Repository: PytLab/MLBox Branch: master Commit: e916cd8ff9c3 Files: 65 Total size: 821.8 KB Directory structure: gitextract_2fv0thbp/ ├── .gitignore ├── README.md ├── Reinforcement Learning/ │ ├── Calculating State Utilities.ipynb │ ├── Calculating Transition Probabilities.ipynb │ ├── Defining Initial Distribution.ipynb │ ├── Policy Iteration Algorithm.ipynb │ ├── T.npy │ └── Value Iteration Algorithm.ipynb ├── classification_and_regression_trees/ │ ├── bikeSpeedVsIq_test.txt │ ├── bikeSpeedVsIq_train.txt │ ├── compare.py │ ├── dot/ │ │ ├── ex0.dot │ │ ├── ex00.dot │ │ ├── ex2.dot │ │ ├── ex2_prune.dot │ │ └── exp2.dot │ ├── ex0.txt │ ├── ex00.txt │ ├── ex2.dot │ ├── ex2.txt │ ├── ex2test.txt │ ├── exp.txt │ ├── exp2.dot │ ├── exp2.txt │ ├── model_tree.py │ ├── notebook/ │ │ ├── 分段函数回归树.ipynb │ │ ├── 后剪枝.ipynb │ │ └── 模型树对分段线性函数进行回归.ipynb │ ├── prune.py │ └── regression_tree.py ├── decision_tree/ │ ├── english_big.txt │ ├── lenses.dot │ ├── lenses.py │ ├── lenses.txt │ ├── sms_tree.dot │ ├── sms_tree.pkl │ ├── sms_tree.py │ ├── sms_tree_2.dot │ └── trees.py ├── linear_regression/ │ ├── abalone.txt │ ├── ex0.txt │ ├── ex1.txt │ ├── lasso_regression.ipynb │ ├── lasso_regression.py │ ├── lasso_traj.ipynb │ ├── lasso_ws │ ├── local_weighted_linear_regression.py │ ├── ridge_regression.ipynb │ ├── ridge_regression.py │ ├── stage_wise_regression.py │ ├── stage_wise_traj.ipynb │ └── standard_linear_regression.py ├── logistic_regression/ │ ├── english_big.txt │ ├── logreg_grad_ascent.py │ ├── logreg_stoch_grad_ascent.py │ ├── sms.py │ └── testSet.txt ├── naive_bayes/ │ ├── bayes.py │ ├── english_big.txt │ └── sms.py └── support_vector_machine/ ├── best_fit.py ├── svm_ga.py ├── svm_platt_smo.py ├── svm_simple_smo.py └── testSet.txt ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python env/ build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ *.egg-info/ .installed.cfg *.egg # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover .hypothesis/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # pyenv .python-version # celery beat schedule file celerybeat-schedule # SageMath parsed files *.sage.py # dotenv .env # virtualenv .venv venv/ ENV/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .DS_Store *.swp ================================================ FILE: README.md ================================================ # MLBox Machine Learning Algorithms implementations # Blogs - [机器学习算法实践-决策树(Decision Tree)](http://pytlab.github.io/2017/07/09/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%AE%97%E6%B3%95%E5%AE%9E%E8%B7%B5-%E5%86%B3%E7%AD%96%E6%A0%91/) - [机器学习算法实践-朴素贝叶斯(Naive Bayes)](http://pytlab.github.io/2017/07/11/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%AE%9E%E8%B7%B5-%E6%9C%B4%E7%B4%A0%E8%B4%9D%E5%8F%B6%E6%96%AF-Naive-Bayes/) - [机器学习算法实践-Logistic回归与梯度上升算法(上)](http://pytlab.github.io/2017/07/13/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%AE%97%E6%B3%95%E5%AE%9E%E8%B7%B5-Logistic%E5%9B%9E%E5%BD%92%E4%B8%8E%E6%A2%AF%E5%BA%A6%E4%B8%8A%E5%8D%87%E7%AE%97%E6%B3%95-%E4%B8%8A/) - [机器学习算法实践-Logistic回归与梯度上升算法(下)](http://pytlab.github.io/2017/07/15/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%AE%97%E6%B3%95%E5%AE%9E%E8%B7%B5-Logistic%E5%9B%9E%E5%BD%92%E4%B8%8E%E6%A2%AF%E5%BA%A6%E4%B8%8A%E5%8D%87%E7%AE%97%E6%B3%95-%E4%B8%8B/) - [机器学习算法实践-支持向量机(SVM)算法原理](http://pytlab.github.io/2017/08/15/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%AE%97%E6%B3%95%E5%AE%9E%E8%B7%B5-%E6%94%AF%E6%8C%81%E5%90%91%E9%87%8F%E6%9C%BA-SVM-%E7%AE%97%E6%B3%95%E5%8E%9F%E7%90%86/) - [机器学习算法实践-SVM核函数和软间隔](http://pytlab.github.io/2017/08/30/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%AE%97%E6%B3%95%E5%AE%9E%E8%B7%B5-SVM%E6%A0%B8%E5%87%BD%E6%95%B0%E5%92%8C%E8%BD%AF%E9%97%B4%E9%9A%94/) - [机器学习算法实践-SVM中的SMO算法](http://pytlab.github.io/2017/09/01/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%AE%97%E6%B3%95%E5%AE%9E%E8%B7%B5-SVM%E4%B8%AD%E7%9A%84SMO%E7%AE%97%E6%B3%95/) - [机器学习算法实践-Platt SMO和遗传算法优化SVM](http://pytlab.github.io/2017/10/15/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%AE%97%E6%B3%95%E5%AE%9E%E8%B7%B5-Platt-SMO%E5%92%8C%E9%81%97%E4%BC%A0%E7%AE%97%E6%B3%95%E4%BC%98%E5%8C%96SVM/) - [机器学习算法实践-标准与局部加权线性回归](http://pytlab.github.io/2017/10/24/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%AE%97%E6%B3%95%E5%AE%9E%E8%B7%B5-%E6%A0%87%E5%87%86%E4%B8%8E%E5%B1%80%E9%83%A8%E5%8A%A0%E6%9D%83%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92/) - [机器学习算法实践-岭回归和LASSO](http://pytlab.github.io/2017/10/27/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%AE%9E%E8%B7%B5-%E5%B2%AD%E5%9B%9E%E5%BD%92%E5%92%8CLASSO%E5%9B%9E%E5%BD%92/) - [机器学习算法实践-树回归](http://pytlab.github.io/2017/11/03/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%AE%97%E6%B3%95%E5%AE%9E%E8%B7%B5-%E6%A0%91%E5%9B%9E%E5%BD%92/) ================================================ FILE: Reinforcement Learning/Calculating State Utilities.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "A MDP is a reinterpretation of Markov chains which includes an agent and a decision making process. A MDP is defined by these components:\n", "1. Set of possible States: S={s0,s1,...,sm}\n", "2. Initial State:s0\n", "3. Set of possible Actions:A={a0,a1,...,an}\n", "4. Transition Model:T(s,a,s′)\n", "5. Reward Function: R(s)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are going to implement MDP in a grid world of 3 x 4 space where our agent/robot is situated at (1,1) in the beginning and needs to reach (3,4) state which is its desired goal state. There is also a fault state at (2,4) which the robot needs to avoid at all costs. The movement of the robot from one state to another earns it a reward. Naturally, the reward for the goal state is the highest and the least for the fault state. The objective of the robot is to maximize its reward and thus plan its movements/actions accordingly. It can move in any direction and this is a stochastic process." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To compare the states, we calculate the utility of these states and this is shown below:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "def state_utility(v, T, u, reward, gamma):\n", " \n", " #v is the state vector\n", " #T is the transition matrix\n", " #u is the utility vector\n", " #reward consists of the rewards earned for moving to a particular state\n", " #gamma is the discount factor by which rewards are discounted over the time\n", "\n", " action_array = np.zeros(4)\n", " for action in range(0, 4):\n", " action_array[action] = np.sum(np.multiply(u, np.dot(v, T[:,:,action])))\n", " return reward + gamma * np.max(action_array)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Utility of state (1,1): 0.7056\n" ] } ], "source": [ "def main():\n", " \n", " #The agent starts from (1, 1)\n", " v = np.array([[0.0, 0.0, 0.0, 0.0, \n", " 0.0, 0.0, 0.0, 0.0, \n", " 1.0, 0.0, 0.0, 0.0]])\n", " \n", " #file loaded from the folder\n", " T = np.load(\"T.npy\")\n", "\n", " #Utility vector\n", " u = np.array([[0.812, 0.868, 0.918, 1.0,\n", " 0.762, 0.0, 0.660, -1.0,\n", " 0.705, 0.655, 0.611, 0.388]])\n", "\n", " #Define the reward for state (1,1)\n", " reward = -0.04\n", " #Assume that the discount factor is equal to 1.0\n", " gamma = 1.0\n", "\n", " #Use the Bellman equation to find the utility of state (1,1)\n", " utility_11 = state_utility(v, T, u, reward, gamma)\n", " print(\"Utility of state (1,1): \" + str(utility_11))\n", "\n", "if __name__ == \"__main__\":\n", " main()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Reinforcement Learning/Calculating Transition Probabilities.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ " 1. Set of possible states : S = {s0,s1,s2,......,sn} \n", " 2. Initial State: s0 \n", " 3. Transition Model: T(s,s')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let’s suppose we have a chain with only two states s0 and s1, where s0 is the initial state. The process is in s0 90% of the time and it can move to s1 the remaining 10% of the time. When the process is in state s1 it will remain there 50% of the time. Given this data we can create a Transition Matrix T as follows:\n", "T=[[0.90 0.10]\n", " [0.50 0.50]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Computing the k-step transition probability:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "T: [[0.9 0.1]\n", " [0.5 0.5]]\n", "T_5: [[0.83504 0.16496]\n", " [0.8248 0.1752 ]]\n", "T_25: [[0.83333333 0.16666667]\n", " [0.83333333 0.16666667]]\n", "T_50: [[0.83333333 0.16666667]\n", " [0.83333333 0.16666667]]\n", "T_100: [[0.83333333 0.16666667]\n", " [0.83333333 0.16666667]]\n" ] } ], "source": [ "import numpy as np\n", "\n", "#Here we declare the Transition Matrix T\n", "T = np.array([[0.90, 0.10],\n", " [0.50, 0.50]])\n", "\n", "#Obtain T after 5 steps\n", "T_5 = np.linalg.matrix_power(T, 5)\n", "\n", "#Obtain T after 25 steps\n", "T_25 = np.linalg.matrix_power(T, 25)\n", "\n", "#Obtain T after 50 steps\n", "T_50 = np.linalg.matrix_power(T, 50)\n", "\n", "#Obtain T after 100 steps\n", "T_100 = np.linalg.matrix_power(T, 100)\n", "\n", "#Print the matrices\n", "print(\"T: \" + str(T))\n", "print(\"T_5: \" + str(T_5))\n", "print(\"T_25: \" + str(T_25))\n", "print(\"T_50: \" + str(T_50))\n", "print(\"T_100: \" + str(T_100))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Reinforcement Learning/Defining Initial Distribution.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Let us now define the initial distribution which represents the state of the system at k=0.\n", "Our system is composed of two states and we can model the initial distribution as a vector with two elements, the first element of the vector represents the probability of staying in the state s0 and the second element the probability of staying in state s1. Let’s suppose that we start from s0, the vector v representing the initial distribution will have this form:\n", "v=(1,0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculating the probability of being in a specific state after k iterations:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "v: [[1. 0.]]\n", "v_1: [[0.9 0.1]]\n", "v_5: [[0.83504 0.16496]]\n", "v_25: [[0.83333333 0.16666667]]\n", "v_50: [[0.83333333 0.16666667]]\n", "v_100: [[0.83333333 0.16666667]]\n" ] } ], "source": [ "import numpy as np\n", "\n", "#Declare the initial distribution\n", "v = np.array([[1.0, 0.0]])\n", "\n", "#Declare the Transition Matrix T(this is the same matrix used as in the file'Calculating Transition Probabilities')\n", "T = np.array([[0.90, 0.10],\n", " [0.50, 0.50]])\n", "\n", "#Obtain T after 5 steps\n", "T_5 = np.linalg.matrix_power(T, 5)\n", "\n", "#Obtain T after 25 steps\n", "T_25 = np.linalg.matrix_power(T, 25)\n", "\n", "#Obtain T after 50 steps\n", "T_50 = np.linalg.matrix_power(T, 50)\n", "\n", "#Obtain T after 100 steps\n", "T_100 = np.linalg.matrix_power(T, 100)\n", "\n", "#Printing the initial distribution\n", "print(\"v: \" + str(v))\n", "print(\"v_1: \" + str(np.dot(v,T)))\n", "print(\"v_5: \" + str(np.dot(v,T_5)))\n", "print(\"v_25: \" + str(np.dot(v,T_25)))\n", "print(\"v_50: \" + str(np.dot(v,T_50)))\n", "print(\"v_100: \" + str(np.dot(v,T_100)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The result after 50 and 100 iterations are the same and v_50 is equal to v_100 no matter which starting distribution we have. The chain converged to equilibrium meaning that as the time progresses it forgets about the starting distribution." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Reinforcement Learning/Policy Iteration Algorithm.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Policy iteration is guaranteed to converge and at convergence, the current policy and its utility function are the optimal policy and the optimal utility function. First of all, we define a policy π which assigns an action to each state. We can assign random actions to this policy, it does not matter.\n", "Once we evaluate the policy we can improve it. The policy improvement is the second and last step of the algorithm. Our environment has a finite number of states and then a finite number of policies. Each iteration yields to a better policy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementing the policy iteration algorithm:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "def return_policy_evaluation(p, u, r, T, gamma):\n", "\n", " #v is the state vector\n", " #T is the transition matrix\n", " #u is the utility vector\n", " #reward consists of the rewards earned for moving to a particular state\n", " #gamma is the discount factor by which rewards are discounted over the time\n", " for s in range(12):\n", " if not np.isnan(p[s]):\n", " v = np.zeros((1,12))\n", " v[0,s] = 1.0\n", " action = int(p[s])\n", " u[s] = r[s] + gamma * np.sum(np.multiply(u, np.dot(v, T[:,:,action])))\n", " return u" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def return_expected_action(u, T, v):\n", " \n", "# It returns an action based on the\n", "# expected utility of doing a in state s, \n", "# according to T and u. This action is\n", "# the one that maximize the expected\n", "# utility.\n", " \n", " actions_array = np.zeros(4)\n", " for action in range(4):\n", " #Expected utility of doing a in state s, according to T and u.\n", " actions_array[action] = np.sum(np.multiply(u, np.dot(v, T[:,:,action])))\n", " return np.argmax(actions_array)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def print_policy(p, shape):\n", " \"\"\"Printing utility.\n", "\n", " Print the policy actions using symbols:\n", " ^, v, <, > up, down, left, right\n", " * terminal states\n", " # obstacles\n", " \"\"\"\n", " counter = 0\n", " policy_string = \"\"\n", " for row in range(shape[0]):\n", " for col in range(shape[1]):\n", " if(p[counter] == -1): policy_string += \" * \" \n", " elif(p[counter] == 0): policy_string += \" ^ \"\n", " elif(p[counter] == 1): policy_string += \" < \"\n", " elif(p[counter] == 2): policy_string += \" v \" \n", " elif(p[counter] == 3): policy_string += \" > \"\n", " elif(np.isnan(p[counter])): policy_string += \" # \"\n", " counter += 1\n", " policy_string += '\\n'\n", " print(policy_string)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " v < > * \n", " ^ # < * \n", " < < ^ v \n", "\n", " ^ > > * \n", " ^ # ^ * \n", " < > ^ v \n", "\n", " > > > * \n", " ^ # ^ * \n", " > > ^ < \n", "\n", " > > > * \n", " ^ # ^ * \n", " > > ^ < \n", "\n", " > > > * \n", " ^ # ^ * \n", " ^ > ^ < \n", "\n", " > > > * \n", " ^ # ^ * \n", " ^ > ^ < \n", "\n", " > > > * \n", " ^ # ^ * \n", " ^ < ^ < \n", "\n", " > > > * \n", " ^ # ^ * \n", " ^ < ^ < \n", "\n", " > > > * \n", " ^ # ^ * \n", " ^ < ^ < \n", "\n", " > > > * \n", " ^ # ^ * \n", " ^ < < < \n", "\n", " > > > * \n", " ^ # ^ * \n", " ^ < < < \n", "\n", " > > > * \n", " ^ # ^ * \n", " ^ < < < \n", "\n", " > > > * \n", " ^ # ^ * \n", " ^ < < < \n", "\n", " > > > * \n", " ^ # ^ * \n", " ^ < < < \n", "\n", " > > > * \n", " ^ # ^ * \n", " ^ < < < \n", "\n", " > > > * \n", " ^ # ^ * \n", " ^ < < < \n", "\n", " > > > * \n", " ^ # ^ * \n", " ^ < < < \n", "\n", " > > > * \n", " ^ # ^ * \n", " ^ < < < \n", "\n", " > > > * \n", " ^ # ^ * \n", " ^ < < < \n", "\n", " > > > * \n", " ^ # ^ * \n", " ^ < < < \n", "\n", " > > > * \n", " ^ # ^ * \n", " ^ < < < \n", "\n", "=================== FINAL RESULT ==================\n", "Iterations: 22\n", "Delta: 9.043213450299348e-08\n", "Gamma: 0.999\n", "Epsilon: 0.0001\n", "===================================================\n", "[0.80796344 0.86539911 0.91653199 1. ]\n", "[ 0.75696624 0. 0.65836281 -1. ]\n", "[0.69968295 0.64882105 0.60471972 0.38150427]\n", "===================================================\n", " > > > * \n", " ^ # ^ * \n", " ^ < < < \n", "\n", "===================================================\n" ] } ], "source": [ "def main():\n", " gamma = 0.999\n", " epsilon = 0.0001\n", " iteration = 0\n", " T = np.load(\"T.npy\")\n", " #Generate the first policy randomly\n", " # NaN=Nothing, -1=Terminal, 0=Up, 1=Left, 2=Down, 3=Right\n", " p = np.random.randint(0, 4, size=(12)).astype(np.float32)\n", " p[5] = np.NaN\n", " p[3] = p[7] = -1\n", " #Utility vectors\n", " u = np.array([0.0, 0.0, 0.0, 0.0,\n", " 0.0, 0.0, 0.0, 0.0,\n", " 0.0, 0.0, 0.0, 0.0])\n", " #Reward vector\n", " r = np.array([-0.04, -0.04, -0.04, +1.0,\n", " -0.04, 0.0, -0.04, -1.0,\n", " -0.04, -0.04, -0.04, -0.04])\n", "\n", " while True:\n", " iteration += 1\n", " #1- Policy evaluation\n", " u_0 = u.copy()\n", " u = return_policy_evaluation(p, u, r, T, gamma)\n", " #Stopping criteria\n", " delta = np.absolute(u - u_0).max()\n", " if delta < epsilon * (1 - gamma) / gamma: break\n", " for s in range(12):\n", " if not np.isnan(p[s]) and not p[s]==-1:\n", " v = np.zeros((1,12))\n", " v[0,s] = 1.0\n", " #2- Policy improvement\n", " a = return_expected_action(u, T, v) \n", " if a != p[s]: p[s] = a\n", " print_policy(p, shape=(3,4))\n", "\n", " print(\"=================== FINAL RESULT ==================\")\n", " print(\"Iterations: \" + str(iteration))\n", " print(\"Delta: \" + str(delta))\n", " print(\"Gamma: \" + str(gamma))\n", " print(\"Epsilon: \" + str(epsilon))\n", " print(\"===================================================\")\n", " print(u[0:4])\n", " print(u[4:8])\n", " print(u[8:12])\n", " print(\"===================================================\")\n", " print_policy(p, shape=(3,4))\n", " print(\"===================================================\")\n", "\n", "if __name__ == \"__main__\":\n", " main()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Reinforcement Learning/Value Iteration Algorithm.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Value Iteration algorithm uses the calculated utilities of all the states and compares them after an equilibrium is reached to calculate which is the best move to be taken. The algorithm reaches an equlibrium and this can be known using a stopping criteria. The stopping criteria taken is when no state's utility gets changed by much between two consecutive iterations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementing the Value Iteration algorithm:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "def state_utility(v, T, u, reward, gamma):\n", " \n", " #v is the state vector\n", " #T is the transition matrix\n", " #u is the utility vector\n", " #reward consists of the rewards earned for moving to a particular state\n", " #gamma is the discount factor by which rewards are discounted over the time\n", "\n", " action_array = np.zeros(4)\n", " for action in range(0, 4):\n", " action_array[action] = np.sum(np.multiply(u, np.dot(v, T[:,:,action])))\n", " return reward + gamma * np.max(action_array)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=================== FINAL RESULT ==================\n", "Iterations: 26\n", "Delta: 9.511968687869743e-06\n", "Gamma: 0.999\n", "Epsilon: 0.01\n", "===================================================\n", "[0.80796341 0.86539911 0.91653199 1. ]\n", "[ 0.75696613 0. 0.65836281 -1. ]\n", "[0.69968168 0.64881721 0.60471137 0.3814863 ]\n", "===================================================\n" ] } ], "source": [ "def main():\n", " \n", " tot_states = 12\n", " gamma = 0.999 \n", " iteration = 0 #Iteration counter\n", " epsilon = 0.01 #Stopping criteria given a small value\n", "\n", " #List containing the data for each iteation\n", " graph_list = list()\n", "\n", " #Transition matrix loaded from file\n", " T = np.load(\"T.npy\")\n", "\n", " #Reward vector\n", " r = np.array([-0.04, -0.04, -0.04, +1.0,\n", " -0.04, 0.0, -0.04, -1.0,\n", " -0.04, -0.04, -0.04, -0.04]) \n", "\n", " #Utility vectors\n", " u = np.array([0.0, 0.0, 0.0, 0.0,\n", " 0.0, 0.0, 0.0, 0.0,\n", " 0.0, 0.0, 0.0, 0.0])\n", " \n", " u1 = np.array([0.0, 0.0, 0.0, 0.0,\n", " 0.0, 0.0, 0.0, 0.0,\n", " 0.0, 0.0, 0.0, 0.0])\n", "\n", " while True:\n", " delta = 0\n", " u = u1.copy()\n", " iteration += 1\n", " graph_list.append(u)\n", " for s in range(tot_states):\n", " reward = r[s]\n", " v = np.zeros((1,tot_states))\n", " v[0,s] = 1.0\n", " u1[s] = state_utility(v, T, u, reward, gamma)\n", " delta = max(delta, np.abs(u1[s] - u[s])) #Stopping criteria checked \n", " \n", " if delta < epsilon * (1 - gamma) / gamma:\n", " print(\"=================== FINAL RESULT ==================\")\n", " print(\"Iterations: \" + str(iteration))\n", " print(\"Delta: \" + str(delta))\n", " print(\"Gamma: \" + str(gamma))\n", " print(\"Epsilon: \" + str(epsilon))\n", " print(\"===================================================\")\n", " print(u[0:4])\n", " print(u[4:8])\n", " print(u[8:12])\n", " print(\"===================================================\")\n", " break\n", "\n", "if __name__ == \"__main__\":\n", " main()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: classification_and_regression_trees/bikeSpeedVsIq_test.txt ================================================ 12.000000 121.010516 19.000000 157.337044 12.000000 116.031825 15.000000 132.124872 2.000000 52.719612 6.000000 39.058368 3.000000 50.757763 20.000000 166.740333 11.000000 115.808227 21.000000 165.582995 3.000000 41.956087 3.000000 34.432370 13.000000 116.954676 1.000000 32.112553 7.000000 50.380243 7.000000 94.107791 23.000000 188.943179 18.000000 152.637773 9.000000 104.122082 18.000000 127.805226 0.000000 83.083232 15.000000 148.180104 3.000000 38.480247 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6.000000 70.711362 23.000000 181.731482 20.000000 151.538932 7.000000 66.067228 6.000000 61.565125 20.000000 184.441687 9.000000 91.569158 9.000000 98.833425 17.000000 144.352866 9.000000 94.498314 15.000000 121.922732 18.000000 166.408274 10.000000 89.571299 8.000000 75.373772 22.000000 161.001478 8.000000 90.594227 5.000000 57.180933 20.000000 161.643007 8.000000 87.197370 8.000000 95.584308 15.000000 126.207221 7.000000 84.528209 18.000000 161.056986 10.000000 86.762615 1.000000 33.325906 9.000000 105.095502 2.000000 22.440421 9.000000 93.449284 14.000000 106.249595 21.000000 163.254385 22.000000 161.746628 20.000000 152.973085 17.000000 122.918987 7.000000 58.536412 1.000000 45.013277 13.000000 137.294148 10.000000 88.123737 2.000000 45.847376 20.000000 163.385797 ================================================ FILE: classification_and_regression_trees/compare.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- from regression_tree import * from model_tree import linear_regression def get_corrcoef(X, Y): # X Y 的协方差 cov = np.mean(X*Y) - np.mean(X)*np.mean(Y) return cov/(np.var(X)*np.var(Y))**0.5 if '__main__' == __name__: # 加载数据 data_train = load_data('bikeSpeedVsIq_train.txt') data_test = load_data('bikeSpeedVsIq_test.txt') dataset_test = np.matrix(data_test) m, n = dataset_test.shape testset = np.ones((m, n+1)) testset[:, 1:] = dataset_test X_test, y_test = testset[:, :-1], testset[:, -1] # 获取标准线性回归模型 w, X, y = linear_regression(data_train) y_lr = X_test*w y_test = np.array(y_test).T y_lr = np.array(y_lr).T[0] corrcoef_lr = get_corrcoef(y_test, y_lr) print('linear regression correlation coefficient: {}'.format(corrcoef_lr)) # 获取模型树回归模型 tree = create_tree(data_train, fleaf, ferr, opt={'err_tolerance': 1, 'n_tolerance': 4}) y_tree = [tree_predict([x], tree) for x in X_test[:, 1].tolist()] corrcoef_tree = get_corrcoef(np.array(y_tree), y_test) print('regression tree correlation coefficient: {}'.format(corrcoef_tree)) plt.scatter(np.array(data_train)[:, 0], np.array(data_train)[:, 1]) # 绘制线性回归曲线 x = np.sort([i for i in X_test[:, 1].tolist()]) y = [np.dot([1.0, i], np.array(w.T).tolist()[0]) for i in x] plt.plot(x, y, c='r') # 绘制回归树回归曲线 y = [tree_predict([i], tree) for i in x] plt.plot(x, y, c='y') plt.show() ================================================ FILE: classification_and_regression_trees/dot/ex0.dot ================================================ digraph decision_tree { "5db27cbb-29af-4987-9cd2-9217c781000d" [label="0: 0.400158"]; "a81daf61-ab07-4e65-8b8a-55ee0bd0b40c" [label="0: 0.208197"]; "1f1412f1-659b-4347-8013-f6e57e634c2b" [label="-0.02"]; "32292eec-1e38-4eff-9700-cba03d93d7d8" [label="1.03"]; "7cee5c66-0140-4be7-ab6e-01245b3c8199" [label="0: 0.609483"]; "3308a031-f17e-494c-b015-9b5f3d904dba" [label="1.98"]; "d53cb038-a0fa-4635-9a07-36d40c33d6b9" [label="0: 0.816742"]; "adeac9bb-ef8a-4a91-a821-bade5e047d0d" [label="2.98"]; 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================================================ FILE: classification_and_regression_trees/exp2.dot ================================================ digraph decision_tree { "c830d5ff-5d25-4637-a268-2bb63f5d4351" [label="0: 0.304401"]; "44889deb-3d44-405b-a7cf-d8dfa5604cb9" [label="[3.4687793552577886, 1.1852174309187824]"]; "4a419f47-2097-4b6e-b01e-047203bf4370" [label="[0.0016985569361161585, 11.964773944276974]"]; "c830d5ff-5d25-4637-a268-2bb63f5d4351" -> "44889deb-3d44-405b-a7cf-d8dfa5604cb9"; "c830d5ff-5d25-4637-a268-2bb63f5d4351" -> "4a419f47-2097-4b6e-b01e-047203bf4370"; } ================================================ FILE: classification_and_regression_trees/exp2.txt ================================================ 0.070670 3.470829 0.534076 6.377132 0.747221 8.949407 0.668970 8.034081 0.586082 6.997721 0.764962 9.318110 0.658125 7.880333 0.346734 4.213359 0.313967 3.762496 0.601418 7.188805 0.404396 4.893403 0.154345 3.683175 0.984061 11.712928 0.597514 7.146694 0.005144 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0.039768 3.429691 0.546308 6.472749 0.786882 9.398951 0.168282 3.564189 0.374900 4.399040 0.737767 8.888536 0.059849 3.431537 0.861891 10.246888 0.597578 7.112627 0.126050 3.611641 0.074795 3.609222 0.634401 7.627416 0.831633 9.926548 0.019095 3.470285 0.396533 4.773104 0.794973 9.492009 0.889088 10.420003 0.003174 3.587139 0.176767 3.554071 0.943730 11.227731 0.758564 8.885337 ================================================ FILE: classification_and_regression_trees/model_tree.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- from regression_tree import * def linear_regression(dataset): ''' 获取标准线性回归系数 ''' dataset = np.matrix(dataset) # 分割数据并添加常数列 X_ori, y = dataset[:, :-1], dataset[:, -1] X_ori, y = np.matrix(X_ori), np.matrix(y) m, n = X_ori.shape X = np.matrix(np.ones((m, n+1))) X[:, 1:] = X_ori # 回归系数 w = (X.T*X).I*X.T*y return w, X, y def fleaf(dataset): ''' 计算给定数据集的线性回归系数 ''' w, _, _ = linear_regression(dataset) return w def ferr(dataset): ''' 对给定数据集进行回归并计算误差 ''' w, X, y = linear_regression(dataset) y_prime = X*w return np.var(y_prime - y) def get_nodes_edges(tree, root_node=None): ''' 返回树中所有节点和边 ''' Node = namedtuple('Node', ['id', 'label']) Edge = namedtuple('Edge', ['start', 'end']) nodes, edges = [], [] if type(tree) is not dict: return nodes, edges if root_node is None: label = '{}: {}'.format(tree['feat_idx'], tree['feat_val']) root_node = Node._make([uuid.uuid4(), label]) nodes.append(root_node) for sub_tree in (tree['left'], tree['right']): if type(sub_tree) is dict: node_label = '{}: {}'.format(sub_tree['feat_idx'], sub_tree['feat_val']) else: node_label = '{}'.format(np.array(sub_tree.T).tolist()[0]) sub_node = Node._make([uuid.uuid4(), node_label]) nodes.append(sub_node) edge = Edge._make([root_node, sub_node]) edges.append(edge) sub_nodes, sub_edges = get_nodes_edges(sub_tree, root_node=sub_node) nodes.extend(sub_nodes) edges.extend(sub_edges) return nodes, edges def dotify(tree): ''' 获取树的Graphviz Dot文件的内容 ''' content = 'digraph decision_tree {\n' nodes, edges = get_nodes_edges(tree) for node in nodes: content += ' "{}" [label="{}"];\n'.format(node.id, node.label) for edge in edges: start, end = edge.start, edge.end content += ' "{}" -> "{}";\n'.format(start.id, end.id) content += '}' return content def tree_predict(data, tree): if type(tree) is not dict: w = tree y = np.matrix(data)*w return y[0, 0] feat_idx, feat_val = tree['feat_idx'], tree['feat_val'] if data[feat_idx+1] < feat_val: return tree_predict(data, tree['left']) else: return tree_predict(data, tree['right']) if '__main__' == __name__: dataset = load_data('exp2.txt') tree = create_tree(dataset, fleaf, ferr, opt={'err_tolerance': 0.1, 'n_tolerance': 4}) # 生成模型树dot文件 with open('exp2.dot', 'w') as f: f.write(dotify(tree)) dataset = np.array(dataset) # 绘制散点图 plt.scatter(dataset[:, 0], dataset[:, 1]) # 绘制回归曲线 x = np.sort(dataset[:, 0]) y = [tree_predict([1.0] + [i], tree) for i in x] plt.plot(x, y, c='r') plt.show() ================================================ FILE: classification_and_regression_trees/notebook/分段函数回归树.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from regression_tree import *" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "dataset = load_data('ex0.txt')\n", "tree = create_tree(dataset, fleaf, ferr)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'feat_idx': 0,\n", " 'feat_val': 0.40015800000000001,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.20819699999999999,\n", " 'left': -0.023838155555555553,\n", " 'right': 1.0289583666666666},\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.609483,\n", " 'left': 1.980035071428571,\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.81674199999999997,\n", " 'left': 2.9836209534883724,\n", " 'right': 3.9871631999999999}}}" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tree" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "dataset = np.array(dataset)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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KvTjJEj0iWo8KHbgBlugR0fpT6FQJEdF6xMBNRFQwDNxERAXDwE1EVDAM3ERE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\nDNxERAXDwE1EVDAM3EREBfP/ATWTEFlkthKIAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(dataset[:, 0], dataset[:, 1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 绘制树回归曲线" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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mSdoKfA2YChARdwDrgCVAP/Aq8KmJKtbq2LwZurtByrsSMyuQNN0y1zTYHsBnM6vIxmdo\nqBruZmaj+ArVstu82efbzexNHO5ldvgw7NzpcDezN3G4l9lIG6RPy5hZDYd7mbnH3czG4HAvM/e4\nm9kYHO5lNjQEJ5wAc+fmXYmZFYzDvcw2b4Zzz4WpU/OuxMwKxuFeZkNDPiVjZnU53MvMFzCZ2Rgc\n7mV17Bhs3eojdzOry+FeVjt2VKelc7ibWR0O97IaaYP0aRkzq8PhXla+gMnMjsPhXlY+cjez43C4\nl9XQEJxxBsyYkXclZlZAqcJd0mJJL0rql/TlOtuvk7Rb0sbk9unsS7X/xx/1a2bHkWYmpinA7cD7\nga3A45L6IuL5mqH3RMSNE1Cj1TM0BG99a95VmFlBpTlyvxToj4iBiDgK/AhYNrFlWUMj0+uZmdWR\nJtznAltGPd6arKt1taRNku6VND+T6qy+V16BAwd8WsbMxpTVG6q/BHoi4h3Ag8DaeoMkrZS0QdKG\n3bt3Z/TSHcidMmbWQJpw3waMPhKfl6x7Q0TsiYgjycPvAu+s90QRsSYieiOit6urq5l6DdzjbmYN\npQn3x4GFks6TdBKwAugbPUDSnFEPlwIvZFeivYkn6TCzBhp2y0TEsKQbgQeAKcBdEfGcpNXAhojo\nAz4naSkwDOwFrpvAmm1oCKZNA//1Y2ZjaBjuABGxDlhXs27VqOWbgZuzLc3GNNIpc4KvQTOz+pwO\nZeRJOsysAYd7GXmSDjNrwOFeNkeOVD/L3UfuZnYcDvey2ZJcT+ZwN7PjcLiXjS9gMrMUHO5l4wuY\nzCwFh3vZDA2BBPPm5V2JmRWYw71shoZgzhw46aS8KzGzAnO4l40n6TCzFBzuZeMLmMwsBYd7mbz+\nerUV0p0yZtaAw71Mdu6Eo0d95G5mDTncy8Qf9WtmKTncy2Skx92nZcysAYd7mfjI3cxScriXydAQ\nzJwJp52WdyVmVnCpwl3SYkkvSuqX9OU626dJuifZvl5ST9aFGu5xN7PUGoa7pCnA7cCHgEXANZIW\n1Qy7HtgXERcA3wZuybpQwz3uZpZamiP3S4H+iBiIiKPAj4BlNWOWAWuT5XuBqyQpuzIN8CQdZpZa\nmnCfC2wZ9Xhrsq7umIgYBvYDZ2VRoCX276/efORuZimkmiA7K5JWAisBups9An3gAfjCFzKsqiSO\nHq3eO9zNLIU04b4NmD/q8bxkXb0xWyWdCMwE9tQ+UUSsAdYA9Pb2RjMFc9ppsKj2lH+HuOIKuOqq\nvKswsxJIE+6PAwslnUc1xFcAf1czpg+4FvgtsBx4KCKaC+9GLr8cfvKTCXlqM7N20TDcI2JY0o3A\nA8AU4K6IeE7SamBDRPQBdwLfl9QP7KX6C8DMzHKS6px7RKwD1tWsWzVq+TDwsWxLMzOzZvkKVTOz\nNuRwNzNrQw53M7M25HA3M2tDDnczszbkcDcza0OaqGuNGr6wtBsYavLLZwEvZ1hOGXifO4P3uTO0\nss8LIqKr0aDcwr0VkjZERG/edUwm73Nn8D53hsnYZ5+WMTNrQw53M7M2VNZwX5N3ATnwPncG73Nn\nmPB9LuU5dzMzO76yHrmbmdlxFDrcJS2W9KKkfklfrrN9mqR7ku3rJfVMfpXZSrHPX5D0vKRNkv5b\nUumnZmq0z6PGXS0pJJW+syLNPkv62+R7/Zyk/5jsGrOW4me7W9LDkp5Kfr6X5FFnViTdJWmXpGfH\n2C5Jtyb/HpskXZJpARFRyBvVz45/CTgfOAl4GlhUM+YfgDuS5RXAPXnXPQn7/NfAKcnyDZ2wz8m4\nGcCjwGNAb951T8L3eSHwFHBG8vjsvOuehH1eA9yQLC8CKnnX3eI+/yVwCfDsGNuXAPcDAi4D1mf5\n+kU+cr8U6I+IgYg4CvwIWFYzZhmwNlm+F7hKkiaxxqw13OeIeDgiXk0ePkZ12sMyS/N9Bvhn4Bbg\n8GQWN0HS7PPfA7dHxD6AiNg1yTVmLc0+B3BasjwT+MMk1pe5iHiU6uRFY1kG3B1VjwGnS5qT1esX\nOdznAltGPd6arKs7JiKGgf3AWZNS3cRIs8+jXU/1N3+ZNdzn5M/V+RHxn5NZ2ARK831+G/A2Sb+R\n9JikxZNW3cRIs89fBz4uaSvVyYH+cXJKy814/7+PS6qZmKx4JH0c6AX+Ku9aJpKkE4BvAdflXMpk\nO5HqqZkrqf519qikP4+IV3KtamJdA3wvIv5F0uVUp+68MCJez7uwMirykfs2YP6ox/OSdXXHSDqR\n6p9yeyaluomRZp+R9DfAV4GlEXFkkmqbKI32eQZwIfCIpArVc5N9JX9TNc33eSvQFxGvRcQg8Huq\nYV9Wafb5euDHABHxW+Bkqp/B0q5S/X9vVpHD/XFgoaTzJJ1E9Q3TvpoxfcC1yfJy4KFI3qkoqYb7\nLOli4N+oBnvZz8NCg32OiP0RMSsieiKih+r7DEsjYkM+5WYizc/2L6getSNpFtXTNAOTWWTG0uzz\nZuAqAEl/RjXcd09qlZOrD/hk0jVzGbA/IrZn9ux5v6Pc4N3mJVSPWF4CvpqsW031PzdUv/k/AfqB\n3wHn513zJOzzfwE7gY3JrS/vmid6n2vGPkLJu2VSfp9F9XTU88AzwIq8a56EfV4E/IZqJ81G4AN5\n19zi/v4Q2A68RvUvseuBzwCfGfU9vj3593gm659rX6FqZtaGinxaxszMmuRwNzNrQw53M7M25HA3\nM2tDDnczszbkcDcza0MOdzOzNuRwNzNrQ/8LfR/vUvqwLfQAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.linspace(0, 1, 50)\n", "y = [tree_predict([i], tree) for i in x]\n", "plt.plot(x, y, c='r')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: classification_and_regression_trees/notebook/后剪枝.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from prune import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 加载数据" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data = load_data('ex2.txt')\n", "tree = create_tree(data, fleaf, ferr)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 判断树结构" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "not_tree(tree)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 对树结构进行塌陷处理" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "53.136107929136443" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "collapse(tree)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 输出树结构" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'feat_idx': 0,\n", " 'feat_val': 0.50854200000000005,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.46324100000000001,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.13062599999999999,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.085111000000000006,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.053763999999999999,\n", " 'left': 4.0916259999999998,\n", " 'right': -2.5443927142857148},\n", " 'right': 6.5098432857142843},\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.37738300000000002,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.3417,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.32889000000000002,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.30031799999999997,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.17652300000000001,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.156273,\n", " 'left': -6.2479000000000013,\n", " 'right': -12.107972500000001},\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.20399300000000001,\n", " 'left': 3.4496025000000001,\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.21832099999999999,\n", " 'left': -11.822278500000001,\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.228628,\n", " 'left': 6.770429,\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.26463900000000001,\n", " 'left': -13.070501,\n", " 'right': 0.40377471428571476}}}}},\n", " 'right': -19.994155200000002},\n", " 'right': 15.059290750000001},\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.35147800000000001,\n", " 'left': -22.693879600000002,\n", " 'right': -15.085111749999999}},\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.44619599999999998,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.41894300000000001,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.388789,\n", " 'left': 3.6584772500000016,\n", " 'right': -0.89235549999999952},\n", " 'right': 14.38417875},\n", " 'right': -12.558604833333334}}},\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.48380299999999998,\n", " 'left': 3.4331330000000007,\n", " 'right': 12.50675925}},\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.73163599999999995,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.64237299999999997,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.61886799999999997,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.58541299999999996,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.56030100000000005,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.53194399999999997,\n", " 'left': 101.73699325000001,\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.546601,\n", " 'left': 110.979946,\n", " 'right': 109.38961049999999}},\n", " 'right': 97.200180249999988},\n", " 'right': 123.2101316},\n", " 'right': 93.673449714285724},\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.66785099999999997,\n", " 'left': 114.15162428571431,\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.70889000000000002,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.69891999999999999,\n", " 'left': 108.92921799999999,\n", " 'right': 104.82495374999999},\n", " 'right': 114.554706}}},\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.95390200000000003,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.76332800000000001,\n", " 'left': 78.085643250000004,\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.79819799999999996,\n", " 'left': 102.35780185714285,\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.83858699999999997,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.81521500000000002,\n", " 'left': 88.784498800000009,\n", " 'right': 81.110151999999999},\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.94882200000000005,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.85642099999999999,\n", " 'left': 95.275843166666661,\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.912161,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.89668300000000001,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.88361500000000004,\n", " 'left': 102.25234449999999,\n", " 'right': 95.181792999999999},\n", " 'right': 104.82540899999999},\n", " 'right': 96.452866999999998}},\n", " 'right': 87.310387500000004}}}},\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.96039799999999997,\n", " 'left': 112.42895575000001,\n", " 'right': 105.24862350000001}}}}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tree" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 使用测试数据进行后剪枝" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "data_test = load_data('ex2test.txt')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "merged\n", "merged\n", "merged\n", "merged\n", "merged\n", "merged\n", "merged\n", "merged\n" ] } ], "source": [ "pruned_tree = postprune(tree, data_test)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'feat_idx': 0,\n", " 'feat_val': 0.50854200000000005,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.46324100000000001,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.13062599999999999,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.085111000000000006,\n", " 'left': 0.77361664285714249,\n", " 'right': 6.5098432857142843},\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.37738300000000002,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.3417,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.32889000000000002,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.30031799999999997,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.17652300000000001,\n", " 'left': -9.1779362500000019,\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.20399300000000001,\n", " 'left': 3.4496025000000001,\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.21832099999999999,\n", " 'left': -11.822278500000001,\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.228628,\n", " 'left': 6.770429,\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.26463900000000001,\n", " 'left': -13.070501,\n", " 'right': 0.40377471428571476}}}}},\n", " 'right': -19.994155200000002},\n", " 'right': 15.059290750000001},\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.35147800000000001,\n", " 'left': -22.693879600000002,\n", " 'right': -15.085111749999999}},\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.44619599999999998,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.41894300000000001,\n", " 'left': 1.3830608750000011,\n", " 'right': 14.38417875},\n", " 'right': -12.558604833333334}}},\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.48380299999999998,\n", " 'left': 3.4331330000000007,\n", " 'right': 12.50675925}},\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.73163599999999995,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.64237299999999997,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.61886799999999997,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.58541299999999996,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.56030100000000005,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.53194399999999997,\n", " 'left': 101.73699325000001,\n", " 'right': 110.18477824999999},\n", " 'right': 97.200180249999988},\n", " 'right': 123.2101316},\n", " 'right': 93.673449714285724},\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.66785099999999997,\n", " 'left': 114.15162428571431,\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.70889000000000002,\n", " 'left': 106.87708587499999,\n", " 'right': 114.554706}}},\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.95390200000000003,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.76332800000000001,\n", " 'left': 78.085643250000004,\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.79819799999999996,\n", " 'left': 102.35780185714285,\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.83858699999999997,\n", " 'left': 84.947325400000011,\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.94882200000000005,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.85642099999999999,\n", " 'left': 95.275843166666661,\n", " 'right': {'feat_idx': 0,\n", " 'feat_val': 0.912161,\n", " 'left': {'feat_idx': 0,\n", " 'feat_val': 0.89668300000000001,\n", " 'left': 98.717068749999996,\n", " 'right': 104.82540899999999},\n", " 'right': 96.452866999999998}},\n", " 'right': 87.310387500000004}}}},\n", " 'right': 108.838789625}}}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pruned_tree" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 生成树结构dot文件用于显示" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "with open('ex2_prune.dot', 'w') as f:\n", " content = dotify(pruned_tree)\n", " f.write(content)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m\u001b[36m__pycache__\u001b[m\u001b[m/ ex2test.txt\r\n", "\u001b[1m\u001b[36mdot\u001b[m\u001b[m/ \u001b[1m\u001b[36mpic\u001b[m\u001b[m/\r\n", "ex0.txt prune.py\r\n", "ex00.txt regression_tree.py\r\n", "ex2.txt 后剪枝.ipynb\r\n", "ex2_prune.dot 分段函数回归树.ipynb\r\n" ] } ], "source": [ "ls" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: classification_and_regression_trees/notebook/模型树对分段线性函数进行回归.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from model_tree import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 加载数据" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "dataset = load_data('exp2.txt')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 创建模型树" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'feat_idx': 0, 'feat_val': 0.30440099999999998, 'left': matrix([[ 3.46877936],\n", " [ 1.18521743]]), 'right': matrix([[ 1.69855694e-03],\n", " [ 1.19647739e+01]])}" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tree = create_tree(dataset, fleaf, ferr, opt={'err_tolerance': 0.1, 'n_tolerance': 4})\n", "tree" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 绘制回归曲线" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "ax = fig.add_subplot(111)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 绘制散点图" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset = np.array(dataset)\n", "ax.scatter(dataset[:, 0], dataset[:, 1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 绘制回归曲线" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = np.sort(np.array(dataset[:, 0]))\n", "y = [tree_predict([1.0] + [i], tree) for i in x]\n", "ax.plot(x, y, c='r')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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4B6tGaRBUJBDNQ5eQKq3Pu9P/vmBGeip/1GtIz0FpAcPcEWUY3bVVZVdRJGwp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\nQmCC\n", "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: classification_and_regression_trees/prune.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- from regression_tree import * def not_tree(tree): ''' 判断是否不是一棵树结构 ''' return type(tree) is not dict def collapse(tree): ''' 对一棵树进行塌陷处理, 得到给定树结构的平均值 ''' if not_tree(tree): return tree ltree, rtree = tree['left'], tree['right'] return (collapse(ltree) + collapse(rtree))/2 def postprune(tree, test_data): ''' 根据测试数据对树结构进行后剪枝 ''' if not_tree(tree): return tree # 若没有测试数据则直接返回树平均值 if not test_data: return collapse(tree) ltree, rtree = tree['left'], tree['right'] if not_tree(ltree) and not_tree(rtree): # 分割数据用于测试 ldata, rdata = split_dataset(test_data, tree['feat_idx'], tree['feat_val']) # 分别计算合并前和合并后的测试数据误差 err_no_merge = (np.sum((np.array(ldata) - ltree)**2) + np.sum((np.array(rdata) - rtree)**2)) err_merge = np.sum((np.array(test_data) - (ltree + rtree)/2)**2) if err_merge < err_no_merge: print('merged') return (ltree + rtree)/2 else: return tree tree['left'] = postprune(tree['left'], test_data) tree['right'] = postprune(tree['right'], test_data) return tree ================================================ FILE: classification_and_regression_trees/regression_tree.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- ''' 回归树实现 ''' import uuid from functools import namedtuple import numpy as np import matplotlib.pyplot as plt def load_data(filename): ''' 加载文本文件中的数据. ''' dataset = [] with open(filename, 'r') as f: for line in f: line_data = [float(data) for data in line.split()] dataset.append(line_data) return dataset def split_dataset(dataset, feat_idx, value): ''' 根据给定的特征编号和特征值对数据集进行分割 ''' ldata, rdata = [], [] for data in dataset: if data[feat_idx] < value: ldata.append(data) else: rdata.append(data) return ldata, rdata def create_tree(dataset, fleaf, ferr, opt=None): ''' 递归创建树结构 dataset: 待划分的数据集 fleaf: 创建叶子节点的函数 ferr: 计算数据误差的函数 opt: 回归树参数. err_tolerance: 最小误差下降值; n_tolerance: 数据切分最小样本数 ''' if opt is None: opt = {'err_tolerance': 1, 'n_tolerance': 4} # 选择最优化分特征和特征值 feat_idx, value = choose_best_feature(dataset, fleaf, ferr, opt) # 触底条件 if feat_idx is None: return value # 创建回归树 tree = {'feat_idx': feat_idx, 'feat_val': value} # 递归创建左子树和右子树 ldata, rdata = split_dataset(dataset, feat_idx, value) ltree = create_tree(ldata, fleaf, ferr, opt) rtree = create_tree(rdata, fleaf, ferr, opt) tree['left'] = ltree tree['right'] = rtree return tree def fleaf(dataset): ''' 计算给定数据的叶节点数值, 这里为均值 ''' dataset = np.array(dataset) return np.mean(dataset[:, -1]) def ferr(dataset): ''' 计算数据集的误差. ''' dataset = np.array(dataset) m, _ = dataset.shape return np.var(dataset[:, -1])*dataset.shape[0] def choose_best_feature(dataset, fleaf, ferr, opt): ''' 选取最佳分割特征和特征值 dataset: 待划分的数据集 fleaf: 创建叶子节点的函数 ferr: 计算数据误差的函数 opt: 回归树参数. err_tolerance: 最小误差下降值; n_tolerance: 数据切分最小样本数 ''' dataset = np.array(dataset) m, n = dataset.shape err_tolerance, n_tolerance = opt['err_tolerance'], opt['n_tolerance'] err = ferr(dataset) best_feat_idx, best_feat_val, best_err = 0, 0, float('inf') # 遍历所有特征 for feat_idx in range(n-1): values = dataset[:, feat_idx] # 遍历所有特征值 for val in values: # 按照当前特征和特征值分割数据 ldata, rdata = split_dataset(dataset.tolist(), feat_idx, val) if len(ldata) < n_tolerance or len(rdata) < n_tolerance: # 如果切分的样本量太小 continue # 计算误差 new_err = ferr(ldata) + ferr(rdata) if new_err < best_err: best_feat_idx = feat_idx best_feat_val = val best_err = new_err # 如果误差变化并不大归为一类 if abs(err - best_err) < err_tolerance: return None, fleaf(dataset) # 检查分割样本量是不是太小 ldata, rdata = split_dataset(dataset.tolist(), best_feat_idx, best_feat_val) if len(ldata) < n_tolerance or len(rdata) < n_tolerance: return None, fleaf(dataset) return best_feat_idx, best_feat_val def get_nodes_edges(tree, root_node=None): ''' 返回树中所有节点和边 ''' Node = namedtuple('Node', ['id', 'label']) Edge = namedtuple('Edge', ['start', 'end']) nodes, edges = [], [] if type(tree) is not dict: return nodes, edges if root_node is None: label = '{}: {}'.format(tree['feat_idx'], tree['feat_val']) root_node = Node._make([uuid.uuid4(), label]) nodes.append(root_node) for sub_tree in (tree['left'], tree['right']): if type(sub_tree) is dict: node_label = '{}: {}'.format(sub_tree['feat_idx'], sub_tree['feat_val']) else: node_label = '{:.2f}'.format(sub_tree) sub_node = Node._make([uuid.uuid4(), node_label]) nodes.append(sub_node) edge = Edge._make([root_node, sub_node]) edges.append(edge) sub_nodes, sub_edges = get_nodes_edges(sub_tree, root_node=sub_node) nodes.extend(sub_nodes) edges.extend(sub_edges) return nodes, edges def dotify(tree): ''' 获取树的Graphviz Dot文件的内容 ''' content = 'digraph decision_tree {\n' nodes, edges = get_nodes_edges(tree) for node in nodes: content += ' "{}" [label="{}"];\n'.format(node.id, node.label) for edge in edges: start, end = edge.start, edge.end content += ' "{}" -> "{}";\n'.format(start.id, end.id) content += '}' return content def tree_predict(data, tree): ''' 根据给定的回归树预测数据值 ''' if type(tree) is not dict: return tree feat_idx, feat_val = tree['feat_idx'], tree['feat_val'] if data[feat_idx] < feat_val: sub_tree = tree['left'] else: sub_tree = tree['right'] return tree_predict(data, sub_tree) if '__main__' == __name__: datafile = 'ex0.txt' dataset = load_data(datafile) tree = create_tree(dataset, fleaf, ferr, opt={'n_tolerance': 4, 'err_tolerance': 1}) dotfile = '{}.dot'.format(datafile.split('.')[0]) with open(dotfile, 'w') as f: content = dotify(tree) f.write(content) dataset = np.array(dataset) # 绘制散点 plt.scatter(dataset[:, 0], dataset[:, 1]) # 绘制回归曲线 x = np.linspace(0, 1, 50) y = [tree_predict([i], tree) for i in x] plt.plot(x, y, c='r') plt.show() ================================================ FILE: decision_tree/english_big.txt ================================================ Urgent! call 09061749602 from Landline. 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IT WAS A GOOD NITE!!,ham ================================================ FILE: decision_tree/lenses.dot ================================================ digraph decision_tree { "99d3b650-7557-420c-be5f-037403909eef" [label="tearRate"]; "ccf5c62e-14ca-4cef-9525-4b8f026622dc" [label="no lenses"]; "6a72f3f9-51ce-4433-b052-34765c65a61e" [label="astigmatic"]; "91ea78df-9cfd-4334-a592-1c8b3c193f0d" [label="age"]; "b5d2e2b7-241b-4c46-a56b-61ba9a1e7678" [label="soft"]; "62193a33-c49d-4bce-b820-1613685e09ce" [label="soft"]; "01240d64-7b96-40fc-9a4b-185cc0fca9d6" [label="prescript"]; "5571119a-43b5-414e-9bf5-c9c62a9dee8c" [label="soft"]; "087246f9-495f-44ef-8ea0-5043b238c1c1" [label="no lenses"]; "c0b04ca3-692d-4498-8292-165ed4997ce5" [label="prescript"]; "8f6cfe1f-a0ea-46de-a456-f3f8b35bca8d" [label="age"]; "4d2b5c7f-e85e-4d44-8da9-0d88de048430" [label="hard"]; "cdc375a5-561f-48c9-a847-ccc73f1cc44c" [label="no lenses"]; "4600fda0-b8a8-45cc-8174-d554de9b7e84" [label="no lenses"]; "08a19fa5-952c-4ab3-a283-dbfe2e3e5870" [label="hard"]; "99d3b650-7557-420c-be5f-037403909eef" -> "ccf5c62e-14ca-4cef-9525-4b8f026622dc" [label="reduced"]; "99d3b650-7557-420c-be5f-037403909eef" -> "6a72f3f9-51ce-4433-b052-34765c65a61e" [label="normal"]; "6a72f3f9-51ce-4433-b052-34765c65a61e" -> "91ea78df-9cfd-4334-a592-1c8b3c193f0d" [label="no"]; "91ea78df-9cfd-4334-a592-1c8b3c193f0d" -> "b5d2e2b7-241b-4c46-a56b-61ba9a1e7678" [label="young"]; "91ea78df-9cfd-4334-a592-1c8b3c193f0d" -> "62193a33-c49d-4bce-b820-1613685e09ce" [label="pre"]; "91ea78df-9cfd-4334-a592-1c8b3c193f0d" -> "01240d64-7b96-40fc-9a4b-185cc0fca9d6" [label="presbyopic"]; "01240d64-7b96-40fc-9a4b-185cc0fca9d6" -> "5571119a-43b5-414e-9bf5-c9c62a9dee8c" [label="hyper"]; "01240d64-7b96-40fc-9a4b-185cc0fca9d6" -> "087246f9-495f-44ef-8ea0-5043b238c1c1" [label="myope"]; "6a72f3f9-51ce-4433-b052-34765c65a61e" -> "c0b04ca3-692d-4498-8292-165ed4997ce5" [label="yes"]; "c0b04ca3-692d-4498-8292-165ed4997ce5" -> "8f6cfe1f-a0ea-46de-a456-f3f8b35bca8d" [label="hyper"]; "8f6cfe1f-a0ea-46de-a456-f3f8b35bca8d" -> "4d2b5c7f-e85e-4d44-8da9-0d88de048430" [label="young"]; "8f6cfe1f-a0ea-46de-a456-f3f8b35bca8d" -> "cdc375a5-561f-48c9-a847-ccc73f1cc44c" [label="pre"]; "8f6cfe1f-a0ea-46de-a456-f3f8b35bca8d" -> "4600fda0-b8a8-45cc-8174-d554de9b7e84" [label="presbyopic"]; "c0b04ca3-692d-4498-8292-165ed4997ce5" -> "08a19fa5-952c-4ab3-a283-dbfe2e3e5870" [label="myope"]; } ================================================ FILE: decision_tree/lenses.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- from trees import DecisionTreeClassifier lense_labels = ['age', 'prescript', 'astigmatic', 'tearRate'] X = [] Y = [] with open('lenses.txt', 'r') as f: for line in f: comps = line.strip().split('\t') X.append(comps[: -1]) Y.append(comps[-1]) clf = DecisionTreeClassifier() clf.create_tree(X, Y, lense_labels) ================================================ FILE: decision_tree/lenses.txt ================================================ young myope no reduced no lenses young myope no normal soft young myope yes reduced no lenses young myope yes normal hard young hyper no reduced no lenses young hyper no normal soft young hyper yes reduced no lenses young hyper yes normal hard pre myope no reduced no lenses pre myope no normal soft pre myope yes reduced no lenses pre myope yes normal hard pre hyper no reduced no lenses pre hyper no normal soft pre hyper yes reduced no lenses pre hyper yes normal no lenses presbyopic myope no reduced no lenses presbyopic myope no normal no lenses presbyopic myope yes reduced no lenses presbyopic myope yes normal hard presbyopic hyper no reduced no lenses presbyopic hyper no normal soft presbyopic hyper yes reduced no lenses presbyopic hyper yes normal no lenses ================================================ FILE: decision_tree/sms_tree.dot ================================================ digraph decision_tree { "959b4c0c-1821-446d-94a1-c619c2decfcd" [label="call"]; "18665160-b058-437f-9b2e-05df2eb55661" [label="to"]; "2eb9860d-d241-45ca-85e6-cbd80fe2ebf7" [label="your"]; "bcbcc17c-9e2a-4bd4-a039-6e51fde5f8fd" [label="areyouunique"]; "ca091fc7-8a4e-4970-9ec3-485a4628ad29" [label="02073162414"]; "aac20872-1aac-499d-b2b5-caf0ef56eff3" [label="ham"]; "18aa8685-a6e8-4d76-bad5-ccea922bb14d" [label="spam"]; "3f7f30b1-4dbb-4459-9f25-358ad3c6d50b" [label="spam"]; "44d1f972-cd97-4636-b6e6-a389bf560656" [label="spam"]; "7f3c8562-69b5-47a9-8ee4-898bd4b6b506" [label="i"]; "a6f22325-8841-4a81-bc04-4e7485117aa1" [label="spam"]; "c181fe42-fd3c-48db-968a-502f8dd462a4" [label="ldn"]; "51b9477a-0326-4774-8622-24d1d869a283" [label="ham"]; "16f6aecd-c675-4291-867c-6c64d27eb3fc" [label="spam"]; "adb05303-813a-4fe0-bf98-c319eb70be48" [label="spam"]; "959b4c0c-1821-446d-94a1-c619c2decfcd" -> "18665160-b058-437f-9b2e-05df2eb55661" [label="0"]; "18665160-b058-437f-9b2e-05df2eb55661" -> "2eb9860d-d241-45ca-85e6-cbd80fe2ebf7" [label="0"]; "2eb9860d-d241-45ca-85e6-cbd80fe2ebf7" -> "bcbcc17c-9e2a-4bd4-a039-6e51fde5f8fd" [label="0"]; "bcbcc17c-9e2a-4bd4-a039-6e51fde5f8fd" -> "ca091fc7-8a4e-4970-9ec3-485a4628ad29" [label="0"]; "ca091fc7-8a4e-4970-9ec3-485a4628ad29" -> "aac20872-1aac-499d-b2b5-caf0ef56eff3" [label="0"]; "ca091fc7-8a4e-4970-9ec3-485a4628ad29" -> "18aa8685-a6e8-4d76-bad5-ccea922bb14d" [label="1"]; "bcbcc17c-9e2a-4bd4-a039-6e51fde5f8fd" -> "3f7f30b1-4dbb-4459-9f25-358ad3c6d50b" [label="1"]; "2eb9860d-d241-45ca-85e6-cbd80fe2ebf7" -> "44d1f972-cd97-4636-b6e6-a389bf560656" [label="1"]; "18665160-b058-437f-9b2e-05df2eb55661" -> "7f3c8562-69b5-47a9-8ee4-898bd4b6b506" [label="1"]; "7f3c8562-69b5-47a9-8ee4-898bd4b6b506" -> "a6f22325-8841-4a81-bc04-4e7485117aa1" [label="0"]; "7f3c8562-69b5-47a9-8ee4-898bd4b6b506" -> "c181fe42-fd3c-48db-968a-502f8dd462a4" [label="1"]; "c181fe42-fd3c-48db-968a-502f8dd462a4" -> "51b9477a-0326-4774-8622-24d1d869a283" [label="0"]; "c181fe42-fd3c-48db-968a-502f8dd462a4" -> "16f6aecd-c675-4291-867c-6c64d27eb3fc" [label="1"]; "959b4c0c-1821-446d-94a1-c619c2decfcd" -> "adb05303-813a-4fe0-bf98-c319eb70be48" [label="1"]; } ================================================ FILE: decision_tree/sms_tree.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- ''' 通过垃圾短信数据训练朴素贝叶斯模型，并进行留存交叉验证 ''' import re import random import os import numpy as np import matplotlib.pyplot as plt from trees import DecisionTreeClassifier ENCODING = 'ISO-8859-1' TRAIN_PERCENTAGE = 0.9 def get_doc_vector(words, vocabulary): ''' 根据词汇表将文档中的词条转换成文档向量 :param words: 文档中的词条列表 :type words: list of str :param vocabulary: 总的词汇列表 :type vocabulary: list of str :return doc_vect: 用于贝叶斯分析的文档向量 :type doc_vect: list of int ''' doc_vect = [0]*len(vocabulary) for word in words: if word in vocabulary: idx = vocabulary.index(word) doc_vect[idx] = 1 return doc_vect def parse_line(line): ''' 解析数据集中的每一行返回词条向量和短信类型. ''' cls = line.split(',')[-1].strip() content = ','.join(line.split(',')[: -1]) word_vect = [word.lower() for word in re.split(r'\W+', content) if word] return word_vect, cls def parse_file(filename): ''' 解析文件中的数据 ''' vocabulary, word_vects, classes = [], [], [] with open(filename, 'r', encoding=ENCODING) as f: for line in f: if line: word_vect, cls = parse_line(line) vocabulary.extend(word_vect) word_vects.append(word_vect) classes.append(cls) vocabulary = list(set(vocabulary)) return vocabulary, word_vects, classes if '__main__' == __name__: clf = DecisionTreeClassifier() vocabulary, word_vects, classes = parse_file('english_big.txt') # 训练数据 & 测试数据 ntest = int(len(classes)*(1-TRAIN_PERCENTAGE)) test_word_vects = [] test_classes = [] for i in range(ntest): idx = random.randint(0, len(word_vects)-1) test_word_vects.append(word_vects.pop(idx)) test_classes.append(classes.pop(idx)) train_word_vects = word_vects train_classes = classes train_dataset = [get_doc_vector(words, vocabulary) for words in train_word_vects] # 生成决策树 if not os.path.exists('sms_tree.pkl'): clf.create_tree(train_dataset, train_classes, vocabulary) clf.dump_tree('sms_tree.pkl') else: clf.load_tree('sms_tree.pkl') # 测试模型 error = 0 for test_word_vect, test_cls in zip(test_word_vects, test_classes): test_data = get_doc_vector(test_word_vect, vocabulary) pred_cls = clf.classify(test_data, feat_names=vocabulary) if test_cls != pred_cls: print('Predict: {} -- Actual: {}'.format(pred_cls, test_cls)) error += 1 print('Error Rate: {}'.format(error/len(test_classes))) ================================================ FILE: decision_tree/sms_tree_2.dot ================================================ digraph decision_tree { "8fbb40df-9b8c-4525-a34a-0ec254360649" [label="call"]; "9c2cf1a6-e34b-4f3c-9cc0-17a4e20f12f7" [label="to"]; "ef9e9738-2596-4bdf-a42d-28d7f5471ca7" [label="your"]; "626c0a8b-c1fe-42d9-ad4f-b79e03cf82f7" [label="from"]; "56bdce7c-b23c-4d52-a802-1c28377ec7f5" [label="explicit"]; "ebe24cea-1310-40fe-b164-25032c942aec" [label="ham"]; "1a56632b-860b-4ace-b604-59b9c3b06405" [label="spam"]; "d7636d96-6f9e-4883-a581-c8919088cbf2" [label="spam"]; "1d1933b4-12e1-41ea-b6c1-46f8bacb851c" [label="spam"]; "ac8ca11e-10f5-4a3f-8c1d-e31933d74a8d" [label="when"]; "00cb082b-b9c3-4417-9d25-209f2b4957c8" [label="spam"]; "b7cc5eda-0d6a-4893-ba1d-78641ed8a949" [label="ham"]; "577ef6a5-eb97-4dc1-9fae-741253db33aa" [label="dead"]; "ae9e2b6c-1bdb-4cdb-aaea-01f0d3e138c6" [label="spam"]; "6c303284-fb0a-44e7-b92d-dcae4ffd828d" [label="ham"]; "8fbb40df-9b8c-4525-a34a-0ec254360649" -> "9c2cf1a6-e34b-4f3c-9cc0-17a4e20f12f7" [label="0"]; "9c2cf1a6-e34b-4f3c-9cc0-17a4e20f12f7" -> "ef9e9738-2596-4bdf-a42d-28d7f5471ca7" [label="0"]; "ef9e9738-2596-4bdf-a42d-28d7f5471ca7" -> "626c0a8b-c1fe-42d9-ad4f-b79e03cf82f7" [label="0"]; "626c0a8b-c1fe-42d9-ad4f-b79e03cf82f7" -> "56bdce7c-b23c-4d52-a802-1c28377ec7f5" [label="0"]; "56bdce7c-b23c-4d52-a802-1c28377ec7f5" -> "ebe24cea-1310-40fe-b164-25032c942aec" [label="0"]; "56bdce7c-b23c-4d52-a802-1c28377ec7f5" -> "1a56632b-860b-4ace-b604-59b9c3b06405" [label="1"]; "626c0a8b-c1fe-42d9-ad4f-b79e03cf82f7" -> "d7636d96-6f9e-4883-a581-c8919088cbf2" [label="1"]; "ef9e9738-2596-4bdf-a42d-28d7f5471ca7" -> "1d1933b4-12e1-41ea-b6c1-46f8bacb851c" [label="1"]; "9c2cf1a6-e34b-4f3c-9cc0-17a4e20f12f7" -> "ac8ca11e-10f5-4a3f-8c1d-e31933d74a8d" [label="1"]; "ac8ca11e-10f5-4a3f-8c1d-e31933d74a8d" -> "00cb082b-b9c3-4417-9d25-209f2b4957c8" [label="0"]; "ac8ca11e-10f5-4a3f-8c1d-e31933d74a8d" -> "b7cc5eda-0d6a-4893-ba1d-78641ed8a949" [label="1"]; "8fbb40df-9b8c-4525-a34a-0ec254360649" -> "577ef6a5-eb97-4dc1-9fae-741253db33aa" [label="1"]; "577ef6a5-eb97-4dc1-9fae-741253db33aa" -> "ae9e2b6c-1bdb-4cdb-aaea-01f0d3e138c6" [label="0"]; "577ef6a5-eb97-4dc1-9fae-741253db33aa" -> "6c303284-fb0a-44e7-b92d-dcae4ffd828d" [label="1"]; } ================================================ FILE: decision_tree/trees.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- # Author: PytLab # Date: 2017-07-07 import copy import uuid import pickle from collections import defaultdict, namedtuple from math import log2 class DecisionTreeClassifier(object): ''' 使用ID3算法划分数据集的决策树分类器 ''' @staticmethod def split_dataset(dataset, classes, feat_idx): ''' 根据某个特征以及特征值划分数据集 :param dataset: 待划分的数据集, 有数据向量组成的列表. :param classes: 数据集对应的类型, 与数据集有相同的长度 :param feat_idx: 特征在特征向量中的索引 :param splited_dict: 保存分割后数据的字典 特征值: [子数据集, 子类型列表] ''' splited_dict = {} for data_vect, cls in zip(dataset, classes): feat_val = data_vect[feat_idx] sub_dataset, sub_classes = splited_dict.setdefault(feat_val, [[], []]) sub_dataset.append(data_vect[: feat_idx] + data_vect[feat_idx+1: ]) sub_classes.append(cls) return splited_dict def get_shanno_entropy(self, values): ''' 根据给定列表中的值计算其Shanno Entropy ''' uniq_vals = set(values) val_nums = {key: values.count(key) for key in uniq_vals} probs = [v/len(values) for k, v in val_nums.items()] entropy = sum([-prob*log2(prob) for prob in probs]) return entropy def choose_best_split_feature(self, dataset, classes): ''' 根据信息增益确定最好的划分数据的特征 :param dataset: 待划分的数据集 :param classes: 数据集对应的类型 :return: 划分数据的增益最大的属性索引 ''' base_entropy = self.get_shanno_entropy(classes) feat_num = len(dataset[0]) entropy_gains = [] for i in range(feat_num): splited_dict = self.split_dataset(dataset, classes, i) new_entropy = sum([ len(sub_classes)/len(classes)*self.get_shanno_entropy(sub_classes) for _, (_, sub_classes) in splited_dict.items() ]) entropy_gains.append(base_entropy - new_entropy) return entropy_gains.index(max(entropy_gains)) def get_majority(classes): ''' 返回类型中占据大多数的类型 ''' cls_num = defaultdict(lambda: 0) for cls in classes: cls_num[cls] += 1 return max(cls_num, key=cls_num.get) def create_tree(self, dataset, classes, feat_names): ''' 根据当前数据集递归创建决策树 :param dataset: 数据集 :param feat_names: 数据集中数据相应的特征名称 :param classes: 数据集中数据相应的类型 :param tree: 以字典形式返回决策树 ''' # 如果数据集中只有一种类型停止树分裂 if len(set(classes)) == 1: return classes[0] # 如果遍历完所有特征，返回比例最多的类型 if len(feat_names) == 0: return get_majority(classes) # 分裂创建新的子树 tree = {} best_feat_idx = self.choose_best_split_feature(dataset, classes) feature = feat_names[best_feat_idx] tree[feature] = {} # 创建用于递归创建子树的子数据集 sub_feat_names = feat_names[:] sub_feat_names.pop(best_feat_idx) splited_dict = self.split_dataset(dataset, classes, best_feat_idx) for feat_val, (sub_dataset, sub_classes) in splited_dict.items(): tree[feature][feat_val] = self.create_tree(sub_dataset, sub_classes, sub_feat_names) self.tree = tree self.feat_names = feat_names return tree def get_nodes_edges(self, tree=None, root_node=None): ''' 返回树中所有节点和边 ''' Node = namedtuple('Node', ['id', 'label']) Edge = namedtuple('Edge', ['start', 'end', 'label']) if tree is None: tree = self.tree if type(tree) is not dict: return [], [] nodes, edges = [], [] if root_node is None: label = list(tree.keys())[0] root_node = Node._make([uuid.uuid4(), label]) nodes.append(root_node) for edge_label, sub_tree in tree[root_node.label].items(): node_label = list(sub_tree.keys())[0] if type(sub_tree) is dict else sub_tree sub_node = Node._make([uuid.uuid4(), node_label]) nodes.append(sub_node) edge = Edge._make([root_node, sub_node, edge_label]) edges.append(edge) sub_nodes, sub_edges = self.get_nodes_edges(sub_tree, root_node=sub_node) nodes.extend(sub_nodes) edges.extend(sub_edges) return nodes, edges def dotify(self, tree=None): ''' 获取树的Graphviz Dot文件的内容 ''' if tree is None: tree = self.tree content = 'digraph decision_tree {\n' nodes, edges = self.get_nodes_edges(tree) for node in nodes: content += ' "{}" [label="{}"];\n'.format(node.id, node.label) for edge in edges: start, label, end = edge.start, edge.label, edge.end content += ' "{}" -> "{}" [label="{}"];\n'.format(start.id, end.id, label) content += '}' return content def classify(self, data_vect, feat_names=None, tree=None): ''' 根据构建的决策树对数据进行分类 ''' if tree is None: tree = self.tree if feat_names is None: feat_names = self.feat_names # Recursive base case. if type(tree) is not dict: return tree feature = list(tree.keys())[0] value = data_vect[feat_names.index(feature)] sub_tree = tree[feature][value] return self.classify(data_vect, feat_names, sub_tree) def dump_tree(self, filename, tree=None): ''' 存储决策树 ''' if tree is None: tree = self.tree with open(filename, 'wb') as f: pickle.dump(tree, f) def load_tree(self, filename): ''' 加载树结构 ''' with open(filename, 'rb') as f: tree = pickle.load(f) self.tree = tree return tree ================================================ FILE: linear_regression/abalone.txt ================================================ 1 0.455 0.365 0.095 0.514 0.2245 0.101 0.15 15 1 0.35 0.265 0.09 0.2255 0.0995 0.0485 0.07 7 -1 0.53 0.42 0.135 0.677 0.2565 0.1415 0.21 9 1 0.44 0.365 0.125 0.516 0.2155 0.114 0.155 10 0 0.33 0.255 0.08 0.205 0.0895 0.0395 0.055 7 0 0.425 0.3 0.095 0.3515 0.141 0.0775 0.12 8 -1 0.53 0.415 0.15 0.7775 0.237 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0.750899 4.268081 1.000000 0.694756 4.278096 1.000000 0.870158 4.517640 1.000000 0.276457 3.555461 1.000000 0.017761 3.055026 1.000000 0.802046 4.354819 1.000000 0.559275 3.894387 1.000000 0.941305 4.597773 1.000000 0.856877 4.523616 ================================================ FILE: linear_regression/lasso_regression.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 1, delta = 1466.2236239668678\n", "Iteration: 2, delta = 178.09595140235479\n", "Iteration: 3, delta = 122.9449681776814\n", "Iteration: 4, delta = 87.31358567622465\n", "Iteration: 5, delta = 62.69665876827412\n", "Iteration: 6, delta = 45.42239934676854\n", "Iteration: 7, delta = 33.158544205951785\n", "Iteration: 8, delta = 24.39248658850147\n", "Iteration: 9, delta = 18.094550266671376\n", "Iteration: 10, delta = 13.54904027267321\n", "Iteration: 11, delta = 10.252379488046245\n", "Iteration: 12, delta = 7.848669709154365\n", "Iteration: 13, delta = 6.086454636246799\n", "Iteration: 14, delta = 4.788458307723886\n", "Iteration: 15, delta = 3.8299053756531976\n", "Iteration: 16, delta = 3.122873178243026\n", "Iteration: 17, delta = 2.6050408381288435\n", "Iteration: 18, delta = 2.2316904209010318\n", "Iteration: 19, delta = 1.9701101960431515\n", "Iteration: 20, delta = 1.795752091414215\n", "Iteration: 21, delta = 1.6896477099976437\n", "Iteration: 22, delta = 1.6367076550845923\n", "Iteration: 23, delta = 1.6246251283932907\n", "Iteration: 24, delta = 1.6431809395098753\n", "Iteration: 25, delta = 1.6838062089104824\n", "Iteration: 26, delta = 1.7393038856876046\n", "Iteration: 27, delta = 1.8036633204969803\n", "Iteration: 28, delta = 1.8719259246654474\n", "Iteration: 29, delta = 1.94007652367236\n", "Iteration: 30, delta = 2.00494617126742\n", "Iteration: 31, delta = 2.0641193924147956\n", "Iteration: 32, delta = 2.1158432226679906\n", "Iteration: 33, delta = 2.1589378902735916\n", "Iteration: 34, delta = 2.1954186438620127\n", "Iteration: 35, delta = 2.1792291130373087\n", "Iteration: 36, delta = 2.161131873532213\n", "Iteration: 37, delta = 2.138616526509395\n", "Iteration: 38, delta = 2.1107247941065452\n", "Iteration: 39, delta = 2.077248416043858\n", "Iteration: 40, delta = 2.0384724195671424\n", "Iteration: 41, delta = 1.994984235397851\n", "Iteration: 42, delta = 1.94753433583287\n", "Iteration: 43, delta = 1.8969373637091849\n", "Iteration: 44, delta = 1.8440046355945015\n", "Iteration: 45, delta = 1.7895004867714306\n", "Iteration: 46, delta = 1.7341163178480201\n", "Iteration: 47, delta = 1.6784574320126922\n", "Iteration: 48, delta = 1.6230388169699381\n", "Iteration: 49, delta = 1.5682869251297689\n", "Iteration: 50, delta = 1.5145452458264117\n", "Iteration: 51, delta = 1.4620820576578808\n", "Iteration: 52, delta = 1.4110992154357973\n", "Iteration: 53, delta = 1.361741184467519\n", "Iteration: 54, delta = 1.3141038041451338\n", "Iteration: 55, delta = 1.268242460967258\n", "Iteration: 56, delta = 1.2241794934923291\n", "Iteration: 57, delta = 1.1819107512205846\n", "Iteration: 58, delta = 1.1414112967506753\n", "Iteration: 59, delta = 1.1026402839911498\n", "Iteration: 60, delta = 1.0655450714154995\n", "Iteration: 61, delta = 1.0300646433558995\n", "Iteration: 62, delta = 0.9961324178657378\n", "Iteration: 63, delta = 0.9636785196698838\n", "Iteration: 64, delta = 0.9326315931257341\n", "Iteration: 65, delta = 0.9029202244089447\n", "Iteration: 66, delta = 0.8744740354454734\n", "Iteration: 67, delta = 0.84722450494246\n", "Iteration: 68, delta = 0.8211055648937418\n", "Iteration: 69, delta = 0.7960540142573791\n", "Iteration: 70, delta = 0.7720097853969037\n", "Iteration: 71, delta = 0.7489160933666881\n", "Iteration: 72, delta = 0.7267194932596794\n", "Iteration: 73, delta = 0.7053698665672528\n", "Iteration: 74, delta = 0.6777633374424568\n", "Iteration: 75, delta = 0.6420322119474804\n", "Iteration: 76, delta = 0.6227444190744791\n", "Iteration: 77, delta = 0.6042345772832505\n", "Iteration: 78, delta = 0.586459970063288\n", "Iteration: 79, delta = 0.5694015038363887\n", "Iteration: 80, delta = 0.5530330654548834\n", "Iteration: 81, delta = 0.5373242592779661\n", "Iteration: 82, delta = 0.5222424281671465\n", "Iteration: 83, delta = 0.507754099076692\n", "Iteration: 84, delta = 0.4938259987636684\n", "Iteration: 85, delta = 0.4804257497094113\n", "Iteration: 86, delta = 0.4675223292865667\n", "Iteration: 87, delta = 0.45508635469013825\n", "Iteration: 88, delta = 0.44309024068775216\n", "Iteration: 89, delta = 0.4315082654932212\n", "Iteration: 90, delta = 0.42031657128563893\n", "Iteration: 91, delta = 0.4094931191989417\n", "Iteration: 92, delta = 0.399017613593287\n", "Iteration: 93, delta = 0.38887140662154707\n", "Iteration: 94, delta = 0.37903739121543367\n", "Iteration: 95, delta = 0.3694998884739107\n", "Iteration: 96, delta = 0.3602445337601239\n", "Iteration: 97, delta = 0.3512581645982209\n", "Iteration: 98, delta = 0.3425287125085106\n", "Iteration: 99, delta = 0.3340451002179634\n", "Iteration: 100, delta = 0.32579714515327396\n", "Iteration: 101, delta = 0.3177754697128421\n", "Iteration: 102, delta = 0.3099714185511857\n", "Iteration: 103, delta = 0.30237698284577164\n", "Iteration: 104, delta = 0.29498473141893555\n", "Iteration: 105, delta = 0.28778774842476196\n", "Iteration: 106, delta = 0.2807795772657755\n", "Iteration: 107, delta = 0.2739541703631403\n", "Iteration: 108, delta = 0.26730584434994853\n", "Iteration: 109, delta = 0.2608292402769621\n", "Iteration: 110, delta = 0.25451928839697757\n", "Iteration: 111, delta = 0.24837117712013423\n", "Iteration: 112, delta = 0.24238032574362478\n", "Iteration: 113, delta = 0.23654236056131595\n", "Iteration: 114, delta = 0.23085309402540588\n", "Iteration: 115, delta = 0.22530850659109092\n", "Iteration: 116, delta = 0.21990473097775975\n", "Iteration: 117, delta = 0.21463803852952879\n", "Iteration: 118, delta = 0.20950482743864995\n", "Iteration: 119, delta = 0.20450161259304878\n", "Iteration: 120, delta = 0.19962501683926348\n", "Iteration: 121, delta = 0.1948717634638797\n", "Iteration: 122, delta = 0.19023866974180237\n", "Iteration: 123, delta = 0.18572264138401806\n", "Iteration: 124, delta = 0.1813206677618382\n", "Iteration: 125, delta = 0.17702981779780202\n", "Iteration: 126, delta = 0.1728472364031859\n", "Iteration: 127, delta = 0.1687701413927698\n", "Iteration: 128, delta = 0.1647958207904594\n", "Iteration: 129, delta = 0.16092163045755115\n", "Iteration: 130, delta = 0.1571449919990755\n", "Iteration: 131, delta = 0.15346339087727756\n", "Iteration: 132, delta = 0.14987437472564125\n", "Iteration: 133, delta = 0.14637555178751427\n", "Iteration: 134, delta = 0.1429645894920668\n", "Iteration: 135, delta = 0.1396392131052835\n", "Iteration: 136, delta = 0.1363972044705406\n", "Iteration: 137, delta = 0.1332364007942033\n", "Iteration: 138, delta = 0.13015469348692932\n", "Iteration: 139, delta = 0.12715002702884703\n", "Iteration: 140, delta = 0.12422039787293215\n", "Iteration: 141, delta = 0.1213638533536141\n", "Iteration: 142, delta = 0.11857849063085268\n", "Iteration: 143, delta = 0.11586245562853037\n", "Iteration: 144, delta = 0.11321394199057977\n", "Iteration: 145, delta = 0.11063119004825239\n", "Iteration: 146, delta = 0.1081124857935265\n", "Iteration: 147, delta = 0.10565615985365184\n", "Iteration: 148, delta = 0.10326058648411163\n", "Iteration: 149, delta = 0.10092418256476776\n", "Iteration: 150, delta = 0.09864540659987142\n", "Correlation coefficient: 0.7255254877587117\n" ] } ], "source": [ "run lasso_regression.py" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: linear_regression/lasso_regression.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- import itertools from math import exp import numpy as np import matplotlib.pyplot as plt from standard_linear_regression import load_data, standarize, get_corrcoef def lasso_regression(X, y, lambd=0.2, threshold=0.1): ''' 通过坐标下降(coordinate descent)法获取LASSO回归系数 ''' # 计算残差平方和 rss = lambda X, y, w: (y - X*w).T*(y - X*w) # 初始化回归系数w. m, n = X.shape w = np.matrix(np.zeros((n, 1))) r = rss(X, y, w) # 使用坐标下降法优化回归系数w niter = itertools.count(1) for it in niter: for k in range(n): # 计算常量值z_k和p_k z_k = (X[:, k].T*X[:, k])[0, 0] p_k = 0 for i in range(m): p_k += X[i, k]*(y[i, 0] - sum([X[i, j]*w[j, 0] for j in range(n) if j != k])) if p_k < -lambd/2: w_k = (p_k + lambd/2)/z_k elif p_k > lambd/2: w_k = (p_k - lambd/2)/z_k else: w_k = 0 w[k, 0] = w_k r_prime = rss(X, y, w) delta = abs(r_prime - r)[0, 0] r = r_prime #print('Iteration: {}, delta = {}'.format(it, delta)) if delta < threshold: break return w def lasso_traj(X, y, ntest=30): ''' 获取回归系数轨迹矩阵 ''' _, n = X.shape ws = np.zeros((ntest, n)) for i in range(ntest): w = lasso_regression(X, y, lambd=exp(i-10)) ws[i, :] = w.T print('lambda = e^({}), w = {}'.format(i-10, w.T[0, :])) return ws if '__main__' == __name__: X, y = load_data('abalone.txt') X, y = standarize(X), standarize(y) # w = lasso_regression(X, y, lambd=10) # # y_prime = X*w # # 计算相关系数 # corrcoef = get_corrcoef(np.array(y.reshape(1, -1)), # np.array(y_prime.reshape(1, -1))) # print('Correlation coefficient: {}'.format(corrcoef)) ntest = 30 # 绘制轨迹 ws = lasso_traj(X, y, ntest) fig = plt.figure() ax = fig.add_subplot(111) lambdas = [i-10 for i in range(ntest)] ax.plot(lambdas, ws) plt.show() ================================================ FILE: linear_regression/lasso_traj.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "lambda = e^(-10), w = [[ 0.01660577 -0.0477072 0.41365826 0.15553501 1.11561007 -1.28671007\n", " -0.26342221 0.47832296]]\n", "lambda = e^(-9), w = [[ 0.01660576 -0.04770667 0.41365771 0.15553499 1.11560914 -1.28670963\n", " -0.26342191 0.47832323]]\n", "lambda = e^(-8), w = [[ 0.01660572 -0.04770526 0.41365621 0.15553496 1.11560659 -1.28670843\n", " -0.2634211 0.47832395]]\n", "lambda = e^(-7), w = [[ 0.01660563 -0.04770141 0.41365214 0.15553486 1.11559967 -1.28670516\n", " -0.26341888 0.4783259 ]]\n", "lambda = e^(-6), w = [[ 0.01660536 -0.04769094 0.41364108 0.1555346 1.11558085 -1.28669629\n", " -0.26341285 0.47833122]]\n", "lambda = e^(-5), w = [[ 0.01660465 -0.04766249 0.41361102 0.15553388 1.11552969 -1.28667218\n", " -0.26339646 0.47834566]]\n", "lambda = e^(-4), w = [[ 0.0166027 -0.04758516 0.41352929 0.15553192 1.11539062 -1.28660663\n", " -0.2633519 0.47838494]]\n", "lambda = e^(-3), w = [[ 0.01659742 -0.04737496 0.41330714 0.1555266 1.1150126 -1.28642845\n", " -0.26323077 0.47849169]]\n", "lambda = e^(-2), w = [[ 0.01658305 -0.04680356 0.41270328 0.15551213 1.11398503 -1.2859441\n", " -0.26290153 0.47878188]]\n", "lambda = e^(-1), w = [[ 0.01653884 -0.04549953 0.41113634 0.15545032 1.11529733 -1.28616464\n", " -0.26296376 0.47804841]]\n", "lambda = e^(0), w = [[ 0.0164328 -0.04121551 0.4066233 0.15534385 1.10760417 -1.2825516\n", " -0.2605118 0.48023131]]\n", "lambda = e^(1), w = [[ 0.01613973 -0.02951034 0.39416606 0.1550324 1.09055472 -1.27419467\n", " -0.25476964 0.4847405 ]]\n", "lambda = e^(2), w = [[ 0.0153508 0. 0.36263561 0.15424449 1.03915517 -1.24943788\n", " -0.23788783 0.49878292]]\n", "lambda = e^(3), w = [[ 0.01325127 0. 0.35059049 0.15288981 0.88507194 -1.17206133\n", " -0.18460861 0.53975315]]\n", "lambda = e^(4), w = [[ 0.00765438 0. 0.32095796 0.14970101 0.37822709 -0.92846213\n", " -0.01885195 0.68362244]]\n", "lambda = e^(5), w = [[ 0. 0. 0.26279139 0.14535091 0. -0.60187314\n", " 0. 0.78431832]]\n", "lambda = e^(6), w = [[ 0. 0. 0.07663304 0.12601456 0. -0.25525521\n", " 0. 0.63220029]]\n", "lambda = e^(7), w = [[ 0. 0. 0. 0.06282233 0. 0. 0.\n", " 0.44495654]]\n", "lambda = e^(8), w = [[ 0. 0. 0. 0. 0. 0. 0.\n", " 0.27074402]]\n", "lambda = e^(9), w = [[ 0. 0. 0. 0. 0. 0. 0. 0.]]\n", "lambda = e^(10), w = [[ 0. 0. 0. 0. 0. 0. 0. 0.]]\n", "lambda = e^(11), w = [[ 0. 0. 0. 0. 0. 0. 0. 0.]]\n", "lambda = e^(12), w = [[ 0. 0. 0. 0. 0. 0. 0. 0.]]\n", "lambda = e^(13), w = [[ 0. 0. 0. 0. 0. 0. 0. 0.]]\n", "lambda = e^(14), w = [[ 0. 0. 0. 0. 0. 0. 0. 0.]]\n", "lambda = e^(15), w = [[ 0. 0. 0. 0. 0. 0. 0. 0.]]\n", "lambda = e^(16), w = [[ 0. 0. 0. 0. 0. 0. 0. 0.]]\n", "lambda = e^(17), w = [[ 0. 0. 0. 0. 0. 0. 0. 0.]]\n", "lambda = e^(18), w = [[ 0. 0. 0. 0. 0. 0. 0. 0.]]\n", "lambda = e^(19), w = [[ 0. 0. 0. 0. 0. 0. 0. 0.]]\n" ] }, { "data": { "image/png": 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L/zNXDbny5a7f+AmYQPthwN9mnjfgxRfo/h2q7GInMyGTrMQssj3ZZHmyyE7MvjLuySY/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\nJiQNhuTBkHc9JOd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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "run lasso_regression.py" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: linear_regression/local_weighted_linear_regression.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- from math import exp import numpy as np import matplotlib.pyplot as plt from standard_linear_regression import load_data, get_corrcoef def lwlr(x, X, Y, k): ''' 局部加权线性回归，给定一个点，获取相应权重矩阵并返回回归系数 ''' m = X.shape[0] # 创建针对x的权重矩阵 W = np.matrix(np.zeros((m, m))) for i in range(m): xi = np.array(X[i][0]) x = np.array(x) W[i, i] = exp((np.linalg.norm(x - xi))/(-2*k**2)) # 获取此点相应的回归系数 xWx = X.T*W*X if np.linalg.det(xWx) == 0: print('xWx is a singular matrix') return w = xWx.I*X.T*W*Y return w if '__main__' == __name__: k = 0.03 X, Y = load_data('ex0.txt') y_prime = [] for x in X.tolist(): w = lwlr(x, X, Y, k).reshape(1, -1).tolist()[0] y_prime.append(np.dot(x, w)) corrcoef = get_corrcoef(np.array(Y.reshape(1, -1)), np.array(y_prime)) print('Correlation coefficient: {}'.format(corrcoef)) fig = plt.figure() ax = fig.add_subplot(111) # 绘制数据点 x = X[:, 1].reshape(1, -1).tolist()[0] y = Y.reshape(1, -1).tolist()[0] ax.scatter(x, y) # 绘制拟合直线 x, y = list(zip(*sorted(zip(x, y_prime), key=lambda x: x[0]))) ax.plot(x, y, c='r') plt.show() ================================================ FILE: linear_regression/ridge_regression.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best w: [[ 0.01013554]\n", " [-0.01118452]\n", " [ 0.68269791]\n", " [-0.18625909]\n", " [ 0.56556456]\n", " [-0.76039839]\n", " [-0.0587618 ]\n", " [ 0.40240551]], best error: 0.7421407031499246\n", "Correlation coefficient: 0.6748460114148546\n" ] }, { "data": { "image/png": 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O5eSTT6asrGxY+tqpq27lkZ+tY+7SUpZePyOu637nnXd44YUXWLJkCRdccEFc163Sk4hs\nMMYsGkzdlPwl8L+s/xc6I52JDkMNksf1kBvKJS+UR2GkkNxwLtnBbHxO9Fe4BoNT6BA4OUBReRFO\ntsNeay8djR2MCoyiMKMwmoBOkKKyHOYuK2PzmmpmLSk55j6Cjub000+nrq6ON998k6KiIhYsWBC3\ndSs1kJRMAC9c9UKiQ0iY/lp0hv5bet2v6a5z5Hx3mcHgGheD6Zk3xuDiggHXuLi4uMYlHAnT2dVJ\nsCtIZ2d0HOwKEuoKEQqGCHYG6WjuoKOpg3BHr6dw2UA2uPkuwcwgwYwgTf4maiO11HXU0bap7TPx\n22JTmFFIUaCIqflTmVM0h5OKTmJG4Qz8tv9Y/4R9Ov1zk6hYf5DXH9rOVf9jIWLFp+UhIqxYsYKG\nhgaeffZZCgoKKC8vj8u6lRpISp4CWr9+Pa7rnoCI4ut4/vb9HuCNOer0kePu6e7Bdd2jzjuOQyQS\nwXGcw6aPLAuHw3R1dX3mYSlH8vl8FBYWMnr06J6huLiYvLy8o54L74x0UtdZd9hQ21FLfVc9BzsO\nsr1hO3WddQB4LA/TC6Yzd9Rc5hZFh8l5k7Ete4C/ct+2vbOf1X/YyrlfnMnsJSXHtY7+dHZ2cs89\n99DR0cE3v/lNCgsL47p+lT6O5RRQSiaAn/3sZ4TD4YErqsOICJZlRc/3x4buedu2ewaPx9PvtMfj\nIRAI9AwZGRmHzXeX2fbxHYQHYozhYMdBNtdtjg71m9lSt4W2cLTlEPAEmD1qNgtGL+DCiRcys3Dm\noK8jGGN48l/ep/FgBzf+4+K4XRDuVl9fzz333ENWVhZf//rXCQQCcV2/Sg9pnwDa2j57mmC4nYiL\nk32tc6Cynou6R4y7p3sf5FOVa1z2tOw5LCl8XPcxEROhPLecSyZdwsWTLmZS3qQB19V9QXjO0lLO\nifMFYYDKykruv/9+ysvLufHGG09YolSpK+0TgFIDaexq5JW9r/D87udZf2A9BsOswlmsmLSCi8sv\nZlx2/x22vfGXT/hwTTUrv7+I0RNz4x7bBx98wNNPP82pp57KpZdeGvf1q9SmCUCpY3Cw/SAvVr7I\nC5Uv8FHdRwAsGL2AFZNWcOHECxkVGHVY/WBHmAd/8g65RYG4XhDu7eWXX+bNN99kxYoVnH766XFf\nv0pdmgCUOk5VLVU8X/k8z+9+noqmCmyxObv0bFbOWMmSkiU9F5BP5AVhiP7w7ZFHHmHbtm1ccMEF\nnHnmmSl9mk7FjyYApeJgR+MOntv1HE9VPEV9Vz1js8Zy5bQruXLqlYzOHM2T//o+jQdOzAVhgHA4\nzFNPPcWWLVs47bTT9PnCalA0ASgVR2E3zJqqNTy6/VHe3v82ttgsLVvKZXlXU3FvhDlnlXLODfG/\nIAzRlsArr7zCW2+9xcyZM/X5wmpAmgCUOkGqWqp4fMfjPFnxJA1dDVxY/UUmVy3k/H+YxowZE07Y\ndt99912ef/55ysrKuP7668nKyjph21LJTROAUidY2AnzatWrPLH5Kaa+eBGt/kaaV2zk8mmXs7R0\nKV47/t/St27dyuOPP05ubi433XST/lhM9UkTgFLD6M3XNrPxL4dYN+MZNhSuJt+fz4pJK7h8yuXM\nGTUnrhdv9+7dy0MPPYSIcMMNN1BWVha3davUoAlAqWFkjOHJf32f+po2Sq+ANTzHq1WvEnSCTMqb\nxOVTLueyyZcxNmtsXLZXV1fHAw88QFtbG1dffTUzZ86My3pVatAEoNQwa6nrZNVvPqRhXzuLr5jC\n1GUFvLznZZ7Z+QzvH3ofQTht7Gl8bsrnOH/i+WR5h3YOv62tjT//+c/s37+fSy65hFNPPTVOe6KS\nnSYApRIgHHR49U9bqVh/iCmnFLP8S7PwZXioaq3ir7v+yrM7n2Vf015yCXDapKWcV34+Z5eeTbYv\n+7i2FwqFeOyxx/jkk08466yzWL58ud4mqjQBKBVPJhzGaWnBaW7GaW7G7ZluwWmJlTW34LS34XZ2\nUeFMZZt/EdmRBk6ueZRA635MVxduVxc40QcVRWyoz4GGXAsZU0xR+UwmTzuV3AlT8I4bh3fsWKzc\n3AGvHziOw6pVq9iwYQMlJSUsW7aMadOm6Y/G0ljaJ4DQnj0wgvdrpBj0e39Mf8pelY9cvzl8mTEm\nVt1El3Uv773MmOhy18U4Lhg3Ou0aMC7GcaB72nUhEsFEIphwBBMOYyJhTCQSLQ+Ho+WRCCYUwu3s\nxO3swHR0Rqc7OnA7OzGdHbjdZZ2dmM6jP1zIysrCysvFzs5BAhlYGQHqMibwvpyBwWJR/nZK8zqQ\njAysQAbi9RFubKC2civNVTuRg3Xktjh4jujB3MrMxFs+kaxTTyPzjMVkLjoVO/uzp46MMWzcuJHX\nX3+dpqYmTQRpLu0TwLYFpwz4oVVpzuvFCgR6BskMYAUyPy3LDCCBaJmdm4OVm4udm4edl4udlxed\nz8vDzslB+vlhVktdJ8//9iPqqts47bJJLFpR3me/QcYYttVu4Y2PnuWjLWvo2ldNUQtMDxcysyFA\n3o4DSCgMHg+Bk04ic/HpZC0+g8CCk7F8vp71OI7Dpk2bWLt2bU8iOOecc5g+fbomgjSS9gmg+bnn\nIAkeCDMyDPLAcCwHkF5VP3PgETliWqJjic731BfptRzEtkEsxLbAsqLTlnw6bVvR13s80QNybCy9\nx72XDdO58kjIYc2D29n+7gEmzS/i/K/Mxhc4+oP4djfvZvXe1azes5rN9Zvxhg3zDvg491Axs3aH\nyd55AHEN4veTufAUMhefQdYZi8mYPRux7Z5E8MYbb9DY2Mi4ceNYtmyZJoI0kfYJQKmRxBjDR2uq\n+dujFeQVB7jk706iYOzg7gKq76xnw8ENrDuwjnUH1rGzeSeBLsOCfT7OOVjI9F1BsvZGn4Bm5eaS\ndfppZC5eTNYZZ2JPGM9HH33E2rVrexLBOeecw4wZMzQRpDBNAEqNQDWfNPLi7zYTCbvMXlJCybR8\nxk3NI5D96WkcjAHXATcMbgTEAm9mT8uprrOO9QfXs/7AetYdWMeu5l3ktRsWVvs5c38OU3e0k1kX\nfSCSZ8wYss44g4zFp7OroJA3N35AY2MjxcXFzJo1i+nTp1NSUqJ3DqUYTQBKjRTBNqj7JDrUbqOt\nupo1WxZQ3TYFx0QP/IXeakp8WynxbaHE8yFZduPh67A8kJEPgfzoOCOvZ7rOl8F6t5114QY+Dtbz\nScd+ChrCzK00LNhjMXcvZLZHAHDLy6g94yx25OSwr7UVYwzZ2dlMmzaNGTNmMHnyZHy9rimo5KQJ\nQKnhFuqA/Ruhdnt0qNsOtZ9AS/WndSwPFE6Bomk4vkIOtRaxr3E0+xpGsb8hn3Akem0gN7uLktHt\nlIztpLigHY/TjB1qwhNuxA41YIfqsTobkGATdDVH74yKCQO7vV62BrLYmp3HVq+XjkMhplW6nFRp\nmFVl8Ecg6PNRNWMyB6ZM4VAgh7AxeDweJk2axPTp05k+fTp5eXnD/EdU8aAJQKkTzRio2wEVL8OO\nl2HPW+AEo8u8mVA0DYpmQHFsKJoBhZOgn07iXMelrrqNfTuaokNFE8HYN/e+iIDtsbC9FrZH8NgG\n23awJYKHIDZdeNwOPG47ltNKl9NCsx2m0XbpCoWhvZWCxhYmHmiiuLGFpoIA1WUl7BtfRmdGJgCj\nRxUyaeo0SktLKSkpobCwUE8XJQFNAEqdCME22L02etCveAWa9kbLi2bA1PNh8jkwehbklkXvThoC\n4xoaDrTTuL8DJ+LihF2ciEsk3N+0gxM2OGGHSNjFDTtI0MWKONhhFzvi4nFcvK7BMhDpPWCIGIeI\n24kJtWAiDbT6mmnODtKeKbh2dF9sXApzMpk4ZRqTps2kpKSE/Px8vaA8wgx7AhCRi4H/AGzgHmPM\nPx+x3A/cDywE6oFrjTGVA61XE4BKKGOip3O6v+XvfRucEHizogf7qedHh4KJiQkv4hLa10ZoTyuh\n6lac5iBuWxinLYzp6qf14BHEa2NCDjgDf/YjxuEgHRyUFuqsZpqkhVZPOyZ2zPc4LpniUFiYSXFp\nKWVT5zKudAL5+fl6PSFBjiUBHP2G5MFtzAbuAi4AqoF1IvKMMebjXtW+DjQaY6aKyHXA/wauHeq2\n+/PHtyqJuCO3ZXO8Uu171mE/Cegpk/6Xi/SqB4L01JHuMhGsWD3LIjotghWrb/XUAY8t2JaFxxJs\nS/BYgi/cQt6Bt8mteZ3s6tfxtu0DIDJqBpFTvolMPR/PpDOxfRkn5o9yFE5LkOCeVkJ7WwjtaSG0\nry36FR6w8/14CjPwlmSRke3DyvJi5Xixs3xY2V7sbC9Wthfx2YgIkXCY1to62g/W0lbbQEd9I12N\nrXQ1tRFu6yDc2onbFSHDziLLl0eZncNUaxwBeyqW46FB2qizWqiVVuqkhb2N7VQ2VbJuS2VPvB7X\nwu8IPhf8ApkBm/yibHJHF1IwdjQFJRMpKC4hM5CJbdvD/vdUcWgBiMgZwP8yxlwUm/8BgDHm573q\nvBir87aIeIADQLEZYOPH2wKY9eMX6Aw7x/w6lX4El5NkN0utDznH3sQCqcAjLi0mwJvuXNa683jd\nmc8+ig57nccSfB4Lv8eKjW38HotMn03AZ5Pl8xDw2WT6bDJ9ntjYJtB72htb5o/V8376mgzbwjnU\nQXB3M6G9rYT2tOA0xa4xeARfaQ6+iTn4J+Tim5CLnTvwt+26vZWse/YJdm/cQGdL82eW+wKZ5Iwq\nIrtwFNmFo8jML8R1XUJdXYSCXYQ6uwgHu3A7I9ghwQ5aeCMefI4Xn2SAx4/j8RDx2AQ9hi7LpU26\neoaI9P+ZtI3gMTY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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "run ridge_regression.py" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: linear_regression/ridge_regression.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- from math import exp import numpy as np import matplotlib.pyplot as plt from standard_linear_regression import load_data, get_corrcoef, standarize def ridge_regression(X, y, lambd=0.2): ''' 获取岭回归系数 ''' XTX = X.T*X m, _ = XTX.shape I = np.matrix(np.eye(m)) w = (XTX + lambd*I).I*X.T*y return w def ridge_traj(X, y, ntest=30): ''' 获取岭轨迹矩阵 ''' _, n = X.shape ws = np.zeros((ntest, n)) for i in range(ntest): w = ridge_regression(X, y, lambd=exp(i-10)) ws[i, :] = w.T return ws if '__main__' == __name__: ntest = 30 # 加载数据 X, y = load_data('abalone.txt') # 中心化 & 标准化 X, y = standarize(X), standarize(y) # 测试数据和训练数据 w_test, errors = [], [] for i in range(ntest): lambd = exp(i - 10) # 训练数据 X_train, y_train = X[: 180, :], y[: 180, :] # 测试数据 X_test, y_test = X[180: -1, :], y[180: -1, :] # 岭回归系数 w = ridge_regression(X_train, y_train, lambd) error = np.std(X_test*w - y_test) w_test.append(w) errors.append(error) # 选择误差最小的回归系数 w_best, e_best = min(zip(w_test, errors), key=lambda x: x[1]) print('Best w: {}, best error: {}'.format(w_best, e_best)) y_prime = X*w_best # 计算相关系数 corrcoef = get_corrcoef(np.array(y.reshape(1, -1)), np.array(y_prime.reshape(1, -1))) print('Correlation coefficient: {}'.format(corrcoef)) # 绘制岭轨迹 ws = ridge_traj(X, y, ntest) fig = plt.figure() ax = fig.add_subplot(111) lambdas = [i-10 for i in range(ntest)] ax.plot(lambdas, ws) plt.show() ================================================ FILE: linear_regression/stage_wise_regression.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt from standard_linear_regression import load_data, get_corrcoef from standard_linear_regression import standarize def stagewise_regression(X, y, eps=0.01, niter=100): ''' 通过向前逐步回归获取回归系数 ''' m, n = X.shape w = np.matrix(np.zeros((n, 1))) min_error = float('inf') all_ws = np.matrix(np.zeros((niter, n))) # 计算残差平方和 rss = lambda X, y, w: (y - X*w).T*(y - X*w) for i in range(niter): print('{}: w = {}'.format(i, w.T[0, :])) for j in range(n): for sign in [-1, 1]: w_test = w.copy() w_test[j, 0] += eps*sign test_error = rss(X, y, w_test) if test_error < min_error: min_error = test_error w = w_test all_ws[i, :] = w.T return all_ws if '__main__' == __name__: X, y = load_data('abalone.txt') X, y = standarize(X), standarize(y) epsilon = 0.005 niter = 1000 all_ws = stagewise_regression(X, y, eps=epsilon, niter=niter) w = all_ws[-1, :] y_prime = X*w.T # 计算相关系数 corrcoef = get_corrcoef(np.array(y.reshape(1, -1)), np.array(y_prime.reshape(1, -1))) print('Correlation coefficient: {}'.format(corrcoef)) # 绘制逐步线性回归回归系数变化轨迹 fig = plt.figure() ax = fig.add_subplot(111) ax.plot(list(range(niter)), all_ws) plt.show() ================================================ FILE: linear_regression/stage_wise_traj.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: w = [[ 0. 0. 0. 0. 0. 0. 0. 0.]]\n", "1: w = [[-0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005]]\n", "2: w = [[-0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01]]\n", "3: w = [[-0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015]]\n", "4: w = [[-0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02]]\n", "5: w = [[-0.025 0.025 0.025 0.025 0.025 0.025 0.025 0.025]]\n", "6: w = [[-0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03]]\n", "7: w = [[-0.03 0.035 0.035 0.035 0.035 0.035 0.035 0.035]]\n", "8: w = [[-0.025 0.04 0.04 0.04 0.04 0.04 0.04 0.04 ]]\n", "9: w = [[-0.025 0.045 0.045 0.045 0.045 0.045 0.045 0.045]]\n", "10: w = [[-0.025 0.05 0.05 0.05 0.05 0.05 0.05 0.05 ]]\n", "11: w = [[-0.025 0.055 0.055 0.055 0.055 0.055 0.055 0.055]]\n", "12: w = [[-0.025 0.06 0.06 0.06 0.06 0.06 0.06 0.06 ]]\n", "13: w = [[-0.02 0.065 0.065 0.065 0.065 0.065 0.065 0.065]]\n", "14: w = [[-0.02 0.07 0.07 0.07 0.07 0.06 0.07 0.07]]\n", "15: w = [[-0.02 0.075 0.075 0.075 0.075 0.055 0.075 0.075]]\n", "16: w = [[-0.02 0.08 0.08 0.08 0.08 0.05 0.08 0.08]]\n", "17: w = [[-0.02 0.085 0.085 0.085 0.085 0.045 0.085 0.085]]\n", "18: w = [[-0.015 0.09 0.09 0.09 0.09 0.04 0.08 0.09 ]]\n", "19: w = [[-0.015 0.095 0.095 0.095 0.085 0.035 0.075 0.095]]\n", "20: w = [[-0.015 0.1 0.1 0.1 0.08 0.03 0.07 0.1 ]]\n", "21: w = [[-0.015 0.105 0.105 0.105 0.075 0.025 0.065 0.105]]\n", "22: w = [[-0.015 0.11 0.11 0.11 0.07 0.02 0.06 0.11 ]]\n", "23: w = [[-0.015 0.115 0.115 0.115 0.065 0.015 0.055 0.115]]\n", "24: w = [[-0.015 0.12 0.12 0.12 0.06 0.01 0.05 0.12 ]]\n", "25: w = [[-0.015 0.12 0.125 0.125 0.055 0.005 0.045 0.125]]\n", "26: w = [[ -1.50000000e-02 1.20000000e-01 1.30000000e-01 1.30000000e-01\n", " 5.00000000e-02 -1.73472348e-18 4.00000000e-02 1.30000000e-01]]\n", "27: w = [[-0.015 0.12 0.135 0.135 0.045 -0.005 0.035 0.135]]\n", "28: w = [[-0.015 0.12 0.14 0.14 0.04 -0.01 0.03 0.14 ]]\n", "29: w = [[-0.01 0.12 0.145 0.145 0.035 -0.015 0.025 0.145]]\n", "30: w = [[-0.01 0.12 0.15 0.15 0.03 -0.02 0.02 0.15]]\n", "31: w = [[-0.01 0.12 0.155 0.155 0.025 -0.025 0.015 0.155]]\n", "32: w = [[-0.01 0.12 0.16 0.16 0.02 -0.03 0.01 0.16]]\n", "33: w = [[-0.01 0.12 0.165 0.165 0.015 -0.035 0.005 0.165]]\n", "34: w = [[ -1.00000000e-02 1.20000000e-01 1.70000000e-01 1.70000000e-01\n", " 1.00000000e-02 -4.00000000e-02 -1.73472348e-18 1.70000000e-01]]\n", "35: w = [[-0.01 0.12 0.175 0.175 0.005 -0.045 -0.005 0.175]]\n", "36: w = [[ -1.00000000e-02 1.20000000e-01 1.80000000e-01 1.80000000e-01\n", " -1.73472348e-18 -5.00000000e-02 -1.00000000e-02 1.80000000e-01]]\n", "37: w = [[-0.01 0.12 0.185 0.185 -0.005 -0.055 -0.015 0.185]]\n", "38: w = [[-0.01 0.12 0.19 0.19 -0.01 -0.06 -0.02 0.19]]\n", "39: w = [[-0.01 0.12 0.195 0.195 -0.015 -0.065 -0.025 0.195]]\n", "40: w = [[-0.01 0.12 0.2 0.2 -0.02 -0.07 -0.03 0.2 ]]\n", "41: w = [[-0.01 0.12 0.205 0.205 -0.025 -0.075 -0.035 0.205]]\n", "42: w = [[-0.01 0.12 0.21 0.21 -0.025 -0.08 -0.04 0.21 ]]\n", "43: w = [[-0.01 0.12 0.215 0.215 -0.03 -0.085 -0.045 0.215]]\n", "44: w = [[-0.01 0.12 0.22 0.22 -0.035 -0.09 -0.05 0.22 ]]\n", "45: w = [[-0.005 0.12 0.225 0.225 -0.04 -0.095 -0.055 0.225]]\n", "46: w = [[-0.005 0.12 0.23 0.23 -0.04 -0.1 -0.06 0.23 ]]\n", "47: w = [[-0.005 0.115 0.235 0.235 -0.04 -0.105 -0.065 0.235]]\n", "48: w = [[-0.005 0.11 0.24 0.24 -0.04 -0.11 -0.07 0.24 ]]\n", "49: w = [[-0.005 0.105 0.245 0.245 -0.04 -0.115 -0.075 0.245]]\n", "50: w = [[-0.005 0.1 0.25 0.25 -0.04 -0.12 -0.08 0.25 ]]\n", "51: w = [[-0.005 0.095 0.255 0.25 -0.035 -0.125 -0.085 0.255]]\n", "52: w = [[-0.005 0.09 0.26 0.25 -0.03 -0.13 -0.09 0.26 ]]\n", "53: w = [[-0.005 0.085 0.265 0.25 -0.025 -0.135 -0.095 0.265]]\n", "54: w = [[-0.005 0.08 0.27 0.25 -0.02 -0.14 -0.1 0.27 ]]\n", "55: w = [[-0.005 0.075 0.275 0.25 -0.015 -0.145 -0.105 0.275]]\n", "56: w = [[-0.005 0.07 0.28 0.25 -0.01 -0.15 -0.11 0.28 ]]\n", "57: w = [[-0.005 0.065 0.285 0.25 -0.005 -0.155 -0.115 0.285]]\n", "58: w = [[ -5.00000000e-03 6.00000000e-02 2.90000000e-01 2.50000000e-01\n", " 1.73472348e-18 -1.60000000e-01 -1.20000000e-01 2.90000000e-01]]\n", "59: w = [[-0.005 0.055 0.295 0.25 0.005 -0.165 -0.125 0.295]]\n", "60: w = [[-0.005 0.05 0.3 0.25 0.01 -0.17 -0.13 0.3 ]]\n", "61: w = [[-0.005 0.045 0.305 0.25 0.015 -0.175 -0.135 0.305]]\n", "62: w = [[-0.005 0.04 0.31 0.25 0.02 -0.18 -0.14 0.31 ]]\n", "63: w = [[-0.005 0.035 0.315 0.245 0.025 -0.185 -0.14 0.315]]\n", "64: w = [[-0.005 0.03 0.32 0.245 0.03 -0.19 -0.145 0.32 ]]\n", "65: w = [[-0.005 0.025 0.325 0.24 0.035 -0.195 -0.145 0.325]]\n", "66: w = [[-0.005 0.02 0.33 0.24 0.04 -0.2 -0.15 0.33 ]]\n", "67: w = [[-0.005 0.015 0.335 0.24 0.045 -0.205 -0.155 0.335]]\n", "68: w = [[-0.005 0.01 0.34 0.235 0.05 -0.21 -0.155 0.34 ]]\n", "69: w = [[-0.005 0.005 0.345 0.235 0.05 -0.215 -0.155 0.345]]\n", "70: w = [[ -5.00000000e-03 -1.73472348e-18 3.50000000e-01 2.35000000e-01\n", " 5.00000000e-02 -2.20000000e-01 -1.55000000e-01 3.50000000e-01]]\n", "71: w = [[-0.005 -0.005 0.355 0.235 0.055 -0.225 -0.16 0.355]]\n", "72: w = [[-0.005 -0.01 0.36 0.23 0.06 -0.23 -0.16 0.36 ]]\n", "73: w = [[-0.005 -0.015 0.365 0.23 0.06 -0.235 -0.16 0.365]]\n", "74: w = [[-0.005 -0.02 0.37 0.23 0.06 -0.24 -0.16 0.37 ]]\n", "75: w = [[-0.005 -0.025 0.375 0.225 0.065 -0.245 -0.16 0.375]]\n", "76: w = [[ 1.73472348e-18 -3.00000000e-02 3.80000000e-01 2.25000000e-01\n", " 6.50000000e-02 -2.50000000e-01 -1.60000000e-01 3.80000000e-01]]\n", "77: w = [[ 1.73472348e-18 -3.50000000e-02 3.85000000e-01 2.25000000e-01\n", " 6.50000000e-02 -2.55000000e-01 -1.60000000e-01 3.85000000e-01]]\n", "78: w = [[ 1.73472348e-18 -4.00000000e-02 3.90000000e-01 2.25000000e-01\n", " 6.50000000e-02 -2.60000000e-01 -1.60000000e-01 3.90000000e-01]]\n", "79: w = [[ 1.73472348e-18 -4.50000000e-02 3.95000000e-01 2.25000000e-01\n", " 6.50000000e-02 -2.65000000e-01 -1.60000000e-01 3.95000000e-01]]\n", "80: w = [[ 1.73472348e-18 -5.00000000e-02 4.00000000e-01 2.20000000e-01\n", " 7.00000000e-02 -2.70000000e-01 -1.60000000e-01 4.00000000e-01]]\n", "81: w = [[ 1.73472348e-18 -5.50000000e-02 4.05000000e-01 2.20000000e-01\n", " 7.00000000e-02 -2.75000000e-01 -1.60000000e-01 4.05000000e-01]]\n", "82: w = [[ 1.73472348e-18 -6.00000000e-02 4.10000000e-01 2.20000000e-01\n", " 7.00000000e-02 -2.80000000e-01 -1.60000000e-01 4.10000000e-01]]\n", "83: w = [[ 1.73472348e-18 -6.50000000e-02 4.15000000e-01 2.20000000e-01\n", " 7.00000000e-02 -2.85000000e-01 -1.60000000e-01 4.15000000e-01]]\n", "84: w = [[ 1.73472348e-18 -7.00000000e-02 4.20000000e-01 2.15000000e-01\n", " 7.50000000e-02 -2.90000000e-01 -1.60000000e-01 4.20000000e-01]]\n", "85: w = [[ 1.73472348e-18 -7.50000000e-02 4.25000000e-01 2.15000000e-01\n", " 7.50000000e-02 -2.95000000e-01 -1.60000000e-01 4.25000000e-01]]\n", "86: w = [[ 1.73472348e-18 -8.00000000e-02 4.30000000e-01 2.15000000e-01\n", " 7.50000000e-02 -3.00000000e-01 -1.60000000e-01 4.30000000e-01]]\n", "87: w = [[ 1.73472348e-18 -8.50000000e-02 4.35000000e-01 2.10000000e-01\n", " 8.00000000e-02 -3.05000000e-01 -1.60000000e-01 4.35000000e-01]]\n", "88: w = [[ 1.73472348e-18 -9.00000000e-02 4.35000000e-01 2.10000000e-01\n", " 8.50000000e-02 -3.10000000e-01 -1.60000000e-01 4.40000000e-01]]\n", "89: w = [[ 1.73472348e-18 -9.50000000e-02 4.35000000e-01 2.10000000e-01\n", " 9.00000000e-02 -3.15000000e-01 -1.60000000e-01 4.45000000e-01]]\n", "90: w = [[ 1.73472348e-18 -1.00000000e-01 4.35000000e-01 2.10000000e-01\n", " 9.50000000e-02 -3.20000000e-01 -1.60000000e-01 4.50000000e-01]]\n", "91: w = [[ 1.73472348e-18 -1.05000000e-01 4.40000000e-01 2.10000000e-01\n", " 9.50000000e-02 -3.25000000e-01 -1.60000000e-01 4.55000000e-01]]\n", "92: w = [[ 1.73472348e-18 -1.10000000e-01 4.45000000e-01 2.05000000e-01\n", " 9.50000000e-02 -3.30000000e-01 -1.60000000e-01 4.60000000e-01]]\n", "93: w = [[ 1.73472348e-18 -1.10000000e-01 4.45000000e-01 2.05000000e-01\n", " 9.50000000e-02 -3.35000000e-01 -1.60000000e-01 4.65000000e-01]]\n", "94: w = [[ 1.73472348e-18 -1.10000000e-01 4.45000000e-01 2.05000000e-01\n", " 1.00000000e-01 -3.40000000e-01 -1.65000000e-01 4.70000000e-01]]\n", "95: w = [[ 1.73472348e-18 -1.10000000e-01 4.45000000e-01 2.05000000e-01\n", " 1.00000000e-01 -3.45000000e-01 -1.65000000e-01 4.75000000e-01]]\n", "96: w = [[ 1.73472348e-18 -1.10000000e-01 4.45000000e-01 2.05000000e-01\n", " 1.05000000e-01 -3.50000000e-01 -1.65000000e-01 4.80000000e-01]]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "97: w = [[ 1.73472348e-18 -1.15000000e-01 4.45000000e-01 2.05000000e-01\n", " 1.05000000e-01 -3.55000000e-01 -1.65000000e-01 4.85000000e-01]]\n", "98: w = [[ 1.73472348e-18 -1.15000000e-01 4.45000000e-01 2.05000000e-01\n", " 1.05000000e-01 -3.60000000e-01 -1.65000000e-01 4.90000000e-01]]\n", "99: w = [[ 1.73472348e-18 -1.15000000e-01 4.45000000e-01 2.05000000e-01\n", " 1.05000000e-01 -3.65000000e-01 -1.60000000e-01 4.95000000e-01]]\n", "100: w = [[ 1.73472348e-18 -1.20000000e-01 4.45000000e-01 2.05000000e-01\n", " 1.05000000e-01 -3.70000000e-01 -1.60000000e-01 5.00000000e-01]]\n", "101: w = [[ 1.73472348e-18 -1.20000000e-01 4.45000000e-01 2.05000000e-01\n", " 1.05000000e-01 -3.75000000e-01 -1.60000000e-01 5.05000000e-01]]\n", "102: w = [[ 1.73472348e-18 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5.40000000e-01]]\n", "109: w = [[ 1.73472348e-18 -1.30000000e-01 4.45000000e-01 2.05000000e-01\n", " 1.05000000e-01 -4.15000000e-01 -1.50000000e-01 5.45000000e-01]]\n", "110: w = [[ 1.73472348e-18 -1.30000000e-01 4.45000000e-01 2.05000000e-01\n", " 1.05000000e-01 -4.20000000e-01 -1.50000000e-01 5.50000000e-01]]\n", "111: w = [[ 0.005 -0.13 0.445 0.205 0.11 -0.425 -0.15 0.555]]\n", "112: w = [[ 0.005 -0.135 0.445 0.205 0.11 -0.43 -0.145 0.56 ]]\n", "113: w = [[ 0.005 -0.14 0.45 0.2 0.11 -0.435 -0.145 0.565]]\n", "114: w = [[ 0.005 -0.14 0.45 0.2 0.11 -0.44 -0.145 0.57 ]]\n", "115: w = [[ 0.005 -0.14 0.45 0.2 0.11 -0.445 -0.145 0.575]]\n", "116: w = [[ 0.005 -0.14 0.45 0.2 0.11 -0.45 -0.145 0.58 ]]\n", "117: w = [[ 0.005 -0.14 0.45 0.2 0.11 -0.455 -0.145 0.585]]\n", "118: w = [[ 0.005 -0.14 0.45 0.2 0.11 -0.46 -0.145 0.59 ]]\n", "119: w = [[ 0.005 -0.14 0.45 0.2 0.11 -0.465 -0.145 0.595]]\n", "120: w = [[ 0.005 -0.14 0.45 0.2 0.11 -0.47 -0.14 0.6 ]]\n", "121: w = [[ 0.005 -0.145 0.45 0.2 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0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "886: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "887: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "888: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "889: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "890: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "891: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "892: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "893: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "894: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "895: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "896: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "897: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "898: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "899: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "900: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "901: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "902: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "903: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "904: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "905: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "906: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "907: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "908: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "909: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "910: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "911: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "912: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "913: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "914: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "915: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "916: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "917: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "918: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "919: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "920: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "921: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "922: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "923: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "924: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "925: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "926: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "927: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "928: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "929: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "930: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "931: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "932: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "933: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "934: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "935: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "936: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "937: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "938: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "939: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "940: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "941: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "942: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "943: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "944: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "945: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "946: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "947: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "948: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "949: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "950: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "951: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "952: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "953: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "954: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "955: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "956: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "957: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "958: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "959: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "960: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "961: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "962: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "963: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "964: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "965: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "966: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "967: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "968: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "969: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "970: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "971: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "972: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "973: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "974: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "975: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "976: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "977: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "978: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "979: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "980: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "981: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "982: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "983: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "984: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "985: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "986: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "987: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "988: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "989: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "990: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "991: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "992: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "993: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "994: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "995: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "996: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "997: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "998: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "999: w = [[ 0.015 -0.065 0.425 0.155 0.885 -1.175 -0.205 0.555]]\n", "Correlation coefficient: 0.7248527452816016\n" ] }, { "data": { "image/png": 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auzGi+wijy6nVnuWnKcwrZfScbphkCAchRANIYlRRmH+V3V9sxGQu4oGzZjL+949c+eBD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\n71Sec98ElqJyig5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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "run stage_wise_regression.py" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: linear_regression/standard_linear_regression.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt def load_data(filename): ''' 加载数据 ''' X, Y = [], [] with open(filename, 'r') as f: for line in f: splited_line = [float(i) for i in line.split()] x, y = splited_line[: -1], splited_line[-1] X.append(x) Y.append(y) X, Y = np.matrix(X), np.matrix(Y).T return X, Y def standarize(X): ''' 中心化 & 标准化数据 (零均值, 单位标准差) ''' std_deviation = np.std(X, 0) mean = np.mean(X, 0) return (X - mean)/std_deviation def std_linreg(X, Y): xTx = X.T*X if np.linalg.det(xTx) == 0: print('xTx is a singular matrix') return return xTx.I*X.T*Y def get_corrcoef(X, Y): # X Y 的协方差 cov = np.mean(X*Y) - np.mean(X)*np.mean(Y) return cov/(np.var(X)*np.var(Y))**0.5 if '__main__' == __name__: # 加载数据 X, Y = load_data('abalone.txt') X, Y = standarize(X), standarize(Y) w = std_linreg(X, Y) Y_prime = X*w print('w: {}'.format(w)) # 计算相关系数 corrcoef = get_corrcoef(np.array(Y.reshape(1, -1)), np.array(Y_prime.reshape(1, -1))) print('Correlation coeffient: {}'.format(corrcoef)) #fig = plt.figure() #ax = fig.add_subplot(111) ## 绘制数据点 #x = X[:, 1].reshape(1, -1).tolist()[0] #y = Y.reshape(1, -1).tolist()[0] #ax.scatter(x, y) ## 绘制拟合直线 #x1, x2 = min(x), max(x) #y1 = (np.matrix([1, x1])*w).tolist()[0][0] #y2 = (np.matrix([1, x2])*w).tolist()[0][0] #ax.plot([x1, x2], [y1, y2], c='r') #plt.show() ================================================ FILE: logistic_regression/english_big.txt ================================================ Urgent! call 09061749602 from Landline. 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M221BP. 2yr warranty. 150ppm. 16 . p p3.99,spam Please call our customer service representative on FREEPHONE 0808 145 4742 between 9am-11pm as you have WON a guaranteed 1000 cash or 5000 prize!,spam You are a winner U have been specially selected 2 receive 1000 cash or a 4* holiday (flights inc) speak to a live operator 2 claim 0871277810810,spam "Hey sorry I didntgive ya a a bellearlier hunny,just been in bedbut mite go 2 thepub l8tr if uwana mt up?loads a luv Jenxxx.",ham "Are you comingdown later?",ham "HEY HEY WERETHE MONKEESPEOPLE SAY WE MONKEYAROUND! HOWDY GORGEOUS, HOWU DOIN? FOUNDURSELF A JOBYET SAUSAGE?LOVE JEN XXX",ham "CHA QUITEAMUZING THATSCOOL BABE,PROBPOP IN & CU SATTHEN HUNNY 4BREKKIE! LOVE JEN XXX. PSXTRA LRG PORTIONS 4 ME PLEASE ",ham "HEY BABE! FAR 2 SPUN-OUT 2 SPK AT DA MO... DEAD 2 DA WRLD. BEEN SLEEPING ON DA SOFA ALL DAY, HAD A COOL NYTHO, TX 4 FONIN HON, CALL 2MWEN IM BK FRMCLOUD 9! J X",ham "CHEERS U TEX MECAUSE U WEREBORED! YEAH OKDEN HUNNY R UIN WK SAT?SOUNDS LIKEYOUR HAVIN GR8FUN J! KEEP UPDAT COUNTINLOTS OF LOVEME XXXXX.",ham "EY! CALM DOWNON THEACUSATIONS.. ITXT U COS IWANA KNOW WOTU R DOIN AT THEW/END... HAVENTCN U IN AGES..RING ME IF UR UP4 NETHING SAT.LOVE J XXX.",ham "YEH I AM DEF UP4 SOMETHING SAT,JUST GOT PAYED2DAY & I HAVBEEN GIVEN A50 PAY RISE 4MY WORK & HAVEBEEN MADE PRESCHOOLCO-ORDINATOR 2I AM FEELINGOOD LUV",ham "Hi its Kate it was lovely to see you tonight and ill phone you tomorrow. I got to sing and a guy gave me his card! xxx",ham "Thinking of u ;) x",ham Me too! Have a lovely night xxx,ham Hey hun-onbus goin 2 meet him. He wants 2go out 4a meal but I donyt feel like it cuz have 2 get last bus home!But hes sweet latelyxxx,ham Hi mate its RV did u hav a nice hol just a message 3 say hello coz havent sent u 1 in ages started driving so stay off roads!RVx,ham IM FINE BABES AINT BEEN UP 2 MUCH THO! SAW SCARY MOVIE YEST ITS QUITE FUNNY! WANT 2MRW AFTERNOON? AT TOWN OR MALL OR SUMTHIN?xx,ham I notice you like looking in the shit mirror youre turning into a right freak,ham IM LATE TELLMISS IM ON MY WAY,ham Been up to ne thing interesting. Did you have a good birthday? When are u wrking nxt? I started uni today.,ham IM GONNAMISSU SO MUCH!!I WOULD SAY IL SEND U A POSTCARD BUTTHERES ABOUTAS MUCH CHANCE OF MEREMEMBERIN ASTHERE IS OFSI NOT BREAKIN HIS CONTRACT!! LUV Yaxx,ham Thanx 4 the time weve spent 2geva, its bin mint! Ur my Baby and all I want is u!xxxx,ham You stayin out of trouble stranger!!saw Dave the other day hes sorted now!still with me bloke when u gona get a girl MR!ur mum still Thinks we will get 2GETHA! ,ham THANX 4 PUTTIN DA FONE DOWN ON ME!!,ham I know dat feelin had it with Pete! Wuld get with em , nuther place nuther time mayb?,ham U 2.,ham Thanx u darlin!im cool thanx. A few bday drinks 2 nite. 2morrow off! Take care c u soon.xxx,ham HIYA COMIN 2 BRISTOL 1 ST WEEK IN APRIL. LES GOT OFF + RUDI ON NEW YRS EVE BUT I WAS SNORING.THEY WERE DRUNK! U BAK AT COLLEGE YET? MY WORK SENDS INK 2 BATH.,ham Sez, hows u & de arab boy? Hope u r all good give my love 2 evry1 love ya eshxxxxxxxxxxx,ham THING R GOOD THANX GOT EXAMS IN MARCH IVE DONE NO REVISION? IS FRAN STILL WITH BOYF? IVE GOTTA INTERVIW 4 EXETER BIT WORRIED!x,ham I love u 2 babe! R u sure everything is alrite. Is he being an idiot? Txt bak girlie,ham I luv u soo much u dont understand how special u r 2 me ring u 2morrow luv u xxx,ham NOT MUCH NO FIGHTS. IT WAS A GOOD NITE!!,ham JADE ITS PAUL. Y DIDNT U TXT ME? DO U REMEMBER ME FROM BARMED? I WANT 2 TALK 2 U! TXT ME,ham LOOK AT AMY URE A BEAUTIFUL, INTELLIGENT WOMAN AND I LIKE U A LOT. I KNOW U DONT LIKE ME LIKE THAT SO DONT WORRY.,ham "Getting tickets 4 walsall tue 6 th march. My mate is getting me them on sat. ill pay my treat. Want 2 go. Txt bak .Terry",ham Alrite jod hows the revision goin? Keris bin doin a smidgin. N e way u wanna cum over after college?xx,ham Wot about on wed nite I am 3 then but only til 9!,ham Pete,is this your phone still? Its Jenny from college and Leanne.what are you up to now?:),ham Sorry about that this is my mates phone and i didnt write it love Kate,ham Have you finished work yet? :),ham HMM yeah if your not too grooved out! And im looking forward to my pound special :),ham Thank god they are in bed!,ham "Hey! do u fancy meetin me at 4 at cha hav a lil beverage on me. if not txt or ring me and we can meet up l8r. quite tired got in at 3 v.pist ;) love Pete x x x",ham IM REALY SOZ IMAT MY MUMS 2NITE WHAT ABOUT 2MORO ,ham Hi darlin im on helens fone im gonna b up the princes 2 nite please come up tb love Kate,ham "ER, ENJOYIN INDIANS AT THE MO..yeP. SaLL gOoD HehE ;> hows bout u shexy? Pete Xx",ham No it was cancelled yeah baby! Well that sounds important so i understand my darlin give me a ring later on this fone love Kate x,ham HI DARLIN ITS KATE ARE U UP FOR DOIN SOMETHIN TONIGHT? IM GOING TO A PUB CALLED THE SWAN OR SOMETHING WITH MY PARENTS FOR ONE DRINK SO PHONE ME IF U CAN,ham Im in inperialmusic listening2the weirdest track ever byleafcutter john-sounds like insects being molested&someone plumbing,remixed by evil men on acid!,ham HI DARLIN HOW WAS WORK DID U GET INTO TROUBLE? IJUST TALKED TO YOUR MUM ALL MORNING! I HAD A REALLY GOOD TIME LAST NIGHT IM GOIN OUT SOON BUT CALL ME IF U CAN,ham HI DARLIN IM MISSIN U HOPE YOU ARE HAVING A GOOD TIME. WHEN ARE U BACK AND WHAT TIME IF U CAN GIVE ME A CALL AT HOME. JESS XX,ham HI BABE U R MOST LIKELY TO BE IN BED BUT IM SO SORRY ABOUT TONIGHT! I REALLY WANNA SEE U TOMORROW SO CALL ME AT 9. LOVE ME XXX,ham Hi im having the most relaxing time ever! we have to get up at 7am every day! was the party good the other night? I get home tomorrow at 5ish.,ham "OH FUCK. JUSWOKE UP IN A BED ON A BOATIN THE DOCKS. SLEPT WID 25 YEAR OLD. SPINOUT! GIV U DA GOSSIP L8R. XXX",ham "BOO BABE! U ENJOYIN YOURJOB? U SEEMED 2 B GETTIN ON WELL HUNNY!HOPE URE OK?TAKE CARE & ILLSPEAK 2U SOONLOTS OF LOVEME XXXX.",ham "HELLO U.CALL WEN U FINISH WRK.I FANCY MEETIN UP WIV U ALL TONITE AS I NEED A BREAK FROM DABOOKS. DID 4 HRS LAST NITE+2 TODAY OF WRK!",ham "ALRITE HUNNY!WOT U UP 2 2NITE? DIDNT END UP GOIN DOWN TOWN JUS DA PUB INSTEAD! JUS CHILLIN AT DA MO IN ME BEDROOM!LOVE JEN XXX.",ham U WILL SWITCH YOUR FONE ON DAMMIT!!,ham "Hi darlin i cantdo anythingtomorrow as myparents aretaking me outfor a meal. when are u free? Katexxx",ham "NOT ENUFCREDEIT TOCALL.SHALL ILEAVE UNI AT 6 +GET A BUS TO YOR HOUSE?",ham "Pete can you please ring meive hardly gotany credit",ham "CAN I PLEASE COME UP NOW IMIN TOWN.DONTMATTER IF URGOIN OUTL8R,JUST REALLYNEED 2DOCD.PLEASE DONTPLEASE DONTIGNORE MYCALLS,U NO THECD ISV.IMPORTANT TOME 4 2MORO",ham Ill be at yours in about 3 mins but look out for me,ham "Hi darlin did youPhone me? Im atHome if youwanna chat.",ham WOT U WANNA DO THEN MISSY?,ham Sary just need Tim in the bollox &it hurt him a lot so he tol me!,ham HOW ARE U? I HAVE MISSED U! I HAVENT BEEN UP 2 MUCH A BIT BORED WITH THE HOLIDAY WANT 2 GO BAK 2 COLLEGE! SAD ISNT IT?xx,ham How u doin baby girl ?? hope u are okay every time I call ure phone is off! I miss u get in touch,ham ILL B DOWN SOON,ham Hello, hello, hi lou sorry it took so long 2 reply- I left mobile at friends in Lancaster, just got it bak Neway im sorry I couldnt make ur bday 2 hun!,ham OH YEAH,AND HAV A GREAT TIME IN NEWQUAY-SEND ME A POSTCARD !1 LOOK AFTER ALL THE GIRLS WHILE IM GONE(U KNOW THE 1IM TALKIN BOUT!)xx,ham CHEERS LOU! YEAH WAS A GOODNITE SHAME U NEVA CAME! C YA GAILxx,ham Wot u up 2? Thout u were gonna call me!! Txt bak luv K,ham 2 celebrate my bday, y else?,ham Fine if thats the way u feel. Thats the way its gota b,ham MY NEW YEARS EVE WAS OK. I WENT TO A PARTY WITH MY BOYFRIEND. WHO IS THIS SI THEN HEY,ham I have no money 4 steve mate! !,ham IM GONNA MISS U SO MUCH,ham Jane babes not goin 2 wrk, feel ill after lst nite. Foned in already cover 4 me chuck.:-),ham Hiya do u like the hlday pics looked horrible in them so took mo out! Hows the camp Amrca thing? Speak soon Serena:),ham HELLOGORGEOUS, HOWS U? MY FONE WAS ON CHARGE LST NITW WEN U TEXD ME. HOPEU AD A NICE WKEND AS IM SURE U DID LOOKIN 4WARD 2 C-IN U 2MRW LUV JAZ,ham I REALLY NEED 2 KISS U I MISS U MY BABY FROM UR BABY 4EVA,ham ALRITE SAM ITS NIC JUST CHECKIN THAT THIS IS UR NUMBER-SO IS IT?T.B*,ham PISS IS TALKING IS SOMEONE THAT REALISE U THAT POINT THIS AT IS IT.(NOW READ IT BACKWARDS),ham U GOIN OUT 2NITE?,ham Okay name ur price as long as its legal! Wen can I pick them up? Y u ave x ams xx,ham Where wuld I be without my baby? The thought alone mite break me and I dont wanna go crazy but everyboy needs his lady xxxxxxxx,ham Hey no I ad a crap nite was borin without ya 2 boggy with me u boring biatch! Thanx but u wait til nxt time il ave ya ,ham "Hello-/@drivby-:0quit edrunk sorry iff pthis makes no senrd-dnot no how ^ dancce 2 drum n basq!ihave fun 2nhite x ros xxxxxxx",ham Hi its Kate how is your evening? I hope i can see you tomorrow for a bit but i have to bloody babyjontet! Txt back if u can. :) xxx,ham WHO ARE YOU SEEING?,ham Ok im not sure what time i finish tomorrow but i wanna spend the evening with you cos that would be vewy vewy lubly! Love me xxx,ham Oh thats late! Well have a good night and i will give u a call tomorrow. Iam now going to go to sleep night night,ham Cheers for the message Zogtorius. Ive been staring at my phone for an age deciding whether to text or not.,ham I cant wait for cornwall. Hope tonight isnt too bad as well but its rock night shite. Anyway im going for a kip now have a good night. Speak to you soon.,ham Hi babe its me thanks for coming even though it didnt go that well!i just wanted my bed! Hope to see you soon love and kisses xxx,ham Just gettin a bit arty with my collages at the mo, well tryin 2 ne way! Got a roast in a min lovely i shall enjoy that!,ham MY NO. IN LUTON 0125698789 RING ME IF UR AROUND! H*,ham HEY THERE BABE, HOW U DOIN? WOT U UP 2 2NITE LOVE ANNIE X.,ham "Si.como no?!listened2the plaid album-quite gd&the new air1 which is hilarious-also boughtbraindancea comp.ofstuff on aphexs ;abel,u hav2hear it!c u sn xxxx",ham HI DARLIN IVE JUST GOT BACK AND I HAD A REALLY NICE NIGHT AND THANKS SO MUCH FOR THE LIFT SEE U TOMORROW XXX,ham I hope you arnt pissed off but id would really like to see you tomorrow. Love me xxxxxxxxxxxxxX,ham SO IS TH GOWER MATE WHICH IS WHERE I AM!?! HOW R U MAN? ALL IS GOOD IN WALES ILL B BACK MORROW. C U THIS WK? WHO WAS THE MSG 4? RANDOM!,ham Hope youre not having too much fun without me!! see u tomorrow love jess x,ham "AH POOR BABY!HOPE URFEELING BETTERSN LUV! PROBTHAT OVERDOSE OF WORK HEY GO CAREFUL SPK 2 U SN LOTS OF LOVEJEN XXX.",ham "Hey j! r u feeling any better, hopeSo hunny. i amnow feelin ill & ithink i may have tonsolitusaswell! damn iam layin in bedreal bored. lotsof luv me xxxx",ham WOT U UP 2 J?,ham "HI BABE UAWAKE?FEELLIKW SHIT.JUSTFOUND OUT VIA ALETTER THATMUM GOTMARRIED 4thNOV.BEHIND OURBACKS FUCKINNICE!SELFISH,DEVIOUSBITCH.ANYWAY,IL CALL U",ham "SHIT BABE.. THASA BIT MESSED UP.YEH, SHE SHUDVETOLD U. DID URGRAN KNOW?NEWAY, ILLSPEAK 2 U2MORO WEN IM NOT ASLEEP...",ham "HEY KATE, HOPE UR OK... WILL GIVE U A BUZ WEDLUNCH. GO OUTSOMEWHERE 4 ADRINK IN TOWN..CUD GO 2WATERSHD 4 A BIT? PPL FROMWRK WILL BTHERE. LOVE PETEXXX.",ham "Petey boy whereare you me and all your friendsare in theKingshead come down if you canlove Nic",ham "Aww you must be nearly dead!Well Jez isComing over toDo some workAnd that whillTake forever!",ham "alright babe, justthought id sayhey! how u doin?nearly the endof me wk offdam nevamind!We will have 2Hook up sn if uwant m8? loveJen x.",ham Right it wasnt you who phoned it was someone with a number like yours!,ham Y?WHERE U AT DOGBREATH? ITS JUST SOUNDING LIKE JAN C THATS AL!!!!!!!!!,ham R U &SAM P IN EACHOTHER. IF WE MEET WE CAN GO 2 MY HOUSE,ham Babes I think I got ur brolly I left it in English wil bring it in 2mrw 4 u luv Franxx,ham WHAT TIME U WRKIN?,ham HEY GIRL. HOW R U? HOPE U R WELL ME AN DEL R BAK! AGAIN LONG TIME NO C! GIVE ME A CALL SUM TIME FROM LUCYxx,ham I HAVE A DATE ON SUNDAY WITH WILL!!,ham Hi the way I was with u 2day, is the normal way&this is the real me. UR unique&I hope I know u 4 the rest of mylife. Hope u find wot was lost.,ham HI HUN! IM NOT COMIN 2NITE-TELL EVERY1 IM SORRY 4 ME, HOPE U AVA GOODTIME!OLI RANG MELNITE IFINK IT MITE B SORTED,BUT IL EXPLAIN EVERYTHIN ON MON.L8RS.x,ham Thanx 4 2day! U r a goodmate I THINK UR RITE SARY! ASUSUAL!1 U CHEERED ME UP! LOVE U FRANYxxxxx,ham Hey darlin.. i can pick u up at college if u tell me wen & where 2 mt.. love Pete xx,ham HAPPY NEW YEAR MY NO.1 MAN,ham ALRITE,ham Wot u up 2 u weirdo?,ham U havent lost me ill always b here 4u.i didnt intend 2 hurt u but I never knew how u felt about me when Iwas+marine&thats what itried2tell urmom.i careabout u,ham Wot is u up 2 then bitch?,ham PLEASSSSSSSEEEEEE TEL ME V AVENT DONE SPORTSx,ham No got new job at bar in airport on satsgettin 4.47per hour but means no lie in! keep in touch,ham Im just wondering what your doing right now?,ham I love u 2 my little pocy bell I am sorry but I love u,ham HELLO PEACH! MY CAKE TASTS LUSH!,ham THANX4 TODAY CER IT WAS NICE 2 CATCH UP BUT WE AVE 2 FIND MORE TIME MORE OFTEN OH WELL TAKE CARE C U SOON.C,ham PICK UR FONE UP NOW U DUMB?,ham There the size of elephant tablets & u shove um up ur ass!!,ham I walked an hour 2 c u! doesnt that show I care y wont u believe im serious?,ham Try neva mate!!,ham Im cool ta luv but v.tired 2 cause i have been doin loads of planning all wk, we have got our social services inspection at the nursery! Take care & spk sn x.,ham Sounds better than my evening im just doing my costume. Im not sure what time i finish tomorrow but i will txt you at the end.,ham Oh ok i didnt know what you meant. Yep i am baby jontin,ham Awww dat is sweet! We can think of something to do he he! Have a nice time tonight ill probably txt u later cos im lonely :( xxx.,ham cud u tell ppl im gona b a bit l8 cos 2 buses hav gon past cos they were full & im still waitin 4 1. Pete x,ham SORRY IM STIL FUCKED AFTER LAST NITE WENT TOBED AT 430 GOT UP 4 WORK AT 630,ham How are you babes. Hope your doing ok. I had a shit nights sleep. I fell asleep at 5.Im knackered and im dreading work tonight. What are thou upto tonight. X,ham Ok darlin i supose it was ok i just worry too much.i have to do some film stuff my mate and then have to babysit again! But you can call me there.xx,ham Mmm thats better now i got a roast down me! id b better if i had a few drinks down me 2! Good indian?,ham YO YO YO BYATCH WHASSUP?,ham Hi Jon, Pete here, Ive bin 2 Spain recently & hav sum dinero left, Bill said u or ur rents mayb interested in it, I hav 12,000pes, so around 48, tb, James.,ham ELLO BABE U OK?,ham Hi juan. Im coming home on fri hey. Of course i expect a welcome party and lots of presents. Ill phone u when i get back. Loads of love nicky x x x x x x x x x,ham hi my darlin im on my way to London and we have just been smashed into by another driver! and have a big dent! im really missing u what have u been up to? xxx,ham hi baby im cruisin with my girl friend what r u up 2? give me a call in and hour at home if thats alright or fone me on this fone now love jenny xxx,ham "GRAN ONLYFOUND OUT AFEW DAYS AGO.CUSOON HONI",ham "CHEERS FOR CALLIN BABE.SOZI CULDNT TALKBUT I WANNATELL U DETAILS LATER WENWECAN CHAT PROPERLY X",ham "HEY DAS COOL... IKNOW ALL 2 WELLDA PERIL OF STUDENTFINANCIAL CRISIS!SPK 2 U L8R.",ham WOT STUDENT DISCOUNT CAN U GET ON BOOKS?,ham "NONE!NOWHERE IKNO DOESDISCOUNT!SHITINNIT",ham "What are youdoing later? Sar xxx",ham "Im on gloucesterroad what are uup to later?",ham "Im at arestaurant eating squid! i will be out about 10:30 wanna dosomething or is that to late?",ham "Hi missed your Call and my mumHas beendropping red wine all over theplace! what is your adress?",ham Al he does is moan at me if n e thin goes wrong its my fault&al de arguments r my fault&fed up of him of himso y bother? Hav 2go, thanx.xx,ham Now u sound like manky scouse boy steve,like! I is travelling on da bus home.wot has u inmind 4 recreation dis eve?,ham DO U WANT 2 MEET UP 2MORRO,ham HEY MATE! HOWS U HONEY?DID U AVE GOOD HOLIDAY? GIMMI DE GOSS!x,ham Ding me on ya break fassyole! Blacko from londn,ham Ill call u 2mrw at ninish, with my address that icky American freek wont stop callin me 2 bad Jen k eh?,ham Hi neva worry bout da truth coz the truth will lead me 2 ur heart. Its the least a unique person like u deserve. Sleep tight or morning,ham U R THE MOST BEAUTIFUL GIRL IVE EVER SEEN. U R MY BABY COME AND C ME IN THE COMMON ROOM,ham WHEN THE FIRST STRIKE IS A RED ONE. THE BIRD + ANTELOPE BEGIN TOPLAY IN THE FIELDOF SELFINDEPENDENCE BELIEVE THIS + THE FLOWER OF CONTENTION WILL GROW.RANDOM!,ham THATS ALRITE GIRL, U KNOW GAIL IS NEVA WRONG!!TAKE CARE SWEET AND DONT WORRY.C U L8TR HUN!LOVE Yaxxx,ham DO NOT B LATE LOVE MUM,ham Where @,ham Hi Petey!noim ok just wanted 2 chat coz avent spoken 2 u 4 a long time-hope ur doin alrite.have good nit at js love ya am.x,ham OH RITE. WELL IM WITH MY BEST MATE PETE, WHO I WENT OUT WITH 4 A WEEK+ NOW WERE 2GEVA AGAIN. ITS BEEN LONGER THAN A WEEK.,ham Thank u. IT BETTER WORK OUT CAUSE I WILL FEEL USED OTHERWISE,ham Nah im goin 2 the wrks with j wot bout u?,ham Im sorry bout last nite it wasnt ur fault it was me, spouse it was pmt or sumthin! U 4give me? I think u shldxxxx,ham Lmao!nice 1,ham Nite nite pocay wocay luv u more than n e thing 4eva I promise ring u 2morrowxxxx,ham "ME 2 BABE I FEEL THE SAME LETS JUST 4GET ABOUT IT+BOTH TRY +CHEER UP+NOT FIT SOO MUCHXXLOVE U LOCAXX",ham FRAN I DECIDED 2 GO N E WAY IM COMPLETELY BROKE AN KNACKERED I GOT UP BOUT 3 C U 2MRW LOVE JANX P.S THIS IS MY DADS FONE, -NO CREDIT,ham CERI U REBEL! SWEET DREAMZ ME LITTLE BUDDY!! C YA 2MORO! WHO NEEDS BLOKES,ham G.W.R,ham Yeah, probably but not sure. Ilol let u know, but personally I wuldnt bother, then again if ur goin to then I mite as well!!,ham No b4 Thursday,ham HI DARLIN I FINISH AT 3 DO U 1 2 PICK ME UP OR MEET ME? TEXT BACK ON THIS NUMBER LUV KATE XXX,ham HI DARLIN I HOPE YOU HAD A NICE NIGHT I WISH I HAD COME CANT WAIT TO SEE YOU LOVE FRAN PS I WANT DIRTY ANAL SEX AND I WANT A 10 MAN GANG BANG,ham Hello darlin ive finished college now so txt me when u finish if u can love Kate xxx,ham Have you had a good day? Mine was really busy are you up to much tomorrow night?,ham Hi its me you are probably having too much fun to get this message but i thought id txt u cos im bored! and james has been farting at me all night,ham make that 3! 4 fucks sake?! x,ham ZOE IT JUST HIT ME 2 IM FUCKING SHITIN MYSELF IL DEFO TRY MY HARDEST 2 CUM 2MOROW LUV U MILLIONS LEKDOG,ham WE REGRET TO INFORM U THAT THE NHS HAS MADE A MISTAKE.U WERE NEVER ACTUALLY BORN.PLEASE REPORT 2 YOR LOCAL HOSPITAL 2B TERMINATED.WE R SORRY 4 THE INCONVENIENCE,ham No probs hon! How u doinat the mo?,ham Yeh. Indians was nice. Tho it did kane me off a bit he he. We shud go out 4 a drink sometime soon. Mite hav 2 go 2 da works 4 a laugh soon. Love Pete x x,ham HI ITS KATE CAN U GIVE ME A RING ASAP XXX,ham HI ITS JESS I DONT KNOW IF YOU ARE AT WORK BUT CALL ME WHEN U CAN IM AT HOME ALL EVE. XXX,ham HI BABE IM AT HOME NOW WANNA DO SOMETHING? XX,ham Hi baby ive just got back from work and i was wanting to see u allday! I hope i didnt piss u off on the phone today. If u are up give me a call xxx,ham 26th OF JULY,ham hi baby im sat on the bloody bus at the mo and i wont be home until about 7:30 wanna do somethin later? call me later ortxt back jess xx,ham Bugis oso near wat... ,ham Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...,ham I dunno until when... Lets go learn pilates...,ham Den only weekdays got special price... Haiz... Cant eat liao... Cut nails oso muz wait until i finish drivin wat, lunch still muz eat wat... ,ham Meet after lunch la...,ham Yunny i'm walking in citylink now faster come down... Me very hungry...,ham 5 nights...We nt staying at port step liao...Too ex,ham Hey pple...$700 or $900 for 5 nights...Excellent location wif breakfast hamper!!!,ham Yun ah.the ubi one say if wan call by tomorrow.call 67441233 look for irene.ere only got bus8,22,65,61,66,382. Ubi cres,ubi tech park.6ph for 1st 5wkg days.n,ham Hey tmr maybe can meet you at yck,ham Oh...i asked for fun. Haha...take care. ,ham We are supposed to meet to discuss abt our trip... Thought xuhui told you? In the afternoon. Thought we can go for lesson after that,ham I can. But it will tell quite long, cos i haven't finish my film yet...,ham Haha... can... But i'm having dinner with my cousin...,ham Oh... Kay... On sat right?,ham I need... Coz i never go before ,ham Hey next sun 1030 there's a basic yoga course... at bugis... We can go for that... Pilates intro next sat.... Tell me what time you r free,ham I am going to sao mu today. Will be done only at 12 ,ham Hey gals...U all wanna meet 4 dinner at nte? ,ham Jos ask if u wana meet up?,ham Haiyoh... Maybe your hamster was jealous of million,ham is your hamster dead? Hey so tmr i meet you at 1pm orchard mrt? ,ham Hey i've booked the pilates and yoga lesson already... Haha,ham Yup... I havent been there before... You want to go for the yoga? I can call up to book ,ham K... Must book a not huh? so going for yoga basic on sunday?,ham Hey so this sat are we going for the intro pilates only? Or the kickboxing too? ,ham Sat right? Okay thanks... ,ham Yup... From what i remb... I think should be can book... ,ham Book which lesson? then you msg me... I will call up after work or sth... I'm going to get specs. My membership is PX3748,ham We can go 4 e normal pilates after our intro... ,ham Sun ah... Thk mayb can if dun have anythin on... Thk have to book e lesson... E pilates is at orchard mrt u noe hor... ,ham Thk shld b can... Ya, i wana go 4 lessons... Haha, can go for one whole stretch...,ham Sounds gd... Haha... Can... Wah, u yan jiu so fast liao...,ham Wah... Okie okie... Muz make use of e unlimited... Haha... ,ham Kaiez... Enjoy ur tuition... Gee... Thk e second option sounds beta... I'll go yan jiu den msg u...,ham K.. I yan jiu liao... Sat we can go 4 bugis vill one frm 10 to 3 den hop to parco 4 nb. Sun can go cine frm 1030 to 2, den hop to orc mrt 4 hip hop at 4...,ham Okies... I'll go yan jiu too... We can skip ard oso, go cine den go mrt one, blah blah blah... ,ham Nope... Juz off from work...,ham Oh oh... Wasted... Den muz chiong on sat n sun liao...,ham Haha... Sounds crazy, dunno can tahan anot...,ham Hmmm.. Thk sure got time to hop ard... Ya, can go 4 free abt... Muz call u to discuss liao... ,ham Oh... I was thkin of goin yogasana at 10 den no nd to go at 3 den can rush to parco 4 nb... Okie lor, u call me when ready...,ham After my work ah... Den 6 plus lor... U workin oso rite... Den go orchard lor, no other place to go liao...,ham Aiyo... U always c our ex one... I dunno abt mei, she haven reply... First time u reply so fast... Y so lucky not workin huh, got bao by ur sugardad ah...gee.. ,ham Ya, i'm referin to mei's ex wat... No ah, waitin 4 u to treat, somebody shld b rich liao...So gd, den u dun have to work frm tmr onwards...,ham Oh oh... Den muz change plan liao... Go back have to yan jiu again...,ham Hey... Thk we juz go accordin to wat we discussed yest lor, except no kb on sun... Cos there's nt much lesson to go if we attend kb on sat...,ham Den wat will e schedule b lk on sun?,ham Eh den sat u book e kb liao huh...,ham Yup... Hey then one day on fri we can ask miwa and jiayin take leave go karaoke ,ham Yup... Ok i go home look at the timings then i msg again... Xuhui going to learn on 2nd may too but her lesson is at 8am,ham Hey i booked the kb on sat already... what other lessons are we going for ah? Keep your sat night free we need to meet and confirm our lodging ,ham Hey... are you going to quit soon? Xuhui and i working till end of the month ,ham oh ya... Got hip hop open. Haha i was thinking can go for jazz then zoom to cine... Actually tonight i'm free leh... And there's a kb lesson tonight,ham Hey so whats the plan this sat? ,ham Can call me at 10:10 to make sure dat i've woken up...,ham Hey i will be late ah... Meet you at 945+,ham Hey tmr meet at bugis 930 ?,ham Ha... Then we must walk to everywhere... Cannot take tram. My cousin said can walk to vic market from our hotel ,ham Haha... Yup hopefully we will lose a few kg by mon. after hip hop can go orchard and weigh again,ham Hey i've booked the 2 lessons on sun liao...,ham Hmmm... Guess we can go 4 kb n power yoga... Haha, dunno we can tahan power yoga anot... Thk got lo oso, forgot liao...,ham Oh... Okie lor...We go on sat... ,ham Oh... Haha... Den we shld had went today too... Gee, nvm la... Kaiez, i dun mind goin jazz oso... Scared hiphop open cant catch up... ,ham I dun thk i'll quit yet... Hmmm, can go jazz ? Yogasana oso can... We can go meet em after our lessons den... ,ham Aiyo... Her lesson so early... I'm still sleepin, haha... Okie, u go home liao den confirm w me lor...,ham I'm nt goin, got somethin on, unless they meetin 4 dinner lor... Haha, i wonder who will go tis time...,ham Wa, ur openin sentence very formal... Anyway, i'm fine too, juz tt i'm eatin too much n puttin on weight...Haha... So anythin special happened?,ham Thk some of em find wtc too far... Weiyi not goin... E rest i dunno yet... R ur goin 4 dinner den i might b able to join...,ham I'm eatin now lor, but goin back to work soon... E mountain deer show huh... I watch b4 liao, very nice...,ham Goin to workout lor... Muz lose e fats... ,ham Yupz... I've oredi booked slots 4 my weekends liao... ,ham I noe la... U wana pei bf oso rite... K lor, other days den...,ham U meet other fren dun wan meet me ah... Muz b a guy rite...,ham Oh... Icic... K lor, den meet other day...,ham Haha... Where got so fast lose weight, thk muz go 4 a month den got effect... Gee,later we go aust put bk e weight.,ham U attend ur driving lesson how many times a wk n which day?,ham Ay wana meet on sat? wkg on sat?,ham Shopping? Eh ger i toking abt syd leh...Haha,ham It's only $140 ard... rest all ard $180 at least...Which is price 4 2 bedrm ($900),ham Hmm...Bad news...Hype park plaza $700 studio taken...Only left 2 bedrm-$900...,ham Okay... I booked all already... Including the one at bugis.,ham Yun ah.now wkg where?btw if go nus sc. wana specialise in wad?,ham Hey you still want to go for yogasana? Coz if we end at cine then can go bathe and hav the steam bath,ham Hey we can go jazz power yoga hip hop kb and yogasana ,ham Can meh? Thgt some will clash... Really ah, i dun mind... I dun seen to have lost any weight... Gee...,ham Oh... Lk tt den we take e one tt ends at cine lor... Dun wan yogasana oso can... ,ham Wa... U so efficient... Gee... Thanx...,ham Hmmm.... Mayb can try e shoppin area one, but forgot e name of hotel...,ham Huh... Hyde park not in mel ah, opps, got confused... Anyway, if tt's e best choice den we juz have to take it...,ham I only work from mon to thurs but Sat i cant leh... Booked liao... Which other day u free?,ham Until 545 lor... Ya, can go 4 dinner together...,ham Mon okie lor... Haha, best is cheap n gd food la, ex oso okie... Depends on whether wana eat western or chinese food... Den which u prefer... ,ham Chinatown got porridge, claypot rice, yam cake, fishhead beehoon... Either we eat cheap den go cafe n tok or go nydc or somethin...,ham Today i'm not workin but not free oso... Gee... Thgt u workin at ur fren's shop ? ,ham My fri ah... Okie lor,goin 4 my drivin den go shoppin after tt...,ham Hey...Great deal...Farm tour 9am to 5pm $95/pax, $50 deposit by 16 May,ham haha but no money leh... Later got to go for tuition... Haha and looking for empty slots for driving lessons,ham My tuition is at 330. Hm we go for the 1120 to 1205 one? Do you mind?,ham Hey... What time is your driving on fri? We go for evaluation on fri?,ham Nvm... I'm going to wear my sport shoes anyway... I'm going to be late leh.,ham Hey are we going for the lo lesson or gym? ,ham Okay... We wait ah,ham Hey come online! Use msn... We are all there,ham Oh yah... We never cancel leh... Haha ,ham K. Did you call me just now ah? ,ham Okay lor... Wah... like that def they wont let us go... Haha... What did they say in the terms and conditions?,ham Okay lor... Will they still let us go a not ah? Coz they will not know until later. We drop our cards into the box right?,ham You want to go? ,ham Kay... Since we are out already ,ham Haha okay... Today weekend leh... ,ham Haha... They cant what... At the most tmr forfeit... haha so how?,ham Hey... Why dont we just go watch x men and have lunch... Haha ,ham Haha... Really oh no... How? Then will they deduct your lesson tmr?,ham Hey i will be really pretty late... You want to go for the lesson first? I will join you. I'm only reaching tp mrt,ham Yunny... I'm goin to be late,ham Ya! when are taking ure practical lessons? I start in june.. ,ham Hey... Very inconvenient for your sis a not huh?,ham Hey i will be late... i'm at amk. Need to drink tea or coffee,ham Hey you gave them your photo when you registered for driving ah? Tmr wanna meet at yck? ,ham Ok... The theory test? when are going to book? I think it's on 21 may. Coz thought wanna go out with jiayin. But she isnt free,ham Haha... dont be angry with yourself... Take it as a practice for the real thing. =) ,ham Haha... Hope can hear the receipt sound... Gd luck!,ham Huh? 6 also cannot? Then only how many mistakes?,ham Nope... Think i will go for it on monday... Sorry i replied so late,ham Hey.. Something came up last min.. Think i wun be signing up tmr.. Hee,ham I am thinking of going down to reg for pract lessons.. Flung my advance.. Haha wat time u going?,ham Hey gals.. Anyone of u going down to e driving centre tmr?,ham Ok no prob,ham Nope i waiting in sch 4 daddy... ,ham Dunno lei he neva say...,ham called dad oredi...,ham Me hungry buy some food good lei... But mum n yun dun wan juz buy a little bit... ,ham Dad went out oredi... ,ham still attending da talks?,ham Then she buying today? no need to c meh...,ham Yup i'm free...,ham Nope... C then...,ham Is fujitsu s series lifebook good?,ham Is toshiba portege m100 gd?,ham Yun buying... But school got offer 2000 plus only...,ham Got fujitsu, ibm, hp, toshiba... Got a lot of model how to say...,ham Okie... Thanx...,ham How come i din c ... Yup i cut my hair...,ham No wonder... Cos i dun rem seeing a silver car... But i thk i saw a black one...,ham Guai... shd haf seen him when he's naughty... so free today? Can go jogging...,ham Ok... C ya...,ham Nope i'm not drivin... I neva develop da photos lei...,ham I wan but too early lei... Me outside now wun b home so early... Neva mind then...,ham 645,ham dun need to pick ur gf?,ham Okie...,ham I'm at home n ready...,ham Ok...,ham Dunno lei... I thk mum lazy to go out... I neva ask her yet...,ham Mum say we wan to go then go... Then she can shun bian watch da glass exhibition... ,ham Dun need to use dial up juz open da browser n surf...,ham When did i use soc... I use it only at home... dunno how 2 type it in word ar...,ham Can a not?,ham Juz go google n search 4 qet...,ham Wat time r going to xin's hostel?,ham Ok...,ham Huh means computational science... Y they like dat one push here n there...,ham I not at home now lei...,ham Eatin my lunch...,ham takin linear algebra today?,ham bot notes oredi... Cos i juz rem i got...,ham Wah lucky man... Then can save money... Hee...,ham Y so late but i need to go n get da laptop...,ham Where to get those?,ham Then cant get da laptop? My matric card wif lei... ,ham I dunno they close oredi not... v ma fan...,ham Hi dis is yijue i would be happy to work wif all for gek1510...,ham comin to fetch us oredi...,ham Got smaller capacity one? Quite ex...,ham Ok thanx...,ham Wat doing now?,ham Got meh... When?,ham neva tell me how i noe... I'm not at home in da aft wat... ,ham Mum ask to buy food home...,ham Leave it wif me lar... wan to carry meh so heavy... Is da num 98321561 familiar to ?,ham I tot it's my group mate... Lucky i havent reply... Wat time do need to leave... ,ham Yup he msg me: is tat yijue? Then i tot it's my group mate cos we meeting today mah... I'm askin if leaving earlier or wat mah cos mayb haf to walk v far...,ham Yup...,ham I'm stuck in da middle of da row on da right hand side of da lt... ,ham Black shirt n blue jeans... I thk i c ...,ham Not directly behind... Abt 4 rows behind ...,ham go home liao? Ask dad to pick me up at 6...,ham I'm done oredi...,ham I wait 4 inside da car park...,ham Yup... How noe leh...,ham Oic... I saw him too but i tot he din c me... I found a group liao...,ham Aiyo a bit pai seh noe... Scared he dun rem who i am then die... Hee... But he become better lookin oredi leh...,ham How i noe... Did specify da domain as nusstu... still in sch...,ham When login dat time... Dad fetching home now?,ham I come n pick up... Come out immediately aft ur lesson...,ham Eh ur laptop got no stock lei... He say mon muz come again to take a look c got a not...,ham ready then call me...,ham still got lessons? in sch?,ham Shall i get my pouch?,ham I'm on da bus going home...,ham I was at bugis juz now wat... But now i'm walking home oredi... so late then reply... I oso saw a top dat i like but din buy... Where r now? ,ham Still at west coast... Haiz... 'll take forever to come back...,ham R comin back for dinner?,ham Eat at old airport road... But now 630 oredi... Got a lot of pple...,ham Y bishan lei... I tot say lavender? ,ham Free any day but i finish at 6 on mon n thurs...,ham I prefer my free days... Tues, wed, fri oso can... ask those workin lor...,ham But we havent got da topic yet rite?,ham So can collect ur laptop?,ham Ok good then i later come find ... C lucky i told to go earlier... Later pple take finish no more again...,ham Eat jap done oso aft ur lect wat... got lect at 12 rite... ,ham So pay first lar... Then when is da stock comin...,ham What's ur pin?,ham collecting ur laptop then going to configure da settings izzit?,ham Cos daddy arranging time c wat time fetch mah...,ham Juz now havent woke up so a bit blur blur... Can? Dad went out liao... I cant cum now oso...,ham So li hai... Me bored now da lecturer repeating last weeks stuff waste time... ,ham How i noe... She's in da car now... Later then c lar... I'm wearing shorts...,ham Huh y lei...,ham eatin later but i'm eatin wif my frens now lei... going home first? ,ham Havent still waitin as usual... come back sch oredi?,ham No break time one... How... I come out n get my stuff fr ?,ham Tmr then brin lar... Aiya later i come n c lar... Mayb neva set properly got da help sheet wif ...,ham I dunno lei... Like dun haf...,ham I'm going out to buy mum's present ar.,ham Havent shopping now lor i juz arrive only,ham Beauty sleep can help ur pimples too.,ham Up to ... wan come then come lor... But i din c any stripes skirt...,ham Then mum's repent how?,ham Got c... I lazy to type... I forgot in lect... I saw a pouch but like not v nice...,ham Ok i found dis pierre cardin one which looks normal costs 20 its on sale.,ham Y wan to go there? C doctor?,ham I'm wif him now buying tix lar...,ham How much r willing to pay?,ham Hi this is yijue... It's regarding the 3230 textbook it's intro to algorithms second edition... I'm selling it for $50...,ham I'll be in sch fr 4-6... I dun haf da book in sch... It's at home...,ham Ok... Thanx... Gd nite 2 too...,ham Wat uniform? In where get?,ham Yup i've finished c there...,ham Never y lei... I v lazy... Got wat? Dat day send me da url cant work one...,ham We're done...,ham Anything lar...,ham Go where n buy? Juz buy when we get there lar.,ham Bishan lar nearer... No need buy so early cos if buy now i gotta park my car...,ham Do u noe how 2 send files between 2 computers?,ham I gotta collect da car at 6 lei.,ham 2 laptop... I noe infra but too slow lar... I wan fast one,ham Dunno da next show aft 6 is 850. Toa payoh got 650.,ham Tmr timin still da same wat cos i got lesson until 6...,ham Wat time wan today?,ham Aiya we discuss later lar... Pick up at 4 is it?,ham I'm in a movie... Collect car oredi...,ham I anything lor...,ham Nothin comes to my mind. help me buy hanger lor. Ur laptop not heavy?,ham Ok lor wat time finish?,ham Huh but i got lesson at 4 lei n i was thinkin of going to sch earlier n i tot of parkin at kent vale... ,ham v ma fan...,ham come lt 25 n pass to me lar,ham Y lei?,ham I wanted to ask to wait 4 me to finish lect. Cos my lect finishes in an hour anyway.,ham Aiyo cos i sms then neva reply so i wait 4 to reply lar. I tot havent finish ur lab wat.,ham Auntie huai juan never pick up her phone,ham I not free today i haf 2 pick my parents up tonite...,ham Ok..,ham Dunno lei... I might b eatin wif my frens... If wan to eat then i wait 4 lar,ham How come?,ham Then ask dad to pick up lar... wan 2 stay until 6 meh...,ham Oh ok wait 4 me there... My lect havent finish,ham I'm done...,ham In da car park ,ham log off 4 wat. It's sdryb8i,ham thk of wat to eat tonight.,ham Aiyo please got time meh.,ham Can... I'm free...,ham Ok.,ham Anything lor. Juz both of us lor.,ham Ok c then.,ham I'll be late...,ham Kent vale lor... wait 4 me there ar?,ham Ard 530 lor. I ok then message lor.,ham I'm done. C there.,ham staff.science.nus.edu.sg/~phyhcmk/teaching/pc1323,ham Dunno lei shd b driving lor cos i go sch 1 hr oni.,ham wait 4 me in sch i finish ard 5..,ham Ok can...,ham dun wan to watch infernal affair?,ham Cos darren say considering mah so i ask ...,ham mean it's confirmed... I tot they juz say oni... Ok then... ,ham Me n him so funny...,ham Anything lor is she coming?,ham Ok lor. I'm in town now lei.,ham With my sis lor... We juz watched italian job.,ham C movie is juz last minute decision mah. Juz watch 2 lar but i tot not interested.,ham Wat so late still early mah. Or we juz go 4 dinner lor. Aiya i dunno...,ham If i not meeting all rite then i'll go home lor. If dun feel like comin it's ok.,ham Can all decide faster cos my sis going home liao..,ham Yup not comin :-(,ham He neva grumble but i sad lor... Hee... Buy tmr lor aft lunch. But we still meetin 4 lunch tmr a not. Neva hear fr them lei. got a lot of work ar?,ham I jokin oni lar.. busy then i wun disturb .,ham Ok lor...,ham I'm in a movie. Call me 4 wat?,ham R going 4 today's meeting?,ham Do noe if ben is going?,ham Dunno lei all decide lor. How abt leona? Oops i tot ben is going n i msg him.,ham Neva mind it's ok..,ham So wat's da decision?,ham Then wait 4 me at bus stop aft ur lect lar. If i dun c then i go get my car then come back n pick .,ham Ok i juz receive..,ham I borrow ur bag ok.,ham Can. Dunno wat to get 4 her...,ham Ok. But i finish at 6.,ham I thk gotta go home by urself. Cos i'll b going out shopping 4 my frens present. ,ham Or i go home first lar wait 4 me lor.. I put down my stuff first..,ham predict wat time 'll finish buying?,ham Ok... But bag again..,ham I only haf msn. It's yijue@hotmail.com,ham I'm in school now n i'll be in da lab doing some stuff give me a call when r done.,ham Anything lar then not going home 4 dinner?,ham When can come out?,ham I havent lei.. Next mon can?,ham That day say cut ur hair at paragon, is it called hair sense? Do noe how much is a hair cut? ,ham Dunno i juz askin cos i got a card got 20% off 4 a salon called hair sense so i tot it's da one cut ur hair. ,ham Yup it's at paragon... I havent decided whether 2 cut yet... Hee...,ham Ok lor but not too early. Me still having project meeting now.,ham Ard 6 like dat lor.,ham I not busy juz dun wan 2 go so early.. Hee.. ,ham Okie wan meet at bishan? Cos me at bishan now. I'm not driving today.,ham Doing project w frens lor. ,ham I'm not coming home 4 dinner.,ham Ard 530 like dat lor. We juz meet in mrt station then dun haf to come out.,ham Ok lor reaching then message me.,ham Happy birthday... May all ur dreams come true...,ham I forgot 2 ask all smth.. There's a card on da present lei... How? all want 2 write smth or sign on it?,ham give me some time to walk there.,ham all write or wat..,ham Then come n pick me at 530 ar?,ham Thanx 4 sending me home...,ham Is ur lecture over?,ham Sorry me going home first... Daddy come fetch later...,ham Okie.. Thanx..,ham Will b going to esplanade fr home?,ham We confirm eating at esplanade?,ham 7 at esplanade.. Do mind giving me a lift cos i got no car today..,ham got wat to buy tell us then no need to come in again.,ham Wat's da model num of ur phone?,ham Hmm thinking lor...,ham Ok lor... Sony ericsson salesman... I ask shuhui then she say quite gd 2 use so i considering...,ham say until like dat i dun buy ericsson oso cannot oredi lar...,ham Jokin only lar... :-) depends on which phone my father can get lor...,ham No need to buy lunch for me.. I eat maggi mee..,ham I know she called me,ham Do all wan 2 meet up n combine all the parts? How's da rest of da project going?,ham I'm doing da intro covers energy trends n pros n cons... Brief description of nuclear fusion n oso brief history of iter n jet got abt 7 n half pages..,ham Yup i shd haf ard 10 pages if i add figures... all got how many pages?,ham That's y we haf to combine n c how lor...,ham cThen i thk shd b enuff.. Still got conclusion n contents pg n references.. I'll b doing da contents pg n cover pg..,ham Now? I'm going out 4 dinner soon..,ham Oh ok.. Wat's ur email?,ham I've sent my part..,ham I emailed yifeng my part oredi.. Can get it fr him..,ham No need to ke qi... too bored izzit y suddenly thk of this...,ham TaKe CaRE n gET WeLL sOOn,ham no home work to do meh... ,ham Huh so late... Fr dinner?,ham Yup i'm elaborating on the safety aspects and some other issues..,ham Oic... Then better quickly go bathe n settle down...,ham I'm hungry buy smth home...,ham But i juz remembered i gotta bathe my dog today..,ham Ok lor thanx... in school?,ham Ok thanx... Take care then...,ham Oops i thk i dun haf enuff... I go check then tell ..,ham only send me the contents page...,ham Wat time finish?,ham But i haf enuff space got like 4 mb...,ham Where r e meeting tmr?,ham Oh ok no prob..,ham Huh i cant thk of more oredi how many pages do we have?,ham Can send me a copy of da report?,ham I thk 50 shd be ok he said plus minus 10.. Did leave a line in between paragraphs?,ham How abt making some of the pics bigger?,ham Nope i'll come online now..,ham How r going to send it to me?,ham Forgot to tell smth.. Can like number the sections so that it's clearer..,ham I havent add yet right.. ,ham They said dun haf passport or smth like dat.. Or juz send to my email account.. ,ham Hey r still online? I've finished the formatting...,ham Can i meet at 5.. As 4 where depends on where wan 2 in lor..,ham Ok.. finishing soon?,ham So 'll be submitting da project tmr rite?,ham Ok lor... But buy wat?,ham Or go buy wif him then i meet later can?,ham Huh so early.. Then having dinner outside izzit?,ham Must come later.. I normally bathe him in da afternoon mah..,ham Crazy ar he's married. like gd looking guys not me. My frens like say he's korean leona's fave but i dun thk he is. Aft some thinking mayb most prob i'll go.,ham Nope but i'll b going 2 sch on fri quite early lor cos mys sis got paper in da morn :-),ham Yar but they say got some error.,ham If u dun drive then how i go 2 sch.,ham Yup,ham Anything...,ham Then u drive lor.,ham Ok,ham Wat time liao, where still got.,ham Gd luck 4 ur exams :-),ham Is ur paper today in e morn or aft?,ham U studying in sch or going home? Anyway i'll b going 2 sch later.,ham Oh ok..,ham In sch but neva mind u eat 1st lor..,ham Call me when u finish then i come n pick u.,ham Hui xin is in da lib.,ham Okie...,ham U wan 2 haf lunch i'm in da canteen now.,ham Haf u found him? I feel so stupid da v cam was working.,ham Where r we meeting?,ham I went to ur hon lab but no one is there.,ham I've reached sch already...,ham Can u look 4 me in da lib i got stuff havent finish yet.,ham Is ur changes 2 da report big? Cos i've already made changes 2 da previous report.,ham Dun b sad.. It's over.. Dun thk abt it already. Concentrate on ur other papers k.,ham Din i tell u jus now 420,ham I fetch yun or u fetch?,ham All e best 4 ur exam later.,ham Thanx 4 e brownie it's v nice...,ham Ok i'm coming home now.,ham Who u talking about?,ham Sorry sent blank msg again. Yup but trying 2 do some serious studying now.,ham Yup but not studying surfing lor. I'm in e lazy mode today.,ham I've reached already.,ham Yup no more already... Thanx 4 printing n handing it up.,ham Ok,ham Ok i've sent u da latest version of da project.,ham Mine here like all fr china then so noisy.,ham Here got lots of hair dresser fr china.,ham All e best 4 ur driving tmr :-),ham He said i look pretty wif long hair wat. But i thk he's cutting quite short 4 me leh.,ham Yup i thk they r e teacher said that will make my face look longer. Darren ask me not 2 cut too short.,ham Ok lar i double check wif da hair dresser already he said wun cut v short. He said will cut until i look nice.,ham Thanx...,ham Shant disturb u anymore... Jia you...,ham Depends on individual lor e hair dresser say pretty but my parents say look gong. U kaypoh.. I also dunno wat she collecting.,ham But i'll b going 2 sch on mon. My sis need 2 take smth.,ham The length is e same but e top shorter n i got a fringe now. I thk i'm not going liao. Too lazy. Dun wan 2 distract u also.,ham Y dun cut too short leh. U dun like ah? She failed. She's quite sad.,ham I might go 2 sch. Yar at e salon now v boring.,ham Is ur paper in e morn or aft tmr?,ham Nothing. Can...,ham Shb b ok lor... Thanx...,ham Really... I tot ur paper ended long ago... But wat u copied jus now got use? U happy lar... I still haf 2 study :-(,ham Really dun bluff me leh... U sleep early too. Nite...,ham Huh so slow i tot u reach long ago liao... U 2 more days only i 4 more leh...,ham Wif my family booking tour package.,ham How's ur paper?,ham Haha mayb u're rite... U know me well. Da feeling of being liked by someone is gd lor. U faster go find one then all gals in our group attached liao.,ham My phone,ham Aiyar hard 2 type. U later free then tell me then i call n scold n tell u.,ham Yar i wanted 2 scold u yest but late already... I where got zhong se qing you? If u ask me b4 he ask me then i'll go out w u all lor. N u still can act so real.,ham That's y i said it's bad dat all e gals know u... Wat u doing now?,ham U horrible gal... U knew dat i was going out wif him yest n u still come n ask me...,ham Aiyar sorry lor forgot 2 tell u...,ham I'm meeting Darren...,ham U coming 2 pick me?,ham Can do lor...,ham It's ok lar. U sleep early too... Nite...,ham Nvm take ur time.,ham Okie,ham I also thk too fast... Xy suggest one not me. U dun wan it's ok. Going 2 rain leh where got gd.,ham Nothing, smsing u n xy lor. Sorry lor da guys neva c u in person but they sort of know u lor. So u wan 2 meet them xy ask me 2 bring u along 4 our next meeting.,ham Dunno he jus say go lido. Same time 930.,ham U know we watchin at lido?,ham Wat time do u wan 2 meet me later?,ham Now got tv 2 watch meh? U no work today?,ham Then we wait 4 u lor... No need 2 feel bad lar...,ham Doing nothing, then u not having dinner w us?,ham Ok...,ham Ok i also wan 2 watch e 9 pm show...,ham Sian... Aft meeting supervisor got work 2 do liao... U working now?,ham Quite late lar... Ard 12 anyway i wun b drivin...,ham Ok but tell me half an hr b4 u come i need 2 prepare.,ham Ok...,ham Then ur sis how?,ham Ard 515 like dat. Y?,ham U repeat e instructions again. Wat's e road name of ur house?,ham Ok lor...,ham I wake up long ago already... Dunno, what other thing?,ham We r outside already.,ham Ok...,ham Ok lor...,ham I'm coming home 4 dinner.,ham Sorry i din lock my keypad.,ham Wat would u like 4 ur birthday?,ham I'm leaving my house now...,ham Ok i msg u b4 i leave my house.,ham Ok i thk i got it. Then u wan me 2 come now or wat?,ham He like not v shock leh. Cos telling shuhui is like telling leona also. Like dat almost all know liao. He got ask me abt ur reaction lor.,ham Yar... I tot u knew dis would happen long ago already.,ham I thk u dun haf 2 hint in e forum already lor... Cos i told ron n darren is going 2 tell shuhui.,ham Quite lor. But dun tell him wait he get complacent...,ham I dun believe u. I thk u told him.,ham Yar he quite clever but aft many guesses lor. He got ask me 2 bring but i thk darren not so willing 2 go. Aiya they thk leona still not attach wat.,ham Yup. Wun believe wat? U really neva c e msg i sent shuhui?,ham Watch lor. I saw a few swatch one i thk quite ok. Ard 116 but i need 2nd opinion leh...,ham Then she dun believe wat?,ham Ron say fri leh. N he said ding tai feng cant make reservations. But he said wait lor.,ham Y she dun believe leh? I tot i told her it's true already. I thk she muz c us tog then she believe.,ham Dunno dat's wat he told me. Ok lor...,ham Orh i tot u say she now still dun believe.,ham Cos i was out shopping wif darren jus now n i called him 2 ask wat present he wan lor. Then he started guessing who i was wif n he finally guessed darren lor.,ham Yup i thk cine is better cos no need 2 go down 2 plaza mah.,ham Watching tv lor. Nice one then i like lor.,ham Then i buy.,ham U buy newspapers already?,ham No objection. My bf not coming.,ham Anything lor if they all go then i go lor...,ham U coming back 4 dinner rite? Dad ask me so i re confirm wif u...,ham Enjoy urself tmr...,ham Wat r u doing?,ham Shuhui has bought ron's present it's a swatch watch...,ham Yeah it's jus rite...,ham Ha... U jus ate honey ar? So sweet...,ham Yar lor actually we quite fast... Cos da ge slow wat... Haha...,ham Watching tv lor... Y she so funny we bluff her 4 wat. Izzit because she thk it's impossible between us?,ham Okie...,ham Hey happy birthday...,ham Dunno leh cant remember mayb lor. So wat time r we meeting tmr?,ham U say leh... Of course nothing happen lar. Not say v romantic jus a bit only lor. I thk e nite scenery not so nice leh.,ham Nothing but we jus tot u would ask cos u ba gua... But we went mt faber yest... Yest jus went out already mah so today not going out... Jus call lor...,ham Of cos can lar i'm not so ba dao ok... 1 pm lor... Y u never ask where we go ah... I said u would ask on fri but he said u will ask today...,ham Then u ask darren go n pick u lor... But i oso sian tmr haf 2 meet lect...,ham Nothing lor... A bit bored too... Then y dun u go home early 2 sleep today...,ham U still havent got urself a jacket ah?,ham How come u got nothing to do?,ham Quite ok but a bit ex... U better go eat smth now else i'll feel guilty...,ham Here got ur favorite oyster... N got my favorite sashimi... Ok lar i dun say already... Wait ur stomach start rumbling...,ham Ok lar... Joking wif u oni...,ham Okie but i scared u say i fat... Then u dun wan me already...,ham Yup having my lunch buffet now.. U eat already?,ham U wake up already? Thanx 4 e tau sar piah it's quite nice.,ham Jus telling u dat i'll b leaving 4 shanghai on 21st instead so we'll haf more time 2 meet up cya...,ham Ok...,ham I'm home...,ham Okie,ham U sleeping now.. Or you going to take? Haha.. I got spys wat.. Me online checking n replying mails lor..,ham Then u better go sleep.. Dun disturb u liao.. U wake up then msg me lor..,ham What u mean u almost done? Done wif sleeping? But i tot u going to take a nap.. Yup i send her liao so i'm picking her up at ard 4 smth lor..,ham My house here e sky quite dark liao... If raining then got excuse not 2 run already rite... Hee...,ham Nope wif my sis lor... Aft bathing my dog then i can bathe... Looks like it's going 2 rain soon.,ham Wow so healthy. Old airport rd lor. Cant thk of anything else. But i'll b bathing my dog later.,ham U too...,ham Ok. C u then.,ham Yup song bro. No creative. Neva test quality. He said check review online.,ham Wewa is 130. Iriver 255. All 128 mb.,ham We left already we at orchard now.,ham I tot u outside cos darren say u come shopping. Of course we nice wat. We jus went sim lim look at mp3 player.,ham U in town alone?,ham Anytime...,ham Ok... Sweet dreams...,ham Jus came back fr lunch wif my sis only. U leh?,ham In e msg jus now. U said thanks for gift.,ham U're welcome... Caught u using broken english again...,ham Yup bathe liao...,ham I'm home.,ham Ok... U enjoy ur shows...,ham Dunno cos i was v late n when i reach they inside already... But we ate spageddies lor... It's e gals who r laughing at me lor...,ham Finish already... Yar they keep saying i mushy... I so embarrassed ok...,ham Nope but i'm going home now then go pump petrol lor... Like going 2 rain soon...,ham Hey they r not watching movie tonight so i'll prob b home early...,ham Dunno y u ask me.,ham I've reached home n i bathe liao... U can call me now...,ham Yup i'm still having coffee wif my frens... My fren drove she'll give me a lift...,ham Ok...,ham I like dis sweater fr mango but no more my size already so irritating.,ham Yup n her fren lor. I'm meeting my fren at 730.,ham Anyway i'm going shopping on my own now. Cos my sis not done yet. Dun disturb u liao.,ham I'm ok wif it cos i like 2 try new things. But i scared u dun like mah. Cos u said not too loud.,ham U dun say so early hor... U c already then say...,ham Got but got 2 colours lor. One colour is quite light n e other is darker lor. Actually i'm done she's styling my hair now.,ham It's ok i noe u're busy but i'm really too bored so i msg u. I oso dunno wat colour she choose 4 me one.,ham Hey i'm bored... So i'm thinking of u... So wat r u doing?,ham Ok lor. Msg me b4 u call.,ham But really quite funny lor wat... Then u shd haf run shorter distance wat...,ham I'm in town now so i'll jus take mrt down later.,ham What time is ur flight tmr?,ham Aiyo u so poor thing... Then u dun wan 2 eat? U bathe already?,ham U so lousy, run already come back then half dead... Hee...,ham Eh u remember how 2 spell his name... Yes i did. He v naughty make until i v wet.,ham Finish liao... U?,ham U can call now...,ham 10 min later k...,ham Wat makes u thk i'll fall down. But actually i thk i'm quite prone 2 falls. Lucky my dad at home i ask him come n fetch me already.,ham Aiyah e rain like quite big leh. If drizzling i can at least run home.,ham That's y u haf 2 keep me busy...,ham Yup next stop.,ham Yar else i'll thk of all sorts of funny things.,ham Aiyah then i wait lor. Then u entertain me. Hee...,ham Yup. Izzit still raining heavily cos i'm in e mrt i can't c outside.,ham Yup, no need. I'll jus wait 4 e rain 2 stop.,ham I'm not driving... Raining! Then i'll get caught at e mrt station lor.,ham I'm on my way home. Went to change batt 4 my watch then go shop a bit lor.,ham Then wat r u doing now? Busy wif work?,ham Yup but it's not giving me problems now so mayb i'll jus leave it...,ham Ok... But they said i've got wisdom teeth hidden inside n mayb need 2 remove.,ham Thanx a lot 4 ur help!,ham Ok.,ham Happy birthday... May u find ur prince charming soon n dun work too hard...,ham R u here yet? I'm wearing blue shirt n black pants.,ham Or u ask they all if next sat can a not. If all of them can make it then i'm ok lor.,ham Ok that's great thanx a lot.,ham I'm e person who's doing e sms survey...,ham Hi this is yijue, can i meet u at 11 tmr?,ham Hmm... Dunno leh, mayb a bag 4 goigng out dat is not too small. Or jus anything except perfume, smth dat i can keep.,ham Eh u send wrongly lar...,ham Ok,ham Aiyah sorry lor... I watch tv watch until i forgot 2 check my phone.,ham Eh sorry leh... I din c ur msg. Not sad already lar. Me watching tv now. U still in office?,ham My supervisor find 4 me one lor i thk his students. I havent ask her yet. Tell u aft i ask her.,ham I got to video tape pple type in message lor. U so free wan 2 help me? Hee... Cos i noe u wan 2 watch infernal affairs so ask u along. Asking shuhui oso.,ham Ok thanx...,ham U gd lor go shopping i got stuff to do. U wan 2 watch infernal affairs a not? Come lar...,ham U sick still can go shopping?,ham Horrible gal. Me in sch doing some stuff. How come u got mc?,ham Die... I accidentally deleted e msg i suppose 2 put in e sim archive. Haiz... I so sad...,ham Ok no prob. Take ur time.,ham His frens go then he in lor. Not alone wif my mum n sis lor.,ham Yar lor he wan 2 go c horse racing today mah, so eat earlier lor. I ate chicken rice. U?,ham U really pig leh sleep so much. My dad wake me up at 10 smth 2 eat lunch today.,ham Yup. Thk of u oso boring wat.,ham Nope watching tv at home... Not going out. V bored...,ham Wat r u doing?,ham Ok set let u noe e details later...,ham U free on sat rite? U wan 2 watch infernal affairs wif me n darren n mayb xy?,ham Same to u...,ham Okie,ham Ok then u tell me wat time u coming later lor.,ham I knew it... U slept v late yest? Wake up so late...,ham Wat's my dear doing? Sleeping ah?,ham Nite...,ham Ok. Me watching tv too.,ham At home also.,ham Ok. I only ask abt e movie. U wan ktv oso?,ham I called but no one pick up e phone. I ask both of them already they said ok.,ham Then ur physics get a-?,ham Aiyah ok wat as long as got improve can already wat...,ham I'm reaching home in 5 min.,ham Wat u doing there?,ham Ok... Help me ask if she's working tmr a not?,ham Ok... Then r we meeting later?,ham Shopping lor. Them raining mah hard 2 leave orchard.,ham Long beach lor. Expected... U having dinner now?,ham Havent stuck at orchard in my dad's car. Going 4 dinner now. U leh? So r they free tonight?,ham How? Izzit still raining?,ham Thank u!,ham Thanx a lot...,ham Although i told u dat i'm into baig face watches now but i really like e watch u gave cos it's fr u. Thanx 4 everything dat u've done today, i'm touched...,ham East coast,ham Left dessert. U wan me 2 go suntec look 4 u?,ham U can call me now...,ham I jus reached home. I go bathe first. But my sis using net tell u when she finishes k...,ham Mum not going robinson already.,ham Nvm it's ok...,ham If u sending her home first it's ok lor. I'm not ready yet.,ham Esplanade lor. Where else...,ham I'm home. Ard wat time will u reach?,ham Jus ans me lar. U'll noe later.,ham It's ok i wun b angry. Msg u aft i come home tonight.,ham So u wan 2 come for our dinner tonight a not?,ham Hi di is yijue we're meeting at 7 pm at esaplanade tonight.,ham R we still meeting 4 dinner tonight?,ham Piggy, r u awake? I bet u're still sleeping. I'm going 4 lunch now...,ham Is xy in ur car when u picking me up?,ham We not leaving yet. Ok lor then we go elsewhere n eat. U thk...,ham Taka lor. Wat time u wan 2 come n look 4 us?,ham Xy trying smth now. U eat already? We havent...,ham Then u go back urself lor...,ham Thanx but my birthday is over already.,ham Yup i thk so until e shop closes lor.,ham Hi i won't b ard 4 christmas. But do enjoy n merry x'mas.,ham We not watching movie already. Xy wants 2 shop so i'm shopping w her now.,ham I'm going 2 orchard now laready me reaching soon. U reaching?,ham Ok lor. Anyway i thk we cant get tickets now cos like quite late already. U wan 2 go look 4 ur frens a not? Darren is wif them now...,ham Aiyah u did ok already lar. E nydc at wheellock?,ham I thk 530 lor. But dunno can get tickets a not. Wat u doing now?,ham I tot u reach liao. He said t-shirt.,ham U reach orchard already? U wan 2 go buy tickets first?,ham Aft i finish my lunch then i go str down lor. Ard 3 smth lor. U finish ur lunch already?,ham Havent planning to buy later. I check already lido only got 530 show in e afternoon. U finish work already?,ham Is xy going 4 e lunch?,ham I'm going 4 lunch now wif my family then aft dat i go str 2 orchard lor.,ham Play w computer? Aiyah i tok 2 u lor?,ham Ha... Both of us doing e same thing. But i got tv 2 watch. U can thk of where 2 go tonight or u already haf smth in mind...,ham Sitting ard nothing to do lor. U leh busy w work?,ham Ok lor.,ham My sis is catching e show in e afternoon so i'm not watching w her. So c u wan 2 watch today or tmr lor.,ham Then dun wear jeans lor...,ham Depends on where u going lor.,ham Ok i go change also...,ham Anything lor but toa payoh got place 2 walk meh...,ham Ok lor. I ned 2 go toa payoh 4 a while 2 return smth u wan 2 send me there or wat?,ham I anything lor.,ham Jus finish watching tv... U?,ham Happy new year to u too!,ham Ok,ham Havent.,ham Ok.,ham I reach home safe n sound liao...,ham I'll reach in ard 20 mins ok...,ham Ok... Let u noe when i leave my house.,ham I finished my lunch already. U wake up already?,ham Ok lor.,ham I'm watching lotr w my sis dis aft. So u wan 2 meet me 4 dinner at nite a not?,ham As usual u can call me ard 10 smth.,ham U call me alter at 11 ok.,ham U wake up already? Wat u doing? U picking us up later rite? I'm taking sq825, reaching ard 7 smth 8 like dat. U can check e arrival time. C ya soon...,ham Thanx. Yup we coming back on sun. Finish dinner going back 2 hotel now. Time flies, we're tog 4 exactly a mth today. Hope we'll haf many more mths to come...,ham Sorry to trouble u again. Can buy 4d for my dad again? 1405, 1680, 1843. All 2 big 1 small, sat n sun. Thanx.,ham No need lar. Jus testing e phone card. Dunno network not gd i thk. Me waiting 4 my sis 2 finish bathing so i can bathe. Dun disturb u liao u cleaning ur room.,ham Yup ok thanx...,ham Do u noe wat time e place dat sells 4d closes?,ham Ok...,ham No my mum went 2 dentist.,ham No it's waiting in e car dat's bored wat. Cos wait outside got nothing 2 do. At home can do my stuff or watch tv wat.,ham U having lunch alone? I now so bored...,ham Waiting in e car 4 my mum lor. U leh? Reach home already?,ham Ok.,ham My trip was ok but quite tiring lor. Uni starts today but it's ok 4 me cos i'm not taking any modules but jus concentrating on my final yr project.,ham I place all ur points on e cultures module already.,ham U definitely need a module from e humanities dis sem izzit? U wan 2 take other modules 1st?,ham Yup,ham Dis is yijue. I jus saw ur mail. In case huiming havent sent u my num. Dis is my num.,ham I'm leaving my house now.,ham Ok i msg u b4 i leave my house.,ham Haf u eaten? Wat time u wan me 2 come?,ham Okie,ham Jus finish blowing my hair. U finish dinner already?,ham Daddy, shu shu is looking 4 u... U wan me 2 tell him u're not in singapore or wat?,ham Ok no prob...,ham At home watching tv lor.,ham Aiyar u so poor thing... I give u my support k... Jia you! I'll think of u...,ham Waiting 4 my tv show 2 start lor... U leh still busy doing ur report?,ham But i'm surprised she still can guess right lor...,ham How come she can get it? Should b quite diff to guess rite...,ham How come guoyang go n tell her? Then u told her?,ham Ok...,ham Ok... Ur typical reply...,ham If e timing can, then i go w u lor...,ham Ok no problem... Yup i'm going to sch at 4 if i rem correctly...,ham Watching tv lor...,ham Ok...,ham Ok lor then we go tog lor...,ham Yup. Anything lor, if u dun wan it's ok...,ham Jus finish bathing...,ham Ok lor... Or u wan me go look 4 u?,ham Yar lor... Keep raining non stop... Or u wan 2 go elsewhere?,ham Wat r u doing now?,ham Ok no prob... I'll come after lunch then...,ham Sorry i'm not free...,ham Anytime lor...,ham Ok then i'll let him noe later n ask him call u tmr...,ham Havent mus ask if u can 1st wat. Of meet 4 lunch den u n him meet can already lor. Or u wan 2 go ask da ge 1st then confirm w me asap?,ham Darren was saying dat if u meeting da ge den we dun meet 4 dinner. Cos later u leave xy will feel awkward. Den u meet him 4 lunch lor.,ham Call me when u're done...,ham I ask if u meeting da ge tmr nite...,ham Did u receive my msg?,ham R u meeting da ge at nite tmr?,ham Can come my room but cannot come my house cos my house still messy... Haha...,ham Yar lor wait 4 my mum 2 finish sch then have lunch lor... I whole morning stay at home clean my room now my room quite clean... Hee...,ham Jus finish my lunch on my way home lor... I tot u dun wan 2 stay in sch today...,ham Yup ok...,ham Ok then i come n pick u at engin?,ham Anything lor... U decide...,ham E admin building there? I might b slightly earlier... I'll call u when i'm reaching...,ham No need lar i go engin? Cos my sis at arts today...,ham Dunno, my dad said he coming home 2 bring us out 4 lunch. Yup i go w u lor. I call u when i reach school lor...,ham Ard 4 lor...,ham Oic cos me n my sis got no lunch today my dad went out... So dunno whether 2 eat in sch or wat...,ham Then u going ikea str aft dat?,ham Wat time u finish ur lect today?,ham Change again... It's e one next to escalator...,ham Shuhui say change 2 suntec steamboat? U noe where? Where r u now?,ham R u still working now?,ham 7 lor... Change 2 suntec... Wat time u coming?,ham Yar lor... How u noe? U used dat route too?,ham I've reached home finally...,ham U calling me right? Call my hand phone...,ham Aiyar dun disturb u liao... Thk u have lots 2 do aft ur cupboard come...,ham Huh so fast... Dat means u havent finished painting?,ham U still painting ur wall?,ham Ok... I din get ur msg...,ham Hey elaine, is today's meeting still on?,ham Hi elaine, is today's meeting confirmed?,ham Sorry i now then c ur msg... Yar lor so poor thing... But only 4 one night... Tmr u'll have a brand new room 2 sleep in...,ham "I;m reaching in another 2 stops.",ham Huh but i cant go 2 ur house empty handed right?,ham OK i'm waliking ard now... Do u wan me 2 buy anything go ur house?,ham Yup ok...,ham Ok... Take ur time n enjoy ur dinner...,ham So gd got free ice cream... I oso wan...,ham Same as u... Dun wan... Y u dun like me already ah... Wat u doing now? Still eating?,ham Horrible u eat macs eat until u forgot abt me already rite... U take so long 2 reply. I thk it's more toot than b4 so b prepared. Now wat shall i eat?,ham Die... Now i have e toot fringe again...,ham Horrible bf... I now v hungry...,ham URGENT! 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BT-national-rate,spam ================================================ FILE: logistic_regression/logreg_grad_ascent.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- import os from math import exp import numpy as np import matplotlib.pyplot as plt class LogisticRegressionClassifier(object): ''' 使用梯度上升算法Logistic回归分类器 ''' @staticmethod def sigmoid(x): ''' Sigmoid 阶跃函数 ''' return 1.0/(1 + np.exp(-x)) def gradient_ascent(self, dataset, labels, max_iter=10000): ''' 使用梯度上升优化Logistic回归模型参数 :param dataset: 数据特征矩阵 :type dataset: MxN numpy matrix :param labels: 数据集对应的类型向量 :type labels: Nx1 numpy matrix ''' dataset = np.matrix(dataset) vlabels = np.matrix(labels).reshape(-1, 1) m, n = dataset.shape w = np.ones((n, 1)) alpha = 0.001 ws = [] for i in range(max_iter): error = vlabels - self.sigmoid(dataset*w) w += alpha*dataset.T*error ws.append(w.reshape(1, -1).tolist()[0]) self.w = w return w, np.array(ws) def classify(self, data, w=None): ''' 对未知数据进行预测 ''' if w is None: w = self.w data = np.matrix(data) prob = self.sigmoid((data*w).tolist()[0][0]) return round(prob) def load_data(filename): dataset, labels = [], [] with open(filename, 'r') as f: for line in f: splited_line = [float(i) for i in line.strip().split('\t')] data, label = [1.0] + splited_line[: -1], splited_line[-1] dataset.append(data) labels.append(label) dataset = np.array(dataset) labels = np.array(labels) return dataset, labels def snapshot(w, dataset, labels, pic_name): ''' 绘制类型分割线图 ''' if not os.path.exists('./snapshots'): os.mkdir('./snapshots') fig = plt.figure() ax = fig.add_subplot(111) pts = {} for data, label in zip(dataset.tolist(), labels.tolist()): pts.setdefault(label, [data]).append(data) for label, data in pts.items(): data = np.array(data) plt.scatter(data[:, 1], data[:, 2], label=label, alpha=0.5) # 分割线绘制 def get_y(x, w): w0, w1, w2 = w return (-w0 - w1*x)/w2 x = [-4.0, 3.0] y = [get_y(i, w) for i in x] plt.plot(x, y, linewidth=2, color='#FB4A42') pic_name = './snapshots/{}'.format(pic_name) fig.savefig(pic_name) plt.close(fig) if '__main__' == __name__: clf = LogisticRegressionClassifier() dataset, labels = load_data('testSet.txt') w, ws = clf.gradient_ascent(dataset, labels, max_iter=50000) m, n = ws.shape # 绘制分割线 for i in range(300): if i % (30) == 0: print('{}.png saved'.format(i)) snapshot(ws[i].tolist(), dataset, labels, '{}.png'.format(i)) fig = plt.figure() for i in range(n): label = 'w{}'.format(i) ax = fig.add_subplot(n, 1, i+1) ax.plot(ws[:, i], label=label) ax.legend() fig.savefig('w_traj.png') ================================================ FILE: logistic_regression/logreg_stoch_grad_ascent.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- import random import numpy as np import matplotlib.pyplot as plt from logreg_grad_ascent import LogisticRegressionClassifier as BaseClassifer from logreg_grad_ascent import load_data, snapshot class LogisticRegressionClassifier(BaseClassifer): def stoch_gradient_ascent(self, dataset, labels, max_iter=150): ''' 使用随机梯度上升算法优化Logistic回归模型参数 ''' dataset = np.matrix(dataset) m, n = dataset.shape w = np.matrix(np.ones((n, 1))) ws = [] for i in range(max_iter): data_indices = list(range(m)) random.shuffle(data_indices) for j, idx in enumerate(data_indices): data, label = dataset[idx], labels[idx] error = label - self.sigmoid((data*w).tolist()[0][0]) alpha = 4/(1 + j + i) + 0.01 w += alpha*data.T*error ws.append(w.T.tolist()[0]) self.w = w return w, np.array(ws) if '__main__' == __name__: clf = LogisticRegressionClassifier() dataset, labels = load_data('testSet.txt') w, ws = clf.stoch_gradient_ascent(dataset, labels, max_iter=500) m, n = ws.shape # 绘制分割线 for i, w in enumerate(ws): if i % (m//10) == 0: print('{}.png saved'.format(i)) snapshot(w.tolist(), dataset, labels, '{}.png'.format(i)) fig = plt.figure() for i in range(n): label = 'w{}'.format(i) ax = fig.add_subplot(n, 1, i+1) ax.plot(ws[:, i], label=label) ax.legend() fig.savefig('stoch_grad_ascent_params.png') ================================================ FILE: logistic_regression/sms.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- ''' 通过垃圾短信数据训练Logistic回归模型，并进行留存交叉验证 ''' import re import random import numpy as np import matplotlib.pyplot as plt from logreg_stoch_grad_ascent import LogisticRegressionClassifier ENCODING = 'ISO-8859-1' TRAIN_PERCENTAGE = 0.9 def get_doc_vector(words, vocabulary): ''' 根据词汇表将文档中的词条转换成文档向量 :param words: 文档中的词条列表 :type words: list of str :param vocabulary: 总的词汇列表 :type vocabulary: list of str :return doc_vect: 用于贝叶斯分析的文档向量 :type doc_vect: list of int ''' doc_vect = [0]*len(vocabulary) for word in words: if word in vocabulary: idx = vocabulary.index(word) doc_vect[idx] += 1 return doc_vect def parse_line(line): ''' 解析数据集中的每一行返回词条向量和短信类型. ''' cls = line.split(',')[-1].strip() content = ','.join(line.split(',')[: -1]) word_vect = [word.lower() for word in re.split(r'\W+', content) if word] return word_vect, cls def parse_file(filename): ''' 解析文件中的数据 ''' vocabulary, word_vects, classes = [], [], [] with open(filename, 'r', encoding=ENCODING) as f: for line in f: if line: word_vect, cls = parse_line(line) vocabulary.extend(word_vect) word_vects.append(word_vect) classes.append(cls) vocabulary = list(set(vocabulary)) return vocabulary, word_vects, classes if '__main__' == __name__: clf = LogisticRegressionClassifier() vocabulary, word_vects, classes = parse_file('english_big.txt') # 训练数据 & 测试数据 ntest = int(len(classes)*(1-TRAIN_PERCENTAGE)) test_word_vects = [] test_classes = [] for i in range(ntest): idx = random.randint(0, len(word_vects)-1) test_word_vects.append(word_vects.pop(idx)) test_classes.append(classes.pop(idx)) train_word_vects = word_vects train_classes = classes # 将类型标签改为0\1 f = lambda x: 1 if x == 'spam' else 0 train_classes = list(map(f, train_classes)) test_classes = list(map(f, test_classes)) train_dataset = [get_doc_vector(words, vocabulary) for words in train_word_vects] # 训练LR模型 clf.stoch_gradient_ascent(train_dataset, train_classes) # 测试模型 error = 0 for test_word_vect, test_cls in zip(test_word_vects, test_classes): test_data = get_doc_vector(test_word_vect, vocabulary) pred_cls = clf.classify(test_data) if test_cls != pred_cls: print('Predict: {} -- Actual: {}'.format(pred_cls, test_cls)) error += 1 print('Error Rate: {}'.format(error/len(test_classes))) ================================================ FILE: logistic_regression/testSet.txt ================================================ -0.017612 14.053064 0 -1.395634 4.662541 1 -0.752157 6.538620 0 -1.322371 7.152853 0 0.423363 11.054677 0 0.406704 7.067335 1 0.667394 12.741452 0 -2.460150 6.866805 1 0.569411 9.548755 0 -0.026632 10.427743 0 0.850433 6.920334 1 1.347183 13.175500 0 1.176813 3.167020 1 -1.781871 9.097953 0 -0.566606 5.749003 1 0.931635 1.589505 1 -0.024205 6.151823 1 -0.036453 2.690988 1 -0.196949 0.444165 1 1.014459 5.754399 1 1.985298 3.230619 1 -1.693453 -0.557540 1 -0.576525 11.778922 0 -0.346811 -1.678730 1 -2.124484 2.672471 1 1.217916 9.597015 0 -0.733928 9.098687 0 -3.642001 -1.618087 1 0.315985 3.523953 1 1.416614 9.619232 0 -0.386323 3.989286 1 0.556921 8.294984 1 1.224863 11.587360 0 -1.347803 -2.406051 1 1.196604 4.951851 1 0.275221 9.543647 0 0.470575 9.332488 0 -1.889567 9.542662 0 -1.527893 12.150579 0 -1.185247 11.309318 0 -0.445678 3.297303 1 1.042222 6.105155 1 -0.618787 10.320986 0 1.152083 0.548467 1 0.828534 2.676045 1 -1.237728 10.549033 0 -0.683565 -2.166125 1 0.229456 5.921938 1 -0.959885 11.555336 0 0.492911 10.993324 0 0.184992 8.721488 0 -0.355715 10.325976 0 -0.397822 8.058397 0 0.824839 13.730343 0 1.507278 5.027866 1 0.099671 6.835839 1 -0.344008 10.717485 0 1.785928 7.718645 1 -0.918801 11.560217 0 -0.364009 4.747300 1 -0.841722 4.119083 1 0.490426 1.960539 1 -0.007194 9.075792 0 0.356107 12.447863 0 0.342578 12.281162 0 -0.810823 -1.466018 1 2.530777 6.476801 1 1.296683 11.607559 0 0.475487 12.040035 0 -0.783277 11.009725 0 0.074798 11.023650 0 -1.337472 0.468339 1 -0.102781 13.763651 0 -0.147324 2.874846 1 0.518389 9.887035 0 1.015399 7.571882 0 -1.658086 -0.027255 1 1.319944 2.171228 1 2.056216 5.019981 1 -0.851633 4.375691 1 -1.510047 6.061992 0 -1.076637 -3.181888 1 1.821096 10.283990 0 3.010150 8.401766 1 -1.099458 1.688274 1 -0.834872 -1.733869 1 -0.846637 3.849075 1 1.400102 12.628781 0 1.752842 5.468166 1 0.078557 0.059736 1 0.089392 -0.715300 1 1.825662 12.693808 0 0.197445 9.744638 0 0.126117 0.922311 1 -0.679797 1.220530 1 0.677983 2.556666 1 0.761349 10.693862 0 -2.168791 0.143632 1 1.388610 9.341997 0 0.317029 14.739025 0 ================================================ FILE: naive_bayes/bayes.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- from collections import defaultdict import numpy as np class NaiveBayesClassifier(object): ''' 朴素贝叶斯分类器 ''' def train(self, dataset, classes): ''' 训练朴素贝叶斯模型 :param dataset: 所有的文档数据向量 :type dataset: MxN matrix containing all doc vectors. :param classes: 所有文档的类型 :type classes: 1xN list :return cond_probs: 训练得到的条件概率矩阵 :type cond_probs: MxK matrix :return cls_probs: 各种类型的概率 :type cls_probs: 1xK list ''' # 按照不同类型记性分类 sub_datasets = defaultdict(lambda: []) cls_cnt = defaultdict(lambda: 0) for doc_vect, cls in zip(dataset, classes): sub_datasets[cls].append(doc_vect) cls_cnt[cls] += 1 # 计算类型概率 cls_probs = {k: v/len(classes) for k, v in cls_cnt.items()} # 计算不同类型的条件概率 cond_probs = {} dataset = np.array(dataset) for cls, sub_dataset in sub_datasets.items(): sub_dataset = np.array(sub_dataset) # Improve the classifier. cond_prob_vect = np.log((np.sum(sub_dataset, axis=0) + 1)/(np.sum(dataset) + 2)) cond_probs[cls] = cond_prob_vect return cond_probs, cls_probs def classify(self, doc_vect, cond_probs, cls_probs): ''' 使用朴素贝叶斯将doc_vect进行分类. 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Delivery within 28days. T Cs Box177. M221BP. 2yr warranty. 150ppm. 16 . p p3.99,spam Please call our customer service representative on FREEPHONE 0808 145 4742 between 9am-11pm as you have WON a guaranteed 1000 cash or 5000 prize!,spam You are a winner U have been specially selected 2 receive 1000 cash or a 4* holiday (flights inc) speak to a live operator 2 claim 0871277810810,spam "Hey sorry I didntgive ya a a bellearlier hunny,just been in bedbut mite go 2 thepub l8tr if uwana mt up?loads a luv Jenxxx.",ham "Are you comingdown later?",ham "HEY HEY WERETHE MONKEESPEOPLE SAY WE MONKEYAROUND! HOWDY GORGEOUS, HOWU DOIN? FOUNDURSELF A JOBYET SAUSAGE?LOVE JEN XXX",ham "CHA QUITEAMUZING THATSCOOL BABE,PROBPOP IN & CU SATTHEN HUNNY 4BREKKIE! LOVE JEN XXX. PSXTRA LRG PORTIONS 4 ME PLEASE ",ham "HEY BABE! FAR 2 SPUN-OUT 2 SPK AT DA MO... DEAD 2 DA WRLD. BEEN SLEEPING ON DA SOFA ALL DAY, HAD A COOL NYTHO, TX 4 FONIN HON, CALL 2MWEN IM BK FRMCLOUD 9! J X",ham "CHEERS U TEX MECAUSE U WEREBORED! YEAH OKDEN HUNNY R UIN WK SAT?SOUNDS LIKEYOUR HAVIN GR8FUN J! KEEP UPDAT COUNTINLOTS OF LOVEME XXXXX.",ham "EY! CALM DOWNON THEACUSATIONS.. ITXT U COS IWANA KNOW WOTU R DOIN AT THEW/END... HAVENTCN U IN AGES..RING ME IF UR UP4 NETHING SAT.LOVE J XXX.",ham "YEH I AM DEF UP4 SOMETHING SAT,JUST GOT PAYED2DAY & I HAVBEEN GIVEN A50 PAY RISE 4MY WORK & HAVEBEEN MADE PRESCHOOLCO-ORDINATOR 2I AM FEELINGOOD LUV",ham "Hi its Kate it was lovely to see you tonight and ill phone you tomorrow. I got to sing and a guy gave me his card! xxx",ham "Thinking of u ;) x",ham Me too! Have a lovely night xxx,ham Hey hun-onbus goin 2 meet him. He wants 2go out 4a meal but I donyt feel like it cuz have 2 get last bus home!But hes sweet latelyxxx,ham Hi mate its RV did u hav a nice hol just a message 3 say hello coz havent sent u 1 in ages started driving so stay off roads!RVx,ham IM FINE BABES AINT BEEN UP 2 MUCH THO! SAW SCARY MOVIE YEST ITS QUITE FUNNY! WANT 2MRW AFTERNOON? AT TOWN OR MALL OR SUMTHIN?xx,ham I notice you like looking in the shit mirror youre turning into a right freak,ham IM LATE TELLMISS IM ON MY WAY,ham Been up to ne thing interesting. Did you have a good birthday? When are u wrking nxt? I started uni today.,ham IM GONNAMISSU SO MUCH!!I WOULD SAY IL SEND U A POSTCARD BUTTHERES ABOUTAS MUCH CHANCE OF MEREMEMBERIN ASTHERE IS OFSI NOT BREAKIN HIS CONTRACT!! LUV Yaxx,ham Thanx 4 the time weve spent 2geva, its bin mint! Ur my Baby and all I want is u!xxxx,ham You stayin out of trouble stranger!!saw Dave the other day hes sorted now!still with me bloke when u gona get a girl MR!ur mum still Thinks we will get 2GETHA! ,ham THANX 4 PUTTIN DA FONE DOWN ON ME!!,ham I know dat feelin had it with Pete! Wuld get with em , nuther place nuther time mayb?,ham U 2.,ham Thanx u darlin!im cool thanx. A few bday drinks 2 nite. 2morrow off! Take care c u soon.xxx,ham HIYA COMIN 2 BRISTOL 1 ST WEEK IN APRIL. LES GOT OFF + RUDI ON NEW YRS EVE BUT I WAS SNORING.THEY WERE DRUNK! U BAK AT COLLEGE YET? MY WORK SENDS INK 2 BATH.,ham Sez, hows u & de arab boy? Hope u r all good give my love 2 evry1 love ya eshxxxxxxxxxxx,ham THING R GOOD THANX GOT EXAMS IN MARCH IVE DONE NO REVISION? IS FRAN STILL WITH BOYF? IVE GOTTA INTERVIW 4 EXETER BIT WORRIED!x,ham I love u 2 babe! R u sure everything is alrite. Is he being an idiot? Txt bak girlie,ham I luv u soo much u dont understand how special u r 2 me ring u 2morrow luv u xxx,ham NOT MUCH NO FIGHTS. IT WAS A GOOD NITE!!,ham JADE ITS PAUL. Y DIDNT U TXT ME? DO U REMEMBER ME FROM BARMED? I WANT 2 TALK 2 U! TXT ME,ham LOOK AT AMY URE A BEAUTIFUL, INTELLIGENT WOMAN AND I LIKE U A LOT. I KNOW U DONT LIKE ME LIKE THAT SO DONT WORRY.,ham "Getting tickets 4 walsall tue 6 th march. My mate is getting me them on sat. ill pay my treat. Want 2 go. Txt bak .Terry",ham Alrite jod hows the revision goin? Keris bin doin a smidgin. N e way u wanna cum over after college?xx,ham Wot about on wed nite I am 3 then but only til 9!,ham Pete,is this your phone still? Its Jenny from college and Leanne.what are you up to now?:),ham Sorry about that this is my mates phone and i didnt write it love Kate,ham Have you finished work yet? :),ham HMM yeah if your not too grooved out! And im looking forward to my pound special :),ham Thank god they are in bed!,ham "Hey! do u fancy meetin me at 4 at cha hav a lil beverage on me. if not txt or ring me and we can meet up l8r. quite tired got in at 3 v.pist ;) love Pete x x x",ham IM REALY SOZ IMAT MY MUMS 2NITE WHAT ABOUT 2MORO ,ham Hi darlin im on helens fone im gonna b up the princes 2 nite please come up tb love Kate,ham "ER, ENJOYIN INDIANS AT THE MO..yeP. SaLL gOoD HehE ;> hows bout u shexy? Pete Xx",ham No it was cancelled yeah baby! Well that sounds important so i understand my darlin give me a ring later on this fone love Kate x,ham HI DARLIN ITS KATE ARE U UP FOR DOIN SOMETHIN TONIGHT? IM GOING TO A PUB CALLED THE SWAN OR SOMETHING WITH MY PARENTS FOR ONE DRINK SO PHONE ME IF U CAN,ham Im in inperialmusic listening2the weirdest track ever byleafcutter john-sounds like insects being molested&someone plumbing,remixed by evil men on acid!,ham HI DARLIN HOW WAS WORK DID U GET INTO TROUBLE? IJUST TALKED TO YOUR MUM ALL MORNING! I HAD A REALLY GOOD TIME LAST NIGHT IM GOIN OUT SOON BUT CALL ME IF U CAN,ham HI DARLIN IM MISSIN U HOPE YOU ARE HAVING A GOOD TIME. WHEN ARE U BACK AND WHAT TIME IF U CAN GIVE ME A CALL AT HOME. JESS XX,ham HI BABE U R MOST LIKELY TO BE IN BED BUT IM SO SORRY ABOUT TONIGHT! I REALLY WANNA SEE U TOMORROW SO CALL ME AT 9. LOVE ME XXX,ham Hi im having the most relaxing time ever! we have to get up at 7am every day! was the party good the other night? I get home tomorrow at 5ish.,ham "OH FUCK. JUSWOKE UP IN A BED ON A BOATIN THE DOCKS. SLEPT WID 25 YEAR OLD. SPINOUT! GIV U DA GOSSIP L8R. XXX",ham "BOO BABE! U ENJOYIN YOURJOB? U SEEMED 2 B GETTIN ON WELL HUNNY!HOPE URE OK?TAKE CARE & ILLSPEAK 2U SOONLOTS OF LOVEME XXXX.",ham "HELLO U.CALL WEN U FINISH WRK.I FANCY MEETIN UP WIV U ALL TONITE AS I NEED A BREAK FROM DABOOKS. DID 4 HRS LAST NITE+2 TODAY OF WRK!",ham "ALRITE HUNNY!WOT U UP 2 2NITE? DIDNT END UP GOIN DOWN TOWN JUS DA PUB INSTEAD! JUS CHILLIN AT DA MO IN ME BEDROOM!LOVE JEN XXX.",ham U WILL SWITCH YOUR FONE ON DAMMIT!!,ham "Hi darlin i cantdo anythingtomorrow as myparents aretaking me outfor a meal. when are u free? Katexxx",ham "NOT ENUFCREDEIT TOCALL.SHALL ILEAVE UNI AT 6 +GET A BUS TO YOR HOUSE?",ham "Pete can you please ring meive hardly gotany credit",ham "CAN I PLEASE COME UP NOW IMIN TOWN.DONTMATTER IF URGOIN OUTL8R,JUST REALLYNEED 2DOCD.PLEASE DONTPLEASE DONTIGNORE MYCALLS,U NO THECD ISV.IMPORTANT TOME 4 2MORO",ham Ill be at yours in about 3 mins but look out for me,ham "Hi darlin did youPhone me? Im atHome if youwanna chat.",ham WOT U WANNA DO THEN MISSY?,ham Sary just need Tim in the bollox &it hurt him a lot so he tol me!,ham HOW ARE U? I HAVE MISSED U! I HAVENT BEEN UP 2 MUCH A BIT BORED WITH THE HOLIDAY WANT 2 GO BAK 2 COLLEGE! SAD ISNT IT?xx,ham How u doin baby girl ?? hope u are okay every time I call ure phone is off! I miss u get in touch,ham ILL B DOWN SOON,ham Hello, hello, hi lou sorry it took so long 2 reply- I left mobile at friends in Lancaster, just got it bak Neway im sorry I couldnt make ur bday 2 hun!,ham OH YEAH,AND HAV A GREAT TIME IN NEWQUAY-SEND ME A POSTCARD !1 LOOK AFTER ALL THE GIRLS WHILE IM GONE(U KNOW THE 1IM TALKIN BOUT!)xx,ham CHEERS LOU! YEAH WAS A GOODNITE SHAME U NEVA CAME! C YA GAILxx,ham Wot u up 2? Thout u were gonna call me!! Txt bak luv K,ham 2 celebrate my bday, y else?,ham Fine if thats the way u feel. Thats the way its gota b,ham MY NEW YEARS EVE WAS OK. I WENT TO A PARTY WITH MY BOYFRIEND. WHO IS THIS SI THEN HEY,ham I have no money 4 steve mate! !,ham IM GONNA MISS U SO MUCH,ham Jane babes not goin 2 wrk, feel ill after lst nite. Foned in already cover 4 me chuck.:-),ham Hiya do u like the hlday pics looked horrible in them so took mo out! Hows the camp Amrca thing? Speak soon Serena:),ham HELLOGORGEOUS, HOWS U? MY FONE WAS ON CHARGE LST NITW WEN U TEXD ME. HOPEU AD A NICE WKEND AS IM SURE U DID LOOKIN 4WARD 2 C-IN U 2MRW LUV JAZ,ham I REALLY NEED 2 KISS U I MISS U MY BABY FROM UR BABY 4EVA,ham ALRITE SAM ITS NIC JUST CHECKIN THAT THIS IS UR NUMBER-SO IS IT?T.B*,ham PISS IS TALKING IS SOMEONE THAT REALISE U THAT POINT THIS AT IS IT.(NOW READ IT BACKWARDS),ham U GOIN OUT 2NITE?,ham Okay name ur price as long as its legal! Wen can I pick them up? Y u ave x ams xx,ham Where wuld I be without my baby? The thought alone mite break me and I dont wanna go crazy but everyboy needs his lady xxxxxxxx,ham Hey no I ad a crap nite was borin without ya 2 boggy with me u boring biatch! Thanx but u wait til nxt time il ave ya ,ham "Hello-/@drivby-:0quit edrunk sorry iff pthis makes no senrd-dnot no how ^ dancce 2 drum n basq!ihave fun 2nhite x ros xxxxxxx",ham Hi its Kate how is your evening? I hope i can see you tomorrow for a bit but i have to bloody babyjontet! Txt back if u can. :) xxx,ham WHO ARE YOU SEEING?,ham Ok im not sure what time i finish tomorrow but i wanna spend the evening with you cos that would be vewy vewy lubly! Love me xxx,ham Oh thats late! Well have a good night and i will give u a call tomorrow. Iam now going to go to sleep night night,ham Cheers for the message Zogtorius. Ive been staring at my phone for an age deciding whether to text or not.,ham I cant wait for cornwall. Hope tonight isnt too bad as well but its rock night shite. Anyway im going for a kip now have a good night. Speak to you soon.,ham Hi babe its me thanks for coming even though it didnt go that well!i just wanted my bed! Hope to see you soon love and kisses xxx,ham Just gettin a bit arty with my collages at the mo, well tryin 2 ne way! Got a roast in a min lovely i shall enjoy that!,ham MY NO. IN LUTON 0125698789 RING ME IF UR AROUND! H*,ham HEY THERE BABE, HOW U DOIN? WOT U UP 2 2NITE LOVE ANNIE X.,ham "Si.como no?!listened2the plaid album-quite gd&the new air1 which is hilarious-also boughtbraindancea comp.ofstuff on aphexs ;abel,u hav2hear it!c u sn xxxx",ham HI DARLIN IVE JUST GOT BACK AND I HAD A REALLY NICE NIGHT AND THANKS SO MUCH FOR THE LIFT SEE U TOMORROW XXX,ham I hope you arnt pissed off but id would really like to see you tomorrow. Love me xxxxxxxxxxxxxX,ham SO IS TH GOWER MATE WHICH IS WHERE I AM!?! HOW R U MAN? ALL IS GOOD IN WALES ILL B BACK MORROW. C U THIS WK? WHO WAS THE MSG 4? RANDOM!,ham Hope youre not having too much fun without me!! see u tomorrow love jess x,ham "AH POOR BABY!HOPE URFEELING BETTERSN LUV! PROBTHAT OVERDOSE OF WORK HEY GO CAREFUL SPK 2 U SN LOTS OF LOVEJEN XXX.",ham "Hey j! r u feeling any better, hopeSo hunny. i amnow feelin ill & ithink i may have tonsolitusaswell! damn iam layin in bedreal bored. lotsof luv me xxxx",ham WOT U UP 2 J?,ham "HI BABE UAWAKE?FEELLIKW SHIT.JUSTFOUND OUT VIA ALETTER THATMUM GOTMARRIED 4thNOV.BEHIND OURBACKS FUCKINNICE!SELFISH,DEVIOUSBITCH.ANYWAY,IL CALL U",ham "SHIT BABE.. THASA BIT MESSED UP.YEH, SHE SHUDVETOLD U. DID URGRAN KNOW?NEWAY, ILLSPEAK 2 U2MORO WEN IM NOT ASLEEP...",ham "HEY KATE, HOPE UR OK... WILL GIVE U A BUZ WEDLUNCH. GO OUTSOMEWHERE 4 ADRINK IN TOWN..CUD GO 2WATERSHD 4 A BIT? PPL FROMWRK WILL BTHERE. LOVE PETEXXX.",ham "Petey boy whereare you me and all your friendsare in theKingshead come down if you canlove Nic",ham "Aww you must be nearly dead!Well Jez isComing over toDo some workAnd that whillTake forever!",ham "alright babe, justthought id sayhey! how u doin?nearly the endof me wk offdam nevamind!We will have 2Hook up sn if uwant m8? loveJen x.",ham Right it wasnt you who phoned it was someone with a number like yours!,ham Y?WHERE U AT DOGBREATH? ITS JUST SOUNDING LIKE JAN C THATS AL!!!!!!!!!,ham R U &SAM P IN EACHOTHER. IF WE MEET WE CAN GO 2 MY HOUSE,ham Babes I think I got ur brolly I left it in English wil bring it in 2mrw 4 u luv Franxx,ham WHAT TIME U WRKIN?,ham HEY GIRL. HOW R U? HOPE U R WELL ME AN DEL R BAK! AGAIN LONG TIME NO C! GIVE ME A CALL SUM TIME FROM LUCYxx,ham I HAVE A DATE ON SUNDAY WITH WILL!!,ham Hi the way I was with u 2day, is the normal way&this is the real me. UR unique&I hope I know u 4 the rest of mylife. Hope u find wot was lost.,ham HI HUN! IM NOT COMIN 2NITE-TELL EVERY1 IM SORRY 4 ME, HOPE U AVA GOODTIME!OLI RANG MELNITE IFINK IT MITE B SORTED,BUT IL EXPLAIN EVERYTHIN ON MON.L8RS.x,ham Thanx 4 2day! U r a goodmate I THINK UR RITE SARY! ASUSUAL!1 U CHEERED ME UP! LOVE U FRANYxxxxx,ham Hey darlin.. i can pick u up at college if u tell me wen & where 2 mt.. love Pete xx,ham HAPPY NEW YEAR MY NO.1 MAN,ham ALRITE,ham Wot u up 2 u weirdo?,ham U havent lost me ill always b here 4u.i didnt intend 2 hurt u but I never knew how u felt about me when Iwas+marine&thats what itried2tell urmom.i careabout u,ham Wot is u up 2 then bitch?,ham PLEASSSSSSSEEEEEE TEL ME V AVENT DONE SPORTSx,ham No got new job at bar in airport on satsgettin 4.47per hour but means no lie in! keep in touch,ham Im just wondering what your doing right now?,ham I love u 2 my little pocy bell I am sorry but I love u,ham HELLO PEACH! MY CAKE TASTS LUSH!,ham THANX4 TODAY CER IT WAS NICE 2 CATCH UP BUT WE AVE 2 FIND MORE TIME MORE OFTEN OH WELL TAKE CARE C U SOON.C,ham PICK UR FONE UP NOW U DUMB?,ham There the size of elephant tablets & u shove um up ur ass!!,ham I walked an hour 2 c u! doesnt that show I care y wont u believe im serious?,ham Try neva mate!!,ham Im cool ta luv but v.tired 2 cause i have been doin loads of planning all wk, we have got our social services inspection at the nursery! Take care & spk sn x.,ham Sounds better than my evening im just doing my costume. Im not sure what time i finish tomorrow but i will txt you at the end.,ham Oh ok i didnt know what you meant. Yep i am baby jontin,ham Awww dat is sweet! We can think of something to do he he! Have a nice time tonight ill probably txt u later cos im lonely :( xxx.,ham cud u tell ppl im gona b a bit l8 cos 2 buses hav gon past cos they were full & im still waitin 4 1. Pete x,ham SORRY IM STIL FUCKED AFTER LAST NITE WENT TOBED AT 430 GOT UP 4 WORK AT 630,ham How are you babes. Hope your doing ok. I had a shit nights sleep. I fell asleep at 5.Im knackered and im dreading work tonight. What are thou upto tonight. X,ham Ok darlin i supose it was ok i just worry too much.i have to do some film stuff my mate and then have to babysit again! But you can call me there.xx,ham Mmm thats better now i got a roast down me! id b better if i had a few drinks down me 2! Good indian?,ham YO YO YO BYATCH WHASSUP?,ham Hi Jon, Pete here, Ive bin 2 Spain recently & hav sum dinero left, Bill said u or ur rents mayb interested in it, I hav 12,000pes, so around 48, tb, James.,ham ELLO BABE U OK?,ham Hi juan. Im coming home on fri hey. Of course i expect a welcome party and lots of presents. Ill phone u when i get back. Loads of love nicky x x x x x x x x x,ham hi my darlin im on my way to London and we have just been smashed into by another driver! and have a big dent! im really missing u what have u been up to? xxx,ham hi baby im cruisin with my girl friend what r u up 2? give me a call in and hour at home if thats alright or fone me on this fone now love jenny xxx,ham "GRAN ONLYFOUND OUT AFEW DAYS AGO.CUSOON HONI",ham "CHEERS FOR CALLIN BABE.SOZI CULDNT TALKBUT I WANNATELL U DETAILS LATER WENWECAN CHAT PROPERLY X",ham "HEY DAS COOL... IKNOW ALL 2 WELLDA PERIL OF STUDENTFINANCIAL CRISIS!SPK 2 U L8R.",ham WOT STUDENT DISCOUNT CAN U GET ON BOOKS?,ham "NONE!NOWHERE IKNO DOESDISCOUNT!SHITINNIT",ham "What are youdoing later? Sar xxx",ham "Im on gloucesterroad what are uup to later?",ham "Im at arestaurant eating squid! i will be out about 10:30 wanna dosomething or is that to late?",ham "Hi missed your Call and my mumHas beendropping red wine all over theplace! what is your adress?",ham Al he does is moan at me if n e thin goes wrong its my fault&al de arguments r my fault&fed up of him of himso y bother? Hav 2go, thanx.xx,ham Now u sound like manky scouse boy steve,like! I is travelling on da bus home.wot has u inmind 4 recreation dis eve?,ham DO U WANT 2 MEET UP 2MORRO,ham HEY MATE! HOWS U HONEY?DID U AVE GOOD HOLIDAY? GIMMI DE GOSS!x,ham Ding me on ya break fassyole! Blacko from londn,ham Ill call u 2mrw at ninish, with my address that icky American freek wont stop callin me 2 bad Jen k eh?,ham Hi neva worry bout da truth coz the truth will lead me 2 ur heart. Its the least a unique person like u deserve. Sleep tight or morning,ham U R THE MOST BEAUTIFUL GIRL IVE EVER SEEN. U R MY BABY COME AND C ME IN THE COMMON ROOM,ham WHEN THE FIRST STRIKE IS A RED ONE. THE BIRD + ANTELOPE BEGIN TOPLAY IN THE FIELDOF SELFINDEPENDENCE BELIEVE THIS + THE FLOWER OF CONTENTION WILL GROW.RANDOM!,ham THATS ALRITE GIRL, U KNOW GAIL IS NEVA WRONG!!TAKE CARE SWEET AND DONT WORRY.C U L8TR HUN!LOVE Yaxxx,ham DO NOT B LATE LOVE MUM,ham Where @,ham Hi Petey!noim ok just wanted 2 chat coz avent spoken 2 u 4 a long time-hope ur doin alrite.have good nit at js love ya am.x,ham OH RITE. WELL IM WITH MY BEST MATE PETE, WHO I WENT OUT WITH 4 A WEEK+ NOW WERE 2GEVA AGAIN. ITS BEEN LONGER THAN A WEEK.,ham Thank u. IT BETTER WORK OUT CAUSE I WILL FEEL USED OTHERWISE,ham Nah im goin 2 the wrks with j wot bout u?,ham Im sorry bout last nite it wasnt ur fault it was me, spouse it was pmt or sumthin! U 4give me? I think u shldxxxx,ham Lmao!nice 1,ham Nite nite pocay wocay luv u more than n e thing 4eva I promise ring u 2morrowxxxx,ham "ME 2 BABE I FEEL THE SAME LETS JUST 4GET ABOUT IT+BOTH TRY +CHEER UP+NOT FIT SOO MUCHXXLOVE U LOCAXX",ham FRAN I DECIDED 2 GO N E WAY IM COMPLETELY BROKE AN KNACKERED I GOT UP BOUT 3 C U 2MRW LOVE JANX P.S THIS IS MY DADS FONE, -NO CREDIT,ham CERI U REBEL! SWEET DREAMZ ME LITTLE BUDDY!! C YA 2MORO! WHO NEEDS BLOKES,ham G.W.R,ham Yeah, probably but not sure. Ilol let u know, but personally I wuldnt bother, then again if ur goin to then I mite as well!!,ham No b4 Thursday,ham HI DARLIN I FINISH AT 3 DO U 1 2 PICK ME UP OR MEET ME? TEXT BACK ON THIS NUMBER LUV KATE XXX,ham HI DARLIN I HOPE YOU HAD A NICE NIGHT I WISH I HAD COME CANT WAIT TO SEE YOU LOVE FRAN PS I WANT DIRTY ANAL SEX AND I WANT A 10 MAN GANG BANG,ham Hello darlin ive finished college now so txt me when u finish if u can love Kate xxx,ham Have you had a good day? Mine was really busy are you up to much tomorrow night?,ham Hi its me you are probably having too much fun to get this message but i thought id txt u cos im bored! and james has been farting at me all night,ham make that 3! 4 fucks sake?! x,ham ZOE IT JUST HIT ME 2 IM FUCKING SHITIN MYSELF IL DEFO TRY MY HARDEST 2 CUM 2MOROW LUV U MILLIONS LEKDOG,ham WE REGRET TO INFORM U THAT THE NHS HAS MADE A MISTAKE.U WERE NEVER ACTUALLY BORN.PLEASE REPORT 2 YOR LOCAL HOSPITAL 2B TERMINATED.WE R SORRY 4 THE INCONVENIENCE,ham No probs hon! How u doinat the mo?,ham Yeh. Indians was nice. Tho it did kane me off a bit he he. We shud go out 4 a drink sometime soon. Mite hav 2 go 2 da works 4 a laugh soon. Love Pete x x,ham HI ITS KATE CAN U GIVE ME A RING ASAP XXX,ham HI ITS JESS I DONT KNOW IF YOU ARE AT WORK BUT CALL ME WHEN U CAN IM AT HOME ALL EVE. XXX,ham HI BABE IM AT HOME NOW WANNA DO SOMETHING? XX,ham Hi baby ive just got back from work and i was wanting to see u allday! I hope i didnt piss u off on the phone today. If u are up give me a call xxx,ham 26th OF JULY,ham hi baby im sat on the bloody bus at the mo and i wont be home until about 7:30 wanna do somethin later? call me later ortxt back jess xx,ham Bugis oso near wat... ,ham Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...,ham I dunno until when... Lets go learn pilates...,ham Den only weekdays got special price... Haiz... Cant eat liao... Cut nails oso muz wait until i finish drivin wat, lunch still muz eat wat... ,ham Meet after lunch la...,ham Yunny i'm walking in citylink now faster come down... Me very hungry...,ham 5 nights...We nt staying at port step liao...Too ex,ham Hey pple...$700 or $900 for 5 nights...Excellent location wif breakfast hamper!!!,ham Yun ah.the ubi one say if wan call by tomorrow.call 67441233 look for irene.ere only got bus8,22,65,61,66,382. Ubi cres,ubi tech park.6ph for 1st 5wkg days.n,ham Hey tmr maybe can meet you at yck,ham Oh...i asked for fun. Haha...take care. ,ham We are supposed to meet to discuss abt our trip... Thought xuhui told you? In the afternoon. Thought we can go for lesson after that,ham I can. But it will tell quite long, cos i haven't finish my film yet...,ham Haha... can... But i'm having dinner with my cousin...,ham Oh... Kay... On sat right?,ham I need... Coz i never go before ,ham Hey next sun 1030 there's a basic yoga course... at bugis... We can go for that... Pilates intro next sat.... Tell me what time you r free,ham I am going to sao mu today. Will be done only at 12 ,ham Hey gals...U all wanna meet 4 dinner at nte? ,ham Jos ask if u wana meet up?,ham Haiyoh... Maybe your hamster was jealous of million,ham is your hamster dead? Hey so tmr i meet you at 1pm orchard mrt? ,ham Hey i've booked the pilates and yoga lesson already... Haha,ham Yup... I havent been there before... You want to go for the yoga? I can call up to book ,ham K... Must book a not huh? so going for yoga basic on sunday?,ham Hey so this sat are we going for the intro pilates only? Or the kickboxing too? ,ham Sat right? Okay thanks... ,ham Yup... From what i remb... I think should be can book... ,ham Book which lesson? then you msg me... I will call up after work or sth... I'm going to get specs. My membership is PX3748,ham We can go 4 e normal pilates after our intro... ,ham Sun ah... Thk mayb can if dun have anythin on... Thk have to book e lesson... E pilates is at orchard mrt u noe hor... ,ham Thk shld b can... Ya, i wana go 4 lessons... Haha, can go for one whole stretch...,ham Sounds gd... Haha... Can... Wah, u yan jiu so fast liao...,ham Wah... Okie okie... Muz make use of e unlimited... Haha... ,ham Kaiez... Enjoy ur tuition... Gee... Thk e second option sounds beta... I'll go yan jiu den msg u...,ham K.. I yan jiu liao... Sat we can go 4 bugis vill one frm 10 to 3 den hop to parco 4 nb. Sun can go cine frm 1030 to 2, den hop to orc mrt 4 hip hop at 4...,ham Okies... I'll go yan jiu too... We can skip ard oso, go cine den go mrt one, blah blah blah... ,ham Nope... Juz off from work...,ham Oh oh... Wasted... Den muz chiong on sat n sun liao...,ham Haha... Sounds crazy, dunno can tahan anot...,ham Hmmm.. Thk sure got time to hop ard... Ya, can go 4 free abt... Muz call u to discuss liao... ,ham Oh... I was thkin of goin yogasana at 10 den no nd to go at 3 den can rush to parco 4 nb... Okie lor, u call me when ready...,ham After my work ah... Den 6 plus lor... U workin oso rite... Den go orchard lor, no other place to go liao...,ham Aiyo... U always c our ex one... I dunno abt mei, she haven reply... First time u reply so fast... Y so lucky not workin huh, got bao by ur sugardad ah...gee.. ,ham Ya, i'm referin to mei's ex wat... No ah, waitin 4 u to treat, somebody shld b rich liao...So gd, den u dun have to work frm tmr onwards...,ham Oh oh... Den muz change plan liao... Go back have to yan jiu again...,ham Hey... Thk we juz go accordin to wat we discussed yest lor, except no kb on sun... Cos there's nt much lesson to go if we attend kb on sat...,ham Den wat will e schedule b lk on sun?,ham Eh den sat u book e kb liao huh...,ham Yup... Hey then one day on fri we can ask miwa and jiayin take leave go karaoke ,ham Yup... Ok i go home look at the timings then i msg again... Xuhui going to learn on 2nd may too but her lesson is at 8am,ham Hey i booked the kb on sat already... what other lessons are we going for ah? Keep your sat night free we need to meet and confirm our lodging ,ham Hey... are you going to quit soon? Xuhui and i working till end of the month ,ham oh ya... Got hip hop open. Haha i was thinking can go for jazz then zoom to cine... Actually tonight i'm free leh... And there's a kb lesson tonight,ham Hey so whats the plan this sat? ,ham Can call me at 10:10 to make sure dat i've woken up...,ham Hey i will be late ah... Meet you at 945+,ham Hey tmr meet at bugis 930 ?,ham Ha... Then we must walk to everywhere... Cannot take tram. My cousin said can walk to vic market from our hotel ,ham Haha... Yup hopefully we will lose a few kg by mon. after hip hop can go orchard and weigh again,ham Hey i've booked the 2 lessons on sun liao...,ham Hmmm... Guess we can go 4 kb n power yoga... Haha, dunno we can tahan power yoga anot... Thk got lo oso, forgot liao...,ham Oh... Okie lor...We go on sat... ,ham Oh... Haha... Den we shld had went today too... Gee, nvm la... Kaiez, i dun mind goin jazz oso... Scared hiphop open cant catch up... ,ham I dun thk i'll quit yet... Hmmm, can go jazz ? Yogasana oso can... We can go meet em after our lessons den... ,ham Aiyo... Her lesson so early... I'm still sleepin, haha... Okie, u go home liao den confirm w me lor...,ham I'm nt goin, got somethin on, unless they meetin 4 dinner lor... Haha, i wonder who will go tis time...,ham Wa, ur openin sentence very formal... Anyway, i'm fine too, juz tt i'm eatin too much n puttin on weight...Haha... So anythin special happened?,ham Thk some of em find wtc too far... Weiyi not goin... E rest i dunno yet... R ur goin 4 dinner den i might b able to join...,ham I'm eatin now lor, but goin back to work soon... E mountain deer show huh... I watch b4 liao, very nice...,ham Goin to workout lor... Muz lose e fats... ,ham Yupz... I've oredi booked slots 4 my weekends liao... ,ham I noe la... U wana pei bf oso rite... K lor, other days den...,ham U meet other fren dun wan meet me ah... Muz b a guy rite...,ham Oh... Icic... K lor, den meet other day...,ham Haha... Where got so fast lose weight, thk muz go 4 a month den got effect... Gee,later we go aust put bk e weight.,ham U attend ur driving lesson how many times a wk n which day?,ham Ay wana meet on sat? wkg on sat?,ham Shopping? Eh ger i toking abt syd leh...Haha,ham It's only $140 ard... rest all ard $180 at least...Which is price 4 2 bedrm ($900),ham Hmm...Bad news...Hype park plaza $700 studio taken...Only left 2 bedrm-$900...,ham Okay... I booked all already... Including the one at bugis.,ham Yun ah.now wkg where?btw if go nus sc. wana specialise in wad?,ham Hey you still want to go for yogasana? Coz if we end at cine then can go bathe and hav the steam bath,ham Hey we can go jazz power yoga hip hop kb and yogasana ,ham Can meh? Thgt some will clash... Really ah, i dun mind... I dun seen to have lost any weight... Gee...,ham Oh... Lk tt den we take e one tt ends at cine lor... Dun wan yogasana oso can... ,ham Wa... U so efficient... Gee... Thanx...,ham Hmmm.... Mayb can try e shoppin area one, but forgot e name of hotel...,ham Huh... Hyde park not in mel ah, opps, got confused... Anyway, if tt's e best choice den we juz have to take it...,ham I only work from mon to thurs but Sat i cant leh... Booked liao... Which other day u free?,ham Until 545 lor... Ya, can go 4 dinner together...,ham Mon okie lor... Haha, best is cheap n gd food la, ex oso okie... Depends on whether wana eat western or chinese food... Den which u prefer... ,ham Chinatown got porridge, claypot rice, yam cake, fishhead beehoon... Either we eat cheap den go cafe n tok or go nydc or somethin...,ham Today i'm not workin but not free oso... Gee... Thgt u workin at ur fren's shop ? ,ham My fri ah... Okie lor,goin 4 my drivin den go shoppin after tt...,ham Hey...Great deal...Farm tour 9am to 5pm $95/pax, $50 deposit by 16 May,ham haha but no money leh... Later got to go for tuition... Haha and looking for empty slots for driving lessons,ham My tuition is at 330. Hm we go for the 1120 to 1205 one? Do you mind?,ham Hey... What time is your driving on fri? We go for evaluation on fri?,ham Nvm... I'm going to wear my sport shoes anyway... I'm going to be late leh.,ham Hey are we going for the lo lesson or gym? ,ham Okay... We wait ah,ham Hey come online! Use msn... We are all there,ham Oh yah... We never cancel leh... Haha ,ham K. Did you call me just now ah? ,ham Okay lor... Wah... like that def they wont let us go... Haha... What did they say in the terms and conditions?,ham Okay lor... Will they still let us go a not ah? Coz they will not know until later. We drop our cards into the box right?,ham You want to go? ,ham Kay... Since we are out already ,ham Haha okay... Today weekend leh... ,ham Haha... They cant what... At the most tmr forfeit... haha so how?,ham Hey... Why dont we just go watch x men and have lunch... Haha ,ham Haha... Really oh no... How? Then will they deduct your lesson tmr?,ham Hey i will be really pretty late... You want to go for the lesson first? I will join you. I'm only reaching tp mrt,ham Yunny... I'm goin to be late,ham Ya! when are taking ure practical lessons? I start in june.. ,ham Hey... Very inconvenient for your sis a not huh?,ham Hey i will be late... i'm at amk. Need to drink tea or coffee,ham Hey you gave them your photo when you registered for driving ah? Tmr wanna meet at yck? ,ham Ok... The theory test? when are going to book? I think it's on 21 may. Coz thought wanna go out with jiayin. But she isnt free,ham Haha... dont be angry with yourself... Take it as a practice for the real thing. =) ,ham Haha... Hope can hear the receipt sound... Gd luck!,ham Huh? 6 also cannot? Then only how many mistakes?,ham Nope... Think i will go for it on monday... Sorry i replied so late,ham Hey.. Something came up last min.. Think i wun be signing up tmr.. Hee,ham I am thinking of going down to reg for pract lessons.. Flung my advance.. Haha wat time u going?,ham Hey gals.. Anyone of u going down to e driving centre tmr?,ham Ok no prob,ham Nope i waiting in sch 4 daddy... ,ham Dunno lei he neva say...,ham called dad oredi...,ham Me hungry buy some food good lei... But mum n yun dun wan juz buy a little bit... ,ham Dad went out oredi... ,ham still attending da talks?,ham Then she buying today? no need to c meh...,ham Yup i'm free...,ham Nope... C then...,ham Is fujitsu s series lifebook good?,ham Is toshiba portege m100 gd?,ham Yun buying... But school got offer 2000 plus only...,ham Got fujitsu, ibm, hp, toshiba... Got a lot of model how to say...,ham Okie... Thanx...,ham How come i din c ... Yup i cut my hair...,ham No wonder... Cos i dun rem seeing a silver car... But i thk i saw a black one...,ham Guai... shd haf seen him when he's naughty... so free today? Can go jogging...,ham Ok... C ya...,ham Nope i'm not drivin... I neva develop da photos lei...,ham I wan but too early lei... Me outside now wun b home so early... Neva mind then...,ham 645,ham dun need to pick ur gf?,ham Okie...,ham I'm at home n ready...,ham Ok...,ham Dunno lei... I thk mum lazy to go out... I neva ask her yet...,ham Mum say we wan to go then go... Then she can shun bian watch da glass exhibition... ,ham Dun need to use dial up juz open da browser n surf...,ham When did i use soc... I use it only at home... dunno how 2 type it in word ar...,ham Can a not?,ham Juz go google n search 4 qet...,ham Wat time r going to xin's hostel?,ham Ok...,ham Huh means computational science... Y they like dat one push here n there...,ham I not at home now lei...,ham Eatin my lunch...,ham takin linear algebra today?,ham bot notes oredi... Cos i juz rem i got...,ham Wah lucky man... Then can save money... Hee...,ham Y so late but i need to go n get da laptop...,ham Where to get those?,ham Then cant get da laptop? My matric card wif lei... ,ham I dunno they close oredi not... v ma fan...,ham Hi dis is yijue i would be happy to work wif all for gek1510...,ham comin to fetch us oredi...,ham Got smaller capacity one? Quite ex...,ham Ok thanx...,ham Wat doing now?,ham Got meh... When?,ham neva tell me how i noe... I'm not at home in da aft wat... ,ham Mum ask to buy food home...,ham Leave it wif me lar... wan to carry meh so heavy... Is da num 98321561 familiar to ?,ham I tot it's my group mate... Lucky i havent reply... Wat time do need to leave... ,ham Yup he msg me: is tat yijue? Then i tot it's my group mate cos we meeting today mah... I'm askin if leaving earlier or wat mah cos mayb haf to walk v far...,ham Yup...,ham I'm stuck in da middle of da row on da right hand side of da lt... ,ham Black shirt n blue jeans... I thk i c ...,ham Not directly behind... Abt 4 rows behind ...,ham go home liao? Ask dad to pick me up at 6...,ham I'm done oredi...,ham I wait 4 inside da car park...,ham Yup... How noe leh...,ham Oic... I saw him too but i tot he din c me... I found a group liao...,ham Aiyo a bit pai seh noe... Scared he dun rem who i am then die... Hee... But he become better lookin oredi leh...,ham How i noe... Did specify da domain as nusstu... still in sch...,ham When login dat time... Dad fetching home now?,ham I come n pick up... Come out immediately aft ur lesson...,ham Eh ur laptop got no stock lei... He say mon muz come again to take a look c got a not...,ham ready then call me...,ham still got lessons? in sch?,ham Shall i get my pouch?,ham I'm on da bus going home...,ham I was at bugis juz now wat... But now i'm walking home oredi... so late then reply... I oso saw a top dat i like but din buy... Where r now? ,ham Still at west coast... Haiz... 'll take forever to come back...,ham R comin back for dinner?,ham Eat at old airport road... But now 630 oredi... Got a lot of pple...,ham Y bishan lei... I tot say lavender? ,ham Free any day but i finish at 6 on mon n thurs...,ham I prefer my free days... Tues, wed, fri oso can... ask those workin lor...,ham But we havent got da topic yet rite?,ham So can collect ur laptop?,ham Ok good then i later come find ... C lucky i told to go earlier... Later pple take finish no more again...,ham Eat jap done oso aft ur lect wat... got lect at 12 rite... ,ham So pay first lar... Then when is da stock comin...,ham What's ur pin?,ham collecting ur laptop then going to configure da settings izzit?,ham Cos daddy arranging time c wat time fetch mah...,ham Juz now havent woke up so a bit blur blur... Can? Dad went out liao... I cant cum now oso...,ham So li hai... Me bored now da lecturer repeating last weeks stuff waste time... ,ham How i noe... She's in da car now... Later then c lar... I'm wearing shorts...,ham Huh y lei...,ham eatin later but i'm eatin wif my frens now lei... going home first? ,ham Havent still waitin as usual... come back sch oredi?,ham No break time one... How... I come out n get my stuff fr ?,ham Tmr then brin lar... Aiya later i come n c lar... Mayb neva set properly got da help sheet wif ...,ham I dunno lei... Like dun haf...,ham I'm going out to buy mum's present ar.,ham Havent shopping now lor i juz arrive only,ham Beauty sleep can help ur pimples too.,ham Up to ... wan come then come lor... But i din c any stripes skirt...,ham Then mum's repent how?,ham Got c... I lazy to type... I forgot in lect... I saw a pouch but like not v nice...,ham Ok i found dis pierre cardin one which looks normal costs 20 its on sale.,ham Y wan to go there? C doctor?,ham I'm wif him now buying tix lar...,ham How much r willing to pay?,ham Hi this is yijue... It's regarding the 3230 textbook it's intro to algorithms second edition... I'm selling it for $50...,ham I'll be in sch fr 4-6... I dun haf da book in sch... It's at home...,ham Ok... Thanx... Gd nite 2 too...,ham Wat uniform? In where get?,ham Yup i've finished c there...,ham Never y lei... I v lazy... Got wat? Dat day send me da url cant work one...,ham We're done...,ham Anything lar...,ham Go where n buy? Juz buy when we get there lar.,ham Bishan lar nearer... No need buy so early cos if buy now i gotta park my car...,ham Do u noe how 2 send files between 2 computers?,ham I gotta collect da car at 6 lei.,ham 2 laptop... I noe infra but too slow lar... I wan fast one,ham Dunno da next show aft 6 is 850. Toa payoh got 650.,ham Tmr timin still da same wat cos i got lesson until 6...,ham Wat time wan today?,ham Aiya we discuss later lar... Pick up at 4 is it?,ham I'm in a movie... Collect car oredi...,ham I anything lor...,ham Nothin comes to my mind. help me buy hanger lor. Ur laptop not heavy?,ham Ok lor wat time finish?,ham Huh but i got lesson at 4 lei n i was thinkin of going to sch earlier n i tot of parkin at kent vale... ,ham v ma fan...,ham come lt 25 n pass to me lar,ham Y lei?,ham I wanted to ask to wait 4 me to finish lect. Cos my lect finishes in an hour anyway.,ham Aiyo cos i sms then neva reply so i wait 4 to reply lar. I tot havent finish ur lab wat.,ham Auntie huai juan never pick up her phone,ham I not free today i haf 2 pick my parents up tonite...,ham Ok..,ham Dunno lei... I might b eatin wif my frens... If wan to eat then i wait 4 lar,ham How come?,ham Then ask dad to pick up lar... wan 2 stay until 6 meh...,ham Oh ok wait 4 me there... My lect havent finish,ham I'm done...,ham In da car park ,ham log off 4 wat. It's sdryb8i,ham thk of wat to eat tonight.,ham Aiyo please got time meh.,ham Can... I'm free...,ham Ok.,ham Anything lor. Juz both of us lor.,ham Ok c then.,ham I'll be late...,ham Kent vale lor... wait 4 me there ar?,ham Ard 530 lor. I ok then message lor.,ham I'm done. C there.,ham staff.science.nus.edu.sg/~phyhcmk/teaching/pc1323,ham Dunno lei shd b driving lor cos i go sch 1 hr oni.,ham wait 4 me in sch i finish ard 5..,ham Ok can...,ham dun wan to watch infernal affair?,ham Cos darren say considering mah so i ask ...,ham mean it's confirmed... I tot they juz say oni... Ok then... ,ham Me n him so funny...,ham Anything lor is she coming?,ham Ok lor. I'm in town now lei.,ham With my sis lor... We juz watched italian job.,ham C movie is juz last minute decision mah. Juz watch 2 lar but i tot not interested.,ham Wat so late still early mah. Or we juz go 4 dinner lor. Aiya i dunno...,ham If i not meeting all rite then i'll go home lor. If dun feel like comin it's ok.,ham Can all decide faster cos my sis going home liao..,ham Yup not comin :-(,ham He neva grumble but i sad lor... Hee... Buy tmr lor aft lunch. But we still meetin 4 lunch tmr a not. Neva hear fr them lei. got a lot of work ar?,ham I jokin oni lar.. busy then i wun disturb .,ham Ok lor...,ham I'm in a movie. Call me 4 wat?,ham R going 4 today's meeting?,ham Do noe if ben is going?,ham Dunno lei all decide lor. How abt leona? Oops i tot ben is going n i msg him.,ham Neva mind it's ok..,ham So wat's da decision?,ham Then wait 4 me at bus stop aft ur lect lar. If i dun c then i go get my car then come back n pick .,ham Ok i juz receive..,ham I borrow ur bag ok.,ham Can. Dunno wat to get 4 her...,ham Ok. But i finish at 6.,ham I thk gotta go home by urself. Cos i'll b going out shopping 4 my frens present. ,ham Or i go home first lar wait 4 me lor.. I put down my stuff first..,ham predict wat time 'll finish buying?,ham Ok... But bag again..,ham I only haf msn. It's yijue@hotmail.com,ham I'm in school now n i'll be in da lab doing some stuff give me a call when r done.,ham Anything lar then not going home 4 dinner?,ham When can come out?,ham I havent lei.. Next mon can?,ham That day say cut ur hair at paragon, is it called hair sense? Do noe how much is a hair cut? ,ham Dunno i juz askin cos i got a card got 20% off 4 a salon called hair sense so i tot it's da one cut ur hair. ,ham Yup it's at paragon... I havent decided whether 2 cut yet... Hee...,ham Ok lor but not too early. Me still having project meeting now.,ham Ard 6 like dat lor.,ham I not busy juz dun wan 2 go so early.. Hee.. ,ham Okie wan meet at bishan? Cos me at bishan now. I'm not driving today.,ham Doing project w frens lor. ,ham I'm not coming home 4 dinner.,ham Ard 530 like dat lor. We juz meet in mrt station then dun haf to come out.,ham Ok lor reaching then message me.,ham Happy birthday... May all ur dreams come true...,ham I forgot 2 ask all smth.. There's a card on da present lei... How? all want 2 write smth or sign on it?,ham give me some time to walk there.,ham all write or wat..,ham Then come n pick me at 530 ar?,ham Thanx 4 sending me home...,ham Is ur lecture over?,ham Sorry me going home first... Daddy come fetch later...,ham Okie.. Thanx..,ham Will b going to esplanade fr home?,ham We confirm eating at esplanade?,ham 7 at esplanade.. Do mind giving me a lift cos i got no car today..,ham got wat to buy tell us then no need to come in again.,ham Wat's da model num of ur phone?,ham Hmm thinking lor...,ham Ok lor... Sony ericsson salesman... I ask shuhui then she say quite gd 2 use so i considering...,ham say until like dat i dun buy ericsson oso cannot oredi lar...,ham Jokin only lar... :-) depends on which phone my father can get lor...,ham No need to buy lunch for me.. I eat maggi mee..,ham I know she called me,ham Do all wan 2 meet up n combine all the parts? How's da rest of da project going?,ham I'm doing da intro covers energy trends n pros n cons... Brief description of nuclear fusion n oso brief history of iter n jet got abt 7 n half pages..,ham Yup i shd haf ard 10 pages if i add figures... all got how many pages?,ham That's y we haf to combine n c how lor...,ham cThen i thk shd b enuff.. Still got conclusion n contents pg n references.. I'll b doing da contents pg n cover pg..,ham Now? I'm going out 4 dinner soon..,ham Oh ok.. Wat's ur email?,ham I've sent my part..,ham I emailed yifeng my part oredi.. Can get it fr him..,ham No need to ke qi... too bored izzit y suddenly thk of this...,ham TaKe CaRE n gET WeLL sOOn,ham no home work to do meh... ,ham Huh so late... Fr dinner?,ham Yup i'm elaborating on the safety aspects and some other issues..,ham Oic... Then better quickly go bathe n settle down...,ham I'm hungry buy smth home...,ham But i juz remembered i gotta bathe my dog today..,ham Ok lor thanx... in school?,ham Ok thanx... Take care then...,ham Oops i thk i dun haf enuff... I go check then tell ..,ham only send me the contents page...,ham Wat time finish?,ham But i haf enuff space got like 4 mb...,ham Where r e meeting tmr?,ham Oh ok no prob..,ham Huh i cant thk of more oredi how many pages do we have?,ham Can send me a copy of da report?,ham I thk 50 shd be ok he said plus minus 10.. Did leave a line in between paragraphs?,ham How abt making some of the pics bigger?,ham Nope i'll come online now..,ham How r going to send it to me?,ham Forgot to tell smth.. Can like number the sections so that it's clearer..,ham I havent add yet right.. ,ham They said dun haf passport or smth like dat.. Or juz send to my email account.. ,ham Hey r still online? I've finished the formatting...,ham Can i meet at 5.. As 4 where depends on where wan 2 in lor..,ham Ok.. finishing soon?,ham So 'll be submitting da project tmr rite?,ham Ok lor... But buy wat?,ham Or go buy wif him then i meet later can?,ham Huh so early.. Then having dinner outside izzit?,ham Must come later.. I normally bathe him in da afternoon mah..,ham Crazy ar he's married. like gd looking guys not me. My frens like say he's korean leona's fave but i dun thk he is. Aft some thinking mayb most prob i'll go.,ham Nope but i'll b going 2 sch on fri quite early lor cos mys sis got paper in da morn :-),ham Yar but they say got some error.,ham If u dun drive then how i go 2 sch.,ham Yup,ham Anything...,ham Then u drive lor.,ham Ok,ham Wat time liao, where still got.,ham Gd luck 4 ur exams :-),ham Is ur paper today in e morn or aft?,ham U studying in sch or going home? Anyway i'll b going 2 sch later.,ham Oh ok..,ham In sch but neva mind u eat 1st lor..,ham Call me when u finish then i come n pick u.,ham Hui xin is in da lib.,ham Okie...,ham U wan 2 haf lunch i'm in da canteen now.,ham Haf u found him? I feel so stupid da v cam was working.,ham Where r we meeting?,ham I went to ur hon lab but no one is there.,ham I've reached sch already...,ham Can u look 4 me in da lib i got stuff havent finish yet.,ham Is ur changes 2 da report big? Cos i've already made changes 2 da previous report.,ham Dun b sad.. It's over.. Dun thk abt it already. Concentrate on ur other papers k.,ham Din i tell u jus now 420,ham I fetch yun or u fetch?,ham All e best 4 ur exam later.,ham Thanx 4 e brownie it's v nice...,ham Ok i'm coming home now.,ham Who u talking about?,ham Sorry sent blank msg again. Yup but trying 2 do some serious studying now.,ham Yup but not studying surfing lor. I'm in e lazy mode today.,ham I've reached already.,ham Yup no more already... Thanx 4 printing n handing it up.,ham Ok,ham Ok i've sent u da latest version of da project.,ham Mine here like all fr china then so noisy.,ham Here got lots of hair dresser fr china.,ham All e best 4 ur driving tmr :-),ham He said i look pretty wif long hair wat. But i thk he's cutting quite short 4 me leh.,ham Yup i thk they r e teacher said that will make my face look longer. Darren ask me not 2 cut too short.,ham Ok lar i double check wif da hair dresser already he said wun cut v short. He said will cut until i look nice.,ham Thanx...,ham Shant disturb u anymore... Jia you...,ham Depends on individual lor e hair dresser say pretty but my parents say look gong. U kaypoh.. I also dunno wat she collecting.,ham But i'll b going 2 sch on mon. My sis need 2 take smth.,ham The length is e same but e top shorter n i got a fringe now. I thk i'm not going liao. Too lazy. Dun wan 2 distract u also.,ham Y dun cut too short leh. U dun like ah? She failed. She's quite sad.,ham I might go 2 sch. Yar at e salon now v boring.,ham Is ur paper in e morn or aft tmr?,ham Nothing. Can...,ham Shb b ok lor... Thanx...,ham Really... I tot ur paper ended long ago... But wat u copied jus now got use? U happy lar... I still haf 2 study :-(,ham Really dun bluff me leh... U sleep early too. Nite...,ham Huh so slow i tot u reach long ago liao... U 2 more days only i 4 more leh...,ham Wif my family booking tour package.,ham How's ur paper?,ham Haha mayb u're rite... U know me well. Da feeling of being liked by someone is gd lor. U faster go find one then all gals in our group attached liao.,ham My phone,ham Aiyar hard 2 type. U later free then tell me then i call n scold n tell u.,ham Yar i wanted 2 scold u yest but late already... I where got zhong se qing you? If u ask me b4 he ask me then i'll go out w u all lor. N u still can act so real.,ham That's y i said it's bad dat all e gals know u... Wat u doing now?,ham U horrible gal... U knew dat i was going out wif him yest n u still come n ask me...,ham Aiyar sorry lor forgot 2 tell u...,ham I'm meeting Darren...,ham U coming 2 pick me?,ham Can do lor...,ham It's ok lar. U sleep early too... Nite...,ham Nvm take ur time.,ham Okie,ham I also thk too fast... Xy suggest one not me. U dun wan it's ok. Going 2 rain leh where got gd.,ham Nothing, smsing u n xy lor. Sorry lor da guys neva c u in person but they sort of know u lor. So u wan 2 meet them xy ask me 2 bring u along 4 our next meeting.,ham Dunno he jus say go lido. Same time 930.,ham U know we watchin at lido?,ham Wat time do u wan 2 meet me later?,ham Now got tv 2 watch meh? U no work today?,ham Then we wait 4 u lor... No need 2 feel bad lar...,ham Doing nothing, then u not having dinner w us?,ham Ok...,ham Ok i also wan 2 watch e 9 pm show...,ham Sian... Aft meeting supervisor got work 2 do liao... U working now?,ham Quite late lar... Ard 12 anyway i wun b drivin...,ham Ok but tell me half an hr b4 u come i need 2 prepare.,ham Ok...,ham Then ur sis how?,ham Ard 515 like dat. Y?,ham U repeat e instructions again. Wat's e road name of ur house?,ham Ok lor...,ham I wake up long ago already... Dunno, what other thing?,ham We r outside already.,ham Ok...,ham Ok lor...,ham I'm coming home 4 dinner.,ham Sorry i din lock my keypad.,ham Wat would u like 4 ur birthday?,ham I'm leaving my house now...,ham Ok i msg u b4 i leave my house.,ham Ok i thk i got it. Then u wan me 2 come now or wat?,ham He like not v shock leh. Cos telling shuhui is like telling leona also. Like dat almost all know liao. He got ask me abt ur reaction lor.,ham Yar... I tot u knew dis would happen long ago already.,ham I thk u dun haf 2 hint in e forum already lor... Cos i told ron n darren is going 2 tell shuhui.,ham Quite lor. But dun tell him wait he get complacent...,ham I dun believe u. I thk u told him.,ham Yar he quite clever but aft many guesses lor. He got ask me 2 bring but i thk darren not so willing 2 go. Aiya they thk leona still not attach wat.,ham Yup. Wun believe wat? U really neva c e msg i sent shuhui?,ham Watch lor. I saw a few swatch one i thk quite ok. Ard 116 but i need 2nd opinion leh...,ham Then she dun believe wat?,ham Ron say fri leh. N he said ding tai feng cant make reservations. But he said wait lor.,ham Y she dun believe leh? I tot i told her it's true already. I thk she muz c us tog then she believe.,ham Dunno dat's wat he told me. Ok lor...,ham Orh i tot u say she now still dun believe.,ham Cos i was out shopping wif darren jus now n i called him 2 ask wat present he wan lor. Then he started guessing who i was wif n he finally guessed darren lor.,ham Yup i thk cine is better cos no need 2 go down 2 plaza mah.,ham Watching tv lor. Nice one then i like lor.,ham Then i buy.,ham U buy newspapers already?,ham No objection. My bf not coming.,ham Anything lor if they all go then i go lor...,ham U coming back 4 dinner rite? Dad ask me so i re confirm wif u...,ham Enjoy urself tmr...,ham Wat r u doing?,ham Shuhui has bought ron's present it's a swatch watch...,ham Yeah it's jus rite...,ham Ha... U jus ate honey ar? So sweet...,ham Yar lor actually we quite fast... Cos da ge slow wat... Haha...,ham Watching tv lor... Y she so funny we bluff her 4 wat. Izzit because she thk it's impossible between us?,ham Okie...,ham Hey happy birthday...,ham Dunno leh cant remember mayb lor. So wat time r we meeting tmr?,ham U say leh... Of course nothing happen lar. Not say v romantic jus a bit only lor. I thk e nite scenery not so nice leh.,ham Nothing but we jus tot u would ask cos u ba gua... But we went mt faber yest... Yest jus went out already mah so today not going out... Jus call lor...,ham Of cos can lar i'm not so ba dao ok... 1 pm lor... Y u never ask where we go ah... I said u would ask on fri but he said u will ask today...,ham Then u ask darren go n pick u lor... But i oso sian tmr haf 2 meet lect...,ham Nothing lor... A bit bored too... Then y dun u go home early 2 sleep today...,ham U still havent got urself a jacket ah?,ham How come u got nothing to do?,ham Quite ok but a bit ex... U better go eat smth now else i'll feel guilty...,ham Here got ur favorite oyster... N got my favorite sashimi... Ok lar i dun say already... Wait ur stomach start rumbling...,ham Ok lar... Joking wif u oni...,ham Okie but i scared u say i fat... Then u dun wan me already...,ham Yup having my lunch buffet now.. U eat already?,ham U wake up already? Thanx 4 e tau sar piah it's quite nice.,ham Jus telling u dat i'll b leaving 4 shanghai on 21st instead so we'll haf more time 2 meet up cya...,ham Ok...,ham I'm home...,ham Okie,ham U sleeping now.. Or you going to take? Haha.. I got spys wat.. Me online checking n replying mails lor..,ham Then u better go sleep.. Dun disturb u liao.. U wake up then msg me lor..,ham What u mean u almost done? Done wif sleeping? But i tot u going to take a nap.. Yup i send her liao so i'm picking her up at ard 4 smth lor..,ham My house here e sky quite dark liao... If raining then got excuse not 2 run already rite... Hee...,ham Nope wif my sis lor... Aft bathing my dog then i can bathe... Looks like it's going 2 rain soon.,ham Wow so healthy. Old airport rd lor. Cant thk of anything else. But i'll b bathing my dog later.,ham U too...,ham Ok. C u then.,ham Yup song bro. No creative. Neva test quality. He said check review online.,ham Wewa is 130. Iriver 255. All 128 mb.,ham We left already we at orchard now.,ham I tot u outside cos darren say u come shopping. Of course we nice wat. We jus went sim lim look at mp3 player.,ham U in town alone?,ham Anytime...,ham Ok... Sweet dreams...,ham Jus came back fr lunch wif my sis only. U leh?,ham In e msg jus now. U said thanks for gift.,ham U're welcome... Caught u using broken english again...,ham Yup bathe liao...,ham I'm home.,ham Ok... U enjoy ur shows...,ham Dunno cos i was v late n when i reach they inside already... But we ate spageddies lor... It's e gals who r laughing at me lor...,ham Finish already... Yar they keep saying i mushy... I so embarrassed ok...,ham Nope but i'm going home now then go pump petrol lor... Like going 2 rain soon...,ham Hey they r not watching movie tonight so i'll prob b home early...,ham Dunno y u ask me.,ham I've reached home n i bathe liao... U can call me now...,ham Yup i'm still having coffee wif my frens... My fren drove she'll give me a lift...,ham Ok...,ham I like dis sweater fr mango but no more my size already so irritating.,ham Yup n her fren lor. I'm meeting my fren at 730.,ham Anyway i'm going shopping on my own now. Cos my sis not done yet. Dun disturb u liao.,ham I'm ok wif it cos i like 2 try new things. But i scared u dun like mah. Cos u said not too loud.,ham U dun say so early hor... U c already then say...,ham Got but got 2 colours lor. One colour is quite light n e other is darker lor. Actually i'm done she's styling my hair now.,ham It's ok i noe u're busy but i'm really too bored so i msg u. I oso dunno wat colour she choose 4 me one.,ham Hey i'm bored... So i'm thinking of u... So wat r u doing?,ham Ok lor. Msg me b4 u call.,ham But really quite funny lor wat... Then u shd haf run shorter distance wat...,ham I'm in town now so i'll jus take mrt down later.,ham What time is ur flight tmr?,ham Aiyo u so poor thing... Then u dun wan 2 eat? U bathe already?,ham U so lousy, run already come back then half dead... Hee...,ham Eh u remember how 2 spell his name... Yes i did. He v naughty make until i v wet.,ham Finish liao... U?,ham U can call now...,ham 10 min later k...,ham Wat makes u thk i'll fall down. But actually i thk i'm quite prone 2 falls. Lucky my dad at home i ask him come n fetch me already.,ham Aiyah e rain like quite big leh. If drizzling i can at least run home.,ham That's y u haf 2 keep me busy...,ham Yup next stop.,ham Yar else i'll thk of all sorts of funny things.,ham Aiyah then i wait lor. Then u entertain me. Hee...,ham Yup. Izzit still raining heavily cos i'm in e mrt i can't c outside.,ham Yup, no need. I'll jus wait 4 e rain 2 stop.,ham I'm not driving... Raining! Then i'll get caught at e mrt station lor.,ham I'm on my way home. Went to change batt 4 my watch then go shop a bit lor.,ham Then wat r u doing now? Busy wif work?,ham Yup but it's not giving me problems now so mayb i'll jus leave it...,ham Ok... But they said i've got wisdom teeth hidden inside n mayb need 2 remove.,ham Thanx a lot 4 ur help!,ham Ok.,ham Happy birthday... May u find ur prince charming soon n dun work too hard...,ham R u here yet? I'm wearing blue shirt n black pants.,ham Or u ask they all if next sat can a not. If all of them can make it then i'm ok lor.,ham Ok that's great thanx a lot.,ham I'm e person who's doing e sms survey...,ham Hi this is yijue, can i meet u at 11 tmr?,ham Hmm... Dunno leh, mayb a bag 4 goigng out dat is not too small. Or jus anything except perfume, smth dat i can keep.,ham Eh u send wrongly lar...,ham Ok,ham Aiyah sorry lor... I watch tv watch until i forgot 2 check my phone.,ham Eh sorry leh... I din c ur msg. Not sad already lar. Me watching tv now. U still in office?,ham My supervisor find 4 me one lor i thk his students. I havent ask her yet. Tell u aft i ask her.,ham I got to video tape pple type in message lor. U so free wan 2 help me? Hee... Cos i noe u wan 2 watch infernal affairs so ask u along. Asking shuhui oso.,ham Ok thanx...,ham U gd lor go shopping i got stuff to do. U wan 2 watch infernal affairs a not? Come lar...,ham U sick still can go shopping?,ham Horrible gal. Me in sch doing some stuff. How come u got mc?,ham Die... I accidentally deleted e msg i suppose 2 put in e sim archive. Haiz... I so sad...,ham Ok no prob. Take ur time.,ham His frens go then he in lor. Not alone wif my mum n sis lor.,ham Yar lor he wan 2 go c horse racing today mah, so eat earlier lor. I ate chicken rice. U?,ham U really pig leh sleep so much. My dad wake me up at 10 smth 2 eat lunch today.,ham Yup. Thk of u oso boring wat.,ham Nope watching tv at home... Not going out. V bored...,ham Wat r u doing?,ham Ok set let u noe e details later...,ham U free on sat rite? U wan 2 watch infernal affairs wif me n darren n mayb xy?,ham Same to u...,ham Okie,ham Ok then u tell me wat time u coming later lor.,ham I knew it... U slept v late yest? Wake up so late...,ham Wat's my dear doing? Sleeping ah?,ham Nite...,ham Ok. Me watching tv too.,ham At home also.,ham Ok. I only ask abt e movie. U wan ktv oso?,ham I called but no one pick up e phone. I ask both of them already they said ok.,ham Then ur physics get a-?,ham Aiyah ok wat as long as got improve can already wat...,ham I'm reaching home in 5 min.,ham Wat u doing there?,ham Ok... Help me ask if she's working tmr a not?,ham Ok... Then r we meeting later?,ham Shopping lor. Them raining mah hard 2 leave orchard.,ham Long beach lor. Expected... U having dinner now?,ham Havent stuck at orchard in my dad's car. Going 4 dinner now. U leh? So r they free tonight?,ham How? Izzit still raining?,ham Thank u!,ham Thanx a lot...,ham Although i told u dat i'm into baig face watches now but i really like e watch u gave cos it's fr u. Thanx 4 everything dat u've done today, i'm touched...,ham East coast,ham Left dessert. U wan me 2 go suntec look 4 u?,ham U can call me now...,ham I jus reached home. I go bathe first. But my sis using net tell u when she finishes k...,ham Mum not going robinson already.,ham Nvm it's ok...,ham If u sending her home first it's ok lor. I'm not ready yet.,ham Esplanade lor. Where else...,ham I'm home. Ard wat time will u reach?,ham Jus ans me lar. U'll noe later.,ham It's ok i wun b angry. Msg u aft i come home tonight.,ham So u wan 2 come for our dinner tonight a not?,ham Hi di is yijue we're meeting at 7 pm at esaplanade tonight.,ham R we still meeting 4 dinner tonight?,ham Piggy, r u awake? I bet u're still sleeping. I'm going 4 lunch now...,ham Is xy in ur car when u picking me up?,ham We not leaving yet. Ok lor then we go elsewhere n eat. U thk...,ham Taka lor. Wat time u wan 2 come n look 4 us?,ham Xy trying smth now. U eat already? We havent...,ham Then u go back urself lor...,ham Thanx but my birthday is over already.,ham Yup i thk so until e shop closes lor.,ham Hi i won't b ard 4 christmas. But do enjoy n merry x'mas.,ham We not watching movie already. Xy wants 2 shop so i'm shopping w her now.,ham I'm going 2 orchard now laready me reaching soon. U reaching?,ham Ok lor. Anyway i thk we cant get tickets now cos like quite late already. U wan 2 go look 4 ur frens a not? Darren is wif them now...,ham Aiyah u did ok already lar. E nydc at wheellock?,ham I thk 530 lor. But dunno can get tickets a not. Wat u doing now?,ham I tot u reach liao. He said t-shirt.,ham U reach orchard already? U wan 2 go buy tickets first?,ham Aft i finish my lunch then i go str down lor. Ard 3 smth lor. U finish ur lunch already?,ham Havent planning to buy later. I check already lido only got 530 show in e afternoon. U finish work already?,ham Is xy going 4 e lunch?,ham I'm going 4 lunch now wif my family then aft dat i go str 2 orchard lor.,ham Play w computer? Aiyah i tok 2 u lor?,ham Ha... Both of us doing e same thing. But i got tv 2 watch. U can thk of where 2 go tonight or u already haf smth in mind...,ham Sitting ard nothing to do lor. U leh busy w work?,ham Ok lor.,ham My sis is catching e show in e afternoon so i'm not watching w her. So c u wan 2 watch today or tmr lor.,ham Then dun wear jeans lor...,ham Depends on where u going lor.,ham Ok i go change also...,ham Anything lor but toa payoh got place 2 walk meh...,ham Ok lor. I ned 2 go toa payoh 4 a while 2 return smth u wan 2 send me there or wat?,ham I anything lor.,ham Jus finish watching tv... U?,ham Happy new year to u too!,ham Ok,ham Havent.,ham Ok.,ham I reach home safe n sound liao...,ham I'll reach in ard 20 mins ok...,ham Ok... Let u noe when i leave my house.,ham I finished my lunch already. U wake up already?,ham Ok lor.,ham I'm watching lotr w my sis dis aft. So u wan 2 meet me 4 dinner at nite a not?,ham As usual u can call me ard 10 smth.,ham U call me alter at 11 ok.,ham U wake up already? Wat u doing? U picking us up later rite? I'm taking sq825, reaching ard 7 smth 8 like dat. U can check e arrival time. C ya soon...,ham Thanx. Yup we coming back on sun. Finish dinner going back 2 hotel now. Time flies, we're tog 4 exactly a mth today. Hope we'll haf many more mths to come...,ham Sorry to trouble u again. Can buy 4d for my dad again? 1405, 1680, 1843. All 2 big 1 small, sat n sun. Thanx.,ham No need lar. Jus testing e phone card. Dunno network not gd i thk. Me waiting 4 my sis 2 finish bathing so i can bathe. Dun disturb u liao u cleaning ur room.,ham Yup ok thanx...,ham Do u noe wat time e place dat sells 4d closes?,ham Ok...,ham No my mum went 2 dentist.,ham No it's waiting in e car dat's bored wat. Cos wait outside got nothing 2 do. At home can do my stuff or watch tv wat.,ham U having lunch alone? I now so bored...,ham Waiting in e car 4 my mum lor. U leh? Reach home already?,ham Ok.,ham My trip was ok but quite tiring lor. Uni starts today but it's ok 4 me cos i'm not taking any modules but jus concentrating on my final yr project.,ham I place all ur points on e cultures module already.,ham U definitely need a module from e humanities dis sem izzit? U wan 2 take other modules 1st?,ham Yup,ham Dis is yijue. I jus saw ur mail. In case huiming havent sent u my num. Dis is my num.,ham I'm leaving my house now.,ham Ok i msg u b4 i leave my house.,ham Haf u eaten? Wat time u wan me 2 come?,ham Okie,ham Jus finish blowing my hair. U finish dinner already?,ham Daddy, shu shu is looking 4 u... U wan me 2 tell him u're not in singapore or wat?,ham Ok no prob...,ham At home watching tv lor.,ham Aiyar u so poor thing... I give u my support k... Jia you! I'll think of u...,ham Waiting 4 my tv show 2 start lor... U leh still busy doing ur report?,ham But i'm surprised she still can guess right lor...,ham How come she can get it? Should b quite diff to guess rite...,ham How come guoyang go n tell her? Then u told her?,ham Ok...,ham Ok... Ur typical reply...,ham If e timing can, then i go w u lor...,ham Ok no problem... Yup i'm going to sch at 4 if i rem correctly...,ham Watching tv lor...,ham Ok...,ham Ok lor then we go tog lor...,ham Yup. Anything lor, if u dun wan it's ok...,ham Jus finish bathing...,ham Ok lor... Or u wan me go look 4 u?,ham Yar lor... Keep raining non stop... Or u wan 2 go elsewhere?,ham Wat r u doing now?,ham Ok no prob... I'll come after lunch then...,ham Sorry i'm not free...,ham Anytime lor...,ham Ok then i'll let him noe later n ask him call u tmr...,ham Havent mus ask if u can 1st wat. Of meet 4 lunch den u n him meet can already lor. Or u wan 2 go ask da ge 1st then confirm w me asap?,ham Darren was saying dat if u meeting da ge den we dun meet 4 dinner. Cos later u leave xy will feel awkward. Den u meet him 4 lunch lor.,ham Call me when u're done...,ham I ask if u meeting da ge tmr nite...,ham Did u receive my msg?,ham R u meeting da ge at nite tmr?,ham Can come my room but cannot come my house cos my house still messy... Haha...,ham Yar lor wait 4 my mum 2 finish sch then have lunch lor... I whole morning stay at home clean my room now my room quite clean... Hee...,ham Jus finish my lunch on my way home lor... I tot u dun wan 2 stay in sch today...,ham Yup ok...,ham Ok then i come n pick u at engin?,ham Anything lor... U decide...,ham E admin building there? I might b slightly earlier... I'll call u when i'm reaching...,ham No need lar i go engin? Cos my sis at arts today...,ham Dunno, my dad said he coming home 2 bring us out 4 lunch. Yup i go w u lor. I call u when i reach school lor...,ham Ard 4 lor...,ham Oic cos me n my sis got no lunch today my dad went out... So dunno whether 2 eat in sch or wat...,ham Then u going ikea str aft dat?,ham Wat time u finish ur lect today?,ham Change again... It's e one next to escalator...,ham Shuhui say change 2 suntec steamboat? U noe where? Where r u now?,ham R u still working now?,ham 7 lor... Change 2 suntec... Wat time u coming?,ham Yar lor... How u noe? U used dat route too?,ham I've reached home finally...,ham U calling me right? Call my hand phone...,ham Aiyar dun disturb u liao... Thk u have lots 2 do aft ur cupboard come...,ham Huh so fast... Dat means u havent finished painting?,ham U still painting ur wall?,ham Ok... I din get ur msg...,ham Hey elaine, is today's meeting still on?,ham Hi elaine, is today's meeting confirmed?,ham Sorry i now then c ur msg... Yar lor so poor thing... But only 4 one night... Tmr u'll have a brand new room 2 sleep in...,ham "I;m reaching in another 2 stops.",ham Huh but i cant go 2 ur house empty handed right?,ham OK i'm waliking ard now... Do u wan me 2 buy anything go ur house?,ham Yup ok...,ham Ok... Take ur time n enjoy ur dinner...,ham So gd got free ice cream... I oso wan...,ham Same as u... Dun wan... Y u dun like me already ah... Wat u doing now? Still eating?,ham Horrible u eat macs eat until u forgot abt me already rite... U take so long 2 reply. I thk it's more toot than b4 so b prepared. Now wat shall i eat?,ham Die... Now i have e toot fringe again...,ham Horrible bf... I now v hungry...,ham URGENT! 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BT-national-rate,spam ================================================ FILE: naive_bayes/sms.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- ''' 通过垃圾短信数据训练朴素贝叶斯模型，并进行留存交叉验证 ''' import re import random import numpy as np import matplotlib.pyplot as plt from bayes import NaiveBayesClassifier ENCODING = 'ISO-8859-1' TRAIN_PERCENTAGE = 0.9 def get_doc_vector(words, vocabulary): ''' 根据词汇表将文档中的词条转换成文档向量 :param words: 文档中的词条列表 :type words: list of str :param vocabulary: 总的词汇列表 :type vocabulary: list of str :return doc_vect: 用于贝叶斯分析的文档向量 :type doc_vect: list of int ''' doc_vect = [0]*len(vocabulary) for word in words: if word in vocabulary: idx = vocabulary.index(word) doc_vect[idx] = 1 return doc_vect def parse_line(line): ''' 解析数据集中的每一行返回词条向量和短信类型. ''' cls = line.split(',')[-1].strip() content = ','.join(line.split(',')[: -1]) word_vect = [word.lower() for word in re.split(r'\W+', content) if word] return word_vect, cls def parse_file(filename): ''' 解析文件中的数据 ''' vocabulary, word_vects, classes = [], [], [] with open(filename, 'r', encoding=ENCODING) as f: for line in f: if line: word_vect, cls = parse_line(line) vocabulary.extend(word_vect) word_vects.append(word_vect) classes.append(cls) vocabulary = list(set(vocabulary)) return vocabulary, word_vects, classes if '__main__' == __name__: clf = NaiveBayesClassifier() vocabulary, word_vects, classes = parse_file('english_big.txt') # 训练数据 & 测试数据 ntest = int(len(classes)*(1-TRAIN_PERCENTAGE)) test_word_vects = [] test_classes = [] for i in range(ntest): idx = random.randint(0, len(word_vects)-1) test_word_vects.append(word_vects.pop(idx)) test_classes.append(classes.pop(idx)) train_word_vects = word_vects train_classes = classes train_dataset = [get_doc_vector(words, vocabulary) for words in train_word_vects] # 训练贝叶斯模型 cond_probs, cls_probs = clf.train(train_dataset, train_classes) # 测试模型 error = 0 for test_word_vect, test_cls in zip(test_word_vects, test_classes): test_data = get_doc_vector(test_word_vect, vocabulary) pred_cls = clf.classify(test_data, cond_probs, cls_probs) if test_cls != pred_cls: print('Predict: {} -- Actual: {}'.format(pred_cls, test_cls)) error += 1 print('Error Rate: {}'.format(error/len(test_classes))) # 绘制不同类型的概率分布曲线 fig = plt.figure() ax = fig.add_subplot(111) for cls, probs in cond_probs.items(): ax.scatter(np.arange(0, len(probs)), probs*cls_probs[cls], label=cls, alpha=0.3) ax.legend() plt.show() ================================================ FILE: support_vector_machine/best_fit.py ================================================ best_fit = [ (0, [0.9643380556912553, -0.14557889594528595, -5.0], 0.4416388939912057), (1, [0.8451392281387395, -0.530532486565706, -3.9638318670576735], 0.6463029769746251), (2, [0.825598436736688, -0.2999511480214949, -3.6901270772238517], 0.8080972667695274), (3, [0.9604298974108452, -0.49535906204201274, -4.491691104594331], 0.8983470449949169), (4, [0.9604298974108452, -0.49535906204201274, -4.491691104594331], 0.8983470449949169), (5, [0.9272105520273568, -0.3644357596482659, -4.296187683284457], 1.0141401297915413), (6, [0.9272105520273568, -0.3644357596482659, -4.296187683284457], 1.0141401297915413), (7, [0.9272105520273568, -0.3644357596482659, -4.296187683284457], 1.0141401297915413), (8, [0.9272105520273568, -0.3644357596482659, -4.296187683284457], 1.0141401297915413), (9, [0.9272105520273568, -0.3644357596482659, -4.296187683284457], 1.0141401297915413), (10, [0.9272105520273568, -0.3644357596482659, -4.296187683284457], 1.0141401297915413), (11, [1.0034196384953589, -0.2823644357596482, -4.765395894428153], 1.0816394576914647), (12, [1.0034196384953589, -0.2823644357596482, -4.765395894428153], 1.0816394576914647), (13, [1.0034196384953589, -0.2823644357596482, -4.765395894428153], 1.0816394576914647), (14, [1.0034196384953589, -0.2823644357596482, -4.765395894428153], 1.0816394576914647), (15, [1.0034196384953589, -0.2823644357596482, -4.765395894428153], 1.0816394576914647), (16, [1.0034196384953589, -0.36052760136785533, -4.6089931573802545], 1.110434001542444), (17, [1.0034196384953589, -0.36052760136785533, -4.6089931573802545], 1.110434001542444), (18, [1.0034196384953589, -0.36052760136785533, -4.6089931573802545], 1.110434001542444), (19, [1.0034196384953589, -0.36052760136785533, -4.6089931573802545], 1.110434001542444), (20, [0.972154372252076, -0.3644357596482659, -4.56989247311828], 1.1577890544568241), (21, [0.972154372252076, -0.3644357596482659, -4.56989247311828], 1.1577890544568241), (22, [0.972154372252076, -0.3644357596482659, -4.56989247311828], 1.1577890544568241), (23, [0.972154372252076, -0.3644357596482659, -4.56989247311828], 1.1577890544568241), (24, [1.0190522716170003, -0.3527112848070346, -4.765395894428153], 1.2134358514384123), (25, [1.0190522716170003, -0.3527112848070346, -4.765395894428153], 1.2134358514384123), (26, [1.0190522716170003, -0.3527112848070346, -4.765395894428153], 1.2134358514384123), (27, [1.048363458720078, -0.35857352222765027, -4.872922776148583], 1.2191491757577673), (28, [1.048363458720078, -0.35857352222765027, -4.872922776148583], 1.2191491757577673), (29, [1.048363458720078, -0.35857352222765027, -4.872922776148583], 1.2191491757577673), (30, [1.048363458720078, -0.35857352222765027, -4.872922776148583], 1.2191491757577673), (31, [1.048363458720078, -0.35857352222765027, -4.872922776148583], 1.2191491757577673), (32, [1.048363458720078, -0.35857352222765027, -4.872922776148583], 1.2191491757577673), (33, [1.048363458720078, -0.35857352222765027, -4.872922776148583], 1.2191491757577673), (34, [1.048363458720078, -0.35857352222765027, -4.872922776148583], 1.2191491757577673), (35, [1.048363458720078, -0.35857352222765027, -4.872922776148583], 1.2191491757577673), 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================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- ''' 使用遗传算法框架GAFT优化SVM. GAFT项目地址: https://github.com/PytLab/gaft ''' import random import numpy as np import matplotlib.pyplot as plt from gaft import GAEngine from gaft.components import GAIndividual, GAPopulation from gaft.operators import RouletteWheelSelection, UniformCrossover, FlipBitBigMutation from gaft.analysis.fitness_store import FitnessStore from gaft.analysis.console_output import ConsoleOutput def load_data(filename): dataset, labels = [], [] with open(filename, 'r') as f: for line in f: x, y, label = [float(i) for i in line.strip().split()] dataset.append([x, y]) labels.append(label) return dataset, labels def get_w(alphas, dataset, labels): ''' 通过已知数据点和拉格朗日乘子获得分割超平面参数w ''' alphas, dataset, labels = np.array(alphas), np.array(dataset), np.array(labels) yx = labels.reshape(1, -1).T*np.array([1, 1])*dataset w = np.dot(yx.T, alphas) return w.tolist() # Population definition. indv_template = GAIndividual(ranges=[(-2, 2), (-2, 2), (-5, 5)], encoding='binary', eps=[0.001, 0.001, 0.005]) population = GAPopulation(indv_template=indv_template, size=600).init() # Genetic operators. selection = RouletteWheelSelection() crossover = UniformCrossover(pc=0.8, pe=0.5) mutation = FlipBitBigMutation(pm=0.1, pbm=0.55, alpha=0.6) engine = GAEngine(population=population, selection=selection, crossover=crossover, mutation=mutation, analysis=[ConsoleOutput, FitnessStore]) # 加载数据 dataset, labels = load_data('testSet.txt') @engine.fitness_register def fitness(indv): w, b = indv.variants[: -1], indv.variants[-1] min_dis = min([y*(np.dot(w, x) + b) for x, y in zip(dataset, labels)]) return float(min_dis) if '__main__' == __name__: engine.run(300) variants = engine.population.best_indv(engine.fitness).variants w = variants[: -1] b = variants[-1] # 分类数据点 classified_pts = {'+1': [], '-1': []} for point, label in zip(dataset, labels): if label == 1.0: classified_pts['+1'].append(point) else: classified_pts['-1'].append(point) fig = plt.figure() ax = fig.add_subplot(111) # 绘制数据点 for label, pts in classified_pts.items(): pts = np.array(pts) ax.scatter(pts[:, 0], pts[:, 1], label=label) # 绘制分割线 x1, _ = max(dataset, key=lambda x: x[0]) x2, _ = min(dataset, key=lambda x: x[0]) a1, a2 = w y1, y2 = (-b - a1*x1)/a2, (-b - a1*x2)/a2 ax.plot([x1, x2], [y1, y2]) plt.show() ================================================ FILE: support_vector_machine/svm_platt_smo.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- import random import numpy as np import matplotlib.pyplot as plt class SVMUtil(object): ''' Struct to save all important values in SVM. ''' def __init__(self, dataset, labels, C, tolerance=0.001): self.dataset, self.labels, self.C = dataset, labels, C self.m, self.n = np.array(dataset).shape self.alphas = np.zeros(self.m) self.b = 0 self.tolerance = tolerance # Cached errors ,f(x_i) - y_i self.errors = [self.get_error(i) for i in range(self.m)] def f(self, x): '''SVM分类器函数 y = w^Tx + b ''' # Kernel function vector. x = np.matrix(x).T data = np.matrix(self.dataset) ks = data*x # Predictive value. wx = np.matrix(self.alphas*self.labels)*ks fx = wx + self.b return fx[0, 0] def get_error(self, i): ''' 获取第i个数据对应的误差. ''' x, y = self.dataset[i], self.labels[i] fx = self.f(x) return fx - y def update_errors(self): ''' 更新所有的误差值. ''' self.errors = [self.get_error(i) for i in range(self.m)] def meet_kkt(self, i): alpha = self.alphas[i] x = self.dataset[i] if alpha == 0: return self.f(x) >= 1 elif alpha == self.C: return self.f(x) <= 1 else: return self.f(x) == 1 def load_data(filename): dataset, labels = [], [] with open(filename, 'r') as f: for line in f: x, y, label = [float(i) for i in line.strip().split()] dataset.append([x, y]) labels.append(label) return dataset, labels def clip(alpha, L, H): ''' 修建alpha的值到L和H之间. ''' if alpha < L: return L elif alpha > H: return H else: return alpha def select_j_rand(i, m): ''' 在m中随机选择除了i之外剩余的数 ''' l = list(range(m)) seq = l[: i] + l[i+1:] return random.choice(seq) def select_j(i, svm_util): ''' 通过最大化步长的方式来获取第二个alpha值的索引. ''' errors = svm_util.errors valid_indices = [i for i, a in enumerate(svm_util.alphas) if 0 < a < svm_util.C] if len(valid_indices) > 1: j = -1 max_delta = 0 for k in valid_indices: if k == i: continue delta = abs(errors[i] - errors[j]) if delta > max_delta: j = k max_delta = delta else: j = select_j_rand(i, svm_util.m) return j def get_w(alphas, dataset, labels): ''' 通过已知数据点和拉格朗日乘子获得分割超平面参数w ''' alphas, dataset, labels = np.array(alphas), np.array(dataset), np.array(labels) yx = labels.reshape(1, -1).T*np.array([1, 1])*dataset w = np.dot(yx.T, alphas) return w.tolist() def take_step(i, j, svm_util): ''' 对选定的一对alpha对进行优化. ''' svm_util.update_errors() alphas, dataset, labels = svm_util.alphas, svm_util.dataset, svm_util.labels C, b = svm_util.C, svm_util.b a_i, x_i, y_i, E_i = alphas[i], dataset[i], labels[i], svm_util.errors[i] a_j, x_j, y_j, E_j = alphas[j], dataset[j], labels[j], svm_util.errors[j] K_ii, K_jj, K_ij = np.dot(x_i, x_i), np.dot(x_j, x_j), np.dot(x_i, x_j) eta = K_ii + K_jj - 2*K_ij if eta <= 0: print('WARNING eta <= 0') return 0 a_i_old, a_j_old = a_i, a_j a_j_new = a_j_old + y_j*(E_i - E_j)/eta # 对alpha进行修剪 if y_i != y_j: L = max(0, a_j_old - a_i_old) H = min(C, C + a_j_old - a_i_old) else: L = max(0, a_i_old + a_j_old - C) H = min(C, a_j_old + a_i_old) a_j_new = clip(a_j_new, L, H) a_i_new = a_i_old + y_i*y_j*(a_j_old - a_j_new) if abs(a_j_new - a_j_old) < 0.00001: #print('WARNING alpha_j not moving enough') return 0 alphas[i], alphas[j] = a_i_new, a_j_new svm_util.update_errors() # 更新阈值b b_i = -E_i - y_i*K_ii*(a_i_new - a_i_old) - y_j*K_ij*(a_j_new - a_j_old) + b b_j = -E_j - y_i*K_ij*(a_i_new - a_i_old) - y_j*K_jj*(a_j_new - a_j_old) + b if 0 < a_i_new < C: b = b_i elif 0 < a_j_new < C: b = b_j else: b = (b_i + b_j)/2 svm_util.b = b print(b) return 1 def examine_example(i, svm_util): ''' 给定第一个alpha， 检测对应alpha是否符合KKT条件并选取第二个alpha进行迭代. ''' E_i, y_i, alpha = svm_util.errors[i], svm_util.labels[i], svm_util.alphas[i] r = E_i*y_i C, tolerance = svm_util.C, svm_util.tolerance # 是否违反KKT条件 if (r < -tolerance and alpha < C) or (r > tolerance and alpha > 0): j = select_j(i, svm_util) #j = select_j_rand(i, svm_util.m) return take_step(i, j, svm_util) else: return 0 def platt_smo(dataset, labels, C, max_iter): ''' Platt SMO算法实现，使用启发式方法对alpha对进行选择. :param dataset: 所有特征数据向量 :param labels: 所有的数据标签 :param C: 软间隔常数, 0 <= alpha_i <= C :param max_iter: 外层循环最大迭代次数 ''' # 初始化SVM工具对象 svm_util = SVMUtil(dataset, labels, C) it = 0 # 遍历所有alpha的标记 entire = True pair_changed = 0 while (it < max_iter): #and (pair_changed > 0 or entire): pair_changed = 0 if entire: for i in range(svm_util.m): pair_changed += examine_example(i, svm_util) print('Full set - iter: {}, pair changed: {}'.format(i, pair_changed)) else: alphas = svm_util.alphas non_bound_indices = [i for i in range(svm_util.m) if alphas[i] > 0 and alphas[i] < C] for i in non_bound_indices: pair_changed += examine_example(i, svm_util) print('Non-bound - iter:{}, pair changed: {}'.format(i, pair_changed)) it += 1 if entire: entire = False elif pair_changed == 0: entire = True print('iteration number: {}'.format(it)) return svm_util.alphas, svm_util.b if '__main__' == __name__: # 加载训练数据 dataset, labels = load_data('testSet.txt') # 使用简化版SMO算法优化SVM alphas, b = platt_smo(dataset, labels, 0.8, 40) # 分类数据点 classified_pts = {'+1': [], '-1': []} for point, label in zip(dataset, labels): if label == 1.0: classified_pts['+1'].append(point) else: classified_pts['-1'].append(point) fig = plt.figure() ax = fig.add_subplot(111) # 绘制数据点 for label, pts in classified_pts.items(): pts = np.array(pts) ax.scatter(pts[:, 0], pts[:, 1], label=label) # 绘制分割线 w = get_w(alphas, dataset, labels) x1, _ = max(dataset, key=lambda x: x[0]) x2, _ = min(dataset, key=lambda x: x[0]) a1, a2 = w y1, y2 = (-b - a1*x1)/a2, (-b - a1*x2)/a2 ax.plot([x1, x2], [y1, y2]) # 绘制支持向量 for i, alpha in enumerate(alphas): if abs(alpha) > 1e-3: x, y = dataset[i] ax.scatter([x], [y], s=150, c='none', alpha=0.7, linewidth=1.5, edgecolor='#AB3319') plt.show() ================================================ FILE: support_vector_machine/svm_simple_smo.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- import random import numpy as np import matplotlib.pyplot as plt def load_data(filename): dataset, labels = [], [] with open(filename, 'r') as f: for line in f: x, y, label = [float(i) for i in line.strip().split()] dataset.append([x, y]) labels.append(label) return dataset, labels def clip(alpha, L, H): ''' 修建alpha的值到L和H之间. ''' if alpha < L: return L elif alpha > H: return H else: return alpha def select_j(i, m): ''' 在m中随机选择除了i之外剩余的数 ''' l = list(range(m)) seq = l[: i] + l[i+1:] return random.choice(seq) def get_w(alphas, dataset, labels): ''' 通过已知数据点和拉格朗日乘子获得分割超平面参数w ''' alphas, dataset, labels = np.array(alphas), np.array(dataset), np.array(labels) yx = labels.reshape(1, -1).T*np.array([1, 1])*dataset w = np.dot(yx.T, alphas) return w.tolist() def simple_smo(dataset, labels, C, max_iter): ''' 简化版SMO算法实现，未使用启发式方法对alpha对进行选择. :param dataset: 所有特征数据向量 :param labels: 所有的数据标签 :param C: 软间隔常数, 0 <= alpha_i <= C :param max_iter: 外层循环最大迭代次数 ''' dataset = np.array(dataset) m, n = dataset.shape labels = np.array(labels) # 初始化参数 alphas = np.zeros(m) b = 0 it = 0 def f(x): "SVM分类器函数 y = w^Tx + b" # Kernel function vector. x = np.matrix(x).T data = np.matrix(dataset) ks = data*x # Predictive value. wx = np.matrix(alphas*labels)*ks fx = wx + b return fx[0, 0] all_alphas, all_bs = [], [] while it < max_iter: pair_changed = 0 for i in range(m): a_i, x_i, y_i = alphas[i], dataset[i], labels[i] fx_i = f(x_i) E_i = fx_i - y_i j = select_j(i, m) a_j, x_j, y_j = alphas[j], dataset[j], labels[j] fx_j = f(x_j) E_j = fx_j - y_j K_ii, K_jj, K_ij = np.dot(x_i, x_i), np.dot(x_j, x_j), np.dot(x_i, x_j) eta = K_ii + K_jj - 2*K_ij if eta <= 0: print('WARNING eta <= 0') continue # 获取更新的alpha对 a_i_old, a_j_old = a_i, a_j a_j_new = a_j_old + y_j*(E_i - E_j)/eta # 对alpha进行修剪 if y_i != y_j: L = max(0, a_j_old - a_i_old) H = min(C, C + a_j_old - a_i_old) else: L = max(0, a_i_old + a_j_old - C) H = min(C, a_j_old + a_i_old) a_j_new = clip(a_j_new, L, H) a_i_new = a_i_old + y_i*y_j*(a_j_old - a_j_new) if abs(a_j_new - a_j_old) < 0.00001: print('WARNING alpha_j not moving enough') continue alphas[i], alphas[j] = a_i_new, a_j_new # 更新阈值b #import ipdb; ipdb.set_trace() b_i = -E_i - y_i*K_ii*(a_i_new - a_i_old) - y_j*K_ij*(a_j_new - a_j_old) + b b_j = -E_j - y_i*K_ij*(a_i_new - a_i_old) - y_j*K_jj*(a_j_new - a_j_old) + b if 0 < a_i_new < C: b = b_i elif 0 < a_j_new < C: b = b_j else: b = (b_i + b_j)/2 all_alphas.append(alphas) all_bs.append(b) pair_changed += 1 print('INFO iteration:{} i:{} pair_changed:{}'.format(it, i, pair_changed)) if pair_changed == 0: it += 1 else: it = 0 print('iteration number: {}'.format(it)) return alphas, b if '__main__' == __name__: # 加载训练数据 dataset, labels = load_data('testSet.txt') # 使用简化版SMO算法优化SVM alphas, b = simple_smo(dataset, labels, 0.6, 40) # 分类数据点 classified_pts = {'+1': [], '-1': []} for point, label in zip(dataset, labels): if label == 1.0: classified_pts['+1'].append(point) else: classified_pts['-1'].append(point) fig = plt.figure() ax = fig.add_subplot(111) # 绘制数据点 for label, pts in classified_pts.items(): pts = np.array(pts) ax.scatter(pts[:, 0], pts[:, 1], label=label) # 绘制分割线 w = get_w(alphas, dataset, labels) x1, _ = max(dataset, key=lambda x: x[0]) x2, _ = min(dataset, key=lambda x: x[0]) a1, a2 = w y1, y2 = (-b - a1*x1)/a2, (-b - a1*x2)/a2 ax.plot([x1, x2], [y1, y2]) # 绘制支持向量 for i, alpha in enumerate(alphas): if abs(alpha) > 1e-3: x, y = dataset[i] ax.scatter([x], [y], s=150, c='none', alpha=0.7, linewidth=1.5, edgecolor='#AB3319') plt.show() ================================================ FILE: support_vector_machine/testSet.txt ================================================ 3.542485 1.977398 -1 3.018896 2.556416 -1 7.551510 -1.580030 1 2.114999 -0.004466 -1 8.127113 1.274372 1 7.108772 -0.986906 1 8.610639 2.046708 1 2.326297 0.265213 -1 3.634009 1.730537 -1 0.341367 -0.894998 -1 3.125951 0.293251 -1 2.123252 -0.783563 -1 0.887835 -2.797792 -1 7.139979 -2.329896 1 1.696414 -1.212496 -1 8.117032 0.623493 1 8.497162 -0.266649 1 4.658191 3.507396 -1 8.197181 1.545132 1 1.208047 0.213100 -1 1.928486 -0.321870 -1 2.175808 -0.014527 -1 7.886608 0.461755 1 3.223038 -0.552392 -1 3.628502 2.190585 -1 7.407860 -0.121961 1 7.286357 0.251077 1 2.301095 -0.533988 -1 -0.232542 -0.547690 -1 3.457096 -0.082216 -1 3.023938 -0.057392 -1 8.015003 0.885325 1 8.991748 0.923154 1 7.916831 -1.781735 1 7.616862 -0.217958 1 2.450939 0.744967 -1 7.270337 -2.507834 1 1.749721 -0.961902 -1 1.803111 -0.176349 -1 8.804461 3.044301 1 1.231257 -0.568573 -1 2.074915 1.410550 -1 -0.743036 -1.736103 -1 3.536555 3.964960 -1 8.410143 0.025606 1 7.382988 -0.478764 1 6.960661 -0.245353 1 8.234460 0.701868 1 8.168618 -0.903835 1 1.534187 -0.622492 -1 9.229518 2.066088 1 7.886242 0.191813 1 2.893743 -1.643468 -1 1.870457 -1.040420 -1 5.286862 -2.358286 1 6.080573 0.418886 1 2.544314 1.714165 -1 6.016004 -3.753712 1 0.926310 -0.564359 -1 0.870296 -0.109952 -1 2.369345 1.375695 -1 1.363782 -0.254082 -1 7.279460 -0.189572 1 1.896005 0.515080 -1 8.102154 -0.603875 1 2.529893 0.662657 -1 1.963874 -0.365233 -1 8.132048 0.785914 1 8.245938 0.372366 1 6.543888 0.433164 1 -0.236713 -5.766721 -1 8.112593 0.295839 1 9.803425 1.495167 1 1.497407 -0.552916 -1 1.336267 -1.632889 -1 9.205805 -0.586480 1 1.966279 -1.840439 -1 8.398012 1.584918 1 7.239953 -1.764292 1 7.556201 0.241185 1 9.015509 0.345019 1 8.266085 -0.230977 1 8.545620 2.788799 1 9.295969 1.346332 1 2.404234 0.570278 -1 2.037772 0.021919 -1 1.727631 -0.453143 -1 1.979395 -0.050773 -1 8.092288 -1.372433 1 1.667645 0.239204 -1 9.854303 1.365116 1 7.921057 -1.327587 1 8.500757 1.492372 1 1.339746 -0.291183 -1 3.107511 0.758367 -1 2.609525 0.902979 -1 3.263585 1.367898 -1 2.912122 -0.202359 -1 1.731786 0.589096 -1 2.387003 1.573131 -1