the first time they've ever heard that (uh huh - doesn't sound like it from the reviews here). A little too expensive to replace yearly..\n\n\n\n\nB0007T27D2:nice_feel,_but_terrible_tracking--huge_lag_time:frequent_amazon_shopper\n\n\nB0007T27D2\n\n\nKensington 72414 PilotMouse Bluetooth Mini Mouse (PC/Mac): Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nNice feel, but terrible tracking--huge lag time\n\n\nOctober 28, 2006\n\n\nFrequent Amazon Shopper\n\n\nNew Hampshire\n\n\nI've had this mouse for about a month...use it with my Mac G4 at home...and I'm exhausted. It feels very nice in the hand and I really like the scrolling ball feature (hence the 2 stars), but it's just not predictable. Sometimes it glides along smoothly, most other times it tracks across the monitor like you're mousing over pebbles. I've tried a number of different surfaces: desktop (light woodgrain); smooth, medium gray fabric mouse pad; mouse pad made specifically for optical mice (better than the other two) and I have even tried picking the thing up and shaking it to knock it into submission--to no avail. It still lags and I have a hard time honing in on an object to select it (or to check a box or pick up a puzzle piece in a jigsaw puzzle...)\n\nI use Photoshop and other mousing intensive apps all day at work and at home and I need a mouse that is precise (for silhouetting, etc.). This just isn't it. You shouldn't have to think about mousing when you're doing it, it should just happen--immediately and smoothly, corded or cordless. And a \"newer\" technology should work better that a gummed up \"old\" tracking ball mouse!\n\nMaybe it doesn't like my older Mac. Maybe it's the bluetooth adapter. Whatever it is, it's not working...\n\nI'll be trying the Logitech MX1000 next. It has a recharging base and is Laser (instead of optical). I'm hoping the higher price will translate into better performance\n\n\n\n\nB0007T27D2:the_mouse_with_parkinson's_disease:r._t._gillfillan\n\n\nB0007T27D2\n\n\nKensington 72414 PilotMouse Bluetooth Mini Mouse (PC/Mac): Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nThe mouse with Parkinson's disease\n\n\nSeptember 19, 2006\n\n\nR. T. Gillfillan\n\n\nsan francisco ca\n\n\nFor my PowerBook - my first bluetooth mouse (a macally bt) worked for about nine months and then a bad battery caused major leakage all over my desk. So for the replacement I read the reviews and went with the Kensington bluetooth mini - primarily for the on/off functionality.\n\nI like the size and shape, the on/off switch and the scroll works just keen. However, tracking is a nightmare - especially when CPU usage is high (applications like Photoshop, Acrobat or using a VPN to access my work files). I tried troubleshooting, adjusting the track rate, but nope. The cursor continues to stutter across the screen with this mouse.\n\nI think I'm going to throw in the towel and go back to wired\n\n\n\n\nB0007T27D2:just_ok:robert_ford\n\n\nB0007T27D2\n\n\nKensington 72414 PilotMouse Bluetooth Mini Mouse (PC/Mac): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n2.0\n\n\nJust OK\n\n\nMay 1, 2006\n\n\nRobert Ford\n\n\nCapitola, CA\n\n\nOne of the reasons I chose this mouse, was because Apple recommended it as one that would offer true smooth scrolling. It turns out that this mouse has the same click scroll as many others. It jerks one line at a time ... the clicking is quite loud I might add. Also, I have to set the tracking speed very low because the cursor jerks all over the screen if I don't. Sometimes just clicking on System Preferences corrects the problem, but if I had it to do over, I'd buy something else\n\n\n\n\nB00065ANYW:mine_died_too....:robin_d._cook\n\n\nB00065ANYW\n\n\nSanDisk ULTRA II HIGH PERFORMANCE 4GB (SDCFH-4096-901, Retail Package): Electronics\n\n\nelectronics\n\n\n7 of 8\n\n\n1.0\n\n\nMine died too....\n\n\nJuly 18, 2006\n\n\nRobin D. Cook\n\n\nArlington, VA USA\n\n\nI bought this in April for my new Canon 30D and it worked great until several months ago. When I would go back to scroll through the recently taken pictures on my camera it would start saying \"Data Corrupted\". It started out slowly then it would lose every third picture. I tried to reformat the card and got an error message. Now my camera does not recognize it nor does the camera software on my computer. It was a wonderful card while it lasted but I too am hesitate to get another one. Hopefully mine was a lemon too.....but then again\n\n\n\n\nB00065ANYW:died_after_4_months:k._bauman\n\n\nB00065ANYW\n\n\nSanDisk ULTRA II HIGH PERFORMANCE 4GB (SDCFH-4096-901, Retail Package): Electronics\n\n\nelectronics\n\n\n7 of 7\n\n\n1.0\n\n\nDied after 4 months\n\n\nJuly 11, 2006\n\n\nK. Bauman\n\n\nMichigan, USA\n\n\nThis seemed like a great card at a great price. Unfortunately after 4 months, during the middle of a paid shoot, all of the images instantly dissappeared. I stopped using the card, put it aside until I got home. I tried to use software to recover the images. Only a few could be recovered, the rest were gone. I've not been able to re-format the card in either a canon eos 5d, digital rebel, or using software on my computer. The card is now completely un-usable.\n\nAll of my older cards continue to work just fine. Hopefully this is just a lemon, although I need to replace it, and am obviously apprehensive about purchasing another one from Sandisk\n\n\n\n\nB0000Y7WBQ:poor_setup,_terrible_support,_good_range:j._macfarlane\n\n\nB0000Y7WBQ\n\n\nNetgear WG302 ProSafe 802.11g Wireless Access Point: Electronics\n\n\nelectronics\n\n\n11 of 11\n\n\n1.0\n\n\nPoor setup, terrible support, good range\n\n\nFebruary 19, 2005\n\n\nJ. Macfarlane\n\n\nCalifornia\n\n\nI purchased 3 of these APs with the expectation to replace 3 Cisco \"b\" APs and drop in. The setup used Radius for authentication which is claimed to be supported in both the online info and docs. After config (the web UI constantly has problems with cookies and forced you to re-login) the system would work. One quark was the IP address field for the radius server wouldn't accept more than one character in the forms. Up and running. However, upon reboot, the WG302 loses it Radius info and then cannot authenticate a client again. SHOWSTOPPER. After logging this with Netgear support - their response is this is an unsupported config and requires premium support. While this would be fine if it was a config issue, it's not ok given they claim support for these features and this was a required feature for purchase. They give no further support now.\n\nI would HIGHLY recommend against the extra cost of this unit given it's buggy and has poor support. Config is difficult (I had to learn how to use the command line config), the upgrades are not well documented (they imply you have to import some obscure file with tftp - you don't but...), however the unit does have good range and works (No RADIUS of course) in a basic config.\n\n\n\n\nB0001V3IB6:disappointing:hester_d'souza\n\n\nB0001V3IB6\n\n\nPlantronics TELEPHONE HEADSET SYSTEM ( S11 ): Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n2.0\n\n\nDisappointing\n\n\nJanuary 26, 2006\n\n\nHester D'Souza\n\n\nVancouver, BC, Canada\n\n\nFirst - a disclaimer: I know very little about telephone headsets, but decided to buy one because I had a series of long telephone meetings coming up.\n\nI walked to my local Staples store, expecting to find a variety of prices and products - and thinking that I'd pay perhaps $30 to $50 at the low end of the market.\n\nAt the store, I found that only the computer-based headsets were that cheap - and the only model that worked on a real, old-fashioned land line was the Plantronics S11 on display (it was priced at $130 Canadian).\n\nSince work was paying for it, I didn't mind spending the extra money, but expected that, at that price, the quality and ease of use would be quite high.\n\nWhen I brough it back to the office and installed it, I found that:\n\n- The regular handset and the headset cannot be plugged in at the same time. That means that every time I want to use the headset, I need to fumble around at the back of the unit to switch wires.\n\n- The volume of both ingoing and outgoing conversation is very low - even when I turn up the volume controls to max. The people I spoke with complained enough that I switched back to my regular handset.\n\n- The microphone, although it's somewhat adjustable, is short and inflexible enough that I can't get it very close enough to my mouth. When I try to move it closer, the earpiece turns and slips off my ear.\n\n- The headset itself - that is, the plastic, adjustable arch and rubber pad opposite the speaker - is very awkward and prone to slipping off my head. It might have been better to design it like a regular two-sided earphone unit - so that it would be more balanced and stay secure.\n\nI don't know whether these comments apply only to the S11 or to most headsets for traditional phones in general.\n\nOverall, however, if I'd been buying the S11 with my own money, I would likely have returned it and just gone back to using the regular phone handset\n\n\n\n\nB000A6X9H0:frustration_doesn't_cover_it!:marilyn_patten\n\n\nB000A6X9H0\n\n\nHP Scanjet 4850 Photo Scanner (L1950A#B1H): Electronics\n\n\nelectronics\n\n\n4 of 5\n\n\n1.0\n\n\nFrustration doesn't cover it!\n\n\nSeptember 15, 2006\n\n\nMarilyn Patten\n\n\nNashua, NH United States\n\n\nSince I first wrote this review I discovered that the fault was not with the scanner but with my computer. After formatting my computer I was able to install the scanner and software without any problems. I am still learning to use it but so far I have decided not to return it. It works well and I like it very much. I apologize if my review has negatively effected anyone's choice\n\n\n\n\nB000A6X9H0:multiple_failures:michael_j._dietz\n\n\nB000A6X9H0\n\n\nHP Scanjet 4850 Photo Scanner (L1950A#B1H): Electronics\n\n\nelectronics\n\n\n14 of 15\n\n\n2.0\n\n\nMultiple failures\n\n\nMarch 15, 2006\n\n\nMichael J. Dietz\n\n\nTorreon, NM United States\n\n\nI have approximately 10,000 slides to scan. I purchased the 4850 and after about 3,000 slides began to get three to four dark blue lines across the image. After reporting it to HP they sent me a replacement TMA. The new TMA worked fine for the first 450 slides and then BINGO, I now am getting two dark blue lines again. I think the machine is not engineered to handle the work load I have.\n\n\n\n\nB00002MZ8M:poor_quality!:russ_baehr\n\n\nB00002MZ8M\n\n\nFirst Act Discovery FG130 Student Acoustic Guitar: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nPoor Quality!\n\n\nMarch 14, 2006\n\n\nRuss Baehr\n\n\nSeattle, WA USA\n\n\nMy daughter really didn't use it that much but within about 5 weeks it started to fall apart. The wood veneer on the base pulled up and splintered from tightening the strings to tune it. A low price for a guitar but I don't think it's even worth this price. \n\n\n\n\nB0008JFJRM:bad_static,_poor_reception!!:faithful_shopper_\"jzk\"\n\n\nB0008JFJRM\n\n\nMotorola MD7091 5.8GHz Digital 2-line Phone: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nBad Static, Poor Reception!!\n\n\nNovember 12, 2006\n\n\nFaithful Shopper \"JZK\"\n\n\nCt\n\n\nThese phones had so much static, you could not hear!!! We ended up having to return the phones and find something that we could actually use!\n\n\n\n\nB0008JFJRM:fatal_design_flaw:tideguy\n\n\nB0008JFJRM\n\n\nMotorola MD7091 5.8GHz Digital 2-line Phone: Electronics\n\n\nelectronics\n\n\n4 of 5\n\n\n1.0\n\n\nfatal design flaw\n\n\nAugust 9, 2006\n\n\ntideguy\n\n\n\n\n\nI bought this because it seemed like it would satisfy my need for a 2-line phone with answering capability. Turns out, I cannot keep it, due to one boneheaded design flaw that makes it unusable for me.\n\nThe good: it's nice looking, compact, has good sound, and has a selection of cute little ringtones.\n\nThe bad: This machine WILL NOT RECORD INCOMING MESSAGES SILENTLY. It broadcasts both the OGM and the ICM being left by the caller through the speaker. There is no way I know of to defeat this. You can turn the volume down from loud to medium loud, but you cannot set the machine to record messages silently, in the background. \n\nDo you think you might ever not want other people in the room to hear the messages being left on your recorder? Would you ever want to sleep without being disturbed by the sound of incoming messages?\n\nThen this one isn't for you.\n\nMine is for sale.\n\n\n\n\nB0008JFJRM:motorola_lost_a_loyal_customer:sven\n\n\nB0008JFJRM\n\n\nMotorola MD7091 5.8GHz Digital 2-line Phone: Electronics\n\n\nelectronics\n\n\n8 of 10\n\n\n1.0\n\n\nMotorola lost a loyal customer\n\n\nMay 4, 2006\n\n\nSven\n\n\nUSA\n\n\nMy very first cell phone was Motorola, and it served me great, so I became attached to the brand based on that experience ,and for that reason alone bought Motorola SD4591 Digital Corded/Cordless System. As we've put the wireless network in house the cordless phone started playing tricks. The battery life got very short very soon too. I was told I need to get different batteries NiH instead of NiCD. Naturally the microwave was playing the tricks on cordless too. As we have two lines in house and was told by several people to move to 5.8Hz 2 line phone I decided to get Motorola MD7091 5.8GHz Digital 2-line Phone and remained loyal to Motorola. And boy was I wrong again. What others are saying about caller ID not showing as soon as I have another phone on the same line besides Motorola MD 7091 is true in my case too. I don't get it does Motorola expect us to switch phones to their system now around the house if we want to used one of their phones??? This is really cheap trick\n\nNaturally, I could blame all this on poor quality of NiCD batteries, Wireless Network, Microwaves and phone company, and other brands' phones connected to my line. But what is the common thread through the entire ordeal: MOTOROLA. I will give other brands a try now, not yet sure which ones\n\n\n\n\nB0008JFJRM:don't_get_the_cordless_sets!!:d._clarke\n\n\nB0008JFJRM\n\n\nMotorola MD7091 5.8GHz Digital 2-line Phone: Electronics\n\n\nelectronics\n\n\n11 of 13\n\n\n1.0\n\n\ndon't get the cordless sets!!\n\n\nMarch 30, 2006\n\n\nD. Clarke\n\n\n\n\n\nNow, imagine my astonishment. After receiving this beautifully designed futuristic looking set from my favorite communication device manufacturers, I set it up only to realize that the cordless phone only works when there is a direct line of site to the base!\nUnbelievable!! I live in a compact apartment ok, and believe me when I say it isn't 20 feet between the base and my bed room, and last night after setting up, I retreated to my room took up the cordless handset to make a call only to realize that there was no connection to the base.\nThis phone in a nutshell, looks good, but it sucks!!\n\n\n\n\nB0000ACKR5:good_looking,_clumsy_to_use:k._hackmann\n\n\nB0000ACKR5\n\n\nBelkin Leather Flip Case For iPaq PDAs: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n2.0\n\n\nGood looking, clumsy to use\n\n\nMay 5, 2005\n\n\nK. Hackmann\n\n\nMissouri, USA\n\n\nI had high hopes for this case when I bought it, but it just hasn't worked out. The leather has a nice feel, and my iPAQ fit snugly in it's slot. However the stylus on my iPAQ h2215 was impossible to get to without partially removing the iPAQ from it's case. This became so frustrating that I eventually discontinued using this case.\n\nIt does have three slots for credit cards, and a larger slot that you could tuck a few folded dollar bills or business cards in.\n\nMaybe a different model of iPAQ would work better, but it hasn't been a good fit for me\n\n\n\n\nB00006L4XU:useless_-_does_not_retract:james_murphy\n\n\nB00006L4XU\n\n\nTargus PA215U Retractable Flat FireWire Cable: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n1.0\n\n\nUseless - does not retract\n\n\nApril 6, 2004\n\n\nJames Murphy\n\n\nPelham, MA USA\n\n\nI agree with the previous review. This phone cord worked twice. Now, it only retracts about half way, and keeps getting worse. I'm returning it for a refund. Don't waste your time with this\n\n\n\n\nB00006L4XU:do_not_wast_your_time_with_this_product...period:alan\n\n\nB00006L4XU\n\n\nTargus PA215U Retractable Flat FireWire Cable: Electronics\n\n\nelectronics\n\n\n4 of 5\n\n\n1.0\n\n\nDO NOT Wast your time with this product...period\n\n\nAugust 8, 2003\n\n\nAlan\n\n\nCarol Stream, IL USA\n\n\nI am on my second one and I will be returning this one also. Both of them worked once and then would not retract fully after that. I was so mad I almost threw it against the wall. I thought the first one was a defect, but then when I got the second one I realized it was a design flaw... If you buy the Targus one keep your receipt because I guarantee you will be returning it. What I don't get is how Targus has the guts to release this product and also put on it: Satisfaction Guaranteed. What a joke..\n\n\n\n\nB000A3WS16:stupidity_at_its_best:n._caraccio\n\n\nB000A3WS16\n\n\nApple 30 GB iPod video White�(5th Generation): Electronics\n\n\nelectronics\n\n\n1 of 5\n\n\n1.0\n\n\nstupidity at its best\n\n\nNovember 24, 2006\n\n\nN. Caraccio\n\n\nRutherford, NJ\n\n\nipods are the leading sellers of portable players and everybody knows that but guess what ipod does not support the leading and best quality video format divx and avi hmm thats kinda retarded no? if any of you out there use file sharing programs such as limewire or any torrent programs if u download any of those videos the best ones and the majority are divx and avi too bad for you unless ur willing to sit around for an hour or two and convert your video to mpeg4. Also the video you convert may have a few complications, the audio and video may not be sychronized...at all and for those of you who have already purchased this piece of garbage im sorry and i have great sympathy for you so if ud like a program that will convert ur video for free and results in pretty good quality go to download.com and search for jodix video converter. This is not from speculations I have experienced this all myself with my own purchase of the video ipod and was a decision i regreted. Suggestion check out the creative zen vision M sure it may be thicker but the price is amazing for its features and conveniences of transfering files into it\n\n\n\n\nB000A3WS16:love_it.._hate_it:a._pereyra\n\n\nB000A3WS16\n\n\nApple 30 GB iPod video White�(5th Generation): Electronics\n\n\nelectronics\n\n\n2 of 6\n\n\n1.0\n\n\nlove it.. hate it\n\n\nNovember 13, 2006\n\n\nA. Pereyra\n\n\nMiami, FL United States\n\n\nI bought with lots of ilussion this this Ipod, i put all my music in it and use it!! after a 5 months it stop working, it didn't charge the battery. I took it to best buy where I bought it and they told me that it has \"external damage\" and didn' repair it. I send it to Apple service and return it without services because the same reason. My Ipod didn't have that damage, it has only a few scratches on the screen. After lots of calls and fight they told me that to fix it I would have to pay US$250.00 that is almost what it cost new. TERRIBLE SERVICE FROM APPLE.. SHORT LIVE.. DON'T RECOMMENDED. Now I'm looking for another option for MP3 player to buy.\nIf you know one please let me know it.\n\n\n\n\n\nB0001F22PA:avoid_if_you_live_in_or_near_a_major_city:pats_fan_bob\n\n\nB0001F22PA\n\n\nBelkin F8V3080 TuneCast II Mobile FM Transmitter: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\navoid if you live in or near a major city\n\n\nNovember 21, 2006\n\n\nPats Fan Bob\n\n\nBoston, MA\n\n\nLike other fm transmitters I have tried, this one leaves much to be desired. Living in a major urban area, I was unable to find any fm channel that did not produce significant static and generally poor sound quality. Product is easy to use, but I consider the sound quality unacceptable.\n\n\n\n\nB0007TIYCU:great_until_it_broke,_no_response_from_uniden:e._smith\n\n\nB0007TIYCU\n\n\nUniden CLX465 5.8 GHz DSS Expandable Cordless Phone with Color LCD Handset and Dual Keypads (Silver): Electronics\n\n\nelectronics\n\n\n4 of 5\n\n\n1.0\n\n\nGreat until it broke, no response from Uniden\n\n\nJanuary 29, 2006\n\n\nE. Smith\n\n\nBermuda\n\n\nThe phone worked for a month or two, and then the handset stopped seeing the base. When I followed the instructions in the manual to fix this (re-register the handset to the base), neither could see each other.\nThe base still gets the land-line signal and works with speakerphone, but that is useless to me since I need the handset.\n\nWhen I contacted Uniden through their email support, I got no response.\n\nI will be buying another phone that is not a Uniden\n\n\n\n\nB000BI2180:the_vendor_delivered_a_defective_unit.:w._h._frohlich_\"musical\"\n\n\nB000BI2180\n\n\nPalm Z22 Handheld: Electronics\n\n\nelectronics\n\n\n0 of 2\n\n\n1.0\n\n\nThe vendor delivered a defective unit.\n\n\nNovember 22, 2006\n\n\nW. H. Frohlich \"Musical\"\n\n\nOakland, CA\n\n\nI'm pissed. I feel I've been ripped off. The 3rd party vendor delivered a defective unit. I want to return it. I have been unable to determine how to return this unit for a refund. I may never buy through Amazon again\n\n\n\n\nB000BMXJTG:did_not_work_in_sony_psp:gadget_dad\n\n\nB000BMXJTG\n\n\nLexar 1 GB Memory Stick Duo Pro ( MSDP1GB331 ): Electronics\n\n\nelectronics\n\n\n3 of 8\n\n\n1.0\n\n\nDid Not Work in Sony PSP\n\n\nJanuary 7, 2006\n\n\nGadget Dad\n\n\nCalifornia\n\n\nThis product did not reliabley work in a new Sony PSP running the current Sony software. Don't know if the design is flawed or the unit I received was defective\n\n\n\n\nB0000TNWU6:be_careful:r._perry\n\n\nB0000TNWU6\n\n\nBelkin F8E462 Voice Recorder for iPod: Electronics\n\n\nelectronics\n\n\n4 of 9\n\n\n1.0\n\n\nbe careful\n\n\nJanuary 19, 2005\n\n\nR. Perry\n\n\nAsia\n\n\nGot it and plugged it into my IPod and there was a major incompatibility. The screen showed up that their was a voice recorder in the IPod but when I went to record I received an error message that the IPod could not locate a microphone. Perhaps an IPod problem? The Ipod is a 4th Generation 40 gig that is fully updated. I think Belkin needs to engineer the recorders to work with all the new IPods, with the exception of the minis. Have not been able to resolve the issue. I am sure IPod will state that it is a Belkin problem and that Belkin will throw the ball back to IPod????\n\n\n\n\nB0007WTHLO:lack_of_bass,_too_much_treble.:will\n\n\nB0007WTHLO\n\n\nEtymotic Research ER6iC Isolator Earphones (White): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n2.0\n\n\nLack of bass, too much treble.\n\n\nNovember 1, 2006\n\n\nWill\n\n\nTexas, USA\n\n\nI just received my pair today, and I have to say, I'm extremely dissapointed. Yes, they are comfortable, but the bass is almost absent, no matter how far you jam them into your ears, the bass isn't nearly what it were hyped to be, it definently isn't enough for the amount of mid & hi's they produce. Unless you like your bass to a minimum and treble to a max, I'd recommend another pair of earphones. If you're just looking for comfort and you don't like hardly any bass, these are your headphones. But, as others said, you can find much better earphones for a much lower price\n\n\n\n\nB000B9TV8W:fleecing_loyal_apple_buyers:nicholas_r._thompson_\"beernose\"\n\n\nB000B9TV8W\n\n\nApple MA183G/A Armband Blue for iPod Nano 1st Generation: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n2.0\n\n\nfleecing loyal apple buyers\n\n\nOctober 7, 2006\n\n\nNicholas R. Thompson \"Beernose\"\n\n\nChicago, IL\n\n\nThe first thing I did when opening this item was check to make sure it actually WAS an Apple product. Its REALLY cheap looking. Looks like something you'd find for [...] at a flea market or in the \"stocking stuffers\" section of the dollar store at Christmas time. The Nano is small so almost anything will hold it, including a rubber band and an envelope which is about as good as this unit.\n\nThis looks more like the freebie armband that should come free with the unit. Certainly not a [...] accessory. It just cheapens the Apple image.\n\nIf OEM Apple products are your thing, then go ahead and try this. But if you're looking for something as sweet as your nano that stays put while you workout, look for something else. I'm returning mine this weekend to spend my money elsewhere.\n\n\n\n\nB000BQQW9G:don't_consider_this_product:a._charity\n\n\nB000BQQW9G\n\n\nApple MA125G/A iPod Universal Dock Adapter 3-Pack for 30 GB iPod with Video: Electronics\n\n\nelectronics\n\n\n1 of 4\n\n\n1.0\n\n\nDon't consider this product\n\n\nAugust 17, 2006\n\n\nA. Charity\n\n\nNorthwest, WA USA\n\n\n3 more of the useless flimsy holders you get in the IPod box!\n\n\n\n\nB000AY5ZIS:extremely_poor_software_package_that_makes_it_very_disappointing:sami_ben_grine\n\n\nB000AY5ZIS\n\n\nLogitech Cordless Desktop MX 5000 Laser (967558-0403): Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n1.0\n\n\nExtremely poor software package that makes it very disappointing\n\n\nOctober 22, 2006\n\n\nSami Ben Grine\n\n\nWalnut Creek, CA USA\n\n\nI had a very poor experience with the Microsoft Media Center infrared keyboard and I wanted to replace it with a bluetooth solution that would - I thought - get rid of all connection problems.\n\nI was wrong. The hardware is very nice but the software keeps on dropping connections. \n\nAS usual, I was very careful: I always read manuals and properly install the software before using any product - I don't want to fail the installation for a very simple reason: the package doesn't come cheap... I am a software developer - I have half a dozen computers at home - and I did everything as instructed. I found the software to be very average in terms of usability (why do they try to do all these fancy effects??), but that's not very important when it works fine. \n\nUnfortunately, after just a couple of days, the keyboard stopped working. I finally managed to get it back by removing the batteries and rebooting the computer (both step were required - the on/off switch didn't work - same thing for the connect button which is totally ineffective). Now, the mouse keeps on dropping connections. After a couple of hours of inactivity, the mouse simply stops responding.\n\nThis is simply unacceptable. Logitech knows that this product doesn't work and keeps on charging customers for something that is simply not usable because it wasn't tested properly.\n\nIt is a very frustrating experience because the hardware is great. Don't buy this product, you could be very disappointed.\n\nNote that I have seen another installation done by professional Windows system administrators that had the exact same problems.\n\nNow don't get me wrong: the hardware is very nice. I would highly recommend the Logitech VX Revolution Cordless Laser Mouse for Notebooks \nwhich is great and had none of the issues I have mentioned there\n\n\n\n\nB000AY5ZIS:severe_connection_issues:daniel_laughland\n\n\nB000AY5ZIS\n\n\nLogitech Cordless Desktop MX 5000 Laser (967558-0403): Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n2.0\n\n\nSevere Connection Issues\n\n\nOctober 19, 2006\n\n\nDaniel Laughland\n\n\nWaukesha, WI\n\n\nThis hardware is well-made and I have heard very good things about the non-bluetooth version of the mouse. When it's connected and working, it does its job very well. Unfortuntately the MX5000 set suffers from some very poorly made software that makes it very difficult to use effectively. At seemingly random intervals, the mouse will start lagging or stop altogether. If this happens during gameplay, you're in a bad way. The keyboard also suffers from a truly annoying bug. When it comes off of its sleep state, it will double the first key you press. I have also had the keyboard just reset out of the blue. Add in that the SetPoint software for the LCD screen only fully works with two pieces of software (MusicMatch and Windows Messenger), and you have a very disappointing desktop package. The MX1000 is a great mouse on its own, but stay away from this Bluetooth combo. It is unstable and very frustrating to use\n\n\n\n\nB00064L17Q:hardware_is_fine_-_software_is_worst_i_have_ever_dealt_with:duce\n\n\nB00064L17Q\n\n\nBelkin F8T003 Bluetooth USB Adapter - 10 Meters: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nHardware is fine - Software is worst i have ever dealt with\n\n\nNovember 17, 2006\n\n\nDuce\n\n\n\n\n\nThe unit works fine as a Bluetooth Radio.\nThe software and drivers!!!! do not install properly at all!!!\nI am a network administrator & pretty savvy about PCs - i could not come up with any scenario to get the software to work!!!\n\nSave your money and just buy the unit - forget the software\n\n\n\n\nB00064L17Q:waste_of_time_waste_of_money:donald_l._mader\n\n\nB00064L17Q\n\n\nBelkin F8T003 Bluetooth USB Adapter - 10 Meters: Electronics\n\n\nelectronics\n\n\n0 of 5\n\n\n1.0\n\n\nWASTE OF TIME WASTE OF MONEY\n\n\nJune 12, 2006\n\n\nDonald L. Mader\n\n\nLos Angeles, Ca United States\n\n\nDOES NOT WORK AT ALL DONT THROW AWAY YOUR MONEY SHOULD READ 0 STAR\n\n\n\n\nB00007MHMN:vonage_is_a_sinking_ship:ben_franklin\n\n\nB00007MHMN\n\n\nVonage DigitalVoice Service: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nVonage is a sinking ship\n\n\nOctober 3, 2006\n\n\nBen Franklin\n\n\nMinneapolis\n\n\nI had Vonage since April, 2006. I finally switched back to Qwest after unending problems and hours and hours on the phone (my cell phone, as my Vonage line didn't work) with tech support. Here is my experience:\n\n1) It took about 2 months and about 2 hours on the phone for them to switch my existing phone # over to Vonage. Don't be too excited about getting the first month free- if your moving an existing number it will take about that long to get it switched over.\n\n2) After my number was finally switched over, outgoing calls were constantly being dropped after about 20-30 seconds. Incoming calls would also be dropped occasionally after about a minute. This resulted in having to call the person back, saying \"sorry the call was dropped- we switched to this cheaper phone service and have been having problems....\"\nThis is with the highest speed cable internet service available (8mbps download, 600kbps upload) and a wireless 'g' system which has provided top notch internet service consistently while I had Vonage problems. I spent no less than 4 - 5 calls and over 5 hours on the phone with tech support trying to fix the problem. After talking with tech support, sometimes the problem would be fixed temporarily- while I was on the phone with them- but return shortly thereafter.\n\n3) As a result of the problems I had, I asked to be credited for the months in which my phone service was not working. Be warned, they are very hard-nosed about providing any credits for poor or no service. I spoke to a tech support person about getting a credit for a month of no service, and he told me to wait until the problem had been fixed so they could determine how much credit should be given. After the problem had been fixed (temporarily), I called back and they told me they could only provide credit for the current month, not previous months of no or poor service. THEY ARE NOT CUSTOMER FRIENDLY ABOUT PROVIDING REFUNDS FOR POOR OR NO SERVICE.\n\n4) Finally, in September, still with dropped calls from Vonage, I switched back to Qwest to get reliable phone service. I found customer service there has improved and is much better than Vonage. And they gave me 3 months free promotion for coming back. \n\nIn the end, I am happy to pay the extra $10/month (after the first 3 months free) to get phone service that works. I notice that $5 of the extra cost is for fees, etc. that the phone company has to charge but Vonage does not, which is unfortunate. In any case, I guess you get what you pay for.\n\nOne last thing: Vonage has never made a profit and many business analysts say with their current business plan, they never will. This means eventually they will either go out of business, or have to raise rates.\n\nSave yourself the problems I had- pay the extra $ to get phone service that works\n\n\n\n\nB00007MHMN:stay_away_from_vonage:crispy\n\n\nB00007MHMN\n\n\nVonage DigitalVoice Service: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nStay Away from Vonage\n\n\nJuly 17, 2006\n\n\nCrispy\n\n\n\n\n\nI bought vonage service thinking that finally there is a company which will let me control everything from their web portal, and provide features that traditional phone companies do not. Good thing I kept my number with traditional phone company and got a second line with Vonage.\n\nIts voice quality was really bad, and cancelling it was a pain to say the least. It took me 4 months to cancell it. Waiting forever on hold. Please read internet forums/blogs before you decide to \"save\" money\n\n\n\n\nB00007MHMN:stay_away_from_vonage!:j._w.\n\n\nB00007MHMN\n\n\nVonage DigitalVoice Service: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n1.0\n\n\nStay away from Vonage!\n\n\nJune 14, 2006\n\n\nJ. W.\n\n\nChandler, AZ United States\n\n\nStay away from Vonage! After more than 1 month, still don't have phone number transferred. Can't get thru to customer service...very long wait. Paying 2 phone bills...so much for saving cost and so-called service. Called to cancel service, and customer service rep promised to put account/billing on hold until phone number transfer is done...finally something positive from customer service department\n\n\n\n\nB000HZBR64:don't_buy:kai_flache\n\n\nB000HZBR64\n\n\nBose Companion 3 Series II multimedia speaker system \u001a Graphite/Silver: Electronics\n\n\nelectronics\n\n\n2 of 9\n\n\n1.0\n\n\nDon't buy\n\n\nOctober 22, 2006\n\n\nKai Flache\n\n\n\n\n\nVery disappointing speakers!\nRip off.\nI returned them. \n\n1.\tSatellites produce static noise even with the computer turned off. You have to press the mute button to get rid of it.\n2.\tExtremely rumble base; not crisp. Even turned all the way down it is too prominent. Maybe good for war games.\n3.\tMissing mid tone. Satellites sound like they are coming out off a tin can.\n4.\tVolume is a bit underpowered. Will not knock your socks off\n\n\n\n\nB000066HO4:headphone_jack_becomes_loose:d._crook\n\n\nB000066HO4\n\n\nSony SRF-M80V S2 Sports Walkman Arm Band Radio with FM/AM, TV and Weather Channels: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n1.0\n\n\nHeadphone jack becomes loose\n\n\nMarch 26, 2005\n\n\nD. Crook\n\n\nCalifornia\n\n\nI too bought one and had the headphone jack become loose. I bought another thinking I had a faulty one, but now I'm having the same issue with this one. The loose jack causes the reception to go in and out...and finally just out completely. I agreed with the previous opinions on this fatal flaw\n\n\n\n\nB000F49ZHU:decent_features,_bad_sound:d._cohen\n\n\nB000F49ZHU\n\n\nPanasonic KX-TG5623B Black 5.8 GHz FHSS GigaRange Digital Cordless Telephone with Three Handsets: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nDecent features, bad sound\n\n\nNovember 21, 2006\n\n\nD. Cohen\n\n\n\n\n\n1. There was a consistent buzzing on the phone even when I tried multiple jacks for the base. \n2. Phone features worked as advertised. but the Voice Enhancer is useless and actually makes it sound worse.\n3. It was kind of a pain to sync the phone books which are separate on each handset.\n\nI returned it mainly because of the buzzing\n\n\n\n\nB00009ZUDD:this_is_a_substandard_product.__pass_on_it.:sadak\n\n\nB00009ZUDD\n\n\nMagnavox CD Alarm/Clock Radio (MCR23017) (MCR230): Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nThis is a substandard product. Pass on it.\n\n\nSeptember 28, 2006\n\n\nSadak\n\n\nMissouri, USA\n\n\nI bought this and found that if you like to burn your own CD's to play in it, it has a problem reading the tracks. It sometimes spins up and doesn't play, and it always has a very audible noise on spin up and track find. I usually wake to that sound before the music starts. I AM NOT A LIGHT SLEEPER. This is my second one, and I've basically been using it for the radio wake function for the last few months. Do yourself a favor and find a different product\n\n\n\n\nB00009ZUDD:not_as_advertised:juliebw_\"woods682\"\n\n\nB00009ZUDD\n\n\nMagnavox CD Alarm/Clock Radio (MCR23017) (MCR230): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n1.0\n\n\nnot as advertised\n\n\nFebruary 15, 2005\n\n\nJulieBW \"woods682\"\n\n\nGeneseo, NY USA\n\n\nThis product DOES NOT have the skip feature included in the product descriptio\n\n\n\n\nB00006HYWR:here's_what_your_buying!:chris_hooker_\"c-dawg\"\n\n\nB00006HYWR\n\n\nCreative Labs NOMAD MuVo 64 MB MP3 Player: Electronics\n\n\nelectronics\n\n\n0 of 3\n\n\n1.0\n\n\nHERE'S WHAT YOUR BUYING!\n\n\nJune 4, 2005\n\n\nChris Hooker \"C-Dawg\"\n\n\nLivonia, NY USA\n\n\nYOUR BUYING 128 MB WORTH OF FRUSTRATION AND $10 WORTH OF GAS FROM BACK AND FORTH TO WAL-MART.\n\nI bought my Creative MuVo and had it for a month or so. LOVED IT. had everything i needed, great mp3 worked great. One day, plugged it in to put a song on it and all of the sudden it starts smoking. Big hole in it from all the smoke. So, i return it at walmart for a new one. TWO DAYS LATER i plug it in my usb to put a song on it - smokes again!!!!!!! i returned it and got my money back... got an ipod shuffle, buy that much better mp3 player wont start on fire...\n\n\n\n\nB0002MRN4W:no_insert_or_escape_keys!!!:vitas_the_fetus\n\n\nB0002MRN4W\n\n\nLogitech Cordless Desktop LX 300: Electronics\n\n\nelectronics\n\n\n\n\n\n1.0\n\n\nNo INSERT or ESCAPE keys!!!\n\n\nNovember 2, 2006\n\n\nVitas the Fetus\n\n\n\n\n\nThe keyboard and mouse work fine, except for some completely absurd reason, Logitech decided to remove the INSERT and ESCAPE keys from the regular keyboard and make them little buttons on the top of the keyboard instead. Look at the picture and you will see what I mean -- the DELETE key is double-size and takes up the space where the insert key would be; INSERT is moved above the home key and is a little recessed round button. ESCAPE is in the usual place, but it too is a little button instead of a legitimate key. Very annoying. Definately returning it\n\n\n\n\nB000HCGAFK:corrupted:michael_haynes\n\n\nB000HCGAFK\n\n\nSanDisk - Flash memory card - 2 GB - microSD: Electronics\n\n\nelectronics\n\n\n5 of 16\n\n\n1.0\n\n\nCorrupted\n\n\nAugust 28, 2006\n\n\nMichael Haynes\n\n\n\n\n\nOrdered it, took about 5 days to get to me, and it's corrupted and cannot format itself and cannot put files on it\n\n\n\n\nB00005TQ08:mouse_works_great_..._except_for_the_middle_\"wheel\"_button.:j._kim\n\n\nB00005TQ08\n\n\nMicrosoft Intellimouse Optical Mouse: Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nMouse works great ... EXCEPT FOR THE MIDDLE \"wheel\" button.\n\n\nNovember 23, 2006\n\n\nJ. Kim\n\n\nPhiladelphia, PA United States\n\n\nI was really happy with the mouse for all of 10 minutes.\nThe scroll wheel is really \"push happy\" such that when I roll the wheel, the middle button registers a \"click\" very often. In order to prevent the inadvertant click, I have to roll the wheel with the lightest of light touches. It's significantly more senstive than my older logitech wheel, and was really really annoying to have the browser repeatedly register the middle button (which is unix is the default \"paste\" so I'd have pages and pages of text that I \"selected\" be pasted on to the browser or editor while what I really wanted was just to scroll the buffer!!!\n\nToo bad, the mouse was really high quality overall.\n\n\n\n\nB0000C3GWV:don't_buy!!:robert_lezenby_\"orgelgrinder\"\n\n\nB0000C3GWV\n\n\nJBL Duet PC Speakers (2-Speaker, Silver): Electronics\n\n\nelectronics\n\n\n1 of 8\n\n\n1.0\n\n\nDon't Buy!!\n\n\nMarch 21, 2006\n\n\nRobert Lezenby \"Orgelgrinder\"\n\n\nOhio USA\n\n\nThe JBL Speakers worked fine for 3 weeks and all of a sudden, they quit. In the future, I will buy this type of merchandise locally so that I have some recourse\n\n\n\n\nB0000C3GWV:blah_sound_overall:j._e._mildren_\"coffee_drinker_extraordinaire\"\n\n\nB0000C3GWV\n\n\nJBL Duet PC Speakers (2-Speaker, Silver): Electronics\n\n\nelectronics\n\n\n1 of 4\n\n\n1.0\n\n\nBlah sound overall\n\n\nMarch 17, 2006\n\n\nJ. E. Mildren \"Coffee drinker extraordinaire\"\n\n\nLawrence, Kan.\n\n\nI was really excited for these affordable speakers after reading the rave reviews, however, I have been extremely disappointed in them. I may have had too high of expectations, but these things put out NO bass whatsoever (it just sounds muffled and cruddy), and the overall sound is extremely poor. I'm really surprised as I've owned JBL speakers in my car that I was extremely pleased with, so I'm amazed at the lacking quality in these. I would recommend either finding another similarly priced set with good reviews or paying a little extra and getting something with a sub included\n\n\n\n\nB0000C3GWV:where's_the_headphone_jack,_jack?:d._g._springs\n\n\nB0000C3GWV\n\n\nJBL Duet PC Speakers (2-Speaker, Silver): Electronics\n\n\nelectronics\n\n\n0 of 4\n\n\n2.0\n\n\nWhere's the headphone jack, Jack?\n\n\nMarch 16, 2006\n\n\nD. G. Springs\n\n\n\n\n\nThese speakers sound great - but sometimes you want to crank it up and in the office that's a no-no. So you reach for your phones and... no place to plug them in! How lame is that? Also whoever designed the Volume/On/Off knob is an idiot. The thing is so streamlined and slippery that when you try to turn it, the whole speaker just slips away from you. They're so light that they don't have enough resistance to stay put. So you have to hold it in place with one hand while you turn the knob with the other (unless you velcro them to your desk or something). Too bad because - like I said - they sound great\n\n\n\n\nB0000BVUZ2:does_not_retract:r._abramson\n\n\nB0000BVUZ2\n\n\nTargus PA206U Mini Retractable RJ-11 Cord (Plastic Casing): Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n1.0\n\n\nDoes not retract\n\n\nOctober 11, 2005\n\n\nR. Abramson\n\n\n\n\n\nAgree with the prior reviewers. I can not get it to retract any longer and that was only after 3 uses! \n\n\n\n\nB00032Q1IK:don't_buy_the_generic_one:feliciano\n\n\nB00032Q1IK\n\n\nLexar Media Single-Slot USB 2.0 Multi-Card Reader (Model RW023-001): Electronics\n\n\nelectronics\n\n\n\n\n\n2.0\n\n\nDon't buy the generic one\n\n\nSeptember 5, 2006\n\n\nFeliciano\n\n\nVenezuela\n\n\nI have bought several of these. Some came in a fancy blister, and others in a coushing bag. Stay away from those. Be sure you're buying the sealed one because the physical design is a lot different.\n\nI give the generic two stars just because it works. I give four stars to the original because as others reviewers have said it's a little difficult to remove the card\n\n\n\n\nB00032Q1IK:i_don't_know_what_i_can_do_with_this:y._kim_\"flyshoe\"\n\n\nB00032Q1IK\n\n\nLexar Media Single-Slot USB 2.0 Multi-Card Reader (Model RW023-001): Electronics\n\n\nelectronics\n\n\n5 of 6\n\n\n2.0\n\n\nI don't know what I can do with this\n\n\nApril 6, 2006\n\n\nY. Kim \"flyshoe\"\n\n\nUSA\n\n\nI bought this for easy transfer of pictures from my digital camera with SD memory card anywhere not home and sometimes from other peoples memory card (xD and memory stick)..\n\nFirst of all I was disappointed with the flimsy, plastic design and the size of it. But it would have been ok if it worked!..IT DOESNT READ my SD card. And as menetioned in other people's review hard to insert and take out the cards! I'm scared if the cards get scratch and ruined whenever i do it. I wish I have bought this after reading amazon reviews...it's useless now. I'm lost how I can get sd card work on this and if I do, scared of frequent use for the flimsy design\n\n\n" }, { "path": "nlp_class/electronics/positive.review", "content": "\n\nB00006HYUB:everyone_should_own_one:d._john_\"looser\"\n\n\nB00006HYUB\n\n\nAPC Back-UPS ES 500 Backup Battery and Surge Protector: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nEveryone should own one\n\n\nJuly 31, 2006\n\n\nD. John \"Looser\"\n\n\nPA\n\n\nI purchased this unit due to frequent blackouts in my area and 2 power supplies going bad. It will run my cable modem, router, PC, and LCD monitor for 5 minutes. This is more than enough time to save work and shut down. Equally important, I know that my electronics are receiving clean power.\n\nI feel that this investment is minor compared to the loss of valuable data or the failure of equipment due to a power spike or an irregular power supply.\n\nAs always, Amazon had it to me in <2 business days\n\n\n\n\nB00006HYUB:breif_review:scott_park\n\n\nB00006HYUB\n\n\nAPC Back-UPS ES 500 Backup Battery and Surge Protector: Electronics\n\n\nelectronics\n\n\n11 of 12\n\n\n5.0\n\n\nBreif Review\n\n\nSeptember 26, 2005\n\n\nScott Park\n\n\nNew York, NY USA\n\n\nI ordered 3 APC Back-UPS ES 500s on the recommendation of an employee of mine who used to work at APC. I've had them for about a month now without any problems. They've functioned properly through a few unexpected power interruptions. I'll gladly order more if the need arises.\n\nPros:\n - Large plug spacing, good for power adapters\n - Simple design\n - Long cord\n\nCons:\n - No line conditioning (usually an expensive option\n\n\n\n\nB00006HYUB:works_fine.:dee_richards_\"deeanalyst\"\n\n\nB00006HYUB\n\n\nAPC Back-UPS ES 500 Backup Battery and Surge Protector: Electronics\n\n\nelectronics\n\n\n0 of 2\n\n\n4.0\n\n\nWorks fine.\n\n\nSeptember 15, 2005\n\n\nDee Richards \"deeanalyst\"\n\n\nMobile, AL\n\n\nWish the unit had a separate online/offline light. When power to the unit is missing, the single red light turns off only when the warning sounds. The warning sound is like a lot of sounds you hear in the house so it isn't always easy to tell what is happening\n\n\n\n\nB000067VBM:affordable_and_effective:jason_dickson_\"needs_more_money\"\n\n\nB000067VBM\n\n\n50-pack Slim CD/DVD Jewel Cases (5mm) Clear: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n5.0\n\n\nAffordable and effective\n\n\nJuly 7, 2005\n\n\nJason Dickson \"Needs More Money\"\n\n\nMaryville, TN USA\n\n\nCheaper than thick CD cases and less prone to breakage, these \"slim\" cases by Memorex protect your CDs. Most buyers will get them for use in CD or DVD burning, and these are an affordable choice from a quality company for that purpose.\n\nYou may want to consider larger cases depending on your tastes, but otherwise these will probably satisfy your needs\n\n\n\n\nB00008SCFU:it_is_a_good_product_:kiran_k._singarapu\n\n\nB00008SCFU\n\n\nKingston 256 MB Secure Digital Card ( SD/256 ): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nIt is a good product \n\n\nFebruary 24, 2006\n\n\nKiran K. Singarapu\n\n\n\n\n\nHi\n\nI brought 256 MB Kingston SD card from Amazon.com recently. According to my openion Amazon is one of the best online shopping market, they deliver the products intime and the items i brought so far are working very well. 256 MB Kingston SD card working very good with out giving any problem. \nOne can always go to Amazon for online shopping.\nbye\nkira\n\n\n\n\nB0007P4G76:perfect:r._johnson\n\n\nB0007P4G76\n\n\nXtreme TB264DKO 264-Disc CD/DVD Case: Electronics\n\n\nelectronics\n\n\n2 of 6\n\n\n5.0\n\n\nperfect\n\n\nMarch 18, 2006\n\n\nR. Johnson\n\n\nChicago\n\n\nThis product was perfect. it's a sturdy case that holds my large collection of cd's and dvd's.\n\nMy other cases were flimsy and wore out fast. This one has a hard outside shell and the inside holds my dvd's nicely.\n\nSo i have no complaints. and at the price i paid i'd say this was a great deal\n\n\n\n\nB0000DK3I4:kingston_1gb_sd_card_review:eugene_ong\n\n\nB0000DK3I4\n\n\nKingston 512 MB Secure Digital Card (SD/512): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nKingston 1GB SD Card Review\n\n\nNovember 9, 2006\n\n\nEugene Ong\n\n\n\n\n\nGreat value for your money. Kingston has always been a reliable memory chip company and the same goes for this product.\n\n\n\n\nB0000DK3I4:very_good_sd_card:gabriel_gore_\"geebi\"\n\n\nB0000DK3I4\n\n\nKingston 512 MB Secure Digital Card (SD/512): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nVery good SD Card\n\n\nNovember 9, 2006\n\n\nGabriel Gore \"geebi\"\n\n\nAlgood, TN\n\n\nI like it very much, I use it in an outdoor trail camera for wild game and it hold several pictures and has been in the camera in all kinds of weather. I would buy Kingston again\n\n\n\n\nB0002MSTWC:flash_card:allen_l._mccartney\n\n\nB0002MSTWC\n\n\nLexar Media 512 MB 80x Pro Series Compact Flash Card (CF512-80-380): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nFlash Card\n\n\nAugust 18, 2005\n\n\nAllen L. Mccartney\n\n\nElk Grove, CA\n\n\nI had no problems with this card and the delivery was prompt...\n\nThank You Muc\n\n\n\n\nB0002MSTWC:works_great_for_me!:robert_farlee\n\n\nB0002MSTWC\n\n\nLexar Media 512 MB 80x Pro Series Compact Flash Card (CF512-80-380): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nWorks great for me!\n\n\nJuly 8, 2005\n\n\nRobert Farlee\n\n\nMinneapolis, MN USA\n\n\nAfter going through the reviews, I bought this CF card for my Canon Digital Rebel. So far it has worked fine, though I don't pretend to be an expert digital photographer. When the card is empty, it shows 127 shots available. It seems to read reasonably fast, though it takes a bit longer than I was used to with my point-and-shoot Olympus and its SmartMedia card.\n\n\n\n\nB0006G0RSI:good_little_speakers!:folsom_mom\n\n\nB0006G0RSI\n\n\nAltec Lansing inMotion iM3 - Portable speakers - 4 Watt (total): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nGood little speakers!\n\n\nApril 14, 2006\n\n\nFolsom Mom\n\n\nFolsom, Ca.\n\n\nI am very happy with this product. It folds super slim, so traveling with it is a breeze! Pretty good sound - not Bose quality, but for the price, very respectable! I've had it almost a year, and it has been along on many weekend get-aways, and works great. I use it alot, so it was a good purchase for me\n\n\n\n\nB0006ZSW1S:excellent_option_to_avoid_camera_cables:k._koesling\n\n\nB0006ZSW1S\n\n\nKodak 8 - in - 1 Multi-card Reader, KRW8IN1SCS: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nExcellent option to avoid camera cables\n\n\nNovember 19, 2006\n\n\nK. Koesling\n\n\nCaracas, Venezuela\n\n\nI bought this reader because I wanted to avoid having my camera and all its cable attached to my pc to download the pictures; and with this reader I could leave my camera (and its cables) in its case and just download the picture with the memory card, I recomend this product....\n\n\n\n\nB0001DI6FW:does_the_job:ks\n\n\nB0001DI6FW\n\n\nTargus PAUH212U 7 Port USB 2.0 Hub: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n5.0\n\n\nDoes the job\n\n\nOctober 22, 2005\n\n\nks\n\n\nNew Jersey\n\n\nIt's a simple device, so there's not to say about it other than it does the job. You should note that the LEDs on the device remain lit as long as the device is plugged into your PC and the PC is powered (they glow a bright blue).\n\n\n\n\nB0000CD08M:great_product:h._knox_\"h._knox\"\n\n\nB0000CD08M\n\n\nSanDisk SDCFH-512-901 512 MB Ultra II CompactFlash Card (Retail Package): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGreat Product\n\n\nFebruary 20, 2006\n\n\nH. Knox \"H. Knox\"\n\n\nBaltimore, Md.\n\n\nI was thinking about buying a 1G card, but after reading one of the reviews,I bought two 512 card,just in case that something\nwent wrong. Which to me made a lot of sense. I'm please\n\n\n\n\nB0000CD08M:a_sandisk_fan:audra_m._fischenich_\"miranda1776\"\n\n\nB0000CD08M\n\n\nSanDisk SDCFH-512-901 512 MB Ultra II CompactFlash Card (Retail Package): Electronics\n\n\nelectronics\n\n\n1 of 7\n\n\n5.0\n\n\nA SanDisk Fan\n\n\nOctober 20, 2005\n\n\nAudra M. Fischenich \"Miranda1776\"\n\n\nMountain View, CA\n\n\nI have always been very pleased with the SanDisk products. I would highly recommend them\n\n\n\n\nB000BKE8QQ:almost_perfect:shawn_fitzgibbons\n\n\nB000BKE8QQ\n\n\nAltec Lansing inMotion iM716 Headphones: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nAlmost perfect\n\n\nSeptember 8, 2006\n\n\nShawn Fitzgibbons\n\n\n\n\n\nThese earphones are great quality. The sound is good and they fit comfortably in the ear once you get used to them. Two minuses make them only 4 out of 5 stars: first, the customer support is weak. There is supposed to be a website for the product for customer support and replacement parts, but Altec Lansing his it linked to a seemingly unrelated product; second, the volume control is a little heavy unless you have somewhere to clip it to (this might be a problem for runners)\n\n\n\n\nB0002MSTW2:fast_reader/writer_:shawn_m._matheson_\"smatheso\"\n\n\nB0002MSTW2\n\n\nLexar Media 1 GB 80x Pro Series Compact Flash Card (CF1GB-80-380): Electronics\n\n\nelectronics\n\n\n9 of 10\n\n\n5.0\n\n\nFast Reader/Writer \n\n\nMarch 3, 2006\n\n\nShawn M. Matheson \"smatheso\"\n\n\nSpokane, WA USA\n\n\nThis CF card is so fast that I can run my shutter on my camera non stop until the card is full at a frame rate of 3.5 fps. No lag or slow down. That is amazing. It makes my camera perform better. (Canon Rebel XT\n\n\n\n\nB00083XXZS:sandisk_2_gb_ultra_ii_memory_stick_pro_duo:l._m._kasik\n\n\nB00083XXZS\n\n\nSanDisk 2 GB Ultra II Memory Stick PRO Duo: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nSanDisk 2 GB Ultra II Memory Stick Pro Duo\n\n\nAugust 27, 2006\n\n\nL. M. Kasik\n\n\nSouthern Ca...USA\n\n\nfast and reliable..well worth the mone\n\n\n\n\nB00083XXZS:big_&_fast!:ikesingle\n\n\nB00083XXZS\n\n\nSanDisk 2 GB Ultra II Memory Stick PRO Duo: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nBig & Fast!\n\n\nJuly 31, 2006\n\n\niKESingle\n\n\nBay Area, CA USA\n\n\nPerhaps the ultimate addition to a good camera. I used my camera with a SanDisk Pro Duo (the blue one...) and while most times it was fine, when I needed to take pictures in quick succession - it let me down. The Ultra II is noticably faster. You will not regret buying this\n\n\n\n\nB00016L0VQ:not_the_right_item:charles_w._fain_\"chuck\"\n\n\nB00016L0VQ\n\n\nSandisk 512 MB Secure Digital Ultra II (SDSDH-512-901, Retail Package): Electronics\n\n\nelectronics\n\n\n0 of 6\n\n\n5.0\n\n\nNot the Right Item\n\n\nAugust 6, 2006\n\n\nCharles W. Fain \"Chuck\"\n\n\nFlorida\n\n\nThe Sandisk 512 MB Secure Digitial Ultra II (SDSDH-512-901) sent to me was not the item I ordered. I returned the item, unopened\n\n\n\n\nB00065AO0K:nice_product,_does_the_job_nicely_._._.:jennifer_l._heupel_\"dvd_fan\"\n\n\nB00065AO0K\n\n\nSanDisk SDSDH-1024-901 1 GB Ultra II Secure Digital Memory Card (Retail Package): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nNice product, does the job nicely . . .\n\n\nNovember 14, 2006\n\n\nJennifer L. Heupel \"DVD Fan\"\n\n\nLaramie WY\n\n\ndefinitely recommend this memory card, download time is great . . \n\n\n\n\nB00065AO0K:fast,_really_faster_than_my_old_ones:bill_\"bill\"\n\n\nB00065AO0K\n\n\nSanDisk SDSDH-1024-901 1 GB Ultra II Secure Digital Memory Card (Retail Package): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nfast, really faster than my old ones\n\n\nNovember 13, 2006\n\n\nBill \"Bill\"\n\n\nLouisiana\n\n\nI am happy to have several Sandisk products, and all of them are excellent.\n\n\n\n\nB00065AO0K:the_perfect_add_on_for_your_camera:pamela_deshazo\n\n\nB00065AO0K\n\n\nSanDisk SDSDH-1024-901 1 GB Ultra II Secure Digital Memory Card (Retail Package): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nThe Perfect Add On For Your Camera\n\n\nNovember 12, 2006\n\n\nPamela DeShazo\n\n\n\n\n\nThe SanDisk SDSDH-1024-901 is absoultely worth the extra money if you love to take many pictures without worrying about how many pictures are left for you to take. It is great to use with mini-videos if your camera has this feature\n\n\n\n\nB0000B3AKR:cheap_option:b._finzi\n\n\nB0000B3AKR\n\n\nViking USB00512L2 512 MB USB 2.0 Portable Mini Flash Memory Device: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nCheap option\n\n\nFebruary 23, 2006\n\n\nB. Finzi\n\n\nBrazil\n\n\nThe cheapest option I found at the time but an excelent pen drive. Small and with a good style\n\n\n\n\nB0000B3AKR:it's_great,_but_has_one_small_problem.:t._domingo\n\n\nB0000B3AKR\n\n\nViking USB00512L2 512 MB USB 2.0 Portable Mini Flash Memory Device: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nit's great, but has one small problem.\n\n\nFebruary 21, 2006\n\n\nT. Domingo\n\n\nKent, WA USA\n\n\nIt works great! I really like it. There is one small problem though. The lid is REALLY hard to take off! I've had it now for about a month and have gotten lots of use out of it and it has loosened up a little, but not much. There has got to be a way to make a lid that stays on, but isn't quite so hard to remove when a person wants to use it. I appreciate being provided with a forum to voice my opinion\n\n\n\n\nB00064V6R6:card_reader:c._legarra\n\n\nB00064V6R6\n\n\nSanDisk CF Type I/II ImageMate USB 2.0 Reader/Writer (Model SDDR-92-A15): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n4.0\n\n\nCard Reader\n\n\nAugust 7, 2006\n\n\nC. Legarra\n\n\nCa - USA\n\n\nWorks well with or without software installed. Good for home use and school or traveling.\n\n\n\n\nB000BBCTHU:wish_i_had_gotten_a_bigger_one!:a._hart\n\n\nB000BBCTHU\n\n\nWacom Graphire4 4x5 USB Tablet (Silver): Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nwish I had gotten a bigger one!\n\n\nNovember 19, 2006\n\n\nA. Hart\n\n\n\n\n\nThis technically works well. I am finding learning to use it difficult. I really wish I had spent a little more and gotten a bigger one!!!!! It is a lot smaller than I thought it would be and would be alot easier if it was bigger\n\n\n\n\nB000BBCTHU:excellent_beginners_tablet:4nr\n\n\nB000BBCTHU\n\n\nWacom Graphire4 4x5 USB Tablet (Silver): Electronics\n\n\nelectronics\n\n\n8 of 8\n\n\n5.0\n\n\nExcellent beginners tablet\n\n\nAugust 21, 2006\n\n\n4nR\n\n\nthe intuos line\n\n\nI was considering purchasing a tablet and Wacom was consistantly recommended. I was originally hesitant to buy the graphire line because I didnt think it would be very good for drawing compared to the top line (the intuos line). I was pleasantly surprised to be proven wrong. Installing a driver was a piece of cake and I began drawing away in minutes. It is very responsive and works with all my drawing programs (paintshop pro, painter, photoshop). The mouse is pretty useless however, and I would have preferred if they had included some extra pen tips instead (they seem to wear out, though I do press pretty hard). Other than that, its an amazing product\n\n\n\n\nB00070QI1S:kingston_1gb_sd_card_review:eugene_ong\n\n\nB00070QI1S\n\n\nKingston 512MB Elite Pro Secure Digital Memory Card ( SD/512-S ): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nKingston 1GB SD Card Review\n\n\nNovember 9, 2006\n\n\nEugene Ong\n\n\n\n\n\nGreat value for your money. Kingston has always been a reliable memory chip company and the same goes for this product.\n\n\n\n\nB0002IWC9C:great:p._valbuena_\"paulty\"\n\n\nB0002IWC9C\n\n\nSony BCG-34HE4 Super-Quick Worldwide Battery Charger with 4 AA NiMH Batteries: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\ngreat\n\n\nNovember 10, 2006\n\n\nP. Valbuena \"paulty\"\n\n\nSan Jose, CA\n\n\nGreat batteries for cameras. They charge quickly, and it's handy to keep the extra 2 spare in the charger and with me all the time \"just in case\"\n\n\n\n\nB000ER23M2:an_unexpected_surprise:c._s.\n\n\nB000ER23M2\n\n\nLogitech NuLOOQ Navigator (965162-0403): Electronics\n\n\nelectronics\n\n\n5 of 7\n\n\n5.0\n\n\nAn unexpected surprise\n\n\nJuly 23, 2006\n\n\nC. S.\n\n\nAlbany, NY\n\n\nI bought the NuLOOQ product a few weeks ago with little expectation and actually with some doubts it'd do anything for me, as I'm a keyboard junkie. The product description was a bit confusing as it seems like ti does a lot. But what a surprise. Just adjusting the brush size using the ipod like scroll disc on the device is worth the price. The pop-up menu thing is cool. I don't use it as much for putting tools, but putting my files and folders into that menu keeps things in one place. I guess the product also lets you pan and zoom around, but I don't seem to use that as much. I still like using my mouse to pan and zoom around.\n\nHey, I was skeptical about the product like anyone else, but found myself a rare surprise. Good job Logitech\n\n\n\n\nB0000AKVHF:great_product!:firas_alragom\n\n\nB0000AKVHF\n\n\nSanDisk SDDR-88-A15 8-in-1 USB 2.0 Hi-speed Reader (Retail Package): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGreat product!\n\n\nSeptember 1, 2006\n\n\nFiras Alragom\n\n\nKuwait\n\n\nI've had this product for well over a year now and was using it with my XP machine and am now using it with my Apple Mac Mini. Works great with both Windows and Mac OS X, with no hassles. The removable stand is a nice touch, as you can pack the reader into a laptop bag or lay it horizontally if you prefer. Highly recommended\n\n\n\n\nB0002KQRBY:good_quality_discs`:vrdumbos\n\n\nB0002KQRBY\n\n\nVelocity DVD-R 4X 4.7GB, 10-Spindle: Electronics\n\n\nelectronics\n\n\n13 of 15\n\n\n5.0\n\n\nGood Quality Discs`\n\n\nNovember 8, 2004\n\n\nvrdumbos\n\n\nDenver, CO\n\n\nI have burnt a few discs and so far no coasters, They are burning at 8X in my NEC 2500A burner. That is pretty considering that it is advertised at 4X. Hopefully the burn quality also will be good and will last for a few years\n\n\n\n\nB0000AKVJC:fast_delivery,_good_product:marc_hutchins\n\n\nB0000AKVJC\n\n\nSanDisk SDSDB-512-A10 512 MB Secure Digital Card (Retail Package): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nfast delivery, good product\n\n\nNovember 11, 2006\n\n\nMarc Hutchins\n\n\n\n\n\nshipping was outrageous, thought it qualified for free shipping since it is the size of a F'n postage stamp, but i ended up paying 1 cent less for postage than I did for the item, could have bought it at walmart for less this way. Buyer beware, I got took\n\n\n\n\nB000AY0HTU:wonderful_keyboard:john_rosney\n\n\nB000AY0HTU\n\n\nLogitech G15 Gaming Keyboard: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nWonderful Keyboard\n\n\nNovember 10, 2006\n\n\nJohn Rosney\n\n\n\n\n\nThis is the second G15 keyboard I've purchased, not because the first one broke, because it's such a great keyboard. Now I keep one at home, and take one with me when I go LANning. The only issue with the keyboard is the LCD is pretty useless is fast paced FPS games, otherwise it's good for keeping the clock up so one doesn't lose track of the time\n\n\n\n\nB000A2K3XW:very_good_tv:rick_d._tucker\n\n\nB000A2K3XW\n\n\nSony KDFE42A10 42\" LCD Rear Projection Television: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nVery Good TV\n\n\nNovember 20, 2006\n\n\nRick D. Tucker\n\n\nOklahoma, USA\n\n\nI'm very happy with the set. Although not listed in the specs, it must have a QAM tuner, because I am able to watch unscrambled digital broadcasts through my cable service even though all I have is basic cable. I was thrilled when I made this discovery. Watching football games in HD, without paying for digital cable, almost made me weep with happiness.\n\nThe picture quality for analog channels is only so-so. I really wish the remote was illuminated. Also, Amazon took much longer to deliver the TV than was promised. Other than these negatives, I'm very happy\n\n\n\n\nB000A2K3XW:very_happy_football_fan_!!!:theodore_m._amenta_\"breckenridge_realtor\"\n\n\nB000A2K3XW\n\n\nSony KDFE42A10 42\" LCD Rear Projection Television: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nVery happy football fan !!!\n\n\nSeptember 21, 2006\n\n\nTheodore M. Amenta \"Breckenridge Realtor\"\n\n\nBreckenridge Colorado\n\n\nMy new TV finally arrived and I am very pleased with it. I picked this one based primarily on all of the reviews I could find online, the vast majority of which were very positive (cNet has lots of reviews as well). Also, I have two friends who are cable guys and they see all kinds of TVs, all day every day, and both of them bought this exact set for themselves.\n\nMy cable provider does not offer many High Def stations yet so the image quality of Standard Def video was the primary criterion for me. Most reviews were very positive in this regard but there were some that were the exact opposite. My first impression had me rather worried. I have since come to believe that poor quality Standard Def video is just a matter of garbage in = garbage out. I began to suspect this when I noticed that some stations looked much better than others. In fact, some shows and commercials on any given station looked much better than those which preceded or followed them. I became convinced as I watched a cable news show where the host and two contributors were each on different cameras being displayed at the same time. The video of the host looked OK, the video of one contributor was poor, and the video of the other contributor looked great. The video quality of the various individuals remained consistent when they were occasionally displayed individually on the entire screen. The set certainly has the ability of displaying fantastic Standard Def video. I expect that I just never noticed the variability of video quality on lesser TVs.\n\nThe quality of the Hi Def Video is breath taking.\n\nSet-up was simple and I have not even bothered yet to read the manual. Of course the display can be adjusted in many ways, but it looks good to me with the factory presets and I do not intend to adjust the display anytime soon. Flipping through the on screen menu, I stumbled across the altitude setting which was a nice surprise since I live at 10,000 ft. above sea level.\n\nI was a little disappointed with the delivery situation. I thought I had ordered the TV in time to have it by opening day kick-off. Instead it arrived just in time for the third weekend of the football season, and I was told that it could easily have been another week beyond that. I blame the shipper (Eagle USA) for this, not Amazon. I say this because I could track the package enough to know that Amazon had the TV picked up right away. But then Eagle USA kept it sitting in Dallas for almost 2 weeks, and then in Denver for another week, before finally calling me to arrange for delivery. I believe that if I had not called and emailed Amazon to complain about the delay, it would have taken longer. Eagle USA told Amazon that they had tried to call me sooner but I know that this is not true for two reasons: 1) I had called Eagle USA the same day they told Amazon this and Eagle USA told me that they had no intention of calling me until a day or so prior to delivery, and 2) my livelihood depends on people being able to reach me at all times and I have a very good message and caller ID tracking system in place.\n\nAll in all, a great set at a great price, I just wish I had ordered and started pushing the issue sooner\n\n\n\n\nB000B622CW:if_i_knew_then_what_i_know_now..._i'd_still_buy_it._:geoffrey_grace\n\n\nB000B622CW\n\n\nGriffin Technology 4026-LCDT iTrip LCD FM Transmitter for iPod: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nIf I knew then what I know now... I'd still buy it. \n\n\nSeptember 11, 2006\n\n\nGeoffrey Grace\n\n\nEast Lansing, MI\n\n\nSo, I was a little weary to buy an iTrip after hearing some mixed reviews. It seemed that a perosn either had horrible luck with theirs... or the iTrip worked perfectly. Knowing my luck, it wouldn't work for me at all. I recieved my iTrip and my luck started right away. I tossed the iTrip onto my iPod and..................... nothing. A bad omen? Well, after I exchanged my iTrip for a new one I tried it out on a short drive. I live in East Lansing, MI... and we have a very crowded FM dial. I couldn't find a completely blank station as recommended... but it didn't matter. My iTrip delivered decent sound through the station. As others have mentioned, charging your iPod while you use your FM transmitter will really help. It's not a night-and-day difference, but it's a noticeable improvement. Sure, I got some static and the occasional interference from a strong signal, but you know what? Through a good 40 minutes of driving, the majority of the time I had satisfactory sound. This was also on the weaker-signaled LX mode, not DX. Supposedly DX provides a better, stronger signal. \nThis product is dependant on several outside factors. The reception your car gets and the availability of a clear FM channel. To rate this product poorly because of factors out of it's control is unfair. There were times when I had downright poor sound... and times where it was comparable to a tape deck. I love good, crisp sound as much as anyone does... but I'll take somewhat degraded sound over trying to find good music on the radio anyday. \n\nMy recommendation after owning this product for a short time *but it has been used quite a bit already, as I do a lot of driving*, I say: If you can, buy a tape deck adaptor... but if it's not an option and you don't want to put the money into an \"ipod to car\" setup, this is a viable solution indeed. In my opinion, an iTrip is a better option than listening to plain, old radio.\n\n\n\n\nB000050AOP:flawless:kirk_w_horne\n\n\nB000050AOP\n\n\nHewlett Packard CD-Writer C4503A 9510i 12x8x32 Internal EIDE Kit: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nFlawless\n\n\nFebruary 5, 2002\n\n\nKirk W Horne\n\n\nGraham, NC United States\n\n\nI have used the 9510i for about 2 years and have found it to be flawless in performance. The Disaster Recovery software provided with this drive is it's biggest star. You can format your hard drive, put in the restore disk that you create for your system, and in about 15 minutes your system is put back to EXACTLY the way it was when you created the disk. Or you can simply replace selected files if you want. It does not miss a single thing. Fanstastic. The software is extremely user friendly and guides you through the process easier than an "ABC" book. I would highly recommend this drive for performance and ease of use\n\n\n\n\nB0009O5ITU:happy_with_the_purchse_for_the_price:j._fregelette\n\n\nB0009O5ITU\n\n\nPhilips HTS3410/37 DVD Home Theater System: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n4.0\n\n\nhappy with the purchse for the price\n\n\nMarch 25, 2006\n\n\nJ. fregelette\n\n\n\n\n\nNice sound, I will admitt for the price, I get big sound, with quality. Only thing is a humming, hissing sound when machine is idle. Ohtherwise, worth it. \n\n\n\n\nB0001Q5SFU:product_desciption_could_be_more_specific:r._david_maurer_\"david_maurer\"\n\n\nB0001Q5SFU\n\n\nTargus PAUH216U 4 Port Slim USB 2.0 Hub: Electronics\n\n\nelectronics\n\n\n3 of 10\n\n\n5.0\n\n\nProduct desciption could be more specific\n\n\nMay 28, 2006\n\n\nR. David Maurer \"David Maurer\"\n\n\nSt. Louis metro area\n\n\nI,ve seen complaints about the ac adapter you \"must\" plug in. Hey guys, try it without an outside energy source, I just hope I,m not gonna fry something but it seems to work fine just plugged into a powered USB port. I have a VERY high end system though with wattage to sprare, but no more PCI slots, Damn 7900 512 gtx graphics cards, why do 2 of em have to take up so much room. Oh well, trade offs are a bi---, but a reality and 7 extra USB slots are awfully nice\n\n\n\n\nB0001Q5SFU:does_the_job:ks\n\n\nB0001Q5SFU\n\n\nTargus PAUH216U 4 Port Slim USB 2.0 Hub: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n5.0\n\n\nDoes the job\n\n\nOctober 22, 2005\n\n\nks\n\n\nNew Jersey\n\n\nIt's a simple device, so there's not to say about it other than it does the job. You should note that the LEDs on the device remain lit as long as the device is plugged into your PC and the PC is powered (they glow a bright blue).\n\n\n\n\nB00029BQ98:great_budget_tv:c._d._pak\n\n\nB00029BQ98\n\n\nSamsung LT-P1545 15\" Flat-Panel LCD TV: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nGreat Budget TV\n\n\nNovember 14, 2006\n\n\nC. D. Pak\n\n\n\n\n\nI bought this TV for college, and it fits perfectly in my dorm. The picture quality is pretty decent, though compared to traditional TV, it is not as sharp.\n\nThe audio quality is very good. I was able to play Final Fantasy 12 with this TV, and the audio lives well for my expectations.\n\nNo lag while playing my Playstation 2 console...\n\nThe only downside is that if you want to switch from AV mode back to TV mode, you need to press \"SOURCE\" on the remote or the TV 4 times...and it's annoying waiting for it...but besides that, it's a very nice budget TV for anyone that wants a TV that can save you some money and space\n\n\n\n\nB0002IOIMQ:charger_&_batteries_are_great:charley\n\n\nB0002IOIMQ\n\n\nSony BCG-34HRMF4 Super Quick Charger with LCD Indicator and 4 AA Batteries: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nCharger & Batteries are great\n\n\nNovember 9, 2006\n\n\nCharley\n\n\nArkansas\n\n\nWe used this charger with a DC inverter on a 6000 mile road trip to charge batteries for a Sony DSC-H5 camera and the batteries were almost unbelievable with lasting power between charges. The 2700mAh battery is great\n\n\n\n\nB000BVB2FK:a_great_buy:nikos_antoniou\n\n\nB000BVB2FK\n\n\nWacom Intuos3 6X11 Pen Tablet: Electronics\n\n\nelectronics\n\n\n7 of 7\n\n\n5.0\n\n\nA great buy\n\n\nAugust 30, 2006\n\n\nNikos Antoniou\n\n\nL.I. N.Y.\n\n\nThe intuos3 6x11 tablet is nice because its like having a giant mouse pad. The mouse itself has five buttons that can be programed to do different tasks, opening programs is one of them. It also has a scroll wheel. On the pad there are more buttons that can open programs among other things. Also there are touch sensitive strips on the tablet that can be used for scrolling. I use the pen inside of art progams and it is a real joy to work with. The pressure sensitivity is is a very nice feature alowing you to go from a thin line to a thick line and vice versa. Its main high point for me is the ability it gives to draw curved lines and detailed lines. It fits nicely in hand and has comfortable rubber grip. Also you can flip the pen over and use that side as an eraser. The only complaint I have is that on the pen there are two buttons and one of them (the lower one) is placed in a spot where when I'm drawing my finger frequently hits it, because of this I simply had to make the button inactive. Over all I feel that Wacom has done a very good job of developing this product. I feel that it is of high quality and well worth the price. I would definetly save up the extra few dollars to get this larger tablet, it is nice to have if you have the desk space. I've heard Wacom is the best in the pen tablet industry and now that I own one I believe it\n\n\n\n\nB0009RGLSE:great_product:d._tennison_\"donny_alaska\"\n\n\nB0009RGLSE\n\n\nSanDisk 2 GB SD Memory Card ( SDSDB-2048-A10, Retail Package): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGREAT Product\n\n\nNovember 5, 2006\n\n\nD. Tennison \"Donny_Alaska\"\n\n\nAnchorage, AK USA\n\n\nFabulous product --- now I can store over 700 photos in the 8 mega pixel range. Happy I purchased this one -- very trouble free\n\n\n\n\nB00009R6VB:almost_a_must,_if_you_use_the_battery_grip:chris_hann\n\n\nB00009R6VB\n\n\nHand Strap E1: Electronics\n\n\nelectronics\n\n\n10 of 10\n\n\n4.0\n\n\nAlmost a must, if you use the battery grip\n\n\nJanuary 12, 2006\n\n\nChris Hann\n\n\nAlameda, CA United States\n\n\nI wouldn't be without this. When I first bought the 20d it was my first digital SLR and I spent a few weeks adding and removing the battery grip and this strap. In the end I settled for using both and wouldn't have it any other way. With the battery grip and hand strap in place I find the camera much more secure and comfortable to hold. That's all there is to it, that's what it does and it does it well. \n\nOn the down side, and it's a small and transient down side, actually fitting it to the camera and getting it adjusted to the exact right length for your hand is a bit fiddly and you'd sort of expect it to be insecure. I think the trick, security wise, is to make sure the bottom strap goes through the center buckle first, then it is locked in by the top strap which is already held securely at the junction with the neck strap (if used). I also don't like the attachment to the neck strap, I don't like having a fairly sharp edged piece of metal where it can rub against the chasis of the camera in normal use.\n\nI use this on a 20d which typically has a 24-200 or 80-400 lens attached. You can only use this if you also use the battery grip, or buy a third party plate which attaches to the tripod mount and provides the lower mounting location. But since I always use the battery grip I don't have an issue. \n\nIf Canon read this, you could do with a better solution for the attachment and adjustment on an accessory of this price, you normally pay close attention to detail and it's a bit lacking here\n\n\n\n\nB0007PGADE:no_coasters_yet:jose_e._gonzalez\n\n\nB0007PGADE\n\n\nMemorex DVD+R 16x 4.7GB 50 Pack Spindle: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nNo Coasters Yet\n\n\nSeptember 6, 2006\n\n\nJose E. Gonzalez\n\n\nEagle Pass, Tx\n\n\nI have bought 50 and 100 pack spindles of Memorex and so far I have not had any problems. They work just fine on my Sony dvd burner\n\n\n\n\nB0007PGADE:burns_right_every_time:mts4by5\n\n\nB0007PGADE\n\n\nMemorex DVD+R 16x 4.7GB 50 Pack Spindle: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nBurns right every time\n\n\nAugust 23, 2006\n\n\nmts4by5\n\n\n\n\n\nMemorex DVD+R's are the best. Price keeps dropping and never have a problem\n\n\n\n\nB0007PGADE:great_dvd_media:joel_laika\n\n\nB0007PGADE\n\n\nMemorex DVD+R 16x 4.7GB 50 Pack Spindle: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nGreat DVD media\n\n\nJuly 15, 2006\n\n\nJoel Laika\n\n\n\n\n\nI have burned over 100 of these in the past 6 months, I have only had 1 burn badly. Haven't found a dvd player yet that they won't play in\n\n\n\n\nB0007WKB14:baby_this_baby.:bv1953_\"8to16to35\"\n\n\nB0007WKB14\n\n\nPanasonic DMR-ES10S DIGA Series DVD Recorder (Silver): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nBaby this baby.\n\n\nMay 23, 2006\n\n\nBV1953 \"8to16to35\"\n\n\nPanasonic brand\n\n\nThis is an excellent machine. I bought two of them. There are a few things you need to do. I have had 100% satisfaction using FujiFilm DVD-R up to 8x. I have had success with FujiFilm DVD+R up to 8x. I`ve also had tremendous success with TDK DVD+R up to 4x and 8x. Some other brands may not work. One thing I`ve learned about this machine is that you have to baby it a little. Don`t go recording 3 or 4 -R or +R DVDs every day. Give it a rest between recordings. One or two per day is okay. Definitely give it room to breathe. If you stack your machines on top of each other, put something in between so they can breathe. Be sure not to block the ventilation holes on the sides. You definitely need to buy a Panasonic DVD player to play back your DVDs - other brand DVD players may not play your recordings properly.\n\nOne of the greatest things about this machine is that it records DVD-RAM. They are re-recordable for many, many times and each program can be erased when watched. You can even edit the recordings on the DVD-RAM. Cut out commercials and fit more on a disc or remove the commercials for when you watch the program. Only works on the DVD-RAM. I have over 50 DVD-RAM discs (Panasonic brand) - they sure take up less space than VCR tapes!\n\nGet one or two of these recorders and a Panasonic DVD player and you`ve got it made! Throw away your VCRs forever! Just remember to baby them\n\n\n\n\nB0007WKB14:great_price,_easy_to_use!!:furtherburn\n\n\nB0007WKB14\n\n\nPanasonic DMR-ES10S DIGA Series DVD Recorder (Silver): Electronics\n\n\nelectronics\n\n\n10 of 11\n\n\n5.0\n\n\nGreat price, easy to use!!\n\n\nJanuary 10, 2006\n\n\nfurtherburn\n\n\nCity of Rock, NY\n\n\nI bought this for my folks for Christmas. It took no time at all to hook up, and making DVDs was simple. Everything from recording off the TV, to recording movies and shows stored on the DVR, to transferring home movies from the original VHS tapes. I'm sure for about $300 more you could purchase some higher end recorders, but for under $200, you really can't go wrong. The only flaw I could find is that the initial hookup (according to the book) didn't seem quite right. After taking a whole 2 minutes to look up the easy to read instructions online, everything went smoothly. Because of that, I'm still going to give it a full 5 stars..it's not as if those 2 whole minutes ruined our life. Really, if you're new to this technology, you're not going to have any problems..believe me..A++\n\n\n\n\nB000EDK8V4:incredible_picture!_with_one_flaw_on_old_dvds:no-nonsense_consumer\n\n\nB000EDK8V4\n\n\nSony DVPNS75H Single Disc Upscaling DVD Player: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nIncredible picture! with one flaw on old DVDs\n\n\nNovember 11, 2006\n\n\nNo-Nonsense Consumer\n\n\nNew York\n\n\nThe other effusive reviews are spot-on... the picture is fabulous on new DVDs (Lord of the Rings, Nemo, Harry Potter). A $20 Cables to Go HDMI cable does a great job. We have the Sony KDF-42E2000 42\" 3LCD Rear Projection HDTV and are thrilled with the combination. A few annoyances: The machine responds and loads slowly, but that's not too bad; a minor sacrifice for the incredibly low price. And, certain DVDs of old movies (Some Like it Hot, The King and I) stretch the picture horizontally, making things look squat. I'm sure there's a solution, but just can't figure it out. Any tips\n\n\n\n\nB000DZDMB6:vhs_to_dvd_and_ipod:wps\n\n\nB000DZDMB6\n\n\nTransfer Video Directly To DVD Capture Video for Psp Or Apple Ipod: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nVHS TO DVD AND IPOD\n\n\nNovember 6, 2006\n\n\nWPS\n\n\nLas Vegas, Nv.\n\n\nVHS to DVD disc is just one easy setup. I set mine to record and recode just before I go to sleep. When I wake up a finished disc awaits. With the software tools that are provided you can sharpen and correct VHS that has degraded and bring back life as when it was first transfered to tape. I have taken many VHS tapes that were never released as DVD and now have the start of a great library. Editing is a little tricky until you get the hang of it. The product works well. IPOD old music videos and movies for a real treat. Sound is perfect\n\n\n\n\nB00006F2WE:this_review_is_not_for_the_technologically_well_versed:\"etollis\"\n\n\nB00006F2WE\n\n\nCanon S830D Photo Printer: Electronics\n\n\nelectronics\n\n\n19 of 20\n\n\n5.0\n\n\nThis review is not for the technologically well versed\n\n\nDecember 17, 2002\n\n\n\"etollis\"\n\n\nSan Jose, California United States\n\n\nI've had this photo printer a few weeks now. I don't consider myself technically proficient. I totally scored despite gross ignorance with this machine. You cannot go wrong. The photo quality is astonishing. I've printed professional quality photos and framed them. The results are fantastic. For someone who is techno-challenged, I had this up and running very quickly\n\n\n\n\nB000050AQ7:great_to_conserve_space:l._forgione_\"lauren\"\n\n\nB000050AQ7\n\n\nMemorex 32021926 Clear Slim Jewel Cases (25-Pack): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\ngreat to conserve space\n\n\nFebruary 23, 2006\n\n\nL. Forgione \"Lauren\"\n\n\n\n\n\ntwo of these jewel cases equal the size of a regular cd case. buy them and conserve space. there great\n\n\n\n\nB000050AQ7:fast_and_friendly:doyle_\"mustang_fan\"\n\n\nB000050AQ7\n\n\nMemorex 32021926 Clear Slim Jewel Cases (25-Pack): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nFast and friendly\n\n\nFebruary 23, 2006\n\n\nDoyle \"Mustang fan\"\n\n\nDAYTON, OHIO United States\n\n\nGot my order quickly and found out that these cases are much BETTER than the ones sold here locally, and they were CHEAPER !!\n\n\n\n\nB000050AQ7:great_space_saver!:mr._dip\n\n\nB000050AQ7\n\n\nMemorex 32021926 Clear Slim Jewel Cases (25-Pack): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nGreat space saver!\n\n\nJune 9, 2005\n\n\nMr. Dip\n\n\n\n\n\nI like keeping all my stuff in one place, but don't like CD folders, so these slim cases are great. I accidentaly sat on one of them and although it did crack, the case didn't break, and the cd was fine. All in all, a great product\n\n\n\n\nB0000645V2:work_fine:d._hennes_\"garetjax\"\n\n\nB0000645V2\n\n\nMemorex CD/DVD Slim Jewel Cases (Assorted Colors, 25-Pack): Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n4.0\n\n\nWork fine\n\n\nNovember 26, 2004\n\n\nD. Hennes \"Garetjax\"\n\n\nCincinnati, OH USA\n\n\nI've used these (and similar) cases for years. No problems, even with the cats occasionally knocking them around and walking on them. \n1) I've never had one crack. \n2) They hold the disc securely, but not with so much force that you risk damaging the disc when removing it.\n3) As long as you open them properly - from the front (clear) side by pulling near the corner(s) while holding the back (colored) side - they open fine\n\n\n\n\nB00063RT5K:okay_for_the_price:buyer\n\n\nB00063RT5K\n\n\nTech B BTAA4801 AA Alkaline Batteries 48-Pack: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nOkay for the price\n\n\nJuly 30, 2006\n\n\nbuyer\n\n\n\n\n\nThey are okay for eight or so dollars, but they don't last very long. Then again, I used these for a digital camera so maybe that's why they get drained so quickly. However, for the price I still would buy them again\n\n\n\n\nB00063RT5K:a_great_price_for_a_great_battery:jason_p._givens_\"jpgivens\"\n\n\nB00063RT5K\n\n\nTech B BTAA4801 AA Alkaline Batteries 48-Pack: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n5.0\n\n\nA great price for a great battery\n\n\nJuly 28, 2005\n\n\nJason P. Givens \"jpgivens\"\n\n\nLeander, TX\n\n\nMy daughter uses a Cochlear Implant that has the option of being powered by 3 AA batteries. We have tried several brands trying to find the ones that last the longest. We have tried Energizer, Duracell, etc. She needs to replace them every day and a half. Since the Implant is a high power draining device, every extra hour counts. These batteries are one of the best values out there.\n\n\n\n\nB000COB58K:prior_customer:j._mageean\n\n\nB000COB58K\n\n\n3-Year Service Plan for Personal Electronics $200 to $599.99: Electronics\n\n\nelectronics\n\n\n55 of 58\n\n\n5.0\n\n\nPrior customer\n\n\nMay 15, 2006\n\n\nJ. Mageean\n\n\nMountain View, CA USA\n\n\nI have not had N.E.W. coverage for this Apple product however I had a washing machine covered by them. The washer turned out to be one of those machines that had one problem after another sometimes the same. N.E.W. used a couple of different service companies to repair it and finally wrote it off. Throughout, N.E.W. was great and made every effort to fix the machine. When the finally wrote it off they sent me a check for the amount that I paid for it. They did not try to discount the settlement price for the time that I actually had the machine\n\n\n\n\nB00083Y4HO:iogear_universal_memory_drive_hi-speed_usb_2.0_memory_card_reader/writer:g_e_geth_\"get\"\n\n\nB00083Y4HO\n\n\nIOGEAR Universal Memory Drive Hi-Speed USB 2.0 Memory Card Reader/Writer: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nIOGEAR Universal Memory Drive Hi-Speed USB 2.0 Memory Card Reader/Writer\n\n\nNovember 5, 2006\n\n\nG E Geth \"GET\"\n\n\nAW\n\n\nEsaki si ta campion, un tremendo idea di e sistema pa por load up un potret digital ariba bo PC. hopi bon y facil. Esaki ta pone cu bo por saca bo potret digital y pone ariba bo PC via e systema aki di chip.(Tremendo\n\n\n\n\nB000B6KXM8:overall-worth_the_hassle:dogmom\n\n\nB000B6KXM8\n\n\nSony RM-AX4000 Home Theater Remote Control: Electronics\n\n\nelectronics\n\n\n1 of 3\n\n\n4.0\n\n\nOverall-Worth the hassle\n\n\nJuly 29, 2006\n\n\nDogmom\n\n\nFairfield, CT\n\n\nInitial setup could be complex for some, but considering all it is capable of i'd say it's worth the time investment.\n\n\n\n\nB00007LTAQ:excellent:gabriel_a.\n\n\nB00007LTAQ\n\n\nTargus PAUM01U Ultra Mini Retractable Optical Mouse: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nExcellent\n\n\nApril 19, 2006\n\n\nGabriel A.\n\n\nColumbus, OH\n\n\nI've had it for over a month now and it works as well as I expected it to work. I was expecting random quirks here and there as I continued using it, but none whatsoever\n\n\n\n\nB000092YQW:great_value:djhack1639_\"not_just_another_web_geek\"\n\n\nB000092YQW\n\n\nSony MDR-EX71SL Fontopia In-The-Ear Headphones (Black): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGreat value\n\n\nNovember 9, 2006\n\n\nDjHack1639 \"not just another web geek\"\n\n\nUpstate NY, USA\n\n\nThese are great earbuds. I have alswys used Sony DJ headphones in the past, and these are the first pair of buds I have purchased - I am impressed.\n\nFor the money, the sound quality is very good - full and well rounded with good bass and the treble is not too bright.\n\nThey are pretty comfortable in the ears, but it does take a little getting used to. The cord is rather thin and very flexible, but seems to be sturdy enough for most use. The included 'case' is a little tough to figure out, but could be good for keeping them clean in a purse or backpack.\n\nDefinitely recommended\n\n\n\n\nB0002SAF96:love_it:george_woodward_\"gman0417\"\n\n\nB0002SAF96\n\n\nLogitech NASCAR Racing Wheel with Vibration Feedback - USB ( 963339-0403 ): Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n5.0\n\n\nLove it\n\n\nSeptember 20, 2006\n\n\nGeorge Woodward \"gman0417\"\n\n\nNJ\n\n\nI bought this for my kids to use with Nascar Racing. Controls are easily reprogramable. Nice product for the money\n\n\n\n\nB0009UDABW:video_express_met_my_needs:m._savela\n\n\nB0009UDABW\n\n\nADS Tech Video Xpress USBAV-191-EF: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nVideo Express met my needs\n\n\nNovember 5, 2006\n\n\nM. Savela\n\n\n\n\n\nGreat product for getting all those old home videos on DV\n\n\n\n\nB0009UDABW:ads_tech_video_:johnny_page_\"gopher_central___reader\"\n\n\nB0009UDABW\n\n\nADS Tech Video Xpress USBAV-191-EF: Electronics\n\n\nelectronics\n\n\n3 of 5\n\n\n5.0\n\n\nADS Tech Video \n\n\nAugust 8, 2006\n\n\nJohnny Page \"Gopher Central Reader\"\n\n\nNewark,De. USA\n\n\nThis little unit is easy to use and ready to go!!!! Not one problem with it so far, I've been using it for over two months now\n\n\n\n\nB0000CFY7I:good_buy:k._faber\n\n\nB0000CFY7I\n\n\nMemorex High Performance AA Alkaline Batteries, 50 Pack: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nGood Buy\n\n\nMarch 15, 2006\n\n\nK. Faber\n\n\nPeachtree City\n\n\nI use these batteries when I need power immediately and I don't have a set of rechargeables ready to go. I also use them as backups - so I don't get caught without power and have to pay $1 a battery at supermarkets and convenience stores. \n\nUnfortunately, my kids keep stealing them instead of recharging their own set\n\n\n\n\nB000EOJ792:movietime:l._thomas_\"lthomas\"\n\n\nB000EOJ792\n\n\nSony HT-DDW700 Complete 5.1-Channel DVD Home Theater System: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nmovietime\n\n\nNovember 10, 2006\n\n\nL. Thomas \"lthomas\"\n\n\nMaryland\n\n\nExcellent sound system, reminds you of being in a movie theatre. Great, Great system\n\n\n\n\nB000EH4Y56:good_performance_for_$150;_but_cheaply_made_(in_china).:t._agoston_\"tawmbooks\"\n\n\nB000EH4Y56\n\n\nSony MHCEC70 Mini Shelf System with 3-Disc CD Changer, Cassette Deck, and AM/FM Tuner: Electronics\n\n\nelectronics\n\n\n8 of 8\n\n\n4.0\n\n\nGood performance for $150; but cheaply made (in China).\n\n\nOctober 2, 2006\n\n\nT. Agoston \"tawmbooks\"\n\n\nConnecticut, USA\n\n\nI've owned many Sony electronics in the past and have always liked their styling and functionality -- lots of hardy Walkmen, useful shortwave and portable radios, stylish TVs and high quality cassette recorders. I still have a Sony mini component stereo model FH-E656 (made in Japan) which I bought in 1992 for US$400+; but rather than pay to fix its worn-out CD player, I decided to purchase this new Sony MHCEC70 component stereo (made in China).\n\nPLUSES: \ndecent sound\ngood functionality\neasy set-up and intuitive operation\nsimple remote -- but only covers basic functions\n\nCONS:\nvery short speaker leads\nunattractive styling (for my tastes) -- resembles a boombox\nonly holds 3 CDs vs 5 for comparable Panasonic units\n\nAt first glance, I was disappointed by the cheap construction, skimpy manual and very short speaker leads -- the manufacturer seemed to be counting pennies. But then I thought: in 2006 for $150, I can't expect the same quality as I received in 1992 for $400. Everything is made more cheaply now and almost all in China. So, for the money, it suits my purposes as a nice sounding stereo unit for the bedroom. The real test will be how long the CD changer works -- to be safe, this time I bought the 2 year replacement warranty. I haven't blasted it at full volume, but it seems to have plenty of power when needed.\n\nI originally bought a $300 Panasonic unit (SC-PM71SD MP3 SD Micro System) which was returned because it didn't have the full data recording feature as advertised (data recording was crippled by DRM limitations). This Sony stereo costs half as much, and has almost all the same functionality\n\n\n\n\nB00083JQY0:great_memory:dorothy_d._burns_\"dottie\"\n\n\nB00083JQY0\n\n\nSony 1 GB Memory Stick PRO Duo Media (MSXM1GST): Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n5.0\n\n\nGreat Memory\n\n\nJuly 8, 2006\n\n\nDorothy D. Burns \"Dottie\"\n\n\nAltadena, CA USA\n\n\nA memory Stick is a s memory stick but when you put it in a Sony Camera - Fantastic\n\n\n\n\nB000068IHH:excellent_product:s._davis\n\n\nB000068IHH\n\n\nCase Logic DMB-5 CD Sports Pack: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nexcellent product\n\n\nJune 30, 2006\n\n\nS. Davis\n\n\n\n\n\nThis portable cd holder is perfect for me. I work out regularly and it has been perfect for the gym. My cd player fit fine and there are 3 slots for extra cd's. I like the feature of the headphones attaching to the outside of the pouch. I would recommend it to anyone\n\n\n\n\nB00007KDVJ:works_great,_poor/inaccurate_documentation:rcp\n\n\nB00007KDVJ\n\n\nLinksys WAP54G Wireless-G Access Point: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n4.0\n\n\nWorks Great, Poor/Inaccurate Documentation\n\n\nSeptember 10, 2006\n\n\nRCP\n\n\nNote to Brian: The WAP54G *can* be configured as a bridge. I have two of them in just such a configuration. Took me about 10 minutes to set them up.\n\n\n(Note to Brian: The WAP54G *can* be configured as a bridge. I have two of them in just such a configuration. Took me about 10 minutes to set them up.)\n\nMajor modes of operation are:\n\n1. Wireless Access Point (its default set-up)\n2. Wireless Client (connect a wired ethernet device via cable to the WAP54G and you now have a wireless ethernet device)\n3. Wireless Repeater (extend range of wireless coverage)\n4. Wireless Bridge (connect two ethernet networks without running a cable between them -- acts as one end of a \"virtual\" cable; you need two to get the job done)\n\nA pair of wireless bridges is just what I needed but hesitated to buy the WAP54G because of Brian's comment that this wouldn't work. Because of my newness to the wireless world it took a bit of investigation to come to the conclusion that Brian was wrong. I bought a pair and they do work.\n\nI rate it 4 instead of 5 stars for a few reasons.\n\n1. I understand that higher-end, more expensive, equipment can be had with more range than this box. Gotta leave some room to rate more powerful equipment!\n2. One must dig very deeply into the users guide (a PDF on the CD) to find the default IP address in order to configure this device. Luckily I had read elsewhere what that IP address is (192.168.1.245). Other manufacturers make this info readily available, even going so far as printing it on the device itself. This will be critical info to any Linux user. Also, the users guide says that the default userid for logging in is \"admin\". Well, it's not. The only allowed userid is an empty field. This had me going for a few minutes.\n3. Something that 99% of the world won't care about -- I like to have more access to the firmware. For example other Linksys products allow one to telnet to the box and do low-level analysis of the box and the network it's attached to. I think there are third-party firmware loads that enable this\n\n\n\n\nB000652LUG:good_item:mandi_rodriguez\n\n\nB000652LUG\n\n\nSanDisk SDDR-103 MobileMate SD+ 5-in-1 Mobile Reader (SD/MiniSD/MMC/RS-MMC/TransFlash, Retail Package): Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\ngood item\n\n\nNovember 12, 2006\n\n\nMandi Rodriguez\n\n\n\n\n\ni love the mobilemate sd. I haven't had any problems yet. It's small compact and gets the job done\n\n\n\n\nB00070QI1I:very_good_sd_card:gabriel_gore_\"geebi\"\n\n\nB00070QI1I\n\n\nKingston 1GB SecureDigital Flash Card ( SD/1GB ): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nVery good SD Card\n\n\nNovember 9, 2006\n\n\nGabriel Gore \"geebi\"\n\n\nAlgood, TN\n\n\nI like it very much, I use it in an outdoor trail camera for wild game and it hold several pictures and has been in the camera in all kinds of weather. I would buy Kingston again\n\n\n\n\n0899336795:be_sure_to_pick_the_right_software_package:a._thatcher\n\n\n0899336795\n\n\nThe Blue Logger & Street Atlas USA 2006 or The Blue Logger & Street Atlas USA 2006 Handheld: Electronics\n\n\nelectronics\n\n\n7 of 13\n\n\n4.0\n\n\nBe sure to pick the right software package\n\n\nDecember 28, 2004\n\n\nA. Thatcher\n\n\n\n\n\nThe first peron to review this incorrectly stated that it doesn't come with software for handhleds. That person chose to buy it without and save some $$ and then complained about having to pay extra for it because he/she bought the wrong software package. Delorme did the right thing and offered us options. I just ordered mine and I have no plans on using with a HH so I'm very happy that I didn't have to buy the HH software. Just don't be dumb and read what you are buying before you buy it. Nemerous people have highly recommended this to me, although I have heard that MS Mappoint has better maps and should be used instead when you have it as an option. Storing 50,000 data points without a connection to a computer is awesome. I will be able to take the unit with me on a bike ride and analize the workout later hopefully looking at the route, elevation, etc. Too bad it is only in Yellow..\n\n\n\n\nB0000BZ40B:totally_secure:jeanette_cefalo_\"darwen\"\n\n\nB0000BZ40B\n\n\nDLO Action Jacket for 3G iPod Graphite: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n4.0\n\n\nTotally Secure\n\n\nJanuary 7, 2005\n\n\nJeanette Cefalo \"darwen\"\n\n\nBehind You\n\n\nI got this jacket a month into having my ipod. I had to buy it because I had been using the one that comes with the ipod and when I pulled the iPod out of that case it was easy to drop. One time I did drop it and within a month of owning the iPod I had it in fro repairs.\n\nThis case protected my iPod of another 11 months just fine. My complaint with it tough is the plastic peice that protects the screen and pod from falling out. It is stiched on the inside so when the ipod is in there is creates friction. Acrilic and plastic do not mux and you get a slightly scraped serface.\n\nIt will protect your pod though\n\n\n\n\nB00003006E:great_product;_lacking_directions:j._levine_\"jordan\"\n\n\nB00003006E\n\n\nLinksys EZXS55W EtherFast 10/100 5-Port Workgroup Switch: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n4.0\n\n\nGreat product; lacking directions\n\n\nMarch 8, 2006\n\n\nJ. Levine \"Jordan\"\n\n\nManhattan, New York, USA\n\n\nThe product works perfectly. The only problem is, the manual only includes directions for the most common setups. To use one port as an \"uplink\" to a router, for example, you need to plug the uplink into the ethernet port labeled \"1\" and then leave port \"2\" open. The directions do not state this, and I had to call Linksys to get it to work.\n\nOther than that, it is fast, reliable, and small. Perfect\n\n\n\n\nB00007LTB6:works_better_than_i_anticipated!:b._t._denyer\n\n\nB00007LTB6\n\n\nD-Link DWL-G650 Wireless Cardbus Adapter, 802.11g, 108Mbps: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nWorks better than I anticipated!\n\n\nMarch 29, 2006\n\n\nB. T. Denyer\n\n\nAtlantic Beach, FL United States\n\n\nMy setup:\n- Dell Latitude PP01L (Intel chip)\n- Win XP Pro\n- D-Link DI-624 Wireless Router (with two walls inbetween the laptop and the antenna)\n- Enabled the WEP feature\n\nI unplugged the old wireless card and simply plugged this in. Windows found the new card and did its Plug-n-Play thing. Not only was I was able to get on the Internet immediately, but the card picked up two other signals in the neighborhood that I couldn't 'see' with my 11MB/sec Cisco Aironet card. This I sort of expected, but was still a bit surprised being that my closest neighbor is beyond the range of most wireless routers.\n\nThe CD installed just fine, but when I went to the D-Link site to check for an updated driver it said I was up-to-date. I ran Windows Update and it claimed I had an updated driver available for my new D-Link card. I've never had a problem with D-Link's website (or customer service, while we're on the subject), so I was a bit taken aback that Microsoft was ahead of D-Link's site. \n\nEither way... install the card and get onto the internet, then have Windows Update check for possible updates.\n\nBe sure to enable the encryption feature. If you are running a small network of computers where you are sharing folders then you want to be sure that your neighbors can't go snooping through your stuff. Or worse, lift the signal, and ultimately your personal information.\n\nI've had no problems with my current set-up. I HIGHLY recommend this card to bring your laptop/desktop up-to-date\n\n\n\n\nB00007LTAU:it's_called_the_ultra_mini_for_a_reason!:c._dray\n\n\nB00007LTAU\n\n\nTargus PA050U USB 2.0 High-Speed Hub (4-port): Electronics\n\n\nelectronics\n\n\n5 of 6\n\n\n5.0\n\n\nIt's called the Ultra Mini for a reason!\n\n\nMarch 17, 2006\n\n\nC. Dray\n\n\nSacramento, CA United States\n\n\nThis review pertains specifically to the Targus PAUH217U 4 Port Ultra Mini USB 2.0 Hub. \nThe cord is about 2.5 feet long - long enough to reach from my pc, which sits on the ground, to the top of the desk. It has a cable which is split into two USB plugs - one just to transmit data, the second to power the unit. The second plug does not need to be used unless you plan on using some heavy-duty hardware, and if so what are you buying a hub called the ultra mini for anyways? If you insist on trying to run a blow dryer off the thing, then it does have an input for a separate power source, which you will have to buy separately. \n\nIn short, If you have an old school machine with USB ports which are inaccessible /inconvenient to use, this little hub will give you quick access while taking up minimal space on your desk\n\n\n\n\nB0007Z1M50:very_pleased:charles_harris\n\n\nB0007Z1M50\n\n\nLogitech MX518 Gaming Optical Mouse - Metal: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nVery pleased\n\n\nNovember 9, 2006\n\n\nCharles Harris\n\n\n\n\n\nI had read the reviews of this mouse prior to buying it. It had gotten very good reviews. Ive ben playing with it for about a month now and i find it meets all my needs and then some. Highly recommend it\n\n\n\n\nB0007Z1M50:excellent_mouse:p._mcclory\n\n\nB0007Z1M50\n\n\nLogitech MX518 Gaming Optical Mouse - Metal: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nexcellent mouse\n\n\nNovember 2, 2006\n\n\nP. MCCLORY\n\n\nPittsburgh, PA USA\n\n\nI've been a gamer for almost 20 years. This is the best mouse I've ever owned. It is fast, accurate, well formed and constructed. I would definitely recommend it.\nThe DPI adjustment is a nice touch. I prefer corded mice due to cordless being to heavy, this mouse is just right\n\n\n\n\nB00024VTKE:i_love_this_player!:kit\n\n\nB00024VTKE\n\n\niRiver iFP 799T 1 GB Flash MP3 Player: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nI love this player!\n\n\nJuly 30, 2006\n\n\nKit\n\n\nCambridge, MA United States\n\n\nFor those of you who bought MiniDisc recorders to move away from analog tape recording, this product is wonderful. Especially good for recording lectures, voice lessons, etc. It works quite well with the built-in mono mike, or you could use an external one.\n\nOne of the few players that lets you record from a line input or an external mike -- you toggle which setting it is in the settings section.\n\nI record my radio shows by plugging in the line-out from the station's tape deck to my iRiver.\n\nFor those who recorded live music with their MiniDiscs, it's a little trickier. You have to set the record input before -- you can't change it on the fly.\n\nI've recorded live DJ shows with the built-in and AGC on. Of course, the booming bass distorts horribly, but overall an easy record of the show. Very easy to keep in a pocket rather than to be obviously recording a show.\n\nI've also tried external mikes with the record input turned down very low with very good results. A bit of trial and error which I'm still refining with later iRiver products.\n\nThe single AA batter lasts a good long time. Summer School involves 2, 2-hour classes, and I can record several days of them before battery is low. If the battery runs out during recording, fear not, the file is saved!\n\nThe negatives are the requirement to go through the special iRiver music program, but there are now firmware updates that will change your iRiver into the typical USB external drag-and-drop drive.\n\nFiles are recorded in a .REC file which one then converts to a .mp3 file after transfer to your computer. I don't know how different this format is because it takes up exactly the same amout of space as the .mp3 file. But you can't trasnfer .mp3 files off your iRiver, even if you made them yourself, so don't think you can use this to transfer .mp3 files from one computer to another.\n\nAlso, the molding where the line-in input is sometimes gets in the way of certain plugs such as some RCA to 1/8\" adaptors, so make sure you pick one that alows it to fit in securely all the way.\n\nI've had mine for just over a year now and use it every day, whether just playing mp3s or recording classes. I'm only just starting to have some trouble with the on/off/play/pause button. After so many presses, it's starting to wear out. But it's still working reliably. I just have to press a little more precisely and pay more attention.\n\nI have the 1G model (iFP-799) which is great. I was able to record a 12-hour radio show marathon I produced with it. Previous marathons required a stack of cassette tapes or minidiscs.\n\nI'm quite happy with iRiver and plan on buying from them in the future\n\n\n\n\nB00068NF0I:love_satellite_radio:d._rhoads_\"catlvr4\"\n\n\nB00068NF0I\n\n\nJVC KT-SR2000 Plug 'n' Play Sirius Satellite Radio Receiver: Electronics\n\n\nelectronics\n\n\n10 of 14\n\n\n5.0\n\n\nLove satellite radio\n\n\nSeptember 4, 2005\n\n\nD. Rhoads \"catlvr4\"\n\n\nBellefonte, pa USA\n\n\nI got this for hubby and me over the summer. We used it for our trip to Rehoboth beach and we loved every minute of it. WE have a tape deck in the car and used it w/ a cassette adaptor. We had not problems whatsoever, in fact it sounds much better thru the cassette adaptor than over the fm transmitter. \nI did try it thru the fm transmitter and it was a pain to switch from one station to the next, but i don't think anybody can improve upon that. I had the same problem w/ my itrip. \nWe chose sirius for several reasons...i liked the classic vinyl station, my dad introduced me to that and hubby likes nascar and this is switching over in 2007.\nThe only problem i had was i couldn't read the red lettering in the daylight w/ the sunglasses on, but i didn't give them less than 5 stars because of this. \nthis is the way to go, plus it has a suction mount, not a permanent mount. \nalso, if you look on ebay, someone is selling something to adapt your mount to the bolt on your car seat!!\ndefinitely go satellite, the music is great and little/ no commercials depending on the stations.\n\n\n\n\nB0000AZ67Z:cruzer:m._winograd_\"mo\"\n\n\nB0000AZ67Z\n\n\nSanDisk SDCZ2-256-A10 256 MB Cruzer Mini USB 2.0 Flash Drive (Retail Package): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nCruzer\n\n\nAugust 9, 2006\n\n\nM. Winograd \"Mo\"\n\n\nBrooklyn, NY United States\n\n\nWorks good, small, and comes with handy little stickers to put on so you can write contact info in case you lose it, though slightly costl\n\n\n\n\nB00008VFAF:great_usb_hub_and_a_great_price:j._howard_\"blackcat725\"\n\n\nB00008VFAF\n\n\nD-Link QuickMedia DUB-H7 7-Port USB 2.0 Hub: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGreat USB Hub AND a Great Price\n\n\nNovember 10, 2006\n\n\nJ. Howard \"blackcat725\"\n\n\nNortheast Ohio, USA\n\n\nI purchased this hub back in September. I plugged it in and I'm running two printers and my mouse off of it at the moment, I've had no problems. It doesn't take up too much room even with 7 ports.\n\nMy only complaint isn't with Amazon, it's with D-Link. After I sent in the rebate info, I got a card from D-Link saying my request wasn't valid because I hadn't purchased it from the right store. But I called them, and in about 10 minutes I had the problem cleared up and had my rebate a week later. I HIGHLY recommend keeping copies of all the rebate info, just in case..\n\n\n\n\nB0000DD7PE:good_for_canon_a95:cristian_anuch_juri\n\n\nB0000DD7PE\n\n\nSandisk 2GB Compactflash Card Type I (SDCFB-2048-A10, Retail Package): Electronics\n\n\nelectronics\n\n\n6 of 13\n\n\n5.0\n\n\nGood for Canon a95\n\n\nAugust 16, 2005\n\n\nCristian Anuch Juri\n\n\nSantiago, Chile\n\n\nFantastic, take all the videos and pictures you want with the best quality\n\n\n\n\nB00008EM7W:imac_user:frank_lorusso\n\n\nB00008EM7W\n\n\nMemorex CD/DVD Compact LabelMaker System: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\niMac user\n\n\nApril 8, 2006\n\n\nFrank LoRusso\n\n\nRochester, NY\n\n\nThe product is a good value. It performed well and was fairly easy to use. I only wish it would allow importing play lists from iTunes, all text had to be manually input. Overall, I'm very satisfied with the product\n\n\n\n\nB00062WV7C:this_is_best_cf_card_have_ever_owned!_won't_regret!:g._xuan\n\n\nB00062WV7C\n\n\nKingston CF/4GB-S 4 GB ElitePro CompactFlash Card: Electronics\n\n\nelectronics\n\n\n14 of 17\n\n\n5.0\n\n\nThis is best CF card have ever owned! Won't regret!\n\n\nMarch 31, 2005\n\n\nG. Xuan\n\n\nLos Angeles, CA\n\n\nI previously owned the 1GB Lexar 40X CF card and thought that was the fastest a CF could get. I was completely wrong. This card is one fast sucker, and it can hold an enormous amount of images. In JPEG mode I am able to store over 1000 images. In RAW mode, which I'm converting to thanks to this large capacity card, I am able to hold 444 images all on my 20D!!! If your thinking of buying this card, don't hesitate. It is one awsome piece of equipment, and one heck of an investment!! \n\n\n\n\nB00005MIS8:excellent_card_value:bill_goodwin_\"cameraman\"\n\n\nB00005MIS8\n\n\nViking CF512M 512 MB CompactFlash Card: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nExcellent Card Value\n\n\nAugust 5, 2005\n\n\nBill Goodwin \"cameraman\"\n\n\nSt. Paul, MN USA\n\n\nThis is an excellent buy and is a quality item. The service was fast and the card works as expected\n\n\n\n\nB00006IW1X:a_lot_of_cd's:blue_van_\"blue_van\"\n\n\nB00006IW1X\n\n\nMemorex 700MB/80-Minute 52x CD-R Media (100-Pack Spindle): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nA lot of Cd's\n\n\nNovember 10, 2006\n\n\nblue van \"blue van\"\n\n\n\n\n\nA lot of space -- My computer is over 5yrs old so I use these CD's as a great back up for storing all my old documents, my ebooks, and my pics.\n\n\n\n\nB00008VF63:cheap_&_good:critic\n\n\nB00008VF63\n\n\nSony MDR-E828LP Fontopia Earbuds with Winding Case: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nCheap & Good\n\n\nNovember 5, 2006\n\n\nCritic\n\n\nNY, USA\n\n\nThese earphones are the best I ever had. I never had to try other pairs of earphone because I absolutely loved these that I keep buying it! After 6 years I buy the same one everytime. Of course I never use the winding case and it just sits there but heck it usually stores my other earphones that I brought but can't seem to throw them out because it costed money. It seems that many people are complaining about the electrical shocks from the earphones and I can answer why thats happening. It's actually static electricity. I experienced it myself a couple of times because I was wearing a wool sweater and the wool peacoat jacket that caused static electricity to form. It's actually not the earphones fault but when your wearing lots of clothes or a scarf with the earphones I suggest you switch to headphones to prevent being shocked. The bass is pretty decent when comparied to panasonic or jvc earphones. Hey for 10 bucks or less you can't except Sony to go all out and improve their cheapest model. I abuse my earphones so for 10 bucks a year I think these are the best rather than buying something thats $100 dollars or over and later regret it because it got either lost, broken or stolen.\n\n\n\n\nB0000VYJOM:very_reliable,_but_not_very_well_made.:b._wickens\n\n\nB0000VYJOM\n\n\nLexar Media RW020-001 USB 2.0 CompactFlash Reader: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nVery reliable, but not very well made.\n\n\nOctober 6, 2006\n\n\nB. Wickens\n\n\nSan Rafael, CA\n\n\nAfter having two SanDisk card readers, and one LaCie card reader die on me, I invested in this Lexar card reader. For the money, it has worked flawlessy with Pro compact flash cards up to 4GB in size. I am very impressed with the transfer speeds, and the unit is not too big for traveling. The door that covers the slot, however, is very flimsy and easily deattaches (but fortunately easily reattaches too?) I use Mac OS X 10.4.8 and it works just fine. I also used it with Windows XP without any installation or transfer issues\n\n\n\n\nB0000VYJOM:fast_and_does_what_it_should:chicken\n\n\nB0000VYJOM\n\n\nLexar Media RW020-001 USB 2.0 CompactFlash Reader: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n5.0\n\n\nFast and does what it should\n\n\nApril 18, 2005\n\n\nChicken\n\n\nBackwoods, NH\n\n\nA simple, cheap investment. This CF reader actually does what it should, i.e. transfer info from compact flash to the computer. Previous Kodak 1.0 multi-card reader had issues after a few weeks, and stopped working after 2 months. This one is much faster and is still working. Ahhhhh\n\n\n\n\nB0000VYJOM:one_of_the_good_high_speed_cf_readers_on_the_market:amazon_fan\n\n\nB0000VYJOM\n\n\nLexar Media RW020-001 USB 2.0 CompactFlash Reader: Electronics\n\n\nelectronics\n\n\n26 of 32\n\n\n5.0\n\n\nOne of the good high speed CF readers on the market\n\n\nOctober 16, 2004\n\n\nAmazon Fan\n\n\nNJ, USA\n\n\nJust some specs missing from amazon's description:\n\nUSB 2.0 CompactFlash Reader\n\nSmall and travels with ease\nLED status indicator indicates when files are being transferred \nData transfer speeds up to 480Mb/s \nBackward compatible with USB 1.1 \nShips with removable USB 2.0 cable \n\nFeatures \n\nDesigned for large data transfers, like images, music and video \nData transfer 5 to 6 times faster than using USB 1.1 \nPortable - weighs only 1.5 ounces \nProtective cover case keeps reader pins away from external hazards \nCompatible with Windows 2000/ME/XP and Mac OS X and 9.1 \nNo drivers required with most current operating systems\n\n2-year Limited Warranty \n\n\n\n\nB0000D8Q8W:adorama_slinger_bag_**_great_**:dr._ronald_berman_\"photoaz\"\n\n\nB0000D8Q8W\n\n\nAdorama Slinger Bag, Single Strap Backpack / Shoulder Bag, Navy.: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nAdorama Slinger Bag ** GREAT **\n\n\nNovember 9, 2006\n\n\nDr. Ronald Berman \"PhotoAz\"\n\n\nScottsdale, Az\n\n\nI purchased the Adorama Slinger Bag after first purchasing the Tamrac Velocity 5. The Tamrac bag is a nice bag and can be worn as either a shoulder bag or a waist bag. Unfotunately, it lacked sufficient support when worn as waist bag and did not have an attached pouch to carry a water bottle. The Slinger Bag addressed both of these deficiencies. When worn as a waist bag, it also uses the shoulder strap to keep the bag firmly in place which also helps to balance the weight of the bag. There are two water pouches on the Slinger Bag (which is a real requirement as I live in Arizona).\n\nThe Adorama Slinger Bag is a great travel bag. It is light weight and has great utility. I highly recommend it.\n\n\n\n\nB00083LFSU:great:malachite\n\n\nB00083LFSU\n\n\nFujifilm 1 GB xD-Picture Card Flash Media Type M ( 600002298 ): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nGreat\n\n\nNovember 9, 2006\n\n\nMalachite\n\n\n\n\n\nThis was the least expensive 1 GB memory card I have ever seen and it works as well as it should\n\n\n\n\nB00009R89Y:a_solution_that_works:brian_koch_\"aspiring_photographer\"\n\n\nB00009R89Y\n\n\nLowepro Street and Field Pouch 50 AW - Pouch ( for camera ) - TXP ripstop - black: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nA Solution That Works\n\n\nNovember 10, 2006\n\n\nBrian Koch \"Aspiring Photographer\"\n\n\nMI United States\n\n\nI have been trying to find a way to easily carry a lot of camera equipment for quite some time. I have tried backpacks, shoulder bags, duffel bags, but none of them really worked all that well. Then I saw the Lowepro Street and Field system with completely customizable pouches, vests, belts, and accessories. It works great, you only get the pouches you need so there is no waisted space and you can organize them however you want according to how often they are used (put frequently used items in easier to access places.) \n\nThe variety alone of this system is worth the investment, but the quality and ease of use is also very high which is a big plus. You can get a case for any lens, camera body, or accessory you have. I even have cases for my cell phone and water bottle. One word of caution, be sure to measure your lens with the lens hood on backwards if you use them to make sure that the entire thing will fit into the pouch, because sometimes the lens will fit, but then the lens with the lens hood will not. I often carry 5-6 lenses, extension tubes, teleconverters, extra memory cards and batteries, and a bunch of cleaning supplies and other gadgets, which weights a ton. But when they are evenly distributed on this vest/belt Lowepro system it is actually very manageable. \n\nThe only down side is that everyone will be staring at you because you have thousands of dollars of camera equipment strapped to your body. But if you can overlook that, this is the way to go. I have used this system at many zoos without ever having problems, in fact just unzipping a single lens and switching seems safer to me than opening an entire bag of equipment every time you want to switch lenses. Many of the cases also come with a built in rain/snow cover so if the weather changes all of your equipment is not destroyed. You can also put just a few pouches on a normal belt when you are just going to take photos in the back yard or don't need to take all of your equipment, which is nice. I really can't say enough about how much easier this makes traveling with a lot of equipment, if you have been hopelessly searching for a better solution, consider this, you won't regret it.\n\n\n\n\nB0002ZAIMI:works_great:b._rivers\n\n\nB0002ZAIMI\n\n\nApple M9837LL/A iPod USB Power Adapter: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n5.0\n\n\nWorks Great\n\n\nAugust 25, 2006\n\n\nB. Rivers\n\n\n\n\n\nGreat for if you are not near a computer and need to charge your Ipo\n\n\n\n\nB0007SL4IW:great_product,_worth_the_price.:a._whitlock_\".o[badmilk]o.\"\n\n\nB0007SL4IW\n\n\nMemorex DVD+R 16x 4.7GB 100 Pack Spindle: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nGreat product, worth the price.\n\n\nJuly 31, 2006\n\n\nA. Whitlock \".o[badmilk]o.\"\n\n\nFort Bragg, NC\n\n\nThe Memorex DVD+R 16x discs are great. I have a LaCie external 16x burner that I use with my mac and I have not yet burned a bad disc. I would reccommend this product to anyone looking for lots of media for a good price\n\n\n\n\nB0002VRFNC:protects_your_investment:l._t._beasimer_\"lb\"\n\n\nB0002VRFNC\n\n\nCanon LP1319 Soft Lens Case for 16-35mm f/2.8L USM, 17-40mm f/4L USM, 10-22mm f/3.5-4.5 EF/EFS Lenses: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nProtects your investment\n\n\nJuly 15, 2006\n\n\nL. T. Beasimer \"LB\"\n\n\nDallas, TX\n\n\nManufactured of a soft gray felt like material, a tough leather bottom, and draw string closure at the top, this lens pouch helps protect your lens from dust and the elements. I store my lenses in a camera bag, and these cases fit nicely around the lens taking up little precious space.\n\nThis case is meant for use with the Canon 16-35mm f/2.8L USM, 17-40mm f/4L USM, 10-22mm f/3.5-4.5 EF/EFS lenses. These L lenses normally come with the case when sold new. There is no need to buy one as Amazon often suggests. However if your case is worn, replacing it to protect your lens is recommended.\n\nPROS:\nProtects your glass investment\n\nCONS:\nI cannot think of an\n\n\n\n\nB00062WV86:ok:tomasz_mielcarek\n\n\nB00062WV86\n\n\nKingston CF/2GB-S 2 GB ElitePro CompactFlash Card: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nOK\n\n\nNovember 9, 2006\n\n\nTomasz Mielcarek\n\n\nPoznan, Poland (Central Europe\n\n\nThis CF card is perfect for anybody who needs a high capacity (e.g. for hi-res pics) but doesn't care about the transfer speed. It may be a bit annoying though - especially if you try to download all the 2GBs from the card to your hard drive because it takes some time. I do still consider it a real good purchase ;\n\n\n\n\nB00062WV86:works_great!:craig_j._llorens_\"cjllorens\"\n\n\nB00062WV86\n\n\nKingston CF/2GB-S 2 GB ElitePro CompactFlash Card: Electronics\n\n\nelectronics\n\n\n4 of 7\n\n\n5.0\n\n\nWorks great!\n\n\nMarch 2, 2006\n\n\nCraig J. Llorens \"cjllorens\"\n\n\nHouston, TX, USA\n\n\nThis memory card handles the job really well. Unless maybe you're a professional sports photographer, it's plenty fast enough. No problems using it with my Canon Rebel XT. It holds about 550 pictures (JPEGs) at the highest resolution (8.2 MP)\n\n\n\n\nB000GEDMNW:verry_happy:s._w._yard\n\n\nB000GEDMNW\n\n\nAcer AL1706AB 17\" LCD Monitor: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nVERRY HAPPY\n\n\nNovember 10, 2006\n\n\nS. W. Yard\n\n\nMesa,az\n\n\nI have only had this monitor for a couple of day's \nbut I am verry happy with it. It came well packaged and you cant beat the price. I shopped around and this was the one for me.\nI dont know about the other review but with in 5 min of opening the box I was using the monitor and it worked verry well!!\nTHANK'S AMAZON\n\n\n\n\nB000GEDMNW:great_monitor:charles_k._dick\n\n\nB000GEDMNW\n\n\nAcer AL1706AB 17\" LCD Monitor: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nGreat Monitor\n\n\nNovember 2, 2006\n\n\nCharles K. Dick\n\n\nMN USA\n\n\nI had a little trouble getting my system to recognize it when first set up...but after a little tweaking, it works beautifully. Previously owned regular CRT...what an improvement! Superb clarity and brightness. For the money you just can't go wrong with this one\n\n\n\n\nB0002A9SJ2:buffer_marks:n._bishop\n\n\nB0002A9SJ2\n\n\nAleratec DVD/CD Disc Repair Plus: Electronics\n\n\nelectronics\n\n\n3 of 7\n\n\n4.0\n\n\nBuffer Marks\n\n\nSeptember 7, 2006\n\n\nN. Bishop\n\n\n\n\n\nI use the pink circles to buff the cd/dvd, and it leaves the circular marks all over the cd, as I thought it would. Well, I thought that those marks would disappear after I used the repair paste with the yellow circles, but they're still there. Is it supposed to be like this? The reason I bought the thing was to make the cds look like new, but now they just look even more scratched with the buffer marks all over.\n\nCould someone answer this question for me please\n\n\n\n\nB0002A9SJ2:repairs_discs_to_useable_condition_as_described:sherri_michelle_lechten_\"smlechten\"\n\n\nB0002A9SJ2\n\n\nAleratec DVD/CD Disc Repair Plus: Electronics\n\n\nelectronics\n\n\n21 of 21\n\n\n4.0\n\n\nRepairs discs to useable condition as described\n\n\nApril 10, 2006\n\n\nSherri Michelle Lechten \"smlechten\"\n\n\nStrongsville, OH USA\n\n\nWith boys aged 2 & 3, and some poor disc storage procedures when I'm driving, it appeared I was in need of one of these units. Generally my toddler boys are not permitted to handle discs, but it occassionally happens, and doesn't take long for scratches, damage and finger prints to hinder performance, and when changing children's cds in the car I don't always store the disc properly and that leads to damage also. I read through a lot of reviews on this item and the skipdoctor products, and finally decided on this unit because it runs on a/c power, is fully contained and automatic, and was about $10 less expensive than the most equivalent skipdoctor unit. Both items had mixed reviews - mostly positive, but I found that most people who tried to make the units work and put in some time and effort were satisfied with both products. I'm very satisfied with my purchase it has repaired two discs that could not be played at all, and one that skipped. \n\nThis product repairs a disc to \"useable\" condition, it does not repair it so that scratches are not visible. I can still see, but not necessarily feel the scratches. It's also important to remember that some scratches do not affect performance. If a disc plays well, but shows scratches - I just clean off the finger prints with a cloth or use the unit to clean the disc and see if the performance is satisfactory before attempting a full repair. \n\nI agree that the instructions with the unit could be better. I saved the instructions provided by another reviewer to use with the provided instructions for clarity. Also, some severely scratched and damaged discs do take many passes to repair, so I agree it is time consuming to acheive a full repair for some items - but it is possible. I noticed that I could cut down some of the repair time and solution usage by repeating the buffing/sanding (pink disc) process several times in a row for deeper scratches before using the repair. I ran one disc through 3 times in a row, then used the repair cream solution with yellow pads and it played fine. My lesser damaged disc only required one pass with the buffer and one with the repair until it worked great. I usually clean the disc (manually with the cloth or with the machine depending on severity of grime), then buff, then repair. It can be time consuming to repair a disc to functioning, but it does save the disc and my discs play like new despite the scratches that are still visable. I think this was good purchase and not overly complicated to use. \n\n\n\n\nB0000CD0B7:san_disk_compact_flash_card:mary_s._hutto\n\n\nB0000CD0B7\n\n\nSanDisk SDCFH-1024-901 1 GB Ultra II CompactFlash Card (Retail Package): Electronics\n\n\nelectronics\n\n\n0 of 3\n\n\n5.0\n\n\nsan disk compact flash card\n\n\nAugust 13, 2006\n\n\nMary S. Hutto\n\n\nKula, HI United States\n\n\nSo far I havent filled up my disc yet using it. The\ndisc that came with it would fill up after 3 minutes of \ntalking. So far its great\n\n\n\n\nB00004WHFL:great_deal_for_the_money!:\"joepie\"\n\n\nB00004WHFL\n\n\nFranklin EBM-901 eBookman (Metallic Black): Electronics\n\n\nelectronics\n\n\n13 of 13\n\n\n5.0\n\n\nGreat deal for the money!\n\n\nJanuary 30, 2004\n\n\n\"joepie\"\n\n\nLancaster, CA United States\n\n\nI've read other reviews for this product that have people complaining about losing their data when the batteries are changed. THIS IS NOT TRUE if you're carefull. First of all, my ebookman came with a bright blue warning flier stating that your ebookman needs to be turned OFF when you change the batteries. Pretty simple, huh? Also, don't wait for your battery to completely die before you change it. The main menu screen has a battery meter in the bottom right hand corner, and I just switch out the batteries when the meter reaches 10%. I primarily use my ebookman to read ebooks and I love it\n\n\n\n\nB000075UZ7:reliable_blank_cd-rs:jerkymom\n\n\nB000075UZ7\n\n\nMemorex 700MB/80-Minute 52x Data CD-R Media (50-Pack Spindle): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nReliable Blank CD-Rs\n\n\nNovember 9, 2006\n\n\nJerkymom\n\n\nHancock, MD USA\n\n\nI've been using these Memorex CD-Rs for years and have no problems with their reliability. Have used them with multiple CD and DVD burners without compatibility issues. This was another re-order and I continue to be happy with this product\n\n\n\n\nB000075UZ7:memorex_700mb/80-minute_52x_data_cd-r_media_(50-pack_spindle:sharon_swatzenberg\n\n\nB000075UZ7\n\n\nMemorex 700MB/80-Minute 52x Data CD-R Media (50-Pack Spindle): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nMemorex 700MB/80-Minute 52x Data CD-R Media (50-Pack Spindle\n\n\nNovember 3, 2006\n\n\nSharon Swatzenberg\n\n\nReno, NV United States\n\n\nthe product arrive to me in very good condition, will recommend this product to anyone who needs blank cd\n\n\n\n\nB00003IE4J:energizer_seems_the_most_reliable:christopher_r._chadwick\n\n\nB00003IE4J\n\n\nEnergizer E91BP-4 AA Batteries (4-Pack): Electronics\n\n\nelectronics\n\n\n4 of 6\n\n\n5.0\n\n\nEnergizer seems the most reliable\n\n\nJune 22, 2000\n\n\nChristopher R. Chadwick\n\n\nNevada\n\n\nFor the most part I just want to keep it short. I usually always by Energizer cause they're reasonably priced and seem to last much MUCH longer then most other battery products that I have used in the past\n\n\n\n\nB00003IE4J:wonderful_batteries:jerry_eubank\n\n\nB00003IE4J\n\n\nEnergizer E91BP-4 AA Batteries (4-Pack): Electronics\n\n\nelectronics\n\n\n1 of 3\n\n\n4.0\n\n\nWonderful Batteries\n\n\nJune 20, 2000\n\n\nJerry Eubank\n\n\nDenver\n\n\nThese batteries are great, and seem to fit my Rio 500 better than some discount batteries. Good price too\n\n\n\n\nB00006B7DA:how_long_will_it_last?:audio_dave_\"disappointed\"\n\n\nB00006B7DA\n\n\nD-Link DUB-H4 4-Port USB 2.0 Hub: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nHow long will it last?\n\n\nOctober 15, 2006\n\n\nAudio Dave \"Disappointed\"\n\n\nWestern Mass.\n\n\nI have been using this D-Link hub for about six months. The power light started flickering after about three months and now doesn't light at all. The hub works great, but I wonder how long it will last\n\n\n\n\nB00006B7DA:does_everything_it_should:s._chandler\n\n\nB00006B7DA\n\n\nD-Link DUB-H4 4-Port USB 2.0 Hub: Electronics\n\n\nelectronics\n\n\n5 of 6\n\n\n5.0\n\n\nDoes everything it should\n\n\nFebruary 24, 2006\n\n\nS. Chandler\n\n\nSan Antonio, TX\n\n\nI use my USB hub every day and it works fine with every device I plug into it\n\n\n\n\nB000E62H2O:great_value:rachael_baker\n\n\nB000E62H2O\n\n\nUniden TRU9488 Expandable Corded/Cordless Combination System with Digital Answering System, Dual Keypad, and Call Waiting/Caller ID: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nGreat value\n\n\nNovember 10, 2006\n\n\nRachael Baker\n\n\nSouth Carolina\n\n\nI'm very happy with this product. It's great value for the price. The corded phone with answering machine means there's always a phone that's not dead and perfect for the home office area. The cordless phone can be used to check the answering machine messages. Lot's of features\n\n\n\n\nB00064V6SK:unit_works_great,_software_hogs_resources:dmi7b5\n\n\nB00064V6SK\n\n\nSandisk SDDR-99 ImageMate 5-in-1 Reader/Writer (Retail Package): Electronics\n\n\nelectronics\n\n\n19 of 19\n\n\n4.0\n\n\nUnit works great, software hogs resources\n\n\nMay 8, 2006\n\n\ndmi7b5\n\n\nUSA\n\n\nThe unit itself works great. Much faster transfers than using the usb cable directly to my camera. However, the software includes a \"monitoring\" feature, which checks every second or so, to see if you've plugged in a card. I opened up Windows Task Manager to discover that, each time it checked, it was consuming 25% of system resources (on/off each second). No wonder my computer has been slower lately, especially if I have my image editing software open (duh).\n\nMy recommendation would be to skip the San Disk software and access the card reader directly from My Computer, as mentioned in another review below\n\n\n\n\nB00004Z5QU:you_cannot_beat_a_belkin_cable:gr8stogie\n\n\nB00004Z5QU\n\n\nBelkin A3L791-S 25-Foot RJ45 CAT 5e Snagless Molded Patch Cable (Blue): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nYou cannot beat a Belkin Cable\n\n\nMarch 30, 2005\n\n\nGr8Stogie\n\n\nSt. Louis, MO\n\n\nGreat quality, excellent construction and strong RJ45 plugs. I have worked with a decent share of Cat5 and I have never had to cut and terminate a Belkin cable due to regular wear and tear\n\n\n\n\nB0002LHSG6:great_purchase!!:magnum\n\n\nB0002LHSG6\n\n\nPanasonic DVD-RAM 4.7GB single-sided DVD-RAM discs 10-pack: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nGREAT PURCHASE!!\n\n\nNovember 6, 2006\n\n\nMAGNUM\n\n\nNC\n\n\nMY HUSBAND & I SEARCHED & SEARCHED FOR DVD'S THAT WERE COMPATABLE TO OUR DVD RECORDED. NO ONE COULD HELP. THEN I THOUGHT, \"I'LL JUST CHECK ON AMAZON,\" AND SURE ENOUGH THERE THEY WERE!!!!!!!!!!!!! WHAT A RELIEF!!!!!!!!!! I WOULD HATE TO OWN A DVD RECORDER & NOT BE ABLE TO RECORD ANYTHING!!!!!\n\nTHANK YOU!!!\n\n\n\n\nB000BP8AY2:mp3_player_is_good:phil_m._fuller\n\n\nB000BP8AY2\n\n\nSanDisk Sansa M250 2 GB MP3 Player (Black): Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nMP3 Player is good\n\n\nNovember 9, 2006\n\n\nPhil M. Fuller\n\n\n\n\n\nThis unit is a good buy. It is priced low, but functions like you paid a higher price. The negatives are that it isn't compatible with Itunes, and the earphones are not very ergonomically designed. Overall, it is a good product\n\n\n\n\nB00006HOKR:great_am_reception_--_assume_you'll_be_buying_new_earphones:steve_h�sler_\"steve_h�sler\"\n\n\nB00006HOKR\n\n\nSangean DT200V AM/FM/TV Portable Pocket-Size Radio: Electronics\n\n\nelectronics\n\n\n7 of 7\n\n\n4.0\n\n\nGreat AM reception -- assume you'll be buying new earphones\n\n\nJune 3, 2006\n\n\nSteve H�sler \"Steve H�sler\"\n\n\nSparks, MD United States\n\n\nI'll try to keep this short and sweet. If you have been frustrated trying to get AM reception on the go, then you've found the right radio for you. I can't tell you how many pieces of junk I've bought that barely pulled-in even relatively close AM stations unless I constantly twisted the radio to the perfect angle needed to get reception. Luckily (?) those other radios also died pretty quickly. This Sangean product solves the AM reception problem and seems very durable. I also own their CCRadio and this pocket product lives up to its reputation. I love the fact that it also gives you the option to broadcast through the built-in speaker, just like transistor radios of the 50's and 60's--a seemingly forgotten feature. As for listening through the supplied earphones, forget it. Sangean should be ashamed to ship this product with the utter junk earphones that are supplied. Never have I heard tinnier sound, even for AM listening. Not not mention that the cord is annoyingly short. I immediatey invested another $35 in decent pair of Koss earbuds and stored the supplied earphones in the neareast trash bin. Now I happily and easily listen to AM and FM stations even over the roar of the lawnmower. There might be alternatives for those of you who plan on listening to FM exclusivey, but if you spend even a little time on the AM dial, I really don't think you have a choice but to pick up this radio\n\n\n\n\nB00008DHSN:nice_for_the_price:owen\n\n\nB00008DHSN\n\n\nJVC HAG101 Closed Ear Headphones (Black): Electronics\n\n\nelectronics\n\n\n2 of 5\n\n\n5.0\n\n\nNice for the price\n\n\nApril 8, 2006\n\n\nOWEN\n\n\nOVERLAND PARK, KS\n\n\nSound great. Nice bank for the buck.\n\nDid the I-plop follower think they would fit inside his ear? Maybe he is bitter with the DRM and I-Tune scam that Job's is screwing people over with. He's worse than Microsoft. I \"love\" followers\n\n\n\n\nB000EF3D4Q:good_but_can_be_annoying:k._franklin\n\n\nB000EF3D4Q\n\n\nDLO 009-3031 Transpod All-in-One Car Solution for iPod (Black): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nGood but can be annoying\n\n\nNovember 5, 2006\n\n\nK. Franklin\n\n\n\n\n\nThis was my second DLO transpod. I had an older white one that I bought used. I figured I would \"upgrade\" to this one since my iPod and my car's interior are black. overall the thing performed similarly to my previous one, but with a few little changes.\n\nFor one the output of this unit is much lower than its older counterparts. That normally doesn't matter if you choose a good empty frequency, but as you start traveling you notice interference much more than you would expect. \n\nNext, I can't turn it up any where near the volume I used to be able to on the older one without getting hissing and static. It seems that the decreased level on the output also increased the amount of electronic noise when using it.\n\nLast, the most annoying part of this whole product, is that it turns off the radio transmitter after a period of no use. I have paused my iPod to take a phone call only to have very loud (since the level is so low on it you have to make up for it with stereo volume) static and sometimes radio stations coming through. Very embarrassing. Or when you stop your car and then come back, as soon as the radio turns on you are met with the same very loud noise.\n\nSince my car doesn't leave me many options for stereo replacement, this is the best product for my specific situation, though I may try and get that old one back from my friend...maybe an \"upgrade\" to a newer one will settle the deal\n\n\n\n\nB00006JPFT:bad_information_below:\"kitkatbreak\"\n\n\nB00006JPFT\n\n\nCoby CA-706 CD/MD/MP3 Car Kit Adapter: Electronics\n\n\nelectronics\n\n\n5 of 7\n\n\n4.0\n\n\nbad information below\n\n\nApril 29, 2004\n\n\n\"kitkatbreak\"\n\n\nNew York, NY\n\n\nYou must have received CA-709 (without the cassette component) by mistake. CA-706 is prepackaged with the cassette component\n\n\n\n\nB000ARJIYC:replacement_batteries_for_p402a/1b_cordless_phone:joy\n\n\nB000ARJIYC\n\n\nPanasonic Type-30 HHR-P402A/1B Cordless Telephone Rechargeable Battery Replacement: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nreplacement batteries for P402A/1B cordless phone\n\n\nNovember 9, 2006\n\n\nJoy\n\n\n\n\n\nWorked perfectly.\n Battery does not run low after a few hours off the base\n\n\n\n\nB00006HMPH:i_like_it!:stephanie_heffner_\"mt\"\n\n\nB00006HMPH\n\n\nLogitech Deluxe Access Keyboard: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n4.0\n\n\nI like it!\n\n\nSeptember 7, 2004\n\n\nStephanie Heffner \"MT\"\n\n\nNC\n\n\nI use this keyboard at work and as I am a medical transcriptionist, it is all I use all day l o n g . . .I really like it and I'm going to buy one for my home as well. It is very easy to type on and I like the wrist support\n\n\n\n\nB0009E1YPM:a_great_receiver_for_the_price:cary_j._conrad\n\n\nB0009E1YPM\n\n\nPanasonic SA-XR55K 6.1-Channel A/V Home Theater Receiver, Black: Electronics\n\n\nelectronics\n\n\n0 of 3\n\n\n4.0\n\n\nA Great Receiver for the Price\n\n\nAugust 27, 2006\n\n\nCary J. Conrad\n\n\nTurtletown, TN\n\n\nThis receiver is a replacement for an older, more expensive, defective Sony that I had. It was easy to install and performs comparably well with more expensive units. I am satisfied with the product\n\n\n\n\nB0009E1YPM:home_theater_solution:michael_lopez_\"ixplore\"\n\n\nB0009E1YPM\n\n\nPanasonic SA-XR55K 6.1-Channel A/V Home Theater Receiver, Black: Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n5.0\n\n\nHome Theater Solution\n\n\nJune 13, 2006\n\n\nMichael Lopez \"iXplore\"\n\n\nSun Diego\n\n\nThe reviews sounded too good to be true. The reviews are right on. This system is perfect for home theater. I have the latest Panasonic plasma. This receiver is the perfect compliment. Here is why...\n\nI could only start with at 2.1 set up - L/C/R and no sub woofer. Great digital sound effects with a one button switch to stereo for easy comparison.\n\nTons of input/output connectors. Unless you need HDMI, the available connectors are perfect. The speaker connectors are screw down/banana plug type for easy, secure connection. Lots of component video, S-VHS and RCA video inputs. Lots of analog audio connections AND two digital coax and one optical. I use the digital coax for my DVD/CD and for my cable box. \n\nReally easy set up. I could assign inputs to the various selections through the front of the receiver. The manual is written in easy to understand language with lots of pictures.\n\nDid I mention the sound. Frys offers Polk Audio R15 for an indecently low price. With this receiver the sound is rich - great bass and treble without any tweaking of levels. Adding a Cerwin Vega center channel brought out the digital sound effects and this has them all.\n\nBest of all, my fashion master wife pulled the receiver out of the box before I got home and pronounced it beautiful. It was the only low profile receiver I could find that fit in the armoire we use as an enterntainment station.\n\nThe end result - I sat down and just watched Hellboy for the first time all over again in the middle of the installation. All for less than the cost of me and the Missus going to the movies for a few weekends. Get this receiver\n\n\n\n\nB0001J3Q9W:works_great:jerry\n\n\nB0001J3Q9W\n\n\nBelkin F8V7067-APL Mobile Power Cord for iPod: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nWorks Great\n\n\nJuly 10, 2006\n\n\nJerry\n\n\nDunmore, PA\n\n\nThis item does exactly what it's suppose to do. It also makes the signal stronger therefore you get better reception. Worth the money. Only drawback would be, there is a lot of cord hanging around\n\n\n\n\nB00029U12Q:budget_solution._in_no_means_good_sound_without_tweaks.:emil_anticevic_\"eanticev\"\n\n\nB00029U12Q\n\n\nSony SS-MF650H 3-Way Floorstanding Speakers (Pair): Electronics\n\n\nelectronics\n\n\n8 of 10\n\n\n4.0\n\n\nBudget solution. In no means good sound without tweaks.\n\n\nSeptember 23, 2005\n\n\nEmil Anticevic \"eanticev\"\n\n\nDC USA\n\n\nThe Sony SS-MF650H are a low-cost solution to good directional sound for a very small room.\n\nThough I have not had the opportunity to test the ultra-high (up to 50kHz) frequency, the highs on these speakers sound very well, maybe only slightly boxy - note that the speakers are rather directional so the sound does not spread.\n\nThe sound is very flat. The lack of depth makes it feel as if the sound is coming from a flat wall or a small box rather than from a space, especially if you stand further away (over 5 ft.) from the speakers. It seems that the speakers have a much lower than published sensitivity level. However, don't get discouraged because with most speakers/headphones it may be that the speakers need some time to burn in (loosen the diaphragm) to get a bit more \"sustain\" to their output rather than seem to have a very sharp cutoff.\n\nThe bass is very punchy, and not deep. I would definitely recommend a subwoofer for these speakers, as their highs are very good, and midranges are nice but maybe a bit flat. The subwoofer will party compensate for the problem, and you could even completely cut off the bottom frequency band and suffer little ill effect (use the LFE on your receiver for the sub-woofer... don't worry, low frequency bass is not perceived by humans as directional so your surround or stereo won't suffer). In other words, you will need a lot of tweaking and additions to make these sound nicer than their out-of-the box quality.\n\nIgnore the 50kHz rating for ultra-high-frequency sound as a positive feature, as no receiver you will buy at the budget for these speakers will be rated for that output. Most are rated for 20 Hz - 20kHz, at a reasonable RMS.\n\nYou get what you pay for... for the price tag on them, the SSMF650H are good. They perform reasonably well on highs and mids (but in no means studio reference sound), and are crisp and punchy on the bass. They do however seem to lack depth and have low sensitivity - especially so on the bass... the further you move away from them (and especially if you stand up above them), the sound quality suffers.\n\nI would recommend Wharfedale Diamond 8.4 speakers as a better choice if you're willing to expand your budget a bit. Polk is also not a bad choice for cheap sound.\n\nI also demo-ed the ~$100 per pair CAT SDAT LEB-404 (I think I saw them on overstock for $80 with free shipping). They are a bit shady, but excellent for the price - seems that the 20 Hz bass rating on them is actually somewhat accurate. If you're looking for deep and bass-heavy sound without a sub-woofer, and are willing to take a hit on mids and highs a bit this is your \"budget solution\" instead of the SS-MF650H. I was pleasantly surprised. They also have poor sensitivity though, so if you have a big room shop around for something better.\n\n\n\n\nB00029U12Q:excellent_sound,_great_price:realguy1\n\n\nB00029U12Q\n\n\nSony SS-MF650H 3-Way Floorstanding Speakers (Pair): Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n5.0\n\n\nExcellent sound, great price\n\n\nJuly 27, 2005\n\n\nrealguy1\n\n\nWashington, DC USA\n\n\nI listen to a wide range of music, from hard rock, techno/electronic, to classical, and I've found it difficult to locate reasonably priced speakers that can handle all of this -- at least until I ordered this set. I was apprehensive about buying online (and also about the Sony brand for speakers), but I'm very happy with this purchase. I've been very impressed with the sound both at the high and low ends - the highs are crisp and the bass is tight, not boomy. The soundstage is expansive (of course, this depends a lot on your receiver as well) and imaging good. I'd definitely recommend this speaker set\n\n\n\n\nB00064L1AI:good_product:yiqing_lu\n\n\nB00064L1AI\n\n\nSamsung SIR-T451 High Definition Terrestrial Tuner: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGood product\n\n\nJuly 22, 2006\n\n\nYiqing Lu\n\n\n\n\n\nThis first time I use the HD product in my home video system and have the expected results. After I installed it and it immediately scan all the signal channels in my local area and have good quality. So far I place the antenna in door just in the first floor. I don't have too many channels in my living area in OH\n\n\n\n\nB000261N6M:newer_model_region-free_code!!:a._patel\n\n\nB000261N6M\n\n\nCoby DVD-224 Compact DVD Player: Electronics\n\n\nelectronics\n\n\n13 of 15\n\n\n4.0\n\n\nNewer Model Region-Free Code!!\n\n\nSeptember 14, 2006\n\n\nA. Patel\n\n\nHouston, TX USA\n\n\nI bought my player Sep 2006 and the other region-free code did not work. It is actually a DVD-224M (see manual) though it says DVD-224 on the player. Here is how to do it:\n\nIf you have the DVD-224M model (says DVD-224 on the box, DVD-224M on the manual...), the hack is: \n\nMake sure you have no DVD in the tray. \nOn the remote control: \n1) Press \"TITLE\" \n2) Press \"1\" and then \"9\" \nOn the TV a large box with a small number will appear. \nThe number indicates for which DVD region the machine has been set, for example \"2\" for Germany or \"0\" for no region code. \n3) Press the up arrow or down arrow to raise or lower the number. \n4) Press \"ENTER\" \nDone. \n\n\n\n\nB000028F42:dual_voltage_/__dual_wattage:r._bougie_\"the_boogie_man\"\n\n\nB000028F42\n\n\nRecoton ADF1650 50/1600W Dual Wattage Travel Converter: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n4.0\n\n\ndual voltage / dual wattage\n\n\nJuly 12, 2006\n\n\nR. BOUGIE \"the boogie man\"\n\n\nCT, USA\n\n\nworked very well as planed\njust heavy to carr\n\n\n\n\nB0000AZK4G:first_time_to_use_a_crimp_tool:a._bess\n\n\nB0000AZK4G\n\n\nTRENDnet Crimp Tool CAT5/6 Cut & Strip: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n4.0\n\n\nFirst time to use a crimp tool\n\n\nMarch 2, 2006\n\n\nA. Bess\n\n\nBowie, Tx. USA\n\n\nI would have given it a five star but sense it's the only one I have ever used I had nothing to compare with. I have used it nine tines and it worked perfect every time. Sturdily made. \n\n\n\n\nB000AMLXHW:slingbox_first_and_second_impressions.:timothy_j._yatsko\n\n\nB000AMLXHW\n\n\nSling Media Slingbox Classic: Electronics\n\n\nelectronics\n\n\n1 of 3\n\n\n4.0\n\n\nSlingbox first and second impressions.\n\n\nNovember 10, 2006\n\n\nTimothy J. Yatsko\n\n\n\n\n\nThe idea is excellent, the execution is flawed. What is not made clear at the time of purchase is that you will need a bridge or gaming adapter as well as a high speed internet connection at home. The adapter should be built into the box. \nAnother disappointing factor is that the quality of the video is almost unwatchable if the connection is not robust. There should be a cache memory built in so that a clear, unchoppy video and sound come through.\n\n\n\n\nB000AMLXHW:slingbox_review:joseph_r._warren\n\n\nB000AMLXHW\n\n\nSling Media Slingbox Classic: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nSlingbox Review\n\n\nNovember 10, 2006\n\n\nJoseph R. Warren\n\n\n\n\n\nOne of the best gadget-products I've ever bought. The installation and connection was completely flawless. It worked the first time and has been working since (and I even used the Powerline ethernet adapter to connect back to my router). I have my cable TV on one input and my security system on the secondary input. Using my cellular Data connection on my laptop, I can watch tv or look at my security system from anywhere in the country\n\n\n\n\nB000AMLXHW:wors_great_but_should_be_able_to_upgrade/trade_in:z._tommy\n\n\nB000AMLXHW\n\n\nSling Media Slingbox Classic: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nWors great but should be able to upgrade/trade in\n\n\nNovember 9, 2006\n\n\nZ. Tommy\n\n\nHong Kong\n\n\nBox worked perfectly as expected. However, it would be nice to be able to upgrade firmware to the new HD like performance or via a trade-in program to allow old customers to upgrade to the new Sling boxes\n\n\n\n\nB000E5XHGK:great:christian_behrens\n\n\nB000E5XHGK\n\n\nApple MA242LL/A iPod AV Connection Kit: Electronics\n\n\nelectronics\n\n\n6 of 25\n\n\n5.0\n\n\nGreat\n\n\nJanuary 23, 2006\n\n\nChristian Behrens\n\n\nCaracas, DF Venezuela\n\n\nThis box is perfect to have all the basic ipod accesories at once. I have the 60 mb video ipod and all the accesories works perfect\n\n\n\n\nB0009U6YQK:trust_the_description.:samuel_chell\n\n\nB0009U6YQK\n\n\nCTA DIGITAL iPod Fold-Up Amplified Aluminum Speakers ( White ): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nTrust the description.\n\n\nJuly 1, 2006\n\n\nSamuel Chell\n\n\nKenosha,, WI United States\n\n\n(Despite some of the negative reviews of this item, it continues to perform well with my regular-sized iPod. In fact, it has more volume, depth, and presence than three other portable speakers--each costing 50 to 75% more--to which I've compared it. The only downside is that the item is no longer sold postage-free from Amazon, diminishing its \"bargain\" appeal somewhat.)\n\nThese modest-sized and priced speakers perform as advertised: the sound is more than adequate; they're small enough that I can take The Stephen Colbert Report with me on walks; when not in use, they fold up into a mini Porta-John. My only misgiving is whether I should have spent 2-3X more for a unit with a dock connector. But for under twenty bucks you simply won't find a better sounding, more compact unit\n\n\n\n\nB0009U6YQK:pretty_good_sound:e._riddle\n\n\nB0009U6YQK\n\n\nCTA DIGITAL iPod Fold-Up Amplified Aluminum Speakers ( White ): Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nPretty good sound\n\n\nJune 30, 2006\n\n\nE. Riddle\n\n\n\n\n\nI liked these speakers they are small compact they work great with my Mp3 Player and you can't really beat the price. My only complaint is that they don't come with an adapter but I just bought rechargeable batteries and the system work great I use them everyda\n\n\n\n\nB0007XJSQC:best_headphone_value_ever:d._wendel\n\n\nB0007XJSQC\n\n\nSennheiser HD201 Headphones: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nBest headphone value EVER\n\n\nNovember 2, 2006\n\n\nD. Wendel\n\n\nBoston, MA\n\n\nThese headphones are lightweight, fairly comfortable, and sound head and shoulders better than any pair of $20 headphones I've ever heard. Here are the aspects people always want to know about:\n\nBASS: these don't pound your eardrums in, but the bass that is there is infinitely cleaner and snappier than some of those boomy headphones I've heard. You can really follow the bass line with these, rather than just having your eyeballs fuzz out from vibration.\n\nMIDS/HIGHS: this is the reason to buy these headphones. Not too bright, but very clear, warm, smooth, soothing, and musical. The first time I listened to music on these I was able to pick out the click of the pick against the guitar string, something I'd never heard before on that song. In fact, I blame these headphones on my new addiction to, well, headphones.\n\nThese obviously can't play as loudly as $200 headphones, and lack a little of the very lowest and very highest reach, but everything in the middle is VERY good. I don't think you can find headphones that are this nice to listen to for under $100. And being under $20, you can buy a pair to try without feeling like you might be wasting a lot of money. I should go work for these guys or something =)\n\n\n\n\nB00000J4M9:xbox_system_link:c._m._noevere_\"mcpospartan117\"\n\n\nB00000J4M9\n\n\nNetgear EN104TP 4-Port 10 Mbps Ethernet Hub RJ-45 with Uplink Button: Electronics\n\n\nelectronics\n\n\n0 of 3\n\n\n5.0\n\n\nXbox system link\n\n\nSeptember 11, 2004\n\n\nC. M. NOEVERE \"MCPOSpartan117\"\n\n\nNC\n\n\nI was wondering if anyone knew if this hub would work for linking Xbox's together so you can have more than 4 people play at once. I know that I could link 2 with a crossover cable but I want to link 4 together so that I can have a gigantic 16 player Halo match (It's a very popular Xbox game for those who don't know\n\n\n\n\nB000067S46:good_high_speed_cd-rws:\"kingsbrother\"\n\n\nB000067S46\n\n\nTDK High Speed Data CD-RW (5-Pack): Electronics\n\n\nelectronics\n\n\n21 of 21\n\n\n5.0\n\n\nGood High Speed CD-RWs\n\n\nOctober 19, 2002\n\n\n\"kingsbrother\"\n\n\nVA\n\n\nNothing much to say about them. They're high quality high speed rewritable discs that do what they are supposed to do. I've been using these kind of rewritable discs for a while now and I haven't had any problems with them what so ever even when I burn at higher than 10x. They come with cases too so you won't have to worry about scratching the CDs\n\n\n\n\nB0000A8Y5K:perfect_headset_for_me!:toonces\n\n\nB0000A8Y5K\n\n\nPanasonic KX-TCA92 Hands-free Headset with Comfort Fit and Fold Design: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nPerfect headset for me!\n\n\nJuly 9, 2006\n\n\nToonces\n\n\nGulf coast of Florida, USA\n\n\nI matched this model number up with the phone I have, and it is a perfect fit for the phone and for me. I stay on the telephone for lengthy conference calls most of my workday, and the headset types that go in the ear ended up causing soreness. This headset sits gently over my head with the earpiece lying on my ear. I'm told I can be heard very clearly without echo or fadeout. I would highly recommend this headset\n\n\n\n\nB00005UK9X:better_than_the_windshield_suction_mount:a._touchard\n\n\nB00005UK9X\n\n\nGarmin Portable Friction Mount for GPS 010-10306-00: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nBetter than the windshield suction mount\n\n\nNovember 1, 2006\n\n\nA. Touchard\n\n\n\n\n\nI bought this because my windshield suction mount could not be trusted. I've had it fall off more than once. Then you drive around wondering if your $700 Nuvi is going to fall and break. Not Fun! The friction mount works for me. For everyday driving, even aggresive driving, it stays put. Someone on this site left feedback about using it with their Nuvi 360 and I used their review to make the purchase. I'm glad I did. \n\n\n\n\nB0007MWE2S:danny:chang_h._lee_\"danny\"\n\n\nB0007MWE2S\n\n\nCables Unlimited 6-Foot Digital Male-to-Male HDMI Cable: Electronics\n\n\nelectronics\n\n\n17 of 17\n\n\n5.0\n\n\nDanny\n\n\nAugust 9, 2006\n\n\nChang H. Lee \"Danny\"\n\n\nAtlanta, GA USA\n\n\nI think this is as good as the expensive over $60 HDMI cables. I wouldn't spend money on the expensive ones. First of all, I'm not a cable junkie nor an expert. However, according to my research, there's no difference in performance b/w the expensive version nor the $12, $15, $20 version when it comes to HDMI since it's all digital.. hole buch of zeros and ones. The durability of the cables... I don't know. But unless you're someone who constantly plugs and unplugs the cables for some reason, it wouldn't matter. Normally, you'd connect the cable and keep it that way for a long period of time, so the durability is not really relevant. Bottomline: This is as good as the expensive ones. I first tried out the expensive Monster brand which was $85. Then I returned it after purchasing this, for the quality was the same. Don't buy the expensive ones\n\n\n\n\nB000067SVG:must_have!:pamelars24\n\n\nB000067SVG\n\n\nTargus PA220U Retractable USB Cord: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nMust Have!\n\n\nJuly 16, 2004\n\n\npamelars24\n\n\nFairfax, VA United States\n\n\nThis is wonderful for laptop users that travel or like to move your laptop around a lot. If I leave my laptop somewhere I hate having to worry about tripping over wires and wires everywhere just look bad. This is really small and compact! Perfect for traveling too. I've used it for about 8 months now and it hasnt had one problem...it still recoils well\n\n\n\n\nB00005BIDS:great_product:\"agator82\"\n\n\nB00005BIDS\n\n\nJensen JTH930 Earbud Telephone Headset with Inline Microphone: Electronics\n\n\nelectronics\n\n\n4 of 6\n\n\n5.0\n\n\nGreat product\n\n\nDecember 23, 2002\n\n\n\"agator82\"\n\n\nOcklawaha, FL USA\n\n\nThis product is a nice headset that did fit nicely on my ear (lucky fit I guess) and was easy to work with. It is compact and feels quite natural for me. Not to mention it is designed to work on either ear so in the middle of a conversation you can just change ears\n\n\n\n\nB0002GWP46:good_value_for_the_money:maggie_jarpey\n\n\nB0002GWP46\n\n\nHP DeskJet 3845 Color Inkjet Printer: Electronics\n\n\nelectronics\n\n\n5 of 6\n\n\n5.0\n\n\ngood value for the money\n\n\nMarch 9, 2006\n\n\nMaggie Jarpey\n\n\n\n\n\nI am pleased with this printer. Considering the price, it is a good value. The cartridges are kind of expensive, but I guess most cartridges are these days\n\n\n\n\nB0002GWP46:the_little_engine_that_could!:peter_c_h\n\n\nB0002GWP46\n\n\nHP DeskJet 3845 Color Inkjet Printer: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nthe little engine that could!\n\n\nMarch 1, 2006\n\n\nPeter C H\n\n\nSoCal\n\n\nPROS:\n\nsmall\nreliable\nvery good quality with pictures\nease of use\ngood software\neasy install\naffordable\n\nCONS:\n\nink tends to run out quickly with pictures\nslow rate of printing\nusb cable not included\n\noverall: 5 amazonian stars for the average user\n\n\n\n\nB00067OF80:excellent_quality,_a_great_buy!:takin_care_of_business_\"the_deal_guy\"\n\n\nB00067OF80\n\n\nSennheiser EH-150 Evolution Hi-Fi Stereo Headphones: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nExcellent Quality, A Great Buy!\n\n\nNovember 16, 2006\n\n\nTakin Care of Business \"The Deal Guy\"\n\n\nLos Angeles, CA\n\n\nThis is truly a no-brainer, excellent headphones at a great price, wonderful all around phones, would make a great gift also\n\n\n\n\nB00067OF80:great_detail_in_these_inexpensive_phones:bruce_young_\"seeking_the_balance\"\n\n\nB00067OF80\n\n\nSennheiser EH-150 Evolution Hi-Fi Stereo Headphones: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n4.0\n\n\nGreat detail in these inexpensive phones\n\n\nNovember 9, 2006\n\n\nBruce Young \"seeking the balance\"\n\n\nSF Bay area\n\n\nI have been using these for a while now and they seem pretty good to me. (disclaimer: I am not an audio professional, nor do I have any expensive headphones to compare with.)\n\nThey have great detail in both low and high end. In fact, I can hear things in some of my music that I never noticed before in either my small speakers or in the several foam on-ear style or earbud styles. This is good when listening to original CDs, or HQ mp3/AAC, but it did reveal some audio glitches and artifacts in lower quality mp3 that I had just not heard before...\n\nI tested these with a frequency response program on my Mac, show as having 25-15,600 Hz (in my ears, your ears may vary...) . This range is better than most of my other inexpensive headphones and earbuds.\n\nThey do a good job of blocking out external noises-- great if you are in a bit of a noisy space.\n\nAnd, yes, they were a bit tight on the head initially. To fix that, I got a stack of cd/dvd cases that was a bit wider than my head and allowed these headphones to be expanded by them for several overnight stretches (pun intended). This has helped too lessen the pressure they put on my ears and head to the point that they are now OK to wear for long periods. NOTE: this procedure is at your own risk, as I do not think it is officially endorsed by the mfg. ;-) Do not overstretch them, as the headband material, while flexible, undoubtedly has limits to the stretch it can take.\n\nGreat Bargain Headphones\n\n\n\n\nB00067OF80:superb_sound_quality:shloky\n\n\nB00067OF80\n\n\nSennheiser EH-150 Evolution Hi-Fi Stereo Headphones: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nSuperb Sound Quality\n\n\nSeptember 5, 2006\n\n\nShloky\n\n\n\n\n\nNot an audiophile, but someone looking to spend a decent amount of money on a quality set of headphones. This product nails all the requirements - great sound, great comfort, well built. I'll be buying more of these guys for friends and family.\n\n\n\n\nB000EWD4TS:uninstall_the_u3_and_it's_fine:david_park\n\n\nB000EWD4TS\n\n\nSanDisk 2 GB Cruzer Micro with U3 ( SDCZ6-2048-A10, Retail Package): Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n5.0\n\n\nUninstall the U3 and it's fine\n\n\nNovember 8, 2006\n\n\nDavid Park\n\n\nAtlanta, GA\n\n\nDon't let the reviews fool you. At this price, it's worth a little research. Apparently U3 is annoying a lot of people and the people at U3 have created an uninstall download for you to get. So Google it, download it, and buy the USB drive. The size of this USB drive and the fact that you'll never have to worry about losing the cap along with an excellent price make it worth buying. After you uninstall the U3 software, it works like any other drive. It works great, it just takes you ten minutes of a little extra work. \n\nI'm very satisfied so far and have carried audio, video, and a lot, lot more on a drive the size of a pen cap. Brilliant...\n\n\n\n\nB00006I5RA:more_than_i_was_expecting:a._larochelle\n\n\nB00006I5RA\n\n\nSennheiser HD 457 Traditional Open Hi-Fi Stereo Headphones (Silver): Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n5.0\n\n\nMore than I was expecting\n\n\nFebruary 6, 2006\n\n\nA. Larochelle\n\n\nQuebec City, Qc, Canada\n\n\nI bought these headphones a week ago and I am very pleased with them. At the electronic store, I listen to 3 pairs of Sennheiser (HD-433 ; HD-437 and HD-457). I hesitated between the 437 and the 457 but went for the 457 (10 more buck$). At the store the sound was OK as they were playing from the Sennheiser display rack. When I got home, I plugged these in my computer soundcard jack and the sound was a whole lot better. I am very impressed of the volume and sound quality I can get from them on my computer. My soundcard is an onboard Soundmax AC97 on an Asus K8V-X motherboard. I set the sound option in Windows XP on \"headphones\" and played \"Metallica- Nothing else matters\" with WinAmp. WOW !!! I was really impressed with the bass response and sound clarity in general. I only use half of the volume available in Windows XP and it`s more than enough for me.\nI do agree with some other reviews that the bass could be more precise but I found out that it varies a lot depending of the sound source you`re using. The sound is better on my computer than on my Harman Kardon amplifier and is a lot better on my MP3 player than with the earbuds that came with it.\nOverall, I would say that these headphones are very good for the inexpensive price. They blow away all the competition in the same price range. Compared to my old Panasonic, it`s a drastic improvement especially for the bass response.\nWhen it comes to music, I love bass a lot but I would recommand to others that you would listen to them from a source that you could adjust the bass level (bass knob or equalizer), so that way you won`t get too much bass from them.\nVery good headphones ... Go get a pair ... at this price you can`t go wrong. I highly recommand them for computer users (music, DVD movies and games)\n\n\n\n\nB000F4C2CA:great_phone:realguy1\n\n\nB000F4C2CA\n\n\nPanasonic KX-TG5631S 5.8 GHz FHSS GigaRange Digital Cordless Answering System: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGreat phone\n\n\nNovember 17, 2006\n\n\nrealguy1\n\n\nWashington, DC USA\n\n\nThis is one of the best wireless phones I've had -- good call quality, good speakerphone quality and good range\n\n\n\n\nB000F4C2CA:dependable:kids_&_corgis\n\n\nB000F4C2CA\n\n\nPanasonic KX-TG5631S 5.8 GHz FHSS GigaRange Digital Cordless Answering System: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nDependable\n\n\nNovember 11, 2006\n\n\nKids & Corgis\n\n\nOrefield, PA USA\n\n\nPanasonic makes good quality products and this is no exception. Easy to use, very compact, no interference with cordless usage\n\n\n\n\nB000F4C2CA:great_phone:j._benson\n\n\nB000F4C2CA\n\n\nPanasonic KX-TG5631S 5.8 GHz FHSS GigaRange Digital Cordless Answering System: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGreat Phone\n\n\nNovember 9, 2006\n\n\nJ. Benson\n\n\n\n\n\nI bought this phone to replace my older Panasonic because I needed a new answering machine. I love that I can retrieve messages from the handset. The handset is a bit smaller than earlier models, but just took some getting used to. Would definitely recommend\n\n\n\n\nB000F4C2CA:high_quality_for_low_price:m._jeffrey_mcmahon_\"herculodge\"\n\n\nB000F4C2CA\n\n\nPanasonic KX-TG5631S 5.8 GHz FHSS GigaRange Digital Cordless Answering System: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nHigh Quality for Low Price\n\n\nNovember 7, 2006\n\n\nM. JEFFREY MCMAHON \"herculodge\"\n\n\nTorrance, CA USA\n\n\nI picked this up for 50 dollars to replace a 75-dollar Uniden that had a sticky \"talk\" button and muffled sound qaulity. The Panasonic blows the Uniden away. The sound is clear, crisp, and intimate, the ring selection is varied (I prefer the nonobtrusive default setting). The controls are easy to use. The quality and finish of the materials is high. I am now a loyal Panasonic phone buyer\n\n\n\n\nB000BJ3BRO:great,_great_quality;_even_better_value.:l._baisden\n\n\nB000BJ3BRO\n\n\nOlympus WS-320M 1 GB Digital Voice Recorder and Music Player: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nGreat, great quality; even better value.\n\n\nNovember 4, 2006\n\n\nL. Baisden\n\n\n\n\n\nIt's hard to believe the quality of the sound from this very small device. MP3's sound great as do the voice recordings. With 1 gig of space you can record an entire day of seminar speakers and still have space left. The built in STEREO OR MONO mics are great...PLUS, YOU CAN USE IT TO CARRY FILES TO AND FROM WORK.\n\nThe function buttons are logical and easily accessed, even with my big fingers. Priced right, I'm glad I purchased. Received it on time. Totally happy with my purchase. \n\nMy only wish is that it came with a case and a neck or wrist strap. It's so small it gets tossed into whatever pocket is available and that can lead to scratches from keys, etc\n\n\n\n\nB000BJ3BRO:great_size,_great_price,_great_performance...:frank_curtin_\"the_smart_guys\"\n\n\nB000BJ3BRO\n\n\nOlympus WS-320M 1 GB Digital Voice Recorder and Music Player: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nGreat size, great price, great performance...\n\n\nNovember 3, 2006\n\n\nFrank Curtin \"The Smart Guys\"\n\n\nGrapevine, TX USA\n\n\nI am a success coach and I record all my coaching calls as a standard practice. This recorder has performed fantastically in all situations. I get about 9 - 12 continuous hours of battery operation (depending on battery quality) and the unit captures voices flawlessly during my calls (I have an adapter for the phone). I have used this in a seminar setting and have had great results. I highly recommend using rechargeable batteries (always have one extra on hand) when you are going to use the unit for extended periods of time. Recharge before your event each day and you won't miss a thing. The unit only uses one battery and it's always a great idea to have a spare. The unit does not audibly warn when the battery is about to expire, so keeping a fresh charge should provide you with uninterrupted recordings. Each stop and restart begins a new file. For me that works great! If you are looking for one continuous file, do not shut it off. You can also buy software that will let you merge the files for your later use. You would be hard pressed to find a better product for the money\n\n\n\n\nB0001F1ZY4:great_product_at_great_price:mohamed_iqbal\n\n\nB0001F1ZY4\n\n\nSanDisk SDMSV-512-A10 512 MB MemoryStick Pro (Retail Package): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGreat product at great price\n\n\nFebruary 24, 2006\n\n\nMohamed Iqbal\n\n\n\n\n\nA good stuff at great price and as usual Amazon was fast to send the item\n\n\n\n\nB00092M1SS:good_basic_player:k._braat\n\n\nB00092M1SS\n\n\nRCA Lyra RD2315 512 MB Personal Digital Audio Player with Voice, Line-in and FM Recording: Electronics\n\n\nelectronics\n\n\n20 of 21\n\n\n4.0\n\n\nGood basic player\n\n\nSeptember 29, 2005\n\n\nK. Braat\n\n\nPortage, MI United States\n\n\nI decided to replace my iRiver 190T after a 3rd party cable adapter malfunctioned and fused the pin connection to the USB cable together. I wanted USB 2.0 for the faster connection and a larger capacity, but still desired standard user replaceable batteries on a flash based player so I picked this up on sale.\n\nI may still search for a used iRiver 190T after playing with the Lyra RD2315. I like the ruggedness of the 190T a bit more than the RCA Lyra RD2315.\n\nPros:\nUser replaceable AAA batteries\nExpandable SD slot up to 512 MB\nDecent sound for biking and general yard work\nScreen is able to be positioned at 0 or 180 degrees for easy reading from belt clip/lanyard\nLine in recording and voice for short memos\n3 different color backlights for user preference\nDecent battery life\n\nCons:\nHeadphones are worthless. Placing them in ear prevents clips from holding them in place\nSlow reaction on interface compared to older iRiver player \"Please Wait...\"\nBelt clip is going to break after minimal usage (like ink pen cap clips do)\nDoesn't seem like USB 2.0 speed, just normal USB 1.1\nNo armband or case available\n\nI used firmware 3.11 for my testing after upgrading from version 1.11\n\n\n\n\nB0002ILKMW:updated_from_a_17\"_crt_and_wanted_something_to_match_imac_20\":j._m_mccormick_\"liquid100percent\"\n\n\nB0002ILKMW\n\n\nApple Cinema 20\" Flat-Panel Display: Electronics\n\n\nelectronics\n\n\n7 of 7\n\n\n5.0\n\n\nupdated from a 17\" CRT and wanted something to match iMac 20\"\n\n\nApril 3, 2006\n\n\nJ. M MCCORMICK \"liquid100percent\"\n\n\nSanto Andre, Brazil\n\n\nwhen I bought my Intel iMac w/20\" LCD suddenly everything else looked dull in comparison. This Apple 20\" is very close in spec to my iMac 20\" screen. Stores like Best Buy do not have good quality 20+\" LCDs in yet. You want the 16x9 profile also, the regular square LCDs look dorky in comparison and typically have lower brightness and contrast ratios. The newer stuff is very bright and clear. It is obvious when you go to a store and look at the LCD screens. The cheap $200 LCDs just don't look good in comparison to the new stuff.\n\nIntel iMac screen:\n1680x1050\n280 cd/m2 (brightness in candle power)\n800:1 Contrast ratio\n\nApple 20\" LCD\n1680x1050\n250 cd/m2 \n400:1 Contrast\n14ms response time (www.apple.com/displays/specs.html)\n\nOther comparables:\nGateway 21\" 1680x1050 $599\n300 cd/m2 1000:1 contrast\n\nDell 24\" 2405FPW $800\n1920 x 1200\n500 cd/m2\n1000:1 contrast\n\nYou should be aiming for at least 300 cd/m2 on brightness and close to 1000:1 contrast. These Apple LCDs have been out for awhile and are starting to show their age in comparison to the brand new products that have just started to come out. If you are using your LCD for a TV you may need the brightness, but for computer work the Mac 20\" LCD is just about right. \n\nI paid a few $$$ more ($750 total price) for the Apple LCD, but I feel I made the correct purchase. I just didn't like the way the others looked and it had to look good against my iMac. I actually like this screen better than the one on my iMac and it seems just as bright even if the specs say that it is not. Amazon also boxes the shipment (besides the Apple box) so your screen should arrive undamaged\n\n\n\n\nB0002ILKMW:a_solid_display_that_compliments_any_computer:clorman_borno_\"robk2\"\n\n\nB0002ILKMW\n\n\nApple Cinema 20\" Flat-Panel Display: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nA solid display that compliments any computer\n\n\nOctober 25, 2005\n\n\nClorman Borno \"robk2\"\n\n\nServing Overseas\n\n\nI've been slowly making my way to the Mac side after nearly 15 years of unadulterated Windows/DOS use. The process has been slow mainly because of the price and availability of Apple products: High and Scarce.\n\nThe best thing about Apple is that their customer forums are full of people like you and me, and if you have a problem, someone else has most likely had it, too. Those forums helped in my decision to purchase this monitor.\n\nI had been looking closely at the 23\" monitor. When I read about a majority of the displays getting a pink tint after only a few hours of use, I realized I would either wait until Apple re-engineered the monitor, or look at other options.\n\nWith the 20\" Aluminum, I didn't have to look far. I wanted a monitor that would compliment my G4 laptop (Powerbook), but I didn't want to break the bank, either. The answer was turning to ebay. $600 out the door. Mint condition. Saved over $150, and the monitor works and looks great!\n\nThe 20\" and 30\" models have had no problems. If you can buy them new, go for it. If you can buy new, but would rather spend that extra money on something else, check out the ebay auctions\n\n\n\n\nB000EM6MP6:nice_phone:big_d\n\n\nB000EM6MP6\n\n\nPanasonic KX-TG2620B 2.4 GHz FHSS GigaRange Digital Cordless Telephone: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nNice Phone\n\n\nOctober 31, 2006\n\n\nBig D\n\n\nKentucky\n\n\nI was looking for a basic cordless phone with out an answering machine and this one is great. It has good reception and I like the size of it. This phone is just what I was looking for.\n\n\n\n\nB00004T8R2:best_in_class:ds\n\n\nB00004T8R2\n\n\nPanasonic RP-HT21 Portable Headphones: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n4.0\n\n\nBest in class\n\n\nMarch 21, 2006\n\n\nDS\n\n\nU.S.A.\n\n\nThese are the best portable headphones in this price range. Good bass and smooth, natural sound. In fact they would probably still be the best bang for the buck at three times the price. If you can afford more, consider the Sennheiser PX100 for about 40-50 dollars. But if you're on a budget or don't want to worry about losing or breaking a more expensive pair of headphones, these are definitely the ones to get\n\n\n\n\nB00009X3RN:changing_to_hdtv:oliver_schwallenstecker\n\n\nB00009X3RN\n\n\nOmniMount Cantilever TV Mount (up to 80 Pounds): Electronics\n\n\nelectronics\n\n\n0 of 5\n\n\n5.0\n\n\nChanging to HDTV\n\n\nFebruary 20, 2006\n\n\nOliver Schwallenstecker\n\n\n\n\n\nExcellent mount for the 2 TV's I've purchased. Was suprised to buy one about a month ago from amazon.com for 149.95 then was able to buy this one for 124.95. Did I pay too much for the first mount? Ollie...........\n\n\n\n\nB0007CNYMC:great_but_pricy!:s._fiorenza_\"stefio5559\"\n\n\nB0007CNYMC\n\n\nRewritable DVD-RAM Disc Without Cartridge: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nGreat but pricy!\n\n\nNovember 9, 2006\n\n\nS. Fiorenza \"Stefio5559\"\n\n\nSt Petersburg, FL\n\n\nThis was needed for my Panasonic DVD recorder. Works well - hope it holds up as advertised.... Looks like high quality and works well. So far - everything is as expected - and THAT is good\n\n\n\n\nB000EH8K8S:next_best_thing_to_a_built-in_connector:cj_\"minidiva\"\n\n\nB000EH8K8S\n\n\nKensington Pico FM Transmitter for iPod 5G and Nano (Black): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n4.0\n\n\nNext Best Thing to a Built-in Connector\n\n\nMay 7, 2006\n\n\nCJ \"MiniDiva\"\n\n\nAtlanta, GA USA\n\n\nIt's not listed in the product description but the Pico works with the iPod Mini too. Like the other reviewers, I found it easy to setup: tune the radio, turn on iPod, connect the Pico, tune the Pico, done). In my car, the static interference was less noticeable when the unit was placed in close proximity to the radio. I noticed this as well with my home stereo. Maybe the next generation Pico needs a larger antennae, or my iPod needed charging. Regarding sound quality, my other car has a built-in iPod connector. The Pico sound quality is comparable, somewhat muted, but enjoyable. The Pico does offer the advantage of displaying the iPod menu to select tunes. I've read mixed reviews on other FM transmitters, but I am pleased with the Pico\n\n\n\n\nB0009JR5IM:excellent_for_what_i_use_this_for:robert_keeney\n\n\nB0009JR5IM\n\n\nKensington 33164 Stereo Dock, Charger, and Transmitter for iPod, iPod Mini, iPod Nano, or iPod Photo: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nExcellent for what I use this for\n\n\nNovember 9, 2006\n\n\nRobert Keeney\n\n\nGrapevine, Texas\n\n\nI ordered this device to sit on a player piano, which can play ipod encoded MP3 files, once they are transferred from the special CDs that are designed for the piano. The remote is limited, as are all remotes for ipods, but for the special use I have, this is a great product. It allows me to sit across the room and pause the piano or select the next piece of music\n\n\n\n\nB00005K4PO:good_capability,_fair_price:rick_galati\n\n\nB00005K4PO\n\n\nBrother PT-1750 Electronic Labeling System: Electronics\n\n\nelectronics\n\n\n27 of 28\n\n\n4.0\n\n\nGood capability, fair price\n\n\nSeptember 16, 2004\n\n\nRick Galati\n\n\nLake St. Louis, Missouri United States\n\n\nI was looking for an inexpensive, yet high quality, long lasting, and durable way to identify dozens upon dozens of electrical wires behind the instrument panel of my airplane. A fellow pilot made me aware of the family of small electronic labelers available in the marketplace. The Brother 1750 fits my needs perfectly. Offering two lines of crisp, highly readable print gives a professional appearance to all labeling needs. My experience is quite different than some reviewers who have complained about wasted tape. In fact, I found that little wasted tape is generated if you simply adjust the margins to a particular task and this option is clearly spelled out in the instruction booklet. Since I purchased the 1750, I have found many uses for it around the home office and I wonder how I got by without one in the past! My only complaint about the keyboard is that among the characters, it does not offer a dash or hyphen, so when one is required, I find myself using a star instead. That is a minor complaint though. The Brother 1750 gets considerable mileage on AA battery power alone, so I have not felt the need to purchase the adapter\n\n\n\n\nB0009AFVZK:maybe_other_reviewers_were_talking_about_another_phone.:superjuan_\"juan\"\n\n\nB0009AFVZK\n\n\nAT&T 992 Two-Line Corded Speakerphone with Caller ID: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nMaybe other reviewers were talking about another phone.\n\n\nSeptember 12, 2006\n\n\nSuperjuan \"Juan\"\n\n\nWashington, DC\n\n\nWe recently purchased 4 of these phones and all of them work fine. The CID works perfectly for both lines. The interface/menu takes a few minutes to learn and get used to but I wouldn't call it \"non-intuitive\".\n\nThe Redial button (as you can see in the picture) is right above the number pad. If you can't find it, you might want to buy a phone with less buttons and less features. And it's pretty simple to redial a number in the CID display... when you see/get to the number press Enter twice (Once to bring up the \"MENU\", and Once more to \"DIAL\"). In fact you can also save that number into the phone's memory through the menu (press Enter once and scroll through the options)\n\n\n\n\nB0001RM3WU:very_nice_product:jorge_osorio_guerrero\n\n\nB0001RM3WU\n\n\nGarmin Etrex Windshield Mounting Bracket: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nVery nice product\n\n\nNovember 10, 2006\n\n\nJorge Osorio Guerrero\n\n\nChile\n\n\nAt first, I was not sure about if this Windshield Mounting Bracket was going to stay in place while I drive trough the streets. However, I was very surprised when I found out that the Bracket stayed put all day going from one place to another, even under some vibration. The suction rubber system is quite strong. It has 2 angles for adjustment, and you can rotate the GPS clockwise/counterclockwise.\n\nPros: Strong suction rubber system, GPS mounting system\nCons: Lack of a 4th angle for adjustment (the GPS can only be adjusted in the windshield plane, so you can not turn it towards your face for easy viewing - that's why I give 4 stars\n\n\n\n\nB0001RM3WU:garmin_etrex_windshield_mounting_bracket:k._y._leung\n\n\nB0001RM3WU\n\n\nGarmin Etrex Windshield Mounting Bracket: Electronics\n\n\nelectronics\n\n\n8 of 8\n\n\n4.0\n\n\nGarmin Etrex Windshield Mounting Bracket\n\n\nFebruary 20, 2006\n\n\nK. Y. Leung\n\n\nKoP, PA\n\n\nVery strong suction cup provides secure mounting. Quick and easy clip in and release for taking the GPS with you. Product would be better if the pivoting arms were longer and able to swivel sideways. Otherwise, this is a well-designed and quality product\n\n\n\n\nB0001JXAYS:height_dimensions_inaccurate:pro_\"rob\"\n\n\nB0001JXAYS\n\n\nPanasonic SA-XR70S 6.1 Channel Surround Sound Home Theater Audio / Video Receiver - Silver: Electronics\n\n\nelectronics\n\n\n1 of 3\n\n\n5.0\n\n\nHeight dimensions inaccurate\n\n\nApril 1, 2006\n\n\nPro \"Rob\"\n\n\nUSA\n\n\nThe forums speak highly of these units-xr70, and I was going to get one because of the small form factor. Amazon's description for the height at 2.5\" is incorrect, according to Panasonic's website. Should be listed as 3 9/32\"H. Makes a difference if you're short on space\n\n\n\n\nB0001JXAYS:i_just_love_this_system:busybee64_\"busybee64\"\n\n\nB0001JXAYS\n\n\nPanasonic SA-XR70S 6.1 Channel Surround Sound Home Theater Audio / Video Receiver - Silver: Electronics\n\n\nelectronics\n\n\n5 of 6\n\n\n5.0\n\n\nI just love this system\n\n\nMarch 22, 2006\n\n\nbusybee64 \"busybee64\"\n\n\nCA USA\n\n\nI was paranoid about having buyer's remorse from this unit. For such a cheap price I was sure I would end up regretting the purchase. What is the tradeoff? Shoot if know, but I haven't regretted buying it one bit. The receiver is perfect for home theater, particularly DD 5.1 shows. There is absolutely no discernable speaker hiss with digital content, which is important for TV watching that has a high range of both loud and silent moments.\n\nAs for power, it's not an even match with some of the more power hungry and expensive analog amplifiers out there, but it easily crosses the threshold of annoying the neighbors.\n\nDigital is something to get used to. On conventional receivers, you turn the sound up high and it becomes somewhat sloppy, or what some call \"warm\". Not so with the digital Panny. It's distortion free up to very high levels. I wouldn't call it bright, but the response is very linear and precise. I like hearing all the details in music and cinema, even at loud volume. Others don't.\n\nThe weaknesses are very minor:\n\n-The FM tuner is nothing special.\n-It lacks fancy DSP modes of expensive receivers.\n-No fancy on screen display (must do most of the tweaking with knobs)\n-It does not upconvert video.\n-The remote control is an el cheapo.\n\nFor this price, though, who cares\n\n\n\n\nB0001JXAYS:great_receiver:toby_a._dingle_\"tadingle\"\n\n\nB0001JXAYS\n\n\nPanasonic SA-XR70S 6.1 Channel Surround Sound Home Theater Audio / Video Receiver - Silver: Electronics\n\n\nelectronics\n\n\n12 of 12\n\n\n5.0\n\n\nGreat Receiver\n\n\nOctober 30, 2005\n\n\nToby A. Dingle \"tadingle\"\n\n\nCalifornia\n\n\nI purchased this receiver from Fry's Electronics in San Diego, CA for $199.99. At first, I was a little hesitant about purchasing an all digital receiver. When I got it home, and connected it, I was amazed. This receiver will blow you away with the precision of it's home theater capabilities. I am using four Polk R30's as my fronts, and rear surround, and two Sony center channels for my front center, and rear center channels. I also have a KLH 10inch Tremor Series powered subwoofer. I estimate the entire system to cost about $650.00 total. I have colleague that has a Bose home theater system, and my little system blows his out of the water, and he paid much more for the Bose. If you want a great receiver a minimal price, this is the way to go. By the way, did I mention that it has four digital inputs, an optical output, and HDMI in and out connections. BUY IT!!!\n\n\n\n\nB0007ZFM38:great_item.:joshua_cole\n\n\nB0007ZFM38\n\n\nLogitech 980356-0403 Stereo USB Headset 250: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGreat item.\n\n\nNovember 10, 2006\n\n\nJoshua Cole\n\n\n\n\n\nThe logitech stereo usb noice cancelling headset is awesome... I never recieved the one I ordered from ANT online, but the one I bought later at walmart owns me\n\n\n\n\nB000F49RAA:excellent_value_for_money:mahadevan_virudhagiri\n\n\nB000F49RAA\n\n\nLogitech mm50 Portable Speakers for iPod Black: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nExcellent Value for money\n\n\nNovember 3, 2006\n\n\nMahadevan Virudhagiri\n\n\nEast Hartford, CT\n\n\nI did a lot of research and finally ended up with this product. \n\n1) BOSE is an excellent piece - but main drawbacks - portability and lack of audio input from other devices\n\n2) Altec Lansing - basically a roun cylinder shaped one and I didnt get that in a black color for my 30 GB Black Video Ipod and the sound wasnt that impressive as Bose (or for that matter Logitech mm 50)\n\n3) Apple's ipod Dock - its a bulky one, that is definitely not portable and costlier than the Bose. \n\nI went to BestBuy to listen to everything and Logitech mm 50 really outweighs the other ones on these - price, portability and sound quality, yea, its not like Bose or Apple's dock - but really good for such a small piece. \n\nThe only thing I find a little difficult is, I am not able to switch playlists with the remote, its just for play/pause/next/prev and sounds, would've been completely happy if had all that I could do in the ipod. But, I am very happy with the buy and will definitely recommend to bu\n\n\n\n\nB000IT600W:the_best_i've_seen!:c._medeiros\n\n\nB000IT600W\n\n\nKensington Digital FM Transmitter and Auto Charger for iPod (Black): Electronics\n\n\nelectronics\n\n\n7 of 8\n\n\n5.0\n\n\nThe Best I've Seen!\n\n\nNovember 8, 2006\n\n\nC. Medeiros\n\n\nMassachusetts\n\n\nI've had 2 other FM transmitters (A Belkin and a Griffin iTrip). Neither came close to the quality of this transmitter. Neither charged the iPod (The Belkin required batteries, which only lasted about 15 hours of use), while the iTrip worked OK at first, but then started getting static no matter what station I used. In fact, I had to have the iPod in hand for the static to go away (and then, it was still not clear). The sound isn't as loud as some (it takes over the volume control, so you cant use the volume on the iPod.) Still, it's not nearly as soft as some I've seen. I found most music came through at a good volume (I did adjust a few songs in iTunes to pump them up). This unit, although it seems cheap and flimsy at first, is able to get a consistant, strong signal, even on stations which usually come in clearly. It powers through them, and the result is a very clean sound. It is a little hard to read at the regular angle, but once you set the 3 presets, you don't need to see the screen. Just set your radio to the same 1-2-3 configuration. Also, turning it at a 90 degree angle lets you see the screen much clearer. In all, I couldn't be happier\n\n\n\n\nB000ELE4Q6:low_price_and_get_the_job_done:epsilon_delta\n\n\nB000ELE4Q6\n\n\nToshiba D-RW2 DVD Player/Recorder: Electronics\n\n\nelectronics\n\n\n7 of 13\n\n\n5.0\n\n\nlow price and get the job done\n\n\nJuly 15, 2006\n\n\nEpsilon Delta\n\n\nCA USA\n\n\nat such low price, this player is really pretty good.\n\nit gets the job done, even though the UI is a little bit basic.\n\nit can record in DVD+R and DVD-R, and has a 10 hour mode. Although in the 10 hour mode, image is not so clear but still pretty good if view on the computer using 320 x 240 resolution.\n\nand at such a low price, you can get two or three to record different programs and archive some shows, and it is still cheaper than the Media Center PC. Good thing is the disc is already a DVD playable in any player. No need to burn it as opposed to if you are using Tivo or Media Center PC.\n\n\n\n\nB000BUWNH2:see_above:ajs\n\n\nB000BUWNH2\n\n\nCanon CLI-8 4-Pack Color Ink Tanks for Pixma printers: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nSee above\n\n\nNovember 3, 2006\n\n\nAJS\n\n\nCA USA\n\n\nQuite acceptable. In line with prior purchases at outlet where PIXMA was sol\n\n\n\n\nB00023C6SE:incredible_headphones:e._kelly\n\n\nB00023C6SE\n\n\nSony MDR-DS3000 Infrared Wireless Digital Surround Headphones: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nIncredible Headphones\n\n\nSeptember 24, 2006\n\n\nE. Kelly\n\n\nUSA\n\n\nI've had these headphones for just over two years, and I'm happy to see that they're one of the few pairs marketed as \"surround sound\" that have done quite well. The reason behind that is because they're truly excellent headphones. The range is spectacular and the inputs even more so. You're given the option of RCA and Toslink (optical), which is all you really need. \n\nMovies really do sound like movies...you hear things behind, in front, and to your left and right when you're connected via optical. Take note that DTS also sounds 'boomier' and a tad crisper than Dolby D does, especially in Gladiator. \n\nHonestly, I've nothing to complain about. As a college guy, I couldn't bring my 1010 watt system from home. Which, I might add, never sounded as good as my headphones, mostly because headphones will always have the advantage of being closer to you physically. Listening to music at extremely high bit rates and using the optical connection from my Macbook Pro truly does some incredible things. You'd be amazed at Coldplay's \"The Scientist\" if you heard it on these headphones. It's indescribable.\n\nAnd by the way, I'm a slight audiophile.\n\nYou seriously can't go wrong with these headphones. And $65 for each additional pair of headphones, allowing you to add as many as you want to one base station? Even better...good idea for families in apartments.\n\nIncredibly advanced technology, incredible sound, and incredible results. Can't go wrong here.\n\nOh, and they're extremely comfy\n\n\n\n\nB0007NIJBC:great_cam,_good_video_and_audio_:d._milios\n\n\nB0007NIJBC\n\n\nLogitech QuickCam Communicate STX: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n4.0\n\n\nGreat cam, good video and audio \n\n\nNovember 21, 2006\n\n\nD. Milios\n\n\nNY, NY\n\n\nI just recently purchased the Quickcam STX for my sister who lives overseas in Greece. It's a great way for us to connect especially since we only see each other on an annual basis. Surprisingly, the video and audio is quite good. I do have a Mac and she doesn't have any issue calling me from her pc. We use Aol instant messenger (Video and Audio) or Yahoo Messenger (Video) to connect. I still prefer the iSight for Macs. Unfortunately, you can't use an iSight on a pc. Wish Apple could make the iSight compatible with PC's. Anyhow the STX is a great cam for what it's worth.\n\n\n\n\nB0007NIJBC:great_image_and_voice_quality:wookjin_na_\"woody\"\n\n\nB0007NIJBC\n\n\nLogitech QuickCam Communicate STX: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n5.0\n\n\nGreat image and voice quality\n\n\nNovember 9, 2006\n\n\nWookjin Na \"Woody\"\n\n\nCollege Station, TX USA\n\n\nThis samll web cam is doing a great job connecting my families overseas. The flexible shoe is really handy and the image and voice quality is beyond my expectation\n\n\n\n\nB00007AKAZ:satisfied_customer:tb\n\n\nB00007AKAZ\n\n\nLinksys USB2HUB4 USB 4-Port Hub: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nsatisfied customer\n\n\nFebruary 20, 2006\n\n\nTB\n\n\nLubbock, TX United States\n\n\nhave had no problems with this item, and it's a good price for the powered kind of USB hubs, which I have found are pretty necessary for most USB items. \n\n\n\n\nB0009Q4PHO:love_'em!:james_sexton\n\n\nB0009Q4PHO\n\n\nUltimate Ears Super.fi 5 Pro Earphones (Black): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nLove 'em!\n\n\nSeptember 18, 2006\n\n\nJames Sexton\n\n\nCentral NY\n\n\nI found the sound fantastic. Just got these after usinf Shure E3s for about a year. I liked those but find these even better. When I first tried them I found the base so much stronger than the E3s as many have alluded to. Now I run my iPod at normal equalizer settings. I find the balance of the set dead on. The fit is great, of course your milage may vary. In fact they are so good at excluding air conduction that bone conduction becomes very noticable. This means when I walk with them I can hear my footsteps. This would be a real problem if I jogged (low impact workouts such as elipticals and bicycles are not affected)\n\n\n\n\nB000H72MMU:much_better_than_the_belkin_tunebase_fm:u._lehmann_\"movie_nerd\"\n\n\nB000H72MMU\n\n\nMonster iCarPlay Plus Wireless FM Transmitter/Charger for iPod: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n4.0\n\n\nMuch better than the Belkin Tunebase FM\n\n\nNovember 2, 2006\n\n\nU. Lehmann \"Movie Nerd\"\n\n\n\n\n\nThis is my second FM transmitter and works much better than the Belkin that I ended up returning because it simply wasn't working.\n\nIt plugs firmly into my cigarette lighter and stays there, unlike the Belkin (I drive a 2001 BMW 325ci). It is easy to use with the preset FM stations. It works right out of the box, you'll be ready to go within seconds. The sound is crisp, with some static interference, which can be expected in a big city. Unmounting my car's antenna helps a little but doesn't completely cure the interference. I have a factory BMW Business radio which picks up FM signals pretty much without an antenna.\nThere is nothing to mount the iPod so you'll either have to set it in your cup holder or purchase a case separately.\n\nI wouldn't spend more than $45 on any FM transmitter as there will always be minor interference in urban areas. I won't be getting rid of my CD changer just yet\n\n\n\n\nB000COJ8NY:amazing_pda,_great_features_at_a_great_price!:mr._tech_genius\n\n\nB000COJ8NY\n\n\nDell Axim X51v PDA 624 MHz Processor, 256MB ROM / 64MB SDRAM, 3.7 inches 480*640 VGA TFT Color LCD, BlueTooth / WiFi 802.11b/Infrared: Electronics\n\n\nelectronics\n\n\n12 of 12\n\n\n5.0\n\n\nAmazing PDA, Great Features at a Great Price!\n\n\nSeptember 3, 2006\n\n\nMr. Tech Genius\n\n\nOhio\n\n\nI have had my Axim x51v for about three months. I purchased it from Dell for $480 with aluminum hardcase, extra 1100mAh battery, and auto power cord.\n\nStrengths: Just about everything. The processor is absolutely incredible. I heard (unconfirmed) that the Intel 624 MHz PXA270 is faster than a 2GHz Pentium 3! It sure seems like it. This PDA blazes through movies, photos, games, word processing, and anything else I can throw at it. \nThe other definite high point for this PDA is the screen. Once you see a VGA screen like this, you will be astounded at the difference it can make over a QVGA screen. I have to get about 4 inches from the screen to even see the pixels. The screen is crystal clear. \nThe battery life is phenomenal, even with the standard low capacity battery. Playing a movie at full brightness and full volume will last about 2 hours, extended to about 4.5 hours on low brightness and headphones. Turned off with a 1GB SD card drains the battery about 2% per day. \nWiFi is amazing. It connects consistently and is moderately easy to set up. Once connected, it keeps the signal well and is, while not fast, reasonable for speed\nJust about everything on this PDA is almost perfect.\n\nWeaknesses: Not enough programs to start with, Windows Media Player 10 (Download TCPMP, it's way better)\n\nProblems: Don't use Odyssey Client, it makes the PDA start up really slow. Also, in ActiveSync, On the PDA, select Menu, Add Server Source. On all of the screens, put random characters in each field, and deselect any check boxes that tell it what to synchronize. Then go to menu, schedule and select manual for both. Then go to menu, options, and delete the entry that you just added (the one that has no boxes under it checked) and delete it. It fixes a myriad of problems and makes the PDA run faster, and it still syncs automatically in the cradle.\n\nConclusion: An awesome PDA that does just about everything. Combine it with a BT keyboard and perhaps even a BT mouse, and you have an unbeatable combination of power and portability. Everything works extremely well\n\n\n\n\nB000G0OQD6:digital_wireless_phone:w._l._holman_\"wlh\"\n\n\nB000G0OQD6\n\n\nPhilips 6.0 Dect Cordless Phone Answering Machine with 2 Handsets (Dect2252g/37): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n4.0\n\n\nDigital wireless phone\n\n\nNovember 4, 2006\n\n\nW. L. Holman \"WLH\"\n\n\nDeep South\n\n\nThis phone has noticably greater range and clarity than our other wireless phones. The message center works well, and the intercom feature is useful. The phone is a little small, and similar to cell phones rather than a house wireless phone.\nThere are two generic rechargable NiCAD batteries, I think they will be easier to find than some of the packaged NiCADs that come with most wireless phones\n\n\n\n\nB000G0OQD6:phone_is_awesome!:t._hawkins\n\n\nB000G0OQD6\n\n\nPhilips 6.0 Dect Cordless Phone Answering Machine with 2 Handsets (Dect2252g/37): Electronics\n\n\nelectronics\n\n\n10 of 11\n\n\n5.0\n\n\nPhone is awesome!\n\n\nJuly 6, 2006\n\n\nT. Hawkins\n\n\nDenver, CO\n\n\nThis phone is small, great battery life, awesome range, and no interference. The features are subtstanial and the quality is amazing. From one that has always had Uniden Cordless phones this is a substantial upgrade. \n\n\n\n\nB00008UVAC:yes!:s._a._rader\n\n\nB00008UVAC\n\n\nRCA RCU410MS Universal 4 Device Remote Control (Platinum): Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n5.0\n\n\nyes!\n\n\nJuly 23, 2004\n\n\nS. A. Rader\n\n\n\n\n\ni bought one version from best buy, then i bought another with different buttons, then i bought this platinum one because its so cool looking, just make sure you save the code\n\n\n\n\nB000COCQXS:not_quite_truth_in_advertising:j._riley\n\n\nB000COCQXS\n\n\n1-Year Replacement Plan for Electronics Products $150 to $174.99: Electronics\n\n\nelectronics\n\n\n9 of 9\n\n\n4.0\n\n\nnot Quite truth in advertising\n\n\nJuly 14, 2006\n\n\nJ. Riley\n\n\n\n\n\nsent check for full price fairly promptly, but not including tax, which ads say they will d\n\n\n\n\nB000E1DSAE:very_good!:smart.man\n\n\nB000E1DSAE\n\n\nSennheiser CX300-B Earbuds (Silver): Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n5.0\n\n\nVery Good!\n\n\nNovember 10, 2006\n\n\nsmart.man\n\n\nNY\n\n\nBest earphones I ever had. Better then Sony MDR-EX51LP Fontopia Headphones. 2 years warranty\n\n\n\n\nB000E1DSAE:great_sound:rroliver\n\n\nB000E1DSAE\n\n\nSennheiser CX300-B Earbuds (Silver): Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n4.0\n\n\ngreat sound\n\n\nNovember 9, 2006\n\n\nrroliver\n\n\nOH\n\n\nEarbud fit system is comfortable, seals out noise, for many ear canal sizes. Sound is great\n\n\n\n\nB000A3IAKY:better_than_logitechs_higher_priced_offering:art_corvelet_\"bsc\"\n\n\nB000A3IAKY\n\n\nCreative Webcam Live! Motion- Titanium: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n5.0\n\n\nBetter than Logitechs higher priced offering\n\n\nMay 3, 2006\n\n\nArt Corvelet \"BSC\"\n\n\nPerry Hall, MD United States\n\n\nI'm very pleased with this camera. I updated it with the new drivers and software on the Creative site (from January 2006) immediately and I have not had any of the problems others mention. The image quality is excellent with good lighting and the camera is fast since it is a true USB 2.0 camera. It will work with USB 1, but be sure you use USB 2.0 for better quality.\n\nVideo calls run a bit choppy if there is a lot of movement, but for basic conversations, it is fine. I use 8 Mbs cable and I connect with people using the same or high speed DSL.\n\nThis camera takes great video & photos and has an excellent one click panoramic option that does all of the stitching for you. If you want to take a large group shot, it is really cool. The other features like security and remote monitoring are very useful as well. I don't use the face tracking. \n\nThe base deserves a mention too. I use this on my notebook and on my desktop. It's cool that the base works with both notebook screens and on flat surfaces. Highly recommended for the price now with the current rebate Amazon is offering\n\n\n\n\nB000HCRVSK:great_mouse,_mediocre_keyboard.:o._sverloov\n\n\nB000HCRVSK\n\n\nLogitech Cordless Desktop MX3200 Laser: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nGreat mouse, mediocre keyboard.\n\n\nNovember 17, 2006\n\n\nO. Sverloov\n\n\n\n\n\nI wanted a combo and this seems to be the best one on the market now days.\nThe mouse is very good, a tad small, but still very comfortable. Not as good as Logitech� MX(tm)500 for my hand (I'd say I have a medium size hand), but still a very solid product. Side buttons are very useful. No need to install any additional drivers for XP if you are ok with the default behavior. Just plug it in and use.\nThe keyboard is so-so. It is not bad, but just not very comfortable, especially compared to Microsoft keyboards. (I use Microsoft 4000 for work and it really shines). The labeling is very small, Insert/Delete keys are not standard and I hate Vista ready button, if you hit it in the middle of the game you are lucky not to crash.\nWould I buy it again? Yes. Would I recommend it? Yes, but with some reservations.\n\n\n\n\n\nB000AAQZPY:get_a_free_42\"_plasma_tv_instead!:jason\n\n\nB000AAQZPY\n\n\nSharp Aquos LC-15B8US 15\" EDTV-Ready LCD Flat Panel TV: Electronics\n\n\nelectronics\n\n\n2 of 91\n\n\n4.0\n\n\nGet a free 42\" Plasma TV instead!\n\n\nMay 8, 2006\n\n\nJason\n\n\n\n\n\nWhy settle for less, when you can get a brand new original Panasonic or Samsung 42 inch plasma TV for Free! I got mine from this web site: awesomestufffree.com/plasma-t\n\n\n\n\nB00009MK3T:great_sound,_some_suggestions_for_durability:matt\n\n\nB00009MK3T\n\n\nBose® around-ear headphones - Silver: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n4.0\n\n\nGreat sound, some suggestions for durability\n\n\nAugust 22, 2006\n\n\nMatt\n\n\nIllinois\n\n\nLike most people here, I found the Bose Triport headphones to have excellent sound but the plastic on mine also broke in the recess next to the earcups. What I found is that this is caused by the tension of the headband frame pushing into the plastic. In fact, the plastic broke from just sitting in my drawer for a week with the headband fully extended.\n\nMy recommendation is to store these headphones with the earcups fully retracted, to reduce the tension of the headband frame on that piece of plastic. Bose replaced them with no hassles, no receipt, no registration, just sent them back and got a brand new pair. I think that was well worth the money spent over 2 years ago\n\n\n\n\nB0009OOBV6:great_speaker_system_:m._j._mahelsky\n\n\nB0009OOBV6\n\n\nCreative Labs 51MF5005AA000 TravelDock Speakers for Zen Micro: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n5.0\n\n\nGreat Speaker System \n\n\nJuly 30, 2006\n\n\nM. J. Mahelsky\n\n\nNew York, NY\n\n\nI recently received the Zen Microphoto as a gift. Since I travel fairly often, I decided to purchase the Traveldock after reading the reviews. It's great. The Microphoto fits in easily and comes out just as simply. The sound is rich, loud enough, and better than any other travel speakers that I own. Many people comment on the bass issue. For travel speakers, the Traveldock has the best bass to date, in my opinion. True bass enthusiasts could purchase the subwoofer (for an additional $60 from Creative's website). I'm very satisfied with the sound and the convenience. The jacks are easy to reach and use, which are on the back of the unit. Also, the Microphoto charges while docked, whether it's playing or not. There's even an attachable FM antenna that plugs in to give you reception when docked (since you are not using the earphones which serve as an antenna also). By the way, it comes with a remote control which allows you to switch between FM and MP3's, adjust volume, and play/pause/FF/Rew. Overall, this is a very nice addition to the Zen Microphoto.\n\n\n\n\nB0009OOBV6:awesome_speakers!:boris\n\n\nB0009OOBV6\n\n\nCreative Labs 51MF5005AA000 TravelDock Speakers for Zen Micro: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n4.0\n\n\nawesome speakers!\n\n\nJuly 26, 2006\n\n\nboris\n\n\nHon, HI\n\n\nAgain, Creative Labs has combined all you need into one package: line in jack, usb port, fm antenna, and even a jack for a sub-woofer! for a small portable speaker, this system gets pretty loud and clear. The stereo effect button works great, the design is very stylish and the remote is a nice finishing touch. now for the cons;like most reviewers,i too, have to say the bass is lacking a bit, and the stand has a bit of trouble holding the speakers up due to small rubber feet that doesnt grip very well. and the last thing is the speakers itself doesnt have any grill or guard on it and it looks like it could be damaged easily, but these are minor things i can overlook and all in all for the price, this speaker system is very good. \ni tried the Altec Lansing portable speakers for the same price and i returned it the next day. it didnt even get half as loud without distortion and it didnt have as many cool features either.\nright now, im using the speakers with the zen nano plus and it works great! im saving up for the zen micro now and if it sounds good with the nano, then i know the TravelDock will sound amazing with the micro that it was intended for.\n one more thing, if your going to use the line-in cable for a device other than the zen micro, be sure to insert the BLACK end of the cable into the device your want to play and the WHITE end into the speakers line-in jack. i read a reviewer who said the speakers sounded tinny like a transistor radio and its because she had the wrong ends of the cable attached. i did it myself and i figured it out. unlike the reviewer who returned the speakers before taking the time to learn how to use it before returning it and giving it a bad review. \nI love these speakers and i even use them for my t.v and they sound great for that too\n\n\n\n\nB000065UTR:superb_sound:sound_man_\"sounds\"\n\n\nB000065UTR\n\n\nJensen JHT525 5.1 Home Theater Speaker System: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n5.0\n\n\nSuperb Sound\n\n\nSeptember 18, 2006\n\n\nSound Man \"Sounds\"\n\n\nAtlanta, GA\n\n\nI bought these speakers based on price because I just wanted to get some budget speakers to start off with on my home theater. It turns out that these speakers have amazing sound quality easily rivaling speakers that cost 10x the price.\n\nI really can't find one bad thing to say. Even at high volumes they have a crystal clear pristine sound. The sub woofer pumps a nice rich heavy bass. Take advantage of the low price on these speakers, you won't find a better deal.\n\nI'm using these speakers with a Harman Kardon AVR 240 receiver and I also have two Polk R15's as my front left and right\n\n\n\n\nB000065UTR:nice_system:t._doran_\"thetomcat\"\n\n\nB000065UTR\n\n\nJensen JHT525 5.1 Home Theater Speaker System: Electronics\n\n\nelectronics\n\n\n3 of 6\n\n\n4.0\n\n\nnice system\n\n\nAugust 25, 2006\n\n\nT. Doran \"thetomcat\"\n\n\nwestchester ny\n\n\nnice little system but only 4 ohms so doesnt handle a lot of power. Good sound for an apartment\n\n\n\n\nB000065UTR:great_speakers:wisconsin_dad\n\n\nB000065UTR\n\n\nJensen JHT525 5.1 Home Theater Speaker System: Electronics\n\n\nelectronics\n\n\n9 of 9\n\n\n5.0\n\n\nGreat Speakers\n\n\nAugust 19, 2006\n\n\nWisconsin Dad\n\n\nFond du Lac, Wisconsin United States\n\n\nFor the cost, you can't go wrong with these speakers from Jensen. We love them, and in shopping around found them to be one of the best home theater values. The sound is great, and the speakers are perfect for just about any home theater need\n\n\n\n\nB0002XJUZG:the_printer_with_an_unsatiable_appetite:d._kaczor\n\n\nB0002XJUZG\n\n\nHP No. 100 Gray Photo Ink Cartridge (C9368A): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n4.0\n\n\nThe Printer With An Unsatiable Appetite\n\n\nAugust 24, 2006\n\n\nD. Kaczor\n\n\nBuffalo, New York\n\n\nPurchased this iterm for my HP8450 printer. Pictures are outstanding and the cartridge does the job.Only problem is the cartridge gets eaten up in no time at all. Cost per print no bargain. Problem isn't with the catridge, but with the printer and print process. Now we know why HP has printers at such reasonable price. They eat you up and spit you out via the price of cartidges. That's where the real profit lies. Amazon is tops in price and service delivery\n\n\n\n\nB00008RGYL:canon_makes_the_best_photo_paper_out_there!:creative_creature\n\n\nB00008RGYL\n\n\nCanon 7980A019 Borderless Photo Paper (5x7, 20 Sheets): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nCanon makes the BEST photo paper out there!\n\n\nJuly 26, 2006\n\n\nCreative Creature\n\n\nNew York\n\n\nAbout 2 1/2 years ago I finally got into the digital age. I began printing my own pictures and have tried every brand of photo paper out there (Kodak, HP, store brands, etc. etc.). By far, the most superior paper is Canon. They make fabulous photo paper that is reasonably priced and prints GREAT photos. \n\nI did a wedding album using Canon photo paper and the couple I gave it to thought that the photos were as good as the photographer they'd hired, which speaks volumes to the quality of Canon paper.\n\nI'd highly recommend that people use this brand. You won't be sorry\n\n\n\n\nB0000C8CG6:excellent_sound_for_those_willing_to_take_a_risk:idiot900\n\n\nB0000C8CG6\n\n\nSHURE E2 High Energy In-Ear Earphones: Electronics\n\n\nelectronics\n\n\n7 of 10\n\n\n5.0\n\n\nExcellent sound for those willing to take a risk\n\n\nFebruary 6, 2006\n\n\nIdiot900\n\n\nNew York, NY United States\n\n\nEchoing most other reviews here, this is an excellent set of earphones that are well worth the money. But buying them is a risk! If you don't wear these earphones correctly, they sound terrible, easily shown up by any pair of throwaway phones. Those unsatisfied with the sound quality (especially those complaining of lack of bass) either haven't put them in correctly or have oddly shaped ears.\n\nLearning how to put them in properly takes time. Be prepared to spend a few hours fiddling with them and trying the different included cushions. Once you teach yourself how to put them in, it will only take a few seconds. Some tips: Before inserting the earbuds, draw the phones up from behind your ear, holding the earbuds in the correct orientation (drivers pointing medially), so the wires fall naturally between your head and pinna and the earbuds are just outside your ear canals. With one hand, pull the superoposterior part of the pinna in a superoposterior direction while inserting the earphone with the other. This straightens the ear canal and makes it easier to insert the earphone. (Your doctor uses the same maneuver when he/she examines you with an otoscope.) You may also wish to twist the earphone during insertion so that it lodges itself better. If the phones are properly inserted, they will have sealed your ear canals and you may want to relieve pressure via your Eustachian tubes (that is, swallow), just as on a climbing plane or skyscraper elevator.\n\nIt may seem like it takes a lot of work (and possibly learning a bit about anatomy) to get these earphones to work as intended. It does, and success is not guaranteed. But it's worth it! And to save your hearing, don't forget to turn down the volume, since your music will no longer have to compete with outside noise\n\n\n\n\nB0007UVYLW:amazing_picture_with_one_regret:s._winograd\n\n\nB0007UVYLW\n\n\nSharp LC-37D7U 37\" Aquos Widescreen LCD TV: Electronics\n\n\nelectronics\n\n\n10 of 12\n\n\n4.0\n\n\nAmazing picture with one regret\n\n\nSeptember 5, 2005\n\n\nS. Winograd\n\n\n\n\n\nThis was the largest set that would fit into my space. I also looked at the Westinghouse 37 inch HD moniter at Best Buy. The first time, I looked at the Westinghouse the picture looked a bit fuzzy. Then I read the review on CNET which said the Westinghouse picture was actually better than the Sharp. I went back to Best Buy and asked the sales person if the picture on the Westinghouse could be improved. He said it was as it good as it could be. He than said the Sharp was twice the price of the Westinghouse, so I should expect a better picture on the Sharp.In any event, I purchased the Sharp on Amazon during the recent Electronic Express Sale. On HD, the picture is amazing. It's also quite good on regular TV. \nMy only regret is that Toshiba has just come out with a 37inch LCD with a DVD built in. If I had known, I would have waited to check out that model\n\n\n\n\nB0009Z66QI:dead_pixels:sys_man\n\n\nB0009Z66QI\n\n\nViewSonic VA902B 19\" LCD Monitor (Black): Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n4.0\n\n\nDead Pixels\n\n\nJune 5, 2006\n\n\nSys Man\n\n\nRichmond, MI\n\n\nWe bought 3 of these and every one has dead pixels. It's not a ton, but one monitor has three that are red in the center of the screen, and other has a few that are bright green in the corner. Not a huge deal, but there are some in all 3\n\n\n\n\nB0006U6GH0:great_sound:avid_reader_\"dave\"\n\n\nB0006U6GH0\n\n\nJBL Creature II Powered 3-Piece Multimedia Speaker System (Black): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGreat Sound\n\n\nNovember 9, 2006\n\n\nAvid Reader \"Dave\"\n\n\nNew York\n\n\nThis is my second purchase of these speakers.They provide great sound especially for the price.My kids use them with their computers and Ipods.I recommend them to everyone\n\n\n\n\nB0006U6GH0:killer_sound:carlene_y._quarre_\"qmaui\"\n\n\nB0006U6GH0\n\n\nJBL Creature II Powered 3-Piece Multimedia Speaker System (Black): Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n5.0\n\n\nKiller Sound\n\n\nJuly 23, 2006\n\n\nCarlene Y. Quarre \"QMaui\"\n\n\nMaui, HI\n\n\nReally don't think you can get better sound for your dollar. Not only look cool, but sound great. Have only had for two months, but no issues.\n\n\n\n\nB000BGZMY2:great_drive:michael_a._gilhooly\n\n\nB000BGZMY2\n\n\nSeagate 160 GB 3.5\" USB 2.0 External Hard Drive ( ST3160203U2-RK ): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGreat Drive\n\n\nNovember 4, 2006\n\n\nMichael A. Gilhooly\n\n\n\n\n\nBefore i bought this product, i was contemplating which brand name hard drive i should go with. I was told Seagate was top of the line. It truly is. No problems at all, easy to setup and use, and a fair price. I highly recommend Seagate over any other brand name (I've heard problems with other brands)\n\n\n\n\nB000BGZMY2:impressive:animate_~_\"rob\"\n\n\nB000BGZMY2\n\n\nSeagate 160 GB 3.5\" USB 2.0 External Hard Drive ( ST3160203U2-RK ): Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n5.0\n\n\nImpressive\n\n\nJuly 18, 2006\n\n\nanimate ~ \"Rob\"\n\n\nFayetteville, NC\n\n\nThis is a great little piece of hardware. \n\nI bought it for my MP3's and movies, and I'm very pleased. It's totally \"plug-n-play\"; there aren't even any drivers that need updating, assuming you have XP up to par. \n\nIt gives 160GB (well, 149 in actuality) of space to work with. After moving nearly 50 gigs over to it at once, on the first day, it froze up. After that minor incident, I've had no problems, though, and my machine runs alot faster because of it. With my 100G of music already on it, I'm very happy with it. It has given me no fuss--it doesn't even give me any noise! It's as silent as a fart, and that's a big plus.\n\nI found mine at Wal-Mart for $114 and I think that I made a good purchase. If you're on a budget, this is about as good as it gets. Seagate's 300GB is also highly recommended, as I've worked with it before. A+\n\n\n\n\nB000BGZMY2:very_pleased_with_this_drive:hypn0toad\n\n\nB000BGZMY2\n\n\nSeagate 160 GB 3.5\" USB 2.0 External Hard Drive ( ST3160203U2-RK ): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nVery Pleased with this Drive\n\n\nJune 11, 2006\n\n\nhypn0toad\n\n\nRaleigh, NC USA\n\n\nThis drive is whisper quiet, easy to install and use, and in my experience, plenty fast. I recently encoded my CD collection in flac format and am using this drive to store files. I also used foobar2000/LAME to convert the flac files to MP3, all on this drive. During the process of converting the files, which is pretty IO-intensive, I found the drive to be completely reliable and the speed to be more than adequate\n\n\n\n\nB0002T74QC:great_for_a_small_radio:ruby\n\n\nB0002T74QC\n\n\nBoston Acoustics Receptor Clock Radio (Charcoal): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nGreat for a small radio\n\n\nOctober 27, 2006\n\n\nRuby\n\n\nOhio\n\n\nI purchased this radio to keep on my kitchen counter. I chose Boston Acoustics over Tivoli because you can't preset the stations in the Tivoli and I like to flip through commercials. Other reviewers wrote that the bass can be overpowering. I listen to rock music and do not notice the bass. My complaint is that this radio advertises itself as picking up weak stations very clearly. It does not pick up stations much better then the other radios in my house. Also if you are listening to the radio you can't see the time display.\n\nThis radio is great if you have limited space. It looks good and sounds like a much larger radio. It has a lot of power for a small radio.\n\n\n\n\nB0007LJHVK:garmin_dash_mount:michael_clements\n\n\nB0007LJHVK\n\n\nGarmin Dash Mount for StreetPilot c320, c330, and c340 Vehicle GPS Navigators: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nGarmin Dash Mount\n\n\nNovember 9, 2006\n\n\nMichael Clements\n\n\nIowa\n\n\nExcellent accessory, portable and you do not have to apply anything permanent to your vehicle....highly recommended\n\n\n\n\nB0000BVV37:good_product!:joel_moreno_\"jo_black\"\n\n\nB0000BVV37\n\n\nTRENDnet Bluetooth USB Adapter: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGood product!\n\n\nNovember 6, 2006\n\n\nJoel Moreno \"Jo Black\"\n\n\nSanto Domingo, R.D.\n\n\nWow! Now I can transfer all I have in my PC to my cellphone. It's awesome... I need no cable to transfer stuff from my PC to my cellphone and from my cellphne to my PC. You can get this item in a very special price. Get it!!\n\n\n\n\nB00006343J:high-end_speaker_connectors:david_c._kraus_\"stereo_lover\"\n\n\nB00006343J\n\n\nMonster Cable QL GAS-H QuickLock Gold Angled Spade Connectors (2 Pair): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nHigh-End Speaker Connectors\n\n\nAugust 18, 2006\n\n\nDavid C. Kraus \"Stereo Lover\"\n\n\n\n\n\nI own a set of older Martin Logan speakers. Their quality is legendary with a price to match. So I was interested in maximizing performance. When I called their office for advice, one of the tech guys (excellent service, by the way) told me the Monster banana plugs were great, not the best or most expensive available, but a good value. That was enough for me. My speakers are bi-wired so I purchased two sets of four plugs. Installation was a snap and the sound is wonderful. Add to that the free shipping, no tax and a total price saving of $12.00 and you can't do better than Amazon!\n\n\n\n\nB00004SDB1:stick_with_brother_cartridges--not_the_cheaper_versions:elena_mauceri_\"www.powerfulweightlosssolutions.com\"\n\n\nB00004SDB1\n\n\nBrother TN460 High Yield Toner Cartridge: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nStick with Brother cartridges--not the cheaper versions\n\n\nOctober 8, 2006\n\n\nElena Mauceri \"www.PowerfulWeightlossSolutions.com\"\n\n\n\n\n\nThis ain't cheap, but it's worth it. I've learned my lesson buying cheaper toner cartridges, but they don't even come close to the quality and longevity of the Brother brand. You get what you pay for..\n\n\n\n\nB00004SDB1:very_nice:alexander_pruss\n\n\nB00004SDB1\n\n\nBrother TN460 High Yield Toner Cartridge: Electronics\n\n\nelectronics\n\n\n10 of 12\n\n\n5.0\n\n\nvery nice\n\n\nApril 19, 2004\n\n\nAlexander Pruss\n\n\nWashington, DC United States\n\n\nNo problems with these, and great price at amazon\n\n\n\n\nB0000T7162:great_little_mp3_player:m._duggan_cooley\n\n\nB0000T7162\n\n\niRock! 860 256MB MP3/WMA Player with FM Tuner: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n4.0\n\n\nGreat little MP3 Player\n\n\nDecember 20, 2004\n\n\nM. Duggan Cooley\n\n\nGainesville, FL USA\n\n\nThis is an awesome little MP3 player. I enjoy both the MP3 player and the FM radio. It is very easy to use and syncs well with Windows Media Player. It is perfect for going to the gym because it is so small. It came with a great leather case that fits it well and prevents damage and scratches. My only problem with the player is that I can't fit more than 66-69 songs although it says there is about 70MB of space left on the player\n\n\n\n\nB000BKI6NM:excellent_and_stable:mr._henry_williams_\"digitalhen\"\n\n\nB000BKI6NM\n\n\nD-Link Super G with MIMO Wireless Router DI-634M - wireless router ( DI-634M ): Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n5.0\n\n\nExcellent and stable\n\n\nSeptember 15, 2006\n\n\nMr. Henry Williams \"digitalhen\"\n\n\nJersey City, NJ USA\n\n\nI've been using this router for a few days. I just had to set it up - I haven't had any cause to touch it since, it's very reliable. Highly recommended\n\n\n\n\nB00004TDN2:visor_deluxe:john_d._whelan_\"visor_man\"\n\n\nB00004TDN2\n\n\nHandspring Visor Deluxe (Graphite): Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nVisor Deluxe\n\n\nSeptember 30, 2005\n\n\nJohn D. Whelan \"Visor Man\"\n\n\nNH\n\n\nUnit works about as expected, with possible exception of a few areas of the screen that are not too sensitive to the touches from the stylus. So far I am working around that. This is my 3rd Deluxe and the 1st one to give any operational problem of any sort. Generally a good product\n\n\n\n\nB00004TDN2:wowzas!:simone_r._worsdale_\"simone\"\n\n\nB00004TDN2\n\n\nHandspring Visor Deluxe (Graphite): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nWowzas!\n\n\nJune 16, 2005\n\n\nSimone R. Worsdale \"Simone\"\n\n\nMiami, FL\n\n\nYeah, so apparently this was a really good PDA to buy. I BOUGHT THIS THREE YEARS AGO AND IT STILL WORKS! Ive been searching for a new PDA since I thought this one was so out of date, and when reading all the reviews I came across many new \"state of the art\" PDAs that broke after only a few months. This baby has been great to me for three long years, so if you are looking for a reliable buy, go for this. Definately NOT flashy, very basic. All I use it for is appointments, dates, and to-dos. GREAT FOR STUDENTS WHO WANT TO USE A PDA AS AN AGENDA! \n\nGood luck on your PDA search!\n\n\n\n\nB00083MW2S:just_great:gustavo_diaz_\"guss\"\n\n\nB00083MW2S\n\n\nPalmOne 3218WW Aluminum Hard Case for LifeDrive Mobile Manager: Electronics\n\n\nelectronics\n\n\n0 of 2\n\n\n5.0\n\n\nJust great\n\n\nFebruary 24, 2006\n\n\nGustavo Diaz \"Guss\"\n\n\nDoral, Florida\n\n\nI am working with my new PDA the Live Drive and is great to have akk ny videos, music and to write my notes and documents before my shift or meeting, and also the wireless connection is so great. I like the option that you can use it as an external drive\n\n\n\n\nB0001407XU:great_drive:denise_m._godfrey\n\n\nB0001407XU\n\n\nSeagate ST3200822A-RK 200 GB ATA Internal Hard Drive: Electronics\n\n\nelectronics\n\n\n0 of 2\n\n\n5.0\n\n\nGreat Drive\n\n\nJuly 14, 2006\n\n\nDenise M. Godfrey\n\n\nLithia, FL USA\n\n\nI am extremely happy with this purchase and will most likely purchase another one of these, soon\n\n\n\n\nB0007OWNBS:beware_if_you_run_xp:robert_gordon_\"batj\"\n\n\nB0007OWNBS\n\n\nNetgear RangeMax Wireless PCI Adapter (WPN311): Electronics\n\n\nelectronics\n\n\n5 of 6\n\n\n4.0\n\n\nBeware if you run XP\n\n\nJuly 11, 2005\n\n\nRobert Gordon \"batj\"\n\n\nColorado Springs\n\n\nNetgear clearly has not figured out how to make their RangeMax adapter work well with the Microsoft XP operating system. If you're not a techie, I'd stay away until Microsoft has certified the WPN311. Here's what I experienced:\n\n1. Installed using the Netgear WPN311 automated configuration.\n2. After installation completion and boot up, CPU utilization was at a solid 100%. Executable from Netgear named WLANCFG5.EXE taking up a bunch of the CPU and Microsoft routines CSRSS.EXE, NET.EXE, and NET1.EXE taking the rest.\n3. Emailed Netgear and received response to use Task Manager to delete WLANCFG5.EXE. Did so for all users.\n4. Hey! CPU utilization returned to normal!\n5. Sent next email to Netgear pointing out that this solution is just a \"patch\" and WLANCFG5 would return when I re-booted.\n6. Received response to de-install and re-install software for WPN311.\n7. Did so, but then Netgear adapter software could not find my MIMO wireless router! And usage of the Netgear adapter software doesn't allow you to use the Microsoft Wireless Zero Configurator (WZC).\n8. Went to microsoft.com and looked up error 871122 \"Error message when you try to run the Wireless Network Setup Wizard\" and followed the instruction to allow usage of WZC.\n9. Then able to add my wireless router and everything is ok.\n\nThe functionality works great. It's the getting there that's not too pretty good.....\n\n\n\n\nB00003CWDQ:buy_with_confidence.:straight_shooter_\"redpoppy\"\n\n\nB00003CWDQ\n\n\nMonster Cable MP HTS800 Home Theater PowerCenter HTS800: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n5.0\n\n\nBuy with confidence.\n\n\nAugust 29, 2006\n\n\nStraight Shooter \"redpoppy\"\n\n\nUSA\n\n\nI lost my previous RadioShack surge strip and the various pieces of equipment attached to it in the Queens power failure and wanted something dependable. The price is great for its rating and all the stuff included, and it is kind of cool-looking too! Recommend without reservation\n\n\n\n\nB00003CWDQ:faint_high-pitched_tone:daniel_von_kohorn\n\n\nB00003CWDQ\n\n\nMonster Cable MP HTS800 Home Theater PowerCenter HTS800: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n4.0\n\n\nFaint high-pitched tone\n\n\nMarch 23, 2006\n\n\nDaniel Von Kohorn\n\n\nBoston, MA USA\n\n\nI'm using the Monster Cable MP Home Theater PowerCenter HTS800 to remove line noise in the electrical current to a high-end tube pre-amp. The line noise is removed beautifully; however, the PowerCenter itself creates a high-pitched tone. The tone comes out of the PowerCenter device directly. If you are planning to use this product on a high-end sound system, I recommend placing it in a location that is acoustically isolated. \n\n\n\n\nB000E1FYQA:nice_buds_for_the_price:infusion\n\n\nB000E1FYQA\n\n\nSennheiser CX300-B Earbuds (Black): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n4.0\n\n\nNice buds for the price\n\n\nNovember 9, 2006\n\n\nInfusion\n\n\nCA, USA\n\n\nFor the background, I've owned & used various IEM (In-Ear-Monitor) headphones over the years: Ety ER4p, Altec Lansing iM716, Shure E2c, Sony EX71, Koss KSC75. Here's my quick impression after using these headphones for a week:\n\nPros:\n1. Very good bass & well balance (not as bloated as the Sony EX71, the ER4p has tight but the least bass-feel/effect.) Mid-range is good. Treble is just a little bit harsh right out of the box (but can be tamed with EQ.)\n2. Easy to drive (with the iM716 phones, I'd need to crank up the vol.)\n3. Very light weight\n4. Comfortable tips (3 sizes included)\n5. Decent isolation from outside noise (the ER4p &iM716 have better isolation)\n6. Moderately priced ($50)\n\nCons:\n1. Cables are thin so handle with care.\n2. Some people may not like the \"phonendoscope effect\" (cables frictions.) This is very common with IEM headphones, some are more than others.\n3. No pouch or case\n\nPS: For bass-heads, you will like these IEMs - from an owner of Beyer DT770-80's experience :\n\n\n\n\nB000E1FYQA:people_need_to_understand...:a._sako\n\n\nB000E1FYQA\n\n\nSennheiser CX300-B Earbuds (Black): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nPeople need to understand...\n\n\nNovember 7, 2006\n\n\nA. Sako\n\n\nLos Angeles, CA USA\n\n\nAll in-ear headphones will make some noise when the wires brush up against something. Sound travels along wires... it's physics, there's nothing you can do about that. Now, these earphones are incredibly light-weight and the wires are thin so that actually reduces the noise traveling through the wires. \n\nThese headphones are called \"asymmetric,\" meaning that the right earbud is on a longer wire than the left. You wear this by putting the wire on the right earbud BEHIND your neck. This leaves the area in front of you clear of wires that could get tangled up in your hands. This also makes it easy to each lunch while listening to music so your food doesn't spill on the wire.\n\nI paid about 60 bucks and for that price, I love these headphones. Face it, these are low to mid-range in terms of price, but perform more like mid-range to high range if you ask me. The cable is thin and flexible yet seems pretty resistant to damage. The buds are comfy and I like the asymmetric design. People need to realize that there are premium headphones for $500 or more. I think people need to keep perspective and not have TOO high expectations from only a $60 pair\n\n\n\n\nB0001YFX7U:consistent..:yong_oh_\"bookie\"\n\n\nB0001YFX7U\n\n\nRidata 8x 4.7 GB DVD+R Spindle (100 Discs): Electronics\n\n\nelectronics\n\n\n1 of 6\n\n\n5.0\n\n\nConsistent..\n\n\nSeptember 9, 2005\n\n\nYong Oh \"Bookie\"\n\n\nFairfax, VA\n\n\nThis is the 4th spindle I've ordered and I haven't had a problem with any of my DVDs. \n\n\n\n\nB000A1GAVW:ipod_home/wall_charger_-_great:cookie_queen\n\n\nB000A1GAVW\n\n\niPower iPod Home and Travel Charger for iPod Nano, G5 Video, G4, Photo & Mini: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nIpod Home/Wall Charger - GREAT\n\n\nNovember 9, 2006\n\n\nCookie Queen\n\n\nHealdsburg, CA\n\n\nThis little charger works great. It is the second one of it's kind that I have purchased. The last one took a lot of abuse and therefore I needed to replace it. Would buy it again. Charges quickly and is quite handy\n\n\n\n\nB000FRW8KS:nice!!_this_is_sweet!:ribbla\n\n\nB000FRW8KS\n\n\nFang Gamepad by Ideazon: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n5.0\n\n\nNice!! This is sweet!\n\n\nJuly 13, 2006\n\n\nRibbla\n\n\n\n\n\nThe Fang is amazing!!!!! I just got it,theres nothing like it. The keyz are in the perfect spot to kill all your fellow gamers. all you need is the fang and a mouse and your good to go. you have to get this if you want to stand a chance in FPS games.\n\n\n\n\nB000EP5MOA:excellent_ipod,_my_favorite_so_far.:\n\n\nB000EP5MOA\n\n\nApple 4 GB iPod Nano Green (2nd Generation): Electronics\n\n\nelectronics\n\n\n7 of 7\n\n\n5.0\n\n\nExcellent iPod, my favorite so far.\n\n\nNovember 22, 2006\n\n\n\n\n\n\n\n\nI love the new nanos. I don't think I have anything bad to say about it besides it not having a radio. It is small and very thin. You barely feel it in your pocket, which might annoy some people but not me. I'd rather get this than any Zen products or Sandisk Sansas because it looks way better and it way thinner. The screen is really bright with the full color display. The new earbuds are A LOT more comfortable than the old ones. The new ones aren't as bulky and actually fit in my small ears with out them slipping out. I don't know why people hate the sound of the earbuds but they are certainly loud and good enough for me! The new color coating is scratch resistant, but not the click wheel and the Apple symbol. But you don't notice the scratches on the white parts. The longer battery life is twice as long as the first generation nano, 24 hours! The new 4 GB color nanos are the same price as the 2 GB of the 2nd gen nanos. I'm so happy I waited after the 1st gen nanos came out. I would never get the 1st gen nanos now since they scratch easily, have a dark screen, and have a short battery life. The new nanos are great deals and great gifts. I recommend this mp3 player to anyone that wants a good, reliable, scratch resistant, cool looking music player! 5/5 star\n\n\n\n\nB0007UQ6LK:works_great_with_my_nokia_e62:farhan_n._hassan\n\n\nB0007UQ6LK\n\n\nSanDisk 1GB miniSD Card (SDSDM-1024-A10M, Retail Package): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nWorks Great with my Nokia E62\n\n\nNovember 10, 2006\n\n\nFarhan N. Hassan\n\n\nTracy, CA\n\n\nI am satisfied. Works great with my Phone Nokia E6\n\n\n\n\nB0007UQ6LK:works_like_a_charm:y._beverly_\"tech_gadget_addict\"\n\n\nB0007UQ6LK\n\n\nSanDisk 1GB miniSD Card (SDSDM-1024-A10M, Retail Package): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nWorks like a charm\n\n\nNovember 3, 2006\n\n\nY. Beverly \"tech gadget addict\"\n\n\nBaltimore, Maryland United States\n\n\nI have to say, when I first purchased this little goodie, I just assumed I was buying more memory for my Motorola Q. I had no idea just how useful and reliable this would be. It has proven to be not only invaluable, but a very good investment, based on price and usage. Buy one, no TWO. These things are great\n\n\n\n\nB00005NIMJ:great_product:daniel_f._boehlke_\"usadan\"\n\n\nB00005NIMJ\n\n\nLogitech Trackman Wheel Optical: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGreat Product\n\n\nNovember 2, 2006\n\n\nDaniel F. Boehlke \"usadan\"\n\n\nSan Francisco, CA United States\n\n\nI'm a big fan of trackballs and this one is my favorite on the market right now. Just love it\n\n\n\n\nB0006ZBHWY:works_with_a_macbook:david_relyea\n\n\nB0006ZBHWY\n\n\nWestern Digital 80 GB Passport 2.5-inch External Hard Drive: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nworks with a MacBook\n\n\nJuly 31, 2006\n\n\nDavid Relyea\n\n\nGlendale, CA\n\n\nI thought I'd weigh in, as the reviews suggesting noncompliance with Apple products seemed ominous. I bought a Passport, and the USB outlet on my Macbook powers it perfectly well. I don't need an external power supply.\n\nBtw, I get a transfer rate of about 2 Mb/s, or 1 Gb/5 minutes. This seems a little slow, but it doesn't bother me much. The Passport is small, has a nice rubber coating to prevent shock, and overall is worth the purchase price\n\n\n\n\nB00020T4UK:higher_quality_ink-jet_printable_dvd_discs:j._ferreira\n\n\nB00020T4UK\n\n\nVerbatim DVD-R 4.7GB 8X DataLifePlus White Inkjet Printable, Hub Printable 50pk Spindle: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nHigher Quality Ink-jet Printable DVD discs\n\n\nMarch 17, 2006\n\n\nJ. Ferreira\n\n\nProvidence, RI\n\n\nThese Verbatim discs have the DataLife Plus layer of protection that will allow them to last 100+ years. I'm a professional photographer and it's crucial to store my client's digital photos and artwork onto a disc that will remain stable in years to come. I've tried many brands and types of discs and have lost data (read errors) many times but so far with the hundreds of Verbatim discs I've burned so far, I haven't had a problem yet. The Kodak brand discs were also excellent, but Kodak discontinued making them. SONY also makes a good disc. These discs can be printed on directly with an ink jet printer such as the Epson R200/R300/R320, and the surface area is printable right up to the hub! I have no reason to switch to any other brand as long as Verbatim continues to make a great disc\n\n\n\n\nB00020T4UK:hub_printable!!:gaijin_\"...yep_it's_me\"\n\n\nB00020T4UK\n\n\nVerbatim DVD-R 4.7GB 8X DataLifePlus White Inkjet Printable, Hub Printable 50pk Spindle: Electronics\n\n\nelectronics\n\n\n5 of 6\n\n\n5.0\n\n\nHub Printable!!\n\n\nAugust 7, 2005\n\n\nGaijin \"...yep it's me\"\n\n\nHilo, HI United States\n\n\nThese Verbatims are terrific. As you may know, working with DVD-R media is almost a gamble...cheap media will save you a few pennies but either give you coasters or media that plays on few players. Sticking to name brands helps, but even then, some brands rely on outside sources for their media and while one lot/batch may be good, the next batch from the same brand may be from somewhere else. Usually, you're safe with media made in Japan. These Verbatim's are made in Taiwan but, they are great..I've bought one every couple months and I'm on my 3rd spindle now.\n\nThey have a wonderful printing surface that as mentioned, dries nearly instantly in Epson printers (I use an R200). Hub printable so you can print all the way to the edge which gives your discs a terrific look when printing nice disc art or photos. Not one coaster so far, burned with Apple Powerbook 2X Superdrive as well as a Lacie D2 Firewire writer. Though the media is 8X, it works in my Panasonic DVD Recorder (DMR-E55) which recommends up to 4X media perfectly). So far, I have not encountered one person who has not been able to play the discs (Samsung, Sanyo, Panasonic, Sony, Apex, Toshiba DVD Players, PS2, XBox, no problems) . Over 134 discs so far, and they look great, play great, and are a great price for hub printable discs. Highly recommended...I'm ordering more!\n\n\n\n\nB000BBYH8O:two_thumbs_up_for_quickcam_notebook_pro:david_k._bowen_\"boholiday\"\n\n\nB000BBYH8O\n\n\nLogitech Quickcam for Notebooks Pro: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nTwo Thumbs up for Quickcam Notebook Pro\n\n\nNovember 3, 2006\n\n\nDavid K. Bowen \"boholiday\"\n\n\nMiami, Florida\n\n\nNotebooks Pro is the highest rated webcam (for notebooks) on the market and for good reason. Audio and Video quality is excellent. You can talk normal and be 2 or 3 feet away with no problem. The echo cancellation feature is a true must for webcams, especially with the use of video. On the negative side the USB cable is too short which limits interchangeability with your desktop. Also, I have the Quickcam Fusion for my desktop which is a 5 star rated product and without doubt the best of the best webcams overall. Logitech ships that product with Quickcam 10 software which is probably a nice update to Quickcam 9 the Notebook Pro uses. Unfortunately, Logitech provides little detail and info on these technical and software issues. B\n\n\n\n\nB000BBYH8O:logitech.._i_am_pleased:bhavya_k._desai\n\n\nB000BBYH8O\n\n\nLogitech Quickcam for Notebooks Pro: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nLogitech.. I am pleased\n\n\nOctober 20, 2006\n\n\nBhavya K. Desai\n\n\nNew York\n\n\nI had the previous version also. It is so far the best webcam I ever owned. Don't even bother to compare it with cheap creative ones. The frame rate and picture quality is awesome. Works in very low light conditions and has great software which can even make gieco or alien out of you. \n\nThey keep on working for years. My old one \"Quickcam for notebooks Pro\" still a champion after use of 3 years now. I got bored and gave it to my uncle. And boy he loves it too. Newer one as mentioned by other reviewers holds much better.\n\nDo I sound Like a logitech sales person? Well I must admit that I am very happy with their webcams. Amazon offers get price and you should get it.\n\nOne more thing. It's features outweighs its small size :). My Parents back in other part of world can see even tiny things in my room which they shouldn't :).\n\n\n\n\n\n\nB00006B9CR:great_mouse_-_esp._if_you_have_carpel_tunnel_in_shoulder:d._forster_\"computer_user\"\n\n\nB00006B9CR\n\n\nLogitech Cordless Optical Trackman (USB/PS/2): Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n5.0\n\n\ngreat mouse - esp. if you have carpel tunnel in shoulder\n\n\nAugust 6, 2006\n\n\nD. Forster \"computer user\"\n\n\nFulton, MO\n\n\nI got this mouse because I have developed carpel tunnel pain in my shoulder from years and years of mouse activity. I'm happy to say it is nearly gone (the pain) - I ordered one for my office and will now get one for home. It does take a bit of getting used to (after years of movable mouse action) - but it can be done.\n\nGood luck\n\n\n\n\nB000ELSVOM:great_cable_at_a_decent_price:r._denardis\n\n\nB000ELSVOM\n\n\nCreative Zen Vision: M Video Cable: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n4.0\n\n\nGreat Cable at a decent price\n\n\nNovember 5, 2006\n\n\nR. DeNardis\n\n\nPittsburgh\n\n\nUnlike many other cables that splice A/V output, this cable does so while retaining the details of that output. I.e., both Left and Right channels are extracted and outputted, not simply the Left channel with audio mixed down to mono. Also, it works on some computers, too!\n\nOn the minus side, I haven't gotten it to work on the television. In order to do so, you also have to buy the adapter for the Creative Zen player.\n\nI bought this so my Creative Sleek Photo would be compatible with an X-Rocker chair with built in speakers, and found that CD players and many other audio units became compatible, too. For that, it was worth the cost\n\n\n\n\nB0001WW3F2:good,_could_be_easily_improved:r._johnson\n\n\nB0001WW3F2\n\n\nGriffin Technology RadioShark AM / FM Desktop Radio with Time-Shift Recording: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\ngood, could be easily improved\n\n\nNovember 5, 2006\n\n\nR. JOHNSON\n\n\nPortland, OR US\n\n\nI am running the RadioShark on a G4 PowerBook w/ Mac OS X 10.4. I am very happy with the programmed recording - saves to a variety of formats, etc. Very handy! The unit did all that it promised, but it could have had much better software. A Dashboard Widget is also included that controls basic operation, but not recording or EQ.\n\nNeeded additions:\n1. Station presets are selected from a drop down menu??!? One has to go to the RS menu bar (top of screen) to find the favorite Stations - kind of klunky. Better to have \"radio buttons\" associated with the App window. To be fair, the Widget does have better station selection, but the button is labeled with a 'heart'...\n2. No station \"scan\" function. One has to dink through the entire spectrum manually - wow - hard to understand this lack of functionality.\n3. Some additional audio filter/edit functions could be included. There is a multi-band EQ that works well, mostly (see comment below). At minimum there should be a hiss filter and a hum filter with variable levels of effect. Also, one should be able to edit out unessential material from an audio file. Though, for Mac OS X there is a nice freeware app that can do this: AUDACITY.\n\nOddities\n1. When first installed, the output had static in it which was worse with the EQ on. Griffin Tech Support was not very helpful - as with most tech support offerings, they failed to grasp the actual problem and replied with unhelpful suggestions. Re-installed software and it began working well.\n2. Initially did not seem to work well thru an external (powered) hub; now has no problems. Though, unit does work okay with a USB extension cable\n\n\n\n\nB000BVCTS4:itrip_is_a_trip!:crackerjack\n\n\nB000BVCTS4\n\n\nGriffin Technology 9631-NANOTRIP iTrip Nano LCD/FM Transmitter for iPod Nano, Black: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\niTrip is a trip!\n\n\nNovember 9, 2006\n\n\nCrackerjack\n\n\nColumbia, SC\n\n\nThis is a total bargain. Not only was the price right, it works perfectly in the car, at home converting every radio to a stereo system for my iPod Nano. When they're put together it looks like one unit and if, by chance, you use a Motorola Rzr phone the charger works with he iTrip and recharges the Nano, eliminating the need for any other charger devices\n\n\n\n\nB000DIM9AS:just_right_:kennth_c._schneider\n\n\nB000DIM9AS\n\n\nCoby TF-TV505 5\" LCD Color Television: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nJust right \n\n\nNovember 10, 2006\n\n\nKennth C. Schneider\n\n\n\n\n\nThe over-the-air picture is crisp, colors are bright and the size is just right. Pretty good viewing angle as well\n\n\n\n\nB0000C8Y55:wireless_logitech_mouse:jeffrey_d._smith\n\n\nB0000C8Y55\n\n\nLogitech Cordless Optical Mouse for Notebooks- Silver: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nWireless Logitech Mouse\n\n\nMarch 29, 2006\n\n\nJeffrey D. Smith\n\n\n\n\n\nI totally recommend this mouse. I purchased the 1st mouse a few months about and just now bought another one for my other laptop. I like it that you have an assortment of colors you can chose from also. The battery life is pretty good also\n\n\n\n\nB0002DQUHC:mac_users_beware:bud_lengtat\n\n\nB0002DQUHC\n\n\nD-Link DWL-G122 Compact Wireless USB Adapter, 802.11g, 54Mbps: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n4.0\n\n\nMac users beware\n\n\nNovember 9, 2006\n\n\nBud Lengtat\n\n\nGrass Valley, CA USA\n\n\nI use this with an iMac. It does not come with drivers for Mac OS X, nor does D-Link offer any help. They refuse to admit it will work with Macs. I was pointed toward a driver made available at [...]. I remember having trouble getting it to work, but once it was working it has worked well and consistently. The iMac connects to an AEBS a couple rooms away through the D-Link\n\n\n\n\nB0006B486K:thank_goodness_i_found_these!:fred_schendel_\"fredproggh\"\n\n\nB0006B486K\n\n\nKoss KSC75 Portable Stereophone Headphones: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n5.0\n\n\nThank goodness I found these!\n\n\nOctober 1, 2006\n\n\nFred Schendel \"fredproggh\"\n\n\nChattanooga, TN\n\n\nI have been going nuts trying to find a decent set of headphones for my new iPod. All I wanted was decent sound for a decent price, but after trying various models of ear-buds and standard phones and returning them all I figured I was going to have to shell out some big bucks after all. But, then I decided to pull the trigger on some cans made by a great old company from my youth that used to be THE only choice for headphones- KOSS of course! And man- they still have it. These aren't the greatest phones I've ever heard, but for $20 (and it seems you can now get them for half that!) they easily compare to some well-known sets costing 3 times as much or more. Finally, no awful hi-mid peaks that threaten to end my career as an audio engineer *cough cough Sony cough*. They are fairly comfortable- I think I would rather have the traditional over-the-head band but you get used to these pretty quick. These sound nice, full- the bass doesn't thump like a lot of people like but to my ears it's smooth. And most important, you can listen for hours without ear fatigue. Without a doubt, these are the best budget phones I've ever owned! My bottom line- anyone who doesn't grab these for $10 bucks a pop is insane. Period *g\n\n\n\n\nB000065BPA:very_nice_product:l._jackson\n\n\nB000065BPA\n\n\nSennheiser HD-212 Headphones (Silver): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nVery nice product\n\n\nAugust 10, 2006\n\n\nL. Jackson\n\n\nMojave, CA USA\n\n\nI've tried several different brands of headsets and this one wins. It reduces noise, has excellent sound and is comfortable for hours at a time. My office is between the shop, where we make rocket engines, and the flight line on the airport, where airplanes are tested. Sometimes jet aircraft sit outside my window and perform engine run-ups, or idle the APU for hours. This headset reduces the white noise while still allowing me to hear the phone ring even when a local F-4 Phantom II tests the afterburner.\n\n\n\n\nB00006B9ZB:has_not_shown_me_trouble_yet...:d._farano\n\n\nB00006B9ZB\n\n\nLexar Media 128 MB Secure Digital Card: Electronics\n\n\nelectronics\n\n\n4 of 6\n\n\n5.0\n\n\nHas not shown me trouble yet...\n\n\nJune 26, 2004\n\n\nD. Farano\n\n\nEast Rockaway, NY United States\n\n\nGood price. Good company. I use this on my Kodak CX7300 and it's fine. Good product to buy\n\n\n\n\nB00006B9ZB:excellent_product:\"deechan\"\n\n\nB00006B9ZB\n\n\nLexar Media 128 MB Secure Digital Card: Electronics\n\n\nelectronics\n\n\n6 of 7\n\n\n5.0\n\n\nExcellent product\n\n\nJune 29, 2003\n\n\n\"deechan\"\n\n\nAdelaise, SA Australia\n\n\nWorks as advertised, have not had a single problem with it since installing it in my Tungsten T\n\n\n\n\nB0002DG0OA:exactly_what_i_wanted:a._scott-stoltz\n\n\nB0002DG0OA\n\n\nAudiovox D1708PK 7\" Ultra Slim Line Portable DVD Player with Bonus Pack: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n5.0\n\n\nExactly what I wanted\n\n\nNovember 3, 2006\n\n\nA. Scott-stoltz\n\n\n\n\n\nBought this a few months ago for my 3yr old son. He loves it. We have used it in the car as well as when he goes to meetings with me. Never had a single problem with it\n\n\n\n\nB0002DG0OA:great_value!:j._rauber-hanes\n\n\nB0002DG0OA\n\n\nAudiovox D1708PK 7\" Ultra Slim Line Portable DVD Player with Bonus Pack: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n5.0\n\n\nGREAT VALUE!\n\n\nSeptember 12, 2006\n\n\nJ. Rauber-Hanes\n\n\nColumbus,Ohio\n\n\nWe bought this product for a long car trip and the kids loved it. I've also used it and thought the sound and picture were wonderful. It was exactly what we were looking for and for a fantastic price! We are very happy\n\n\n\n\nB00006JKW5:best_value_for_fiber_optic_audio:atomicwasteland\n\n\nB00006JKW5\n\n\nMonster Cable THX-Certified Fiber Optic Digital Interconnect Cable (4 Feet): Electronics\n\n\nelectronics\n\n\n52 of 52\n\n\n5.0\n\n\nBest value for Fiber Optic Audio\n\n\nOctober 17, 2004\n\n\natomicwasteland\n\n\nFalls Church, VA USA\n\n\nI've bought several of these cables and have been very happy with my purchase. I've used them for hooking up my Gaming Systems, TV, Cable Box, and Receiver/DVD Player. (Note: I had to buy a small 3 in 1 Fiber Optic Switch Box to handle all these cables -my Receiver and TV only had one digital audio connection each.)\n\nI believe that these cables are the best name brand VALUES currently around for Fiber Optic Audio.\n\nWhat do you REALLY get for a \"high end\" fiber optic cable that is triple the cost of this cable? I don't know. I purchased one (which was \"top of the line\") and obscenely expensive, and of course it sounds great, but these cables sound great too. Plus, these cables ARE Monster cables, AND they are THX certified!\n\nWhat does THX certification mean? Below is a blurb taken directly from the THX website describing it:\n---\n\"THX is on the forefront of entertainment technology, offering products and services of unparalleled quality for post-production, distribution and playback of audio and visual content. THX Certification is considered a mark of excellence in the presentation technology industry, offering consumers a promise that the cinemas they visit and the products they purchase have been evaluated and tested to meet the highest standards for picture and sound performance.\"\n\n---\nA digital signal is a digital signal. This is fiber optic cable -it uses light to transmit the digital data -it doesn't degrade like analog based on the quality of the cable. So these cables are NOT likely to give you a worse audio signal than more expensive cables, they just are going be a little less durable, and probably use slightly less high quality materials. Are they fragile? Not at all, but the top of the line cable that I purchased was so thick that I probably could have stepped on it on cement and it would have been fine. This THX cable is thinner and I could imagine it breaking more easily. If you work somewhere (like a studio) where there is a good chance of rolling things over the cables from time to time on a hard floor then you might have a good reason for a more durable cable. (Same goes for cables that are going to be manhandled regularly -i.e. a lot of plugging/unplugging and bending of the ends).\n\nOverall, I'm very happy with these cables and can I afford to buy a bunch for all the fiber optic audio connections I am now using (i.e. Receiver, TV, Cable Box, Xbox/Playstation/Gamecube)\n\n(P.S. for first time users, don't forget to pull off the tiny plastic coverings on each end of the fiber optic cables. These are there to protect the cable during transit/storage and will prevent the light from passing unless removed. Throw these tips away; you'll never need them again.\n\n\n\n\nB00023CJKE:great_phone:charlotte_b._street_\"charlotte_street\"\n\n\nB00023CJKE\n\n\nAT&T E5865 5.8 GHz DSS Expandable Cordless Phone with Answering System and Dual Keypads: Electronics\n\n\nelectronics\n\n\n13 of 14\n\n\n5.0\n\n\nGreat phone\n\n\nApril 2, 2005\n\n\nCharlotte B. Street \"Charlotte Street\"\n\n\nIdaho\n\n\nI have had this phone now about 3 months and I am VERY HAPPY with it. IT has all the things and want and many more, The hand sets are very clear and I love the fact I can check voice mail on any hand set that goes with it I do not have to run to the main answering machine to check the voice mail. The range of the hand sets is great had no problem at all with this. So far I love everything with this phone, It is much better than either of the last two phone I have had. I have walked over a 1/2 block from the house and it still works clearly, I have never had a phone that worked so far away from home\n\n\n\n\nB0000BVYTV:not_bad,_given_the_price_i_paid!:j._chung_\"calegend\"\n\n\nB0000BVYTV\n\n\nAntec USB-Powered Notebook Cooler: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nNot bad, given the price I paid!\n\n\nSeptember 4, 2006\n\n\nJ. Chung \"calegend\"\n\n\nCA, United States\n\n\nI purchased the Notebook Cooler approximately one month ago, after reading online testimonials of laptops becoming damaged from overheating. Thankfully, the same time I read that, Circuit City was having a sale on this item for only $15, after mail-in rebate. Fast forward one month and my opinion of the Cooler so far: not bad... \n\nMy laptop is noticeably cooler to the touch now, and the internal fan rarely comes on. The Notebook Cooler itself is smartly designed, with an aluminum topping and two fans that draw away heat. I'll admit, the USB connector cable is a bit flimsy, and I can see why some folks have complained. For those users who travel frequently (or anyone who foresees plugging and unplugging the cable often), you may want to exercise extra caution. The blue LED light that some reviewer claims is too \"strong\" is a minor quibble--if you really wanted to, you could always tape over the light and problem solved. As for the fan noise, I would say the low setting is comparable to \"white noise,\" which you don't really notice until you purposely try to listen for it! The high setting, on the other hand, is a bit loud, and I can see people having to turn up the volume on their speakers or TV to compensate. Usually, though, I only use the high setting when it's really hot in the room, or if I'm running intensive programs like Photoshop or some video game. \n\nOverall, I'd say I'm very satisfied with this product. (Though, I am somewhat disappointed that Antec needs 3 months to process my $15 rebate check.) \n\n\n\n\nB000F1O0X2:excellent_mouse:michael_g._reno_\"gte987t\"\n\n\nB000F1O0X2\n\n\nLogitech MX 400 Performance Laser Mouse: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nExcellent Mouse\n\n\nNovember 9, 2006\n\n\nMichael G. Reno \"gte987t\"\n\n\nAtlanta, GA\n\n\nThe Logitech MX 400 mouse in my opinion was a good buy. It is very reliable, accurate, scrolls smooth, and is precise and responsive on every surface I have used it on. It is also light weight and comfortable, making it the purfect mouse for the price\n\n\n\n\nB00001P4ZR:as_sturdy_as_it_looks,_and_with_very_good_sound_for_the_price:n._winocur_\"marik_steele\"\n\n\nB00001P4ZR\n\n\nKoss R-10 Home Headphones: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nAs sturdy as it looks, and with very good sound for the price\n\n\nOctober 26, 2005\n\n\nN. Winocur \"Marik_Steele\"\n\n\nUSA\n\n\nI bought two pairs of these when I saw them in a store on sale for under $15 each. They've both gotten extensive use over the past couple years. The cushioning on the outside is cloth inside a black material that I suppose imitates leather in looks and feel. This outer material on the cushions has worn off in flakes on both pairs, which makes it look raggedy, but the comfort and sound quality isn't affected by it so I don't consider it a big deal. The headband adjust on one side of one pair seems to have had something break inside it which lets that earmuff go farther down than it \"should,\" but likewise, it's something to be expected with this much use and hasn't affected comfort.\n\nThe cable is also thicker than most headphone cables I'm used to, which is good news for people who find themselves constantly rolling over cables with their chairs and such.\n\nI've compared the sound quality to the Koss QZ-50's (which I own) and a library's Koss UR-10's or 15's (can't remember which). Though the QZ-50's do sound better when they have their active noise cancelling feature turned on, the UR-20's are the most comfortable of the three, have the richest bass, are the most durable, and won't eat up your AAA batteries like the QZ-50's\n\n\n\n\nB000BSOBG0:perfect_for_the_office:hunter400_\"hunter400\"\n\n\nB000BSOBG0\n\n\nDigilife Fold-Up Compact Speakers for Apple iPod (Travel Size): Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n5.0\n\n\nPerfect for the office\n\n\nJune 19, 2006\n\n\nHunter400 \"hunter400\"\n\n\nNew York, NY USA\n\n\nI recently bought these speakers because I wanted to play my iPod in the office. The sound is better then expected. While I would get a better speaker system for my home, for the office or on the go, these will do very nicely\n\n\n\n\nB00008YBCJ:adorable:l._plank\n\n\nB00008YBCJ\n\n\nD-Link - Antenna cable - N-Series connector (M) - N-Series connector (F) - 30 ft: Electronics\n\n\nelectronics\n\n\n10 of 11\n\n\n5.0\n\n\nAdorable\n\n\nJanuary 9, 2003\n\n\nL. Plank\n\n\nMissouri\n\n\nMyself and my grandchildren love to hold these adorable pets, their fun. lifelike and surprisingly sturdy. Get one!\n\n\n\n\nB000GABCY2:fantastic_mouse!:paul_grandmaison\n\n\nB000GABCY2\n\n\nLogitech V450 Cordless Laser Mouse: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nFantastic mouse!\n\n\nNovember 17, 2006\n\n\npaul grandmaison\n\n\nWalpole, NH USA\n\n\nWhat a relief to find this mouse after having bought a Microsoft 6000 lazer mouse which caused my tendonitis to flare up again really bad. I had originally gotten really bad tendonitis from another Microsoft \"ergonomic\" mouse a year ago, which became chronic, but barely noticeable until I tried the 6000.\n\nI also tried a Logitech V200, which is a good mouse, just not nearly as comfortable as the 450. The buttons on both of these Logitech mice press down really nicely and are effortless. This was not so on the MS 6000.\n\nIf you are looking for the ultimate in a mouse for a notebook, the Logitech V450 is it. My hands are fairly large, and for the $15 more than the v200, it is definitely worth it as I spend a lot of time on computers\n\n\n\n\nB000GABCY2:great_freaking_mouse:anh_lai\n\n\nB000GABCY2\n\n\nLogitech V450 Cordless Laser Mouse: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nGreat freaking mouse\n\n\nAugust 17, 2006\n\n\nAnh Lai\n\n\n\n\n\nThis laser version tracks much better on odd surfaces compared to the Logitech V200 I have.\n\nThe Setpoint software allows you to use Setpoint's mouse sensativity independent of the touchpad settings. Finally!!! I dont have to change mouse sensativities when I unplug it. The Setpoint sensativity is an offset to window's sensativity setting for the touchpad. So basically, set your touchpad sensativity first, then adjust the Setpoint sensativity. They will be work just the way you want everytime you unplug/plug the mouse and going back and forth between touchpad and mouse.\n\nPerfect product\n\n\n\n\nB000BYZNMK:outstanding:jo_i._williams\n\n\nB000BYZNMK\n\n\nJBL On Stage II with RF Remote: Electronics\n\n\nelectronics\n\n\n2 of 8\n\n\n5.0\n\n\nOutstanding\n\n\nJuly 31, 2006\n\n\nJo I. Williams\n\n\nMississippi\n\n\nJBL speaker system for IPOD is an outstanding product, I would highly recommend it\n\n\n\n\nB000BBCKOC:great_the_device_but_you_still_need_to_know_how_to_best_use_it!:s._lim\n\n\nB000BBCKOC\n\n\nNeatReceipts Scanalizer Professional 2.5 Mobile Scanner & Software Combination for Business Cards, Expense Management and Reporting, Document Organization, Scanning & Copying: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nGreat the device but you still need to know how to best use it!\n\n\nOctober 22, 2006\n\n\nS. Lim\n\n\nCA, USA\n\n\nThis is a product too unique for me not too try. I've struggling with lost receipts and always in a panic mode when tax day comes. There's just no other easy way I've found to keep track of all my receipts. \n\nEven with some of the negativity about the accuracy on the receipt recognition, I still wanted to try this out for myself.\n\nI noticed the following will increase the accuracy of the receipt scanning:\n\n1) Scan your receipt ASAP once you have it. Receipts are pretty cheap in quality and will fade over time. Also for that matter, you risk the chance of loosing the receipt the longer you don't scan it in. =)\n\n2) Ink quality on the receipt matters.\nIt does the best job when the dots of the letters/number are together and dark.\n\n3)wording on the receipt.\ne.g. the receipt can say visa/mc then scanner will pick it as visa.\n\nBusiness card scans have been excellent for me. Be sure to upgrade your sw to the lastest version. Mine is 2.55 at the time of writing\n\n\n\n\nB00003CWA0:great_product_for_the_price:j._farkas\n\n\nB00003CWA0\n\n\nMonster Cable IDL100-2M Interlink Datalink 100 6.56-Foot Low-Loss S/PDIF Style Digital Coaxial Cable: Electronics\n\n\nelectronics\n\n\n3 of 6\n\n\n5.0\n\n\nGreat product for the price\n\n\nJuly 17, 2001\n\n\nJ. Farkas\n\n\nSan Diego, CA United States\n\n\nI have used this and the optical cable. For $20 less, it sounds just as good. Monster cable is a great price point investment for anyone who wants to get the most out of their home theater system\n\n\n\n\nB00006FXR9:zenith_zhdtv1_hdtv-uhf_digital_indoor_antenna:c._tran\n\n\nB00006FXR9\n\n\nZenith ZHDTV1 HDTV-UHF Digital Indoor Antenna (Silver and Black): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nZenith ZHDTV1 HDTV-UHF Digital Indoor Antenna\n\n\nNovember 11, 2006\n\n\nC. Tran\n\n\nTexas\n\n\nExcellent antenna. Why pay so much more for other indoor antennas such as Terk or Samsung. I tried both of those but ended up with the Zenith at a fraction of the price and with much better reception\n\n\n\n\nB00006FXR9:great_antenna_for_hdtv_ota:henri_c._clinch\n\n\nB00006FXR9\n\n\nZenith ZHDTV1 HDTV-UHF Digital Indoor Antenna (Silver and Black): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGreat antenna for HDTV OTA\n\n\nNovember 9, 2006\n\n\nHenri C. Clinch\n\n\nNew York\n\n\nI live in Manhattan on the 7th floor of an apartment building surrounded by taller buildings on all sides and this antenna provides great reception. I use it hooked up to an HP 3200 lcd tv with built-in tuner. It took me less than five minutes from package being opened to finding the best place for the antenna to sit for reception. The cable is a few feet long and the antenna itself light. I have it resting atop my center channel speaker. $20 is a great price to watch the hd content the major networks provide- especially for football\n\n\n\n\nB00004Z5Z8:does_the_job:michael_kobylarz\n\n\nB00004Z5Z8\n\n\n3M Gel Wrist Rest for Keyboard, Blue: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nDoes the job\n\n\nNovember 9, 2006\n\n\nMichael Kobylarz\n\n\nSterling Heights, MI United States\n\n\nThis is very comfortable and works very well. It's heavy enough that it doesn't move around or push the keyboard, as some of the other types of wrist rests that I've used have done. Yet it's cushiony while sturdy enough to provide support. \n\n\n\n\nB00004Z5Z8:comfort_for_the_keyboard:srf_\"chinamom\"\n\n\nB00004Z5Z8\n\n\n3M Gel Wrist Rest for Keyboard, Blue: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nComfort for the keyboard\n\n\nFebruary 28, 2006\n\n\nSRF \"ChinaMom\"\n\n\nLivonia, MI USA\n\n\nI type medical reports for a living, up to 10 hours a day and 7 days a week. Without the wrist rest, my wrists would not be able to hold up. The wrist rest is the perfect size. It stays in place on the desk. The gel continues to be supportive, even after a year of working on it. The only reason I did not give it 5 stars is because of the back of the wrist rest that lays on the desk has a horrible smell. I can't smell it while typing, only when I pick it up for cleaning. I wish there was the option for half stars - I'd give this 4-1/2 stars\n\n\n\n\nB0001DBEHY:ique3600:r._fenton_\"old_time_sailor\"\n\n\nB0001DBEHY\n\n\nGarmin Suction Cup Mount including speaker w/12V cable (iQue 3600 not included): Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n4.0\n\n\niQue3600\n\n\nSeptember 28, 2005\n\n\nR. Fenton \"Old Time Sailor\"\n\n\nCape Coral FL.\n\n\nWorks well....Customer support from Garmin....more than excellent...One complaint..Very short battery life...Requires a 12 volt charging adapter...Windshield suction cup charge adapter is a must to use as a G.P.S....Also...Get a minimum 512 MB SD card for combo marine and street navigation....One plus is the brightness,unit size,and screen size....Do not keep it in your breast pocket while on the boat...Dont float..not waterproof..nuff said\n\n\n\n\nB0001DBEHY:quality_item:george_p._ivy\n\n\nB0001DBEHY\n\n\nGarmin Suction Cup Mount including speaker w/12V cable (iQue 3600 not included): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nQuality item\n\n\nSeptember 25, 2005\n\n\nGeorge P. Ivy\n\n\nLas Vegas, NV USA\n\n\nThis product took less than 5 minutes to unbox, install, and work properly. Well built, and functions exactly as I would have hoped. I am impressed with the ability of the suction cup to stay attached, even in the summer heat of Las Vegas\n\n\n\n\nB0002GDIII:fine_product:marcos_kirsch\n\n\nB0002GDIII\n\n\nApple AirPort Express with Air Tunes (M9470LL/A): Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nFine product\n\n\nOctober 30, 2006\n\n\nMarcos Kirsch\n\n\nAustin, TX USA\n\n\nThis is a very simple product that works as advertised. Easy to set up within MacOS X. I use the AirTunes functionality which is very cool. I don't use the USB printing. While more expensive than other 802.11g routers/access points, the simplicity + AirTunes functionality make it worth the extra money\n\n\n\n\nB00067OEVS:good_phone_system,_for_the_price:john_s._johnston_\"fiction_reader\"\n\n\nB00067OEVS\n\n\nUniden CXAI5198 5.8 GHz Analog Cordless Phone with Corded Base and Answering System (Silver and Black): Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nGood Phone System, for the Price\n\n\nNovember 9, 2006\n\n\nJohn S. Johnston \"Fiction Reader\"\n\n\nNewnan, GA\n\n\nThis Uniden phone/answering system has been trouble free since we bought it three months ago. Here's a list of the other things about this phone that I like most:\n1. The corded handset base unit can be used to make and receive calls even when the electricity is off. This, along with price, was the main reason I bought this phone system. This feature is unusual for a cordless phone system.\n2. The controls on the answering machine are simple and intuitive enough so that you don't have to refer to the manual when you need to set the clock or change the outgoing recording.\n3. Also, the answering machine has operated flawlessly so far.\n4. The battery life of the cordless handset has been excellent between charges. At least a week, sometimes more.\n5. Voice quality of the cordless handset and the corded base handset are very good.\n\nHowever, there is something about this phone system that I don't like.\n--The cordless handset has a pretty limited range. I had to locate the base unit in a central location in my 2,300 square foot house to get the handset to work in all rooms.\n\nBut overall I think the phone is a good buy, if you can live with the distance restrictions of the cordless handset\n\n\n\n\nB0002K6Y9E:excellent_value_for_the_price:trader_john_\"trader_john\"\n\n\nB0002K6Y9E\n\n\nKensington Noise Cancelling Headphones ( 33084 ): Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n4.0\n\n\nExcellent value for the price\n\n\nApril 24, 2006\n\n\nTrader John \"Trader John\"\n\n\nNew Jersey USA\n\n\nVery effective inside a plane comparing to the other noise cancelling headphone I have. Sound is ok for what it does. However, in a moving vehicle, it actually distorts the music which is a surprise. My other noise-cancelling headphone does not do that. It appears to have a relative long lag time in creating the opposite noise-cancelling wave. By the time the opposite wave applies to the music, the intruding noise is over. Otherwise, no complains\n\n\n\n\nB000A6LN0A:best_prints_ever!:colby_crossley\n\n\nB000A6LN0A\n\n\nHP Photosmart 475 Compact Photo Printer (Q7011A#ABA): Electronics\n\n\nelectronics\n\n\n18 of 18\n\n\n5.0\n\n\nBEST PRINTS EVER!\n\n\nJanuary 13, 2006\n\n\nColby Crossley\n\n\nOmaha, NE USA\n\n\nI was debating over the PictureMate, Kodak Printer Dock 3, Sony DDP-FP50, and the HP Photosmart 475. I made the choice of the HP and I LOVE IT! The prints are 4800x1200dpi and look like real lab prints. I use the HP 4x6 boarderless Premium Plus photo paper with the HP Ink and the prints are just wonderful. Forget the other bad reviews because this printer beats them all. (...) The HP produces bright, vivid pictures designed to last a lifetime. The PictureMate is also alot larger than the HP 475. The 1.5GB hard drive is a plus as well. HP said that the remote is not included but they lied, they come with the remote and battery. Overall, this printer is the best I have own\n\n\n\n\nB0002IQ1F8:excellent_quality-_but_do_read_this:poweruser\n\n\nB0002IQ1F8\n\n\nVerbatim Shiny Silver DataLife Plus 8x DVD-R Spindle, 50 Discs: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nExcellent Quality- But do read this\n\n\nOctober 6, 2006\n\n\nPowerUser\n\n\nLos Angeles, CA USA\n\n\nThese DVDs are absolutely top quality, and you really need to use bad DVDs to know how bad they can get. I ordered a pack a while ago and have burnt 7 discs so far. Not a single coaster, and Nero data verification (I always do that for DVDs) has not brought up any errors ever. BTW, the discs had a mixture of both large and very small files. The burner is the DVD multi drive on my Dell Inspiron Laptop (1505).\n\nHowever, I should mention (to those who haven't bought this product) why they are called Shiny Silver. You know that print on the top of DVDs with lines and that matte finish? Well, there's no such thing on these DVDs. The top looks a lot like the writing surface on the other side (other than the characteristic DVD color). Anyone who has seen unbranded DVDs will know exactly what I'm talking about- No special surface; however a felt tip marker works nicely to label the disks. Huge cost cutting here I guess, but definitely not in the quality department. I will buy again\n\n\n\n\nB0002IQ1F8:i_have_to_agree_with_many_on_this.......:mary_l_martin\n\n\nB0002IQ1F8\n\n\nVerbatim Shiny Silver DataLife Plus 8x DVD-R Spindle, 50 Discs: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n5.0\n\n\nI have to agree with many on this.......\n\n\nJuly 3, 2006\n\n\nMary L Martin\n\n\n\n\n\nI burn a ton of DVDs and have used almost every brand out there. Many have errors, even the top brand names like Memorex...etc.\n\nI have found the least amount of errors with this brand and the shelf life seems to be lasting. \n\nI've switched all DVD media purchases to Verbatim. They are by far, the best of them all. Would recommend to everyone\n\n\n\n\nB000B5XU06:sweet_sounding_plugs!:cubik_dervish_\"mroddyssey\"\n\n\nB000B5XU06\n\n\nUltimate Ears Super.fi 3 Studio Earphones (Black): Electronics\n\n\nelectronics\n\n\n6 of 8\n\n\n4.0\n\n\nSweet sounding plugs!\n\n\nAugust 31, 2006\n\n\ncubik dervish \"mroddyssey\"\n\n\nburque, usa\n\n\nFirst off, these puppies sound good! However, there are things anyone intending to buy these earphones should know:\n\nThe sound is loud and crisp but definetely mid-trebley, as with most in-ear headphones. The low end is there, but for full effect, it takes to position the earphones just so in your ear canal at the right volume. Since they tend to slide out of the ear canal as one moves, this sweet spot is not very wide. That said, however, I recognize the difficlulty of delivering earth shaking bass in a device this small. Most bass-rich systems utilize either massive speakers/ woofers or chambered enclosures (or both) to deliver. Since the size of this type of headphones doesn't permit for either big speakers or special chambers, maybe the thing to do is to use the human cranium as the resonating chamber! I am yet to find any in ear headphones that really deliver bass to die for. Such is the difficulty to get big bass in so compact a package. Maybe the 5 and 7 series are better in this regard (for the price they better be). So if you really need earth shaking bass in your playback, look into buying those pricey phones. The lack of such bass is the only reason \ni didn't give it five stars.\n\nAlso, the lack of colored LEFT (blue or white) & RIGHT (red) tags on the earphones make it tricky to put on for the first time. I didn't realize I had them reversed until the pain got so unbearable I just had to check. So be careful.\n\nRiding while using them can be dangerous, since the sound isolation is so great one can barely hear jets thundering overhead. \n\nBang for the buck, ( I paid $69.99 on amazon), I really recommend these headphones to all but big bass aficionados.\n\n\n\n\nB0002VMDJI:great_add-on:a._j._dong\n\n\nB0002VMDJI\n\n\nPanasonic KX-TGA650B 5.8GHZ Expandable 2-Line Handset for use with KX-TG6500B/6502B (Black): Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\ngreat add-on\n\n\nNovember 11, 2006\n\n\nA. J. Dong\n\n\nSan Marcos, CA USA\n\n\nI purchase two of these phones to add to our base unit. Very easy set up. Quality product\n\n\n\n\nB00006HODF:woks_great!!!:\"siddhaarth\"\n\n\nB00006HODF\n\n\nLexar Media 128 MB Memory Stick (MS128-281): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nWoks Great!!!\n\n\nApril 28, 2004\n\n\n\"siddhaarth\"\n\n\nJefferson City, TN United States\n\n\nI had slight confusion whether to choose the sony 64 MB memory stick, which is nearly the same price or this one, which has twice the memory capacity of the previous one. I choose this one for the better deal and to add to my surprise J&R Electronics shipped it so quickly(You guys Rock!!!). This one comes par with sony memory media in every field. So why choose the one with lesser memory capacity. Choose this one and feed your camera with some memory space\n\n\n\n\nB0007W2IW4:very_good:a._vilaverde\n\n\nB0007W2IW4\n\n\nIomega 33215 250 GB External Hard Drive: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nVery good\n\n\nNovember 3, 2006\n\n\nA. Vilaverde\n\n\nPA, USA\n\n\nDelivered on time, no problems setting it up (just followed the instructions to format it because I didn't want the default format it comes with, which is less safe). It's been running fine ever since I bought it. I can't comment on the speed of access because, since I leave it backing up over night, I don't really care. It's all done in the morning anyway, so I don't really need anything faster\n\n\n\n\nB000EHMIPO:a_very_good_dvd_recorder:raja_bhat_\"raja\"\n\n\nB000EHMIPO\n\n\nSamsung DVD-R145 DVD Recorder: Electronics\n\n\nelectronics\n\n\n5 of 8\n\n\n4.0\n\n\nA very good DVD Recorder\n\n\nOctober 2, 2006\n\n\nRaja Bhat \"Raja\"\n\n\n\n\n\nI like this unit. It is hooked up to an S-Video in from my Digital Cable Box, and uses a component video out to a Mitsubishi HDTV. Picture quality is very good; I'm very used to my high-quality Sony DVD player DVP-NS700P, but this one is very close. There is just a touch of graininess to the picture from the Cable box, but then I've not compared with other units. I think its more than acceptable. A DVD I burned played fine on the Sony..but...when I recorded one program, deleted it, and then recorded again, it would only play on an older Sony DVD player. My Sony DVD player is kind of touchy, anyhow. \nAll told, I'd recommend this DVD recorder without hesitation\n\n\n\n\nB000EHMIPO:by_far_the_best_multi_on_the_market_today:gary_strickfaden\n\n\nB000EHMIPO\n\n\nSamsung DVD-R145 DVD Recorder: Electronics\n\n\nelectronics\n\n\n92 of 95\n\n\n5.0\n\n\nBy Far The Best Multi On The Market Today\n\n\nJuly 6, 2006\n\n\nGary Strickfaden\n\n\nLynnwood, WA USA\n\n\nI don't normally write reviews on products but in this case I feel I must share the value and features of this product. You can't go wrong in selecting this player/recorder for your DVD needs. The quick start guide is just that \"quick\" and well planned. The people at Samsung know how to write manuals and this manual is the best I have seen well documented with pictures (in full color) and just that step-by-step for the novice. The setup diagram is excellent. The features are numerous you can throw any discs you have left over from other recording machines and through it in takes three seconds to intialize (any disc) and away you go. One touch record starts immediately. The quality is superb especially in High Definition. I couldn't wait to get it after reading the reviews from the last Electronics Trade Show so I was able to get it from back East about three weeks ago. Finalizing discs is a snap and easy I have had difficulty formatting on other machines but the Samsung is so easy the on-screen menu is good and the remote is easy to navigate even with my big fingers! It will play mp3s, CDs, computer made DivX movies, Region 1 DVDs, Picture discs from your computer and you can make your own movies from your camcorder or video camera and burn on the fly. You can also program your other electronics into the remote but you must know the manufacturer's codes which they list in a handy guide. If you are shopping for a multi recorder/player search no more get this one you won't be disappointed\n\n\n\n\nB00006B9W4:greattt:pankajkumar_patel\n\n\nB00006B9W4\n\n\nCyber Acoustics CA-3095 Speaker System (3-Piece, Black): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nGREATTT\n\n\nOctober 24, 2005\n\n\nPankajkumar patel\n\n\n\n\n\nI bought this set of speakers expecting a piece of junk...I WAS TOTALLY WRONG!!! These are great, they give the bass right where you want it. Sometimes i have to turn the bass all the way down and it is still too much. Watch out these come on sale instores for around $8.00 sometimes. That's where i got my 1st pair\n\n\n\n\nB00004Z0BO:diane:diane_\"diane\"\n\n\nB00004Z0BO\n\n\nSennheiser MX500 Lightweight In-Ear Headphones (Blue): Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n4.0\n\n\nDiane\n\n\nNovember 5, 2006\n\n\nDiane \"Diane\"\n\n\nCincinnati, OH\n\n\nThe sound range and clarity is quite good, they are comfortable. My only hesitation is the cord is too short for my purposes, I'd like to use it with my computer not a MP3 attached to my belt\n\n\n\n\nB0001A99LK:it;s_my_baby!:j._grabinski_\"xoclarinetloverxo\"\n\n\nB0001A99LK\n\n\nApple 4 GB iPod Mini Pink M9435LL/A: Electronics\n\n\nelectronics\n\n\n7 of 8\n\n\n5.0\n\n\nIt;s my baby!\n\n\nMarch 31, 2006\n\n\nJ. Grabinski \"xoClarinetLOVERxo\"\n\n\nCHentusterky\n\n\nI got my mini about nine monthes ago right before they were discontinued (which, in my opinion, was a big mistake on Apple's part) and I absolutely love it! Mine is pink, and the color is so nice, the design sleek and seamless all around. It's smooth metal casing is really nice and different. The clickwheel is smooth and I have had no problems with it.\n\nIt holds a little less than 1,000 songs due to different formatting, but it's still a good amount; iTunes is easily understandable and functional as well. The ipod mini is approx. 1/2 of an inch think and slides into pockets easily, while not being as flimsy as the nano feels to me. The clickwheel is a great and fast way to scroll through hundreds of songs, raher than, as on my brother's mp3 player, needing to press \"next\" everytime you change the song. The backlight is fine, and does it's job; my one complaint on that would be that, as I mainly use it on the bus at 6:00 am, it is quite bright and hurts my eyes.\n\nAnother huge plus to owning a member of the ipiod \"family\" is the countless accesories that have become availible exclusively to them. I myself have a case, an iSkin which I have come to adore, high quality headphones and the iBass, which is another great speaker product for ipods. I've actually invested waay more on accessorites for my mini than it cost itself, but that's how much I love it!\n\nFor having so many great features, this is priced very reasonably. Even when I got it and it was $200, I still felt that it was well worth the money. BUY THIS NOW!!!! it's amazing! \n\n\n\n\nB00009KH63:simply_the_best_trackball:d._s._kerr\n\n\nB00009KH63\n\n\nKensington Expert Mouse Optical USB Trackball for PC or Mac 64325: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nSimply the best trackball\n\n\nNovember 9, 2006\n\n\nD. S. Kerr\n\n\n\n\n\nI haven't written any reviews for Amazon before, but when prompted for my feedback on this trackball, I had to respond. I've been using one of these or its predecessors since the early 90's when Kensington first came out with the \"Turbo Mouse\". It was great then, and now it's better. This is as solid, smooth, and precise of an input device as you can get. I don't understand those others with buttons on the side that when you press them the trackball slides across your desk. This baby stays right where you put it because your button presses go straight down. You can use it with just the tips of your fingers or your whole hand -- your choice. The optical version is far superior to the old mechanical ones that needed frequent cleaning to stay in tip top performance. The only drawback is the $100 retail price, but frankly it's worth that. It's a no brainer at $80 which is what I paid through Amazon to Tiger Direct. If you need (or want) a high performance trackball that you'll be able to use with many computers to come, get this one\n\n\n\n\nB0007TYZE6:an_ipod_must_have!:allison_crouch_\"ajcrouch6\"\n\n\nB0007TYZE6\n\n\nMACALLY IceTune Stereo Speaker and Charger: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n5.0\n\n\nAn IPOD must have!\n\n\nApril 9, 2006\n\n\nAllison Crouch \"ajcrouch6\"\n\n\nGreenwood, IN United States\n\n\nThese speakers are awesome for the price. They charge your ipod even when the unit is off. The sound quality is great for around 50 bucks. I bought these for traveling and they are small enough to fit in a suitcase without taking up too much room. They also stack so they don't take up much counter space. I can't say enough about the sound quality. They are great\n\n\n\n\nB000F3YSQ4:easy__to_use_scanner_for_pdf_files:antonio_pietrangeli\n\n\nB000F3YSQ4\n\n\nFujitsu ScanSnap S500 Clr 18PPM/36IPM Dupl Scanner: Electronics\n\n\nelectronics\n\n\n16 of 16\n\n\n5.0\n\n\nEASY TO USE SCANNER FOR PDF FILES\n\n\nJuly 24, 2006\n\n\nAntonio Pietrangeli\n\n\n\n\n\nI work for a small sized consulting engineer's firm where everyday we have to scan some hundreds of pages of text (magazine articles, old projects, letters from the client, books, etc).\n\nWe got four years ago our first fujitsu scanner ( still working ) and we have been impressed with its speed, small size and since it wase easy to use to obtain good quality PDF files.\n\nA couple of years we moved to the scansnap series. We now have four of them.\n\nPROS :\n- incredibly easy to use ( you don't need to teach anybody )\n- fast for PDF scanning\n- great value for money ( includes acrobat 7.0 bundled )\n- very small, perfect for a desk\n- good construction quality, no one of our 4 scanners ever broke\n\nCONS :\n- quality of the feeding roller is good but not professional. If you have to scan hundreds of pages you have to be on the scanner to avoid paper jams, double-feeding that some time occurs.\n- not great quality for photographs scanning ( however difficult because it is not flatbed ).\n\nI strongly suggest it for small-medium sized businesses to scan documents.\n\nIf you have to scan hundreds of pages everyday or you need a high quality scanner for photos you can find something better. \n\n\n\n\n\nB0002ZLYYY:incredible_sound:gbw_\"gbw\"\n\n\nB0002ZLYYY\n\n\nBose Wave Music System - Graphite Gray: Electronics\n\n\nelectronics\n\n\n13 of 14\n\n\n5.0\n\n\nIncredible Sound\n\n\nJanuary 20, 2006\n\n\nGBW \"GBW\"\n\n\nCT\n\n\nThe CD sound is like being at the concert. Hooking the radio up to a television makes the home theater experience even better. The fm radio receiption is even clear. Great quality product. Worth the money\n\n\n\n\nB00014B4OQ:could_have_been_perfect_if...:consumer_\"consumer\"\n\n\nB00014B4OQ\n\n\nAcoustic Research AW771 900 MHz Wireless Stereo Headphones: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n4.0\n\n\nCould have been perfect if...\n\n\nSeptember 3, 2006\n\n\nConsumer \"Consumer\"\n\n\nOrlando, Fl United States\n\n\nThese are some great headphones with a few minor flaws:\n\nPros:\n1. Great sound quality\n2. Very comfortable\n3. Light\n4. Worry-free charging\n\nCons:\n1. Achieving good wireless connection is not always easy, could require pressing the tune-in button several times\n2. Interference noises could sneak into the broadcast\n3. Turning them off could be annoying (requires precise timing)\n\nMy experience: I've had two pairs of these for over a year and I like them very much. I enjoy great sound watching TV or listening to music while everybody is asleep. This is really great, I can enjoy music at comfortable levels at any time of the day or night. The sound is much better and clearer than the sound produced by the speakers of the TV. You have the same clarity and volume even though you might be moving around the rooms away from the TV (closet or the garage for example). The headphones are light and very comfortable. I tried other brands and the aw771 were the most comfortable by far. So, overall wonderful product. However, there a few flaws to be aware of. Consider the following before buying. It might require several attempts before the unit tunes in at the right channel. Sometimes, turning the headphones ON results in poor reception if any at all. To correct this, you have to press the only button (power, search, and turn off) and wait until the unit picks the signal at the right channel. You might have to repeat this operation two or three times and it is a bit annoying. Once you get good reception, everything is ok although you might get some interference (alien noises) when you turn your head or move around. The last problem is with the turn off button (again the only multifunction button). You need to press and hold the button for a certain amount of seconds and release it in order to turn off the unit. That is not always easy and usually the unit comes back on. You think you turned it off but it is still on and if it's not in the charger base you drain the batteries completely. Well, I hope my description is helpful. \n\nRegardless of the flaws, I recommend the AW771. I use them every day and best of all I enjoy great sound when other people are asleep. \n\n\n\n\nB00006SG3V:satisfied:b._coburn_\"starving_student\"\n\n\nB00006SG3V\n\n\nBose AM10IIi-BK Home Theater Speaker System: Electronics\n\n\nelectronics\n\n\n11 of 14\n\n\n4.0\n\n\nSatisfied\n\n\nOctober 3, 2005\n\n\nB. Coburn \"starving student\"\n\n\nSeattle\n\n\nI have always run all of my media through my pc and my former speaker set (a Midiland 5.1 system) was thoroughly aged and falling apart I decided it was time to upgrade. I wanted something that had incredible sound with both music and dvd's. I shopped around and came to the conclusion I would aim for the form factor speakers. When considering this bose system, this MUST be taken into account. \n\nFirst off, this system is expensive. For just under 1000 dollars (doesn't 999 sound sooo much better than 1g?) you get 5 bi-directional midrange speakers, the subwoofer unit, and speaker cable. At this price it is very disappointing that bose does not include some sort of receiver, but no matter this leaves room for the customer to customize his/her choice to suit any personal needs and/or budget. Bose includes 3 colors (white, silver, black) and all the speakers are well packaged and aesthetically pleasing with the exception of the sub unit (a large rectangular box meant to save space but does not). \n \nWhat brought me to look into this system was the specific size of the speakers and there general attractiveness. For their size the bose do a very good job of reproducing any sound-granted they are paired with a competent receiver. Anyone who has any qualms about this system should first consider what they are running their sound through, without competent equipment these speakers will not be on par with what you heard in the store. \n\nOnce I got the system home it was very easy to set up and install. Bose packages the speakers with clear instructions and all of the wires are meant to be hidden and include markings as to what speakers they belong to-a very handy feature especially when one is trying to hide this system. The only serious issue I have with this system ended up being with my receiver (an Onkyo) and not related at all with the speakers. \n\nSince I do run this through my computer (presently a laptop) I had a few troubles setting up a new sound card and getting it too mesh with my receiver, but when that was all said and done, I was extremely please with the system. With a bose the tweaking one needs to do is very minimal and thus why this deserves the higher price tag-its target audience is usually those more affluent and unable to spend hours tweaking individual speaker levels and sound ranges. \n\nMy test soundtracks included: Gladiator, Matrix (II or III), Snatch, and music tracks ranging from techno, rock, classical, and alternative. Everything sounded great with enough low, high, and midrange response. The second complaint I have with this system is not differentiating in the least between any of the satellites. I found myself turning the response on the center channel up so the front speakers weren't drowning out the voices. That was solved with a little tweaking so nothing major there. \n\nAll in all this speaker system is a great investment in my mind satisfying all of my needs (though draining my bank account) and providing me with great surround sound.\n\nGood: Great range and volume of sound. Easy set-up. Well built speakers-aesthetically and physically. No interference or feedback.\n\nBad: Subwoofer awkward, no dedicated center channel, pricey, no included receiver.\n\nFor their size these speakers more than compete with any full-blown tower 5.1 system. Here is one satisfied customer\n\n\n\n\nB000EWARBG:please_read_before_you_run_your_mouth:angel_matos_\"computing_since_1968\"\n\n\nB000EWARBG\n\n\nSanDisk 1 GB Cruzer Micro with U3 ( SDCZ6-1024-A10, Retail Package): Electronics\n\n\nelectronics\n\n\n9 of 16\n\n\n4.0\n\n\nPlease READ before you run your mouth\n\n\nSeptember 30, 2006\n\n\nAngel Matos \"computing since 1968\"\n\n\nDa Bronx in NYC\n\n\nLadies and Gentlemen,\n\nJust a quick note to say that it is a pity that people buy 'toys' before exploring what it is that they're buying. I have seen people knock the SanDisk U3 drives, as well as U3 drive made by other companies. Well the reality is that:\n\na- The U3 software has a way of stopping the software's auto-boot.\n\nb- There is Uninstall software available at at SanDisk.com and at U3.com, and I'm guessing that those company's that offer U3 and have a web site probably have the 'Uninstaller' on their site.\n\nc- There is a great deal of U3 software available (free and pay-for-it) at U3.com. I know that SanDisk has a software site, so do Verbatim & Memorex.\n\nd- For those who want portable software but don't want U3 or don't have a U3 drive, go to http://portableapps.com. Note: some of the software here is the same used by the U3 folks, without using the U3 LaunchPad.\n\ne- Quality of drives: remember that when you buy most of the electronics 'toys' available today, you are buying Chinese c*** (junk). Both the manufacturing and Quality Control (QC) are garbage. Unless you are paying serious change for something ($500+) you will get passable junk. If you are expecting \"made in Japan quality\" your kidding yourself. This is the reason you will see many Brand Name goods, also selling with No-Name labels (i.e Coby). Same production line, different labels. It is also one of the reason folks are impressed with Apple iPods. Apple demands excellent QC, and the destruction of goods that don't meet the standard. In exchange you are willing to pay a 20% premium for this level of QC. Like everything in life, ya gets whats ya pay for.\n\ng- Since the beginning of Windows (v1.0, et al) there have always been buggy and uncooperative software programs, there will always be pain in the a** software. One of the reasons XP has such a prominent 'roll-back' feature. And also the reason many, if not MOST, power users run multiple machines. Of course, these days we have to fill Billy G's pockets by having to buy multiple paks of XP. Most of my power-user friends run at least three or four machines, each one with a different use. It shouldn't be this way, but the reality is that for folk that use or explore a great deal of software, for many purposes (MP3's, scanning, Video, controlling radios, composing music, et al), one machine won't cut it because XP ain't as stable as folks think. The more programs, and the more varied in type, the more instability is introduced into ANY Windows installation. Why do you think businesses run Win2000 or Win2003, because there are less software 'toys' that run under 2K, and the 'admin' can prevent you from loading a lot of software 'toys'.\n\nh- In the end, understand what you are buying before you buy so that you'll have better luck getting what you want, and you won't look so ignorant when you say something that anyone who has bothered to go to the web site(s) knows is dead wrong, or just plain stupid.\n\ni- When you truly understand the product, you'll be able to write TRULY useful reviews, and that what all readers want, useful information.\n\nTo one and all, may your 'toys' keep you happy\n\n\n\n\nB00064C58U:wireless_lan_usb_antenna:anatoly_karp_\"ktolikde\"\n\n\nB00064C58U\n\n\nLinksys WUSB54AG Wireless A/G USB Network Adapter: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nWireless LAN USB antenna\n\n\nJuly 3, 2006\n\n\nAnatoly Karp \"Ktolikde\"\n\n\nDe, USA\n\n\nVery easy to use. Expend my wireless network access from two marginal to five with very good signal level. No external power required. Highly recomend it.\n\n\n\n\nB00064C58U:easy_to_install,_easy_to_use:jase_t._wolfe\n\n\nB00064C58U\n\n\nLinksys WUSB54AG Wireless A/G USB Network Adapter: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n4.0\n\n\nEasy to Install, Easy to Use\n\n\nFebruary 10, 2006\n\n\nJase T. Wolfe\n\n\nSeattle, WA USA\n\n\nCould not have been easier to install - literally just put the included CD in the drive, and plug the device into your USB 2.0 port. Installed and self-configured in minutes. Very small, light, no additional power cords to worry about, no additional software (beyond the automatically installed drivers) to install - quick and easy. Only issue I found is that the antenna is permanent, so you could never swap for replacement/better antenna like you can on some other wireless NICs\n\n\n\n\nB000CSH44K:very_durable:dave_f\n\n\nB000CSH44K\n\n\niSkin eVo3 for iPod with Video 30GB (Eclipse): Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nVery Durable\n\n\nNovember 5, 2006\n\n\nDave F\n\n\n\n\n\nThis cover is very durable, however it is somewhat difficult to get the ipod into the case. The case covers the entire ipod except for small vent holes so it is very water resistant, but the case covers the locking button making it harder to lock and unlock the ipod\n\n\n\n\nB000F8J3AK:a_nice_whole_house_telephone_system:m._kravitz\n\n\nB000F8J3AK\n\n\nPanasonic KX-TG5771S 5.8 GHz FHSS GigaRange Expandable Digital Cordless Answering System: Electronics\n\n\nelectronics\n\n\n13 of 13\n\n\n5.0\n\n\nA nice whole house telephone system\n\n\nSeptember 11, 2006\n\n\nM. Kravitz\n\n\nNew Jersey\n\n\nI purchased this answering machine with the intention of expanding the quantity of phones to cover the entire house. To start, I bought only one additional phone. I'll buy the rest later. So far I am very pleased. The sound quality is excellent and there are numerous bonus features that I have found very useful. Like the talking caller ID and being able to screen messages from any of the phones. I also like being able to program a phone directory into one phone and transfering (download) a single entry or the entire list to the other phones. And each phone is also a speaker phone and a full intercom.\n\nYou can name the phones for the rooms they will occupy which will help when you use the intercom to call a specific room. So you would page the \"Living Rm\" rather than phone #3, etc.\n\nIf you have caller ID, you can program special rings and special call ID colors for the callers in your phone directory but that information must be programmed into each phone. It doesn't transfer when you download the directory to the other phones. \n\nAlso, on another Panasonic wireless phone that I own, the incoming call displayed the name I had programmed into the phone directory. Just like your cell phone will say \"home\", etc. On this phone, the caller ID displays the telephone company info only. \n\nI think the phone is great, I just wanted to clarify some points that are not crystal clear in the literature.\n\n\n\n\n\nB000ERAON2:strongly_recommended:j._solis\n\n\nB000ERAON2\n\n\n2GB Data Traveler USB Drive: Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n5.0\n\n\nStrongly recommended\n\n\nOctober 25, 2006\n\n\nJ. Solis\n\n\nMiami, FL\n\n\nKingston's 2GB Data Traveler USB drive is perfect to storage files. I have looked for a 2 GB USB and found this one to be the cheapest based on its characteristices. I think it is a bargain price\n\n\n\n\nB000BJHH1K:great_product_at_an_excellent_price_from_amazon:s._kadamati\n\n\nB000BJHH1K\n\n\nKensington 33165 SX 2000 Speakers and Dock for iPod & iPod Nano: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nGreat product at an excellent price from Amazon\n\n\nNovember 17, 2006\n\n\nS. Kadamati\n\n\n\n\n\nI love these speakers and the price was great\n\n\n\n\nB000BJHH1K:high_quality_-_good_price:peter_la_barbera_\"plabjazz\"\n\n\nB000BJHH1K\n\n\nKensington 33165 SX 2000 Speakers and Dock for iPod & iPod Nano: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nHigh Quality - Good Price\n\n\nOctober 16, 2006\n\n\nPeter La Barbera \"plabjazz\"\n\n\nCalifornia\n\n\nI'm very happy with the Kensington SX 2000. I listen mostly to jazz and the midrange is as good as the higher end Bose or Apple speaker systems. Pumping up the volume does not distort in any way either. Plus, I have my speaker on a divider that equally services both ends of the open space\n\n\n\n\nB000068CNM:good_ol'_belkin:travis_craig_hellstrom_\"thellstrombooks\"\n\n\nB000068CNM\n\n\nBelkin 7-Outlet Surgemaster Home Series - F9H710-06: Electronics\n\n\nelectronics\n\n\n4 of 5\n\n\n5.0\n\n\nGood Ol' Belkin\n\n\nJanuary 11, 2006\n\n\nTravis Craig Hellstrom \"THellstromBooks\"\n\n\nHickory, NC United States\n\n\nI love Belkin and their surge protectors are top quality. I specifically look for their brand and I am always very impressed. This model has been especially good to me over the last 3 years and I would highly recommend it\n\n\n\n\nB0001PFQ76:thanks_for_the_memories:mayavi\n\n\nB0001PFQ76\n\n\nHP DVD Movie Writer dc4000: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n5.0\n\n\nThanks for the memories\n\n\nAugust 8, 2005\n\n\nMayavi\n\n\nAtlanta, GA USA\n\n\nI am a camcorder buff. Ever since my children were born, I have \nhad the camcorder attached to my hand most days because I know\nthat life is fleeting and memories are too precious to be lost.\n\nI had a huge collection of video tapes and was able to transfer\nthe entire collection to DVD. Now I know that they will last at\nleast through my life time.. Also, I find that using the Showbiz\napplication, I can create customized DVDs for my friends and\nthat has saved me a ton of money in gifts..\n\nThe only problem we had with this product was that we had\nlimited hard disk memory and had to acquire some more. \nAnd I have probably created more than a 100 DVDs by now.\n\nThanks HP!\n\n\n\n\nB0001PFQ76:wonderful_idea,_with_good_tech_support:thomas_taylor_\"run_man\"\n\n\nB0001PFQ76\n\n\nHP DVD Movie Writer dc4000: Electronics\n\n\nelectronics\n\n\n5 of 6\n\n\n4.0\n\n\nWonderful idea, with good tech support\n\n\nMarch 22, 2005\n\n\nThomas Taylor \"Run Man\"\n\n\nPhoenix, AZ\n\n\nThis little device is a great invention from HP. I had problems installing the unit and was on the telephone with HP Tech support (India) for 1.75 hours, I spent very little time on hold.\n\nThe largest draw back to the unit is my computer acts like the DC4000 is not there from time to time and required me to reboot the computer in order to use this nifty device\n\n\n\n\nB0000E6S0Y:great_speaker_value_--lower_cost,_higher_performance:king_lemuel_\"trust,_but_verify\"\n\n\nB0000E6S0Y\n\n\nJBL Northridge E30 2-Way 6-Inch Bookshelf Speakers, Pair (Black Ash): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nGreat speaker value --lower cost, higher performance\n\n\nNovember 2, 2006\n\n\nKing Lemuel \"Trust, but verify\"\n\n\nPuyallup, WA\n\n\nI recently began upgrading my home audio by buying at Ebay 2 pairs of JBL E20s and 1 Pair E30s, an E25 center speaker and a Harmon Kardon AVR. I got the first pair of e20s a week ago and the 2nd pair of E20s and the E30 yesterday and the remaining stuff should be delivered by the weekend. \n\nThe E20s and E30s are listed as \"Bookshelf Speakers.\" The E20s ARE bookshelf speakers. As I wrote in my review of the E20s, the E30s can only be thought of as bookshelf speakers if you last name is Kong and your first name is King! The E20s are heavy at 7lbs each, not flimsy little boxes as some cheapo bookshelves are. The E30s are HEAVY. HEAVY! 16 lbs each. (That is what my 7 qt lodge cast iron dutch oven weighs!) They are 15 inches high and 12 inches deep. They need to be placed on small end tables or speaker stands 18 to 24 inches off the floor. \n\nI am very pleased with both the E20s and E30s. For small speakers, the E20s have some oomph and can be cranked up. The E30s have OOOMMPHHHH. They WANT to be cranked up. Yesterday I tried them out by listening to my mp3 files of the Animals Singles and Paul Revere and the Raiders Kicks. When cranked up, the E30s sound at least 1000% better than the the E20s. I hooked them up to my vintage JVC AV receiver circa 1989 that has 80 watts front and 20 watts rear power. At a little over half volume my ears began to hurt. You can really feel the bass and the drums kick. \n\nI also listened to about 2 hours of The Doors via Rhapsody with the audio out from my computer to the audio in on my receiver. I have been listening to this music since it was first played on the airwaves back in the 60s. The E30s are simply incredible! They brought some very familiar tunes to life and made them new and fresh.\n\nMy biggest surprise with the E30s is that they sound very good at lower volumes, not as good as the E20s, but still very good. As I write, I am watching/listening to a DVD of Vivaldi's 4 Seasons concertos performed by Herbert Von Karajan, the BPO and Anne-Sophie Mutter on fiddle and am running the E30s and E20s at about 15% volume--pretty low since the 4 speakers are sharing the front 80 watts of my receiver. Yet I can still HEAR everything at low volumes including the resonating violins and the harpsacord. Since we normally do not rock the house down all the time and we do often listen at lower levels, I am very pleased at how good they sound without having to blow out my eardrums. \n\nI bought the E30s for under $90 including s/h. Back in the early 80s or late 70s I spent more than four times this amount (adjusted for inflation) for 2 large, heavy Advent Loudspeakers with 8\" woofers. The E30s clearly sound better than I ever remember the Advents sounding including better bass response, and I liked the Advents! Over time, our audio choices have gotten better and cheaper. You still can spend mega bucks and buy a $10,000 McIntosh system (or even drop that much on a turntable magnetic cartridge as I saw at one website!) It all depends on what your budget is. The JBLs are a great value offering tremendous perfomance for those who are budget minded\n\n\n\n\nB000EN62YG:quiet,_fast,_and_easy:s._slavin\n\n\nB000EN62YG\n\n\nSeagate ST3300601CB-RK 300 GB External Hard Drive with FireWire and USB 2.0 Interface: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n5.0\n\n\nQuiet, fast, and easy\n\n\nNovember 10, 2006\n\n\nS. Slavin\n\n\n\n\n\nI repartitioned the disk so I have a Macintosh partition and a Windows partition. I back up both my Mac and my PC to this disk. It is fast and easy to use\n\n\n\n\nB000EN62YG:fast_and_quiet:c._leung\n\n\nB000EN62YG\n\n\nSeagate ST3300601CB-RK 300 GB External Hard Drive with FireWire and USB 2.0 Interface: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nFast and quiet\n\n\nNovember 3, 2006\n\n\nC. Leung\n\n\nUSA\n\n\nI use the drive to back up important data. I write a few GB to the drive each time. I was surprise by how fast data transfered onto the hard drive through the USB2.0 interface. It is quiet and I have no complain\n\n\n\n\nB000EN62YG:fast_and_quiet:s._sourelis\n\n\nB000EN62YG\n\n\nSeagate ST3300601CB-RK 300 GB External Hard Drive with FireWire and USB 2.0 Interface: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n5.0\n\n\nFast and quiet\n\n\nOctober 29, 2006\n\n\nS. Sourelis\n\n\nChicago, IL\n\n\nUsed for backing up a huge quantity of high resolution digital images. The drive is fast and quiet in it's operation, while taking up very little space on the desktop. I highly recommend this drive and this brand of drives\n\n\n\n\nB0002Z45DQ:perfect_connection_were_you_are:v._iparraguirre_\"lambas\"\n\n\nB0002Z45DQ\n\n\nD-Link DWL-G730AP Wireless Pocket Router/AP w/ Client Mode, 802.11g, 54Mbps: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nPerfect connection were you are\n\n\nSeptember 9, 2006\n\n\nV. Iparraguirre \"Lambas\"\n\n\nLima, Peru\n\n\nThis equipment is simply and perfect a viable connection to the world \"where you are\"!. My work forces to me to travel to very distant sites where the communication was difficult and this creates a access point to me for connected to the world via Interne\n\n\n\n\nB00003CWEY:monster_cable_s-video_mvs3-1m:graham_mercer\n\n\nB00003CWEY\n\n\nMonster Cable MVSV3-2M Monster Super Video 3 Double-Shielded S-Video Cable (6.56 Feet): Electronics\n\n\nelectronics\n\n\n1 of 3\n\n\n5.0\n\n\nMonster Cable S-Video MVS3-1M\n\n\nFebruary 4, 2002\n\n\nGraham Mercer\n\n\nConnecticut, USA\n\n\nNoel Lee's Monster Cable products Inc., come up with the goods here! A cable of stunning, high resolution that snaps the picture into such clarity that you'll think you're watching scenes through a glass window and not through a television/monitor, a bold claim indeed, BUT, quite true never-the-less. I was totally unprepared for such a dramatic change for the better. This cable brings to the viewer ALL the picture, and, yes, that does mean \"Warts and all\"so, be prepared to see the smallest of detail, like strings and wire that enabled special effects crews to do their work \"Un-seen\". Who really needs HDTV when cables like Monster's MVS3-1M are this good and can bring a vastly superior standard to an existing television/monitor for only a minute fraction of the cost. Thanks Noel, well done\n\n\n\n\nB00001P4ZD:fits_my_fat_head...:\n\n\nB00001P4ZD\n\n\nKoss KTX-Pro Portable Stereophone: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n4.0\n\n\nFits my fat head...\n\n\nAugust 21, 2002\n\n\n\n\n\n\n\n\nI stretched them till they fit comfortably on my wide head. They sound great to my ear and I use them alot\n\n\n\n\nB00001P4ZD:the_headphone_to_compare_all_others_to.:chris_kelley\n\n\nB00001P4ZD\n\n\nKoss KTX-Pro Portable Stereophone: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nThe Headphone to compare all others to.\n\n\nApril 11, 2002\n\n\nChris Kelley\n\n\nBoston, MA\n\n\nI have searched and searched and searched for a pair of portable headphones that sound as good as these with lots of failure. IMO, you cannot beat the sound of a set of "over-the-head" headphones. I have tried all styles: earbuds, behind the neck, clips, even the ear plugs. Nothing gives the sound and comfort of the KTX Pros (granted, I have never tried the sportapros or portapros, but from what I have read, they are the same headphone. Except they are UGLY!). The KTX Pros have both great lows and great highs. Plus, you can't beat that price\n\n\n\n\nB0002XN3VS:it's_very_good:b._v._michael\n\n\nB0002XN3VS\n\n\nPNY Attache 1GB USB 2.0 Flash Drive ( PFD01GU20RF ): Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n5.0\n\n\nIt's very good\n\n\nSeptember 5, 2006\n\n\nB. V. Michael\n\n\n\n\n\nThis is an affordable option that provides a lot of memory and allows USB 2.0 protocol for very fast transfer. If you need to transfer data from one digital product to another, this is very handy and I think that every person dealing a lot with data should have one.\n\n\n\n\nB00005MNHI:excellent_value_for_money!:b._ma_\"yospeed\"\n\n\nB00005MNHI\n\n\nKoss R-200 Studio Quality Home Stereophone: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nExcellent value for money!\n\n\nAugust 2, 2006\n\n\nB. Ma \"yospeed\"\n\n\nSan Francisco\n\n\nI basically bought these because they were cheap. Cheap things are not always bad and these headphones are a great example. The UR-20 provides excellent sound for both music and movies. It has excellent bass and sound isolation. I am by no means an audiophile but I know good sound when I hear it and these headphones produce great sound, especially for its price.\n\n\n\n\nB0000Y3KS0:for_the_price,_tough_to_beat:scottmo\n\n\nB0000Y3KS0\n\n\nMaxell AA Alkaline Batteries Value Pack - 40 Pack: Electronics\n\n\nelectronics\n\n\n15 of 15\n\n\n4.0\n\n\nFor the price, tough to beat\n\n\nJanuary 10, 2005\n\n\nScottMo\n\n\nStony Brook, NY USA\n\n\nNot a bad battery deal. While they don't last as long as some other batteries I've tried, their cost per battery more than makes up for it. For those that care about such things, they contain no mercury, no cadmium, and are made in China\n\n\n\n\nB0000642RX:wonderful_-_no_more_carpal_tunnel_problems:susanna_hutcheson_\"see_http://www.powerwriting.com\"\n\n\nB0000642RX\n\n\nMicrosoft Natural Keyboard Elite: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nWonderful - No more Carpal Tunnel Problems\n\n\nOctober 11, 2006\n\n\nSusanna Hutcheson \"see http://www.powerwriting.com\"\n\n\nWichita, KS United States\n\n\nI've had this keyboard a number of years and would never go back to anything else. I have (or had) severe carpal tunnel syndrom. This keyboard has eliminated that problem entirely. I can't think of a downside. Yes, it takes a bit of getting used to and yes, the keys are a bit noisy. But I have no problem with that. It just reminds me I'm working.\n\nThe number pad is excellent too.\n\nI'm getting ready to get another one to use with my laptop.\n\nYou can't destroy this keyboard. If you value your wrists and hands, get this\n\n\n\n\nB000F1UFYU:great_phone:tazewell_carrington_\"carrington\"\n\n\nB000F1UFYU\n\n\nPanasonic KX-TG5633B 5.8 GHz FHSS GigaRange Digital Cordless Answering System with Three Handsets: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n5.0\n\n\ngreat phone\n\n\nNovember 9, 2006\n\n\nTazewell Carrington \"Carrington\"\n\n\nCharlottesville, VA United States\n\n\nWe previously had an AT&T cordless w/answering machine (1 handset) and always had trouble locating the one receiver - kind of like misplacing the TV remote. With 3 handsets, we are spoiled. Also our house has a few phone jacks but they are all located where we did not want a phone and no jacks in our den or living room. With the abilty to locate the phones anywhere in the house where there is an outlet, we were able to place one in our living room and another in our bedroom (which we can turn the ringer off).\n\n\n\n\nB00005N5Y2:everything_you_want_in_a_corded_phone!:a._calhoun\n\n\nB00005N5Y2\n\n\nNorthwestern Bell 76510 Corded Speakerphone with Caller ID: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nEverything you want in a corded phone!\n\n\nOctober 14, 2006\n\n\nA. Calhoun\n\n\nDes Plaines, IL\n\n\nCorded phones with features are becoming harder to find now-a-days. My home needed one corded phone for sure fire communications during power outages, mobile phone down for whatever reason, etc. This is one of the few corded phones with caller ID and no answering machine built in. The greatest feature of this phone is it has a HOLD button so one can place a call on hold while one gets high tech cordless, etc. Also with the built in speaker phone this phone is great in the kitchen. Highly recommend this for your one corded phone in the house\n\n\n\n\nB000E60HKS:wow!!!!!!!!:robrt_maisonet_\"bobbie_bobbie\"\n\n\nB000E60HKS\n\n\nUniden TCX905 Accessory Handset and Charger for the T9 Series: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n5.0\n\n\nWOW!!!!!!!!\n\n\nAugust 19, 2006\n\n\nRobrt Maisonet \"BOBBIE BOBBIE\"\n\n\nBERKELEY, CA.\n\n\nWow, who would've thought that you could get such amazing products, at affordable prices. The staff's friendly and responds quickly to any questions that you may have. This is one amazing phone system (Uniden TCX905), I forgot that it's a cordless. \n\n\n\n\nB000EA8U12:my_son_loves_it!:t._ellison\n\n\nB000EA8U12\n\n\nCreative Zen Sleek Photo 20 GB Multimedia Player (Aluminum/White): Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n5.0\n\n\nMy son loves it!\n\n\nNovember 9, 2006\n\n\nT. Ellison\n\n\n\n\n\nBought this for my son who is in Iraq. He doesn't leave his tent w/o it!!\nStands up fine to the dust and heat with no problem. Has more room than he expected. The price was LESS than the smaller one he wanted from a competitor to Amazon.com.\n\nSO glad I found this one on Amazon instead\n\n\n\n\nB0009MYS9S:i_payed_my_$45_for_these_headphones_and_enjoyed_them.:anthony_t._vu\n\n\nB0009MYS9S\n\n\nLogitech Wireless Headphones for MP3: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nI payed my $45 for these headphones and enjoyed them.\n\n\nSeptember 15, 2006\n\n\nAnthony T. Vu\n\n\nLongview, TX\n\n\nI'm going to cover compatiblity, sound, and comfort.\n\nOVERVIEW.\n\nAlthough I wish these were as cheap as the IPOD, $39 + $6 shipping isnt bad either. I have done the heat treatment (using boiling water) and the headphones seemed to open up more. When the headphones came in the box, the ear pieces were really close together. So I can easily see how these bands can crack easily when put on your head.\n\nI plan to heat treat these again to spread them out more.\n\nSOUND.\n\nSound quality is excellent.\n\nCOMFORT.\n\nThe only thing I don't really like is this style of headphone. I don't really like the band to go around the back of my head, causing my ears to support the headphones. I like the ones that go over the top of your head.\n\nCOMPATIBILITY.\n\nI have a Inspiron DELL Laptop and these suckers can connect to it easily without using a transmitter. These can also connect to just about any MP3 player of all sizes (PSP also) because it comes with a small 8 mm jack extension cord.\n\nFEATURES.\n\nCoolest features with this compared to my other IR sony wireless headphones is that this can go through walls! About 3X feet away\n\n\n\n\nB0009MYS9S:not_the_most_comfortable,_but_work_great_...:s._eric_asberry_\"certified_computer_geek\"\n\n\nB0009MYS9S\n\n\nLogitech Wireless Headphones for MP3: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nNot the most comfortable, but work great ...\n\n\nSeptember 12, 2006\n\n\nS. Eric Asberry \"Certified Computer Geek\"\n\n\nRoswell, GA USA\n\n\nI am overall quite pleased with these headphones. The sound quality is excellent. I've not had any problems with interference, background hiss or anything of that nature.\n\nBattery life has been excellent. I have not actually ran them all the way down to see just how long they'll last, but I use them to listen to music at work for the better part of 8 hours a day and they haven't failed me yet.\n\nThe only reason I would not give them 5 stars is that they can be a little uncomfortable after prolonged use. I'm not really a big fan of this \"sport\" style of headphone, and probably wouldn't have tried these if I hadn't gotten them on clearance. But, I found that after experimenting a little I was able to get them situated to feel pretty comfortable, even wearing them the better part of a workday\n\n\n\n\nB000243DNA:good_quality_product_and_nice_design:v._t._d'onofrio\n\n\nB000243DNA\n\n\nKensington Slim Type Keyboard - PC ( 64365 ): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGood quality product and Nice design\n\n\nOctober 28, 2006\n\n\nV. T. D'onofrio\n\n\nArlington, VA United States\n\n\nThe keyboard is good quality and the best product I could find for the price. I recommend if you are looking for a low profile keyboard w/numeric entry pad that takes a mimimum anount of desk space. The keypad has the feel of a large notebook computer. So makes a great docking station keyboard if you are used to the slim kepads on most modern laptop computers.\n\n\n\n\nB000HKIPVE:a_fine_monitor_for_a_very_fair_price:duane_p._schroeder\n\n\nB000HKIPVE\n\n\nViewSonic VA1912WB 19-inch Widescreen LCD Monitor (Black): Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n4.0\n\n\nA FINE monitor for a very fair price\n\n\nNovember 18, 2006\n\n\nDuane P. Schroeder\n\n\nMooresville, NC\n\n\nFirst off, let me say that this is a VERY impressive monitor. My points of reference for that statement are my other three LCD monitors: three Samsung models, two of which are 913Ns and the other is a 712N. These two Samsung models are advertised to have the same contrast ratio rating as this ViewSonic, and their advertised brightness ratings are 20 points higher than this ViewSonic. Surprisingly, I actually can see the brightness difference between the Samsungs and this ViewSonic, but, for my taste and needs, the Samsungs are simply too bright, and I end up turning down their brightnesses anyway. For high light, daytime use, I have found that I do like to have the brightness all the way up on the ViewSonic, but I truly do not see any need for it to be any brighter than it is capable of being.\n\nInstallation was simple: in fact, something that might be helpful to those of you who are buying this would be that, interestingly again, the drivers that are on the ViewSonic website are actually older than the ones that came with my installation CD, even though the driver package that is posted on the ViewSonic website claims to have been posted only a few weeks ago. When I first installed this monitor and turned it on, I truly do not think the picture was as clear, or the graphics as rich, as any of the Samsung monitors that I own. But, and this is another tip for new users of this product, the default setting for the ViewSonic screen contrast was set MUCH lower than the maximum setting. When I increased the contrast up to its maximum setting, I believe the clarity and quality of the picture to be nearly equal, if not fully equal, to those on the other LCDs that I own.\n\nOne big plus about this monitor is that it comes with a DVI cable included, so you save that added expense (this isn't true with all ViewSonics). And, especially with the digital connection, I have no doubts about ViewSonic's claim that this monitor has nearly twice (5ms versus 8ms) the refresh rate as the Samsungs. I have to admit that I wasn't sure I needed a widescreen display, but I believe it's going to be a challenge going back to the standard ones! This is a really a fine monitor!\n\nNow for a few small negatives. One reason I haven't purchased ViewSonics in the past is that they put a fairly tall bar on the bottom of these screens to accommodate speakers. I already have a fine speaker system set up and connected to my computer, so I really have no use for monitor speakers. Additionally, those ViewSonic speakers always raise the monitor height up much higher than I'm comfortable with. Strains my neck. The reason I really didn't worry about it on this monitor was because, while there are speakers, they appear to be nearly half the height of the normal ones on other ViewSonic monitors. With that said, I have to admit that even this speaker accommodation bar added a bit more to the screen height than I had expected. For example, on the 17 inch Samsung monitor, the bottom of the screen is 3 3/4\" from the desktop. On the 19 inch Samsungs, it is 4 inches. On this ViewSonic, the bottom of the screen is 5 1/2 inches above the desktop. It's not bad, but it's going to take a bit of getting used to. If all else fails, other monitor stands are available.\n\nAs with most electronic products these days, there is very little in the way of documentation. The 87 billion language installation card tells diddly. The .PDF manual that is installed on the computer simply says the standard self-explanatory things. However, I found the interactive help program on the manufacturer's website be quite useful in helping fix a few small problems, surprisingly.\n\nAll in all, I'm very impressed so far, and I'm really looking forward to using this monitor! By the by, shipping from Amazon.com was amazingly fast: it shipped the next day, and it arrived the day after that. I can't complain about that speed! My only question is why they would need a weight surcharge on this but could ship an extremely heavy wood lathe for free? Go figure! Anyway, a solid four stars for this monitor\n\n\n\n\nB00019EYVG:great_access_point_-_after_firmware_upgrade:big_bear_\"bigbearbear\"\n\n\nB00019EYVG\n\n\nD-Link DWL-2100AP Wireless Access Point w/ SNMP, AES, 802.11g, 108Mbps: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n4.0\n\n\nGreat Access Point - after firmware upgrade\n\n\nDecember 28, 2004\n\n\nBig Bear \"bigbearbear\"\n\n\nSan Jose, CA USA\n\n\nI'm using this AP, along with a Netgear 802.11g PC Card to test streaming live/recorded TV to other parts of my house. Seems like a capable product even if I didn't use the 108Mbps feature.\n\nHowever, be sure to update the firmware or the AP might not work correctly. When I first received it, configuration was easy enough but it had trouble working with my Netgear and HP Centrino notebook. Both notebooks will get a signal and even connect be failed to obtain an IP address from my network's DNS server. A firmware upgrade solved the problem and the AP now streams TV realibly from my living room to the kitchen.\n\nI noticed that there is are hordes of wireless APs around my house, I guess most neighbours are using some form of 802.11 wireless as well. However, this D-Link AP is able to function correctly abeit all the other APs around (2 using the same channel as mine I've found).\n\n\nOverall, recommended if you want a good AP. Just be sure to update the firmware\n\n\n\n\nB0007TIYES:best_5.8ghz_i've_tried_though_still_not_as_good_as_900_mhz_dss:ottokrat\n\n\nB0007TIYES\n\n\nUniden CellLink ELBT595 5.8 GHz Digital Expandable Cordless Flip Phone with Color LCD, Answering System, and Bluetooth Capability (Titanium): Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n4.0\n\n\nBest 5.8GHz I've tried though still not as good as 900 MHz DSS\n\n\nSeptember 5, 2006\n\n\nOttokrat\n\n\nSan Jose, CA\n\n\nThe sound quality is pretty darn good, except that if you pace around while talking/listening, there are these annoying clicking/scratching sounds. They are quite, and, if you stand still they go away. I've had other phones that click/scratch while pacing around, and they actually dropped some of the sound during the clicking - at least this phone doesn't do that. \n\nMy 900 MHz DSS Uniden didn't have these sounds (while pacing or otherwise), and still had greater range. I don't know why the new technology can't measure up. Still though, this is the best 5.8 I've tried. (Yet I'd rather buy another 900MHz DSS if there were any on the market anymore. All of today's 900MHz are analog instead of digital.)\n\nAnother annoying thing is that you can't recharge the phone with the belt clip attached, so the clip goes on and off (and on and off). (Many (cell) phones have a charger that allows this.)\n\nThe plastic is a bit cheap. Don't drop this phone. My friend has one and it hasn't worked since it was dropped.\n\n(I haven't tried the bluetooth yet, and have yet to see how good the battery life is.\n\n\n\n\nB0007TIYES:works_great_with_other_handsets:bill_kearney_\"wkearney99\"\n\n\nB0007TIYES\n\n\nUniden CellLink ELBT595 5.8 GHz Digital Expandable Cordless Flip Phone with Color LCD, Answering System, and Bluetooth Capability (Titanium): Electronics\n\n\nelectronics\n\n\n6 of 7\n\n\n4.0\n\n\nworks great with other handsets\n\n\nFebruary 25, 2006\n\n\nBill Kearney \"wkearney99\"\n\n\nBethesda, MD USA\n\n\nI got this base and added 6 TCX805 handsets to it. The whole setup works great. I got this base because it supports bluetooth through to my cell phone. That features works well, there's a slightly longer pause as it connects through to the cell phone but that's trivial. I will say this, you need to keep your cell phone within about 20' of this base. I just take mine off, turn on Bluetooth and it automagically pairs up with the base.\n\nSo if you want the cell dialing feature and like the more traditional upright handset, get this base and expand it with other handsets. \n\n\n\n\nB00001ZWXA:a_great_product:eagerreader\n\n\nB00001ZWXA\n\n\nBelkin F3U134-10 USB Extension Cable (10-Feet): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nA Great Product\n\n\nAugust 22, 2005\n\n\nEagerreader\n\n\nPA USA\n\n\nThis product is great - allowed me to connect a distant printer to my computer by hooking two of the extension cables together. Worked right away - no problems\n\n\n\n\nB00005A9AU:great_headset_for_me:greg_n\n\n\nB00005A9AU\n\n\nPlantronics DSP-400 Digitally-Enhanced USB Foldable Stereo Headset and Software: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n5.0\n\n\nGreat Headset for me\n\n\nAugust 21, 2006\n\n\nGreg N\n\n\nHouston, TX USA\n\n\nThis is a great headset for my use. It is very clear. I use it for work, and I only make use it periodically throughout the day, making about 10 calls daily so I don't wear it all the time\n\n\n\n\nB000ELSY0I:excellent_lcd!:video_game_nut_\"steve\"\n\n\nB000ELSY0I\n\n\nSamsung LNS3251D 32\" Wide LCD HDTV with Integrated ATSC Tuner: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n5.0\n\n\nExcellent LCD!\n\n\nNovember 13, 2006\n\n\nVideo Game Nut \"Steve\"\n\n\nChicago, IL United States\n\n\nResearched a lot before I bought this TV and for the money, this one was hands down the best 32\" LCD. Also, Amazon had the best price by far and it shipped to my house in a timely manner with no major issues. \nI mainly use this TV for my XBOX 360. You will NOT be disappointed if you are planning on doing the same. The game mode does enhance the picture a great deal as well. I provides sort of an Anti Aliasing effect smoothing out some of the rough edges. All in all, after owning the tv for about 2 months now, I couldn't be happier. To get around the poor speakers, I just plug in a nice set of headphones and games sound GREAT. \nI've also hooked up an HD antenna and regular cable. HD channels have a very good picture over the air. Some football games almost look better on this than on my 42\" Samsung DLP downstairs with the HD Cable. There's also a nice Antenna button on the remote which quickly swaps between cable and the Over the Air Antenna. \nThe only drawbacks to the TV are the 1 component input and the fact you can't use the PIP with the cable connected, only the antenna. \n\n\n\n\nB0007Y79G2:really_grew_on_me.:jordan_\"jordan\"\n\n\nB0007Y79G2\n\n\nCreative Zen V 4 GB Portable Media Player (Black/Blue): Electronics\n\n\nelectronics\n\n\n8 of 8\n\n\n5.0\n\n\nReally grew on me.\n\n\nNovember 7, 2006\n\n\nJordan \"Jordan\"\n\n\nSacramento\n\n\nI had a Zen Vision:M but I returned it because I decided I didn't need quite that much storage capacity and the hard drive seemed to sputter and whirr too often and I wanted the peace of mind that comes with owning a flash player compared to a hard drive player, so I decided to go with the Zen V since it's interface is almost identical to that of the Zen Vision M's(which I liked).\n\nThe first thing you will notice is the physical design. Initially I wasn't that impressed, but it's really grown on me. The height x width dimensions are comparitively much much smaller than the iPod nano. You can actually wrap your entire hand around the device, and the soft finish feels very nice in your hand. The second color that fills the seams of the player is a very nice touch as well.\n\nThe music is organized your standard Artist-Album-Song interface that is becoming quite a bit stale in my opinion. Still, it's as simple as you can get I suppose. The device officially supports album art although it seems to be very hit-or-miss in that regard in my experience. The screen is vibrant and colorful but is very hard to see in bright sunlight.\n\n The middle joystick is used for navigation and it works very well in that regard, although wedging your thumb underneath to move upward can be tricky sometimes and result in an accidental button push. A dedicated volume control switch is found on the side and a hold switch on top. \n\nThe player syncs with WMP11 via USB connection. Syncing it is a snap although I was not impressed with the transfer speeds. Also, if you sync the tracks on your player back to another computer, album art will not be attached to the music tracks.\n\nIf you are a newbie to buying MP3 players make note of the different sized models. The 2GB model for instance advertises that it holds 1000 songs but these figures are theoretical maximums based on low quality files. Most of my files are mid bit-rate WMAs and I can fit about 500 songs on the player, half as much as advertised. \n\nSummary:\n\nPros:\nExcellent physical design\nEasy to use interface\nGood navigation\nGreat sound quality\n\nCons:\nOn-the-go playlist creation is flawed\nAlbum art support hit-or-miss\nSlow transfer speeds from WMP11\nAccesories rarely available in brick-and-mortar stores\n\nUltimately, I recommend the Zen V over the iPod nano mostly on the strength of it's vastly superior physical design. \n\n\n\n\n\n\n\nB0001EMLXE:love_them:jessica_huerta\n\n\nB0001EMLXE\n\n\nAltec Lansing FX6021 3-Piece System: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nLove them\n\n\nNovember 6, 2006\n\n\nJessica Huerta\n\n\n\n\n\nI have had these speakers for over a month and I love them they sound awesome. They were delivered quickly and they were brand new as described\n\n\n\n\nB0001EMLXE:better_than_expected:doctor_dudemento\n\n\nB0001EMLXE\n\n\nAltec Lansing FX6021 3-Piece System: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nBetter than expected\n\n\nSeptember 25, 2006\n\n\nDoctor Dudemento\n\n\nSeattle, WA USA\n\n\nI read the other reviews for these speakers so I had very high expectations. I can only say WOW! The premium price (compared with lesser 2.1 speaker systems) is absolutely justified\n\n\n\n\nB0001EMLXE:pinnacle_of_2.1_pc_speakers,_best_for_their_price:eli_smith\n\n\nB0001EMLXE\n\n\nAltec Lansing FX6021 3-Piece System: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n5.0\n\n\nPinnacle of 2.1 PC speakers, best for their price\n\n\nAugust 29, 2006\n\n\nEli Smith\n\n\nSanta Rosa, CA USA\n\n\nFor reference, I've owned a set of Labtec APX-6420 2.1 speakers for about 6-7 years now. With the purchase of a a new computer (Mac Pro + Cinema Display) I decided it was also time to upgrade my speakers. I spent a very long time mulling over different brands and models, listening to them in stores whenever possible. Of the major \"PC speaker\" makers, I recall sampling Altec Lansing, Logitech, Klipsch, Bose, Creative, and JBL. Aside from listening in stores (a poor environment) I also took into account personal experience with the brands (mostly the first three I listed) in regular \"home\" situations.\n\nI initially wanted the Klipsch ProMedia 2.1's because of the fantastic reviews they've gotten on Amazon and elsewhere, but was turned off to them because of the apparently poor build quality (parts breaking very quickly), along with reports of buzzing, the power switch being on the back of the subwoofer, my impression of them in stores, and experience with a friend's ProMedia 4.1's (which neither I nor him really cared for, and which recently broke).\n\nI tried as many Logitech speakers as I could in many different situations and decided I HATE ALL OF THEM (with the sole exception of the Z-5500's which I own and love)! The bass especially sucks on their speakers.\n\nSo... through all this I eventually came to the conclusion that Altec Langsing was the brand I wanted to go with. And if that was so, it had to be the FX6021's which I had been eyeing for years. The first time I heard them in a store I declared them my favorite of the selection at the time and the same is still true. These are probably the best sub-$200 2.1 PC speakers you can buy. Even at their MSRP of $250 they're probably worth it. These speakers a very nice!\n\nThe sound is very clear and sharp. Get ready to hear all the distortion in your low bitrate digital audio files. The speakers especially shine when listen to live recordings or anything that was produced by real instruments (i.e. not techno, though that sounds good too).\n\nSubwoofer is NOT \"boomy\" or \"buzzy\" like others have reported. I never use the equalizer in iTunes and have the bass at about half. It's very deep, rich, and discrete. Doesn't shake the floor or cause my neighbors to complain--yes, I live in an apartment too and bass is a concern of mine. For those who complain about the bass: maybe you shouldn't have gotten 2.1 speakers? Maybe your units were defective? Or perhaps the subwoofer was placed in a very poor position? I know sound is highly subjective, but those reviews just don't sound right to me.\n\nPROs:\n* Tall, sleek, and sexy satellites provide a nice wall of sound and match the Cinema Display perfectly.\n* Bass and treble have a *large* range of adjustability. 50% of either is plenty enough for me.\n* Power button on control pod\n* This is important: NO BUZZING. Not while they're on, not while they off. 100% silent.\n\nCONs:\n* Volume adjustment is digital and moves in \"notches\"; these notches aren't always small enough. I much prefer a regular analog knob.\n* Remote is a bit useless\n* While they look good and perform well, they feel a little cheap\n* Get ready for lots of cables hanging from your desk and all over the floor (on the other hand, long cables are a PRO as well)\n\nAnd finally, don't be fooled by specs. Yes, other speakers may have more watts of power and THX certification, but that really doesn't matter as much as it might seem. These are more than loud enough for any sane person (100% volume would probably wake up the neighborhood). I really didn't pay any attention to the specs, I bought them because they sound the best\n\n\n\n\nB0001EMLXE:the_best!!!:ali_hasan_\"filmmaker_-_teacher_-_political_commentator/writer\"\n\n\nB0001EMLXE\n\n\nAltec Lansing FX6021 3-Piece System: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nThe Best!!!\n\n\nAugust 9, 2006\n\n\nAli Hasan \"Filmmaker - Teacher - Political Commentator/Writer\"\n\n\nSurf City, California & Eagle, Colorado\n\n\nI've owned these Altec's for over three years now, and I have yet to ever coming close to replacing them - as a matter of fact, when facing the decision of buying another set of speakers for a new computer, I went ahead and still purchased these babies!\n\nSOUND QUALITY --- the sound quality is very crisp and excellent overall - I have used mine for video editing, and I'm very pleased with how clear the dialogue audio has come through, in addition to the special effects sound - unless you need a five speaker system (or comparable) then I'm confident that this is the most crisp sound you'll find in any speaker\n\nVOLUME --- these suckers can get LOUD - just take my word for it\n\nCOMPATIBILITY --- This is the part I love most - there is no software to install and no special plugs - the speakers plug right in, and very easily, into the audio jack, just like a pair of headphones would --- thus, hooking in a computer, iPod, cd-player, etc, is easy as heck, making these speakers extremely universal for any sound equipment\n\nCONTROLS --- the speaker system comes with a seperate remote, giving one access to adjust the volume, bass, and treble, all seperately from the computer's controls -- the knob to control the volume, and other functions, is easy to use and makes for quick adjustments\n\nSIZE --- the speakers are extremely thin and the subwoofer is a very manageable size -- placing these speakers is easy\n\nPRICE --- believe me - it's a bargain at under $200!!!\n\nOverall, I give my full recommendation here -- you'll love these speakers\n\n\n\n\nB000AY7A1I:works_pretty_well_with_stereo_hook-up:john_a._krumm\n\n\nB000AY7A1I\n\n\nCreative Sound Blaster X-Fi XtremeMusic Card: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nworks pretty well with stereo hook-up\n\n\nOctober 12, 2006\n\n\nJohn A. Krumm\n\n\nJuneau, AK\n\n\nMy sound improvement wasn't as dramatic as many people describe, likely because I already had my computer wired to a good reciever and speakers, but I did notice an improvement over the integrated system I have once I made some adjustments. If you listen to rock, pop, blues or jazz, this card can make everything seem a bit brighter and punchier. It also seems to improve Pandora internet radio streams even more than my mp3's, though I have to keep the volume at about 3/4 in the Pandora player to prevent distortion. If you mostly listen to classical on a good stereo, I'm not sure if this card will do much for the sound, but it might\n\n\n\n\nB0006ZSXJE:wow:jimmy_l._yelvington_\"wombat\"\n\n\nB0006ZSXJE\n\n\nRazer RZ-1600 Diamondback Precision Gaming Mouse- Chameleon Green: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nWOW\n\n\nAugust 10, 2005\n\n\nJimmy L. Yelvington \"Wombat\"\n\n\nRockwood, TN\n\n\nI wondered what kind of mouse I would get for $60. I kept thinking, what could be so special about it. Well, I don't know where to begin. I like it so much, I'm getting ready to purchase another one just to use at work. I used a Microsoft Explorer mouse prior and thought I'd never find a mouse that was as comforable to use and had all the extra buttons I needed. This mouse completely puts the Explorer to shame. It took me a few minutes to get used to how quickly it responds but I soon caught on. I really didn't care for the glowing or the chameleon paint on it. That's not why I got it, although it does look pretty cool. As for gaming, I really didn't notice much improvment in my skill but it REALLY does feel better in your hand. The bottom of the mouse it curved in so you can grip it much easier.\n\nMy only negative comment towards it is how cheesy the driver and gui is for it as it looks \"homemade\".\n\nI would highly recommend this mouse for anyone who spends any time at all on a PC\n\n\n\n\nB0006ZSXJE:razor_diamondback_it's_not_perfect_but_at_1600dpi_who_cares!:c._mccomas\n\n\nB0006ZSXJE\n\n\nRazer RZ-1600 Diamondback Precision Gaming Mouse- Chameleon Green: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nRazor Diamondback it's not perfect but at 1600dpi who cares!\n\n\nApril 30, 2005\n\n\nC. Mccomas\n\n\n\n\n\nI got the Razor Diamondback a few months back and have been very pleased with it's performance. For gaming it's great, but I do not recommended it for everyday mousing around. \n\nThe resolution on the Diamondback was the selling point for me, and if that's something your interested in you will NOT be disappointed. It's really super sensitive and has improved my accuracy quite a bit. I do have a few gripes with it's design, but with 1600dpi that can be overlooked! Oh, it looks cool when the lights are out the chameleon green paint is a trip.\n\nDesign problems that I have noticed... Buttons squeak, button placement is not optimal, it's kinda small for people with big hands, programmable buttons take to much force to operate which can cause erratic mouse movement. If you have a second mouse to do your work with this makes a great gaming specific mouse, highly recommended\n\n\n\n\nB0001D3JXG:upgraded_to_solve_security__issue.:phillip_todd\n\n\nB0001D3JXG\n\n\nLinksys WPC54GS Wireless-G Notebook Adapter with SpeedBooster: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nUpgraded to solve security issue.\n\n\nNovember 13, 2006\n\n\nPhillip Todd\n\n\n\n\n\nI decided to upgrade my laptop with the wpc54gs from the wpc54g. I did this because the \"g\" card would randomly disconnect when the WEP or WPA security was activated. Living in a home near about 6 other networks I really wanted the router and wireless network secured. So, I purchased this card to see if it would fix my problem. I can happily say it solved the random disconnect with WPA security turned on. The range is just fine and all my network connections seem solid. Have not had a disconnect yet, over the last 5 hours.\n\nThe reason I took away one star is the fact on the Wireless Network Monitor V2.1 there seems to be a flaw. When I connect to the network and internet the monitor says it is hooked to the network but unable to find the internet. Obvious by my submission of this review, the internet can be found and used.\n\nAll in all a good product that functions just fine in my A45-S121 Toshiba laptop.\n\nPhil (Mesa, AZ\n\n\n\n\nB0001D3JXG:great_product:d._walton\n\n\nB0001D3JXG\n\n\nLinksys WPC54GS Wireless-G Notebook Adapter with SpeedBooster: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGreat Product\n\n\nNovember 2, 2006\n\n\nD. Walton\n\n\nHaines, AK\n\n\nThis was easy to set-up and use. It receives the signal from the router in all the rooms in my house\n\n\n\n\nB0006GDCD0:great_device:alex_napolitano_\"sa\"\n\n\nB0006GDCD0\n\n\nD-Link Securicam Network DCS-950G Wireless Internet Camera: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\ngreat device\n\n\nJuly 5, 2006\n\n\nAlex Napolitano \"sa\"\n\n\nMiami, FL USA\n\n\ncamera works great! the first one I ordered did not function correctly but Amazon promptly replaced it. resolution is good and audio is a great bonus that works very well. a great feature for me that I found was not available on all other devices was the ability to have video stored on a network folder. additionally, there is no software required to run this\n\n\n\n\nB00006GETD:does_what's_expected:alan_deikman\n\n\nB00006GETD\n\n\nKingston 512 MB Type I CompactFlash Card (CF/512): Electronics\n\n\nelectronics\n\n\n1 of 6\n\n\n5.0\n\n\nDoes what's expected\n\n\nAugust 1, 2005\n\n\nAlan Deikman\n\n\nFremont, CA United States\n\n\nA device like this either works or it doesn't. This one happens to work\n\n\n\n\nB0007O7DT0:lexar_jdp_review:gary_lawera\n\n\nB0007O7DT0\n\n\nLexar JDP1GB-231 JumpDrive Lightning 1 GB, 100X: Electronics\n\n\nelectronics\n\n\n4 of 6\n\n\n5.0\n\n\nLexar JDP Review\n\n\nSeptember 26, 2005\n\n\nGary Lawera\n\n\n\n\n\nI had no problem with the product. The directions were pretty simple. \n\nProblem: I never received my rebate information!!!\n\n\n\n\n\nB00006HOGN:great_headphones,_cheap_construction.:a._t._zak\n\n\nB00006HOGN\n\n\nPanasonic RP-HC70 Noise Cancelling Headphones: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n4.0\n\n\nGreat headphones, cheap construction.\n\n\nMarch 14, 2006\n\n\nA. T. Zak\n\n\nFlorida, U.S.A.\n\n\nThe first week I had these, the left earpiece's plastic \"rails\" which hold the earpiece in its slot broke. The parts that seem to need the most strength structurally are fasioned with cheap plastic.\n\nSound quality is great, with music playing and noise cancelling active, a normal conversation is barely audible in the background; great for crowded offices. Battery life is good, I've gotten 24+ hours using a NiMH rechargeable\n\n\n\n\nB00006HOGN:very_impressive_for_the_price:john_simmer_\"simmerin1\"\n\n\nB00006HOGN\n\n\nPanasonic RP-HC70 Noise Cancelling Headphones: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nVery impressive for the price\n\n\nJanuary 21, 2006\n\n\nJohn Simmer \"simmerin1\"\n\n\nminnesota\n\n\nI ordered these headphones from Amazon based primarily on user reviews of a number of noise cancelling headphones. I am quite pleased with my purchase. Though not stellar audiophile quality, the sound is pretty darn good, and with some minor EQ it's excellent. The noise cancelling effect seems to work very well: much better than I expected. Furthermore, the headphones are very comfortable and seem to be solidly constructed. I highly recommend them\n\n\n\n\nB00030CHP8:very_good_standard_sd_card_at_an_excellent_price!:j._sharp\n\n\nB00030CHP8\n\n\nPNY PSD1GRF3 1 GB Secure Digital Card: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nVery good standard SD card at an excellent price!\n\n\nNovember 11, 2006\n\n\nJ. Sharp\n\n\n\n\n\nI recently bought a new 6.0 megapixel digital camera (Canon PowerShot S3 IS) which came with an extremely small and practically useless 16MB secure digital card so I bought a couple of these 1GB PNY PSD1GRF3 secure digital cards since they were on sale for less than $20 apiece. These SD cards are made in Japan by Toshiba (they are then rebranded and sold by PNY, Lexar, DaneElec, etc.) and I have been very pleased with them so far. They work flawlessly with no write-speed delay in my new camera while shooting still pictures or while shooting video with stereo sound, and the card reader in my HP Pavilion M7480N Media Center PC (running Windows XP Professional service pack 2) reads the pictures and video into my Adobe Photoshop Elements 4.0 and Premiere Elements 2.0 software programs with no problems. The only drawback is that the read-speed on these standard SD cards is a bit slow when compared to the newer and faster SD cards...it takes several minutes to transfer all the pictures or video from a full 1GB card onto my PC...but I find this to be a small inconvenience rather than a big problem. So, if a little longer read time is not a problem for you, then save yourself some money and buy this card. Overall, a very good standard SD card at an excellent price! \n\n\n\n\nB0009AFFE2:great_product:ryan_t._duffy\n\n\nB0009AFFE2\n\n\nLexar SD1GB-40-231 1GB Platinum Secure Digital: Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n5.0\n\n\nGreat Product\n\n\nJanuary 15, 2006\n\n\nRyan T. Duffy\n\n\nPhiladelphia, PA\n\n\nThere isn't too much to write about this except is delivers as stated. Great product for the price\n\n\n\n\nB0000CEPC0:works_great_with_macintosh:jeff_g._\"microdoc\"\n\n\nB0000CEPC0\n\n\nD-Link DCS-900 10/100TX Internet Camera, Home Security: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nWorks Great with Macintosh\n\n\nNovember 2, 2006\n\n\nJeff G. \"MicroDoc\"\n\n\nWisconsin\n\n\nI set up a small security program with my Macintosh. Intially I needed a PC to place the settings in the camera, however once I got it hooked up to my security system it works beautifully. I would recommend you leave the lights on though because if you don't you will be getting just a black, fuzzy dark image back if you are using this camera for security. This camera is a very simple easy to use camera without the fuss of zooming, pan, tilt etc. If you need a simple camera you can't go wrong with the D-link DCS-900 (Wireless or wired version)\n\n\n\n\nB000BSHI92:excellent_service.:chris_schaefer\n\n\nB000BSHI92\n\n\nPower Strip Liberator Flat 18'' (5-Pack): Electronics\n\n\nelectronics\n\n\n2 of 7\n\n\n5.0\n\n\nExcellent service.\n\n\nFebruary 25, 2006\n\n\nChris Schaefer\n\n\nHouston, Texas USA\n\n\nWhat can you say? I placed my order with Amazon.com. A few days later the product arrived at my door. Perfect\n\n\n\n\nB000A2AMUQ:good_quality_sound_for_portable_use:dendoc_\"dendoc\"\n\n\nB000A2AMUQ\n\n\nMUSICASE-IPOD/MP3 Accessory Speaker & Carrying Case: Electronics\n\n\nelectronics\n\n\n14 of 14\n\n\n4.0\n\n\nGood Quality Sound for Portable Use\n\n\nFebruary 28, 2006\n\n\ndendoc \"dendoc\"\n\n\nWestern U.S.\n\n\nThe quality of materials: zipper, hard case, strap and light\nweight are outstanding. When I connected my MP3 player, the\nsound was impressive for a-carry along- portable stereo speaker.\nNext, I took the MP3 out of the speaker case and loaded my DT\n200V pocket radio; again the sound was very good and even loud\nat a low volume setting. \nNow the minor negatives: changing the volume, etc. \nrequires going into the zipped case. \nOther than the ipod, be careful in\nselecting this speaker. The player, radio... cannot be \nmore than 4\" high, 2.5\" wide and .75\" thick; \nlarger and you may not be able to zip closed \nthe speaker case, which is necessary.\nMy items just do fit. So the Quality Sound \nand Materials make this a fine product, \nespecially for the money.\nOTHER PRODUCTS REVIEWS HAVE GREATLY HELPED ME \nDECIDE WHAT TO BUY, THIS IS WHY I DID THIS REVIEW. \nPLEASE DO THE SAME FOR OTHERS AS YOU BUY PRODUCTS.\n\n\n\n\nB0002IG356:perfect_keyboard,_good_mouse:stephen_craton_\"melchior.us\"\n\n\nB0002IG356\n\n\nMicrosoft Optical Desktop Elite for Bluetooth: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n5.0\n\n\nPerfect Keyboard, Good Mouse\n\n\nJune 21, 2006\n\n\nStephen Craton \"Melchior.us\"\n\n\nIndiana\n\n\nI bought this package purely for the keyboard. I wanted a new one with wireless technology and I was driven to Bluetooth just because it is so reliable. I do not use the keyboard and mouse together, however.\n\nThe keyboard is one of the most comfortable keyboards I've ever used. I am a computer programmer so having a nice keyboard is essential. The pad at the bottom is very comfortable and the keys are very well spaced and organized. The only real compliant is they cut some corners with the home, end, page up, page down, and delete keys. They moved them around from the conventional way which I still haven't gotten used to but it's fine.\n\nI have experience no lag with either the mouse or the keyboard. I play a game called StepMania (a sort of Dance Dance Revolution clone for PC) which requires very accurate timing with the keyboard. I've experienced no problems here, it's spot on and probably the fastest ever. The only problem, really, is it takes a while to register if you haven't use the keyboard for a while since it must turn back on from sleep mode and reconnect. After that however, there are no problems at all.\n\nI do not use the mouse with the keyboard. I decided to use it as a mouse with my iBook since it had built-in bluetooth. I had absolutely no problems connecting the two devices and it works flawlessly. I don't use the mouse very much, but the times I do use it I experience no problems.\n\nBoth the range on the mouse and the keyboard are extremely nice. I can go anywhere in my room with either the mouse or keyboard and experience no problems.\n\nThe battery life on the keyboard is also very nice, esspecially since it has the built-in sleep feature. I have had to replace the batteries once since I got the unit in January of 06 (currently June of 06). I have not had to replace the mouse batteries yet, but I do not use it very much.\n\nOthers complained about not being able to use the keyboard in the BIOS or startup screens, but I haven't had any issues with that. It depends on how recent your motherboard is, and mine is relatively new so I had no problems.\n\nI highly recommend this setup to anyone wanting a wireless keyboard or mouse, or if you want to split and use the mouse with a laptop and keyboard with a desktop. Either way, you cannot go wrong with this package, it is extremely excellent\n\n\n\n\nB00005LAC5:does_the_job:natataka\n\n\nB00005LAC5\n\n\nJensen J052W Indoor / Outdoor 2-Way Speakers, White: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n4.0\n\n\ndoes the job\n\n\nOctober 9, 2006\n\n\nnatataka\n\n\nTorrance, CA\n\n\n i recently purchased these speakers for my patio setup. the sound quality wont blow your mind, but it does not sound bad. its large and lound enough to hear for an outdoor speaker for the price. it has a switch in the back for outdoor/indoor. not really sure what that does. very easy installation with the brackets included. (no screws provided) overall great unit for thye pric\n\n\n\n\nB00006RVNW:solid_and_clear:tony\n\n\nB00006RVNW\n\n\nMonster Cable ULT V1000 CV-4 THX Ultra 1000 Component Video Cable: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nSolid and Clear\n\n\nAugust 25, 2005\n\n\nTony\n\n\nVirginia\n\n\nI agree that these cables are way too expensive!! However that being said, they really do perform well. I have been using them for almost a year and they are just solid. Colors always look their best. I know it was a silly purchase but my DVD's look great.\n\n\n\n\nB00067KETY:good_phone_-_short_battery_life:cubbins\n\n\nB00067KETY\n\n\nAT&T 959 Speakerphone with Caller ID (White/Gray): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n4.0\n\n\nGood phone - short battery life\n\n\nJuly 26, 2006\n\n\nCubbins\n\n\nOhio\n\n\nOverall, a good phone. Low cost. Well built. Many features. Speaker works OK for the price. Features such as caller ID and the speaker phone are powered by batteries. As stated by another reviewer a set of batteries only lasts a few months, even if you do not use the battery-powered speaker phone often. This phone would be a great phone (for the price) if it did not use batteries, but was a plug-in model\n\n\n\n\nB0007QOK9Y:best_value_for_money.:d._weerakoon_\"ddw\"\n\n\nB0007QOK9Y\n\n\nActiontec USB00020C-01A Internet Phone Wizard: Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n5.0\n\n\nBest value for money.\n\n\nMay 9, 2006\n\n\nD. Weerakoon \"DDW\"\n\n\nUSA\n\n\nFor a long time I was looking for a phone to use with skype. For the price and convenience, Actiontec Internet phone wizard was the best I found and ordered it from Amazon ($42.99 with free shipping). After receiving the modem I have been using it for almost a month now and I am extremely happy. I have connected my codeless phone to it and everything seems to be working fine\n\n\n\n\nB00009V2PG:great_product:virgil_dahlstrom_\"vasind\"\n\n\nB00009V2PG\n\n\nPanasonic KX-TCA86 Folding Headset with Noise-Canceling Microphone: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nGreat product\n\n\nJuly 26, 2006\n\n\nVirgil Dahlstrom \"vasind\"\n\n\nMinneapolis, Mn.\n\n\nThis headset is everything that I hoped it would be and the seller rates 100\n\n\n\n\nB0000BXJVW:best_price:hayley\n\n\nB0000BXJVW\n\n\nCanon Borderless Photo Paper Plus, Glossy (7980A022, 4x6, 120 Sheets): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nbest price\n\n\nNovember 4, 2006\n\n\nHayley\n\n\nConnecticut\n\n\nyou can never go wrong with this paper. I always buy it here, the price in stores is outrageous\n\n\n\n\nB0000BXJVW:highly_recommended:b._v._michael\n\n\nB0000BXJVW\n\n\nCanon Borderless Photo Paper Plus, Glossy (7980A022, 4x6, 120 Sheets): Electronics\n\n\nelectronics\n\n\n5 of 6\n\n\n5.0\n\n\nHighly recommended\n\n\nSeptember 2, 2006\n\n\nB. V. Michael\n\n\n\n\n\nMy experince shows that it is always better to use paper from the same brand as your printer. Then the result is really outstanding print after print. The paper is really top quality and regardless of your needs it will satisfy them. Bravo Canon for this first class product\n\n\n\n\nB000CS7RYM:how_networking_technology_should_work:css\n\n\nB000CS7RYM\n\n\nSling Media SlingLink Ethernet Connection Bridge: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nhow networking technology SHOULD work\n\n\nNovember 8, 2006\n\n\ncss\n\n\nNeedham Heights, MA USA\n\n\nAgree with the reviewer who called this a 'Perfect Product'. It is.\n\na) Take it out of box\nb) Plug it in\nc) You're networked\n\nElapsed time - approximate 15 seconds.\n\nCompared to the pain I went through trying to get my SlingBox working with a Linksys game adapter, installing SlingLink renewed my faith in technology. Awesome\n\n\n\n\nB000CS7RYM:super_easy_setup_and_works_like_a_charm:bearcat\n\n\nB000CS7RYM\n\n\nSling Media SlingLink Ethernet Connection Bridge: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nSuper easy setup and works like a charm\n\n\nOctober 7, 2006\n\n\nBearcat\n\n\nOhio\n\n\nI purchased this to use with my newly acquired Slingbox Pro. All I had to do was plug these into wall outlets and connect the Lan wires. Worked like a charm...couldn't believe it was that easy. Great performance and good price from Amazon\n\n\n\n\nB0006FS2E0:great_for_the_price:pauly_long\n\n\nB0006FS2E0\n\n\nIOGEAR GBU211 Bluetooth To USB Adapter: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGreat for the price\n\n\nAugust 11, 2006\n\n\nPauly Long\n\n\nLittleton, CO United States\n\n\nBottom line up front:\n\nWorks very well, very low price, works much better with the \nincluded drivers than with the generic windows xp driver\n\nDetailed review:\n\nI bought this so I could use my new Treo 700w with my non-bluetooth-enabled laptop. The price was my main motivation in buying it.\n\nInitially I just plugged it in and used the default windows xp drivers and it worked okay, however it didn't always work and i'd have to unplug/replug for my phone and the device to find and talk to each other.\n\nAfter getting tired of that, I decided to install the drivers that ship with the product. I've had a much better experience since i did that. I was afraid the software would be really clunky given the cheap price, but it's actually quite good and gives you a TON of options that the standard XP driver does not provide.\n\nIf you use a this with a laptop and you dock/undock it, you'll have much better luck if you stop the adapter prior to docking/undocking. Otherwise, you'll probably end up having to unplug/replug it\n\n\n\n\nB00006HZ0K:logitech_mx500_optical_mouse:hardware_traveler\n\n\nB00006HZ0K\n\n\nLogitech MX500 Optical Mouse: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nLogitech MX500 optical mouse\n\n\nJuly 6, 2005\n\n\nHardware Traveler\n\n\nCalifornia, USA\n\n\nThis is a corded mouse; fits comfortably in your hand. Moves smoothly across table surface; works well on a mousepad as well if you need wrist support.\n\nWorked out of the box (except for the application button) for the different buttons on the mouse. The Internet buttons allow for a page forward/ back with your thumb. There are also line up/down buttonw above and below the scroll wheel that allow for more control in scrolling.\n\nThe scroll wheel feel good, better than some other mice I have used.\n\nI opted to use the native Windows XP mouse driver, so the application button does not work, but the Windows taskbar is not much of a hardship :-); and I prefer not to clutter up my system with another set of marginal utility software\n\n\n\n\nB0007ZFLYS:good,_not_great:juan_m._camacho_arturo_\"harkon\"\n\n\nB0007ZFLYS\n\n\nLogitech Quickcam for Notebooks ( 961404-0403 ): Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\ngood, not great\n\n\nNovember 10, 2006\n\n\nJuan M. Camacho Arturo \"HarKon\"\n\n\nDavie, FL United States\n\n\nIt has very good definition but is slow, even in the night is not that good and is slower\n\n\n\n\nB00006JHZ0:compatible_with_more_models:wayne-in-seattle\n\n\nB00006JHZ0\n\n\nMotorola Ni-Cd Rechargeable Battery for T5000 Series Radios (53615): Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n5.0\n\n\nCompatible with more models\n\n\nFebruary 16, 2006\n\n\nWayne-in-Seattle\n\n\nSeattle, WA\n\n\nWhile the product description states this battery is \"Compatible with Motorola T5320, T5400, T5420, T5600, T5620, T5700, T5720, T5800, and T5820 2-way radios\" the 53615 is compatible with many more. To bad Amazon couldn't put this online for us.\n\nFrom the packaging:\nT4800 Series\nT4900 Series\nT5000\nT5320\nT5400 Series\nT5500 Series\nT5600 Series\nT5700 Series\nT5800 Series\nT5900 Series\nT6500 Series\nFV500 Series\n\n\n\n\n\nB000F8L506:excellent_phone_tons_of_features:j._sundquist\n\n\nB000F8L506\n\n\nPanasonic KX-TG5672B 5.8 GHz FHSS GigaRange Digital Cordless Answering System with Dual Handsets: Electronics\n\n\nelectronics\n\n\n16 of 16\n\n\n5.0\n\n\nExcellent phone Tons of features\n\n\nOctober 14, 2006\n\n\nJ. Sundquist\n\n\nNew York\n\n\nI purchased this phone system as a gift. I set it up and used it extensively. The handsets have features that you are used to finding in a cell phone. Many different ring tones to choose from, the antenna lights up when a call comes in. You can even set special colors and ring tones for specific callers so you'll know when they're calling without picking up the handset.\n\nTalking caller ID is great too. No more rushing to find the phone only to discover it's a sales pitch or a call that could wait.\n\nI set this phone up on a voice over IP cable modem phone and the calls are loud and clear. \n\nThe phone is running next to an 802.11b/g wireless computer network and has NO interference at all. \n\nOne suggestion to make the batteries last longer: Do NOT leave the phone sitting in the charger all the time. This will basically be constantly charging the batteries and will wear them out MUCH faster. Instead leave the phone off the charger until the battery alert beeps and the phone displays \"recharge\" on the screen. Then let it get a full charge. You'll find that the batteries will not need to be replaced for a year or more instead of 6 months or so\n\n\n\n\nB0009Q4PIS:music_like_never_before:s._huesman_\"music_collector\"\n\n\nB0009Q4PIS\n\n\nUltimate Ears Super.fi 5 EB Earphones (Black): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nMusic Like Never Before\n\n\nNovember 1, 2006\n\n\nS. Huesman \"music collector\"\n\n\nKentucky, USA\n\n\nI was looking for a new pair of headphones to compliment my brand new ipod nano when I ran across these on a website. I was a little hesitant at first because I don't think I ever spent over $40 on a pair of headphones. I finally decided to invest in these because I spend most of my free time listening to music. I received these the other day, and immediately plugged them into my ipod. Please don't let the breaking in period discourage you! After these become comfortable, you will hear music you have never heard before even when you are listening to songs you have heard thousands of time.\n\nI started with all my heavy bass tracks, and couldn't believe how they sounded with these headphones. I then wanted to try some of my jazz and blues tracks to see how these tracks sounded. This is the first time in my life John Coltrane has given me goosebumps!\n\nIf you are debating on whether to spend this kind of money let me make it easy for you. If you are just a casual music listener, I would say just go with a good pair of cheap ones, you will save a lot of money. However if you are a passionate music lover and collector, these headphones will change your music completely. After getting comfortable with these, I would have paid much more than I actually did\n\n\n\n\nB0009Q4PIS:for_extreme_bassheads:douglas_a._greenberg\n\n\nB0009Q4PIS\n\n\nUltimate Ears Super.fi 5 EB Earphones (Black): Electronics\n\n\nelectronics\n\n\n12 of 12\n\n\n4.0\n\n\nFor extreme bassheads\n\n\nFebruary 28, 2006\n\n\nDouglas A. Greenberg\n\n\nBerkeley, CA USA\n\n\nThis is the Ultimate Ears Super fi model especially designed for \"bassheads,\" featuring a disproportionately large bass driver complemented by a more conventional driver for the middle and high frequencies. I don't think UE would ever claim that what results is a balanced sound signature. Yes, the mids and highs are present and the quality is clean and reasonably detailed, but these frequencies are definitely recessed and with some kinds of music this can result in an unnatural kind of reproduction.\n\nOn the bright side, however, the bass reproduction is marvelous. Not only is the quantity/extent of the bass frequencies as powerful as any headphones or earphones I've ever used, the quality is excellent. The bass is tight and pleasant, neither boomy nor thumpy. This overbalance toward bass frequencies renders the UE 5 EB's ideal for use when listening to music in which the bass is presented \"front and center,\" including techno bass (aka \"car audio bass\"), drum n' bass, and many types of rap/hip-hop. For music in which the basslines are prominent but comprise part of a more balanced presentation overall, opinions will vary. With rock music, for example, the strong bass is certainly a plus, but I have found that sometimes the bass overpowers the middle frequencies too strongly, and some detail is lost.\n\nIt should be noted that this bass-dominated sound signature renders the EB's a good choice for people who own iPods and feel that their music player is weak on bass. Without any EQ adjustment, the iPod suddenly is transformed into a player where the bass is PRESENT. \n\nIn terms of build, the EB's superficially seem well-constructed and designed. I have heard complaints, however, that the thinness of the wires and earpieces render them vulnerable to breakage. Be careful out there! Thoughtfully, EB has includes a variety of ear tips with these IEM's (including foam, flanged, and plain) so that users with different sized ear canals can get a good fit. The EB's don't extend deeply into the ear canal, which is a mixed blessing. On the one hand, this means that if you get a good fit of the earpiece into the entrance to the ear canal, comfort is good and isolation decent. However, if the fit is not quite right both comfort and isolation will prove frustrating. Making the right choice of ear tip is absolutely essential when using the EB's.\n\nOh, and some people think that the EB's look kind of strange when worn. Larger than the higher-priced Super fi 5 Pro model, the EB's stick out a bit. Your call as to whether this is a problem.\n\nLike all of the UE IEM's in this series, the EB's are packaged with a nice set of accessories, including a 1/4\" adapter plug, a sound attenuator, a soft travel case, an earwax loop, and a silvery metal storage case which includes a winder that helps head off what is always a problem with IEM's, cord tangling.\n\nIt's unlikely that the EB's will prove satisfactory as your \"everyday\" earphone unless you listen solely to bass-dominant types of music. The truth is, however, that even for bass-heavy music the overall sound signature of the Super fi 5 Pros is likely to be more satisfactory for most users (I own both). But as a second pair specifically for use with bass-y music, the EB's are well-designed and definitely fun\n\n\n\n\nB0001FV30K:amazin:anil_k._pali\n\n\nB0001FV30K\n\n\nDLO TransPod All-in-One Car Solution for iPod White: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nAmazin\n\n\nNovember 10, 2006\n\n\nAnil K. Pali\n\n\nChicago,USA\n\n\nwe have this for our 30gb ipod and it works fine in both our cars.....very handy and a must buy!!\n\n\n\n\nB0002XJBGE:excellent_for_auto-racing_games:andrew_martin\n\n\nB0002XJBGE\n\n\nLogitech Cordless Rumblepad 2 with Vibration Feedback (963326-0403): Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nExcellent for Auto-Racing games\n\n\nNovember 20, 2006\n\n\nAndrew Martin\n\n\nParadox, Canada\n\n\nThis is the 2nd Gamepad I've owned (My first PC was in 1993). The 1st gamepad was the Gravis Gamepad (4-button + D-Pad). I recently upraded and there is no GAME/MIDI port on the new system, plus I need more buttons for Need For Speed Most Wanted, etc.\n\nI love this controller. I've had it for two days and have had nothing but excellent results. I've only used it in NFS games (I have them all).\n\nThe WiFi works flawlessly and I have a WiFi network in my house. Installation is simple and takes maybe 3mins.\n\nThe rumble only works on games which have forced feedback software. This is totally understandable. The feedback in NFS-MW is very slight but that's not the controllers fault, it's a matter of programming by EA Games.\n\nI like the weight of the controller. It's not feather light, so with that motor and battery weight added, it feels more like a premium device (just like the 67$ with tax I paid for it - canadian dollars). I didn't like how my gravis gamepad as so light, so this is a step up.\n\nThe analog sticks are a great for racing as they are progressive like a steering wheel. They are rubber gripped for non-slip action. I am used to the D-Pad, so I have mine setup in NFS to use both as a directional control without having to press the MODE button to switch because you don't have time during a race to press a button before switching.\n\nIt works as well as my old gamepad, but feels betetr and has extra buttons and the other features listed make it worth the money. If you in the USA, you'll be getting a much better deal. It lists for 60$ cnd here but only 30$ US in the states.\n\nCONS:\n\nIt's not as comfortable to hold as I would like but its much better than my Gravis Gamepad and the buttons are easier to hit on the new one.\n\nCONCLUSION:\n\nI'm very satisfied overall and I knew I may have to get used to the way it feels when I hold this controller since changing from the old SNES style Gravis Gamepad.\n\n4/5 rating only because it could be easier to hold\n\n\n\n\nB0007U8Y9M:really_a_great_phone:yogi_bear\n\n\nB0007U8Y9M\n\n\nPanasonic GigaRange KX-TG5576M 5.8 GHz DSS Expandable Cordless Phone with Answering System and Color LCD on Handset (Metallic Grey): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nReally a great phone\n\n\nNovember 12, 2006\n\n\nYogi bear\n\n\nDC area\n\n\nWe have had this phone for almost a year and I love it. We have the base station which is also an answereing machine plus we have a sattelite phone. I wish we had more satelite phones. I love the talking feature because I just listen and know who is calling. I also love the programming feature. I never have to look aup a tel number that I call regularly anymore. I just wish the extra phones were not so expensive to add on. \nGreat phone buy it\n\n\n\n\nB0002A9U7M:no_management_needed:norm_cimon_\"programmer\"\n\n\nB0002A9U7M\n\n\nNetgear GS605 5-port Gigabit Ethernet Switch (10/100/1000 Mbps): Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nNo management needed\n\n\nNovember 11, 2006\n\n\nNorm Cimon \"programmer\"\n\n\nLa Grande, OR USA\n\n\nThe system worked out of the box allowing me to quickly link up additional devices in an in-home office which had a single network connection. Haven't measured simultaneous throughput to multiple computers yet, but from simple observation connectivity seems transparent. Should allow for movement to gigabit switching in the future as well\n\n\n\n\nB00004Z5PY:excellent_producs:a._wilson_\"latins3\"\n\n\nB00004Z5PY\n\n\nBelkin F3U133-16 USB 2.0 A/B Cable (16 Feet): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nExcellent producs\n\n\nNovember 9, 2006\n\n\nA. wilson \"latins3\"\n\n\nboston, ma\n\n\nThis cable is of the best quality, quick shipment and at a price that beats all the retail stores - you can't go wrong\n\n\n\n\nB0009J4X14:stylish_design_that_works_great_too!!!:n._saboor_\"gadgetguru\"\n\n\nB0009J4X14\n\n\nVtech I5871 - Expandable System w/ digital Answering Device, Color Handset Display & Dual Caller ID: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nStylish design that works great too!!!\n\n\nNovember 3, 2006\n\n\nN. Saboor \"Gadgetguru\"\n\n\nCA - USA\n\n\nI did buy a set of 2 \"Base station and extra handset\". Its a great design, tons of features, MP3 ring ton, Picture Caller ID, Sync with your PC for phone book, Answering machine, etc.\nI am very happy with.\nThank you so much for such great product with a great price\n\n\n\n\nB0009J4X14:best_phone_i_have_used:d._l._neumann\n\n\nB0009J4X14\n\n\nVtech I5871 - Expandable System w/ digital Answering Device, Color Handset Display & Dual Caller ID: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nBest phone I have used\n\n\nNovember 3, 2006\n\n\nD. L. Neumann\n\n\nPortland, OR\n\n\nI work at a home office so I am on the phone lot. I always buy cordless phones I can use with a headset so I can have both arms free while I talk with a min of background noise intrusion. In other words, I don't talk with the phone cradled between my shoulder and ear. And I don't use speaker phone features. Let's just get that out of the way now since those are things some have complained about that I couldn't care less about.\n\nThis phone first of all looks good and FEELS good. It was built with high quality plastics and metals on all surfaces. It has a nice solid weight to itself. Nothing tacky or cheap about it. It looks far better in person than it ever did in pictures. And it looks pretty good in pictures. I like how the bases are designed such that the antennas serve a double purpose as cradle supports for when the phone is docked. Clever design. The charging base light and back-lit buttons all look very slick.\n\nThe screen is attractive. A big step up from back-lit LCDs or no screen at all like my previous phone.\n\nI like that you can very quickly find and hit Mute, adjust volume, and page through your caller history.\n\nThe belt clip goes deep so the phone stays snugly attached to you pocket when you are walking around.\n\nI know many 5.8 GHz phones don't mention that only one direction of communication is at that frequency vs. both directions (as 2.4 GHz sometimes gets used too). I don't know for sure if this phone falls into that category, but I was in the market for a 5.8 GHz phone to avoid interference with my WiFi networks as well as to avoid microwave oven interference with my calls. One thing's for sure: this phone definitely is immune from MY microwave oven. In the past when I was on a call heating up my coffee, static and voice quality went way down unless I got far away from the oven. With this new phone I can stand right by it while on High w/o ANY impact on voice quality.\n\nOne other very cool feature is the spare battery charger. While it's nice that the battery can replace a unit that runs down in a handset right away, the best part is if you have a power failure. Your phone will keep truckin' and there is never a need to replace that battery with some short lived 9volt waste of time.\n\nFinally, I like how the phone by a bedside basically doubles as a digital clock. The phone base unit picks up the current time from the caller ID info provided by the phone company and uses that info to automatically set and broadcast the current time to all headsets and base station. No need to manually set the clock. And when not in use, sitting it is dock, the phone's screen displays the time.\n\nSo far I've found the voice quality to be best of any cordless phone I've owned\n\n\n\n\nB00008Y1A7:great_item:a._sokolowski_\"mom_of_3\"\n\n\nB00008Y1A7\n\n\nSony SRS-A202 Active Speakers with Built-in Mega Bass Sound: Electronics\n\n\nelectronics\n\n\n13 of 22\n\n\n5.0\n\n\nGREAT ITEM\n\n\nSeptember 16, 2004\n\n\nA. Sokolowski \"Mom of 3\"\n\n\nNJ\n\n\nI bought this for my kids computer when I bought a new one for hubby and myself... these speakers sound better then the more expensive ones I have.... \n \n I highly recommend these speaker\n\n\n\n\nB000BI7NHY:mixed_results_with_my_t/x:l._prosky\n\n\nB000BI7NHY\n\n\nPalm TX Handheld: Electronics\n\n\nelectronics\n\n\n5 of 6\n\n\n4.0\n\n\nMIXED RESULTS WITH MY T/X\n\n\nNovember 10, 2006\n\n\nL. Prosky\n\n\n\n\n\ni wanted a top quality palm with the bells and whistles. this is pretty close to it but i had a few problems with the email and web features. granted i am not a techie, but connecting and checking email should be more straightforward. all the other features are top notch and overall i love my palm\n\n\n\n\nB00005LACD:works_great!:s._stokesbary\n\n\nB00005LACD\n\n\nJensen JP20 160-Watt DC-to-AC Power Inverter: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n5.0\n\n\nWorks Great!\n\n\nJuly 20, 2006\n\n\nS. Stokesbary\n\n\n\n\n\nI purchased this item for a long trip so I could charge and/or play my Zen mp3 player in the car without worrying about battery life on the trip. It worked great. I am also planning to use it for my notebook in the car the same way because the car adapter for my notebook costs $100\n\n\n\n\nB00001P4YG:the_headphone_to_compare_all_others_to.:chris_kelley\n\n\nB00001P4YG\n\n\nKoss KTX-4 Collapsible Portable Stereophone: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nThe Headphone to compare all others to.\n\n\nApril 11, 2002\n\n\nChris Kelley\n\n\nBoston, MA\n\n\nI have searched and searched and searched for a pair of portable headphones that sound as good as these with lots of failure. IMO, you cannot beat the sound of a set of "over-the-head" headphones. I have tried all styles: earbuds, behind the neck, clips, even the ear plugs. Nothing gives the sound and comfort of the KTX Pros (granted, I have never tried the sportapros or portapros, but from what I have read, they are the same headphone. Except they are UGLY!). The KTX Pros have both great lows and great highs. Plus, you can't beat that price\n\n\n\n\nB0000A1GO9:nice_but_not_solid:y._simonov\n\n\nB0000A1GO9\n\n\nPNY E-Commerce 512 MB USB 2.0 Portable Drive (P-FD512U20-RF): Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nnice but not solid\n\n\nAugust 19, 2006\n\n\nY. Simonov\n\n\nusa\n\n\nFor the price is is great.\nit's working fine but the plastic is not solid and has a feeling of low quality. \n\n\n\n\nB0000A1GO9:convenient_and_very_usable:technofreak\n\n\nB0000A1GO9\n\n\nPNY E-Commerce 512 MB USB 2.0 Portable Drive (P-FD512U20-RF): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n4.0\n\n\nConvenient and very usable\n\n\nJuly 18, 2006\n\n\nTechnofreak\n\n\nSpringfield MO\n\n\nI thought that this flash drive was above average in performance. Works well between a Macintosh and a PC. I liked the feature--missing from my last flash drive--of easily emptying the drive after use without having to some fancy erasing techniques. In other words, the PNY worked like a real hard drive. I would like the manufacuters to explain in detail how to attach the tread to the laniard. It took me a while to figure it out\n\n\n\n\nB000F4CVQC:a_great_audio_compliment:k._briscoe_\"save_bluth\"\n\n\nB000F4CVQC\n\n\nBose SoundDock digital music system for iPod (Black): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nA great audio compliment\n\n\nNovember 9, 2006\n\n\nK. Briscoe \"Save Bluth\"\n\n\nChicago, IL United States\n\n\nWith so many of these ipod speaker docks on the market, it was sort of struggle to decide. Ultimately, I felt very comfortable going with the Bose brand. Plus, in my opinion, this is about the sleakest and best-looking unit out there (I went with black; I'm not an Apple-white drone). The sound is very, very good for small to medium size spaces. There are not a lot of features to the unit, which I think is a good thing - it just focuses on the sound and the small footprint. Mini remote control work well and again the small size makes it unimposing in this world of too many remotes. Power cord is plenty long and I like the fact I can easily move the thing to a different room if I need music there\n\n\n\n\nB000F4CVQC:great_quality,_not-so-great_price:a._sands_\"commander_pudding\"\n\n\nB000F4CVQC\n\n\nBose SoundDock digital music system for iPod (Black): Electronics\n\n\nelectronics\n\n\n1 of 3\n\n\n4.0\n\n\nGreat quality, not-so-great price\n\n\nNovember 3, 2006\n\n\nA. Sands \"Commander Pudding\"\n\n\nMinnesota\n\n\nWell, the bose sounddock is a wonderful music dock. Its VERY simple. just put your ipod on there and ROCK OUT TO THE BEAT. The bose quality is very obvious, and it looks like you could spend 300 dollars on it, which brings me to my next point. ITS TOO EXPENSIVE. my recomendation is to buy an FM transmitter and listend to your ipod on any Fm raido. The only disadvantage of an Fm transmitter to this is that this will charge your ipod, and its slightly easier(but thats what iPod is all about, isnt it...?)\n\nBasically, im saying buy it if you have a lot of mone\n\n\n\n\nB0007RA6NW:great_little_radio:henrybmn_\"henry_b\"\n\n\nB0007RA6NW\n\n\nSony SRF-M97 Radio Walkman: Electronics\n\n\nelectronics\n\n\n18 of 19\n\n\n5.0\n\n\ngreat little radio\n\n\nMarch 10, 2006\n\n\nHenrybMN \"Henry B\"\n\n\nMinneapolis, MN United States\n\n\nI'm a jogger, and like to listen to All Things Considered while I run miles and miles. This radio is great because the light weight is unobtrusive. It has a convenient \"lock\" button to prevent accidentally changing the station with a bump. I previously had a similar size unit from Aiwa, this Sony has MUCH MUCH better reception\n\n\n\n\nB00083PGFS:convient,_bring_extra_batteries.:j._mullin\n\n\nB00083PGFS\n\n\nApple M9861G/A iPod Camera Connector: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n4.0\n\n\nConvient, bring extra batteries.\n\n\nJuly 18, 2006\n\n\nJ. Mullin\n\n\nIowa\n\n\nThis works well, allows you to view imported photos, but it is a slow proccess best left for the hotel room. Just plan on taking plenty of batteries and the charger for your ipod.\n\n\n\n\nB0000B35CK:beware_type-m:j._lamberg\n\n\nB0000B35CK\n\n\nFujifilm 512 MB XD Type M Picture Card ( 600002308 ): Electronics\n\n\nelectronics\n\n\n5 of 6\n\n\n5.0\n\n\nBeware Type-M\n\n\nAugust 29, 2006\n\n\nJ. Lamberg\n\n\nSaint Paul, MN USA\n\n\nThis card is the largest size that will allow clean video with FinePix cameras from 2004 or earlier. The picture-to-picture lag time is minimal with this card and video is excellent. \n\nFrom Fuji Website - \"NOTE: for owners of FinePix cameras made before 2004, check notice below for the operating compatibility of older FinePix cameras with Type M and H.\n\n\n\n\nB00004Z5XH:i_didn't_believe_it,_but_it's_true...:p._summersgill\n\n\nB00004Z5XH\n\n\nCanon 1029A004 Photo Paper Pro for BJC-8200 (8.5\"x11\", 15 Sheets ): Electronics\n\n\nelectronics\n\n\n13 of 13\n\n\n5.0\n\n\nI didn't believe it, but it's true...\n\n\nNovember 24, 2003\n\n\nP. Summersgill\n\n\nSan Rafael, CA USA\n\n\nI was devout in my belief that I could get similar photo output from my Canon S800 with less expensive paper. I bought those big boxes of 100 sheets of Kodak's photo quality glossy from Costco. My prints were blotchy. Then I got my hands on some of Canon's Photo Paper Pro, and my pictures were magically crystal clear. The difference is amazing. I never would've thought paper would make this much difference. I've since bought Canon's 15 sheet packs multiple times. I now use the Kodak stuff for drafts or to give prints to people who don't care much about the quality of the output, while I save the Canon Photo Paper Pro for prints we want to frame\n\n\n\n\nB0006IEV7E:very_fast:d._smolarek\n\n\nB0006IEV7E\n\n\nSONY MSX-M1GN 1GB High Speed Memory Stick(R) PRO Duo Media ( Red ): Electronics\n\n\nelectronics\n\n\n11 of 14\n\n\n5.0\n\n\nVery Fast\n\n\nMay 4, 2005\n\n\nD. Smolarek\n\n\nChicago, IL USA\n\n\nThis stick is fantastic for high resolution photos and is lightning quick. Do you need the extra speed the MSX-M1GN gives you? Maybe, maybe not. But at least you have it when and if you need the feature. Highly recommend it for the money. \n\n\n\n\nB000C28VPM:great_little_speakers:scott_james\n\n\nB000C28VPM\n\n\nCreative TravelSound 400 Portable Speaker System ( 51MF5030AA000 ): Electronics\n\n\nelectronics\n\n\n9 of 9\n\n\n5.0\n\n\ngreat little speakers\n\n\nMay 5, 2006\n\n\nScott James\n\n\nCalifornia\n\n\nThose people giving these speakers low reviews because they don't have enough bass are idiots. I don't know what people expect of these little speakers, but it is difficult to imagine them delivering as much bass as they do. These are amazing little speakers that have accompanied me in all of my travels; from Paris to camping in Northern California. I cannot imagine going everywhere without them. They are not equivalent to a $10k stereo system, but they are not meant to be. All I need is my Zen touch or Zen micro and these speakers. Do not listen to the other reviews, these speakers will make a decent stereo of your MP3 player. Enjoy them anywhere\n\n\n\n\nB000CRUZ2O:works_well_most_of_the_time:sanjeevp_\"bottom_line\"\n\n\nB000CRUZ2O\n\n\nActiontec VoSKY Skype Call Center ( USB07051C-01 ): Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nWorks Well Most of the Time\n\n\nNovember 11, 2006\n\n\nSanjeevP \"Bottom Line\"\n\n\nUSA\n\n\nI have used VoSKY Skype Call Center (USB07051C-01) for about a year now as my only home Skype In phone (paying $3.16 a month) after getting rid of Verizon (for which I was paying $50.00 a month).\n\nPROS\nIt works most of the time although sound quality is not as good as regular phone.\nEasy to set up.\n\nCONS\nOnce in a while the phone will keep on ringing even after you hang up and then you have to exit out of the Vosky software and start again.\nYou need a computer.\n\nIn summary good value for money and the best way to use Skype as your phone until phones that can be used without computer like Skype Wi-Fi phones Netgear and Belkin come down in cost and their bugs are worked out; or a phone adapter like this product comes out that plugs directly to the router\n\n\n\n\nB000CRUZ2O:makes_using_skype_that_much_easier:david_mozealous\n\n\nB000CRUZ2O\n\n\nActiontec VoSKY Skype Call Center ( USB07051C-01 ): Electronics\n\n\nelectronics\n\n\n1 of 4\n\n\n4.0\n\n\nMakes using Skype that much easier\n\n\nAugust 25, 2006\n\n\nDavid Mozealous\n\n\nSan Diego, Ca United States\n\n\nAlthough I could go on and on about how Skype has changed my life forever, I will try to address the issue at hand, and that is the Polycom Communicator. Basically I love the concept of the Polycom Communicator! When I first started using Skype I used a USB headset that works great. The problem is that it is a serious PITA to put the thing on when someone calls because I have to either a. Try to untangle the cord before accepting the skype connection, or b. answer the skype connection then try to untangle the cord. If I chose option a. envariably I would miss half the calls, if I chose option b. everyone would always comment that I sounded so desheveled when I tried to answer the call. Well, now with the new Polycom communicator all I have to do is answer the call.\n\nAs far as the quality and performance of the Polycom communicator...that is the only reason I didn't give the item 4 stars. It seems ocasionally that when you are in large conference calls that the Polycom communicator will cause feedback to other peoples lines. It is really sporadic though, and I can't seem to reproduce it consistently. Also, the driver for the Polycom Communicator has crashed on me a couple of times.\n\nSo despite the downfalls that I mentioned above, there is no way I could give up my Polycom Communicator now. I simply love it, I just wish those other issues never happened\n\n\n\n\nB000092WYS:a_monster_of_a_cable.:tom_->_\"'the_sadhu'\"\n\n\nB000092WYS\n\n\nMonster Cable DVI400-5M DVI-D Video Cable: Electronics\n\n\nelectronics\n\n\n0 of 2\n\n\n5.0\n\n\nA Monster of a Cable.\n\n\nAugust 5, 2006\n\n\ntom -> \"'the sadhu'\"\n\n\nsame world as you\n\n\nWell, actually I bought this nice cable, at Amazon Marketplace great offering that it was, for an HDTV Tuner, which bombed in my neighborhood. So, I never did get to unpack the cable. But it's in the closet, and it knows it's a good cable. Great cable, @ great price, thru Amazon\n\n\n\n\nB0007Z6RAA:perfect_for_your_laptop:franklin_villalobos\n\n\nB0007Z6RAA\n\n\nLogitech R-20 Speakers: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n4.0\n\n\nPerfect for your laptop\n\n\nJune 30, 2006\n\n\nFranklin Villalobos\n\n\nLima, Peru\n\n\nI'm a Creative fan so these are the first Logitech speakers I ever bought and I was pleasantly surprised. I was looking for a set of speakers for my laptop but since the portable models are more expensive (and probably will not sound as good since being portable means no sub-woofer) I decided for this not-as-portable model.\nWell, these are perfect to keep on your desktop (though the ideal placement of any sub-woofer is on the floor close to a corner), as it has a small sized sub-woofer all controls in one the speakers.\n\nThese speakers sound really nice, slightly superior sound to Creative's corresponding model (SBS 350) but with less output power (Creative's SBS 350 sub-woofer is larger and more powerful)\n\nPros:\n\n- Headphone jack and POWER button in the left speaker\n- No \"wired remote\" (which adds more clutter to your desktop)\n- Nice design for the price\n- Even cheaper than Creative's SBS 350\n\n\nCons:\n\n- Small sub-woofer\n- No \"wired remote\" which means you have to keep one of the speakers close.\n- No bass control (which usually means too much bass if your audio source has no equalization control\n\n\n\n\nB00030A066:great_card:mark_twain\n\n\nB00030A066\n\n\nACP-EP Memory Mobile Storage 512MB MultiMedia MMC Card - EPMM/512: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nGreat card\n\n\nMarch 1, 2005\n\n\nMark Twain\n\n\nSouthern California\n\n\nI bought one of these to replace the standard 32MB card that came with my camera. Wow, now I can store a ton of pictures. The price on the EP Memory cards are awesome too\n\n\n\n\nB00076QA56:fits_quest_2_perfectly..:steven_bryant_\"tek\"\n\n\nB00076QA56\n\n\nGarmin 010-10500-00 Carry Case, Quest, Deluxe Leather: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nFits Quest 2 perfectly..\n\n\nNovember 3, 2006\n\n\nSteven Bryant \"TEK\"\n\n\nLouisville, KY\n\n\nI have the Quest 2, but I've heard that this also fits the Quest. I bought it because, while my Dell laptop backpack is well padded, I wanted to make sure that my Quest 2 GPS was protected \"just in case\". This case fits perfectly, literally like a glove. When I got it, I was sceptical; it looked to small, but I was wrong; the dimensions are perfect. And there is more than enough padding to protect the GPS from harm, yet it is small enough to stick in a jacket pocket, which I sometimes have to (late day meetings where I leave my backpack at work). All in all, it's another great product from Garmin\n\n\n\n\nB00002ND67:they_withstand_punishment_year_after_year.:nina_goodall\n\n\nB00002ND67\n\n\nLasko 20\" Gray Box Fan: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nThey withstand punishment year after year.\n\n\nMay 30, 2006\n\n\nNina Goodall\n\n\nNew York, New York United States\n\n\nI have been using these fans for the stalls in our horse barn for years. It keeps the flies away and cools down the whole barn. The barn is dusty and the fans collect a lot of dirt. I wash them down each year and they are like new. The fans I have now are on their third barn year. In a normal house I'm sure they would last forever. They are a little noisy, but the horses don't care\n\n\n\n\nB000196ENC:does_the_job:lovesstories\n\n\nB000196ENC\n\n\nMagellan RoadMate GPS Dash Mount: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n4.0\n\n\nDoes the job\n\n\nOctober 30, 2006\n\n\nLovesStories\n\n\nSacramento, CA USA\n\n\nI purchased this dashmount so I could swap the GPS between vehicles without having to install a permanent mount. I don't know what the \"feet\" of the base is made of, but it is basically like a rubber version of a lead blanket - it is heavy yet is very pliable. This is useful since my dash (a 2004 F-250) has an uneven surface. I can press and mold the feet to put the base in a certain position and under most circumstances it'll stay there. Occasionally, on long or \"bouncy\" trips, it'll creep out of position a couple inches and I'll have to move it back. I've seen some comments complaining of that - but I don't see it as a problem since common sense says it won't stay perfectly still since it isn't bolted in place. Overall this mount has worked very well.\n\nMy wife is going on a business trip next week and will be borrowing the GPS - I don't recommend she take the mount though - since the thing must way at least a couple pounds - too bulky and heavy to put in a suit case in my opinion\n\n\n\n\nB000196ENC:dash_mount_--_very_helpful:j._redfearn\n\n\nB000196ENC\n\n\nMagellan RoadMate GPS Dash Mount: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n5.0\n\n\nDash Mount -- very helpful\n\n\nAugust 16, 2006\n\n\nJ. Redfearn\n\n\nLongview, TX\n\n\nI got the dash mount because a friend recommended it. I thought the windshield mount worked fine except it was difficult to remove from the windshield. I felt like I should remove it each time I parked somewhere. Also, there was a fair amount of vibration since it usually was dangling. The dash mount is very heavy and flexible enough to \"mold\" to fit the surface of the dash. However, it kept slipping off the surface of my Toyota Avalon until I found a little sticky pad to put it on. That solved that problem. Anyway, it is very easy to move it to the floorboard and cover it up when I stop, thus effectively hiding it from the temtation of a robber\n\n\n\n\nB00030GSJY:this_is_an_awesome_joystick!:bill_homan_\"bilnv\"\n\n\nB00030GSJY\n\n\nSaitek X52 Flight Control System: Electronics\n\n\nelectronics\n\n\n5 of 9\n\n\n5.0\n\n\nThis is an awesome joystick!\n\n\nAugust 19, 2006\n\n\nbill homan \"bilnv\"\n\n\nSpring Creek, NV USA\n\n\nThis is my first HOTAS, but I did a good bit of research. It is very easy to set up, and the SST Programming is very helpful setting the shift key. The joystick is easy to move, which is nice in a close dogfight. I am very glad that I bought this HOTAS, because I have had NO problems with it. I highly recommend this system to anyone\n\n\n\n\nB00003CWDH:pricey...:maxtone_witherball\n\n\nB00003CWDH\n\n\nMonster Cable MP AV 800 PowerCenter AV 800 with Surge Protection: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nPricey...\n\n\nJuly 3, 2006\n\n\nMaxtone Witherball\n\n\n\n\n\n...for a surge protector. But as far as I can tell, it's doing its job. Plus it looks kind of neat\n\n\n\n\nB00005V3PY:very_nice_set...almost_too_nice...:gbw_-_dtown\n\n\nB00005V3PY\n\n\nFirst Act Discovery FP601 Marching Band Kit: Electronics\n\n\nelectronics\n\n\n20 of 22\n\n\n4.0\n\n\nVery Nice Set...Almost too Nice...\n\n\nApril 13, 2004\n\n\nGBW - DTown\n\n\nPennsylvania\n\n\nWe bought this drum set for our 2 year old who is obsessed with the concept of drumming & marching. This is a first class set and as noted is almost too nice. This is a huge step above the tupperware and pan lids our little guy used to use for his personal parades. The drum is a bit heavy,large, cumbersonme for a two year old and of course is LOUD. The cymbals are also loud but do have a professional sound quality. Do recommend this purchase. It does come a bit steep at $30 but is a very nice looking and performing set. If you little one gets hooked on drumming via the pots and pans, spoil him/her with this first-class set that they should be able to use well into their childhood\n\n\n\n\nB0002ZH71S:it_really_works:m._sweeney_\"wybnormal\"\n\n\nB0002ZH71S\n\n\nNetgear WGXB102 54Mbps Wall-Plugged Wireless Range Extender Kit: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nIt really works\n\n\nNovember 5, 2006\n\n\nM. Sweeney \"Wybnormal\"\n\n\nOrange, California USA\n\n\nI found this as the perfect solution to fill in some dead airt space in my house downstairs given the internet connection is upstairs and so far, other wireless solutions have been marginal. I really wondered if the Ethernet over powerlines would really work or if the 802.11G would really work. \n\nSo I get my new Extender kit, unbox it and plug everything in without bothering to read the directions. Lets be real, this stuff SHOULD be plug and play and IT WAS. It came right up and worked and has worked for the past three months flawlessly. I have decent range downstairs, it works with the Macs and the PCs and doesnt seem to care a whit. I can highly recommend using this product as the primary connection or as I did, extending an existing network without having to pull cable or put in multiple 802.11 bridges\n\n\n\n\nB0000AZ67Y:no_problems._very_satisfied.:christopher_altermatt\n\n\nB0000AZ67Y\n\n\nSanDisk SDCZ2-128-A10 128 MB Cruzer Mini USB 2.0 Flash Drive: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nNo problems. Very Satisfied.\n\n\nNovember 2, 2004\n\n\nChristopher Altermatt\n\n\nBaltimore, MD, USA\n\n\nI have had no problems with my Mini Cruzer USB flash drive. It has been recognized without exception by multiple computers and has performed flawlessly\n\n\n\n\nB0000632H5:sony_never_dissappoints_!:kartiq_subramanian\n\n\nB0000632H5\n\n\nSony Mini-DV Cassettes (DVC) Premium series 60 Minute (3-Pack): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nSony never dissappoints !\n\n\nNovember 5, 2006\n\n\nKartiq Subramanian\n\n\nNew Jersey USA\n\n\nI actually bought this for a friend of my mine. He says 'Sony Never Disappoints'\n\n\n\n\nB000A1IUFQ:so_cool_it's_absurd:alexander_clemens\n\n\nB000A1IUFQ\n\n\nPalm 3207NA GPS Adapter Kit for Select Palm PDAs: Electronics\n\n\nelectronics\n\n\n23 of 27\n\n\n5.0\n\n\nSo cool it's absurd\n\n\nNovember 16, 2005\n\n\nAlexander Clemens\n\n\nSan Francisco, CA United States\n\n\nIf you've got a Treo 650, You Don't Need A GPS Device. Simple as that. The smaller-than-a-small-filet-mignon gps device talks via bluetooth to your 650, which attaches to your windshield via a well-engineered holster. The charger charges 'em both, or not, as you see fit. The instructions are clear and to the point. The installation instructions are slightly out of date - you don't need a card reader to make it happen, after all - and the user experience is, in my case, outstanding. If you've got a 650 and you ever drive somewhere unfamiliar, BUY IT\n\n\n\n\nB00005T3N3:a_little_expensive_but_worth_the_money:a._c._ege_\"ace_doh\"\n\n\nB00005T3N3\n\n\nBose UFS-20 Universal Floor Stands (pair) - Black: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nA little expensive but worth the money\n\n\nNovember 14, 2006\n\n\nA. C. Ege \"ace doh\"\n\n\nStockton, CA USA\n\n\nWhen I purchased this product a couple weeks ago I wanted to make sure this purchase was worthwhile. I studied many other speaker stands and it just didn't seem I would have been getting the same quality. \n\nI received this pair a few days ago and originally thought they may be a little complicated to put together. They only took about twenty minutes to assemble and put my cube speakers on. The stands look great and go well with the cube speakers. I am happy I made this investment and will enjoy these for years to come. \n\n\n\n\nB0001NA3S0:good_bacup_drive:howard_weiss_\"hrap\"\n\n\nB0001NA3S0\n\n\nWestern Digital 250 GB USB 2.0/Firewire External Hard Drive with Dual Option Backup and USB ( WDXB2500JBRNN ): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\ngood bacup drive\n\n\nNovember 20, 2006\n\n\nHoward Weiss \"hrap\"\n\n\nWasington D.C. area\n\n\nI have this drive and been sucessfully using it with Backup on my Mac. I would recomend it for off line storag\n\n\n\n\nB0001NA3S0:excellent_value_for_the_price:a._hicks\n\n\nB0001NA3S0\n\n\nWestern Digital 250 GB USB 2.0/Firewire External Hard Drive with Dual Option Backup and USB ( WDXB2500JBRNN ): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nExcellent value for the price\n\n\nNovember 10, 2006\n\n\nA. Hicks\n\n\n\n\n\njust enough space to save video, photo, music etc... all your files in one place and also is compatible with mac... i love it.\n\n\n\n\nB00001OPJW:great_cassette_player:jonm4\n\n\nB00001OPJW\n\n\nTeac W790R Dual Auto-Reverse Cassette Deck with Pitch Control: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nGreat cassette player\n\n\nNovember 2, 2006\n\n\nJonM4\n\n\nMichigan\n\n\nLook around in your local electronics stores and you'll find either HUGELY expensive players or single tape units.\n\nThis player allows tape-to-tape duplication as well as the ability to put two of your old collection in and listen uninterrupted. Sure, it's not a 100-disk CD carousel...but how else are you going to listen to some of your best stuff (that you probably taped yourself?).\n \nThis, or the other TEAC dual-cassette units, should find a place on your shelf\n\n\n\n\nB000E9BXDU:a_beautiful,_versatile_monitor!:mark_hernberg\n\n\nB000E9BXDU\n\n\nAcer AL1916AB 19\" LCD Monitor: Electronics\n\n\nelectronics\n\n\n10 of 10\n\n\n5.0\n\n\nA Beautiful, Versatile Monitor!\n\n\nAugust 5, 2006\n\n\nMark Hernberg\n\n\nIowa City, Iowa United States\n\n\nAfter having used CRTs roughly the size of small cars for my entire life, I finally jumped on the LCD-flatscreen bandwagon. This Acer 19\" LCD makes me happy I did!\n\nMostly using my computer for gaming and movies the color, brightness, and size of the screen really matters to me. This monitor really nails all three of those areas. It provides vivid, crisp colors; it doesn't wash out the picture with super-brightness (what my old monitor did), nor is its display too dark to catch the action; and the 19\" screen seems to have the perfect balance of immense screen size yet incredibly low desk size\n\n\n\n\nB0002378EG:nice_component_cable:perry_sweeten\n\n\nB0002378EG\n\n\nMonster Cable ULT V1000 CV-16 THX Ultra 1000 Component Video Cable: Electronics\n\n\nelectronics\n\n\n0 of 2\n\n\n5.0\n\n\nNice Component Cable\n\n\nMarch 10, 2006\n\n\nPerry Sweeten\n\n\nRochester, MN United States\n\n\nMonster has went over the top on this THX component video cable. Instantly noticed a superior picture\n\n\n\n\nB00009NDCX:epson_paper_-_it_doesn't_get_any_better:ward_f._rosen_jr.\n\n\nB00009NDCX\n\n\nEpson S041727 Premium Gloss Photo Paper (4x6, 100 Sheets): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nEpson Paper - it doesn't get any better\n\n\nNovember 11, 2006\n\n\nWard F. Rosen Jr.\n\n\nHouston, TX\n\n\ne'nuf said - Epson always leads the way with printers and papers. You can't go wrong!!!\n\nOrder was filled exactly as requested, and arrived in better than expected time. The packaging was excellent.\n\nI'll be back for more soon\n\n\n\n\nB00009NDCX:perfect_in_price_and_quality:abdoreza_dashtpeyma_\"reza_d.\"\n\n\nB00009NDCX\n\n\nEpson S041727 Premium Gloss Photo Paper (4x6, 100 Sheets): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nPerfect in price and quality\n\n\nNovember 10, 2006\n\n\nAbdoreza Dashtpeyma \"Reza D.\"\n\n\nLos Angeles, CA\n\n\nI have bought 2 packages of them, 100 sheet each. Quite satisfied.\nThe printing are really excellent and professional.\n\n\n\n\nB00009NDCX:excellent_paper,_excellent_price:evan_d._hoffman_\"whatwhat\"\n\n\nB00009NDCX\n\n\nEpson S041727 Premium Gloss Photo Paper (4x6, 100 Sheets): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nExcellent paper, excellent price\n\n\nApril 19, 2006\n\n\nEvan D. Hoffman \"whatwhat\"\n\n\nBellerose, NY USA\n\n\nI bought this paper after my mom gave me a box of Epson 8.5x11 paper she didn't need. I was really impressed by the quality of the pictures rendered on that paper by my printer. When the paper ran out I looked for an economical replacement and found the price of most photo papers to be prohibitive - on the order of 40 cents per page. Since you can only get 2 4x6 pics from a letter-sized sheet of paper, that comes to about 20 cents per print, not counting the cost of ink. Finally I stumbled upon this paper - Epson S041727 - and the price per print was around 12 cents, which rivals the cheapest Internet photo printing services I've found.\n\nIf you print a lot of 4x6 pictures, this is a great value for a great product\n\n\n\n\nB00009NDCX:epson_photo_paper:david_s._cuda_jr._\"d_cuda\"\n\n\nB00009NDCX\n\n\nEpson S041727 Premium Gloss Photo Paper (4x6, 100 Sheets): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nEpson Photo Paper\n\n\nMarch 10, 2006\n\n\nDavid S. Cuda Jr. \"D Cuda\"\n\n\nIndiana\n\n\nHands down Epson Paper is the best photo paper on the market.\nAt this price its a no brainer.A++++++++++\n\n\n\n\nB00004VX14:works_great,_but_its_another_wire!:john_ellsworth\n\n\nB00004VX14\n\n\nGarmin Cigarette Lighter Adapter for Most eTrex Models and Geko: Electronics\n\n\nelectronics\n\n\n9 of 9\n\n\n5.0\n\n\nWorks great, but its another wire!\n\n\nJanuary 4, 2003\n\n\nJohn Ellsworth\n\n\nNorthport, NY\n\n\nThis adapter is functional, is easy to use, enables continued use of backlight, and saves the batteries for walking with the eMap after arriving in a strange town. When I want to connect other media while driving with my family, I use a 12v 3-Outlet Car Power Adapter. Despite another wire, it's a worthwhile accessory\n\n\n\n\nB0006LA5S0:good_for_backing_up_digital_pics:jk\n\n\nB0006LA5S0\n\n\nPNY 2 GB Attache USB Hard Drive: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGood for backing up digital pics\n\n\nOctober 28, 2006\n\n\nJK\n\n\nNYC\n\n\nI have no complaints about this flash drive. It does what it should, is easy to use, has enough storage to backup all my digital pics from my hard drive (so far!), and is not expensive.\n\n\n\n\nB00000JDF6:go_cables!:robert_pace_\"hugs_and_curses!\"\n\n\nB00000JDF6\n\n\nBelkin A3L791-S 50-Foot RJ45 CAT 5e Snagless Molded Patch Cable (Blue): Electronics\n\n\nelectronics\n\n\n9 of 9\n\n\n5.0\n\n\nGo cables!\n\n\nSeptember 6, 2005\n\n\nRobert Pace \"Hugs and curses!\"\n\n\nPompton Lakes, NJ\n\n\nYup... They connect my cable modem to my router, and my router to my computers. They transmit data back and forth. So far they have failed to burst into flames or snap off and poke me in the eye. So go patch cables (yay).\n\n\n\n\nB000BBE9DM:awesome_when_you_figure_out_how_to_use_it:meggerz\n\n\nB000BBE9DM\n\n\nWacom Graphire4 6x8 USB Tablet ( CTE640S ) - Silver: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nAwesome when you figure out how to use it\n\n\nNovember 5, 2006\n\n\nMeggerz\n\n\nwww.meggerz.com\n\n\nI love it but its been a battle trying to get used to the sensetivity and pressure of holding the pen. I'm glad I bought it but it is something I cannot use all the time\n\n\n\n\nB000BBE9DM:one_of_the_best_things_i_have_purchased:daniel_geer\n\n\nB000BBE9DM\n\n\nWacom Graphire4 6x8 USB Tablet ( CTE640S ) - Silver: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nOne of the best things I have purchased\n\n\nOctober 23, 2006\n\n\nDaniel Geer\n\n\nLansing, Michigan United States\n\n\nThis has helped me significantly in drawing in the computer, for programs such as Adobe Illustrator, Adobe Photoshop and Macromedia Flash. Drawing with a mouse is like drawing with a brick - so buy this piece of equipment if you need to draw in the computer!\n\nIt also works well as an overall mouse for your computer. It even comes with a mouse if you prefer to use that instead of the pen. Plugs right into your USB drive and works almost instantly\n\n\n\n\nB00008L3J1:well_designed:horn_and_sax_guy\n\n\nB00008L3J1\n\n\nBelkin Retractable USB Light (F8E448): Electronics\n\n\nelectronics\n\n\n7 of 7\n\n\n4.0\n\n\nWell Designed\n\n\nFebruary 22, 2005\n\n\nHorn and Sax Guy\n\n\nDenver, CO United States\n\n\nI bought this to illuminate the keyboard on my 12\" Apple Powerbook when I work in the dark. It works well, and shines impressively bright and wide. I even use it to read by simply sitting it on its back on my lap as I read in bed and pointing the lamp at my book. It's too bad the lamp's arm isn't telescopic or it'd make for a perfectly good clip-on booklight right out of the box.\n\nThis device is well built, folds into a form that protects it from damage, even when thrown in a laptop case full of stuff that might crush something built less solidly. And it has a nice retracting USB cable. The retraction device for the cable, like most I've seen on devices like this, can be a bit finnicky but with the right pulling technique works correctly about 90% of the time.\n\nUpdate, May, 2005. The light died. No idea why. So, maybe it's not as good as I thought. :\n\n\n\n\nB000062STU:you'll_need_a_dvd_burner...:james_rose\n\n\nB000062STU\n\n\nAdaptec 1890600 Fireconnect 4300 Kit AFW-4300: Electronics\n\n\nelectronics\n\n\n2 of 5\n\n\n5.0\n\n\nYou'll need a DVD burner...\n\n\nSeptember 1, 2004\n\n\nJames Rose\n\n\nSeattle, WA\n\n\n...to really take advandage of this. Installation was super easy, although I ran into the TI thing like another reviewer. Oh well. Not much more I can say about this product; it works and that's all that really matters\n\n\n\n\nB000CSQRYS:satisfied_user:hiker_dude\n\n\nB000CSQRYS\n\n\nGarmin GPSMap 76CSx Handheld Marine GPS Navigator: Electronics\n\n\nelectronics\n\n\n9 of 10\n\n\n5.0\n\n\nSatisfied User\n\n\nSeptember 2, 2006\n\n\nHiker Dude\n\n\nSeattle, WA\n\n\nI found this device to be relatively easy and comfortable to use (one-handed, no less), and accurate to within 15' +/- under tree cover of greater than 90% mature alder and fir - once locked onto satellites under clearer overhead conditions in Seattle WA and using an external Gilsson antenna mounted atop a hat. Satellite aquisition under the trees did not happen the few times I tried. The display is excellent under sunny conditions. Downloading waypoint data into a text file was simple. I haven't used it yet under driving conditions, so I can't speak to using related features. Were it damaged or stolen, I'd buy another one without hesitation\n\n\n\n\nB000A4AWK4:black_ink_anyone?:michael_kerner_\"michael_kerner\"\n\n\nB000A4AWK4\n\n\nHP 21 Black US Inkjet Print Cartridge EAS Sensormatic: Electronics\n\n\nelectronics\n\n\n3 of 8\n\n\n4.0\n\n\nBlack Ink Anyone?\n\n\nAugust 9, 2006\n\n\nMichael Kerner \"Michael Kerner\"\n\n\nBrooklyn, New York U.S.A.\n\n\nThere are so many times where the actual ink, can be the difference between a good copy, and a not-so-good copy on paper. With HP black ink cartridge #21, it is very convient, and easy to setup into your HP deskjet 3930 series printer. This cartridge is very inexpensive, and affordable for HP printers. I think of it as a must for your HP printers. While there are cartridges that are difficult to open, and install into your printers, this one installs in a snap. I really recommend this for my new HP printer, and so will you if you own a HP printer for this ink as well.\n\nPrice: B\n\nConvience: B+\n\nOverall: B 1/2+\n\n\n\n\nB00006JPFP:nice_power_adapter:carlos_lerzundy\n\n\nB00006JPFP\n\n\nCoby CA-979 Universal AC Power Adapter: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nNice Power Adapter\n\n\nNovember 4, 2006\n\n\nCarlos Lerzundy\n\n\nNC, USA\n\n\nIt works with all my devices, it has several outputs for your different devices, no complain with it...\n\n\n\n\n\n\nB0009F3SGE:i_love_my_nano:cher_holt-fortin_\"grinchymom\"\n\n\nB0009F3SGE\n\n\nCreative Zen Nano Plus 1 GB MP3 Player (Blue): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nI love my nano\n\n\nNovember 10, 2006\n\n\nCher Holt-Fortin \"grinchymom\"\n\n\nCentral New York\n\n\nSo far it has been terrific. Radio and downloads are easy to sort out and use.\nDownloading was hard, but I finally got it. The sound quality is the best thing, however. It is better than my big stereo.\nNapster doesn't do this mp3 however\n\n\n\n\nB000EPNB52:my_first_mp3_player~~like_it_very_much!:c._thatcher_\"heartforjc\"\n\n\nB000EPNB52\n\n\nApple 4 GB iPod Nano Pink (2nd Generation): Electronics\n\n\nelectronics\n\n\n12 of 15\n\n\n5.0\n\n\nMy First MP3 Player~~Like it very much!\n\n\nNovember 17, 2006\n\n\nC. Thatcher \"heartforjc\"\n\n\nBeautiful Ohio\n\n\nLet's just say I am 39 and holding and have wanted an MP3 for years. My birthday was last week and I bought myslef the pink 4GB Nano. It is easy to use, but I have to agree it takes some time on itunes to import CD's, the ipod updates fast but the CD import is slow. The sound quality is good. I like that it is small and lightweight. The screen is bright and easy to read...even with not so young eyes. I looked at other brands but am glad I went with the leader. I didn't need or want to watch movies on a tiny screen...just tunes. I'm sure 1000 songs will hold me. \n\nI looked all over at prices and Amazon wins! The free shipping is a great bonus also.\n\nThe case may look delicate but it feels very sturdy to me, I have had it in my pocket and it is just fine. Would highly recommend this MP3. It is cute to boot! This not so young person thinks it is WAY COOL\n\n\n\n\nB000EPNB52:awesome!:c._e._pugh\n\n\nB000EPNB52\n\n\nApple 4 GB iPod Nano Pink (2nd Generation): Electronics\n\n\nelectronics\n\n\n9 of 10\n\n\n5.0\n\n\nAwesome!\n\n\nNovember 3, 2006\n\n\nC. E. Pugh\n\n\n\n\n\nI had been storing my music on a 2GB card in my palm pilot. I was having a lot of trouble transferring the music, with many errors, not to mention how long it took to transfer. I finally decided to get an MP3 player. A friend kept telling me about the Sandisk Sanso. There were some great features, but the bad far, far outweighed the good. Too many of the same problems over and over, and many people returning them. \nI got my Apple iPod last night and have hardly put it down! I did a lot of research first and was hard pressesd to find negative feedback about this item. This thing is great! I was up till 3AM listening to my music. The sound quality is so much better than I had expected! \nI really like the case I got (Tuffwrap Accent by XtremeMac- Walmart and Radio Shack) because it is really tough rubber and all of the holes are in the right place for the new nano. The only thing is that the unit does not slide up into it completely because of the square corners of the nano. The case has more rounded corners in the top, so the nano does not line up just right in the case. It is good enough and you can still use the controls. It is only about a 1/4 of an inch at most. I am just picky about it lining up correctly so this is a bit annoying. But the case is still great as far as protection. \nI also bought the Belkin TuneBase FM transmitter for the car. It is similar to the Monster version. I did not like the Belkin version at all. I ended up going and getting the Monster FM transmitter at Radio Shack. It works great! I like the fact that my Ipod lays in the console rather than sitting in a dock (some of you may prefer the dock). I like that I can pick up the Ipod and use it sort of like a remote to the radio because I can operate it easily without looking down at the dock. I can even hold it while driving if I want to flip through the songs while driving rather than having to reach over to the dock. The sound of this transmitter is great. Some areas you might get a bit of static or station bleed through while driving, but this is minimal and is usually not a problem. I find this happened a lot less with Monster than with the Belkin. The Belkin had a nasty hiss over all of the songs. \nPeople are talking about this unit not having a radio on it, but I find that I have so many favorite songs on it that I do not need a radio. When I do have a radio, all I do is channel surf for songs I like anyway. This way, I already have all the songs I like at my fingertips!\nI think these headphones really make this nano great! I was so surprised when I first heard this little unit play! For something so tiny, it rocks! I am really glad I followed my gut and went with this unit instead of dealing with frustration first by getting the Sanso. This new nano is a must have!\n\n\n\n\nB00000J0O2:it_does_the_job:\n\n\nB00000J0O2\n\n\nHP Microfine Toner Cartridge for LaserJet 5L/6L/3100/3150 (C3906A, Black): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nIt does the job\n\n\nJune 20, 2000\n\n\n\n\n\n\n\n\nWell the most important thing about buying ink and toner for your printer is price, and Amazon.com has beaten every on-line price I have seen. As for the toner itself, Hewlett Packard toner is top. I buy this product for two reasons, 1. It's what fits in my printer, and 2. I know when I buy the C3906A, I am getting what I payed for, a quality cartrige that lasts. One last note, twords the end of the cartrige life, a small line appears in the content of my page letting me know its allmost time for a new one. simply shake the cartrige, reorder, and by the time the new one arrives, the old one is finished\n\n\n\n\nB000A4AW1I:wish_i'd_bought_this_sooner!:r._kent_bailey\n\n\nB000A4AW1I\n\n\nCruzer Titanium 2GB USB Flash Drive (SDCZ3-2048-A10, Retail Package): Electronics\n\n\nelectronics\n\n\n6 of 7\n\n\n5.0\n\n\nWish I'd bought this sooner!\n\n\nApril 17, 2006\n\n\nR. Kent Bailey\n\n\nTampa, FL United States\n\n\nI'm not much of a techie. But, I do have lots of documents and pictures that I can't bear to lose should my computer decide to konk out one day. \n\nI used to back up my most important files on CD: an overly complex and tedious process involving multiple CDs and lots of time. But, with this 2GB flash drive backing up my important documents is just a matter of plugging the device in and selecting which files and folders to synchronize. The files are copied over to the device quickly, and easily.\n\nThe 2 Gig storage capacity allows me to backup all of my most important photos and documents on one handy removeable drive. \n \nScanDisk even includes a \"Synch\" software that simplifies the process of identifying and copying your important data. (The software also helps you copy over your Outlook messages, folders, appointments, tasks, and other data that used to be difficult for me to find on my hard drive.) \n\nAlso, I can imagine that the use of the Synch software with this little drive would made it very easy to keep the data on your work machine syched up with the data on your home machine.\n\nPhysically, the unit is small, sleek with a case that feels substantial. The retractable connector functions smoothly and allows the case to protect the connector when not in use. \n\nThis thing is great. I only wish I'd started using it sooner. \n\n\n\n\nB00074Z9OQ:happy_customer:susan_l._o'dea_\"catniip58\"\n\n\nB00074Z9OQ\n\n\nApple M9758G/A iPod Shuffle Sport Case: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nHappy Customer\n\n\nFebruary 23, 2006\n\n\nSusan L. O'Dea \"Catniip58\"\n\n\nIN\n\n\nI ordered the apple ipod shuffle sport case because I wanted something to protect my ipod from the elements and just general bumping and scraping. It works great, and you can use your ipod without taking it out of the case. I haven't taken my ipod out of the case except to charge it, or to change my playlist. It's a \"must have item\" for anyone with an ipod shuffle\n\n\n\n\nB0002A59Z4:nicely_done_package/_great_value:p._j._snyder_\"av8or\"\n\n\nB0002A59Z4\n\n\nCH Products Throttle Quadrant USB (300-133): Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n5.0\n\n\nNicely Done package/ great value\n\n\nApril 30, 2005\n\n\nP. J. Snyder \"AV8OR\"\n\n\nMilford,CT\n\n\nAfter reading the Dec. '04 review in Computer Pilot I ordered it. Fine tuning is a bit involved but repetition makes the curve easier. Configuring from one a/c type throttle to another is easy with the downloadable manager from CH prod. Compared to the GoFlight modular system this is great value. Unless $$ is no object and you're not interested in building a home cockpit, the CH line of av-sim controls is well suited to the aspiring and advaced flt. simmer\n\n\n\n\nB000058E2B:use_it,_love_it:tri_nguyen_\"ucdavis\"\n\n\nB000058E2B\n\n\nCompaq iPAQ 3635 Pocket PC Bundle (with Compact Flash Jacket): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nUSE IT, LOVE IT\n\n\nNovember 7, 2004\n\n\nTri Nguyen \"UCDAVIS\"\n\n\nDavis, California\n\n\nI have had my Pocket PC for quite some time now, and I love the convenience that it offers. \n\nPlus:\n\n- Great color screen\n- Fast and easy to use.\n- Cradle adaptor is nice so you can able to sync files from pc to pocket pc.\n- Includes Word, Notes, Calender, Calculator, Recorder, Window Media Player, etc... (HELPFUL)\n- Expandable\n\nCon\n\n- Although it is expandable, you can't use your memory card to do anything but save materials. I am unable to listen to 256 mb songs... but i have to copy and paste song from the card to the pc every time I get sick of listening to the same song.\n- Low battery life!\n\nOther than that... it is great... user-friendly... and helpful if you are busy student or a college student...\n\nI use it to:\n\n1) types notes in class (why carry a laptop when you can get this)\n2) MP3 player... besides the issue above, music quality is great... easy to transfer from pc to pocket pc.\n3) calender and agenda... (keep important dates in check and be on tops of things\n\n\n\n\nB000058E2B:why_i_still_like_this_pda,_continued:\n\n\nB000058E2B\n\n\nCompaq iPAQ 3635 Pocket PC Bundle (with Compact Flash Jacket): Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nWhy I still like this PDA, continued\n\n\nApril 4, 2004\n\n\n\n\n\n\n\n\nI found out the way to copy photos from my CompactFlash card onto the iPAQ. It's not hard but more complex than necessary. I also found out how to send the iPAQ in to have its screen replaced. A hassle. Its screen and/or stylus should work together so they don't produce scratches in the first place\n\n\n\n\nB000ELTJ8E:motorola_v3_usb_cable_with_4.0_phonetools_cd:b._kelly_\"system_user\"\n\n\nB000ELTJ8E\n\n\nUSB Data Cable for Motorola V3 Razr V180 V188 V220 V190 V195 includes Cable PLUS Motorola Phone Tools 4.0 Software: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n5.0\n\n\nMotorola V3 USB Cable with 4.0 Phonetools Cd\n\n\nMay 6, 2006\n\n\nB. Kelly \"System User\"\n\n\nDe Soto, MO\n\n\nExcellent service, five days is all I had to wait and saved a lot of money. Couldn't be happier.\nIm a new Razr owner so everything is new to me. CD installed without any problems (Windows XP). I had a cable so I now have a spair. I just wanted the Phonetools CD.\nI would gladly do business again-Thanks\n\n\n\n\nB0000512E6:nice_80mm_fan!:codey_f.\n\n\nB0000512E6\n\n\nAntec OEM Small Dc Fan Dc Brushless Case Cooling Fan for Mini-Tower Desktop: Electronics\n\n\nelectronics\n\n\n6 of 7\n\n\n4.0\n\n\nNice 80mm fan!\n\n\nFebruary 8, 2001\n\n\nCodey F.\n\n\nBothell, WA USA\n\n\nI also ordered the Antec SX-1030 (which has yet to be shipped! arrgghh!!). According to Antec, the 1030 includes 2 fans, but has space for 2 more, which is where this one comes in! For those of you wondering what size "small" is, it's an 80mm fan. Good airflow (don't know actual CFM) and pretty quiet\n\n\n\n\nB0000C1ZNV:terrific_card_in_my_old_a20m:west_coast_paddler\n\n\nB0000C1ZNV\n\n\nNetgear WG511T Super G 108 Mbps Wireless PC Card: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nTerrific card in my old A20m\n\n\nNovember 17, 2006\n\n\nWest Coast Paddler\n\n\nPacific NW\n\n\nI'm a D-Link refugee and this card has been just great. My home connection has been very good, I'm in a basement and have no idea where the router is!\n\nSwapping wireless networks is a breeze as I travel around - it can be easily configured to automatically find one wherever you are.\n\nVery reliable, quality construction, easy to install and no problems with W2K.\n\nThis is my 2nd Netgear card; also have the FA511 wired adapter about which I can say the same things (except the wireless bits).\n\nDefinitely recommended and worth the extra cost if there is any\n\n\n\n\nB0006TIA02:no_more_dropped_connections!!!:scott_gibbons\n\n\nB0006TIA02\n\n\nD-Link DGL-4300 Wireless Gaming Router, 4-Port Gigabit Switch, 802.11g, 108Mbps, 5dBi: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n5.0\n\n\nNo more dropped connections!!!\n\n\nJuly 24, 2006\n\n\nScott Gibbons\n\n\nChicago\n\n\nI had originally bought a Linksys, without doing any research beforehand... thinking all routers were more or less equal... oops! Hardware was poor, the interface was wonky, customer service was a joke. I was re-booting it every few minutes under moderate usage, because it keep dropping my DSL connection or completely locking up. I just replaced it with a DGL-4300 based on all the positive reviews, and have been very happy so far. I don't need or use any of the advanced features for gaming; but already I've been very impressed with the DGL-4300 since it hasn't dropped any connections since I bought it last week. You get what you pay for\n\n\n\n\nB000F5K3Y8:not_bad_for_the_price.:l._lee_\"m3beanie\"\n\n\nB000F5K3Y8\n\n\nMustang MV-TILT3 Tilt Wall Mount Adjustable for TV's up to 60-inches: Electronics\n\n\nelectronics\n\n\n11 of 11\n\n\n4.0\n\n\nNot bad for the price.\n\n\nSeptember 8, 2006\n\n\nL. Lee \"M3Beanie\"\n\n\nOld Bridge, NJ\n\n\nI don't get why other mounts cost twice as much. I bought this to hang my 42\" Panasonic and it is great. Can someone tell me why the Omnimount costs so much more? I don't see any \"functionality\" differences. And with the Mustang Tilt mount, I have been tilting it at least 10 times for either adjustments or disconnecting/reconnecting cables...It is so simple\n\n\n\n\nB000FDVJTI:great_phone_:keri_e._davies\n\n\nB000FDVJTI\n\n\nUniden TRU9466 2-Line Expandable Cordless System with Dual Keypad and Call Waiting/Caller ID: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n5.0\n\n\nGreat Phone \n\n\nAugust 27, 2006\n\n\nKeri E. Davies\n\n\nLafayette, Louisiana United States\n\n\nI got this phone about 1 month ago and I like all the features of the phone,Volume control ,Caller ID,Large display window and you can add up to 10 more hand set\n\n\n\n\nB0002IVPC2:good_cell_phone_accessory:dwight_eddy\n\n\nB0002IVPC2\n\n\nUltra Cord 9-Piece USB Cell Phone Charger Kit ULT31438 - Cellular phone charging kit: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n4.0\n\n\nGood Cell Phone Accessory\n\n\nSeptember 15, 2005\n\n\nDwight Eddy\n\n\nBedford, NH\n\n\nHandy traveler accessory. Retractable USB cord very nice. Had a small problem with Moto adapter, one of the locking pins would not release. Was able to get release wire reseated under release button and all working fine now\n\n\n\n\nB000B9RXPA:love_it:shelley_cadamy_\"shelley\"\n\n\nB000B9RXPA\n\n\nApple MA186G/A Armband Red for iPod Nano 1st Generation: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nLove it\n\n\nJuly 20, 2006\n\n\nShelley Cadamy \"Shelley\"\n\n\nOklahoma City, OK United States\n\n\nI found this on clearance at another retailer (I guess not everyone likes pink), and it's fabulous. I thought it might be just for skinny-armed people, but not so! I've got plenty of room, it fits great, and it doesn't move. Still looking for a solution to keep the earbud cord out of the way, though. Makes mowing the lawn WAY more fun\n\n\n\n\nB00067OEPE:perfect_for_dj's:dj_mori\n\n\nB00067OEPE\n\n\nSennheiser EH-250 Digital DJ Headphones: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nPerfect for DJ'S\n\n\nMay 21, 2006\n\n\nDj Mori\n\n\nBogota, Colombia\n\n\nI have been a Dj for a almost 5 years, and this are the best headphones I've ever had. The H250 headphones are light, confortable, and great acustic properties. It is true that the bass is a little loud, but thats exactly what you need if you're playing in loud and crowded clubs\n\n\n\n\nB000AP1RE8:amazing_for_the_value:julian9596\n\n\nB000AP1RE8\n\n\nStudio Condenser Mic with USB Output - SAM C01U: Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n5.0\n\n\namazing for the value\n\n\nSeptember 13, 2006\n\n\njulian9596\n\n\nMaryland,USA\n\n\nWhen i first came across this particular microphone i had my doubts. Considering the price of it i just couldn't see it holding up for what i wanted to do. to my surprise this mic exceeded my expectations. For recording vocals this mic is amazing. a real professional sound for a extremely low price. i definitely recommend this mic to anyone out there looking for an in expensive way to make professional sounding material\n\n\n\n\nB000AP1RE8:not_bad_at_all!:tracy_latham_\"wretchasketch\"\n\n\nB000AP1RE8\n\n\nStudio Condenser Mic with USB Output - SAM C01U: Electronics\n\n\nelectronics\n\n\n14 of 14\n\n\n4.0\n\n\nNot bad at all!\n\n\nApril 11, 2006\n\n\nTracy Latham \"wretchasketch\"\n\n\nNashville, TN USA\n\n\nI have some decent mics in my cabinet, as well as a nice preamp, A/D converter and fire wire interface for my home multitracking setup. I wasn't expecting as good or better quality than I can already get, but I hoped the Samson C01B USB Studio Condenser would at least give me a decent sound. The idea was to have freedom and portablility that my studio set up prohibits. I was not disappointed. Installation was no more complicated that plugging the cable into a USB port on my laptop running XP and the other end into the mic. It is far from the best mic I have, but it is certainly not the worst. I have read reviews that say the recording quality is too thin, but I found that not to be the case. The noise floor when recording with the Samson is a little high, and it is lacking in headroom compared to any traditional mic through my Millenia preamp. I have to record at slightly lower levels than I am used to, but for $79, I am not complaining. There is no significant latency recording 16 bits at 44100hz in Fasoft N-Track Studio. The ability to plug this thing in and record at a whim is quite liberating. Way to go, Samson\n\n\n\n\nB000EIDLWC:works_ok:keith_rust\n\n\nB000EIDLWC\n\n\nAmerican Red Cross FR350 Emergency Radio: Electronics\n\n\nelectronics\n\n\n5 of 7\n\n\n4.0\n\n\nWorks OK\n\n\nJuly 14, 2006\n\n\nKeith Rust\n\n\nArizona\n\n\nCouldn't get any SW stations, but AM and FM worked great and the hand crank charged up the cell phone, after a lot of cranking\n\n\n\n\nB00000JDF5:does_its_job-5_stars:nick\n\n\nB00000JDF5\n\n\nBelkin A3L791-S 3-Foot RJ45 CAT 5e Snagless Molded Patch Cable (Blue): Electronics\n\n\nelectronics\n\n\n5 of 8\n\n\n5.0\n\n\nDoes its job-5 Stars\n\n\nApril 30, 2002\n\n\nNick\n\n\nSantee, CA United States\n\n\nNo problems so far; does its job.(if you are setting up a wireless network, get windows XP. 1 click and you're set up.\n\n\n\n\nB000BSLTNI:scare_those_critters_away:i_own_4_cats\n\n\nB000BSLTNI\n\n\nSensor Plug: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nscare those critters away\n\n\nNovember 10, 2006\n\n\nI own 4 cats\n\n\nLindenhurst N.Y.\n\n\nI bought this to scare away the opossums that come into my back porch at night. I used a 3 prong adapter with this to plug in a lamp and a radio.\nWhen they pass it, suddenly music and a light goes on and they scoot away fast. It is great to use if you want a lamp turned on when you walk in front of it, or anything else you want to use it for\n\n\n\n\nB000ARUXAU:good_product,_good_service:rc&dc\n\n\nB000ARUXAU\n\n\nKingston 512 MB Mini Secure Digital (miniSD) Memory Card ( SDM/512 ): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGood product, good service\n\n\nFebruary 24, 2006\n\n\nRC&DC\n\n\nKansas City, MO\n\n\nProduct-wise, the card has worked fine, but then memory cards generally either work or they don't. Product was shipped in a timely manner, and the price was reasonable\n\n\n\n\nB000ACGE3U:multiplier_of_power_source:r._foyder_\"loyal_customer\"\n\n\nB000ACGE3U\n\n\nPower Sentry 5-Outlet PowerSquid Power Multiplier: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nMultiplier of power source\n\n\nNovember 4, 2006\n\n\nR. Foyder \"loyal customer\"\n\n\nQuakertown,PA United States\n\n\nOriginally purchased to supplement & provide a tangle free connection to \npower source.After reading product manual,PowerSquid should be connected\nto dedicated power source 110 volt outlet.It is not recommended that \nPowerSquid be used with temporary power strip application...with or without\nsurge protection. Use of Power Sentry 5-outlet PowerSquid should be used in \naccordance to sound electrical application. Do not overload or connect grounding \ncheaters in non-grounded 110 volt systems.Use with stand alone generators\n should be closely monitored and PowerSquid not be used beyond generator's \ncapacity\n\n\n\n\nB0000A2QBX:very_good_case!:c._mota_\"homesick\"\n\n\nB0000A2QBX\n\n\nSamsonite L35 Notebook Case (198111261): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nvery good case!\n\n\nNovember 9, 2006\n\n\nC. Mota \"homesick\"\n\n\nchicago, il United States\n\n\nvery good case for the price. Actually looks more expensive. Can't go wrong\n\n\n\n\nB0000A2QBX:exactly_what_i_expected:julie_b._lewandowski\n\n\nB0000A2QBX\n\n\nSamsonite L35 Notebook Case (198111261): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nExactly What I Expected\n\n\nNovember 5, 2006\n\n\nJulie B. Lewandowski\n\n\nGeorgia, USA\n\n\nI wanted a case that was small, slim, and just carried the laptop. Perfect! I have a briefcase for files. I didn't need anything bulky. This case was exactly what I was looking for, for the right price. I have a new Gateway Tablet P.C. and with the larger battery and battery placement, this is a snug fit, but at least I know the computer isn't loose inside. I really like this case\n\n\n\n\nB0000A2QBX:excelente_maletin_para_notebook_con_14\"_de_pantalla:eduardo_gibaja_\"lalogi\"\n\n\nB0000A2QBX\n\n\nSamsonite L35 Notebook Case (198111261): Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nExcelente maletin para Notebook con 14\" de pantalla\n\n\nNovember 4, 2006\n\n\nEduardo Gibaja \"Lalogi\"\n\n\nLima, Peru\n\n\nCuando compr� una Toshiba Satellite M105 con pantalla de 14\", eleg� este maletin porque lei que sus dimensiones se ajustaban al equipo. El interior est� forrado y cuenta con las divisiones suficientes para llevar el cargador y los cables de poder de la notebook. Asimismo puedo incluir documentos, mouse y otros accesorios. El asa es c�moda y la correa incluida es ancha y ajustable al largo que uno desee. Estoy satisfecho con esta compra\n\n\n\n\nB0002VMDIY:best_of_the_bunch,_and_pretty_good_anyway:mcq_\"mcq\"\n\n\nB0002VMDIY\n\n\nPanasonic KX-TG6500B 5.8 GHz DSS Expandable 2-Line Cordless Phone (Black): Electronics\n\n\nelectronics\n\n\n23 of 23\n\n\n4.0\n\n\nBest of the bunch, and pretty good anyway\n\n\nOctober 13, 2005\n\n\nMcQ \"McQ\"\n\n\nOrlando, FL\n\n\nThe 6500B was the last one I tried of three. I've had cordless Panasonic phones break too often in the past so was reluctant to buy one again. But the other choices had major drawbacks. I really needed a two-line phone that wasn't 2.4Ghz.\n\nThe first I tried was the Uniden TRU8866. It had a bad echo on the extra handset and other people complained about that too. This phone uses phone company voice mail (no built-in ans mach) - which is fine - but I had the VM system go bonkers so lost all trust in it. For over a week messages accumulated but we got no stutter-tone so didn't know there were any new messages - arrgghh. Not the phones fault I know but it required VM for messaging so nope. \n\nNext I tried the new Motorola MD7081. I really wanted to like this phone. It's a 2-line 5.8Ghz ans machine like the Panasonic 6500B. I liked it's simplicity and easy switching between line 1 and 2. Had battery backup for use during power outages and the best voice quality of the three. But, it's answering system has no message counter. That alone is no big deal but it didn't have any indicator to say whether you had any saved messages. When a new message arrived it blinked and such - all good. But, if I got home, say, and listened to a message for my wife, the blinking stopped and no indication of \"1 saved message\" or anything. So, I would have to write a note to her to check the messages. We're so used to relying on seeing a message counter and just listening to them if it's not zero that we thought this would become a nuisance. I also would have liked an auto-line-selection so it would automatically answer the ringing line or select a non-busy outgoing line. But, that would have messed up their simpler style button function - hmmm.\n\nThe Panasonic, I am surprised to say, ended up being my favorite. Hopefully it will be reliable. I was always bothered by Panasonic's phones losing the date/time with any little power outage. Happily, this phone sets its date/time from caller-id info even though the manual implies it wouldn't work quite that way. With the right settings and phone company options (busy-forward), it elegantly supports two lines as if they are one. I really like not thinking about which line is what - just pick up and talk.\n\nThe 6500B has good voice quality and reasonable range. It's much lighter than earlier caller-id handsets which I like. I also like their shape of handset the best. My gripes are the limit of 4 handsets - I would like 6 or 7. Also, can't name the handsets so with intercom you have to remember each number\n\n\n\n\nB0006BKH3S:monster_ipod_fm_transmitter:k._uyeda_\"fish_hunter\"\n\n\nB0006BKH3S\n\n\nMonster A-IP-FM-CH-PS Wireless FM Transmitter: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nMonster iPod FM Transmitter\n\n\nOctober 29, 2006\n\n\nK. Uyeda \"fish hunter\"\n\n\nWahiawa, HI USA\n\n\nI love my new Monster FM Transmitter! It's very easy to use and the reception is great\n\n\n\n\nB0000DK662:no_like_48gx_but_good_enough:leonardo_gurlino_\"gurlinol\"\n\n\nB0000DK662\n\n\nHewlett Packard 49G+ Graphing Calculator: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nNo like 48GX but good enough\n\n\nNovember 3, 2006\n\n\nLeonardo Gurlino \"gurlinol\"\n\n\nMaturin, Venezuela\n\n\nBesides all opion regarding this new calculator, I would like to say, it is very nice, good design, options, features, a huge screen and modern color. So far, I haven't navigate deep inside it, it is not similar as 48GX, so you've got to expend some time to know how to use it. I bought it since there is not a better calculator than this (48GX is UNIQUE but obsolete\n\n\n\n\nB0002YK9SW:surround_sound:robin_n._decato\n\n\nB0002YK9SW\n\n\nM-Audio Revolution 5.1 High-Definition Surround Sound Card: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nSurround Sound\n\n\nFebruary 22, 2006\n\n\nRobin N. Decato\n\n\nBrooklyn, NY\n\n\nEasy to install, gret price and now I have surround sound on my PC. woowho\n\n\n\n\nB00001P4XA:big_sound_for_small_ears!!:gym_rat\n\n\nB00001P4XA\n\n\nKoss \"The Plug\" Portable Headphones: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nBig Sound for Small Ears!!\n\n\nNovember 10, 2006\n\n\nGym Rat\n\n\n\n\n\nI thought I was going to have to try to track down some headphones for kids until I ran across these! They are super comfortable and seem to block the world out. I've been using them for about 3 months now and couldn't be happier eventhough I've got a healthy dose of ear wax building up on them\n\n\n\n\nB000E873QM:good,_simple,_inexpensive_plug-n-play_mp3_player._great_for_audiobooks_and_holding_data!_:practice_everyday_\"bright_blessings\"\n\n\nB000E873QM\n\n\nCOBY MP-C827 MP3 Player w/128 MB Flash Memory & USB Drive: Electronics\n\n\nelectronics\n\n\n12 of 12\n\n\n5.0\n\n\nGood, simple, inexpensive plug-n-play mp3 player. Great for audiobooks and holding data! \n\n\nSeptember 5, 2006\n\n\nPractice Everyday \"Bright Blessings\"\n\n\nHouston, TX USA\n\n\nThis isn't my first mp3 player. I have a couple of the years and since my last one died I was looking for a quick, cheap replacement. I also needed a flash drive for work. Don't underestimate this, if you use the net at a library or cafe then you'll realize that 1. not all players/drives are recognized as drives and/or 2. you've got to carry an extra cord with you to plug into the usb port, which gets annoying quickly. when you clean out your purse and discover you don't have it with you. With this you get both in one. It has a built in usb port which is recognized fine in most operating systems, even win98. The sound quality is good, I can't tell the difference between this and my $100.00 player. The controls do take a few seconds to get used to, but once I did I find I preferred them. They are easy to use one-handed.\n\nAn unexpected bonus is that its great for audiobooks! If you turn off the player, it starts back up exactly where you left it, be it in the middle of a song or in the middle of a chapter! Its small without being too tiny, its design is simple enough that there is nothing to fall off or get lost and technophobes won't have a heart-attack. Its doesn't hold a lot of music at only 128mb or have a shuffle feature, but if you need to carry more than an album or 2, or need extra features then you'll want more than this player anyway. \n\nThe best feature is the price. I found this one at my local ToysRUs for $15.00. It also plays both mp3's and wma. It won't play AAC but if most of your music is this format you'll better off with an ipod.\n\nThis would make a good first player for a kid or for someone who needs a usb drive and music without all the bells and whistles. \n\nI'm giving it 5 stars because it does exactly what I need it to do without any hassle\n\n\n\n\nB000068CS0:great_product:gary_albert_\"albert\"\n\n\nB000068CS0\n\n\nSanDisk SDSDB-128-A10 Secure Digital 128MB (Retail Package): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nGreat Product\n\n\nFebruary 16, 2006\n\n\nGary Albert \"ALbert\"\n\n\nMiami, FL\n\n\nI decided to purchase this item because I needed to get SD memory for my g/friends digital camera, so I chose this one because of the price and the brand. SanDisk is known for their memory. Well, no need to say more, this thing works great. I will buy another one but with more space anyday. Thanks alot San Disk\n\n\n\n\nB000068CS0:great_for_canon_a-series_dig,_cameras:benito\n\n\nB000068CS0\n\n\nSanDisk SDSDB-128-A10 Secure Digital 128MB (Retail Package): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nGreat for Canon A-series dig, cameras\n\n\nSeptember 9, 2005\n\n\nBenito\n\n\nWest Atlanta\n\n\njust got this one at sears, and was only $20 so i grabbed it. the Canon A400 i have only came with 16mb so i needed more space. the 128 is much faster and easy to format than my old one. love it!!! go out and buy one today! GET SANDISK NOT LEXAR, THEY BREAK ALL THE TIME!!!!!\n\n\n\n\nB000068CS0:how_many_pictures_will_it_store?:brent_d._payne_\"lyricvault_com\"\n\n\nB000068CS0\n\n\nSanDisk SDSDB-128-A10 Secure Digital 128MB (Retail Package): Electronics\n\n\nelectronics\n\n\n13 of 14\n\n\n5.0\n\n\nHow many pictures will it store?\n\n\nNovember 5, 2004\n\n\nBrent D. Payne \"LyricVault_com\"\n\n\nSpokane, WA USA\n\n\nHow many pictures will it store? It is the most commonly asked question from digital camera enthusiast but usually the question that is the most difficult to get a straight answer about. Well, considering I worked in the memory industry for over 7 years I can help clarify this perplexing question and do so unbiased as I have since changed industries. \n\nThe SanDisk SDSDB-128-A10 Secure Digital 128MB, like most 128MB cards, will store on average 142 pictures when used with a 2 megapixel camera, 106 images when used with a 3 megapixel camera, 64 pictures when used with a 4 megapixel camera, 51 images when used with a 5 megapixel camera, and 40 pictures when used with a 6 megapixel camera. These numbers are based off the assumption that you are going to shoot your images at the highest quality JPEG setting available for the camera and understand that they are estimates and may be off by as much as 10 percent due to numerous factors including the complexity of the scene being shot and the compression algorithm used by your specific camera.\n\nI truly hope this review was helpful to you in determining whether this card is the right capacity for your specific needs\n\n\n\n\nB000CO77TQ:on_the_contract:env-youreverythinghomepage-com\n\n\nB000CO77TQ\n\n\n4-Year Service Plan for Televisions $1,500 to $1,999.99: Electronics\n\n\nelectronics\n\n\n20 of 20\n\n\n4.0\n\n\nOn the Contract\n\n\nAugust 24, 2006\n\n\nenv-YourEverythingHomepage-com\n\n\nLos Angeles, CA, formerly Rochester, MN USA\n\n\nIf you buy this service plan, print out the contract (found at the top of this page) and keep your receipt. I did not purchase the plan, but looked over the contract intensely since I nearly got it.\n\nSome notes on the contract:\n1. It is only good in the United States (including Washington, DC, but MINUS MAINE), but it does not include Guam, Virgin Islands, etc. So if you are in another country or outside the 50 states + D.C., this contract will be nothing but a waste of money.\n2. This contract is transferable and the directions to do so are simple.\n3. They will only fix/replace/etc IF the manufacturing warranty no longer covers the item. That is, if the manu. warranty is still good, you have to use it.\n4. If the item needs to be shipped, shipping/handling will only be covered if the manufacturer would cover those costs in their warranty period. If the item is being replaced, \"You will be responsible for the delivery or cost of delivery of the covered Product to Our authorized service center. Reimbursement will be issued in the form of a check payable to You.\" I'm uncertain if that means you cover those costs or they reimburse you.\n5. No lemon policy of 3 service repairs for the SAME defect, and a upon the time of a 4th repair, as determined by them, they will \"replace it with a product of comparable performance. Repairs performed while the Product is under manufacturer's warranty DO NOT APPLY.\" (Doesn't sound like a great no lemon policy... your product would have to breakdown in the same way 4 times in the number of years they cover you -- many electronics are typically already covered by the manufacturer for 1 year.)\n6. There are numerous conditions that they will not repair for, but they all sound typical of such plans (such as, they don't cover accidents and they don't cover \"the elements or acts of God\" ... sorry, I had to note that second one since it stuck out to me from the other things). But, in all seriousness, read the list before you purchase.\n\nSo, now that I have summarized much of the contract, if you want to get the service warranty, PLEASE READ THE CONTRACT SINCE I DIDN'T COVER EVERYTHING!!! (Don't hold me liable since I don't have much money anyways.) Also, I think this is typical in warranty contracts, but you will occassionally see the phrase \"at our discretion\" in the contract.\n\nAssuming they aren't jerks with their discretion and aren't too tough to deal with, I would give the warranty 4 stars (minus 1 star for the crappy lemon policy). I didn't get the coverage because the product I was going to get it for had just made into the next price category (so it was pricy for my product, being at the lower price end of the scale)\n\n\n\n\nB000F8J3AA:great_phones!:howard_b._weiner\n\n\nB000F8J3AA\n\n\nPanasonic KX-TG5761S 5.8 GHz FHSS GigaRange Expandable Digital Cordless Telephone: Electronics\n\n\nelectronics\n\n\n6 of 16\n\n\n5.0\n\n\nGreat Phones!\n\n\nJuly 28, 2006\n\n\nHoward B. Weiner\n\n\nSeattle, WA USA\n\n\nThere is absolutely nothing that we don't like about these Panasonic phones. The sound quality is excellent as is the range. Very happy customers\n\n\n\n\nB000196C3E:converts_hiking_to_auto_usage:wilbur_d._wong_\"wilbur_wong\"\n\n\nB000196C3E\n\n\nGarmin Automotive Navigation Kit with City Select North America: Electronics\n\n\nelectronics\n\n\n21 of 23\n\n\n5.0\n\n\nConverts hiking to Auto usage\n\n\nDecember 7, 2005\n\n\nWilbur D. Wong \"Wilbur Wong\"\n\n\nRichmond, CA USA\n\n\nI have had mine for a week. I had a non mapping garmin gps 12 for years, which was misplaced. I bought the navigation kit at the same time I ordered a 60 CS. It makes what I bought for backpacking usable as a car navigation tool both in the city and out in the boonies. I had no problems loading software and maps, upload or download from/to my gps.\n\nI am impressed by the softwares ability to \"recalculate\" in navigation mode. If I ignore the suggested route, it continually adapts to suggest a new route based on where I am.\n\nI am sure that some portions of the database is not current as far as new roads etc. are concerned but I am impressed with the degree of information that is currently contained. I have gotten messages regarding keeping right for example (to avoid being caught in what becomes a left turn only lane), and knowledge of one-way streets etc.\n\nThe real test is yet to come to negotiate San Francisco's endless no left turns, one way streets, construction and limited freeway on ramps!!\n\n\n\n\nB00030AX3Q:where_comfort,_precision,_and_great_price_meet:seagull\n\n\nB00030AX3Q\n\n\nLogitech Precision Plug and Play Gamepad: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nWhere comfort, precision, and great price meet\n\n\nMay 3, 2006\n\n\nSeagull\n\n\nsomewhere on the West Coast\n\n\nThree months later, I'm still amazed at how cheap this amazing piece of hardware was. Granted, it doesn't vibrate, nor have a zillion buttons, but it definitely performs admirably. It is comfortable and the buttons respond well. The unit feels well-built, and connects to the computer easily via USB. And as a bonus, it looks pretty too. :\n\n\n\n\nB000065BP9:good_value:crookdimwit\n\n\nB000065BP9\n\n\nSennheiser HD 202 Headphones: Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n4.0\n\n\nGood value\n\n\nNovember 13, 2006\n\n\ncrookdimwit\n\n\nWashington, DC USA\n\n\nI bought this based on the overall positive reviews. Good sound quality, and a comfortable fit. I didn't find them to be too tight at all. For a low-end pair of headphones, I think they're pretty impressive\n\n\n\n\nB000065BP9:exacly_as_expected:the_headphone_man\n\n\nB000065BP9\n\n\nSennheiser HD 202 Headphones: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n4.0\n\n\nExacly As Expected\n\n\nNovember 9, 2006\n\n\nThe Headphone Man\n\n\n\n\n\nThe cups of these heaphone are a little small and they do fit snuggly on my head but after stretching them out a bit I must say they are very comfortable. If you dont like them at first wear them for a while, they will grow on you.\n\n\n\n\nB00005NIMR:great_price_and_fast_shipping_from_eektechnology:mark\n\n\nB00005NIMR\n\n\nBelkin F3N401-06-ICE IEEE 1394 4-Pin/6-Pin 400 Mbps FireWire Cable (6 Feet): Electronics\n\n\nelectronics\n\n\n6 of 8\n\n\n5.0\n\n\nGREAT price and fast shipping from eektechnology\n\n\nMarch 23, 2006\n\n\nMark\n\n\nSan Francisco, CA\n\n\nIf you went to circuit city, you would see this is $30 there. Here its only 8.99! With fast shipping I only paid about $16 total, half the price of circuit city. Those type of stores (best buy, circuit city, compusa) are a total ripoff when it comes to these cables. Buy from Amazon.com (eektech will ship) and you save lots of $$. Thanks eek for great service\n\n\n\n\nB00005NIMR:great_cable_for_the_money:m._cloutier\n\n\nB00005NIMR\n\n\nBelkin F3N401-06-ICE IEEE 1394 4-Pin/6-Pin 400 Mbps FireWire Cable (6 Feet): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nGreat cable for the money\n\n\nFebruary 24, 2006\n\n\nM. Cloutier\n\n\nWA, United States\n\n\nWorks great, good quality from Belkin as usual. Hard to beat for the money\n\n\n\n\nB0009N5MCY:better_than_sliced_bread:guy_brunt_\"mebgb\"\n\n\nB0009N5MCY\n\n\nLogitech V200 Cordless Mouse - Silver ( 931379-0403 ): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nBetter than Sliced Bread\n\n\nNovember 9, 2006\n\n\nGuy Brunt \"mebgb\"\n\n\nCamp Hill, PA United States\n\n\nI'm using the product as I respond to this request for a review. Sufficiently compact for ease of packing in my laptop case. No technical issues (so far). Tracking is excellent. Has worked flawlessly under daily working conditions. Recommend highly\n\n\n\n\nB0009N5MCY:easy_to_use:georgenewlife\n\n\nB0009N5MCY\n\n\nLogitech V200 Cordless Mouse - Silver ( 931379-0403 ): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nEasy to use\n\n\nAugust 9, 2006\n\n\ngeorgenewlife\n\n\nnew york\n\n\nThis mouse works perfectly fast and easy without problems even if you don't install the software.\nIf you need a cordless mouse this is what you have to bu\n\n\n\n\nB0009N5MCY:excellent_mouse...software_needs_improvement:k._arens\n\n\nB0009N5MCY\n\n\nLogitech V200 Cordless Mouse - Silver ( 931379-0403 ): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nExcellent Mouse...Software Needs Improvement\n\n\nJuly 26, 2006\n\n\nK. Arens\n\n\n\n\n\nAs the title states, this is an excellent wireless mouse. Works right away with Windows XP, no driver install or software needed for two-button and normal scroll capabilities. Software allows for extra button assignment, but could use some work. I have had some trouble finding the menus I want when I want to find them. For example, I have an issue where I cannot find the extra button assignment menu after plugging in the USB receiver after a system start-up, however I am able to find those menus in the software if I remove and reinsert the receiver.\nBut as I said before, superb mouse function and tracking...this thing works on upholstery, leather, my jeans, and thick carpet!!\n\n\n\n\nB0009N5MCY:a_good_wireless_mouse_for_a_notebook_but_consider_the_bluetooth_option_v270.:cpt|kirk\n\n\nB0009N5MCY\n\n\nLogitech V200 Cordless Mouse - Silver ( 931379-0403 ): Electronics\n\n\nelectronics\n\n\n7 of 8\n\n\n4.0\n\n\nA Good Wireless Mouse for a Notebook but consider the Bluetooth option V270.\n\n\nJune 16, 2006\n\n\nCpt|Kirk\n\n\nStarship Enterprise\n\n\nThe V200 is another great Logitech product. It is far from the king of the mouse, but when it comes to the notebook it doesn't get much better than this. It takes two AA batteries (so get some rechargeable batteries to save on costs) which slide into the top of the mouse by pulling back on the palm rest to reveal two slots. The mouse doesn't weigh too much heavier than the two mouse batteries themselves. This is a two button mouse with a tilt wheel. With the setpoint software you can alter what the buttons do. Overall the design is good. There could be a problem installing setpoint. It may hang on install. If so you need to use the task manager to quit the install and then try it again. It should work. The USB receiver is 2.4 GHz and has a 10 Meter range. Wow. However there is one major problem with a wireless mouse for a notebook. USB dongles and receivers that are plugged into the side of the laptop are just begging to be snapped off. For this reason you should have the USB receiver on the side opposite the mouse hand, if you can, to avoid accidental contact. It should also not be hanging over the edge of the table. If you press it down lightly, then that is it, game over for the receiver and you will have to get a new 2.4 GHz receiver. So okay you will have to spend on a new receiver, if you break the old one. The receiver plugs into a small slot under the mouse using a magnet. When it is in the holder the mouse turns off. It doesn't fit in smoothly. It creates a block shape in the bottom of the mouse. Anyway you are not going to be using it when it is clipped in. The other thing is that it can pop out sometimes. Just happens if the mouse just gets knocked a certain way. I am sure most notebook users are cautious with their notebooks and devices. If you are then a mouse like this and USB should last for a very long time. There is really nothing wrong with it. Wireless mini mouse. FAB! However there is one last thing to say. If you don't like the idea of a USB receiver then GET A BLUETOOTH VERSION OF THIS MOUSE INSTEAD. It is called the V270. That is if your notebook has internal Bluetooth or else you will need to get a Bluetooth USB receiver and be in the same boat when it comes to watching your USB receivers. But if you have a Bluetooth internal receiver with your notebook then by all means head over to the V270 and forget about this USB option. The USB design isn't a fault with the mouse; it is just a fault with all notebooks that is not really a fault. You just got to switch to internal Bluetooth or learn to take care of your receivers.\n\n\n\n\nB000A6X9CU:exceptional_product:r._howland\n\n\nB000A6X9CU\n\n\nHP Photosmart 8250 Printer (Q3470A#ABA): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nexceptional product\n\n\nNovember 3, 2006\n\n\nR. HOWLAND\n\n\nShadyside, OH USA\n\n\nWhen I purchased this product I was a little sceptical, however, once I unpacked it and started using it, it was so absolutely easy to use, and the pictures have such a fine quality, and you can insert 4 different sizes of memory cards in addition to computer connection. So, I see it as a win/win product. The only draw back is the weight and size. I just do not regret this purchase with the price an added bonus as wel\n\n\n\n\nB000IK5X2C:great_way_to_hold_and_hear_your_2g_nano:darwin_\"computer_consultant\"\n\n\nB000IK5X2C\n\n\nApple iPod Lanyard Headphones for 2nd Generation nano: Electronics\n\n\nelectronics\n\n\n9 of 9\n\n\n4.0\n\n\nGreat way to hold and hear your 2G Nano\n\n\nOctober 6, 2006\n\n\nDarwin \"Computer consultant\"\n\n\nRedmond, WA United States\n\n\nFirst and foremost--I am not an audiophile, so I can't comment with any authority on the quality of the headphones themselves. They are fine for me and my listening habits--I find myself increasingly listening to podcasts when commuting via bus for which these headphones are perfectly fine, although when I do listen to my music the bass and treble response seems fine to me.\n\nThat being said, what I wanted was a convenient way to carry my 2G Nano so I could listen and manipulate the controls with the iPod handy yet out of the way. This lanyard does an excellent job of providing that and I would recommend it to anyone.\n\nSo why four stars? Well, for a few reasons:\n\n--Only available in white. I have a black 2G 8GB Nano. I'd have liked to match it.\n\n--No lock or release on the base that connects to the Nano. While the connector does fit snug with the Nano and doesn't disconnect so easily from the Nano that I'd fear it might fall off while walking about, I would have felt more secure with some sort of lock or release mechanism holding it in place.\n\nFor this reason I would not recommend the lanyard to anyone doing any sort of strenuous activity or exercise--but hanging loose around the neck really isn't the best place for the Nano then anyway. The armband would be more appropriate.\n\n--NOWHERE on the packaging did it say this was for the 2G Nano! I had bought a 1G Nano lanyard at a local store and had to return it when I discovered it was incompatible (the dock connector and headphone jack on the 1G and 2G Nanos are NOT the same distance apart). When this 2G lanyard arrived, I could find nothing on the package that said \"for 2G Nano\", so I opened it very gingerly thinking I had received the wrong lanyard and would need to repackage it and send it back. I was very relieved when it did fit! Regardless, there should be a very clear \"For 2G Nanos only!\" label on the box to alleviate 2G Nano owners' fears and to warn 1G Nano owners.\n\nAll in all, I am very happy with the lanyard. It keeps the Nano handy while leaving my hands free, and that was what I was looking for\n\n\n\n\nB000117DF8:works_great_---_mac_and_pc:reader_\"reader_r._reader\"\n\n\nB000117DF8\n\n\nLexar Media 512 MB JumpDrive Sport Portable USB 2.0 Drive (PC/Mac): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nWorks Great --- Mac and PC\n\n\nMay 20, 2006\n\n\nReader \"Reader R. Reader\"\n\n\nAnytown, USA\n\n\nIt's been through the wringer, and has had no problems after about a year. I came here looking for a replacement cap, but can't find one on Amazon\n\n\n\n\nB000636JD8:works_fine:r._ward_\"ry1nik1\"\n\n\nB000636JD8\n\n\nTRENDnet 54Mbps 802.11G Wireless USB Adapter: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nWorks fine\n\n\nJuly 11, 2006\n\n\nR. Ward \"ry1nik1\"\n\n\nVirginia\n\n\nI downloaded the driver from Trendnet's website before installing the adapter. I did not encounter any problems at all, including the issue that some users had with it disconnecting when they plugged in another USB device\n\n\n\n\nB00004SY4H:sonic_delight!__but..._a_warning!:mark_j._foster_\"cto\"\n\n\nB00004SY4H\n\n\nSennheiser HD600 Traditional Over-Ear Open Dynamic Hi-Fi Professional Stereo Headphones (Black): Electronics\n\n\nelectronics\n\n\n9 of 24\n\n\n5.0\n\n\nSonic Delight! But... a Warning!\n\n\nOctober 20, 2005\n\n\nMark J. Foster \"CTO\"\n\n\nPalo Alto, CA USA\n\n\nThe Sennheiser HD600s have only one thing wrong with them: they'll change you from someone who enjoys good audio... into an audiophile! I've been known as a videophile for many years over on one of the top AV forums, but I've always been satisfied with merely \"decent\" audio. Until now.\n\nI'd previously ripped all my CDs into the (previously quite impressive) Windows Media format at 192K bps, but that's now useless. With the phenomenal ability of the Sennheiser HD600s to reveal all the subtle nuances of your music, listening to compressed discs is like listening to your music through a thick layer of vaseline. Here we go again... it's time to rip everything into lossless format.\n\nI cannot recommend these headphones more highly - they are simply extraordinary. When your tastes become accustomed to this level of quality, though, life's going to get a lot more expensive, as you try to update everything to match your new level of sonic differentiation.\n\nStill, this is a bloody miracle. My ears have finally been opened. Wow\n\n\n\n\nB00004SY4H:sennheiser_hd-600:mr._s._st_thomas_\"suckerfly\"\n\n\nB00004SY4H\n\n\nSennheiser HD600 Traditional Over-Ear Open Dynamic Hi-Fi Professional Stereo Headphones (Black): Electronics\n\n\nelectronics\n\n\n11 of 24\n\n\n5.0\n\n\nSennheiser HD-600\n\n\nApril 2, 2005\n\n\nMr. S. St Thomas \"suckerfly\"\n\n\nUK\n\n\nHaving been a buyer of Sennheiser products for over 10 years, I feel it is time I endorse a product I truly believe in, and have never been let down by. Including service and customer satisfaction.\n\nEach time buying a Sennheiser product, I found myself stepping up to the next level of their headphones, because they simply keep improving their technology to the point that what you used to own by them gets overwhelmed by the latest headphone they offer. When I bought the HD-600's, I had just come away from years owning the HD-25. When these encountered a technical problem, I replaced them with a backup choice, the HD-25SP's. The difference in sound going 'lower' down their scale of products was just too much to bear. So I bit the bullet and went higher. \n\nWhether you use Sennheiser's merely for pleasure, or for studio work, the accuracy of their sound, and what you get for your money cannot be beat. My HD-600's ran into a small technical glitch (and Sennheiser were extremely helpful in getting this fixed and replaced within 2 days of receipt), and I swear I nearly cried in their absence. HD-600's give you a clear idea of how things were recorded, how good your system is, and what truly sounds inferior. You never listen to things quite the same way again.\n\nThe reason my reviews of Paul McCartney and John Lennon albums from the 70's all comment on how inferior the recording quality is (especially compared to the so called ''Economy Class Beatles'' George Harrison's and Ringo Starr's albums from the same decade), is down to what HD-600's reveal. My main source of media tends to be Vinyl, because Compact Discs and DVD's still cannot capture what analogue/turntable equipment captures. Vinyl is simply warmer and more 'round' in comparison to its digital counterparts. And when listening to badly recorded albums, HD-600's make you wonder if you spent more on your headphones than the artist did using a studio. HD-600's are the reason I mention time and time again you are not getting value for money when buying Lennon and McCartney solo albums. Listening to ''Take IT Away'' from 1982's Tug Of War reveals an annoying tape hiss coming from the left side of the stereofield. Which sounds much like someone forgot to 'silence' a track that had a recording on it, before the instrument actually showed up in the song. It sounds totally like electrical / amp noise coming from equipment, and Geoff Emerick or George Martin just simply forgot to keep this track silent until the instrument that occupied that track appeared in the song. \n\nIt gets much worse than 'Take It Away' on Paul McCartney albums, and even more painfully so on John Lennon albums. But listening pleasure can be found recording wise on their solo albums BACK TO THE EGG, RAM, DOUBLE FANTASY, MILK AND HONEY, and some portions of BAND ON THE RUN, and WALLS AND BRIDGES. #9 Dream is simply a gorgeous recording, it's just too bad the rest of the album doesn't sound as good as that one song.\n\nWhen you listen to your CD's, vinyl, DVD's in whatever room of your house your speakers are, the room dictates what you hear, no matter what you've spent on speakers, power amps, woofers, sub-dividers, etc. Whether you have wall to wall carpeting, high ceilings, low ceilings, plaster or cement, wood floors, a more long than wide room, it all affects how sound reaches your ear. Which is why on a great set of headphones, you are more likely to hear HOW those sounds were meant to be heard, how they were recorded, and how much time was spent doing it. On speakers, McCartney's JET sounds quite full, though the snare is a little thin. But when hearing the album with HD-600's, the drums nearly become non-existent, and all the inconsistencies of the recording become so apparent, you wonder if the mix was sabotaged by someone. McCartney on drums is not a miracle, especially when you hear many of the mistakes being made by someone not as confident on that instrument as they are on Bass. \n\nCan I review anything without bringing up Lennon and McCartney? Quite possibly Dog Food. But so far, they come up pretty regularly. Makes you wonder if I'm trying to prove a point doesn't it.\n\nAlbums that are phenomenally recorded get due justice with Sennhesier HD-600's. They truly do. Some noteworthy recordings from the 70's and 80's when recording technology (for popular music) was just beginning to reach new highs, that are all HD-600 friendly:\n\nIsao Tomita's The Firebird (1976)\nGeorge Harrison's eponymous album (1979)\nSteely Dan's Gaucho (1980)\nJoe Walsh's The Smoker You Drink, The Player You Get (1973)\n10CC's The Original Soundtrack (1975)\nGodley & Creme's Consequences (1977)\nSupertramp's Crime of the Century (1974)\nXTC's Black Sea (1980)\nPeter Gabriel's 3rd album (1980)\nKate Bush's The Dreaming (1982)\nEarth Wind and Fire's All 'n' All (1977)\nLevel 42's The Pursuit of Accidents (1982)\nBilly Cobham's Spectrum (1972)\nFrank Zappa's One Size Fits All (1975)\nRingo Starr's Goodnight Vienna (1974)\n\nand a truly incredible sounding album from the 90's, Prince and The New Power Generation's ''Symbol'' album from 1992.\n\nAlbums NOT HD-600 friendly:\nTodd Rundgren's Hermit of Mink Hollow (1978)\nBlue Oyster Cult's Agents of Fortune (1976)\nPaul McCartney's Venus and Mars (1975 - and quite a few others)\nJohn Lennon's Imagine (1971 - and avoid 1970's Instant Karma like the plague, the piano track will hurt you . . )\nYes's Fragile (often the guitars and keyboards hit frequencies that cause a lot of crackling and clipping)\n\nand surprisingly enough, many remasters of albums once on vinyl or antiquated CD reveal the errors of fixing something that ain't broken. \n\n\nIf you have to save up for a pair, or do some heavy searching for HD-600's that are affordable, do so. In over 10 years I have never been disappointed by anything Sennheiser distributed, especially when I kept up with their pace.\n\n\n\n\n\n\n\nB0002Y22VE:great_for_the_price:len_egdish_\"leneggs\"\n\n\nB0002Y22VE\n\n\nMagellan RoadMate 300 Vehicle GPS with Voice Prompting: Electronics\n\n\nelectronics\n\n\n22 of 23\n\n\n4.0\n\n\nGREAT for the price\n\n\nMarch 10, 2006\n\n\nLen Egdish \"leneggs\"\n\n\nKansas City\n\n\nI just purchased one of these and was a little sceptical after reading some of the reviews that stated how difficult it was to load the information into the unit. I had no problem at all. In fact it was quite simple. I am truly amazed at how acurate and easy to use this unit is. For the price, you can't beat it\n\n\n\n\nB0009I6KCA:very_good_for_the_price:hari_manoharan\n\n\nB0009I6KCA\n\n\nTranscend 1GB 80x Secure Digital (SD) Card: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n5.0\n\n\nVery good for the price\n\n\nJune 11, 2006\n\n\nHari Manoharan\n\n\nFremont, California\n\n\nI bought the Transcend 1 GByte card for my PDA. My Axim x51v immediately recognized it and is working flawless for the past few weeks. I am planning to get the 4GB model too which is currently retailing for $90 to $110 elsewhere.\n\nHar\n\n\n\n\nB0007Y6AF8:no_mp3_support_but_very_high_quality_device:buyerlsd_\"buyerlsd\"\n\n\nB0007Y6AF8\n\n\nSony ICD-MX20 Memory Stick Pro Duo Digital Voice Recorder: Electronics\n\n\nelectronics\n\n\n16 of 18\n\n\n4.0\n\n\nNo MP3 support but very high quality device\n\n\nDecember 29, 2005\n\n\nBuyerLSD \"buyerlsd\"\n\n\nedinburgh\n\n\nI was mislead with Amazon review which said it supports conversion to MP3 format with the software supplied, it does not. The help quite clearly states \"You cannot convert a file into an MP3 file\". It will convert to .wav then you can use a 3rd party program to go to MP3.\n\nIf it had MP3 support (at least conversion on the PC from the Sony proprietary format which is .msv) I would have given it 5 stars due to the good software supplied and the amazing sound quality possible. It you record in full quality mode (1.25 hours with built in 32meg memory) and listen to it again with good headphones it does sound amazing. The Voice Up (so you hear each person in a conversation) works very well as do other nice features.\n\nBut it is let down with the software supplied not performing MP3 conversion. Going to .wav then .mp3 with another program is a pain and simply converting to .wav to get away from proprietary .msv takes up too much space.\n\nBut I would buy one again, as it is very easy to use, small, and very high quality. I just hope Sony would listen to consumers and support MP3\n\n\n\n\nB000069LI9:perfect!:michelle_sanders_\"busybusymomma\"\n\n\nB000069LI9\n\n\nHP No. 56 Black Ink Cartridge (C6656AN): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nPerfect!\n\n\nNovember 3, 2006\n\n\nMichelle Sanders \"busybusymomma\"\n\n\nIndiana USA\n\n\nThis is the same cartridge I can purchase elsewhere, but cheaper. It works well in our printer with no issues whatsoever and my HP printer prints fantastic photos when I use HP ink and HP premium photo paper\n\n\n\n\nB000069LI9:hp_no._56_black_ink_cartridge_(c6656an):carrie_schultz\n\n\nB000069LI9\n\n\nHP No. 56 Black Ink Cartridge (C6656AN): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nHP No. 56 Black Ink Cartridge (C6656AN)\n\n\nFebruary 23, 2006\n\n\nCarrie Schultz\n\n\n\n\n\nNo complaints - I'm pleased with the quality and price\n\n\n\n\nB000BI2CIO:this_was_just_what_i_wanted:marie\n\n\nB000BI2CIO\n\n\nLogitech Wireless Music System for PC: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nThis was just what I wanted\n\n\nAugust 16, 2006\n\n\nMarie\n\n\nMissouri\n\n\nBefore I bought this device, I was trying to enjoy the \"Hearts of Space\" website, \"This American Life\" and other NPR shows available for online streaming. Only problem was, I didn't like being shackled to my computer in order to hear them. I did some research on the internet, read a lot of reviews and ultimately decided on the Logitech Wireless Music System. I would like to echo all the positive comments recorded here.\n\nIt was indeed quite fast and easy to set up. No lengthy manuals to read (thank goodness.) I did not find it necessary to load the CD software that came with the device. If I want to listen through the stereo, I plug in the USB. If I want to listen through the computer, I simply unplug the USB. It's very simple.\n\nI am 100% satisfied with the sound quality. I would not be able to tell the difference between FM radio and the online streaming. However, I do have to turn up my stereo about twice as loud when I am sending internet audio streams to it using this device, than when I am listening to a CD or the radio. On my stereo (which isn't fancy), this does not cause diminished sound quality, so I'm perfectly OK with it. I just have to remember to turn down the volume before switching back to CD or radio listening.\n\nIf you are wondering whether the device will be compatible with your stereo, check for two things. First, your stereo has to have a set of two holes side-by-side that are for the twin plugs generally colored white and red. I'm sorry I do not know the technical names for these things, but there's a picture of the red-and-white plugs on this Amazon page in the Manufacturer's \"Product Description\" section (just scroll up until you see it.) Second, your stereo has to have a button that will let you switch to \"Auxiliary\" input. My stereo has this, but my portable CD player/cassette player/radio (AKA boom box) does not. If it is important to you to be able to use this on a portable player you can carry around the house and/or outside, be sure to check your player for these two things.\n\nOverall I am very satisfied and quite happy with the product\n\n\n\n\nB00000JBLH:personal_productivity_tool:thomas_crowell\n\n\nB00000JBLH\n\n\nHP HP12C Financial Calculator: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nPersonal Productivity Tool\n\n\nNovember 5, 2006\n\n\nThomas Crowell\n\n\n\n\n\nThe HP12C has been an essential in my personal productivity tool kit for over 20 years. I bought my first one when I was a consultant and it lasted until August 2006. It withstood all the travel and constant use well. I use it for simple and complex math; statistical analysis and financial analysis. It is much faster and simpler to operate than a PC. My new one has all the same great features. I highly recommend this for someone who is frequently required to perform mathematical and financial analysis.\n\n\n\n\nB0006J59YW:you_will_need_your_own_charger:rick_levesque_\"waja\"\n\n\nB0006J59YW\n\n\nIogear Wireless Bluetooth Mini Mouse: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nYou will need your own charger\n\n\nAugust 5, 2006\n\n\nRick Levesque \"Waja\"\n\n\nSilicon Valley, CA USA\n\n\nI bought this mouse and have used it for over a year now. The nice part about this mouse is the range and the fact that when it works, it works beautifully. When it doesn't work, it is erratic and locks up. Turn it on and off and while the LED will light up, it will still not work well. This is the hint that the battery is low. The mouse comes with two rechargable NiMH AAA batteries. These are 650mAh batteries and are not the best. I bought 4 1000mAh AccuPower NiMH batteries. I keep 2 in the charger. These batteries work great and when fully charged will give me a month of use at about 8 hours a day. But as soon the mouse starts to get erratic, you have to swap the batteries. Do this and you will really like this mouse. \nAnother nice feature for this mouse is the range. You can use your laptop as a DVD player connected to your TV and sitting out of the way. The mouse makes a nice remote control\n\n\n\n\nB0009EUFI4:almost_like_prints_from_the_photo_shop:w._aona_\"koa_man\"\n\n\nB0009EUFI4\n\n\nEpson PictureMate Deluxe Viewer Edition Photo Printer: Electronics\n\n\nelectronics\n\n\n8 of 8\n\n\n5.0\n\n\nAlmost like prints from the photo shop\n\n\nAugust 27, 2006\n\n\nW. Aona \"Koa Man\"\n\n\nHonolulu, Hawaii USA\n\n\nThis is a great printer. I have used it several times and it makes great pictures. I had some pictures taken at a low resolution and the print still turned out looking great. Tried this on a regular printer and the print didn't look near as great. A great feature is proof sheet print of the pictures you have. You can also add a border to your pictures which makes things nice for postcards.\n\nColors are great and vivid. Also the prints look like that purchased from a photo shop. I have not tried printing to non Epson paper so I can't say how prints would look. I know that Epson printers look the best printed on Epson paper. \n\n\n\n\nB0002UE9WI:good_piece_for_good_price:boopathy_subramaniam\n\n\nB0002UE9WI\n\n\nCreative Labs WebCam Live!: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nGood Piece for good price\n\n\nNovember 9, 2006\n\n\nBoopathy Subramaniam\n\n\n\n\n\nThe camers is good in picture quality and easy to install and use. It's multi utility for desktop and notepad is a very good feature which I personally expected. I have a complete satisfaction\n\n\n\n\nB000AM6QHO:it_solved_my_problem:jarome\n\n\nB000AM6QHO\n\n\nLogitech Z-5450 Digital 5.1 Speaker System: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nIt solved my problem\n\n\nNovember 9, 2006\n\n\nJARome\n\n\nTennessee USA\n\n\nAlthough the rear speakers need a power plug, this system solved my problem of being unable to run a signal wire to the back speakers from the front. The sound is OK, not awesome, but not bad. Better than the TV speakers by a long shot. They can also be nicely controlled with my Harmony remote\n\n\n\n\nB000AM6QHO:logitech_z-5450_digital_5.1_speaker_system:wei_feng\n\n\nB000AM6QHO\n\n\nLogitech Z-5450 Digital 5.1 Speaker System: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nLogitech Z-5450 Digital 5.1 Speaker System\n\n\nNovember 9, 2006\n\n\nWei Feng\n\n\n\n\n\nvery good, worth the money. If you want a louder one, go for Z 535\n\n\n\n\nB000AY5Y5W:play_games_and_want_to_move_faster?_get_one:brett_christoffel_\"bmc\"\n\n\nB000AY5Y5W\n\n\nLogitech G7 Laser Cordless Mouse: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nPlay Games and want to move faster? Get one\n\n\nNovember 10, 2006\n\n\nBrett Christoffel \"BMC\"\n\n\nPlano, Texas United States\n\n\nNever been game player much since pong-Purhcased BF2 last December for the PC and loved it. Flying was limited though-util I purchased my G7 from NE and have been enjoying MUCHO fast speeds, easily adjusted, and for internet parusing it has a built in back button. Have to change out battery every two days but gives plenty of warning and is always charged when I need to replace. Absolutely the best mouse I have ever owned.\n\n\n\n\nB0002CPBUK:excellent_keyboard!:peter_brogan_\"pete\"\n\n\nB0002CPBUK\n\n\nMicrosoft Digital Media Pro Keyboard: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n5.0\n\n\nExcellent Keyboard!\n\n\nAugust 25, 2006\n\n\nPeter Brogan \"Pete\"\n\n\nBlackwood, NJ USA\n\n\nI love this keyboard. \nAfter searching through just about every keyboard available, I was torn between this and a gaming keyboard that was triple the price. I'm so glad that I chose this one.\nIt is excellent! The quick access buttons are great and you can set them up almost any way you want. It's a great keyboard for gaming, office use, media or just ordinary, everyday use.\nThey keystrokes are effortless and near silent. The slide zoom feature is cool, but I don't use it much. It's better to have something and not need it than to need it and not have it though.\nI really love the quick access buttons which allow me to pull up my files, e-mail or web pages with immediate ease. The only thing I don't like about this keyboard is the sleep button. I NEVER use this and I can't see any way to reasign it. However, it is out of the way and not a big enough deal to lower my rating of this sweet keyboard.\nAside from all of that, how can anyone complain about a keyboard of this quality for such a great price. And it was delivered the day after I ordered it.\nTo top it off, it feels sturdy and well made. I would reccomend this keyboard to anyone. It is a great all around keyboard with either USB or PS2 connectability\n\n\n\n\nB000CSCLMK:wow_-_buy_one.:jeff_hutchison\n\n\nB000CSCLMK\n\n\nCreative Zen Vision:M 30 GB MP3 and Video Player (White): Electronics\n\n\nelectronics\n\n\n7 of 7\n\n\n5.0\n\n\nWow - buy one.\n\n\nNovember 20, 2006\n\n\nJeff Hutchison\n\n\nNorman, OK United States\n\n\nI have been into MP3s since 1996 - I used winplay, and l3enc, and thought it was the most amazing thing ever. I bought one of the first CD based MP3 players, and then a Nomad Jukebox with a whopping 6 gig hard disk... next was the 10 gig model, and then I upgraded to the Nomad Zen 20gig with USB 2.0. The Zen went with me everywhere - Africa, Europe, My living room. \n\nAfter about 70,000 hours of use and abuse, it finally died. \n\nI needed a replacement, so I looked at iPod, and Zune, but why would I stray from Creative Labs? Besides, it's better in every way - better screen than the ipod, better interface than the Zune, and a better battery than either one.\n\nWhen I plugged it into my computer, I was blown away. XP found the player, and without any drivers or proprietary (think iTunes) software, I was able to drop my non-DRM ;) mp3 files right onto the generous space available. I was listening to my music in mere minutes without installing anything, or ever being forced to reboot my PC. I transferred a couple of movies and I'm looking forward to my next flight\n\n\n\n\nB000CSCLMK:a_little_pricy,_but_it_was_worth_it.:j._willig_\"cosmo_jiggy\"\n\n\nB000CSCLMK\n\n\nCreative Zen Vision:M 30 GB MP3 and Video Player (White): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nA little pricy, but it was worth it.\n\n\nNovember 13, 2006\n\n\nJ. Willig \"Cosmo Jiggy\"\n\n\nSumter, SC USA\n\n\nThis is my first time ever buying a really expensive MP3 player. I bought it three weeks ago. If I hadn't seen the Creative Zen Vision:M, I probably would have bought the Sandisk Sansa E260. I love being able to bring my cartoon/tv shows over to a friends house so we can watch them on the tv. There is one problem I've had with it. I'm not sure if anyone else has had this, but there is some slight static noise that happens once in a while when I'm listening to music. I don't think it's the MP3s, because I've never ran into the problem with my other MP3 players. If only they would've added the video cable for the TV and kept the charger, then I would've rated it a 5\n\n\n\n\nB0002YXTRK:a_great_value:m._mitchell\n\n\nB0002YXTRK\n\n\nAMW M520B Portable DVD/CD/MP3 Player with 10.2-Inch Widescreen LCD (Black): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nA great Value\n\n\nApril 18, 2006\n\n\nM. Mitchell\n\n\nTallahassee, FL\n\n\nBought this DVD player 4 days ago and have enjoyed it ever since. It has many features and a good picture. You can't beat the large screen. I like the zoom feature especially. Another feature I like is that the battery fits flush with the dvd player in back so that you don't have a bulky protrusion when it's attached.The sound is not loud as others have said but I have yet to find a portable DVD player that has good sound. You'll have to use external speakers or ear phones(included). Another thing I notice is that DVD-RW or DVD+/- R are not supported. They play but the menu system does not work. You have to access the file manually. That being said I cannot find this size dvd player anywhere else for less than $299. The shipping was fast and free. There is a stamp on the manual stating that the unit is sold \"as-is\" but the unit I got was brand new and not refurbished. So far so good. I don't thonk you will be disappointed\n\n\n\n\nB00005AC8J:great_card_at_great_price:ashu\n\n\nB00005AC8J\n\n\nViking CF256M 256 MB CompactFlash Card: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nGreat Card at great price\n\n\nAugust 16, 2005\n\n\nAshu\n\n\nSunnyvale, CA USA\n\n\nI have been using this card for last 6 months, no problem so far. Rebate takes some time to arrive\n\n\n\n\nB000068CNR:a_must_for_home_theater_or_multimedia_centers.:ghenghis\n\n\nB000068CNR\n\n\nSurgeMaster Gold Series w/Tel, 9 Outlet, 2950 Joules, 10' Cord, 360 Rotating Plug, (Unlimited CEW & Data Recovery): Electronics\n\n\nelectronics\n\n\n13 of 16\n\n\n5.0\n\n\nA must for Home Theater or Multimedia centers.\n\n\nMarch 5, 2005\n\n\nGhenghis\n\n\nCharlotte, NC\n\n\nI was really pushing the limits of my mid-range Monster surge protector with 2 TVs, a 2.4Gig P4 with desktop TV, DSL, two printers, and .....drumroll please.....a fridge all crammmed into a corner office setup about 7' wide. The Belkin Gold unit with RF/EMI suppression up to 75db cured all the rattle and hum problems with speakers, satellite signal, the \"dirty\" line into my modem, and a week later I haven't had to reboot my system once. Amazing!\n\nI had already had a great experience with a Belkin SurgeMaster on my Home Theater setup, so I went one level up with this \"Gold\" unit. One of the better investments I've made lately. I paid $60 at the local Office Depot, the price Amazon is advertising is ridiculously low at $35. Belkin.com's price is about $70 so my advice is to buy this unit now before somebody at Amazon wakes up and jacks the price.\n\n\n\n\nB000ANQXKS:quality_recorder_is_easy_to_use:r._campbell_\"intruder_films\"\n\n\nB000ANQXKS\n\n\nM-Audio MicroTrack 24/96 Professional 2-Channel Mobile Digital Recorder: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nQuality recorder is easy to use\n\n\nNovember 6, 2006\n\n\nR. Campbell \"Intruder Films\"\n\n\n\n\n\nI bought this recorder to use as backup for real-time event video recording. I have also used it concert settings. The record quality is astounding, full range of dynamics, using both the stereo mini-plug input and the 1/4\" inputs. It is mostly being used now to plug into sound boards to record long event audio. First thing I did was to upgrade to a 4GB Compactflash card. The provided card is too limited.\n\nBattery life is adequate for hour plus recordings, and connection and downloading to PC is a breeze. The WAV files are really high quality.\n\nIt all works as advertised. The only minor glitch I have had, is that when you first try to start a recording, it gives a \"media full\" message, even tho the media is empty. Then you select record again and it records just fine. I went several iterations with Tech Support, who were very friendly and helpful, but the problem has not been resolved. I also suspect the 3rd part flash card may be contributing to this glitch. I would buy this recorder again\n\n\n\n\nB0001H29OW:great_value_for_money:integral_yogi_\"auro\"\n\n\nB0001H29OW\n\n\nTargus CVR200 15 Notebook Slip Case: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGreat value for money\n\n\nNovember 9, 2006\n\n\nIntegral Yogi \"Auro\"\n\n\nState College, PA USA\n\n\nI am glad I bought this backpack. It exceeded all my expectations. I strongly recommend this one\n\n\n\n\nB000F2M16E:great_product!:t._m._p.\n\n\nB000F2M16E\n\n\nCreative Zen Micro Photo 4 GB MP3 Player Black: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGreat Product!\n\n\nNovember 5, 2006\n\n\nT. M. P.\n\n\nCalifornia\n\n\n+Built very sturdy, with no flimsy plastic parts \n+Fits right into the palm of your hand, though it may be a bit thick\n+Music quality and screen clarity is superb, superior to other counterparts. \n+Easy to use software. \n+FM radio and voice recorder is a definite plus!\n\n-Controls takes getting used to\n-Can't listem to music while viewing pictures.\n\nDefinitely a great buy. You will not be disappointed\n\n\n\n\nB0003QER7S:this_is_great_for_just_one_system.:deborah_drake\n\n\nB0003QER7S\n\n\nLogitech Harmony 676 Universal Remote Control: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nThis is great for just one system.\n\n\nNovember 9, 2006\n\n\nDeborah Drake\n\n\n\n\n\nI got this less robust remote because I only wanted to use it on one system. And I was able to set it up and program all the different buttons, and it all fits nicely in my hand. They have great customer support available on the weekends. The best part is everything goes on and off with one button! \n\nTHE DRAWBACKS: a couple of buttons you can't program or use for anything. And my goofey tv has 3 component modes and this thing is set up for 2, so getting it to sink up right is tricky, and it's constantly getting out of sink.\n\n\n\n\nB00006346B:monster_cable:w._r._classen\n\n\nB00006346B\n\n\nMonster Cable XP-CI BIG-100 CL Rated Monster XP Clear Jacket Compact Speaker Cable (100 Foot Spool): Electronics\n\n\nelectronics\n\n\n0 of 2\n\n\n4.0\n\n\nMonster Cable\n\n\nNovember 9, 2006\n\n\nW. R. Classen\n\n\n\n\n\nI must say Moster XP speaker wire makes a huge sound inprovement on mt 6.1 home theater setup my side and rear channels are so clear and crisp .\nI have these runing threw my walls and ceiling and the intall was a brezz do the jacket by these cables you will love them for your surond speakers\nWayne mn\n\n\n\n\nB000E8VQ74:perfect_for_a_soldier_deployed_to_iraq!:shadow_doc_\"shadow_doc\"\n\n\nB000E8VQ74\n\n\niSkin Eclipse 30GB Video iPod Protector - Black (EVO3EC1-A): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nPerfect for a soldier deployed to Iraq!\n\n\nSeptember 26, 2006\n\n\nShadow Doc \"Shadow Doc\"\n\n\nIraq\n\n\nI am a soldier serving in Iraq. I bought an Ipod while deployed and figured it would get ruined by the dirt, dust and my combat duties. I was fortunate to find the iSkin Eclipse. It is perfect for this environment. It completely encases the ipod to protect it from dirt and dust. It also has a hard exterior faceplate the protects the ipod from damage. \n\nThe Eclipse also makes it easy to use the controls; even when playing the new games put out by itunes.\n\nTo put it simply I am one happy soldier, thanks to the iskin Eclipse\n\n\n\n\nB000E8VQ74:safe:gary_a._fiebelkorn_jr._\"beef_man\"\n\n\nB000E8VQ74\n\n\niSkin Eclipse 30GB Video iPod Protector - Black (EVO3EC1-A): Electronics\n\n\nelectronics\n\n\n1 of 6\n\n\n5.0\n\n\nsafe\n\n\nAugust 24, 2006\n\n\nGary A. Fiebelkorn Jr. \"beef man\"\n\n\nwch, Ohio usa\n\n\nIt helps proteck your ipod veary well. I already broke one ipod some how but with this case it well never happen agai\n\n\n\n\nB00004Y2ZF:the_little_details_:juju4242_\"music_lover\"\n\n\nB00004Y2ZF\n\n\nMonster Reference PowerCenter HTS 3500 MKII: Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n4.0\n\n\nThe little details \n\n\nOctober 5, 2006\n\n\nJuju4242 \"Music Lover\"\n\n\nNYC\n\n\nOk, so I upgraded form a 30 inch LCD to a 50\" Plasma screen, all the sales guys kept trying to get me to buy a power center. I came home did some research and chose this one, if it didn't improve sound or picture at least i knew that the equipment was protected from power surges. \n\nI hooked everything up last light and turns it one. OMG!!!! amazing, my Yamaha receiver and Wharfdale Diamonds 8.2 no longer sounded muddy but insted bright and even toned, the kick from the sub was tight no sloppy and precise, it turned a good system into an eye opener. The Sony progressive scan and LG plasma now are like looking at real people no a screen , the HD stiff is simply the best , digital cable it good and the dvd's are quite near HD. All in all I would highly reccomend this to anyone. \n\nThe blue light is quite bright but i placed a piece of blue painters tape over it and it is no longer a problem. the voltage meter light is adjustable and not an issue. I now know the voltage in my home ranges between 123.5 to 121\n\n\n\n\nB00004Y2ZF:good_quality,_cool_looking_filter/protector_for_little_more_than_a_strip_form-factor.:cfcubed_\"cfcubed\"\n\n\nB00004Y2ZF\n\n\nMonster Reference PowerCenter HTS 3500 MKII: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n4.0\n\n\nGood quality, cool looking filter/protector for little more than a strip form-factor.\n\n\nSeptember 14, 2006\n\n\ncfcubed \"cfcubed\"\n\n\nNJ\n\n\nNeeded better protection than my $10 surge protector for my HT setup now that I have a couple DVRs & a plasma. Filtering would be a nice side benefit too. Very little $$ more than the power-strip design units I was looking at (& now I see it dropped even lower!).\n\nPros: Build quality, fit & finish, plenty of outlets & diversity of outlets for me, nice form-factor compared to power-strip design, cool looking LEDs & meter.\n\nCons: Those cool looking LEDs cannot be dimmed & are too bright (esp the blue one!) if facing HT viewers. I just blocked the LEDs w/a black card. CABLE/SAT cable filtering no good for my digital cable provider (Cablevision)... Caused more artifacts on HD channels, so couldn't use the cable line filter.\n\nBottom line: Seems a great deal esp at the new even lower pricing @ Amazon\n\n\n\n\nB00068E56G:tunes_for_the_tent!!:p._miller\n\n\nB00068E56G\n\n\nLogic3 i-Station Dock and Speaker Station for iPod: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nTunes for the tent!!\n\n\nNovember 9, 2006\n\n\nP. Miller\n\n\nRust-Free Central Washington State\n\n\nActually 4 and a half stars...I borrowed one of these Logic3 amplified Ipod speaker systems for a weekend camping trip and was impressed with the sound and portability. I decided to buy one of my own after looked at several other makes and models all of which were more money. Plenty of connectivity along with base adapters to hold most Ipod models securely. The little subwoofer and 'ambiance' switch make decent sound even at non-annoyingly low volume levels. Close to being a steal at the $59.00 I paid for i\n\n\n\n\nB0000DCRHA:excellent_for_skype_and_very_serviceable_for_music._:joe_hutnik\n\n\nB0000DCRHA\n\n\nSennheiser PC150 Noise Cancelling PC Headset: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nExcellent for Skype and very serviceable for music. \n\n\nNovember 9, 2006\n\n\nJoe Hutnik\n\n\nNJ, USA\n\n\nExcellent sound from phones and microphone. Comfortable on head and ears. Plenty of cable. On cable controls are handy.\n\n\n\n\nB000GISUAS:great:darkhaven_\"(mainly_horror_fiction_&_herbals_-_wheelsofterror.com,_darkhaven,_fictionaddiction.net_reviewer)\"\n\n\nB000GISUAS\n\n\nSansa E 200 Travel Case Kit: Electronics\n\n\nelectronics\n\n\n9 of 9\n\n\n4.0\n\n\nGreat\n\n\nSeptember 18, 2006\n\n\nDarkhaven \"(Mainly horror fiction & herbals - Wheelsofterror.com, DarkHaven, Fictionaddiction.net reviewer)\"\n\n\nFL\n\n\nI was looking for something suitable to house my sansa, and this fit the bill better than most. The leather case is superb, looking sharp, snug and protective. I enjoy the fact you can still easily access the buttons and menus through it. Both the leather case and the larger case have an atachee on the back, if you wished to connect them to the cord that comes with it. The cleaning cloth works as well as any lense cloth, great for cleaning. The screen protector is a pain in the rear to apply without bubbles, still haven't figured out how yet, and I may trim it to make it more visually appealing. But, really, if you invest in a product such as this, it really makes sense to protect the easily scratched screen! The larger case is compact and protective, having a small storage area, yet the cover is a bit unsightly because of the cheap, generic looking Sansa logo. Only other complaint is that the leather case goes on a belt, but not as a removable type. Only if you put it on when putting on the belt, which can be impractical in some circumstances. This way, I suppose, it wouldn't be able to fall off\n\n\n\n\nB00001RME1:easy_and_affordable!:asinthia_marshall\n\n\nB00001RME1\n\n\nInvent It! Iron-On Transfers: Electronics\n\n\nelectronics\n\n\n4 of 5\n\n\n4.0\n\n\nEasy and Affordable!\n\n\nOctober 22, 2001\n\n\nAsinthia Marshall\n\n\nDollar Bay, MI United States\n\n\nI wasnt sure how these Iron-Ons would work since one of the ratings on them was negative. But I just did a practice iron-on on my daughters shirts. It was so easy and they peeled off very well. My girls love their shirts with their own pictures on them. These are a great buy and worth their money\n\n\n\n\nB00001RME1:invent_it!_iron-on_transfers:david_price\n\n\nB00001RME1\n\n\nInvent It! Iron-On Transfers: Electronics\n\n\nelectronics\n\n\n13 of 13\n\n\n4.0\n\n\nInvent it! Iron-on transfers\n\n\nJune 12, 2000\n\n\nDavid Price\n\n\nToledo, Ohio\n\n\nWhile thinking of ideas for my church's 75th anniversary celebration, I decided to make T-shirts and sweatshirts using iron-on transfers and my ink jet printer. I chose the "invent it!" brand from Hammermill papers because it was the most cost-effective (cheapest). I created the design on my computer and tried it out on the transfer paper. The results were outstanding for a do-it-yourself project. I followed the instructions included with the transfers and it was a piece of cake. One thing that I did find out - keep the transfers dry after they have been printed. One got wet in the rain and it spotted very badly wherever a rain drop came in contact. This product also does a very good job in black and white, not just in color. The shirts that I did, laundered according to the instructions, came out of the wash lookin' good. I won't hesitate to use this product again in the future. However, I am now having trouble buying it locally, since my previous source no longer handles this product\n\n\n\n\nB0007LVW22:absolutely_thrilled!:mrs.c\n\n\nB0007LVW22\n\n\nYamaha YSP-1 Digital Sound Projector: Electronics\n\n\nelectronics\n\n\n29 of 31\n\n\n5.0\n\n\nAbsolutely Thrilled!\n\n\nDecember 5, 2005\n\n\nmrs.c\n\n\nColumbus, OH\n\n\nWe purchased the YSP-1 in August as a companion to our Hitachi Ultravision wall-mounted television. What a fabulous piece of audio equipment!\n\nWhen we began searching for a replacement for our Infiniti surround system, my husband and I had different concerns. He is an audiophile that demands wide range digital sound with full depth of field and I was ready for aestethic simplicity (no speakers throughout the room and wires everywhere). When we found the YSP-1 we thought it was too good to be true. One speaker instead of five? Wall-mounted? Doesn't take up any floor space? We visited two Yamaha dealers to see it for ourselves. One had the YSP-1 set up in a room environment. The other, on the salesroom floor. In both set-ups it performed consistantly. In both demonstrations however, my husband felt the low-end sound could use enhancement. So, we made the purchase and opted to use the YSP-1 with our existing subwoofer. Installation was fairly simple, provided you have the necessary tools. The most important, and time-consuming part was leveling and centering the speaker under the television. Now that it is installed and in use in our home, I feel the low-end sound is quite adequate and prefer not to use the sub-woofer. My husband though, prefers the earth-shaking action sequences in movies to mimic their intended theatre quality sound. More than movies the sound quality you get when listening to CDs is absolutely stunning. Overall, we both got what we wanted. Expert sound designation and sleek, minimalistic styling (with the added bonus of more floor and wall space). \n\n\n\n\nB000BB80TQ:you_bought_the_ipod_basically_for_the_looks.__buy_this_for_the_same_reason:ganesh_kumar_eswaran_\"ganesh_kumar\"\n\n\nB000BB80TQ\n\n\nApple MA072G/A Dock for iPod Nano 1st Generation: Electronics\n\n\nelectronics\n\n\n3 of 6\n\n\n5.0\n\n\nyou bought the IPOD basically for the looks. buy this for the same reason\n\n\nMarch 18, 2006\n\n\nGanesh Kumar Eswaran \"Ganesh Kumar\"\n\n\nLansdale, PA, USA\n\n\nYou bought the IPOD Nano basically for its looks. Buy this for the same reason. Its so cute and fits your Nano exactly as you wish. I bought an item from another company and though it was cheaper, it looked dirty on my computer table. This one has the \"Apple\" look\n\n\n\n\nB000BYQDM4:perfect_headphones_for_urban_dwellers_who_use_mass_transit:r._feld\n\n\nB000BYQDM4\n\n\niRhythms A-9800 Passive Noise Reducing Ear Bud Headphones: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n4.0\n\n\nperfect headphones for urban dwellers who use mass transit\n\n\nJuly 3, 2006\n\n\nR. Feld\n\n\nNew York, NY United States\n\n\nI chose these by reading other reviews on Amazon. I am pleased with them and recommend them. Good sound, light weight. The best feature is that they do NOT leak sound yet you can still hear loud noises around you. This is perfect for an ipod-using NYC subway rider. Don't want to annoy others by leaking sound, but you have to be able to hear if there is exterior noise, at least enough to know to turn down the sound and find out what's going on.\n\nOnly con - Once you lose or wear out the removable spongy pieces I'm not sure where you can get replacements\n\n\n\n\nB000BYQDM4:i_love_them:daycare_lady\n\n\nB000BYQDM4\n\n\niRhythms A-9800 Passive Noise Reducing Ear Bud Headphones: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nI love them\n\n\nJuly 3, 2006\n\n\ndaycare lady\n\n\n\n\n\nThese ear buds are great for people with small ears. I've tried several and these are by far the best. It comes with 3 sizes of buds and the smallest work best for me\n\n\n\n\nB000F8GX5I:great_product_for_a_fair_price:b._manchester_\"electronics_expert\"\n\n\nB000F8GX5I\n\n\nPanasonic KX-TG5653B 5.8 GHz FHSS GigaRange Digital Cordless Answering System with Three Handsets: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGreat product for a fair price\n\n\nNovember 5, 2006\n\n\nB. Manchester \"electronics expert\"\n\n\nCanton, NY United States\n\n\nThis is a terrific phone. It has a ton of great features and the three handsets is very convenient. This is the only phone that I use now. I only keep one analog phone for power outages. If you're looking to clean up a little bit of phone clutter this is a great way to do it\n\n\n\n\nB000F8GX5I:great_phone_system_at_a_great_price!:b._rojas_\"abro\"\n\n\nB000F8GX5I\n\n\nPanasonic KX-TG5653B 5.8 GHz FHSS GigaRange Digital Cordless Answering System with Three Handsets: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nGreat phone system at a great price!\n\n\nSeptember 27, 2006\n\n\nB. Rojas \"abro\"\n\n\nRoyersford, PA United States\n\n\nWe are very satisfied with our purchase. This phone system is great and very easy to use. Super clarity, volume, etc. Also, super fast shipping from Amazon. I chose Super Saver shipping and it arrived in less than 24 hours\n\n\n\n\nB0001OTBUK:great_cam,_and_its_not_expensive!:matthew_berry\n\n\nB0001OTBUK\n\n\nLabtec WebCam Pro: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n5.0\n\n\nGreat Cam, and its not expensive!\n\n\nSeptember 27, 2006\n\n\nMatthew Berry\n\n\nMaryland USA\n\n\nI saw the other reviews and was hoping I would have the same experience as the happy reviewers. Well, I got the cam and it was easy to install like they say. It just takes a little tinkering, with the PC options before you use it. With the mic and software it comes with, you get a lot for your money! If I bought a cam in the store, I would have been mad to lose out on this PC camera. It is SWEET\n\n\n\n\nB0000CEPE8:no_speed_boost_but_great_range_boost.:hyeonsuh_koo_\"koo\"\n\n\nB0000CEPE8\n\n\nD-Link DWL-G120 Wireless USB Adapter, 802.11g, 54Mbps: Electronics\n\n\nelectronics\n\n\n2 of 4\n\n\n4.0\n\n\nno speed boost but great range boost.\n\n\nMarch 13, 2006\n\n\nhyeonsuh koo \"koo\"\n\n\nseoul,korea\n\n\nok mimo is becomming popular ....i dont give a darn.\n\nmy home net work relies on a cable modem(moto4200)+wlanRouter\n\nmy house is 2 story high and it's made of steelbar re-enforced\n\nconcrete... with brick wall here and there...\n\nsince the internet performenc is lower then 9mbits max...\n\nall i wanted was penatration through the concrete floor..\n\ni 'v used pen type? usb adapters and so on ...but they were\n\nalways short in range ...but g120 was another demention ...\n\nallover the house allrange !when i put this close to the wall\n\nadjacent to the negihbor's house, i was able to update my \n\nneighbor's router firmware (i've done it twice already)\n\nspeed is not the main concern in my case .\n\nbut you guys should know about it's response speed . its a\n\nlittle slower then pcmcia types even if i use usb2.0 connection\n\nbut i think thats because my lap top is running xp-pro on\n\nceleron 800/256mbram. as long as you have a good pc \n\n(faster then mine~!)\n\nyou wont have any problems. it's not that hot dont worry!\n\ni wont give this product 5stars because it's not small enough.\n\nwho needs mimo. !\n\nps: use the zero config not the stupid utility\n\n\n\n\nB00004Z0C7:worked_as_expected:william_cameron_\"crazywilie\"\n\n\nB00004Z0C7\n\n\nMaxell HP20 Headphone Extension Cord with Adapters: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nWorked as expected\n\n\nNovember 9, 2006\n\n\nWilliam Cameron \"crazywilie\"\n\n\nKoalarado, CO\n\n\nThis extension cord did what it was supposed to do and did it just fine. No problems whatsoever\n\n\n\n\nB000068CNU:price_went_up:louis\n\n\nB000068CNU\n\n\nBelkin Components F9M923-08 9-Outlet Maximum SurgeMaster: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n4.0\n\n\nPrice went up\n\n\nJune 27, 2006\n\n\nLouis\n\n\nNew York, New York United States\n\n\nAs soon as I bought this from Amazon, the price went up from $21 to $ 37. I was going to buy another one, but the price is too high. Overall, this is a great product it has everything you need. \n\nHowever, I wouldn't buy it at the current price, I'm waiting for the price to go down before I buy another one\n\n\n\n\nB0000899WI:excellent_memory:carlos_moya\n\n\nB0000899WI\n\n\nKingston 512MB PC2700 CL2.5 SODIMM UNB ( KVR333X64SC25/512 ): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nexcellent memory\n\n\nJanuary 29, 2006\n\n\nCarlos Moya\n\n\n\n\n\nwork fine with my hp pavillion zd7050. increase the performance of my laptop\n\n\n\n\nB00029U0YK:the_best_option_for_the_'best'_fit_......:evitzee\n\n\nB00029U0YK\n\n\nShure Universal Fit 20 Pack Replacement Foams for E1c, E3c and E5c Earphones: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nThe best option for the 'best' fit ......\n\n\nNovember 13, 2006\n\n\nEvitzee\n\n\nFredericksburg, TX\n\n\nI tried all the rubber vinyl plugs that came with the E3C's, but they all had the shortcoming of not effectively sealing the ear canal making bass very poor, and they were uncomfortable. The yellow foam plugs are easy to use and work just like the ear foams many people use in industrial applications. They effectively seal the ear canal and are very comfortable. Although they seem a bit expensive I've found that once they get dirty you can use water and a bit of dish detergent to clean them. Just soak, squeeze, rinse and let them air dry and they are as good as new. You can do this three or four times, then just toss them away. \n\nHighly recommended\n\n\n\n\nb000062vuo:incredible_sound,_unbeatable_value:greg_robertson\n\n\nb000062vuo\n\n\nKlipsch ProMedia 2.1 THX Certified Computer 3-Speaker System (Black): Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n5.0\n\n\nIncredible Sound, Unbeatable Value\n\n\nOctober 30, 2006\n\n\nGreg Robertson\n\n\nWest Quincy, MA\n\n\nOnce THE speaker of choice for every hard rockin' audiophile with a big enough garage, Klipsch fell off the landscape for a while after being sold to a corporate conglomerate that didn't know the real value of what they'd bought. But after another buyout by a small group of genuine audiophiles, Klipsch is back on top, from a sound quality standpoint -- and it shows in the ProMedia 2.1 THX.\n\nI have my trio connected to a new Mac Pro desktop unit and the sound is absolutely magical. Yes, there are more complex set-ups, with 5 or 7 speakers spread out across the room, but if you just want the magic without the madness, the Klipsch ProMedia 2.1 THX is an incredible (and very affordable) solution that's quick and easy to configure in about 5 minutes.\n\nOf course, there are other 3-speaker desktop systems by makers like Logitech, Cyber Acoustics, and Intec, but here's the difference: Klipsch is a true speaker company that connects great speakers to your computer, while those others are computer peripherals companies that one day decided to make speakers. NOT the same thing. The only brands that are true competitors to Klipsch in this area are Bose, Harman Kardon, JBL, and Altec Lansing. I find that the Altec Lansing and JBL 3-speaker sets both sound nasal and \"tinny\" on the high end, while the Bose and Harman Kardon sets sound great, but are more expensive. So, after a good look (and listen) around, I went with Klipsch.\n\nIn any case, this is an incredible sound set-up for your computer-based music player and should not be overlooked. Enjoy\n\n\n\n\nB00003CWG3:fantastic_splitter:francesco_esposito_\"francesco__esposito\"\n\n\nB00003CWG3\n\n\nMonster Cable TGHZ-3RF 3-Way Low-Loss RF Splitter for TV and Satellite, 2 GHz: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n5.0\n\n\nFantastic Splitter\n\n\nJuly 9, 2005\n\n\nFrancesco Esposito \"Francesco Esposito\"\n\n\nDetroit, Michigan\n\n\nThis splitter was set-up as part of a configuration that splits one line from the street into three and then into sixteen ports and a line to the cable modem for a fraternity house. I used this brand of splitters and a moterola amplifier all the way through, and the results were amazing. The picture quality is crystal clear, even when almost all of the ports are in use, and the internet is blazing fast. Compared to the set-up we used to have, with some cheap splitters and a radio shack amplifier, these pieces are WELL WORTH THE SMALL EXTRA COST\n\n\n\n\nB00003CWE5:buy_these_cables!:\n\n\nB00003CWE5\n\n\nMonster Cable MV2CV-1M Monster Video 2 Component Video Cable (1 Meter): Electronics\n\n\nelectronics\n\n\n4 of 12\n\n\n5.0\n\n\nBuy these cables!\n\n\nMarch 15, 2001\n\n\n\n\n\n\n\n\nThese cables are super with my new DVD player. There is absolutely no interference from my other home theater equipment. The picture is soo clear, and vivid. The cables themselves are really made well, they could hold up 100 pounds of weight no doubt. If you get these Monster cables you won't be sorry\n\n\n\n\nB0002WPSCG:great_system:j._king\n\n\nB0002WPSCG\n\n\nLogitech X-530 5.1 5-PieceSystem with Subwoofer Speaker: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGreat System\n\n\nNovember 4, 2006\n\n\nJ. King\n\n\n\n\n\nI previously had a 2.1 sound system for my computer, that were pretty good a few years ago when I bought them. When I decided to replace them with these I was rather happy with my choice. The sound is extremely clear and crisp, whether it be music or games, and I am very pleased with them. The only downside to them is how many extra cords there are behind my desk, but you have to expect that with a 5.1 sound system\n\n\n\n\nB0001GZ87I:great_value_for_money:integral_yogi_\"auro\"\n\n\nB0001GZ87I\n\n\nTargus CVR400 15 CityLite Notebook Case: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGreat value for money\n\n\nNovember 9, 2006\n\n\nIntegral Yogi \"Auro\"\n\n\nState College, PA USA\n\n\nI am glad I bought this backpack. It exceeded all my expectations. I strongly recommend this one\n\n\n\n\nB00085ESA0:i_think_they_are_great:m._baggesen\n\n\nB00085ESA0\n\n\nPhilips HN 110 Folding Noise Canceling Headphones: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nI think they are great\n\n\nNovember 9, 2006\n\n\nM. Baggesen\n\n\nDallas, Texas\n\n\nThese are really great. Super noise cancelling. Affordable. They cover my ears completely without problem. At first, the headset was a bit tight, so I stretched the top headband upwards a little to relax or slightly loosen it. No more problem, while still having a snug fit. These headphones also fold up very compact, have two plug adapters and a nice durable sack for storage.\n\nIf you buy these, just make sure they fit well for your ears. If so, these are great; if not, try another type. Like all things that touch the body, it is best to try at a local store, before you buy it. \n\nThis is the lowest price I have seen for this product.\n\n\n\n\n\nB0009I6K7K:i'm_a_satisfied_customer.:linda_mayfield-hayes_\"slendah\"\n\n\nB0009I6K7K\n\n\nAltec Lansing inMotion IM3C Portable Audio System for iPod and iPod Photo: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nI'm a satisfied customer.\n\n\nNovember 9, 2006\n\n\nLinda Mayfield-Hayes \"Slendah\"\n\n\nNew Castle, DE\n\n\nI am happy with my purchase. I only bought it because I don't like the idea of going deaf listening to loud music through earphones. I would not use this product to entertain at a party, but it serves my needs\n\n\n\n\nB00009KAPW:life_saver:cairo_commuter\n\n\nB00009KAPW\n\n\nBelkin F8E464 Backup Battery Pack for iPod: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nlife saver\n\n\nNovember 22, 2006\n\n\nCairo commuter\n\n\nConnecticut, USA\n\n\nOn a recent trip, I was 23 hours in transit and couldn't have made it without this little device. It powered my movies and TV shows for at least 12 hours or so without a flicker. I was worried that the four AA batteries would make it heavy to carry but it didn't matter when the device was sitting on my tray table. Besides, it was comforting to know that AA batteries are ubiquitous throughout the world. By the way, I substituted the original batteries with Duracells on the advice of previous reviewers\n\n\n\n\nB00009KAPW:good:sweetiepie54\n\n\nB00009KAPW\n\n\nBelkin F8E464 Backup Battery Pack for iPod: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nGood\n\n\nOctober 2, 2006\n\n\nSweetiePie54\n\n\nBoulder, Co, USA\n\n\nI bought this battery pack for a 10 day trip to Italy and I am very happy with it. I never had to change my batteries once and I listened to it all the time. Reading other people's reviews I threw out the batteries it came with and used different ones and it worked great. The only complaint I have is the suction cups aren't that great. Several times my Ipod came off the suction cups and being on a plane with minimal room and people sitting next to me trying to get my ipod off the floor is not fun. \n\nOverall, I recommend this product. It is a little bulky, but if you're someone like me who cannot live without music, it's a great product to have if you aren't going to be able to charge your Ipod\n\n\n\n\nB000E5CZ4U:performs_just_fine:andrew_to_\"amto\"\n\n\nB000E5CZ4U\n\n\nKensington 33350 Entertainment Dock 500 with Charger for iPod: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n4.0\n\n\nPerforms just fine\n\n\nAugust 24, 2006\n\n\nAndrew To \"amto\"\n\n\nBoston\n\n\nI have seen many reviews about problem with the volume control. I experienced the same thing until I realized that I mistaken the up & down buttons with the + & - buttons. The volume works just fine on my wife's old Sony receiver. The remote is quite nice and it does everything the iPod control could do. As stated in others' reviews, you can't read the screen from a distance, thus rendering the ability to scroll through the menu with the remote a bit pointless. However, it would be worse if the remote couldn't scroll through menus.\n\nIt is compatible with the latest video iPod. I have the 60 gig iPod but I do notice a slight hiss coming out from the speakers. I listen to classical music and the hiss bothers me. I bought this for my wife and she is not as picky as I am in terms of sound quality. Then again, I don't expect high fidelity quality from this small setup. Since I have this little gadget for my wife, I bought the Squeezebox for myself from Slim Devices to stream music which sounds a lot better.\n\nIn summary, this dock does a good job and the design is not bad. The construction is solid and I like the light on the remote. If you are looking for great sound quality, I haven't encountered any dock that is better than this one, so this might be your best bet. The Apple A/V cables work fine as well but it is more expensive than the dock and I see more value in this dock\n\n\n\n\nB0002SAF3C:awesome!!:shobi_lawalata_\"absolution81\"\n\n\nB0002SAF3C\n\n\nLogitech MediaPlay Cordless Mouse- Blue: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nawesome!!\n\n\nJune 9, 2006\n\n\nShobi Lawalata \"absolution81\"\n\n\nBerkeley, CA\n\n\nI'm really happy with this purchase. I was worried that it would not work with Rhapsody (which is the media player I use the most), but it proved to work well with it. In fact, it works with all the media player I've thrown at it, including Windows Media Player, MediaLife, RealPlayer, iTunes, Rhapsody, DivX Player, WinDVD, and PowerDVD. \n\nAll the buttons are functional (and I worship functionality - hence the 5 stars), although Firefox users have to deal with extra configuration steps to make the back and forward button work. No biggies, though, since setting up is really easy. Basically all you have to do is open the SetPoint software that was installed with the driver, point to the intended buttons that you want to program, and assign a corresponding keystroke to it. In this case, it's Alt+Right for forward button and Alt+Left for back button. Click \"Apply\" and you're good to go a-browsin'!!\n\nBattery life so far is really good - it's been going on the same set of Duracell AA batteries since I got it 3 months ago, and still going strong. \n\nThe RF range is really good, although admittedly my room isn't big enough to really put its range to the test. Dorm-dweller users should be happy with the range, though. You could just lie on your bed and only lift one finger to control your music or movie.\n\nThis mouse is also great looking; not cheap-plasticky but actually sharp and slick. It does feel a bit odd to the grip when using as a remote control, but as a mouse it's really sturdy and ergonomic. \n\nI can't comment on installation since it's been a while since I first used it, but I guess no impression is good impression - if it was troublesome I'm sure I'd remember.\n\nI'd second the other reviewer's gripe about how most cordless keyboards come with a mouse, and this fantastic mouse doesn't come with a cordless keyboard... But it's a great buy, all in all\n\n\n\n\nB00004VXO5:increasing_tech._data:ray_hatton\n\n\nB00004VXO5\n\n\nUniden Washington 40 Channel CB AM/SSB Base Station: Electronics\n\n\nelectronics\n\n\n7 of 12\n\n\n4.0\n\n\nincreasing tech. data\n\n\nJanuary 31, 2001\n\n\nRay Hatton\n\n\nTracy,Mn\n\n\nIt would seem, even to a non-tech, that someone has switched your Uniden mobile/base tech. data around. Secondly, terms such as "standard" mean less than trustworthy when applied to sensitivity and selectivity. Please don't feel I berate these 2 fine products.However as I am a com/tech. I would like the actual bench test measurements for an informed recom/purchases.Also I would like to know If Cobra 1000 or 2000 series are availible as they still have the best selectivity and sensitivity that I have had on my bench yet.Thank Yo\n\n\n\n\nB00005B8SD:powerful_for_the_price:\"saellys\"\n\n\nB00005B8SD\n\n\nJensen JB7 Over-the-Earbuds: Electronics\n\n\nelectronics\n\n\n13 of 14\n\n\n4.0\n\n\nPowerful for the price\n\n\nAugust 29, 2002\n\n\n\"saellys\"\n\n\nSpringfield, MO United States\n\n\nUntil an accident caused one bud to stop emitting sound, these were my favorite pair of earphones. They were comfortable (I often left them in when I went to sleep), affordable, lightweight, durable, and had good sound quality. I used them with my Sony CFD-V5 boombox, my Oritron OP5034 portable CD player, and my RCA RP2410 MP3-CD player. On all three, bass and detail was great, and there was no distortion at a higher volume. All in all, these earphones were a great value; the only possible downside is a bit of fuzziness in the sound, which I got used to rather quickly and began to prefer over my other earphones (it lent a bit of warmth to the music). I plan to buy another pair in the near future, and I highly recommend that you do so as well\n\n\n\n\nB00000J0HX:great_service,_cartridge_like_a_new_one:les_barrett\n\n\nB00000J0HX\n\n\nHP No. 45 Black Inkjet Print Cartridge (51645A): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nGreat Service, Cartridge Like a New One\n\n\nJune 26, 2003\n\n\nLes Barrett\n\n\nLeavenworth, ks United States\n\n\nOrdering was fast and easy. Could not detect any inferiority in the cartridges and will order them again\n\n\n\n\nB0002ILKWM:apple_30\"_cinema_display_and_pny_6600gt:kustard_king\n\n\nB0002ILKWM\n\n\nApple Cinema 30\" HD Flat-Panel Display: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n4.0\n\n\nApple 30\" Cinema Display and PNY 6600GT\n\n\nJanuary 31, 2006\n\n\nKustard King\n\n\nNew York City\n\n\nI received it today, and it's a monster. If you've seen one in a store, it looks twice as big on your desk. Until a few hours ago, I was running two 20\" aluminum cinema displays on a PNY 6600GT. After doing online research about using the 30\" display under Windows XP, I found that the \"functional\" cheap solution being used is in fact the PNY 6600GT (Verto 128MB DDR3 with two DVI connectors). Considering that the other two most noted solutions, QuadroFX 4000 AGP or Parhelia DL256 PCI, are going for $600+ on ebay, I was willing to give my old beater a try. Besides, I didn't feel like buying another AGP card with PCIE taking over the world, or sacrificing the only PCI slot in my Shuttle XPC (SB75G2).\n\nTo my great relief, the monitor worked on the card right away, even showing me the POST and Windows start-up screen (it did for the 20\" displays, too). But to my absolute delight, the card is actually running both the 30\" and a 20\" in 32-bit color. The 20\" looks absurdly small next to the 30\", like I didn't water it enough or something. The 30\" is enormous enough that I think it might be better were it curved! Maybe I'm just sitting too close.\n\nThe only problem with the 6600GT is that the DVI jacks are so near each other that I had to take the plastic housing off of the 20\" plug to fit it below the 30\". I'm not a gamer, so I can't say whether the card performs well for that. It feels as zippy as with the 20\" on regular windowing tasks. I use Eclipse, Flash, Photoshop, etc., but no video editing or 3D stuff.\n\nThe screen had a slight warm cast that I didn't like. Nvidia's control panel let me adjust it manually, and now my 30\" and 20\" look almost identical. My biggest fear was of course dead pixels. By the good grace of lucky ducks, this is the third panel I've gotten from Apple with no dead pixels. Brightness is uniform but for a very very subtle drop in the far lower left and right edges. It's not apparent unless you put white there and actively look for it.\n\nIn any case, I bought it to get more programming panels on the screen at once, and it does a fine job at that. I'm using the 20\" for browsing and, alas, a few panels that STILL don't fit. Apple, where's 3840 x 2400?? I strip one star cuz it's crazy expensive.\n\n\n\n\nB0002ILKWM:apple_cinema_30\"_hd_flat-panel_display:victor_bono\n\n\nB0002ILKWM\n\n\nApple Cinema 30\" HD Flat-Panel Display: Electronics\n\n\nelectronics\n\n\n19 of 19\n\n\n5.0\n\n\nApple Cinema 30\" HD Flat-Panel Display\n\n\nJanuary 12, 2006\n\n\nVictor Bono\n\n\n\n\n\nThis screen is great.\nUpgraded from a 23\" and the increase in realestate is awesome.\nYou can play games with crystal clarity; have so many pages up and read them clearly.\nYou will need a graphics card that supports Dual-link DVI, not a card that says dual DVI.\nI installed the GeForce 7800 GT on my PC and it works flawlessly!\nThe Quadro cards are great, but big $$$.\nThe 7800GT can be had for under $300!\n\nHere's a list of cards for PC and Apple:\nNVIDIA Geforce 6800Ultra DDL AGP for Powermac G5\nNVIDIA Geforce 6800GT DDL AGP for Powermac G5\nNVIDIA Geforce 6600 PCI Express for Powermac G5(PCI Express)\nNVIDIA Geforce 7800GT PCI Express for Powermac G5(PCI Express)\nNVIDIA QuadroFX 4500 PCI Express for Powermac G5(PCI Express)\n\nFor the PC Windows platform, the following graphic cards support the Apple 30\" Cinema Display:\n\nNVIDIA QuadroFX 2000\nNVIDIA QuadroFX 3000\nNVIDIA QuadroFX 3400\nNVIDIA QuadroFX 3450\nNVIDIA QuadroFX 4000\nNVIDIA QuadroFX 4400\nNVIDIA QuadroFX 4500\nNVIDIA Geforce 7800GT\nNVIDIA Geforce 7800GTX\nNVIDIA Geforce 7800GTX 512\n\n\n\n\n\nB00009AKLI:just_what_the_doctor_ordered:r._dewitt_\"cowboy\"\n\n\nB00009AKLI\n\n\nTargus DVD301 9 Sport Portable DVD Player Case: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nJust what the doctor ordered\n\n\nNovember 10, 2006\n\n\nR. DeWitt \"Cowboy\"\n\n\nCuero, Tx USA\n\n\nOutstanding item. Plenty of room to carry dvds. Enough padding to protect the player, but not too bulky\n\n\n\n\nB00012O7NS:good_for_madrid:richard_r._harman\n\n\nB00012O7NS\n\n\nGarmin MapSource European City Navigator v8: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n4.0\n\n\nGood for Madrid\n\n\nJune 19, 2006\n\n\nRichard R. Harman\n\n\nElkridge, MD United States\n\n\nI ordered v8 as soon as it was advertised by Garmin but they shipped v7 instead. Six weeks later I got the v8 update dvd. My wife and kids had to make due with v7 while in Madrid. She was pleased with the accuracy and said it enabled her to travel around with confidence. However, she noted that Madrid was undergoing quite a bit of major road construction and she got stuck in multiple traffic jams on the major highways. In the end, she would veer away from the major roads and let the unit re-calculate her route. \n\n\n\n\nB00004Z0C8:good_product_within_its_limits:john_hunter\n\n\nB00004Z0C8\n\n\nGarmin MapSource CD ROM (USA TOPO): Electronics\n\n\nelectronics\n\n\n22 of 22\n\n\n4.0\n\n\nGood product within its limits\n\n\nAugust 24, 2006\n\n\nJohn Hunter\n\n\nCleveland, TN\n\n\nIf you understand this product's limits before you buy it, you'll be happy. It shows topographical contours, rivers and most streams for the entire United States. In addition to that, it shows roads that were in existance 10-20 years ago. These major roads are also named.\n\nThe limit is that newer roads don't show up. It also doesn't support auto-routing, even if your GPS does. Garmin sells other products that are tailored for road navigation but you have to do some independant research or logical deduction to figure out that this package isn't suited for it.\n\nThe product support could be better. Neither the manual nor web site tell you how to load all 3 CDs on your computer so you don't have to swap CDs when transitioning between zones. The company's web site doesn't give a good explanation of what the software can and can't do.\n\nYou'll be disappointed if your expectations aren't grounded in reality. Garmin could help alleviate this problem by making this information readily available\n\n\n\n\nB00005MNSS:very_nice_monitor,_good_price_too:\n\n\nB00005MNSS\n\n\nEnvision EN-7100E/SI 17\" LCD High-Resolution Color Display: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nVery nice monitor, good price too\n\n\nDecember 19, 2003\n\n\n\n\n\n\n\n\nI am very pleased with my purchase of the Envision EN-7100 monitor. It arrived in perfect condition, no dead pixels. I resisted spending the money on a new monitor for a long time, but now I'm glad I made the switch to LCD. The color and clarity is wonderful. Most of all, I love having all this extra space on my desk. For the money, this is a great monitor\n\n\n\n\nB00003CWET:monster_makes_the_difference!:unionmemberhhh@yahoo.com\n\n\nB00003CWET\n\n\nMonster Cable MVSV2-1M High-Resolution S-Video Cable (1 Meter): Electronics\n\n\nelectronics\n\n\n17 of 19\n\n\n5.0\n\n\nMonster makes the difference!\n\n\nJuly 25, 2000\n\n\nunionmemberhhh@yahoo.com\n\n\nMidwestern USA\n\n\nI just recently bought a Sony DVP-S560D model DVD player to replace my older, more out of date player. I hooked it up, and to my surprise, it wasn't any better than my old player as far as quality. Then I purchased S-Video cables from Monster Video and now the colors are more vibrant, the picture is in higher-res, and I can see every little detail on the screen. The picture quality that these S-Video cables produce are surpassed only by component video cables, which cost many times more the price. If you are considering buying these cables then just consider that for the price of only one DVD, you can get better performance and picture quality\n\n\n\n\nB0007KXFH8:worth_the_money:carlos_r._insuaste_\"carlos\"\n\n\nB0007KXFH8\n\n\nGarmin GPS10 Bluetooth Enabled Wireless GPS with Mount and Software: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nWorth The money\n\n\nAugust 3, 2006\n\n\nCarlos R. Insuaste \"Carlos\"\n\n\nNJ\n\n\nI wanted to thank amazon for their quick response to my first order not arriving. within a week they sent me another. I have been using it everyday, and have not gotton lost. I install wood flooring in NJ PA NY, works like a charm. I use it with my palm treo 700P.\n\n\n\n\nB00004SSS0:good_ink_but:computer_illiterate_in_chicago\n\n\nB00004SSS0\n\n\nHP No. 78 Tri-Color Inkjet Print Cartridge (C6578DN): Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nGood Ink But\n\n\nDecember 31, 2005\n\n\nComputer illiterate in Chicago\n\n\nChicago\n\n\nFor the most part, I do find HP ink to be the best. However, every now and then a new ink cartridge is bad. One of the colors doesn't print for some reason. My opinion is that these ink cartridges are just too expensive to be getting any bad ones\n\n\n\n\nB000CR3RJW:perfect_for_all_your_mp3s!!!:ben_a.\n\n\nB000CR3RJW\n\n\nMultimedia Card, Mobile 1GB, Dual: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nPerfect for all your mp3s!!!\n\n\nOctober 23, 2006\n\n\nBen A.\n\n\nWest Lafayette, IN USA\n\n\nI used this card on my Nokia N70 for storing mp3 files. Granted I can't fit my entire music collection on it, but if I wanted to, I'd buy an iPod. This is perfect for listening to my favourite songs on the go, as well as taking pictures and video clips! Highly recommended\n\n\n\n\nB00012F7HS:excellent_product:raja_sekhar_matukumalli_\"simple\"\n\n\nB00012F7HS\n\n\nSony Power Charger with 4 Ni-MH AA Batteries: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nExcellent Product\n\n\nNovember 12, 2006\n\n\nRaja Sekhar Matukumalli \"Simple\"\n\n\nUS\n\n\nI purchased this product to use with my Camera. The charging lasts very good time. Would recommend this product to others. Good work Amazon\n\n\n\n\nB00004Y2VS:good_product:brett_godfrey_\"phoenix_risen\"\n\n\nB00004Y2VS\n\n\nMonster Cable XPMS-50 Monster XP Clear Jacket (Compact Speaker Cable 50-Foot Piece): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGood product\n\n\nApril 14, 2006\n\n\nBrett Godfrey \"Phoenix Risen\"\n\n\nWestminster, Colorado\n\n\nI love sound systems. So when I got my new reciever (Sony STR-DE898/B) I hooked up the new speaker wires with the THX Certified Gold Plated speaker connectors and wow what a difference. I miscalculated how much wire I needed so my rear right speaker does not have the new speaker wire, I can hear a light and day difference in sound quality. Needless to say I'm ordering more to complete the job. The monster fiber optic cable is a must as well. I can hear sounds that I normally couldn't on my old system. I like testing sound with: The Hulk (the scene were he tears up the tanks), Saving Private Ryan (taking the beach), and Open Range (Gunfight at the end of the movie). In all these movies there are small sounds that you can distinctly hear now that I could not before the new wire and connectors. Excellent product\n\n\n\n\nB000IF4TPY:more_bang_for_your_buck.:xeticus\n\n\nB000IF4TPY\n\n\nCreative Zen Vision W 30 GB Widescreen Multimedia Player (Black): Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n5.0\n\n\nMore bang for your buck.\n\n\nNovember 4, 2006\n\n\nXeticus\n\n\nfort lauderdale, fl\n\n\nThis is my first mp3 player so I researched it carefully before making my decision. The gorgeous screen, the wide number of codecs it supports and the 30gb hard drive were all selling points. I'm so glad I bought it. Videos look great on the screen. Some need to be converted over which is a slow process but in general I love it. The built in speaker doesn't do it justice. Even on the included earbuds the sounds quality is amazing. Making playlists with the creative software is very easy. My biggest problem is the lag of accessories for it. No case, no screen protectors out for it. I'd love to see a docking station that lets you record to it as a dvr the way the archos players do. But for the money it looks to me to be a far superior player to the Ipod or the Zune.\n\n\n\n\nB00005B8M3:wrong_adapter_for_garmin_330c:m._lamonica_\"meekomagic\"\n\n\nB00005B8M3\n\n\nGarmin 010-10085-00 Cigarette Lighter Adapter: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n5.0\n\n\nWrong adapter for Garmin 330c\n\n\nNovember 10, 2006\n\n\nM. LAMONICA \"meekomagic\"\n\n\nCentral Florida\n\n\nThis adapter can not be used with the Garmin 330\n\n\n\n\nB000EEDXJM:nice_armband:s._tobison\n\n\nB000EEDXJM\n\n\niSnug iPod Armband: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nNice Armband\n\n\nJuly 31, 2006\n\n\nS. Tobison\n\n\n\n\n\nA little bouncy while running, but a convenient product. Fits perfectly around all of my MP3 players (large and small)\n\n\n\n\nB000050FZP:excellent_basic_phone:old_lady\n\n\nB000050FZP\n\n\nAT&T 210 Trimline Corded Phone (White): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nExcellent basic phone\n\n\nAugust 23, 2006\n\n\nOld lady\n\n\nNew Jersey USA\n\n\nThis is a great phone. Memory is easy to progam. The phone has 3 memory keys for emergenies and 9 additional memory keys for personal numbers. It can be used on a desk or hung on the wall. The only thing I don't like about the phone is the buttons on the handset. However, I knew about this feature before purchasing the phone. I would recommend this phone to anyone who is looking for a good basic phone\n\n\n\n\nB00067IDLA:excellent_portable_mouse:aglaiapoo\n\n\nB00067IDLA\n\n\nAnycom Inc CC3141 Anycom BTM-100 Bluetooth Mobile Mouse with usb Charge: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nExcellent portable mouse\n\n\nApril 18, 2006\n\n\naglaiapoo\n\n\nGreece/Japan/USA depending\n\n\nI did a lot of research on Amazon.com before I finally decided on this mouse. I wanted to replace my (extremely) small Kensington USB mouse with a retractable cable. At home, I use a Wacom tablet. Next month I am traveling abroad, so I wanted a slightly bigger mouse that can handle more frequent use than the Kensington. Wireless was key for portability. I also wanted something rechargeable, and I didn't want the USB adapter sticking out.\n\nI received it today, and I'm on my first charge. So no comments on batteries (which can be replaced with other AAA NiMH for optimal performance if desired anyway).\n\n- I have large but skinny hands. Mouse is small, but its long length helps in terms of where to place your fingers. Of course convenience is not comparable to a full-size mouse, but if you want portability, this is about the right size.\n\n- I have a PowerBook that runs Tiger (10.4). Excellent Bluetooth connection. Also very good response, very little delay. Only reservation is for the scroll wheel, which requires subtle pressure for accurate scrolling - this is ever so slightly uncomfortable if you have large hands, because the mouse is flat and your hand is adjusted over the mouse so that it is a bit hard to apply adequate vertical pressure on the scroll wheel from that position. It is hard to explain, but not a big issue for me. \n\n- USB charger works well. The added size of the USB adapter helps with comfortability (still flat, but becomes an otherwise full-sized mouse) - despite the fact that the cable attaches to the bottom of the mouse.\n\n- As mentioned by others, installing the batteries is a challenge. Pull the notch on the bottom of the mouse as you slide the top (part with the Anycom label) away from the clickers. Once I figured it out, not so hard. \n\nOverall, I don't seek perfection anyway, so this mouse does the job adequately for me. I haven't tried any other Bluetooth mouse before, so I'm more or less in awe\n\n\n\n\nB00006B9QF:sandisk_is_reliable:m._donelson_\"bigsleep39\"\n\n\nB00006B9QF\n\n\nSanDisk 512 MB CompactFlash Card, SDCFB-512-A10 (Retail Package): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nSanDisk Is Reliable\n\n\nJuly 9, 2006\n\n\nM. Donelson \"bigsleep39\"\n\n\nAnn Arbor, MI United States\n\n\nThis card has been working great for me so far after over a month of heavy use. \n\n512 MB is more than enough space for anyone with a 5 MB or less camera, assuming you transfer photos off your camera fairly regularly and are not shooting hundreds of photos at a time. With the 1200x1600p setting I use on my camera most often, this card holds over 400 photos, and it will hold even more for those using a more email-friendly setting. Do the math to figure out if 512 is the number for you, but I've had no problems with SanDisk products\n\n\n\n\nB000A5TAT2:very_easy_to_use_and_quick_to_acquire_satellites:t._milligan\n\n\nB000A5TAT2\n\n\nGarmin Streetpilot C340 Portable GPS Navigation System, Traffic Ready: Electronics\n\n\nelectronics\n\n\n22 of 22\n\n\n5.0\n\n\nVery easy to use and quick to acquire satellites\n\n\nNovember 16, 2006\n\n\nT. Milligan\n\n\nCalifornia\n\n\nI have to admit, I'm a GPS junkie. I've had a variety of GPS units over the past decade and currently own 3 - the c340 being my latest acquisition for use solely when driving.\n\nI am greatly impressed with this unit. It was fast to get going right out of the box and after the initial satellite acquisition and almanac download (for those who are interested, when a GPS unit is turned off and moved a significant distance, say 300+ miles, from where it last thought it was, it needs to recalibrate itself by downloading the current almanac of satellite positions from the GPS satellites. This can take a few minutes.) In any case, after the initial automatic recalibration, the unit acquired satellites quickly whenever I turned it on.\n\nThe user interface is very easy to use. I've used Hertz NeverLost systems for years and have always thought they had a good interface. I think Garmin's is better.\n\nOne highly useful feature is the ability not just to search for Points of Interest (POI), but to have the unit search for those points of interest that are along the route you are currently navigating. This allows you to identify, say, hotels along your route while you are driving and then, using the telephone info provided by the unit, to call the hotels up and see if you can negotiate a lower rate before you arrive - \"let your fingers do the walking...\"\n\nIf you don't tell the unit where to look, though, it can take quite a while to search for a Point of Interest. I didn't know which city to specify and I did a search for \"Eagle Rock Rec\" (I was too lazy to spell out \"Eagle Rock Recreation Center\" which turned out to be about 175 miles away) and the unit chugged away for several minutes but eventually came back with the right entry for me to navigate to. I later realized that even though I didn't know the exact city the destination was in, I did know a city that was near the desired destination. When I specified that information, the c340 took only a few seconds to find the right location.\n\nThe advanced user can also create your own POI lists complete with custom icons and load those into the c340. I have done this for all In-N-Out Burger and Krispy Kreme locations. The advantage to having your own custom POIs even though these places are in the built-in POI list is that the custom POIs are visible on the c340 map screen at certain zoom levels. If you are a Geocacher, you can do the same with geocaches, but you'll still need your handheld unit to find the cache.\n\nThe newer (and more expensive) c5xx units evidently have the Sirf circuitry in them which is more sensitive, allowing signal acquisition indoors, but I found the sensitivity of the c340 to be fine - especially since I have limited my indoor driving lately :-). I haven't tried the c340 under a thick tree canopy with cloud cover yet, so I might change my tune. The comparable c5xx unit at this price point, the c530, lacks the c340's text-to-speech capabilities (this prevents the c530 from announcing street names).\n\nSince I already have a Garmin handheld GPS unit as well as Garmin's MapSource Topo mapping software, I used the unlock code that came with the c340 to unlock my MapSource maps for the unit. I then loaded the topo maps for the entire Western US onto an SD card (between 300-500 MB) and plugged it into the c340 to see what would happen. It worked! So now I have the c340's excellent built-in maps and POIs for on-road trips *and* Topo Maps available to me for off-road adventures.\n\nOne thing you should do if you are in the US is enable the unit's WAAS capability (the default is for WAAS to be disabled). Enabling this capability increases the accuracy of the unit when certain satellites are in range.\n\nAlso, since this unit has a touch-screen, fingerprints on the screen are inevitable. You should *never* use an ammonia-based product (like most window cleaners) to clean electronic screens - it will cause them to fog. Instead, use those little antiseptic alcohol towelettes. They are cheap and if you don't use them for cleaning your touch-screen they might come in handy for first-aid.\n\nI give this unit a highly positive rating. I like it\n\n\n\n\nB00005RZOR:hp_photo_paper_/_glossy:cherie_wetmiller\n\n\nB00005RZOR\n\n\nHP Premium Photo Paper, Glossy (C6979A, 8.5x11, 50 Sheets): Electronics\n\n\nelectronics\n\n\n7 of 7\n\n\n5.0\n\n\nHp Photo paper / glossy\n\n\nJune 10, 2002\n\n\nCherie Wetmiller\n\n\nWest Sand Lake, New York United States\n\n\nThis paper works excellent in the hp series printers. I have tryed many other makes of Photo paper and they just don't cut it. Some other makes don't dry in a timely manner, others have a dull finish them, some have a red tone. This HP Photo paper allows you to print store developed quality photos from your hp printer. You get what you pay for... This product is worth the money....\n\n\n\n\nB00005RZP3:great_for_power,_not_compatibility:\"skphoton\"\n\n\nB00005RZP3\n\n\nSocket Communications Low Power Wireless LAN CF Card: Electronics\n\n\nelectronics\n\n\n9 of 9\n\n\n4.0\n\n\nGreat for power, not compatibility\n\n\nJune 25, 2003\n\n\n\"skphoton\"\n\n\nAtlanta, GA\n\n\nI've used this card for a while now and i'd say that i like it overall. the power usage is great. the drivers have been vastly improved and now you are able to scan for networks. Those of you who are interested in wardriving should look elsewhere. If you want to use netstumbler or pocketwarrior in windows or kismet and airsnort in linux, either go for the linksys wcf12 (prism based) or lucent orinoco based pcmcia card. if you're looking to run just 802.11b and check email, stream music, browse the web, etc, this card will be perfect plus it'll save you some battery power\n\n\n\n\nB00006I5FR:memory_card_for_a_college_student:mmaclover\n\n\nB00006I5FR\n\n\nLexar Media 512 MB Secure Digital Memory Card: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nMemory Card for a college student\n\n\nJuly 3, 2006\n\n\nMMaclover\n\n\nGeorgia\n\n\nI am a typical college student, I am not a photgrapher to be or anything, so this digital card was perfect for me. I get about 330 pictures on my second best setting and have never come close to taking that many pictures. I believe this card is great for anyone who is not obessed with taking pictures, or to someone who loads them on quickly. :\n\n\n\n\nB000FFEP92:excellent!:flopi\n\n\nB000FFEP92\n\n\nKingston 2GB U3 DataTraveler Smart USB 2.0 Flash Drive ( DTIU32TGB ): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nExcellent!\n\n\nNovember 9, 2006\n\n\nFlopi\n\n\nArgentina\n\n\nEasy to use, nice (in appearance), very comfortable (lots of space) and at an excellent price! I highly reccomend it\n\n\n\n\nB0000899ZA:excelent_product_from_top_to_bottom:fernando_pecchio_\"fepe\"\n\n\nB0000899ZA\n\n\nApple M8799LL/A AirPort Extreme Base Station with Modem and Antenna Port: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nExcelent Product from top to bottom\n\n\nNovember 6, 2006\n\n\nFernando Pecchio \"Fepe\"\n\n\nCaracas, Venezuela\n\n\nI recommend this product! is an excellent purchase option. Excellent product quality in performance and looks. No product out there in the market looks as good as this one\n\n\n\n\nB000095SBE:good_but_not_perfect:k._chen\n\n\nB000095SBE\n\n\nSony DR-220DP Hands-Free Monaural PC Headset: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n4.0\n\n\nGood but not perfect\n\n\nJuly 8, 2006\n\n\nK. Chen\n\n\nPhily, PA\n\n\nThe sound is fine, but the mic is not so good, too much background noise. The headset is quite \"loose\" but it's ok when you get used to it.\n\n\n\n\nB00009KO14:this_buds_for_you:s._lennon\n\n\nB00009KO14\n\n\nApple M9128G/A iPod Remote and Earbuds: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n5.0\n\n\nThis Buds for You\n\n\nMarch 21, 2006\n\n\nS. Lennon\n\n\nNY\n\n\nThese are the only earphones worth buying for your iPod. I tried to get some cheapies after I lost my originals, but they were horrible. These are a little expensicve, but so worth it.\n\nI don't use the pause/skip attachment thing though. It makes the cord too long\n\n\n\n\nB0009RKL62:very_dependable_-_error_free:j._gustek\n\n\nB0009RKL62\n\n\nTDK Electronics DVD+R47FCB100 Single-Sided 16x DVD+R Spindle, 100 Discs: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nvery dependable - error free\n\n\nNovember 9, 2006\n\n\nJ. Gustek\n\n\n\n\n\nI'm using them a lot.Never failed - keeping up to 16x speed\n\n\n\n\nB0009RKL62:great_value_excellent_service:p._benza\n\n\nB0009RKL62\n\n\nTDK Electronics DVD+R47FCB100 Single-Sided 16x DVD+R Spindle, 100 Discs: Electronics\n\n\nelectronics\n\n\n1 of 3\n\n\n5.0\n\n\nGreat value excellent service\n\n\nAugust 23, 2006\n\n\nP. Benza\n\n\nRiverview, FL United States\n\n\nHigh quality disks. Never a bad burn. Fast delivery. Buy this from this selle\n\n\n\n\nB0007LZGF6:cute_and_easy_to_get_going:dorrie_wheeler_\"author_of_be_my_sorority_sister,_founder_of_thabiz.com\"\n\n\nB0007LZGF6\n\n\nSanDisk SDMSG-1024 Pro Duo 1 GB Gaming Memory Stick: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nCute and easy to get going\n\n\nAugust 31, 2006\n\n\nDorrie Wheeler \"Author of Be My Sorority Sister, founder of Thabiz.com\"\n\n\nVirginia Beach, Va United States\n\n\nI opened it, I put it in my PSP and that was it. I had plenty of space on the memory stick and I personally didn't have to do any special formatting I just popped it in and that was it.\n\nI did want to add, buy this online because in my comparison shopping it was much more expensive in brick and morter stores I visited\n\n\n\n\nB00029U13A:good_value:a._ku_\"anonymous\"\n\n\nB00029U13A\n\n\nSony SCD-CE595 5-Disc CD/Super Audio CD Player: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n4.0\n\n\nGood value\n\n\nAugust 24, 2006\n\n\nA. Ku \"anonymous\"\n\n\nAtlanta, GA\n\n\nEasy to install and use but the remote does not have an on/off switch\n\n\n\n\nB00029U13A:cd_player_review:pamela_j._pauwels_\"twinprincess\"\n\n\nB00029U13A\n\n\nSony SCD-CE595 5-Disc CD/Super Audio CD Player: Electronics\n\n\nelectronics\n\n\n1 of 4\n\n\n4.0\n\n\nCd Player Review\n\n\nJuly 5, 2006\n\n\nPamela J. Pauwels \"Twinprincess\"\n\n\nHobart, IN\n\n\nThe cd player works well, just wish the random play would work for all of the cd's instead of only one\n\n\n\n\nB00006ANWH:tim:timothy_h._wright\n\n\nB00006ANWH\n\n\nKLH 525 II Platinum-II 125-Watt Deluxe Center Channel Speaker: Electronics\n\n\nelectronics\n\n\n0 of 5\n\n\n4.0\n\n\nTim\n\n\nJune 26, 2006\n\n\nTimothy H. Wright\n\n\n\n\n\nThe product is fine however when i ordered i added a gift card to the payment. The gift card was not used and the purchase was paid in full with my credit card. Is my gift card still valid\n\n\n\n\nB00006B7HB:a_great_porduct:w._humbert\n\n\nB00006B7HB\n\n\nMicrosoft Wheel Mouse Optical: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nA great porduct\n\n\nMarch 1, 2006\n\n\nW. Humbert\n\n\nDoylestown, OH USA\n\n\nThe Microsoft optical wheel mouse is a great product. It is worth every penny of the purchase price. A great value! It is so much better than my old mouse with the ball in it. The optical wheel mouse is much more accurate too. The best purchase I ever made\n\n\n\n\nB000AA2IC8:great_web_cam:c._mcnicoll\n\n\nB000AA2IC8\n\n\nLogitech Quickcam Fusion (961403-0403): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nGreat Web Cam\n\n\nNovember 9, 2006\n\n\nC. McNicoll\n\n\nBoston, MA USA\n\n\nGreat quality. I had a problem initially with XP Media Center compatibilty, but it works now after downloading an update from Logitech's website. Recently, the sound is cutting out when communicating with my girlfriend across the country. I think it might just be her interet connection though (we both have the same web cam). As long as we restart the video call, then the sound comes back. I am using Windows Live Messenger as the video call program. I have never used the cartoons that it comes with. The video quality is very good when recording a video for email\n\n\n\n\nB00008ZPJQ:headset:m._gabriel_affandy\n\n\nB00008ZPJQ\n\n\nLogitech 980158-0403 Internet Chat Headset: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n4.0\n\n\nheadset\n\n\nNovember 5, 2006\n\n\nM. Gabriel Affandy\n\n\nGreenbelt, MD United States\n\n\nThe headset works great, comfortable. I just wish they had better sound quality...for instance when playing music\n\n\n\n\nB000AP05BO:my_best_flights_ever...:mr._john_c._mcmillan_\"chasemac\"\n\n\nB000AP05BO\n\n\nBose� QuietComfort� 2 Acoustic Noise Cancelling� Headphones, with free MP3 player (a $50 value): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nMy Best flights ever...\n\n\nNovember 3, 2006\n\n\nMr. John C. Mcmillan \"Chasemac\"\n\n\nLondon, GB\n\n\nI bought these headphones on the recommendation of a friend, and he was sooo right! The moment when the noice-cancelling algorithm kicks-in on a noisy flight is just sublime. I frequently use them without input, just tuning out the background noise on the aircraft...they even work well on squalling rug-rats a few rows away...\n\n\n\n\nB000066E6Y:excellent_product_except_cables_came_with_it:d._zhou\n\n\nB000066E6Y\n\n\nMotorola 484095-001-00 Signal Booster: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nExcellent Product except cables came with it\n\n\nSeptember 12, 2006\n\n\nD. Zhou\n\n\n\n\n\nThis amplifier works as stated. Increased my modem downstream from -7 to +9. No obvious loss of noise ratio and upstream signals. All TV channels are much sharper! The cables came with the amplifier are two RG59 cable lines. For minimum loss of broadband signals, I suggest you replace one of them with R6 cable to carry the cable signals. Use the RG59 for the power supply only\n\n\n\n\nB00005AW1H:plain_vanilla:engineer\n\n\nB00005AW1H\n\n\nLinksys WUSB11 Wireless-B USB Network Adapter: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nPlain Vanilla\n\n\nNovember 12, 2006\n\n\nEngineer\n\n\nColts Neck, NJ\n\n\nIf you want very basic capability this is the wireless adapter for you. Slow but steady and inexpensive\n\n\n\n\nB00005AW1H:works_great:k._booth\n\n\nB00005AW1H\n\n\nLinksys WUSB11 Wireless-B USB Network Adapter: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nWorks great\n\n\nNovember 4, 2006\n\n\nK. Booth\n\n\n\n\n\nI bought this for my Tivo, hooked it up the day it arrived and haven't touched it since. Great value\n\n\n\n\nB0007MWE1E:works_for_me:p._cao_\"college_student\"\n\n\nB0007MWE1E\n\n\nCables Unlimited 6-Foot Digital Audio/Visual HDMI-to-DVI-D Single Link Cable: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nworks for me\n\n\nNovember 22, 2006\n\n\nP. Cao \"College Student\"\n\n\nSocal\n\n\nPeople buy expensive av cables because of most people would think that more expensive cables will work better and last longer. I've found that it's more a matter of quality control. I'll roll the dice with cheap (price) cables untill they fail on me\n\n\n\n\nB0007MWE1E:great_cable_for_less_money:steve\n\n\nB0007MWE1E\n\n\nCables Unlimited 6-Foot Digital Audio/Visual HDMI-to-DVI-D Single Link Cable: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nGreat cable for less money\n\n\nOctober 28, 2006\n\n\nSteve\n\n\nCalifornia\n\n\nGreat cable that works as advertised. Why spend $80 on a name brand cable which performs no better or no worse. Connected a third gen Mot 6412 DVR with HD to a Sony 36XBR800. Great picture. A name brand cable will not transmit your digital signal 6' any better than this Cables Unlimited brand will\n\n\n\n\nB000BHWV2M:a_great_buy.:james_hatsis_\"ahr\"\n\n\nB000BHWV2M\n\n\nEpson PictureMate Print Pack (2 Ink Cartridges/270 sheets of Glossy Photo Paper) - T5570-270: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n5.0\n\n\nA great buy.\n\n\nFebruary 19, 2006\n\n\nJames Hatsis \"AHR\"\n\n\nGreensboro, GA USA\n\n\nThe only thing you will need when you buy this package is some extra paper! I love the fact that Epson advertises realistic figures for this package. Some other printer makers do not do this!\n\n\n\n\nB000AUQICO:i__recommend__buying_the_roady:eric_m._green_\"emg\"\n\n\nB000AUQICO\n\n\nDelphi XM Roady XT Satellite Radio Receiver: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n4.0\n\n\ni recommend buying the roady\n\n\nOctober 14, 2006\n\n\nEric M. Green \"EMG\"\n\n\nWILMINGTON,DE USA\n\n\nI installed the roady in my car a few days ago.my wife has a xm radio in her new vehile so i was some what familiar with the xm radio.the only reason i didn't give the roady 5 stars was that the signal to the indash radio is poor.on the front of my in dash stereo there is a aux. input which i plugged my roady into and the roady plays crystal clear,however this connected is not mentioned in the roady instruction manual.i have three vehicles and all three have an aux. input on the front of the cd player that i plug my mp3 player into.xm radio was running a special and i got the unit and the car kit for 40.00.all in all the roady is awesome\n\n\n\n\nB00000J47L:excellent_rechargeables:lance_jurgensen\n\n\nB00000J47L\n\n\nEnergizer NH15BP-4 ACCU 2500mAh Rechargeable AA Batteries (Four-pack): Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n5.0\n\n\nExcellent rechargeables\n\n\nAugust 7, 2006\n\n\nLance Jurgensen\n\n\nWest Salem, WI USA\n\n\nI have purchased these and other rechargeable batteries in the past. Generally I like the PowerEx batteries the best, but these Energizer batteries have really outdone the others. The seem to have the best capacity and last the longest of all the batteries I have. I have used them in my Canon S2-IS digital camera and have taken over 400 pictures during a weeklong vacation with no problems, and no recharging during the vacation\n\n\n\n\nB000A6TNKC:a_top-notch_universal_dvd_player:marshall_schwartz_\"ms_from_phx\"\n\n\nB000A6TNKC\n\n\nSony DVPNS90V HDMI/SACD Single Disc DVD Player: Electronics\n\n\nelectronics\n\n\n30 of 31\n\n\n5.0\n\n\nA Top-Notch Universal DVD Player\n\n\nMay 13, 2006\n\n\nMarshall Schwartz \"ms from phx\"\n\n\nPhoenix, AZ USA\n\n\nThere's no need to take anybody else's word for specs on this Sony DVD player when you can view or download, for free, the user's manual at http://esupport.sony.com/US/perl/model-documents.pl?mdl=DVPNS90V&LOC=3. I've been running this Sony DVP-NS90V player through a Sony widescreen HDTV and a Pioneer amplifier/receiver for several days now, and I find almost none of the problems and limitations mentioned by previous reviewers. One, for example, states that there is \"no bass management,\" yet you can (a) route more or less signal to the subwoofer by selecting \"small\" or \"large\" for the front speaker size (see page 76 of the user's manual), and also (b) increase or decrease the bass from -15 dB to +5 dB (see page 77 of the manual). Whether you prefer analog to digital audio, or you think SACD's are tops, is a matter of opinion, taste and choice and not an objective evaluation of a playback unit. I, for one, prefer DVD-Audio, and I mention this for an important reason: Some product ads and the user's manual clearly warn that this unit \"will NOT play DVD-Audio discs.\" Happy surprize: it most certainly will! For my DVD-A's that have optional Dolby 5.1 and/or DTS tracks, the player simply ignores the primary DVD-A format (normally output via the 6 analog cable connectors) but still plays the Dolby or the DTS via the digital audio (optical or coaxial) connectors. For \"pure\" DVD-A's (without Dolby 5.1 or DTS tracks), it conveniently outputs the audio BOTH via the 6 analog AND the digital connectors. And in all cases above, the audio is full multi-channel surround, and not downmixed to 2-channel PCM, nor Prologic pseudo-surround. The only DVD-A function it lacks is the video slideshow often included on DVD-A's, and so displays only the \"jacket image\" onscreen. The Quick Setup allows any DVD newbie to be up and running in a short time. And the Custom Setup is so detailed with so many fine tweaks and trouble-shooters that it's bound to please even the high-end audio-video geek-freaks. In short, you'll find this to be a superior universal player, provided you read the user's manual (all 84 pages of it!), follow it, and connect equally high-quality, compatible equipment capable of exploiting its many fine features\n\n\n\n\nB000FYZQSW:successor_to_the_lengendary_xr55!:eduardo_nietzsche\n\n\nB000FYZQSW\n\n\nPanasonic SA-XR57S Digital Home Theater Receiver with HDMI, Silver: Electronics\n\n\nelectronics\n\n\n32 of 34\n\n\n5.0\n\n\nSuccessor to the lengendary xr55!\n\n\nJuly 1, 2006\n\n\nEduardo Nietzsche\n\n\nHouston\n\n\n...what it has that the xr55 does not: HDMI, dual-amping of front mains now available during 5.1 playback, one extra set of component inputs, and all-channel stereo (called \"party mode\" on other receivers). \n\nOtherwise the internal components are identical, so expect similar performance---in other words, outstanding SQ when matched with the right speakers, blows away all other receivers under five hundred bucks and most under a thousand. \n\nSee my lengthy review of the xr55 for more details. \n\nBoutique audio shops do not want you to know about this product, because it'd put them out of business...they make much higher margins selling you receivers costing several times this Panasonic's prices, since their profits are usually a fixed percentage of the street price. \n\n\n\n\nB0007KNZ8M:best_buy:rk\n\n\nB0007KNZ8M\n\n\nBrother HL-2070N Network Monochrome Laser Printer (Black): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nbest buy\n\n\nNovember 5, 2006\n\n\nRK\n\n\nRochester,NY\n\n\nThis is the second Brother HL-2070N printer i\"ve owned. The first died during a storm and I forgot about electrical surges. The only negative on this printer is a slight curling of the pages. It is fast and reliable. The prints are crystal clear. I would recommend this product to everyone\n\n\n\n\nB00005K3KE:decent_buy_for_the_price:\n\n\nB00005K3KE\n\n\nSony SPP-A946 900 MHz Analog Cordless Phone with Answering Device (Gray): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nDecent buy for the price\n\n\nMay 17, 2002\n\n\n\n\n\n\n\n\nMuch cheaper than other Sony machine. Works fine -- then again, I wasn't looking for anything fancy. Don't have problems with the speakerphone or message recording as the others did. I like the dual keypads\n\n\n\n\nB000086A2I:great_but_quirky_large-format_linux_printer:r._d_johnson\n\n\nB000086A2I\n\n\nEpson Stylus Photo 1280 Inkjet Printer (Silver): Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n4.0\n\n\nGreat but quirky large-format linux printer\n\n\nJune 2, 2006\n\n\nR. D Johnson\n\n\nCedar Ridge, CA USA\n\n\nI've had my Epson 1280 for over two years now and consider it money well spent. I run exclusively on Linux (currently Gentoo) and the Linux support for this printer (using CUPS) is superb. All modes, resolutions, etc. are supported. I use this printer a lot for engineering work printing B-size (tabloid) drawings and it works great for that.\n\nLike just about every review states, this printer slurps ink like Homer Simpson slurps beer. Because I don't print many color photos this is not a problem for me--if I was doing that I'd get the Niagara system other reviewers cite. I've researched the Niagara system and it looks like a good deal for heavy users. \n\nMy main complaint with this printer--and the reason why it only gets four stars--is the ease with which the heads clog up. This printer wants to be used constantly. If you let it sit for a day or two it's almost guaranteed that one or more of the jets will clog up and you'll get streaks in the output. Then you have to either press the front-panel control or, in more extreme cases, run the head-unclogging utility (on Linux I use mtink) to force the printer to 'blow out' the plugged jets. When the printer does this it blasts ink through the heads wasting even more of the pricey ink. Do this enough and the reservoir the printer blasts the ink into (a sponge-sandpaper-like thing) will overflow and cause the head to smear ink all over the paper. That's when you get out the Windex and paper towels and clean up the mess (google the web for details.) Because of this 'trait' I would not recommend this printer for casual users.\n\nStill, for B-sized printing and high-quality photos, and considering how well supported it is under Linux, there's not much else better out there\n\n\n\n\nB000086A2I:big_work_horse,_colors_just_right:adam_w._chapin_\"chipmunkartist\"\n\n\nB000086A2I\n\n\nEpson Stylus Photo 1280 Inkjet Printer (Silver): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nBig Work Horse, Colors Just Right\n\n\nFebruary 21, 2006\n\n\nAdam W. Chapin \"chipmunkartist\"\n\n\nGrand Rapids, MI\n\n\nHaving owned a 1280 for a year and a half thus far, I can say that this printer is for somone who wants a reliable and problem free large format printer that produces on target results. The drivers at least for Mac OS X are easy to understand and reliable. The ability to manage the printer such as doing a nozzle check and a head clean right from the application that is going to be printing, is a vey nice touch as well. However, it is the colors that this printer gets high reviews from me. By using mat or glossy photo paper and making the proper print settings, one can get nice rich colors that are on cue. Thus this printer makes good for doing proofs. As an artist and Graphic Designer, having this quality is a blessing since this print cost $ 400 at the time of parchase. Compare that to profesional quality workhorses going from $2000 on up. Also being able to print up to 13 x 19 save me loads of time since that cuts down on the number of trips to my local print service to have them do my print outs on there large format printers. The only minuses are that one needs a generous amout of desktop realestate to acomadate the 1280 as it is almost twice the size of a normal 8 1/2 x 11 printer. Also one needs be prepared to shell out some payolla as the ink cartriges can get pricey. But what inkjet dosen't have that problem? Anyway that little expences is nullified by savings of not having to go to my print service so often. So any one looking for a good large format printer that produces some good printouts and not have to pay an arm and a leg for it, the 1280 is a very good cadadate for that job. So I'm giving 5 stars on the Epson 128\n\n\n\n\nB00008XETC:quality_cable:b._blom\n\n\nB00008XETC\n\n\nMonster Cable DVI400-1M DVI-D Video Cable: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nQuality Cable\n\n\nSeptember 13, 2006\n\n\nB. Blom\n\n\nColorado, USA\n\n\nI ordered the 4 meter cable which was pictured with 24 pin connectors and received one with 18 pins. Found out that the missing middle 6 pins are used to transmit audio which my monitor doesn't support anyway. Originally, the DVI-D specification allowed for both a video signal and an audio signal, but most manufacturer's have only used the video portion. If you need a DVI-D cable, an 18 pin connector will most likely work since most devices won't process the audio signal anyway. There are a few (very few) devices that do use all 24 pins, but with the advent of HDMI, nobody really makes them anymore\n\n\n\n\nB00008XETC:my_review:baonam_truong\n\n\nB00008XETC\n\n\nMonster Cable DVI400-1M DVI-D Video Cable: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n5.0\n\n\nMy Review\n\n\nJuly 8, 2006\n\n\nBaonam Truong\n\n\n\n\n\nProduct came in as describe. Super clear picture quality as it's supposed to be as Monster Cable brand\n\n\n\n\nB000E1FYRO:great_value_for_money!:gerald_khoo\n\n\nB000E1FYRO\n\n\nSennheiser HD 205 Studio Monitor DJ Headphones w/ Swivel Ear Cup: Electronics\n\n\nelectronics\n\n\n10 of 10\n\n\n5.0\n\n\nGreat value for money!\n\n\nMarch 17, 2006\n\n\nGerald Khoo\n\n\nSingapore\n\n\nThe Sennheiser HD 205 is great value for money. The design is not only comfortable, but takes out a lot of ambient noise. This headphone does not have active noise reduction but does a good job with its closed design headphone.\n\nThe right-side is able to swivel backwards, so you can answer a phone call and not need to remove the whole headphone. This swivel was designed for DJ's to do sound mixing, but works well for other purposes as well.\n\nI also like its strong bass and great clarity, where I could hear sounds in music that I did not hear before. This headphones not only works well for music, but does great with your favourite action movie. You will hear how close the sound is, and have bullets whizzing past you and swords meeting like they are just around you.\n\nBelow are the technical specifications:\n\nAcounstic principle: Dynamic, closed\nEar coupling: Supra-aural\nFrequency response: 14-20,000 Hz\nMax SPL at 1kHz, 1 Vrms: 112 dB\nTHD at 1kHz, 100 dB SPL: < 0.5%\nImpedance: 32 Ohms\nWeight (without cable): 206 g\nSingnal cable: 3 m copper cable (OFC), single-sided\nConnector/adapter: 3.5 mm stereo jack with 6.3 mm adapter\n\nThis headphones comes with a 2 year warranty and a protective pouch for the headphones\n\n\n\n\nB0001W9IIC:very_good_for_this_money:myopinion\n\n\nB0001W9IIC\n\n\nPanasonic KX-TG5050W 5.8 GHz Cordless Phone (White): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nVery good for this money\n\n\nNovember 20, 2005\n\n\nmyopinion\n\n\n\n\n\nI bought this phone at Amazon, and I am happy with it. Provides very good sound quality, comfortable navigation,address book and Caller ID.With its 5.8 GHz frequency speed, it is much better than my previous one which was Panasonic 2.4GHz and did not have light signal on handset when you have a new message. \n\n\n\n\nB00003WGP5:good_product:guillermo_castro_\"castor\"\n\n\nB00003WGP5\n\n\nGarmin eTrex GPS with Waterproof Exterior (Yellow): Electronics\n\n\nelectronics\n\n\n0 of 2\n\n\n4.0\n\n\ngood product\n\n\nNovember 3, 2006\n\n\nGuillermo Castro \"castor\"\n\n\nHonduras\n\n\ntakes a little time to start operating, very good for the price a little trouble using it inside ca\n\n\n\n\nB00003WGP5:excelent__for_its_price:domingo_rodriguez\n\n\nB00003WGP5\n\n\nGarmin eTrex GPS with Waterproof Exterior (Yellow): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nexcelent for its price\n\n\nAugust 24, 2006\n\n\nDomingo Rodriguez\n\n\nDominican Republic\n\n\nThis simple device is reliable for fixing positions. And If you know about coordinates, you can set it in, and bingo. You can compensate what it lacks of by using coordinates from a map while you paid unexpensive\n\n\n\n\nB0000C20XD:fantastic_speakers:kenneth_e._aldridge\n\n\nB0000C20XD\n\n\nAltec Lansing VS4121 2.1 Computer Speaker System, 3 piece: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nFantastic Speakers\n\n\nOctober 16, 2006\n\n\nKenneth E. Aldridge\n\n\nFranklin, Oh.\n\n\nWhat a wonderful surprize. It took about ten minutes to install them to my HP Media center computer. They replace speakers that came with my 19\" monitor which were lousy to say the least. (speaker make with held) But the vs 4121s surpassed all my expectations. While I am not an audiophile, I do admire and expect good faithful sound reproduction and these speakers more than do the job. I don't understand how the more expensive ones can do any better. Suffice to say I am completely satsfied and would highly recommend them to anyone looking for their moneys worth\n\n\n\n\nB000BNDEZY:fire_and_forget:knowledge_seeker\n\n\nB000BNDEZY\n\n\nBuffalo Technology AirStation Turbo G High Power Wireless Ethernet Converter ( WLI-TX4-G54HP ): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nFire and forget\n\n\nOctober 23, 2006\n\n\nKnowledge Seeker\n\n\nFlorida, USA\n\n\nThis was a breeze to setup, even with WPA2 to ensure privacy. I also hooked up an Xbox360 and it has never failed in since I bought it about 6 month ago.\n\nMuch better value then your average gaming adaptor since it has multiple ports so you can hook up all your gizmos at the entertainment center rather then buying a wireless adaptor for each (i.e. Tivo, PS2, etc...)\n\nI love it, and for the price, you could not beat it.\n\n\n\n\nB00006I56A:portability_is_nice:c._hui\n\n\nB00006I56A\n\n\nCoby CX-TV1 5\" Black-and-White TV with AM/FM Tuner: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nportability is nice\n\n\nAugust 4, 2006\n\n\nC. Hui\n\n\n\n\n\nHaving a TV in the car make the carpool-commute a little easier. Obviously, the driver can't watch it, but the passengers can. All of the chords and connectors are included. Great price\n\n\n\n\nB000CS7UTY:a_little_pricy,_but_it_was_worth_it.:j._willig_\"cosmo_jiggy\"\n\n\nB000CS7UTY\n\n\nCreative Zen Vision:M 30 GB MP3 and Video Player (Pink): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nA little pricy, but it was worth it.\n\n\nNovember 13, 2006\n\n\nJ. Willig \"Cosmo Jiggy\"\n\n\nSumter, SC USA\n\n\nThis is my first time ever buying a really expensive MP3 player. I bought it three weeks ago. If I hadn't seen the Creative Zen Vision:M, I probably would have bought the Sandisk Sansa E260. I love being able to bring my cartoon/tv shows over to a friends house so we can watch them on the tv. There is one problem I've had with it. I'm not sure if anyone else has had this, but there is some slight static noise that happens once in a while when I'm listening to music. I don't think it's the MP3s, because I've never ran into the problem with my other MP3 players. If only they would've added the video cable for the TV and kept the charger, then I would've rated it a 5\n\n\n\n\nB0001H4BMU:don't_overlook_this_player:wiseguy_945\n\n\nB0001H4BMU\n\n\nSamsung DVD-HD841 Up-Converting DVD Player: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n5.0\n\n\nDon't overlook this player\n\n\nJune 30, 2006\n\n\nWiseguy 945\n\n\nOmaha, NE\n\n\nEarlier this week I received the factory referbished Samsung HD841. I have had a few days to use it and compare it to my other players, mainly my Philips 642, which have been excellent players for me. This Model does well. I don't understand why this model has such a rough reveiw. Note, this model has the DVI output, If your TV has an HDMI port, you will want to check out the Samsung HD 850 (identical otherwise). First, this model has component, composite and S-video hook ups, as well as the DVI port (still have to run seperate sound out with DVI). Also, this model does feature digital coax sound out and optical sound out as well for systems that can handle 5.1 THX. Many upconvert models do not have the digital outs on lower priced models. The picture quality is great, at 480p comparable with my Philips. Again, The upconvert works well out the DVI hook up to my TV, but to be honest, it is hard to tell any difference between it and a good 480p progressive scan player, at least on my tv which is a 23\" 720p LCD. Menus also are a bit difficult to figure out at first as well. The user manual is less than impressive at explaining thing, in fact, it does not have an index. But, it the main thing is that the player is does perform well, and is a name brand product from samsung (I have had some offbrand experience and have had perfomace problems). THis player also appears to have DivX available on it, not noted in the product discription, so that was a suprise as well. This will allow the use of MP4 movie files. The remote again is bigger and at first more difficult than my other players, but after a day or so it actually is better because many of the features are available to change at the push of a button instread of going through the menus. In all, I will continue to explore this new player, but in all it seems to be a quality product that has received some bad flack. So give it a chane, the prices are good currently, quality has been good, and it is a Samsung.\nNote to the technosavy: I have not had luck using the component upconvert code that is available on other web sites, so I am using my DVI input for upconvert. Also, the new Blue ray (HD 1080p) DVD players are out, so if you have the expensive hi res tvs that can handle this, you might want to wait and save till you can get the new DVD players. \nNote 2: the factory referbished models come with a DVI cable, a 20-30 dollar value that many don't come with\n\n\n\n\nB000BYCNM8:quality_at_a_good_price:brent_t._horton\n\n\nB000BYCNM8\n\n\nLCD Articulating Wall Mount for Screens Up To 22 In with vesa 75/100MM: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nQuality at a good price\n\n\nAugust 25, 2006\n\n\nBrent T. Horton\n\n\nCleveland,Oh\n\n\nThis was one of the cheapest tv mounts and it had all the options the expensive ones had it will pull away from the wall as well as allow the screen to pivot up and down.It also compacts up close to the wall.\nI dont know what other features someone would want in a mount.\nIt is also very smooth when it moves and there is not any deflection or sloppiness in the construction, altough I only put a 14\" lcd on it\n\n\n\n\nB00063ZVJQ:i'd_have_given_it_5_stars_except_for_one_irritating_feature:kevin_corn_\"rareanduniquebooksonline.com\"\n\n\nB00063ZVJQ\n\n\nPalm Cradle Kit for Handhelds (3199WW): Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nI'd have given it 5 stars except for one irritating feature\n\n\nNovember 10, 2006\n\n\nKevin Corn \"rareanduniquebooksonline.com\"\n\n\nIndianapolis,, IN United States\n\n\nThere is a light that ALWAYS stays on and it is distracting, as others have noted. Otherwise, it is great. Good design, works well, no complaints. But that light...WHY did they design it with that?\n\n\n\n\nB00076QA6K:very_nice:paul_talbert\n\n\nB00076QA6K\n\n\nGarmin Protective GPS Case for GPSMap 60C / 60CS: Electronics\n\n\nelectronics\n\n\n14 of 14\n\n\n4.0\n\n\nVery nice\n\n\nSeptember 26, 2005\n\n\nPaul Talbert\n\n\nColorado USA\n\n\nProtects the Garmin's 60 screen from scratches, but interferes with the button operations a little. Worth the hassle to protect the screen\n\n\n\n\nB0002XGA3Q:very_pleased:cleany\n\n\nB0002XGA3Q\n\n\nPolycom SoundStation2W 2.4 GHz Cordless Table-Top Conference Phone: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n4.0\n\n\nVery pleased\n\n\nJuly 29, 2005\n\n\nCleany\n\n\nMilwaukkee, WI\n\n\nThis unit works very well and we are very pleased with it. Nice construction, good sound quality on both ends. We're concerned about battery life as in # of recharges before performance decreases, but no indication right now that is an issue. I would have give the product 5 stars if it had been cheaper. This class of device is overpriced in general. Amazon's price was very good, but overall still seems over priced. I would definately buy another from Amazon as these are 25% more at other places.\n\n\n\n\nB0000524PB:good_cables,_but_way_overpriced.:evan_arsenault_\"jaded_moon\"\n\n\nB0000524PB\n\n\nMonster Cable GameLink 400 Component-Video Cable for PlayStation 2 (10 feet): Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n4.0\n\n\nGood cables, but way overpriced.\n\n\nFebruary 14, 2006\n\n\nEvan Arsenault \"Jaded Moon\"\n\n\nFramingham, MA USA\n\n\nThis cable does improve the image quality but not by much. I was dissappointed to see very little difference between my standard PS2 stock cable and the moster cable I bought. This is not to say that the Monster cable is bad, but it just isn't worth the asking price. If you see on for under $20 then I recommend picking it up, but other than that, just avoid them because there isn't much better out there\n\n\n\n\nB0007KX4UQ:what_is_this_for?__high_quality_audio_and_video:ricardo_carreon_quinones_\"http://ricardosblog.com\"\n\n\nB0007KX4UQ\n\n\nApple iPod Dock for Color Display iPods: Electronics\n\n\nelectronics\n\n\n69 of 70\n\n\n4.0\n\n\nWhat is this for? High quality Audio and Video\n\n\nJune 30, 2005\n\n\nRicardo Carreon Quinones \"http://ricardosblog.com\"\n\n\nSao Paulo, Brazil\n\n\nBREAKING NEWS: If you are buying a new video playback capable iPod, please look for the new iPod Universal dock, which has s-Video output for both photos and video. It is also ready to accept a control remote so you can control your iPod from your sofa.\n\nIs this expensive? The answer is probably yes. This dock was included on the original iPod Photo of late 2004 (priced $500-$600), the dock was removed as Apple lowered the prices this year to their current levels for color iPods. However, for a couple of uses: high quality home audio and Slide Shows on hi-res TVs, it is a great alternative.\n\nThis dock basically serves three purposes:\n- The traditional synch & charge function available since the early days.\n- It helps you get \"Line\" output out of an iPod. Line output is a cleaner, higher quality sound output than the usual headphone output out of the miniplug jack. It is the best way to connect your iPod to a home stereo or home theater.\n- The super Video out for color iPods. This function is something you cannot get without the dock. So, if you like to show your pictures to your friends & family using a hi-res TV (Plasma, LCD or a Flat Screen) this is a great tool to use. As an alternative, if you do not want to use s-Video and are OK with regular digital video out, a cheaper alternative is the \"Apple iPod AV Cable\" at $19 from the Apple Store or similar alternatives.\n\nIn summary, buy this if you hook your iPod to high quality audio equipment or if you regularly do hi-res slide shows on your TV. If you don't do it, it's just too expensive as a synch/charger, since anything you need is in the iPod box\n\n\n\n\nB0000BZOGY:well_built_and_roomy:j._stephen_shaw\n\n\nB0000BZOGY\n\n\nTargus TXL717 17 XL Rolling Notebook Case: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nWell built and roomy\n\n\nNovember 25, 2006\n\n\nJ. Stephen Shaw\n\n\nBethesda MD\n\n\nI bought this backpack for walking to work each day with my laptop and various other items I need to carry back and forth (umbrella, lunch, spare battery, paper, etc). It's well designed and constructed and has fulfilled all my expectations. Its size allows me to carry a hefty foldup umbrella which can lie horizontally. I was concerned it size might make it intrusive or awkward, but it's not.\n\nMy only minor complaint is that the buckles on the shoulder straps slip gradually and need to be tightened every couple of days\n\n\n\n\nB0000BZOGY:great!excellent!:n._m._ramsay\n\n\nB0000BZOGY\n\n\nTargus TXL717 17 XL Rolling Notebook Case: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGreat!Excellent!\n\n\nNovember 2, 2006\n\n\nN. M. Ramsay\n\n\n\n\n\nThis bag is fantastic,comfortable to carry, fits loads in,protects my laptop,doubles as hand luggage.No complaints at all. Best bag I've ever bought\n\n\n\n\nB0000A1OG9:can't_go_wrong_with_batteries:j._corsi\n\n\nB0000A1OG9\n\n\nRAYO30PKAAA Pro Battery: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\ncan't go wrong with batteries\n\n\nDecember 23, 2005\n\n\nJ. Corsi\n\n\nalbany ny\n\n\nSeems nowadays everything uses AAA batteries so this 30 pack is perfect for just about anyone that needs them. You know who you are. The ones with the video games, remote controls, clocks, and whatever else you need. Expecially good to get during the holidays, you should never be without\n\n\n\n\nB000FH2BVO:good_service!!!:joseph_spivey\n\n\nB000FH2BVO\n\n\nSanDisk 1 GB Ultra II MicroSD Card (SDSDQU-1024-A10M, Retail Package): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n4.0\n\n\nGood Service!!!\n\n\nAugust 7, 2006\n\n\nJoseph Spivey\n\n\n\n\n\nReceived it pretty quick, but it was SDSDQU-1024-E10M. There isn't any difference just it comes from Europe instead of the U.S. That was pretty odd but otherwise it works\n\n\n\n\nB00067ARYQ:works_for_10_days_then_quits:daniel_brockman\n\n\nB00067ARYQ\n\n\nNetgear WG111T 108 Mbps Wireless USB 2.0 Adapter: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n4.0\n\n\nWorks for 10 days then quits\n\n\nMay 7, 2006\n\n\nDaniel Brockman\n\n\nPetaluma, CA USA\n\n\nMy experience, with several repetitions: The adapter, continuously plugged in, continuously on, works for about 10 days, then stops working and drops the connection. It won't restart. The only way to recover is to unplug it, plug in your spare (you do have a spare, of course), and let the Netgear WG111T cool off for a couple of days. Then you plug it in again, and it works for about 10 days...\n\n\n\n\nB0002GRUJ6:great_product_for_the_money:scott_r._bigham_\"scott\"\n\n\nB0002GRUJ6\n\n\nStarTech.com 6FT HDMI TO HDMI DIGITAL VIDEO ( HDMIMM6 ): Electronics\n\n\nelectronics\n\n\n8 of 8\n\n\n5.0\n\n\nGreat Product For The Money\n\n\nMarch 15, 2006\n\n\nScott R. Bigham \"Scott\"\n\n\nHolts Summit, MO\n\n\nA super picture with this cable! For the price, you've got to try this before spending hundreds of dollars on others. I give it 2 thumbs up\n\n\n\n\nB0000DJEK7:never_leave_home_without_it.:tv_guy_\"tv_guy\"\n\n\nB0000DJEK7\n\n\nMagellan RoadMate 700 Vehicle GPS with Windshield Mount: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nNever leave home without it.\n\n\nNovember 5, 2006\n\n\nTV Guy \"TV Guy\"\n\n\nSouthfield MI\n\n\nI purchased my first Roadmate in August 2004. Since then I have taken it to various places in North America. It's gotten me out of some tight spaces. I've never gotten lost while using it.\n\nI purchased the second one when the voice interface on the first failed. I sent it in for repair. (The extended warranty had just run out about 2 weeks earlier) The new RoadMate arrived in two days and has worked fine ever since. The old Roadmate was returned to me repaired in about a week.\n\nI gave the first one to my wife and she has used it with success ever since.\n\nPros: Easy to use. Amazing detail.\nCons: Sometimes takes a long time to acquire satellites\n Original mount is useless after about a month. I got aftermarket\n tripod for each unit and they seem to work fine. Software updates \n are expensive. \n\n\n\n\nB00005ATME:it's_worth_it._:m._aydin_\"mahmut4@yahoo\"\n\n\nB00005ATME\n\n\nCase Logic Nylon 144-Disc CD/DVD Wallet: Electronics\n\n\nelectronics\n\n\n5 of 6\n\n\n5.0\n\n\nIt's worth it. \n\n\nSeptember 30, 2005\n\n\nM. Aydin \"mahmut4@yahoo\"\n\n\nPhiladelphia, PA United States\n\n\nI have case logic CD wallets from 7+ years ago that are still in good shape...despite the abuse I put them through. I bought this one only because of CD collection expansion. I find simply wiping off the outside cover with a wet sponge keeps it clean. The nylon covers really hold up over time. Remember- the \"128 cd capacity\" refers to CDs without their accompaning booklets. It's really 64 CDs with booklets.\n\n\n\n\nB0000D8LSW:the_power_of_charging:bruce_aguilar\n\n\nB0000D8LSW\n\n\nApple iPod FireWire Power Adapter: Electronics\n\n\nelectronics\n\n\n8 of 10\n\n\n5.0\n\n\nThe Power of Charging\n\n\nAugust 11, 2005\n\n\nBruce Aguilar\n\n\nHollywood, CA\n\n\nThis charger is designed for every iPod EXCEPT the Shuffle. The Shuffle connects via USB, this charger uses FireWire.\n\nEvery iPod charges while connected to a computer via FireWire or USB 2.0, but this charger allows you to charge your iPod in a wall outlet. In addition to the charger you will need a connector cable. You can use the one that came with your iPod or buy a second one. Hook 'em up, plug it in the wall and get on with more important things while your iPod charges.\n\nFor international travelers, the plug pops off and you can purchase the correct plug for the country you will be visiting. Great feature!\n\nI have an iPod Dock connected to my stereo and use an extra connection cable along with this plug to ensure that my iPod doesn't run of of juice durring parites and long playing sessions. I highly recommend the Power Adapter to iPod users who want the flexability to use their iPod on the road or in conjunction with a stereo\n\n\n\n\nB00005NWV1:works_great:a._luptak\n\n\nB00005NWV1\n\n\nNETGEAR FA511 32-bit CarBus PC Card Mobile (10/100): Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n5.0\n\n\nWorks great\n\n\nNovember 20, 2004\n\n\nA. Luptak\n\n\nPittsburgh, PA United States\n\n\nI didn't buy this on Amazon but wanted to say this device is great. The only bad thing was MY laptop is old! Can't go wrong with this one\n\n\n\n\nB000ELC0GM:great_product_-_great_service:lisa_abdill_\"south_orange_soccer_mom\"\n\n\nB000ELC0GM\n\n\nSony SLVD370P DVD/VCR Progressive Scan Combo Player: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nGreat Product - Great service\n\n\nNovember 4, 2006\n\n\nLisa Abdill \"South Orange Soccer Mom\"\n\n\nNew Jersey\n\n\nOur new VCR/DVD player is wonderful. Set up was easy and I am even able to record-- something that the set-up configuration on my last model was unable to do. Delivery was even faster than expected! Quality is teriffic and price was reasonable\n\n\n\n\nB000ELC0GM:super_sony:e._provost_\"rapide_eduardo\"\n\n\nB000ELC0GM\n\n\nSony SLVD370P DVD/VCR Progressive Scan Combo Player: Electronics\n\n\nelectronics\n\n\n11 of 11\n\n\n5.0\n\n\nSuper Sony\n\n\nSeptember 3, 2006\n\n\nE. Provost \"Rapide Eduardo\"\n\n\nPrairie Village, Kansas USA\n\n\nMy old DVD would not play some of the many new formats, i.e. DVD-R, etc. This unit plays every DVD I've put in it with no hassles. The VCR has an auto tracking adjustment feature that seems to be fool proof, no stutters or static in the sound. And the icing on the cake is the remote. I understand it! Plus it controls my television functions. Excellent price and standard excellent Amazon service. Well done\n\n\n\n\nB000A7NRPI:excellent_product:ankush_mittal_\"ciao,\"\n\n\nB000A7NRPI\n\n\nAltec Lansing VS4221 3-Piece Speaker System Music & Gaming System with Remote: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nExcellent Product\n\n\nNovember 9, 2006\n\n\nAnkush Mittal \"Ciao,\"\n\n\n\n\n\nI bought these speakers some time back. I was a bit skeptical because the sub woofer looked a little wierd. but once I plugged it in they sounded really good..\n\nthe remote control is really handy.......\n\nIf you are looking for nice speakers for dorms or not an apartment these are it....they are really awesome......\n\nThey go really loud...I owned JBL Creatures 2 before and I prefer these to my old ones.... These Altec Lansings are the best speakers I have ever owned and trust me I have gone through the lot of the\n\n\n\n\nB00030099O:great_sound_at_affordable_price:evan_walter\n\n\nB00030099O\n\n\nCreative Labs USB SoundBlaster Live! 24-Bit External Sound Card: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nGreat sound at affordable price\n\n\nJuly 31, 2006\n\n\nEvan Walter\n\n\nArlington, TX United States\n\n\nI've been using SBL soundcards for about 3 years. I use it for recording live and with sound fonts. This usb device is easy to set up and the sound quality is great! One installation tip. Initially after I connected it and installed the software, I was not getting any sound, although the software was installed successfully. I rebooted, and everything was detected and has worked fine since. So just remember to reboot after installation. \n\n\n\n\nB0001FTVE0:unexpected_awesomeness:k._shazer\n\n\nB0001FTVE0\n\n\nSennheiser HD-595 Premier Headphone: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nUnexpected Awesomeness\n\n\nOctober 19, 2006\n\n\nK. Shazer\n\n\n\n\n\nI am a audiophile and am very picky when it comes to buying my toys. I bought these by accident when i was acually trying to buy the hd650's it was too late to cancell my order and since it was half the price i decided to try them out. I first hooked them up to my denon amp and listened to some cds and they sounded awesome. i could hear things in songs i have never heard before. I then hooked them up to my cheapy portable philips cd player just to see what it would sound like. to my surprise it sounded just as good as hooking them up to my denon amp. There was plenty of bass and highs and mids came out very good. the only draw back is that it is open and it bugs people that are sitting close to you. I am glad i got these instead of the 650's cause my friend owned the 650's and if you don't have a amp hooked up to it your out of luck\n\n\n\n\nB00064V6XK:brilliant!:g._l._knowles_\"ellisgeorgee\"\n\n\nB00064V6XK\n\n\nSanDisk MOBILEMATE MS - 4 IN 1 ( SDDR-107-A10M, Retail Package): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nBrilliant!\n\n\nNovember 2, 2006\n\n\nG. L. Knowles \"EllisGeorgee\"\n\n\nMA\n\n\nThis little doohickey is exactly what I needed to read my memory sticks. Load the software, plug it into the USB port and you're off and running! The MobileMate is SUPER SPEEDY! The files/pictures pop right up with no lag time. And you can read all forms of memory sticks with this one reader.\n\nExcellent value for the price\n\n\n\n\nB000F4C2AM:very_good_value:j._kolberg\n\n\nB000F4C2AM\n\n\nPanasonic DMR-ES15S DVD Recorder with DV Input: Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n4.0\n\n\nVery good value\n\n\nOctober 20, 2006\n\n\nJ. Kolberg\n\n\nNY United States\n\n\nI'm very happy with this model. I use it mostly as a VCR replacement to record broadcast TV shows. You can't beat the price for this feature-set. I'll try to describe some aspects that aren't mentioned in the other reviews. The owner's manual can also be found online in PDF format.\n\nIt can schedule upto 16 programs. It does not support VCR+ or a TViO-style program guides. You can enter a title into each schedule that appears both on the schedule list and direct navigator chapter menus. It even sorts the main schedule listing and indicates which events are on the current disk. Every on-screen menu shows you which remote control buttons are functional on that screen.\n\nThe commercial skip button advances about 60 seconds which isn't quite granular enough, but 2 clicks and a little fast forwarding works well. The remote operations have a very fast feeling response compared to tape players. \n\nMy only complaint: The remote sensor on the recorder seems to be all the way on right-hand side. When the unit is in our video cabinet, the sensor is in a deadzone for half the room. We can't shift the position much because the disk tray slot is all the way on the left side and won't open if it's too close to the wall. Of course this probably means nothing to you.\n\nAt the highest quality setting (XP) it made a very good (near perfect I'd say) copy from a 8mm video camera using analog inputs. At that setting, only one hour of content fits on a DVD. The next lower setting (SP) which can hold two hours per disk, the copy from the video camera had some contrast problems. Most TV shows recorded in EP (8 hrs) are acceptable quality although you do see the blockiness and some motion blur at times. This is especially pronounced on animated shows. The four-hour LP setting had no apparent digital artifacts on recorded TV. It also has a Flex-mode option I haven't tried yet in which you tell it how long to record and it will fit the recording to the freespace available on the disk at the highest possible quality. Probably would be good for recording a TV movie.\n\nIt's too bad other vendors haven't adopted the -RAM format because most of the features on this box only work on that kind of media, but it won't play on most other equipment. The other formats seem lame by comparison, but this unit can play and record on them. \n\nWatching one show while another is recording (or the same show that's recording, but from the beginning) is very easy to do. Most of the operations are very sensible and explained on-screen pretty well, but you should spend some time in the manual if you've never had a dvd recorder before. It can do a lot of stuff.\n\nOne interesting feature is that no matter what input you are using, the signal is sent through the s-video output.\n\nIt can't burn music CD's which seems like it would have been a trivially easy feature to add. That and a battery backup (so I won't lose my schedule) and wider angle of view for the remote sensor would required get the fifth review star\n\n\n\n\nB0000E2PMB:monster_connections:andrew_shirley_\"rentino\"\n\n\nB0000E2PMB\n\n\nMonster Cable MP HTS 1000 PowerCenter with Clean Power Stage 2 (v.2.0): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nMonster Connections\n\n\nJuly 18, 2006\n\n\nAndrew Shirley \"Rentino\"\n\n\nOrange, NJ\n\n\nI purchased the Monster Cable MP HTS 1000 without reading the reviews,(which I don't usually do) for the simple reason of where I purchased my hdtv, Samsung HLS-5687w they were trying to sell me but for a $150, which was too much. I plugged in my equipment to the monster and at first there was no picture only sound. I have directv and they have a connection which allows you to receive digital and analog signals through the same cable. That connection needs to be placed after the monster in the connection loop. After switching around the cables I had a picture again. I can't really see an improvement in the picture quality (HD is beautiful) I bought the monster basically for the protection\n\n\n\n\nB000GTPRPS:hp_50g_review_comments:daniel_r._renninger_\"daniel\"\n\n\nB000GTPRPS\n\n\nHP 50G Graphing Calculator: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nHP 50G Review Comments\n\n\nNovember 5, 2006\n\n\nDaniel R. Renninger \"Daniel\"\n\n\nWest Mifflin, PA, USA\n\n\nThe 50G is the successor to the HP 49G. They both accept only a maximum of 1GB SecureDigital flash cards since they only have an approximate 1GB operating system file address space. Only about 0.9GB of the SecureDigital flash cards (a little more on the HP 49G than on the HP 50G) are accessible. The manuals only vaguely state that they recognice FAT16 and FAT32 SecureDigital flash cards. The HP 50G has a black case with a black metallic faceplate. The white and orange shift key labels on the metallic black faceplate have much better contrast and are easier to distinguish than the HP 49G red (or tomato) and turquiose shift key labels on the tan anodized faceplate. The black HP 50G looks tastefully geared for serious users. They still have no display backlighting or a color display. The HP 50G keyboard feels a little more compliant than the HP 49G but it would be nicer if they were as buttery smooth as the original early 1970's series of the HP hand held calculators\n\n\n\n\nB0009HTB0Y:ultra_convenient:erik_craig_malin\n\n\nB0009HTB0Y\n\n\nSandisk SDSDPH-1024-901 1 GB Ultra II SD Plus USB (Retail Package): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nUltra Convenient\n\n\nNovember 3, 2006\n\n\nErik Craig Malin\n\n\nSanta Clarita, CA USA\n\n\nThe convenience of this product is terrific. Whenever I show people how it snaps in half, they're amazed at the ingeniousness of it and they say how they want to get one. Now that all computers have USB ports, I can put my photos into any computer anytime I want without worrying about the docking station. A must-have product for every digital camera!!\n\n\n\n\nB0009HTB0Y:it_is_worth_it:constantin_aldea\n\n\nB0009HTB0Y\n\n\nSandisk SDSDPH-1024-901 1 GB Ultra II SD Plus USB (Retail Package): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nIt is worth it\n\n\nAugust 24, 2006\n\n\nConstantin Aldea\n\n\nGlen Ridge, New Jersey\n\n\nGreat product. It's two in one. You have it in your camera and take photos, next you can plug it into the USB port and download data. It's worth the money\n\n\n\n\nB0002SQ0A4:high_priced_sounds:butch\n\n\nB0002SQ0A4\n\n\nLogitech x-230 2.1 2-Piece Dual Drive Speakers with Ported Subwoofer: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nHigh priced sounds\n\n\nNovember 16, 2006\n\n\nButch\n\n\nIllinois\n\n\nFirst time I heard these I was floored by the output. Nice high, mid and low bass sounds no matter what you have going thru them be it music, games or movies. Definately worth the low low price...\n\nFirst person WWII shooter games like Call of Duty sound awesome with these speaker\n\n\n\n\nB0002V8KX6:linksys_pap2:jake_a._fitzpatrick_\"jfitzpa503\"\n\n\nB0002V8KX6\n\n\nLinksys PAP2 Phone Adapter for Vonage Internet Phone Service: Electronics\n\n\nelectronics\n\n\n13 of 13\n\n\n4.0\n\n\nLinksys PAP2\n\n\nAugust 12, 2006\n\n\nJake A. Fitzpatrick \"jfitzpa503\"\n\n\nPortland, OR USA\n\n\nI am going to strictly comment on the Linksys PAP2. A lot of the reviews on here have been for the Vonage service. The Linksys PAP2 is NOT THE Vonage service, but one of many adapters you can use. \n\nI am extreamly satisfied with my Linksys PAP2. It is small, light weight, and perfect for those who need to travel. The voice quality is excellent and people say I do not sound like I am talking over VOIP. There is no line noise, static, popping, etc. \n\nI am giving this product 4 stars. Why? Because there is only ONE ethernet port. If you have a router, just connect the PAP2 to one of your available ethernet ports and you are good to go. If you do not have a router, consider getting another Vonage phone adapter with built in 4-port router. There are several.\n\n\n\n\nB0002V8KX6:vonage_--_wonderful_service:amber_mist_\"miss_burlesque\"\n\n\nB0002V8KX6\n\n\nLinksys PAP2 Phone Adapter for Vonage Internet Phone Service: Electronics\n\n\nelectronics\n\n\n3 of 5\n\n\n5.0\n\n\nVonage -- Wonderful Service\n\n\nJuly 19, 2006\n\n\nAmber Mist \"Miss Burlesque\"\n\n\nBoston, MA USA\n\n\nI read the other reviews and don't understand where they are coming from UNLESS they are all by AT&T or BELL employees. I've had Vonage for 3 years and have nothing but good things to say about it. I have local numbers for Seattle, Salt Lake City, Boston and San Francisco so my friends can stay in touch. These numbers only cost me $4.99/each a month. Customer service is excellent. They stay right on the phone with you until everything works perfectly. This is especially nice after they added 911 service\n\n\n\n\nB00003CWA6:simple_physics:\n\n\nB00003CWA6\n\n\nMonster Cable ILS100-1M Interlink LightSpeed 100 High-Performance Digital Fiber-Optic Cable, Toslink-to-Toslink (1 Meter): Electronics\n\n\nelectronics\n\n\n10 of 31\n\n\n4.0\n\n\nsimple physics\n\n\nJuly 24, 2000\n\n\n\n\n\n\n\n\nI'm using Monster optical cable as well, but with different reasons behind. Anybody with a basic knowledge of physics or optics should know that under normal condition light is not susceptible to interference from magnetic field or EMF\n\n\n\n\nB00009EFQH:exactly_same_as_the_description:i._huang\n\n\nB00009EFQH\n\n\nTargus PA496U DEFCON VPKL - Video Port Key Lock: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nExactly same as the description\n\n\nMarch 3, 2006\n\n\nI. Huang\n\n\nCalifornia\n\n\nThis is the best lock you can find for laptops, but I am still not very confident with it. It looks flimpsy but better than others\n\n\n\n\nB000IVDTSG:works_well_at_a_great_price:glenn_ware\n\n\nB000IVDTSG\n\n\nSling Media Slingbox AV ( SB240-100 ): Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n5.0\n\n\nWorks well at a great price\n\n\nOctober 21, 2006\n\n\nGlenn Ware\n\n\nLittleton, Colorado USA\n\n\nInstallation on a normal home network setup is very easy. My setup was a little more complex due to voip router, and 2nd wireless router. Sling has instructions on their web site but I still had to mess around with my settings in order to get it to work. To work on multiple routers you have to do port forwarding on both routers to port 5001. Once I worked this out everything is great. \n\nVideo quality is good in the house, haven't tried it from another location. I'm running the Sling AV with Netgear Ethernet power adaptors to my router and then watching TV via wireless to my laptop. Now we can watch TV in any room of the house. My wife loves being able to watch the news in the morning while in the kitchen. She also loves being able to surf while having a show running in a small window on the side. \n\nControl of my Dish DVR is very good. We can manage any feature of our Satelite/DVR box. This will come in handy when we travel as we have our sons Seaseme Street shows on the DVR and now we can take those with us on the road. \n\nOverall, I'm pleased with the Sling AV. It offers good performance at a great price point. Now Sling just needs to get the Mac version out so I can watch TV on my beautiful 15.4 inch Mac Book Pro\n\n\n\n\nB0001GU81E:quality_product_-_should_be_higher_capacity:seacidal_\"seacidal\"\n\n\nB0001GU81E\n\n\nBrother Toner Cartridge (TN570): Electronics\n\n\nelectronics\n\n\n6 of 15\n\n\n4.0\n\n\nQuality product - should be higher capacity\n\n\nSeptember 11, 2005\n\n\nSeacidal \"Seacidal\"\n\n\nVentura, CA USA\n\n\nThis is a quality built toner cartridge, but for the money, it should have a great toner capacity\n\n\n\n\nB0009MFQ12:fantastic:christopher_thomas\n\n\nB0009MFQ12\n\n\nMagellan RoadMate 760 Portable GPS Vehicle Navigation System: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nFantastic\n\n\nNovember 24, 2006\n\n\nChristopher Thomas\n\n\nChicago, IL USA\n\n\nI bought this item about 4 months ago and do not have a single complaint. I use it in Chicago. A lot of reviews say it takes a long time to acquire a satellite signal, not true at all. I think the longest I have ever waited was 20 seconds. It does have trouble getting a signal downtown sometimes, when I am surrounded by tall buildings (Wacker Dr.) But this is expected and can be corrected with the purchase of an external antenna.\nOverall, this GPS works perfectly. The auto-reroute is fantastic, it is very quick to recalculate my route if I miss a turn, and the voice prompts give you plenty of warning in advance of a turn. You will not be disappointed with this purchase\n\n\n\n\nB0000UI304:amazing_clarity_and_bass:a._jadczak\n\n\nB0000UI304\n\n\nKlipsch ProMedia Ultra 5.1 Multimedia Speaker System, (6 Speakers, Black): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nAmazing clarity and bass\n\n\nNovember 9, 2006\n\n\nA. Jadczak\n\n\nUSA\n\n\nSoundwise the Klipsch Promedia Ultra 5.1s were everything that they were supposed to be. The bass is very powerful and can even be overpowering if not adjusted. The sound is crystal clear and with a decent sound card all of the settings can be tweaked until perfection. Setup was a breeze and no instructions are needed although they are provided just in case.\n\nSomething to note is the speaker wire that comes with the speakers is a little on the small size and thicker wires should be be purchased if you are picky about your sound. The wires themselves are long enough and there shouldn't be any trouble reaching anywhere assuming you are putting these in a normal sized room. One last thing to know is that there is no optical input for these speakers and only 1/8\" plugs for hooking up the sound source. If you want optical you're going to have to go elsewhere.\n\nPlease note that buying these speakers will not make up for having a mediocre sound card or poor music quality. If you are using most types of on board sound or an ancient soundblaster card it is time to upgrade or else you are just throwing away your money. Same goes for music quality, if you are listening to an mp3 that is below 128kbps the poor sound quality is because of the shoddy encoding, not the speakers. If you can't spare the hard drive space for 192kbps its time to get a new hard drive.\n\nOverall these speakers are a great deal. While the price tag does seem a bit steep it is well worth the money for anyone that is serious about their music or movies. For those of you who like to game these speakers will treat you good, however in my past experience a good set of headphones is better than a full blown sound system. Lastly for those of you who like to play your music and bass full blast, don't bother spending the money. These speakers should only be purchased by people who actually care about sound quality. If you are more worried about loudness, do yourself a favor and buy some cheaper speakers, you won't even notice the difference\n\n\n\n\nB0001EMLZW:financial_calculator:katz_blue_\"kat\"\n\n\nB0001EMLZW\n\n\nTexas Instruments BA II Plus Professional Calculator: Electronics\n\n\nelectronics\n\n\n0 of 2\n\n\n4.0\n\n\nFinancial Calculator\n\n\nAugust 16, 2006\n\n\nKatz Blue \"Kat\"\n\n\nLas Vegas, NV\n\n\nIt has some very useful addition from the previous model. It has cut the time of doing my homework in half\n\n\n\n\nB000BMAL10:cool!:c._havens_\"commuter\"\n\n\nB000BMAL10\n\n\nApple 1 GB iPod Nano Black: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nCool!\n\n\nNovember 9, 2006\n\n\nC. Havens \"Commuter\"\n\n\nDenver\n\n\nGreat for frequent air travel to pass the time. Minor complaints - instructions are sparse (had to get my son to show me how to use iTunes & load songs), but still haven't figured out how to arrange songs on the iPod. My problem\n\n\n\n\nB00006I5V3:quite_a_company_for_a_college_student:cupiemayo_\"cupiemayo\"\n\n\nB00006I5V3\n\n\nTargus TSB312 16 Sport Deluxe Notebook Backpack: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n4.0\n\n\nQuite a company for a college student\n\n\nDecember 21, 2005\n\n\ncupiemayo \"cupiemayo\"\n\n\nSanta Barbara, California\n\n\nThis backpack has been such a useful companion for me. There are lots of space inside even for huge text books, and compartment is arranged just right to hold variety of stuff---CDs, DVDs, paperback books, pens, cell phones, water bottles, etc., etc. There are numerous creative ways of storing stuff. Indeed my whole life as a student can be inside at once.\n\nAs a notebook backpack, this would not provide the best protection. Padding is fine, but depending on how much stuff you have a notebook inside could be squeezed a bit more than you desire. Also, the clip to fix the cover flap onto the backpack almost is broken after a year of daily use for me. I have had Dell 14\" and 15\" laptops with the backpack and fitting was just fine for securely hold it inside. Wearing comfort is pretty good, and things are quite adjustable for your body.\n\nI would recommend this backpack if carrying more than a laptop, e.g., testbooks, is essential for you.\n\nP.S., I ended up using a warranty support to fix the buckle. Targus's customer support is first rate---prompt, courteous, and reliable. They actually replaced the backpack with a newer version of the same product. Their service totally make them highly recommended\n\n\n\n\nB000A3X2GQ:just_a_little_wobbly:k._danforth_\"yy2wry\"\n\n\nB000A3X2GQ\n\n\nBelkin TuneDok Car Holder for iPod Black: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n4.0\n\n\njust a little wobbly\n\n\nMarch 24, 2006\n\n\nK. Danforth \"yy2wry\"\n\n\nChandler\n\n\nI had to 'fix' this ipod holder with a bicycle hand grip to stop it from shaking my ipod. I have a decent car and the roads are just ok but this thing shakes and shakes. I also put one of those beer coolers around the base of the foam hand grip and its now rock solid. Oh yeah and it holds the ipod just right too. I also put some of those velcro dots on the bottom for a little more security from my cup holder\n\n\n\n\nB000A3X2GQ:looks_cool_even_with_a_white_ipod.:j._o._s._rodriguez\n\n\nB000A3X2GQ\n\n\nBelkin TuneDok Car Holder for iPod Black: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n5.0\n\n\nLooks cool even with a white iPod.\n\n\nOctober 23, 2005\n\n\nJ. O. S. Rodriguez\n\n\nMexico\n\n\nI hadn't seen this product in stores as I usually would see the white TuneDok which seemed pretty cool but I decided to buy this one online to go better with my black car (I have a white 40GB 4th generation iPod).\nThe holder is everything you would expect and it did work a little better than I expected. It's very easy to just place the iPod in the holder and very easy to take it off. You can do it with one hand. You just pull back the small tab of the suction thing on the back and your iPod is easily removed and ready to go. The additional cup adapters is an added bonus for those who need them, but the standard one was enough for me.\n\nThe part that is black and holds the iPod is what seems to be the best thing about the product. As far as I know the cups are the same grey color for both the white TuneCok and this black version, so the difference is only in the iPod holder and not the cups.\n\nThe only \"bad\" thing I could say is that the cup itself is made of a rubbery plastic that scratches easily. In fact when I was trying to figure out what the adapters were for (before reading the manual..oops), I scratched it quite a LOT trying to fit the two additional cups together. That doesn't affect functionality so it doesn't make me give it less than 5 stars. A small issue you might want to consider as well is that the TuneDok doesn't hold iPods in their cases. So if you're like me and carry your iPod everywhere in a case you might wan to consider having a case that is easy to put on the iPod and remove it to avoid the hassle everytime you want to place the iPod on the TuneDok.\n\nI like how the black TuneDok looks with my white iPod. It kind of makes the iPod stand out more than the TuneDok itself. I think the $15 price is pretty reasonable for the white TuneDok, and this one is too if you're willing to pay the extra $5 for it to go with your U2 iPod. But as I said, I think my white iPod looks cooler in my black car with the black TuneDok anyway.\nEdit: I bought it for $20, but now it's $15. One more good reason to get this one.\n\nI'm not too sure, but it seems like the new 5G iPod fits in the TuneDok. If they do, I bet this product is going to turn into a top seller soon enough as the demand for the black iPods is greater than the white one. Maybe someone who gets their 5G iPod can confirm this, so don't buy this for your 5G iPod unless you're already sure or can try it before you buy it\n\n\n\n\nB00006IRUC:jf_review:james_e._floyd_\"arctic_dude\"\n\n\nB00006IRUC\n\n\nEpson Premium Glossy Photo Paper (S041667, 8.5x11, 50 Sheets): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nJF Review\n\n\nAugust 11, 2005\n\n\nJames E. Floyd \"Arctic Dude\"\n\n\nWoodbury,MN USA\n\n\nWorks better than any other glossy paper I have used with my Epson 960\n\n\n\n\nB0000TSII2:great_notebook_case:w._c._swanke_\"willie_c.\"\n\n\nB0000TSII2\n\n\nPort PR600 3.1 Notebook Backpack - Black (1680D Ballistic Nylon): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nGreat notebook case\n\n\nJune 22, 2006\n\n\nW. C. Swanke \"Willie C.\"\n\n\nHendersonville, TN United States\n\n\nI purchased this for my wife who wanted a case for her \nHP Pavilion Notebook PC (DV5139US), screen size 15.4\", ht 1.8\", wdth 14.1\", depth 10.4 \". The bag is for personal use, not business, and use will be moderate. She did want some extra compartments for organization. \n\nThis case has met all her needs, it is light weight, constructed of durable material, well organized. Her notebook fits in this top loading case with ease. She is satisfied with this purchase. We would recommend purchasing this case.\n\n\n\n\n\nB00076SC4S:1_gig_of_sony_memory_is_not_too_much!!_i_have_2_for_each_camera!:ryan_hanan\n\n\nB00076SC4S\n\n\nSony 1 GB Memory Stick Pro ( MSX1GS/GST ): Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n5.0\n\n\n1 gig of Sony memory is NOT too much!! I have 2 for each camera!\n\n\nNovember 9, 2006\n\n\nRyan Hanan\n\n\nOakland, CA USA\n\n\nI have owned many Sony cameras in the past and am currently using two right now. The only problem I have encountered with any digital camera is that when you think you have enough memory, you run out when you need it most. That is why I upgraded all of my cameras from 256 and 512 meg memory sticks to the Sony 1 gig. Actually I carry a spare 1 gig too where ever I go. \n\nLook, when you take video with your digital camera or you use a high file size (3-6 megapixels) per picture, which you should always do to get the best possible prints made, your camera will need a ton of storage space. Especially if you are on vacation or encounter a great photo moment or maybe even something you hadn't expected like a wildfire, a plane crash, a bank robbery or maybe even a cat in the park having kittens. You can always \"resize\" you photos to a smaller size later when you save then to your computer if you want to save space. Trust me, take large megapixel pics now and you will be amazed at how many shots came out better that you expected.\n\nYou never know. I have tons of pictures that people ask how did I ever get such good pictures of such amazing things. I have a helicopter crash, police car up a power poll, deer in downtown Oakland, house fire before the Fire Dept got there, tons of wild animals, any many more things I saw while I was driving somewhere or walking in a park. If you want great photos you have to be prepared when the moment arrives!\n\nMatching a camera with its name brand memory is also a great idea. No problems ever!! The Sony memory sticks I have from 5 to 6 years ago still work great with no problems. They make it fast and easy to transfer data from your camera to your computer. They are tiny wafer thin and extremely light weight and durable. I have first hand knowledge that products with the SONY name on it are first rate and high quality. If you want quality buy SONY\n\n\n\n\nB0007VJ7I8:everything_is_labled_for_the_game:michael_a._kortz\n\n\nB0007VJ7I8\n\n\nIdeazon Battlefield 2 Limited Edition Keyset for Zboard: Electronics\n\n\nelectronics\n\n\n7 of 7\n\n\n4.0\n\n\neverything is labled for the game\n\n\nJuly 19, 2005\n\n\nMichael A. Kortz\n\n\n\n\n\nat first, the keyboard looked cheap and the keys were in slightly different places than normal, but after time, I got used to the layout, and found that the keys actually work surprisingly smooth. It probably helped my game a bit. The extra bit on the front of the board did break off after 2 weeks, but I didn't like using it anyway\n\n\n\n\nB00008VF91:looks_great:john_mclaurin\n\n\nB00008VF91\n\n\nMonster Cable THX SP 16-100 Standard THX-Certified Speaker Cable (100 Feet): Electronics\n\n\nelectronics\n\n\n1 of 4\n\n\n5.0\n\n\nLooks great\n\n\nAugust 30, 2006\n\n\nJohn McLaurin\n\n\nSan Diego, CA USA\n\n\nI bought this speaker cable because I wanted it to fall in a straight line and hug the walls without curling all over the place. This cable does just that. I'm very happy with it. If that is your goal too, I can recommend this cable.\n\nRegarding the long standing debate of whether Monster cable sounds better than cheaper cable, I'm not an audio engineer and don't have an opinion\n\n\n\n\nB00019068G:best_fm_transmitter_for_ipod_i've_used:marybeth_carroll\n\n\nB00019068G\n\n\nMonster Cable A-IP-FM-CH iCarPlay Wireless FM Transmitter for iPod: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nBest FM Transmitter for iPod I've used\n\n\nOctober 25, 2006\n\n\nMaryBeth Carroll\n\n\nBellmore, New York United States\n\n\nI've used the iTrip by Belkin, the TunecastII by Belkin, and this Monster iCarPlay for getting my tunes to my car speakers. This is the best design and works the best of the three. I like that it can charge my iPod and that the transmitter itself is inline. The signal seems to be decent enough, though of course it crackles sometimes or you hear a bit of static. This gets more pronounced the louder you make it.\n\nI think this is the safest for car use. The iTrip is a nightmare because you have to change the station through the iPod. So if you're taking a car trip (I live in NY but went to college in MI -- that's a 12 hour drive) and signals are coming in and out and you need to adjust, you really can't do it safely with the iTrip. The Tunecast is a bit better, but that runs on batteries (the optional charger/power supply that I got for it didn't work). Also, you can tune into any FM station with the up/down buttons, which is good, but you really only need the lower stations. The iCarPlay has 8 frequencies it can transmit over and only 1 button. Though I feel limited by the 8, I don't think going higher would help because there's more congestion. I like the single button because there's no confusion whether you're going up or down and you can press the button, then easily tune the radio up a notch. The other limitation I see is its iPod specific (uses dock connector) whereas the Tunecast just hooks into the headphone jack and it only works in cigarette lighters so you can't use it without a car (or something else with that power source). But considering I bought it to use in my car, it does what it should and does a decent job of it\n\n\n\n\nB00000J09V:best_answerer_i've_had:ralph_g._merrill\n\n\nB00000J09V\n\n\nGE 29869GE2 Digital Answering System with 4 Mailboxes: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n4.0\n\n\nBest answerer I've had\n\n\nAugust 31, 2006\n\n\nRalph G. Merrill\n\n\nBoulder, CO USA\n\n\nWorks precisely as advertised. None of the complaints in previous reviews hold for my experience. Specifically, battery backup works and sound quality of messages is excellent. Only negative: too many words spoken by the machine and they sometimes seem to overlap\n\n\n\n\nB00000J09V:great_item:al\n\n\nB00000J09V\n\n\nGE 29869GE2 Digital Answering System with 4 Mailboxes: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n5.0\n\n\nGreat Item\n\n\nAugust 24, 2006\n\n\nAl\n\n\nMissouri\n\n\nWe replaced a tape unit with this one and are so pleased. No more waiting for the tape to rewind and this one keeps the messages once played until you remove them. Also very accurate on time. We love it!\n\n\n\n\nB00000J09V:i_like_it._i_recommend_it._:arthur_radley_\"boo\"\n\n\nB00000J09V\n\n\nGE 29869GE2 Digital Answering System with 4 Mailboxes: Electronics\n\n\nelectronics\n\n\n8 of 8\n\n\n5.0\n\n\nI like it. I recommend it. \n\n\nAugust 15, 2006\n\n\nArthur Radley \"Boo\"\n\n\nNorth Carolina\n\n\n\nThe things I like most:\n\n1. My recorded greetings (two greetings allowed) and my caller's messages are clear and clean. \n\n2. The unit's appearance and styling is sleek and modern. The controls layout is good with everything in view and accessible on the top. My last machine had some of its controls on the sides causing me to have to pick it up and turn it around and around looking for a particular control. This machine is better in that respect. \n\n3. Learning to use it is a very easy and intuitive process especially if you have used other machines. The included instructions clearly explain how to use its less obvious features.\n\nThings I didn't like: Nothing, really. \n\nBut some minor annoyances include:\n\n1. The electronic accent of the generated voice of the machine was a little hard to understand at first. But I quickly \"learned\" its accent. Your callers will never hear it after you record your own greeting which will be clear and sound like you. Otherwise, your callers will hear a default message in the machine's voice.\n\n2. I installed the 9 volt battery first and the unit would not turn on when plugged into AC. Removing the battery, then plugging in the AC, then installing the 9 volt battery allowed it to power up and work normally. The battery installation instructions and general installation instructions did not indicate any particular installation sequence requirements regarding the battery\n\n\n\n\nB000F73ZNM:very_good_phone:ray\n\n\nB000F73ZNM\n\n\nUniden CEZAI998 EZ-Dial Large Button Cordless Phone with Caller ID: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nVery good phone\n\n\nNovember 9, 2006\n\n\nRay\n\n\nLancaster, CA\n\n\nBought it for my elderly mother, who is getting hard of hearing. The volume control on each handset has helped her a lot. She also says that the clarity from the handset is better than anything phone she's had before. The extra large size base unit dial buttons are also very helpful for her\n\n\n\n\nB000FH5BYS:excellent_product_and_service!:carol_mcwhirter_\"missmac\"\n\n\nB000FH5BYS\n\n\nSamsung TR50X3BX Adjustable DLP TV Stand for HL-S5086, HL-S5087, HL-S5686, HL-S5687, HL-S6186 and HL-S6187: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nExcellent Product and Service!\n\n\nNovember 5, 2006\n\n\nCarol McWhirter \"missmac\"\n\n\nDallas\n\n\nWe received our DLP TV stand in record time. We had wanted to get it for a new tv that was being delivered that weekend. We ordered on I think a Tuesday and it was at our office on Friday. It was in excellent shape and we had no trouble putting it together. The tv fit perfectly! \n\n\n\n\nB0007QDGZ8:digital_clock:sandy_smith\n\n\nB0007QDGZ8\n\n\nSony ICF-C492 Large Display AM/FM Clock Radio: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nDigital clock\n\n\nNovember 12, 2006\n\n\nsandy smith\n\n\n\n\n\nThe numbers on the clock are not bright enough to see in the day light I wish they were brighter and bigger\n\n\n\n\nB00005T3UH:don't_upgrade_the_reb_1100!!:bina\n\n\nB00005T3UH\n\n\nRCA eBook Reader (REB1100): Electronics\n\n\nelectronics\n\n\n24 of 26\n\n\n4.0\n\n\nDON'T Upgrade the REB 1100!!\n\n\nNovember 6, 2004\n\n\nBina\n\n\n\n\n\nJust a note to future readers - \nIf you take Gemstar up on their offer to \"upgrade\" this model you will receive some nice new features, however what they don't tell you is that your machine will become PROPRIETARY! All the books you had before will become unreadable and your ebook will only be able to \"read\" Gemstar books in the future. This means you are force to buy all future books from the Gemstar catalog only! \nThe new features that come with this upgrade to the 1150 model are hardly worth being locked into one ebook vendor forever. The openness of this little model is it's beauty - it's the last one of it's kind, as all future models are proprietary\n\n\n\n\nB0001FV3AK:how_can_you_go_wrong?:diane_c._stoltz\n\n\nB0001FV3AK\n\n\nSanDisk 512 MB Cruzer Titanium (SDCZ3-512-A10, Retail Package): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nHow can you go wrong?\n\n\nNovember 2, 2006\n\n\nDiane C. Stoltz\n\n\n\n\n\nThis was a very highly rated item on CNET. At the price I paid for it at Amazon it was like they were GIVING it away\n\n\n\n\nB00083LJ5Y:spectacular_capacity:j._goodwin\n\n\nB00083LJ5Y\n\n\nSony 2 GB High Speed Memory Stick PRO Duo Media (MSX-M2GN): Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n5.0\n\n\nSpectacular capacity\n\n\nAugust 8, 2006\n\n\nJ. Goodwin\n\n\nJacksonville\n\n\nJust spent the weekend photographing and video recording lots of activity and didn't even get close to capacity.\nThis is fantastic\n\n\n\n\nB00083LJ5Y:ipod_uses_30_gb_hard_drive;_2gb_memory_stick_is_flash_memory:v._calvin_hoe\n\n\nB00083LJ5Y\n\n\nSony 2 GB High Speed Memory Stick PRO Duo Media (MSX-M2GN): Electronics\n\n\nelectronics\n\n\n4 of 20\n\n\n5.0\n\n\niPod uses 30 GB hard drive; 2gb memory stick is flash memory\n\n\nMay 17, 2006\n\n\nV. Calvin Hoe\n\n\nColorado Springs, CO United States\n\n\n2gb of flash memory will not be destroyed at high altitude or dropped short of shattering the memory stick. A 30gb hard drive as in the iPod 30g (that my son has) is subject to air pressure, shock and temperature domain that a flash memory device will not be subject to. Because the hard drive spins to lift the heads above the disk, moisture from condensation will cause the head to crash if, brought in from a cold car and turned on. I do not know about the iPod but some larger devices have dew point detector that will not spin up unless the air in the device is at safe temp.\n\nTHAT's WHY..\n\n\n\n\nB0007U6IFY:very_pleased_at_the_price_and_sound.:j._collins_\"world_traveler\"\n\n\nB0007U6IFY\n\n\nLogitech mm22 Portable Speakers for iPod: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nVery pleased at the price and sound.\n\n\nAugust 14, 2006\n\n\nJ. Collins \"World Traveler\"\n\n\nCamp Humphreys, Korea\n\n\nI am pleased at the sound of these speakers. I feel that for the price, it was a good purchase. The carrying case that is included is a nice addition. You can use the included A/C adapter or run it on batteries. Not only do I use these speakers with my iPod, but also for my Mac Book laptop. I don't need an overbearing sound, but these portable speakers are just right\n\n\n\n\nB00008SCFL:muy_bueno..._recomendado:s._daniel_\"saint_luke\"\n\n\nB00008SCFL\n\n\nNetgear WGR614 802.11g Wireless Router: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n5.0\n\n\nMuy bueno... Recomendado\n\n\nNovember 9, 2006\n\n\nS. DANIEL \"Saint Luke\"\n\n\nCaracas, Venezuela\n\n\nKit completo para colocar una peque�a red inal�mbrica casera, sin problemas. Sencillo de instalar, tanto que cualquier bolsa sin experiencia puede hacerse con este kit. Tiene se�al fuerte, sin atenuaciones.\n\nSaludo\n\n\n\n\nB00003CWEI:best_cable_to_connect_your_dvd_player_!:steve\n\n\nB00003CWEI\n\n\nMonster Cable MV3CV-1M Monster Video 3 High-Resolution Component-Video Cable: Electronics\n\n\nelectronics\n\n\n6 of 9\n\n\n5.0\n\n\nBEST CABLE TO CONNECT YOUR DVD PLAYER !\n\n\nAugust 2, 2001\n\n\nSteve\n\n\nOhio\n\n\nI purchased a Sony WEGA 27 inch tv. I took advantage of it's rear component input labeled video4. I connected my Sony DVP360 Dvd player. With dvd viewing, the movie Dinosaur, Bug's Life, Toy Story 1 and 2, and Vegas Vacation, the colors were very vibrant, details were dynamic, and the picture quality was very accurate in at least 480 lines of resolution. I have viewed these same movies on larger screen HDTV's such as the Pioneer Elite and was disappointed??? Glass picture tubes provide a more realistic picture. I can't imagine that I can do any better for my television than these cables. I am completely happy with dvd movie watching on my Sony WEGA with these Monster component 3 cables\n\n\n\n\nB00005B9W6:repairs_xbox_games_perfectly:greg\n\n\nB00005B9W6\n\n\nSkipdoctor CD Repair Kit: Electronics\n\n\nelectronics\n\n\n4 of 5\n\n\n5.0\n\n\nRepairs xbox games perfectly\n\n\nDecember 26, 2005\n\n\nGreg\n\n\nNew Jersey\n\n\nI had a game that would not even play on the xbox because it was so scratched. After using the Game Dr. It works perfectly. It is like i bought a brand new game. The Game Dr. was pretty easy to use too\n\n\n\n\nB000BMFYGW:fantastic_!!!!:han32some_\"k32k\"\n\n\nB000BMFYGW\n\n\nPanasonic PT-AE900U Home Theater Projector: Electronics\n\n\nelectronics\n\n\n5 of 11\n\n\n5.0\n\n\nFantastic !!!!\n\n\nMarch 9, 2006\n\n\nHan32some \"k32k\"\n\n\nBurlington, MA\n\n\nThis is a fantastic home projector. I am a very picky electronics fan. This exceeded my expectations. The colors are RICH, bright & great. \n\nPanasonic produces best colors. I own their camera, camcorder & now this. I am going to buy their HDMI DVD playe\n\n\n\n\nB00005T6GZ:truoble_free_internet:j._murphy_\"average_guy\"\n\n\nB00005T6GZ\n\n\nLinksys BEFCMU10 EtherFast Cable Modem: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nTruoble Free Internet\n\n\nNovember 4, 2006\n\n\nJ. Murphy \"Average Guy\"\n\n\nChandler, Arizona\n\n\nI have paired this with my Linkysys wireless-g router and I have not had any problems with either. The modem installed without any issues. Prior to this modem I was using an RCA which began having connection problems. Since the Linksys install I have rock solid connection to the internet and a more stable wireless network. Previously I would expirience intermittant \"drop outs\" and would have to reboot. Currently I couldnt be more satisfied has been a good 3 month\n\n\n\n\nB00005T6GZ:linksys_befcmu10_etherfast_cable_modem:c._ildefosno_\"gadget_guy\"\n\n\nB00005T6GZ\n\n\nLinksys BEFCMU10 EtherFast Cable Modem: Electronics\n\n\nelectronics\n\n\n5 of 6\n\n\n5.0\n\n\nLinksys BEFCMU10 EtherFast Cable Modem\n\n\nAugust 3, 2006\n\n\nC. Ildefosno \"Gadget Guy\"\n\n\nGeneva, IL\n\n\nThe product was easy to load onto my Windows based computer using Windows XP. I connected everything as instructed and as soon as I plugged in the TV cable to the back of the modem, My internet explorer picked up the signal and put me at the log in page for new accounts of my cable provider. \n\nThe modem comes with both a USB and 100T ethernet adapter port, so use whatever way works best for you. I tried them both and they appear to work at the same speed. I am guessing it all depends on the speeds your cable provide is delivering.\n\nAmazon had the best price on the product if you have the time to wait for their free shipping. I ordered some stuff from them in the past and the free shipping seemed to take forever.\n\nYou may want to factor in paying the additional cost for shipping when buying this product if you need it right away. The price, even with the additional shipping charge is still a little less expensive than Best Buy. The cost difference between buying it at a Best Buy store and Amazon online will not be much, and you'll have to wait a few days to have it in your hands, but you get to avoid the drive to the store. I went this route, paid the extra shipping and waited for my package to arrive. If time is not an issue, then use the free shipping method and you will discover that Amazons Price can't be beat, it is the lowest price anywhere. \n\n\n\n\nB00003CW9R:great_cables:\"topgun8\"\n\n\nB00003CW9R\n\n\nMonster Cable I400MKII-2M Interlink 400 MkII Advanced Bandwidth Balanced Audio Interconnect: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n4.0\n\n\nGreat Cables\n\n\nFebruary 7, 2003\n\n\n\"topgun8\"\n\n\nChicago, IL USA\n\n\nI bought these for my dvd player. They work wonderfully! The old analog cables that came with the dvd player looked alright but [didn't measure up}. These monster cables definately enhance the sound of my dvd movies, especially the dialogue. The old cables that I had made my movies sound a bit muffled. But the monster cables intensified the clarity from the dvd player to my reciever. For those of us who cannot afford 5 speaker digital surround sound, I would definately recommend these cables if you use regular 2 channel stereo for your movies or CDs\n\n\n\n\nB00012OJ22:very_usefull_comes_with_adapter_included_:juan_seminario\n\n\nB00012OJ22\n\n\nSanDisk SDMSPD-512-A10 512 MB MemoryStick Pro Duo (Retail Package): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nVery Usefull comes with adapter included \n\n\nAugust 15, 2006\n\n\nJuan Seminario\n\n\nPeru\n\n\nI PURCHASED 2 CARDS FOR ABOUT us$ 42 FOR MY psp they work Ok. I had other sandisk products and dont have any complaints. Best of all these cards came with the Adapter Memory Stick Pro duo to Memory Stick. So I could use it with my videocamera. Its nice when one company starts addresing the multiple formats problems.\n\n\n\n\nB0009SY78Y:working_just_as_new_and_new_firmwave_version_4.32:zhichao_huang_\"zhi_chao_huang\"\n\n\nB0009SY78Y\n\n\nRemanufactured Magellan RoadMate 700 20 GB Vehicle GPS Navigation System with Windshield Mount: Electronics\n\n\nelectronics\n\n\n5 of 7\n\n\n5.0\n\n\nworking just as new and new firmwave version 4.32\n\n\nMarch 24, 2006\n\n\nZhichao Huang \"zhi chao huang\"\n\n\n\n\n\nI just bought a refurbished Roadmate 700 a few days ago. It took minutes to locate my current location, which was totally normal. \n\nI kinda feared that I would have the GPS with the older version 3.2, as some people said earlier. But in fact, the one that I got is with firmwave version 4.32, the newest version. The voice is working good too.\n\nI am happy with this product, even though it is a refurbished one\n\n\n\n\nB00009ZOHD:great_value:alyssa_a._lappen\n\n\nB00009ZOHD\n\n\nMAXELL 723849 Alkaline Battery Multipacks (AAA 20pk): Electronics\n\n\nelectronics\n\n\n11 of 11\n\n\n5.0\n\n\nGreat value\n\n\nAugust 12, 2006\n\n\nAlyssa A. Lappen\n\n\nEarth\n\n\nIf you go through a lot of AAA batteries, this is one of the best deals on the market, short of investing in a recharging unit and a couple of rechargeable batteries. A very good value\n\n\n\n\nB0000AZK27:wicked_good_back_pack:p._b._panagore\n\n\nB0000AZK27\n\n\nKensington 62238 Contour Backpack: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nWicked Good Back Pack\n\n\nAugust 10, 2006\n\n\nP. B. Panagore\n\n\nMaine\n\n\nA sore back and a sore shoulder forced me to search for a better means to carry my laptop. I took a month to research laptop backpacks and finally settled on the Kensington Contour. I didn't want to buy a Kensington. I wanted a backpack making company with a name I knew, like Jansport,or Kelty. I've been a backpacker and was familiar with their products. I settled on the Kensington because it had the qualities I was seeking: comfort, quality materials and design, water proofing, laptop padding, urban black, rugged exterior, sophisticated appearance, lots of pockets, adjustable lumbar support with back padding and a water bottle pocket. I write and work in media for a living. Mostly I work at home, but once a week I travel to cities. This pack does exactly what I want it to do - have places for everything I need, protect my precious laptop, and look hip. \n\nSo now it is a few months later. I still like my pack, but twice now the hardware has broken. The first time it was just the zipper-handle on the little outside pocket. It fell off. Kennisington immediately replaced the pack for free. Last week at the airport the tab that holds and adjusts the left shoulder strap-- broke when slung the pack across my back. I haven't contacted them yet\n\n\n\n\nB000CS7U1C:a_bit_bulky,_but_it's_all_good:ma_rowena_v._reyes\n\n\nB000CS7U1C\n\n\nCreative Zen Vision:M 30 GB MP3 and Video Player (Black): Electronics\n\n\nelectronics\n\n\n7 of 7\n\n\n5.0\n\n\nA bit bulky, but it's all good\n\n\nNovember 15, 2006\n\n\nMa Rowena V. Reyes\n\n\nQuezon City, Metro Manila Philippines\n\n\nAs a journalist, I need to tote around a microcassette recorder with me wherever I went. Now, with the Zen's microphone/recording feature, I'm able to use it to record my interviews, and it's easier to transcribe because of its clear, crisp recording quality.\n\nYou can also play .avi files, so unlike iPod users, there's no need to convert them in order to view them. \n\nMany might find it a bit bulky compared to the new iPod, but with its added features, it's a small flaw that's easy to overlook.\n\n\n\n\n\nB00005T3B5:good_deal:bin_liu\n\n\nB00005T3B5\n\n\nTDK MiniDV Tapes, 60 Minute (3-Pack): Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n4.0\n\n\nGood deal\n\n\nFebruary 26, 2006\n\n\nBin Liu\n\n\nUSA\n\n\nSo far I used one tape of this pack. The video quality is pretty good\n\n\n\n\nB0002LEMWE:outstanding_media:kenneth_wallace\n\n\nB0002LEMWE\n\n\nVerbatim 3 Pack 8.5GB DVD+R Double Layer DVDs with Jewel Case (95014): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nOutstanding media\n\n\nMarch 8, 2006\n\n\nKenneth Wallace\n\n\nBlairsville, GA USA\n\n\nShort review: Have burned about twenty disks, all work perfectly. Have made home dvds out of them, all work in three different regular dvd players that I've tried. Very satisfied with them\n\n\n\n\nB0002LEMWE:great_dual_layer_discs_(just_update_your_firmware_first):a._clark\n\n\nB0002LEMWE\n\n\nVerbatim 3 Pack 8.5GB DVD+R Double Layer DVDs with Jewel Case (95014): Electronics\n\n\nelectronics\n\n\n8 of 8\n\n\n5.0\n\n\nGreat Dual Layer Discs (Just update your firmware first)\n\n\nNovember 24, 2005\n\n\nA. Clark\n\n\n\n\n\nTo avoid burning a coaster, update the firm ware on your dual layer compatable drive first ([...]). The Verbatim discs seem to have fewer compatability problems than the other brands I've tried. \n\nIf your home DVD player is not dual layer compatable, you may experience some problems. It's normal for the movie to stop momentarily when it switches to the second layer. For best results with movies, I burn these as slow as possible. For data storage, they will burn at 8X no problem. \n\nThese discs work as advertised. If you're torn between Verbatim and another brand, I would opt for Verbatim discs. They really do work the best\n\n\n\n\nB000EWHH7I:the_best_usb_drive_out_there:s._j._wilson\n\n\nB000EWHH7I\n\n\nSanDisk 2 GB Cruzer Titanium ( SDCZ7-2048-A10, Retail Package): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nThe best USB drive out there\n\n\nNovember 10, 2006\n\n\nS. J. WILSON\n\n\nSan Diego, CA\n\n\nThe SanDisk Cruzer TI is the most durable thumb drive on the market. I had a 512MB Cruzer TI before this one. I dropped it stepped on it and was just not too careful with it. Through all that it never failed me. I needed an upgrade and picked this one, because the 4GB was not available. It move data fast and the new U3 stuff is very interesting. I am not all that used to the U3 stuff yet, but I am sure I will start to use it to its fullest soon. Co-workers of mine buy the less expensive USB drives and break them. The SanDisk Cruzer TI is the absolute best\n\n\n\n\nB000EWHH7I:my_last_\"jump_drive\":k._b._jensen_\"advanced_user\"\n\n\nB000EWHH7I\n\n\nSanDisk 2 GB Cruzer Titanium ( SDCZ7-2048-A10, Retail Package): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nMy last \"jump drive\"\n\n\nNovember 6, 2006\n\n\nK. B. Jensen \"Advanced User\"\n\n\nPedro Bay, Alaska\n\n\nI had a small 512 Sandisk a while back, so when I saw a review for the Titanium 2GB Cruzer in a popular computer mag, I decided to upgrade from my Lexar 1GB. So far, I'm very impressed. Just did a 1.26GB transfer that took 140 seconds. I was a big leery of the included software, but was pleased to see that if I didn't care for it, it was easily removed. I have not had an issue with either the software or the drive, and am very impressed with the design of the retractable interface that requires pressure to expose or retract. This is a very good drive, and contrary to the negative comments, I would encourage anyone looking for a fast, stylish, practical jump drive to seriously conside the Titanium Cruzer\n\n\n\n\nB00023763Y:excellent_product:jose_castillo\n\n\nB00023763Y\n\n\nTargus CVR217 Neoprene Slip Case for 17\"\" Notebooks (Black/Blue)\": Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nExcellent Product\n\n\nNovember 9, 2006\n\n\nJose Castillo\n\n\n\n\n\nI am satisfied by the purchase of this Targus CVR600 15.4 Groove Notebook Backpack since it is super wide, comfortable and with many compartments to keep objects apart of the laptop. I recommend it to you\n\n\n\n\nB0001DQ5YG:jbl_creature_ii-fabulous!:ruth_foreman\n\n\nB0001DQ5YG\n\n\nJBL Creature II 3-Piece Powered Speaker System: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nJBL Creature II-FABULOUS!\n\n\nNovember 5, 2006\n\n\nRuth Foreman\n\n\nThe Sunshine State\n\n\nGreat buy for a wonderful speaker system. Works beautifully with my MAC Mini for great sound. Would not hesitate to purchase again. Great contemporary look on my desk. So easy to turn the volume up or down either from my screen or the subtle touch control on the Creature. I highly recommend\n\n\n\n\nB0001DQ5YG:great_speakers:clark_kent\n\n\nB0001DQ5YG\n\n\nJBL Creature II 3-Piece Powered Speaker System: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGreat Speakers\n\n\nNovember 2, 2006\n\n\nClark Kent\n\n\n\n\n\nI recommend these speakers - have had them for 3 months now and they are great - sound quality is great. \nPower converter gets pretty hot though so i suggest putting it on something non flammable - don't think it would catch anything on fire but just to be safe. Speakers are not super loud but are great if you just want quality sound.\n\n\n\n\nB00003CWEJ:great_cable_at_good_price:panukorn_vichaikul\n\n\nB00003CWEJ\n\n\nMonster Cable MV3CV-2M Monster Video 3 2-meter High-Resolution Component Video Cable: Electronics\n\n\nelectronics\n\n\n9 of 12\n\n\n5.0\n\n\nGreat Cable at Good Price\n\n\nNovember 17, 2001\n\n\nPanukorn Vichaikul\n\n\nKissimmee, FL USA\n\n\nRecently bought a Analog Big Screen TV. Switched from S-Video to Component cables. There is a big difference. The picture looks more film like with Component cables. Do not hesitate to upgrade. Stay away from cheap Wally World brands. You get what you pay for. The S-Video cable I used was Monster Cable M1000 sv\n\n\n\n\nB00009EBYA:best_$80_i_ever_spent_on_portable_electronics:p._bergin_\"flying_coyote\"\n\n\nB00009EBYA\n\n\nTargus PA870U Universal Wireless Keyboard: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nBest $80 I ever spent on portable electronics\n\n\nApril 13, 2005\n\n\nP. Bergin \"Flying Coyote\"\n\n\nBrooklyn, NY United States\n\n\nI got one of these a few years ago. It's been to several countries, on camping trips, bicycle trips, subways, etc etc and held together very well. I pop this and the handspring into my pocket and off I go, not lugging around a huge laptop anymore.\n\nThe typing is comfortable, this is just like a laptop keyboard, and while I too have worried about the little popup part where you connect the handspring, it's taken a beating so far. I'm buying another preemptively because I'll be so bummed when I do eventually manage to break the current one.\n\nThe hinge in the middle is annoying - you have to put the thing on a book or other flat surface to hold it in your lap. I think that when the new one comes, I'll try to create some kind of deadbolt mechanism to hold the middle hinge when it's open and then it will be the single most perfect gadget ever\n\n\n\n\nB0002TJDPW:good_investment_:david_andrew_moller_\"dave\"\n\n\nB0002TJDPW\n\n\nSaitek PC Gamer's Keyboard: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n5.0\n\n\nGood Investment \n\n\nAugust 2, 2006\n\n\nDavid Andrew Moller \"Dave\"\n\n\nSan Luis Obispo, CA\n\n\nLet me just preface this review by mentioning that I read many customer reviews(here and elsewhere) on this product before purchasing it. I focused on the negative reviews as to develop a good idea of any potential design flaws. After using this keyboard for almost 6 months I believe that Saitek delivers an attractive design with solid quality. \n\nCommand Pad: Many reviews bash this feature. It is quite a basic pad as compared to some, but it gets the job done. My only main gripe with the pad was how it failed to deliver some windows macros such as ctrl-alt-delete. During games such as WoW and CoD2 I had no problems whatsoever. The rumor about the horrible software and it being buggy was nonexistent for me. The customers who claimed it to be \"impossible\" and \"buggy\" seem to be computer illiterate at best, and judging from the frequency of grammatical errors in their reviews, possible English illiterate as well. If you are familiar with computer hardware then you will be fine with this software and pad.\n\nKeyboard: The quietness, response, and overall feel of the keys rival any Logitech design. I am a huge fan/customer of Logitech equipment, and thus can confidently state that this board is of equal material quality. Saitek took a more simple route with this design(no LCD screen etc...) and the price shows it.\n\n*I almost laughed out loud after recently reading some of the passionate and disgruntled reviews concerning the lack of key-symbol illumination. It is obvious that these reviewers did not know much about the product they purchased. Unfortunately failing to be informed about a product one buys totally demerits any kind of negative opinion resulting from that lack of knowledge. I would guess that 90% of the negative reviews about this board capitalize on this point resulting in a much lower overall rating than deserved. \n\nLast and probably least comes the aesthetics, of course the most subjective part of any review. I found this setup to be very sharp looking. In the dark, the vivid key definition from the backlights allows any experienced typist to quickly find whatever he/she is looking for. This eliminates the need for symbol illumination. Although symbol illumination might be a nice touch, especially for the keyboard deficient, I feel that Saitek sacrificed this option to provide very durable lettering. I have lost some lettering on Logitech keyboards sooner than the 6 months I've been using this one(Although frequency of gaming has fluctuated). Saitek Engineers probably figured that most gamers would know the keyboard well enough to not need both key definition and symbol illumination, apparently an oversight on their part. \n\nI will recap this lengthy review by recommending this keyboard to any capable computer enthusiast who desires a good value. \n\n\n\n\nB000ALAHSO:excellent_monitor!:brendan\n\n\nB000ALAHSO\n\n\nViewSonic Optiquest Q7b 17\" LCD Monitor: Electronics\n\n\nelectronics\n\n\n9 of 9\n\n\n5.0\n\n\nExcellent Monitor!\n\n\nSeptember 27, 2006\n\n\nBrendan\n\n\nwww\n\n\nUpgraded from 19\" Viewsonic CRT. This monitor is superb! Recommendation: go with native resolution, enable Clear Type through display settings, and download the monitor drivers from Viewsonic (XP uses standard drivers, not as good as Viewsonic's imo), then restart, makes a huge difference over settings out of the box.\n\n\n\n\nB0002INL4M:they_finally_stay_in_my_ears!:alan_e._moore_\"choklat_luvr\"\n\n\nB0002INL4M\n\n\nShure PA755 Triple-Flange Sleeves for Shure Sound-Isolating Earphones (1 Pair): Electronics\n\n\nelectronics\n\n\n8 of 8\n\n\n5.0\n\n\nThey finally stay in my ears!\n\n\nOctober 17, 2005\n\n\nAlan E. Moore \"Choklat Luvr\"\n\n\nSanford, FL\n\n\nI love my Shure E3C earphones and up until recently was pretty happy with the foam sleeves. However, I was constantly having to reset them in my ears when engaged in strenuous activities. \n\nThe first time I tried these triple flange sleeves I thought I had wasted my money. Later I pulled my ear up and they slid in a lot further... and suddenly I was in heaven. I get the full sound that I love so much complete with throbbing bass line AND the things stay in my ears until I pop them out!! These are the perfect complement to a wonderful headset :\n\n\n\n\nB0000C4DX6:ok_product,_so-so_design:theodore\n\n\nB0000C4DX6\n\n\nKensington 64338 Comfort Type USB/PS2 Keyboard (PC/Mac): Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n4.0\n\n\nOk product, So-so design\n\n\nMarch 9, 2006\n\n\nTheodore\n\n\n\n\n\nIt says the keyboard is supposed to be easier and more comfortable to type with. I don't think it is that comfortable. It's a little hard getting used to, so be prepared to hit the backspace a couple of times. The keyboard is 100mA and uses USB connection. There is a USB to PS/2 converter included. Overall, it is an ok product for the money, as it is the cheapest USB keyboard I've found\n\n\n\n\nB00005ATMG:as_promised:a._student\n\n\nB00005ATMG\n\n\nCase Logic Nylon 280-Disc CD/DVD Wallet: Electronics: Case Logic\n\n\nelectronics\n\n\n0 of 1\n\n\n4.0\n\n\nAs promised\n\n\nNovember 10, 2006\n\n\nA. Student\n\n\n\n\n\nArrived in time, decent quality for the price. Is a bit difficult to close though, as the zip isn't awesome. But as I said, it's good for the price\n\n\n\n\nB00005ATMG:cd/dvd_wallet:neeshal_n._desai_\"nee\"\n\n\nB00005ATMG\n\n\nCase Logic Nylon 280-Disc CD/DVD Wallet: Electronics: Case Logic\n\n\nelectronics\n\n\n0 of 1\n\n\n4.0\n\n\nCd/DVD Wallet\n\n\nNovember 6, 2006\n\n\nNeeshal N. Desai \"Nee\"\n\n\nNY\n\n\nWorks great, just like ti should, can add more sheets to store more. \n\n\n\n\nB000E8POQ8:good_skin...good_price.:k._kim\n\n\nB000E8POQ8\n\n\niSkin Eclipse 60GB/80GB Video iPod Protector - Black (EVO3EC1-B): Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nGood skin...good price.\n\n\nNovember 14, 2006\n\n\nK. Kim\n\n\n\n\n\nThis is a very good skin for 60 GB & 80 GB owners. It completely covers the entire iPod without sacrificing functionality. What separates this skin from other skins it the Visor (a plastic shield which protects the screen). My only problem with this skin is the belt clip. It is impossible to remove and difficult to use. A better belt clip would make this the perfect skin. This is a very good price from TechGNet. \n\n\n\n\nB000BFHWX2:a_new_definition_to_sound_quality:gabriel_meguira\n\n\nB000BFHWX2\n\n\nCreative Sound Blaster X-fi Elite Pro ( 70SB055000002 ): Electronics\n\n\nelectronics\n\n\n19 of 21\n\n\n5.0\n\n\nA New definition to Sound Quality\n\n\nOctober 24, 2005\n\n\nGabriel Meguira\n\n\nisrael\n\n\nI have this card for a week, and what I can tell you is that this card has a quality that I've never heard from any sound card - the sound is so clean, balanced and accurrate.\nof coure, you will need a good set of spekaers.\nthe CMSS surround is much better than Dolby Prologic 2 and DTS neo 6 for both music and movies.\nif you plan to connect the DIN cable for Integrated speakers control with the Megaworks or Gigaworks, you should know that it's not working, but Creative is going to add support for those speakers in the following drivers.\nI didn't like the new 24-bit Crystalizer sound, but belive me you will not need that feature, just use good mp3 player like Winamp 5+ MAD mp3 decoder plug and ASIO plug for the output.\nI had many sound card like the Audigy 2 , Audigy 2 ZS, Audigy 4 Pro, ESI Juli@, M-audio Revolution...\nand none of theme even close to this card!!!\nworth every $$$!!!!\nI recommend you buying from amazon beacuse of thier great service and warrnty, I could't do that because I am outside USA\n\n\n\n\nB0000AOWVC:great_product:keith_graves\n\n\nB0000AOWVC\n\n\nBelkin F8V366-APL Ipod Mobile Cassette Adapter: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n5.0\n\n\nGreat Product\n\n\nFebruary 24, 2006\n\n\nKeith Graves\n\n\nBrattleboro, VT\n\n\nThe Belkin cassette adapter was my 2nd attempt at a product that would allow me to play my iPod through car cassette deck. The first, non-Belkin, product (can you say Monster here?) was crap...stopped working properly after two weeks. The Belkin adapter has performed flawlessly for 2+ months now\n\n\n\n\nB000069K8O:great_all_purpose_remote:b._tierney\n\n\nB000069K8O\n\n\nOne For All URC 9910 Premium Learning Line 8-Device IR/RF Upgradeable Universal Remote Control: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nGreat all purpose remote\n\n\nNovember 9, 2006\n\n\nB. Tierney\n\n\n\n\n\nI have long struggled to find the perfect remote. The URC9910 isn't it, but its pretty good. Its no match for my Harmony 880 but it also costs a whole bunch less and works through walls. I have 2 URC9910s, both programmed identically for my main HT. The cabinet also contains a second reciever and a sound distribution center for the other rooms in the house. The kids can be using one URC9910 with the TV/HT and I can be in the basement or outside and control the volume of my music with the RF part of the second URC9910 remote. For the price you can't beat it. All buttons AFIK are progammable or mappable. Setting it us is a pain and I had to find some pretty obscure codes on the internet to get my TV to do its thing so that is where it looses a star. Otherwise its a great performer and with the price, I don't mind if my kids use it, whereas, they aren't allowed to use my Harmony 880.\n\nIf cost is no object, then there are better remotes. For the price, I can't find one better\n\n\n\n\nB0009MX5R4:great_dvds:christopher_\"chris\"\n\n\nB0009MX5R4\n\n\nMemorex 16X DVD-R (100-Pack Spindle): Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nGreat DVDs\n\n\nNovember 3, 2006\n\n\nChristopher \"Chris\"\n\n\nChicago, IL\n\n\nOnly thing I did not care for was the design on the top of the disk. Other than that, these are quality DVD\n\n\n\n\nB000I5LAFQ:elegant!:g._mettetal\n\n\nB000I5LAFQ\n\n\nCanon PIXMA MP600 All-in-One Photo Printer (1451B002): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nElegant!\n\n\nNovember 9, 2006\n\n\nG. Mettetal\n\n\nMishawaka, IN USA\n\n\nI bought this to replace an Epsom printer that refused to print accurate color, and an old Canon scanner. I am very, very pleased! It looks terrific (my daughter said, \"Wow, cool new printer!\"). Set up was easy. And the printing is fast and absolutely beautiful, even in draft mode. Better yet, the cartridges are much cheaper than my Epson ones. Scanning is easy and good, too. I don't use fax, so can't rate that function.\n\nOverall, I am thrilled with this printer. It was well worth the $180\n\n\n\n\nB00061RWR2:2_cents_per_page.:lincoln_thompson_\"lincoln\"\n\n\nB00061RWR2\n\n\nHP Q5949X Black Toner Cartridge (HP LaserJet 1320): Electronics\n\n\nelectronics\n\n\n7 of 7\n\n\n5.0\n\n\n2 Cents per page.\n\n\nMay 18, 2006\n\n\nLincoln Thompson \"Lincoln\"\n\n\nKentucky\n\n\nI received the correct Q5949X cartridge.\nMy calculations for cost per page is only 2 cents per page, which is contrary to another reviewer's claim of 12 cents per page.\n$120.00 divided by 6000 pages equals 2 cents per page.\n\n\n\n\nB0002XM01C:it_will_do.:melissa_beeman\n\n\nB0002XM01C\n\n\nKensington FM Transmitter/Auto Charger for iPod (White): Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n4.0\n\n\nIt will do.\n\n\nMarch 27, 2006\n\n\nMelissa Beeman\n\n\nMesa, AZ USA\n\n\nIf you are like me and dont really care about the \"best\" sound quality you can possibly have, and don't mind the tiny bit of static (keep in mind I live in a city) then this is for you. Its simple, it works, and Im glad that I bought it, and that it didnt break the bank. I like it\n\n\n\n\nB0000A1EDV:it_does_exactly_what_i_needed:p._heath\n\n\nB0000A1EDV\n\n\nTargus PA243U Notebook Portable Lapdesk: Electronics\n\n\nelectronics\n\n\n12 of 16\n\n\n5.0\n\n\nIt Does Exactly What I Needed\n\n\nNovember 22, 2005\n\n\nP. Heath\n\n\nHurst, TX United States\n\n\nI have a Gateway CX2610, and it always got hot in my lap after about 10 or 15 minutes. Enter this Targus lapdesk. When folded out, it provides a nice buffer between my lap and the heat of the laptop even when using the laptop for upwards of two hours.\n\nBut wait, there's more! I had also found my laptop awkward to use at a desk because of the poor ergonomics. However, I use this lapdesk folded with the adjustable leg to reposition the keyboard to a better angle for typing. It also puts the screen at a better height as well.\n\nThe desk also works well folded in half with my laptop converted to tablet mode. So, it's a very versatile accessory to go with my versatile laptop. I highly recommend it\n\n\n\n\nB000068P8R:cyber_acoustics_computer_speakers:wu-tang_assassin\n\n\nB000068P8R\n\n\nCyber Acoustics CA-3550 2.1 Computer Speaker System (3 Speakers, Black): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nCyber Acoustics Computer Speakers\n\n\nDecember 30, 2005\n\n\nWu-Tang_Assassin\n\n\nCentral Cali\n\n\nI got the newer model of these, with the blue light on the speakers . All I can say is that these speakers are worth the money. You get what you pay for. These speakers have pretty good highs in them, bass wise there decent. They hit alright, and sub decent. You pay more money, you get a better system you know. This system here isn't much money, so it isn't going to be the best. So there isn't no reason for the bad reviews. I think they work really good for the price their tagged at. So if your looking for a system with some good highs, and decent bass these are for you\n\n\n\n\nB00013WH0M:good_overall_buy:r._ball_\"rkssball\"\n\n\nB00013WH0M\n\n\nPNY PSD512RF 512MB SD Secure Digital Flash Memory Card: Electronics\n\n\nelectronics\n\n\n21 of 21\n\n\n5.0\n\n\nGood Overall Buy\n\n\nSeptember 3, 2005\n\n\nR. Ball \"rkssball\"\n\n\nConcord, MI (USA\n\n\nI recently purchased a PNY 512 MB Secure Digital Card from Best Buy when it was on sale for $29.99 each card. I had been given a SanDisk 256 mb card for christmas the past year, which had gone curropt. The PNY is so much better and so much cheaper for virtually the same quality or better than my old SanDisk. I would recommend this product to anybody who is fed up with SanDisk or any other high priced memory manufacturer.\n\nSteve\n\n\n\n\nB00022RV9E:an_excellent_value!:g._butler_\"paaudiophile\"\n\n\nB00022RV9E\n\n\nSony STRDE197 Stereo Receiver: Electronics\n\n\nelectronics\n\n\n6 of 7\n\n\n5.0\n\n\nAn Excellent Value!\n\n\nOctober 19, 2005\n\n\nG. Butler \"PaAudiophile\"\n\n\nPa\n\n\nThis was part of my Stereo update. I selected this receiver for quality, value and ease of operation. I matched it up with the Sony CDPCX355 300 CD player and a fresh pair of those Bose 301-V speakers. Years ago I used to be an audiophile and I now think I'm going to enjoy getting back into my hobby\n\n\n\n\nB00005T3CU:unbreakable:kellyandpeanut\n\n\nB00005T3CU\n\n\nSony KV-36FS17 36\" Flat-Screen TV: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nUnbreakable\n\n\nOctober 20, 2006\n\n\nKellyandPeanut\n\n\nNC\n\n\nI bought a 32 inch 5 years ago. in 2003 when movie I was trying to move it across the room and it flipped face first in the floor. Mind you I had paid like $200 for Sear's sorry 3-5 year? warranty and it turned out they wouldn't have replaced it or probably fixed it even though my sister worked there. I heard a sickening crash (like the picture tube/glass breaking). It still works and only has a dent it the top left. I think I got the remote a little wet years ago and it never worked again. Not a big deal since I have a directv remote,but I forgot I even had PIP because of no remote. From now on if I can afford it I will buy sony only. I bought an xplode cd player 2 years ago with no problems except a dead remote that I never use\n\n\n\n\nB00005T3CU:sony_36":j._d._bremer\n\n\nB00005T3CU\n\n\nSony KV-36FS17 36\" Flat-Screen TV: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nSony 36"\n\n\nSeptember 18, 2002\n\n\nJ. D. Bremer\n\n\nSt Charles, MO USA\n\n\nThis TV weighs a ton, took three adult males to pick it up off of the floor and load it into an entertainment armoire. I have owned several Sony TVs and really thought hard about this purchase because of firmware issues that I have had with a 27" Sony. Mainly compared this model with the corresponding Panasonic Tau, absolutely hated the Tau remote and am very familiar with the Sony. Note that the remote was not the deciding criteria, but it was a consideration. I have no real complaints about this TV, the menu system is nice and the picture very good. I do not normally use the built-in speakers so really can't comment about their sound. This TV has a 16:9 enhanced mode that must be selected from the menus, I tried it once using a DVD as source input and liked the widescreen format that the DVD player provides better than the 16:9 mode. The 27" Sony that I have has an auto-volume feature, this 36" does not and I do miss it. I would purchase this unit again. Note that there is a very similar Sony model that has upgraded sound, but not consider it because the added width would not fit in the armoire\n\n\n\n\nB0002ZPTBI:these_are_really_a_killer_buy:steven_f._archibald\n\n\nB0002ZPTBI\n\n\nAURA PRO BASS SHAKER EACH: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nThese are really a killer buy\n\n\nMarch 30, 2005\n\n\nSteven F. Archibald\n\n\n\n\n\nThese are amazing products for low freq. they'll really get down word of advise don't use them for higher bass freq sounds more like a grinding noise after about 75 htz. Enjo\n\n\n\n\nB00080L62S:fast:\n\n\nB00080L62S\n\n\nSony 4 GB High Speed Memory Stick PRO Media (MSX-4GN): Electronics\n\n\nelectronics\n\n\n4 of 16\n\n\n4.0\n\n\nFast\n\n\nNovember 24, 2005\n\n\n\n\n\n\n\n\nThe 4GB high speed memory card is the best it is fast and it can store so much data I cant even imagine using all of the memory but the only reason I gave it 4 stars is because it's over 700\n\n\n\n\nB0009HGVZW:great_tapes:joel_clark\n\n\nB0009HGVZW\n\n\nSony DVM-60 PR/5 Premium Minidv Videocassette (5DVM60PRL): Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n5.0\n\n\ngreat tapes\n\n\nJuly 26, 2006\n\n\nJoel Clark\n\n\nLivonia, Michigan United States\n\n\nI bought these here and have had the for a little bit now. they work great. I will buy more when needed.\n\n\n\n\nB0007TIYA2:works_just_fine:sf\n\n\nB0007TIYA2\n\n\nUniden DCT648-3 2.4 GHz Digital Expandable Cordless Phone with 3 Handsets and Digital Answering System (Silver/Black): Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nWorks just fine\n\n\nNovember 3, 2006\n\n\nSF\n\n\nCA, USA\n\n\nNo problems w/ the phone. There is no mute button and it's annoying to hit two buttons to mute/unmute. I also couldn't find a way to change ringer volume but it's easy to change the ringer melody and there is one that's really soft. \nThe menu is not as intuitive as it could be but overall it works just fine as a phone.\n\n\n\n\nB0009STJIC:a_great_product:janet_smith_\"smitty\"\n\n\nB0009STJIC\n\n\nLogitech V200 Cordless Mouse - Blue ( 931409-0403 ): Electronics\n\n\nelectronics\n\n\n1 of 3\n\n\n5.0\n\n\nA great product\n\n\nMarch 8, 2006\n\n\nJanet Smith \"Smitty\"\n\n\nBrick,NJ\n\n\nI really like this mouse. It is easy to use and is one of the most comfortable on my hand. I have gone through a lot of mice and have finally found the one for me\n\n\n\n\nB00009EFQL:the_price_is_right_!:j._derosa_\"defgerl\"\n\n\nB00009EFQL\n\n\nTargus PA238U Ergonomic Monitor Stand: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nThe price is right !\n\n\nOctober 10, 2005\n\n\nJ. DeRosa \"defgerl\"\n\n\n\n\n\nThis stand works great for bringing the monitor up to eye level. If you want to sit comfortably...get rid of back and neck strain...this Targus Stand is definately the way to go. And...the price is right\n\n\n\n\nB00009EFQL:an_eyesaver:nucleusaccumbens789\n\n\nB00009EFQL\n\n\nTargus PA238U Ergonomic Monitor Stand: Electronics\n\n\nelectronics\n\n\n8 of 8\n\n\n5.0\n\n\nAn eyesaver\n\n\nJanuary 30, 2005\n\n\nNucleusAccumbens789\n\n\nWinston Salem, NC\n\n\nThis monitor stand is worth every penny, and considering the price, that really isnt that many pennies. What a monitor stand does is it raises the monitor to make it easier on your neck to view. I have an IBM laptop and I place it on top of the stand. With the stand, I probably have to look down at most about 10-15 degrees. Before the stand, I would have to look down at about 35 degrees. This does not seem like much, but if you spend alot of time staring at a computer screen, your neck will be sore. Buy this stand and your neck will thank you. \n\n\n\n\nB0007O7DTK:like_a_glove:wikipedia\n\n\nB0007O7DTK\n\n\nSpeck Products ToughSkin for iPod 4G & iPod Photo - Black: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nLike a Glove\n\n\nMarch 17, 2006\n\n\nWikipedia\n\n\nGermany\n\n\nThe case itself fits very snug around the IPOD, and even gives it a better look. The only complaint that I have is that the plastic window that covers the dial pops open, and even falls off at times. I still give the case a 4 star rating, because I am a soldier in Iraq, and this case is doing a very good job of keeping my IPOD clean. If a product can keep something clean in Iraq, then I am guessing that it can keep things clean anywhere. \n\n\n\n\nB0007O7DTK:works_as_expected:stanley_j._judd_\"jess1963\"\n\n\nB0007O7DTK\n\n\nSpeck Products ToughSkin for iPod 4G & iPod Photo - Black: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nWorks as expected\n\n\nFebruary 24, 2006\n\n\nStanley J. Judd \"Jess1963\"\n\n\nHonolulu\n\n\nWhat can I say, it's a rubber case that stretches over my Ipod. There's little room for malfunction. White would've looked better but isn't available through Amazon or its merchants without paying more than the product cost for shipping. It has little hard clear plastic windows over the screen and dial, the dial covering pops open. so far (several weeks now) they work fine without scratching\n\n\n\n\nB00068E570:my_first_review_of_a_product!:joel__brown_\"joel\"\n\n\nB00068E570\n\n\nSony SRS-P11Q Passive Speaker System, White: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nMy first review of a product!\n\n\nNovember 6, 2006\n\n\nJoel Brown \"Joel\"\n\n\nBloomington, IN United States\n\n\nThese speakers were purchased for office use and occasionally for on the road. They are a great look and compact. The sound is decent and should not be confused with higher end speakers. They serve the purpose for which they were intended as far as I am concerned as I purchased them to play my NANO I-pod. I do recommend them. \n\n\n\n\nB0001MHYK6:great_sound,_not_so_great_comfort.:jonathan_d._schmidt\n\n\nB0001MHYK6\n\n\nSennheiser PC155 Traditional Binaural PC Headset with USB and Noise canceling: Electronics\n\n\nelectronics\n\n\n16 of 16\n\n\n4.0\n\n\nGreat sound, not so great comfort.\n\n\nMay 2, 2006\n\n\nJonathan D. Schmidt\n\n\nColumbus, Ohio USA\n\n\nI agree with most of the reviews here. The PC155 is a great headset overall. The sound quality is great. Volume is more than adequate and fidelity is excellent. I've listened to a great deal of MP3 music through these babies and I am very impressed. Spacial/stereo imaging is great and I've actually heard things in the songs that I have never heard before! It actually shows the imperfections of my MP3 and makes me realize that I probably need to turn up the bit rate when ripping my CDs. The controls are also simple to use, both on the cord and on the PC.\n\nAudio recording quality is great as well. I have tried it with Dragon and recognition works great with it. Playback of recorded sound shows that the sound is a little on the dark side, however. Ps and Bs are a little too hot even though I have tried different mike positions and used the included wind sock.\n\nMy biggest problem with the set is in the comfort department. I first feel obligated to tell you that I do not normally wear full coverage headphones. Usually, I wear high-quality buds. So, perhaps I just have to get used to it. However, I feel as though my ears are being squeezed too much with these headphones. Instead of feeling like they are comfortably secure, they feel as though they are too tight and they make my ears hot... even after only 15 minutes.\n\nSo, despite my positive review of the sound quality, I am going to send these back and try the PC165. They supposedly have bigger ear cups, which will hopefully translate to more comfort? Other people here have not had problems with the comfort of the 155s so it is probably just me.\n\nUPDATE: I received the PC165 set and there is a big difference. The sound is fuller and bigger (not louder) on the 165s. Also, the comfort is much better. They don't pinch my ears like the 155s. The cord going to the headset is also thicker, which means fewer tangles and less chance of damage. If you're thinking of the 155s, I would get the 165s instead\n\n\n\n\nB0001MHYK6:high_quality_headset_for_internet_telephone:henry_d._friedman_\"henry\"\n\n\nB0001MHYK6\n\n\nSennheiser PC155 Traditional Binaural PC Headset with USB and Noise canceling: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nHigh quality headset for internet telephone\n\n\nMarch 18, 2006\n\n\nHenry D. Friedman \"Henry\"\n\n\nSyracuse NY\n\n\nI purchased this headset for use with my Mac dG5 and Skype internet telephone. The combination is killer. I cannot compare to other similar equipment as this is my first and only, but for me, the quality is far better than what I have with my phone, and it was really as easy as plug and play. I wish that the headset had thicker and stronger wires; I wish that the headset was more sold. On the otherhand, I have not had any problems with the equipment up to now. And the sound quality during telephone conversations is steller, which is important to me, especially as my hearing starts to deminish. I saw some comments about the headset as an audiophile listening headphone. IMO, this is too complementary. For its price and features, a good buy and good sound quality, but IMO, this is not an audiophile headphone. \n\n\n\n\nB00070FHXS:tunebase_works_great:patricia_long-fosaaen\n\n\nB00070FHXS\n\n\nBelkin TuneBase FM Auto Charger/FM Transmitter for iPod Mini: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nTuneBase Works Great\n\n\nAugust 18, 2006\n\n\nPatricia Long-Fosaaen\n\n\n\n\n\nAll 3 kids have a Tunebase. It works great, easy to use. It also charges the iPod while we travel\n\n\n\n\nB00066UTBS:excellent_value:spirit_of_76\n\n\nB00066UTBS\n\n\nAKG K 26 P - Headphones ( ear-cup ): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nExcellent value\n\n\nAugust 25, 2006\n\n\nSpirit of 76\n\n\nNew York, NY\n\n\nI've gone through a ton of headphones in recent years. They've included Koss \"The Plug,\" Sennheiser PX100, Koss UR29, Koss SportaPro and a few others. All had their strong and weak points. So far, this has been the closest I've had to a good balance.\n\nFor my tastes (not necessarily anybody else's), strong bass is one of the most important things. The PX100 felt very weak in this area, despite what everybody else seemed to say -- weaker than my old Sony MDR-NC20 (which are still going strong after 6 years and remain my backup headphone of choice thanks to their decent sound and folding ability). I had problems keeping \"The Plug\" sealed in my ears, despite the \"hold the outer ear while the Plug seats\" advice. They're also a very a bad choice for iPods because their very low impedance overdrives the amplifier circuit. The SportaPros had great bass, but leaked way too much sound. The UR29 had good bass and very good isolation, but were a little uncomfortable, warm, heavy and too bulky to use on the go. Going back a few years, I had a pair of AKG K240s that had disappointing bass. Ditto for a pair of Sennheiser HD-420s.\n\nThe K26P seems to combine the better aspects of all these phones. The bass is among the strongest. When I listen to the 1812 Overture, the \"cannon blasts\" come through loud and clear, even at moderately low volumes, with no distortion at all. Isolation isn't as good as the UR29, but not too bad. Certainly a lot more portable, easy to stow in my bag at all times. The PX100s fold smaller, but they're a pain in the neck to stow in their hard case. The higher efficiency of the K26P means I can set my iPod at a lower volume. Instead of 55-60% of max volume with the UR29 or the stock earbuds, I now run at 35-40% comfortably. In terms of comfort, they're comfortable enough for hours of wearing, after a couple of slight modifications. If they press too hard on your hears, first extend the headband all the way, then GENTLY straighten each half a little bit by gripping a couple of inches of steel band at a time and unbending it. After a few tries, it should be perfectly fitted to your head. Don't bend too hard or grip the plastic parts or you can break the phones. Be patient. So it takes a couple of days of trial and error, but in the end, you get good-sounding phones that are wonderfully comfortable. You'd be surprised how little pressure is needed to produce a good seal. You'll know if you've unbent too far because either the phones will slide down or you lose bass. Here's an easy test: with a finger on each side, press the earcups harder onto your ears. If the bass doesn't get stronger as you press (the way it does with all open-air headphones), that's as good a seal as you need. I hated the little, square foam pads on the plastic sliders and quickly peeled them off. All they seem to do is create two pressure points on top of the head. Other phones with similar headbands (like the Koss SportaPro/PortaPro) don't use these pads, so I don't think they're really necessary. Better to let the headband rest directly on the scalp and distribute the pressure over the entire headband, and so far my suspicions seem to have been correct. It has been far more comfortable for me without the pads, especially after more than half an hour. In fact, without the pads, I barely feel the headband at all.\n\nOverall, I'm satisified, especially at its low price point. I hope to use these for many years to come until either they break or something better comes along. By the way, don't be put off by comments about the warranty being invalid in the US and Canada. Read the whole warranty sheet! A separate section on the sheet covers a limited warranty for these countries, so yes, it is covered\n\n\n\n\nB000189W8M:excellant_unit:r._myers\n\n\nB000189W8M\n\n\nGarmin GPSMap 60CS Handheld GPS Navigator (56 MB): Electronics\n\n\nelectronics\n\n\n5 of 6\n\n\n5.0\n\n\nExcellant unit\n\n\nMarch 2, 2006\n\n\nR. Myers\n\n\n\n\n\nI had been wanting a nice color GPS unit for a while. My wife purchased this one for me and I love it. I purchased the City Select Maps to use with the autoroute system. It would be nice if the maps were included with unit. I've had it out on several caching trips now and it hasn't let me down yet. Make sure you take the time to read through the instruction book so you will know how to use the many features that the unit has built-in\n\n\n\n\nB0002OCR5K:great_carrying_case:nevets616\n\n\nB0002OCR5K\n\n\nApple M9603G/A iPod Carrying Case with Belt Clip: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n5.0\n\n\nGreat carrying case\n\n\nSeptember 2, 2005\n\n\nNevets616\n\n\nSo. Cali\n\n\nThis is a overall convient carrying case. IT DOES NOT SCRATCH you ipod in anyway. This is a great case for jogging or everyday use, it has a belt clip that works very well. great and simple case, i highly recommend it for everyday use\n\n\n\n\nB0007QN18U:best_buy:shankar_rathnam\n\n\nB0007QN18U\n\n\nSennheiser RS 130 Wireless Surround Sound Headphones: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nBest BUY\n\n\nNovember 20, 2006\n\n\nShankar Rathnam\n\n\nSpartanburg, SC United States\n\n\nThis is a wonderful product. The surround effect is phoenomenal and I just love it. The extra $50 is worth it for the quality sound that it delivers\n\n\n\n\nB00006JQ06:inexpensive,_great_performer,_outstanding_value:james_w._welch\n\n\nB00006JQ06\n\n\nSony SRF-59 FM/AM Radio Walkman with Sony MDR Headphones: Electronics\n\n\nelectronics\n\n\n4 of 5\n\n\n5.0\n\n\nInexpensive, Great Performer, Outstanding Value\n\n\nSeptember 30, 2006\n\n\nJames W. Welch\n\n\n\n\n\nI initially purchased this model then returned it in favor of the Sony SRF-M37V because of its five one-button pre-sets which make it a no-brainer to use while exercising. However, the SRF-59, perhaps because of its analog tuning, suffers less from co-channel and adjacent channel interference on FM. It's audio is slightly brighter and more musical with a decent pair of headphones or earbuds than its digitally tuned cousin. With this in mind, I purchased another SRF-59 yesterday and spent all last evening listening to it. I literally could not make myself shut it off and go to bed I was having so much fun scanning the FM dial. There was a program which aired last night on a local channel which featured bass-heavy 12\" remixes of eighties new wave hits with, blessfully, very limited commercial interruption. Over the course of a three hour listening session, I gradually cranked up the volume. As I was too lazy to fish out decent earbuds, it became apparent that with bass-heavy audio at very loud volumes the SRF-59's tiny amplifier was insufficient to drive stock Walkman style headphones and peak audio was clipped which simply necessitated turning the volume down a tad to restore full musicality (I understand that decent quality earbuds require less power to achieve the same level of high sound output, therefore there will be no audio clipping). Nevertheless, the sound quality from this bargain-priced Walkman through less than ordinary headphones was astounding. The party is literally in your head, and the soundstage ranges from ear to ear, which is somewhat disconcerting when it is located above your sinuses. I also made one pass through the AM band and there the SRF-59 was no slouch either. I hauled in 1200 WOAI San Antonio which is approximately 1000 miles from me. Though no true DX machine, after all how could it be, it is at the least a competent performer on the AM band as well (I suspect early in the evening AM DXing conditions were as good as they have been in a long time). For more on this model and others visit Xin Feng's website. I highly recommend this model for cheap thrills! \n\n\n\n\nB00006JQ06:nice_radio:w._peters_\"retired_geek\"\n\n\nB00006JQ06\n\n\nSony SRF-59 FM/AM Radio Walkman with Sony MDR Headphones: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n4.0\n\n\nNice radio\n\n\nJuly 4, 2006\n\n\nW. Peters \"retired geek\"\n\n\nRTP, NC USA\n\n\nA good basic AM/FM radio. Not digital tuning, but it works fine. Reasonably good reception\n\n\n\n\nB000F1YF9Q:great_picture_quality:busy_mom/attorney\n\n\nB000F1YF9Q\n\n\nToshiba SD-P1700 Portable DVD Player with 7\" Widescreen LCD: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n4.0\n\n\nGreat picture quality\n\n\nNovember 10, 2006\n\n\nBusy Mom/Attorney\n\n\nSan Diego, CA\n\n\nI bought this item rather than the cheaper products advertised because I trust Toshiba and because the picture quality was better than average. Overall, I am very satisfied with the product. We have used it on several aiplane trips and my toddler is very entertained. We haven't had any problems with the mechanics of the player. We also bought a case (which I highly recommend) so that we have enough Baby Einstein to keep her happy to our destination. Also, the battery has never run out, and charges quickly. My only problem with it is that the volume doesn't go up loud enough without earphones to hear it very well in a roaring commercial airliner. But, my daughter mostly looks at the pictures anyway, so it wasn't that big a deal for us. \n\n\n\n\nB000F1YF9Q:excellent_player,_really_great_purchase!!:linda_w._scherf\n\n\nB000F1YF9Q\n\n\nToshiba SD-P1700 Portable DVD Player with 7\" Widescreen LCD: Electronics\n\n\nelectronics\n\n\n10 of 10\n\n\n5.0\n\n\nExcellent player, really great purchase!!\n\n\nSeptember 3, 2006\n\n\nLinda W. Scherf\n\n\nMcMinnville, OR\n\n\nThis DVD player is great for the road, and even at home when you don't want to be bothered. Unless the are much loud noises happening around you, the players' speakers work fine. It comes with all of the accesories you'd want, including a car outlet, wall adapter, remote, and the battery. It charges in under 4 hours, and plays for over 3 hours. \n\nThe player is sleek and so is the battery, but together are a bit bulky, not bad. Screen quality is good, great for a portable. Made of good quality materials. I've had it for a month and nothing is showing signs of wear-n-tear.\n\nA recommended accessory to purchase is a carrying case, which will haul all your add-ons that are included. I bought the targus sport, which is great also. For 160 bucks, it's well worth it!\n\n\n\n\nB00026C9HE:finally_a_cordless_phone_with_good_volume:j._katzenstein_\"pathological_bookbuyer\"\n\n\nB00026C9HE\n\n\nUniden EZI996 900 MHz Cordless Phone for the Visually or Hearing Impaired, with Caller ID: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nFinally a cordless phone with good volume\n\n\nNovember 22, 2006\n\n\nJ. Katzenstein \"pathological bookbuyer\"\n\n\nLexington, Ma\n\n\nI do not have a hearing problem, but my previous cordless phones were inaudible even at high volume.. this phone fills the bill -- at least so far! The only caveat: no speakerphone, which would be handy considering the amount of time most of us spend on hold.\n\n\n\n\nB00062VYR0:lcd_dvd:a._bellatorre\n\n\nB00062VYR0\n\n\nAudiovox D1812PK 8\" Portable LCD DVD Player with Car Kit: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nLCD DVD\n\n\nNovember 3, 2006\n\n\nA. Bellatorre\n\n\nSan Diego, Ca United States\n\n\nThis car kit works very well. The screen is a bit larger than other DVD players of similar type. The only down side is that the battery only charges for about 2 hours (shorter than many movies). So, if you are going to use this in the car, you need to be able to charge it\n\n\n\n\nB00062VYR0:a_must_for_trip_w/_small_children:kim_walden\n\n\nB00062VYR0\n\n\nAudiovox D1812PK 8\" Portable LCD DVD Player with Car Kit: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n4.0\n\n\nA MUST for trip w/ small children\n\n\nOctober 17, 2006\n\n\nKim Walden\n\n\nMS, USA\n\n\nWe bought this unit from Sam's Club right before a trip to Disney World. (8 hours in the car with a toddler--eek!) However, it was/is perfect! We still use it for trips around town when we're in massive traffic or our son is being really fussy. The case that keeps the dvd player on the back of the seat needs a little work, but we figured out a way to make it work for us. Also, the remote doesn't work well, but we don't really need to use it as a DVD usually lasts long enough between stops\n\n\n\n\nB000067SPP:good:j._mendoza_\"ipsnatch\"\n\n\nB000067SPP\n\n\nStartech 6ft Premium USB 2.0 Ab High Speed Certified Device Cable: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nGood\n\n\nMarch 1, 2006\n\n\nJ. Mendoza \"ipsnatch\"\n\n\nNew Rochelle, NY\n\n\nThe cable was a good price, and it fit my printer perfect. Worth the few pennies I paid\n\n\n\n\nB0000A1EE8:nice_little_gadget!:lisa_a._fluitt\n\n\nB0000A1EE8\n\n\nCreative Labs TravelSound i300 MP3 Players: Electronics\n\n\nelectronics\n\n\n5 of 13\n\n\n4.0\n\n\nNice little gadget!\n\n\nNovember 27, 2005\n\n\nLisa A. Fluitt\n\n\nCa\n\n\nThese speakers are great for the common traveller and have great clean music. the only reason I did not give this 5 stars is because it doesn;t fofill the height of volume I wished. It is pretty loud, but I like really loud. I do not own this but one of my friends does I hoe to get logi3 i station for my ippod it seems awsome according to the reviews\n\n\n\n\nB0000A1EE8:one_word_fits_perfect,_surprised!:brett_a._stern_\"emscarsnchicks\"\n\n\nB0000A1EE8\n\n\nCreative Labs TravelSound i300 MP3 Players: Electronics\n\n\nelectronics\n\n\n7 of 7\n\n\n5.0\n\n\none word fits perfect, surprised!\n\n\nJune 24, 2005\n\n\nBrett A. Stern \"EMScarsnchicks\"\n\n\nBergen County, NJ\n\n\nThese speakers are not what i had expected from their size, price, and looks. Their quality, even at high volumes, exceeds many other speakers i've seen at double the price tag. The reason I bought these was because of the other reviews and the size. Another reason was the proposed battery life, 35 hours on 4 aaa batteries, not bad at all. So far its been very close. I have heard the other speaker (systems) and yes the JBL OnStage and the Bose Sounddock are both very good and do live up to their reputations, but your stuck with using them only when there is an outlet nearby\n\n\n\n\nB000AMDO0Q:mostly_pleased,_but....:2wanna\n\n\nB000AMDO0Q\n\n\nCuisinart TOB-30BC Toaster Oven Broiler: Electronics\n\n\nelectronics\n\n\n7 of 7\n\n\n4.0\n\n\nMostly Pleased, but....\n\n\nApril 24, 2006\n\n\n2Wanna\n\n\nNorth Richland Hills, TX United States\n\n\nI spent a lot of time reviewing Toaster Ovens to replace our 8 year old Delonghi Alfredo one. Most of the others had so-so features or bad enough reviews that made me pass on them; however, with the 5-star review and Cuisinart name, I chose the TOB-30BC. It toasts, it bakes, it looks nice and it has the knobs that my hubby likes in lieu of digital. Overall, it's a great oven and we have no complaints with it's cooking abilities or the sturdiness of the unit; but less than a month later, one of the knobs broke off the unit. They are completely plastic and no metal reinforcement to prevent this from happening. My hubby sent an email to Cuisinart and they are sending a replacement knob, but one would have expected better engineering from a name like Cuisinart\n\n\n\n\nB00006B9WD:cheap_and_clever:l._maurer_\"resident__xprt\"\n\n\nB00006B9WD\n\n\nCyber Acoustics CA2015 2.0 Computer Speakers(2-Speaker, White): Electronics\n\n\nelectronics\n\n\n10 of 10\n\n\n4.0\n\n\nCheap and clever\n\n\nMarch 16, 2006\n\n\nL. Maurer \"Resident xprt\"\n\n\nPort Charlotte, FL\n\n\nWhen we needed speaker systems for a batch of new workstations, price was the main consideration. The price for these units was well below budget. Since our main application for them was for audio-enhanced CAT (Computer Assisted Training), High fidelity and volume were not issues. They worked admirably for the purpose.\n\nThe use of a USB port for the power supply was a clever twist. One of my aggravating support woes is keeping speaker power adapters matched up with corresponding speakers. I had been buying unpowered (unamplified) units with no manual volume control. The USB trick gives me just what I want\n\n\n\n\nB000BFO320:can't_remember_the_last_time_i_changed_the_batteries....:d._neakarse\n\n\nB000BFO320\n\n\nLogitech LX5 Cordless Optical Mouse: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n5.0\n\n\nCan't remember the last time I changed the batteries....\n\n\nJune 27, 2006\n\n\nD. Neakarse\n\n\nNew Lenox, IL United States\n\n\nI've purchased several Logitech mice(cordless optical)and I've been satisfied with all of them, except for the battery life. I had to change the batteries in my previous mouse once a month. This new Logitech mouse is terrific. It's been several months and I have not had to change the batteries yet ... and I am not even using the on-off switch (I want to test the battery life without the switch first). Battery issues aside, the mouse is smooth, light and accurate. It was simple to install and I'd buy it again in a heartbeat\n\n\n\n\nB00008ZEKC:i_love_it!:brittany_steinhauer_\"~brit~\"\n\n\nB00008ZEKC\n\n\nCoby CX-CD375 Micro Stereo System with Top-Loading CD Player: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n5.0\n\n\ni love it!\n\n\nAugust 14, 2006\n\n\nBrittany Steinhauer \"~Brit~\"\n\n\nMount Juliet, TN 37122\n\n\nthis stereo works great and its cute and small so it doesnt take up alot of room, it lights up blue when u turn it on in the dark and i love the remote, now i dont have to get up to switch the station\n\n\n\n\nB000BS264Y:jabra_bt150_review:bryan_a._walters_\"gamegamer43\"\n\n\nB000BS264Y\n\n\nBT150 Bt Headset: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n4.0\n\n\nJabra BT150 Review\n\n\nJanuary 4, 2006\n\n\nBryan A. Walters \"GameGamer43\"\n\n\n\n\n\nI bought this headset and I must say it's a great buy. First off it's lightweight weighing in at about 16 grams. It's small which definately helps when your wearing it around. At times it's like you don't even feel liek your wearing anything. Exactly how a headset should be. I've place and recieved many calls with it already and having been using the the traditional wired headsets of the past this headset sound better and has no wires. The only problem I have found so far is that it takes some time getting used to the button placement and answering calls with the headset. Not to mention you only hear the standard ringtone rather than your custom ringtones. But that a small price to pay for such a great, inexpensive headset like this one\n\n\n\n\nB00005T3RC:better_than_expected:kg\n\n\nB00005T3RC\n\n\nSony MDR-E829V Fontopia Earbuds with In-line Volume Control: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nBetter than expected\n\n\nJune 16, 2006\n\n\nKG\n\n\n\n\n\nI was looking for comfy earphones to replace my Phillips one I got awhile back cos the rubber coating had fallen off and I stumbled on this one. I was a bit skeptical about the volume control thinking it would give out after a few times of sliding up and down but it works wonderfully and is so light and responsive to touch as well as being sturdy. \n\nI started out using the foam that came with it but that proved to be a bother and pretty much useless as the plastic earbuds are comfy enough. And they stay in your ears like glue. No more readjusting and I loved that.\n\nFor the price, this SO worth it.\n\n\n\n\nB00005T3RC:best_earbuds_foe_under_$10:harold_orville_peterson_\"arcangel\"\n\n\nB00005T3RC\n\n\nSony MDR-E829V Fontopia Earbuds with In-line Volume Control: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nBest Earbuds Foe Under $10\n\n\nNovember 7, 2005\n\n\nHarold Orville Peterson \"ArcAngel\"\n\n\nLos Angeles\n\n\nI bought These earbuds for the first time 3 years ago for $15 now u can get them for under $10 They last more than six months of hard wear and tear before they need replacing awsome i've been using them for a long time and don't plan on changin\n\n\n\n\nB000271MV2:nice_battery._poor_seller.:angela_b._schmith_\"purple_unicorn\"\n\n\nB000271MV2\n\n\nLenmar DVD-U9 NoMem Lithium Ion Battery for Portable DVD Players: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n5.0\n\n\nNice Battery. Poor seller.\n\n\nAugust 23, 2005\n\n\nAngela B. Schmith \"Purple Unicorn\"\n\n\nSandy, UT USA\n\n\nLenmar Is great to work with, When Beach Audio will not do exchanges for faulty equipment. Beach Audio told me I would need to purchase another one then when they get the faulty one back they would refund my money for the second one that I had to purchase. POOR RETURN POLICY. So I just sent it to Lenmar under the warranty and they sent me a new one. Only took one week to get it. GO LENMAR.\nI've been able to watch on a 10in. Portable DVD Two (2hr) movies on just this battery with a little to time to spare. ***NICE BATTERY*** \n\n\n\n\nB0000D8IYC:great_surge_protector,_especially_for_transformers:the_mango_man\n\n\nB0000D8IYC\n\n\nAPC PF11VT3 11-outlet SurgeArrest with Tel2/Splitter and Coax Protection: Electronics\n\n\nelectronics\n\n\n9 of 9\n\n\n5.0\n\n\nGreat surge protector, especially for transformers\n\n\nMarch 9, 2006\n\n\nThe Mango Man\n\n\nSoutheast of disorder..\n\n\nI really likethe design on this Protector, and bought 3.\n\nIt allows you to plug multiple transformers in, and still have room for regular plugs. Slipping plus in is a firm push, but not that tough. \n\nI also like the fact that it gives you a warning light if you get up around 12 amps.. Cool, but you have to keep in mind a 15 amp circuit shoulldn't have 12 amps to one plug..\n\nThe cord restraint system slides out to hold all the cords, then wedges backin to hold them in place. A bit hard to set up, and a zip tie could achieve the same thing, altough this is way easier if you change things\n\n\n\n\nB0000D8IYC:very_useful,_but_hard_to_get_plugs_in_and_out:your_average_gadgeteer\n\n\nB0000D8IYC\n\n\nAPC PF11VT3 11-outlet SurgeArrest with Tel2/Splitter and Coax Protection: Electronics\n\n\nelectronics\n\n\n9 of 9\n\n\n4.0\n\n\nVery useful, but hard to get plugs in and out\n\n\nMarch 8, 2006\n\n\nYour Average Gadgeteer\n\n\nMcLean, VA USA\n\n\nOverall, I like this surge protector and would buy it again. I bought it primarily because of the 11 outlets, six of which are spaced out to accommodate big brick-style transformer plugs. I did a lot of digging to find the surge protector that had the most outlets in this configuration, and this unit always came up. It has worked just fine for the month or so I've owned it. I particularly appreciate the angled plug, which allows you to use the second plug in a wall outlet without a problem.\n\nI only have two quibbles with the APC--the color and the stiffness caused by the plug-activated safety shutters. Plugging and unplugging cords requires some force to overcome the safety shutters, which could be a problem if you are trying to get something plugged or unplugged while the unit is in an awkward position or hard-to-reach location. On a much more minor note, the black unit and cord stand out quite a bit against light-colored walls and carpets, even when tucked behind a desk.\n\nI don't use the phone or coax plugs, so I don't know if my unit has the problem (or \"feature\") other reviewers have reported with Internet signal degradation\n\n\n\n\nB000ELV2MA:satisfied_with_my_mp830_:robert_c._hall\n\n\nB000ELV2MA\n\n\nCanon PIXMA MP830 Office All-In-One Printer: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n5.0\n\n\nSatisfied with my MP830 \n\n\nNovember 11, 2006\n\n\nRobert C. Hall\n\n\n\n\n\nMy mp830 has performed well in the time that I've had it. My family members have found it easy to use as well. I was saddened at the early demise of my Epson cx6400 which suffered from a common problem of dried ink clogging the feeder tube. I chose the Canon after reading good reviews, and I got it for an excellent price right here on Amazon plus free shipping. It still sells for more at my local Office Depot and Best Buy. In truth I haven't tried all of it's claimed capabilities, but I'm happy with what it's been able to do thus far. \n\n\n\n\nB0001YFYYM:a_good_lapel_mic:j._horneman\n\n\nB0001YFYYM\n\n\nSony ECM-C115 Omnidirectional Clip-On Business Microphone: Electronics\n\n\nelectronics\n\n\n21 of 21\n\n\n4.0\n\n\nA good lapel mic\n\n\nOctober 13, 2005\n\n\nJ. Horneman\n\n\nSarasota, FL\n\n\n I use this in my videography business, primarily for weddings. So far I've never had a problem, and the mic has always given me a clear recording of voices from a lapel position.\n I use it in conjunction with my iRiver-795, to record the sound directly, and it's worked like a dream... recommende\n\n\n\n\nB000EUGX7A:you_get_what_you_pay_for_with_these_speakers.:chillwater\n\n\nB000EUGX7A\n\n\nCreative GigaWorks T20 2.0 Speakers ( 51MF1545AA001 ): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nYou get what you pay for with these speakers.\n\n\nNovember 14, 2006\n\n\nchillwater\n\n\nMIAMI, FL USA\n\n\nThese 2.0 speakers sound great (and not just for their size), are adjustable, small, and good looking. I'm about half deaf and every speakers with a separate woofer had too much bass when I turned the volume up so I could understand human speech. I'm happy I spent the money. These are real quality in my opinion\n\n\n\n\nB0000AZJVP:great_headset:j._drechsler\n\n\nB0000AZJVP\n\n\nSennheiser PC130 Headset with Noise Cancelling Microphone: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nGreat headset\n\n\nAugust 28, 2006\n\n\nJ. Drechsler\n\n\n\n\n\nI recently bought an altec lancing headset for $20 and it was the worst headset investment I've made. Horrible support for the ears and very uncomfortable to wear and they eventually broke within a week or two. So I bought this headset and I have been quite pleased with it. The mic output is extremely crisp. While talking with clients they said that it sounds like I'm talking on the telephone with them or better. They are very comfortable and rest on the top of your head unlike many headsets like my prior pair which have a support on the back of the neck which does nothing. All in all a good headset.\n\n\n\n\nB00067LYFW:nice_fm_transmitter:r._anand\n\n\nB00067LYFW\n\n\niRiver AFT 100 Mobile FM Transmitter: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nNice FM Transmitter\n\n\nNovember 10, 2006\n\n\nR. Anand\n\n\n\n\n\nWorks good for me. Some time one can hear some interference but over all a pretty good product\n\n\n\n\nB0007LI13U:excellent:diego_castillo_jovel\n\n\nB0007LI13U\n\n\nNyko Stereo Link for iPod: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nExcellent\n\n\nNovember 5, 2006\n\n\nDiego Castillo Jovel\n\n\nManagua, Nicaragua\n\n\nThis product is awsome, i intalled it on my car with a sony stereo system, i placed the end of the ilink cable inside of a compartment of my car so i could put my ipod in, the sound quality that the cable delivers its outstanding, besides, it does not look like a cheap cable, it has a thick rubber coating that gives it some stiffness....\nIf you are realy thinking on connecting your ipod to your home or car stereo this is the right solution\n\n\n\n\nB00005V9GR:the_reviews_are_in...:\"gjen128\"\n\n\nB00005V9GR\n\n\nKingston Technology CF/256 256MB Compactflash Card: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n4.0\n\n\nThe reviews are in...\n\n\nDecember 22, 2003\n\n\n\"gjen128\"\n\n\nManila, Philippines\n\n\nI've been using this CF card on my Nikon 4300 for a couple of months now. It writes fast compared to my other CF (Canon stock, from another camera). However, start up of the camera takes longer (because of the size), and as more pictures are stored, there is a noticeable degradation in speed in writing. I've also used this on a Dell Axim, and it worked great! No problem in formatting\n\n\n\n\nB0000E2RP9:awesome_-_:bryan_m._whitehead\n\n\nB0000E2RP9\n\n\nGarmin City Select v7 CD-ROM Map - North America: Electronics\n\n\nelectronics\n\n\n4 of 6\n\n\n5.0\n\n\nAwesome - \n\n\nFebruary 24, 2006\n\n\nBryan M. Whitehead\n\n\n\n\n\nI'm still discovering the power of this program - at least when teamed with the Garmin 60CS. Auto routing, waypoints, detailed maps, assistance in finding the nearest gas station... the list is nearly endless.\n\nI've recommended this combination to several of my friends who are also consultants and who operate in a broad geography\n\n\n\n\nB00005T39Y:one_of_the_best_that_i_have_used:kay_galo_\"kay\"\n\n\nB00005T39Y\n\n\nSony CPA-9C MiniDisc and Discman Cassette Adapter: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nOne of the Best that i have Used\n\n\nNovember 3, 2006\n\n\nKay Galo \"Kay\"\n\n\n\n\n\nWell, I have used 3-4 cassette adapters, till i heard this one, only words that i can say is you have to hear it to identify the difference.\ni have been using it with my cd player and also with IPOD, with others i had to make sure that the volume on ipod was 100% but with this one hey 85% is too much.\n\nWell i showed it to 1 of my friend and he also loved it, and took mine and told me to get another.\n\nDon't hesitate to buy this one\n\n\n\n\nB00005T39Y:work_great_for_its_price:mohammed_raffi_ajmulkhan\n\n\nB00005T39Y\n\n\nSony CPA-9C MiniDisc and Discman Cassette Adapter: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nWork Great for its price\n\n\nNovember 3, 2006\n\n\nMohammed Raffi Ajmulkhan\n\n\n\n\n\nI bought this from amazon for $15 and it works great with my car audio.\nIt is easy to use and working fine till now. The audio quality is decent and surely recommend this\n\n\n\n\nB00001WRSJ:great_headphones_for_the_money:optimist\n\n\nB00001WRSJ\n\n\nSony MDR-V6 Monitor Series Headphones with CCAW Voice Coil: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nGreat headphones for the money\n\n\nNovember 5, 2006\n\n\nOptimist\n\n\nKansas\n\n\nThere are lots of reviews here and most appear to be on-target relative to my experience with these headphones. But here are my 2 cents:\nI got these to replace some aging Sony MDR-V2's that I purchased maybe 10 years ago. The ear cushions of the MDR-V2's had begun to deteriorate, and I figured it was time to see what had improved in 10 years. I just compared them to the older V2s and I can say that the V6 is definitely better. They fully surround the ears (V2s just sat on your ear, pressing on them). The V6s also seem to offer more detailed sound. I concur that they sound a little bright, but I can take care of this on my iPod by setting the EQ to ReduceTreble (but I like the brightness, so sometimes I don't do that). I do NOT concur with other review comments about weak bass. They appear to have the right amount. (IMHO - it is a shame that bass has been overemphasized in the past decade, at the expense of flatness and non-coloring of sound). I also concur that the sensitivity of these is quite good (plenty loud for me with the iPod - and I listen at good levels - albeit not ear damaging ones).\nAs a last comment, I notice Amazon has these for about $65. I paid $99 at a local store. Buy from Amazon ! (no - I don't work for or invest in them ;-\n\n\n\n\nB00001WRSJ:great,_uncolored_sound:michael_a._baxter\n\n\nB00001WRSJ\n\n\nSony MDR-V6 Monitor Series Headphones with CCAW Voice Coil: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nGreat, uncolored sound\n\n\nJuly 20, 2006\n\n\nMichael A. Baxter\n\n\n\n\n\nI bought these after reading over a hundred reviews for them, and I agree with the consensus that they deliver a flat, uncolored sound with tight, but not boomy, or overpowering bass. By the way, I've owned the Sony MDR-V600's for a couple of years now thinking they had replaced these, but they are actually more of a consumer model that seem to be comparable in terms of price and specifications, but are actually quite inferior to these in my opinion. These have been used by Musicians, studio engineers, and on air talent for 20 years now\n\n\n\n\nB00076TH8S:woop:w._downer_\"monkeyfootfever\"\n\n\nB00076TH8S\n\n\nRazer Diamondback Salamander Red 1600 DPI Gaming Mouse: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nWOOP\n\n\nNovember 6, 2006\n\n\nW. Downer \"MonkeyFootFeVeR\"\n\n\nFargo, ND USA\n\n\nthe razor diamondback is awsome no problems with install. and great sensetivity when doing everything\n\n\n\n\nB000637U86:griffin_isqueez:m._haefele\n\n\nB000637U86\n\n\nGriffin iSqueez Cradle for iPod or iPod Mini: Electronics\n\n\nelectronics\n\n\n8 of 8\n\n\n4.0\n\n\nGriffin iSqueez\n\n\nOctober 2, 2005\n\n\nM. Haefele\n\n\nRochester, NY USA\n\n\nAlthough there are more expensive options, if you're just looking for something to keep your iPod from sliding all over in the car, the iSqueez is a great option.\n\nI use this with the Monster iCarPlay FM Transmitter/Charger, which uses the dock connector. I can't get my iPod mini to fit all the way down in where it should because of this, but it still fits in there well enough to stay in place.\n\nI find that it holds the iPod at a great angle for viewing the screen from where you're sitting (though I'd recommend putting the passenger in charge of finding music)\n\n\n\n\nB0001ZY6Z4:good_dvd_player,_and_i_got_it_free!:steve\n\n\nB0001ZY6Z4\n\n\nAudiovox VBP700 7\" DVD Player in a Bag: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGood DVD Player, and I got it free!\n\n\nMay 14, 2006\n\n\nSteve\n\n\n\n\n\nThis player is very convenient for long trips. If you want to save the money, you can get it Free from this web site: awesomestufffree.com/portabledv\n\n\n\n\nB0001F21IS:great,_except_the_skins:john_winebarger_\"techie_and_trekkie\"\n\n\nB0001F21IS\n\n\nSanDisk SDCZ4-256-A10 256 MB Cruzer Micro with Skins (Retail Package): Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nGreat, except the skins\n\n\nNovember 25, 2006\n\n\nJohn Winebarger \"Techie and Trekkie\"\n\n\nGloucester, VA\n\n\nI'm a section editor at a high school newspaper and I have used this drive every day both in that class and in all my others for over a year and it has never let me down. It's small, sleek and elegant and has a quick transfer rate.\n\nThe only issue is with the caps ans skins, which get annoyingly loose over time. I carry it around on a little pouch on my keys that I keep on my belt loop. When I pull it out of the pouch it will sometimes pull off the cap and skin. It's a minor annoyance for an otherwise great flash drive.\n\nI just went out and bought the 1GB version and I use them both\n\n\n\n\nB0001F21IS:cruzer_w/_skins:daniel_moran_\"dan_moran\"\n\n\nB0001F21IS\n\n\nSanDisk SDCZ4-256-A10 256 MB Cruzer Micro with Skins (Retail Package): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nCruzer w/ Skins\n\n\nSeptember 22, 2006\n\n\nDaniel Moran \"Dan Moran\"\n\n\nMemphis, TN\n\n\nAwesome product for the price. You need to get a SMALL ring (like from a key fob from a car) to use it on your own key ring\n\n\n\n\nB00005RI9V:sound_good,_worked_out_of_the_box:j._hamann\n\n\nB00005RI9V\n\n\nJBL SoundPoint SP6C 2-Way 6.5-Inch In-Ceiling Loudspeakers, Pair (Off-White): Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n5.0\n\n\nSound good, worked out of the box\n\n\nMarch 3, 2006\n\n\nJ. Hamann\n\n\nDallas, TX USA\n\n\nGood sound quality for the sub-$150 (per pair) price tag. Easy to install\n\n\n\n\nB0001FV364:easy_and_cheap!:walter_correa_iii\n\n\nB0001FV364\n\n\nSanDisk Cruzer Micro 512 MB USB 2.0 Flash Drive (SDCZ4-512-A10, Retail Package): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nEasy and Cheap!\n\n\nNovember 9, 2006\n\n\nWalter Correa III\n\n\nWailuku, HI United States\n\n\ni can't believe the unbelievable prices of flash drives these days. i just bought this one from Wal-mart for $20.00 dollars. that's way cheaper than on amazon.com. plus i've noticed that amazon.com has switched to third retailers and some don't even ship to hawaii, or do ship to hawaii at a hefty price. this switching of amazon having the lowest price to third retailers is really turning me off. possibly even towards purchasing books. \n\n\n\n\nB000F76W78:i_own_it_and_love_it!:marsha_collier_\"dealingdiva\"\n\n\nB000F76W78\n\n\nNetgear SPH101 Skype Wi-Fi Phone: Electronics\n\n\nelectronics\n\n\n0 of 2\n\n\n4.0\n\n\nI own it and love it!\n\n\nOctober 31, 2006\n\n\nMarsha Collier \"dealingdiva\"\n\n\nLos Angeles, CA USA\n\n\nSmall, convenient and classy. I love the Netgear Wi-Fi phone. Yes, it isn't perfect, but due to the uplink with Skype, they can update the software in the phone. I'm hoping they will be able to make the phone more compatible with Starbucks and hotel wi-fi. That would make the phone perfect. In the meanwhile, I'm enjoying making phone calls all hours of the day to my friends round the world\n\n\n\n\nB000AZ0QC2:lived_up_to_the_reviews_i_read_before_buying,:emil_g._enriquez\n\n\nB000AZ0QC2\n\n\nCanon PIXMA MP800 All In One Photo Printer: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n5.0\n\n\nLived up to the reviews I read before buying,\n\n\nNovember 5, 2006\n\n\nEmil G. Enriquez\n\n\n\n\n\nIt certainly did. Excellent color qualit\n\n\n\n\nB0006698SS:works_like_a_champ..setup_in_5_minutes:ryan_kern-mount\n\n\nB0006698SS\n\n\nD-Link DWL-G710 Wireless Range Extender, 802.11g, 54Mbps: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nWorks like a champ..setup in 5 minutes\n\n\nOctober 16, 2006\n\n\nRyan Kern-Mount\n\n\nGrass Valley, CA USA\n\n\nAfter I read some folk's horror stories, I was really reluctant to purchase this device. Well, it took me only 5 minutes to configure the DWL-G710, and another 10 minutes to find the best place for it in the house. It was really simple and it worked well with our Comcast/Netgear wireless cable modem.\n\nExcellent product\n\n\n\n\nB00001W0D4:great_sound_quality_and_durability!:ichaucer\n\n\nB00001W0D4\n\n\nSony MDR-E827G Sports Series Fontopia Water-Resistant Ear-Bud Stereo Headphones: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nGreat sound quality and durability!\n\n\nJune 13, 2005\n\n\niChaucer\n\n\nPittsburgh, Pennsylvania, USA\n\n\nI'm not entirely sure how other people are finding ways to damage their earphones, but I have used the same pair since the summer of 2000 (purchased from Amazon), and I have never had any difficulties besides losing the essential earpiece covers, which were easily replaced. When they are wound up in their little yellow case, the headphones' wires are well protected from stress unlike other models that don't offer such security.\n\nCompared to other headphones I have used, especially noise cancelling of both active and passive varieties, these phones have superior bass response, and all levels of sound seem reasonably clear.\n\nI have used these earphones with many different devices over five years: CD players, MP3 players (including iPods), Pocket PCs, desktops, and notebooks. They always sound great, EXCEPT - due to their greater sensitivity to sound, I often am able to detect the hiss caused by poorly designed audio hardware, which I otherwise wouldn't hear with over-ear phones. Pocket PCs especially seem prone to this, as their audio hardware is crammed so tightly in with noisy backlit screens and processors\n\n\n\n\nB000EWHEM6:excellent_unit:kenneth_w._hoehn\n\n\nB000EWHEM6\n\n\nSanDisk 4 GB Cruzer Micro with U3 ( SDCZ6-4096-A10, Retail Package): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nExcellent unit\n\n\nNovember 9, 2006\n\n\nKenneth W. Hoehn\n\n\n\n\n\nUnit performs well, is reasonably fast, and has on-board security software that will allow the setting of a password. If you choose this option, loading each time upon insertion is a little slower, and you must 'stop' the unit prior to removing it when operating under the password protection. The few times I have forgotten I've not lost any data, but it scolds me for doing so upon the next use. \n\n\n\n\nB00000J0D5:using_it_for_caller_i.d._on_direct_t.v.......:mr._john_m._ruof_\"kofa\"\n\n\nB00000J0D5\n\n\nRCA RC930 Caller-ID-Compatible Wireless Modem Jack: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n5.0\n\n\nusing it for caller i.d. on direct t.v.......\n\n\nDecember 26, 2005\n\n\nMr. John M. Ruof \"kofa\"\n\n\npinal county,az.\n\n\npurchased the unit 9/05 because there wasn't a tele jack near the t.v.... thus far has worked w/o a hitch connected to my direct t.v. black box to show caller i.d. on the screen...\n\n\n\n\nB00004Y2Z2:buy_these_cables!:\n\n\nB00004Y2Z2\n\n\nMonster Cable MV2CV-8M Monster Video 2 Component Video Cable 8 meter: Electronics\n\n\nelectronics\n\n\n4 of 12\n\n\n5.0\n\n\nBuy these cables!\n\n\nMarch 15, 2001\n\n\n\n\n\n\n\n\nThese cables are super with my new DVD player. There is absolutely no interference from my other home theater equipment. The picture is soo clear, and vivid. The cables themselves are really made well, they could hold up 100 pounds of weight no doubt. If you get these Monster cables you won't be sorry\n\n\n\n\nB000BMXJR8:a_good_accessory_for_your_ipod:r._urmeyev\n\n\nB000BMXJR8\n\n\nGriffin Technology 4031-RDGC RoadTrip FM Transmitter & Auto Charger and Cradle for iPod (Charcoal): Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nA good accessory for your iPod\n\n\nOctober 29, 2006\n\n\nR. Urmeyev\n\n\nDearborn, MI\n\n\nI purchased my iTrip about 4 months ago and now can give an objective review of the product. The iTrip is a good choice over competitors' products and offers a good value for the money. Radio reception is good; I especially like the changing channels feature. The fact that it charges the iPod while in use even makes it better. The only negative thing that I've discovered is that sometimes it disconnects from the iPod slot and I have to push it back. That's mostly due to rattles and I imagine will get worse with time. \n\nOverall, good product, good value for the money, and if carefully handled, will last a long time. \n\n\n\n\nB000BMXJR8:good_buy:s._ward\n\n\nB000BMXJR8\n\n\nGriffin Technology 4031-RDGC RoadTrip FM Transmitter & Auto Charger and Cradle for iPod (Charcoal): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n4.0\n\n\nGood Buy\n\n\nSeptember 30, 2006\n\n\nS. Ward\n\n\nLos Angeles, CA\n\n\nI bought mine about a week ago and I haven't had any problems with it. The only reason I gave it 4 stars instead of 5 was because it's a little hard to plug in & take out. But other then that I'm 100% pleased with it. It's not CD quality but it's very close. Good buy\n\n\n\n\nB00006B9HC:alot_for_the_money:brad_\"brad\"\n\n\nB00006B9HC\n\n\nNetgear FVS318NA VPN Firewall Router with 8-Port Switch: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n4.0\n\n\nalot for the money\n\n\nJune 21, 2005\n\n\nBrad \"Brad\"\n\n\nTexas\n\n\nThis review concerns the \"v3\" hardware and firmware v3.0_20 - I did not purchase from Amazon.\n\nFor the most part, everything works pretty well. I have discovered a bug where the log is not cleared after a scheduled-email out, which leads to strange behavior for the log-emailing function. I can always go into the web-interface to view or clear it with no problem. Another thing to note regarding the log is that VPN (not pass-thru) has its own separate log area, which does not get emailed out.\n\nAs expected, Netgear support is mediocre. Level 1 support can only handle very simple issues and Level 2 support is VERY slow. I recommend you use the incident-submission area of the \"My Netgear\" website, so you can more easily track/respond to support incidents.\n\nFor those of you using the FR114p model, this is basically the same thing with 8-connection VPN service minus the printer port.\n\nI figure, after a few more firmware revisions, this will be a really good unit\n\n\n\n\nB000087LI2:ique_navigation_kit:brett\n\n\nB000087LI2\n\n\nGarmin Auto Navigation Kit: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nIque Navigation kit\n\n\nAugust 28, 2005\n\n\nBrett\n\n\nIowa\n\n\nThe navigation kit works well although you do need a mostly flat area to set it on. Our Toyota Highlander lacks enough room between the windsheild and the dash to set it up on top, but we placed it below the gear shift on the center console and it worked quite well. The Ique was able to hold signels with no problem. I think the suction base might be a better solution for cars with out a low flat dash area\n\n\n\n\nB000EPJL1A:iit_'gets_'er_done':s._boehning_\"sueb\"\n\n\nB000EPJL1A\n\n\nApple 1 GB Shuffle Metal (2nd Generation): Electronics\n\n\nelectronics\n\n\n3 of 5\n\n\n4.0\n\n\niit 'gets 'er done'\n\n\nNovember 24, 2006\n\n\nS. Boehning \"SueB\"\n\n\nPoconos, PA\n\n\nI love the iShuffle. It does the job. Not many features, but makes my 2-hour commute livable.\n\ni put all my podcasts up top and list the songs in alpahbetical order (by artist name) so all is organized. It's like flying blind, but who said blind was impossible? Certainly not Helen Keller\n\n\n\n\nB000EPJL1A:amazon_is_charging_more_than_apple_for_the_shuffle.:tara_vance\n\n\nB000EPJL1A\n\n\nApple 1 GB Shuffle Metal (2nd Generation): Electronics\n\n\nelectronics\n\n\n11 of 13\n\n\n5.0\n\n\nAmazon is charging more than Apple for the shuffle.\n\n\nNovember 21, 2006\n\n\nTara Vance\n\n\nFlorida\n\n\nI just thought people should know that you can by the shuffle from Apple and have it engraved and shipped for free for $79.00 (everyday price). So Amazon is not really offering a great deal here\n\n\n\n\nB000233YDU:i_love_it!:c._carey_\"geek\"\n\n\nB000233YDU\n\n\nSyntax Olevia LT30HV 30\" HD-Ready Flat-Panel LCD TV: Electronics\n\n\nelectronics\n\n\n9 of 9\n\n\n5.0\n\n\nI love it!\n\n\nOctober 26, 2005\n\n\nC. Carey \"geek\"\n\n\nSeattle, WA USA\n\n\nThis is the best device I have bought for at least a couple decades. I am a degreed engineer, and I love gadgets. Just the best. I have had it about 3 months, and I love it. Attractive base, all the connections, removable speakers, picture quality is among the best LCD that I have viewed. I highly recommend it\n\n\n\n\nB0002V9IKK:creative_sb260_speakers:sing_y._leung_\"wilthing1\"\n\n\nB0002V9IKK\n\n\nCreative SBS260 Speakers: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\ncreative sb260 speakers\n\n\nNovember 10, 2006\n\n\nSing Y. Leung \"wilthing1\"\n\n\nnew york\n\n\n the speakers sounds excellent for it's price but the power button has a defect. you need to push it a certain way to power it on\n\n\n\n\nB0002V9IKK:very_good_for_the_price:j._kokkonen_\"jolly_llama\"\n\n\nB0002V9IKK\n\n\nCreative SBS260 Speakers: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nVery good for the price\n\n\nNovember 9, 2006\n\n\nJ. Kokkonen \"Jolly Llama\"\n\n\nProvo, UT\n\n\nIt doesn't have spectacular bass power, but I am happy for the product I got at such a feasible pric\n\n\n\n\nB0002V9IKK:good_speaker,_fair_price:jim_p._yang_\"slimjim05\"\n\n\nB0002V9IKK\n\n\nCreative SBS260 Speakers: Electronics\n\n\nelectronics\n\n\n5 of 7\n\n\n5.0\n\n\nGood speaker, Fair price\n\n\nAugust 8, 2006\n\n\nJim P. Yang \"slimjim05\"\n\n\nFresno, CA\n\n\nPro:\nVery clear, stylish, eazy assemble.\n\nCon:\nNone I can think of\n\n\n\n\nB00061IYJC:great_noise-canceling_headphones.:shawn_milochik_\"the_freelance_pen\"\n\n\nB00061IYJC\n\n\nPhilips HN060/37 Noise-Canceling Earbuds: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGreat noise-canceling headphones.\n\n\nOctober 2, 2006\n\n\nShawn Milochik \"The Freelance Pen\"\n\n\nUSA\n\n\nI have had and used two pairs of these headphones over the last year. I have been very happy with them, and even bought the second pair to keep as a backup. \n\nThe Good: They are not only noise-cancelling, but they also are rubber earbuds which fit snugly and comfortably in the ear, blocking more noise. If you put them on without any audio source, in only dulls background noise, but I think that's the best you can expect from any pair. While listening to audio, nearly everything is blocked out unless you really listen for it. There is a neck strap with the noise-cancelling component in the middle and the two earbuds hanging from each side. You put it around your neck like a necklace, put the buds in your ears, and use the power and volume controls on the noise-cancelling piece.\n\nThe Bad: Nothing is actually \"bad\" about them, but they're not quite perfect. The earbuds will easily slip out if you're sweating, or if they get at all greasy from your skin or earwax. However, they pop right off and you can wash them with a little soap and water in about 10 seconds. I do this daily when I use them. However, that doesn't make them the best solution for walking or jogging. Once one starts slipping, it generally won't stay in well until it (and your ears) have been cleaned, which detracts from the noise-cancelling effect. \n\nOther Thoughts: The cord is plenty long enough to reach an iPod or whatever at about belt level. However, if the player is in a pants pocket, it could be a little close. Also, I often wear it when I'm not using it, because you can forget it's there, and also it beats putting it into a pocket to get tangled. I had an issue once because I was sitting at a desk, and it dangled to the floor. When I stood up, I stood on the end and the force of my neck going up while my foot held it down damaged the wiring -- I began to get a slight buzz in one ear. However, I blame myself for that -- it was a mighty tug.\n\nI bought mine at Target, where they were labelled $24.99, on \"clearance\" from $49.99. A couple of months later they were back to $49.99. Once they dropped again to $34.99 (another \"clearance\" price), I bought a second pair as a backup. I'm glad I did -- I'm still using the second pair. Battery life is great. I don't know how many hours I get, but I can go at least a couple of weeks using my iPod for at least a couple of hours per day, listening to podcasts and audiobooks\n\n\n\n\nB000EEZEVC:perfect._inexpensive.:don_jones_\"author,_speaker,_scripting_guru\"\n\n\nB000EEZEVC\n\n\nSanDisk SDSDM-2048-A10M 2 GB Mini SD Card (Retail Package): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nPerfect. Inexpensive.\n\n\nNovember 3, 2006\n\n\nDon Jones \"Author, Speaker, Scripting Guru\"\n\n\nLas Vegas, NV\n\n\nWorks great in my Cingular 8125 phone. I can't believe how inexpensive these have become! More space than I'll likely every need, and no speed issues (noting that the 8125 is no speed demon itself; I can't attest to this product's performance in something that's inherently faster)\n\n\n\n\nB000EEZEVC:perfect._repeat_customer:steven_j._smith\n\n\nB000EEZEVC\n\n\nSanDisk SDSDM-2048-A10M 2 GB Mini SD Card (Retail Package): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nPerfect. Repeat Customer\n\n\nSeptember 16, 2006\n\n\nSteven J. Smith\n\n\n\n\n\nI ordered the 2 gb card twice....Fast shipping and both worked perfectly\n\n\n\n\nB00005B60G:good_functionality_and_quality:julien_pierre_\"software_engineer_and_amateur_musician\"\n\n\nB00005B60G\n\n\nPolycom Soundpoint Pro SE-225 2-Line Professional Conference Phone with Caller ID: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n4.0\n\n\nGood functionality and quality\n\n\nJuly 17, 2005\n\n\nJulien Pierre \"Software engineer and amateur musician\"\n\n\nSanta Clara, California\n\n\nThis phone has excellent voice quality, including in conference mode. The only reasons it doesn't get 5 stars from me is that :\na) it is unable to operate if there is a power outage - as opposed to perhaps just losing some of its functionality . Just make sure it isn't the only phone in your home.\nb) there is a bug which occasionnally renders it unable to operate. I can't figure out what causes it. It may be my cats that walk on it at night and press a weird sequence wit their paws :). The only cure I have found is to pull the power plug, which reboots it\n\n\n\n\nB0000C3GWU:nice_office_speakers:j._won_\"batzflashboy\"\n\n\nB0000C3GWU\n\n\nJBL Duet Speakers, White: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n4.0\n\n\nNice Office Speakers\n\n\nJuly 31, 2006\n\n\nJ. Won \"batzflashboy\"\n\n\nDallas, United States\n\n\nAmazon has a nice price on the JBL Duet Speakers (July 2006 - 36.95) dealmac is a site I used and returned to amazon.\n\nI purchased these for my office desk where I wanted a crisp sound for low volume daily listening. Anything from Ambient, to Rock and Jazz. They produce sounds nicely, nice tight crisp. If these came with a woofer unit these would be ideal speakers, as the 2-pieces alone do not produce any significant bass at all. Just the \"hints\" of bass in the music but no thud, if that's what you are hoping for. I did turn these up loud to listen to their full sound and they do sound good. These are perfect for offices and just daily music, sound is superb. Not for audiophiles or for people who are expecting to shake everything on their desks. \n\nBuild quality is \"ok\". I got the white speakers and they are the nice \"Mac\" white plastic. The JBL logo on the speaker itself is awfully big. And the silver volume knob is made of that cheap silver flake paint over plastic which is abit tacky. One of the soft rubber pads on the bottom of these (meant to not scratch your desk) did fall off during opening but they are glued on so no big deal in placing it back on. \n\nThe design is very modern and cool tho. They look nice on my desk.\n\nYou will need to keep these on for abit to let the speakers fully... break in. They sound awful right out of the box and after maybe one hour of keeping these on they will sound better.\n\nOn the low end of speakers out right now, these will probably sound the best and are most modern design, compared to the black boxes out there\n\n\n\n\nB00006B8ZB:surprised_what_a_difference_this_makes.__much_more_pleasant_working_on_laptop.:karen_f._hensley_\"kfh\"\n\n\nB00006B8ZB\n\n\nTargus PA230U Compact Universal 6 Inch Monitor Stand - Black: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nSurprised what a difference this makes. Much more pleasant working on laptop.\n\n\nJanuary 5, 2006\n\n\nKaren F. Hensley \"kfh\"\n\n\nSeattle, WA USA\n\n\nI bought this laptop stand when I found out that my laptop could drive dual screens so I could have different items on the two screens and drag stuff between them. So now I have the center of the screens at the same height, side-by-side. It is really nice to have twice the screen real-estate.\n\nI figured that mostly I would put background stuff on the laptop screen (like email) and use my big hi-resolution screen for what I was activly working on. But, now that the laptop screen is up at eye level, I tend to use it just as often as the big monitor! It turns out that my dislike of the laptop screen had more to do with having to look down at it, even though I was not aware of that until I got it raised. \n\n\n\n\nB00006B8ZB:works_great!:travis_craig_hellstrom_\"thellstrombooks\"\n\n\nB00006B8ZB\n\n\nTargus PA230U Compact Universal 6 Inch Monitor Stand - Black: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nWorks great!\n\n\nSeptember 26, 2005\n\n\nTravis Craig Hellstrom \"THellstromBooks\"\n\n\nHickory, NC United States\n\n\nOne of the least expensive ways out there to raise your computer screen to eye level. I am 6'3, so when I sit I am still pretty high in the air. With this stand I am able to sit with great posture and look straight ahead to my screen. I would definitely suggest getting one of these if you've ever thought about raising your monitor up off the desk\n\n\n\n\nB00005A9AV:not_perfect_but_really_good:merddyn_\"merddyn\"\n\n\nB00005A9AV\n\n\nPlantronics DSP-500 Digitally-Enhanced USB Gaming/Multimedia Stereo Headset and Software: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nnot perfect but really good\n\n\nSeptember 12, 2006\n\n\nMerddyn \"merddyn\"\n\n\nHouston, Tx United States\n\n\nI'm overly critical of sound quality. At home i have the Tritton surround sound headset running off optical connection. this doesnt' compare to that but the Tritton is $99 too. \n\nFor the $ this is really nice. Sound, microphone and wire quality are all good. Controls are easy to use and the software is easy to use too. They're also fairly comfortable. Good headset for work\n\n\n\n\nB0000WA8CI:great_printer_for_the_photographer:richard_rowell\n\n\nB0000WA8CI\n\n\nEpson Stylus Photo R800 Inkjet Printer: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n5.0\n\n\nGreat Printer for the Photographer\n\n\nFebruary 28, 2006\n\n\nRichard Rowell\n\n\nWoodlawn, TN USA\n\n\nI love this printer. I purchased it to print 8X10s from my digital camera. The ink doesn't fade over time like my HP did.\n\n\n\n\nB00006G96O:all_in_all_this_is_an_excellent_phone:evanprod\n\n\nB00006G96O\n\n\nPanasonic KX-TGA271V 2.4 GHz DSS Accessory Handset for Panasonic KX-TG2700 Series (Violet): Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nAll in all this is an excellent phone\n\n\nJune 23, 2006\n\n\nEvanprod\n\n\nPrinceton, NJ\n\n\nAs you may already know, this is an accessory handset which will only work if you have one of these Panasonic models: KX-TG2700/KX-TG2720, KX-TG2730/KX-TG2740. \n\nThe Panasonic KX-TGA271V, does not need (or use) a telephone jack. It simply plugs into any standard electrical socket and works off the base unit with one of the models mentioned above.\n\nThis phone was also very easy to register. (It had to be charged for about 2 hours before it could be registered...) I simply had to press the \"MUTE\" button on my base unit and then press the \"PLAY\" button on the phone (the registration directions were even displayed on the LCD display on the phone!) Please know that I did find the Panasonic instaltion manual/registration directions to be a little confusing.\n\nSome features that are NOT mentioned in this description which ARE included are: finger grips/ergonomic sizing on the sides of the phone, the opportunity to play your answering machine messages off this phone (the base unit to the KX-TG2730 has a digital answering machine, and I suspect the other Panasonic models do as well) ---- THIS WAS THE MAIN REASON WHY I PURCHASED THIS PHONE, YES THE KX-TGA271V WILL PLAY YOUR ANSWERING MACHINE MESSAGES OFF THIS PHONE THROUGH THE SPEAKER ON THE BACK OF IT!!!, a separate \"call waiting\" dedicated button is also included.\n\nAlso, this phone will use any standard headset, it does NOT need a Panasonic branded headset. This was very important for me because I ofen use a headset and I do not want to purchase another one. I have since learned that ALL cordless phone headsets can be used on ANY type of cordless phone! \n\nPlease know that the batteries on this phone are not interchangeable with the KX-TG2730 system (that is the model that I have.) Also, the KX-TG2730 phone can't be charged in this charger, and vice versa. That is (likely) because the KX-TGA271V phone is slightly smaller and weighs less. \n\nThe face of the phone is white with a purple trim. The back of the phone is also purple on the bottom and white on the top half. The \"white\" in the phone is sort of a shiny metalic white, and the \"purple\" is not shiny at all. Some of the buttons are also slightly smaller than the buttons on the KX-TG2730 model. And the LCD screen is not flat, it is indented slightly, and bordered in silver. \n\nI purchased this phone from an Amazon.com merchant and am very pleased with it. I noticed that another Amazon.com merchant is selling the same phone that I purchased for less ($30.), however that merchant has HORRIFIC feedback and I was only too glad to pay a little extra for the confidence of knowing that I would receive a phone in a timely manner that would work! \n\nPanasonic makes very good phones. The only (big) problem is the battery life. The batteries often die after less than 90 minutes. I have solved this problem by purchasing a second set of batteries. However, this is still not a perfect solution. So, I had to purchase another set of cordless phones (Uniden.) I now keep two phones in each of the rooms that I had each of the Panasonic phones, and so far have not run in to any problems. I hope Panasonic will offer batteries with longer lives soon.\n\nAll in all this is an excellent phone. I hope the extra details that I provided will help a prospective buyer make the right decision. Amazon.com rarely provides specific details to these types of questions (does Amazon.com only employ braindead dimwits?) and the Panasonic Website does not offer this info\n\n\n\n\nB00024ENHK:perfect_17\"_laptop_case:j._sadki\n\n\nB00024ENHK\n\n\nTargus TCG200 17 CityGear Miami Messenger Notebook Case: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nPerfect 17\" Laptop case\n\n\nNovember 9, 2006\n\n\nJ. Sadki\n\n\n\n\n\nI recently bought an HP 17\" widescreen laptop and needed to find a case for traveling. This case is all that and more...on one side is a detachable cell phone case...the other side a HIDDEN drink holder. It has a pocket that it slides into if not needed- that came in handy in the airport.\n\nThis case looks extremely sturdy (well built!)and dirt just seems to wipe right off of it. It holds my 17\" laptop, PDA, Cell phone, computer mouse, one-time-use cameras, Ipod, and all the wires and chargers for those electronics. I even fit two books, keys, a folder filled with directions and plane tickets, a notebook, and two dvd's in here. (all of that at the same time) Granted, all that stuff makes it pretty heavy to lug around the airport, so if you are doing that routinely you are going to want something with wheels or backpack form. The strap on this bag is extremely sturdy and cushiony. The strap was long enough for me to wear it diagonally accross my chest if I wanted, but it is not detachable (which means it isn't likely to fall off easily).\n\nThis bag is extremely well-designed. There is a strap for attaching it to wheeled luggage...it even fits under the window seat of the airplane and those areas for storage are much smaller than the aisle seats. Very well padded..I knocked my brand new computer around a bit during the travels and kept opening the case to make sure it was okay! Of course it was fine because there are 3 heavy duty layers of padding.\n\nBecause this bag does all that and more, it is large. It becomes larger the more you stuff it (obviously). However, I am 5'1\" and I carried it fine from North Carolina to New York. It looks great, if I needed to go to a meeting with my laptop I would gladly take it in this case. It looks professional enough.\n\nhappy buying!\nps. let me add that finding a decently priced case for a 17\" laptop that has all the features this one does is nearly impossible. Also, in the stores I checked this was sold for about 30 dollars more than here on amazon!! If you have a smaller laptop, this case will work just fine too! It has an adjustable divider. You will just gain more room for other stuff. I look foward to long usage of this case.\n\n\n\n\nB0003NN80S:escort's_windshield_suction_cup_mount_for_radar_detectors:michael_maxwell\n\n\nB0003NN80S\n\n\nEscort Windshield Suction Cup Mount for Radar and Laser Detectors: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n5.0\n\n\nEscort's Windshield Suction Cup Mount For Radar Detectors\n\n\nSeptember 6, 2005\n\n\nMichael Maxwell\n\n\nEastern NC, USA\n\n\nVery reasonable price and a must have as a spare for those that leave affixed to windshield year-round. Fast shipping. A great shopping experience\n\n\n\n\nB00008V6TI:this_is_a_one_of_a_kind_player._i_love_it.:j._a._mischo\n\n\nB00008V6TI\n\n\nGrundig Eton Ego 4000 Plus Portable CD / MP3 Player with Built-In Removable Speakers: Electronics\n\n\nelectronics\n\n\n5 of 6\n\n\n4.0\n\n\nThis is a one of a kind player. I love it.\n\n\nMarch 1, 2005\n\n\nJ. A. Mischo\n\n\nPasadena, Ca United States\n\n\nI've read the other reviews of this product and feel a need to defend this gadget. I've owned mine for about three years and I love it. I use it nightly. The application I find this perfect for is Audio Books stored on MP3. This of one of the only small, portable MP3 players that has it's own speakers. I go to bed listening to audio books and I don't want to wear a pair of earphones or lug a set of extra powered speakers around. However just slightly larger than a CD Walkman when folded closed it is small enough to work great on an airline trip, where headphones are preferable to the endless roar of the planes engines.\n\nThe big gripes I've seen repeated in the other reviews are:\n 1) Poor radio reception.\n 2) No Bass from the mini speakers.\n 3) Short Battery life.\n\n1) I never use this to listen to the radio, so I won't comment on it good or bad. But there are thousands of good and cheep radio's on the market so if you need a radio, go buy a radio.\n\n2) I'll agree with the poor bass reproduction in the speakers. Small cone's yield small bass. It's a physics thing. However when I travel and use this to play music in a hotel room, it's been good enough to satisfy the want of a little background music.\n\n3) Battery Life. I'm surprised by the times listed by other reviewers. I get about 30-35 hours of use when I'm on the road using the AA's. When at home or in a hotel, the AC adapter removes the concern about battery life completely. \n\nPerhaps this is because I primarily play MP3 disks which it spins up to read into it's buffer then spins down until it needs more data. \n\nAnyway I wanted to toss in my two cents, as this is a great gadget for audio book listeners, and I would hate to see someone miss out on it because some reviewers expected the performance of a boom box or home stereo out of a Walkman sized device.. It's my hope that we'll see more devices like this on the market and the price point to come down a bit. But bottom line, I've got my monies worth, and if anyone doesn't want theirs, I'd be glad to take it off their hands.\n\n\n\n\nB00006DY6M:works_fine:ms_scout\n\n\nB00006DY6M\n\n\nFujifilm 128 MB XD Picture Card: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nWorks Fine\n\n\nAugust 12, 2006\n\n\nMs_Scout\n\n\nTexas USA\n\n\nI have used this 128 MB XD Memory card and it works fine. The pictures are good. I have had no problems. I now have this memory card and the standard 16 MB card that came with my camera. Good deal.\n\n\n\n\nB0006ZO2CG:please_with_my_daugther_ipod_nano:dany_\"daniela,\"\n\n\nB0006ZO2CG\n\n\nAltec Lansing inMotion iMmini Portable Audio System for iPod Mini: Electronics\n\n\nelectronics\n\n\n3 of 6\n\n\n4.0\n\n\nPlease with my daugther ipod nano\n\n\nMarch 15, 2006\n\n\nDany \"Daniela,\"\n\n\nCaracas , Vzla\n\n\nReview this product over the internet, and it is just what I was looking for my daughter of twelve years of age, pros. is portable and rechargable; cons. the sound is not as good as the Boose but with less volume it is quite acceptabl\n\n\n\n\nB00000J4EL:unbelievably_durable:ahmet_cuneyt_ucisik\n\n\nB00000J4EL\n\n\nAllsop Raindrop Mouse Pad (Blue): Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n5.0\n\n\nUnbelievably Durable\n\n\nNovember 11, 2000\n\n\nAhmet Cuneyt Ucisik\n\n\nIstanbul, Turkey\n\n\nI had bought one of these five years ago when I was in the States and am still happily using it at home. Unbelievably durable for a mouse pad heavily used for hours everyday for five full years. Fed up with giveaway mouse pads sliding with my hand movements, I was searching for something decent to use in the office and amazingly found the exact same item. It is a good example that how little things make a big difference\n\n\n\n\nB0007Y79B2:just_perfect:andrea_corcuera_smalley_\"low_techie\"\n\n\nB0007Y79B2\n\n\nApple 4 GB iPod Nano Black: Electronics\n\n\nelectronics\n\n\n5 of 6\n\n\n5.0\n\n\nJust perfect\n\n\nNovember 11, 2006\n\n\nAndrea Corcuera Smalley \"low techie\"\n\n\nParis, France\n\n\nI bought this Ipod 4G to replace my 2G one that got stolen and chose more memory as the 2G got filled up too quickly. Can say that this is probably the best size in terms of memory. Otherwise, as usual with Macs, very easy to use, even for low-tech people like myself, beautiful design, small and light, not too fragile, just great on the go\n\n\n\n\nB0007Y79B2:ipod_is_great!:v._antonov_\"four_eyes_man\"\n\n\nB0007Y79B2\n\n\nApple 4 GB iPod Nano Black: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n5.0\n\n\niPod is great!\n\n\nNovember 10, 2006\n\n\nV. Antonov \"four_eyes_man\"\n\n\nUS\n\n\nJust what I expected - good quality, convenient and holds lots of musi\n\n\n\n\nB0006BKG98:flawed,_but_still_the_best_case_for_the_ipod_20_gb:k._w._schreiter\n\n\nB0006BKG98\n\n\nContour Design iSee 20 Case for iPod: Electronics\n\n\nelectronics\n\n\n9 of 9\n\n\n5.0\n\n\nFlawed, but still the best case for the iPod 20 GB\n\n\nDecember 3, 2005\n\n\nK. W. Schreiter\n\n\nConshohocken, PA\n\n\nI researched cases upon receipt of my 20 GB iPod and deemed this the best. This case consists of a thin plastic sleeve inside a separate clear, hard shell. The hard case protects the iPod from scratches and minor falls while still allowing easy access to the controls and inputs. It also does not obscure the screen. An enclosed plastic belt clip attaches to the back of the unit. \nThis case is not compatible with a Bose Sounddock as the user must remove the iPod from the case. Also, Contour Design only enclosed one of the thin plastic sleeves. I spent $5 at their website for 5 more sleeves and gave some to friends with the same case. \nThe price seems high for a couple pieces of plastic but a case is essential and this is the best one for the 20 GB iPod. \n\n\n\n\nB000ELCQ8Y:great:e._p._fern�ndez_\"eponce\"\n\n\nB000ELCQ8Y\n\n\nSony Memory Stick Pro Duo 2 GB - High Speed: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nGreat\n\n\nAugust 12, 2006\n\n\nE. P. Fern�ndez \"Eponce\"\n\n\nCosta Rica\n\n\nIt's great if you have High Speed enabled devices. It's very fast a 2GB is sufficient for almost purpose\n\n\n\n\nB0000ALFCI:works_for_me:j._chen_\"jonc\"\n\n\nB0000ALFCI\n\n\nLogitech Dual Action Game Pad USB: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nWorks for me\n\n\nOctober 27, 2006\n\n\nJ. Chen \"JonC\"\n\n\nIrvina, CA USA\n\n\nI've had this controller for over a year now, and it still works great and reliably as ever. After many months, the analog sticks occasionally drifted a little, but a quick jiggle and they'd be back to center. \n\nThe profiler software is excellent, allowing you to fully map the controller to your keyboard and even mouse. This allows you to play any game with the controller, even ones which don't naturally support gamepads like Splinter Cell. With the \"shift\" profile function, you can effectively double the number of buttons which I found great for playing a complicated flight sim like X-Wing Alliance, completely keyboard free, using only the gamepad.\n\nOnly downside: The D-pad is hard to control precisely. It's difficult to press only Up, Down, Left or Right. You almost always end up pressing a diagonal direction instead. This made the D-pad more or less unusable for games like Street Fighter, but then the analog stick was a perfect alternative anyway\n\n\n\n\nB000EZP1QE:razor-sharp_image_and_good_features:eric\n\n\nB000EZP1QE\n\n\nSamsung 215TW 21\" LCD Monitor: Electronics\n\n\nelectronics\n\n\n37 of 40\n\n\n5.0\n\n\nRazor-sharp image and good features\n\n\nApril 30, 2006\n\n\nEric\n\n\nPortland, OR, USA\n\n\nFirst, I should say that I'm surprised at how sharp and clear the image is. The display card at CompUSA said this monitor has a 1000:1 contrast ratio, and I'm not sure what that means, but it definately seems to translate to a crystal clear picture. The contrast ratio is higher than most other flat-panels I looked at, which have a contrast ratio of around 500:1 to 700:1 (including Samsung's own 20.1\" non-widescreen model). Everything is crystal-clear, which makes it easy on the eyes and good for watching movies. It is also good for gaming - Half-life 2 and Unreal Tournament 2004, for example, both look great.\n\nIts wide-screen resolution of 1680x1050 is more than decent for most tasks. It also has a feature that lets you change the aspect ratio to 4:3 (which makes a square picture centered on the screen), so if you have any games or other applications that don't support widescreen modes, you can use 4:3 mode so that the picture doesn't look stretched and distorted. Plus, the monitor has DVI, analog VGA, S-Video, and RCA inputs (which would allow you to plug in a DVD player if you want). The monitor can also rotate 90 degrees, and it has built-in speakers and a headphone jack.\n\nBeing a flat-panel display, it is also lightweight and compact, which is ideal for areas where space is limited. This is my first flat-panel display, and I was surprised at how lightweight it really is.\n\nAll of these features, especially the sharp image, make this a great monitor, and it is worth the money\n\n\n\n\nB00005QSRF:12_v__adaptor:r._kilmartin\n\n\nB00005QSRF\n\n\nMagellan Meridian External Power Cable with Cigarette Lighter Adapter: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\n12 V Adaptor\n\n\nAugust 16, 2006\n\n\nR. Kilmartin\n\n\nPa. USA\n\n\nThis was a defective unit. Got new unit and it works as expected\n\n\n\n\nB00005QSRF:a_must_have_if_you_bought_the_car_bracket:d._johnson_\"chi-town_dale\"\n\n\nB00005QSRF\n\n\nMagellan Meridian External Power Cable with Cigarette Lighter Adapter: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nA must have if you bought the car bracket\n\n\nJanuary 6, 2006\n\n\nD. Johnson \"Chi-Town Dale\"\n\n\nWheaton, Illinois USA\n\n\nThe den of horrors that is the Magellan car bracket requires you to have this power cord. If you don't then you will be fighting to change batteries and remount your GPS and that is no fun at all.\nI have used mine for over six months and it works without a hitch. A nice improvement would be a retractable cord facility so that you don't have excess cordage lying around your dashboard. Right now I loop the excess around the steering wheel but then again I never change the adjustment on the steering wheel so no problem with the wire getting caught up in the adjusting mechanism. Others may run into this problem. The alternative is a shorter wire but then it would make the power unit useless for some vehicles. A minor hitch but not earthshaking.\n\n\n\n\nB00007KDVI:works_out_of_the_box:adam\n\n\nB00007KDVI\n\n\nLinksys WRT54G Wireless-G Router: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nWorks out of the box\n\n\nNovember 16, 2006\n\n\nAdam\n\n\nFL United States\n\n\nPluged it in and good to go, I skipped the installation disk in fear of what nonsense is loaded on that... went to the admin screen, a little overwhelmed with ALL the features, it could be more stremlined, for a novice user they would be entirely lost, perhaps that is whats on the install disk (easier setup), but my experience self, got through just fine, enabled the maximum security features (more so then the easy setup button, which itself is good but does not suffice my network requirments).\n\nHeard lots of issues, was weary, but out of the box works great, no issues, no dropped connections. And for that reason, since it works I do NOT intend to flash the firmware to the latest, as the saying goes if it isnt boke don't fix it.\n\nBTW home network is a mix of print server, wired PC, wireless Apple powerbook, everything running smoothly\n\n\n\n\nB00004VX39:garmin_carry_case:mab\n\n\nB00004VX39\n\n\nGarmin Carry Case for eTrex Series & Summit: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n5.0\n\n\nGarmin Carry Case\n\n\nSeptember 14, 2006\n\n\nmab\n\n\nIlinois\n\n\nI'm glad that I bought the carry case with my Garmin eTrex, because it has kept it clean and easy to hold and carry. I haven't used the belt clip yet, but I liked that option\n\n\n\n\nB000BKLNHS:happy_so_far:b._bowman\n\n\nB000BKLNHS\n\n\nAltec Lansing XM3020 Docking/Speaker System for the MyFi, AirWare, Tao: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nHappy so far\n\n\nNovember 2, 2006\n\n\nB. Bowman\n\n\nLittle Rock, AR USA\n\n\nAfter reading the online reviews of this product vs. the portable product I went with this one. Portability is not much of a need, I plan on being around a power outlet. The sound is in line with a radio/boom box of the same size and it's about the same weight. I have a lot of trees around my house so I have to extend the antenna several feet out the back door in order to get a good signal. Keep that in mind if you have signal issues with your satellite unit. I have my unit placed next to the back door in the kitchen and I can hear it all over the first floor of my home. So far I'm happy that I bought it\n\n\n\n\nB000BKLNHS:great_speaker_system_for_the_price!:s._taylor\n\n\nB000BKLNHS\n\n\nAltec Lansing XM3020 Docking/Speaker System for the MyFi, AirWare, Tao: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nGreat Speaker System for the Price!\n\n\nJuly 31, 2006\n\n\nS. Taylor\n\n\nIndiana\n\n\nI was surprised at the sound quality of this small speaker system. I purchased it to use in my bedroom on my nightstand and am very pleased. You can hear the music in several other rooms if the volume is turned up, without distortion.\n\nI am very pleased with my purchase and would recommend the speaker system to others\n\n\n\n\nB000CSF6MM:almost_perfect...if_you_don't_mind_lint:shannon_cox\n\n\nB000CSF6MM\n\n\nISkin eVo3 Sonic - Case ( for digital player ) - silicone - vibrant blue - iPod with video (5G) 30GB: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n4.0\n\n\nAlmost perfect...if you don't mind lint\n\n\nFebruary 23, 2006\n\n\nShannon Cox\n\n\nDoylestown, PA\n\n\nThis case is a veritable dirt magnet. It attracts more lint than I ever knew existed on my desk. However, it protects very well. One thing I was worried about was the possibility of the hard plastic front of the case scratching my screen. However, it doesn't even touch the ipod. Also I worried about a loss of sensitivity for my clickwheel. While it isn't quite as sensitive, it makes it more precise in my opinion, seeing as how I all of a sudden found myself at the end of my artists lists when I wanted in the S range without the case on. This blue case even looks good on my black ipod, even though I was worried the color would appear muted. It's still vibrant. \n\nOne complaint that some have already brought up is the covered hold button. Truthfully this doesn't bother me, because I have long enough fingernails to dig in and push the button. But those without could have a great deal of difficulty. Also, the hole for the headset isn't quite large enough, and sometimes my headphones will pop out (I'm not using the earbuds that came with the ipod, so I do not know how those would fare). \n\nOverall, it's a great case that you won't lose your grip on. It protects well, and you never have to take it off, even to plug it in. I would recommend it, though it is a bit pricy. \n\nIn a bit of an update, with this case on, my iPod survived a ferret attack, with only one little tiny puncture mark on the outside of the case. I think this case is well worth the money. My iPod could have been destroyed, but it's perfect\n\n\n\n\nB0000C9ZJY:absolutely_worth_it:jt_\"i_may_not_have_gone_where_i_intended_to_go,_but_i_think_i_have_ended_up_where_i_intended_to_be._(adams)\"\n\n\nB0000C9ZJY\n\n\nHP OfficeJet 5510 All-in-One Printer, Fax, Scanner, Copier: Electronics\n\n\nelectronics\n\n\n10 of 12\n\n\n4.0\n\n\nAbsolutely worth it\n\n\nOctober 28, 2005\n\n\nJT \"I may not have gone where I intended to go, but I think I have ended up where I intended to be. (Adams)\"\n\n\nTampa Bay, FL United States\n\n\nPrinters have gotten inexpensive. Let's face it; the game is, cheap printers and then the consumer buys cartridge after cartridge of ink refills. I'm fine with that.\n\nFor the $149 Amazon price, this machine actually *does* do everything I need it to: a fax machine for those rare occasions I need to fax a physical piece of paper. A great inkject that doesn't consumer *that* much ink. It copies and scans. I have used every function, am pleased with them all.\n\nSure, it feels a little flimsy; it is a small machine meant to do work that you are used to seeing a huge piece of hardware accommodate in a standard office. However, it has held up well to some serious use in the past couple months, so that flimsiness isn't really even a complaint. And I work from an office in my home three weeks out of each month, so I am more than glad I can reproduce all the functionality my \"real\" office provides me.\n\nAll in all, worth the money\n\n\n\n\nB0001MR13G:decent_phone_-_i'm_satisfied:j._b._mccord\n\n\nB0001MR13G\n\n\nAT&T E5965C 5.8 GHz DSS Expandable Cordless Phone with Answering System: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nDecent Phone - I'm Satisfied\n\n\nNovember 20, 2006\n\n\nJ. B. Mccord\n\n\nSan Diego, CA\n\n\nWe bought this phone and two additional handsets eleven months ago. One handset gave us problems from the start, but was promptly replaced when returned for service. The system works fine in our 2-story, 2,300 sq. ft. home. I did notice some reception problems carrying the handset into the closed garage with the base station upstairs on the opposite end of the house. As a test I took the handset down the street about 100 yards away - it still got a clear dialtone to the base located in upstairs bedroom facing the street. I like the features, including paging system, call transfer, and handset speakerphone. Being able to have a phone at any 110v outlet location is a big plus. I think the sound quality is very good, and the controls well laid out and pretty intuitive. I agree that having to enter memory numbers into each handset is a drag.\nOverall I feel it works as advertised, and we'll be using it for a long time\n\n\n\n\nB0001MR13G:great_phone:karen_v.\n\n\nB0001MR13G\n\n\nAT&T E5965C 5.8 GHz DSS Expandable Cordless Phone with Answering System: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nGreat Phone\n\n\nSeptember 30, 2006\n\n\nKaren V.\n\n\nNew Jersey\n\n\nI bought this phone over 6 months ago and absolutely love it! It has great sound quality. I love the features it has such as speakerphone, caller ID, and it's phone directory. The only thing that is not great about this phone is that the base and the headset each have their own memory and you will need to enter people twice-into both directories. Oher than that, this is a GREAT phone. I have owned panasonic and GE cordless phones in the past and had problems. I even bought and returned 2 phones before trying and keeping this phone. \n\n\n\n\nB000A1AQOO:amazon_fails_to_deliver:scott_burgess\n\n\nB000A1AQOO\n\n\nLinksys WTR54GS Wireless G Travel Router with Speedbooster: Electronics\n\n\nelectronics\n\n\n0 of 14\n\n\n5.0\n\n\namazon fails to deliver\n\n\nJuly 13, 2006\n\n\nScott Burgess\n\n\n\n\n\namazon attempted to extort second payment for undeliverd items claiming they would refund late\n\n\n\n\nB000EEZ2R8:i_got_one_heck_of_a_deal!!:r._signs_\"doc2toxic\"\n\n\nB000EEZ2R8\n\n\nBelkin F8Z063-BLK TuneBase FM for iPod Nano (Black): Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n5.0\n\n\nI got one heck of a deal!!\n\n\nOctober 18, 2006\n\n\nR. Signs \"Doc2toxic\"\n\n\nSuffolk, Va\n\n\nThis FM transmitter is the bomb diggiti!! \n\nPROS: The clarity it great and the fact that it has a long neck makes it the best! \n\nPROS: If you own a Chyrsler PT Cruiser, you will love this device. The neck brings the IPod even with the top of the steering column. So it right into view on the right. You don't have to look down or fumble for you I-Pod in between the seats. \n\nPROS: Very stable once it is in the outlet.\n\nPROS: GREAT reception!\n\nCONS: Would be nice if it had a on/off switch on the base for those outlets that stay on even when the car is off. The green light on the device my attract unwanted attention.\n\nCONS: I have to take off my protector everytime I use the IPod in my car\n\n\n\n\nB000095SLZ:4_games_not_t5_compatible._others_are_fun...:daniel_b._widdis\n\n\nB000095SLZ\n\n\nPalmOne P10914U Games Essentials Card: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n4.0\n\n\n4 games not T5 compatible. Others are fun...\n\n\nSeptember 17, 2005\n\n\nDaniel B. Widdis\n\n\n\n\n\nOn the plus side, it's a great value for the games that do work on the T5. SimCity alone is probably worth the price.\n\nOn the down side, although Amazon claims it's compatible with the \"Tungsten Series\" that was probably written before the T5 came out. Four of the games appear to act as \"trial\" versions, although it's impossible to enter the registration codes. See (...) for an explanation.\n\nEven with only 4 working games past the trial period, it's a good buy. Just know you're not getting what's advertised if you have a T5\n\n\n\n\nB00006BBEL:quality_does_not_cost_--_it_pays!:g._m._shaheen\n\n\nB00006BBEL\n\n\nHP Premium Plus Photo Paper, High Gloss (50, 8.5x11 Inch Sheets): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nQuality does not cost -- It pays!\n\n\nJanuary 30, 2006\n\n\nG. M. Shaheen\n\n\n\n\n\nHP Premium Plus photo paper is the only way to go if you desire tremendous photos from your home printer without any scratching, scuffing or blemishes. I have tried less expensive paper from HP as well as other manufacturers. The difference in quality is obvious. If you want friends and family to think you know what you are doing, share photo's on HP Premium Plus glossy paper. \n\nJay Shahee\n\n\n\n\nB00083CZD4:flash_drive_:valentian_acosta_espana\n\n\nB00083CZD4\n\n\nMemorex 1GB M Flyer TravelDrive USB 2.0 Flash Drive ( 32509560 ): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nFlash drive \n\n\nOctober 5, 2005\n\n\nValentian Acosta Espana\n\n\n\n\n\nExcelent, hard drive, good price and good time for seller.\nthanks\n\n\n\n\nB000A2BLEC:almost_free!!__just_pay_for_shipping:t._m._weddle\n\n\nB000A2BLEC\n\n\niPod USB Travel Power Charger Adapter for Nano, Shuffle, 4G, Mini, Photo and U2- HHI Brand: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n5.0\n\n\nAlmost free!! Just pay for shipping\n\n\nAugust 9, 2006\n\n\nT. M. Weddle\n\n\nOhio\n\n\nI ordered one from an Amazon partner for $ .01 plus shipping, then with my A9 discount the price was even lower. So I got one for less than the shipping cost. True, the seller made a little on the shipping - maybe $4.50 not counting time. Think how much Apple must be making! The packaging didn't mention Nano so with a bit of trepidation I plugged it into the wall and my iPod and my Nano liked it! Charging happened - it must have been made before the Nano came out. As other reviewers have said, buy this one and avoid a HIGH price elsewhere\n\n\n\n\nB0009GHRBK:must_have:keith_mutter\n\n\nB0009GHRBK\n\n\nMagellan 980806 Roadmate 300 / 700 Carrying Case: Electronics\n\n\nelectronics\n\n\n25 of 26\n\n\n5.0\n\n\nMust Have\n\n\nOctober 2, 2005\n\n\nKeith Mutter\n\n\n\n\n\nI purchased the pouch & the carring case shortly after. I own more than one car and have four family menbers that are always on the go.The case holds the GPS, mount and all the different cables.The foam insert holds the GPS while still in the pouch or without.The velcro staps hold the mount & the ac cord.This way when you are looking for it,everthing is in one place.The case is made well and very plain looking on the outside,so not to draw any attention\n\n\n\n\nB00078GLJY:as_advertised:glenn_a._brottman_md_\"doc\"\n\n\nB00078GLJY\n\n\nOPPO OPDV971H Digital HD-Ready Up-Converting DVD Player: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nAs advertised\n\n\nNovember 9, 2006\n\n\nGlenn A. Brottman MD \"doc\"\n\n\nNew York, NY\n\n\nVery easy to set up, great product. Picture is great on my samsung 50 inch plasma\n\n\n\n\nB00078GLJY:first_rate_machine:juan_c._jaramillo\n\n\nB00078GLJY\n\n\nOPPO OPDV971H Digital HD-Ready Up-Converting DVD Player: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nFirst rate machine\n\n\nNovember 9, 2006\n\n\nJuan C. Jaramillo\n\n\nWashington DC\n\n\nIt is a pleasure to use this DVD player. The quality of the image is superb. It upgrades definition on DVDs and the difference is very (favorably) noticed. As compared to that obtaibed from a less than a year old Sony player (with HDMI), the difference in image quality absolutely huge. \n\nYes, it is more expensive. But the extra quality is worth every penny; and then more\n\n\n\n\nB0009K9FZW:great_modem_for_those_with_voip.:mike_l.\n\n\nB0009K9FZW\n\n\nSB5120 Surfboard Cable Modem Docsis 1.1 and 2.0 Certified USB: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGreat modem for those with VOIP.\n\n\nNovember 9, 2006\n\n\nMike L.\n\n\nNew Jersey\n\n\nI had a problem with my Sunrocket VOIP and I read that someone had bought this modem and that it solved her problem. I gave it a shot and guess what no more dropped calls. Thank you Motorola!!!!!!!!!\n\n\n\n\nB00007LAYN:quality_cable:b._blom\n\n\nB00007LAYN\n\n\nMonster Cable DVI400-2M DVI-D Video Cable: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nQuality Cable\n\n\nSeptember 13, 2006\n\n\nB. Blom\n\n\nColorado, USA\n\n\nI ordered the 4 meter cable which was pictured with 24 pin connectors and received one with 18 pins. Found out that the missing middle 6 pins are used to transmit audio which my monitor doesn't support anyway. Originally, the DVI-D specification allowed for both a video signal and an audio signal, but most manufacturer's have only used the video portion. If you need a DVI-D cable, an 18 pin connector will most likely work since most devices won't process the audio signal anyway. There are a few (very few) devices that do use all 24 pins, but with the advent of HDMI, nobody really makes them anymore\n\n\n\n\nB00007LAYN:my_review:baonam_truong\n\n\nB00007LAYN\n\n\nMonster Cable DVI400-2M DVI-D Video Cable: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n5.0\n\n\nMy Review\n\n\nJuly 8, 2006\n\n\nBaonam Truong\n\n\n\n\n\nProduct came in as describe. Super clear picture quality as it's supposed to be as Monster Cable brand\n\n\n\n\nB0002SAF4G:great!!!:karen_\"prettybluluv\"\n\n\nB0002SAF4G\n\n\nLabtec Pulse 485 2.1 Multimedia Speaker System: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nGreat!!!\n\n\nAugust 24, 2006\n\n\nKaren \"PrettyBluLuv\"\n\n\nNew York\n\n\nI am not really a big tech person so I don't have the mega system hooked up to my computer and DVD/TV but I do know that since I got this I have been blasting my music like crazy. I don't really mind too much that the power and volume is under the desk because I have enough space down there and all I have to is lean down... Anyway I personally feel the only way it could be better was if there was a remote for it and it gets hella loud (considering its computer speakers) I don't even have to turn them all the way up to hear them when I'm in the shower so I am pretty much happy with them\n\n\n\n\nB0002SAF4G:totally_awesome_for_the_price!!!:sara_smile_\"pw_rocker\"\n\n\nB0002SAF4G\n\n\nLabtec Pulse 485 2.1 Multimedia Speaker System: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nTOTALLY AWESOME FOR THE PRICE!!!\n\n\nAugust 3, 2006\n\n\nSara Smile \"PW rocker\"\n\n\nCincinnati, OH\n\n\nThese speakers are amazing, I am truly shocked to receive something so sweet at an unbelievable price, amazon thank you for the hook up, my speaker system rocks. My delivery experience with amazon was great. Can't thank you enough, keep up the great service, truly\n\n\n\n\nB0002SAF4G:great_computer_speakers_for_a_great_price!:brian_kloppenborg_\"electronics_junkie_:d\"\n\n\nB0002SAF4G\n\n\nLabtec Pulse 485 2.1 Multimedia Speaker System: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n5.0\n\n\nGreat computer speakers for a Great price!\n\n\nJune 8, 2006\n\n\nBrian Kloppenborg \"Electronics Junkie :D\"\n\n\nLa Mesa, Ca\n\n\nI needed new computer speakers after my Cyber Acoustics died after only 3 months. SO i wasn't going to buy the same brand since it died so fast. I use a Mac i wanted some nice sounding speakers, but couldn't decide to go with either the Sound Sticks, Creatures, or these. Well since i am saving up for a new Laptop, i wanted to save as much money as possible. Plus me and my dad had bought this brand over 7 years ago and they STILL work and sound great so i decided to give these a chance. Well first thing i did when i got them was turn the volume up to Max and Sub base up to Max. (Yes i underestimated these speakers) When i turned a song in in iTunes i nearley got blasted out of my chair! I was supprised by the sound clarity was pretty good even at higher volume, and sub was powerful shaking my whole desk! \n\nMoral one: Do not underestimate speakers my good brands even with low wattage.\n\nI highley suggest these speakers for people who don't have much to spend or don't want to spend alot of money. If you want better quality i would go with Sound sticks or Creatures. But these look like they will serve me well\n\n\n\n\nB00006OLCT:perfect_for_price:k._horvath\n\n\nB00006OLCT\n\n\nLogitech QuickCam Messenger WebCam: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n4.0\n\n\nPerfect for price\n\n\nJune 30, 2005\n\n\nK. Horvath\n\n\nOkinawa, JP\n\n\nThe camera is perfect. If using the camera for keeping in touch with friends and family this is the camera for you. The picture is clear not to grainy. The only heads up is lighting. Try to use the camera in a well light area. We are stationed in Japan and use the camera almost daily to spend time with family back home. The camera has added great joy to the family especially when feeling homesick. The best part is the price won't break the bank\n\n\n\n\nB00005JAFE:cheap_and_convenient:v_garrison\n\n\nB00005JAFE\n\n\nCanon BCI-6Y Yellow Ink Tank: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n5.0\n\n\nCheap and Convenient\n\n\nMarch 14, 2006\n\n\nV Garrison\n\n\nSteubenville, OH\n\n\nAround here Canon ink is difficult to locate and expensive when you actually find someone who carries it. It may take a week or two to arrive using Amazon's free shipping option, but I get fantatstic results from my Canon Pixma MP780 (that I also bought from Amazon). Most of my printing is borderless 8 1/2x11 and the quality is always great. Definitely worth it. If turnaround time is a problem, buy two so you always have a spare.\n\n\n\n\nB0002ZAILY:ipod_shuffle:thea_d._marlborough\n\n\nB0002ZAILY\n\n\nApple 512 MB iPod Shuffle: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\niPod Shuffle\n\n\nNovember 14, 2006\n\n\nThea D. Marlborough\n\n\nVan Nuys, CA United States\n\n\nGave as a gift. Excellent value. Tune capacity more than sufficient, easy to use and sound quality is right up there. Must get one for myself\n\n\n\n\nB000EMCZ3Y:panasonic_kx-tg2620w_2.4_ghz_fhss_cordless_telephone:huey_precise\n\n\nB000EMCZ3Y\n\n\nPanasonic KX-TG2620W 2.4 GHz FHSS GigaRange Digital Cordless Telephone: Electronics\n\n\nelectronics\n\n\n7 of 8\n\n\n5.0\n\n\nPanasonic KX-TG2620W 2.4 GHz FHSS cordless telephone\n\n\nAugust 20, 2006\n\n\nHuey Precise\n\n\nDallas, Texas USA\n\n\nOutstanding product as you would expect from Panasonic. Incoming calls loud and clear. The scroll telephone directory is convenient and easy to use. I do not like the \"beep\" sound when the hand-set is placed back in the cradle (it cannot be turned off\n\n\n\n\nB0009NZ6M0:never_had_a_problem.:mr._michael_lucien_whaley_\"mlwhaleyus\"\n\n\nB0009NZ6M0\n\n\nVerbatim DVD+R 4.7GB LightScribe 30pk Spindle: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nnever had a problem.\n\n\nJuly 12, 2006\n\n\nMr. Michael Lucien Whaley \"mlwhaleyus\"\n\n\nColorado Springs, CO\n\n\nI often have problems with memorex brand but have never had a problem with Verbati\n\n\n\n\nB000AKX10G:a_must_with_the_garmin_gps_i5:anonymous\n\n\nB000AKX10G\n\n\nGarmin Automotive GPS Friction Mounting Kit: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nA must with the Garmin GPS i5\n\n\nNovember 11, 2006\n\n\nAnonymous\n\n\nUSA\n\n\nI don't think I would have used my i5 without this. I just wish it came with the GPS instead of having to buy it separately. Great to easily move my GPS from car to car. Highly recommend\n\n\n\n\nB000AKX10G:grips_like_a_spider!:d._ralston\n\n\nB000AKX10G\n\n\nGarmin Automotive GPS Friction Mounting Kit: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGrips like a spider!\n\n\nNovember 9, 2006\n\n\nD. Ralston\n\n\nCalifornia USA\n\n\nA great solution to theft prevention for this GPS. The legs fold up to make a compact unit that easily fits into the glove box.\n\nAs for the grip on the dash, it works very well. There is a little bit of creep on slick dash boards, but it sticks better than the Garmin bean bag on my 2720.\n\n\n\n\nB000077CEB:a_perfect_addition_to_my_mobile_aresenal...:robert_gordon_\"macaddict_extraordinaire\"\n\n\nB000077CEB\n\n\nSanDisk SDDR-80-784 Firewire CF Reader: Electronics\n\n\nelectronics\n\n\n15 of 16\n\n\n5.0\n\n\nA perfect addition to my mobile aresenal...\n\n\nApril 7, 2003\n\n\nRobert Gordon \"Macaddict Extraordinaire\"\n\n\nDecatur, GA United States\n\n\nThe reader is fast. Example: I took 56-2MP images from my camera recently. I plugged this reader into my G4, plugged in the card, the card mounted, I dragged the images to my Pictures folder, and they copied immediately. No waiting. I was amazed, because with my [MIA] USB reader, it could take a while to transfer that many images, over a minute. A copy dialog didn't even pop up, because it transferred so fast. It doesn't hurt that it's a cutie too. 5 stars\n\n\n\n\nB000A2BKAM:hey,_it's_canon.:kamran_grasselli_\"kam\"\n\n\nB000A2BKAM\n\n\nCanon CLI-8C Cyan Ink Tank: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nHey, it's Canon.\n\n\nNovember 3, 2006\n\n\nKamran Grasselli \"Kam\"\n\n\nWashington, DC\n\n\nIt's a Canon product made for another Canon product -- how can you go wrong?\n\n\n\n\nB000A2BKAM:combine_shipping_for_free_shipping_of_ink_etc:m._s._willis\n\n\nB000A2BKAM\n\n\nCanon CLI-8C Cyan Ink Tank: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\ncombine shipping for free shipping of ink etc\n\n\nAugust 18, 2006\n\n\nM. S. Willis\n\n\nSacramento, ca USA\n\n\nGood printer ink, always try to use factory ink and these are with a good price. I bought all the colors and paper at the same time all good price and free shipping. Save gas driving to local stores\n\n\n\n\nB000EDI3Y8:sony_dvd_player:byron_l._lagoy_\"b._lagoy\"\n\n\nB000EDI3Y8\n\n\nSony DVPNS55P/B Single Disc DVD Player: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n4.0\n\n\nSony DVD Player\n\n\nNovember 9, 2006\n\n\nByron L. Lagoy \"B. LaGoy\"\n\n\nSanta Rosa, CA\n\n\nOperation of the Sony DVPNSS550/B single disc DVD player has been straight forward. Picture quality is very good, and the desired basic features have functioned without problems. In other words, in the 2 1/2 months since we have owned this player, quality and reliability have been high. Price too was very reasonable. That means durability is the last quality to consider, which is why I have given the player a 4 rather than a 5. There is no reason a machine like this should not be problem free for years. But we won't know that for years. \n\n\n\n\nB00005NHGP:great_for_sony_mavica_cameras:abilene_police_dept\n\n\nB00005NHGP\n\n\nMemorex 210 MB 24x Pocket CD-R Mini-Discs (50-Pack Spindle): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nGreat for Sony Mavica Cameras\n\n\nJuly 31, 2002\n\n\nAbilene Police Dept\n\n\nAbilene, KS United States\n\n\nThese mini CD-Rs work great in our Sony Mavica CD Cameras at a cost of only .42 cents apiece\n\n\n\n\nB0002YGJY0:great_multi-function_with_a_little_room_for_improvement:andrew_balthazor_\"iraqi_war_vet\"\n\n\nB0002YGJY0\n\n\nHP OfficeJet 7410 All-in-One: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n4.0\n\n\nGreat multi-function with a little room for improvement\n\n\nAugust 1, 2006\n\n\nAndrew Balthazor \"Iraqi War Vet\"\n\n\nHouston, TX USA\n\n\nThis is a great multi-function printer/scanner/copier/fax machine for a small office or home business that has a light to moderately light document workload.\n\nPrint quality is excellent, scanning quality is excellent, copying quality is excellent, faxing is faxing.\n\nThe control software is fine; not great, but it does the job. The networking features, including built-in wireless, makes this printer a snap to integrate into your office / business using a wireless network. \n\nSome concerns: after heavy usage the top document feeder, used for scanning, faxing, or photocopying a stack of loose documents, begins to jam. The rollers need to be cleaned to avoid this, and getting jammed paper out of the top document feeder is very difficult. \n\nHowever, the fact that this piece of equipment even has a document feeder for this sort of functionality is pretty good. \n\nThe fax log keeps a record of outgoing faxes and you can re-send them easily enough, but you can't re-print faxes received; the fax log merely logs their reception, but not the fax itself.\n\nOverall it's a good multi-function at a reasonable price with a lot of functionality built into it. Would recommend to anyone who only has a light document load, with maybe the occasional large document scanning / copying requirement\n\n\n\n\nB000089GN3:wow!:zach\n\n\nB000089GN3\n\n\nSennheiser PX 100 Collapsible Headphones: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nWow!\n\n\nNovember 17, 2006\n\n\nZach\n\n\nLander, WY USA\n\n\nAs long as I own a pair of these headphones, I will never use bass boost or a graphic equalizer again. They sound PERFECT. I cannot fathom a more balanced, natural sound. These babies put you in the recording studio, or in the concert hall! Bass is crisp, taut, and strong. You can feel it. Yet it is not overbearing or boomy. Midrange is balanced so well that you'd swear you were standing next to the lead guitarist, and the treble is so crisp and clear that you will rediscover the percussion sections of your favorite tunes. The folding feature is handy.\n\nThe only things to be aware of are the following: First, you have to break them in. They start sounding better after a few hours of use. Second, they don't have an inline volume control, which I will dearly miss from my Koss KTXpro1s. Finally, plenty of sound escapes. So don't crank them too much in the library or at Grandma Edith's funeral. \n\nOther than that, you have no excuse. Put your iPod's lousy white earbuds in the garbage disposal where they belong, and order these (or if you don't want to fork out $40, order the Koss KTX Pro titanium phones - which are almost as good as these, and definitely the best in the sub-$20 price range.) \n\n\n\n\n\nB000EGJZ2O:good_sound,_exellent_noise_insulation.:e.t._\"the_tyrminator\"\n\n\nB000EGJZ2O\n\n\nCreative Headphones EP-630: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nGood sound, exellent noise insulation.\n\n\nNovember 8, 2006\n\n\nE.T. \"The Tyrminator\"\n\n\n\n\n\nGood sound for the price, and very durable. Discrete design. I prefer the \"plug\" design to other headphones. They don't fall out, and I can listen to music at low volume at home or in a plane, it makes almost no difference. Just be careful in the traffic ;-)\n\nThe sound is good, especially for music where the bass is most important, but the treble is also good for pop and rock. The middle range is a bit dry for e.g. classical music, but still good enough for an mp3-player. \n\nI have two pairs.\n\n\n\n\n\nB000EGJZ2O:creative_earphones_ep-630:jason_cerundolo\n\n\nB000EGJZ2O\n\n\nCreative Headphones EP-630: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nCreative Earphones EP-630\n\n\nOctober 4, 2006\n\n\nJason Cerundolo\n\n\nPasadena, CA\n\n\nThese earphones are excellent quality. Noise that gets in from outside is so low that I can barely hear what someone next to me is telling me. In addition, the frequency response and quality of the sound is comparable to the much larger and bulkier insulated headphones. \n\nThese earphones do in fact rest in your ear. It can be slightly uncomfortable to put them in the first time, but you quickly get used to it, especially when sound is playing. In addition, the package includes three sets of differently sized earpieces including the default mid-sized piece shown.\n\n\n\n\nB000EPHP4U:good_but_possible_defect?:rick_t._nguyen_\"info_addict\"\n\n\nB000EPHP4U\n\n\nApple 8 GB iPod Nano Black (2nd Generation): Electronics\n\n\nelectronics\n\n\n5 of 8\n\n\n4.0\n\n\nGood but possible defect?\n\n\nNovember 12, 2006\n\n\nRick T. Nguyen \"Info Addict\"\n\n\nRedondo Beach, CA United States\n\n\nI just got my NANO last week and the minute I turned it on (before even transfering any music to it) I could hear a faint noise/buzz from the nano. It comes on when the power is on and goes off as soon as I turn off the unit. Has anyone experienced this with theirs? My first generation nano does not experience this noise. I plan to take it to an Apple store and try to get a replacement.\n\nJust a bit frustrating when you plopped down the $250 and the new toy already appears to be defective. Lesson learned for everyone else, if you don't have to get a new toy right when it comes out...wait...let them get the bugs sorted out first on new adopters like myself\n\n\n\n\nB000EPHP4U:ipod_hater_that_has_reformed_his_way_of_thinking:mike_mcauley\n\n\nB000EPHP4U\n\n\nApple 8 GB iPod Nano Black (2nd Generation): Electronics\n\n\nelectronics\n\n\n3 of 5\n\n\n4.0\n\n\nIpod hater that has reformed his way of thinking\n\n\nNovember 5, 2006\n\n\nMike Mcauley\n\n\nIdaho, USA\n\n\nI have never been a fan of the Ipod. I have been an Iriver H120, H320, and H340 owner. I had a Toshiba Gigabeat and currently own an Archos AV500. The reason for owning a Nano is because of the size of the AV500. It is too big to pack around unless I am on the bike or have a backpack or coat on. The Nano solves this problem and other than not having a remote, (soon), I am very happy with it. It loads from Itunes very easily and I have had no problems with it not playing certain files, (and I would reencode them if there was a problem.) Apple, don't let me down\n\n\n\n\nB00004Z7G9:classic_from_the_golden_age_of_high-fidelity_adiophilia:b._lynch_\"the_onewhoknows\"\n\n\nB00004Z7G9\n\n\nKoss Pro-4AA Studio Quality Headphones: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n5.0\n\n\nClassic from the golden age of High-Fidelity Adiophilia\n\n\nJanuary 13, 2006\n\n\nB. Lynch \"the_onewhoknows\"\n\n\nUSA\n\n\nThe KOSS Pro-4AA's were not made for iPod/MP3 headphone users. TO begin with, they reveal the low fidelity of MP3 files (compared with LP's or CD's). Also, they require a 1/4\"-to-3.5mm (or other) adapter in order to plug into most recent audio playback and computer equipment.\n\nThese Headphones have been around a long time. Check out the old photos of the \"Watergate\" hearings from 1973/1974, and notice that the Senators & attorneys were all listening through KOSS Pro 4AA headphones. Their longevity is mainly due to their extermely high fidelity audio reproduction. Unfortunately, their age has made them somewhat inconvenient for most listeners, since they are very heavy and a bit cumbersome.\n\nNEGATIVES:\n1) Weight (about 3 or 4 times as heavy as most contemporary studio-reference headphones)\n2) Tight headband - as noted, the fit is fatiguing if your 'hat size' is greater than 7 1/2.\n3) The coiled cord is extremely difficult to replace (it's actually less expensive to replace the entire set of headphones of you can get a good discount).\n\nPOSITIVES:\n1) AUDIO FIDELITY. Unless you want to spend at least $300 for a set of headphones, the sound quality and fidelity of the Pro-4AA's is almost impossible to beat.\n2) VALUE. As noted above, for the price you pay, these are vastly superior to any other headphones that are comparably priced.\n3) DURABLE. My first pair lasted 30 years. They were dropped and knocked around, and finally gave out when (It think) a voltage spike surged in the left earphone, fusing a coil.\n4) ISOLATION. THe paddded earphones block out external sound very well.\n\nThe sound fidelity is preserved over a wide volume range. These sound great at low and high volumes.\n\nThe biggest Positive (and the reason why I prefer to use Pro-4AA's for audio mixing, other studio applications, and serious listeining) is that they are extremely unlikely to be stolen. Size matters: they are heavy, and bulky so they're not easily concealed; and since iPOD or MP3 players use the mini plugs, these require additional hardware. If you just want 'jogging/exercise music', then these will not work well. If you want hi-fidelity at a bargain price, the Pro-4AA's are still the best choice around.\n\n\n\n\n\nB0009N7OKW:cool_mouse...with_a_minor_gripe:rajkumar_srikrishnan_\"crush04\"\n\n\nB0009N7OKW\n\n\nLogitech V200 Cordless Mouse - Black ( 931380-0403 ): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nCool Mouse...with a minor gripe\n\n\nNovember 9, 2006\n\n\nRajkumar Srikrishnan \"crush04\"\n\n\nSan Diego, CA\n\n\nBought this mouse over 3 months ago, and its still going strong. The batteries last forever (I've never replaced them yet). My only problem is that at times, I will turn off the mouse by attaching the receiver to the bottom, but it won't seem to turn off! I reset the mouse and then attach the receiver again, and this always does the trick. Other than that, very ergonomic, extremely smooth and precise.\n\nI like the solid feel of a slightly weighty mouse, and with the batteries, this mouse feels just right. If only it weren't for the mouse not turning off sometimes, I'd give it a 5\n\n\n\n\nB0009N7OKW:great_addition_to_a_laptop:a._lashway\n\n\nB0009N7OKW\n\n\nLogitech V200 Cordless Mouse - Black ( 931380-0403 ): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\ngreat addition to a laptop\n\n\nNovember 4, 2006\n\n\nA. Lashway\n\n\nNorthampton, MA USA\n\n\nthis is a great little product to take the place of the builtin finger pad on a laptop.....the USB just plugs in and the mouse is the perfect size for comfortable us\n\n\n\n\nB0009N7OKW:awesome_wireless_mouse:wendela38\n\n\nB0009N7OKW\n\n\nLogitech V200 Cordless Mouse - Black ( 931380-0403 ): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nAwesome wireless mouse\n\n\nOctober 4, 2006\n\n\nWendela38\n\n\nWaukesha, WI\n\n\nI previously had purchased a different brand of wireless mouse, I can't\nrecall the brand, but it was awful (battery contacts wouldn't stay in contact\nand it was hard to use). I was using my laptop at my brother's house and he\nlent me his Logitech V200. It was AWESOME! Just plug in and use!\n\nI went home that nite and looked it up here and saw that others had had \nsimilar experiences, so I bought one. I've had it for about a month now\nand love it. The scroll wheel being able to lean left & right for sideways\nscrolling is a nifty feature.\n\nI recommend this product highly\n\n\n\n\nB00067KT94:critical_al:alexander_don-doncow_\"critical_al\"\n\n\nB00067KT94\n\n\nCreative Webcam Live! Pro USB 2.0 WebCam: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nCritical Al\n\n\nNovember 2, 2006\n\n\nAlexander Don-Doncow \"Critical Al\"\n\n\nSan Francisco,CA USA\n\n\nI have not used this device all that much but it seems to work well.\nIt lives up to its claims. The \"face tracking\" feature is not always fast and accurate, but generaly works (in good light).\nThis camera does require a significant amount of light to give good results, but then again what camera doesn't.\nImage quality is about as good as you will get from this type of device\n\n\n\n\nB000FIVDIU:good_monitor_for_the_price:kerry_kobashi\n\n\nB000FIVDIU\n\n\nViewSonic Optiquest Q9b 19\" LCD Monitor: Electronics\n\n\nelectronics\n\n\n12 of 13\n\n\n4.0\n\n\nGood monitor for the price\n\n\nSeptember 21, 2006\n\n\nKerry Kobashi\n\n\n\n\n\nMy first purchase of the Q9b resulted in a return as the display wasn't mounted properly within the plastic housing. This resulted in the screen being tilted downwards about 1/4\" and made the viewing unpleasant. The screen also wasn't very bright even with the brightness controlled up at 100%. Having to disassemble the unit to place it back in the box required the base stand to be taken apart but that was next to impossible without breaking it. Suffice to say, quality control missed this one.\n\nAfter getting a new Q9b replacement, I unfortunately had a bad pixel. After using the Q9b for a week now, I noticed some red aliasing between characters. The crispness of the display is fine but I changed display fonts to 120dpi to make it look better. The control buttons on the monitor are too small. The built-in speakers leave less than desired and useless. \nThe Q9b does not have DVI ports. At 1280x1024 mode its fine for doing basic computing like web surfing and office applications. Viewing DVD movies at full 1280x1024 mode was difficult to watch. At smaller sizes (400x300) was fine.\n\nAll in all, the Q9b is not a bad monitor for the price for general computing work\n\n\n\n\nB000B5KUX6:psp:mr._mansoor_syed\n\n\nB000B5KUX6\n\n\nSanDisk Gaming Memory Stick PRO Duo 2GB ( SDMSG-2048 ): Electronics\n\n\nelectronics\n\n\n5 of 12\n\n\n5.0\n\n\npsp\n\n\nMay 24, 2006\n\n\nMr. Mansoor Syed\n\n\nLondon\n\n\nYou idiot, its not only for PSPs - the card can go in anything that has a memory stick duo slot [phones + cameras + PSP + TVs + computers\n\n\n\n\nB00005JAFD:nothing_beats_oem_products._._.:r._shunk\n\n\nB00005JAFD\n\n\nCanon BCI-6M Magenta Ink Tank: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nNothing beats OEM products. . .\n\n\nJuly 10, 2006\n\n\nR. Shunk\n\n\nBlacklick, OH United States\n\n\nAfter ordering non-OEM inks from several different places on the web (and having some problems with them), I discovered that Amazon sold OEM at great prices.\n\nI went to my local office supply store and purchased a complete set of 5 inks for my Canon Pixma iP4000 at a cost of $75. Later that day I found them on Amazon at almost 1/2 the price!\n\nAlso, when it comes to OEM inks, I discovered that they seem to last almost 3 times as long as the generic and refilled versions.\n\n\n\n\nB00004Z6HO:looks_great_on_my_hp_printer:red_queen\n\n\nB00004Z6HO\n\n\nEpson S041069 Photo-Quality Inkjet Paper (13x19, 100 Sheets): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nLooks great on my HP printer\n\n\nNovember 5, 2006\n\n\nRed Queen\n\n\nUpstate NY, USA\n\n\nHP didn't make 13x19 paper when I bought my 13x19 HP printer. Way to go, HP. They may make it now, but I never check, b/c I'm totally satisifed w.this paper. Runs smooth and looks great\n\n\n\n\nB00065HM1Y:grundig_fr200:d._kulas_\"deb_k\"\n\n\nB00065HM1Y\n\n\nGrundig FR200 Emergency Radio (Yellow): Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nGrundig FR200\n\n\nAugust 27, 2006\n\n\nD. Kulas \"deb k\"\n\n\nMONSON, MA USA\n\n\nProduct was as described...reception was strong and weak depending on your location...was able to receive all frequencies..rated this a 4 instead of a 5 only because when the wind blew the reception would get fuzzy...could get annoying on a very windy day..radio is bulky and heavy..\n\n\n\n\nB000ATU8M6:cobra_gps:james_a._rose\n\n\nB000ATU8M6\n\n\nCobra Nav One 4500 Mobile GPS Navigation System with Traffic: Electronics\n\n\nelectronics\n\n\n11 of 13\n\n\n5.0\n\n\nCobra GPS\n\n\nMarch 16, 2006\n\n\nJames A. Rose\n\n\nFairfield, CT United States\n\n\nI have used GPS units from Cobra, Garmin, and Lowrance. I consider this Cobra unit to be one of the best. I started with the Cobra but it was stolen after a month. Then I bought the Lowrance but it was too heavy and the mount was a sgood as the Cobra. I returned the Lowrance and bought the Garmin 340.\n\nAdvantages of the Cobra is that it has a large screen and a solid mount (despite its size). The interface isn't quite as intuitive as Garmin but once you learn it it is actually better. The traffic service is also nice (costs more with the Garmin). The Cobra is the most capable of the GPS units I mentioned but overall I am happier with the Garmin because of it's smaller size and lower price\n\n\n\n\nB000ATU8M6:cobra_4500:dogmike_\"dogmike\"\n\n\nB000ATU8M6\n\n\nCobra Nav One 4500 Mobile GPS Navigation System with Traffic: Electronics\n\n\nelectronics\n\n\n17 of 19\n\n\n4.0\n\n\nCobra 4500\n\n\nDecember 20, 2005\n\n\nDogMike \"DogMike\"\n\n\nNJ\n\n\nThe unit operates as good as any other GPS system available, plus it has a great big screen of 5 inches. I am very impressed with the GPS, but the mount used to attach the unit to the window or dash is very poorly designed. The unit falls off the window and from the dash, plus the power cord comes out too easily. The unit deserves a five star rating, but because of the mounting system and power cord coming out too easily a four star rating was provided.\n\n\n\n\nB000ATU8M6:businessweek_ranks_it_above_garmin:i._mihaljevic\n\n\nB000ATU8M6\n\n\nCobra Nav One 4500 Mobile GPS Navigation System with Traffic: Electronics\n\n\nelectronics\n\n\n83 of 93\n\n\n5.0\n\n\nBusinessWeek ranks it above Garmin\n\n\nOctober 4, 2005\n\n\nI. Mihaljevic\n\n\nUnited States\n\n\nI would highly recommend reading the BusinessWeek review on this product that came out on September 12, 2005. BusinessWeek as reviewed all the major GPS navigation options on the market and ranks the Cobra NAV ONE 4500 as number one. Here is an excerpt from the BusinessWeek article:\n\n\"Cobra's NAV ONE 4500 and Garmin's StreetPilot 2720, which each sell for $800 to $1,000, use a more straightforward approach. The traffic info travels along with FM radio broadcasts from Clear Channel stations in 48 of the top 50 markets. An FM antenna and receiver pick it off the air and feed it to the navigation system.\"\n\n\"I preferred the Cobra, mostly for its big, bright 5-inch screen and its built-in radio receiver. (The Garmin receiver is $200 extra and needs to be installed between your car antenna and radio.) When you encounter traffic, a pop-up on the screen and a voice give you three options: Keep driving, reroute me, or give me a couple of new routes and let me pick one.\n\n\n\n\nB0001M4E72:helping_with_my_tmj:mimi\n\n\nB0001M4E72\n\n\nPlantronics M220C Mobile Headset for Cordless Phones: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nHelping with my TMJ\n\n\nJanuary 29, 2006\n\n\nMiMi\n\n\nBarrington, NH United States\n\n\nIf money was no object, I would prefer the totally wireless headset phone, however, this has been a great alternative. I have TMJ, and activities like talking with the phone crooked in my neck, while holding my toddler, while preparing dinner, compound my pain...go figure! I find that having the phone clipped on my hip with the cord hanging is kind of a pain, but, the product works great. I have had no problems hearing anyone, nor has anyone had difficulty hearing me. The earpiece is comfortable and easy to get on and off, even for someone with elfishly small ears like me! \n\n\n\n\nB00001W0DI:great_headphone_for_good_price.:j._chung_\"inspire\"\n\n\nB00001W0DI\n\n\nSony MDR-V600 Studio Monitor Series Headphones with Circum-Aural Earcup Design: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n5.0\n\n\nGreat headphone for good price.\n\n\nOctober 11, 2006\n\n\nJ. Chung \"iNSPiRE\"\n\n\n\n\n\nI've owned this headphone for about a year now, and I've got to say, after going through multiple headphones, this beats all others I've tried. If this one ever broke (I doubt that'll happen anytime soon), I'll surely buy another one again!\n\nPros:\n\n- Comfortable to wear. The ear cushions are indeed comfortable to wear for extended periods of time. Even if you wear glasses, you won't feel uncomfortable.\n\n- Clear treble, middle, and bass. You can hear all the subtle sounds in your song clearly. Even with completely flat EQ setting, the headset does very good job with sound (provided you increase the volume up a bit more). As with any headphones, take time to 'break it in' to have it function the best it can (it means, to leave music playing on the headphone for very extended period of time non-stop, about 24 or 32 hours).\n\n- Sturdy wires. This headphone seems to have been designed based on recording studio-level headphones in this regard. The wire is very thick and long. Also, it's single wire format.\n\nCons:\n\n- Ear padding is perhaps, too soft. This means it won't fully cover your ear area, and some of sound may leak out. So note that when you listen to your music loud in public places. Although, at first few weeks, the ear padding is relatively hard. If you are looking for a headphone that will ensure your music will never leak out, look into getting in-ear canal type headphones instead.\n\n- As some others have pointed out, tilting your head too forward or backward will make your headphone just drop from where it is at. So be cautious of your movements.\n\n- You might not want to wear this kind of headphone during summer due to poor ventilation. But this affects all closed headphones, so it's not a huge negative point.\n\n- The headphone is quite big and on a small head, you may have a difficult time keeping it up on your head. The size of the headphone is designed for a fully grown average adult head.\n\nThat's all I've got to say, and overall, this headset is definitely worth your money\n\n\n\n\nB0000AFX1G:takes_a_lickin_and_keeps_on_tickin:james_stuber\n\n\nB0000AFX1G\n\n\nPNY 128 MB Attache USB 2.0 Portable Flash Memory Drive: Electronics\n\n\nelectronics\n\n\n4 of 5\n\n\n5.0\n\n\nTakes a lickin and keeps on tickin\n\n\nSeptember 27, 2004\n\n\nJames Stuber\n\n\nPendleton, IN USA\n\n\nI think everyone knows that these drives have become invaluable. Mine has become a necessitiy between my computers at work and at home and any where I can get an open computer. I have W2K at home with USB 1 and XP Pro at work with USB 2. Never had any type of compatibility issue. This 128 MB size is the minimum most can get by with in my opinion. I plan on upgrading to at least the 256 soon.\n\nThe amazing this is that I have just removed my USB drive from a clean pair of pants that went through the wash and dry cycle. And this was the second time. I can't believe it still works.\n\nMaybe I'll have to start using that ridiculus cord and wear it around my neck.\n\nThrough away your zip and floppy disks and get one of these things. Just make sure you back it up often\n\n\n\n\nB0002IQ1FS:excellent_value_for_dvd-r:chad_atkinson\n\n\nB0002IQ1FS\n\n\nVerbatim 8x 4.7 GB DVD-R Spindle (100 Discs): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nExcellent Value for DVD-R\n\n\nSeptember 18, 2005\n\n\nChad Atkinson\n\n\n\n\n\nI am very pleased with the quality of the product. I have burned about a dozen discs and have had zero coasters. After the rebate, the price was very competitive\n\n\n\n\nB0001FV2YM:people_complain_too_much:david_b._nichols_\"the_truth\"\n\n\nB0001FV2YM\n\n\nBelkin TuneCast II FM Transmitter: Electronics\n\n\nelectronics\n\n\n1 of 3\n\n\n5.0\n\n\nPeople complain too much\n\n\nMay 12, 2006\n\n\nDavid B. Nichols \"The Truth\"\n\n\nHarrisburg, PA\n\n\nHey, don't listen to those other schmucks. So maybe there are a few of these that are exceptable, but the rest just complain too much. I love this thing! I got it for Christmas, and now for $20, you can't beat that. I use a standard charger for it in my car, so I never have to worry about batteries. It seems that the best way to get it to sound the best, is work with your mp3 player or whatever your using (equalizer). I have also used it on my portable DVD player in the car and it sounds awesome! For the price, I highly recommend this product\n\n\n\n\nB0000AOWVP:solid_keyboard_and_mouse:d._e._lee\n\n\nB0000AOWVP\n\n\nMicrosoft Wireless Optical Desktop Pro: Electronics\n\n\nelectronics\n\n\n7 of 8\n\n\n4.0\n\n\nSolid keyboard and mouse\n\n\nFebruary 23, 2006\n\n\nD. E. Lee\n\n\nTexas\n\n\nI've used the keyboard/mouse for a little over a month now and have generally found it to do all I would wish. Of course, any wireless keyboard will at times not be as responsive as a wired keyboard/mouse setup, but this is more than offset by the lack of wires and the ability to pull the keyboard to wherever I wish.\n\nThe only drawback, and it is minor, is getting used to the fact that there are no lights on the keyboard to indicate when CAPS LOCK or NUM LOCK is on. I have to look over at the wireless receiver for this. Additionally, it would be nice if there was an option for the keyboard to be backlit. Yes, it would draw down batteries more quickly, but as an option it would be user selected.\n\nAll in all, a good wireless keyboard/mouse combination, and one I am quite glad I purchased.\n\nLife is not a journey to the grave with the intention of arriving safely in a pretty and well-preserved body. \nBut rather to skid in broadside, thoroughly used up, totally worn out, and loudly proclaiming,\n\n\"WOW, Lord! What a ride!\"\n\n\n\n\nB000089GN4:can't_be_beaten_for_quality_or_price...!:wendy_hay\n\n\nB000089GN4\n\n\nSennheiser PX 200 Headphones (Silver): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\ncan't be beaten for quality or price...!\n\n\nNovember 3, 2006\n\n\nWendy Hay\n\n\nTrinidad\n\n\nI read about these headphones in a magazine and i was intrigued by the reviews. having a headphone fetish (odd, i know), i decided to order them seeing that they were relatively inexpensive.. this is a decision i am thoroughly glad i made. these little headphones are absolutely incredible and they even outmatch, in quality, my larger studio-size headphones i have at home. the bass is the best i've ever heard in a headphone, rich and deep and true. the mids and heights are also brilliantly emulated. the mere fact that it is portable with its own compact and innovative carrying case, means that you can take the excellent sound with you wherever you go. i definitely recommend these pones to anyone who values outstanding quality in their music and games.....5 stars \n\n\n\n\nB000068IH5:perfect_for_anyone_who_can_afford_the_bose_soundock:m._krasko_\"bghd123\"\n\n\nB000068IH5\n\n\nMonster Cable 126130 iCable for iPod: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nPerfect for anyone who can afford the Bose Soundock\n\n\nFebruary 20, 2006\n\n\nM. Krasko \"Bghd123\"\n\n\n\n\n\nAfter buying my ipod, i felt my stereo system wasnt geting anyuse. So I felt that instead of buying the Bose Soundock, which is about $300, your better off only spending about $25 and getting just as good quality\n\n\n\n\nB0007NZQ2W:absolute_necessity!:j._robertson_\"jenr\"\n\n\nB0007NZQ2W\n\n\nGriffin Technology 4002-TJUICE TuneJuice Battery Backup for iPod and iPod mini: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nAbsolute necessity!\n\n\nMay 22, 2006\n\n\nJ. Robertson \"JenR\"\n\n\nCalifornia\n\n\nI was unsure at first if this product would work with my iPod nano, but after checking out Griffin's website, I decided to go ahead and order it. It works great! I like to play Solitaire and read text files on my nano, so the battery charge runs down much faster than it would if I didn't use the screen so much. TuneJuice means I don't ever have to worry about that again. I just keep a couple of 9V batteries with me, and I can keep listening, playing, reading or whatever on my nano as long as I want. Love it.\n-------\nI posted the above review on May 22, 2006 after having had the TuneJuice for almost 3 weeks. Two of those weeks were spent on vacation and this little gadget got a *lot* of use. \n\nNow, about 5 months after first receiving the TuneJuice, I'm about to order my second one - to replace the first one. I still love it, and did get a *lot* of use out of it, but snapping 9V batteries on and off so many times caused the wires leading from inside the battery compartment to the 'snaps' that connect to the battery itself to break off. Despite that, I feel like I got my money's worth and I'm buying another one\n\n\n\n\nB000ETVKHE:this_players_meets_all_my_needs:robert_j._apke\n\n\nB000ETVKHE\n\n\nSandisk SDMX4-4096 Sansa e260 4 GB MP3 Player with SD Expansion Slot: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nThis players meets all my needs\n\n\nNovember 25, 2006\n\n\nRobert J. Apke\n\n\nCincinnati, Ohio United States\n\n\nI have been using my Sandisk player with no problems downloading songs or pictures. I have also made some observations that makes this player function more efficently.\n-use MTP music files because they use less file space.\n-adjust the file size on pictures to use less memory.\n-use the Speck toughskin case to protect display window (extra).\n-use the AC Sandisk USB charger instead of the computer USB port(extra).\nI do not use the raio or the video settings because of no interest, and the large amount of memory the video consummes.\nThis player does fine using the earphones or hooked up to a audio center.\n\nI think that the 2MG card will be on my list of upgrades.\n\nThe only upgrade I am made was to buy a better set of earphones. I did not like the pair supplied with the unit. My earphones have a volume control on the output cord which makes it much easier to control.\n\nI am not sorry about buying this item from Amazon.\n\n\n\n\nB000ETVKHE:it's_not_perfect,_but_it's_very_good:jenny_b._\"loyal_amazon_customer\"\n\n\nB000ETVKHE\n\n\nSandisk SDMX4-4096 Sansa e260 4 GB MP3 Player with SD Expansion Slot: Electronics\n\n\nelectronics\n\n\n5 of 7\n\n\n4.0\n\n\nIt's not perfect, but it's very good\n\n\nNovember 16, 2006\n\n\nJenny B. \"Loyal Amazon customer\"\n\n\nNew York, NY USA\n\n\nPros: Easy to use, last for days without charging (depending on the use, of course... but i was traveling for 5 days and used it a lot without charging, and it lasted the trip), enough memory space, has radio, works as a recorder, small\nCons: Stopped working, froze twice since i bought it 3 month ago. When it's on, tunes that i had deleted will still appear on my play list. If I hit play on such song, the player just skips and plays the next song on the list. \n\nBut overall i'm happy with my MP3. I'd recommend it\n\n\n\n\nB0001OHH6A:it_is_better!:michael_j._foley\n\n\nB0001OHH6A\n\n\nMonster Cable HDMI 400 High Resolution A/V Cable - 6 Meters: Electronics\n\n\nelectronics\n\n\n5 of 7\n\n\n5.0\n\n\nIt IS better!\n\n\nNovember 16, 2006\n\n\nMichael J. Foley\n\n\nSherman Oaks, CA United States\n\n\nI didn't want to spend the money on this either, but it IS better. I really don't know why, but it IS. No question about it. PERFECT picture and sound! I have a Yamaha RX-V2700 receiver which has HDMI hookups. I tried a HDMI cable that came with my DirecTV box (from my Toshiba HD DVD player) and it was seriously lacking. This hookup is MUCH better! I have a Monster Cable HDMI hooked up to the new receiver that runs to my Sony 46\" flat panel as well (more $ but....). I'm finally satisfied. I'm watching (and listening to) \"Batman Begins\" (In True HD sound) on HD DVD and it is superb!!!! This is just awesome! The stuff of fantasy. You only have to buy this/these cables once, so why not buy the BEST?! I highly recommend this move. \n\n\n\n\nB000623HE8:best_remote_i've_ever_owned:kooldak_\"a_customer\"\n\n\nB000623HE8\n\n\nLogitech Harmony 680 Universal Remote Control: Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n5.0\n\n\nBest remote I've ever owned\n\n\nMarch 25, 2005\n\n\nkooldak \"a customer\"\n\n\nChama, NM\n\n\nThis remote will do everything you need or want. Simple to use and great customer service. I had a couple of \"glitches\" (buttons that didn't work quite right) when I first programmed the device, but the Harmony support team helped me through those areas. The changes needed are now permanently in my data area on the Harmony web site so if I ever need to, all I have to do is reload the info.\n\nLike another reviewer, I also have a TiVO (2 actually) and the only button that wouldn't work on my Harmony 680 was the \"clear\" button. Customer service fixed that in a jiffy, too.\n\nI have owned several multiple device remotes over the years and never had one so good that I could really put away all my other remotes. I now only use one - the Harmony 680. Fantastic device! I'd give it 6 stars if I could\n\n\n\n\nB00006JI6Y:nice_organizer_for_a_low_price:s.c.\n\n\nB00006JI6Y\n\n\nHP/Compaq CQP121 HP Nylon Folio Case - Black (High Density Nylon): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nNice organizer for a low price\n\n\nFebruary 17, 2006\n\n\nS.C.\n\n\nNewark, DE, USA\n\n\nI paid about a dollar for this holder. Holds and protects the iPaq nicely. Can hold some credit cards as well as ID. Overall a decent product for a low price\n\n\n\n\nB00000J53H:very_satisfied_apc_customer:scott_g._howard_\"scottgh\"\n\n\nB00000J53H\n\n\nAPC Notebook Surge Arrest Protector (PNOTE1): Electronics\n\n\nelectronics\n\n\n21 of 22\n\n\n5.0\n\n\nVery Satisfied APC Customer\n\n\nMarch 16, 2000\n\n\nScott G. Howard \"scottgh\"\n\n\nAlabaster, AL USA\n\n\nThis is perfect for anyone who travels with a laptop. It's small and fits nicely next to the ac adapter in my laptop case. It protects my laptop from surges through the AC outlet and phone jacks. I have been pleased with APC products for about six years. They provide excellent customer support. I highly recommend their products\n\n\n\n\nB00083J67C:very_high_priced_but_works_good.:dave_h.\n\n\nB00083J67C\n\n\nMonster MBL CLNKIT-SM Screen Clean Mini: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nVery high priced but works good.\n\n\nNovember 18, 2006\n\n\nDave H.\n\n\nFL, USA\n\n\nIt also seems like the product goes a long way and will last a long time. I've used it four times already on two different laptop screens and the first opened bottle (out of two that come with it) still looks almost full. If the price was a little more consumer friendly I'd give it five stars. I mean come on. When you can currently buy other computer accessories like a brand name 50-pack of CD-R's for less than $14, the profit margin on something like this is probably 200% or more\n\n\n\n\nB0000DBJ8A:very_helpful:n._burt_\"nikkles\"\n\n\nB0000DBJ8A\n\n\nApple Wireless Pro Keyboard: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n5.0\n\n\nVery Helpful\n\n\nMay 20, 2006\n\n\nN. Burt \"nikkles\"\n\n\nWisconsin\n\n\nI found this keyboard to be very helpful. I am a graduate student and spend a lot of my time typing papers. While my laptop can be used to type, when you type for hours at a time it gets very uncomfortable. This keyboard is a great size and has a nice feel for long days spent typing. The bluetooth is also reliable and allows me to get some distance from the computer if I would like without stopping the connection. I would suggest getting a mouse to accompany it. Overall, a very useful and reliable product that makes my life much easier\n\n\n\n\nB0000YKA9C:no_complaints!:user\n\n\nB0000YKA9C\n\n\nPOCKETEC 929 80GB Portable HDD USB 2.0: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nNo complaints!\n\n\nAugust 17, 2006\n\n\nUser\n\n\nSaranac Lake, NY United States\n\n\nI had it now for 12 months and no problems with it. Its tiny lightweight and great speed. My old computer has to work quite hard to recognize it, but my laptop has no problems. \nOne minor detail.. You cannot hook too many other stuff on the USB at the same time.\nI just ordered another one\n\n\n\n\nB000G1Q9GW:excellent!:mark_sanders_\"silverspurr\"\n\n\nB000G1Q9GW\n\n\nCreative Zen Aurvana In-Ear Headphones: Electronics\n\n\nelectronics\n\n\n9 of 9\n\n\n5.0\n\n\nExcellent!\n\n\nJuly 24, 2006\n\n\nMark Sanders \"Silverspurr\"\n\n\nnew york city, new york United States\n\n\nHave been using them for a week now. \nIncredible sound for the size. Bass is incredible.... mids are accurate, highs are accurate. These are the way to go if you're using an MP3 player. I use them with the Creative ZEN Vision M 30 (\"The Ipod killer\") \n \nWhile walking, riding a bus or train in in N.Y.C. seals out external noise amazingly VERY WELL!!!\n\nPros:\nAwesome accurate sound / Incredibly light / don't protrude out of your ears / comes with \"2 sets\" of three different sizes of silicone plugs / plugs are ultra soft, comfortable and fit perfectly / secure well built storage case / cleaning tool / barely uses battery power! / Not the yup-tard WHITE COLOR.... much cooler BLACK!\n\nCons: None so far.\n\nI bought them directly from Amazon for $75.99 with \"free\" 4 day ultra fast shipping\n\n\n\n\nB000G1Q9GW:this_is_a_good_product_-_good_for_creative:albeniz\n\n\nB000G1Q9GW\n\n\nCreative Zen Aurvana In-Ear Headphones: Electronics\n\n\nelectronics\n\n\n8 of 8\n\n\n5.0\n\n\nThis is a good product - good for Creative\n\n\nJuly 20, 2006\n\n\nAlbeniz\n\n\nVenezuela\n\n\nExcellent sound, much much much........ better that the one that comes with any players, Zens or ipod.\nFor those that have ever used safety earplugs in the industry, they feel like them and really isolate the noises.\nAfter that the sound is excellent compare to many others and amazon offers a very good price here.\nalso good to have 3 sizes. 2 pairs of each + airplane adapter, case, cleaner, very good product.\n\n\n\n\nB00008RUKX:wireless_on_the_desktop:ned_\"java_ned\"\n\n\nB00008RUKX\n\n\nLinksys WMP55AG Dual-Band Wireless A+G PCI Adapter: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n4.0\n\n\nWireless on the Desktop\n\n\nJanuary 20, 2005\n\n\nNed \"java_ned\"\n\n\nEldersburg, Maryland United States\n\n\nI just recently move from dial-up to DSL. Going DSL was not enough; I did not want to have cables running though the house so I started looking into going wireless. To make a long story short, after a little investigation I decided to make the leap into wireless technology. I purchased the WAP55AG router and the adapter cards, WMP55AG and WPC55AG.\n\nOnce the WAP55AG router was installed and set up, the installation of the WMP55AG PCI Adapter on each of the desktops were simple.\n\nI had no difficulty setting up the hardware and software and with a few hours, I was on the net through the router. One desktop is running XP professional and the other desktops are running XP Home edition. \n\nThe LinkSys Web site has all the documentation you need for installation and setup. There is a \"Knowledge Base\" page that covers a lot of the products and answers to a lot of questions about various products and configurations; I found it to be very helpful.\n\nPulled from LinkSys Web Site:\n----------------------------------------\nThe Linksys Dual-Band Wireless A+G PCI Adapter installs in most desktops and lets you put your computer almost anywhere in the building, without the cost and hassle of running network cables. Now you don't have to drill holes in your walls and climb through the attic or cellar to get connected to the network. Once you're connected, you can keep in touch with your e-mail; access the Internet, use instant messaging to chat with friends, and share files and other resources such as printers and network storage with other computers on the network. \n\nThe Dual-Band Wireless A+G PCI Adapter connects you with Wireless-A (802.11a) and Wireless-G (draft 802.11g) networks at an incredible 54Mbps! And for added versatility, it can also interoperate with all the 11Mbps Wireless-B (802.11b) products found in homes, businesses, and public wireless hotspots around the country. And whichever mode you choose, your wireless communications are protected by up to 152-bit encryption, so your data stays secure. \n\nFeatures of the product:\n--------------------------------\nOne adapter connects to either 2.4 GHz (802.11b or draft 802.11g) or 5 GHz (802.11a) networks\nAdvanced security features with up to 152-Bit WEP encryption\nDetailed monitoring and performance utility\nIEEE 802.11a, 802.11b and draft 802.11g compliant\n \nSystem Requirements (Minimum):\n---------------------------------------------\n200MHz of Faster Processor\n64MB RAM\nAvailable PCI Slot\nCD-ROM Drive\nWindows 98SE, Me, 2000 or X\n\n\n\n\nB000BFO4OC:everything_i_was_hoping_for:codex57\n\n\nB000BFO4OC\n\n\nLogitech MX 610 Cordless Laser Mouse (931350-0403): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nEverything I was hoping for\n\n\nAugust 31, 2006\n\n\nCodex57\n\n\nCalifornia\n\n\nI am very happy with this mouse and would recommend it to everyone. It looks good, but more importantly, it feels good. Button placement, weight, smoothness, everything was done well. Coming from wired mice, the lack of a wire is very freeing. I am also especially happy with how small the USB radio receiver is. I am using this mouse on a laptop and love how inobtrusive it is.\n\nWhat's strange is I bought the S 530 desktop set for Mac at the same time. The mouse in that set looks nearly identical (except for color). However, it has a different weight and simply doesn't feel quite as comfortable to use. It only fits the hand the same but once you move it, it lacks the same comfort and ease of use as this mouse\n\n\n\n\nB000246U1C:great_product,_very_satisified!!!:charles_cochrane\n\n\nB000246U1C\n\n\nAcoustic Research AW-811 Indoor/Outdoor Wireless Speaker (single): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGreat product, very satisified!!!\n\n\nOctober 4, 2006\n\n\nCharles Cochrane\n\n\n\n\n\nReceived this speaker as a replacement for the Advent AW-810 which did not work at all. This one was much easier to install and calibrate! Very happy with range and sound quality. Highly recommended\n\n\n\n\nB000779RZU:good_product:j._griffith_\"jacki_-_shopaholic\"\n\n\nB000779RZU\n\n\nSony MDR-EX51LP Fontopia In-The-Ear Headphones (White): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nGood product\n\n\nNovember 3, 2006\n\n\nJ. Griffith \"Jacki - shopaholic\"\n\n\nIdaho Falls, Idaho United States\n\n\nThese are very comfortable to wear, and the sound is great. I use them out jogging, and they stay put, without any pressure on the ear. The tight fit blocks out any outside noise, so be careful around traffic, as you cannot hear cars. I would highly recommend these to anyone\n\n\n\n\nB000779RZU:good_sound_quality,_but...:bruce_d._miller_\"telecom_entrepreneur\"\n\n\nB000779RZU\n\n\nSony MDR-EX51LP Fontopia In-The-Ear Headphones (White): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n4.0\n\n\nGood Sound Quality, but...\n\n\nSeptember 28, 2006\n\n\nBruce D. Miller \"Telecom Entrepreneur\"\n\n\nBoston, MA\n\n\nI bought these Ear Headphones primarily to use when running and biking at the club. The sound quality is acceptable, not exceptional. The construction is a little flimsy and the big issue is that the cord is shorter for the left ear than the right. That places all the force on the left side and that one tends to pull out. I have no idea why Sony thought that this was a good design. For the money they could also have included a case rather than a clip for the cord. I would give these a B- good/not great\n\n\n\n\nB00004VUGJ:logitech_cordless_trackman_wheel:dcedte\n\n\nB00004VUGJ\n\n\nLogitech Cordless TrackMan Wheel: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nLogitech Cordless TrackMan Wheel\n\n\nNovember 19, 2006\n\n\ndcedte\n\n\n\n\n\nThe Logitech Cordless TrackMan Wheel was purchased in Sept. 2006. I have had this for two months at the time this review is being written.\n\nI bought this to replace the corded version of the same item. The cordless works as well as the corded one. The trackball doesn't has quite as smooth a roll to it as the corded one did, but the difference is minimal. I wonder if this may be because it is new and the other one had had years of use.\n\nI find the shape of the Logitech Trackman to fit the hand very naturally. It is also much less strenuous on the wrist than a normal mouse, which is why I use it.\n\nI replaced the corded one so that we could easily move the mouse to different locations for use. This is for home use with multiple users, with different mouse location preferences. I took the corded one to use at work, where it still performs flawlessly 8+ hours a day.\n\nI also have a Logitech cordless keyboard.\n\nThere are two places where both these items could be improved:\n- Each one of these items needs it own receiver. It would save some of my limited home desk space if these could be combined into one unit.\n- I initially had the receivers out of sight behing an LCD monitor. I found that it was necessary to move them away from the monitor to get them to work reliably. This is a documented requirement in the owners manual, but that necessity takes away from the convenience of the wireless. The receivers, as stated earlier, take up space I would rather not spare\n\n\n\n\nB00004VUGJ:works_great_but...:j._fields\n\n\nB00004VUGJ\n\n\nLogitech Cordless TrackMan Wheel: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nWorks great but...\n\n\nNovember 13, 2006\n\n\nJ. Fields\n\n\n\n\n\nWe have a TrackMan corded model and loved it so I got this one for my work computer. It works just as well as the corded one when I'm in my office. For travel, the part you plug in is a little bulky so I usually leave it at home when I travel. It's not a small, USB stick that goes in the back. It's a long, corded, mouse looking device that sends/recieves the signal to the TrackMan. I don't think they market it as a portable device so it works just as advertised but it would be nice if it just had a USB stick instead of the other for travel. All that said, I love the trackball over a mouse so this is just perfect for me\n\n\n\n\nB0002MK9X4:concerned_with_other_reviews:magda\n\n\nB0002MK9X4\n\n\nHP PhotoSmart 375 Compact Photo Printer: Electronics\n\n\nelectronics\n\n\n21 of 22\n\n\n5.0\n\n\nconcerned with other reviews\n\n\nSeptember 3, 2005\n\n\nmagda\n\n\nsanfrancisco\n\n\nmy canon 510 got glowing reviews just about across the board, but not this printer. i have very little space, and needed something small just for photos. so, even though there were many negative reviews i went ahead and bought it. couldn't be happier! colour fabulous! and when you print on stock paper you can use photo to do some extra artwork on. I LOVE IT\n\n\n\n\nB00003CWG2:pleased:michael_j._corso\n\n\nB00003CWG2\n\n\nMonster Cable TGHZ-2RF Two Gigahertz Low-Loss RF Splitters For TV & Satellite 2-Way 2 Gigahertz RF Splitter: Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n5.0\n\n\nPLeased\n\n\nJanuary 15, 2006\n\n\nMichael J. Corso\n\n\n\n\n\nProduct nicely made , looks like quality product and performs fine\n\n\n\n\nB000EDIKVO:excellent_hand_crank_radio:g._ward_\"macboy\"\n\n\nB000EDIKVO\n\n\nAmerican Red Cross FR300 Emergency Radio: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nExcellent Hand Crank Radio\n\n\nNovember 7, 2006\n\n\nG. Ward \"macboy\"\n\n\nQueens, NY USA\n\n\nMy wife and I used the FR300 during the week-long Queens blackout this past summer and this little radio delivered. One 2 minute winding lasted for an hour on medium volume. The cell phone charger is cool, but it takes a lot of winding. The light is good in a pinch, but not particularly useful\n\n\n\n\nB000BW3TK0:great_updated_product:outdoors_\"backpacker\"\n\n\nB000BW3TK0\n\n\nGarmin MapSource City Navigator North America, v.8: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nGreat updated product\n\n\nNovember 4, 2006\n\n\nOutdoors \"backpacker\"\n\n\nUtah\n\n\nThis map set met all my expectations, and was far more current than I had expected. Software for turn by turn directions on roadways while driving works great with the Garmin Ctrex GPS. Recommended. To load many states you will need large memory card, 1 GB. \n\n\n\n\nB000CO77SC:on_the_contract:env-youreverythinghomepage-com\n\n\nB000CO77SC\n\n\n2-Year Service Plan for Televisions $200 to $999.99: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n4.0\n\n\nOn the Contract\n\n\nAugust 24, 2006\n\n\nenv-YourEverythingHomepage-com\n\n\nLos Angeles, CA, formerly Rochester, MN USA\n\n\nIf you buy this service plan, print out the contract and keep your receipt. I did not purchase the plan, but looked over the contract intensely since I nearly got it.\n\nSome notes on the contract:\n1. It is only good in the United States (including Washington, DC, but MINUS MAINE), but it does not include Guam, Virgin Islands, etc. So if you are in another country or outside the 50 states + D.C., this contract will be nothing but a waste of money.\n2. This contract is transferable and the directions to do so are simple.\n3. They will only fix/replace/etc IF the manufacturing warranty no longer covers the item. That is, if the manu. warranty is still good, you have to use it.\n4. If the item needs to be shipped, shipping/handling will only be covered if the manufacturer would cover those costs in their warranty period. If the item is being replaced, \"You will be responsible for the delivery or cost of delivery of the covered Product to Our authorized service center. Reimbursement will be issued in the form of a check payable to You.\" I'm uncertain if that means you cover those costs or they reimburse you.\n5. No lemon policy of 3 service repairs for the SAME defect, and a upon the time of a 4th repair, as determined by them, they will \"replace it with a product of comparable performance. Repairs performed while the Product is under manufacturer's warranty DO NOT APPLY.\" (Doesn't sound like a great no lemon policy... your product would have to breakdown in the same way 4 times in the number of years they cover you -- many electronics are typically already covered by the manufacturer for 1 year.)\n6. There are numerous conditions that they will not repair for, but they all sound typical of such plans (such as, they don't cover accidents and they don't cover \"the elements or acts of God\" ... sorry, I had to note that second one since it stuck out to me from the other things). But, in all seriousness, read the list before you purchase.\n\nSo, now that I have summarized much of the contract, if you want to get the service warranty, PLEASE READ THE CONTRACT SINCE I DIDN'T COVER EVERYTHING!!! (Don't hold me liable since I don't have much money anyways.) Also, I think this is typical in warranty contracts, but you will occassionally see the phrase \"at our discretion\" in the contract.\n\nAssuming they aren't jerks with their discretion and aren't too tough to deal with, I would give the warranty 4 stars (minus 1 star for the crappy lemon policy). I didn't get the coverage because the product I was going to get it for had just made into the next price category (so it was pricy for my product, being at the lower price end of the scale)\n\n\n\n\nB000196ENM:works_as_designed_-_and_what_a_difference!:m._j._cerone_\"look_for_me_and_donate_at_www.rideforroswell.org\"\n\n\nB000196ENM\n\n\nMagellan 980637 External Antenna: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nWorks as designed - and what a difference!\n\n\nMarch 22, 2006\n\n\nM. J. Cerone \"Look for me and donate at www.RideForRoswell.org\"\n\n\n30 miles SSE of Buffalo, NY\n\n\nEasy enough to pop off the normal ant (RoadMate 700) and pop on this magmount version... and what a difference it made! Signals really are remarkedly improved. **HIGHLY** suggested for folks in hilly areas to nearly guarentee you'll get the birds\n\n\n\n\nB0002LEMZG:pretty_helpful:jeremy_thompson_\"atx\"\n\n\nB0002LEMZG\n\n\nKensington 60148 USB 2.0 Universal Laptop Docking Station (PC/Mac): Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n4.0\n\n\nPretty helpful\n\n\nMarch 27, 2006\n\n\nJeremy Thompson \"ATX\"\n\n\nCHARLOTTE, NC United States\n\n\nA lot cheaper than model-specific docking stations, and 'does what it says on the tin'. Quite stylish too, as docking stations go\n\n\n\n\nB000F9NXW8:nice_communications_device:william_christiansen_\"wpc\"\n\n\nB000F9NXW8\n\n\nMidland GXT600VP4 5-Watt 22-Channel GMRS with NOAA All-Hazard Weather Alert: Electronics\n\n\nelectronics\n\n\n6 of 7\n\n\n4.0\n\n\nNice communications device\n\n\nAugust 19, 2006\n\n\nWilliam Christiansen \"WPC\"\n\n\nWoodburn, Oregon United States\n\n\nWe used the GTX600VP4 to stay in contact with two vehicles while traveling. The 5-watt power gave us ample range. Does it reach 18 or 20 miles, don't know. But 5 to 8 miles between cars was not a problem. Range was somewhat reduced over hilly terrain and varied from poor to good in the Rocky Mountains. It's a great way to stay in touch when traveling. The batteries are rechargeable, both A/C & D/C. The ear pieces were very good for privacy. Very easy to use and well worth the price. A good investment.\n\n\n\n\nB000EIBVKG:does_everything_you'd_need_in_a_disaster:j._weber\n\n\nB000EIBVKG\n\n\nAmerican Red Cross FR400 Emergency Radio: Electronics\n\n\nelectronics\n\n\n10 of 10\n\n\n5.0\n\n\nDoes Everything You'd Need In A Disaster\n\n\nSeptember 11, 2006\n\n\nJ. Weber\n\n\n\n\n\nI was hesitant to get this, since other reviewers gave it poor ratings on reception. Now that I bought it, I'm very glad I have it. It does all the things you might want in a disaster, inluding a flashlight, red flashing light (especially if your car breaks down at night), a siren (if you're trapped in a building), AC, battery, and crank power, and multiple radio bands. While some reviewers complained of not getting a lot of stations, in a disaster, you only need a few stations, as everyone will be broadcasting disaster news. Between the AM, FM, TV, and NOAA bands, my radio will received a huge number of stations--many more than would ever be needed in a disaster. (I didn't try the cellphone charging function.) It comes in a carrying case.\nOnly minor criticisms: The case seems like an afterthought: it doesn't hold the AC adapter; the knobs tend to turn the radio on when putting it in the case, and the knobs could get broken off if the case is hit (not that protective). However, it comes in a strong box which is perhaps better storage for everything.\nOverall, this is all you should need for light, siren, and broadcast information from multiple power sources in a disaster\n\n\n\n\nB0002L5R78:great_value:john_w._montgomery\n\n\nB0002L5R78\n\n\nHDMI 2M (6 Feet) Super High Resolution Cable: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nGreat Value\n\n\nJuly 31, 2006\n\n\nJohn W. Montgomery\n\n\nSan Diego, CA United States\n\n\nI just recieved my HDMI cable and am very impressed. The price is just what it should be about $5 and makes me wonder how somebody would spend over $100 for this cable at a store. The service was excellent and the cable arrived in 4 days! I highly recommend this cable. I just plugged it into my cable box and the other end into the TV and WOW what a great picture all around. The color is just so much more vivid using HDMI compared to component 3 wire connectors. Get this cable for your system and stay away from those high priced others\n\n\n\n\nB0002F8WKS:nice:cdaddy_\"cdaddy\"\n\n\nB0002F8WKS\n\n\nLogitech Media Keyboard: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nnice\n\n\nNovember 9, 2006\n\n\nCDADDY \"CDADDY\"\n\n\nHELL,CO\n\n\nThis is the perfect keyboard ( I know cuz I am typing on it right now ) I recommend if you like media boards\n\n\n\n\nB00006HXF4:great_value_for_great_product!:james_martin_\"star_trek_nerd\"\n\n\nB00006HXF4\n\n\nSanDisk SDSDB-256-A10 256 MB Secure Digital Card (Retail Package): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nGreat Value For Great Product!\n\n\nMarch 15, 2006\n\n\nJames Martin \"Star Trek Nerd\"\n\n\nFresno, CA USA\n\n\nSanDisk has done it again. They never seem to let me down with their products. I was in the market for another memory chip since I kept filling up my smaller 128 MB chip. The price was the deciding factor for me, as being a college student the financial situation was tight. But this product was perfect, right price, excellecnt storage space, good name. Get this product if you need more space for your digital camera\n\n\n\n\nB0000AN4EG:garmin_:r._nielsen\n\n\nB0000AN4EG\n\n\nGarmin StreetPilot 2610 In-Car GPS Receiver with 128 MB CompactFlash Card: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n5.0\n\n\nGARMIN \n\n\nAugust 9, 2006\n\n\nR. Nielsen\n\n\n\n\n\nFast shipping, Very happy with the GARMIN. Their tech support is good..they answer all questions....WILL buy from AMAZON again... will buy a GARMIn agai\n\n\n\n\nB00016V3VI:ipod_#in_!:sea_chelle_\"still_shopper\"\n\n\nB00016V3VI\n\n\nGriffin Technology 1072-POD PowerPod Firewire Auto Charger for iPod: Electronics\n\n\nelectronics\n\n\n1 of 3\n\n\n4.0\n\n\nIPOD #in !\n\n\nMarch 22, 2006\n\n\nSea Chelle \"still shopper\"\n\n\nBrooklyn, NY\n\n\nThis is great! My only problem is the knob for adjusting. It's to loose\n\n\n\n\nB000A6NV0U:great_mouse,_great_price:clide\n\n\nB000A6NV0U\n\n\nMicrosoft Comfort Optical Mouse 3000 ( D1T-00002 ): Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n5.0\n\n\nGreat mouse, Great Price\n\n\nJune 19, 2006\n\n\nCLiDE\n\n\n\n\n\nI bought this mouse originally as a replacement for a $9.99 mouse, and I'll never turn back. THIS MOUSE IS OUTSTANDING FOR GAMING. It's pretty damned accurate, and it easily fits in my hand. Good price, too\n\n\n\n\nB000EEZCEG:works_out_of_the_box_with_the_cingular_3125:j._hansen_\"tech_hound\"\n\n\nB000EEZCEG\n\n\nSanDisk 1 GB MicroSD Card (Retail Package): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nWorks out of the box with the Cingular 3125\n\n\nNovember 19, 2006\n\n\nJ. Hansen \"tech hound\"\n\n\n\n\n\nI you own a SmartPhone, you'll need one of these. Also happy this came with an adapter card, to plug in the MicroSD into it to move data from my Mac to the phone.\n\n\n\n\nB000EEZCEG:expanded_memory_for_motorola_razr_v3m_for_verizon:richard_b._cuciti\n\n\nB000EEZCEG\n\n\nSanDisk 1 GB MicroSD Card (Retail Package): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nExpanded Memory for Motorola RAZR V3m for Verizon\n\n\nNovember 9, 2006\n\n\nRichard B. Cuciti\n\n\nLaguna Beach, CA USA\n\n\nThe SanDisk 1GB Micro SD Card arrived quickly with intact sturdy packaging. I installed it in my Motorala RAZR V3m as expanded memory for my music library. Be sure to remove the RAZR battery to clear the access slot for the Micro SD. The V3m recognized the expanded memory without any problems. I used Motorola Phone Tools to transfer my entire music library. The program converts the mp3 file formats before transfer. The entire music library...a full 1GB...transferred reasonably quickly. The V3m is slow to access \"My Music\" using menu function #3 under \"Get Tunes & Tones\". Function #6 \"Manage Music\" accesses the library reasonably quickly and works well. The new corded Motorola SYN1301B stereo earbuds work well in this configuration. Sound quality is surprisingly good, but the large diameter earbuds are flat...not tapered... and do not seat very well in the ear canal. The SanDisk 1GB MicroSD Card provides great value at a very reasonable price for full optimization of the V3m as cell phone with \"MP3\" player\n\n\n\n\nB000FGI9RU:wonderful_sound:deadman_\"deadman\"\n\n\nB000FGI9RU\n\n\nJBL DE893BABA Platinum Series Powered Speakers, Black (Pair): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nWonderful sound\n\n\nNovember 9, 2006\n\n\nDeadman \"Deadman\"\n\n\nOnamia, MN USA\n\n\nI upgraded from some speakers I purchased when I built my system. The sound difference was wonderful. It is a clean crisp fresh sound. Worth every penny\n\n\n\n\nB000BDDBLG:no-brainer_if_you_already_have_bluetooth,_not_quite_perfect:michael_ilnyckyj\n\n\nB000BDDBLG\n\n\nLogitech V270 Cordless Optical Mouse for Bluetooth - Bright Silver: Electronics\n\n\nelectronics\n\n\n0 of 1\n\n\n4.0\n\n\nNo-brainer if you already have bluetooth, not quite perfect\n\n\nNovember 6, 2006\n\n\nMichael Ilnyckyj\n\n\n\n\n\nPros:\nGreat battery life\nCompact, but not too small\nNo extra dongle\n\nCons:\nIncluded software is worthless, but you don't really need to use it\n$40 is a bit much considering that you can get cordless keyboard/mouse sets for around $30 nowaday\n\n\n\n\nB0002ZSCJY:saved_me_from_a_ticket!!:\n\n\nB0002ZSCJY\n\n\nEscort Passport 8500 X50 Radar and Laser Detector (Blue Display): Electronics\n\n\nelectronics\n\n\n4 of 11\n\n\n5.0\n\n\nSaved me from a ticket!!\n\n\nOctober 3, 2006\n\n\n\n\n\n\n\n\nOkay so it was about 8:45 at night. I was coming on to a 1/2 mile long bridge that goes over a lake where I live. I was doing 60mph, the speed limit is 40. As I start to approach the bridge I start getting a small K-Band warning. I instantly slow down to the speed limit. As I start crossing the bridge the warning keeps increasing so I slow down even more to 35 mph. Then it gets to full K-Band. I look to my left where there is a service parking area, and in the pitch dark I see the reflectors of the one and only Crown Vic Police Car. As I pass the police car, I start flashing all the oncoming cars to tell them that there is a cop ahead. \n\nIf I didn't have this detector he would have gotten me doing 20 over the limit. Thank you Escort!\n\n\n\n\nB0002ZSCJY:driving_worry_free:j._chu\n\n\nB0002ZSCJY\n\n\nEscort Passport 8500 X50 Radar and Laser Detector (Blue Display): Electronics\n\n\nelectronics\n\n\n10 of 11\n\n\n5.0\n\n\nDriving worry free\n\n\nAugust 14, 2006\n\n\nJ. Chu\n\n\nLexington, MA\n\n\nThis is my second Escort Passport 8500 X50. The first one is in another car. It saved me many times for getting caught though I'm not maniac driver. The detection range is very good, it gives you plenty time to adjust your speed before you see the police car. I love the blue color and it only costs $30 more than red. The only problem that I have experienced on my first one is sometimes I got laser false warning signals, especially on raining day with defog is on. I heard someone mentioned that turbo charged car can causes X50 Pop and Laser false warning signals, and my first X50 is in my turbo charged car. I'm not sure if Pop alerts were false but since it won't help much, I have it turned off. Overall, it's very good detector, you will get payback if you own one. \n\n\n\n\nB0007N55OG:juan_yactayo_california:j._y._vazquez\n\n\nB0007N55OG\n\n\nSony MDR-XD400 Stereo Headphones: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nJuan Yactayo California\n\n\nNovember 9, 2006\n\n\nJ. Y. Vazquez\n\n\nBakersfield California\n\n\nThe best headphones I ever had, good fit excellent sound on my SoundBlaster X-FI!!\n\n\n\n\nB00021XIJW:mine_works_great:jason_a._bell_\"jbeldar\"\n\n\nB00021XIJW\n\n\nLinksys Wireless-G Range Expander WRE54G: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n4.0\n\n\nMine works great\n\n\nNovember 21, 2006\n\n\nJason A. Bell \"jbeldar\"\n\n\nSeattle, WA\n\n\nI got mine to work in about 30 min following S Huffs directions below (followed exactly). There was only one thing not mentioned that I had to do to get it to work. Once you get into the settings on the range extender to enable WEP and put in the key and passphrase, I had to turn WEP back on on my router before saving the settings on the range extender. Basically just put step 6 before you save settings in step 5, this is what worked for me at any rate.\nI've had it for about 4 months now and it has worked as advertised. I gave it 4 stars because set up was still kind of a pain.\n\nFrom S Huff: The tips that Anthony Taylor left helped me get it working... but I still had some issues. I've added onto his tips from his review below \n\n1-Disable WEP on your Router or AP \n2-Hold the auto-configuration button for 30 seconds(if everything goes well both lights on the expander will turn blue) and unplug while still holding button. Plug back in after 10-15 secs. \n3-open your Internet explorer browser and type 192.168.1.240 (default ip of expander), a window will pop up. leave the user name blank and put admin as a password (this is default password). \n4-make sure that the default gateway and subnet mask settings are identical with your Router or AP,change password on the expander if desired & save settings. (all settings should be correct due to auto configuration) \n5-Enable WEP on the expander, once again remember use same settings that you will be putting in your router or AP in step 6 -- aka same passphrase), save settings again. \n5.5 - As soon as you enable WEP on your expander - you will loose your connection to it as it's now setup for WEP and your router isn't - keep this in mind - drove me nuts. \n6-Go to your Router or Ap settings and enable WEP. \n7 - UNPLUG your expander for 10 to 15 seconds and plug it back in -- Bang you're DONE. Enjoy \n\n\n\n\nB000BQQL24:it's_a_dock!!:p._giever\n\n\nB000BQQL24\n\n\nApple MA045G/B iPod Universal Dock: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n5.0\n\n\nIt's a dock!!\n\n\nAugust 19, 2006\n\n\nP. Giever\n\n\nKew Gardens, NY\n\n\nWorks with Ipod skin,no need to take off cover.Looks good.Has connections for Audio,TV. Cables and remote are additional. Included are 5 adapters for all Ipods that have doc connector.\n Paid 29.95 from Amazon including shipping.It is what it is.It's a dock\n\n\n\n\nB0009WDM2W:flash_drive:tracy_l._vasquez\n\n\nB0009WDM2W\n\n\nMemorex TravelDrive 1GB USB 2.0 Flash Drive ( 32509060 ): Electronics\n\n\nelectronics\n\n\n1 of 4\n\n\n5.0\n\n\nFlash Drive\n\n\nAugust 3, 2006\n\n\nTracy L. Vasquez\n\n\nGurnee, IL\n\n\nThis is a great storage device. I use it to store documents to take with me when I travel and I keep it in my purse. I love the lanyard, that helps me to keep track of the drive. Worth every $$$ I paid for it!\n\n\n\n\nB00005ATZJ:make_a_lot_of_nice_greeting_cards:b._yoder\n\n\nB00005ATZJ\n\n\nBurlington Half-Fold Greeting Cards and Envelopes (Model 475, 50-Count): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nMake a LOT of nice greeting cards\n\n\nMarch 17, 2006\n\n\nB. Yoder\n\n\n\n\n\nThis is a very nice set of paper and envelopes. The price is really great. The paper is a little lighter than the ones that cost almost double. The colors come out very bright and the folding is easy. Overall it is a good deal\n\n\n\n\nB000DZEUUI:wonderful_phone:d._robison\n\n\nB000DZEUUI\n\n\nUniden TRU9460 Expandable Cordless System with Call Waiting/Caller ID: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nwonderful phone\n\n\nOctober 29, 2006\n\n\nD. Robison\n\n\nDuluth, MN\n\n\nI have always had uniden cordless telephone and have always liked them. I had purchased this one to replace my 2.4ghz uniden cordless telephone which would interfere with my wireless modem on my computer. This one doesn't. I also like the feature of the intercom feature. My daughter has one of the handsets downstairs in her room and if she isn't on the phone, I can contact her to talk to her. \n \nMy daughter also likes the mute feature where she can put somebody on hold if she is on the phone to ask me a question so that person on the other end doesn't hear her.\n\nThe phone has many other features but those are 2 usefule ones I like along with the caller ID\n\n\n\n\nB00006B9H8:excellent_initial_quality_switch:kaushik_goswami_\"gadgetlover\"\n\n\nB00006B9H8\n\n\nNetgear FS605 5-Port 10/100 Fast Ethernet Switch: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nExcellent initial quality switch\n\n\nSeptember 12, 2006\n\n\nKaushik Goswami \"gadgetlover\"\n\n\nOK USA\n\n\nI am extremely pleased with the purchase. I am using it in my office. Simple plug and play (no brainer)!!\nIf I get the MIR the cost comes to $5. None can better that. Transaction with Amazon was flawless as usual\n\n\n\n\nB000E5GKW8:patience_and_then_this_thing_rocks!:s._c._bower\n\n\nB000E5GKW8\n\n\nShure E2c-n Sound Isolating Earphones (Black): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\npatience and then this thing rocks!\n\n\nNovember 4, 2006\n\n\nS. C. Bower\n\n\nNC\n\n\nLike most other reviews, I will agree and say it make take a try or two to get the right feel and the right fittings. What helped me is when I looped the wire around my ear and put the earphone in. Now it is crystal clear sound and a perfect fit. Movies are amazing! My music collection never sounded so good. If you want to get away from it all for awhile, slip these babies in your ears and chill\n\n\n\n\nB0000512IE:adds_another_dimension_to_x-plane_in_os_x:gregory_farrell_\"star_man\"\n\n\nB0000512IE\n\n\nCH Products Pro Pedals USB Flight Simulator Pedals ( 300-111 ): Electronics\n\n\nelectronics\n\n\n6 of 6\n\n\n5.0\n\n\nAdds another dimension to X-Plane in OS X\n\n\nMarch 3, 2006\n\n\nGregory Farrell \"Star Man\"\n\n\nWichita, KS USA\n\n\nI bought USB rudder pedals and yoke for use with X-Plane on a Mac Dual G5.\nPlug and play (no drivers needed), easily configured in x-plane, works beautifully.\nGreat price! Thanks Amazon! Shipping was 2 days early! Kudos to Fedex!\nI am a private pilot, and recommend this product\n\n\n\n\nB000090WE9:conference..._in_style:niki_r._\"globe_trekin\"\n\n\nB000090WE9\n\n\nPanasonic KX-TS105B Corded Speakerphone (Black): Electronics\n\n\nelectronics\n\n\n8 of 10\n\n\n4.0\n\n\nConference... in style\n\n\nJuly 14, 2006\n\n\nNiki R. \"Globe Trekin\"\n\n\nStow, OH\n\n\nI needed a simple solution for my office. I wanted to be able to use the phone, but not get tied down by the handset, or the headset. Here was my solution, Since the TS105B has the 2.5mm port for a headset, I purchased a Jabra A210 Bluetooth adapter along with this phone and used one of my spare bluetooth headset to stay on conference calls and yet be able to move around my office. So far... WOW!!! its worked out great. The phone is also good by itself, plus it was inexpensive through AMAZON. For anyone interested in going the route I took... you will not be disappointed. \n\nOh yes... you are limited to about 25 ft, or in my case as I work in a hospital where there are alot of other signals and lead lined walls... 15ft with max length at about 20 before you begin to hear static. \n\nThe phone was a great buy, as was the Jabra A210 adapter. \n\n\n\n\nB0009EXVNA:rainbow_effect:d._p._jividen_\"foxhound42001\"\n\n\nB0009EXVNA\n\n\nSamsung HL-R5067W 50\" HD-Ready DLP TV: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n5.0\n\n\nRainbow effect\n\n\nJuly 6, 2006\n\n\nD. P. Jividen \"Foxhound42001\"\n\n\nAbilene, TX USA\n\n\nI have had this TV since Mid-March 2006. I absolutley love it!! I did extensive research before I bought a large screen TV and found that this Samsung seemed to give you the most bang for your buck. I read some reviews saying that DLP technology was plagued with problems from the rainbow effect, especially in movies with lots of dark scenes and lots of moving action. One reviewer specifically said to try playing \"Bourne Supremecy\" to see an example of the rainbow effect. This was the first dvd I played on this tv and I did not once have a problem with rainbow effect. Overall I have no complaints with this TV. Samsung has produced an excellent product. I recommend either satellite or HD programming for the best picture. Regular cable comes in sort of grainy.\nAs far as where to buy it, I recommend Best Buy. I originally was going to buy from Amazon because when I first looked they were offering free shipping. When I decided to buy their was no longer free shipping, so off to Best Buy I went where they price matched me with amazon's price no questions asked. Best Buy offers an extended warranty for around $400 that covers bulb replacement or any other problems. It will also cover the rainbow effect on the slight chance that you have any problem with it. Overall a wonderful product\n\n\n\n\nB00005UPF6:cellphone_interference!:tom_rix_\"rixth\"\n\n\nB00005UPF6\n\n\nKoss EQ50 3-Band Stereo Equalizer: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n4.0\n\n\nCellphone Interference!\n\n\nOctober 3, 2005\n\n\nTom Rix \"Rixth\"\n\n\nMasterton, New Zealand\n\n\nThere is a little story before this:\n\nI bought a pair of Sony MDR-XD100's (they are full cup headphones) because I lost the earbuds which came with my mp3 player (Sony HD5). I took them out of the package and found that, even at full volume on the player, it cannot drive the headphones sufficently. I went to a few electronics stores then found the KOSS EQ50.\n\nOn with the review:\n\nYes, there is a slight hiss, but I find it alright. The biggest gripe I have is that whenever someone within about 2-3 ft (IE, on a bus or subway) uses a cellphone, this devices picks it up. You can get the same effect by using a cellphone next to an unshielded pair or speakers. I do most of my listening at home so this won't bo too much of an issue.\n\nIt also does a great job of amplifying the signal, it works very well, I can now listen to my XD100's properly (and they are very good!). I haven't used the EQ much but what I can say is that it is very, very sensitive. Raising the bass slider a third above normal makes a massive difference. At a half, my headphones start to distort.\n\nIf your looking for a nice little EQ and/or preamp this is the product for you. It has its drawbacks though, mainly the problem with cellphone interference and of course, the hiss but it isn't too loud & I can live with it.\n\nI reccomend this product if your looking for a budget portable equalizer or headphone amplifer\n\n\n\n\nB0009N5MDI:ipod_bluetooth:david_wright\n\n\nB0009N5MDI\n\n\nLogitech 980397-0403 Wireless Headphones for iPod, Gray: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nipod bluetooth\n\n\nNovember 5, 2006\n\n\nDavid Wright\n\n\nScarsdale, NY\n\n\nThis does what it's advertized too. I like not having wires. The iPod in my bag, and just the headset. It would be a bit better if you could control the power on the ipod from the headset, ie turn off the headset, receiver and ipod all from the headset. Instead, I have to get the ipod back out, turn it off, turn the receiver off, then turn the headset off.\n\nOtherwise, the sound is good and the headset hasn't broken for me yet\n\n\n\n\nB0009N5MDI:a_bargain,_if_you_trust_customer_applied_eco's:joe_murphy_\"computer_geek_since_1975\"\n\n\nB0009N5MDI\n\n\nLogitech 980397-0403 Wireless Headphones for iPod, Gray: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nA bargain, if you trust customer applied ECO's\n\n\nSeptember 29, 2006\n\n\nJoe Murphy \"computer geek since 1975\"\n\n\nPortland, OR USofA\n\n\nI picked up 4 pairs of these on Amazon, and have applied the CA and Nylon tape fix to the first pair (took about 10 minutes to do, let it cure overnight). Headphones look ok, with just the right amount of Geekhood proudly announced afterwards (reminiscent of the last generation of Geeks with the tape on the glasses). Sound is very good (better than most earbuds), they establish link quite quickly with the dedicated Logitech transiever, and range is outstanding. My only beef is how long I have to hold down the power switch to turn them on, and so far I have not successfully established a connections with my Treo 650. 1 star held back because of need of customer applied ECO, and they are only partially functional with the Nano (can't use the advance track feature of the headphones)\n\n\n\n\nB0002Y6CVA:great_for_lots_of_storage:g._wynn\n\n\nB0002Y6CVA\n\n\nCase Logic Nylon 336-Disc CD Wallet (Black): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGreat for lots of storage\n\n\nNovember 12, 2006\n\n\nG. Wynn\n\n\nWA United States\n\n\nI am ordering my third of this capacity and have one in the 200 capacity range from Case Logic. This is a great product and is manufactured well. Holds the discs well and is easy to take them in and out. I don't transport them so I can't comment on that. Like others have said it is not easy if you want to move pages around but you can do it and it does make it nice for organizing as you fill it up. Get this if you need a lot of storage\n\n\n\n\nB000068BUI:perfect!:michael_e._zelenak\n\n\nB000068BUI\n\n\nLabtec AC10REGN 20-foot Headphone Extension Cord with Miniplug and 3.5mm Jack: Electronics\n\n\nelectronics\n\n\n7 of 7\n\n\n5.0\n\n\nPerfect!\n\n\nMay 11, 2006\n\n\nMichael E. Zelenak\n\n\nZelienople (Pittsburgh\n\n\nThis headphone extension is perfect! It is not as long as described (about 15 feet) but that's O.K. with me. The connections are tight (the plugs don't pull apart). It maintains sonic quality. It also retains it's elasticity. Using this extension cord my Grado SR-60's sound better than any wireless phones. You won't be dissapointed! \n\n\n\n\nB0007P4G7G:not_bad..._for_a_couple_bucks:andrew_cherry_\"programmer,_cg_artist\"\n\n\nB0007P4G7G\n\n\nXtreme TBCDW128 128-Disc CD/DVD Case: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nNot bad... for a couple bucks\n\n\nAugust 4, 2006\n\n\nAndrew Cherry \"Programmer, CG Artist\"\n\n\nHampton Roads, VA, USA\n\n\nThis is a pretty decent cheapo case. If you want something that you'll use every day in the car or at your DJ gigs, then you may want to get something a little sturdier. For my [archival] purposes, however, it works just fine\n\n\n\n\nB000F2PHDI:excellent_hdtv_:marc_j._levin_\"wally_the_woodworker\"\n\n\nB000F2PHDI\n\n\nSamsung HL-S6187W 61\" 1080p DLP HDTV: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n5.0\n\n\nExcellent HDTV \n\n\nNovember 22, 2006\n\n\nMarc J. Levin \"Wally the Woodworker\"\n\n\nPA\n\n\nPurchase/Service:\n\nFor $2,200 at Amazon, this was a great purchase since it would have cost over $2,700 at a retail store (including tax and shipping). Eagle arrived 10 days earlier than the Amazon estimate, unloaded the TV right to the TV stand, and after hooking it up and making sure the controls worked and the picture / sound worked, I signed for it. And, Eagle asked if I wanted the packaging carted away.\n\nVery pleased with the service part so far.....\n\nMy Set-up:\n\nS6187 61\" HDTV\nSony AV and 5.1 surround (this is not a digital unit.....I wasn't sure it would work but there are three options to hook-up a home theater: 1) Basic connection; 2) Optical; 3) HDMI)\nSony DVD\nMotorola HD-CATV box (We use Comcast)\nSony VCR\n\nExperience:\n\nAs far as the tv, the HD channels look sharp and clear, however the non HD channels aren't as sharp as my 36\" Sony CRT TV (there are several settings on the Samsung for 16:9, 4:3 and wide format; setting to 4:3 or wide for non HD channels improves the picture quality a little). But it doesn't effect the enjoyment since I purchased this for the HD, and it blows me away.\n\nThe TV controls are simple to use, and the on screen menu's are easy to navigate. \n\nThe sound out of the TV is not adequate for surround sound, but I hooked up a 5.1 reciever and it makes HD come to life.\n\nI like the fact that you can connect components to an AV and/or the TV, so there is room for 3 additional components with my set-up. \n\nI also hooked up my laptop (there is 2 connections for the video and audio on the back of the TV). After changing the video settings on the Laptop (the mannual does a nice job for XP users), it was incredible! \n\nThe camcorder hooks up to the side of the TV, so there is no need to move the unit away from the wall. It was a simple set-up, and again, the picture quality just blew me away!!!\n\nI highly recommend this TV to anyome looking for a 50\" plus TV\n\n\n\n\nB000A2BJ44:ink_toner:donald_peeples_\"jack_of_all_trades\"\n\n\nB000A2BJ44\n\n\nCanon PG-40 Black FINE Ink Cartridge: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nInk Toner\n\n\nNovember 3, 2006\n\n\nDonald Peeples \"jack of all trades\"\n\n\nPoconos\n\n\nThe ink toner I received was exactly as described and was delivered in a timely fashion. \n\n\n\n\nB000A2BJ44:evaluating_canon_pixma_ink:john_l._friedman\n\n\nB000A2BJ44\n\n\nCanon PG-40 Black FINE Ink Cartridge: Electronics\n\n\nelectronics\n\n\n4 of 5\n\n\n4.0\n\n\nEvaluating Canon PiXma Ink\n\n\nMarch 16, 2006\n\n\nJohn L. Friedman\n\n\nAZ, USA\n\n\nA very good in supply is obtained at all times without leakage of ink or smudging of the paper or clogging of the printer. The reason it is not rated 5 stars is that the ink supply is utilized too rapidly and the expensive cartriges have to replaced too frequently\n\n\n\n\nB0001BXVA4:wicked_easy_to_set_up!:av_mainiac\n\n\nB0001BXVA4\n\n\nNetgear USB Mini Print Server (PS121): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nWicked easy to set up!\n\n\nJuly 10, 2006\n\n\nAV Mainiac\n\n\nPortland, ME USA\n\n\nThis was by far the easiest computer peripheral I have ever setup! Works flawlessly with my Netgear wireless router\n\n\n\n\nB000CSDDLI:not_perfect,_but_the_best_ipod_case_i_have_found:m._hendrickx\n\n\nB000CSDDLI\n\n\niSkin eVo3 for iPod with Video 30GB (Arctic): Electronics\n\n\nelectronics\n\n\n16 of 16\n\n\n4.0\n\n\nNot perfect, but the best iPod case I have found\n\n\nFebruary 18, 2006\n\n\nM. Hendrickx\n\n\nChicago, il United States\n\n\nIt is remarkable that, even though there are hundreds of iPod cases in existence, none of them are near-perfect. I tried four different cases, both hard plastic and silicone variants, and the iskin case comes the closest, protecting the ipod but at the same time not rendering the ipod less enjoyable to use.\n\nThe case is basically a hybrid of hard plastic and soft silicone. The ipod first slips into a silicone sleeve, which covers every part of the ipod except for the earphone plug and the screen. In addition, there is a hard plastic plate that is then inserted into seems on the front of the silicone case, and this provides the screen protection. Due to the double-seam in the silicone, the plastic plate never comes in contact with the ipod, so you don't have to worry about it scratching.\n\nThe case even has a flap that covers the dock connector, preventing dust from entering, yet you can still use the universal dock without removing the case.\n\nThe most unique part of this case is that it covers the clickwheel, but the silicone is so thin that it hardly impedes the scrolling. Actually, I think it provides just enough resistance so that you will overshoot your target much less than with the bare clickwheel.\n\nThe only real downside I found with the iskin is the clip that comes with it--I would not trust it at all, so I took it off. It would be nice to have a better clip that you can actually use, and it would be really great to have an armband available for it as well, but as I said above, I have yet to find a perfect ipod case. Like most silicone cases, this one does also attract lint in your pockets, etc., but it is not as bad as other cases.\n\nUnfortunately, even though this is a great case, it is not readily available. I couldn't find it in any store, and Amazon only sells it through Marketplace sellers. I ordered from Applelinks, and they delivered it to me in four days. Whatever you do, don't order from the iSkin company directly. I have heard countless horror stories of people waiting a month for their cases. They should re-dedicate themselves to manufacturing these great iskins and let others worry about selling them\n\n\n\n\nB00005S83B:hp_57_tri-color_ink_cartridge:linda_\"linda\"\n\n\nB00005S83B\n\n\nHP 57 Tri-Color Ink Cartridge (C6657AN): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nHP 57 Tri-Color Ink Cartridge\n\n\nMarch 8, 2006\n\n\nLinda \"Linda\"\n\n\nSharps Chapel, TN\n\n\nOrdered the ink cartridge and had it within 4 days. Love the service at Amazon\n\n\n\n\nB00005T3EJ:look_no_further...:\n\n\nB00005T3EJ\n\n\nMonster Cable MB AA 1850-4 Ultra-High Capacity Rechargeable Monster PowerCells: Electronics\n\n\nelectronics\n\n\n4 of 34\n\n\n5.0\n\n\nLook no Further...\n\n\nJanuary 29, 2002\n\n\n\n\n\n\n\n\nThese are the BEST. PERIOD! Look no further.. because you will not find a better value in rechargable batteries\n\n\n\n\nB00005JAFA:stick_with_the_best:j._kaplan_\"jkaplan29\"\n\n\nB00005JAFA\n\n\nCanon BCI-6BK Black Ink Tank: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nstick with the best\n\n\nAugust 15, 2006\n\n\nJ. Kaplan \"jkaplan29\"\n\n\nElmira, N.Y.\n\n\ni used canon ink and then decided to try one of the cheap imitations. it worked well for a few months and then my photos came out of my canon i900d with color changes. after 3 conversations with canon customer care, we decided it was not the printer, but the cheaper ink. i switched back to canon, bit the bullet on the price, and everything is fine. i found decent prices on amazon, particularly at eek industries\n\n\n\n\nB0009F4ORQ:great_product:k._odom_\"reader\"\n\n\nB0009F4ORQ\n\n\nCreative Zen Nano Plus 1 GB MP3 Player Red: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n4.0\n\n\ngreat product\n\n\nNovember 3, 2006\n\n\nK. Odom \"reader\"\n\n\nOklahoma City, OK USA\n\n\nthis is a nifty very small musical device that I use when exercising. its great - holds all the songs I can listen to and is so portable. I really like i\n\n\n\n\nB0009F4ORQ:very_cute!!!!:terri\n\n\nB0009F4ORQ\n\n\nCreative Zen Nano Plus 1 GB MP3 Player Red: Electronics\n\n\nelectronics\n\n\n5 of 7\n\n\n5.0\n\n\nVery cute!!!!\n\n\nAugust 26, 2006\n\n\nTerri\n\n\nChicago, IL USA\n\n\nI was so happy with my Creative Zen Nano that I bought one as a gift. I love the color choice and the sound is great. Love the pause button. It came with a protective case that attaches to the arm band. You do not need to buy one!! Amazon delivered it in 2 days\n\n\n\n\nB0009Q5BUE:hp__photosmart_335_printer:gary_balint\n\n\nB0009Q5BUE\n\n\nPhotosmart 335 Compact Photo Printer (Q6377A#ABA): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nHP Photosmart 335 Printer\n\n\nAugust 16, 2006\n\n\nGary Balint\n\n\n\n\n\nThis printer was purchased for my wife's birthday and arrived\nin two to three days. Considering money spent the printer\nworks quite well. Pictures come out very good overall. An\nHP 95 or HP 97 cartridge is used for color photos. It contains\nthree color inks but no black. Still, photos will easily \nsatisfy the average owner. An HP 100 cartridge can be used\nfor black and white photos. Yes, the color cartridge must be\nexchanged for the black one when converting from color to\nblack and white and vise versa. Even so, we find the printer\nfun and very simple to use. Just keep in mind that the price\nof ink will soon exceed the price of the printer if used\noften. What we do is use it for special occasions or when\nthe grandchildren come over.\n\nGary \n\n\n\n\nB0007WCA4K:ideal_for_small_spaces:foyen\n\n\nB0007WCA4K\n\n\nToshiba 27AF45 27\" Flat Screen TV (Silver): Electronics\n\n\nelectronics\n\n\n0 of 4\n\n\n4.0\n\n\nIdeal for small spaces\n\n\nJune 28, 2006\n\n\nFoyen\n\n\nNY, NY\n\n\nThis is perfect for use in kitchen space and has incredible pictue quality\n\n\n\n\nB00003CWA7:simple_physics:\n\n\nB00003CWA7\n\n\nMonster Cable ILS100-2M Interlink LightSpeed 100 High-Performance Digital Fiber-Optic Cable, Toslink-to-Toslink (2 Meters): Electronics\n\n\nelectronics\n\n\n10 of 31\n\n\n4.0\n\n\nsimple physics\n\n\nJuly 24, 2000\n\n\n\n\n\n\n\n\nI'm using Monster optical cable as well, but with different reasons behind. Anybody with a basic knowledge of physics or optics should know that under normal condition light is not susceptible to interference from magnetic field or EMF\n\n\n\n\nB000F8J3A0:good_sound_quality,_good_battery_life:riemahe\n\n\nB000F8J3A0\n\n\nPanasonic KX-TG5673B 5.8 GHz FHSS GigaRange Digital Cordless Telephone with Dual Handsets: Electronics\n\n\nelectronics\n\n\n7 of 7\n\n\n5.0\n\n\nGood sound quality, good battery life\n\n\nAugust 29, 2006\n\n\nRiemahe\n\n\nDenver, CO\n\n\nSound quality is very good, and I haven't run out of battery so far. The base is somewhat smaller than what I expected from the pictures. The handsets are well designed; on my previous phone it happened that the \"mute\" function became engaged when I kept the phone between my head and my shoulders - this did not happen with these so far. Speakerphone on the base is a nice feature and makes the phone usable even during power outages. Answering machine works well. Easy to copy phonebook entries between phones.\n\n\n\n\nB00009EFRB:a_better_mousetrap:two_wheel_touring\n\n\nB00009EFRB\n\n\nTargus PA492U DEFCON VPCL - Video Port Combination Lock: Electronics\n\n\nelectronics\n\n\n3 of 5\n\n\n5.0\n\n\nA better mousetrap\n\n\nDecember 13, 2005\n\n\nTwo Wheel Touring\n\n\nNorth Carolina\n\n\nThis does exactly what it is supposed to do. It is light, easy to pack, easy to use, and probably difficult to crack. \n\n(Though would-be PC pilferers haven't tried taking a hacksaw to my cable lock so I cannot comment as to its ability to withstand saws, blow-torches, or any other criminal's tool.)\n\nOther reviewers baffled me as mine is as described by Amazon. There is neither an alarm nor batteries. Why bother with an alarm if the PC is locked with steel cable? Seems like overkill\n\n\n\n\nB000BBAKSA:excellent_webcam_-_no_video_lag_-_superior_clarity:wordchipper\n\n\nB000BBAKSA\n\n\nLogitech QuickCam Orbit MP Webcam ( 961422-0403 ): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nExcellent webcam - no video lag - superior clarity\n\n\nNovember 18, 2006\n\n\nWordchipper\n\n\nFargo, ND, USA\n\n\nI tried both Microsoft's LifeCam VX-3000 and VX-6000. Had 'em both home and ran trials with my Intel Pentium D Dual Core 3.00GHz CPU with 2 gigs of ram and, with both of those, the video lagged the audio (distractingly so). Spent the better part of an hour on the phone with a friendly Microsoft customer service rep one evening (got through on their toll free line)and he pointed me to new software for the VX-6000. Downloaded the 50 meg plus file, installed it, but the VX-6000 still produced out-of-sync video vs. audio when I created a simple 640 by 480 video file for viewing on Media Player or editing in Windows Movie Maker. \n\nSo, I talked to some technicians at our company and then talked to the webcam guru at the local assembler that built my, I believe, excellent computer. All recommended Logitech when buying webcams. I then marched back to Best Buy and traded my $99 Microsoft VX-6000 for a Logitech Quickcam Orbit MP for BB's price of $139.95. You can find this Logitech unit cheaper all over the place, including, of course, here at Amazon. The package I bought included an inexpensive headset, which I haven't used so far.\n\nAlthough I haven't tried the Orbit in a video phone call yet, from the moment I turned it on I had that satisfing good feeling that I bought a winner. Installed fairly easily with a couple of bumps. Got a \"USB composite device has not passed Windows Logo testing\" screen a couple of times..BUT..I received the SAME screen on, get this, the Microsoft cams! I just continued installing and have not experienced system problems so far.\n\nThe audio sync with video in making a recording is right on (remember, that was primary reason for taking back the Microsoft cameras) AND the picture clarity on my LCD flat panel monitor at the 640 webcam capture setting produced a gorgeous picture. Excellent detail and smoothness of video. Besides the superior audio sync with video, the Orbit MP is remarkably better than the VX-6000 and way above the $49 Microsoft VX-3000 in all other criteria too, in my opinion after comparison testing.\n\nThe Orbit's special effects are interesting and fun. The pan, tilt, etc., excellent. The face tracking is vigorous and alive - fun to watch the little Orbit camera turn - but I haven't tested in actual live conferencing yet.\n\nAll in all - for webcams, I'm now saying what more experienced webcam users told me - head for Logitech. They've certianly hooked me with their apparent superior performance. The Orbit MP is a keeper! Not going to waste anymore time searching and testing. And, my advice to Microsoft, you'd better talk to the outfit that bought your name for those Lifecams. I've relied on the Microsoft name on peripherals, keyboards, etc., and I've generally been very satisfied. But, in webcams, with me, the Microsoft brand now takes a back seat to Logitech.\n\n\n\n\n\nB000BBAKSA:great_little_cam:b._veltman\n\n\nB000BBAKSA\n\n\nLogitech QuickCam Orbit MP Webcam ( 961422-0403 ): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nGreat little cam\n\n\nAugust 9, 2006\n\n\nB. Veltman\n\n\nHampton, VA\n\n\nEasy to operate and good focus. Just a little problem with tracking for the goofy faces but overall a nice little cam\n\n\n\n\nB00079V9XG:incredibly_useful_gizmo:librarian\n\n\nB00079V9XG\n\n\nGarmin iQue M5 Integrated Pocket PC and GPS: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nIncredibly Useful Gizmo\n\n\nOctober 21, 2006\n\n\nLibrarian\n\n\nSouthfield, Michigan United States\n\n\nGarmin iQue M5\n\nI'm very pleased with my little iQue M5 GPS/PDA. While it's not perfect, it's pretty darn amazing. I had actually purchased a Lowrence iWay 350 GPS, but then my husband discovered the iQue on sale at Costco for about [...] more. With the iQue offering so many more features, I returned the Lowrence and purchased the iQue. With my husband and I both having the same machines, we can beam information to each other or send them over Bluetooth.\n\nIt has taken many hours of playing around with it to get to learn the device, as it isn't very intuitive and the manuals, especially for the PDA, are limited. My husband has spent a lot of time learning to use the GPS, and he's learning its idiosyncrasies as far as planning routes and getting it to avoid routing through construction areas. It's very sophisticated and the maps are great - better detail than the Lowrence. We purchased a 2 Gig memory card and downloaded the entire map database onto it, and there were still 300 gigs to spare. I had no trouble downloading the maps or the Active Sync software. The basic GPS functions are easy to use once you get the hang of it, the voice directions are clear and concise, and it re-routes quickly if you go off course. You can plan your route in advance without having the GPS activated. This is a good idea, since it can take up to 5 minutes for the unit to locate the satellites, and you can be on your way if you've viewed your route before starting out.\n\nI used the GPS on a trip Stratford, Canada, and it was wonderful having the Canadian maps as well as the US maps available. I even used it handheld while walking to my bed-and-breakfast from the theater. Every once in awhile there is a glitch (it said the theater was on the left, which would have taken me into the Avon River!), but it's generally right on target and I find most of the problems are operator error, such as turning too soon. The \"Route to Home\" feature is very convenient. Granted, the iQue doesn't always take you on the shortest route, but it will get you where you're going and if you're \"directionally challenged\" like I am, it becomes indispensible.\n\nThe car mounting bracket works fine. It's popped off occasionally in hot weather, but generally stays put. The bracket would be easy to move to another vehicle. The GPS would work in a cup-holder if you needed to use it in a rental car.\n\nI've spent more time learning the PDA portion, which uses Windows Mobile 2003 2nd Edition. The iQue uses Outlook as its interface, so you'll have to have it installed on your computer. Not having used Outlook before, I installed Outlook on my laptop and spent some time getting familiar with it, which was helpful. \n\nThe PDA has so many functions that it's hard to even describe what this little machine can do. It will organize all of your calendar functions and contacts, checks e-mail, accesses the Internet if you get a Wi-Fi card, recognizes handwriting, has a voice recorder, interfaces with other Bluetooth units, sends alarms, has Word and Excel, even has a couple of good games. I got a separate SD card for music and downloaded a lot of songs, and the sound through headphones is quite acceptable (it has bass boost). It uses Windows Media Player so you can organize all of your playlists. You can download any programs that are available for Windows Mobile 2003, including TV shows and videos. I never thought I'd find much use for a PDA but I'm using it every day. No more little notes on scraps of paper or a separate pocket calendar, address book or even an IPod. Everything is in one handy-dandy little machine. And even though it isn't that intuitive, once you've learned the basic conventions they translate to most of the programs.\n\nWe've called Garmin Tech Support a few times and, although they are only open during regular business hours, we found American staff who are very knowledgeable about the product. Since a good GPS will run at least [...], this was a great bargain at around [...] We've purchased screen protectors and are hoping it won't wear out too quickly, as I've heard Palm Pilots tend to do. I'm really enjoying the iQue.\n\n\n\n\n\n\nB00079V9XG:decent_gps_but_now_in_discontinued_status:debbie_b\n\n\nB00079V9XG\n\n\nGarmin iQue M5 Integrated Pocket PC and GPS: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n4.0\n\n\nDecent GPS but now in DISCONTINUED status\n\n\nJuly 16, 2006\n\n\nDebbie B\n\n\nHamilton, NJ United States\n\n\nWe bought this about a month ago from Amazon. The product itself seems fine - we used it on a vacation drive from NJ to Myrtle Beach and back. At times, it was a help (like when there was a traffic jam on 95 in VA and we needed an alternate route). However, you definitely have to keep an eye on it as it doesn't always recommend the best or shortest route. Also, it was missing certain stores, restaurants, etc. that had been around for awhile.\n\nWe liked that it is a PDA though. My husband drives limos so he wanted a mapping device for help but we hope to load some games into it. This way, when he has to wait around, he will have something to do. \n\nCONS: \n- You should be tech-savvy to get one of these set up\n- Match sure you get patches as unit will lock up\n- Manufacturer has DISCONTINUED this model. They still offer some support but be warned that this is a DISCONTINUED model.\n- Since it became DISCONTINUED, it dropped in price about $60 two weeks after I bought it which annoys me somewhat\n- Will be spending extra to get bigger memory card\n- Came with EXPIRED Outlook coupon - why bother including this?\n\nPROS\n- Nice display and controls\n- Came with mount and dock (some don't - you have to buy extra)\n- We had no trouble with the suction cup mount (had to pry it off the window with a knife one time) but agree that most placement seems to block driver view through windshield\n- Also a PD\n\n\n\n\nB000AYH0QS:vtech_model_ip811_phone:r._shwarts\n\n\nB000AYH0QS\n\n\nVTech Accessory Handset - Black/ Silver: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nVtech Model IP811 Phone\n\n\nAugust 12, 2006\n\n\nR. Shwarts\n\n\n\n\n\nThis phone was an additional phone for the Model IP811-2 phone system used for Vonage. This system has every feature that I could want, including range and quality, there are no negatives. I am highly recommending this phone and system\n\n\n\n\nB000ETXOC8:great_sound_quality_-_smokin'_video:charles_horne_\"ceh007\"\n\n\nB000ETXOC8\n\n\nSanDisk SDMX4-2048 Sansa e250 2 GB MP3 Player with SD Expansion Slot: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nGreat Sound Quality - Smokin' Video\n\n\nNovember 25, 2006\n\n\nCHARLES HORNE \"ceh007\"\n\n\nSmall Town, USA\n\n\nNot much said about the fantastic quality of the video picture. You wouldn't think that you would actually be able to watch video on a screen this small but the resolution and quality of the picture is amazing. I have shown it to an I-Pod owner and was told the video on this player is definately better than the I-Pod.\nThe unit is not perfect, it does freeze occasionally, but the re-set function has always unlocked it. The video sometimes converts in a jerky fashion also. But the conversion software was able to handle very large files. I suspect a little tweaking might fix the conversions.\nSound quality is great. Combine this with Sennheiser CX300-B Earbuds and you'll love it.\nThis is my forth MP3 type player amd the extra money over the lower end players definately yields a better product. The previous player, Creative Zen Nano Plus 1 GB MP3, quit working the week the warranty ran out.\nThumbs up on this one\n\n\n\n\nB00065H0QQ:works_well_with_garmin_gps:a._duncan\n\n\nB00065H0QQ\n\n\nSabrent - USB / serial cable - DB-9 (M) - 4 pin USB Type A (M) - 1 ft: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nWorks well with Garmin GPS\n\n\nNovember 10, 2006\n\n\nA. Duncan\n\n\n\n\n\nI purchased this cable solely to connect my GPSmap 76S to my HP laptop. So far it has performed perfectly. I have not tried to use it for other applications yet, so I can't comment on how well it would work in other situations. \n\n\n\n\nB0001DBHNA:canon_i-9900_photo_printer:d._r._ferguson_\"doctor_jazz\"\n\n\nB0001DBHNA\n\n\nCanon I-9900 Photo Printer: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nCanon I-9900 Photo Printer\n\n\nNovember 5, 2006\n\n\nD. R. Ferguson \"Doctor Jazz\"\n\n\nSpotsylvania, VA USA\n\n\nI find this printer to be better than HP or Epson as there are not a lot of bells and whistles, just a solid printer that performs as I anticipated\n\n\n\n\nB00086HTP0:i_couldn't_live_without_it:lorraine\n\n\nB00086HTP0\n\n\nCreative Zen Micro 6 GB MP3 Player Black: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nI couldn't live without it\n\n\nOctober 27, 2006\n\n\nLorraine\n\n\n\n\n\nThe zen micro is brilliant I have dropped it a load of times and it still works fine i've dropped it on the kitchen tiles twice, i've dropped it a few times in the yard and it still works perfect. I had to buy new headphones for it which made the sound quality much better. It is very easy to transfer files to it I always use windows media player to transfer files as it is easier to use than the creative software. I use it everyday and the battery lasts a good while for me.\nOverall the zen micro is a very good product\n\n\n\n\nB0006A2SNG:works_well:ian_baker\n\n\nB0006A2SNG\n\n\nFirst Alert MotionSense PIR720R Light Control: Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n5.0\n\n\nWorks well\n\n\nAugust 12, 2006\n\n\nIan Baker\n\n\nEtna, NH USA\n\n\nI bought four of these. So far they appear to work well. They do not work with compact flourescent lights well, but are fine with incandescent bulbs\n\n\n\n\nB000AYHY46:very_pleased_w/_this_monitor:c._t._wallace\n\n\nB000AYHY46\n\n\nHP F2105 21\" LCD Flat Panel Monitor: Electronics\n\n\nelectronics\n\n\n11 of 16\n\n\n5.0\n\n\nVery pleased w/ this monitor\n\n\nJanuary 1, 2006\n\n\nC. T. Wallace\n\n\nDavis, CA United States\n\n\nI bought this screen as a replacement for my 8ms 17\" Hyundai L90D+(which is a great low-priced screen for gaming), which was the replacement for a Dell fp2100 (that I gave to my wife). Anyways, this is an excellent monitor with excellent color reproduction and response times (more than enough for my flight sims). Movies are beautiful and the rather weird resolution hasn't been a problem. The design is probably the neatest looking out there (subjective I realize). The ONLY gripe I have w/ this monitor is lack of swivel (sigh). Other than that I'm very pleased with everything about this screen\n\n\n\n\nB000AYHY46:works_great_on_a_mac_as_substitute_for_apple_cinema_display:bill_atkinson_\"techhead\"\n\n\nB000AYHY46\n\n\nHP F2105 21\" LCD Flat Panel Monitor: Electronics\n\n\nelectronics\n\n\n9 of 15\n\n\n5.0\n\n\nWorks great on a Mac as substitute for Apple Cinema Display\n\n\nNovember 14, 2005\n\n\nBill Atkinson \"TechHead\"\n\n\nClear Creek, IN\n\n\nWow...just got one of these at a great price, hooked it up to my Mac G4 via a DVID cable, and it boots up at native resolution (1680 x 1050) automatically - no muss, no fuss. Text is crisp and clean\n\n\n\n\nB000EP8POE:great_features:t._toister\n\n\nB000EP8POE\n\n\nPanasonic KX-TG5621S 5.8 GHz FHSS GigaRange Digital Cordless Telephone: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nGreat features\n\n\nOctober 28, 2006\n\n\nT. Toister\n\n\nLos Angeles, CA United States\n\n\nMy favorite thing about this phone is that the caller ID captures 11 digit numbers (1-xxx-xxx-xxxx) which are then very easy to store into the phone book and call. My old phone only captured 10 digits (xxx-xxx-xxxx) which meant I had to reenter everything manually because of course you have to dial a 1 first. This new phone also allows me to toggle between 11 digits and 7 digits on the same number. I put together my phone book just by capturing calls as they came in. Very handy. As far as being a phone, it does everything well and after a couple of months I am still as happy as could be with it\n\n\n\n\nB00001W0FJ:it_rocks_man:\n\n\nB00001W0FJ\n\n\nPlantronics H51M12 Supra Monaural Headset and Modular Amplifier: Electronics\n\n\nelectronics\n\n\n1 of 23\n\n\n5.0\n\n\nIT rocks man\n\n\nOctober 1, 2000\n\n\n\n\n\n\n\n\nThis is the rockinest think i've ever seen or buyed dudes!!! check it ou\n\n\n\n\nB000CSWHCY:garmin_gpsmap_60cx:gary\n\n\nB000CSWHCY\n\n\nGarmin GPSMap 60Cx Handheld GPS Navigator: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n4.0\n\n\nGarmin GPSMap 60Cx\n\n\nNovember 13, 2006\n\n\nGary\n\n\nCT USA\n\n\nThis is my first GPS and I wanted a unit that could be used in a variety of environments such as biking, hiking, kayaking, and in the car to. I found this unit to be easy to use with a large display and convenient controls. Also the 2 times I called Garmin with questions I was able to talk to someone who is in the USA as opposed to a person in a foreign country which can be frustrating to some. On the down side to get the most out of this unit you still need to buy additional mapping software\n\n\n\n\nB000CSWHCY:out_of_the_box,_this_thing_rocks!:ryan_park\n\n\nB000CSWHCY\n\n\nGarmin GPSMap 60Cx Handheld GPS Navigator: Electronics\n\n\nelectronics\n\n\n15 of 16\n\n\n5.0\n\n\nOut of the box, this thing rocks!\n\n\nJuly 17, 2006\n\n\nRyan Park\n\n\nSalt Lake City, UT\n\n\nOk.. here's the deal. I've been an avid Magellan user for the past 3 years. I love my Magellan. I've never liked the Garmins because of their clumsy interface (the buttons placed above the display NEVER made sense to me) and how the menu structure was built. \n\nHowever.... We're planning a 2000 mile road trip from Utah up to the Oregon coast. We needed something with expandable memory so we could upload more than one state map at a time. My wife bought me the Magellan eXplorist 600 for Fathers Day. I forced myself to use it for two days (and believe me, it was torture). I could go on and on about why I sent it back. \nWhat I ordered as a replacement was the GPSMap 60Cx. Now keep in mind that I felt like someone who had turned to the dark side. \nI couldn't be happier with my decision. \n\nWhile I agree with most of the other reviews about the lack of base maps, and the expense of additional ones, this unit ROCKS! It acquires satellites within seconds. It has expandable memory. It has a color screen. It manages Geocaches. It auto-routes. It stores 1000 waypoints. It has SiRF Technology. It is a USB interface. It has a belt clip. It changes display contrast at night (automatically). It slices... It dices....\nSuddenly the Dark Side isn't so dark!!\n\nThis is a great unit that I would recommend to anyone. \n\n\n\n\nB000ETTFRG:first_mp3_player_i_ever_owned_and_not_disappointed:m._mcqueen\n\n\nB000ETTFRG\n\n\nSanDisk SDMX4-6144 Sansa e270 6 GB MP3 Player with SD Expansion Slot: Electronics\n\n\nelectronics\n\n\n9 of 9\n\n\n4.0\n\n\nFirst MP3 player I ever owned and not disappointed\n\n\nNovember 21, 2006\n\n\nM. McQueen\n\n\nKentucky\n\n\nI had been talking about wanting an MP3 player, but I did NOT want an iPod, as I heard you cannot add files in Windows format and that's what all of my music is. I received this e260 4 GB MP3 player for my birthday in June 2006, and this is the first MP3 player I have ever owned. Although there are a few little quirks, overall I am not disappointed and would recommend this MP3 player.\n\nLike many rechargeable battery-powered items, it required a lengthy initial charge before first use. After that, the future charges don't seem to take as long. The MP3 player plays for hours on end without needing a new charge. Although I've never run it completely down, I have used it continuously for about 4 hours with no battery issues.\n\nI installed the software on my computer, and the disk is where you will find the owner's manual. I was a bit irritated that there was no hard copy of the owner's manual, and just a few weeks ago it came up as an issue when my MP3 player froze and I had to turn on the computer to look up the troubleshooting guide (turns out if the player freezes you need to hold the power button down for about 15 seconds to reset it). \n\nIt came pre-loaded with 20 songs, only one of which I had ever heard of (and it was one of those no-name bands that played once on Saturday Night Live). Although my first instinct was to delete the songs to make room for my own choices, I decided to leave them on there until I need the space. About three of the songs have grown on me a little bit over the past 5 months. I currently have 517 songs on there and still have room for more.\n\nAs far as performance goes, it performs pretty well. The buttons are a little difficult to get used to, as you navigate through the functions and the song lists by scrolling a wheel. Not too bad, but when you turn on the player and it begins at the beginning of the list and you want a song that's in the middle and you have a lot of songs on there, you will be scrolling a lot. \n\nI do not use all the functions that came with this MP3 player. It has music, fm radio, photo, voice, video and a settings option. I have used the music function and the voice recorder option, both with good results. The only downside to the voice recorder is that you can't get the recordings you made off the MP3 player and onto your computer. It would have been nice to be able to record things and sync them onto your computer. As for syncing music files, I have not had any problems, although I did have one instance where it shows a song was downloaded, but when you try to play it, it skips to the next song alphabetically in the list. I don't know what caused this. Also, you can only store a song as one genre, which is a little annoying. Be sure you have your genres picked before you sync because once the song is on the MP3 player, you cannot change the genre.\n\nIt came with a headset, but I have never used it. Instead I purchased some Panasonic folding travel speakers to use with it. \n\nI do wish the MP3 player had some alternate form of charging other than needing to be connected to my computer's USB drive to charge. Another con is that a scratch of some sort appeared on the inside surface of the screen. I can't remember it being dropped or anything, so I don't know what caused this, but it's pretty noticeable. It doesn't seem to affect the operation, just the aesthetics.\n\nOverall, the cons are little irritations to me and I would recommend this player to someone who doesn't want an iPod\n\n\n\n\nB000ETTFRG:it's_not_perfect,_but_it's_very_good:jenny_b._\"loyal_amazon_customer\"\n\n\nB000ETTFRG\n\n\nSanDisk SDMX4-6144 Sansa e270 6 GB MP3 Player with SD Expansion Slot: Electronics\n\n\nelectronics\n\n\n5 of 7\n\n\n4.0\n\n\nIt's not perfect, but it's very good\n\n\nNovember 16, 2006\n\n\nJenny B. \"Loyal Amazon customer\"\n\n\nNew York, NY USA\n\n\nPros: Easy to use, last for days without charging (depending on the use, of course... but i was traveling for 5 days and used it a lot without charging, and it lasted the trip), enough memory space, has radio, works as a recorder, small\nCons: Stopped working, froze twice since i bought it 3 month ago. When it's on, tunes that i had deleted will still appear on my play list. If I hit play on such song, the player just skips and plays the next song on the list. \n\nBut overall i'm happy with my MP3. I'd recommend it\n\n\n\n\nB0007YG8JQ:best_traveling_mouse_yet:cameron_crosby_\"gadget_freak\"\n\n\nB0007YG8JQ\n\n\nAnycom Bluetooth Mouse & USB Adapter Bundle: Electronics\n\n\nelectronics\n\n\n5 of 6\n\n\n5.0\n\n\nBest traveling mouse yet\n\n\nJanuary 9, 2006\n\n\nCameron Crosby \"Gadget Freak\"\n\n\n\n\n\nI've never written a review, but I've relied heavily upon the voluntary opinions of other Amazon buyers, so I felt a really good product like the Anycom Bluetooth mouse deserves an unsolicited review.\n\nI know experience and taste vary by individual, but this mouse is awesome from my viewpoint.\n\nPros:\n\n- Bluetooth - connects immediately; provides smooth accurate tracking\n- Size - very portable; some may find it too narrow; I have a hardtime going back to a wider mouse\n- Battery life - I get at least 2 days out of it before charing and I'm a fairly heavy user\n- USB charge attachment - allows you to continue to work while recharging\n- Product Design/Quality - Nicely engineered; good quality material- not tinny - it's built to take abuse\n\nCons:\n- Haven't really found any - It took me awhile to figure out how to connect until I read the manual line by line (I don't tend to do that) then I realized it was pretty straight forward.\n\n\nIf you're looking for a quality bluetooth traveling mouse this is the one.\n\n\n\n\nB000A6LNVY:a_dandy_little_printer:christine_b\n\n\nB000A6LNVY\n\n\nHP Photosmart 8050 Printer (Q6351A#ABA): Electronics\n\n\nelectronics\n\n\n10 of 10\n\n\n5.0\n\n\nA dandy little printer\n\n\nJuly 4, 2006\n\n\nChristine B\n\n\nCanada\n\n\nI was in the market for a new inkjet and finally settled on this printer. The photo output is awesome and it's very fast!\n\nThere are only two things I would comment on that new users might not be aware of. I was ready to return the printer as I thought it was defective until I found out what the problems were.\n\n1) When the cartridges dry out, it will tell you that the cartridges are incompatible. This was confusing as I did have the correct cartridges in the printer. A \"replace cartridge\" message would have been less ambiguous.\n\n2) The printer has so far appeared \"dead\" once. When I went to turn it on, it would not do so. The solution is on HP's website - you must remove the rear door and do a hard reset. I did this and the printer worked fine again.\n\nThe printer does not come with a USB cable, but those can be easily bought for $14-20.\n\n\n\n\nB0000DC643:like_an_electronic_glove...:g._rummell\n\n\nB0000DC643\n\n\nBelkin Nostromo Speedpad n52: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n5.0\n\n\nlike an electronic glove...\n\n\nOctober 22, 2006\n\n\nG. Rummell\n\n\n\n\n\nI'm a skeptic. No, I mean I'm REALLY a skeptic... especially in today's marketplace (because nothing is built like it used to be). I don't buy anything without checking it out first, and even then, I expect that it's probably going to fail me when I do. Yeah, wonderful view there, huh?\n\nWell, needless to say, I was pleasantly (and quite happily) surprised when this device didn't fail me out of the box. I've been using my speedpad for almost a year now, and I have to say that it has been by far more solid, responsive and intuitive than I ever expected it to be. I will not play a PC game without it plugged in now, that's how well this gamepad has worked for me.\n\nHere are the ins and outs:\n\n1) Solid construction. As others have stated, this device feels very solid and is just generally \"sturdy as hell\". This, alone, surprised me. Not too many electronic devices are solidly made these days (not too many at all!)... so I'm glad to see that this one was. Belkin did an excellent job in this department.\n\n2) Software. Do yourself a favor, when you get this device home, don't bother putting in the CD that comes with it. Instead, go directly to Belkin's website first-thing, download the newer version of the software (which is near exactly the same as what's on the CD, but updated) and install that *before* plugging in your speedpad for the first time. Do that and you'll have everything updated immediately with little to no trouble at all.\n\nI've had no problems with the software for this hardware (and that's generally the part I have trouble with the most). The applications (profiler and button editor) function perfectly, and macros are a snap to program. Kudos to Belkin's programmers on writing the software suite for this, they did a good job.\n\n4) Sticky keys. Most people have experienced this.. myself included. Being possibly a byproduct of the solid construction of the product, basically, what happens is the keys stick just a little bit coming out of the box. Though just \"excercising\" the device (pressing the buttons down quickly and solidly over and over) for a few seconds after plugging it in each time I went to use it cleared up this issue within the first week of using the gamepad, and I've never had it happen since. \n\n5) Other buttons. \n* The mouse scroll wheel can be an annoyance, and I don't use it much at all because it's hard to scroll up and down with it (Belkin may just have made that particular button a little *too* solid). \n* The orange button above the d-pad also isn't that easy to press, though if you keep you pinky finger positioned on the side of the keypad for leverage, it's not impossible to press this button (in fact, I use it as my default \"map\" button in most of the games I play). It's perfect for those functions you use enough to need a button for but not enough to need an easy spam button.\n* I haven't experienced any problems hitting the 'space' button below the d-pad that others have experienced. My thumb taps that button as easily as it taps the space bar on a standard keyboard. My only complaint with it is that I can't simply twist my thumb a bit to hit the key, I have to actually lift my thumb off the d-pad to do it - and that's not much of a complaint at all, really.\n\nThe d-pad itself is a godsend! I'm a true console gamer, having used nothing but d-pads for movement since all the time spent playing my old NES as a kid (and using a joystick before that with my even older Atari). D-pad is all I know, really. In fact, the one thing that's kept me away from PC gaming all these years is I've never, ever been able to accustom myself to using arrow keys or WASD for movement (and probably never will get accustomed to that either). For the longest time I was looking for a device that could allow me to keep the sensitivity of mouse aiming (to keep up with other PC players) while giving me an actual d-pad for movement... and that is EXACTLY what this gamepad did for me. \n\nAs I said at the top of this review, I will never play a PC game without my Nostromo speedpad plugged in. This gamepad has provided the one benefit that other pads haven't and that I really needed - a controller interface that's actually functional and intuitive enough to use that I forget I'm using it most of the time, allowing me to immerse myself completely in whatever game I'm playing... which is really the point of gaming in the first place, isn't it? \n\nThe Nostromo Speedpad gets 5 stars from me. I would recommend it to anyone who's looking for the type of benefits I've listed. And, for that matter, I'm recommending it right now in fact...\n\n\n\n\nB000095SB6:sony_earbuds_are_great:michael_chellson_\"mike_c\"\n\n\nB000095SB6\n\n\nSony MDR-EX51LP Fontopia Headphones: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nSony earbuds are great\n\n\nNovember 10, 2006\n\n\nMichael Chellson \"MIke C\"\n\n\nColorado\n\n\nThe sound of these earbuds are great..and they are a value for the price.\n\nHowever the \"in the ear\" design causes them to catch sweat, and they won't last when you sweat into them alot. After 9-12 months of wearing them in the GYM they will stop working. I have gone through 3 pairs in the last 2 years\n\n\n\n\nB000I28RU0:chip_is_fine_-_fast_delivery:m._mortimer\n\n\nB000I28RU0\n\n\n1GB Kingston MicroSD TransFlash 1 GB Memory Card: Electronics\n\n\nelectronics\n\n\n2 of 3\n\n\n5.0\n\n\nChip is fine - Fast Delivery\n\n\nOctober 16, 2006\n\n\nM. Mortimer\n\n\nSan Francisco\n\n\nUsing this Kingston chip on my Pearl 8100 phone and it has worked flawlessly.\n\nAnd eSmartBuy delivered it within about 3 days of my order. Can't beat that\n\n\n\n\nB00006RVPW:stable,_fast,_and_quiet:steven_hsu\n\n\nB00006RVPW\n\n\nNetgear GS108 8-Port 10/100/1000 Copper Gigabit Ethernet Switch: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n5.0\n\n\nSTABLE, FAST, and QUIET\n\n\nMay 10, 2006\n\n\nSteven Hsu\n\n\nNY USA\n\n\nI work in a computer lab where we constantly re-image devices (using Norton Ghost). I have 3 laptops and 1 DELL GX620 desktop and all of them have Gigabit connection. When I connected those 4 to this switch and run the imaging process simultaneous, I get the same performance (0 differences) as I run 1 laptop to the GX620. This testing was done with Cat5 cables that I pick randomly from my lab. I can't wait to add more devices and using Cat6 cables to get my work done faster\n\n\n\n\nB00005UK9M:antenna_works_ok,_garmin_needs_a_better_antenna_plug:ragamuffin\n\n\nB00005UK9M\n\n\nGarmin 010-10052-05 Magnetic or Suction Mount Antenna: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n4.0\n\n\nAntenna works OK, Garmin needs a better antenna plug\n\n\nNovember 11, 2006\n\n\nragamuffin\n\n\nSeattle, WA\n\n\nThe antenna works well and obtains more satellites than the GPS units internal antenna. This easily solved the issue of poor reception when the GPS is inside of a car; however, the best accuracy I am able to obtain is 13 feet with this external antenna vs. the 9 feet using the GPS's internal antenna when in an open area. Garmin's external antenna connector plug does not attach very firmly and can disconnect easily when the units are moved around (on my GPS). I feel the antenna was worth purchasing\n\n\n\n\nB00005UK9M:magnetic_or_suction_mount_antenna:r._w._boone_\"roaming_golfer\"\n\n\nB00005UK9M\n\n\nGarmin 010-10052-05 Magnetic or Suction Mount Antenna: Electronics\n\n\nelectronics\n\n\n7 of 7\n\n\n4.0\n\n\nMagnetic or Suction Mount Antenna\n\n\nMarch 17, 2006\n\n\nR. W. Boone \"Roaming golfer\"\n\n\nCoventry, RI\n\n\nI use the Suctions Mount Antenna because I remove from sight whenever I leave my car which is idea for my purposes. The antenna does greatly improve the reception and maintain a satilite lock. The only draw back is keeping it attached to windshield in cold weather untill the car warms up\n\n\n\n\nB0007ZYU9U:fine,_but_not_what_i_expected...:otto_burgess_\"the_movie_guy\"\n\n\nB0007ZYU9U\n\n\nCoby TF-DVD8500 8.5\" TFT Portable DVD/CD/MP3 Player with Swivel Screen: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n4.0\n\n\nfine, but not what i expected...\n\n\nAugust 9, 2006\n\n\notto burgess \"the movie guy\"\n\n\nmiamisburg, ohio\n\n\nthis unit has a/v in. my thought was that one could play video games on it, or view images from a camcorder/digital camera. however, all video sent to it gets distorted to fit the wide screen. so it does not allow me to do all i wanted. it is also a bit on the loud side. if your getting this for the car i suggest the coby 7050 unit. i got one of those also, and it works much better for the kids being a tablet form factor. it also does not make much noise\n\n\n\n\nB000023VUE:good_companion_with_d-link_router_di-704:y._ho_\"yifong\"\n\n\nB000023VUE\n\n\nD-Link DSS-5+ 5-Port 10/100 Switch, Desktop: Electronics\n\n\nelectronics\n\n\n3 of 4\n\n\n5.0\n\n\nGood companion with D-Link Router DI-704\n\n\nMay 15, 2001\n\n\nY. Ho \"Yifong\"\n\n\nBudd Lake, NJ United States\n\n\nThis is a great swicth with low price. I use it together with D-Link DI-704 4-port internet sharing router to build my home network with 1 PC, two servers, one PowerMac and a laptop connected to cable modem ISP. Connecting via the swicth for internet sharing doesn't show any noticeable slow-response as compared to connecting via the router directly. It works well by itself as we as with D-Link DI-704\n\n\n\n\nB0007Q3QPS:excellent_phones:g._bergeron_\"gary\"\n\n\nB0007Q3QPS\n\n\nPanasonic KX-TG5432M 5.8 GHz Platinum Cordless Phone with Answering System and Dual Handsets: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nExcellent Phones\n\n\nNovember 13, 2006\n\n\nG. Bergeron \"Gary\"\n\n\nBaytown, Tx\n\n\nI would highly recommend this phone set. Easy to program and crystal clear reception. A definite 5 star\n\n\n\n\nB00065ANYW:excellent_card:james_g._driscoll\n\n\nB00065ANYW\n\n\nSanDisk ULTRA II HIGH PERFORMANCE 4GB (SDCFH-4096-901, Retail Package): Electronics\n\n\nelectronics\n\n\n4 of 5\n\n\n5.0\n\n\nExcellent Card\n\n\nApril 23, 2006\n\n\nJames G. Driscoll\n\n\nSanta Clara, CA United States\n\n\nWorks well with Canon 10D, 30D, and S40. Fairly fast write speed. And 4GB - pretty big (1600 JPGs in the 30D!)\n\n\n\n\nB00004TS2A:excellent_color:\"splash_62\"\n\n\nB00004TS2A\n\n\nEpson T009201 Stylus Photo Color Cartridge: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n5.0\n\n\nExcellent Color\n\n\nMarch 5, 2003\n\n\n\"splash_62\"\n\n\nMassachusetts\n\n\nFor the Epson Photo 1270, the photo quality and color are excellent. I have printed digital photos that people cannot believe come from an inkjet printer\n\n\n\n\nB000069K98:easy_to_be_up_&_running:linda_k._trier\n\n\nB000069K98\n\n\nD-Link DI-604 Cable/DSL Router, 4-Port Switch: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nEasy to be up & running\n\n\nNovember 4, 2006\n\n\nLinda K. Trier\n\n\n\n\n\nThis product was very easy to set-up. Hooked up 4 computers and had all up and running in no time. Been running 3 months with NO problems what so ever. Would highly recommend\n\n\n\n\nB00002MZ8M:student_acoustic_guitar:bharanidharan_natarajan\n\n\nB00002MZ8M\n\n\nFirst Act Discovery FG130 Student Acoustic Guitar: Electronics\n\n\nelectronics\n\n\n0 of 2\n\n\n5.0\n\n\nstudent acoustic guitar\n\n\nMarch 20, 2006\n\n\nBharanidharan Natarajan\n\n\n\n\n\nI am amazed by the quality of the guitar. The product is extremely good. The service by amazon was ultimate. Thanks a lot Amazon\n\n\n\n\nB0000ACKR5:better_than_most:s._ettinger\n\n\nB0000ACKR5\n\n\nBelkin Leather Flip Case For iPaq PDAs: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n4.0\n\n\nbetter than most\n\n\nJanuary 27, 2004\n\n\nS. Ettinger\n\n\nBoulder, CO United States\n\n\nSince there are very few cases that fit iPAQ PDAs, I recommend you get this one. Depending on type of your iPAQ (1900 series vs 3000 series), you might have varying degrees of comfort trying to shove your PDA into the case. Once you're done, however, it's very easy to use and carry. It comes with a belt clip that you may or may not use. The case itself is slim enough to fit snugly in your pocket, unlike so many other cases. The obvious disadvantage is that if you drop it on a hard surface, this case won't provide much in a way of side protection (klutzes be warned). The leather feels surprisingly good, almost a lambskin kind of quality. The credit card space is not very useful, but you can shove a couple of business cards in there. A solid 4 stars until anything better becomes available\n\n\n\n\nB00006L4XU:awesome_accessory:\"vc_george06\"\n\n\nB00006L4XU\n\n\nTargus PA215U Retractable Flat FireWire Cable: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nAwesome Accessory\n\n\nJuly 29, 2002\n\n\n\"vc_george06\"\n\n\nTexas\n\n\nThis is the perfect product for someone who travels with portable computers. You don't have to tote around long phone cords it retracts with no jamming! Excellent travel accessory\n\n\n\n\nB00079PNU6:the_sound_of_silence:cherif_elassar\n\n\nB00079PNU6\n\n\nSennheiser PXC 300 Noise Cancelling Headphones: Electronics\n\n\nelectronics\n\n\n4 of 9\n\n\n5.0\n\n\nThe Sound of Silence\n\n\nNovember 4, 2006\n\n\nCherif Elassar\n\n\n\n\n\nWorks exactly as advertised - I travel a lot and I can finally enjoy those long travel hours in planes calmly.\nWorth the \"relatively\" high price tag\n\n\n\n\nB0001F22PA:satisfied_customer:bud\n\n\nB0001F22PA\n\n\nBelkin F8V3080 TuneCast II Mobile FM Transmitter: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nSatisfied customer\n\n\nNovember 12, 2006\n\n\nBud\n\n\nIllinois\n\n\nUnit's range shorter than expected, but I feel that I got what I wanted, and paid for. Shipping and delivery were great\n\n\n\n\nB000BI2180:excellent_organizer!!:d._griblin\n\n\nB000BI2180\n\n\nPalm Z22 Handheld: Electronics\n\n\nelectronics\n\n\n4 of 4\n\n\n5.0\n\n\nExcellent organizer!!\n\n\nNovember 13, 2006\n\n\nD. Griblin\n\n\nKansas\n\n\nI was looking for a very basic palm that would become my calendar and contact list and this is perfect for both!! It's very easy to learn and enter stuff. I'm a first time PDA owner and this is an excellent product!\n\n\n\n\nB00005V8Q8:free_upgrades_at_garmin_web_site:j\n\n\nB00005V8Q8\n\n\nGarmin MapSource CD ROM (Americas BlueChart): Electronics\n\n\nelectronics\n\n\n12 of 16\n\n\n5.0\n\n\nfree upgrades at Garmin web site\n\n\nNovember 12, 2005\n\n\nJ\n\n\n\n\n\nIf you ORIGINALLY & NEWLY unlocked a NEW locked MapSource CD-ROM/DVD-ROM product ON or AFTER the release date of an update for that same product, you are eligible to unlock on that specific update what you unlocked on the original for no charge. In other words, you can update to the latest data for free! visit the garmin web site for more inf\n\n\n\n\nB0007WTHLO:the_isolation_is_great:douglas_r._dickeson_\"mooseboy\"\n\n\nB0007WTHLO\n\n\nEtymotic Research ER6iC Isolator Earphones (White): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n4.0\n\n\nThe isolation is great\n\n\nNovember 9, 2006\n\n\nDouglas R. Dickeson \"Mooseboy\"\n\n\nLincoln, NE USA\n\n\nIf you're looking for isolation from ambient noise, these are great. I use them as In Ear Monitors for my onstage use (rock band), and if I use the foam inserts, I can stand right in front of the horn section and not get my head knocked off. \n\nI've read some people complain that the bass response is not quite what they expected, but frankly it doesn't bother me a bit.\n\nThe only thing that's awkward is removing them when they're fully inserted for a while. They're rather small, and getting hold of them can be a bit tricky. I'm using small bits of tape for \"pull tabs\", and it seems to work pretty well.\n\nOverall, I'm impressed with this product since it's affordable and does exactly what it's advertised to do\n\n\n\n\nB000B9TV8W:love_it:shelley_cadamy_\"shelley\"\n\n\nB000B9TV8W\n\n\nApple MA183G/A Armband Blue for iPod Nano 1st Generation: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n4.0\n\n\nLove it\n\n\nJuly 20, 2006\n\n\nShelley Cadamy \"Shelley\"\n\n\nOklahoma City, OK United States\n\n\nI found this on clearance at another retailer (I guess not everyone likes pink), and it's fabulous. I thought it might be just for skinny-armed people, but not so! I've got plenty of room, it fits great, and it doesn't move. Still looking for a solution to keep the earbud cord out of the way, though. Makes mowing the lawn WAY more fun\n\n\n\n\nB0009VLF6S:how_to_deregister_phone:bernardo_zamora_\"el_ber\"\n\n\nB0009VLF6S\n\n\nUniden TCX805 Accessory Handset For One- or Two-Line Systems (Silver/Black): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nHow to deregister phone\n\n\nSeptember 14, 2006\n\n\nBernardo Zamora \"el_ber\"\n\n\nUSA\n\n\nThe phone works great!. I have three of these and a base. The best phones I have had. The other phones I have are the TCX800 model (previous version), equally good.\n\nI had trouble deregistering one of the phones, as it has to be close to the original base (kept telling me out of range). I found out that pressing END and #, also deregisters\n\n\n\n\nB000AY5ZIS:simple_review...:jeff_in_georgia\n\n\nB000AY5ZIS\n\n\nLogitech Cordless Desktop MX 5000 Laser (967558-0403): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nSimple Review...\n\n\nSeptember 7, 2006\n\n\nJeff in Georgia\n\n\nGeorgia, USA\n\n\nI just use the keyboard like a business user. I don't use the media stuff. I really like the tactile feel of the keyboard and how it works. I like that it's not huge and doesn't have all those extra ridiculous buttons that I never use. The rechargeable mouse is handy as well. Prior to this, I had two Microsoft wireless combos in a row, and in both cases the mouse gradually began flaking out on me. I don't know if it's because it's Logitech or it's bluetooth, but the keyboard/mouse are as responsive as were they wired (after they \"wake up\").\n\n\n\n\nB0000AI0OE:small,_inexpensive,_reliable:owen_mundy\n\n\nB0000AI0OE\n\n\nTripp lite 150W POWER INVERTER 1 OUTLET ( PV150 ): Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n5.0\n\n\nSmall, inexpensive, reliable\n\n\nOctober 4, 2005\n\n\nOwen Mundy\n\n\nSan Diego, CA\n\n\nI used the Tripp lite to charge my PowerBook and digital camera during a cross country trip to grad school. It worked great and I had no problems. The only thing I could have used was a second outlet\n\n\n\n\nB000056SPM:works_perfect_much_better_than_a_joystick:dprime\n\n\nB000056SPM\n\n\nCH Products Flight Sim Yoke USB ( 200-615 ): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nWorks perfect much better than a joystick\n\n\nOctober 12, 2006\n\n\nDPrime\n\n\nTexas\n\n\nEsactly what I was wanting. When you turn there is resistance (springs I think) and everything works great. If you use flight sims you need this product. Works awsome\n\n\n\n\nB0000V6YXQ:awesome_leather_case:n._tran_\"sleepy_med_student\"\n\n\nB0000V6YXQ\n\n\nHP Leather Belt Case for iPAQ H1900 & H2200: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nawesome leather case\n\n\nAugust 30, 2005\n\n\nN. Tran \"sleepy med student\"\n\n\nChicago, IL\n\n\nnicely designed leather case w/ belt clip. i bought this for my hp hx2415. pockets good for SD memory cards. belt clip hooks very tightly to belt, so you don't have to worry about dropping pda. \n\n\n\n\nB0000V6YXQ:excelente_producto:miguel_kingsley\n\n\nB0000V6YXQ\n\n\nHP Leather Belt Case for iPAQ H1900 & H2200: Electronics\n\n\nelectronics\n\n\n1 of 2\n\n\n5.0\n\n\nExcelente producto\n\n\nAugust 18, 2005\n\n\nMiguel Kingsley\n\n\n\n\n\nEste estuche lo compr\u001a para mi iPaq hx2415 y es muy c\u001amodo de llevar porque no es grande y adem\u001as el equipo se mantiene seguro por el dise\u001ao que tiene\n\n\n\n\nB0007QCT0G:excellent_quality_and_design:pravin_k._pant_\"pravin_pant\"\n\n\nB0007QCT0G\n\n\nMAXXUM from SwissGear by Wenger Computer Backpack Red: Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nExcellent quality and design\n\n\nNovember 7, 2006\n\n\nPravin K. Pant \"Pravin Pant\"\n\n\nJamaica Plain, MA\n\n\nThis backpack is ideal for carrying my huge science textbooks as well as keeping my MacBook safe. Its many pockets and nooks are ideal for carrying and organizing just about everything I need. As a chronically disorganized person, the bag helps me maintain some sense of organization and order in my belongings. The strap is nicely padded and it's elastic, so shocks are well absorbed.\n\nA nice touch is a zippered 'cavity' at the top, which is an ideal place to store a laptop charger. \n\nThe build quality seems excellent so far - it looks to be a very durable product. I've always had good luck with Wenger products in the past. \n\nThe best part about this backpack is that it doesn't scream 'There's a computer in here!'.\n\n\n\n\nB0007QCT0G:versatile_backback,_good_protection,_excellent_capacity:matthew_spewak\n\n\nB0007QCT0G\n\n\nMAXXUM from SwissGear by Wenger Computer Backpack Red: Electronics\n\n\nelectronics\n\n\n\n\n\n4.0\n\n\nVersatile Backback, good protection, excellent capacity\n\n\nOctober 31, 2006\n\n\nMatthew Spewak\n\n\n\n\n\nGreat all around backpack for lugging laptop and books to school. It has a fairly large capacity but it also has tension straps so you can compact it. One of the best features is the open compartment which is perfect for gym shoes or clothes\n\n\n\n\nB000F49ZHU:good_quality_phones:nora\n\n\nB000F49ZHU\n\n\nPanasonic KX-TG5623B Black 5.8 GHz FHSS GigaRange Digital Cordless Telephone with Three Handsets: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n4.0\n\n\nGood quality phones\n\n\nNovember 6, 2006\n\n\nNora\n\n\nCalifornia\n\n\nGreat sound quality; great signal quality. I can hear everyone well, and the people on the other end of the call can also hear me well. The main charging station is about 40 feet away at the opposite end of the house from my farthest charger, with a couple walls in between, and I have no problems with signal.\n\nThe handsets are smaller than I expected, but that's actually a bonus since I hadn't bought a new phone in a while and thought my old handset was too big and clunky. The handsets are about 6 inches long not including the antenna part, 1 and 7/8 inches at the widest point by the display, and 1 and 1/8 inches thick at the thickest point.\n\nDoesn't interfere with my wireless internet or any of my other electronics. I've used the phone while sitting next to my laptop, while both I and the chargers were next to TV and stereos, and with my cell phone going at the same time - no interference.\n\nThe speakerphone part isn't that great - the person on the other end can hear other sounds in the room, plus they say it sounds like I'm speaking from a great distance away. But I've never had a phone with a good speakerphone, so wasn't expecting that to work anyway.\n\nWould have been great if they'd included a headset that goes with it since the handsets do have headset jacks. I love the extra handsets that only need to be plugged into a power outlet - solved my lack of working jacks problem!!\n\nThe display lights up when it rings and when you press any of the keys. The keys are not backlit. The menus are fairly intuitive - if you've ever had a cell phone you can figure this out pretty easily. It's very easy to send phone numbers that you've programmed into one handset to all the other handsets or only to a specific handset.\n\nWhen I first hooked everything up, the phones got no dial tone, but the problem was with the phone cord that plugs into the phone jack in the wall. For some reason, the cord they provided didn't work, so when I swapped it out with the cord I had, all the phones worked fine.\n\nI'd buy these again and recommend to others to buy\n\n\n\n\nB0000A1GOF:no_complaints:michelle_collazo\n\n\nB0000A1GOF\n\n\nLogitech MX 310 Optical Mouse: Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nNo complaints\n\n\nMay 18, 2006\n\n\nMichelle Collazo\n\n\nCT & FL\n\n\nThis is definitely a comfortable mouse if you don't have big hands. It is a pretty small mouse but it contours very nicely, even if you are left-handed. The extra buttons are handy for lazy people like me who don't like using the keyboard or clicking on buttons to go forward, backward, and switch between programs. The button in the middle though (the one that lets you switch programs) is out of the way. It takes just a bit more effort to use, meaning that you would have to remove your hand from the default contour to use that button. I actually hardly ever use that button. I can't live without the forward/backward buttons though. I find myself man-handling other mice thinking they too have those neat, little buttons. The mouse has a really good response to movement as well. I've used it for gaming as well, though I admit I'm not a hardcore gamer. I really recommend this mouse. I can't imagine spending more money and getting a remarkably better mouse than this one\n\n\n\n\nB0000A1GOF:all_around_xlnt_mouse!:peter_c_h\n\n\nB0000A1GOF\n\n\nLogitech MX 310 Optical Mouse: Electronics\n\n\nelectronics\n\n\n1 of 3\n\n\n5.0\n\n\nALL AROUND XLNT MOUSE!\n\n\nMarch 1, 2006\n\n\nPeter C H\n\n\nSoCal\n\n\nPROS:\n\nxlnt quality\nlong cord\nusb or ps2 connection\nxlnt accuracy\nvery comfortable feel\neasy access to side buttons\ngreat gaming mouse\naffordable\n\nCONS:\n\nnone\n\nthis is by far the best mouse i've ever owned bar none\n\n\n\n\nB00005TQ08:great_mouse:deborah_lampkins\n\n\nB00005TQ08\n\n\nMicrosoft Intellimouse Optical Mouse: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nGreat mouse\n\n\nNovember 9, 2006\n\n\nDeborah Lampkins\n\n\nRoyal Oak MI\n\n\nI love it. You can switch back and forth from left hand to right hand and it was a great price\n\n\n\n\nB0002HS1A2:great_bang_for_your_buck!:m._presto\n\n\nB0002HS1A2\n\n\nGarmin GPS 18 Deluxe GPS Sensor with nRoute and City Select Navigation Software (USB Interface): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nGreat bang for your buck!\n\n\nJuly 30, 2006\n\n\nM. Presto\n\n\n\n\n\nThis is an excellent product. If you drive a delivery vehicle or are in route sales this product is for you. I am in route sales and use my laptop every minute of the day. I have an extensive territory and found it difficult to find certain places. The Garmin GPS 18 plugs right into the USB port and is powered by the computer so no need for additional attachments. I'll admit \"NROUTE\" took a little getting used to (for example it has a list of all the streets and names of places so if it isnt on the list you're not going to find it) but once you figure out how it works its easy as pie. Instead of spelling Hoover Street you just type Hoover and it will give you a list of the possible locations. A great buy for only 100 bucks! If you use a laptop in your vehicle and are in need of GPS, dont spend $500 on a 2 inch screen unit when you can get an awesome GPS sensor for only $100\n\n\n\n\nB000B63RIU:excellent_three_in_one_for_its_price_range:r._smith\n\n\nB000B63RIU\n\n\nHP Officejet 5610 All-in-One Printer, Copier, Scanner, Fax: Electronics\n\n\nelectronics\n\n\n9 of 10\n\n\n5.0\n\n\nExcellent three in one for its price range\n\n\nNovember 14, 2006\n\n\nR. Smith\n\n\n\n\n\nObviously, there are far better printers on the market. But if you're looking for a quality 3-in-one in the $100 to $150 range, this one seems to be at the top of the pack.\n\nThere are nicer printers out there in this price range. However, you will be severely penalized when it comes to purchasing ink. Hewlett-Packard printers tend to be the most friendly when it comes to buying refilled cartridges or refilling your own cartridges.\n\nIt prints fast, it prints well, and I enjoy the double-sided printing feature. This printer has been consistently rated by users as being accurate in its color prints, although I've done little color printing myself.\n\nThe scans are not the greatest as they leave small artifacts at regular intervals when you zoom in in Photoshop. The artifact is a vertical line which bisects the picture and offsets each side by a few pixels. They're not really noticeable to the naked eye and you won't see them unless you're editing the picture and have blown it up considerably. Still, it's probably not the best for archiving your photos. (I expect there's a bur on the plastic gear that rolls down the rack & pinion)\n\nHowever, flaws are to be expected in this price range and the listed flaws of this printer are nominal. \n\nGiven my research two months ago, and performance I've encountered after purchasing this printer, and the ease with which I was able to refill the ink cartridges; I'd say this is the best printer you can buy for $100,(as of Nov 2006) if you don't want to be extorted into paying exorbitant ink prices, for say, an Epson\n\n\n\n\nB0000C3GWV:good_product:robert_khachikyan\n\n\nB0000C3GWV\n\n\nJBL Duet PC Speakers (2-Speaker, Silver): Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\ngood product\n\n\nMarch 12, 2006\n\n\nRobert Khachikyan\n\n\nLA, CA, USA\n\n\nSince I've seen musicians use JBL speakers for their concerts, that gave me a trust in their product. With that, I became an owner to a desktop JBL pc cpeaker. This product has been satisfactory and by far I have no complaints from it\n\n\n\n\nB0000BVUZ2:perfect_for_the_frequent_traveler:michelle\n\n\nB0000BVUZ2\n\n\nTargus PA206U Mini Retractable RJ-11 Cord (Plastic Casing): Electronics\n\n\nelectronics\n\n\n1 of 1\n\n\n5.0\n\n\nPerfect for the frequent traveler\n\n\nSeptember 6, 2005\n\n\nMichelle\n\n\nMadison, WI\n\n\nNote: This is a review of the Targus PA225U Retractable Phone & Ethernet Cord (Plastic Casing). It's the rectangular model, not the oval shaped one.\n\nI'm on the road 50-75% of the time for work and have used this on almost every trip. I initially bought it because I forgot my regular network cord while traveling and needed to buy something quickly. I like to be able to move around in the hotel room and use this cord along with a cheap little plastic cable joiner to connect it to the hotel internet connection so I can sit on the couch or bed while connected to the internet. I've experimented with a travel wireless router but keep coming back to the wired connection when the wireless connection has problems. I've used it for over a year and have never had any problems with the retraction\n\n\n\n\nB0000BVUZ2:must_have!:pamelars24\n\n\nB0000BVUZ2\n\n\nTargus PA206U Mini Retractable RJ-11 Cord (Plastic Casing): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nMust Have!\n\n\nJuly 16, 2004\n\n\npamelars24\n\n\nFairfax, VA United States\n\n\nThis is wonderful for laptop users that travel or like to move your laptop around a lot. If I leave my laptop somewhere I hate having to worry about tripping over wires and wires everywhere just look bad. This is really small and compact! Perfect for traveling too. I've used it for about 8 months now and it hasnt had one problem...it still recoils well\n\n\n\n\nB0007ZFLYI:does_what_its_supposed_too_but...:aamina_masood_\"am15\"\n\n\nB0007ZFLYI\n\n\nLogitech 961400-0403 Quickcam for Notebooks Deluxe: Electronics\n\n\nelectronics\n\n\n\n\n\n5.0\n\n\nDoes what its supposed too but...\n\n\nNovember 8, 2006\n\n\nAamina Masood \"am15\"\n\n\nFrisco, TX United States\n\n\nI just got my quickcam and it was very easy to setup. I had some trouble focussing though since it is manual focus and the ring was stuck. However, with some nudging it finally loosened up and now the picture is crystal clear. It is so small and just excellent. Great work, Logitech\n\n\n\n\nB00006JHWA:canon's_best_value_for_photo_printing..._truly_amazing:d._m._hefner\n\n\nB00006JHWA\n\n\nCanon Photo Paper Plus, Glossy (7980A006, 8.5x11, 20 Sheets): Electronics\n\n\nelectronics\n\n\n2 of 2\n\n\n5.0\n\n\nCANON'S BEST VALUE FOR PHOTO PRINTING... TRULY AMAZING\n\n\nJanuary 25, 2006\n\n\nD. M. HEFNER\n\n\nWV\n\n\nI have the Canon MP500 printer and Canon Rebel XT camera - both capable of producing amazing results, but paper makes a HUUUUGE diference in photo printing. I have both the Canon Photo Plus Glossy and the Photo Pro papers. If you are looking to print out very impressive photos, then I would recommend getting the 8x10 Photo Plus Glossy - definitely nice enough to frame. And with Plus glossy, you're definitely saving $ instead of paying $$$ to have professional photos done at a professional or retail store. And I even scanned a professional photo that I had done last year with my MP500 and printed using Plus Glossy, and the print is significantly better than the original. \n\nPros: Best Value Canon has to offer. Great, great prints, my favorite paper overall. As good as Pro, and less expensive. About 55 cents per page in a 30 page pack of Pro compared to 40 cents per page in a 20 page pack of Plus.\n\nCons: None at all so far. have not seen if photos last over time\n\n\n\n\nB00006JHWA:best_paper_for_canon_printers:b._rutledge_\"sjsdad\"\n\n\nB00006JHWA\n\n\nCanon Photo Paper Plus, Glossy (7980A006, 8.5x11, 20 Sheets): Electronics\n\n\nelectronics\n\n\n17 of 18\n\n\n5.0\n\n\nBest paper for Canon printers\n\n\nFebruary 23, 2004\n\n\nB. Rutledge \"sjsdad\"\n\n\nLas Vegas, NV USA\n\n\nI tested my new Canon i960 with a variety of papers. Like most printers, the output is much better when using the manufacture's own paper (as opposed to the cheaper generic paper), but there is no need to buy the most expensive "Pro" paper as neither I nor my wife could discern ANY difference between the two. There may be a benifit to the "Pro" paper that cannot be seen (lasts longer?) but at almost twice the cost it is not worth it - this paper will produce the highest quality prints on your Canon printer (I think they look better than the ones I get from the photo lab)\n\n\n\n\nB00032Q1IK:does_its_job_well:kris_\"kristen\"\n\n\nB00032Q1IK\n\n\nLexar Media Single-Slot USB 2.0 Multi-Card Reader (Model RW023-001): Electronics\n\n\nelectronics\n\n\n3 of 3\n\n\n4.0\n\n\nDoes its job well\n\n\nApril 12, 2006\n\n\nKris \"Kristen\"\n\n\nSunrise, FL USA\n\n\nI've had this for at least 8 months now. We've used it with xD, sD, Memory Stick Pro, Pro Duo(with the adapter) and a few misc. cards that friends' cameras may be using.\n\nI've never had a problem getting cards in or out, nor any problems with reading the data on them. \n\nMy only possible complaint is that the cap for the usb connector doesn't stay on as well as I'd like or have a place to store it while the device is in use. Short of that, I can't place a single complaint about the unit. It has been a well spent $20\n\n\n\n\nB0007Q7MUI:holy_molly!!!:franklin_p._helton\n\n\nB0007Q7MUI\n\n\nHolux GM-270 - GPS receiver module: Electronics\n\n\nelectronics\n\n\n5 of 5\n\n\n5.0\n\n\nHoly Molly!!!\n\n\nSeptember 11, 2005\n\n\nFranklin P. Helton\n\n\nFort Worth, TX\n\n\nThis reciever is great! It even gets reception on 6 satelites inside my office building. I have played with many GPS units and have yet to see a reciever do that. This is a definte buy. It is extremely accurate also. I highly recomend it\n\n\n\n\nB0007Q7MUI:holux_gm-270_ultra:j._ryan\n\n\nB0007Q7MUI\n\n\nHolux GM-270 - GPS receiver module: Electronics\n\n\nelectronics\n\n\n11 of 11\n\n\n4.0\n\n\nHolux Gm-270 Ultra\n\n\nJuly 5, 2005\n\n\nJ. Ryan\n\n\nIllinois\n\n\nThe GPS works in both my Dell Axim X5 and in my HP Compaq nc6000 with a compact flash adapter card. I have used it with MS Streets and Trips and ArcPad\n\n\n" }, { "path": "nlp_class/extra_reading.txt", "content": "LEARNING THE NAIVE BAYES CLASSIFIER WITH OPTIMIZATION MODELS\nhttps://pdfs.semanticscholar.org/059c/36439a84c8d51443022352a94e2751c60d1c.pdf\n\nRANDOM FORESTS\nhttps://www.stat.berkeley.edu/~breiman/randomforest2001.pdf\n\nExplaining AdaBoost\nhttp://rob.schapire.net/papers/explaining-adaboost.pdf\n\nUnderstanding logistic regression analysis\nhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3936971/\n\nIndexing by Latent Semantic Analysis\nhttp://lsa.colorado.edu/papers/JASIS.lsi.90.pdf\n\nLanguage Modeling\nhttp://www.cs.columbia.edu/~mcollins/lm-spring2013.pdf\n\nNatural Language Processing with Python – Analyzing Text with the Natural Language Toolkit\nhttps://www.nltk.org/book/\n\nNLTK Documentation\nhttps://media.readthedocs.org/pdf/nltk/latest/nltk.pdf" }, { "path": "nlp_class/lsa.py", "content": "# Latent semantic analysis visualization for NLP class, which can be found at:\n# https://deeplearningcourses.com/c/data-science-natural-language-processing-in-python\n# https://www.udemy.com/data-science-natural-language-processing-in-python\n\n# Author: http://lazyprogrammer.me\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport nltk\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom nltk.stem import WordNetLemmatizer\nfrom sklearn.decomposition import TruncatedSVD\n\n\nwordnet_lemmatizer = WordNetLemmatizer()\n\ntitles = [line.rstrip() for line in open('all_book_titles.txt')]\n\n# copy tokenizer from sentiment example\nstopwords = set(w.rstrip() for w in open('stopwords.txt'))\n\n# note: an alternative source of stopwords\n# from nltk.corpus import stopwords\n# stopwords.words('english')\n\n# add more stopwords specific to this problem\nstopwords = stopwords.union({\n 'introduction', 'edition', 'series', 'application',\n 'approach', 'card', 'access', 'package', 'plus', 'etext',\n 'brief', 'vol', 'fundamental', 'guide', 'essential', 'printed',\n 'third', 'second', 'fourth', })\ndef my_tokenizer(s):\n s = s.lower() # downcase\n tokens = nltk.tokenize.word_tokenize(s) # split string into words (tokens)\n tokens = [t for t in tokens if len(t) > 2] # remove short words, they're probably not useful\n tokens = [wordnet_lemmatizer.lemmatize(t) for t in tokens] # put words into base form\n tokens = [t for t in tokens if t not in stopwords] # remove stopwords\n tokens = [t for t in tokens if not any(c.isdigit() for c in t)] # remove any digits, i.e. \"3rd edition\"\n return tokens\n\n\n# create a word-to-index map so that we can create our word-frequency vectors later\n# let's also save the tokenized versions so we don't have to tokenize again later\nword_index_map = {}\ncurrent_index = 0\nall_tokens = []\nall_titles = []\nindex_word_map = []\nerror_count = 0\nfor title in titles:\n try:\n title = title.encode('ascii', 'ignore').decode('utf-8') # this will throw exception if bad characters\n all_titles.append(title)\n tokens = my_tokenizer(title)\n all_tokens.append(tokens)\n for token in tokens:\n if token not in word_index_map:\n word_index_map[token] = current_index\n current_index += 1\n index_word_map.append(token)\n except Exception as e:\n print(e)\n print(title)\n error_count += 1\n\n\nprint(\"Number of errors parsing file:\", error_count, \"number of lines in file:\", len(titles))\nif error_count == len(titles):\n print(\"There is no data to do anything with! Quitting...\")\n exit()\n\n\n# now let's create our input matrices - just indicator variables for this example - works better than proportions\ndef tokens_to_vector(tokens):\n x = np.zeros(len(word_index_map))\n for t in tokens:\n i = word_index_map[t]\n x[i] = 1\n return x\n\nN = len(all_tokens)\nD = len(word_index_map)\nX = np.zeros((D, N)) # terms will go along rows, documents along columns\ni = 0\nfor tokens in all_tokens:\n X[:,i] = tokens_to_vector(tokens)\n i += 1\n\ndef main():\n svd = TruncatedSVD()\n Z = svd.fit_transform(X)\n plt.scatter(Z[:,0], Z[:,1])\n for i in range(D):\n plt.annotate(s=index_word_map[i], xy=(Z[i,0], Z[i,1]))\n plt.show()\n\nif __name__ == '__main__':\n main()\n\n" }, { "path": "nlp_class/nb.py", "content": "# Naive Bayes spam detection for NLP class, which can be found at:\n# https://deeplearningcourses.com/c/data-science-natural-language-processing-in-python\n# https://www.udemy.com/data-science-natural-language-processing-in-python\n# dataset: https://archive.ics.uci.edu/ml/datasets/Spambase\n\n# Author: http://lazyprogrammer.me\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nfrom sklearn.naive_bayes import MultinomialNB\nimport pandas as pd\nimport numpy as np\n\n# Note: technically multinomial NB is for \"counts\", but the documentation says\n# it will work for other types of \"counts\", like tf-idf, so it should\n# also work for our \"word proportions\"\n\ndata = pd.read_csv('spambase.data').values # use pandas for convenience\nnp.random.shuffle(data) # shuffle each row in-place, but preserve the row\n\nX = data[:,:48]\nY = data[:,-1]\n\n# last 100 rows will be test\nXtrain = X[:-100,]\nYtrain = Y[:-100,]\nXtest = X[-100:,]\nYtest = Y[-100:,]\n\nmodel = MultinomialNB()\nmodel.fit(Xtrain, Ytrain)\nprint(\"Classification rate for NB:\", model.score(Xtest, Ytest))\n\n\n\n##### you can use ANY model! #####\nfrom sklearn.ensemble import AdaBoostClassifier\n\nmodel = AdaBoostClassifier()\nmodel.fit(Xtrain, Ytrain)\nprint(\"Classification rate for AdaBoost:\", model.score(Xtest, Ytest))" }, { "path": "nlp_class/sentiment.py", "content": "# This code is for my NLP Udemy class, which can be found at:\n# https://deeplearningcourses.com/c/data-science-natural-language-processing-in-python\n# https://www.udemy.com/data-science-natural-language-processing-in-python\n# It is written in such a way that tells a story.\n# i.e. So you can follow a thought process of starting from a\n# simple idea, hitting an obstacle, overcoming it, etc.\n# i.e. It is not optimized for anything.\n\n# Author: http://lazyprogrammer.me\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport nltk\nimport numpy as np\nfrom sklearn.utils import shuffle\n\nfrom nltk.stem import WordNetLemmatizer\nfrom sklearn.linear_model import LogisticRegression\nfrom bs4 import BeautifulSoup\n\n\nwordnet_lemmatizer = WordNetLemmatizer()\n\n# from http://www.lextek.com/manuals/onix/stopwords1.html\nstopwords = set(w.rstrip() for w in open('stopwords.txt'))\n\n# note: an alternative source of stopwords\n# from nltk.corpus import stopwords\n# stopwords.words('english')\n\n# load the reviews\n# data courtesy of http://www.cs.jhu.edu/~mdredze/datasets/sentiment/index2.html\npositive_reviews = BeautifulSoup(open('electronics/positive.review').read(), features=\"html5lib\")\npositive_reviews = positive_reviews.findAll('review_text')\n\nnegative_reviews = BeautifulSoup(open('electronics/negative.review').read(), features=\"html5lib\")\nnegative_reviews = negative_reviews.findAll('review_text')\n\n\n\n# first let's just try to tokenize the text using nltk's tokenizer\n# let's take the first review for example:\n# t = positive_reviews[0]\n# nltk.tokenize.word_tokenize(t.text)\n#\n# notice how it doesn't downcase, so It != it\n# not only that, but do we really want to include the word \"it\" anyway?\n# you can imagine it wouldn't be any more common in a positive review than a negative review\n# so it might only add noise to our model.\n# so let's create a function that does all this pre-processing for us\n\ndef my_tokenizer(s):\n s = s.lower() # downcase\n tokens = nltk.tokenize.word_tokenize(s) # split string into words (tokens)\n tokens = [t for t in tokens if len(t) > 2] # remove short words, they're probably not useful\n tokens = [wordnet_lemmatizer.lemmatize(t) for t in tokens] # put words into base form\n tokens = [t for t in tokens if t not in stopwords] # remove stopwords\n return tokens\n\n\n# create a word-to-index map so that we can create our word-frequency vectors later\n# let's also save the tokenized versions so we don't have to tokenize again later\nword_index_map = {}\ncurrent_index = 0\npositive_tokenized = []\nnegative_tokenized = []\norig_reviews = []\n\nfor review in positive_reviews:\n orig_reviews.append(review.text)\n tokens = my_tokenizer(review.text)\n positive_tokenized.append(tokens)\n for token in tokens:\n if token not in word_index_map:\n word_index_map[token] = current_index\n current_index += 1\n\nfor review in negative_reviews:\n orig_reviews.append(review.text)\n tokens = my_tokenizer(review.text)\n negative_tokenized.append(tokens)\n for token in tokens:\n if token not in word_index_map:\n word_index_map[token] = current_index\n current_index += 1\n\nprint(\"len(word_index_map):\", len(word_index_map))\n\n# now let's create our input matrices\ndef tokens_to_vector(tokens, label):\n x = np.zeros(len(word_index_map) + 1) # last element is for the label\n for t in tokens:\n i = word_index_map[t]\n x[i] += 1\n x = x / x.sum() # normalize it before setting label\n x[-1] = label\n return x\n\nN = len(positive_tokenized) + len(negative_tokenized)\n# (N x D+1 matrix - keeping them together for now so we can shuffle more easily later\ndata = np.zeros((N, len(word_index_map) + 1))\ni = 0\nfor tokens in positive_tokenized:\n xy = tokens_to_vector(tokens, 1)\n data[i,:] = xy\n i += 1\n\nfor tokens in negative_tokenized:\n xy = tokens_to_vector(tokens, 0)\n data[i,:] = xy\n i += 1\n\n# shuffle the data and create train/test splits\n# try it multiple times!\norig_reviews, data = shuffle(orig_reviews, data)\n\nX = data[:,:-1]\nY = data[:,-1]\n\n# last 100 rows will be test\nXtrain = X[:-100,]\nYtrain = Y[:-100,]\nXtest = X[-100:,]\nYtest = Y[-100:,]\n\nmodel = LogisticRegression()\nmodel.fit(Xtrain, Ytrain)\nprint(\"Train accuracy:\", model.score(Xtrain, Ytrain))\nprint(\"Test accuracy:\", model.score(Xtest, Ytest))\n\n\n# let's look at the weights for each word\n# try it with different threshold values!\nthreshold = 0.5\nfor word, index in iteritems(word_index_map):\n weight = model.coef_[0][index]\n if weight > threshold or weight < -threshold:\n print(word, weight)\n\n\n# check misclassified examples\npreds = model.predict(X)\nP = model.predict_proba(X)[:,1] # p(y = 1 | x)\n\n# since there are many, just print the \"most\" wrong samples\nminP_whenYis1 = 1\nmaxP_whenYis0 = 0\nwrong_positive_review = None\nwrong_negative_review = None\nwrong_positive_prediction = None\nwrong_negative_prediction = None\nfor i in range(N):\n p = P[i]\n y = Y[i]\n if y == 1 and p < 0.5:\n if p < minP_whenYis1:\n wrong_positive_review = orig_reviews[i]\n wrong_positive_prediction = preds[i]\n minP_whenYis1 = p\n elif y == 0 and p > 0.5:\n if p > maxP_whenYis0:\n wrong_negative_review = orig_reviews[i]\n wrong_negative_prediction = preds[i]\n maxP_whenYis0 = p\n\nprint(\"Most wrong positive review (prob = %s, pred = %s):\" % (minP_whenYis1, wrong_positive_prediction))\nprint(wrong_positive_review)\nprint(\"Most wrong negative review (prob = %s, pred = %s):\" % (maxP_whenYis0, wrong_negative_prediction))\nprint(wrong_negative_review)\n\n" }, { "path": "nlp_class/spam2.py", "content": "# https://deeplearningcourses.com/c/data-science-natural-language-processing-in-python\n# https://www.udemy.com/data-science-natural-language-processing-in-python\n\n# Author: http://lazyprogrammer.me\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.svm import SVC\nfrom wordcloud import WordCloud\n\n\n# data from:\n# https://www.kaggle.com/uciml/sms-spam-collection-dataset\n# file contains some invalid chars\n# depending on which version of pandas you have\n# an error may be thrown\ndf = pd.read_csv('../large_files/spam.csv', encoding='ISO-8859-1')\n\n# drop unnecessary columns\ndf = df.drop([\"Unnamed: 2\", \"Unnamed: 3\", \"Unnamed: 4\"], axis=1)\n\n# rename columns to something better\ndf.columns = ['labels', 'data']\n\n# create binary labels\ndf['b_labels'] = df['labels'].map({'ham': 0, 'spam': 1})\nY = df['b_labels'].values\n\n# split up the data\ndf_train, df_test, Ytrain, Ytest = train_test_split(df['data'], Y, test_size=0.33)\n\n# try multiple ways of calculating features\ntfidf = TfidfVectorizer(decode_error='ignore')\nXtrain = tfidf.fit_transform(df_train)\nXtest = tfidf.transform(df_test)\n\n# count_vectorizer = CountVectorizer(decode_error='ignore')\n# Xtrain = count_vectorizer.fit_transform(df_train)\n# Xtest = count_vectorizer.transform(df_test)\n\n\n\n# create the model, train it, print scores\nmodel = MultinomialNB()\nmodel.fit(Xtrain, Ytrain)\nprint(\"train score:\", model.score(Xtrain, Ytrain))\nprint(\"test score:\", model.score(Xtest, Ytest))\n# exit()\n\n\n# visualize the data\ndef visualize(label):\n words = ''\n for msg in df[df['labels'] == label]['data']:\n msg = msg.lower()\n words += msg + ' '\n wordcloud = WordCloud(width=600, height=400).generate(words)\n plt.imshow(wordcloud)\n plt.axis('off')\n plt.show()\n\nvisualize('spam')\nvisualize('ham')\n\n\n# see what we're getting wrong\nX = tfidf.transform(df['data'])\ndf['predictions'] = model.predict(X)\n\n# things that should be spam\nsneaky_spam = df[(df['predictions'] == 0) & (df['b_labels'] == 1)]['data']\nfor msg in sneaky_spam:\n print(msg)\n\n# things that should not be spam\nnot_actually_spam = df[(df['predictions'] == 1) & (df['b_labels'] == 0)]['data']\nfor msg in not_actually_spam:\n print(msg)\n\n\n" }, { "path": "nlp_class/spambase.data", "content": 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}, { "path": "nlp_class/stopwords.txt", "content": "a\nabout\nabove\nacross\nafter\nagain\nagainst\nall\nalmost\nalone\nalong\nalready\nalso\nalthough\nalways\namong\nan\nand\nanother\nany\nanybody\nanyone\nanything\nanywhere\nare\narea\nareas\naround\nas\nask\nasked\nasking\nasks\nat\naway\nb\nback\nbacked\nbacking\nbacks\nbe\nbecame\nbecause\nbecome\nbecomes\nbeen\nbefore\nbegan\nbehind\nbeing\nbeings\nbest\nbetter\nbetween\nbig\nboth\nbut\nby\nc\ncame\ncan\ncannot\ncase\ncases\ncertain\ncertainly\nclear\nclearly\ncome\ncould\nd\ndid\ndiffer\ndifferent\ndifferently\ndo\ndoes\ndone\ndown\ndown\ndowned\ndowning\ndowns\nduring\ne\neach\nearly\neither\nend\nended\nending\nends\nenough\neven\nevenly\never\nevery\neverybody\neveryone\neverything\neverywhere\nf\nface\nfaces\nfact\nfacts\nfar\nfelt\nfew\nfind\nfinds\nfirst\nfor\nfour\nfrom\nfull\nfully\nfurther\nfurthered\nfurthering\nfurthers\ng\ngave\ngeneral\ngenerally\nget\ngets\ngive\ngiven\ngives\ngo\ngoing\ngood\ngoods\ngot\ngreat\ngreater\ngreatest\ngroup\ngrouped\ngrouping\ngroups\nh\nhad\nhas\nhave\nhaving\nhe\nher\nhere\nherself\nhigh\nhigh\nhigh\nhigher\nhighest\nhim\nhimself\nhis\nhow\nhowevhowevhoif\nimportant\nin\ninterest\ninterested\ninteresting\ninterests\ninto\nis\nit\nits\nitself\nj\njust\nk\nkeep\nkeeps\nkind\nknew\nknow\nknown\nknows\nl\nlarge\nlargely\nlast\nlater\nlatest\nleast\nless\nlet\nlets\nlike\nlikely\nlong\nlonger\nlongest\nm\nmade\nmake\nmaking\nman\nmany\nmay\nme\nmember\nmembers\nmen\nmight\nmore\nmost\nmostly\nmr\nmrs\nmuch\nmust\nmy\nmyself\nn\nnnnnnsary\nneed\nneeded\nneeding\nneeds\nnever\nnew\nnew\nnewer\nnewest\nnext\nno\nnobody\nnon\nnoone\nnot\nnothing\nnow\nnowhere\nnumber\nnumbers\no\nof\noff\noften\nold\nolder\noldest\non\nonce\none\nonly\nopen\nopened\nopening\nopens\nor\norder\nordered\nordering\norders\nother\nothers\nour\nout\nover\np\npart\nparted\nparting\nparts\nper\nperhaps\nplace\nplaces\npoint\npointed\npointing\npoints\npossible\npresent\npresented\npresenting\npresents\nproblem\nproblems\nput\nputs\nq\nquite\nr\nrather\nreally\nright\nright\nroom\nrooms\ns\nsaid\nsame\nsaw\nsay\nsays\nsecond\nseconds\nsee\nseem\nseemed\nseeming\nseems\nsees\nseveral\nshall\nshe\nshould\nshow\nshowed\nshowing\nshows\nside\nsides\nsince\nsmall\nsmaller\nsmallest\nso\nsome\nsomebody\nsomeone\nsomething\nsomewhere\nstate\nstates\nstill\nstill\nsuch\nsure\nt\ntake\ntaken\nthan\nthat\nthe\ntheir\nthem\nhen\nthere\ntherefore\nthese\nthey\nthing\nthings\nthink\nthinks\nthink\nthose\nthough\nthought\nthoughts\nthree\nthrough\nthus\nto\ntoday\ntogether\ntoo\ntook\ntoward\nturn\nturned\nturning\nturns\ntwo\nu\nunder\nuntil\nup\nupon\nus\nuse\nused\nuses\nv\nvery\nw\nwant\nwanted\nwanting\nwants\nwas\nway\nways\nwe\nwell\nwells\nwent\nwere\nwhat\nwhen\nwhere\nwhether\nwhich\nwhile\nwho\nwhole\nwhose\nwhy\nwill\nwith\nwithin\nwithout\nwork\nworked\nworking\nworks\nwould\nx\ny\nyear\nyears\nyet\nyyyyyyyung\nyounger\nyoungest\nyour\nyours\nz\n" }, { "path": "nlp_class2/bow_classifier.py", "content": "# Course URL:\n# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport sys\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier\nfrom gensim.models import KeyedVectors\n\n\n# data from https://www.cs.umb.edu/~smimarog/textmining/datasets/\n# alternate source: https://lazyprogrammer.me/course_files/deepnlp_classification_data.zip\ntrain = pd.read_csv('../large_files/r8-train-all-terms.txt', header=None, sep='\\t')\ntest = pd.read_csv('../large_files/r8-test-all-terms.txt', header=None, sep='\\t')\ntrain.columns = ['label', 'content']\ntest.columns = ['label', 'content']\n\n\n\nclass GloveVectorizer:\n def __init__(self):\n # load in pre-trained word vectors\n print('Loading word vectors...')\n word2vec = {}\n embedding = []\n idx2word = []\n with open('../large_files/glove.6B/glove.6B.50d.txt') as f:\n # is just a space-separated text file in the format:\n # word vec[0] vec[1] vec[2] ...\n for line in f:\n values = line.split()\n word = values[0]\n vec = np.asarray(values[1:], dtype='float32')\n word2vec[word] = vec\n embedding.append(vec)\n idx2word.append(word)\n print('Found %s word vectors.' % len(word2vec))\n\n # save for later\n self.word2vec = word2vec\n self.embedding = np.array(embedding)\n self.word2idx = {v:k for k,v in enumerate(idx2word)}\n self.V, self.D = self.embedding.shape\n\n def fit(self, data):\n pass\n\n def transform(self, data):\n X = np.zeros((len(data), self.D))\n n = 0\n emptycount = 0\n for sentence in data:\n tokens = sentence.lower().split()\n vecs = []\n for word in tokens:\n if word in self.word2vec:\n vec = self.word2vec[word]\n vecs.append(vec)\n if len(vecs) > 0:\n vecs = np.array(vecs)\n X[n] = vecs.mean(axis=0)\n else:\n emptycount += 1\n n += 1\n print(\"Numer of samples with no words found: %s / %s\" % (emptycount, len(data)))\n return X\n\n def fit_transform(self, data):\n self.fit(data)\n return self.transform(data)\n\n\n\n\nclass Word2VecVectorizer:\n def __init__(self):\n print(\"Loading in word vectors...\")\n self.word_vectors = KeyedVectors.load_word2vec_format(\n '../large_files/GoogleNews-vectors-negative300.bin',\n binary=True\n )\n print(\"Finished loading in word vectors\")\n\n def fit(self, data):\n pass\n\n def transform(self, data):\n # determine the dimensionality of vectors\n v = self.word_vectors.get_vector('king')\n self.D = v.shape[0]\n\n X = np.zeros((len(data), self.D))\n n = 0\n emptycount = 0\n for sentence in data:\n tokens = sentence.split()\n vecs = []\n m = 0\n for word in tokens:\n try:\n # throws KeyError if word not found\n vec = self.word_vectors.get_vector(word)\n vecs.append(vec)\n m += 1\n except KeyError:\n pass\n if len(vecs) > 0:\n vecs = np.array(vecs)\n X[n] = vecs.mean(axis=0)\n else:\n emptycount += 1\n n += 1\n print(\"Numer of samples with no words found: %s / %s\" % (emptycount, len(data)))\n return X\n\n\n def fit_transform(self, data):\n self.fit(data)\n return self.transform(data)\n\n\n\nvectorizer = GloveVectorizer()\n# vectorizer = Word2VecVectorizer()\nXtrain = vectorizer.fit_transform(train.content)\nYtrain = train.label\n\nXtest = vectorizer.transform(test.content)\nYtest = test.label\n\n\n\n# create the model, train it, print scores\nmodel = RandomForestClassifier(n_estimators=200)\nmodel.fit(Xtrain, Ytrain)\nprint(\"train score:\", model.score(Xtrain, Ytrain))\nprint(\"test score:\", model.score(Xtest, Ytest))\n\n\n" }, { "path": "nlp_class2/extra_reading.txt", "content": "Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global Vectors for Word Representation\nhttps://nlp.stanford.edu/pubs/glove.pdf\n\nNeural Word Embedding as Implicit Matrix Factorization\nhttp://papers.nips.cc/paper/5477-neural-word-embedding-as-implicit-matrix-factorization.pdf\n\nHierarchical Softmax\nhttp://www.iro.umontreal.ca/~lisa/pointeurs/hierarchical-nnlm-aistats05.pdf\n\nMore about Hierarchical Softmax\nhttp://papers.nips.cc/paper/3583-a-scalable-hierarchical-distributed-language-model.pdf\n\nDistributed Representations of Words and Phrases and their Compositionality\nhttps://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf" }, { "path": "nlp_class2/glove.py", "content": "# Course URL:\n# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport os\nimport json\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom datetime import datetime\nfrom sklearn.utils import shuffle\nfrom util import find_analogies\n\n\nimport sys\nsys.path.append(os.path.abspath('..'))\nfrom rnn_class.util import get_wikipedia_data\nfrom rnn_class.brown import get_sentences_with_word2idx_limit_vocab, get_sentences_with_word2idx\n\n# using ALS, what's the least # files to get correct analogies?\n# use this for word2vec training to make it faster\n# first tried 20 files --> not enough\n# how about 30 files --> some correct but still not enough\n# 40 files --> half right but 50 is better\n\nclass Glove:\n def __init__(self, D, V, context_sz):\n self.D = D\n self.V = V\n self.context_sz = context_sz\n\n def fit(self, sentences, cc_matrix=None, learning_rate=1e-4, reg=0.1, xmax=100, alpha=0.75, epochs=10, gd=False):\n # build co-occurrence matrix\n # paper calls it X, so we will call it X, instead of calling\n # the training data X\n # TODO: would it be better to use a sparse matrix?\n t0 = datetime.now()\n V = self.V\n D = self.D\n\n if not os.path.exists(cc_matrix):\n X = np.zeros((V, V))\n N = len(sentences)\n print(\"number of sentences to process:\", N)\n it = 0\n for sentence in sentences:\n it += 1\n if it % 10000 == 0:\n print(\"processed\", it, \"/\", N)\n n = len(sentence)\n for i in range(n):\n # i is not the word index!!!\n # j is not the word index!!!\n # i just points to which element of the sequence (sentence) we're looking at\n wi = sentence[i]\n\n start = max(0, i - self.context_sz)\n end = min(n, i + self.context_sz)\n\n # we can either choose only one side as context, or both\n # here we are doing both\n\n # make sure \"start\" and \"end\" tokens are part of some context\n # otherwise their f(X) will be 0 (denominator in bias update)\n if i - self.context_sz < 0:\n points = 1.0 / (i + 1)\n X[wi,0] += points\n X[0,wi] += points\n if i + self.context_sz > n:\n points = 1.0 / (n - i)\n X[wi,1] += points\n X[1,wi] += points\n\n # left side\n for j in range(start, i):\n wj = sentence[j]\n points = 1.0 / (i - j) # this is +ve\n X[wi,wj] += points\n X[wj,wi] += points\n\n # right side\n for j in range(i + 1, end):\n wj = sentence[j]\n points = 1.0 / (j - i) # this is +ve\n X[wi,wj] += points\n X[wj,wi] += points\n\n # save the cc matrix because it takes forever to create\n np.save(cc_matrix, X)\n else:\n X = np.load(cc_matrix)\n\n print(\"max in X:\", X.max())\n\n # weighting\n fX = np.zeros((V, V))\n fX[X < xmax] = (X[X < xmax] / float(xmax)) ** alpha\n fX[X >= xmax] = 1\n\n print(\"max in f(X):\", fX.max())\n\n # target\n logX = np.log(X + 1)\n\n print(\"max in log(X):\", logX.max())\n\n print(\"time to build co-occurrence matrix:\", (datetime.now() - t0))\n\n # initialize weights\n W = np.random.randn(V, D) / np.sqrt(V + D)\n b = np.zeros(V)\n U = np.random.randn(V, D) / np.sqrt(V + D)\n c = np.zeros(V)\n mu = logX.mean()\n\n\n costs = []\n sentence_indexes = range(len(sentences))\n for epoch in range(epochs):\n delta = W.dot(U.T) + b.reshape(V, 1) + c.reshape(1, V) + mu - logX\n cost = ( fX * delta * delta ).sum()\n costs.append(cost)\n print(\"epoch:\", epoch, \"cost:\", cost)\n\n if gd:\n # gradient descent method\n # update W\n # oldW = W.copy()\n for i in range(V):\n # for j in range(V):\n # W[i] -= learning_rate*fX[i,j]*(W[i].dot(U[j]) + b[i] + c[j] + mu - logX[i,j])*U[j]\n W[i] -= learning_rate*(fX[i,:]*delta[i,:]).dot(U)\n W -= learning_rate*reg*W\n # print \"updated W\"\n\n # update b\n for i in range(V):\n # for j in range(V):\n # b[i] -= learning_rate*fX[i,j]*(W[i].dot(U[j]) + b[i] + c[j] + mu - logX[i,j])\n b[i] -= learning_rate*fX[i,:].dot(delta[i,:])\n # b -= learning_rate*reg*b\n # print \"updated b\"\n\n # update U\n for j in range(V):\n # for i in range(V):\n # U[j] -= learning_rate*fX[i,j]*(W[i].dot(U[j]) + b[i] + c[j] + mu - logX[i,j])*W[i]\n U[j] -= learning_rate*(fX[:,j]*delta[:,j]).dot(W)\n U -= learning_rate*reg*U\n # print \"updated U\"\n\n # update c\n for j in range(V):\n # for i in range(V):\n # c[j] -= learning_rate*fX[i,j]*(W[i].dot(U[j]) + b[i] + c[j] + mu - logX[i,j])\n c[j] -= learning_rate*fX[:,j].dot(delta[:,j])\n # c -= learning_rate*reg*c\n # print \"updated c\"\n\n else:\n # ALS method\n\n # update W\n # fast way\n # t0 = datetime.now()\n for i in range(V):\n # matrix = reg*np.eye(D) + np.sum((fX[i,j]*np.outer(U[j], U[j]) for j in range(V)), axis=0)\n matrix = reg*np.eye(D) + (fX[i,:]*U.T).dot(U)\n # assert(np.abs(matrix - matrix2).sum() < 1e-5)\n vector = (fX[i,:]*(logX[i,:] - b[i] - c - mu)).dot(U)\n W[i] = np.linalg.solve(matrix, vector)\n # print \"fast way took:\", (datetime.now() - t0)\n\n # slow way\n # t0 = datetime.now()\n # for i in range(V):\n # matrix2 = reg*np.eye(D)\n # vector2 = 0\n # for j in range(V):\n # matrix2 += fX[i,j]*np.outer(U[j], U[j])\n # vector2 += fX[i,j]*(logX[i,j] - b[i] - c[j])*U[j]\n # print \"slow way took:\", (datetime.now() - t0)\n\n # assert(np.abs(matrix - matrix2).sum() < 1e-5)\n # assert(np.abs(vector - vector2).sum() < 1e-5)\n # W[i] = np.linalg.solve(matrix, vector)\n # print \"updated W\"\n\n # update b\n for i in range(V):\n denominator = fX[i,:].sum() + reg\n # assert(denominator > 0)\n numerator = fX[i,:].dot(logX[i,:] - W[i].dot(U.T) - c - mu)\n # for j in range(V):\n # numerator += fX[i,j]*(logX[i,j] - W[i].dot(U[j]) - c[j])\n b[i] = numerator / denominator\n # print \"updated b\"\n\n # update U\n for j in range(V):\n # matrix = reg*np.eye(D) + np.sum((fX[i,j]*np.outer(W[i], W[i]) for i in range(V)), axis=0)\n matrix = reg*np.eye(D) + (fX[:,j]*W.T).dot(W)\n # assert(np.abs(matrix - matrix2).sum() < 1e-8)\n vector = (fX[:,j]*(logX[:,j] - b - c[j] - mu)).dot(W)\n # matrix = reg*np.eye(D)\n # vector = 0\n # for i in range(V):\n # matrix += fX[i,j]*np.outer(W[i], W[i])\n # vector += fX[i,j]*(logX[i,j] - b[i] - c[j])*W[i]\n U[j] = np.linalg.solve(matrix, vector)\n # print \"updated U\"\n\n # update c\n for j in range(V):\n denominator = fX[:,j].sum() + reg\n numerator = fX[:,j].dot(logX[:,j] - W.dot(U[j]) - b - mu)\n # for i in range(V):\n # numerator += fX[i,j]*(logX[i,j] - W[i].dot(U[j]) - b[i])\n c[j] = numerator / denominator\n # print \"updated c\"\n\n self.W = W\n self.U = U\n\n plt.plot(costs)\n plt.show()\n\n def save(self, fn):\n # function word_analogies expects a (V,D) matrx and a (D,V) matrix\n arrays = [self.W, self.U.T]\n np.savez(fn, *arrays)\n\n\ndef main(we_file, w2i_file, use_brown=True, n_files=100):\n if use_brown:\n cc_matrix = \"cc_matrix_brown.npy\"\n else:\n cc_matrix = \"cc_matrix_%s.npy\" % n_files\n\n # hacky way of checking if we need to re-load the raw data or not\n # remember, only the co-occurrence matrix is needed for training\n if os.path.exists(cc_matrix):\n with open(w2i_file) as f:\n word2idx = json.load(f)\n sentences = [] # dummy - we won't actually use it\n else:\n if use_brown:\n keep_words = set([\n 'king', 'man', 'woman',\n 'france', 'paris', 'london', 'rome', 'italy', 'britain', 'england',\n 'french', 'english', 'japan', 'japanese', 'chinese', 'italian',\n 'australia', 'australian', 'december', 'november', 'june',\n 'january', 'february', 'march', 'april', 'may', 'july', 'august',\n 'september', 'october',\n ])\n sentences, word2idx = get_sentences_with_word2idx_limit_vocab(n_vocab=5000, keep_words=keep_words)\n else:\n sentences, word2idx = get_wikipedia_data(n_files=n_files, n_vocab=2000)\n \n with open(w2i_file, 'w') as f:\n json.dump(word2idx, f)\n\n V = len(word2idx)\n model = Glove(100, V, 10)\n\n # alternating least squares method\n model.fit(sentences, cc_matrix=cc_matrix, epochs=20)\n\n # gradient descent method\n # model.fit(\n # sentences,\n # cc_matrix=cc_matrix,\n # learning_rate=5e-4,\n # reg=0.1,\n # epochs=500,\n # gd=True,\n # )\n model.save(we_file)\n\n\nif __name__ == '__main__':\n we = 'glove_model_50.npz'\n w2i = 'glove_word2idx_50.json'\n # we = 'glove_model_brown.npz'\n # w2i = 'glove_word2idx_brown.json'\n main(we, w2i, use_brown=False)\n \n # load back embeddings\n npz = np.load(we)\n W1 = npz['arr_0']\n W2 = npz['arr_1']\n\n with open(w2i) as f:\n word2idx = json.load(f)\n idx2word = {i:w for w,i in word2idx.items()}\n\n for concat in (True, False):\n print(\"** concat:\", concat)\n\n if concat:\n We = np.hstack([W1, W2.T])\n else:\n We = (W1 + W2.T) / 2\n\n\n find_analogies('king', 'man', 'woman', We, word2idx, idx2word)\n find_analogies('france', 'paris', 'london', We, word2idx, idx2word)\n find_analogies('france', 'paris', 'rome', We, word2idx, idx2word)\n find_analogies('paris', 'france', 'italy', We, word2idx, idx2word)\n find_analogies('france', 'french', 'english', We, word2idx, idx2word)\n find_analogies('japan', 'japanese', 'chinese', We, word2idx, idx2word)\n find_analogies('japan', 'japanese', 'italian', We, word2idx, idx2word)\n find_analogies('japan', 'japanese', 'australian', We, word2idx, idx2word)\n find_analogies('december', 'november', 'june', We, word2idx, idx2word)\n\n" }, { "path": "nlp_class2/glove_svd.py", "content": "# Course URL:\n# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport os\nimport json\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.decomposition import TruncatedSVD\n\nfrom datetime import datetime\nfrom sklearn.utils import shuffle\nfrom util import find_analogies\n\n\nimport sys\nsys.path.append(os.path.abspath('..'))\nfrom rnn_class.util import get_wikipedia_data\nfrom rnn_class.brown import get_sentences_with_word2idx_limit_vocab, get_sentences_with_word2idx\n\n\nclass Glove:\n def __init__(self, D, V, context_sz):\n self.D = D\n self.V = V\n self.context_sz = context_sz\n\n def fit(self, sentences, cc_matrix=None):\n # build co-occurrence matrix\n # paper calls it X, so we will call it X, instead of calling\n # the training data X\n # TODO: would it be better to use a sparse matrix?\n t0 = datetime.now()\n V = self.V\n D = self.D\n\n if not os.path.exists(cc_matrix):\n X = np.zeros((V, V))\n N = len(sentences)\n print(\"number of sentences to process:\", N)\n it = 0\n for sentence in sentences:\n it += 1\n if it % 10000 == 0:\n print(\"processed\", it, \"/\", N)\n n = len(sentence)\n for i in range(n):\n # i is not the word index!!!\n # j is not the word index!!!\n # i just points to which element of the sequence (sentence) we're looking at\n wi = sentence[i]\n\n start = max(0, i - self.context_sz)\n end = min(n, i + self.context_sz)\n\n # we can either choose only one side as context, or both\n # here we are doing both\n\n # make sure \"start\" and \"end\" tokens are part of some context\n # otherwise their f(X) will be 0 (denominator in bias update)\n if i - self.context_sz < 0:\n points = 1.0 / (i + 1)\n X[wi,0] += points\n X[0,wi] += points\n if i + self.context_sz > n:\n points = 1.0 / (n - i)\n X[wi,1] += points\n X[1,wi] += points\n\n # left side\n for j in range(start, i):\n wj = sentence[j]\n points = 1.0 / (i - j) # this is +ve\n X[wi,wj] += points\n X[wj,wi] += points\n\n # right side\n for j in range(i + 1, end):\n wj = sentence[j]\n points = 1.0 / (j - i) # this is +ve\n X[wi,wj] += points\n X[wj,wi] += points\n\n # save the cc matrix because it takes forever to create\n np.save(cc_matrix, X)\n else:\n X = np.load(cc_matrix)\n\n print(\"max in X:\", X.max())\n\n # target\n logX = np.log(X + 1)\n\n print(\"max in log(X):\", logX.max())\n\n print(\"time to build co-occurrence matrix:\", (datetime.now() - t0))\n\n # subtract global mean\n mu = logX.mean()\n\n model = TruncatedSVD(n_components=D)\n Z = model.fit_transform(logX - mu)\n S = np.diag(model.explained_variance_)\n Sinv = np.linalg.inv(S)\n self.W = Z.dot(Sinv)\n self.U = model.components_.T\n\n # calculate cost once\n delta = self.W.dot(S).dot(self.U.T) + mu - logX\n cost = (delta * delta).sum()\n print(\"svd cost:\", cost)\n\n def save(self, fn):\n # function word_analogies expects a (V,D) matrx and a (D,V) matrix\n arrays = [self.W, self.U.T]\n np.savez(fn, *arrays)\n\n\ndef main(we_file, w2i_file, use_brown=True, n_files=100):\n if use_brown:\n cc_matrix = \"cc_matrix_brown.npy\"\n else:\n cc_matrix = \"cc_matrix_%s.npy\" % n_files\n\n # hacky way of checking if we need to re-load the raw data or not\n # remember, only the co-occurrence matrix is needed for training\n if os.path.exists(cc_matrix):\n with open(w2i_file) as f:\n word2idx = json.load(f)\n sentences = [] # dummy - we won't actually use it\n else:\n if use_brown:\n keep_words = set([\n 'king', 'man', 'woman',\n 'france', 'paris', 'london', 'rome', 'italy', 'britain', 'england',\n 'french', 'english', 'japan', 'japanese', 'chinese', 'italian',\n 'australia', 'australian', 'december', 'november', 'june',\n 'january', 'february', 'march', 'april', 'may', 'july', 'august',\n 'september', 'october',\n ])\n sentences, word2idx = get_sentences_with_word2idx_limit_vocab(n_vocab=5000, keep_words=keep_words)\n else:\n sentences, word2idx = get_wikipedia_data(n_files=n_files, n_vocab=2000)\n \n with open(w2i_file, 'w') as f:\n json.dump(word2idx, f)\n\n V = len(word2idx)\n model = Glove(100, V, 10)\n\n # alternating least squares method\n model.fit(sentences, cc_matrix=cc_matrix)\n model.save(we_file)\n\n\nif __name__ == '__main__':\n we = 'glove_svd_50.npz'\n w2i = 'glove_word2idx_50.json'\n # we = 'glove_svd_brown.npz'\n # w2i = 'glove_word2idx_brown.json'\n main(we, w2i, use_brown=False)\n \n # load back embeddings\n npz = np.load(we)\n W1 = npz['arr_0']\n W2 = npz['arr_1']\n\n with open(w2i) as f:\n word2idx = json.load(f)\n idx2word = {i:w for w,i in word2idx.items()}\n\n for concat in (True, False):\n print(\"** concat:\", concat)\n\n if concat:\n We = np.hstack([W1, W2.T])\n else:\n We = (W1 + W2.T) / 2\n\n\n find_analogies('king', 'man', 'woman', We, word2idx, idx2word)\n find_analogies('france', 'paris', 'london', We, word2idx, idx2word)\n find_analogies('france', 'paris', 'rome', We, word2idx, idx2word)\n find_analogies('paris', 'france', 'italy', We, word2idx, idx2word)\n find_analogies('france', 'french', 'english', We, word2idx, idx2word)\n find_analogies('japan', 'japanese', 'chinese', We, word2idx, idx2word)\n find_analogies('japan', 'japanese', 'italian', We, word2idx, idx2word)\n find_analogies('japan', 'japanese', 'australian', We, word2idx, idx2word)\n find_analogies('december', 'november', 'june', We, word2idx, idx2word)\n\n" }, { "path": "nlp_class2/glove_tf.py", "content": "# Course URL:\n# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport os\nimport json\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\n\nfrom datetime import datetime\nfrom sklearn.utils import shuffle\nfrom util import find_analogies\n\nimport sys\nsys.path.append(os.path.abspath('..'))\nfrom rnn_class.util import get_wikipedia_data\nfrom rnn_class.brown import get_sentences_with_word2idx_limit_vocab, get_sentences_with_word2idx\n\nif tf.__version__.startswith('2'):\n tf.compat.v1.disable_eager_execution()\n\n\n\nclass Glove:\n def __init__(self, D, V, context_sz):\n self.D = D\n self.V = V\n self.context_sz = context_sz\n\n def fit(self, sentences, cc_matrix=None, learning_rate=1e-4, reg=0.1, xmax=100, alpha=0.75, epochs=10):\n # build co-occurrence matrix\n # paper calls it X, so we will call it X, instead of calling\n # the training data X\n # TODO: would it be better to use a sparse matrix?\n t0 = datetime.now()\n V = self.V\n D = self.D\n\n if not os.path.exists(cc_matrix):\n X = np.zeros((V, V))\n N = len(sentences)\n print(\"number of sentences to process:\", N)\n it = 0\n for sentence in sentences:\n it += 1\n if it % 10000 == 0:\n print(\"processed\", it, \"/\", N)\n n = len(sentence)\n for i in range(n):\n # i is not the word index!!!\n # j is not the word index!!!\n # i just points to which element of the sequence (sentence) we're looking at\n wi = sentence[i]\n\n start = max(0, i - self.context_sz)\n end = min(n, i + self.context_sz)\n\n # we can either choose only one side as context, or both\n # here we are doing both\n\n # make sure \"start\" and \"end\" tokens are part of some context\n # otherwise their f(X) will be 0 (denominator in bias update)\n if i - self.context_sz < 0:\n points = 1.0 / (i + 1)\n X[wi,0] += points\n X[0,wi] += points\n if i + self.context_sz > n:\n points = 1.0 / (n - i)\n X[wi,1] += points\n X[1,wi] += points\n\n # left side\n for j in range(start, i):\n wj = sentence[j]\n points = 1.0 / (i - j) # this is +ve\n X[wi,wj] += points\n X[wj,wi] += points\n\n # right side\n for j in range(i + 1, end):\n wj = sentence[j]\n points = 1.0 / (j - i) # this is +ve\n X[wi,wj] += points\n X[wj,wi] += points\n\n # save the cc matrix because it takes forever to create\n np.save(cc_matrix, X)\n else:\n X = np.load(cc_matrix)\n\n print(\"max in X:\", X.max())\n\n # weighting\n fX = np.zeros((V, V))\n fX[X < xmax] = (X[X < xmax] / float(xmax)) ** alpha\n fX[X >= xmax] = 1\n\n print(\"max in f(X):\", fX.max())\n\n # target\n logX = np.log(X + 1)\n\n print(\"max in log(X):\", logX.max())\n\n print(\"time to build co-occurrence matrix:\", (datetime.now() - t0))\n\n # initialize weights\n W = np.random.randn(V, D) / np.sqrt(V + D)\n b = np.zeros(V)\n U = np.random.randn(V, D) / np.sqrt(V + D)\n c = np.zeros(V)\n mu = logX.mean()\n\n # initialize weights, inputs, targets placeholders\n tfW = tf.Variable(W.astype(np.float32))\n tfb = tf.Variable(b.reshape(V, 1).astype(np.float32))\n tfU = tf.Variable(U.astype(np.float32))\n tfc = tf.Variable(c.reshape(1, V).astype(np.float32))\n tfLogX = tf.compat.v1.placeholder(tf.float32, shape=(V, V))\n tffX = tf.compat.v1.placeholder(tf.float32, shape=(V, V))\n\n delta = tf.matmul(tfW, tf.transpose(a=tfU)) + tfb + tfc + mu - tfLogX\n cost = tf.reduce_sum(input_tensor=tffX * delta * delta)\n regularized_cost = cost\n for param in (tfW, tfU):\n regularized_cost += reg*tf.reduce_sum(input_tensor=param * param)\n\n train_op = tf.compat.v1.train.MomentumOptimizer(\n learning_rate,\n momentum=0.9\n ).minimize(regularized_cost)\n # train_op = tf.train.AdamOptimizer(1e-3).minimize(regularized_cost)\n init = tf.compat.v1.global_variables_initializer()\n session = tf.compat.v1.InteractiveSession()\n session.run(init)\n\n costs = []\n sentence_indexes = range(len(sentences))\n for epoch in range(epochs):\n c, _ = session.run((cost, train_op), feed_dict={tfLogX: logX, tffX: fX})\n print(\"epoch:\", epoch, \"cost:\", c)\n costs.append(c)\n\n # save for future calculations\n self.W, self.U = session.run([tfW, tfU])\n\n plt.plot(costs)\n plt.show()\n\n def save(self, fn):\n # function word_analogies expects a (V,D) matrx and a (D,V) matrix\n arrays = [self.W, self.U.T]\n np.savez(fn, *arrays)\n\n\ndef main(we_file, w2i_file, use_brown=True, n_files=50):\n if use_brown:\n cc_matrix = \"cc_matrix_brown.npy\"\n else:\n cc_matrix = \"cc_matrix_%s.npy\" % n_files\n\n # hacky way of checking if we need to re-load the raw data or not\n # remember, only the co-occurrence matrix is needed for training\n if os.path.exists(cc_matrix):\n with open(w2i_file) as f:\n word2idx = json.load(f)\n sentences = [] # dummy - we won't actually use it\n else:\n if use_brown:\n keep_words = set([\n 'king', 'man', 'woman',\n 'france', 'paris', 'london', 'rome', 'italy', 'britain', 'england',\n 'french', 'english', 'japan', 'japanese', 'chinese', 'italian',\n 'australia', 'australian', 'december', 'november', 'june',\n 'january', 'february', 'march', 'april', 'may', 'july', 'august',\n 'september', 'october',\n ])\n sentences, word2idx = get_sentences_with_word2idx_limit_vocab(n_vocab=5000, keep_words=keep_words)\n else:\n sentences, word2idx = get_wikipedia_data(n_files=n_files, n_vocab=2000)\n \n with open(w2i_file, 'w') as f:\n json.dump(word2idx, f)\n\n V = len(word2idx)\n model = Glove(100, V, 10)\n model.fit(sentences, cc_matrix=cc_matrix, epochs=200)\n model.save(we_file)\n\n\nif __name__ == '__main__':\n we = 'glove_model_50.npz'\n w2i = 'glove_word2idx_50.json'\n main(we, w2i, use_brown=False)\n\n # load back embeddings\n npz = np.load(we)\n W1 = npz['arr_0']\n W2 = npz['arr_1']\n\n with open(w2i) as f:\n word2idx = json.load(f)\n idx2word = {i:w for w,i in word2idx.items()}\n\n for concat in (True, False):\n print(\"** concat:\", concat)\n\n if concat:\n We = np.hstack([W1, W2.T])\n else:\n We = (W1 + W2.T) / 2\n\n\n find_analogies('king', 'man', 'woman', We, word2idx, idx2word)\n find_analogies('france', 'paris', 'london', We, word2idx, idx2word)\n find_analogies('france', 'paris', 'rome', We, word2idx, idx2word)\n find_analogies('paris', 'france', 'italy', We, word2idx, idx2word)\n find_analogies('france', 'french', 'english', We, word2idx, idx2word)\n find_analogies('japan', 'japanese', 'chinese', We, word2idx, idx2word)\n find_analogies('japan', 'japanese', 'italian', We, word2idx, idx2word)\n find_analogies('japan', 'japanese', 'australian', We, word2idx, idx2word)\n find_analogies('december', 'november', 'june', We, word2idx, idx2word)\n" }, { "path": "nlp_class2/glove_theano.py", "content": "# Course URL:\n# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport os\nimport json\nimport numpy as np\nimport theano\nimport theano.tensor as T\nimport matplotlib.pyplot as plt\n\nfrom datetime import datetime\nfrom sklearn.utils import shuffle\nfrom util import find_analogies\n\nimport sys\nsys.path.append(os.path.abspath('..'))\nfrom rnn_class.util import get_wikipedia_data\nfrom rnn_class.brown import get_sentences_with_word2idx_limit_vocab, get_sentences_with_word2idx\n\n\ndef momentum_updates(cost, params, lr=1e-4, mu=0.9):\n grads = T.grad(cost, params)\n velocities = [theano.shared(\n np.zeros_like(p.get_value()).astype(np.float32)\n ) for p in params]\n updates = []\n for p, v, g in zip(params, velocities, grads):\n newv = mu*v - lr*g\n newp = p + newv\n updates.append((p, newp))\n updates.append((v, newv))\n return updates\n\n\nclass Glove:\n def __init__(self, D, V, context_sz):\n self.D = D\n self.V = V\n self.context_sz = context_sz\n\n def fit(self, sentences, cc_matrix=None, learning_rate=1e-4, reg=0.1, xmax=100, alpha=0.75, epochs=10, gd=False, use_theano=False, use_tensorflow=False):\n # build co-occurrence matrix\n # paper calls it X, so we will call it X, instead of calling\n # the training data X\n # TODO: would it be better to use a sparse matrix?\n t0 = datetime.now()\n V = self.V\n D = self.D\n\n if not os.path.exists(cc_matrix):\n X = np.zeros((V, V))\n N = len(sentences)\n print(\"number of sentences to process:\", N)\n it = 0\n for sentence in sentences:\n it += 1\n if it % 10000 == 0:\n print(\"processed\", it, \"/\", N)\n n = len(sentence)\n for i in range(n):\n # i is not the word index!!!\n # j is not the word index!!!\n # i just points to which element of the sequence (sentence) we're looking at\n wi = sentence[i]\n\n start = max(0, i - self.context_sz)\n end = min(n, i + self.context_sz)\n\n # we can either choose only one side as context, or both\n # here we are doing both\n\n # make sure \"start\" and \"end\" tokens are part of some context\n # otherwise their f(X) will be 0 (denominator in bias update)\n if i - self.context_sz < 0:\n points = 1.0 / (i + 1)\n X[wi,0] += points\n X[0,wi] += points\n if i + self.context_sz > n:\n points = 1.0 / (n - i)\n X[wi,1] += points\n X[1,wi] += points\n\n # left side\n for j in range(start, i):\n wj = sentence[j]\n points = 1.0 / (i - j) # this is +ve\n X[wi,wj] += points\n X[wj,wi] += points\n\n # right side\n for j in range(i + 1, end):\n wj = sentence[j]\n points = 1.0 / (j - i) # this is +ve\n X[wi,wj] += points\n X[wj,wi] += points\n\n # save the cc matrix because it takes forever to create\n np.save(cc_matrix, X)\n else:\n X = np.load(cc_matrix)\n\n print(\"max in X:\", X.max())\n\n # weighting\n fX = np.zeros((V, V))\n fX[X < xmax] = (X[X < xmax] / float(xmax)) ** alpha\n fX[X >= xmax] = 1\n\n print(\"max in f(X):\", fX.max())\n\n # target\n logX = np.log(X + 1)\n\n # cast\n fX = fX.astype(np.float32)\n logX = logX.astype(np.float32)\n\n print(\"max in log(X):\", logX.max())\n\n print(\"time to build co-occurrence matrix:\", (datetime.now() - t0))\n\n # initialize weights\n W = np.random.randn(V, D) / np.sqrt(V + D)\n b = np.zeros(V)\n U = np.random.randn(V, D) / np.sqrt(V + D)\n c = np.zeros(V)\n mu = logX.mean()\n\n # initialize weights, inputs, targets placeholders\n thW = theano.shared(W.astype(np.float32))\n thb = theano.shared(b.astype(np.float32))\n thU = theano.shared(U.astype(np.float32))\n thc = theano.shared(c.astype(np.float32))\n thLogX = T.matrix('logX')\n thfX = T.matrix('fX')\n\n params = [thW, thb, thU, thc]\n\n thDelta = thW.dot(thU.T) + T.reshape(thb, (V, 1)) + T.reshape(thc, (1, V)) + mu - thLogX\n thCost = ( thfX * thDelta * thDelta ).sum()\n\n # regularization\n regularized_cost = thCost + reg*((thW * thW).sum() + (thU * thU).sum())\n\n updates = momentum_updates(regularized_cost, params, learning_rate)\n\n train_op = theano.function(\n inputs=[thfX, thLogX],\n updates=updates,\n )\n\n cost_op = theano.function(inputs=[thfX, thLogX], outputs=thCost)\n\n costs = []\n sentence_indexes = range(len(sentences))\n for epoch in range(epochs):\n train_op(fX, logX)\n cost = cost_op(fX, logX)\n costs.append(cost)\n print(\"epoch:\", epoch, \"cost:\", cost)\n\n\n self.W = thW.get_value()\n self.U = thU.get_value()\n\n plt.plot(costs)\n plt.show()\n\n def save(self, fn):\n # function word_analogies expects a (V,D) matrx and a (D,V) matrix\n arrays = [self.W, self.U.T]\n np.savez(fn, *arrays)\n\n\ndef main(we_file, w2i_file, use_brown=True, n_files=50):\n if use_brown:\n cc_matrix = \"cc_matrix_brown.npy\"\n else:\n cc_matrix = \"cc_matrix_%s.npy\" % n_files\n\n # hacky way of checking if we need to re-load the raw data or not\n # remember, only the co-occurrence matrix is needed for training\n if os.path.exists(cc_matrix):\n with open(w2i_file) as f:\n word2idx = json.load(f)\n sentences = [] # dummy - we won't actually use it\n else:\n if use_brown:\n keep_words = set([\n 'king', 'man', 'woman',\n 'france', 'paris', 'london', 'rome', 'italy', 'britain', 'england',\n 'french', 'english', 'japan', 'japanese', 'chinese', 'italian',\n 'australia', 'australian', 'december', 'november', 'june',\n 'january', 'february', 'march', 'april', 'may', 'july', 'august',\n 'september', 'october',\n ])\n sentences, word2idx = get_sentences_with_word2idx_limit_vocab(n_vocab=5000, keep_words=keep_words)\n else:\n sentences, word2idx = get_wikipedia_data(n_files=n_files, n_vocab=2000)\n \n with open(w2i_file, 'w') as f:\n json.dump(word2idx, f)\n\n V = len(word2idx)\n model = Glove(100, V, 10)\n model.fit(\n sentences,\n cc_matrix=cc_matrix,\n learning_rate=1e-4,\n reg=0.1,\n epochs=200,\n )\n model.save(we_file)\n\n\nif __name__ == '__main__':\n we = 'glove_model_50.npz'\n w2i = 'glove_word2idx_50.json'\n # we = 'glove_model_brown.npz'\n # w2i = 'glove_word2idx_brown.json'\n main(we, w2i, use_brown=False)\n \n # load back embeddings\n npz = np.load(we)\n W1 = npz['arr_0']\n W2 = npz['arr_1']\n\n with open(w2i) as f:\n word2idx = json.load(f)\n idx2word = {i:w for w,i in word2idx.items()}\n\n for concat in (True, False):\n print(\"** concat:\", concat)\n\n if concat:\n We = np.hstack([W1, W2.T])\n else:\n We = (W1 + W2.T) / 2\n\n\n find_analogies('king', 'man', 'woman', We, word2idx, idx2word)\n find_analogies('france', 'paris', 'london', We, word2idx, idx2word)\n find_analogies('france', 'paris', 'rome', We, word2idx, idx2word)\n find_analogies('paris', 'france', 'italy', We, word2idx, idx2word)\n find_analogies('france', 'french', 'english', We, word2idx, idx2word)\n find_analogies('japan', 'japanese', 'chinese', We, word2idx, idx2word)\n find_analogies('japan', 'japanese', 'italian', We, word2idx, idx2word)\n find_analogies('japan', 'japanese', 'australian', We, word2idx, idx2word)\n find_analogies('december', 'november', 'june', We, word2idx, idx2word)\n" }, { "path": "nlp_class2/logistic.py", "content": "# Course URL:\n# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport random\nfrom datetime import datetime\n\nimport os\nimport sys\nsys.path.append(os.path.abspath('..'))\nfrom rnn_class.util import get_wikipedia_data\nfrom rnn_class.brown import get_sentences_with_word2idx_limit_vocab, get_sentences_with_word2idx\n\nfrom markov import get_bigram_probs\n\n\nif __name__ == '__main__':\n # load in the data\n # note: sentences are already converted to sequences of word indexes\n # note: you can limit the vocab size if you run out of memory\n sentences, word2idx = get_sentences_with_word2idx_limit_vocab(2000)\n # sentences, word2idx = get_sentences_with_word2idx()\n\n # vocab size\n V = len(word2idx)\n print(\"Vocab size:\", V)\n\n # we will also treat beginning of sentence and end of sentence as bigrams\n # START -> first word\n # last word -> END\n start_idx = word2idx['START']\n end_idx = word2idx['END']\n\n\n # a matrix where:\n # row = last word\n # col = current word\n # value at [row, col] = p(current word | last word)\n bigram_probs = get_bigram_probs(sentences, V, start_idx, end_idx, smoothing=0.1)\n\n\n # train a logistic model\n W = np.random.randn(V, V) / np.sqrt(V)\n\n losses = []\n epochs = 1\n lr = 1e-1\n \n def softmax(a):\n a = a - a.max()\n exp_a = np.exp(a)\n return exp_a / exp_a.sum(axis=1, keepdims=True)\n\n # what is the loss if we set W = log(bigram_probs)?\n W_bigram = np.log(bigram_probs)\n bigram_losses = []\n\n\n t0 = datetime.now()\n for epoch in range(epochs):\n # shuffle sentences at each epoch\n random.shuffle(sentences)\n\n j = 0 # keep track of iterations\n for sentence in sentences:\n # convert sentence into one-hot encoded inputs and targets\n sentence = [start_idx] + sentence + [end_idx]\n n = len(sentence)\n inputs = np.zeros((n - 1, V))\n targets = np.zeros((n - 1, V))\n inputs[np.arange(n - 1), sentence[:n-1]] = 1\n targets[np.arange(n - 1), sentence[1:]] = 1\n\n # get output predictions\n predictions = softmax(inputs.dot(W))\n\n # do a gradient descent step\n W = W - lr * inputs.T.dot(predictions - targets)\n\n # keep track of the loss\n loss = -np.sum(targets * np.log(predictions)) / (n - 1)\n losses.append(loss)\n\n # keep track of the bigram loss\n # only do it for the first epoch to avoid redundancy\n if epoch == 0:\n bigram_predictions = softmax(inputs.dot(W_bigram))\n bigram_loss = -np.sum(targets * np.log(bigram_predictions)) / (n - 1)\n bigram_losses.append(bigram_loss)\n\n\n if j % 10 == 0:\n print(\"epoch:\", epoch, \"sentence: %s/%s\" % (j, len(sentences)), \"loss:\", loss)\n j += 1\n\n print(\"Elapsed time training:\", datetime.now() - t0)\n plt.plot(losses)\n\n # plot a horizontal line for the bigram loss\n avg_bigram_loss = np.mean(bigram_losses)\n print(\"avg_bigram_loss:\", avg_bigram_loss)\n plt.axhline(y=avg_bigram_loss, color='r', linestyle='-')\n\n\n # plot smoothed losses to reduce variability\n def smoothed_loss(x, decay=0.99):\n y = np.zeros(len(x))\n last = 0\n for t in range(len(x)):\n z = decay * last + (1 - decay) * x[t]\n y[t] = z / (1 - decay ** (t + 1))\n last = z\n return y\n\n plt.plot(smoothed_loss(losses))\n plt.show()\n\n # plot W and bigram probs side-by-side\n # for the most common 200 words\n plt.subplot(1,2,1)\n plt.title(\"Logistic Model\")\n plt.imshow(softmax(W))\n plt.subplot(1,2,2)\n plt.title(\"Bigram Probs\")\n plt.imshow(bigram_probs)\n plt.show()\n\n\n\n\n" }, { "path": "nlp_class2/markov.py", "content": "# Course URL:\n# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\n\nimport os\nimport sys\nsys.path.append(os.path.abspath('..'))\nfrom rnn_class.brown import get_sentences_with_word2idx_limit_vocab, get_sentences_with_word2idx\n\n\n\ndef get_bigram_probs(sentences, V, start_idx, end_idx, smoothing=1):\n # structure of bigram probability matrix will be:\n # (last word, current word) --> probability\n # we will use add-1 smoothing\n # note: we'll always ignore this from the END token\n bigram_probs = np.ones((V, V)) * smoothing\n for sentence in sentences:\n for i in range(len(sentence)):\n \n if i == 0:\n # beginning word\n bigram_probs[start_idx, sentence[i]] += 1\n else:\n # middle word\n bigram_probs[sentence[i-1], sentence[i]] += 1\n\n # if we're at the final word\n # we update the bigram for last -> current\n # AND current -> END token\n if i == len(sentence) - 1:\n # final word\n bigram_probs[sentence[i], end_idx] += 1\n\n # normalize the counts along the rows to get probabilities\n bigram_probs /= bigram_probs.sum(axis=1, keepdims=True)\n return bigram_probs\n\n\n\nif __name__ == '__main__':\n # load in the data\n # note: sentences are already converted to sequences of word indexes\n # note: you can limit the vocab size if you run out of memory\n sentences, word2idx = get_sentences_with_word2idx_limit_vocab(10000)\n # sentences, word2idx = get_sentences_with_word2idx()\n\n # vocab size\n V = len(word2idx)\n print(\"Vocab size:\", V)\n\n # we will also treat beginning of sentence and end of sentence as bigrams\n # START -> first word\n # last word -> END\n start_idx = word2idx['START']\n end_idx = word2idx['END']\n\n\n # a matrix where:\n # row = last word\n # col = current word\n # value at [row, col] = p(current word | last word)\n bigram_probs = get_bigram_probs(sentences, V, start_idx, end_idx, smoothing=0.1)\n\n\n # a function to calculate normalized log prob score\n # for a sentence\n def get_score(sentence):\n score = 0\n for i in range(len(sentence)):\n if i == 0:\n # beginning word\n score += np.log(bigram_probs[start_idx, sentence[i]])\n else:\n # middle word\n score += np.log(bigram_probs[sentence[i-1], sentence[i]])\n # final word\n score += np.log(bigram_probs[sentence[-1], end_idx])\n\n # normalize the score\n return score / (len(sentence) + 1)\n\n\n # a function to map word indexes back to real words\n idx2word = dict((v, k) for k, v in iteritems(word2idx))\n def get_words(sentence):\n return ' '.join(idx2word[i] for i in sentence)\n\n\n # when we sample a fake sentence, we want to ensure not to sample\n # start token or end token\n sample_probs = np.ones(V)\n sample_probs[start_idx] = 0\n sample_probs[end_idx] = 0\n sample_probs /= sample_probs.sum()\n\n # test our model on real and fake sentences\n while True:\n # real sentence\n real_idx = np.random.choice(len(sentences))\n real = sentences[real_idx]\n\n # fake sentence\n fake = np.random.choice(V, size=len(real), p=sample_probs)\n\n print(\"REAL:\", get_words(real), \"SCORE:\", get_score(real))\n print(\"FAKE:\", get_words(fake), \"SCORE:\", get_score(fake))\n\n # input your own sentence\n custom = input(\"Enter your own sentence:\\n\")\n custom = custom.lower().split()\n\n # check that all tokens exist in word2idx (otherwise, we can't get score)\n bad_sentence = False\n for token in custom:\n if token not in word2idx:\n bad_sentence = True\n\n if bad_sentence:\n print(\"Sorry, you entered words that are not in the vocabulary\")\n else:\n # convert sentence into list of indexes\n custom = [word2idx[token] for token in custom]\n print(\"SCORE:\", get_score(custom))\n\n\n cont = input(\"Continue? [Y/n]\")\n if cont and cont.lower() in ('N', 'n'):\n break\n\n" }, { "path": "nlp_class2/ner.txt", "content": "@paulwalk\tO\nIt\tO\n's\tO\nthe\tO\nview\tO\nfrom\tO\nwhere\tO\nI\tO\n'm\tO\nliving\tO\nfor\tO\ntwo\tO\nweeks\tO\n.\tO\nEmpire\tB-facility\nState\tI-facility\nBuilding\tI-facility\n=\tO\nESB\tB-facility\n.\tO\nPretty\tO\nbad\tO\nstorm\tO\nhere\tO\nlast\tO\nevening\tO\n.\tO\n\t\nFrom\tO\nGreen\tO\nNewsfeed\tO\n:\tO\nAHFA\tB-other\nextends\tO\ndeadline\tO\nfor\tO\nSage\tB-other\nAward\tI-other\nto\tO\nNov\tO\n.\tO\n5\tO\nhttp://tinyurl.com/24agj38\tO\n\t\nPxleyes\tB-company\nTop\tO\n50\tO\nPhotography\tO\nContest\tO\nPictures\tO\nof\tO\nAugust\tO\n2010\tO\n...\tO\nhttp://bit.ly/bgCyZ0\tO\n#photography\tO\n\t\ntoday\tO\nis\tO\nmy\tO\nlast\tO\nday\tO\nat\tO\nthe\tO\noffice\tO\n.\tO\n\t\n4Dbling\tB-person\n's\tO\nplace\tO\ntil\tO\nmonday\tO\n,\tO\nparty\tO\nparty\tO\nparty\tO\n.\tO\n<\tO\n3\tO\n\t\nwatching\tO\nthe\tO\nVMA\tB-tvshow\npre-show\tO\nagain\tO\nlol\tO\nit\tO\nwas\tO\nn't\tO\neven\tO\na\tO\ngood\tO\nshow\tO\nthe\tO\nfirst\tO\ntime\tO\n...\tO\nso\tO\nbored\tO\n!\tO\n\t\n27\tO\nfollowers\tO\n!\tO\n30\tO\nfollowers\tO\nis\tO\nmy\tO\ngoal\tO\nfor\tO\ntoday\tO\n!\tO\n\t\nThis\tO\nis\tO\nthe\tO\n2nd\tO\nhospital\tO\nive\tO\nbeen\tO\nin\tO\ntoday\tO\n,\tO\nbut\tO\nive\tO\njust\tO\nseen\tO\na\tO\ndoctor\tO\nwho\tO\nwas\tO\nan\tO\nolder\tO\nversion\tO\nof\tO\njustin\tO\n:'\tO\n)\tO\n\t\nFriday\tO\nNight\tO\nEats\tO\nhttp://twitpic.com/2pdvtr\tO\n\t\nGotta\tO\ndress\tO\nup\tO\nfor\tO\nlondon\tB-other\nfashion\tI-other\nweek\tI-other\nand\tO\nparty\tO\nin\tO\nstyle\tO\n!\tO\n\t\n@Suzie55\tO\nwhispering\tO\ncause\tO\nI\tO\nmay\tO\nhave\tO\nhad\tO\n1\tO\ntoo\tO\nmany\tO\nvodka\tO\n's\tO\nlast\tO\nnight\tO\nand\tO\nam\tO\na\tO\nlil\tO\nfragile\tO\n,\tO\nhold\tO\nme\tO\n?\tO\n\t\n#NFL\tO\nWeek\tO\n2\tO\npicks\tO\nblog\tO\nis\tO\nup\tO\non\tO\n@The_Score\tO\nwebsite\tO\n...\tO\nhttp://blogs.thescore.com/nfl/2010/09/17/pizzolaw2/\tO\n\t\nI\tO\nlike\tO\n@SouljaBoy\tO\nsongs\tO\nafter\tO\na\tO\nfew\tO\ndrinks\tO\n\t\n...\tO\nasked\tO\nme\tO\na\tO\nquestion\tO\nfor\tO\nonce\tO\n,\tO\nbut\tO\nno\tO\n!\tO\nYou\tO\nask\tO\nme\tO\nabout\tO\nfucking\tO\ndonuts\tO\nyou\tO\ndonut\tO\n...\tO\nhave\tO\na\tO\nnice\tO\nday\tO\n:P\tO\n\t\nevery\tO\nyear\tO\nyour\tO\n\"\tO\nbestfriend\tO\n\"\tO\nget\tO\nyo\tO\nass\tO\nin\tO\ntrouble\tO\nsmdh\tO\n#doinme\tO\n\t\nRT\tO\n@midgetmegs\tO\n:\tO\nhate\tO\npeople\tO\nwho\tO\nwrite\tO\ndrunk\tO\nstatus'.\tO\nmust\tO\nbe\tO\nhaving\tO\na\tO\ngreat\tO\ntime\tO\nif\tO\nyour\tO\nsat\tO\nwriting\tO\nit\tO\non\tO\nfacebook\tB-company\n.....\tO\n\t\n@MrzEndy\tO\ntru\tO\ntru\tO\nI\tO\n'm\tO\nleavin\tO\nagain\tO\non\tO\nTuesday\tO\nyo\tO\n\t\nThe\tO\nguys\tO\nare\tO\ncooking\tO\nabout\tO\na\tO\ndozen\tO\npizzas\tO\nat\tO\nour\tO\nhouse\tO\nfor\tO\nthe\tO\ntailgate\tO\ntonight\tO\n.\tO\nNonetheless\tO\nit\tO\nsmells\tO\nfresh\tO\nin\tO\nthis\tO\nplace\tO\n.\tO\n\t\nI\tO\nheart\tO\nPark(ing\tB-other\n)\tI-other\nDay\tI-other\n!\tO\nPhoto\tO\nof\tO\nmeter\tO\nshark\tO\n:\tO\nhttp://bit.ly/aQun1b\tO\n\t\nhows\tO\neveryones\tO\nday\tO\ngoin\tO\n?\tO\n\t\n@Phoebe1_\tO\nand\tO\ni\tO\nalso\tO\nloved\tO\nthe\tO\nlast\tO\nyears\tO\neurovision\tB-tvshow\nentry\tO\n!!\tO\nhoppaa\tO\n!\tO\nthey\tO\nwere\tO\ncool\tO\ntoo\tO\n!!\tO\n\t\nMetroPCS\tB-company\nhas\tO\nthe\tO\nworst\tO\ncustomer\tO\nservice\tO\nI\tO\n've\tO\nexperienced\tO\nin\tO\nmonths\tO\n(\tO\ntrying\tO\nto\tO\nhelp\tO\nsomeone\tO\norder\tO\na\tO\nphone\tO\n)\tO\n#metropcs\tO\n#fail\tO\n\t\n@xSophieBx\tO\nHe\tO\nhas\tO\na\tO\nhabit\tO\nof\tO\ntweeting\tO\nat\tO\nthe\tO\nsame\tO\ntime\tO\nas\tO\nyou\tO\nlol\tO\n\t\nA\tO\nTRUE\tO\nfriend\tO\nwalks\tO\nin\tO\nwhen\tO\neverybody\tO\nelse\tO\nwalks\tO\nout\tO\n!\tO\n\t\nYay\tO\n.\tO\n@penguin\tO\nis\tO\ncoming\tO\nto\tO\nsee\tO\nme\tO\nnext\tO\nweek\tO\n...\tO\nplotting\tO\nmy\tO\ncooking\tO\nnow\tO\n!\tO\n\t\n@_mistercory\tO\n*Smiles\tO\nup\tO\nat\tO\nhim*\tO\nCan\tO\nwe\tO\njust\tO\n...\tO\nsit\tO\ntogether\tO\nfor\tO\na\tO\nwhile\tO\nbefore\tO\nround\tO\ntwo\tO\n?\tO\n\t\nthe\tO\ntears\tO\nmay\tO\nhave\tO\nbeen\tO\nand\tO\ngone\tO\n,\tO\nbut\tO\nthe\tO\nfeeling\tO\nstill\tO\nstay\tO\nthe\tO\nsame\tO\n.\tO\n\t\n@BieberEgypt\tO\nI\tO\n've\tO\ngot\tO\nsome\tO\nbad\tO\nnews\tO\n,\tO\nYeah\tO\nI\tO\ncant\tO\ncome\tO\nto\tO\nthe\tO\nmeeting\tO\ntomorrow\tO\n.\tO\n:\tO\nS\tO\n\t\n@abzmedic\tO\nI\tO\n'm\tO\nmissing\tO\nautumn\tO\nbecause\tO\nof\tO\nwork\tO\n.\tO\n:(\tO\nI\tO\nmust\tO\nget\tO\ninto\tO\nthe\tO\nforests\tO\ntomorrow\tO\n!\tO\n\t\nFellas\tO\nDo\tO\nn't\tO\njust\tO\nlick\tO\nthe\tO\nPussyY\tO\n,\tO\nLOVE\tO\nIT\tO\n!\tO\nenjoy\tO\nit\tO\n.\tO\nlike\tO\nu\tO\njust\tO\ngot\tO\nmarried\tO\n<\tO\nthe\tO\npreacher\tO\nsaid\tO\nu\tO\nmay\tO\nkiss\tO\nthe\tO\nbride\tO\n\t\nRT\tO\n@Sexstrology\tO\n:\tO\nPisces\tB-other\ntend\tO\nto\tO\nescape\tO\ninto\tO\nfantasy\tO\nand\tO\nday\tO\ndreams\tO\n.\tO\nThere\tO\nthey\tO\nare\tO\nfree\tO\n.\tO\n\t\nCant\tO\nwait\tO\nfor\tO\nthe\tO\nravens\tB-sportsteam\ngame\tO\ntomorrow\tO\n....\tO\ngo\tO\nray\tB-person\nrice\tI-person\n!!!!!!!\tO\n\t\nI\tO\njust\tO\ntook\t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\n\t\n@KimberleyW1983\tO\nThanks\tO\n,\tO\nc4\tO\nis\tO\ngoing\tO\non\tO\n!\tO\nLol\tO\n.\tO\nDVD\tO\nOut\tO\n25th\tO\nOctober\tO\n!\tO\n\t\nWhoo\tO\nwho\tO\nready\tO\nfor\tO\nsome\tO\nSeahawk\tB-sportsteam\nfootball\tO\n?\tO\nCan't\tO\nwait\tO\nto\tO\nwatch\tO\nmy\tO\ndirtbags\tO\non\tO\nsunday\tO\n.\tO\n\t\nwill\tO\ngo\tO\nat\tO\nmy\tO\nB-day\tO\nparty\tO\n.\tO\nFucking\tO\ncan't\tO\nbelieve\tO\nit\tO\n!\tO\n\t\nMay\tO\neveryone\tO\nwho\tO\nis\tO\ncelebrating\tO\nYom\tB-other\nKippur\tI-other\nhave\tO\nan\tO\neasy\tO\nand\tO\nmeaningful\tO\nfast\tO\n!\tO\n#notaboutbirthcontrol\tO\n\t\nonly\tO\non\tO\nthe\tO\n#LateLate\tO\ncan\tO\ni\tO\nfind\tO\nrespect\tO\nfor\tO\n#desbishop\tO\n,\tO\nhe\tO\n's\tO\nactually\tO\npretty\tO\nfunny\tO\n,\tO\nnever\tO\nliked\tO\nhim\tO\nbefore\tO\nthis\tO\npoint\tO\n.\tO\n\t\n@borneobaby\tO\nMe\tO\nneither\tO\n.\tO\nI\tO\nwill\tO\nthink\tO\nof\tO\nwitty\tO\nreplies\tO\na\tO\nweek\tO\nlater\tO\n!\tO\nMy\tO\nmind\tO\nwill\tO\nbe\tO\na\tO\ncomplete\tO\nblank\tO\nI\tO\nknow\tO\nit\tO\n!\tO\n\t\nRT\tO\n@claywsyx6\tO\n:\tO\nTHE\tO\nFOOTBALL\tO\nFEVER\tO\n:\tO\nOhio\tB-geo-loc\nhead\tO\ncoach\tO\nFrank\tB-person\nSolich\tI-person\nsays\tO\nOhio\tB-sportsteam\nState\tI-sportsteam\nknows\tO\nthey\tO\nhave\tO\na\tO\nspecial\tO\nteam\tO\nand\tO\nseason\tO\nunderway\tO\n.\tO\nLong\tO\n...\tO\n\t\nAlways\tO\nwondered\tO\nwhich\tO\nspecies\tO\nhad\tO\nthe\tO\nhighest\tO\nbiomass\tO\nRT\tO\n@NatGeoSociety\tO\n:\tO\nFriday\tO\nFact\tO\n:\tO\nThe\tO\nworld\tO\n's\tO\ntermites\tO\noutweigh\tO\nthe\tO\nworld\tO\n's\tO\npeople\tO\n\t\n<\tO\n3\tO\nit\tO\nRT\tO\n@Djcheapshot\tO\n:\tO\nTonite\tO\nI\tO\nm\tO\nDJing\tO\nat\tO\nMai\tB-facility\nTai\tI-facility\nin\tO\nLong\tB-geo-loc\nBeach\tI-geo-loc\n.\tO\nI'm\tO\nconsidering\tO\nwearing\tO\nMY\tO\nTIE\tO\n!!\tO\nGet\tO\nit\tO\n?\tO\nMy\tO\ntie\tO\n=\tO\nMai\tO\nTai\tO\n?\tO\nNo\tO\n?\tO\nSorry\tO\n.\tO\nBye\tO\n.\tO\n\t\nFrench\tO\nIV\tO\nembarrassed\tO\nme\tO\ntoday\tO\n..\tO\nnever\tO\nagain\tO\nwill\tO\nI\tO\ngo\tO\ninto\tO\na\tO\nbattle\tO\nunprepared\tO\n.\tO\n\t\n@BPadalecki1990\tO\nDo\tO\nyou\tO\nhave\tO\nany\tO\npic\tO\n's\tO\nfrom\tO\nyour\tO\nwedding\tO\nday\tO\nhun\tO\nI\tO\nwould\tO\nlike\tO\nto\tO\nsee\tO\nthem\tO\n\t\nLooks\tO\nlike\tO\nwe\tO\nwill\tO\nbe\tO\nliving\tO\nin\tO\nthe\tO\nstudio\tO\nfor\tO\nthe\tO\nnext\tO\ncouple\tO\nof\tO\nmonths\tO\n\t\nHappy\tO\nDay\tO\n!!!....\tO\n:)\tO\n\t\n@yanxfan24\tO\nWe\tO\nshould\tO\nwin\tO\nbut\tO\nI\tO\nsaid\tO\nthat\tO\non\tO\nTuesday\tO\n.\tO\nThese\tO\ngames\tO\nare\tO\nnormally\tO\npretty\tO\npoor\tO\nthough\tO\n.\tO\nProbably\tO\n0-0\tO\nlol\tO\n\t\nYou'll\tO\ncome\tO\nback\tO\nwhen\tO\nthey\tO\ncall\tO\nyou\tO\n...\tO\nNo\tO\nneed\tO\nto\tO\nsay\tO\ngoodbye\tO\n.\tO\n\t\n@lauramcblain94_\tO\nlololololol\tO\ntonight\tO\nwas\tO\nworse\tO\nthan\tO\nstressful\tO\n!\tO\nxo\tO\n\t\n#FF\tO\nTHE\tO\nFUTURE\tO\nOF\tO\nTHE\tO\n@WWE\tO\n@JTG1284\tO\nHE\tO\n#SofaKingFlyy\tO\nHE\tO\nONE\tO\nOF\tO\nMY\tO\nALL\tO\nTIME\tO\nFAVS\tO\n....\tO\n#ReallyDoe\tO\n\t\n@DavidHayemaker\tO\nLiked\tO\nyour\tO\nspot\tO\non\tO\nRadio\tB-other\n1\tI-other\nthe\tO\nother\tO\nday\tO\n.\tO\n\t\nRT\tO\n@ContrarianDave\tO\n:\tO\nHere\tO\n's\tO\nmy\tO\nstory\tO\non\tO\ntoday\tO\n's\tO\nForensic\tO\nScience\tO\nComm\tO\n.\tO\nmeeting\tO\n,\tO\nwhere\tO\nWillingham\tB-geo-loc\nprobe\tO\ntook\tO\na\tO\nremarkable\tO\nturn\tO\n.\tO\nhttp://bi\tO\n...\tO\n\t\nRT\tO\n@MATTHARDYBRAND\tO\n:\tO\nThx\tO\nto\tO\neveryone\tO\nwho\tO\nwished\tO\nmy\tO\nDad\tO\n,\tO\n"\tO\nThe\tO\nLegend\tO\n"\tO\na\tO\nHappy\tO\n88th\tO\nB-Day\tO\njust\tO\na\tO\ncouple\tO\ndays\tO\nago\tO\n..\tO\nHe\tO\n's\tO\nmy\tO\ninspiration\tO\n&\tO\nme\tO\n...\tO\n\t\n@imRockey\tO\nI\tO\nwill\tO\nlet\tO\nyou\tO\nknow\tO\nwhen\tO\nyou\tO\ncan\tO\ntake\tO\nanother\tO\nvisit\tO\n;\tO\ntoday\tO\nwas\tO\na\tO\nlittle\tO\nquiet\tO\n.\tO\n\t\n@tonygarcha\tO\nHurray\tO\n!\tO\nEnjoy\tO\nyour\tO\ntime\tO\nin\tO\nHarrison\tB-geo-loc\n.\tO\n\t\nOff\tO\nwork\tO\nI\tO\nguess\tO\n?\tO\nTold\tO\nto\tO\nleave\tO\nan\tO\nhour\tO\nand\tO\nhalf\tO\nbefore\tO\nI\tO\nwas\tO\nscheduled\tO\nto\tO\n.\tO\nMy\tO\npaycheck\tO\nis\tO\ngoing\tO\nto\tO\nbe\tO\nweak\tO\n.\tO\nNot\tO\nhappy\tO\n.\tO\n\t\nYup\tO\n,\tO\nit\tO\nis\tO\n:)\tO\n|\tO\nRT\tO\n@iSuboohi\tO\n:\tO\n-\tO\nWhen\tO\nsomeone/anyone\tO\nmakes\tO\nyou\tO\nfeel\tO\nspecial\tO\n,\tO\nit\tO\n's\tO\nsheer\tO\nbliss\tO\n:)\tO\n\t\nRT\tO\n@Quotealicious\tO\n:\tO\nWhen\tO\never\tO\ni\tO\nsee\tO\na\tO\ncute\tO\nlove\tO\nstory\tO\nmovie\tO\n,\tO\nIt\tO\n's\tO\nalways\tO\nyou\tO\nthat\tO\ncomes\tO\ninto\tO\nmy\tO\nmind\tO\n#Quotealicious\tO\n\t\nI\tO\nhad\tO\na\tO\nwonderful\tO\nday\tO\ntoday\tO\nand\tO\ni\tO\nhope\tO\nu\tO\nall\tO\ndid\tO\n,\tO\ni\tO\nwish\tO\nu\tO\nall\tO\ngood\tO\nnight\tO\nand\tO\nsweet\tO\ndreams\tO\nxoxoxo\tO\n\t\nTheres\tO\nnothing\tO\nfunny\tO\nabout\tO\ntesticles\tO\nCooper\tB-person\nas\tO\nyou\tO\nwill\tO\nfind\tO\nout\tO\nin\tO\nmy\tO\noffice\tO\ntomorrow\tO\n.\tO\nLmfao\tO\n.\tO\n\t\nWelcome\tO\nSaturday\tO\n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long\tO\nand\tO\nwonderful\tO\nrelationship\tO\nwith\tO\nit\tB-tvshow\n's\tI-tvshow\nalways\tI-tvshow\nsunny\tI-tvshow\n.\tO\nSince\tO\nI\tO\ndo\tO\nown\tO\nseason\tO\n1\tO\nand\tO\n2\tO\non\tO\nDVD\tO\nalready\tO\n.\tO\n\t\n@LesColyer\tO\nWho\tO\ndo\tO\nyou\tO\nfilm\tO\nfor\tO\n?\tO\nI\tO\nwas\tO\non\tO\nSKY\tB-other\nnews\tI-other\n2\tO\nweeks\tO\nago\tO\n#checkmeout\tO\n\t\n@snufflesgirl25\tO\n@mo9x\tO\nWell\tO\n.\tO\njust\tO\nbeg\tO\n@100monkeysmusic\tO\n..\tO\nPLEASE\tO\ncome\tO\nto\tO\nthe\tO\n#SBLeurope\tO\nNext\tO\nyear\tO\n!.\tO\nWe\tO\nneed\tO\nyou\tO\nthere\tO\n!\tO\n*puss\tO\nin\tO\nboots\tO\neyes*\tO\n\t\nomg\tO\ni\tO\nget\tO\npaid\tO\ntoday\tO\nyesss\tO\n\t\nRT\tO\n@Leonsays\tO\n:\tO\nNeed\tO\na\tO\nnice\tO\nchill\tO\nspot\tO\ntonight\tO\n,\tO\nno\tO\nhype\tO\n...\tO\nAny\tO\nsuggestions\tO\n?\tO\n\t\nso\tO\nfar\tO\nthe\tO\nherring\tO\nis\tO\na\tO\nsuccess\tO\n.\tO\ndefinitely\tO\nneeds\tO\na\tO\ncouple\tO\nmore\tO\ndays\tO\nthough\tO\n\t\nKk\tO\nits\tO\n22:48\tO\nfriday\tO\nnyt\tO\n:D\tO\nreally\tO\ntired\tO\nso\tO\nimma\tO\ngo\tO\nto\tO\nsleep\tO\n:)\tO\ngood\tO\nnyt\tO\nx\tO\ngod\tO\nbles\tO\nxxxxx\tO\n\t\nmothers\tB-other\nday\tI-other\ngreeting\tO\ncards\tO\n:)\tO\nSurrounded\tO\nby\tO\nMothers\tB-other\nDay\tI-other\nand\tO\nDreading\tO\nIt\tO\n?\tO\nhttp://bit.ly/a7d7tg\tO\n\t\n@ConorMc_Ginty\tO\nGaGa\tB-person\nplayed\tO\nhere\tO\na\tO\nfew\tO\nweeks\tO\nback\tO\n.\tO\nShe\tO\nspoke\tO\nout\tO\nagainst\tO\nSB1070\tB-other\nduring\tO\nthe\tO\nshow\tO\n.\tO\nGod\tO\nbless\tO\nher\tO\nfor\tO\nthat\tO\n.\tO\n\t\nRT\tO\n@jaye403\tO\n:\tO\n@BlackCanseco\tO\nyou\tO\nknow\tO\nit\tO\nis\tO\nfunny\tO\nhow\tO\nthose\tO\nppl\tO\nwant\tO\ntheir\tO\nheros\tO\nremembered\tO\n...\tO\nwhen\tO\nthe\tO\ntruth\tO\nis\tO\nare\tO\nless\tO\nthan\tO\nworthy\tO\n\t\nGoodnight\tO\nand\tO\nmay\tO\npeace\tO\nbe\tO\nupon\tO\nyou\tO\nand\tO\nyours\tO\n.\tO\n<\tO\n3\tO\n\t\nRT\tO\n@MarioBB9\tO\n:\tO\nPope\tB-person\nsays\tO\natheists\tO\npick\tO\nand\tO\nchoose\tO\ntheir\tO\nmorals\tO\n.\tO\nCorrect\tO\n.\tO\nToday\tO\nI\tO\nwill\tO\nbe\tO\nfrowning\tO\non\tO\nchild\tO\nabuse\tO\nand\tO\nnot\tO\nhaving\tO\na\tO\nproble\tO\n...\tO\n\t\n@TMCphotographs\tO\nI\tO\nknow\tO\n,\tO\nbut\tO\nthere\tO\nare\tO\nplenty\tO\nof\tO\nmid\tO\nweek\tO\nprogrammes\tO\nthat\tO\nI\tO\ncan\tO\ntake\tO\nthe\tO\npiss\tO\nout\tO\nof\tO\nnow\tO\nthe\tO\nwinter\tO\nis\tO\nalmost\tO\nupon\tO\nus\tO\n.\tO\nxx\tO\n\t\n@miaturner\tO\nOOC\tO\n:\tO\nFeeling\tO\na\tO\nlittle\tO\nvolent\tO\ntoday\tO\n,\tO\nhuh\tO\n?\tO\n\t\nIt\tO\nsucks\tO\nwhen\tO\nyou\tO\nwaiting\tO\nfor\tO\nthe\tO\nbus\tO\nwith\tO\nyour\tO\nbike\tO\n,\tO\nthen\tO\nit\tO\nshows\tO\nup\tO\nwith\tO\ntwo\tO\nbikes\tO\nalready\tO\non\tO\nit\tO\n.\tO\n\t\nLines\tO\nfor\tO\nTonight\tO\n's\tO\ngame\tO\n9:00\tO\npm\tO\nat\tO\nBurnaby\tB-geo-loc\n8Rinks\tO\n.\tO\nPlease\tO\nnote\tO\n,\tO\nthese\tO\nare\tO\nnot\tO\nthe\tO\nlines\tO\nfor\tO\nshowering\tO\ntonight\tO\n!\tO\nhttp://twitpic.com/2pducs\tO\n\t\n@TrustL33\tO\nword\tO\nim\tO\ndefinitely\tO\ngettin\tO\nthat\tO\nthe\tO\nday\tO\nit\tO\ndrops\tO\n\t\nRT\tO\n@cinnzcocky_yep\tO\n\"\tO\nI\tO\ngot\tO\nlots\tO\nof\tO\ngualllluhh\tO\nI\tO\nspend\tO\nit\tO\n\"\tO\n--\tO\n>\tO\ndrinks\tO\non\tO\nu\tO\nnext\tO\ntime\tO\n\t\nI\tO\njust\tO\ntook\tO\n\"\tO\nAfter\tO\ngetting\tO\ntrampled\tO\nat\tO\na\tO\nJustin\tB-person\nBieber\tI-person\nconcert\tO\n,\tO\nyoiu\tO\nwake\tO\nup\tO\nand\tO\n...\"\tO\nand\tO\ngot\tO\n:\tO\nPart\tO\n6\tO\n:)\tO\n!\tO\nTry\tO\nit\tO\n:\tO\nhttp://tinyurl.com/233u5lu\tO\n\t\ncomo\tO\nsaben\tO\nque\tO\ndanger\tO\ndays\tO\nes\tO\nel\tO\nnombre\tO\ndel\tO\ndisco\tO\n?\tO\n\t\nHurry\tO\nup\tO\n!\tO\nBrandy\tB-person\nwill\tO\nbe\tO\nLeaving\tO\nin\tO\n2\tO\ndays\tO\n!\tO\n-\tO\n#Eskorte\tO\nand\tO\n#Massasje\tO\ni\tO\n#Norge\tO\n\t\n#DiggyFollows50\tO\n@Diggy_simmons\tO\nFOLLOW\tO\nME\tO\nPLZ\tO\nCAUSE\tO\nIM\tO\nHAVIN\tO\nA\tO\nBAD\tO\nDAY\tO\nAND\tO\nWOULD\tO\nBE\tO\nSO\tO\nMUCH\tO\nBETTER\tO\nIF\tO\nU\tO\nFOLLOWD\tO\nME\tO\n.\tO\nBUMPING\tO\nAIRBORNE\tO\nRIGHT\tO\nNOW\tO\n\t\nRT\tO\n@LoveQuotesx\tO\n:\tO\nHave\tO\nyou\tO\never\tO\nnoticed\tO\nthat\tO\nthe\tO\nworst\tO\nway\tO\nto\tO\nmiss\tO\nsomeone\tO\nis\tO\nwhen\tO\nthey\tO\nare\tO\nright\tO\nbeside\tO\nyou\tO\nand\tO\nyou\tO\ncan\tO\nnever\tO\nhave\tO\nthem\tO\n?\tO\n\t\nHaving\tO\nmovie\tO\nnight\tO\ntonight\tO\nat\tO\nthe\tO\ncrib\tO\nand\tO\nur\tO\nnot\tO\ninvited\tO\n*BBM\tO\nsticks\tO\ntongue\tO\nout\tO\nsmiley*\tO\n\t\nFRIDAY\tO\n....\tO\nhome\tO\nalone\tO\n!\tO\nxD\tO\nwanna\tO\njoin\tO\nme\tO\n??\tO\n\t\nI\tO\njust\tO\nhad\tO\nto\tO\nstep\tO\ninto\tO\nmy\tO\noffice\tO\n(\tO\nfront\tO\nporch\tO\n)\tO\nagain\tO\n.\tO\nLol\tO\n.\tO\nI\tO\nfeel\tO\nlike\tO\neverything\tO\nhappening\tO\ntoday\tO\nis\tO\nhighly\tO\nunusual\tO\n.\tO\nI\tO\nlike\tO\nit\tO\na\tO\nlot\tO\n!\tO\n\t\n@morgancharlie\tO\nmmm\tO\ndeffo\tO\none\tO\nof\tO\n'\tO\nthose\tO\n'\tO\nthought\tO\nprovoking\tO\ndays\tO\n:/\tO\nNever\tO\nmind\tO\n,\tO\nonwards\tO\n&\tO\nupwards\tO\nwe\tO\nshall\tO\ngo\tO\n!!\tO\n\t\nIf\tO\nI\tO\nhad\tO\nknown\tO\nwhat\tO\nI\tO\nknow\tO\nnow\tO\nabout\tO\ntruck\tO\ndrivers\tO\n,\tO\nI\tO\ndon't\tO\nthink\tO\nI\tO\nwould\tO\nhave\tO\nasked\tO\nthem\tO\nto\tO\nhonk\tO\nas\tO\nmuch\tO\nwhen\tO\nI\tO\nwas\tO\nyounger\tO\n.\tO\n\t\nYay\tO\n.\tO\n@penguin\tO\nis\tO\ncoming\tO\nto\tO\nsee\tO\nme\tO\nnext\tO\nweek\tO\n...\tO\nplotting\tO\nmy\tO\ncooking\tO\nnow\tO\n!\tO\n\t\n@KaulitzClub\tO\nHahaha\tO\n!\tO\nWe\tO\ndance\tO\n,\tO\neat\tO\n,\tO\ndrink\tO\n,\tO\nand\tO\ntalk\tO\nEVERYTIME\tO\nwe\tO\nget\tO\ntogether\tO\n.\tO\nAnd\tO\nit\tO\n's\tO\nbeen\tO\nthis\tO\nway\tO\nfor\tO\nlike\tO\nyears\tO\nnow\tO\n!\tO\n\t\nwas\tO\non\tO\nclub\tB-product\npenguin\tI-product\nwith\tO\n@twilightmarisa\tO\ntill\tO\nshe\tO\ndecided\tO\nto\tO\nlog\tO\nof\tO\nto\tO\ngo\tO\nbed\tO\nand\tO\nleave\tO\nme\tO\nloool\tO\nreminded\tO\nme\tO\nof\tO\nmy\tO\nold\tO\ncp\tO\ndays\tO\nlol\tO\n\t\nohh\tO\nright\tO\n...\tO\nim\tO\nan\tO\nactor\tO\n...\tO\nback\tO\nto\tO\nwork\tO\ntonight\tO\nWOOT\tO\n\t\nSurely\tO\nmags\tO\nwill\tO\nline\tO\nup\tO\nto\tO\nfeature\tO\n.\tO\nRT\tO\n@peoplemag\tO\n:\tO\nLindsay\tB-person\nLohan\tI-person\nfails\tO\ncourt-mandated\tO\ndrug\tO\ntest\tO\n,\tO\nfaces\tO\nmore\tO\njail\tO\ntime\tO\nhttp://ow.ly/2G3R2\tO\n\t\nRT\tO\n@JDay_Sept15\tO\n:\tO\nRT\tO\n@Ashanie_di_Don\tO\n:\tO\n@JDay_Sept15\tO\nlol\tO\ni\tO\nsee\tO\nsomeone\tO\nhyped\tO\nand\tO\nready\tO\nfor\tO\n#FlavaFridays\tO\ntonight\tO\n<\tO\nevery\tO\nweek\tO\n.\tO\nLets\tO\nget\tO\nit\tO\n!\tO\n\t\nOur\tO\nFavorite\tO\nYouTube\tB-company\nVideos\tO\nThis\tO\nWeek\tO\n:\tO\nThe\tB-other\nDrama\tI-other\nEdition\tI-other\n-\tO\nHad\tO\nany\tO\nepic\tO\nmeltdowns\tO\nthis\tO\nweek\tO\n?\tO\nYou\tO\nknow\tO\n,\tO\nof\tO\nthe\tO\ncrying\tO\n...\tO\nhttp://ow.ly/193oP5\tO\n\t\nDoing\tO\n\"\tO\nspring\tO\ncleaning\tO\n\",\tO\nfound\tO\nphoto\tO\nstorage\tO\ntank\tO\nthat\tO\nis\tO\n5x\tO\nsize\tO\nof\tO\nmy\tO\niPhone\tB-product\nwith\tO\nless\tO\ncapacity\tO\nthan\tO\niPhone4\tB-product\n...\tO\n4\tO\nyears\tO\nold\tO\n,\tO\ntechnology\tO\n!\tO\n\t\ne\tO\no\tO\nq\tO\nvai\tO\nser\tO\nhj\tO\n?\tO\ncerveja\tO\nfoi\tO\nontem\tO\n...\tO\nque\tO\ntal\tO\num\tO\nvinho\tO\npra\tO\nvariar\tO\num\tO\npouco\tO\n!!\tO\nfriday\tO\n's\tO\ncelebrate\tO\n!!!\tO\nyupppppp\tO\n\t\n#asdrakesays\tO\n\"\tO\nwhat\tO\n's\tO\na\tO\nstar\tO\nwhen\tO\nhis\tO\nmost\tO\nimportant\tO\nfan\tO\nis\tO\nmissing\tO\n?\"\tO\n\t\nWhat\tO\na\tO\nproductive\tO\nday\tO\n.\tO\nNot\tO\n.\tO\n\t\nFellas\tO\nDon't\tO\njust\tO\nlick\tO\nthe\tO\nPussyY\tO\n,\tO\nLOVE\tO\nIT\tO\n!\tO\nenjoy\tO\nit\tO\n.\tO\nlike\tO\nu\tO\njust\tO\ngot\tO\nmarried\tO\n&\tO\nthe\tO\npreacher\tO\nsaid\tO\nu\tO\nmay\tO\nkiss\tO\nthe\tO\nbride\tO\n\t\nYou\tO\nall\tO\nneed\tO\nto\tO\nknow\tO\njustin\tB-person\nisnt\tO\nthe\tO\ninnocent\tO\n16\tO\nyear\tO\nold\tO\nboy\tO\nyou\tO\nthink\tO\nhe\tO\nis\tO\nhe\tO\nhas\tO\nto\tO\nbe\tO\nlike\tO\nthat\tO\nand\tO\n'\tO\nlove\tO\n'\tO\nhis\tO\nfans\tO\nso\tO\n...\tO\n\t\nWhat\tO\ngame\tO\ncome\tO\non\tO\ntonight\tO\n?\tO\n\t\nPower\tO\nnap\tO\n.\tO\nI\tO\nneed\tO\nit\tO\n.\tO\nIts\tO\nbeen\tO\na\tO\nstressful\tO\nweek\tO\n.\tO\nI'm\tO\nexcited\tO\nfor\tO\nxmas\tB-other\n,\tO\nhaha\tO\n.\tO\nGoodnight\tO\n.\tO\n:)\tO\n\t\n@LH_TCC\tO\nI\tO\nhope\tO\nfor\tO\nAll\tO\nOf\tO\nThe\tO\nLights\tO\n.\tO\nI\tO\nreally\tO\ndo\tO\n.\tO\nCDQ\tO\nof\tO\nRunaway\tO\nbetter\tO\nhave\tO\nthose\tO\nsamples\tO\nfrom\tO\nwhen\tO\nhe\tO\ndid\tO\nit\tO\nLive\tO\ntoo\tO\n.\tO\n\t\n@Viic156\tO\nI'm\tO\nOcutta\tO\nTime\tO\nps\tO\ntambn\tO\nla\tO\namooo\tO\nperoo\tO\nla\tO\notra\tO\ntambn\tO\nme\tO\ngustaa\tO\n\t\nRT\tO\n@yikes77\tO\n:\tO\n@DonnieWahlberg\tO\nI\tO\nhave\tO\na\tO\nfeeling\tO\nthat\tO\nwhen\tO\nit\tO\ncomes\tO\nto\tO\nyou\tO\n,\tO\nyou\tO\nneed\tO\nmore\tO\nthan\tO\njust\tO\nONE\tO\ndate\tO\n.\tO\n-\tO\nYUP\tO\n!!!!\tO\n\t\n@Dazzx\tO\nI\tO\nthink\tO\nyou\tO\nmay\tO\nhave\tO\nmentioned\tO\nit\tO\n.\tO\nOnce\tO\nor\tO\ntwice\tO\n.\tO\n:)\tO\n\t\nBeddy\tO\nbye\tO\nfor\tO\nsleepy\tO\ntime\tO\nxx\tO\n\t\n@Dishfunctional\tO\nyeah\tO\n.\tO\nbig\tO\ntime\tO\ndancer\tO\n.\tO\ni\tO\nmight\tO\nworship\tO\nher\tO\na\tO\nbit\tO\n\t\n@erutan\tO\nStanford\tB-other\n's\tO\nRobert\tB-person\nLaughlin\tI-person\n(\tO\nNobel\tO\nPhysics1998\tO\n)\tO\nhas\tO\na\tO\nbetter\tO\nexplanation\tO\nthan\tO\nI\tO\nhave\tO\ntime\tO\nto\tO\nprovide\tO\nrt\tO\nnow\tO\n.\tO\nhttp://bit.ly/9k1L4N\tO\n\t\n@MsAustinTaylor\tO\nwhats\tO\nbeen\tO\ngoing\tO\non\tO\ntoday\tO\n?\tO\n\t\nNext\tO\ntime\tO\nyou'll\tO\nshow\tO\nrestraint\tO\nyou\tO\ndon't\tO\nget\tO\nanother\tO\nchance\tO\nlife\tO\nis\tO\nno\tO\nnintendo\tB-company\ngame\tO\nbut\tO\nyou\tO\nlied\tO\nagain\tO\nnow\tO\nyou\tO\nget\tO\nto\tO\nwatch\tO\nher\tO\nleave\tO\n\t\nNot\tO\nfeeling\tO\nup\tO\nto\tO\npar\tO\ntoday\tO\n.\tO\n#stressing\tO\n\t\nThe\tB-sportsteam\nWolves\tI-sportsteam\nto\tO\nhost\tO\nthe\tB-sportsteam\nLions\tI-sportsteam\nfor\tO\ngame\tO\ntime\tO\n!\tO\nFollow\tO\nus\tO\nfor\tO\nscores\tO\nand\tO\nupdates\tO\nthrough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\n:\tO\nLeftovers\tO\n:\tO\nThe\tO\nDay\tO\n's\tO\nStray\tO\nLinks\tO\n#Food\tO\nhttp://portfo.li/t/7/rjE\tO\n\t\nRT\tO\n@Lexus4u\tO\n:\tO\nOK\tO\nSO\tO\nWE\tO\nARE\tO\nHAVING\tO\nA\tO\nPOOL\tO\nPARTY\tO\nTONITE\tO\n,\tO\nHOPEFULLY\tO\nI\tO\nCAN\tO\nC\tO\nSUM\tO\nEYE\tO\nCANDY\tO\n!\tO\nLOL\tO\n\t\nMy\tO\nmama\tO\njust\tO\nsurprised\tO\nme\tO\non\tO\nring\tO\nday\tO\n!!\tO\nAhh\tO\ntoday\tO\njust\tO\ngot\tO\nso\tO\nmuch\tO\nbetter\tO\n!\tO\n\t\nDay\tO\n27\tO\n-\tO\nDay\tO\n27-\tO\nWhy\tO\nare\tO\nyou\tO\ndoing\tO\nthis\tO\n30\tO\nday\tO\nchallenge\tO\nim\tO\ndoing\tO\nthis\tO\nbecause\tO\ni\tO\nam\tO\nbored\tO\n:)\tO\nhttp://tumblr.com/xooiuell3\tO\n\t\n@Wes0010\tO\ngood\tO\nidea\tO\n!\tO\ni\tO\nhave\tO\nsome\tO\ni\tO\nfound\tO\nin\tO\nthe\tO\ngolf\tO\nbag\tO\ni\tO\njust\tO\nreceived\tO\nfrom\tO\nmy\tO\naunt\tO\nwho\tO\ndied\tO\n21\tO\nyears\tO\nago\tO\n!\tO\n\t\nIts\tO\n3\tO\no'clock\tO\non\tO\nmy\tO\nfirst\tO\nday\tO\nin\tO\nVegas\tB-geo-loc\nand\tO\nI\tO\nam\tO\nalready\tO\ndrunk\tO\nand\tO\njust\tO\nsang\tO\nmeatloaf\tB-person\nat\tO\nkaroke\tO\n.\tO\n\t\n@Emmas76\tO\nhave\tO\ni\tO\nmissed\tO\nanything\tO\nthese\tO\nlast\tO\n2\tO\nweeks-\tO\nhow\tO\n's\tO\nmusic\tO\npractice\tO\ngoing\tO\n?\tO\n\t\n@DudeImSuchATeen\tO\nOoo\tO\nI\tO\ndo\tO\n!!\tO\nIt\tO\nwould\tO\nmake\tO\nTHiS\tO\nday\tO\nbetter\tO\n:-/\tO\n\t\n@xiaomai_\tO\nKom\tO\ndeze\tO\nmaand\tO\ndenk\tO\nik\tO\nniet\tO\n,\tO\nnext\tO\nweek\tO\nParijs\tO\n,\tO\nen\tO\nallemaal\tO\ntoetsen\tO\nen\tO\npresentaties\tO\nenzo\tO\n>\tO\n.\tO\n<\tO\n'....\tO\n\t\nWell\tO\nhad\tO\nthe\tO\nweigh\tO\nin\tO\ntoday\tO\n.\tO\nI've\tO\nlost\tO\na\tO\npound\tO\n!!!\tO\nI've\tO\nhad\tO\nno\tO\nalcohol\tO\n,\tO\nno\tO\npuddings\tO\n,\tO\nno\tO\ncrisps\tO\n,\tO\nno\tO\nchocolate\tO\n&\tO\nI've\tO\nonly\tO\nlost\tO\na\tO\npound\tO\n!!!!!\tO\n\t\n@maeband\tO\nI'm\tO\nso\tO\nexcited\tO\nto\tO\nsee\tO\nyou\tO\nguys\tO\non\tO\nOct\tO\n.\tO\n12th\tO\nat\tO\nthe\tO\nSouthgate\tB-facility\nHouse\tI-facility\n!\tO\nI've\tO\nbeen\tO\nlistening\tO\nto\tO\nyou\tO\nsince\tO\nD\tO\n:\tO\nB\tO\n.\tO\nYour\tO\nso\tO\namazing\tO\n!!\tO\n\t\nbeen\tO\nlistenin\tO\nto\tO\ntrey\tB-person\nalllll\tO\nweek\tO\n...\tO\ncan\tO\nu\tO\nluv\tO\nsomeone\tO\nu\tO\nnever\tO\nmet\tO\n??\tO\nbcuz\tO\ni\tO\nthink\tO\nim\tO\nin\tO\nluv\tO\nyeeuuuuppp\tO\n!!!\tO\n\t\nLast\tO\nday\tO\n\t\n#birdgang\tO\nin\tO\nfull\tO\nforce\tO\n!!!\tO\nSunday\tO\n10:00\tO\nam\tO\nget\tO\nyo\tO\npopcorn\tO\nready\tO\n@ddockett\tO\nwill\tO\ndominate\tO\n!\tO\n\t\nwell\tO\nmy\tO\n700th\tO\ntweet\tO\nwas\tO\n3\tO\ntweets\tO\nago\tO\n.\tO\n\t\ngym\tO\ntonight\tO\nbecause\tO\nI\tO\nwas\tO\na\tO\nslacker\tO\nlast\tO\nnight\tO\nand\tO\nwent\tO\nout\tO\nto\tO\ndinner\tO\ninstead\tO\nof\tO\nworking\tO\nout\tO\n#fb\tO\n\t\n'\"\tO\ncome\tO\non\tO\nits\tO\nFriday\tO\nnight\tO\n...\tO\nshes\tO\ndreaming\tO\nwhoa\tO\noh\tO\noh\tO\noh\tO\ndreaming\tO\nof\tO\na\tO\nweekend\tO\nwhoa\tO\noh\tO\noh\tO\noh\tO\n\"'\tO\n\t\n@terryronald\tO\nHello\tO\nmate\tO\nhows\tO\nyou\tO\n?\tO\nLong\tO\ntime\tO\nno\tO\ntweet\tO\nWhat\tO\nyou\tO\nbeen\tO\nup\tO\nto\tO\n?\tO\nHows\tO\nthe\tO\nbook\tO\n?\tO\n\t\nBouncing\tO\nmy\tO\nass\tO\nlike\tO\nthrs\tO\nno\tO\ntomorrow\tO\n\t\nRT\tO\n@iLL87WiLL\tO\n:\tO\nWhen\tO\ntrey\tB-person\nsongz\tI-person\nwas\tO\na\tO\nkid\tO\nhis\tO\nmother\tO\nprobably\tO\nwent\tO\ndid\tO\nyou\tO\ndo\tO\nyour\tO\nhomework\tO\n?\tO\nAnd\tO\nhe\tO\nprobably\tO\nwent\tO\n#YUPPP\tO\nlol\tO\n\t\nDear\tO\nsun\tO\n,\tO\nit\tO\n's\tO\napproaching\tO\nthe\tO\nend\tO\nof\tO\nSeptember\tO\nand\tO\nseriously\tO\n,\tO\nyou're\tO\nstill\tO\nunbearable\tO\n.\tO\nGive\tO\nArizona\tB-geo-loc\na\tO\nbreak\tO\n.\tO\nLove\tO\n,\tO\nMackenna\tB-person\n\t\n@since1938\tO\n@Kimmiecoomd\tO\nKudos\tO\nto\tO\nthem\tO\n,\tO\nLois\tB-person\nwas\tO\nalways\tO\n"\tO\nthere\tO\n's\tO\nsomething\tO\nabout\tO\nyou\tO\n..\tO\nwhatever\tO\ntell\tO\nme\tO\nwhen\tO\nyou're\tO\nready\tO\n"\tO\nthing\tO\n.\tO\n\t\nRT\tO\n@MirandaBuzz\tO\n:\tO\nLast\tO\nday\tO\nof\tO\nthis\tO\nseason\tO\n!!!\tO\nNext\tO\nseason\tO\nI\tO\nhope\tO\nI'm\tO\ndriving\tO\nmyself\tO\nto\tO\nwork\tO\n!\tO\nGonna\tO\nmiss\tO\neverybody\tO\nlike\tO\ncrazy\tO\n:)\tO\n\t\n@Mys_Perdita\tO\nha\tO\nye\tO\ntheyr\tO\nmay\tO\nbe\tO\nsevere\tO\nconsiquences\tO\nof\tO\nour\tO\nmeeting\tO\nlike\tO\nthe\tO\nwaypeople\tO\ntalk\tO\nbout\tO\ntheday\tO\nsum1\tO\ntold\tO\nadolf\tO\nhe\tO\nwasnt\tO\nblond\tO\nenuf\tO\n:\tO\nS\tO\n\t\n@SofieB_\tO\nHah\tO\n!\tO\nThat\tO\n's\tO\nthe\tO\none\tO\nprogramme\tO\nmy\tO\ndad\tO\nand\tO\nI\tO\nwatch\tO\ntogether\tO\nevery\tO\nweek\tO\n!\tO\n\t\nAre\tO\nyou\tO\nready\tO\nfor\tO\nthe\tO\nshield\tO\ngame\tO\ntonight\tO\nat\tO\n7\tO\n?\tO\n\t\nFriday\tO\nnight\tO\nis\tO\nthe\tO\nbest\tO\ncus\tO\nu\tO\nknow\tO\nthe\tO\nnest\tO\nday\tO\nu\tO\ncan\tO\nrest\tO\n!!!!!\tO\n:D\tO\n\t\nHonestly\tO\nwhat\tO\nis\tO\nwrong\tO\nwith\tO\nkids\tO\ntoday\tO\n.\tO\nOn\tO\na\tO\npacked\tO\nt\tO\nand\tO\nthere\tO\nare\tO\n2\tO\nolder\tO\nppl\tO\n(\tO\n1\tO\nw/\tO\na\tO\ncane\tO\nmind\tO\nyou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\nsecond\tO\n!\tO\n\t\nWhen\tO\nthe\tO\nlast\tO\ntime\tO\nyou\tO\nran\tO\ninto\tO\nRick\tB-person\nRoss\tI-person\nand\tO\nDrake\tB-person\ntwice\tO\nin\tO\nthe\tO\nsame\tO\nday\tO\nat\tO\n2\tO\ndiff\tO\nvideo\tO\nshoot\tO\nlocations\tO\n.\tO\nToday\tO\nI\tO\ndid\tO\n!\tO\n\t\nImma\tO\nget\tO\nyour\tO\nheart\tO\nracing\tO\nin\tO\nmy\tO\nskin-tight\tO\njeans\tO\nbe\tO\nyour\tO\nteenage\tO\ndream\tO\ntonight\tO\n....\tO\n\t\nRT\tO\n@itsJuliaHardy\tO\n@WanderingRyu\tO\nI\tO\nnoticed\tO\na\tO\nRyu\tO\nshaped\tO\nhole\tO\nin\tO\nthe\tO\nshow\tO\n...\tO\n;)\tO\nDef\tO\ntune\tO\nin\tO\nnext\tO\nweek\tO\nif\tO\nu\tO\ncan\tO\n.\tO\nXX\tO\n\t\nPLANO\tB-other\nBALLOON\tI-other\nFESTIVAL\tI-other\n2010\tI-other\nTO\tO\nSTART\tO\nTODAY\tO\n:\tO\nThe\tB-other\n31st\tI-other\nannual\tI-other\nPlano\tI-other\nBalloon\tI-other\nFestival\tI-other\n,\tO\nPlano\tB-other\nBalloon\tI-other\nFestival\tI-other\n2\tO\n...\tO\nhttp://tinyurl.com/22kg6uz\tO\n\t\ni'll\tO\nmiss\tO\nyooouuu\tO\nmy\tO\naunty\tO\ntersayangg\tO\n....\tO\nSee\tO\nU\tO\nnext\tO\nTime\tO\nyah\tO\n...\tO\nMmuah\tO\n:\tO\n*\tO\n\t\nHPU\tO\npau\tO\nfor\tO\nthe\tO\nday\tO\n!\tO\nHappy\tO\nAloha\tO\nFriday\tO\n!!!\tO\n\t\nMy\tO\nhorrible\tO\ndoctor\tO\nwas\tO\nfeatured\tO\non\tO\nOh\tB-other\nCanada\tI-other\nTeam\tI-other\n's\tO\nblog\tO\n:\tO\nhttp://ohcanadateam.blogspot.com/2010/09/friday-finds-doctor-horrible.html\tO\n\t\n@Cuddle_Factory\tO\nwill\tO\nsend\tO\nagain\tO\ntomorrow\tO\nhoney\tO\ntoo\tO\n\t\nRT\tO\n@GraceThorson\tO\n:\tO\nI\tO\nenjoy\tO\nreading\tO\n,\tO\nbecause\tO\nI\tO\ncan\tO\nvisit\tO\ndifferent\tO\nplaces/time\tO\nperiods\tO\n,\tO\nand\tO\nI\tO\nalways\tO\nlearn\tO\nsomething\tO\nfrom\tO\nit\tO\n.\tO\n@ChristianScroll\tO\n\t\n@chazzy00\tO\nwhat\tO\nwe\tO\non\tO\ntonight\tO\nfriend\tO\n\t\nAnd\tO\nwe\tO\nhad\tO\nsome\tO\namazing\tO\ndessert\tO\nwines\tO\ntoo\tO\n!\tO\nFirst\tO\ntime\tO\nI've\tO\ntried\tO\nthat\tO\n,\tO\nbut\tO\nnow\tO\na\tO\nconvert\tO\n.\tO\nWill\tO\nhave\tO\nto\tO\nadd\tO\nwine\tO\nto\tO\ndessert\tO\nfrom\tO\nnow\tO\non\tO\n!\tO\nYum\tO\n!\tO\n\t\nThis\tO\nweek\tO\nI\tO\nhave\tO\npeed\tO\na\tO\nlot\tO\nand\tO\nhave\tO\neaten\tO\nvery\tO\nwell\tO\n.\tO\nWeekend\tO\n,\tO\nplease\tO\ndon't\tO\nkill\tO\nme\tO\n.\tO\n\t\ni\tO\nhave\tO\na\tO\nfeeling\tO\nthat\tO\nthis\tO\nweekend\tO\nwill\tO\nbe\tO\namazing\tO\n...\tO\nor\tO\nat\tO\nleast\tO\ntonight\tO\nwill\tO\nbe\tO\n.\tO\n\t\nRT\tO\n@JBieber_isHOT\tO\n:\tO\nWhen\tO\nJustin\tB-person\ngets\tO\na\tO\ngirlfriend\tO\n...\tO\nits\tO\ngoing\tO\nto\tO\nbe\tO\nWorld\tB-other\nWar\tI-other\n3\tI-other\n!!!!!!\tO\n\t\n@Iamjustinbieb\tO\ni\tO\nknow\tO\nthat\tO\ntoday\tO\nis\tO\nfollow\tO\nfriday\tO\n.\tO\nso\tO\nim\tO\nguessing\tO\nthats\tO\nwhat\tO\ni\tO\nmeans\tO\n.\tO\nbut\tO\nim\tO\nnot\tO\nsuree\tO\nlmaoo\tO\n\t\nCopa\tB-other\nSudamericana\tI-other\n#2010\tO\n:\tO\nThursday\tO\nRound-Up\tO\n-\tO\nhttp://newzfor.me/?754c\tO\n\t\nRT\tO\n@PrincePoppycock\tO\n:\tO\nA\tO\ngentleman\tO\nwould\tO\nnever\tO\nget\tO\nhis\tO\nlipstick\tO\non\tO\na\tO\nlady\tO\n's\tO\nhand\tO\nwhen\tO\nkissing\tO\nit\tO\n!\tO\n\t\n@keoghsie\tO\nIt\tO\n's\tO\nbeen\tO\nages\tO\n!!\tO\nHow\tO\nare\tO\nyou\tO\n?\tO\nWe've\tO\nbeen\tO\ndoing\tO\nnon-Twitter\tO\nstuff\tO\nfor\tO\na\tO\nwhile\tO\n,\tO\nbut\tO\nnow\tO\nhave\tO\ntime\tO\nfor\tO\nyapping\tO\nagain\tO\n!\tO\nHUrrah\tO\n!!\tO\n\t\nDriving\tO\n,\tO\ndriving\tO\n,\tO\ndriving\tO\naway\tO\nto\tO\nPhil\tB-geo-loc\n.\tO\nTasty\tO\ndinner\tO\ntonight\tO\nwith\tO\nthe\tO\nSociety\tB-other\nof\tI-other\nMining\tI-other\nand\tI-other\nMetallurgy\tI-other\nEngineers\tI-other\n.\tO\n\t\nThis\tO\n.\tO\nRT\tO\n@bethanyshondark\tO\n:\tO\nRT\tO\n@duchess_rebecca\tO\n:\tO\nTwitter\tB-company\nis\tO\ngoing\tO\nto\tO\nbe\tO\nless\tO\nfun\tO\ntonight\tO\nwithout\tO\nthe\tO\nsnarky\tO\nJoos\tO\n.\tO\nAw\tO\n!!\tO\n\t\nTOP\tO\nEVENT\tO\n--\tO\n>\tO\nhttp://schmap.it/98hCOt\tO\n-\tO\nAnd\tO\ntomorrow\tO\nthe\tO\n@BaristaThing\tO\nEvent\tO\ngoes\tO\ndown\tO\n-\tO\nopen\tO\nto\tO\npros\tO\n,\tO\nhome\tO\nbaristas\tO\n,\tO\nc\tO\n...\tO\n(\tO\nvia\tO\n@CoffeeGeek\tO\n)\tO\n\t\n@kajelly_milk\tO\n(\tO\n:\tO\nbut\tO\nhey\tO\n..\tO\nits\tO\nall\tO\ngood\tO\n.\tO\ni\tO\nlike\tO\nhim\tO\nhaha\tO\n&omg\tO\njason\tO\nkept\tO\ntouching\tO\nme\tO\ntoday\tO\nD\tO\n;\tO\ni\tO\nwas\tO\nlike\tO\nman\tO\nstop\tO\ntouching\tO\nO\tO\n:\tO\n\t\n@x_Jamelia_x\tO\nWhen\tO\nr\tO\nu\tO\ngona\tO\nrealise\tO\nwe\tO\ndont\tO\nwant\tO\nu\tO\non\tO\nour\tO\ntvs\tO\n??\tO\nTo\tO\nprove\tO\nmy\tO\npoint\tO\nenter\tO\nur\tO\nname\tO\ninto\tO\nsearch\tO\n..\tO\nUve\tO\nhad\tO\nur\tO\n15mins\tO\n!\tO\n\t\nI\tO\nliked\tO\na\tO\nYouTube\tB-company\nvideo\tO\n--\tO\nTesting\tB-musicartist\nTomorrow\tI-musicartist\nhttp://youtu.be/aOYInsel7Qw?a\tO\n\t\nI\tO\nlove\tO\nto\tO\nsee\tO\na\tO\nchild\tO\n's\tO\nface\tO\nwhen\tO\nthey\tO\nthink\tO\nof\tO\nthe\tO\nfairies\tO\n.\tO\nLike\tO\na\tO\nfairy\tO\ntheir\tO\nlittle\tO\nfaces\tO\nlight\tO\nup\tO\n:)\tO\n\t\nThe\tO\nforced\tO\nads\tO\nin\tO\nngmoco\tB-company\ngames\tO\nare\tO\noutstandingly\tO\nannoying\tO\n!\tO\nLooks\tO\nlike\tO\nit\tO\n's\tO\ntime\tO\nto\tO\ndelete\tO\nall\tO\ntheir\tO\napps\tO\nfrom\tO\nmy\tO\niOS\tB-product\ndevices\tO\n!\tO\n\t\nRT\tO\n@LightCMS\tO\n:\tO\nDay\tO\n6\tO\n:\tO\nWin\tO\n1\tO\nof\tO\n10\tO\niPads\tO\nfrom\tO\n@LightCMS\tO\n-\tO\nRT\tO\nto\tO\nenter\tO\nor\tO\nread\tO\nmore\tO\nhttp://speaklight.com/iPad\tO\n\t\nRT\tO\n@KarlaRay\tO\n:\tO\nWhat\tO\na\tO\nday\tO\nin\tO\n#Collier\tO\n!\tO\nWatch\tO\n@NBC2\tO\nat\tO\n6\tO\nfor\tO\ndetails\tO\non\tO\nthe\tO\nlatest\tO\ngrowhouse\tO\nbust\tO\nand\tO\nthe\tO\ncounty\tO\n's\tO\nfirst-ever\tO\nWest\tO\nNile\tO\nd\tO\n...\tO\n\t\nI\tO\nplayed\tO\nthis\tO\nout\tO\nnot\tO\ntoo\tO\nlong\tO\nago\tO\nlol\tO\n-\tO\nhttp://www.youtube.com/watch?v=aWISNFAvGkw\tO\nI\tO\nreally\tO\ndo\tO\nlike\tO\nit\tO\neven\tO\nthough\tO\nit\tO\n's\tO\nutter\tO\nshit\tO\n.\tO\n\t\n\"\tO\nYou\tO\nmay\tO\nhave\tO\ngiven\tO\nup\tO\non\tO\nme\tO\n,\tO\nbut\tO\nI'm\tO\nnot\tO\ngiven\tO\nup\tO\non\tO\nyou\tO\n.\"\tO\n-The\tO\nBoy\tO\nYou\tO\nUse\tO\nTo\tO\nLove\tO\n\t\nArtful\tO\nPumpkins\tO\nLadies\tO\nLong\tO\nSleeve\tO\n:\tO\nArtful\tO\nPumpkins\tO\nLadies\tO\nLong\tO\nSleeve\tO\n.\tO\nAltered\tO\nvintage\tO\ndesign\tO\n.\tO\nBackgroun\tO\n...\tO\nhttp://bit.ly/9RRYOI\tO\n#tshirts\tO\n\t\nJune\tB-other\nFest\tI-other\n2010\tI-other\n~\tO\nForgotten\tB-facility\nDoor\tI-facility\nLIVE\tO\n!\tO\nhttp://fb.me/G1pof3lO\tO\n\t\n@marciaforbes\tO\nWhen\tO\nU\tO\ndig\tO\ngrave\tO\nalways\tO\ndig\tO\n2\tO\n.\tO\nLike\tO\nhe\tO\nwanted\tO\nto\tO\ndiminish\tO\nBrady\tB-person\n&\tO\nend\tO\nup\tO\ndoing\tO\nthat\tO\nto\tO\nhimself\tO\n<\tO\n>\tO\nTrue\tO\n....\tO\nDat\tO\n\t\nI'm\tO\ntired\tO\nafter\tO\nschool\tO\ntoday\tO\n!\tO\n\t\n\"\tO\nsmell\tO\n.\tO\n(\tO\nthe\tO\nlack\tO\nof\tO\ncould\tO\nonly\tO\nhelp\tO\nwhen\tO\nforced\tO\nto\tO\ngo\tO\ncowtipping\tO\nwith\tO\nram\tO\n.\tO\n)\tO\n\"\tO\n-\tO\n@jenist\tO\nwins\tO\n!\tO\nyour\tO\nmineral\tO\nwater\tO\nis\tO\nin\tO\nthe\tO\nmail\tO\n.\tO\n\t\nfour\tO\nmonths\tO\n<\tO\n3\tO\n\t\nRain\tO\nrain\tO\ngo\tO\naway\tO\ncome\tO\nback\tO\nwhen\tO\nit\tO\n's\tO\nnot\tO\nmy\tO\ndads\tO\nbirthday\tO\n.\tO\n\t\n@justinbieber\tO\nWhen\tO\nyou\tO\nsmile\tO\nI\tO\nsmile\tO\n...\tO\nseriously\tO\nI\tO\ndo\tO\n!!!!\tO\n\t\n@Surfrdan\tO\nto\tO\nbe\tO\nfair\tO\nthough\tO\nit\tO\n's\tO\nroughly\tO\nthe\tO\nmillionth\tO\ntime\tO\nhe\tO\n's\tO\nplayed\tO\nTony\tB-person\nBlair\tI-person\n.\tO\n\t\n@jenniferehle\tO\n:\tO\nDid\tO\nyou\tO\nget\tO\nheavy\tO\nrains\tO\nup\tO\nthere\tO\nyesterday\tO\n?\tO\n\t\nSweet\tO\n!\tO\nRT\tO\n@SimoneSOliver\tO\n:\tO\nHere\tO\n's\tO\nthe\tO\nLondon\tB-other\nFashion\tI-other\nWeek\tI-other\nschedule\tO\n:\tO\nhttp://nyti.ms/bW0N3u\tO\n\t\nRT\tO\n@davehodg\tO\n:\tO\nPope\tB-person\nsays\tO\natheists\tO\npick\tO\n&\tO\nchoose\tO\ntheir\tO\nmorals\tO\n.\tO\nCorrect\tO\n.\tO\nToday\tO\nI\tO\nwill\tO\nbe\tO\nfrowning\tO\non\tO\nchild\tO\nabuse\tO\n&\tO\nnot\tO\nhaving\tO\na\tO\nproblem\tO\nwit\tO\n...\tO\n\t\n@DeezyWest\tO\nraiders\tO\nget\tO\nthere\tO\nfirst\tO\nwin\tO\nthis\tO\nsunday\tO\n#Predictions\tO\n\t\nWHATS\tO\nGOODIE\tO\nEVERY1\tO\nCOOLIN\tO\nCOOLIN\tO\nFEELIN\tO\nTHE\tO\nBREEZE\tO\n#FRIDAY#MANIA\tO\n\t\n@valdary\tO\nsorry\tO\ni\tO\nmeant\tO\nmy\tO\ndad\tO\nin\tO\nthe\tO\nfirst\tO\ntweet\tO\n,\tO\nmy\tO\nmums\tO\nbeen\tO\ndead\tO\n5\tO\nyears\tO\n.\tO\n\t\nGreat\tO\nweek\tO\nfor\tO\nmusic\tO\nwith\tO\n@blindoldfreak\tO\n,\tO\nUNKLE\tB-musicartist\n&\tO\n@trent_reznor\tO\nall\tO\nputting\tO\nout\tO\nnew\tO\nreleases\tO\n.\tO\nAmazing\tO\nstuff\tO\n.\tO\n**Support\tO\ndecent\tO\nmusic**\tO\n\t\n@victorcajiao\tO\nSo\tO\ncan\tO\nI\tO\ncall\tO\nin\tO\nsick\tO\non\tO\nmy\tO\nlast\tO\nday\tO\non\tO\nthe\tO\njob\tO\nso\tO\nI\tO\ncan\tO\ngo\tO\nout\tO\nthere\tO\nand\tO\nlook\tO\nat\tO\nit\tO\n?\tO\n:-)\tO\n\t\nThank\tO\nGod\tO\nit\tO\n's\tO\nfinally\tO\nFriday\tO\n!\tO\nI\tO\nreally\tO\nneeded\tO\nthis\tO\n.\tO\n:-)\tO\n\t\nTelly\tO\nwas\tO\njunk\tO\nlast\tO\nweek\tO\n\t\nLove\tO\nme\tO\nwhen\tO\nI\tO\nleast\tO\ndeserve\tO\nit\tO\n,\tO\nbecause\tO\nthat\tO\n's\tO\nwhen\tO\nI\tO\nneed\tO\nit\tO\nthe\tO\nmost\tO\n.\tO\n\t\nI\tO\nreally\tO\nfeel\tO\nlike\tO\nstaying\tO\nup\tO\nall\tO\nnight\tO\nlistening\tO\nto\tO\nmy\tO\nCDs\tO\n.\tO\nThe\tO\nneighbours\tO\nand\tO\nmy\tO\nboss\tO\nat\tO\nwork\tO\ntomorrow\tO\nwould\tO\ncomplain\tO\n,\tO\nso\tO\nno\tO\ncan\tO\ndo\tO\n:(\tO\n\t\n@SweetBee_90\tO\nwhat\tO\n's\tO\nthe\tO\nlook\tO\nfor\tO\ntonight\tO\n?\tO\n\t\nvolunteering\tO\nfor\tO\nthe\tO\nRotary\tB-facility\nClub\tI-facility\nat\tO\nthe\tO\nPoMo\tO\nTerry\tB-person\nFox\tI-person\nrun\tO\n.\tO\nShould\tO\nbe\tO\nan\tO\nexcellent\tO\ntime\tO\n:\tO\ncome\tO\ndown\tO\nand\tO\ncheck\tO\nit\tO\nout\tO\non\tO\nSunday\tO\nam\tO\n!\tO\n\t\nI\tO\ndistinctly\tO\nremember\tO\nsomeone\tO\ngiving\tO\nme\tO\nchocolates\tO\nlast\tO\nweek\tO\n.\tO\nI\tO\ndon't\tO\nrecall\tO\neating\tO\na\tO\nsingle\tO\npiece\tO\n.\tO\nThey're\tO\nnowhere\tO\nto\tO\nbe\tO\nfound\tO\nof\tO\ncourse\tO\n.\tO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\namerica\tB-geo-loc\nwhen\tO\ni'm\tO\nat\tO\nleast\tO\n23\tO\n.\tO\nno\tO\njoke\tO\n\t\nim\tO\nsick\tO\nhad\tO\na\tO\nbad\tO\nday\tO\nhope\tO\nthee\tO\ngame\tO\nus\tO\ngood\tO\n\t\nfoi\tO\nquando\tO\neu\tO\nte\tO\nencontrei\tO\n,\tO\nouvindo\tO\no\tO\nsom\tO\ndo\tO\nmar\tO\n(\tO\n8\tO\n\t\nI\tO\nwent\tO\nto\tO\nold\tB-company\nnavy\tI-company\ntoday\tO\nand\tO\nbought\tO\ntwo\tO\npairs\tO\nof\tO\npants\tO\n.\tO\nNow\tO\nI\tO\nhave\tO\nthree\tO\npairs\tO\nof\tO\npants\tO\n!\tO\n\t\nHa\tO\nha\tO\ni\tO\ntry\tO\nfor\tO\nyou\tO\nreally\tO\nhm\tO\nlmao\tO\nlame\tO\npretty\tO\nday\tO\n\t\n@HotwifeKay\tO\nafter\tO\nBig\tO\nMaxxx\tO\n@killerCram\tO\n\"\tO\nleft\tO\nhis\tO\nmark\tO\n\"\tO\non\tO\nmy\tO\nhottie\tO\nSlutWife\tO\n:\tO\nhttp://tinyurl.com/25gxuvq\tO\n\t\n@SweetTartelette\tO\nGlad\tO\nto\tO\nhear\tO\nthey\tO\nare\tO\nokay\tO\n.\tO\nA\tO\ndrunk\tO\nran\tO\ninto\tO\nthe\tO\nback\tO\nof\tO\nmy\tO\nson\tO\n's\tO\ncar\tO\nthis\tO\nweek\tO\n.\tO\nHe\tO\nsaid\tO\nit\tO\nwas\tO\nfrightening\tO\n.\tO\nN\tO\n\t\n@IZONModels\tO\nwe\tO\nall\tO\nneed\tO\nit\tO\nfrom\tO\ntime\tO\nto\tO\ntime\tO\n.\tO\n:)\tO\n\t\n#uknouugly\tO\nwhen\tO\n#TeamFollowBack\tO\ndnt\tO\nfollow\tO\nyou\tO\nback\tO\n\t\n@jeffstinco\tO\nor\tO\n@chuckcomeau\tO\nfollow\tO\nme\tO\nfor\tO\nmake\tO\nthis\tO\nbad\tO\nday\tO\na\tO\nHAPPY\tO\nday\tO\n:D\tO\nlove\tO\nyou\tO\nguys\tO\n!!!\tO\n\t\n@sarahXtinchyBBK\tO\nlooool\tO\nu\tO\nhav\tO\nmade\tO\nme\tO\nwant\tO\nmcdz\tO\nagain\tO\nan\tO\nim\tO\ntryin\tO\n2\tO\nstay\tO\noff\tO\nthat\tO\nhad\tO\nit\tO\nall\tO\nweek\tO\nan\tO\nsounds\tO\nnice\tO\n:D\tO\nx\tO\n\t\n...\tO\naround\tO\nall\tO\nday\tO\nisnt\tO\ngood\tO\nfor\tO\nyou\tO\n,\tO\nleast\tO\ni\tO\nget\tO\nhome-relaxing\tO\ntime\tO\n,\tO\nthursday\tO\nand\tO\nfrees\tO\nto\tO\nkeeep\tO\nme\tO\nsane\tO\n:)\tO\n\t\nRT\tO\n@PHANTHANHstic\tO\n\"\tO\nGood\tO\nMorning\tO\n!\tO\nHave\tO\na\tO\npleasant\tO\nand\tO\nhappy\tO\nday\tO\n!\"\tO\n\t\nHey\tO\n....\tO\nmy\tO\nbirthday\tO\nis\tO\nin\tO\nexactly\tO\none\tO\nmonth\tO\n!!!\tO\n:D\tO\n\t\n@KaiyhaBADDass\tO\nfor\tO\nyou\tO\n?\tO\nwhen\tO\nI\tO\nsee\tO\nyou\tO\n...\tO\nseriously\tO\n...\tO\n\t\n@Romanthropy\tO\noi\tO\nu\tO\nthere\tO\n's\tO\na\tO\n#MCRchat\tO\non\tO\ntonight\tO\n.\tO\nNo\tO\nidea\tO\nwhat\tO\nit\tO\nentails\tO\nbut\tO\nif\tO\nit\tO\n's\tO\nnew\tO\nalbum\tO\nconvos\tO\nwere\tO\nin\tO\n!!\tO\n\t\nmiren\tO\nel\tO\nvideo0\tO\none\tO\ntime\tO\n....\tO\nESTA\tO\nSIENDO\tO\nATACADO\tO\nX\tO\nLOS\tO\nNO\tO\nME\tO\nGUSTA\tO\nD\tO\n:\tO\n\t\nRT\tO\n@chloezxy\tO\n:\tO\nhad\tO\na\tO\ngreat\tO\ntime\tO\nhanging\tO\nout\tO\nand\tO\ndinner-ing\tO\nwith\tO\nDL3\tO\npeeps\tO\n!!\tO\n:)\tO\nthey\tO\nare\tO\nreally\tO\nawesome\tO\npeople\tO\n!!\tO\n:D\tO\n\t\nim\tO\nthinking\tO\njalepeno\tO\npoppers\tO\ntonight\tO\n:]\tO\n]\tO\n\t\nToday\tO\nis\tO\ngoin\tO\nto\tO\nslow\tO\nat\tO\nwork\tO\n...\tO\nCan't\tO\nwait\tO\nto\tO\nget\tO\nthis\tO\nweekend\tO\nstarted\tO\n!!!\tO\n\t\n@johngirvin\tO\nyou've\tO\nbeen\tO\nworking\tO\non\tO\nthat\tO\nall\tO\nnight\tO\n!\tO\nI\tO\nsuspect\tO\nyou\tO\nmay\tO\neven\tO\nhave\tO\nopened\tO\na\tO\ngit\tB-product\nmanual\tO\n.\tO\n\t\n@joereist\tO\nhey\tO\nJoe\tB-person\n.\tO\nI'm\tO\ndown\tO\nthe\tO\nroad\tO\nin\tO\nFt\tB-geo-loc\nMitchell\tI-geo-loc\n.\tO\nNo\tO\ntime\tO\nto\tO\nsmoke\tO\nbut\tO\nwaving\tO\n.\tO\nDid\tO\nyou\tO\nget\tO\nthe\tO\nbook\tO\n?\tO\n\t\nCongrats\tO\nto\tO\n@BigBlueSpruce\tO\nand\tO\ntheir\tO\nrecord\tO\nrelease\tO\ntoday\tO\n!\tO\nJoin\tO\nus\tO\nto\tO\ncelebrate\tO\nwith\tO\nthem\tO\nat\tO\n@worldcafelive\tO\nGet\tO\nyour\tO\nCD\tO\n&\tO\ntees\tO\n8PM\tO\ntoday\tO\n!\tO\n\t\nDearrykas\tO\n#1\tO\nrule\tO\nof\tO\nlifenever\tO\ndate\tO\nsomeone\tO\nwho\tO\nlives\tO\nby\tO\nyou\tO\nit\tO\nonly\tO\ncauses\tO\ntroublr\tO\nwhen\tO\nyou\tO\nbreak\tO\nup\tO\n\t\nFRIDAY\tO\n!\tO\nyesshh\tO\n!!\tO\nFiNaLlY\tO\n\t\nJust\tO\nordered\tO\npizza\tO\n.\tO\nFirst\tO\ntime\tO\nsince\tO\nMay\tO\nto\tO\norder\tO\nPizza\tB-company\nHut\tI-company\n.\tO\nDefinitely\tO\nnot\tO\na\tO\nfave\tO\n,\tO\nbut\tO\nPizza\tO\nby\tO\nthe\tO\nChef\tO\ndoesn't\tO\ndeliver\tO\nfive\tO\nhours\tO\naway/\tO\n\t\nDrop\tO\nby\tO\nthe\tO\nsweat\tO\ncheck\tO\nI'll\tO\nbe\tO\nthere\tO\nvolunteering\tO\nRT\tO\n@EricJacksch\tO\nAh\tO\n,\tO\nthe\tO\nweekend\tO\n.\tO\nSee\tO\nyou\tO\nat\tO\nthe\tO\nArmy\tB-other\nRun\tI-other\non\tO\nSunday\tO\n?\tO\n#Ottawa\tO\n\t\nRT\tO\n@TCPB_McDreamy\tO\n:\tO\nGonna\tO\nstudy\tO\nfor\tO\na\tO\nwhile\tO\nbefore\tO\niTake\tO\nthis\tO\nquiz\tO\n...\tO\nwish\tO\nme\tO\nluck\tO\n=)\tO\n(\tO\nluck\tO\n)\tO\n\t\nThis\tO\nweek\tO\nonly\tO\n$5\tO\noff\tO\ncustom\tO\nblogger\tO\ndesign\tO\n!\tO\nhttp://www.bonanzle.com/LoisCollis\tO\n\t\n@sfgiantsfan55\tO\nomg\tO\nTodo\tB-person\nCabio\tI-person\nhas\tO\nbeen\tO\nmy\tO\nfav\tO\nsince\tO\nlike\tO\n`07\tO\ni\tO\nstill\tO\nlisten\tO\nto\tO\ntheir\tO\nCD\tO\nall\tO\nthe\tO\ntime\tO\ni\tO\nwant\tO\nthe\tO\nnew\tO\none\tO\n.\tO\nalejate\tO\nde\tO\nmi\tO\n...\tO\n\t\nI\tO\nknow\tO\nu\tO\nlike\tO\nthat\tO\nturn\tO\nyou\tO\ninto\t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\tO\nfor\tO\nQ&A\tO\n.\tO\nBack\tO\nin\tO\nIone\tB-company\nto\tO\nride\tO\ntomorrow\tO\n!\tO\nhttp://ow.ly/2G4ti\tO\n\t\nnew\tO\noffice\tO\nfurniture-\tO\ncheck\tO\n,\tO\npaintings\tO\nof\tO\nhubby-\tO\ncheck\tO\n,\tO\nneuro\tO\ndrink\tO\nstock-\tO\ncheck\tO\n,\tO\nfinished\tO\ngiving\tO\ninterview-\tO\ncheck\tO\n.\tO\ntime\tO\n2\tO\ngo\tO\nhome\tO\n.\tO\n\t\nThe\tO\nweek\tO\nahead\tO\n-\tO\nhttp://newzfor.me/?7jfc\tO\n\t\nG'mar\tO\nchatima\tO\ntova\tO\n,\tO\ninternet\tO\n!\tO\n\t\n'RT\tO\n@ihatequotes\tO\n:\tO\nFor\tO\nanyone\tO\nhaving\tO\na\tO\nbad\tO\nday\tO\n,\tO\njust\tO\nremember\tO\n:\tO\n\"\tO\nIf\tO\nGod\tB-person\nbrings\tO\nyou\tO\nto\tO\nit\tO\n,\tO\nHe\tO\nwill\tO\nbring\tO\nyou\tO\nthrough\tO\nit\tO\n!\"\tO\n-@KimKardashian\tO\n#i\tO\n...'\tO\n\t\n@Alleycat17\tO\nHAPPY\tO\nBIRTHDAY\tO\n!\tO\n:D\tO\n.\tO\nHope\tO\nyou're\tO\nhaving\tO\nan\tO\namazingrific\tO\nday\tO\n!!!\tO\n:D\tO\n.\tO\n\t\nRT\tO\n@LUNGevity\tO\n:\tO\nThis\tO\nis\tO\ngoing\tO\nto\tO\nbe\tO\nan\tO\namazing\tO\nweekend\tO\nw/3\tO\nevents\tO\non\tO\nthe\tO\nsame\tO\nday\tO\nbenefiting\tO\nlung\tO\ncancer\tO\nresearch\tO\n(\tO\nSun/Sept\tO\n19\tO\n)\tO\n!...\tO\nhttp\tO\n...\tO\n\t\nellwood\tB-person\n's\tO\nsushi\tO\n,\tO\na\tO\nglass\tO\nof\tO\npinot\tO\n,\tO\n"\tO\nstrokes\tB-other\nof\tI-other\ngenius\tI-other\n"\tO\nby\tO\njohn\tB-person\nwertheim\tI-person\n,\tO\nplay\tO\nat\tO\nbarksdale\tB-facility\nin\tO\na\tO\nbit\tO\n,\tO\nlovely\tO\nfriday\tO\nnight\tO\n:)\tO\n\t\nRT\tO\n@Kiara_TheGR8\tO\n:\tO\nSomeone\tO\nplease\tO\nwake\tO\nme\tO\nup\tO\nwhen\tO\nMay\tO\n7\tO\n,\tO\n2011\tO\ngets\tO\nhere\tO\n!\tO\n#thanks\tO\n\t\n@henryandfriends\tO\nooh\tO\nthat\tO\nsounds\tO\nlovely\tO\n:)\tO\nwe\tO\nhad\tO\nlots\tO\nsunshine\tO\n2-\tO\nhope\tO\nit\tO\nstays\tO\n4\tO\ntomo\tO\nwhen\tO\nwe\tO\ngoing\tO\nto\tO\ndo\tO\nthe\tO\ndoggie\tO\nshow\tO\n*paws\tO\nx-d*\tO\n!\tO\nx\tO\nx\tO\n\t\nHaven't\tO\ntweeted\tO\nsince\tO\njune\tO\nwow\tO\n.......................\tO\nbut\tO\nI\tO\ndid\tO\nget\tO\na\tO\nfacebook\tB-company\nso\tO\nwho\tO\ncares\tO\nabout\tO\ntwitter\tB-company\n\t\n'I\tO\njust\tO\ntook\tO\n\"\tO\nJustin\tB-person\nBieber\tI-person\ntakes\tO\n2\tO\nmonths\tO\noff\tO\ncareer\tO\nto\tO\nfinish\tO\nschool\tO\n,\tO\nH\tO\n...\"\tO\nand\tO\ngot\tO\n:\tO\nPart\tO\n25\tO\n:\tO\nThey\tO\ngot\tO\nmy\tO\nback\tO\n.!\tO\nTry\tO\nit\tO\n:\tO\nhttp://bit.ly/bcqNCs'\tO\n\t\nSo\tO\nhere\tO\nin\tO\nBelgium\tB-geo-loc\nits\tO\nnow\tO\n7minutes\tO\nbefore\tO\nmidnight\tO\n,,\tO\nTell\tO\nme\tO\nhow\tO\nlate\tO\nit\tO\nis\tO\nwere\tO\nyou\tO\nlive\tO\n:D\tO\n\t\nRT\tO\n@eljmayes\tO\n:\tO\nLadbrokes\tO\nLabour\tO\nLeadership\tO\nMarket-\tO\nhttp://bit.ly/cD3Rn8\tO\nEd\tB-person\nMiliband\tI-person\n's\tO\nodds\tO\nhave\tO\nshortened\tO\nsignificantly\tO\nin\tO\nthe\tO\nlast\tO\nweek\tO\n.\tO\n\t\nHa\tO\nI'm\tO\nlucky\tO\n.\tO\nI\tO\ngot\tO\nin\tO\nthe\tO\ncar\tO\nlike\tO\nfive\tO\nseconds\tO\nbefore\tO\nit\tO\nstarted\tO\nPOURING\tO\nrain\tO\n.\tO\n\t\nAlways\tO\nkinda\tO\nawkward\tO\nwhen\tO\nyou\tO\nget\tO\nthe\tO\nchat\tO\ngroup\tO\nwrong\tO\nin\tO\nWoW\tB-product\n.\tO\nSometimes\tO\nyou\tO\ndon't\tO\nwant\tO\nthe\tO\ntank\tO\nto\tO\nknow\tO\nyou're\tO\ndissin\tO\n'\tO\nhim\tO\n.\tO\nOops\tO\n.\tO\n\t\nhe\tO\nlikes\tO\nprince\tB-person\n,\tO\npaul\tB-person\nsimon\tI-person\n,\tO\nand\tO\no.d.b.\tB-person\ni\tO\nmay\tO\nhave\tO\nfound\tO\nmy\tO\nsoul\tO\nmate\tO\n.\tO\n\t\n'@stephaniebriggs\tO\nJumped\tO\nin\tO\nthe\tO\ntruck\tO\n,\tO\nflipped\tO\nthe\tO\niPod\tB-product\nover\tO\nto\tO\nshuffle\tO\n,\tO\nand\tO\nit\tO\ngives\tO\nme\tO\n\"\tO\ngoodbye\tB-other\nmoon\tI-other\n\".\tO\nProbably\tO\na\tO\nyear\tO\nsince\tO\nlast\tO\nheard\tO\nit\tO\n!'\tO\n\t\nRT\tO\n@rainnwilson\tO\n:\tO\nHey\tO\n,\tO\nSeattle-ites\tO\n!\tO\nCheck\tO\nit\tO\n:\tO\nhttp://www.officetally.com/rainn-wilson-and-friends-seattle-oct-23\tO\n\t\nThink\tO\nthe\tO\narticle\tO\nshould\tO\nbe\tO\nready\tO\nto\tO\nresubmit\tO\nnext\tO\nweek\tO\n.\tO\nQuite\tO\nexcited\tO\n,\tO\nbut\tO\nalso\tO\nrather\tO\nscared\tO\n.\tO\n\t\nRT\tO\n@ezraklein\tO\n:\tO\nIt\tO\n's\tO\nYom\tB-other\nKippur\tI-other\neve\tO\n,\tO\nso\tO\nit\tO\n's\tO\nthe\tO\ntime\tO\nto\tO\napologize\tO\nto\tO\nanyone\tO\nI've\tO\nwronged\tO\n.\tO\nHopefully\tO\n,\tO\nyou're\tO\non\tO\nTwitter\tB-company\n.\tO\nSorry\tO\n!\tO\n\t\nFREE\tO\n:\tO\nAir\tB-product\nMusic\tI-product\nJump\tI-product\n.\tO\nif\tO\nyou\tO\nlike\tO\nRock\tB-product\nBand\tI-product\nor\tO\nGuitar\tB-product\nHero\tI-product\n,\tO\nyou\tO\nmay\tO\nalso\tO\nlike\tO\nMusic\tB-product\nJump\tI-product\n.\tO\nhttp://appj.mp/AirMusicJump\tO\n\t\n@Cortney_plus2\tO\nding\tO\nding\tO\nding\tO\n!\tO\nfront\tO\n,\tO\nback\tO\nand\tO\nlet\tO\n's\tO\njust\tO\nsay\tO\nI'll\tO\nbe\tO\nputting\tO\nsoph\tO\n's\tO\nhair\tO\nin\tO\na\tO\nbun\tO\nbefore\tO\nthe\tO\nnext\tO\nnap\tO\ntime\tO\n.\tO\n\t\nBack\tO\nat\tO\nwork\tO\n,\tO\nwaiting\tO\nfor\tO\nSunday\tO\nwhen\tO\nMike\tB-person\nVick\tI-person\nget\tO\nhis\tO\nshine\tO\non\tO\nVs\tO\n.\tO\nthe\tB-sportsteam\nLions\tI-sportsteam\n\t\nConflicts\tB-product\nof\tI-product\nLaw\tI-product\n:\tI-product\nCases\tI-product\nand\tI-product\nMaterials\tI-product\n:\tO\nR\tO\n.\tO\nLea\tB-person\nBrilmayer\tI-person\n,\tO\nJack\tB-person\nL\tI-person\n.\tO\nGoldsmith[May\tO\n01\tO\n,\tO\n2011\tO\n]\tO\nhttp://amzn.to/bnabQu\tO\n\t\nWhat\tO\na\tO\nday\tO\n<\tO\n3\tO\n\t\n@Kianerzzz\tO\nshowering\tO\nsleeping\tO\nand\tO\ntanning\tO\nalways\tO\nmake\tO\nme\tO\nfeel\tO\nbetter\tO\nwhen\tO\nI'm\tO\nsick\tO\n.\tO\n\t\nHope\tO\nto\tO\nsee\tO\na\tO\nlot\tO\nof\tO\npeople\tO\nat\tO\ngame\tO\nnight\tO\nat\tO\n@gathering_umc\tO\ntonight\tO\n.\tO\nPlenty\tO\nof\tO\ngames\tO\nto\tO\nchoose\tO\nfrom\tO\njust\tO\nfrom\tO\nmy\tO\nfamily\tO\n.\tO\n:)\tO\n\t\nRT\tO\n@jakeandamir\tO\n:\tO\nIf\tO\nyou\tO\nwould\tO\nhave\tO\ntold\tO\nme\tO\n20\tO\nyears\tO\nago\tO\nthat\tO\nYOM\tB-other\nKIPPUR\tI-other\nwould\tO\nbe\tO\ntrending\tO\non\tO\ntwitter\tB-company\nI'd\tO\nbe\tO\nall\tO\nlike\tO\n"\tO\nwhat\tO\n's\tO\ntwitter\tB-company\n?"\tO\n\t\nso\tO\nmuch\tO\nfor\tO\ntrying\tO\nto\tO\ndo\tO\nmy\tO\nh/w\tO\nbefore\tO\nmy\tO\nbday\tO\nweekend\tO\n.\tO\nworking\tO\ntil\tO\nclose\tO\n:/\tO\n\t\nRT\tO\n@its_QuiB\tO\n@jperiod1015\tO\ncooking\tO\nnext\tO\nweek\tO\n!!:\tO\n))\tO\n<\tO\n>\tO\nlol\tO\nbet\tO\n!\tO\n\t\nRT\tO\n@Quotealicious\tO\n:\tO\nA\tO\nguy\tO\n&\tO\ngirl\tO\ncan\tO\nbe\tO\nfriends\tO\n.\tO\nBut\tO\nsooner\tO\nor\tO\nlater\tO\n,\tO\none\tO\nwill\tO\nfall\tO\nfor\tO\nthe\tO\nother\tO\n.\tO\nMaybe\tO\ntoo\tO\nearly\tO\n,\tO\nmaybe\tO\ntoo\tO\nLATE\tO\nor\tO\nmay\tO\n...\tO\n\t\n@P_Rezzphere\tO\n:\tO\nuh\tO\n...\tO\nFruit\tO\nthis\tO\ntime\tO\n..\tO\nGotta\tO\nstay\tO\nin\tO\nshape\tO\nmy\tO\nbrotha\tO\n...\tO\n\t\n@CHAMBERSfever\tO\ni\tO\nused\tO\nto\tO\nhave\tO\nfishes\tO\nbut\tO\nthey\tO\nlived\tO\nlike\tO\n5\tO\ndays\tO\nwhat\tO\npet\tO\nwould\tO\nyou\tO\nlike\tO\nto\tO\nhave\tO\n?\tO\ni'm\tO\ndeeply\tO\nin\tO\nlove\tO\nwith\tO\nEli\tB-person\n\t\nAfter\tO\nthe\tO\nStorm\tO\n-\tO\nhttp://newzfor.me/?7r7c\tO\n[\tO\nLike\tO\nit\tO\n?\tO\nhttp://bit.ly/cWAfsq\tO\n]\tO\n\t\n@JayDubshow\tO\nit\tO\nshowed\tO\nyou\tO\nClapping\tO\non\tO\nTV\tO\nafter\tO\nthe\tO\nSwagger/Kofi\tB-other\nMatch\tI-other\nLol\tO\n\t\nRT\tO\n@PeterRabbitt\tO\n:\tO\nHAPPY\tO\nBIRTHDAY\tO\nTO\tO\n@DaDaDaphne\tO\nWISHIN\tO\nYOU\tO\nTHE\tO\nBEST\tO\n&\tO\nHOPE\tO\nYOU\tO\nHAVE\tO\nA\tO\nWONDERFUL\tO\nDAY\tO\n\t\n@becky_89\tO\nOh\tO\ndear\tO\n,\tO\nwell\tO\ni'll\tO\ncheer\tO\nyou\tO\nup\tO\non\tO\nWednesday\tO\n!\tO\nxx\tO\n\t\nToday\tO\n's\tO\nVuelter\tB-other\nblog\tI-other\n:\tO\nhttp://bit.ly/choe8Y\tO\n\t\nif\tO\ni\tO\nhit\tO\ntweet\tO\nlimit\tO\nagain\tO\ntoday\tO\n,\tO\ni'm\tO\ngonna\tO\nbe\tO\nso\tO\nfrickin\tO\n'\tO\npissed\tO\n.\tO\n\t\n@eatzombiebabies\tO\nhaha\tO\nmaybe\tO\nwe'll\tO\ndance\tO\noff\tO\nin\tO\nthe\tO\nonline\tO\nstream\tO\ntomorrow\tO\n!\tO\nlol\tO\n\t\n@George_Hill3\tO\nu\tO\nmade\tO\nmy\tO\nday\tO\n:)\tO\n\t\n"\tO\nWhen\tO\nyou\tO\ndon't\tO\nget\tO\nwhat\tO\nyou\tO\nwant\tO\n,\tO\nyou\tO\nsuffer\tO\n.\tO\nIf\tO\nyou\tO\nget\tO\nit\tO\n,\tO\nyou\tO\nsuffer\tO\ntoo\tO\nsince\tO\nyou\tO\ncan't\tO\nhold\tO\non\tO\nto\tO\nit\tO\nforever\tO\n."\tO\n~\tO\nPeaceful\tO\nWarrior\tO\n\t\nRT\tO\n@iSplashInYoMouf\tO\n:\tO\nRE*TWEET\tO\nIF\tO\nYOU\tO\nGOING\tO\nOUT\tO\nTONIGHT\tO\n?\tO\n\t\nReady\tO\nfor\tO\nFriday\tO\nnight\tO\nat\tO\nThe\tB-facility\nPub\tI-facility\n!\tO\nDUDE\tO\nJones\tB-person\nplaying\tO\ninside\tO\nand\tO\nDJ\tB-musicartist\nChris\tI-musicartist\nL\tI-musicartist\nin\tO\nLoft\tO\n\t\n'RT\tO\n@chambermusician\tO\nSprezzatura\tO\nTime\tO\n(\tO\n\"\tO\nBut\tO\n,\tO\nI\tO\ncould\tO\nplay\tO\nit\tO\nin\tO\npractice\tO\n\"\tO\n)\tO\n#music\tO\n#musicians\tO\nhttp://su.pr/3iJ0RE'\tO\n\t\n@luvgh62\tO\nnext\tO\nsun\tO\non\tO\nstelly\tO\nday-boo\tO\n!\tO\n\t\nfound\tO\nsome\tO\nreally\tO\nfunny\tO\npictures\tO\nof\tO\n1st\tO\nyear\tO\n:\tO\nL\tO\ngoing\tO\nto\tO\nput\tO\nthem\tO\non\tO\nfacebook\tB-company\nnow\tO\n!!\tO\n\t\n@bubzeh\tO\nWhat\tO\nare\tO\nyou\tO\nup\tO\nto\tO\ntoday\tO\n?\tO\n\t\n'or\tO\neven\tO\nin\tO\na\tO\n69\tO\n.\tO\nsmother\tO\nme\tO\nw/\tO\nit\tO\nand\tO\nhold\tO\nmy\tO\ndick\tO\nlike\tO\na\tO\njoystick\tO\nwhen\tO\nu\tO\ndo\tO\nit\tO\n.\tO\nshit\tO\n...\tO\nsay\tO\nsome\tO\nshit\tO\nlike\tO\n\"\tO\nsuck\tO\nthis\tO\npussy\tO\n\"\tO\ni\tO\nwont\tO\nlaugh\tO\n.'\tO\n\t\nI\tO\nlove\tO\nit\tO\n,\tO\nI\tO\ncan\tO\ntorment\tO\nmy\tO\nfriends\tO\nand\tO\ntheir\tO\nkids\tO\nlong\tO\ndistance\tO\nvia\tO\nfb\tB-company\nand\tO\ntwitter\tB-company\n.\tO\nMwahaha\tO\n!\tO\n\t\nNeed\tO\nit\tO\nto\tO\nbe\tO\nslammed\tO\nat\tO\nthe\tO\nhaus\tO\ntonight\tO\n\t\n@Jfordj12\tO\nWee\tO\nneed\tO\nu\tO\nto\tO\nmake\tO\nit\tO\nhappen\tO\non\tO\nSunday\tO\n\t\nBest\tO\n#free\tO\n#livemusic\tO\nin\tO\n#Denver\tB-geo-loc\ntonight\tO\nwith\tO\nthe\tO\nreturn\tO\nof\tO\nBreaking\tB-musicartist\n&\tI-musicartist\nEntering\tI-musicartist\n.\tO\nBe\tO\nsure\tO\nto\tO\ncome\tO\ndown\tO\nto\tO\nHi\tB-facility\nPac\tI-facility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y\tB-person\n...\tO\nwhere\tO\nwe\tO\nalways\tO\ngo\tO\nwhen\tO\nwe\tO\ndon't\tO\nknow\tO\nwhere\tO\nto\tO\ngo\tO\n.\tO\n\t\nYEAH\tO\n!\tO\nNo\tO\njury\tO\nduty\tO\n.\tO\nYes\tO\nclass\tO\non\tO\nMonday\tO\n.\tO\nstill\tO\nwilling\tO\nto\tO\nsend\tO\nout\tO\nstudy\tO\nguides\tO\nto\tO\nthose\tO\nwho\tO\nsend\tO\nme\tO\nan\tO\nemail\tO\nrequest\tO\n.\tO\nsorgelaw@yahoo\tO\n.\tO\ncom\tO\n\t\nWhat\tO\nshould\tO\nyou\tO\ndo\tO\nwhen\tO\nyou're\tO\nsued\tO\n.\tO\nHere\tO\nare\tO\na\tO\nfew\tO\ntips\tO\n.\tO\nhttp://www.calitigationblog.com/2010/09/articles/what-to-do-when-you-are-sued/\tO\n\t\nGot\tO\nmy\tO\nphone\tO\ntaken\tO\nup\tO\ntoday\tO\n.\tO\nGuess\tO\nwhy\tO\n.\tO\n@justinbieber\tO\n.\tO\nThat\tO\n's\tO\nwhy\tO\n.\tO\n\t\nMy\tO\neyes\tO\nare\tO\nsupeeer\tO\ndark\tO\ntoday\tO\n..\tO\njohn\tB-person\nsays\tO\nits\tO\ncuz\tO\nim\tO\novertired\tO\n..\tO\n:/\tO\n\t\nRT\tO\n@HRosenfelder\tO\n-\tO\nEducate\tO\nyourself\tO\nabout\tO\nlong-term\tO\ncare\tO\nby\tO\ngoing\tO\nto\tO\nhttp://www.ltcfp.com\tO\n.\tO\n#financial\tO\n\t\nLindsay\tB-person\nLohan\tI-person\nallegedly\tO\nfailed\tO\nher\tO\ndrug\tO\ntest\tO\n.\tO\nSo\tO\nnot\tO\nfetch\tO\n.\tO\nQuick\tO\n,\tO\nlet\tO\n's\tO\nall\tO\nput\tO\non\tO\nour\tO\nshocked\tO\nfaces\tO\nthat\tO\n23\tO\ndays\tO\nof\tO\nrehab\tO\ndidn't\tO\nwork\tO\n!\tO\n\t\nHydrating\tO\nfor\tO\nthe\tO\nbig\tO\ngame\tO\ntomorrow\tO\n:)\tO\n\t\nBeen\tO\nworking\tO\nlike\tO\ncrazy\tO\nthese\tO\npast\tO\ncouple\tO\ndayzz\tO\n-__-\tO\n...\tO\nwell\tO\nat\tO\nleast\tO\nfor\tO\na\tO\nhigh\tO\nschool\tO\nstudent\tO\n...\tO\ngot\tO\nhome\tO\ntoday\tO\nand\tO\nCRASHED\tO\n=D\tO\n\t\nLogging\tO\noff\tO\nfor\tO\ntoday\tO\n.\tO\nAbout\tO\nto\tO\ngo\tO\nenjoy\tO\nmy\tO\n#wkndread\tO\nSandman\tB-product\nSlim\tI-product\nby\tO\nRichard\tB-person\nKadrey\tI-person\n.\tO\nLove\tO\n#wkndreads\tO\n:)\tO\n\t\n\"\tO\nYou'll\tO\nfind\tO\nthat\tO\nwhen\tO\nyou\tO\nseek\tO\nto\tO\nunderstand\tO\nfirst\tO\nyou\tO\nwon't\tO\nfeel\tO\na\tO\nneed\tO\nto\tO\nbe\tO\nunderstood\tO\nas\tO\nmuch\tO\n.\"\tO\n-@TonyGaskins\tO\n\t\nI\tO\nremember\tO\nwhen\tO\nyou\tO\nall\tO\nwere\tO\nthiiis\tO\nbig\tO\n,\tO\nyou\tO\nknow\tO\n?\tO\n\t\nRT\tO\n@bobbyhundreds\tO\n:\tO\nAfter\tO\na\tO\nfew\tO\nepisodes\tO\n,\tO\nI\tO\nthink\tO\nI've\tO\nfigured\tO\nout\tO\nhow\tO\nto\tO\ncatch\tO\na\tO\npredator\tO\n.\tO\nIt\tO\nseems\tO\nyou\tO\njust\tO\nask\tO\nthem\tO\nto\tO\ntake\tO\na\tO\nseat\tO\n.\tO\n\t\nRT\tO\n@OMGTotallyLegit\tO\n:\tO\nHaving\tO\na\tO\ndream\tO\nabout\tO\nsomeone\tO\nthen\tO\nfeeling\tO\nweird\tO\nabout\tO\nthem\tO\nthe\tO\nnext\tO\nday\tO\n.\tO\n#OMGTotallyLegit\tO\n\t\n@ALiNA_BlahxD\tO\nnein\tO\n,\tO\ndas\tO\nist\tO\ndie\tO\nvon\tO\nmeinem\tO\nbruder\tO\n.\tO\naber\tO\nbei\tO\nihm\tO\nstaubt\tO\ndie\tO\nnur\tO\nein\tO\n:D\tO\njetzt\tO\nkann\tO\nich\tO\nschon\tO\none\tO\ntime\tO\nvon\tO\njustin\tB-person\nbieber\tI-person\nund\tO\n\t\n@GOBLUE_FUCKosu\tO\nit\tO\nwas\tO\nour\tO\n10th\tO\ngrade\tO\nyear\tO\n.\tO\nsmh\tO\n\t\n@leesalulu\tO\nperfect\tO\nround\tO\nfor\tO\nus\tO\nthis\tO\nweek\tO\n....\tO\nand\tO\nyes\tO\n,\tO\nI'm\tO\nconvincing\tO\nmyself\tO\nit\tO\n's\tO\nas\tO\nhard\tO\nas\tO\nany\tO\nother\tO\nweek\tO\n!\tO\n\t\nGot\tO\nsome\tO\nclose\tO\nfriends\tO\nfrom\tO\nback\tO\nhome\tO\nover\tO\ntonight\tO\n.\tO\nMight\tO\nget\tO\na\tO\nlittle\tO\ncarried\tO\naway\tO\nwoot\tO\n\t\nThere\tO\nis\tO\nlike\tO\nno\tO\nmayo\tO\non\tO\nthis\tO\nsandwich\tO\n!\tO\nUgh\tO\n....\tO\nnext\tO\ntime\tO\nIm\tO\ngoing\tO\nwith\tO\nmy\tO\ndad\tO\n\t\n@snarkobabble\tO\nI'll\tO\nlook\tO\nfor\tO\nyou\tO\ntonight\tO\n!\tO\n#wineparty\tO\n\t\n@EssieLovesYou\tO\nyesterday\tO\ni\tO\nhad\tO\na\tO\nkings\tB-musicartist\nof\tI-musicartist\nleon\tI-musicartist\nmarathon\tO\n.\tO\ni\tO\nslept\tO\nhappy\tO\nll\tO\n!\tO\n\t\nPublic\tO\nencouraged\tO\nto\tO\nattend\tO\nSaturday\tO\n's\tO\nmemorial\tO\nfor\tO\njazz\tO\ngreat\tO\nCaliman\tB-person\nat\tO\nFirst\tB-facility\nBaptist\tI-facility\non\tO\nHarvard\tB-geo-loc\nhttp://bit.ly/bsnOkT\tO\nMusical\tO\ncelebration\tO\n\t\n@drance\tO\njust\tO\nwait\tO\ntil\tO\nyou\tO\ndiscover\tO\nthat\tO\nall\tO\ntableviews\tO\nscroll\tO\nup\tO\nwhen\tO\nyou\tO\ntap\tO\nthe\tO\ntop\tO\nbar\tO\nwhere\tO\nthe\tO\nclock\tO\nis\tO\n:)\tO\n\t\n#Offline\tO\nFor\tO\nalong\tO\ntime\tO\n...\tO\nmaybe\tO\nnot\tO\nuntil\tO\ntomorrow\tO\n\t\nRT\tO\n@EckoMMA\tO\n:\tO\nCRAZY\tO\n#FF\tO\nDEAL\tO\n!!!\tO\nWe'll\tO\ngive\tO\naway\tO\n50\tO\nfree\tO\ntees\tO\nto\tO\nfollowers\tO\nif\tO\nwe\tO\nhit\tO\n10K\tO\nfollowers\tO\ntoday\tO\n.\tO\nYou\tO\nmust\tO\nbe\tO\nfollowing\tO\n&\tO\nmust\tO\nRT\tO\n!\tO\n\t\nRT\tO\n@Rammsies\tO\n:\tO\nhaha\tO\nthats\tO\nawesome\tO\nRT\tO\n:\tO\n@aplusk\tO\nThis\tO\nmade\tO\nme\tO\nlaugh\tO\ntoday\tO\nhttp://bit.ly/bjOhom\tO\n<\tO\n---\tO\ncourtesy\tO\nof\tO\nsplurb\tO\n.\tO\nWhat\tO\nmade\tO\nyou\tO\nlaugh\tO\n?\tO\n\t\n@batonabike\tO\nI\tO\nfeel\tO\nreally\tO\nsorry\tO\n,\tO\nthe\tO\nratbags\tO\n,\tO\nmaybe\tO\nin\tO\na\tO\nfew\tO\nyears\tO\nwhen\tO\nyou\tO\nare\tO\nstraight\tO\nand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n@MyLifeAsLiz_Liz\tO\n:\tO\nWhen\tO\ndid\tO\nbeing\tO\na\tO\nnerd\tO\nbecome\tO\ncool\tO\n&\tO\nwhy\tO\nwasn't\tO\nit\tO\nwhen\tO\nI\tO\nwas\tO\nin\tO\nhigh\tO\nschool\tO\n?\tO\nI\tO\nwould've\tO\nbeen\tO\nthe\tO\nmost\tO\npopular\tO\nchick\tO\n...\tO\n\t\n@josiery\tO\nHope\tO\nthat\tO\nI\tO\ncan\tO\nmake\tO\nit\tO\non\tO\ntime\tO\n.\tO\nWish\tO\nthat\tO\nI'll\tO\nbe\tO\nan\tO\n'\tO\nS.S\tO\n'\tO\nin\tO\nthe\tO\nend\tO\nof\tO\nthe\tO\nyear\tO\n:)\tO\n\t\n@ethernat\tO\nYou\tO\nknow\tO\nme\tO\n,\tO\nif\tO\nI\tO\ncan\tO\nbe\tO\nkind\tO\nand\tO\nguilt\tO\nyou\tO\nat\tO\nthe\tO\nsame\tO\ntime\tO\n,\tO\nwell\tO\n,\tO\nmy\tO\njob\tO\nis\tO\ndone\tO\n:)\tO\n\t\nCostco\tB-company\nIrvine\tB-geo-loc\nhas\tO\nfresh\tO\nsushi\tO\nsamples\tO\ntoday\tO\n.\tO\nAwesome\tO\n.\tO\n\t\nRT\tO\n@PhilKeoghan\tO\n:\tO\nPrivate\tO\nsneak\tO\npeak\tO\nof\tO\n'\tO\nThe\tB-movie\nRide\tI-movie\n'\tO\nmy\tO\nride\tO\nacross\tO\nUSA\tB-geo-loc\n,\tO\ntonight\tO\nonly\tO\n,\tO\nSan\tB-geo-loc\nFrancisco\tI-geo-loc\n6pm\tO\ncontact\tO\nJennifer\tB-person\n.\tO\nGainza@nmss\tO\n.\tO\norg\tO\n\t\nWonder\tO\nwhat\tO\nthe\tO\nnew\tO\nGreen\tB-musicartist\nDay\tI-musicartist\nalbum\tO\nwill\tO\ncalled\tO\n,\tO\nTre\tO\n's\tO\nPenis\tO\n?\tO\n\t\nRT\tO\n@m_candelaria\tO\n:\tO\nThe\tO\namazing\tO\nFollow\tO\nFriday\tO\nTrain\tO\n:\tO\n@Sketchjobs\tO\n@lollieshopping\tO\n@philadelphiabn\tO\n@elainebiss\tO\n@TableMatters10\tO\n@STALKmyPRETT\tO\n...\tO\n\t\n\"\tO\nAlways\tO\nplay\tO\nthem\tO\nbefore\tO\nthey\tO\ncan\tO\nplay\tO\nyou\tO\n\"\tO\n-\tO\nJoy\tB-person\nHuhn\tI-person\n\t\nRT\tO\n@_RANGO_\tO\n:\tO\nHey\tO\ny'all\tO\n\"\tO\nEast\tB-other\nNu\tI-other\n,\"\tO\nround\tO\nTwo\tO\ntonight\tO\n.\tO\nSee\tO\ny'all\tO\nearly\tO\n!\tO\n(shit\tO\n!!!!\tO\nI\tO\nshould've\tO\nstayed\tO\nlol\tO\n\t\n@colintattum\tO\nIt\tO\n's\tO\nalready\tO\ntomorrow\tO\nin\tO\nAustralia\tB-geo-loc\nso\tO\ncan\tO\nyou\tO\ntell\tO\njust\tO\nme\tO\nnow\tO\n?!\tO\n\t\n@YFarahat\tO\n@n_atalla\tO\nI\tO\nstopped\tO\nbuying\tO\nbottled\tO\nwater\tO\nfor\tO\nlike\tO\n2\tO\nyears\tO\n:)\tO\ninstalled\tO\na\tO\nwater\tO\nfilter\tO\nand\tO\nit\tO\n's\tO\ngreat\tO\n.\tO\nWater\tO\ntaste\tO\nlike\tO\nwater\tO\nnow\tO\n:)\tO\n\t\n@Tink3rbell94\tO\ngoing\tO\nto\tO\nGA\tB-geo-loc\n,\tO\ndon't\tO\nknow\tO\nwhy\tO\nbut\tO\nwhen\tO\nI\tO\nthought\tO\nabout\tO\nit\tO\nATL\tB-geo-loc\nstood\tO\nout\tO\nin\tO\nmy\tO\nmind\tO\n.\tO\nI\tO\nhave\tO\ntime\tO\nto\tO\nplan\tO\nit\tO\nproperly\tO\nthough\tO\n:)\tO\n\t\nThe\tO\npope\tB-person\nisn't\tO\nreally\tO\nmaking\tO\nmuch\tO\nof\tO\nan\tO\neffort\tO\n.\tO\nHe\tO\n's\tO\nwearing\tO\nthe\tO\nsame\tO\nclothes\tO\nas\tO\nyesterday\tO\n.\tO\n\t\n2\tO\ndays\tO\ntill\tO\n#BoardwalkEmpire\tO\n\t\n@JodiWonderland\tO\n@LeighWonderland\tO\n@sharwonderland\tO\n@kaseywonderland\tO\n@CiCiWonderland\tO\nYou\tO\nladies\tO\nwere\tO\ngreat\tO\ntoday\tO\n.\tO\nHave\tO\na\tO\nwicked\tO\nweekend\tO\n.\tO\nxx\tO\n\t\nJustin\tB-person\nTimberlake\tI-person\nft\tO\n.\tO\nBeyonce\tB-person\n-\tO\nUntil\tB-other\nthe\tI-other\nEnd\tI-other\nof\tI-other\nTime\tI-other\nhttp://t.co/kUe6djJ\tO\nvia\tO\n@youtube\tO\n\t\n@ahmong\tO\nI'm\tO\nthinking\tO\nthey\tO\ntrade\tO\nhim\tO\nbefore\tO\nthe\tO\ndeadline\tO\nand\tO\ntry\tO\nto\tO\nblow\tO\nit\tO\nup\tO\nso\tO\nthey\tO\ndon't\tO\nlose\tO\nhim\tO\nfor\tO\nnothing\tO\n.\tO\n\t\n@glorybeful\tO\nWell\tO\n...\tO\nthats\tO\nwhy\tO\nthey\tO\ninvented\tO\n...\tO\ntomorrow\tO\n!\tO\n:o\tO\nD\tO\n\t\nomg\tO\n,\tO\ni'm\tO\nsooooo\tO\nready\tO\nfor\tO\ntonight\tO\n!\tO\n<\tO\n3\tO\n\t\nAny\tO\n1\tO\nknow\tO\nwhen\tO\nseason\tO\n3\tO\nTrue\tB-tvshow\nBlood\tI-tvshow\nis\tO\nback\tO\non\tO\nthe\tO\ntelly\tO\n?\tO\n\t\n@TripleThreat99\tO\nlol\tO\nwhere\tO\nu\tO\nheaded\tO\ntonight\tO\n?\tO\n\t\nRT\tO\n@SavingGorillas\tO\n:\tO\nKwiruka\tB-person\nleft\tO\nher\tO\ngroup\tO\nseveral\tO\ntimes\tO\nafter\tO\ndeath\tO\nof\tO\ninfant-unknown\tO\ncauses-late\tO\nJuly\tO\n;\tO\nlast\tO\nseen\tO\nalive\tO\nAug\tO\n24\tO\n.\tO\n\t\nRT\tO\n@DollarVanDemos\tO\n:\tO\n#BROOKLYN\tO\n!\tO\nStay\tO\nup\tO\nlate\tO\ntonight\tO\nto\tO\ncatch\tO\nthe\tO\n1\tO\nhr\tO\nspecial\tO\nof\tO\nDemos\tB-tvshow\n@\tO\n3AM\tO\non\tO\nBCAT\tB-other\nfeat\tO\n@BlissyCakes\tO\n@YaGirlNicolette\tO\n...\tO\n\t\nSecond-year\tO\nCBs\tO\nJerraud\tB-person\nPowers\tI-person\nand\tO\nJacob\tB-person\nLacey\tI-person\ndon't\tO\nremember\tO\nmuch\tO\nabout\tO\nManning\tB-other\nBowl\tI-other\nI\tI-other\n.\tO\nBoth\tO\nwere\tO\ncollege\tO\n...\tO\nhttp://bit.ly/at71uZ\tO\n\t\nooh\tO\ndidn't\tO\nknow\tO\nwe\tO\nwas\tO\nat\tO\nhome\tO\ntomorrow\tO\n.\tO\nSilly\tO\nbilly\tB-person\n\t\nOk\tO\noff\tO\nto\tO\nbed\tO\nBUT\tO\nWHEN\tO\nI\tO\nWAKE\tO\nUP\tO\nI\tO\nBETTER\tO\nHAVE\tO\nWON\tO\nA\tO\nT-SHIRT\tO\n\t\nRT\tO\n@ATeenageThought\tO\n:\tO\ni\tO\nhate\tO\nit\tO\nwhen\tO\nmy\tO\nparents\tO\nwant\tO\nme\tO\nto\tO\ncome\tO\nhome\tO\nearly\tO\nfrom\tO\na\tO\nparty\tO\n,\tO\nbut\tO\ni\tO\nwanna\tO\nstay\tO\nreally\tO\nlate\tO\n#ATeenageThought\tO\n\t\nYesterday\tO\n's\tO\nshoot\t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collection\tO\n!\tO\nThe\tO\nfurniture\tO\nis\tO\ndivine\tO\n:\tO\nhttp://go.cort.com/5f\tO\n\t\nJust\tO\npuffed\tO\non\tO\nsour\tO\nd\tO\nunintentionally\tO\n...\tO\nI'm\tO\nso\tO\nhigh\tO\nIdk\tO\nwhen\tO\nI\tO\nwill\tO\ncome\tO\nback\tO\nto\tO\nearth\tO\n\t\nWin\tO\ntix\tO\n2\tO\nKELE\tB-person\nand\tO\nDOES\tB-musicartist\nIT\tI-musicartist\nOFFEND\tI-musicartist\nYOU\tI-musicartist\n,\tI-musicartist\nYEAH\tI-musicartist\n?\tI-musicartist\nSept\tO\n21st\tO\n@\tO\nMusic\tB-facility\nBox\tI-facility\n:\tO\ncontest@\tO\ndimmak.com\tO\n|\tO\nAfterparty\tO\nat\tO\nDim\tB-facility\nMak\tI-facility\nTuesdays\tO\n@CinespaceLA\tO\n\t\nI\tO\nwanna\tO\ngo\tO\nto\tO\na\tO\nSHS\tB-sportsteam\nfootball\tO\ngame\tO\ncuz\tO\nI\tO\nain't\tO\nbeen\tO\nto\tO\na\tO\nhigh\tO\nschool\tO\ngame\tO\nin\tO\nyears\tO\n.\tO\n@themystikSAIDso\tO\nmake\tO\nit\tO\nhappen\tO\n\t\nFollow\tO\nFriday\tO\n:\tO\nOnce\tO\nagain\tO\n,\tO\nI\tO\nsuggest\tO\nyou\tO\ntell\tO\neveryone\tO\nyou\tO\nknow\tO\nto\tO\nFOLLOW\tO\nME\tO\n!\tO\nLet\tO\n's\tO\nget\tO\nme\tO\nover\tO\n200\tO\nfollowers\tO\n!\tO\nPorn\tO\nrobots\tO\nwelcome\tO\n!\tO\n#ff\tO\n\t\n@diaryOFcrazy_T\tO\nstill\tO\ntrying\tO\nto\tO\nwarm\tO\nup\tO\nmy\tO\ntoes\tO\nare\tO\ncold\tO\nas\tO\n:)\tO\nbut\tO\njust\tO\nchillin\tO\nno\tO\nrunning\tO\ntoday\tO\n.\tO\n\t\nStoked\tO\nabout\tO\nthe\tO\nawesome\tO\ndate\tO\nnight\tO\nim\tO\nabout\tO\nto\tO\nhave\tO\nwith\tO\n@sssmash\tO\nand\tO\nalso\tO\nvery\tO\nstoked\tO\nabout\tO\nthe\tO\nbro\tO\ntime\tO\ntomorrow\tO\nwith\tO\n@jeffsyourhero\tO\n\t\n@MJay615\tO\njoining\tO\nthe\tO\nawsm\tO\nbefore\tO\nI'm\tO\ninterested\tO\nto\tO\nsee\tO\nwhat\tO\nthey're\tO\nbringing\tO\nto\tO\nit\tO\n\t\nSleeping\tO\ntime\tO\n.\tO\nMorgenochtend\tO\nmaar\tO\neven\tO\nde\tO\nKoperdoos\tO\neen\tO\nschoonmaakbeurtje\tO\ngeven\tO\n...\tO\n\t\nAbout\tO\nto\tO\nget\tO\nready\tO\nfor\tO\nwork...don't\tO\nhave\tO\na\tO\ngood\tO\nfeeling\tO\nabout\tO\ntonight\tO\n#famouslastwords\tO\n\t\n...\tO\nand\tO\nthe\tO\nrules\tO\nare\tO\n?\tO\n#corny\tO\nRT\tO\n@barzangi\tO\n:\tO\nI've\tO\nsaid\tO\nit\tO\nbefore\tO\nand\tO\nI'll\tO\nsay\tO\nit\tO\nagain\tO\n:\tO\nIndian\tB-geo-loc\nfood\tO\nRULES\tO\n.\tO\n\t\n@jodieboulger\tO\nEhe\tO\ngood\tO\n:)\tO\nsee\tO\nyou\tO\ntomorrow\tO\ndude\tO\n!\tO\n\t\nTomorrow\tO\nis\tO\nanother\tO\nday\tO\n!\tO\n\t\nParking\tB-other\nDay\tI-other\n-\tO\nParking\tB-other\nDay\tI-other\nhosted\tO\nby\tO\nthe\tO\nHudson\tB-other\nTMA\tI-other\nat\tO\nTown\tB-facility\nSquare\tI-facility\nPlace\tI-facility\non\tO\nPavonia\tB-geo-loc\nAvenue\tI-geo-loc\nin\tO\nthe\tO\nNewport\tB-geo-loc\n...\tO\nhttp://tumblr.com/xmyiud0zl\tO\n\t\nFive\tO\nBullpens\tO\nto\tO\nStay\tO\nAway\tO\nFrom\tO\nin\tO\nMLB\tB-other\nOdds\tO\n:\tO\nWhen\tO\nyou\tO\nthink\tO\nof\tO\nthe\tO\nworst\tO\nteams\tO\nin\tO\nbaseball\tO\n,\tO\nyou\tO\nthink\tO\nof\tO\nteams\tO\ntha\tO\n...\tO\nhttp://bit.ly/9R73CK\tO\n\t\nNew\tO\nDorky\tO\nStuff\tO\n:\tO\nFriday\tO\nReview\tO\nRundown\tO\n!\tO\nSept\tO\n17th\tO\nEasy\tO\nA\tO\nvs\tO\n.\tO\nDevil\tO\nvs\tO\n.\tO\nA\tO\nWoman\tO\n,\tO\nA\tO\nGun\tO\n,\tO\nand\tO\nA\tO\nNoodleshop\tO\nhttp://ow.ly/193oCy\tO\n\t\nOh\tO\nPEI\tB-person\n,\tO\ni\tO\nalways\tO\nforget\tO\nhow\tO\nbeautiful\tO\nyou\tO\nare\tO\n.\tO\nEspecially\tO\nwhen\tO\nyou\tO\nstop\tO\nhanging\tO\nout\tO\nwith\tO\ntourists\tO\n.\tO\n\t\nworkin\tO\nat\tO\nthe\tO\nol\tO\nSunset\tB-facility\nBistro\tI-facility\ntonight\tO\n...\tO\ncome\tO\nget\tO\nsome\tO\ngrub\tO\n\t\n" }, { "path": "nlp_class2/ner_baseline.py", "content": "# Course URL:\n# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\n\n# data from https://github.com/aritter/twitter_nlp/blob/master/data/annotated/ner.txt\n# data2 from http://schwa.org/projects/resources/wiki/Wikiner#WikiGold\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nfrom sklearn.utils import shuffle\nfrom pos_baseline import LogisticRegression\n\ndef get_data(split_sequences=False):\n word2idx = {}\n tag2idx = {}\n word_idx = 0\n tag_idx = 0\n Xtrain = []\n Ytrain = []\n currentX = []\n currentY = []\n for line in open('ner.txt'):\n line = line.rstrip()\n if line:\n r = line.split()\n word, tag = r\n word = word.lower()\n if word not in word2idx:\n word2idx[word] = word_idx\n word_idx += 1\n currentX.append(word2idx[word])\n \n if tag not in tag2idx:\n tag2idx[tag] = tag_idx\n tag_idx += 1\n currentY.append(tag2idx[tag])\n elif split_sequences:\n Xtrain.append(currentX)\n Ytrain.append(currentY)\n currentX = []\n currentY = []\n\n if not split_sequences:\n Xtrain = currentX\n Ytrain = currentY\n\n print(\"number of samples:\", len(Xtrain))\n Xtrain, Ytrain = shuffle(Xtrain, Ytrain)\n Ntest = int(0.3*len(Xtrain))\n Xtest = Xtrain[:Ntest]\n Ytest = Ytrain[:Ntest]\n Xtrain = Xtrain[Ntest:]\n Ytrain = Ytrain[Ntest:]\n print(\"number of classes:\", len(tag2idx))\n return Xtrain, Ytrain, Xtest, Ytest, word2idx, tag2idx\n\n\n# def get_data2(split_sequences=False):\n# word2idx = {}\n# tag2idx = {}\n# word_idx = 0\n# tag_idx = 0\n# Xtrain = []\n# Ytrain = []\n# for line in open('../large_files/aij-wikiner-en-wp3'):\n# # each line is a full sentence\n# currentX = []\n# currentY = []\n# line = line.rstrip()\n# if not line:\n# continue\n# triples = line.split()\n# for triple in triples:\n# word, _, tag = triple.split('|')\n# if word not in word2idx:\n# word2idx[word] = word_idx\n# word_idx += 1\n# currentX.append(word2idx[word])\n \n# if tag not in tag2idx:\n# tag2idx[tag] = tag_idx\n# tag_idx += 1\n# currentY.append(tag2idx[tag])\n\n# Xtrain.append(currentX)\n# Ytrain.append(currentY)\n\n# if not split_sequences:\n# Xtrain = np.concatenate(Xtrain)\n# Ytrain = np.concatenate(Ytrain)\n\n# print(\"number of samples:\", len(Xtrain))\n# Xtrain, Ytrain = shuffle(Xtrain, Ytrain)\n# Ntest = int(0.3*len(Xtrain))\n# Xtest = Xtrain[:Ntest]\n# Ytest = Ytrain[:Ntest]\n# Xtrain = Xtrain[Ntest:]\n# Ytrain = Ytrain[Ntest:]\n# print(\"number of classes:\", len(tag2idx))\n# return Xtrain, Ytrain, Xtest, Ytest, word2idx, tag2idx\n\n\ndef main():\n Xtrain, Ytrain, Xtest, Ytest, word2idx, tag2idx = get_data()\n\n V = len(word2idx)\n print(\"vocabulary size:\", V)\n K = len(tag2idx)\n\n # train and score\n model = LogisticRegression()\n model.fit(Xtrain, Ytrain, V=V, K=K, epochs=5)\n print(\"training complete\")\n print(\"train score:\", model.score(Xtrain, Ytrain))\n print(\"train f1 score:\", model.f1_score(Xtrain, Ytrain))\n print(\"test score:\", model.score(Xtest, Ytest))\n print(\"test f1 score:\", model.f1_score(Xtest, Ytest))\n\nif __name__ == '__main__':\n main()\n" }, { "path": "nlp_class2/ner_rnn.py", "content": "# Course URL:\n# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nfrom ner_baseline import get_data\nfrom pos_rnn import RNN\n\ndef main():\n Xtrain, Ytrain, Xtest, Ytest, word2idx, tag2idx = get_data(split_sequences=True)\n V = len(word2idx)\n K = len(tag2idx)\n rnn = RNN(10, [10], V, K)\n rnn.fit(Xtrain, Ytrain, epochs=70)\n print(\"train score:\", rnn.score(Xtrain, Ytrain))\n print(\"test score:\", rnn.score(Xtest, Ytest))\n print(\"train f1 score:\", rnn.f1_score(Xtrain, Ytrain))\n print(\"test f1 score:\", rnn.f1_score(Xtest, Ytest))\n \n\nif __name__ == '__main__':\n main()\n" }, { "path": "nlp_class2/ner_tf.py", "content": "# Course URL:\n# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nimport os\nimport sys\nsys.path.append(os.path.abspath('..'))\nfrom pos_baseline import get_data\nfrom sklearn.utils import shuffle\nfrom util import init_weight\nfrom datetime import datetime\nfrom sklearn.metrics import f1_score\n\nfrom tensorflow.contrib.rnn import static_rnn as get_rnn_output\nfrom tensorflow.contrib.rnn import BasicRNNCell, GRUCell\n\n\n\ndef get_data(split_sequences=False):\n word2idx = {}\n tag2idx = {}\n word_idx = 1\n tag_idx = 1\n Xtrain = []\n Ytrain = []\n currentX = []\n currentY = []\n for line in open('ner.txt'):\n line = line.rstrip()\n if line:\n r = line.split()\n word, tag = r\n word = word.lower()\n if word not in word2idx:\n word2idx[word] = word_idx\n word_idx += 1\n currentX.append(word2idx[word])\n \n if tag not in tag2idx:\n tag2idx[tag] = tag_idx\n tag_idx += 1\n currentY.append(tag2idx[tag])\n elif split_sequences:\n Xtrain.append(currentX)\n Ytrain.append(currentY)\n currentX = []\n currentY = []\n\n if not split_sequences:\n Xtrain = currentX\n Ytrain = currentY\n\n print(\"number of samples:\", len(Xtrain))\n Xtrain, Ytrain = shuffle(Xtrain, Ytrain)\n Ntest = int(0.3*len(Xtrain))\n Xtest = Xtrain[:Ntest]\n Ytest = Ytrain[:Ntest]\n Xtrain = Xtrain[Ntest:]\n Ytrain = Ytrain[Ntest:]\n print(\"number of classes:\", len(tag2idx))\n return Xtrain, Ytrain, Xtest, Ytest, word2idx, tag2idx\n\n\n\ndef flatten(l):\n return [item for sublist in l for item in sublist]\n\n\n\n# get the data\nXtrain, Ytrain, Xtest, Ytest, word2idx, tag2idx = get_data(split_sequences=True)\nV = len(word2idx) + 2 # vocab size (+1 for unknown, +1 for pad)\nK = len(set(flatten(Ytrain)) | set(flatten(Ytest))) + 1 # num classes\n\n\n# training config\nepochs = 5\nlearning_rate = 1e-2\nmu = 0.99\nbatch_size = 32\nhidden_layer_size = 10\nembedding_dim = 10\nsequence_length = max(len(x) for x in Xtrain + Xtest)\n\n\n\n# pad sequences\nXtrain = tf.keras.preprocessing.sequence.pad_sequences(Xtrain, maxlen=sequence_length)\nYtrain = tf.keras.preprocessing.sequence.pad_sequences(Ytrain, maxlen=sequence_length)\nXtest = tf.keras.preprocessing.sequence.pad_sequences(Xtest, maxlen=sequence_length)\nYtest = tf.keras.preprocessing.sequence.pad_sequences(Ytest, maxlen=sequence_length)\nprint(\"Xtrain.shape:\", Xtrain.shape)\nprint(\"Ytrain.shape:\", Ytrain.shape)\n\n\n\n# inputs\ninputs = tf.placeholder(tf.int32, shape=(None, sequence_length))\ntargets = tf.placeholder(tf.int32, shape=(None, sequence_length))\nnum_samples = tf.shape(inputs)[0] # useful for later\n\n# embedding\nWe = np.random.randn(V, embedding_dim).astype(np.float32)\n\n# output layer\nWo = init_weight(hidden_layer_size, K).astype(np.float32)\nbo = np.zeros(K).astype(np.float32)\n\n# make them tensorflow variables\ntfWe = tf.Variable(We)\ntfWo = tf.Variable(Wo)\ntfbo = tf.Variable(bo)\n\n# make the rnn unit\nrnn_unit = GRUCell(num_units=hidden_layer_size, activation=tf.nn.relu)\n\n\n# get the output\nx = tf.nn.embedding_lookup(tfWe, inputs)\n\n# converts x from a tensor of shape N x T x D\n# into a list of length T, where each element is a tensor of shape N x D\nx = tf.unstack(x, sequence_length, 1)\n\n# get the rnn output\noutputs, states = get_rnn_output(rnn_unit, x, dtype=tf.float32)\n\n\n# outputs are now of size (T, N, M)\n# so make it (N, T, M)\noutputs = tf.transpose(outputs, (1, 0, 2))\noutputs = tf.reshape(outputs, (sequence_length*num_samples, hidden_layer_size)) # NT x M\n\n# Linear activation, using rnn inner loop last output\nlogits = tf.matmul(outputs, tfWo) + tfbo # NT x K\npredictions = tf.argmax(logits, 1)\npredict_op = tf.reshape(predictions, (num_samples, sequence_length))\nlabels_flat = tf.reshape(targets, [-1])\n\ncost_op = tf.reduce_mean(\n tf.nn.sparse_softmax_cross_entropy_with_logits(\n logits=logits,\n labels=labels_flat\n )\n)\ntrain_op = tf.train.AdamOptimizer(learning_rate).minimize(cost_op)\n\n\n\n\n# init stuff\nsess = tf.InteractiveSession()\ninit = tf.global_variables_initializer()\nsess.run(init)\n\n\n# training loop\ncosts = []\nn_batches = len(Ytrain) // batch_size\nfor i in range(epochs):\n n_total = 0\n n_correct = 0\n\n t0 = datetime.now()\n Xtrain, Ytrain = shuffle(Xtrain, Ytrain)\n cost = 0\n\n for j in range(n_batches):\n x = Xtrain[j*batch_size:(j+1)*batch_size]\n y = Ytrain[j*batch_size:(j+1)*batch_size]\n\n # get the cost, predictions, and perform a gradient descent step\n c, p, _ = sess.run(\n (cost_op, predict_op, train_op),\n feed_dict={inputs: x, targets: y})\n cost += c\n\n # calculate the accuracy\n for yi, pi in zip(y, p):\n # we don't care about the padded entries so ignore them\n yii = yi[yi > 0]\n pii = pi[yi > 0]\n n_correct += np.sum(yii == pii)\n n_total += len(yii)\n\n # print stuff out periodically\n if j % 10 == 0:\n sys.stdout.write(\n \"j/N: %d/%d correct rate so far: %f, cost so far: %f\\r\" %\n (j, n_batches, float(n_correct)/n_total, cost)\n )\n sys.stdout.flush()\n\n # get test acc. too\n p = sess.run(predict_op, feed_dict={inputs: Xtest, targets: Ytest})\n n_test_correct = 0\n n_test_total = 0\n for yi, pi in zip(Ytest, p):\n yii = yi[yi > 0]\n pii = pi[yi > 0]\n n_test_correct += np.sum(yii == pii)\n n_test_total += len(yii)\n test_acc = float(n_test_correct) / n_test_total\n\n print(\n \"i:\", i, \"cost:\", \"%.4f\" % cost,\n \"train acc:\", \"%.4f\" % (float(n_correct)/n_total),\n \"test acc:\", \"%.4f\" % test_acc,\n \"time for epoch:\", (datetime.now() - t0)\n )\n costs.append(cost)\n\nplt.plot(costs)\nplt.show()\n\n\n" }, { "path": "nlp_class2/neural_network.py", "content": "# Course URL:\n# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport random\nfrom datetime import datetime\n\nimport os\nimport sys\nsys.path.append(os.path.abspath('..'))\nfrom rnn_class.util import get_wikipedia_data\nfrom rnn_class.brown import get_sentences_with_word2idx_limit_vocab, get_sentences_with_word2idx\n\nfrom markov import get_bigram_probs\n\n\nif __name__ == '__main__':\n # load in the data\n # note: sentences are already converted to sequences of word indexes\n # note: you can limit the vocab size if you run out of memory\n sentences, word2idx = get_sentences_with_word2idx_limit_vocab(2000)\n # sentences, word2idx = get_sentences_with_word2idx()\n\n # vocab size\n V = len(word2idx)\n print(\"Vocab size:\", V)\n\n # we will also treat beginning of sentence and end of sentence as bigrams\n # START -> first word\n # last word -> END\n start_idx = word2idx['START']\n end_idx = word2idx['END']\n\n\n # a matrix where:\n # row = last word\n # col = current word\n # value at [row, col] = p(current word | last word)\n bigram_probs = get_bigram_probs(sentences, V, start_idx, end_idx, smoothing=0.1)\n\n\n # train a shallow neural network model\n D = 100\n W1 = np.random.randn(V, D) / np.sqrt(V)\n W2 = np.random.randn(D, V) / np.sqrt(D)\n\n losses = []\n epochs = 1\n lr = 1e-2\n \n def softmax(a):\n a = a - a.max()\n exp_a = np.exp(a)\n return exp_a / exp_a.sum(axis=1, keepdims=True)\n\n # what is the loss if we set W = log(bigram_probs)?\n W_bigram = np.log(bigram_probs)\n bigram_losses = []\n\n t0 = datetime.now()\n for epoch in range(epochs):\n # shuffle sentences at each epoch\n random.shuffle(sentences)\n\n j = 0 # keep track of iterations\n for sentence in sentences:\n # convert sentence into one-hot encoded inputs and targets\n sentence = [start_idx] + sentence + [end_idx]\n n = len(sentence)\n inputs = np.zeros((n - 1, V))\n targets = np.zeros((n - 1, V))\n inputs[np.arange(n - 1), sentence[:n-1]] = 1\n targets[np.arange(n - 1), sentence[1:]] = 1\n\n # get output predictions\n hidden = np.tanh(inputs.dot(W1))\n predictions = softmax(hidden.dot(W2))\n\n # do a gradient descent step\n W2 = W2 - lr * hidden.T.dot(predictions - targets)\n dhidden = (predictions - targets).dot(W2.T) * (1 - hidden * hidden)\n W1 = W1 - lr * inputs.T.dot(dhidden)\n\n # keep track of the loss\n loss = -np.sum(targets * np.log(predictions)) / (n - 1)\n losses.append(loss)\n\n # keep track of the bigram loss\n # only do it for the first epoch to avoid redundancy\n if epoch == 0:\n bigram_predictions = softmax(inputs.dot(W_bigram))\n bigram_loss = -np.sum(targets * np.log(bigram_predictions)) / (n - 1)\n bigram_losses.append(bigram_loss)\n\n\n if j % 10 == 0:\n print(\"epoch:\", epoch, \"sentence: %s/%s\" % (j, len(sentences)), \"loss:\", loss)\n j += 1\n\n print(\"Elapsed time training:\", datetime.now() - t0)\n plt.plot(losses)\n\n # plot a horizontal line for the bigram loss\n avg_bigram_loss = np.mean(bigram_losses)\n print(\"avg_bigram_loss:\", avg_bigram_loss)\n plt.axhline(y=avg_bigram_loss, color='r', linestyle='-')\n\n\n # plot smoothed losses to reduce variability\n def smoothed_loss(x, decay=0.99):\n y = np.zeros(len(x))\n last = 0\n for t in range(len(x)):\n z = decay * last + (1 - decay) * x[t]\n y[t] = z / (1 - decay ** (t + 1))\n last = z\n return y\n\n plt.plot(smoothed_loss(losses))\n plt.show()\n\n # plot W and bigram probs side-by-side\n # for the most common 200 words\n plt.subplot(1,2,1)\n plt.title(\"Neural Network Model\")\n plt.imshow(np.tanh(W1).dot(W2))\n plt.subplot(1,2,2)\n plt.title(\"Bigram Probs\")\n plt.imshow(W_bigram)\n plt.show()\n\n\n\n\n" }, { "path": "nlp_class2/neural_network2.py", "content": "# Course URL:\n# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport random\nfrom datetime import datetime\n\nimport os\nimport sys\nsys.path.append(os.path.abspath('..'))\nfrom rnn_class.util import get_wikipedia_data\nfrom rnn_class.brown import get_sentences_with_word2idx_limit_vocab, get_sentences_with_word2idx\n\nfrom markov import get_bigram_probs\n\n\nif __name__ == '__main__':\n # load in the data\n # note: sentences are already converted to sequences of word indexes\n # note: you can limit the vocab size if you run out of memory\n sentences, word2idx = get_sentences_with_word2idx_limit_vocab(2000)\n # sentences, word2idx = get_sentences_with_word2idx()\n\n # vocab size\n V = len(word2idx)\n print(\"Vocab size:\", V)\n\n # we will also treat beginning of sentence and end of sentence as bigrams\n # START -> first word\n # last word -> END\n start_idx = word2idx['START']\n end_idx = word2idx['END']\n\n\n # a matrix where:\n # row = last word\n # col = current word\n # value at [row, col] = p(current word | last word)\n bigram_probs = get_bigram_probs(sentences, V, start_idx, end_idx, smoothing=0.1)\n\n\n # train a shallow neural network model\n D = 100\n W1 = np.random.randn(V, D) / np.sqrt(V)\n W2 = np.random.randn(D, V) / np.sqrt(D)\n\n losses = []\n epochs = 1\n lr = 1e-2\n \n def softmax(a):\n a = a - a.max()\n exp_a = np.exp(a)\n return exp_a / exp_a.sum(axis=1, keepdims=True)\n\n # what is the loss if we set W = log(bigram_probs)?\n W_bigram = np.log(bigram_probs)\n bigram_losses = []\n\n t0 = datetime.now()\n for epoch in range(epochs):\n # shuffle sentences at each epoch\n random.shuffle(sentences)\n\n j = 0 # keep track of iterations\n for sentence in sentences:\n # do not one-hot encoded inputs and targets\n sentence = [start_idx] + sentence + [end_idx]\n n = len(sentence)\n inputs = sentence[:n-1]\n targets = sentence[1:]\n\n # get output predictions\n hidden = np.tanh(W1[inputs])\n predictions = softmax(hidden.dot(W2))\n\n # keep track of the loss\n loss = -np.sum(np.log(predictions[np.arange(n - 1), targets])) / (n - 1)\n losses.append(loss)\n\n # do a gradient descent step\n # do it after loss since the calculation of doutput will overwrite predictions\n # we don't want to make a copy because it would be slow\n doutput = predictions # N x V\n doutput[np.arange(n - 1), targets] -= 1\n W2 = W2 - lr * hidden.T.dot(doutput) # (D x N) (N x V)\n dhidden = doutput.dot(W2.T) * (1 - hidden * hidden) # (N x V) (V x D) * (N x D)\n # # for reference:\n # # original: W1 = W1 - lr * inputs.T.dot(dhidden) # VxN NxD --> VxD\n\n # fastest way\n W1_copy = W1.copy()\n np.subtract.at(W1, inputs, lr * dhidden)\n\n # vs this\n # W1_test = W1_copy.copy()\n # oh_inputs = np.zeros((n - 1, V))\n # oh_inputs[np.arange(n - 1), sentence[:n-1]] = 1\n # W1_test = W1_test - lr * oh_inputs.T.dot(dhidden)\n # assert(np.allclose(W1_test, W1))\n\n # vs this\n # W1_test = W1_copy.copy()\n # i = 0\n # for w in inputs: # don't include end token\n # W1_test[w] = W1_test[w] - lr * dhidden[i]\n # i += 1\n # assert(np.allclose(W1_test, W1))\n\n # keep track of the bigram loss\n # only do it for the first epoch to avoid redundancy\n if epoch == 0:\n bigram_predictions = softmax(W_bigram[inputs])\n bigram_loss = -np.sum(np.log(bigram_predictions[np.arange(n - 1), targets])) / (n - 1)\n bigram_losses.append(bigram_loss)\n\n\n if j % 100 == 0:\n print(\"epoch:\", epoch, \"sentence: %s/%s\" % (j, len(sentences)), \"loss:\", loss)\n j += 1\n\n\n print(\"Elapsed time training:\", datetime.now() - t0)\n plt.plot(losses)\n\n # plot a horizontal line for the bigram loss\n avg_bigram_loss = np.mean(bigram_losses)\n print(\"avg_bigram_loss:\", avg_bigram_loss)\n plt.axhline(y=avg_bigram_loss, color='r', linestyle='-')\n\n\n # plot smoothed losses to reduce variability\n def smoothed_loss(x, decay=0.99):\n y = np.zeros(len(x))\n last = 0\n for t in range(len(x)):\n z = decay * last + (1 - decay) * x[t]\n y[t] = z / (1 - decay ** (t + 1))\n last = z\n return y\n\n plt.plot(smoothed_loss(losses))\n plt.show()\n\n # plot W and bigram probs side-by-side\n # for the most common 200 words\n plt.subplot(1,2,1)\n plt.title(\"Neural Network Model\")\n plt.imshow(np.tanh(W1).dot(W2))\n plt.subplot(1,2,2)\n plt.title(\"Bigram Probs\")\n plt.imshow(W_bigram)\n plt.show()\n\n\n\n\n" }, { "path": "nlp_class2/pmi.py", "content": "# https://deeplearningcourses.com/c/data-science-natural-language-processing-in-python\n# https://www.udemy.com/data-science-natural-language-processing-in-python\n\n# Author: http://lazyprogrammer.me\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport os, sys\nimport string\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.sparse import lil_matrix, csr_matrix, save_npz, load_npz\nfrom scipy.spatial.distance import cosine as cos_dist\nfrom sklearn.metrics.pairwise import pairwise_distances\nfrom glob import glob\nfrom datetime import datetime\n\n\n# input files\nfiles = glob('../large_files/enwiki*.txt')\n\n\n# unfortunately these work different ways\ndef remove_punctuation_2(s):\n return s.translate(None, string.punctuation)\n\ndef remove_punctuation_3(s):\n return s.translate(str.maketrans('','',string.punctuation))\n\nif sys.version.startswith('2'):\n remove_punctuation = remove_punctuation_2\nelse:\n remove_punctuation = remove_punctuation_3\n\n\n# max vocab size\nV = 2000\n\n# context size\ncontext_size = 10\n\n# word counts\nall_word_counts = {}\n\n# get the top V words\nnum_lines = 0\nnum_tokens = 0\nfor f in files:\n for line in open(f):\n # don't count headers, structured data, lists, etc...\n if line and line[0] not in ('[', '*', '-', '|', '=', '{', '}'):\n num_lines += 1\n for word in remove_punctuation(line).lower().split():\n num_tokens += 1\n if word not in all_word_counts:\n all_word_counts[word] = 0\n all_word_counts[word] += 1\nprint(\"num_lines:\", num_lines)\nprint(\"num_tokens:\", num_tokens)\n\n\n# words I really want to keep\nkeep_words = [\n 'king', 'man', 'queen', 'woman',\n 'heir', 'heiress', 'prince', 'princess',\n 'nephew', 'niece', 'uncle', 'aunt',\n 'husband', 'wife', 'brother', 'sister',\n 'tokyo', 'beijing', 'dallas', 'texas',\n 'january', 'february', 'march',\n 'april', 'may', 'june',\n 'july', 'august', 'september',\n 'october', 'november', 'december',\n 'actor', 'actress',\n 'rice', 'bread', 'miami', 'florida',\n 'walk', 'walking', 'swim', 'swimming',\n]\nfor w in keep_words:\n all_word_counts[w] = float('inf')\n\n\n# sort in descending order\nall_word_counts = sorted(all_word_counts.items(), key=lambda x: x[1], reverse=True)\n\n# keep just the top V words\n# save a slot for \nV = min(V, len(all_word_counts))\ntop_words = [w for w, count in all_word_counts[:V-1]] + ['']\n# TODO: try it without UNK at all\n\n# reverse the array to get word2idx mapping\nword2idx = {w:i for i, w in enumerate(top_words)}\nunk = word2idx['']\n\n# for w in ('king', 'man', 'queen', 'woman', 'france', 'paris', \\\n# 'london', 'england', 'italy', 'rome', \\\n# 'france', 'french', 'english', 'england', \\\n# 'japan', 'japanese', 'chinese', 'china', \\\n# 'italian', 'australia', 'australian' \\\n# 'japan', 'tokyo', 'china', 'beijing'):\n# assert(w in word2idx)\n\n\nif not os.path.exists('pmi_counts_%s.npz' % V):\n # init counts\n wc_counts = lil_matrix((V, V))\n\n ### make PMI matrix\n # add counts\n k = 0\n # for line in open('../large_files/text8'):\n for f in files:\n for line in open(f):\n # don't count headers, structured data, lists, etc...\n if line and line[0] not in ('[', '*', '-', '|', '=', '{', '}'):\n line_as_idx = []\n for word in remove_punctuation(line).lower().split():\n if word in word2idx:\n idx = word2idx[word]\n # line_as_idx.append(idx)\n else:\n idx = unk\n # pass\n line_as_idx.append(idx)\n\n for i, w in enumerate(line_as_idx):\n # keep count\n k += 1\n if k % 10000 == 0:\n print(\"%s/%s\" % (k, num_tokens))\n\n start = max(0, i - context_size)\n end = min(len(line_as_idx), i + context_size)\n for c in line_as_idx[start:i]:\n wc_counts[w, c] += 1\n for c in line_as_idx[i+1:end]:\n wc_counts[w, c] += 1\n print(\"Finished counting\")\n\n save_npz('pmi_counts_%s.npz' % V, csr_matrix(wc_counts))\n\nelse:\n wc_counts = load_npz('pmi_counts_%s.npz' % V)\n\n\n# context counts get raised ^ 0.75\nc_counts = wc_counts.sum(axis=0).A.flatten() ** 0.75\nc_probs = c_counts / c_counts.sum()\nc_probs = c_probs.reshape(1, V)\n\n\n# PMI(w, c) = #(w, c) / #(w) / p(c)\n# pmi = wc_counts / wc_counts.sum(axis=1) / c_probs # works only if numpy arrays\npmi = wc_counts.multiply(1.0 / wc_counts.sum(axis=1) / c_probs).tocsr()\n# this operation changes it to a coo_matrix\n# which doesn't have functions we need, e.g log1p()\n# so convert it back to a csr\nprint(\"type(pmi):\", type(pmi))\nlogX = pmi.log1p() # would be logX = np.log(pmi.A + 1) in numpy\nprint(\"type(logX):\", type(logX))\nlogX[logX < 0] = 0\n\n\n### do alternating least squares\n\n\n# latent dimension\nD = 100\nreg = 0.1\n\n\n# initialize weights\nW = np.random.randn(V, D) / np.sqrt(V + D)\nb = np.zeros(V)\nU = np.random.randn(V, D) / np.sqrt(V + D)\nc = np.zeros(V)\nmu = logX.mean()\n\n\ncosts = []\nt0 = datetime.now()\nfor epoch in range(10):\n print(\"epoch:\", epoch)\n delta = W.dot(U.T) + b.reshape(V, 1) + c.reshape(1, V) + mu - logX\n # cost = ( delta * delta ).sum()\n cost = np.multiply(delta, delta).sum()\n # * behaves differently if delta is a \"matrix\" object vs \"array\" object\n costs.append(cost)\n\n ### partially vectorized updates ###\n # update W\n # matrix = reg*np.eye(D) + U.T.dot(U)\n # for i in range(V):\n # vector = (logX[i,:] - b[i] - c - mu).dot(U)\n # W[i] = np.linalg.solve(matrix, vector)\n\n # # update b\n # for i in range(V):\n # numerator = (logX[i,:] - W[i].dot(U.T) - c - mu).sum()\n # b[i] = numerator / V #/ (1 + reg)\n\n # # update U\n # matrix = reg*np.eye(D) + W.T.dot(W)\n # for j in range(V):\n # vector = (logX[:,j] - b - c[j] - mu).dot(W)\n # U[j] = np.linalg.solve(matrix, vector)\n\n # # update c\n # for j in range(V):\n # numerator = (logX[:,j] - W.dot(U[j]) - b - mu).sum()\n # c[j] = numerator / V #/ (1 + reg)\n\n\n ### vectorized updates ###\n # vectorized update W\n matrix = reg*np.eye(D) + U.T.dot(U)\n vector = (logX - b.reshape(V, 1) - c.reshape(1, V) - mu).dot(U).T\n W = np.linalg.solve(matrix, vector).T\n\n # vectorized update b\n b = (logX - W.dot(U.T) - c.reshape(1, V) - mu).sum(axis=1) / V\n\n # vectorized update U\n matrix = reg*np.eye(D) + W.T.dot(W)\n vector = (logX - b.reshape(V, 1) - c.reshape(1, V) - mu).T.dot(W).T\n U = np.linalg.solve(matrix, vector).T\n\n # vectorized update c\n c = (logX - W.dot(U.T) - b.reshape(V, 1) - mu).sum(axis=0) / V\n\n\nprint(\"train duration:\", datetime.now() - t0)\n\nplt.plot(costs)\nplt.show()\n\n\n\n\n### test it\nking = W[word2idx['king']]\nman = W[word2idx['man']]\nqueen = W[word2idx['queen']]\nwoman = W[word2idx['woman']]\n\nvec = king - man + woman\n\n# find closest\n# closest = None\n# min_dist = float('inf')\n# for i in range(len(W)):\n# dist = cos_dist(W[i], vec)\n# if dist < min_dist:\n# closest = i\n# min_dist = dist\n\n# set word embedding matrix\n# W = (W + U) / 2\n\ndistances = pairwise_distances(vec.reshape(1, D), W, metric='cosine').reshape(V)\nidx = distances.argsort()[:10]\n\nprint(\"closest 10:\")\nfor i in idx:\n print(top_words[i], distances[i])\n\nprint(\"dist to queen:\", cos_dist(W[word2idx['queen']], vec))\n\n\n\ndef analogy(pos1, neg1, pos2, neg2):\n # don't actually use pos2 in calculation, just print what's expected\n print(\"testing: %s - %s = %s - %s\" % (pos1, neg1, pos2, neg2))\n for w in (pos1, neg1, pos2, neg2):\n if w not in word2idx:\n print(\"Sorry, %s not in word2idx\" % w)\n return\n\n p1 = W[word2idx[pos1]]\n n1 = W[word2idx[neg1]]\n p2 = W[word2idx[pos2]]\n n2 = W[word2idx[neg2]]\n\n vec = p1 - n1 + n2\n\n distances = pairwise_distances(vec.reshape(1, D), W, metric='cosine').reshape(V)\n idx = distances.argsort()[:10]\n\n # pick the best that's not p1, n1, or n2\n best_idx = -1\n keep_out = [word2idx[w] for w in (pos1, neg1, neg2)]\n for i in idx:\n if i not in keep_out:\n best_idx = i\n break\n\n print(\"got: %s - %s = %s - %s\" % (pos1, neg1, top_words[best_idx], neg2))\n print(\"closest 10:\")\n for i in idx:\n print(top_words[i], distances[i])\n\n print(\"dist to %s:\" % pos2, cos_dist(p2, vec))\n\n\nanalogy('king', 'man', 'queen', 'woman')\nanalogy('miami', 'florida', 'dallas', 'texas')\n# analogy('einstein', 'scientist', 'picasso', 'painter')\nanalogy('china', 'rice', 'england', 'bread')\nanalogy('man', 'woman', 'he', 'she')\nanalogy('man', 'woman', 'uncle', 'aunt')\nanalogy('man', 'woman', 'brother', 'sister')\nanalogy('man', 'woman', 'husband', 'wife')\nanalogy('man', 'woman', 'actor', 'actress')\nanalogy('man', 'woman', 'father', 'mother')\nanalogy('heir', 'heiress', 'prince', 'princess')\nanalogy('nephew', 'niece', 'uncle', 'aunt')\nanalogy('france', 'paris', 'japan', 'tokyo')\nanalogy('france', 'paris', 'china', 'beijing')\nanalogy('february', 'january', 'december', 'november')\nanalogy('france', 'paris', 'italy', 'rome')\nanalogy('paris', 'france', 'rome', 'italy')\nanalogy('france', 'french', 'england', 'english')\nanalogy('japan', 'japanese', 'china', 'chinese')\nanalogy('japan', 'japanese', 'italy', 'italian')\nanalogy('japan', 'japanese', 'australia', 'australian')\nanalogy('walk', 'walking', 'swim', 'swimming')\n" }, { "path": "nlp_class2/pos_baseline.py", "content": "# Course URL:\n# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\n\n# You can get the data from this URL: https://www.clips.uantwerpen.be/conll2000/chunking/\n# If above URL does not work, try this:\n# https://drive.google.com/file/d/0BxGV7C-8DTe5QmF2MTFwN3JjWGc/view?usp=sharing\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport os, sys\nimport numpy as np\nimport theano\nimport theano.tensor as T\nimport matplotlib.pyplot as plt\nfrom sklearn.utils import shuffle\nfrom sklearn.metrics import f1_score\nfrom sklearn.tree import DecisionTreeClassifier\n\n\nclass LogisticRegression:\n def __init__(self):\n pass\n\n def fit(self, X, Y, V=None, K=None, D=50, lr=1e-1, mu=0.99, batch_sz=100, epochs=6):\n if V is None:\n V = len(set(X))\n if K is None:\n K = len(set(Y))\n N = len(X)\n\n W = np.random.randn(V, K) / np.sqrt(V + K)\n b = np.zeros(K)\n self.W = theano.shared(W)\n self.b = theano.shared(b)\n self.params = [self.W, self.b]\n\n thX = T.ivector('X')\n thY = T.ivector('Y')\n\n py_x = T.nnet.softmax(self.W[thX] + self.b)\n prediction = T.argmax(py_x, axis=1)\n\n cost = -T.mean(T.log(py_x[T.arange(thY.shape[0]), thY]))\n grads = T.grad(cost, self.params)\n dparams = [theano.shared(p.get_value()*0) for p in self.params]\n self.cost_predict_op = theano.function(\n inputs=[thX, thY],\n outputs=[cost, prediction],\n allow_input_downcast=True,\n )\n\n updates = [\n (p, p + mu*dp - lr*g) for p, dp, g in zip(self.params, dparams, grads)\n ] + [\n (dp, mu*dp - lr*g) for dp, g in zip(dparams, grads)\n ]\n train_op = theano.function(\n inputs=[thX, thY],\n outputs=[cost, prediction],\n updates=updates,\n allow_input_downcast=True\n )\n\n costs = []\n n_batches = N // batch_sz\n for i in range(epochs):\n X, Y = shuffle(X, Y)\n print(\"epoch:\", i)\n for j in range(n_batches):\n Xbatch = X[j*batch_sz:(j*batch_sz + batch_sz)]\n Ybatch = Y[j*batch_sz:(j*batch_sz + batch_sz)]\n\n c, p = train_op(Xbatch, Ybatch)\n costs.append(c)\n if j % 200 == 0:\n print(\n \"i:\", i, \"j:\", j,\n \"n_batches:\", n_batches,\n \"cost:\", c,\n \"error:\", np.mean(p != Ybatch)\n )\n plt.plot(costs)\n plt.show()\n\n def score(self, X, Y):\n _, p = self.cost_predict_op(X, Y)\n return np.mean(p == Y)\n\n def f1_score(self, X, Y):\n _, p = self.cost_predict_op(X, Y)\n return f1_score(Y, p, average=None).mean()\n\n\ndef get_data(split_sequences=False):\n if not os.path.exists('chunking'):\n print(\"Please create a folder in your local directory called 'chunking'\")\n print(\"train.txt and test.txt should be stored in there.\")\n print(\"Please check the comments to get the download link.\")\n exit()\n elif not os.path.exists('chunking/train.txt'):\n print(\"train.txt is not in chunking/train.txt\")\n print(\"Please check the comments to get the download link.\")\n exit()\n elif not os.path.exists('chunking/test.txt'):\n print(\"test.txt is not in chunking/test.txt\")\n print(\"Please check the comments to get the download link.\")\n exit()\n\n word2idx = {}\n tag2idx = {}\n word_idx = 0\n tag_idx = 0\n Xtrain = []\n Ytrain = []\n currentX = []\n currentY = []\n for line in open('chunking/train.txt'):\n line = line.rstrip()\n if line:\n r = line.split()\n word, tag, _ = r\n if word not in word2idx:\n word2idx[word] = word_idx\n word_idx += 1\n currentX.append(word2idx[word])\n \n if tag not in tag2idx:\n tag2idx[tag] = tag_idx\n tag_idx += 1\n currentY.append(tag2idx[tag])\n elif split_sequences:\n Xtrain.append(currentX)\n Ytrain.append(currentY)\n currentX = []\n currentY = []\n\n if not split_sequences:\n Xtrain = currentX\n Ytrain = currentY\n\n # load and score test data\n Xtest = []\n Ytest = []\n currentX = []\n currentY = []\n for line in open('chunking/test.txt'):\n line = line.rstrip()\n if line:\n r = line.split()\n word, tag, _ = r\n if word in word2idx:\n currentX.append(word2idx[word])\n else:\n currentX.append(word_idx) # use this as unknown\n currentY.append(tag2idx[tag])\n elif split_sequences:\n Xtest.append(currentX)\n Ytest.append(currentY)\n currentX = []\n currentY = []\n if not split_sequences:\n Xtest = currentX\n Ytest = currentY\n\n return Xtrain, Ytrain, Xtest, Ytest, word2idx\n\ndef main():\n Xtrain, Ytrain, Xtest, Ytest, word2idx = get_data()\n\n # convert to numpy arrays\n Xtrain = np.array(Xtrain)\n Ytrain = np.array(Ytrain)\n\n # convert Xtrain to indicator matrix\n N = len(Xtrain)\n V = len(word2idx) + 1\n print(\"vocabulary size:\", V)\n # Xtrain_indicator = np.zeros((N, V))\n # Xtrain_indicator[np.arange(N), Xtrain] = 1\n\n # decision tree\n dt = DecisionTreeClassifier()\n\n # without indicator\n dt.fit(Xtrain.reshape(N, 1), Ytrain)\n print(\"dt train score:\", dt.score(Xtrain.reshape(N, 1), Ytrain))\n p = dt.predict(Xtrain.reshape(N, 1))\n print(\"dt train f1:\", f1_score(Ytrain, p, average=None).mean())\n\n # with indicator -- too slow!!\n # dt.fit(Xtrain_indicator, Ytrain)\n # print(\"dt score:\", dt.score(Xtrain_indicator, Ytrain))\n\n # train and score\n model = LogisticRegression()\n model.fit(Xtrain, Ytrain, V=V)\n print(\"training complete\")\n print(\"lr train score:\", model.score(Xtrain, Ytrain))\n print(\"lr train f1:\", model.f1_score(Xtrain, Ytrain))\n\n\n Ntest = len(Xtest)\n Xtest = np.array(Xtest)\n Ytest = np.array(Ytest)\n # convert Xtest to indicator\n # Xtest_indicator = np.zeros((Ntest, V))\n # Xtest_indicator[np.arange(Ntest), Xtest] = 1\n\n # decision tree test score\n print(\"dt test score:\", dt.score(Xtest.reshape(Ntest, 1), Ytest))\n p = dt.predict(Xtest.reshape(Ntest, 1))\n print(\"dt test f1:\", f1_score(Ytest, p, average=None).mean())\n # print(\"dt test score:\", dt.score(Xtest_indicator, Ytest)) # too slow!\n\n # logistic test score -- too slow!!\n print(\"lr test score:\", model.score(Xtest, Ytest))\n print(\"lr test f1:\", model.f1_score(Xtest, Ytest))\n\nif __name__ == '__main__':\n main()\n" }, { "path": "nlp_class2/pos_hmm.py", "content": "# Course URL:\n# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport os\nimport sys\nsys.path.append(os.path.abspath('..'))\nfrom hmm_class.hmmd_scaled import HMM\n\nfrom pos_baseline import get_data\nfrom sklearn.utils import shuffle\nfrom datetime import datetime\nfrom sklearn.metrics import f1_score\n\n\ndef accuracy(T, Y):\n # inputs are lists of lists\n n_correct = 0\n n_total = 0\n for t, y in zip(T, Y):\n n_correct += np.sum(t == y)\n n_total += len(y)\n return float(n_correct) / n_total\n\n\ndef total_f1_score(T, Y):\n # inputs are lists of lists\n T = np.concatenate(T)\n Y = np.concatenate(Y)\n return f1_score(T, Y, average=None).mean()\n\n\n# def flatten(l):\n# return [item for sublist in l for item in sublist]\n\n\ndef main(smoothing=1e-1):\n # X = words, Y = POS tags\n Xtrain, Ytrain, Xtest, Ytest, word2idx = get_data(split_sequences=True)\n V = len(word2idx) + 1\n\n # find hidden state transition matrix and pi\n M = max(max(y) for y in Ytrain) + 1 #len(set(flatten(Ytrain)))\n A = np.ones((M, M))*smoothing # add-one smoothing\n pi = np.zeros(M)\n for y in Ytrain:\n pi[y[0]] += 1\n for i in range(len(y)-1):\n A[y[i], y[i+1]] += 1\n # turn it into a probability matrix\n A /= A.sum(axis=1, keepdims=True)\n pi /= pi.sum()\n\n # find the observation matrix\n B = np.ones((M, V))*smoothing # add-one smoothing\n for x, y in zip(Xtrain, Ytrain):\n for xi, yi in zip(x, y):\n B[yi, xi] += 1\n B /= B.sum(axis=1, keepdims=True)\n\n hmm = HMM(M)\n hmm.pi = pi\n hmm.A = A\n hmm.B = B\n\n # get predictions\n Ptrain = []\n for x in Xtrain:\n p = hmm.get_state_sequence(x)\n Ptrain.append(p)\n\n Ptest = []\n for x in Xtest:\n p = hmm.get_state_sequence(x)\n Ptest.append(p)\n\n # print results\n print(\"train accuracy:\", accuracy(Ytrain, Ptrain))\n print(\"test accuracy:\", accuracy(Ytest, Ptest))\n print(\"train f1:\", total_f1_score(Ytrain, Ptrain))\n print(\"test f1:\", total_f1_score(Ytest, Ptest))\n\nif __name__ == '__main__':\n main()\n" }, { "path": "nlp_class2/pos_ner_keras.py", "content": "# Course URL:\n# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\nimport sys\nsys.path.append(os.path.abspath('..'))\nfrom pos_baseline import get_data\nfrom sklearn.utils import shuffle\nfrom util import init_weight\nfrom datetime import datetime\nfrom sklearn.metrics import f1_score\n\nfrom keras.models import Model\nfrom keras.layers import Input, Dense, Embedding, LSTM, GRU\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.optimizers import Adam\n\n\nMAX_VOCAB_SIZE = 20000\nMAX_TAGS = 100\n\n\n\ndef get_data_pos(split_sequences=False):\n if not os.path.exists('chunking'):\n print(\"Please create a folder in your local directory called 'chunking'\")\n print(\"train.txt and test.txt should be stored in there.\")\n print(\"Please check the comments to get the download link.\")\n exit()\n elif not os.path.exists('chunking/train.txt'):\n print(\"train.txt is not in chunking/train.txt\")\n print(\"Please check the comments to get the download link.\")\n exit()\n elif not os.path.exists('chunking/test.txt'):\n print(\"test.txt is not in chunking/test.txt\")\n print(\"Please check the comments to get the download link.\")\n exit()\n\n Xtrain = []\n Ytrain = []\n currentX = []\n currentY = []\n for line in open('chunking/train.txt'):\n line = line.rstrip()\n if line:\n r = line.split()\n word, tag, _ = r\n currentX.append(word)\n \n currentY.append(tag)\n elif split_sequences:\n Xtrain.append(currentX)\n Ytrain.append(currentY)\n currentX = []\n currentY = []\n\n if not split_sequences:\n Xtrain = currentX\n Ytrain = currentY\n\n # load and score test data\n Xtest = []\n Ytest = []\n currentX = []\n currentY = []\n for line in open('chunking/test.txt'):\n line = line.rstrip()\n if line:\n r = line.split()\n word, tag, _ = r\n currentX.append(word)\n currentY.append(tag)\n elif split_sequences:\n Xtest.append(currentX)\n Ytest.append(currentY)\n currentX = []\n currentY = []\n if not split_sequences:\n Xtest = currentX\n Ytest = currentY\n\n return Xtrain, Ytrain, Xtest, Ytest\n\n\ndef get_data_ner(split_sequences=False):\n Xtrain = []\n Ytrain = []\n currentX = []\n currentY = []\n for line in open('ner.txt'):\n line = line.rstrip()\n if line:\n r = line.split()\n word, tag = r\n word = word.lower()\n currentX.append(word)\n currentY.append(tag)\n elif split_sequences:\n Xtrain.append(currentX)\n Ytrain.append(currentY)\n currentX = []\n currentY = []\n\n if not split_sequences:\n Xtrain = currentX\n Ytrain = currentY\n\n print(\"number of samples:\", len(Xtrain))\n Xtrain, Ytrain = shuffle(Xtrain, Ytrain)\n Ntest = int(0.3*len(Xtrain))\n Xtest = Xtrain[:Ntest]\n Ytest = Ytrain[:Ntest]\n Xtrain = Xtrain[Ntest:]\n Ytrain = Ytrain[Ntest:]\n return Xtrain, Ytrain, Xtest, Ytest\n\n\n\n\n# get the data\nXtrain, Ytrain, Xtest, Ytest = get_data_ner(split_sequences=True)\n\n\n# convert the sentences (strings) into integers\ntokenizer = Tokenizer(num_words=MAX_VOCAB_SIZE)\ntokenizer.fit_on_texts(Xtrain)\nXtrain = tokenizer.texts_to_sequences(Xtrain)\nXtest = tokenizer.texts_to_sequences(Xtest)\n\n# get word -> integer mapping\nword2idx = tokenizer.word_index\nprint('Found %s unique tokens.' % len(word2idx))\nvocab_size = min(MAX_VOCAB_SIZE, len(word2idx) + 1)\n\n\n# convert the tags (strings) into integers\ntokenizer2 = Tokenizer(num_words=MAX_TAGS)\ntokenizer2.fit_on_texts(Ytrain)\nYtrain = tokenizer2.texts_to_sequences(Ytrain)\nYtest = tokenizer2.texts_to_sequences(Ytest)\n\n# get tag -> integer mapping\ntag2idx = tokenizer2.word_index\nprint('Found %s unique tags.' % len(tag2idx))\nnum_tags = min(MAX_TAGS, len(tag2idx) + 1)\n\n\n# pad sequences\nsequence_length = max(len(x) for x in Xtrain + Xtest)\nXtrain = pad_sequences(Xtrain, maxlen=sequence_length)\nYtrain = pad_sequences(Ytrain, maxlen=sequence_length)\nXtest = pad_sequences(Xtest, maxlen=sequence_length)\nYtest = pad_sequences(Ytest, maxlen=sequence_length)\nprint(\"Xtrain.shape:\", Xtrain.shape)\nprint(\"Ytrain.shape:\", Ytrain.shape)\n\n\n# one-hot the targets\nYtrain_onehot = np.zeros((len(Ytrain), sequence_length, num_tags), dtype='float32')\nfor n, sample in enumerate(Ytrain):\n for t, tag in enumerate(sample):\n Ytrain_onehot[n, t, tag] = 1\n\nYtest_onehot = np.zeros((len(Ytest), sequence_length, num_tags), dtype='float32')\nfor n, sample in enumerate(Ytest):\n for t, tag in enumerate(sample):\n Ytest_onehot[n, t, tag] = 1\n\n\n\n# training config\nepochs = 30\nbatch_size = 32\nhidden_layer_size = 10\nembedding_dim = 10\n\n\n\n\n# build the model\ninput_ = Input(shape=(sequence_length,))\nx = Embedding(vocab_size, embedding_dim)(input_)\nx = GRU(hidden_layer_size, return_sequences=True)(x)\noutput = Dense(num_tags, activation='softmax')(x)\n\n\nmodel = Model(input_, output)\nmodel.compile(\n loss='categorical_crossentropy',\n optimizer=Adam(lr=1e-2),\n metrics=['accuracy']\n)\n\n\nprint('Training model...')\nr = model.fit(\n Xtrain,\n Ytrain_onehot,\n batch_size=batch_size,\n epochs=epochs,\n validation_data=(Xtest, Ytest_onehot)\n)\n\n# plot some data\nplt.plot(r.history['loss'], label='loss')\nplt.plot(r.history['val_loss'], label='val_loss')\nplt.legend()\nplt.show()\n\n# accuracies\nplt.plot(r.history['accuracy'], label='acc')\nplt.plot(r.history['val_accuracy'], label='val_acc')\nplt.legend()\nplt.show()\n\n" }, { "path": "nlp_class2/pos_rnn.py", "content": "# Course URL:\n# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport theano\nimport theano.tensor as T\nimport os\nimport sys\nsys.path.append(os.path.abspath('..'))\nfrom rnn_class.gru import GRU\nfrom pos_baseline import get_data\nfrom sklearn.utils import shuffle\nfrom util import init_weight\nfrom datetime import datetime\nfrom sklearn.metrics import f1_score\n\n\nclass RNN:\n def __init__(self, D, hidden_layer_sizes, V, K):\n self.hidden_layer_sizes = hidden_layer_sizes\n self.D = D\n self.V = V\n self.K = K\n\n def fit(self, X, Y, learning_rate=1e-4, mu=0.99, epochs=30, show_fig=True, activation=T.nnet.relu, RecurrentUnit=GRU, normalize=False):\n D = self.D\n V = self.V\n N = len(X)\n\n We = init_weight(V, D)\n self.hidden_layers = []\n Mi = D\n for Mo in self.hidden_layer_sizes:\n ru = RecurrentUnit(Mi, Mo, activation)\n self.hidden_layers.append(ru)\n Mi = Mo\n\n Wo = init_weight(Mi, self.K)\n bo = np.zeros(self.K)\n\n self.We = theano.shared(We)\n self.Wo = theano.shared(Wo)\n self.bo = theano.shared(bo)\n self.params = [self.Wo, self.bo]\n for ru in self.hidden_layers:\n self.params += ru.params\n\n thX = T.ivector('X')\n thY = T.ivector('Y')\n\n Z = self.We[thX]\n for ru in self.hidden_layers:\n Z = ru.output(Z)\n py_x = T.nnet.softmax(Z.dot(self.Wo) + self.bo)\n\n testf = theano.function(\n inputs=[thX],\n outputs=py_x,\n )\n testout = testf(X[0])\n print(\"py_x.shape:\", testout.shape)\n\n prediction = T.argmax(py_x, axis=1)\n \n cost = -T.mean(T.log(py_x[T.arange(thY.shape[0]), thY]))\n grads = T.grad(cost, self.params)\n dparams = [theano.shared(p.get_value()*0) for p in self.params]\n\n dWe = theano.shared(self.We.get_value()*0)\n gWe = T.grad(cost, self.We)\n dWe_update = mu*dWe - learning_rate*gWe\n We_update = self.We + dWe_update\n if normalize:\n We_update /= We_update.norm(2)\n\n updates = [\n (p, p + mu*dp - learning_rate*g) for p, dp, g in zip(self.params, dparams, grads)\n ] + [\n (dp, mu*dp - learning_rate*g) for dp, g in zip(dparams, grads)\n ] + [\n (self.We, We_update), (dWe, dWe_update)\n ]\n\n self.cost_predict_op = theano.function(\n inputs=[thX, thY],\n outputs=[cost, prediction],\n allow_input_downcast=True,\n )\n\n self.train_op = theano.function(\n inputs=[thX, thY],\n outputs=[cost, prediction],\n updates=updates\n )\n\n costs = []\n sequence_indexes = range(N)\n n_total = sum(len(y) for y in Y)\n for i in range(epochs):\n t0 = datetime.now()\n sequence_indexes = shuffle(sequence_indexes)\n n_correct = 0\n cost = 0\n it = 0\n for j in sequence_indexes:\n c, p = self.train_op(X[j], Y[j])\n cost += c\n n_correct += np.sum(p == Y[j])\n it += 1\n if it % 200 == 0:\n sys.stdout.write(\n \"j/N: %d/%d correct rate so far: %f, cost so far: %f\\r\" %\n (it, N, float(n_correct)/n_total, cost)\n )\n sys.stdout.flush()\n print(\n \"i:\", i, \"cost:\", cost,\n \"correct rate:\", (float(n_correct)/n_total),\n \"time for epoch:\", (datetime.now() - t0)\n )\n costs.append(cost)\n\n if show_fig:\n plt.plot(costs)\n plt.show()\n\n def score(self, X, Y):\n n_total = sum(len(y) for y in Y)\n n_correct = 0\n for x, y in zip(X, Y):\n _, p = self.cost_predict_op(x, y)\n n_correct += np.sum(p == y)\n return float(n_correct) / n_total\n\n def f1_score(self, X, Y):\n P = []\n for x, y in zip(X, Y):\n _, p = self.cost_predict_op(x, y)\n P.append(p)\n Y = np.concatenate(Y)\n P = np.concatenate(P)\n return f1_score(Y, P, average=None).mean()\n\n\ndef flatten(l):\n return [item for sublist in l for item in sublist]\n\n\ndef main():\n Xtrain, Ytrain, Xtest, Ytest, word2idx = get_data(split_sequences=True)\n V = len(word2idx) + 1\n K = len(set(flatten(Ytrain)) | set(flatten(Ytest)))\n rnn = RNN(10, [10], V, K)\n rnn.fit(Xtrain, Ytrain)\n print(\"train score:\", rnn.score(Xtrain, Ytrain))\n print(\"test score:\", rnn.score(Xtest, Ytest))\n print(\"train f1:\", rnn.f1_score(Xtrain, Ytrain))\n print(\"test f1:\", rnn.f1_score(Xtest, Ytest))\n \n\nif __name__ == '__main__':\n main()\n" }, { "path": "nlp_class2/pos_tf.py", "content": "# Course URL:\n# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nimport os\nimport sys\nsys.path.append(os.path.abspath('..'))\nfrom pos_baseline import get_data\nfrom sklearn.utils import shuffle\nfrom util import init_weight\nfrom datetime import datetime\nfrom sklearn.metrics import f1_score\n\nfrom tensorflow.contrib.rnn import static_rnn as get_rnn_output\nfrom tensorflow.contrib.rnn import BasicRNNCell, GRUCell\n\n\n\ndef get_data(split_sequences=False):\n if not os.path.exists('chunking'):\n print(\"Please create a folder in your local directory called 'chunking'\")\n print(\"train.txt and test.txt should be stored in there.\")\n print(\"Please check the comments to get the download link.\")\n exit()\n elif not os.path.exists('chunking/train.txt'):\n print(\"train.txt is not in chunking/train.txt\")\n print(\"Please check the comments to get the download link.\")\n exit()\n elif not os.path.exists('chunking/test.txt'):\n print(\"test.txt is not in chunking/test.txt\")\n print(\"Please check the comments to get the download link.\")\n exit()\n\n word2idx = {}\n tag2idx = {}\n word_idx = 1\n tag_idx = 1\n Xtrain = []\n Ytrain = []\n currentX = []\n currentY = []\n for line in open('chunking/train.txt'):\n line = line.rstrip()\n if line:\n r = line.split()\n word, tag, _ = r\n if word not in word2idx:\n word2idx[word] = word_idx\n word_idx += 1\n currentX.append(word2idx[word])\n \n if tag not in tag2idx:\n tag2idx[tag] = tag_idx\n tag_idx += 1\n currentY.append(tag2idx[tag])\n elif split_sequences:\n Xtrain.append(currentX)\n Ytrain.append(currentY)\n currentX = []\n currentY = []\n\n if not split_sequences:\n Xtrain = currentX\n Ytrain = currentY\n\n # load and score test data\n Xtest = []\n Ytest = []\n currentX = []\n currentY = []\n for line in open('chunking/test.txt'):\n line = line.rstrip()\n if line:\n r = line.split()\n word, tag, _ = r\n if word in word2idx:\n currentX.append(word2idx[word])\n else:\n currentX.append(word_idx) # use this as unknown\n currentY.append(tag2idx[tag])\n elif split_sequences:\n Xtest.append(currentX)\n Ytest.append(currentY)\n currentX = []\n currentY = []\n if not split_sequences:\n Xtest = currentX\n Ytest = currentY\n\n return Xtrain, Ytrain, Xtest, Ytest, word2idx\n\n\ndef flatten(l):\n return [item for sublist in l for item in sublist]\n\n\n\n# get the data\nXtrain, Ytrain, Xtest, Ytest, word2idx = get_data(split_sequences=True)\nV = len(word2idx) + 2 # vocab size (+1 for unknown, +1 b/c start from 1)\nK = len(set(flatten(Ytrain)) | set(flatten(Ytest))) + 1 # num classes\n\n\n# training config\nepochs = 20\nlearning_rate = 1e-2\nmu = 0.99\nbatch_size = 32\nhidden_layer_size = 10\nembedding_dim = 10\nsequence_length = max(len(x) for x in Xtrain + Xtest)\n\n\n\n# pad sequences\nXtrain = tf.keras.preprocessing.sequence.pad_sequences(Xtrain, maxlen=sequence_length)\nYtrain = tf.keras.preprocessing.sequence.pad_sequences(Ytrain, maxlen=sequence_length)\nXtest = tf.keras.preprocessing.sequence.pad_sequences(Xtest, maxlen=sequence_length)\nYtest = tf.keras.preprocessing.sequence.pad_sequences(Ytest, maxlen=sequence_length)\nprint(\"Xtrain.shape:\", Xtrain.shape)\nprint(\"Ytrain.shape:\", Ytrain.shape)\n\n\n\n# inputs\ninputs = tf.placeholder(tf.int32, shape=(None, sequence_length))\ntargets = tf.placeholder(tf.int32, shape=(None, sequence_length))\nnum_samples = tf.shape(inputs)[0] # useful for later\n\n# embedding\nWe = np.random.randn(V, embedding_dim).astype(np.float32)\n\n# output layer\nWo = init_weight(hidden_layer_size, K).astype(np.float32)\nbo = np.zeros(K).astype(np.float32)\n\n# make them tensorflow variables\ntfWe = tf.Variable(We)\ntfWo = tf.Variable(Wo)\ntfbo = tf.Variable(bo)\n\n# make the rnn unit\nrnn_unit = GRUCell(num_units=hidden_layer_size, activation=tf.nn.relu)\n\n\n# get the output\nx = tf.nn.embedding_lookup(tfWe, inputs)\n\n# converts x from a tensor of shape N x T x M\n# into a list of length T, where each element is a tensor of shape N x M\nx = tf.unstack(x, sequence_length, 1)\n\n# get the rnn output\noutputs, states = get_rnn_output(rnn_unit, x, dtype=tf.float32)\n\n\n# outputs are now of size (T, N, M)\n# so make it (N, T, M)\noutputs = tf.transpose(outputs, (1, 0, 2))\noutputs = tf.reshape(outputs, (sequence_length*num_samples, hidden_layer_size)) # NT x M\n\n# final dense layer\nlogits = tf.matmul(outputs, tfWo) + tfbo # NT x K\npredictions = tf.argmax(logits, 1)\npredict_op = tf.reshape(predictions, (num_samples, sequence_length))\nlabels_flat = tf.reshape(targets, [-1])\n\ncost_op = tf.reduce_mean(\n tf.nn.sparse_softmax_cross_entropy_with_logits(\n logits=logits,\n labels=labels_flat\n )\n)\ntrain_op = tf.train.AdamOptimizer(learning_rate).minimize(cost_op)\n\n\n\n\n# init stuff\nsess = tf.InteractiveSession()\ninit = tf.global_variables_initializer()\nsess.run(init)\n\n\n# training loop\ncosts = []\nn_batches = len(Ytrain) // batch_size\nfor i in range(epochs):\n n_total = 0\n n_correct = 0\n\n t0 = datetime.now()\n Xtrain, Ytrain = shuffle(Xtrain, Ytrain)\n cost = 0\n\n for j in range(n_batches):\n x = Xtrain[j*batch_size:(j+1)*batch_size]\n y = Ytrain[j*batch_size:(j+1)*batch_size]\n\n # get the cost, predictions, and perform a gradient descent step\n c, p, _ = sess.run(\n (cost_op, predict_op, train_op),\n feed_dict={inputs: x, targets: y})\n cost += c\n\n # calculate the accuracy\n for yi, pi in zip(y, p):\n # we don't care about the padded entries so ignore them\n yii = yi[yi > 0]\n pii = pi[yi > 0]\n n_correct += np.sum(yii == pii)\n n_total += len(yii)\n\n # print stuff out periodically\n if j % 10 == 0:\n sys.stdout.write(\n \"j/N: %d/%d correct rate so far: %f, cost so far: %f\\r\" %\n (j, n_batches, float(n_correct)/n_total, cost)\n )\n sys.stdout.flush()\n\n # get test acc. too\n p = sess.run(predict_op, feed_dict={inputs: Xtest, targets: Ytest})\n n_test_correct = 0\n n_test_total = 0\n for yi, pi in zip(Ytest, p):\n yii = yi[yi > 0]\n pii = pi[yi > 0]\n n_test_correct += np.sum(yii == pii)\n n_test_total += len(yii)\n test_acc = float(n_test_correct) / n_test_total\n\n print(\n \"i:\", i, \"cost:\", \"%.4f\" % cost,\n \"train acc:\", \"%.4f\" % (float(n_correct)/n_total),\n \"test acc:\", \"%.4f\" % test_acc,\n \"time for epoch:\", (datetime.now() - t0)\n )\n costs.append(cost)\n\nplt.plot(costs)\nplt.show()\n\n\n" }, { "path": "nlp_class2/pretrained_glove.py", "content": "# https://deeplearningcourses.com/c/data-science-natural-language-processing-in-python\n# https://www.udemy.com/data-science-natural-language-processing-in-python\n\n# Author: http://lazyprogrammer.me\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\n# WHERE TO GET THE VECTORS:\n# GloVe: https://nlp.stanford.edu/projects/glove/\n# Direct link: http://nlp.stanford.edu/data/glove.6B.zip\n\nimport numpy as np\nfrom sklearn.metrics.pairwise import pairwise_distances\n\n\ndef dist1(a, b):\n return np.linalg.norm(a - b)\ndef dist2(a, b):\n return 1 - a.dot(b) / (np.linalg.norm(a) * np.linalg.norm(b))\n\n# pick a distance type\ndist, metric = dist2, 'cosine'\n# dist, metric = dist1, 'euclidean'\n\n\n## more intuitive\n# def find_analogies(w1, w2, w3):\n# for w in (w1, w2, w3):\n# if w not in word2vec:\n# print(\"%s not in dictionary\" % w)\n# return\n\n# king = word2vec[w1]\n# man = word2vec[w2]\n# woman = word2vec[w3]\n# v0 = king - man + woman\n\n# min_dist = float('inf')\n# best_word = ''\n# for word, v1 in iteritems(word2vec):\n# if word not in (w1, w2, w3):\n# d = dist(v0, v1)\n# if d < min_dist:\n# min_dist = d\n# best_word = word\n# print(w1, \"-\", w2, \"=\", best_word, \"-\", w3)\n\n\n## faster\ndef find_analogies(w1, w2, w3):\n for w in (w1, w2, w3):\n if w not in word2vec:\n print(\"%s not in dictionary\" % w)\n return\n\n king = word2vec[w1]\n man = word2vec[w2]\n woman = word2vec[w3]\n v0 = king - man + woman\n\n distances = pairwise_distances(v0.reshape(1, D), embedding, metric=metric).reshape(V)\n idxs = distances.argsort()[:4]\n for idx in idxs:\n word = idx2word[idx]\n if word not in (w1, w2, w3): \n best_word = word\n break\n\n print(w1, \"-\", w2, \"=\", best_word, \"-\", w3)\n\n\ndef nearest_neighbors(w, n=5):\n if w not in word2vec:\n print(\"%s not in dictionary:\" % w)\n return\n\n v = word2vec[w]\n distances = pairwise_distances(v.reshape(1, D), embedding, metric=metric).reshape(V)\n idxs = distances.argsort()[1:n+1]\n print(\"neighbors of: %s\" % w)\n for idx in idxs:\n print(\"\\t%s\" % idx2word[idx])\n\n\n\n# load in pre-trained word vectors\nprint('Loading word vectors...')\nword2vec = {}\nembedding = []\nidx2word = []\nwith open('../large_files/glove.6B/glove.6B.50d.txt', encoding='utf-8') as f:\n # is just a space-separated text file in the format:\n # word vec[0] vec[1] vec[2] ...\n for line in f:\n values = line.split()\n word = values[0]\n vec = np.asarray(values[1:], dtype='float32')\n word2vec[word] = vec\n embedding.append(vec)\n idx2word.append(word)\nprint('Found %s word vectors.' % len(word2vec))\nembedding = np.array(embedding)\nV, D = embedding.shape\n\n\nfind_analogies('king', 'man', 'woman')\nfind_analogies('france', 'paris', 'london')\nfind_analogies('france', 'paris', 'rome')\nfind_analogies('paris', 'france', 'italy')\nfind_analogies('france', 'french', 'english')\nfind_analogies('japan', 'japanese', 'chinese')\nfind_analogies('japan', 'japanese', 'italian')\nfind_analogies('japan', 'japanese', 'australian')\nfind_analogies('december', 'november', 'june')\nfind_analogies('miami', 'florida', 'texas')\nfind_analogies('einstein', 'scientist', 'painter')\nfind_analogies('china', 'rice', 'bread')\nfind_analogies('man', 'woman', 'she')\nfind_analogies('man', 'woman', 'aunt')\nfind_analogies('man', 'woman', 'sister')\nfind_analogies('man', 'woman', 'wife')\nfind_analogies('man', 'woman', 'actress')\nfind_analogies('man', 'woman', 'mother')\nfind_analogies('heir', 'heiress', 'princess')\nfind_analogies('nephew', 'niece', 'aunt')\nfind_analogies('france', 'paris', 'tokyo')\nfind_analogies('france', 'paris', 'beijing')\nfind_analogies('february', 'january', 'november')\nfind_analogies('france', 'paris', 'rome')\nfind_analogies('paris', 'france', 'italy')\n\nnearest_neighbors('king')\nnearest_neighbors('france')\nnearest_neighbors('japan')\nnearest_neighbors('einstein')\nnearest_neighbors('woman')\nnearest_neighbors('nephew')\nnearest_neighbors('february')\nnearest_neighbors('rome')\n" }, { "path": "nlp_class2/pretrained_w2v.py", "content": "# https://deeplearningcourses.com/c/data-science-natural-language-processing-in-python\n# https://www.udemy.com/data-science-natural-language-processing-in-python\n\n# Author: http://lazyprogrammer.me\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nfrom gensim.models import KeyedVectors\n\n\n# warning: takes quite awhile\n# https://code.google.com/archive/p/word2vec/\n# direct link: https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing\n# 3 million words and phrases\n# D = 300\nword_vectors = KeyedVectors.load_word2vec_format(\n '../large_files/GoogleNews-vectors-negative300.bin',\n binary=True\n)\n\n\n# convenience\n# result looks like:\n# [('athens', 0.6001024842262268),\n# ('albert', 0.5729557275772095),\n# ('holmes', 0.569324254989624),\n# ('donnie', 0.5690680742263794),\n# ('italy', 0.5673537254333496),\n# ('toni', 0.5666348338127136),\n# ('spain', 0.5661854147911072),\n# ('jh', 0.5661597847938538),\n# ('pablo', 0.5631559491157532),\n# ('malta', 0.5620371103286743)]\ndef find_analogies(w1, w2, w3):\n r = word_vectors.most_similar(positive=[w1, w3], negative=[w2])\n print(\"%s - %s = %s - %s\" % (w1, w2, r[0][0], w3))\n\ndef nearest_neighbors(w):\n r = word_vectors.most_similar(positive=[w])\n print(\"neighbors of: %s\" % w)\n for word, score in r:\n print(\"\\t%s\" % word)\n\n\nfind_analogies('king', 'man', 'woman')\nfind_analogies('france', 'paris', 'london')\nfind_analogies('france', 'paris', 'rome')\nfind_analogies('paris', 'france', 'italy')\nfind_analogies('france', 'french', 'english')\nfind_analogies('japan', 'japanese', 'chinese')\nfind_analogies('japan', 'japanese', 'italian')\nfind_analogies('japan', 'japanese', 'australian')\nfind_analogies('december', 'november', 'june')\nfind_analogies('miami', 'florida', 'texas')\nfind_analogies('einstein', 'scientist', 'painter')\nfind_analogies('china', 'rice', 'bread')\nfind_analogies('man', 'woman', 'she')\nfind_analogies('man', 'woman', 'aunt')\nfind_analogies('man', 'woman', 'sister')\nfind_analogies('man', 'woman', 'wife')\nfind_analogies('man', 'woman', 'actress')\nfind_analogies('man', 'woman', 'mother')\nfind_analogies('heir', 'heiress', 'princess')\nfind_analogies('nephew', 'niece', 'aunt')\nfind_analogies('france', 'paris', 'tokyo')\nfind_analogies('france', 'paris', 'beijing')\nfind_analogies('february', 'january', 'november')\nfind_analogies('france', 'paris', 'rome')\nfind_analogies('paris', 'france', 'italy')\n\nnearest_neighbors('king')\nnearest_neighbors('france')\nnearest_neighbors('japan')\nnearest_neighbors('einstein')\nnearest_neighbors('woman')\nnearest_neighbors('nephew')\nnearest_neighbors('february')\nnearest_neighbors('rome')" }, { "path": "nlp_class2/recursive_tensorflow.py", "content": "# Course URL:\n# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\n# data is from: http://nlp.stanford.edu/sentiment/\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport sys\nimport tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.utils import shuffle\nfrom datetime import datetime\nfrom util import init_weight, get_ptb_data, display_tree\n\nif tf.__version__.startswith('2'):\n tf.compat.v1.disable_eager_execution()\n\n\ndef get_labels(tree):\n # must be returned in the same order as tree logits are returned\n # post-order traversal\n if tree is None:\n return []\n return get_labels(tree.left) + get_labels(tree.right) + [tree.label]\n\n\nclass TNN:\n def __init__(self, V, D, K, activation):\n self.D = D\n self.f = activation\n\n # word embedding\n We = init_weight(V, D)\n\n # linear terms\n W1 = init_weight(D, D)\n W2 = init_weight(D, D)\n\n # bias\n bh = np.zeros(D)\n\n # output layer\n Wo = init_weight(D, K)\n bo = np.zeros(K)\n\n # make them tensorflow variables\n self.We = tf.Variable(We.astype(np.float32))\n self.W1 = tf.Variable(W1.astype(np.float32))\n self.W2 = tf.Variable(W2.astype(np.float32))\n self.bh = tf.Variable(bh.astype(np.float32))\n self.Wo = tf.Variable(Wo.astype(np.float32))\n self.bo = tf.Variable(bo.astype(np.float32))\n self.params = [self.We, self.W1, self.W2, self.Wo]\n\n def fit(self, trees, lr=1e-1, mu=0.9, reg=0.1, epochs=5):\n train_ops = []\n costs = []\n predictions = []\n all_labels = []\n i = 0\n N = len(trees)\n print(\"Compiling ops\")\n for t in trees:\n i += 1\n sys.stdout.write(\"%d/%d\\r\" % (i, N))\n sys.stdout.flush()\n logits = self.get_output(t)\n labels = get_labels(t)\n all_labels.append(labels)\n\n cost = self.get_cost(logits, labels, reg)\n costs.append(cost)\n\n prediction = tf.argmax(input=logits, axis=1)\n predictions.append(prediction)\n\n train_op = tf.compat.v1.train.MomentumOptimizer(lr, mu).minimize(cost)\n train_ops.append(train_op)\n\n # save for later so we don't have to recompile\n self.predictions = predictions\n self.all_labels = all_labels\n self.saver = tf.compat.v1.train.Saver()\n\n init = tf.compat.v1.initialize_all_variables()\n actual_costs = []\n per_epoch_costs = []\n correct_rates = []\n with tf.compat.v1.Session() as session:\n session.run(init)\n\n for i in range(epochs):\n t0 = datetime.now()\n\n train_ops, costs, predictions, all_labels = shuffle(train_ops, costs, predictions, all_labels)\n epoch_cost = 0\n n_correct = 0\n n_total = 0\n j = 0\n N = len(train_ops)\n for train_op, cost, prediction, labels in zip(train_ops, costs, predictions, all_labels):\n _, c, p = session.run([train_op, cost, prediction])\n epoch_cost += c\n actual_costs.append(c)\n n_correct += np.sum(p == labels)\n n_total += len(labels)\n\n j += 1\n if j % 10 == 0:\n sys.stdout.write(\"j: %d, N: %d, c: %f\\r\" % (j, N, c))\n sys.stdout.flush()\n\n print(\n \"epoch:\", i, \"cost:\", epoch_cost,\n \"elapsed time:\", (datetime.now() - t0)\n )\n\n per_epoch_costs.append(epoch_cost)\n correct_rates.append(n_correct / float(n_total))\n\n self.saver.save(session, \"recursive.ckpt\")\n\n plt.plot(actual_costs)\n plt.title(\"cost per train_op call\")\n plt.show()\n\n plt.plot(per_epoch_costs)\n plt.title(\"per epoch costs\")\n plt.show()\n\n plt.plot(correct_rates)\n plt.title(\"correct rates\")\n plt.show()\n\n def get_cost(self, logits, labels, reg):\n cost = tf.reduce_mean(\n input_tensor=tf.nn.sparse_softmax_cross_entropy_with_logits(\n logits=logits,\n labels=labels\n )\n )\n rcost = sum(tf.nn.l2_loss(p) for p in self.params)\n cost += reg*rcost\n return cost\n\n # list_of_logits is an output!\n # it is added to using post-order traversal\n def get_output_recursive(self, tree, list_of_logits, is_root=True):\n if tree.word is not None:\n # this is a leaf node\n x = tf.nn.embedding_lookup(params=self.We, ids=[tree.word])\n else:\n # this node has children\n x1 = self.get_output_recursive(tree.left, list_of_logits, is_root=False)\n x2 = self.get_output_recursive(tree.right, list_of_logits, is_root=False)\n x = self.f(\n tf.matmul(x1, self.W1) +\n tf.matmul(x2, self.W2) +\n self.bh)\n\n logits = tf.matmul(x, self.Wo) + self.bo\n list_of_logits.append(logits)\n\n return x\n\n def get_output(self, tree):\n logits = []\n # try:\n self.get_output_recursive(tree, logits)\n # except Exception as e:\n # display_tree(tree)\n # raise e\n return tf.concat(logits, 0)\n\n def score(self, trees):\n if trees is None:\n predictions = self.predictions\n all_labels = self.all_labels\n else:\n # just build and run the predict_op for each tree\n # and accumulate the total\n predictions = []\n all_labels = []\n\n i = 0\n N = len(trees)\n print(\"Compiling ops\")\n for t in trees:\n\n i += 1\n sys.stdout.write(\"%d/%d\\r\" % (i, N))\n sys.stdout.flush()\n\n logits = self.get_output(t)\n labels = get_labels(t)\n all_labels.append(labels)\n\n prediction = tf.argmax(input=logits, axis=1)\n predictions.append(prediction)\n\n n_correct = 0\n n_total = 0\n with tf.compat.v1.Session() as session:\n self.saver.restore(session, \"recursive.ckpt\")\n for prediction, y in zip(predictions, all_labels):\n p = session.run(prediction)\n n_correct += (p[-1] == y[-1]) # we only care about the root\n n_total += len(y)\n\n return float(n_correct) / n_total\n\n\ndef main():\n train, test, word2idx = get_ptb_data()\n\n train = train[:100]\n test = test[:100]\n\n V = len(word2idx)\n D = 80\n K = 5\n\n model = TNN(V, D, K, tf.nn.relu)\n model.fit(train)\n print(\"train accuracy:\", model.score(None))\n print(\"test accuracy:\", model.score(test))\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "nlp_class2/recursive_theano.py", "content": "# Course URL:\n# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport sys\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport theano\nimport theano.tensor as T\n\nfrom sklearn.utils import shuffle\nfrom util import init_weight, get_ptb_data, display_tree\nfrom datetime import datetime\n\n\ndef adagrad(cost, params, lr, eps=1e-10):\n grads = T.grad(cost, params)\n caches = [theano.shared(np.ones_like(p.get_value())) for p in params]\n new_caches = [c + g*g for c, g in zip(caches, grads)]\n\n c_update = [(c, new_c) for c, new_c in zip(caches, new_caches)]\n g_update = [\n (p, p - lr*g / T.sqrt(new_c + eps)) for p, new_c, g in zip(params, new_caches, grads)\n ]\n updates = c_update + g_update\n return updates\n\n\nclass RecursiveNN:\n def __init__(self, V, D, K):\n self.V = V\n self.D = D\n self.K = K\n\n def fit(self, trees, learning_rate=3*1e-3, mu=0.99, reg=1e-4, epochs=15, activation=T.nnet.relu, train_inner_nodes=False):\n D = self.D\n V = self.V\n K = self.K\n self.f = activation\n N = len(trees)\n\n We = init_weight(V, D)\n Wh = np.random.randn(2, D, D) / np.sqrt(2 + D + D)\n bh = np.zeros(D)\n Wo = init_weight(D, K)\n bo = np.zeros(K)\n\n self.We = theano.shared(We)\n self.Wh = theano.shared(Wh)\n self.bh = theano.shared(bh)\n self.Wo = theano.shared(Wo)\n self.bo = theano.shared(bo)\n self.params = [self.We, self.Wh, self.bh, self.Wo, self.bo]\n\n words = T.ivector('words')\n parents = T.ivector('parents')\n relations = T.ivector('relations')\n labels = T.ivector('labels')\n\n def recurrence(n, hiddens, words, parents, relations):\n w = words[n]\n # any non-word will have index -1\n # if T.ge(w, 0):\n # hiddens = T.set_subtensor(hiddens[n], self.We[w])\n # else:\n # hiddens = T.set_subtensor(hiddens[n], self.f(hiddens[n] + self.bh))\n hiddens = T.switch(\n T.ge(w, 0),\n T.set_subtensor(hiddens[n], self.We[w]),\n T.set_subtensor(hiddens[n], self.f(hiddens[n] + self.bh))\n )\n\n r = relations[n] # 0 = is_left, 1 = is_right\n p = parents[n] # parent idx\n # if T.ge(p, 0):\n # # root will have parent -1\n # hiddens = T.set_subtensor(hiddens[p], hiddens[p] + hiddens[n].dot(self.Wh[r]))\n hiddens = T.switch(\n T.ge(p, 0),\n T.set_subtensor(hiddens[p], hiddens[p] + hiddens[n].dot(self.Wh[r])),\n hiddens\n )\n return hiddens\n\n hiddens = T.zeros((words.shape[0], D))\n\n h, _ = theano.scan(\n fn=recurrence,\n outputs_info=[hiddens],\n n_steps=words.shape[0],\n sequences=T.arange(words.shape[0]),\n non_sequences=[words, parents, relations],\n )\n\n # shape of h that is returned by scan is TxTxD\n # because hiddens is TxD, and it does the recurrence T times\n # technically this stores T times too much data\n py_x = T.nnet.softmax(h[-1].dot(self.Wo) + self.bo)\n\n prediction = T.argmax(py_x, axis=1)\n \n rcost = reg*T.mean([(p*p).sum() for p in self.params])\n if train_inner_nodes:\n # won't work for binary classification\n cost = -T.mean(T.log(py_x[T.arange(labels.shape[0]), labels])) + rcost\n else:\n # print \"K is:\", K\n # premean = T.log(py_x[-1])\n # target = T.zeros(K)\n # target = T.set_subtensor(target[labels[-1]], 1) \n # cost = -T.mean(target * premean)\n\n cost = -T.mean(T.log(py_x[-1, labels[-1]])) + rcost\n # grads = T.grad(cost, self.params)\n # dparams = [theano.shared(p.get_value()*0) for p in self.params]\n\n # updates = [\n # (p, p + mu*dp - learning_rate*g) for p, dp, g in zip(self.params, dparams, grads)\n # ] + [\n # (dp, mu*dp - learning_rate*g) for dp, g in zip(dparams, grads)\n # ]\n\n updates = adagrad(cost, self.params, lr=8e-3)\n\n self.cost_predict_op = theano.function(\n inputs=[words, parents, relations, labels],\n outputs=[cost, prediction],\n allow_input_downcast=True,\n )\n\n self.train_op = theano.function(\n inputs=[words, parents, relations, labels],\n outputs=[h, cost, prediction],\n updates=updates\n )\n\n costs = []\n sequence_indexes = range(N)\n if train_inner_nodes:\n n_total = sum(len(words) for words, _, _, _ in trees)\n else:\n n_total = N\n for i in range(epochs):\n t0 = datetime.now()\n sequence_indexes = shuffle(sequence_indexes)\n n_correct = 0\n cost = 0\n it = 0\n for j in sequence_indexes:\n words, par, rel, lab = trees[j]\n _, c, p = self.train_op(words, par, rel, lab)\n\n if np.isnan(c):\n print(\"Cost is nan! Let's stop here. \\\n Why don't you try decreasing the learning rate?\")\n exit()\n cost += c\n if train_inner_nodes:\n n_correct += np.sum(p == lab)\n else:\n n_correct += (p[-1] == lab[-1])\n it += 1\n if it % 1 == 0:\n sys.stdout.write(\"j/N: %d/%d correct rate so far: %f, cost so far: %f\\r\" % (it, N, float(n_correct)/n_total, cost))\n sys.stdout.flush()\n print(\n \"i:\", i, \"cost:\", cost,\n \"correct rate:\", (float(n_correct)/n_total),\n \"time for epoch:\", (datetime.now() - t0)\n )\n costs.append(cost)\n\n plt.plot(costs)\n plt.draw() # don't block later code\n\n def score(self, trees, idx2word=None):\n n_total = len(trees)\n n_correct = 0\n for words, par, rel, lab in trees:\n _, p = self.cost_predict_op(words, par, rel, lab)\n n_correct += (p[-1] == lab[-1])\n # if idx2word:\n # print_sentence(words, idx2word)\n # print(\"label:\", lab[-1], \"pred:\", p[-1])\n print(\"n_correct:\", n_correct, \"n_total:\", n_total, end=\" \")\n return float(n_correct) / n_total\n\n\ndef add_idx_to_tree(tree, current_idx):\n # post-order labeling of tree nodes\n if tree is None:\n return current_idx\n current_idx = add_idx_to_tree(tree.left, current_idx)\n current_idx = add_idx_to_tree(tree.right, current_idx)\n tree.idx = current_idx\n current_idx += 1\n return current_idx\n\n\ndef tree2list(tree, parent_idx, is_binary=False, is_left=False, is_right=False):\n if tree is None:\n return [], [], [], []\n\n w = tree.word if tree.word is not None else -1\n if is_left:\n r = 0\n elif is_right:\n r = 1\n else:\n r = -1\n words_left, parents_left, relations_left, labels_left = tree2list(tree.left, tree.idx, is_binary, is_left=True)\n words_right, parents_right, relations_right, labels_right = tree2list(tree.right, tree.idx, is_binary, is_right=True)\n\n words = words_left + words_right + [w]\n parents = parents_left + parents_right + [parent_idx]\n relations = relations_left + relations_right + [r]\n if is_binary:\n if tree.label > 2:\n label = 1\n elif tree.label < 2:\n label = 0\n else:\n label = -1 # we will eventually filter these out\n else:\n label = tree.label\n labels = labels_left + labels_right + [label]\n\n return words, parents, relations, labels\n\n\n# def get_sentence(tree):\n# if tree is None:\n# return []\n# w = [tree.word] if tree.word is not None else []\n# return get_sentence(tree.left) + get_sentence(tree.right) + w\n\n\ndef print_sentence(words, idx2word):\n # sentence = ' '.join(get_sentence(tree))\n # print(sentence, \"label:\", tree.label)\n for w in words:\n if w >= 0:\n print(idx2word[w], end=\" \")\n\n\ndef main(is_binary=True):\n train, test, word2idx = get_ptb_data()\n\n for t in train:\n add_idx_to_tree(t, 0)\n train = [tree2list(t, -1, is_binary) for t in train]\n if is_binary:\n train = [t for t in train if t[3][-1] >= 0] # for filtering binary labels\n\n # sanity check\n # check that last node has no parent\n # for t in train:\n # assert(t[1][-1] == -1 and t[2][-1] == -1)\n\n for t in test:\n add_idx_to_tree(t, 0)\n test = [tree2list(t, -1, is_binary) for t in test]\n if is_binary:\n test = [t for t in test if t[3][-1] >= 0] # for filtering binary labels\n\n train = shuffle(train)\n # train = train[:2000]\n n_pos = sum(t[3][-1] for t in train)\n # print(\"num pos train:\", n_pos)\n # idx2word = {v:k for k, v in word2idx.items()}\n # for i in range(4):\n # words, _, _, labels = train[i]\n # print_sentence(words, idx2word)\n # print(\"label:\", labels[-1])\n test = shuffle(test)\n test = test[:1000]\n\n V = len(word2idx)\n print(\"vocab size:\", V)\n D = 10\n K = 2 if is_binary else 5\n\n model = RecursiveNN(V, D, K)\n model.fit(train, learning_rate=1e-2, reg=1e-2, mu=0, epochs=20, activation=T.tanh, train_inner_nodes=False)\n print(\"train accuracy:\", model.score(train))\n print(\"test accuracy:\", model.score(test))\n\n # make sure program doesn't end until we close the plot\n plt.show()\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "nlp_class2/rntn_tensorflow.py", "content": "# Course URL:\n# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\n# data is from: http://nlp.stanford.edu/sentiment/\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport sys\nimport tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.utils import shuffle\nfrom util import init_weight, get_ptb_data, display_tree\n\n\ndef tensor_mul(d, x1, A, x2):\n A = tf.reshape(A, [d, d*d])\n # (1 x d) x (d x dd)\n tmp = tf.matmul(x1, A)\n # (1 x dd)\n tmp = tf.reshape(tmp, [d, d])\n # (d x d)\n tmp = tf.matmul(tmp, tf.transpose(x2))\n # (d x 1)\n return tf.reshape(tmp, [1, d])\n\n\ndef get_labels(tree):\n # must be returned in the same order as tree logits are returned\n # post-order traversal\n if tree is None:\n return []\n return get_labels(tree.left) + get_labels(tree.right) + [tree.label]\n\n\nclass RNTN:\n def __init__(self, V, D, K, activation):\n self.D = D\n self.f = activation\n\n # word embedding\n We = init_weight(V, D)\n\n # quadratic terms\n W11 = np.random.randn(D, D, D) / np.sqrt(3*D)\n W22 = np.random.randn(D, D, D) / np.sqrt(3*D)\n W12 = np.random.randn(D, D, D) / np.sqrt(3*D)\n\n # linear terms\n W1 = init_weight(D, D)\n W2 = init_weight(D, D)\n\n # bias\n bh = np.zeros(D)\n\n # output layer\n Wo = init_weight(D, K)\n bo = np.zeros(K)\n\n # make them tensorflow variables\n self.We = tf.Variable(We.astype(np.float32))\n self.W11 = tf.Variable(W11.astype(np.float32))\n self.W22 = tf.Variable(W22.astype(np.float32))\n self.W12 = tf.Variable(W12.astype(np.float32))\n self.W1 = tf.Variable(W1.astype(np.float32))\n self.W2 = tf.Variable(W2.astype(np.float32))\n self.bh = tf.Variable(bh.astype(np.float32))\n self.Wo = tf.Variable(Wo.astype(np.float32))\n self.bo = tf.Variable(bo.astype(np.float32))\n self.params = [self.We, self.W11, self.W22, self.W12, self.W1, self.W2, self.Wo]\n\n def fit(self, trees, lr=1e-2, mu=0.9, reg=1e-1, epochs=5):\n train_ops = []\n costs = []\n predictions = []\n all_labels = []\n i = 0\n N = len(trees)\n print(\"Compiling ops\")\n for t in trees:\n i += 1\n sys.stdout.write(\"%d/%d\\r\" % (i, N))\n sys.stdout.flush()\n logits = self.get_output(t)\n labels = get_labels(t)\n all_labels.append(labels)\n\n cost = self.get_cost(logits, labels, reg)\n costs.append(cost)\n\n prediction = tf.argmax(logits, 1)\n predictions.append(prediction)\n\n train_op = tf.train.MomentumOptimizer(lr, mu).minimize(cost)\n train_ops.append(train_op)\n\n # save for later so we don't have to recompile if we call score\n self.predictions = predictions\n self.all_labels = all_labels\n\n self.saver = tf.train.Saver()\n init = tf.initialize_all_variables()\n actual_costs = []\n per_epoch_costs = []\n correct_rates = []\n with tf.Session() as session:\n session.run(init)\n\n for i in range(epochs):\n train_ops, costs, predictions, all_labels = shuffle(train_ops, costs, predictions, all_labels)\n epoch_cost = 0\n n_correct = 0\n n_total = 0\n j = 0\n N = len(train_ops)\n for train_op, cost, prediction, labels in zip(train_ops, costs, predictions, all_labels):\n _, c, p = session.run([train_op, cost, prediction])\n epoch_cost += c\n actual_costs.append(c)\n n_correct += np.sum(p == labels)\n n_total += len(labels)\n\n j += 1\n if j % 10 == 0:\n sys.stdout.write(\"j: %d, N: %d, c: %f\\r\" % (j, N, c))\n sys.stdout.flush()\n\n if np.isnan(c):\n exit()\n\n per_epoch_costs.append(epoch_cost)\n correct_rates.append(n_correct / float(n_total))\n\n self.save_path = self.saver.save(session, \"tf_model.ckpt\")\n\n plt.plot(actual_costs)\n plt.title(\"cost per train_op call\")\n plt.show()\n\n plt.plot(per_epoch_costs)\n plt.title(\"per epoch costs\")\n plt.show()\n\n plt.plot(correct_rates)\n plt.title(\"correct rates\")\n plt.show()\n\n def get_cost(self, logits, labels, reg):\n cost = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits, labels))\n rcost = sum(tf.nn.l2_loss(p) for p in self.params)\n cost += reg*rcost\n return cost\n\n # list_of_logits is an output!\n # it is added to using post-order traversal\n def get_output_recursive(self, tree, list_of_logits, is_root=True):\n if tree.word is not None:\n # this is a leaf node\n x = tf.nn.embedding_lookup(self.We, [tree.word])\n else:\n # if tree.left is None or tree.right is None:\n # print(\"This tree node has no word but also has no children:\")\n # display_tree(tree)\n # print(\"\")\n # this node has children\n x1 = self.get_output_recursive(tree.left, list_of_logits, is_root=False)\n x2 = self.get_output_recursive(tree.right, list_of_logits, is_root=False)\n x = self.f(\n tensor_mul(self.D, x1, self.W11, x1) +\n tensor_mul(self.D, x2, self.W22, x2) +\n tensor_mul(self.D, x1, self.W12, x2) +\n tf.matmul(x1, self.W1) +\n tf.matmul(x2, self.W2) +\n self.bh)\n\n logits = tf.matmul(x, self.Wo) + self.bo\n list_of_logits.append(logits)\n\n return x\n\n def get_output(self, tree):\n logits = []\n try:\n self.get_output_recursive(tree, logits)\n except Exception as e:\n display_tree(tree)\n raise e\n return tf.concat(0, logits)\n\n def score(self, trees):\n if trees is None:\n predictions = self.predictions\n all_labels = self.all_labels\n else:\n # just build and run the predict_op for each tree\n # and accumulate the total\n predictions = []\n all_labels = []\n\n i = 0\n N = len(trees)\n print(\"Compiling ops\")\n for t in trees:\n\n i += 1\n sys.stdout.write(\"%d/%d\\r\" % (i, N))\n sys.stdout.flush()\n\n logits = self.get_output(t)\n labels = get_labels(t)\n all_labels.append(labels)\n\n prediction = tf.argmax(logits, 1)\n predictions.append(prediction)\n\n n_correct = 0\n n_total = 0\n with tf.Session() as session:\n self.saver.restore(session, \"tf_model.ckpt\")\n\n for prediction, y in zip(predictions, all_labels):\n p = session.run(prediction)\n # print(\"pred:\", p)\n # print(\"label:\", y)\n # n_correct += np.sum(p == y)\n n_correct += (p[-1] == y[-1]) # we only care about the root\n n_total += len(y)\n\n return float(n_correct) / n_total\n\n\ndef main():\n train, test, word2idx = get_ptb_data()\n\n train = train[:100]\n test = test[:100]\n\n V = len(word2idx)\n D = 80\n K = 5\n\n model = RNTN(V, D, K, tf.nn.relu)\n model.fit(train)\n print(\"train accuracy:\", model.score(None))\n print(\"test accuracy:\", model.score(test))\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "nlp_class2/rntn_tensorflow_rnn.py", "content": "# Course URL:\n# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport sys\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\n\nfrom sklearn.utils import shuffle\nfrom util import init_weight, get_ptb_data, display_tree\nfrom datetime import datetime\nfrom sklearn.metrics import f1_score\n\nif tf.__version__.startswith('2'):\n tf.compat.v1.disable_eager_execution()\n\n\n\nclass RecursiveNN:\n def __init__(self, V, D, K, activation=tf.tanh):\n self.V = V\n self.D = D\n self.K = K\n self.f = activation\n\n def fit(self, trees, test_trees, reg=1e-3, epochs=8, train_inner_nodes=False):\n D = self.D\n V = self.V\n K = self.K\n N = len(trees)\n\n We = init_weight(V, D)\n W11 = np.random.randn(D, D, D) / np.sqrt(3*D)\n W22 = np.random.randn(D, D, D) / np.sqrt(3*D)\n W12 = np.random.randn(D, D, D) / np.sqrt(3*D)\n W1 = init_weight(D, D)\n W2 = init_weight(D, D)\n bh = np.zeros(D)\n Wo = init_weight(D, K)\n bo = np.zeros(K)\n\n self.We = tf.Variable(We.astype(np.float32))\n self.W11 = tf.Variable(W11.astype(np.float32))\n self.W22 = tf.Variable(W22.astype(np.float32))\n self.W12 = tf.Variable(W12.astype(np.float32))\n self.W1 = tf.Variable(W1.astype(np.float32))\n self.W2 = tf.Variable(W2.astype(np.float32))\n self.bh = tf.Variable(bh.astype(np.float32))\n self.Wo = tf.Variable(Wo.astype(np.float32))\n self.bo = tf.Variable(bo.astype(np.float32))\n self.weights = [self.We, self.W11, self.W22, self.W12, self.W1, self.W2, self.Wo]\n\n\n words = tf.compat.v1.placeholder(tf.int32, shape=(None,), name='words')\n left_children = tf.compat.v1.placeholder(tf.int32, shape=(None,), name='left_children')\n right_children = tf.compat.v1.placeholder(tf.int32, shape=(None,), name='right_children')\n labels = tf.compat.v1.placeholder(tf.int32, shape=(None,), name='labels')\n\n # save for later\n self.words = words\n self.left = left_children\n self.right = right_children\n self.labels = labels\n\n def dot1(a, B):\n return tf.tensordot(a, B, axes=[[0], [1]])\n\n def dot2(B, a):\n return tf.tensordot(B, a, axes=[[1], [0]])\n\n def recursive_net_transform(hiddens, n):\n h_left = hiddens.read(left_children[n])\n h_right = hiddens.read(right_children[n])\n return self.f(\n dot1(h_left, dot2(self.W11, h_left)) +\n dot1(h_right, dot2(self.W22, h_right)) +\n dot1(h_left, dot2(self.W12, h_right)) +\n dot1(h_left, self.W1) +\n dot1(h_right, self.W2) +\n self.bh\n )\n\n\n def recurrence(hiddens, n):\n w = words[n]\n # any non-word will have index -1\n\n h_n = tf.cond(\n pred=w >= 0,\n true_fn=lambda: tf.nn.embedding_lookup(params=self.We, ids=w),\n false_fn=lambda: recursive_net_transform(hiddens, n)\n )\n hiddens = hiddens.write(n, h_n)\n n = tf.add(n, 1)\n return hiddens, n\n\n\n def condition(hiddens, n):\n # loop should continue while n < len(words)\n return tf.less(n, tf.shape(input=words)[0])\n\n\n hiddens = tf.TensorArray(\n tf.float32,\n size=0,\n dynamic_size=True,\n clear_after_read=False,\n infer_shape=False\n )\n\n hiddens, _ = tf.while_loop(\n cond=condition,\n body=recurrence,\n loop_vars=[hiddens, tf.constant(0)],\n parallel_iterations=1\n )\n h = hiddens.stack()\n logits = tf.matmul(h, self.Wo) + self.bo\n\n prediction_op = tf.argmax(input=logits, axis=1)\n self.prediction_op = prediction_op\n \n rcost = reg*sum(tf.nn.l2_loss(p) for p in self.weights)\n if train_inner_nodes:\n # filter out -1s\n labeled_indices = tf.compat.v1.where(labels >= 0)\n\n cost_op = tf.reduce_mean(\n input_tensor=tf.nn.sparse_softmax_cross_entropy_with_logits(\n logits=tf.gather(logits, labeled_indices),\n labels=tf.gather(labels, labeled_indices),\n )\n ) + rcost\n else:\n cost_op = tf.reduce_mean(\n input_tensor=tf.nn.sparse_softmax_cross_entropy_with_logits(\n logits=logits[-1],\n labels=labels[-1],\n )\n ) + rcost\n\n train_op = tf.compat.v1.train.AdagradOptimizer(learning_rate=8e-3).minimize(cost_op)\n # train_op = tf.train.MomentumOptimizer(learning_rate=8e-3, momentum=0.9).minimize(cost_op)\n\n # NOTE: If you're using GPU, InteractiveSession breaks\n # AdagradOptimizer and some other optimizers\n # change to tf.Session() if so.\n self.session = tf.compat.v1.Session()\n init_op = tf.compat.v1.global_variables_initializer()\n self.session.run(init_op)\n\n\n costs = []\n sequence_indexes = range(N)\n for i in range(epochs):\n t0 = datetime.now()\n sequence_indexes = shuffle(sequence_indexes)\n n_correct = 0\n n_total = 0\n cost = 0\n it = 0\n for j in sequence_indexes:\n words_, left, right, lab = trees[j]\n # print(\"words_:\", words_)\n # print(\"lab:\", lab)\n c, p, _ = self.session.run(\n (cost_op, prediction_op, train_op),\n feed_dict={\n words: words_,\n left_children: left,\n right_children: right,\n labels: lab\n }\n )\n if np.isnan(c):\n print(\"Cost is nan! Let's stop here. \\\n Why don't you try decreasing the learning rate?\")\n for p in self.params:\n print(p.get_value().sum())\n exit()\n cost += c\n n_correct += (p[-1] == lab[-1])\n n_total += 1\n\n it += 1\n if it % 10 == 0:\n sys.stdout.write(\n \"j/N: %d/%d correct rate so far: %f, cost so far: %f\\r\" %\n (it, N, float(n_correct)/n_total, cost)\n )\n sys.stdout.flush()\n\n\n # calculate the test score\n n_test_correct = 0\n n_test_total = 0\n for words_, left, right, lab in test_trees:\n p = self.session.run(prediction_op, feed_dict={\n words: words_,\n left_children: left,\n right_children: right,\n labels: lab\n })\n n_test_correct += (p[-1] == lab[-1])\n n_test_total += 1\n\n\n print(\n \"i:\", i, \"cost:\", cost,\n \"train acc:\", float(n_correct)/n_total,\n \"test acc:\", float(n_test_correct)/n_test_total,\n \"time for epoch:\", (datetime.now() - t0)\n )\n costs.append(cost)\n\n plt.plot(costs)\n plt.show()\n\n def predict(self, words, left, right, lab):\n return self.session.run(\n self.prediction_op,\n feed_dict={\n self.words: words,\n self.left: left,\n self.right: right,\n self.labels: lab\n }\n )\n\n\n def score(self, trees):\n n_total = len(trees)\n n_correct = 0\n for words, left, right, lab in trees:\n p = self.predict(words, left, right, lab)\n n_correct += (p[-1] == lab[-1])\n return float(n_correct) / n_total\n\n def f1_score(self, trees):\n Y = []\n P = []\n for words, left, right, lab in trees:\n p = self.predict(words, left, right, lab)\n Y.append(lab[-1])\n P.append(p[-1])\n return f1_score(Y, P, average=None).mean()\n\n\ndef add_idx_to_tree(tree, current_idx):\n # post-order labeling of tree nodes\n if tree is None:\n return current_idx\n current_idx = add_idx_to_tree(tree.left, current_idx)\n current_idx = add_idx_to_tree(tree.right, current_idx)\n tree.idx = current_idx\n current_idx += 1\n return current_idx\n\n\ndef tree2list(tree, parent_idx, is_binary=False):\n if tree is None:\n return [], [], [], []\n\n words_left, left_child_left, right_child_left, labels_left = tree2list(tree.left, tree.idx, is_binary)\n words_right, left_child_right, right_child_right, labels_right = tree2list(tree.right, tree.idx, is_binary)\n\n if tree.word is None:\n w = -1\n left = tree.left.idx\n right = tree.right.idx\n else:\n w = tree.word\n left = -1\n right = -1\n\n words = words_left + words_right + [w]\n left_child = left_child_left + left_child_right + [left]\n right_child = right_child_left + right_child_right + [right]\n\n if is_binary:\n if tree.label > 2:\n label = 1\n elif tree.label < 2:\n label = 0\n else:\n label = -1 # we will eventually filter these out\n else:\n label = tree.label\n labels = labels_left + labels_right + [label]\n\n return words, left_child, right_child, labels\n\n\ndef main(is_binary=True):\n train, test, word2idx = get_ptb_data()\n\n for t in train:\n add_idx_to_tree(t, 0)\n train = [tree2list(t, -1, is_binary) for t in train]\n if is_binary:\n train = [t for t in train if t[3][-1] >= 0] # for filtering binary labels\n\n for t in test:\n add_idx_to_tree(t, 0)\n test = [tree2list(t, -1, is_binary) for t in test]\n if is_binary:\n test = [t for t in test if t[3][-1] >= 0] # for filtering binary labels\n\n\n\n train = shuffle(train)\n # train = train[:5000]\n # n_pos = sum(t[3][-1] for t in train)\n # print(\"n_pos train:\", n_pos)\n test = shuffle(test)\n smalltest = test[:1000]\n # n_pos = sum(t[3][-1] for t in test)\n # print(\"n_pos test:\", n_pos)\n\n V = len(word2idx)\n print(\"vocab size:\", V)\n D = 10\n K = 2 if is_binary else 5\n\n model = RecursiveNN(V, D, K)\n model.fit(train, smalltest, reg=1e-3, epochs=20, train_inner_nodes=True)\n print(\"train accuracy:\", model.score(train))\n print(\"test accuracy:\", model.score(test))\n print(\"train f1:\", model.f1_score(train))\n print(\"test f1:\", model.f1_score(test))\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "nlp_class2/rntn_theano.py", "content": "# Course URL:\n# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport sys\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport theano\nimport theano.tensor as T\n\nfrom sklearn.utils import shuffle\nfrom util import init_weight, get_ptb_data, display_tree\nfrom datetime import datetime\nfrom sklearn.metrics import f1_score\n\n\n# helper for adam optimizer\n# use tensorflow defaults\n# def adam(cost, params, lr0=1e-4, beta1=0.9, beta2=0.999, eps=1e-8):\n# grads = T.grad(cost, params)\n# updates = []\n# time = theano.shared(0)\n# new_time = time + 1\n# updates.append((time, new_time))\n# lr = lr0*T.sqrt(1 - beta2**new_time) / (1 - beta1**new_time)\n# for p, g in zip(params, grads):\n# m = theano.shared(p.get_value() * 0.)\n# v = theano.shared(p.get_value() * 0.)\n# new_m = beta1*m + (1 - beta1)*g\n# new_v = beta2*v + (1 - beta2)*g*g\n# new_p = p - lr*new_m / (T.sqrt(new_v) + eps)\n# updates.append((m, new_m))\n# updates.append((v, new_v))\n# updates.append((p, new_p))\n# return updates\n\n\n# def momentum_updates(cost, params, learning_rate=1e-3, mu=0.99):\n# # momentum changes\n# dparams = [theano.shared(p.get_value() * 0.) for p in params]\n\n# updates = []\n# grads = T.grad(cost, params)\n# for p, dp, g in zip(params, dparams, grads):\n# dp_update = mu*dp - learning_rate*g\n# p_update = p + dp_update\n\n# updates.append((dp, dp_update))\n# updates.append((p, p_update))\n# return updates\n\n\n# def rmsprop(cost, params, lr=1e-3, decay=0.999, eps=1e-10):\n# grads = T.grad(cost, params)\n# caches = [theano.shared(np.ones_like(p.get_value())) for p in params]\n# new_caches = [decay*c + (1. - decay)*g*g for c, g in zip(caches, grads)]\n\n# c_update = [(c, new_c) for c, new_c in zip(caches, new_caches)]\n# g_update = [\n# (p, p - lr*g / T.sqrt(new_c + eps)) for p, new_c, g in zip(params, new_caches, grads)\n# ]\n# updates = c_update + g_update\n# return updates\n\n\ndef adagrad(cost, params, lr, eps=1e-10):\n grads = T.grad(cost, params)\n caches = [theano.shared(np.ones_like(p.get_value())) for p in params]\n new_caches = [c + g*g for c, g in zip(caches, grads)]\n\n c_update = [(c, new_c) for c, new_c in zip(caches, new_caches)]\n g_update = [\n (p, p - lr*g / T.sqrt(new_c + eps)) for p, new_c, g in zip(params, new_caches, grads)\n ]\n updates = c_update + g_update\n return updates\n\n\nclass RecursiveNN:\n def __init__(self, V, D, K, activation=T.tanh):\n self.V = V\n self.D = D\n self.K = K\n self.f = activation\n\n def fit(self, trees, test_trees, reg=1e-3, epochs=8, train_inner_nodes=False):\n D = self.D\n V = self.V\n K = self.K\n N = len(trees)\n\n We = init_weight(V, D)\n W11 = np.random.randn(D, D, D) / np.sqrt(3*D)\n W22 = np.random.randn(D, D, D) / np.sqrt(3*D)\n W12 = np.random.randn(D, D, D) / np.sqrt(3*D)\n W1 = init_weight(D, D)\n W2 = init_weight(D, D)\n bh = np.zeros(D)\n Wo = init_weight(D, K)\n bo = np.zeros(K)\n\n self.We = theano.shared(We)\n self.W11 = theano.shared(W11)\n self.W22 = theano.shared(W22)\n self.W12 = theano.shared(W12)\n self.W1 = theano.shared(W1)\n self.W2 = theano.shared(W2)\n self.bh = theano.shared(bh)\n self.Wo = theano.shared(Wo)\n self.bo = theano.shared(bo)\n self.params = [self.We, self.W11, self.W22, self.W12, self.W1, self.W2, self.bh, self.Wo, self.bo]\n\n lr = T.scalar('learning_rate')\n words = T.ivector('words')\n left_children = T.ivector('left_children')\n right_children = T.ivector('right_children')\n labels = T.ivector('labels')\n\n def recurrence(n, hiddens, words, left, right):\n w = words[n]\n # any non-word will have index -1\n hiddens = T.switch(\n T.ge(w, 0),\n T.set_subtensor(hiddens[n], self.We[w]),\n T.set_subtensor(hiddens[n],\n self.f(\n hiddens[left[n]].dot(self.W11).dot(hiddens[left[n]]) +\n hiddens[right[n]].dot(self.W22).dot(hiddens[right[n]]) +\n hiddens[left[n]].dot(self.W12).dot(hiddens[right[n]]) +\n hiddens[left[n]].dot(self.W1) +\n hiddens[right[n]].dot(self.W2) +\n self.bh\n )\n )\n )\n return hiddens\n\n hiddens = T.zeros((words.shape[0], D))\n\n h, _ = theano.scan(\n fn=recurrence,\n outputs_info=[hiddens],\n n_steps=words.shape[0],\n sequences=T.arange(words.shape[0]),\n non_sequences=[words, left_children, right_children],\n )\n\n py_x = T.nnet.softmax(h[-1].dot(self.Wo) + self.bo)\n\n prediction = T.argmax(py_x, axis=1)\n \n rcost = reg*T.sum([(p*p).sum() for p in self.params])\n if train_inner_nodes:\n relevant_labels = labels[labels >= 0]\n cost = -T.mean(T.log(py_x[labels >= 0, relevant_labels])) + rcost\n else:\n cost = -T.mean(T.log(py_x[-1, labels[-1]])) + rcost\n \n\n updates = adagrad(cost, self.params, lr)\n\n self.cost_predict_op = theano.function(\n inputs=[words, left_children, right_children, labels],\n outputs=[cost, prediction],\n allow_input_downcast=True,\n )\n\n self.train_op = theano.function(\n inputs=[words, left_children, right_children, labels, lr],\n outputs=[cost, prediction],\n updates=updates\n )\n\n lr_ = 8e-3 # initial learning rate\n costs = []\n sequence_indexes = range(N)\n # if train_inner_nodes:\n # n_total = sum(len(words) for words, _, _, _ in trees)\n # else:\n # n_total = N\n for i in range(epochs):\n t0 = datetime.now()\n sequence_indexes = shuffle(sequence_indexes)\n n_correct = 0\n n_total = 0\n cost = 0\n it = 0\n for j in sequence_indexes:\n words, left, right, lab = trees[j]\n c, p = self.train_op(words, left, right, lab, lr_)\n if np.isnan(c):\n print(\"Cost is nan! Let's stop here. \\\n Why don't you try decreasing the learning rate?\")\n for p in self.params:\n print(p.get_value().sum())\n exit()\n cost += c\n n_correct += (p[-1] == lab[-1])\n n_total += 1\n it += 1\n if it % 10 == 0:\n sys.stdout.write(\n \"j/N: %d/%d correct rate so far: %f, cost so far: %f\\r\" %\n (it, N, float(n_correct)/n_total, cost)\n )\n sys.stdout.flush()\n\n # calculate the test score\n n_test_correct = 0\n n_test_total = 0\n for words, left, right, lab in test_trees:\n _, p = self.cost_predict_op(words, left, right, lab)\n n_test_correct += (p[-1] == lab[-1])\n n_test_total += 1\n\n print(\n \"i:\", i, \"cost:\", cost,\n \"train acc:\", float(n_correct)/n_total,\n \"test acc:\", float(n_test_correct)/n_test_total,\n \"time for epoch:\", (datetime.now() - t0)\n )\n costs.append(cost)\n\n plt.plot(costs)\n plt.show()\n\n def score(self, trees):\n n_total = len(trees)\n n_correct = 0\n for words, left, right, lab in trees:\n _, p = self.cost_predict_op(words, left, right, lab)\n n_correct += (p[-1] == lab[-1])\n return float(n_correct) / n_total\n\n def f1_score(self, trees):\n Y = []\n P = []\n for words, left, right, lab in trees:\n _, p = self.cost_predict_op(words, left, right, lab)\n Y.append(lab[-1])\n P.append(p[-1])\n return f1_score(Y, P, average=None).mean()\n\n\ndef add_idx_to_tree(tree, current_idx):\n # post-order labeling of tree nodes\n if tree is None:\n return current_idx\n current_idx = add_idx_to_tree(tree.left, current_idx)\n current_idx = add_idx_to_tree(tree.right, current_idx)\n tree.idx = current_idx\n current_idx += 1\n return current_idx\n\n\ndef tree2list(tree, parent_idx, is_binary=False):\n if tree is None:\n return [], [], [], []\n\n words_left, left_child_left, right_child_left, labels_left = tree2list(tree.left, tree.idx, is_binary)\n words_right, left_child_right, right_child_right, labels_right = tree2list(tree.right, tree.idx, is_binary)\n\n if tree.word is None:\n w = -1\n left = tree.left.idx\n right = tree.right.idx\n else:\n w = tree.word\n left = -1\n right = -1\n\n words = words_left + words_right + [w]\n left_child = left_child_left + left_child_right + [left]\n right_child = right_child_left + right_child_right + [right]\n\n if is_binary:\n if tree.label > 2:\n label = 1\n elif tree.label < 2:\n # else:\n label = 0\n else:\n label = -1 # we will eventually filter these out\n else:\n label = tree.label\n labels = labels_left + labels_right + [label]\n\n return words, left_child, right_child, labels\n\n\ndef main(is_binary=True):\n train, test, word2idx = get_ptb_data()\n\n for t in train:\n add_idx_to_tree(t, 0)\n train = [tree2list(t, -1, is_binary) for t in train]\n if is_binary:\n train = [t for t in train if t[3][-1] >= 0] # for filtering binary labels\n\n for t in test:\n add_idx_to_tree(t, 0)\n test = [tree2list(t, -1, is_binary) for t in test]\n if is_binary:\n test = [t for t in test if t[3][-1] >= 0] # for filtering binary labels\n\n # check imbalance\n # pos = 0\n # neg = 0\n # mid = 0\n # label_counts = np.zeros(5)\n # for t in train + test:\n # words, left_child, right_child, labels = t\n # # for l in labels:\n # # if l == 0:\n # # neg += 1\n # # elif l == 1:\n # # pos += 1\n # # else:\n # # mid += 1\n # for l in labels:\n # label_counts[l] += 1\n # # print(\"pos / total:\", float(pos) / (pos + neg + mid))\n # # print(\"mid / total:\", float(mid) / (pos + neg + mid))\n # # print(\"neg / total:\", float(neg) / (pos + neg + mid))\n # print(\"label proportions:\", label_counts / label_counts.sum())\n # exit()\n\n\n train = shuffle(train)\n # train = train[:5000]\n # n_pos = sum(t[3][-1] for t in train)\n # print(\"n_pos train:\", n_pos)\n test = shuffle(test)\n smalltest = test[:1000]\n # n_pos = sum(t[3][-1] for t in test)\n # print(\"n_pos test:\", n_pos)\n\n V = len(word2idx)\n print(\"vocab size:\", V)\n D = 20\n K = 2 if is_binary else 5\n\n model = RecursiveNN(V, D, K)\n model.fit(train, smalltest, epochs=20, train_inner_nodes=True)\n print(\"train accuracy:\", model.score(train))\n print(\"test accuracy:\", model.score(test))\n print(\"train f1:\", model.f1_score(train))\n print(\"test f1:\", model.f1_score(test))\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "nlp_class2/tfidf_tsne.py", "content": "# Course URL:\n# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport json\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.utils import shuffle\nfrom sklearn.manifold import TSNE\nfrom sklearn.decomposition import TruncatedSVD, PCA, KernelPCA\nfrom datetime import datetime\n\nimport os\nimport sys\nsys.path.append(os.path.abspath('..'))\nfrom rnn_class.util import get_wikipedia_data\nfrom rnn_class.brown import get_sentences_with_word2idx_limit_vocab, get_sentences_with_word2idx\n\nfrom util import find_analogies\nfrom sklearn.feature_extraction.text import TfidfTransformer\n\n\ndef main():\n analogies_to_try = (\n ('king', 'man', 'woman'),\n ('france', 'paris', 'london'),\n ('france', 'paris', 'rome'),\n ('paris', 'france', 'italy'),\n )\n\n ### choose a data source ###\n # sentences, word2idx = get_sentences_with_word2idx_limit_vocab(n_vocab=1500)\n sentences, word2idx = get_wikipedia_data(n_files=3, n_vocab=2000, by_paragraph=True)\n # with open('tfidf_word2idx.json', 'w') as f:\n # json.dump(word2idx, f)\n\n notfound = False\n for word_list in analogies_to_try:\n for w in word_list:\n if w not in word2idx:\n print(\"%s not found in vocab, remove it from \\\n analogies to try or increase vocab size\" % w)\n notfound = True\n if notfound:\n exit()\n\n\n # build term document matrix\n V = len(word2idx)\n N = len(sentences)\n\n # create raw counts first\n A = np.zeros((V, N))\n print(\"V:\", V, \"N:\", N)\n j = 0\n for sentence in sentences:\n for i in sentence:\n A[i,j] += 1\n j += 1\n print(\"finished getting raw counts\")\n\n transformer = TfidfTransformer()\n A = transformer.fit_transform(A.T).T\n\n # tsne requires a dense array\n A = A.toarray()\n\n # map back to word in plot\n idx2word = {v:k for k, v in iteritems(word2idx)}\n\n # plot the data in 2-D\n tsne = TSNE()\n Z = tsne.fit_transform(A)\n plt.scatter(Z[:,0], Z[:,1])\n for i in range(V):\n try:\n plt.annotate(s=idx2word[i].encode(\"utf8\").decode(\"utf8\"), xy=(Z[i,0], Z[i,1]))\n except:\n print(\"bad string:\", idx2word[i])\n plt.draw()\n\n ### multiple ways to create vectors for each word ###\n # 1) simply set it to the TF-IDF matrix\n # We = A\n\n # 2) create a higher-D word embedding\n tsne = TSNE(n_components=3)\n We = tsne.fit_transform(A)\n\n # 3) use a classic dimensionality reduction technique\n # svd = KernelPCA(n_components=20, kernel='rbf')\n # We = svd.fit_transform(A)\n\n for word_list in analogies_to_try:\n w1, w2, w3 = word_list\n find_analogies(w1, w2, w3, We, word2idx, idx2word)\n\n plt.show() # pause script until plot is closed\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "nlp_class2/util.py", "content": "# Course URL:\n# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport os\nimport numpy as np\nfrom sklearn.metrics.pairwise import pairwise_distances\n\n\n\ndef init_weight(Mi, Mo):\n return np.random.randn(Mi, Mo) / np.sqrt(Mi + Mo)\n\n\n# slow version\n# def find_analogies(w1, w2, w3, We, word2idx):\n# king = We[word2idx[w1]]\n# man = We[word2idx[w2]]\n# woman = We[word2idx[w3]]\n# v0 = king - man + woman\n\n# def dist1(a, b):\n# return np.linalg.norm(a - b)\n# def dist2(a, b):\n# return 1 - a.dot(b) / (np.linalg.norm(a) * np.linalg.norm(b))\n\n# for dist, name in [(dist1, 'Euclidean'), (dist2, 'cosine')]:\n# min_dist = float('inf')\n# best_word = ''\n# for word, idx in iteritems(word2idx):\n# if word not in (w1, w2, w3):\n# v1 = We[idx]\n# d = dist(v0, v1)\n# if d < min_dist:\n# min_dist = d\n# best_word = word\n# print(\"closest match by\", name, \"distance:\", best_word)\n# print(w1, \"-\", w2, \"=\", best_word, \"-\", w3)\n\n# fast version\ndef find_analogies(w1, w2, w3, We, word2idx, idx2word):\n V, D = We.shape\n\n king = We[word2idx[w1]]\n man = We[word2idx[w2]]\n woman = We[word2idx[w3]]\n v0 = king - man + woman\n\n for dist in ('euclidean', 'cosine'):\n distances = pairwise_distances(v0.reshape(1, D), We, metric=dist).reshape(V)\n # idx = distances.argmin()\n # best_word = idx2word[idx]\n idx = distances.argsort()[:4]\n best_idx = -1\n keep_out = [word2idx[w] for w in (w1, w2, w3)]\n for i in idx:\n if i not in keep_out:\n best_idx = i\n break\n best_word = idx2word[best_idx]\n\n\n print(\"closest match by\", dist, \"distance:\", best_word)\n print(w1, \"-\", w2, \"=\", best_word, \"-\", w3)\n\n\nclass Tree:\n def __init__(self, word, label):\n self.left = None\n self.right = None\n self.word = word\n self.label = label\n\n\ndef display_tree(t, lvl=0):\n prefix = ''.join(['>']*lvl)\n if t.word is not None:\n print(\"%s%s %s\" % (prefix, t.label, t.word))\n else:\n print(\"%s%s -\" % (prefix, t.label))\n # if t.left is None or t.right is None:\n # raise Exception(\"Tree node has no word but left and right child are None\")\n if t.left:\n display_tree(t.left, lvl + 1)\n if t.right:\n display_tree(t.right, lvl + 1)\n\n\ncurrent_idx = 0\ndef str2tree(s, word2idx):\n # take a string that starts with ( and MAYBE ends with )\n # return the tree that it represents\n # EXAMPLE: \"(3 (2 It) (4 (4 (2 's) (4 (3 (2 a) (4 (3 lovely) (2 film))) (3 (2 with) (4 (3 (3 lovely) (2 performances)) (2 (2 by) (2 (2 (2 Buy) (2 and)) (2 Accorsi))))))) (2 .)))\"\n # NOTE: not every node has 2 children (possibly not correct ??)\n # NOTE: not every node has a word\n # NOTE: every node has a label\n # NOTE: labels are 0,1,2,3,4\n # NOTE: only leaf nodes have words\n # s[0] = (, s[1] = label, s[2] = space, s[3] = character or (\n\n # print \"Input string:\", s, \"len:\", len(s)\n\n global current_idx\n\n label = int(s[1])\n if s[3] == '(':\n t = Tree(None, label)\n # try:\n\n # find the string that represents left child\n # it can include trailing characters we don't need, because we'll only look up to )\n child_s = s[3:]\n t.left = str2tree(child_s, word2idx)\n\n # find the string that represents right child\n # can contain multiple ((( )))\n # left child is completely represented when we've closed as many as we've opened\n # we stop at 1 because the first opening paren represents the current node, not children nodes\n i = 0\n depth = 0\n for c in s:\n i += 1\n if c == '(':\n depth += 1\n elif c == ')':\n depth -= 1\n if depth == 1:\n break\n # print \"index of right child\", i\n\n t.right = str2tree(s[i+1:], word2idx)\n\n # except Exception as e:\n # print \"Exception:\", e\n # print \"Input string:\", s\n # raise e\n\n # if t.left is None or t.right is None:\n # raise Exception(\"Tree node has no word but left and right child are None\")\n return t\n else:\n # this has a word, so it's a leaf\n r = s.split(')', 1)[0]\n word = r[3:].lower()\n # print \"word found:\", word\n\n if word not in word2idx:\n word2idx[word] = current_idx\n current_idx += 1\n\n t = Tree(word2idx[word], label)\n return t\n\n\ndef get_ptb_data():\n # like the wikipedia dataset, I want to return 2 things:\n # word2idx mapping, sentences\n # here the sentences should be Tree objects\n\n if not os.path.exists('../large_files/trees'):\n print(\"Please create ../large_files/trees relative to this file.\")\n print(\"train.txt and test.txt should be stored in there.\")\n print(\"Please download the data from http://nlp.stanford.edu/sentiment/\")\n exit()\n elif not os.path.exists('../large_files/trees/train.txt'):\n print(\"train.txt is not in ../large_files/trees/train.txt\")\n print(\"Please download the data from http://nlp.stanford.edu/sentiment/\")\n exit()\n elif not os.path.exists('../large_files/trees/test.txt'):\n print(\"test.txt is not in ../large_files/trees/test.txt\")\n print(\"Please download the data from http://nlp.stanford.edu/sentiment/\")\n exit()\n\n word2idx = {}\n train = []\n test = []\n\n # train set first\n for line in open('../large_files/trees/train.txt'):\n line = line.rstrip()\n if line:\n t = str2tree(line, word2idx)\n # if t.word is None and t.left is None and t.right is None:\n # print \"sentence:\", line\n # display_tree(t)\n # print \"\"\n train.append(t)\n # break\n\n # test set\n for line in open('../large_files/trees/test.txt'):\n line = line.rstrip()\n if line:\n t = str2tree(line, word2idx)\n test.append(t)\n return train, test, word2idx\n\n# get_ptb_data()\n" }, { "path": "nlp_class2/visualize_countries.py", "content": "# Course URL:\n# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport json\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.manifold import TSNE\n\ndef main(we_file='glove_model_50.npz', w2i_file='glove_word2idx_50.json'):\n words = ['japan', 'japanese', 'england', 'english', 'australia', 'australian', 'china', 'chinese', 'italy', 'italian', 'french', 'france', 'spain', 'spanish']\n\n with open(w2i_file) as f:\n word2idx = json.load(f)\n\n npz = np.load(we_file)\n W = npz['arr_0']\n V = npz['arr_1']\n We = (W + V.T) / 2\n\n idx = [word2idx[w] for w in words]\n # We = We[idx]\n\n tsne = TSNE()\n Z = tsne.fit_transform(We)\n Z = Z[idx]\n plt.scatter(Z[:,0], Z[:,1])\n for i in range(len(words)):\n plt.annotate(s=words[i], xy=(Z[i,0], Z[i,1]))\n plt.show()\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "nlp_class2/w2v_word2idx.json", "content": "{\"limited\": 575, \"writings\": 1340, \"four\": 171, \"consists\": 1061, \"oldest\": 1493, \"whose\": 544, \"paris\": 734, \"under\": 111, \"lord\": 1228, \"lack\": 839, \"risk\": 1570, \"rise\": 1216, \"georgia\": 1882, \"every\": 307, \"confederate\": 935, \"vast\": 1953, \"alpine\": 1947, \"school\": 267, \"prize\": 1308, \"crops\": 1788, \"companies\": 1137, \"solution\": 731, \"announced\": 1114, \"force\": 315, \"leaders\": 1799, \"almond\": 1918, \"japanese\": 699, \"surrounding\": 1695, \"second\": 144, \"street\": 1943, \"estimated\": 695, \"machines\": 1725, \"even\": 141, \"established\": 301, \"christ\": 1777, \"asia\": 453, \"above\": 371, \"churches\": 653, \"increasing\": 1052, \"ever\": 822, \"consisting\": 1737, \"told\": 1679, \"men\": 340, \"here\": 1068, \"atoms\": 425, \"anthropology\": 834, \"met\": 871, \"protection\": 1743, \"china\": 824, \"represented\": 918, \"100\": 780, \"obtained\": 1506, \"anarchism\": 1175, \"daughter\": 1048, \"study\": 263, \"changed\": 1237, \"reports\": 1544, \"saint\": 1699, \"military\": 224, \"anarchist\": 963, \"aikido\": 1917, \"changes\": 629, \"golden\": 1510, \"campaign\": 1394, \"schweitzer\": 1623, \"highly\": 670, \"brought\": 646, \"moral\": 1322, \"total\": 382, \"unit\": 875, \"agassi\": 781, \"would\": 60, \"army\": 236, \"hospital\": 1462, \"mexico\": 1980, \"negative\": 1337, \"music\": 280, \"therefore\": 417, \"h\": 1451, \"type\": 415, \"until\": 142, \"separated\": 1902, \"holy\": 1537, \"successful\": 809, \"wars\": 1791, \"award\": 954, \"arms\": 1660, \"teaching\": 1753, \"hold\": 1316, \"must\": 266, \"me\": 1054, \"1990\": 1957, \"1992\": 1694, \"word\": 200, \"room\": 1601, \"1997\": 1769, \"rights\": 504, \"1999\": 1156, \"1998\": 1562, \"work\": 103, \"theories\": 1781, \"era\": 944, \"henry\": 1248, \"already\": 876, \"my\": 813, \"example\": 113, \"give\": 896, \"india\": 723, \"david\": 1133, \"absolute\": 1081, \"ceremony\": 1983, \"end\": 183, \"provide\": 640, \"travel\": 1560, \"song\": 995, \"feature\": 1116, \"machine\": 641, \"how\": 308, \"writers\": 1100, \"widespread\": 1833, \"regional\": 1416, \"description\": 1715, \"turing\": 747, \"after\": 49, \"president\": 372, \"law\": 325, \"types\": 579, \"attempt\": 990, \"third\": 338, \"greek\": 157, \"algorithms\": 1118, \"suggest\": 1824, \"democratic\": 1499, \"order\": 176, \"operations\": 1029, \"interpretation\": 1842, \"office\": 705, \"over\": 71, \"london\": 767, \"japan\": 1042, \"before\": 112, \"personal\": 685, \"christie\": 1235, \"writing\": 352, \"better\": 776, \"production\": 182, \"weeks\": 1311, \"then\": 95, \"them\": 90, \"combination\": 1594, \"grand\": 1581, \"nobel\": 1424, \"effects\": 777, \"they\": 38, \"schools\": 660, \"silver\": 1495, \"bank\": 1258, \"represents\": 1656, \"netherlands\": 1648, \"oxygen\": 1724, \"classified\": 1557, \"slavery\": 1143, \"victory\": 859, \"each\": 110, \"went\": 730, \"side\": 444, \"mean\": 958, \"financial\": 1019, \"series\": 153, \"principles\": 1800, \"used\": 45, \"astatine\": 1350, \"navy\": 1742, \"laboratory\": 1964, \"network\": 1395, \"god\": 220, \"tried\": 1556, \"particles\": 934, \"medicine\": 1329, \"independence\": 665, \"foundation\": 1285, \"linear\": 1901, \"written\": 248, \"given\": 227, \"free\": 379, \"standard\": 337, \"management\": 1711, \"formation\": 910, \"wanted\": 1908, \"created\": 361, \"days\": 487, \"1970s\": 1592, \"uses\": 644, \"rand\": 1262, \"industrial\": 993, \"features\": 630, \"primary\": 601, \"featured\": 1579, \"adopted\": 938, \"another\": 147, \"service\": 451, \"similarly\": 1923, \"top\": 547, \"historian\": 1339, \"approximately\": 567, \"fiction\": 1126, \"needed\": 1074, \"rates\": 1549, \"too\": 763, \"john\": 310, \"urban\": 1577, \"murder\": 1121, \"serve\": 1492, \"took\": 261, \"rejected\": 1466, \"direct\": 845, \"western\": 277, \"somewhat\": 1504, \"distance\": 827, \"anxiety\": 961, \"showed\": 1412, \"tree\": 1955, \"likely\": 905, \"nations\": 745, \"project\": 741, \"matter\": 708, \"historical\": 817, \"k\": 1808, \"powers\": 1297, \"modern\": 143, \"mind\": 1218, \"seen\": 399, \"relatively\": 671, \"forced\": 1342, \"strength\": 1628, \"1968\": 1922, \"latter\": 768, \"responsible\": 1522, \"materials\": 1269, \"forces\": 269, \"blue\": 1834, \"though\": 207, \"object\": 899, \"anatomy\": 1661, \"regular\": 1771, \"germany\": 599, \"letter\": 681, \"phase\": 1954, \"the\": 2, \"medical\": 701, \"metal\": 397, \"treaty\": 1536, \"points\": 863, \"principle\": 1215, \"came\": 355, \"saying\": 1768, \"queen\": 1906, \"radio\": 1086, \"solutions\": 1622, \"earth\": 278, \"alcohols\": 1866, \"finally\": 949, \"just\": 347, \"rich\": 1723, \"axiom\": 1456, \"do\": 174, \"mixture\": 1944, \"wide\": 916, \"de\": 284, \"vowels\": 1363, \"13\": 980, \"coast\": 1152, \"despite\": 478, \"report\": 1039, \"14\": 998, \"runs\": 1914, \"countries\": 249, \"fields\": 1090, \"method\": 577, \"release\": 1229, \"reference\": 1318, \"1960s\": 1088, \"observed\": 1040, \"decided\": 1014, \"result\": 272, \"iran\": 1582, \"best\": 323, \"techniques\": 887, \"antimony\": 1023, \"away\": 698, \"artificial\": 1313, \"r\": 1430, \"approach\": 1135, \"discovery\": 1422, \"we\": 234, \"never\": 373, \"terms\": 349, \"nature\": 446, \"oxidation\": 1685, \"however\": 73, \"southeast\": 1477, \"rivers\": 1883, \"extent\": 1673, \"carbon\": 637, \"suggested\": 819, \"received\": 603, \"climate\": 933, \"aristotles\": 1888, \"reported\": 605, \"country\": 167, \"against\": 160, \"players\": 1773, \"games\": 936, \"planned\": 1790, \"logic\": 1349, \"studio\": 1962, \"adapted\": 1995, \"asked\": 1533, \"appeared\": 869, \"character\": 408, \"kurosawa\": 683, \"arsenic\": 506, \"hitler\": 1646, \"conference\": 1566, \"symbols\": 1381, \"basis\": 785, \"union\": 184, \"three\": 104, \"been\": 39, \"quickly\": 1242, \"much\": 127, \"communion\": 1046, \"interest\": 691, \"basic\": 884, \"expected\": 1259, \"entered\": 1588, \"dry\": 1630, \"life\": 136, \"families\": 772, \"eastern\": 428, \"child\": 1327, \"worked\": 930, \"applied\": 894, \"exception\": 1881, \"east\": 255, \"n\": 1359, \"near\": 330, \"aid\": 1921, \"property\": 604, \"anchor\": 1227, \"launched\": 1840, \"seven\": 838, \"economics\": 1973, \"played\": 561, \"is\": 8, \"it\": 20, \"defeated\": 1179, \"ii\": 426, \"player\": 1720, \"in\": 5, \"ie\": 806, \"if\": 86, \"containing\": 1429, \"perform\": 1550, \"things\": 803, \"make\": 233, \"airport\": 1559, \"complex\": 529, \"azerbaijani\": 1147, \"colonies\": 1545, \"several\": 107, \"european\": 303, \"independent\": 721, \"published\": 196, \"hand\": 687, \"characters\": 514, \"anarchists\": 1999, \"kept\": 1806, \"programs\": 1302, \"ocean\": 1099, \"academic\": 1938, \"rome\": 974, \"greatest\": 1186, \"mother\": 709, \"claims\": 1396, \"alabama\": 459, \"musical\": 1663, \"left\": 319, \"traditions\": 1541, \"proposed\": 586, \"gods\": 1307, \"human\": 181, \"yet\": 762, \"previous\": 945, \"classification\": 1877, \"equipment\": 1702, \"regarded\": 1247, \"royal\": 833, \"countrys\": 1994, \"alchemy\": 1232, \"animation\": 1036, \"board\": 1516, \"has\": 30, \"gave\": 552, \"possible\": 321, \"arab\": 1966, \"possibly\": 1222, \"cultural\": 538, \"birth\": 1047, \"unique\": 1357, \"advanced\": 1239, \"d\": 1433, \"county\": 1431, \"50\": 1091, \"specific\": 558, \"night\": 1392, \"security\": 992, \"portuguese\": 1437, \"right\": 380, \"old\": 289, \"people\": 96, \"staff\": 1960, \"deaf\": 1997, \"dead\": 1188, \"born\": 546, \"election\": 1132, \"anatolia\": 1233, \"for\": 11, \"ice\": 1505, \"continue\": 1397, \"core\": 1709, \"fish\": 1402, 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494, \"orthodox\": 1692, \"camus\": 1665, \"material\": 518, \"front\": 1131, \"republican\": 1338, \"day\": 222, \"worlds\": 784, \"february\": 600, \"university\": 216, \"identified\": 1202, \"magnitude\": 1283, \"related\": 465, \"society\": 331, \"books\": 416, \"measure\": 1401, \"our\": 536, \"agriculture\": 943, \"special\": 735, \"out\": 119, \"critics\": 1488, \"cause\": 477, \"red\": 927, \"completely\": 1326, \"york\": 471, \"philip\": 1680, \"organic\": 1538, \"g\": 1812, \"islamic\": 1276, \"could\": 108, \"times\": 230, \"length\": 761, \"hence\": 1828, \"south\": 128, \"powerful\": 1443, \"jesus\": 1728, \"reached\": 652, \"imperial\": 1772, \"quality\": 1551, \"ancient\": 228, \"festival\": 1117, \"unknown\": 1704, \"system\": 118, \"relations\": 1165, \"their\": 33, \"attack\": 811, \"final\": 381, \"shell\": 1667, \"academy\": 612, \"completed\": 1172, \"amsterdam\": 390, \"environmental\": 1606, \"argon\": 1498, \"aarhus\": 543, \"july\": 435, \"steel\": 1519, \"defense\": 1106, \"visited\": 1959, \"have\": 28, \"need\": 771, \"border\": 1257, \"viewed\": 1974, \"catholic\": 590, \"angle\": 1021, \"able\": 497, \"contact\": 1532, \"instance\": 1225, \"which\": 23, \"subject\": 635, \"worldwide\": 1060, \"centre\": 718, \"who\": 48, \"said\": 244, \"eight\": 1129, \"device\": 1523, \"achilles\": 851, \"class\": 677, \"disease\": 1123, \"face\": 1370, \"normally\": 1864, \"fact\": 491, \"text\": 1013, \"agreed\": 1986, \"charles\": 1012, \"soldiers\": 1464, \"debate\": 1951, \"principal\": 1920, \"apollo\": 316, \"based\": 149, \"knowledge\": 661, \"should\": 268, \"molecules\": 1774, \"fuel\": 1511, \"employed\": 1763, \"local\": 295, \"abba\": 1087, \"means\": 335, \"overall\": 1473, \"ones\": 1038, \"words\": 345, \"argues\": 1739, \"areas\": 312, \"following\": 180, \"processes\": 1400, \"brain\": 1489, \"conditions\": 717, \"married\": 1226, \"taxes\": 1956, \"she\": 125, \"contain\": 1139, \"fixed\": 1972, \"view\": 454, \"europe\": 256, \"albania\": 613, \"exists\": 1793, \"national\": 135, \"humans\": 940, \"edition\": 1683, \"computer\": 398, \"austrian\": 1280, \"creating\": 1818, \"record\": 805, \"nuclear\": 923, \"tend\": 1293, \"state\": 68, \"closed\": 1362, \"limit\": 1889, \"ability\": 1005, \"importance\": 1279, \"armenia\": 418, \"police\": 1266, \"distribution\": 1514, \"career\": 881, \"taking\": 941, \"decay\": 1230, \"equal\": 919, \"etc\": 1204, \"figures\": 1442, \"wall\": 1985, \"ataxia\": 1948, \"table\": 978, \"winning\": 1905, \"provinces\": 1206, \"addition\": 407, \"isotopes\": 1848, \"corresponding\": 1845, \"both\": 78, \"am\": 1718, \"goddess\": 1387, \"sales\": 1886, \"angles\": 1691, \"covered\": 1384, \"general\": 151, \"present\": 365, \"novel\": 571, \"abandoned\": 1895, \"unlike\": 1072, \"appearance\": 1421, \"as\": 9, \"value\": 481, \"will\": 106, \"antigua\": 1585, \"supply\": 1807, \"almost\": 360, \"thus\": 219, \"site\": 1009, \"surface\": 540, \"helped\": 1871, \"claimed\": 858, \"arabic\": 252, \"orbital\": 1584, \"perhaps\": 1108, \"began\": 188, \"radiation\": 1930, \"member\": 572, \"buddhism\": 1936, \"parts\": 383, \"largest\": 215, \"units\": 852, \"party\": 400, \"difficult\": 812, \"practice\": 744, \"novels\": 1289, \"upon\": 385, \"effect\": 549, \"ion\": 1915, \"frequently\": 1328, \"afghan\": 1890, \"scholars\": 964, \"identity\": 1716, \"keep\": 1787, \"off\": 624, \"center\": 531, \"i\": 115, \"well\": 99, \"fighting\": 1762, \"thought\": 324, \"command\": 1093, \"english\": 205, \"position\": 433, \"soviet\": 438, \"less\": 191, \"increasingly\": 1528, \"domestic\": 1677, \"sources\": 883, \"lincolns\": 1766, \"paul\": 1635, \"antisemitism\": 724, \"bell\": 485, \"lake\": 1668, \"hall\": 1447, \"reasons\": 1607, \"other\": 36, \"aruba\": 1604, \"ada\": 666, \"usage\": 1554, \"match\": 1358, \"tests\": 1927, \"increased\": 700, \"articles\": 996, \"increases\": 1574, \"historic\": 1858, \"five\": 329, \"know\": 1008, \"press\": 1857, \"immediately\": 1348, \"prominent\": 1169, \"loss\": 1344, \"lincoln\": 265, \"necessary\": 1004, \"like\": 132, \"success\": 788, \"alkali\": 548, \"james\": 1015, \"become\": 238, \"works\": 214, \"italian\": 1527, \"because\": 91, \"classical\": 616, \"sequence\": 1805, \"village\": 1569, \"authority\": 969, \"growth\": 439, \"band\": 1698, \"proper\": 1878, \"home\": 311, \"empire\": 302, \"peter\": 1375, \"transport\": 1399, \"lead\": 847, \"avoid\": 1643, \"philosopher\": 1638, \"does\": 275, \"leader\": 1526, \"medieval\": 1314, \"expansion\": 1653, \"pressure\": 1345, \"although\": 116, \"stage\": 791, \"gained\": 1649, \"about\": 64, \"actual\": 1651, \"carried\": 1376, \"universe\": 1389, \"albanian\": 1234, \"recognized\": 1300, \"andes\": 1971, \"introduced\": 455, \"software\": 1321, \"own\": 163, \"letters\": 557, \"previously\": 1151, \"washington\": 1136, \"female\": 1245, \"artists\": 802, \"roads\": 1939, \"van\": 1045, \"additional\": 960, \"museum\": 626, \"reduction\": 1900, \"awarded\": 1479, \"continental\": 1112, \"spread\": 1096, \"naturally\": 1765, \"function\": 696, \"north\": 140, \"but\": 32, \"volume\": 1546, \"construction\": 622, \"courts\": 1092, \"highest\": 728, \"he\": 25, \"made\": 88, \"places\": 1271, \"whether\": 583, \"cells\": 1380, \"official\": 542, \"signed\": 1163, \"placed\": 1071, \"below\": 617, \"stories\": 891, \"problem\": 726, \"minutes\": 1841, \"display\": 1854, \"supreme\": 1162, \"deaths\": 1756, \"universal\": 1476, \"abbot\": 1749, \"aristotle\": 611, \"education\": 499, \"autism\": 720, \"functions\": 1182, \"compared\": 840, \"variety\": 642, \"percent\": 715, \"40\": 1710, \"book\": 187, \"branch\": 1521, \"canada\": 758, \"star\": 850, \"june\": 519, \"ultimately\": 1837, \"amphibians\": 1583, \"friends\": 1410, \"exposure\": 1829, \"\\u2013\": 202, \"\\u2014\": 1879, \"bce\": 1655, \"factors\": 1159, \"rule\": 999, \"write\": 1243}" }, { "path": "nlp_class2/word2vec.py", "content": "# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport json\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.special import expit as sigmoid\nfrom sklearn.utils import shuffle\nfrom datetime import datetime\n# from util import find_analogies\n\nfrom scipy.spatial.distance import cosine as cos_dist\nfrom sklearn.metrics.pairwise import pairwise_distances\n\n\nfrom glob import glob\n\nimport os\nimport sys\nimport string\n\nsys.path.append(os.path.abspath('..'))\nfrom rnn_class.brown import get_sentences_with_word2idx_limit_vocab as get_brown\n\n\n\n# unfortunately these work different ways\ndef remove_punctuation_2(s):\n return s.translate(None, string.punctuation)\n\ndef remove_punctuation_3(s):\n return s.translate(str.maketrans('','',string.punctuation))\n\nif sys.version.startswith('2'):\n remove_punctuation = remove_punctuation_2\nelse:\n remove_punctuation = remove_punctuation_3\n\n\n\n\ndef get_wiki():\n V = 20000\n files = glob('../large_files/enwiki*.txt')\n all_word_counts = {}\n for f in files:\n for line in open(f):\n if line and line[0] not in '[*-|=\\{\\}':\n s = remove_punctuation(line).lower().split()\n if len(s) > 1:\n for word in s:\n if word not in all_word_counts:\n all_word_counts[word] = 0\n all_word_counts[word] += 1\n print(\"finished counting\")\n\n V = min(V, len(all_word_counts))\n all_word_counts = sorted(all_word_counts.items(), key=lambda x: x[1], reverse=True)\n\n top_words = [w for w, count in all_word_counts[:V-1]] + ['']\n word2idx = {w:i for i, w in enumerate(top_words)}\n unk = word2idx['']\n\n sents = []\n for f in files:\n for line in open(f):\n if line and line[0] not in '[*-|=\\{\\}':\n s = remove_punctuation(line).lower().split()\n if len(s) > 1:\n # if a word is not nearby another word, there won't be any context!\n # and hence nothing to train!\n sent = [word2idx[w] if w in word2idx else unk for w in s]\n sents.append(sent)\n return sents, word2idx\n\n\n\n\ndef train_model(savedir):\n # get the data\n sentences, word2idx = get_wiki() #get_brown()\n\n\n # number of unique words\n vocab_size = len(word2idx)\n\n\n # config\n window_size = 5\n learning_rate = 0.025\n final_learning_rate = 0.0001\n num_negatives = 5 # number of negative samples to draw per input word\n epochs = 20\n D = 50 # word embedding size\n\n\n # learning rate decay\n learning_rate_delta = (learning_rate - final_learning_rate) / epochs\n\n\n # params\n W = np.random.randn(vocab_size, D) # input-to-hidden\n V = np.random.randn(D, vocab_size) # hidden-to-output\n\n\n # distribution for drawing negative samples\n p_neg = get_negative_sampling_distribution(sentences, vocab_size)\n\n\n # save the costs to plot them per iteration\n costs = []\n\n\n # number of total words in corpus\n total_words = sum(len(sentence) for sentence in sentences)\n print(\"total number of words in corpus:\", total_words)\n\n # for subsampling each sentence\n threshold = 1e-5\n p_drop = 1 - np.sqrt(threshold / p_neg)\n\n\n # train the model\n for epoch in range(epochs):\n # randomly order sentences so we don't always see\n # sentences in the same order\n np.random.shuffle(sentences)\n\n # accumulate the cost\n cost = 0\n counter = 0\n t0 = datetime.now()\n for sentence in sentences:\n # keep only certain words based on p_neg\n sentence = [w for w in sentence \\\n if np.random.random() < (1 - p_drop[w])\n ]\n if len(sentence) < 2:\n continue\n\n\n # randomly order words so we don't always see\n # samples in the same order\n randomly_ordered_positions = np.random.choice(\n len(sentence),\n size=len(sentence),#np.random.randint(1, len(sentence) + 1),\n replace=False,\n )\n\n \n for pos in randomly_ordered_positions:\n # the middle word\n word = sentence[pos]\n\n # get the positive context words/negative samples\n context_words = get_context(pos, sentence, window_size)\n neg_word = np.random.choice(vocab_size, p=p_neg)\n targets = np.array(context_words)\n\n # do one iteration of stochastic gradient descent\n c = sgd(word, targets, 1, learning_rate, W, V)\n cost += c\n c = sgd(neg_word, targets, 0, learning_rate, W, V)\n cost += c\n\n counter += 1\n if counter % 100 == 0:\n sys.stdout.write(\"processed %s / %s\\r\" % (counter, len(sentences)))\n sys.stdout.flush()\n # break\n\n\n # print stuff so we don't stare at a blank screen\n dt = datetime.now() - t0\n print(\"epoch complete:\", epoch, \"cost:\", cost, \"dt:\", dt)\n\n # save the cost\n costs.append(cost)\n\n # update the learning rate\n learning_rate -= learning_rate_delta\n\n\n # plot the cost per iteration\n plt.plot(costs)\n plt.show()\n\n\n # save the model\n if not os.path.exists(savedir):\n os.mkdir(savedir)\n\n with open('%s/word2idx.json' % savedir, 'w') as f:\n json.dump(word2idx, f)\n\n np.savez('%s/weights.npz' % savedir, W, V)\n\n # return the model\n return word2idx, W, V\n\n\ndef get_negative_sampling_distribution(sentences, vocab_size):\n # Pn(w) = prob of word occuring\n # we would like to sample the negative samples\n # such that words that occur more often\n # should be sampled more often\n\n word_freq = np.zeros(vocab_size)\n word_count = sum(len(sentence) for sentence in sentences)\n for sentence in sentences:\n for word in sentence:\n word_freq[word] += 1\n\n # smooth it\n p_neg = word_freq**0.75\n\n # normalize it\n p_neg = p_neg / p_neg.sum()\n\n assert(np.all(p_neg > 0))\n return p_neg\n\n\ndef get_context(pos, sentence, window_size):\n # input:\n # a sentence of the form: x x x x c c c pos c c c x x x x\n # output:\n # the context word indices: c c c c c c\n\n start = max(0, pos - window_size)\n end_ = min(len(sentence), pos + window_size)\n\n context = []\n for ctx_pos, ctx_word_idx in enumerate(sentence[start:end_], start=start):\n if ctx_pos != pos:\n # don't include the input word itself as a target\n context.append(ctx_word_idx)\n return context\n\n\ndef sgd(input_, targets, label, learning_rate, W, V):\n # W[input_] shape: D\n # V[:,targets] shape: D x N\n # activation shape: N\n # print(\"input_:\", input_, \"targets:\", targets)\n activation = W[input_].dot(V[:,targets])\n prob = sigmoid(activation)\n\n # gradients\n gV = np.outer(W[input_], prob - label) # D x N\n gW = np.sum((prob - label)*V[:,targets], axis=1) # D\n\n V[:,targets] -= learning_rate*gV # D x N\n W[input_] -= learning_rate*gW # D\n\n # return cost (binary cross entropy)\n cost = label * np.log(prob + 1e-10) + (1 - label) * np.log(1 - prob + 1e-10)\n return cost.sum()\n\n\ndef load_model(savedir):\n with open('%s/word2idx.json' % savedir) as f:\n word2idx = json.load(f)\n npz = np.load('%s/weights.npz' % savedir)\n W = npz['arr_0']\n V = npz['arr_1']\n return word2idx, W, V\n\n\n\ndef analogy(pos1, neg1, pos2, neg2, word2idx, idx2word, W):\n V, D = W.shape\n\n # don't actually use pos2 in calculation, just print what's expected\n print(\"testing: %s - %s = %s - %s\" % (pos1, neg1, pos2, neg2))\n for w in (pos1, neg1, pos2, neg2):\n if w not in word2idx:\n print(\"Sorry, %s not in word2idx\" % w)\n return\n\n p1 = W[word2idx[pos1]]\n n1 = W[word2idx[neg1]]\n p2 = W[word2idx[pos2]]\n n2 = W[word2idx[neg2]]\n\n vec = p1 - n1 + n2\n\n distances = pairwise_distances(vec.reshape(1, D), W, metric='cosine').reshape(V)\n idx = distances.argsort()[:10]\n\n # pick one that's not p1, n1, or n2\n best_idx = -1\n keep_out = [word2idx[w] for w in (pos1, neg1, neg2)]\n # print(\"keep_out:\", keep_out)\n for i in idx:\n if i not in keep_out:\n best_idx = i\n break\n # print(\"best_idx:\", best_idx)\n\n print(\"got: %s - %s = %s - %s\" % (pos1, neg1, idx2word[best_idx], neg2))\n print(\"closest 10:\")\n for i in idx:\n print(idx2word[i], distances[i])\n\n print(\"dist to %s:\" % pos2, cos_dist(p2, vec))\n\n\ndef test_model(word2idx, W, V):\n # there are multiple ways to get the \"final\" word embedding\n # We = (W + V.T) / 2\n # We = W\n\n idx2word = {i:w for w, i in word2idx.items()}\n\n for We in (W, (W + V.T) / 2):\n print(\"**********\")\n\n analogy('king', 'man', 'queen', 'woman', word2idx, idx2word, We)\n analogy('king', 'prince', 'queen', 'princess', word2idx, idx2word, We)\n analogy('miami', 'florida', 'dallas', 'texas', word2idx, idx2word, We)\n analogy('einstein', 'scientist', 'picasso', 'painter', word2idx, idx2word, We)\n analogy('japan', 'sushi', 'germany', 'bratwurst', word2idx, idx2word, We)\n analogy('man', 'woman', 'he', 'she', word2idx, idx2word, We)\n analogy('man', 'woman', 'uncle', 'aunt', word2idx, idx2word, We)\n analogy('man', 'woman', 'brother', 'sister', word2idx, idx2word, We)\n analogy('man', 'woman', 'husband', 'wife', word2idx, idx2word, We)\n analogy('man', 'woman', 'actor', 'actress', word2idx, idx2word, We)\n analogy('man', 'woman', 'father', 'mother', word2idx, idx2word, We)\n analogy('heir', 'heiress', 'prince', 'princess', word2idx, idx2word, We)\n analogy('nephew', 'niece', 'uncle', 'aunt', word2idx, idx2word, We)\n analogy('france', 'paris', 'japan', 'tokyo', word2idx, idx2word, We)\n analogy('france', 'paris', 'china', 'beijing', word2idx, idx2word, We)\n analogy('february', 'january', 'december', 'november', word2idx, idx2word, We)\n analogy('france', 'paris', 'germany', 'berlin', word2idx, idx2word, We)\n analogy('week', 'day', 'year', 'month', word2idx, idx2word, We)\n analogy('week', 'day', 'hour', 'minute', word2idx, idx2word, We)\n analogy('france', 'paris', 'italy', 'rome', word2idx, idx2word, We)\n analogy('paris', 'france', 'rome', 'italy', word2idx, idx2word, We)\n analogy('france', 'french', 'england', 'english', word2idx, idx2word, We)\n analogy('japan', 'japanese', 'china', 'chinese', word2idx, idx2word, We)\n analogy('china', 'chinese', 'america', 'american', word2idx, idx2word, We)\n analogy('japan', 'japanese', 'italy', 'italian', word2idx, idx2word, We)\n analogy('japan', 'japanese', 'australia', 'australian', word2idx, idx2word, We)\n analogy('walk', 'walking', 'swim', 'swimming', word2idx, idx2word, We)\n\n\n\nif __name__ == '__main__':\n word2idx, W, V = train_model('w2v_model')\n # word2idx, W, V = load_model('w2v_model')\n test_model(word2idx, W, V)\n\n" }, { "path": "nlp_class2/word2vec_tf.py", "content": "# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport json\nimport tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.special import expit as sigmoid\nfrom sklearn.utils import shuffle\nfrom datetime import datetime\n# from util import find_analogies\n\nfrom scipy.spatial.distance import cosine as cos_dist\nfrom sklearn.metrics.pairwise import pairwise_distances\n\n\nfrom glob import glob\n\nimport os\nimport sys\nimport string\n\nif tf.__version__.startswith('2'):\n tf.compat.v1.disable_eager_execution()\n\n\n\n# unfortunately these work different ways\ndef remove_punctuation_2(s):\n return s.translate(None, string.punctuation)\n\ndef remove_punctuation_3(s):\n return s.translate(str.maketrans('','',string.punctuation))\n\nif sys.version.startswith('2'):\n remove_punctuation = remove_punctuation_2\nelse:\n remove_punctuation = remove_punctuation_3\n\n\ndef download_text8(dst):\n pass\n\n\ndef get_text8():\n # download the data if it is not yet in the right place\n path = '../large_files/text8'\n if not os.path.exists(path):\n download_text8(path)\n\n words = open(path).read()\n word2idx = {}\n sents = [[]]\n count = 0\n for word in words.split():\n if word not in word2idx:\n word2idx[word] = count\n count += 1\n sents[0].append(word2idx[word])\n print(\"count:\", count)\n return sents, word2idx\n\n\ndef get_wiki():\n V = 20000\n files = glob('../large_files/enwiki*.txt')\n all_word_counts = {}\n for f in files:\n for line in open(f):\n if line and line[0] not in '[*-|=\\{\\}':\n s = remove_punctuation(line).lower().split()\n if len(s) > 1:\n for word in s:\n if word not in all_word_counts:\n all_word_counts[word] = 0\n all_word_counts[word] += 1\n print(\"finished counting\")\n\n V = min(V, len(all_word_counts))\n all_word_counts = sorted(all_word_counts.items(), key=lambda x: x[1], reverse=True)\n\n top_words = [w for w, count in all_word_counts[:V-1]] + ['']\n word2idx = {w:i for i, w in enumerate(top_words)}\n unk = word2idx['']\n\n sents = []\n for f in files:\n for line in open(f):\n if line and line[0] not in '[*-|=\\{\\}':\n s = remove_punctuation(line).lower().split()\n if len(s) > 1:\n # if a word is not nearby another word, there won't be any context!\n # and hence nothing to train!\n sent = [word2idx[w] if w in word2idx else unk for w in s]\n sents.append(sent)\n return sents, word2idx\n\n\n\n\ndef train_model(savedir):\n # get the data\n sentences, word2idx = get_wiki() #get_text8()\n\n\n # number of unique words\n vocab_size = len(word2idx)\n\n\n # config\n window_size = 10\n learning_rate = 0.025\n final_learning_rate = 0.0001\n num_negatives = 5 # number of negative samples to draw per input word\n samples_per_epoch = int(1e5)\n epochs = 20\n D = 50 # word embedding size\n\n # learning rate decay\n learning_rate_delta = (learning_rate - final_learning_rate) / epochs\n\n # distribution for drawing negative samples\n p_neg = get_negative_sampling_distribution(sentences)\n\n\n # params\n W = np.random.randn(vocab_size, D).astype(np.float32) # input-to-hidden\n V = np.random.randn(D, vocab_size).astype(np.float32) # hidden-to-output\n\n\n # create the model\n tf_input = tf.compat.v1.placeholder(tf.int32, shape=(None,))\n tf_negword = tf.compat.v1.placeholder(tf.int32, shape=(None,))\n tf_context = tf.compat.v1.placeholder(tf.int32, shape=(None,)) # targets (context)\n tfW = tf.Variable(W)\n tfV = tf.Variable(V.T)\n # biases = tf.Variable(np.zeros(vocab_size, dtype=np.float32))\n\n def dot(A, B):\n C = A * B\n return tf.reduce_sum(input_tensor=C, axis=1)\n\n # correct middle word output\n emb_input = tf.nn.embedding_lookup(params=tfW, ids=tf_input) # 1 x D\n emb_output = tf.nn.embedding_lookup(params=tfV, ids=tf_context) # N x D\n correct_output = dot(emb_input, emb_output) # N\n # emb_input = tf.transpose(emb_input, (1, 0))\n # correct_output = tf.matmul(emb_output, emb_input)\n pos_loss = tf.nn.sigmoid_cross_entropy_with_logits(\n labels=tf.ones(tf.shape(input=correct_output)), logits=correct_output)\n\n # incorrect middle word output\n emb_input = tf.nn.embedding_lookup(params=tfW, ids=tf_negword)\n incorrect_output = dot(emb_input, emb_output)\n # emb_input = tf.transpose(emb_input, (1, 0))\n # incorrect_output = tf.matmul(emb_output, emb_input)\n neg_loss = tf.nn.sigmoid_cross_entropy_with_logits(\n labels=tf.zeros(tf.shape(input=incorrect_output)), logits=incorrect_output)\n\n # total loss\n loss = tf.reduce_mean(input_tensor=pos_loss) + tf.reduce_mean(input_tensor=neg_loss)\n\n # output = hidden.dot(tfV)\n\n # loss\n # neither of the built-in TF functions work well\n # per_sample_loss = tf.nn.nce_loss(\n # # per_sample_loss = tf.nn.sampled_softmax_loss(\n # weights=tfV,\n # biases=biases,\n # labels=tfY,\n # inputs=hidden,\n # num_sampled=num_negatives,\n # num_classes=vocab_size,\n # )\n # loss = tf.reduce_mean(per_sample_loss)\n\n # optimizer\n # train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)\n train_op = tf.compat.v1.train.MomentumOptimizer(0.1, momentum=0.9).minimize(loss)\n # train_op = tf.train.AdamOptimizer(1e-2).minimize(loss)\n\n # make session\n session = tf.compat.v1.Session()\n init_op = tf.compat.v1.global_variables_initializer()\n session.run(init_op)\n\n\n # save the costs to plot them per iteration\n costs = []\n\n\n # number of total words in corpus\n total_words = sum(len(sentence) for sentence in sentences)\n print(\"total number of words in corpus:\", total_words)\n\n\n # for subsampling each sentence\n threshold = 1e-5\n p_drop = 1 - np.sqrt(threshold / p_neg)\n\n\n # train the model\n for epoch in range(epochs):\n # randomly order sentences so we don't always see\n # sentences in the same order\n np.random.shuffle(sentences)\n\n # accumulate the cost\n cost = 0\n counter = 0\n inputs = []\n targets = []\n negwords = []\n t0 = datetime.now()\n for sentence in sentences:\n\n # keep only certain words based on p_neg\n sentence = [w for w in sentence \\\n if np.random.random() < (1 - p_drop[w])\n ]\n if len(sentence) < 2:\n continue\n\n\n # randomly order words so we don't always see\n # samples in the same order\n randomly_ordered_positions = np.random.choice(\n len(sentence),\n # size=np.random.randint(1, len(sentence) + 1),\n size=len(sentence),\n replace=False,\n )\n\n\n for j, pos in enumerate(randomly_ordered_positions):\n # the middle word\n word = sentence[pos]\n\n # get the positive context words/negative samples\n context_words = get_context(pos, sentence, window_size)\n neg_word = np.random.choice(vocab_size, p=p_neg)\n\n \n n = len(context_words)\n inputs += [word]*n\n negwords += [neg_word]*n\n # targets = np.concatenate([targets, targets_])\n targets += context_words\n\n # _, c = session.run(\n # (train_op, loss),\n # feed_dict={\n # tf_input: [word],\n # tf_negword: [neg_word],\n # tf_context: targets_,\n # }\n # )\n # cost += c\n\n\n if len(inputs) >= 128:\n _, c = session.run(\n (train_op, loss),\n feed_dict={\n tf_input: inputs,\n tf_negword: negwords,\n tf_context: targets,\n }\n )\n cost += c\n\n # reset\n inputs = []\n targets = []\n negwords = []\n\n counter += 1\n if counter % 100 == 0:\n sys.stdout.write(\"processed %s / %s\\r\" % (counter, len(sentences)))\n sys.stdout.flush()\n # break\n\n\n # print stuff so we don't stare at a blank screen\n dt = datetime.now() - t0\n print(\"epoch complete:\", epoch, \"cost:\", cost, \"dt:\", dt)\n\n # save the cost\n costs.append(cost)\n\n # update the learning rate\n learning_rate -= learning_rate_delta\n\n\n # plot the cost per iteration\n plt.plot(costs)\n plt.show()\n\n # get the params\n W, VT = session.run((tfW, tfV))\n V = VT.T\n\n # save the model\n if not os.path.exists(savedir):\n os.mkdir(savedir)\n\n with open('%s/word2idx.json' % savedir, 'w') as f:\n json.dump(word2idx, f)\n\n np.savez('%s/weights.npz' % savedir, W, V)\n\n # return the model\n return word2idx, W, V\n\n\ndef get_negative_sampling_distribution(sentences):\n # Pn(w) = prob of word occuring\n # we would like to sample the negative samples\n # such that words that occur more often\n # should be sampled more often\n\n word_freq = {}\n word_count = sum(len(sentence) for sentence in sentences)\n for sentence in sentences:\n for word in sentence:\n if word not in word_freq:\n word_freq[word] = 0\n word_freq[word] += 1\n \n # vocab size\n V = len(word_freq)\n\n p_neg = np.zeros(V)\n for j in range(V):\n p_neg[j] = word_freq[j]**0.75\n\n # normalize it\n p_neg = p_neg / p_neg.sum()\n\n assert(np.all(p_neg > 0))\n return p_neg\n\n\ndef get_context(pos, sentence, window_size):\n # input:\n # a sentence of the form: x x x x c c c pos c c c x x x x\n # output:\n # the context word indices: c c c c c c\n\n start = max(0, pos - window_size)\n end_ = min(len(sentence), pos + window_size)\n\n context = []\n for ctx_pos, ctx_word_idx in enumerate(sentence[start:end_], start=start):\n if ctx_pos != pos:\n # don't include the input word itself as a target\n context.append(ctx_word_idx)\n return context\n\n\n\ndef load_model(savedir):\n with open('%s/word2idx.json' % savedir) as f:\n word2idx = json.load(f)\n npz = np.load('%s/weights.npz' % savedir)\n W = npz['arr_0']\n V = npz['arr_1']\n return word2idx, W, V\n\n\n\ndef analogy(pos1, neg1, pos2, neg2, word2idx, idx2word, W):\n V, D = W.shape\n\n # don't actually use pos2 in calculation, just print what's expected\n print(\"testing: %s - %s = %s - %s\" % (pos1, neg1, pos2, neg2))\n for w in (pos1, neg1, pos2, neg2):\n if w not in word2idx:\n print(\"Sorry, %s not in word2idx\" % w)\n return\n\n p1 = W[word2idx[pos1]]\n n1 = W[word2idx[neg1]]\n p2 = W[word2idx[pos2]]\n n2 = W[word2idx[neg2]]\n\n vec = p1 - n1 + n2\n\n distances = pairwise_distances(vec.reshape(1, D), W, metric='cosine').reshape(V)\n idx = distances.argsort()[:10]\n\n # pick one that's not p1, n1, or n2\n best_idx = -1\n keep_out = [word2idx[w] for w in (pos1, neg1, neg2)]\n for i in idx:\n if i not in keep_out:\n best_idx = i\n break\n\n print(\"got: %s - %s = %s - %s\" % (pos1, neg1, idx2word[idx[0]], neg2))\n print(\"closest 10:\")\n for i in idx:\n print(idx2word[i], distances[i])\n\n print(\"dist to %s:\" % pos2, cos_dist(p2, vec))\n\n\ndef test_model(word2idx, W, V):\n # there are multiple ways to get the \"final\" word embedding\n # We = (W + V.T) / 2\n # We = W\n\n idx2word = {i:w for w, i in word2idx.items()}\n\n for We in (W, (W + V.T) / 2):\n print(\"**********\")\n\n analogy('king', 'man', 'queen', 'woman', word2idx, idx2word, We)\n analogy('king', 'prince', 'queen', 'princess', word2idx, idx2word, We)\n analogy('miami', 'florida', 'dallas', 'texas', word2idx, idx2word, We)\n analogy('einstein', 'scientist', 'picasso', 'painter', word2idx, idx2word, We)\n analogy('japan', 'sushi', 'england', 'bread', word2idx, idx2word, We)\n analogy('man', 'woman', 'he', 'she', word2idx, idx2word, We)\n analogy('man', 'woman', 'uncle', 'aunt', word2idx, idx2word, We)\n analogy('man', 'woman', 'brother', 'sister', word2idx, idx2word, We)\n analogy('man', 'woman', 'husband', 'wife', word2idx, idx2word, We)\n analogy('man', 'woman', 'actor', 'actress', word2idx, idx2word, We)\n analogy('man', 'woman', 'father', 'mother', word2idx, idx2word, We)\n analogy('heir', 'heiress', 'prince', 'princess', word2idx, idx2word, We)\n analogy('nephew', 'niece', 'uncle', 'aunt', word2idx, idx2word, We)\n analogy('france', 'paris', 'japan', 'tokyo', word2idx, idx2word, We)\n analogy('france', 'paris', 'china', 'beijing', word2idx, idx2word, We)\n analogy('february', 'january', 'december', 'november', word2idx, idx2word, We)\n analogy('france', 'paris', 'germany', 'berlin', word2idx, idx2word, We)\n analogy('week', 'day', 'year', 'month', word2idx, idx2word, We)\n analogy('week', 'day', 'hour', 'minute', word2idx, idx2word, We)\n analogy('france', 'paris', 'italy', 'rome', word2idx, idx2word, We)\n analogy('paris', 'france', 'rome', 'italy', word2idx, idx2word, We)\n analogy('france', 'french', 'england', 'english', word2idx, idx2word, We)\n analogy('japan', 'japanese', 'china', 'chinese', word2idx, idx2word, We)\n analogy('china', 'chinese', 'america', 'american', word2idx, idx2word, We)\n analogy('japan', 'japanese', 'italy', 'italian', word2idx, idx2word, We)\n analogy('japan', 'japanese', 'australia', 'australian', word2idx, idx2word, We)\n analogy('walk', 'walking', 'swim', 'swimming', word2idx, idx2word, We)\n\n\n\nif __name__ == '__main__':\n word2idx, W, V = train_model('w2v_tf')\n # word2idx, W, V = load_model('w2v_tf')\n test_model(word2idx, W, V)\n\n" }, { "path": "nlp_class2/word2vec_theano.py", "content": "# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python\n# https://udemy.com/natural-language-processing-with-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport json\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.special import expit as sigmoid\nfrom sklearn.utils import shuffle\nfrom datetime import datetime\n# from util import find_analogies\n\nfrom scipy.spatial.distance import cosine as cos_dist\nfrom sklearn.metrics.pairwise import pairwise_distances\n\n\nfrom glob import glob\n\nimport os\nimport sys\nimport string\n\nimport theano\nimport theano.tensor as T\n\n\n\n# unfortunately these work different ways\ndef remove_punctuation_2(s):\n return s.translate(None, string.punctuation)\n\ndef remove_punctuation_3(s):\n return s.translate(str.maketrans('','',string.punctuation))\n\nif sys.version.startswith('2'):\n remove_punctuation = remove_punctuation_2\nelse:\n remove_punctuation = remove_punctuation_3\n\n\n\ndef get_wiki():\n V = 20000\n files = glob('../large_files/enwiki*.txt')\n all_word_counts = {}\n for f in files:\n for line in open(f):\n if line and line[0] not in '[*-|=\\{\\}':\n s = remove_punctuation(line).lower().split()\n if len(s) > 1:\n for word in s:\n if word not in all_word_counts:\n all_word_counts[word] = 0\n all_word_counts[word] += 1\n print(\"finished counting\")\n\n V = min(V, len(all_word_counts))\n all_word_counts = sorted(all_word_counts.items(), key=lambda x: x[1], reverse=True)\n\n top_words = [w for w, count in all_word_counts[:V-1]] + ['']\n word2idx = {w:i for i, w in enumerate(top_words)}\n unk = word2idx['']\n\n sents = []\n for f in files:\n for line in open(f):\n if line and line[0] not in '[*-|=\\{\\}':\n s = remove_punctuation(line).lower().split()\n if len(s) > 1:\n # if a word is not nearby another word, there won't be any context!\n # and hence nothing to train!\n sent = [word2idx[w] if w in word2idx else unk for w in s]\n sents.append(sent)\n return sents, word2idx\n\n\n\n\ndef train_model(savedir):\n # get the data\n sentences, word2idx = get_wiki() #get_text8()\n\n\n # number of unique words\n vocab_size = len(word2idx)\n\n\n # config\n window_size = 5\n learning_rate = 0.025*128\n final_learning_rate = 0.0001*128\n num_negatives = 5 # number of negative samples to draw per input word\n samples_per_epoch = int(1e5)\n epochs = 1\n D = 50 # word embedding size\n\n\n # learning rate decay\n learning_rate_delta = (learning_rate - final_learning_rate) / epochs\n # learning_rate_delta = 0\n\n\n # params\n W = np.random.randn(vocab_size, D) / np.sqrt(D + vocab_size) # input-to-hidden\n V = np.random.randn(D, vocab_size) / np.sqrt(D + vocab_size) # hidden-to-output\n\n\n # theano variables\n thW = theano.shared(W)\n thV = theano.shared(V)\n\n # theano placeholders\n th_pos_word = T.ivector('pos_word')\n th_neg_word = T.ivector('neg_word')\n th_context = T.ivector('context')\n th_lr = T.scalar('learning_rate')\n\n # get the output and loss\n input_words = T.concatenate([th_pos_word, th_neg_word])\n W_subset = thW[input_words]\n dbl_context = T.concatenate([th_context, th_context])\n V_subset = thV[:, dbl_context]\n logits = W_subset.dot(V_subset)\n out = T.nnet.sigmoid(logits)\n\n n = th_pos_word.shape[0]\n th_cost = -T.log(out[:n]).mean() - T.log(1 - out[n:]).mean()\n\n\n # specify the updates\n gW = T.grad(th_cost, W_subset)\n gV = T.grad(th_cost, V_subset)\n W_update = T.inc_subtensor(W_subset, -th_lr*gW)\n V_update = T.inc_subtensor(V_subset, -th_lr*gV)\n updates = [(thW, W_update), (thV, V_update)]\n\n # full update\n # gW, gV = T.grad(th_cost, [thW, thV])\n # vW = theano.shared(np.zeros_like(W))\n # vV = theano.shared(np.zeros_like(V))\n # new_vW = 0.9*vW - th_lr*gW\n # new_vV = 0.9*vV - th_lr*gV\n # W_update = thW + new_vW\n # V_update = thV + new_vV\n # updates = [(thW, W_update), (thV, V_update), (vW, new_vW), (vV, new_vV)]\n\n # make callable functions\n cost_op = theano.function(\n inputs=[th_pos_word, th_neg_word, th_context],\n outputs=th_cost,\n # allow_input_downcast=True\n )\n cost_train_op = theano.function(\n inputs=[th_pos_word, th_neg_word, th_context, th_lr],\n outputs=th_cost,\n updates=updates,\n # allow_input_downcast=True\n )\n\n\n # distribution for drawing negative samples\n p_neg = get_negative_sampling_distribution(sentences, vocab_size)\n\n\n # save the costs to plot them per iteration\n costs = []\n\n\n # number of total words in corpus\n total_words = sum(len(sentence) for sentence in sentences)\n print(\"total number of words in corpus:\", total_words)\n\n\n # keep only certain words based on p_neg\n threshold = 1e-5\n p_drop = 1 - np.sqrt(threshold / p_neg)\n\n\n # train the model\n for epoch in range(epochs):\n # randomly order sentences so we don't always see\n # sentences in the same order\n np.random.shuffle(sentences)\n\n # accumulate the cost\n cost = 0\n counter = 0\n inputs = []\n targets = []\n negwords = []\n t0 = datetime.now()\n for sentence in sentences:\n # keep only certain words based on p_neg\n sentence = [w for w in sentence \\\n if np.random.random() < (1 - p_drop[w])\n ]\n if len(sentence) < 2:\n continue\n\n # randomly order words so we don't always see\n # samples in the same order\n randomly_ordered_positions = np.random.choice(\n len(sentence),\n size=len(sentence),\n replace=False,\n )\n\n \n for pos in randomly_ordered_positions:\n # the middle word\n word = sentence[pos]\n\n # get the positive context words/negative samples\n context_words = get_context(pos, sentence, window_size)\n neg_word = np.random.choice(vocab_size, p=p_neg)\n\n n = len(context_words)\n inputs += [word]*n\n negwords += [neg_word]*n\n targets += context_words\n \n\n if len(inputs) >= 128:\n c = cost_train_op(inputs, negwords, targets, learning_rate)\n cost += c\n\n if np.isnan(c):\n print(\"c is nan:\", c)\n exit()\n\n # reset\n inputs = []\n targets = []\n negwords = []\n\n counter += 1\n if counter % 100 == 0:\n sys.stdout.write(\"processed %s / %s, cost: %s\\r\" % (counter, len(sentences), c))\n sys.stdout.flush()\n\n\n # print stuff so we don't stare at a blank screen\n dt = datetime.now() - t0\n print(\"epoch complete:\", epoch, \"cost:\", cost, \"dt:\", dt)\n\n # save the cost\n costs.append(cost)\n\n # update the learning rate\n learning_rate -= learning_rate_delta\n\n\n # plot the cost per iteration\n plt.plot(costs)\n plt.show()\n\n\n # save the model\n if not os.path.exists(savedir):\n os.mkdir(savedir)\n\n with open('%s/word2idx.json' % savedir, 'w') as f:\n json.dump(word2idx, f)\n\n # don't forget to extract the weights from theano\n W, V = thW.get_value(), thV.get_value()\n np.savez('%s/weights.npz' % savedir, W, V)\n\n # return the model\n return word2idx, W, V\n\n\ndef get_negative_sampling_distribution(sentences, vocab_size):\n # Pn(w) = prob of word occuring\n # we would like to sample the negative samples\n # such that words that occur more often\n # should be sampled more often\n\n word_freq = np.zeros(vocab_size)\n word_count = sum(len(sentence) for sentence in sentences)\n for sentence in sentences:\n for word in sentence:\n word_freq[word] += 1\n\n # smooth it\n p_neg = word_freq**0.75\n\n # normalize it\n p_neg = p_neg / p_neg.sum()\n\n assert(np.all(p_neg > 0))\n return p_neg\n\n\ndef get_context(pos, sentence, window_size):\n # input:\n # a sentence of the form: x x x x c c c pos c c c x x x x\n # output:\n # the context word indices: c c c c c c\n\n start = max(0, pos - window_size)\n end_ = min(len(sentence), pos + window_size)\n\n context = []\n for ctx_pos, ctx_word_idx in enumerate(sentence[start:end_], start=start):\n if ctx_pos != pos:\n # don't include the input word itself as a target\n context.append(ctx_word_idx)\n return context\n # return np.concatenate([sentence[start:pos], sentence[pos+1:end_]])\n\n\ndef load_model(savedir):\n with open('%s/word2idx.json' % savedir) as f:\n word2idx = json.load(f)\n npz = np.load('%s/weights.npz' % savedir)\n W = npz['arr_0']\n V = npz['arr_1']\n return word2idx, W, V\n\n\n\ndef analogy(pos1, neg1, pos2, neg2, word2idx, idx2word, W):\n V, D = W.shape\n\n # don't actually use pos2 in calculation, just print what's expected\n print(\"testing: %s - %s = %s - %s\" % (pos1, neg1, pos2, neg2))\n for w in (pos1, neg1, pos2, neg2):\n if w not in word2idx:\n print(\"Sorry, %s not in word2idx\" % w)\n return\n\n p1 = W[word2idx[pos1]]\n n1 = W[word2idx[neg1]]\n p2 = W[word2idx[pos2]]\n n2 = W[word2idx[neg2]]\n\n vec = p1 - n1 + n2\n\n distances = pairwise_distances(vec.reshape(1, D), W, metric='cosine').reshape(V)\n idx = distances.argsort()[:10]\n\n # pick one that's not p1, n1, or n2\n best_idx = -1\n keep_out = [word2idx[w] for w in (pos1, neg1, neg2)]\n # print(\"keep_out:\", keep_out)\n for i in idx:\n if i not in keep_out:\n best_idx = i\n break\n # print(\"best_idx:\", best_idx)\n\n print(\"got: %s - %s = %s - %s\" % (pos1, neg1, idx2word[best_idx], neg2))\n print(\"closest 10:\")\n for i in idx:\n print(idx2word[i], distances[i])\n\n print(\"dist to %s:\" % pos2, cos_dist(p2, vec))\n\n\ndef test_model(word2idx, W, V):\n # there are multiple ways to get the \"final\" word embedding\n # We = (W + V.T) / 2\n # We = W\n\n idx2word = {i:w for w, i in word2idx.items()}\n\n for We in (W, (W + V.T) / 2):\n print(\"**********\")\n\n analogy('king', 'man', 'queen', 'woman', word2idx, idx2word, We)\n analogy('king', 'prince', 'queen', 'princess', word2idx, idx2word, We)\n analogy('miami', 'florida', 'dallas', 'texas', word2idx, idx2word, We)\n analogy('einstein', 'scientist', 'picasso', 'painter', word2idx, idx2word, We)\n analogy('japan', 'sushi', 'germany', 'bratwurst', word2idx, idx2word, We)\n analogy('man', 'woman', 'he', 'she', word2idx, idx2word, We)\n analogy('man', 'woman', 'uncle', 'aunt', word2idx, idx2word, We)\n analogy('man', 'woman', 'brother', 'sister', word2idx, idx2word, We)\n analogy('man', 'woman', 'husband', 'wife', word2idx, idx2word, We)\n analogy('man', 'woman', 'actor', 'actress', word2idx, idx2word, We)\n analogy('man', 'woman', 'father', 'mother', word2idx, idx2word, We)\n analogy('heir', 'heiress', 'prince', 'princess', word2idx, idx2word, We)\n analogy('nephew', 'niece', 'uncle', 'aunt', word2idx, idx2word, We)\n analogy('france', 'paris', 'japan', 'tokyo', word2idx, idx2word, We)\n analogy('france', 'paris', 'china', 'beijing', word2idx, idx2word, We)\n analogy('february', 'january', 'december', 'november', word2idx, idx2word, We)\n analogy('france', 'paris', 'germany', 'berlin', word2idx, idx2word, We)\n analogy('week', 'day', 'year', 'month', word2idx, idx2word, We)\n analogy('week', 'day', 'hour', 'minute', word2idx, idx2word, We)\n analogy('france', 'paris', 'italy', 'rome', word2idx, idx2word, We)\n analogy('paris', 'france', 'rome', 'italy', word2idx, idx2word, We)\n analogy('france', 'french', 'england', 'english', word2idx, idx2word, We)\n analogy('japan', 'japanese', 'china', 'chinese', word2idx, idx2word, We)\n analogy('china', 'chinese', 'america', 'american', word2idx, idx2word, We)\n analogy('japan', 'japanese', 'italy', 'italian', word2idx, idx2word, We)\n analogy('japan', 'japanese', 'australia', 'australian', word2idx, idx2word, We)\n analogy('walk', 'walking', 'swim', 'swimming', word2idx, idx2word, We)\n\n\n\nif __name__ == '__main__':\n word2idx, W, V = train_model('w2v_model')\n # word2idx, W, V = load_model('w2v_model')\n test_model(word2idx, W, V)\n\n" }, { "path": "nlp_class3/attention.py", "content": "# https://deeplearningcourses.com/c/deep-learning-advanced-nlp\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport os, sys\n\nfrom keras.models import Model\nfrom keras.layers import Input, LSTM, GRU, Dense, Embedding, \\\n Bidirectional, RepeatVector, Concatenate, Activation, Dot, Lambda\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nimport keras.backend as K\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ntry:\n import keras.backend as K\n if len(K.tensorflow_backend._get_available_gpus()) > 0:\n from keras.layers import CuDNNLSTM as LSTM\n from keras.layers import CuDNNGRU as GRU\nexcept:\n pass\n\n\n# make sure we do softmax over the time axis\n# expected shape is N x T x D\n# note: the latest version of Keras allows you to pass in axis arg\ndef softmax_over_time(x):\n assert(K.ndim(x) > 2)\n e = K.exp(x - K.max(x, axis=1, keepdims=True))\n s = K.sum(e, axis=1, keepdims=True)\n return e / s\n\n\n\n# config\nBATCH_SIZE = 64\nEPOCHS = 30\nLATENT_DIM = 400\nLATENT_DIM_DECODER = 400 # idea: make it different to ensure things all fit together properly!\nNUM_SAMPLES = 20000\nMAX_SEQUENCE_LENGTH = 100\nMAX_NUM_WORDS = 20000\nEMBEDDING_DIM = 100\n\n\n\n\n# Where we will store the data\ninput_texts = [] # sentence in original language\ntarget_texts = [] # sentence in target language\ntarget_texts_inputs = [] # sentence in target language offset by 1\n\n\n# load in the data\n# download the data at: http://www.manythings.org/anki/\nt = 0\nfor line in open('../large_files/translation/spa.txt'):\n # only keep a limited number of samples\n t += 1\n if t > NUM_SAMPLES:\n break\n\n # input and target are separated by tab\n if '\\t' not in line:\n continue\n\n # split up the input and translation\n input_text, translation, *rest = line.rstrip().split('\\t')\n\n # make the target input and output\n # recall we'll be using teacher forcing\n target_text = translation + ' '\n target_text_input = ' ' + translation\n\n input_texts.append(input_text)\n target_texts.append(target_text)\n target_texts_inputs.append(target_text_input)\nprint(\"num samples:\", len(input_texts))\n\n\n\n\n\n\n# tokenize the inputs\ntokenizer_inputs = Tokenizer(num_words=MAX_NUM_WORDS)\ntokenizer_inputs.fit_on_texts(input_texts)\ninput_sequences = tokenizer_inputs.texts_to_sequences(input_texts)\n\n# get the word to index mapping for input language\nword2idx_inputs = tokenizer_inputs.word_index\nprint('Found %s unique input tokens.' % len(word2idx_inputs))\n\n# determine maximum length input sequence\nmax_len_input = max(len(s) for s in input_sequences)\n\n# tokenize the outputs\n# don't filter out special characters\n# otherwise and won't appear\ntokenizer_outputs = Tokenizer(num_words=MAX_NUM_WORDS, filters='')\ntokenizer_outputs.fit_on_texts(target_texts + target_texts_inputs) # inefficient, oh well\ntarget_sequences = tokenizer_outputs.texts_to_sequences(target_texts)\ntarget_sequences_inputs = tokenizer_outputs.texts_to_sequences(target_texts_inputs)\n\n# get the word to index mapping for output language\nword2idx_outputs = tokenizer_outputs.word_index\nprint('Found %s unique output tokens.' % len(word2idx_outputs))\n\n# store number of output words for later\n# remember to add 1 since indexing starts at 1\nnum_words_output = len(word2idx_outputs) + 1\n\n# determine maximum length output sequence\nmax_len_target = max(len(s) for s in target_sequences)\n\n\n\n\n# pad the sequences\nencoder_inputs = pad_sequences(input_sequences, maxlen=max_len_input)\nprint(\"encoder_data.shape:\", encoder_inputs.shape)\nprint(\"encoder_data[0]:\", encoder_inputs[0])\n\ndecoder_inputs = pad_sequences(target_sequences_inputs, maxlen=max_len_target, padding='post')\nprint(\"decoder_data[0]:\", decoder_inputs[0])\nprint(\"decoder_data.shape:\", decoder_inputs.shape)\n\ndecoder_targets = pad_sequences(target_sequences, maxlen=max_len_target, padding='post')\n\n\n\n\n\n\n# store all the pre-trained word vectors\nprint('Loading word vectors...')\nword2vec = {}\nwith open(os.path.join('../large_files/glove.6B/glove.6B.%sd.txt' % EMBEDDING_DIM)) as f:\n # is just a space-separated text file in the format:\n # word vec[0] vec[1] vec[2] ...\n for line in f:\n values = line.split()\n word = values[0]\n vec = np.asarray(values[1:], dtype='float32')\n word2vec[word] = vec\nprint('Found %s word vectors.' % len(word2vec))\n\n\n\n\n# prepare embedding matrix\nprint('Filling pre-trained embeddings...')\nnum_words = min(MAX_NUM_WORDS, len(word2idx_inputs) + 1)\nembedding_matrix = np.zeros((num_words, EMBEDDING_DIM))\nfor word, i in word2idx_inputs.items():\n if i < MAX_NUM_WORDS:\n embedding_vector = word2vec.get(word)\n if embedding_vector is not None:\n # words not found in embedding index will be all zeros.\n embedding_matrix[i] = embedding_vector\n\n\n\n\n# create embedding layer\nembedding_layer = Embedding(\n num_words,\n EMBEDDING_DIM,\n weights=[embedding_matrix],\n input_length=max_len_input,\n # trainable=True\n)\n\n\n\n\n\n\n# create targets, since we cannot use sparse\n# categorical cross entropy when we have sequences\ndecoder_targets_one_hot = np.zeros(\n (\n len(input_texts),\n max_len_target,\n num_words_output\n ),\n dtype='float32'\n)\n\n# assign the values\nfor i, d in enumerate(decoder_targets):\n for t, word in enumerate(d):\n if word > 0:\n decoder_targets_one_hot[i, t, word] = 1\n\n\n\n\n\n\n##### build the model #####\n\n# Set up the encoder - simple!\nencoder_inputs_placeholder = Input(shape=(max_len_input,))\nx = embedding_layer(encoder_inputs_placeholder)\nencoder = Bidirectional(LSTM(\n LATENT_DIM,\n return_sequences=True,\n # dropout=0.5 # dropout not available on gpu\n))\nencoder_outputs = encoder(x)\n\n\n# Set up the decoder - not so simple\ndecoder_inputs_placeholder = Input(shape=(max_len_target,))\n\n# this word embedding will not use pre-trained vectors\n# although you could\ndecoder_embedding = Embedding(num_words_output, EMBEDDING_DIM)\ndecoder_inputs_x = decoder_embedding(decoder_inputs_placeholder)\n\n\n\n\n######### Attention #########\n# Attention layers need to be global because\n# they will be repeated Ty times at the decoder\nattn_repeat_layer = RepeatVector(max_len_input)\nattn_concat_layer = Concatenate(axis=-1)\nattn_dense1 = Dense(10, activation='tanh')\nattn_dense2 = Dense(1, activation=softmax_over_time)\nattn_dot = Dot(axes=1) # to perform the weighted sum of alpha[t] * h[t]\n\ndef one_step_attention(h, st_1):\n # h = h(1), ..., h(Tx), shape = (Tx, LATENT_DIM * 2)\n # st_1 = s(t-1), shape = (LATENT_DIM_DECODER,)\n \n # copy s(t-1) Tx times\n # now shape = (Tx, LATENT_DIM_DECODER)\n st_1 = attn_repeat_layer(st_1)\n\n # Concatenate all h(t)'s with s(t-1)\n # Now of shape (Tx, LATENT_DIM_DECODER + LATENT_DIM * 2)\n x = attn_concat_layer([h, st_1])\n\n # Neural net first layer\n x = attn_dense1(x)\n\n # Neural net second layer with special softmax over time\n alphas = attn_dense2(x)\n\n # \"Dot\" the alphas and the h's\n # Remember a.dot(b) = sum over a[t] * b[t]\n context = attn_dot([alphas, h])\n\n return context\n\n\n# define the rest of the decoder (after attention)\ndecoder_lstm = LSTM(LATENT_DIM_DECODER, return_state=True)\ndecoder_dense = Dense(num_words_output, activation='softmax')\n\ninitial_s = Input(shape=(LATENT_DIM_DECODER,), name='s0')\ninitial_c = Input(shape=(LATENT_DIM_DECODER,), name='c0')\ncontext_last_word_concat_layer = Concatenate(axis=2)\n\n\n# Unlike previous seq2seq, we cannot get the output\n# all in one step\n# Instead we need to do Ty steps\n# And in each of those steps, we need to consider\n# all Tx h's\n\n# s, c will be re-assigned in each iteration of the loop\ns = initial_s\nc = initial_c\n\n# collect outputs in a list at first\noutputs = []\nfor t in range(max_len_target): # Ty times\n # get the context using attention\n context = one_step_attention(encoder_outputs, s)\n\n # we need a different layer for each time step\n selector = Lambda(lambda x: x[:, t:t+1])\n xt = selector(decoder_inputs_x)\n \n # combine \n decoder_lstm_input = context_last_word_concat_layer([context, xt])\n\n # pass the combined [context, last word] into the LSTM\n # along with [s, c]\n # get the new [s, c] and output\n o, s, c = decoder_lstm(decoder_lstm_input, initial_state=[s, c])\n\n # final dense layer to get next word prediction\n decoder_outputs = decoder_dense(o)\n outputs.append(decoder_outputs)\n\n\n# 'outputs' is now a list of length Ty\n# each element is of shape (batch size, output vocab size)\n# therefore if we simply stack all the outputs into 1 tensor\n# it would be of shape T x N x D\n# we would like it to be of shape N x T x D\n\ndef stack_and_transpose(x):\n # x is a list of length T, each element is a batch_size x output_vocab_size tensor\n x = K.stack(x) # is now T x batch_size x output_vocab_size tensor\n x = K.permute_dimensions(x, pattern=(1, 0, 2)) # is now batch_size x T x output_vocab_size\n return x\n\n# make it a layer\nstacker = Lambda(stack_and_transpose)\noutputs = stacker(outputs)\n\n# create the model\nmodel = Model(\n inputs=[\n encoder_inputs_placeholder,\n decoder_inputs_placeholder,\n initial_s, \n initial_c,\n ],\n outputs=outputs\n)\n\n\ndef custom_loss(y_true, y_pred):\n # both are of shape N x T x K\n mask = K.cast(y_true > 0, dtype='float32')\n out = mask * y_true * K.log(y_pred)\n return -K.sum(out) / K.sum(mask)\n\n\ndef acc(y_true, y_pred):\n # both are of shape N x T x K\n targ = K.argmax(y_true, axis=-1)\n pred = K.argmax(y_pred, axis=-1)\n correct = K.cast(K.equal(targ, pred), dtype='float32')\n\n # 0 is padding, don't include those\n mask = K.cast(K.greater(targ, 0), dtype='float32')\n n_correct = K.sum(mask * correct)\n n_total = K.sum(mask)\n return n_correct / n_total\n\n\n# compile the model\nmodel.compile(optimizer='adam', loss=custom_loss, metrics=[acc])\n# model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['acc'])\n\n# train the model\nz = np.zeros((len(encoder_inputs), LATENT_DIM_DECODER)) # initial [s, c]\nr = model.fit(\n [encoder_inputs, decoder_inputs, z, z], decoder_targets_one_hot,\n batch_size=BATCH_SIZE,\n epochs=EPOCHS,\n validation_split=0.2\n)\n\n\n\n# plot some data\nplt.plot(r.history['loss'], label='loss')\nplt.plot(r.history['val_loss'], label='val_loss')\nplt.legend()\nplt.show()\n\n# accuracies\nplt.plot(r.history['accuracy'], label='acc')\nplt.plot(r.history['val_accuracy'], label='val_acc')\nplt.legend()\nplt.show()\n\n\n\n##### Make predictions #####\n# As with the poetry example, we need to create another model\n# that can take in the RNN state and previous word as input\n# and accept a T=1 sequence.\n\n# The encoder will be stand-alone\n# From this we will get our initial decoder hidden state\n# i.e. h(1), ..., h(Tx)\nencoder_model = Model(encoder_inputs_placeholder, encoder_outputs)\n\n# next we define a T=1 decoder model\nencoder_outputs_as_input = Input(shape=(max_len_input, LATENT_DIM * 2,))\ndecoder_inputs_single = Input(shape=(1,))\ndecoder_inputs_single_x = decoder_embedding(decoder_inputs_single)\n\n# no need to loop over attention steps this time because there is only one step\ncontext = one_step_attention(encoder_outputs_as_input, initial_s)\n\n# combine context with last word\ndecoder_lstm_input = context_last_word_concat_layer([context, decoder_inputs_single_x])\n\n\n\n\n# lstm and final dense\no, s, c = decoder_lstm(decoder_lstm_input, initial_state=[initial_s, initial_c])\ndecoder_outputs = decoder_dense(o)\n\n\n# note: we don't really need the final stack and tranpose\n# because there's only 1 output\n# it is already of size N x D\n# no need to make it 1 x N x D --> N x 1 x D\n\n\n\n# create the model object\ndecoder_model = Model(\n inputs=[\n decoder_inputs_single,\n encoder_outputs_as_input,\n initial_s, \n initial_c\n ],\n outputs=[decoder_outputs, s, c]\n)\n\n\n\n# map indexes back into real words\n# so we can view the results\nidx2word_eng = {v:k for k, v in word2idx_inputs.items()}\nidx2word_trans = {v:k for k, v in word2idx_outputs.items()}\n\n\n\n\n\ndef decode_sequence(input_seq):\n # Encode the input as state vectors.\n enc_out = encoder_model.predict(input_seq)\n\n # Generate empty target sequence of length 1.\n target_seq = np.zeros((1, 1))\n \n # Populate the first character of target sequence with the start character.\n # NOTE: tokenizer lower-cases all words\n target_seq[0, 0] = word2idx_outputs['']\n\n # if we get this we break\n eos = word2idx_outputs['']\n\n\n # [s, c] will be updated in each loop iteration\n s = np.zeros((1, LATENT_DIM_DECODER))\n c = np.zeros((1, LATENT_DIM_DECODER))\n\n\n # Create the translation\n output_sentence = []\n for _ in range(max_len_target):\n o, s, c = decoder_model.predict([target_seq, enc_out, s, c])\n \n\n # Get next word\n idx = np.argmax(o.flatten())\n\n # End sentence of EOS\n if eos == idx:\n break\n\n word = ''\n if idx > 0:\n word = idx2word_trans[idx]\n output_sentence.append(word)\n\n # Update the decoder input\n # which is just the word just generated\n target_seq[0, 0] = idx\n\n return ' '.join(output_sentence)\n\n\n\n\nwhile True:\n # Do some test translations\n i = np.random.choice(len(input_texts))\n input_seq = encoder_inputs[i:i+1]\n translation = decode_sequence(input_seq)\n print('-')\n print('Input sentence:', input_texts[i])\n print('Predicted translation:', translation)\n print('Actual translation:', target_texts[i])\n\n ans = input(\"Continue? [Y/n]\")\n if ans and ans.lower().startswith('n'):\n break\n\n" }, { "path": "nlp_class3/bilstm_mnist.py", "content": "# https://deeplearningcourses.com/c/deep-learning-advanced-nlp\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport os\nfrom keras.models import Model\nfrom keras.layers import Input, LSTM, GRU, Bidirectional, GlobalMaxPooling1D, Lambda, Concatenate, Dense\nimport keras.backend as K\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ntry:\n import keras.backend as K\n if len(K.tensorflow_backend._get_available_gpus()) > 0:\n from keras.layers import CuDNNLSTM as LSTM\n from keras.layers import CuDNNGRU as GRU\nexcept:\n pass\n\n\ndef get_mnist(limit=None):\n if not os.path.exists('../large_files'):\n print(\"You must create a folder called large_files adjacent to the class folder first.\")\n if not os.path.exists('../large_files/train.csv'):\n print(\"Looks like you haven't downloaded the data or it's not in the right spot.\")\n print(\"Please get train.csv from https://www.kaggle.com/c/digit-recognizer\")\n print(\"and place it in the large_files folder.\")\n\n print(\"Reading in and transforming data...\")\n df = pd.read_csv('../large_files/train.csv')\n data = df.values\n np.random.shuffle(data)\n X = data[:, 1:].reshape(-1, 28, 28) / 255.0 # data is from 0..255\n Y = data[:, 0]\n if limit is not None:\n X, Y = X[:limit], Y[:limit]\n return X, Y\n\n\n\n\n# get data\nX, Y = get_mnist()\n\n# config\nD = 28\nM = 15\n\n\n# input is an image of size 28x28\ninput_ = Input(shape=(D, D))\n\n# up-down\nrnn1 = Bidirectional(LSTM(M, return_sequences=True))\nx1 = rnn1(input_) # output is N x D x 2M\nx1 = GlobalMaxPooling1D()(x1) # output is N x 2M\n\n# left-right\nrnn2 = Bidirectional(LSTM(M, return_sequences=True))\n\n# custom layer\npermutor = Lambda(lambda t: K.permute_dimensions(t, pattern=(0, 2, 1)))\n\nx2 = permutor(input_)\nx2 = rnn2(x2) # output is N x D x 2M\nx2 = GlobalMaxPooling1D()(x2) # output is N x 2M\n\n# put them together\nconcatenator = Concatenate(axis=1)\nx = concatenator([x1, x2]) # output is N x 4M\n\n# final dense layer\noutput = Dense(10, activation='softmax')(x)\n\nmodel = Model(inputs=input_, outputs=output)\n\n# testing\n# o = model.predict(X)\n# print(\"o.shape:\", o.shape)\n\n# compile\nmodel.compile(\n loss='sparse_categorical_crossentropy',\n optimizer='adam',\n metrics=['accuracy']\n)\n\n# train\nprint('Training model...')\nr = model.fit(X, Y, batch_size=32, epochs=10, validation_split=0.3)\n\n\n# plot some data\nplt.plot(r.history['loss'], label='loss')\nplt.plot(r.history['val_loss'], label='val_loss')\nplt.legend()\nplt.show()\n\n# accuracies\nplt.plot(r.history['accuracy'], label='acc')\nplt.plot(r.history['val_accuracy'], label='val_acc')\nplt.legend()\nplt.show()\n\n" }, { "path": "nlp_class3/bilstm_test.py", "content": "# https://deeplearningcourses.com/c/deep-learning-advanced-nlp\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nfrom keras.models import Model\nfrom keras.layers import Input, LSTM, GRU, Bidirectional\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ntry:\n import keras.backend as K\n if len(K.tensorflow_backend._get_available_gpus()) > 0:\n from keras.layers import CuDNNLSTM as LSTM\n from keras.layers import CuDNNGRU as GRU\nexcept:\n pass\n\n\nT = 8\nD = 2\nM = 3\n\n\nX = np.random.randn(1, T, D)\n\n\ninput_ = Input(shape=(T, D))\n# rnn = Bidirectional(LSTM(M, return_state=True, return_sequences=True))\nrnn = Bidirectional(LSTM(M, return_state=True, return_sequences=False))\nx = rnn(input_)\n\nmodel = Model(inputs=input_, outputs=x)\no, h1, c1, h2, c2 = model.predict(X)\nprint(\"o:\", o)\nprint(\"o.shape:\", o.shape)\nprint(\"h1:\", h1)\nprint(\"c1:\", c1)\nprint(\"h2:\", h2)\nprint(\"c2:\", c2)" }, { "path": "nlp_class3/cnn_toxic.py", "content": "# https://deeplearningcourses.com/c/deep-learning-advanced-nlp\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport os\nimport sys\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.layers import Dense, Input, GlobalMaxPooling1D\nfrom keras.layers import Conv1D, MaxPooling1D, Embedding\nfrom keras.models import Model\nfrom sklearn.metrics import roc_auc_score\n\n\n# Download the data:\n# https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge\n# https://lazyprogrammer.me/course_files/toxic_comment_train.csv\n# Download the word vectors:\n# http://nlp.stanford.edu/data/glove.6B.zip\n\n\n# some configuration\nMAX_SEQUENCE_LENGTH = 100\nMAX_VOCAB_SIZE = 20000\nEMBEDDING_DIM = 100\nVALIDATION_SPLIT = 0.2\nBATCH_SIZE = 128\nEPOCHS = 10\n\n\n\n# load in pre-trained word vectors\nprint('Loading word vectors...')\nword2vec = {}\nwith open(os.path.join('../large_files/glove.6B/glove.6B.%sd.txt' % EMBEDDING_DIM)) as f:\n # is just a space-separated text file in the format:\n # word vec[0] vec[1] vec[2] ...\n for line in f:\n values = line.split()\n word = values[0]\n vec = np.asarray(values[1:], dtype='float32')\n word2vec[word] = vec\nprint('Found %s word vectors.' % len(word2vec))\n\n\n\n# prepare text samples and their labels\nprint('Loading in comments...')\n\ntrain = pd.read_csv(\"../large_files/toxic-comment/train.csv\")\nsentences = train[\"comment_text\"].fillna(\"DUMMY_VALUE\").values\npossible_labels = [\"toxic\", \"severe_toxic\", \"obscene\", \"threat\", \"insult\", \"identity_hate\"]\ntargets = train[possible_labels].values\n\n\n\n# convert the sentences (strings) into integers\ntokenizer = Tokenizer(num_words=MAX_VOCAB_SIZE)\ntokenizer.fit_on_texts(sentences)\nsequences = tokenizer.texts_to_sequences(sentences)\n# print(\"sequences:\", sequences); exit()\n\n\nprint(\"max sequence length:\", max(len(s) for s in sequences))\nprint(\"min sequence length:\", min(len(s) for s in sequences))\ns = sorted(len(s) for s in sequences)\nprint(\"median sequence length:\", s[len(s) // 2])\n\nprint(\"max word index:\", max(max(seq) for seq in sequences if len(seq) > 0))\n\n\n# get word -> integer mapping\nword2idx = tokenizer.word_index\nprint('Found %s unique tokens.' % len(word2idx))\n\n# exit()\n\n\n# pad sequences so that we get a N x T matrix\ndata = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)\nprint('Shape of data tensor:', data.shape)\n\n\n\n# prepare embedding matrix\nprint('Filling pre-trained embeddings...')\nnum_words = min(MAX_VOCAB_SIZE, len(word2idx) + 1)\nembedding_matrix = np.zeros((num_words, EMBEDDING_DIM))\nfor word, i in word2idx.items():\n if i < MAX_VOCAB_SIZE:\n embedding_vector = word2vec.get(word)\n if embedding_vector is not None:\n # words not found in embedding index will be all zeros.\n embedding_matrix[i] = embedding_vector\n\n\n\n# load pre-trained word embeddings into an Embedding layer\n# note that we set trainable = False so as to keep the embeddings fixed\nembedding_layer = Embedding(\n num_words,\n EMBEDDING_DIM,\n weights=[embedding_matrix],\n input_length=MAX_SEQUENCE_LENGTH,\n trainable=False\n)\n\n\nprint('Building model...')\n\n# train a 1D convnet with global maxpooling\ninput_ = Input(shape=(MAX_SEQUENCE_LENGTH,))\nx = embedding_layer(input_)\nx = Conv1D(128, 3, activation='relu')(x)\nx = MaxPooling1D(3)(x)\nx = Conv1D(128, 3, activation='relu')(x)\nx = MaxPooling1D(3)(x)\nx = Conv1D(128, 3, activation='relu')(x)\nx = GlobalMaxPooling1D()(x)\nx = Dense(128, activation='relu')(x)\noutput = Dense(len(possible_labels), activation='sigmoid')(x)\n\nmodel = Model(input_, output)\nmodel.compile(\n loss='binary_crossentropy',\n optimizer='rmsprop',\n metrics=['accuracy']\n)\n\nprint('Training model...')\nr = model.fit(\n data,\n targets,\n batch_size=BATCH_SIZE,\n epochs=EPOCHS,\n validation_split=VALIDATION_SPLIT\n)\n\n\n# plot some data\nplt.plot(r.history['loss'], label='loss')\nplt.plot(r.history['val_loss'], label='val_loss')\nplt.legend()\nplt.show()\n\n# accuracies\nplt.plot(r.history['accuracy'], label='acc')\nplt.plot(r.history['val_accuracy'], label='val_acc')\nplt.legend()\nplt.show()\n\n# plot the mean AUC over each label\np = model.predict(data)\naucs = []\nfor j in range(6):\n auc = roc_auc_score(targets[:,j], p[:,j])\n aucs.append(auc)\nprint(np.mean(aucs))\n" }, { "path": "nlp_class3/convert_twitter.py", "content": "# https://deeplearningcourses.com/c/deep-learning-advanced-nlp\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\n# each output line should be:\n# INPUTRESPONSE\nwith open('../large_files/twitter_tab_format.txt', 'w') as f:\n prev_line = None\n # data source: https://github.com/Phylliida/Dialogue-Datasets\n for line in open('../large_files/TwitterLowerAsciiCorpus.txt'):\n line = line.rstrip()\n\n if prev_line and line:\n f.write(\"%s\\t%s\\n\" % (prev_line, line))\n\n # note:\n # between conversations there are empty lines\n # which evaluate to false\n\n prev_line = line\n" }, { "path": "nlp_class3/extra_reading.txt", "content": "https://deeplearningcourses.com/c/deep-learning-advanced-nlp\n\nBidirectional Recurrent Neural Networks\nhttps://maxwell.ict.griffith.edu.au/spl/publications/papers/ieeesp97_schuster.pdf\n\nTranslation Modeling with Bidirectional Recurrent Neural Networks\nhttp://emnlp2014.org/papers/pdf/EMNLP2014003.pdf\n\nSequence to Sequence Learning with Neural Networks\nhttps://arxiv.org/abs/1409.3215\n\nA Neural Conversational Model\nhttps://arxiv.org/abs/1506.05869v3\n\nNeural Machine Translation by Jointly Learning to Align and Translate (Attention)\nhttps://arxiv.org/abs/1409.0473\n\nFeed-Forward Networks with Attention Can Solve Some Long-Term Memory Problems (Simplified Attention)\nhttps://arxiv.org/abs/1512.08756\n\nMemory Networks\nhttps://arxiv.org/abs/1410.3916\n\nTowards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks\nhttp://arxiv.org/abs/1502.05698\n\nEnd-To-End Memory Networks\nhttp://arxiv.org/abs/1503.08895\n\nAsk Me Anything: Dynamic Memory Networks for Natural Language Processing\nhttps://arxiv.org/abs/1506.07285\n\nWaveNet\nhttps://deepmind.com/blog/wavenet-generative-model-raw-audio/\n\nTacotron\nhttps://google.github.io/tacotron/\n\nTacotron 2\nhttps://research.googleblog.com/2017/12/tacotron-2-generating-human-like-speech.html\n\nAn Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling\nhttps://arxiv.org/abs/1803.01271\n(just released March 2018!)\n\nRelational recurrent neural networks\nhttps://arxiv.org/abs/1806.01822\n" }, { "path": "nlp_class3/lstm_toxic.py", "content": "# https://deeplearningcourses.com/c/deep-learning-advanced-nlp\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport os\nimport sys\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom keras.models import Model\nfrom keras.layers import Dense, Embedding, Input\nfrom keras.layers import LSTM, Bidirectional, GlobalMaxPool1D, Dropout\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.optimizers import Adam\nfrom sklearn.metrics import roc_auc_score\n\nimport keras.backend as K\n# if len(K.tensorflow_backend._get_available_gpus()) > 0:\n# from keras.layers import CuDNNLSTM as LSTM\n# from keras.layers import CuDNNGRU as GRU\n\n\n# Download the data:\n# https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge\n# https://lazyprogrammer.me/course_files/toxic_comment_train.csv\n# Download the word vectors:\n# http://nlp.stanford.edu/data/glove.6B.zip\n\n\n# some configuration\nMAX_SEQUENCE_LENGTH = 100\nMAX_VOCAB_SIZE = 20000\nEMBEDDING_DIM = 50\nVALIDATION_SPLIT = 0.2\nBATCH_SIZE = 128\nEPOCHS = 5\n\n\n\n# load in pre-trained word vectors\nprint('Loading word vectors...')\nword2vec = {}\nwith open(os.path.join('../large_files/glove.6B/glove.6B.%sd.txt' % EMBEDDING_DIM)) as f:\n # is just a space-separated text file in the format:\n # word vec[0] vec[1] vec[2] ...\n for line in f:\n values = line.split()\n word = values[0]\n vec = np.asarray(values[1:], dtype='float32')\n word2vec[word] = vec\nprint('Found %s word vectors.' % len(word2vec))\n\n\n\n# prepare text samples and their labels\nprint('Loading in comments...')\n\ntrain = pd.read_csv(\"../large_files/toxic-comment/train.csv\")\nsentences = train[\"comment_text\"].fillna(\"DUMMY_VALUE\").values\npossible_labels = [\"toxic\", \"severe_toxic\", \"obscene\", \"threat\", \"insult\", \"identity_hate\"]\ntargets = train[possible_labels].values\n\n\n\n\n# convert the sentences (strings) into integers\ntokenizer = Tokenizer(num_words=MAX_VOCAB_SIZE)\ntokenizer.fit_on_texts(sentences)\nsequences = tokenizer.texts_to_sequences(sentences)\n\n\n\n# get word -> integer mapping\nword2idx = tokenizer.word_index\nprint('Found %s unique tokens.' % len(word2idx))\n\n\n# pad sequences so that we get a N x T matrix\ndata = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)\nprint('Shape of data tensor:', data.shape)\n\n\n\n# prepare embedding matrix\nprint('Filling pre-trained embeddings...')\nnum_words = min(MAX_VOCAB_SIZE, len(word2idx) + 1)\nembedding_matrix = np.zeros((num_words, EMBEDDING_DIM))\nfor word, i in word2idx.items():\n if i < MAX_VOCAB_SIZE:\n embedding_vector = word2vec.get(word)\n if embedding_vector is not None:\n # words not found in embedding index will be all zeros.\n embedding_matrix[i] = embedding_vector\n\n\n\n# load pre-trained word embeddings into an Embedding layer\n# note that we set trainable = False so as to keep the embeddings fixed\nembedding_layer = Embedding(\n num_words,\n EMBEDDING_DIM,\n weights=[embedding_matrix],\n input_length=MAX_SEQUENCE_LENGTH,\n trainable=False\n)\n\n\n\nprint('Building model...')\n\n# create an LSTM network with a single LSTM\ninput_ = Input(shape=(MAX_SEQUENCE_LENGTH,))\nx = embedding_layer(input_)\nx = LSTM(15, return_sequences=True)(x)\n# x = Bidirectional(LSTM(15, return_sequences=True))(x)\nx = GlobalMaxPool1D()(x)\noutput = Dense(len(possible_labels), activation=\"sigmoid\")(x)\n\nmodel = Model(input_, output)\nmodel.compile(\n loss='binary_crossentropy',\n optimizer=Adam(lr=0.01),\n metrics=['accuracy'],\n)\n\n\nprint('Training model...')\nr = model.fit(\n data,\n targets,\n batch_size=BATCH_SIZE,\n epochs=EPOCHS,\n validation_split=VALIDATION_SPLIT\n)\n\n# plot some data\nplt.plot(r.history['loss'], label='loss')\nplt.plot(r.history['val_loss'], label='val_loss')\nplt.legend()\nplt.show()\n\n# accuracies\nplt.plot(r.history['accuracy'], label='acc')\nplt.plot(r.history['val_accuracy'], label='val_acc')\nplt.legend()\nplt.show()\n\np = model.predict(data)\naucs = []\nfor j in range(6):\n auc = roc_auc_score(targets[:,j], p[:,j])\n aucs.append(auc)\nprint(np.mean(aucs))\n\n" }, { "path": "nlp_class3/memory_network.py", "content": "# https://deeplearningcourses.com/c/deep-learning-advanced-nlp\nfrom __future__ import print_function, division\nfrom builtins import range, input\n\n\nimport numpy as np\nimport keras.backend as K\nimport matplotlib.pyplot as plt\nimport re\nimport tarfile\n\nfrom keras.models import Model\nfrom keras.layers import Dense, Embedding, Input, Lambda, Reshape, add, dot, Activation\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.optimizers import Adam, RMSprop\nfrom keras.utils.data_utils import get_file\n\n\n\n# get the data and open the compressed file using the tarfile library\n# https://research.fb.com/downloads/babi/\npath = get_file(\n 'babi-tasks-v1-2.tar.gz', \n origin='https://s3.amazonaws.com/text-datasets/babi_tasks_1-20_v1-2.tar.gz')\ntar = tarfile.open(path)\n\n\n\n# relevant data in the tar file\n# there's lots more data in there, check it out if you want!\nchallenges = {\n # QA1 with 10,000 samples\n 'single_supporting_fact_10k': 'tasks_1-20_v1-2/en-10k/qa1_single-supporting-fact_{}.txt',\n # QA2 with 10,000 samples\n 'two_supporting_facts_10k': 'tasks_1-20_v1-2/en-10k/qa2_two-supporting-facts_{}.txt',\n}\n\n\n\ndef tokenize(sent):\n '''Return the tokens of a sentence including punctuation.\n\n >>> tokenize('Bob dropped the apple. Where is the apple?')\n ['Bob', 'dropped', 'the', 'apple', '.', 'Where', 'is', 'the', 'apple', '?']\n '''\n return [x.strip() for x in re.split('(\\W+?)', sent) if x.strip()]\n\n\n\n\ndef get_stories(f):\n # data will return a list of triples\n # each triple contains:\n # 1. a story\n # 2. a question about the story\n # 3. the answer to the question\n data = []\n\n # use this list to keep track of the story so far\n story = []\n\n # print a random story, helpful to see the data\n printed = False\n for line in f:\n line = line.decode('utf-8').strip()\n\n # split the line number from the rest of the line\n nid, line = line.split(' ', 1)\n\n # see if we should begin a new story\n if int(nid) == 1:\n story = []\n\n # this line contains a question and answer if it has a tab\n # questionanswer\n # it also tells us which line in the story is relevant to the answer\n # Note: we actually ignore this fact, since the model will learn\n # which lines are important\n # Note: the max line number is not the number of lines of the story\n # since lines with questions do not contain any story\n # one story may contain MULTIPLE questions\n if '\\t' in line:\n q, a, supporting = line.split('\\t')\n q = tokenize(q)\n\n # numbering each line is very useful\n # it's the equivalent of adding a unique token to the front\n # of each sentence\n story_so_far = [[str(i)] + s for i, s in enumerate(story) if s]\n\n # uncomment if you want to see what a story looks like\n # if not printed and np.random.rand() < 0.5:\n # print(\"story_so_far:\", story_so_far)\n # printed = True\n data.append((story_so_far, q, a))\n story.append('')\n else:\n # just add the line to the current story\n story.append(tokenize(line))\n return data\n\n\n# recursively flatten a list\ndef should_flatten(el): \n return not isinstance(el, (str, bytes))\n\ndef flatten(l):\n for el in l:\n if should_flatten(el):\n yield from flatten(el)\n else:\n yield el\n\n\n\n\n\n\n# convert stories from words into lists of word indexes (integers)\n# pad each sequence so that they are the same length\n# we will need to re-pad the stories later so that each story\n# is the same length\ndef vectorize_stories(data, word2idx, story_maxlen, query_maxlen):\n inputs, queries, answers = [], [], []\n for story, query, answer in data:\n inputs.append([[word2idx[w] for w in s] for s in story])\n queries.append([word2idx[w] for w in query])\n answers.append([word2idx[answer]])\n return (\n [pad_sequences(x, maxlen=story_maxlen) for x in inputs],\n pad_sequences(queries, maxlen=query_maxlen),\n np.array(answers)\n )\n\n\n\n# this is like 'pad_sequences' but for entire stories\n# we are padding each story with zeros so every story\n# has the same number of sentences\n# append an array of zeros of size:\n# (max_sentences - num sentences in story, max words in sentence)\ndef stack_inputs(inputs, story_maxsents, story_maxlen):\n for i, story in enumerate(inputs):\n inputs[i] = np.concatenate(\n [\n story, \n np.zeros((story_maxsents - story.shape[0], story_maxlen), 'int')\n ]\n )\n return np.stack(inputs)\n\n\n\n\n\n# make a function to get the data since\n# we want to load both the single supporting fact data\n# and the two supporting fact data later\ndef get_data(challenge_type):\n # input should either be 'single_supporting_fact_10k' or 'two_supporting_facts_10k'\n challenge = challenges[challenge_type]\n\n\n # returns a list of triples of:\n # (story, question, answer)\n # story is a list of sentences\n # question is a sentence\n # answer is a word\n train_stories = get_stories(tar.extractfile(challenge.format('train')))\n test_stories = get_stories(tar.extractfile(challenge.format('test')))\n\n\n # group all the stories together\n stories = train_stories + test_stories\n\n # so we can get the max length of each story, of each sentence, and of each question\n story_maxlen = max((len(s) for x, _, _ in stories for s in x))\n story_maxsents = max((len(x) for x, _, _ in stories))\n query_maxlen = max(len(x) for _, x, _ in stories)\n\n # Create vocabulary of corpus and find size, including a padding element.\n vocab = sorted(set(flatten(stories)))\n vocab.insert(0, '')\n vocab_size = len(vocab)\n\n # Create an index mapping for the vocabulary.\n word2idx = {c:i for i, c in enumerate(vocab)}\n\n # convert stories from strings to lists of integers\n inputs_train, queries_train, answers_train = vectorize_stories(\n train_stories, \n word2idx,\n story_maxlen,\n query_maxlen\n )\n inputs_test, queries_test, answers_test = vectorize_stories(\n test_stories, \n word2idx,\n story_maxlen,\n query_maxlen\n )\n\n # convert inputs into 3-D numpy arrays\n inputs_train = stack_inputs(inputs_train, story_maxsents, story_maxlen)\n inputs_test = stack_inputs(inputs_test, story_maxsents, story_maxlen)\n print(\"inputs_train.shape, inputs_test.shape\", inputs_train.shape, inputs_test.shape)\n\n\n # return model inputs for keras\n return train_stories, test_stories, \\\n inputs_train, queries_train, answers_train, \\\n inputs_test, queries_test, answers_test, \\\n story_maxsents, story_maxlen, query_maxlen, \\\n vocab, vocab_size\n\n\n# get the single supporting fact data\ntrain_stories, test_stories, \\\n inputs_train, queries_train, answers_train, \\\n inputs_test, queries_test, answers_test, \\\n story_maxsents, story_maxlen, query_maxlen, \\\n vocab, vocab_size = get_data('single_supporting_fact_10k')\n\n\n\n\n##### create the model #####\nembedding_dim = 15\n\n\n# turn the story into a sequence of embedding vectors\n# one for each story line\n# treating each story line like a \"bag of words\"\ninput_story_ = Input((story_maxsents, story_maxlen))\nembedded_story = Embedding(vocab_size, embedding_dim)(input_story_)\nembedded_story = Lambda(lambda x: K.sum(x, axis=2))(embedded_story)\nprint(\"input_story_.shape, embedded_story.shape:\", input_story_.shape, embedded_story.shape)\n\n\n# turn the question into an embedding\n# also a bag of words\ninput_question_ = Input((query_maxlen,))\nembedded_question = Embedding(vocab_size, embedding_dim)(input_question_)\nembedded_question = Lambda(lambda x: K.sum(x, axis=1))(embedded_question)\n\n# add a \"sequence length\" of 1 so that it can\n# be dotted with the story later\nembedded_question = Reshape((1, embedding_dim))(embedded_question)\nprint(\"inp_q.shape, emb_q.shape:\", input_question_.shape, embedded_question.shape)\n\n\n# calculate the weights for each story line\n# embedded_story.shape = (N, num sentences, embedding_dim)\n# embedded_question.shape = (N, 1, embedding_dim)\nx = dot([embedded_story, embedded_question], 2)\nx = Reshape((story_maxsents,))(x) # flatten the vector\nx = Activation('softmax')(x)\nstory_weights = Reshape((story_maxsents, 1))(x) # unflatten it again to be dotted later\nprint(\"story_weights.shape:\", story_weights.shape)\n\n\n\nx = dot([story_weights, embedded_story], 1)\nx = Reshape((embedding_dim,))(x) # flatten it again\nans = Dense(vocab_size, activation='softmax')(x)\n\n# make the model\nmodel = Model([input_story_, input_question_], ans)\n\n# compile the model\nmodel.compile(\n optimizer=RMSprop(lr=1e-2),\n loss='sparse_categorical_crossentropy',\n metrics=['accuracy']\n)\n\n# train the model\nr = model.fit(\n [inputs_train, queries_train],\n answers_train,\n epochs=4,\n batch_size=32,\n validation_data=([inputs_test, queries_test], answers_test)\n)\n\n\n# Check how we weight each input sentence given a story and question\ndebug_model = Model([input_story_, input_question_], story_weights)\n\n# choose a random story\nstory_idx = np.random.choice(len(train_stories))\n\n# get weights from debug model\ni = inputs_train[story_idx:story_idx+1]\nq = queries_train[story_idx:story_idx+1]\nw = debug_model.predict([i, q]).flatten()\n\nstory, question, ans = train_stories[story_idx]\nprint(\"story:\\n\")\nfor i, line in enumerate(story):\n print(\"{:1.5f}\".format(w[i]), \"\\t\", \" \".join(line))\n\nprint(\"question:\", \" \".join(question))\nprint(\"answer:\", ans)\n\n\n# pause so we can see the output\ninput(\"Hit enter to continue\\n\\n\")\n\n\n\n##### two supporting facts #####\n\n\n# get the two supporting fact data\ntrain_stories, test_stories, \\\n inputs_train, queries_train, answers_train, \\\n inputs_test, queries_test, answers_test, \\\n story_maxsents, story_maxlen, query_maxlen, \\\n vocab, vocab_size = get_data('two_supporting_facts_10k')\n\n\n\n##### create the model #####\nembedding_dim = 30\n\n\n# make a function for this so we can use it again\ndef embed_and_sum(x, axis=2):\n x = Embedding(vocab_size, embedding_dim)(x)\n x = Lambda(lambda x: K.sum(x, axis))(x)\n return x\n\n# define the inputs\ninput_story_ = Input((story_maxsents, story_maxlen))\ninput_question_ = Input((query_maxlen,))\n\n\n# embed the inputs\nembedded_story = embed_and_sum(input_story_)\nembedded_question = embed_and_sum(input_question_, 1)\n\n\n# final dense will be used in each hop\ndense_layer = Dense(embedding_dim, activation='elu')\n\n\n# define one hop\n# the \"query\" can be the question, or the answer from the previous hop\ndef hop(query, story):\n # query.shape = (embedding_dim,)\n # story.shape = (num sentences, embedding_dim)\n x = Reshape((1, embedding_dim))(query) # make it (1, embedding_dim)\n x = dot([story, x], 2)\n x = Reshape((story_maxsents,))(x) # flatten it for softmax\n x = Activation('softmax')(x)\n story_weights = Reshape((story_maxsents, 1))(x) # unflatten for dotting\n\n # makes a new embedding\n story_embedding2 = embed_and_sum(input_story_)\n x = dot([story_weights, story_embedding2], 1)\n x = Reshape((embedding_dim,))(x)\n x = dense_layer(x)\n return x, story_embedding2, story_weights\n\n\n# do the hops\nans1, embedded_story, story_weights1 = hop(embedded_question, embedded_story)\nans2, _, story_weights2 = hop(ans1, embedded_story)\n\n# get the final answer\nans = Dense(vocab_size, activation='softmax')(ans2)\n\n\n# build the model\nmodel2 = Model([input_story_, input_question_], ans)\n\n# compile the model\nmodel2.compile(\n optimizer=RMSprop(lr=5e-3),\n loss='sparse_categorical_crossentropy',\n metrics=['accuracy']\n)\n\n# fit the model\nr = model2.fit(\n [inputs_train, queries_train],\n answers_train,\n epochs=30,\n batch_size=32,\n validation_data=([inputs_test, queries_test], answers_test)\n)\n\n\n### print story line weights again ###\ndebug_model2 = Model(\n [input_story_, input_question_],\n [story_weights1, story_weights2]\n)\n\n# choose a random story\nstory_idx = np.random.choice(len(train_stories))\n\n# get weights from debug model\ni = inputs_train[story_idx:story_idx+1]\nq = queries_train[story_idx:story_idx+1]\nw1, w2 = debug_model2.predict([i, q])\nw1 = w1.flatten()\nw2 = w2.flatten()\n\nstory, question, ans = train_stories[story_idx]\nprint(\"story:\\n\")\nfor j, line in enumerate(story):\n print(\"{:1.5f}\".format(w1[j]), \"\\t\", \"{:1.5f}\".format(w2[j]), \"\\t\", \" \".join(line))\n\nprint(\"question:\", \" \".join(question))\nprint(\"answer:\", ans)\nprint(\"prediction:\", vocab[ np.argmax(model2.predict([i, q])[0]) ])\n\n\n# plot some data\nplt.plot(r.history['loss'], label='loss')\nplt.plot(r.history['val_loss'], label='val_loss')\nplt.legend()\nplt.show()\n\n# accuracies\nplt.plot(r.history['accuracy'], label='acc')\nplt.plot(r.history['val_accuracy'], label='val_acc')\nplt.legend()\nplt.show()\n" }, { "path": "nlp_class3/poetry.py", "content": "# https://deeplearningcourses.com/c/deep-learning-advanced-nlp\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport os\nimport sys\nimport string\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom keras.models import Model\nfrom keras.layers import Dense, Embedding, Input, LSTM\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.optimizers import Adam, SGD\n\ntry:\n import keras.backend as K\n if len(K.tensorflow_backend._get_available_gpus()) > 0:\n from keras.layers import CuDNNLSTM as LSTM\n from keras.layers import CuDNNGRU as GRU\nexcept:\n pass\n\n\n# some configuration\nMAX_SEQUENCE_LENGTH = 100\nMAX_VOCAB_SIZE = 3000\nEMBEDDING_DIM = 50\nVALIDATION_SPLIT = 0.2\nBATCH_SIZE = 128\nEPOCHS = 2000\nLATENT_DIM = 25\n\n# load in the data\ninput_texts = []\ntarget_texts = []\nfor line in open('../hmm_class/robert_frost.txt'):\n line = line.rstrip()\n if not line:\n continue\n\n input_line = ' ' + line\n target_line = line + ' '\n\n input_texts.append(input_line)\n target_texts.append(target_line)\n\n\nall_lines = input_texts + target_texts\n\n# convert the sentences (strings) into integers\ntokenizer = Tokenizer(num_words=MAX_VOCAB_SIZE, filters='')\ntokenizer.fit_on_texts(all_lines)\ninput_sequences = tokenizer.texts_to_sequences(input_texts)\ntarget_sequences = tokenizer.texts_to_sequences(target_texts)\n\n# find max seq length\nmax_sequence_length_from_data = max(len(s) for s in input_sequences)\nprint('Max sequence length:', max_sequence_length_from_data)\n\n\n# get word -> integer mapping\nword2idx = tokenizer.word_index\nprint('Found %s unique tokens.' % len(word2idx))\nassert('' in word2idx)\nassert('' in word2idx)\n\n\n# pad sequences so that we get a N x T matrix\nmax_sequence_length = min(max_sequence_length_from_data, MAX_SEQUENCE_LENGTH)\ninput_sequences = pad_sequences(input_sequences, maxlen=max_sequence_length, padding='post')\ntarget_sequences = pad_sequences(target_sequences, maxlen=max_sequence_length, padding='post')\nprint('Shape of data tensor:', input_sequences.shape)\n\n\n\n# load in pre-trained word vectors\nprint('Loading word vectors...')\nword2vec = {}\nwith open(os.path.join('../large_files/glove.6B/glove.6B.%sd.txt' % EMBEDDING_DIM)) as f:\n # is just a space-separated text file in the format:\n # word vec[0] vec[1] vec[2] ...\n for line in f:\n values = line.split()\n word = values[0]\n vec = np.asarray(values[1:], dtype='float32')\n word2vec[word] = vec\nprint('Found %s word vectors.' % len(word2vec))\n\n\n\n# prepare embedding matrix\nprint('Filling pre-trained embeddings...')\nnum_words = min(MAX_VOCAB_SIZE, len(word2idx) + 1)\nembedding_matrix = np.zeros((num_words, EMBEDDING_DIM))\nfor word, i in word2idx.items():\n if i < MAX_VOCAB_SIZE:\n embedding_vector = word2vec.get(word)\n if embedding_vector is not None:\n # words not found in embedding index will be all zeros.\n embedding_matrix[i] = embedding_vector\n\n\n\n# one-hot the targets (can't use sparse cross-entropy)\none_hot_targets = np.zeros((len(input_sequences), max_sequence_length, num_words))\nfor i, target_sequence in enumerate(target_sequences):\n for t, word in enumerate(target_sequence):\n if word > 0:\n one_hot_targets[i, t, word] = 1\n\n\n\n# load pre-trained word embeddings into an Embedding layer\nembedding_layer = Embedding(\n num_words,\n EMBEDDING_DIM,\n weights=[embedding_matrix],\n # trainable=False\n)\n\n\n\nprint('Building model...')\n\n# create an LSTM network with a single LSTM\ninput_ = Input(shape=(max_sequence_length,))\ninitial_h = Input(shape=(LATENT_DIM,))\ninitial_c = Input(shape=(LATENT_DIM,))\nx = embedding_layer(input_)\nlstm = LSTM(LATENT_DIM, return_sequences=True, return_state=True)\nx, _, _ = lstm(x, initial_state=[initial_h, initial_c]) # don't need the states here\ndense = Dense(num_words, activation='softmax')\noutput = dense(x)\n\nmodel = Model([input_, initial_h, initial_c], output)\nmodel.compile(\n loss='categorical_crossentropy',\n # optimizer='rmsprop',\n optimizer=Adam(lr=0.01),\n # optimizer=SGD(lr=0.01, momentum=0.9),\n metrics=['accuracy']\n)\n\nprint('Training model...')\nz = np.zeros((len(input_sequences), LATENT_DIM))\nr = model.fit(\n [input_sequences, z, z],\n one_hot_targets,\n batch_size=BATCH_SIZE,\n epochs=EPOCHS,\n validation_split=VALIDATION_SPLIT\n)\n\n# plot some data\nplt.plot(r.history['loss'], label='loss')\nplt.plot(r.history['val_loss'], label='val_loss')\nplt.legend()\nplt.show()\n\n# accuracies\nplt.plot(r.history['accuracy'], label='acc')\nplt.plot(r.history['val_accuracy'], label='val_acc')\nplt.legend()\nplt.show()\n\n\n\n# make a sampling model\ninput2 = Input(shape=(1,)) # we'll only input one word at a time\nx = embedding_layer(input2)\nx, h, c = lstm(x, initial_state=[initial_h, initial_c]) # now we need states to feed back in\noutput2 = dense(x)\nsampling_model = Model([input2, initial_h, initial_c], [output2, h, c])\n\n\n# reverse word2idx dictionary to get back words\n# during prediction\nidx2word = {v:k for k, v in word2idx.items()}\n\n\ndef sample_line():\n # initial inputs\n np_input = np.array([[ word2idx[''] ]])\n h = np.zeros((1, LATENT_DIM))\n c = np.zeros((1, LATENT_DIM))\n\n # so we know when to quit\n eos = word2idx['']\n\n # store the output here\n output_sentence = []\n\n for _ in range(max_sequence_length):\n o, h, c = sampling_model.predict([np_input, h, c])\n\n # print(\"o.shape:\", o.shape, o[0,0,:10])\n # idx = np.argmax(o[0,0])\n probs = o[0,0]\n if np.argmax(probs) == 0:\n print(\"wtf\")\n probs[0] = 0\n probs /= probs.sum()\n idx = np.random.choice(len(probs), p=probs)\n if idx == eos:\n break\n\n # accuulate output\n output_sentence.append(idx2word.get(idx, '' % idx))\n\n # make the next input into model\n np_input[0,0] = idx\n\n return ' '.join(output_sentence)\n\n# generate a 4 line poem\nwhile True:\n for _ in range(4):\n print(sample_line())\n\n ans = input(\"---generate another? [Y/n]---\")\n if ans and ans[0].lower().startswith('n'):\n break\n" }, { "path": "nlp_class3/simple_rnn_test.py", "content": "# https://deeplearningcourses.com/c/deep-learning-advanced-nlp\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nfrom keras.models import Model\nfrom keras.layers import Input, LSTM, GRU\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ntry:\n import keras.backend as K\n if len(K.tensorflow_backend._get_available_gpus()) > 0:\n from keras.layers import CuDNNLSTM as LSTM\n from keras.layers import CuDNNGRU as GRU\nexcept:\n pass\n\n\nT = 8\nD = 2\nM = 3\n\n\nX = np.random.randn(1, T, D)\n\n\ndef lstm1():\n input_ = Input(shape=(T, D))\n rnn = LSTM(M, return_state=True)\n x = rnn(input_)\n\n model = Model(inputs=input_, outputs=x)\n o, h, c = model.predict(X)\n print(\"o:\", o)\n print(\"h:\", h)\n print(\"c:\", c)\n\n\ndef lstm2():\n input_ = Input(shape=(T, D))\n rnn = LSTM(M, return_state=True, return_sequences=True)\n # rnn = GRU(M, return_state=True)\n x = rnn(input_)\n\n model = Model(inputs=input_, outputs=x)\n o, h, c = model.predict(X)\n print(\"o:\", o)\n print(\"h:\", h)\n print(\"c:\", c)\n\n\ndef gru1():\n input_ = Input(shape=(T, D))\n rnn = GRU(M, return_state=True)\n x = rnn(input_)\n\n model = Model(inputs=input_, outputs=x)\n o, h = model.predict(X)\n print(\"o:\", o)\n print(\"h:\", h)\n\n\ndef gru2():\n input_ = Input(shape=(T, D))\n rnn = GRU(M, return_state=True, return_sequences=True)\n x = rnn(input_)\n\n model = Model(inputs=input_, outputs=x)\n o, h = model.predict(X)\n print(\"o:\", o)\n print(\"h:\", h)\n\n\n\nprint(\"lstm1:\")\nlstm1()\nprint(\"lstm2:\")\nlstm2()\nprint(\"gru1:\")\ngru1()\nprint(\"gru2:\")\ngru2()\n\n\n" }, { "path": "nlp_class3/wseq2seq.py", "content": "# # https://deeplearningcourses.com/c/deep-learning-advanced-nlp\n# get the data at: http://www.manythings.org/anki/\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport os, sys\n\nfrom keras.models import Model\nfrom keras.layers import Input, LSTM, GRU, Dense, Embedding\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.utils import to_categorical\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ntry:\n import keras.backend as K\n if len(K.tensorflow_backend._get_available_gpus()) > 0:\n from keras.layers import CuDNNLSTM as LSTM\n from keras.layers import CuDNNGRU as GRU\nexcept:\n pass\n\n\n# some config\nBATCH_SIZE = 64 # Batch size for training.\nEPOCHS = 40 # Number of epochs to train for.\nLATENT_DIM = 256 # Latent dimensionality of the encoding space.\nNUM_SAMPLES = 10000 # Number of samples to train on.\nMAX_NUM_WORDS = 20000\nEMBEDDING_DIM = 100\n\n# Where we will store the data\ninput_texts = [] # sentence in original language\ntarget_texts = [] # sentence in target language\ntarget_texts_inputs = [] # sentence in target language offset by 1\n\n\n# load in the data\n# download the data at: http://www.manythings.org/anki/\nt = 0\nfor line in open('../large_files/translation/spa.txt'):\n # only keep a limited number of samples\n t += 1\n if t > NUM_SAMPLES:\n break\n\n # input and target are separated by tab\n if '\\t' not in line:\n continue\n\n # split up the input and translation\n input_text, translation, *rest = line.rstrip().split('\\t')\n\n # make the target input and output\n # recall we'll be using teacher forcing\n target_text = translation + ' '\n target_text_input = ' ' + translation\n\n input_texts.append(input_text)\n target_texts.append(target_text)\n target_texts_inputs.append(target_text_input)\nprint(\"num samples:\", len(input_texts))\n\n\n# tokenize the inputs\ntokenizer_inputs = Tokenizer(num_words=MAX_NUM_WORDS)\ntokenizer_inputs.fit_on_texts(input_texts)\ninput_sequences = tokenizer_inputs.texts_to_sequences(input_texts)\n\n# get the word to index mapping for input language\nword2idx_inputs = tokenizer_inputs.word_index\nprint('Found %s unique input tokens.' % len(word2idx_inputs))\n\n# determine maximum length input sequence\nmax_len_input = max(len(s) for s in input_sequences)\n\n# tokenize the outputs\n# don't filter out special characters\n# otherwise and won't appear\ntokenizer_outputs = Tokenizer(num_words=MAX_NUM_WORDS, filters='')\ntokenizer_outputs.fit_on_texts(target_texts + target_texts_inputs) # inefficient, oh well\ntarget_sequences = tokenizer_outputs.texts_to_sequences(target_texts)\ntarget_sequences_inputs = tokenizer_outputs.texts_to_sequences(target_texts_inputs)\n\n# get the word to index mapping for output language\nword2idx_outputs = tokenizer_outputs.word_index\nprint('Found %s unique output tokens.' % len(word2idx_outputs))\n\n# store number of output words for later\n# remember to add 1 since indexing starts at 1\nnum_words_output = len(word2idx_outputs) + 1\n\n# determine maximum length output sequence\nmax_len_target = max(len(s) for s in target_sequences)\n\n\n# pad the sequences\nencoder_inputs = pad_sequences(input_sequences, maxlen=max_len_input)\nprint(\"encoder_inputs.shape:\", encoder_inputs.shape)\nprint(\"encoder_inputs[0]:\", encoder_inputs[0])\n\ndecoder_inputs = pad_sequences(target_sequences_inputs, maxlen=max_len_target, padding='post')\nprint(\"decoder_inputs[0]:\", decoder_inputs[0])\nprint(\"decoder_inputs.shape:\", decoder_inputs.shape)\n\ndecoder_targets = pad_sequences(target_sequences, maxlen=max_len_target, padding='post')\n\n\n\n\n\n\n\n# store all the pre-trained word vectors\nprint('Loading word vectors...')\nword2vec = {}\nwith open(os.path.join('../large_files/glove.6B/glove.6B.%sd.txt' % EMBEDDING_DIM)) as f:\n # is just a space-separated text file in the format:\n # word vec[0] vec[1] vec[2] ...\n for line in f:\n values = line.split()\n word = values[0]\n vec = np.asarray(values[1:], dtype='float32')\n word2vec[word] = vec\nprint('Found %s word vectors.' % len(word2vec))\n\n\n\n\n# prepare embedding matrix\nprint('Filling pre-trained embeddings...')\nnum_words = min(MAX_NUM_WORDS, len(word2idx_inputs) + 1)\nembedding_matrix = np.zeros((num_words, EMBEDDING_DIM))\nfor word, i in word2idx_inputs.items():\n if i < MAX_NUM_WORDS:\n embedding_vector = word2vec.get(word)\n if embedding_vector is not None:\n # words not found in embedding index will be all zeros.\n embedding_matrix[i] = embedding_vector\n\n\n\n\n# create embedding layer\nembedding_layer = Embedding(\n num_words,\n EMBEDDING_DIM,\n weights=[embedding_matrix],\n input_length=max_len_input,\n # trainable=True\n)\n\n\n# create targets, since we cannot use sparse\n# categorical cross entropy when we have sequences\ndecoder_targets_one_hot = np.zeros(\n (\n len(input_texts),\n max_len_target,\n num_words_output\n ),\n dtype='float32'\n)\n\n# assign the values\nfor i, d in enumerate(decoder_targets):\n for t, word in enumerate(d):\n if word != 0:\n decoder_targets_one_hot[i, t, word] = 1\n\n\n\n\n##### build the model #####\nencoder_inputs_placeholder = Input(shape=(max_len_input,))\nx = embedding_layer(encoder_inputs_placeholder)\nencoder = LSTM(\n LATENT_DIM,\n return_state=True,\n # dropout=0.5 # dropout not available on gpu\n)\nencoder_outputs, h, c = encoder(x)\n# encoder_outputs, h = encoder(x) #gru\n\n# keep only the states to pass into decoder\nencoder_states = [h, c]\n# encoder_states = [state_h] # gru\n\n# Set up the decoder, using [h, c] as initial state.\ndecoder_inputs_placeholder = Input(shape=(max_len_target,))\n\n# this word embedding will not use pre-trained vectors\n# although you could\ndecoder_embedding = Embedding(num_words_output, EMBEDDING_DIM)\ndecoder_inputs_x = decoder_embedding(decoder_inputs_placeholder)\n\n# since the decoder is a \"to-many\" model we want to have\n# return_sequences=True\ndecoder_lstm = LSTM(\n LATENT_DIM,\n return_sequences=True,\n return_state=True,\n # dropout=0.5 # dropout not available on gpu\n)\ndecoder_outputs, _, _ = decoder_lstm(\n decoder_inputs_x,\n initial_state=encoder_states\n)\n\n# decoder_outputs, _ = decoder_gru(\n# decoder_inputs_x,\n# initial_state=encoder_states\n# )\n\n# final dense layer for predictions\ndecoder_dense = Dense(num_words_output, activation='softmax')\ndecoder_outputs = decoder_dense(decoder_outputs)\n\n# Create the model object\nmodel = Model([encoder_inputs_placeholder, decoder_inputs_placeholder], decoder_outputs)\n\n\ndef custom_loss(y_true, y_pred):\n # both are of shape N x T x K\n mask = K.cast(y_true > 0, dtype='float32')\n out = mask * y_true * K.log(y_pred)\n return -K.sum(out) / K.sum(mask)\n\n\ndef acc(y_true, y_pred):\n # both are of shape N x T x K\n targ = K.argmax(y_true, axis=-1)\n pred = K.argmax(y_pred, axis=-1)\n correct = K.cast(K.equal(targ, pred), dtype='float32')\n\n # 0 is padding, don't include those\n mask = K.cast(K.greater(targ, 0), dtype='float32')\n n_correct = K.sum(mask * correct)\n n_total = K.sum(mask)\n return n_correct / n_total\n\nmodel.compile(optimizer='adam', loss=custom_loss, metrics=[acc])\n\n# Compile the model and train it\n# model.compile(\n# optimizer='rmsprop',\n# loss='categorical_crossentropy',\n# metrics=['accuracy']\n# )\n\n\n\nr = model.fit(\n [encoder_inputs, decoder_inputs], decoder_targets_one_hot,\n batch_size=BATCH_SIZE,\n epochs=EPOCHS,\n validation_split=0.2,\n)\n\n# plot some data\nplt.plot(r.history['loss'], label='loss')\nplt.plot(r.history['val_loss'], label='val_loss')\nplt.legend()\nplt.show()\n\n# accuracies\nplt.plot(r.history['accuracy'], label='acc')\nplt.plot(r.history['val_accuracy'], label='val_acc')\nplt.legend()\nplt.show()\n\n# Save model\nmodel.save('s2s.h5')\n\n\n\n\n##### Make predictions #####\n# As with the poetry example, we need to create another model\n# that can take in the RNN state and previous word as input\n# and accept a T=1 sequence.\n\n# The encoder will be stand-alone\n# From this we will get our initial decoder hidden state\nencoder_model = Model(encoder_inputs_placeholder, encoder_states)\n\ndecoder_state_input_h = Input(shape=(LATENT_DIM,))\ndecoder_state_input_c = Input(shape=(LATENT_DIM,))\ndecoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]\n# decoder_states_inputs = [decoder_state_input_h] # gru\n\ndecoder_inputs_single = Input(shape=(1,))\ndecoder_inputs_single_x = decoder_embedding(decoder_inputs_single)\n\n# this time, we want to keep the states too, to be output\n# by our sampling model\ndecoder_outputs, h, c = decoder_lstm(\n decoder_inputs_single_x,\n initial_state=decoder_states_inputs\n)\n# decoder_outputs, state_h = decoder_lstm(\n# decoder_inputs_single_x,\n# initial_state=decoder_states_inputs\n# ) #gru\ndecoder_states = [h, c]\n# decoder_states = [h] # gru\ndecoder_outputs = decoder_dense(decoder_outputs)\n\n# The sampling model\n# inputs: y(t-1), h(t-1), c(t-1)\n# outputs: y(t), h(t), c(t)\ndecoder_model = Model(\n [decoder_inputs_single] + decoder_states_inputs, \n [decoder_outputs] + decoder_states\n)\n\n# map indexes back into real words\n# so we can view the results\nidx2word_eng = {v:k for k, v in word2idx_inputs.items()}\nidx2word_trans = {v:k for k, v in word2idx_outputs.items()}\n\n\ndef decode_sequence(input_seq):\n # Encode the input as state vectors.\n states_value = encoder_model.predict(input_seq)\n\n # Generate empty target sequence of length 1.\n target_seq = np.zeros((1, 1))\n\n # Populate the first character of target sequence with the start character.\n # NOTE: tokenizer lower-cases all words\n target_seq[0, 0] = word2idx_outputs['']\n\n # if we get this we break\n eos = word2idx_outputs['']\n\n # Create the translation\n output_sentence = []\n for _ in range(max_len_target):\n output_tokens, h, c = decoder_model.predict(\n [target_seq] + states_value\n )\n # output_tokens, h = decoder_model.predict(\n # [target_seq] + states_value\n # ) # gru\n\n # Get next word\n idx = np.argmax(output_tokens[0, 0, :])\n\n # End sentence of EOS\n if eos == idx:\n break\n\n word = ''\n if idx > 0:\n word = idx2word_trans[idx]\n output_sentence.append(word)\n\n # Update the decoder input\n # which is just the word just generated\n target_seq[0, 0] = idx\n\n # Update states\n states_value = [h, c]\n # states_value = [h] # gru\n\n return ' '.join(output_sentence)\n\n\n\nwhile True:\n # Do some test translations\n i = np.random.choice(len(input_texts))\n input_seq = encoder_inputs[i:i+1]\n translation = decode_sequence(input_seq)\n print('-')\n print('Input:', input_texts[i])\n print('Translation:', translation)\n\n ans = input(\"Continue? [Y/n]\")\n if ans and ans.lower().startswith('n'):\n break\n\n" }, { "path": "nlp_v2/WHERE ARE THE NOTEBOOKS.txt", "content": "As stated in the \"where to get the code\" / \"where to get the notebooks\" lecture, the notebooks are NOT on Github.\n\nIf you missed this, please review the lecture for the actual location of the notebooks.\n\nIf, after reviewing it, you still need assistance, please contact info@deeplearningcourses.com." }, { "path": "nlp_v2/extra_reading.txt", "content": "An information-theoretic perspective of tf–idf measures\nhttps://www.sciencedirect.com/science/article/abs/pii/S0306457302000213\n\nA Mathematical Theory of Communication by Claude Shannon\nhttps://people.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf\n\nTextRank: Bringing Order into Texts\nhttps://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf\n\nVariations of the Similarity Function of TextRank for Automated Summarization\nhttps://arxiv.org/abs/1602.03606\n\nGeneric Text Summarization Using Relevance Measure and Latent Semantic Analysis\nhttps://www.cs.bham.ac.uk/~pxt/IDA/text_summary.pdf\n\nUsing Latent Semantic Analysis in Text Summarization and Summary Evaluation\nhttp://textmining.zcu.cz/publications/isim.pdf\n\nSpam Filtering with Naive Bayes – Which Naive Bayes?\nhttp://www2.aueb.gr/users/ion/docs/ceas2006_paper.pdf\n\nSentiment analysis using multinomial logistic regression\nhttps://ieeexplore.ieee.org/document/8226700\n\nLatent Dirichlet Allocation\nhttps://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf\n\nList of Hugging Face Pipelines for NLP\nhttps://lazyprogrammer.me/list-of-hugging-face-pipelines-for-nlp/\n\nIndexing by Latent Semantic Analysis (Latent Semantic Indexing)\nhttp://lsa.colorado.edu/papers/JASIS.lsi.90.pdf\n\nEfficient Estimation of Word Representations in Vector Space (word2vec)\nhttps://arxiv.org/abs/1301.3781\n\nGloVe: Global Vectors for Word Representation (GloVe)\nhttps://nlp.stanford.edu/pubs/glove.pdf\n\nDeep Learning with Tensorflow, a bit more in-depth\nhttps://deeplearningcourses.com/c/deep-learning-tensorflow-2" }, { "path": "numpy_class/classification_example.py", "content": "# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python\n# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python\n# YouTube direct link: http://bit.ly/2LENC50\n\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\n# just in case we need it\nimport numpy as np\n\n\n# import the function that will get the data\n# yes, sklearn comes with built-in datasets!\nfrom sklearn.datasets import load_breast_cancer\n\n# load the data\ndata = load_breast_cancer()\n\n# check the type of 'data'\ntype(data)\n\n# note: it is a Bunch object\n# this basically acts like a dictionary where you can treat the keys like attributes\ndata.keys()\n\n# 'data' (the attribute) means the input data\ndata.data.shape\n# it has 569 samples, 30 features\n\n# 'targets'\ndata.target\n# note how the targets are just 0s and 1s\n# normally, when you have K targets, they are labeled 0..K-1\n\n# their meaning is not lost\ndata.target_names\n\n# there are also 569 corresponding targets\ndata.target.shape\n\n# you can also determinw the meaning of each feature\ndata.feature_names\n\n\n# normally we would put all of our imports at the top\n# but this lets us tell a story\nfrom sklearn.model_selection import train_test_split\n\n\n# split the data into train and test sets\n# this lets us simulate how our model will perform in the future\nX_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.33)\n\n\n# instantiate a classifer and train it\nfrom sklearn.ensemble import RandomForestClassifier\n\n\nmodel = RandomForestClassifier()\nmodel.fit(X_train, y_train)\n\n\n# evaluate the model's performance\nmodel.score(X_train, y_train)\nmodel.score(X_test, y_test)\n\n\n# how you can make predictions\npredictions = model.predict(X_test)\n\n# what did we get?\npredictions\n\n# manually check the accuracy of your predictions\nN = len(y_test)\nnp.sum(predictions == y_test) / N # can also just call np.mean()\n\n\n\n# we can even use deep learning to solve the same problem!\nfrom sklearn.neural_network import MLPClassifier\n\n# you'll learn why scaling is needed in a later course\nfrom sklearn.preprocessing import StandardScaler\n\nscaler = StandardScaler()\nX_train2 = scaler.fit_transform(X_train)\nX_test2 = scaler.transform(X_test)\n\nmodel = MLPClassifier(max_iter=500)\nmodel.fit(X_train2, y_train)\n\n\n# evaluate the model's performance\nmodel.score(X_train2, y_train)\nmodel.score(X_test2, y_test)" }, { "path": "numpy_class/dot_for.py", "content": "# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python\n# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python\nimport numpy as np\nfrom datetime import datetime\n\na = np.random.randn(100)\nb = np.random.randn(100)\nT = 100000\n\ndef slow_dot_product(a, b):\n\tresult = 0\n\tfor e, f in zip(a, b):\n\t\tresult += e*f\n\treturn result\n\nt0 = datetime.now()\nfor t in xrange(T):\n\tslow_dot_product(a, b)\ndt1 = datetime.now() - t0\n\nt0 = datetime.now()\nfor t in xrange(T):\n\ta.dot(b)\ndt2 = datetime.now() - t0\n\nprint \"dt1 / dt2:\", dt1.total_seconds() / dt2.total_seconds()" }, { "path": "numpy_class/exercises/ex1.py", "content": "# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python\n# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python\n\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nA = np.array([\n [0.3, 0.6, 0.1],\n [0.5, 0.2, 0.3],\n [0.4, 0.1, 0.5]])\n\nv = np.ones(3) / 3\n\nnum_iters = 25\ndistances = np.zeros(num_iters)\nfor i in range(num_iters):\n v2 = v.dot(A)\n d = np.linalg.norm(v2 - v)\n distances[i] = d\n v = v2\n\nplt.plot(distances)\nplt.show()" }, { "path": "numpy_class/exercises/ex2.py", "content": "# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python\n# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python\n\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef sampleY(n=1000):\n # draw n samples from uniform dist.\n X = np.random.random(n)\n Y = X.sum()\n return Y\n\n\n# now draw N Y's\nN = 1000\nY_samples = np.zeros(N)\nfor i in range(N):\n Y_samples[i] = sampleY()\n\n\n# now plot the Y_samples\nplt.hist(Y_samples, bins=20)\nplt.show()" }, { "path": "numpy_class/exercises/ex3.py", "content": "# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python\n# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python\n\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n\n# load in the data\ndf = pd.read_csv('../../large_files/train.csv')\ndata = df.values\nX = data[:, 1:] # images\nY = data[:, 0] # labels\n\n# loop through each label\nfor k in range(10):\n Xk = X[Y == k]\n\n # mean image\n Mk = Xk.mean(axis=0)\n\n # reshape into an image\n im = Mk.reshape(28, 28)\n\n # plot the image\n plt.imshow(im, cmap='gray')\n plt.title(\"Label: %s\" % k)\n plt.show()\n" }, { "path": "numpy_class/exercises/ex4.py", "content": "# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python\n# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python\n\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n\n# load in the data\ndf = pd.read_csv('../../large_files/train.csv')\ndata = df.values\n\n# shuffle the images\nnp.random.shuffle(data)\n\nX = data[:, 1:] # images\nY = data[:, 0] # labels\n\n\n# define rotate functions\ndef rotate1(im):\n return np.rot90(im, 3)\n\ndef rotate2(im):\n H, W = im.shape\n im2 = np.zeros((W, H))\n for i in range(H):\n for j in range(W):\n im2[j,H - i - 1] = im[i,j]\n return im2\n\n\nfor i in range(X.shape[0]):\n # get the image\n im = X[i].reshape(28, 28)\n\n # flip the image\n # im = rotate1(im)\n im = rotate2(im)\n\n # plot the image\n plt.imshow(im, cmap='gray')\n plt.title(\"Label: %s\" % Y[i])\n plt.show()\n\n ans = input(\"Continue? [Y/n]: \")\n if ans and ans[0].lower() == 'n':\n break\n" }, { "path": "numpy_class/exercises/ex5.py", "content": "# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python\n# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python\n\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\n\ndef is_symmetric1(A):\n return np.all(A == A.T)\n\n\ndef is_symmetric2(A):\n rows, cols = A.shape\n if rows != cols:\n return False\n\n for i in range(rows):\n for j in range(cols):\n if A[i,j] != A[j,i]:\n return False\n\n return True\n\n\ndef check(A, b):\n print(\"Testing:\", A)\n assert(is_symmetric1(A) == b)\n assert(is_symmetric2(A) == b)\n\n\n# test the functions\nA = np.zeros((3, 3))\ncheck(A, True)\n\nA = np.eye(3)\ncheck(A, True)\n\nA = np.random.randn(3, 2)\nA = A.dot(A.T)\ncheck(A, True)\n\nA = np.array([[1, 2, 3], [2, 4, 5], [3, 5, 6]])\ncheck(A, True)\n\nA = np.random.randn(3, 2)\ncheck(A, False)\n\nA = np.random.randn(3, 3)\ncheck(A, False)\n\nA = np.arange(9).reshape(3, 3)\ncheck(A, False)\n\n" }, { "path": "numpy_class/exercises/ex6.py", "content": "# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python\n# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python\n\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\n# generate unlabeled data\nN = 2000\nX = np.random.random((N, 2))*2 - 1\n\n# generate labels\nY = np.zeros(N)\nY[(X[:,0] < 0) & (X[:,1] > 0)] = 1\nY[(X[:,0] > 0) & (X[:,1] < 0)] = 1\n\n# plot it\nplt.scatter(X[:,0], X[:,1], c=Y)\nplt.show()" }, { "path": "numpy_class/exercises/ex7.py", "content": "# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python\n# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python\n\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef get_donut():\n N = 2000\n R_inner = 5\n R_outer = 10\n\n # distance from origin is radius + random normal\n # angle theta is uniformly distributed between (0, 2pi)\n R1 = np.random.randn(N//2) + R_inner\n theta = 2*np.pi*np.random.random(N//2)\n X_inner = np.concatenate([[R1 * np.cos(theta)], [R1 * np.sin(theta)]]).T\n\n R2 = np.random.randn(N//2) + R_outer\n theta = 2*np.pi*np.random.random(N//2)\n X_outer = np.concatenate([[R2 * np.cos(theta)], [R2 * np.sin(theta)]]).T\n\n X = np.concatenate([ X_inner, X_outer ])\n Y = np.array([0]*(N//2) + [1]*(N//2))\n return X, Y\n\nX, Y = get_donut()\nplt.scatter(X[:,0], X[:,1], c=Y)\nplt.show()" }, { "path": "numpy_class/exercises/ex8.py", "content": "# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python\n# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python\n\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef get_spiral():\n # Idea: radius -> low...high\n # (don't start at 0, otherwise points will be \"mushed\" at origin)\n # angle = low...high proportional to radius\n # [0, 2pi/6, 4pi/6, ..., 10pi/6] --> [pi/2, pi/3 + pi/2, ..., ]\n # x = rcos(theta), y = rsin(theta) as usual\n\n radius = np.linspace(1, 10, 100)\n thetas = np.empty((6, 100))\n for i in range(6):\n start_angle = np.pi*i / 3.0\n end_angle = start_angle + np.pi / 2\n points = np.linspace(start_angle, end_angle, 100)\n thetas[i] = points\n\n # convert into cartesian coordinates\n x1 = np.empty((6, 100))\n x2 = np.empty((6, 100))\n for i in range(6):\n x1[i] = radius * np.cos(thetas[i])\n x2[i] = radius * np.sin(thetas[i])\n\n # inputs\n X = np.empty((600, 2))\n X[:,0] = x1.flatten()\n X[:,1] = x2.flatten()\n\n # add noise\n X += np.random.randn(600, 2)*0.5\n\n # targets\n Y = np.array([0]*100 + [1]*100 + [0]*100 + [1]*100 + [0]*100 + [1]*100)\n return X, Y\n\n\nX, Y = get_spiral()\nplt.scatter(X[:,0], X[:,1], c=Y)\nplt.show()" }, { "path": "numpy_class/exercises/ex9.py", "content": "# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python\n# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python\n\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n\nfrom ex8 import get_spiral\n\n# get the data\nX, Y = get_spiral()\n\n# combine the data into one array\n# data to be concatenated must have same # of dimensions\n# e.g. N x D and N x 1\n# not N x D and N\ndata = np.concatenate((X, np.expand_dims(Y, 1)), axis=1)\n\ndf = pd.DataFrame(data)\ndf.columns = ['x1', 'x2', 'y']\ndf.to_csv('mydata.csv', index=False)" }, { "path": "numpy_class/manual_data_loading.py", "content": "# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python\n# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python\n\n# NOTE: in class, we assumed the current working directory\n# was linear_regression_class\n# in this file, we assume you are running the script\n# from the directory this file is in\n\nimport numpy as np\n\nX = []\n\nfor line in open('../linear_regression_class/data_2d.csv'):\n row = line.split(',')\n sample = map(float, row)\n X.append(sample)\n\nX = np.array(X)\nprint X" }, { "path": "numpy_class/python3/dot_for.py", "content": "# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python\n# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nfrom datetime import datetime\n\na = np.random.randn(100)\nb = np.random.randn(100)\nT = 100000\n\ndef slow_dot_product(a, b):\n result = 0\n for e, f in zip(a, b):\n result += e*f\n return result\n\nt0 = datetime.now()\nfor t in range(T):\n slow_dot_product(a, b)\ndt1 = datetime.now() - t0\n\nt0 = datetime.now()\nfor t in range(T):\n a.dot(b)\ndt2 = datetime.now() - t0\n\nprint(\"dt1 / dt2:\", dt1.total_seconds() / dt2.total_seconds())" }, { "path": "numpy_class/python3/manual_data_loading.py", "content": "# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python\n# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python\n\n# NOTE: in class, we assumed the current working directory\n# was linear_regression_class\n# in this file, we assume you are running the script\n# from the directory this file is in\n\nimport numpy as np\n\nX = []\n\nfor line in open('../../linear_regression_class/data_2d.csv'):\n row = line.split(',')\n sample = list(map(float, row))\n X.append(sample)\n\nX = np.array(X)\nprint(X)" }, { "path": "numpy_class/regression_example.py", "content": "# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python\n# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python\n# YouTube direct link: http://bit.ly/2LENC50\n\n# Get the data from:\n# https://archive.ics.uci.edu/ml/datasets/Airfoil+Self-Noise\n\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\n# just in case we need it\nimport numpy as np\nimport pandas as pd\n\n\n# load the data\n# important note: this is where we will usually put data files\ndf = pd.read_csv('../large_files/airfoil_self_noise.dat', sep='\\t', header=None)\n\n# check the data\ndf.head()\ndf.info()\n\n# get the inputs\ndata = df[[0,1,2,3,4]].values\n\n# get the outputs\ntarget = df[5].values\n\n# tiny update: pandas is moving from .as_matrix() to the equivalent .values\n\n\n# normally we would put all of our imports at the top\n# but this lets us tell a story\nfrom sklearn.model_selection import train_test_split\n\n\n# split the data into train and test sets\n# this lets us simulate how our model will perform in the future\nX_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.33)\n\n\n# instantiate a classifer and train it\nfrom sklearn.linear_model import LinearRegression\n\n\nmodel = LinearRegression()\nmodel.fit(X_train, y_train)\n\n\n# evaluate the model's performance\nprint(model.score(X_train, y_train))\nprint(model.score(X_test, y_test))\n\n\n# how you can make predictions\npredictions = model.predict(X_test)\n\n# what did we get?\npredictions\n\n\n\n# we can even use random forest to solve the same problem!\nfrom sklearn.ensemble import RandomForestRegressor\n\nmodel2 = RandomForestRegressor()\nmodel2.fit(X_train, y_train)\n\n\n# evaluate the model's performance\nprint(model2.score(X_train, y_train))\nprint(model2.score(X_test, y_test))\n\n\n\n\n# we can even use deep learning to solve the same problem!\nfrom sklearn.neural_network import MLPRegressor\n\n# you'll learn why scaling is needed in a later course\nfrom sklearn.preprocessing import StandardScaler\n\nscaler = StandardScaler()\nX_train2 = scaler.fit_transform(X_train)\nX_test2 = scaler.transform(X_test)\nscaler2 = StandardScaler()\ny_train2 = scaler2.fit_transform(np.expand_dims(y_train, -1)).ravel()\ny_test2 = scaler2.fit_transform(np.expand_dims(y_test, -1)).ravel()\n\nmodel = MLPRegressor(max_iter=500)\nmodel.fit(X_train2, y_train2)\n\n\n# evaluate the model's performance\nprint(model.score(X_train2, y_train2))\nprint(model.score(X_test2, y_test2))\n# not as good as a random forest!\n# but not as bad as linear regression\n" }, { "path": "numpy_class/table1.csv", "content": "user_id,email,age\n1,alice@gmail.com,20\n2,bob@gmail.com,25\n3,carol@gmail.com,30" }, { "path": "numpy_class/table2.csv", "content": "user_id,ad_id,click\n1,1,1\n1,2,0\n1,5,0\n2,3,0\n2,4,1\n2,1,0\n3,2,0\n3,1,0\n3,3,0\n3,4,0\n3,5,1" }, { "path": "openai/extra_reading.txt", "content": "How to Set Environment Variables Permanently in Windows, Linux, and Mac\nhttps://lazyprogrammer.me/how-to-set-environment-variables-permanently-in-windows-linux-and-mac/\n\nHow to make your completions outputs consistent with the new seed parameter\nhttps://cookbook.openai.com/examples/reproducible_outputs_with_the_seed_parameter\n\nWhat is Temperature in NLP / LLMs?\nhttps://medium.com/@lazyprogrammerofficial/what-is-temperature-in-nlp-llms-aa2a7212e687\n\nLarge Language Models are Zero-Shot Reasoners (CoT)\nhttps://arxiv.org/abs/2205.11916\n\nChain-of-Thought Prompting Elicits Reasoning in Large Language Models\nhttps://arxiv.org/abs/2201.11903\n\nA much better example of \"ELI5\"\nhttps://www.reddit.com/r/ChatGPT/comments/1c5s51g/my_mother_and_i_had_difficulty_understanding_my\n\nWhat is RAG? — Retrieval-Augmented Generation Explained\nhttps://medium.com/@lazyprogrammerofficial/what-is-rag-retrieval-augmented-generation-explained-148c8bb9c00f\nhttps://lazyprogrammer.me/what-is-rag-retrieval-augmented-generation-explained/" }, { "path": "openai/replies.json", "content": "[\n {\n \"review\": \"(1) His answers are sometimes antagonistic but the guy wants us to think by ourselves. I feel he was guided by questions from students with very little background on the subject. (2) Links are not updated. I understand not updated them on the videos, but on the git repository and the scripts, it should have them updated. (3) Explanations are great, with a few inconsistencies when compared to Gemini.google.com understanding. (4) The course content in general is great.\",\n \"response\": \"(1) I think all students should respect other students taking this course. All students deserve to have their questions answered, and no student can expect to have this course personalized to their own background.\\n\\n(2) This is incorrect, and you were already instructed on the Q&A to ensure you were looking at the correct repository with the most up-to-date files.\\n\\n(3) LLMs are known for hallucinating, and their output cannot be trusted, especially if you don't know what you're doing. Instead, you should be using the Q&A to rectify these issues, which is why it's the #1 rule in 'how to succeed in this course'.\"\n },\n {\n \"review\": \"You should have explained in the introduction video that, you have not yet figured out Stock forecasting, so explaining your (thoughts or beliefs or work) in this course. But marketing in great way , nothing in content other than playing with data.\",\n \"response\": \"Try paying attention and understanding the course. If you still believe there's some magic algorithm to perfectly predict stock prices and I \\\"just haven't figured it out yet\\\", you clearly weren't listening, know absolutely nothing about finance, and hence, spreading misinformation to readers.\"\n },\n {\n \"review\": \"I'm really disappointed. The last update of your codes was 9 years ago. Nothing is running. I tried file after file - nothing works. I don't think you could even continue to sell these courses. Unfortunately, I can no longer cancel the current course, but I will cancel the next course. Your courses do not meet basic standards. Too bad.\",\n \"response\": \"> The last update of your codes was 9 years ago.\\n\\nNOTE: Our friend here just doesn't know how to use Git properly. My ML Github repo was CREATED 9 years ago (long before I even started making courses).\"\n },\n {\n \"review\": \"nao é claro ainda como fazer o donlow de githum\",\n \"response\": \"Thanks for the feedback! Please re-watch the lecture \\\"Where to get the code / notebooks\\\" carefully and follow the instructions. It clearly states 5 times (yes, that many times) that the notebooks are not on Github, but rather, are accessed via the code link.\"\n },\n {\n \"review\": \"It is a good course about RL, as all his courses, but if you are here for the Trading agent, don't buy it... very basic code and does not really work.\",\n \"response\": \"Thanks for your feedback! However, it seems you are basing your rating around your own misconceptions and naivete surrounding trading and finance, rather than the quality of the course itself.\"\n },\n {\n \"review\": \"Need to explain more about the topic. CNN is more theoretical in the course than programming.\",\n \"response\": \"Incorrect. There are equal parts theory and programming. Every \\\"theoretical\\\" concept is implemented in code. And obviously, you must understand \\\"what\\\" you are coding before you code it. Please pay attention to improve your understanding, thanks!\"\n },\n {\n \"review\": \"The content is ok but the links between videos and sections aren't always obvious. I like the fact that it goes in dept on my subjects but the quality of the audio isn't always good enough. I would still recommend to someone that really want to have a better understanding of AI/ML or specifically logistic regression but expect some extra reading if you really want to understand all the concepts well.\",\n \"response\": \"You should probably revisit the \\\"introduction and outline\\\" of the course if you've forgotten the structure of the course. There's only \\\"extra reading\\\" if you do not sufficiently meet the prerequisites.\"\n },\n {\n \"review\": \"I expected some coding and practice but most of the course till now is just theory\",\n \"response\": \"That's incorrect, and this is even visible from simply looking at the lecture titles. There are equal parts theory and code. First we discuss \\\"what\\\" we will code, then we code it. Obviously, you can't write code without knowing what you're coding first... Please pay attention to improve your understanding, thanks!\"\n },\n {\n \"review\": \"It is all over the place ... not very structured and i have IT and python background , still finding difficult to follow ... i wonder how the Deep learning course will be\",\n \"response\": \"Unclear why you're having trouble following the structure of such a simple short course. There are only 4 sections: Numpy, Matplotlib, Pandas, and Scipy. Please pay attention to improve your understanding, thanks!\"\n },\n {\n \"review\": \"Content is good so far, but lecturer seems hung up on the behaviors of the participants which is not helping the instruction time.\",\n \"response\": \"It's important to remember that the course isn't customized for you individually. Therefore, it should be expected that common problems that afflict a non-trivial number of students will be addressed.\"\n },\n {\n \"review\": \"Explanation is not clear as we are beginners. May be improve better to understand clearly.\",\n \"response\": \"Thanks for the feedback! Please ensure you meet the prerequisites as listed twice in the course description and several more times in lectures such as \\\"how to succeed in this course\\\".\"\n },\n {\n \"review\": \"Much was great, some frequently explained by referring to other courses as well as skipping some code blocks or instrumental variables, which were readily explained in chat gpt by asking if to add copious explanatory comments. Most, but certainly not all AI ML course instructors are more concerned with taking the time to explain finer details of the code. Early parts on ANN and CNN were excellent in presentation although this was simpler material. In the course was more presenting and explaining than teaching how to code the models, both necessary individually and together sufficient for a solid learning experience. Perhaps sacrifice some optional topics for more time indepth to the course essentials - quantity versus quality.\",\n \"response\": \"Unclear whether you wrote this comment for the wrong course, as there are no sections about ANNs or CNNs in this course... Furthermore, this course is not about in-depth theory - please check the course description and prerequisites, thanks!\"\n },\n {\n \"review\": \"Some more details about math equations could be added\",\n \"response\": \"Thanks for the feedback! Please ensure you meet the prerequisites as listed twice in the course description and several more times in lectures such as \\\"how to succeed in this course\\\". Furthermore, you may want to read the course description that explains that this course is about Tensorflow 2, not the math behind deep learning. I already have courses on that, so check those out instead.\"\n },\n {\n \"review\": \"Teaches only Syntax and not any ML or theory behind how any of the Neural Network architectures work.\",\n \"response\": \"Incorrect. This course gives an overview of theory, and furthermore, I already have 15+ in-depth DL courses that go into the math behind DL. Luckily, this is all in the course description, which I'm sure you've diligently read. ;)\"\n },\n {\n \"review\": \"It was a good match for my current abilities. I could not access the live python notebooks or links used. I would have appreciated solutions to the end of section exercises. Overall though the instructor is very knowledgeable and the course is free so I can't complain.\",\n \"response\": \"The key is to follow the instructions. All notebooks and exercise solutions are provided, if you can just follow the instructions on how to get them (clicking links). It really is very easy, it just requires paying attention. :)\"\n },\n {\n \"review\": \"The Neural Network part is not very clear\",\n \"response\": \"This course doesn't talk about neural networks...\"\n },\n {\n \"review\": \"need more examples\",\n \"response\": \"The course is full of examples, please use the Q&A if you have difficulties understanding them\"\n },\n {\n \"review\": \"Could use a bit more time spent on explaining certain concepts, like Box-Cox, giving more intuition and explanation of why that is useful\",\n \"response\": \"Please use the Q&A to inquire about your misunderstandings, as stated in \\\"how to succeed in this course\\\".\"\n },\n {\n \"review\": \"Interesting course with lots of examples and lectures. Although some parts of the course become repetitive. There are lectures where he explains the code step by step and then goes on to repeat the same thing in the \\\"... in Python\\\" lectures. It would have been nice if he had proposed other exercises with a different dataset than the lectures, even though he does not provide the solution. It is fine to say that we should try to write the code ourselves first and then check the solution, but when this is reduced to copy and paste from the previous lecture it seems ridiculous to me.\",\n \"response\": \"This is a practical course, meaning that you are shown the code and can do with it what you wish (there's no way to \\\"exercise\\\" writing library code without first being shown the syntax, which doesn't make any sense). Additionally, repetition is a research-backed learning technique: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8442015/ Furthermore, please remember that the course is not customized to you. Therefore, if you personally choose to avoid repetition, you are free to do so, nobody is stopping you.\"\n },\n {\n \"review\": \"I enjoyed the author explanation on the differences between statistics regression and ML regression content. But I think the lecture on the differences was pedantic and unnecessary even. The content list and summary to this lecture is where the distinction could be made. Instead, it was spent ranting within the lecture, how does that provide any value and meaningful experience? Worry not about the review, if the content is good, the review would come.\",\n \"response\": \"> But I think the lecture on the differences was pedantic and unnecessary even.\\n\\nPlease remember, the course is not customized for you personally, it's for all students and therefore addresses common student mistakes, irrespective of your personal needs. Obviously, I have to answer ALL student questions, whether or not you personally find the question/answer helpful.\"\n },\n {\n \"review\": \"The instructor seems inadequately prepared for each segment of the course. There is a noticeable absence of demonstrations that are essential for a comprehensive understanding of the material. Furthermore, there is a disproportionate emphasis on paid websites over free alternatives, raising concerns about potential conflicts of interest due to affiliate marketing associations with these paid platforms.\",\n \"response\": \"Everything is demonstrated in Colab notebooks. I challenge you to list specific examples instead of vague accusations like \\\"inadequately prepared\\\" and \\\"noticeable absence of demonstrations\\\".\\n\\n> disproportionate emphasis on paid websites over free alternatives\\n\\nThat is merely what you are paying attention to and triggered by. Most tools in the course are free or have free alternatives. Furthermore, I don't choose what's free, they are not my companies, so I am unsure why you think this is my fault...?\"\n },\n {\n \"review\": \"I have taken this course and have spent lot of hrs and can tell that, this 12 hrs course will take lot of time to understand and complete (150 hrs). This can be done if we have 100% free time for 4 weeks. Good thing is if you have little idea of Calculus and we flow what is being taught, this course will make us ready to understand ML. Having said that the instructor should again go back and try to minimize the un-necessary or repetitive content.\",\n \"response\": \"There is no unnecessary or repetitive content, and if you believe there is, you should use the Q&A to clear up any misunderstandings as instructed in the \\\"how to succeed in this course\\\" video.\"\n },\n {\n \"review\": \"Content is inconsistent in difficulty. Instructor is not good at explaining highly complicated topics that require a lot of Mathematics.\",\n \"response\": \"Thanks for the feedback! Please ensure you meet the prerequisites as listed twice in the course description and several more times in lectures such as \\\"how to succeed in this course\\\". Please recall that each section is denoted as \\\"beginner\\\", \\\"intermediate\\\", or \\\"advanced\\\" (as stated in the intro). One must choose according to their own skill level. This obvious lack of attention explains why you are having trouble understanding the course (since clearly, even the basic info in the intro was beyond your understanding).\"\n },\n {\n \"review\": \"you are READING the slide nicely.\",\n \"response\": \"Incorrect. The slides provide a summary of the spoken audio, like all courses on this site, all other video-based courses, and all presentations around the world. I'm amused that you would act \\\"surprised\\\" by such a normal occurrence... The slides are not being \\\"read\\\", and anyone taking the course can confirm this fact readily, so this comment is both inaccurate and misleading to potential students.\"\n },\n {\n \"review\": \"There are some good topics explained, but lots of tutorials are just off topic (not about NLP), but various attempts from the instructor the explain to others why they're not able to understand stuff.\",\n \"response\": \"You just have to remember that the course is not customized for you, it's for everyone. The \\\"FAQ\\\" section (meaning \\\"frequently asked questions\\\") answers questions other students are asking. It's unkind to suggest that I shouldn't answer questions from your fellow students.\"\n },\n {\n \"review\": \"It doesn’t make sense to have to make an account outside of Udemy to then have to read an article that gives an analogy about \\\"burgers\\\" just to then inform you that you have to wait 31 days to view the material. Just upload the video like every other instructor and inform us that you have other material. Don’t force us to the material.\",\n \"response\": \"Incorrect. You don't have to \\\"make an account outside of Udemy\\\" to take this course. That is for a different VERSION of the course for students who want to go above and beyond and learn more exciting material, in case they want to learn stuff OUTSIDE the course description [which I'm sure you've diligently read ;)].\"\n },\n {\n \"review\": \"I feel pretty lost, I feel like showing an example of what we're trying to achieve even thought it comes way later in the course would show how the individual parts of the course will play into it.\",\n \"response\": \"This is what happens when you don't follow the instructions, like meeting the prerequisites or using the Q&A.\"\n },\n {\n \"review\": \"Teacher is good at explaining concepts,albeit he has some language problems.\",\n \"response\": \"English is my only language, but based on your comment alone, it is clear that the language problems may be on your end. I suggest improving in this area to better understand the course, thanks!\"\n },\n {\n \"review\": \"Honestly !! this is highly insufficient material. When we open the books of Machine Learning, we are lost in understanding the mathematical notations. However, this course is teaching Integration way below the levels of Class 12. My comment - It needs improvement\",\n \"response\": \"Please make sure you read the course description so you understand the purpose of this course. For example, hard integration problems would not serve that purpose.\"\n },\n {\n \"review\": \"The course has no depth where the instructor explains an intuition and runs off. The exercises given have no solutions and you have to do it either yourself or you suck and they can't help you! I wouldn't recommend it. Feels more like a refresher course than a course for someone to learn from scratch.\",\n \"response\": \"> The course has no depth where the instructor explains an intuition and runs off.\\n\\nIncorrect. Every concept lecture is followed by Python code.\\n\\n> The exercises given have no solutions\\n\\nIncorrect. Exercise solutions are in the videos... please pay attention to improve your understanding, thanks.\"\n },\n {\n \"review\": \"I feel helped by this course, but I am a bit confused about understanding the Markov model, but in other materials I can smoothly. Thank you for making this class, I hope you are always healthy.\",\n \"response\": \"Please note that the sections are clearly marked beginner, intermediate, or advanced. This is so that you can stick to your level without being confused about material that is too advanced for you.\"\n },\n {\n \"review\": \"Thank you for your prompt response. Let's be frank. I'm no novice to this topic, and I took your course hoping to get a fresh perspective. However, I was met with content that seemed hastily put together and felt more like a reference guide rather than a comprehensive educational course. I've previously enrolled in some of your courses, which were of higher quality. My feedback is based on a comparison with your own past materials. I hope you'll take this as an opportunity to review and enhance the course content for the benefit of future students.\",\n \"response\": \"All algorithms are derived from scratch and based on the prerequisites, it is not a \\\"reference guide\\\". It seems strange that someone who is \\\"no novice to this topic\\\" would get those confused...\"\n },\n {\n \"review\": \"Too much talking, less content till now\",\n \"response\": \"It's a video course, I'm not sure how one would avoid talking...\"\n },\n {\n \"review\": \"While this course has multiple sections on how LP believes you should be learning. That time could have been spent reinforcing some of the more difficult concepts with additional examples.\",\n \"response\": \"This is a common misunderstanding of the appendix/FAQ. It's not \\\"That time could have been spent reinforcing some of the more difficult concepts with additional examples\\\". This content doesn't displace any other content.\"\n },\n {\n \"review\": \"Therotical only.....No Example......just copy from book and paste it.......read it.....No Implementation...................\",\n \"response\": \"Incorrect. Everything has been implemented from scratch. Please pay attention to improve your understanding, and please watch the FAQ lecture \\\"Beginner's Coding Tips\\\", thanks!\"\n },\n {\n \"review\": \"I haven't proceeded in the course yet but I wouldn't say I liked the instructor's stance on students asking dumb questions.\",\n \"response\": \"That seems very weird. In the \\\"how to succeed\\\" lecture it clearly states that I encourage any and all questions. Why would you disagree with that?\"\n },\n {\n \"review\": \"If you hang out on YouTube probably you find the same information in the same time\",\n \"response\": \"You can say that about any subject. The real question is, if it's so easy, then why haven't you done so? ;)\"\n },\n {\n \"review\": \"Generally useful but structure of content and direction of course is not always clear. We jump backwards and forwards between methods more than I would like.\",\n \"response\": \"Each section is devoted to a different \\\"method\\\", there's no jumping \\\"back and forth\\\" between them...\"\n },\n {\n \"review\": \"I do not think its advanced stuff at all, nevertheless its good.\",\n \"response\": \"Read the course description to learn what this course is about. In addition, please see the FAQ, which answers questions such as whether this course is for beginners or experts.\"\n },\n {\n \"review\": \"There is constant talk about not having to understand the theory but it seems like the maths goes hand in hand with the models so not sure if it is feasible to just learn the code without understanding why you do certain things\",\n \"response\": \"Because you're not implementing any of that math yourself, only using high level libraries. Please pay more attention to improve your understanding, thanks!\"\n },\n {\n \"review\": \"not get any technical knowledge yet\",\n \"response\": \"Why not read the course description so that you understand what this course is about?\"\n },\n {\n \"review\": \"I came here to learn industry level but it does not meet my expectations. this course suits you well for beginners because you can learn all the math and coding from scratch.\",\n \"response\": \"You simply have an incorrect understanding about what constitutes \\\"industry level\\\" (hint: you are not at this level).\"\n },\n {\n \"review\": \"there could be hands-on session rather than pre written code . This would help in understanding the logic better.\",\n \"response\": \"I've instructed you to code by yourself, not to peek at my prewritten solutions. Therefore, you have simply not followed the instructions. You claim to want to be \\\"hands-on\\\", yet you haven't even done the hands-on work I've prescribed.\"\n },\n {\n \"review\": \"If you are new to AI, don't take this course. Find something else to start with. Most of what I got from this course is exposure to possibilities with recommender systems. It is not the most organized course either\",\n \"response\": \"Why should you be new to AI? You should meet the prerequisites, as instructed. Furthermore, it's not \\\"exposure to possibilities\\\", we are implementing many concrete algorithms. If you're having trouble understanding how the course is organized, it's one algorithm per section. I suggest simply paying more attention. Thanks!\"\n },\n {\n \"review\": \"sometimes a little superficial\",\n \"response\": \"Thanks for your feedback. Please make sure to READ the course description so you understand what this course is about before taking it, thanks!\"\n },\n {\n \"review\": \"I am an industry data scientist with an academic background in Machine learning, I have done several deep learning projects in my school years, I am taking this as a refresher. But equations don't have an explanation of variables, and what they stand for, the instructor repetitively mentions that if you don't know any equations you are not ready for this course but no one knows an equation, how it is derived, and what all the greek symbols mean right off the bat especially if you are away from macadamia for a few years. If you add additional resources(eg: citation as you should!) we can read and understand your variables, also many textbooks use different notations, so you need to make your description clear. Also, I don't like the tone and rudeness of the instructor. He sounds like a mad professor, this is a recorded video, so take away the anger\",\n \"response\": \"> But equations don't have an explanation of variables, and what they stand for\\n\\nThere is literally a lecture titled \\\"What do all these symbols and letters mean?\\\", which is actually a review of previously taught explanations (in other words, variables have been defined multiple times in many cases). Perhaps the problem is that you're simply not paying attention...\\n\\nAt the very least, thank you for making it obvious to the readers here that your claims are unfounded.\\n\\n> Also, I don't like the tone and rudeness of the instructor.\\n\\nYes, I know some students don't like being corrected (as above) and construe all corrections as rude because it's impossible for them to be wrong. But how can a teacher do his job if every correction is interpreted as rude?\"\n },\n {\n \"review\": \"They can't send me slide for this course.\",\n \"response\": \"Incorrect. Slides are available upon request. Simply use the Q&A as instructed in the how to succeed lecture.\"\n },\n {\n \"review\": \"The instructor is poor. Read their responses to other negative reviews. Really off-putting. Constantly stating how people are INCORRECT showing the instructor clearly has no ability to take constructive criticism. If only the instructor could A/B test their own responses. Honestly thought it was a good-ish course. The instructor earned a 1-star here.\",\n \"response\": \"No, \\\"incorrect\\\" is used to denote factually wrong statements. It appears you are too emotional, focusing on your feelings (you're offended that I've corrected others) instead of the facts.\"\n },\n {\n \"review\": \"Please respect yourself, you must be ashamed of yourself because of this. Peyser\",\n \"response\": \"Thanks for your feedback! It'd be great if you could provide specifics regarding what you didn't like about the course...\"\n },\n {\n \"review\": \"Mentor is providing slides for video and he is also fooling student to stay 31 on Udemy but after 31 days I am not finding any slides and note from instructor side.\",\n \"response\": \"Incorrect. Comment speaks for itself really.\"\n },\n {\n \"review\": \"This course kind of breezes over the topics, there are colabs used in the videos that we do not have access to and Section 4 is irrelevant to the course, I would rather see my on the topic and learn something than how to install tools that I already know how to do. Section 4 should actually be on his youtube site. There should also be more links to the tools he speaks of or uses, you have to stop the video and actually go google the tools and search for them. I am still trying to find ffmpegexamples.ipynb that is used for a whole segment of the training but there is no access to. Good course but needs a lot of fine-tuning to be better. I hope to see more added to create better content.\",\n \"response\": \"> there are colabs used in the videos that we do not have access to\\n\\nIncorrect. Lecture 4 is called \\\"COURSE RESOURCES\\\". Any guesses about what this is for?\\n\\n> Section 4 is irrelevant to the course\\n\\nInteresting, who should decide what's relevant to the course? Instructor (who understands the course content) or student (who does not)?\\n\\n> I would rather see my on the topic\\n\\nThis is not even a coherent sentence.\\n\\n> There should also be more links to the tools he speaks of or uses, you have to stop the video and actually go google the tools and search for them\\n\\nAgain, no you do not. This is what happens when you don't pay attention.\\n\\n> I am still trying to find ffmpegexamples.ipynb\\n\\nIf you paid attention, you would have already found it.\\n\\n> Good course but needs a lot of fine-tuning to be better. I hope to see more added to create better content.\\n\\nNo, you just need to follow the instructions and use the Q&A to fix your misunderstandings. I don't see how it could be any simpler.\"\n }\n]\n" }, { "path": "probability/WHERE ARE THE NOTEBOOKS.txt", "content": "As stated in the \"where to get the code\" / \"where to get the notebooks\" lecture, the notebooks are NOT on Github.\n\nIf you missed this, please review the lecture for the actual location of the notebooks.\n\nIf, after reviewing it, you still need assistance, please contact info@deeplearningcourses.com." }, { "path": "probability/extra_reading.txt", "content": "Multivariate Change of Variables\nhttps://math.libretexts.org/Bookshelves/Calculus/Book%3A_Active_Calculus_(Boelkins_et_al.)/11%3A_Multiple_Integrals/11.09%3A_Change_of_Variables" }, { "path": "prophet/extra_reading.txt", "content": "Forecasting at Scale (Facebook Prophet)\nhttps://peerj.com/preprints/3190.pdf" }, { "path": "pytorch/.gitignore", "content": "*rl_trader_models\n*rl_trader_rewards\n*.png\n" }, { "path": "pytorch/WHERE ARE THE NOTEBOOKS.txt", "content": "As stated in the \"where to get the code\" / \"where to get the notebooks\" lecture, the notebooks are NOT on Github.\n\nIf you missed this, please review the lecture for the actual location of the notebooks.\n\nIf, after reviewing it, you still need assistance, please contact info@deeplearningcourses.com." }, { "path": "pytorch/aapl_msi_sbux.csv", "content": 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3.02,58.2\n119.11,82.63,57.88\n119.75,82.88,58.1\n119.25,82.18,58.03\n119.04,82.27,57.85\n120.0,80.73,58.0\n119.99,81.65,58.45\n119.78,81.86,57.89\n120.0,82.36,57.66\n120.08,82.44,57.76\n119.97,84.35,58.44\n121.88,85.29,58.7\n121.94,83.36,58.46\n121.95,82.98,56.12\n121.63,81.7,55.9\n121.35,80.71,55.22\n128.75,80.03,53.9\n128.53,81.0,53.87\n129.08,81.6,55.06\n130.29,81.73,55.73\n131.53,77.34,55.24\n132.04,78.25,55.22\n132.42,77.81,55.81\n132.12,78.37,56.22\n133.29,78.48,56.11\n135.02,78.68,56.58\n135.51,79.4,56.86\n135.345,78.66,56.73\n135.72,79.31,57.35\n136.7,80.15,57.54\n137.11,79.65,57.57\n136.53,79.36,57.64\n136.66,80.27,57.48\n136.93,79.28,56.78\n136.99,78.97,56.87\n139.79,79.98,57.14\n138.96,80.02,57.12\n139.78,80.55,57.1\n139.34,79.97,56.68\n139.52,79.66,56.2\n139.0,80.2,55.74\n138.68,81.37,55.19\n139.14,82.1,54.53\n139.2,81.65,54.63\n138.99,83.36,54.27\n140.46,85.24,54.54\n140.69,85.15,54.8\n139.99,84.72,55.78\n141.46,84.3,55.81\n139.84,83.76,55.54\n141.42,83.59,55.89\n140.92,83.74,55.85\n140.64,83.67,56.81\n140.88,84.0,57.23\n143.8,84.0,57.35\n144.12,84.13,57.54\n143.93,84.87,58.16\n143.66,86.22,58.39\n143.7,84.83,58.44\n144.77,84.52,58.32\n144.02,83.83,58.22\n143.66,84.2,57.92\n143.34,84.25,58.02\n143.17,83.71,57.95\n141.63,83.45,57.88\n141.8,82.84,57.58\n141.05,82.34,57.51\n141.83,83.08,58.08\n141.2,82.64,58.35\n140.68,83.37,59.04\n142.44,84.1,60.08\n142.27,83.72,60.61\n143.64,84.72,61.11\n144.53,85.39,60.96\n143.68,85.38,61.56\n143.79,86.07,61.3\n143.65,85.97,60.06\n146.58,86.16,60.18\n147.51,85.92,60.5\n147.06,86.37,60.59\n146.53,86.1,60.83\n148.96,84.44,60.95\n153.01,83.59,60.94\n153.99,84.77,60.98\n153.26,85.77,60.66\n153.95,85.36,60.27\n156.1,84.21,59.93\n155.7,84.48,60.45\n155.47,83.7,59.98\n150.25,81.85,59.73\n152.54,80.83,59.82\n153.06,80.83,61.36\n153.99,82.93,61.23\n153.8,82.11,61.15\n153.34,82.22,61.89\n153.87,82.27,62.9\n153.61,81.86,63.3\n153.67,82.83,63.26\n152.76,83.57,63.61\n153.18,85.64,63.75\n155.45,86.62,64.57\n153.93,87.31,64.27\n154.45,87.46,64.16\n155.37,86.18,63.5\n154.99,86.65,62.24\n148.98,86.17,62.19\n145.42,86.11,61.29\n146.59,86.04,60.92\n145.16,84.87,60.27\n144.29,84.45,60.09\n142.27,84.72,60.14\n146.34,86.2,60.9\n145.01,85.74,59.86\n145.87,86.24,59.96\n145.63,87.36,59.51\n146.28,88.64,59.81\n145.82,88.42,59.64\n143.73,87.72,58.96\n145.83,88.13,59.18\n143.68,86.8,58.36\n144.02,86.74,58.31\n143.5,86.68,58.25\n144.09,86.83,57.94\n142.73,85.96,57.6\n144.18,87.31,58.04\n145.06,87.23,57.81\n145.53,87.65,57.9\n145.74,88.65,58.54\n147.77,88.33,58.38\n149.04,88.61,58.76\n149.56,88.28,58.33\n150.08,88.45,58.21\n151.02,89.78,58.11\n150.34,89.96,58.03\n150.27,90.52,57.98\n152.09,90.67,58.02\n152.74,91.39,58.55\n153.46,91.84,57.94\n150.56,92.21,59.5\n149.5,91.01,54.0\n148.73,90.68,53.98\n158.59,90.43,54.73\n157.14,90.43,55.43\n155.57,90.4,55.68\n156.39,90.37,55.44\n158.81,89.2,55.63\n160.08,88.58,54.52\n161.06,88.51,53.74\n155.32,86.99,53.07\n157.48,87.48,53.18\n159.85,88.6,53.22\n161.6,87.92,53.15\n160.95,88.19,53.5\n157.86,87.13,53.04\n157.5,87.37,52.7\n157.21,87.2,53.15\n159.78,86.41,54.45\n159.98,86.21,54.08\n159.27,86.51,53.94\n159.86,86.88,54.36\n161.47,87.3,54.4\n162.91,86.94,54.1\n163.35,87.66,54.52\n164.0,88.12,54.86\n164.05,87.87,54.93\n162.08,86.66,55.13\n161.91,85.72,54.31\n161.26,86.35,53.47\n158.63,85.12,53.49\n161.5,87.01,54.02\n160.86,87.74,53.54\n159.65,85.97,54.29\n158.28,84.58,54.53\n159.88,85.48,54.67\n158.67,85.48,54.69\n158.73,85.84,54.62\n156.07,85.65,55.15\n153.39,84.99,55.01\n151.89,84.29,55.09\n150.55,83.5,54.95\n153.14,83.02,55.13\n154.23,84.1,54.99\n153.28,83.69,54.5\n154.12,84.87,53.71\n153.81,85.84,53.81\n154.48,85.69,53.99\n153.48,85.64,53.93\n155.39,86.0,54.6\n155.3,89.44,55.17\n155.84,89.1,55.02\n155.9,89.08,55.42\n156.55,89.26,55.64\n156.0,89.8,55.97\n156.99,89.93,55.72\n159.88,89.36,54.91\n160.47,88.88,54.51\n159.76,89.09,55.21\n155.98,89.65,55.4\n156.25,90.0,54.57\n156.17,89.94,54.27\n157.1,90.23,54.28\n156.41,90.04,54.16\n157.41,90.28,54.91\n163.05,91.19,54.88\n166.72,90.37,55.17\n169.04,90.54,54.84\n166.89,90.56,55.13\n168.11,90.02,54.87\n172.5,94.25,56.03\n174.25,92.43,56.57\n174.81,92.11,57.22\n176.24,92.66,57.91\n175.88,91.61,57.36\n174.67,91.07,57.04\n173.97,91.37,56.64\n171.34,91.02,56.93\n169.08,90.39,56.7\n171.1,90.97,57.24\n170.15,90.95,56.93\n169.98,92.33,56.81\n173.14,92.45,57.26\n174.96,91.83,57.14\n174.97,92.36,56.8\n174.09,92.88,55.91\n173.07,94.53,56.66\n169.48,94.17,57.51\n171.85,94.11,57.82\n171.05,93.03,57.32\n169.8,93.63,58.76\n169.64,90.66,59.34\n169.01,91.29,59.28\n169.32,92.8,59.14\n169.37,92.52,58.61\n172.67,92.33,59.07\n171.7,93.37,59.27\n172.27,93.94,59.49\n172.22,92.2,59.7\n173.97,93.15,58.29\n176.42,94.49,58.03\n174.54,93.28,58.01\n174.35,92.1,57.73\n175.01,91.62,57.58\n175.01,90.76,57.3\n170.57,90.67,57.14\n170.6,90.8,57.27\n171.08,90.57,57.81\n169.23,90.34,57.43\n172.26,90.55,57.63\n172.23,89.91,58.71\n173.03,90.66,58.93\n175.0,91.88,59.61\n174.35,92.82,59.31\n174.33,92.12,59.18\n174.29,92.38,59.82\n175.28,93.55,60.0\n177.09,96.57,60.4\n176.19,95.86,60.56\n179.1,97.28,60.66\n179.26,97.5,61.09\n178.46,97.8,61.26\n177.0,97.33,61.41\n177.04,96.76,61.69\n174.22,95.84,60.83\n171.11,97.68,60.55\n171.51,99.0,57.99\n167.96,99.18,57.02\n166.97,99.8,57.19\n167.43,99.46,56.81\n167.78,99.12,56.0\n160.5,103.87,55.77\n156.49,101.06,54.69\n163.03,102.76,55.61\n159.54,102.63,54.46\n" }, { "path": "pytorch/ann_regression.py", "content": "# -*- coding: utf-8 -*-\n\"\"\"PyTorch Regression.ipynb\n\nAutomatically generated by Colaboratory.\n\nOriginal file is located at\n https://colab.research.google.com/drive/1pEjzEmbnu2wXAhIaBS8PSpi-0cWtR6ov\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\n# Make the dataset\nN = 1000\nX = np.random.random((N, 2)) * 6 - 3 # uniformly distributed between (-3, +3)\nY = np.cos(2*X[:,0]) + np.cos(3*X[:,1])\n\n# Plot it\nfig = plt.figure()\nax = fig.add_subplot(111, projection='3d')\nax.scatter(X[:,0], X[:,1], Y)\nplt.show()\n\n# Build the model\nmodel = nn.Sequential(\n nn.Linear(2, 128),\n nn.ReLU(),\n nn.Linear(128, 1)\n)\n\n# Loss and optimizer\ncriterion = nn.MSELoss()\noptimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n\n# Train the model\ndef full_gd(model, criterion, optimizer, X_train, y_train, epochs=1000):\n # Stuff to store\n train_losses = np.zeros(epochs)\n\n for it in range(epochs):\n # zero the parameter gradients\n optimizer.zero_grad()\n\n # Forward pass\n outputs = model(X_train)\n loss = criterion(outputs, y_train)\n \n # Backward and optimize\n loss.backward()\n optimizer.step()\n\n # Save losses\n train_losses[it] = loss.item()\n \n if (it + 1) % 50 == 0:\n print(f'Epoch {it+1}/{epochs}, Train Loss: {loss.item():.4f}')\n \n return train_losses\n\nX_train = torch.from_numpy(X.astype(np.float32))\ny_train = torch.from_numpy(Y.astype(np.float32).reshape(-1, 1))\ntrain_losses = full_gd(model, criterion, optimizer, X_train, y_train)\n\nplt.plot(train_losses)\nplt.show()\n\n\n# Plot the prediction surface\nfig = plt.figure()\nax = fig.add_subplot(111, projection='3d')\nax.scatter(X[:,0], X[:,1], Y)\n\n# surface plot\nwith torch.no_grad():\n line = np.linspace(-3, 3, 50)\n xx, yy = np.meshgrid(line, line)\n Xgrid = np.vstack((xx.flatten(), yy.flatten())).T\n Xgrid_torch = torch.from_numpy(Xgrid.astype(np.float32))\n Yhat = model(Xgrid_torch).numpy().flatten()\n ax.plot_trisurf(Xgrid[:,0], Xgrid[:,1], Yhat, linewidth=0.2, antialiased=True)\n plt.show()\n\n# Can it extrapolate?\n# Plot the prediction surface\nfig = plt.figure()\nax = fig.add_subplot(111, projection='3d')\nax.scatter(X[:,0], X[:,1], Y)\n\n# surface plot\nwith torch.no_grad():\n line = np.linspace(-5, 5, 50)\n xx, yy = np.meshgrid(line, line)\n Xgrid = np.vstack((xx.flatten(), yy.flatten())).T\n Xgrid_torch = torch.from_numpy(Xgrid.astype(np.float32))\n Yhat = model(Xgrid_torch).numpy().flatten()\n ax.plot_trisurf(Xgrid[:,0], Xgrid[:,1], Yhat, linewidth=0.2, antialiased=True)\n plt.show()" }, { "path": "pytorch/exercises.txt", "content": "Logistic Regression\nhttps://www.kaggle.com/uciml/pima-indians-diabetes-database\nhttps://lazyprogrammer.me/course_files/exercises/diabetes.csv\n\nLinear Regression\nhttps://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html\nhttps://lazyprogrammer.me/course_files/exercises/boston.txt\n\nANN\nhttps://archive.ics.uci.edu/ml/datasets/ecoli (orig)\nhttps://www.kaggle.com/elikplim/ecoli-data-set (alt)\nhttps://lazyprogrammer.me/course_files/exercises/ecoli.csv\n\nCNN\nhttps://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge\nhttps://archive.org/download/fer2013_202311/fer2013.csv\n\nRNN\nFind your own stock price dataset!\n\nNLP\nhttps://www.kaggle.com/crowdflower/twitter-airline-sentiment\nhttps://lazyprogrammer.me/course_files/exercises/AirlineSentimentTweets.csv\n\nRecommender Systems\nhttp://www2.informatik.uni-freiburg.de/~cziegler/BX/\nhttp://lazyprogrammer.me/course_files/exercises/BX-CSV-Dump.zip\n\nTransfer Learning\nhttps://www.kaggle.com/c/dogs-vs-cats\n\nGAN\nhttps://www.kaggle.com/c/dogs-vs-cats\n\nDeepRL\nFind your own stock price dataset!" }, { "path": "pytorch/extra_reading.txt", "content": "Gradient Descent: Convergence Analysis\nhttp://www.stat.cmu.edu/~ryantibs/convexopt-F13/scribes/lec6.pdf\n\nDeep learning improved by biological activation functions\nhttps://arxiv.org/pdf/1804.11237.pdf\n\nBatch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift\nSergey Ioffe, Christian Szegedy\nhttps://arxiv.org/abs/1502.03167\n\nDropout: A Simple Way to Prevent Neural Networks from Overfitting\nhttps://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf\n\nImplementing Dropout\nhttps://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow/\n\nConvolution arithmetic tutorial\nhttps://theano-pymc.readthedocs.io/en/latest/tutorial/conv_arithmetic.html\n\nOn the Practical Computational Power of Finite Precision RNNs for Language Recognition\nhttps://arxiv.org/abs/1805.04908\n\nMassive Exploration of Neural Machine Translation Architectures\nhttps://arxiv.org/abs/1703.03906\n\nPractical Deep Reinforcement Learning Approach for Stock Trading\nhttps://arxiv.org/abs/1811.07522\n\nInceptionism: Going Deeper into Neural Networks\nhttps://ai.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html" }, { "path": "pytorch/plot_rl_rewards.py", "content": "import matplotlib.pyplot as plt\nimport numpy as np\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-m', '--mode', type=str, required=True,\n help='either \"train\" or \"test\"')\nargs = parser.parse_args()\n\na = np.load(f'rl_trader_rewards/{args.mode}.npy')\n\nprint(f\"average reward: {a.mean():.2f}, min: {a.min():.2f}, max: {a.max():.2f}\")\n\nif args.mode == 'train':\n # show the training progress\n plt.plot(a)\nelse:\n # test - show a histogram of rewards\n plt.hist(a, bins=20)\n\nplt.title(args.mode)\nplt.show()" }, { "path": "pytorch/rl_trader.py", "content": "import numpy as np\nimport pandas as pd\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom datetime import datetime\nimport itertools\nimport argparse\nimport re\nimport os\nimport pickle\n\nfrom sklearn.preprocessing import StandardScaler\n\n\n# Let's use AAPL (Apple), MSI (Motorola), SBUX (Starbucks)\ndef get_data():\n # returns a T x 3 list of stock prices\n # each row is a different stock\n # 0 = AAPL\n # 1 = MSI\n # 2 = SBUX\n df = pd.read_csv('aapl_msi_sbux.csv')\n return df.values\n\n\n\n### The experience replay memory ###\nclass ReplayBuffer:\n def __init__(self, obs_dim, act_dim, size):\n self.obs1_buf = np.zeros([size, obs_dim], dtype=np.float32)\n self.obs2_buf = np.zeros([size, obs_dim], dtype=np.float32)\n self.acts_buf = np.zeros(size, dtype=np.uint8)\n self.rews_buf = np.zeros(size, dtype=np.float32)\n self.done_buf = np.zeros(size, dtype=np.uint8)\n self.ptr, self.size, self.max_size = 0, 0, size\n\n def store(self, obs, act, rew, next_obs, done):\n self.obs1_buf[self.ptr] = obs\n self.obs2_buf[self.ptr] = next_obs\n self.acts_buf[self.ptr] = act\n self.rews_buf[self.ptr] = rew\n self.done_buf[self.ptr] = done\n self.ptr = (self.ptr+1) % self.max_size\n self.size = min(self.size+1, self.max_size)\n\n def sample_batch(self, batch_size=32):\n idxs = np.random.randint(0, self.size, size=batch_size)\n return dict(s=self.obs1_buf[idxs],\n s2=self.obs2_buf[idxs],\n a=self.acts_buf[idxs],\n r=self.rews_buf[idxs],\n d=self.done_buf[idxs])\n\n\n\n\n\ndef get_scaler(env):\n # return scikit-learn scaler object to scale the states\n # Note: you could also populate the replay buffer here\n\n states = []\n for _ in range(env.n_step):\n action = np.random.choice(env.action_space)\n state, reward, done, info = env.step(action)\n states.append(state)\n if done:\n break\n\n scaler = StandardScaler()\n scaler.fit(states)\n return scaler\n\n\n\n\ndef maybe_make_dir(directory):\n if not os.path.exists(directory):\n os.makedirs(directory)\n\n\n\n\nclass MLP(nn.Module):\n def __init__(self, n_inputs, n_action, n_hidden_layers=1, hidden_dim=32):\n super(MLP, self).__init__()\n\n M = n_inputs\n self.layers = []\n for _ in range(n_hidden_layers):\n layer = nn.Linear(M, hidden_dim)\n M = hidden_dim\n self.layers.append(layer)\n self.layers.append(nn.ReLU())\n\n # final layer\n self.layers.append(nn.Linear(M, n_action))\n self.layers = nn.Sequential(*self.layers)\n\n def forward(self, X):\n return self.layers(X)\n\n def save_weights(self, path):\n torch.save(self.state_dict(), path)\n\n def load_weights(self, path):\n self.load_state_dict(torch.load(path))\n\n\n\ndef predict(model, np_states):\n with torch.no_grad():\n inputs = torch.from_numpy(np_states.astype(np.float32))\n output = model(inputs)\n # print(\"output:\", output)\n return output.numpy()\n\n\n\ndef train_one_step(model, criterion, optimizer, inputs, targets):\n # convert to tensors\n inputs = torch.from_numpy(inputs.astype(np.float32))\n targets = torch.from_numpy(targets.astype(np.float32))\n\n # zero the parameter gradients\n optimizer.zero_grad()\n\n # Forward pass\n outputs = model(inputs)\n loss = criterion(outputs, targets)\n \n # Backward and optimize\n loss.backward()\n optimizer.step()\n\n\n\nclass MultiStockEnv:\n \"\"\"\n A 3-stock trading environment.\n State: vector of size 7 (n_stock * 2 + 1)\n - # shares of stock 1 owned\n - # shares of stock 2 owned\n - # shares of stock 3 owned\n - price of stock 1 (using daily close price)\n - price of stock 2\n - price of stock 3\n - cash owned (can be used to purchase more stocks)\n Action: categorical variable with 27 (3^3) possibilities\n - for each stock, you can:\n - 0 = sell\n - 1 = hold\n - 2 = buy\n \"\"\"\n def __init__(self, data, initial_investment=20000):\n # data\n self.stock_price_history = data\n self.n_step, self.n_stock = self.stock_price_history.shape\n\n # instance attributes\n self.initial_investment = initial_investment\n self.cur_step = None\n self.stock_owned = None\n self.stock_price = None\n self.cash_in_hand = None\n\n self.action_space = np.arange(3**self.n_stock)\n\n # action permutations\n # returns a nested list with elements like:\n # [0,0,0]\n # [0,0,1]\n # [0,0,2]\n # [0,1,0]\n # [0,1,1]\n # etc.\n # 0 = sell\n # 1 = hold\n # 2 = buy\n self.action_list = list(map(list, itertools.product([0, 1, 2], repeat=self.n_stock)))\n\n # calculate size of state\n self.state_dim = self.n_stock * 2 + 1\n\n self.reset()\n\n\n def reset(self):\n self.cur_step = 0\n self.stock_owned = np.zeros(self.n_stock)\n self.stock_price = self.stock_price_history[self.cur_step]\n self.cash_in_hand = self.initial_investment\n return self._get_obs()\n\n\n def step(self, action):\n assert action in self.action_space\n\n # get current value before performing the action\n prev_val = self._get_val()\n\n # update price, i.e. go to the next day\n self.cur_step += 1\n self.stock_price = self.stock_price_history[self.cur_step]\n\n # perform the trade\n self._trade(action)\n\n # get the new value after taking the action\n cur_val = self._get_val()\n\n # reward is the increase in porfolio value\n reward = cur_val - prev_val\n\n # done if we have run out of data\n done = self.cur_step == self.n_step - 1\n\n # store the current value of the portfolio here\n info = {'cur_val': cur_val}\n\n # conform to the Gym API\n return self._get_obs(), reward, done, info\n\n\n def _get_obs(self):\n obs = np.empty(self.state_dim)\n obs[:self.n_stock] = self.stock_owned\n obs[self.n_stock:2*self.n_stock] = self.stock_price\n obs[-1] = self.cash_in_hand\n return obs\n \n\n\n def _get_val(self):\n return self.stock_owned.dot(self.stock_price) + self.cash_in_hand\n\n\n def _trade(self, action):\n # index the action we want to perform\n # 0 = sell\n # 1 = hold\n # 2 = buy\n # e.g. [2,1,0] means:\n # buy first stock\n # hold second stock\n # sell third stock\n action_vec = self.action_list[action]\n\n # determine which stocks to buy or sell\n sell_index = [] # stores index of stocks we want to sell\n buy_index = [] # stores index of stocks we want to buy\n for i, a in enumerate(action_vec):\n if a == 0:\n sell_index.append(i)\n elif a == 2:\n buy_index.append(i)\n\n # sell any stocks we want to sell\n # then buy any stocks we want to buy\n if sell_index:\n # NOTE: to simplify the problem, when we sell, we will sell ALL shares of that stock\n for i in sell_index:\n self.cash_in_hand += self.stock_price[i] * self.stock_owned[i]\n self.stock_owned[i] = 0\n if buy_index:\n # NOTE: when buying, we will loop through each stock we want to buy,\n # and buy one share at a time until we run out of cash\n can_buy = True\n while can_buy:\n for i in buy_index:\n if self.cash_in_hand > self.stock_price[i]:\n self.stock_owned[i] += 1 # buy one share\n self.cash_in_hand -= self.stock_price[i]\n else:\n can_buy = False\n\n\n\n\n\nclass DQNAgent(object):\n def __init__(self, state_size, action_size):\n self.state_size = state_size\n self.action_size = action_size\n self.memory = ReplayBuffer(state_size, action_size, size=500)\n self.gamma = 0.95 # discount rate\n self.epsilon = 1.0 # exploration rate\n self.epsilon_min = 0.01\n self.epsilon_decay = 0.995\n self.model = MLP(state_size, action_size)\n\n # Loss and optimizer\n self.criterion = nn.MSELoss()\n self.optimizer = torch.optim.Adam(self.model.parameters())\n\n\n def update_replay_memory(self, state, action, reward, next_state, done):\n self.memory.store(state, action, reward, next_state, done)\n\n\n def act(self, state):\n if np.random.rand() <= self.epsilon:\n return np.random.choice(self.action_size)\n act_values = predict(self.model, state)\n return np.argmax(act_values[0]) # returns action\n\n\n def replay(self, batch_size=32):\n # first check if replay buffer contains enough data\n if self.memory.size < batch_size:\n return\n\n # sample a batch of data from the replay memory\n minibatch = self.memory.sample_batch(batch_size)\n states = minibatch['s']\n actions = minibatch['a']\n rewards = minibatch['r']\n next_states = minibatch['s2']\n done = minibatch['d']\n\n # Calculate the target: Q(s',a)\n target = rewards + (1 - done) * self.gamma * np.amax(predict(self.model, next_states), axis=1)\n\n # With the PyTorch API, it is simplest to have the target be the \n # same shape as the predictions.\n # However, we only need to update the network for the actions\n # which were actually taken.\n # We can accomplish this by setting the target to be equal to\n # the prediction for all values.\n # Then, only change the targets for the actions taken.\n # Q(s,a)\n target_full = predict(self.model, states)\n target_full[np.arange(batch_size), actions] = target\n\n # Run one training step\n train_one_step(self.model, self.criterion, self.optimizer, states, target_full)\n\n if self.epsilon > self.epsilon_min:\n self.epsilon *= self.epsilon_decay\n\n\n def load(self, name):\n self.model.load_weights(name)\n\n\n def save(self, name):\n self.model.save_weights(name)\n\n\ndef play_one_episode(agent, env, is_train):\n # note: after transforming states are already 1xD\n state = env.reset()\n state = scaler.transform([state])\n done = False\n\n while not done:\n action = agent.act(state)\n next_state, reward, done, info = env.step(action)\n next_state = scaler.transform([next_state])\n if is_train == 'train':\n agent.update_replay_memory(state, action, reward, next_state, done)\n agent.replay(batch_size)\n state = next_state\n\n return info['cur_val']\n\n\n\nif __name__ == '__main__':\n\n # config\n models_folder = 'rl_trader_models'\n rewards_folder = 'rl_trader_rewards'\n num_episodes = 2000\n batch_size = 32\n initial_investment = 20000\n\n\n parser = argparse.ArgumentParser()\n parser.add_argument('-m', '--mode', type=str, required=True,\n help='either \"train\" or \"test\"')\n args = parser.parse_args()\n\n maybe_make_dir(models_folder)\n maybe_make_dir(rewards_folder)\n\n data = get_data()\n n_timesteps, n_stocks = data.shape\n\n n_train = n_timesteps // 2\n\n train_data = data[:n_train]\n test_data = data[n_train:]\n\n env = MultiStockEnv(train_data, initial_investment)\n state_size = env.state_dim\n action_size = len(env.action_space)\n agent = DQNAgent(state_size, action_size)\n scaler = get_scaler(env)\n\n # store the final value of the portfolio (end of episode)\n portfolio_value = []\n\n if args.mode == 'test':\n # then load the previous scaler\n with open(f'{models_folder}/scaler.pkl', 'rb') as f:\n scaler = pickle.load(f)\n\n # remake the env with test data\n env = MultiStockEnv(test_data, initial_investment)\n\n # make sure epsilon is not 1!\n # no need to run multiple episodes if epsilon = 0, it's deterministic\n agent.epsilon = 0.01\n\n # load trained weights\n agent.load(f'{models_folder}/dqn.ckpt')\n\n # play the game num_episodes times\n for e in range(num_episodes):\n t0 = datetime.now()\n val = play_one_episode(agent, env, args.mode)\n dt = datetime.now() - t0\n print(f\"episode: {e + 1}/{num_episodes}, episode end value: {val:.2f}, duration: {dt}\")\n portfolio_value.append(val) # append episode end portfolio value\n\n # save the weights when we are done\n if args.mode == 'train':\n # save the DQN\n agent.save(f'{models_folder}/dqn.ckpt')\n\n # save the scaler\n with open(f'{models_folder}/scaler.pkl', 'wb') as f:\n pickle.dump(scaler, f)\n\n\n # save portfolio value for each episode\n np.save(f'{rewards_folder}/{args.mode}.npy', portfolio_value)\n" }, { "path": "recommenders/autorec.py", "content": "# https://udemy.com/recommender-systems\n# https://deeplearningcourses.com/recommender-systems\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.utils import shuffle\nfrom scipy.sparse import save_npz, load_npz\n\nimport keras.backend as K\nfrom keras.models import Model\nfrom keras.layers import Input, Dropout, Dense\nfrom keras.regularizers import l2\nfrom keras.optimizers import SGD\n\n# config\nbatch_size = 128\nepochs = 20\nreg = 0.0001\n# reg = 0\n\nA = load_npz(\"Atrain.npz\")\nA_test = load_npz(\"Atest.npz\")\nmask = (A > 0) * 1.0\nmask_test = (A_test > 0) * 1.0\n\n# make copies since we will shuffle\nA_copy = A.copy()\nmask_copy = mask.copy()\nA_test_copy = A_test.copy()\nmask_test_copy = mask_test.copy()\n\nN, M = A.shape\nprint(\"N:\", N, \"M:\", M)\nprint(\"N // batch_size:\", N // batch_size)\n\n# center the data\nmu = A.sum() / mask.sum()\nprint(\"mu:\", mu)\n\n\n\n# build the model - just a 1 hidden layer autoencoder\ni = Input(shape=(M,))\n# bigger hidden layer size seems to help!\nx = Dropout(0.7)(i)\nx = Dense(700, activation='tanh', kernel_regularizer=l2(reg))(x)\n# x = Dropout(0.5)(x)\nx = Dense(M, kernel_regularizer=l2(reg))(x)\n\n\n\ndef custom_loss(y_true, y_pred):\n mask = K.cast(K.not_equal(y_true, 0), dtype='float32')\n diff = y_pred - y_true\n sqdiff = diff * diff * mask\n sse = K.sum(K.sum(sqdiff))\n n = K.sum(K.sum(mask))\n return sse / n\n\n\ndef generator(A, M):\n while True:\n A, M = shuffle(A, M)\n for i in range(A.shape[0] // batch_size + 1):\n upper = min((i+1)*batch_size, A.shape[0])\n a = A[i*batch_size:upper].toarray()\n m = M[i*batch_size:upper].toarray()\n a = a - mu * m # must keep zeros at zero!\n # m2 = (np.random.random(a.shape) > 0.5)\n # noisy = a * m2\n noisy = a # no noise\n yield noisy, a\n\n\ndef test_generator(A, M, A_test, M_test):\n # assumes A and A_test are in corresponding order\n # both of size N x M\n while True:\n for i in range(A.shape[0] // batch_size + 1):\n upper = min((i+1)*batch_size, A.shape[0])\n a = A[i*batch_size:upper].toarray()\n m = M[i*batch_size:upper].toarray()\n at = A_test[i*batch_size:upper].toarray()\n mt = M_test[i*batch_size:upper].toarray()\n a = a - mu * m\n at = at - mu * mt\n yield a, at\n\n\n\nmodel = Model(i, x)\nmodel.compile(\n loss=custom_loss,\n optimizer=SGD(lr=0.08, momentum=0.9),\n # optimizer='adam',\n metrics=[custom_loss],\n)\n\n\nr = model.fit(\n generator(A, mask),\n validation_data=test_generator(A_copy, mask_copy, A_test_copy, mask_test_copy),\n epochs=epochs,\n steps_per_epoch=A.shape[0] // batch_size + 1,\n validation_steps=A_test.shape[0] // batch_size + 1,\n)\nprint(r.history.keys())\n\n\n\n# plot losses\nplt.plot(r.history['loss'], label=\"train loss\")\nplt.plot(r.history['val_loss'], label=\"test loss\")\nplt.legend()\nplt.show()\n\n# plot mse\nplt.plot(r.history['custom_loss'], label=\"train mse\")\nplt.plot(r.history['val_custom_loss'], label=\"test mse\")\nplt.legend()\nplt.show()\n" }, { "path": "recommenders/extra_reading.txt", "content": "How Hacker News ranking really works: scoring, controversy, and penalties\nhttp://www.righto.com/2013/11/how-hacker-news-ranking-really-works.html\n\nThe Evolution Of Hacker News\nhttps://techcrunch.com/2013/05/18/the-evolution-of-hacker-news/\n\nReddit sorting code\nhttps://github.com/reddit-archive/reddit/blob/master/r2/r2/lib/db/_sorts.pyx\n\nRevealed: US spy operation that manipulates social media\nhttps://www.theguardian.com/technology/2011/mar/17/us-spy-operation-social-networks\n\n5G Got me Fired\nhttps://medium.com/@dvorak/5g-got-me-fired-ce407e584c4a\n\nLearning to rank\nhttps://en.wikipedia.org/wiki/Learning_to_rank#Evaluation_measures\n\nHow Not To Sort By Average Rating\nhttps://www.evanmiller.org/how-not-to-sort-by-average-rating.html\n\nWilson score interval\nhttps://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Wilson_score_interval\n\nreddit’s new comment sorting system\nhttps://redditblog.com/2009/10/15/reddits-new-comment-sorting-system/\n\nMarkov Chains Explained Visually\nhttp://setosa.io/ev/markov-chains/\n\nAn algorithmic framework for performing collaborative filtering\nhttps://dl.acm.org/citation.cfm?id=312682\n\nItem-based collaborative filtering recommendation algorithms\nhttps://dl.acm.org/citation.cfm?id=372071\n\nFunkSVD\nhttp://sifter.org/~simon/journal/20061211.html\n\nProbabilistic Matrix Factorization\nhttps://papers.nips.cc/paper/3208-probabilistic-matrix-factorization.pdf\n\nBayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo\nhttps://www.cs.toronto.edu/~amnih/papers/bpmf.pdf\n\nAlgorithms for Non-negative Matrix Factorization\nhttps://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf\n\nLearning the parts of objects by non-negative matrix factorization\nhttp://www.columbia.edu/~jwp2128/Teaching/E4903/papers/nmf_nature.pdf\n\nRestricted Boltzmann Machines for Collaborative Filtering\nhttps://www.cs.toronto.edu/~rsalakhu/papers/rbmcf.pdf\n\nAutoRec: Autoencoders Meet Collaborative Filtering\nhttp://users.cecs.anu.edu.au/~u5098633/papers/www15.pdf\n\nCollaborative Filtering for Implicit Feedback Datasets\nhttp://yifanhu.net/PUB/cf.pdf\n\nNeural Collaborative Filtering\nhttps://arxiv.org/abs/1708.05031" }, { "path": "recommenders/itembased.py", "content": "# https://udemy.com/recommender-systems\n# https://deeplearningcourses.com/recommender-systems\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport pickle\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.utils import shuffle\nfrom datetime import datetime\nfrom sortedcontainers import SortedList\n\n# load in the data\nimport os\nif not os.path.exists('user2movie.json') or \\\n not os.path.exists('movie2user.json') or \\\n not os.path.exists('usermovie2rating.json') or \\\n not os.path.exists('usermovie2rating_test.json'):\n import preprocess2dict\n\n\nwith open('user2movie.json', 'rb') as f:\n user2movie = pickle.load(f)\n\nwith open('movie2user.json', 'rb') as f:\n movie2user = pickle.load(f)\n\nwith open('usermovie2rating.json', 'rb') as f:\n usermovie2rating = pickle.load(f)\n\nwith open('usermovie2rating_test.json', 'rb') as f:\n usermovie2rating_test = pickle.load(f)\n\n\nN = np.max(list(user2movie.keys())) + 1\n# the test set may contain movies the train set doesn't have data on\nm1 = np.max(list(movie2user.keys()))\nm2 = np.max([m for (u, m), r in usermovie2rating_test.items()])\nM = max(m1, m2) + 1\nprint(\"N:\", N, \"M:\", M)\n\nif M > 2000:\n print(\"N =\", N, \"are you sure you want to continue?\")\n print(\"Comment out these lines if so...\")\n exit()\n\n\n# to find the user similarities, you have to do O(M^2 * N) calculations!\n# in the \"real-world\" you'd want to parallelize this\n# note: we really only have to do half the calculations, since w_ij is symmetric\nK = 20 # number of neighbors we'd like to consider\nlimit = 5 # number of common movies users must have in common in order to consider\nneighbors = [] # store neighbors in this list\naverages = [] # each item's average rating for later use\ndeviations = [] # each item's deviation for later use\n\nfor i in range(M):\n # find the K closest items to item i\n users_i = movie2user[i]\n users_i_set = set(users_i)\n\n # calculate avg and deviation\n ratings_i = { user:usermovie2rating[(user, i)] for user in users_i }\n avg_i = np.mean(list(ratings_i.values()))\n dev_i = { user:(rating - avg_i) for user, rating in ratings_i.items() }\n dev_i_values = np.array(list(dev_i.values()))\n sigma_i = np.sqrt(dev_i_values.dot(dev_i_values))\n\n # save these for later use\n averages.append(avg_i)\n deviations.append(dev_i)\n\n sl = SortedList()\n for j in range(M):\n # don't include yourself\n if j != i:\n users_j = movie2user[j]\n users_j_set = set(users_j)\n common_users = (users_i_set & users_j_set) # intersection\n if len(common_users) > limit:\n # calculate avg and deviation\n ratings_j = { user:usermovie2rating[(user, j)] for user in users_j }\n avg_j = np.mean(list(ratings_j.values()))\n dev_j = { user:(rating - avg_j) for user, rating in ratings_j.items() }\n dev_j_values = np.array(list(dev_j.values()))\n sigma_j = np.sqrt(dev_j_values.dot(dev_j_values))\n\n # calculate correlation coefficient\n numerator = sum(dev_i[m]*dev_j[m] for m in common_users)\n w_ij = numerator / (sigma_i * sigma_j)\n\n # insert into sorted list and truncate\n # negate weight, because list is sorted ascending\n # maximum value (1) is \"closest\"\n sl.add((-w_ij, j))\n if len(sl) > K:\n del sl[-1]\n\n # store the neighbors\n neighbors.append(sl)\n\n # print out useful things\n if i % 1 == 0:\n print(i)\n\n\n\n# using neighbors, calculate train and test MSE\n\ndef predict(i, u):\n # calculate the weighted sum of deviations\n numerator = 0\n denominator = 0\n for neg_w, j in neighbors[i]:\n # remember, the weight is stored as its negative\n # so the negative of the negative weight is the positive weight\n try:\n numerator += -neg_w * deviations[j][u]\n denominator += abs(neg_w)\n except KeyError:\n # neighbor may not have been rated by the same user\n # don't want to do dictionary lookup twice\n # so just throw exception\n pass\n\n if denominator == 0:\n prediction = averages[i]\n else:\n prediction = numerator / denominator + averages[i]\n prediction = min(5, prediction)\n prediction = max(0.5, prediction) # min rating is 0.5\n return prediction\n\n\n\ntrain_predictions = []\ntrain_targets = []\nfor (u, m), target in usermovie2rating.items():\n # calculate the prediction for this movie\n prediction = predict(m, u)\n\n # save the prediction and target\n train_predictions.append(prediction)\n train_targets.append(target)\n\ntest_predictions = []\ntest_targets = []\n# same thing for test set\nfor (u, m), target in usermovie2rating_test.items():\n # calculate the prediction for this movie\n prediction = predict(m, u)\n\n # save the prediction and target\n test_predictions.append(prediction)\n test_targets.append(target)\n\n\n# calculate accuracy\ndef mse(p, t):\n p = np.array(p)\n t = np.array(t)\n return np.mean((p - t)**2)\n\nprint('train mse:', mse(train_predictions, train_targets))\nprint('test mse:', mse(test_predictions, test_targets))\n\n\n\n" }, { "path": "recommenders/mf.py", "content": "# https://udemy.com/recommender-systems\n# https://deeplearningcourses.com/recommender-systems\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport pickle\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.utils import shuffle\nfrom datetime import datetime\n\n# load in the data\nimport os\nif not os.path.exists('user2movie.json') or \\\n not os.path.exists('movie2user.json') or \\\n not os.path.exists('usermovie2rating.json') or \\\n not os.path.exists('usermovie2rating_test.json'):\n import preprocess2dict\n\n\nwith open('user2movie.json', 'rb') as f:\n user2movie = pickle.load(f)\n\nwith open('movie2user.json', 'rb') as f:\n movie2user = pickle.load(f)\n\nwith open('usermovie2rating.json', 'rb') as f:\n usermovie2rating = pickle.load(f)\n\nwith open('usermovie2rating_test.json', 'rb') as f:\n usermovie2rating_test = pickle.load(f)\n\n\nN = np.max(list(user2movie.keys())) + 1\n# the test set may contain movies the train set doesn't have data on\nm1 = np.max(list(movie2user.keys()))\nm2 = np.max([m for (u, m), r in usermovie2rating_test.items()])\nM = max(m1, m2) + 1\nprint(\"N:\", N, \"M:\", M)\n\n\n# initialize variables\nK = 10 # latent dimensionality\nW = np.random.randn(N, K)\nb = np.zeros(N)\nU = np.random.randn(M, K)\nc = np.zeros(M)\nmu = np.mean(list(usermovie2rating.values()))\n\n# prediction[i,j] = W[i].dot(U[j]) + b[i] + c.T[j] + mu\n\ndef get_loss(d):\n # d: (user_id, movie_id) -> rating\n N = float(len(d))\n sse = 0\n for k, r in d.items():\n i, j = k\n p = W[i].dot(U[j]) + b[i] + c[j] + mu\n sse += (p - r)*(p - r)\n return sse / N\n\n\n# train the parameters\nepochs = 25\nreg =20. # regularization penalty\ntrain_losses = []\ntest_losses = []\nfor epoch in range(epochs):\n print(\"epoch:\", epoch)\n epoch_start = datetime.now()\n # perform updates\n\n # update W and b\n t0 = datetime.now()\n for i in range(N):\n # for W\n matrix = np.eye(K) * reg\n vector = np.zeros(K)\n\n # for b\n bi = 0\n for j in user2movie[i]:\n r = usermovie2rating[(i,j)]\n matrix += np.outer(U[j], U[j])\n vector += (r - b[i] - c[j] - mu)*U[j]\n bi += (r - W[i].dot(U[j]) - c[j] - mu)\n\n # set the updates\n W[i] = np.linalg.solve(matrix, vector)\n b[i] = bi / (len(user2movie[i]) + reg)\n\n if i % (N//10) == 0:\n print(\"i:\", i, \"N:\", N)\n print(\"updated W and b:\", datetime.now() - t0)\n\n # update U and c\n t0 = datetime.now()\n for j in range(M):\n # for U\n matrix = np.eye(K) * reg\n vector = np.zeros(K)\n\n # for c\n cj = 0\n try:\n for i in movie2user[j]:\n r = usermovie2rating[(i,j)]\n matrix += np.outer(W[i], W[i])\n vector += (r - b[i] - c[j] - mu)*W[i]\n cj += (r - W[i].dot(U[j]) - b[i] - mu)\n\n # set the updates\n U[j] = np.linalg.solve(matrix, vector)\n c[j] = cj / (len(movie2user[j]) + reg)\n\n if j % (M//10) == 0:\n print(\"j:\", j, \"M:\", M)\n except KeyError:\n # possible not to have any ratings for a movie\n pass\n print(\"updated U and c:\", datetime.now() - t0)\n print(\"epoch duration:\", datetime.now() - epoch_start)\n\n\n # store train loss\n t0 = datetime.now()\n train_losses.append(get_loss(usermovie2rating))\n\n # store test loss\n test_losses.append(get_loss(usermovie2rating_test))\n print(\"calculate cost:\", datetime.now() - t0)\n print(\"train loss:\", train_losses[-1])\n print(\"test loss:\", test_losses[-1])\n\n\nprint(\"train losses:\", train_losses)\nprint(\"test losses:\", test_losses)\n\n# plot losses\nplt.plot(train_losses, label=\"train loss\")\nplt.plot(test_losses, label=\"test loss\")\nplt.legend()\nplt.show()\n" }, { "path": "recommenders/mf2.py", "content": "# https://udemy.com/recommender-systems\n# https://deeplearningcourses.com/recommender-systems\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport pickle\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.utils import shuffle\nfrom datetime import datetime\nfrom copy import deepcopy\n\n# load in the data\nimport os\nif not os.path.exists('user2movie.json') or \\\n not os.path.exists('movie2user.json') or \\\n not os.path.exists('usermovie2rating.json') or \\\n not os.path.exists('usermovie2rating_test.json'):\n import preprocess2dict\n\n\nwith open('user2movie.json', 'rb') as f:\n user2movie = pickle.load(f)\n\nwith open('movie2user.json', 'rb') as f:\n movie2user = pickle.load(f)\n\nwith open('usermovie2rating.json', 'rb') as f:\n usermovie2rating = pickle.load(f)\n\nwith open('usermovie2rating_test.json', 'rb') as f:\n usermovie2rating_test = pickle.load(f)\n\n\nN = np.max(list(user2movie.keys())) + 1\n# the test set may contain movies the train set doesn't have data on\nm1 = np.max(list(movie2user.keys()))\nm2 = np.max([m for (u, m), r in usermovie2rating_test.items()])\nM = max(m1, m2) + 1\nprint(\"N:\", N, \"M:\", M)\n\n\n# convert user2movie and movie2user to include ratings\nprint(\"converting...\")\nuser2movierating = {}\nfor i, movies in user2movie.items():\n r = np.array([usermovie2rating[(i,j)] for j in movies])\n user2movierating[i] = (movies, r)\nmovie2userrating = {}\nfor j, users in movie2user.items():\n r = np.array([usermovie2rating[(i,j)] for i in users])\n movie2userrating[j] = (users, r)\n\n# create a movie2user for test set, since we need it for loss\nmovie2userrating_test = {}\nfor (i, j), r in usermovie2rating_test.items():\n if j not in movie2userrating_test:\n movie2userrating_test[j] = [[i], [r]]\n else:\n movie2userrating_test[j][0].append(i)\n movie2userrating_test[j][1].append(r)\nfor j, (users, r) in movie2userrating_test.items():\n movie2userrating_test[j][1] = np.array(r)\nprint(\"conversion done\")\n\n# initialize variables\nK = 10 # latent dimensionality\nW = np.random.randn(N, K)\nb = np.zeros(N)\nU = np.random.randn(M, K)\nc = np.zeros(M)\nmu = np.mean(list(usermovie2rating.values()))\n\n\n\ndef get_loss(m2u):\n # d: movie_id -> (user_ids, ratings)\n N = 0.\n sse = 0\n for j, (u_ids, r) in m2u.items():\n p = W[u_ids].dot(U[j]) + b[u_ids] + c[j] + mu\n delta = p - r\n sse += delta.dot(delta)\n N += len(r)\n return sse / N\n\n\n\n# train the parameters\nepochs = 25\nreg = 20. # regularization penalty\ntrain_losses = []\ntest_losses = []\nfor epoch in range(epochs):\n print(\"epoch:\", epoch)\n epoch_start = datetime.now()\n # perform updates\n\n # update W and b\n t0 = datetime.now()\n for i in range(N):\n m_ids, r = user2movierating[i]\n matrix = U[m_ids].T.dot(U[m_ids]) + np.eye(K) * reg\n vector = (r - b[i] - c[m_ids] - mu).dot(U[m_ids])\n bi = (r - U[m_ids].dot(W[i]) - c[m_ids] - mu).sum()\n\n # set the updates\n W[i] = np.linalg.solve(matrix, vector)\n b[i] = bi / (len(user2movie[i]) + reg)\n\n if i % (N//10) == 0:\n print(\"i:\", i, \"N:\", N)\n print(\"updated W and b:\", datetime.now() - t0)\n\n\n # update U and c\n t0 = datetime.now()\n for j in range(M):\n try:\n u_ids, r = movie2userrating[j]\n matrix = W[u_ids].T.dot(W[u_ids]) + np.eye(K) * reg\n vector = (r - b[u_ids] - c[j] - mu).dot(W[u_ids])\n cj = (r - W[u_ids].dot(U[j]) - b[u_ids] - mu).sum()\n\n # set the updates\n U[j] = np.linalg.solve(matrix, vector)\n c[j] = cj / (len(movie2user[j]) + reg)\n\n if j % (M//10) == 0:\n print(\"j:\", j, \"M:\", M)\n except KeyError:\n # possible not to have any ratings for a movie\n pass\n print(\"updated U and c:\", datetime.now() - t0)\n print(\"epoch duration:\", datetime.now() - epoch_start)\n\n\n # store train loss\n t0 = datetime.now()\n train_losses.append(get_loss(movie2userrating))\n\n # store test loss\n test_losses.append(get_loss(movie2userrating_test))\n print(\"calculate cost:\", datetime.now() - t0)\n print(\"train loss:\", train_losses[-1])\n print(\"test loss:\", test_losses[-1])\n\n\nprint(\"train losses:\", train_losses)\nprint(\"test losses:\", test_losses)\n\n# plot losses\nplt.plot(train_losses, label=\"train loss\")\nplt.plot(test_losses, label=\"test loss\")\nplt.legend()\nplt.show()\n" }, { "path": "recommenders/mf_keras.py", "content": "# https://udemy.com/recommender-systems\n# https://deeplearningcourses.com/recommender-systems\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport pickle\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.utils import shuffle\n\nfrom keras.models import Model\nfrom keras.layers import Input, Embedding, Dot, Add, Flatten\nfrom keras.regularizers import l2\nfrom keras.optimizers import SGD, Adam\n\n# load in the data\ndf = pd.read_csv('../large_files/movielens-20m-dataset/edited_rating.csv')\n\nN = df.userId.max() + 1 # number of users\nM = df.movie_idx.max() + 1 # number of movies\n\n# split into train and test\ndf = shuffle(df)\ncutoff = int(0.8*len(df))\ndf_train = df.iloc[:cutoff]\ndf_test = df.iloc[cutoff:]\n\n# initialize variables\nK = 10 # latent dimensionality\nmu = df_train.rating.mean()\nepochs = 15\nreg = 0. # regularization penalty\n\n\n# keras model\nu = Input(shape=(1,))\nm = Input(shape=(1,))\nu_embedding = Embedding(N, K, embeddings_regularizer=l2(reg))(u) # (N, 1, K)\nm_embedding = Embedding(M, K, embeddings_regularizer=l2(reg))(m) # (N, 1, K)\n\n# subsubmodel = Model([u, m], [u_embedding, m_embedding])\n# user_ids = df_train.userId.values[0:5]\n# movie_ids = df_train.movie_idx.values[0:5]\n# print(\"user_ids.shape\", user_ids.shape)\n# p = subsubmodel.predict([user_ids, movie_ids])\n# print(\"p[0].shape:\", p[0].shape)\n# print(\"p[1].shape:\", p[1].shape)\n# exit()\n\n\nu_bias = Embedding(N, 1, embeddings_regularizer=l2(reg))(u) # (N, 1, 1)\nm_bias = Embedding(M, 1, embeddings_regularizer=l2(reg))(m) # (N, 1, 1)\nx = Dot(axes=2)([u_embedding, m_embedding]) # (N, 1, 1)\n\n# submodel = Model([u, m], x)\n# user_ids = df_train.userId.values[0:5]\n# movie_ids = df_train.movie_idx.values[0:5]\n# p = submodel.predict([user_ids, movie_ids])\n# print(\"p.shape:\", p.shape)\n# exit()\n\n\nx = Add()([x, u_bias, m_bias])\nx = Flatten()(x) # (N, 1)\n\nmodel = Model(inputs=[u, m], outputs=x)\nmodel.compile(\n loss='mse',\n # optimizer='adam',\n # optimizer=Adam(lr=0.01),\n optimizer=SGD(lr=0.08, momentum=0.9),\n metrics=['mse'],\n)\n\nr = model.fit(\n x=[df_train.userId.values, df_train.movie_idx.values],\n y=df_train.rating.values - mu,\n epochs=epochs,\n batch_size=128,\n validation_data=(\n [df_test.userId.values, df_test.movie_idx.values],\n df_test.rating.values - mu\n )\n)\n\n\n# plot losses\nplt.plot(r.history['loss'], label=\"train loss\")\nplt.plot(r.history['val_loss'], label=\"test loss\")\nplt.legend()\nplt.show()\n\n# plot mse\nplt.plot(r.history['mean_squared_error'], label=\"train mse\")\nplt.plot(r.history['val_mean_squared_error'], label=\"test mse\")\nplt.legend()\nplt.show()\n" }, { "path": "recommenders/mf_keras_deep.py", "content": "# https://udemy.com/recommender-systems\n# https://deeplearningcourses.com/recommender-systems\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport pickle\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.utils import shuffle\n\nfrom keras.models import Model\nfrom keras.layers import Input, Embedding, Flatten, Dense, Concatenate\nfrom keras.layers import Dropout, BatchNormalization, Activation\nfrom keras.regularizers import l2\nfrom keras.optimizers import SGD, Adam\n\n# load in the data\ndf = pd.read_csv('../large_files/movielens-20m-dataset/edited_rating.csv')\n\nN = df.userId.max() + 1 # number of users\nM = df.movie_idx.max() + 1 # number of movies\n\n# split into train and test\ndf = shuffle(df)\ncutoff = int(0.8*len(df))\ndf_train = df.iloc[:cutoff]\ndf_test = df.iloc[cutoff:]\n\n# initialize variables\nK = 10 # latent dimensionality\nmu = df_train.rating.mean()\nepochs = 15\n# reg = 0.0001 # regularization penalty\n\n\n# keras model\nu = Input(shape=(1,))\nm = Input(shape=(1,))\nu_embedding = Embedding(N, K)(u) # (N, 1, K)\nm_embedding = Embedding(M, K)(m) # (N, 1, K)\nu_embedding = Flatten()(u_embedding) # (N, K)\nm_embedding = Flatten()(m_embedding) # (N, K)\nx = Concatenate()([u_embedding, m_embedding]) # (N, 2K)\n\n# the neural network\nx = Dense(400)(x)\n# x = BatchNormalization()(x)\nx = Activation('relu')(x)\n# x = Dropout(0.5)(x)\n# x = Dense(100)(x)\n# x = BatchNormalization()(x)\n# x = Activation('relu')(x)\nx = Dense(1)(x)\n\nmodel = Model(inputs=[u, m], outputs=x)\nmodel.compile(\n loss='mse',\n # optimizer='adam',\n # optimizer=Adam(lr=0.01),\n optimizer=SGD(lr=0.08, momentum=0.9),\n metrics=['mse'],\n)\n\nr = model.fit(\n x=[df_train.userId.values, df_train.movie_idx.values],\n y=df_train.rating.values - mu,\n epochs=epochs,\n batch_size=128,\n validation_data=(\n [df_test.userId.values, df_test.movie_idx.values],\n df_test.rating.values - mu\n )\n)\n\n\n# plot losses\nplt.plot(r.history['loss'], label=\"train loss\")\nplt.plot(r.history['val_loss'], label=\"test loss\")\nplt.legend()\nplt.show()\n\n# plot mse\nplt.plot(r.history['mean_squared_error'], label=\"train mse\")\nplt.plot(r.history['val_mean_squared_error'], label=\"test mse\")\nplt.legend()\nplt.show()\n" }, { "path": "recommenders/mf_keras_res.py", "content": "# https://udemy.com/recommender-systems\n# https://deeplearningcourses.com/recommender-systems\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport pickle\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.utils import shuffle\n\nfrom keras.models import Model\nfrom keras.layers import Input, Embedding, Dot, Add, Flatten, Dense, Concatenate\nfrom keras.layers import Dropout, BatchNormalization, Activation\nfrom keras.regularizers import l2\nfrom keras.optimizers import SGD, Adam\n\n# load in the data\ndf = pd.read_csv('../large_files/movielens-20m-dataset/edited_rating.csv')\n\nN = df.userId.max() + 1 # number of users\nM = df.movie_idx.max() + 1 # number of movies\n\n# split into train and test\ndf = shuffle(df)\ncutoff = int(0.8*len(df))\ndf_train = df.iloc[:cutoff]\ndf_test = df.iloc[cutoff:]\n\n# initialize variables\nK = 10 # latent dimensionality\nmu = df_train.rating.mean()\nepochs = 15\nreg = 0. # regularization penalty\n\n\n# keras model\nu = Input(shape=(1,))\nm = Input(shape=(1,))\nu_embedding = Embedding(N, K)(u) # (N, 1, K)\nm_embedding = Embedding(M, K)(m) # (N, 1, K)\n\n\n##### main branch\nu_bias = Embedding(N, 1)(u) # (N, 1, 1)\nm_bias = Embedding(M, 1)(m) # (N, 1, 1)\nx = Dot(axes=2)([u_embedding, m_embedding]) # (N, 1, 1)\nx = Add()([x, u_bias, m_bias])\nx = Flatten()(x) # (N, 1)\n\n\n##### side branch\nu_embedding = Flatten()(u_embedding) # (N, K)\nm_embedding = Flatten()(m_embedding) # (N, K)\ny = Concatenate()([u_embedding, m_embedding]) # (N, 2K)\ny = Dense(400)(y)\ny = Activation('elu')(y)\n# y = Dropout(0.5)(y)\ny = Dense(1)(y)\n\n\n##### merge\nx = Add()([x, y])\n\nmodel = Model(inputs=[u, m], outputs=x)\nmodel.compile(\n loss='mse',\n # optimizer='adam',\n # optimizer=Adam(lr=0.01),\n optimizer=SGD(lr=0.08, momentum=0.9),\n metrics=['mse'],\n)\n\nr = model.fit(\n x=[df_train.userId.values, df_train.movie_idx.values],\n y=df_train.rating.values - mu,\n epochs=epochs,\n batch_size=128,\n validation_data=(\n [df_test.userId.values, df_test.movie_idx.values],\n df_test.rating.values - mu\n )\n)\n\n\n# plot losses\nplt.plot(r.history['loss'], label=\"train loss\")\nplt.plot(r.history['val_loss'], label=\"test loss\")\nplt.legend()\nplt.show()\n\n# plot mse\nplt.plot(r.history['mean_squared_error'], label=\"train mse\")\nplt.plot(r.history['val_mean_squared_error'], label=\"test mse\")\nplt.legend()\nplt.show()" }, { "path": "recommenders/preprocess.py", "content": "# https://udemy.com/recommender-systems\n# https://deeplearningcourses.com/recommender-systems\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport pandas as pd\n\n# https://www.kaggle.com/grouplens/movielens-20m-dataset\ndf = pd.read_csv('../large_files/movielens-20m-dataset/rating.csv')\n\n\n\n# note:\n# user ids are ordered sequentially from 1..138493\n# with no missing numbers\n# movie ids are integers from 1..131262\n# NOT all movie ids appear\n# there are only 26744 movie ids\n# write code to check it yourself!\n\n\n# make the user ids go from 0...N-1\ndf.userId = df.userId - 1\n\n# create a mapping for movie ids\nunique_movie_ids = set(df.movieId.values)\nmovie2idx = {}\ncount = 0\nfor movie_id in unique_movie_ids:\n movie2idx[movie_id] = count\n count += 1\n\n# add them to the data frame\n# takes awhile\ndf['movie_idx'] = df.apply(lambda row: movie2idx[row.movieId], axis=1)\n\ndf = df.drop(columns=['timestamp'])\n\ndf.to_csv('../large_files/movielens-20m-dataset/edited_rating.csv', index=False)" }, { "path": "recommenders/preprocess2dict.py", "content": "# https://udemy.com/recommender-systems\n# https://deeplearningcourses.com/recommender-systems\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport pickle\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.utils import shuffle\n\n# load in the data\n# https://www.kaggle.com/grouplens/movielens-20m-dataset\ndf = pd.read_csv('../large_files/movielens-20m-dataset/very_small_rating.csv')\n\nN = df.userId.max() + 1 # number of users\nM = df.movie_idx.max() + 1 # number of movies\n\n# split into train and test\ndf = shuffle(df)\ncutoff = int(0.8*len(df))\ndf_train = df.iloc[:cutoff]\ndf_test = df.iloc[cutoff:]\n\n# a dictionary to tell us which users have rated which movies\nuser2movie = {}\n# a dicationary to tell us which movies have been rated by which users\nmovie2user = {}\n# a dictionary to look up ratings\nusermovie2rating = {}\nprint(\"Calling: update_user2movie_and_movie2user\")\ncount = 0\ndef update_user2movie_and_movie2user(row):\n global count\n count += 1\n if count % 100000 == 0:\n print(\"processed: %.3f\" % (float(count)/cutoff))\n\n i = int(row.userId)\n j = int(row.movie_idx)\n if i not in user2movie:\n user2movie[i] = [j]\n else:\n user2movie[i].append(j)\n\n if j not in movie2user:\n movie2user[j] = [i]\n else:\n movie2user[j].append(i)\n\n usermovie2rating[(i,j)] = row.rating\ndf_train.apply(update_user2movie_and_movie2user, axis=1)\n\n# test ratings dictionary\nusermovie2rating_test = {}\nprint(\"Calling: update_usermovie2rating_test\")\ncount = 0\ndef update_usermovie2rating_test(row):\n global count\n count += 1\n if count % 100000 == 0:\n print(\"processed: %.3f\" % (float(count)/len(df_test)))\n\n i = int(row.userId)\n j = int(row.movie_idx)\n usermovie2rating_test[(i,j)] = row.rating\ndf_test.apply(update_usermovie2rating_test, axis=1)\n\n# note: these are not really JSONs\nwith open('user2movie.json', 'wb') as f:\n pickle.dump(user2movie, f)\n\nwith open('movie2user.json', 'wb') as f:\n pickle.dump(movie2user, f)\n\nwith open('usermovie2rating.json', 'wb') as f:\n pickle.dump(usermovie2rating, f)\n\nwith open('usermovie2rating_test.json', 'wb') as f:\n pickle.dump(usermovie2rating_test, f)\n" }, { "path": "recommenders/preprocess2sparse.py", "content": "# https://udemy.com/recommender-systems\n# https://deeplearningcourses.com/recommender-systems\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.utils import shuffle\nfrom scipy.sparse import lil_matrix, csr_matrix, save_npz, load_npz\n\n# load in the data\ndf = pd.read_csv('../large_files/movielens-20m-dataset/edited_rating.csv')\n# df = pd.read_csv('../large_files/movielens-20m-dataset/small_rating.csv')\n\nN = df.userId.max() + 1 # number of users\nM = df.movie_idx.max() + 1 # number of movies\n\n# split into train and test\ndf = shuffle(df)\ncutoff = int(0.8*len(df))\ndf_train = df.iloc[:cutoff]\ndf_test = df.iloc[cutoff:]\n\nA = lil_matrix((N, M))\nprint(\"Calling: update_train\")\ncount = 0\ndef update_train(row):\n global count\n count += 1\n if count % 100000 == 0:\n print(\"processed: %.3f\" % (float(count)/cutoff))\n\n i = int(row.userId)\n j = int(row.movie_idx)\n A[i,j] = row.rating\ndf_train.apply(update_train, axis=1)\n\n# mask, to tell us which entries exist and which do not\nA = A.tocsr()\nmask = (A > 0)\nsave_npz(\"Atrain.npz\", A)\n\n# test ratings dictionary\nA_test = lil_matrix((N, M))\nprint(\"Calling: update_test\")\ncount = 0\ndef update_test(row):\n global count\n count += 1\n if count % 100000 == 0:\n print(\"processed: %.3f\" % (float(count)/len(df_test)))\n\n i = int(row.userId)\n j = int(row.movie_idx)\n A_test[i,j] = row.rating\ndf_test.apply(update_test, axis=1)\nA_test = A_test.tocsr()\nmask_test = (A_test > 0)\nsave_npz(\"Atest.npz\", A_test)\n" }, { "path": "recommenders/preprocess_shrink.py", "content": "# https://udemy.com/recommender-systems\n# https://deeplearningcourses.com/recommender-systems\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport pickle\nimport numpy as np\nimport pandas as pd\nfrom collections import Counter\n\n# load in the data\n# https://www.kaggle.com/grouplens/movielens-20m-dataset\ndf = pd.read_csv('../large_files/movielens-20m-dataset/edited_rating.csv')\nprint(\"original dataframe size:\", len(df))\n\nN = df.userId.max() + 1 # number of users\nM = df.movie_idx.max() + 1 # number of movies\n\nuser_ids_count = Counter(df.userId)\nmovie_ids_count = Counter(df.movie_idx)\n\n# number of users and movies we would like to keep\nn = 10000\nm = 2000\n\nuser_ids = [u for u, c in user_ids_count.most_common(n)]\nmovie_ids = [m for m, c in movie_ids_count.most_common(m)]\n\n# make a copy, otherwise ids won't be overwritten\ndf_small = df[df.userId.isin(user_ids) & df.movie_idx.isin(movie_ids)].copy()\n\n# need to remake user ids and movie ids since they are no longer sequential\nnew_user_id_map = {}\ni = 0\nfor old in user_ids:\n new_user_id_map[old] = i\n i += 1\nprint(\"i:\", i)\n\nnew_movie_id_map = {}\nj = 0\nfor old in movie_ids:\n new_movie_id_map[old] = j\n j += 1\nprint(\"j:\", j)\n\nprint(\"Setting new ids\")\ndf_small.loc[:, 'userId'] = df_small.apply(lambda row: new_user_id_map[row.userId], axis=1)\ndf_small.loc[:, 'movie_idx'] = df_small.apply(lambda row: new_movie_id_map[row.movie_idx], axis=1)\n# df_small.drop(columns=['userId', 'movie_idx'])\n# df_small.rename(index=str, columns={'new_userId': 'userId', 'new_movie_idx': 'movie_idx'})\nprint(\"max user id:\", df_small.userId.max())\nprint(\"max movie id:\", df_small.movie_idx.max())\n\nprint(\"small dataframe size:\", len(df_small))\ndf_small.to_csv('../large_files/movielens-20m-dataset/small_rating.csv', index=False)\n" }, { "path": "recommenders/rbm_tf_k.py", "content": "# https://udemy.com/recommender-systems\n# https://deeplearningcourses.com/recommender-systems\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nfrom sklearn.utils import shuffle\n\nimport pandas as pd\nfrom scipy.sparse import lil_matrix, csr_matrix, save_npz, load_npz\nfrom datetime import datetime\n\nif tf.__version__.startswith('2'):\n tf.compat.v1.disable_eager_execution()\n\n\n# is it possible to one-hot encode the data prior to feeding it\n# into the neural network, so that we don't have to do it on the fly?\n# yes, but:\n# 1) scipy sparse doesn't support N-D matrices\n# 2) you can use the 'sparse' standalone package, but it takes very long\n# and you will run out of RAM\n\n\ndef one_hot_encode(X, K):\n # input is N x D\n # output is N x D x K\n N, D = X.shape\n Y = np.zeros((N, D, K))\n for n, d in zip(*X.nonzero()):\n # 0.5...5 --> 1..10 --> 0..9\n k = int(X[n,d]*2 - 1)\n Y[n,d,k] = 1\n return Y\n\ndef one_hot_mask(X, K):\n # input is N x D\n # output is N x D x K\n N, D = X.shape\n Y = np.zeros((N, D, K))\n # if X[n,d] == 0, there's a missing rating\n # so the mask should be all zeros\n # else, it should be all ones\n for n, d in zip(*X.nonzero()):\n Y[n,d,:] = 1\n return Y\n\none_to_ten = np.arange(10) + 1 # [1, 2, 3, ..., 10]\ndef convert_probs_to_ratings(probs):\n # probs is N x D x K\n # output is N x D matrix of predicted ratings\n # N, D, K = probs.shape\n # out = np.zeros((N, D))\n # each predicted rating is a weighted average using the probabilities\n # for n in range(N):\n # for d in range(D):\n # out[n,d] = probs[n,d].dot(one_to_ten) / 2\n # return out\n return probs.dot(one_to_ten) / 2\n\n\n\ndef dot1(V, W):\n # V is N x D x K (batch of visible units)\n # W is D x K x M (weights)\n # returns N x M (hidden layer size)\n return tf.tensordot(V, W, axes=[[1,2], [0,1]])\n\ndef dot2(H, W):\n # H is N x M (batch of hiddens)\n # W is D x K x M (weights transposed)\n # returns N x D x K (visible)\n return tf.tensordot(H, W, axes=[[1], [2]])\n\n\nclass RBM(object):\n def __init__(self, D, M, K):\n self.D = D # input feature size\n self.M = M # hidden size\n self.K = K # number of ratings\n self.build(D, M, K)\n\n\n def build(self, D, M, K):\n # params\n self.W = tf.Variable(tf.random.normal(shape=(D, K, M)) * np.sqrt(2.0 / M))\n self.c = tf.Variable(np.zeros(M).astype(np.float32))\n self.b = tf.Variable(np.zeros((D, K)).astype(np.float32))\n\n # data\n self.X_in = tf.compat.v1.placeholder(tf.float32, shape=(None, D, K))\n self.mask = tf.compat.v1.placeholder(tf.float32, shape=(None, D, K))\n\n # conditional probabilities\n # NOTE: tf.contrib.distributions.Bernoulli API has changed in Tensorflow v1.2\n V = self.X_in\n p_h_given_v = tf.nn.sigmoid(dot1(V, self.W) + self.c)\n self.p_h_given_v = p_h_given_v # save for later\n\n # draw a sample from p(h | v)\n r = tf.random.uniform(shape=tf.shape(input=p_h_given_v))\n H = tf.cast(r < p_h_given_v, dtype=tf.float32)\n\n # draw a sample from p(v | h)\n # note: we don't have to actually do the softmax\n logits = dot2(H, self.W) + self.b\n cdist = tf.compat.v1.distributions.Categorical(logits=logits)\n X_sample = cdist.sample() # shape is (N, D)\n X_sample = tf.one_hot(X_sample, depth=K) # turn it into (N, D, K)\n X_sample = X_sample * self.mask # missing ratings shouldn't contribute to objective\n\n\n # build the objective\n objective = tf.reduce_mean(input_tensor=self.free_energy(self.X_in)) - tf.reduce_mean(input_tensor=self.free_energy(X_sample))\n self.train_op = tf.compat.v1.train.AdamOptimizer(1e-2).minimize(objective)\n # self.train_op = tf.train.GradientDescentOptimizer(1e-3).minimize(objective)\n\n # build the cost\n # we won't use this to optimize the model parameters\n # just to observe what happens during training\n logits = self.forward_logits(self.X_in)\n self.cost = tf.reduce_mean(\n input_tensor=tf.nn.softmax_cross_entropy_with_logits(\n labels=tf.stop_gradient(self.X_in),\n logits=logits,\n )\n )\n\n # to get the output\n self.output_visible = self.forward_output(self.X_in)\n\n initop = tf.compat.v1.global_variables_initializer()\n self.session = tf.compat.v1.Session()\n self.session.run(initop)\n\n def fit(self, X, mask, X_test, mask_test, epochs=10, batch_sz=256, show_fig=True):\n N, D = X.shape\n n_batches = N // batch_sz\n\n\n costs = []\n test_costs = []\n for i in range(epochs):\n t0 = datetime.now()\n print(\"epoch:\", i)\n X, mask, X_test, mask_test = shuffle(X, mask, X_test, mask_test) # everything has to be shuffled accordingly\n for j in range(n_batches):\n x = X[j*batch_sz:(j*batch_sz + batch_sz)].toarray()\n m = mask[j*batch_sz:(j*batch_sz + batch_sz)].toarray()\n\n # both visible units and mask have to be in one-hot form\n # N x D --> N x D x K\n batch_one_hot = one_hot_encode(x, self.K)\n m = one_hot_mask(m, self.K)\n\n _, c = self.session.run(\n (self.train_op, self.cost),\n feed_dict={self.X_in: batch_one_hot, self.mask: m}\n )\n\n if j % 100 == 0:\n print(\"j / n_batches:\", j, \"/\", n_batches, \"cost:\", c)\n print(\"duration:\", datetime.now() - t0)\n\n # calculate the true train and test cost\n t0 = datetime.now()\n sse = 0\n test_sse = 0\n n = 0\n test_n = 0\n for j in range(n_batches):\n x = X[j*batch_sz:(j*batch_sz + batch_sz)].toarray()\n m = mask[j*batch_sz:(j*batch_sz + batch_sz)].toarray()\n\n # only visible input has to be in one-hot form\n xoh = one_hot_encode(x, self.K)\n\n probs = self.get_visible(xoh)\n xhat = convert_probs_to_ratings(probs)\n sse += (m * (xhat - x)*(xhat - x)).sum()\n n += m.sum()\n\n # the test PREDICTIONS come from the train data!\n # X_test and mask_test are only used for targets\n xt = X_test[j*batch_sz:(j*batch_sz + batch_sz)].toarray()\n mt = mask_test[j*batch_sz:(j*batch_sz + batch_sz)].toarray()\n\n test_sse += (mt * (xhat - xt) * (xhat - xt)).sum()\n test_n += mt.sum()\n c = sse/n\n ct = test_sse/test_n\n print(\"train mse:\", c)\n print(\"test mse:\", ct)\n print(\"calculate cost duration:\", datetime.now() - t0)\n costs.append(c)\n test_costs.append(ct)\n if show_fig:\n plt.plot(costs, label='train mse')\n plt.plot(test_costs, label='test mse')\n plt.legend()\n plt.show()\n\n def free_energy(self, V):\n first_term = -tf.reduce_sum(input_tensor=dot1(V, self.b))\n second_term = -tf.reduce_sum(\n # tf.log(1 + tf.exp(tf.matmul(V, self.W) + self.c)),\n input_tensor=tf.nn.softplus(dot1(V, self.W) + self.c),\n axis=1\n )\n return first_term + second_term\n\n def forward_hidden(self, X):\n return tf.nn.sigmoid(dot1(X, self.W) + self.c)\n\n def forward_logits(self, X):\n Z = self.forward_hidden(X)\n return dot2(Z, self.W) + self.b\n\n def forward_output(self, X):\n return tf.nn.softmax(self.forward_logits(X))\n\n def transform(self, X):\n # accepts and returns a real numpy array\n # unlike forward_hidden and forward_output\n # which deal with tensorflow variables\n return self.session.run(self.p_h_given_v, feed_dict={self.X_in: X})\n\n def get_visible(self, X):\n return self.session.run(self.output_visible, feed_dict={self.X_in: X})\n\n\ndef main():\n A = load_npz(\"Atrain.npz\")\n A_test = load_npz(\"Atest.npz\")\n mask = (A > 0) * 1.0\n mask_test = (A_test > 0) * 1.0\n\n N, M = A.shape\n rbm = RBM(M, 50, 10)\n rbm.fit(A, mask, A_test, mask_test)\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "recommenders/rbm_tf_k_faster.py", "content": "# https://udemy.com/recommender-systems\n# https://deeplearningcourses.com/recommender-systems\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nfrom sklearn.utils import shuffle\n\nimport pandas as pd\nfrom scipy.sparse import lil_matrix, csr_matrix, save_npz, load_npz\nfrom datetime import datetime\n\nif tf.__version__.startswith('2'):\n tf.compat.v1.disable_eager_execution()\n\n\ndef dot1(V, W):\n # V is N x D x K (batch of visible units)\n # W is D x K x M (weights)\n # returns N x M (hidden layer size)\n return tf.tensordot(V, W, axes=[[1,2], [0,1]])\n\ndef dot2(H, W):\n # H is N x M (batch of hiddens)\n # W is D x K x M (weights transposed)\n # returns N x D x K (visible)\n return tf.tensordot(H, W, axes=[[1], [2]])\n\n\nclass RBM(object):\n def __init__(self, D, M, K):\n self.D = D # input feature size\n self.M = M # hidden size\n self.K = K # number of ratings\n self.build(D, M, K)\n\n\n def build(self, D, M, K):\n # params\n self.W = tf.Variable(tf.random.normal(shape=(D, K, M)) * np.sqrt(2.0 / M))\n self.c = tf.Variable(np.zeros(M).astype(np.float32))\n self.b = tf.Variable(np.zeros((D, K)).astype(np.float32))\n\n # data\n self.X_in = tf.compat.v1.placeholder(tf.float32, shape=(None, D))\n\n # one hot encode X\n # first, make each rating an int\n X = tf.cast(self.X_in * 2 - 1, tf.int32)\n X = tf.one_hot(X, K)\n\n # conditional probabilities\n # NOTE: tf.contrib.distributions.Bernoulli API has changed in Tensorflow v1.2\n V = X\n p_h_given_v = tf.nn.sigmoid(dot1(V, self.W) + self.c)\n self.p_h_given_v = p_h_given_v # save for later\n\n # draw a sample from p(h | v)\n r = tf.random.uniform(shape=tf.shape(input=p_h_given_v))\n H = tf.cast(r < p_h_given_v, dtype=tf.float32)\n\n # draw a sample from p(v | h)\n # note: we don't have to actually do the softmax\n logits = dot2(H, self.W) + self.b\n cdist = tf.compat.v1.distributions.Categorical(logits=logits)\n X_sample = cdist.sample() # shape is (N, D)\n X_sample = tf.one_hot(X_sample, depth=K) # turn it into (N, D, K)\n\n # mask X_sample to remove missing ratings\n mask2d = tf.cast(self.X_in > 0, tf.float32)\n mask3d = tf.stack([mask2d]*K, axis=-1) # repeat K times in last dimension\n X_sample = X_sample * mask3d\n\n\n # build the objective\n objective = tf.reduce_mean(input_tensor=self.free_energy(X)) - tf.reduce_mean(input_tensor=self.free_energy(X_sample))\n self.train_op = tf.compat.v1.train.AdamOptimizer(1e-2).minimize(objective)\n # self.train_op = tf.train.GradientDescentOptimizer(1e-3).minimize(objective)\n\n # build the cost\n # we won't use this to optimize the model parameters\n # just to observe what happens during training\n logits = self.forward_logits(X)\n self.cost = tf.reduce_mean(\n input_tensor=tf.nn.softmax_cross_entropy_with_logits(\n labels=tf.stop_gradient(X),\n logits=logits,\n )\n )\n\n # to get the output\n self.output_visible = self.forward_output(X)\n\n\n # for calculating SSE\n self.one_to_ten = tf.constant((np.arange(10) + 1).astype(np.float32) / 2)\n self.pred = tf.tensordot(self.output_visible, self.one_to_ten, axes=[[2], [0]])\n mask = tf.cast(self.X_in > 0, tf.float32)\n se = mask * (self.X_in - self.pred) * (self.X_in - self.pred)\n self.sse = tf.reduce_sum(input_tensor=se)\n\n # test SSE\n self.X_test = tf.compat.v1.placeholder(tf.float32, shape=(None, D))\n mask = tf.cast(self.X_test > 0, tf.float32)\n tse = mask * (self.X_test - self.pred) * (self.X_test - self.pred)\n self.tsse = tf.reduce_sum(input_tensor=tse)\n\n\n initop = tf.compat.v1.global_variables_initializer()\n self.session = tf.compat.v1.Session()\n self.session.run(initop)\n\n def fit(self, X, X_test, epochs=10, batch_sz=256, show_fig=True):\n N, D = X.shape\n n_batches = N // batch_sz\n\n\n costs = []\n test_costs = []\n for i in range(epochs):\n t0 = datetime.now()\n print(\"epoch:\", i)\n X, X_test = shuffle(X, X_test) # everything has to be shuffled accordingly\n for j in range(n_batches):\n x = X[j*batch_sz:(j*batch_sz + batch_sz)].toarray()\n\n _, c = self.session.run(\n (self.train_op, self.cost),\n feed_dict={self.X_in: x}\n )\n\n if j % 100 == 0:\n print(\"j / n_batches:\", j, \"/\", n_batches, \"cost:\", c)\n print(\"duration:\", datetime.now() - t0)\n\n # calculate the true train and test cost\n t0 = datetime.now()\n sse = 0\n test_sse = 0\n n = 0\n test_n = 0\n for j in range(n_batches):\n x = X[j*batch_sz:(j*batch_sz + batch_sz)].toarray()\n xt = X_test[j*batch_sz:(j*batch_sz + batch_sz)].toarray()\n\n # number of train ratings\n n += np.count_nonzero(x)\n\n # number of test ratings\n test_n += np.count_nonzero(xt)\n\n # use tensorflow to get SSEs\n sse_j, tsse_j = self.get_sse(x, xt)\n sse += sse_j\n test_sse += tsse_j\n c = sse/n\n ct = test_sse/test_n\n print(\"train mse:\", c)\n print(\"test mse:\", ct)\n print(\"calculate cost duration:\", datetime.now() - t0)\n costs.append(c)\n test_costs.append(ct)\n if show_fig:\n plt.plot(costs, label='train mse')\n plt.plot(test_costs, label='test mse')\n plt.legend()\n plt.show()\n\n def free_energy(self, V):\n first_term = -tf.reduce_sum(input_tensor=dot1(V, self.b))\n second_term = -tf.reduce_sum(\n # tf.log(1 + tf.exp(tf.matmul(V, self.W) + self.c)),\n input_tensor=tf.nn.softplus(dot1(V, self.W) + self.c),\n axis=1\n )\n return first_term + second_term\n\n def forward_hidden(self, X):\n return tf.nn.sigmoid(dot1(X, self.W) + self.c)\n\n def forward_logits(self, X):\n Z = self.forward_hidden(X)\n return dot2(Z, self.W) + self.b\n\n def forward_output(self, X):\n return tf.nn.softmax(self.forward_logits(X))\n\n def transform(self, X):\n # accepts and returns a real numpy array\n # unlike forward_hidden and forward_output\n # which deal with tensorflow variables\n return self.session.run(self.p_h_given_v, feed_dict={self.X_in: X})\n\n def get_visible(self, X):\n return self.session.run(self.output_visible, feed_dict={self.X_in: X})\n\n def get_sse(self, X, Xt):\n return self.session.run(\n (self.sse, self.tsse),\n feed_dict={\n self.X_in: X,\n self.X_test: Xt,\n })\n\n\n\ndef main():\n A = load_npz(\"Atrain.npz\")\n A_test = load_npz(\"Atest.npz\")\n\n N, M = A.shape\n rbm = RBM(M, 50, 10)\n rbm.fit(A, A_test)\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "recommenders/spark.py", "content": "# https://udemy.com/recommender-systems\n# https://deeplearningcourses.com/recommender-systems\n\n### meant to be pasted into console ###\n\n# notes:\n# you may have trouble with full dataset on just your local machine\n# if you want to know what's in an RDD, use .take(n), ex:\n# tmp = p.take(5)\n# print(tmp)\n\nfrom pyspark.mllib.recommendation import ALS, MatrixFactorizationModel, Rating\nimport os\n\n# load in the data\ndata = sc.textFile(\"../large_files/movielens-20m-dataset/small_rating.csv\")\n\n# filter out header\nheader = data.first() #extract header\ndata = data.filter(lambda row: row != header)\n\n# convert into a sequence of Rating objects\nratings = data.map(\n lambda l: l.split(',')\n).map(\n lambda l: Rating(int(l[0]), int(l[1]), float(l[2]))\n)\n\n# split into train and test\ntrain, test = ratings.randomSplit([0.8, 0.2])\n\n# train the model\nK = 10\nepochs = 10\nmodel = ALS.train(train, K, epochs)\n\n# evaluate the model\n\n# train\nx = train.map(lambda p: (p[0], p[1]))\np = model.predictAll(x).map(lambda r: ((r[0], r[1]), r[2]))\nratesAndPreds = train.map(lambda r: ((r[0], r[1]), r[2])).join(p)\n# joins on first item: (user_id, movie_id)\n# each row of result is: ((user_id, movie_id), (rating, prediction))\nmse = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).mean()\nprint(\"train mse: %s\" % mse)\n\n\n# test\nx = test.map(lambda p: (p[0], p[1]))\np = model.predictAll(x).map(lambda r: ((r[0], r[1]), r[2]))\nratesAndPreds = test.map(lambda r: ((r[0], r[1]), r[2])).join(p)\nmse = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).mean()\nprint(\"test mse: %s\" % mse)" }, { "path": "recommenders/spark2.py", "content": "# https://udemy.com/recommender-systems\n# https://deeplearningcourses.com/recommender-systems\n\n# notes:\n# you may have trouble with full dataset on just your local machine\n# if you want to know what's in an RDD, use .take(n), ex:\n# tmp = p.take(5)\n# print(tmp)\n\nfrom pyspark.mllib.recommendation import ALS, MatrixFactorizationModel, Rating\nfrom pyspark import SparkContext\n\n# increase memory\n# SparkContext.setSystemProperty('spark.driver.memory', '10g')\n# SparkContext.setSystemProperty('spark.executor.memory', '10g')\n\nsc = SparkContext(\"local\", \"Your App Name Here\")\n\n\n# load in the data\n# data = sc.textFile(\"../large_files/movielens-20m-dataset/small_rating.csv\")\ndata = sc.textFile(\"../large_files/movielens-20m-dataset/rating.csv.gz\")\n\n# filter out header\nheader = data.first() #extract header\ndata = data.filter(lambda row: row != header)\n\n# convert into a sequence of Rating objects\nratings = data.map(\n lambda l: l.split(',')\n).map(\n lambda l: Rating(int(l[0]), int(l[1]), float(l[2]))\n)\n\n# split into train and test\ntrain, test = ratings.randomSplit([0.8, 0.2])\n\n# train the model\nK = 10\nepochs = 10\nmodel = ALS.train(train, K, epochs)\n\n# evaluate the model\n\n# train\nx = train.map(lambda p: (p[0], p[1]))\np = model.predictAll(x).map(lambda r: ((r[0], r[1]), r[2]))\nratesAndPreds = train.map(lambda r: ((r[0], r[1]), r[2])).join(p)\n# joins on first item: (user_id, movie_id)\n# each row of result is: ((user_id, movie_id), (rating, prediction))\nmse = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).mean()\nprint(\"***** train mse: %s *****\" % mse)\n\n\n# test\nx = test.map(lambda p: (p[0], p[1]))\np = model.predictAll(x).map(lambda r: ((r[0], r[1]), r[2]))\nratesAndPreds = test.map(lambda r: ((r[0], r[1]), r[2])).join(p)\nmse = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).mean()\nprint(\"***** test mse: %s *****\" % mse)" }, { "path": "recommenders/tfidf.py", "content": "import pandas as pd\nimport json\n\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.metrics.pairwise import cosine_similarity, euclidean_distances\n\n\n# get the data from: https://www.kaggle.com/tmdb/tmdb-movie-metadata\n# load in the data\ndf = pd.read_csv('../large_files/tmdb_5000_movies.csv')\n\n\n# convert the relevant data for each movie into a single string\n# to be ingested by TfidfVectorizer\ndef genres_and_keywords_to_string(row):\n genres = json.loads(row['genres'])\n genres = ' '.join(''.join(j['name'].split()) for j in genres)\n\n keywords = json.loads(row['keywords'])\n keywords = ' '.join(''.join(j['name'].split()) for j in keywords)\n return \"%s %s\" % (genres, keywords)\n\n\n# create a new string representation of each movie\ndf['string'] = df.apply(genres_and_keywords_to_string, axis=1)\n\n\n# create a tf-idf vectorizer object\n# remove stopwords automatically\ntfidf = TfidfVectorizer(max_features=2000)\n\n# create a data matrix from the overviews\nX = tfidf.fit_transform(df['string'])\n\n# check the shape of X\nprint(\"X.shape:\", X.shape)\n\n# generate a mapping from movie title -> index (in df)\nmovie2idx = pd.Series(df.index, index=df['title'])\n\n# create a function that generates recommendations\ndef recommend(title):\n # get the row in the dataframe for this movie\n idx = movie2idx[title]\n if type(idx) == pd.Series:\n idx = idx.iloc[0]\n # print(\"idx:\", idx)\n\n # calculate the pairwise similarities for this movie\n query = X[idx]\n scores = cosine_similarity(query, X)\n\n # currently the array is 1 x N, make it just a 1-D array\n scores = scores.flatten()\n\n # get the indexes of the highest scoring movies\n # get the first K recommendations\n # don't return itself!\n recommended_idx = (-scores).argsort()[1:6]\n\n # return the titles of the recommendations\n return df['title'].iloc[recommended_idx]\n\n\nprint(\"\\nRecommendations for 'Scream 3':\")\nprint(recommend('Scream 3'))\n\nprint(\"\\nRecommendations for 'Mortal Kombat':\")\nprint(recommend('Mortal Kombat'))\n\nprint(\"\\nRecommendations for 'Runaway Bride':\")\nprint(recommend('Runaway Bride'))\n" }, { "path": "recommenders/userbased.py", "content": "# https://udemy.com/recommender-systems\n# https://deeplearningcourses.com/recommender-systems\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport pickle\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.utils import shuffle\nfrom datetime import datetime\nfrom sortedcontainers import SortedList\n\n# load in the data\nimport os\nif not os.path.exists('user2movie.json') or \\\n not os.path.exists('movie2user.json') or \\\n not os.path.exists('usermovie2rating.json') or \\\n not os.path.exists('usermovie2rating_test.json'):\n import preprocess2dict\n\n\nwith open('user2movie.json', 'rb') as f:\n user2movie = pickle.load(f)\n\nwith open('movie2user.json', 'rb') as f:\n movie2user = pickle.load(f)\n\nwith open('usermovie2rating.json', 'rb') as f:\n usermovie2rating = pickle.load(f)\n\nwith open('usermovie2rating_test.json', 'rb') as f:\n usermovie2rating_test = pickle.load(f)\n\n\nN = np.max(list(user2movie.keys())) + 1\n# the test set may contain movies the train set doesn't have data on\nm1 = np.max(list(movie2user.keys()))\nm2 = np.max([m for (u, m), r in usermovie2rating_test.items()])\nM = max(m1, m2) + 1\nprint(\"N:\", N, \"M:\", M)\n\nif N > 10000:\n print(\"N =\", N, \"are you sure you want to continue?\")\n print(\"Comment out these lines if so...\")\n exit()\n\n\n# to find the user similarities, you have to do O(N^2 * M) calculations!\n# in the \"real-world\" you'd want to parallelize this\n# note: we really only have to do half the calculations, since w_ij is symmetric\nK = 25 # number of neighbors we'd like to consider\nlimit = 5 # number of common movies users must have in common in order to consider\nneighbors = [] # store neighbors in this list\naverages = [] # each user's average rating for later use\ndeviations = [] # each user's deviation for later use\nfor i in range(N):\n # find the 25 closest users to user i\n movies_i = user2movie[i]\n movies_i_set = set(movies_i)\n\n # calculate avg and deviation\n ratings_i = { movie:usermovie2rating[(i, movie)] for movie in movies_i }\n avg_i = np.mean(list(ratings_i.values()))\n dev_i = { movie:(rating - avg_i) for movie, rating in ratings_i.items() }\n dev_i_values = np.array(list(dev_i.values()))\n sigma_i = np.sqrt(dev_i_values.dot(dev_i_values))\n\n # save these for later use\n averages.append(avg_i)\n deviations.append(dev_i)\n\n sl = SortedList()\n for j in range(N):\n # don't include yourself\n if j != i:\n movies_j = user2movie[j]\n movies_j_set = set(movies_j)\n common_movies = (movies_i_set & movies_j_set) # intersection\n if len(common_movies) > limit:\n # calculate avg and deviation\n ratings_j = { movie:usermovie2rating[(j, movie)] for movie in movies_j }\n avg_j = np.mean(list(ratings_j.values()))\n dev_j = { movie:(rating - avg_j) for movie, rating in ratings_j.items() }\n dev_j_values = np.array(list(dev_j.values()))\n sigma_j = np.sqrt(dev_j_values.dot(dev_j_values))\n\n # calculate correlation coefficient\n numerator = sum(dev_i[m]*dev_j[m] for m in common_movies)\n w_ij = numerator / (sigma_i * sigma_j)\n\n # insert into sorted list and truncate\n # negate weight, because list is sorted ascending\n # maximum value (1) is \"closest\"\n sl.add((-w_ij, j))\n if len(sl) > K:\n del sl[-1]\n\n # store the neighbors\n neighbors.append(sl)\n\n # print out useful things\n if i % 1 == 0:\n print(i)\n\n\n# using neighbors, calculate train and test MSE\n\ndef predict(i, m):\n # calculate the weighted sum of deviations\n numerator = 0\n denominator = 0\n for neg_w, j in neighbors[i]:\n # remember, the weight is stored as its negative\n # so the negative of the negative weight is the positive weight\n try:\n numerator += -neg_w * deviations[j][m]\n denominator += abs(neg_w)\n except KeyError:\n # neighbor may not have rated the same movie\n # don't want to do dictionary lookup twice\n # so just throw exception\n pass\n\n if denominator == 0:\n prediction = averages[i]\n else:\n prediction = numerator / denominator + averages[i]\n prediction = min(5, prediction)\n prediction = max(0.5, prediction) # min rating is 0.5\n return prediction\n\n\ntrain_predictions = []\ntrain_targets = []\nfor (i, m), target in usermovie2rating.items():\n # calculate the prediction for this movie\n prediction = predict(i, m)\n\n # save the prediction and target\n train_predictions.append(prediction)\n train_targets.append(target)\n\ntest_predictions = []\ntest_targets = []\n# same thing for test set\nfor (i, m), target in usermovie2rating_test.items():\n # calculate the prediction for this movie\n prediction = predict(i, m)\n\n # save the prediction and target\n test_predictions.append(prediction)\n test_targets.append(target)\n\n\n# calculate accuracy\ndef mse(p, t):\n p = np.array(p)\n t = np.array(t)\n return np.mean((p - t)**2)\n\nprint('train mse:', mse(train_predictions, train_targets))\nprint('test mse:', mse(test_predictions, test_targets))\n\n\n\n" }, { "path": "rl/approx_control.py", "content": "# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom grid_world import standard_grid, negative_grid\nfrom iterative_policy_evaluation import print_values, print_policy\nfrom sklearn.kernel_approximation import Nystroem, RBFSampler\n\nGAMMA = 0.9\nALPHA = 0.1\nALL_POSSIBLE_ACTIONS = ('U', 'D', 'L', 'R')\nACTION2INT = {a: i for i, a in enumerate(ALL_POSSIBLE_ACTIONS)}\nINT2ONEHOT = np.eye(len(ALL_POSSIBLE_ACTIONS))\n\n\ndef epsilon_greedy(model, s, eps=0.1):\n # we'll use epsilon-soft to ensure all states are visited\n # what happens if you don't do this? i.e. eps=0\n p = np.random.random()\n if p < (1 - eps):\n values = model.predict_all_actions(s)\n return ALL_POSSIBLE_ACTIONS[np.argmax(values)]\n else:\n return np.random.choice(ALL_POSSIBLE_ACTIONS)\n\n\ndef one_hot(k):\n return INT2ONEHOT[k]\n\n\ndef merge_state_action(s, a):\n ai = one_hot(ACTION2INT[a])\n return np.concatenate((s, ai))\n\n\ndef gather_samples(grid, n_episodes=1000):\n samples = []\n for _ in range(n_episodes):\n s = grid.reset()\n while not grid.game_over():\n a = np.random.choice(ALL_POSSIBLE_ACTIONS)\n sa = merge_state_action(s, a)\n samples.append(sa)\n\n r = grid.move(a)\n s = grid.current_state()\n return samples\n\n\nclass Model:\n def __init__(self, grid):\n # fit the featurizer to data\n samples = gather_samples(grid)\n # self.featurizer = Nystroem()\n self.featurizer = RBFSampler()\n self.featurizer.fit(samples)\n dims = self.featurizer.n_components\n\n # initialize linear model weights\n self.w = np.zeros(dims)\n\n def predict(self, s, a):\n sa = merge_state_action(s, a)\n x = self.featurizer.transform([sa])[0]\n return x @ self.w\n\n def predict_all_actions(self, s):\n return [self.predict(s, a) for a in ALL_POSSIBLE_ACTIONS]\n\n def grad(self, s, a):\n sa = merge_state_action(s, a)\n x = self.featurizer.transform([sa])[0]\n return x\n\n\nif __name__ == '__main__':\n # use the standard grid again (0 for every step) so that we can compare\n # to iterative policy evaluation\n # grid = standard_grid()\n grid = negative_grid(step_cost=-0.1)\n\n # print rewards\n print(\"rewards:\")\n print_values(grid.rewards, grid)\n\n model = Model(grid)\n reward_per_episode = []\n state_visit_count = {}\n\n # repeat until convergence\n n_episodes = 20000\n for it in range(n_episodes):\n if (it + 1) % 100 == 0:\n print(it + 1)\n\n s = grid.reset()\n state_visit_count[s] = state_visit_count.get(s, 0) + 1\n episode_reward = 0\n while not grid.game_over():\n a = epsilon_greedy(model, s)\n r = grid.move(a)\n s2 = grid.current_state()\n state_visit_count[s2] = state_visit_count.get(s2, 0) + 1\n\n # get the target\n if grid.game_over():\n target = r\n else:\n values = model.predict_all_actions(s2)\n target = r + GAMMA * np.max(values)\n\n # update the model\n g = model.grad(s, a)\n err = target - model.predict(s, a)\n model.w += ALPHA * err * g\n \n # accumulate reward\n episode_reward += r\n\n # update state\n s = s2\n \n reward_per_episode.append(episode_reward)\n\n plt.plot(reward_per_episode)\n plt.title(\"Reward per episode\")\n plt.show()\n\n # obtain V* and pi*\n V = {}\n greedy_policy = {}\n states = grid.all_states()\n for s in states:\n if s in grid.actions:\n values = model.predict_all_actions(s)\n V[s] = np.max(values)\n greedy_policy[s] = ALL_POSSIBLE_ACTIONS[np.argmax(values)]\n else:\n # terminal state or state we can't otherwise get to\n V[s] = 0\n\n print(\"values:\")\n print_values(V, grid)\n print(\"policy:\")\n print_policy(greedy_policy, grid)\n\n\n print(\"state_visit_count:\")\n state_sample_count_arr = np.zeros((grid.rows, grid.cols))\n for i in range(grid.rows):\n for j in range(grid.cols):\n if (i, j) in state_visit_count:\n state_sample_count_arr[i,j] = state_visit_count[(i, j)]\n df = pd.DataFrame(state_sample_count_arr)\n print(df)\n" }, { "path": "rl/approx_prediction.py", "content": "# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom grid_world import standard_grid, negative_grid\nfrom iterative_policy_evaluation import print_values, print_policy\nfrom sklearn.kernel_approximation import Nystroem, RBFSampler\n\nGAMMA = 0.9\nALPHA = 0.01\nALL_POSSIBLE_ACTIONS = ('U', 'D', 'L', 'R')\n\n\ndef epsilon_greedy(greedy, s, eps=0.1):\n # we'll use epsilon-soft to ensure all states are visited\n # what happens if you don't do this? i.e. eps=0\n p = np.random.random()\n if p < (1 - eps):\n return greedy[s]\n else:\n return np.random.choice(ALL_POSSIBLE_ACTIONS)\n\n\ndef gather_samples(grid, n_episodes=10000):\n samples = []\n for _ in range(n_episodes):\n s = grid.reset()\n samples.append(s)\n while not grid.game_over():\n a = np.random.choice(ALL_POSSIBLE_ACTIONS)\n r = grid.move(a)\n s = grid.current_state()\n samples.append(s)\n return samples\n\n\nclass Model:\n def __init__(self, grid):\n # fit the featurizer to data\n samples = gather_samples(grid)\n # self.featurizer = Nystroem()\n self.featurizer = RBFSampler()\n self.featurizer.fit(samples)\n dims = self.featurizer.n_components\n\n # initialize linear model weights\n self.w = np.zeros(dims)\n\n def predict(self, s):\n x = self.featurizer.transform([s])[0]\n return x @ self.w\n\n def grad(self, s):\n x = self.featurizer.transform([s])[0]\n return x\n\n\nif __name__ == '__main__':\n # use the standard grid again (0 for every step) so that we can compare\n # to iterative policy evaluation\n grid = standard_grid()\n\n # print rewards\n print(\"rewards:\")\n print_values(grid.rewards, grid)\n\n # state -> action\n greedy_policy = {\n (2, 0): 'U',\n (1, 0): 'U',\n (0, 0): 'R',\n (0, 1): 'R',\n (0, 2): 'R',\n (1, 2): 'R',\n (2, 1): 'R',\n (2, 2): 'R',\n (2, 3): 'U',\n }\n\n model = Model(grid)\n mse_per_episode = []\n\n # repeat until convergence\n n_episodes = 10000\n for it in range(n_episodes):\n if (it + 1) % 100 == 0:\n print(it + 1)\n\n s = grid.reset()\n Vs = model.predict(s)\n n_steps = 0\n episode_err = 0\n while not grid.game_over():\n a = epsilon_greedy(greedy_policy, s)\n r = grid.move(a)\n s2 = grid.current_state()\n\n # get the target\n if grid.is_terminal(s2):\n target = r\n else:\n Vs2 = model.predict(s2)\n target = r + GAMMA * Vs2\n\n # update the model\n g = model.grad(s)\n err = target - Vs\n model.w += ALPHA * err * g\n \n # accumulate error\n n_steps += 1\n episode_err += err*err\n\n # update state\n s = s2\n Vs = Vs2\n \n mse = episode_err / n_steps\n mse_per_episode.append(mse)\n\n plt.plot(mse_per_episode)\n plt.title(\"MSE per episode\")\n plt.show()\n\n # obtain predicted values\n V = {}\n states = grid.all_states()\n for s in states:\n if s in grid.actions:\n V[s] = model.predict(s)\n else:\n # terminal state or state we can't otherwise get to\n V[s] = 0\n\n print(\"values:\")\n print_values(V, grid)\n print(\"policy:\")\n print_policy(greedy_policy, grid)\n" }, { "path": "rl/bayesian_bandit.py", "content": "# From the course: Bayesin Machine Learning in Python: A/B Testing\n# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing\n# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.stats import beta\n\n\n# np.random.seed(2)\nNUM_TRIALS = 2000\nBANDIT_PROBABILITIES = [0.2, 0.5, 0.75]\n\n\nclass Bandit:\n def __init__(self, p):\n self.p = p\n self.a = 1\n self.b = 1\n self.N = 0 # for information only\n\n def pull(self):\n return np.random.random() < self.p\n\n def sample(self):\n return np.random.beta(self.a, self.b)\n\n def update(self, x):\n self.a += x\n self.b += 1 - x\n self.N += 1\n\n\ndef plot(bandits, trial):\n x = np.linspace(0, 1, 200)\n for b in bandits:\n y = beta.pdf(x, b.a, b.b)\n plt.plot(x, y, label=f\"real p: {b.p:.4f}, win rate = {b.a - 1}/{b.N}\")\n plt.title(f\"Bandit distributions after {trial} trials\")\n plt.legend()\n plt.show()\n\n\ndef experiment():\n bandits = [Bandit(p) for p in BANDIT_PROBABILITIES]\n\n sample_points = [5,10,20,50,100,200,500,1000,1500,1999]\n rewards = np.zeros(NUM_TRIALS)\n for i in range(NUM_TRIALS):\n # Thompson sampling\n j = np.argmax([b.sample() for b in bandits])\n\n # plot the posteriors\n if i in sample_points:\n plot(bandits, i)\n\n # pull the arm for the bandit with the largest sample\n x = bandits[j].pull()\n\n # update rewards\n rewards[i] = x\n\n # update the distribution for the bandit whose arm we just pulled\n bandits[j].update(x)\n\n # print total reward\n print(\"total reward earned:\", rewards.sum())\n print(\"overall win rate:\", rewards.sum() / NUM_TRIALS)\n print(\"num times selected each bandit:\", [b.N for b in bandits])\n\n\nif __name__ == \"__main__\":\n experiment()\n" }, { "path": "rl/bayesian_normal.py", "content": "# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.stats import norm\n\n\nnp.random.seed(1)\nNUM_TRIALS = 2000\nBANDIT_MEANS = [1, 2, 3]\n\n\nclass Bandit:\n def __init__(self, true_mean):\n self.true_mean = true_mean\n # parameters for mu - prior is N(0,1)\n self.m = 0\n self.lambda_ = 1\n self.tau = 1\n self.N = 0\n\n def pull(self):\n return np.random.randn() / np.sqrt(self.tau) + self.true_mean\n\n def sample(self):\n return np.random.randn() / np.sqrt(self.lambda_) + self.m\n\n def update(self, x):\n self.m = (self.tau * x + self.lambda_ * self.m) / (self.tau + self.lambda_)\n self.lambda_ += self.tau\n self.N += 1\n\n\ndef plot(bandits, trial):\n x = np.linspace(-3, 6, 200)\n for b in bandits:\n y = norm.pdf(x, b.m, np.sqrt(1. / b.lambda_))\n plt.plot(x, y, label=f\"real mean: {b.true_mean:.4f}, num plays: {b.N}\")\n plt.title(f\"Bandit distributions after {trial} trials\")\n plt.legend()\n plt.show()\n\n\ndef run_experiment():\n bandits = [Bandit(m) for m in BANDIT_MEANS]\n\n sample_points = [5,10,20,50,100,200,500,1000,1500,1999]\n rewards = np.empty(NUM_TRIALS)\n for i in range(NUM_TRIALS):\n # Thompson sampling\n j = np.argmax([b.sample() for b in bandits])\n\n # plot the posteriors\n if i in sample_points:\n plot(bandits, i)\n\n # pull the arm for the bandit with the largest sample\n x = bandits[j].pull()\n\n # update the distribution for the bandit whose arm we just pulled\n bandits[j].update(x)\n\n # update rewards\n rewards[i] = x\n\n cumulative_average = np.cumsum(rewards) / (np.arange(NUM_TRIALS) + 1)\n\n # plot moving average ctr\n plt.plot(cumulative_average)\n for m in BANDIT_MEANS:\n plt.plot(np.ones(NUM_TRIALS)*m)\n plt.show()\n\n return cumulative_average\n\nif __name__ == '__main__':\n run_experiment()\n\n\n" }, { "path": "rl/bayesian_starter.py", "content": "# From the course: Bayesin Machine Learning in Python: A/B Testing\n# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing\n# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.stats import beta\n\n\n# np.random.seed(2)\nNUM_TRIALS = 2000\nBANDIT_PROBABILITIES = [0.2, 0.5, 0.75]\n\n\nclass Bandit:\n def __init__(self, p):\n self.p = p\n self.a = # TODO\n self.b = # TODO\n self.N = 0 # for information only\n\n def pull(self):\n return np.random.random() < self.p\n\n def sample(self):\n return # TODO - draw a sample from Beta(a, b)\n\n def update(self, x):\n self.a = # TODO\n self.b = # TODO\n self.N += 1\n\n\ndef plot(bandits, trial):\n x = np.linspace(0, 1, 200)\n for b in bandits:\n y = beta.pdf(x, b.a, b.b)\n plt.plot(x, y, label=f\"real p: {b.p:.4f}, win rate = {b.a - 1}/{b.N}\")\n plt.title(f\"Bandit distributions after {trial} trials\")\n plt.legend()\n plt.show()\n\n\ndef experiment():\n bandits = [Bandit(p) for p in BANDIT_PROBABILITIES]\n\n sample_points = [5,10,20,50,100,200,500,1000,1500,1999]\n rewards = np.zeros(NUM_TRIALS)\n for i in range(NUM_TRIALS):\n # Thompson sampling\n j = # TODO\n\n # plot the posteriors\n if i in sample_points:\n plot(bandits, i)\n\n # pull the arm for the bandit with the largest sample\n x = bandits[j].pull()\n\n # update rewards\n rewards[i] = x\n\n # update the distribution for the bandit whose arm we just pulled\n bandits[j].update(x)\n\n # print total reward\n print(\"total reward earned:\", rewards.sum())\n print(\"overall win rate:\", rewards.sum() / NUM_TRIALS)\n print(\"num times selected each bandit:\", [b.N for b in bandits])\n\n\nif __name__ == \"__main__\":\n experiment()\n" }, { "path": "rl/cartpole.py", "content": "# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport gym\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.kernel_approximation import RBFSampler\n\n\nGAMMA = 0.99\nALPHA = 0.1\n\n\ndef epsilon_greedy(model, s, eps=0.1):\n # we'll use epsilon-soft to ensure all states are visited\n # what happens if you don't do this? i.e. eps=0\n p = np.random.random()\n if p < (1 - eps):\n values = model.predict_all_actions(s)\n return np.argmax(values)\n else:\n return model.env.action_space.sample()\n\n\ndef gather_samples(env, n_episodes=10000):\n samples = []\n for _ in range(n_episodes):\n s, info = env.reset()\n done = False\n truncated = False\n while not (done or truncated):\n a = env.action_space.sample()\n sa = np.concatenate((s, [a]))\n samples.append(sa)\n\n s, r, done, truncated, info = env.step(a)\n return samples\n\n\nclass Model:\n def __init__(self, env):\n # fit the featurizer to data\n self.env = env\n samples = gather_samples(env)\n self.featurizer = RBFSampler()\n self.featurizer.fit(samples)\n dims = self.featurizer.n_components\n\n # initialize linear model weights\n self.w = np.zeros(dims)\n\n def predict(self, s, a):\n sa = np.concatenate((s, [a]))\n x = self.featurizer.transform([sa])[0]\n return x @ self.w\n\n def predict_all_actions(self, s):\n return [self.predict(s, a) for a in range(self.env.action_space.n)]\n\n def grad(self, s, a):\n sa = np.concatenate((s, [a]))\n x = self.featurizer.transform([sa])[0]\n return x\n\n\ndef test_agent(model, env, n_episodes=20):\n reward_per_episode = np.zeros(n_episodes)\n for it in range(n_episodes):\n done = False\n truncated = False\n episode_reward = 0\n s, info = env.reset()\n while not (done or truncated):\n a = epsilon_greedy(model, s, eps=0)\n s, r, done, truncated, info = env.step(a)\n episode_reward += r\n reward_per_episode[it] = episode_reward\n return np.mean(reward_per_episode)\n\n\ndef watch_agent(model, env, eps):\n done = False\n truncated = False\n episode_reward = 0\n s, info = env.reset()\n while not (done or truncated):\n a = epsilon_greedy(model, s, eps=eps)\n s, r, done, truncated, info = env.step(a)\n episode_reward += r\n print(\"Episode reward:\", episode_reward)\n\n\nif __name__ == '__main__':\n # instantiate environment\n env = gym.make(\"CartPole-v1\", render_mode=\"rgb_array\")\n\n model = Model(env)\n reward_per_episode = []\n\n # watch untrained agent\n watch_agent(model, env, eps=0)\n\n # repeat until convergence\n n_episodes = 1500\n for it in range(n_episodes):\n s, info = env.reset()\n episode_reward = 0\n done = False\n truncated = False\n while not (done or truncated):\n a = epsilon_greedy(model, s)\n s2, r, done, truncated, info = env.step(a)\n\n # get the target\n if done:\n target = r\n else:\n values = model.predict_all_actions(s2)\n target = r + GAMMA * np.max(values)\n\n # update the model\n g = model.grad(s, a)\n err = target - model.predict(s, a)\n model.w += ALPHA * err * g\n \n # accumulate reward\n episode_reward += r\n\n # update state\n s = s2\n\n if (it + 1) % 50 == 0:\n print(f\"Episode: {it + 1}, Reward: {episode_reward}\")\n\n # early exit\n if it > 20 and np.mean(reward_per_episode[-20:]) == 200:\n print(\"Early exit\")\n break\n \n reward_per_episode.append(episode_reward)\n\n # test trained agent\n test_reward = test_agent(model, env)\n print(f\"Average test reward: {test_reward}\")\n\n plt.plot(reward_per_episode)\n plt.title(\"Reward per episode\")\n plt.show()\n\n # watch trained agent\n env = gym.make(\"CartPole-v1\", render_mode=\"human\")\n watch_agent(model, env, eps=0)\n\n" }, { "path": "rl/cartpole_gym0.19.py", "content": "# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport gym\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.kernel_approximation import RBFSampler\n\n\nGAMMA = 0.99\nALPHA = 0.1\n\n\ndef epsilon_greedy(model, s, eps=0.1):\n # we'll use epsilon-soft to ensure all states are visited\n # what happens if you don't do this? i.e. eps=0\n p = np.random.random()\n if p < (1 - eps):\n values = model.predict_all_actions(s)\n return np.argmax(values)\n else:\n return model.env.action_space.sample()\n\n\ndef gather_samples(env, n_episodes=10000):\n samples = []\n for _ in range(n_episodes):\n s = env.reset()\n done = False\n while not done:\n a = env.action_space.sample()\n sa = np.concatenate((s, [a]))\n samples.append(sa)\n\n s, r, done, info = env.step(a)\n return samples\n\n\nclass Model:\n def __init__(self, env):\n # fit the featurizer to data\n self.env = env\n samples = gather_samples(env)\n self.featurizer = RBFSampler()\n self.featurizer.fit(samples)\n dims = self.featurizer.n_components\n\n # initialize linear model weights\n self.w = np.zeros(dims)\n\n def predict(self, s, a):\n sa = np.concatenate((s, [a]))\n x = self.featurizer.transform([sa])[0]\n return x @ self.w\n\n def predict_all_actions(self, s):\n return [self.predict(s, a) for a in range(self.env.action_space.n)]\n\n def grad(self, s, a):\n sa = np.concatenate((s, [a]))\n x = self.featurizer.transform([sa])[0]\n return x\n\n\ndef test_agent(model, env, n_episodes=20):\n reward_per_episode = np.zeros(n_episodes)\n for it in range(n_episodes):\n done = False\n episode_reward = 0\n s = env.reset()\n while not done:\n a = epsilon_greedy(model, s, eps=0)\n s, r, done, info = env.step(a)\n episode_reward += r\n reward_per_episode[it] = episode_reward\n return np.mean(reward_per_episode)\n\n\ndef watch_agent(model, env, eps):\n done = False\n episode_reward = 0\n s = env.reset()\n while not done:\n a = epsilon_greedy(model, s, eps=eps)\n s, r, done, info = env.step(a)\n env.render()\n episode_reward += r\n print(\"Episode reward:\", episode_reward)\n\n\nif __name__ == '__main__':\n # instantiate environment\n env = gym.make(\"CartPole-v0\")\n\n model = Model(env)\n reward_per_episode = []\n\n # watch untrained agent\n watch_agent(model, env, eps=0)\n\n # repeat until convergence\n n_episodes = 1500\n for it in range(n_episodes):\n s = env.reset()\n episode_reward = 0\n done = False\n while not done:\n a = epsilon_greedy(model, s)\n s2, r, done, info = env.step(a)\n\n # get the target\n if done:\n target = r\n else:\n values = model.predict_all_actions(s2)\n target = r + GAMMA * np.max(values)\n\n # update the model\n g = model.grad(s, a)\n err = target - model.predict(s, a)\n model.w += ALPHA * err * g\n \n # accumulate reward\n episode_reward += r\n\n # update state\n s = s2\n\n if (it + 1) % 50 == 0:\n print(f\"Episode: {it + 1}, Reward: {episode_reward}\")\n\n # early exit\n if it > 20 and np.mean(reward_per_episode[-20:]) == 200:\n print(\"Early exit\")\n break\n \n reward_per_episode.append(episode_reward)\n\n # test trained agent\n test_reward = test_agent(model, env)\n print(f\"Average test reward: {test_reward}\")\n\n plt.plot(reward_per_episode)\n plt.title(\"Reward per episode\")\n plt.show()\n\n # watch trained agent\n watch_agent(model, env, eps=0)\n\n" }, { "path": "rl/comparing_epsilons.py", "content": "# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nclass Bandit:\n def __init__(self, m):\n self.m = m\n self.mean = 0\n self.N = 0\n\n def pull(self):\n return np.random.randn() + self.m\n\n def update(self, x):\n self.N += 1\n self.mean = (1 - 1.0/self.N)*self.mean + 1.0/self.N*x\n\n\ndef run_experiment(m1, m2, m3, eps, N):\n bandits = [Bandit(m1), Bandit(m2), Bandit(m3)]\n\n data = np.empty(N)\n \n for i in range(N):\n # epsilon greedy\n p = np.random.random()\n if p < eps:\n j = np.random.choice(3)\n else:\n j = np.argmax([b.mean for b in bandits])\n x = bandits[j].pull()\n bandits[j].update(x)\n\n # for the plot\n data[i] = x\n cumulative_average = np.cumsum(data) / (np.arange(N) + 1)\n\n # plot moving average ctr\n plt.plot(cumulative_average)\n plt.plot(np.ones(N)*m1)\n plt.plot(np.ones(N)*m2)\n plt.plot(np.ones(N)*m3)\n plt.xscale('log')\n plt.show()\n\n for b in bandits:\n print(b.mean)\n\n return cumulative_average\n\nif __name__ == '__main__':\n c_1 = run_experiment(1.0, 2.0, 3.0, 0.1, 100000)\n c_05 = run_experiment(1.0, 2.0, 3.0, 0.05, 100000)\n c_01 = run_experiment(1.0, 2.0, 3.0, 0.01, 100000)\n\n # log scale plot\n plt.plot(c_1, label='eps = 0.1')\n plt.plot(c_05, label='eps = 0.05')\n plt.plot(c_01, label='eps = 0.01')\n plt.legend()\n plt.xscale('log')\n plt.show()\n\n\n # linear plot\n plt.plot(c_1, label='eps = 0.1')\n plt.plot(c_05, label='eps = 0.05')\n plt.plot(c_01, label='eps = 0.01')\n plt.legend()\n plt.show()\n\n" }, { "path": "rl/comparing_explore_exploit_methods.py", "content": "# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom comparing_epsilons import Bandit\nfrom optimistic_initial_values import run_experiment as run_experiment_oiv\nfrom ucb1 import run_experiment as run_experiment_ucb\n\nclass BayesianBandit:\n def __init__(self, true_mean):\n self.true_mean = true_mean\n # parameters for mu - prior is N(0,1)\n self.predicted_mean = 0\n self.lambda_ = 1\n self.sum_x = 0 # for convenience\n self.tau = 1\n\n def pull(self):\n return np.random.randn() + self.true_mean\n\n def sample(self):\n return np.random.randn() / np.sqrt(self.lambda_) + self.predicted_mean\n\n def update(self, x):\n self.lambda_ += self.tau\n self.sum_x += x\n self.predicted_mean = self.tau*self.sum_x / self.lambda_\n\n\ndef run_experiment_decaying_epsilon(m1, m2, m3, N):\n bandits = [Bandit(m1), Bandit(m2), Bandit(m3)]\n\n data = np.empty(N)\n \n for i in range(N):\n # epsilon greedy\n p = np.random.random()\n if p < 1.0/(i+1):\n j = np.random.choice(3)\n else:\n j = np.argmax([b.mean for b in bandits])\n x = bandits[j].pull()\n bandits[j].update(x)\n\n # for the plot\n data[i] = x\n cumulative_average = np.cumsum(data) / (np.arange(N) + 1)\n\n # plot moving average ctr\n plt.plot(cumulative_average)\n plt.plot(np.ones(N)*m1)\n plt.plot(np.ones(N)*m2)\n plt.plot(np.ones(N)*m3)\n plt.xscale('log')\n plt.show()\n\n for b in bandits:\n print(b.mean)\n\n return cumulative_average\n\n\ndef run_experiment(m1, m2, m3, N):\n bandits = [BayesianBandit(m1), BayesianBandit(m2), BayesianBandit(m3)]\n\n data = np.empty(N)\n \n for i in range(N):\n # optimistic initial values\n j = np.argmax([b.sample() for b in bandits])\n x = bandits[j].pull()\n bandits[j].update(x)\n\n # for the plot\n data[i] = x\n cumulative_average = np.cumsum(data) / (np.arange(N) + 1)\n\n # plot moving average ctr\n plt.plot(cumulative_average)\n plt.plot(np.ones(N)*m1)\n plt.plot(np.ones(N)*m2)\n plt.plot(np.ones(N)*m3)\n plt.xscale('log')\n plt.show()\n\n return cumulative_average\n\nif __name__ == '__main__':\n m1 = 1.0\n m2 = 2.0\n m3 = 3.0\n eps = run_experiment_decaying_epsilon(m1, m2, m3, 100000)\n oiv = run_experiment_oiv(m1, m2, m3, 100000)\n ucb = run_experiment_ucb(m1, m2, m3, 100000)\n bayes = run_experiment(m1, m2, m3, 100000)\n\n # log scale plot\n plt.plot(eps, label='decaying-epsilon-greedy')\n plt.plot(oiv, label='optimistic')\n plt.plot(ucb, label='ucb1')\n plt.plot(bayes, label='bayesian')\n plt.legend()\n plt.xscale('log')\n plt.show()\n\n\n # linear plot\n plt.plot(eps, label='decaying-epsilon-greedy')\n plt.plot(oiv, label='optimistic')\n plt.plot(ucb, label='ucb1')\n plt.plot(bayes, label='bayesian')\n plt.legend()\n plt.show()\n\n" }, { "path": "rl/epsilon_greedy.py", "content": "# From the course: Bayesin Machine Learning in Python: A/B Testing\n# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing\n# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nNUM_TRIALS = 10000\nEPS = 0.1\nBANDIT_PROBABILITIES = [0.2, 0.5, 0.75]\n\n\nclass Bandit:\n def __init__(self, p):\n # p: the win rate\n self.p = p\n self.p_estimate = 0.\n self.N = 0. # num samples collected so far\n\n def pull(self):\n # draw a 1 with probability p\n return np.random.random() < self.p\n\n def update(self, x):\n self.N += 1.\n self.p_estimate = ((self.N - 1)*self.p_estimate + x) / self.N\n\n\ndef experiment():\n bandits = [Bandit(p) for p in BANDIT_PROBABILITIES]\n\n rewards = np.zeros(NUM_TRIALS)\n num_times_explored = 0\n num_times_exploited = 0\n num_optimal = 0\n optimal_j = np.argmax([b.p for b in bandits])\n print(\"optimal j:\", optimal_j)\n\n for i in range(NUM_TRIALS):\n\n # use epsilon-greedy to select the next bandit\n if np.random.random() < EPS:\n num_times_explored += 1\n j = np.random.randint(len(bandits))\n else:\n num_times_exploited += 1\n j = np.argmax([b.p_estimate for b in bandits])\n\n if j == optimal_j:\n num_optimal += 1\n\n # pull the arm for the bandit with the largest sample\n x = bandits[j].pull()\n\n # update rewards log\n rewards[i] = x\n\n # update the distribution for the bandit whose arm we just pulled\n bandits[j].update(x)\n\n \n\n # print mean estimates for each bandit\n for b in bandits:\n print(\"mean estimate:\", b.p_estimate)\n\n # print total reward\n print(\"total reward earned:\", rewards.sum())\n print(\"overall win rate:\", rewards.sum() / NUM_TRIALS)\n print(\"num_times_explored:\", num_times_explored)\n print(\"num_times_exploited:\", num_times_exploited)\n print(\"num times selected optimal bandit:\", num_optimal)\n\n # plot the results\n cumulative_rewards = np.cumsum(rewards)\n win_rates = cumulative_rewards / (np.arange(NUM_TRIALS) + 1)\n plt.plot(win_rates)\n plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES))\n plt.show()\n\nif __name__ == \"__main__\":\n experiment()\n" }, { "path": "rl/epsilon_greedy_starter.py", "content": "# From the course: Bayesin Machine Learning in Python: A/B Testing\n# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing\n# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nNUM_TRIALS = 10000\nEPS = 0.1\nBANDIT_PROBABILITIES = [0.2, 0.5, 0.75]\n\n\nclass Bandit:\n def __init__(self, p):\n # p: the win rate\n self.p = p\n self.p_estimate = # TODO\n self.N = # TODO\n\n def pull(self):\n # draw a 1 with probability p\n return np.random.random() < self.p\n\n def update(self, x):\n self.N = # TODO\n self.p_estimate = # TODO\n\n\ndef experiment():\n bandits = [Bandit(p) for p in BANDIT_PROBABILITIES]\n\n rewards = np.zeros(NUM_TRIALS)\n num_times_explored = 0\n num_times_exploited = 0\n num_optimal = 0\n optimal_j = np.argmax([b.p for b in bandits])\n print(\"optimal j:\", optimal_j)\n\n for i in range(NUM_TRIALS):\n\n # use epsilon-greedy to select the next bandit\n if np.random.random() < EPS:\n num_times_explored += 1\n j = # TODO\n else:\n num_times_exploited += 1\n j = # TODO\n\n if j == optimal_j:\n num_optimal += 1\n\n # pull the arm for the bandit with the largest sample\n x = bandits[j].pull()\n\n # update rewards log\n rewards[i] = x\n\n # update the distribution for the bandit whose arm we just pulled\n bandits[j].update(x)\n\n \n\n # print mean estimates for each bandit\n for b in bandits:\n print(\"mean estimate:\", b.p_estimate)\n\n # print total reward\n print(\"total reward earned:\", rewards.sum())\n print(\"overall win rate:\", rewards.sum() / NUM_TRIALS)\n print(\"num_times_explored:\", num_times_explored)\n print(\"num_times_exploited:\", num_times_exploited)\n print(\"num times selected optimal bandit:\", num_optimal)\n\n # plot the results\n cumulative_rewards = np.cumsum(rewards)\n win_rates = cumulative_rewards / (np.arange(NUM_TRIALS) + 1)\n plt.plot(win_rates)\n plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES))\n plt.show()\n\nif __name__ == \"__main__\":\n experiment()\n" }, { "path": "rl/extra_reading.txt", "content": "Finite-time Analysis of the Multiarmed Bandit Problem\nhttps://homes.di.unimi.it/cesa-bianchi/Pubblicazioni/ml-02.pdf\n\nA Nice Lecture for Students Who Claim \"RL Doesn't Use Math\"\nhttps://www.youtube.com/watch?v=dhEF5pfYmvc\n\nHacking Google reCAPTCHA v3 using Reinforcement Learning\nhttps://arxiv.org/pdf/1903.01003.pdf\n\nPractical Deep Reinforcement Learning Approach for Stock Trading\nhttps://arxiv.org/abs/1811.07522\n\nReinforcement Learning: A Tutorial Survey and Recent Advances - Abhijit Gosavi\nhttp://web.mst.edu/~gosavia/joc.pdf\n\nAlgorithms for Reinforcement Learning - Csaba Szepesv´ari\nhttp://old.sztaki.hu/~szcsaba/papers/RLAlgsInMDPs-lecture.pdf\n\nMarkov Decision Processes in Artificial Intelligence\nhttps://zodml.org/sites/default/files/Markov_Decision_Processes_and_Artificial_Intelligence.pdf\n\nMDP Preliminaries\nhttp://nanjiang.cs.illinois.edu/files/cs598/note1.pdf\n\nConcentration Inequalities and Multi-Armed Bandits\nhttp://nanjiang.cs.illinois.edu/files/cs598/note_bandit.pdf\n\nNotes on Tabular Methods\nhttp://nanjiang.cs.illinois.edu/files/cs598/note3.pdf\n\nNotes on State Abstractions\nhttp://nanjiang.cs.illinois.edu/files/cs598/note4.pdf\n\nNotes on Fitted Q-iteration\nhttp://nanjiang.cs.illinois.edu/files/cs598/note5.pdf\n\nConvergence of Stochastic Iterative Dynamic Programming Algorithms\nhttps://papers.nips.cc/paper/764-convergence-of-stochastic-iterative-dynamic-programming-algorithms.pdf\n\nSutton & Barto\nhttp://incompleteideas.net/sutton/book/the-book-2nd.html\n\nFinite-Sample Analysis of Proximal Gradient TD Algorithms\nhttps://marek.petrik.us/pub/Liu2015.pdf\n\nFinite Sample Analyses for TD(0) with Function Approximation\nhttps://arxiv.org/pdf/1704.01161.pdf\n\nMastering the game of Go with deep neural networks and tree search - Silver, D. et al.\nhttps://storage.googleapis.com/deepmind-media/alphago/AlphaGoNaturePaper.pdf\n\nLearning Rates for Q-learning\nhttp://www.jmlr.org/papers/volume5/evendar03a/evendar03a.pdf" }, { "path": "rl/grid_world.py", "content": "# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\n\n\nACTION_SPACE = ('U', 'D', 'L', 'R')\n\n\nclass Grid: # Environment\n def __init__(self, rows, cols, start):\n self.rows = rows\n self.cols = cols\n self.i = start[0]\n self.j = start[1]\n\n def set(self, rewards, actions):\n # rewards should be a dict of: (i, j): r (row, col): reward\n # actions should be a dict of: (i, j): A (row, col): list of possible actions\n self.rewards = rewards\n self.actions = actions\n\n def set_state(self, s):\n self.i = s[0]\n self.j = s[1]\n\n def current_state(self):\n return (self.i, self.j)\n\n def is_terminal(self, s):\n return s not in self.actions\n\n def reset(self):\n # put agent back in start position\n self.i = 2\n self.j = 0\n return (self.i, self.j)\n\n def get_next_state(self, s, a):\n # this answers: where would I end up if I perform action 'a' in state 's'?\n i, j = s[0], s[1]\n\n # if this action moves you somewhere else, then it will be in this dictionary\n if a in self.actions[(i, j)]:\n if a == 'U':\n i -= 1\n elif a == 'D':\n i += 1\n elif a == 'R':\n j += 1\n elif a == 'L':\n j -= 1\n return i, j\n\n def move(self, action):\n # check if legal move first\n if action in self.actions[(self.i, self.j)]:\n if action == 'U':\n self.i -= 1\n elif action == 'D':\n self.i += 1\n elif action == 'R':\n self.j += 1\n elif action == 'L':\n self.j -= 1\n # return a reward (if any)\n return self.rewards.get((self.i, self.j), 0)\n\n def undo_move(self, action):\n # these are the opposite of what U/D/L/R should normally do\n if action == 'U':\n self.i += 1\n elif action == 'D':\n self.i -= 1\n elif action == 'R':\n self.j -= 1\n elif action == 'L':\n self.j += 1\n # raise an exception if we arrive somewhere we shouldn't be\n # should never happen\n assert(self.current_state() in self.all_states())\n\n def game_over(self):\n # returns true if game is over, else false\n # true if we are in a state where no actions are possible\n return (self.i, self.j) not in self.actions\n\n def all_states(self):\n # possibly buggy but simple way to get all states\n # either a position that has possible next actions\n # or a position that yields a reward\n return set(self.actions.keys()) | set(self.rewards.keys())\n\n\ndef standard_grid():\n # define a grid that describes the reward for arriving at each state\n # and possible actions at each state\n # the grid looks like this\n # x means you can't go there\n # s means start position\n # number means reward at that state\n # . . . 1\n # . x . -1\n # s . . .\n g = Grid(3, 4, (2, 0))\n rewards = {(0, 3): 1, (1, 3): -1}\n actions = {\n (0, 0): ('D', 'R'),\n (0, 1): ('L', 'R'),\n (0, 2): ('L', 'D', 'R'),\n (1, 0): ('U', 'D'),\n (1, 2): ('U', 'D', 'R'),\n (2, 0): ('U', 'R'),\n (2, 1): ('L', 'R'),\n (2, 2): ('L', 'R', 'U'),\n (2, 3): ('L', 'U'),\n }\n g.set(rewards, actions)\n return g\n\n\ndef negative_grid(step_cost=-0.1):\n # in this game we want to try to minimize the number of moves\n # so we will penalize every move\n g = standard_grid()\n g.rewards.update({\n (0, 0): step_cost,\n (0, 1): step_cost,\n (0, 2): step_cost,\n (1, 0): step_cost,\n (1, 2): step_cost,\n (2, 0): step_cost,\n (2, 1): step_cost,\n (2, 2): step_cost,\n (2, 3): step_cost,\n })\n return g\n\n\n\n\n\nclass WindyGrid:\n def __init__(self, rows, cols, start):\n self.rows = rows\n self.cols = cols\n self.i = start[0]\n self.j = start[1]\n\n def set(self, rewards, actions, probs):\n # rewards should be a dict of: (i, j): r (row, col): reward\n # actions should be a dict of: (i, j): A (row, col): list of possible actions\n self.rewards = rewards\n self.actions = actions\n self.probs = probs\n\n def set_state(self, s):\n self.i = s[0]\n self.j = s[1]\n\n def current_state(self):\n return (self.i, self.j)\n\n def is_terminal(self, s):\n return s not in self.actions\n\n def move(self, action):\n s = (self.i, self.j)\n a = action\n\n next_state_probs = self.probs[(s, a)]\n next_states = list(next_state_probs.keys())\n next_probs = list(next_state_probs.values())\n next_state_idx = np.random.choice(len(next_states), p=next_probs)\n s2 = next_states[next_state_idx]\n\n # update the current state\n self.i, self.j = s2\n\n # return a reward (if any)\n return self.rewards.get(s2, 0)\n\n def game_over(self):\n # returns true if game is over, else false\n # true if we are in a state where no actions are possible\n return (self.i, self.j) not in self.actions\n\n def all_states(self):\n # possibly buggy but simple way to get all states\n # either a position that has possible next actions\n # or a position that yields a reward\n return set(self.actions.keys()) | set(self.rewards.keys())\n\n\ndef windy_grid():\n g = WindyGrid(3, 4, (2, 0))\n rewards = {(0, 3): 1, (1, 3): -1}\n actions = {\n (0, 0): ('D', 'R'),\n (0, 1): ('L', 'R'),\n (0, 2): ('L', 'D', 'R'),\n (1, 0): ('U', 'D'),\n (1, 2): ('U', 'D', 'R'),\n (2, 0): ('U', 'R'),\n (2, 1): ('L', 'R'),\n (2, 2): ('L', 'R', 'U'),\n (2, 3): ('L', 'U'),\n }\n\n # p(s' | s, a) represented as:\n # KEY: (s, a) --> VALUE: {s': p(s' | s, a)}\n probs = {\n ((2, 0), 'U'): {(1, 0): 1.0},\n ((2, 0), 'D'): {(2, 0): 1.0},\n ((2, 0), 'L'): {(2, 0): 1.0},\n ((2, 0), 'R'): {(2, 1): 1.0},\n ((1, 0), 'U'): {(0, 0): 1.0},\n ((1, 0), 'D'): {(2, 0): 1.0},\n ((1, 0), 'L'): {(1, 0): 1.0},\n ((1, 0), 'R'): {(1, 0): 1.0},\n ((0, 0), 'U'): {(0, 0): 1.0},\n ((0, 0), 'D'): {(1, 0): 1.0},\n ((0, 0), 'L'): {(0, 0): 1.0},\n ((0, 0), 'R'): {(0, 1): 1.0},\n ((0, 1), 'U'): {(0, 1): 1.0},\n ((0, 1), 'D'): {(0, 1): 1.0},\n ((0, 1), 'L'): {(0, 0): 1.0},\n ((0, 1), 'R'): {(0, 2): 1.0},\n ((0, 2), 'U'): {(0, 2): 1.0},\n ((0, 2), 'D'): {(1, 2): 1.0},\n ((0, 2), 'L'): {(0, 1): 1.0},\n ((0, 2), 'R'): {(0, 3): 1.0},\n ((2, 1), 'U'): {(2, 1): 1.0},\n ((2, 1), 'D'): {(2, 1): 1.0},\n ((2, 1), 'L'): {(2, 0): 1.0},\n ((2, 1), 'R'): {(2, 2): 1.0},\n ((2, 2), 'U'): {(1, 2): 1.0},\n ((2, 2), 'D'): {(2, 2): 1.0},\n ((2, 2), 'L'): {(2, 1): 1.0},\n ((2, 2), 'R'): {(2, 3): 1.0},\n ((2, 3), 'U'): {(1, 3): 1.0},\n ((2, 3), 'D'): {(2, 3): 1.0},\n ((2, 3), 'L'): {(2, 2): 1.0},\n ((2, 3), 'R'): {(2, 3): 1.0},\n ((1, 2), 'U'): {(0, 2): 0.5, (1, 3): 0.5},\n ((1, 2), 'D'): {(2, 2): 1.0},\n ((1, 2), 'L'): {(1, 2): 1.0},\n ((1, 2), 'R'): {(1, 3): 1.0},\n }\n g.set(rewards, actions, probs)\n return g\n\n\ndef windy_grid_no_wind():\n g = windy_grid()\n g.probs[((1, 2), 'U')] = {(0, 2): 1.0}\n return g\n\n\n\ndef windy_grid_penalized(step_cost=-0.1):\n g = WindyGrid(3, 4, (2, 0))\n rewards = {\n (0, 0): step_cost,\n (0, 1): step_cost,\n (0, 2): step_cost,\n (1, 0): step_cost,\n (1, 2): step_cost,\n (2, 0): step_cost,\n (2, 1): step_cost,\n (2, 2): step_cost,\n (2, 3): step_cost,\n (0, 3): 1,\n (1, 3): -1\n }\n actions = {\n (0, 0): ('D', 'R'),\n (0, 1): ('L', 'R'),\n (0, 2): ('L', 'D', 'R'),\n (1, 0): ('U', 'D'),\n (1, 2): ('U', 'D', 'R'),\n (2, 0): ('U', 'R'),\n (2, 1): ('L', 'R'),\n (2, 2): ('L', 'R', 'U'),\n (2, 3): ('L', 'U'),\n }\n\n # p(s' | s, a) represented as:\n # KEY: (s, a) --> VALUE: {s': p(s' | s, a)}\n probs = {\n ((2, 0), 'U'): {(1, 0): 1.0},\n ((2, 0), 'D'): {(2, 0): 1.0},\n ((2, 0), 'L'): {(2, 0): 1.0},\n ((2, 0), 'R'): {(2, 1): 1.0},\n ((1, 0), 'U'): {(0, 0): 1.0},\n ((1, 0), 'D'): {(2, 0): 1.0},\n ((1, 0), 'L'): {(1, 0): 1.0},\n ((1, 0), 'R'): {(1, 0): 1.0},\n ((0, 0), 'U'): {(0, 0): 1.0},\n ((0, 0), 'D'): {(1, 0): 1.0},\n ((0, 0), 'L'): {(0, 0): 1.0},\n ((0, 0), 'R'): {(0, 1): 1.0},\n ((0, 1), 'U'): {(0, 1): 1.0},\n ((0, 1), 'D'): {(0, 1): 1.0},\n ((0, 1), 'L'): {(0, 0): 1.0},\n ((0, 1), 'R'): {(0, 2): 1.0},\n ((0, 2), 'U'): {(0, 2): 1.0},\n ((0, 2), 'D'): {(1, 2): 1.0},\n ((0, 2), 'L'): {(0, 1): 1.0},\n ((0, 2), 'R'): {(0, 3): 1.0},\n ((2, 1), 'U'): {(2, 1): 1.0},\n ((2, 1), 'D'): {(2, 1): 1.0},\n ((2, 1), 'L'): {(2, 0): 1.0},\n ((2, 1), 'R'): {(2, 2): 1.0},\n ((2, 2), 'U'): {(1, 2): 1.0},\n ((2, 2), 'D'): {(2, 2): 1.0},\n ((2, 2), 'L'): {(2, 1): 1.0},\n ((2, 2), 'R'): {(2, 3): 1.0},\n ((2, 3), 'U'): {(1, 3): 1.0},\n ((2, 3), 'D'): {(2, 3): 1.0},\n ((2, 3), 'L'): {(2, 2): 1.0},\n ((2, 3), 'R'): {(2, 3): 1.0},\n ((1, 2), 'U'): {(0, 2): 0.5, (1, 3): 0.5},\n ((1, 2), 'D'): {(2, 2): 1.0},\n ((1, 2), 'L'): {(1, 2): 1.0},\n ((1, 2), 'R'): {(1, 3): 1.0},\n }\n g.set(rewards, actions, probs)\n return g\n\n\n\ndef grid_5x5(step_cost=-0.1):\n g = Grid(5, 5, (4, 0))\n rewards = {(0, 4): 1, (1, 4): -1}\n actions = {\n (0, 0): ('D', 'R'),\n (0, 1): ('L', 'R'),\n (0, 2): ('L', 'R'),\n (0, 3): ('L', 'D', 'R'),\n (1, 0): ('U', 'D', 'R'),\n (1, 1): ('U', 'D', 'L'),\n (1, 3): ('U', 'D', 'R'),\n (2, 0): ('U', 'D', 'R'),\n (2, 1): ('U', 'L', 'R'),\n (2, 2): ('L', 'R', 'D'),\n (2, 3): ('L', 'R', 'U'),\n (2, 4): ('L', 'U', 'D'),\n (3, 0): ('U', 'D'),\n (3, 2): ('U', 'D'),\n (3, 4): ('U', 'D'),\n (4, 0): ('U', 'R'),\n (4, 1): ('L', 'R'),\n (4, 2): ('L', 'R', 'U'),\n (4, 3): ('L', 'R'),\n (4, 4): ('L', 'U'),\n }\n g.set(rewards, actions)\n\n # non-terminal states\n visitable_states = actions.keys()\n for s in visitable_states:\n g.rewards[s] = step_cost\n\n return g\n\n" }, { "path": "rl/iterative_policy_evaluation_deterministic.py", "content": "# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nfrom grid_world import standard_grid, ACTION_SPACE\n\nSMALL_ENOUGH = 1e-3 # threshold for convergence\n\n\ndef print_values(V, g):\n for i in range(g.rows):\n print(\"---------------------------\")\n for j in range(g.cols):\n v = V.get((i,j), 0)\n if v >= 0:\n print(\" %.2f|\" % v, end=\"\")\n else:\n print(\"%.2f|\" % v, end=\"\") # -ve sign takes up an extra space\n print(\"\")\n\n\ndef print_policy(P, g):\n for i in range(g.rows):\n print(\"---------------------------\")\n for j in range(g.cols):\n a = P.get((i,j), ' ')\n print(\" %s |\" % a, end=\"\")\n print(\"\")\n\n\n\nif __name__ == '__main__':\n\n ### define transition probabilities and grid ###\n # the key is (s, a, s'), the value is the probability\n # that is, transition_probs[(s, a, s')] = p(s' | s, a)\n # any key NOT present will considered to be impossible (i.e. probability 0)\n transition_probs = {}\n\n # to reduce the dimensionality of the dictionary, we'll use deterministic\n # rewards, r(s, a, s')\n # note: you could make it simpler by using r(s') since the reward doesn't\n # actually depend on (s, a)\n rewards = {}\n\n grid = standard_grid()\n for i in range(grid.rows):\n for j in range(grid.cols):\n s = (i, j)\n if not grid.is_terminal(s):\n for a in ACTION_SPACE:\n s2 = grid.get_next_state(s, a)\n transition_probs[(s, a, s2)] = 1\n if s2 in grid.rewards:\n rewards[(s, a, s2)] = grid.rewards[s2]\n\n ### fixed policy ###\n policy = {\n (2, 0): 'U',\n (1, 0): 'U',\n (0, 0): 'R',\n (0, 1): 'R',\n (0, 2): 'R',\n (1, 2): 'U',\n (2, 1): 'R',\n (2, 2): 'U',\n (2, 3): 'L',\n }\n print_policy(policy, grid)\n\n # initialize V(s) = 0\n V = {}\n for s in grid.all_states():\n V[s] = 0\n\n gamma = 0.9 # discount factor\n\n # repeat until convergence\n it = 0\n while True:\n biggest_change = 0\n for s in grid.all_states():\n if not grid.is_terminal(s):\n old_v = V[s]\n new_v = 0 # we will accumulate the answer\n for a in ACTION_SPACE:\n for s2 in grid.all_states():\n\n # action probability is deterministic\n action_prob = 1 if policy.get(s) == a else 0\n \n # reward is a function of (s, a, s'), 0 if not specified\n r = rewards.get((s, a, s2), 0)\n new_v += action_prob * transition_probs.get((s, a, s2), 0) * (r + gamma * V[s2])\n\n # after done getting the new value, update the value table\n V[s] = new_v\n biggest_change = max(biggest_change, np.abs(old_v - V[s]))\n\n print(\"iter:\", it, \"biggest_change:\", biggest_change)\n print_values(V, grid)\n it += 1\n\n if biggest_change < SMALL_ENOUGH:\n break\n print(\"\\n\\n\")\n" }, { "path": "rl/iterative_policy_evaluation_probabilistic.py", "content": "# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nfrom grid_world import windy_grid, ACTION_SPACE\n\nSMALL_ENOUGH = 1e-3 # threshold for convergence\n\n\ndef print_values(V, g):\n for i in range(g.rows):\n print(\"---------------------------\")\n for j in range(g.cols):\n v = V.get((i,j), 0)\n if v >= 0:\n print(\" %.2f|\" % v, end=\"\")\n else:\n print(\"%.2f|\" % v, end=\"\") # -ve sign takes up an extra space\n print(\"\")\n\n\ndef print_policy(P, g):\n for i in range(g.rows):\n print(\"---------------------------\")\n for j in range(g.cols):\n a = P.get((i,j), ' ')\n print(\" %s |\" % a, end=\"\")\n print(\"\")\n\n\n\nif __name__ == '__main__':\n\n ### define transition probabilities and grid ###\n # the key is (s, a, s'), the value is the probability\n # that is, transition_probs[(s, a, s')] = p(s' | s, a)\n # any key NOT present will considered to be impossible (i.e. probability 0)\n # we can take this from the grid object and convert it to the format we want\n transition_probs = {}\n\n # to reduce the dimensionality of the dictionary, we'll use deterministic\n # rewards, r(s, a, s')\n # note: you could make it simpler by using r(s') since the reward doesn't\n # actually depend on (s, a)\n rewards = {}\n\n grid = windy_grid()\n for (s, a), v in grid.probs.items():\n for s2, p in v.items():\n transition_probs[(s, a, s2)] = p\n rewards[(s, a, s2)] = grid.rewards.get(s2, 0)\n\n ### probabilistic policy ###\n policy = {\n (2, 0): {'U': 0.5, 'R': 0.5},\n (1, 0): {'U': 1.0},\n (0, 0): {'R': 1.0},\n (0, 1): {'R': 1.0},\n (0, 2): {'R': 1.0},\n (1, 2): {'U': 1.0},\n (2, 1): {'R': 1.0},\n (2, 2): {'U': 1.0},\n (2, 3): {'L': 1.0},\n }\n print_policy(policy, grid)\n\n # initialize V(s) = 0\n V = {}\n for s in grid.all_states():\n V[s] = 0\n\n gamma = 0.9 # discount factor\n\n # repeat until convergence\n it = 0\n while True:\n biggest_change = 0\n for s in grid.all_states():\n if not grid.is_terminal(s):\n old_v = V[s]\n new_v = 0 # we will accumulate the answer\n for a in ACTION_SPACE:\n for s2 in grid.all_states():\n\n # action probability is deterministic\n action_prob = policy[s].get(a, 0)\n \n # reward is a function of (s, a, s'), 0 if not specified\n r = rewards.get((s, a, s2), 0)\n new_v += action_prob * transition_probs.get((s, a, s2), 0) * (r + gamma * V[s2])\n\n # after done getting the new value, update the value table\n V[s] = new_v\n biggest_change = max(biggest_change, np.abs(old_v - V[s]))\n\n print(\"iter:\", it, \"biggest_change:\", biggest_change)\n print_values(V, grid)\n it += 1\n\n if biggest_change < SMALL_ENOUGH:\n break\n print(\"V:\", V)\n print(\"\\n\\n\")\n\n # sanity check\n # at state (1, 2), value is 0.5 * 0.9 * 1 + 0.5 * (-1) = -0.05\n\n" }, { "path": "rl/linear_rl_trader.py", "content": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom datetime import datetime\nimport itertools\nimport argparse\nimport re\nimport os\nimport pickle\n\nfrom sklearn.preprocessing import StandardScaler\n\n\n# Let's use AAPL (Apple), MSI (Motorola), SBUX (Starbucks)\ndef get_data():\n # returns a T x 3 list of stock prices\n # each row is a different stock\n # 0 = AAPL\n # 1 = MSI\n # 2 = SBUX\n df = pd.read_csv('../tf2.0/aapl_msi_sbux.csv')\n return df.values\n\n\n\n\n\ndef get_scaler(env):\n # return scikit-learn scaler object to scale the states\n # Note: you could also populate the replay buffer here\n\n states = []\n for _ in range(env.n_step):\n action = np.random.choice(env.action_space)\n state, reward, done, info = env.step(action)\n states.append(state)\n if done:\n break\n\n scaler = StandardScaler()\n scaler.fit(states)\n return scaler\n\n\n\n\ndef maybe_make_dir(directory):\n if not os.path.exists(directory):\n os.makedirs(directory)\n\n\n\nclass LinearModel:\n \"\"\" A linear regression model \"\"\"\n def __init__(self, input_dim, n_action):\n self.W = np.random.randn(input_dim, n_action) / np.sqrt(input_dim)\n self.b = np.zeros(n_action)\n\n # momentum terms\n self.vW = 0\n self.vb = 0\n\n self.losses = []\n\n def predict(self, X):\n # make sure X is N x D\n assert(len(X.shape) == 2)\n return X.dot(self.W) + self.b\n\n def sgd(self, X, Y, learning_rate=0.01, momentum=0.9):\n # make sure X is N x D\n assert(len(X.shape) == 2)\n\n # the loss values are 2-D\n # normally we would divide by N only\n # but now we divide by N x K\n num_values = np.prod(Y.shape)\n\n # do one step of gradient descent\n # we multiply by 2 to get the exact gradient\n # (not adjusting the learning rate)\n # i.e. d/dx (x^2) --> 2x\n Yhat = self.predict(X)\n gW = 2 * X.T.dot(Yhat - Y) / num_values\n gb = 2 * (Yhat - Y).sum(axis=0) / num_values\n\n # update momentum terms\n self.vW = momentum * self.vW - learning_rate * gW\n self.vb = momentum * self.vb - learning_rate * gb\n\n # update params\n self.W += self.vW\n self.b += self.vb\n\n mse = np.mean((Yhat - Y)**2)\n self.losses.append(mse)\n\n def load_weights(self, filepath):\n npz = np.load(filepath)\n self.W = npz['W']\n self.b = npz['b']\n\n def save_weights(self, filepath):\n np.savez(filepath, W=self.W, b=self.b)\n\n\n\n\nclass MultiStockEnv:\n \"\"\"\n A 3-stock trading environment.\n State: vector of size 7 (n_stock * 2 + 1)\n - # shares of stock 1 owned\n - # shares of stock 2 owned\n - # shares of stock 3 owned\n - price of stock 1 (using daily close price)\n - price of stock 2\n - price of stock 3\n - cash owned (can be used to purchase more stocks)\n Action: categorical variable with 27 (3^3) possibilities\n - for each stock, you can:\n - 0 = sell\n - 1 = hold\n - 2 = buy\n \"\"\"\n def __init__(self, data, initial_investment=20000):\n # data\n self.stock_price_history = data\n self.n_step, self.n_stock = self.stock_price_history.shape\n\n # instance attributes\n self.initial_investment = initial_investment\n self.cur_step = None\n self.stock_owned = None\n self.stock_price = None\n self.cash_in_hand = None\n\n self.action_space = np.arange(3**self.n_stock)\n\n # action permutations\n # returns a nested list with elements like:\n # [0,0,0]\n # [0,0,1]\n # [0,0,2]\n # [0,1,0]\n # [0,1,1]\n # etc.\n # 0 = sell\n # 1 = hold\n # 2 = buy\n self.action_list = list(map(list, itertools.product([0, 1, 2], repeat=self.n_stock)))\n\n # calculate size of state\n self.state_dim = self.n_stock * 2 + 1\n\n self.reset()\n\n\n def reset(self):\n self.cur_step = 0\n self.stock_owned = np.zeros(self.n_stock)\n self.stock_price = self.stock_price_history[self.cur_step]\n self.cash_in_hand = self.initial_investment\n return self._get_obs()\n\n\n def step(self, action):\n assert action in self.action_space\n\n # get current value before performing the action\n prev_val = self._get_val()\n\n # update price, i.e. go to the next day\n self.cur_step += 1\n self.stock_price = self.stock_price_history[self.cur_step]\n\n # perform the trade\n self._trade(action)\n\n # get the new value after taking the action\n cur_val = self._get_val()\n\n # reward is the increase in porfolio value\n reward = cur_val - prev_val\n\n # done if we have run out of data\n done = self.cur_step == self.n_step - 1\n\n # store the current value of the portfolio here\n info = {'cur_val': cur_val}\n\n # conform to the Gym API\n return self._get_obs(), reward, done, info\n\n\n def _get_obs(self):\n obs = np.empty(self.state_dim)\n obs[:self.n_stock] = self.stock_owned\n obs[self.n_stock:2*self.n_stock] = self.stock_price\n obs[-1] = self.cash_in_hand\n return obs\n \n\n\n def _get_val(self):\n return self.stock_owned.dot(self.stock_price) + self.cash_in_hand\n\n\n def _trade(self, action):\n # index the action we want to perform\n # 0 = sell\n # 1 = hold\n # 2 = buy\n # e.g. [2,1,0] means:\n # buy first stock\n # hold second stock\n # sell third stock\n action_vec = self.action_list[action]\n\n # determine which stocks to buy or sell\n sell_index = [] # stores index of stocks we want to sell\n buy_index = [] # stores index of stocks we want to buy\n for i, a in enumerate(action_vec):\n if a == 0:\n sell_index.append(i)\n elif a == 2:\n buy_index.append(i)\n\n # sell any stocks we want to sell\n # then buy any stocks we want to buy\n if sell_index:\n # NOTE: to simplify the problem, when we sell, we will sell ALL shares of that stock\n for i in sell_index:\n self.cash_in_hand += self.stock_price[i] * self.stock_owned[i]\n self.stock_owned[i] = 0\n if buy_index:\n # NOTE: when buying, we will loop through each stock we want to buy,\n # and buy one share at a time until we run out of cash\n can_buy = True\n while can_buy:\n for i in buy_index:\n if self.cash_in_hand > self.stock_price[i]:\n self.stock_owned[i] += 1 # buy one share\n self.cash_in_hand -= self.stock_price[i]\n else:\n can_buy = False\n\n\n\n\n\nclass DQNAgent(object):\n def __init__(self, state_size, action_size):\n self.state_size = state_size\n self.action_size = action_size\n self.gamma = 0.95 # discount rate\n self.epsilon = 1.0 # exploration rate\n self.epsilon_min = 0.01\n self.epsilon_decay = 0.995\n self.model = LinearModel(state_size, action_size)\n\n def act(self, state):\n if np.random.rand() <= self.epsilon:\n return np.random.choice(self.action_size)\n act_values = self.model.predict(state)\n return np.argmax(act_values[0]) # returns action\n\n\n def train(self, state, action, reward, next_state, done):\n if done:\n target = reward\n else:\n target = reward + self.gamma * np.amax(self.model.predict(next_state), axis=1)\n\n target_full = self.model.predict(state)\n target_full[0, action] = target\n\n # Run one training step\n self.model.sgd(state, target_full)\n\n if self.epsilon > self.epsilon_min:\n self.epsilon *= self.epsilon_decay\n\n\n def load(self, name):\n self.model.load_weights(name)\n\n\n def save(self, name):\n self.model.save_weights(name)\n\n\ndef play_one_episode(agent, env, is_train):\n # note: after transforming states are already 1xD\n state = env.reset()\n state = scaler.transform([state])\n done = False\n\n while not done:\n action = agent.act(state)\n next_state, reward, done, info = env.step(action)\n next_state = scaler.transform([next_state])\n if is_train == 'train':\n agent.train(state, action, reward, next_state, done)\n state = next_state\n\n return info['cur_val']\n\n\n\nif __name__ == '__main__':\n\n # config\n models_folder = 'linear_rl_trader_models'\n rewards_folder = 'linear_rl_trader_rewards'\n num_episodes = 2000\n batch_size = 32\n initial_investment = 20000\n\n\n parser = argparse.ArgumentParser()\n parser.add_argument('-m', '--mode', type=str, required=True,\n help='either \"train\" or \"test\"')\n args = parser.parse_args()\n\n maybe_make_dir(models_folder)\n maybe_make_dir(rewards_folder)\n\n data = get_data()\n n_timesteps, n_stocks = data.shape\n\n n_train = n_timesteps // 2\n\n train_data = data[:n_train]\n test_data = data[n_train:]\n\n env = MultiStockEnv(train_data, initial_investment)\n state_size = env.state_dim\n action_size = len(env.action_space)\n agent = DQNAgent(state_size, action_size)\n scaler = get_scaler(env)\n\n # store the final value of the portfolio (end of episode)\n portfolio_value = []\n\n if args.mode == 'test':\n # then load the previous scaler\n with open(f'{models_folder}/scaler.pkl', 'rb') as f:\n scaler = pickle.load(f)\n\n # remake the env with test data\n env = MultiStockEnv(test_data, initial_investment)\n\n # make sure epsilon is not 1!\n # no need to run multiple episodes if epsilon = 0, it's deterministic\n agent.epsilon = 0.01\n\n # load trained weights\n agent.load(f'{models_folder}/linear.npz')\n\n # play the game num_episodes times\n for e in range(num_episodes):\n t0 = datetime.now()\n val = play_one_episode(agent, env, args.mode)\n dt = datetime.now() - t0\n print(f\"episode: {e + 1}/{num_episodes}, episode end value: {val:.2f}, duration: {dt}\")\n portfolio_value.append(val) # append episode end portfolio value\n\n # save the weights when we are done\n if args.mode == 'train':\n # save the DQN\n agent.save(f'{models_folder}/linear.npz')\n\n # save the scaler\n with open(f'{models_folder}/scaler.pkl', 'wb') as f:\n pickle.dump(scaler, f)\n\n # plot losses\n plt.plot(agent.model.losses)\n plt.show()\n\n\n # save portfolio value for each episode\n np.save(f'{rewards_folder}/{args.mode}.npy', portfolio_value)\n" }, { "path": "rl/monte_carlo.py", "content": "# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nfrom grid_world import standard_grid, negative_grid\nfrom iterative_policy_evaluation import print_values, print_policy\n\nGAMMA = 0.9\n\n# NOTE: this is only policy evaluation, not optimization\n\ndef play_game(grid, policy, max_steps=20):\n # returns a list of states and corresponding returns\n\n # reset game to start at a random position\n # we need to do this, because given our current deterministic policy\n # we would never end up at certain states, but we still want to measure their value\n start_states = list(grid.actions.keys())\n start_idx = np.random.choice(len(start_states))\n grid.set_state(start_states[start_idx])\n\n s = grid.current_state()\n\n # keep track of all states and rewards encountered\n states = [s]\n rewards = [0]\n\n steps = 0\n while not grid.game_over():\n a = policy[s]\n r = grid.move(a)\n next_s = grid.current_state()\n\n # update states and rewards lists\n states.append(next_s)\n rewards.append(r)\n\n steps += 1\n if steps >= max_steps:\n break\n\n # update state\n # note: there is no need to store the final terminal state\n s = next_s\n\n # we want to return:\n # states = [s(0), s(1), ..., S(T)]\n # rewards = [R(0), R(1), ..., R(T)]\n\n return states, rewards\n\n\nif __name__ == '__main__':\n # use the standard grid again (0 for every step) so that we can compare\n # to iterative policy evaluation\n grid = standard_grid()\n\n # print rewards\n print(\"rewards:\")\n print_values(grid.rewards, grid)\n\n # state -> action\n policy = {\n (2, 0): 'U',\n (1, 0): 'U',\n (0, 0): 'R',\n (0, 1): 'R',\n (0, 2): 'R',\n (1, 2): 'R',\n (2, 1): 'R',\n (2, 2): 'R',\n (2, 3): 'U',\n }\n\n # initialize V(s) and returns\n V = {}\n returns = {} # dictionary of state -> list of returns we've received\n states = grid.all_states()\n for s in states:\n if s in grid.actions:\n returns[s] = []\n else:\n # terminal state or state we can't otherwise get to\n V[s] = 0\n\n # repeat\n for _ in range(100):\n # generate an episode using pi\n states, rewards = play_game(grid, policy)\n G = 0\n T = len(states)\n for t in range(T - 2, -1, -1):\n s = states[t]\n r = rewards[t+1]\n G = r + GAMMA * G # update return\n\n # we'll use first-visit Monte Carlo\n if s not in states[:t]:\n returns[s].append(G)\n V[s] = np.mean(returns[s])\n\n print(\"values:\")\n print_values(V, grid)\n print(\"policy:\")\n print_policy(policy, grid)\n" }, { "path": "rl/monte_carlo_es.py", "content": "# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom grid_world import standard_grid, negative_grid\nfrom iterative_policy_evaluation import print_values, print_policy\n\nGAMMA = 0.9\nALL_POSSIBLE_ACTIONS = ('U', 'D', 'L', 'R')\n\n# NOTE: this script implements the Monte Carlo Exploring-Starts method\n# for finding the optimal policy\n\n\ndef play_game(grid, policy, max_steps=20):\n # reset game to start at a random position\n # we need to do this if we have a deterministic policy\n # we would never end up at certain states, but we still want to measure their value\n # this is called the \"exploring starts\" method\n start_states = list(grid.actions.keys())\n start_idx = np.random.choice(len(start_states))\n grid.set_state(start_states[start_idx])\n\n s = grid.current_state()\n a = np.random.choice(ALL_POSSIBLE_ACTIONS) # first action is uniformly random\n\n states = [s]\n actions = [a]\n rewards = [0]\n\n for _ in range(max_steps):\n r = grid.move(a)\n s = grid.current_state()\n\n rewards.append(r)\n states.append(s)\n \n if grid.game_over():\n break\n else:\n a = policy[s]\n actions.append(a)\n\n # we want to return:\n # states = [s(0), s(1), ..., s(T-1), s(T)]\n # actions = [a(0), a(1), ..., a(T-1), ]\n # rewards = [ 0, R(1), ..., R(T-1), R(T)]\n\n return states, actions, rewards\n\n\ndef max_dict(d):\n # returns the argmax (key) and max (value) from a dictionary\n # put this into a function since we are using it so often\n\n # find max val\n max_val = max(d.values())\n\n # find keys corresponding to max val\n max_keys = [key for key, val in d.items() if val == max_val]\n\n ### slow version\n # max_keys = []\n # for key, val in d.items():\n # if val == max_val:\n # max_keys.append(key)\n\n return np.random.choice(max_keys), max_val\n\n\nif __name__ == '__main__':\n # use the standard grid again (0 for every step) so that we can compare\n # to iterative policy evaluation\n grid = standard_grid()\n # try the negative grid too, to see if agent will learn to go past the \"bad spot\"\n # in order to minimize number of steps\n # grid = negative_grid(step_cost=-0.1)\n\n # print rewards\n print(\"rewards:\")\n print_values(grid.rewards, grid)\n\n # state -> action\n # initialize a random policy\n policy = {}\n for s in grid.actions.keys():\n policy[s] = np.random.choice(ALL_POSSIBLE_ACTIONS)\n\n # initialize Q(s,a) and returns\n Q = {}\n sample_counts = {}\n states = grid.all_states()\n for s in states:\n if s in grid.actions: # not a terminal state\n Q[s] = {}\n sample_counts[s] = {}\n for a in ALL_POSSIBLE_ACTIONS:\n Q[s][a] = 0\n sample_counts[s][a] = 0\n else:\n # terminal state or state we can't otherwise get to\n pass\n\n # repeat until convergence\n deltas = []\n for it in range(10000):\n if it % 1000 == 0:\n print(it)\n\n # generate an episode using pi\n biggest_change = 0\n states, actions, rewards = play_game(grid, policy)\n\n # create a list of only state-action pairs for lookup\n states_actions = list(zip(states, actions))\n\n T = len(states)\n G = 0\n for t in range(T - 2, -1, -1):\n # retrieve current s, a, r tuple\n s = states[t]\n a = actions[t]\n\n # update G\n G = rewards[t+1] + GAMMA * G\n\n # check if we have already seen (s, a) (\"first-visit\")\n if (s, a) not in states_actions[:t]:\n old_q = Q[s][a]\n sample_counts[s][a] += 1\n lr = 1 / sample_counts[s][a]\n Q[s][a] = old_q + lr * (G - old_q)\n\n # update policy\n policy[s] = max_dict(Q[s])[0]\n\n # update delta\n biggest_change = max(biggest_change, np.abs(old_q - Q[s][a]))\n deltas.append(biggest_change)\n\n plt.plot(deltas)\n plt.show()\n\n print(\"final policy:\")\n print_policy(policy, grid)\n\n # find V\n V = {}\n for s, Qs in Q.items():\n V[s] = max_dict(Q[s])[1]\n\n print(\"final values:\")\n print_values(V, grid)\n" }, { "path": "rl/monte_carlo_no_es.py", "content": "# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom grid_world import standard_grid, negative_grid\nfrom iterative_policy_evaluation import print_values, print_policy\n\nGAMMA = 0.9\nALL_POSSIBLE_ACTIONS = ('U', 'D', 'L', 'R')\n\n\n\ndef epsilon_greedy(policy, s, eps=0.1):\n p = np.random.random()\n if p < (1 - eps):\n return policy[s]\n else:\n return np.random.choice(ALL_POSSIBLE_ACTIONS)\n\n\ndef play_game(grid, policy, max_steps=20):\n\n # start state\n s = grid.reset()\n\n # choose action\n a = epsilon_greedy(policy, s)\n\n states = [s]\n actions = [a]\n rewards = [0]\n\n for _ in range(max_steps):\n r = grid.move(a)\n s = grid.current_state()\n\n rewards.append(r)\n states.append(s)\n \n if grid.game_over():\n break\n else:\n a = epsilon_greedy(policy, s)\n actions.append(a)\n\n # we want to return:\n # states = [s(0), s(1), ..., s(T-1), s(T)]\n # actions = [a(0), a(1), ..., a(T-1), ]\n # rewards = [ 0, R(1), ..., R(T-1), R(T)]\n\n return states, actions, rewards\n\n\ndef max_dict(d):\n # returns the argmax (key) and max (value) from a dictionary\n # put this into a function since we are using it so often\n\n # find max val\n max_val = max(d.values())\n\n # find keys corresponding to max val\n max_keys = [key for key, val in d.items() if val == max_val]\n\n ### slow version\n # max_keys = []\n # for key, val in d.items():\n # if val == max_val:\n # max_keys.append(key)\n\n return np.random.choice(max_keys), max_val\n\n\nif __name__ == '__main__':\n # use the standard grid again (0 for every step) so that we can compare\n # to iterative policy evaluation\n grid = standard_grid()\n # try the negative grid too, to see if agent will learn to go past the \"bad spot\"\n # in order to minimize number of steps\n # grid = negative_grid(step_cost=-0.1)\n\n # print rewards\n print(\"rewards:\")\n print_values(grid.rewards, grid)\n\n # state -> action\n # initialize a random policy\n policy = {}\n for s in grid.actions.keys():\n policy[s] = np.random.choice(ALL_POSSIBLE_ACTIONS)\n\n # initialize Q(s,a) and returns\n Q = {}\n sample_counts = {}\n state_sample_count = {}\n states = grid.all_states()\n for s in states:\n if s in grid.actions: # not a terminal state\n Q[s] = {}\n sample_counts[s] = {}\n state_sample_count[s] = 0\n for a in ALL_POSSIBLE_ACTIONS:\n Q[s][a] = 0\n sample_counts[s][a] = 0\n else:\n # terminal state or state we can't otherwise get to\n pass\n\n # repeat until convergence\n deltas = []\n for it in range(10000):\n if it % 1000 == 0:\n print(it)\n\n # generate an episode using pi\n biggest_change = 0\n states, actions, rewards = play_game(grid, policy)\n\n # create a list of only state-action pairs for lookup\n states_actions = list(zip(states, actions))\n\n T = len(states)\n G = 0\n for t in range(T - 2, -1, -1):\n # retrieve current s, a, r tuple\n s = states[t]\n a = actions[t]\n\n # update G\n G = rewards[t+1] + GAMMA * G\n\n # check if we have already seen (s, a) (\"first-visit\")\n if (s, a) not in states_actions[:t]:\n old_q = Q[s][a]\n sample_counts[s][a] += 1\n lr = 1 / sample_counts[s][a]\n Q[s][a] = old_q + lr * (G - old_q)\n\n # update policy\n policy[s] = max_dict(Q[s])[0]\n\n # update state sample count\n state_sample_count[s] += 1\n\n # update delta\n biggest_change = max(biggest_change, np.abs(old_q - Q[s][a]))\n deltas.append(biggest_change)\n\n plt.plot(deltas)\n plt.show()\n\n print(\"final policy:\")\n print_policy(policy, grid)\n\n # find V\n V = {}\n for s, Qs in Q.items():\n V[s] = max_dict(Q[s])[1]\n\n print(\"final values:\")\n print_values(V, grid)\n\n print(\"state_sample_count:\")\n state_sample_count_arr = np.zeros((grid.rows, grid.cols))\n for i in range(grid.rows):\n for j in range(grid.cols):\n if (i, j) in state_sample_count:\n state_sample_count_arr[i,j] = state_sample_count[(i, j)]\n df = pd.DataFrame(state_sample_count_arr)\n print(df)\n" }, { "path": "rl/optimistic.py", "content": "# From the course: Bayesin Machine Learning in Python: A/B Testing\n# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing\n# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nNUM_TRIALS = 10000\nEPS = 0.1\nBANDIT_PROBABILITIES = [0.2, 0.5, 0.75]\n\n\nclass Bandit:\n def __init__(self, p):\n # p: the win rate\n self.p = p\n self.p_estimate = 5.\n self.N = 1. # num samples collected so far\n\n def pull(self):\n # draw a 1 with probability p\n return np.random.random() < self.p\n\n def update(self, x):\n self.N += 1.\n self.p_estimate = ((self.N - 1)*self.p_estimate + x) / self.N\n\n\ndef experiment():\n bandits = [Bandit(p) for p in BANDIT_PROBABILITIES]\n\n rewards = np.zeros(NUM_TRIALS)\n for i in range(NUM_TRIALS):\n # use optimistic initial values to select the next bandit\n j = np.argmax([b.p_estimate for b in bandits])\n\n # pull the arm for the bandit with the largest sample\n x = bandits[j].pull()\n\n # update rewards log\n rewards[i] = x\n\n # update the distribution for the bandit whose arm we just pulled\n bandits[j].update(x)\n\n\n # print mean estimates for each bandit\n for b in bandits:\n print(\"mean estimate:\", b.p_estimate)\n\n # print total reward\n print(\"total reward earned:\", rewards.sum())\n print(\"overall win rate:\", rewards.sum() / NUM_TRIALS)\n print(\"num times selected each bandit:\", [b.N for b in bandits])\n\n # plot the results\n cumulative_rewards = np.cumsum(rewards)\n win_rates = cumulative_rewards / (np.arange(NUM_TRIALS) + 1)\n plt.ylim([0, 1])\n plt.plot(win_rates)\n plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES))\n plt.show()\n\nif __name__ == \"__main__\":\n experiment()\n" }, { "path": "rl/optimistic_initial_values.py", "content": "# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom comparing_epsilons import run_experiment as run_experiment_eps\n\n\nclass Bandit:\n def __init__(self, m, upper_limit):\n self.m = m\n self.mean = upper_limit\n self.N = 1\n\n def pull(self):\n return np.random.randn() + self.m\n\n def update(self, x):\n self.N += 1\n self.mean = (1 - 1.0/self.N)*self.mean + 1.0/self.N*x\n\n\ndef run_experiment(m1, m2, m3, N, upper_limit=10):\n bandits = [Bandit(m1, upper_limit), Bandit(m2, upper_limit), Bandit(m3, upper_limit)]\n\n data = np.empty(N)\n \n for i in range(N):\n # optimistic initial values\n j = np.argmax([b.mean for b in bandits])\n x = bandits[j].pull()\n bandits[j].update(x)\n\n # for the plot\n data[i] = x\n cumulative_average = np.cumsum(data) / (np.arange(N) + 1)\n\n # plot moving average ctr\n plt.plot(cumulative_average)\n plt.plot(np.ones(N)*m1)\n plt.plot(np.ones(N)*m2)\n plt.plot(np.ones(N)*m3)\n plt.xscale('log')\n plt.show()\n\n for b in bandits:\n print(b.mean)\n\n return cumulative_average\n\nif __name__ == '__main__':\n c_1 = run_experiment_eps(1.0, 2.0, 3.0, 0.1, 100000)\n oiv = run_experiment(1.0, 2.0, 3.0, 100000)\n\n # log scale plot\n plt.plot(c_1, label='eps = 0.1')\n plt.plot(oiv, label='optimistic')\n plt.legend()\n plt.xscale('log')\n plt.show()\n\n\n # linear plot\n plt.plot(c_1, label='eps = 0.1')\n plt.plot(oiv, label='optimistic')\n plt.legend()\n plt.show()\n\n" }, { "path": "rl/optimistic_starter.py", "content": "# From the course: Bayesin Machine Learning in Python: A/B Testing\n# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing\n# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nNUM_TRIALS = 10000\nEPS = 0.1\nBANDIT_PROBABILITIES = [0.2, 0.5, 0.75]\n\n\nclass Bandit:\n def __init__(self, p):\n # p: the win rate\n self.p = p\n self.p_estimate = # TODO\n self.N = # TODO\n\n def pull(self):\n # draw a 1 with probability p\n return np.random.random() < self.p\n\n def update(self, x):\n # TODO\n self.p_estimate = # TODO\n\n\ndef experiment():\n bandits = [Bandit(p) for p in BANDIT_PROBABILITIES]\n\n rewards = np.zeros(NUM_TRIALS)\n for i in range(NUM_TRIALS):\n # use optimistic initial values to select the next bandit\n j = # TODO\n\n # pull the arm for the bandit with the largest sample\n x = bandits[j].pull()\n\n # update rewards log\n rewards[i] = x\n\n # update the distribution for the bandit whose arm we just pulled\n bandits[j].update(x)\n\n\n # print mean estimates for each bandit\n for b in bandits:\n print(\"mean estimate:\", b.p_estimate)\n\n # print total reward\n print(\"total reward earned:\", rewards.sum())\n print(\"overall win rate:\", rewards.sum() / NUM_TRIALS)\n print(\"num times selected each bandit:\", [b.N for b in bandits])\n\n # plot the results\n cumulative_rewards = np.cumsum(rewards)\n win_rates = cumulative_rewards / (np.arange(NUM_TRIALS) + 1)\n plt.ylim([0, 1])\n plt.plot(win_rates)\n plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES))\n plt.show()\n\nif __name__ == \"__main__\":\n experiment()\n" }, { "path": "rl/plot_rl_rewards.py", "content": "import matplotlib.pyplot as plt\nimport numpy as np\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-m', '--mode', type=str, required=True,\n help='either \"train\" or \"test\"')\nargs = parser.parse_args()\n\na = np.load(f'linear_rl_trader_rewards/{args.mode}.npy')\n\nprint(f\"average reward: {a.mean():.2f}, min: {a.min():.2f}, max: {a.max():.2f}\")\n\nif args.mode == 'train':\n # show the training progress\n plt.plot(a)\nelse:\n # test - show a histogram of rewards\n plt.hist(a, bins=20)\n\nplt.title(args.mode)\nplt.show()" }, { "path": "rl/policy_iteration_deterministic.py", "content": "# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nfrom grid_world import standard_grid, ACTION_SPACE\nfrom iterative_policy_evaluation_deterministic import print_values, print_policy\n\nSMALL_ENOUGH = 1e-3\nGAMMA = 0.9\n\n\n# copied from iterative_policy_evaluation\ndef get_transition_probs_and_rewards(grid):\n ### define transition probabilities and grid ###\n # the key is (s, a, s'), the value is the probability\n # that is, transition_probs[(s, a, s')] = p(s' | s, a)\n # any key NOT present will considered to be impossible (i.e. probability 0)\n transition_probs = {}\n\n # to reduce the dimensionality of the dictionary, we'll use deterministic\n # rewards, r(s, a, s')\n # note: you could make it simpler by using r(s') since the reward doesn't\n # actually depend on (s, a)\n rewards = {}\n\n for i in range(grid.rows):\n for j in range(grid.cols):\n s = (i, j)\n if not grid.is_terminal(s):\n for a in ACTION_SPACE:\n s2 = grid.get_next_state(s, a)\n transition_probs[(s, a, s2)] = 1\n if s2 in grid.rewards:\n rewards[(s, a, s2)] = grid.rewards[s2]\n\n return transition_probs, rewards\n\n\ndef evaluate_deterministic_policy(grid, policy, initV=None):\n # initialize V(s) = 0\n if initV is None:\n V = {}\n for s in grid.all_states():\n V[s] = 0\n else:\n # it's faster to use the existing V(s) since the value won't change\n # that much from one policy to the next\n V = initV\n\n # repeat until convergence\n it = 0\n while True:\n biggest_change = 0\n for s in grid.all_states():\n if not grid.is_terminal(s):\n old_v = V[s]\n new_v = 0 # we will accumulate the answer\n for a in ACTION_SPACE:\n for s2 in grid.all_states():\n\n # action probability is deterministic\n action_prob = 1 if policy.get(s) == a else 0\n \n # reward is a function of (s, a, s'), 0 if not specified\n r = rewards.get((s, a, s2), 0)\n new_v += action_prob * transition_probs.get((s, a, s2), 0) * (r + GAMMA * V[s2])\n\n # after done getting the new value, update the value table\n V[s] = new_v\n biggest_change = max(biggest_change, np.abs(old_v - V[s]))\n it += 1\n\n if biggest_change < SMALL_ENOUGH:\n break\n return V\n\n\nif __name__ == '__main__':\n\n grid = standard_grid()\n transition_probs, rewards = get_transition_probs_and_rewards(grid)\n\n # print rewards\n print(\"rewards:\")\n print_values(grid.rewards, grid)\n\n # state -> action\n # we'll randomly choose an action and update as we learn\n policy = {}\n for s in grid.actions.keys():\n policy[s] = np.random.choice(ACTION_SPACE)\n\n # initial policy\n print(\"initial policy:\")\n print_policy(policy, grid)\n\n # repeat until convergence - will break out when policy does not change\n V = None\n while True:\n\n # policy evaluation step - we already know how to do this!\n V = evaluate_deterministic_policy(grid, policy, initV=V)\n\n # policy improvement step\n is_policy_converged = True\n for s in grid.actions.keys():\n old_a = policy[s]\n new_a = None\n best_value = float('-inf')\n\n # loop through all possible actions to find the best current action\n for a in ACTION_SPACE:\n v = 0\n for s2 in grid.all_states():\n # reward is a function of (s, a, s'), 0 if not specified\n r = rewards.get((s, a, s2), 0)\n v += transition_probs.get((s, a, s2), 0) * (r + GAMMA * V[s2])\n\n if v > best_value:\n best_value = v\n new_a = a\n\n # new_a now represents the best action in this state\n policy[s] = new_a\n if new_a != old_a:\n is_policy_converged = False\n\n if is_policy_converged:\n break\n\n # once we're done, print the final policy and values\n print(\"values:\")\n print_values(V, grid)\n print(\"policy:\")\n print_policy(policy, grid)\n" }, { "path": "rl/policy_iteration_probabilistic.py", "content": "# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nfrom grid_world import windy_grid, windy_grid_penalized, ACTION_SPACE\nfrom iterative_policy_evaluation import print_values, print_policy\n\nSMALL_ENOUGH = 1e-3\nGAMMA = 0.9\n\n\n# copied from iterative_policy_evaluation\ndef get_transition_probs_and_rewards(grid):\n ### define transition probabilities and grid ###\n # the key is (s, a, s'), the value is the probability\n # that is, transition_probs[(s, a, s')] = p(s' | s, a)\n # any key NOT present will considered to be impossible (i.e. probability 0)\n transition_probs = {}\n\n # to reduce the dimensionality of the dictionary, we'll use deterministic\n # rewards, r(s, a, s')\n # note: you could make it simpler by using r(s') since the reward doesn't\n # actually depend on (s, a)\n rewards = {}\n\n for (s, a), v in grid.probs.items():\n for s2, p in v.items():\n transition_probs[(s, a, s2)] = p\n rewards[(s, a, s2)] = grid.rewards.get(s2, 0)\n\n return transition_probs, rewards\n\n\ndef evaluate_deterministic_policy(grid, policy, initV=None):\n # initialize V(s) = 0\n if initV is None:\n V = {}\n for s in grid.all_states():\n V[s] = 0\n else:\n # it's faster to use the existing V(s) since the value won't change\n # that much from one policy to the next\n V = initV\n\n # repeat until convergence\n it = 0\n while True:\n biggest_change = 0\n for s in grid.all_states():\n if not grid.is_terminal(s):\n old_v = V[s]\n new_v = 0 # we will accumulate the answer\n for a in ACTION_SPACE:\n for s2 in grid.all_states():\n\n # action probability is deterministic\n action_prob = 1 if policy.get(s) == a else 0\n \n # reward is a function of (s, a, s'), 0 if not specified\n r = rewards.get((s, a, s2), 0)\n new_v += action_prob * transition_probs.get((s, a, s2), 0) * (r + GAMMA * V[s2])\n\n # after done getting the new value, update the value table\n V[s] = new_v\n biggest_change = max(biggest_change, np.abs(old_v - V[s]))\n it += 1\n\n if biggest_change < SMALL_ENOUGH:\n break\n return V\n\n\nif __name__ == '__main__':\n\n grid = windy_grid_penalized(-0.1)\n # grid = windy_grid()\n transition_probs, rewards = get_transition_probs_and_rewards(grid)\n\n # print rewards\n print(\"rewards:\")\n print_values(grid.rewards, grid)\n\n # state -> action\n # we'll randomly choose an action and update as we learn\n policy = {}\n for s in grid.actions.keys():\n policy[s] = np.random.choice(ACTION_SPACE)\n\n # initial policy\n print(\"initial policy:\")\n print_policy(policy, grid)\n\n # repeat until convergence - will break out when policy does not change\n V = None\n while True:\n\n # policy evaluation step - we already know how to do this!\n V = evaluate_deterministic_policy(grid, policy, initV=V)\n\n # policy improvement step\n is_policy_converged = True\n for s in grid.actions.keys():\n old_a = policy[s]\n new_a = None\n best_value = float('-inf')\n\n # loop through all possible actions to find the best current action\n for a in ACTION_SPACE:\n v = 0\n for s2 in grid.all_states():\n # reward is a function of (s, a, s'), 0 if not specified\n r = rewards.get((s, a, s2), 0)\n v += transition_probs.get((s, a, s2), 0) * (r + GAMMA * V[s2])\n\n if v > best_value:\n best_value = v\n new_a = a\n\n # new_a now represents the best action in this state\n policy[s] = new_a\n if new_a != old_a:\n is_policy_converged = False\n\n if is_policy_converged:\n break\n\n # once we're done, print the final policy and values\n print(\"values:\")\n print_values(V, grid)\n print(\"policy:\")\n print_policy(policy, grid)\n" }, { "path": "rl/q_learning.py", "content": "# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom grid_world import standard_grid, negative_grid\nfrom iterative_policy_evaluation import print_values, print_policy\nfrom monte_carlo_es import max_dict\n\nGAMMA = 0.9\nALPHA = 0.1\nALL_POSSIBLE_ACTIONS = ('U', 'D', 'L', 'R')\n\n\ndef epsilon_greedy(Q, s, eps=0.1):\n if np.random.random() < eps:\n return np.random.choice(ALL_POSSIBLE_ACTIONS)\n else:\n a_opt = max_dict(Q[s])[0]\n return a_opt\n\n\nif __name__ == '__main__':\n # grid = standard_grid()\n grid = negative_grid(step_cost=-0.1)\n\n # print rewards\n print(\"rewards:\")\n print_values(grid.rewards, grid)\n\n # initialize Q(s,a)\n Q = {}\n states = grid.all_states()\n for s in states:\n Q[s] = {}\n for a in ALL_POSSIBLE_ACTIONS:\n Q[s][a] = 0\n\n # let's also keep track of how many times Q[s] has been updated\n update_counts = {}\n\n # repeat until convergence\n reward_per_episode = []\n for it in range(10000):\n if it % 2000 == 0:\n print(\"it:\", it)\n\n # begin a new episode\n s = grid.reset()\n episode_reward = 0\n while not grid.game_over():\n # perform action and get next state + reward\n a = epsilon_greedy(Q, s, eps=0.1)\n r = grid.move(a)\n s2 = grid.current_state()\n\n # update reward\n episode_reward += r\n\n # update Q(s,a)\n maxQ = max_dict(Q[s2])[1]\n Q[s][a] = Q[s][a] + ALPHA*(r + GAMMA*maxQ - Q[s][a])\n\n # we would like to know how often Q(s) has been updated too\n update_counts[s] = update_counts.get(s,0) + 1\n\n # next state becomes current state\n s = s2\n\n # log the reward for this episode\n reward_per_episode.append(episode_reward)\n\n plt.plot(reward_per_episode)\n plt.title(\"reward_per_episode\")\n plt.show()\n\n # determine the policy from Q*\n # find V* from Q*\n policy = {}\n V = {}\n for s in grid.actions.keys():\n a, max_q = max_dict(Q[s])\n policy[s] = a\n V[s] = max_q\n\n # what's the proportion of time we spend updating each part of Q?\n print(\"update counts:\")\n total = np.sum(list(update_counts.values()))\n for k, v in update_counts.items():\n update_counts[k] = float(v) / total\n print_values(update_counts, grid)\n\n print(\"values:\")\n print_values(V, grid)\n print(\"policy:\")\n print_policy(policy, grid)\n\n" }, { "path": "rl/sarsa.py", "content": "# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom grid_world import standard_grid, negative_grid\nfrom iterative_policy_evaluation import print_values, print_policy\nfrom monte_carlo_es import max_dict\n\nGAMMA = 0.9\nALPHA = 0.1\nALL_POSSIBLE_ACTIONS = ('U', 'D', 'L', 'R')\n\n\ndef epsilon_greedy(Q, s, eps=0.1):\n if np.random.random() < eps:\n return np.random.choice(ALL_POSSIBLE_ACTIONS)\n else:\n a_opt = max_dict(Q[s])[0]\n return a_opt\n\n\nif __name__ == '__main__':\n # grid = standard_grid()\n grid = negative_grid(step_cost=-0.1)\n\n # print rewards\n print(\"rewards:\")\n print_values(grid.rewards, grid)\n\n # initialize Q(s,a)\n Q = {}\n states = grid.all_states()\n for s in states:\n Q[s] = {}\n for a in ALL_POSSIBLE_ACTIONS:\n Q[s][a] = 0\n\n # let's also keep track of how many times Q[s] has been updated\n update_counts = {}\n\n # repeat until convergence\n reward_per_episode = []\n for it in range(10000):\n if it % 2000 == 0:\n print(\"it:\", it)\n\n # begin a new episode\n s = grid.reset()\n a = epsilon_greedy(Q, s, eps=0.1)\n episode_reward = 0\n while not grid.game_over():\n # perform action and get next state + reward\n r = grid.move(a)\n s2 = grid.current_state()\n\n # update reward\n episode_reward += r\n\n # get next action\n a2 = epsilon_greedy(Q, s2, eps=0.1)\n\n # update Q(s,a)\n Q[s][a] = Q[s][a] + ALPHA*(r + GAMMA*Q[s2][a2] - Q[s][a])\n\n # we would like to know how often Q(s) has been updated too\n update_counts[s] = update_counts.get(s,0) + 1\n\n # next state becomes current state\n s = s2\n a = a2\n\n # log the reward for this episode\n reward_per_episode.append(episode_reward)\n\n plt.plot(reward_per_episode)\n plt.title(\"reward_per_episode\")\n plt.show()\n\n # determine the policy from Q*\n # find V* from Q*\n policy = {}\n V = {}\n for s in grid.actions.keys():\n a, max_q = max_dict(Q[s])\n policy[s] = a\n V[s] = max_q\n\n # what's the proportion of time we spend updating each part of Q?\n print(\"update counts:\")\n total = np.sum(list(update_counts.values()))\n for k, v in update_counts.items():\n update_counts[k] = float(v) / total\n print_values(update_counts, grid)\n\n print(\"values:\")\n print_values(V, grid)\n print(\"policy:\")\n print_policy(policy, grid)\n\n" }, { "path": "rl/td0_prediction.py", "content": "# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom grid_world import standard_grid, negative_grid\nfrom iterative_policy_evaluation import print_values, print_policy\n\nSMALL_ENOUGH = 1e-3\nGAMMA = 0.9\nALPHA = 0.1\nALL_POSSIBLE_ACTIONS = ('U', 'D', 'L', 'R')\n\n\ndef epsilon_greedy(policy, s, eps=0.1):\n # we'll use epsilon-soft to ensure all states are visited\n # what happens if you don't do this? i.e. eps=0\n p = np.random.random()\n if p < (1 - eps):\n return policy[s]\n else:\n return np.random.choice(ALL_POSSIBLE_ACTIONS)\n\n\nif __name__ == '__main__':\n # use the standard grid again (0 for every step) so that we can compare\n # to iterative policy evaluation\n grid = standard_grid()\n\n # print rewards\n print(\"rewards:\")\n print_values(grid.rewards, grid)\n\n # state -> action\n policy = {\n (2, 0): 'U',\n (1, 0): 'U',\n (0, 0): 'R',\n (0, 1): 'R',\n (0, 2): 'R',\n (1, 2): 'R',\n (2, 1): 'R',\n (2, 2): 'R',\n (2, 3): 'U',\n }\n\n # initialize V(s) and returns\n V = {}\n states = grid.all_states()\n for s in states:\n V[s] = 0\n\n # store max change in V(s) per episode\n deltas = []\n\n # repeat until convergence\n n_episodes = 10000\n for it in range(n_episodes):\n # begin a new episode\n s = grid.reset()\n \n delta = 0\n while not grid.game_over():\n a = epsilon_greedy(policy, s)\n\n r = grid.move(a)\n s_next = grid.current_state()\n\n # update V(s)\n v_old = V[s]\n V[s] = V[s] + ALPHA*(r + GAMMA*V[s_next] - V[s])\n delta = max(delta, np.abs(V[s] - v_old))\n \n # next state becomes current state\n s = s_next\n\n # store delta\n deltas.append(delta)\n\n plt.plot(deltas)\n plt.show()\n\n print(\"values:\")\n print_values(V, grid)\n print(\"policy:\")\n print_policy(policy, grid)\n" }, { "path": "rl/tic_tac_toe.py", "content": "# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python\n# Simple reinforcement learning algorithm for learning tic-tac-toe\n# Use the update rule: V(s) = V(s) + alpha*(V(s') - V(s))\n# Use the epsilon-greedy policy:\n# action|s = argmax[over all actions possible from state s]{ V(s) } if rand > epsilon\n# action|s = select random action from possible actions from state s if rand < epsilon\n#\n#\n# INTERESTING THINGS TO TRY:\n#\n# Currently, both agents use the same learning strategy while they play against each other.\n# What if they have different learning rates?\n# What if they have different epsilons? (probability of exploring)\n# Who will converge faster?\n# What if one agent doesn't learn at all?\n# Poses an interesting philosophical question: If there's no one around to challenge you,\n# can you reach your maximum potential?\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nLENGTH = 3\n\n\nclass Agent:\n def __init__(self, eps=0.1, alpha=0.5):\n self.eps = eps # probability of choosing random action instead of greedy\n self.alpha = alpha # learning rate\n self.verbose = False\n self.state_history = []\n \n def setV(self, V):\n self.V = V\n\n def set_symbol(self, sym):\n self.sym = sym\n\n def set_verbose(self, v):\n # if true, will print values for each position on the board\n self.verbose = v\n\n def reset_history(self):\n self.state_history = []\n\n def take_action(self, env):\n # choose an action based on epsilon-greedy strategy\n r = np.random.rand()\n best_state = None\n if r < self.eps:\n # take a random action\n if self.verbose:\n print(\"Taking a random action\")\n\n possible_moves = []\n for i in range(LENGTH):\n for j in range(LENGTH):\n if env.is_empty(i, j):\n possible_moves.append((i, j))\n idx = np.random.choice(len(possible_moves))\n next_move = possible_moves[idx]\n else:\n # choose the best action based on current values of states\n # loop through all possible moves, get their values\n # keep track of the best value\n pos2value = {} # for debugging\n next_move = None\n best_value = -1\n for i in range(LENGTH):\n for j in range(LENGTH):\n if env.is_empty(i, j):\n # what is the state if we made this move?\n env.board[i,j] = self.sym\n state = env.get_state()\n env.board[i,j] = 0 # don't forget to change it back!\n pos2value[(i,j)] = self.V[state]\n if self.V[state] > best_value:\n best_value = self.V[state]\n best_state = state\n next_move = (i, j)\n\n # if verbose, draw the board w/ the values\n if self.verbose:\n print(\"Taking a greedy action\")\n for i in range(LENGTH):\n print(\"------------------\")\n for j in range(LENGTH):\n if env.is_empty(i, j):\n # print the value\n print(\" %.2f|\" % pos2value[(i,j)], end=\"\")\n else:\n print(\" \", end=\"\")\n if env.board[i,j] == env.x:\n print(\"x |\", end=\"\")\n elif env.board[i,j] == env.o:\n print(\"o |\", end=\"\")\n else:\n print(\" |\", end=\"\")\n print(\"\")\n print(\"------------------\")\n\n # make the move\n env.board[next_move[0], next_move[1]] = self.sym\n\n def update_state_history(self, s):\n # cannot put this in take_action, because take_action only happens\n # once every other iteration for each player\n # state history needs to be updated every iteration\n # s = env.get_state() # don't want to do this twice so pass it in\n self.state_history.append(s)\n\n def update(self, env):\n # we want to BACKTRACK over the states, so that:\n # V(prev_state) = V(prev_state) + alpha*(V(next_state) - V(prev_state))\n # where V(next_state) = reward if it's the most current state\n #\n # NOTE: we ONLY do this at the end of an episode\n # not so for all the algorithms we will study\n reward = env.reward(self.sym)\n target = reward\n for prev in reversed(self.state_history):\n value = self.V[prev] + self.alpha*(target - self.V[prev])\n self.V[prev] = value\n target = value\n self.reset_history()\n\n\n# this class represents a tic-tac-toe game\n# is a CS101-type of project\nclass Environment:\n def __init__(self):\n self.board = np.zeros((LENGTH, LENGTH))\n self.x = -1 # represents an x on the board, player 1\n self.o = 1 # represents an o on the board, player 2\n self.winner = None\n self.ended = False\n self.num_states = 3**(LENGTH*LENGTH)\n\n def is_empty(self, i, j):\n return self.board[i,j] == 0\n\n def reward(self, sym):\n # no reward until game is over\n if not self.game_over():\n return 0\n\n # if we get here, game is over\n # sym will be self.x or self.o\n return 1 if self.winner == sym else 0\n\n def get_state(self):\n # returns the current state, represented as an int\n # from 0...|S|-1, where S = set of all possible states\n # |S| = 3^(BOARD SIZE), since each cell can have 3 possible values - empty, x, o\n # some states are not possible, e.g. all cells are x, but we ignore that detail\n # this is like finding the integer represented by a base-3 number\n k = 0\n h = 0\n for i in range(LENGTH):\n for j in range(LENGTH):\n if self.board[i,j] == 0:\n v = 0\n elif self.board[i,j] == self.x:\n v = 1\n elif self.board[i,j] == self.o:\n v = 2\n h += (3**k) * v\n k += 1\n return h\n\n def game_over(self, force_recalculate=False):\n # returns true if game over (a player has won or it's a draw)\n # otherwise returns false\n # also sets 'winner' instance variable and 'ended' instance variable\n if not force_recalculate and self.ended:\n return self.ended\n \n # check rows\n for i in range(LENGTH):\n for player in (self.x, self.o):\n if self.board[i].sum() == player*LENGTH:\n self.winner = player\n self.ended = True\n return True\n\n # check columns\n for j in range(LENGTH):\n for player in (self.x, self.o):\n if self.board[:,j].sum() == player*LENGTH:\n self.winner = player\n self.ended = True\n return True\n\n # check diagonals\n for player in (self.x, self.o):\n # top-left -> bottom-right diagonal\n if self.board.trace() == player*LENGTH:\n self.winner = player\n self.ended = True\n return True\n # top-right -> bottom-left diagonal\n if np.fliplr(self.board).trace() == player*LENGTH:\n self.winner = player\n self.ended = True\n return True\n\n # check if draw\n if np.all((self.board == 0) == False):\n # winner stays None\n self.winner = None\n self.ended = True\n return True\n\n # game is not over\n self.winner = None\n return False\n\n def is_draw(self):\n return self.ended and self.winner is None\n\n # Example board\n # -------------\n # | x | | |\n # -------------\n # | | | |\n # -------------\n # | | | o |\n # -------------\n def draw_board(self):\n for i in range(LENGTH):\n print(\"-------------\")\n for j in range(LENGTH):\n print(\" \", end=\"\")\n if self.board[i,j] == self.x:\n print(\"x \", end=\"\")\n elif self.board[i,j] == self.o:\n print(\"o \", end=\"\")\n else:\n print(\" \", end=\"\")\n print(\"\")\n print(\"-------------\")\n\n\n\nclass Human:\n def __init__(self):\n pass\n\n def set_symbol(self, sym):\n self.sym = sym\n\n def take_action(self, env):\n while True:\n # break if we make a legal move\n move = input(\"Enter coordinates i,j for your next move (i,j=0..2): \")\n i, j = move.split(',')\n i = int(i)\n j = int(j)\n if env.is_empty(i, j):\n env.board[i,j] = self.sym\n break\n\n def update(self, env):\n pass\n\n def update_state_history(self, s):\n pass\n\n\n# recursive function that will return all\n# possible states (as ints) and who the corresponding winner is for those states (if any)\n# (i, j) refers to the next cell on the board to permute (we need to try -1, 0, 1)\n# impossible games are ignored, i.e. 3x's and 3o's in a row simultaneously\n# since that will never happen in a real game\ndef get_state_hash_and_winner(env, i=0, j=0):\n results = []\n\n for v in (0, env.x, env.o):\n env.board[i,j] = v # if empty board it should already be 0\n if j == 2:\n # j goes back to 0, increase i, unless i = 2, then we are done\n if i == 2:\n # the board is full, collect results and return\n state = env.get_state()\n ended = env.game_over(force_recalculate=True)\n winner = env.winner\n results.append((state, winner, ended))\n else:\n results += get_state_hash_and_winner(env, i + 1, 0)\n else:\n # increment j, i stays the same\n results += get_state_hash_and_winner(env, i, j + 1)\n\n return results\n\n# play all possible games\n# need to also store if game is over or not\n# because we are going to initialize those values to 0.5\n# NOTE: THIS IS SLOW because MANY possible games lead to the same outcome / state\n# def get_state_hash_and_winner(env, turn='x'):\n# results = []\n\n# state = env.get_state()\n# # board_before = env.board.copy()\n# ended = env.game_over(force_recalculate=True)\n# winner = env.winner\n# results.append((state, winner, ended))\n\n# # DEBUG\n# # if ended:\n# # if winner is not None and env.win_type.startswith('col'):\n# # env.draw_board()\n# # print \"Winner:\", 'x' if winner == -1 else 'o', env.win_type\n# # print \"\\n\\n\"\n# # assert(np.all(board_before == env.board))\n\n# if not ended:\n# if turn == 'x':\n# sym = env.x\n# next_sym = 'o'\n# else:\n# sym = env.o\n# next_sym = 'x'\n\n# for i in xrange(LENGTH):\n# for j in xrange(LENGTH):\n# if env.is_empty(i, j):\n# env.board[i,j] = sym\n# results += get_state_hash_and_winner(env, next_sym)\n# env.board[i,j] = 0 # reset it\n# return results\n\n\ndef initialV_x(env, state_winner_triples):\n # initialize state values as follows\n # if x wins, V(s) = 1\n # if x loses or draw, V(s) = 0\n # otherwise, V(s) = 0.5\n V = np.zeros(env.num_states)\n for state, winner, ended in state_winner_triples:\n if ended:\n if winner == env.x:\n v = 1\n else:\n v = 0\n else:\n v = 0.5\n V[state] = v\n return V\n\n\ndef initialV_o(env, state_winner_triples):\n # this is (almost) the opposite of initial V for player x\n # since everywhere where x wins (1), o loses (0)\n # but a draw is still 0 for o\n V = np.zeros(env.num_states)\n for state, winner, ended in state_winner_triples:\n if ended:\n if winner == env.o:\n v = 1\n else:\n v = 0\n else:\n v = 0.5\n V[state] = v\n return V\n\n\ndef play_game(p1, p2, env, draw=False):\n # loops until the game is over\n current_player = None\n while not env.game_over():\n # alternate between players\n # p1 always starts first\n if current_player == p1:\n current_player = p2\n else:\n current_player = p1\n\n # draw the board before the user who wants to see it makes a move\n if draw:\n if draw == 1 and current_player == p1:\n env.draw_board()\n if draw == 2 and current_player == p2:\n env.draw_board()\n\n # current player makes a move\n current_player.take_action(env)\n\n # update state histories\n state = env.get_state()\n p1.update_state_history(state)\n p2.update_state_history(state)\n\n if draw:\n env.draw_board()\n\n # do the value function update\n p1.update(env)\n p2.update(env)\n\n\nif __name__ == '__main__':\n # train the agent\n p1 = Agent()\n p2 = Agent()\n\n # set initial V for p1 and p2\n env = Environment()\n state_winner_triples = get_state_hash_and_winner(env)\n\n\n Vx = initialV_x(env, state_winner_triples)\n p1.setV(Vx)\n Vo = initialV_o(env, state_winner_triples)\n p2.setV(Vo)\n\n # give each player their symbol\n p1.set_symbol(env.x)\n p2.set_symbol(env.o)\n\n T = 10000\n for t in range(T):\n if t % 200 == 0:\n print(t)\n play_game(p1, p2, Environment())\n\n # play human vs. agent\n # do you think the agent learned to play the game well?\n human = Human()\n human.set_symbol(env.o)\n while True:\n p1.set_verbose(True)\n play_game(p1, human, Environment(), draw=2)\n # I made the agent player 1 because I wanted to see if it would\n # select the center as its starting move. If you want the agent\n # to go second you can switch the human and AI.\n answer = input(\"Play again? [Y/n]: \")\n if answer and answer.lower()[0] == 'n':\n break\n\n" }, { "path": "rl/ucb1.py", "content": "# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python\n# https://books.google.ca/books?id=_ATpBwAAQBAJ&lpg=PA201&ots=rinZM8jQ6s&dq=hoeffding%20bound%20gives%20probability%20%22greater%20than%201%22&pg=PA201#v=onepage&q&f=false\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nNUM_TRIALS = 100000\nEPS = 0.1\nBANDIT_PROBABILITIES = [0.2, 0.5, 0.75]\n\n\nclass Bandit:\n def __init__(self, p):\n # p: the win rate\n self.p = p\n self.p_estimate = 0.\n self.N = 0. # num samples collected so far\n\n def pull(self):\n # draw a 1 with probability p\n return np.random.random() < self.p\n\n def update(self, x):\n self.N += 1.\n self.p_estimate = ((self.N - 1)*self.p_estimate + x) / self.N\n\n\ndef ucb(mean, n, nj):\n return mean + np.sqrt(2*np.log(n) / nj)\n\n\ndef run_experiment():\n bandits = [Bandit(p) for p in BANDIT_PROBABILITIES]\n rewards = np.empty(NUM_TRIALS)\n total_plays = 0\n\n # initialization: play each bandit once\n for j in range(len(bandits)):\n x = bandits[j].pull()\n total_plays += 1\n bandits[j].update(x)\n \n for i in range(NUM_TRIALS):\n j = np.argmax([ucb(b.p_estimate, total_plays, b.N) for b in bandits])\n x = bandits[j].pull()\n total_plays += 1\n bandits[j].update(x)\n\n # for the plot\n rewards[i] = x\n cumulative_average = np.cumsum(rewards) / (np.arange(NUM_TRIALS) + 1)\n\n # plot moving average ctr\n plt.plot(cumulative_average)\n plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES))\n plt.xscale('log')\n plt.show()\n\n # plot moving average ctr linear\n plt.plot(cumulative_average)\n plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES))\n plt.show()\n\n for b in bandits:\n print(b.p_estimate)\n\n print(\"total reward earned:\", rewards.sum())\n print(\"overall win rate:\", rewards.sum() / NUM_TRIALS)\n print(\"num times selected each bandit:\", [b.N for b in bandits])\n\n return cumulative_average\n\nif __name__ == '__main__':\n run_experiment()\n\n" }, { "path": "rl/ucb1_starter.py", "content": "# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python\n# https://books.google.ca/books?id=_ATpBwAAQBAJ&lpg=PA201&ots=rinZM8jQ6s&dq=hoeffding%20bound%20gives%20probability%20%22greater%20than%201%22&pg=PA201#v=onepage&q&f=false\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nNUM_TRIALS = 100000\nEPS = 0.1\nBANDIT_PROBABILITIES = [0.2, 0.5, 0.75]\n\n\nclass Bandit:\n def __init__(self, p):\n # p: the win rate\n self.p = p\n self.p_estimate = 0.\n self.N = 0. # num samples collected so far\n\n def pull(self):\n # draw a 1 with probability p\n return np.random.random() < self.p\n\n def update(self, x):\n self.N += 1.\n self.p_estimate = ((self.N - 1)*self.p_estimate + x) / self.N\n\n\ndef ucb(mean, n, nj):\n return # TODO\n\n\ndef run_experiment():\n bandits = [Bandit(p) for p in BANDIT_PROBABILITIES]\n rewards = np.empty(NUM_TRIALS)\n total_plays = 0\n\n # initialization: play each bandit once\n for j in range(len(bandits)):\n x = bandits[j].pull()\n total_plays += 1\n bandits[j].update(x)\n \n for i in range(NUM_TRIALS):\n j = # TODO\n x = bandits[j].pull()\n total_plays += 1\n bandits[j].update(x)\n\n # for the plot\n rewards[i] = x\n cumulative_average = np.cumsum(rewards) / (np.arange(NUM_TRIALS) + 1)\n\n # plot moving average ctr\n plt.plot(cumulative_average)\n plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES))\n plt.xscale('log')\n plt.show()\n\n # plot moving average ctr linear\n plt.plot(cumulative_average)\n plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES))\n plt.show()\n\n for b in bandits:\n print(b.p_estimate)\n\n print(\"total reward earned:\", rewards.sum())\n print(\"overall win rate:\", rewards.sum() / NUM_TRIALS)\n print(\"num times selected each bandit:\", [b.N for b in bandits])\n\n return cumulative_average\n\nif __name__ == '__main__':\n run_experiment()\n\n" }, { "path": "rl/value_iteration.py", "content": "# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python\n# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nfrom grid_world import windy_grid, ACTION_SPACE\nfrom iterative_policy_evaluation import print_values, print_policy\n\nSMALL_ENOUGH = 1e-3\nGAMMA = 0.9\n\n# copied from iterative_policy_evaluation\ndef get_transition_probs_and_rewards(grid):\n ### define transition probabilities and grid ###\n # the key is (s, a, s'), the value is the probability\n # that is, transition_probs[(s, a, s')] = p(s' | s, a)\n # any key NOT present will considered to be impossible (i.e. probability 0)\n transition_probs = {}\n\n # to reduce the dimensionality of the dictionary, we'll use deterministic\n # rewards, r(s, a, s')\n # note: you could make it simpler by using r(s') since the reward doesn't\n # actually depend on (s, a)\n rewards = {}\n\n for (s, a), v in grid.probs.items():\n for s2, p in v.items():\n transition_probs[(s, a, s2)] = p\n rewards[(s, a, s2)] = grid.rewards.get(s2, 0)\n\n return transition_probs, rewards\n\nif __name__ == '__main__':\n grid = windy_grid()\n transition_probs, rewards = get_transition_probs_and_rewards(grid)\n\n # print rewards\n print(\"rewards:\")\n print_values(grid.rewards, grid)\n\n # initialize V(s)\n V = {}\n states = grid.all_states()\n for s in states:\n V[s] = 0\n\n # repeat until convergence\n # V[s] = max[a]{ sum[s',r] { p(s',r|s,a)[r + gamma*V[s']] } }\n it = 0\n while True:\n biggest_change = 0\n for s in grid.all_states():\n if not grid.is_terminal(s):\n old_v = V[s]\n new_v = float('-inf')\n\n for a in ACTION_SPACE:\n v = 0\n for s2 in grid.all_states():\n # reward is a function of (s, a, s'), 0 if not specified\n r = rewards.get((s, a, s2), 0)\n v += transition_probs.get((s, a, s2), 0) * (r + GAMMA * V[s2])\n\n # keep v if it's better\n if v > new_v:\n new_v = v\n\n V[s] = new_v\n biggest_change = max(biggest_change, np.abs(old_v - V[s]))\n\n it += 1\n if biggest_change < SMALL_ENOUGH:\n break\n\n # find a policy that leads to optimal value function\n policy = {}\n for s in grid.actions.keys():\n best_a = None\n best_value = float('-inf')\n # loop through all possible actions to find the best current action\n for a in ACTION_SPACE:\n v = 0\n for s2 in grid.all_states():\n # reward is a function of (s, a, s'), 0 if not specified\n r = rewards.get((s, a, s2), 0)\n v += transition_probs.get((s, a, s2), 0) * (r + GAMMA * V[s2])\n\n # best_a is the action associated with best_value\n if v > best_value:\n best_value = v\n best_a = a\n policy[s] = best_a\n\n # our goal here is to verify that we get the same answer as with policy iteration\n print(\"values:\")\n print_values(V, grid)\n print(\"policy:\")\n print_policy(policy, grid)\n" }, { "path": "rl2/a3c/main.py", "content": "import gym\nimport sys\nimport os\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nimport itertools\nimport shutil\nimport threading\nimport multiprocessing\n\nfrom nets import create_networks\nfrom worker import Worker\n\n\ngym_minor_version = int(gym.__version__.split('.')[1])\nif gym_minor_version >= 19:\n exit(\"Please install OpenAI Gym 0.19.0 or earlier\")\n\nif tf.__version__.startswith('2'):\n exit(\"Please install Tensorflow 1.x\")\n\n\nENV_NAME = \"Breakout-v0\"\nMAX_GLOBAL_STEPS = 5e6\nSTEPS_PER_UPDATE = 5\n\n\ndef Env():\n return gym.envs.make(ENV_NAME)\n\n# Depending on the game we may have a limited action space\nif ENV_NAME == \"Pong-v0\" or ENV_NAME == \"Breakout-v0\":\n NUM_ACTIONS = 4 # env.action_space.n returns a bigger number\nelse:\n env = Env()\n NUM_ACTIONS = env.action_space.n\n env.close()\n\n\ndef smooth(x):\n # last 100\n n = len(x)\n y = np.zeros(n)\n for i in range(n):\n start = max(0, i - 99)\n y[i] = float(x[start:(i+1)].sum()) / (i - start + 1)\n return y\n\n\n# Set the number of workers\nNUM_WORKERS = multiprocessing.cpu_count()\n\nwith tf.device(\"/cpu:0\"):\n\n # Keeps track of the number of updates we've performed\n # https://www.tensorflow.org/api_docs/python/tf/train/global_step\n global_step = tf.Variable(0, name=\"global_step\", trainable=False)\n\n # Global policy and value nets\n with tf.variable_scope(\"global\") as vs:\n policy_net, value_net = create_networks(NUM_ACTIONS)\n\n # Global step iterator\n global_counter = itertools.count()\n\n # Save returns\n returns_list = []\n\n # Create workers\n workers = []\n for worker_id in range(NUM_WORKERS):\n worker = Worker(\n name=\"worker_{}\".format(worker_id),\n env=Env(),\n policy_net=policy_net,\n value_net=value_net,\n global_counter=global_counter,\n returns_list=returns_list,\n discount_factor = 0.99,\n max_global_steps=MAX_GLOBAL_STEPS)\n workers.append(worker)\n\nwith tf.Session() as sess:\n sess.run(tf.global_variables_initializer())\n coord = tf.train.Coordinator()\n\n # Start worker threads\n worker_threads = []\n for worker in workers:\n worker_fn = lambda: worker.run(sess, coord, STEPS_PER_UPDATE)\n t = threading.Thread(target=worker_fn)\n t.start()\n worker_threads.append(t)\n\n # Wait for all workers to finish\n coord.join(worker_threads, stop_grace_period_secs=300)\n\n # Plot the smoothed returns\n x = np.array(returns_list)\n y = smooth(x)\n plt.plot(x, label='orig')\n plt.plot(y, label='smoothed')\n plt.legend()\n plt.show()\n\n" }, { "path": "rl2/a3c/nets.py", "content": "import tensorflow as tf\n\n\ndef build_feature_extractor(input_):\n # We only want to create the weights once\n # In all future calls we should set reuse = True\n\n # scale the inputs from 0..255 to 0..1\n input_ = tf.to_float(input_) / 255.0\n\n # conv layers\n conv1 = tf.contrib.layers.conv2d(\n input_,\n 16, # num output feature maps\n 8, # kernel size\n 4, # stride\n activation_fn=tf.nn.relu,\n scope=\"conv1\")\n conv2 = tf.contrib.layers.conv2d(\n conv1,\n 32, # num output feature maps\n 4, # kernel size\n 2, # stride\n activation_fn=tf.nn.relu,\n scope=\"conv2\")\n\n # image -> feature vector\n flat = tf.contrib.layers.flatten(conv2)\n\n # dense layer\n fc1 = tf.contrib.layers.fully_connected(\n inputs=flat,\n num_outputs=256,\n scope=\"fc1\")\n\n return fc1\n\nclass PolicyNetwork:\n def __init__(self, num_outputs, reg=0.01):\n self.num_outputs = num_outputs\n\n # Graph inputs\n # After resizing we have 4 consecutive frames of size 84 x 84\n self.states = tf.placeholder(shape=[None, 84, 84, 4], dtype=tf.uint8, name=\"X\")\n # Advantage = G - V(s)\n self.advantage = tf.placeholder(shape=[None], dtype=tf.float32, name=\"y\")\n # Selected actions\n self.actions = tf.placeholder(shape=[None], dtype=tf.int32, name=\"actions\")\n\n # Since we set reuse=False here, that means we MUST\n # create the PolicyNetwork before creating the ValueNetwork\n # ValueNetwork will use reuse=True\n with tf.variable_scope(\"shared\", reuse=False):\n fc1 = build_feature_extractor(self.states)\n\n # Use a separate scope for output and loss\n with tf.variable_scope(\"policy_network\"):\n self.logits = tf.contrib.layers.fully_connected(fc1, num_outputs, activation_fn=None)\n self.probs = tf.nn.softmax(self.logits)\n\n # Sample an action\n cdist = tf.distributions.Categorical(logits=self.logits)\n self.sample_action = cdist.sample()\n\n # Add regularization to increase exploration\n self.entropy = -tf.reduce_sum(self.probs * tf.log(self.probs), axis=1)\n\n # Get the predictions for the chosen actions only\n batch_size = tf.shape(self.states)[0]\n gather_indices = tf.range(batch_size) * tf.shape(self.probs)[1] + self.actions\n self.selected_action_probs = tf.gather(tf.reshape(self.probs, [-1]), gather_indices)\n\n self.loss = tf.log(self.selected_action_probs) * self.advantage + reg * self.entropy\n self.loss = -tf.reduce_sum(self.loss, name=\"loss\")\n\n # training\n self.optimizer = tf.train.RMSPropOptimizer(0.00025, 0.99, 0.0, 1e-6)\n\n # we'll need these later for running gradient descent steps\n self.grads_and_vars = self.optimizer.compute_gradients(self.loss)\n self.grads_and_vars = [[grad, var] for grad, var in self.grads_and_vars if grad is not None]\n\n\nclass ValueNetwork:\n def __init__(self):\n # Placeholders for our input\n # After resizing we have 4 consecutive frames of size 84 x 84\n self.states = tf.placeholder(shape=[None, 84, 84, 4], dtype=tf.uint8, name=\"X\")\n # The TD target value\n self.targets = tf.placeholder(shape=[None], dtype=tf.float32, name=\"y\")\n\n # Since we set reuse=True here, that means we MUST\n # create the PolicyNetwork before creating the ValueNetwork\n # PolictyNetwork will use reuse=False\n with tf.variable_scope(\"shared\", reuse=True):\n fc1 = build_feature_extractor(self.states)\n\n # Use a separate scope for output and loss\n with tf.variable_scope(\"value_network\"):\n self.vhat = tf.contrib.layers.fully_connected(\n inputs=fc1,\n num_outputs=1,\n activation_fn=None)\n self.vhat = tf.squeeze(self.vhat, squeeze_dims=[1], name=\"vhat\")\n\n self.loss = tf.squared_difference(self.vhat, self.targets)\n self.loss = tf.reduce_sum(self.loss, name=\"loss\")\n\n # training\n self.optimizer = tf.train.RMSPropOptimizer(0.00025, 0.99, 0.0, 1e-6)\n\n # we'll need these later for running gradient descent steps\n self.grads_and_vars = self.optimizer.compute_gradients(self.loss)\n self.grads_and_vars = [[grad, var] for grad, var in self.grads_and_vars if grad is not None]\n\n\n# Should use this to create networks\n# to ensure they're created in the correct order\ndef create_networks(num_outputs):\n policy_network = PolicyNetwork(num_outputs=num_outputs)\n value_network = ValueNetwork()\n return policy_network, value_network\n" }, { "path": "rl2/a3c/thread_example.py", "content": "import itertools\nimport threading\nimport time\nimport multiprocessing\nimport numpy as np\n\n\nclass Worker:\n def __init__(self, id_, global_counter):\n self.id = id_\n self.global_counter = global_counter\n self.local_counter = itertools.count()\n\n def run(self): \n while True:\n time.sleep(np.random.rand()*2)\n global_step = next(self.global_counter)\n local_step = next(self.local_counter)\n print(\"Worker({}): {}\".format(self.id, local_step))\n if global_step >= 20:\n break\n\nglobal_counter = itertools.count()\nNUM_WORKERS = multiprocessing.cpu_count()\n\n# create the workers\nworkers = []\nfor worker_id in range(NUM_WORKERS):\n worker = Worker(worker_id, global_counter)\n workers.append(worker)\n\n# start the threads\nworker_threads = []\nfor worker in workers:\n worker_fn = lambda: worker.run()\n t = threading.Thread(target=worker_fn)\n t.start()\n worker_threads.append(t)\n\n\n# join the threads\nfor t in worker_threads:\n t.join()\n\nprint(\"DONE!\")" }, { "path": "rl2/a3c/worker.py", "content": "import gym\nimport sys\nimport os\nimport numpy as np\nimport tensorflow as tf\n\nfrom nets import create_networks\n\n\nclass Step:\n def __init__(self, state, action, reward, next_state, done):\n self.state = state\n self.action = action\n self.reward = reward\n self.next_state = next_state\n self.done = done\n\n\n# Transform raw images for input into neural network\n# 1) Convert to grayscale\n# 2) Resize\n# 3) Crop\nclass ImageTransformer:\n def __init__(self):\n with tf.variable_scope(\"image_transformer\"):\n self.input_state = tf.placeholder(shape=[210, 160, 3], dtype=tf.uint8)\n self.output = tf.image.rgb_to_grayscale(self.input_state)\n self.output = tf.image.crop_to_bounding_box(self.output, 34, 0, 160, 160)\n self.output = tf.image.resize_images(\n self.output,\n [84, 84],\n method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)\n self.output = tf.squeeze(self.output)\n\n def transform(self, state, sess=None):\n sess = sess or tf.get_default_session()\n return sess.run(self.output, { self.input_state: state })\n\n\n# Create initial state by repeating the same frame 4 times\ndef repeat_frame(frame):\n return np.stack([frame] * 4, axis=2)\n\n\n# Create next state by shifting each frame by 1\n# Throw out the oldest frame\n# And concatenate the newest frame\ndef shift_frames(state, next_frame):\n return np.append(state[:,:,1:], np.expand_dims(next_frame, 2), axis=2)\n\n\n# Make a Tensorflow op to copy weights from one scope to another\ndef get_copy_params_op(src_vars, dst_vars):\n src_vars = list(sorted(src_vars, key=lambda v: v.name))\n dst_vars = list(sorted(dst_vars, key=lambda v: v.name))\n\n ops = []\n for s, d in zip(src_vars, dst_vars):\n op = d.assign(s)\n ops.append(op)\n\n return ops\n\n\ndef make_train_op(local_net, global_net):\n \"\"\"\n Use gradients from local network to update the global network\n \"\"\"\n\n # Idea:\n # We want a list of gradients and corresponding variables\n # e.g. [[g1, g2, g3], [v1, v2, v3]]\n # Since that's what the optimizer expects.\n # But we would like the gradients to come from the local network\n # And the variables to come from the global network\n # So we want to make a list like this:\n # [[local_g1, local_g2, local_g3], [global_v1, global_v2, global_v3]]\n\n # First get only the gradients\n local_grads, _ = zip(*local_net.grads_and_vars)\n\n # Clip gradients to avoid large values\n local_grads, _ = tf.clip_by_global_norm(local_grads, 5.0)\n\n # Get global vars\n _, global_vars = zip(*global_net.grads_and_vars)\n\n # Combine local grads and global vars\n local_grads_global_vars = list(zip(local_grads, global_vars))\n\n # Run a gradient descent step, e.g.\n # var = var - learning_rate * grad\n return global_net.optimizer.apply_gradients(\n local_grads_global_vars,\n global_step=tf.train.get_global_step())\n\n\n# Worker object to be run in a thread\n# name (String) should be unique for each thread\n# env (OpenAI Gym Environment) should be unique for each thread\n# policy_net (PolicyNetwork) should be a global passed to every worker\n# value_net (ValueNetwork) should be a global passed to every worker\n# returns_list (List) should be a global passed to every worker\nclass Worker:\n def __init__(\n self,\n name,\n env,\n policy_net,\n value_net,\n global_counter,\n returns_list,\n discount_factor=0.99,\n max_global_steps=None):\n\n self.name = name\n self.env = env\n self.global_policy_net = policy_net\n self.global_value_net = value_net\n self.global_counter = global_counter\n self.discount_factor = discount_factor\n self.max_global_steps = max_global_steps\n self.global_step = tf.train.get_global_step()\n self.img_transformer = ImageTransformer()\n\n # Create local policy and value networks that belong only to this worker\n with tf.variable_scope(name):\n # self.policy_net = PolicyNetwork(num_outputs=policy_net.num_outputs)\n # self.value_net = ValueNetwork()\n self.policy_net, self.value_net = create_networks(policy_net.num_outputs)\n\n # We will use this op to copy the global network weights\n # back to the local policy and value networks\n self.copy_params_op = get_copy_params_op(\n tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=\"global\"),\n tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.name+'/'))\n\n # These will take the gradients from the local networks\n # and use those gradients to update the global network\n self.vnet_train_op = make_train_op(self.value_net, self.global_value_net)\n self.pnet_train_op = make_train_op(self.policy_net, self.global_policy_net)\n\n self.state = None # Keep track of the current state\n self.total_reward = 0. # After each episode print the total (sum of) reward\n self.returns_list = returns_list # Global returns list to plot later\n\n def run(self, sess, coord, t_max):\n with sess.as_default(), sess.graph.as_default():\n # Assign the initial state\n self.state = repeat_frame(self.img_transformer.transform(self.env.reset()))\n\n try:\n while not coord.should_stop():\n # Copy weights from global networks to local networks\n sess.run(self.copy_params_op)\n\n # Collect some experience\n steps, global_step = self.run_n_steps(t_max, sess)\n\n # Stop once the max number of global steps has been reached\n if self.max_global_steps is not None and global_step >= self.max_global_steps:\n coord.request_stop()\n return\n\n # Update the global networks using local gradients\n self.update(steps, sess)\n\n except tf.errors.CancelledError:\n return\n\n def sample_action(self, state, sess):\n # Make input N x D (N = 1)\n feed_dict = { self.policy_net.states: [state] }\n actions = sess.run(self.policy_net.sample_action, feed_dict)\n # Prediction is a 1-D array of length N, just want the first value\n return actions[0]\n\n def get_value_prediction(self, state, sess):\n # Make input N x D (N = 1)\n feed_dict = { self.value_net.states: [state] }\n vhat = sess.run(self.value_net.vhat, feed_dict)\n # Prediction is a 1-D array of length N, just want the first value\n return vhat[0]\n\n def run_n_steps(self, n, sess):\n steps = []\n for _ in range(n):\n # Take a step\n action = self.sample_action(self.state, sess)\n next_frame, reward, done, _ = self.env.step(action)\n\n # Shift the state to include the latest frame\n next_state = shift_frames(self.state, self.img_transformer.transform(next_frame))\n\n # Save total return\n if done:\n print(\"Total reward:\", self.total_reward, \"Worker:\", self.name)\n self.returns_list.append(self.total_reward)\n if len(self.returns_list) > 0 and len(self.returns_list) % 100 == 0:\n print(\"*** Total average reward (last 100):\", np.mean(self.returns_list[-100:]), \"Collected so far:\", len(self.returns_list))\n self.total_reward = 0.\n else:\n self.total_reward += reward\n\n # Save step\n step = Step(self.state, action, reward, next_state, done)\n steps.append(step)\n\n # Increase local and global counters\n global_step = next(self.global_counter)\n\n if done:\n self.state = repeat_frame(self.img_transformer.transform(self.env.reset()))\n break\n else:\n self.state = next_state\n return steps, global_step\n\n def update(self, steps, sess):\n \"\"\"\n Updates global policy and value networks using the local networks' gradients\n \"\"\"\n\n # In order to accumulate the total return\n # We will use V_hat(s') to predict the future returns\n # But we will use the actual rewards if we have them\n # Ex. if we have s1, s2, s3 with rewards r1, r2, r3\n # Then G(s3) = r3 + V(s4)\n # G(s2) = r2 + r3 + V(s4)\n # G(s1) = r1 + r2 + r3 + V(s4)\n reward = 0.0\n if not steps[-1].done:\n reward = self.get_value_prediction(steps[-1].next_state, sess)\n\n # Accumulate minibatch samples\n states = []\n advantages = []\n value_targets = []\n actions = []\n\n # loop through steps in reverse order\n for step in reversed(steps):\n reward = step.reward + self.discount_factor * reward\n advantage = reward - self.get_value_prediction(step.state, sess)\n # Accumulate updates\n states.append(step.state)\n actions.append(step.action)\n advantages.append(advantage)\n value_targets.append(reward)\n\n feed_dict = {\n self.policy_net.states: np.array(states),\n self.policy_net.advantage: advantages,\n self.policy_net.actions: actions,\n self.value_net.states: np.array(states),\n self.value_net.targets: value_targets,\n }\n\n # Train the global estimators using local gradients\n global_step, pnet_loss, vnet_loss, _, _ = sess.run([\n self.global_step,\n self.policy_net.loss,\n self.value_net.loss,\n self.pnet_train_op,\n self.vnet_train_op,\n ], feed_dict)\n\n # Theoretically could plot these later\n return pnet_loss, vnet_loss\n" }, { "path": "rl2/atari/dqn_tf.py", "content": "# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python\n# https://www.udemy.com/deep-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport copy\nimport gym\nimport os\nimport sys\nimport random\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nfrom gym import wrappers\nfrom datetime import datetime\nfrom scipy.misc import imresize\n\n\n\ngym_minor_version = int(gym.__version__.split('.')[1])\nif gym_minor_version >= 19:\n exit(\"Please install OpenAI Gym 0.19.0 or earlier\")\n\nif tf.__version__.startswith('2'):\n exit(\"Please install Tensorflow 1.x\")\n\n##### testing only\n# MAX_EXPERIENCES = 10000\n# MIN_EXPERIENCES = 1000\n\n\nMAX_EXPERIENCES = 500000\nMIN_EXPERIENCES = 50000\nTARGET_UPDATE_PERIOD = 10000\nIM_SIZE = 84\nK = 4 #env.action_space.n\n\n\n# Transform raw images for input into neural network\n# 1) Convert to grayscale\n# 2) Resize\n# 3) Crop\nclass ImageTransformer:\n def __init__(self):\n with tf.variable_scope(\"image_transformer\"):\n self.input_state = tf.placeholder(shape=[210, 160, 3], dtype=tf.uint8)\n self.output = tf.image.rgb_to_grayscale(self.input_state)\n self.output = tf.image.crop_to_bounding_box(self.output, 34, 0, 160, 160)\n self.output = tf.image.resize_images(\n self.output,\n [IM_SIZE, IM_SIZE],\n method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)\n self.output = tf.squeeze(self.output)\n\n def transform(self, state, sess=None):\n sess = sess or tf.get_default_session()\n return sess.run(self.output, { self.input_state: state })\n\n\ndef update_state(state, obs_small):\n return np.append(state[:,:,1:], np.expand_dims(obs_small, 2), axis=2)\n\n\n\nclass ReplayMemory:\n def __init__(self, size=MAX_EXPERIENCES, frame_height=IM_SIZE, frame_width=IM_SIZE, \n agent_history_length=4, batch_size=32):\n \"\"\"\n Args:\n size: Integer, Number of stored transitions\n frame_height: Integer, Height of a frame of an Atari game\n frame_width: Integer, Width of a frame of an Atari game\n agent_history_length: Integer, Number of frames stacked together to create a state\n batch_size: Integer, Number of transitions returned in a minibatch\n \"\"\"\n self.size = size\n self.frame_height = frame_height\n self.frame_width = frame_width\n self.agent_history_length = agent_history_length\n self.batch_size = batch_size\n self.count = 0\n self.current = 0\n \n # Pre-allocate memory\n self.actions = np.empty(self.size, dtype=np.int32)\n self.rewards = np.empty(self.size, dtype=np.float32)\n self.frames = np.empty((self.size, self.frame_height, self.frame_width), dtype=np.uint8)\n self.terminal_flags = np.empty(self.size, dtype=np.bool)\n \n # Pre-allocate memory for the states and new_states in a minibatch\n self.states = np.empty((self.batch_size, self.agent_history_length, \n self.frame_height, self.frame_width), dtype=np.uint8)\n self.new_states = np.empty((self.batch_size, self.agent_history_length, \n self.frame_height, self.frame_width), dtype=np.uint8)\n self.indices = np.empty(self.batch_size, dtype=np.int32)\n \n def add_experience(self, action, frame, reward, terminal):\n \"\"\"\n Args:\n action: An integer-encoded action\n frame: One grayscale frame of the game\n reward: reward the agend received for performing an action\n terminal: A bool stating whether the episode terminated\n \"\"\"\n if frame.shape != (self.frame_height, self.frame_width):\n raise ValueError('Dimension of frame is wrong!')\n self.actions[self.current] = action\n self.frames[self.current, ...] = frame\n self.rewards[self.current] = reward\n self.terminal_flags[self.current] = terminal\n self.count = max(self.count, self.current+1)\n self.current = (self.current + 1) % self.size\n \n def _get_state(self, index):\n if self.count is 0:\n raise ValueError(\"The replay memory is empty!\")\n if index < self.agent_history_length - 1:\n raise ValueError(\"Index must be min 3\")\n return self.frames[index-self.agent_history_length+1:index+1, ...]\n \n def _get_valid_indices(self):\n for i in range(self.batch_size):\n while True:\n index = random.randint(self.agent_history_length, self.count - 1)\n if index < self.agent_history_length:\n continue\n if index >= self.current and index - self.agent_history_length <= self.current:\n continue\n if self.terminal_flags[index - self.agent_history_length:index].any():\n continue\n break\n self.indices[i] = index\n \n def get_minibatch(self):\n \"\"\"\n Returns a minibatch of self.batch_size transitions\n \"\"\"\n if self.count < self.agent_history_length:\n raise ValueError('Not enough memories to get a minibatch')\n \n self._get_valid_indices()\n \n for i, idx in enumerate(self.indices):\n self.states[i] = self._get_state(idx - 1)\n self.new_states[i] = self._get_state(idx)\n \n return np.transpose(self.states, axes=(0, 2, 3, 1)), \\\n self.actions[self.indices], \\\n self.rewards[self.indices], \\\n np.transpose(self.new_states, axes=(0, 2, 3, 1)), \\\n self.terminal_flags[self.indices]\n\n\nclass DQN:\n def __init__(self, K, conv_layer_sizes, hidden_layer_sizes, scope):\n\n self.K = K\n self.scope = scope\n\n with tf.variable_scope(scope):\n\n # inputs and targets\n self.X = tf.placeholder(tf.float32, shape=(None, IM_SIZE, IM_SIZE, 4), name='X')\n\n # tensorflow convolution needs the order to be:\n # (num_samples, height, width, \"color\")\n\n self.G = tf.placeholder(tf.float32, shape=(None,), name='G')\n self.actions = tf.placeholder(tf.int32, shape=(None,), name='actions')\n\n # calculate output and cost\n # convolutional layers\n # these built-in layers are faster and don't require us to\n # calculate the size of the output of the final conv layer!\n Z = self.X / 255.0\n for num_output_filters, filtersz, poolsz in conv_layer_sizes:\n Z = tf.contrib.layers.conv2d(\n Z,\n num_output_filters,\n filtersz,\n poolsz,\n activation_fn=tf.nn.relu\n )\n\n # fully connected layers\n Z = tf.contrib.layers.flatten(Z)\n for M in hidden_layer_sizes:\n Z = tf.contrib.layers.fully_connected(Z, M)\n\n # final output layer\n self.predict_op = tf.contrib.layers.fully_connected(Z, K)\n\n selected_action_values = tf.reduce_sum(\n self.predict_op * tf.one_hot(self.actions, K),\n reduction_indices=[1]\n )\n\n # cost = tf.reduce_mean(tf.square(self.G - selected_action_values))\n cost = tf.reduce_mean(tf.losses.huber_loss(self.G, selected_action_values))\n self.train_op = tf.train.AdamOptimizer(1e-5).minimize(cost)\n # self.train_op = tf.train.AdagradOptimizer(1e-2).minimize(cost)\n # self.train_op = tf.train.RMSPropOptimizer(2.5e-4, decay=0.99, epsilon=1e-3).minimize(cost)\n # self.train_op = tf.train.RMSPropOptimizer(0.00025, 0.99, 0.0, 1e-6).minimize(cost)\n # self.train_op = tf.train.MomentumOptimizer(1e-3, momentum=0.9).minimize(cost)\n # self.train_op = tf.train.GradientDescentOptimizer(1e-4).minimize(cost)\n\n self.cost = cost\n\n\n def copy_from(self, other):\n mine = [t for t in tf.trainable_variables() if t.name.startswith(self.scope)]\n mine = sorted(mine, key=lambda v: v.name)\n theirs = [t for t in tf.trainable_variables() if t.name.startswith(other.scope)]\n theirs = sorted(theirs, key=lambda v: v.name)\n\n ops = []\n for p, q in zip(mine, theirs):\n op = p.assign(q)\n ops.append(op)\n self.session.run(ops)\n\n\n def save(self):\n params = [t for t in tf.trainable_variables() if t.name.startswith(self.scope)]\n params = self.session.run(params)\n np.savez('tf_dqn_weights.npz', *params)\n\n\n def load(self):\n params = [t for t in tf.trainable_variables() if t.name.startswith(self.scope)]\n npz = np.load('tf_dqn_weights.npz')\n ops = []\n for p, (_, v) in zip(params, npz.iteritems()):\n ops.append(p.assign(v))\n self.session.run(ops)\n\n\n def set_session(self, session):\n self.session = session\n\n def predict(self, states):\n return self.session.run(self.predict_op, feed_dict={self.X: states})\n\n def update(self, states, actions, targets):\n c, _ = self.session.run(\n [self.cost, self.train_op],\n feed_dict={\n self.X: states,\n self.G: targets,\n self.actions: actions\n }\n )\n return c\n\n def sample_action(self, x, eps):\n if np.random.random() < eps:\n return np.random.choice(self.K)\n else:\n return np.argmax(self.predict([x])[0])\n\n\ndef learn(model, target_model, experience_replay_buffer, gamma, batch_size):\n # Sample experiences\n states, actions, rewards, next_states, dones = experience_replay_buffer.get_minibatch()\n\n # Calculate targets\n next_Qs = target_model.predict(next_states)\n next_Q = np.amax(next_Qs, axis=1)\n targets = rewards + np.invert(dones).astype(np.float32) * gamma * next_Q\n\n # Update model\n loss = model.update(states, actions, targets)\n return loss\n\n\ndef play_one(\n env,\n sess,\n total_t,\n experience_replay_buffer,\n model,\n target_model,\n image_transformer,\n gamma,\n batch_size,\n epsilon,\n epsilon_change,\n epsilon_min):\n\n t0 = datetime.now()\n\n # Reset the environment\n obs = env.reset()\n obs_small = image_transformer.transform(obs, sess)\n state = np.stack([obs_small] * 4, axis=2)\n loss = None\n\n\n total_time_training = 0\n num_steps_in_episode = 0\n episode_reward = 0\n\n done = False\n while not done:\n\n # Update target network\n if total_t % TARGET_UPDATE_PERIOD == 0:\n target_model.copy_from(model)\n print(\"Copied model parameters to target network. total_t = %s, period = %s\" % (total_t, TARGET_UPDATE_PERIOD))\n\n\n # Take action\n action = model.sample_action(state, epsilon)\n obs, reward, done, _ = env.step(action)\n obs_small = image_transformer.transform(obs, sess)\n next_state = update_state(state, obs_small)\n\n # Compute total reward\n episode_reward += reward\n\n # Save the latest experience\n experience_replay_buffer.add_experience(action, obs_small, reward, done) \n\n # Train the model, keep track of time\n t0_2 = datetime.now()\n loss = learn(model, target_model, experience_replay_buffer, gamma, batch_size)\n dt = datetime.now() - t0_2\n\n # More debugging info\n total_time_training += dt.total_seconds()\n num_steps_in_episode += 1\n\n\n state = next_state\n total_t += 1\n\n epsilon = max(epsilon - epsilon_change, epsilon_min)\n\n return total_t, episode_reward, (datetime.now() - t0), num_steps_in_episode, total_time_training/num_steps_in_episode, epsilon\n\n\ndef smooth(x):\n # last 100\n n = len(x)\n y = np.zeros(n)\n for i in range(n):\n start = max(0, i - 99)\n y[i] = float(x[start:(i+1)].sum()) / (i - start + 1)\n return y\n\n\nif __name__ == '__main__':\n\n # hyperparams and initialize stuff\n conv_layer_sizes = [(32, 8, 4), (64, 4, 2), (64, 3, 1)]\n hidden_layer_sizes = [512]\n gamma = 0.99\n batch_sz = 32\n num_episodes = 3500\n total_t = 0\n experience_replay_buffer = ReplayMemory()\n episode_rewards = np.zeros(num_episodes)\n\n\n\n # epsilon\n # decays linearly until 0.1\n epsilon = 1.0\n epsilon_min = 0.1\n epsilon_change = (epsilon - epsilon_min) / 500000\n\n\n\n # Create environment\n env = gym.envs.make(\"Breakout-v0\")\n \n\n\n # Create models\n model = DQN(\n K=K,\n conv_layer_sizes=conv_layer_sizes,\n hidden_layer_sizes=hidden_layer_sizes,\n scope=\"model\")\n target_model = DQN(\n K=K,\n conv_layer_sizes=conv_layer_sizes,\n hidden_layer_sizes=hidden_layer_sizes,\n scope=\"target_model\"\n )\n image_transformer = ImageTransformer()\n\n\n\n with tf.Session() as sess:\n model.set_session(sess)\n target_model.set_session(sess)\n sess.run(tf.global_variables_initializer())\n\n\n print(\"Populating experience replay buffer...\")\n obs = env.reset()\n\n for i in range(MIN_EXPERIENCES):\n\n action = np.random.choice(K)\n obs, reward, done, _ = env.step(action)\n obs_small = image_transformer.transform(obs, sess) # not used anymore\n experience_replay_buffer.add_experience(action, obs_small, reward, done)\n\n if done:\n obs = env.reset()\n\n\n # Play a number of episodes and learn!\n t0 = datetime.now()\n for i in range(num_episodes):\n\n total_t, episode_reward, duration, num_steps_in_episode, time_per_step, epsilon = play_one(\n env,\n sess,\n total_t,\n experience_replay_buffer,\n model,\n target_model,\n image_transformer,\n gamma,\n batch_sz,\n epsilon,\n epsilon_change,\n epsilon_min,\n )\n episode_rewards[i] = episode_reward\n\n last_100_avg = episode_rewards[max(0, i - 100):i + 1].mean()\n print(\"Episode:\", i,\n \"Duration:\", duration,\n \"Num steps:\", num_steps_in_episode,\n \"Reward:\", episode_reward,\n \"Training time per step:\", \"%.3f\" % time_per_step,\n \"Avg Reward (Last 100):\", \"%.3f\" % last_100_avg,\n \"Epsilon:\", \"%.3f\" % epsilon\n )\n sys.stdout.flush()\n print(\"Total duration:\", datetime.now() - t0)\n\n model.save()\n\n # Plot the smoothed returns\n y = smooth(episode_rewards)\n plt.plot(episode_rewards, label='orig')\n plt.plot(y, label='smoothed')\n plt.legend()\n plt.show()\n\n\n" }, { "path": "rl2/atari/dqn_theano.py", "content": "# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python\n# https://www.udemy.com/deep-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport copy\nimport gym\nimport os\nimport sys\nimport random\nimport numpy as np\nimport theano\nimport theano.tensor as T\nfrom theano.tensor.nnet import conv2d\nimport matplotlib.pyplot as plt\nfrom gym import wrappers\nfrom datetime import datetime\nfrom scipy.misc import imresize\n\n\n\n\n##### testing only\n# MAX_EXPERIENCES = 10000\n# MIN_EXPERIENCES = 1000\n\n\nMAX_EXPERIENCES = 500000\nMIN_EXPERIENCES = 50000\nTARGET_UPDATE_PERIOD = 10000\nIM_SIZE = 84\nK = 4 #env.action_space.n\n\n\ndef rgb2gray(rgb):\n r, g, b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2]\n gray = 0.2989 * r + 0.5870 * g + 0.1140 * b\n return gray.astype(np.uint8)\n\n\n# TODO: can this be converted into a Theano function?\ndef downsample_image(A):\n B = A[34:194] # select the important parts of the image\n B = rgb2gray(B) # convert to grayscale\n \n # downsample image\n # changing aspect ratio doesn't significantly distort the image\n # nearest neighbor interpolation produces a much sharper image\n # than default bilinear\n B = imresize(B, size=(IM_SIZE, IM_SIZE), interp='nearest')\n return B\n\n\ndef update_state(state, obs):\n obs_small = downsample_image(obs)\n return np.append(state[1:], np.expand_dims(obs_small, 0), axis=0)\n\n\nclass ReplayMemory:\n def __init__(self, size=MAX_EXPERIENCES, frame_height=IM_SIZE, frame_width=IM_SIZE, \n agent_history_length=4, batch_size=32):\n \"\"\"\n Args:\n size: Integer, Number of stored transitions\n frame_height: Integer, Height of a frame of an Atari game\n frame_width: Integer, Width of a frame of an Atari game\n agent_history_length: Integer, Number of frames stacked together to create a state\n batch_size: Integer, Number of transitions returned in a minibatch\n \"\"\"\n self.size = size\n self.frame_height = frame_height\n self.frame_width = frame_width\n self.agent_history_length = agent_history_length\n self.batch_size = batch_size\n self.count = 0\n self.current = 0\n \n # Pre-allocate memory\n self.actions = np.empty(self.size, dtype=np.int32)\n self.rewards = np.empty(self.size, dtype=np.float32)\n self.frames = np.empty((self.size, self.frame_height, self.frame_width), dtype=np.uint8)\n self.terminal_flags = np.empty(self.size, dtype=np.bool)\n \n # Pre-allocate memory for the states and new_states in a minibatch\n self.states = np.empty((self.batch_size, self.agent_history_length, \n self.frame_height, self.frame_width), dtype=np.uint8)\n self.new_states = np.empty((self.batch_size, self.agent_history_length, \n self.frame_height, self.frame_width), dtype=np.uint8)\n self.indices = np.empty(self.batch_size, dtype=np.int32)\n \n def add_experience(self, action, frame, reward, terminal):\n \"\"\"\n Args:\n action: An integer-encoded action\n frame: One grayscale frame of the game\n reward: reward the agend received for performing an action\n terminal: A bool stating whether the episode terminated\n \"\"\"\n if frame.shape != (self.frame_height, self.frame_width):\n raise ValueError('Dimension of frame is wrong!')\n self.actions[self.current] = action\n self.frames[self.current, ...] = frame\n self.rewards[self.current] = reward\n self.terminal_flags[self.current] = terminal\n self.count = max(self.count, self.current+1)\n self.current = (self.current + 1) % self.size\n \n def _get_state(self, index):\n if self.count is 0:\n raise ValueError(\"The replay memory is empty!\")\n if index < self.agent_history_length - 1:\n raise ValueError(\"Index must be min 3\")\n return self.frames[index-self.agent_history_length+1:index+1, ...]\n \n def _get_valid_indices(self):\n for i in range(self.batch_size):\n while True:\n index = random.randint(self.agent_history_length, self.count - 1)\n if index < self.agent_history_length:\n continue\n if index >= self.current and index - self.agent_history_length <= self.current:\n continue\n if self.terminal_flags[index - self.agent_history_length:index].any():\n continue\n break\n self.indices[i] = index\n \n def get_minibatch(self):\n \"\"\"\n Returns a minibatch of self.batch_size transitions\n \"\"\"\n if self.count < self.agent_history_length:\n raise ValueError('Not enough memories to get a minibatch')\n \n self._get_valid_indices()\n \n for i, idx in enumerate(self.indices):\n self.states[i] = self._get_state(idx - 1)\n self.new_states[i] = self._get_state(idx)\n \n return self.states, \\\n self.actions[self.indices], \\\n self.rewards[self.indices], \\\n self.new_states, \\\n self.terminal_flags[self.indices]\n\n\ndef init_filter(shape):\n w = np.random.randn(*shape) * np.sqrt(2.0 / np.prod(shape[1:]))\n return w.astype(np.float32)\n\n\ndef adam(cost, params, lr0=1e-5, beta1=0.9, beta2=0.999, eps=1e-8):\n # cast\n lr0 = np.float32(lr0)\n beta1 = np.float32(beta1)\n beta2 = np.float32(beta2)\n eps = np.float32(eps)\n one = np.float32(1)\n zero = np.float32(0)\n\n grads = T.grad(cost, params)\n updates = []\n time = theano.shared(zero)\n new_time = time + one\n updates.append((time, new_time))\n lr = lr0*T.sqrt(one - beta2**new_time) / (one - beta1**new_time)\n for p, g in zip(params, grads):\n m = theano.shared(p.get_value() * zero)\n v = theano.shared(p.get_value() * zero)\n new_m = beta1*m + (one - beta1)*g\n new_v = beta2*v + (one - beta2)*g*g\n new_p = p - lr*new_m / (T.sqrt(new_v) + eps)\n updates.append((m, new_m))\n updates.append((v, new_v))\n updates.append((p, new_p))\n return updates\n\n\nclass ConvLayer(object):\n def __init__(self, mi, mo, filtsz=5, stride=2, f=T.nnet.relu):\n # mi = input feature map size\n # mo = output feature map size\n sz = (mo, mi, filtsz, filtsz)\n W0 = init_filter(sz)\n self.W = theano.shared(W0)\n b0 = np.zeros(mo, dtype=np.float32)\n self.b = theano.shared(b0)\n self.stride = (stride, stride)\n self.params = [self.W, self.b]\n self.f = f\n # self.cut = cut\n\n def forward(self, X):\n conv_out = conv2d(\n input=X,\n filters=self.W,\n subsample=self.stride,\n # border_mode='half',\n border_mode='valid',\n )\n # cut off 1 pixel from each edge\n # to make the output the same size as input\n # like tensorflow\n # if self.cut:\n # conv_out = conv_out[:, : ,:self.cut ,:self.cut]\n return self.f(conv_out + self.b.dimshuffle('x', 0, 'x', 'x'))\n\nclass HiddenLayer:\n def __init__(self, M1, M2, f=T.nnet.relu):\n W = np.random.randn(M1, M2) * np.sqrt(2 / M1)\n self.W = theano.shared(W.astype(np.float32))\n self.b = theano.shared(np.zeros(M2).astype(np.float32))\n self.params = [self.W, self.b]\n self.f = f\n\n def forward(self, X):\n a = X.dot(self.W) + self.b\n return self.f(a)\n\nclass DQN:\n def __init__(self, K, conv_layer_sizes, hidden_layer_sizes):\n self.K = K\n\n # inputs and targets\n X = T.ftensor4('X')\n G = T.fvector('G')\n actions = T.ivector('actions')\n\n # create the graph\n self.conv_layers = []\n num_input_filters = 4 # number of filters / color channels\n current_size = IM_SIZE\n for num_output_filters, filtersz, stride in conv_layer_sizes:\n ### not using this currently, it didn't make a difference ###\n # cut = None\n # if filtersz % 2 == 0: # if even\n # cut = (current_size + stride - 1) // stride\n layer = ConvLayer(num_input_filters, num_output_filters, filtersz, stride)\n current_size = (current_size + stride - 1) // stride\n # print(\"current_size:\", current_size)\n self.conv_layers.append(layer)\n num_input_filters = num_output_filters\n\n # get conv output size\n Z = X / 255.0\n for layer in self.conv_layers:\n Z = layer.forward(Z)\n conv_out = Z.flatten(ndim=2)\n conv_out_op = theano.function(inputs=[X], outputs=conv_out, allow_input_downcast=True)\n test = conv_out_op(np.random.randn(1, 4, IM_SIZE, IM_SIZE))\n flattened_ouput_size = test.shape[1]\n\n\n # build fully connected layers\n self.layers = []\n M1 = flattened_ouput_size\n print(\"flattened_ouput_size:\", flattened_ouput_size)\n for M2 in hidden_layer_sizes:\n layer = HiddenLayer(M1, M2)\n self.layers.append(layer)\n M1 = M2\n\n # final layer\n layer = HiddenLayer(M1, K, lambda x: x)\n self.layers.append(layer)\n\n # collect params for copy\n self.params = []\n for layer in (self.conv_layers + self.layers):\n self.params += layer.params\n \n\n # calculate final output and cost\n Z = conv_out\n for layer in self.layers:\n Z = layer.forward(Z)\n Y_hat = Z\n\n selected_action_values = Y_hat[T.arange(actions.shape[0]), actions]\n cost = T.mean((G - selected_action_values)**2)\n\n # create train function\n updates = adam(cost, self.params)\n\n # compile functions\n self.train_op = theano.function(\n inputs=[X, G, actions],\n outputs=cost,\n updates=updates,\n allow_input_downcast=True\n )\n\n self.predict_op = theano.function(\n inputs=[X],\n outputs=Y_hat,\n allow_input_downcast=True\n )\n\n def copy_from(self, other):\n my_params = self.params\n other_params = other.params\n for p, q in zip(my_params, other_params):\n actual = q.get_value()\n p.set_value(actual)\n\n def predict(self, X):\n return self.predict_op(X)\n\n def update(self, states, actions, targets):\n return self.train_op(states, targets, actions)\n\n def sample_action(self, x, eps):\n if np.random.random() < eps:\n return np.random.choice(self.K)\n else:\n return np.argmax(self.predict([x])[0])\n\n\n\n\ndef learn(model, target_model, experience_replay_buffer, gamma, batch_size):\n # Sample experiences\n states, actions, rewards, next_states, dones = experience_replay_buffer.get_minibatch()\n\n # Calculate targets\n next_Qs = target_model.predict(next_states)\n next_Q = np.amax(next_Qs, axis=1)\n targets = rewards + np.invert(dones).astype(np.float32) * gamma * next_Q\n\n # Update model\n loss = model.update(states, actions, targets)\n return loss\n\n\ndef play_one(\n env,\n total_t,\n experience_replay_buffer,\n model,\n target_model,\n gamma,\n batch_size,\n epsilon,\n epsilon_change,\n epsilon_min):\n\n t0 = datetime.now()\n\n # Reset the environment\n obs = env.reset()\n obs_small = downsample_image(obs)\n state = np.stack([obs_small] * 4, axis=0)\n loss = None\n\n\n total_time_training = 0\n num_steps_in_episode = 0\n episode_reward = 0\n\n done = False\n while not done:\n\n # Update target network\n if total_t % TARGET_UPDATE_PERIOD == 0:\n target_model.copy_from(model)\n print(\"Copied model parameters to target network. total_t = %s, period = %s\" % (total_t, TARGET_UPDATE_PERIOD))\n\n # Take action\n action = model.sample_action(state, epsilon)\n obs, reward, done, _ = env.step(action)\n obs_small = downsample_image(obs)\n next_state = np.append(state[1:], np.expand_dims(obs_small, 0), axis=0)\n\n episode_reward += reward\n\n # Save the latest experience\n experience_replay_buffer.add_experience(action, obs_small, reward, done)\n\n # Train the model, keep track of time\n t0_2 = datetime.now()\n loss = learn(model, target_model, experience_replay_buffer, gamma, batch_size)\n dt = datetime.now() - t0_2\n\n total_time_training += dt.total_seconds()\n num_steps_in_episode += 1\n\n\n state = next_state\n total_t += 1\n\n epsilon = max(epsilon - epsilon_change, epsilon_min)\n\n return total_t, episode_reward, (datetime.now() - t0), num_steps_in_episode, total_time_training/num_steps_in_episode, epsilon\n\n\ndef smooth(x):\n # last 100\n n = len(x)\n y = np.zeros(n)\n for i in range(n):\n start = max(0, i - 99)\n y[i] = float(x[start:(i+1)].sum()) / (i - start + 1)\n return y\n\n\nif __name__ == '__main__':\n\n # hyperparams and initialize stuff\n conv_layer_sizes = [(32, 8, 4), (64, 4, 2), (64, 3, 1)]\n hidden_layer_sizes = [512]\n gamma = 0.99\n batch_sz = 32\n num_episodes = 5000\n total_t = 0\n experience_replay_buffer = ReplayMemory()\n episode_rewards = np.zeros(num_episodes)\n step_counts = np.zeros(num_episodes)\n\n\n\n # epsilon\n # decays linearly until 0.1\n epsilon = 1.0\n epsilon_min = 0.1\n epsilon_change = (epsilon - epsilon_min) / 500000\n\n\n\n # Create environment\n env = gym.envs.make(\"Breakout-v0\")\n \n\n\n # Create models\n model = DQN(\n K=K,\n conv_layer_sizes=conv_layer_sizes,\n hidden_layer_sizes=hidden_layer_sizes,\n )\n target_model = DQN(\n K=K,\n conv_layer_sizes=conv_layer_sizes,\n hidden_layer_sizes=hidden_layer_sizes,\n )\n\n\n print(\"Populating experience replay buffer...\")\n obs = env.reset()\n obs_small = downsample_image(obs)\n for i in range(MIN_EXPERIENCES):\n\n action = np.random.choice(K)\n obs, reward, done, _ = env.step(action)\n obs_small = downsample_image(obs)\n experience_replay_buffer.add_experience(action, obs_small, reward, done)\n\n if done:\n obs = env.reset()\n\n\n # Play a number of episodes and learn!\n t0 = datetime.now()\n for i in range(num_episodes):\n\n total_t, episode_reward, duration, num_steps_in_episode, time_per_step, epsilon = play_one(\n env,\n total_t,\n experience_replay_buffer,\n model,\n target_model,\n gamma,\n batch_sz,\n epsilon,\n epsilon_change,\n epsilon_min,\n )\n episode_rewards[i] = episode_reward\n step_counts[i] = num_steps_in_episode\n\n last_100_avg = episode_rewards[max(0, i - 100):i + 1].mean()\n last_100_avg_steps = step_counts[max(0, i - 100):i + 1].mean()\n print(\"Episode:\", i,\n \"Duration:\", duration,\n \"Num steps:\", num_steps_in_episode,\n \"Reward:\", episode_reward,\n \"Training time per step:\", \"%.3f\" % time_per_step,\n \"Avg Reward (Last 100):\", \"%.3f\" % last_100_avg,\n \"Avg Steps (Last 100):\", \"%.1f\" % last_100_avg_steps,\n \"Epsilon:\", \"%.3f\" % epsilon\n )\n sys.stdout.flush()\n print(\"Total duration:\", datetime.now() - t0)\n\n # Plot the smoothed returns\n y = smooth(episode_rewards)\n plt.plot(episode_rewards, label='orig')\n plt.plot(y, label='smoothed')\n plt.legend()\n plt.show()\n\n\n" }, { "path": "rl2/cartpole/dqn_tf.py", "content": "# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python\n# https://www.udemy.com/deep-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport gym\nimport os\nimport sys\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nfrom gym import wrappers\nfrom datetime import datetime\nfrom q_learning_bins import plot_running_avg\n\ngym_minor_version = int(gym.__version__.split('.')[1])\nif gym_minor_version >= 19:\n exit(\"Please install OpenAI Gym 0.19.0 or earlier\")\n\nif tf.__version__.startswith('2'):\n exit(\"Please install Tensorflow 1.x\")\n\n\n# global counter\nglobal_iters = 0\n\n\n# a version of HiddenLayer that keeps track of params\nclass HiddenLayer:\n def __init__(self, M1, M2, f=tf.nn.tanh, use_bias=True):\n self.W = tf.Variable(tf.random_normal(shape=(M1, M2)))\n self.params = [self.W]\n self.use_bias = use_bias\n if use_bias:\n self.b = tf.Variable(np.zeros(M2).astype(np.float32))\n self.params.append(self.b)\n self.f = f\n\n def forward(self, X):\n if self.use_bias:\n a = tf.matmul(X, self.W) + self.b\n else:\n a = tf.matmul(X, self.W)\n return self.f(a)\n\n\nclass DQN:\n def __init__(self, D, K, hidden_layer_sizes, gamma, max_experiences=10000, min_experiences=100, batch_sz=32):\n self.K = K\n\n # create the graph\n self.layers = []\n M1 = D\n for M2 in hidden_layer_sizes:\n layer = HiddenLayer(M1, M2)\n self.layers.append(layer)\n M1 = M2\n\n # final layer\n layer = HiddenLayer(M1, K, lambda x: x)\n self.layers.append(layer)\n\n # collect params for copy\n self.params = []\n for layer in self.layers:\n self.params += layer.params\n\n # inputs and targets\n self.X = tf.placeholder(tf.float32, shape=(None, D), name='X')\n self.G = tf.placeholder(tf.float32, shape=(None,), name='G')\n self.actions = tf.placeholder(tf.int32, shape=(None,), name='actions')\n\n # calculate output and cost\n Z = self.X\n for layer in self.layers:\n Z = layer.forward(Z)\n Y_hat = Z\n self.predict_op = Y_hat\n\n selected_action_values = tf.reduce_sum(\n Y_hat * tf.one_hot(self.actions, K),\n reduction_indices=[1]\n )\n\n cost = tf.reduce_sum(tf.square(self.G - selected_action_values))\n self.train_op = tf.train.AdamOptimizer(1e-2).minimize(cost)\n # self.train_op = tf.train.AdagradOptimizer(1e-2).minimize(cost)\n # self.train_op = tf.train.MomentumOptimizer(1e-3, momentum=0.9).minimize(cost)\n # self.train_op = tf.train.GradientDescentOptimizer(1e-4).minimize(cost)\n\n # create replay memory\n self.experience = {'s': [], 'a': [], 'r': [], 's2': [], 'done': []}\n self.max_experiences = max_experiences\n self.min_experiences = min_experiences\n self.batch_sz = batch_sz\n self.gamma = gamma\n\n def set_session(self, session):\n self.session = session\n\n def copy_from(self, other):\n # collect all the ops\n ops = []\n my_params = self.params\n other_params = other.params\n for p, q in zip(my_params, other_params):\n actual = self.session.run(q)\n op = p.assign(actual)\n ops.append(op)\n # now run them all\n self.session.run(ops)\n\n def predict(self, X):\n X = np.atleast_2d(X)\n return self.session.run(self.predict_op, feed_dict={self.X: X})\n\n def train(self, target_network):\n # sample a random batch from buffer, do an iteration of GD\n if len(self.experience['s']) < self.min_experiences:\n # don't do anything if we don't have enough experience\n return\n\n # randomly select a batch\n idx = np.random.choice(len(self.experience['s']), size=self.batch_sz, replace=False)\n # print(\"idx:\", idx)\n states = [self.experience['s'][i] for i in idx]\n actions = [self.experience['a'][i] for i in idx]\n rewards = [self.experience['r'][i] for i in idx]\n next_states = [self.experience['s2'][i] for i in idx]\n dones = [self.experience['done'][i] for i in idx]\n next_Q = np.max(target_network.predict(next_states), axis=1)\n targets = [r + self.gamma*next_q if not done else r for r, next_q, done in zip(rewards, next_Q, dones)]\n\n # call optimizer\n self.session.run(\n self.train_op,\n feed_dict={\n self.X: states,\n self.G: targets,\n self.actions: actions\n }\n )\n\n def add_experience(self, s, a, r, s2, done):\n if len(self.experience['s']) >= self.max_experiences:\n self.experience['s'].pop(0)\n self.experience['a'].pop(0)\n self.experience['r'].pop(0)\n self.experience['s2'].pop(0)\n self.experience['done'].pop(0)\n self.experience['s'].append(s)\n self.experience['a'].append(a)\n self.experience['r'].append(r)\n self.experience['s2'].append(s2)\n self.experience['done'].append(done)\n\n def sample_action(self, x, eps):\n if np.random.random() < eps:\n return np.random.choice(self.K)\n else:\n X = np.atleast_2d(x)\n return np.argmax(self.predict(X)[0])\n\n\ndef play_one(env, model, tmodel, eps, gamma, copy_period):\n global global_iters\n observation = env.reset()\n done = False\n totalreward = 0\n iters = 0\n while not done and iters < 2000:\n # if we reach 2000, just quit, don't want this going forever\n # the 200 limit seems a bit early\n action = model.sample_action(observation, eps)\n prev_observation = observation\n observation, reward, done, info = env.step(action)\n\n totalreward += reward\n if done:\n reward = -200\n\n # update the model\n model.add_experience(prev_observation, action, reward, observation, done)\n model.train(tmodel)\n\n iters += 1\n global_iters += 1\n\n if global_iters % copy_period == 0:\n tmodel.copy_from(model)\n\n return totalreward\n\n\ndef main():\n env = gym.make('CartPole-v0')\n gamma = 0.99\n copy_period = 50\n\n D = len(env.observation_space.sample())\n K = env.action_space.n\n sizes = [200,200]\n model = DQN(D, K, sizes, gamma)\n tmodel = DQN(D, K, sizes, gamma)\n init = tf.global_variables_initializer()\n session = tf.InteractiveSession()\n session.run(init)\n model.set_session(session)\n tmodel.set_session(session)\n\n\n if 'monitor' in sys.argv:\n filename = os.path.basename(__file__).split('.')[0]\n monitor_dir = './' + filename + '_' + str(datetime.now())\n env = wrappers.Monitor(env, monitor_dir)\n\n\n N = 500\n totalrewards = np.empty(N)\n costs = np.empty(N)\n for n in range(N):\n eps = 1.0/np.sqrt(n+1)\n totalreward = play_one(env, model, tmodel, eps, gamma, copy_period)\n totalrewards[n] = totalreward\n if n % 100 == 0:\n print(\"episode:\", n, \"total reward:\", totalreward, \"eps:\", eps, \"avg reward (last 100):\", totalrewards[max(0, n-100):(n+1)].mean())\n\n print(\"avg reward for last 100 episodes:\", totalrewards[-100:].mean())\n print(\"total steps:\", totalrewards.sum())\n\n plt.plot(totalrewards)\n plt.title(\"Rewards\")\n plt.show()\n\n plot_running_avg(totalrewards)\n\n\nif __name__ == '__main__':\n main()\n\n\n" }, { "path": "rl2/cartpole/dqn_theano.py", "content": "# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python\n# https://www.udemy.com/deep-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport gym\nimport os\nimport sys\nimport numpy as np\nimport theano\nimport theano.tensor as T\nimport matplotlib.pyplot as plt\nfrom gym import wrappers\nfrom datetime import datetime\nfrom q_learning_bins import plot_running_avg\n\ngym_minor_version = int(gym.__version__.split('.')[1])\nif gym_minor_version >= 19:\n exit(\"Please install OpenAI Gym 0.19.0 or earlier\")\n\n\n# global counter\nglobal_iters = 0\n\n\n# helper for adam optimizer\n# use tensorflow defaults\ndef adam(cost, params, lr0=1e-2, beta1=0.9, beta2=0.999, eps=1e-8):\n grads = T.grad(cost, params)\n updates = []\n time = theano.shared(0)\n new_time = time + 1\n updates.append((time, new_time))\n lr = lr0*T.sqrt(1 - beta2**new_time) / (1 - beta1**new_time)\n for p, g in zip(params, grads):\n m = theano.shared(p.get_value() * 0.)\n v = theano.shared(p.get_value() * 0.)\n new_m = beta1*m + (1 - beta1)*g\n new_v = beta2*v + (1 - beta2)*g*g\n new_p = p - lr*new_m / (T.sqrt(new_v) + eps)\n updates.append((m, new_m))\n updates.append((v, new_v))\n updates.append((p, new_p))\n return updates\n\n\n# a version of HiddenLayer that keeps track of params\nclass HiddenLayer:\n def __init__(self, M1, M2, f=T.tanh, use_bias=True):\n self.W = theano.shared(np.random.randn(M1, M2) * np.sqrt(2 / M1))\n self.params = [self.W]\n self.use_bias = use_bias\n if use_bias:\n self.b = theano.shared(np.zeros(M2))\n self.params += [self.b]\n self.f = f\n\n def forward(self, X):\n if self.use_bias:\n a = X.dot(self.W) + self.b\n else:\n a = X.dot(self.W)\n return self.f(a)\n\n\nclass DQN:\n def __init__(self, D, K, hidden_layer_sizes, gamma, max_experiences=10000, min_experiences=100, batch_sz=32):\n self.K = K\n lr = 1e-2\n mu = 0.\n decay = 0.99\n\n # create the graph\n self.layers = []\n M1 = D\n for M2 in hidden_layer_sizes:\n layer = HiddenLayer(M1, M2)\n self.layers.append(layer)\n M1 = M2\n\n # final layer\n layer = HiddenLayer(M1, K, lambda x: x)\n self.layers.append(layer)\n\n # collect params for copy\n self.params = []\n for layer in self.layers:\n self.params += layer.params\n\n # inputs and targets\n X = T.matrix('X')\n G = T.vector('G')\n actions = T.ivector('actions')\n\n # calculate output and cost\n Z = X\n for layer in self.layers:\n Z = layer.forward(Z)\n Y_hat = Z\n\n selected_action_values = Y_hat[T.arange(actions.shape[0]), actions]\n cost = T.sum((G - selected_action_values)**2) \n\n # create train function\n updates = adam(cost, self.params)\n\n # compile functions\n self.train_op = theano.function(\n inputs=[X, G, actions],\n updates=updates,\n allow_input_downcast=True\n )\n self.predict_op = theano.function(\n inputs=[X],\n outputs=Y_hat,\n allow_input_downcast=True\n )\n\n # create replay memory\n self.experience = {'s': [], 'a': [], 'r': [], 's2': [], 'done': []}\n self.max_experiences = max_experiences\n self.min_experiences = min_experiences\n self.batch_sz = batch_sz\n self.gamma = gamma\n\n def copy_from(self, other):\n my_params = self.params\n other_params = other.params\n for p, q in zip(my_params, other_params):\n actual = q.get_value()\n p.set_value(actual)\n\n def predict(self, X):\n X = np.atleast_2d(X)\n return self.predict_op(X)\n\n def train(self, target_network):\n # sample a random batch from buffer, do an iteration of GD\n if len(self.experience['s']) < self.min_experiences:\n # don't do anything if we don't have enough experience\n return\n\n # randomly select a batch\n idx = np.random.choice(len(self.experience['s']), size=self.batch_sz, replace=False)\n # print(\"idx:\", idx)\n states = [self.experience['s'][i] for i in idx]\n actions = [self.experience['a'][i] for i in idx]\n rewards = [self.experience['r'][i] for i in idx]\n next_states = [self.experience['s2'][i] for i in idx]\n dones = [self.experience['done'][i] for i in idx]\n next_Q = np.max(target_network.predict(next_states), axis=1)\n targets = [r + self.gamma*next_q if not done else r for r, next_q, done in zip(rewards, next_Q, dones)]\n\n # call optimizer\n self.train_op(states, targets, actions)\n\n def add_experience(self, s, a, r, s2, done):\n if len(self.experience['s']) >= self.max_experiences:\n self.experience['s'].pop(0)\n self.experience['a'].pop(0)\n self.experience['r'].pop(0)\n self.experience['s2'].pop(0)\n self.experience['done'].pop(0)\n self.experience['s'].append(s)\n self.experience['a'].append(a)\n self.experience['r'].append(r)\n self.experience['s2'].append(s2)\n self.experience['done'].append(done)\n\n def sample_action(self, x, eps):\n if np.random.random() < eps:\n return np.random.choice(self.K)\n else:\n X = np.atleast_2d(x)\n return np.argmax(self.predict(X)[0])\n\n\ndef play_one(env, model, tmodel, eps, gamma, copy_period):\n global global_iters\n observation = env.reset()\n done = False\n totalreward = 0\n iters = 0\n while not done and iters < 2000:\n # if we reach 2000, just quit, don't want this going forever\n # the 200 limit seems a bit early\n action = model.sample_action(observation, eps)\n prev_observation = observation\n observation, reward, done, info = env.step(action)\n\n totalreward += reward\n if done:\n reward = -200\n\n # update the model\n model.add_experience(prev_observation, action, reward, observation, done)\n model.train(tmodel)\n\n iters += 1\n global_iters += 1\n\n if global_iters % copy_period == 0:\n tmodel.copy_from(model)\n\n return totalreward\n\n\ndef main():\n env = gym.make('CartPole-v0')\n gamma = 0.99\n copy_period = 50\n\n D = len(env.observation_space.sample())\n K = env.action_space.n\n sizes = [200,200]\n model = DQN(D, K, sizes, gamma)\n tmodel = DQN(D, K, sizes, gamma)\n\n if 'monitor' in sys.argv:\n filename = os.path.basename(__file__).split('.')[0]\n monitor_dir = './' + filename + '_' + str(datetime.now())\n env = wrappers.Monitor(env, monitor_dir)\n\n N = 500\n totalrewards = np.empty(N)\n costs = np.empty(N)\n for n in range(N):\n eps = 1.0/np.sqrt(n+1)\n totalreward = play_one(env, model, tmodel, eps, gamma, copy_period)\n totalrewards[n] = totalreward\n if n % 100 == 0:\n print(\"episode:\", n, \"total reward:\", totalreward, \"eps:\", eps, \"avg reward (last 100):\", totalrewards[max(0, n-100):(n+1)].mean())\n\n print(\"avg reward for last 100 episodes:\", totalrewards[-100:].mean())\n print(\"total steps:\", totalrewards.sum())\n\n plt.plot(totalrewards)\n plt.title(\"Rewards\")\n plt.show()\n\n plot_running_avg(totalrewards)\n\n\nif __name__ == '__main__':\n main()\n\n\n" }, { "path": "rl2/cartpole/pg_tf.py", "content": "# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python\n# https://www.udemy.com/deep-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n# Inspired by https://github.com/dennybritz/reinforcement-learning\n\nimport gym\nimport os\nimport sys\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nfrom gym import wrappers\nfrom datetime import datetime\nfrom q_learning_bins import plot_running_avg\n\ngym_minor_version = int(gym.__version__.split('.')[1])\nif gym_minor_version >= 19:\n exit(\"Please install OpenAI Gym 0.19.0 or earlier\")\n\nif tf.__version__.startswith('2'):\n exit(\"Please install Tensorflow 1.x\")\n\n\n# so you can test different architectures\nclass HiddenLayer:\n def __init__(self, M1, M2, f=tf.nn.tanh, use_bias=True):\n self.W = tf.Variable(tf.random_normal(shape=(M1, M2)))\n self.use_bias = use_bias\n if use_bias:\n self.b = tf.Variable(np.zeros(M2).astype(np.float32))\n self.f = f\n\n def forward(self, X):\n if self.use_bias:\n a = tf.matmul(X, self.W) + self.b\n else:\n a = tf.matmul(X, self.W)\n return self.f(a)\n\n\n# approximates pi(a | s)\nclass PolicyModel:\n def __init__(self, D, K, hidden_layer_sizes):\n # create the graph\n # K = number of actions\n self.layers = []\n M1 = D\n for M2 in hidden_layer_sizes:\n layer = HiddenLayer(M1, M2)\n self.layers.append(layer)\n M1 = M2\n\n # final layer\n # layer = HiddenLayer(M1, K, lambda x: x, use_bias=False)\n layer = HiddenLayer(M1, K, tf.nn.softmax, use_bias=False)\n self.layers.append(layer)\n\n # inputs and targets\n self.X = tf.placeholder(tf.float32, shape=(None, D), name='X')\n self.actions = tf.placeholder(tf.int32, shape=(None,), name='actions')\n self.advantages = tf.placeholder(tf.float32, shape=(None,), name='advantages')\n\n # calculate output and cost\n Z = self.X\n for layer in self.layers:\n Z = layer.forward(Z)\n p_a_given_s = Z\n # action_scores = Z\n # p_a_given_s = tf.nn.softmax(action_scores)\n # self.action_scores = action_scores\n self.predict_op = p_a_given_s\n\n # self.one_hot_actions = tf.one_hot(self.actions, K)\n\n selected_probs = tf.log(\n tf.reduce_sum(\n p_a_given_s * tf.one_hot(self.actions, K),\n reduction_indices=[1]\n )\n )\n\n # self.selected_probs = selected_probs\n cost = -tf.reduce_sum(self.advantages * selected_probs)\n # self.cost = cost\n # self.train_op = tf.train.AdamOptimizer(1e-1).minimize(cost)\n self.train_op = tf.train.AdagradOptimizer(1e-1).minimize(cost)\n # self.train_op = tf.train.MomentumOptimizer(1e-4, momentum=0.9).minimize(cost)\n # self.train_op = tf.train.GradientDescentOptimizer(1e-4).minimize(cost)\n\n def set_session(self, session):\n self.session = session\n\n def partial_fit(self, X, actions, advantages):\n X = np.atleast_2d(X)\n actions = np.atleast_1d(actions)\n advantages = np.atleast_1d(advantages)\n self.session.run(\n self.train_op,\n feed_dict={\n self.X: X,\n self.actions: actions,\n self.advantages: advantages,\n }\n )\n\n def predict(self, X):\n X = np.atleast_2d(X)\n return self.session.run(self.predict_op, feed_dict={self.X: X})\n\n def sample_action(self, X):\n p = self.predict(X)[0]\n return np.random.choice(len(p), p=p)\n\n\n# approximates V(s)\nclass ValueModel:\n def __init__(self, D, hidden_layer_sizes):\n # create the graph\n self.layers = []\n M1 = D\n for M2 in hidden_layer_sizes:\n layer = HiddenLayer(M1, M2)\n self.layers.append(layer)\n M1 = M2\n\n # final layer\n layer = HiddenLayer(M1, 1, lambda x: x)\n self.layers.append(layer)\n\n # inputs and targets\n self.X = tf.placeholder(tf.float32, shape=(None, D), name='X')\n self.Y = tf.placeholder(tf.float32, shape=(None,), name='Y')\n\n # calculate output and cost\n Z = self.X\n for layer in self.layers:\n Z = layer.forward(Z)\n Y_hat = tf.reshape(Z, [-1]) # the output\n self.predict_op = Y_hat\n\n cost = tf.reduce_sum(tf.square(self.Y - Y_hat))\n # self.train_op = tf.train.AdamOptimizer(1e-2).minimize(cost)\n # self.train_op = tf.train.MomentumOptimizer(1e-2, momentum=0.9).minimize(cost)\n self.train_op = tf.train.GradientDescentOptimizer(1e-4).minimize(cost)\n\n def set_session(self, session):\n self.session = session\n\n def partial_fit(self, X, Y):\n X = np.atleast_2d(X)\n Y = np.atleast_1d(Y)\n self.session.run(self.train_op, feed_dict={self.X: X, self.Y: Y})\n\n def predict(self, X):\n X = np.atleast_2d(X)\n return self.session.run(self.predict_op, feed_dict={self.X: X})\n\n\ndef play_one_td(env, pmodel, vmodel, gamma):\n observation = env.reset()\n done = False\n totalreward = 0\n iters = 0\n\n while not done and iters < 2000:\n # if we reach 2000, just quit, don't want this going forever\n # the 200 limit seems a bit early\n action = pmodel.sample_action(observation)\n prev_observation = observation\n observation, reward, done, info = env.step(action)\n\n # if done:\n # reward = -200\n\n # update the models\n V_next = vmodel.predict(observation)[0]\n G = reward + gamma*V_next\n advantage = G - vmodel.predict(prev_observation)\n pmodel.partial_fit(prev_observation, action, advantage)\n vmodel.partial_fit(prev_observation, G)\n\n if reward == 1: # if we changed the reward to -200\n totalreward += reward\n iters += 1\n\n return totalreward\n\n\n\ndef play_one_mc(env, pmodel, vmodel, gamma):\n observation = env.reset()\n done = False\n totalreward = 0\n iters = 0\n\n states = []\n actions = []\n rewards = []\n\n reward = 0\n while not done and iters < 2000:\n # if we reach 2000, just quit, don't want this going forever\n # the 200 limit seems a bit early\n action = pmodel.sample_action(observation)\n\n states.append(observation)\n actions.append(action)\n rewards.append(reward)\n\n prev_observation = observation\n observation, reward, done, info = env.step(action)\n\n if done:\n reward = -200\n\n if reward == 1: # if we changed the reward to -200\n totalreward += reward\n iters += 1\n\n # save the final (s,a,r) tuple\n action = pmodel.sample_action(observation)\n states.append(observation)\n actions.append(action)\n rewards.append(reward)\n\n returns = []\n advantages = []\n G = 0\n for s, r in zip(reversed(states), reversed(rewards)):\n returns.append(G)\n advantages.append(G - vmodel.predict(s)[0])\n G = r + gamma*G\n returns.reverse()\n advantages.reverse()\n\n # update the models\n pmodel.partial_fit(states, actions, advantages)\n vmodel.partial_fit(states, returns)\n\n return totalreward\n\n\ndef main():\n env = gym.make('CartPole-v0')\n D = env.observation_space.shape[0]\n K = env.action_space.n\n pmodel = PolicyModel(D, K, [])\n vmodel = ValueModel(D, [10])\n init = tf.global_variables_initializer()\n session = tf.InteractiveSession()\n session.run(init)\n pmodel.set_session(session)\n vmodel.set_session(session)\n gamma = 0.99\n\n if 'monitor' in sys.argv:\n filename = os.path.basename(__file__).split('.')[0]\n monitor_dir = './' + filename + '_' + str(datetime.now())\n env = wrappers.Monitor(env, monitor_dir)\n\n N = 1000\n totalrewards = np.empty(N)\n costs = np.empty(N)\n for n in range(N):\n totalreward = play_one_mc(env, pmodel, vmodel, gamma)\n totalrewards[n] = totalreward\n if n % 100 == 0:\n print(\"episode:\", n, \"total reward:\", totalreward, \"avg reward (last 100):\", totalrewards[max(0, n-100):(n+1)].mean())\n\n print(\"avg reward for last 100 episodes:\", totalrewards[-100:].mean())\n print(\"total steps:\", totalrewards.sum())\n\n plt.plot(totalrewards)\n plt.title(\"Rewards\")\n plt.show()\n\n plot_running_avg(totalrewards)\n\n\nif __name__ == '__main__':\n main()\n\n" }, { "path": "rl2/cartpole/pg_theano.py", "content": "# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python\n# https://www.udemy.com/deep-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n# Inspired by https://github.com/dennybritz/reinforcement-learning\n\nimport gym\nimport os\nimport sys\nimport numpy as np\nimport theano\nimport theano.tensor as T\nimport matplotlib.pyplot as plt\nfrom gym import wrappers\nfrom datetime import datetime\nfrom q_learning_bins import plot_running_avg\n\ngym_minor_version = int(gym.__version__.split('.')[1])\nif gym_minor_version >= 19:\n exit(\"Please install OpenAI Gym 0.19.0 or earlier\")\n\n\n# so you can test different architectures\nclass HiddenLayer:\n def __init__(self, M1, M2, f=T.tanh, use_bias=True):\n self.W = theano.shared(np.random.randn(M1, M2) * np.sqrt(2 / M1))\n self.params = [self.W]\n self.use_bias = use_bias\n if use_bias:\n self.b = theano.shared(np.zeros(M2))\n self.params += [self.b]\n self.f = f\n\n def forward(self, X):\n if self.use_bias:\n a = X.dot(self.W) + self.b\n else:\n a = X.dot(self.W)\n return self.f(a)\n\n\n# approximates pi(a | s)\nclass PolicyModel:\n def __init__(self, D, K, hidden_layer_sizes):\n # learning rate and other hyperparams\n lr = 1e-4\n\n # create the graph\n # K = number of actions\n self.layers = []\n M1 = D\n for M2 in hidden_layer_sizes:\n layer = HiddenLayer(M1, M2)\n self.layers.append(layer)\n M1 = M2\n\n # final layer\n layer = HiddenLayer(M1, K, lambda x: x, use_bias=False)\n self.layers.append(layer)\n\n # get all params for gradient later\n params = []\n for layer in self.layers:\n params += layer.params\n\n # inputs and targets\n X = T.matrix('X')\n actions = T.ivector('actions')\n advantages = T.vector('advantages')\n\n # calculate output and cost\n Z = X\n for layer in self.layers:\n Z = layer.forward(Z)\n action_scores = Z\n p_a_given_s = T.nnet.softmax(action_scores)\n\n selected_probs = T.log(p_a_given_s[T.arange(actions.shape[0]), actions])\n cost = -T.sum(advantages * selected_probs)\n \n # specify update rule\n grads = T.grad(cost, params)\n updates = [(p, p - lr*g) for p, g in zip(params, grads)]\n\n # compile functions\n self.train_op = theano.function(\n inputs=[X, actions, advantages],\n updates=updates,\n allow_input_downcast=True\n )\n self.predict_op = theano.function(\n inputs=[X],\n outputs=p_a_given_s,\n allow_input_downcast=True\n )\n\n def partial_fit(self, X, actions, advantages):\n X = np.atleast_2d(X)\n actions = np.atleast_1d(actions)\n advantages = np.atleast_1d(advantages)\n self.train_op(X, actions, advantages)\n\n def predict(self, X):\n X = np.atleast_2d(X)\n return self.predict_op(X)\n\n def sample_action(self, X):\n p = self.predict(X)[0]\n nonans = np.all(~np.isnan(p))\n assert(nonans)\n return np.random.choice(len(p), p=p)\n\n\n# approximates V(s)\nclass ValueModel:\n def __init__(self, D, hidden_layer_sizes):\n # constant learning rate is fine\n lr = 1e-4\n\n # create the graph\n self.layers = []\n M1 = D\n for M2 in hidden_layer_sizes:\n layer = HiddenLayer(M1, M2)\n self.layers.append(layer)\n M1 = M2\n\n # final layer\n layer = HiddenLayer(M1, 1, lambda x: x)\n self.layers.append(layer)\n\n # get all params for gradient later\n params = []\n for layer in self.layers:\n params += layer.params\n\n # inputs and targets\n X = T.matrix('X')\n Y = T.vector('Y')\n\n # calculate output and cost\n Z = X\n for layer in self.layers:\n Z = layer.forward(Z)\n Y_hat = T.flatten(Z)\n cost = T.sum((Y - Y_hat)**2)\n\n # specify update rule\n grads = T.grad(cost, params)\n updates = [(p, p - lr*g) for p, g in zip(params, grads)]\n\n # compile functions\n self.train_op = theano.function(\n inputs=[X, Y],\n updates=updates,\n allow_input_downcast=True\n )\n self.predict_op = theano.function(\n inputs=[X],\n outputs=Y_hat,\n allow_input_downcast=True\n )\n\n def partial_fit(self, X, Y):\n X = np.atleast_2d(X)\n Y = np.atleast_1d(Y)\n self.train_op(X, Y)\n\n def predict(self, X):\n X = np.atleast_2d(X)\n return self.predict_op(X)\n\n\ndef play_one_td(env, pmodel, vmodel, gamma):\n observation = env.reset()\n done = False\n totalreward = 0\n iters = 0\n\n while not done and iters < 2000:\n # if we reach 2000, just quit, don't want this going forever\n # the 200 limit seems a bit early\n action = pmodel.sample_action(observation)\n prev_observation = observation\n observation, reward, done, info = env.step(action)\n\n if done:\n reward = -200\n\n # update the models\n V_next = vmodel.predict(observation)\n G = reward + gamma*np.max(V_next)\n advantage = G - vmodel.predict(prev_observation)\n pmodel.partial_fit(prev_observation, action, advantage)\n vmodel.partial_fit(prev_observation, G)\n\n if reward == 1: # if we changed the reward to -200\n totalreward += reward\n iters += 1\n\n return totalreward\n\n\ndef play_one_mc(env, pmodel, vmodel, gamma):\n observation = env.reset()\n done = False\n totalreward = 0\n iters = 0\n\n states = []\n actions = []\n rewards = []\n\n reward = 0\n while not done and iters < 2000:\n # if we reach 2000, just quit, don't want this going forever\n # the 200 limit seems a bit early\n action = pmodel.sample_action(observation)\n\n states.append(observation)\n actions.append(action)\n rewards.append(reward)\n\n prev_observation = observation\n observation, reward, done, info = env.step(action)\n\n if done:\n reward = -200\n\n if reward == 1: # if we changed the reward to -200\n totalreward += reward\n iters += 1\n\n # save the final (s,a,r) tuple\n action = pmodel.sample_action(observation)\n states.append(observation)\n actions.append(action)\n rewards.append(reward)\n\n returns = []\n advantages = []\n G = 0\n for s, r in zip(reversed(states), reversed(rewards)):\n returns.append(G)\n advantages.append(G - vmodel.predict(s)[0])\n G = r + gamma*G\n returns.reverse()\n advantages.reverse()\n\n # update the models\n pmodel.partial_fit(states[1:], actions[1:], advantages[1:])\n vmodel.partial_fit(states, returns)\n\n return totalreward\n\n\ndef main():\n env = gym.make('CartPole-v0')\n D = env.observation_space.shape[0]\n K = env.action_space.n\n pmodel = PolicyModel(D, K, [])\n vmodel = ValueModel(D, [10])\n gamma = 0.99\n\n if 'monitor' in sys.argv:\n filename = os.path.basename(__file__).split('.')[0]\n monitor_dir = './' + filename + '_' + str(datetime.now())\n env = wrappers.Monitor(env, monitor_dir)\n\n N = 1000\n totalrewards = np.empty(N)\n costs = np.empty(N)\n for n in range(N):\n totalreward = play_one_mc(env, pmodel, vmodel, gamma)\n totalrewards[n] = totalreward\n if n % 100 == 0:\n print(\"episode:\", n, \"total reward:\", totalreward, \"avg reward (last 100):\", totalrewards[max(0, n-100):(n+1)].mean())\n\n print(\"avg reward for last 100 episodes:\", totalrewards[-100:].mean())\n print(\"total steps:\", totalrewards.sum())\n\n plt.plot(totalrewards)\n plt.title(\"Rewards\")\n plt.show()\n\n plot_running_avg(totalrewards)\n\n\nif __name__ == '__main__':\n main()\n\n" }, { "path": "rl2/cartpole/q_learning.py", "content": "# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python\n# https://www.udemy.com/deep-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n# Inspired by https://github.com/dennybritz/reinforcement-learning\n\n# Works best w/ multiply RBF kernels at var=0.05, 0.1, 0.5, 1.0\n\nimport gym\nimport os\nimport sys\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom gym import wrappers\nfrom datetime import datetime\nfrom sklearn.pipeline import FeatureUnion\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.kernel_approximation import RBFSampler\nfrom q_learning_bins import plot_running_avg\n\ngym_minor_version = int(gym.__version__.split('.')[1])\nif gym_minor_version >= 19:\n exit(\"Please install OpenAI Gym 0.19.0 or earlier\")\n\n\nclass SGDRegressor:\n def __init__(self, D):\n self.w = np.random.randn(D) / np.sqrt(D)\n self.lr = 0.1\n\n def partial_fit(self, X, Y):\n self.w += self.lr*(Y - X.dot(self.w)).dot(X)\n\n def predict(self, X):\n return X.dot(self.w)\n\n\n\nclass FeatureTransformer:\n def __init__(self, env):\n # observation_examples = np.array([env.observation_space.sample() for x in range(10000)])\n # NOTE!! state samples are poor, b/c you get velocities --> infinity\n observation_examples = np.random.random((20000, 4))*2 - 1\n scaler = StandardScaler()\n scaler.fit(observation_examples)\n\n # Used to converte a state to a featurizes represenation.\n # We use RBF kernels with different variances to cover different parts of the space\n featurizer = FeatureUnion([\n (\"rbf1\", RBFSampler(gamma=0.05, n_components=1000)),\n (\"rbf2\", RBFSampler(gamma=1.0, n_components=1000)),\n (\"rbf3\", RBFSampler(gamma=0.5, n_components=1000)),\n (\"rbf4\", RBFSampler(gamma=0.1, n_components=1000))\n ])\n feature_examples = featurizer.fit_transform(scaler.transform(observation_examples))\n\n self.dimensions = feature_examples.shape[1]\n self.scaler = scaler\n self.featurizer = featurizer\n\n def transform(self, observations):\n scaled = self.scaler.transform(observations)\n return self.featurizer.transform(scaled)\n\n\n# Holds one SGDRegressor for each action\nclass Model:\n def __init__(self, env, feature_transformer):\n self.env = env\n self.models = []\n self.feature_transformer = feature_transformer\n for i in range(env.action_space.n):\n model = SGDRegressor(feature_transformer.dimensions)\n self.models.append(model)\n\n def predict(self, s):\n X = self.feature_transformer.transform(np.atleast_2d(s))\n result = np.stack([m.predict(X) for m in self.models]).T\n return result\n\n def update(self, s, a, G):\n X = self.feature_transformer.transform(np.atleast_2d(s))\n self.models[a].partial_fit(X, [G])\n\n def sample_action(self, s, eps):\n if np.random.random() < eps:\n return self.env.action_space.sample()\n else:\n return np.argmax(self.predict(s))\n\n\ndef play_one(env, model, eps, gamma):\n observation = env.reset()\n done = False\n totalreward = 0\n iters = 0\n while not done and iters < 2000:\n # if we reach 2000, just quit, don't want this going forever\n # the 200 limit seems a bit early\n action = model.sample_action(observation, eps)\n prev_observation = observation\n observation, reward, done, info = env.step(action)\n\n if done:\n reward = -200\n\n # update the model\n next = model.predict(observation)\n # print(next.shape)\n assert(next.shape == (1, env.action_space.n))\n G = reward + gamma*np.max(next)\n model.update(prev_observation, action, G)\n\n if reward == 1: # if we changed the reward to -200\n totalreward += reward\n iters += 1\n\n return totalreward\n\n\ndef main():\n env = gym.make('CartPole-v0')\n ft = FeatureTransformer(env)\n model = Model(env, ft)\n gamma = 0.99\n\n if 'monitor' in sys.argv:\n filename = os.path.basename(__file__).split('.')[0]\n monitor_dir = './' + filename + '_' + str(datetime.now())\n env = wrappers.Monitor(env, monitor_dir)\n\n\n N = 500\n totalrewards = np.empty(N)\n costs = np.empty(N)\n for n in range(N):\n eps = 1.0/np.sqrt(n+1)\n totalreward = play_one(env, model, eps, gamma)\n totalrewards[n] = totalreward\n if n % 100 == 0:\n print(\"episode:\", n, \"total reward:\", totalreward, \"eps:\", eps, \"avg reward (last 100):\", totalrewards[max(0, n-100):(n+1)].mean())\n\n print(\"avg reward for last 100 episodes:\", totalrewards[-100:].mean())\n print(\"total steps:\", totalrewards.sum())\n\n plt.plot(totalrewards)\n plt.title(\"Rewards\")\n plt.show()\n\n plot_running_avg(totalrewards)\n\n\nif __name__ == '__main__':\n main()\n\n" }, { "path": "rl2/cartpole/q_learning_bins.py", "content": "# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python\n# https://www.udemy.com/deep-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# if builtins is not defined\n# sudo pip install -U future\n\nimport gym\nimport os\nimport sys\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom gym import wrappers\nfrom datetime import datetime\n\ngym_minor_version = int(gym.__version__.split('.')[1])\nif gym_minor_version >= 19:\n exit(\"Please install OpenAI Gym 0.19.0 or earlier\")\n\n\n# turns list of integers into an int\n# Ex.\n# build_state([1,2,3,4,5]) -> 12345\ndef build_state(features):\n return int(\"\".join(map(lambda feature: str(int(feature)), features)))\n\ndef to_bin(value, bins):\n return np.digitize(x=[value], bins=bins)[0]\n\n\nclass FeatureTransformer:\n def __init__(self):\n # Note: to make this better you could look at how often each bin was\n # actually used while running the script.\n # It's not clear from the high/low values nor sample() what values\n # we really expect to get.\n self.cart_position_bins = np.linspace(-2.4, 2.4, 9)\n self.cart_velocity_bins = np.linspace(-2, 2, 9) # (-inf, inf) (I did not check that these were good values)\n self.pole_angle_bins = np.linspace(-0.4, 0.4, 9)\n self.pole_velocity_bins = np.linspace(-3.5, 3.5, 9) # (-inf, inf) (I did not check that these were good values)\n\n def transform(self, observation):\n # returns an int\n cart_pos, cart_vel, pole_angle, pole_vel = observation\n return build_state([\n to_bin(cart_pos, self.cart_position_bins),\n to_bin(cart_vel, self.cart_velocity_bins),\n to_bin(pole_angle, self.pole_angle_bins),\n to_bin(pole_vel, self.pole_velocity_bins),\n ])\n\n\nclass Model:\n def __init__(self, env, feature_transformer):\n self.env = env\n self.feature_transformer = feature_transformer\n\n num_states = 10**env.observation_space.shape[0]\n num_actions = env.action_space.n\n self.Q = np.random.uniform(low=-1, high=1, size=(num_states, num_actions))\n\n def predict(self, s):\n x = self.feature_transformer.transform(s)\n return self.Q[x]\n\n def update(self, s, a, G):\n x = self.feature_transformer.transform(s)\n self.Q[x,a] += 1e-2*(G - self.Q[x,a])\n\n def sample_action(self, s, eps):\n if np.random.random() < eps:\n return self.env.action_space.sample()\n else:\n p = self.predict(s)\n return np.argmax(p)\n\n\ndef play_one(model, eps, gamma):\n observation = env.reset()\n done = False\n totalreward = 0\n iters = 0\n while not done and iters < 10000:\n action = model.sample_action(observation, eps)\n prev_observation = observation\n observation, reward, done, info = env.step(action)\n\n totalreward += reward\n\n if done and iters < 199:\n reward = -300\n\n # update the model\n G = reward + gamma*np.max(model.predict(observation))\n model.update(prev_observation, action, G)\n\n iters += 1\n\n return totalreward\n\n\ndef plot_running_avg(totalrewards):\n N = len(totalrewards)\n running_avg = np.empty(N)\n for t in range(N):\n running_avg[t] = totalrewards[max(0, t-100):(t+1)].mean()\n plt.plot(running_avg)\n plt.title(\"Running Average\")\n plt.show()\n\n\nif __name__ == '__main__':\n env = gym.make('CartPole-v0')\n ft = FeatureTransformer()\n model = Model(env, ft)\n gamma = 0.9\n\n if 'monitor' in sys.argv:\n filename = os.path.basename(__file__).split('.')[0]\n monitor_dir = './' + filename + '_' + str(datetime.now())\n env = wrappers.Monitor(env, monitor_dir)\n\n N = 10000\n totalrewards = np.empty(N)\n for n in range(N):\n eps = 1.0/np.sqrt(n+1)\n totalreward = play_one(model, eps, gamma)\n totalrewards[n] = totalreward\n if n % 100 == 0:\n print(\"episode:\", n, \"total reward:\", totalreward, \"eps:\", eps)\n print(\"avg reward for last 100 episodes:\", totalrewards[-100:].mean())\n print(\"total steps:\", totalrewards.sum())\n\n plt.plot(totalrewards)\n plt.title(\"Rewards\")\n plt.show()\n\n plot_running_avg(totalrewards)\n" }, { "path": "rl2/cartpole/random_search.py", "content": "# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python\n# https://www.udemy.com/deep-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport gym\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ngym_minor_version = int(gym.__version__.split('.')[1])\nif gym_minor_version >= 19:\n exit(\"Please install OpenAI Gym 0.19.0 or earlier\")\n\n\ndef get_action(s, w):\n return 1 if s.dot(w) > 0 else 0\n\n\ndef play_one_episode(env, params):\n observation = env.reset()\n done = False\n t = 0\n\n while not done and t < 10000:\n # env.render()\n t += 1\n action = get_action(observation, params)\n observation, reward, done, info = env.step(action)\n if done:\n break\n\n return t\n\n\ndef play_multiple_episodes(env, T, params):\n episode_lengths = np.empty(T)\n\n for i in range(T):\n episode_lengths[i] = play_one_episode(env, params)\n\n avg_length = episode_lengths.mean()\n print(\"avg length:\", avg_length)\n return avg_length\n\n\ndef random_search(env):\n episode_lengths = []\n best = 0\n params = None\n for t in range(100):\n new_params = np.random.random(4)*2 - 1\n avg_length = play_multiple_episodes(env, 100, new_params)\n episode_lengths.append(avg_length)\n\n if avg_length > best:\n params = new_params\n best = avg_length\n return episode_lengths, params\n\n\nif __name__ == '__main__':\n env = gym.make('CartPole-v0')\n episode_lengths, params = random_search(env)\n plt.plot(episode_lengths)\n plt.show()\n\n # play a final set of episodes\n print(\"***Final run with final weights***\")\n play_multiple_episodes(env, 100, params)\n" }, { "path": "rl2/cartpole/save_a_video.py", "content": "# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python\n# https://www.udemy.com/deep-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport gym\nfrom gym import wrappers\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ngym_minor_version = int(gym.__version__.split('.')[1])\nif gym_minor_version >= 19:\n exit(\"Please install OpenAI Gym 0.19.0 or earlier\")\n \n\n\ndef get_action(s, w):\n return 1 if s.dot(w) > 0 else 0\n\n\ndef play_one_episode(env, params):\n observation = env.reset()\n done = False\n t = 0\n\n while not done and t < 10000:\n t += 1\n action = get_action(observation, params)\n observation, reward, done, info = env.step(action)\n if done:\n break\n\n return t\n\n\ndef play_multiple_episodes(env, T, params):\n episode_lengths = np.empty(T)\n\n for i in range(T):\n episode_lengths[i] = play_one_episode(env, params)\n\n avg_length = episode_lengths.mean()\n print(\"avg length:\", avg_length)\n return avg_length\n\n\ndef random_search(env):\n episode_lengths = []\n best = 0\n params = None\n for t in range(100):\n new_params = np.random.random(4)*2 - 1\n avg_length = play_multiple_episodes(env, 100, new_params)\n episode_lengths.append(avg_length)\n\n if avg_length > best:\n params = new_params\n best = avg_length\n return episode_lengths, params\n\n\nif __name__ == '__main__':\n env = gym.make('CartPole-v0')\n episode_lengths, params = random_search(env)\n plt.plot(episode_lengths)\n plt.show()\n\n # play a final set of episodes\n env = wrappers.RecordVideo(env, 'my_awesome_dir')\n print(\"***Final run with final weights***:\", play_one_episode(env, params))\n" }, { "path": "rl2/cartpole/td_lambda.py", "content": "# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python\n# https://www.udemy.com/deep-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport gym\nimport os\nimport sys\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom gym import wrappers\nfrom datetime import datetime\nfrom q_learning import FeatureTransformer\nfrom q_learning_bins import plot_running_avg\n\ngym_minor_version = int(gym.__version__.split('.')[1])\nif gym_minor_version >= 19:\n exit(\"Please install OpenAI Gym 0.19.0 or earlier\")\n \n\n\nclass SGDRegressor:\n def __init__(self, D):\n self.w = np.random.randn(D) / np.sqrt(D)\n\n def partial_fit(self, x, y, e, lr=1e-1):\n self.w += lr*(y - x.dot(self.w))*e\n\n def predict(self, X):\n X = np.array(X)\n return X.dot(self.w)\n\n\n# Holds one SGDRegressor for each action\nclass Model:\n def __init__(self, env, feature_transformer):\n self.env = env\n self.models = []\n self.feature_transformer = feature_transformer\n\n sample_feature = feature_transformer.transform( [env.reset()] )\n D = sample_feature.shape[1]\n\n for i in range(env.action_space.n):\n # model = SGDRegressor(learning_rate=\"constant\")\n # model.partial_fit(feature_transformer.transform( [env.reset()] ), [0])\n model = SGDRegressor(D)\n self.models.append(model)\n \n self.eligibilities = np.zeros((env.action_space.n, D))\n\n def reset(self):\n self.eligibilities = np.zeros_like(self.eligibilities)\n\n def predict(self, s):\n X = self.feature_transformer.transform([s])\n result = np.stack([m.predict(X) for m in self.models]).T\n return result\n\n def update(self, s, a, G, gamma, lambda_):\n X = self.feature_transformer.transform([s])\n # assert(len(X.shape) == 2)\n\n # slower\n # for action in range(self.env.action_space.n):\n # if action != a:\n # self.eligibilities[action] *= gamma*lambda_\n # else:\n # self.eligibilities[a] = grad + gamma*lambda_*self.eligibilities[a]\n\n self.eligibilities *= gamma*lambda_\n self.eligibilities[a] += X[0]\n self.models[a].partial_fit(X[0], G, self.eligibilities[a])\n\n def sample_action(self, s, eps):\n if np.random.random() < eps:\n return self.env.action_space.sample()\n else:\n return np.argmax(self.predict(s))\n\n\n# returns a list of states_and_rewards, and the total reward\ndef play_one(model, env, eps, gamma, lambda_):\n observation = env.reset()\n done = False\n totalreward = 0\n states_actions_rewards = []\n iters = 0\n model.reset()\n while not done and iters < 1000000:\n action = model.sample_action(observation, eps)\n prev_observation = observation\n observation, reward, done, info = env.step(action)\n\n if done:\n reward = -300\n\n # update the model\n next = model.predict(observation)\n assert(next.shape == (1, env.action_space.n))\n G = reward + gamma*np.max(next[0])\n model.update(prev_observation, action, G, gamma, lambda_)\n\n states_actions_rewards.append((prev_observation, action, reward))\n\n if reward == 1: # if we changed the reward to -200\n totalreward += reward\n\n iters += 1\n\n # if iters > 0 and iters % 1000 == 0:\n # print(iters)\n # if done:\n # print \"finished in < 1000 steps!\"\n\n return states_actions_rewards, totalreward\n\n\nif __name__ == '__main__':\n env = gym.make('CartPole-v0')\n ft = FeatureTransformer(env)\n model = Model(env, ft)\n gamma = 0.999\n lambda_ = 0.7\n\n if 'monitor' in sys.argv:\n filename = os.path.basename(__file__).split('.')[0]\n monitor_dir = './' + filename + '_' + str(datetime.now())\n env = wrappers.Monitor(env, monitor_dir)\n\n\n N = 500\n totalrewards = np.empty(N)\n # costs = np.empty(N)\n for n in range(N):\n # eps = 1.0/(0.1*n+1)\n # eps = 0.1*(0.97**n)\n eps = 1.0/np.sqrt(n+1)\n # eps = 0.1\n states_actions_rewards, totalreward = play_one(model, env, eps, gamma, lambda_)\n totalrewards[n] = totalreward\n if n % 100 == 0:\n print(\"episode:\", n, \"total reward:\", totalreward, \"eps:\", eps, \"avg reward (last 100):\", totalrewards[max(0, n-100):(n+1)].mean())\n print(\"avg reward for last 100 episodes:\", totalrewards[-100:].mean())\n print(\"total steps:\", totalrewards.sum())\n\n plt.plot(totalrewards)\n plt.title(\"Rewards\")\n plt.show()\n\n plot_running_avg(totalrewards)\n\n\n" }, { "path": "rl2/cartpole/tf_warmup.py", "content": "# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python\n# https://www.udemy.com/deep-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n\nimport numpy as np\nimport tensorflow as tf\nimport q_learning\n\nif tf.__version__.startswith('2'):\n exit(\"Please install Tensorflow 1.x\")\n\n\nclass SGDRegressor:\n def __init__(self, D):\n print(\"Hello TensorFlow!\")\n lr = 0.1\n\n # create inputs, targets, params\n # matmul doesn't like when w is 1-D\n # so we make it 2-D and then flatten the prediction\n self.w = tf.Variable(tf.random_normal(shape=(D, 1)), name='w')\n self.X = tf.placeholder(tf.float32, shape=(None, D), name='X')\n self.Y = tf.placeholder(tf.float32, shape=(None,), name='Y')\n\n # make prediction and cost\n Y_hat = tf.reshape( tf.matmul(self.X, self.w), [-1] )\n delta = self.Y - Y_hat\n cost = tf.reduce_sum(delta * delta)\n\n # ops we want to call later\n self.train_op = tf.train.GradientDescentOptimizer(lr).minimize(cost)\n self.predict_op = Y_hat\n \n # start the session and initialize params\n init = tf.global_variables_initializer()\n self.session = tf.InteractiveSession()\n self.session.run(init)\n\n def partial_fit(self, X, Y):\n self.session.run(self.train_op, feed_dict={self.X: X, self.Y: Y})\n\n def predict(self, X):\n return self.session.run(self.predict_op, feed_dict={self.X: X})\n\n\nif __name__ == '__main__':\n q_learning.SGDRegressor = SGDRegressor\n q_learning.main()\n" }, { "path": "rl2/cartpole/theano_warmup.py", "content": "# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python\n# https://www.udemy.com/deep-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n\nimport numpy as np\nimport theano\nimport theano.tensor as T\nimport q_learning\n\n\nclass SGDRegressor:\n def __init__(self, D):\n print(\"Hello Theano!\")\n w = np.random.randn(D) / np.sqrt(D)\n self.w = theano.shared(w)\n self.lr = 0.1\n\n X = T.matrix('X')\n Y = T.vector('Y')\n Y_hat = X.dot(self.w)\n delta = Y - Y_hat\n cost = delta.dot(delta)\n grad = T.grad(cost, self.w)\n updates = [(self.w, self.w - self.lr*grad)]\n\n self.train_op = theano.function(\n inputs=[X, Y],\n updates=updates,\n )\n self.predict_op = theano.function(\n inputs=[X],\n outputs=Y_hat,\n )\n\n def partial_fit(self, X, Y):\n self.train_op(X, Y)\n\n def predict(self, X):\n return self.predict_op(X)\n\n\nif __name__ == '__main__':\n q_learning.SGDRegressor = SGDRegressor\n q_learning.main()\n" }, { "path": "rl2/extra_reading.txt", "content": "Random Features for Large-Scale Kernel Machines\nhttp://www.robots.ox.ac.uk/~vgg/rg/papers/randomfeatures.pdf\n\nReflections on Random Kitchen Sinks\nhttp://www.argmin.net/2017/12/05/kitchen-sinks/\n\nWeighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning\nhttps://papers.nips.cc/paper/3495-weighted-sums-of-random-kitchen-sinks-replacing-minimization-with-randomization-in-learning\n\nThis guy generated some nice plots and code to demonstrate that RBFSampler works like a real RBF Kernel\nhttps://www.kaggle.com/sy2002/rbfsampler-actually-is-not-using-any-rbfs\n\nSutton & Barto\nhttp://incompleteideas.net/sutton/book/the-book-2nd.html\n\nImplementation Details of the TD(λ) Procedure for the Case of Vector Predictions and Backpropagation\nhttp://incompleteideas.net/papers/sutton-89.pdf\n\nPolicy Gradient Methods for Reinforcement Learning with Function Approximation\nhttps://homes.cs.washington.edu/~todorov/courses/amath579/reading/PolicyGradient.pdf\n\nPlaying Atari with Deep Reinforcement Learning\nhttps://www.cs.toronto.edu/~vmnih/docs/dqn.pdf\n\nAsynchronous Methods for Deep Reinforcement Learning\nhttps://arxiv.org/pdf/1602.01783.pdf" }, { "path": "rl2/gym_tutorial.py", "content": "# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python\n# https://www.udemy.com/deep-reinforcement-learning-in-python\nimport gym\n# Wiki:\n# https://github.com/openai/gym/wiki/CartPole-v0\n# Environment page:\n# https://gym.openai.com/envs/CartPole-v0\n\ngym_minor_version = int(gym.__version__.split('.')[1])\nif gym_minor_version >= 19:\n exit(\"Please install OpenAI Gym 0.19.0 or earlier\")\n\n\n# get the environment\nenv = gym.make('CartPole-v0')\n\n# put yourself in the start state\n# it also returns the state\nenv.reset()\n# Out[50]: array([-0.04533731, -0.03231478, -0.01469216, 0.04151 ])\n\n# what do the state variables mean?\n# Num Observation Min Max\n# 0 Cart Position -2.4 2.4\n# 1 Cart Velocity -Inf Inf\n# 2 Pole Angle ~ -41.8° ~ 41.8°\n# 3 Pole Velocity At Tip -Inf Inf\n\nbox = env.observation_space\n\n# In [53]: box\n# Out[53]: Box(4,)\n\n# In [54]: box.\n# box.contains box.high box.sample box.to_jsonable\n# box.from_jsonable box.low box.shape\n\nenv.action_space\n\n# In [71]: env.action_space\n# Out[71]: Discrete(2)\n\n# In [72]: env.action_space.\n# env.action_space.contains env.action_space.n env.action_space.to_jsonable\n# env.action_space.from_jsonable env.action_space.sample\n\n# pick an action\naction = env.action_space.sample()\n\n# do an action\nobservation, reward, done, info = env.step(action)\n\n\n# run through an episode\ndone = False\nwhile not done:\n observation, reward, done, _ = env.step(env.action_space.sample())\n\n\n\n" }, { "path": "rl2/mountaincar/n_step.py", "content": "# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python\n# https://www.udemy.com/deep-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n#\n# Note: gym changed from version 0.7.3 to 0.8.0\n# MountainCar episode length is capped at 200 in later versions.\n# This means your agent can't learn as much in the earlier episodes\n# since they are no longer as long. \n#\n# Adapt Q-Learning script to use N-step method instead\n\nimport gym\nimport os\nimport sys\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom gym import wrappers\nfrom datetime import datetime\n\n# code we already wrote\nimport q_learning\nfrom q_learning import plot_cost_to_go, FeatureTransformer, Model, plot_running_avg\n\n\nclass SGDRegressor:\n def __init__(self, **kwargs):\n self.w = None\n self.lr = 1e-2\n\n def partial_fit(self, X, Y):\n if self.w is None:\n D = X.shape[1]\n self.w = np.random.randn(D) / np.sqrt(D)\n self.w += self.lr*(Y - X.dot(self.w)).dot(X)\n\n def predict(self, X):\n return X.dot(self.w)\n\n# replace SKLearn Regressor\nq_learning.SGDRegressor = SGDRegressor\n\n# calculate everything up to max[Q(s,a)]\n# Ex.\n# R(t) + gamma*R(t+1) + ... + (gamma^(n-1))*R(t+n-1) + (gamma^n)*max[Q(s(t+n), a(t+n))]\n# def calculate_return_before_prediction(rewards, gamma):\n# ret = 0\n# for r in reversed(rewards[1:]):\n# ret += r + gamma*ret\n# ret += rewards[0]\n# return ret\n\n# returns a list of states_and_rewards, and the total reward\ndef play_one(model, eps, gamma, n=5):\n observation = env.reset()[0]\n done = False\n totalreward = 0\n rewards = []\n states = []\n actions = []\n iters = 0\n # array of [gamma^0, gamma^1, ..., gamma^(n-1)]\n multiplier = np.array([gamma]*n)**np.arange(n)\n # while not done and iters < 200:\n while not done and iters < 10000:\n # in earlier versions of gym, episode doesn't automatically\n # end when you hit 200 steps\n action = model.sample_action(observation, eps)\n\n states.append(observation)\n actions.append(action)\n\n prev_observation = observation\n observation, reward, done, truncated, info = env.step(action)\n\n rewards.append(reward)\n\n # update the model\n if len(rewards) >= n:\n # return_up_to_prediction = calculate_return_before_prediction(rewards, gamma)\n return_up_to_prediction = multiplier.dot(rewards[-n:])\n action_values = model.predict(observation)[0]\n # print(\"action_values.shape:\", action_values.shape)\n G = return_up_to_prediction + (gamma**n)*np.max(action_values)\n # print(\"G:\", G)\n model.update(states[-n], actions[-n], G)\n\n # if len(rewards) > n:\n # rewards.pop(0)\n # states.pop(0)\n # actions.pop(0)\n # assert(len(rewards) <= n)\n\n totalreward += reward\n iters += 1\n\n # empty the cache\n if n == 1:\n rewards = []\n states = []\n actions = []\n else:\n rewards = rewards[-n+1:]\n states = states[-n+1:]\n actions = actions[-n+1:]\n # unfortunately, new version of gym cuts you off at 200 steps\n # even if you haven't reached the goal.\n # it's not good to do this UNLESS you've reached the goal.\n # we are \"really done\" if position >= 0.5\n if observation[0] >= 0.5:\n # we actually made it to the goal\n # print(\"made it!\")\n while len(rewards) > 0:\n G = multiplier[:len(rewards)].dot(rewards)\n model.update(states[0], actions[0], G)\n rewards.pop(0)\n states.pop(0)\n actions.pop(0)\n else:\n # we did not make it to the goal\n # print(\"didn't make it...\")\n while len(rewards) > 0:\n guess_rewards = rewards + [-1]*(n - len(rewards))\n G = multiplier.dot(guess_rewards)\n model.update(states[0], actions[0], G)\n rewards.pop(0)\n states.pop(0)\n actions.pop(0)\n\n return totalreward\n\n\nif __name__ == '__main__':\n env = gym.make('MountainCar-v0')\n ft = FeatureTransformer(env)\n model = Model(env, ft, \"constant\")\n gamma = 0.99\n\n if 'monitor' in sys.argv:\n filename = os.path.basename(__file__).split('.')[0]\n monitor_dir = './' + filename + '_' + str(datetime.now())\n env = wrappers.Monitor(env, monitor_dir)\n\n\n N = 300\n totalrewards = np.empty(N)\n costs = np.empty(N)\n for n in range(N):\n # eps = 1.0/(0.1*n+1)\n eps = 0.1*(0.97**n)\n totalreward = play_one(model, eps, gamma)\n totalrewards[n] = totalreward\n print(\"episode:\", n, \"total reward:\", totalreward)\n print(\"avg reward for last 100 episodes:\", totalrewards[-100:].mean())\n print(\"total steps:\", -totalrewards.sum())\n\n plt.plot(totalrewards)\n plt.title(\"Rewards\")\n plt.show()\n\n plot_running_avg(totalrewards)\n\n # plot the optimal state-value function\n plot_cost_to_go(env, model)\n\n" }, { "path": "rl2/mountaincar/pg_tf.py", "content": "# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python\n# https://www.udemy.com/deep-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport gym\nimport os\nimport sys\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nfrom gym import wrappers\nfrom datetime import datetime\nfrom q_learning import plot_running_avg, FeatureTransformer, plot_cost_to_go\n\ngym_minor_version = int(gym.__version__.split('.')[1])\nif gym_minor_version >= 19:\n exit(\"Please install OpenAI Gym 0.19.0 or earlier\")\n\nif tf.__version__.startswith('2'):\n exit(\"Please install Tensorflow 1.x\")\n\n\n# so you can test different architectures\nclass HiddenLayer:\n def __init__(self, M1, M2, f=tf.nn.tanh, use_bias=True, zeros=False):\n if zeros:\n W = np.zeros((M1, M2), dtype=np.float32)\n else:\n W = tf.random_normal(shape=(M1, M2)) * np.sqrt(2. / M1, dtype=np.float32)\n self.W = tf.Variable(W)\n\n self.use_bias = use_bias\n if use_bias:\n self.b = tf.Variable(np.zeros(M2).astype(np.float32))\n\n self.f = f\n\n def forward(self, X):\n if self.use_bias:\n a = tf.matmul(X, self.W) + self.b\n else:\n a = tf.matmul(X, self.W)\n return self.f(a)\n\n\n# approximates pi(a | s)\nclass PolicyModel:\n def __init__(self, D, ft, hidden_layer_sizes=[]):\n self.ft = ft\n\n ##### hidden layers #####\n M1 = D\n self.hidden_layers = []\n for M2 in hidden_layer_sizes:\n layer = HiddenLayer(M1, M2)\n self.hidden_layers.append(layer)\n M1 = M2\n\n # final layer mean\n self.mean_layer = HiddenLayer(M1, 1, lambda x: x, use_bias=False, zeros=True)\n\n # final layer variance\n self.stdv_layer = HiddenLayer(M1, 1, tf.nn.softplus, use_bias=False, zeros=False)\n\n # inputs and targets\n self.X = tf.placeholder(tf.float32, shape=(None, D), name='X')\n self.actions = tf.placeholder(tf.float32, shape=(None,), name='actions')\n self.advantages = tf.placeholder(tf.float32, shape=(None,), name='advantages')\n\n # get final hidden layer\n Z = self.X\n for layer in self.hidden_layers:\n Z = layer.forward(Z)\n\n # calculate output and cost\n mean = self.mean_layer.forward(Z)\n stdv = self.stdv_layer.forward(Z) + 1e-5 # smoothing\n\n # make them 1-D\n mean = tf.reshape(mean, [-1])\n stdv = tf.reshape(stdv, [-1]) \n\n norm = tf.contrib.distributions.Normal(mean, stdv)\n self.predict_op = tf.clip_by_value(norm.sample(), -1, 1)\n\n log_probs = norm.log_prob(self.actions)\n cost = -tf.reduce_sum(self.advantages * log_probs + 0.1*norm.entropy())\n self.train_op = tf.train.AdamOptimizer(1e-3).minimize(cost)\n\n def set_session(self, session):\n self.session = session\n\n def partial_fit(self, X, actions, advantages):\n X = np.atleast_2d(X)\n X = self.ft.transform(X)\n \n actions = np.atleast_1d(actions)\n advantages = np.atleast_1d(advantages)\n self.session.run(\n self.train_op,\n feed_dict={\n self.X: X,\n self.actions: actions,\n self.advantages: advantages,\n }\n )\n\n def predict(self, X):\n X = np.atleast_2d(X)\n X = self.ft.transform(X)\n return self.session.run(self.predict_op, feed_dict={self.X: X})\n\n def sample_action(self, X):\n p = self.predict(X)[0]\n return p\n\n\n# approximates V(s)\nclass ValueModel:\n def __init__(self, D, ft, hidden_layer_sizes=[]):\n self.ft = ft\n self.costs = []\n\n # create the graph\n self.layers = []\n M1 = D\n for M2 in hidden_layer_sizes:\n layer = HiddenLayer(M1, M2)\n self.layers.append(layer)\n M1 = M2\n\n # final layer\n layer = HiddenLayer(M1, 1, lambda x: x)\n self.layers.append(layer)\n\n # inputs and targets\n self.X = tf.placeholder(tf.float32, shape=(None, D), name='X')\n self.Y = tf.placeholder(tf.float32, shape=(None,), name='Y')\n\n # calculate output and cost\n Z = self.X\n for layer in self.layers:\n Z = layer.forward(Z)\n Y_hat = tf.reshape(Z, [-1]) # the output\n self.predict_op = Y_hat\n\n cost = tf.reduce_sum(tf.square(self.Y - Y_hat))\n self.cost = cost\n self.train_op = tf.train.AdamOptimizer(1e-1).minimize(cost)\n\n def set_session(self, session):\n self.session = session\n\n def partial_fit(self, X, Y):\n X = np.atleast_2d(X)\n X = self.ft.transform(X)\n Y = np.atleast_1d(Y)\n self.session.run(self.train_op, feed_dict={self.X: X, self.Y: Y})\n cost = self.session.run(self.cost, feed_dict={self.X: X, self.Y: Y})\n self.costs.append(cost)\n\n def predict(self, X):\n X = np.atleast_2d(X)\n X = self.ft.transform(X)\n return self.session.run(self.predict_op, feed_dict={self.X: X})\n\n\ndef play_one_td(env, pmodel, vmodel, gamma):\n observation = env.reset()\n done = False\n totalreward = 0\n iters = 0\n\n while not done and iters < 2000:\n # if we reach 2000, just quit, don't want this going forever\n # the 200 limit seems a bit early\n action = pmodel.sample_action(observation)\n prev_observation = observation\n observation, reward, done, info = env.step([action])\n\n totalreward += reward\n\n # update the models\n if done:\n G = reward\n else:\n V_next = vmodel.predict(observation)\n G = reward + gamma*V_next\n\n advantage = G - vmodel.predict(prev_observation)\n pmodel.partial_fit(prev_observation, action, advantage)\n vmodel.partial_fit(prev_observation, G)\n\n iters += 1\n\n return totalreward, iters\n\n\ndef main():\n env = gym.make('MountainCarContinuous-v0')\n ft = FeatureTransformer(env, n_components=100)\n D = ft.dimensions\n pmodel = PolicyModel(D, ft, [])\n vmodel = ValueModel(D, ft, [])\n init = tf.global_variables_initializer()\n session = tf.InteractiveSession()\n session.run(init)\n pmodel.set_session(session)\n vmodel.set_session(session)\n gamma = 0.95\n\n if 'monitor' in sys.argv:\n filename = os.path.basename(__file__).split('.')[0]\n monitor_dir = './' + filename + '_' + str(datetime.now())\n env = wrappers.Monitor(env, monitor_dir)\n\n N = 50\n totalrewards = np.empty(N)\n costs = np.empty(N)\n for n in range(N):\n totalreward, num_steps = play_one_td(env, pmodel, vmodel, gamma)\n totalrewards[n] = totalreward\n if n % 1 == 0:\n print(\"episode:\", n, \"total reward: %.1f\" % totalreward, \"num steps: %d\" % num_steps, \"avg reward (last 100): %.1f\" % totalrewards[max(0, n-100):(n+1)].mean())\n\n print(\"avg reward for last 100 episodes:\", totalrewards[-100:].mean())\n\n plt.plot(totalrewards)\n plt.title(\"Rewards\")\n plt.show()\n\n plot_running_avg(totalrewards)\n plot_cost_to_go(env, vmodel)\n\n\nif __name__ == '__main__':\n main()\n\n" }, { "path": "rl2/mountaincar/pg_tf_random.py", "content": "# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python\n# https://www.udemy.com/deep-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport gym\nimport os\nimport sys\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nfrom gym import wrappers\nfrom datetime import datetime\nfrom q_learning import plot_running_avg, FeatureTransformer\n\ngym_minor_version = int(gym.__version__.split('.')[1])\nif gym_minor_version >= 19:\n exit(\"Please install OpenAI Gym 0.19.0 or earlier\")\n\nif tf.__version__.startswith('2'):\n exit(\"Please install Tensorflow 1.x\")\n\n\n# so you can test different architectures\nclass HiddenLayer:\n def __init__(self, M1, M2, f=tf.nn.tanh, use_bias=True, zeros=False):\n if zeros:\n W = np.zeros((M1, M2)).astype(np.float32)\n self.W = tf.Variable(W)\n else:\n self.W = tf.Variable(tf.random_normal(shape=(M1, M2)))\n\n self.params = [self.W]\n\n self.use_bias = use_bias\n if use_bias:\n self.b = tf.Variable(np.zeros(M2).astype(np.float32))\n self.params.append(self.b)\n self.f = f\n\n def forward(self, X):\n if self.use_bias:\n a = tf.matmul(X, self.W) + self.b\n else:\n a = tf.matmul(X, self.W)\n return self.f(a)\n\n\n# approximates pi(a | s)\nclass PolicyModel:\n def __init__(self, ft, D, hidden_layer_sizes_mean=[], hidden_layer_sizes_var=[]):\n\n # save inputs for copy\n self.ft = ft\n self.D = D\n self.hidden_layer_sizes_mean = hidden_layer_sizes_mean\n self.hidden_layer_sizes_var = hidden_layer_sizes_var\n\n ##### model the mean #####\n self.mean_layers = []\n M1 = D\n for M2 in hidden_layer_sizes_mean:\n layer = HiddenLayer(M1, M2)\n self.mean_layers.append(layer)\n M1 = M2\n\n # final layer\n layer = HiddenLayer(M1, 1, lambda x: x, use_bias=False, zeros=True)\n self.mean_layers.append(layer)\n\n\n ##### model the variance #####\n self.var_layers = []\n M1 = D\n for M2 in hidden_layer_sizes_var:\n layer = HiddenLayer(M1, M2)\n self.var_layers.append(layer)\n M1 = M2\n\n # final layer\n layer = HiddenLayer(M1, 1, tf.nn.softplus, use_bias=False, zeros=False)\n self.var_layers.append(layer)\n\n # gather params\n self.params = []\n for layer in (self.mean_layers + self.var_layers):\n self.params += layer.params\n\n # inputs and targets\n self.X = tf.placeholder(tf.float32, shape=(None, D), name='X')\n self.actions = tf.placeholder(tf.float32, shape=(None,), name='actions')\n self.advantages = tf.placeholder(tf.float32, shape=(None,), name='advantages')\n\n def get_output(layers):\n Z = self.X\n for layer in layers:\n Z = layer.forward(Z)\n return tf.reshape(Z, [-1])\n\n # calculate output and cost\n mean = get_output(self.mean_layers)\n std = get_output(self.var_layers) + 1e-4 # smoothing\n\n # note: the 'variance' is actually standard deviation\n norm = tf.contrib.distributions.Normal(mean, std)\n self.predict_op = tf.clip_by_value(norm.sample(), -1, 1)\n\n\n def set_session(self, session):\n self.session = session\n\n def init_vars(self):\n init_op = tf.variables_initializer(self.params)\n self.session.run(init_op)\n\n # def partial_fit(self, X, actions, advantages):\n # X = np.atleast_2d(X)\n # X = self.ft.transform(X)\n \n # actions = np.atleast_1d(actions)\n # advantages = np.atleast_1d(advantages)\n # self.session.run(\n # self.train_op,\n # feed_dict={\n # self.X: X,\n # self.actions: actions,\n # self.advantages: advantages,\n # }\n # )\n\n def predict(self, X):\n X = np.atleast_2d(X)\n X = self.ft.transform(X)\n \n return self.session.run(self.predict_op, feed_dict={self.X: X})\n\n def sample_action(self, X):\n p = self.predict(X)[0]\n # print(\"action:\", p)\n return p\n\n def copy(self):\n clone = PolicyModel(self.ft, self.D, self.hidden_layer_sizes_mean, self.hidden_layer_sizes_mean)\n clone.set_session(self.session)\n clone.init_vars() # tf will complain if we don't do this\n clone.copy_from(self)\n return clone\n\n def copy_from(self, other):\n # collect all the ops\n ops = []\n my_params = self.params\n other_params = other.params\n for p, q in zip(my_params, other_params):\n actual = self.session.run(q)\n op = p.assign(actual)\n ops.append(op)\n # now run them all\n self.session.run(ops)\n\n def perturb_params(self):\n ops = []\n for p in self.params:\n v = self.session.run(p)\n noise = np.random.randn(*v.shape) / np.sqrt(v.shape[0]) * 5.0\n if np.random.random() < 0.1:\n # with probability 0.1 start completely from scratch\n op = p.assign(noise)\n else:\n op = p.assign(v + noise)\n ops.append(op)\n self.session.run(ops)\n\n\n\ndef play_one(env, pmodel, gamma):\n observation = env.reset()\n done = False\n totalreward = 0\n iters = 0\n\n while not done and iters < 2000:\n # if we reach 2000, just quit, don't want this going forever\n # the 200 limit seems a bit early\n action = pmodel.sample_action(observation)\n # oddly, the mountain car environment requires the action to be in\n # an object where the actual action is stored in object[0]\n observation, reward, done, info = env.step([action])\n\n totalreward += reward\n iters += 1\n return totalreward\n\n\ndef play_multiple_episodes(env, T, pmodel, gamma, print_iters=False):\n totalrewards = np.empty(T)\n\n for i in range(T):\n totalrewards[i] = play_one(env, pmodel, gamma)\n\n if print_iters:\n print(i, \"avg so far:\", totalrewards[:(i+1)].mean())\n\n avg_totalrewards = totalrewards.mean()\n print(\"avg totalrewards:\", avg_totalrewards)\n return avg_totalrewards\n\n\ndef random_search(env, pmodel, gamma):\n totalrewards = []\n best_avg_totalreward = float('-inf')\n best_pmodel = pmodel\n num_episodes_per_param_test = 3\n for t in range(100):\n tmp_pmodel = best_pmodel.copy()\n\n tmp_pmodel.perturb_params()\n\n avg_totalrewards = play_multiple_episodes(\n env,\n num_episodes_per_param_test,\n tmp_pmodel,\n gamma\n )\n totalrewards.append(avg_totalrewards)\n\n if avg_totalrewards > best_avg_totalreward:\n best_pmodel = tmp_pmodel\n best_avg_totalreward = avg_totalrewards\n return totalrewards, best_pmodel\n\n\ndef main():\n env = gym.make('MountainCarContinuous-v0')\n ft = FeatureTransformer(env, n_components=100)\n D = ft.dimensions\n pmodel = PolicyModel(ft, D, [], [])\n # init = tf.global_variables_initializer()\n session = tf.InteractiveSession()\n # session.run(init)\n pmodel.set_session(session)\n pmodel.init_vars()\n gamma = 0.99\n\n if 'monitor' in sys.argv:\n filename = os.path.basename(__file__).split('.')[0]\n monitor_dir = './' + filename + '_' + str(datetime.now())\n env = wrappers.Monitor(env, monitor_dir)\n\n totalrewards, pmodel = random_search(env, pmodel, gamma)\n print(\"max reward:\", np.max(totalrewards))\n\n # play 100 episodes and check the average\n avg_totalrewards = play_multiple_episodes(env, 100, pmodel, gamma, print_iters=True)\n print(\"avg reward over 100 episodes with best models:\", avg_totalrewards)\n\n plt.plot(totalrewards)\n plt.title(\"Rewards\")\n plt.show()\n\n\nif __name__ == '__main__':\n main()\n\n" }, { "path": "rl2/mountaincar/pg_theano.py", "content": "# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python\n# https://www.udemy.com/deep-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport gym\nimport os\nimport sys\nimport numpy as np\nimport theano\nimport theano.tensor as T\nimport matplotlib.pyplot as plt\nfrom gym import wrappers\nfrom datetime import datetime\nfrom q_learning import plot_running_avg, FeatureTransformer, plot_cost_to_go\n\n\n\n# helper for adam optimizer\n# use tensorflow defaults\ndef adam(cost, params, lr0=1e-3, beta1=0.9, beta2=0.999, eps=1e-8):\n grads = T.grad(cost, params)\n updates = []\n time = theano.shared(0)\n new_time = time + 1\n updates.append((time, new_time))\n lr = lr0*T.sqrt(1 - beta2**new_time) / (1 - beta1**new_time)\n for p, g in zip(params, grads):\n m = theano.shared(p.get_value() * 0.)\n v = theano.shared(p.get_value() * 0.)\n new_m = beta1*m + (1 - beta1)*g\n new_v = beta2*v + (1 - beta2)*g*g\n new_p = p - lr*new_m / (T.sqrt(new_v) + eps)\n updates.append((m, new_m))\n updates.append((v, new_v))\n updates.append((p, new_p))\n return updates\n\n\n# so you can test different architectures\nclass HiddenLayer:\n def __init__(self, M1, M2, f=T.nnet.relu, use_bias=True, zeros=False):\n if zeros:\n W = np.zeros((M1, M2))\n else:\n W = np.random.randn(M1, M2) * np.sqrt(2. / M1)\n self.W = theano.shared(W)\n self.params = [self.W]\n self.use_bias = use_bias\n if use_bias:\n self.b = theano.shared(np.zeros(M2))\n self.params += [self.b]\n self.f = f\n\n def forward(self, X):\n if self.use_bias:\n a = X.dot(self.W) + self.b\n else:\n a = X.dot(self.W)\n return self.f(a)\n\n\n# approximates pi(a | s)\nclass PolicyModel:\n def __init__(self, D, ft, hidden_layer_sizes=[]):\n self.ft = ft\n\n ##### hidden layers #####\n M1 = D\n self.hidden_layers = []\n for M2 in hidden_layer_sizes:\n layer = HiddenLayer(M1, M2)\n self.hidden_layers.append(layer)\n M1 = M2\n\n # final layer mean\n self.mean_layer = HiddenLayer(M1, 1, lambda x: x, use_bias=False, zeros=True)\n\n # final layer variance\n self.var_layer = HiddenLayer(M1, 1, T.nnet.softplus, use_bias=False, zeros=False)\n\n # get all params for gradient later\n params = self.mean_layer.params + self.var_layer.params\n for layer in self.hidden_layers:\n params += layer.params\n\n # inputs and targets\n X = T.matrix('X')\n actions = T.vector('actions')\n advantages = T.vector('advantages')\n target_value = T.vector('target_value')\n\n # get final hidden layer\n Z = X\n for layer in self.hidden_layers:\n Z = layer.forward(Z)\n\n mean = self.mean_layer.forward(Z).flatten()\n var = self.var_layer.forward(Z).flatten() + 1e-5 # smoothing\n\n # can't find Theano log pdf, we will make it\n def log_pdf(actions, mean, var):\n k1 = T.log(2*np.pi*var)\n k2 = (actions - mean)**2 / var\n return -0.5*(k1 + k2)\n\n def entropy(var):\n return 0.5*T.log(2*np.pi*np.e*var)\n\n log_probs = log_pdf(actions, mean, var)\n cost = -T.sum(advantages * log_probs + 0.1*entropy(var))\n updates = adam(cost, params)\n\n # compile functions\n self.train_op = theano.function(\n inputs=[X, actions, advantages],\n updates=updates,\n allow_input_downcast=True\n )\n\n # alternatively, we could create a RandomStream and sample from\n # the Gaussian using Theano code\n self.predict_op = theano.function(\n inputs=[X],\n outputs=[mean, var],\n allow_input_downcast=True\n )\n\n def partial_fit(self, X, actions, advantages):\n X = np.atleast_2d(X)\n X = self.ft.transform(X)\n actions = np.atleast_1d(actions)\n advantages = np.atleast_1d(advantages)\n self.train_op(X, actions, advantages)\n\n def predict(self, X):\n X = np.atleast_2d(X)\n X = self.ft.transform(X)\n return self.predict_op(X)\n\n def sample_action(self, X):\n pred = self.predict(X)\n mu = pred[0][0]\n v = pred[1][0]\n a = np.random.randn()*np.sqrt(v) + mu\n return min(max(a, -1), 1)\n\n\n# approximates V(s)\nclass ValueModel:\n def __init__(self, D, ft, hidden_layer_sizes=[]):\n self.ft = ft\n\n # create the graph\n self.layers = []\n M1 = D\n for M2 in hidden_layer_sizes:\n layer = HiddenLayer(M1, M2)\n self.layers.append(layer)\n M1 = M2\n\n # final layer\n layer = HiddenLayer(M1, 1, lambda x: x)\n self.layers.append(layer)\n\n # get all params for gradient later\n params = []\n for layer in self.layers:\n params += layer.params\n\n # inputs and targets\n X = T.matrix('X')\n Y = T.vector('Y')\n\n # calculate output and cost\n Z = X\n for layer in self.layers:\n Z = layer.forward(Z)\n Y_hat = T.flatten(Z)\n cost = T.sum((Y - Y_hat)**2)\n\n # specify update rule\n updates = adam(cost, params, lr0=1e-1)\n\n # compile functions\n self.train_op = theano.function(\n inputs=[X, Y],\n updates=updates,\n allow_input_downcast=True\n )\n self.predict_op = theano.function(\n inputs=[X],\n outputs=Y_hat,\n allow_input_downcast=True\n )\n\n def partial_fit(self, X, Y):\n X = np.atleast_2d(X)\n X = self.ft.transform(X)\n Y = np.atleast_1d(Y)\n self.train_op(X, Y)\n\n def predict(self, X):\n X = np.atleast_2d(X)\n X = self.ft.transform(X)\n return self.predict_op(X)\n\n\ndef play_one_td(env, pmodel, vmodel, gamma):\n observation = env.reset()\n done = False\n totalreward = 0\n iters = 0\n\n while not done and iters < 2000:\n # if we reach 2000, just quit, don't want this going forever\n # the 200 limit seems a bit early\n action = pmodel.sample_action(observation)\n prev_observation = observation\n observation, reward, done, info = env.step([action])\n\n totalreward += reward\n\n # update the models\n if done:\n G = reward\n else:\n V_next = vmodel.predict(observation)\n G = reward + gamma*V_next\n\n advantage = G - vmodel.predict(prev_observation)\n pmodel.partial_fit(prev_observation, action, advantage)\n vmodel.partial_fit(prev_observation, G)\n\n iters += 1\n\n return totalreward\n\n\ndef main():\n env = gym.make('MountainCarContinuous-v0')\n ft = FeatureTransformer(env, n_components=100)\n D = ft.dimensions\n pmodel = PolicyModel(D, ft)\n vmodel = ValueModel(D, ft)\n gamma = 0.99\n\n if 'monitor' in sys.argv:\n filename = os.path.basename(__file__).split('.')[0]\n monitor_dir = './' + filename + '_' + str(datetime.now())\n env = wrappers.Monitor(env, monitor_dir)\n\n N = 50\n totalrewards = np.empty(N)\n costs = np.empty(N)\n for n in range(N):\n totalreward = play_one_td(env, pmodel, vmodel, gamma)\n totalrewards[n] = totalreward\n if n % 1 == 0:\n print(\"episode:\", n, \"total reward: %.1f\" % totalreward, \"avg reward (last 100): %.1f\" % totalrewards[max(0, n-100):(n+1)].mean())\n\n print(\"avg reward for last 100 episodes:\", totalrewards[-100:].mean())\n\n plt.plot(totalrewards)\n plt.title(\"Rewards\")\n plt.show()\n\n plot_running_avg(totalrewards)\n plot_cost_to_go(env, vmodel)\n\n\nif __name__ == '__main__':\n main()\n\n" }, { "path": "rl2/mountaincar/pg_theano_random.py", "content": "# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python\n# https://www.udemy.com/deep-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport gym\nimport os\nimport sys\nimport numpy as np\nimport theano\nimport theano.tensor as T\nimport matplotlib.pyplot as plt\nfrom gym import wrappers\nfrom datetime import datetime\nfrom q_learning import plot_running_avg, FeatureTransformer\n\ngym_minor_version = int(gym.__version__.split('.')[1])\nif gym_minor_version >= 19:\n exit(\"Please install OpenAI Gym 0.19.0 or earlier\")\n\n\n\n# so you can test different architectures\nclass HiddenLayer:\n def __init__(self, M1, M2, f=T.nnet.relu, use_bias=True, zeros=False):\n if zeros:\n W = np.zeros((M1, M2))\n else:\n W = np.random.randn(M1, M2) * np.sqrt(2 / M1)\n self.W = theano.shared(W)\n self.params = [self.W]\n self.use_bias = use_bias\n if use_bias:\n self.b = theano.shared(np.zeros(M2))\n self.params += [self.b]\n self.f = f\n\n def forward(self, X):\n if self.use_bias:\n a = X.dot(self.W) + self.b\n else:\n a = X.dot(self.W)\n return self.f(a)\n\n\n# approximates pi(a | s)\nclass PolicyModel:\n def __init__(self, ft, D, hidden_layer_sizes_mean=[], hidden_layer_sizes_var=[]):\n # save inputs for copy\n self.ft = ft\n self.D = D\n self.hidden_layer_sizes_mean = hidden_layer_sizes_mean\n self.hidden_layer_sizes_var = hidden_layer_sizes_var\n\n ##### model the mean #####\n self.mean_layers = []\n M1 = D\n for M2 in hidden_layer_sizes_mean:\n layer = HiddenLayer(M1, M2)\n self.mean_layers.append(layer)\n M1 = M2\n\n # final layer\n layer = HiddenLayer(M1, 1, lambda x: x, use_bias=False, zeros=True)\n self.mean_layers.append(layer)\n\n\n ##### model the variance #####\n self.var_layers = []\n M1 = D\n for M2 in hidden_layer_sizes_var:\n layer = HiddenLayer(M1, M2)\n self.var_layers.append(layer)\n M1 = M2\n\n # final layer\n layer = HiddenLayer(M1, 1, T.nnet.softplus, use_bias=False, zeros=False)\n self.var_layers.append(layer)\n\n # get all params for gradient later\n params = []\n for layer in (self.mean_layers + self.var_layers):\n params += layer.params\n self.params = params\n\n # inputs and targets\n X = T.matrix('X')\n actions = T.vector('actions')\n advantages = T.vector('advantages')\n\n # calculate output and cost\n def get_output(layers):\n Z = X\n for layer in layers:\n Z = layer.forward(Z)\n return Z.flatten()\n\n mean = get_output(self.mean_layers)\n var = get_output(self.var_layers) + 1e-4 # smoothing\n\n # alternatively, we could create a RandomStream and sample from\n # the Gaussian using Theano code\n self.predict_op = theano.function(\n inputs=[X],\n outputs=[mean, var],\n allow_input_downcast=True\n )\n\n def predict(self, X):\n X = np.atleast_2d(X)\n X = self.ft.transform(X)\n return self.predict_op(X)\n\n def sample_action(self, X):\n pred = self.predict(X)\n mu = pred[0][0]\n v = pred[1][0]\n a = np.random.randn()*np.sqrt(v) + mu\n return min(max(a, -1), 1)\n\n def copy(self):\n clone = PolicyModel(self.ft, self.D, self.hidden_layer_sizes_mean, self.hidden_layer_sizes_mean)\n clone.copy_from(self)\n return clone\n\n def copy_from(self, other):\n # self is being copied from other\n for p, q in zip(self.params, other.params):\n v = q.get_value()\n p.set_value(v)\n\n def perturb_params(self):\n for p in self.params:\n v = p.get_value()\n noise = np.random.randn(*v.shape) / np.sqrt(v.shape[0]) * 5.0\n if np.random.random() < 0.1:\n # with probability 0.1 start completely from scratch\n p.set_value(noise)\n else:\n p.set_value(v + noise)\n\n\ndef play_one(env, pmodel, gamma):\n observation = env.reset()\n done = False\n totalreward = 0\n iters = 0\n\n while not done and iters < 2000:\n # if we reach 2000, just quit, don't want this going forever\n # the 200 limit seems a bit early\n action = pmodel.sample_action(observation)\n # oddly, the mountain car environment requires the action to be in\n # an object where the actual action is stored in object[0]\n observation, reward, done, info = env.step([action])\n\n totalreward += reward\n iters += 1\n return totalreward\n\n\ndef play_multiple_episodes(env, T, pmodel, gamma, print_iters=False):\n totalrewards = np.empty(T)\n\n for i in range(T):\n totalrewards[i] = play_one(env, pmodel, gamma)\n\n if print_iters:\n print(i, \"avg so far:\", totalrewards[:(i+1)].mean())\n\n avg_totalrewards = totalrewards.mean()\n print(\"avg totalrewards:\", avg_totalrewards)\n return avg_totalrewards\n\n\ndef random_search(env, pmodel, gamma):\n totalrewards = []\n best_avg_totalreward = float('-inf')\n best_pmodel = pmodel\n num_episodes_per_param_test = 3\n for t in range(100):\n tmp_pmodel = best_pmodel.copy()\n\n tmp_pmodel.perturb_params()\n\n avg_totalrewards = play_multiple_episodes(\n env,\n num_episodes_per_param_test,\n tmp_pmodel,\n gamma\n )\n totalrewards.append(avg_totalrewards)\n\n if avg_totalrewards > best_avg_totalreward:\n best_pmodel = tmp_pmodel\n best_avg_totalreward = avg_totalrewards\n return totalrewards, best_pmodel\n\n\ndef main():\n env = gym.make('MountainCarContinuous-v0')\n ft = FeatureTransformer(env, n_components=100)\n D = ft.dimensions\n pmodel = PolicyModel(ft, D, [], [])\n gamma = 0.99\n\n if 'monitor' in sys.argv:\n filename = os.path.basename(__file__).split('.')[0]\n monitor_dir = './' + filename + '_' + str(datetime.now())\n env = wrappers.Monitor(env, monitor_dir)\n\n\n totalrewards, pmodel = random_search(env, pmodel, gamma)\n\n print(\"max reward:\", np.max(totalrewards))\n\n # play 100 episodes and check the average\n avg_totalrewards = play_multiple_episodes(env, 100, pmodel, gamma, print_iters=True)\n print(\"avg reward over 100 episodes with best models:\", avg_totalrewards)\n\n plt.plot(totalrewards)\n plt.title(\"Rewards\")\n plt.show()\n\n\nif __name__ == '__main__':\n main()\n\n" }, { "path": "rl2/mountaincar/q_learning.py", "content": "# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python\n# https://www.udemy.com/deep-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n#\n# This takes 4min 30s to run in Python 2.7\n# But only 1min 30s to run in Python 3.5!\n#\n# Note: gym changed from version 0.7.3 to 0.8.0\n# MountainCar episode length is capped at 200 in later versions.\n# This means your agent can't learn as much in the earlier episodes\n# since they are no longer as long.\n\nimport gym\nimport os\nimport sys\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom gym import wrappers\nfrom datetime import datetime\nfrom sklearn.pipeline import FeatureUnion\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.kernel_approximation import RBFSampler\nfrom sklearn.linear_model import SGDRegressor\n\n\n# SGDRegressor defaults:\n# loss='squared_loss', penalty='l2', alpha=0.0001,\n# l1_ratio=0.15, fit_intercept=True, n_iter=5, shuffle=True,\n# verbose=0, epsilon=0.1, random_state=None, learning_rate='invscaling',\n# eta0=0.01, power_t=0.25, warm_start=False, average=False\n\n# Inspired by https://github.com/dennybritz/reinforcement-learning\nclass FeatureTransformer:\n def __init__(self, env, n_components=500):\n observation_examples = np.array([env.observation_space.sample() for x in range(10000)])\n scaler = StandardScaler()\n scaler.fit(observation_examples)\n\n # Used to converte a state to a featurizes represenation.\n # We use RBF kernels with different variances to cover different parts of the space\n featurizer = FeatureUnion([\n (\"rbf1\", RBFSampler(gamma=5.0, n_components=n_components)),\n (\"rbf2\", RBFSampler(gamma=2.0, n_components=n_components)),\n (\"rbf3\", RBFSampler(gamma=1.0, n_components=n_components)),\n (\"rbf4\", RBFSampler(gamma=0.5, n_components=n_components))\n ])\n example_features = featurizer.fit_transform(scaler.transform(observation_examples))\n\n self.dimensions = example_features.shape[1]\n self.scaler = scaler\n self.featurizer = featurizer\n\n def transform(self, observations):\n # print \"observations:\", observations\n scaled = self.scaler.transform(observations)\n # assert(len(scaled.shape) == 2)\n return self.featurizer.transform(scaled)\n\n\n# Holds one SGDRegressor for each action\nclass Model:\n def __init__(self, env, feature_transformer, learning_rate):\n self.env = env\n self.models = []\n self.feature_transformer = feature_transformer\n for i in range(env.action_space.n):\n model = SGDRegressor(learning_rate=learning_rate)\n model.partial_fit(feature_transformer.transform( [env.reset()[0]] ), [0])\n self.models.append(model)\n\n def predict(self, s):\n X = self.feature_transformer.transform([s])\n result = np.stack([m.predict(X) for m in self.models]).T\n assert(len(result.shape) == 2)\n return result\n\n def update(self, s, a, G):\n X = self.feature_transformer.transform([s])\n assert(len(X.shape) == 2)\n self.models[a].partial_fit(X, [G])\n\n def sample_action(self, s, eps):\n # eps = 0\n # Technically, we don't need to do epsilon-greedy\n # because SGDRegressor predicts 0 for all states\n # until they are updated. This works as the\n # \"Optimistic Initial Values\" method, since all\n # the rewards for Mountain Car are -1.\n if np.random.random() < eps:\n return self.env.action_space.sample()\n else:\n return np.argmax(self.predict(s))\n\n\n# returns a list of states_and_rewards, and the total reward\ndef play_one(model, env, eps, gamma):\n observation = env.reset()[0]\n done = False\n totalreward = 0\n iters = 0\n while not done and iters < 10000:\n action = model.sample_action(observation, eps)\n prev_observation = observation\n observation, reward, done, truncated, info = env.step(action)\n\n # update the model\n if done:\n G = reward\n else:\n Qnext = model.predict(observation)\n # assert(next.shape == (1, env.action_space.n))\n G = reward + gamma*np.max(Qnext[0])\n\n model.update(prev_observation, action, G)\n\n totalreward += reward\n iters += 1\n\n return totalreward\n\n\ndef plot_cost_to_go(env, estimator, num_tiles=20):\n x = np.linspace(env.observation_space.low[0], env.observation_space.high[0], num=num_tiles)\n y = np.linspace(env.observation_space.low[1], env.observation_space.high[1], num=num_tiles)\n X, Y = np.meshgrid(x, y)\n # both X and Y will be of shape (num_tiles, num_tiles)\n Z = np.apply_along_axis(lambda _: -np.max(estimator.predict(_)), 2, np.dstack([X, Y]))\n # Z will also be of shape (num_tiles, num_tiles)\n\n fig = plt.figure(figsize=(10, 5))\n ax = fig.add_subplot(111, projection='3d')\n surf = ax.plot_surface(X, Y, Z,\n rstride=1, cstride=1, cmap=matplotlib.cm.coolwarm, vmin=-1.0, vmax=1.0)\n ax.set_xlabel('Position')\n ax.set_ylabel('Velocity')\n ax.set_zlabel('Cost-To-Go == -V(s)')\n ax.set_title(\"Cost-To-Go Function\")\n fig.colorbar(surf)\n plt.show()\n\n\ndef plot_running_avg(totalrewards):\n N = len(totalrewards)\n running_avg = np.empty(N)\n for t in range(N):\n running_avg[t] = totalrewards[max(0, t-100):(t+1)].mean()\n plt.plot(running_avg)\n plt.title(\"Running Average\")\n plt.show()\n\n\ndef main(show_plots=True):\n env = gym.make('MountainCar-v0')\n ft = FeatureTransformer(env)\n model = Model(env, ft, \"constant\")\n gamma = 0.99\n\n if 'monitor' in sys.argv:\n filename = os.path.basename(__file__).split('.')[0]\n monitor_dir = './' + filename + '_' + str(datetime.now())\n env = wrappers.Monitor(env, monitor_dir)\n\n\n N = 300\n totalrewards = np.empty(N)\n for n in range(N):\n eps = 1.0/(0.1*n+1)\n # eps = 0.1*(0.97**n)\n if n == 199:\n print(\"eps:\", eps)\n # eps = 1.0/np.sqrt(n+1)\n totalreward = play_one(model, env, eps, gamma)\n totalrewards[n] = totalreward\n if (n + 1) % 10 == 0:\n print(\"episode:\", n, \"total reward:\", totalreward)\n print(\"avg reward for last 100 episodes:\", totalrewards[-100:].mean())\n print(\"total steps:\", -totalrewards.sum())\n\n if show_plots:\n plt.plot(totalrewards)\n plt.title(\"Rewards\")\n plt.show()\n\n plot_running_avg(totalrewards)\n\n # plot the optimal state-value function\n plot_cost_to_go(env, model)\n\n\nif __name__ == '__main__':\n # for i in range(10):\n # main(show_plots=False)\n main()" }, { "path": "rl2/mountaincar/td_lambda.py", "content": "# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python\n# https://www.udemy.com/deep-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n#\n# Note: gym changed from version 0.7.3 to 0.8.0\n# MountainCar episode length is capped at 200 in later versions.\n# This means your agent can't learn as much in the earlier episodes\n# since they are no longer as long.\n#\n# Adapt Q-Learning script to use TD(lambda) method instead\n\nimport gym\nimport os\nimport sys\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom gym import wrappers\nfrom datetime import datetime\n\n# code we already wrote\nfrom q_learning import plot_cost_to_go, FeatureTransformer, plot_running_avg\n\ngym_minor_version = int(gym.__version__.split('.')[1])\nif gym_minor_version >= 19:\n exit(\"Please install OpenAI Gym 0.19.0 or earlier\")\n\n\nclass BaseModel:\n def __init__(self, D):\n self.w = np.random.randn(D) / np.sqrt(D)\n\n def partial_fit(self, input_, target, eligibility, lr=1e-2):\n self.w += lr*(target - input_.dot(self.w))*eligibility\n\n def predict(self, X):\n X = np.array(X)\n return X.dot(self.w)\n\n\n# Holds one BaseModel for each action\nclass Model:\n def __init__(self, env, feature_transformer):\n self.env = env\n self.models = []\n self.feature_transformer = feature_transformer\n\n D = feature_transformer.dimensions\n self.eligibilities = np.zeros((env.action_space.n, D))\n for i in range(env.action_space.n):\n model = BaseModel(D)\n self.models.append(model)\n\n def predict(self, s):\n X = self.feature_transformer.transform([s])\n assert(len(X.shape) == 2)\n result = np.stack([m.predict(X) for m in self.models]).T\n assert(len(result.shape) == 2)\n return result\n\n def update(self, s, a, G, gamma, lambda_):\n X = self.feature_transformer.transform([s])\n assert(len(X.shape) == 2)\n self.eligibilities *= gamma*lambda_\n self.eligibilities[a] += X[0]\n self.models[a].partial_fit(X[0], G, self.eligibilities[a])\n\n def sample_action(self, s, eps):\n if np.random.random() < eps:\n return self.env.action_space.sample()\n else:\n return np.argmax(self.predict(s))\n\n\n# returns a list of states_and_rewards, and the total reward\ndef play_one(model, env, eps, gamma, lambda_):\n observation = env.reset()\n done = False\n totalreward = 0\n iters = 0\n # while not done and iters < 200:\n while not done and iters < 10000:\n action = model.sample_action(observation, eps)\n prev_observation = observation\n observation, reward, done, info = env.step(action)\n\n # update the model\n Qnext = model.predict(observation)\n assert(Qnext.shape == (1, env.action_space.n))\n G = reward + gamma*np.max(Qnext[0])\n model.update(prev_observation, action, G, gamma, lambda_)\n\n totalreward += reward\n iters += 1\n\n return totalreward\n\n\nif __name__ == '__main__':\n env = gym.make('MountainCar-v0')\n ft = FeatureTransformer(env)\n model = Model(env, ft)\n gamma = 0.9999\n lambda_ = 0.7\n\n if 'monitor' in sys.argv:\n filename = os.path.basename(__file__).split('.')[0]\n monitor_dir = './' + filename + '_' + str(datetime.now())\n env = wrappers.Monitor(env, monitor_dir)\n\n\n N = 300\n totalrewards = np.empty(N)\n costs = np.empty(N)\n for n in range(N):\n # eps = 1.0/(0.1*n+1)\n eps = 0.1*(0.97**n)\n # eps = 0.5/np.sqrt(n+1)\n totalreward = play_one(model, env, eps, gamma, lambda_)\n totalrewards[n] = totalreward\n print(\"episode:\", n, \"total reward:\", totalreward)\n print(\"avg reward for last 100 episodes:\", totalrewards[-100:].mean())\n print(\"total steps:\", -totalrewards.sum())\n\n plt.plot(totalrewards)\n plt.title(\"Rewards\")\n plt.show()\n\n plot_running_avg(totalrewards)\n\n # plot the optimal state-value function\n plot_cost_to_go(env, model)\n\n" }, { "path": "rl2v2/extra_reading.txt", "content": "Gymnasium Library\nhttps://gymnasium.farama.org/\n\nStable Baselines 3\nhttps://github.com/DLR-RM/stable-baselines3\n\nReinforcement Learning Prerequisites\nhttps://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python" }, { "path": "rl3/a2c/a2c.py", "content": "# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence\nimport time\nimport joblib\nimport numpy as np\nimport tensorflow as tf\nimport os\n\nif tf.__version__.startswith('2'):\n exit(\"Please install Tensorflow 1.x\")\n\n\ndef set_global_seeds(i):\n tf.set_random_seed(i)\n np.random.seed(i)\n\n\ndef cat_entropy(logits):\n a0 = logits - tf.reduce_max(logits, 1, keepdims=True)\n ea0 = tf.exp(a0)\n z0 = tf.reduce_sum(ea0, 1, keepdims=True)\n p0 = ea0 / z0\n return tf.reduce_sum(p0 * (tf.log(z0) - a0), 1)\n\n\ndef find_trainable_variables(key):\n with tf.variable_scope(key):\n return tf.trainable_variables()\n\n\ndef discount_with_dones(rewards, dones, gamma):\n discounted = []\n r = 0\n for reward, done in zip(rewards[::-1], dones[::-1]):\n r = reward + gamma * r * (1. - done) # fixed off by one bug\n discounted.append(r)\n return discounted[::-1]\n\n\n\nclass Agent:\n def __init__(self, Network, ob_space, ac_space, nenvs, nsteps, nstack,\n ent_coef=0.01, vf_coef=0.5, max_grad_norm=0.5, lr=7e-4,\n alpha=0.99, epsilon=1e-5, total_timesteps=int(80e6)):\n config = tf.ConfigProto(intra_op_parallelism_threads=nenvs,\n inter_op_parallelism_threads=nenvs)\n config.gpu_options.allow_growth = True\n sess = tf.Session(config=config)\n nbatch = nenvs * nsteps\n\n A = tf.placeholder(tf.int32, [nbatch])\n ADV = tf.placeholder(tf.float32, [nbatch])\n R = tf.placeholder(tf.float32, [nbatch])\n LR = tf.placeholder(tf.float32, [])\n\n step_model = Network(sess, ob_space, ac_space, nenvs, 1, nstack, reuse=False)\n train_model = Network(sess, ob_space, ac_space, nenvs, nsteps, nstack, reuse=True)\n\n neglogpac = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=train_model.pi, labels=A)\n pg_loss = tf.reduce_mean(ADV * neglogpac)\n vf_loss = tf.reduce_mean(tf.squared_difference(tf.squeeze(train_model.vf), R) / 2.0)\n entropy = tf.reduce_mean(cat_entropy(train_model.pi))\n loss = pg_loss - entropy * ent_coef + vf_loss * vf_coef\n\n params = find_trainable_variables(\"model\")\n grads = tf.gradients(loss, params)\n if max_grad_norm is not None:\n grads, grad_norm = tf.clip_by_global_norm(grads, max_grad_norm)\n grads_and_params = list(zip(grads, params))\n trainer = tf.train.RMSPropOptimizer(learning_rate=LR, decay=alpha, epsilon=epsilon)\n _train = trainer.apply_gradients(grads_and_params)\n\n def train(states, rewards, actions, values):\n advs = rewards - values\n feed_dict = {train_model.X: states, A: actions, ADV: advs, R: rewards, LR: lr}\n policy_loss, value_loss, policy_entropy, _ = sess.run(\n [pg_loss, vf_loss, entropy, _train],\n feed_dict\n )\n return policy_loss, value_loss, policy_entropy\n\n def save(save_path):\n ps = sess.run(params)\n joblib.dump(ps, save_path)\n\n def load(load_path):\n loaded_params = joblib.load(load_path)\n restores = []\n for p, loaded_p in zip(params, loaded_params):\n restores.append(p.assign(loaded_p))\n ps = sess.run(restores)\n\n self.train = train\n self.train_model = train_model\n self.step_model = step_model\n self.step = step_model.step\n self.value = step_model.value\n self.save = save\n self.load = load\n tf.global_variables_initializer().run(session=sess)\n\n\nclass Runner:\n def __init__(self, env, agent, nsteps=5, nstack=4, gamma=0.99):\n self.env = env\n self.agent = agent\n nh, nw, nc = env.observation_space.shape\n nenv = env.num_envs\n self.batch_ob_shape = (nenv * nsteps, nh, nw, nc * nstack)\n self.state = np.zeros((nenv, nh, nw, nc * nstack), dtype=np.uint8)\n self.nc = nc\n obs = env.reset()\n self.update_state(obs)\n self.gamma = gamma\n self.nsteps = nsteps\n self.dones = [False for _ in range(nenv)]\n self.total_rewards = [] # store all workers' total rewards\n self.real_total_rewards = []\n\n def update_state(self, obs):\n # Do frame-stacking here instead of the FrameStack wrapper to reduce IPC overhead\n self.state = np.roll(self.state, shift=-self.nc, axis=3)\n self.state[:, :, :, -self.nc:] = obs\n\n def run(self):\n mb_states, mb_rewards, mb_actions, mb_values, mb_dones = [], [], [], [], []\n for n in range(self.nsteps):\n actions, values = self.agent.step(self.state)\n mb_states.append(np.copy(self.state))\n mb_actions.append(actions)\n mb_values.append(values)\n mb_dones.append(self.dones)\n obs, rewards, dones, infos = self.env.step(actions)\n for done, info in zip(dones, infos):\n if done:\n self.total_rewards.append(info['reward'])\n if info['total_reward'] != -1:\n self.real_total_rewards.append(info['total_reward'])\n self.dones = dones\n for n, done in enumerate(dones):\n if done:\n self.state[n] = self.state[n] * 0\n self.update_state(obs)\n mb_rewards.append(rewards)\n mb_dones.append(self.dones)\n # batch of steps to batch of rollouts\n mb_states = np.asarray(mb_states, dtype=np.uint8).swapaxes(1, 0).reshape(self.batch_ob_shape)\n mb_rewards = np.asarray(mb_rewards, dtype=np.float32).swapaxes(1, 0)\n mb_actions = np.asarray(mb_actions, dtype=np.int32).swapaxes(1, 0)\n mb_values = np.asarray(mb_values, dtype=np.float32).swapaxes(1, 0)\n mb_dones = np.asarray(mb_dones, dtype=np.bool).swapaxes(1, 0)\n mb_dones = mb_dones[:, 1:]\n last_values = self.agent.value(self.state).tolist()\n # discount/bootstrap off value fn\n for n, (rewards, dones, value) in enumerate(zip(mb_rewards, mb_dones, last_values)):\n rewards = rewards.tolist()\n dones = dones.tolist()\n if dones[-1] == 0:\n rewards = discount_with_dones(rewards + [value], dones + [0], self.gamma)[:-1]\n else:\n rewards = discount_with_dones(rewards, dones, self.gamma)\n mb_rewards[n] = rewards\n mb_rewards = mb_rewards.flatten()\n mb_actions = mb_actions.flatten()\n mb_values = mb_values.flatten()\n return mb_states, mb_rewards, mb_actions, mb_values\n\n\ndef learn(network, env, seed, new_session=True, nsteps=5, nstack=4, total_timesteps=int(80e6),\n vf_coef=0.5, ent_coef=0.01, max_grad_norm=0.5, lr=7e-4,\n epsilon=1e-5, alpha=0.99, gamma=0.99, log_interval=1000):\n tf.reset_default_graph()\n set_global_seeds(seed)\n\n nenvs = env.num_envs\n env_id = env.env_id\n save_name = os.path.join('models', env_id + '.save')\n ob_space = env.observation_space\n ac_space = env.action_space\n agent = Agent(Network=network, ob_space=ob_space, ac_space=ac_space, nenvs=nenvs,\n nsteps=nsteps, nstack=nstack,\n ent_coef=ent_coef, vf_coef=vf_coef,\n max_grad_norm=max_grad_norm,\n lr=lr, alpha=alpha, epsilon=epsilon, total_timesteps=total_timesteps)\n if os.path.exists(save_name):\n agent.load(save_name)\n\n runner = Runner(env, agent, nsteps=nsteps, nstack=nstack, gamma=gamma)\n\n nbatch = nenvs * nsteps\n tstart = time.time()\n for update in range(1, total_timesteps // nbatch + 1):\n states, rewards, actions, values = runner.run()\n policy_loss, value_loss, policy_entropy = agent.train(\n states, rewards, actions, values)\n nseconds = time.time() - tstart\n fps = int((update * nbatch) / nseconds)\n if update % log_interval == 0 or update == 1:\n print(' - - - - - - - ')\n print(\"nupdates\", update)\n print(\"total_timesteps\", update * nbatch)\n print(\"fps\", fps)\n print(\"policy_entropy\", float(policy_entropy))\n print(\"value_loss\", float(value_loss))\n\n # total reward\n r = runner.total_rewards[-100:] # get last 100\n tr = runner.real_total_rewards[-100:]\n if len(r) == 100:\n print(\"avg reward (last 100):\", np.mean(r))\n if len(tr) == 100:\n print(\"avg total reward (last 100):\", np.mean(tr))\n print(\"max (last 100):\", np.max(tr))\n\n agent.save(save_name)\n\n env.close()\n agent.save(save_name)\n" }, { "path": "rl3/a2c/atari_wrappers.py", "content": "# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence\nimport numpy as np\nfrom collections import deque\nimport gym\nfrom gym import spaces\nimport cv2 # opencv-python\n\n\nclass NoopResetEnv(gym.Wrapper):\n def __init__(self, env, noop_max=30):\n \"\"\"Sample initial states by taking random number of no-ops on reset.\n No-op is assumed to be action 0.\n \"\"\"\n gym.Wrapper.__init__(self, env)\n self.noop_max = noop_max\n self.override_num_noops = None\n self.noop_action = 0\n assert env.unwrapped.get_action_meanings()[0] == 'NOOP'\n\n def reset(self, **kwargs):\n \"\"\" Do no-op action for a number of steps in [1, noop_max].\"\"\"\n self.env.reset(**kwargs)\n if self.override_num_noops is not None:\n noops = self.override_num_noops\n else:\n noops = self.unwrapped.np_random.randint(1, self.noop_max + 1) # pylint: disable=E1101\n assert noops > 0\n obs = None\n for _ in range(noops):\n obs, _, done, _ = self.env.step(self.noop_action)\n if done:\n obs = self.env.reset(**kwargs)\n return obs\n\n def step(self, ac):\n return self.env.step(ac)\n\n\nclass FireResetEnv(gym.Wrapper):\n def __init__(self, env):\n \"\"\"Take action on reset for environments that are fixed until firing.\"\"\"\n gym.Wrapper.__init__(self, env)\n assert env.unwrapped.get_action_meanings()[1] == 'FIRE'\n assert len(env.unwrapped.get_action_meanings()) >= 3\n\n def reset(self, **kwargs):\n self.env.reset(**kwargs)\n obs, _, done, _ = self.env.step(1)\n if done:\n self.env.reset(**kwargs)\n obs, _, done, _ = self.env.step(2)\n if done:\n self.env.reset(**kwargs)\n return obs\n\n def step(self, ac):\n return self.env.step(ac)\n\n\nclass EpisodicLifeEnv(gym.Wrapper):\n def __init__(self, env):\n \"\"\"Make end-of-life == end-of-episode, but only reset on true game over.\n Done by DeepMind for the DQN and co. since it helps value estimation.\n \"\"\"\n gym.Wrapper.__init__(self, env)\n self.lives = 0\n self.was_real_done = True\n\n def step(self, action):\n obs, reward, done, info = self.env.step(action)\n self.was_real_done = done\n # check current lives, make loss of life terminal,\n # then update lives to handle bonus lives\n lives = self.env.unwrapped.ale.lives()\n if lives < self.lives and lives > 0:\n # for Qbert sometimes we stay in lives == 0 condtion for a few frames\n # so its important to keep lives > 0, so that we only reset once\n # the environment advertises done.\n done = True\n self.lives = lives\n return obs, reward, done, info\n\n def reset(self, **kwargs):\n \"\"\"Reset only when lives are exhausted.\n This way all states are still reachable even though lives are episodic,\n and the learner need not know about any of this behind-the-scenes.\n \"\"\"\n if self.was_real_done:\n obs = self.env.reset(**kwargs)\n else:\n # no-op step to advance from terminal/lost life state\n obs, _, _, _ = self.env.step(0)\n self.lives = self.env.unwrapped.ale.lives()\n return obs\n\n\nclass MaxAndSkipEnv(gym.Wrapper):\n def __init__(self, env, skip=4):\n \"\"\"Return only every `skip`-th frame\"\"\"\n gym.Wrapper.__init__(self, env)\n # most recent raw observations (for max pooling across time steps)\n self._obs_buffer = np.zeros((2,) + env.observation_space.shape, dtype='uint8')\n self._skip = skip\n\n def step(self, action):\n \"\"\"Repeat action, sum reward, and max over last observations.\"\"\"\n total_reward = 0.0\n done = None\n for i in range(self._skip):\n obs, reward, done, info = self.env.step(action)\n if i == self._skip - 2:\n self._obs_buffer[0] = obs\n if i == self._skip - 1:\n self._obs_buffer[1] = obs\n total_reward += reward\n if done:\n break\n # Note that the observation on the done=True frame\n # doesn't matter\n max_frame = self._obs_buffer.max(axis=0)\n\n return max_frame, total_reward, done, info\n\n def reset(self, **kwargs):\n return self.env.reset(**kwargs)\n\n\nclass ClipRewardEnv(gym.RewardWrapper):\n def reward(self, reward):\n \"\"\"Bin reward to {+1, 0, -1} by its sign.\"\"\"\n return np.sign(reward)\n\n\n# class WarpFrame(gym.ObservationWrapper):\n# def __init__(self, env):\n# \"\"\"Warp frames to 84x84 as done in the Nature paper and later work.\"\"\"\n# gym.ObservationWrapper.__init__(self, env)\n# self.width = 84\n# self.height = 84\n# self.observation_space = spaces.Box(low=0, high=255, shape=(self.height, self.width, 1))\n\n# def _observation(self, frame):\n# frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)\n# frame = cv2.resize(frame, (self.width, self.height), interpolation=cv2.INTER_AREA)\n# return frame[:, :, None]\nclass WarpFrame(gym.ObservationWrapper):\n def __init__(self, env, width=84, height=84, grayscale=True):\n \"\"\"Warp frames to 84x84 as done in the Nature paper and later work.\"\"\"\n gym.ObservationWrapper.__init__(self, env)\n self.width = width\n self.height = height\n self.grayscale = grayscale\n if self.grayscale:\n self.observation_space = spaces.Box(low=0, high=255,\n shape=(self.height, self.width, 1), dtype=np.uint8)\n else:\n self.observation_space = spaces.Box(low=0, high=255,\n shape=(self.height, self.width, 3), dtype=np.uint8)\n\n def observation(self, frame):\n if self.grayscale:\n frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)\n frame = cv2.resize(frame, (self.width, self.height), interpolation=cv2.INTER_AREA)\n if self.grayscale:\n frame = np.expand_dims(frame, -1)\n return frame\n\n\nclass FrameStack(gym.Wrapper):\n def __init__(self, env, k):\n \"\"\"Stack k last frames.\n\n Returns lazy array, which is much more memory efficient.\n\n See Also\n --------\n baselines.common.atari_wrappers.LazyFrames\n \"\"\"\n gym.Wrapper.__init__(self, env)\n self.k = k\n self.frames = deque([], maxlen=k)\n shp = env.observation_space.shape\n self.observation_space = spaces.Box(low=0, high=255, shape=(shp[0], shp[1], shp[2] * k))\n\n def reset(self):\n ob = self.env.reset()\n for _ in range(self.k):\n self.frames.append(ob)\n return self._get_ob()\n\n def step(self, action):\n ob, reward, done, info = self.env.step(action)\n self.frames.append(ob)\n return self._get_ob(), reward, done, info\n\n def _get_ob(self):\n assert len(self.frames) == self.k\n return LazyFrames(list(self.frames))\n\n\nclass LazyFrames:\n def __init__(self, frames):\n \"\"\"This object ensures that common frames between the observations are only stored once.\n It exists purely to optimize memory usage which can be huge for DQN's 1M frames replay\n buffers.\n\n This object should only be converted to numpy array before being passed to the model.\n\n You'd not believe how complex the previous solution was.\"\"\"\n self._frames = frames\n\n def __array__(self, dtype=None):\n out = np.concatenate(self._frames, axis=2)\n if dtype is not None:\n out = out.astype(dtype)\n return out\n\n\ndef make_atari(env_id):\n env = gym.make(env_id)\n assert 'NoFrameskip' in env.spec.id\n env = NoopResetEnv(env, noop_max=30)\n env = MaxAndSkipEnv(env, skip=4)\n return env\n\n\ndef wrap_deepmind(env, episode_life=True, clip_rewards=True, frame_stack=False):\n \"\"\"Configure environment for DeepMind-style Atari.\n \"\"\"\n if episode_life:\n env = EpisodicLifeEnv(env)\n\n if 'FIRE' in env.unwrapped.get_action_meanings():\n env = FireResetEnv(env)\n env = WarpFrame(env)\n\n if clip_rewards:\n env = ClipRewardEnv(env)\n\n if frame_stack:\n env = FrameStack(env, 4)\n\n return env\n\n\nclass Monitor(gym.Wrapper):\n def __init__(self, env, rank=0):\n gym.Wrapper.__init__(self, env=env)\n self.rank = rank\n self.rewards = []\n self.total_reward = []\n self.summaries_dict = {'reward': 0, 'episode_length': 0, 'total_reward': 0, 'total_episode_length': 0}\n env = self.env\n while True:\n if hasattr(env, 'was_real_done'):\n self.episodic_env = env\n if not hasattr(env, 'env'):\n break\n env = env.env\n\n def reset(self):\n self.summaries_dict['reward'] = -1\n self.summaries_dict['episode_length'] = -1\n self.summaries_dict['total_reward'] = -1\n self.summaries_dict['total_episode_length'] = -1\n self.rewards = []\n env = self.env\n if self.episodic_env.was_real_done:\n self.summaries_dict['total_reward'] = -1\n self.summaries_dict['total_episode_length'] = -1\n self.total_reward = []\n return self.env.reset()\n\n def step(self, action):\n observation, reward, done, info = self.env.step(action)\n self.rewards.append(reward)\n self.total_reward.append(reward)\n if done:\n # print(\"Done! R = %s, N = %s\" % (sum(self.rewards), len(self.rewards)))\n self.summaries_dict['reward'] = sum(self.rewards)\n self.summaries_dict['episode_length'] = len(self.rewards)\n\n if self.episodic_env.was_real_done:\n self.summaries_dict['total_reward'] = sum(self.total_reward)\n self.summaries_dict['total_episode_length'] = len(self.total_reward)\n info = self.summaries_dict.copy() # otherwise it will be overwritten\n # if done:\n # print(\"info:\", info)\n return observation, reward, done, info\n" }, { "path": "rl3/a2c/main.py", "content": "# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence\nfrom subproc_vec_env import SubprocVecEnv\nfrom atari_wrappers import make_atari, wrap_deepmind, Monitor\n\nfrom neural_network import CNN\nfrom a2c import learn\n\nimport os\n\nimport gym\nimport argparse\nimport logging\n\ngym_minor_version = int(gym.__version__.split('.')[1])\nif gym_minor_version >= 19:\n exit(\"Please install OpenAI Gym 0.19.0 or earlier\")\n\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Mute missing instructions errors\n\nMODEL_PATH = 'models'\nSEED = 0\n\n\ndef get_args():\n # Get some basic command line arguements\n parser = argparse.ArgumentParser()\n parser.add_argument('-e', '--env', help='environment ID', default='BreakoutNoFrameskip-v4')\n parser.add_argument('-s', '--steps', help='training steps', type=int, default=int(80e6))\n parser.add_argument('--nenv', help='No. of environments', type=int, default=16)\n return parser.parse_args()\n\n\ndef train(env_id, num_timesteps, num_cpu):\n def make_env(rank):\n def _thunk():\n env = make_atari(env_id)\n env.seed(SEED + rank)\n gym.logger.setLevel(logging.WARN)\n env = wrap_deepmind(env)\n\n # wrap the env one more time for getting total reward\n env = Monitor(env, rank)\n return env\n return _thunk\n\n env = SubprocVecEnv([make_env(i) for i in range(num_cpu)])\n learn(CNN, env, SEED, total_timesteps=int(num_timesteps * 1.1))\n env.close()\n pass\n\n\ndef main():\n args = get_args()\n os.makedirs(MODEL_PATH, exist_ok=True)\n train(args.env, args.steps, num_cpu=args.nenv)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "rl3/a2c/neural_network.py", "content": "# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence\nimport numpy as np\nimport tensorflow as tf\n\n\ndef sample(logits):\n noise = tf.random_uniform(tf.shape(logits))\n return tf.argmax(logits - tf.log(-tf.log(noise)), 1)\n\n\ndef conv(inputs, nf, ks, strides, gain=1.0):\n return tf.layers.conv2d(inputs=inputs, filters=nf, kernel_size=ks,\n strides=(strides, strides), activation=tf.nn.relu,\n kernel_initializer=tf.orthogonal_initializer(gain=gain))\n\n\ndef dense(inputs, n, act=tf.nn.relu, gain=1.0):\n return tf.layers.dense(inputs=inputs, units=n, activation=act,\n kernel_initializer=tf.orthogonal_initializer(gain))\n\n\nclass CNN:\n\n def __init__(self, sess, ob_space, ac_space, nenv, nsteps, nstack, reuse=False):\n gain = np.sqrt(2)\n nbatch = nenv * nsteps\n nh, nw, nc = ob_space.shape\n ob_shape = (nbatch, nh, nw, nc * nstack)\n X = tf.placeholder(tf.uint8, ob_shape) # obs\n X_normal = tf.cast(X, tf.float32) / 255.0\n with tf.variable_scope(\"model\", reuse=reuse):\n h1 = conv(X_normal, 32, 8, 4, gain)\n h2 = conv(h1, 64, 4, 2, gain)\n h3 = conv(h2, 64, 3, 1, gain)\n h3 = tf.layers.flatten(h3)\n h4 = dense(h3, 512, gain=gain)\n pi = dense(h4, ac_space.n, act=None)\n vf = dense(h4, 1, act=None)\n\n v0 = vf[:, 0]\n a0 = sample(pi)\n # self.initial_state = [] # State reserved for LSTM\n\n def step(ob):\n a, v = sess.run([a0, v0], {X: ob})\n return a, v#, [] # dummy state\n\n def value(ob):\n return sess.run(v0, {X: ob})\n\n self.X = X\n self.pi = pi\n self.vf = vf\n self.step = step\n self.value = value\n" }, { "path": "rl3/a2c/play.py", "content": "# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence\nimport argparse\nimport os\nimport numpy as np\nfrom atari_wrappers import make_atari, wrap_deepmind, Monitor\nfrom a2c import Agent\nfrom neural_network import CNN\nimport imageio\nimport time\n\n\ndef get_args():\n # Get some basic command line arguements\n parser = argparse.ArgumentParser()\n parser.add_argument('-e', '--env', help='environment ID', default='BreakoutNoFrameskip-v4')\n return parser.parse_args()\n\n\ndef get_agent(env, nsteps=5, nstack=1, total_timesteps=int(80e6),\n vf_coef=0.5, ent_coef=0.01, max_grad_norm=0.5, lr=7e-4,\n epsilon=1e-5, alpha=0.99):\n # Note: nstack=1 since frame_stack=True, during training frame_stack=False\n agent = Agent(Network=CNN, ob_space=env.observation_space,\n ac_space=env.action_space, nenvs=1, nsteps=nsteps, nstack=nstack,\n ent_coef=ent_coef, vf_coef=vf_coef, max_grad_norm=max_grad_norm,\n lr=lr, alpha=alpha, epsilon=epsilon, total_timesteps=total_timesteps)\n return agent\n\n\ndef main():\n env_id = get_args().env\n env = make_atari(env_id)\n env = wrap_deepmind(env, frame_stack=True, clip_rewards=False, episode_life=True)\n env = Monitor(env)\n # rewards will appear higher than during training since rewards are not clipped\n\n agent = get_agent(env)\n\n # check for save path\n save_path = os.path.join('models', env_id + '.save')\n agent.load(save_path)\n\n obs = env.reset()\n renders = []\n while True:\n obs = np.expand_dims(obs.__array__(), axis=0)\n a, v = agent.step(obs)\n obs, reward, done, info = env.step(a)\n env.render()\n if done:\n print(info)\n env.reset()\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "rl3/a2c/subproc_vec_env.py", "content": "# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence\nimport numpy as np\nfrom multiprocessing import Process, Pipe\n\n\ndef worker(remote, parent_remote, env_fn_wrapper):\n parent_remote.close()\n env = env_fn_wrapper.x()\n while True:\n cmd, data = remote.recv()\n if cmd == 'step':\n ob, reward, done, info = env.step(data)\n if done:\n ob = env.reset()\n remote.send((ob, reward, done, info))\n elif cmd == 'reset':\n ob = env.reset()\n remote.send(ob)\n elif cmd == 'reset_task':\n ob = env.reset_task()\n remote.send(ob)\n elif cmd == 'close':\n remote.close()\n break\n elif cmd == 'get_spaces':\n remote.send((env.action_space, env.observation_space))\n elif cmd == 'get_id':\n remote.send(env.spec.id)\n else:\n raise NotImplementedError\n\n\nclass CloudpickleWrapper():\n \"\"\"\n Uses cloudpickle to serialize contents (otherwise multiprocessing tries to use pickle)\n \"\"\"\n\n def __init__(self, x):\n self.x = x\n\n def __getstate__(self):\n import cloudpickle\n return cloudpickle.dumps(self.x)\n\n def __setstate__(self, ob):\n import pickle\n self.x = pickle.loads(ob)\n\n\nclass SubprocVecEnv():\n def __init__(self, env_fns):\n \"\"\"\n envs: list of gym environments to run in subprocesses\n \"\"\"\n self.closed = False\n nenvs = len(env_fns)\n self.remotes, self.work_remotes = zip(*[Pipe() for _ in range(nenvs)])\n self.ps = [Process(target=worker, args=(work_remote, remote, CloudpickleWrapper(env_fn)))\n for (work_remote, remote, env_fn) in zip(self.work_remotes, self.remotes, env_fns)]\n for p in self.ps:\n p.daemon = True # if the main process crashes, we should not cause things to hang\n p.start()\n for remote in self.work_remotes:\n remote.close()\n\n self.remotes[0].send(('get_spaces', None))\n self.action_space, self.observation_space = self.remotes[0].recv()\n\n self.remotes[0].send(('get_id', None))\n self.env_id = self.remotes[0].recv()\n\n def step(self, actions):\n for remote, action in zip(self.remotes, actions):\n remote.send(('step', action))\n results = [remote.recv() for remote in self.remotes]\n obs, rews, dones, infos = zip(*results)\n # print(\"Infos:\", infos)\n # for done, info in zip(dones, infos):\n # if done:\n # # print(\"Total reward:\", info['reward'], \"Num steps:\", info['episode_length'])\n # print(\"Returned info:\", info, \"Done:\", done)\n return np.stack(obs), np.stack(rews), np.stack(dones), infos\n\n def reset(self):\n for remote in self.remotes:\n remote.send(('reset', None))\n return np.stack([remote.recv() for remote in self.remotes])\n\n def reset_task(self):\n for remote in self.remotes:\n remote.send(('reset_task', None))\n return np.stack([remote.recv() for remote in self.remotes])\n\n def close(self):\n if self.closed:\n return\n\n for remote in self.remotes:\n remote.send(('close', None))\n for p in self.ps:\n p.join()\n self.closed = True\n\n @property\n def num_envs(self):\n return len(self.remotes)\n" }, { "path": "rl3/ddpg.py", "content": "# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence\nimport numpy as np\nimport tensorflow as tf\nimport gym\nimport matplotlib.pyplot as plt\nfrom datetime import datetime\n\ngym_minor_version = int(gym.__version__.split('.')[1])\nif gym_minor_version >= 19:\n exit(\"Please install OpenAI Gym 0.19.0 or earlier\")\n\nif tf.__version__.startswith('2'):\n exit(\"Please install Tensorflow 1.x\")\n\n\n### avoid crashing on Mac\n# doesn't seem to work\nfrom sys import platform as sys_pf\nif sys_pf == 'darwin':\n import matplotlib\n matplotlib.use(\"TkAgg\")\n\n\n# simple feedforward neural net\ndef ANN(x, layer_sizes, hidden_activation=tf.nn.relu, output_activation=None):\n for h in layer_sizes[:-1]:\n x = tf.layers.dense(x, units=h, activation=hidden_activation)\n return tf.layers.dense(x, units=layer_sizes[-1], activation=output_activation)\n\n\n# get all variables within a scope\ndef get_vars(scope):\n return [x for x in tf.global_variables() if scope in x.name]\n\n\n### Create both the actor and critic networks at once ###\n### Q(s, mu(s)) returns the maximum Q for a given state s ###\ndef CreateNetworks(\n s, a,\n num_actions,\n action_max,\n hidden_sizes=(300,),\n hidden_activation=tf.nn.relu, \n output_activation=tf.tanh):\n\n with tf.variable_scope('mu'):\n mu = action_max * ANN(s, list(hidden_sizes)+[num_actions], hidden_activation, output_activation)\n with tf.variable_scope('q'):\n input_ = tf.concat([s, a], axis=-1) # (state, action)\n q = tf.squeeze(ANN(input_, list(hidden_sizes)+[1], hidden_activation, None), axis=1)\n with tf.variable_scope('q', reuse=True):\n # reuse is True, so it reuses the weights from the previously defined Q network\n input_ = tf.concat([s, mu], axis=-1) # (state, mu(state))\n q_mu = tf.squeeze(ANN(input_, list(hidden_sizes)+[1], hidden_activation, None), axis=1)\n return mu, q, q_mu\n\n\n### The experience replay memory ###\nclass ReplayBuffer:\n def __init__(self, obs_dim, act_dim, size):\n self.obs1_buf = np.zeros([size, obs_dim], dtype=np.float32)\n self.obs2_buf = np.zeros([size, obs_dim], dtype=np.float32)\n self.acts_buf = np.zeros([size, act_dim], dtype=np.float32)\n self.rews_buf = np.zeros(size, dtype=np.float32)\n self.done_buf = np.zeros(size, dtype=np.float32)\n self.ptr, self.size, self.max_size = 0, 0, size\n\n def store(self, obs, act, rew, next_obs, done):\n self.obs1_buf[self.ptr] = obs\n self.obs2_buf[self.ptr] = next_obs\n self.acts_buf[self.ptr] = act\n self.rews_buf[self.ptr] = rew\n self.done_buf[self.ptr] = done\n self.ptr = (self.ptr+1) % self.max_size\n self.size = min(self.size+1, self.max_size)\n\n def sample_batch(self, batch_size=32):\n idxs = np.random.randint(0, self.size, size=batch_size)\n return dict(s=self.obs1_buf[idxs],\n s2=self.obs2_buf[idxs],\n a=self.acts_buf[idxs],\n r=self.rews_buf[idxs],\n d=self.done_buf[idxs])\n\n\n### Implement the DDPG algorithm ###\ndef ddpg(\n env_fn,\n ac_kwargs=dict(),\n seed=0,\n save_folder=None,\n num_train_episodes=100,\n test_agent_every=25,\n replay_size=int(1e6),\n gamma=0.99, \n decay=0.995,\n mu_lr=1e-3,\n q_lr=1e-3,\n batch_size=100,\n start_steps=10000, \n action_noise=0.1,\n max_episode_length=1000):\n\n tf.set_random_seed(seed)\n np.random.seed(seed)\n\n env, test_env = env_fn(), env_fn()\n\n # comment out this line if you don't want to record a video of the agent\n if save_folder is not None:\n test_env = gym.wrappers.Monitor(test_env, save_folder)\n\n # get size of state space and action space\n num_states = env.observation_space.shape[0]\n num_actions = env.action_space.shape[0]\n\n # Maximum value of action\n # Assumes both low and high values are the same\n # Assumes all actions have the same bounds\n # May NOT be the case for all environments\n action_max = env.action_space.high[0]\n\n # Create Tensorflow placeholders (neural network inputs)\n X = tf.placeholder(dtype=tf.float32, shape=(None, num_states)) # state\n A = tf.placeholder(dtype=tf.float32, shape=(None, num_actions)) # action\n X2 = tf.placeholder(dtype=tf.float32, shape=(None, num_states)) # next state\n R = tf.placeholder(dtype=tf.float32, shape=(None,)) # reward\n D = tf.placeholder(dtype=tf.float32, shape=(None,)) # done\n\n # Main network outputs\n with tf.variable_scope('main'):\n mu, q, q_mu = CreateNetworks(X, A, num_actions, action_max, **ac_kwargs)\n \n # Target networks\n with tf.variable_scope('target'):\n # We don't need the Q network output with arbitrary input action A\n # because that's not actually used in our loss functions\n # NOTE 1: The state input is X2, NOT X\n # We only care about max_a{ Q(s', a) }\n # Where this is equal to Q(s', mu(s'))\n # This is because it's used in the target calculation: r + gamma * max_a{ Q(s',a) }\n # Where s' = X2\n # NOTE 2: We ignore the first 2 networks for the same reason\n _, _, q_mu_targ = CreateNetworks(X2, A, num_actions, action_max, **ac_kwargs)\n\n # Experience replay memory\n replay_buffer = ReplayBuffer(obs_dim=num_states, act_dim=num_actions, size=replay_size)\n\n\n # Target value for the Q-network loss\n # We use stop_gradient to tell Tensorflow not to differentiate\n # q_mu_targ wrt any params\n # i.e. consider q_mu_targ values constant\n q_target = tf.stop_gradient(R + gamma * (1 - D) * q_mu_targ)\n\n # DDPG losses\n mu_loss = -tf.reduce_mean(q_mu)\n q_loss = tf.reduce_mean((q - q_target)**2)\n\n # Train each network separately\n mu_optimizer = tf.train.AdamOptimizer(learning_rate=mu_lr)\n q_optimizer = tf.train.AdamOptimizer(learning_rate=q_lr)\n mu_train_op = mu_optimizer.minimize(mu_loss, var_list=get_vars('main/mu'))\n q_train_op = q_optimizer.minimize(q_loss, var_list=get_vars('main/q'))\n\n # Use soft updates to update the target networks\n target_update = tf.group(\n [tf.assign(v_targ, decay*v_targ + (1 - decay)*v_main)\n for v_main, v_targ in zip(get_vars('main'), get_vars('target'))\n ]\n )\n\n # Copy main network params to target networks\n target_init = tf.group(\n [tf.assign(v_targ, v_main)\n for v_main, v_targ in zip(get_vars('main'), get_vars('target'))\n ]\n )\n\n # boilerplate (and copy to the target networks!)\n sess = tf.Session()\n sess.run(tf.global_variables_initializer())\n sess.run(target_init)\n\n def get_action(s, noise_scale):\n a = sess.run(mu, feed_dict={X: s.reshape(1,-1)})[0]\n a += noise_scale * np.random.randn(num_actions)\n return np.clip(a, -action_max, action_max)\n\n test_returns = []\n def test_agent(num_episodes=5):\n t0 = datetime.now()\n n_steps = 0\n for j in range(num_episodes):\n s, episode_return, episode_length, d = test_env.reset(), 0, 0, False\n while not (d or (episode_length == max_episode_length)):\n # Take deterministic actions at test time (noise_scale=0)\n test_env.render()\n s, r, d, _ = test_env.step(get_action(s, 0))\n episode_return += r\n episode_length += 1\n n_steps += 1\n print('test return:', episode_return, 'episode_length:', episode_length)\n test_returns.append(episode_return)\n # print(\"test steps per sec:\", n_steps / (datetime.now() - t0).total_seconds())\n\n\n # Main loop: play episode and train\n returns = []\n q_losses = []\n mu_losses = []\n num_steps = 0\n for i_episode in range(num_train_episodes):\n\n # reset env\n s, episode_return, episode_length, d = env.reset(), 0, 0, False\n\n while not (d or (episode_length == max_episode_length)):\n # For the first `start_steps` steps, use randomly sampled actions\n # in order to encourage exploration.\n if num_steps > start_steps:\n a = get_action(s, action_noise)\n else:\n a = env.action_space.sample()\n\n # Keep track of the number of steps done\n num_steps += 1\n if num_steps == start_steps:\n print(\"USING AGENT ACTIONS NOW\")\n\n # Step the env\n s2, r, d, _ = env.step(a)\n episode_return += r\n episode_length += 1\n\n # Ignore the \"done\" signal if it comes from hitting the time\n # horizon (that is, when it's an artificial terminal signal\n # that isn't based on the agent's state)\n d_store = False if episode_length == max_episode_length else d\n\n # Store experience to replay buffer\n replay_buffer.store(s, a, r, s2, d_store)\n\n # Assign next state to be the current state on the next round\n s = s2\n\n # Perform the updates\n for _ in range(episode_length):\n batch = replay_buffer.sample_batch(batch_size)\n feed_dict = {\n X: batch['s'],\n X2: batch['s2'],\n A: batch['a'],\n R: batch['r'],\n D: batch['d']\n }\n\n # Q network update\n # Note: plot the Q loss if you want\n ql, _, _ = sess.run([q_loss, q, q_train_op], feed_dict)\n q_losses.append(ql)\n\n # Policy update\n # (And target networks update)\n # Note: plot the mu loss if you want\n mul, _, _ = sess.run([mu_loss, mu_train_op, target_update], feed_dict)\n mu_losses.append(mul)\n\n print(\"Episode:\", i_episode + 1, \"Return:\", episode_return, 'episode_length:', episode_length)\n returns.append(episode_return)\n\n # Test the agent\n if i_episode > 0 and i_episode % test_agent_every == 0:\n test_agent()\n\n # on Mac, plotting results in an error, so just save the results for later\n # if you're not on Mac, feel free to uncomment the below lines\n np.savez('ddpg_results.npz', train=returns, test=test_returns, q_losses=q_losses, mu_losses=mu_losses)\n\n # plt.plot(returns)\n # plt.plot(smooth(np.array(returns)))\n # plt.title(\"Train returns\")\n # plt.show()\n\n # plt.plot(test_returns)\n # plt.plot(smooth(np.array(test_returns)))\n # plt.title(\"Test returns\")\n # plt.show()\n\n # plt.plot(q_losses)\n # plt.title('q_losses')\n # plt.show()\n\n # plt.plot(mu_losses)\n # plt.title('mu_losses')\n # plt.show()\n\n\ndef smooth(x):\n # last 100\n n = len(x)\n y = np.zeros(n)\n for i in range(n):\n start = max(0, i - 99)\n y[i] = float(x[start:(i+1)].sum()) / (i - start + 1)\n return y\n\n\nif __name__ == '__main__':\n import argparse\n parser = argparse.ArgumentParser()\n # parser.add_argument('--env', type=str, default='HalfCheetah-v2')\n parser.add_argument('--env', type=str, default='Pendulum-v0')\n parser.add_argument('--hidden_layer_sizes', type=int, default=300)\n parser.add_argument('--num_layers', type=int, default=1)\n parser.add_argument('--gamma', type=float, default=0.99)\n parser.add_argument('--seed', type=int, default=0)\n parser.add_argument('--num_train_episodes', type=int, default=200)\n parser.add_argument('--save_folder', type=str, default='ddpg_monitor')\n args = parser.parse_args()\n\n\n ddpg(\n lambda : gym.make(args.env),\n ac_kwargs=dict(hidden_sizes=[args.hidden_layer_sizes]*args.num_layers),\n gamma=args.gamma,\n seed=args.seed,\n save_folder=args.save_folder,\n num_train_episodes=args.num_train_episodes,\n )\n" }, { "path": "rl3/es_flappy.py", "content": "# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom datetime import datetime\n\n# import multiprocessing\n# from multiprocessing.dummy import Pool\n\n# INSTRUCTIONS FOR INSTALLING PLE:\n# https://pygame-learning-environment.readthedocs.io/en/latest/user/home.html\nfrom ple import PLE\nfrom ple.games.flappybird import FlappyBird\n\nimport sys\n\n\n# thread pool for parallelization\n# pool = Pool(4)\n\n\nHISTORY_LENGTH = 1\n\n\nclass Env:\n def __init__(self):\n self.game = FlappyBird(pipe_gap=125)\n self.env = PLE(self.game, fps=30, display_screen=False)\n self.env.init()\n self.env.getGameState = self.game.getGameState # maybe not necessary\n\n # by convention we want to use (0,1)\n # but the game uses (None, 119)\n self.action_map = self.env.getActionSet() #[None, 119]\n\n def step(self, action):\n action = self.action_map[action]\n reward = self.env.act(action)\n done = self.env.game_over()\n obs = self.get_observation()\n # don't bother returning an info dictionary like gym\n return obs, reward, done\n\n def reset(self):\n self.env.reset_game()\n return self.get_observation()\n\n def get_observation(self):\n # game state returns a dictionary which describes\n # the meaning of each value\n # we only want the values\n obs = self.env.getGameState()\n return np.array(list(obs.values()))\n\n def set_display(self, boolean_value):\n self.env.display_screen = boolean_value\n\n\n# make a global environment to be used throughout the script\nenv = Env()\n\n\n### neural network\n\n# hyperparameters\nD = len(env.reset())*HISTORY_LENGTH\nM = 50\nK = 2\n\ndef softmax(a):\n c = np.max(a, axis=1, keepdims=True)\n e = np.exp(a - c)\n return e / e.sum(axis=-1, keepdims=True)\n\ndef relu(x):\n return x * (x > 0)\n\nclass ANN:\n def __init__(self, D, M, K, f=relu):\n self.D = D\n self.M = M\n self.K = K\n self.f = f\n\n def init(self):\n D, M, K = self.D, self.M, self.K\n self.W1 = np.random.randn(D, M) / np.sqrt(D)\n # self.W1 = np.zeros((D, M))\n self.b1 = np.zeros(M)\n self.W2 = np.random.randn(M, K) / np.sqrt(M)\n # self.W2 = np.zeros((M, K))\n self.b2 = np.zeros(K)\n\n def forward(self, X):\n Z = self.f(X.dot(self.W1) + self.b1)\n return softmax(Z.dot(self.W2) + self.b2)\n\n def sample_action(self, x):\n # assume input is a single state of size (D,)\n # first make it (N, D) to fit ML conventions\n X = np.atleast_2d(x)\n P = self.forward(X)\n p = P[0] # the first row\n # return np.random.choice(len(p), p=p)\n return np.argmax(p)\n\n def get_params(self):\n # return a flat array of parameters\n return np.concatenate([self.W1.flatten(), self.b1, self.W2.flatten(), self.b2])\n\n def get_params_dict(self):\n return {\n 'W1': self.W1,\n 'b1': self.b1,\n 'W2': self.W2,\n 'b2': self.b2,\n }\n\n def set_params(self, params):\n # params is a flat list\n # unflatten into individual weights\n D, M, K = self.D, self.M, self.K\n self.W1 = params[:D * M].reshape(D, M)\n self.b1 = params[D * M:D * M + M]\n self.W2 = params[D * M + M:D * M + M + M * K].reshape(M, K)\n self.b2 = params[-K:]\n\n\ndef evolution_strategy(\n f,\n population_size,\n sigma,\n lr,\n initial_params,\n num_iters):\n\n # assume initial params is a 1-D array\n num_params = len(initial_params)\n reward_per_iteration = np.zeros(num_iters)\n\n params = initial_params\n for t in range(num_iters):\n t0 = datetime.now()\n N = np.random.randn(population_size, num_params)\n\n ### slow way\n R = np.zeros(population_size) # stores the reward\n\n # loop through each \"offspring\"\n for j in range(population_size):\n params_try = params + sigma*N[j]\n R[j] = f(params_try)\n\n ### fast way\n # R = pool.map(f, [params + sigma*N[j] for j in range(population_size)])\n # R = np.array(R)\n\n m = R.mean()\n s = R.std()\n if s == 0:\n # we can't apply the following equation\n print(\"Skipping\")\n continue\n\n A = (R - m) / s\n reward_per_iteration[t] = m\n params = params + lr/(population_size*sigma) * np.dot(N.T, A)\n\n # update the learning rate\n lr *= 0.992354\n # sigma *= 0.99\n\n print(\"Iter:\", t, \"Avg Reward: %.3f\" % m, \"Max:\", R.max(), \"Duration:\", (datetime.now() - t0))\n\n return params, reward_per_iteration\n\n\ndef reward_function(params):\n model = ANN(D, M, K)\n model.set_params(params)\n \n # play one episode and return the total reward\n episode_reward = 0\n episode_length = 0 # not sure if it will be used\n done = False\n obs = env.reset()\n obs_dim = len(obs)\n if HISTORY_LENGTH > 1:\n state = np.zeros(HISTORY_LENGTH*obs_dim) # current state\n state[-obs_dim:] = obs\n else:\n state = obs\n while not done:\n # get the action\n action = model.sample_action(state)\n\n # perform the action\n obs, reward, done = env.step(action)\n\n # update total reward\n episode_reward += reward\n episode_length += 1\n\n # update state\n if HISTORY_LENGTH > 1:\n state = np.roll(state, -obs_dim)\n state[-obs_dim:] = obs\n else:\n state = obs\n return episode_reward\n\n\nif __name__ == '__main__':\n model = ANN(D, M, K)\n\n if len(sys.argv) > 1 and sys.argv[1] == 'play':\n # play with a saved model\n j = np.load('es_flappy_results.npz')\n best_params = np.concatenate([j['W1'].flatten(), j['b1'], j['W2'].flatten(), j['b2']])\n\n # in case initial shapes are not correct\n D, M = j['W1'].shape\n K = len(j['b2'])\n model.D, model.M, model.K = D, M, K\n else:\n # train and save\n model.init()\n params = model.get_params()\n best_params, rewards = evolution_strategy(\n f=reward_function,\n population_size=30,\n sigma=0.1,\n lr=0.03,\n initial_params=params,\n num_iters=300,\n )\n\n # plot the rewards per iteration\n # plt.plot(rewards)\n # plt.show()\n model.set_params(best_params)\n np.savez(\n 'es_flappy_results.npz',\n train=rewards,\n **model.get_params_dict(),\n )\n\n # play 5 test episodes\n env.set_display(True)\n for _ in range(5):\n print(\"Test:\", reward_function(best_params))\n\n" }, { "path": "rl3/es_mnist.py", "content": "# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom datetime import datetime\n\nimport multiprocessing\nfrom multiprocessing.dummy import Pool\n\n\n# thread pool for parallelization\npool = Pool(4)\n\n# get the data from: https://www.kaggle.com/c/digit-recognizer\n# although you can feel free to use any dataset\ndf = pd.read_csv('../large_files/train.csv')\n\n# convert to numpy\ndata = df.values.astype(np.float32)\n\n# randomize and split the data\nnp.random.shuffle(data)\n\nX = data[:, 1:] / 255.\nY = data[:, 0].astype(np.int32)\n\nXtrain = X[:-1000]\nYtrain = Y[:-1000]\nXtest = X[-1000:]\nYtest = Y[-1000:]\nprint(\"Finished loading in and splitting data\")\n\n# layer sizes\nD = Xtrain.shape[1]\nM = 100\nK = len(set(Y))\n\n\ndef softmax(a):\n c = np.max(a, axis=1, keepdims=True)\n e = np.exp(a - c)\n return e / e.sum(axis=-1, keepdims=True)\n\n\ndef relu(x):\n return x * (x > 0)\n\n\ndef log_likelihood(Y, P):\n # assume Y is not one-hot encoded\n N = len(Y)\n return np.log(P[np.arange(N), Y]).mean()\n\n\nclass ANN:\n def __init__(self, D, M, K):\n self.D = D\n self.M = M\n self.K = K\n\n def init(self):\n D, M, K = self.D, self.M, self.K\n self.W1 = np.random.randn(D, M) / np.sqrt(D)\n self.b1 = np.zeros(M)\n self.W2 = np.random.randn(M, K) / np.sqrt(M)\n self.b2 = np.zeros(K)\n\n def forward(self, X):\n Z = np.tanh(X.dot(self.W1) + self.b1)\n return softmax(Z.dot(self.W2) + self.b2)\n\n def score(self, X, Y):\n P = np.argmax(self.forward(X), axis=1)\n return np.mean(Y == P)\n\n def get_params(self):\n # return a flat array of parameters\n return np.concatenate([self.W1.flatten(), self.b1, self.W2.flatten(), self.b2])\n\n def set_params(self, params):\n # params is a flat list\n # unflatten into individual weights\n D, M, K = self.D, self.M, self.K\n self.W1 = params[:D * M].reshape(D, M)\n self.b1 = params[D * M:D * M + M]\n self.W2 = params[D * M + M:D * M + M + M * K].reshape(M, K)\n self.b2 = params[-K:]\n\n\ndef evolution_strategy(\n f,\n population_size,\n sigma,\n lr,\n initial_params,\n num_iters):\n\n # assume initial params is a 1-D array\n num_params = len(initial_params)\n reward_per_iteration = np.zeros(num_iters)\n\n params = initial_params\n for t in range(num_iters):\n t0 = datetime.now()\n N = np.random.randn(population_size, num_params)\n\n # ### slow way\n # R = np.zeros(population_size) # stores the reward\n\n # # loop through each \"offspring\"\n # for j in range(population_size):\n # params_try = params + sigma*N[j]\n # R[j] = f(params_try)\n\n ### fast way\n R = pool.map(f, [params + sigma*N[j] for j in range(population_size)])\n R = np.array(R)\n\n m = R.mean()\n A = (R - m) / R.std()\n reward_per_iteration[t] = m\n params = params + lr/(population_size*sigma) * np.dot(N.T, A)\n print(\"Iter:\", t, \"Avg Reward:\", m, \"Duration:\", (datetime.now() - t0))\n\n return params, reward_per_iteration\n\n\ndef reward_function(params):\n model = ANN(D, M, K)\n model.set_params(params)\n # Ptrain = model.forward(Xtrain)\n # return log_likelihood(Ytrain, Ptrain)\n return model.score(Xtrain, Ytrain)\n\n\n\nif __name__ == '__main__':\n model = ANN(D, M, K)\n model.init()\n params = model.get_params()\n best_params, rewards = evolution_strategy(\n f=reward_function,\n population_size=50,\n sigma=0.1,\n lr=0.2,\n initial_params=params,\n num_iters=600,\n )\n\n # plot the rewards per iteration\n plt.plot(rewards)\n plt.show()\n\n # final train and test accuracy\n model.set_params(best_params)\n print(\"Train score:\", model.score(Xtrain, Ytrain))\n print(\"Test score:\", model.score(Xtest, Ytest))\n\n" }, { "path": "rl3/es_mujoco.py", "content": "# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom datetime import datetime\n\nimport multiprocessing\nfrom multiprocessing.dummy import Pool\n\nimport gym\nimport sys\n\ngym_minor_version = int(gym.__version__.split('.')[1])\nif gym_minor_version >= 19:\n exit(\"Please install OpenAI Gym 0.19.0 or earlier\")\n\n\n\n# environment\nENV_NAME = 'HalfCheetah-v2'\n\n\n# thread pool for parallelization\npool = Pool(4)\n\n\n### neural network\n\n# hyperparameters\nenv = gym.make(ENV_NAME)\nD = len(env.reset())\nM = 300\nK = env.action_space.shape[0]\naction_max = env.action_space.high[0]\n\n\ndef relu(x):\n return x * (x > 0)\n\n# def output_activation(x):\n# return action_max * np.tanh(x)\n\nclass ANN:\n def __init__(self, D, M, K, f=relu):\n self.D = D\n self.M = M\n self.K = K\n self.f = f\n\n def init(self):\n D, M, K = self.D, self.M, self.K\n self.W1 = np.random.randn(D, M) / np.sqrt(D)\n # self.W1 = np.zeros((D, M))\n self.b1 = np.zeros(M)\n self.W2 = np.random.randn(M, K) / np.sqrt(M)\n # self.W2 = np.zeros((M, K))\n self.b2 = np.zeros(K)\n\n def forward(self, X):\n Z = self.f(X.dot(self.W1) + self.b1)\n return np.tanh(Z.dot(self.W2) + self.b2) * action_max\n\n def sample_action(self, x):\n # assume input is a single state of size (D,)\n # first make it (N, D) to fit ML conventions\n X = np.atleast_2d(x)\n Y = self.forward(X)\n return Y[0] # the first row\n\n def get_params(self):\n # return a flat array of parameters\n return np.concatenate([self.W1.flatten(), self.b1, self.W2.flatten(), self.b2])\n\n def get_params_dict(self):\n return {\n 'W1': self.W1,\n 'b1': self.b1,\n 'W2': self.W2,\n 'b2': self.b2,\n }\n\n def set_params(self, params):\n # params is a flat list\n # unflatten into individual weights\n D, M, K = self.D, self.M, self.K\n self.W1 = params[:D * M].reshape(D, M)\n self.b1 = params[D * M:D * M + M]\n self.W2 = params[D * M + M:D * M + M + M * K].reshape(M, K)\n self.b2 = params[-K:]\n\n\ndef evolution_strategy(\n f,\n population_size,\n sigma,\n lr,\n initial_params,\n num_iters):\n\n # assume initial params is a 1-D array\n num_params = len(initial_params)\n reward_per_iteration = np.zeros(num_iters)\n\n params = initial_params\n for t in range(num_iters):\n t0 = datetime.now()\n N = np.random.randn(population_size, num_params)\n\n # ### slow way\n # R = np.zeros(population_size) # stores the reward\n\n # # loop through each \"offspring\"\n # for j in range(population_size):\n # params_try = params + sigma*N[j]\n # R[j] = f(params_try)\n\n ### fast way\n R = pool.map(f, [params + sigma*N[j] for j in range(population_size)])\n R = np.array(R)\n\n m = R.mean()\n s = R.std()\n if s == 0:\n # we can't apply the following equation\n print(\"Skipping\")\n continue\n\n A = (R - m) / s\n reward_per_iteration[t] = m\n params = params + lr/(population_size*sigma) * np.dot(N.T, A)\n\n # update the learning rate\n # lr *= 0.992354\n # sigma *= 0.99\n\n print(\"Iter:\", t, \"Avg Reward: %.3f\" % m, \"Max:\", R.max(), \"Duration:\", (datetime.now() - t0))\n\n return params, reward_per_iteration\n\n\ndef reward_function(params, display=False):\n model = ANN(D, M, K)\n model.set_params(params)\n\n env = gym.make(ENV_NAME)\n if display:\n env = gym.wrappers.Monitor(env, 'es_monitor')\n \n # play one episode and return the total reward\n episode_reward = 0\n episode_length = 0 # not sure if it will be used\n done = False\n state = env.reset()\n while not done:\n # display the env\n if display:\n env.render()\n\n # get the action\n action = model.sample_action(state)\n\n # perform the action\n state, reward, done, _ = env.step(action)\n\n # update total reward\n episode_reward += reward\n episode_length += 1\n\n return episode_reward\n\n\nif __name__ == '__main__':\n model = ANN(D, M, K)\n\n if len(sys.argv) > 1 and sys.argv[1] == 'play':\n # play with a saved model\n j = np.load('es_mujoco_results.npz')\n best_params = np.concatenate([j['W1'].flatten(), j['b1'], j['W2'].flatten(), j['b2']])\n\n # in case initial shapes are not correct\n D, M = j['W1'].shape\n K = len(j['b2'])\n model.D, model.M, model.K = D, M, K\n else:\n # train and save\n model.init()\n params = model.get_params()\n best_params, rewards = evolution_strategy(\n f=reward_function,\n population_size=30,\n sigma=0.1,\n lr=0.03,\n initial_params=params,\n num_iters=300,\n )\n\n # plot the rewards per iteration\n # plt.plot(rewards)\n # plt.show()\n model.set_params(best_params)\n np.savez(\n 'es_mujoco_results.npz',\n train=rewards,\n **model.get_params_dict(),\n )\n\n # play test episode\n print(\"Test:\", reward_function(best_params, display=True))\n\n" }, { "path": "rl3/es_simple.py", "content": "# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef evolution_strategy(\n f,\n population_size,\n sigma,\n lr,\n initial_params,\n num_iters):\n\n # assume initial params is a 1-D array\n num_params = len(initial_params)\n reward_per_iteration = np.zeros(num_iters)\n\n params = initial_params\n for t in range(num_iters):\n N = np.random.randn(population_size, num_params)\n R = np.zeros(population_size) # stores the reward\n\n # loop through each \"offspring\"\n for j in range(population_size):\n params_try = params + sigma*N[j]\n R[j] = f(params_try)\n\n m = R.mean()\n A = (R - m) / R.std()\n reward_per_iteration[t] = m\n params = params + lr/(population_size*sigma) * np.dot(N.T, A)\n\n return params, reward_per_iteration\n\n\ndef reward_function(params):\n x0 = params[0]\n x1 = params[1]\n x2 = params[2]\n return -(x0**2 + 0.1*(x1 - 1)**2 + 0.5*(x2 + 2)**2)\n\n\nif __name__ == '__main__':\n best_params, rewards = evolution_strategy(\n f=reward_function,\n population_size=50,\n sigma=0.1,\n lr=1e-3,\n initial_params=np.random.randn(3),\n num_iters=500,\n )\n\n # plot the rewards per iteration\n plt.plot(rewards)\n plt.show()\n\n # final params\n print(\"Final params:\", best_params)\n\n" }, { "path": "rl3/extra_reading.txt", "content": "How do I sample from a discrete (categorical) distribution in log space?\nhttps://stats.stackexchange.com/questions/64081/how-do-i-sample-from-a-discrete-categorical-distribution-in-log-space\n\nA2C (Advantage Actor-Critic)\nhttps://openai.com/blog/baselines-acktr-a2c/\n\nDDPG (Deep Deterministic Policy Gradient)\n\"Continuous control with deep reinforcement learning\"\nhttps://arxiv.org/abs/1509.02971\n\nDeterministic Policy Gradient Algorithms\nhttp://proceedings.mlr.press/v32/silver14.pdf\n\nES (Evolution Strategies)\n\"Evolution Strategies as a Scalable Alternative to Reinforcement Learning\"\nhttps://arxiv.org/abs/1703.03864\n\nTrust Region Evolution Strategies\nhttps://www.microsoft.com/en-us/research/uploads/prod/2018/11/trust-region-evolution-strategies.pdf\n\nAddressing Function Approximation Error in Actor-Critic Methods\nhttps://arxiv.org/abs/1802.09477" }, { "path": "rl3/flappy2envs.py", "content": "# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom datetime import datetime\n\nfrom ple import PLE\nfrom ple.games.flappybird import FlappyBird\n\nimport sys\n\nfrom threading import Thread\n\n\n\nHISTORY_LENGTH = 1\n\n\nclass Env:\n def __init__(self):\n self.game = FlappyBird(pipe_gap=125)\n self.env = PLE(self.game, fps=30, display_screen=True)\n self.env.init()\n self.env.getGameState = self.game.getGameState # maybe not necessary\n\n # by convention we want to use (0,1)\n # but the game uses (None, 119)\n self.action_map = self.env.getActionSet() #[None, 119]\n\n def step(self, action):\n action = self.action_map[action]\n reward = self.env.act(action)\n done = self.env.game_over()\n obs = self.get_observation()\n # don't bother returning an info dictionary like gym\n return obs, reward, done\n\n def reset(self):\n self.env.reset_game()\n return self.get_observation()\n\n def get_observation(self):\n # game state returns a dictionary which describes\n # the meaning of each value\n # we only want the values\n obs = self.env.getGameState()\n return np.array(list(obs.values()))\n\n def set_display(self, boolean_value):\n self.env.display_screen = boolean_value\n\n\n# make a global environment to be used throughout the script\nenv = Env()\n\n\n### neural network\n\n# hyperparameters\nD = len(env.reset())*HISTORY_LENGTH\nM = 50\nK = 2\n\ndef softmax(a):\n c = np.max(a, axis=1, keepdims=True)\n e = np.exp(a - c)\n return e / e.sum(axis=-1, keepdims=True)\n\ndef relu(x):\n return x * (x > 0)\n\nclass ANN:\n def __init__(self, D, M, K, f=relu):\n self.D = D\n self.M = M\n self.K = K\n self.f = f\n\n def init(self):\n D, M, K = self.D, self.M, self.K\n self.W1 = np.random.randn(D, M) / np.sqrt(D)\n # self.W1 = np.zeros((D, M))\n self.b1 = np.zeros(M)\n self.W2 = np.random.randn(M, K) / np.sqrt(M)\n # self.W2 = np.zeros((M, K))\n self.b2 = np.zeros(K)\n\n def forward(self, X):\n Z = self.f(X.dot(self.W1) + self.b1)\n return softmax(Z.dot(self.W2) + self.b2)\n\n def sample_action(self, x):\n # assume input is a single state of size (D,)\n # first make it (N, D) to fit ML conventions\n X = np.atleast_2d(x)\n P = self.forward(X)\n p = P[0] # the first row\n # return np.random.choice(len(p), p=p)\n return np.argmax(p)\n\n def score(self, X, Y):\n P = np.argmax(self.forward(X), axis=1)\n return np.mean(Y == P)\n\n def get_params(self):\n # return a flat array of parameters\n return np.concatenate([self.W1.flatten(), self.b1, self.W2.flatten(), self.b2])\n\n def get_params_dict(self):\n return {\n 'W1': self.W1,\n 'b1': self.b1,\n 'W2': self.W2,\n 'b2': self.b2,\n }\n\n def set_params(self, params):\n # params is a flat list\n # unflatten into individual weights\n D, M, K = self.D, self.M, self.K\n self.W1 = params[:D * M].reshape(D, M)\n self.b1 = params[D * M:D * M + M]\n self.W2 = params[D * M + M:D * M + M + M * K].reshape(M, K)\n self.b2 = params[-K:]\n\n\n\nenv1, env2 = Env(), Env()\n\n\n\n\ndef reward_function(params, env):\n model = ANN(D, M, K)\n model.set_params(params)\n \n # play one episode and return the total reward\n episode_reward = 0\n episode_length = 0 # not sure if it will be used\n done = False\n obs = env.reset()\n obs_dim = len(obs)\n if HISTORY_LENGTH > 1:\n state = np.zeros(HISTORY_LENGTH*obs_dim) # current state\n state[obs_dim:] = obs\n else:\n state = obs\n while not done:\n # get the action\n action = model.sample_action(state)\n\n # perform the action\n obs, reward, done = env.step(action)\n\n # update total reward\n episode_reward += reward\n episode_length += 1\n\n # update state\n if HISTORY_LENGTH > 1:\n state = np.roll(state, -obs_dim)\n state[-obs_dim:] = obs\n else:\n state = obs\n print(\"Reward:\", episode_reward)\n\n\nif __name__ == '__main__':\n\n j = np.load('es_flappy_results.npz')\n best_params = np.concatenate([j['W1'].flatten(), j['b1'], j['W2'].flatten(), j['b2']])\n\n # in case D isn't correct\n D, M = j['W1'].shape\n K = len(j['b2'])\n \n t1 = Thread(target=reward_function, args=(best_params, env1))\n t2 = Thread(target=reward_function, args=(best_params, env2))\n t1.start()\n t2.start()\n t1.join()\n t2.join()\n\n\n" }, { "path": "rl3/gym_review.py", "content": "# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence\nimport gym\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ngym_minor_version = int(gym.__version__.split('.')[1])\nif gym_minor_version >= 19:\n exit(\"Please install OpenAI Gym 0.19.0 or earlier\")\n\n\n\ndef get_action(s, w):\n return 1 if s.dot(w) > 0 else 0\n\n\ndef play_one_episode(env, params):\n observation = env.reset()\n done = False\n t = 0\n r = 0\n\n while not done and t < 10000:\n t += 1\n action = get_action(observation, params)\n observation, reward, done, info = env.step(action)\n r += reward\n\n return r\n\n\ndef play_multiple_episodes(env, T, params):\n episode_rewards = np.empty(T)\n\n for i in range(T):\n episode_rewards[i] = play_one_episode(env, params)\n\n avg_reward = episode_rewards.mean()\n print(\"avg reward:\", avg_reward)\n return avg_reward\n\n\ndef random_search(env):\n episode_rewards = []\n best = 0\n params = None\n for t in range(100):\n new_params = np.random.random(4)*2 - 1\n avg_reward = play_multiple_episodes(env, 100, new_params)\n episode_rewards.append(avg_reward)\n\n if avg_reward > best:\n params = new_params\n best = avg_reward\n return episode_rewards, params\n\n\nif __name__ == '__main__':\n env = gym.make('CartPole-v0')\n episode_rewards, params = random_search(env)\n plt.plot(episode_rewards)\n plt.show()\n\n # play a final set of episodes\n print(\"***Final run with final weights***\")\n play_multiple_episodes(env, 100, params)\n" }, { "path": "rl3/plot_ddpg_result.py", "content": "# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence\nimport json\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ndef smooth(x):\n # last 100\n n = len(x)\n y = np.zeros(n)\n for i in range(n):\n start = max(0, i - 99)\n y[i] = float(x[start:(i+1)].sum()) / (i - start + 1)\n return y\n\nj = np.load('ddpg_results.npz')\n\nreturns = j['train']\ntest_returns = j['test']\nq_losses = j['q_losses']\nmu_losses = j['mu_losses']\n\nplt.plot(returns)\nplt.plot(smooth(np.array(returns)))\nplt.title(\"Train returns\")\nplt.show()\n\nplt.plot(test_returns)\nplt.plot(smooth(np.array(test_returns)))\nplt.title(\"Test returns\")\nplt.show()\n\nplt.plot(q_losses)\nplt.title('q_losses')\nplt.show()\n\nplt.plot(mu_losses)\nplt.title('mu_losses')\nplt.show()" }, { "path": "rl3/plot_es_flappy_results.py", "content": "# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ndef smooth(x):\n # last 100\n n = len(x)\n y = np.zeros(n)\n for i in range(n):\n start = max(0, i - 99)\n y[i] = float(x[start:(i+1)].sum()) / (i - start + 1)\n return y\n\nj = np.load('es_flappy_results.npz')\n\nreturns = j['train']\n\nplt.plot(returns)\nplt.plot(smooth(np.array(returns)))\nplt.title(\"Train returns\")\nplt.show()" }, { "path": "rl3/plot_es_mujoco_results.py", "content": "# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ndef smooth(x):\n # last 100\n n = len(x)\n y = np.zeros(n)\n for i in range(n):\n start = max(0, i - 99)\n y[i] = float(x[start:(i+1)].sum()) / (i - start + 1)\n return y\n\nj = np.load('es_mujoco_results.npz')\n\nreturns = j['train']\n\nplt.plot(returns)\nplt.plot(smooth(np.array(returns)))\nplt.title(\"Train returns\")\nplt.show()" }, { "path": "rl3/sample_test.py", "content": "# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nlogits = np.log([0.1, 0.2, 0.3, 0.4])\n\nsamples = []\n\nfor _ in range(10000):\n noise = np.random.random(len(logits))\n sample = np.argmax(logits - np.log(-np.log(noise)))\n samples.append(sample)\n\n\nplt.hist(samples)\nplt.show()" }, { "path": "rl3v2/extra_reading.txt", "content": "=== PART 1 ===\n\nES (Evolution Strategies)\n\"Evolution Strategies as a Scalable Alternative to Reinforcement Learning\"\nhttps://arxiv.org/abs/1703.03864\n\nTrust Region Evolution Strategies\nhttps://www.microsoft.com/en-us/research/uploads/prod/2018/11/trust-region-evolution-strategies.pdf\n\nThe CMA Evolution Strategy: A Tutorial\nhttps://arxiv.org/pdf/1604.00772\n\nSimple random search provides a competitive approach to reinforcement learning (Augmented Random Search)\nhttps://arxiv.org/abs/1803.07055\n\n=== PART 2 ===\n\nDDPG (Deep Deterministic Policy Gradient)\n\"Continuous control with deep reinforcement learning\"\nhttps://arxiv.org/abs/1509.02971\n\nDeterministic Policy Gradient Algorithms\nhttp://proceedings.mlr.press/v32/silver14.pdf\n\nAddressing Function Approximation Error in Actor-Critic Methods\nhttps://arxiv.org/abs/1802.09477" }, { "path": "rl3v2/visualize_es.py", "content": "import numpy as np\nimport matplotlib.pyplot as plt\n\n# Objective function to minimize (you can change this)\ndef f(x, y):\n # return np.sin(x) + np.cos(y)\n return -((x - 1)**2 + y**2)\n\n# Evolution Strategies optimizer (simple version)\ndef evolution_strategies(\n f, bounds, pop_size=50, sigma=0.3, alpha=0.03, iterations=100\n):\n dim = 2\n mu = np.random.uniform(bounds[0], bounds[1], size=dim)\n \n history = []\n\n for gen in range(iterations):\n # Sample noise\n noise = np.random.randn(pop_size, dim)\n population = mu + sigma * noise\n fitness = np.array([f(x[0], x[1]) for x in population])\n\n history.append((population.copy(), mu.copy()))\n\n # Normalize fitness for weighting\n fitness_norm = (fitness - np.mean(fitness)) / (np.std(fitness) + 1e-8)\n mu += alpha / (pop_size * sigma) * np.dot(noise.T, fitness_norm)\n \n return history\n\n# Visualization function\ndef visualize_es(history, bounds, f, resolution=100):\n x = np.linspace(bounds[0], bounds[1], resolution)\n y = np.linspace(bounds[0], bounds[1], resolution)\n X, Y = np.meshgrid(x, y)\n Z = f(X, Y)\n\n plt.figure(figsize=(8, 6))\n for i, (pop, mu) in enumerate(history):\n plt.clf()\n plt.contourf(X, Y, Z, levels=50, cmap='viridis')\n plt.colorbar(label=\"f(x, y)\")\n plt.scatter(pop[:, 0], pop[:, 1], c='white', s=20, label='Population')\n plt.scatter(mu[0], mu[1], c='red', s=80, label='Mean', edgecolors='black')\n plt.title(f\"Evolution Strategies - Step {i+1}\")\n plt.xlim(bounds[0], bounds[1])\n plt.ylim(bounds[0], bounds[1])\n plt.xlabel('x')\n plt.ylabel('y')\n plt.legend()\n # plt.pause(0.1)\n plt.waitforbuttonpress()\n plt.show()\n\n# Run\nbounds = (-5, 5)\nhistory = evolution_strategies(f, bounds, iterations=80)\nvisualize_es(history, bounds, f)\n" }, { "path": "rl3v2/visualize_hill_climbing.py", "content": "import numpy as np\nimport matplotlib.pyplot as plt\n\n# Objective function to minimize (you can change this)\ndef f(x, y):\n # return np.sin(x) + np.cos(y)\n return -((x - 1)**2 + y**2)\n\n# Evolution Strategies optimizer (simple version)\ndef hill_climb(\n f, bounds, pop_size=1, sigma=0.3, alpha=0.3, iterations=100\n):\n dim = 2\n mu = np.random.uniform(bounds[0], bounds[1], size=dim)\n \n history = []\n best_f = f(mu)\n\n for gen in range(iterations):\n # Sample noise\n noise = np.random.randn(pop_size, dim)\n population = mu + sigma * noise\n fitness = np.array([f(x[0], x[1]) for x in population])\n\n history.append((population.copy(), mu.copy()))\n\n # Update point if it's better\n if fitness[0] > best_f:\n best_f = fitness[0]\n mu = population.flatten()\n \n return history\n\n# Visualization function\ndef visualize_es(history, bounds, f, resolution=100):\n x = np.linspace(bounds[0], bounds[1], resolution)\n y = np.linspace(bounds[0], bounds[1], resolution)\n X, Y = np.meshgrid(x, y)\n Z = f(X, Y)\n\n plt.figure(figsize=(8, 6))\n for i, (pop, mu) in enumerate(history):\n plt.clf()\n plt.contourf(X, Y, Z, levels=50, cmap='viridis')\n plt.colorbar(label=\"f(x, y)\")\n plt.scatter(pop[:, 0], pop[:, 1], c='white', s=20, label='Population')\n plt.scatter(mu[0], mu[1], c='red', s=80, label='Mean', edgecolors='black')\n plt.title(f\"Hill Climbing - Step {i+1}\")\n plt.xlim(bounds[0], bounds[1])\n plt.ylim(bounds[0], bounds[1])\n plt.xlabel('x')\n plt.ylabel('y')\n plt.legend()\n # plt.pause(0.1)\n plt.waitforbuttonpress()\n plt.show()\n\n# Run\nbounds = (-5, 5)\nhistory = hill_climb(f, bounds, iterations=80)\nvisualize_es(history, bounds, f)\n" }, { "path": "rnn_class/WHERE ARE THE NOTEBOOKS.txt", "content": "As stated in the \"where to get the code\" / \"where to get the notebooks\" lecture, the notebooks are NOT on Github.\n\nIf you missed this, please review the lecture for the actual location of the notebooks.\n\nIf, after reviewing it, you still need assistance, please contact info@deeplearningcourses.com." }, { "path": "rnn_class/__init__.py", "content": "" }, { "path": "rnn_class/batch_gru.py", "content": "# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python\n# https://udemy.com/deep-learning-recurrent-neural-networks-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport theano\nimport theano.tensor as T\n\nfrom util import init_weight\n\n\nclass GRU:\n def __init__(self, Mi, Mo, activation):\n self.Mi = Mi\n self.Mo = Mo\n self.f = activation\n\n # numpy init\n Wxr = init_weight(Mi, Mo)\n Whr = init_weight(Mo, Mo)\n br = np.zeros(Mo)\n Wxz = init_weight(Mi, Mo)\n Whz = init_weight(Mo, Mo)\n bz = np.zeros(Mo)\n Wxh = init_weight(Mi, Mo)\n Whh = init_weight(Mo, Mo)\n bh = np.zeros(Mo)\n h0 = np.zeros(Mo)\n\n # theano vars\n self.Wxr = theano.shared(Wxr)\n self.Whr = theano.shared(Whr)\n self.br = theano.shared(br)\n self.Wxz = theano.shared(Wxz)\n self.Whz = theano.shared(Whz)\n self.bz = theano.shared(bz)\n self.Wxh = theano.shared(Wxh)\n self.Whh = theano.shared(Whh)\n self.bh = theano.shared(bh)\n self.h0 = theano.shared(h0)\n self.params = [self.Wxr, self.Whr, self.br, self.Wxz, self.Whz, self.bz, self.Wxh, self.Whh, self.bh, self.h0]\n\n def get_ht(self, xWxr_t, xWxz_t, xWxh_t, h_t1):\n r = T.nnet.sigmoid(xWxr_t + h_t1.dot(self.Whr) + self.br)\n z = T.nnet.sigmoid(xWxz_t + h_t1.dot(self.Whz) + self.bz)\n hhat = self.f(xWxh_t + (r * h_t1).dot(self.Whh) + self.bh)\n h = (1 - z) * h_t1 + z * hhat\n return h\n\n def recurrence(self, xWxr_t, xWxz_t, xWxh_t, is_start, h_t1, h0):\n h_t = T.switch(\n T.eq(is_start, 1),\n self.get_ht(xWxr_t, xWxz_t, xWxh_t, h0),\n self.get_ht(xWxr_t, xWxz_t, xWxh_t, h_t1)\n )\n return h_t\n\n def output(self, Xflat, startPoints):\n # Xflat should be (NT, D)\n # calculate X after multiplying input weights\n XWxr = Xflat.dot(self.Wxr)\n XWxz = Xflat.dot(self.Wxz)\n XWxh = Xflat.dot(self.Wxh)\n\n h, _ = theano.scan(\n fn=self.recurrence,\n sequences=[XWxr, XWxz, XWxh, startPoints],\n outputs_info=[self.h0],\n non_sequences=[self.h0],\n n_steps=Xflat.shape[0],\n )\n return h\n" }, { "path": "rnn_class/batch_parity.py", "content": "# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python\n# https://udemy.com/deep-learning-recurrent-neural-networks-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport theano\nimport theano.tensor as T\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.utils import shuffle\nfrom util import init_weight, all_parity_pairs_with_sequence_labels\n\n\nclass SimpleRNN:\n def __init__(self, M):\n self.M = M # hidden layer size\n\n def fit(self, X, Y, batch_sz=20, learning_rate=1.0, mu=0.99, reg=1.0, activation=T.tanh, epochs=100, show_fig=False):\n D = X[0].shape[1] # X is of size N x T(n) x D\n K = len(set(Y.flatten()))\n N = len(Y)\n M = self.M\n self.f = activation\n\n # initial weights\n Wx = init_weight(D, M)\n Wh = init_weight(M, M)\n bh = np.zeros(M)\n h0 = np.zeros(M)\n Wo = init_weight(M, K)\n bo = np.zeros(K)\n\n # make them theano shared\n self.Wx = theano.shared(Wx)\n self.Wh = theano.shared(Wh)\n self.bh = theano.shared(bh)\n self.h0 = theano.shared(h0)\n self.Wo = theano.shared(Wo)\n self.bo = theano.shared(bo)\n self.params = [self.Wx, self.Wh, self.bh, self.h0, self.Wo, self.bo]\n\n thX = T.fmatrix('X') # will represent multiple batches concatenated\n thY = T.ivector('Y')\n thStartPoints = T.ivector('start_points')\n\n XW = thX.dot(self.Wx)\n\n # startPoints will contain 1 where a sequence starts and 0 otherwise\n # Ex. if I have 3 sequences: [[1,2,3], [4,5], [6,7,8]]\n # Then I will concatenate these into one X: [1,2,3,4,5,6,7,8]\n # And startPoints will be [1,0,0,1,0,1,0,0]\n\n # One possible solution: loop through index\n # def recurrence(t, h_t1, XW, h0, startPoints):\n # # returns h(t)\n\n # # if at a boundary, state should be h0\n # h_t = T.switch(\n # T.eq(startPoints[t], 1),\n # self.f(XW[t] + h0.dot(self.Wh) + self.bh),\n # self.f(XW[t] + h_t1.dot(self.Wh) + self.bh)\n # )\n # return h_t\n\n # h, _ = theano.scan(\n # fn=recurrence,\n # outputs_info=[self.h0],\n # sequences=T.arange(XW.shape[0]),\n # non_sequences=[XW, self.h0, thStartPoints],\n # n_steps=XW.shape[0],\n # )\n\n # other solution - loop through all sequences simultaneously\n def recurrence(xw_t, is_start, h_t1, h0):\n # if at a boundary, state should be h0\n h_t = T.switch(\n T.eq(is_start, 1),\n self.f(xw_t + h0.dot(self.Wh) + self.bh),\n self.f(xw_t + h_t1.dot(self.Wh) + self.bh)\n )\n return h_t\n\n h, _ = theano.scan(\n fn=recurrence,\n outputs_info=[self.h0],\n sequences=[XW, thStartPoints],\n non_sequences=[self.h0],\n n_steps=XW.shape[0],\n )\n\n # h is of shape (T*batch_sz, M)\n py_x = T.nnet.softmax(h.dot(self.Wo) + self.bo)\n prediction = T.argmax(py_x, axis=1)\n\n cost = -T.mean(T.log(py_x[T.arange(thY.shape[0]), thY]))\n grads = T.grad(cost, self.params)\n dparams = [theano.shared(p.get_value()*0) for p in self.params]\n\n updates = [\n (p, p + mu*dp - learning_rate*g) for p, dp, g in zip(self.params, dparams, grads)\n ] + [\n (dp, mu*dp - learning_rate*g) for dp, g in zip(dparams, grads)\n ]\n\n # self.predict_op = theano.function(inputs=[thX, thStartPoints], outputs=prediction)\n self.train_op = theano.function(\n inputs=[thX, thY, thStartPoints],\n outputs=[cost, prediction, py_x],\n updates=updates\n )\n\n costs = []\n n_batches = N // batch_sz\n sequenceLength = X.shape[1]\n\n # if each sequence was of variable length, we would need to\n # initialize this inside the loop for every new batch\n startPoints = np.zeros(sequenceLength*batch_sz, dtype=np.int32)\n for b in range(batch_sz):\n startPoints[b*sequenceLength] = 1\n for i in range(epochs):\n X, Y = shuffle(X, Y)\n n_correct = 0\n cost = 0\n for j in range(n_batches):\n Xbatch = X[j*batch_sz:(j+1)*batch_sz].reshape(sequenceLength*batch_sz, D)\n Ybatch = Y[j*batch_sz:(j+1)*batch_sz].reshape(sequenceLength*batch_sz).astype(np.int32)\n c, p, rout = self.train_op(Xbatch, Ybatch, startPoints)\n # print \"p:\", p\n cost += c\n # P = p.reshape(batch_sz, sequenceLength)\n for b in range(batch_sz):\n idx = sequenceLength*(b + 1) - 1\n if p[idx] == Ybatch[idx]:\n n_correct += 1\n # else:\n # print \"pred:\", p[idx], \"actual:\", Ybatch[idx]\n if i % 10 == 0:\n print(\"shape y:\", rout.shape)\n print(\"i:\", i, \"cost:\", cost, \"classification rate:\", (float(n_correct)/N))\n if n_correct == N:\n print(\"i:\", i, \"cost:\", cost, \"classification rate:\", (float(n_correct)/N))\n break\n costs.append(cost)\n\n if show_fig:\n plt.plot(costs)\n plt.show()\n\n\n\ndef parity(B=12, learning_rate=1e-3, epochs=3000):\n X, Y = all_parity_pairs_with_sequence_labels(B)\n\n rnn = SimpleRNN(4)\n rnn.fit(X, Y,\n batch_sz=10,\n learning_rate=learning_rate,\n epochs=epochs,\n activation=T.nnet.sigmoid,\n show_fig=False\n )\n\n\nif __name__ == '__main__':\n parity()\n\n" }, { "path": "rnn_class/batch_units.py", "content": "# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python\n# https://udemy.com/deep-learning-recurrent-neural-networks-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport theano\nimport theano.tensor as T\n\ndef init_weight(Mi, Mo):\n return np.random.randn(Mi, Mo) * np.sqrt(2.0 / Mi)\n\n\nclass SimpleRecurrentLayer:\n def __init__(self, Mi, Mo, activation):\n self.Mi = Mi\n self.Mo = Mo\n self.f = activation\n\n # numpy init\n Wxh = init_weight(Mi, Mo)\n Whh = init_weight(Mo, Mo)\n b = np.zeros(Mo)\n h0 = np.zeros(Mo)\n\n # theano vars\n self.Wxh = theano.shared(Wxh)\n self.Whh = theano.shared(Whh)\n self.b = theano.shared(b)\n self.h0 = theano.shared(h0)\n self.params = [self.Wxh, self.Whh, self.b, self.h0]\n\n def get_ht(self, xWxh_t, h_t1):\n return self.f(xWxh_t + h_t1.dot(self.Whh) + self.b)\n\n def recurrence(self, xWxh_t, is_start, h_t1, h0):\n h_t = T.switch(\n T.eq(is_start, 1),\n self.get_ht(xWxh_t, h0),\n self.get_ht(xWxh_t, h_t1)\n )\n return h_t\n\n def output(self, Xflat, startPoints):\n # Xflat should be (NT, D)\n # calculate X after multiplying input weights\n XWxh = Xflat.dot(self.Wxh)\n\n h, _ = theano.scan(\n fn=self.recurrence,\n sequences=[XWxh, startPoints],\n outputs_info=[self.h0],\n non_sequences=[self.h0],\n n_steps=Xflat.shape[0],\n )\n return h\n\n\nclass GRU:\n def __init__(self, Mi, Mo, activation):\n self.Mi = Mi\n self.Mo = Mo\n self.f = activation\n\n # numpy init\n Wxr = init_weight(Mi, Mo)\n Whr = init_weight(Mo, Mo)\n br = np.zeros(Mo)\n Wxz = init_weight(Mi, Mo)\n Whz = init_weight(Mo, Mo)\n bz = np.zeros(Mo)\n Wxh = init_weight(Mi, Mo)\n Whh = init_weight(Mo, Mo)\n bh = np.zeros(Mo)\n h0 = np.zeros(Mo)\n\n # theano vars\n self.Wxr = theano.shared(Wxr)\n self.Whr = theano.shared(Whr)\n self.br = theano.shared(br)\n self.Wxz = theano.shared(Wxz)\n self.Whz = theano.shared(Whz)\n self.bz = theano.shared(bz)\n self.Wxh = theano.shared(Wxh)\n self.Whh = theano.shared(Whh)\n self.bh = theano.shared(bh)\n self.h0 = theano.shared(h0)\n self.params = [self.Wxr, self.Whr, self.br, self.Wxz, self.Whz, self.bz, self.Wxh, self.Whh, self.bh, self.h0]\n\n def get_ht(self, xWxr_t, xWxz_t, xWxh_t, h_t1):\n r = T.nnet.sigmoid(xWxr_t + h_t1.dot(self.Whr) + self.br)\n z = T.nnet.sigmoid(xWxz_t + h_t1.dot(self.Whz) + self.bz)\n hhat = self.f(xWxh_t + (r * h_t1).dot(self.Whh) + self.bh)\n h = (1 - z) * h_t1 + z * hhat\n return h\n\n def recurrence(self, xWxr_t, xWxz_t, xWxh_t, is_start, h_t1, h0):\n h_t = T.switch(\n T.eq(is_start, 1),\n self.get_ht(xWxr_t, xWxz_t, xWxh_t, h0),\n self.get_ht(xWxr_t, xWxz_t, xWxh_t, h_t1)\n )\n return h_t\n\n def output(self, Xflat, startPoints):\n # Xflat should be (NT, D)\n # calculate X after multiplying input weights\n XWxr = Xflat.dot(self.Wxr)\n XWxz = Xflat.dot(self.Wxz)\n XWxh = Xflat.dot(self.Wxh)\n\n h, _ = theano.scan(\n fn=self.recurrence,\n sequences=[XWxr, XWxz, XWxh, startPoints],\n outputs_info=[self.h0],\n non_sequences=[self.h0],\n n_steps=Xflat.shape[0],\n )\n return h\n\n\n\nclass LSTM:\n def __init__(self, Mi, Mo, activation):\n self.Mi = Mi\n self.Mo = Mo\n self.f = activation\n\n # numpy init\n Wxi = init_weight(Mi, Mo)\n Whi = init_weight(Mo, Mo)\n Wci = init_weight(Mo, Mo)\n bi = np.zeros(Mo)\n Wxf = init_weight(Mi, Mo)\n Whf = init_weight(Mo, Mo)\n Wcf = init_weight(Mo, Mo)\n bf = np.zeros(Mo)\n Wxc = init_weight(Mi, Mo)\n Whc = init_weight(Mo, Mo)\n bc = np.zeros(Mo)\n Wxo = init_weight(Mi, Mo)\n Who = init_weight(Mo, Mo)\n Wco = init_weight(Mo, Mo)\n bo = np.zeros(Mo)\n c0 = np.zeros(Mo)\n h0 = np.zeros(Mo)\n\n # theano vars\n self.Wxi = theano.shared(Wxi)\n self.Whi = theano.shared(Whi)\n self.Wci = theano.shared(Wci)\n self.bi = theano.shared(bi)\n self.Wxf = theano.shared(Wxf)\n self.Whf = theano.shared(Whf)\n self.Wcf = theano.shared(Wcf)\n self.bf = theano.shared(bf)\n self.Wxc = theano.shared(Wxc)\n self.Whc = theano.shared(Whc)\n self.bc = theano.shared(bc)\n self.Wxo = theano.shared(Wxo)\n self.Who = theano.shared(Who)\n self.Wco = theano.shared(Wco)\n self.bo = theano.shared(bo)\n self.c0 = theano.shared(c0)\n self.h0 = theano.shared(h0)\n self.params = [\n self.Wxi,\n self.Whi,\n self.Wci,\n self.bi,\n self.Wxf,\n self.Whf,\n self.Wcf,\n self.bf,\n self.Wxc,\n self.Whc,\n self.bc,\n self.Wxo,\n self.Who,\n self.Wco,\n self.bo,\n self.c0,\n self.h0,\n ]\n\n def get_ht_ct(self, xWxi_t, xWxf_t, xWxc_t, xWxo_t, h_t1, c_t1):\n i_t = T.nnet.sigmoid(xWxi_t + h_t1.dot(self.Whi) + c_t1.dot(self.Wci) + self.bi)\n f_t = T.nnet.sigmoid(xWxf_t + h_t1.dot(self.Whf) + c_t1.dot(self.Wcf) + self.bf)\n c_t = f_t * c_t1 + i_t * T.tanh(xWxc_t + h_t1.dot(self.Whc) + self.bc)\n o_t = T.nnet.sigmoid(xWxo_t + h_t1.dot(self.Who) + c_t.dot(self.Wco) + self.bo)\n h_t = o_t * T.tanh(c_t)\n return h_t, c_t\n\n def recurrence(self, xWxi_t, xWxf_t, xWxc_t, xWxo_t, is_start, h_t1, c_t1, h0, c0):\n h_t_c_t = T.switch(\n T.eq(is_start, 1),\n self.get_ht_ct(xWxi_t, xWxf_t, xWxc_t, xWxo_t, h0, c0),\n self.get_ht_ct(xWxi_t, xWxf_t, xWxc_t, xWxo_t, h_t1, c_t1)\n )\n return h_t_c_t[0], h_t_c_t[1]\n\n def output(self, Xflat, startPoints):\n # Xflat should be (NT, D)\n # calculate X after multiplying input weights\n XWxi = Xflat.dot(self.Wxi)\n XWxf = Xflat.dot(self.Wxf)\n XWxc = Xflat.dot(self.Wxc)\n XWxo = Xflat.dot(self.Wxo)\n\n [h, c], _ = theano.scan(\n fn=self.recurrence,\n sequences=[XWxi, XWxf, XWxc, XWxo, startPoints],\n outputs_info=[self.h0, self.c0],\n non_sequences=[self.h0, self.c0],\n n_steps=Xflat.shape[0],\n )\n return h\n" }, { "path": "rnn_class/batch_wiki.py", "content": "# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python\n# https://udemy.com/deep-learning-recurrent-neural-networks-in-python\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport os\nimport sys\nimport theano\nimport theano.tensor as T\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport json\n\nfrom datetime import datetime\nfrom sklearn.utils import shuffle\nfrom batch_units import GRU, LSTM\nfrom util import init_weight, get_wikipedia_data\nfrom brown import get_sentences_with_word2idx_limit_vocab\n\n\nclass RNN:\n def __init__(self, D, hidden_layer_sizes, V):\n self.hidden_layer_sizes = hidden_layer_sizes\n self.D = D\n self.V = V\n\n def fit(self, X, learning_rate=1e-4, mu=0.99, epochs=10, batch_sz=100, show_fig=True, activation=T.nnet.relu, RecurrentUnit=LSTM):\n D = self.D\n V = self.V\n N = len(X)\n\n We = init_weight(V, D)\n self.hidden_layers = []\n Mi = D\n for Mo in self.hidden_layer_sizes:\n ru = RecurrentUnit(Mi, Mo, activation)\n self.hidden_layers.append(ru)\n Mi = Mo\n\n Wo = init_weight(Mi, V)\n bo = np.zeros(V)\n\n self.We = theano.shared(We)\n self.Wo = theano.shared(Wo)\n self.bo = theano.shared(bo)\n self.params = [self.We, self.Wo, self.bo]\n for ru in self.hidden_layers:\n self.params += ru.params\n\n thX = T.ivector('X') # will represent multiple batches concatenated\n thY = T.ivector('Y') # represents next word\n thStartPoints = T.ivector('start_points')\n\n Z = self.We[thX]\n for ru in self.hidden_layers:\n Z = ru.output(Z, thStartPoints)\n py_x = T.nnet.softmax(Z.dot(self.Wo) + self.bo)\n prediction = T.argmax(py_x, axis=1)\n\n cost = -T.mean(T.log(py_x[T.arange(thY.shape[0]), thY]))\n grads = T.grad(cost, self.params)\n dparams = [theano.shared(p.get_value()*0) for p in self.params]\n\n updates = [\n (p, p + mu*dp - learning_rate*g) for p, dp, g in zip(self.params, dparams, grads)\n ] + [\n (dp, mu*dp - learning_rate*g) for dp, g in zip(dparams, grads)\n ]\n\n # self.predict_op = theano.function(inputs=[thX, thStartPoints], outputs=prediction)\n self.train_op = theano.function(\n inputs=[thX, thY, thStartPoints],\n outputs=[cost, prediction],\n updates=updates\n )\n\n costs = []\n n_batches = N // batch_sz\n for i in range(epochs):\n t0 = datetime.now()\n X = shuffle(X)\n n_correct = 0\n n_total = 0\n cost = 0\n\n for j in range(n_batches):\n # construct input sequence and output sequence as\n # concatenatation of multiple input sequences and output sequences\n # input X should be a list of 2-D arrays or one 3-D array\n # N x T(n) x D - batch size x sequence length x num features\n # sequence length can be variable\n sequenceLengths = []\n input_sequence = []\n output_sequence = []\n for k in range(j*batch_sz, (j+1)*batch_sz):\n # don't always add the end token\n if np.random.random() < 0.01 or len(X[k]) <= 1:\n input_sequence += [0] + X[k]\n output_sequence += X[k] + [1]\n sequenceLengths.append(len(X[k]) + 1)\n else:\n input_sequence += [0] + X[k][:-1]\n output_sequence += X[k]\n sequenceLengths.append(len(X[k]))\n n_total += len(output_sequence)\n\n startPoints = np.zeros(len(output_sequence), dtype=np.int32)\n last = 0\n for length in sequenceLengths:\n startPoints[last] = 1\n last += length\n\n c, p = self.train_op(input_sequence, output_sequence, startPoints)\n cost += c\n for pj, xj in zip(p, output_sequence):\n if pj == xj:\n n_correct += 1\n if j % 1 == 0:\n sys.stdout.write(\"j/n_batches: %d/%d correct rate so far: %f\\r\" % (j, n_batches, float(n_correct)/n_total))\n sys.stdout.flush()\n print(\"i:\", i, \"cost:\", cost, \"correct rate:\", (float(n_correct)/n_total), \"time for epoch:\", (datetime.now() - t0))\n costs.append(cost)\n\n if show_fig:\n plt.plot(costs)\n plt.show()\n\n\n\ndef train_wikipedia(we_file='word_embeddings.npy', w2i_file='wikipedia_word2idx.json', RecurrentUnit=GRU):\n # there are 32 files\n ### note: you can pick between Wikipedia data and Brown corpus\n ### just comment one out, and uncomment the other!\n # sentences, word2idx = get_wikipedia_data(n_files=100, n_vocab=2000)\n sentences, word2idx = get_sentences_with_word2idx_limit_vocab()\n print(\"finished retrieving data\")\n print(\"vocab size:\", len(word2idx), \"number of sentences:\", len(sentences))\n rnn = RNN(30, [30], len(word2idx))\n rnn.fit(sentences, learning_rate=2*1e-4, epochs=10, show_fig=True, activation=T.nnet.relu)\n\n np.save(we_file, rnn.We.get_value())\n with open(w2i_file, 'w') as f:\n json.dump(word2idx, f)\n\n\n\ndef find_analogies(w1, w2, w3, we_file='word_embeddings.npy', w2i_file='wikipedia_word2idx.json'):\n We = np.load(we_file)\n with open(w2i_file) as f:\n word2idx = json.load(f)\n\n king = We[word2idx[w1]]\n man = We[word2idx[w2]]\n woman = We[word2idx[w3]]\n v0 = king - man + woman\n\n def dist1(a, b):\n return np.linalg.norm(a - b)\n def dist2(a, b):\n return 1 - a.dot(b) / (np.linalg.norm(a) * np.linalg.norm(b))\n\n for dist, name in [(dist1, 'Euclidean'), (dist2, 'cosine')]:\n min_dist = float('inf')\n best_word = ''\n for word, idx in iteritems(word2idx):\n if word not in (w1, w2, w3):\n v1 = We[idx]\n d = dist(v0, v1)\n if d < min_dist:\n min_dist = d\n best_word = word\n print(\"closest match by\", name, \"distance:\", best_word)\n print(w1, \"-\", w2, \"=\", best_word, \"-\", w3)\n\n\n\nif __name__ == '__main__':\n if not os.path.exists('working_files'):\n os.mkdir('working_files')\n we = 'working_files/batch_gru_word_embeddings.npy'\n w2i = 'working_files/batch_wikipedia_word2idx.json'\n train_wikipedia(we, w2i, RecurrentUnit=LSTM)\n find_analogies('king', 'man', 'woman', we, w2i)\n find_analogies('france', 'paris', 'london', we, w2i)\n find_analogies('france', 'paris', 'rome', we, w2i)\n find_analogies('paris', 'france', 'italy', we, w2i)\n\n" }, { "path": "rnn_class/brown.py", "content": "# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python\n# https://udemy.com/deep-learning-recurrent-neural-networks-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nfrom nltk.corpus import brown\nimport operator\n\nKEEP_WORDS = set([\n 'king', 'man', 'queen', 'woman',\n 'italy', 'rome', 'france', 'paris',\n 'london', 'britain', 'england',\n])\n\n\ndef get_sentences():\n # returns 57340 of the Brown corpus\n # each sentence is represented as a list of individual string tokens\n return brown.sents()\n\n\ndef get_sentences_with_word2idx():\n sentences = get_sentences()\n indexed_sentences = []\n\n i = 2\n word2idx = {'START': 0, 'END': 1}\n for sentence in sentences:\n indexed_sentence = []\n for token in sentence:\n token = token.lower()\n if token not in word2idx:\n word2idx[token] = i\n i += 1\n\n indexed_sentence.append(word2idx[token])\n indexed_sentences.append(indexed_sentence)\n\n print(\"Vocab size:\", i)\n return indexed_sentences, word2idx\n\n\ndef get_sentences_with_word2idx_limit_vocab(n_vocab=2000, keep_words=KEEP_WORDS):\n sentences = get_sentences()\n indexed_sentences = []\n\n i = 2\n word2idx = {'START': 0, 'END': 1}\n idx2word = ['START', 'END']\n\n word_idx_count = {\n 0: float('inf'),\n 1: float('inf'),\n }\n\n for sentence in sentences:\n indexed_sentence = []\n for token in sentence:\n token = token.lower()\n if token not in word2idx:\n idx2word.append(token)\n word2idx[token] = i\n i += 1\n\n # keep track of counts for later sorting\n idx = word2idx[token]\n word_idx_count[idx] = word_idx_count.get(idx, 0) + 1\n\n indexed_sentence.append(idx)\n indexed_sentences.append(indexed_sentence)\n\n\n\n # restrict vocab size\n\n # set all the words I want to keep to infinity\n # so that they are included when I pick the most\n # common words\n for word in keep_words:\n word_idx_count[word2idx[word]] = float('inf')\n\n sorted_word_idx_count = sorted(word_idx_count.items(), key=operator.itemgetter(1), reverse=True)\n word2idx_small = {}\n new_idx = 0\n idx_new_idx_map = {}\n for idx, count in sorted_word_idx_count[:n_vocab]:\n word = idx2word[idx]\n print(word, count)\n word2idx_small[word] = new_idx\n idx_new_idx_map[idx] = new_idx\n new_idx += 1\n # let 'unknown' be the last token\n word2idx_small['UNKNOWN'] = new_idx \n unknown = new_idx\n\n assert('START' in word2idx_small)\n assert('END' in word2idx_small)\n for word in keep_words:\n assert(word in word2idx_small)\n\n # map old idx to new idx\n sentences_small = []\n for sentence in indexed_sentences:\n if len(sentence) > 1:\n new_sentence = [idx_new_idx_map[idx] if idx in idx_new_idx_map else unknown for idx in sentence]\n sentences_small.append(new_sentence)\n\n return sentences_small, word2idx_small\n\n\n\n" }, { "path": "rnn_class/exercises.txt", "content": "Logistic Regression\nhttps://www.kaggle.com/uciml/pima-indians-diabetes-database\nhttps://lazyprogrammer.me/course_files/exercises/diabetes.csv\n\nLinear Regression\nhttps://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html\nhttps://lazyprogrammer.me/course_files/exercises/boston.txt\n\nANN\nhttps://archive.ics.uci.edu/ml/datasets/ecoli (orig)\nhttps://www.kaggle.com/elikplim/ecoli-data-set (alt)\nhttps://lazyprogrammer.me/course_files/exercises/ecoli.csv\n\nRNN\nFind your own stock price dataset!\n\nNLP\nhttps://www.kaggle.com/crowdflower/twitter-airline-sentiment\nhttps://lazyprogrammer.me/course_files/exercises/AirlineSentimentTweets.csv" }, { "path": "rnn_class/extra_reading.txt", "content": "Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling, Chung 2014\nhttps://arxiv.org/pdf/1412.3555v1.pdf" }, { "path": "rnn_class/gru.py", "content": "# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python\n# https://udemy.com/deep-learning-recurrent-neural-networks-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport theano\nimport theano.tensor as T\n\nfrom util import init_weight\n\n\nclass GRU:\n def __init__(self, Mi, Mo, activation):\n self.Mi = Mi\n self.Mo = Mo\n self.f = activation\n\n # numpy init\n Wxr = init_weight(Mi, Mo)\n Whr = init_weight(Mo, Mo)\n br = np.zeros(Mo)\n Wxz = init_weight(Mi, Mo)\n Whz = init_weight(Mo, Mo)\n bz = np.zeros(Mo)\n Wxh = init_weight(Mi, Mo)\n Whh = init_weight(Mo, Mo)\n bh = np.zeros(Mo)\n h0 = np.zeros(Mo)\n\n # theano vars\n self.Wxr = theano.shared(Wxr)\n self.Whr = theano.shared(Whr)\n self.br = theano.shared(br)\n self.Wxz = theano.shared(Wxz)\n self.Whz = theano.shared(Whz)\n self.bz = theano.shared(bz)\n self.Wxh = theano.shared(Wxh)\n self.Whh = theano.shared(Whh)\n self.bh = theano.shared(bh)\n self.h0 = theano.shared(h0)\n self.params = [self.Wxr, self.Whr, self.br, self.Wxz, self.Whz, self.bz, self.Wxh, self.Whh, self.bh, self.h0]\n\n def recurrence(self, x_t, h_t1):\n r = T.nnet.sigmoid(x_t.dot(self.Wxr) + h_t1.dot(self.Whr) + self.br)\n z = T.nnet.sigmoid(x_t.dot(self.Wxz) + h_t1.dot(self.Whz) + self.bz)\n hhat = self.f(x_t.dot(self.Wxh) + (r * h_t1).dot(self.Whh) + self.bh)\n h = (1 - z) * h_t1 + z * hhat\n return h\n\n def output(self, x):\n # input X should be a matrix (2-D)\n # rows index time\n h, _ = theano.scan(\n fn=self.recurrence,\n sequences=x,\n outputs_info=[self.h0],\n n_steps=x.shape[0],\n )\n return h\n" }, { "path": "rnn_class/gru_nonorm_part1_wikipedia_word2idx.json", "content": "{\"limited\": 618, \"writings\": 1936, \"magnetic\": 1974, \"dynasty\": 1588, \"four\": 175, \"asian\": 1966, \"consists\": 1176, \"oldest\": 1710, \"whose\": 543, \"paris\": 896, \"edward\": 1538, \"under\": 90, \"lord\": 1179, \"1958\": 1840, \"risk\": 1267, \"rise\": 1090, \"every\": 327, \"jack\": 1996, \"formula\": 1666, \"school\": 240, \"prize\": 1219, \"companies\": 785, \"solution\": 1609, \"red\": 392, \"expanded\": 1602, \"force\": 247, \"leaders\": 1341, \"japanese\": 511, \"second\": 126, \"street\": 889, \"estimated\": 904, \"even\": 147, \"established\": 301, \"captain\": 1896, \"christ\": 1492, \"selected\": 1956, \"asia\": 972, \"above\": 435, \"new\": 43, \"increasing\": 1190, \"ever\": 886, \"told\": 1529, \"men\": 391, \"here\": 1143, \"atoms\": 1997, \"met\": 995, \"protection\": 1390, \"china\": 429, \"active\": 838, \"100\": 841, \"obtained\": 1872, \"voice\": 1507, \"daughter\": 1137, \"study\": 374, \"changed\": 940, \"reports\": 1721, \"military\": 185, \"changes\": 687, \"golden\": 1687, \"campaign\": 1045, \"highly\": 875, \"brought\": 677, \"actions\": 1576, \"total\": 303, \"unit\": 890, \"charge\": 1370, \"dna\": 1271, \"would\": 64, \"army\": 244, \"mexico\": 1345, \"negative\": 1474, \"music\": 177, \"therefore\": 569, \"type\": 456, \"until\": 130, \"holy\": 1107, \"successful\": 806, \"wars\": 1349, \"award\": 681, \"berlin\": 1689, \"arms\": 1524, \"hold\": 1254, \"must\": 281, \"me\": 973, \"1990\": 816, \"1993\": 924, \"1992\": 864, \"1995\": 837, \"1994\": 818, \"1997\": 744, \"rights\": 436, \"1999\": 595, \"1998\": 694, \"work\": 119, \"theories\": 1616, \"era\": 862, \"henry\": 777, \"my\": 628, \"example\": 125, \"give\": 839, \"hebrew\": 1909, \"india\": 483, \"david\": 530, \"end\": 170, \"provide\": 583, \"travel\": 1515, \"feature\": 1158, \"machine\": 1083, \"how\": 348, \"hot\": 1789, \"writers\": 1441, \"flow\": 1590, \"regional\": 1020, \"after\": 47, \"president\": 203, \"law\": 194, \"types\": 696, \"attempt\": 993, \"third\": 339, \"lost\": 509, \"greek\": 365, \"maintain\": 1856, \"green\": 883, \"democratic\": 1163, \"order\": 192, \"operations\": 847, \"singersongwriter\": 1281, \"over\": 66, \"london\": 342, \"japan\": 748, \"before\": 118, \"personal\": 731, \"writing\": 652, \"better\": 848, \"production\": 282, \"weeks\": 1597, \"strength\": 1848, \"then\": 91, \"them\": 100, \"combination\": 1628, \"grand\": 1078, \"break\": 1955, \"band\": 516, \"effects\": 709, \"they\": 40, \"schools\": 695, \"silver\": 1773, \"bank\": 961, \"netherlands\": 1722, \"oxygen\": 1719, \"l\": 1003, \"victory\": 1448, \"each\": 113, \"went\": 608, \"side\": 480, \"mean\": 1358, \"financial\": 893, \"series\": 145, \"principles\": 1668, \"carry\": 1802, \"used\": 51, \"navy\": 962, \"message\": 1958, \"network\": 562, \"god\": 323, \"tried\": 1676, \"content\": 1415, \"medicine\": 1620, \"independence\": 798, \"foundation\": 1444, \"linear\": 1911, \"given\": 220, \"free\": 360, \"standard\": 349, \"management\": 1135, \"formation\": 1422, \"wanted\": 1501, \"widespread\": 1866, \"created\": 354, \"days\": 399, \"1970s\": 1120, \"moving\": 1491, \"user\": 1809, \"industrial\": 1099, \"features\": 635, \"primary\": 673, \"featured\": 1140, \"adopted\": 906, \"another\": 152, \"electronic\": 1619, \"service\": 287, \"similarly\": 1937, \"top\": 544, \"historian\": 1598, \"approximately\": 766, \"fiction\": 1094, \"needed\": 1111, \"rates\": 1601, \"too\": 701, \"john\": 158, \"urban\": 1236, \"serve\": 1440, \"took\": 262, \"rejected\": 1788, \"direct\": 808, \"technique\": 1873, \"western\": 278, \"shortly\": 1825, \"distance\": 1194, \"target\": 1603, \"showed\": 1532, \"programs\": 948, \"tree\": 1685, \"likely\": 884, \"nations\": 468, \"project\": 561, \"matter\": 1037, \"classes\": 1691, \"historical\": 678, \"powers\": 1015, \"bridge\": 1399, \"palace\": 1908, \"ran\": 1969, \"modern\": 172, \"mind\": 1457, \"seen\": 433, \"ocean\": 1385, \"relatively\": 955, \"forced\": 1008, \"traditionally\": 1656, \"1968\": 1296, \"1969\": 1300, \"1964\": 1478, \"1965\": 1450, \"latter\": 1027, \"1967\": 1354, \"1960\": 1487, \"1961\": 1673, \"1962\": 1553, \"1963\": 1559, \"materials\": 1183, \"forces\": 269, \"blue\": 834, \"though\": 221, \"object\": 1091, \"regular\": 963, \"germany\": 381, \"letter\": 901, \"phase\": 1580, \"zealand\": 1427, \"singer\": 987, \"episode\": 1222, \"medical\": 932, \"metal\": 786, \"treaty\": 946, \"points\": 674, \"principle\": 1497, \"came\": 357, \"saying\": 1631, \"pope\": 793, \"queen\": 1533, \"attempts\": 1574, \"radio\": 486, \"earth\": 502, \"just\": 343, \"constant\": 1542, \"do\": 193, \"wide\": 933, \"de\": 161, \"stop\": 1891, \"13\": 858, \"coast\": 752, \"despite\": 513, \"report\": 907, \"dr\": 1674, \"runs\": 1651, \"countries\": 224, \"fields\": 1332, \"method\": 685, \"architecture\": 1647, \"release\": 761, \"secretary\": 1581, \"reference\": 1036, \"1960s\": 1189, \"observed\": 1426, \"decided\": 1126, \"result\": 288, \"best\": 322, \"techniques\": 1299, \"said\": 213, \"capacity\": 1664, \"away\": 725, \"r\": 641, \"approach\": 1051, \"discovery\": 1723, \"we\": 271, \"never\": 407, \"terms\": 432, \"korea\": 1519, \"nature\": 538, \"however\": 72, \"news\": 974, \"suggested\": 1122, \"received\": 418, \"climate\": 1124, \"brown\": 1286, \"reported\": 743, \"country\": 179, \"cup\": 1359, \"against\": 121, \"players\": 594, \"games\": 330, \"planned\": 1768, \"studio\": 1816, \"asked\": 1367, \"appeared\": 671, \"character\": 492, \"2nd\": 1980, \"represented\": 1174, \"conference\": 1260, \"engines\": 1902, \"basis\": 842, \"union\": 277, \"three\": 97, \"been\": 41, \"quickly\": 1240, \"commission\": 1209, \"much\": 160, \"interest\": 750, \"basic\": 820, \"expected\": 1310, \"entered\": 1421, \"life\": 141, \"families\": 1460, \"eastern\": 472, \"child\": 1175, \"worked\": 990, \"applied\": 1025, \"east\": 234, \"n\": 759, \"near\": 390, \"aid\": 1618, \"property\": 928, \"k\": 1082, \"launched\": 1394, \"seven\": 732, \"played\": 369, \"is\": 8, \"it\": 18, \"defeated\": 1449, \"ii\": 252, \"player\": 219, \"in\": 5, \"ie\": 944, \"if\": 80, \"containing\": 1543, \"perform\": 1853, \"things\": 1159, \"make\": 245, \"amount\": 967, \"complex\": 607, \"split\": 1923, \"liberal\": 1655, \"several\": 115, \"european\": 229, \"independent\": 598, \"published\": 208, \"rail\": 1890, \"hand\": 613, \"characters\": 713, \"cycle\": 1626, \"kept\": 1680, \"1979\": 1071, \"1978\": 1246, \"1977\": 1317, \"1976\": 1223, \"1975\": 1172, \"1974\": 1287, \"1973\": 1247, \"1972\": 1262, \"1971\": 1273, \"1970\": 1188, \"academic\": 953, \"rome\": 819, \"greatest\": 1309, \"mother\": 832, \"claims\": 1204, \"the\": 2, \"musical\": 986, \"left\": 266, \"proposed\": 789, \"gods\": 1513, \"human\": 211, \"yet\": 891, \"previous\": 879, \"sciences\": 1952, \"regarded\": 1412, \"royal\": 533, \"countrys\": 1663, \"spread\": 1320, \"board\": 914, \"has\": 31, \"gave\": 637, \"bands\": 1376, \"possible\": 385, \"arab\": 1733, \"possibly\": 1611, \"cultural\": 757, \"birth\": 1162, \"unique\": 1379, \"advanced\": 1384, \"county\": 941, \"50\": 1088, \"specific\": 692, \"officer\": 1884, \"night\": 902, \"security\": 690, \"awarded\": 1473, \"right\": 366, \"old\": 259, \"deal\": 1456, \"people\": 104, \"crown\": 1987, \"dead\": 1138, \"born\": 501, \"election\": 729, \"for\": 11, \"ice\": 971, \"individuals\": 980, \"core\": 1578, \"translated\": 1813, \"post\": 1695, \"super\": 1061, \"properties\": 1086, \"census\": 1622, \"attacks\": 1416, \"months\": 615, \"o\": 1720, \"magazine\": 1146, \"islands\": 386, \"efforts\": 1313, \"slightly\": 1726, \"duke\": 1845, \"raised\": 1606, \"presence\": 1198, \"civil\": 487, \"material\": 655, \"son\": 409, \"down\": 318, \"1990s\": 1233, \"methods\": 873, \"support\": 230, \"initial\": 1239, \"why\": 1880, \"joseph\": 1462, \"way\": 217, \"resulted\": 1316, \"call\": 1234, \"was\": 10, \"war\": 77, \"head\": 470, \"form\": 127, \"offer\": 1732, \"attempted\": 1623, \"becoming\": 1220, \"differences\": 1512, \"failure\": 1777, \"heat\": 1600, \"solar\": 1483, \"removed\": 1446, \"true\": 753, \"versions\": 1307, \"inside\": 1342, \"maximum\": 1697, \"devices\": 1504, \"economic\": 289, \"reform\": 1702, \"proved\": 1645, \"evidence\": 517, \"exist\": 1042, \"ship\": 1252, \"physical\": 691, \"no\": 70, \"whereas\": 1505, \"when\": 53, \"actor\": 496, \"conditions\": 792, \"republic\": 397, \"digital\": 1277, \"test\": 952, \"brothers\": 1865, \"models\": 1177, \"football\": 616, \"fell\": 1699, \"reign\": 1659, \"authorities\": 1837, \"died\": 451, \"billion\": 978, \"longer\": 760, \"together\": 460, \"time\": 62, \"serious\": 1670, \"songs\": 1014, \"concept\": 778, \"doctor\": 1847, \"chain\": 1925, \"dance\": 1724, \"global\": 1038, \"focus\": 1461, \"manager\": 1380, \"battle\": 482, \"competition\": 1395, \"1948\": 1662, \"1949\": 1843, \"zone\": 1755, \"1942\": 1921, \"1940\": 1868, \"1941\": 1775, \"father\": 512, \"1947\": 1780, \"1944\": 1764, \"1945\": 1388, \"environment\": 1201, \"0\": 1291, \"finally\": 1063, \"suffered\": 1826, \"division\": 747, \"supported\": 880, \"string\": 1897, \"advantage\": 1919, \"choice\": 1675, 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\"final\": 448, \"academy\": 1210, \"completed\": 1103, \"environmental\": 1749, \"july\": 345, \"steel\": 1750, \"individual\": 609, \"visited\": 1931, \"providing\": 1718, \"have\": 29, \"need\": 784, \"border\": 1057, \"catholic\": 705, \"able\": 574, \"contact\": 1617, \"instance\": 1333, \"which\": 24, \"korean\": 1851, \"subject\": 686, \"worldwide\": 1551, \"combat\": 1778, \"centre\": 856, \"who\": 45, \"dutch\": 931, \"eight\": 1013, \"device\": 1644, \"class\": 552, \"disease\": 1290, \"face\": 1494, \"normally\": 1994, \"fact\": 611, \"text\": 783, \"agreed\": 1321, \"charles\": 659, \"bring\": 1893, \"soldiers\": 1297, \"staff\": 1540, \"based\": 164, \"knowledge\": 851, \"should\": 314, \"fuel\": 1558, \"employed\": 1904, \"communist\": 1624, \"local\": 255, \"meant\": 1801, \"means\": 422, \"overall\": 1514, \"joint\": 1739, \"ones\": 1117, \"words\": 493, \"areas\": 298, \"evolution\": 1621, \"ended\": 1150, \"h\": 746, \"married\": 1009, \"teams\": 926, \"she\": 133, \"contain\": 1251, \"fixed\": 1728, \"view\": 600, \"europe\": 274, \"official\": 458, \"exists\": 1918, \"acquired\": 1991, \"national\": 114, \"humans\": 1208, \"edition\": 799, \"computer\": 471, \"greece\": 1713, \"operated\": 1982, \"creating\": 1592, \"record\": 499, \"nuclear\": 693, \"state\": 84, \"closed\": 1466, \"limit\": 1922, \"ability\": 1119, \"opening\": 1585, \"importance\": 1572, \"key\": 702, \"police\": 844, \"problem\": 767, \"career\": 1022, \"taking\": 1002, \"equal\": 1192, \"drug\": 1792, \"etc\": 1259, \"figures\": 1436, \"otherwise\": 1785, \"wall\": 1762, \"supreme\": 1477, \"carbon\": 1211, \"kings\": 1304, \"winning\": 1682, \"addition\": 400, \"poet\": 1736, \"controlled\": 1594, \"both\": 76, \"am\": 1669, \"finished\": 1879, \"covered\": 1905, \"improved\": 1889, \"general\": 148, \"present\": 475, \"wood\": 1960, \"novel\": 751, \"unlike\": 1335, \"appearance\": 1378, \"as\": 9, \"value\": 547, \"will\": 111, \"almost\": 437, \"thus\": 256, \"site\": 791, \"surface\": 714, \"helped\": 1429, \"claimed\": 923, \"arabic\": 1941, \"perhaps\": 1432, \"began\": 178, \"administration\": 1105, \"cross\": 1419, \"member\": 419, \"parts\": 450, \"largest\": 275, \"units\": 773, \"party\": 204, \"difficult\": 1017, \"ball\": 1280, \"columbia\": 1910, \"upon\": 393, \"effect\": 534, \"student\": 1589, \"frequently\": 1206, \"indigenous\": 1988, \"scholars\": 1123, \"identity\": 1472, \"keep\": 1383, \"off\": 420, \"center\": 466, \"records\": 882, \"i\": 96, \"well\": 108, \"fighting\": 1711, \"thought\": 527, \"command\": 1085, \"english\": 124, \"position\": 439, \"soviet\": 416, \"less\": 257, \"increasingly\": 1561, \"executive\": 1360, \"domestic\": 1458, \"sources\": 800, \"seats\": 1895, \"paul\": 624, \"web\": 994, \"density\": 1943, \"supply\": 1470, \"smith\": 1225, \"lake\": 957, \"killed\": 863, \"reasons\": 1677, \"book\": 191, \"footballer\": 1030, \"match\": 1763, \"increased\": 564, \"government\": 99, \"five\": 316, \"know\": 1468, \"press\": 292, \"immediately\": 1363, \"prominent\": 1406, \"loss\": 1109, \"necessary\": 1173, \"like\": 144, \"success\": 774, \"journalist\": 1793, \"martin\": 1330, \"become\": 246, \"works\": 344, \"page\": 1989, \"italian\": 612, \"because\": 110, \"classical\": 861, \"sequence\": 1452, \"scottish\": 1577, \"authority\": 827, \"growth\": 658, \"home\": 241, \"empire\": 355, \"peter\": 921, \"transport\": 1152, \"lead\": 632, \"avoid\": 1712, \"does\": 290, \"leader\": 859, \"medieval\": 1496, \"journal\": 917, \"expansion\": 1467, \"pressure\": 831, \"host\": 1414, \"although\": 120, \"stage\": 981, \"gained\": 1498, \"about\": 68, \"actual\": 1575, \"carried\": 1156, \"freedom\": 1182, \"recognized\": 1364, \"angeles\": 1867, \"statement\": 1706, \"emperor\": 717, \"software\": 662, \"own\": 180, \"letters\": 977, \"previously\": 1062, \"washington\": 1130, \"female\": 988, \"artists\": 1274, \"1986\": 1004, \"1987\": 1059, \"1984\": 1065, \"1985\": 1087, \"1982\": 1112, \"1983\": 1132, \"1980\": 1040, \"1981\": 1166, \"museum\": 721, \"1988\": 1079, \"imperial\": 1237, \"function\": 488, \"north\": 149, \"stadium\": 1864, \"but\": 35, \"volume\": 959, \"construction\": 741, \"courts\": 1714, \"highest\": 770, \"he\": 25, \"made\": 86, \"places\": 1365, \"whether\": 636, \"cells\": 1001, \"j\": 548, \"signed\": 1018, \"placed\": 937, \"below\": 657, \"frequency\": 1772, \"stories\": 930, \"distribution\": 1282, \"piece\": 1978, \"minutes\": 1912, \"display\": 1725, \"irish\": 1055, \"universal\": 1707, \"education\": 465, \"functions\": 919, \"compared\": 1039, \"variety\": 801, \"agriculture\": 1784, \"guitarist\": 1833, \"percent\": 736, \"40\": 1410, \"other\": 38, \"branch\": 1555, \"u\": 1932, \"canada\": 699, \"star\": 596, \"june\": 337, \"ultimately\": 1886, \"friends\": 1508, \"christians\": 1794, \"\\u2013\": 37, \"\\u2014\": 969, \"factors\": 1509, \"rule\": 542, \"write\": 1530}" }, { "path": "rnn_class/lstm.py", "content": "# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python\n# https://udemy.com/deep-learning-recurrent-neural-networks-in-pythonfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport theano\nimport theano.tensor as T\n\nfrom util import init_weight\n\n\nclass LSTM:\n def __init__(self, Mi, Mo, activation):\n self.Mi = Mi\n self.Mo = Mo\n self.f = activation\n\n # numpy init\n Wxi = init_weight(Mi, Mo)\n Whi = init_weight(Mo, Mo)\n Wci = init_weight(Mo, Mo)\n bi = np.zeros(Mo)\n Wxf = init_weight(Mi, Mo)\n Whf = init_weight(Mo, Mo)\n Wcf = init_weight(Mo, Mo)\n bf = np.zeros(Mo)\n Wxc = init_weight(Mi, Mo)\n Whc = init_weight(Mo, Mo)\n bc = np.zeros(Mo)\n Wxo = init_weight(Mi, Mo)\n Who = init_weight(Mo, Mo)\n Wco = init_weight(Mo, Mo)\n bo = np.zeros(Mo)\n c0 = np.zeros(Mo)\n h0 = np.zeros(Mo)\n\n # theano vars\n self.Wxi = theano.shared(Wxi)\n self.Whi = theano.shared(Whi)\n self.Wci = theano.shared(Wci)\n self.bi = theano.shared(bi)\n self.Wxf = theano.shared(Wxf)\n self.Whf = theano.shared(Whf)\n self.Wcf = theano.shared(Wcf)\n self.bf = theano.shared(bf)\n self.Wxc = theano.shared(Wxc)\n self.Whc = theano.shared(Whc)\n self.bc = theano.shared(bc)\n self.Wxo = theano.shared(Wxo)\n self.Who = theano.shared(Who)\n self.Wco = theano.shared(Wco)\n self.bo = theano.shared(bo)\n self.c0 = theano.shared(c0)\n self.h0 = theano.shared(h0)\n self.params = [\n self.Wxi,\n self.Whi,\n self.Wci,\n self.bi,\n self.Wxf,\n self.Whf,\n self.Wcf,\n self.bf,\n self.Wxc,\n self.Whc,\n self.bc,\n self.Wxo,\n self.Who,\n self.Wco,\n self.bo,\n self.c0,\n self.h0,\n ]\n\n def recurrence(self, x_t, h_t1, c_t1):\n i_t = T.nnet.sigmoid(x_t.dot(self.Wxi) + h_t1.dot(self.Whi) + c_t1.dot(self.Wci) + self.bi)\n f_t = T.nnet.sigmoid(x_t.dot(self.Wxf) + h_t1.dot(self.Whf) + c_t1.dot(self.Wcf) + self.bf)\n c_t = f_t * c_t1 + i_t * T.tanh(x_t.dot(self.Wxc) + h_t1.dot(self.Whc) + self.bc)\n o_t = T.nnet.sigmoid(x_t.dot(self.Wxo) + h_t1.dot(self.Who) + c_t.dot(self.Wco) + self.bo)\n h_t = o_t * T.tanh(c_t)\n return h_t, c_t\n\n def output(self, x):\n # input X should be a matrix (2-D)\n # rows index time\n [h, c], _ = theano.scan(\n fn=self.recurrence,\n sequences=x,\n outputs_info=[self.h0, self.c0],\n n_steps=x.shape[0],\n )\n return h\n" }, { "path": "rnn_class/mlp_parity.py", "content": "# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python\n# https://udemy.com/deep-learning-recurrent-neural-networks-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport theano\nimport theano.tensor as T\nimport matplotlib.pyplot as plt\n\nfrom util import init_weight, all_parity_pairs\nfrom sklearn.utils import shuffle\n\n\nclass HiddenLayer(object):\n def __init__(self, M1, M2, an_id):\n self.id = an_id\n self.M1 = M1\n self.M2 = M2\n W = init_weight(M1, M2)\n b = np.zeros(M2)\n self.W = theano.shared(W, 'W_%s' % self.id)\n self.b = theano.shared(b, 'b_%s' % self.id)\n self.params = [self.W, self.b]\n\n def forward(self, X):\n return T.nnet.relu(X.dot(self.W) + self.b)\n\n\nclass ANN(object):\n def __init__(self, hidden_layer_sizes):\n self.hidden_layer_sizes = hidden_layer_sizes\n\n def fit(self, X, Y, learning_rate=1e-2, mu=0.99, reg=1e-12, epochs=400, batch_sz=20, print_period=1, show_fig=False):\n\n # X = X.astype(np.float32)\n Y = Y.astype(np.int32)\n\n # initialize hidden layers\n N, D = X.shape\n K = len(set(Y))\n self.hidden_layers = []\n M1 = D\n count = 0\n for M2 in self.hidden_layer_sizes:\n h = HiddenLayer(M1, M2, count)\n self.hidden_layers.append(h)\n M1 = M2\n count += 1\n W = init_weight(M1, K)\n b = np.zeros(K)\n self.W = theano.shared(W, 'W_logreg')\n self.b = theano.shared(b, 'b_logreg')\n\n # collect params for later use\n self.params = [self.W, self.b]\n for h in self.hidden_layers:\n self.params += h.params\n\n # for momentum\n dparams = [theano.shared(np.zeros(p.get_value().shape)) for p in self.params]\n\n # for rmsprop\n cache = [theano.shared(np.zeros(p.get_value().shape)) for p in self.params]\n\n # set up theano functions and variables\n thX = T.matrix('X')\n thY = T.ivector('Y')\n pY = self.forward(thX)\n\n rcost = reg*T.sum([(p*p).sum() for p in self.params])\n cost = -T.mean(T.log(pY[T.arange(thY.shape[0]), thY])) + rcost\n prediction = self.predict(thX)\n grads = T.grad(cost, self.params)\n\n # momentum only\n updates = [\n (p, p + mu*dp - learning_rate*g) for p, dp, g in zip(self.params, dparams, grads)\n ] + [\n (dp, mu*dp - learning_rate*g) for dp, g in zip(dparams, grads)\n ]\n\n train_op = theano.function(\n inputs=[thX, thY],\n outputs=[cost, prediction],\n updates=updates,\n )\n\n n_batches = N // batch_sz\n costs = []\n for i in range(epochs):\n X, Y = shuffle(X, Y)\n for j in range(n_batches):\n Xbatch = X[j*batch_sz:(j*batch_sz+batch_sz)]\n Ybatch = Y[j*batch_sz:(j*batch_sz+batch_sz)]\n\n c, p = train_op(Xbatch, Ybatch)\n\n if j % print_period == 0:\n costs.append(c)\n e = np.mean(Ybatch != p)\n print(\"i:\", i, \"j:\", j, \"nb:\", n_batches, \"cost:\", c, \"error rate:\", e)\n \n if show_fig:\n plt.plot(costs)\n plt.show()\n\n def forward(self, X):\n Z = X\n for h in self.hidden_layers:\n Z = h.forward(Z)\n return T.nnet.softmax(Z.dot(self.W) + self.b)\n\n def predict(self, X):\n pY = self.forward(X)\n return T.argmax(pY, axis=1)\n\n\ndef wide():\n X, Y = all_parity_pairs(12)\n model = ANN([2048])\n model.fit(X, Y, learning_rate=1e-4, print_period=10, epochs=300, show_fig=True)\n\ndef deep():\n # Challenge - find a deeper, slimmer network to solve the problem\n X, Y = all_parity_pairs(12)\n model = ANN([1024]*2)\n model.fit(X, Y, learning_rate=1e-3, print_period=10, epochs=100, show_fig=True)\n\nif __name__ == '__main__':\n wide()\n # deep()\n" }, { "path": "rnn_class/poetry_classifier.py", "content": "# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python\n# https://udemy.com/deep-learning-recurrent-neural-networks-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport theano\nimport theano.tensor as T\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.utils import shuffle\nfrom util import init_weight, get_poetry_classifier_data\n\n\nclass SimpleRNN:\n def __init__(self, M, V):\n self.M = M # hidden layer size\n self.V = V # vocabulary size\n\n def fit(self, X, Y, learning_rate=1.0, mu=0.99, reg=1.0, activation=T.tanh, epochs=500, show_fig=False):\n M = self.M\n V = self.V\n K = len(set(Y))\n print(\"V:\", V)\n\n X, Y = shuffle(X, Y)\n Nvalid = 10\n Xvalid, Yvalid = X[-Nvalid:], Y[-Nvalid:]\n X, Y = X[:-Nvalid], Y[:-Nvalid]\n N = len(X)\n\n # initial weights\n Wx = init_weight(V, M)\n Wh = init_weight(M, M)\n bh = np.zeros(M)\n h0 = np.zeros(M)\n Wo = init_weight(M, K)\n bo = np.zeros(K)\n\n thX, thY, py_x, prediction = self.set(Wx, Wh, bh, h0, Wo, bo, activation)\n\n cost = -T.mean(T.log(py_x[thY]))\n grads = T.grad(cost, self.params)\n dparams = [theano.shared(p.get_value()*0) for p in self.params]\n lr = T.scalar('learning_rate')\n\n updates = [\n (p, p + mu*dp - lr*g) for p, dp, g in zip(self.params, dparams, grads)\n ] + [\n (dp, mu*dp - lr*g) for dp, g in zip(dparams, grads)\n ]\n\n self.train_op = theano.function(\n inputs=[thX, thY, lr],\n outputs=[cost, prediction],\n updates=updates,\n allow_input_downcast=True,\n )\n\n costs = []\n for i in range(epochs):\n X, Y = shuffle(X, Y)\n n_correct = 0\n cost = 0\n for j in range(N):\n # we set 0 to start and 1 to end\n # print \"X[%d]:\" % j, X[j], \"len:\", len(X[j])\n c, p = self.train_op(X[j], Y[j], learning_rate)\n # print \"p:\", p, \"y:\", Y[j]\n cost += c\n if p == Y[j]:\n n_correct += 1\n # update the learning rate\n learning_rate *= 0.9999\n\n # calculate validation accuracy\n n_correct_valid = 0\n for j in range(Nvalid):\n p = self.predict_op(Xvalid[j])\n if p == Yvalid[j]:\n n_correct_valid += 1\n print(\"i:\", i, \"cost:\", cost, \"correct rate:\", (float(n_correct)/N), end=\" \")\n print(\"validation correct rate:\", (float(n_correct_valid)/Nvalid))\n costs.append(cost)\n\n if show_fig:\n plt.plot(costs)\n plt.show()\n \n def save(self, filename):\n np.savez(filename, *[p.get_value() for p in self.params])\n\n @staticmethod\n def load(filename, activation):\n # TODO: would prefer to save activation to file too\n npz = np.load(filename)\n Wx = npz['arr_0']\n Wh = npz['arr_1']\n bh = npz['arr_2']\n h0 = npz['arr_3']\n Wo = npz['arr_4']\n bo = npz['arr_5']\n V, M = Wx.shape\n rnn = SimpleRNN(M, V)\n rnn.set(Wx, Wh, bh, h0, Wo, bo, activation)\n return rnn\n\n def set(self, Wx, Wh, bh, h0, Wo, bo, activation):\n self.f = activation\n\n # redundant - see how you can improve it\n self.Wx = theano.shared(Wx)\n self.Wh = theano.shared(Wh)\n self.bh = theano.shared(bh)\n self.h0 = theano.shared(h0)\n self.Wo = theano.shared(Wo)\n self.bo = theano.shared(bo)\n self.params = [self.Wx, self.Wh, self.bh, self.h0, self.Wo, self.bo]\n\n thX = T.ivector('X')\n thY = T.iscalar('Y')\n\n def recurrence(x_t, h_t1):\n # returns h(t), y(t)\n h_t = self.f(self.Wx[x_t] + h_t1.dot(self.Wh) + self.bh)\n y_t = T.nnet.softmax(h_t.dot(self.Wo) + self.bo)\n return h_t, y_t\n\n [h, y], _ = theano.scan(\n fn=recurrence,\n outputs_info=[self.h0, None],\n sequences=thX,\n n_steps=thX.shape[0],\n )\n\n py_x = y[-1, 0, :] # only interested in the final classification of the sequence\n prediction = T.argmax(py_x)\n self.predict_op = theano.function(\n inputs=[thX],\n outputs=prediction,\n allow_input_downcast=True,\n )\n return thX, thY, py_x, prediction\n\n\ndef train_poetry():\n X, Y, V = get_poetry_classifier_data(samples_per_class=500)\n rnn = SimpleRNN(30, V)\n rnn.fit(X, Y, learning_rate=1e-6, show_fig=True, activation=T.nnet.relu, epochs=1000)\n\nif __name__ == '__main__':\n train_poetry()\n\n" }, { "path": "rnn_class/rrnn_language.py", "content": "# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python\n# https://udemy.com/deep-learning-recurrent-neural-networks-in-python\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport theano\nimport theano.tensor as T\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.utils import shuffle\nfrom util import init_weight, get_robert_frost\n\n\nclass SimpleRNN:\n def __init__(self, D, M, V):\n self.D = D # dimensionality of word embedding\n self.M = M # hidden layer size\n self.V = V # vocabulary size\n\n def fit(self, X, learning_rate=10., mu=0.9, reg=0., activation=T.tanh, epochs=500, show_fig=False):\n N = len(X)\n D = self.D\n M = self.M\n V = self.V\n\n # initial weights\n We = init_weight(V, D)\n Wx = init_weight(D, M)\n Wh = init_weight(M, M)\n bh = np.zeros(M)\n h0 = np.zeros(M)\n # z = np.ones(M)\n Wxz = init_weight(D, M)\n Whz = init_weight(M, M)\n bz = np.zeros(M)\n Wo = init_weight(M, V)\n bo = np.zeros(V)\n\n thX, thY, py_x, prediction = self.set(We, Wx, Wh, bh, h0, Wxz, Whz, bz, Wo, bo, activation)\n\n lr = T.scalar('lr')\n\n cost = -T.mean(T.log(py_x[T.arange(thY.shape[0]), thY]))\n grads = T.grad(cost, self.params)\n dparams = [theano.shared(p.get_value()*0) for p in self.params]\n\n updates = []\n for p, dp, g in zip(self.params, dparams, grads):\n new_dp = mu*dp - lr*g\n updates.append((dp, new_dp))\n\n new_p = p + new_dp\n updates.append((p, new_p))\n\n self.predict_op = theano.function(inputs=[thX], outputs=prediction)\n self.train_op = theano.function(\n inputs=[thX, thY, lr],\n outputs=[cost, prediction],\n updates=updates\n )\n\n costs = []\n for i in range(epochs):\n X = shuffle(X)\n n_correct = 0\n n_total = 0\n cost = 0\n for j in range(N):\n if np.random.random() < 0.1:\n input_sequence = [0] + X[j]\n output_sequence = X[j] + [1]\n else:\n input_sequence = [0] + X[j][:-1]\n output_sequence = X[j]\n n_total += len(output_sequence)\n\n # we set 0 to start and 1 to end\n c, p = self.train_op(input_sequence, output_sequence, learning_rate)\n # print \"p:\", p\n cost += c\n # print \"j:\", j, \"c:\", c/len(X[j]+1)\n for pj, xj in zip(p, output_sequence):\n if pj == xj:\n n_correct += 1\n print(\"i:\", i, \"cost:\", cost, \"correct rate:\", (float(n_correct)/n_total))\n if (i + 1) % 500 == 0:\n learning_rate /= 2\n costs.append(cost)\n\n if show_fig:\n plt.plot(costs)\n plt.show()\n \n def save(self, filename):\n np.savez(filename, *[p.get_value() for p in self.params])\n\n @staticmethod\n def load(filename, activation):\n # TODO: would prefer to save activation to file too\n npz = np.load(filename)\n We = npz['arr_0']\n Wx = npz['arr_1']\n Wh = npz['arr_2']\n bh = npz['arr_3']\n h0 = npz['arr_4']\n Wxz = npz['arr_5']\n Whz = npz['arr_6']\n bz = npz['arr_7']\n Wo = npz['arr_8']\n bo = npz['arr_9']\n V, D = We.shape\n _, M = Wx.shape\n rnn = SimpleRNN(D, M, V)\n rnn.set(We, Wx, Wh, bh, h0, Wxz, Whz, bz, Wo, bo, activation)\n return rnn\n\n def set(self, We, Wx, Wh, bh, h0, Wxz, Whz, bz, Wo, bo, activation):\n self.f = activation\n\n # redundant - see how you can improve it\n self.We = theano.shared(We)\n self.Wx = theano.shared(Wx)\n self.Wh = theano.shared(Wh)\n self.bh = theano.shared(bh)\n self.h0 = theano.shared(h0)\n self.Wxz = theano.shared(Wxz)\n self.Whz = theano.shared(Whz)\n self.bz = theano.shared(bz)\n self.Wo = theano.shared(Wo)\n self.bo = theano.shared(bo)\n self.params = [self.We, self.Wx, self.Wh, self.bh, self.h0, self.Wxz, self.Whz, self.bz, self.Wo, self.bo]\n\n thX = T.ivector('X')\n Ei = self.We[thX] # will be a TxD matrix\n thY = T.ivector('Y')\n\n def recurrence(x_t, h_t1):\n # returns h(t), y(t)\n hhat_t = self.f(x_t.dot(self.Wx) + h_t1.dot(self.Wh) + self.bh)\n z_t = T.nnet.sigmoid(x_t.dot(self.Wxz) + h_t1.dot(self.Whz) + self.bz)\n h_t = (1 - z_t) * h_t1 + z_t * hhat_t\n y_t = T.nnet.softmax(h_t.dot(self.Wo) + self.bo)\n return h_t, y_t\n\n [h, y], _ = theano.scan(\n fn=recurrence,\n outputs_info=[self.h0, None],\n sequences=Ei,\n n_steps=Ei.shape[0],\n )\n\n py_x = y[:, 0, :]\n prediction = T.argmax(py_x, axis=1)\n self.predict_op = theano.function(\n inputs=[thX],\n outputs=[py_x, prediction],\n allow_input_downcast=True,\n )\n return thX, thY, py_x, prediction\n\n\n def generate(self, word2idx):\n # convert word2idx -> idx2word\n idx2word = {v:k for k,v in iteritems(word2idx)}\n V = len(word2idx)\n\n # generate 4 lines at a time\n n_lines = 0\n\n # why? because using the START symbol will always yield the same first word!\n X = [ 0 ]\n while n_lines < 4:\n # print \"X:\", X\n PY_X, _ = self.predict_op(X)\n PY_X = PY_X[-1].flatten()\n P = [ np.random.choice(V, p=PY_X)]\n X = np.concatenate([X, P]) # append to the sequence\n # print \"P.shape:\", P.shape, \"P:\", P\n P = P[-1] # just grab the most recent prediction\n if P > 1:\n # it's a real word, not start/end token\n word = idx2word[P]\n print(word, end=\" \")\n elif P == 1:\n # end token\n n_lines += 1\n X = [0]\n print('')\n\n\ndef train_poetry():\n # students: tanh didn't work but you should try it\n sentences, word2idx = get_robert_frost()\n rnn = SimpleRNN(50, 50, len(word2idx))\n rnn.fit(sentences, learning_rate=1e-4, show_fig=True, activation=T.nnet.relu, epochs=2000)\n rnn.save('RRNN_D50_M50_epochs2000_relu.npz')\n\ndef generate_poetry():\n sentences, word2idx = get_robert_frost()\n rnn = SimpleRNN.load('RRNN_D50_M50_epochs2000_relu.npz', T.nnet.relu)\n rnn.generate(word2idx)\n\n\nif __name__ == '__main__':\n train_poetry()\n generate_poetry()\n\n" }, { "path": "rnn_class/srn_language.py", "content": "# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python\n# https://udemy.com/deep-learning-recurrent-neural-networks-in-python\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport theano\nimport theano.tensor as T\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.utils import shuffle\nfrom util import init_weight, get_robert_frost, get_wikipedia_data\n\n\nclass SimpleRNN:\n def __init__(self, D, M, V):\n self.D = D # dimensionality of word embedding\n self.M = M # hidden layer size\n self.V = V # vocabulary size\n\n def fit(self, X, learning_rate=1., mu=0.99, reg=1.0, activation=T.tanh, epochs=500, show_fig=False):\n N = len(X)\n D = self.D\n M = self.M\n V = self.V\n self.f = activation\n\n # initial weights\n We = init_weight(V, D)\n Wx = init_weight(D, M)\n Wh = init_weight(M, M)\n bh = np.zeros(M)\n h0 = np.zeros(M)\n Wo = init_weight(M, V)\n bo = np.zeros(V)\n\n # make them theano shared\n self.We = theano.shared(We)\n self.Wx = theano.shared(Wx)\n self.Wh = theano.shared(Wh)\n self.bh = theano.shared(bh)\n self.h0 = theano.shared(h0)\n self.Wo = theano.shared(Wo)\n self.bo = theano.shared(bo)\n self.params = [self.We, self.Wx, self.Wh, self.bh, self.h0, self.Wo, self.bo]\n\n thX = T.ivector('X')\n Ei = self.We[thX] # will be a TxD matrix\n thY = T.ivector('Y')\n\n # sentence input:\n # [START, w1, w2, ..., wn]\n # sentence target:\n # [w1, w2, w3, ..., END]\n\n def recurrence(x_t, h_t1):\n # returns h(t), y(t)\n h_t = self.f(x_t.dot(self.Wx) + h_t1.dot(self.Wh) + self.bh)\n y_t = T.nnet.softmax(h_t.dot(self.Wo) + self.bo)\n return h_t, y_t\n\n [h, y], _ = theano.scan(\n fn=recurrence,\n outputs_info=[self.h0, None],\n sequences=Ei,\n n_steps=Ei.shape[0],\n )\n\n py_x = y[:, 0, :]\n prediction = T.argmax(py_x, axis=1)\n\n cost = -T.mean(T.log(py_x[T.arange(thY.shape[0]), thY]))\n grads = T.grad(cost, self.params)\n dparams = [theano.shared(p.get_value()*0) for p in self.params]\n\n updates = []\n for p, dp, g in zip(self.params, dparams, grads):\n new_dp = mu*dp - learning_rate*g\n updates.append((dp, new_dp))\n\n new_p = p + new_dp\n updates.append((p, new_p))\n\n self.predict_op = theano.function(inputs=[thX], outputs=prediction)\n self.train_op = theano.function(\n inputs=[thX, thY],\n outputs=[cost, prediction],\n updates=updates\n )\n\n costs = []\n n_total = sum((len(sentence)+1) for sentence in X)\n for i in range(epochs):\n X = shuffle(X)\n n_correct = 0\n cost = 0\n for j in range(N):\n # problem! many words --> END token are overrepresented\n # result: generated lines will be very short\n # we will try to fix in a later iteration\n # BAD! magic numbers 0 and 1...\n input_sequence = [0] + X[j]\n output_sequence = X[j] + [1]\n\n # we set 0 to start and 1 to end\n c, p = self.train_op(input_sequence, output_sequence)\n # print \"p:\", p\n cost += c\n # print \"j:\", j, \"c:\", c/len(X[j]+1)\n for pj, xj in zip(p, output_sequence):\n if pj == xj:\n n_correct += 1\n print(\"i:\", i, \"cost:\", cost, \"correct rate:\", (float(n_correct)/n_total))\n costs.append(cost)\n\n if show_fig:\n plt.plot(costs)\n plt.show()\n \n def save(self, filename):\n np.savez(filename, *[p.get_value() for p in self.params])\n\n @staticmethod\n def load(filename, activation):\n # TODO: would prefer to save activation to file too\n npz = np.load(filename)\n We = npz['arr_0']\n Wx = npz['arr_1']\n Wh = npz['arr_2']\n bh = npz['arr_3']\n h0 = npz['arr_4']\n Wo = npz['arr_5']\n bo = npz['arr_6']\n V, D = We.shape\n _, M = Wx.shape\n rnn = SimpleRNN(D, M, V)\n rnn.set(We, Wx, Wh, bh, h0, Wo, bo, activation)\n return rnn\n\n def set(self, We, Wx, Wh, bh, h0, Wo, bo, activation):\n self.f = activation\n\n # redundant - see how you can improve it\n self.We = theano.shared(We)\n self.Wx = theano.shared(Wx)\n self.Wh = theano.shared(Wh)\n self.bh = theano.shared(bh)\n self.h0 = theano.shared(h0)\n self.Wo = theano.shared(Wo)\n self.bo = theano.shared(bo)\n self.params = [self.We, self.Wx, self.Wh, self.bh, self.h0, self.Wo, self.bo]\n\n thX = T.ivector('X')\n Ei = self.We[thX] # will be a TxD matrix\n thY = T.ivector('Y')\n\n def recurrence(x_t, h_t1):\n # returns h(t), y(t)\n h_t = self.f(x_t.dot(self.Wx) + h_t1.dot(self.Wh) + self.bh)\n y_t = T.nnet.softmax(h_t.dot(self.Wo) + self.bo)\n return h_t, y_t\n\n [h, y], _ = theano.scan(\n fn=recurrence,\n outputs_info=[self.h0, None],\n sequences=Ei,\n n_steps=Ei.shape[0],\n )\n\n py_x = y[:, 0, :]\n prediction = T.argmax(py_x, axis=1)\n self.predict_op = theano.function(\n inputs=[thX],\n outputs=prediction,\n allow_input_downcast=True,\n )\n\n def generate(self, pi, word2idx):\n # convert word2idx -> idx2word\n idx2word = {v:k for k,v in iteritems(word2idx)}\n V = len(pi)\n\n # generate 4 lines at a time\n n_lines = 0\n\n # why? because using the START symbol will always yield the same first word!\n X = [ np.random.choice(V, p=pi) ]\n print(idx2word[X[0]], end=\" \")\n\n while n_lines < 4:\n # print \"X:\", X\n P = self.predict_op(X)[-1]\n X += [P]\n if P > 1:\n # it's a real word, not start/end token\n word = idx2word[P]\n print(word, end=\" \")\n elif P == 1:\n # end token\n n_lines += 1\n print('')\n if n_lines < 4:\n X = [ np.random.choice(V, p=pi) ] # reset to start of line\n print(idx2word[X[0]], end=\" \")\n\n\ndef train_poetry():\n sentences, word2idx = get_robert_frost()\n rnn = SimpleRNN(30, 30, len(word2idx))\n rnn.fit(sentences, learning_rate=1e-4, show_fig=True, activation=T.nnet.relu, epochs=2000)\n rnn.save('RNN_D30_M30_epochs2000_relu.npz')\n\ndef generate_poetry():\n sentences, word2idx = get_robert_frost()\n rnn = SimpleRNN.load('RNN_D30_M30_epochs2000_relu.npz', T.nnet.relu)\n\n # determine initial state distribution for starting sentences\n V = len(word2idx)\n pi = np.zeros(V)\n for sentence in sentences:\n pi[sentence[0]] += 1\n pi /= pi.sum()\n\n rnn.generate(pi, word2idx)\n\ndef wikipedia():\n sentences, word2idx = get_wikipedia_data()\n print(\"finished retrieving data\")\n print(\"vocab size:\", len(word2idx), \"number of sentences:\", len(sentences))\n rnn = SimpleRNN(20, 15, len(word2idx))\n rnn.fit(sentences, learning_rate=1e-4, show_fig=True, activation=T.nnet.relu)\n\nif __name__ == '__main__':\n train_poetry()\n generate_poetry()\n # wikipedia()\n\n" }, { "path": "rnn_class/srn_language_tf.py", "content": "# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python\n# https://udemy.com/deep-learning-recurrent-neural-networks-in-python\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.utils import shuffle\nfrom util import init_weight, get_robert_frost, get_wikipedia_data\n\n\nclass SimpleRNN:\n def __init__(self, D, M, V, f, session):\n self.D = D # dimensionality of word embedding\n self.M = M # hidden layer size\n self.V = V # vocabulary size\n self.f = f\n self.session = session\n\n def set_session(self, session):\n self.session = session\n\n def build(self, We, Wx, Wh, bh, h0, Wo, bo):\n # make them tf Variables\n self.We = tf.Variable(We)\n self.Wx = tf.Variable(Wx)\n self.Wh = tf.Variable(Wh)\n self.bh = tf.Variable(bh)\n self.h0 = tf.Variable(h0)\n self.Wo = tf.Variable(Wo)\n self.bo = tf.Variable(bo)\n self.params = [self.We, self.Wx, self.Wh, self.bh, self.h0, self.Wo, self.bo]\n\n # for easy access\n V = self.V\n D = self.D\n M = self.M\n\n # placeholders\n self.tfX = tf.placeholder(tf.int32, shape=(None,), name='X')\n self.tfY = tf.placeholder(tf.int32, shape=(None,), name='Y')\n\n # convert word indexes to word vectors\n # this would be equivalent to doing\n # We[tfX] in Numpy / Theano\n # or:\n # X_one_hot = one_hot_encode(X)\n # X_one_hot.dot(We)\n XW = tf.nn.embedding_lookup(We, self.tfX)\n\n # multiply it by input->hidden so we don't have to do\n # it inside recurrence\n XW_Wx = tf.matmul(XW, self.Wx)\n\n\n def recurrence(h_t1, XW_Wx_t):\n # returns h(t), y(t)\n h_t1 = tf.reshape(h_t1, (1, M))\n h_t = self.f(XW_Wx_t + tf.matmul(h_t1, self.Wh) + self.bh)\n h_t = tf.reshape(h_t, (M,))\n return h_t\n\n h = tf.scan(\n fn=recurrence,\n elems=XW_Wx,\n initializer=self.h0,\n )\n\n # output\n logits = tf.matmul(h, self.Wo) + self.bo\n prediction = tf.argmax(logits, 1)\n self.output_probs = tf.nn.softmax(logits)\n\n nce_weights = tf.transpose(self.Wo, [1,0]) # needs to be VxD, not DxV\n nce_biases = self.bo\n\n h = tf.reshape(h, (-1, M))\n labels = tf.reshape(self.tfY, (-1, 1))\n\n self.cost = tf.reduce_mean(\n tf.nn.sampled_softmax_loss(\n weights=nce_weights,\n biases=nce_biases,\n labels=labels,\n inputs=h,\n num_sampled=50, # number of negative samples\n num_classes=V\n )\n )\n\n self.predict_op = prediction\n self.train_op = tf.train.AdamOptimizer(1e-2).minimize(self.cost)\n # self.train_op = tf.train.MomentumOptimizer(1e-3, 0.9).minimize(self.cost)\n\n # init all variables\n init = tf.global_variables_initializer()\n self.session.run(init)\n\n\n def fit(self, X, epochs=500, show_fig=False):\n N = len(X)\n D = self.D\n M = self.M\n V = self.V\n\n # initial weights\n We = init_weight(V, D).astype(np.float32)\n Wx = init_weight(D, M).astype(np.float32)\n Wh = init_weight(M, M).astype(np.float32)\n bh = np.zeros(M).astype(np.float32)\n h0 = np.zeros(M).astype(np.float32)\n Wo = init_weight(M, V).astype(np.float32)\n bo = np.zeros(V).astype(np.float32)\n\n # build tensorflow functions\n self.build(We, Wx, Wh, bh, h0, Wo, bo)\n\n # sentence input:\n # [START, w1, w2, ..., wn]\n # sentence target:\n # [w1, w2, w3, ..., END]\n\n costs = []\n n_total = sum((len(sentence)+1) for sentence in X)\n for i in range(epochs):\n X = shuffle(X)\n n_correct = 0\n cost = 0\n for j in range(N):\n # problem! many words --> END token are overrepresented\n # result: generated lines will be very short\n # we will try to fix in a later iteration\n # BAD! magic numbers 0 and 1...\n input_sequence = [0] + X[j]\n output_sequence = X[j] + [1]\n\n # we set 0 to start and 1 to end\n _, c, p = self.session.run(\n (self.train_op, self.cost, self.predict_op),\n feed_dict={self.tfX: input_sequence, self.tfY: output_sequence}\n )\n # print \"p:\", p\n cost += c\n # print \"j:\", j, \"c:\", c/len(X[j]+1)\n for pj, xj in zip(p, output_sequence):\n if pj == xj:\n n_correct += 1\n print(\"i:\", i, \"cost:\", cost, \"correct rate:\", (float(n_correct)/n_total))\n costs.append(cost)\n\n if show_fig:\n plt.plot(costs)\n plt.show()\n\n def predict(self, prev_words):\n # don't use argmax, so that we can sample\n # from this probability distribution\n return self.session.run(\n self.output_probs,\n feed_dict={self.tfX: prev_words}\n )\n \n def save(self, filename):\n actual_params = self.session.run(self.params)\n np.savez(filename, *[p for p in actual_params])\n\n @staticmethod\n def load(filename, activation, session):\n # TODO: would prefer to save activation to file too\n npz = np.load(filename)\n We = npz['arr_0']\n Wx = npz['arr_1']\n Wh = npz['arr_2']\n bh = npz['arr_3']\n h0 = npz['arr_4']\n Wo = npz['arr_5']\n bo = npz['arr_6']\n V, D = We.shape\n _, M = Wx.shape\n rnn = SimpleRNN(D, M, V, activation, session)\n rnn.build(We, Wx, Wh, bh, h0, Wo, bo)\n return rnn\n\n def generate(self, pi, word2idx):\n # convert word2idx -> idx2word\n idx2word = {v:k for k,v in iteritems(word2idx)}\n V = len(pi)\n\n # generate 4 lines at a time\n n_lines = 0\n\n # why? because using the START symbol will always yield the same first word!\n X = [ np.random.choice(V, p=pi) ]\n print(idx2word[X[0]], end=\" \")\n\n while n_lines < 4:\n probs = self.predict(X)[-1]\n word_idx = np.random.choice(V, p=probs)\n X.append(word_idx)\n if word_idx > 1:\n # it's a real word, not start/end token\n word = idx2word[word_idx]\n print(word, end=\" \")\n elif word_idx == 1:\n # end token\n n_lines += 1\n print('')\n if n_lines < 4:\n X = [ np.random.choice(V, p=pi) ] # reset to start of line\n print(idx2word[X[0]], end=\" \")\n\n\ndef train_poetry(session, dims, savefile):\n sentences, word2idx = get_robert_frost()\n rnn = SimpleRNN(dims, dims, len(word2idx), tf.nn.relu, session)\n rnn.fit(sentences, epochs=17, show_fig=True)\n rnn.save(savefile)\n\n\ndef generate_poetry(session, savefile):\n sentences, word2idx = get_robert_frost()\n rnn = SimpleRNN.load(savefile, tf.nn.relu, session)\n\n # determine initial state distribution for starting sentences\n V = len(word2idx)\n pi = np.zeros(V)\n for sentence in sentences:\n pi[sentence[0]] += 1\n pi /= pi.sum()\n\n rnn.generate(pi, word2idx)\n\n\nif __name__ == '__main__':\n dims = 50\n savefile = 'RNN_D50_M50_tf.npz'\n session = tf.InteractiveSession()\n train_poetry(session, dims, savefile)\n generate_poetry(session, savefile)\n\n" }, { "path": "rnn_class/srn_parity.py", "content": "# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python\n# https://udemy.com/deep-learning-recurrent-neural-networks-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport theano\nimport theano.tensor as T\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.utils import shuffle\nfrom util import init_weight, all_parity_pairs_with_sequence_labels\n\n\nclass SimpleRNN:\n def __init__(self, M):\n self.M = M # hidden layer size\n\n def fit(self, X, Y, learning_rate=0.1, mu=0.99, reg=1.0, activation=T.tanh, epochs=100, show_fig=False):\n D = X[0].shape[1] # X is of size N x T(n) x D\n K = len(set(Y.flatten()))\n N = len(Y)\n M = self.M\n self.f = activation\n\n # initial weights\n Wx = init_weight(D, M)\n Wh = init_weight(M, M)\n bh = np.zeros(M)\n h0 = np.zeros(M)\n Wo = init_weight(M, K)\n bo = np.zeros(K)\n\n # make them theano shared\n self.Wx = theano.shared(Wx)\n self.Wh = theano.shared(Wh)\n self.bh = theano.shared(bh)\n self.h0 = theano.shared(h0)\n self.Wo = theano.shared(Wo)\n self.bo = theano.shared(bo)\n self.params = [self.Wx, self.Wh, self.bh, self.h0, self.Wo, self.bo]\n\n thX = T.fmatrix('X')\n thY = T.ivector('Y')\n\n def recurrence(x_t, h_t1):\n # returns h(t), y(t)\n h_t = self.f(x_t.dot(self.Wx) + h_t1.dot(self.Wh) + self.bh)\n y_t = T.nnet.softmax(h_t.dot(self.Wo) + self.bo)\n return h_t, y_t\n\n [h, y], _ = theano.scan(\n fn=recurrence,\n outputs_info=[self.h0, None],\n sequences=thX,\n n_steps=thX.shape[0],\n )\n\n py_x = y[:, 0, :]\n prediction = T.argmax(py_x, axis=1)\n\n cost = -T.mean(T.log(py_x[T.arange(thY.shape[0]), thY]))\n grads = T.grad(cost, self.params)\n dparams = [theano.shared(p.get_value()*0) for p in self.params]\n\n updates = [\n (p, p + mu*dp - learning_rate*g) for p, dp, g in zip(self.params, dparams, grads)\n ] + [\n (dp, mu*dp - learning_rate*g) for dp, g in zip(dparams, grads)\n ]\n\n self.predict_op = theano.function(inputs=[thX], outputs=prediction)\n self.train_op = theano.function(\n inputs=[thX, thY],\n outputs=[cost, prediction, y],\n updates=updates\n )\n\n costs = []\n for i in range(epochs):\n X, Y = shuffle(X, Y)\n n_correct = 0\n cost = 0\n for j in range(N):\n c, p, rout = self.train_op(X[j], Y[j])\n # print \"p:\", p\n cost += c\n if p[-1] == Y[j,-1]:\n n_correct += 1\n print(\"shape y:\", rout.shape)\n print(\"i:\", i, \"cost:\", cost, \"classification rate:\", (float(n_correct)/N))\n costs.append(cost)\n if n_correct == N:\n break\n\n if show_fig:\n plt.plot(costs)\n plt.show()\n\n\n\ndef parity(B=12, learning_rate=1e-4, epochs=200):\n X, Y = all_parity_pairs_with_sequence_labels(B)\n\n rnn = SimpleRNN(20)\n rnn.fit(X, Y, learning_rate=learning_rate, epochs=epochs, activation=T.nnet.relu, show_fig=False)\n\n\nif __name__ == '__main__':\n parity()\n\n" }, { "path": "rnn_class/srn_parity_tf.py", "content": "# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python\n# https://udemy.com/deep-learning-recurrent-neural-networks-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.utils import shuffle\nfrom util import init_weight, all_parity_pairs_with_sequence_labels\n\n\nclass SimpleRNN:\n def __init__(self, M):\n self.M = M # hidden layer size\n\n def fit(self, X, Y, learning_rate=1.0, mu=0.99, reg=1.0, activation=tf.tanh, epochs=100, show_fig=False):\n N, T, D = X.shape\n K = len(set(Y.flatten()))\n M = self.M\n self.f = activation\n\n # initial weights\n Wx = init_weight(D, M).astype(np.float32)\n Wh = init_weight(M, M).astype(np.float32)\n bh = np.zeros(M, dtype=np.float32)\n h0 = np.zeros(M, dtype=np.float32)\n Wo = init_weight(M, K).astype(np.float32)\n bo = np.zeros(K, dtype=np.float32)\n\n # make them theano shared\n self.Wx = tf.Variable(Wx)\n self.Wh = tf.Variable(Wh)\n self.bh = tf.Variable(bh)\n self.h0 = tf.Variable(h0)\n self.Wo = tf.Variable(Wo)\n self.bo = tf.Variable(bo)\n\n tfX = tf.placeholder(tf.float32, shape=(T, D), name='X')\n tfY = tf.placeholder(tf.int32, shape=(T,), name='Y')\n\n XWx = tf.matmul(tfX, self.Wx)\n\n def recurrence(h_t1, xw_t):\n # matmul() only works with 2-D objects\n # we want to return a 1-D object of size M\n # so that the final result is T x M\n # not T x 1 x M\n h_t = self.f(xw_t + tf.matmul(tf.reshape(h_t1, (1, M)), self.Wh) + self.bh)\n return tf.reshape(h_t, (M,))\n\n h = tf.scan(\n fn=recurrence,\n elems=XWx,\n initializer=self.h0,\n )\n\n logits = tf.matmul(h, self.Wo) + self.bo\n\n cost = tf.reduce_mean(\n tf.nn.sparse_softmax_cross_entropy_with_logits(\n labels=tfY,\n logits=logits,\n )\n )\n\n predict_op = tf.argmax(logits, 1)\n train_op = tf.train.AdamOptimizer(1e-2).minimize(cost)\n\n init = tf.global_variables_initializer()\n with tf.Session() as session:\n session.run(init)\n\n costs = []\n for i in range(epochs):\n X, Y = shuffle(X, Y)\n n_correct = 0\n batch_cost = 0\n for j in range(N):\n _, c, p = session.run([train_op, cost, predict_op], feed_dict={tfX: X[j].reshape(T, D), tfY: Y[j]})\n batch_cost += c\n if p[-1] == Y[j,-1]:\n n_correct += 1\n print(\"i:\", i, \"cost:\", batch_cost, \"classification rate:\", (float(n_correct)/N))\n costs.append(batch_cost)\n if n_correct == N:\n break\n\n if show_fig:\n plt.plot(costs)\n plt.show()\n\n\n\ndef parity(B=12, learning_rate=1e-4, epochs=200):\n X, Y = all_parity_pairs_with_sequence_labels(B)\n X = X.astype(np.float32)\n\n rnn = SimpleRNN(20)\n rnn.fit(X, Y, learning_rate=learning_rate, epochs=epochs, activation=tf.nn.relu, show_fig=False)\n\n\nif __name__ == '__main__':\n parity()\n\n" }, { "path": "rnn_class/tf_parity.py", "content": "# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python\n# https://udemy.com/deep-learning-recurrent-neural-networks-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# from tensorflow.python.ops import rnn as rnn_module\n\n######## This only works for pre-1.0 versions ##########\n# from tensorflow.python.ops.rnn import rnn as get_rnn_output\n# from tensorflow.python.ops.rnn_cell import BasicRNNCell, GRUCell\n########################################################\n\n########## This works for TensorFlow v1.0 ##############\nfrom tensorflow.contrib.rnn import static_rnn as get_rnn_output\nfrom tensorflow.contrib.rnn import BasicRNNCell, GRUCell\n########################################################\n\nfrom sklearn.utils import shuffle\nfrom util import init_weight, all_parity_pairs_with_sequence_labels, all_parity_pairs\n\n\ndef x2sequence(x, T, D, batch_sz):\n # Permuting batch_size and n_steps\n x = tf.transpose(x, (1, 0, 2))\n # Reshaping to (n_steps*batch_size, n_input)\n x = tf.reshape(x, (T*batch_sz, D))\n # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input)\n # x = tf.split(0, T, x) # v0.1\n x = tf.split(x, T) # v1.0\n # print \"type(x):\", type(x)\n return x\n\nclass SimpleRNN:\n def __init__(self, M):\n self.M = M # hidden layer size\n\n\n def fit(self, X, Y, batch_sz=20, learning_rate=0.1, mu=0.9, activation=tf.nn.sigmoid, epochs=100, show_fig=False):\n N, T, D = X.shape # X is of size N x T(n) x D\n K = len(set(Y.flatten()))\n M = self.M\n self.f = activation\n\n # initial weights\n # note: Wx, Wh, bh are all part of the RNN unit and will be created\n # by BasicRNNCell\n Wo = init_weight(M, K).astype(np.float32)\n bo = np.zeros(K, dtype=np.float32)\n\n # make them tf variables\n self.Wo = tf.Variable(Wo)\n self.bo = tf.Variable(bo)\n\n # tf Graph input\n tfX = tf.placeholder(tf.float32, shape=(batch_sz, T, D), name='inputs')\n tfY = tf.placeholder(tf.int64, shape=(batch_sz, T), name='targets')\n\n # turn tfX into a sequence, e.g. T tensors all of size (batch_sz, D)\n sequenceX = x2sequence(tfX, T, D, batch_sz)\n\n # create the simple rnn unit\n rnn_unit = BasicRNNCell(num_units=self.M, activation=self.f)\n\n # Get rnn cell output\n # outputs, states = rnn_module.rnn(rnn_unit, sequenceX, dtype=tf.float32)\n outputs, states = get_rnn_output(rnn_unit, sequenceX, dtype=tf.float32)\n\n # outputs are now of size (T, batch_sz, M)\n # so make it (batch_sz, T, M)\n outputs = tf.transpose(outputs, (1, 0, 2))\n outputs = tf.reshape(outputs, (T*batch_sz, M))\n\n # Linear activation, using rnn inner loop last output\n logits = tf.matmul(outputs, self.Wo) + self.bo\n predict_op = tf.argmax(logits, 1)\n targets = tf.reshape(tfY, (T*batch_sz,))\n\n cost_op = tf.reduce_mean(\n tf.nn.sparse_softmax_cross_entropy_with_logits(\n logits=logits,\n labels=targets\n )\n )\n train_op = tf.train.MomentumOptimizer(learning_rate, momentum=mu).minimize(cost_op)\n\n costs = []\n n_batches = N // batch_sz\n \n init = tf.global_variables_initializer()\n with tf.Session() as session:\n session.run(init)\n for i in range(epochs):\n X, Y = shuffle(X, Y)\n n_correct = 0\n cost = 0\n for j in range(n_batches):\n Xbatch = X[j*batch_sz:(j+1)*batch_sz]\n Ybatch = Y[j*batch_sz:(j+1)*batch_sz]\n \n _, c, p = session.run([train_op, cost_op, predict_op], feed_dict={tfX: Xbatch, tfY: Ybatch})\n cost += c\n for b in range(batch_sz):\n idx = (b + 1)*T - 1\n n_correct += (p[idx] == Ybatch[b][-1])\n if i % 10 == 0:\n print(\"i:\", i, \"cost:\", cost, \"classification rate:\", (float(n_correct)/N))\n if n_correct == N:\n print(\"i:\", i, \"cost:\", cost, \"classification rate:\", (float(n_correct)/N))\n break\n costs.append(cost)\n\n if show_fig:\n plt.plot(costs)\n plt.show()\n\n\n\ndef parity(B=12, learning_rate=1., epochs=1000):\n X, Y = all_parity_pairs_with_sequence_labels(B)\n\n rnn = SimpleRNN(4)\n rnn.fit(X, Y,\n batch_sz=len(Y),\n learning_rate=learning_rate,\n epochs=epochs,\n activation=tf.nn.sigmoid,\n show_fig=False\n )\n\n\nif __name__ == '__main__':\n parity()\n\n" }, { "path": "rnn_class/util.py", "content": "# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python\n# https://udemy.com/deep-learning-recurrent-neural-networks-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport string\nimport os\nimport sys\nimport operator\nfrom nltk import pos_tag, word_tokenize\nfrom datetime import datetime\n\ndef init_weight(Mi, Mo):\n return np.random.randn(Mi, Mo) / np.sqrt(Mi + Mo)\n\ndef all_parity_pairs(nbit):\n # total number of samples (Ntotal) will be a multiple of 100\n # why did I make it this way? I don't remember.\n N = 2**nbit\n remainder = 100 - (N % 100)\n Ntotal = N + remainder\n X = np.zeros((Ntotal, nbit))\n Y = np.zeros(Ntotal)\n for ii in range(Ntotal):\n i = ii % N\n # now generate the ith sample\n for j in range(nbit):\n if i % (2**(j+1)) != 0:\n i -= 2**j\n X[ii,j] = 1\n Y[ii] = X[ii].sum() % 2\n return X, Y\n\ndef all_parity_pairs_with_sequence_labels(nbit):\n X, Y = all_parity_pairs(nbit)\n N, t = X.shape\n\n # we want every time step to have a label\n Y_t = np.zeros(X.shape, dtype=np.int32)\n for n in range(N):\n ones_count = 0\n for i in range(t):\n if X[n,i] == 1:\n ones_count += 1\n if ones_count % 2 == 1:\n Y_t[n,i] = 1\n\n X = X.reshape(N, t, 1).astype(np.float32)\n return X, Y_t\n\n# unfortunately Python 2 and 3 translates work differently\ndef remove_punctuation_2(s):\n return s.translate(None, string.punctuation)\n\ndef remove_punctuation_3(s):\n return s.translate(str.maketrans('','',string.punctuation))\n\nif sys.version.startswith('2'):\n remove_punctuation = remove_punctuation_2\nelse:\n remove_punctuation = remove_punctuation_3\n\n\ndef get_robert_frost():\n word2idx = {'START': 0, 'END': 1}\n current_idx = 2\n sentences = []\n for line in open('../hmm_class/robert_frost.txt'):\n line = line.strip()\n if line:\n tokens = remove_punctuation(line.lower()).split()\n sentence = []\n for t in tokens:\n if t not in word2idx:\n word2idx[t] = current_idx\n current_idx += 1\n idx = word2idx[t]\n sentence.append(idx)\n sentences.append(sentence)\n return sentences, word2idx\n\ndef my_tokenizer(s):\n s = remove_punctuation(s)\n s = s.lower() # downcase\n return s.split()\n\ndef get_wikipedia_data(n_files, n_vocab, by_paragraph=False):\n prefix = '../large_files/'\n\n if not os.path.exists(prefix):\n print(\"Are you sure you've downloaded, converted, and placed the Wikipedia data into the proper folder?\")\n print(\"I'm looking for a folder called large_files, adjacent to the class folder, but it does not exist.\")\n print(\"Please download the data from https://dumps.wikimedia.org/\")\n print(\"Quitting...\")\n exit()\n\n input_files = [f for f in os.listdir(prefix) if f.startswith('enwiki') and f.endswith('txt')]\n\n if len(input_files) == 0:\n print(\"Looks like you don't have any data files, or they're in the wrong location.\")\n print(\"Please download the data from https://dumps.wikimedia.org/\")\n print(\"Quitting...\")\n exit()\n\n # return variables\n sentences = []\n word2idx = {'START': 0, 'END': 1}\n idx2word = ['START', 'END']\n current_idx = 2\n word_idx_count = {0: float('inf'), 1: float('inf')}\n\n if n_files is not None:\n input_files = input_files[:n_files]\n\n for f in input_files:\n print(\"reading:\", f)\n for line in open(prefix + f):\n line = line.strip()\n # don't count headers, structured data, lists, etc...\n if line and line[0] not in ('[', '*', '-', '|', '=', '{', '}'):\n if by_paragraph:\n sentence_lines = [line]\n else:\n sentence_lines = line.split('. ')\n for sentence in sentence_lines:\n tokens = my_tokenizer(sentence)\n for t in tokens:\n if t not in word2idx:\n word2idx[t] = current_idx\n idx2word.append(t)\n current_idx += 1\n idx = word2idx[t]\n word_idx_count[idx] = word_idx_count.get(idx, 0) + 1\n sentence_by_idx = [word2idx[t] for t in tokens]\n sentences.append(sentence_by_idx)\n\n # restrict vocab size\n sorted_word_idx_count = sorted(word_idx_count.items(), key=operator.itemgetter(1), reverse=True)\n word2idx_small = {}\n new_idx = 0\n idx_new_idx_map = {}\n for idx, count in sorted_word_idx_count[:n_vocab]:\n word = idx2word[idx]\n print(word, count)\n word2idx_small[word] = new_idx\n idx_new_idx_map[idx] = new_idx\n new_idx += 1\n # let 'unknown' be the last token\n word2idx_small['UNKNOWN'] = new_idx \n unknown = new_idx\n\n assert('START' in word2idx_small)\n assert('END' in word2idx_small)\n assert('king' in word2idx_small)\n assert('queen' in word2idx_small)\n assert('man' in word2idx_small)\n assert('woman' in word2idx_small)\n\n # map old idx to new idx\n sentences_small = []\n for sentence in sentences:\n if len(sentence) > 1:\n new_sentence = [idx_new_idx_map[idx] if idx in idx_new_idx_map else unknown for idx in sentence]\n sentences_small.append(new_sentence)\n\n return sentences_small, word2idx_small\n\ndef get_tags(s):\n tuples = pos_tag(word_tokenize(s))\n return [y for x, y in tuples]\n\ndef get_poetry_classifier_data(samples_per_class, load_cached=True, save_cached=True):\n datafile = 'poetry_classifier_data.npz'\n if load_cached and os.path.exists(datafile):\n npz = np.load(datafile)\n X = npz['arr_0']\n Y = npz['arr_1']\n V = int(npz['arr_2'])\n return X, Y, V\n\n word2idx = {}\n current_idx = 0\n X = []\n Y = []\n for fn, label in zip(('../hmm_class/edgar_allan_poe.txt', '../hmm_class/robert_frost.txt'), (0, 1)):\n count = 0\n for line in open(fn):\n line = line.rstrip()\n if line:\n print(line)\n # tokens = remove_punctuation(line.lower()).split()\n tokens = get_tags(line)\n if len(tokens) > 1:\n # scan doesn't work nice here, technically could fix...\n for token in tokens:\n if token not in word2idx:\n word2idx[token] = current_idx\n current_idx += 1\n sequence = np.array([word2idx[w] for w in tokens])\n X.append(sequence)\n Y.append(label)\n count += 1\n print(count)\n # quit early because the tokenizer is very slow\n if count >= samples_per_class:\n break\n if save_cached:\n np.savez(datafile, X, Y, current_idx)\n return X, Y, current_idx\n\n\ndef get_stock_data():\n input_files = os.listdir('stock_data')\n min_length = 2000\n\n # first find the latest start date\n # so that each time series can start at the same time\n max_min_date = datetime(2000, 1, 1)\n line_counts = {}\n for f in input_files:\n n = 0\n for line in open('stock_data/%s' % f):\n # pass\n n += 1\n line_counts[f] = n\n if n > min_length:\n # else we'll ignore this symbol, too little data\n # print 'stock_data/%s' % f, 'num lines:', n\n last_line = line\n date = line.split(',')[0]\n date = datetime.strptime(date, '%Y-%m-%d')\n if date > max_min_date:\n max_min_date = date\n\n print(\"max min date:\", max_min_date)\n\n # now collect the data up to min date\n all_binary_targets = []\n all_prices = []\n for f in input_files:\n if line_counts[f] > min_length:\n prices = []\n binary_targets = []\n first = True\n last_price = 0\n for line in open('stock_data/%s' % f):\n if first:\n first = False\n continue\n date, price = line.split(',')[:2]\n date = datetime.strptime(date, '%Y-%m-%d')\n if date < max_min_date:\n break\n prices.append(float(price))\n target = 1 if last_price < price else 0\n binary_targets.append(target)\n last_price = price\n all_prices.append(prices)\n all_binary_targets.append(binary_targets)\n\n # D = number of symbols\n # T = length of series\n return np.array(all_prices).T, np.array(all_binary_targets).T # make it T x D\n\n" }, { "path": "rnn_class/visualize_embeddings.py", "content": "# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python\n# https://udemy.com/deep-learning-recurrent-neural-networks-in-python\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport json\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.manifold import TSNE\nfrom sklearn.decomposition import PCA, TruncatedSVD\n\ndef main(we_file='word_embeddings.npy', w2i_file='wikipedia_word2idx.json', Model=PCA):\n We = np.load(we_file)\n V, D = We.shape\n with open(w2i_file) as f:\n word2idx = json.load(f)\n idx2word = {v:k for k,v in iteritems(word2idx)}\n\n model = Model()\n Z = model.fit_transform(We)\n plt.scatter(Z[:,0], Z[:,1])\n for i in range(V):\n plt.annotate(s=idx2word[i], xy=(Z[i,0], Z[i,1]))\n plt.show()\n\n\nif __name__ == '__main__':\n # main(Model=TSNE)\n\n # D=80, M=80\n # main(we_file='gru_nonorm_part1_word_embeddings.npy', w2i_file='gru_nonorm_part1_wikipedia_word2idx.json', Model=TSNE)\n main(we_file='working_files/batch_gru_word_embeddings.npy', w2i_file='working_files/batch_wikipedia_word2idx.json', Model=TSNE)\n" }, { "path": "rnn_class/wiki.py", "content": "# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python\n# https://udemy.com/deep-learning-recurrent-neural-networks-in-python\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport sys\nimport theano\nimport theano.tensor as T\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport json\n\nfrom datetime import datetime\nfrom sklearn.utils import shuffle\nfrom gru import GRU\nfrom lstm import LSTM\nfrom util import init_weight, get_wikipedia_data\nfrom brown import get_sentences_with_word2idx_limit_vocab\n\n\nclass RNN:\n def __init__(self, D, hidden_layer_sizes, V):\n self.hidden_layer_sizes = hidden_layer_sizes\n self.D = D\n self.V = V\n\n def fit(self, X, learning_rate=1e-5, mu=0.99, epochs=10, show_fig=True, activation=T.nnet.relu, RecurrentUnit=GRU, normalize=True):\n D = self.D\n V = self.V\n N = len(X)\n\n We = init_weight(V, D)\n self.hidden_layers = []\n Mi = D\n for Mo in self.hidden_layer_sizes:\n ru = RecurrentUnit(Mi, Mo, activation)\n self.hidden_layers.append(ru)\n Mi = Mo\n\n Wo = init_weight(Mi, V)\n bo = np.zeros(V)\n\n self.We = theano.shared(We)\n self.Wo = theano.shared(Wo)\n self.bo = theano.shared(bo)\n self.params = [self.Wo, self.bo]\n for ru in self.hidden_layers:\n self.params += ru.params\n\n thX = T.ivector('X')\n thY = T.ivector('Y')\n\n Z = self.We[thX]\n for ru in self.hidden_layers:\n Z = ru.output(Z)\n py_x = T.nnet.softmax(Z.dot(self.Wo) + self.bo)\n\n prediction = T.argmax(py_x, axis=1)\n # let's return py_x too so we can draw a sample instead\n self.predict_op = theano.function(\n inputs=[thX],\n outputs=[py_x, prediction],\n allow_input_downcast=True,\n )\n \n cost = -T.mean(T.log(py_x[T.arange(thY.shape[0]), thY]))\n grads = T.grad(cost, self.params)\n dparams = [theano.shared(p.get_value()*0) for p in self.params]\n\n dWe = theano.shared(self.We.get_value()*0)\n gWe = T.grad(cost, self.We)\n dWe_update = mu*dWe - learning_rate*gWe\n We_update = self.We + dWe_update\n if normalize:\n We_update /= We_update.norm(2)\n\n updates = [\n (p, p + mu*dp - learning_rate*g) for p, dp, g in zip(self.params, dparams, grads)\n ] + [\n (dp, mu*dp - learning_rate*g) for dp, g in zip(dparams, grads)\n ] + [\n (self.We, We_update), (dWe, dWe_update)\n ]\n\n self.train_op = theano.function(\n inputs=[thX, thY],\n outputs=[cost, prediction],\n updates=updates\n )\n\n costs = []\n for i in range(epochs):\n t0 = datetime.now()\n X = shuffle(X)\n n_correct = 0\n n_total = 0\n cost = 0\n for j in range(N):\n if np.random.random() < 0.01 or len(X[j]) <= 1:\n input_sequence = [0] + X[j]\n output_sequence = X[j] + [1]\n else:\n input_sequence = [0] + X[j][:-1]\n output_sequence = X[j]\n n_total += len(output_sequence)\n\n # test:\n \n try:\n # we set 0 to start and 1 to end\n c, p = self.train_op(input_sequence, output_sequence)\n except Exception as e:\n PYX, pred = self.predict_op(input_sequence)\n print(\"input_sequence len:\", len(input_sequence))\n print(\"PYX.shape:\",PYX.shape)\n print(\"pred.shape:\", pred.shape)\n raise e\n # print \"p:\", p\n cost += c\n # print \"j:\", j, \"c:\", c/len(X[j]+1)\n for pj, xj in zip(p, output_sequence):\n if pj == xj:\n n_correct += 1\n if j % 200 == 0:\n sys.stdout.write(\"j/N: %d/%d correct rate so far: %f\\r\" % (j, N, float(n_correct)/n_total))\n sys.stdout.flush()\n print(\"i:\", i, \"cost:\", cost, \"correct rate:\", (float(n_correct)/n_total), \"time for epoch:\", (datetime.now() - t0))\n costs.append(cost)\n\n if show_fig:\n plt.plot(costs)\n plt.show()\n\n\ndef train_wikipedia(we_file='word_embeddings.npy', w2i_file='wikipedia_word2idx.json', RecurrentUnit=GRU):\n # there are 32 files\n ### note: you can pick between Wikipedia data and Brown corpus\n ### just comment one out, and uncomment the other!\n # sentences, word2idx = get_wikipedia_data(n_files=100, n_vocab=2000)\n sentences, word2idx = get_sentences_with_word2idx_limit_vocab()\n\n print(\"finished retrieving data\")\n print(\"vocab size:\", len(word2idx), \"number of sentences:\", len(sentences))\n rnn = RNN(30, [30], len(word2idx))\n rnn.fit(sentences, learning_rate=1e-5, epochs=10, show_fig=True, activation=T.nnet.relu)\n\n np.save(we_file, rnn.We.get_value())\n with open(w2i_file, 'w') as f:\n json.dump(word2idx, f)\n\ndef find_analogies(w1, w2, w3, we_file='word_embeddings.npy', w2i_file='wikipedia_word2idx.json'):\n We = np.load(we_file)\n with open(w2i_file) as f:\n word2idx = json.load(f)\n\n king = We[word2idx[w1]]\n man = We[word2idx[w2]]\n woman = We[word2idx[w3]]\n v0 = king - man + woman\n\n def dist1(a, b):\n return np.linalg.norm(a - b)\n def dist2(a, b):\n return 1 - a.dot(b) / (np.linalg.norm(a) * np.linalg.norm(b))\n\n for dist, name in [(dist1, 'Euclidean'), (dist2, 'cosine')]:\n min_dist = float('inf')\n best_word = ''\n for word, idx in iteritems(word2idx):\n if word not in (w1, w2, w3):\n v1 = We[idx]\n d = dist(v0, v1)\n if d < min_dist:\n min_dist = d\n best_word = word\n print(\"closest match by\", name, \"distance:\", best_word)\n print(w1, \"-\", w2, \"=\", best_word, \"-\", w3)\n\nif __name__ == '__main__':\n we = 'lstm_word_embeddings2.npy'\n w2i = 'lstm_wikipedia_word2idx2.json'\n train_wikipedia(we, w2i, RecurrentUnit=GRU)\n find_analogies('king', 'man', 'woman', we, w2i)\n find_analogies('france', 'paris', 'london', we, w2i)\n find_analogies('france', 'paris', 'rome', we, w2i)\n find_analogies('paris', 'france', 'italy', we, w2i)\n\n\n\n\n" }, { "path": "stats/extra_reading.txt", "content": "The Unbiased Estimate of the Covariance Matrix\nhttps://lazyprogrammer.me/covariance-matrix-divide-by-n-or-n-1/" }, { "path": "supervised_class/app.py", "content": "from __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python\n# https://www.udemy.com/data-science-supervised-machine-learning-in-python\nimport pickle\nimport numpy as np\nimport os\nimport json\n\nimport tornado.ioloop\nimport tornado.web\n\nif not os.path.exists('mymodel.pkl'):\n exit(\"Can't run without the model!\")\n\nwith open('mymodel.pkl', 'rb') as f:\n model = pickle.load(f)\n\nclass MainHandler(tornado.web.RequestHandler):\n def get(self):\n self.write(\"Hello, Tornado!\")\n\nclass PredictionHandler(tornado.web.RequestHandler):\n # predict one sample at a time\n def post(self):\n # print \"body:\", self.request.body\n # print \"arguments:\", self.request.arguments\n # will look like this:\n # body: three=four&one=two\n # arguments: {'three': ['four'], 'one': ['two']}\n params = self.request.arguments\n x = np.array(list(map(float, params['input'])))\n y = model.predict([x])[0]\n self.write(json.dumps({'prediction': y.item()}))\n self.finish()\n\nif __name__ == \"__main__\":\n application = tornado.web.Application([\n (r\"/\", MainHandler),\n (r\"/predict\", PredictionHandler),\n ])\n application.listen(8888)\n tornado.ioloop.IOLoop.current().start()\n" }, { "path": "supervised_class/app_caller.py", "content": "from __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python\n# https://www.udemy.com/data-science-supervised-machine-learning-in-python\nimport requests\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom util import get_data\n\n# make a prediction from our own server!\n# in reality this could be coming from any client\n\nX, Y = get_data()\nN = len(Y)\nwhile True:\n i = np.random.choice(N)\n r = requests.post(\"http://localhost:8888/predict\", data={'input': X[i]})\n print(\"RESPONSE:\")\n print(r.content)\n j = r.json()\n print(j)\n print(\"target:\", Y[i])\n\n plt.imshow(X[i].reshape(28, 28), cmap='gray')\n plt.title(\"Target: %d, Prediction: %d\" % (Y[i], j['prediction']))\n plt.show()\n\n response = input(\"Continue? (Y/n)\\n\")\n if response in ('n', 'N'):\n break\n" }, { "path": "supervised_class/app_trainer.py", "content": "from __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python\n# https://www.udemy.com/data-science-supervised-machine-learning-in-python\nimport pickle\nimport numpy as np\nfrom util import get_data\nfrom sklearn.ensemble import RandomForestClassifier\n\nif __name__ == '__main__':\n X, Y = get_data()\n Ntrain = len(Y) // 4\n Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain]\n\n model = RandomForestClassifier()\n model.fit(Xtrain, Ytrain)\n\n # just in case you're curious\n Xtest, Ytest = X[Ntrain:], Y[Ntrain:]\n print(\"test accuracy:\", model.score(Xtest, Ytest))\n\n with open('mymodel.pkl', 'wb') as f:\n pickle.dump(model, f)\n" }, { "path": "supervised_class/bayes.py", "content": "# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python\n# https://www.udemy.com/data-science-supervised-machine-learning-in-python\n# This is an example of a Bayes classifier on MNIST data.\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom util import get_data\nfrom datetime import datetime\nfrom scipy.stats import norm\nfrom scipy.stats import multivariate_normal as mvn\n\nclass Bayes(object):\n def fit(self, X, Y, smoothing=1e-2):\n N, D = X.shape\n self.gaussians = dict()\n self.priors = dict()\n labels = set(Y)\n for c in labels:\n current_x = X[Y == c]\n self.gaussians[c] = {\n 'mean': current_x.mean(axis=0),\n 'cov': np.cov(current_x.T) + np.eye(D)*smoothing,\n }\n self.priors[c] = float(len(Y[Y == c])) / len(Y)\n\n def score(self, X, Y):\n P = self.predict(X)\n return np.mean(P == Y)\n\n def predict(self, X):\n N, D = X.shape\n K = len(self.gaussians)\n P = np.zeros((N, K))\n for c, g in iteritems(self.gaussians):\n mean, cov = g['mean'], g['cov']\n P[:,c] = mvn.logpdf(X, mean=mean, cov=cov) + np.log(self.priors[c])\n return np.argmax(P, axis=1)\n\n\nif __name__ == '__main__':\n X, Y = get_data(10000)\n Ntrain = len(Y) // 2\n Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain]\n Xtest, Ytest = X[Ntrain:], Y[Ntrain:]\n\n model = Bayes()\n t0 = datetime.now()\n model.fit(Xtrain, Ytrain)\n print(\"Training time:\", (datetime.now() - t0))\n\n t0 = datetime.now()\n print(\"Train accuracy:\", model.score(Xtrain, Ytrain))\n print(\"Time to compute train accuracy:\", (datetime.now() - t0), \"Train size:\", len(Ytrain))\n\n t0 = datetime.now()\n print(\"Test accuracy:\", model.score(Xtest, Ytest))\n print(\"Time to compute test accuracy:\", (datetime.now() - t0), \"Test size:\", len(Ytest))\n\n # plot the mean of each class\n for c, g in iteritems(model.gaussians):\n plt.imshow(g['mean'].reshape(28, 28))\n plt.title(c)\n plt.show()\n" }, { "path": "supervised_class/dt.py", "content": "# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python\n# https://www.udemy.com/data-science-supervised-machine-learning-in-python\n# Decision Tree for continuous-vector input, binary output\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nfrom util import get_data, get_xor, get_donut\nfrom datetime import datetime\n\n\ndef entropy(y):\n # assume y is binary - 0 or 1\n N = len(y)\n s1 = (y == 1).sum()\n if 0 == s1 or N == s1:\n return 0\n p1 = float(s1) / N\n p0 = 1 - p1\n return -p0*np.log2(p0) - p1*np.log2(p1)\n\n\nclass TreeNode:\n def __init__(self, depth=1, max_depth=None):\n print('depth:', depth)\n self.depth = depth\n self.max_depth = max_depth\n if self.max_depth is not None and self.max_depth < self.depth:\n raise Exception(\"depth > max_depth\")\n\n def fit(self, X, Y):\n if len(Y) == 1 or len(set(Y)) == 1:\n # base case, only 1 sample\n # another base case\n # this node only receives examples from 1 class\n # we can't make a split\n self.col = None\n self.split = None\n self.left = None\n self.right = None\n self.prediction = Y[0]\n\n else:\n D = X.shape[1]\n cols = range(D)\n\n max_ig = 0\n best_col = None\n best_split = None\n for col in cols:\n ig, split = self.find_split(X, Y, col)\n # print \"ig:\", ig\n if ig > max_ig:\n max_ig = ig\n best_col = col\n best_split = split\n\n if max_ig == 0:\n # nothing we can do\n # no further splits\n self.col = None\n self.split = None\n self.left = None\n self.right = None\n self.prediction = np.round(Y.mean())\n else:\n self.col = best_col\n self.split = best_split\n\n if self.depth == self.max_depth:\n self.left = None\n self.right = None\n self.prediction = [\n np.round(Y[X[:,best_col] < self.split].mean()),\n np.round(Y[X[:,best_col] >= self.split].mean()),\n ]\n else:\n # print \"best split:\", best_split\n left_idx = (X[:,best_col] < best_split)\n # print \"left_idx.shape:\", left_idx.shape, \"len(X):\", len(X)\n Xleft = X[left_idx]\n Yleft = Y[left_idx]\n self.left = TreeNode(self.depth + 1, self.max_depth)\n self.left.fit(Xleft, Yleft)\n\n right_idx = (X[:,best_col] >= best_split)\n Xright = X[right_idx]\n Yright = Y[right_idx]\n self.right = TreeNode(self.depth + 1, self.max_depth)\n self.right.fit(Xright, Yright)\n\n def find_split(self, X, Y, col):\n # print \"finding split for col:\", col\n x_values = X[:, col]\n sort_idx = np.argsort(x_values)\n x_values = x_values[sort_idx]\n y_values = Y[sort_idx]\n\n # Note: optimal split is the midpoint between 2 points\n # Note: optimal split is only on the boundaries between 2 classes\n\n # if boundaries[i] is true\n # then y_values[i] != y_values[i+1]\n # nonzero() gives us indices where arg is true\n # but for some reason it returns a tuple of size 1\n boundaries = np.nonzero(y_values[:-1] != y_values[1:])[0]\n best_split = None\n max_ig = 0\n for b in boundaries:\n split = (x_values[b] + x_values[b+1]) / 2\n ig = self.information_gain(x_values, y_values, split)\n if ig > max_ig:\n max_ig = ig\n best_split = split\n return max_ig, best_split\n\n def information_gain(self, x, y, split):\n # assume classes are 0 and 1\n # print \"split:\", split\n y0 = y[x < split]\n y1 = y[x >= split]\n N = len(y)\n y0len = len(y0)\n if y0len == 0 or y0len == N:\n return 0\n p0 = float(len(y0)) / N\n p1 = 1 - p0 #float(len(y1)) / N\n # print \"entropy(y):\", entropy(y)\n # print \"p0:\", p0\n # print \"entropy(y0):\", entropy(y0)\n # print \"p1:\", p1\n # print \"entropy(y1):\", entropy(y1)\n return entropy(y) - p0*entropy(y0) - p1*entropy(y1)\n\n def predict_one(self, x):\n # use \"is not None\" because 0 means False\n if self.col is not None and self.split is not None:\n feature = x[self.col]\n if feature < self.split:\n if self.left:\n p = self.left.predict_one(x)\n else:\n p = self.prediction[0]\n else:\n if self.right:\n p = self.right.predict_one(x)\n else:\n p = self.prediction[1]\n else:\n # corresponds to having only 1 prediction\n p = self.prediction\n return p\n\n def predict(self, X):\n N = len(X)\n P = np.zeros(N)\n for i in range(N):\n P[i] = self.predict_one(X[i])\n return P\n\n\n# This class is kind of redundant\nclass DecisionTree:\n def __init__(self, max_depth=None):\n self.max_depth = max_depth\n\n def fit(self, X, Y):\n self.root = TreeNode(max_depth=self.max_depth)\n self.root.fit(X, Y)\n\n def predict(self, X):\n return self.root.predict(X)\n\n def score(self, X, Y):\n P = self.predict(X)\n return np.mean(P == Y)\n\n\nif __name__ == '__main__':\n X, Y = get_data()\n\n # try donut and xor\n # from sklearn.utils import shuffle\n # X, Y = get_xor()\n # # X, Y = get_donut()\n # X, Y = shuffle(X, Y)\n\n # only take 0s and 1s since we're doing binary classification\n idx = np.logical_or(Y == 0, Y == 1)\n X = X[idx]\n Y = Y[idx]\n\n # split the data\n Ntrain = len(Y) // 2\n Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain]\n Xtest, Ytest = X[Ntrain:], Y[Ntrain:]\n \n model = DecisionTree()\n # model = DecisionTree(max_depth=7)\n t0 = datetime.now()\n model.fit(Xtrain, Ytrain)\n print(\"Training time:\", (datetime.now() - t0))\n\n t0 = datetime.now()\n print(\"Train accuracy:\", model.score(Xtrain, Ytrain))\n print(\"Time to compute train accuracy:\", (datetime.now() - t0))\n\n t0 = datetime.now()\n print(\"Test accuracy:\", model.score(Xtest, Ytest))\n print(\"Time to compute test accuracy:\", (datetime.now() - t0))\n" }, { "path": "supervised_class/dt_without_recursion.py", "content": "# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python\n# https://www.udemy.com/data-science-supervised-machine-learning-in-python\n# Decision Tree for continuous-vector input, binary output\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nfrom util import get_data, get_xor, get_donut\nfrom datetime import datetime\n\n\ndef entropy(y):\n # assume y is binary - 0 or 1\n N = len(y)\n s1 = (y == 1).sum()\n if 0 == s1 or N == s1:\n return 0\n p1 = float(s1) / N\n p0 = 1 - p1\n # return -p0*np.log2(p0) - p1*np.log2(p1)\n return 1 - p0*p0 - p1*p1\n\n\nclass DecisionTree:\n def __init__(self, depth=0, max_depth=None):\n # print 'depth:', depth\n # self.depth = depth\n self.max_depth = max_depth\n self.root = {} # is a tree node\n # each node will have the attributes (k-v pairs):\n # - col\n # - split\n # - left\n # - right\n # - prediction\n\n def fit(self, X, Y):\n\n current_node = self.root\n depth = 0\n queue = []\n # origX = X\n # origY = Y\n while True:\n\n if len(Y) == 1 or len(set(Y)) == 1:\n # base case, only 1 sample\n # another base case\n # this node only receives examples from 1 class\n # we can't make a split\n # self.col = None\n # self.split = None\n # self.left = None\n # self.right = None\n # self.prediction = Y[0]\n current_node['col'] = None\n current_node['split'] = None\n current_node['left'] = None\n current_node['right'] = None\n current_node['prediction'] = Y[0]\n\n else:\n D = X.shape[1]\n cols = range(D)\n\n max_ig = 0\n best_col = None\n best_split = None\n for col in cols:\n ig, split = self.find_split(X, Y, col)\n # print \"ig:\", ig\n if ig > max_ig:\n max_ig = ig\n best_col = col\n best_split = split\n\n if max_ig == 0:\n # nothing we can do\n # no further splits\n # self.col = None\n # self.split = None\n # self.left = None\n # self.right = None\n # self.prediction = np.round(Y.mean())\n current_node['col'] = None\n current_node['split'] = None\n current_node['left'] = None\n current_node['right'] = None\n current_node['prediction'] = np.round(Y.mean())\n else:\n # self.col = best_col\n # self.split = best_split\n current_node['col'] = best_col\n current_node['split'] = best_split\n\n # if self.depth == self.max_depth:\n if depth == self.max_depth:\n # self.left = None\n # self.right = None\n # self.prediction = [\n # np.round(Y[X[:,best_col] < self.split].mean()),\n # np.round(Y[X[:,best_col] >= self.split].mean()),\n # ]\n current_node['left'] = None\n current_node['right'] = None\n current_node['prediction'] = [\n np.round(Y[X[:,best_col] < self.split].mean()),\n np.round(Y[X[:,best_col] >= self.split].mean()),\n ]\n else:\n # print \"best split:\", best_split\n left_idx = (X[:,best_col] < best_split)\n # print \"left_idx.shape:\", left_idx.shape, \"len(X):\", len(X)\n # TODO: bad but I can't figure out a better way atm\n Xleft = X[left_idx]\n Yleft = Y[left_idx]\n # self.left = TreeNode(self.depth + 1, self.max_depth)\n # self.left.fit(Xleft, Yleft)\n new_node = {}\n current_node['left'] = new_node\n left_data = {\n 'node': new_node,\n 'X': Xleft,\n 'Y': Yleft,\n }\n queue.insert(0, left_data)\n\n right_idx = (X[:,best_col] >= best_split)\n Xright = X[right_idx]\n Yright = Y[right_idx]\n # self.right = TreeNode(self.depth + 1, self.max_depth)\n # self.right.fit(Xright, Yright)\n new_node = {}\n current_node['right'] = new_node\n right_data = {\n 'node': new_node,\n 'X': Xright,\n 'Y': Yright,\n }\n queue.insert(0, right_data)\n\n # setup for the next iteration of the loop\n # idea is, queue stores list of work to be done\n if len(queue) == 0:\n break\n\n next_data = queue.pop()\n current_node = next_data['node']\n X = next_data['X']\n Y = next_data['Y']\n\n def find_split(self, X, Y, col):\n # print \"finding split for col:\", col\n x_values = X[:, col]\n sort_idx = np.argsort(x_values)\n x_values = x_values[sort_idx]\n y_values = Y[sort_idx]\n\n # Note: optimal split is the midpoint between 2 points\n # Note: optimal split is only on the boundaries between 2 classes\n\n # if boundaries[i] is true\n # then y_values[i] != y_values[i+1]\n # nonzero() gives us indices where arg is true\n # but for some reason it returns a tuple of size 1\n boundaries = np.nonzero(y_values[:-1] != y_values[1:])[0]\n best_split = None\n max_ig = 0\n last_ig = 0\n for b in boundaries:\n split = (x_values[b] + x_values[b+1]) / 2\n ig = self.information_gain(x_values, y_values, split)\n if ig < last_ig:\n break\n last_ig = ig\n if ig > max_ig:\n max_ig = ig\n best_split = split\n return max_ig, best_split\n\n def information_gain(self, x, y, split):\n # assume classes are 0 and 1\n # print \"split:\", split\n y0 = y[x < split]\n y1 = y[x >= split]\n N = len(y)\n y0len = len(y0)\n if y0len == 0 or y0len == N:\n return 0\n p0 = float(len(y0)) / N\n p1 = 1 - p0 #float(len(y1)) / N\n # print \"entropy(y):\", entropy(y)\n # print \"p0:\", p0\n # print \"entropy(y0):\", entropy(y0)\n # print \"p1:\", p1\n # print \"entropy(y1):\", entropy(y1)\n return entropy(y) - p0*entropy(y0) - p1*entropy(y1)\n\n def predict_one(self, x):\n # use \"is not None\" because 0 means False\n # if self.col is not None and self.split is not None:\n # feature = x[self.col]\n # if feature < self.split:\n # if self.left:\n # p = self.left.predict_one(x)\n # else:\n # p = self.prediction[0]\n # else:\n # if self.right:\n # p = self.right.predict_one(x)\n # else:\n # p = self.prediction[1]\n # else:\n # # corresponds to having only 1 prediction\n # p = self.prediction\n p = None\n current_node = self.root\n while True:\n if current_node['col'] is not None and current_node['split'] is not None:\n feature = x[current_node['col']]\n if feature < current_node['split']:\n if current_node['left']:\n current_node = current_node['left']\n else:\n p = current_node['prediction'][0]\n break\n else:\n if current_node['right']:\n current_node = current_node['right']\n else:\n p = current_node['prediction'][1]\n break\n else:\n # corresponds to having only 1 prediction\n p = current_node['prediction']\n break\n return p\n\n def predict(self, X):\n N = len(X)\n P = np.zeros(N)\n for i in range(N):\n P[i] = self.predict_one(X[i])\n return P\n\n def score(self, X, Y):\n P = self.predict(X)\n return np.mean(P == Y)\n\n\nif __name__ == '__main__':\n X, Y = get_data()\n\n # try donut and xor\n # from sklearn.utils import shuffle\n # X, Y = get_xor()\n # # X, Y = get_donut()\n # X, Y = shuffle(X, Y)\n\n # only take 0s and 1s since we're doing binary classification\n idx = np.logical_or(Y == 0, Y == 1)\n X = X[idx]\n Y = Y[idx]\n\n # split the data\n Ntrain = len(Y) // 2\n Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain]\n Xtest, Ytest = X[Ntrain:], Y[Ntrain:]\n \n model = DecisionTree()\n # model = DecisionTree(max_depth=7)\n t0 = datetime.now()\n model.fit(Xtrain, Ytrain)\n print(\"Training time:\", (datetime.now() - t0))\n\n t0 = datetime.now()\n print(\"Train accuracy:\", model.score(Xtrain, Ytrain))\n print(\"Time to compute train accuracy:\", (datetime.now() - t0))\n\n t0 = datetime.now()\n print(\"Test accuracy:\", model.score(Xtest, Ytest))\n print(\"Time to compute test accuracy:\", (datetime.now() - t0))\n\n # test SKLearn\n from sklearn.tree import DecisionTreeClassifier\n model = DecisionTreeClassifier()\n t0 = datetime.now()\n model.fit(Xtrain, Ytrain)\n print(\"SK: Training time:\", (datetime.now() - t0))\n\n t0 = datetime.now()\n print(\"Train accuracy:\", model.score(Xtrain, Ytrain))\n print(\"SK: Time to compute train accuracy:\", (datetime.now() - t0))\n\n t0 = datetime.now()\n print(\"Test accuracy:\", model.score(Xtest, Ytest))\n print(\"SK: Time to compute test accuracy:\", (datetime.now() - t0))\n" }, { "path": "supervised_class/knn.py", "content": "# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python\n# https://www.udemy.com/data-science-supervised-machine-learning-in-python\n# This is an example of a K-Nearest Neighbors classifier on MNIST data.\n# We try k=1...5 to show how we might choose the best k.\n# sudo pip install sortedcontainers (if you don't have it)\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sortedcontainers import SortedList\n# Note: You can't use SortedDict because the key is distance\n# if 2 close points are the same distance away, one will be overwritten\nfrom util import get_data\nfrom datetime import datetime\n\n\nclass KNN(object):\n def __init__(self, k):\n self.k = k\n\n def fit(self, X, y):\n self.X = X\n self.y = y\n\n def predict(self, X):\n y = np.zeros(len(X))\n for i,x in enumerate(X): # test points\n sl = SortedList() # stores (distance, class) tuples\n for j,xt in enumerate(self.X): # training points\n diff = x - xt\n d = diff.dot(diff)\n if len(sl) < self.k:\n # don't need to check, just add\n sl.add( (d, self.y[j]) )\n else:\n if d < sl[-1][0]:\n del sl[-1]\n sl.add( (d, self.y[j]) )\n # print \"input:\", x\n # print \"sl:\", sl\n\n # vote\n votes = {}\n for _, v in sl:\n # print \"v:\", v\n votes[v] = votes.get(v,0) + 1\n # print \"votes:\", votes, \"true:\", Ytest[i]\n max_votes = 0\n max_votes_class = -1\n for v,count in iteritems(votes):\n if count > max_votes:\n max_votes = count\n max_votes_class = v\n y[i] = max_votes_class\n return y\n\n def score(self, X, Y):\n P = self.predict(X)\n return np.mean(P == Y)\n\n\nif __name__ == '__main__':\n X, Y = get_data(2000)\n Ntrain = 1000\n Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain]\n Xtest, Ytest = X[Ntrain:], Y[Ntrain:]\n train_scores = []\n test_scores = []\n ks = (1,2,3,4,5)\n for k in ks:\n print(\"\\nk =\", k)\n knn = KNN(k)\n t0 = datetime.now()\n knn.fit(Xtrain, Ytrain)\n print(\"Training time:\", (datetime.now() - t0))\n\n t0 = datetime.now()\n train_score = knn.score(Xtrain, Ytrain)\n train_scores.append(train_score)\n print(\"Train accuracy:\", train_score)\n print(\"Time to compute train accuracy:\", (datetime.now() - t0), \"Train size:\", len(Ytrain))\n\n t0 = datetime.now()\n test_score = knn.score(Xtest, Ytest)\n print(\"Test accuracy:\", test_score)\n test_scores.append(test_score)\n print(\"Time to compute test accuracy:\", (datetime.now() - t0), \"Test size:\", len(Ytest))\n\n plt.plot(ks, train_scores, label='train scores')\n plt.plot(ks, test_scores, label='test scores')\n plt.legend()\n plt.show()" }, { "path": "supervised_class/knn_donut.py", "content": "# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python\n# https://www.udemy.com/data-science-supervised-machine-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nfrom knn import KNN\nfrom util import get_donut\nimport matplotlib.pyplot as plt\n\nif __name__ == '__main__':\n X, Y = get_donut()\n\n # display the data\n plt.scatter(X[:,0], X[:,1], s=100, c=Y, alpha=0.5)\n plt.show()\n\n # get the accuracy\n model = KNN(3)\n model.fit(X, Y)\n print(\"Accuracy:\", model.score(X, Y))" }, { "path": "supervised_class/knn_fail.py", "content": "# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python\n# https://www.udemy.com/data-science-supervised-machine-learning-in-python\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom knn import KNN\n\ndef get_data():\n width = 8\n height = 8\n N = width * height\n X = np.zeros((N, 2))\n Y = np.zeros(N)\n n = 0\n start_t = 0\n for i in range(width):\n t = start_t\n for j in range(height):\n X[n] = [i, j]\n Y[n] = t\n n += 1\n t = (t + 1) % 2 # alternate between 0 and 1\n start_t = (start_t + 1) % 2\n return X, Y\n\n\nif __name__ == '__main__':\n X, Y = get_data()\n\n # display the data\n plt.scatter(X[:,0], X[:,1], s=100, c=Y, alpha=0.5)\n plt.show()\n\n # get the accuracy\n model = KNN(3)\n model.fit(X, Y)\n print(\"Train accuracy:\", model.score(X, Y))\n" }, { "path": "supervised_class/knn_vectorized.py", "content": "# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python\n# https://www.udemy.com/data-science-supervised-machine-learning-in-python\n# This is an example of a K-Nearest Neighbors classifier on MNIST data.\n# We try k=1...5 to show how we might choose the best k.\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom util import get_data\nfrom datetime import datetime\nfrom sklearn.metrics.pairwise import pairwise_distances\n\n\nclass KNN(object):\n def __init__(self, k):\n self.k = k\n\n def fit(self, X, y):\n self.X = X\n self.y = y\n\n def predict(self, X):\n N = len(X)\n y = np.zeros(N)\n\n # returns distances in a matrix\n # of shape (N_test, N_train)\n distances = pairwise_distances(X, self.X)\n \n\n # now get the minimum k elements' indexes\n # https://stackoverflow.com/questions/16817948/i-have-need-the-n-minimum-index-values-in-a-numpy-array\n idx = distances.argsort(axis=1)[:, :self.k]\n\n # now determine the winning votes\n # each row of idx contains indexes from 0..Ntrain\n # corresponding to the indexes of the closest samples\n # from the training set\n # NOTE: if you don't \"believe\" this works, test it\n # in your console with simpler arrays\n votes = self.y[idx]\n\n # now y contains the classes in each row\n # e.g.\n # sample 0 --> [class0, class1, class1, class0, ...]\n # unfortunately there's no good way to vectorize this\n # https://stackoverflow.com/questions/19201972/can-numpy-bincount-work-with-2d-arrays\n for i in range(N):\n y[i] = np.bincount(votes[i]).argmax()\n\n return y\n\n def score(self, X, Y):\n P = self.predict(X)\n return np.mean(P == Y)\n\n\nif __name__ == '__main__':\n X, Y = get_data(2000)\n Ntrain = 1000\n Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain]\n Xtest, Ytest = X[Ntrain:], Y[Ntrain:]\n train_scores = []\n test_scores = []\n ks = (1,2,3,4,5)\n for k in ks:\n print(\"\\nk =\", k)\n knn = KNN(k)\n t0 = datetime.now()\n knn.fit(Xtrain, Ytrain)\n print(\"Training time:\", (datetime.now() - t0))\n\n t0 = datetime.now()\n train_score = knn.score(Xtrain, Ytrain)\n train_scores.append(train_score)\n print(\"Train accuracy:\", train_score)\n print(\"Time to compute train accuracy:\", (datetime.now() - t0), \"Train size:\", len(Ytrain))\n\n t0 = datetime.now()\n test_score = knn.score(Xtest, Ytest)\n print(\"Test accuracy:\", test_score)\n test_scores.append(test_score)\n print(\"Time to compute test accuracy:\", (datetime.now() - t0), \"Test size:\", len(Ytest))\n\n plt.plot(ks, train_scores, label='train scores')\n plt.plot(ks, test_scores, label='test scores')\n plt.legend()\n plt.show()\n\n" }, { "path": "supervised_class/knn_xor.py", "content": "# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python\n# https://www.udemy.com/data-science-supervised-machine-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nfrom knn import KNN\nfrom util import get_xor\nimport matplotlib.pyplot as plt\n\nif __name__ == '__main__':\n X, Y = get_xor()\n\n # display the data\n plt.scatter(X[:,0], X[:,1], s=100, c=Y, alpha=0.5)\n plt.show()\n\n # get the accuracy\n model = KNN(3)\n model.fit(X, Y)\n print(\"Accuracy:\", model.score(X, Y))" }, { "path": "supervised_class/multinomialnb.py", "content": "# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python\n# https://www.udemy.com/data-science-supervised-machine-learning-in-python\n# This is an example of a Naive Bayes classifier on MNIST data.\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nfrom util import get_data\nfrom datetime import datetime\n\nclass MultinomialNB(object):\n def fit(self, X, Y, smoothing=1.0):\n # one-hot encode Y\n K = len(set(Y)) # number of classes\n N = len(Y) # number of samples\n labels = Y\n Y = np.zeros((N, K))\n Y[np.arange(N), labels] = 1\n\n # D x K matrix of feature counts\n # feature_counts[d,k] = count of feature d in class k\n feature_counts = X.T.dot(Y) + smoothing\n class_counts = Y.sum(axis=0)\n\n self.weights = np.log(feature_counts) - np.log(feature_counts.sum(axis=0))\n self.priors = np.log(class_counts) - np.log(class_counts.sum())\n\n def score(self, X, Y):\n P = self.predict(X)\n return np.mean(P == Y)\n\n def predict(self, X):\n P = X.dot(self.weights) + self.priors\n return np.argmax(P, axis=1)\n\n\nif __name__ == '__main__':\n X, Y = get_data(10000)\n Ntrain = len(Y) // 2\n Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain]\n Xtest, Ytest = X[Ntrain:], Y[Ntrain:]\n\n model = MultinomialNB()\n t0 = datetime.now()\n model.fit(Xtrain, Ytrain)\n print(\"Training time:\", (datetime.now() - t0))\n\n t0 = datetime.now()\n print(\"Train accuracy:\", model.score(Xtrain, Ytrain))\n print(\"Time to compute train accuracy:\", (datetime.now() - t0), \"Train size:\", len(Ytrain))\n\n t0 = datetime.now()\n print(\"Test accuracy:\", model.score(Xtest, Ytest))\n print(\"Time to compute test accuracy:\", (datetime.now() - t0), \"Test size:\", len(Ytest))\n" }, { "path": "supervised_class/nb.py", "content": "# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python\n# https://www.udemy.com/data-science-supervised-machine-learning-in-python\n# This is an example of a Naive Bayes classifier on MNIST data.\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nfrom util import get_data\nfrom datetime import datetime\nfrom scipy.stats import norm\nfrom scipy.stats import multivariate_normal as mvn\n\nclass NaiveBayes(object):\n def fit(self, X, Y, smoothing=1e-2):\n self.gaussians = dict()\n self.priors = dict()\n labels = set(Y)\n for c in labels:\n current_x = X[Y == c]\n self.gaussians[c] = {\n 'mean': current_x.mean(axis=0),\n 'var': current_x.var(axis=0) + smoothing,\n }\n self.priors[c] = float(len(Y[Y == c])) / len(Y)\n\n def score(self, X, Y):\n P = self.predict(X)\n return np.mean(P == Y)\n\n def predict(self, X):\n N, D = X.shape\n K = len(self.gaussians)\n P = np.zeros((N, K))\n for c, g in iteritems(self.gaussians):\n mean, var = g['mean'], g['var']\n P[:,c] = mvn.logpdf(X, mean=mean, cov=var) + np.log(self.priors[c])\n return np.argmax(P, axis=1)\n\n\nif __name__ == '__main__':\n X, Y = get_data(10000)\n Ntrain = len(Y) // 2\n Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain]\n Xtest, Ytest = X[Ntrain:], Y[Ntrain:]\n\n model = NaiveBayes()\n t0 = datetime.now()\n model.fit(Xtrain, Ytrain)\n print(\"Training time:\", (datetime.now() - t0))\n\n t0 = datetime.now()\n print(\"Train accuracy:\", model.score(Xtrain, Ytrain))\n print(\"Time to compute train accuracy:\", (datetime.now() - t0), \"Train size:\", len(Ytrain))\n\n t0 = datetime.now()\n print(\"Test accuracy:\", model.score(Xtest, Ytest))\n print(\"Time to compute test accuracy:\", (datetime.now() - t0), \"Test size:\", len(Ytest))\n" }, { "path": "supervised_class/perceptron.py", "content": "# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python\n# https://www.udemy.com/data-science-supervised-machine-learning-in-python\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom util import get_data as get_mnist\nfrom datetime import datetime\n\n\ndef get_data():\n w = np.array([-0.5, 0.5])\n b = 0.1\n X = np.random.random((300, 2))*2 - 1\n Y = np.sign(X.dot(w) + b)\n return X, Y\n\n\ndef get_simple_xor():\n X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n Y = np.array([0, 1, 1, 0])\n return X, Y\n\n\nclass Perceptron:\n def fit(self, X, Y, learning_rate=1.0, epochs=1000):\n # solution\n # self.w = np.array([-0.5, 0.5])\n # self.b = 0.1\n\n # initialize random weights\n D = X.shape[1]\n self.w = np.random.randn(D)\n self.b = 0\n\n N = len(Y)\n costs = []\n for epoch in range(epochs):\n # determine which samples are misclassified, if any\n Yhat = self.predict(X)\n incorrect = np.nonzero(Y != Yhat)[0]\n if len(incorrect) == 0:\n # we are done!\n break\n\n # choose a random incorrect sample\n i = np.random.choice(incorrect)\n self.w += learning_rate*Y[i]*X[i]\n self.b += learning_rate*Y[i]\n\n # cost is incorrect rate\n c = len(incorrect) / float(N)\n costs.append(c)\n print(\"final w:\", self.w, \"final b:\", self.b, \"epochs:\", (epoch+1), \"/\", epochs)\n plt.plot(costs)\n plt.show()\n\n def predict(self, X):\n return np.sign(X.dot(self.w) + self.b)\n\n def score(self, X, Y):\n P = self.predict(X)\n return np.mean(P == Y)\n\n\nif __name__ == '__main__':\n # linearly separable data\n X, Y = get_data()\n plt.scatter(X[:,0], X[:,1], c=Y, s=100, alpha=0.5)\n plt.show()\n Ntrain = len(Y) // 2\n Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain]\n Xtest, Ytest = X[Ntrain:], Y[Ntrain:]\n\n model = Perceptron()\n t0 = datetime.now()\n model.fit(Xtrain, Ytrain)\n print(\"Training time:\", (datetime.now() - t0))\n\n t0 = datetime.now()\n print(\"Train accuracy:\", model.score(Xtrain, Ytrain))\n print(\"Time to compute train accuracy:\", (datetime.now() - t0), \"Train size:\", len(Ytrain))\n\n t0 = datetime.now()\n print(\"Test accuracy:\", model.score(Xtest, Ytest))\n print(\"Time to compute test accuracy:\", (datetime.now() - t0), \"Test size:\", len(Ytest))\n\n\n # mnist\n X, Y = get_mnist()\n idx = np.logical_or(Y == 0, Y == 1)\n X = X[idx]\n Y = Y[idx]\n Y[Y == 0] = -1\n model = Perceptron()\n t0 = datetime.now()\n model.fit(X, Y, learning_rate=1e-2)\n print(\"MNIST train accuracy:\", model.score(X, Y))\n\n\n # xor data\n print(\"\")\n print(\"XOR results:\")\n X, Y = get_simple_xor()\n Y[Y == 0] = -1\n model.fit(X, Y)\n print(\"XOR accuracy:\", model.score(X, Y))\n\n" }, { "path": "supervised_class/regression.py", "content": "# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python\n# https://www.udemy.com/data-science-supervised-machine-learning-in-python\n# Works with Python 2 and 3\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.neighbors import KNeighborsRegressor\nfrom sklearn.tree import DecisionTreeRegressor\n\nN = 200\nX = np.linspace(0, 10, N).reshape(N, 1)\nY = np.sin(X)\n\nNtrain = 20\nidx = np.random.choice(N, Ntrain)\nXtrain = X[idx]\nYtrain = Y[idx]\n\nknn = KNeighborsRegressor(n_neighbors=2, weights='distance')\nknn.fit(Xtrain, Ytrain)\nYknn = knn.predict(X)\n\ndt = DecisionTreeRegressor()\ndt.fit(Xtrain, Ytrain)\nYdt = dt.predict(X)\n\nplt.scatter(Xtrain, Ytrain) # show the training points\nplt.plot(X, Y) # show the original data\nplt.plot(X, Yknn, label='KNN')\nplt.plot(X, Ydt, label='Decision Tree')\nplt.legend()\nplt.show()\n\n\n" }, { "path": "supervised_class/util.py", "content": "# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python\n# https://www.udemy.com/data-science-supervised-machine-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport pandas as pd\n\ndef get_data(limit=None):\n print(\"Reading in and transforming data...\")\n df = pd.read_csv('../large_files/train.csv')\n data = df.values\n np.random.shuffle(data)\n X = data[:, 1:] / 255.0 # data is from 0..255\n Y = data[:, 0]\n if limit is not None:\n X, Y = X[:limit], Y[:limit]\n return X, Y\n\ndef get_xor():\n X = np.zeros((200, 2))\n X[:50] = np.random.random((50, 2)) / 2 + 0.5 # (0.5-1, 0.5-1)\n X[50:100] = np.random.random((50, 2)) / 2 # (0-0.5, 0-0.5)\n X[100:150] = np.random.random((50, 2)) / 2 + np.array([[0, 0.5]]) # (0-0.5, 0.5-1)\n X[150:] = np.random.random((50, 2)) / 2 + np.array([[0.5, 0]]) # (0.5-1, 0-0.5)\n Y = np.array([0]*100 + [1]*100)\n return X, Y\n\ndef get_donut():\n N = 200\n R_inner = 5\n R_outer = 10\n\n # distance from origin is radius + random normal\n # angle theta is uniformly distributed between (0, 2pi)\n R1 = np.random.randn(N//2) + R_inner\n theta = 2*np.pi*np.random.random(N//2)\n X_inner = np.concatenate([[R1 * np.cos(theta)], [R1 * np.sin(theta)]]).T\n\n R2 = np.random.randn(N//2) + R_outer\n theta = 2*np.pi*np.random.random(N//2)\n X_outer = np.concatenate([[R2 * np.cos(theta)], [R2 * np.sin(theta)]]).T\n\n X = np.concatenate([ X_inner, X_outer ])\n Y = np.array([0]*(N//2) + [1]*(N//2))\n return X, Y\n" }, { "path": "supervised_class2/adaboost.py", "content": "# https://deeplearningcourses.com/c/machine-learning-in-python-random-forest-adaboost\n# https://www.udemy.com/machine-learning-in-python-random-forest-adaboost\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.tree import DecisionTreeClassifier\nfrom rf_classification import get_data\n\n\nclass AdaBoost:\n def __init__(self, M):\n self.M = M\n\n def fit(self, X, Y):\n self.models = []\n self.alphas = []\n\n N, _ = X.shape\n W = np.ones(N) / N\n\n for m in range(self.M):\n tree = DecisionTreeClassifier(max_depth=1)\n tree.fit(X, Y, sample_weight=W)\n P = tree.predict(X)\n\n err = W.dot(P != Y)\n alpha = 0.5*(np.log(1 - err) - np.log(err))\n\n W = W*np.exp(-alpha*Y*P) # vectorized form\n W = W / W.sum() # normalize so it sums to 1\n\n self.models.append(tree)\n self.alphas.append(alpha)\n\n def predict(self, X):\n # NOT like SKLearn API\n # we want accuracy and exponential loss for plotting purposes\n N, _ = X.shape\n FX = np.zeros(N)\n for alpha, tree in zip(self.alphas, self.models):\n FX += alpha*tree.predict(X)\n return np.sign(FX), FX\n\n def score(self, X, Y):\n # NOT like SKLearn API\n # we want accuracy and exponential loss for plotting purposes\n P, FX = self.predict(X)\n L = np.exp(-Y*FX).mean()\n return np.mean(P == Y), L\n\n\nif __name__ == '__main__':\n \n X, Y = get_data()\n Y[Y == 0] = -1 # make the targets -1,+1\n Ntrain = int(0.8*len(X))\n Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain]\n Xtest, Ytest = X[Ntrain:], Y[Ntrain:]\n\n T = 200\n train_errors = np.empty(T)\n test_losses = np.empty(T)\n test_errors = np.empty(T)\n for num_trees in range(T):\n if num_trees == 0:\n train_errors[num_trees] = None\n test_errors[num_trees] = None\n test_losses[num_trees] = None\n continue\n if num_trees % 20 == 0:\n print(num_trees)\n\n model = AdaBoost(num_trees)\n model.fit(Xtrain, Ytrain)\n acc, loss = model.score(Xtest, Ytest)\n acc_train, _ = model.score(Xtrain, Ytrain)\n train_errors[num_trees] = 1 - acc_train\n test_errors[num_trees] = 1 - acc\n test_losses[num_trees] = loss\n\n if num_trees == T - 1:\n print(\"final train error:\", 1 - acc_train)\n print(\"final test error:\", 1 - acc)\n\n plt.plot(test_errors, label='test errors')\n plt.plot(test_losses, label='test losses')\n plt.legend()\n plt.show()\n\n plt.plot(train_errors, label='train errors')\n plt.plot(test_errors, label='test errors')\n plt.legend()\n plt.show()\n" }, { "path": "supervised_class2/bagging_classification.py", "content": "# https://deeplearningcourses.com/c/machine-learning-in-python-random-forest-adaboost\n# https://www.udemy.com/machine-learning-in-python-random-forest-adaboost\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.utils import shuffle\nfrom util import plot_decision_boundary\n\nnp.random.seed(10)\n\n# create the data\nN = 500\nD = 2\nX = np.random.randn(N, D)\n\n# 2 gaussians\n# sep = 1.5\n# X[:N/2] += np.array([sep, sep])\n# X[N/2:] += np.array([-sep, -sep])\n# Y = np.array([0]*(N/2) + [1]*(N/2))\n\n# noisy XOR\nsep = 2\nX[:125] += np.array([sep, sep])\nX[125:250] += np.array([sep, -sep])\nX[250:375] += np.array([-sep, -sep])\nX[375:] += np.array([-sep, sep])\nY = np.array([0]*125 + [1]*125 + [0]*125 + [1]*125)\n\n# plot the data\nplt.scatter(X[:,0], X[:,1], s=100, c=Y, alpha=0.5)\nplt.show()\n\n# lone decision tree\nmodel = DecisionTreeClassifier()\nmodel.fit(X, Y)\nprint(\"score for 1 tree:\", model.score(X, Y))\n\n# plot data with boundary\nplt.scatter(X[:,0], X[:,1], s=100, c=Y, alpha=0.5)\nplot_decision_boundary(X, model)\nplt.show()\n\n\n# create the bagged model\nclass BaggedTreeClassifier:\n def __init__(self, B):\n self.B = B\n\n def fit(self, X, Y):\n N = len(X)\n self.models = []\n for b in range(self.B):\n idx = np.random.choice(N, size=N, replace=True)\n Xb = X[idx]\n Yb = Y[idx]\n\n model = DecisionTreeClassifier(max_depth=2)\n model.fit(Xb, Yb)\n self.models.append(model)\n\n def predict(self, X):\n # no need to keep a dictionary since we are doing binary classification\n predictions = np.zeros(len(X))\n for model in self.models:\n predictions += model.predict(X)\n return np.round(predictions / self.B)\n\n def score(self, X, Y):\n P = self.predict(X)\n return np.mean(Y == P)\n\n\nmodel = BaggedTreeClassifier(200)\nmodel.fit(X, Y)\n\nprint(\"score for bagged model:\", model.score(X, Y))\n\n# plot data with boundary\nplt.scatter(X[:,0], X[:,1], s=100, c=Y, alpha=0.5)\nplot_decision_boundary(X, model)\nplt.show()\n\n\n\n" }, { "path": "supervised_class2/bagging_regression.py", "content": "# https://deeplearningcourses.com/c/machine-learning-in-python-random-forest-adaboost\n# https://www.udemy.com/machine-learning-in-python-random-forest-adaboost\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.tree import DecisionTreeRegressor\nfrom sklearn.utils import shuffle\n\n\n# create the data\nT = 100\nx_axis = np.linspace(0, 2*np.pi, T)\ny_axis = np.sin(x_axis)\n\n# get the training data\nN = 30\nidx = np.random.choice(T, size=N, replace=False)\nXtrain = x_axis[idx].reshape(N, 1)\nYtrain = y_axis[idx]\n\n# try a lone decision tree\nmodel = DecisionTreeRegressor()\nmodel.fit(Xtrain, Ytrain)\nprediction = model.predict(x_axis.reshape(T, 1))\nprint(\"score for 1 tree:\", model.score(x_axis.reshape(T, 1), y_axis))\n\n# plot the lone decision tree's predictions\nplt.plot(x_axis, prediction)\nplt.plot(x_axis, y_axis)\nplt.show()\n\n# now try bagging\nclass BaggedTreeRegressor:\n def __init__(self, B):\n self.B = B\n\n def fit(self, X, Y):\n N = len(X)\n self.models = []\n for b in range(self.B):\n idx = np.random.choice(N, size=N, replace=True)\n Xb = X[idx]\n Yb = Y[idx]\n\n model = DecisionTreeRegressor()\n model.fit(Xb, Yb)\n self.models.append(model)\n\n def predict(self, X):\n predictions = np.zeros(len(X))\n for model in self.models:\n predictions += model.predict(X)\n return predictions / self.B\n\n def score(self, X, Y):\n d1 = Y - self.predict(X)\n d2 = Y - Y.mean()\n return 1 - d1.dot(d1) / d2.dot(d2)\n\n\nmodel = BaggedTreeRegressor(200)\nmodel.fit(Xtrain, Ytrain)\nprint(\"score for bagged tree:\", model.score(x_axis.reshape(T, 1), y_axis))\nprediction = model.predict(x_axis.reshape(T, 1))\n\n# plot the bagged regressor's predictions\nplt.plot(x_axis, prediction)\nplt.plot(x_axis, y_axis)\nplt.show()\n" }, { "path": "supervised_class2/bias_variance_demo.py", "content": "# https://deeplearningcourses.com/c/machine-learning-in-python-random-forest-adaboost\n# https://www.udemy.com/machine-learning-in-python-random-forest-adaboost\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import mean_squared_error as mse\n\nNUM_DATASETS = 50\nNOISE_VARIANCE = 0.5\nMAX_POLY = 12\nN = 25\nNtrain = int(0.9*N)\n\nnp.random.seed(2)\n\n# make a dataset with x^D, x^(D-1), ..., x^0\ndef make_poly(x, D):\n N = len(x)\n X = np.empty((N, D+1))\n for d in range(D+1):\n X[:,d] = x**d\n if d > 1:\n X[:,d] = (X[:,d] - X[:,d].mean()) / X[:,d].std()\n return X\n\ndef f(X):\n return np.sin(X)\n\n\nx_axis = np.linspace(-np.pi, np.pi, 100)\ny_axis = f(x_axis)\n\n# plot the data\n# plt.plot(x_axis, y_axis)\n# plt.show()\n\n# f(x) = sin(x) from x = [-pi, +pi]\nX = np.linspace(-np.pi, np.pi, N)\nnp.random.shuffle(X)\nf_X = f(X)\n\n# just need to do this once\nXpoly = make_poly(X, MAX_POLY)\n\n# array to store all the scores\ntrain_scores = np.zeros((NUM_DATASETS, MAX_POLY))\ntest_scores = np.zeros((NUM_DATASETS, MAX_POLY))\n# squared_biases = np.zeros((NUM_DATASETS, MAX_POLY))\n# test_predictions = np.zeros((N - Ntrain, NUM_DATASETS, MAX_POLY))\ntrain_predictions = np.zeros((Ntrain, NUM_DATASETS, MAX_POLY))\nprediction_curves = np.zeros((100, NUM_DATASETS, MAX_POLY))\n\n# create the model\nmodel = LinearRegression()\n\nfor k in range(NUM_DATASETS):\n Y = f_X + np.random.randn(N)*NOISE_VARIANCE\n\n Xtrain = Xpoly[:Ntrain]\n Ytrain = Y[:Ntrain]\n\n Xtest = Xpoly[Ntrain:]\n Ytest = Y[Ntrain:]\n\n for d in range(MAX_POLY):\n model.fit(Xtrain[:,:d+2], Ytrain)\n predictions = model.predict(Xpoly[:,:d+2])\n\n # debug\n x_axis_poly = make_poly(x_axis, d+1)\n prediction_axis = model.predict(x_axis_poly)\n # plt.plot(x_axis, prediction_axis)\n # plt.show()\n\n prediction_curves[:,k,d] = prediction_axis\n\n train_prediction = predictions[:Ntrain]\n test_prediction = predictions[Ntrain:]\n\n train_predictions[:,k,d] = train_prediction # use this to calculate bias/variance later\n\n train_score = mse(train_prediction, Ytrain)\n test_score = mse(test_prediction, Ytest)\n\n train_scores[k,d] = train_score\n test_scores[k,d] = test_score\n\n# show all prediction curves for each polynomial degree\n# along with the mean curve\nfor d in range(MAX_POLY):\n for k in range(NUM_DATASETS):\n plt.plot(x_axis, prediction_curves[:,k,d], color='green', alpha=0.5)\n plt.plot(x_axis, prediction_curves[:,:,d].mean(axis=1), color='blue', linewidth=2.0)\n plt.title(\"All curves for degree = %d\" % (d+1))\n plt.show()\n\n# calculate the squared bias\navg_train_prediction = np.zeros((Ntrain, MAX_POLY))\nsquared_bias = np.zeros(MAX_POLY)\nf_Xtrain = f_X[:Ntrain]\nfor d in range(MAX_POLY):\n for i in range(Ntrain):\n avg_train_prediction[i,d] = train_predictions[i,:,d].mean()\n squared_bias[d] = ((avg_train_prediction[:,d] - f_Xtrain)**2).mean()\n\n# calculate the variance\nvariances = np.zeros((Ntrain, MAX_POLY))\nfor d in range(MAX_POLY):\n for i in range(Ntrain):\n delta = train_predictions[i,:,d] - avg_train_prediction[i,d]\n variances[i,d] = delta.dot(delta) / len(delta)\nvariance = variances.mean(axis=0)\n\n# make bias-variance plots\ndegrees = np.arange(MAX_POLY) + 1\nbest_degree = np.argmin(test_scores.mean(axis=0)) + 1\nplt.plot(degrees, squared_bias, label='squared bias')\nplt.plot(degrees, variance, label='variance')\nplt.plot(degrees, test_scores.mean(axis=0), label='test scores')\nplt.plot(degrees, squared_bias + variance, label='squared bias + variance')\nplt.axvline(x=best_degree, linestyle='--', label='best complexity')\nplt.legend()\nplt.show()\n\n# train score vs test score\nplt.plot(degrees, train_scores.mean(axis=0), label='train scores')\nplt.plot(degrees, test_scores.mean(axis=0), label='test scores')\nplt.axvline(x=best_degree, linestyle='--', label='best complexity')\nplt.legend()\nplt.show()\n" }, { "path": "supervised_class2/bootstrap.py", "content": "# https://deeplearningcourses.com/c/machine-learning-in-python-random-forest-adaboost\n# https://www.udemy.com/machine-learning-in-python-random-forest-adaboost\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.stats import norm, t\n\nB = 200\nN = 20\nX = np.random.randn(N)\n\nprint(\"sample mean of X:\", X.mean())\n\nindividual_estimates = np.empty(B)\nfor b in range(B):\n sample = np.random.choice(X, size=N)\n individual_estimates[b] = sample.mean()\n\n\nbmean = individual_estimates.mean()\nbstd = individual_estimates.std()\nlower = bmean + norm.ppf(0.025)*bstd # norm.ppf(0.025) == -1.96\nupper = bmean + norm.ppf(0.975)*bstd # norm.ppf(0.975) == +1.96\n\n# traditional way of calculating CI\nlower2 = X.mean() + norm.ppf(0.025)*X.std()/np.sqrt(N)\nupper2 = X.mean() + norm.ppf(0.975)*X.std()/np.sqrt(N)\n\nprint(\"bootstrap mean of X:\", bmean)\n\nplt.hist(individual_estimates, bins=20)\nplt.axvline(x=lower, linestyle='--', color='g', label=\"lower bound for 95%% CI (bootstrap)\")\nplt.axvline(x=upper, linestyle='--', color='g', label=\"upper bound for 95%% CI (bootstrap)\")\nplt.axvline(x=lower2, linestyle='--', color='r', label=\"lower bound for 95%% CI\")\nplt.axvline(x=upper2, linestyle='--', color='r', label=\"upper bound for 95%% CI\")\nplt.legend()\nplt.show()\n" }, { "path": "supervised_class2/extra_reading.txt", "content": "RANDOM FORESTS\nhttps://www.stat.berkeley.edu/~breiman/randomforest2001.pdf\n\nA Short Introduction to Boosting\nhttps://cseweb.ucsd.edu/~yfreund/papers/IntroToBoosting.pdf\n\nExplaining AdaBoost\nhttp://rob.schapire.net/papers/explaining-adaboost.pdf\n\nImproved Boosting Algorithms Using Confidence-rated Predictions\nhttps://sci2s.ugr.es/keel/pdf/algorithm/articulo/1999-ML-Improved%20boosting%20algorithms%20using%20confidence-rated%20predictions%20(Schapire%20y%20Singer).pdf\n\nWhy does the bootstrap work?\nhttp://www.stat.cmu.edu/~larry/=sml/Boot.pdf" }, { "path": "supervised_class2/knn_dt_demo.py", "content": "# https://deeplearningcourses.com/c/machine-learning-in-python-random-forest-adaboost\n# https://www.udemy.com/machine-learning-in-python-random-forest-adaboost\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier\nfrom sklearn.neighbors import KNeighborsRegressor, KNeighborsClassifier\nfrom sklearn.utils import shuffle\n\nN = 20\nNtrain = 12\n\n# create the data\nX = np.linspace(0, 2*np.pi, N).reshape(N, 1)\nY = np.sin(3*X)\nX, Y = shuffle(X, Y)\n\nXtrain = X[:Ntrain]\nYtrain = Y[:Ntrain]\n\n# decision tree - low bias, high variance\nmodel = DecisionTreeRegressor() # default max_depth=None\nmodel.fit(Xtrain, Ytrain)\n\nT = 50\nXaxis = np.linspace(0, 2*np.pi, T)\nYaxis = np.sin(3*Xaxis)\n\nplt.scatter(Xtrain, Ytrain, s=50, alpha=0.7, c='blue')\nplt.scatter(Xtrain, model.predict(Xtrain.reshape(Ntrain, 1)), s=50, alpha=0.7, c='green')\nplt.title(\"decision tree - low bias, high variance\")\n# plt.show()\n\n# plt.scatter(X, Y)\n# plt.scatter(X, model.predict(X.reshape(N, 1)))\n# plt.show()\n\nplt.plot(Xaxis, Yaxis)\nplt.plot(Xaxis, model.predict(Xaxis.reshape(T, 1)))\nplt.show()\n\n\n# decision tree - high bias, low variance\nmodel = DecisionTreeRegressor(max_depth=1)\nmodel.fit(Xtrain, Ytrain)\n\nplt.scatter(Xtrain, Ytrain, s=50, alpha=0.7, c='blue')\nplt.scatter(Xtrain, model.predict(Xtrain.reshape(Ntrain, 1)), s=50, alpha=0.7, c='green')\nplt.plot(Xaxis, Yaxis)\nplt.plot(Xaxis, model.predict(Xaxis.reshape(T, 1)))\nplt.title(\"decision tree - high bias, low variance\")\nplt.show()\n\n\n# knn - low bias, high variance\nmodel = KNeighborsRegressor(n_neighbors=1)\nmodel.fit(Xtrain, Ytrain)\n\nplt.scatter(Xtrain, Ytrain, s=50, alpha=0.7, c='blue')\nplt.scatter(Xtrain, model.predict(Xtrain.reshape(Ntrain, 1)), s=50, alpha=0.7, c='green')\nplt.plot(Xaxis, Yaxis)\nplt.plot(Xaxis, model.predict(Xaxis.reshape(T, 1)))\nplt.title(\"knn - low bias, high variance\")\nplt.show()\n\n# knn - high bias, low variance\nmodel = KNeighborsRegressor(n_neighbors=10)\nmodel.fit(Xtrain, Ytrain)\n\nplt.scatter(Xtrain, Ytrain, s=50, alpha=0.7, c='blue')\nplt.scatter(Xtrain, model.predict(Xtrain.reshape(Ntrain, 1)), s=50, alpha=0.7, c='green')\nplt.plot(Xaxis, Yaxis)\nplt.plot(Xaxis, model.predict(Xaxis.reshape(T, 1)))\nplt.title(\"knn - high bias, low variance\")\nplt.show()\n\n\n# classification\n\n# generate the data\nN = 100\nD = 2\nX = np.random.randn(N, D)\nX[:N//2] += np.array([1, 1]) # center it at (1,1)\nX[N//2:] += np.array([-1, -1]) # center it at (-1, -1)\n\nY = np.array([0]*(N//2) + [1]*(N//2))\n\n\ndef plot_decision_boundary(X, model):\n h = .02 # step size in the mesh\n # create a mesh to plot in\n x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n np.arange(y_min, y_max, h))\n\n\n # Plot the decision boundary. For that, we will assign a color to each\n # point in the mesh [x_min, m_max]x[y_min, y_max].\n Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n\n # Put the result into a color plot\n Z = Z.reshape(xx.shape)\n plt.contour(xx, yy, Z, cmap=plt.cm.Paired)\n\n\n# look at the data\nplt.scatter(X[:,0], X[:,1], s=50, c=Y, alpha=0.7)\nplt.show()\n\n# dt - low bias, high variance\nmodel = DecisionTreeClassifier()\nmodel.fit(X, Y)\n\nplt.scatter(X[:,0], X[:,1], s=50, c=Y, alpha=0.7)\nplot_decision_boundary(X, model)\nplt.title(\"dt - low bias, high variance\")\nplt.show()\n\n# dt - high bias, low variance\nmodel = DecisionTreeClassifier(max_depth=2)\nmodel.fit(X, Y)\n\nplt.scatter(X[:,0], X[:,1], s=50, c=Y, alpha=0.7)\nplot_decision_boundary(X, model)\nplt.title(\"dt - high bias, low variance\")\nplt.show()\n\n\n# knn - low bias, high variance\nmodel = KNeighborsClassifier(n_neighbors=1)\nmodel.fit(X, Y)\n\nplt.scatter(X[:,0], X[:,1], s=50, c=Y, alpha=0.7)\nplot_decision_boundary(X, model)\nplt.title(\"knn - low bias, high variance\")\nplt.show()\n\n# knn - high bias, low variance\nmodel = KNeighborsClassifier(n_neighbors=20)\nmodel.fit(X, Y)\n\nplt.scatter(X[:,0], X[:,1], s=50, c=Y, alpha=0.7)\nplot_decision_boundary(X, model)\nplt.title(\"knn - high bias, low variance\")\nplt.show()\n" }, { "path": "supervised_class2/rf_classification.py", "content": "# https://deeplearningcourses.com/c/machine-learning-in-python-random-forest-adaboost\n# https://www.udemy.com/machine-learning-in-python-random-forest-adaboost\n# mushroom data from:\n# https://archive.ics.uci.edu/ml/datasets/Mushroom\n# put all files in the folder ../large_files/\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.preprocessing import LabelEncoder, StandardScaler\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import cross_val_score\n\nNUMERICAL_COLS = ()\nCATEGORICAL_COLS = np.arange(22) + 1 # 1..22 inclusive\n\n# transforms data from dataframe to numerical matrix\n# one-hot encodes categories and normalizes numerical columns\n# we want to use the scales found in training when transforming the test set\n# so only call fit() once\n# call transform() for any subsequent data\nclass DataTransformer:\n def fit(self, df):\n self.labelEncoders = {}\n self.scalers = {}\n for col in NUMERICAL_COLS:\n scaler = StandardScaler()\n scaler.fit(df[col].reshape(-1, 1))\n self.scalers[col] = scaler\n\n for col in CATEGORICAL_COLS:\n encoder = LabelEncoder()\n # in case the train set does not have 'missing' value but test set does\n values = df[col].tolist()\n values.append('missing')\n encoder.fit(values)\n self.labelEncoders[col] = encoder\n\n # find dimensionality\n self.D = len(NUMERICAL_COLS)\n for col, encoder in iteritems(self.labelEncoders):\n self.D += len(encoder.classes_)\n print(\"dimensionality:\", self.D)\n\n def transform(self, df):\n N, _ = df.shape\n X = np.zeros((N, self.D))\n i = 0\n for col, scaler in iteritems(self.scalers):\n X[:,i] = scaler.transform(df[col].values.reshape(-1, 1)).flatten()\n i += 1\n\n for col, encoder in iteritems(self.labelEncoders):\n # print \"transforming col:\", col\n K = len(encoder.classes_)\n X[np.arange(N), encoder.transform(df[col]) + i] = 1\n i += K\n return X\n\n def fit_transform(self, df):\n self.fit(df)\n return self.transform(df)\n\n\ndef replace_missing(df):\n # standard method of replacement for numerical columns is median\n for col in NUMERICAL_COLS:\n if np.any(df[col].isnull()):\n med = np.median(df[ col ][ df[col].notnull() ])\n df.loc[ df[col].isnull(), col ] = med\n\n # set a special value = 'missing'\n for col in CATEGORICAL_COLS:\n if np.any(df[col].isnull()):\n print(col)\n df.loc[ df[col].isnull(), col ] = 'missing'\n\n\ndef get_data():\n df = pd.read_csv('../large_files/mushroom.data', header=None)\n\n # replace label column: e/p --> 0/1\n # e = edible = 0, p = poisonous = 1\n df[0] = df.apply(lambda row: 0 if row[0] == 'e' else 1, axis=1)\n\n # check if there is missing data\n replace_missing(df)\n\n # transform the data\n transformer = DataTransformer()\n\n X = transformer.fit_transform(df)\n Y = df[0].values\n return X, Y\n\n\nif __name__ == '__main__':\n X, Y = get_data()\n\n # do a quick baseline test\n baseline = LogisticRegression()\n print(\"CV baseline:\", cross_val_score(baseline, X, Y, cv=8).mean())\n\n # single tree\n tree = DecisionTreeClassifier()\n print(\"CV one tree:\", cross_val_score(tree, X, Y, cv=8).mean())\n\n model = RandomForestClassifier(n_estimators=20) # try 10, 20, 50, 100, 200\n print(\"CV forest:\", cross_val_score(model, X, Y, cv=8).mean())\n" }, { "path": "supervised_class2/rf_regression.py", "content": "# https://deeplearningcourses.com/c/machine-learning-in-python-random-forest-adaboost\n# https://www.udemy.com/machine-learning-in-python-random-forest-adaboost\n# uses house dataset from https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n# Alternate data source: https://archive.org/download/housing_202405/housing.data\n# put all files in the folder ../large_files\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.preprocessing import LabelEncoder, StandardScaler\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.tree import DecisionTreeRegressor\nfrom sklearn.model_selection import cross_val_score\n\nNUMERICAL_COLS = [\n 'crim', # numerical\n 'zn', # numerical\n 'nonretail', # numerical\n 'nox', # numerical\n 'rooms', # numerical\n 'age', # numerical\n 'dis', # numerical\n 'rad', # numerical\n 'tax', # numerical\n 'ptratio', # numerical\n 'b', # numerical\n 'lstat', # numerical\n]\n\nNO_TRANSFORM = ['river']\n\n# transforms data from dataframe to numerical matrix\n# we want to use the scales found in training when transforming the test set\n# so only call fit() once\n# call transform() for any subsequent data\nclass DataTransformer:\n def fit(self, df):\n self.scalers = {}\n for col in NUMERICAL_COLS:\n scaler = StandardScaler()\n scaler.fit(df[col].values.reshape(-1, 1))\n self.scalers[col] = scaler\n\n def transform(self, df):\n N, _ = df.shape\n D = len(NUMERICAL_COLS) + len(NO_TRANSFORM)\n X = np.zeros((N, D))\n i = 0\n for col, scaler in iteritems(self.scalers):\n X[:,i] = scaler.transform(df[col].values.reshape(-1, 1)).flatten()\n i += 1\n for col in NO_TRANSFORM:\n X[:,i] = df[col]\n i += 1\n return X\n\n def fit_transform(self, df):\n self.fit(df)\n return self.transform(df)\n\n\ndef get_data():\n df = pd.read_csv('housing.data', header=None, delim_whitespace=True)\n df.columns = [\n 'crim', # numerical\n 'zn', # numerical\n 'nonretail', # numerical\n 'river', # binary\n 'nox', # numerical\n 'rooms', # numerical\n 'age', # numerical\n 'dis', # numerical\n 'rad', # numerical\n 'tax', # numerical\n 'ptratio', # numerical\n 'b', # numerical\n 'lstat', # numerical\n 'medv', # numerical -- this is the target\n ]\n\n # transform the data\n transformer = DataTransformer()\n\n # shuffle the data\n N = len(df)\n train_idx = np.random.choice(N, size=int(0.7*N), replace=False)\n test_idx = [i for i in range(N) if i not in train_idx]\n df_train = df.loc[train_idx]\n df_test = df.loc[test_idx]\n\n Xtrain = transformer.fit_transform(df_train)\n Ytrain = np.log(df_train['medv'].values)\n Xtest = transformer.transform(df_test)\n Ytest = np.log(df_test['medv'].values)\n return Xtrain, Ytrain, Xtest, Ytest\n\n\nif __name__ == '__main__':\n Xtrain, Ytrain, Xtest, Ytest = get_data()\n\n model = RandomForestRegressor(n_estimators=100) # try 10, 20, 50, 100, 200\n model.fit(Xtrain, Ytrain)\n predictions = model.predict(Xtest)\n\n # plot predictions vs targets\n plt.scatter(Ytest, predictions)\n plt.xlabel(\"target\")\n plt.ylabel(\"prediction\")\n ymin = np.round( min( min(Ytest), min(predictions) ) )\n ymax = np.ceil( max( max(Ytest), max(predictions) ) )\n print(\"ymin:\", ymin, \"ymax:\", ymax)\n r = range(int(ymin), int(ymax) + 1)\n plt.plot(r, r)\n plt.show()\n\n plt.plot(Ytest, label='targets')\n plt.plot(predictions, label='predictions')\n plt.legend()\n plt.show()\n\n # do a quick baseline test\n baseline = LinearRegression()\n single_tree = DecisionTreeRegressor()\n print(\"CV single tree:\", cross_val_score(single_tree, Xtrain, Ytrain, cv=5).mean())\n print(\"CV baseline:\", cross_val_score(baseline, Xtrain, Ytrain, cv=5).mean())\n print(\"CV forest:\", cross_val_score(model, Xtrain, Ytrain, cv=5).mean())\n\n # test score\n single_tree.fit(Xtrain, Ytrain)\n baseline.fit(Xtrain, Ytrain)\n print(\"test score single tree:\", single_tree.score(Xtest, Ytest))\n print(\"test score baseline:\", baseline.score(Xtest, Ytest))\n print(\"test score forest:\", model.score(Xtest, Ytest))\n\n" }, { "path": "supervised_class2/rf_vs_bag.py", "content": "# https://deeplearningcourses.com/c/machine-learning-in-python-random-forest-adaboost\n# https://www.udemy.com/machine-learning-in-python-random-forest-adaboost\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.ensemble import RandomForestRegressor, BaggingRegressor, RandomForestClassifier, BaggingClassifier\nfrom util import BaggedTreeRegressor, BaggedTreeClassifier\n\n# make simple regression data\nN = 15\nD = 100\nX = (np.random.random((N, D)) - 0.5)*10\nY = X.sum(axis=1)**2 + 0.5*np.random.randn(N)\nNtrain = N//2\nXtrain = X[:Ntrain]\nYtrain = Y[:Ntrain]\nXtest = X[Ntrain:]\nYtest = Y[Ntrain:]\n\n# from rf_classification import get_data\n# X, Y = get_data()\n# Ntrain = int(0.8*len(X))\n# Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain]\n# Xtest, Ytest = X[Ntrain:], Y[Ntrain:]\n\n# from rf_regression import get_data\n# Xtrain, Ytrain, Xtest, Ytest = get_data()\n\nT = 300\ntest_error_rf = np.empty(T)\ntest_error_bag = np.empty(T)\nfor num_trees in range(T):\n if num_trees == 0:\n test_error_rf[num_trees] = None\n test_error_bag[num_trees] = None\n else:\n rf = RandomForestRegressor(n_estimators=num_trees)\n # rf = RandomForestClassifier(n_estimators=num_trees)\n rf.fit(Xtrain, Ytrain)\n test_error_rf[num_trees] = rf.score(Xtest, Ytest)\n\n bg = BaggedTreeRegressor(n_estimators=num_trees)\n # bg = BaggedTreeClassifier(n_estimators=num_trees)\n bg.fit(Xtrain, Ytrain)\n test_error_bag[num_trees] = bg.score(Xtest, Ytest)\n\n if num_trees % 10 == 0:\n print(\"num_trees:\", num_trees)\n\nplt.plot(test_error_rf, label='rf')\nplt.plot(test_error_bag, label='bag')\nplt.legend()\nplt.show()\n" }, { "path": "supervised_class2/rf_vs_bag2.py", "content": "# https://deeplearningcourses.com/c/machine-learning-in-python-random-forest-adaboost\n# https://www.udemy.com/machine-learning-in-python-random-forest-adaboost\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestRegressor, BaggingRegressor, RandomForestClassifier, BaggingClassifier\nfrom util import BaggedTreeRegressor, BaggedTreeClassifier\n\n# make simple regression data\n# N = 15\n# D = 100\n# X = (np.random.random((N, D)) - 0.5)*10\n# Y = X.sum(axis=1)**2 + 0.5*np.random.randn(N)\n# Ntrain = N/2\n# Xtrain = X[:Ntrain]\n# Ytrain = Y[:Ntrain]\n# Xtest = X[Ntrain:]\n# Ytest = Y[Ntrain:]\n\nfrom rf_classification import get_data\nX, Y = get_data()\nNtrain = int(0.8*len(X))\nXtrain, Ytrain = X[:Ntrain], Y[:Ntrain]\nXtest, Ytest = X[Ntrain:], Y[Ntrain:]\n\n# from rf_regression import get_data\n# Xtrain, Ytrain, Xtest, Ytest = get_data()\n\nclass NotAsRandomForest:\n def __init__(self, n_estimators):\n self.B = n_estimators\n\n def fit(self, X, Y, M=None):\n N, D = X.shape\n if M is None:\n M = int(np.sqrt(D))\n\n self.models = []\n self.features = []\n for b in range(self.B):\n tree = DecisionTreeClassifier()\n\n # sample features\n features = np.random.choice(D, size=M, replace=False)\n\n # sample training samples\n idx = np.random.choice(N, size=N, replace=True)\n Xb = X[idx]\n Yb = Y[idx]\n\n tree.fit(Xb[:, features], Yb)\n self.features.append(features)\n self.models.append(tree)\n\n def predict(self, X):\n N = len(X)\n P = np.zeros(N)\n for features, tree in zip(self.features, self.models):\n P += tree.predict(X[:, features])\n return np.round(P / self.B)\n\n def score(self, X, Y):\n P = self.predict(X)\n return np.mean(P == Y)\n\n\nT = 500\ntest_error_prf = np.empty(T)\ntest_error_rf = np.empty(T)\ntest_error_bag = np.empty(T)\nfor num_trees in range(T):\n if num_trees == 0:\n test_error_prf[num_trees] = None\n test_error_rf[num_trees] = None\n test_error_bag[num_trees] = None\n else:\n rf = RandomForestClassifier(n_estimators=num_trees)\n rf.fit(Xtrain, Ytrain)\n test_error_rf[num_trees] = rf.score(Xtest, Ytest)\n\n bg = BaggedTreeClassifier(n_estimators=num_trees)\n bg.fit(Xtrain, Ytrain)\n test_error_bag[num_trees] = bg.score(Xtest, Ytest)\n\n prf = NotAsRandomForest(n_estimators=num_trees)\n prf.fit(Xtrain, Ytrain)\n test_error_prf[num_trees] = prf.score(Xtest, Ytest)\n\n if num_trees % 10 == 0:\n print(\"num_trees:\", num_trees)\n\nplt.plot(test_error_rf, label='rf')\nplt.plot(test_error_prf, label='pseudo rf')\nplt.plot(test_error_bag, label='bag')\nplt.legend()\nplt.show()\n" }, { "path": "supervised_class2/util.py", "content": "# https://deeplearningcourses.com/c/machine-learning-in-python-random-forest-adaboost\n# https://www.udemy.com/machine-learning-in-python-random-forest-adaboost\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n\ndef plot_decision_boundary(X, model):\n h = .02 # step size in the mesh\n # create a mesh to plot in\n x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n np.arange(y_min, y_max, h))\n\n\n # Plot the decision boundary. For that, we will assign a color to each\n # point in the mesh [x_min, m_max]x[y_min, y_max].\n Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n\n # Put the result into a color plot\n Z = Z.reshape(xx.shape)\n plt.contour(xx, yy, Z, cmap=plt.cm.Paired)\n\n\nclass BaggedTreeRegressor:\n def __init__(self, n_estimators, max_depth=None):\n self.B = n_estimators\n self.max_depth = max_depth\n\n def fit(self, X, Y):\n N = len(X)\n self.models = []\n for b in range(self.B):\n idx = np.random.choice(N, size=N, replace=True)\n Xb = X[idx]\n Yb = Y[idx]\n\n model = DecisionTreeRegressor(max_depth=self.max_depth)\n model.fit(Xb, Yb)\n self.models.append(model)\n\n def predict(self, X):\n predictions = np.zeros(len(X))\n for model in self.models:\n predictions += model.predict(X)\n return predictions / self.B\n\n def score(self, X, Y):\n d1 = Y - self.predict(X)\n d2 = Y - Y.mean()\n return 1 - d1.dot(d1) / d2.dot(d2)\n\n\nclass BaggedTreeClassifier:\n def __init__(self, n_estimators, max_depth=None):\n self.B = n_estimators\n self.max_depth = max_depth\n\n def fit(self, X, Y):\n N = len(X)\n self.models = []\n for b in range(self.B):\n idx = np.random.choice(N, size=N, replace=True)\n Xb = X[idx]\n Yb = Y[idx]\n\n model = DecisionTreeClassifier(max_depth=self.max_depth)\n model.fit(Xb, Yb)\n self.models.append(model)\n\n def predict(self, X):\n # no need to keep a dictionary since we are doing binary classification\n predictions = np.zeros(len(X))\n for model in self.models:\n predictions += model.predict(X)\n return np.round(predictions / self.B)\n\n def score(self, X, Y):\n P = self.predict(X)\n return np.mean(Y == P)\n" }, { "path": "svm_class/crossval.py", "content": "# https://deeplearningcourses.com/c/support-vector-machines-in-python\n# https://www.udemy.com/support-vector-machines-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nfrom datetime import datetime\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.datasets import load_breast_cancer\nfrom sklearn.svm import SVC\n\n# load the data\ndata = load_breast_cancer()\n\nfor C in (0.5, 1.0, 5.0, 10.0):\n pipeline = Pipeline([('scaler', StandardScaler()), ('svm', SVC(C=C))])\n scores = cross_val_score(pipeline, data.data, data.target, cv=5)\n print(\"C:\", C, \"mean:\", scores.mean(), \"std:\", scores.std())\n" }, { "path": "svm_class/extra_reading.txt", "content": "Pattern Recognition and Machine Learning\nhttps://amzn.to/2DeexU0\n\nLearning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond\nhttps://amzn.to/2FdyP2s\n\nConvex Optimization\nhttp://stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf\n\nA Tutorial on Support Vector Machines for Pattern Recognition\nhttps://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/svmtutorial.pdf\n\nSequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines\nhttps://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-98-14.pdf\n\nFast Training of Support Vector Machines using Sequential Minimal Optimization\nhttps://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/smo-book.pdf\n\nConvex Analysis\nhttps://press.princeton.edu/titles/1815.html\n\nGeneralized Lagrange multiplier method for solving problems of optimum allocation of resources\nhttps://web.archive.org/web/20110724151508/http://or.journal.informs.org/cgi/reprint/11/3/399\n\nDuality in Linear Programming\nhttp://www.civilized.com/files/duality.pdf\n\nLinear programming\nhttps://en.wikipedia.org/wiki/Linear_programming#Duality\n\nKarush–Kuhn–Tucker conditions\nhttps://en.wikipedia.org/wiki/Karush–Kuhn–Tucker_conditions\n\nA Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods\nhttps://www.researchgate.net/publication/2478380_A_Study_on_Sigmoid_Kernels_for_SVM_and_the_Training_of_non-PSD_Kernels_by_SMO-type_Methods\n\nText Classification using String Kernels\nhttp://www.jmlr.org/papers/volume2/lodhi02a/lodhi02a.pdf\n\nA Comparison of Methods for Multi-class Support Vector Machines\nhttps://www.csie.ntu.edu.tw/~cjlin/papers/multisvm.pdf\n\nA Tutorial on Support Vector Regression\nhttps://alex.smola.org/papers/2003/SmoSch03b.pdf\n\nLIBSVM -- A Library for Support Vector Machines\nhttps://www.csie.ntu.edu.tw/~cjlin/libsvm/\n\nRandom Features for Large-Scale Kernel Machines\nhttp://www.robots.ox.ac.uk/~vgg/rg/papers/randomfeatures.pdf\n\nReflections on Random Kitchen Sinks\nhttp://www.argmin.net/2017/12/05/kitchen-sinks/\n\nWeighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning\nhttps://papers.nips.cc/paper/3495-weighted-sums-of-random-kitchen-sinks-replacing-minimization-with-randomization-in-learning\n\nUsing the Nyström Method to Speed Up Kernel Machines\nhttps://papers.nips.cc/paper/1866-using-the-nystrom-method-to-speed-up-kernel-machines\n\nNyström Method vs Random Fourier Features: A Theoretical and Empirical Comparison\nhttps://papers.nips.cc/paper/4588-nystrom-method-vs-random-fourier-features-a-theoretical-and-empirical-comparison\n\nThis guy generated some nice plots and code to demonstrate that RBFSampler works like a real RBF Kernel\nhttps://www.kaggle.com/sy2002/rbfsampler-actually-is-not-using-any-rbfs" }, { "path": "svm_class/fake_neural_net.py", "content": "# https://deeplearningcourses.com/c/support-vector-machines-in-python\n# https://www.udemy.com/support-vector-machines-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\n\nfrom sklearn.svm import SVC\nfrom util import getKaggleMNIST\nfrom datetime import datetime\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.svm import LinearSVC\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.cluster import KMeans\nfrom sklearn.mixture import GaussianMixture\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.utils import shuffle\nfrom scipy import stats\nfrom sklearn.linear_model import LogisticRegression\n\n\nclass SigmoidFeaturizer:\n def __init__(self, gamma=1.0, n_components=100, method='random'):\n self.M = n_components\n self.gamma = gamma\n assert(method in ('normal', 'random', 'kmeans', 'gmm'))\n self.method = method\n\n def _subsample_data(self, X, Y, n=10000):\n if Y is not None:\n X, Y = shuffle(X, Y)\n return X[:n], Y[:n]\n else:\n X = shuffle(X)\n return X[:n]\n\n def fit(self, X, Y=None):\n if self.method == 'random':\n N = len(X)\n idx = np.random.randint(N, size=self.M)\n self.samples = X[idx]\n elif self.method == 'normal':\n # just sample from N(0,1)\n D = X.shape[1]\n self.samples = np.random.randn(self.M, D) / np.sqrt(D)\n elif self.method == 'kmeans':\n X, Y = self._subsample_data(X, Y)\n\n print(\"Fitting kmeans...\")\n t0 = datetime.now()\n kmeans = KMeans(n_clusters=len(set(Y)))\n kmeans.fit(X)\n print(\"Finished fitting kmeans, duration:\", datetime.now() - t0)\n\n # calculate the most ambiguous points\n # we will do this by finding the distance between each point\n # and all cluster centers\n # and return which points have the smallest variance\n dists = kmeans.transform(X) # returns an N x K matrix\n variances = dists.var(axis=1)\n idx = np.argsort(variances) # smallest to largest\n idx = idx[:self.M]\n self.samples = X[idx]\n elif self.method == 'gmm':\n X, Y = self._subsample_data(X, Y)\n\n print(\"Fitting GMM\")\n t0 = datetime.now()\n gmm = GaussianMixture(\n n_components=len(set(Y)),\n covariance_type='spherical',\n reg_covar=1e-6)\n gmm.fit(X)\n print(\"Finished fitting GMM, duration:\", datetime.now() - t0)\n\n # calculate the most ambiguous points\n probs = gmm.predict_proba(X)\n ent = stats.entropy(probs.T) # N-length vector of entropies\n idx = np.argsort(-ent) # negate since we want biggest first\n idx = idx[:self.M]\n self.samples = X[idx]\n return self\n\n def transform(self, X):\n Z = X.dot(self.samples.T) # (Ntest x D) x (D x Nsamples) -> (Ntest x Nsamples)\n return np.tanh(self.gamma * Z)\n # return self.gamma * Z * (Z > 0)\n\n def fit_transform(self, X, Y=None):\n return self.fit(X, Y).transform(X)\n\n\n# get the data: https://www.kaggle.com/c/digit-recognizer\nXtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()\n\n# with SGD\npipeline = Pipeline([\n ('scaler', StandardScaler()),\n ('sigmoid', SigmoidFeaturizer(gamma=0.05, n_components=2000, method='normal')),\n # ('linear', SGDClassifier(max_iter=1e6, tol=1e-5))\n ('linear', LogisticRegression()) # takes longer\n])\n\n# with Linear SVC\n# n_components = 3000\n# pipeline = Pipeline([\n# ('scaler', StandardScaler()),\n# ('sigmoid', SigmoidFeaturizer(n_components=n_components)),\n# ('linear', LinearSVC())\n# ])\n\n# let's do some cross-validation instead, why not\nX = np.vstack((Xtrain, Xtest))\nY = np.concatenate((Ytrain, Ytest))\nscores = cross_val_score(pipeline, X, Y, cv=5)\nprint(scores)\nprint(\"avg:\", np.mean(scores))\n\n# t0 = datetime.now()\n# pipeline.fit(Xtrain, Ytrain)\n# print(\"train duration:\", datetime.now() - t0)\n# t0 = datetime.now()\n# print(\"train score:\", pipeline.score(Xtrain, Ytrain), \"duration:\", datetime.now() - t0)\n# t0 = datetime.now()\n# print(\"test score:\", pipeline.score(Xtest, Ytest), \"duration:\", datetime.now() - t0)\n" }, { "path": "svm_class/kernel_svm_gradient_primal.py", "content": "from __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.datasets import load_breast_cancer\nfrom sklearn.preprocessing import StandardScaler\nfrom datetime import datetime\nfrom util import get_spiral, get_xor, get_donut, get_clouds\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\n# kernels\ndef linear(X1, X2, c=0):\n return X1.dot(X2.T) + c\n\ndef rbf(X1, X2, gamma=None):\n if gamma is None:\n gamma = 1.0 / X1.shape[-1] # 1 / D\n # gamma = 0.05\n # gamma = 5. # for donut and spiral\n if np.ndim(X1) == 1 and np.ndim(X2) == 1:\n result = np.exp(-gamma * np.linalg.norm(X1 - X2)**2)\n elif (np.ndim(X1) > 1 and np.ndim(X2) == 1) or (np.ndim(X1) == 1 and np.ndim(X2) > 1):\n result = np.exp(-gamma * np.linalg.norm(X1 - X2, axis=1)**2)\n elif np.ndim(X1) > 1 and np.ndim(X2) > 1:\n result = np.exp(-gamma * np.linalg.norm(X1[:, np.newaxis] - X2[np.newaxis, :], axis=2)**2)\n return result\n\ndef sigmoid(X1, X2, gamma=0.05, c=1):\n return np.tanh(gamma * X1.dot(X2.T) + c)\n\n\nclass KernelSVM:\n def __init__(self, kernel=linear, C=1.0):\n self.C = C\n self.kernel = kernel\n\n def _objective(self, margins):\n return 0.5 * self.u.dot(self.K.dot(self.u)) + \\\n self.C * np.maximum(0, 1 - margins).sum()\n\n def fit(self, X, Y, lr=1e-5, n_iters=400):\n N, D = X.shape\n self.N = N\n self.u = np.random.randn(N)\n self.b = 0\n\n # setup kernel matrix\n self.X = X\n self.Y = Y\n self.K = self.kernel(X, X)\n\n # gradient descent\n losses = []\n for _ in range(n_iters):\n margins = Y * (self.u.dot(self.K) + self.b)\n loss = self._objective(margins)\n losses.append(loss)\n \n idx = np.where(margins < 1)[0]\n grad_u = self.K.dot(self.u) - self.C * Y[idx].dot(self.K[idx])\n self.u -= lr * grad_u\n grad_b = -self.C * Y[idx].sum()\n self.b -= lr * grad_b\n\n self.support_ = np.where((Y * (self.u.dot(self.K) + self.b)) <= 1)[0]\n print(\"num SVs:\", len(self.support_))\n\n # print(\"w:\", self.w)\n # print(\"b:\", self.b)\n\n # hist of margins\n m = Y * (self.u.dot(self.K) + self.b)\n plt.hist(m, bins=20)\n plt.show()\n\n plt.plot(losses)\n plt.title(\"loss per iteration\")\n plt.show()\n\n def _decision_function(self, X):\n return self.u.dot(self.kernel(self.X, X)) + self.b\n\n def predict(self, X):\n return np.sign(self._decision_function(X))\n\n def score(self, X, Y):\n P = self.predict(X)\n return np.mean(Y == P)\n\n\ndef plot_decision_boundary(model, X, Y, resolution=100, colors=('b', 'k', 'r')):\n np.warnings.filterwarnings('ignore')\n fig, ax = plt.subplots()\n\n # Generate coordinate grid of shape [resolution x resolution]\n # and evaluate the model over the entire space\n x_range = np.linspace(X[:,0].min(), X[:,0].max(), resolution)\n y_range = np.linspace(X[:,1].min(), X[:,1].max(), resolution)\n grid = [[model._decision_function(np.array([[xr, yr]])) for yr in y_range] for xr in x_range]\n grid = np.array(grid).reshape(len(x_range), len(y_range))\n \n # Plot decision contours using grid and\n # make a scatter plot of training data\n ax.contour(x_range, y_range, grid.T, (-1, 0, 1), linewidths=(1, 1, 1),\n linestyles=('--', '-', '--'), colors=colors)\n ax.scatter(X[:,0], X[:,1],\n c=Y, lw=0, alpha=0.3, cmap='seismic')\n \n # Plot support vectors (non-zero alphas)\n # as circled points (linewidth > 0)\n mask = model.support_\n ax.scatter(X[:,0][mask], X[:,1][mask],\n c=Y[mask], cmap='seismic')\n\n # debug\n ax.scatter([0], [0], c='black', marker='x')\n\n # debug\n # x_axis = np.linspace(X[:,0].min(), X[:,0].max(), 100)\n # w = model.w\n # b = model.b\n # # w[0]*x + w[1]*y + b = 0\n # y_axis = -(w[0]*x_axis + b)/w[1]\n # plt.plot(x_axis, y_axis, color='purple')\n # margin_p = (1 - w[0]*x_axis - b)/w[1]\n # plt.plot(x_axis, margin_p, color='orange')\n # margin_n = -(1 + w[0]*x_axis + b)/w[1]\n # plt.plot(x_axis, margin_n, color='orange')\n\n plt.show()\n\n\ndef clouds():\n X, Y = get_clouds()\n Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33)\n return Xtrain, Xtest, Ytrain, Ytest, linear, 1e-5, 500\n\n\ndef medical():\n data = load_breast_cancer()\n X, Y = data.data, data.target\n Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33)\n return Xtrain, Xtest, Ytrain, Ytest, linear, 1e-3, 200\n\ndef xor():\n X, Y = get_xor()\n Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33)\n kernel = lambda X1, X2: rbf(X1, X2, gamma=3.)\n return Xtrain, Xtest, Ytrain, Ytest, kernel, 1e-3, 500\n\ndef donut():\n X, Y = get_donut()\n Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33)\n kernel = lambda X1, X2: rbf(X1, X2, gamma=1.)\n return Xtrain, Xtest, Ytrain, Ytest, kernel, 1e-3, 300\n\ndef spiral():\n X, Y = get_spiral()\n Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33)\n kernel = lambda X1, X2: rbf(X1, X2, gamma=5.)\n return Xtrain, Xtest, Ytrain, Ytest, kernel, 1e-3, 500\n\n\nif __name__ == '__main__':\n Xtrain, Xtest, Ytrain, Ytest, kernel, lr, n_iters = donut()\n print(\"Possible labels:\", set(Ytrain))\n\n # make sure the targets are (-1, +1)\n Ytrain[Ytrain == 0] = -1\n Ytest[Ytest == 0] = -1\n\n # scale the data\n scaler = StandardScaler()\n Xtrain = scaler.fit_transform(Xtrain)\n Xtest = scaler.transform(Xtest)\n\n # now we'll use our custom implementation\n model = KernelSVM(kernel=kernel, C=1.0)\n\n t0 = datetime.now()\n model.fit(Xtrain, Ytrain, lr=lr, n_iters=n_iters)\n print(\"train duration:\", datetime.now() - t0)\n t0 = datetime.now()\n print(\"train score:\", model.score(Xtrain, Ytrain), \"duration:\", datetime.now() - t0)\n t0 = datetime.now()\n print(\"test score:\", model.score(Xtest, Ytest), \"duration:\", datetime.now() - t0)\n\n if Xtrain.shape[1] == 2:\n plot_decision_boundary(model, Xtrain, Ytrain)\n" }, { "path": "svm_class/linear_svm_gradient.py", "content": "# https://deeplearningcourses.com/c/support-vector-machines-in-python\n# https://www.udemy.com/support-vector-machines-in-python\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.datasets import load_breast_cancer\nfrom sklearn.preprocessing import StandardScaler\nfrom datetime import datetime\nfrom util import get_clouds\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nclass LinearSVM:\n def __init__(self, C=1.0):\n self.C = C\n\n def _objective(self, margins):\n return 0.5 * self.w.dot(self.w) + self.C * np.maximum(0, 1 - margins).sum()\n\n def fit(self, X, Y, lr=1e-5, n_iters=400):\n N, D = X.shape\n self.N = N\n self.w = np.random.randn(D)\n self.b = 0\n\n # gradient descent\n losses = []\n for _ in range(n_iters):\n margins = Y * self._decision_function(X)\n loss = self._objective(margins)\n losses.append(loss)\n \n idx = np.where(margins < 1)[0]\n grad_w = self.w - self.C * Y[idx].dot(X[idx])\n self.w -= lr * grad_w\n grad_b = -self.C * Y[idx].sum()\n self.b -= lr * grad_b\n\n self.support_ = np.where((Y * self._decision_function(X)) <= 1)[0]\n print(\"num SVs:\", len(self.support_))\n\n print(\"w:\", self.w)\n print(\"b:\", self.b)\n\n # hist of margins\n # m = Y * self._decision_function(X)\n # plt.hist(m, bins=20)\n # plt.show()\n\n plt.plot(losses)\n plt.title(\"loss per iteration\")\n plt.show()\n\n def _decision_function(self, X):\n return X.dot(self.w) + self.b\n\n def predict(self, X):\n return np.sign(self._decision_function(X))\n\n def score(self, X, Y):\n P = self.predict(X)\n return np.mean(Y == P)\n\n\ndef plot_decision_boundary(model, X, Y, resolution=100, colors=('b', 'k', 'r')):\n np.warnings.filterwarnings('ignore')\n fig, ax = plt.subplots()\n\n # Generate coordinate grid of shape [resolution x resolution]\n # and evaluate the model over the entire space\n x_range = np.linspace(X[:,0].min(), X[:,0].max(), resolution)\n y_range = np.linspace(X[:,1].min(), X[:,1].max(), resolution)\n grid = [[model._decision_function(np.array([[xr, yr]])) for yr in y_range] for xr in x_range]\n grid = np.array(grid).reshape(len(x_range), len(y_range))\n \n # Plot decision contours using grid and\n # make a scatter plot of training data\n ax.contour(x_range, y_range, grid.T, (-1, 0, 1), linewidths=(1, 1, 1),\n linestyles=('--', '-', '--'), colors=colors)\n ax.scatter(X[:,0], X[:,1],\n c=Y, lw=0, alpha=0.3, cmap='seismic')\n \n # Plot support vectors (non-zero alphas)\n # as circled points (linewidth > 0)\n mask = model.support_\n ax.scatter(X[:,0][mask], X[:,1][mask],\n c=Y[mask], cmap='seismic')\n\n # debug\n ax.scatter([0], [0], c='black', marker='x')\n\n # debug\n # x_axis = np.linspace(X[:,0].min(), X[:,0].max(), 100)\n # w = model.w\n # b = model.b\n # # w[0]*x + w[1]*y + b = 0\n # y_axis = -(w[0]*x_axis + b)/w[1]\n # plt.plot(x_axis, y_axis, color='purple')\n # margin_p = (1 - w[0]*x_axis - b)/w[1]\n # plt.plot(x_axis, margin_p, color='orange')\n # margin_n = -(1 + w[0]*x_axis + b)/w[1]\n # plt.plot(x_axis, margin_n, color='orange')\n\n plt.show()\n\n\ndef clouds():\n X, Y = get_clouds()\n Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33)\n return Xtrain, Xtest, Ytrain, Ytest, 1e-3, 200\n\n\ndef medical():\n data = load_breast_cancer()\n X, Y = data.data, data.target\n Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33)\n return Xtrain, Xtest, Ytrain, Ytest, 1e-3, 200\n\n\nif __name__ == '__main__':\n Xtrain, Xtest, Ytrain, Ytest, lr, n_iters = clouds()\n print(\"Possible labels:\", set(Ytrain))\n\n # make sure the targets are (-1, +1)\n Ytrain[Ytrain == 0] = -1\n Ytest[Ytest == 0] = -1\n\n # scale the data\n scaler = StandardScaler()\n Xtrain = scaler.fit_transform(Xtrain)\n Xtest = scaler.transform(Xtest)\n\n # now we'll use our custom implementation\n model = LinearSVM(C=1.0)\n\n t0 = datetime.now()\n model.fit(Xtrain, Ytrain, lr=lr, n_iters=n_iters)\n print(\"train duration:\", datetime.now() - t0)\n t0 = datetime.now()\n print(\"train score:\", model.score(Xtrain, Ytrain), \"duration:\", datetime.now() - t0)\n t0 = datetime.now()\n print(\"test score:\", model.score(Xtest, Ytest), \"duration:\", datetime.now() - t0)\n\n if Xtrain.shape[1] == 2:\n plot_decision_boundary(model, Xtrain, Ytrain)\n" }, { "path": "svm_class/rbfnetwork.py", "content": "# https://deeplearningcourses.com/c/support-vector-machines-in-python\n# https://www.udemy.com/support-vector-machines-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nfrom sklearn.svm import SVC\nfrom util import getKaggleMNIST\nfrom datetime import datetime\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.kernel_approximation import RBFSampler\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.svm import LinearSVC\nfrom sklearn.pipeline import FeatureUnion\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.kernel_approximation import Nystroem\n\n# get the data: https://www.kaggle.com/c/digit-recognizer\nXtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()\n\n# linear SGD classifier\n# pipeline = Pipeline([('linear', SGDClassifier(max_iter=1e6, tol=1e-5))])\n\n# linear SVC - a bit faster than SVC with linear kernel\n# pipeline = Pipeline([('linear', LinearSVC())])\n\n# one RBFSampler with linear SGD classifier\n# pipeline = Pipeline([\n# ('rbf', RBFSampler(gamma=0.01, n_components=1000)),\n# ('linear', SGDClassifier(max_iter=1e6, tol=1e-5))])\n\n# multiple RBFSamplers\n# n_components = 2000\n# featurizer = FeatureUnion([\n# (\"rbf1\", RBFSampler(gamma=0.01, n_components=n_components)),\n# (\"rbf2\", RBFSampler(gamma=0.005, n_components=n_components)),\n# (\"rbf3\", RBFSampler(gamma=0.001, n_components=n_components)),\n# ])\n# pipeline = Pipeline([('rbf', featurizer), ('linear', SGDClassifier(max_iter=1e6, tol=1e-5))])\n\n# Nystroem approximation\n# pipeline = Pipeline([\n# ('rbf', Nystroem(gamma=0.05, n_components=1000)),\n# ('linear', SGDClassifier(max_iter=1e6, tol=1e-5))])\n\n# multiple Nystroem\nn_components = 1000\nfeaturizer = FeatureUnion([\n (\"rbf0\", Nystroem(gamma=0.05, n_components=n_components)),\n (\"rbf1\", Nystroem(gamma=0.01, n_components=n_components)),\n (\"rbf2\", Nystroem(gamma=0.005, n_components=n_components)),\n (\"rbf3\", Nystroem(gamma=0.001, n_components=n_components)),\n ])\npipeline = Pipeline([('rbf', featurizer), ('linear', SGDClassifier(max_iter=1e6, tol=1e-5))])\n\n\nt0 = datetime.now()\npipeline.fit(Xtrain, Ytrain)\nprint(\"train duration:\", datetime.now() - t0)\nt0 = datetime.now()\nprint(\"train score:\", pipeline.score(Xtrain, Ytrain), \"duration:\", datetime.now() - t0)\nt0 = datetime.now()\nprint(\"test score:\", pipeline.score(Xtest, Ytest), \"duration:\", datetime.now() - t0)\n" }, { "path": "svm_class/real_neural_net.py", "content": "# https://deeplearningcourses.com/c/support-vector-machines-in-python\n# https://www.udemy.com/support-vector-machines-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\n\nfrom util import getKaggleMNIST\nfrom datetime import datetime\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.linear_model import LogisticRegression\n\n# get the data: https://www.kaggle.com/c/digit-recognizer\nXtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()\n\n# scale first\npipeline = Pipeline([\n # ('scaler', StandardScaler()),\n ('mlp', MLPClassifier(hidden_layer_sizes=(500,), activation='tanh')),\n # ('lr', LogisticRegression()),\n])\n\n\n\nt0 = datetime.now()\npipeline.fit(Xtrain, Ytrain)\nprint(\"train duration:\", datetime.now() - t0)\nt0 = datetime.now()\nprint(\"train score:\", pipeline.score(Xtrain, Ytrain), \"duration:\", datetime.now() - t0)\nt0 = datetime.now()\nprint(\"test score:\", pipeline.score(Xtest, Ytest), \"duration:\", datetime.now() - t0)\n" }, { "path": "svm_class/regression.py", "content": "# https://deeplearningcourses.com/c/support-vector-machines-in-python\n# https://www.udemy.com/support-vector-machines-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nfrom datetime import datetime\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.svm import SVR\n\n# get the data: https://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+Strength\ndf = pd.read_excel('../large_files/Concrete_Data.xls')\ndf.columns = list(range(df.shape[1]))\n\nX = df[[0,1,2,3,4,5,6,7]].values\nY = df[8].values\n\n# split the data into train and test sets\n# this lets us simulate how our model will perform in the future\nXtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33)\n\n# scale the data\nscaler = StandardScaler()\nXtrain = scaler.fit_transform(Xtrain)\nXtest = scaler.transform(Xtest)\n\ntarget_scaler = StandardScaler()\nYtrain = target_scaler.fit_transform(Ytrain.reshape(-1, 1)).flatten()\nYtest = target_scaler.transform(Ytest.reshape(-1, 1)).flatten()\n\nmodel = SVR(kernel='rbf')\nmodel.fit(Xtrain, Ytrain)\nprint(\"train score:\", model.score(Xtrain, Ytrain))\nprint(\"test score:\", model.score(Xtest, Ytest))\n" }, { "path": "svm_class/svm_gradient.py", "content": "# https://deeplearningcourses.com/c/support-vector-machines-in-python\n# https://www.udemy.com/support-vector-machines-in-python\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.datasets import load_breast_cancer\nfrom sklearn.preprocessing import StandardScaler\nfrom datetime import datetime\nfrom util import get_spiral, get_xor, get_donut, get_clouds, plot_decision_boundary\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\n# kernels\ndef linear(X1, X2, c=0):\n return X1.dot(X2.T) + c\n\ndef rbf(X1, X2, gamma=None):\n if gamma is None:\n gamma = 1.0 / X1.shape[-1] # 1 / D\n # gamma = 0.05\n # gamma = 5. # for donut and spiral\n if np.ndim(X1) == 1 and np.ndim(X2) == 1:\n result = np.exp(-gamma * np.linalg.norm(X1 - X2)**2)\n elif (np.ndim(X1) > 1 and np.ndim(X2) == 1) or (np.ndim(X1) == 1 and np.ndim(X2) > 1):\n result = np.exp(-gamma * np.linalg.norm(X1 - X2, axis=1)**2)\n elif np.ndim(X1) > 1 and np.ndim(X2) > 1:\n result = np.exp(-gamma * np.linalg.norm(X1[:, np.newaxis] - X2[np.newaxis, :], axis=2)**2)\n return result\n\ndef sigmoid(X1, X2, gamma=0.05, c=1):\n return np.tanh(gamma * X1.dot(X2.T) + c)\n\n\nclass SVM:\n def __init__(self, kernel, C=1.0):\n self.kernel = kernel\n self.C = C\n\n def _train_objective(self):\n return np.sum(self.alphas) - 0.5 * np.sum(self.YYK * np.outer(self.alphas, self.alphas))\n\n def fit(self, X, Y, lr=1e-5, n_iters=400):\n # we need these to make future predictions\n self.Xtrain = X\n self.Ytrain = Y\n self.N = X.shape[0]\n self.alphas = np.random.random(self.N)\n self.b = 0\n\n # kernel matrix\n self.K = self.kernel(X, X)\n self.YY = np.outer(Y, Y)\n self.YYK = self.K * self.YY\n\n # gradient ascent\n losses = []\n for _ in range(n_iters):\n loss = self._train_objective()\n losses.append(loss)\n grad = np.ones(self.N) - self.YYK.dot(self.alphas)\n self.alphas += lr * grad\n\n # clip\n self.alphas[self.alphas < 0] = 0\n self.alphas[self.alphas > self.C] = self.C\n\n # distrbution of bs\n idx = np.where((self.alphas) > 0 & (self.alphas < self.C))[0]\n bs = Y[idx] - (self.alphas * Y).dot(self.kernel(X, X[idx]))\n self.b = np.mean(bs)\n\n plt.plot(losses)\n plt.title(\"loss per iteration\")\n plt.show()\n\n def _decision_function(self, X):\n return (self.alphas * self.Ytrain).dot(self.kernel(self.Xtrain, X)) + self.b\n\n def predict(self, X):\n return np.sign(self._decision_function(X))\n\n def score(self, X, Y):\n P = self.predict(X)\n return np.mean(Y == P)\n\n\ndef medical():\n data = load_breast_cancer()\n X, Y = data.data, data.target\n Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33)\n return Xtrain, Xtest, Ytrain, Ytest, rbf, 1e-3, 200\n\ndef medical_sigmoid():\n data = load_breast_cancer()\n X, Y = data.data, data.target\n Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33)\n return Xtrain, Xtest, Ytrain, Ytest, sigmoid, 1e-3, 200\n\ndef xor():\n X, Y = get_xor()\n Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33)\n kernel = lambda X1, X2: rbf(X1, X2, gamma=5.)\n return Xtrain, Xtest, Ytrain, Ytest, kernel, 1e-2, 300\n\ndef donut():\n X, Y = get_donut()\n Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33)\n kernel = lambda X1, X2: rbf(X1, X2, gamma=5.)\n return Xtrain, Xtest, Ytrain, Ytest, kernel, 1e-2, 300\n\ndef spiral():\n X, Y = get_spiral()\n Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33)\n kernel = lambda X1, X2: rbf(X1, X2, gamma=5.)\n return Xtrain, Xtest, Ytrain, Ytest, kernel, 1e-2, 300\n\ndef clouds():\n X, Y = get_clouds()\n Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33)\n return Xtrain, Xtest, Ytrain, Ytest, linear, 1e-5, 400\n\n\nif __name__ == '__main__':\n Xtrain, Xtest, Ytrain, Ytest, kernel, lr, n_iters = spiral()\n print(\"Possible labels:\", set(Ytrain))\n\n # make sure the targets are (-1, +1)\n Ytrain[Ytrain == 0] = -1\n Ytest[Ytest == 0] = -1\n\n # scale the data\n scaler = StandardScaler()\n Xtrain = scaler.fit_transform(Xtrain)\n Xtest = scaler.transform(Xtest)\n\n # now we'll use our custom implementation\n model = SVM(kernel=kernel, C=1.0)\n\n t0 = datetime.now()\n model.fit(Xtrain, Ytrain, lr=lr, n_iters=n_iters)\n print(\"train duration:\", datetime.now() - t0)\n t0 = datetime.now()\n print(\"train score:\", model.score(Xtrain, Ytrain), \"duration:\", datetime.now() - t0)\n t0 = datetime.now()\n print(\"test score:\", model.score(Xtest, Ytest), \"duration:\", datetime.now() - t0)\n\n if Xtrain.shape[1] == 2:\n plot_decision_boundary(model)\n" }, { "path": "svm_class/svm_medical.py", "content": "# https://deeplearningcourses.com/c/support-vector-machines-in-python\n# https://www.udemy.com/support-vector-machines-in-python\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nfrom sklearn.svm import SVC\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.datasets import load_breast_cancer\nfrom sklearn.preprocessing import StandardScaler\n\n# load the data\ndata = load_breast_cancer()\n\n# split the data into train and test sets\n# this lets us simulate how our model will perform in the future\nXtrain, Xtest, Ytrain, Ytest = train_test_split(data.data, data.target, test_size=0.33)\n\n# scale the data\nscaler = StandardScaler()\nXtrain = scaler.fit_transform(Xtrain)\nXtest = scaler.transform(Xtest)\n\nmodel = SVC(kernel='rbf')\n# model = SVC()\nmodel.fit(Xtrain, Ytrain)\nprint(\"train score:\", model.score(Xtrain, Ytrain))\nprint(\"test score:\", model.score(Xtest, Ytest))\n" }, { "path": "svm_class/svm_mnist.py", "content": "# https://deeplearningcourses.com/c/support-vector-machines-in-python\n# https://www.udemy.com/support-vector-machines-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nfrom sklearn.svm import SVC\nfrom util import getKaggleMNIST\nfrom datetime import datetime\n\n# get the data: https://www.kaggle.com/c/digit-recognizer\nXtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()\n\n# model = SVC()\nmodel = SVC(C=5., gamma=.05)\n\nt0 = datetime.now()\nmodel.fit(Xtrain, Ytrain)\nprint(\"train duration:\", datetime.now() - t0)\nt0 = datetime.now()\nprint(\"train score:\", model.score(Xtrain, Ytrain), \"duration:\", datetime.now() - t0)\nt0 = datetime.now()\nprint(\"test score:\", model.score(Xtest, Ytest), \"duration:\", datetime.now() - t0)\n" }, { "path": "svm_class/svm_smo.py", "content": "# https://deeplearningcourses.com/c/support-vector-machines-in-python\n# https://www.udemy.com/support-vector-machines-in-python\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.datasets import load_breast_cancer\nfrom sklearn.preprocessing import StandardScaler\nfrom datetime import datetime\nfrom util import get_spiral, get_xor, get_donut, get_clouds, plot_decision_boundary\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\n# kernels\ndef linear(X1, X2):\n return X1.dot(X2.T)\n\ndef rbf(X1, X2):\n # gamma = 1.0 / X1.shape[-1] # 1 / D\n gamma = 5. # for donut and spiral\n if np.ndim(X1) == 1 and np.ndim(X2) == 1:\n result = np.exp(-gamma * np.linalg.norm(X1 - X2)**2)\n elif (np.ndim(X1) > 1 and np.ndim(X2) == 1) or (np.ndim(X1) == 1 and np.ndim(X2) > 1):\n result = np.exp(-gamma * np.linalg.norm(X1 - X2, axis=1)**2)\n elif np.ndim(X1) > 1 and np.ndim(X2) > 1:\n result = np.exp(-gamma * np.linalg.norm(X1[:, np.newaxis] - X2[np.newaxis, :], axis=2)**2)\n return result\n\ndef sigmoid(X1, X2, gamma=0.05, c=1):\n return np.tanh(gamma * X1.dot(X2.T) + c)\n\n\nclass SVM:\n def __init__(self, kernel, C=1.0):\n self.kernel = kernel\n self.C = C\n\n def _loss(self, X, Y):\n # return -np.sum(self.alphas) + \\\n # 0.5 * np.sum(np.outer(Y, Y) * self.kernel(X, X) * np.outer(self.alphas, self.alphas))\n return -np.sum(self.alphas) + \\\n 0.5 * np.sum(self.YYK * np.outer(self.alphas, self.alphas))\n\n def _take_step(self, i1, i2):\n # returns True if model params changed, False otherwise\n\n # Skip if chosen alphas are the same\n if i1 == i2:\n return False\n \n alph1 = self.alphas[i1]\n alph2 = self.alphas[i2]\n y1 = self.Ytrain[i1]\n y2 = self.Ytrain[i2]\n E1 = self.errors[i1]\n E2 = self.errors[i2]\n s = y1 * y2\n \n # Compute L & H, the bounds on new possible alpha values\n if (y1 != y2):\n L = max(0, alph2 - alph1)\n H = min(self.C, self.C + alph2 - alph1)\n elif (y1 == y2):\n L = max(0, alph1 + alph2 - self.C)\n H = min(self.C, alph1 + alph2)\n if (L == H):\n return False\n\n # Compute kernel & 2nd derivative eta\n k11 = self.kernel(self.Xtrain[i1], self.Xtrain[i1])\n k12 = self.kernel(self.Xtrain[i1], self.Xtrain[i2])\n k22 = self.kernel(self.Xtrain[i2], self.Xtrain[i2])\n eta = k11 + k22 - 2 * k12\n \n # Usual case - eta is non-negative\n if eta > 0:\n a2 = alph2 + y2 * (E1 - E2) / eta\n # Clip a2 based on bounds L & H\n if (a2 < L):\n a2 = L\n elif (a2 > H):\n a2 = H\n # else a2 remains unchanged\n \n # Unusual case - eta is negative\n # alpha2 should be set to whichever extreme (L or H) that yields the lowest\n # value of the objective\n else:\n print(\"***** eta < 0 *****\")\n # keep it to assign it back later\n alphas_i2 = self.alphas[i2]\n # alphas_adj = self.alphas.copy()\n # alphas_adj[i2] = L\n self.alphas[i2] = L\n # objective function output with a2 = L\n Lobj = self._loss(self.Xtrain, self.Ytrain)\n # alphas_adj[i2] = H\n self.alphas[i2] = H\n # objective function output with a2 = H\n Hobj = self._loss(self.Xtrain, self.Ytrain)\n if Lobj < Hobj - self.eps:\n a2 = L\n elif Lobj > Hobj + self.eps:\n a2 = H\n else:\n a2 = alph2\n\n # now assign it back\n self.alphas[i2] = alphas_i2\n \n # Push a2 to 0 or C if very close\n if a2 < 1e-8:\n a2 = 0.0\n elif a2 > (self.C - 1e-8):\n a2 = self.C\n \n # If examples can't be optimized within epsilon (eps), skip this pair\n if (np.abs(a2 - alph2) < self.eps * (a2 + alph2 + self.eps)):\n return False\n \n # Calculate new alpha 1 (a1)\n a1 = alph1 + s * (alph2 - a2)\n \n # Update threshold b to reflect newly calculated alphas\n # Calculate both possible thresholds\n b1 = E1 + y1 * (a1 - alph1) * k11 + y2 * (a2 - alph2) * k12 + self.b\n b2 = E2 + y1 * (a1 - alph1) * k12 + y2 * (a2 - alph2) * k22 + self.b\n \n # Set new threshold based on if a1 or a2 is bound by L and/or H\n if 0 < a1 and a1 < self.C:\n b_new = b1\n elif 0 < a2 and a2 < self.C:\n b_new = b2\n # Average thresholds if both are bound\n else:\n b_new = (b1 + b2) * 0.5\n\n # Update model object with new alphas & threshold\n self.alphas[i1] = a1\n self.alphas[i2] = a2\n \n # Update error cache\n # Error cache for optimized alphas is set to 0 if they're unbound\n for index, alph in zip([i1, i2], [a1, a2]):\n if 0.0 < alph < self.C:\n self.errors[index] = 0.0\n \n # Set non-optimized errors based on equation 12.11 in SMO book\n # non_opt = [n for n in range(self.N) if (n != i1 and n != i2 and self.alphas[n] < self.C and self.alphas[n] > 0)] #new\n non_opt = [n for n in range(self.N) if (n != i1 and n != i2)] # old\n self.errors[non_opt] = self.errors[non_opt] + \\\n y1*(a1 - alph1)*self.kernel(self.Xtrain[i1], self.Xtrain[non_opt]) + \\\n y2*(a2 - alph2)*self.kernel(self.Xtrain[i2], self.Xtrain[non_opt]) + self.b - b_new\n \n # Update model threshold\n self.b = b_new\n \n return True\n\n def _examine_example(self, i2):\n # returns True (1) if alphas changed, False (0) otherwise\n y2 = self.Ytrain[i2]\n alph2 = self.alphas[i2]\n E2 = self.errors[i2]\n r2 = E2 * y2\n\n # Proceed if error is within specified tolerance (tol)\n if ((r2 < -self.tol and alph2 < self.C) or (r2 > self.tol and alph2 > 0)):\n \n if len(self.alphas[(self.alphas != 0) & (self.alphas != self.C)]) > 1:\n # Use 2nd choice heuristic is choose max difference in error\n if self.errors[i2] > 0:\n i1 = np.argmin(self.errors)\n elif self.errors[i2] <= 0:\n i1 = np.argmax(self.errors)\n if self._take_step(i1, i2):\n return 1\n \n # Loop through non-zero and non-C alphas, starting at a random point\n # e.g. [1,2,3,4,5] -> [4,5,1,2,3]\n for i1 in np.roll(np.where((self.alphas != 0) & (self.alphas != self.C))[0],\n np.random.choice(np.arange(self.N))):\n if self._take_step(i1, i2):\n return 1\n \n # loop through all alphas, starting at a random point\n for i1 in np.roll(np.arange(self.N), np.random.choice(np.arange(self.N))):\n if self._take_step(i1, i2):\n return 1\n \n return 0\n\n def fit(self, X, Y, tol=0.00001, eps=0.01):\n # we need these to make future predictions\n self.tol = tol\n self.eps = eps\n self.Xtrain = X\n self.Ytrain = Y\n self.N = X.shape[0]\n self.alphas = np.zeros(self.N)\n self.b = 0.\n self.errors = self._decision_function(self.Xtrain) - self.Ytrain\n\n # kernel matrix\n self.K = self.kernel(X, X)\n self.YY = np.outer(Y, Y)\n self.YYK = self.K * self.YY\n\n iter_ = 0\n numChanged = 0\n examineAll = 1\n losses = []\n\n while numChanged > 0 or examineAll:\n print(\"iter:\", iter_)\n iter_ += 1\n numChanged = 0\n if examineAll:\n # loop over all training examples\n for i in range(self.alphas.shape[0]):\n examine_result = self._examine_example(i)\n numChanged += examine_result\n if examine_result:\n loss = self._loss(self.Xtrain, self.Ytrain)\n losses.append(loss)\n else:\n # loop over examples where alphas are not already at their limits\n for i in np.where((self.alphas != 0) & (self.alphas != self.C))[0]:\n examine_result = self._examine_example(i)\n numChanged += examine_result\n if examine_result:\n loss = self._loss(self.Xtrain, self.Ytrain)\n losses.append(loss)\n if examineAll == 1:\n examineAll = 0\n elif numChanged == 0:\n examineAll = 1\n\n plt.plot(losses)\n plt.title(\"loss per iteration\")\n plt.show()\n\n def _decision_function(self, X):\n return (self.alphas * self.Ytrain).dot(self.kernel(self.Xtrain, X)) - self.b\n \n\n def predict(self, X):\n return np.sign(self._decision_function(X))\n\n def score(self, X, Y):\n P = self.predict(X)\n return np.mean(Y == P)\n\n\ndef get_data():\n ### medical data\n # load the data\n # data = load_breast_cancer()\n # X, Y = data.data, data.target\n\n # X, Y = get_xor()\n # X, Y = get_donut()\n # X, Y = get_spiral()\n X, Y = get_clouds()\n\n # split the data into train and test sets\n # this lets us simulate how our model will perform in the future\n Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33)\n return Xtrain, Xtest, Ytrain, Ytest\n\n\nif __name__ == '__main__':\n # np.random.seed(3)\n Xtrain, Xtest, Ytrain, Ytest = get_data()\n print(\"Possible labels:\", set(Ytrain))\n\n # make sure the targets are (-1, +1)\n Ytrain[Ytrain == 0] = -1\n Ytest[Ytest == 0] = -1\n\n # scale the data\n scaler = StandardScaler()\n Xtrain = scaler.fit_transform(Xtrain)\n Xtest = scaler.transform(Xtest)\n\n # now we'll use our custom implementation\n model = SVM(kernel=linear)\n\n t0 = datetime.now()\n model.fit(Xtrain, Ytrain)\n print(\"train duration:\", datetime.now() - t0)\n t0 = datetime.now()\n print(\"train score:\", model.score(Xtrain, Ytrain), \"duration:\", datetime.now() - t0)\n t0 = datetime.now()\n print(\"test score:\", model.score(Xtest, Ytest), \"duration:\", datetime.now() - t0)\n\n if Xtrain.shape[1] == 2:\n plot_decision_boundary(model)\n" }, { "path": "svm_class/svm_spam.py", "content": "# https://deeplearningcourses.com/c/support-vector-machines-in-python\n# https://www.udemy.com/support-vector-machines-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nfrom sklearn.svm import SVC\nfrom datetime import datetime\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\nfrom sklearn.model_selection import train_test_split\nfrom wordcloud import WordCloud\n\n# data from:\n# https://www.kaggle.com/uciml/sms-spam-collection-dataset\n# file contains some invalid chars\n# depending on which version of pandas you have\n# an error may be thrown\ndf = pd.read_csv('../large_files/spam.csv', encoding='ISO-8859-1')\n\n# drop unnecessary columns\ndf = df.drop([\"Unnamed: 2\", \"Unnamed: 3\", \"Unnamed: 4\"], axis=1)\n\n# rename columns to something better\ndf.columns = ['labels', 'data']\n\n# create binary labels\ndf['b_labels'] = df['labels'].map({'ham': 0, 'spam': 1})\nY = df['b_labels'].values\n\n# try multiple ways of calculating features\ntfidf = TfidfVectorizer(decode_error='ignore')\nX = tfidf.fit_transform(df['data'])\n\n# count_vectorizer = CountVectorizer(decode_error='ignore')\n# X = count_vectorizer.fit_transform(df['data'])\n\n# split the data\nXtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33)\n\nmodel = SVC(kernel='linear', C=2.)\n\nt0 = datetime.now()\nmodel.fit(Xtrain, Ytrain)\nprint(\"train duration:\", datetime.now() - t0)\nt0 = datetime.now()\nprint(\"train score:\", model.score(Xtrain, Ytrain), \"duration:\", datetime.now() - t0)\nt0 = datetime.now()\nprint(\"test score:\", model.score(Xtest, Ytest), \"duration:\", datetime.now() - t0)\n\n\n\n# visualize the data\ndef visualize(label):\n words = ''\n for msg in df[df['labels'] == label]['data']:\n msg = msg.lower()\n words += msg + ' '\n wordcloud = WordCloud(width=600, height=400).generate(words)\n plt.imshow(wordcloud)\n plt.axis('off')\n plt.title(label)\n plt.show()\n\nvisualize('spam')\nvisualize('ham')\n\n\n# see what we're getting wrong\ndf['predictions'] = model.predict(X)\n\n# things that should be spam\nprint(\"*** things that should be spam ***\")\nsneaky_spam = df[(df['predictions'] == 0) & (df['b_labels'] == 1)]['data']\nfor msg in sneaky_spam:\n print(msg)\n\n# things that should not be spam\nprint(\"*** things that should not be spam ***\")\nnot_actually_spam = df[(df['predictions'] == 1) & (df['b_labels'] == 0)]['data']\nfor msg in not_actually_spam:\n print(msg)\n\n" }, { "path": "svm_class/util.py", "content": "# https://deeplearningcourses.com/c/support-vector-machines-in-python\n# https://www.udemy.com/support-vector-machines-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom sklearn.utils import shuffle\nfrom sklearn.preprocessing import StandardScaler\n\n\ndef getKaggleMNIST():\n # MNIST data:\n # column 0 is labels\n # column 1-785 is data, with values 0 .. 255\n # total size of CSV: (42000, 784)\n train = pd.read_csv('../large_files/train.csv').values.astype(np.float32)\n train = shuffle(train)\n\n Xtrain = train[:-1000,1:]\n Ytrain = train[:-1000,0].astype(np.int32)\n\n Xtest = train[-1000:,1:]\n Ytest = train[-1000:,0].astype(np.int32)\n\n # scale the data\n Xtrain /= 255.\n Xtest /= 255.\n # scaler = StandardScaler()\n # Xtrain = scaler.fit_transform(Xtrain)\n # Xtest = scaler.transform(Xtest)\n\n return Xtrain, Ytrain, Xtest, Ytest\n\n\ndef get_spiral():\n # Idea: radius -> low...high\n # (don't start at 0, otherwise points will be \"mushed\" at origin)\n # angle = low...high proportional to radius\n # [0, 2pi/6, 4pi/6, ..., 10pi/6] --> [pi/2, pi/3 + pi/2, ..., ]\n # x = rcos(theta), y = rsin(theta) as usual\n\n radius = np.linspace(1, 10, 100)\n thetas = np.empty((6, 100))\n for i in range(6):\n start_angle = np.pi*i / 3.0\n end_angle = start_angle + np.pi / 2\n points = np.linspace(start_angle, end_angle, 100)\n thetas[i] = points\n\n # convert into cartesian coordinates\n x1 = np.empty((6, 100))\n x2 = np.empty((6, 100))\n for i in range(6):\n x1[i] = radius * np.cos(thetas[i])\n x2[i] = radius * np.sin(thetas[i])\n\n # inputs\n X = np.empty((600, 2))\n X[:,0] = x1.flatten()\n X[:,1] = x2.flatten()\n\n # add noise\n X += np.random.randn(600, 2)*0.5\n\n # targets\n Y = np.array([0]*100 + [1]*100 + [0]*100 + [1]*100 + [0]*100 + [1]*100)\n return X, Y\n\n\ndef get_xor():\n X = np.zeros((200, 2))\n X[:50] = np.random.random((50, 2)) / 2 + 0.5 # (0.5-1, 0.5-1)\n X[50:100] = np.random.random((50, 2)) / 2 # (0-0.5, 0-0.5)\n X[100:150] = np.random.random((50, 2)) / 2 + np.array([[0, 0.5]]) # (0-0.5, 0.5-1)\n X[150:] = np.random.random((50, 2)) / 2 + np.array([[0.5, 0]]) # (0.5-1, 0-0.5)\n Y = np.array([0]*100 + [1]*100)\n return X, Y\n\n\ndef get_donut():\n N = 200\n R_inner = 5\n R_outer = 10\n\n # distance from origin is radius + random normal\n # angle theta is uniformly distributed between (0, 2pi)\n R1 = np.random.randn(N//2) + R_inner\n theta = 2*np.pi*np.random.random(N//2)\n X_inner = np.concatenate([[R1 * np.cos(theta)], [R1 * np.sin(theta)]]).T\n\n R2 = np.random.randn(N//2) + R_outer\n theta = 2*np.pi*np.random.random(N//2)\n X_outer = np.concatenate([[R2 * np.cos(theta)], [R2 * np.sin(theta)]]).T\n\n X = np.concatenate([ X_inner, X_outer ])\n Y = np.array([0]*(N//2) + [1]*(N//2))\n return X, Y\n\n\ndef get_clouds():\n N = 1000\n c1 = np.array([2, 2])\n c2 = np.array([-2, -2])\n # c1 = np.array([0, 3])\n # c2 = np.array([0, 0])\n X1 = np.random.randn(N, 2) + c1\n X2 = np.random.randn(N, 2) + c2\n X = np.vstack((X1, X2))\n Y = np.array([-1]*N + [1]*N)\n return X, Y\n\n\ndef plot_decision_boundary(model, resolution=100, colors=('b', 'k', 'r')):\n np.warnings.filterwarnings('ignore')\n fig, ax = plt.subplots()\n\n # Generate coordinate grid of shape [resolution x resolution]\n # and evaluate the model over the entire space\n x_range = np.linspace(model.Xtrain[:,0].min(), model.Xtrain[:,0].max(), resolution)\n y_range = np.linspace(model.Xtrain[:,1].min(), model.Xtrain[:,1].max(), resolution)\n grid = [[model._decision_function(np.array([[xr, yr]])) for yr in y_range] for xr in x_range]\n grid = np.array(grid).reshape(len(x_range), len(y_range))\n \n # Plot decision contours using grid and\n # make a scatter plot of training data\n ax.contour(x_range, y_range, grid.T, (-1, 0, 1), linewidths=(1, 1, 1),\n linestyles=('--', '-', '--'), colors=colors)\n ax.scatter(model.Xtrain[:,0], model.Xtrain[:,1],\n c=model.Ytrain, lw=0, alpha=0.3, cmap='seismic')\n \n # Plot support vectors (non-zero alphas)\n # as circled points (linewidth > 0)\n mask = model.alphas > 0.\n ax.scatter(model.Xtrain[:,0][mask], model.Xtrain[:,1][mask],\n c=model.Ytrain[mask], cmap='seismic')\n\n # debug\n ax.scatter([0], [0], c='black', marker='x')\n \n plt.show()\n" }, { "path": "tensorflow/input_data.py", "content": "\"\"\"Functions for downloading and reading MNIST data.\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nimport gzip\nimport os\nimport numpy\nfrom six.moves import urllib\nfrom six.moves import xrange # pylint: disable=redefined-builtin\nSOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'\ndef maybe_download(filename, work_directory):\n \"\"\"Download the data from Yann's website, unless it's already here.\"\"\"\n if not os.path.exists(work_directory):\n os.mkdir(work_directory)\n filepath = os.path.join(work_directory, filename)\n if not os.path.exists(filepath):\n filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)\n statinfo = os.stat(filepath)\n print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')\n return filepath\ndef _read32(bytestream):\n dt = numpy.dtype(numpy.uint32).newbyteorder('>')\n return numpy.frombuffer(bytestream.read(4), dtype=dt)\ndef extract_images(filename):\n \"\"\"Extract the images into a 4D uint8 numpy array [index, y, x, depth].\"\"\"\n print('Extracting', filename)\n with gzip.open(filename) as bytestream:\n magic = _read32(bytestream)\n if magic != 2051:\n raise ValueError(\n 'Invalid magic number %d in MNIST image file: %s' %\n (magic, filename))\n num_images = _read32(bytestream)\n rows = _read32(bytestream)\n cols = _read32(bytestream)\n buf = bytestream.read(rows * cols * num_images)\n data = numpy.frombuffer(buf, dtype=numpy.uint8)\n data = data.reshape(num_images, rows, cols, 1)\n return data\ndef dense_to_one_hot(labels_dense, num_classes=10):\n \"\"\"Convert class labels from scalars to one-hot vectors.\"\"\"\n num_labels = labels_dense.shape[0]\n index_offset = numpy.arange(num_labels) * num_classes\n labels_one_hot = numpy.zeros((num_labels, num_classes))\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n return labels_one_hot\ndef extract_labels(filename, one_hot=False):\n \"\"\"Extract the labels into a 1D uint8 numpy array [index].\"\"\"\n print('Extracting', filename)\n with gzip.open(filename) as bytestream:\n magic = _read32(bytestream)\n if magic != 2049:\n raise ValueError(\n 'Invalid magic number %d in MNIST label file: %s' %\n (magic, filename))\n num_items = _read32(bytestream)\n buf = bytestream.read(num_items)\n labels = numpy.frombuffer(buf, dtype=numpy.uint8)\n if one_hot:\n return dense_to_one_hot(labels)\n return labels\nclass DataSet(object):\n def __init__(self, images, labels, fake_data=False):\n if fake_data:\n self._num_examples = 10000\n else:\n assert images.shape[0] == labels.shape[0], (\n \"images.shape: %s labels.shape: %s\" % (images.shape,\n labels.shape))\n self._num_examples = images.shape[0]\n # Convert shape from [num examples, rows, columns, depth]\n # to [num examples, rows*columns] (assuming depth == 1)\n assert images.shape[3] == 1\n images = images.reshape(images.shape[0],\n images.shape[1] * images.shape[2])\n # Convert from [0, 255] -> [0.0, 1.0].\n images = images.astype(numpy.float32)\n images = numpy.multiply(images, 1.0 / 255.0)\n self._images = images\n self._labels = labels\n self._epochs_completed = 0\n self._index_in_epoch = 0\n @property\n def images(self):\n return self._images\n @property\n def labels(self):\n return self._labels\n @property\n def num_examples(self):\n return self._num_examples\n @property\n def epochs_completed(self):\n return self._epochs_completed\n def next_batch(self, batch_size, fake_data=False):\n \"\"\"Return the next `batch_size` examples from this data set.\"\"\"\n if fake_data:\n fake_image = [1.0 for _ in xrange(784)]\n fake_label = 0\n return [fake_image for _ in xrange(batch_size)], [\n fake_label for _ in xrange(batch_size)]\n start = self._index_in_epoch\n self._index_in_epoch += batch_size\n if self._index_in_epoch > self._num_examples:\n # Finished epoch\n self._epochs_completed += 1\n # Shuffle the data\n perm = numpy.arange(self._num_examples)\n numpy.random.shuffle(perm)\n self._images = self._images[perm]\n self._labels = self._labels[perm]\n # Start next epoch\n start = 0\n self._index_in_epoch = batch_size\n assert batch_size <= self._num_examples\n end = self._index_in_epoch\n return self._images[start:end], self._labels[start:end]\ndef read_data_sets(train_dir, fake_data=False, one_hot=False):\n class DataSets(object):\n pass\n data_sets = DataSets()\n if fake_data:\n data_sets.train = DataSet([], [], fake_data=True)\n data_sets.validation = DataSet([], [], fake_data=True)\n data_sets.test = DataSet([], [], fake_data=True)\n return data_sets\n TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'\n TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'\n TEST_IMAGES = 't10k-images-idx3-ubyte.gz'\n TEST_LABELS = 't10k-labels-idx1-ubyte.gz'\n VALIDATION_SIZE = 5000\n local_file = maybe_download(TRAIN_IMAGES, train_dir)\n train_images = extract_images(local_file)\n local_file = maybe_download(TRAIN_LABELS, train_dir)\n train_labels = extract_labels(local_file, one_hot=one_hot)\n local_file = maybe_download(TEST_IMAGES, train_dir)\n test_images = extract_images(local_file)\n local_file = maybe_download(TEST_LABELS, train_dir)\n test_labels = extract_labels(local_file, one_hot=one_hot)\n validation_images = train_images[:VALIDATION_SIZE]\n validation_labels = train_labels[:VALIDATION_SIZE]\n train_images = train_images[VALIDATION_SIZE:]\n train_labels = train_labels[VALIDATION_SIZE:]\n data_sets.train = DataSet(train_images, train_labels)\n data_sets.validation = DataSet(validation_images, validation_labels)\n data_sets.test = DataSet(test_images, test_labels)\n return data_sets\n" }, { "path": "tf2.0/.gitignore", "content": "rl_trader_working*.py\n*rl_trader_models\n*rl_trader_rewards\n" }, { "path": "tf2.0/WHERE ARE THE NOTEBOOKS.txt", "content": "As stated in the \"where to get the code\" / \"where to get the notebooks\" lecture, the notebooks are NOT on Github.\n\nIf you missed this, please review the lecture for the actual location of the notebooks.\n\nIf, after reviewing it, you still need assistance, please contact info@deeplearningcourses.com." }, { "path": "tf2.0/aapl_msi_sbux.csv", "content": 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2\t\"volkswagen dasher\"\n22.0 6 146.0 97.00 2815. 14.5 77 3\t\"datsun 810\"\n21.5 4 121.0 110.0 2600. 12.8 77 2\t\"bmw 320i\"\n21.5 3 80.00 110.0 2720. 13.5 77 3\t\"mazda rx-4\"\n43.1 4 90.00 48.00 1985. 21.5 78 2\t\"volkswagen rabbit custom diesel\"\n36.1 4 98.00 66.00 1800. 14.4 78 1\t\"ford fiesta\"\n32.8 4 78.00 52.00 1985. 19.4 78 3\t\"mazda glc deluxe\"\n39.4 4 85.00 70.00 2070. 18.6 78 3\t\"datsun b210 gx\"\n36.1 4 91.00 60.00 1800. 16.4 78 3\t\"honda civic cvcc\"\n19.9 8 260.0 110.0 3365. 15.5 78 1\t\"oldsmobile cutlass salon brougham\"\n19.4 8 318.0 140.0 3735. 13.2 78 1\t\"dodge diplomat\"\n20.2 8 302.0 139.0 3570. 12.8 78 1\t\"mercury monarch ghia\"\n19.2 6 231.0 105.0 3535. 19.2 78 1\t\"pontiac phoenix lj\"\n20.5 6 200.0 95.00 3155. 18.2 78 1\t\"chevrolet malibu\"\n20.2 6 200.0 85.00 2965. 15.8 78 1\t\"ford fairmont (auto)\"\n25.1 4 140.0 88.00 2720. 15.4 78 1\t\"ford fairmont (man)\"\n20.5 6 225.0 100.0 3430. 17.2 78 1\t\"plymouth volare\"\n19.4 6 232.0 90.00 3210. 17.2 78 1\t\"amc concord\"\n20.6 6 231.0 105.0 3380. 15.8 78 1\t\"buick century special\"\n20.8 6 200.0 85.00 3070. 16.7 78 1\t\"mercury zephyr\"\n18.6 6 225.0 110.0 3620. 18.7 78 1\t\"dodge aspen\"\n18.1 6 258.0 120.0 3410. 15.1 78 1\t\"amc concord d/l\"\n19.2 8 305.0 145.0 3425. 13.2 78 1\t\"chevrolet monte carlo landau\"\n17.7 6 231.0 165.0 3445. 13.4 78 1\t\"buick regal sport coupe (turbo)\"\n18.1 8 302.0 139.0 3205. 11.2 78 1\t\"ford futura\"\n17.5 8 318.0 140.0 4080. 13.7 78 1\t\"dodge magnum xe\"\n30.0 4 98.00 68.00 2155. 16.5 78 1\t\"chevrolet chevette\"\n27.5 4 134.0 95.00 2560. 14.2 78 3\t\"toyota corona\"\n27.2 4 119.0 97.00 2300. 14.7 78 3\t\"datsun 510\"\n30.9 4 105.0 75.00 2230. 14.5 78 1\t\"dodge omni\"\n21.1 4 134.0 95.00 2515. 14.8 78 3\t\"toyota celica gt liftback\"\n23.2 4 156.0 105.0 2745. 16.7 78 1\t\"plymouth sapporo\"\n23.8 4 151.0 85.00 2855. 17.6 78 1\t\"oldsmobile starfire sx\"\n23.9 4 119.0 97.00 2405. 14.9 78 3\t\"datsun 200-sx\"\n20.3 5 131.0 103.0 2830. 15.9 78 2\t\"audi 5000\"\n17.0 6 163.0 125.0 3140. 13.6 78 2\t\"volvo 264gl\"\n21.6 4 121.0 115.0 2795. 15.7 78 2\t\"saab 99gle\"\n16.2 6 163.0 133.0 3410. 15.8 78 2\t\"peugeot 604sl\"\n31.5 4 89.00 71.00 1990. 14.9 78 2\t\"volkswagen scirocco\"\n29.5 4 98.00 68.00 2135. 16.6 78 3\t\"honda accord lx\"\n21.5 6 231.0 115.0 3245. 15.4 79 1\t\"pontiac lemans v6\"\n19.8 6 200.0 85.00 2990. 18.2 79 1\t\"mercury zephyr 6\"\n22.3 4 140.0 88.00 2890. 17.3 79 1\t\"ford fairmont 4\"\n20.2 6 232.0 90.00 3265. 18.2 79 1\t\"amc concord dl 6\"\n20.6 6 225.0 110.0 3360. 16.6 79 1\t\"dodge aspen 6\"\n17.0 8 305.0 130.0 3840. 15.4 79 1\t\"chevrolet caprice classic\"\n17.6 8 302.0 129.0 3725. 13.4 79 1\t\"ford ltd landau\"\n16.5 8 351.0 138.0 3955. 13.2 79 1\t\"mercury grand marquis\"\n18.2 8 318.0 135.0 3830. 15.2 79 1\t\"dodge st. regis\"\n16.9 8 350.0 155.0 4360. 14.9 79 1\t\"buick estate wagon (sw)\"\n15.5 8 351.0 142.0 4054. 14.3 79 1\t\"ford country squire (sw)\"\n19.2 8 267.0 125.0 3605. 15.0 79 1\t\"chevrolet malibu classic (sw)\"\n18.5 8 360.0 150.0 3940. 13.0 79 1\t\"chrysler lebaron town @ country (sw)\"\n31.9 4 89.00 71.00 1925. 14.0 79 2\t\"vw rabbit custom\"\n34.1 4 86.00 65.00 1975. 15.2 79 3\t\"maxda glc deluxe\"\n35.7 4 98.00 80.00 1915. 14.4 79 1\t\"dodge colt hatchback custom\"\n27.4 4 121.0 80.00 2670. 15.0 79 1\t\"amc spirit dl\"\n25.4 5 183.0 77.00 3530. 20.1 79 2\t\"mercedes benz 300d\"\n23.0 8 350.0 125.0 3900. 17.4 79 1\t\"cadillac eldorado\"\n27.2 4 141.0 71.00 3190. 24.8 79 2\t\"peugeot 504\"\n23.9 8 260.0 90.00 3420. 22.2 79 1\t\"oldsmobile cutlass salon brougham\"\n34.2 4 105.0 70.00 2200. 13.2 79 1\t\"plymouth horizon\"\n34.5 4 105.0 70.00 2150. 14.9 79 1\t\"plymouth horizon tc3\"\n31.8 4 85.00 65.00 2020. 19.2 79 3\t\"datsun 210\"\n37.3 4 91.00 69.00 2130. 14.7 79 2\t\"fiat strada custom\"\n28.4 4 151.0 90.00 2670. 16.0 79 1\t\"buick skylark limited\"\n28.8 6 173.0 115.0 2595. 11.3 79 1\t\"chevrolet citation\"\n26.8 6 173.0 115.0 2700. 12.9 79 1\t\"oldsmobile omega brougham\"\n33.5 4 151.0 90.00 2556. 13.2 79 1\t\"pontiac phoenix\"\n41.5 4 98.00 76.00 2144. 14.7 80 2\t\"vw rabbit\"\n38.1 4 89.00 60.00 1968. 18.8 80 3\t\"toyota corolla tercel\"\n32.1 4 98.00 70.00 2120. 15.5 80 1\t\"chevrolet chevette\"\n37.2 4 86.00 65.00 2019. 16.4 80 3\t\"datsun 310\"\n28.0 4 151.0 90.00 2678. 16.5 80 1\t\"chevrolet citation\"\n26.4 4 140.0 88.00 2870. 18.1 80 1\t\"ford fairmont\"\n24.3 4 151.0 90.00 3003. 20.1 80 1\t\"amc concord\"\n19.1 6 225.0 90.00 3381. 18.7 80 1\t\"dodge aspen\"\n34.3 4 97.00 78.00 2188. 15.8 80 2\t\"audi 4000\"\n29.8 4 134.0 90.00 2711. 15.5 80 3\t\"toyota corona liftback\"\n31.3 4 120.0 75.00 2542. 17.5 80 3\t\"mazda 626\"\n37.0 4 119.0 92.00 2434. 15.0 80 3\t\"datsun 510 hatchback\"\n32.2 4 108.0 75.00 2265. 15.2 80 3\t\"toyota corolla\"\n46.6 4 86.00 65.00 2110. 17.9 80 3\t\"mazda glc\"\n27.9 4 156.0 105.0 2800. 14.4 80 1\t\"dodge colt\"\n40.8 4 85.00 65.00 2110. 19.2 80 3\t\"datsun 210\"\n44.3 4 90.00 48.00 2085. 21.7 80 2\t\"vw rabbit c (diesel)\"\n43.4 4 90.00 48.00 2335. 23.7 80 2\t\"vw dasher (diesel)\"\n36.4 5 121.0 67.00 2950. 19.9 80 2\t\"audi 5000s (diesel)\"\n30.0 4 146.0 67.00 3250. 21.8 80 2\t\"mercedes-benz 240d\"\n44.6 4 91.00 67.00 1850. 13.8 80 3\t\"honda civic 1500 gl\"\n40.9 4 85.00 ? 1835. 17.3 80 2\t\"renault lecar deluxe\"\n33.8 4 97.00 67.00 2145. 18.0 80 3\t\"subaru dl\"\n29.8 4 89.00 62.00 1845. 15.3 80 2\t\"vokswagen rabbit\"\n32.7 6 168.0 132.0 2910. 11.4 80 3\t\"datsun 280-zx\"\n23.7 3 70.00 100.0 2420. 12.5 80 3\t\"mazda rx-7 gs\"\n35.0 4 122.0 88.00 2500. 15.1 80 2\t\"triumph tr7 coupe\"\n23.6 4 140.0 ? 2905. 14.3 80 1\t\"ford mustang cobra\"\n32.4 4 107.0 72.00 2290. 17.0 80 3\t\"honda accord\"\n27.2 4 135.0 84.00 2490. 15.7 81 1\t\"plymouth reliant\"\n26.6 4 151.0 84.00 2635. 16.4 81 1\t\"buick skylark\"\n25.8 4 156.0 92.00 2620. 14.4 81 1\t\"dodge aries wagon (sw)\"\n23.5 6 173.0 110.0 2725. 12.6 81 1\t\"chevrolet citation\"\n30.0 4 135.0 84.00 2385. 12.9 81 1\t\"plymouth reliant\"\n39.1 4 79.00 58.00 1755. 16.9 81 3\t\"toyota starlet\"\n39.0 4 86.00 64.00 1875. 16.4 81 1\t\"plymouth champ\"\n35.1 4 81.00 60.00 1760. 16.1 81 3\t\"honda civic 1300\"\n32.3 4 97.00 67.00 2065. 17.8 81 3\t\"subaru\"\n37.0 4 85.00 65.00 1975. 19.4 81 3\t\"datsun 210 mpg\"\n37.7 4 89.00 62.00 2050. 17.3 81 3\t\"toyota tercel\"\n34.1 4 91.00 68.00 1985. 16.0 81 3\t\"mazda glc 4\"\n34.7 4 105.0 63.00 2215. 14.9 81 1\t\"plymouth horizon 4\"\n34.4 4 98.00 65.00 2045. 16.2 81 1\t\"ford escort 4w\"\n29.9 4 98.00 65.00 2380. 20.7 81 1\t\"ford escort 2h\"\n33.0 4 105.0 74.00 2190. 14.2 81 2\t\"volkswagen jetta\"\n34.5 4 100.0 ? 2320. 15.8 81 2\t\"renault 18i\"\n33.7 4 107.0 75.00 2210. 14.4 81 3\t\"honda prelude\"\n32.4 4 108.0 75.00 2350. 16.8 81 3\t\"toyota corolla\"\n32.9 4 119.0 100.0 2615. 14.8 81 3\t\"datsun 200sx\"\n31.6 4 120.0 74.00 2635. 18.3 81 3\t\"mazda 626\"\n28.1 4 141.0 80.00 3230. 20.4 81 2\t\"peugeot 505s turbo diesel\"\n30.7 6 145.0 76.00 3160. 19.6 81 2\t\"volvo diesel\"\n25.4 6 168.0 116.0 2900. 12.6 81 3\t\"toyota cressida\"\n24.2 6 146.0 120.0 2930. 13.8 81 3\t\"datsun 810 maxima\"\n22.4 6 231.0 110.0 3415. 15.8 81 1\t\"buick century\"\n26.6 8 350.0 105.0 3725. 19.0 81 1\t\"oldsmobile cutlass ls\"\n20.2 6 200.0 88.00 3060. 17.1 81 1\t\"ford granada gl\"\n17.6 6 225.0 85.00 3465. 16.6 81 1\t\"chrysler lebaron salon\"\n28.0 4 112.0 88.00 2605. 19.6 82 1\t\"chevrolet cavalier\"\n27.0 4 112.0 88.00 2640. 18.6 82 1\t\"chevrolet cavalier wagon\"\n34.0 4 112.0 88.00 2395. 18.0 82 1\t\"chevrolet cavalier 2-door\"\n31.0 4 112.0 85.00 2575. 16.2 82 1\t\"pontiac j2000 se hatchback\"\n29.0 4 135.0 84.00 2525. 16.0 82 1\t\"dodge aries se\"\n27.0 4 151.0 90.00 2735. 18.0 82 1\t\"pontiac phoenix\"\n24.0 4 140.0 92.00 2865. 16.4 82 1\t\"ford fairmont futura\"\n23.0 4 151.0 ? 3035. 20.5 82 1\t\"amc concord dl\"\n36.0 4 105.0 74.00 1980. 15.3 82 2\t\"volkswagen rabbit l\"\n37.0 4 91.00 68.00 2025. 18.2 82 3\t\"mazda glc custom l\"\n31.0 4 91.00 68.00 1970. 17.6 82 3\t\"mazda glc custom\"\n38.0 4 105.0 63.00 2125. 14.7 82 1\t\"plymouth horizon miser\"\n36.0 4 98.00 70.00 2125. 17.3 82 1\t\"mercury lynx l\"\n36.0 4 120.0 88.00 2160. 14.5 82 3\t\"nissan stanza xe\"\n36.0 4 107.0 75.00 2205. 14.5 82 3\t\"honda accord\"\n34.0 4 108.0 70.00 2245 16.9 82 3\t\"toyota corolla\"\n38.0 4 91.00 67.00 1965. 15.0 82 3\t\"honda civic\"\n32.0 4 91.00 67.00 1965. 15.7 82 3\t\"honda civic (auto)\"\n38.0 4 91.00 67.00 1995. 16.2 82 3\t\"datsun 310 gx\"\n25.0 6 181.0 110.0 2945. 16.4 82 1\t\"buick century limited\"\n38.0 6 262.0 85.00 3015. 17.0 82 1\t\"oldsmobile cutlass ciera (diesel)\"\n26.0 4 156.0 92.00 2585. 14.5 82 1\t\"chrysler lebaron medallion\"\n22.0 6 232.0 112.0 2835 14.7 82 1\t\"ford granada l\"\n32.0 4 144.0 96.00 2665. 13.9 82 3\t\"toyota celica gt\"\n36.0 4 135.0 84.00 2370. 13.0 82 1\t\"dodge charger 2.2\"\n27.0 4 151.0 90.00 2950. 17.3 82 1\t\"chevrolet camaro\"\n27.0 4 140.0 86.00 2790. 15.6 82 1\t\"ford mustang gl\"\n44.0 4 97.00 52.00 2130. 24.6 82 2\t\"vw pickup\"\n32.0 4 135.0 84.00 2295. 11.6 82 1\t\"dodge rampage\"\n28.0 4 120.0 79.00 2625. 18.6 82 1\t\"ford ranger\"\n31.0 4 119.0 82.00 2720. 19.4 82 1\t\"chevy s-10\"\n" }, { "path": "tf2.0/daily-minimum-temperatures-in-me.csv", "content": "\"Date\",\"Daily minimum temperatures in Melbourne, Australia, 1981-1990\"\r\n\"1981-01-01\",20.7\r\n\"1981-01-02\",17.9\r\n\"1981-01-03\",18.8\r\n\"1981-01-04\",14.6\r\n\"1981-01-05\",15.8\r\n\"1981-01-06\",15.8\r\n\"1981-01-07\",15.8\r\n\"1981-01-08\",17.4\r\n\"1981-01-09\",21.8\r\n\"1981-01-10\",20.0\r\n\"1981-01-11\",16.2\r\n\"1981-01-12\",13.3\r\n\"1981-01-13\",16.7\r\n\"1981-01-14\",21.5\r\n\"1981-01-15\",25.0\r\n\"1981-01-16\",20.7\r\n\"1981-01-17\",20.6\r\n\"1981-01-18\",24.8\r\n\"1981-01-19\",17.7\r\n\"1981-01-20\",15.5\r\n\"1981-01-21\",18.2\r\n\"1981-01-22\",12.1\r\n\"1981-01-23\",14.4\r\n\"1981-01-24\",16.0\r\n\"1981-01-25\",16.5\r\n\"1981-01-26\",18.7\r\n\"1981-01-27\",19.4\r\n\"1981-01-28\",17.2\r\n\"1981-01-29\",15.5\r\n\"1981-01-30\",15.1\r\n\"1981-01-31\",15.4\r\n\"1981-02-01\",15.3\r\n\"1981-02-02\",18.8\r\n\"1981-02-03\",21.9\r\n\"1981-02-04\",19.9\r\n\"1981-02-05\",16.6\r\n\"1981-02-06\",16.8\r\n\"1981-02-07\",14.6\r\n\"1981-02-08\",17.1\r\n\"1981-02-09\",25.0\r\n\"1981-02-10\",15.0\r\n\"1981-02-11\",13.7\r\n\"1981-02-12\",13.9\r\n\"1981-02-13\",18.3\r\n\"1981-02-14\",22.0\r\n\"1981-02-15\",22.1\r\n\"1981-02-16\",21.2\r\n\"1981-02-17\",18.4\r\n\"1981-02-18\",16.6\r\n\"1981-02-19\",16.1\r\n\"1981-02-20\",15.7\r\n\"1981-02-21\",16.6\r\n\"1981-02-22\",16.5\r\n\"1981-02-23\",14.4\r\n\"1981-02-24\",14.4\r\n\"1981-02-25\",18.5\r\n\"1981-02-26\",16.9\r\n\"1981-02-27\",17.5\r\n\"1981-02-28\",21.2\r\n\"1981-03-01\",17.8\r\n\"1981-03-02\",18.6\r\n\"1981-03-03\",17.0\r\n\"1981-03-04\",16.0\r\n\"1981-03-05\",13.3\r\n\"1981-03-06\",14.3\r\n\"1981-03-07\",11.4\r\n\"1981-03-08\",16.3\r\n\"1981-03-09\",16.1\r\n\"1981-03-10\",11.8\r\n\"1981-03-11\",12.2\r\n\"1981-03-12\",14.7\r\n\"1981-03-13\",11.8\r\n\"1981-03-14\",11.3\r\n\"1981-03-15\",10.6\r\n\"1981-03-16\",11.7\r\n\"1981-03-17\",14.2\r\n\"1981-03-18\",11.2\r\n\"1981-03-19\",16.9\r\n\"1981-03-20\",16.7\r\n\"1981-03-21\",8.1\r\n\"1981-03-22\",8.0\r\n\"1981-03-23\",8.8\r\n\"1981-03-24\",13.4\r\n\"1981-03-25\",10.9\r\n\"1981-03-26\",13.4\r\n\"1981-03-27\",11.0\r\n\"1981-03-28\",15.0\r\n\"1981-03-29\",15.7\r\n\"1981-03-30\",14.5\r\n\"1981-03-31\",15.8\r\n\"1981-04-01\",16.7\r\n\"1981-04-02\",16.8\r\n\"1981-04-03\",17.5\r\n\"1981-04-04\",17.1\r\n\"1981-04-05\",18.1\r\n\"1981-04-06\",16.6\r\n\"1981-04-07\",10.0\r\n\"1981-04-08\",14.9\r\n\"1981-04-09\",15.9\r\n\"1981-04-10\",13.0\r\n\"1981-04-11\",7.6\r\n\"1981-04-12\",11.5\r\n\"1981-04-13\",13.5\r\n\"1981-04-14\",13.0\r\n\"1981-04-15\",13.3\r\n\"1981-04-16\",12.1\r\n\"1981-04-17\",12.4\r\n\"1981-04-18\",13.2\r\n\"1981-04-19\",13.8\r\n\"1981-04-20\",10.6\r\n\"1981-04-21\",9.0\r\n\"1981-04-22\",10.0\r\n\"1981-04-23\",9.8\r\n\"1981-04-24\",11.5\r\n\"1981-04-25\",8.9\r\n\"1981-04-26\",7.4\r\n\"1981-04-27\",9.9\r\n\"1981-04-28\",9.3\r\n\"1981-04-29\",9.9\r\n\"1981-04-30\",7.4\r\n\"1981-05-01\",8.6\r\n\"1981-05-02\",11.9\r\n\"1981-05-03\",14.0\r\n\"1981-05-04\",8.6\r\n\"1981-05-05\",10.0\r\n\"1981-05-06\",13.5\r\n\"1981-05-07\",12.0\r\n\"1981-05-08\",10.5\r\n\"1981-05-09\",10.7\r\n\"1981-05-10\",8.1\r\n\"1981-05-11\",10.1\r\n\"1981-05-12\",10.6\r\n\"1981-05-13\",5.3\r\n\"1981-05-14\",6.6\r\n\"1981-05-15\",8.5\r\n\"1981-05-16\",11.2\r\n\"1981-05-17\",9.8\r\n\"1981-05-18\",5.9\r\n\"1981-05-19\",3.2\r\n\"1981-05-20\",2.1\r\n\"1981-05-21\",3.4\r\n\"1981-05-22\",5.4\r\n\"1981-05-23\",9.6\r\n\"1981-05-24\",11.5\r\n\"1981-05-25\",12.3\r\n\"1981-05-26\",12.6\r\n\"1981-05-27\",11.0\r\n\"1981-05-28\",11.2\r\n\"1981-05-29\",11.4\r\n\"1981-05-30\",11.8\r\n\"1981-05-31\",12.8\r\n\"1981-06-01\",11.6\r\n\"1981-06-02\",10.6\r\n\"1981-06-03\",9.8\r\n\"1981-06-04\",11.2\r\n\"1981-06-05\",5.7\r\n\"1981-06-06\",7.1\r\n\"1981-06-07\",2.5\r\n\"1981-06-08\",3.5\r\n\"1981-06-09\",4.6\r\n\"1981-06-10\",11.0\r\n\"1981-06-11\",5.7\r\n\"1981-06-12\",7.7\r\n\"1981-06-13\",10.4\r\n\"1981-06-14\",11.4\r\n\"1981-06-15\",9.2\r\n\"1981-06-16\",6.1\r\n\"1981-06-17\",2.7\r\n\"1981-06-18\",4.3\r\n\"1981-06-19\",6.3\r\n\"1981-06-20\",3.8\r\n\"1981-06-21\",4.4\r\n\"1981-06-22\",7.1\r\n\"1981-06-23\",4.8\r\n\"1981-06-24\",5.8\r\n\"1981-06-25\",6.2\r\n\"1981-06-26\",7.3\r\n\"1981-06-27\",9.2\r\n\"1981-06-28\",10.2\r\n\"1981-06-29\",9.5\r\n\"1981-06-30\",9.5\r\n\"1981-07-01\",10.7\r\n\"1981-07-02\",10.0\r\n\"1981-07-03\",6.5\r\n\"1981-07-04\",7.0\r\n\"1981-07-05\",7.4\r\n\"1981-07-06\",8.1\r\n\"1981-07-07\",6.6\r\n\"1981-07-08\",8.3\r\n\"1981-07-09\",8.9\r\n\"1981-07-10\",4.6\r\n\"1981-07-11\",6.8\r\n\"1981-07-12\",5.7\r\n\"1981-07-13\",6.1\r\n\"1981-07-14\",7.0\r\n\"1981-07-15\",7.2\r\n\"1981-07-16\",6.3\r\n\"1981-07-17\",8.8\r\n\"1981-07-18\",5.0\r\n\"1981-07-19\",7.4\r\n\"1981-07-20\",10.1\r\n\"1981-07-21\",12.0\r\n\"1981-07-22\",9.0\r\n\"1981-07-23\",8.9\r\n\"1981-07-24\",9.8\r\n\"1981-07-25\",9.0\r\n\"1981-07-26\",9.2\r\n\"1981-07-27\",7.7\r\n\"1981-07-28\",8.0\r\n\"1981-07-29\",6.1\r\n\"1981-07-30\",3.5\r\n\"1981-07-31\",3.2\r\n\"1981-08-01\",5.7\r\n\"1981-08-02\",7.7\r\n\"1981-08-03\",9.0\r\n\"1981-08-04\",10.0\r\n\"1981-08-05\",6.2\r\n\"1981-08-06\",6.9\r\n\"1981-08-07\",6.5\r\n\"1981-08-08\",6.8\r\n\"1981-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1990-09-12\",12.6\r\n\"1990-09-13\",9.8\r\n\"1990-09-14\",7.2\r\n\"1990-09-15\",10.1\r\n\"1990-09-16\",10.4\r\n\"1990-09-17\",3.7\r\n\"1990-09-18\",7.3\r\n\"1990-09-19\",11.6\r\n\"1990-09-20\",16.3\r\n\"1990-09-21\",9.6\r\n\"1990-09-22\",6.8\r\n\"1990-09-23\",5.2\r\n\"1990-09-24\",10.6\r\n\"1990-09-25\",16.3\r\n\"1990-09-26\",9.8\r\n\"1990-09-27\",4.6\r\n\"1990-09-28\",11.1\r\n\"1990-09-29\",8.7\r\n\"1990-09-30\",10.0\r\n\"1990-10-01\",11.3\r\n\"1990-10-02\",10.5\r\n\"1990-10-03\",9.9\r\n\"1990-10-04\",11.0\r\n\"1990-10-05\",14.0\r\n\"1990-10-06\",9.2\r\n\"1990-10-07\",9.8\r\n\"1990-10-08\",6.0\r\n\"1990-10-09\",9.8\r\n\"1990-10-10\",9.2\r\n\"1990-10-11\",11.8\r\n\"1990-10-12\",10.3\r\n\"1990-10-13\",7.5\r\n\"1990-10-14\",7.7\r\n\"1990-10-15\",15.8\r\n\"1990-10-16\",14.6\r\n\"1990-10-17\",10.5\r\n\"1990-10-18\",11.3\r\n\"1990-10-19\",10.9\r\n\"1990-10-20\",6.4\r\n\"1990-10-21\",10.9\r\n\"1990-10-22\",9.0\r\n\"1990-10-23\",10.9\r\n\"1990-10-24\",12.4\r\n\"1990-10-25\",11.6\r\n\"1990-10-26\",13.3\r\n\"1990-10-27\",14.4\r\n\"1990-10-28\",18.4\r\n\"1990-10-29\",13.6\r\n\"1990-10-30\",14.9\r\n\"1990-10-31\",14.8\r\n\"1990-11-01\",15.4\r\n\"1990-11-02\",11.8\r\n\"1990-11-03\",13.0\r\n\"1990-11-04\",11.1\r\n\"1990-11-05\",12.5\r\n\"1990-11-06\",18.3\r\n\"1990-11-07\",19.2\r\n\"1990-11-08\",15.4\r\n\"1990-11-09\",13.1\r\n\"1990-11-10\",11.5\r\n\"1990-11-11\",8.6\r\n\"1990-11-12\",12.6\r\n\"1990-11-13\",13.8\r\n\"1990-11-14\",14.6\r\n\"1990-11-15\",13.2\r\n\"1990-11-16\",12.3\r\n\"1990-11-17\",8.8\r\n\"1990-11-18\",10.7\r\n\"1990-11-19\",9.9\r\n\"1990-11-20\",8.3\r\n\"1990-11-21\",15.0\r\n\"1990-11-22\",12.2\r\n\"1990-11-23\",10.5\r\n\"1990-11-24\",11.1\r\n\"1990-11-25\",13.0\r\n\"1990-11-26\",12.9\r\n\"1990-11-27\",8.8\r\n\"1990-11-28\",14.7\r\n\"1990-11-29\",14.7\r\n\"1990-11-30\",12.7\r\n\"1990-12-01\",13.3\r\n\"1990-12-02\",13.2\r\n\"1990-12-03\",16.2\r\n\"1990-12-04\",17.3\r\n\"1990-12-05\",20.5\r\n\"1990-12-06\",20.2\r\n\"1990-12-07\",19.4\r\n\"1990-12-08\",15.5\r\n\"1990-12-09\",14.1\r\n\"1990-12-10\",11.0\r\n\"1990-12-11\",11.1\r\n\"1990-12-12\",14.0\r\n\"1990-12-13\",11.4\r\n\"1990-12-14\",12.5\r\n\"1990-12-15\",13.4\r\n\"1990-12-16\",13.6\r\n\"1990-12-17\",13.9\r\n\"1990-12-18\",17.2\r\n\"1990-12-19\",14.7\r\n\"1990-12-20\",15.4\r\n\"1990-12-21\",13.1\r\n\"1990-12-22\",13.2\r\n\"1990-12-23\",13.9\r\n\"1990-12-24\",10.0\r\n\"1990-12-25\",12.9\r\n\"1990-12-26\",14.6\r\n\"1990-12-27\",14.0\r\n\"1990-12-28\",13.6\r\n\"1990-12-29\",13.5\r\n\"1990-12-30\",15.7\r\n\"1990-12-31\",13.0\r\n\r\nDaily minimum temperatures in Melbourne, Australia, 1981-1990\r\n\r\n" }, { "path": "tf2.0/exercises.txt", "content": "Logistic Regression\nhttps://www.kaggle.com/uciml/pima-indians-diabetes-database\nhttps://lazyprogrammer.me/course_files/exercises/diabetes.csv\n\nLinear Regression\nhttps://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html\nhttps://lazyprogrammer.me/course_files/exercises/boston.txt\n\nANN\nhttps://archive.ics.uci.edu/ml/datasets/ecoli (orig)\nhttps://www.kaggle.com/elikplim/ecoli-data-set (alt)\nhttps://lazyprogrammer.me/course_files/exercises/ecoli.csv\n\nCNN\nhttps://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge\nhttps://archive.org/download/fer2013_202311/fer2013.csv\n\nRNN\nFind your own stock price dataset!\n\nNLP\nhttps://www.kaggle.com/crowdflower/twitter-airline-sentiment\nhttps://lazyprogrammer.me/course_files/exercises/AirlineSentimentTweets.csv\n\nRecommender Systems\nhttp://www2.informatik.uni-freiburg.de/~cziegler/BX/\nhttp://lazyprogrammer.me/course_files/exercises/BX-CSV-Dump.zip\n\nTransfer Learning\nhttps://www.kaggle.com/c/dogs-vs-cats\n\nGAN\nhttps://www.kaggle.com/c/dogs-vs-cats\n\nDeepRL\nFind your own stock price dataset!" }, { "path": "tf2.0/extra_reading.txt", "content": "Gradient Descent: Convergence Analysis\nhttp://www.stat.cmu.edu/~ryantibs/convexopt-F13/scribes/lec6.pdf\n\nDeep learning improved by biological activation functions\nhttps://arxiv.org/pdf/1804.11237.pdf\n\nBatch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift\nSergey Ioffe, Christian Szegedy\nhttps://arxiv.org/abs/1502.03167\n\nDropout: A Simple Way to Prevent Neural Networks from Overfitting\nhttps://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf\n\nConvolution arithmetic tutorial\nhttps://theano-pymc.readthedocs.io/en/latest/tutorial/conv_arithmetic.html\n\nOn the Practical Computational Power of Finite Precision RNNs for Language Recognition\nhttps://arxiv.org/abs/1805.04908\n\nMassive Exploration of Neural Machine Translation Architectures\nhttps://arxiv.org/abs/1703.03906\n\nPractical Deep Reinforcement Learning Approach for Stock Trading\nhttps://arxiv.org/abs/1811.07522\n\nInceptionism: Going Deeper into Neural Networks\nhttps://ai.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html\n\nThe Loss Surfaces of Multilayer Networks\nhttps://arxiv.org/pdf/1412.0233.pdf\n\nTensorflow Developer Certificate Installation Guide\nhttps://www.tensorflow.org/static/extras/cert/Setting_Up_TF_Developer_Certificate_Exam.pdf\n\nTensorflow Developer Certificate Candidate Handbook\nhttps://www.tensorflow.org/extras/cert/TF_Certificate_Candidate_Handbook.pdf\n" }, { "path": "tf2.0/fake_util.py", "content": "# Used for an example only\n\ndef my_useful_function():\n print(\"hello world\")" }, { "path": "tf2.0/keras_trader.py", "content": "import numpy as np\nimport pandas as pd\n\n# must do this BEFORE importing keras\nimport os\nos.environ[\"KERAS_BACKEND\"] = \"jax\"\n\nfrom keras.models import Model\nfrom keras.layers import Dense, Input\nfrom keras.optimizers import Adam\n\nfrom datetime import datetime\nimport itertools\nimport argparse\nimport re\nimport pickle\n\nfrom sklearn.preprocessing import StandardScaler\n\n\nimport keras.backend as K\nprint(\"Using backend:\", K.backend())\n\n# import tensorflow as tf\n# if tf.__version__.startswith('2'):\n# tf.compat.v1.disable_eager_execution()\n\n\n\n# Let's use AAPL (Apple), MSI (Motorola), SBUX (Starbucks)\ndef get_data():\n # returns a T x 3 list of stock prices\n # each row is a different stock\n # 0 = AAPL\n # 1 = MSI\n # 2 = SBUX\n df = pd.read_csv('aapl_msi_sbux.csv')\n return df.values\n\n\n\n### The experience replay memory ###\nclass ReplayBuffer:\n def __init__(self, obs_dim, act_dim, size):\n self.obs1_buf = np.zeros([size, obs_dim], dtype=np.float32)\n self.obs2_buf = np.zeros([size, obs_dim], dtype=np.float32)\n self.acts_buf = np.zeros(size, dtype=np.uint8)\n self.rews_buf = np.zeros(size, dtype=np.float32)\n self.done_buf = np.zeros(size, dtype=np.uint8)\n self.ptr, self.size, self.max_size = 0, 0, size\n\n def store(self, obs, act, rew, next_obs, done):\n self.obs1_buf[self.ptr] = obs\n self.obs2_buf[self.ptr] = next_obs\n self.acts_buf[self.ptr] = act\n self.rews_buf[self.ptr] = rew\n self.done_buf[self.ptr] = done\n self.ptr = (self.ptr+1) % self.max_size\n self.size = min(self.size+1, self.max_size)\n\n def sample_batch(self, batch_size=32):\n idxs = np.random.randint(0, self.size, size=batch_size)\n return dict(s=self.obs1_buf[idxs],\n s2=self.obs2_buf[idxs],\n a=self.acts_buf[idxs],\n r=self.rews_buf[idxs],\n d=self.done_buf[idxs])\n\n\n\n\n\ndef get_scaler(env):\n # return scikit-learn scaler object to scale the states\n # Note: you could also populate the replay buffer here\n\n states = []\n for _ in range(env.n_step):\n action = np.random.choice(env.action_space)\n state, reward, done, info = env.step(action)\n states.append(state)\n if done:\n break\n\n scaler = StandardScaler()\n scaler.fit(states)\n return scaler\n\n\n\n\ndef maybe_make_dir(directory):\n if not os.path.exists(directory):\n os.makedirs(directory)\n\n\n\n\ndef mlp(input_dim, n_action, n_hidden_layers=1, hidden_dim=32):\n \"\"\" A multi-layer perceptron \"\"\"\n\n # input layer\n i = Input(shape=(input_dim,))\n x = i\n\n # hidden layers\n for _ in range(n_hidden_layers):\n x = Dense(hidden_dim, activation='relu')(x)\n \n # final layer\n x = Dense(n_action)(x)\n\n # make the model\n model = Model(i, x)\n\n model.compile(loss='mse', optimizer='adam')\n print((model.summary()))\n return model\n\n\n\n\nclass MultiStockEnv:\n \"\"\"\n A 3-stock trading environment.\n State: vector of size 7 (n_stock * 2 + 1)\n - # shares of stock 1 owned\n - # shares of stock 2 owned\n - # shares of stock 3 owned\n - price of stock 1 (using daily close price)\n - price of stock 2\n - price of stock 3\n - cash owned (can be used to purchase more stocks)\n Action: categorical variable with 27 (3^3) possibilities\n - for each stock, you can:\n - 0 = sell\n - 1 = hold\n - 2 = buy\n \"\"\"\n def __init__(self, data, initial_investment=20000):\n # data\n self.stock_price_history = data\n self.n_step, self.n_stock = self.stock_price_history.shape\n\n # instance attributes\n self.initial_investment = initial_investment\n self.cur_step = None\n self.stock_owned = None\n self.stock_price = None\n self.cash_in_hand = None\n\n self.action_space = np.arange(3**self.n_stock)\n\n # action permutations\n # returns a nested list with elements like:\n # [0,0,0]\n # [0,0,1]\n # [0,0,2]\n # [0,1,0]\n # [0,1,1]\n # etc.\n # 0 = sell\n # 1 = hold\n # 2 = buy\n self.action_list = list(map(list, itertools.product([0, 1, 2], repeat=self.n_stock)))\n\n # calculate size of state\n self.state_dim = self.n_stock * 2 + 1\n\n self.reset()\n\n\n def reset(self):\n self.cur_step = 0\n self.stock_owned = np.zeros(self.n_stock)\n self.stock_price = self.stock_price_history[self.cur_step]\n self.cash_in_hand = self.initial_investment\n return self._get_obs()\n\n\n def step(self, action):\n assert action in self.action_space\n\n # get current value before performing the action\n prev_val = self._get_val()\n\n # perform the trade\n self._trade(action)\n\n # update price, i.e. go to the next day\n self.cur_step += 1\n self.stock_price = self.stock_price_history[self.cur_step]\n\n # get the new value after taking the action\n cur_val = self._get_val()\n\n # reward is the increase in porfolio value\n reward = cur_val - prev_val\n\n # done if we have run out of data\n done = self.cur_step == self.n_step - 1\n\n # store the current value of the portfolio here\n info = {'cur_val': cur_val}\n\n # conform to the Gym API\n return self._get_obs(), reward, done, info\n\n\n def _get_obs(self):\n obs = np.empty(self.state_dim)\n obs[:self.n_stock] = self.stock_owned\n obs[self.n_stock:2*self.n_stock] = self.stock_price\n obs[-1] = self.cash_in_hand\n return obs\n \n\n\n def _get_val(self):\n return self.stock_owned.dot(self.stock_price) + self.cash_in_hand\n\n\n def _trade(self, action):\n # index the action we want to perform\n # 0 = sell\n # 1 = hold\n # 2 = buy\n # e.g. [2,1,0] means:\n # buy first stock\n # hold second stock\n # sell third stock\n action_vec = self.action_list[action]\n\n # determine which stocks to buy or sell\n sell_index = [] # stores index of stocks we want to sell\n buy_index = [] # stores index of stocks we want to buy\n for i, a in enumerate(action_vec):\n if a == 0:\n sell_index.append(i)\n elif a == 2:\n buy_index.append(i)\n\n # sell any stocks we want to sell\n # then buy any stocks we want to buy\n if sell_index:\n # NOTE: to simplify the problem, when we sell, we will sell ALL shares of that stock\n for i in sell_index:\n self.cash_in_hand += self.stock_price[i] * self.stock_owned[i]\n self.stock_owned[i] = 0\n if buy_index:\n # NOTE: when buying, we will loop through each stock we want to buy,\n # and buy one share at a time until we run out of cash\n can_buy = True\n while can_buy:\n for i in buy_index:\n if self.cash_in_hand > self.stock_price[i]:\n self.stock_owned[i] += 1 # buy one share\n self.cash_in_hand -= self.stock_price[i]\n else:\n can_buy = False\n\n\n\n\n\nclass DQNAgent(object):\n def __init__(self, state_size, action_size):\n self.state_size = state_size\n self.action_size = action_size\n self.memory = ReplayBuffer(state_size, action_size, size=500)\n self.gamma = 0.95 # discount rate\n self.epsilon = 1.0 # exploration rate\n self.epsilon_min = 0.01\n self.epsilon_decay = 0.995\n self.model = mlp(state_size, action_size)\n\n\n def update_replay_memory(self, state, action, reward, next_state, done):\n self.memory.store(state, action, reward, next_state, done)\n\n\n def act(self, state):\n if np.random.rand() <= self.epsilon:\n return np.random.choice(self.action_size)\n act_values = self.model.predict(state, verbose=0)\n return np.argmax(act_values[0]) # returns action\n\n\n def replay(self, batch_size=32):\n # first check if replay buffer contains enough data\n if self.memory.size < batch_size:\n return\n\n # sample a batch of data from the replay memory\n minibatch = self.memory.sample_batch(batch_size)\n states = minibatch['s']\n actions = minibatch['a']\n rewards = minibatch['r']\n next_states = minibatch['s2']\n done = minibatch['d']\n\n # Calculate the tentative target: Q(s',a)\n target = rewards + (1 - done) * self.gamma * np.amax(self.model.predict(next_states, verbose=0), axis=1)\n\n # With the Keras API, the target (usually) must have the same\n # shape as the predictions.\n # However, we only need to update the network for the actions\n # which were actually taken.\n # We can accomplish this by setting the target to be equal to\n # the prediction for all values.\n # Then, only change the targets for the actions taken.\n # Q(s,a)\n target_full = self.model.predict(states, verbose=0)\n target_full[np.arange(batch_size), actions] = target\n\n # Run one training step\n self.model.train_on_batch(states, target_full)\n\n if self.epsilon > self.epsilon_min:\n self.epsilon *= self.epsilon_decay\n\n\n def load(self, name):\n self.model.load_weights(name)\n\n\n def save(self, name):\n self.model.save_weights(name)\n\n\n\ndef play_one_episode(agent, env, is_train):\n # note: after transforming states are already 1xD\n state = env.reset()\n state = scaler.transform([state])\n done = False\n\n while not done:\n action = agent.act(state)\n next_state, reward, done, info = env.step(action)\n next_state = scaler.transform([next_state])\n if is_train == 'train':\n agent.update_replay_memory(state, action, reward, next_state, done)\n agent.replay(batch_size)\n state = next_state\n\n return info['cur_val']\n\n\n\nif __name__ == '__main__':\n\n # config\n models_folder = 'rl_trader_models'\n rewards_folder = 'rl_trader_rewards'\n model_file = 'dqn.weights.h5'\n num_episodes = 2000\n batch_size = 32\n initial_investment = 20000\n\n\n parser = argparse.ArgumentParser()\n parser.add_argument('-m', '--mode', type=str, required=True,\n help='either \"train\" or \"test\"')\n args = parser.parse_args()\n\n maybe_make_dir(models_folder)\n maybe_make_dir(rewards_folder)\n\n data = get_data()\n n_timesteps, n_stocks = data.shape\n\n n_train = n_timesteps // 2\n\n train_data = data[:n_train]\n test_data = data[n_train:]\n\n env = MultiStockEnv(train_data, initial_investment)\n state_size = env.state_dim\n action_size = len(env.action_space)\n agent = DQNAgent(state_size, action_size)\n scaler = get_scaler(env)\n\n # store the final value of the portfolio (end of episode)\n portfolio_value = []\n\n if args.mode == 'test':\n # then load the previous scaler\n with open(f'{models_folder}/scaler.pkl', 'rb') as f:\n scaler = pickle.load(f)\n\n # remake the env with test data\n env = MultiStockEnv(test_data, initial_investment)\n\n # make sure epsilon is not 1!\n # no need to run multiple episodes if epsilon = 0, it's deterministic\n agent.epsilon = 0.01\n\n # load trained weights\n agent.load(f'{models_folder}/{model_file}')\n\n # play the game num_episodes times\n for e in range(num_episodes):\n t0 = datetime.now()\n val = play_one_episode(agent, env, args.mode)\n dt = datetime.now() - t0\n print(f\"episode: {e + 1}/{num_episodes}, episode end value: {val:.2f}, duration: {dt}\")\n portfolio_value.append(val) # append episode end portfolio value\n\n # save the weights when we are done\n if args.mode == 'train':\n # save the DQN\n agent.save(f'{models_folder}/{model_file}')\n\n # save the scaler\n with open(f'{models_folder}/scaler.pkl', 'wb') as f:\n pickle.dump(scaler, f)\n\n\n # save portfolio value for each episode\n np.save(f'{rewards_folder}/{args.mode}.npy', portfolio_value)\n" }, { "path": "tf2.0/mlp_trader.py", "content": "import numpy as np\nimport pandas as pd\n\nfrom sklearn.neural_network import MLPRegressor\nfrom sklearn.preprocessing import StandardScaler\n\nfrom datetime import datetime\nimport itertools\nimport argparse\nimport re\nimport os\nimport pickle\n\n\n# Let's use AAPL (Apple), MSI (Motorola), SBUX (Starbucks)\ndef get_data():\n # returns a T x 3 list of stock prices\n # each row is a different stock\n # 0 = AAPL\n # 1 = MSI\n # 2 = SBUX\n df = pd.read_csv('aapl_msi_sbux.csv')\n return df.values\n\n\n\n### The experience replay memory ###\nclass ReplayBuffer:\n def __init__(self, obs_dim, act_dim, size):\n self.obs1_buf = np.zeros([size, obs_dim], dtype=np.float32)\n self.obs2_buf = np.zeros([size, obs_dim], dtype=np.float32)\n self.acts_buf = np.zeros(size, dtype=np.uint8)\n self.rews_buf = np.zeros(size, dtype=np.float32)\n self.done_buf = np.zeros(size, dtype=np.uint8)\n self.ptr, self.size, self.max_size = 0, 0, size\n\n def store(self, obs, act, rew, next_obs, done):\n self.obs1_buf[self.ptr] = obs\n self.obs2_buf[self.ptr] = next_obs\n self.acts_buf[self.ptr] = act\n self.rews_buf[self.ptr] = rew\n self.done_buf[self.ptr] = done\n self.ptr = (self.ptr+1) % self.max_size\n self.size = min(self.size+1, self.max_size)\n\n def sample_batch(self, batch_size=32):\n idxs = np.random.randint(0, self.size, size=batch_size)\n return dict(s=self.obs1_buf[idxs],\n s2=self.obs2_buf[idxs],\n a=self.acts_buf[idxs],\n r=self.rews_buf[idxs],\n d=self.done_buf[idxs])\n\n\n\n\n\ndef get_scaler(env):\n # return scikit-learn scaler object to scale the states\n # Note: you could also populate the replay buffer here\n\n states = []\n for _ in range(env.n_step):\n action = np.random.choice(env.action_space)\n state, reward, done, info = env.step(action)\n states.append(state)\n if done:\n break\n\n scaler = StandardScaler()\n scaler.fit(states)\n return scaler\n\n\n\n\ndef maybe_make_dir(directory):\n if not os.path.exists(directory):\n os.makedirs(directory)\n\n\n\n\ndef mlp(input_dim, n_action, n_hidden_layers=1, hidden_dim=32):\n \"\"\" A multi-layer perceptron \"\"\"\n\n model = MLPRegressor(\n hidden_layer_sizes=n_hidden_layers * [hidden_dim],\n )\n\n # since we'll be first using this to make a prediction with random weights\n # we need to know the output size\n\n # so we'll just start by fitting on some dummy data\n X = np.random.randn(100, input_dim)\n Y = np.random.randn(100, n_action)\n model.partial_fit(X, Y)\n\n return model\n\n\n\n\nclass MultiStockEnv:\n \"\"\"\n A 3-stock trading environment.\n State: vector of size 7 (n_stock * 2 + 1)\n - # shares of stock 1 owned\n - # shares of stock 2 owned\n - # shares of stock 3 owned\n - price of stock 1 (using daily close price)\n - price of stock 2\n - price of stock 3\n - cash owned (can be used to purchase more stocks)\n Action: categorical variable with 27 (3^3) possibilities\n - for each stock, you can:\n - 0 = sell\n - 1 = hold\n - 2 = buy\n \"\"\"\n def __init__(self, data, initial_investment=20000):\n # data\n self.stock_price_history = data\n self.n_step, self.n_stock = self.stock_price_history.shape\n\n # instance attributes\n self.initial_investment = initial_investment\n self.cur_step = None\n self.stock_owned = None\n self.stock_price = None\n self.cash_in_hand = None\n\n self.action_space = np.arange(3**self.n_stock)\n\n # action permutations\n # returns a nested list with elements like:\n # [0,0,0]\n # [0,0,1]\n # [0,0,2]\n # [0,1,0]\n # [0,1,1]\n # etc.\n # 0 = sell\n # 1 = hold\n # 2 = buy\n self.action_list = list(map(list, itertools.product([0, 1, 2], repeat=self.n_stock)))\n\n # calculate size of state\n self.state_dim = self.n_stock * 2 + 1\n\n self.reset()\n\n\n def reset(self):\n self.cur_step = 0\n self.stock_owned = np.zeros(self.n_stock)\n self.stock_price = self.stock_price_history[self.cur_step]\n self.cash_in_hand = self.initial_investment\n return self._get_obs()\n\n\n def step(self, action):\n assert action in self.action_space\n\n # get current value before performing the action\n prev_val = self._get_val()\n\n # perform the trade\n self._trade(action)\n\n # update price, i.e. go to the next day\n self.cur_step += 1\n self.stock_price = self.stock_price_history[self.cur_step]\n\n # get the new value after taking the action\n cur_val = self._get_val()\n\n # reward is the increase in porfolio value\n reward = cur_val - prev_val\n\n # done if we have run out of data\n done = self.cur_step == self.n_step - 1\n\n # store the current value of the portfolio here\n info = {'cur_val': cur_val}\n\n # conform to the Gym API\n return self._get_obs(), reward, done, info\n\n\n def _get_obs(self):\n obs = np.empty(self.state_dim)\n obs[:self.n_stock] = self.stock_owned\n obs[self.n_stock:2*self.n_stock] = self.stock_price\n obs[-1] = self.cash_in_hand\n return obs\n \n\n\n def _get_val(self):\n return self.stock_owned.dot(self.stock_price) + self.cash_in_hand\n\n\n def _trade(self, action):\n # index the action we want to perform\n # 0 = sell\n # 1 = hold\n # 2 = buy\n # e.g. [2,1,0] means:\n # buy first stock\n # hold second stock\n # sell third stock\n action_vec = self.action_list[action]\n\n # determine which stocks to buy or sell\n sell_index = [] # stores index of stocks we want to sell\n buy_index = [] # stores index of stocks we want to buy\n for i, a in enumerate(action_vec):\n if a == 0:\n sell_index.append(i)\n elif a == 2:\n buy_index.append(i)\n\n # sell any stocks we want to sell\n # then buy any stocks we want to buy\n if sell_index:\n # NOTE: to simplify the problem, when we sell, we will sell ALL shares of that stock\n for i in sell_index:\n self.cash_in_hand += self.stock_price[i] * self.stock_owned[i]\n self.stock_owned[i] = 0\n if buy_index:\n # NOTE: when buying, we will loop through each stock we want to buy,\n # and buy one share at a time until we run out of cash\n can_buy = True\n while can_buy:\n for i in buy_index:\n if self.cash_in_hand > self.stock_price[i]:\n self.stock_owned[i] += 1 # buy one share\n self.cash_in_hand -= self.stock_price[i]\n else:\n can_buy = False\n\n\n\n\n\nclass DQNAgent(object):\n def __init__(self, state_size, action_size):\n self.state_size = state_size\n self.action_size = action_size\n self.memory = ReplayBuffer(state_size, action_size, size=500)\n self.gamma = 0.95 # discount rate\n self.epsilon = 1.0 # exploration rate\n self.epsilon_min = 0.01\n self.epsilon_decay = 0.995\n self.model = mlp(state_size, action_size)\n\n\n def update_replay_memory(self, state, action, reward, next_state, done):\n self.memory.store(state, action, reward, next_state, done)\n\n\n def act(self, state):\n if np.random.rand() <= self.epsilon:\n return np.random.choice(self.action_size)\n act_values = self.model.predict(state)\n return np.argmax(act_values[0]) # returns action\n\n def replay(self, batch_size=32):\n # first check if replay buffer contains enough data\n if self.memory.size < batch_size:\n return\n\n # sample a batch of data from the replay memory\n minibatch = self.memory.sample_batch(batch_size)\n states = minibatch['s']\n actions = minibatch['a']\n rewards = minibatch['r']\n next_states = minibatch['s2']\n done = minibatch['d']\n\n # Calculate the tentative target: Q(s',a)\n target = rewards + (1 - done) * self.gamma * np.amax(self.model.predict(next_states), axis=1)\n\n # With the Keras API, the target (usually) must have the same\n # shape as the predictions.\n # However, we only need to update the network for the actions\n # which were actually taken.\n # We can accomplish this by setting the target to be equal to\n # the prediction for all values.\n # Then, only change the targets for the actions taken.\n # Q(s,a)\n target_full = self.model.predict(states)\n target_full[np.arange(batch_size), actions] = target\n\n # Run one training step\n self.model.partial_fit(states, target_full)\n\n if self.epsilon > self.epsilon_min:\n self.epsilon *= self.epsilon_decay\n\n\n def load(self, name):\n with open(name, \"rb\") as f:\n self.model = pickle.load(f)\n\n\n def save(self, name):\n with open(name, \"wb\") as f:\n pickle.dump(self.model, f)\n\n\ndef play_one_episode(agent, env, is_train):\n # note: after transforming states are already 1xD\n state = env.reset()\n state = scaler.transform([state])\n done = False\n\n while not done:\n action = agent.act(state)\n next_state, reward, done, info = env.step(action)\n next_state = scaler.transform([next_state])\n if is_train == 'train':\n agent.update_replay_memory(state, action, reward, next_state, done)\n agent.replay(batch_size)\n state = next_state\n\n return info['cur_val']\n\n\n\nif __name__ == '__main__':\n\n # config\n models_folder = 'rl_trader_models'\n rewards_folder = 'rl_trader_rewards'\n num_episodes = 2000\n batch_size = 32\n initial_investment = 20000\n\n\n parser = argparse.ArgumentParser()\n parser.add_argument('-m', '--mode', type=str, required=True,\n help='either \"train\" or \"test\"')\n args = parser.parse_args()\n\n maybe_make_dir(models_folder)\n maybe_make_dir(rewards_folder)\n\n data = get_data()\n n_timesteps, n_stocks = data.shape\n\n n_train = n_timesteps // 2\n\n train_data = data[:n_train]\n test_data = data[n_train:]\n\n env = MultiStockEnv(train_data, initial_investment)\n state_size = env.state_dim\n action_size = len(env.action_space)\n agent = DQNAgent(state_size, action_size)\n scaler = get_scaler(env)\n\n # store the final value of the portfolio (end of episode)\n portfolio_value = []\n\n if args.mode == 'test':\n # then load the previous scaler\n with open(f'{models_folder}/scaler.pkl', 'rb') as f:\n scaler = pickle.load(f)\n\n # remake the env with test data\n env = MultiStockEnv(test_data, initial_investment)\n\n # make sure epsilon is not 1!\n # no need to run multiple episodes if epsilon = 0, it's deterministic\n agent.epsilon = 0.01\n\n # load trained weights\n agent.load(f'{models_folder}/mlp.pkl')\n\n # play the game num_episodes times\n for e in range(num_episodes):\n t0 = datetime.now()\n val = play_one_episode(agent, env, args.mode)\n dt = datetime.now() - t0\n print(f\"episode: {e + 1}/{num_episodes}, episode end value: {val:.2f}, duration: {dt}\")\n portfolio_value.append(val) # append episode end portfolio value\n\n # save the weights when we are done\n if args.mode == 'train':\n # save the DQN\n agent.save(f'{models_folder}/mlp.pkl')\n\n # save the scaler\n with open(f'{models_folder}/scaler.pkl', 'wb') as f:\n pickle.dump(scaler, f)\n\n\n # save portfolio value for each episode\n np.save(f'{rewards_folder}/{args.mode}.npy', portfolio_value)\n" }, { "path": "tf2.0/moore.csv", "content": "1971,2300\n1972,3500\n1973,2500\n1973,2500\n1974,4100\n1974,4500\n1974,8000\n1975,3510\n1976,5000\n1976,8500\n1976,6500\n1978,9000\n1978,29000\n1979,17500\n1979,29000\n1979,68000\n1981,11500\n1982,55000\n1982,134000\n1983,22000\n1984,63000\n1984,190000\n1985,275000\n1985,25000\n1985,16000\n1986,110000\n1986,375000\n1986,30000\n1987,385000\n1987,730000\n1987,273000\n1987,553000\n1988,180000\n1988,250000\n1989,600000\n1989,1000000\n1989,1180235\n1989,310000\n1990,1200000\n1991,1350000\n1991,35000\n1992,600000\n1992,900000\n1993,2800000\n1993,3100000\n1994,578977\n1994,2500000\n1995,2500000\n1999,111000\n1995,5500000\n1996,4300000\n1997,10000000\n1997,7500000\n1997,8800000\n1998,7500000\n1999,9500000\n1999,13500000\n2000,21000000\n2000,21000000\n1999,27400000\n1999,21300000\n1999,22000000\n2000,42000000\n2001,191000000\n2001,45000000\n2002,55000000\n2004,112000000\n2004,400000000\n2005,169000000\n2006,184000000\n2005,228000000\n2006,362000000\n2007,540000000\n2008,47000000\n2003,54300000\n2003,105900000\n2002,220000000\n2005,165000000\n2005,250000000\n2006,291000000\n2007,169000000\n2003,410000000\n2008,600000000\n2009,760000000\n2011,1870000000\n2012,432000000\n2007,463000000\n2007,26000000\n2008,230000000\n2004,592000000\n2007,411000000\n2008,731000000\n2008,758000000\n2007,789000000\n2009,904000000\n2010,1000000000\n2012,2990000000\n2013,1000000000\n2011,1160000000\n2010,1170000000\n2010,1200000000\n2012,1200000000\n2012,1303000000\n2010,1400000000\n2012,1400000000\n2014,1400000000\n2006,1700000000\n2015,1750000000\n2013,1860000000\n2015,1900000000\n2008,1900000000\n2010,2000000000\n2014,2000000000\n2015,2000000000\n2015,3000000000\n2012,2100000000\n2011,2270000000\n2010,2300000000\n2014,2600000000\n2011,2600000000\n2012,2750000000\n2014,3000000000\n2016,3000000000\n2017,5300000000\n2017,5300000000\n2018,8500000000\n2012,3100000000\n2016,3200000000\n2016,3300000000\n2015,3990000000\n2013,4200000000\n2017,4300000000\n2014,4310000000\n2017,4800000000\n2017,4800000000\n2017,4800000000\n2012,5000000000\n2013,5000000000\n2014,5560000000\n2017,6100000000\n2018,6900000000\n2016,4000000000\n2018,6900000000\n2017,5500000000\n2018,5500000000\n2017,7000000000\n2015,7100000000\n2017,8000000000\n2016,7200000000\n2017,8000000000\n2016,8000000000\n2017,9700000000\n2017,250000000\n2015,10000000000\n2017,5450000000\n2018,10000000000\n2017,4300000000\n2017,18000000000\n2017,19200000000\n2018,8876000000\n2018,23600000000\n2018,9000000000\n" }, { "path": "tf2.0/plot_rl_rewards.py", "content": "import matplotlib.pyplot as plt\nimport numpy as np\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-m', '--mode', type=str, required=True,\n help='either \"train\" or \"test\"')\nargs = parser.parse_args()\n\na = np.load(f'rl_trader_rewards/{args.mode}.npy')\n\nprint(f\"average reward: {a.mean():.2f}, min: {a.min():.2f}, max: {a.max():.2f}\")\n\nif args.mode == 'train':\n # show the training progress\n plt.plot(a)\nelse:\n # test - show a histogram of rewards\n plt.hist(a, bins=20)\n\nplt.title(args.mode)\nplt.show()" }, { "path": "tf2.0/rl_trader.py", "content": "import numpy as np\nimport pandas as pd\n\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import Dense, Input\nfrom tensorflow.keras.optimizers import Adam\n\nfrom datetime import datetime\nimport itertools\nimport argparse\nimport re\nimport os\nimport pickle\n\nfrom sklearn.preprocessing import StandardScaler\n\n\nimport tensorflow as tf\n# if tf.__version__.startswith('2'):\n# tf.compat.v1.disable_eager_execution()\n\n\n# Let's use AAPL (Apple), MSI (Motorola), SBUX (Starbucks)\ndef get_data():\n # returns a T x 3 list of stock prices\n # each row is a different stock\n # 0 = AAPL\n # 1 = MSI\n # 2 = SBUX\n df = pd.read_csv('aapl_msi_sbux.csv')\n return df.values\n\n\n\n### The experience replay memory ###\nclass ReplayBuffer:\n def __init__(self, obs_dim, act_dim, size):\n self.obs1_buf = np.zeros([size, obs_dim], dtype=np.float32)\n self.obs2_buf = np.zeros([size, obs_dim], dtype=np.float32)\n self.acts_buf = np.zeros(size, dtype=np.uint8)\n self.rews_buf = np.zeros(size, dtype=np.float32)\n self.done_buf = np.zeros(size, dtype=np.uint8)\n self.ptr, self.size, self.max_size = 0, 0, size\n\n def store(self, obs, act, rew, next_obs, done):\n self.obs1_buf[self.ptr] = obs\n self.obs2_buf[self.ptr] = next_obs\n self.acts_buf[self.ptr] = act\n self.rews_buf[self.ptr] = rew\n self.done_buf[self.ptr] = done\n self.ptr = (self.ptr+1) % self.max_size\n self.size = min(self.size+1, self.max_size)\n\n def sample_batch(self, batch_size=32):\n idxs = np.random.randint(0, self.size, size=batch_size)\n return dict(s=self.obs1_buf[idxs],\n s2=self.obs2_buf[idxs],\n a=self.acts_buf[idxs],\n r=self.rews_buf[idxs],\n d=self.done_buf[idxs])\n\n\n\n\n\ndef get_scaler(env):\n # return scikit-learn scaler object to scale the states\n # Note: you could also populate the replay buffer here\n\n states = []\n for _ in range(env.n_step):\n action = np.random.choice(env.action_space)\n state, reward, done, info = env.step(action)\n states.append(state)\n if done:\n break\n\n scaler = StandardScaler()\n scaler.fit(states)\n return scaler\n\n\n\n\ndef maybe_make_dir(directory):\n if not os.path.exists(directory):\n os.makedirs(directory)\n\n\n\n\ndef mlp(input_dim, n_action, n_hidden_layers=1, hidden_dim=32):\n \"\"\" A multi-layer perceptron \"\"\"\n\n # input layer\n i = Input(shape=(input_dim,))\n x = i\n\n # hidden layers\n for _ in range(n_hidden_layers):\n x = Dense(hidden_dim, activation='relu')(x)\n \n # final layer\n x = Dense(n_action)(x)\n\n # make the model\n model = Model(i, x)\n\n model.compile(loss='mse', optimizer='adam')\n print((model.summary()))\n return model\n\n\n\n\nclass MultiStockEnv:\n \"\"\"\n A 3-stock trading environment.\n State: vector of size 7 (n_stock * 2 + 1)\n - # shares of stock 1 owned\n - # shares of stock 2 owned\n - # shares of stock 3 owned\n - price of stock 1 (using daily close price)\n - price of stock 2\n - price of stock 3\n - cash owned (can be used to purchase more stocks)\n Action: categorical variable with 27 (3^3) possibilities\n - for each stock, you can:\n - 0 = sell\n - 1 = hold\n - 2 = buy\n \"\"\"\n def __init__(self, data, initial_investment=20000):\n # data\n self.stock_price_history = data\n self.n_step, self.n_stock = self.stock_price_history.shape\n\n # instance attributes\n self.initial_investment = initial_investment\n self.cur_step = None\n self.stock_owned = None\n self.stock_price = None\n self.cash_in_hand = None\n\n self.action_space = np.arange(3**self.n_stock)\n\n # action permutations\n # returns a nested list with elements like:\n # [0,0,0]\n # [0,0,1]\n # [0,0,2]\n # [0,1,0]\n # [0,1,1]\n # etc.\n # 0 = sell\n # 1 = hold\n # 2 = buy\n self.action_list = list(map(list, itertools.product([0, 1, 2], repeat=self.n_stock)))\n\n # calculate size of state\n self.state_dim = self.n_stock * 2 + 1\n\n self.reset()\n\n\n def reset(self):\n self.cur_step = 0\n self.stock_owned = np.zeros(self.n_stock)\n self.stock_price = self.stock_price_history[self.cur_step]\n self.cash_in_hand = self.initial_investment\n return self._get_obs()\n\n\n def step(self, action):\n assert action in self.action_space\n\n # get current value before performing the action\n prev_val = self._get_val()\n\n # perform the trade\n self._trade(action)\n\n # update price, i.e. go to the next day\n self.cur_step += 1\n self.stock_price = self.stock_price_history[self.cur_step]\n\n # get the new value after taking the action\n cur_val = self._get_val()\n\n # reward is the increase in porfolio value\n reward = cur_val - prev_val\n\n # done if we have run out of data\n done = self.cur_step == self.n_step - 1\n\n # store the current value of the portfolio here\n info = {'cur_val': cur_val}\n\n # conform to the Gym API\n return self._get_obs(), reward, done, info\n\n\n def _get_obs(self):\n obs = np.empty(self.state_dim)\n obs[:self.n_stock] = self.stock_owned\n obs[self.n_stock:2*self.n_stock] = self.stock_price\n obs[-1] = self.cash_in_hand\n return obs\n \n\n\n def _get_val(self):\n return self.stock_owned.dot(self.stock_price) + self.cash_in_hand\n\n\n def _trade(self, action):\n # index the action we want to perform\n # 0 = sell\n # 1 = hold\n # 2 = buy\n # e.g. [2,1,0] means:\n # buy first stock\n # hold second stock\n # sell third stock\n action_vec = self.action_list[action]\n\n # determine which stocks to buy or sell\n sell_index = [] # stores index of stocks we want to sell\n buy_index = [] # stores index of stocks we want to buy\n for i, a in enumerate(action_vec):\n if a == 0:\n sell_index.append(i)\n elif a == 2:\n buy_index.append(i)\n\n # sell any stocks we want to sell\n # then buy any stocks we want to buy\n if sell_index:\n # NOTE: to simplify the problem, when we sell, we will sell ALL shares of that stock\n for i in sell_index:\n self.cash_in_hand += self.stock_price[i] * self.stock_owned[i]\n self.stock_owned[i] = 0\n if buy_index:\n # NOTE: when buying, we will loop through each stock we want to buy,\n # and buy one share at a time until we run out of cash\n can_buy = True\n while can_buy:\n for i in buy_index:\n if self.cash_in_hand > self.stock_price[i]:\n self.stock_owned[i] += 1 # buy one share\n self.cash_in_hand -= self.stock_price[i]\n else:\n can_buy = False\n\n\n\n\n\nclass DQNAgent(object):\n def __init__(self, state_size, action_size):\n self.state_size = state_size\n self.action_size = action_size\n self.memory = ReplayBuffer(state_size, action_size, size=500)\n self.gamma = 0.95 # discount rate\n self.epsilon = 1.0 # exploration rate\n self.epsilon_min = 0.01\n self.epsilon_decay = 0.995\n self.model = mlp(state_size, action_size)\n\n\n def update_replay_memory(self, state, action, reward, next_state, done):\n self.memory.store(state, action, reward, next_state, done)\n\n\n def act(self, state):\n if np.random.rand() <= self.epsilon:\n return np.random.choice(self.action_size)\n act_values = self.model.predict(state, verbose=0)\n return np.argmax(act_values[0]) # returns action\n\n @tf.function\n def replay(self, batch_size=32):\n # first check if replay buffer contains enough data\n if self.memory.size < batch_size:\n return\n\n # sample a batch of data from the replay memory\n minibatch = self.memory.sample_batch(batch_size)\n states = minibatch['s']\n actions = minibatch['a']\n rewards = minibatch['r']\n next_states = minibatch['s2']\n done = minibatch['d']\n\n # Calculate the tentative target: Q(s',a)\n target = rewards + (1 - done) * self.gamma * np.amax(self.model.predict(next_states, verbose=0), axis=1)\n\n # With the Keras API, the target (usually) must have the same\n # shape as the predictions.\n # However, we only need to update the network for the actions\n # which were actually taken.\n # We can accomplish this by setting the target to be equal to\n # the prediction for all values.\n # Then, only change the targets for the actions taken.\n # Q(s,a)\n target_full = self.model.predict(states, verbose=0)\n target_full[np.arange(batch_size), actions] = target\n\n # Run one training step\n self.model.train_on_batch(states, target_full)\n\n if self.epsilon > self.epsilon_min:\n self.epsilon *= self.epsilon_decay\n\n\n def load(self, name):\n self.model.load_weights(name)\n\n\n def save(self, name):\n self.model.save_weights(name)\n\n\n\ndef play_one_episode(agent, env, is_train):\n # note: after transforming states are already 1xD\n state = env.reset()\n state = scaler.transform([state])\n done = False\n\n while not done:\n action = agent.act(state)\n next_state, reward, done, info = env.step(action)\n next_state = scaler.transform([next_state])\n if is_train == 'train':\n agent.update_replay_memory(state, action, reward, next_state, done)\n agent.replay(batch_size)\n state = next_state\n\n return info['cur_val']\n\n\n\nif __name__ == '__main__':\n\n # config\n models_folder = 'rl_trader_models'\n rewards_folder = 'rl_trader_rewards'\n model_file = 'dqn.weights.h5'\n num_episodes = 2000\n batch_size = 32\n initial_investment = 20000\n\n\n parser = argparse.ArgumentParser()\n parser.add_argument('-m', '--mode', type=str, required=True,\n help='either \"train\" or \"test\"')\n args = parser.parse_args()\n\n maybe_make_dir(models_folder)\n maybe_make_dir(rewards_folder)\n\n data = get_data()\n n_timesteps, n_stocks = data.shape\n\n n_train = n_timesteps // 2\n\n train_data = data[:n_train]\n test_data = data[n_train:]\n\n env = MultiStockEnv(train_data, initial_investment)\n state_size = env.state_dim\n action_size = len(env.action_space)\n agent = DQNAgent(state_size, action_size)\n scaler = get_scaler(env)\n\n # store the final value of the portfolio (end of episode)\n portfolio_value = []\n\n if args.mode == 'test':\n # then load the previous scaler\n with open(f'{models_folder}/scaler.pkl', 'rb') as f:\n scaler = pickle.load(f)\n\n # remake the env with test data\n env = MultiStockEnv(test_data, initial_investment)\n\n # make sure epsilon is not 1!\n # no need to run multiple episodes if epsilon = 0, it's deterministic\n agent.epsilon = 0.01\n\n # load trained weights\n agent.load(f'{models_folder}/{model_file}')\n\n # play the game num_episodes times\n for e in range(num_episodes):\n t0 = datetime.now()\n val = play_one_episode(agent, env, args.mode)\n dt = datetime.now() - t0\n print(f\"episode: {e + 1}/{num_episodes}, episode end value: {val:.2f}, duration: {dt}\")\n portfolio_value.append(val) # append episode end portfolio value\n\n # save the weights when we are done\n if args.mode == 'train':\n # save the DQN\n agent.save(f'{models_folder}/{model_file}')\n\n # save the scaler\n with open(f'{models_folder}/scaler.pkl', 'wb') as f:\n pickle.dump(scaler, f)\n\n\n # save portfolio value for each episode\n np.save(f'{rewards_folder}/{args.mode}.npy', portfolio_value)" }, { "path": "tf2.0/sbux.csv", "content": "date,open,high,low,close,volume,Name\n2013-02-08,27.92,28.325,27.92,28.185,7146296,SBUX\n2013-02-11,28.26,28.26,27.93,28.07,5457354,SBUX\n2013-02-12,28.0,28.275,27.975,28.13,8665592,SBUX\n2013-02-13,28.23,28.23,27.75,27.915,7022056,SBUX\n2013-02-14,27.765,27.905,27.675,27.775,8899188,SBUX\n2013-02-15,27.805,27.85,27.085,27.17,18195730,SBUX\n2013-02-19,27.18,27.305,27.01,27.225,11760912,SBUX\n2013-02-20,27.3,27.42,26.59,26.655,12472506,SBUX\n2013-02-21,26.535,26.82,26.26,26.675,13896450,SBUX\n2013-02-22,26.85,27.105,26.64,27.085,11487316,SBUX\n2013-02-25,27.2,27.355,26.6,26.605,12333954,SBUX\n2013-02-26,26.715,26.93,26.425,26.64,10607724,SBUX\n2013-02-27,26.625,27.4875,26.54,27.285,12056302,SBUX\n2013-02-28,27.325,27.585,27.225,27.425,10394356,SBUX\n2013-03-01,27.315,27.465,27.0,27.435,8451764,SBUX\n2013-03-04,27.385,27.86,27.33,27.85,10193852,SBUX\n2013-03-05,28.0,28.4,28.0,28.255,12931844,SBUX\n2013-03-06,28.38,28.745,28.325,28.55,14925144,SBUX\n2013-03-07,28.55,29.25,28.545,29.125,18237018,SBUX\n2013-03-08,29.335,29.485,29.0725,29.335,14215718,SBUX\n2013-03-11,29.2,29.465,29.165,29.305,9897766,SBUX\n2013-03-12,29.225,29.275,28.99,29.14,11670100,SBUX\n2013-03-13,29.165,29.4,29.14,29.2925,7435340,SBUX\n2013-03-14,29.26,29.375,28.705,28.84,14723066,SBUX\n2013-03-15,28.68,28.93,28.63,28.83,15102742,SBUX\n2013-03-18,28.53,28.71,28.375,28.465,10521204,SBUX\n2013-03-19,28.255,28.49,27.98,28.415,13337034,SBUX\n2013-03-20,28.61,28.805,28.5,28.715,9620874,SBUX\n2013-03-21,28.65,28.71,28.375,28.525,8307328,SBUX\n2013-03-22,28.65,28.875,28.58,28.69,8720670,SBUX\n2013-03-25,28.765,28.915,28.075,28.345,10580234,SBUX\n2013-03-26,28.495,28.58,28.355,28.525,6128410,SBUX\n2013-03-27,28.43,28.475,28.105,28.455,7456828,SBUX\n2013-03-28,28.465,28.63,28.43,28.475,7620390,SBUX\n2013-04-01,28.565,28.67,28.325,28.435,7009632,SBUX\n2013-04-02,28.595,29.165,28.575,29.13,13495550,SBUX\n2013-04-03,29.2,29.45,28.69,28.85,11272606,SBUX\n2013-04-04,28.805,29.155,28.805,29.055,7568480,SBUX\n2013-04-05,28.605,28.9382,28.3592,28.9,8993596,SBUX\n2013-04-08,28.915,29.06,28.73,29.06,7343972,SBUX\n2013-04-09,28.98,29.0,28.59,28.705,8361158,SBUX\n2013-04-10,28.82,29.155,28.8045,28.9,8577388,SBUX\n2013-04-11,28.975,29.495,28.9,29.2875,10416656,SBUX\n2013-04-12,29.355,29.855,29.3,29.545,10418310,SBUX\n2013-04-15,29.33,29.72,28.8,28.855,10700276,SBUX\n2013-04-16,28.97,29.47,28.86,29.28,8849328,SBUX\n2013-04-17,29.015,29.275,28.825,29.085,7207368,SBUX\n2013-04-18,29.18,29.24,28.75,28.86,8776706,SBUX\n2013-04-19,28.79,29.29,28.77,29.2025,8605504,SBUX\n2013-04-22,29.21,29.435,28.99,29.32,5547302,SBUX\n2013-04-23,29.42,29.95,29.3899,29.695,11323422,SBUX\n2013-04-24,29.845,29.985,29.565,29.915,10410890,SBUX\n2013-04-25,30.0,30.32,29.955,30.25,16550532,SBUX\n2013-04-26,29.65,30.195,29.6,30.0,14970972,SBUX\n2013-04-29,30.175,30.46,30.065,30.29,7400546,SBUX\n2013-04-30,30.315,30.455,30.155,30.42,8041092,SBUX\n2013-05-01,30.275,30.34,29.975,30.07,6294596,SBUX\n2013-05-02,30.22,30.34,29.8,30.19,6237460,SBUX\n2013-05-03,30.425,30.985,30.35,30.935,9835550,SBUX\n2013-05-06,31.0,31.24,30.96,31.24,6997134,SBUX\n2013-05-07,31.265,31.265,30.855,31.095,7495628,SBUX\n2013-05-08,31.17,31.22,30.865,31.205,5507696,SBUX\n2013-05-09,31.33,31.45,31.02,31.18,8276152,SBUX\n2013-05-10,31.285,31.595,31.195,31.5485,7480820,SBUX\n2013-05-13,31.6,31.615,31.305,31.41,5906892,SBUX\n2013-05-14,31.46,31.785,31.39,31.76,7510580,SBUX\n2013-05-15,31.6592,32.1,31.61,32.035,9654546,SBUX\n2013-05-16,32.0,32.0325,31.725,31.775,6661036,SBUX\n2013-05-17,31.885,32.07,31.75,32.065,6934282,SBUX\n2013-05-20,32.015,32.305,31.89,31.915,7211790,SBUX\n2013-05-21,32.02,32.23,31.94,32.125,6588946,SBUX\n2013-05-22,32.075,32.465,31.98,32.075,11012050,SBUX\n2013-05-23,31.845,31.91,31.515,31.76,8258348,SBUX\n2013-05-24,31.54,31.7,31.305,31.68,6507242,SBUX\n2013-05-28,32.105,32.35,32.02,32.13,7438516,SBUX\n2013-05-29,31.895,32.05,31.5575,31.815,7560250,SBUX\n2013-05-30,31.935,32.0221,31.7275,31.735,8798456,SBUX\n2013-05-31,31.605,32.1725,31.395,31.57,10205308,SBUX\n2013-06-03,31.63,31.77,31.31,31.73,11417578,SBUX\n2013-06-04,31.305,32.07,31.305,31.665,8622564,SBUX\n2013-06-05,31.52,31.65,31.155,31.17,8408538,SBUX\n2013-06-06,31.175,31.5275,31.16,31.51,8625308,SBUX\n2013-06-07,31.705,32.545,31.705,32.52,13752898,SBUX\n2013-06-10,32.83,33.155,32.74,33.055,12412340,SBUX\n2013-06-11,32.735,33.0525,32.655,32.71,7897496,SBUX\n2013-06-12,32.91,33.0,32.13,32.225,10069920,SBUX\n2013-06-13,32.27,33.095,32.035,32.985,11909482,SBUX\n2013-06-14,32.975,33.335,32.725,32.8,9854434,SBUX\n2013-06-17,33.095,33.28,32.8575,33.015,8115800,SBUX\n2013-06-18,33.0,33.565,32.975,33.5475,7451352,SBUX\n2013-06-19,33.51,33.74,33.205,33.205,10644698,SBUX\n2013-06-20,32.91,33.16,32.52,32.61,11606540,SBUX\n2013-06-21,32.855,32.96,32.115,32.345,14709502,SBUX\n2013-06-24,32.0,32.289,31.59,32.005,9913028,SBUX\n2013-06-25,32.265,32.68,32.22,32.37,9856852,SBUX\n2013-06-26,32.685,33.3546,32.4455,32.9,9545442,SBUX\n2013-06-27,33.135,33.25,32.825,32.845,6292594,SBUX\n2013-06-28,32.755,33.125,32.7,32.755,10836506,SBUX\n2013-07-01,33.045,33.245,32.91,33.12,6692040,SBUX\n2013-07-02,33.12,33.6,33.04,33.395,6765528,SBUX\n2013-07-03,33.215,33.84,33.2,33.65,5635188,SBUX\n2013-07-05,33.935,34.0,33.45,33.86,5189244,SBUX\n2013-07-08,33.97,34.41,33.94,34.145,8029920,SBUX\n2013-07-09,34.45,34.5,33.995,34.065,7459914,SBUX\n2013-07-10,33.935,34.06,33.725,34.05,8090792,SBUX\n2013-07-11,34.34,34.7575,34.175,34.67,8385798,SBUX\n2013-07-12,34.62,34.86,34.39,34.86,9706492,SBUX\n2013-07-15,34.845,34.95,34.765,34.83,6180564,SBUX\n2013-07-16,34.82,34.86,34.54,34.76,6068146,SBUX\n2013-07-17,34.87,34.9076,33.945,34.1,12147310,SBUX\n2013-07-18,34.225,34.505,34.02,34.24,8537754,SBUX\n2013-07-19,34.285,34.555,34.2,34.395,7196954,SBUX\n2013-07-22,34.54,34.605,34.275,34.51,6559682,SBUX\n2013-07-23,34.62,34.66,33.725,33.83,8465332,SBUX\n2013-07-24,34.06,34.215,33.15,33.305,14033776,SBUX\n2013-07-25,33.47,34.15,33.365,34.085,15971984,SBUX\n2013-07-26,36.3,36.76,35.925,36.68,32293248,SBUX\n2013-07-29,36.375,36.54,36.05,36.225,12801986,SBUX\n2013-07-30,36.45,36.49,35.855,35.965,9808846,SBUX\n2013-07-31,36.07,36.25,35.61,35.6445,11248506,SBUX\n2013-08-01,36.135,36.865,36.04,36.74,12849582,SBUX\n2013-08-02,36.75,37.135,36.58,37.115,9394576,SBUX\n2013-08-05,37.11,37.135,36.855,36.985,7606188,SBUX\n2013-08-06,36.83,36.925,36.39,36.4,7956060,SBUX\n2013-08-07,36.18,36.575,36.0825,36.095,7068922,SBUX\n2013-08-08,36.385,36.7,36.2,36.47,5923772,SBUX\n2013-08-09,36.295,36.6154,36.26,36.4,5366324,SBUX\n2013-08-12,36.14,36.565,36.125,36.465,5473182,SBUX\n2013-08-13,36.545,36.545,35.9674,36.32,5377794,SBUX\n2013-08-14,36.29,36.29,35.895,35.925,4932052,SBUX\n2013-08-15,35.655,35.675,35.155,35.37,7198878,SBUX\n2013-08-16,35.49,35.695,35.275,35.355,6929830,SBUX\n2013-08-19,35.29,35.6475,35.09,35.145,6349108,SBUX\n2013-08-20,35.18,35.5125,35.0558,35.33,6046820,SBUX\n2013-08-21,35.21,35.7,35.14,35.3565,6917088,SBUX\n2013-08-22,35.54,36.13,35.505,35.95,5399676,SBUX\n2013-08-23,36.0325,36.095,35.79,35.985,5869740,SBUX\n2013-08-26,36.035,36.105,35.8,35.94,4928868,SBUX\n2013-08-27,35.45,35.685,35.055,35.08,8021076,SBUX\n2013-08-28,35.045,35.66,34.93,35.48,6249812,SBUX\n2013-08-29,35.365,35.92,35.25,35.59,5509920,SBUX\n2013-08-30,35.69,35.7,35.165,35.26,5666988,SBUX\n2013-09-03,35.85,36.07,35.595,35.8,7249600,SBUX\n2013-09-04,35.71,36.135,35.5455,36.07,6502234,SBUX\n2013-09-05,36.125,36.38,36.005,36.025,4931220,SBUX\n2013-09-06,36.185,36.325,35.465,35.785,6356670,SBUX\n2013-09-09,35.84,36.245,35.835,36.22,4936828,SBUX\n2013-09-10,36.49,37.16,36.465,37.1075,11779224,SBUX\n2013-09-11,37.165,37.7499,37.165,37.695,9982644,SBUX\n2013-09-12,37.75,37.955,37.5603,37.835,7628560,SBUX\n2013-09-13,37.795,37.85,37.325,37.785,5767336,SBUX\n2013-09-16,38.285,38.3,37.43,37.62,8787132,SBUX\n2013-09-17,37.63,38.12,37.585,38.02,6661016,SBUX\n2013-09-18,37.94,38.81,37.775,38.665,9488762,SBUX\n2013-09-19,38.76,38.9225,38.15,38.175,8042062,SBUX\n2013-09-20,38.13,38.5,38.035,38.06,9620500,SBUX\n2013-09-23,38.055,38.19,37.535,37.68,6783882,SBUX\n2013-09-24,37.765,38.655,37.739000000000004,38.275,7633976,SBUX\n2013-09-25,38.42,38.505,38.0352,38.17,7261490,SBUX\n2013-09-26,38.32,38.6395,38.16,38.59,4988814,SBUX\n2013-09-27,38.455,38.74,38.2,38.665,6200278,SBUX\n2013-09-30,38.25,38.5425,38.025,38.485,8828424,SBUX\n2013-10-01,38.49,38.67,38.28,38.58,5205160,SBUX\n2013-10-02,38.295,38.615,38.105,38.595,6088422,SBUX\n2013-10-03,38.48,38.73,38.16,38.435,8164122,SBUX\n2013-10-04,38.4,38.73,38.255,38.7,5226696,SBUX\n2013-10-07,38.3,38.695,38.205,38.4305,5106784,SBUX\n2013-10-08,38.555,39.015,37.72,37.765,12663250,SBUX\n2013-10-09,37.695,37.9,37.2263,37.63,9482078,SBUX\n2013-10-10,38.145,38.655,38.065,38.56,6638096,SBUX\n2013-10-11,38.61,38.92,38.44,38.91,5530376,SBUX\n2013-10-14,38.7675,39.16,38.625,39.05,5986662,SBUX\n2013-10-15,38.915,38.9475,38.3,38.355,8146874,SBUX\n2013-10-16,38.6,39.1125,38.445,39.02,10103938,SBUX\n2013-10-17,38.835,39.425,38.725,39.3675,6562580,SBUX\n2013-10-18,39.635,39.835,39.485,39.655,9049968,SBUX\n2013-10-21,39.555,39.955,39.335,39.73,6770290,SBUX\n2013-10-22,39.95,40.54,39.8,40.45,8419570,SBUX\n2013-10-23,40.225,40.425,39.945,40.025,7908094,SBUX\n2013-10-24,39.68,39.72,39.255,39.525,13508366,SBUX\n2013-10-25,39.735,40.0,39.635,39.98,7251840,SBUX\n2013-10-28,40.1,40.215,39.2625,39.355,11242732,SBUX\n2013-10-29,39.605,39.81,39.525,39.81,7898436,SBUX\n2013-10-30,40.315,40.425,39.88,40.415,17518716,SBUX\n2013-10-31,39.345,40.81,39.2859,40.525,20491552,SBUX\n2013-11-01,40.77,40.77,39.835,40.185,12005206,SBUX\n2013-11-04,40.375,40.39,40.105,40.185,8285184,SBUX\n2013-11-05,40.18,41.17,40.075,40.995,10092844,SBUX\n2013-11-06,41.195,41.25,40.485,40.565,8562294,SBUX\n2013-11-07,40.65,40.675,39.5,39.535,13283288,SBUX\n2013-11-08,39.755,40.625,39.7,40.6,11487736,SBUX\n2013-11-11,40.6,40.71,40.49,40.495,5146208,SBUX\n2013-11-12,40.38,40.5249,40.06,40.3075,6613072,SBUX\n2013-11-13,39.62,40.75,39.595,40.7305,12519058,SBUX\n2013-11-14,40.765,40.77,40.3925,40.57,7647688,SBUX\n2013-11-15,40.625,40.725,40.39,40.595,6445400,SBUX\n2013-11-18,40.51,40.67,40.105,40.27,8322628,SBUX\n2013-11-19,40.005,40.46,39.93,39.96,8725944,SBUX\n2013-11-20,40.065,40.125,39.66,39.845,8734150,SBUX\n2013-11-21,39.9,40.825,39.875,40.765,13139094,SBUX\n2013-11-22,40.85,40.85,40.55,40.675,7926170,SBUX\n2013-11-25,40.685,40.74,40.255,40.355,9001126,SBUX\n2013-11-26,40.43,40.825,40.2025,40.755,8767864,SBUX\n2013-11-27,40.695,40.955,40.6,40.81,4752000,SBUX\n2013-11-29,40.925,41.185,40.685,40.73,4387144,SBUX\n2013-12-02,40.745,40.8445,40.455,40.535,5773800,SBUX\n2013-12-03,40.37,40.5775,40.145,40.275,7893264,SBUX\n2013-12-04,40.195,40.36,39.72,39.75,10206454,SBUX\n2013-12-05,39.78,40.125,39.665,39.86,6569194,SBUX\n2013-12-06,40.2975,40.375,39.855,39.97,6736632,SBUX\n2013-12-09,40.13,40.23,39.8025,39.865,7936162,SBUX\n2013-12-10,39.495,39.535,38.34,38.69,26329504,SBUX\n2013-12-11,38.8,38.9975,38.145,38.2,15751996,SBUX\n2013-12-12,38.14,38.545,38.0,38.24,9651038,SBUX\n2013-12-13,38.415,38.465,38.045,38.175,8052582,SBUX\n2013-12-16,38.015,38.49,37.955,38.23,9348824,SBUX\n2013-12-17,38.29,38.29,37.955,38.045,7008900,SBUX\n2013-12-18,38.065,38.845,38.05,38.84,10268256,SBUX\n2013-12-19,39.12,39.25,38.51,38.575,11406282,SBUX\n2013-12-20,38.76,38.99,38.622,38.83,12707400,SBUX\n2013-12-23,38.985,39.17,38.675,39.16,7624552,SBUX\n2013-12-24,39.05,39.375,38.975,39.285,3896612,SBUX\n2013-12-26,39.365,39.5125,39.2195,39.44,4391338,SBUX\n2013-12-27,39.645,39.65,39.23,39.285,4506128,SBUX\n2013-12-30,39.395,39.415,38.93,39.275,4734486,SBUX\n2013-12-31,39.215,39.39,39.005,39.195,6016240,SBUX\n2014-01-02,39.035,39.135,38.5025,38.585,8528022,SBUX\n2014-01-03,38.74,38.885,38.47,38.475,6545626,SBUX\n2014-01-06,38.45,38.675,38.005,38.085,10604900,SBUX\n2014-01-07,38.325,38.705,38.235,38.605,8180398,SBUX\n2014-01-08,38.64,39.0735,38.595,39.015,10190576,SBUX\n2014-01-09,39.035,39.05,38.4,38.8,8370276,SBUX\n2014-01-10,38.78,38.995,38.475,38.835,6475950,SBUX\n2014-01-13,38.69,38.695,37.38,37.56,14730286,SBUX\n2014-01-14,37.56,38.1775,37.375,37.73,18668208,SBUX\n2014-01-15,37.77,38.165,37.705,38.095,8721564,SBUX\n2014-01-16,37.91,38.0975,37.565,37.645,9110924,SBUX\n2014-01-17,37.5,37.73,37.335,37.45,13007820,SBUX\n2014-01-21,37.525,37.535,36.63,36.825,18777272,SBUX\n2014-01-22,36.975,37.1,36.77,36.8,13989730,SBUX\n2014-01-23,36.74,36.865,35.84,36.695,30451212,SBUX\n2014-01-24,37.365,38.08,37.055,37.49,33218428,SBUX\n2014-01-27,37.615,37.615,37.01,37.105,18212030,SBUX\n2014-01-28,37.285,37.3975,36.835,36.945,11103896,SBUX\n2014-01-29,36.75,36.83,35.73,35.78,16002076,SBUX\n2014-01-30,36.14,36.215,35.655,35.955,15181598,SBUX\n2014-01-31,35.445,35.9705,35.435,35.56,12287056,SBUX\n2014-02-03,35.5,35.755,34.335,34.485,20353852,SBUX\n2014-02-04,35.0,35.6,34.685,35.325,21066236,SBUX\n2014-02-05,35.045,35.37,34.805,35.245,10968328,SBUX\n2014-02-06,35.33,36.4,35.28,36.18,13819714,SBUX\n2014-02-07,36.805,37.245,36.345,37.0175,14752770,SBUX\n2014-02-10,37.155,37.59,37.155,37.4,12948390,SBUX\n2014-02-11,37.435,37.595,36.895,37.25,16165852,SBUX\n2014-02-12,37.245,37.53,36.85,36.955,9973052,SBUX\n2014-02-13,36.78,37.345,36.695,37.345,8477256,SBUX\n2014-02-14,37.175,37.545,37.005,37.515,8266438,SBUX\n2014-02-18,37.495,37.5,36.9675,36.985,10967746,SBUX\n2014-02-19,36.92,37.14,36.615,36.66,9802100,SBUX\n2014-02-20,36.7,36.855,36.24,36.775,8553032,SBUX\n2014-02-21,36.89,36.93,36.26,36.28,11298258,SBUX\n2014-02-24,36.33,36.44,36.01,36.28,11953270,SBUX\n2014-02-25,36.25,36.33,35.275,35.275,18641246,SBUX\n2014-02-26,35.4,36.11,34.975,35.89,19156536,SBUX\n2014-02-27,35.81,36.12,35.715,36.095,11298448,SBUX\n2014-02-28,35.77,35.9663,35.25,35.48,22465506,SBUX\n2014-03-03,35.005,35.365,35.0,35.235,12248352,SBUX\n2014-03-04,35.715,35.98,35.525,35.83,12255104,SBUX\n2014-03-05,35.995,36.045,35.44,35.65,9783410,SBUX\n2014-03-06,35.915,36.6675,35.885,36.345,13781048,SBUX\n2014-03-07,36.495,36.55,36.105,36.535,8409372,SBUX\n2014-03-10,36.625,36.82,36.395,36.78,8648776,SBUX\n2014-03-11,36.995,37.704,36.925,37.515,18368750,SBUX\n2014-03-12,37.275,37.83,37.25,37.815,10450862,SBUX\n2014-03-13,37.92,38.21,37.015,37.215,11380782,SBUX\n2014-03-14,37.045,37.445,37.0118,37.135,8962602,SBUX\n2014-03-17,37.405,37.495,36.91,37.09,11019894,SBUX\n2014-03-18,37.175,37.42,37.025,37.3,5997520,SBUX\n2014-03-19,37.5,38.665,37.41,37.955,24983140,SBUX\n2014-03-20,38.145,38.565,37.8,38.4775,13851476,SBUX\n2014-03-21,39.105,39.32,38.31,38.355,18036904,SBUX\n2014-03-24,38.45,38.555,37.605,37.885,10018724,SBUX\n2014-03-25,38.22,38.255,37.145,37.305,10568158,SBUX\n2014-03-26,37.465,37.495,36.76,36.77,8371118,SBUX\n2014-03-27,36.535,36.98,36.345,36.7,12675658,SBUX\n2014-03-28,36.645,37.03,36.525,36.85,6582750,SBUX\n2014-03-31,36.95,37.2,36.585,36.69,7721754,SBUX\n2014-04-01,36.82,37.49,36.705,37.005,8741780,SBUX\n2014-04-02,37.175,37.185,36.62,36.835,8275518,SBUX\n2014-04-03,36.93,37.17,36.41,36.545,7165454,SBUX\n2014-04-04,36.775,36.945,35.66,35.775,11708546,SBUX\n2014-04-07,35.6,35.69,35.025,35.215,11089724,SBUX\n2014-04-08,35.32,35.8475,35.25,35.74,10679090,SBUX\n2014-04-09,35.815,36.34,35.6,36.24,9055586,SBUX\n2014-04-10,36.37,36.37,35.085,35.11,13375470,SBUX\n2014-04-11,34.895,35.065,34.34,34.365,16368844,SBUX\n2014-04-14,34.815,35.02,34.35,34.655,12176480,SBUX\n2014-04-15,34.98,35.05,33.965,34.445,15461760,SBUX\n2014-04-16,34.825,35.44,34.58,35.395,11278644,SBUX\n2014-04-17,35.285,35.695,35.07,35.075,9245966,SBUX\n2014-04-21,35.155,35.3,34.925,35.24,6098442,SBUX\n2014-04-22,35.265,35.6375,35.165,35.5745,8862690,SBUX\n2014-04-23,35.73,35.735,35.11,35.195,8881548,SBUX\n2014-04-24,35.76,36.02,35.055,35.545,15577016,SBUX\n2014-04-25,36.0,36.125,35.47,35.725,18122130,SBUX\n2014-04-28,35.945,36.01,34.9,35.465,10733556,SBUX\n2014-04-29,35.59,35.725,35.1325,35.32,9830164,SBUX\n2014-04-30,35.31,35.38,35.06,35.31,7283078,SBUX\n2014-05-01,35.375,35.6,35.16,35.56,7919194,SBUX\n2014-05-02,35.625,35.7625,35.285,35.3,8306490,SBUX\n2014-05-05,35.25,35.5275,35.09,35.46,5486598,SBUX\n2014-05-06,35.365,35.415,34.665,34.79,10926786,SBUX\n2014-05-07,34.89,34.9975,34.57,34.87,10961996,SBUX\n2014-05-08,34.8,35.2475,34.67,34.79,7531344,SBUX\n2014-05-09,34.85,35.155,34.7612,35.145,6623136,SBUX\n2014-05-12,35.32,35.62,35.2,35.575,7227204,SBUX\n2014-05-13,35.485,35.67,35.45,35.58,5802916,SBUX\n2014-05-14,35.59,35.6,35.015,35.085,8586022,SBUX\n2014-05-15,35.13,35.165,34.64,34.925,9124686,SBUX\n2014-05-16,35.0,35.52,34.905,35.47,9294826,SBUX\n2014-05-19,35.28,35.56,35.14,35.51,6757810,SBUX\n2014-05-20,35.38,35.448,34.9201,35.115,8699878,SBUX\n2014-05-21,35.355,35.375,35.035,35.2,6089286,SBUX\n2014-05-22,35.175,35.885,35.15,35.7,7359636,SBUX\n2014-05-23,36.145,36.255,35.915,35.99,7166310,SBUX\n2014-05-27,36.32,36.89,36.27,36.83,10100398,SBUX\n2014-05-28,36.65,36.785,36.4575,36.635,8212030,SBUX\n2014-05-29,36.76,36.78,36.325,36.555,6448878,SBUX\n2014-05-30,36.58,36.75,36.265,36.62,6879534,SBUX\n2014-06-02,36.61,37.0275,36.58,36.925,5926156,SBUX\n2014-06-03,36.86,37.175,36.79,37.09,6768354,SBUX\n2014-06-04,37.06,37.35,36.82,37.335,6495704,SBUX\n2014-06-05,37.18,37.57,37.1,37.36,5188766,SBUX\n2014-06-06,37.53,37.77,37.4,37.665,6204706,SBUX\n2014-06-09,37.69,37.7,37.325,37.59,5701018,SBUX\n2014-06-10,37.625,37.72,37.16,37.3,6640912,SBUX\n2014-06-11,37.07,37.435,36.925,37.4,7510866,SBUX\n2014-06-12,37.345,37.35,36.865,36.98,7193748,SBUX\n2014-06-13,36.935,37.4475,36.765,37.345,7728298,SBUX\n2014-06-16,37.24,37.58,37.23,37.545,6759676,SBUX\n2014-06-17,37.515,37.825,37.495,37.655,5778600,SBUX\n2014-06-18,37.6,37.8424,37.3,37.78,5786570,SBUX\n2014-06-19,38.275,38.75,38.23,38.615,12555078,SBUX\n2014-06-20,38.815,38.815,38.205,38.3,12446594,SBUX\n2014-06-23,38.345,38.4,38.0955,38.365,4780230,SBUX\n2014-06-24,38.45,38.875,38.27,38.715,10912302,SBUX\n2014-06-25,38.52,39.09,38.48,39.06,7813834,SBUX\n2014-06-26,39.005,39.165,38.735,39.03,6983266,SBUX\n2014-06-27,38.98,39.175,38.895,38.97,8669268,SBUX\n2014-06-30,39.05,39.095,38.54,38.69,9610248,SBUX\n2014-07-01,38.945,39.1,38.8,39.04,8073888,SBUX\n2014-07-02,39.19,39.2045,38.955,39.095,4794618,SBUX\n2014-07-03,39.195,39.7,39.07,39.53,6662616,SBUX\n2014-07-07,39.39,39.69,39.305,39.345,7443924,SBUX\n2014-07-08,39.32,39.45,39.125,39.28,7802080,SBUX\n2014-07-09,39.27,39.74,39.1801,39.725,7783444,SBUX\n2014-07-10,39.305,39.525,39.095,39.425,4720762,SBUX\n2014-07-11,39.48,39.495,39.07,39.3,4254768,SBUX\n2014-07-14,39.49,39.49,39.21,39.28,4562120,SBUX\n2014-07-15,39.325,39.575,39.23,39.445,8369728,SBUX\n2014-07-16,39.53,39.53,39.155,39.365,8734258,SBUX\n2014-07-17,39.06,39.23,38.56,38.62,8446350,SBUX\n2014-07-18,38.85,39.075,38.625,38.97,6744520,SBUX\n2014-07-21,38.93,38.965,38.585,38.805,5021858,SBUX\n2014-07-22,39.165,39.515,39.1,39.37,6457244,SBUX\n2014-07-23,39.255,39.6395,39.2,39.57,6450280,SBUX\n2014-07-24,39.795,40.32,39.575,40.225,16129286,SBUX\n2014-07-25,39.2,39.66,39.0,39.37,18984366,SBUX\n2014-07-28,39.4,39.5,39.0865,39.18,8012924,SBUX\n2014-07-29,39.245,39.625,39.205,39.325,7937248,SBUX\n2014-07-30,39.5,39.59,39.26,39.45,8911708,SBUX\n2014-07-31,39.26,39.35,38.76,38.84,8147584,SBUX\n2014-08-01,38.75,38.945,38.29,38.49,7798186,SBUX\n2014-08-04,38.63,38.88,38.515,38.765,6282674,SBUX\n2014-08-05,38.585,38.73,38.215,38.395,6696400,SBUX\n2014-08-06,38.25,38.72,38.135,38.565,5935218,SBUX\n2014-08-07,38.665,38.72,38.28,38.355,5427964,SBUX\n2014-08-08,38.315,38.835,38.255,38.81,5957988,SBUX\n2014-08-11,38.835,39.125,38.825,38.935,4522400,SBUX\n2014-08-12,38.935,39.0718,38.76,38.91,4723844,SBUX\n2014-08-13,39.05,39.05,38.565,38.62,6935144,SBUX\n2014-08-14,38.74,38.75,38.08,38.31,10892582,SBUX\n2014-08-15,38.61,38.64,38.185,38.455,8090044,SBUX\n2014-08-18,38.595,38.93,38.59,38.795,6845416,SBUX\n2014-08-19,38.9,39.1,38.8,39.06,4919030,SBUX\n2014-08-20,39.005,39.235,38.87,39.015,4668758,SBUX\n2014-08-21,38.905,39.085,38.695,38.735,4811992,SBUX\n2014-08-22,38.62,38.8,38.525,38.64,4552328,SBUX\n2014-08-25,38.82,39.11,38.755,38.985,5698322,SBUX\n2014-08-26,39.005,39.215,38.8775,38.895,5513856,SBUX\n2014-08-27,38.955,39.135,38.83,38.96,4904018,SBUX\n2014-08-28,38.895,39.015,38.755,38.905,3958032,SBUX\n2014-08-29,39.035,39.05,38.76,38.905,4657356,SBUX\n2014-09-02,38.85,38.99,38.58,38.74,6197468,SBUX\n2014-09-03,38.78,38.965,38.35,38.395,6796736,SBUX\n2014-09-04,38.4,38.6863,38.38,38.58,5749790,SBUX\n2014-09-05,38.505,38.985,38.405,38.975,8026608,SBUX\n2014-09-08,38.88,38.98,38.665,38.835,4492776,SBUX\n2014-09-09,38.725,38.875,38.49,38.56,5114572,SBUX\n2014-09-10,38.505,38.7,38.375,38.605,5918672,SBUX\n2014-09-11,38.49,38.5,38.04,38.06,9365624,SBUX\n2014-09-12,37.915,38.035,37.47,37.735,14619348,SBUX\n2014-09-15,37.63,37.67,37.195,37.46,10845708,SBUX\n2014-09-16,37.42,37.725,37.265,37.545,8041214,SBUX\n2014-09-17,37.67,37.9105,37.5525,37.67,7475730,SBUX\n2014-09-18,37.795,37.945,37.5,37.865,8028914,SBUX\n2014-09-19,37.985,38.165,37.885,38.035,12040726,SBUX\n2014-09-22,38.025,38.025,37.165,37.3,7859646,SBUX\n2014-09-23,37.145,37.44,36.89,36.9775,7739386,SBUX\n2014-09-24,37.265,37.715,37.175,37.66,8599714,SBUX\n2014-09-25,37.59,37.64,37.045,37.06,8010814,SBUX\n2014-09-26,37.0,37.615,36.97,37.585,7659794,SBUX\n2014-09-29,37.275,37.76,37.225,37.635,6376526,SBUX\n2014-09-30,37.78,37.925,37.51,37.73,7800904,SBUX\n2014-10-01,37.84,37.955,37.205,37.305,8119064,SBUX\n2014-10-02,37.205,37.5,36.89,37.225,8573520,SBUX\n2014-10-03,37.4,38.05,37.385,37.945,8229168,SBUX\n2014-10-06,38.01,38.115,37.55,37.5725,5221748,SBUX\n2014-10-07,37.45,37.525,37.01,37.025,6383432,SBUX\n2014-10-08,37.12,37.71,36.815,37.63,6716308,SBUX\n2014-10-09,37.52,37.91,37.18,37.24,9343828,SBUX\n2014-10-10,37.13,37.895,37.05,37.23,10495940,SBUX\n2014-10-13,37.105,37.22,36.005,36.095,12431414,SBUX\n2014-10-14,36.205,36.79,36.105,36.37,10995022,SBUX\n2014-10-15,36.015,36.465,35.635,36.19,13622008,SBUX\n2014-10-16,35.59,36.435,35.385,36.32,9738832,SBUX\n2014-10-17,36.675,36.955,36.44,36.77,9581060,SBUX\n2014-10-20,36.805,37.375,36.7025,37.35,7957028,SBUX\n2014-10-21,37.5,37.5925,37.07,37.18,12312930,SBUX\n2014-10-22,37.24,37.4925,37.08,37.3,6469670,SBUX\n2014-10-23,37.575,37.755,37.38,37.42,6837730,SBUX\n2014-10-24,37.45,37.955,37.175,37.905,7359034,SBUX\n2014-10-27,38.005,38.19,37.91,37.985,5796276,SBUX\n2014-10-28,38.17,38.535,37.94,38.525,8150988,SBUX\n2014-10-29,38.55,38.8325,38.11,38.27,8794902,SBUX\n2014-10-30,38.09,38.725,38.055,38.66,13388970,SBUX\n2014-10-31,37.625,38.37,37.46,37.78,35889908,SBUX\n2014-11-03,37.985,38.135,37.735,38.05,10606936,SBUX\n2014-11-04,37.805,38.47,37.75,38.355,10420764,SBUX\n2014-11-05,38.505,38.695,38.295,38.33,7403396,SBUX\n2014-11-06,38.485,38.775,38.33,38.725,6167276,SBUX\n2014-11-07,38.82,39.045,38.605,38.895,8248994,SBUX\n2014-11-10,38.82,38.95,38.585,38.825,6555370,SBUX\n2014-11-11,38.915,39.1,38.625,38.865,5201438,SBUX\n2014-11-12,38.705,39.005,38.635,38.925,5312194,SBUX\n2014-11-13,39.095,39.24,38.7059,38.945,6842248,SBUX\n2014-11-14,39.04,39.15,38.865,39.06,6039282,SBUX\n2014-11-17,38.97,39.235,38.8225,38.915,5899020,SBUX\n2014-11-18,38.815,39.0,38.6575,38.785,5755816,SBUX\n2014-11-19,38.72,39.04,38.7,38.91,5231186,SBUX\n2014-11-20,38.83,39.32,38.825,39.1,6287096,SBUX\n2014-11-21,39.565,39.96,39.385,39.88,14027718,SBUX\n2014-11-24,39.95,40.41,39.855,40.26,9426200,SBUX\n2014-11-25,40.28,40.46,40.025,40.105,8118386,SBUX\n2014-11-26,40.06,40.155,39.7,39.85,6196752,SBUX\n2014-11-28,40.215,40.82,40.205,40.605,6766674,SBUX\n2014-12-01,40.5,40.75,40.31,40.425,8627478,SBUX\n2014-12-02,40.345,40.425,40.045,40.185,7656398,SBUX\n2014-12-03,40.21,40.375,40.035,40.235,11201396,SBUX\n2014-12-04,40.3,41.15,40.195,40.655,14070910,SBUX\n2014-12-05,41.25,41.96,41.215,41.785,14788168,SBUX\n2014-12-08,42.1,42.1,41.55,41.9,11770652,SBUX\n2014-12-09,41.535,41.645,41.1,41.515,8725620,SBUX\n2014-12-10,41.45,42.02,41.275,41.33,10096320,SBUX\n2014-12-11,41.45,42.01,41.3275,41.56,10058296,SBUX\n2014-12-12,41.37,41.985,41.285,41.625,9164230,SBUX\n2014-12-15,41.135,41.25,40.345,40.445,16286986,SBUX\n2014-12-16,40.245,40.545,39.555,39.565,13981874,SBUX\n2014-12-17,39.565,40.295,39.22,40.2175,10161624,SBUX\n2014-12-18,40.775,40.82,39.63,40.015,17582942,SBUX\n2014-12-19,40.1,40.145,39.705,39.72,17444212,SBUX\n2014-12-22,39.925,40.275,39.885,40.27,7583856,SBUX\n2014-12-23,40.75,41.025,40.565,40.715,8147722,SBUX\n2014-12-24,40.735,40.93,40.59,40.635,2602398,SBUX\n2014-12-26,40.715,41.2,40.69,40.915,4550364,SBUX\n2014-12-29,40.83,41.275,40.75,41.19,4796662,SBUX\n2014-12-30,41.035,41.44,40.895,40.895,5253454,SBUX\n2014-12-31,41.095,41.665,41.0,41.025,7628772,SBUX\n2015-01-02,41.065,41.4875,40.445,40.72,6906098,SBUX\n2015-01-05,40.07,40.335,39.745,39.94,11623796,SBUX\n2015-01-06,40.17,40.195,39.28,39.615,7664340,SBUX\n2015-01-07,39.875,40.615,39.7,40.59,9732554,SBUX\n2015-01-08,41.165,41.65,41.01,41.245,13170548,SBUX\n2015-01-09,40.495,40.755,39.56,39.895,27556706,SBUX\n2015-01-12,40.145,40.415,39.91,40.115,10021486,SBUX\n2015-01-13,40.74,41.07,40.065,40.435,11040702,SBUX\n2015-01-14,40.025,40.39,39.805,40.21,9295084,SBUX\n2015-01-15,40.3,40.45,39.595,39.79,8126602,SBUX\n2015-01-16,39.63,40.39,39.5,40.305,9015502,SBUX\n2015-01-20,40.4,40.735,40.165,40.6125,10738304,SBUX\n2015-01-21,40.525,40.815,40.265,40.645,10844182,SBUX\n2015-01-22,40.68,41.42,40.445,41.37,23913056,SBUX\n2015-01-23,43.25,44.35,43.22,44.11,38107194,SBUX\n2015-01-26,44.045,44.25,43.705,44.06,14098574,SBUX\n2015-01-27,43.89,44.605,43.725,44.17,10995808,SBUX\n2015-01-28,44.35,44.795,43.745,43.7825,11963202,SBUX\n2015-01-29,44.005,44.65,43.785,44.525,12475860,SBUX\n2015-01-30,44.29,44.47,43.695,43.765,10070456,SBUX\n2015-02-02,43.84,44.045,42.93,43.995,13638832,SBUX\n2015-02-03,43.99,44.245,43.465,44.245,9252426,SBUX\n2015-02-04,44.0,44.715,43.995,44.35,11496698,SBUX\n2015-02-05,44.355,44.885,44.355,44.82,7598672,SBUX\n2015-02-06,44.75,44.8375,44.34,44.5,7835332,SBUX\n2015-02-09,44.255,44.6035,44.07,44.41,6911614,SBUX\n2015-02-10,44.685,45.69,44.665,45.59,12469500,SBUX\n2015-02-11,45.58,45.895,45.3525,45.395,6466910,SBUX\n2015-02-12,45.545,45.97,45.045,45.9125,7197558,SBUX\n2015-02-13,45.995,45.995,45.4525,45.79,6109522,SBUX\n2015-02-17,45.885,46.12,45.63,46.015,6386900,SBUX\n2015-02-18,46.195,46.665,46.0,46.5,6541986,SBUX\n2015-02-19,46.575,46.89,46.505,46.585,6109176,SBUX\n2015-02-20,46.73,46.835,46.485,46.755,6462662,SBUX\n2015-02-23,46.925,46.964,46.54,46.79,5854572,SBUX\n2015-02-24,46.63,46.995,46.58,46.725,6337888,SBUX\n2015-02-25,46.65,47.415,46.625,47.13,8120660,SBUX\n2015-02-26,47.15,47.4113,47.04,47.275,6816352,SBUX\n2015-02-27,47.395,47.4,46.635,46.7425,8658404,SBUX\n2015-03-02,46.665,47.275,46.665,47.1125,7947018,SBUX\n2015-03-03,47.05,47.105,46.6,47.0,7578374,SBUX\n2015-03-04,46.905,47.095,46.005,46.53,7774534,SBUX\n2015-03-05,46.64,46.98,46.5303,46.815,5848750,SBUX\n2015-03-06,46.65,46.8475,45.94,46.1075,6814414,SBUX\n2015-03-09,46.195,46.565,46.045,46.52,5984880,SBUX\n2015-03-10,46.08,46.4549,45.885,46.09,6076984,SBUX\n2015-03-11,46.06,46.49,45.61,45.71,8185894,SBUX\n2015-03-12,45.985,46.765,45.92,46.69,7295344,SBUX\n2015-03-13,46.34,47.185,46.34,46.645,5835252,SBUX\n2015-03-16,47.0,47.31,46.87,47.0225,6772606,SBUX\n2015-03-17,46.88,47.24,46.8,47.1925,5508796,SBUX\n2015-03-18,47.0,48.285,46.675,47.92,15429928,SBUX\n2015-03-19,48.38,49.6,48.19,48.88,22020618,SBUX\n2015-03-20,49.245,49.45,48.505,48.73,17360612,SBUX\n2015-03-23,48.94,48.95,48.355,48.685,7985986,SBUX\n2015-03-24,48.555,49.165,48.38,48.9575,7718488,SBUX\n2015-03-25,49.07,49.24,47.885,47.885,9907170,SBUX\n2015-03-26,47.675,47.875,46.83,47.54,10344304,SBUX\n2015-03-27,47.5,47.975,47.375,47.535,7993350,SBUX\n2015-03-30,48.02,48.25,47.75,47.99,6830270,SBUX\n2015-03-31,47.835,48.1,47.345,47.35,8717754,SBUX\n2015-04-01,47.14,47.25,46.28,46.51,14125350,SBUX\n2015-04-02,46.71,47.3175,46.61,47.195,8863018,SBUX\n2015-04-06,46.925,47.4,46.725,47.26,6058894,SBUX\n2015-04-07,47.205,47.48,46.98,47.035,5354670,SBUX\n2015-04-08,46.92,47.64,46.92,47.615,6827888,SBUX\n2015-04-09,47.65,47.99,47.25,47.96,7109621,SBUX\n2015-04-10,48.6,48.6,47.88,48.17,6643106,SBUX\n2015-04-13,48.56,48.89,48.38,48.5,8171030,SBUX\n2015-04-14,48.52,48.71,47.97,48.3,5952424,SBUX\n2015-04-15,48.81,48.81,48.13,48.14,5162169,SBUX\n2015-04-16,48.23,48.48,48.16,48.245,5312499,SBUX\n2015-04-17,47.9,48.0,47.39,47.62,7539865,SBUX\n2015-04-20,47.9,48.12,47.7,47.97,4868425,SBUX\n2015-04-21,48.35,48.4799,48.02,48.37,6213360,SBUX\n2015-04-22,48.5,48.6,47.98,48.335,7248119,SBUX\n2015-04-23,48.55,49.7,48.28,49.43,15866051,SBUX\n2015-04-24,51.32,52.09,50.62,51.84,22284881,SBUX\n2015-04-27,51.81,51.94,50.76,50.87,11222608,SBUX\n2015-04-28,50.6,50.8,49.9801,50.61,8882901,SBUX\n2015-04-29,50.42,50.95,50.2,50.65,7161992,SBUX\n2015-04-30,50.63,50.68,49.43,49.58,8492048,SBUX\n2015-05-01,49.95,50.42,49.68,50.29,5916509,SBUX\n2015-05-04,50.3,50.93,50.27,50.445,7493420,SBUX\n2015-05-05,49.94,50.05,49.36,49.405,10691207,SBUX\n2015-05-06,49.68,49.7,48.57,48.93,8033489,SBUX\n2015-05-07,48.74,49.55,48.72,49.35,5681417,SBUX\n2015-05-08,49.99,50.4265,49.46,49.78,6039840,SBUX\n2015-05-11,49.71,50.22,49.38,49.5,5047180,SBUX\n2015-05-12,49.15,49.99,49.0,49.71,5868552,SBUX\n2015-05-13,49.85,50.15,49.425,49.59,4927094,SBUX\n2015-05-14,49.98,50.59,49.67,50.555,7339742,SBUX\n2015-05-15,50.79,50.85,50.39,50.8,6016694,SBUX\n2015-05-18,50.65,51.29,50.56,51.18,8999761,SBUX\n2015-05-19,51.48,51.715,50.96,51.42,6976052,SBUX\n2015-05-20,51.31,51.44,50.43,51.03,5644662,SBUX\n2015-05-21,50.94,51.45,50.78,51.33,5084042,SBUX\n2015-05-22,51.33,51.65,51.21,51.48,5857672,SBUX\n2015-05-26,51.38,51.78,50.66,50.84,7369923,SBUX\n2015-05-27,51.04,51.7,50.91,51.59,6213573,SBUX\n2015-05-28,51.84,51.94,51.445,51.81,5874382,SBUX\n2015-05-29,51.95,52.23,51.45,51.96,9399112,SBUX\n2015-06-01,51.96,52.46,51.67,52.22,7075082,SBUX\n2015-06-02,51.98,52.3,51.66,51.73,7877799,SBUX\n2015-06-03,52.0,52.27,51.67,52.12,5522702,SBUX\n2015-06-04,51.87,52.18,51.57,51.72,6230805,SBUX\n2015-06-05,51.57,52.44,51.27,52.19,7123248,SBUX\n2015-06-08,52.0,52.23,51.49,51.53,6320181,SBUX\n2015-06-09,51.35,51.7,51.1,51.54,5034038,SBUX\n2015-06-10,51.8,52.86,51.66,52.69,8003611,SBUX\n2015-06-11,52.81,53.0,52.44,52.49,6030167,SBUX\n2015-06-12,52.41,52.74,52.16,52.63,5236747,SBUX\n2015-06-15,52.23,52.46,52.01,52.27,5554964,SBUX\n2015-06-16,52.27,53.14,52.2,52.965,6106529,SBUX\n2015-06-17,53.09,53.47,52.72,53.24,6735294,SBUX\n2015-06-18,53.5,54.28,53.4,54.11,10712142,SBUX\n2015-06-19,54.08,54.44,53.84,53.93,10609714,SBUX\n2015-06-22,54.325,54.43,53.88,53.9,7100665,SBUX\n2015-06-23,54.04,54.18,53.72,54.115,5679984,SBUX\n2015-06-24,53.75,53.97,53.5194,53.71,5524969,SBUX\n2015-06-25,54.09,54.45,54.0,54.07,5389863,SBUX\n2015-06-26,54.46,54.75,54.3,54.62,6637183,SBUX\n2015-06-29,53.87,54.39,53.5144,53.55,6534077,SBUX\n2015-06-30,54.15,54.3,53.14,53.615,9793969,SBUX\n2015-07-01,53.86,54.21,53.6,53.89,6107698,SBUX\n2015-07-02,54.03,54.7,53.9501,54.24,5684667,SBUX\n2015-07-06,53.64,54.405,53.63,54.305,5396439,SBUX\n2015-07-07,54.29,54.54,53.36,54.375,9462256,SBUX\n2015-07-08,53.86,54.02,53.3101,53.39,8139303,SBUX\n2015-07-09,54.07,54.5,53.88,54.05,7681013,SBUX\n2015-07-10,54.54,54.732,54.18,54.57,8087800,SBUX\n2015-07-13,55.0,55.89,54.9,55.7,7477170,SBUX\n2015-07-14,55.96,56.06,55.52,55.75,7028025,SBUX\n2015-07-15,55.92,55.95,55.285,55.34,8212573,SBUX\n2015-07-16,56.06,56.16,55.66,55.74,7305520,SBUX\n2015-07-17,55.9,55.9,55.37,55.69,8715431,SBUX\n2015-07-20,55.73,56.74,55.7,56.21,8029646,SBUX\n2015-07-21,56.38,56.47,55.78,56.2,6717452,SBUX\n2015-07-22,56.43,56.87,56.25,56.69,6876059,SBUX\n2015-07-23,56.98,57.0,56.16,56.56,12439229,SBUX\n2015-07-24,59.12,59.31,57.15,57.29,14559687,SBUX\n2015-07-27,57.21,57.47,56.85,56.98,8993523,SBUX\n2015-07-28,57.39,57.4,56.56,57.14,8689215,SBUX\n2015-07-29,57.38,57.8,57.14,57.51,8470512,SBUX\n2015-07-30,57.3,58.15,57.01,58.06,7337948,SBUX\n2015-07-31,58.44,58.44,57.73,57.93,6519528,SBUX\n2015-08-03,58.62,58.96,58.0386,58.19,7664002,SBUX\n2015-08-04,58.25,58.72,58.03,58.7,9113083,SBUX\n2015-08-05,59.15,59.3198,58.83,59.01,7349063,SBUX\n2015-08-06,59.13,59.2,57.09,57.23,11064470,SBUX\n2015-08-07,57.33,57.36,56.51,57.2,7781995,SBUX\n2015-08-10,57.29,57.626000000000005,55.75,56.27,12029150,SBUX\n2015-08-11,55.79,56.435,55.24,56.35,8062553,SBUX\n2015-08-12,55.69,56.4,54.95,56.38,10075571,SBUX\n2015-08-13,56.52,57.25,56.51,56.85,6731474,SBUX\n2015-08-14,56.95,57.12,56.66,57.1,4803903,SBUX\n2015-08-17,57.0,57.76,56.73,57.74,5768362,SBUX\n2015-08-18,57.96,58.06,57.66,57.83,5575441,SBUX\n2015-08-19,57.58,58.08,57.115,57.59,6044193,SBUX\n2015-08-20,57.0,57.15,55.77,55.81,7470885,SBUX\n2015-08-21,54.72,54.86,52.601000000000006,52.84,20211503,SBUX\n2015-08-24,48.05,52.67,42.05,50.34,27158813,SBUX\n2015-08-25,52.96,53.61,51.05,51.09,19659002,SBUX\n2015-08-26,52.99,54.15,51.27,53.96,15517591,SBUX\n2015-08-27,54.74,56.21,54.41,55.95,15987923,SBUX\n2015-08-28,55.84,56.31,55.2,55.63,7584826,SBUX\n2015-08-31,55.23,55.47,54.5,54.71,7971204,SBUX\n2015-09-01,52.82,54.36,52.74,53.5,13424932,SBUX\n2015-09-02,54.47,55.29,53.751000000000005,55.26,9891071,SBUX\n2015-09-03,55.72,55.76,54.475,54.69,7968868,SBUX\n2015-09-04,53.87,54.57,53.84,54.28,6994267,SBUX\n2015-09-08,55.31,55.45,54.53,55.21,8051710,SBUX\n2015-09-09,55.9,56.0,54.57,54.69,8406656,SBUX\n2015-09-10,54.34,55.69,54.33,55.37,8924778,SBUX\n2015-09-11,55.19,56.54,55.03,56.53,8363110,SBUX\n2015-09-14,56.54,56.91,56.05,56.29,5464463,SBUX\n2015-09-15,56.43,57.21,56.115,56.91,6741341,SBUX\n2015-09-16,56.83,57.35,56.21,57.26,6593362,SBUX\n2015-09-17,57.32,58.1,57.04,57.28,7525349,SBUX\n2015-09-18,56.49,57.63,56.28,56.84,16268035,SBUX\n2015-09-21,57.2,57.84,56.96,57.54,6220131,SBUX\n2015-09-22,56.85,57.25,56.7,57.12,8585093,SBUX\n2015-09-23,57.16,57.93,57.05,57.79,6829205,SBUX\n2015-09-24,57.38,58.54,57.17,58.37,10027330,SBUX\n2015-09-25,58.92,58.96,57.74,57.99,10627026,SBUX\n2015-09-28,58.01,58.43,55.6201,55.77,11548114,SBUX\n2015-09-29,55.85,56.3,54.81,55.72,9392065,SBUX\n2015-09-30,56.4,56.9,55.61,56.84,9799610,SBUX\n2015-10-01,56.99,57.5,55.89,57.48,8497124,SBUX\n2015-10-02,56.99,58.09,56.5406,58.08,9036765,SBUX\n2015-10-05,58.49,59.18,58.07,59.04,8198998,SBUX\n2015-10-06,58.82,59.14,58.22,58.69,5642949,SBUX\n2015-10-07,58.62,58.83,57.9,58.78,8138313,SBUX\n2015-10-08,58.78,59.71,58.39,59.46,6834836,SBUX\n2015-10-09,59.47,60.11,59.3,60.07,7969884,SBUX\n2015-10-12,60.35,60.89,60.04,60.54,6430301,SBUX\n2015-10-13,60.34,60.745,60.0161,60.16,6262774,SBUX\n2015-10-14,60.0,60.17,58.43,58.82,8365604,SBUX\n2015-10-15,58.95,59.83,58.08,59.69,9745919,SBUX\n2015-10-16,59.96,60.29,59.455,59.93,12860812,SBUX\n2015-10-19,60.13,61.29,59.8745,60.97,8117213,SBUX\n2015-10-20,61.22,61.36,60.56,60.88,6089097,SBUX\n2015-10-21,61.07,61.12,60.16,60.53,6063923,SBUX\n2015-10-22,60.96,61.7099,60.17,61.49,9182031,SBUX\n2015-10-23,62.11,62.8,61.6201,62.61,8205994,SBUX\n2015-10-26,62.98,63.84,62.97,63.43,9751716,SBUX\n2015-10-27,63.37,63.41,62.19,62.71,8973243,SBUX\n2015-10-28,63.11,63.52,62.42,63.51,9627260,SBUX\n2015-10-29,63.42,63.5,61.713,62.5,14839093,SBUX\n2015-10-30,63.69,64.0,62.26,62.57,16822302,SBUX\n2015-11-02,63.01,63.1,62.12,62.24,8547237,SBUX\n2015-11-03,62.0,62.975,61.65,62.8,8847718,SBUX\n2015-11-04,63.0355,63.0355,61.34,61.96,9085091,SBUX\n2015-11-05,62.17,62.46,62.01,62.28,6144979,SBUX\n2015-11-06,62.05,62.24,61.61,61.97,6616305,SBUX\n2015-11-09,61.75,61.97,60.86,61.34,6838326,SBUX\n2015-11-10,61.54,62.32,61.21,62.18,6689040,SBUX\n2015-11-11,62.55,62.57,61.81,61.87,4437315,SBUX\n2015-11-12,61.34,61.65,60.75,61.07,6793779,SBUX\n2015-11-13,60.89,61.345,59.61,59.74,8821593,SBUX\n2015-11-16,59.5,60.69,59.5,60.68,8096603,SBUX\n2015-11-17,60.95,61.56,60.435,60.55,6695251,SBUX\n2015-11-18,60.66,61.865,60.33,61.8,7215255,SBUX\n2015-11-19,61.8,61.93,61.39,61.46,5154366,SBUX\n2015-11-20,61.96,62.15,61.58,61.99,8302476,SBUX\n2015-11-23,62.14,63.19,62.1,62.64,8493485,SBUX\n2015-11-24,62.06,62.37,61.2199,61.96,7908223,SBUX\n2015-11-25,62.05,62.5,62.0,62.19,4549913,SBUX\n2015-11-27,62.19,62.38,61.93,62.18,2447902,SBUX\n2015-11-30,62.1,62.29,61.201,61.39,9863771,SBUX\n2015-12-01,61.08,61.68,60.51,61.37,10910838,SBUX\n2015-12-02,61.63,61.71,61.115,61.22,6587454,SBUX\n2015-12-03,61.37,61.4468,59.15,59.55,12056103,SBUX\n2015-12-04,59.86,61.87,59.6,61.75,9100588,SBUX\n2015-12-07,61.75,61.95,61.44,61.89,5967809,SBUX\n2015-12-08,61.69,62.43,61.52,62.16,6664947,SBUX\n2015-12-09,61.71,62.538999999999994,60.82,61.18,8541573,SBUX\n2015-12-10,61.13,62.14,61.01,61.87,6623896,SBUX\n2015-12-11,60.86,61.19,59.6,59.82,11489255,SBUX\n2015-12-14,60.04,60.14,58.61,59.92,13453719,SBUX\n2015-12-15,60.55,60.68,59.97,59.98,7842073,SBUX\n2015-12-16,60.32,60.5,59.51,60.35,9281835,SBUX\n2015-12-17,60.66,60.83,59.47,59.515,9079430,SBUX\n2015-12-18,59.2,59.5,58.27,58.62,18099462,SBUX\n2015-12-21,58.89,59.615,58.66,59.54,7187470,SBUX\n2015-12-22,59.94,60.07,59.275,59.99,6501424,SBUX\n2015-12-23,60.26,60.37,59.96,60.34,4510229,SBUX\n2015-12-24,60.37,60.51,60.17,60.32,2215418,SBUX\n2015-12-28,60.02,60.33,59.58,60.19,4437236,SBUX\n2015-12-29,60.46,61.32,60.35,61.13,5477335,SBUX\n2015-12-30,61.22,61.4,60.75,60.82,3973912,SBUX\n2015-12-31,60.65,60.81,60.02,60.03,4960875,SBUX\n2016-01-04,58.77,58.83,57.6,58.26,13521544,SBUX\n2016-01-05,58.79,58.79,57.98,58.65,9617778,SBUX\n2016-01-06,57.7,58.53,57.64,58.13,8266322,SBUX\n2016-01-07,56.88,57.91,56.16,56.69,11140877,SBUX\n2016-01-08,57.41,57.73,56.53,56.63,10427021,SBUX\n2016-01-11,57.0,58.12,56.78,57.82,10757313,SBUX\n2016-01-12,58.39,59.53,58.18,59.46,12375826,SBUX\n2016-01-13,59.8,60.0,57.8001,57.87,11303603,SBUX\n2016-01-14,57.51,59.43,56.92100000000001,58.98,11444106,SBUX\n2016-01-15,57.07,58.39,56.75,58.0,15246127,SBUX\n2016-01-19,58.67,59.39,58.12,58.55,12288950,SBUX\n2016-01-20,57.57,57.96,54.94,56.92,22786359,SBUX\n2016-01-21,57.84,59.38,57.67,59.03,20888519,SBUX\n2016-01-22,57.55,59.4,57.41,59.17,32820193,SBUX\n2016-01-25,59.36,59.38,57.61,57.71,13554262,SBUX\n2016-01-26,57.92,58.865,57.8,58.61,8898100,SBUX\n2016-01-27,58.87,58.96,57.255,57.63,12491252,SBUX\n2016-01-28,58.29,59.42,58.0,59.285,11832368,SBUX\n2016-01-29,59.78,60.88,59.64,60.77,13224438,SBUX\n2016-02-01,60.66,61.785,60.27,61.4,9529094,SBUX\n2016-02-02,60.66,60.9,60.18,60.695,9407352,SBUX\n2016-02-03,60.88,61.13,58.5,59.53,12254460,SBUX\n2016-02-04,59.41,59.4487,57.99,58.29,13944926,SBUX\n2016-02-05,58.1,58.2,54.25,54.49,24529008,SBUX\n2016-02-08,53.09,54.47,52.63,54.14,21457492,SBUX\n2016-02-09,53.19,55.2886,53.17,54.42,11605059,SBUX\n2016-02-10,55.28,56.35,55.01,55.14,11663942,SBUX\n2016-02-11,53.89,55.39,53.55,54.92,12106062,SBUX\n2016-02-12,55.56,56.04,55.04,55.86,8680205,SBUX\n2016-02-16,56.79,56.85,55.98,56.41,11594766,SBUX\n2016-02-17,56.7,57.66,56.16,57.63,11955188,SBUX\n2016-02-18,57.57,57.57,56.67,56.96,8493953,SBUX\n2016-02-19,56.92,57.86,56.52,57.67,9033620,SBUX\n2016-02-22,58.63,58.95,58.17,58.87,8390689,SBUX\n2016-02-23,58.45,58.9,58.0,58.46,7064095,SBUX\n2016-02-24,57.21,58.35,56.28,58.11,10780882,SBUX\n2016-02-25,58.46,58.75,58.0,58.75,6262127,SBUX\n2016-02-26,59.0,59.21,57.92,58.34,7473374,SBUX\n2016-02-29,58.25,59.15,58.1,58.21,7645081,SBUX\n2016-03-01,58.77,60.2,58.5,60.04,9183562,SBUX\n2016-03-02,59.83,60.0,58.83,59.56,8856392,SBUX\n2016-03-03,59.12,59.2,58.2,59.04,8262455,SBUX\n2016-03-04,59.14,59.19,58.23,58.7,8344773,SBUX\n2016-03-07,58.44,58.67,57.31,58.0,9204624,SBUX\n2016-03-08,57.58,58.23,57.26,57.6,8127426,SBUX\n2016-03-09,57.78,57.97,56.79,57.07,9734589,SBUX\n2016-03-10,57.51,57.86,56.92,57.52,7023785,SBUX\n2016-03-11,58.1,58.1,56.57,57.59,15497560,SBUX\n2016-03-14,57.58,58.78,57.5,58.65,9053250,SBUX\n2016-03-15,58.32,59.1566,58.17,59.08,7428469,SBUX\n2016-03-16,58.65,59.82,58.65,59.67,8069427,SBUX\n2016-03-17,59.47,59.98,59.37,59.55,7734658,SBUX\n2016-03-18,59.91,60.45,59.4295,59.7,14313578,SBUX\n2016-03-21,59.56,59.8609,59.015,59.1,6487185,SBUX\n2016-03-22,59.0,59.55,58.57,59.38,8246837,SBUX\n2016-03-23,59.14,59.395,58.69,58.83,5794495,SBUX\n2016-03-24,58.7,58.79,58.28,58.36,5948307,SBUX\n2016-03-28,58.56,59.47,58.4,58.96,5791603,SBUX\n2016-03-29,58.82,59.735,58.82,59.55,6031947,SBUX\n2016-03-30,60.0,60.26,59.51,60.01,5723499,SBUX\n2016-03-31,59.77,60.21,59.68,59.7,5622834,SBUX\n2016-04-01,59.61,61.17,59.41,61.02,9401126,SBUX\n2016-04-04,61.1,61.1839,60.08,60.25,5799864,SBUX\n2016-04-05,59.88,60.23,59.44,60.04,4994792,SBUX\n2016-04-06,60.02,60.91,59.91,60.83,5667253,SBUX\n2016-04-07,60.59,61.54,60.54,61.17,8239174,SBUX\n2016-04-08,61.5,61.64,60.7,61.04,5064894,SBUX\n2016-04-11,61.22,61.5,60.78,60.9,6103358,SBUX\n2016-04-12,58.95,59.68,58.37,59.5,17565750,SBUX\n2016-04-13,60.32,61.08,59.75,60.21,9898527,SBUX\n2016-04-14,60.26,60.4,59.91,60.13,5157368,SBUX\n2016-04-15,60.24,60.6246,60.01,60.51,5965310,SBUX\n2016-04-18,60.69,61.07,60.355,60.89,7228573,SBUX\n2016-04-19,61.16,61.25,60.48,60.9,7283570,SBUX\n2016-04-20,61.04,61.43,60.85,60.9,5558770,SBUX\n2016-04-21,60.9,61.1,60.48,60.64,12799083,SBUX\n2016-04-22,59.01,59.1,57.03,57.68,29836693,SBUX\n2016-04-25,57.62,57.96,57.58,57.77,8428038,SBUX\n2016-04-26,58.05,58.67,57.56,57.72,8839067,SBUX\n2016-04-27,57.51,57.65,56.62,56.9,12390767,SBUX\n2016-04-28,56.59,57.36,56.32,56.42,9196560,SBUX\n2016-04-29,56.02,56.43,55.29,56.23,12133364,SBUX\n2016-05-02,56.29,57.37,56.11,57.36,8616189,SBUX\n2016-05-03,56.7,57.059,56.14,56.25,7854260,SBUX\n2016-05-04,55.98,56.65,55.8,56.39,6508507,SBUX\n2016-05-05,56.37,56.77,56.01,56.25,6215367,SBUX\n2016-05-06,55.96,56.32,55.38,56.31,6378690,SBUX\n2016-05-09,56.32,56.93,56.23,56.64,5976229,SBUX\n2016-05-10,56.85,57.6,56.71,57.49,7931185,SBUX\n2016-05-11,57.13,57.39,56.09,56.23,8118920,SBUX\n2016-05-12,56.57,56.79,55.82,56.3,6870281,SBUX\n2016-05-13,56.43,56.6,55.73,55.82,5466041,SBUX\n2016-05-16,55.7,55.77,55.2,55.53,9536192,SBUX\n2016-05-17,55.38,55.64,54.51,54.88,10530018,SBUX\n2016-05-18,54.76,55.17,54.38,54.8,7468496,SBUX\n2016-05-19,54.43,54.615,54.19,54.55,7582848,SBUX\n2016-05-20,54.88,55.3736,54.58,54.62,8430783,SBUX\n2016-05-23,54.62,54.8171,54.291000000000004,54.6,7352054,SBUX\n2016-05-24,54.74,55.62,54.68,55.44,7748697,SBUX\n2016-05-25,55.2,55.46,54.95,55.15,8126058,SBUX\n2016-05-26,55.55,55.8699,54.95,55.29,9451708,SBUX\n2016-05-27,55.36,55.55,55.1,55.15,6631120,SBUX\n2016-05-31,55.5,55.5,54.7,54.89,12043976,SBUX\n2016-06-01,54.76,55.49,54.72,54.82,8761577,SBUX\n2016-06-02,54.9,55.0,54.455,54.62,8307488,SBUX\n2016-06-03,54.71,55.08,54.4,54.61,6649224,SBUX\n2016-06-06,54.72,55.87,54.69,55.59,9900917,SBUX\n2016-06-07,55.65,56.1,55.28,55.3,7173072,SBUX\n2016-06-08,55.5,55.5,54.9,55.22,7623851,SBUX\n2016-06-09,55.15,55.61,55.06,55.58,5927854,SBUX\n2016-06-10,54.92,55.2,54.5,54.865,8118651,SBUX\n2016-06-13,54.79,55.6,54.76,55.04,7928722,SBUX\n2016-06-14,55.05,55.58,55.0101,55.57,8036517,SBUX\n2016-06-15,55.64,56.09,55.27,55.35,7447317,SBUX\n2016-06-16,54.9,55.59,54.41,55.53,7968033,SBUX\n2016-06-17,55.61,55.62,55.04,55.31,9503017,SBUX\n2016-06-20,55.77,56.28,55.38,55.38,7286681,SBUX\n2016-06-21,55.52,56.03,55.45,55.81,7445109,SBUX\n2016-06-22,55.88,55.98,55.49,55.61,7215411,SBUX\n2016-06-23,55.98,56.195,55.9,56.13,5569431,SBUX\n2016-06-24,54.05,55.57,54.01,54.68,14654672,SBUX\n2016-06-27,54.2,54.48,53.41,53.69,11650798,SBUX\n2016-06-28,54.1,54.9,53.95,54.85,8416950,SBUX\n2016-06-29,55.42,56.945,55.36,56.74,11103787,SBUX\n2016-06-30,56.81,57.19,56.516000000000005,57.12,10215193,SBUX\n2016-07-01,57.04,57.36,56.845,56.99,8330308,SBUX\n2016-07-05,56.81,56.96,56.55,56.77,7274208,SBUX\n2016-07-06,56.52,57.11,56.32,56.75,8474188,SBUX\n2016-07-07,56.66,57.0,56.47,56.91,6813347,SBUX\n2016-07-08,56.92,57.0,56.36,56.51,12655500,SBUX\n2016-07-11,56.8,56.92,56.06,56.32,10394366,SBUX\n2016-07-12,56.65,57.6,56.505,57.48,10998486,SBUX\n2016-07-13,56.8,57.26,56.35,56.48,12183638,SBUX\n2016-07-14,57.0,57.68,56.97,57.59,11353599,SBUX\n2016-07-15,57.69,57.74,57.125,57.41,8494000,SBUX\n2016-07-18,57.59,57.59,56.85,56.92,7614635,SBUX\n2016-07-19,56.85,57.135,56.545,56.76,8719772,SBUX\n2016-07-20,57.0,57.66,56.705,57.54,9446396,SBUX\n2016-07-21,57.62,57.67,57.0375,57.6,16151699,SBUX\n2016-07-22,57.6,58.24,57.2,57.9,23899275,SBUX\n2016-07-25,57.72,58.09,57.5,57.95,10486019,SBUX\n2016-07-26,58.58,58.84,58.2,58.31,10106663,SBUX\n2016-07-27,58.38,58.4,57.67,57.85,6551777,SBUX\n2016-07-28,57.88,58.31,57.74,58.21,6830059,SBUX\n2016-07-29,58.18,58.43,57.92,58.05,6914907,SBUX\n2016-08-01,58.0,58.05,57.43,57.63,7997576,SBUX\n2016-08-02,57.25,57.34,56.54,56.73,7574622,SBUX\n2016-08-03,56.46,56.59,55.72,55.94,11484792,SBUX\n2016-08-04,56.05,56.29,55.38,55.42,11193748,SBUX\n2016-08-05,55.8,56.12,55.52,55.9,9206197,SBUX\n2016-08-08,55.97,55.99,55.17,55.36,9129307,SBUX\n2016-08-09,55.39,55.71,55.18,55.2,7136683,SBUX\n2016-08-10,55.37,55.71,55.11,55.62,6991077,SBUX\n2016-08-11,55.75,55.96,55.46,55.47,6191412,SBUX\n2016-08-12,55.27,55.745,55.23,55.47,5039757,SBUX\n2016-08-15,55.65,55.7,55.18,55.25,5968728,SBUX\n2016-08-16,55.25,55.57,54.92,55.37,5751178,SBUX\n2016-08-17,55.77,55.92,55.43,55.8,7410631,SBUX\n2016-08-18,55.78,55.9,55.49,55.53,5390990,SBUX\n2016-08-19,55.46,55.56,54.85,54.94,8981214,SBUX\n2016-08-22,54.98,55.92,54.95,55.85,8837808,SBUX\n2016-08-23,56.17,56.54,56.0,56.4,7827873,SBUX\n2016-08-24,57.0,57.98,56.95,57.09,13200460,SBUX\n2016-08-25,57.04,57.45,56.9,57.29,6686589,SBUX\n2016-08-26,57.48,57.83,56.995,57.29,6940511,SBUX\n2016-08-29,57.22,57.48,56.61,56.8,7026700,SBUX\n2016-08-30,56.66,56.75,56.01,56.4,6377668,SBUX\n2016-08-31,56.31,56.42,55.905,56.23,6996894,SBUX\n2016-09-01,56.3,56.56,55.83,56.31,6230148,SBUX\n2016-09-02,56.52,56.65,55.985,56.18,7441463,SBUX\n2016-09-06,56.18,56.42,55.69,56.02,6472907,SBUX\n2016-09-07,56.19,56.6,56.12,56.32,11428644,SBUX\n2016-09-08,56.1,56.15,55.2,55.3,12673626,SBUX\n2016-09-09,55.14,55.2,54.3,54.35,10658120,SBUX\n2016-09-12,53.92,54.79,53.92,54.71,11002544,SBUX\n2016-09-13,54.39,54.55,53.75,53.98,10050215,SBUX\n2016-09-14,54.26,54.35,53.8,53.9,6707850,SBUX\n2016-09-15,53.96,54.13,53.54,54.11,8080426,SBUX\n2016-09-16,53.94,54.09,53.41,53.74,10207750,SBUX\n2016-09-19,53.96,53.9739,52.9,53.01,9231628,SBUX\n2016-09-20,53.4,53.435,53.05,53.3,8731499,SBUX\n2016-09-21,53.43,54.03,53.28,53.98,9213637,SBUX\n2016-09-22,54.2,54.6,53.965,54.39,9096709,SBUX\n2016-09-23,54.0,54.56,54.0,54.43,6945147,SBUX\n2016-09-26,54.28,54.34,53.86,54.04,7755629,SBUX\n2016-09-27,54.0,54.21,53.82,54.19,6463747,SBUX\n2016-09-28,53.88,54.08,53.53,53.98,8593496,SBUX\n2016-09-29,53.88,53.995,52.91,53.45,11993517,SBUX\n2016-09-30,53.65,54.385,53.56,54.14,13767754,SBUX\n2016-10-03,54.1,54.15,53.665,53.84,5479037,SBUX\n2016-10-04,54.13,54.13,53.41,53.53,6176358,SBUX\n2016-10-05,53.5,53.76,53.275,53.35,7451916,SBUX\n2016-10-06,53.3,53.48,53.03,53.14,6130270,SBUX\n2016-10-07,53.37,53.605,53.0,53.46,7279723,SBUX\n2016-10-10,53.53,53.6,53.27,53.3,7224335,SBUX\n2016-10-11,53.13,53.4,52.74,52.92,9720407,SBUX\n2016-10-12,53.01,53.47,52.78,53.16,6320462,SBUX\n2016-10-13,52.88,53.13,52.6662,52.95,6958128,SBUX\n2016-10-14,53.12,53.37,52.96,53.08,6430136,SBUX\n2016-10-17,52.94,53.145,52.69,52.76,5223526,SBUX\n2016-10-18,53.24,53.3197,52.59,52.61,6550399,SBUX\n2016-10-19,52.91,53.74,52.9,53.15,9095261,SBUX\n2016-10-20,53.36,53.74,52.91,53.59,9286800,SBUX\n2016-10-21,53.42,53.7,53.25,53.63,6767204,SBUX\n2016-10-24,53.9,54.46,53.8939,54.18,6919714,SBUX\n2016-10-25,54.1,54.17,53.5,53.67,6052830,SBUX\n2016-10-26,53.6,53.84,53.355,53.63,5817798,SBUX\n2016-10-27,53.6,53.83,53.13,53.59,7899957,SBUX\n2016-10-28,53.65,53.84,53.11,53.53,6620333,SBUX\n2016-10-31,53.7,53.7,53.055,53.07,9142509,SBUX\n2016-11-01,53.14,53.21,52.085,52.5,15425819,SBUX\n2016-11-02,52.34,53.46,52.31,52.98,10851658,SBUX\n2016-11-03,52.99,53.0,51.34,51.77,21847292,SBUX\n2016-11-04,51.43,53.74,50.84,52.75,21956848,SBUX\n2016-11-07,53.5,54.68,53.19,54.49,14916848,SBUX\n2016-11-08,54.4,54.79,54.115,54.62,9351994,SBUX\n2016-11-09,53.2,54.82,52.8,54.58,13727777,SBUX\n2016-11-10,54.64,54.817,53.51,53.57,13621701,SBUX\n2016-11-11,53.43,53.99,53.25,53.93,8436435,SBUX\n2016-11-14,53.93,54.47,53.5,54.22,10489826,SBUX\n2016-11-15,54.09,54.69,53.9,54.59,9588036,SBUX\n2016-11-16,54.33,55.52,54.26,55.44,10779155,SBUX\n2016-11-17,55.215,55.9,55.06,55.85,8744504,SBUX\n2016-11-18,55.72,56.12,55.42,55.77,8740953,SBUX\n2016-11-21,55.51,56.16,55.51,56.1,8004000,SBUX\n2016-11-22,56.32,57.15,55.88,57.12,10268720,SBUX\n2016-11-23,56.91,57.64,56.9,57.59,8183628,SBUX\n2016-11-25,57.7,57.7,57.255,57.43,3228848,SBUX\n2016-11-28,57.0,57.86,56.76,57.59,8750925,SBUX\n2016-11-29,57.64,58.21,57.5,58.17,10582850,SBUX\n2016-11-30,58.19,58.25,57.86,57.97,9527959,SBUX\n2016-12-01,57.34,58.52,57.2,58.51,12381607,SBUX\n2016-12-02,56.648999999999994,57.75,56.57,57.21,16869957,SBUX\n2016-12-05,56.96,57.84,56.96,57.5,7701167,SBUX\n2016-12-06,57.66,57.7,57.14,57.44,7035674,SBUX\n2016-12-07,57.54,58.85,57.45,58.76,9094812,SBUX\n2016-12-08,59.0,59.25,58.4118,58.65,7972498,SBUX\n2016-12-09,58.92,58.95,58.43,58.75,7091577,SBUX\n2016-12-12,58.54,58.79,58.34,58.77,7736198,SBUX\n2016-12-13,58.99,59.54,58.66,59.31,8878080,SBUX\n2016-12-14,59.03,59.25,58.6,58.75,9022867,SBUX\n2016-12-15,57.9675,58.21,57.52,57.71,11837756,SBUX\n2016-12-16,58.01,58.07,57.56,57.66,10611461,SBUX\n2016-12-19,57.44,57.98,57.44,57.65,6433824,SBUX\n2016-12-20,57.81,58.06,57.32,57.7,4888284,SBUX\n2016-12-21,57.5,57.87,57.41,57.44,5380537,SBUX\n2016-12-22,57.31,57.4,56.72,57.11,6777656,SBUX\n2016-12-23,57.29,57.36,56.89,57.01,4298476,SBUX\n2016-12-27,56.99,57.3869,56.81,56.86,4186157,SBUX\n2016-12-28,56.8,56.9,56.25,56.35,5548726,SBUX\n2016-12-29,56.35,56.47,56.135,56.32,3781721,SBUX\n2016-12-30,56.28,56.45,55.4,55.52,8344508,SBUX\n2017-01-03,55.91,55.95,55.04,55.35,7809307,SBUX\n2017-01-04,55.56,56.195,55.38,55.99,7796290,SBUX\n2017-01-05,56.08,56.53,55.8099,56.46,7602321,SBUX\n2017-01-06,56.63,57.27,56.08,57.13,8587812,SBUX\n2017-01-09,57.26,58.335,57.25,58.2,12640515,SBUX\n2017-01-10,58.22,58.26,57.83,57.88,6672024,SBUX\n2017-01-11,57.8,58.12,57.59,58.1,6027960,SBUX\n2017-01-12,58.0,58.13,57.64,58.03,4733015,SBUX\n2017-01-13,58.03,58.1,57.65,57.85,4745840,SBUX\n2017-01-17,57.62,58.25,57.41,58.0,5734666,SBUX\n2017-01-18,58.32,58.58,58.03,58.45,7375725,SBUX\n2017-01-19,58.31,58.45,57.715,57.89,7850480,SBUX\n2017-01-20,58.14,58.2,57.41,57.66,7651562,SBUX\n2017-01-23,57.42,57.9,57.15,57.76,6814368,SBUX\n2017-01-24,57.93,58.5,57.76,58.44,10704103,SBUX\n2017-01-25,58.67,58.93,58.45,58.7,7124547,SBUX\n2017-01-26,58.7,59.0,58.26,58.46,12382416,SBUX\n2017-01-27,55.75,56.59,55.65,56.12,28884899,SBUX\n2017-01-30,56.0,56.24,55.58,55.9,13322010,SBUX\n2017-01-31,55.8,55.87,54.88,55.22,14307985,SBUX\n2017-02-01,55.49,55.5,53.81,53.9,18796871,SBUX\n2017-02-02,54.04,54.39,53.85,53.87,15289650,SBUX\n2017-02-03,54.21,55.1,54.01,55.06,14161693,SBUX\n2017-02-06,55.01,55.75,54.9,55.73,13029829,SBUX\n2017-02-07,55.79,55.84,55.1801,55.24,9910498,SBUX\n2017-02-08,55.19,55.4957,55.1,55.22,11681938,SBUX\n2017-02-09,55.23,56.12,55.21,55.81,11106757,SBUX\n2017-02-10,55.73,56.395,55.56,56.22,11178950,SBUX\n2017-02-13,56.5,56.66,56.03,56.11,8027939,SBUX\n2017-02-14,56.02,56.61,56.02,56.58,8865947,SBUX\n2017-02-15,56.56,56.88,56.305,56.86,6967179,SBUX\n2017-02-16,56.96,56.99,56.53,56.73,8524519,SBUX\n2017-02-17,56.8,57.57,56.71,57.35,11008366,SBUX\n2017-02-21,57.41,57.81,57.4,57.54,8289185,SBUX\n2017-02-22,57.52,57.85,57.35,57.57,7876599,SBUX\n2017-02-23,57.6,57.79,57.39,57.64,7178627,SBUX\n2017-02-24,57.61,57.71,57.145,57.48,7806190,SBUX\n2017-02-27,57.24,57.3,56.66,56.78,7702400,SBUX\n2017-02-28,56.71,57.06,56.55,56.87,8750655,SBUX\n2017-03-01,57.27,57.4,56.94,57.14,7197973,SBUX\n2017-03-02,57.07,57.19,56.85,57.12,6595418,SBUX\n2017-03-03,56.7,57.26,56.7,57.1,7738064,SBUX\n2017-03-06,56.78,56.81,56.33,56.68,9159983,SBUX\n2017-03-07,56.58,56.75,56.02,56.2,10890313,SBUX\n2017-03-08,56.15,56.35,55.54,55.74,13061632,SBUX\n2017-03-09,55.75,55.8,54.81,55.19,17844248,SBUX\n2017-03-10,55.39,55.4,54.415,54.53,13886431,SBUX\n2017-03-13,54.57,54.8847,54.4,54.63,9090230,SBUX\n2017-03-14,54.62,54.74,54.19,54.27,7892888,SBUX\n2017-03-15,54.39,54.65,54.09,54.54,8712006,SBUX\n2017-03-16,54.85,54.985,54.66,54.8,8074278,SBUX\n2017-03-17,55.04,56.13,54.95,55.78,15822141,SBUX\n2017-03-20,55.87,56.05,55.51,55.81,7948425,SBUX\n2017-03-21,56.05,56.45,55.485,55.54,8030642,SBUX\n2017-03-22,55.68,56.04,55.51,55.89,8452070,SBUX\n2017-03-23,56.15,56.425,55.78,55.85,7357207,SBUX\n2017-03-24,56.11,57.38,55.9,56.81,15763000,SBUX\n2017-03-27,56.66,57.45,56.46,57.23,8861799,SBUX\n2017-03-28,57.1,57.52,57.025,57.35,8331644,SBUX\n2017-03-29,57.17,57.85,57.13,57.54,7001635,SBUX\n2017-03-30,57.45,58.3,57.42,58.16,8677916,SBUX\n2017-03-31,58.105,58.66,58.06,58.39,9156707,SBUX\n2017-04-03,58.28,58.47,57.89,58.44,8989831,SBUX\n2017-04-04,58.37,58.41,58.01,58.32,6474781,SBUX\n2017-04-05,57.96,59.27,57.81,58.22,13656221,SBUX\n2017-04-06,58.13,58.36,57.73,57.92,9112533,SBUX\n2017-04-07,57.8,58.25,57.55,58.02,6773750,SBUX\n2017-04-10,58.0,58.21,57.73,57.95,5266312,SBUX\n2017-04-11,57.74,58.16,57.38,57.88,5855091,SBUX\n2017-04-12,57.88,57.895,57.48,57.58,5450298,SBUX\n2017-04-13,57.61,57.79,57.45,57.51,4871717,SBUX\n2017-04-17,57.68,58.25,57.68,58.08,5626042,SBUX\n2017-04-18,57.59,58.48,57.59,58.35,5449699,SBUX\n2017-04-19,58.6,59.49,58.5,59.04,12000639,SBUX\n2017-04-20,59.71,60.335,59.56,60.08,12445862,SBUX\n2017-04-21,60.2,60.69,60.15,60.61,8799985,SBUX\n2017-04-24,61.0,61.38,60.89,61.11,10721276,SBUX\n2017-04-25,60.65,61.21,59.9237,60.96,11031475,SBUX\n2017-04-26,61.23,61.75,60.98,61.56,8525419,SBUX\n2017-04-27,61.63,61.94,61.19,61.3,15285342,SBUX\n2017-04-28,59.41,60.18,58.99,60.06,25046130,SBUX\n2017-05-01,60.0,60.6,59.7619,60.18,10910341,SBUX\n2017-05-02,60.15,60.52,60.04,60.5,9152088,SBUX\n2017-05-03,60.52,60.665,60.255,60.59,7706367,SBUX\n2017-05-04,60.65,60.85,60.39,60.83,6874286,SBUX\n2017-05-05,60.86,60.99,60.58,60.95,6443309,SBUX\n2017-05-08,61.07,61.07,60.7,60.94,5588371,SBUX\n2017-05-09,60.58,61.08,60.57,60.98,5806562,SBUX\n2017-05-10,60.88,60.98,60.21,60.66,7198414,SBUX\n2017-05-11,60.45,60.51,60.03,60.27,5516893,SBUX\n2017-05-12,60.28,60.34,59.83,59.93,5647531,SBUX\n2017-05-15,60.42,60.49,60.125,60.45,5904094,SBUX\n2017-05-16,60.68,60.7,59.88,59.98,6303480,SBUX\n2017-05-17,59.97,60.3239,59.55,59.73,7581230,SBUX\n2017-05-18,59.73,59.94,58.87,59.82,8602411,SBUX\n2017-05-19,59.94,61.92,59.94,61.36,12530995,SBUX\n2017-05-22,61.03,61.72,61.03,61.23,6392124,SBUX\n2017-05-23,61.4,61.5,60.86,61.15,5622524,SBUX\n2017-05-24,61.4,62.0,60.94,61.89,7283789,SBUX\n2017-05-25,62.01,63.11,61.9,62.9,8931804,SBUX\n2017-05-26,63.01,63.42,62.97,63.3,6097990,SBUX\n2017-05-30,63.07,63.41,63.01,63.26,7094586,SBUX\n2017-05-31,63.27,63.61,63.08,63.61,7314634,SBUX\n2017-06-01,63.51,63.82,63.35,63.75,6058263,SBUX\n2017-06-02,63.88,64.68,63.7,64.57,7840374,SBUX\n2017-06-05,64.85,64.87,64.18,64.27,6809284,SBUX\n2017-06-06,64.22,64.35,64.05,64.16,5448439,SBUX\n2017-06-07,64.13,64.295,63.34,63.5,8364994,SBUX\n2017-06-08,63.44,63.58,62.02,62.24,11289266,SBUX\n2017-06-09,62.37,62.48,61.8745,62.19,11240487,SBUX\n2017-06-12,61.8,61.99,60.63,61.29,11071593,SBUX\n2017-06-13,61.12,61.255,60.59,60.92,9384906,SBUX\n2017-06-14,60.67,60.82,59.86,60.27,9703332,SBUX\n2017-06-15,59.92,60.28,59.51,60.09,7515980,SBUX\n2017-06-16,59.89,60.16,59.47,60.14,11522438,SBUX\n2017-06-19,60.35,61.0,60.11,60.9,6778024,SBUX\n2017-06-20,60.98,61.0,59.7,59.86,6985666,SBUX\n2017-06-21,60.0,60.31,59.71,59.96,6027647,SBUX\n2017-06-22,60.09,60.1,59.4,59.51,5602002,SBUX\n2017-06-23,59.76,60.17,59.58,59.81,6469495,SBUX\n2017-06-26,60.02,60.15,59.33,59.64,5674637,SBUX\n2017-06-27,59.54,59.69,58.81,58.96,5652429,SBUX\n2017-06-28,59.06,59.25,58.8,59.18,5419169,SBUX\n2017-06-29,59.17,59.18,57.955,58.36,7421177,SBUX\n2017-06-30,58.68,58.95,58.29,58.31,8117066,SBUX\n2017-07-03,58.9,58.99,58.25,58.25,4575268,SBUX\n2017-07-05,58.43,58.5,57.8,57.94,7773566,SBUX\n2017-07-06,57.8,57.92,57.4,57.6,8886648,SBUX\n2017-07-07,57.79,58.36,57.54,58.04,7278250,SBUX\n2017-07-10,58.18,58.35,57.75,57.81,4832094,SBUX\n2017-07-11,57.91,58.08,57.53,57.9,5422330,SBUX\n2017-07-12,58.21,58.71,58.02,58.54,7141916,SBUX\n2017-07-13,58.8,58.87,58.12,58.38,8460245,SBUX\n2017-07-14,58.4,58.92,58.28,58.76,5441377,SBUX\n2017-07-17,58.73,58.87,58.28,58.33,6774471,SBUX\n2017-07-18,58.12,58.58,57.69,58.21,7857464,SBUX\n2017-07-19,58.15,58.41,57.9,58.11,8203557,SBUX\n2017-07-20,58.41,58.84,58.0,58.03,10546701,SBUX\n2017-07-21,57.92,58.26,57.83,57.98,6717235,SBUX\n2017-07-24,58.0,58.25,57.93,58.02,7442589,SBUX\n2017-07-25,58.215,58.84,57.98,58.55,7933137,SBUX\n2017-07-26,58.8,58.84,57.7847,57.94,8775889,SBUX\n2017-07-27,58.25,59.66,57.93,59.5,23286716,SBUX\n2017-07-28,55.23,55.96,53.41,54.0,53454789,SBUX\n2017-07-31,54.48,54.68,53.95,53.98,20299407,SBUX\n2017-08-01,54.57,54.79,53.97,54.73,18120912,SBUX\n2017-08-02,54.75,55.45,54.6702,55.43,14764854,SBUX\n2017-08-03,55.64,56.12,55.5,55.68,13331459,SBUX\n2017-08-04,55.97,56.05,55.09,55.44,9179779,SBUX\n2017-08-07,55.6,55.93,55.42,55.63,7253947,SBUX\n2017-08-08,55.55,55.58,54.36,54.52,11095259,SBUX\n2017-08-09,54.43,54.43,53.3,53.74,16717719,SBUX\n2017-08-10,53.52,53.7453,52.99,53.07,13235301,SBUX\n2017-08-11,53.05,53.4,53.05,53.18,9235033,SBUX\n2017-08-14,53.6,53.6,53.18,53.22,7426467,SBUX\n2017-08-15,53.41,53.42,52.89,53.15,6674597,SBUX\n2017-08-16,53.26,53.92,53.25,53.5,7667081,SBUX\n2017-08-17,53.32,53.78,52.99,53.04,7451679,SBUX\n2017-08-18,52.92,53.085,52.58,52.7,10370499,SBUX\n2017-08-21,53.14,53.52,52.8,53.15,12753196,SBUX\n2017-08-22,53.49,54.74,53.39,54.45,14547613,SBUX\n2017-08-23,53.96,54.1,53.65,54.08,11838533,SBUX\n2017-08-24,54.26,54.445,53.77,53.94,8716699,SBUX\n2017-08-25,54.62,54.69,54.19,54.36,8601120,SBUX\n2017-08-28,54.54,54.58,54.02,54.4,6643652,SBUX\n2017-08-29,54.04,54.29,54.0,54.1,6212403,SBUX\n2017-08-30,54.04,54.62,54.04,54.52,5596567,SBUX\n2017-08-31,54.68,54.99,54.57,54.86,8245287,SBUX\n2017-09-01,54.9,55.155,54.88,54.93,7696302,SBUX\n2017-09-05,54.95,55.2,54.6,55.13,9178048,SBUX\n2017-09-06,55.12,55.275,54.13,54.31,11372298,SBUX\n2017-09-07,54.42,54.675,53.25,53.47,13801890,SBUX\n2017-09-08,53.39,53.78,53.05,53.49,11774483,SBUX\n2017-09-11,53.79,54.13,53.61,54.02,9314079,SBUX\n2017-09-12,53.98,54.05,53.33,53.54,10339446,SBUX\n2017-09-13,53.55,54.72,53.4866,54.29,12787883,SBUX\n2017-09-14,54.38,54.71,54.1,54.53,8260770,SBUX\n2017-09-15,54.44,54.79,54.16,54.67,10744783,SBUX\n2017-09-18,54.8,54.97,54.57,54.69,6348360,SBUX\n2017-09-19,54.84,54.88,54.56,54.62,5187121,SBUX\n2017-09-20,54.58,55.18,54.58,55.15,7080035,SBUX\n2017-09-21,55.05,55.17,54.86,55.01,7178462,SBUX\n2017-09-22,55.05,55.2,54.86,55.09,6950029,SBUX\n2017-09-25,54.98,55.22,54.7,54.95,10242655,SBUX\n2017-09-26,55.06,55.22,54.795,55.13,8021851,SBUX\n2017-09-27,55.25,55.6,54.96,54.99,8671309,SBUX\n2017-09-28,54.81,55.08,54.45,54.5,7607473,SBUX\n2017-09-29,54.31,54.47,53.36,53.71,11944370,SBUX\n2017-10-02,53.86,54.04,53.75,53.81,5955980,SBUX\n2017-10-03,54.0,54.34,53.9,53.99,6137069,SBUX\n2017-10-04,54.12,54.2,53.69,53.93,5569058,SBUX\n2017-10-05,54.06,54.97,53.92,54.6,7994274,SBUX\n2017-10-06,54.51,55.45,54.245,55.17,10576278,SBUX\n2017-10-09,55.37,55.49,54.96,55.02,5822893,SBUX\n2017-10-10,55.1,55.8492,55.08,55.42,6573918,SBUX\n2017-10-11,55.46,55.8,55.26,55.64,8881897,SBUX\n2017-10-12,55.67,56.27,55.309,55.97,7234267,SBUX\n2017-10-13,56.0,56.43,55.61,55.72,6231132,SBUX\n2017-10-16,55.67,55.8,54.89,54.91,7256893,SBUX\n2017-10-17,54.86,55.23,54.29,54.51,11227337,SBUX\n2017-10-18,54.46,55.43,54.22,55.21,8299509,SBUX\n2017-10-19,55.08,55.5423,54.9,55.4,5720179,SBUX\n2017-10-20,55.31,55.4,54.175,54.57,11741092,SBUX\n2017-10-23,54.77,54.935,54.18,54.27,10111071,SBUX\n2017-10-24,54.37,54.56,54.02,54.28,7818490,SBUX\n2017-10-25,54.24,54.39,53.66,54.16,8281219,SBUX\n2017-10-26,54.5,55.75,54.5,54.91,12211440,SBUX\n2017-10-27,54.83,55.12,54.68,54.88,9922611,SBUX\n2017-10-30,54.79,55.23,54.4,55.17,6430949,SBUX\n2017-10-31,55.16,55.305,54.7,54.84,8857829,SBUX\n2017-11-01,55.1,55.59,54.94,55.13,7189764,SBUX\n2017-11-02,55.15,55.39,54.77,54.87,16879022,SBUX\n2017-11-03,54.16,56.94,54.05,56.03,28773774,SBUX\n2017-11-06,55.99,56.69,55.63,56.57,10835659,SBUX\n2017-11-07,56.33,57.29,56.33,57.22,11167447,SBUX\n2017-11-08,57.03,58.01,57.0,57.91,13533654,SBUX\n2017-11-09,57.2,57.66,56.785,57.36,14758907,SBUX\n2017-11-10,57.36,57.39,56.54,57.04,7930318,SBUX\n2017-11-13,56.81,57.14,56.55,56.64,7648192,SBUX\n2017-11-14,56.47,57.0,56.41,56.93,7758215,SBUX\n2017-11-15,56.82,57.06,56.51,56.7,8880505,SBUX\n2017-11-16,56.92,57.42,56.75,57.24,8310611,SBUX\n2017-11-17,57.24,57.43,56.84,56.93,6311519,SBUX\n2017-11-20,56.67,57.05,56.58,56.81,6360086,SBUX\n2017-11-21,56.96,57.56,56.88,57.26,6284259,SBUX\n2017-11-22,57.07,57.18,56.795,57.14,7309073,SBUX\n2017-11-24,57.19,57.19,56.71,56.8,3479177,SBUX\n2017-11-27,56.83,56.9207,55.751000000000005,55.91,10580296,SBUX\n2017-11-28,56.03,56.72,55.9101,56.66,9734431,SBUX\n2017-11-29,56.71,57.68,56.7,57.51,10017914,SBUX\n2017-11-30,57.64,58.1399,57.47,57.82,11509224,SBUX\n2017-12-01,57.5,57.71,56.461000000000006,57.32,12756391,SBUX\n2017-12-04,57.54,59.19,57.5,58.76,13302050,SBUX\n2017-12-05,59.25,59.68,58.91,59.34,11295644,SBUX\n2017-12-06,59.38,59.83,59.23,59.28,8458198,SBUX\n2017-12-07,59.12,59.275,58.761,59.14,6033792,SBUX\n2017-12-08,58.52,58.845,58.1,58.61,9950491,SBUX\n2017-12-11,58.39,59.35,58.29,59.07,10286560,SBUX\n2017-12-12,58.99,59.36,58.87,59.27,6042917,SBUX\n2017-12-13,59.44,59.89,59.3,59.49,7726299,SBUX\n2017-12-14,59.73,60.05,59.44,59.7,8946111,SBUX\n2017-12-15,59.25,59.37,58.1574,58.29,22595018,SBUX\n2017-12-18,58.44,58.786,57.89,58.03,8751620,SBUX\n2017-12-19,58.13,58.575,57.93,58.01,7946435,SBUX\n2017-12-20,58.22,58.29,57.69,57.73,7188717,SBUX\n2017-12-21,57.94,58.145,57.49,57.58,5974474,SBUX\n2017-12-22,57.57,57.91,57.12,57.3,7148723,SBUX\n2017-12-26,57.27,57.5799,57.05,57.14,5546208,SBUX\n2017-12-27,57.19,57.65,57.18,57.27,4812173,SBUX\n2017-12-28,57.47,58.0,57.3,57.81,5044505,SBUX\n2017-12-29,57.74,57.97,57.42,57.43,5365646,SBUX\n2018-01-02,57.95,58.21,57.48,57.63,7215978,SBUX\n2018-01-03,57.93,58.96,57.8,58.71,7478356,SBUX\n2018-01-04,58.99,59.41,58.73,58.93,5775921,SBUX\n2018-01-05,59.25,59.69,59.07,59.61,6047686,SBUX\n2018-01-08,59.48,59.67,58.56,59.31,6335782,SBUX\n2018-01-09,59.2,59.47,58.86,59.18,5233353,SBUX\n2018-01-10,59.24,60.13,58.855,59.82,8656454,SBUX\n2018-01-11,59.76,60.02,59.4541,60.0,5806282,SBUX\n2018-01-12,60.4,60.51,59.65,60.4,6989075,SBUX\n2018-01-16,60.33,61.1,60.3,60.56,8040748,SBUX\n2018-01-17,61.0,61.33,60.52,60.66,8433771,SBUX\n2018-01-18,61.43,61.44,60.735,61.09,9170903,SBUX\n2018-01-19,61.21,61.46,60.95,61.26,8361853,SBUX\n2018-01-22,61.04,61.47,60.77,61.41,11945783,SBUX\n2018-01-23,61.32,61.91,61.14,61.69,10806783,SBUX\n2018-01-24,61.51,61.94,60.2326,60.83,11911867,SBUX\n2018-01-25,61.03,61.2,60.4,60.55,16225618,SBUX\n2018-01-26,57.94,58.17,56.55,57.99,51851690,SBUX\n2018-01-29,57.55,58.35,56.91,57.02,18899867,SBUX\n2018-01-30,56.96,57.54,56.74,57.19,14341155,SBUX\n2018-01-31,57.23,57.45,56.7,56.81,13118364,SBUX\n2018-02-01,56.28,56.42,55.89,56.0,14690146,SBUX\n2018-02-02,55.9,56.32,55.7,55.77,15358909,SBUX\n2018-02-05,55.53,56.26,54.57,54.69,16059955,SBUX\n2018-02-06,53.685,56.06,53.56,55.61,17415065,SBUX\n2018-02-07,55.08,55.43,54.44,54.46,13927022,SBUX\n" }, { "path": "tf2.0/xor3d.py", "content": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\n\n\ndef get_label(x, i1, i2, i3):\n # x = sequence\n if x[i1] < 0 and x[i2] < 0 and x[i3] < 0:\n return 1\n if x[i1] < 0 and x[i2] > 0 and x[i3] > 0:\n return 1\n if x[i1] > 0 and x[i2] < 0 and x[i3] > 0:\n return 1\n if x[i1] > 0 and x[i2] > 0 and x[i3] < 0:\n return 1\n return 0\n\n\nN = 2000\nX = np.random.random((N, 3))*2 - 1\n\nY = np.zeros(N)\nfor i in range(N):\n x = X[i]\n y = get_label(x, 0, 1, 2)\n Y[i] = y\n\n\nfig = plt.figure()\nax = fig.add_subplot(111, projection='3d')\nax.scatter(X[:,0], X[:,1], X[:,2], c=Y)\nplt.show()" }, { "path": "timeseries/WHERE ARE THE NOTEBOOKS.txt", "content": "As stated in the \"where to get the code\" / \"where to get the notebooks\" lecture, the notebooks are NOT on Github.\n\nIf you missed this, please review the lecture for the actual location of the notebooks.\n\nIf, after reviewing it, you still need assistance, please contact info@deeplearningcourses.com." }, { "path": "timeseries/extra_reading.txt", "content": "Estimating Box-Cox power transformation parameter via goodness of fit tests\nhttps://arxiv.org/pdf/1401.3812.pdf\n\nLinear Regression\nhttps://deeplearningcourses.com/c/data-science-linear-regression-in-python/\n\nLogistic Regression\nhttps://deeplearningcourses.com/c/data-science-logistic-regression-in-python/\n\nSupport Vector Machines\nhttps://deeplearningcourses.com/c/support-vector-machines-in-python\n\nRandom Forests\nhttps://deeplearningcourses.com/c/machine-learning-in-python-random-forest-adaboost\n\nDeep Learning and Tensorflow 2\nhttps://deeplearningcourses.com/c/deep-learning-tensorflow-2\n\nGaussian Processes for Regression and Classification\nhttps://www.cs.toronto.edu/~radford/ftp/val6gp.pdf\n\nHow Does Backpropagation Work? (In-Depth)\nhttps://deeplearningcourses.com/c/data-science-deep-learning-in-python/\nhttps://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow/\n\nForecasting at Scale (Facebook Prophet)\nhttps://peerj.com/preprints/3190.pdf\n\nStatistical and Machine Learning forecasting methods: Concerns and ways forward\nhttps://journals.plos.org/plosone/article%3Fid%3D10.1371/journal.pone.0194889" }, { "path": "transformers/WHERE ARE THE NOTEBOOKS.txt", "content": "As stated in the \"where to get the code\" / \"where to get the notebooks\" lecture, the notebooks are NOT on Github.\n\nIf you missed this, please review the lecture for the actual location of the notebooks.\n\nIf, after reviewing it, you still need assistance, please contact info@deeplearningcourses.com." }, { "path": "transformers/extra_reading.txt", "content": "Attention Is All You Need\nhttps://arxiv.org/abs/1706.03762\n\nBERT: Pre-training of Deep Bidirectional Transformers for Language Understanding\nhttps://arxiv.org/abs/1810.04805v2\n\nImproving Language Understanding by Generative Pre-Training (GPT)\nhttps://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf\n\nImproving Language Understanding with Unsupervised Learning\nhttps://openai.com/blog/language-unsupervised/\n\nLanguage Models are Unsupervised Multitask Learners (GPT-2)\nhttps://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf\n\nBetter Language Models and Their Implications\nhttps://openai.com/blog/better-language-models/\n\nLanguage Models are Few-Shot Learners (GPT-3)\nhttps://arxiv.org/abs/2005.14165\n\nList of Hugging Face Pipelines for NLP\nhttps://lazyprogrammer.me/list-of-hugging-face-pipelines-for-nlp/\n\nBitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models\nhttps://arxiv.org/abs/2106.10199\n\nTranslation Datasets\nhttps://opus.nlpl.eu/KDE4.php\n\nLayer Normalization\nhttps://arxiv.org/abs/1607.06450" }, { "path": "unsupervised_class/__init__.py", "content": "" }, { "path": "unsupervised_class/books.py", "content": "# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python\n# https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport networkx as nx\nimport nltk\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom nltk.stem import WordNetLemmatizer\nfrom sklearn.decomposition import PCA\nfrom sklearn.manifold import TSNE, LocallyLinearEmbedding as LLE\nfrom sklearn.feature_extraction.text import TfidfTransformer\n\nwordnet_lemmatizer = WordNetLemmatizer()\n\ntitles = [line.rstrip() for line in open('../nlp_class/all_book_titles.txt')]\n\n# copy tokenizer from sentiment example\nstopwords = set(w.rstrip() for w in open('../nlp_class/stopwords.txt'))\n# add more stopwords specific to this problem\nstopwords = stopwords.union({\n 'introduction', 'edition', 'series', 'application',\n 'approach', 'card', 'access', 'package', 'plus', 'etext',\n 'brief', 'vol', 'fundamental', 'guide', 'essential', 'printed',\n 'third', 'second', 'fourth', })\ndef my_tokenizer(s):\n s = s.lower() # downcase\n tokens = nltk.tokenize.word_tokenize(s) # split string into words (tokens)\n tokens = [t for t in tokens if len(t) > 2] # remove short words, they're probably not useful\n tokens = [wordnet_lemmatizer.lemmatize(t) for t in tokens] # put words into base form\n tokens = [t for t in tokens if t not in stopwords] # remove stopwords\n tokens = [t for t in tokens if not any(c.isdigit() for c in t)] # remove any digits, i.e. \"3rd edition\"\n return tokens\n\n\n# create a word-to-index map so that we can create our word-frequency vectors later\n# let's also save the tokenized versions so we don't have to tokenize again later\nword_index_map = {}\ncurrent_index = 0\nall_tokens = []\nall_titles = []\nindex_word_map = []\nprint(\"num titles:\", len(titles))\nprint(\"first title:\", titles[0])\nfor title in titles:\n try:\n title = title.encode('ascii', 'ignore') # this will throw exception if bad characters\n title = title.decode('utf-8')\n all_titles.append(title)\n tokens = my_tokenizer(title)\n all_tokens.append(tokens)\n for token in tokens:\n if token not in word_index_map:\n word_index_map[token] = current_index\n current_index += 1\n index_word_map.append(token)\n except Exception as e:\n print(e)\n\n\n\n# now let's create our input matrices - just indicator variables for this example - works better than proportions\ndef tokens_to_vector(tokens):\n x = np.zeros(len(word_index_map))\n for t in tokens:\n i = word_index_map[t]\n x[i] += 1\n return x\n\nN = len(all_tokens)\nD = len(word_index_map)\nX = np.zeros((D, N)) # terms will go along rows, documents along columns\ni = 0\nfor tokens in all_tokens:\n X[:,i] = tokens_to_vector(tokens)\n i += 1\n\ndef d(u, v):\n diff = u - v\n return diff.dot(diff)\n\ndef cost(X, R, M):\n cost = 0\n for k in range(len(M)):\n # method 1\n # for n in range(len(X)):\n # cost += R[n,k]*d(M[k], X[n])\n\n # method 2\n diff = X - M[k]\n sq_distances = (diff * diff).sum(axis=1)\n cost += (R[:,k] * sq_distances).sum()\n return cost\n\ndef plot_k_means(X, K, index_word_map, max_iter=20, beta=1.0, show_plots=True):\n N, D = X.shape\n M = np.zeros((K, D))\n R = np.zeros((N, K))\n exponents = np.empty((N, K))\n\n # initialize M to random\n for k in range(K):\n M[k] = X[np.random.choice(N)]\n\n costs = np.zeros(max_iter)\n for i in range(max_iter):\n # step 1: determine assignments / resposibilities\n # is this inefficient?\n for k in range(K):\n for n in range(N):\n # R[n,k] = np.exp(-beta*d(M[k], X[n])) / np.sum( np.exp(-beta*d(M[j], X[n])) for j in range(K) )\n exponents[n,k] = np.exp(-beta*d(M[k], X[n]))\n\n R = exponents / exponents.sum(axis=1, keepdims=True)\n\n # step 2: recalculate means\n for k in range(K):\n M[k] = R[:,k].dot(X) / R[:,k].sum()\n\n costs[i] = cost(X, R, M)\n if i > 0:\n if np.abs(costs[i] - costs[i-1]) < 10e-5:\n break\n\n if show_plots:\n # plt.plot(costs)\n # plt.title(\"Costs\")\n # plt.show()\n\n random_colors = np.random.random((K, 3))\n colors = R.dot(random_colors)\n plt.figure(figsize=(80.0, 80.0))\n plt.scatter(X[:,0], X[:,1], s=300, alpha=0.9, c=colors)\n annotate1(X, index_word_map)\n # plt.show()\n plt.savefig(\"test.png\")\n\n\n # print out the clusters\n hard_responsibilities = np.argmax(R, axis=1) # is an N-size array of cluster identities\n # let's \"reverse\" the order so it's cluster identity -> word index\n cluster2word = {}\n for i in range(len(hard_responsibilities)):\n word = index_word_map[i]\n cluster = hard_responsibilities[i]\n if cluster not in cluster2word:\n cluster2word[cluster] = []\n cluster2word[cluster].append(word)\n\n # print out the words grouped by cluster\n for cluster, wordlist in cluster2word.items():\n print(\"cluster\", cluster, \"->\", wordlist)\n\n return M, R\n\n# def annotate2(X, index_word_map, k=0.1):\n# N, D = X.shape\n\n# # create graph\n# G = nx.DiGraph()\n# data_nodes = []\n# init_pos = {}\n# for i in range(N):\n# x, y = X[i]\n# label = index_word_map[i]\n# data_str = 'data_{0}'.format(label)\n# G.add_node(data_str)\n# G.add_node(label)\n# G.add_edge(label, data_str)\n# data_nodes.append(data_str)\n# init_pos[data_str] = (x, y)\n# init_pos[label] = (x, y)\n# pos = nx.spring_layout(G, pos=init_pos, fixed=data_nodes, k=k)\n\n# # undo spring_layout's rescaling\n# pos_after = np.vstack([pos[d] for d in data_nodes])\n# pos_before = np.vstack([init_pos[d] for d in data_nodes])\n# scale, shift_x = np.polyfit(pos_after[:,0], pos_before[:,0], 1)\n# scale, shift_y = np.polyfit(pos_after[:,1], pos_before[:,1], 1)\n# shift = np.array([shift_x, shift_y])\n# for key, val in pos.items():\n# pos[key] = (val*scale) + shift\n\n\n# for label, data_str in G.edges():\n# plt.annotate(\n# label,\n# xy=pos[data_str], xycoords='data',\n# xytext=pos[label], textcoords='data',\n# arrowprops=dict(arrowstyle=\"->\", color='black'),\n# )\n\n# # expand limits\n# all_pos = np.vstack(pos.values())\n# x_span, y_span = np.ptp(all_pos, axis=0)\n# mins = np.min(all_pos-x_span*0.15, 0)\n# maxs = np.max(all_pos+y_span*0.15, 0)\n# plt.xlim([mins[0], maxs[0]])\n# plt.ylim([mins[1], maxs[1]])\n\n\ndef annotate1(X, index_word_map, eps=0.1):\n N, D = X.shape\n placed = np.empty((N, D))\n for i in range(N):\n x, y = X[i]\n\n # if x, y is too close to something already plotted, move it\n close = []\n\n x, y = X[i]\n for retry in range(3):\n for j in range(i):\n diff = np.array([x, y]) - placed[j]\n\n # if something is close, append it to the close list\n if diff.dot(diff) < eps:\n close.append(placed[j])\n\n if close:\n # then the close list is not empty\n x += (np.random.randn() + 0.5) * (1 if np.random.rand() < 0.5 else -1)\n y += (np.random.randn() + 0.5) * (1 if np.random.rand() < 0.5 else -1)\n close = [] # so we can start again with an empty list\n else:\n # nothing close, let's break\n break\n\n placed[i] = (x, y)\n\n plt.annotate(\n s=index_word_map[i],\n xy=(X[i,0], X[i,1]),\n xytext=(x, y),\n arrowprops={\n 'arrowstyle' : '->',\n 'color' : 'black',\n }\n )\n\nprint(\"vocab size:\", current_index)\n\ntransformer = TfidfTransformer()\nX = transformer.fit_transform(X).toarray()\n\nreducer = TSNE()\nZ = reducer.fit_transform(X)\nplot_k_means(Z[:,:2], current_index//10, index_word_map, show_plots=True)\n" }, { "path": "unsupervised_class/choose_k.py", "content": "# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python\n# https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom kmeans import plot_k_means, get_simple_data, cost\n\n\ndef main():\n X = get_simple_data()\n\n plt.scatter(X[:,0], X[:,1])\n plt.show()\n\n costs = np.empty(10)\n costs[0] = None\n for k in range(1, 10):\n M, R = plot_k_means(X, k, show_plots=False)\n c = cost(X, R, M)\n costs[k] = c\n\n plt.plot(costs)\n plt.title(\"Cost vs K\")\n plt.show()\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "unsupervised_class/evolution.py", "content": "# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python\n# https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport random\nimport numpy as np\nimport scipy.spatial.distance as ssd\nimport matplotlib.pyplot as plt\nfrom scipy.cluster.hierarchy import dendrogram, linkage\n\n\n# our genetic code\ncode = ['A', 'T', 'C', 'G']\n\n\n# convert list of integers to corresponding letters\ndef to_code(a):\n return [code[i] for i in a]\n\n\n# distance between 2 DNA strands\ndef dist(a, b):\n return sum(i != j for i, j in zip(a, b))\n\n\n# generate offspring by modifying some characters in the code\ndef generate_offspring(parent):\n return [maybe_modify(c) for c in parent]\n\n\n# modify letter c with probability ~1/1000\ndef maybe_modify(c):\n if np.random.random() < 0.001:\n return np.random.choice(code)\n return c\n \n\n\n# create 3 distinct ancestors\np1 = to_code(np.random.randint(4, size=1000))\np2 = to_code(np.random.randint(4, size=1000))\np3 = to_code(np.random.randint(4, size=1000))\n\n\n# create offspring\nnum_generations = 99\nmax_offspring_per_generation = 1000\ncurrent_generation = [p1, p2, p3]\nfor i in range(num_generations):\n\n next_generation = []\n for parent in current_generation:\n # each parent will have between 1 and 3 children\n num_offspring = np.random.randint(3) + 1\n\n # generate the offspring\n for _ in range(num_offspring):\n child = generate_offspring(parent)\n next_generation.append(child)\n\n current_generation = next_generation\n\n # limit the number of offspring\n random.shuffle(current_generation)\n current_generation = current_generation[:max_offspring_per_generation]\n\n print(\"Finished creating generation %d / %d, size = %d\" % (i + 2, num_generations + 1, len(current_generation)))\n\n\n\n# create distance matrix\n# note: you can also use scipy's pdist for this\n# but NOT sklearn's pairwise_distances function\n# which does not return a symmetric matrix\nN = len(current_generation)\ndist_matrix = np.zeros((N, N))\nfor i in range(N):\n for j in range(N):\n if i == j:\n continue\n elif j > i:\n a = current_generation[i]\n b = current_generation[j]\n dist_matrix[i,j] = dist(a, b)\n else:\n dist_matrix[i,j] = dist_matrix[j,i]\n\ndist_array = ssd.squareform(dist_matrix)\n\nZ = linkage(dist_array, 'ward')\nplt.title(\"Ward\")\ndendrogram(Z)\nplt.show()\n\nZ = linkage(dist_array, 'single')\nplt.title(\"Single\")\ndendrogram(Z)\nplt.show()\n\nZ = linkage(dist_array, 'complete')\nplt.title(\"Complete\")\ndendrogram(Z)\nplt.show()\n" }, { "path": "unsupervised_class/gmm.py", "content": "# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python\n# https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom scipy.stats import multivariate_normal\n\n\ndef gmm(X, K, max_iter=20, smoothing=1e-2):\n N, D = X.shape\n M = np.zeros((K, D))\n R = np.zeros((N, K))\n C = np.zeros((K, D, D))\n pi = np.ones(K) / K # uniform\n\n # initialize M to random, initialize C to spherical with variance 1\n for k in range(K):\n M[k] = X[np.random.choice(N)]\n C[k] = np.eye(D)\n\n lls = []\n weighted_pdfs = np.zeros((N, K)) # we'll use these to store the PDF value of sample n and Gaussian k\n for i in range(max_iter):\n # step 1: determine assignments / resposibilities\n # this is the slow way\n # for k in range(K):\n # for n in range(N):\n # weighted_pdfs[n,k] = pi[k]*multivariate_normal.pdf(X[n], M[k], C[k])\n\n # for k in range(K):\n # for n in range(N):\n # R[n,k] = weighted_pdfs[n,k] / weighted_pdfs[n,:].sum()\n\n # a faster way to do step 1: \"vectorization\"\n for k in range(K):\n weighted_pdfs[:,k] = pi[k]*multivariate_normal.pdf(X, M[k], C[k])\n R = weighted_pdfs / weighted_pdfs.sum(axis=1, keepdims=True)\n\n # step 2: recalculate params\n for k in range(K):\n Nk = R[:,k].sum()\n pi[k] = Nk / N\n M[k] = R[:,k].dot(X) / Nk\n\n ## faster\n delta = X - M[k] # N x D\n Rdelta = np.expand_dims(R[:,k], -1) * delta # multiplies R[:,k] by each col. of delta - N x D\n C[k] = Rdelta.T.dot(delta) / Nk + np.eye(D)*smoothing # D x D\n ## slower\n # C[k] = np.sum(R[n,k]*np.outer(X[n] - M[k], X[n] - M[k]) for n in range(N)) / Nk + np.eye(D)*smoothing\n\n\n ll = np.log(weighted_pdfs.sum(axis=1)).sum()\n lls.append(ll)\n if i > 0:\n if np.abs(lls[i] - lls[i-1]) < 0.1:\n break\n\n plt.plot(lls)\n plt.title(\"Log-Likelihood\")\n plt.show()\n\n random_colors = np.random.random((K, 3))\n colors = R.dot(random_colors)\n plt.scatter(X[:,0], X[:,1], c=colors)\n plt.show()\n\n print(\"pi:\", pi)\n print(\"means:\", M)\n print(\"covariances:\", C)\n return R\n\n\ndef main():\n # assume 3 means\n D = 2 # so we can visualize it more easily\n s = 4 # separation so we can control how far apart the means are\n mu1 = np.array([0, 0])\n mu2 = np.array([s, s])\n mu3 = np.array([0, s])\n\n N = 2000 # number of samples\n X = np.zeros((N, D))\n X[:1200, :] = np.random.randn(1200, D)*2 + mu1\n X[1200:1800, :] = np.random.randn(600, D) + mu2\n X[1800:, :] = np.random.randn(200, D)*0.5 + mu3\n\n # what does it look like without clustering?\n plt.scatter(X[:,0], X[:,1])\n plt.show()\n\n K = 3\n gmm(X, K)\n\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "unsupervised_class/gmm_mnist.py", "content": "# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python\n# https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python\n\n# data is from https://www.kaggle.com/c/digit-recognizer\n# each image is a D = 28x28 = 784 dimensional vector\n# there are N = 42000 samples\n# you can plot an image by reshaping to (28,28) and using plt.imshow()\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n# from kmeans import plot_k_means, get_simple_data\nfrom datetime import datetime\n# from gmm import gmm\nfrom sklearn.mixture import GaussianMixture\nfrom kmeans_mnist import get_data, purity, DBI\n\n\ndef main():\n X, Y = get_data(10000)\n print(\"Number of data points:\", len(Y))\n\n model = GaussianMixture(n_components=10)\n model.fit(X)\n M = model.means_\n R = model.predict_proba(X)\n\n print(\"Purity:\", purity(Y, R)) # max is 1, higher is better\n print(\"DBI:\", DBI(X, M, R)) # lower is better\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "unsupervised_class/hcluster.py", "content": "# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python\n# https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom scipy.cluster.hierarchy import dendrogram, linkage\n\n\ndef main():\n D = 2 # so we can visualize it more easily\n s = 4 # separation so we can control how far apart the means are\n mu1 = np.array([0, 0])\n mu2 = np.array([s, s])\n mu3 = np.array([0, s])\n\n N = 900 # number of samples\n X = np.zeros((N, D))\n X[:300, :] = np.random.randn(300, D) + mu1\n X[300:600, :] = np.random.randn(300, D) + mu2\n X[600:, :] = np.random.randn(300, D) + mu3\n\n Z = linkage(X, 'ward')\n print(\"Z.shape:\", Z.shape)\n # Z has the format [idx1, idx2, dist, sample_count]\n # therefore, its size will be (N-1, 4)\n\n # from documentation:\n # A (n-1) by 4 matrix Z is returned. At the i-th iteration,\n # clusters with indices Z[i, 0] and Z[i, 1] are combined to\n # form cluster n + i. A cluster with an index less than n\n # corresponds to one of the original observations.\n # The distance between clusters Z[i, 0] and Z[i, 1] is given\n # by Z[i, 2]. The fourth value Z[i, 3] represents the number\n # of original observations in the newly formed cluster.\n plt.title(\"Ward\")\n dendrogram(Z)\n plt.show()\n\n Z = linkage(X, 'single')\n plt.title(\"Single\")\n dendrogram(Z)\n plt.show()\n\n Z = linkage(X, 'complete')\n plt.title(\"Complete\")\n dendrogram(Z)\n plt.show()\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "unsupervised_class/kmeans.py", "content": "# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python\n# https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics.pairwise import pairwise_distances\n\n\ndef d(u, v):\n diff = u - v\n return diff.dot(diff)\n\n\ndef cost(X, R, M):\n cost = 0\n for k in range(len(M)):\n # method 1\n # for n in range(len(X)):\n # cost += R[n,k]*d(M[k], X[n])\n\n # method 2\n diff = X - M[k]\n sq_distances = (diff * diff).sum(axis=1)\n cost += (R[:,k] * sq_distances).sum()\n return cost\n\n\ndef plot_k_means(X, K, max_iter=20, beta=3.0, show_plots=False):\n N, D = X.shape\n # R = np.zeros((N, K))\n exponents = np.empty((N, K))\n\n # initialize M to random\n initial_centers = np.random.choice(N, K, replace=False)\n M = X[initial_centers]\n\n costs = []\n k = 0\n for i in range(max_iter):\n k += 1\n # step 1: determine assignments / resposibilities\n # is this inefficient?\n for k in range(K):\n for n in range(N):\n exponents[n,k] = np.exp(-beta*d(M[k], X[n]))\n R = exponents / exponents.sum(axis=1, keepdims=True)\n\n\n # step 2: recalculate means\n # decent vectorization\n # for k in range(K):\n # M[k] = R[:,k].dot(X) / R[:,k].sum()\n # oldM = M\n\n # full vectorization\n M = R.T.dot(X) / R.sum(axis=0, keepdims=True).T\n # print(\"diff M:\", np.abs(M - oldM).sum())\n\n c = cost(X, R, M)\n costs.append(c)\n if i > 0:\n if np.abs(costs[-1] - costs[-2]) < 1e-5:\n break\n\n if len(costs) > 1:\n if costs[-1] > costs[-2]:\n pass\n # print(\"cost increased!\")\n # print(\"M:\", M)\n # print(\"R.min:\", R.min(), \"R.max:\", R.max())\n\n if show_plots:\n plt.plot(costs)\n plt.title(\"Costs\")\n plt.show()\n\n random_colors = np.random.random((K, 3))\n colors = R.dot(random_colors)\n plt.scatter(X[:,0], X[:,1], c=colors)\n plt.show()\n\n print(\"Final cost\", costs[-1])\n return M, R\n\n\ndef get_simple_data():\n # assume 3 means\n D = 2 # so we can visualize it more easily\n s = 4 # separation so we can control how far apart the means are\n mu1 = np.array([0, 0])\n mu2 = np.array([s, s])\n mu3 = np.array([0, s])\n\n N = 900 # number of samples\n X = np.zeros((N, D))\n X[:300, :] = np.random.randn(300, D) + mu1\n X[300:600, :] = np.random.randn(300, D) + mu2\n X[600:, :] = np.random.randn(300, D) + mu3\n return X\n\n\ndef main():\n X = get_simple_data()\n\n # what does it look like without clustering?\n plt.scatter(X[:,0], X[:,1])\n plt.show()\n\n K = 3 # luckily, we already know this\n plot_k_means(X, K, beta=1.0, show_plots=True)\n\n K = 3 # luckily, we already know this\n plot_k_means(X, K, beta=3.0, show_plots=True)\n\n K = 3 # luckily, we already know this\n plot_k_means(X, K, beta=10.0, show_plots=True)\n\n K = 5 # what happens if we choose a \"bad\" K?\n plot_k_means(X, K, max_iter=30, show_plots=True)\n\n K = 5 # what happens if we change beta?\n plot_k_means(X, K, max_iter=30, beta=0.3, show_plots=True)\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "unsupervised_class/kmeans_fail.py", "content": "# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python\n# https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nfrom kmeans import plot_k_means\n\n\ndef donut():\n N = 1000\n D = 2\n\n R_inner = 5\n R_outer = 10\n\n # distance from origin is radius + random normal\n # angle theta is uniformly distributed between (0, 2pi)\n R1 = np.random.randn(N//2) + R_inner\n theta = 2*np.pi*np.random.random(N//2)\n X_inner = np.concatenate([[R1 * np.cos(theta)], [R1 * np.sin(theta)]]).T\n\n R2 = np.random.randn(N//2) + R_outer\n theta = 2*np.pi*np.random.random(N//2)\n X_outer = np.concatenate([[R2 * np.cos(theta)], [R2 * np.sin(theta)]]).T\n\n X = np.concatenate([ X_inner, X_outer ])\n return X\n\n\ndef main():\n # donut\n X = donut()\n plot_k_means(X, 2, beta=0.1, show_plots=True)\n\n # elongated clusters\n X = np.zeros((1000, 2))\n X[:500,:] = np.random.multivariate_normal([0, 0], [[1, 0], [0, 20]], 500)\n X[500:,:] = np.random.multivariate_normal([5, 0], [[1, 0], [0, 20]], 500)\n plot_k_means(X, 2, beta=0.1, show_plots=True)\n\n # different density\n X = np.zeros((1000, 2))\n X[:950,:] = np.array([0,0]) + np.random.randn(950, 2)\n X[950:,:] = np.array([3,0]) + np.random.randn(50, 2)\n plot_k_means(X, 2, show_plots=True)\n\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "unsupervised_class/kmeans_mnist.py", "content": "# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python\n# https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python\n\n# data is from https://www.kaggle.com/c/digit-recognizer\n# each image is a D = 28x28 = 784 dimensional vector\n# there are N = 42000 samples\n# you can plot an image by reshaping to (28,28) and using plt.imshow()\n\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom .kmeans import plot_k_means, get_simple_data\nfrom datetime import datetime\n\ndef get_data(limit=None):\n print(\"Reading in and transforming data...\")\n df = pd.read_csv('../large_files/train.csv')\n data = df.values\n np.random.shuffle(data)\n X = data[:, 1:] / 255.0 # data is from 0..255\n Y = data[:, 0]\n if limit is not None:\n X, Y = X[:limit], Y[:limit]\n return X, Y\n\n\n# hard labels\ndef purity2(Y, R):\n # maximum purity is 1, higher is better\n C = np.argmax(R, axis=1) # cluster assignments\n\n N = len(Y) # number of data pts\n K = len(set(Y)) # number of labels\n\n total = 0.0\n for k in range(K):\n max_intersection = 0\n for j in range(K):\n intersection = ((C == k) & (Y == j)).sum()\n if intersection > max_intersection:\n max_intersection = intersection\n total += max_intersection\n return total / N\n\n\ndef purity(Y, R):\n # maximum purity is 1, higher is better\n N, K = R.shape\n p = 0\n for k in range(K):\n best_target = -1 # we don't strictly need to store this\n max_intersection = 0\n for j in range(K):\n intersection = R[Y==j, k].sum()\n if intersection > max_intersection:\n max_intersection = intersection\n best_target = j\n p += max_intersection\n return p / N\n\n\n# hard labels\ndef DBI2(X, R):\n N, D = X.shape\n _, K = R.shape\n\n # get sigmas, means first\n sigma = np.zeros(K)\n M = np.zeros((K, D))\n assignments = np.argmax(R, axis=1)\n for k in range(K):\n Xk = X[assignments == k]\n M[k] = Xk.mean(axis=0)\n # assert(Xk.mean(axis=0).shape == (D,))\n n = len(Xk)\n diffs = Xk - M[k]\n sq_diffs = diffs * diffs\n sigma[k] = np.sqrt( sq_diffs.sum() / n )\n\n\n # calculate Davies-Bouldin Index\n dbi = 0\n for k in range(K):\n max_ratio = 0\n for j in range(K):\n if k != j:\n numerator = sigma[k] + sigma[j]\n denominator = np.linalg.norm(M[k] - M[j])\n ratio = numerator / denominator\n if ratio > max_ratio:\n max_ratio = ratio\n dbi += max_ratio\n return dbi / K\n\n\n\ndef DBI(X, M, R):\n # ratio between sum of std deviations between 2 clusters / distance between cluster means\n # lower is better\n N, D = X.shape\n K, _ = M.shape\n\n # get sigmas first\n sigma = np.zeros(K)\n for k in range(K):\n diffs = X - M[k] # should be NxD\n squared_distances = (diffs * diffs).sum(axis=1) # now just N\n weighted_squared_distances = R[:,k]*squared_distances\n sigma[k] = np.sqrt( weighted_squared_distances.sum() / R[:,k].sum() )\n\n # calculate Davies-Bouldin Index\n dbi = 0\n for k in range(K):\n max_ratio = 0\n for j in range(K):\n if k != j:\n numerator = sigma[k] + sigma[j]\n denominator = np.linalg.norm(M[k] - M[j])\n ratio = numerator / denominator\n if ratio > max_ratio:\n max_ratio = ratio\n dbi += max_ratio\n return dbi / K\n\n\ndef main():\n # mnist data\n X, Y = get_data(10000)\n\n # simple data\n # X = get_simple_data()\n # Y = np.array([0]*300 + [1]*300 + [2]*300)\n\n print(\"Number of data points:\", len(Y))\n M, R = plot_k_means(X, len(set(Y)))\n # Exercise: Try different values of K and compare the evaluation metrics\n print(\"Purity:\", purity(Y, R))\n print(\"Purity 2 (hard clusters):\", purity2(Y, R))\n print(\"DBI:\", DBI(X, M, R))\n print(\"DBI 2 (hard clusters):\", DBI2(X, R))\n\n # plot the mean images\n # they should look like digits\n for k in range(len(M)):\n im = M[k].reshape(28, 28)\n plt.imshow(im, cmap='gray')\n plt.show()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "unsupervised_class/kmeans_visualize.py", "content": "# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python\n# https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef d(u, v):\n diff = u - v\n return diff.dot(diff)\n\n\ndef cost(X, R, M):\n cost = 0\n for k in range(len(M)):\n for n in range(len(X)):\n cost += R[n,k]*d(M[k], X[n])\n return cost\n\n\ndef plot_k_means(X, K, max_iter=20, beta=1.0):\n N, D = X.shape\n M = np.zeros((K, D))\n R = np.ones((N, K)) / K\n\n # initialize M to random\n for k in range(K):\n M[k] = X[np.random.choice(N)]\n\n grid_width = 5\n grid_height = max_iter / grid_width\n random_colors = np.random.random((K, 3))\n plt.figure()\n\n costs = np.zeros(max_iter)\n for i in range(max_iter):\n # moved the plot inside the for loop\n colors = R.dot(random_colors)\n plt.subplot(grid_width, grid_height, i+1)\n plt.scatter(X[:,0], X[:,1], c=colors)\n\n # step 1: determine assignments / resposibilities\n # is this inefficient?\n for k in range(K):\n for n in range(N):\n R[n,k] = np.exp(-beta*d(M[k], X[n])) / np.sum( np.exp(-beta*d(M[j], X[n])) for j in range(K) )\n\n # step 2: recalculate means\n for k in range(K):\n M[k] = R[:,k].dot(X) / R[:,k].sum()\n\n costs[i] = cost(X, R, M)\n if i > 0:\n if np.abs(costs[i] - costs[i-1]) < 1e-5:\n break\n plt.show()\n\n\ndef main():\n # assume 3 means\n D = 2 # so we can visualize it more easily\n s = 4 # separation so we can control how far apart the means are\n mu1 = np.array([0, 0])\n mu2 = np.array([s, s])\n mu3 = np.array([0, s])\n\n N = 900 # number of samples\n X = np.zeros((N, D))\n X[:300, :] = np.random.randn(300, D) + mu1\n X[300:600, :] = np.random.randn(300, D) + mu2\n X[600:, :] = np.random.randn(300, D) + mu3\n\n # what does it look like without clustering?\n plt.scatter(X[:,0], X[:,1])\n plt.show()\n\n K = 3 # luckily, we already know this\n plot_k_means(X, K)\n\n # K = 5 # what happens if we choose a \"bad\" K?\n # plot_k_means(X, K, max_iter=30)\n\n # K = 5 # what happens if we change beta?\n # plot_k_means(X, K, max_iter=30, beta=0.3)\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "unsupervised_class/neural_kmeans.py", "content": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom kmeans import get_simple_data\nfrom sklearn.preprocessing import StandardScaler\n\n\n# get the data and standardize it\nX = get_simple_data()\nscaler = StandardScaler()\nX = scaler.fit_transform(X)\n\n# get shapes\nN, D = X.shape\nK = 3\n\n# initialize parameters\nW = np.random.randn(D, K)\n\n# set hyperparameters\nn_epochs = 100\nlearning_rate = 0.001\nlosses = []\n\n# training loop\nfor i in range(n_epochs):\n loss = 0\n for j in range(N):\n h = W.T.dot(X[j]) # K-length vector\n k = np.argmax(h) # winning neuron\n\n # accumulate loss\n loss += (W[:,k] - X[j]).dot(W[:,k] - X[j])\n\n # weight update\n W[:,k] += learning_rate * (X[j] - W[:,k])\n\n losses.append(loss)\n\n\n# plot losses\nplt.plot(losses)\nplt.show()\n\n# show cluster assignments\nH = np.argmax(X.dot(W), axis=1)\nplt.scatter(X[:,0], X[:,1], c=H, alpha=0.5)\nplt.show()\n" }, { "path": "unsupervised_class/tweets.py", "content": "# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python\n# https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python\n# data from https://www.kaggle.com/benhamner/clinton-trump-tweets\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport re\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom scipy.spatial.distance import pdist\nfrom scipy.cluster.hierarchy import dendrogram, linkage, fcluster\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n# from sklearn.metrics.pairwise import pairwise_distances ### fast, but result not symmetric\n\n\n\n# load stopwords\n# selected after observing results without stopwords\nstopwords = [\n 'the',\n 'about',\n 'an',\n 'and',\n 'are',\n 'at',\n 'be',\n 'can',\n 'for',\n 'from',\n 'if',\n 'in',\n 'is',\n 'it',\n 'of',\n 'on',\n 'or',\n 'that',\n 'this',\n 'to',\n 'you',\n 'your',\n 'with',\n]\n\n\n# find urls and twitter usernames within a string\nurl_finder = re.compile(r\"(?:\\@|https?\\://)\\S+\")\n\n\ndef filter_tweet(s):\n s = s.lower() # downcase\n s = url_finder.sub(\"\", s) # remove urls and usernames\n return s\n\n\n\n### load data ###\ndf = pd.read_csv('../large_files/tweets.csv')\ntext = df.text.tolist()\ntext = [filter_tweet(s) for s in text]\n\n\n# transform the text into a data matrix\ntfidf = TfidfVectorizer(max_features=100, stop_words=stopwords)\nX = tfidf.fit_transform(text).asformat('array')\n\n\n# subsample for efficiency\n# remember: calculating distances is O(N^2)\nN = X.shape[0]\nidx = np.random.choice(N, size=2000, replace=False)\nx = X[idx]\nlabels = df.handle[idx].tolist()\n\n\n# proportions of each label\n# so we can be confident that each is represented equally\npTrump = sum(1.0 if e == 'realDonaldTrump' else 0.0 for e in labels) / len(labels)\nprint(\"proportion @realDonaldTrump: %.3f\" % pTrump)\nprint(\"proportion @HillaryClinton: %.3f\" % (1 - pTrump))\n\n\n# transform the data matrix into pairwise distances list\ndist_array = pdist(x)\n\n\n# calculate hierarchy\nZ = linkage(dist_array, 'ward')\nplt.title(\"Ward\")\ndendrogram(Z, labels=labels)\nplt.show()\n\n### hits max recursion depth\n# Z = linkage(dist_array, 'single')\n# plt.title(\"Single\")\n# dendrogram(Z, labels=labels)\n# plt.show()\n\n# Z = linkage(dist_array, 'complete')\n# plt.title(\"Complete\")\n# dendrogram(Z, labels=labels)\n# plt.show()\n\n\n# convert labels to (1, 2), not (0, 1)\n# since that's what's returned by fcluster\nY = np.array([1 if e == 'realDonaldTrump' else 2 for e in labels])\n\n\n# get cluster assignments\n# threshold 9 was chosen empirically to yield 2 clusters\nC = fcluster(Z, 9, criterion='distance') # returns 1, 2, ..., K\ncategories = set(C)\n# sanity check: should be {1, 2}\nprint(\"values in C:\", categories)\n\n\n### calculate the purity of our clusters ###\ndef purity(true_labels, cluster_assignments, categories):\n # maximum purity is 1, higher is better\n N = len(true_labels)\n\n total = 0.0\n for k in categories:\n max_intersection = 0\n for j in categories:\n intersection = ((cluster_assignments == k) & (true_labels == j)).sum()\n if intersection > max_intersection:\n max_intersection = intersection\n total += max_intersection\n return total / N\n\nprint(\"purity:\", purity(Y, C, categories))\n\n\n# we know the smaller cluster is the trump cluster\n#\n# important note: we call it the trump cluster from\n# observing AFTER the fact that most tweets in this\n# cluster are by trump\n# we do NOT use those labels to create the cluster\n#\n# we create the clusters using a distance-based\n# algorithm which knows nothing about the labels,\n# just the tf-idf scores.\n#\n# it just so happens that most of the tweets in\n# one of the clusters is by trump, and that this\n# cluster is very small\nif (C == 1).sum() < (C == 2).sum():\n d = 1\n h = 2\nelse:\n d = 2\n h = 1\n\nactually_donald = ((C == d) & (Y == 1)).sum()\ndonald_cluster_size = (C == d).sum()\nprint(\"purity of @realDonaldTrump cluster:\", float(actually_donald) / donald_cluster_size)\n\nactually_hillary = ((C == h) & (Y == 2)).sum()\nhillary_cluster_size = (C == h).sum()\nprint(\"purity of @HillaryClinton cluster:\", float(actually_hillary) / hillary_cluster_size)\n\n\n# just for interest, how would a classifier do?\n# note: classification is always easier\nfrom sklearn.ensemble import RandomForestClassifier\n\nrf = RandomForestClassifier()\nrf.fit(X, df.handle)\nprint(\"classifier score:\", rf.score(X, df.handle))\n\n# same as mnist\n# classifier yields almost 100% accuracy\n# but purity of clustering is much lower\n\n\n\n# what words have the highest tf-idf in cluster 1? in cluster 2?\nw2i = tfidf.vocabulary_\n\n# tf-idf vectorizer todense() returns a matrix rather than array\n# matrix always wants to be 2-D, so we convert to array in order to flatten\nd_avg = np.array(x[C == d].mean(axis=0)).flatten()\nd_sorted = sorted(w2i.keys(), key=lambda w: -d_avg[w2i[w]])\n\nprint(\"\\nTop 10 'Donald cluster' words:\")\nprint(\"\\n\".join(d_sorted[:10]))\n\nh_avg = np.array(x[C == h].mean(axis=0)).flatten()\nh_sorted = sorted(w2i.keys(), key=lambda w: -h_avg[w2i[w]])\n\nprint(\"\\nTop 10 'Hillary cluster' words:\")\nprint(\"\\n\".join(h_sorted[:10]))\n\n\n" }, { "path": "unsupervised_class2/__init__.py", "content": "" }, { "path": "unsupervised_class2/autoencoder.py", "content": "# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python\n# https://www.udemy.com/unsupervised-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport theano\nimport theano.tensor as T\nimport matplotlib.pyplot as plt\n\nfrom sklearn.utils import shuffle\nfrom util import relu, error_rate, getKaggleMNIST, init_weights\n\n\ndef T_shared_zeros_like32(p):\n # p is a Theano shared itself\n return theano.shared(np.zeros_like(p.get_value(), dtype=np.float32))\n\ndef momentum_updates(cost, params, mu, learning_rate):\n # momentum changes\n dparams = [T_shared_zeros_like32(p) for p in params]\n\n updates = []\n grads = T.grad(cost, params)\n for p, dp, g in zip(params, dparams, grads):\n dp_update = mu*dp - learning_rate*g\n p_update = p + dp_update\n\n updates.append((dp, dp_update))\n updates.append((p, p_update))\n return updates\n\n\nclass AutoEncoder(object):\n def __init__(self, M, an_id):\n self.M = M\n self.id = an_id\n\n def fit(self, X, learning_rate=0.5, mu=0.99, epochs=1, batch_sz=100, show_fig=False):\n # cast to float\n mu = np.float32(mu)\n learning_rate = np.float32(learning_rate)\n\n N, D = X.shape\n n_batches = N // batch_sz\n\n W0 = init_weights((D, self.M))\n self.W = theano.shared(W0, 'W_%s' % self.id)\n self.bh = theano.shared(np.zeros(self.M, dtype=np.float32), 'bh_%s' % self.id)\n self.bo = theano.shared(np.zeros(D, dtype=np.float32), 'bo_%s' % self.id)\n self.params = [self.W, self.bh, self.bo]\n self.forward_params = [self.W, self.bh]\n\n # TODO: technically these should be reset before doing backprop\n self.dW = theano.shared(np.zeros(W0.shape), 'dW_%s' % self.id)\n self.dbh = theano.shared(np.zeros(self.M), 'dbh_%s' % self.id)\n self.dbo = theano.shared(np.zeros(D), 'dbo_%s' % self.id)\n self.dparams = [self.dW, self.dbh, self.dbo]\n self.forward_dparams = [self.dW, self.dbh]\n\n X_in = T.matrix('X_%s' % self.id)\n X_hat = self.forward_output(X_in)\n\n # attach it to the object so it can be used later\n # must be sigmoidal because the output is also a sigmoid\n H = T.nnet.sigmoid(X_in.dot(self.W) + self.bh)\n self.hidden_op = theano.function(\n inputs=[X_in],\n outputs=H,\n )\n\n # save this for later so we can call it to\n # create reconstructions of input\n self.predict = theano.function(\n inputs=[X_in],\n outputs=X_hat,\n )\n\n cost = -(X_in * T.log(X_hat) + (1 - X_in) * T.log(1 - X_hat)).flatten().mean()\n cost_op = theano.function(\n inputs=[X_in],\n outputs=cost,\n )\n\n \n\n updates = momentum_updates(cost, self.params, mu, learning_rate)\n train_op = theano.function(\n inputs=[X_in],\n updates=updates,\n )\n\n costs = []\n print(\"training autoencoder: %s\" % self.id)\n print(\"epochs to do:\", epochs)\n for i in range(epochs):\n print(\"epoch:\", i)\n X = shuffle(X)\n for j in range(n_batches):\n batch = X[j*batch_sz:(j*batch_sz + batch_sz)]\n train_op(batch)\n the_cost = cost_op(batch) # technically we could also get the cost for Xtest here\n if j % 10 == 0:\n print(\"j / n_batches:\", j, \"/\", n_batches, \"cost:\", the_cost)\n costs.append(the_cost)\n if show_fig:\n plt.plot(costs)\n plt.show()\n\n def forward_hidden(self, X):\n Z = T.nnet.sigmoid(X.dot(self.W) + self.bh)\n return Z\n\n def forward_output(self, X):\n Z = self.forward_hidden(X)\n Y = T.nnet.sigmoid(Z.dot(self.W.T) + self.bo)\n return Y\n\n @staticmethod\n def createFromArrays(W, bh, bo, an_id):\n ae = AutoEncoder(W.shape[1], an_id)\n ae.W = theano.shared(W, 'W_%s' % ae.id)\n ae.bh = theano.shared(bh, 'bh_%s' % ae.id)\n ae.bo = theano.shared(bo, 'bo_%s' % ae.id)\n ae.params = [ae.W, ae.bh, ae.bo]\n ae.forward_params = [ae.W, ae.bh]\n return ae\n\n\nclass DNN(object):\n def __init__(self, hidden_layer_sizes, UnsupervisedModel=AutoEncoder):\n self.hidden_layers = []\n count = 0\n for M in hidden_layer_sizes:\n ae = UnsupervisedModel(M, count)\n self.hidden_layers.append(ae)\n count += 1\n\n\n def fit(self, X, Y, Xtest, Ytest,\n pretrain=True,\n train_head_only=False,\n learning_rate=0.1,\n mu=0.99,\n reg=0.0,\n epochs=1,\n batch_sz=100):\n\n # cast to float32\n learning_rate = np.float32(learning_rate)\n mu = np.float32(mu)\n reg = np.float32(reg)\n\n # greedy layer-wise training of autoencoders\n pretrain_epochs = 2\n if not pretrain:\n pretrain_epochs = 0\n\n current_input = X\n for ae in self.hidden_layers:\n ae.fit(current_input, epochs=pretrain_epochs)\n\n # create current_input for the next layer\n current_input = ae.hidden_op(current_input)\n\n # initialize logistic regression layer\n N = len(Y)\n K = len(set(Y))\n W0 = init_weights((self.hidden_layers[-1].M, K))\n self.W = theano.shared(W0, \"W_logreg\")\n self.b = theano.shared(np.zeros(K, dtype=np.float32), \"b_logreg\")\n\n self.params = [self.W, self.b]\n if not train_head_only:\n for ae in self.hidden_layers:\n self.params += ae.forward_params\n\n X_in = T.matrix('X_in')\n targets = T.ivector('Targets')\n pY = self.forward(X_in)\n\n squared_magnitude = [(p*p).sum() for p in self.params]\n reg_cost = T.sum(squared_magnitude)\n cost = -T.mean( T.log(pY[T.arange(pY.shape[0]), targets]) ) + reg*reg_cost\n prediction = self.predict(X_in)\n cost_predict_op = theano.function(\n inputs=[X_in, targets],\n outputs=[cost, prediction],\n )\n\n updates = momentum_updates(cost, self.params, mu, learning_rate)\n train_op = theano.function(\n inputs=[X_in, targets],\n updates=updates,\n )\n\n n_batches = N // batch_sz\n costs = []\n print(\"supervised training...\")\n for i in range(epochs):\n print(\"epoch:\", i)\n X, Y = shuffle(X, Y)\n for j in range(n_batches):\n Xbatch = X[j*batch_sz:(j*batch_sz + batch_sz)]\n Ybatch = Y[j*batch_sz:(j*batch_sz + batch_sz)]\n train_op(Xbatch, Ybatch)\n the_cost, the_prediction = cost_predict_op(Xtest, Ytest)\n error = error_rate(the_prediction, Ytest)\n print(\"j / n_batches:\", j, \"/\", n_batches, \"cost:\", the_cost, \"error:\", error)\n costs.append(the_cost)\n plt.plot(costs)\n plt.show()\n\n def predict(self, X):\n return T.argmax(self.forward(X), axis=1)\n\n def forward(self, X):\n current_input = X\n for ae in self.hidden_layers:\n Z = ae.forward_hidden(current_input)\n current_input = Z\n\n # logistic layer\n Y = T.nnet.softmax(T.dot(current_input, self.W) + self.b)\n return Y\n\n\ndef main():\n Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()\n # dnn = DNN([1000, 750, 500])\n # dnn.fit(Xtrain, Ytrain, Xtest, Ytest, epochs=3)\n # vs\n dnn = DNN([1000, 750, 500])\n dnn.fit(Xtrain, Ytrain, Xtest, Ytest, pretrain=True, train_head_only=False, epochs=3)\n # note: try training the head only too! what does that mean?\n\n\ndef test_single_autoencoder():\n Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()\n\n autoencoder = AutoEncoder(300, 0)\n autoencoder.fit(Xtrain, epochs=2, show_fig=True)\n\n done = False\n while not done:\n i = np.random.choice(len(Xtest))\n x = Xtest[i]\n y = autoencoder.predict([x])\n plt.subplot(1,2,1)\n plt.imshow(x.reshape(28,28), cmap='gray')\n plt.title('Original')\n\n plt.subplot(1,2,2)\n plt.imshow(y.reshape(28,28), cmap='gray')\n plt.title('Reconstructed')\n\n plt.show()\n\n ans = input(\"Generate another?\")\n if ans and ans[0] in ('n' or 'N'):\n done = True\n\n\nif __name__ == '__main__':\n main()\n # test_single_autoencoder()\n" }, { "path": "unsupervised_class2/autoencoder_tf.py", "content": "# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python\n# https://www.udemy.com/unsupervised-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\n\nfrom sklearn.utils import shuffle\nfrom util import error_rate, getKaggleMNIST\n\nif tf.__version__.startswith('2'):\n tf.compat.v1.disable_eager_execution()\n\n\nclass AutoEncoder(object):\n def __init__(self, D, M, an_id):\n self.M = M\n self.id = an_id\n self.build(D, M)\n\n def set_session(self, session):\n self.session = session\n\n def build(self, D, M):\n self.W = tf.Variable(tf.random.normal(shape=(D, M)))\n self.bh = tf.Variable(np.zeros(M).astype(np.float32))\n self.bo = tf.Variable(np.zeros(D).astype(np.float32))\n\n self.X_in = tf.compat.v1.placeholder(tf.float32, shape=(None, D))\n self.Z = self.forward_hidden(self.X_in) # for transform() later\n self.X_hat = self.forward_output(self.X_in)\n\n\n # using the naive formulation for cross-entropy\n # will have numerical stability issues if X_hat = 0 or 1\n logits = self.forward_logits(self.X_in)\n self.cost = tf.reduce_mean(\n input_tensor=tf.nn.sigmoid_cross_entropy_with_logits(\n labels=self.X_in,\n logits=logits,\n )\n )\n\n self.train_op = tf.compat.v1.train.AdamOptimizer(1e-1).minimize(self.cost)\n # self.train_op = tf.train.MomentumOptimizer(1e-3, momentum=0.9).minimize(self.cost)\n\n def fit(self, X, epochs=1, batch_sz=100, show_fig=False):\n N, D = X.shape\n n_batches = N // batch_sz\n\n costs = []\n print(\"training autoencoder: %s\" % self.id)\n for i in range(epochs):\n print(\"epoch:\", i)\n X = shuffle(X)\n for j in range(n_batches):\n batch = X[j*batch_sz:(j*batch_sz + batch_sz)]\n _, c = self.session.run((self.train_op, self.cost), feed_dict={self.X_in: batch})\n if j % 10 == 0:\n print(\"j / n_batches:\", j, \"/\", n_batches, \"cost:\", c)\n costs.append(c)\n if show_fig:\n plt.plot(costs)\n plt.show()\n\n def transform(self, X):\n # accepts and returns a real numpy array\n # unlike forward_hidden and forward_output\n # which deal with tensorflow variables\n return self.session.run(self.Z, feed_dict={self.X_in: X})\n\n def predict(self, X):\n # accepts and returns a real numpy array\n # unlike forward_hidden and forward_output\n # which deal with tensorflow variables\n return self.session.run(self.X_hat, feed_dict={self.X_in: X})\n\n def forward_hidden(self, X):\n Z = tf.nn.sigmoid(tf.matmul(X, self.W) + self.bh)\n return Z\n\n def forward_logits(self, X):\n Z = self.forward_hidden(X)\n return tf.matmul(Z, tf.transpose(a=self.W)) + self.bo\n\n def forward_output(self, X):\n return tf.nn.sigmoid(self.forward_logits(X))\n\n\nclass DNN(object):\n def __init__(self, D, hidden_layer_sizes, K, UnsupervisedModel=AutoEncoder):\n self.hidden_layers = []\n count = 0\n input_size = D\n for output_size in hidden_layer_sizes:\n ae = UnsupervisedModel(input_size, output_size, count)\n self.hidden_layers.append(ae)\n count += 1\n input_size = output_size\n self.build_final_layer(D, hidden_layer_sizes[-1], K)\n\n def set_session(self, session):\n self.session = session\n for layer in self.hidden_layers:\n layer.set_session(session)\n\n def build_final_layer(self, D, M, K):\n # initialize logistic regression layer\n self.W = tf.Variable(tf.random.normal(shape=(M, K)))\n self.b = tf.Variable(np.zeros(K).astype(np.float32))\n\n self.X = tf.compat.v1.placeholder(tf.float32, shape=(None, D))\n labels = tf.compat.v1.placeholder(tf.int32, shape=(None,))\n self.Y = labels\n logits = self.forward(self.X)\n\n self.cost = tf.reduce_mean(\n input_tensor=tf.nn.sparse_softmax_cross_entropy_with_logits(\n logits=logits,\n labels=labels\n )\n )\n self.train_op = tf.compat.v1.train.AdamOptimizer(1e-2).minimize(self.cost)\n self.prediction = tf.argmax(input=logits, axis=1)\n\n def fit(self, X, Y, Xtest, Ytest, pretrain=True, epochs=1, batch_sz=100):\n N = len(X)\n\n # greedy layer-wise training of autoencoders\n pretrain_epochs = 1\n if not pretrain:\n pretrain_epochs = 0\n\n current_input = X\n for ae in self.hidden_layers:\n ae.fit(current_input, epochs=pretrain_epochs)\n\n # create current_input for the next layer\n current_input = ae.transform(current_input)\n\n n_batches = N // batch_sz\n costs = []\n print(\"supervised training...\")\n for i in range(epochs):\n print(\"epoch:\", i)\n X, Y = shuffle(X, Y)\n for j in range(n_batches):\n Xbatch = X[j*batch_sz:(j*batch_sz + batch_sz)]\n Ybatch = Y[j*batch_sz:(j*batch_sz + batch_sz)]\n self.session.run(\n self.train_op,\n feed_dict={self.X: Xbatch, self.Y: Ybatch}\n )\n c, p = self.session.run(\n (self.cost, self.prediction),\n feed_dict={self.X: Xtest, self.Y: Ytest\n })\n error = error_rate(p, Ytest)\n if j % 10 == 0:\n print(\"j / n_batches:\", j, \"/\", n_batches, \"cost:\", c, \"error:\", error)\n costs.append(c)\n plt.plot(costs)\n plt.show()\n\n def forward(self, X):\n current_input = X\n for ae in self.hidden_layers:\n Z = ae.forward_hidden(current_input)\n current_input = Z\n\n # logistic layer\n logits = tf.matmul(current_input, self.W) + self.b\n return logits\n\n\ndef test_pretraining_dnn():\n Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()\n # dnn = DNN([1000, 750, 500])\n # dnn.fit(Xtrain, Ytrain, Xtest, Ytest, epochs=3)\n # vs\n Xtrain = Xtrain.astype(np.float32)\n Xtest = Xtest.astype(np.float32)\n _, D = Xtrain.shape\n K = len(set(Ytrain))\n dnn = DNN(D, [1000, 750, 500], K)\n init_op = tf.compat.v1.global_variables_initializer()\n with tf.compat.v1.Session() as session:\n session.run(init_op)\n dnn.set_session(session)\n dnn.fit(Xtrain, Ytrain, Xtest, Ytest, pretrain=True, epochs=10)\n\n\ndef test_single_autoencoder():\n Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()\n Xtrain = Xtrain.astype(np.float32)\n Xtest = Xtest.astype(np.float32)\n\n _, D = Xtrain.shape\n autoencoder = AutoEncoder(D, 300, 0)\n init_op = tf.compat.v1.global_variables_initializer()\n with tf.compat.v1.Session() as session:\n session.run(init_op)\n autoencoder.set_session(session)\n autoencoder.fit(Xtrain, show_fig=True)\n\n done = False\n while not done:\n i = np.random.choice(len(Xtest))\n x = Xtest[i]\n y = autoencoder.predict([x])\n plt.subplot(1,2,1)\n plt.imshow(x.reshape(28,28), cmap='gray')\n plt.title('Original')\n\n plt.subplot(1,2,2)\n plt.imshow(y.reshape(28,28), cmap='gray')\n plt.title('Reconstructed')\n\n plt.show()\n\n ans = input(\"Generate another?\")\n if ans and ans[0] in ('n' or 'N'):\n done = True\n\n\n\nif __name__ == '__main__':\n # test_single_autoencoder()\n test_pretraining_dnn()\n" }, { "path": "unsupervised_class2/compare_pca_svd.py", "content": "# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python\n# https://www.udemy.com/unsupervised-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.decomposition import PCA, TruncatedSVD\nfrom util import getKaggleMNIST\n\n\nX, Y, _, _ = getKaggleMNIST()\nm = X.mean(axis=0)\ns = X.std(axis=0)\nnp.place(s, s == 0, 1)\nX = (X - m) / s\n\npca = PCA()\nsvd = TruncatedSVD()\n\nZ1 = pca.fit_transform(X)\nZ2 = svd.fit_transform(X)\n\nplt.subplot(1,2,1)\nplt.scatter(Z1[:,0], Z1[:,1], c=Y)\nplt.subplot(1,2,2)\nplt.scatter(Z2[:,0], Z2[:,1], c=Y)\nplt.show()\n" }, { "path": "unsupervised_class2/extra_reading.txt", "content": "Visualizing Data using t-SNE\nhttp://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf\n\nReducing the Dimensionality of Data with Neural Networks\nhttps://www.cs.toronto.edu/~hinton/science.pdf\n\nA fast learning algorithm for deep belief nets\nhttps://www.cs.toronto.edu/~hinton/absps/fastnc.pdf\n\nWhy Does Unsupervised Pre-training Help Deep Learning?\nhttp://www.jmlr.org/papers/volume11/erhan10a/erhan10a.pdf\n\nA Better Way to Pretrain Deep Boltzmann Machines\nhttp://www.cs.toronto.edu/~hinton/absps/DBM_pretrain.pdf\n\nOn Deep Generative Models with Applications to Recognition\nhttp://www.cs.toronto.edu/~hinton/absps/ranzato_cvpr2011.pdf\n\nLEARNING A BETTER REPRESENTATION OF SPEECH SOUND WAVES USING RESTRICTED BOLTZMANN MACHINES\nhttp://www.cs.toronto.edu/~hinton/absps/jaitly_ICASSP2011.pdf\n\nRectified Linear Units Improve Restricted Boltzmann Machines\nhttp://www.cs.toronto.edu/~hinton/absps/reluICML.pdf\n\nGenerative versus discriminative training of RBMs for classification of fMRI images\nhttp://www.cs.toronto.edu/~hinton/absps/fmrinips.pdf\n\nRestricted Boltzmann Machines for Collaborative Filtering\nhttp://www.cs.toronto.edu/~hinton/absps/netflix.pdf\n\nOn Contrastive Divergence Learning\nhttp://www.cs.toronto.edu/~hinton/absps/cdmiguel.pdf" }, { "path": "unsupervised_class2/gaussian_nb.py", "content": "# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nfrom sklearn.decomposition import PCA\n# from sklearn.naive_bayes import GaussianNB # doesn't have smoothing\nfrom scipy.stats import norm\nfrom scipy.stats import multivariate_normal as mvn\nfrom util import getKaggleMNIST\n\n\nclass GaussianNB(object):\n def fit(self, X, Y, smoothing=1e-2):\n self.gaussians = dict()\n self.priors = dict()\n labels = set(Y)\n for c in labels:\n current_x = X[Y == c]\n self.gaussians[c] = {\n 'mean': current_x.mean(axis=0),\n 'var': current_x.var(axis=0) + smoothing,\n }\n self.priors[c] = float(len(Y[Y == c])) / len(Y)\n\n def score(self, X, Y):\n P = self.predict(X)\n return np.mean(P == Y)\n\n def predict(self, X):\n N, D = X.shape\n K = len(self.gaussians)\n P = np.zeros((N, K))\n for c, g in iteritems(self.gaussians):\n mean, var = g['mean'], g['var']\n P[:,c] = mvn.logpdf(X, mean=mean, cov=var) + np.log(self.priors[c])\n return np.argmax(P, axis=1)\n\n\n# get data\nXtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()\n\n# try NB by itself\nmodel1 = GaussianNB()\nmodel1.fit(Xtrain, Ytrain)\nprint(\"NB train score:\", model1.score(Xtrain, Ytrain))\nprint(\"NB test score:\", model1.score(Xtest, Ytest))\n\n# try NB with PCA first\npca = PCA(n_components=50)\nZtrain = pca.fit_transform(Xtrain)\nZtest = pca.transform(Xtest)\n\nmodel2 = GaussianNB()\nmodel2.fit(Ztrain, Ytrain)\nprint(\"NB+PCA train score:\", model2.score(Ztrain, Ytrain))\nprint(\"NB+PCA test score:\", model2.score(Ztest, Ytest))\n" }, { "path": "unsupervised_class2/pca.py", "content": "# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python\n# https://www.udemy.com/unsupervised-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.decomposition import PCA\nfrom util import getKaggleMNIST\n\ndef main():\n Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()\n\n pca = PCA()\n reduced = pca.fit_transform(Xtrain)\n plt.scatter(reduced[:,0], reduced[:,1], s=100, c=Ytrain, alpha=0.5)\n plt.show()\n\n plt.plot(pca.explained_variance_ratio_)\n plt.show()\n\n # cumulative variance\n # choose k = number of dimensions that gives us 95-99% variance\n cumulative = []\n last = 0\n for v in pca.explained_variance_ratio_:\n cumulative.append(last + v)\n last = cumulative[-1]\n plt.plot(cumulative)\n plt.show()\n\nif __name__ == '__main__':\n main()" }, { "path": "unsupervised_class2/pca_impl.py", "content": "# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python\n# https://www.udemy.com/unsupervised-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom util import getKaggleMNIST\n\n# get the data\nXtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()\n\n# decompose covariance\ncovX = np.cov(Xtrain.T)\nlambdas, Q = np.linalg.eigh(covX)\n\n\n# lambdas are sorted from smallest --> largest\n# some may be slightly negative due to precision\nidx = np.argsort(-lambdas)\nlambdas = lambdas[idx] # sort in proper order\nlambdas = np.maximum(lambdas, 0) # get rid of negatives\nQ = Q[:,idx]\n\n\n# plot the first 2 columns of Z\nZ = Xtrain.dot(Q)\nplt.scatter(Z[:,0], Z[:,1], s=100, c=Ytrain, alpha=0.3)\nplt.show()\n\n\n# plot variances\nplt.plot(lambdas)\nplt.title(\"Variance of each component\")\nplt.show()\n\n# cumulative variance\nplt.plot(np.cumsum(lambdas))\nplt.title(\"Cumulative variance\")\nplt.show()" }, { "path": "unsupervised_class2/rbm.py", "content": "# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python\n# https://www.udemy.com/unsupervised-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport theano\nimport theano.tensor as T\nimport matplotlib.pyplot as plt\n\nfrom sklearn.utils import shuffle\nfrom theano.tensor.shared_randomstreams import RandomStreams\nfrom util import relu, error_rate, getKaggleMNIST, init_weights\nfrom autoencoder import DNN\n\n\nclass RBM(object):\n def __init__(self, M, an_id):\n self.M = M\n self.id = an_id\n self.rng = RandomStreams()\n\n def fit(self, X, learning_rate=0.1, epochs=1, batch_sz=100, show_fig=False):\n # cast to float32\n learning_rate = np.float32(learning_rate)\n\n\n N, D = X.shape\n n_batches = N // batch_sz\n\n W0 = init_weights((D, self.M))\n self.W = theano.shared(W0, 'W_%s' % self.id)\n self.c = theano.shared(np.zeros(self.M), 'c_%s' % self.id)\n self.b = theano.shared(np.zeros(D), 'b_%s' % self.id)\n self.params = [self.W, self.c, self.b]\n self.forward_params = [self.W, self.c]\n\n X_in = T.matrix('X_%s' % self.id)\n\n # attach it to the object so it can be used later\n # must be sigmoidal because the output is also a sigmoid\n H = T.nnet.sigmoid(X_in.dot(self.W) + self.c)\n self.hidden_op = theano.function(\n inputs=[X_in],\n outputs=H,\n )\n\n # we won't use this cost to do any updates\n # but we would like to see how this cost function changes\n # as we do contrastive divergence\n X_hat = self.forward_output(X_in)\n cost = -(X_in * T.log(X_hat) + (1 - X_in) * T.log(1 - X_hat)).mean()\n cost_op = theano.function(\n inputs=[X_in],\n outputs=cost,\n )\n\n # do one round of Gibbs sampling to obtain X_sample\n H = self.sample_h_given_v(X_in)\n X_sample = self.sample_v_given_h(H)\n\n # define the objective, updates, and train function\n objective = T.mean(self.free_energy(X_in)) - T.mean(self.free_energy(X_sample))\n\n # need to consider X_sample constant because you can't take the gradient of random numbers in Theano\n updates = [(p, p - learning_rate*T.grad(objective, p, consider_constant=[X_sample])) for p in self.params]\n train_op = theano.function(\n inputs=[X_in],\n updates=updates,\n )\n\n costs = []\n print(\"training rbm: %s\" % self.id)\n for i in range(epochs):\n print(\"epoch:\", i)\n X = shuffle(X)\n for j in range(n_batches):\n batch = X[j*batch_sz:(j*batch_sz + batch_sz)]\n train_op(batch)\n the_cost = cost_op(X) # technically we could also get the cost for Xtest here\n print(\"j / n_batches:\", j, \"/\", n_batches, \"cost:\", the_cost)\n costs.append(the_cost)\n if show_fig:\n plt.plot(costs)\n plt.show()\n\n def free_energy(self, V):\n return -V.dot(self.b) - T.sum(T.log(1 + T.exp(V.dot(self.W) + self.c)), axis=1)\n\n def sample_h_given_v(self, V):\n p_h_given_v = T.nnet.sigmoid(V.dot(self.W) + self.c)\n h_sample = self.rng.binomial(size=p_h_given_v.shape, n=1, p=p_h_given_v)\n return h_sample\n\n def sample_v_given_h(self, H):\n p_v_given_h = T.nnet.sigmoid(H.dot(self.W.T) + self.b)\n v_sample = self.rng.binomial(size=p_v_given_h.shape, n=1, p=p_v_given_h)\n return v_sample\n\n def forward_hidden(self, X):\n return T.nnet.sigmoid(X.dot(self.W) + self.c)\n\n def forward_output(self, X):\n Z = self.forward_hidden(X)\n Y = T.nnet.sigmoid(Z.dot(self.W.T) + self.b)\n return Y\n\n @staticmethod\n def createFromArrays(W, c, b, an_id):\n rbm = AutoEncoder(W.shape[1], an_id)\n rbm.W = theano.shared(W, 'W_%s' % rbm.id)\n rbm.c = theano.shared(c, 'c_%s' % rbm.id)\n rbm.b = theano.shared(b, 'b_%s' % rbm.id)\n rbm.params = [rbm.W, rbm.c, rbm.b]\n rbm.forward_params = [rbm.W, rbm.c]\n return rbm\n\n\ndef main():\n Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()\n dnn = DNN([1000, 750, 500], UnsupervisedModel=RBM)\n dnn.fit(Xtrain, Ytrain, Xtest, Ytest, epochs=3)\n\n # we compare with no pretraining in autoencoder.py\n\n\nif __name__ == '__main__':\n main()" }, { "path": "unsupervised_class2/rbm_tf.py", "content": "# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python\n# https://www.udemy.com/unsupervised-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nfrom sklearn.utils import shuffle\nfrom util import getKaggleMNIST\nfrom autoencoder_tf import DNN\n\n\nclass RBM(object):\n def __init__(self, D, M, an_id):\n self.D = D\n self.M = M\n self.id = an_id\n self.build(D, M)\n\n def set_session(self, session):\n self.session = session\n\n def build(self, D, M):\n # params\n self.W = tf.Variable(tf.random.normal(shape=(D, M)) * np.sqrt(2.0 / M))\n # note: without limiting variance, you get numerical stability issues\n self.c = tf.Variable(np.zeros(M).astype(np.float32))\n self.b = tf.Variable(np.zeros(D).astype(np.float32))\n\n # data\n self.X_in = tf.compat.v1.placeholder(tf.float32, shape=(None, D))\n\n # conditional probabilities\n # NOTE: tf.contrib.distributions.Bernoulli API has changed in Tensorflow v1.2\n V = self.X_in\n p_h_given_v = tf.nn.sigmoid(tf.matmul(V, self.W) + self.c)\n self.p_h_given_v = p_h_given_v # save for later\n # self.rng_h_given_v = tf.contrib.distributions.Bernoulli(\n # probs=p_h_given_v,\n # dtype=tf.float32\n # )\n r = tf.random.uniform(shape=tf.shape(input=p_h_given_v))\n H = tf.cast(r < p_h_given_v, dtype=tf.float32)\n\n p_v_given_h = tf.nn.sigmoid(tf.matmul(H, tf.transpose(a=self.W)) + self.b)\n # self.rng_v_given_h = tf.contrib.distributions.Bernoulli(\n # probs=p_v_given_h,\n # dtype=tf.float32\n # )\n r = tf.random.uniform(shape=tf.shape(input=p_v_given_h))\n X_sample = tf.cast(r < p_v_given_h, dtype=tf.float32)\n\n\n # build the objective\n objective = tf.reduce_mean(input_tensor=self.free_energy(self.X_in)) - tf.reduce_mean(input_tensor=self.free_energy(X_sample))\n self.train_op = tf.compat.v1.train.AdamOptimizer(1e-2).minimize(objective)\n # self.train_op = tf.train.GradientDescentOptimizer(1e-3).minimize(objective)\n\n # build the cost\n # we won't use this to optimize the model parameters\n # just to observe what happens during training\n logits = self.forward_logits(self.X_in)\n self.cost = tf.reduce_mean(\n input_tensor=tf.nn.sigmoid_cross_entropy_with_logits(\n labels=self.X_in,\n logits=logits,\n )\n )\n\n def fit(self, X, epochs=1, batch_sz=100, show_fig=False):\n N, D = X.shape\n n_batches = N // batch_sz\n\n costs = []\n print(\"training rbm: %s\" % self.id)\n for i in range(epochs):\n print(\"epoch:\", i)\n X = shuffle(X)\n for j in range(n_batches):\n batch = X[j*batch_sz:(j*batch_sz + batch_sz)]\n _, c = self.session.run((self.train_op, self.cost), feed_dict={self.X_in: batch})\n if j % 10 == 0:\n print(\"j / n_batches:\", j, \"/\", n_batches, \"cost:\", c)\n costs.append(c)\n if show_fig:\n plt.plot(costs)\n plt.show()\n\n def free_energy(self, V):\n b = tf.reshape(self.b, (self.D, 1))\n first_term = -tf.matmul(V, b)\n first_term = tf.reshape(first_term, (-1,))\n\n second_term = -tf.reduce_sum(\n # tf.log(1 + tf.exp(tf.matmul(V, self.W) + self.c)),\n input_tensor=tf.nn.softplus(tf.matmul(V, self.W) + self.c),\n axis=1\n )\n\n return first_term + second_term\n\n def forward_hidden(self, X):\n return tf.nn.sigmoid(tf.matmul(X, self.W) + self.c)\n\n def forward_logits(self, X):\n Z = self.forward_hidden(X)\n return tf.matmul(Z, tf.transpose(a=self.W)) + self.b\n\n def forward_output(self, X):\n return tf.nn.sigmoid(self.forward_logits(X))\n\n def transform(self, X):\n # accepts and returns a real numpy array\n # unlike forward_hidden and forward_output\n # which deal with tensorflow variables\n return self.session.run(self.p_h_given_v, feed_dict={self.X_in: X})\n\n\ndef main():\n Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()\n\n # same as autoencoder_tf.py\n Xtrain = Xtrain.astype(np.float32)\n Xtest = Xtest.astype(np.float32)\n _, D = Xtrain.shape\n K = len(set(Ytrain))\n dnn = DNN(D, [1000, 750, 500], K, UnsupervisedModel=RBM)\n init_op = tf.compat.v1.global_variables_initializer()\n with tf.compat.v1.Session() as session:\n session.run(init_op)\n dnn.set_session(session)\n dnn.fit(Xtrain, Ytrain, Xtest, Ytest, pretrain=True, epochs=10)\n\n\nif __name__ == '__main__':\n main()" }, { "path": "unsupervised_class2/sk_mlp.py", "content": "# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow\n# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow\nfrom __future__ import print_function, division\nfrom future.utils import iteritems\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\n\nimport numpy as np\nfrom sklearn.neural_network import MLPRegressor\nfrom util import getKaggleMNIST\n\n\n\n# get data\nX, _, Xt, _ = getKaggleMNIST()\n\n# create the model and train it\nmodel = MLPRegressor()\nmodel.fit(X, X)\n\n# test the model\nprint(\"Train R^2:\", model.score(X, X))\nprint(\"Test R^2:\", model.score(Xt, Xt))\n\nXhat = model.predict(X)\nmse = ((Xhat - X)**2).mean()\nprint(\"Train MSE:\", mse)\n\nXhat = model.predict(Xt)\nmse = ((Xhat - Xt)**2).mean()\nprint(\"Test MSE:\", mse)" }, { "path": "unsupervised_class2/tsne_books.py", "content": "# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python\n# https://www.udemy.com/unsupervised-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport nltk\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom nltk.stem import WordNetLemmatizer\nfrom sklearn.manifold import TSNE\n\n\nwordnet_lemmatizer = WordNetLemmatizer()\n\ntitles = [line.rstrip() for line in open('../nlp_class/all_book_titles.txt')]\n\n# copy tokenizer from sentiment example\nstopwords = set(w.rstrip() for w in open('../nlp_class/stopwords.txt'))\n# add more stopwords specific to this problem\nstopwords = stopwords.union({\n 'introduction', 'edition', 'series', 'application',\n 'approach', 'card', 'access', 'package', 'plus', 'etext',\n 'brief', 'vol', 'fundamental', 'guide', 'essential', 'printed',\n 'third', 'second', 'fourth', })\ndef my_tokenizer(s):\n s = s.lower() # downcase\n tokens = nltk.tokenize.word_tokenize(s) # split string into words (tokens)\n tokens = [t for t in tokens if len(t) > 2] # remove short words, they're probably not useful\n tokens = [wordnet_lemmatizer.lemmatize(t) for t in tokens] # put words into base form\n tokens = [t for t in tokens if t not in stopwords] # remove stopwords\n tokens = [t for t in tokens if not any(c.isdigit() for c in t)] # remove any digits, i.e. \"3rd edition\"\n return tokens\n\n\n# create a word-to-index map so that we can create our word-frequency vectors later\n# let's also save the tokenized versions so we don't have to tokenize again later\nword_index_map = {}\ncurrent_index = 0\nall_tokens = []\nall_titles = []\nindex_word_map = []\nfor title in titles:\n try:\n title = title.encode('ascii', 'ignore') # this will throw exception if bad characters\n title = title.decode('utf-8')\n all_titles.append(title)\n tokens = my_tokenizer(title)\n all_tokens.append(tokens)\n for token in tokens:\n if token not in word_index_map:\n word_index_map[token] = current_index\n current_index += 1\n index_word_map.append(token)\n except Exception as e:\n print(e)\n\n\n\n# now let's create our input matrices - just indicator variables for this example - works better than proportions\ndef tokens_to_vector(tokens):\n x = np.zeros(len(word_index_map))\n for t in tokens:\n i = word_index_map[t]\n x[i] = 1\n return x\n\nN = len(all_tokens)\nD = len(word_index_map)\nX = np.zeros((D, N)) # terms will go along rows, documents along columns\ni = 0\nfor tokens in all_tokens:\n X[:,i] = tokens_to_vector(tokens)\n i += 1\nprint(\"X.shape:\", X.shape)\n\ndef main():\n tsne = TSNE(perplexity=40)\n Z = tsne.fit_transform(X)\n plt.scatter(Z[:,0], Z[:,1])\n for i in range(D):\n plt.annotate(s=index_word_map[i], xy=(Z[i,0], Z[i,1]))\n plt.show()\n\nif __name__ == '__main__':\n main()\n\n" }, { "path": "unsupervised_class2/tsne_donut.py", "content": "# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python\n# https://www.udemy.com/unsupervised-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.manifold import TSNE\n\n\ndef get_donut_data():\n N = 600\n R_inner = 10\n R_outer = 20\n\n # distance from origin is radius + random normal\n # angle theta is uniformly distributed between (0, 2pi)\n R1 = np.random.randn(N//2) + R_inner\n theta = 2*np.pi*np.random.random(N//2)\n X_inner = np.concatenate([[R1 * np.cos(theta)], [R1 * np.sin(theta)]]).T\n\n R2 = np.random.randn(N//2) + R_outer\n theta = 2*np.pi*np.random.random(N//2)\n X_outer = np.concatenate([[R2 * np.cos(theta)], [R2 * np.sin(theta)]]).T\n\n X = np.concatenate([ X_inner, X_outer ])\n Y = np.array([0]*(N//2) + [1]*(N//2))\n return X, Y\n\n\ndef main():\n X, Y = get_donut_data()\n\n plt.scatter(X[:,0], X[:,1], s=100, c=Y, alpha=0.5)\n plt.show()\n\n tsne = TSNE(perplexity=40)\n Z = tsne.fit_transform(X)\n plt.scatter(Z[:,0], Z[:,1], s=100, c=Y, alpha=0.5)\n plt.show()\n\nif __name__ == '__main__':\n main()" }, { "path": "unsupervised_class2/tsne_mnist.py", "content": "# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python\n# https://www.udemy.com/unsupervised-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.manifold import TSNE\nfrom util import getKaggleMNIST\n\nimport os\nimport sys\nsys.path.append(os.path.abspath('..'))\nfrom unsupervised_class.kmeans_mnist import purity\nfrom sklearn.mixture import GaussianMixture\n\n\ndef main():\n Xtrain, Ytrain, _, _ = getKaggleMNIST()\n\n sample_size = 1000\n X = Xtrain[:sample_size]\n Y = Ytrain[:sample_size]\n\n tsne = TSNE()\n Z = tsne.fit_transform(X)\n plt.scatter(Z[:,0], Z[:,1], s=100, c=Y, alpha=0.5)\n plt.show()\n\n # purity measure from unsupervised machine learning pt 1\n # maximum purity is 1, higher is better\n gmm = GaussianMixture(n_components=10)\n gmm.fit(X)\n Rfull = gmm.predict_proba(X)\n print(\"Rfull.shape:\", Rfull.shape)\n print(\"full purity:\", purity(Y, Rfull))\n\n # now try the same thing on the reduced data\n gmm.fit(Z)\n Rreduced = gmm.predict_proba(Z)\n print(\"reduced purity:\", purity(Y, Rreduced))\n\nif __name__ == '__main__':\n main()" }, { "path": "unsupervised_class2/tsne_visualization.py", "content": "# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python\n# https://www.udemy.com/unsupervised-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom sklearn.manifold import TSNE\n\n\nif __name__ == '__main__':\n # define the centers of each Gaussian cloud\n centers = np.array([\n [ 1, 1, 1],\n [ 1, 1, -1],\n [ 1, -1, 1],\n [ 1, -1, -1],\n [-1, 1, 1],\n [-1, 1, -1],\n [-1, -1, 1],\n [-1, -1, -1],\n ])*3\n\n # create the clouds, Gaussian samples centered at\n # each of the centers we just made\n data = []\n pts_per_cloud = 100\n for c in centers:\n cloud = np.random.randn(pts_per_cloud, 3) + c\n data.append(cloud)\n data = np.concatenate(data)\n\n # visualize the clouds in 3-D\n # add colors / labels so we can track where the points go\n colors = np.array([[i]*pts_per_cloud for i in range(len(centers))]).flatten()\n fig = plt.figure()\n ax = fig.add_subplot(111, projection='3d')\n ax.scatter(data[:,0], data[:,1], data[:,2], c=colors)\n plt.show()\n\n\n # perform dimensionality reduction\n tsne = TSNE()\n transformed = tsne.fit_transform(data)\n\n # visualize the clouds in 2-D\n plt.scatter(transformed[:,0], transformed[:,1], c=colors)\n plt.show()\n\n\n\n" }, { "path": "unsupervised_class2/tsne_xor.py", "content": "# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python\n# https://www.udemy.com/unsupervised-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.manifold import TSNE\n\n\ndef get_xor_data():\n X1 = np.random.random((100, 2))\n X2 = np.random.random((100, 2)) - np.array([1, 1])\n X3 = np.random.random((100, 2)) - np.array([1, 0])\n X4 = np.random.random((100, 2)) - np.array([0, 1])\n X = np.vstack((X1, X2, X3, X4))\n Y = np.array([0]*200 + [1]*200)\n return X, Y\n\n\ndef main():\n X, Y = get_xor_data()\n\n plt.scatter(X[:,0], X[:,1], s=100, c=Y, alpha=0.5)\n plt.show()\n\n tsne = TSNE(perplexity=40)\n Z = tsne.fit_transform(X)\n plt.scatter(Z[:,0], Z[:,1], s=100, c=Y, alpha=0.5)\n plt.show()\n\nif __name__ == '__main__':\n main()" }, { "path": "unsupervised_class2/umap_transformer.py", "content": "# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python\n# https://www.udemy.com/unsupervised-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom datetime import datetime\nfrom util import getKaggleMNIST\nfrom sklearn.linear_model import LogisticRegression\nfrom umap import UMAP\n\n# get the data\nXtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()\n\nprint(\"Score without transformation:\")\nmodel = LogisticRegression()\nmodel.fit(Xtrain, Ytrain)\nprint(model.score(Xtrain, Ytrain))\nprint(model.score(Xtest, Ytest))\n\n\numapper = UMAP(n_neighbors=5, n_components=10)\nt0 = datetime.now()\nZtrain = umapper.fit_transform(Xtrain)\nprint(\"umap fit_transform took:\", datetime.now() - t0)\nt0 = datetime.now()\nZtest = umapper.transform(Xtest)\nprint(\"umap transform took:\", datetime.now() - t0)\n\nprint(\"Score with transformation\")\nmodel = LogisticRegression()\nt0 = datetime.now()\nmodel.fit(Ztrain, Ytrain)\nprint(\"logistic regression fit took:\", datetime.now() - t0)\nprint(model.score(Ztrain, Ytrain))\nprint(model.score(Ztest, Ytest))" }, { "path": "unsupervised_class2/unsupervised.py", "content": "# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python\n# https://www.udemy.com/unsupervised-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport theano\nimport theano.tensor as T\nimport matplotlib.pyplot as plt\n\nfrom sklearn.utils import shuffle\nfrom sklearn.manifold import TSNE\nfrom sklearn.decomposition import PCA\nfrom theano.tensor.shared_randomstreams import RandomStreams\nfrom util import relu, error_rate, getKaggleMNIST, init_weights\nfrom autoencoder import AutoEncoder, momentum_updates\nfrom rbm import RBM\n\n\nclass DBN(object):\n def __init__(self, hidden_layer_sizes, UnsupervisedModel=AutoEncoder):\n self.hidden_layers = []\n count = 0\n for M in hidden_layer_sizes:\n ae = UnsupervisedModel(M, count)\n self.hidden_layers.append(ae)\n count += 1\n\n def fit(self, X, pretrain_epochs=1):\n self.D = X.shape[1] # save for later\n\n current_input = X\n for ae in self.hidden_layers:\n ae.fit(current_input, epochs=pretrain_epochs)\n\n # create current_input for the next layer\n current_input = ae.hidden_op(current_input)\n\n # return it here so we can use directly after fitting without calling forward\n return current_input\n\n def forward(self, X):\n Z = X\n for ae in self.hidden_layers:\n Z = ae.forward_hidden(Z)\n return Z\n\n def fit_to_input(self, k, learning_rate=1.0, mu=0.99, epochs=100000):\n # This is not very flexible, as you would ideally\n # like to be able to activate any node in any hidden\n # layer, not just the last layer.\n # Exercise for students: modify this function to be able\n # to activate neurons in the middle layers.\n\n # cast hyperperams\n learning_rate = np.float32(learning_rate)\n mu = np.float32(mu)\n\n # randomly initialize an image\n X0 = init_weights((1, self.D))\n\n # make the image a shared so theano can update it\n X = theano.shared(X0, 'X_shared')\n\n # get the output of the neural network\n Y = self.forward(X)\n\n # t = np.zeros(self.hidden_layers[-1].M)\n # t[k] = 1\n\n # # choose Y[0] b/c it's shape 1xD, we want just a D-size vector, not 1xD matrix\n # cost = -(t*T.log(Y[0]) + (1 - t)*(T.log(1 - Y[0]))).sum()\n\n # k = which output node to look at\n # there is only 1 image, so we select the 0th row of X\n cost = -T.log(Y[0,k])\n\n updates = momentum_updates(cost, [X], mu, learning_rate)\n train = theano.function(\n inputs=[],\n outputs=[cost, Y],\n updates=updates,\n )\n\n costs = []\n for i in range(epochs):\n if i % 10000 == 0:\n print(\"epoch:\", i)\n the_cost, out = train()\n if i == 0:\n print(\"out.shape:\", out.shape)\n costs.append(the_cost)\n plt.plot(costs)\n plt.show()\n\n return X.get_value()\n\n def save(self, filename):\n arrays = [p.get_value() for layer in self.hidden_layers for p in layer.params]\n np.savez(filename, *arrays)\n\n @staticmethod\n def load(filename, UnsupervisedModel=AutoEncoder):\n dbn = DBN([], UnsupervisedModel)\n npz = np.load(filename)\n dbn.hidden_layers = []\n count = 0\n for i in range(0, len(npz.files), 3):\n W = npz['arr_%s' % i]\n bh = npz['arr_%s' % (i + 1)]\n bo = npz['arr_%s' % (i + 2)]\n\n if i == 0:\n dbn.D = W.shape[0]\n\n ae = UnsupervisedModel.createFromArrays(W, bh, bo, count)\n dbn.hidden_layers.append(ae)\n count += 1\n return dbn\n\n\ndef main():\n Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()\n dbn = DBN([1000, 750, 500], UnsupervisedModel=AutoEncoder)\n # dbn = DBN([1000, 750, 500, 10])\n output = dbn.fit(Xtrain, pretrain_epochs=2)\n print(\"output.shape\", output.shape)\n\n # sample before using t-SNE because it requires lots of RAM\n sample_size = 600\n tsne = TSNE()\n reduced = tsne.fit_transform(output[:sample_size])\n plt.scatter(reduced[:,0], reduced[:,1], s=100, c=Ytrain[:sample_size], alpha=0.5)\n plt.title(\"t-SNE visualization on data transformed by DBN\")\n plt.show()\n\n # t-SNE on raw data\n reduced = tsne.fit_transform(Xtrain[:sample_size])\n plt.scatter(reduced[:,0], reduced[:,1], s=100, c=Ytrain[:sample_size], alpha=0.5)\n plt.title(\"t-SNE visualization on raw data\")\n plt.show()\n\n pca = PCA()\n reduced = pca.fit_transform(output)\n plt.scatter(reduced[:,0], reduced[:,1], s=100, c=Ytrain, alpha=0.5)\n plt.title(\"PCA visualization on data transformed by DBN\")\n plt.show()\n\nif __name__ == '__main__':\n main()" }, { "path": "unsupervised_class2/util.py", "content": "# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python\n# https://www.udemy.com/unsupervised-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn.utils import shuffle\n\ndef relu(x):\n return x * (x > 0)\n\n\ndef error_rate(p, t):\n return np.mean(p != t)\n\n\ndef getKaggleMNIST():\n # MNIST data:\n # column 0 is labels\n # column 1-785 is data, with values 0 .. 255\n # total size of CSV: (42000, 1, 28, 28)\n train = pd.read_csv('../large_files/train.csv').values.astype(np.float32)\n train = shuffle(train)\n\n Xtrain = train[:-1000,1:] / 255\n Ytrain = train[:-1000,0].astype(np.int32)\n\n Xtest = train[-1000:,1:] / 255\n Ytest = train[-1000:,0].astype(np.int32)\n return Xtrain, Ytrain, Xtest, Ytest\n\n\ndef init_weights(shape):\n w = np.random.randn(*shape) / np.sqrt(sum(shape))\n return w.astype(np.float32)" }, { "path": "unsupervised_class2/vanishing.py", "content": "# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python\n# https://www.udemy.com/unsupervised-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport theano\nimport theano.tensor as T\nimport matplotlib.pyplot as plt\n\nfrom sklearn.utils import shuffle\nfrom autoencoder import T_shared_zeros_like32, momentum_updates\nfrom util import relu, error_rate, getKaggleMNIST, init_weights\n\n\nclass HiddenLayer(object):\n def __init__(self, D, M):\n W = init_weights((D, M))\n b = np.zeros(M, dtype=np.float32)\n self.W = theano.shared(W)\n self.b = theano.shared(b)\n self.params = [self.W, self.b]\n\n def forward(self, X):\n # we want to use the sigmoid so we can observe\n # the vanishing gradient!\n return T.nnet.sigmoid(X.dot(self.W) + self.b)\n\n\nclass ANN(object):\n def __init__(self, hidden_layer_sizes):\n self.hidden_layer_sizes = hidden_layer_sizes\n\n def fit(self, X, Y, learning_rate=0.01, mu=0.99, epochs=30, batch_sz=100):\n # cast to float32\n learning_rate = np.float32(learning_rate)\n mu = np.float32(mu)\n\n N, D = X.shape\n K = len(set(Y))\n\n self.hidden_layers = []\n mi = D\n for mo in self.hidden_layer_sizes:\n h = HiddenLayer(mi, mo)\n self.hidden_layers.append(h)\n mi = mo\n\n # initialize logistic regression layer\n W = init_weights((mo, K))\n b = np.zeros(K, dtype=np.float32)\n self.W = theano.shared(W)\n self.b = theano.shared(b)\n\n self.params = [self.W, self.b]\n self.allWs = []\n for h in self.hidden_layers:\n self.params += h.params\n self.allWs.append(h.W)\n self.allWs.append(self.W)\n\n X_in = T.matrix('X_in')\n targets = T.ivector('Targets')\n pY = self.forward(X_in)\n\n cost = -T.mean( T.log(pY[T.arange(pY.shape[0]), targets]) )\n prediction = self.predict(X_in)\n\n updates = momentum_updates(cost, self.params, mu, learning_rate)\n train_op = theano.function(\n inputs=[X_in, targets],\n outputs=[cost, prediction],\n updates=updates,\n )\n\n n_batches = N // batch_sz\n costs = []\n lastWs = [W.get_value() for W in self.allWs]\n W_changes = []\n print(\"supervised training...\")\n for i in range(epochs):\n print(\"epoch:\", i)\n X, Y = shuffle(X, Y)\n for j in range(n_batches):\n Xbatch = X[j*batch_sz:(j*batch_sz + batch_sz)]\n Ybatch = Y[j*batch_sz:(j*batch_sz + batch_sz)]\n c, p = train_op(Xbatch, Ybatch)\n if j % 100 == 0:\n print(\"j / n_batches:\", j, \"/\", n_batches, \"cost:\", c, \"error:\", error_rate(p, Ybatch))\n costs.append(c)\n\n # log changes in all Ws\n W_change = [np.abs(W.get_value() - lastW).mean() for W, lastW in zip(self.allWs, lastWs)]\n W_changes.append(W_change)\n lastWs = [W.get_value() for W in self.allWs]\n\n W_changes = np.array(W_changes)\n plt.subplot(2,1,1)\n for i in range(W_changes.shape[1]):\n plt.plot(W_changes[:,i], label='layer %s' % i)\n plt.legend()\n # plt.show()\n\n plt.subplot(2,1,2)\n plt.plot(costs)\n plt.show()\n\n def predict(self, X):\n return T.argmax(self.forward(X), axis=1)\n\n def forward(self, X):\n Z = X\n for h in self.hidden_layers:\n Z = h.forward(Z)\n Y = T.nnet.softmax(Z.dot(self.W) + self.b)\n return Y\n\n\ndef main():\n Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()\n dnn = ANN([1000, 750, 500])\n dnn.fit(Xtrain, Ytrain)\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "unsupervised_class2/visualize_features.py", "content": "# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python\n# https://www.udemy.com/unsupervised-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport theano\nimport theano.tensor as T\nimport matplotlib.pyplot as plt\n\nfrom sklearn.utils import shuffle\nfrom theano.tensor.shared_randomstreams import RandomStreams\nfrom util import relu, error_rate, getKaggleMNIST, init_weights\nfrom unsupervised import DBN\nfrom rbm import RBM\n\n\ndef main(loadfile=None, savefile=None):\n Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()\n if loadfile:\n dbn = DBN.load(loadfile)\n else:\n dbn = DBN([1000, 750, 500, 10]) # AutoEncoder is default\n # dbn = DBN([1000, 750, 500, 10], UnsupervisedModel=RBM)\n dbn.fit(Xtrain, pretrain_epochs=2)\n\n if savefile:\n dbn.save(savefile)\n\n # first layer features\n # initial weight is D x M\n W = dbn.hidden_layers[0].W.eval()\n for i in range(dbn.hidden_layers[0].M):\n imgplot = plt.imshow(W[:,i].reshape(28, 28), cmap='gray')\n plt.show()\n should_quit = input(\"Show more? Enter 'n' to quit\\n\")\n if should_quit == 'n':\n break\n\n # features learned in the last layer\n for k in range(dbn.hidden_layers[-1].M):\n # activate the kth node\n X = dbn.fit_to_input(k)\n imgplot = plt.imshow(X.reshape(28, 28), cmap='gray')\n plt.show()\n if k < dbn.hidden_layers[-1].M - 1:\n should_quit = input(\"Show more? Enter 'n' to quit\\n\")\n if should_quit == 'n':\n break\n\n\nif __name__ == '__main__':\n # to load a saved file\n # main(loadfile='rbm15.npz')\n\n # to neither load nor save\n main()\n\n # to save a trained unsupervised deep network\n # main(savefile='rbm15.npz')" }, { "path": "unsupervised_class2/xwing.py", "content": "# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python\n# https://www.udemy.com/unsupervised-deep-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport theano\nimport theano.tensor as T\nimport matplotlib.pyplot as plt\n\nfrom sklearn.utils import shuffle\nfrom util import relu, error_rate, getKaggleMNIST, init_weights\nfrom autoencoder import momentum_updates\n\n# new additions used to compare purity measure using GMM\nimport os\nimport sys\nsys.path.append(os.path.abspath('..'))\nfrom unsupervised_class.kmeans_mnist import purity\nfrom sklearn.mixture import GaussianMixture\n\nclass Layer(object):\n def __init__(self, m1, m2):\n W = init_weights((m1, m2))\n bi = np.zeros(m2, dtype=np.float32)\n bo = np.zeros(m1, dtype=np.float32)\n self.W = theano.shared(W)\n self.bi = theano.shared(bi)\n self.bo = theano.shared(bo)\n self.params = [self.W, self.bi, self.bo]\n\n def forward(self, X):\n return T.nnet.sigmoid(X.dot(self.W) + self.bi)\n\n def forwardT(self, X):\n return T.nnet.sigmoid(X.dot(self.W.T) + self.bo)\n\n\nclass DeepAutoEncoder(object):\n def __init__(self, hidden_layer_sizes):\n self.hidden_layer_sizes = hidden_layer_sizes\n\n def fit(self, X, learning_rate=0.5, mu=0.99, epochs=50, batch_sz=100, show_fig=False):\n # cast hyperparams\n learning_rate = np.float32(learning_rate)\n mu = np.float32(mu)\n\n N, D = X.shape\n n_batches = N // batch_sz\n\n mi = D\n self.layers = []\n self.params = []\n for mo in self.hidden_layer_sizes:\n layer = Layer(mi, mo)\n self.layers.append(layer)\n self.params += layer.params\n mi = mo\n\n X_in = T.matrix('X')\n X_hat = self.forward(X_in)\n\n cost = -(X_in * T.log(X_hat) + (1 - X_in) * T.log(1 - X_hat)).mean()\n cost_op = theano.function(\n inputs=[X_in],\n outputs=cost,\n )\n\n updates = momentum_updates(cost, self.params, mu, learning_rate)\n train_op = theano.function(\n inputs=[X_in],\n outputs=cost,\n updates=updates,\n )\n\n costs = []\n for i in range(epochs):\n print(\"epoch:\", i)\n X = shuffle(X)\n for j in range(n_batches):\n batch = X[j*batch_sz:(j*batch_sz + batch_sz)]\n c = train_op(batch)\n if j % 100 == 0:\n print(\"j / n_batches:\", j, \"/\", n_batches, \"cost:\", c)\n costs.append(c)\n if show_fig:\n plt.plot(costs)\n plt.show()\n\n def forward(self, X):\n Z = X\n for layer in self.layers:\n Z = layer.forward(Z)\n\n self.map2center = theano.function(\n inputs=[X],\n outputs=Z,\n )\n\n for i in range(len(self.layers)-1, -1, -1):\n Z = self.layers[i].forwardT(Z)\n\n return Z\n\n\ndef main():\n Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()\n dae = DeepAutoEncoder([500, 300, 2])\n dae.fit(Xtrain)\n mapping = dae.map2center(Xtrain)\n plt.scatter(mapping[:,0], mapping[:,1], c=Ytrain, s=100, alpha=0.5)\n plt.show()\n\n # purity measure from unsupervised machine learning pt 1\n # NOTE: this will take a long time (i.e. just leave it overnight)\n gmm = GaussianMixture(n_components=10)\n gmm.fit(Xtrain)\n print(\"Finished GMM training\")\n responsibilities_full = gmm.predict_proba(Xtrain)\n print(\"full purity:\", purity(Ytrain, responsibilities_full))\n\n gmm.fit(mapping)\n responsibilities_reduced = gmm.predict_proba(mapping)\n print(\"reduced purity:\", purity(Ytrain, responsibilities_reduced))\n\n\nif __name__ == '__main__':\n main()\n\n" }, { "path": "unsupervised_class3/autoencoder_tf.py", "content": "# https://deeplearningcourses.com/c/deep-learning-gans-and-variational-autoencoders\n# https://www.udemy.com/deep-learning-gans-and-variational-autoencoders\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport util\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\n\n\nclass Autoencoder:\n def __init__(self, D, M):\n # represents a batch of training data\n self.X = tf.placeholder(tf.float32, shape=(None, D))\n\n # input -> hidden\n self.W = tf.Variable(tf.random_normal(shape=(D, M)) * np.sqrt(2.0 / M))\n self.b = tf.Variable(np.zeros(M).astype(np.float32))\n\n # hidden -> output\n self.V = tf.Variable(tf.random_normal(shape=(M, D)) * np.sqrt(2.0 / D))\n self.c = tf.Variable(np.zeros(D).astype(np.float32))\n\n # construct the reconstruction\n self.Z = tf.nn.relu(tf.matmul(self.X, self.W) + self.b)\n logits = tf.matmul(self.Z, self.V) + self.c\n self.X_hat = tf.nn.sigmoid(logits)\n\n # compute the cost\n self.cost = tf.reduce_sum(\n tf.nn.sigmoid_cross_entropy_with_logits(\n labels=self.X,\n logits=logits\n )\n )\n\n # make the trainer\n self.train_op = tf.train.RMSPropOptimizer(learning_rate=0.001).minimize(self.cost)\n\n # set up session and variables for later\n self.init_op = tf.global_variables_initializer()\n self.sess = tf.InteractiveSession()\n self.sess.run(self.init_op)\n\n def fit(self, X, epochs=30, batch_sz=64):\n costs = []\n n_batches = len(X) // batch_sz\n print(\"n_batches:\", n_batches)\n for i in range(epochs):\n print(\"epoch:\", i)\n np.random.shuffle(X)\n for j in range(n_batches):\n batch = X[j*batch_sz:(j+1)*batch_sz]\n _, c, = self.sess.run((self.train_op, self.cost), feed_dict={self.X: batch})\n c /= batch_sz # just debugging\n costs.append(c)\n if j % 100 == 0:\n print(\"iter: %d, cost: %.3f\" % (j, c))\n plt.plot(costs)\n plt.show()\n\n def predict(self, X):\n return self.sess.run(self.X_hat, feed_dict={self.X: X})\n\n\ndef main():\n X, Y = util.get_mnist()\n\n model = Autoencoder(784, 300)\n model.fit(X)\n\n # plot reconstruction\n done = False\n while not done:\n i = np.random.choice(len(X))\n x = X[i]\n im = model.predict([x]).reshape(28, 28)\n plt.subplot(1,2,1)\n plt.imshow(x.reshape(28, 28), cmap='gray')\n plt.title(\"Original\")\n plt.subplot(1,2,2)\n plt.imshow(im, cmap='gray')\n plt.title(\"Reconstruction\")\n plt.show()\n\n ans = input(\"Generate another?\")\n if ans and ans[0] in ('n' or 'N'):\n done = True\n\nif __name__ == '__main__':\n main()\n\n" }, { "path": "unsupervised_class3/autoencoder_theano.py", "content": "# https://deeplearningcourses.com/c/deep-learning-gans-and-variational-autoencoders\n# https://www.udemy.com/deep-learning-gans-and-variational-autoencoders\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport util\nimport numpy as np\nimport theano\nimport theano.tensor as T\nimport matplotlib.pyplot as plt\n\n\nclass Autoencoder:\n def __init__(self, D, M):\n # represents a batch of training data\n self.X = T.matrix('X')\n\n # input -> hidden\n self.W = theano.shared(np.random.randn(D, M) * np.sqrt(2.0 / M))\n self.b = theano.shared(np.zeros(M))\n\n # hidden -> output\n self.V = theano.shared(np.random.randn(M, D) * np.sqrt(2.0 / D))\n self.c = theano.shared(np.zeros(D))\n\n # construct the reconstruction\n self.Z = T.nnet.relu(self.X.dot(self.W) + self.b)\n self.X_hat = T.nnet.sigmoid(self.Z.dot(self.V) + self.c)\n\n # compute the cost\n self.cost = T.sum(\n T.nnet.binary_crossentropy(\n output=self.X_hat,\n target=self.X,\n )\n )\n\n # define the updates\n params = [self.W, self.b, self.V, self.c]\n grads = T.grad(self.cost, params)\n\n # rmsprop\n decay = 0.9\n learning_rate = 0.001\n\n # for rmsprop\n cache = [theano.shared(np.ones_like(p.get_value())) for p in params]\n new_cache = [decay*c + (1-decay)*g*g for p, c, g in zip(params, cache, grads)]\n\n updates = [\n (c, new_c) for c, new_c in zip(cache, new_cache)\n ] + [\n (p, p - learning_rate*g/T.sqrt(new_c + 1e-10)) for p, new_c, g in zip(params, new_cache, grads)\n ]\n\n\n # now define callable functions\n self.train_op = theano.function(\n inputs=[self.X],\n outputs=self.cost,\n updates=updates\n )\n\n self.predict = theano.function(\n inputs=[self.X],\n outputs=self.X_hat\n )\n\n def fit(self, X, epochs=30, batch_sz=64):\n costs = []\n n_batches = len(X) // batch_sz\n print(\"n_batches:\", n_batches)\n for i in range(epochs):\n print(\"epoch:\", i)\n np.random.shuffle(X)\n for j in range(n_batches):\n batch = X[j*batch_sz:(j+1)*batch_sz]\n c = self.train_op(batch)\n c /= batch_sz # just debugging\n costs.append(c)\n if j % 100 == 0:\n print(\"iter: %d, cost: %.3f\" % (j, c))\n plt.plot(costs)\n plt.show()\n\n\ndef main():\n X, Y = util.get_mnist()\n\n model = Autoencoder(784, 300)\n model.fit(X)\n\n # plot reconstruction\n done = False\n while not done:\n i = np.random.choice(len(X))\n x = X[i]\n im = model.predict([x]).reshape(28, 28)\n plt.subplot(1,2,1)\n plt.imshow(x.reshape(28, 28), cmap='gray')\n plt.title(\"Original\")\n plt.subplot(1,2,2)\n plt.imshow(im, cmap='gray')\n plt.title(\"Reconstruction\")\n plt.show()\n\n ans = input(\"Generate another?\")\n if ans and ans[0] in ('n' or 'N'):\n done = True\n\nif __name__ == '__main__':\n main()\n\n" }, { "path": "unsupervised_class3/bayes_classifier_gaussian.py", "content": "# https://deeplearningcourses.com/c/deep-learning-gans-and-variational-autoencoders\n# https://www.udemy.com/deep-learning-gans-and-variational-autoencoders\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport util\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.stats import multivariate_normal as mvn\n\n\ndef clamp_sample(x):\n x = np.minimum(x, 1)\n x = np.maximum(x, 0)\n return x\n\n\nclass BayesClassifier:\n def fit(self, X, Y):\n # assume classes are numbered 0...K-1\n self.K = len(set(Y))\n\n self.gaussians = []\n self.p_y = np.zeros(self.K)\n for k in range(self.K):\n Xk = X[Y == k]\n self.p_y[k] = len(Xk)\n mean = Xk.mean(axis=0)\n cov = np.cov(Xk.T)\n g = {'m': mean, 'c': cov}\n self.gaussians.append(g)\n # normalize p(y)\n self.p_y /= self.p_y.sum()\n\n def sample_given_y(self, y):\n g = self.gaussians[y]\n return clamp_sample( mvn.rvs(mean=g['m'], cov=g['c']) )\n\n def sample(self):\n y = np.random.choice(self.K, p=self.p_y)\n return clamp_sample( self.sample_given_y(y) )\n\n\nif __name__ == '__main__':\n X, Y = util.get_mnist()\n clf = BayesClassifier()\n clf.fit(X, Y)\n\n for k in range(clf.K):\n # show one sample for each class\n # also show the mean image learned\n\n sample = clf.sample_given_y(k).reshape(28, 28)\n mean = clf.gaussians[k]['m'].reshape(28, 28)\n\n plt.subplot(1,2,1)\n plt.imshow(sample, cmap='gray')\n plt.title(\"Sample\")\n plt.subplot(1,2,2)\n plt.imshow(mean, cmap='gray')\n plt.title(\"Mean\")\n plt.show()\n\n # generate a random sample\n sample = clf.sample().reshape(28, 28)\n plt.imshow(sample, cmap='gray')\n plt.title(\"Random Sample from Random Class\")\n plt.show()\n" }, { "path": "unsupervised_class3/bayes_classifier_gmm.py", "content": "# https://deeplearningcourses.com/c/deep-learning-gans-and-variational-autoencoders\n# https://www.udemy.com/deep-learning-gans-and-variational-autoencoders\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport util\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.mixture import BayesianGaussianMixture\n\n\ndef clamp_sample(x):\n x = np.minimum(x, 1)\n x = np.maximum(x, 0)\n return x\n\n\nclass BayesClassifier:\n def fit(self, X, Y):\n # assume classes are numbered 0...K-1\n self.K = len(set(Y))\n\n self.gaussians = []\n self.p_y = np.zeros(self.K)\n for k in range(self.K):\n print(\"Fitting gmm\", k)\n Xk = X[Y == k]\n self.p_y[k] = len(Xk)\n gmm = BayesianGaussianMixture(n_components=10)\n gmm.fit(Xk)\n self.gaussians.append(gmm)\n # normalize p(y)\n self.p_y /= self.p_y.sum()\n\n def sample_given_y(self, y):\n gmm = self.gaussians[y]\n sample = gmm.sample()\n # note: sample returns a tuple containing 2 things:\n # 1) the sample\n # 2) which cluster it came from\n # we'll use (2) to obtain the means so we can plot\n # them like we did in the previous script\n # we cheat by looking at \"non-public\" params in\n # the sklearn source code\n mean = gmm.means_[sample[1]]\n return clamp_sample( sample[0].reshape(28, 28) ), mean.reshape(28, 28)\n\n def sample(self):\n y = np.random.choice(self.K, p=self.p_y)\n return clamp_sample( self.sample_given_y(y) )\n\n\nif __name__ == '__main__':\n X, Y = util.get_mnist()\n clf = BayesClassifier()\n clf.fit(X, Y)\n\n for k in range(clf.K):\n # show one sample for each class\n # also show the mean image learned\n\n sample, mean = clf.sample_given_y(k)\n\n plt.subplot(1,2,1)\n plt.imshow(sample, cmap='gray')\n plt.title(\"Sample\")\n plt.subplot(1,2,2)\n plt.imshow(mean, cmap='gray')\n plt.title(\"Mean\")\n plt.show()\n\n # generate a random sample\n sample, mean = clf.sample()\n plt.subplot(1,2,1)\n plt.imshow(sample, cmap='gray')\n plt.title(\"Random Sample from Random Class\")\n plt.subplot(1,2,2)\n plt.imshow(mean, cmap='gray')\n plt.title(\"Corresponding Cluster Mean\")\n plt.show()\n" }, { "path": "unsupervised_class3/dcgan_tf.py", "content": "# https://deeplearningcourses.com/c/deep-learning-gans-and-variational-autoencoders\n# https://www.udemy.com/deep-learning-gans-and-variational-autoencoders\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport os\nimport util\nimport scipy as sp\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nfrom datetime import datetime\n\n\n# some constants\nLEARNING_RATE = 0.0002\nBETA1 = 0.5\nBATCH_SIZE = 64\nEPOCHS = 2\nSAVE_SAMPLE_PERIOD = 50\n\n\n# make dir to save samples\nif not os.path.exists('samples'):\n os.mkdir('samples')\n\n\ndef lrelu(x, alpha=0.2):\n return tf.maximum(alpha*x, x)\n\n\nclass ConvLayer:\n def __init__(self, name, mi, mo, apply_batch_norm, filtersz=5, stride=2, f=tf.nn.relu):\n # mi = input feature map size\n # mo = output feature map size\n # self.W = tf.Variable(0.02*tf.random_normal(shape=(filtersz, filtersz, mi, mo)))\n # self.b = tf.Variable(np.zeros(mo, dtype=np.float32))\n self.W = tf.get_variable(\n \"W_%s\" % name,\n shape=(filtersz, filtersz, mi, mo),\n # initializer=tf.contrib.layers.xavier_initializer(),\n initializer=tf.truncated_normal_initializer(stddev=0.02),\n )\n self.b = tf.get_variable(\n \"b_%s\" % name,\n shape=(mo,),\n initializer=tf.zeros_initializer(),\n )\n self.name = name\n self.f = f\n self.stride = stride\n self.apply_batch_norm = apply_batch_norm\n self.params = [self.W, self.b]\n\n def forward(self, X, reuse, is_training):\n # print(\"**************** reuse:\", reuse)\n conv_out = tf.nn.conv2d(\n X,\n self.W,\n strides=[1, self.stride, self.stride, 1],\n padding='SAME'\n )\n conv_out = tf.nn.bias_add(conv_out, self.b)\n\n # apply batch normalization\n if self.apply_batch_norm:\n conv_out = tf.contrib.layers.batch_norm(\n conv_out,\n decay=0.9, \n updates_collections=None,\n epsilon=1e-5,\n scale=True,\n is_training=is_training,\n reuse=reuse,\n scope=self.name,\n )\n return self.f(conv_out)\n\n\nclass FractionallyStridedConvLayer:\n def __init__(self, name, mi, mo, output_shape, apply_batch_norm, filtersz=5, stride=2, f=tf.nn.relu):\n # mi = input feature map size\n # mo = output feature map size\n # NOTE!!! shape is specified in the OPPOSITE way from regular conv\n # self.W = tf.Variable(0.02*tf.random_normal(shape=(filtersz, filtersz, mo, mi)))\n # self.b = tf.Variable(np.zeros(mo, dtype=np.float32))\n self.W = tf.get_variable(\n \"W_%s\" % name,\n shape=(filtersz, filtersz, mo, mi),\n # initializer=tf.contrib.layers.xavier_initializer(),\n initializer=tf.random_normal_initializer(stddev=0.02),\n )\n self.b = tf.get_variable(\n \"b_%s\" % name,\n shape=(mo,),\n initializer=tf.zeros_initializer(),\n )\n self.f = f\n self.stride = stride\n self.name = name\n self.output_shape = output_shape\n self.apply_batch_norm = apply_batch_norm\n self.params = [self.W, self.b]\n\n def forward(self, X, reuse, is_training):\n conv_out = tf.nn.conv2d_transpose(\n value=X,\n filter=self.W,\n output_shape=self.output_shape,\n strides=[1, self.stride, self.stride, 1],\n )\n conv_out = tf.nn.bias_add(conv_out, self.b)\n\n # apply batch normalization\n if self.apply_batch_norm:\n conv_out = tf.contrib.layers.batch_norm(\n conv_out,\n decay=0.9, \n updates_collections=None,\n epsilon=1e-5,\n scale=True,\n is_training=is_training,\n reuse=reuse,\n scope=self.name,\n )\n\n return self.f(conv_out)\n\n\nclass DenseLayer(object):\n def __init__(self, name, M1, M2, apply_batch_norm, f=tf.nn.relu):\n self.W = tf.get_variable(\n \"W_%s\" % name,\n shape=(M1, M2),\n initializer=tf.random_normal_initializer(stddev=0.02),\n )\n self.b = tf.get_variable(\n \"b_%s\" % name,\n shape=(M2,),\n initializer=tf.zeros_initializer(),\n )\n self.f = f\n self.name = name\n self.apply_batch_norm = apply_batch_norm\n self.params = [self.W, self.b]\n\n def forward(self, X, reuse, is_training):\n a = tf.matmul(X, self.W) + self.b\n\n # apply batch normalization\n if self.apply_batch_norm:\n a = tf.contrib.layers.batch_norm(\n a,\n decay=0.9, \n updates_collections=None,\n epsilon=1e-5,\n scale=True,\n is_training=is_training,\n reuse=reuse,\n scope=self.name,\n )\n return self.f(a)\n\n\nclass DCGAN:\n def __init__(self, img_length, num_colors, d_sizes, g_sizes):\n\n # save for later\n self.img_length = img_length\n self.num_colors = num_colors\n self.latent_dims = g_sizes['z']\n\n # define the input data\n self.X = tf.placeholder(\n tf.float32,\n shape=(None, img_length, img_length, num_colors),\n name='X'\n )\n self.Z = tf.placeholder(\n tf.float32,\n shape=(None, self.latent_dims),\n name='Z'\n )\n\n # note: by making batch_sz a placeholder, we can specify a variable\n # number of samples in the FS-conv operation where we are required\n # to pass in output_shape\n # we need only pass in the batch size via feed_dict\n self.batch_sz = tf.placeholder(tf.int32, shape=(), name='batch_sz')\n\n # build the discriminator\n logits = self.build_discriminator(self.X, d_sizes)\n\n # build generator\n self.sample_images = self.build_generator(self.Z, g_sizes)\n\n # get sample logits\n with tf.variable_scope(\"discriminator\") as scope:\n scope.reuse_variables()\n sample_logits = self.d_forward(self.sample_images, True)\n\n # get sample images for test time (batch norm is different)\n with tf.variable_scope(\"generator\") as scope:\n scope.reuse_variables()\n self.sample_images_test = self.g_forward(\n self.Z, reuse=True, is_training=False\n )\n\n # build costs\n self.d_cost_real = tf.nn.sigmoid_cross_entropy_with_logits(\n logits=logits,\n labels=tf.ones_like(logits)\n )\n self.d_cost_fake = tf.nn.sigmoid_cross_entropy_with_logits(\n logits=sample_logits,\n labels=tf.zeros_like(sample_logits)\n )\n self.d_cost = tf.reduce_mean(self.d_cost_real) + tf.reduce_mean(self.d_cost_fake)\n self.g_cost = tf.reduce_mean(\n tf.nn.sigmoid_cross_entropy_with_logits(\n logits=sample_logits,\n labels=tf.ones_like(sample_logits)\n )\n )\n real_predictions = tf.cast(logits > 0, tf.float32)\n fake_predictions = tf.cast(sample_logits < 0, tf.float32)\n num_predictions = 2.0*BATCH_SIZE\n num_correct = tf.reduce_sum(real_predictions) + tf.reduce_sum(fake_predictions)\n self.d_accuracy = num_correct / num_predictions\n\n\n # optimizers\n self.d_params = [t for t in tf.trainable_variables() if t.name.startswith('d')]\n self.g_params = [t for t in tf.trainable_variables() if t.name.startswith('g')]\n\n self.d_train_op = tf.train.AdamOptimizer(\n LEARNING_RATE, beta1=BETA1\n ).minimize(\n self.d_cost, var_list=self.d_params\n )\n self.g_train_op = tf.train.AdamOptimizer(\n LEARNING_RATE, beta1=BETA1\n ).minimize(\n self.g_cost, var_list=self.g_params\n )\n\n # show_all_variables()\n # exit()\n\n # set up session and variables for later\n self.init_op = tf.global_variables_initializer()\n self.sess = tf.InteractiveSession()\n self.sess.run(self.init_op)\n\n\n def build_discriminator(self, X, d_sizes):\n with tf.variable_scope(\"discriminator\") as scope:\n\n # build conv layers\n self.d_convlayers = []\n mi = self.num_colors\n dim = self.img_length\n count = 0\n for mo, filtersz, stride, apply_batch_norm in d_sizes['conv_layers']:\n # make up a name - used for get_variable\n name = \"convlayer_%s\" % count\n count += 1\n\n layer = ConvLayer(name, mi, mo, apply_batch_norm, filtersz, stride, lrelu)\n self.d_convlayers.append(layer)\n mi = mo\n print(\"dim:\", dim)\n dim = int(np.ceil(float(dim) / stride))\n\n\n mi = mi * dim * dim\n # build dense layers\n self.d_denselayers = []\n for mo, apply_batch_norm in d_sizes['dense_layers']:\n name = \"denselayer_%s\" % count\n count += 1\n\n layer = DenseLayer(name, mi, mo, apply_batch_norm, lrelu)\n mi = mo\n self.d_denselayers.append(layer)\n\n\n # final logistic layer\n name = \"denselayer_%s\" % count\n self.d_finallayer = DenseLayer(name, mi, 1, False, lambda x: x)\n\n # get the logits\n logits = self.d_forward(X)\n\n # build the cost later\n return logits\n\n\n def d_forward(self, X, reuse=None, is_training=True):\n # encapsulate this because we use it twice\n output = X\n for layer in self.d_convlayers:\n output = layer.forward(output, reuse, is_training)\n output = tf.contrib.layers.flatten(output)\n for layer in self.d_denselayers:\n output = layer.forward(output, reuse, is_training)\n logits = self.d_finallayer.forward(output, reuse, is_training)\n return logits\n\n\n def build_generator(self, Z, g_sizes):\n with tf.variable_scope(\"generator\") as scope:\n\n # determine the size of the data at each step\n dims = [self.img_length]\n dim = self.img_length\n for _, _, stride, _ in reversed(g_sizes['conv_layers']):\n dim = int(np.ceil(float(dim) / stride))\n dims.append(dim)\n\n # note: dims is actually backwards\n # the first layer of the generator is actually last\n # so let's reverse it\n dims = list(reversed(dims))\n print(\"dims:\", dims)\n self.g_dims = dims\n\n\n # dense layers\n mi = self.latent_dims\n self.g_denselayers = []\n count = 0\n for mo, apply_batch_norm in g_sizes['dense_layers']:\n name = \"g_denselayer_%s\" % count\n count += 1\n\n layer = DenseLayer(name, mi, mo, apply_batch_norm)\n self.g_denselayers.append(layer)\n mi = mo\n\n # final dense layer\n mo = g_sizes['projection'] * dims[0] * dims[0]\n name = \"g_denselayer_%s\" % count\n layer = DenseLayer(name, mi, mo, not g_sizes['bn_after_project'])\n self.g_denselayers.append(layer)\n\n\n # fs-conv layers\n mi = g_sizes['projection']\n self.g_convlayers = []\n\n # output may use tanh or sigmoid\n num_relus = len(g_sizes['conv_layers']) - 1\n activation_functions = [tf.nn.relu]*num_relus + [g_sizes['output_activation']]\n\n for i in range(len(g_sizes['conv_layers'])):\n name = \"fs_convlayer_%s\" % i\n mo, filtersz, stride, apply_batch_norm = g_sizes['conv_layers'][i]\n f = activation_functions[i]\n output_shape = [self.batch_sz, dims[i+1], dims[i+1], mo]\n print(\"mi:\", mi, \"mo:\", mo, \"outp shape:\", output_shape)\n layer = FractionallyStridedConvLayer(\n name, mi, mo, output_shape, apply_batch_norm, filtersz, stride, f\n )\n self.g_convlayers.append(layer)\n mi = mo\n\n # get the output\n self.g_sizes = g_sizes\n return self.g_forward(Z)\n\n\n def g_forward(self, Z, reuse=None, is_training=True):\n # dense layers\n output = Z\n for layer in self.g_denselayers:\n output = layer.forward(output, reuse, is_training)\n\n # project and reshape\n output = tf.reshape(\n output,\n [-1, self.g_dims[0], self.g_dims[0], self.g_sizes['projection']],\n )\n\n # apply batch norm\n if self.g_sizes['bn_after_project']:\n output = tf.contrib.layers.batch_norm(\n output,\n decay=0.9, \n updates_collections=None,\n epsilon=1e-5,\n scale=True,\n is_training=is_training,\n reuse=reuse,\n scope='bn_after_project'\n )\n\n # pass through fs-conv layers\n for layer in self.g_convlayers:\n output = layer.forward(output, reuse, is_training)\n\n return output\n\n\n def fit(self, X):\n d_costs = []\n g_costs = []\n\n N = len(X)\n n_batches = N // BATCH_SIZE\n total_iters = 0\n for i in range(EPOCHS):\n print(\"epoch:\", i)\n np.random.shuffle(X)\n for j in range(n_batches):\n t0 = datetime.now()\n\n if type(X[0]) is str:\n # is celeb dataset\n batch = util.files2images(\n X[j*BATCH_SIZE:(j+1)*BATCH_SIZE]\n )\n\n else:\n # is mnist dataset\n batch = X[j*BATCH_SIZE:(j+1)*BATCH_SIZE]\n\n Z = np.random.uniform(-1, 1, size=(BATCH_SIZE, self.latent_dims))\n\n # train the discriminator\n _, d_cost, d_acc = self.sess.run(\n (self.d_train_op, self.d_cost, self.d_accuracy),\n feed_dict={self.X: batch, self.Z: Z, self.batch_sz: BATCH_SIZE},\n )\n d_costs.append(d_cost)\n\n # train the generator\n _, g_cost1 = self.sess.run(\n (self.g_train_op, self.g_cost),\n feed_dict={self.Z: Z, self.batch_sz: BATCH_SIZE},\n )\n # g_costs.append(g_cost1)\n _, g_cost2 = self.sess.run(\n (self.g_train_op, self.g_cost),\n feed_dict={self.Z: Z, self.batch_sz: BATCH_SIZE},\n )\n g_costs.append((g_cost1 + g_cost2)/2) # just use the avg\n\n print(\" batch: %d/%d - dt: %s - d_acc: %.2f\" % (j+1, n_batches, datetime.now() - t0, d_acc))\n\n\n # save samples periodically\n total_iters += 1\n if total_iters % SAVE_SAMPLE_PERIOD == 0:\n print(\"saving a sample...\")\n samples = self.sample(64) # shape is (64, D, D, color)\n\n # for convenience\n d = self.img_length\n \n if samples.shape[-1] == 1:\n # if color == 1, we want a 2-D image (N x N)\n samples = samples.reshape(64, d, d)\n flat_image = np.empty((8*d, 8*d))\n\n k = 0\n for i in range(8):\n for j in range(8):\n flat_image[i*d:(i+1)*d, j*d:(j+1)*d] = samples[k].reshape(d, d)\n k += 1\n\n # plt.imshow(flat_image, cmap='gray')\n else:\n # if color == 3, we want a 3-D image (N x N x 3)\n flat_image = np.empty((8*d, 8*d, 3))\n k = 0\n for i in range(8):\n for j in range(8):\n flat_image[i*d:(i+1)*d, j*d:(j+1)*d] = samples[k]\n k += 1\n # plt.imshow(flat_image)\n \n # plt.savefig('samples/samples_at_iter_%d.png' % total_iters)\n sp.misc.imsave(\n 'samples/samples_at_iter_%d.png' % total_iters,\n flat_image,\n )\n\n # save a plot of the costs\n plt.clf()\n plt.plot(d_costs, label='discriminator cost')\n plt.plot(g_costs, label='generator cost')\n plt.legend()\n plt.savefig('cost_vs_iteration.png')\n\n def sample(self, n):\n Z = np.random.uniform(-1, 1, size=(n, self.latent_dims))\n samples = self.sess.run(self.sample_images_test, feed_dict={self.Z: Z, self.batch_sz: n})\n return samples\n\n\n\ndef celeb():\n X = util.get_celeb()\n # just loads a list of filenames, we will load them in dynamically\n # because there are many\n dim = 64\n colors = 3\n\n # for celeb\n d_sizes = {\n 'conv_layers': [\n (64, 5, 2, False),\n (128, 5, 2, True),\n (256, 5, 2, True),\n (512, 5, 2, True)\n ],\n 'dense_layers': [],\n }\n g_sizes = {\n 'z': 100,\n 'projection': 512,\n 'bn_after_project': True,\n 'conv_layers': [\n (256, 5, 2, True),\n (128, 5, 2, True),\n (64, 5, 2, True),\n (colors, 5, 2, False)\n ],\n 'dense_layers': [],\n 'output_activation': tf.tanh,\n }\n\n # setup gan\n # note: assume square images, so only need 1 dim\n gan = DCGAN(dim, colors, d_sizes, g_sizes)\n gan.fit(X)\n\n\ndef mnist():\n X, Y = util.get_mnist()\n X = X.reshape(len(X), 28, 28, 1)\n dim = X.shape[1]\n colors = X.shape[-1]\n\n # for mnist\n d_sizes = {\n 'conv_layers': [(2, 5, 2, False), (64, 5, 2, True)],\n 'dense_layers': [(1024, True)],\n }\n g_sizes = {\n 'z': 100,\n 'projection': 128,\n 'bn_after_project': False,\n 'conv_layers': [(128, 5, 2, True), (colors, 5, 2, False)],\n 'dense_layers': [(1024, True)],\n 'output_activation': tf.sigmoid,\n }\n\n\n # setup gan\n # note: assume square images, so only need 1 dim\n gan = DCGAN(dim, colors, d_sizes, g_sizes)\n gan.fit(X)\n # samples = gan.sample(1) # just making sure it works\n\n # since training will take a considerable\n # amount of time, let's just save some\n # samples to disk rather than plotting now\n\n\nif __name__ == '__main__':\n # celeb()\n mnist()\n" }, { "path": "unsupervised_class3/dcgan_theano.py", "content": "# https://deeplearningcourses.com/c/deep-learning-gans-and-variational-autoencoders\n# https://www.udemy.com/deep-learning-gans-and-variational-autoencoders\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport os\nimport util\nimport scipy as sp\nimport numpy as np\nimport theano\nimport theano.tensor as T\nimport matplotlib.pyplot as plt\n\nfrom datetime import datetime\nfrom theano.tensor.nnet.bn import batch_normalization_train, batch_normalization_test\nfrom theano.tensor.nnet import conv2d\n\n\n# some constants\nLEARNING_RATE = 0.0002\nBETA1 = 0.5\nBETA2 = 0.999\nEPSILON = 1e-8\nBATCH_SIZE = 64\nEPOCHS = 2\nBATCH_SIZE = 64\nSAVE_SAMPLE_PERIOD = 50\n\n\n# make dir to save samples\nif not os.path.exists('samples'):\n os.mkdir('samples')\n\n\n# make it callable with only 1 input\ndef lrelu(x, alpha=0.2):\n return T.nnet.relu(x, alpha)\n\n\n# helper for adam optimizer\ndef adam(params, grads):\n updates = []\n time = theano.shared(0)\n new_time = time + 1\n updates.append((time, new_time))\n lr = LEARNING_RATE*T.sqrt(1 - BETA2**new_time) / (1 - BETA1**new_time)\n for p, g in zip(params, grads):\n m = theano.shared(p.get_value() * 0.)\n v = theano.shared(p.get_value() * 0.)\n new_m = BETA1*m + (1 - BETA1)*g\n new_v = BETA2*v + (1 - BETA2)*g*g\n new_p = p - lr*new_m / (T.sqrt(new_v) + EPSILON)\n updates.append((m, new_m))\n updates.append((v, new_v))\n updates.append((p, new_p))\n return updates\n\n\n# helper for batch norm\ndef batch_norm(\n input_,\n gamma,\n beta,\n running_mean,\n running_var,\n is_training,\n axes='per-activation'):\n\n if is_training:\n # returns:\n # batch-normalized output\n # batch mean\n # batch variance\n # running mean (for later use as population mean estimate)\n # running var (for later use as population var estimate)\n out, _, _, new_running_mean, new_running_var = batch_normalization_train(\n input_,\n gamma,\n beta,\n running_mean=running_mean,\n running_var=running_var,\n axes=axes,\n running_average_factor=0.9,\n )\n else:\n new_running_mean = None\n new_running_var = None # just to ensure we don't try to use them\n out = batch_normalization_test(\n input_,\n gamma,\n beta,\n running_mean,\n running_var,\n axes=axes,\n )\n return out, new_running_mean, new_running_var\n\n\nclass ConvLayer:\n def __init__(self, mi, mo, apply_batch_norm, filtersz=5, stride=2, f=T.nnet.relu):\n # mi = input feature map size\n # mo = output feature map size\n W = 0.02*np.random.randn(mo, mi, filtersz, filtersz)\n self.W = theano.shared(W)\n self.b = theano.shared(np.zeros(mo))\n self.params = [self.W, self.b]\n self.updates = [] # in case we do batch norm\n\n if apply_batch_norm:\n self.gamma = theano.shared(np.ones(mo))\n self.beta = theano.shared(np.zeros(mo))\n self.params += [self.gamma, self.beta]\n\n self.running_mean = theano.shared(np.zeros(mo))\n self.running_var = theano.shared(np.zeros(mo))\n\n self.f = f\n self.stride = stride\n self.apply_batch_norm = apply_batch_norm\n \n\n def forward(self, X, is_training):\n conv_out = conv2d(\n input=X,\n filters=self.W,\n subsample=(self.stride, self.stride),\n border_mode='half',\n )\n conv_out += self.b.dimshuffle('x', 0, 'x', 'x')\n\n # apply batch normalization\n if self.apply_batch_norm:\n conv_out, new_running_mean, new_running_var = batch_norm(\n conv_out,\n self.gamma,\n self.beta,\n self.running_mean,\n self.running_var,\n is_training,\n 'spatial'\n )\n if is_training:\n self.updates = [\n (self.running_mean, new_running_mean),\n (self.running_var, new_running_var),\n ]\n return self.f(conv_out)\n\n\n# regular convolution expects output size to be:\n# new_dim = floor( (old_dim - filter_sz) / stride ) + 1\n\n# therefore, for fs-conv, output size should be:\n# new_dim = stride * (old_dim - 1) + filter_sz\n\n\nclass FractionallyStridedConvLayer:\n def __init__(self, mi, mo, output_shape, apply_batch_norm, filtersz=5, stride=2, f=T.nnet.relu):\n # mi = input feature map size\n # mo = output feature map size\n self.filter_shape = (mi, mo, filtersz, filtersz)\n W = 0.02*np.random.randn(*self.filter_shape)\n self.W = theano.shared(W)\n self.b = theano.shared(np.zeros(mo))\n self.params = [self.W, self.b]\n self.updates = [] # in case we do batch norm\n\n if apply_batch_norm:\n self.gamma = theano.shared(np.ones(mo))\n self.beta = theano.shared(np.zeros(mo))\n self.params += [self.gamma, self.beta]\n\n self.running_mean = theano.shared(np.zeros(mo))\n self.running_var = theano.shared(np.zeros(mo))\n\n self.f = f\n self.stride = stride\n self.output_shape = output_shape\n self.apply_batch_norm = apply_batch_norm\n self.params = [self.W, self.b]\n\n def forward(self, X, is_training):\n conv_out = T.nnet.abstract_conv.conv2d_grad_wrt_inputs(\n X, self.W,\n input_shape=self.output_shape,\n filter_shape=self.filter_shape,\n border_mode='half',\n subsample=(self.stride, self.stride)\n )\n conv_out += self.b.dimshuffle('x', 0, 'x', 'x')\n\n # apply batch normalization\n if self.apply_batch_norm:\n conv_out, new_running_mean, new_running_var = batch_norm(\n conv_out,\n self.gamma,\n self.beta,\n self.running_mean,\n self.running_var,\n is_training,\n 'spatial'\n )\n if is_training:\n self.updates = [\n (self.running_mean, new_running_mean),\n (self.running_var, new_running_var),\n ]\n\n return self.f(conv_out)\n\n\nclass DenseLayer(object):\n def __init__(self, M1, M2, apply_batch_norm, f=T.nnet.relu):\n W = 0.02*np.random.randn(M1, M2)\n self.W = theano.shared(W)\n self.b = theano.shared(np.zeros(M2))\n self.params = [self.W, self.b]\n self.updates = [] # in case we do batch norm\n\n if apply_batch_norm:\n self.gamma = theano.shared(np.ones(M2))\n self.beta = theano.shared(np.zeros(M2))\n self.params += [self.gamma, self.beta]\n\n self.running_mean = theano.shared(np.zeros(M2))\n self.running_var = theano.shared(np.zeros(M2))\n\n self.f = f\n self.apply_batch_norm = apply_batch_norm\n self.params = [self.W, self.b]\n\n def forward(self, X, is_training):\n a = X.dot(self.W) + self.b\n\n # apply batch normalization\n if self.apply_batch_norm:\n a, new_running_mean, new_running_var = batch_norm(\n a,\n self.gamma,\n self.beta,\n self.running_mean,\n self.running_var,\n is_training,\n 'spatial'\n )\n if is_training:\n self.updates = [\n (self.running_mean, new_running_mean),\n (self.running_var, new_running_var),\n ]\n\n return self.f(a)\n\n\nclass DCGAN:\n def __init__(self, img_length, num_colors, d_sizes, g_sizes):\n\n # save for later\n self.img_length = img_length\n self.num_colors = num_colors\n self.latent_dims = g_sizes['z']\n\n # define the input data\n self.X = T.tensor4('placeholderX')\n self.Z = T.matrix('placeholderZ')\n\n # build the discriminator\n p_real_given_real = self.build_discriminator(self.X, d_sizes)\n\n # build generator\n self.sample_images = self.build_generator(self.Z, g_sizes)\n\n # get sample predictions\n p_real_given_fake = self.d_forward(self.sample_images, True)\n\n # sample with batch norm in test mode\n self.sample_images_test = self.g_forward(self.Z, False)\n\n # build costs\n self.d_cost_real = T.nnet.binary_crossentropy(\n output=p_real_given_real,\n target=T.ones_like(p_real_given_real),\n )\n self.d_cost_fake = T.nnet.binary_crossentropy(\n output=p_real_given_fake,\n target=T.zeros_like(p_real_given_fake),\n )\n self.d_cost = T.mean(self.d_cost_real) + T.mean(self.d_cost_fake)\n\n self.g_cost = T.mean(\n T.nnet.binary_crossentropy(\n output=p_real_given_fake,\n target=T.ones_like(p_real_given_fake),\n )\n )\n real_predictions = T.sum(T.eq(T.round(p_real_given_real), 1))\n fake_predictions = T.sum(T.eq(T.round(p_real_given_fake), 0))\n num_predictions = 2.0*BATCH_SIZE\n num_correct = real_predictions + fake_predictions\n self.d_accuracy = num_correct / num_predictions\n\n\n # optimizers\n d_grads = T.grad(self.d_cost, self.d_params)\n d_updates = adam(self.d_params, d_grads)\n # add batch norm updates\n for layer in self.d_convlayers + self.d_denselayers + [self.d_finallayer]:\n d_updates += layer.updates\n self.train_d = theano.function(\n inputs=[self.X, self.Z],\n outputs=[self.d_cost, self.d_accuracy],\n updates=d_updates,\n )\n\n g_grads = T.grad(self.g_cost, self.g_params)\n g_updates = adam(self.g_params, g_grads)\n # add batch norm updates\n for layer in self.g_denselayers + self.g_convlayers:\n g_updates += layer.updates\n g_updates += self.g_bn_updates\n self.train_g = theano.function(\n inputs=[self.Z],\n outputs=self.g_cost,\n updates=g_updates,\n )\n\n # make a function to get sample images\n self.get_sample_images = theano.function(\n inputs=[self.Z],\n outputs=self.sample_images_test,\n )\n\n\n def build_discriminator(self, X, d_sizes):\n self.d_params = []\n\n # build conv layers\n self.d_convlayers = []\n mi = self.num_colors\n dim = self.img_length\n print(\"*** conv layer image sizes:\")\n for mo, filtersz, stride, apply_batch_norm in d_sizes['conv_layers']:\n layer = ConvLayer(mi, mo, apply_batch_norm, filtersz, stride, lrelu)\n self.d_convlayers.append(layer)\n self.d_params += layer.params\n mi = mo\n print(\"dim:\", dim)\n dim = int(np.ceil(float(dim) / stride))\n\n # for 'valid' border mode\n # dim = int(np.floor( (dim - filtersz) / stride ) ) + 1\n\n # for 'full' border mode\n # dim = int(np.ceil( (dim + filtersz - 1) / stride ) )\n\n print(\"final dim before flatten:\", dim)\n\n\n mi = mi * dim * dim\n # build dense layers\n self.d_denselayers = []\n for mo, apply_batch_norm in d_sizes['dense_layers']:\n layer = DenseLayer(mi, mo, apply_batch_norm, lrelu)\n mi = mo\n self.d_denselayers.append(layer)\n self.d_params += layer.params\n\n\n # final logistic layer\n self.d_finallayer = DenseLayer(mi, 1, False, T.nnet.sigmoid)\n self.d_params += self.d_finallayer.params\n\n # get the logits\n p_real_given_x = self.d_forward(X, True)\n\n # build the cost later\n return p_real_given_x\n\n\n def d_forward(self, X, is_training):\n # encapsulate this because we use it twice\n output = X\n for layer in self.d_convlayers:\n output = layer.forward(output, is_training)\n output = output.flatten(ndim=2)\n for layer in self.d_denselayers:\n output = layer.forward(output, is_training)\n output = self.d_finallayer.forward(output, is_training)\n return output\n\n\n def build_generator(self, Z, g_sizes):\n self.g_params = []\n\n # determine the size of the data at each step\n dims = [self.img_length]\n dim = self.img_length\n for _, filtersz, stride, _ in reversed(g_sizes['conv_layers']):\n dim = int(np.ceil(float(dim) / stride))\n\n # for 'valid' border mode\n # dim = int(np.floor( (dim - filtersz) / stride ) ) + 1\n\n # for 'full' border mode\n # dim = int(np.ceil( (dim + filtersz - 1) / stride ) )\n\n dims.append(dim)\n\n # note: dims is actually backwards\n # the first layer of the generator is actually last\n # so let's reverse it\n dims = list(reversed(dims))\n print(\"dims:\", dims)\n self.g_dims = dims\n\n\n # dense layers\n mi = self.latent_dims\n self.g_denselayers = []\n for mo, apply_batch_norm in g_sizes['dense_layers']:\n layer = DenseLayer(mi, mo, apply_batch_norm)\n self.g_denselayers.append(layer)\n self.g_params += layer.params\n mi = mo\n\n # final dense layer\n mo = g_sizes['projection'] * dims[0] * dims[0]\n layer = DenseLayer(mi, mo, not g_sizes['bn_after_project'])\n self.g_denselayers.append(layer)\n self.g_params += layer.params\n\n\n # fs-conv layers\n mi = g_sizes['projection']\n self.g_convlayers = []\n\n # output may use tanh or sigmoid\n num_relus = len(g_sizes['conv_layers']) - 1\n activation_functions = [T.nnet.relu]*num_relus + [g_sizes['output_activation']]\n\n for i in range(len(g_sizes['conv_layers'])):\n mo, filtersz, stride, apply_batch_norm = g_sizes['conv_layers'][i]\n f = activation_functions[i]\n output_shape = [BATCH_SIZE, mo, dims[i+1], dims[i+1]]\n print(\"mi:\", mi, \"mo:\", mo, \"outp shape:\", output_shape)\n layer = FractionallyStridedConvLayer(\n mi, mo, output_shape, apply_batch_norm, filtersz, stride, f\n )\n self.g_convlayers.append(layer)\n self.g_params += layer.params\n mi = mo\n\n # apply batch norm\n if g_sizes['bn_after_project']:\n self.gamma = theano.shared(np.ones(g_sizes['projection']))\n self.beta = theano.shared(np.zeros(g_sizes['projection']))\n self.running_mean = theano.shared(np.zeros(g_sizes['projection']))\n self.running_var = theano.shared(np.zeros(g_sizes['projection']))\n self.g_params += [self.gamma, self.beta]\n\n # get the output\n self.g_sizes = g_sizes\n return self.g_forward(Z, True)\n\n\n def g_forward(self, Z, is_training):\n # dense layers\n output = Z\n for layer in self.g_denselayers:\n output = layer.forward(output, is_training)\n\n # project and reshape\n # remember! (N, color, D, D)\n output = output.reshape(\n [-1, self.g_sizes['projection'], self.g_dims[0], self.g_dims[0]],\n )\n\n # apply batch norm\n if self.g_sizes['bn_after_project']:\n output, new_running_mean, new_running_var = batch_norm(\n output,\n self.gamma,\n self.beta,\n self.running_mean,\n self.running_var,\n is_training,\n 'spatial'\n )\n if is_training:\n self.g_bn_updates = [\n (self.running_mean, new_running_mean),\n (self.running_var, new_running_var),\n ]\n else:\n self.g_bn_updates = []\n\n # pass through fs-conv layers\n for layer in self.g_convlayers:\n output = layer.forward(output, is_training)\n\n return output\n\n\n def fit(self, X):\n d_costs = []\n g_costs = []\n\n N = len(X)\n n_batches = N // BATCH_SIZE\n total_iters = 0\n for i in range(EPOCHS):\n print(\"epoch:\", i)\n np.random.shuffle(X)\n for j in range(n_batches):\n t0 = datetime.now()\n\n if type(X[0]) is str:\n # is celeb dataset\n batch = util.files2images_theano(\n X[j*BATCH_SIZE:(j+1)*BATCH_SIZE]\n )\n\n else:\n # is mnist dataset\n batch = X[j*BATCH_SIZE:(j+1)*BATCH_SIZE]\n\n Z = np.random.uniform(-1, 1, size=(BATCH_SIZE, self.latent_dims))\n\n # train the discriminator\n d_cost, d_acc = self.train_d(batch, Z)\n d_costs.append(d_cost)\n\n # train the generator\n g_cost1 = self.train_g(Z)\n g_cost2 = self.train_g(Z)\n g_costs.append((g_cost1 + g_cost2)/2) # just use the avg\n\n print(\" batch: %d/%d - dt: %s - d_acc: %.2f\" % (j+1, n_batches, datetime.now() - t0, d_acc))\n\n # save samples periodically\n total_iters += 1\n if total_iters % SAVE_SAMPLE_PERIOD == 0:\n print(\"saving a sample...\")\n samples = self.sample(64) # shape is (64, D, D, color)\n\n # for convenience\n d = self.img_length\n \n if samples.shape[-1] == 1:\n # if color == 1, we want a 2-D image (N x N)\n samples = samples.reshape(64, d, d)\n flat_image = np.empty((8*d, 8*d))\n\n k = 0\n for i in range(8):\n for j in range(8):\n flat_image[i*d:(i+1)*d, j*d:(j+1)*d] = samples[k].reshape(d, d)\n k += 1\n\n # plt.imshow(flat_image, cmap='gray')\n else:\n # if color == 3, we want a 3-D image (N x N x 3)\n flat_image = np.empty((8*d, 8*d, 3))\n k = 0\n for i in range(8):\n for j in range(8):\n # note: we have to change it back to (D, D, color)\n flat_image[i*d:(i+1)*d, j*d:(j+1)*d] = samples[k].transpose((1, 2, 0))\n k += 1\n # plt.imshow(flat_image)\n \n # plt.savefig('samples/samples_at_iter_%d.png' % total_iters)\n sp.misc.imsave(\n 'samples/samples_at_iter_%d.png' % total_iters,\n flat_image,\n )\n\n # save a plot of the costs\n plt.clf()\n plt.plot(d_costs, label='discriminator cost')\n plt.plot(g_costs, label='generator cost')\n plt.legend()\n plt.savefig('cost_vs_iteration.png')\n\n def sample(self, n):\n Z = np.random.uniform(-1, 1, size=(n, self.latent_dims))\n return self.get_sample_images(Z)\n\n\n\ndef celeb():\n X = util.get_celeb()\n # just loads a list of filenames, we will load them in dynamically\n # because there are many\n dim = 64\n colors = 3\n\n # for celeb\n d_sizes = {\n 'conv_layers': [\n (64, 5, 2, False),\n (128, 5, 2, True),\n (256, 5, 2, True),\n (512, 5, 2, True)\n ],\n 'dense_layers': [],\n }\n g_sizes = {\n 'z': 100,\n 'projection': 512,\n 'bn_after_project': True,\n 'conv_layers': [\n (256, 5, 2, True),\n (128, 5, 2, True),\n (64, 5, 2, True),\n (colors, 5, 2, False)\n ],\n 'dense_layers': [],\n 'output_activation': T.tanh,\n }\n\n # setup gan\n # note: assume square images, so only need 1 dim\n gan = DCGAN(dim, colors, d_sizes, g_sizes)\n gan.fit(X)\n\n\ndef mnist():\n X, Y = util.get_mnist()\n X = X.reshape(len(X), 1, 28, 28) # remember! (N, color, D, D)\n dim = X.shape[2]\n colors = X.shape[1]\n\n # for mnist\n d_sizes = {\n 'conv_layers': [(2, 5, 2, False), (64, 5, 2, True)],\n 'dense_layers': [(1024, True)],\n }\n g_sizes = {\n 'z': 100,\n 'projection': 128,\n 'bn_after_project': False,\n 'conv_layers': [(128, 5, 2, True), (colors, 5, 2, False)],\n 'dense_layers': [(1024, True)],\n 'output_activation': T.nnet.sigmoid,\n }\n\n\n # setup gan\n # note: assume square images, so only need 1 dim\n gan = DCGAN(dim, colors, d_sizes, g_sizes)\n gan.fit(X)\n\n # since training will take a considerable\n # amount of time, let's just save some\n # samples to disk rather than plotting now\n\n\nif __name__ == '__main__':\n celeb()\n # mnist()\n" }, { "path": "unsupervised_class3/extra_reading.txt", "content": "Generative Visual Manipulation on the Natural Image Manifold\nhttps://www.youtube.com/watch?v=9c4z6YsBGQ0\n\nGenerative Adversarial Nets (Goodfellow 2014)\nhttps://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf\n\nRadford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks\nhttps://arxiv.org/abs/1511.06434v2\n\nStriving for Simplicity: The All Convolutional Net (Springenberg et al., 2015)\nhttps://arxiv.org/abs/1412.6806\n\n" }, { "path": "unsupervised_class3/parameterize_guassian.py", "content": "# https://deeplearningcourses.com/c/deep-learning-gans-and-variational-autoencoders\n# https://www.udemy.com/deep-learning-gans-and-variational-autoencoders\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.stats import multivariate_normal as mvn\n\n\ndef softplus(x):\n # log1p(x) == log(1 + x)\n return np.log1p(np.exp(x))\n\n\n# we're going to make a neural network\n# with the layer sizes (4, 3, 2)\n# like a toy version of a encoder\n\nW1 = np.random.randn(4, 3)\nW2 = np.random.randn(3, 2*2)\n\n# why 2 * 2?\n# we need 2 components for the mean,\n# and 2 components for the standard deviation!\n\n# ignore bias terms for simplicity.\n\ndef forward(x, W1, W2):\n hidden = np.tanh(x.dot(W1))\n output = hidden.dot(W2) # no activation!\n mean = output[:2]\n stddev = softplus(output[2:])\n return mean, stddev\n\n\n# make a random input\nx = np.random.randn(4)\n\n# get the parameters of the Gaussian\nmean, stddev = forward(x, W1, W2)\nprint(\"mean:\", mean)\nprint(\"stddev:\", stddev)\n\n# draw samples\nsamples = mvn.rvs(mean=mean, cov=stddev**2, size=10000)\n\n# plot the samples\nplt.scatter(samples[:,0], samples[:,1], alpha=0.5)\nplt.show()\n\n\n" }, { "path": "unsupervised_class3/test_stochastic_tensor.py", "content": "# https://deeplearningcourses.com/c/deep-learning-gans-and-variational-autoencoders\n# https://www.udemy.com/deep-learning-gans-and-variational-autoencoders\n\n# a simple script to see what StochasticTensor outputs\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\n\nst = tf.contrib.bayesflow.stochastic_tensor\nNormal = tf.contrib.distributions.Normal\n\n# sample N samples from N(5,3*3)\nN = 10000\nmean = np.ones(N)*5\nscale = np.ones(N)*3\n\n\nI = tf.Variable(np.ones(N))\n\n\nwith st.value_type(st.SampleValue()):\n X = st.StochasticTensor(Normal(loc=mean, scale=scale))\n\n# cannot session.run a stochastic tensor\n# but we can session.run a tensor\nY = I * X\n\n\ninit_op = tf.global_variables_initializer()\nwith tf.Session() as session:\n session.run(init_op)\n Y_val = session.run(Y)\n\n print(\"Sample mean:\", Y_val.mean())\n print(\"Sample std dev:\", Y_val.std())\n\n plt.hist(Y_val, bins=20)\n plt.show()\n" }, { "path": "unsupervised_class3/util.py", "content": "# https://deeplearningcourses.com/c/deep-learning-gans-and-variational-autoencoders\n# https://www.udemy.com/deep-learning-gans-and-variational-autoencoders\n\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport os\nimport requests\nimport zipfile\nimport numpy as np\nimport pandas as pd\n\ntry:\n # new version doesn't support\n from scipy.misc import imread, imsave, imresize\nexcept:\n from PIL import Image\n def imread(fn):\n im = Image.open(fn)\n return np.array(im)\n\n def imsave(fn, arr):\n im = Image.fromarray(arr)\n im.save(fn)\n\n def imresize(arr, sz):\n im = Image.fromarray(arr)\n im.resize(sz)\n return np.array(im)\n\n\nfrom glob import glob\nfrom tqdm import tqdm\nfrom sklearn.utils import shuffle\n\n\ndef get_mnist(limit=None):\n if not os.path.exists('../large_files'):\n print(\"You must create a folder called large_files adjacent to the class folder first.\")\n if not os.path.exists('../large_files/train.csv'):\n print(\"Looks like you haven't downloaded the data or it's not in the right spot.\")\n print(\"Please get train.csv from https://www.kaggle.com/c/digit-recognizer\")\n print(\"and place it in the large_files folder.\")\n\n print(\"Reading in and transforming data...\")\n df = pd.read_csv('../large_files/train.csv')\n data = df.values\n # np.random.shuffle(data)\n X = data[:, 1:] / 255.0 # data is from 0..255\n Y = data[:, 0]\n X, Y = shuffle(X, Y)\n if limit is not None:\n X, Y = X[:limit], Y[:limit]\n return X, Y\n\n\ndef get_celeb(limit=None):\n if not os.path.exists('../large_files'):\n os.mkdir('../large_files')\n\n # eventual place where our final data will reside\n if not os.path.exists('../large_files/img_align_celeba-cropped'):\n\n # check for original data\n if not os.path.exists('../large_files/img_align_celeba'):\n # download the file and place it here\n if not os.path.exists('../large_files/img_align_celeba.zip'):\n print(\"Downloading img_align_celeba.zip...\")\n download_file(\n '0B7EVK8r0v71pZjFTYXZWM3FlRnM',\n '../large_files/img_align_celeba.zip'\n )\n\n # unzip the file\n print(\"Extracting img_align_celeba.zip...\")\n with zipfile.ZipFile('../large_files/img_align_celeba.zip') as zf:\n zip_dir = zf.namelist()[0]\n zf.extractall('../large_files')\n\n\n # load in the original images\n filenames = glob(\"../large_files/img_align_celeba/*.jpg\")\n N = len(filenames)\n print(\"Found %d files!\" % N)\n\n\n # crop the images to 64x64\n os.mkdir('../large_files/img_align_celeba-cropped')\n print(\"Cropping images, please wait...\")\n\n for i in range(N):\n crop_and_resave(filenames[i], '../large_files/img_align_celeba-cropped')\n if i % 1000 == 0:\n print(\"%d/%d\" % (i, N))\n\n\n # make sure to return the cropped version\n filenames = glob(\"../large_files/img_align_celeba-cropped/*.jpg\")\n return filenames\n\n\ndef crop_and_resave(inputfile, outputdir):\n # theoretically, we could try to find the face\n # but let's be lazy\n # we assume that the middle 108 pixels will contain the face\n im = imread(inputfile)\n height, width, color = im.shape\n edge_h = int( round( (height - 108) / 2.0 ) )\n edge_w = int( round( (width - 108) / 2.0 ) )\n\n cropped = im[edge_h:(edge_h + 108), edge_w:(edge_w + 108)]\n small = imresize(cropped, (64, 64))\n\n filename = inputfile.split('/')[-1]\n imsave(\"%s/%s\" % (outputdir, filename), small)\n\n\ndef scale_image(im):\n # scale to (-1, +1)\n return (im / 255.0)*2 - 1\n\n\ndef files2images_theano(filenames):\n # theano wants images to be of shape (C, D, D)\n # tensorflow wants (D, D, C) which is what scipy imread\n # uses by default\n return [scale_image(imread(fn).transpose((2, 0, 1))) for fn in filenames]\n\n\ndef files2images(filenames):\n return [scale_image(imread(fn)) for fn in filenames]\n\n\n# functions for downloading file from google drive\ndef save_response_content(r, dest):\n # unfortunately content-length is not provided in header\n total_iters = 1409659 # in KB\n print(\"Note: units are in KB, e.g. KKB = MB\")\n # because we are reading 1024 bytes at a time, hence\n # 1KB == 1 \"unit\" for tqdm\n with open(dest, 'wb') as f:\n for chunk in tqdm(\n r.iter_content(1024),\n total=total_iters,\n unit='KB',\n unit_scale=True):\n if chunk: # filter out keep-alive new chunks\n f.write(chunk)\n\n\ndef get_confirm_token(response):\n for key, value in response.cookies.items():\n if key.startswith('download_warning'):\n return value\n return None\n\n\ndef download_file(file_id, dest):\n drive_url = \"https://docs.google.com/uc?export=download\"\n session = requests.Session()\n response = session.get(drive_url, params={'id': file_id}, stream=True)\n token = get_confirm_token(response)\n\n if token:\n params = {'id': file_id, 'confirm': token}\n response = session.get(drive_url, params=params, stream=True)\n\n save_response_content(response, dest)\n\n\n" }, { "path": "unsupervised_class3/vae_tf.py", "content": "# https://deeplearningcourses.com/c/deep-learning-gans-and-variational-autoencoders\n# https://www.udemy.com/deep-learning-gans-and-variational-autoencoders\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport util\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\n\nst = None\ntry:\n st = tf.contrib.bayesflow.stochastic_tensor\nexcept:\n # doesn't exist in later versions of TF\n # we will use the reparameterization trick instead\n # watch the later lecture on the reparameterization trick\n # to learn about it.\n pass\nNormal = tf.contrib.distributions.Normal\nBernoulli = tf.contrib.distributions.Bernoulli\n\n\n\nclass DenseLayer(object):\n def __init__(self, M1, M2, f=tf.nn.relu):\n # self.M1 = M1\n # self.M2 = M2\n\n self.W = tf.Variable(tf.random_normal(shape=(M1, M2)) * 2 / np.sqrt(M1))\n self.b = tf.Variable(np.zeros(M2).astype(np.float32))\n self.f = f\n\n def forward(self, X):\n return self.f(tf.matmul(X, self.W) + self.b)\n\n\nclass VariationalAutoencoder:\n def __init__(self, D, hidden_layer_sizes):\n # hidden_layer_sizes specifies the size of every layer\n # in the encoder\n # up to the final hidden layer Z\n # the decoder will have the reverse shape\n\n \n # represents a batch of training data\n self.X = tf.placeholder(tf.float32, shape=(None, D))\n\n # encoder\n self.encoder_layers = []\n M_in = D\n for M_out in hidden_layer_sizes[:-1]:\n h = DenseLayer(M_in, M_out)\n self.encoder_layers.append(h)\n M_in = M_out\n\n\n # for convenience, we'll refer to the final encoder size as M\n # also the input to the decoder size\n M = hidden_layer_sizes[-1]\n\n # the encoder's final layer output is unbounded\n # so there is no activation function\n # we also need 2 times as many units as specified by M_out\n # since there needs to be M_out means + M_out variances\n h = DenseLayer(M_in, 2 * M, f=lambda x: x)\n self.encoder_layers.append(h)\n\n # get the mean and variance / std dev of Z.\n # note that the variance must be > 0\n # we can get a sigma (standard dev) > 0 from an unbounded variable by\n # passing it through the softplus function.\n # add a small amount for smoothing.\n current_layer_value = self.X\n for layer in self.encoder_layers:\n current_layer_value = layer.forward(current_layer_value)\n self.means = current_layer_value[:, :M]\n self.stddev = tf.nn.softplus(current_layer_value[:, M:]) + 1e-6\n\n # get a sample of Z\n # we need to use a stochastic tensor\n # in order for the errors to be backpropagated past this point\n if st is None:\n # doesn't exist in later versions of Tensorflow\n # we'll use the same trick we use in Theano\n standard_normal = Normal(\n loc=np.zeros(M, dtype=np.float32),\n scale=np.ones(M, dtype=np.float32)\n )\n e = standard_normal.sample(tf.shape(self.means)[0])\n self.Z = e * self.stddev + self.means\n\n # note: this also works because Tensorflow\n # now does the \"magic\" for you\n # n = Normal(\n # loc=self.means,\n # scale=self.stddev,\n # )\n # self.Z = n.sample()\n else:\n with st.value_type(st.SampleValue()):\n self.Z = st.StochasticTensor(Normal(loc=self.means, scale=self.stddev))\n # to get back Q(Z), the distribution of Z\n # we will later use self.Z.distribution\n\n\n # decoder\n self.decoder_layers = []\n M_in = M\n for M_out in reversed(hidden_layer_sizes[:-1]):\n h = DenseLayer(M_in, M_out)\n self.decoder_layers.append(h)\n M_in = M_out\n\n # the decoder's final layer should technically go through a sigmoid\n # so that the final output is a binary probability (e.g. Bernoulli)\n # but Bernoulli accepts logits (pre-sigmoid) so we will take those\n # so no activation function is needed at the final layer\n h = DenseLayer(M_in, D, f=lambda x: x)\n self.decoder_layers.append(h)\n\n # get the logits\n current_layer_value = self.Z\n for layer in self.decoder_layers:\n current_layer_value = layer.forward(current_layer_value)\n logits = current_layer_value\n posterior_predictive_logits = logits # save for later\n\n # get the output\n self.X_hat_distribution = Bernoulli(logits=logits)\n\n # take samples from X_hat\n # we will call this the posterior predictive sample\n self.posterior_predictive = self.X_hat_distribution.sample()\n self.posterior_predictive_probs = tf.nn.sigmoid(logits)\n\n # take sample from a Z ~ N(0, 1)\n # and put it through the decoder\n # we will call this the prior predictive sample\n standard_normal = Normal(\n loc=np.zeros(M, dtype=np.float32),\n scale=np.ones(M, dtype=np.float32)\n )\n\n Z_std = standard_normal.sample(1)\n current_layer_value = Z_std\n for layer in self.decoder_layers:\n current_layer_value = layer.forward(current_layer_value)\n logits = current_layer_value\n\n prior_predictive_dist = Bernoulli(logits=logits)\n self.prior_predictive = prior_predictive_dist.sample()\n self.prior_predictive_probs = tf.nn.sigmoid(logits)\n\n\n # prior predictive from input\n # only used for generating visualization\n self.Z_input = tf.placeholder(tf.float32, shape=(None, M))\n current_layer_value = self.Z_input\n for layer in self.decoder_layers:\n current_layer_value = layer.forward(current_layer_value)\n logits = current_layer_value\n self.prior_predictive_from_input_probs = tf.nn.sigmoid(logits)\n\n\n # now build the cost\n if st is None:\n kl = -tf.log(self.stddev) + 0.5*(self.stddev**2 + self.means**2) - 0.5\n kl = tf.reduce_sum(kl, axis=1)\n else:\n kl = tf.reduce_sum(\n tf.contrib.distributions.kl_divergence(\n self.Z.distribution, standard_normal\n ),\n 1\n )\n expected_log_likelihood = tf.reduce_sum(\n self.X_hat_distribution.log_prob(self.X),\n 1\n )\n\n # equivalent\n # expected_log_likelihood = -tf.nn.sigmoid_cross_entropy_with_logits(\n # labels=self.X,\n # logits=posterior_predictive_logits\n # )\n # expected_log_likelihood = tf.reduce_sum(expected_log_likelihood, 1)\n\n\n\n self.elbo = tf.reduce_sum(expected_log_likelihood - kl)\n self.train_op = tf.train.RMSPropOptimizer(learning_rate=0.001).minimize(-self.elbo)\n\n # set up session and variables for later\n self.init_op = tf.global_variables_initializer()\n self.sess = tf.InteractiveSession()\n self.sess.run(self.init_op)\n\n\n def fit(self, X, epochs=30, batch_sz=64):\n costs = []\n n_batches = len(X) // batch_sz\n print(\"n_batches:\", n_batches)\n for i in range(epochs):\n print(\"epoch:\", i)\n np.random.shuffle(X)\n for j in range(n_batches):\n batch = X[j*batch_sz:(j+1)*batch_sz]\n _, c, = self.sess.run((self.train_op, self.elbo), feed_dict={self.X: batch})\n c /= batch_sz # just debugging\n costs.append(c)\n if j % 100 == 0:\n print(\"iter: %d, cost: %.3f\" % (j, c))\n plt.plot(costs)\n plt.show()\n\n def transform(self, X):\n return self.sess.run(\n self.means,\n feed_dict={self.X: X}\n )\n\n def prior_predictive_with_input(self, Z):\n return self.sess.run(\n self.prior_predictive_from_input_probs,\n feed_dict={self.Z_input: Z}\n )\n\n def posterior_predictive_sample(self, X):\n # returns a sample from p(x_new | X)\n return self.sess.run(self.posterior_predictive, feed_dict={self.X: X})\n\n def prior_predictive_sample_with_probs(self):\n # returns a sample from p(x_new | z), z ~ N(0, 1)\n return self.sess.run((self.prior_predictive, self.prior_predictive_probs))\n\n\ndef main():\n X, Y = util.get_mnist()\n # convert X to binary variable\n X = (X > 0.5).astype(np.float32)\n\n vae = VariationalAutoencoder(784, [200, 100])\n vae.fit(X)\n\n # plot reconstruction\n done = False\n while not done:\n i = np.random.choice(len(X))\n x = X[i]\n im = vae.posterior_predictive_sample([x]).reshape(28, 28)\n plt.subplot(1,2,1)\n plt.imshow(x.reshape(28, 28), cmap='gray')\n plt.title(\"Original\")\n plt.subplot(1,2,2)\n plt.imshow(im, cmap='gray')\n plt.title(\"Sampled\")\n plt.show()\n\n ans = input(\"Generate another?\")\n if ans and ans[0] in ('n' or 'N'):\n done = True\n\n # plot output from random samples in latent space\n done = False\n while not done:\n im, probs = vae.prior_predictive_sample_with_probs()\n im = im.reshape(28, 28)\n probs = probs.reshape(28, 28)\n plt.subplot(1,2,1)\n plt.imshow(im, cmap='gray')\n plt.title(\"Prior predictive sample\")\n plt.subplot(1,2,2)\n plt.imshow(probs, cmap='gray')\n plt.title(\"Prior predictive probs\")\n plt.show()\n\n ans = input(\"Generate another?\")\n if ans and ans[0] in ('n' or 'N'):\n done = True\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "unsupervised_class3/vae_theano.py", "content": "# https://deeplearningcourses.com/c/deep-learning-gans-and-variational-autoencoders\n# https://www.udemy.com/deep-learning-gans-and-variational-autoencoders\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport util\nimport numpy as np\nimport theano\nimport theano.tensor as T\nimport matplotlib.pyplot as plt\n\nfrom theano.tensor.shared_randomstreams import RandomStreams\n\n\nclass DenseLayer(object):\n def __init__(self, M1, M2, f=T.nnet.relu):\n self.W = theano.shared(np.random.randn(M1, M2) * np.sqrt(2.0 / M1))\n self.b = theano.shared(np.zeros(M2))\n self.f = f\n self.params = [self.W, self.b]\n\n def forward(self, X):\n return self.f(X.dot(self.W) + self.b)\n\n\nclass VariationalAutoencoder:\n def __init__(self, D, hidden_layer_sizes):\n # hidden_layer_sizes specifies the size of every layer\n # in the encoder\n # up to the final hidden layer Z\n # the decoder will have the reverse shape\n\n \n # represents a batch of training data\n self.X = T.matrix('X')\n\n # encoder\n self.encoder_layers = []\n M_in = D\n for M_out in hidden_layer_sizes[:-1]:\n h = DenseLayer(M_in, M_out)\n self.encoder_layers.append(h)\n M_in = M_out\n\n\n # for convenience, we'll refer to the final encoder size as M\n # also the input to the decoder size\n M = hidden_layer_sizes[-1]\n\n # the encoder's final layer output is unbounded\n # so there is no activation function\n # we also need 2 times as many units as specified by M_out\n # since there needs to be M_out means + M_out variances\n h = DenseLayer(M_in, 2 * M, f=lambda x: x)\n self.encoder_layers.append(h)\n\n # get the mean and variance / std dev of Z.\n # note that the variance must be > 0\n # we can get a sigma (standard dev) > 0 from an unbounded variable by\n # passing it through the softplus function.\n # add a small amount for smoothing.\n current_layer_value = self.X\n for layer in self.encoder_layers:\n current_layer_value = layer.forward(current_layer_value)\n self.means = current_layer_value[:, :M]\n self.stddev = T.nnet.softplus(current_layer_value[:, M:]) + 1e-6\n\n # get a sample of Z\n self.rng = RandomStreams()\n eps = self.rng.normal((self.means.shape[0], M))\n self.Z = self.means + self.stddev * eps\n\n\n # decoder\n self.decoder_layers = []\n M_in = M\n for M_out in reversed(hidden_layer_sizes[:-1]):\n h = DenseLayer(M_in, M_out)\n self.decoder_layers.append(h)\n M_in = M_out\n\n # the decoder's final layer should go through a sigmoid\n h = DenseLayer(M_in, D, f=T.nnet.sigmoid)\n self.decoder_layers.append(h)\n\n # get the posterior predictive\n current_layer_value = self.Z\n for layer in self.decoder_layers:\n current_layer_value = layer.forward(current_layer_value)\n self.posterior_predictive_probs = current_layer_value\n\n # take samples from X_hat\n # we will call this the posterior predictive sample\n self.posterior_predictive = self.rng.binomial(\n size=self.posterior_predictive_probs.shape,\n n=1,\n p=self.posterior_predictive_probs\n )\n\n # take sample from a Z ~ N(0, 1)\n # and put it through the decoder\n # we will call this the prior predictive sample\n Z_std = self.rng.normal((1, M))\n current_layer_value = Z_std\n for layer in self.decoder_layers:\n current_layer_value = layer.forward(current_layer_value)\n self.prior_predictive_probs = current_layer_value\n\n self.prior_predictive = self.rng.binomial(\n size=self.prior_predictive_probs.shape,\n n=1,\n p=self.prior_predictive_probs\n )\n\n # prior predictive from input\n # only used for generating visualization\n Z_input = T.matrix('Z_input')\n current_layer_value = Z_input\n for layer in self.decoder_layers:\n current_layer_value = layer.forward(current_layer_value)\n prior_predictive_probs_from_Z_input = current_layer_value\n\n\n # now build the cost\n # https://stats.stackexchange.com/questions/7440/kl-divergence-between-two-univariate-gaussians\n # https://stats.stackexchange.com/questions/60680/kl-divergence-between-two-multivariate-gaussians\n kl = -T.log(self.stddev) + 0.5*(self.stddev**2 + self.means**2) - 0.5\n kl = T.sum(kl, axis=1)\n expected_log_likelihood = -T.nnet.binary_crossentropy(\n output=self.posterior_predictive_probs,\n target=self.X,\n )\n expected_log_likelihood = T.sum(expected_log_likelihood, axis=1)\n self.elbo = T.sum(expected_log_likelihood - kl)\n\n \n\n # define the updates\n params = []\n for layer in self.encoder_layers:\n params += layer.params\n for layer in self.decoder_layers:\n params += layer.params\n\n grads = T.grad(-self.elbo, params)\n\n # rmsprop\n decay = 0.9\n learning_rate = 0.001\n\n # for rmsprop\n cache = [theano.shared(np.ones_like(p.get_value())) for p in params]\n\n new_cache = [decay*c + (1-decay)*g*g for p, c, g in zip(params, cache, grads)]\n\n updates = [\n (c, new_c) for c, new_c in zip(cache, new_cache)\n ] + [\n (p, p - learning_rate*g/T.sqrt(new_c + 1e-10)) for p, new_c, g in zip(params, new_cache, grads)\n ]\n\n\n # now define callable functions\n self.train_op = theano.function(\n inputs=[self.X],\n outputs=self.elbo,\n updates=updates\n )\n\n # returns a sample from p(x_new | X)\n self.posterior_predictive_sample = theano.function(\n inputs=[self.X],\n outputs=self.posterior_predictive,\n )\n\n # returns a sample from p(x_new | z), z ~ N(0, 1)\n self.prior_predictive_sample_with_probs = theano.function(\n inputs=[],\n outputs=[self.prior_predictive, self.prior_predictive_probs]\n )\n\n # return mean of q(z | x)\n self.transform = theano.function(\n inputs=[self.X],\n outputs=self.means\n )\n\n # returns a sample from p(x_new | z), from a given z\n self.prior_predictive_with_input = theano.function(\n inputs=[Z_input],\n outputs=prior_predictive_probs_from_Z_input\n )\n\n\n def fit(self, X, epochs=30, batch_sz=64):\n costs = []\n n_batches = len(X) // batch_sz\n print(\"n_batches:\", n_batches)\n for i in range(epochs):\n print(\"epoch:\", i)\n np.random.shuffle(X)\n for j in range(n_batches):\n batch = X[j*batch_sz:(j+1)*batch_sz]\n c = self.train_op(batch)\n c /= batch_sz # just debugging\n costs.append(c)\n if j % 100 == 0:\n print(\"iter: %d, cost: %.3f\" % (j, c))\n plt.plot(costs)\n plt.show()\n\n\ndef main():\n X, Y = util.get_mnist()\n # convert X to binary variable\n X = (X > 0.5).astype(np.float32)\n\n vae = VariationalAutoencoder(784, [200, 100])\n vae.fit(X)\n\n # plot reconstruction\n done = False\n while not done:\n i = np.random.choice(len(X))\n x = X[i]\n im = vae.posterior_predictive_sample([x]).reshape(28, 28)\n plt.subplot(1,2,1)\n plt.imshow(x.reshape(28, 28), cmap='gray')\n plt.title(\"Original\")\n plt.subplot(1,2,2)\n plt.imshow(im, cmap='gray')\n plt.title(\"Sampled\")\n plt.show()\n\n ans = input(\"Generate another?\")\n if ans and ans[0] in ('n' or 'N'):\n done = True\n\n # plot output from random samples in latent space\n done = False\n while not done:\n im, probs = vae.prior_predictive_sample_with_probs()\n im = im.reshape(28, 28)\n probs = probs.reshape(28, 28)\n plt.subplot(1,2,1)\n plt.imshow(im, cmap='gray')\n plt.title(\"Prior predictive sample\")\n plt.subplot(1,2,2)\n plt.imshow(probs, cmap='gray')\n plt.title(\"Prior predictive probs\")\n plt.show()\n\n ans = input(\"Generate another?\")\n if ans and ans[0] in ('n' or 'N'):\n done = True\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "unsupervised_class3/visualize_latent_space.py", "content": "# https://deeplearningcourses.com/c/deep-learning-gans-and-variational-autoencoders\n# https://www.udemy.com/deep-learning-gans-and-variational-autoencoders\nfrom __future__ import print_function, division\nfrom builtins import range, input\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport util\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom vae_tf import VariationalAutoencoder\n# from vae_theano import VariationalAutoencoder\n\nif __name__ == '__main__':\n X, Y = util.get_mnist()\n # convert X to binary variable\n X = (X > 0.5).astype(np.float32)\n\n for i in range(len(X)):\n plt.imshow(X[i].reshape(28, 28), cmap='gray')\n plt.title(\"Label: %s\" % Y[i])\n plt.show()\n ans = input(\"Show another? [Y/n]\")\n if ans and ans[0].lower().startswith('n'):\n break\n\n\n vae = VariationalAutoencoder(784, [200, 100, 2])\n vae.fit(X.copy())\n # fit will shuffle the data\n # so we need to copy to prevent messing up the order\n # for plotting later, we need Z and Y to correspond\n\n Z = vae.transform(X)\n plt.scatter(Z[:,0], Z[:,1], c=Y, s=10)\n plt.show()\n\n\n # plot what image is reproduced for different parts of Z\n n = 20 # number of images per side\n x_values = np.linspace(-3, 3, n)\n y_values = np.linspace(-3, 3, n)\n image = np.empty((28 * n, 28 * n))\n\n # build Z first so we don't have to keep\n # re-calling the predict function\n # it is particularly slow in theano\n Z2 = []\n for i, x in enumerate(x_values):\n for j, y in enumerate(y_values):\n z = [x, y]\n Z2.append(z)\n X_recon = vae.prior_predictive_with_input(Z2)\n\n k = 0\n for i, x in enumerate(x_values):\n for j, y in enumerate(y_values):\n x_recon = X_recon[k]\n k += 1\n # convert from NxD == 1 x 784 --> 28 x 28\n x_recon = x_recon.reshape(28, 28)\n image[(n - i - 1) * 28:(n - i) * 28, j * 28:(j + 1) * 28] = x_recon\n plt.imshow(image, cmap='gray')\n plt.show()\n\n" } ]