[
  {
    "path": "Chapter 1/affinity_dataset.txt",
    "content": "0 0 1 1 1\n1 1 0 1 0\n1 0 1 1 0\n0 0 1 1 1\n0 1 0 0 1\n0 1 0 0 0\n1 0 0 0 1\n1 0 0 0 1\n0 0 0 1 1\n0 0 1 1 1\n1 1 0 0 1\n0 1 0 0 0\n0 0 0 0 1\n0 0 1 0 1\n0 1 0 0 1\n0 0 1 1 1\n1 0 0 0 1\n0 0 1 1 1\n1 1 0 0 0\n0 1 0 0 0\n0 0 1 0 0\n0 1 0 0 1\n0 1 0 0 0\n0 1 0 0 1\n0 0 1 1 1\n0 0 1 1 0\n0 0 1 0 1\n0 0 0 0 1\n0 1 0 0 0\n0 1 0 1 0\n1 1 1 0 1\n1 1 0 0 1\n0 0 1 1 1\n0 0 1 0 1\n0 0 1 1 1\n0 0 1 1 0\n0 1 1 0 1\n0 0 1 1 0\n0 1 0 0 1\n0 0 0 0 1\n0 0 1 0 1\n1 1 0 1 1\n1 0 0 0 1\n0 0 1 1 1\n0 1 0 0 0\n0 1 0 1 1\n0 1 0 0 0\n0 1 0 0 0\n0 0 1 1 0\n0 0 1 1 1\n0 1 0 1 0\n0 1 1 0 0\n0 0 1 1 0\n0 0 1 1 1\n1 0 0 0 0\n0 1 0 1 0\n1 0 0 0 1\n0 1 0 0 0\n0 0 0 0 1\n0 0 1 1 1\n0 1 1 1 1\n1 1 0 0 0\n0 0 1 0 1\n1 0 0 0 1\n1 1 0 0 0\n0 1 1 0 0\n0 0 0 0 1\n0 1 0 0 0\n0 0 1 1 1\n0 1 0 0 1\n1 0 0 0 1\n1 0 0 0 1\n0 1 0 0 1\n0 0 1 1 1\n1 0 1 0 1\n1 1 0 0 1\n0 1 0 0 1\n1 1 1 0 1\n0 0 1 1 1\n1 0 0 0 0\n0 0 1 1 1\n1 1 0 1 0\n0 0 1 0 0\n0 0 1 0 1\n0 1 0 0 0\n1 1 0 0 0\n0 0 0 1 0\n0 0 0 1 1\n0 1 0 0 0\n0 1 0 0 0\n1 1 0 0 1\n0 0 1 0 0\n0 1 0 0 1\n1 1 0 1 0\n1 0 0 0 1\n0 1 0 0 0\n0 0 1 1 0\n0 1 1 0 0\n0 0 1 1 0\n0 0 0 0 1\n"
  },
  {
    "path": "Chapter 1/ch1_affinity.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"This dataset has 100 samples and 5 features\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"dataset_filename = \\\"affinity_dataset.txt\\\"\\n\",\n    \"X = np.loadtxt(dataset_filename)\\n\",\n    \"n_samples, n_features = X.shape\\n\",\n    \"print(\\\"This dataset has {0} samples and {1} features\\\".format(n_samples, n_features))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 0.  0.  1.  1.  1.]\\n\",\n      \" [ 1.  1.  0.  1.  0.]\\n\",\n      \" [ 1.  0.  1.  1.  0.]\\n\",\n      \" [ 0.  0.  1.  1.  1.]\\n\",\n      \" [ 0.  1.  0.  0.  1.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(X[:5])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# The names of the features, for your reference.\\n\",\n    \"features = [\\\"bread\\\", \\\"milk\\\", \\\"cheese\\\", \\\"apples\\\", \\\"bananas\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In our first example, we will compute the Support and Confidence of the rule \\\"If a person buys Apples, they also buy Bananas\\\".\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"36 people bought Apples\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# First, how many rows contain our premise: that a person is buying apples\\n\",\n    \"num_apple_purchases = 0\\n\",\n    \"for sample in X:\\n\",\n    \"    if sample[3] == 1:  # This person bought Apples\\n\",\n    \"        num_apple_purchases += 1\\n\",\n    \"print(\\\"{0} people bought Apples\\\".format(num_apple_purchases))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"21 cases of the rule being valid were discovered\\n\",\n      \"15 cases of the rule being invalid were discovered\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# How many of the cases that a person bought Apples involved the people purchasing Bananas too?\\n\",\n    \"# Record both cases where the rule is valid and is invalid.\\n\",\n    \"rule_valid = 0\\n\",\n    \"rule_invalid = 0\\n\",\n    \"for sample in X:\\n\",\n    \"    if sample[3] == 1:  # This person bought Apples\\n\",\n    \"        if sample[4] == 1:\\n\",\n    \"            # This person bought both Apples and Bananas\\n\",\n    \"            rule_valid += 1\\n\",\n    \"        else:\\n\",\n    \"            # This person bought Apples, but not Bananas\\n\",\n    \"            rule_invalid += 1\\n\",\n    \"print(\\\"{0} cases of the rule being valid were discovered\\\".format(rule_valid))\\n\",\n    \"print(\\\"{0} cases of the rule being invalid were discovered\\\".format(rule_invalid))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The support is 21 and the confidence is 0.583.\\n\",\n      \"As a percentage, that is 58.3%.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Now we have all the information needed to compute Support and Confidence\\n\",\n    \"support = rule_valid  # The Support is the number of times the rule is discovered.\\n\",\n    \"confidence = rule_valid / num_apple_purchases\\n\",\n    \"print(\\\"The support is {0} and the confidence is {1:.3f}.\\\".format(support, confidence))\\n\",\n    \"# Confidence can be thought of as a percentage using the following:\\n\",\n    \"print(\\\"As a percentage, that is {0:.1f}%.\\\".format(100 * confidence))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from collections import defaultdict\\n\",\n    \"# Now compute for all possible rules\\n\",\n    \"valid_rules = defaultdict(int)\\n\",\n    \"invalid_rules = defaultdict(int)\\n\",\n    \"num_occurences = defaultdict(int)\\n\",\n    \"\\n\",\n    \"for sample in X:\\n\",\n    \"    for premise in range(n_features):\\n\",\n    \"        if sample[premise] == 0: continue\\n\",\n    \"        # Record that the premise was bought in another transaction\\n\",\n    \"        num_occurences[premise] += 1\\n\",\n    \"        for conclusion in range(n_features):\\n\",\n    \"            if premise == conclusion:  # It makes little sense to measure if X -> X.\\n\",\n    \"                continue\\n\",\n    \"            if sample[conclusion] == 1:\\n\",\n    \"                # This person also bought the conclusion item\\n\",\n    \"                valid_rules[(premise, conclusion)] += 1\\n\",\n    \"            else:\\n\",\n    \"                # This person bought the premise, but not the conclusion\\n\",\n    \"                invalid_rules[(premise, conclusion)] += 1\\n\",\n    \"support = valid_rules\\n\",\n    \"confidence = defaultdict(float)\\n\",\n    \"for premise, conclusion in valid_rules.keys():\\n\",\n    \"    confidence[(premise, conclusion)] = valid_rules[(premise, conclusion)] / num_occurences[premise]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Rule: If a person buys bread they will also buy milk\\n\",\n      \" - Confidence: 0.519\\n\",\n      \" - Support: 14\\n\",\n      \"\\n\",\n      \"Rule: If a person buys milk they will also buy cheese\\n\",\n      \" - Confidence: 0.152\\n\",\n      \" - Support: 7\\n\",\n      \"\\n\",\n      \"Rule: If a person buys apples they will also buy cheese\\n\",\n      \" - Confidence: 0.694\\n\",\n      \" - Support: 25\\n\",\n      \"\\n\",\n      \"Rule: If a person buys milk they will also buy apples\\n\",\n      \" - Confidence: 0.196\\n\",\n      \" - Support: 9\\n\",\n      \"\\n\",\n      \"Rule: If a person buys bread they will also buy apples\\n\",\n      \" - Confidence: 0.185\\n\",\n      \" - Support: 5\\n\",\n      \"\\n\",\n      \"Rule: If a person buys apples they will also buy bread\\n\",\n      \" - Confidence: 0.139\\n\",\n      \" - Support: 5\\n\",\n      \"\\n\",\n      \"Rule: If a person buys apples they will also buy bananas\\n\",\n      \" - Confidence: 0.583\\n\",\n      \" - Support: 21\\n\",\n      \"\\n\",\n      \"Rule: If a person buys apples they will also buy milk\\n\",\n      \" - Confidence: 0.250\\n\",\n      \" - Support: 9\\n\",\n      \"\\n\",\n      \"Rule: If a person buys milk they will also buy bananas\\n\",\n      \" - Confidence: 0.413\\n\",\n      \" - Support: 19\\n\",\n      \"\\n\",\n      \"Rule: If a person buys cheese they will also buy bananas\\n\",\n      \" - Confidence: 0.659\\n\",\n      \" - Support: 27\\n\",\n      \"\\n\",\n      \"Rule: If a person buys cheese they will also buy bread\\n\",\n      \" - Confidence: 0.098\\n\",\n      \" - Support: 4\\n\",\n      \"\\n\",\n      \"Rule: If a person buys cheese they will also buy apples\\n\",\n      \" - Confidence: 0.610\\n\",\n      \" - Support: 25\\n\",\n      \"\\n\",\n      \"Rule: If a person buys cheese they will also buy milk\\n\",\n      \" - Confidence: 0.171\\n\",\n      \" - Support: 7\\n\",\n      \"\\n\",\n      \"Rule: If a person buys bananas they will also buy apples\\n\",\n      \" - Confidence: 0.356\\n\",\n      \" - Support: 21\\n\",\n      \"\\n\",\n      \"Rule: If a person buys bread they will also buy bananas\\n\",\n      \" - Confidence: 0.630\\n\",\n      \" - Support: 17\\n\",\n      \"\\n\",\n      \"Rule: If a person buys bananas they will also buy cheese\\n\",\n      \" - Confidence: 0.458\\n\",\n      \" - Support: 27\\n\",\n      \"\\n\",\n      \"Rule: If a person buys milk they will also buy bread\\n\",\n      \" - Confidence: 0.304\\n\",\n      \" - Support: 14\\n\",\n      \"\\n\",\n      \"Rule: If a person buys bananas they will also buy milk\\n\",\n      \" - Confidence: 0.322\\n\",\n      \" - Support: 19\\n\",\n      \"\\n\",\n      \"Rule: If a person buys bread they will also buy cheese\\n\",\n      \" - Confidence: 0.148\\n\",\n      \" - Support: 4\\n\",\n      \"\\n\",\n      \"Rule: If a person buys bananas they will also buy bread\\n\",\n      \" - Confidence: 0.288\\n\",\n      \" - Support: 17\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for premise, conclusion in confidence:\\n\",\n    \"    premise_name = features[premise]\\n\",\n    \"    conclusion_name = features[conclusion]\\n\",\n    \"    print(\\\"Rule: If a person buys {0} they will also buy {1}\\\".format(premise_name, conclusion_name))\\n\",\n    \"    print(\\\" - Confidence: {0:.3f}\\\".format(confidence[(premise, conclusion)]))\\n\",\n    \"    print(\\\" - Support: {0}\\\".format(support[(premise, conclusion)]))\\n\",\n    \"    print(\\\"\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def print_rule(premise, conclusion, support, confidence, features):\\n\",\n    \"    premise_name = features[premise]\\n\",\n    \"    conclusion_name = features[conclusion]\\n\",\n    \"    print(\\\"Rule: If a person buys {0} they will also buy {1}\\\".format(premise_name, conclusion_name))\\n\",\n    \"    print(\\\" - Confidence: {0:.3f}\\\".format(confidence[(premise, conclusion)]))\\n\",\n    \"    print(\\\" - Support: {0}\\\".format(support[(premise, conclusion)]))\\n\",\n    \"    print(\\\"\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Rule: If a person buys milk they will also buy apples\\n\",\n      \" - Confidence: 0.196\\n\",\n      \" - Support: 9\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"premise = 1\\n\",\n    \"conclusion = 3\\n\",\n    \"print_rule(premise, conclusion, support, confidence, features)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[((0, 1), 14),\\n\",\n      \" ((1, 2), 7),\\n\",\n      \" ((3, 2), 25),\\n\",\n      \" ((1, 3), 9),\\n\",\n      \" ((0, 2), 4),\\n\",\n      \" ((3, 0), 5),\\n\",\n      \" ((4, 1), 19),\\n\",\n      \" ((3, 1), 9),\\n\",\n      \" ((1, 4), 19),\\n\",\n      \" ((2, 4), 27),\\n\",\n      \" ((2, 0), 4),\\n\",\n      \" ((2, 3), 25),\\n\",\n      \" ((2, 1), 7),\\n\",\n      \" ((4, 3), 21),\\n\",\n      \" ((0, 4), 17),\\n\",\n      \" ((4, 2), 27),\\n\",\n      \" ((1, 0), 14),\\n\",\n      \" ((3, 4), 21),\\n\",\n      \" ((0, 3), 5),\\n\",\n      \" ((4, 0), 17)]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Sort by support\\n\",\n    \"from pprint import pprint\\n\",\n    \"pprint(list(support.items()))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from operator import itemgetter\\n\",\n    \"sorted_support = sorted(support.items(), key=itemgetter(1), reverse=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Rule #1\\n\",\n      \"Rule: If a person buys cheese they will also buy bananas\\n\",\n      \" - Confidence: 0.659\\n\",\n      \" - Support: 27\\n\",\n      \"\\n\",\n      \"Rule #2\\n\",\n      \"Rule: If a person buys bananas they will also buy cheese\\n\",\n      \" - Confidence: 0.458\\n\",\n      \" - Support: 27\\n\",\n      \"\\n\",\n      \"Rule #3\\n\",\n      \"Rule: If a person buys apples they will also buy cheese\\n\",\n      \" - Confidence: 0.694\\n\",\n      \" - Support: 25\\n\",\n      \"\\n\",\n      \"Rule #4\\n\",\n      \"Rule: If a person buys cheese they will also buy apples\\n\",\n      \" - Confidence: 0.610\\n\",\n      \" - Support: 25\\n\",\n      \"\\n\",\n      \"Rule #5\\n\",\n      \"Rule: If a person buys bananas they will also buy apples\\n\",\n      \" - Confidence: 0.356\\n\",\n      \" - Support: 21\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for index in range(5):\\n\",\n    \"    print(\\\"Rule #{0}\\\".format(index + 1))\\n\",\n    \"    (premise, conclusion) = sorted_support[index][0]\\n\",\n    \"    print_rule(premise, conclusion, support, confidence, features)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"sorted_confidence = sorted(confidence.items(), key=itemgetter(1), reverse=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Rule #1\\n\",\n      \"Rule: If a person buys apples they will also buy cheese\\n\",\n      \" - Confidence: 0.694\\n\",\n      \" - Support: 25\\n\",\n      \"\\n\",\n      \"Rule #2\\n\",\n      \"Rule: If a person buys cheese they will also buy bananas\\n\",\n      \" - Confidence: 0.659\\n\",\n      \" - Support: 27\\n\",\n      \"\\n\",\n      \"Rule #3\\n\",\n      \"Rule: If a person buys bread they will also buy bananas\\n\",\n      \" - Confidence: 0.630\\n\",\n      \" - Support: 17\\n\",\n      \"\\n\",\n      \"Rule #4\\n\",\n      \"Rule: If a person buys cheese they will also buy apples\\n\",\n      \" - Confidence: 0.610\\n\",\n      \" - Support: 25\\n\",\n      \"\\n\",\n      \"Rule #5\\n\",\n      \"Rule: If a person buys apples they will also buy bananas\\n\",\n      \" - Confidence: 0.583\\n\",\n      \" - Support: 21\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for index in range(5):\\n\",\n    \"    print(\\\"Rule #{0}\\\".format(index + 1))\\n\",\n    \"    (premise, conclusion) = sorted_confidence[index][0]\\n\",\n    \"    print_rule(premise, conclusion, support, confidence, features)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "Chapter 1/ch1_affinity_create.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import numpy as np\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"X = np.zeros((100, 5), dtype='bool')\\n\",\n      \"features = [\\\"bread\\\", \\\"milk\\\", \\\"cheese\\\", \\\"apples\\\", \\\"bananas\\\"]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"for i in range(X.shape[0]):\\n\",\n      \"    if np.random.random() < 0.3:\\n\",\n      \"        # A bread winner\\n\",\n      \"        X[i][0] = 1\\n\",\n      \"        if np.random.random() < 0.5:\\n\",\n      \"            # Who likes milk\\n\",\n      \"            X[i][1] = 1\\n\",\n      \"        if np.random.random() < 0.2:\\n\",\n      \"            # Who likes cheese\\n\",\n      \"            X[i][2] = 1\\n\",\n      \"        if np.random.random() < 0.25:\\n\",\n      \"            # Who likes apples\\n\",\n      \"            X[i][3] = 1\\n\",\n      \"        if np.random.random() < 0.5:\\n\",\n      \"            # Who likes bananas\\n\",\n      \"            X[i][4] = 1\\n\",\n      \"    else:\\n\",\n      \"        # Not a bread winner\\n\",\n      \"        if np.random.random() < 0.5:\\n\",\n      \"            # Who likes milk\\n\",\n      \"            X[i][1] = 1\\n\",\n      \"            if np.random.random() < 0.2:\\n\",\n      \"                # Who likes cheese\\n\",\n      \"                X[i][2] = 1\\n\",\n      \"            if np.random.random() < 0.25:\\n\",\n      \"                # Who likes apples\\n\",\n      \"                X[i][3] = 1\\n\",\n      \"            if np.random.random() < 0.5:\\n\",\n      \"                # Who likes bananas\\n\",\n      \"                X[i][4] = 1\\n\",\n      \"        else:\\n\",\n      \"            if np.random.random() < 0.8:\\n\",\n      \"                # Who likes cheese\\n\",\n      \"                X[i][2] = 1\\n\",\n      \"            if np.random.random() < 0.6:\\n\",\n      \"                # Who likes apples\\n\",\n      \"                X[i][3] = 1\\n\",\n      \"            if np.random.random() < 0.7:\\n\",\n      \"                # Who likes bananas\\n\",\n      \"                X[i][4] = 1\\n\",\n      \"    if X[i].sum() == 0:\\n\",\n      \"        X[i][4] = 1  # Must buy something, so gets bananas\\n\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 3\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"print(X[:5])\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"[[False False  True  True  True]\\n\",\n        \" [ True  True False  True False]\\n\",\n        \" [ True False  True  True False]\\n\",\n        \" [False False  True  True  True]\\n\",\n        \" [False  True False False  True]]\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 5\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"np.savetxt(\\\"affinity_dataset.txt\\\", X, fmt='%d')\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 7\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}"
  },
  {
    "path": "Chapter 1/ch1_oner_application.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The OneR algorithm is quite simple but can be quite effective, showing the power of using even basic statistics in many applications.\\n\",\n    \"The algorithm is:\\n\",\n    \"\\n\",\n    \"* For each variable\\n\",\n    \"    * For each value of the variable\\n\",\n    \"        * The prediction based on this variable goes the most frequent class\\n\",\n    \"        * Compute the error of this prediction\\n\",\n    \"    * Sum the prediction errors for all values of the variable\\n\",\n    \"* Use the variable with the lowest error\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Iris Plants Database\\n\",\n      \"\\n\",\n      \"Notes\\n\",\n      \"-----\\n\",\n      \"Data Set Characteristics:\\n\",\n      \"    :Number of Instances: 150 (50 in each of three classes)\\n\",\n      \"    :Number of Attributes: 4 numeric, predictive attributes and the class\\n\",\n      \"    :Attribute Information:\\n\",\n      \"        - sepal length in cm\\n\",\n      \"        - sepal width in cm\\n\",\n      \"        - petal length in cm\\n\",\n      \"        - petal width in cm\\n\",\n      \"        - class:\\n\",\n      \"                - Iris-Setosa\\n\",\n      \"                - Iris-Versicolour\\n\",\n      \"                - Iris-Virginica\\n\",\n      \"    :Summary Statistics:\\n\",\n      \"    ============== ==== ==== ======= ===== ====================\\n\",\n      \"                    Min  Max   Mean    SD   Class Correlation\\n\",\n      \"    ============== ==== ==== ======= ===== ====================\\n\",\n      \"    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n\",\n      \"    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n\",\n      \"    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n\",\n      \"    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)\\n\",\n      \"    ============== ==== ==== ======= ===== ====================\\n\",\n      \"    :Missing Attribute Values: None\\n\",\n      \"    :Class Distribution: 33.3% for each of 3 classes.\\n\",\n      \"    :Creator: R.A. Fisher\\n\",\n      \"    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n\",\n      \"    :Date: July, 1988\\n\",\n      \"\\n\",\n      \"This is a copy of UCI ML iris datasets.\\n\",\n      \"http://archive.ics.uci.edu/ml/datasets/Iris\\n\",\n      \"\\n\",\n      \"The famous Iris database, first used by Sir R.A Fisher\\n\",\n      \"\\n\",\n      \"This is perhaps the best known database to be found in the\\n\",\n      \"pattern recognition literature.  Fisher's paper is a classic in the field and\\n\",\n      \"is referenced frequently to this day.  (See Duda & Hart, for example.)  The\\n\",\n      \"data set contains 3 classes of 50 instances each, where each class refers to a\\n\",\n      \"type of iris plant.  One class is linearly separable from the other 2; the\\n\",\n      \"latter are NOT linearly separable from each other.\\n\",\n      \"\\n\",\n      \"References\\n\",\n      \"----------\\n\",\n      \"   - Fisher,R.A. \\\"The use of multiple measurements in taxonomic problems\\\"\\n\",\n      \"     Annual Eugenics, 7, Part II, 179-188 (1936); also in \\\"Contributions to\\n\",\n      \"     Mathematical Statistics\\\" (John Wiley, NY, 1950).\\n\",\n      \"   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\\n\",\n      \"     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n\",\n      \"   - Dasarathy, B.V. (1980) \\\"Nosing Around the Neighborhood: A New System\\n\",\n      \"     Structure and Classification Rule for Recognition in Partially Exposed\\n\",\n      \"     Environments\\\".  IEEE Transactions on Pattern Analysis and Machine\\n\",\n      \"     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n\",\n      \"   - Gates, G.W. (1972) \\\"The Reduced Nearest Neighbor Rule\\\".  IEEE Transactions\\n\",\n      \"     on Information Theory, May 1972, 431-433.\\n\",\n      \"   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\\\"s AUTOCLASS II\\n\",\n      \"     conceptual clustering system finds 3 classes in the data.\\n\",\n      \"   - Many, many more ...\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Load our dataset\\n\",\n    \"from sklearn.datasets import load_iris\\n\",\n    \"#X, y = np.loadtxt(\\\"X_classification.txt\\\"), np.loadtxt(\\\"y_classification.txt\\\")\\n\",\n    \"dataset = load_iris()\\n\",\n    \"X = dataset.data\\n\",\n    \"y = dataset.target\\n\",\n    \"print(dataset.DESCR)\\n\",\n    \"n_samples, n_features = X.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Our attributes are continuous, while we want categorical features to use OneR. We will perform a *preprocessing* step called discretisation. At this stage, we will perform a simple procedure: compute the mean and determine whether a value is above or below the mean.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Compute the mean for each attribute\\n\",\n    \"attribute_means = X.mean(axis=0)\\n\",\n    \"assert attribute_means.shape == (n_features,)\\n\",\n    \"X_d = np.array(X >= attribute_means, dtype='int')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"There are (112,) training samples\\n\",\n      \"There are (38,) testing samples\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Now, we split into a training and test set\\n\",\n    \"from sklearn.cross_validation import train_test_split\\n\",\n    \"\\n\",\n    \"# Set the random state to the same number to get the same results as in the book\\n\",\n    \"random_state = 14\\n\",\n    \"\\n\",\n    \"X_train, X_test, y_train, y_test = train_test_split(X_d, y, random_state=random_state)\\n\",\n    \"print(\\\"There are {} training samples\\\".format(y_train.shape))\\n\",\n    \"print(\\\"There are {} testing samples\\\".format(y_test.shape))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from collections import defaultdict\\n\",\n    \"from operator import itemgetter\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def train(X, y_true, feature):\\n\",\n    \"    \\\"\\\"\\\"Computes the predictors and error for a given feature using the OneR algorithm\\n\",\n    \"    \\n\",\n    \"    Parameters\\n\",\n    \"    ----------\\n\",\n    \"    X: array [n_samples, n_features]\\n\",\n    \"        The two dimensional array that holds the dataset. Each row is a sample, each column\\n\",\n    \"        is a feature.\\n\",\n    \"    \\n\",\n    \"    y_true: array [n_samples,]\\n\",\n    \"        The one dimensional array that holds the class values. Corresponds to X, such that\\n\",\n    \"        y_true[i] is the class value for sample X[i].\\n\",\n    \"    \\n\",\n    \"    feature: int\\n\",\n    \"        An integer corresponding to the index of the variable we wish to test.\\n\",\n    \"        0 <= variable < n_features\\n\",\n    \"        \\n\",\n    \"    Returns\\n\",\n    \"    -------\\n\",\n    \"    predictors: dictionary of tuples: (value, prediction)\\n\",\n    \"        For each item in the array, if the variable has a given value, make the given prediction.\\n\",\n    \"    \\n\",\n    \"    error: float\\n\",\n    \"        The ratio of training data that this rule incorrectly predicts.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    # Check that variable is a valid number\\n\",\n    \"    n_samples, n_features = X.shape\\n\",\n    \"    assert 0 <= feature < n_features\\n\",\n    \"    # Get all of the unique values that this variable has\\n\",\n    \"    values = set(X[:,feature])\\n\",\n    \"    # Stores the predictors array that is returned\\n\",\n    \"    predictors = dict()\\n\",\n    \"    errors = []\\n\",\n    \"    for current_value in values:\\n\",\n    \"        most_frequent_class, error = train_feature_value(X, y_true, feature, current_value)\\n\",\n    \"        predictors[current_value] = most_frequent_class\\n\",\n    \"        errors.append(error)\\n\",\n    \"    # Compute the total error of using this feature to classify on\\n\",\n    \"    total_error = sum(errors)\\n\",\n    \"    return predictors, total_error\\n\",\n    \"\\n\",\n    \"# Compute what our predictors say each sample is based on its value\\n\",\n    \"#y_predicted = np.array([predictors[sample[feature]] for sample in X])\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"def train_feature_value(X, y_true, feature, value):\\n\",\n    \"    # Create a simple dictionary to count how frequency they give certain predictions\\n\",\n    \"    class_counts = defaultdict(int)\\n\",\n    \"    # Iterate through each sample and count the frequency of each class/value pair\\n\",\n    \"    for sample, y in zip(X, y_true):\\n\",\n    \"        if sample[feature] == value:\\n\",\n    \"            class_counts[y] += 1\\n\",\n    \"    # Now get the best one by sorting (highest first) and choosing the first item\\n\",\n    \"    sorted_class_counts = sorted(class_counts.items(), key=itemgetter(1), reverse=True)\\n\",\n    \"    most_frequent_class = sorted_class_counts[0][0]\\n\",\n    \"    # The error is the number of samples that do not classify as the most frequent class\\n\",\n    \"    # *and* have the feature value.\\n\",\n    \"    n_samples = X.shape[1]\\n\",\n    \"    error = sum([class_count for class_value, class_count in class_counts.items()\\n\",\n    \"                 if class_value != most_frequent_class])\\n\",\n    \"    return most_frequent_class, error\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The best model is based on variable 2 and has error 37.00\\n\",\n      \"{'variable': 2, 'predictor': {0: 0, 1: 2}}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Compute all of the predictors\\n\",\n    \"all_predictors = {variable: train(X_train, y_train, variable) for variable in range(X_train.shape[1])}\\n\",\n    \"errors = {variable: error for variable, (mapping, error) in all_predictors.items()}\\n\",\n    \"# Now choose the best and save that as \\\"model\\\"\\n\",\n    \"# Sort by error\\n\",\n    \"best_variable, best_error = sorted(errors.items(), key=itemgetter(1))[0]\\n\",\n    \"print(\\\"The best model is based on variable {0} and has error {1:.2f}\\\".format(best_variable, best_error))\\n\",\n    \"\\n\",\n    \"# Choose the bset model\\n\",\n    \"model = {'variable': best_variable,\\n\",\n    \"         'predictor': all_predictors[best_variable][0]}\\n\",\n    \"print(model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def predict(X_test, model):\\n\",\n    \"    variable = model['variable']\\n\",\n    \"    predictor = model['predictor']\\n\",\n    \"    y_predicted = np.array([predictor[int(sample[variable])] for sample in X_test])\\n\",\n    \"    return y_predicted\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0 0 0 2 2 2 0 2 0 2 2 0 2 2 0 2 0 2 2 2 0 0 0 2 0 2 0 2 2 0 0 0 2 0 2 0 2\\n\",\n      \" 2]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"y_predicted = predict(X_test, model)\\n\",\n    \"print(y_predicted)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The test accuracy is 65.8%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Compute the accuracy by taking the mean of the amounts that y_predicted is equal to y_test\\n\",\n    \"accuracy = np.mean(y_predicted == y_test) * 100\\n\",\n    \"print(\\\"The test accuracy is {:.1f}%\\\".format(accuracy))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.metrics import classification_report\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"             precision    recall  f1-score   support\\n\",\n      \"\\n\",\n      \"          0       0.94      1.00      0.97        17\\n\",\n      \"          1       0.00      0.00      0.00        13\\n\",\n      \"          2       0.40      1.00      0.57         8\\n\",\n      \"\\n\",\n      \"avg / total       0.51      0.66      0.55        38\\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/usr/local/lib/python3.4/dist-packages/sklearn/metrics/metrics.py:1771: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\\n\",\n      \"  'precision', 'predicted', average, warn_for)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(classification_report(y_test, y_predicted))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "Chapter 10/Chapter 10 Clusterer.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"490\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"import numpy as np\\n\",\n    \"data_folder = os.path.join(os.path.expanduser(\\\"~\\\"), \\\"Data\\\", \\\"websites\\\", \\\"textonly\\\")\\n\",\n    \"\\n\",\n    \"documents = [open(os.path.join(data_folder, filename)).read() for filename in os.listdir(data_folder)]\\n\",\n    \"len(documents)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Pretty printing has been turned OFF\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"pprint([document[:100] for document in documents[:5]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.cluster import KMeans\\n\",\n    \"from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.pipeline import Pipeline\\n\",\n    \"\\n\",\n    \"n_clusters = 10\\n\",\n    \"\\n\",\n    \"pipeline = Pipeline([('feature_extraction', TfidfVectorizer(max_df=0.4)),\\n\",\n    \"                     ('clusterer', KMeans(n_clusters=n_clusters))\\n\",\n    \"                     ])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Pipeline(steps=[('feature_extraction', TfidfVectorizer(analyzer='word', binary=False, charset=None,\\n\",\n       \"        charset_error=None, decode_error='strict',\\n\",\n       \"        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\\n\",\n       \"        lowercase=True, max_df=0.4, max_features=None, min_df=1,\\n\",\n       \"        ngram_range=(... n_init=10,\\n\",\n       \"    n_jobs=1, precompute_distances=True, random_state=None, tol=0.0001,\\n\",\n       \"    verbose=0))])\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipeline.fit(documents)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"labels = pipeline.predict(documents)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Cluster 0 contains 1 samples\\n\",\n      \"Cluster 1 contains 2 samples\\n\",\n      \"Cluster 2 contains 439 samples\\n\",\n      \"Cluster 3 contains 1 samples\\n\",\n      \"Cluster 4 contains 2 samples\\n\",\n      \"Cluster 5 contains 3 samples\\n\",\n      \"Cluster 6 contains 27 samples\\n\",\n      \"Cluster 7 contains 2 samples\\n\",\n      \"Cluster 8 contains 12 samples\\n\",\n      \"Cluster 9 contains 1 samples\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from collections import Counter\\n\",\n    \"c = Counter(labels)\\n\",\n    \"for cluster_number in range(n_clusters):\\n\",\n    \"    print(\\\"Cluster {} contains {} samples\\\".format(cluster_number, c[cluster_number]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"c[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"381.97317261989065\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipeline.named_steps['clusterer'].inertia_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inertia_scores = []\\n\",\n    \"n_cluster_values = list(range(2, 20))\\n\",\n    \"for n_clusters in n_cluster_values:\\n\",\n    \"    cur_inertia_scores = []\\n\",\n    \"    X = TfidfVectorizer(max_df=0.4).fit_transform(documents)\\n\",\n    \"    for i in range(30):\\n\",\n    \"        km = KMeans(n_clusters=n_clusters).fit(X)\\n\",\n    \"        cur_inertia_scores.append(km.inertia_)\\n\",\n    \"    inertia_scores.append(cur_inertia_scores)\\n\",\n    \"inertia_scores = np.array(inertia_scores)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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bnlbdqvqJ6mFNV1o4EQAAAAAAbCQlHAAA2O+mf6lOq76gembTFy6cCAAAAAAANo4S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\\n5+rEpi+oHt50ydqzDqvpWl15QHMgT6b5xOpDffx45v3Ve6u3XclrPnTY/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     \"text/plain\": [\n       \"<matplotlib.figure.Figure object at 0x7f0bc5ae9240>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"from matplotlib import pyplot as plt\\n\",\n    \"\\n\",\n    \"inertia_means = np.mean(inertia_scores, axis=1)\\n\",\n    \"inertia_stderr = np.std(inertia_scores, axis=1)\\n\",\n    \"\\n\",\n    \"fig = plt.figure(figsize=(40,20))\\n\",\n    \"plt.errorbar(n_cluster_values, inertia_means, inertia_stderr, color='green')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Pipeline(steps=[('feature_extraction', TfidfVectorizer(analyzer='word', binary=False, charset=None,\\n\",\n       \"        charset_error=None, decode_error='strict',\\n\",\n       \"        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\\n\",\n       \"        lowercase=True, max_df=0.4, max_features=None, min_df=1,\\n\",\n       \"        ngram_range=(... n_init=10,\\n\",\n       \"    n_jobs=1, precompute_distances=True, random_state=None, tol=0.0001,\\n\",\n       \"    verbose=0))])\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"n_clusters = 6\\n\",\n    \"\\n\",\n    \"pipeline = Pipeline([('feature_extraction', TfidfVectorizer(max_df=0.4)),\\n\",\n    \"                     ('clusterer', KMeans(n_clusters=n_clusters))\\n\",\n    \"                     ])\\n\",\n    \"pipeline.fit(documents)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"labels = pipeline.predict(documents)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Cluster 0 contains 21 samples\\n\",\n      \"  Most important terms\\n\",\n      \"  1) korea (score: 0.1591)\\n\",\n      \"  2) korean (score: 0.1573)\\n\",\n      \"  3) south (score: 0.1336)\\n\",\n      \"  4) kim (score: 0.1088)\\n\",\n      \"  5) north (score: 0.1077)\\n\",\n      \"\\n\",\n      \"Cluster 1 contains 33 samples\\n\",\n      \"  Most important terms\\n\",\n      \"  1) iran (score: 0.1047)\\n\",\n      \"  2) netanyahu (score: 0.0869)\\n\",\n      \"  3) nuclear (score: 0.0660)\\n\",\n      \"  4) he (score: 0.0615)\\n\",\n      \"  5) his (score: 0.0550)\\n\",\n      \"\\n\",\n      \"Cluster 2 contains 156 samples\\n\",\n      \"  Most important terms\\n\",\n      \"  1) palestinian (score: 0.0196)\\n\",\n      \"  2) israel (score: 0.0138)\\n\",\n      \"  3) security (score: 0.0118)\\n\",\n      \"  4) bank (score: 0.0115)\\n\",\n      \"  5) its (score: 0.0103)\\n\",\n      \"\\n\",\n      \"Cluster 3 contains 31 samples\\n\",\n      \"  Most important terms\\n\",\n      \"  1) browser (score: 0.2786)\\n\",\n      \"  2) able (score: 0.2429)\\n\",\n      \"  3) you (score: 0.2018)\\n\",\n      \"  4) css (score: 0.1925)\\n\",\n      \"  5) sheets (score: 0.1925)\\n\",\n      \"\\n\",\n      \"Cluster 4 contains 48 samples\\n\",\n      \"  Most important terms\\n\",\n      \"  1) al (score: 0.0862)\\n\",\n      \"  2) isis (score: 0.0723)\\n\",\n      \"  3) islamic (score: 0.0664)\\n\",\n      \"  4) iraq (score: 0.0657)\\n\",\n      \"  5) state (score: 0.0580)\\n\",\n      \"\\n\",\n      \"Cluster 5 contains 201 samples\\n\",\n      \"  Most important terms\\n\",\n      \"  1) he (score: 0.0448)\\n\",\n      \"  2) we (score: 0.0306)\\n\",\n      \"  3) they (score: 0.0294)\\n\",\n      \"  4) his (score: 0.0254)\\n\",\n      \"  5) who (score: 0.0253)\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"c = Counter(labels)\\n\",\n    \"\\n\",\n    \"terms = pipeline.named_steps['feature_extraction'].get_feature_names()\\n\",\n    \"\\n\",\n    \"for cluster_number in range(n_clusters):\\n\",\n    \"    print(\\\"Cluster {} contains {} samples\\\".format(cluster_number, c[cluster_number]))\\n\",\n    \"    print(\\\"  Most important terms\\\")\\n\",\n    \"    centroid = pipeline.named_steps['clusterer'].cluster_centers_[cluster_number]\\n\",\n    \"    most_important = centroid.argsort()\\n\",\n    \"    for i in range(5):\\n\",\n    \"        term_index = most_important[-(i+1)]\\n\",\n    \"        print(\\\"  {0}) {1} (score: {2:.4f})\\\".format(i+1, terms[term_index], centroid[term_index]))\\n\",\n    \"    print()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.031820420989154712\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.metrics import silhouette_score\\n\",\n    \"X = pipeline.named_steps['feature_extraction'].transform(documents)\\n\",\n    \"silhouette_score(X, labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"14327\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"len(terms)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"Y = pipeline.transform(documents) \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"km = KMeans(n_clusters=n_clusters)\\n\",\n    \"labels = km.fit_predict(Y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Cluster 0 contains 89 samples\\n\",\n      \"Cluster 1 contains 65 samples\\n\",\n      \"Cluster 2 contains 7 samples\\n\",\n      \"Cluster 3 contains 24 samples\\n\",\n      \"Cluster 4 contains 290 samples\\n\",\n      \"Cluster 5 contains 15 samples\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"c = Counter(labels)\\n\",\n    \"for cluster_number in range(n_clusters):\\n\",\n    \"    print(\\\"Cluster {} contains {} samples\\\".format(cluster_number, c[cluster_number]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.56564461613141714\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"silhouette_score(Y, labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(490, 6)\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"Y.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Evidence Accumulation Clustering\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from scipy.sparse import csr_matrix\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def create_coassociation_matrix(labels):\\n\",\n    \"    rows = []\\n\",\n    \"    cols = []\\n\",\n    \"    unique_labels = set(labels)\\n\",\n    \"    for label in unique_labels:\\n\",\n    \"        indices = np.where(labels == label)[0]\\n\",\n    \"        for index1 in indices:\\n\",\n    \"            for index2 in indices:\\n\",\n    \"                rows.append(index1)\\n\",\n    \"                cols.append(index2)\\n\",\n    \"    data = np.ones((len(rows),))\\n\",\n    \"    return csr_matrix((data, (rows, cols)), dtype='float')\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"C = create_coassociation_matrix(labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<490x490 sparse matrix of type '<class 'numpy.float64'>'\\n\",\n       \"\\twith 97096 stored elements in Compressed Sparse Row format>\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"C\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"((490, 490), 240100)\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"C.shape, C.shape[0] * C.shape[1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.4043981674302374\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"len(C.nonzero()[0]) / (C.shape[0] * C.shape[1])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from scipy.sparse.csgraph import minimum_spanning_tree\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"mst = minimum_spanning_tree(C)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<490x490 sparse matrix of type '<class 'numpy.float64'>'\\n\",\n       \"\\twith 484 stored elements in Compressed Sparse Row format>\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"mst\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"pipeline = Pipeline([('feature_extraction', TfidfVectorizer(max_df=0.4)),\\n\",\n    \"                     ('clusterer', KMeans(n_clusters=3))\\n\",\n    \"                     ])\\n\",\n    \"pipeline.fit(documents)\\n\",\n    \"labels2 = pipeline.predict(documents)\\n\",\n    \"C2 = create_coassociation_matrix(labels2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"matrix([[ 1. ,  0.5,  0.5, ...,  0.5,  0.5,  0.5],\\n\",\n       \"        [ 0.5,  1. ,  1. , ...,  1. ,  1. ,  1. ],\\n\",\n       \"        [ 0.5,  1. ,  1. , ...,  1. ,  1. ,  1. ],\\n\",\n       \"        ..., \\n\",\n       \"        [ 0.5,  1. ,  1. , ...,  1. ,  1. ,  1. ],\\n\",\n       \"        [ 0.5,  1. ,  1. , ...,  1. ,  1. ,  1. ],\\n\",\n       \"        [ 0.5,  1. ,  1. , ...,  1. ,  1. ,  1. ]])\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"C_sum = (C + C2) / 2\\n\",\n    \"#C_sum.data = C_sum.data\\n\",\n    \"C_sum.todense()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<490x490 sparse matrix of type '<class 'numpy.float64'>'\\n\",\n       \"\\twith 489 stored elements in Compressed Sparse Row format>\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"mst = minimum_spanning_tree(-C_sum)\\n\",\n    \"mst\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<490x490 sparse matrix of type '<class 'numpy.float64'>'\\n\",\n       \"\\twith 481 stored elements in Compressed Sparse Row format>\"\n      ]\n     },\n     \"execution_count\": 33,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"#mst.data[mst.data < 1] = 0\\n\",\n    \"mst.data[mst.data > -1] = 0\\n\",\n    \"mst.eliminate_zeros()\\n\",\n    \"mst\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from scipy.sparse.csgraph import connected_components\\n\",\n    \"number_of_clusters, labels = connected_components(mst)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"\\n\",\n    \"from sklearn.base import BaseEstimator, ClusterMixin\\n\",\n    \"\\n\",\n    \"class EAC(BaseEstimator, ClusterMixin):\\n\",\n    \"    def __init__(self, n_clusterings=10, cut_threshold=0.5, n_clusters_range=(3, 10)):\\n\",\n    \"        self.n_clusterings = n_clusterings\\n\",\n    \"        self.cut_threshold = cut_threshold\\n\",\n    \"        self.n_clusters_range = n_clusters_range\\n\",\n    \"    \\n\",\n    \"    def fit(self, X, y=None):\\n\",\n    \"        C = sum((create_coassociation_matrix(self._single_clustering(X))\\n\",\n    \"                 for i in range(self.n_clusterings)))\\n\",\n    \"        mst = minimum_spanning_tree(-C)\\n\",\n    \"        mst.data[mst.data > -self.cut_threshold] = 0\\n\",\n    \"        mst.eliminate_zeros()\\n\",\n    \"        self.n_components, self.labels_ = connected_components(mst)\\n\",\n    \"        return self\\n\",\n    \"    \\n\",\n    \"    def _single_clustering(self, X):\\n\",\n    \"        n_clusters = np.random.randint(*self.n_clusters_range)\\n\",\n    \"        km = KMeans(n_clusters=n_clusters)\\n\",\n    \"        return km.fit_predict(X)\\n\",\n    \"    \\n\",\n    \"    def fit_predict(self, X):\\n\",\n    \"        self.fit(X)\\n\",\n    \"        return self.labels_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"pipeline = Pipeline([('feature_extraction', TfidfVectorizer(max_df=0.4)),\\n\",\n    \"                     ('clusterer', EAC())\\n\",\n    \"                     ])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Pipeline(steps=[('feature_extraction', TfidfVectorizer(analyzer='word', binary=False, charset=None,\\n\",\n       \"        charset_error=None, decode_error='strict',\\n\",\n       \"        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\\n\",\n       \"        lowercase=True, max_df=0.4, max_features=None, min_df=1,\\n\",\n       \"        ngram_range=(...ocabulary=None)), ('clusterer', EAC(cut_threshold=0.5, n_clusterings=10, n_clusters_range=(3, 10)))])\"\n      ]\n     },\n     \"execution_count\": 37,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipeline.fit(documents)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"labels = pipeline.named_steps['clusterer'].labels_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"c = Counter(labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Counter({0: 490})\"\n      ]\n     },\n     \"execution_count\": 40,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"c\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Online Learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.cluster import MiniBatchKMeans\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"vec = TfidfVectorizer(max_df=0.4)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X = vec.fit_transform(documents)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"mbkm = MiniBatchKMeans(random_state=14, n_clusters=3)\\n\",\n    \"batch_size = 500\\n\",\n    \"\\n\",\n    \"indices = np.arange(0, X.shape[0])\\n\",\n    \"for iteration in range(100):\\n\",\n    \"    sample = np.random.choice(indices, size=batch_size, replace=True)\\n\",\n    \"    mbkm.partial_fit(X[sample[:batch_size]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 74,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"mbkm = MiniBatchKMeans(random_state=14, n_clusters=3)\\n\",\n    \"batch_size = 10\\n\",\n    \"\\n\",\n    \"for iteration in range(int(X.shape[0] / batch_size)):\\n\",\n    \"    start = batch_size * iteration\\n\",\n    \"    end = batch_size * (iteration + 1)\\n\",\n    \"    mbkm.partial_fit(X[start:end])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"401.6913550000382\"\n      ]\n     },\n     \"execution_count\": 45,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"labels_mbkm = mbkm.predict(X)\\n\",\n    \"mbkm.inertia_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"392.6561949236311\"\n      ]\n     },\n     \"execution_count\": 46,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"km = KMeans(random_state=14, n_clusters=3)\\n\",\n    \"labels_km = km.fit_predict(X)\\n\",\n    \"km.inertia_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.metrics import adjusted_mutual_info_score, homogeneity_score\\n\",\n    \"from sklearn.metrics import mutual_info_score, v_measure_score\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.39531764243316064\"\n      ]\n     },\n     \"execution_count\": 48,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"v_measure_score(labels_mbkm, labels_km)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(490, 14327)\"\n      ]\n     },\n     \"execution_count\": 49,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([2, 2, 0, 2, 1, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\\n\",\n       \"       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\\n\",\n       \"       2, 2, 2, 0, 2, 2, 2, 2, 2, 0, 0, 1, 2, 0, 2, 2, 2, 1, 2, 2, 2, 2, 2,\\n\",\n       \"       2, 2, 1, 1, 1, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 0,\\n\",\n       \"       2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2, 1, 0, 0, 2, 2, 2, 2, 2, 2,\\n\",\n       \"       2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 0, 0, 2, 1, 0, 0, 2, 2, 1,\\n\",\n       \"       2, 0, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0,\\n\",\n       \"       0, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 1, 2, 2, 0, 0, 2, 0, 2, 2, 0, 2,\\n\",\n       \"       2, 2, 2, 2, 0, 2, 0, 2, 2, 2, 0, 2, 2, 2, 2, 0, 0, 2, 2, 2, 0, 2, 2,\\n\",\n       \"       2, 1, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 0,\\n\",\n       \"       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 0, 2, 0, 2, 0, 2, 2,\\n\",\n       \"       2, 2, 2, 0, 2, 2, 0, 2, 2, 2, 2, 2, 0, 2, 2, 0, 1, 2, 2, 2, 2, 2, 2,\\n\",\n       \"       0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 0, 2, 2, 2,\\n\",\n       \"       2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 0, 0, 1, 2, 2, 2, 2, 0, 2, 2, 2, 2,\\n\",\n       \"       2, 2, 0, 2, 1, 2, 2, 2, 2, 1, 2, 0, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2,\\n\",\n       \"       0, 2, 0, 2, 2, 0, 2, 2, 2, 1, 2, 2, 0, 2, 2, 1, 2, 2, 0, 0, 2, 2, 0,\\n\",\n       \"       2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 0, 0, 2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0,\\n\",\n       \"       0, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2,\\n\",\n       \"       2, 2, 2, 2, 2, 2, 0, 0, 1, 2, 0, 1, 2, 2, 2, 2, 2, 2, 0, 0, 2, 0, 2,\\n\",\n       \"       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 0, 2, 2, 2, 0, 0, 1, 2, 2, 2,\\n\",\n       \"       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 0, 2,\\n\",\n       \"       2, 0, 0, 0, 2, 2, 0], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 50,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"labels_mbkm\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.feature_extraction.text import HashingVectorizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class PartialFitPipeline(Pipeline):\\n\",\n    \"    def partial_fit(self, X, y=None):\\n\",\n    \"        Xt = X\\n\",\n    \"        for name, transform in self.steps[:-1]:\\n\",\n    \"            Xt = transform.transform(Xt)\\n\",\n    \"        return self.steps[-1][1].partial_fit(Xt, y=y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 67,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"pipeline = PartialFitPipeline([('feature_extraction', HashingVectorizer()),\\n\",\n    \"                             ('clusterer', MiniBatchKMeans(random_state=14, n_clusters=3))\\n\",\n    \"                             ])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 75,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"batch_size = 10\\n\",\n    \"\\n\",\n    \"for iteration in range(int(len(documents) / batch_size)):\\n\",\n    \"    start = batch_size * iteration\\n\",\n    \"    end = batch_size * (iteration + 1)\\n\",\n    \"    pipeline.partial_fit(documents[start:end])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 76,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0, 2, 2, 0, 1, 0, 2, 2, 2, 2, 1, 2, 2, 0, 2, 2, 0, 2, 0, 2, 2, 1, 2,\\n\",\n       \"       2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2,\\n\",\n       \"       2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 0, 2, 1, 2, 2, 2, 1, 2,\\n\",\n       \"       2, 2, 2, 1, 1, 1, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 0, 2, 2, 1, 0, 0, 2,\\n\",\n       \"       1, 2, 2, 2, 1, 0, 0, 2, 2, 2, 2, 2, 0, 2, 2, 2, 1, 2, 2, 0, 2, 2, 0,\\n\",\n       \"       0, 2, 0, 2, 2, 0, 0, 2, 2, 2, 1, 0, 0, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1,\\n\",\n       \"       2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 0, 2, 2, 2, 0, 2, 0, 2, 2, 2, 2, 2,\\n\",\n       \"       2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 0, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2,\\n\",\n       \"       2, 2, 2, 2, 2, 0, 2, 0, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2,\\n\",\n       \"       2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 2, 2, 2, 0, 0, 0, 1, 0, 2,\\n\",\n       \"       1, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 0, 2, 0, 2, 2, 2,\\n\",\n       \"       0, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 0,\\n\",\n       \"       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 0, 2, 2, 2, 2,\\n\",\n       \"       2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2,\\n\",\n       \"       2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\\n\",\n       \"       2, 1, 2, 0, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2,\\n\",\n       \"       0, 2, 2, 1, 0, 1, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2,\\n\",\n       \"       2, 2, 2, 1, 2, 2, 2, 0, 0, 0, 2, 0, 2, 2, 0, 0, 2, 1, 2, 2, 2, 2, 2,\\n\",\n       \"       0, 2, 0, 2, 0, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2,\\n\",\n       \"       0, 2, 0, 2, 2, 2, 2, 1, 0, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2,\\n\",\n       \"       2, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\\n\",\n       \"       0, 2, 2, 2, 2, 2, 2], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 76,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"labels = pipeline.predict(documents)\\n\",\n    \"labels\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "Chapter 11/Chapter 11 (CIFAR).ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"data_folder = os.path.join(os.path.expanduser(\\\"~\\\"), \\\"Data\\\", \\\"cifar-10-batches-py\\\")\\n\",\n    \"batch1_filename = os.path.join(data_folder, \\\"data_batch_1\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import pickle\\n\",\n    \"# Bigfix thanks to: http://stackoverflow.com/questions/11305790/pickle-incompatability-of-numpy-arrays-between-python-2-and-3\\n\",\n    \"def unpickle(filename):\\n\",\n    \"    with open(filename, 'rb') as fo:\\n\",\n    \"        return pickle.load(fo, encoding='latin1')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"batch1 = unpickle(batch1_filename)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"image_index = 100\\n\",\n    \"image = batch1['data'][image_index]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"image = image.reshape((32,32, 3), order='F')\\n\",\n    \"import numpy as np\\n\",\n    \"image = np.rot90(image, -1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"from matplotlib import pyplot as plt\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.image.AxesImage at 0x7fe57222a2e8>\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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zPAzenuy5BJlVG8XPbd09ds543p+1+R63Ee81kQBVdfJ5HR+mnU/j6m\\nnd1busa712bC57gtzx5aOWKMaVufZP7v0UqfTb9Zp9Nr0X96+unU6jqYDrnT4DVvhAL3QYw0bDam\\nS4YgE/dYQJ/RLErm2T2fnCN02tE58I/CdMc6UWYUTEhtaR9Tm/UrHcrF9HI1Xt/1NMU665nDsfYx\\nfj7TGm8K+H8A4P8FsAF4APDl90XvK2A6vSYe8+mReR6jYAz6M8deD2hD0rjDqTdrsc3p7oFr6Ysw\\nz+jL6vIoXr/1U2qQsrCWTZC/Fjo0TMW8hmEsoxn0kb5tsjF0lrdqJv0b9rmPSbsdjVnmY6/UDfta\\n6WcqZu5NAV8AfDWA/3v8ml1O0r3Taz7tSWUnHTt2gi5eKedb4MCNpZQlYSYl+H0EahZ+VFZ9Ovtu\\nSPOws1bQD9MwXWDGZEYZH0u0yGgyyX4gRNI09WksoXtGkNOb7Zo7r9Yf+SsvPE7zbYzhT0IkP5DQ\\nJSQg8PHOZTxTB/MjgeJPkhn3v8P7NE7JaXN5+psxRx+8t13uce9mTitlBmwxdknHO9N2uQ2Fwhja\\ntREscOJ1ZOcZn6gzJjLrDFkbJYIi7csjv5yuu8B+0r0p4AuA/xH1e/Ffey64umhsyd+Z90ZD8pzY\\nvZY4owbO4s7cqLNl4Uz2wY3yOxq3HvmNpPeR1O/fdZ2sU0VHhrO+HTygGbzZL9J0sKmFFNTjzt6/\\nt8A/oqcH25mxdm7IRHI9onnWF47acxYfeHOV/qsA/AiAfxHAXwLwtwF83zxKrlIfr3Gerz/26l3+\\nro8/GTIUpNbUMxIyVQELMDxHPZR9bpQc+98/Zh+/yxbXzuq4d+P2GpclZwB819fZyDg2iXdIj1Hn\\nXVFHQwabRqLy3xU/d4fh71ii/aaA/5F2/XEAfwbVaCeA/+7v/BYJ+LN+zpfhZ/2cL5Pn4aKa+G7i\\nZyV+TKcLfofuQy7BMvA/lvpjUE/yTu+zO+CuQyztKjn9k1cLMUzyNfWp5Eo69lnn4/f5VHomcYW5\\nZHadajxM0z8gNW3TDMCxX6T9dyascu3vqMy2DT/1ye/Hp37g+4d5HKV5xv0zAFYAPwHgnwXwFwH8\\nF+0KAOVb/txfzjMN6xVd14vP6CvUB+9Rbzs1xbADTjCtCLt1k0KjhrQlNTK0hTJ0yzUtcDsQjySY\\noWWWRrbEtwN/yEC+zBGuhiYbnwyVfE+xzEk2PQ0l9XftTzZfW7a+/l3SZOkLxTVhmW4pk+sycZdf\\nD/i44Cfed21ly+1Hrqnz9RVneTTmF3zeT0mTehMJ/zmoUp3T+W+hYB+6mWTP8Pgmkv0gq2PnmI9O\\ns6Trq0OCLu9RoJBYT+/kYMZkXXufb5J2MoUTn89YN+KMUz5FF1Mcb3Tp6cxomw13+vxdyJna28/0\\nnXB9LR6B/SgFEEbnqgz6wf3a1JsA/u8D+JJ7ImRgP4mFU/HStML9fUDviZkBOUqXLgIGZeJgMY+k\\nbNT9HdDm8syZQ5Re0dgV+16UiJmzDIBVaec34s4JffnzeNzuwg53FI7zeMwe9bwuJkz8iFETcDRb\\nMxYETzMtd8qdAbsrxBuC/UiSzOjs6LBpkoYT3wnGcwbBzyNpBSeJKQkx7jC5+mw7Gz+7p4zhxvg+\\niUPQVdAnfkjqImQ6b8847cdDrmidT4AzUptPqDWdcZXyd76vH3W88fh9yMyS+GdZ1dPvlpuA1obJ\\npNEo3rBzTNI55X3ImBLJOkL7JMcIxlHU9Mis0caQAcPo64/c33JiZsL6edW+SXXnxkazUaJDZja4\\n75mB9UlWLA414Rb2fk1Z05/aY/r7/vn4SzqOwJNVy+5pN8/cAdrHxpt17tR6mmVppAyFQOT/nFRv\\nk3cJl6DkpXbqPDWfTBJvasmPxjUuat6LTjHPNGrpwowep2DP2r2T8sYvWQ+TkTRup8nCoQPOPrKf\\ndOEOntlvzgTOryl5OsALYAZsPAtzEK8H+7xzx6xmIBoyF/scO27aIefvsgbLJFca9w5LUz8zMo9L\\nGrDLPNLnr2c6X6JqZwzmRPHO1cF5QHTph2tOw5iWmUAahjXPp4XGSfduAU9wVI/APpIox2CntCIr\\nMHPwKzl548yNao7ijNTgBuOrifQcaSFH7oxVWNObcp/kMWGl0yzqqjWexbd/422eX/LuEZ37bndy\\nJWxWZVO/QZX1n4nW+zPs+DHu6Yx2JyR7Fu6cZI9/Y9j4LpP2OV39uzi3n0j+LK3kHcc7XlnXl+2M\\nZEslzmE86sqiDHS+HTV3fo75bNzpUOlR6L9vKbWNJ88l+5ZA0lKJOne+T5xp16x+zpXv3RrtxiJD\\nvSYibQ72BATZuy6JEeyBkVbgaUryS2ngi10RdbC8MuZBebiOHRxJ9KnawrfxrFrC+UlhwrndXwNr\\n2LCc7bV7eRbs+fLs4/gtzB2GuyPg65uj6TZ+eabVM/cRmpY7A/ZUtT8FdnIgGb4zQUZgzyR2n394\\nnWoCY1fDFZNOYpkNeRwxGnk3Af94AQjNmUuoU3eluPrMONmPct8YelyESV6n3SPG88lux0xLkfpI\\nwmHg5+5DJ6LwexvuScfw7tXBYDWC/5xkl5hZkt07Q8y0UuPcfAZ2dRkXL4HGPB3r96YAP1R7JT+f\\n0xmNP3/IIh6fbz+KKe+yipN3lN4fu2OqpjRN/Gw/PQL7yP9tAjy693SI5bzxqkdym4LdI8VzzETN\\nSvLNpCtfOu1gTKYLc6ZhI52Tvm3i3yfB55s2kkpj/0N+kWsFlsl11xNTYfOx+5ym3s2AfeLdAb12\\nn0L3Lgs/yXHQFG/dPT3gz9TEsNR9I81lyFGL5WmeoyXvgKclzclwc4CfSSFZ2SZAncwzu7zVUHmg\\nC3UpWNk3W4jl48DQ7CnRd+RipfQfzlbM3SG5rOpnB5yE02cObRw9j3kn7skX3mSGvOE0UXMl+HeV\\n4mt6mM6YAfT+I8k9c49Wr33ogzeDpZiU3x/nUAb3mpbqH2cAlGlVQeElffYaGYy/5kcmasyBfESN\\nNdVszrt7NLVxGidh/K7RjvdstBtJ9o4BdOCMNXMg5U9pEAcTrvFV5FvnVon0SU/jnZHus9Vg0Wd0\\nzNiZheQdbA0o46l2pBGoj+UT7BmJBXjadMnQzj0/wsp9Vm85fJdN3d3j3jHo3+0x1Sdq6uz8fHRl\\nsFWyxpvMqTzy3RzPM2DeA3I610MCnR78fQJneApLoXwl/WjfnHkitHX0JQ93Wjsar8Kj7i968N8J\\n9lOy4My77MOaR4dKmuC25Q6jDQk57jxPqtIPwX0n2KMbdVVN54RG4EAzW4QdK3xs2T62lD+iex3w\\nhGOj13xzDIUf+5Jc6SAfQpz1cMt1rPhOY2t8S5GkmJQ/A3tejiTOzI2bQFHZjdXvcI+U5j1DmOkn\\n3r1Xo11mDR5ZiPO6manrRyr6qd0T9fWkc58G+33zXfnbhP5x/rOx+dkFIDBa+Uhy9p1NZa6HvbuS\\nCZMmnQPc89p0IPFoKR3TSd2ZT22V8Y63PuzZgEMy7nLvZbfc8HnkN3CZzX6c1mChdJLfSBLquy7x\\nEOa0euDTmqV9ol7yWYNR6ONjw+ckRFhYSTwmIrvTuJgCfDYl+rjltn06zp2UCSpjHoveLrtz0aI1\\n+4R7T9+WyyTgcaMNpbyZnLPTdMUa+CiZwiPe4uENge6OMjs2mfj8XFVRUHIyeme4Aoopq5+xzutg\\nXPb49sx98ylc7rMLURowj4xmpi40LNeDrY+YSGApg2AUaOgHAONfTZdS7aAr7UkslTf4qGexN+Vx\\noL8nztN9W+4s2z5wFrQEGNOdNZrofTWGqEmk8Kq3oKayHYDVVxtDnu27KI0z6ZyG8XkebZbweWUx\\n+V4pHn+bnOvO0CBVlRjmejtUCgy5o5iMyduJb19nwiKJS6UqMekLV52euYw0h57UyHr7rvh4TcG6\\ncsfR0c7UegbA9Hgqn/xjktXFBj/jRtUw69wNEmQ6FUgOZfGyuGcQ8aCWPnVOP8A26ziUf5SI/Qp5\\n2JZBMl6qx7Ie14VPo2Yw+pZmLsF76Z4JabLqpqxIs378p9HmmM4Y4H6u3mc+nfFJ6O1B/5hT7d6O\\n61nxPLDrm/F54p5+Wi70jiGRo/5pPPU08mLAUgwADcg5SfJ+mlWRTli6v4Ee13Ftj0ukdyhgQWbo\\nm0slG7t/Pgf68QmnTbdh0HcHT/atNHsSH4rPdoNTqEyyLTH+8gxL4J4RxMzGLgX7RL2/lwFYyX6k\\n6mdM9ox6zsdw9QLpmNonlPBKXSq5ZrRS1HMSKRfmpj1QRWEPISD+Sl4v3e2cfy7ZbfhMyvgO6RnN\\nAIrJvHTuPMCZBULonEn6Y9c30xzu8a1v60xs2yFJH0yVA+r8vGxOiMmcAfdY0p9M6w0d84OkOPN4\\neHyLvgeVPlSvY3OTzi39uMDLAy/dTUB0S3OoOChYf87dgkSATl5id6p5LtL6IgePTNGuQvbUyH7w\\nnHWBbByfpeptAZbW8SEdAXT2venJWiWRIWp96yeWiwtSjXQH1BwhnrJSHV+fwpXuZu6I+2BrqjO2\\nIHbvZwyfYV1ezDqklewxDktd25fKoDIM0whERCPZEErOKGjBPbgflimXWF1+qToPk/s9Y/oRMX4w\\nbwEX3XiVY2QD3r/epmyuA2V9tvTk6x7m06Djx3cF8nsMdm+S/v362pMecZW4Tk3PwK5+vWV8JHWS\\n53s7Spfn4P09gUfuLhZvpF/XmUtyb9M8N6ZUBtLfE1gh8mkJKclUn03DlyGjsWd7I6ZHQnNSLiY2\\nf5yC/B1i9a24NyHvCSV8lDpe/T7r0mmiKBmcX149451tqe/43VvvHHP1u2doTceg4zAxzax+GJxk\\nwqfSvOmU4zQkkItEgY4IxFEZQsbJe+M/isx9Io6d7f1HHOxv6s5I+G8D8KMA/obx+ymon4f+O6jf\\nk/vJZzMcMeRHO9vCo04fmcRJFp6Hi8s80izuc13kXn3vOmfG5NDAreLWg890dA/KOGbOyPLhVNIn\\naTj7Wy7po9QeSfu+HD4/m/bwK6zUfiYwSSGCf5rCR9M9htYzgP9jAH5J8PsdqID/mQD+p/acEjTi\\n4NWVCfhHKr6m7jqmaaz5ytbxS32XDBUScTCr8HONMSvfZHjTMbAIIhvMq9LjTxOXJC2/AEZBZdLo\\nwD0fltm8ehpmz5FxQDiOZ+6BKQXOn6r2g076UQP/HEvn3BnAfx+A/yf4/XIA397uvx3Av3dftqV/\\ndGO/s+bK+8JMARq3NmZjhDcgo3dGog2KmwKgiVABaBKJUolopW8vrft6KmZMPwJxS6NEOgp6rSTT\\nWnqpHiW/dRlDidN5Lg1XJp1CHQH/bQH8XRjs3laKjx3Dfw6qmo92/ZzHk3C/ft+NV9PBWIgTI7r0\\n5rK6Hx+/afVnFvdRmr5Dj/iQjOUHacZlRjZOTMOG0nRHNPbp6lN819sDcilehuH1PXXPsexBuOsl\\nVH+Ww0dJur9N+t6Glf60SHZEZquQDlLhsVjnCdtBx+/yNEfq+4xzvIk6P7AoHxa+l4KdlO8yLonk\\nKvDbN30YC9S+TidNXVSq95pJZqH3+fvwgd6BBuD4fDfMsfdBojdd/21KdeveZDPNu3aPlfA/CuCn\\nAfjHAD4XwI9lgf777/gWuf/CT3wZvvATP6/ntLHGg18vD3pVdtRos5nKsSQcpxulnpVACpRe8uj9\\ngI5ib6xsygEk8WInT+nt62BUxg7gut4Wfbl8rE4VLxBQsYfUVUvX513m1feELs8y0Rq69p31hbdI\\nS9Lun/rk9+FTP/A/Py69xH0cwHcD+Dnt+RsA/BMAvw/VYPeT0Rvuyrd9z1/ps3NqKWWXIWXKtdV4\\n1dnMrV9EBfVNxmGiZqBNR07iW8MexbBGhMyUBHWmE5nodj5cfsTg92fc27JmdREKat7R8D6/9gXq\\nihcX7dh6S+m15VRrvSs/37NEts05WMzT22MGTH8giTNQ2yW+Uo7gZ57eEqMaLG/K2j0E/ILP++dT\\nKs6o9N8J4JMAfhaAfwjg1wP4vQD+LdRpuX+zPZ8i3n+9aGT0gRMivZRjdYz8+77csBpBVnnRv3+2\\n40mfHlxYlcbHp5RaNTVXWT1W82FApFvu08LOVOm4HTWng5qhrTcK2nTst9iytLXNo18gNaU/c+9m\\nVZs1bB7NPNj2ezvqvPajkSHbguZ8nmdU+q8Z+P/io4hn1veKepyp8oWQjV3rQ5+67cBOsjiXqWZ9\\n+uO0tZOJgitPAAAgAElEQVTq3m1+61W8XgHsO7ZIjsIN6KUkA8hhOIL9ZH8fDXFG4EUrH0DJ+Z6x\\nk82MZq0uxEZo2/ztguVNwT+ObTcmURK+V+/fOhtyuwpjnueUivdz4k0ZS3Xt2CoXjqQwBs++MWZh\\nx0cLZ3n3V99pqfP3Bq1e0/FpzRahWHrSehi0+lF9Wb9MexmQY+goYoh10qn4uvBMwPpFZjgH/5st\\nnjpyAyEjfnn72IM87pG6p9xhu57L7x1/PfbACcMyHNEwLc+7jqy50W9mrMo1gDNMpF6Z3pEmYf2T\\nb+NYVbcknbIAlHGErCNGrShxkbZsreCwrUQLsca4GMfeRanj66Lbbx+FlvNE1oxD93ZU+0R1rql3\\nxCijGmlNbwr6rqK7V2cMw9a9tzPtnIvSPgxPqsQwFU19HWR+8g69ujiS3Pk7Hb9n0q+X8l5rsJJd\\nFxhZly+kcRoBawhZ2TleUgEzCMyk11GH7RnceKovmwb06RSTVT42ftsC817XS1JPp3v/1qblsr4S\\n8475z92TSvhpNZitmTMgjtJO8+7ObrJjqzg2zRaFzDQBvwXSA8RyXIItuefFIc82jtccD9Ral4e/\\n7+snk1Qj6WUlcTIc6qSyqdPSXpjxZl83+VBlzFB7ulLBmpzVVSyzIToA40i652mP8n4bekaftqac\\n9+1z7ok/ROElFnPDnPuPl2Z2xaSYtnVmXFliOnma/dSXvc8kcc5xnWR29FhrdmnTQ0dLUfv00zKK\\nthB/fOF7U3YZe0fpGrWWjoymgcWwVsqN35H1u1uE99K/Fq90glGVB5/H7OkUPTPm8bYX30jbjTSg\\nc/m9BwlvO5r1Y2lrpbCXnKmEnzA5DzIrja0knElxDOLwHyN9KGoIvbSn4t/YMFHCoZCo71Rg5uF7\\nyd5L+uAc0Amq1mTagW55VYaUJ620tqe4XbboKcDuHjlD66V81KCSfFnliKc6Orqy2rlH0sfXA+n6\\nSKl72oVCzL6aNnJPd4hl8V2L61QNOT3wMgk82tDWN2bPbii0fN+Jzqr1o/3iBuSkado33KE9O2CG\\nVukbHurQuZ7J+MmwkiRjwB7qvWcA7Mb15vIugD2iqk/LMxT9Ox6DWmaWhgmgl0cL/ruEbRJYipEA\\nenBKEIDhwp5TbsQ4jFA0HkrLgXtiCd86dtF7fuPntBNLMoXKHOZqVcnxUki32SRd6hnuLWelyTtH\\ng9cqPISKkKOMyEtXL9F9ESh4+OeWh9Mogo7rlszGetEUHUOK/ckxANPpiFMokldfLznIPT8fAD3y\\ncwt6btm+CWY6kAkRaSmmvp7QST32FLUAGKguU/ekH6Kwh8+KHGAOKVd9aVeMDQR7yCL2yKjqWY0i\\ngL5Lvz+XLB0KkM030yp6aW41nAh0V44gVT34PTMZ14yhwQGD0+A6iB2omPuxdBX/gDDqW9qkk4eN\\neXS5doLNSjdPVFdUTfQON5GYIyNdIjwe7bpm5X4Rz/Y7j/p3arTLpJRr8M54U9/5TtGDPaZbf6PG\\nSVaqoXRpxvy9/8CI10mpUv0oK7OWiUxYX872XBLJntDp8y9dnXipzmkX9+zDeZqU/hLe6RJb8TOG\\nP6/KmoVEJaadH8jR1anhkLGfjxiRLap71Vf7wCXrJySBA7+3abDLifDXO7J7OgkfvrrptEokoL5D\\nso9KPFMZ+/uMS/bLZsmprOrv0syWybbyi2xT4YrOTuA0khz8nolQyNeHcXXNDMtuDyV/FXU+tEF0\\nNHiIpwP1zHKgWZUQ15z+Gstrw7gMkjSzOsmSGgkaG9i3udUwYpufl7pTlySR94Vz+b1jwHsg1ioq\\n5l795UrhGeg7BmIHmLi00x6M1516m6jx9l5U8lDZEjcjp9WCLasrd0sv9M4e/NRAEmsSrlr8kk8D\\nZOJ7nhGgvP4P+lEGfAroJVuPgsSkizKpKXj8lRBeO7CGuhgBR9pvXCYbzrW/2+sRmMGg/c+7yOh6\\n4iQ/MjQduCc/lz4DOtBLkxnY1R0XMG3IyARSpjBY8BHvkY9Loa8P6crqofoP9sM7zUBTIEebpSnJ\\nmwzzaGDv2iCtl0l5OlDryxmg0jwE29yhoyYV6tu89u3B6dGQ0VAXV4mwOfaSNNy7fpGW6rQb24Xy\\nVM/m9G6t9KbOrUJrqyuVKHx1nT/cp1ZMm9tAik9X88V4B9Kd71OQtffdMKvXehz45NqHI/POlcJ9\\n+KCMcF5j2Po2xiXx79rDGogmadsqsOAJDCyqC2MmbkFeHGD79Q4K+pinCzfTeh1pCVOwbW4Ndm5a\\nLjKhM9rnEaDHwz19PD98eDIJ328iyXeoxU5n3+l9QJEra9ZY+X3+HCtvwNHj10Xs+DghbqhukSlT\\nAsC+8Y0QoWhjsPkNs3MMhohkNkTbgFJGnCcc8jWRuqCUSa6EAbJ/U5nz7cc9DRIuCWaxnAmSKETy\\n+ptI965MJwAY1P6u5YSWPr15Px27Jx/DZ29tJ3fPIaJtlHGD2PuwYm/SmEOuahulkBuLah76fgyI\\nPLNOwib0VTyOtvD6vNPDKkf5CfAZrLlWRM4n11Ucoybvl7v5GF7LMgJYBECgyLRFmkfKnNExhKhN\\nzVdpZmr9zFFSlAnjHjBRl/+Be0/fhw+NnXT2WefOG7AvbM9A+t43NkglhjijYtrFMq4jlmmi6asO\\n7AJEmy+/DyfKUDa2ZPo1w1if8mPgZ+EGgNGvnpYurA1/91qV4oEnCaRz3gqMIfMvQL+WwqY/0AbT\\nd5PVla7Nj/uAo7Fd+q2981WUY7+5e08fk2TQwDGlueSdca9+fDbWACiEyVWh7B05L6tBhDQGU0WZ\\n5M6efQfkTmElqwFzKdq/QhUVE89+c5cBXlV3GgJeQONydql7aSnMh9r/eA3vTMFj7hkDtM73i0H7\\n0eT9COxAygi6vOIy28HOzDNu2NfsfbZ8916Givd6AIYXHxSurpHj2Gog0bN8R6DuOvYEmHmaLd7I\\nMusYg+/QLv3Ymd1D6a412eKrgIBS2tDCCZrS36OCjZp4F6BaaaqRjBS3KwQTbYrTiVdmJO2eGU1x\\nTMavgU9tCPbZlFFvQt129WjtIwP6zc0Rc/YEWIJs+x+p2JZhcPjkaDAb+hEgt+49qfTV+UoeL3Ht\\nXV6Rs0Ya3fs0cwVpxhzyZ771HbdrQCfkfIft6CgKds6jk+zmns9GL6WAN9GwnwWhvQr9YTVesUwg\\nFNGKeJ3as9f2W+p1oUX8mCHI55Up1NkE9OieG/NNKs/HKZ3/Edh9USOozU03xDtyJzdrmfp4U/eE\\nRrsxBx5XuAc2Wb9TKnzvZuvOc2toDJO4ZGUdE6k7yALTkbJGBpAs1w17y+VQB/JZuynrBvRS+MeA\\nt7MLdsqNTF4N5O1XHA2RNggB5CS7+i0LgfYFy7IAtIOWBQstwEKohlBjajT0RECmoLeuW88faI0M\\nxMUd+CPrFxPQW89TAj4BeCbZ5b7vY/e4J52Wi63likR5M3mfc5I9+vXvZ8CODZA3SJdyUOFSgwuZ\\new7jOlpiibdqawNfVOn9FncKQC8oZdd7m7ZhUqIctLBo8VAM+E15MlautgAdMixEKEsFeylLvS8F\\nWAoWLA3hS0ePpB2AeIaxp0x9wChslk6guLQyl/QBpyXeJ+kzegHLAN+KgH8/Y/gc6D5ADtLwbAd+\\nHCTEzYAcn48YRvcs+YRu77KIkDCdgeCvSR7aadrPbH5ho52q9A3xbkxrgb5j3/lZM+plSEHZd8ck\\nUPbqZ8sUmkLKEg2BIOwLYVlWrMuCZV2BUkBL0YHJsgDYQbTAzru7uiBfV/0YPnO+oFk992BPypWk\\nN/LvHw+kr5s67NPv6qBdXTPf6Z50DB9nHkZTVL0rw4ocS+ED0LZ46VTdYbwJQypAN8UStkymDSp/\\nk33iJV6DZZ7MO4KXzvuO3QF+0ksE6DsKh9/3PF6xBMEZ5eLzuu7AutY01tUDdwdoWdo4vmA6fx4F\\nwSHwsy3O/cO59m0ZmSGaehdYLlSyPjBKzzGLnl5LS9dvaDDSmrj3J+GHlZ1JRvMqGl2ctA0yK+5s\\n0wnkYz+3COV4vrPn+iY96aj9AR8+j5ErIm29tG/Wbj7ppU3T7aVg3zfs+45t27HvG7Z9x77vRoKP\\nfqzGa1gEwKdAbNKdjW/8vCwL1suKy3rBuq5YLxdc2nXf1yr91xVLWQEUrAvnYNFMrdlL1yWOWyNT\\nvSeAhm+TPOVMXZ9rjGMqc9V/fsZBi3e2Oox72gMw/OOJKDM1PktrsGuNa4ZDOz+ScG5HlxuPhbyc\\nZ+hQFDvMWLKPD9GEaclmNGN1XoAPqBrfMm0MoOwF+7Zj2zZs+1av5lclvjIAvfeAt/mVWLexPcO0\\nG4N+WRZcLldcLxdcLhdcrhfslwsu+469AR+lAGsB0QWFlqb217LHBU5lIAVNdfs26fy8G2tzI7B3\\nmU2Yxznoj9Kt76wKl8P7HtCfAfy3AfilqF+I5Y9J/k4AvxHAj7fnrwfwFzJCUj+ah+ldok5G9U7S\\nSiS9S8KA32xvzFZR8WYPl+5wbnU+PFAGEq8hrNF87Dy6Wt13AXxVYAiF6mxAoSoJ931vQL/hdrvh\\ntm243W71eduwbz0T2LYNZTcgR2Ms5rk7lpu0BE6lN2P4dV1xuV5xu15wvVxx3a7Yr1fs+47r5doY\\nSktzqQyiYAnt2O5ZdZ4OS4QwaKOfk7znwW7DeW3i3pVvNp1ZzFTGhPdnQH8G8H8MwB8E8MdDlt/Y\\nfmM3ATaNXpRwM5Lm6XJLDnNQ9AYUuDFjvR9ZhWsHP6rSyDxqnNEc+wzsrOIz2IpI2zou14gsxRgQ\\nFfAV1DfcthtuDzc83G643R4qA7jdsLXr7baJX9k3GS4o6IvQwtlJCRnw1Et4Vu3XdcWz6zNsz67Y\\nrxv2fRO7gKRNwLIQ9n1p/r4enIQrBWUyPtY4toYHQqCLZ+Pad2Npffxu5kbrPjKpXq88fOvdbEio\\n7gzgvw/Ax1O6DlxamEPpXtzd/COG/iTaKUFOwrdKZKtwt+nFclydUjviomP1EIGR6FhewdNPyRWm\\nxUjcYqbNaiBy11KoqunbJmB+uD3g4UF/t4fs+YbdAD6eLa8Ah2g96So6J+mBy3rB9uyGbXuGfWPA\\nFykdp7MsC9Z1lXcd2O3cYybhKbSc7Tx5R0r6ywjslpZx/KEQO+UG6UfMI/SNO92bjOG/DsCvA/BD\\nAH4bgH86C9xJs+gJIBYgO8yxj5qrxl5RiEMCA3oHPE0rqtyZVPY0WwniOZBT4ynSWkJslfYk8+ZF\\nVGsxpjG3D+o8QE6l3263CujXD3h4eI3Xr+vv4fUDXr9+hdevH/Dw+jVeP7zGtm3glXzU6knos2AP\\nC2yiZHcW+suKbbuJZFep3iQ7EZZ1wbqtNQwbJqMaz6AXhj1oBgB+AY5n3pkb4TO1IcFrazkhZxX7\\nSXoB4L6vHgufkXss4P8QgN/V7n83gN8P4DfEQB0HHIIW6HlWBGnSzuLXVxx3WJdSCR2BSA8wKOHZ\\npdWPzfojDmND56ulsvdxGOG1CaNil6LTZUI3hP56S9VC38blNwZ8A/urV6/w+tUrvGq/169f49XL\\nl3j1+jX2263lzSftWCnMdFpAj6U7/y6XC/ZmH6hDkVqepS3KWahK9m29VMOhqPq2agODtu9TZFEe\\nbxQ8TWEEpxO71krWWc+4bO//mPbHgP6xgP8xc/+tAL47C/Rdf+KPyP0Xf+JL8bO/5MtwxP1scaNf\\nF5ZsGF8xhQMUoyqbca5NN1XNmhpIg/3NeT/rQd9rCXPNIW1U1lJ4fI3quYuqb9V/VCPdwwO22w37\\ndmtAUsnNy12XZcFCzbC2LNjWFWSGFroVtwiAFwv2JRu3m/cgXNa1TsPJtQKcr8u6VFp4mOBKb+oq\\n0fIw9D+WsuelehInLKUW4eIExYjgkN7B3Lt342HFJz/5SfzgD3xymh/weMB/LoAfafe/AsDfyAL9\\nql/7tZ1fp7o2FzXvexorS9NKS5tuEXV0Rk/CaY/bz78mT8PsDHx3JX8fldNKUj9/vpvls9ttw8Ot\\nAn7b6nw8mjGMJTWDvgKvzpWrpsGMoeizBXtjFLJOnipUqakCZO4v61qn4y5rm49vwG+r79a2rp5B\\nr+p7Puw61RBZu2RSN3iNwJ4a5lggaA5w/aZo/R1QBs/YPFFnmcdXfuVX4Cu/8ivk+b/6L39/Gu4M\\n4L8TwL8B4F8A8A8B/OcAvhrAlzRK/j6A33SUyAyk9nlWRcM0yL/zjIRgD5YjCU9J3NFxSsVdNKrr\\nLUkaY7p4KDKz4DMFHuzt0kAe59LrYptNpuNEnY4SngjrsmBroLusK7ZGV6fStzpalqUBnAG6qJRv\\nFUlauDqGX1dcDNDrbxFpr2mS5OMkqbTfgOlS8ujC3a/O+/S45n3j+3waYBsTONIu5vRaN6P9MSP4\\nc4D/msTv284k3q01R95emfouEp/yuvCLZPr4fa4l7xz2OfSvzhLexS3mwTSyk+6GAZCNF2YBDD0R\\n9JA4rMI31b5Z4/dtdwts9n3Dxgtv2DJedmF2C0HG0OuyYF9X7PtuAGet8swkGPCLDAX4XlTSYJD0\\nEl6lvFXpV5OeO2JLVjtGxst+I8d1mTGJYkJksezziMkHD+mjtj+avGeUjo5Ad/aZnK406RM84GnX\\n0gPIxleZdLcSLk9jIj0nubtU/aV7r8ArPtpkvbctY09XlnYN34GerG2qhMZsqrxI9Drfvt22et38\\nSrqy74BMd7V/C0+FLXX+e1+xM+BF+2iAJ+jUmQW9ATxZ4o1b1xXXy4rrWpfX2jG9qvTeNuDKLB1/\\nIDm7TuI9Jq+OpbAdYqb5RIrOHLiZZTICtw0yL9dZef+ed8sZKWYEcHz28Uq4twawMwPtkYpk/YqR\\n8hqnp8H6e3XEAiA91KBzuaS3P5htqqLSO2t8XVyz2fXyu120w4BuFvLFz38vC6EDuhkGsAq+Lmxs\\nq/fdARrmbm1Ad+P3ixrwOB1aorETIc0z4Dmu28wNpXu4779T6GmjQOrxAjCyXWZIpwf+qAznIP+E\\nEj473UOvmXRv0TzXh8NfN6WV1sekr3QVXHz61a8oUYklNtcuxuWt976jMIPo6yRpSDNFt/MS2odb\\nW0jzuhrpXHDTgQlOPV+XukcdlxX7Tj3QAYCKWPN5m+u6sJV9rXvew1QaP/MY/soqfbTWLwvWhVTK\\nowmzWOfSLsdi2jOO0AYHvKNT5X2pGh2xz/TfBTi7W84uwOqHH+bdiOw7t8s9qUqvsxm1ELIRIpl3\\n9UVl58dvXQU8Sgj0uYhP2jmCapUwn3pvpX+QDMP+GjUWuDntpZ0Qg2INXVYvMtJc/EL1FgUyH06x\\nti2rZalr2O3iG75flkVV8Qbite2AWxaSqcPSwFpaXsu6VLCbuFWzyKbhmGoFnQhVCRg4fOYkyJto\\nBe/Lvak2M3fvFPB+SyUB1PZcmTHJbH92r2qF+4l6PkwkfTWvZDeEGgQdAt9klOoCQapnY9AqkSvQ\\nsVRA7nsPerfF1dJvtVLOs43Jmd6NCHb7rYK+PrPqz8Y3McStl3aajR6FVUcPlZZlWXC5qkrP03B2\\n+a0a+8xwoJSgaVkmewcgKDTXCX7xUXDvir4nk/AKGgP6qD+fAr/vHJbxdxz9FNgdhQPCkwRGwI9/\\nI1M66HCsAYlWIMBoEh4LSpO4DBybnpfw7NcTTia9mvwCmA0tEfzLwhb3te56u17qtterAXzyq9tj\\nL7heLn7MbhbneAmuG3f8oKi0uhhUXFe/PXO9h098VByF65u6dwt4NzD3QJcFMCfGIK7pKN5Mwtzt\\nxrRUXtUOnJhbT/TipqmoB7uV7unQwajeRCi0tOOhFuxh/rpqH20Rzh6lo0m1KG3AgnUtKIWwLKVq\\nEAz6djYeP68LiYS/Xi+4Xq+4Xq94dr1iWdd2sk5/mAbRImN3nYbLrPJKn9LpDaFSbVZGjJsgi/oR\\ndrZPjdVJ9n2se8Lvw9c/FuiFjkdZs8aLUvOtuVjPR4aeAGSWWBQCnC2rvRbWXIiAhUBlQSFg2XjR\\ni9V5ioCegZMCnyWrF60N6LyenZphsKr661qBe12rAe7Z9Yrrs2d49uxZncuXjT266o8ZD4/1K/C9\\nhOeiGSJUy+DuXcaSPfdO6vsRfeSx3equIccp1w/z2Pde93RjeK6EEkCPvIKOVOBeYeN83oRidR3G\\n3UB+4LIeljI18tJ9kJSY8Kg0sBNQFpQCUekXIpeAHJIxTBmqTtMiQwexFpcdZd9QCgH7Xjeo7RCr\\nfD21pkn3Z1d81rNnbS5fD8vcC6/+q+oET+NVg5012lGoA56XaGp9WFxFQjsdK4ahA6X95f8/4r+5\\nNzfoPd0Y3k5dBKDLOetAitgp2N9V683qNp0+YSrmYE65/yC8GuBZwi3gY622hSZGO2ulDyohaxvN\\naGfXyAMA9g1loQp6IpS9DmPWthyWx+MM9s/6rGdY20o9WexTitwDaPPsyWIdUubjKteRb6V9UjfI\\nR4VvQwg8Fl7TY7jeYo6PUe+f+EMURe67OVUDoF55meQgdj9N+03ckepIcm83SpjpRdHD+wMrhWCT\\nkSddp6MOqDRTdUuwoF9aNuE8emEElVEUWppRjYC2Nr7G29v6e3M6zb5h36vuzdJ33/d2uMatzuUP\\njHYgkmW4EfTLsmK5bFjXC5Z1x7pfsJaCZQVWlBoeKxYCdrQTrUvVTM5NP9f6z/bNcD2P/H0/zALQ\\nxOfUErApJ3JT1vGK+4HO7kkX3lTnge9PegkVMNhtlI9orFp4DvRHTJgCSb1aaLbPSgMBetwyv/fM\\nqIK8SIJOWbAZyFi2uJcqGevOtbUtkWUJjAbIbQdK2VTibjtAQP0ghJGcYrBDA/kmp9Ps2w37tjkt\\nYt933LYbHm5XvH54qICf1KNIdV4zz4BfV1y2C9ZLwXqp230LgLUltrTVgVgbie37edOjtrkqR2Du\\nApJph5IgKTYKp30E+iymeRrQNjIfVWVnfrzXGfek34cX0t1iEV88BZkBx7SM9yteszqbgjwY33ia\\nUT4nxXPH/B15YQacbq+FZEwEYH7QS3wbnneaLWwBv1xw3eoxVbftVsGJBtJtw23bANTxONalghyr\\nS7eq4m3TzVYPvty3rfGfgm1rkv3hQTQK2UDDTKhVstVCyEp51kzWFdtlx2UvuLA2wsUloJRV7rn+\\nC9ftnU4ZQJS+xaMqVrhPBbaVso09oxbmchz2VMup2j1LewZ9TfZxwH8PKr2pWSJfRWEsNytSrYuc\\ncUzXFQ84SLRYuxCGNIrPLWu7w4m5PyFoL0KmOYtP6HelS+8j86nj70UNauuK/XJRK/myg7amprdN\\nNlViMtB5GW3lTQUQsG/7TY7I4njbXjfnrDez+WVdnboegT27rutaz8svBXwQdjXoknRwyzhK4V/e\\nhtqO9X23eMdpWyHesMswQ+j72mwQWdobQ84o9e5VB3CryrPQsVrOyVkg4CkB30lNwwAEBMPAk/QZ\\nSCckvdOdTW6UBIvqtrkXdd4lR61xaoh+i6M/cDMyP9+F6m2R4U5RzxbajeGbOr9f6jFRbCXfNpUU\\n+75hu91QWEeWayWT16+7PfXbJqfblrJj2fSwClHLSTfgsNS26+0dI2hAV7pXPNvbyjwBsQ5VKm3N\\nuLcQlkKAHGGdt/dYla/2h7Mfd3VtKzeZgCnSEzJwj1T0s86q8rXbKPCVxrPjlycfw3PPSq4h7Gj+\\nuPcYNcRR3FhH3lrcAd0pIP0Js1zpVuVSlV+Zmk3dayIldC6+lI52plO3rK7Y1xWXy469XCq4jSWc\\nx93bZvbFt3TqGXjNkMeMgaX87YaHre7AK/sOnmKMajsDng2HqxlirExHmBFgCe/VeGuIbOfr7c1v\\n31GacdAzwHlHT7Hgok3ic/0PgB7jZgNYwJkJ9I3jAol6EQlv9QQDfAv6s0zkPar0rM7XZ63/O/if\\nVMpBwx+CHSnYcwZQHHb1VBhS0AttrNQVaXXLHExBTOeCv2/R47jVnUvXwLPvO648Xr/djOVdd9XZ\\nnXQW8PtOIuGrdG9n2jdL/N5sA6XRz1em5XI1X5dpc/WXbasbc4xaToFJiH4TjHnLVhnE3pjEXhbs\\nZcdS9rpGYNCOafUaTcpWOfedtOeU8OCY9kEfbWn37CFRG9GkdQC9qPMwanwrXE2esoIeuqeX8OZa\\nDOjjyMhV0EGKefpH1DzuKyGZin8UjnPspufEOHM+Z+LhAan6y1Nc61rH7rwjjTtdKbsY23h8WD/8\\nQG28v6N+7aVmw4djLOuKlefVm/Gopld1cLtensft4F8z4jWVoPuxtAJRXVS06PBkkaOv2mpCWVGo\\ndZlVo6/5WZgWTgAVFLAuLQw00T5YfNZYCvZcp530JzbeZfF4vH9y7v/dAt5JJZJKK7I5xI9jI8h5\\nm+S4GJZxTIJ06ZE8yyaVe1wG0IMKTyW7leRJ2gepgSjMba9rncdeFsgqulKXuLLk3stewbVVgO1m\\nDXxU0zk9ANibOr3Ll2X1Jyq7W9+vm34q6O2PpB407KIHcrRpxmVVCz9xXB0AoJfcTWKTffaVqrql\\n2YJdIuCsGEpaYsb1Jwah+YCg10JcGDP/HgEuKDoxXfm0++FhZbkHu1ZKZwIZjo3y9M3zcB7/cdLd\\njrNHbW6BzfcUQU72YeS0bnJK2Upf97Qvy4K1rMAKtZyzAiEqfT3BVqbz9gVrWeDOuUcD4bqgKtxr\\nU/s37HvB0tR+OWJLNIZFQE9NMlsLu2cCEOBXZkFqCFyjlG8SXrRBZjLj+ppL+Shg+oFml44VTHYI\\nOZHs1nME9HO66NjZxTln9qUA73otvbnPFip46Gkle+4qKXTpjr7BNQL6nNZE2sdxXBKLg8+YfozT\\nH200yqKXAXJvJCNRqTvW2rtlXat6LSp9wbbr12SXjc+y21XCQw+rpIWwsIoPAtGOZV/klFzalwr2\\n1tkIAAmTaYAnBX38ESO+gT9qKnpQhjkVN0h4Px9vmCPpfVeXTaMaqtSkkj+ygJkrfVNxI01iHUj0\\nmWBeVsYAACAASURBVF8i3Rn0R+5JJbzlkJab+mpl7hsrOmEAxA2lXucpYQuCP1U2ZpGq3Xc4K/Gd\\nf2T196RJaB2+SceyuOGPSviaAQP11ox223bDtjVDXzFLcBmQZFXplr6slS/Y9g37vgD7DuzV6r9E\\n6T4AOwNdb0nG6ctgHC8mAamBXo3v710LIAI9tfhIMlaKR+keko33zYPtirlkp9R/5LKBxWOkO/DU\\nY3hEdboEfw3Xj7ijC1L9hJqepjiLcgD2yGe6PhANc9IJ2sMJoFP6UKUbL7wx9rYGvnrOHNePV+k3\\nbNtSF7yYzS5uZ+PSvtGOIve8yo72DdgJtO06VGlDAAE88S+M5VmtaaoERQbTwM6n2ZLZNy/xkkrj\\nRVgjLfLIzVX7yBYs8HXoVk70Kdnq3OVhIw7o5ZkRAEPp/lEawxeEg/4ARO6cG0pMQU/wsQz4JVTi\\n+TF8XoF0+G6Q1mRft8ZO0qWs7AqWusmkeTWVmOe9q0G9uHPq17V9e27fnEpfUCQvBhkz4VIKaNnq\\nYp5tB9EGbNpaS9sCyxKe2gcrYEBdk/ISX8OSSvUm4buxf6tHu/jMj8sngNFW0J5VggTmtMwCquF4\\nnV8f+HkWlI/nD6V8nLIL0n1ESuaeVqU3go3QyhGkXhnVL/XTErMKK8ldT04xp9hYuorrOr47leEQ\\nsb7STTQpcZl+NnBCQ7HaSa23vSiAq4quH6J4/uI5PvP8OZ6/eIGXL1/i5cv60cjXDw/VaLe2ue7L\\ninXbsN42rJcNgBkvozEZHnMTmiUdJtwu5+Ix4FeR0qsY3ZzxzuTB++OFMbT63dvMQlXlW4eOQw2p\\nQ35vay26QR9w/OFAQhegG4fxJ7qt0CrmHrpEFoCZVzfS3ZGW2xd6uhMsfCSm5YLLhq32K0LI3o8S\\nSwrX8/hwVzSqX/nKx2+VLg1hTJkqKWHtrjkb0SdGppyB2zkpoOmXLj3uXLKJhb8Oe3vA7Va/8/78\\nxXM8f/4cL168wIuXL/Hy1Su8ev0aDwbw67pivV2wrhsul/qlGqJdgUWLUrQAVFiTYMm8gPa97mhD\\n+CpN+KmUhgP+sl4q4Nne0NbJ8zQiaAGZjky0yOetMiejp66pBuHNKy8nh4jXDBjIph2LqPvKnnW6\\nE6pXsmBA6EMpldAOmzkzbHwb03KfD+CPA/ipjbZvAfBNAH4KgD8J4F8C8A8A/EocfB9eKsVJ8FbM\\nMNSNz5ZzxecIfFf9ozqCArkHvwUl7JNPAP07K939ECYEj8KEcd2rNRqp2IFJtbo/3Db53nuV4FWS\\nP3/+HJ/5TAN9AzxL+FIKlls7Yvp2w3q54NakPM+FE9XTcfmceIikL8BasDZgLku/512m18yae2Ui\\nXkWv++DXylxIZxT4tByi0oxfjUHw8GVZTL3wndZODnzVkLJalutAe/eigzuL0/ugEl79hMnz1l6j\\nPZ0C+6RQTnM4Id2BY8A/APhPAPw1AD8JwA8D+EsAfn27fgOA3w7gd7Sfc7OjLIoL1avyURs4Ko4L\\nP0A6d4yqxnsrvd2hxH+cwC6x0Tmv1mi8Zt4W1pIR1XpSzaL3D9FDp+ZpttvthtcPD3j1+jVevnyJ\\nV69e4uXLl3hhVPoXL1/Wb8G/fsDrhwegVFX6tt7wcKugv90uuFx2LMuOUhYsa6naTin1oAwYoBqC\\nlH+RGthkcww1iayzCWzIWwTwq0r4Znm0Er7OQLCQaOkx4FnbERUsHnlegmA8B4hMtXf8ncxD8XD1\\nEr5dCw8/GgMww4AMExSz6cjLNdszBjvgGPD/uP0A4NMA/haAzwPwy1G/KAsA3w7ge5EAviMqPgTa\\nT9naT0n3Qb4GNuaMCjeG53llCZ0Qbo09aeZZ+SJx8uxf+CEJt2PbUdY8CqoRrkr4B7x89QovXr6o\\nKvyLF3jx/HlV61+8wMsXXqUHqG5vvd2wbjfctirhb9tWDX5AAxfM9n1jUW9UsoFLr+Ss6v5nV+Dp\\nPX8fnkTCtw08bXHPsvNBHZpH1RxWyNLeoJfZMXQjPRUAUXlX41/mTB6yxdYr5DaogB115kAkPAPd\\n7KYZgfyMkHuMu2cM/3EAPxfApwB8DoAfbf4/2p7vd6yVMeAS1dpJ+QTsEsY8DzMrnA7HM2N3IjWq\\nmCiGd/eJF31n2ZQsHTZ9r5ZPRbgIJvMe7Ees8mm+BWY5K5qE35qEf/UKL168xPPnL/CZ55/Bi+fP\\n8eLlC7w0Y/iq0t+qsex2weW24dZ+1YK/Y1l3ASVRwWKKAOoldJx2o0a3eyb9rLR+Xnppn65a/Bge\\n9ZuXda6/ruqrdWSZRf3xev4aQIdvXqoPJIt1o1czFbm4S7svjWn5PlSFQ2USPH1YQd9024Ns7gH9\\n21xa+5MA/GkAvwXATyR0ndMnTATr7H7fjHd2hT4h2d37wbTGcOzeEiXMge4KI0OsZCHPAfuOefP4\\njqW63MuPVfoNrx8e8PLVa7x4+RKfef4cn/70p/HixXOn4r9qEv716wcsC2G9PODCoN8a8LcNy7Y2\\nUFfr+w5UIx0UbLpZxxx2waBn+oNl3YaLaSzNmm8NT/rlW2u38KCv7+q/HboDUO0yWslEA3tOaKZ4\\nn/ersbmN8ywmDAMcDHYzdOQjtawwu1fSnwG5dWcAf0UF+58A8Geb348C+Gmo6v7nAvixLOJ3fccf\\nk/sv/sSX4Is/8XMBJOCSzqJyUlWtpMpLz4Aff1LozJkeEZBf+lIApjFjKm5ZkTAZknskz2rxCeUl\\nVYN1gYvdd85AaktU1wvWywWX6wULLbhcrnXfevZrH4ikZTVgVqMal08kWPvjliazFbsNjxZQXXMP\\nAHsBLWifr97rJ65uIqgrkNvmHDCDa+fx7W0tQd1H36QqryFgbcv1DeruHYAMz7btIm0ZWrzj0qYf\\nlObphxj5LjZd5Wn6xwS4mZ4SQ//gD/wAPvWpHxymEcievv92AP8E1XjH7hua3+9DHbv/ZPRj+PJd\\n3/O9hwRYFVfGiO3qK6pvvCytzD9W5kgaBaoGkr3dSIfT8NlGET/2Rbg3LM5dVDXwnbnI9eXLl/j0\\nZz6NT3/6J/DpT3+6/X6iSfgXeP26Sfb24+dlWfCxj30MH/vsj+GzP/YxfOyzP7tdP4br9Vlbx67r\\n2d26dlbPjSGuWtiVWZFtT1AIaxlUNeyty2rm7s2vffDicr3g2hgU77df13XUMIDUe2gPHmqY2iWp\\n8x5sOTCko9pUGqNgCU0mRe5bGk7b2wi3ojHH+c/g3rsv+Ff+5TSZIwn/VQB+DYC/DuCvNr+vB/B7\\nAXwXgN8AnZa7w2XVaQxC2dtTwlvHSW/FCe64kVQF4wBxFV+fgA5VuJE7FU46EYczWkE37KiywS9h\\njWpyk/DmFJptv+KyVVX9cr1WKX+1kv4qB1LKcVXLKunDdWSuF2BnitoshZVeZKc2dqCedK1z/QUL\\n0E7VXUtBWXbstGBfSFT6fd+x3XZcLjfc2scr19V8U48USFS5rhy9xdODtEAOA3HKePHKuS2Yl+y2\\nM7QYpC3LB3LouRzTAammHLUIc3O8Z9TTe9YdAf77ISu1O/eLT+cyc0Yq8DPQS/foZlOPM4l+xr/L\\nUzRVgpj1TafI5UPUGFQCCICDoZHMn8JvqHUJu8OOpRfPc9s17LIvvX3lZb1gvexY9w3rvmNdSE6l\\nWS/XBvRrA9IlHD6xyK471lT4F4EPS68xjtQqK1IjtpmXdmwWz71XoNZdeNu2Y193bNuKdd2wbStu\\ntxsusjrPf4jDHqG1NGPgWgAswNLO4Hdz3xRH4o3+Dn1WewhNAugDgLEFX9tQDbpFUy9GdIT8+77R\\nkzXx6twT75YDgu4afFXKi38HSsPzJqB/u85K+AZ20+glISQ+6TfiWEqbcXyL0I/VbD3oeBEgJ+Ed\\nQNm4xmr5ZcW+X3C51P3s61KPo2LpHsfyMn8uW1PVXmCZkYxBC1S6N3CHFQ6APDEe2NZRT6ndqX4w\\nk/YdCwgL1ZV7+7bJYZi3mz8l1//Ib68tazuYtynWTPsUMAywAos8Nbhpi6hBzrQvp+FAn7vejqDD\\nthFqub+dKMLUPTHgM1BQ904q9wSa3w3ovXYh3LVYFV6nzWI9OzVNOooCpXqoGhjJ1w0RngVYpVSl\\nuVqv5YRYke4r1v2C/bJjLVdc9lLHylaym3Po1gur8FZ6muOqhN4E7EGVl/3q9l1bwCDnl6JUsIOw\\ng3lKXYJDRO1Ls5sC2e2Z52O9dA+9nJNXSj2Ql9DqpcgCnrF0h0rdoq3aGdNEyptFWmFfvl/JMTia\\nyt4ZsEe7kCZLId7j3HuQ8HCc0T7bsWzOIXNkPwb0M2ai5FHwbZ2A1dDmU1woo+6Sf5bpGHOFid/N\\nPIT60XvqgG5Veh3DX7Due5XwbQ5/WaKV/tp+VaX38+rG8Ojo8g+ySYQt0ooEYZAq0XSnF/vv7epU\\nfyJsMg43y2pJD8B0n6FuPz6kk410O8UFPBCMESwkmbzaoqUDP8HtQ2Zpy2CkmjDbXeJGG2GHlsPL\\ncmlRFVvVRXXesaU3cu8U8PaEVFtLlSGaQlCrDKP2UvPPXVT8JZk8XHcCTlTJql9kRM1X7nTxC6DT\\nQe25qal7G6NVFbV2OAEPl9XcZ+WwnpTcE+rmGV1s0qTd5YLL5YrrdfMqrEhoakY7tnZfzJz6Au76\\nsqilo86onoC5Jxk/6zJatY6DYc/qq7nnxQYk90UE5rKQrMm3i3d4FqFqJnVfgF4v7ZAPu9e/UW9W\\nUSo7Vcbjwe6BDy6LNAI/a0UXc2/9XR2S6U+mnnkaUplA6BezvqKBpm+Bdw54CzTRpwB4rmrBJtJE\\nGOGsEGEarotTgj+/G/nbS99YbpmrWfXGqqKVijt0aog7B1uR/f20eA6z9VpveB87dz7eAXe5XrE1\\nY1gsGgCR8LJxpR0jzWP0YjqdLF0tyX0oe1yMI8+I4bX+0A7ERHKPYgHvQb8u+uHM69UcjX1pH8Bs\\nn622UlFsieLnAS8LAYAc8K5ByD2LXkK2z9h3fUMI0yvF9CsFfWcTGhmefSc+dE8n4QXsxXnJnWWe\\nAXzJg/ExccEVk4y7JNUS/ENaaX4mpnReoBQv8RyYLeChc8P2/kxjia3MMUVg2/VbbCzh+Tz4veym\\n73lpREQV8LzIxi1tJQHnXngBzO7u5R3/9qop6Xjaj6trmv4z0nLdS/s67S5fqS2lHpsVAa+MpAL+\\ner3ier3g4aFer80Iue/XtvnGaiFcNm5NBbeeBx+lewA+N1TxPbhwHUs/qfUcBjKezRiNQ67F5Od4\\nTBAKnZDw/f/IvVvAW+5ogSHOgNBifKq+eJS6IQAoV99J8+7SnHoEKV+gUgoQ7myVMAG1FsYB3I2P\\nTVm7hjavNGzrUgRse5H6ZTCsa12kUkodFUtf57QaAxJ13hw8qTBom1c2PQKLP1+1N4DuxV9BhIuM\\no3fs+yofmSCQT6edsrNte9sGu6FsfN2wbwz8am9YrcZgxu8V8Fdcrzds2xXbtuF6vTZm4oHu1xIU\\nlfLF3HeAjwuezCgk3NsKLk4LIM7G5cL9SEEeQR96oMWDkfReYBxoi8094RjeOtPJYerHvj20wlkQ\\nULiGPJKkxji34I7gV4B79Z6jhS2PBvzd6jsGv3NZYyvgRdoTKjCa6lfPgmtj+H2HLO9kDcPlCZXw\\nSybhoQtedj1Vx4PW/4gI2+VSQX9pBkJuo6aN1K/R8ocq9XnfNuztyza7hLkBe1FVXvbXL1iXusvu\\n2bNnuF6f4dmzm6PRns/HGhYzRK5jEqQWfTZtmgG/SFuzdmfBmQC93ZfiQe6YhgW57VeGkUtfSO+D\\n3ekE4p9wDG9d7x9BDzDmZ6UwhjBw52Zf0d8sG57SokE90IsJKg0FSEeIxfHZFZHscZlnbv329Djp\\nTuq3NZUYYAnfjFZX6VZwfdBcL7JibRXrPvecUqpmtu2lfnLqpkdo7dveDr9UKb3v9dvx27Zjv15w\\ncSOmWsYbH8N1q5+wqt+tq/f7rX6hdud8btVPFuIY0K8N9JdLXYRTwf5ZhvkYI6rUsc5auBY1gIZ7\\n9qo8g58Bvpt7nakxFWyNck5IwDGOCHBvDM4lu2I7Gn1jQ4/de5LwYzcX7P6lXV5J4TlP9CwDiiDX\\n9CzIGaB+5FJMPAVkBHoG+Jq+AbwMAzzYidhoV2ngTr1e9Gx6McRBr5yfrI83Z8pRW41WVeIigL61\\nr8faM/P2IPmJKIANUFWaJA2b1sPDQ/0U9a1+rHJ7uGFrV/54pZt7J5L7y+WCh4ebbO1lBmQlrkxZ\\nmo1EYt8pKtU92EsAu4Kf7Rhsv+Bz93TRlQG6eZ5pBwpwC/z6LuvL0c9L+jMK/TsG/MtXr4bvjnBN\\n0WMUVAxSVhqOwsdxQy/PxaeEZ743QC42jB3nOSZQO8QyAH3mOD2nygfVns+Z3263dmXpyaC6ta/F\\nWi3Af4K5ZlONcfVbkaWek/fwgIeHB7xuh2Y8PDzgdrs5qS6fld73+rFH82yv1N7xNBk7twGm1Kk4\\nlokEoOz5gptlIVxWts7rXDxvs7Uf2FyWsPxW2qAoA21DPm7h0dQcg1tsGQb03Gal2GOreKhnNEAi\\n7TRkNEoxZpP0F3niYZu5lzX4ki7n8Z4l/KuXY8A7lw0/OmtkJoeZy2WW8TudQ72fNMwttH48Jn7F\\nvG3MwXY2PTxi6ekssYRjKc/nzDPo5Z7Vb9lTXufrI+C1wNxp28cqGuBf8aEZr1/j1atXDfBFLO6s\\nQu9FAb/tO65tG+u1jcfrYRVcN6VJr7YcFlQX2xS/1r4CPqysM0C+rDotp4tu/P76uPRWwY+2468t\\n5yU0WoCCXSQuq9kC+GJnGMKzkdhW5Y/t2NSx1ml0Ky2fpy/KvI3LQ63QR+zf+ZDVu3cr4V++9B4D\\neuI0nIxcTiA+zm/rGP6EiwJfsjEqvPyJTEDvoqT3Ut4DPp4a07mko3ijnar0ejx1k+ybfhKaJTd3\\n2l7Cs8rJYFfV+/XrKt1fvnzVDtF41b48a6fodMpuoUWl+JU/dnGVveuZsXJZlvrVWCPV+YM1CxHK\\nvk8Bf73aZcF+G68HuP9pOhpmJaon4RZr9CuuDu3MBDM6nv6T+4r4uvd/b8zeYtiA3vU36WQEu0Jv\\nNAC1faSYP8Pwxj2pSp/CUEBu0W6ntZrLSsPg7q4TwGt9avLmOVfhPTGRFH6OyzGlIQXg4QTXxWxE\\njNLdqHJx/E5ogN/9mfQ7S/dibScMqJCfyaZKXTbS3fDwwJL9JV68qKfm1PPwvAGLpygd4Pc6rr5e\\nK+gvFz6IQ+foqa2W0/N02jCltZ8HvH5RNpPwPdh15SCZRTuVyfC03uL34K/1Ha98Kw74RqoL4Hf3\\nvLWy077X8Duq1C7FgFy6Agt4+JeQF8NN10a6+77WaxQj97Qq/VDCQ0Dq7xWCfXGKgJvjWLUeSDXk\\ng3xtPM9lI+jh/DmeVr5K+2al55ViLG2aIconlqj0CGp9e64dzhrTbvVLMtsmw4iFJRdph9dsPFPa\\ndzRjmqr0r169wsuXL/D8+YsGeC4zD1fq07IsTZ2vX7O5tpNpLtcN160Cs5QrLhd7xFX9HNYGlu6E\\nrYG9fot+d5J9XewY3qr0/OODO5KddOb4bTmTf13aNOLSttzWr+h6wOtVAa73G5+u22YqbhubAneH\\n9Sq4yUl63jWYgb6g78Al3NtFX/557t6L0S5K1npLgjrGATk1OpOrqpq6K/XBY2XYPETC0wzsgWD2\\nN9xFOG8KeKPKm3tnyAnEkiRvwc4GHGspZ1W+PoOAdVlQ1gUL9DttrGHUjmw7TpH06scsVKV/8eIl\\nXrx4gYeH1135uezLslTpzsznuuGyXes4/srLXHlIUZmR/aT1DmCnui12a4BHKW1VXa/W15V2UcJn\\nW2ct2P3HKjmuGP6WpQN5aWq8A/y+Y2vPCxsmbwRgk/bfm3YmUh2k21u5uwDojHVW0psO6fqha7P+\\nd+SedgyPXNvuFxJk41uWnvosYZyUj1LT3hZT6cwgbL42SqbWhwKEvGyl20bgMWsH+AOVngHOWVkp\\nzwC1H4nkeyJCuaxYcamdrX2yqU5NtZWrhTffqCGOVfrXrx/w6tVrOQTz+fPnIuFlIY+p7wr4mvdl\\n26qkv2zYLpfKgABZIVfKVQC/rgt2AnZUkO+tfvYlA7zer21qLoK+N9RZsGud8xCA06hHZy1ilCxt\\nXT/fOxV+3yvQyy7rEtAE014KlmUH7WaMboQPkQK9NmtpcRPQO+Ocv3Jeto/5acmxe7ffh08IYB8K\\nfuQKnBNu57gh1WRYZ7Gp27AmxcIy1+eR5dzrFJoH2cDiHcbuNm4ppsGZKexJRh3B7VL1QFEV2SDH\\nwoSSjh2mteq42ZZ2wd7GmywBedvp9XrBs2fP8Fmf9YDb7WOd8U1X8NV19JfLWhfztOulrby7Xq94\\n9uyZ/HgNfJWqaz0AYyGUbUHZN+zbirJvDfBLMo63m2fayr71YiT2VRiBqve+t4kEl0VEhG0rRqJH\\nSa9jeje6NnXBDGlf60k7BUUPSWkCo/C96arcGytdvlNxbmpbYPuJoYcNikl/zdw7BnzwCDjUZvAq\\njL/qe8/zWgaEXjXqkBuMIEwYAaUo+L3qPvATBswDMVMmKbBelZNztpq2bGqL4LahzDsubqWbhw5W\\n/V9Qjd/GEh3GtJE+gCDfmpeVbBcBap3T33Exh2NQsw2wxsLr+PmEHV1Xf1HG8ewZnl0VjLzSr+yE\\nUhZg3VH2BvY2X59K+LA91l5XAX291oVFXDauQwbx0qQ0YaMtvKtXBdnuJCpcnbe6KAuWpZ3NtxYU\\n8MYhnraD3FvpTaY7an9iBuA1xfynjOQM4p9OwmfjaohibV62wkYpHEFrWUb8jIyJ099GHaPYlHpC\\nqUn2wKkMjo2EB45rnVuGwIcfxigzHUfKEqQNUT0ppgjYjXRfVcrXNOq1lPbBiaXS4iX8VdT0uiDn\\nYj4kYc+jV+1B5sTZKt/AKBJeNrxcRQKjLKjbYXdgV6ARSqrOy0m36+DXAL9emIb28UnSOtQFNPV4\\nrW1Xfwd0WNDb2QnTUFz3RCjLggX1sJ06TNzbFB2r3RWZddmDfr+Av05Dbf1HkXP0WcIzsyhyHj8c\\nPZ0oHLp3u7TWdkhHjZHtBPfll/pswWtAC3i1oa0w4okMNo6QbZSoIRQf1zs74DIEOr+qdRQj7W0Y\\nq85bRYWJ6bSeU2C3jKsEVlVkPF0ZkT0CyvzWavRSYlhaKdg5Ho9t9/1ad8OhWvB1T/piPuzYtrCu\\netz0IkdPr6LWX59dcb02ld6Muxnc8UqAm3uPEp6n+HiJMJ9353+6LJdMmVl13wnYCKgCnuuDh0oq\\nzXVJrNfoanspAyxgsLf22NrCImqGSyr8aZ02BdnUeOnu3CdZ6DWwwy7ttbYXc+0EYu6eTMIrP6tP\\nAEzvzsbv/YjaQZ+BzpXUjeFNjGJSaNmIeowatz7HKvNqvD1JUG0uls6symO5RtqAHWfa3JmRRKBb\\n+pt6iQpoHbOvfkprXRGBXpoqysZFVpGfta2mKFWCyccdF925tiyLgn5Z0l89X55V+SbdzRi70s0f\\nrNB7IgwkfH62HQ9dXJlZpV9sgxWg1H0I7TAibDCSnaWnSNEgcEzrsEF1IRLNqnZnHSLte/3YBlFp\\nQ5UdBQuo7K2cKqi0q3iw21V8us+/708fAcC7J686Z2B3qjkUTBawXQYKAxnzd+fHh+euZrxKRNYu\\nIBc/ZpeFE53I9mk6a2zjyMRjcNgDDtvAga3yXDwpPHdCw5iIrfa82rDeO0v1akHS4NU6MgOdx/BV\\npa+70cp+bVmQzFGPQM0LXfRT0SR+q0yBxV+zrLc64hV2+kxOwovxzjCbaJ+wW2mt9tHtHYCq9STq\\nPI+btb7dszYmpK+2eqeFsGCR/ihn7y8F28Za1S4gJqrMk/gwP5Y+mXrHSgEPQ9ppQIhBz6Ad78lK\\nT2KMaz4inTNJD7nvJKPozICO3Q2EQv7dM4KAFteDXTWJFiI7N3jqZhKe9OKYGD8XZvfgTpitP7CW\\nemehN+vMOT6PV7t461q33V4V7Jd2EqyV2vY+zhDENDvjGt+z9GZmQyRGxzWA26n3nJcDeJ6/36Sk\\n0rHs9RMae6ncuzjhIvZxedb9B2bquFZ81U6qAQVop/jSAtBe6nhhI8mdGWzhIRj/bFcg0UVMHF7t\\nl2mh5917OZc+dGnpwNnVu8L1jx4sqn5mwB5J4qiFxKFB5SWattM2IuZd3ZO/0z/umVwMBb5RDIx2\\nYugiPgKKVLLy2nAe3zI4uOO3+AsBpf5B1SyKdjzAWN4r2G+XC1CKORE3nFs3ABkvONLxNAnNpjrg\\nSmemH6vxam/8jg/nqLTvewE1qVqWggXab2pZFJz2nutiYclsnv0si4K9VrllsCRqOxrT4LX0bFxj\\nP152G6+6+YhVdb2WUmS3oZ48pBuiuIPwdJ5dj3Lk3ssx1QB60MNoN1z5AjAv7Z2AtIY7C3oJ4xT7\\nkGPnCdvA3oioHSIIDPclKsuABN7cOOHKksLE8veG9wQZJZ3Urh6z89Wr2S5q1+CjAWBhPZrXszc/\\n3ne+rgu2bcV2qQtpCgOejXX2yp2fQU7Qexl/r2pAE2JMHTVNrYh6DaDsKLQ0NXapY+ClAPuiDKxd\\n67CkqcqcKjNVw1yVIcA/u/a3kp7pbJGgFcn9zIF9L44BKMCL2zq87TqltnfXth03ni5UdMuxfGNe\\nhoBeeIzcEeA/H8AfB/BTW018C4BvAvA7AfxGAD/ewn09gL8wT6onJwe9Wi6d+O2kdgOfBbpVvU2a\\nfcYlf2fUaYKCvX5sQYvRDesE+HYqUSHKIIigEMkTyFDBp8xNU61+CwM3m35bF2fwWrSPCs0V8Lr6\\nb1+obUklbOuCdV+dhGEm4+big8psVwVa6eq/jqMA0/XkXSFlvItSVW+V7FUDqGNgarM7e6VrIWG6\\nXtnSZ1Kvdh+OK+chFBPkmtNKUk3R7pbj9fUC+r3IUly3Br8Z4goovfrDPnXTDi/gkjrk0rLxv0NU\\npQAAIABJREFU48AdAf4B9auxfw31G/E/DOAvtRr5xvYbOrvNdESKQIyNWI3zC4AjLK3EdkDXNEaZ\\npUsPi9MZRKpbm0B9bJ2ZNQ4WQlbjk85RtQPuaMyIc7W3dwp4iBSg1kGwK7hEhV9JVPg6TbWIuroQ\\nS1W41qAFoNLWj1M1OtVVZgtW29natcUSDcHeC8CMaiLaRJPyvtymgKBw1TapKxsJfD7Nvitzot1o\\nLlSlO4odr/ftTJKnXu2BlojvjXbpz6yTwgEF2IrdQdckufXrJH9bGtt6R9VoyFBEvv7Z5tDuecjF\\nTNWKqSN3BPh/3H4A8GkAfwvA52mJz7sozeM7viOzcMYvKPBqPVh9tpoAq+En6XHMww0dmhQpDehF\\nm0O0duMn5SPzUqQBtOOzVCZyn1qWUUSgsBS0fdWiVFYmB7i0RI1vC0/sZhE3nm501SKZIQXX2cob\\nSBpzFOOemWFh6c1keqR3jhqx3VX4qWXgXFaz7Ko19Y4dC+q6ewW7Mj8dVsB1ldjOdsrNclSnS7m+\\nwPeiD7jSVUbEJ/yU7qrjesh131U/LeDzBAkw93GtvJ5BUNhC2GZZTDu+5TH8xwH8XAA/iPoZ6a8D\\n8OsA/BCA3wbgnx4lMAO9hulVYtWsPJQF3BboA6nZxebw7U9nvGs08BFI8q0087rm15rOfGSSx1cC\\nMLAKpuNu+ZSx66C2YzbtZtnbdxnac4ugYNcxPBvZlsVKYpgxswegk4ZOrVVafN8PrRDqrPefsV7f\\nGwpKot03ra9J+t1pSVwOe1+vedvyAhWz190uo3WNGsFufcgEqW3Cavy22SO+WLWHMNAdqrHV9BaA\\n+Ou87Z79hNHqPDwz4cb5W5/vETNzZwH/kwD8KQC/BVXS/yEAv6u9+90Afj+A3xAj/a8/+L1y/zk/\\n/eP4aT/944egL2lnKfm71rh8X4V90skSzqfSo2hbt8rkJY72rDCi4vLvJJSRlp1qa8iQ6Sojge2s\\ngKTZvMtOzVDFqkKtQWuwWxcyK810zXylsZj7WqY4pLAbYSwTiGNzXUuOw+vonb3vFzo1CS+Iqsxt\\nL4ZW1HG6o42ZmaHTtq3Lf9cdcHFnnKeCL6Y8dlBggoj13YK+XWt5SMpVpIxkTgxu+xSWFTAHig7r\\nl2p/3Isyyb/3v/8t/B9/9+909RndGcBfAfxpAN8B4M82vx8z778VwHdnEf+1X/jVaYLZUDsdXnfc\\nNryHh1QpSLA9Yi+stofVevxKglGM1SXpQd2P0+00lj+Qkc+185qDPLTxdSlUP4a4LyhLlUp8EMRq\\nDXTWLtDATiY5Lq9mxOPIpkIX6GetAVlcRCa0A5LVvsQQmr9zYWxYpqblDaq8bWG8s4WK24fQDHVo\\nmh2EWRVjd+nz5j+BARomaioquc9au/l0NhLTlK4PZ33R2iEa410W0Vip2DpqeoGscFwkzs/4wi/C\\nz/yiny2p/sU/92eSvI4BTwD+KIC/CeAPGP/PBfAj7f5XAPgbWeQIVQov3VoZe5uh/8DlzZRXcE5Z\\n6byOmlpUSBjLKbEF3T7rlJeepdY2dpi8Y/0ARYFe6pRUKVXd41VxK68jJ7XGW8luEhPQS9owZlGZ\\nGbEx2W5g/YtjxHLPIDd+R+H4hQC9kbdQ/Yw0yS6+Boii4GIbBIO/iF1AtaRUf7BaDxdYrPu+qtKu\\nYuuEk+R8HfCZueQCh4d4nYBoIC7MuIUeJcQua6aF+xD/5u4I8F8F4NcA+OsA/mrz+88AfA2AL2nk\\n/H0Av+kwJ6U9SJ3w3nE0E3kiVe06fd1dnEFe9FrwN8ztMl4K6niSraqP7Q8Zb1E5h1Lerh6ravgQ\\n8FK4UsVds6izsYnILD1deVmrmYKT+gjAb3Vei6rST6RmzD5c+/YJIG8PfVtq4nYYBapjW5HoaGBP\\nQG6fwZqIMVqxtPcEe0Hg6tpoMMUGNt0g7p2y/UqKYDUrn4x3kjYPGfmXgD7GtcmYI7ucsfKEOwL8\\n90M+iu3c95xJ3I7n7NjQAV+O64WXLkYSUBepX613qIlxQ1FUIMz43Kj0fcz+Wa6mQ6qUtau4egm/\\nikrGXa7R4soUPoskgGfNwQCdVfoBrSpxuovklc5vZGFbeOdfVEGX+nVStoS0zKbjwhK9aUrFakgZ\\n6BvgjSpfpSJ0lkcwkGyJUiXA2AJMv4gg7zhCTM8YDu0QUBii+ln2bv/VuHr4pi+Dz9euENSvBpFq\\nHRP3ZCvt0nG77eAG7FYldOoT4IdbGOGcwvusJox0Z8LiUMIg24NcLfbKI8h1Ul4YI9Kd9FAK/mTS\\numgHoUC5LQFppdTnovkIEEwnsLWjsOxktQdkKLs1UHbaVucU5Cq9vZGzj6FlURtKEUav5YugZ7WZ\\nFLCN0VV7jGeg0ssSTuhlO/HozKjzKtOtPYPrg1DtPztPE5q0hyW3MsVJ97ZwaDES3uRp752RFaoZ\\nnHHvdvOMua+SimSoGMN46WCuoFq5pOEyrifMo2ZjGEPf0izRibmis/Qb1j5wnYRvf7gxbGcVThxU\\n+tVI5UZVwljitS3VsMyTmYyNbzquWz/QhgNONgcDmmhWVrGYo93Ha2kqkxjH1UVWlbPzFmBpnw70\\n1PnJPLQZ9xaWmqbjyF9e8mz7h8e9OXimeGljmLLulrSMSEfTUimddPfpecZdgc+Hm9YszJDFMQC9\\nRvpm7ukkPOAACSh5pcyuvJEmlO0gn0yyKxe0LYxaeUUHEJqP76yWg3uQG7Cbq6jwiUrPFnsi34Cu\\nMduz/EjvPX2WzgZggiowKDJnWx9V4ttDHvQZOu9r/Pp6NLlOpuM0XozU/ggDLmYZs98II/fF+7Gk\\nJpBKeNJaM7j0dWeYu2gATrp7UiNY7bOq9FkhR04lNJpAUCmvXyVyi4qQ1/097h1vj9W5zVq9poLB\\n8rtWXSfZedLSlU9jWl5e33iI+3D6LFyaRLDUN8zZDTPq2k8Ap/PwrManVni7d1tUbqWFJRpL4mLK\\nYhkXk15cl4tg96o6i6kS31sp30n3YoIa2pRI5yg+lHvCh/Yj2za6WaWT8gxw46dqsQEQNSbQGlLu\\nOfuG7GwtgNVyarWwSmULZSVwYy5OPd+xlEV6vCyj5dYnEkv70mhlOjty7FqPibJ1RhN7t4C3e3f5\\nwAAwYe3eTBOhwIn3wtFgO5FybXeFBTk/9z1PAG7SZGoKcbO0EGTjFJ8AGOjameQQBzPPvropFGVY\\nVYTugVrPEh0o2VhnpDMT34G6o99wNhPGjrdtelxMe06nEY1wjkxWtkNyAiZc15VFyjIYdbzNzFak\\nuAW11aSmgIcBo9YrE9xXSayHYIeIqqkrStDi9qVu7yULdNUpqlQ34/YAdpk0LWbLuOkhNpwvw9w9\\nmYTX3mClWOvYnXSvV49V35vIvgLvCe4lugW9C8+ptYZnaW+ZSAwvPyPV+dlJd9Ips9Vyce7A4HIW\\nodFC3cn3YrQdBrtcWzotvVbpCnSRbv7qtQF9jn2mA7HngD5gm22heJ+lZ/qASHpWWW39C9CBVNqD\\nAujVHxb8sPXe/K1JfrDD0g5rIGlaNZCCMVF/y0LYyyI0WMbOTMB+FUim1oxEKqSg9+3AQSLd71nC\\nu7lX5lTSwQGZC0/A3q294YoF10uRxmSnnaXXJ1Xl79cei7QHMw3Y5mk3yjRy8DcubwxyvK+ceM28\\ndEo0DWbXPAvk1BWmsn6ebOeep/Pw/Gwkkl3kwgASwJt71yd6EZc4JjhECa7XBgyogvTyfsqsReIb\\nRupV9wD4DvQZ4MP1UD1m5qeaFG9JRWsjV5JWPQSlpX4uq54VKL1VGsDct6O4uFNwNYsobKAH0JZ7\\nK/A7I6uSPnVPLuGlMxtYFlOx7cZcB1JFulCU4v4K8mqiXOHb3qdsriY8tfkwBm0HdlHpFOz1CKiY\\ni5avV+lNPTHI+asidt23SHer1qtWJKBYFCCL4WQ5cHW45GkdwRym5k0wAX5gFKRP9pnClZl2N3bv\\n1Hs/VlfjlmlvBHVekYtUsrMmJXyV+6ahr8ClD5v/QvWM+lJQ2rGcHuT2WcGuHJmzUNADCnxtZtvu\\n8PcT92Rj+Hr2tu0erVPHMTz6wlD4K9xfEuf7AHZEFd87q3JGqa/MxF5hGIqhg3gzC6ALa0g2tVgg\\nmOUp2rlI64XTL63HCcB38/kj+ayQnb7UOhOaFgK1I6hBhH1hSRQFPSNOelfPGMaYr68K35Vu6lWj\\nZ+kS2GDXX88AnvuCkfSAUeHJtTO3Q+iK6CpFJLxOERZQWAeieYkqTxXM9WcXxXjAi5pPfF+9i6Mg\\nciO9dxi5Q8o/rYQvXm1jQ0QNW9Jrjcrg1WcBItkW8x2tB2kvrSLQfbw8pJPuwP/X3rWF3Hdc9d/M\\nPt+/SYwopdi0abGxFlvBhxpMCtYbqNiXqi/6KCo+tSoIWn2yPqkFwbc+VAWt4AVFK/qgrdiiJYkU\\nk1qtTRM1avrPDRSxiP2fPTM+rFlr/dbsfc75cvm+k9K9YJ99Ofsye8381m1mrwn50YL/Pvn0TpJo\\nQjVHB3NzfR5aHgVqDODjotpfdTt1rynY0bqfiJ7SCllAzRpPn5riBhuuC8CsEYNg4Y8xAMdtV5e9\\nWKTxmdejL8/Ho3l/7HmO3h5QGwcc8RK0vLfb4cW64B+sjpyRoVMLdfAvAJ/9uNg0XbBEOpSM9Shm\\nDtCVAv5iolG5JoURmOQSf71V2aCVIbWSDlixV1wIOBYYDno9PprzqnejkNAGadVhlkU2rd5BbiY9\\nvSfdh2xEeD94v3frPzzFqJ3bZ2VZXNfsNLU0FKYjn9y3dN5rfQT+G+gUNFQzXD0nlMmhvuI03Mju\\nn+T9F67XoOHB+1ayBI6euzfoyibggMcddFSblmy+bv3cmMeQwNz975RkEsxUKlKqyDUj54qpxokm\\n2VbkFqX/pRTPG/np7dyVpey68ji7Sb8jwCdqTKFCF9spNIR1f80luSYHUNOLv7kWpg5GUgC6jwhz\\nUaCX+oisRPsKdgWWdcMpuLRR2jP82WExAPd7D2DXBufXVL+OhQU9hPmXEmdojTzkOgnalQQV19P4\\nJsqYyyj+4aJBQXbzPSgDEj4B7P0csKBKZBQNoju0ixEcIKAT+NEwNBcrZ0vdVQLFaSb5jr0mBbvO\\nLivJL6b+PXx12RL29Y1YCAiPEg5qa7JKFlmJXk4a3s00XQ9gT0NKpq7ZTauyyZacKZr6R1MImTIE\\nmToEdOgRAr7WdQCR/q/lbR3gUNAvP3s1X94A3/x75hUN7eAfNDsXQl9ouGYBtgHI2g2on+qapu8n\\n2NmDpj8M+sA+uO3iwnVRpPXD4RlugmtZaFvLBoR24LcdRKpp93Zwwcq2u1fJ+eEltapwIS9ZgTUz\\ncM25g11SW2WaT741nUTCc9ml5jntlqNGtAcrDgni1G2uDPzFPXPPcbpiwMcKMbACoXK1O4tnLgkT\\nC8jFxnBtMM0SA9L83f24+Mwu6XVbhLhrb1EQzUxjK6sWsHnDC2a9gXs5fNbAjv4QKDxIs/cKsnNG\\nsAcBMYJerlG+dHbaC4zC08cAIPDT64UqxHiM5b42Ov57GFyzRimt/8lKgIEfNHpvJGYhskBSTjS/\\nVsHsc717MkjPC0cxEMoVt2ZFJsgEHK3ps5Mln9B57XJtqFnaoIJdn1uqJ68stSI1nVTSRTeb9geJ\\n68vA38xSaf25p+j6TPr+YxIcXrFjquUxQUTQSqadkuXrLhU9pXISBgMmWdWzqSNgOvCjCafA689p\\nBBB42TVQp2Cfkg6jZWDJQ6JTEU30ZADvzzYh0+j0Gq/hdxiEJwvF0ZTP1Ii9UkZhEQUyCwHQ/c2k\\ntPOojuMNA41AVfAGq88An4zfoe5dDi9IZaRrV1IEQ7AzbsuFOp7dXSHKfz9ZibuG15RiO7RcUWtG\\ny2N++m7el4rSp58qpQKpodk8U7zoizgb197TrtA2/vLR8OuAt8SP/R01zbImYvTphnWkUjThtPJL\\nSSgFmCpQSkPpwqEAfWaShoqe2rdvq2hVs0jwpWtlpqqYznL9YEO1PGnLOMJOpnXgHCQmt4MJGTV2\\nMg3VHEVmgrim9w9gyAMcu7BYO60Af6ndR8SOoKe1FqnzZZEwI2mphmsWN9Fnu/Y362NNu2s5WcsP\\ngtTNedfypSjYC4G+z8BaCfQyaXsX2jL5Rc4ZyJJzoFI6dK/zPmXWNKG1jJoZeD7b61wF7ChFXqxB\\nWiKl3zYXVLX9YM4HWvjpo5ty6EKh64vSozdocCMT8ExTxrTL2E2ealnnKR+J/auSgTk3lLlFkLWG\\n1CcqERT3hH9wKZjYvCcBoEBPTX1e1yo+IroDOy2XGLSjoZFUpVC7QyUy938D/sUYWyWDH6+ax10M\\nLIC+HvC0yuj89G0/oGxI8VijsoHHUMBAGraXGF+hCHZ22YJ/r/Xbj6lwbl0gC+hJu1om2RLWCvpG\\nFoBOie3fPjRMyvIsz/RoPecnlDnyHOgubBqkDKkUJBR719Z6AqM+LqU1ifIbswbALrR8YgT0Nixm\\njb3HMbpSwN9++2227eaZAEHbgwBeQZ4N6JqBNb4g3w2opUiW0FIw93WpJczdZXN0aQMIvtwQ8CPN\\nGTRLb4A6hZPnkptoNF00TwEJINZWTJjIM3z+8SXR8+1NvdH7OEUahbai0ReBz8SgYkSTzTRq8xXQ\\n6thuF5SpK63Ybx/uc5IaeHi1WyzLM/lY6DqzdQcdTctk5nHymBAy3Nrq1wIMeJ6qS7Zv8Pz2fb3b\\nXXTAu3voaynjNBdMU5F1KZiyrOdSdKr4HszrSxcKwW1aWaslmlNCy5oh5zTTrxTwd9z2Ct8hQJgG\\n7D86RbEBaZow7TqQ5OJwXz1WurkmoK8myXkOr1rinF5hNpDuy9lcXgsQRujxkNnM4KfvlwFQZbGp\\nyZqdNf7wHDXlCWzm1thlifg5avATgA8gZmANoDezPtm7GNiHbL9R55ywKVcpmuTL5g2qG+pu6xfw\\n2qLutC3vma3dGZup5CoQpvBJswsAAXpcZDbcCCF++9Ya8lSQ5+LrXJCLLKVITvsiSLdYwirY25Kz\\nWr9TzVbPp+j6NHxooB30Pcil2tzSLhPg7foA+q7haaI9n21zmLSvVJRaLE94KT4NUKkxqCMzgmjL\\no8d1iW0NIOVBG/TRdGquG6ajz82NdvGA4S35W4DwH2no4EIc0PAgUz5oeDWTDwGeDyUd6hvBzhre\\ntD6ZppcBfwT4AWCzjwqyxhaatXWuLa0XneVnDMiFvIOW7juHbZ/j/gIXFzvT7BcXDnjnawRd0PB5\\nduDPBXOuMmCnNCB1F8Pa3xHQN/0SMckUYVlcxXyJpHbXBnj+PNSyq6aElB3wy/WgOYO+TzRpHwO3\\nLcCuFoBqf7MGVDDQjCHjQB5nMvlvtvbkFtLeamiktfmYd6dmr7Ic6jvuD41XzWxcwpQPGv2Ahld3\\nyW/ObF6a9wPAQpAo+asd+pwzvBf5xcwzux5udY1daSP4da3c8zaWkTKQkYPvHeYHSMNcAXSeZoed\\ncjZtLsD3ZZom46MJ2L7fkJCn2TX7NCHPM6bc9+eClCqQCuoMpNxkgkyOtpNmb41FaAIHbKN4OExX\\nbNKThteMLzkhZwWPFHYEOW+DG3wwsJNFQiv3ffZjwa8vFXMptl0q+/xRGHAjwxCISXA/UBuGagsN\\nFrmGbx4cQrP4BdCVgGF7adYz/vg/9+dXzHfOZKo8M40+BsG8IEGQsFcygh5L7S7zt3Ng0t/r2Kiv\\nKMSVZ8slzKBqdczAh/Fb1yn1bt5p6lH2qfPC+841uj5RpD1PE2zyTe2eIwEgYNfY0i7EmqKgjeb1\\nNE/itxvwJ+Q8I+eClGa0VFAhvnxJDTKicuAfuSgs1IDUv5WgRnOCrk/DB8D7OiftkmPAO+iBwZxP\\n1uwp6KbBGt8PQJ8lSKKgl2N1eawUnwBQgU/7bhZTX20HnsxhnlBsRsTWG6lEaJu6MUlHUtkL9bVW\\nZYtvnBzsAegYQD4uJihJw4djnbOG/RSKswr41g6C3s5R/6eXeTTrRz9T3Z8R5DodVLTeim2PQNfn\\niou4w0551r9Jl/Y2mZa+mKaorfswWZ73Pmtd06fO2h2nC0+DnVMmxSYB5zypGe9AV9A3JNSWJLhc\\nmmcdHqwWDlBK3bhFEayKc/vwdwyAt/zplveNujimtUV9eIa8N1rrCgFc4nfmzKVgngns82zHfD3H\\n/XlGqRzI6/22vZGZL4wIrJSSzPJaGxJJ6EaNU6LDaugtVX20qv2YVix/BMMC59iinBs1O4N77ZiW\\nYDTt1XduLQbtDhGD/xitaXSLzVB3WuhaWzHp9T1aUzBUTE3akLpfu2nCxW6Hi50G32R7t9sZwNn6\\nVF+fwS1BW98fFx4lKkNwI9DFpM8C9gaU2jDlSinQYODmtbZzdRncunNBdYqudqTdzm+/DngfVTf1\\nEXYaAVcfKqkmgqxNQ3Uy7QCQb9nPVYDokF2KkkogZZJ1KZh2BdO8c41Samh4VSOowbkgM7xrUTXT\\nWUvJ/6qB1LwGUiV3JdFbjVqdwM4j5uyZB45ZCQ3LBPT+HHokoBqZQd/PHwNmPEx5zRwffe01On7d\\nyP8awJ5C2eP7m9netfcYWd/tdjKt9uQANT+45xlsRfhVkgznKqWsgloFSdDwOiQ8pWhhzm5x7ucZ\\n+/2M/a095nmPucyhh2nU7BoJ4l4I9WFSa6uf+q7RKcDfBuBjAF4B4AaADwH4OQCvBPB7AL4awBMA\\nfgAr00Vzl8US5ImG0Y7gp8CJ1CgBnrbBQSTEIJuZsiq1q9yzTL1LpCIr2EvBVCp2uxK69yrNAlpL\\ntUYOZbatmtvs4FO8AaswsLWa01GxIpjZGPz1YQ39P4A+3m28N5Z/L44pB9tifwn08T2PAX4N/NZd\\nufDH1/z4RgK0C8CcbV/bzBrQb9y4gYvdDlM333UchWpwfdOqDYnqNwHhE22L3Siw2RoYPgDz+NEY\\nT6q4tZ9xa7/Hfj+LdTkX1N7uxkAd74MBPpj7p+gU4P8PwHcA+N9+7t8AeDuAdwL4MID3AXgPgJ/t\\nS7w5jZRjQDPI08K8T2GBNW43YxQUHCcy67E3xpQyUq3dB6vIU0UuE6apGuCnKsyfSsWuxkoppXfl\\n6X4u3oC1MVd3I2qqhBmPJWikPqTAHtZMfIy1PEDgRwT2oW1rJittYb15aONywB8F+yU0PBCBPm4f\\nczuP3VMDcKrt3dxeavcbN24Y8G3yzb7mHPAeS6iLwVmn3KdVlyql2B1MPUIK+P1csN+7y8kanusp\\nrFmQvsSABwTsgGj4CcB/QQD/bf34bwL4KFYBzxrewZxWzPrgN/VUUTEANQLeKykwxHw5raRs3yjn\\nLL55puj8xJVQK8qs0fy+2P5kH2BYtDj5J5DBPGcg9F4Ex6FFtPrqcEWx2+BgRzT/+WaBnDEtbgT9\\nHYJBIICr6a7HjgDdzj8B+KUAECkYrRRQ3R66TtpWiFUk/0Z97Dpj0GsUnvvZBZzoQrrXcamLeMGi\\nB+TAtp4L+Ic8tQ+w0a7kUir2s/QWSfyIFE3jGlqumRdszr9UQbsM4O8AvBHA+wH8I4BXA3im//9M\\n31/evA87TAmrII9gZ/9WwQ8Hu5myDnp7edtw+EifpnyRpoG3qZuEZdrFwTm8bczv6yzBvTy7iW8R\\n4x6gq7V6pcN9LW1AaoaGqvPoDL+AM48q0O9MPPHT4nV0zN2N6PoE07lf4Mc5Mqz++uXBPu6Px8bt\\ncRZUFvT+PvH92Ecet4/57wJ4vz9vW1VUAWO1Ydpi6Y2xI60XPsT1pcet16c129ax/nOpYRErwN2W\\nURU0u3/nSUpmca7xaY0uA/gKmRr6KwD8OcTED+VYKRuANR8+r/ru3fohwMtr+bHBh+3HG1LEiLED\\nyK2h5mxddtxPP3WtW0zy+rrsCqZ5xryXkVFzLshzxpxmaQRJh/P6s9hH15JEH9QH5DDoYwW5T6Ig\\nNJgrP8DrFWADRwBG5eoXx6BQBPZifQTovH0Z0I+meVQAPuBFadSa7EPzOQz4aZqWPvzFhfEuEXio\\n1qDBwrkWlFl861qqWT5aVWH7iGK1LmNth7Q/96G1cx8dWqp+4adWYIwJ2SNX/HcTAifo+UTp/xvA\\nnwG4F6LV7wLwNIDXAHh27YIPfOADtn3ffd+E+++/3yt2csDru42+bQA8UgS/aYCRKW7qZwxMpn0e\\nU19o8M48yyCJKc+Yp0n6UXtDnOcZKReUWdE9G3DM6uDgV0jAIKXUIFUAPGnY0SeLIcpRu18ecKvH\\nVwBc6b965LwXCvzxf+7i0m3tqZE2sKx3Ds6xuT1q91HL37hxI/J3FH5NNbBaeLMtwa1gi4lANgLO\\nBapafbC22hpQKlBak0E3tUleh74fglSDqb7mvz/+2UfxL489ilN0CvCvAjBDIvC3A/guAL8A4E8A\\n/BCAX+7rP167+N3vfpdtm7mWPQq9ZJgc5PfTwTrSJRUlut1kTcQmmBSgTXuQV278sq6UGWWeUcpM\\nFS6RVPXraxn8/B7Y84QLpBVt8QEl3ujr0EioRZi9woAfeBcanWsO2z7y/yGyWENjHjvPGWD6Hrwd\\n2Xzcr1wLtvkAl7W6RhD2jepPAZpzxq1bt+x8/u8LX/iCFmwAsOxLpHy2cRuq4UspdK4CeQn4Qxxt\\nK9ut98HrIrfR+FTfluCNxW06AxaBwQTgjV/3Znztm99iT/rwn35otTSnAP8aSFAu9+WDAP4SwMMA\\nfh/Aj8K75RaUM338kuBgZ1PFGO/saOTjtg70nICae2p/bXjcEPjBJBydyGRWwNkHNxo5dTPOlv0e\\ne6p4jt7X6lFVD/DEFNKszSVgU+25I+DXDLhEAbpo1A+gHqwDlxlxe/zV5zbe6U/IgKQGg/uKLaUA\\nlhHoDP41IcDnAQCPXPOhqxHw+qxFjfb7ax/5PM8LYaD1uN/vJVmF8mcF8CzAWaiHbyxOgD0IuMEK\\n7VG9zn35Fl6GaiVqr9QrpVhhl2YEe+J7vniT/lMAvnHl+H8C+M5TN9ehscCKuQ6QKQsN29vkAAAH\\nEUlEQVR4w3QN1NC84pPk+W45u+ZYU+50/7Fh6+3VRNWAmgZnLGJaRtBLg6nabVJ60G7ss+cIvjYQ\\nfWawJop9wReAx7zpG2mAuUn/AGZqhCbY6J2P7DGz7N7DnwnN/EZp8xH8lwG4v2O0DHjYqo5X3+12\\nyL2e19yDQxp+PK6afb/fy2CbnGP5h/vWWtE4KBsEufPZtgdBp+sYc1BAqjb2fclsI9+LSE4mhP+1\\n92AN3Kzdnw9d6Ug7/rzVG9pgwgbtZLaNb6tZnoHWtBHwPROtlaIRH7fILNNuEu1v72Avc3FTvq9V\\nw3vXXF0ZjRc1/Ogbs38oy2wld4GoFYoB6L7nVr3yb+BloLVGsVYXeq4yPFFIQlMxtdANpMfGxXsl\\nhrsPLoGb9FMHuvvfDHjXsM16RIyvB8A+z3MI4KnVwGBnbc9Wn7aNRh9lrYF9fJcR9A5UTeQS95H6\\nxCCY0BMoGsC5dyquubH0OmPv9gRdMeBdwwe/hxuqMZ+lLh0DTNPB0iuRpGesDy179N3dfyJzvpYI\\nwtGk1yGQ+72DWjV5ALt+2MGNgbR8P99HWsm9oe+h2jypFbfeFZeGCg420ugeJBKGg/ZudF7rpzZ7\\nQv8vDecNQE/KywHoOecF6Nf8/6X/7kE2vYcCXLcNtHBwp5QssKZg94h/CttWNytC2SzONrRTqss1\\ngbHWReiLZtnJ8O5H2UZu5uoycBPSYmbZAPxeIZfA94KuFPDiw0uDM82HGrW6Lfo/H9ecb0k8nbT8\\nOiuSa8cuFzo1X/qza1MJXoPGneeyMOd1uxb1wVn6V7cUglmvYPd3MXOePtZpTb+SYs1AgO/SXrU8\\na/2gzYN7pOcS2M1kX2GZy1Xjtx7gPmV3HxCAsrBmSAsvHtdBF/vOPbUZa3jW3no/66Om55bioyDH\\nLrxxPYKdy+0ljuDj561ZB+NQ2wB8HdyjgOd120kgG1nGjbS1EX3dOqDxCaPtakrlEiLg2jT8xz/+\\nAN72tvtRKzEeayYhA0ZS+Sb03F2NgWSiBN5Uh9xqo4qHMOWhBx7EW++9N5jlNtCmeHRWwL7Hft6b\\nhnfTjwRVH6yxNOfddDbzk3zLeZ7x7NPP4KvuerUnBOmVm0fAkz8fwcSVzhrUhQcW1/i5TDdvPoXX\\n3v3afs3ShGxNpEZTs57qT7Wvgv2YlmcNHz85FbDf/NxN3PM19xjYOUp+SLiwhTG2rbV2tgb60PXX\\n+X7z5lN43evuPgh2vW7dotAPa3Q9kQCYkCcJ2uVUkVs2S2u0DjR99j8/9hm86c1f766FVmNiN/c4\\nnf6e7iWiBx548FLnXbbgp2hVv/R7P/TgQ8fLcOrmL+aEqJTx3LPPYSGbnycPXiqePfXU075zLBp0\\nQHu/WNLb/vu/PfmCrmfL79C2HcPg7nVAmyHY/7z5uafCNf3k4B4efs5CFx8o+PrhkR5/9DOXO/EI\\nXRvgN9poo/PTlxbgr0YxbbTRFw1dJQQ+Cv+ibqONNrpe+hiAbz93ITbaaKONNtpoo4022mijLzr6\\nHgCfAfAYJB3WOekJAH8P+fjnb6/52b8B+az4U3TslZBUYZ8F8BcAvvKMZXkvgCchvHkYUm/XQa8H\\n8FeQxCr/AOAn+vFz8OZQWd6L6+fNbQAeAvAIgE8D+MV+/Fxt5lI0AXgcwBsAXEAK/5ZjF1wx/SuE\\nYeegbwHwVkSQvQ/Az/Tt9wD4pTOW5ecB/NQ1PZ/pLkiCFQC4E8CjkDZyDt4cKsu5eHNHX+8APAjJ\\nJ/mi+HLV3XL3QQD/BIA9gN8F8L1X/MxTdK7Oub+G5ANkeifk82P09fedsSzAeXjzNEQRAMDnAfwT\\ngLtxHt4cKgtwHt4cyif5gvly1YC/G8B/0P6TcAaegxqAjwD4BIAfO2M5lC6VG/Aa6ccBfBLAr+M8\\npuIbIJbHQzg/b7QsOkT0HLzJEAH0DNzVeFF8uWrAv0SDPl8y+mZIJb4DwLsgpu3LhcbRntdN7wdw\\nD8SkfQrAr1zz8+8E8IcAfhLA/wz/XTdv7gTwB70sn8f5eKP5JF8H4FvxPPJJHqKrBvznIIEQpddD\\ntPy5SAdGPwfgjyAuxzlJcwMCR3IDXhM9C29Av4br5c0FBOwfhKdLOxdvtCy/TWU5J2+A9XySwAvg\\ny1UD/hMA3gQxj24A+EFIPrxz0B0AvrxvfxmA70YMWp2DNDcgcCQ34DXRa2j7+3F9vEkQM/nTAH6V\\njp+DN4fKcg7evAruOmg+yYfx8mozq/QOSLTzccg0VeeieyD+0COQLpfrLsvvALgJ4BYkrvHDkB6D\\nj+D6u1jGsvwIgN+CdFl+EtKIrstnfjvEdH0EsdvrHLxZK8s7cB7efANkPohH+rN/uh8/V5vZaKON\\nNtpoo4022mijjTbaaKONNtpoo4022mijjTbaaKONNtpoo4022mijL136f293JUSsY7wHAAAAAElF\\nTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fe58c2f6278>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.imshow(image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##Application\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"batches = []\\n\",\n    \"for i in range(1, 6):\\n\",\n    \"    batch_filename = os.path.join(data_folder, \\\"data_batch_{}\\\".format(i))\\n\",\n    \"    batches.append(unpickle(batch1_filename))\\n\",\n    \"    break    #IMPORTANT -- see chapter for explanation of this line\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X = np.vstack([batch['data'] for batch in batches])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X = np.array(X) / X.max()\\n\",\n    \"X = X.astype(np.float32)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.preprocessing import OneHotEncoder\\n\",\n    \"y = np.hstack(batch['labels'] for batch in batches).flatten()\\n\",\n    \"y = OneHotEncoder().fit_transform(y.reshape(y.shape[0],1)).todense()\\n\",\n    \"y = y.astype(np.float32)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.cross_validation import train_test_split\\n\",\n    \"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X_train = X_train.reshape(-1, 3, 32, 32)\\n\",\n    \"X_test = X_test.reshape(-1, 3, 32, 32)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from lasagne import layers\\n\",\n    \"layers=[\\n\",\n    \"        ('input', layers.InputLayer),\\n\",\n    \"        ('conv1', layers.Conv2DLayer),\\n\",\n    \"        ('pool1', layers.MaxPool2DLayer),\\n\",\n    \"        ('conv2', layers.Conv2DLayer),\\n\",\n    \"        ('pool2', layers.MaxPool2DLayer),\\n\",\n    \"        ('conv3', layers.Conv2DLayer),\\n\",\n    \"        ('pool3', layers.MaxPool2DLayer),\\n\",\n    \"        ('hidden4', layers.DenseLayer),\\n\",\n    \"        ('hidden5', layers.DenseLayer),\\n\",\n    \"        ('output', layers.DenseLayer),\\n\",\n    \"        ]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from nolearn.lasagne import NeuralNet\\n\",\n    \"from lasagne.nonlinearities import sigmoid, softmax\\n\",\n    \"nnet = NeuralNet(layers=layers,\\n\",\n    \"                 input_shape=(None, 3, 32, 32),\\n\",\n    \"                 conv1_num_filters=32,\\n\",\n    \"                 conv1_filter_size=(3, 3),\\n\",\n    \"                 conv2_num_filters=64,\\n\",\n    \"                 conv2_filter_size=(2, 2),\\n\",\n    \"                 conv3_num_filters=128,\\n\",\n    \"                 conv3_filter_size=(2, 2),\\n\",\n    \"                 pool1_ds=(2,2),\\n\",\n    \"                 pool2_ds=(2,2),\\n\",\n    \"                 pool3_ds=(2,2),\\n\",\n    \"                 hidden4_num_units=500,\\n\",\n    \"                 hidden5_num_units=500,\\n\",\n    \"                 output_num_units=10,\\n\",\n    \"                 output_nonlinearity=softmax,\\n\",\n    \"                 update_learning_rate=0.01,\\n\",\n    \"                 update_momentum=0.9,\\n\",\n    \"                 regression=True,\\n\",\n    \"                 max_epochs=3,\\n\",\n    \"                 verbose=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"  input             \\t(None, 3, 32, 32)   \\tproduces    3072 outputs\\n\",\n      \"  conv1             \\t(None, 32, 30, 30)  \\tproduces   28800 outputs\\n\",\n      \"  pool1             \\t(None, 32, 15, 15)  \\tproduces    7200 outputs\\n\",\n      \"  conv2             \\t(None, 64, 14, 14)  \\tproduces   12544 outputs\\n\",\n      \"  pool2             \\t(None, 64, 7, 7)    \\tproduces    3136 outputs\\n\",\n      \"  conv3             \\t(None, 128, 6, 6)   \\tproduces    4608 outputs\\n\",\n      \"  pool3             \\t(None, 128, 3, 3)   \\tproduces    1152 outputs\\n\",\n      \"  hidden4           \\t(None, 500)         \\tproduces     500 outputs\\n\",\n      \"  hidden5           \\t(None, 500)         \\tproduces     500 outputs\\n\",\n      \"  output            \\t(None, 10)          \\tproduces      10 outputs\\n\",\n      \"\\n\",\n      \" Epoch  |  Train loss  |  Valid loss  |  Train / Val  |  Valid acc  |  Dur\\n\",\n      \"--------|--------------|--------------|---------------|-------------|-------\\n\",\n      \"     1  |  \\u001b[94m  0.090018\\u001b[0m  |  \\u001b[32m  0.090022\\u001b[0m  |     0.999963  |             |  103.9s\\n\",\n      \"     2  |  \\u001b[94m  0.089988\\u001b[0m  |  \\u001b[32m  0.090000\\u001b[0m  |     0.999858  |             |  103.8s\\n\",\n      \"     3  |  \\u001b[94m  0.089961\\u001b[0m  |  \\u001b[32m  0.089982\\u001b[0m  |     0.999759  |             |  104.2s\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/usr/local/lib/python3.4/dist-packages/Lasagne-0.1dev-py3.4.egg/lasagne/init.py:30: UserWarning: The uniform initializer no longer uses Glorot et al.'s approach to determine the bounds, but defaults to the range (-0.01, 0.01) instead. Please use the new GlorotUniform initializer to get the old behavior. GlorotUniform is now the default for all layers.\\n\",\n      \"  warnings.warn(\\\"The uniform initializer no longer uses Glorot et al.'s \\\"\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"NeuralNet(X_tensor_type=<function tensor4 at 0x7fe55b73a0d0>,\\n\",\n       \"     batch_iterator_test=<nolearn.lasagne.BatchIterator object at 0x7fe559e70710>,\\n\",\n       \"     batch_iterator_train=<nolearn.lasagne.BatchIterator object at 0x7fe559e706d8>,\\n\",\n       \"     conv1_filter_size=(3, 3), conv1_num_filters=32,\\n\",\n       \"     conv2_filter_size=(2, 2), conv2_num_filters=64,\\n\",\n       \"     conv3_filter_size=(2, 2), conv3_num_filters=128, eval_size=0.2,\\n\",\n       \"     hidden4_num_units=500, hidden5_num_units=500,\\n\",\n       \"     input_shape=(None, 3, 32, 32),\\n\",\n       \"     layers=[('input', <class 'lasagne.layers.input.InputLayer'>), ('conv1', <class 'lasagne.layers.conv.Conv2DLayer'>), ('pool1', <class 'lasagne.layers.pool.MaxPool2DLayer'>), ('conv2', <class 'lasagne.layers.conv.Conv2DLayer'>), ('pool2', <class 'lasagne.layers.pool.MaxPool2DLayer'>), ('conv3', <class..., <class 'lasagne.layers.dense.DenseLayer'>), ('output', <class 'lasagne.layers.dense.DenseLayer'>)],\\n\",\n       \"     loss=None, max_epochs=3, more_params={},\\n\",\n       \"     objective=<class 'lasagne.objectives.Objective'>,\\n\",\n       \"     objective_loss_function=<function mse at 0x7fe559e622f0>,\\n\",\n       \"     on_epoch_finished=(), on_training_finished=(),\\n\",\n       \"     output_nonlinearity=<theano.tensor.nnet.nnet.Softmax object at 0x7fe57cfddba8>,\\n\",\n       \"     output_num_units=10, pool1_ds=(2, 2), pool2_ds=(2, 2),\\n\",\n       \"     pool3_ds=(2, 2), regression=True,\\n\",\n       \"     update=<function nesterov_momentum at 0x7fe559e628c8>,\\n\",\n       \"     update_learning_rate=0.01, update_momentum=0.9,\\n\",\n       \"     use_label_encoder=False, verbose=1,\\n\",\n       \"     y_tensor_type=TensorType(float32, matrix))\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"nnet.fit(X_train, y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0.0364246254873\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/usr/local/lib/python3.4/dist-packages/sklearn/metrics/metrics.py:1771: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\\n\",\n      \"  'precision', 'predicted', average, warn_for)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.metrics import f1_score\\n\",\n    \"y_pred = nnet.predict(X_test)\\n\",\n    \"print(f1_score(y_test.argmax(axis=1), y_pred.argmax(axis=1)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "Chapter 11/Chapter 11 (Theano and Lasagne).ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##Cifar Dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"data_folder = os.path.join(os.path.expanduser(\\\"~\\\"), \\\"Data\\\", \\\"cifar-10-batches-py\\\")\\n\",\n    \"batch1_filename = os.path.join(data_folder, \\\"data_batch_1\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import pickle\\n\",\n    \"# Bigfix thanks to: http://stackoverflow.com/questions/11305790/pickle-incompatability-of-numpy-arrays-between-python-2-and-3\\n\",\n    \"def unpickle(filename):\\n\",\n    \"    with open(filename, 'rb') as fo:\\n\",\n    \"        return pickle.load(fo, encoding='latin1')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"batch1 = unpickle(batch1_filename)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"image_index = 100\\n\",\n    \"image = batch1['data'][image_index]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"image = image.reshape((32,32, 3), order='F')\\n\",\n    \"import numpy as np\\n\",\n    \"image = np.rot90(image, -1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"from matplotlib import pyplot as plt\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.image.AxesImage at 0x7f12f47efac8>\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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zPAzenuy5BJlVG8XPbd09ds543p+1+R63Ee81kQBVdfJ5HR+mnU/j6m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\\nTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f12f48b8b70>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.imshow(image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##Theano\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import theano\\n\",\n    \"from theano import tensor as T\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"a = T.dscalar()\\n\",\n    \"b = T.dscalar()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"c = T.sqrt(a ** 2 + b ** 2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"f = theano.function([a,b], c)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array(5.0)\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"f(3, 4)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##Deep Neural Networks\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.datasets import load_iris\\n\",\n    \"iris = load_iris()\\n\",\n    \"X = iris.data.astype(np.float32)\\n\",\n    \"y_true = iris.target.astype(np.int32)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.cross_validation import train_test_split\\n\",\n    \"X_train, X_test, y_train, y_test = train_test_split(X, y_true, random_state=14)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import lasagne\\n\",\n    \"input_layer = lasagne.layers.InputLayer(shape=(10, X.shape[1]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/usr/local/lib/python3.4/dist-packages/Lasagne-0.1dev-py3.4.egg/lasagne/init.py:30: UserWarning: The uniform initializer no longer uses Glorot et al.'s approach to determine the bounds, but defaults to the range (-0.01, 0.01) instead. Please use the new GlorotUniform initializer to get the old behavior. GlorotUniform is now the default for all layers.\\n\",\n      \"  warnings.warn(\\\"The uniform initializer no longer uses Glorot et al.'s \\\"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"hidden_layer = lasagne.layers.DenseLayer(input_layer, num_units=12, nonlinearity=lasagne.nonlinearities.sigmoid)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"output_layer = lasagne.layers.DenseLayer(hidden_layer, num_units=3, \\n\",\n    \"                                     nonlinearity=lasagne.nonlinearities.softmax)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import theano.tensor as T\\n\",\n    \"net_input = T.matrix('net_input')\\n\",\n    \"net_output = output_layer.get_output(net_input)\\n\",\n    \"true_output = T.ivector('true_output')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"loss = T.mean(T.nnet.categorical_crossentropy(net_output, true_output))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/usr/local/lib/python3.4/dist-packages/Lasagne-0.1dev-py3.4.egg/lasagne/layers/helper.py:52: UserWarning: get_all_layers() has been changed to return layers in topological order. The former implementation is still available as get_all_layers_old(), but will be removed before the first release of Lasagne. To ignore this warning, use `warnings.filterwarnings('ignore', '.*topo.*')`.\\n\",\n      \"  warnings.warn(\\\"get_all_layers() has been changed to return layers in \\\"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"all_params = lasagne.layers.get_all_params(output_layer)\\n\",\n    \"updates = lasagne.updates.sgd(loss, all_params, learning_rate=0.1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import theano\\n\",\n    \"train = theano.function([net_input, true_output], loss, updates=updates)\\n\",\n    \"get_output = theano.function([net_input], net_output)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"for n in range(1000):\\n\",\n    \"    train(X_train, y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"y_output = get_output(X_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"y_pred = np.argmax(y_output, axis=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.metrics import f1_score\\n\",\n    \"print(f1_score(y_test, y_pred))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from PIL import Image, ImageDraw, ImageFont\\n\",\n    \"from skimage.transform import resize\\n\",\n    \"from skimage import transform as tf\\n\",\n    \"from skimage.measure import label, regionprops\\n\",\n    \"from sklearn.utils import check_random_state\\n\",\n    \"from sklearn.preprocessing import OneHotEncoder\\n\",\n    \"from sklearn.cross_validation import train_test_split\\n\",\n    \"\\n\",\n    \"def create_captcha(text, shear=0, size=(100, 24)):\\n\",\n    \"    im = Image.new(\\\"L\\\", size, \\\"black\\\")\\n\",\n    \"    draw = ImageDraw.Draw(im)\\n\",\n    \"    font = ImageFont.truetype(r\\\"Coval.otf\\\", 22)\\n\",\n    \"    draw.text((2, 2), text, fill=1, font=font)\\n\",\n    \"    image = np.array(im)\\n\",\n    \"    affine_tf = tf.AffineTransform(shear=shear)\\n\",\n    \"    image = tf.warp(image, affine_tf)\\n\",\n    \"    return image / image.max()\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def segment_image(image):\\n\",\n    \"    labeled_image = label(image > 0)\\n\",\n    \"    subimages = []\\n\",\n    \"    for region in regionprops(labeled_image):\\n\",\n    \"        start_x, start_y, end_x, end_y = region.bbox\\n\",\n    \"        subimages.append(image[start_x:end_x,start_y:end_y])\\n\",\n    \"    if len(subimages) == 0:\\n\",\n    \"        return [image,]\\n\",\n    \"    return subimages\\n\",\n    \"\\n\",\n    \"random_state = check_random_state(14)\\n\",\n    \"letters = list(\\\"ABCDEFGHIJKLMNOPQRSTUVWXYZ\\\")\\n\",\n    \"shear_values = np.arange(0, 0.5, 0.05)\\n\",\n    \"\\n\",\n    \"def generate_sample(random_state=None):\\n\",\n    \"    random_state = check_random_state(random_state)\\n\",\n    \"    letter = random_state.choice(letters)\\n\",\n    \"    shear = random_state.choice(shear_values)\\n\",\n    \"    return create_captcha(letter, shear=shear, size=(20, 20)), letters.index(letter)\\n\",\n    \"dataset, targets = zip(*(generate_sample(random_state) for i in range(3000)))\\n\",\n    \"dataset = np.array(dataset, dtype='float')\\n\",\n    \"targets =  np.array(targets)\\n\",\n    \"\\n\",\n    \"onehot = OneHotEncoder()\\n\",\n    \"y = onehot.fit_transform(targets.reshape(targets.shape[0],1))\\n\",\n    \"y = y.todense().astype(np.float32)\\n\",\n    \"\\n\",\n    \"dataset = np.array([resize(segment_image(sample)[0], (20, 20)) for sample in dataset])\\n\",\n    \"X = dataset.reshape((dataset.shape[0], dataset.shape[1] * dataset.shape[2]))\\n\",\n    \"X = X / X.max()\\n\",\n    \"X = X.astype(np.float32)\\n\",\n    \"\\n\",\n    \"X_train, X_test, y_train, y_test = \\\\\\n\",\n    \"    train_test_split(X, y, train_size=0.9, random_state=14)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from lasagne import layers\\n\",\n    \"layers=[\\n\",\n    \"        ('input', layers.InputLayer),\\n\",\n    \"        ('hidden', layers.DenseLayer),\\n\",\n    \"        ('output', layers.DenseLayer),\\n\",\n    \"        ]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from lasagne import updates\\n\",\n    \"from nolearn.lasagne import NeuralNet\\n\",\n    \"from lasagne.nonlinearities import sigmoid, softmax\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"net1 = NeuralNet(layers=layers,\\n\",\n    \"                 input_shape=X.shape,\\n\",\n    \"                 hidden_num_units=100,\\n\",\n    \"                 output_num_units=26,\\n\",\n    \"                 hidden_nonlinearity=sigmoid,\\n\",\n    \"                 output_nonlinearity=softmax,\\n\",\n    \"                 hidden_b=np.zeros((100,), dtype=np.float64),\\n\",\n    \"                 update=updates.momentum,\\n\",\n    \"                 update_learning_rate=0.9,\\n\",\n    \"                 update_momentum=0.1,\\n\",\n    \"                 regression=True,\\n\",\n    \"                 max_epochs=1000,\\n\",\n    \"                 )\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"NeuralNet(X_tensor_type=<function matrix at 0x7f12ed0819d8>,\\n\",\n       \"     batch_iterator_test=<nolearn.lasagne.BatchIterator object at 0x7f12e5039c50>,\\n\",\n       \"     batch_iterator_train=<nolearn.lasagne.BatchIterator object at 0x7f12e5039c18>,\\n\",\n       \"     eval_size=0.2, hidden_b=array([ 0.,  0., ...,  0.,  0.]),\\n\",\n       \"     hidden_nonlinearity=<theano.tensor.elemwise.Elemwise object at 0x7f12ecd109e8>,\\n\",\n       \"     hidden_num_units=100, input_shape=(3000, 400),\\n\",\n       \"     layers=[('input', <class 'lasagne.layers.input.InputLayer'>), ('hidden', <class 'lasagne.layers.dense.DenseLayer'>), ('output', <class 'lasagne.layers.dense.DenseLayer'>)],\\n\",\n       \"     loss=None, max_epochs=1000, more_params={},\\n\",\n       \"     objective=<class 'lasagne.objectives.Objective'>,\\n\",\n       \"     objective_loss_function=<function mse at 0x7f12e8300598>,\\n\",\n       \"     on_epoch_finished=(), on_training_finished=(),\\n\",\n       \"     output_nonlinearity=<theano.tensor.nnet.nnet.Softmax object at 0x7f12ecd195c0>,\\n\",\n       \"     output_num_units=26, regression=True,\\n\",\n       \"     update=<function momentum at 0x7f12e8300ae8>,\\n\",\n       \"     update_learning_rate=0.9, update_momentum=0.1,\\n\",\n       \"     use_label_encoder=False, verbose=0,\\n\",\n       \"     y_tensor_type=TensorType(float32, matrix))\"\n      ]\n     },\n     \"execution_count\": 34,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"net1.fit(X_train, y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"y_pred = net1.predict(X_test)\\n\",\n    \"y_pred = y_pred.argmax(axis=1)\\n\",\n    \"assert len(y_pred) == len(X_test)\\n\",\n    \"if len(y_test.shape) > 1:\\n\",\n    \"    y_test = y_test.argmax(axis=1)\\n\",\n    \"print(f1_score(y_test, y_pred))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "Chapter 12/CH12 MapReduce Basics.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##Intuition\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"a = [[1,2,1], [3,2], [4,9,1,0,2]]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"sums = map(sum, a)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"sums = [] \\n\",\n    \"for sublist in a: \\n\",\n    \"    results = sum(sublist) \\n\",\n    \"    sums.append(results)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def add(a, b):\\n\",\n    \"    return a + b \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"25\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from functools import reduce\\n\",\n    \"print(reduce(add, sums, 0))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"initial = 0\\n\",\n    \"current_result = initial\\n\",\n    \"for element in sums:\\n\",\n    \"    current_result = add(current_result, element)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##Basic Example\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from collections import defaultdict\\n\",\n    \"\\n\",\n    \"def map_word_count(document_id, document):\\n\",\n    \"    counts = defaultdict(int)\\n\",\n    \"    for word in document.split():\\n\",\n    \"        counts[word] += 1\\n\",\n    \"    for word in counts:\\n\",\n    \"        yield (word, counts[word])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def shuffle_words(results_generators):\\n\",\n    \"    records = defaultdict(list)\\n\",\n    \"    for results in results_generators:\\n\",\n    \"        for word, count in results:\\n\",\n    \"            records[word].append(count)\\n\",\n    \"    for word in records:\\n\",\n    \"        yield (word, records[word])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def reduce_counts(word, list_of_counts):\\n\",\n    \"    return (word, sum(list_of_counts))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.datasets import fetch_20newsgroups\\n\",\n    \"dataset = fetch_20newsgroups(subset='train')\\n\",\n    \"documents = dataset.data[:50]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"map_results = map(map_word_count, range(len(documents)), documents)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"shuffle_results = shuffle_words(map_results)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"reduce_results = [reduce_counts(word, list_of_counts) for word, list_of_counts in shuffle_results]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[('coming', 1), (\\\"couldn't\\\", 4), ('Jose,', 1), ('{As', 1), ('185c', 1)]\\n\",\n      \"5036\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(reduce_results[:5])\\n\",\n    \"print(len(reduce_results))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from joblib import Parallel, delayed\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def map_word_count(document_id, document):\\n\",\n    \"    counts = defaultdict(int)\\n\",\n    \"    for word in document.split():\\n\",\n    \"        counts[word] += 1\\n\",\n    \"    return list(counts.items())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"map_results = Parallel(n_jobs=2)(delayed(map_word_count)(i, document)\\n\",\n    \"                                 for i, document in enumerate(documents))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"shuffle_results = shuffle_words(map_results)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[('coming', [1]),\\n\",\n       \" (\\\"couldn't\\\", [1, 1, 1, 1]),\\n\",\n       \" ('Jose,', [1]),\\n\",\n       \" ('{As', [1]),\\n\",\n       \" ('185c', [1]),\\n\",\n       \" ('burst', [5]),\\n\",\n       \" ('context.', [1]),\\n\",\n       \" ('copy,', [1]),\\n\",\n       \" ('**********************************************************************',\\n\",\n       \"  [1]),\\n\",\n       \" ('Modular', [1]),\\n\",\n       \" ('Yeah,', [1]),\\n\",\n       \" ('parking', [1]),\\n\",\n       \" ('Prices!', [1]),\\n\",\n       \" ('em', [1]),\\n\",\n       \" ('record,', [1]),\\n\",\n       \" ('program', [1]),\\n\",\n       \" ('>philosophically<', [1]),\\n\",\n       \" ('kind', [1, 1]),\\n\",\n       \" ('opinions', [2, 1, 1]),\\n\",\n       \" ('cubic', [1]),\\n\",\n       \" ('vision', [1]),\\n\",\n       \" ('later', [1, 1, 1]),\\n\",\n       \" ('$3495,', [1]),\\n\",\n       \" ('she', [2, 1]),\\n\",\n       \" ('xray@is.rice.edu', [1]),\\n\",\n       \" ('up', [2, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 3]),\\n\",\n       \" ('Callison', [1]),\\n\",\n       \" ('v8', [1]),\\n\",\n       \" ('No', [6, 1]),\\n\",\n       \" ('disobeys', [1]),\\n\",\n       \" ('term?', [1]),\\n\",\n       \" ('login', [1]),\\n\",\n       \" ('Most', [1, 1, 1, 3, 1]),\\n\",\n       \" ('kept', [1]),\\n\",\n       \" ('(Repost)', [1]),\\n\",\n       \" ('mean', [1, 1, 1]),\\n\",\n       \" ('luck,', [1]),\\n\",\n       \" ('punisher.caltech.edu', [1]),\\n\",\n       \" ('nCUBE', [1]),\\n\",\n       \" ('result', [1]),\\n\",\n       \" ('Problems???', [1]),\\n\",\n       \" ('(I', [2, 1]),\\n\",\n       \" ('Grow', [1]),\\n\",\n       \" ('Goalie', [1]),\\n\",\n       \" ('Binoculars', [1]),\\n\",\n       \" ('boots),', [1]),\\n\",\n       \" ('multiple', [3]),\\n\",\n       \" ('At', [1, 1, 1]),\\n\",\n       \" ('Nearby', [1]),\\n\",\n       \" (\\\"won't-\\\", [1]),\\n\",\n       \" ('however', [1]),\\n\",\n       \" ('one', [1, 3, 2, 8, 1, 1, 1, 2, 5, 1, 2, 3, 1, 1]),\\n\",\n       \" ('Vijay', [2]),\\n\",\n       \" ('great.', [1, 1]),\\n\",\n       \" ('stuff', [1, 1, 1]),\\n\",\n       \" ('problem.', [1]),\\n\",\n       \" ('movies', [4]),\\n\",\n       \" ('associated', [1]),\\n\",\n       \" ('continues', [1]),\\n\",\n       \" ('Call', [1, 1]),\\n\",\n       \" ('(David', [2, 1]),\\n\",\n       \" ('hand-cocked', [1]),\\n\",\n       \" ('Brewers', [1]),\\n\",\n       \" ('btw.', [1]),\\n\",\n       \" ('game,', [1]),\\n\",\n       \" (\\\">(there's\\\", [1]),\\n\",\n       \" ('boy,', [1]),\\n\",\n       \" ('safest', [1]),\\n\",\n       \" ('add', [2, 1, 1, 2, 1]),\\n\",\n       \" ('mos.', [2]),\\n\",\n       \" ('references', [1, 1]),\\n\",\n       \" ('Negev', [1]),\\n\",\n       \" ('nuclear', [1, 5]),\\n\",\n       \" ('stack@translab.its.uci.edu', [1]),\\n\",\n       \" ('thought.', [1]),\\n\",\n       \" ('this;', [1]),\\n\",\n       \" ('racers,', [1]),\\n\",\n       \" 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('Askew)', [1]),\\n\",\n       \" ('maybe', [1, 1, 1, 1, 1]),\\n\",\n       \" ...]\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"list(shuffle_results)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "Chapter 12/Chapter 12 (NB Predict).ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import os\\n\",\n      \"import re\\n\",\n      \"import numpy as np\\n\",\n      \"from collections import defaultdict\\n\",\n      \"from operator import itemgetter\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"word_search_re = re.compile(r\\\"[\\\\w']+\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def load_model(model_filename):\\n\",\n      \"    model = defaultdict(lambda: defaultdict(float))\\n\",\n      \"    with open(model_filename) as inf:\\n\",\n      \"        for line in inf:\\n\",\n      \"            word, values = line.split(maxsplit=1)\\n\",\n      \"            word = eval(word)\\n\",\n      \"            values = eval(values)\\n\",\n      \"            model[word] = values\\n\",\n      \"    return model\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 3\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"model_filename = os.path.join(os.path.expanduser(\\\"~\\\"), \\\"models\\\", \\\"part-00000\\\")\\n\",\n      \"model = load_model(model_filename)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 4\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"model[\\\"i\\\"][\\\"male\\\"], model[\\\"i\\\"][\\\"female\\\"]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 5,\n       \"text\": [\n        \"(409.7987003114851, 513.3231594734408)\"\n       ]\n      }\n     ],\n     \"prompt_number\": 5\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def nb_predict(model, document):\\n\",\n      \"    words = word_search_re.findall(document)\\n\",\n      \"    probabilities = defaultdict(lambda : 0)\\n\",\n      \"    for word in set(words):\\n\",\n      \"        probabilities[\\\"male\\\"] += np.log(model[word].get(\\\"male\\\", 1e-5))\\n\",\n      \"        probabilities[\\\"female\\\"] += np.log(model[word].get(\\\"female\\\", 1e-5))\\n\",\n      \"    # Now find the most likely gender\\n\",\n      \"    most_likely_genders = sorted(probabilities.items(), key=itemgetter(1), reverse=True)\\n\",\n      \"    return most_likely_genders[0][0]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 6\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"new_post = \\\"\\\"\\\" Every day should be a half day.  Took the afternoon off to hit the dentist, and while I was out I managed to get my oil changed, too.  Remember that business with my car dealership this winter?  Well, consider this the epilogue.  The friendly fellas at the Valvoline Instant Oil Change on Snelling were nice enough to notice that my dipstick was broken, and the metal piece was too far down in its little dipstick tube to pull out.  Looks like I'm going to need a magnet.   Damn you, Kline Nissan, daaaaaaammmnnn yooouuuu....   Today I let my boss know that I've submitted my Corps application.  The news has been greeted by everyone in the company with a level of enthusiasm that really floors me.     The back deck has finally been cleared off by the construction company working on the place.  This company, for anyone who's interested, consists mainly of one guy who spends his days cursing at his crew of Spanish-speaking laborers.  Construction of my deck began around the time Nixon was getting out of office.\\n\",\n      \"\\\"\\\"\\\"\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 7\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"nb_predict(model, new_post)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 8,\n       \"text\": [\n        \"'male'\"\n       ]\n      }\n     ],\n     \"prompt_number\": 8\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"testing_folder = os.path.join(os.path.expanduser(\\\"~\\\"), \\\"Data\\\", \\\"blogposts_testing\\\")\\n\",\n      \"testing_filenames = []\\n\",\n      \"for filename in os.listdir(testing_folder):\\n\",\n      \"    testing_filenames.append(os.path.join(testing_folder, filename))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 9\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def nb_predict_many(model, input_filename):\\n\",\n      \"    with open(input_filename) as inf:\\n\",\n      \"        # remove leading and trailing whitespace\\n\",\n      \"        for line in inf:\\n\",\n      \"            tokens = line.split()\\n\",\n      \"            actual_gender = eval(tokens[0])\\n\",\n      \"            blog_post = eval(\\\" \\\".join(tokens[1:]))\\n\",\n      \"            yield actual_gender, nb_predict(model, blog_post)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 10\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def nb_predict(model, document):\\n\",\n      \"    words = word_search_re.findall(document)\\n\",\n      \"    probabilities = defaultdict(lambda : 1)\\n\",\n      \"    for word in set(words):\\n\",\n      \"        probabilities[\\\"male\\\"] += np.log(model[word].get(\\\"male\\\", 1e-15))\\n\",\n      \"        probabilities[\\\"female\\\"] += np.log(model[word].get(\\\"female\\\", 1e-15))\\n\",\n      \"    # Now find the most likely gender\\n\",\n      \"    most_likely_genders = sorted(probabilities.items(), key=itemgetter(1), reverse=True)\\n\",\n      \"    return most_likely_genders\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 11\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"y_true = []\\n\",\n      \"y_pred = []\\n\",\n      \"for testing_filename in testing_filenames:\\n\",\n      \"    for actual_gender, ratios in nb_predict_many(model, testing_filename):\\n\",\n      \"        predicted_gender = ratios[0][0]\\n\",\n      \"        y_true.append(actual_gender == \\\"female\\\")\\n\",\n      \"        y_pred.append(predicted_gender == \\\"female\\\")\\n\",\n      \"y_true = np.array(y_true, dtype='int')\\n\",\n      \"y_pred = np.array(y_pred, dtype='int')\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 13\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from sklearn.metrics import f1_score\\n\",\n      \"print(\\\"f1={:.4f}\\\".format(f1_score(y_true, y_pred, pos_label=None)))\\n\",\n      \"print(\\\"acc={:.4f}\\\".format(np.mean(y_true == y_pred)))\\n\",\n      \"      \\n\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"f1=0.5540\\n\",\n        \"acc=0.5765\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 14\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"aws_model_filename = os.path.join(os.path.expanduser(\\\"~\\\"), \\\"models\\\", \\\"model_aws\\\")\\n\",\n      \"aws_model = load_model(aws_model_filename)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 15\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"y_true = []\\n\",\n      \"y_pred = []\\n\",\n      \"for testing_filename in testing_filenames:\\n\",\n      \"    for actual_gender, predicted_gender in nb_predict_many(aws_model, testing_filename):\\n\",\n      \"        predicted_gender = ratios[0][0]\\n\",\n      \"        y_true.append(actual_gender == \\\"female\\\")\\n\",\n      \"        y_pred.append(predicted_gender == \\\"female\\\")\\n\",\n      \"        #print(\\\"Actual: {0}\\\\tPredicted: {1}\\\".format(actual_gender, predicted_gender))\\n\",\n      \"        if len(y_true) > 500:\\n\",\n      \"            break\\n\",\n      \"y_true = np.array(y_true, dtype='int')\\n\",\n      \"y_pred = np.array(y_pred, dtype='int')\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 16\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"print(\\\"f1={:.4f}\\\".format(f1_score(y_true, y_pred, pos_label=None)))\\n\",\n      \"print(\\\"acc={:.4f}\\\".format(np.mean(y_true == y_pred)))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"f1=0.8144\\n\",\n        \"acc=0.8734\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stderr\",\n       \"text\": [\n        \"/usr/local/lib/python3.4/dist-packages/sklearn/metrics/metrics.py:1771: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\\n\",\n        \"  'precision', 'predicted', average, warn_for)\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 17\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"print(list(zip(y_true, y_pred))[:10])\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"[(0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0)]\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 18\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from sklearn.metrics import confusion_matrix\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 19\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"confusion_matrix(y_true, y_pred)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 20,\n       \"text\": [\n        \"array([[614,   0],\\n\",\n        \"       [ 89,   0]])\"\n       ]\n      }\n     ],\n     \"prompt_number\": 20\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}"
  },
  {
    "path": "Chapter 12/Chapter 12 (Test load).ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import os\\n\",\n      \"filename = os.path.join(os.path.expanduser(\\\"~\\\"), \\\"Data\\\", \\\"blogs\\\", \\\"1005545.male.25.Engineering.Sagittarius.xml\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"all_posts = []\\n\",\n      \"with open(filename) as inf:\\n\",\n      \"    # remove leading and trailing whitespace\\n\",\n      \"    post_start = False\\n\",\n      \"    post = []\\n\",\n      \"    for line in inf:\\n\",\n      \"        line = line.strip()\\n\",\n      \"        if line == \\\"<post>\\\":\\n\",\n      \"            post_start = True\\n\",\n      \"        elif line == \\\"</post>\\\":\\n\",\n      \"            post_start = False\\n\",\n      \"            all_posts.append(\\\"\\\\n\\\".join(post))\\n\",\n      \"            post = []\\n\",\n      \"        elif post_start:\\n\",\n      \"            post.append(line)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 3\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"len(all_posts)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 4,\n       \"text\": [\n        \"80\"\n       ]\n      }\n     ],\n     \"prompt_number\": 4\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}"
  },
  {
    "path": "Chapter 12/extract_posts.py",
    "content": "import os\nimport re\nfrom mrjob.job import MRJob\nfrom mrjob.step import MRStep\n\nword_search_re = re.compile(r\"[\\w']+\")\n\n\nclass ExtractPosts(MRJob):\n\n    post_start = False\n    post = []\n\n    def mapper(self, key, line):\n        filename = os.environ[\"map_input_file\"]\n        gender = filename.split(\".\")[1]\n        try:\n            docnum = int(filename[0])\n        except:\n            docnum = 8\n        if filename.startswith(\"51\"):\n            # remove leading and trailing whitespace\n            line = line.strip()\n            if line == \"<post>\":\n                self.post_start = True\n            elif line == \"</post>\":\n                self.post_start = False\n                yield gender, repr(\"\\n\".join(self.post))\n                self.post = []\n            elif self.post_start:\n                self.post.append(line)\n\n\n\nif __name__ == '__main__':\n    ExtractPosts.run()\n"
  },
  {
    "path": "Chapter 12/nb_predict.py",
    "content": "import os\nimport re\nimport numpy as np\nfrom mrjob.job import MRJob\nfrom mrjob.step import MRStep\nfrom operator import itemgetter\n\nword_search_re = re.compile(r\"[\\w']+\")\n\n\nclass NaiveBayesTrainer(MRJob):\n    \n    def steps(self):\n        return [\n            MRStep(mapper=self.extract_words_mapping,\n                   reducer=self.reducer_count_words),\n            MRStep(mapper=self.predict_mapper),\n            ]\n\n    def extract_words_mapping(self, key, value):\n        tokens = value.split()\n        gender = eval(tokens[0])\n        blog_post = eval(\" \".join(tokens[1:]))\n        all_words = word_search_re.findall(blog_post)\n        all_words = [word.lower() for word in all_words]\n        #for word in all_words:\n        for word in all_words:\n            #yield \"{0}:{1}\".format(gender, word.lower()), 1\n            #yield (gender, word.lower()), (1. / len(all_words))\n            # Occurence probability\n            yield (gender, word), 1\n\n    def reducer_count_words(self, key, counts):\n        s = sum(counts)\n        gender, word = key #.split(\":\")\n        yield word, (gender, s)\n\n    def predict_mapper(self, key, value):\n        per_gender = {}\n        for value in values:\n            gender, s = value\n            per_gender[gender] = s\n        yield word, per_gender\n\n    def ratio_mapper(self, word, value):\n        counts = dict(value)\n        sum_of_counts = float(np.mean(counts.values()))\n        maximum_score = max(counts.items(), key=itemgetter(1))\n        current_ratio = maximum_score[1] / sum_of_counts\n        yield None, (word, sum_of_counts, value)\n    \n    def sorter_reducer(self, key, values):\n        ranked_list = sorted(values, key=itemgetter(1), reverse=True)\n        n_printed = 0\n        for word, sum_of_counts, scores in ranked_list:\n            if n_printed < 20:\n                print((n_printed + 1), word, scores)\n                n_printed += 1\n            yield word, dict(scores)\n\nif __name__ == '__main__':\n    NaiveBayesTrainer.run()\n"
  },
  {
    "path": "Chapter 12/nb_train.py",
    "content": "import os\nimport re\nimport numpy as np\nfrom mrjob.job import MRJob\nfrom mrjob.step import MRStep\nfrom operator import itemgetter\n\nword_search_re = re.compile(r\"[\\w']+\")\n\n\nclass NaiveBayesTrainer(MRJob):\n    \n    def steps(self):\n        return [\n            MRStep(mapper=self.extract_words_mapping,\n                   reducer=self.reducer_count_words),\n            MRStep(reducer=self.compare_words_reducer),\n            ]\n\n    def extract_words_mapping(self, key, value):\n        tokens = value.split()\n        gender = eval(tokens[0])\n        blog_post = eval(\" \".join(tokens[1:]))\n        all_words = word_search_re.findall(blog_post)\n        all_words = [word.lower() for word in all_words]\n        #for word in all_words:\n        for word in all_words:\n            #yield \"{0}:{1}\".format(gender, word.lower()), 1\n            #yield (gender, word.lower()), (1. / len(all_words))\n            # Occurence probability\n            yield (gender, word), 1. / len(all_words)\n\n    def reducer_count_words(self, key, counts):\n        s = sum(counts)\n        gender, word = key #.split(\":\")\n        yield word, (gender, s)\n\n    def compare_words_reducer(self, word, values):\n        per_gender = {}\n        for value in values:\n            gender, s = value\n            per_gender[gender] = s\n        yield word, per_gender\n\n    def ratio_mapper(self, word, value):\n        counts = dict(value)\n        sum_of_counts = float(np.mean(counts.values()))\n        maximum_score = max(counts.items(), key=itemgetter(1))\n        current_ratio = maximum_score[1] / sum_of_counts\n        yield None, (word, sum_of_counts, value)\n    \n    def sorter_reducer(self, key, values):\n        ranked_list = sorted(values, key=itemgetter(1), reverse=True)\n        n_printed = 0\n        for word, sum_of_counts, scores in ranked_list:\n            if n_printed < 20:\n                print((n_printed + 1), word, scores)\n                n_printed += 1\n            yield word, dict(scores)\n\nif __name__ == '__main__':\n    NaiveBayesTrainer.run()\n"
  },
  {
    "path": "Chapter 2/Ionosphere Nearest Neighbour.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/bob\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"home_folder = os.path.expanduser(\\\"~\\\")\\n\",\n    \"print(home_folder)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/bob/Data/Ionosphere/ionosphere.data\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Change this to the location of your dataset\\n\",\n    \"data_folder = os.path.join(home_folder, \\\"Data\\\", \\\"Ionosphere\\\")\\n\",\n    \"data_filename = os.path.join(data_folder, \\\"ionosphere.data\\\")\\n\",\n    \"print(data_filename)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import csv\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"# Size taken from the dataset and is known\\n\",\n    \"X = np.zeros((351, 34), dtype='float')\\n\",\n    \"y = np.zeros((351,), dtype='bool')\\n\",\n    \"\\n\",\n    \"with open(data_filename, 'r') as input_file:\\n\",\n    \"    reader = csv.reader(input_file)\\n\",\n    \"    for i, row in enumerate(reader):\\n\",\n    \"        # Get the data, converting each item to a float\\n\",\n    \"        data = [float(datum) for datum in row[:-1]]\\n\",\n    \"        # Set the appropriate row in our dataset\\n\",\n    \"        X[i] = data\\n\",\n    \"        # 1 if the class is 'g', 0 otherwise\\n\",\n    \"        y[i] = row[-1] == 'g'\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"There are 263 samples in the training dataset\\n\",\n      \"There are 88 samples in the testing dataset\\n\",\n      \"Each sample has 34 features\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.cross_validation import train_test_split\\n\",\n    \"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=14)\\n\",\n    \"print(\\\"There are {} samples in the training dataset\\\".format(X_train.shape[0]))\\n\",\n    \"print(\\\"There are {} samples in the testing dataset\\\".format(X_test.shape[0]))\\n\",\n    \"print(\\\"Each sample has {} features\\\".format(X_train.shape[1]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.neighbors import KNeighborsClassifier\\n\",\n    \"\\n\",\n    \"estimator = KNeighborsClassifier()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\\n\",\n       \"           metric_params=None, n_neighbors=5, p=2, weights='uniform')\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"estimator.fit(X_train, y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The accuracy is 86.4%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"y_predicted = estimator.predict(X_test)\\n\",\n    \"accuracy = np.mean(y_test == y_predicted) * 100\\n\",\n    \"print(\\\"The accuracy is {0:.1f}%\\\".format(accuracy))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.cross_validation import cross_val_score\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The average accuracy is 82.3%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"scores = cross_val_score(estimator, X, y, scoring='accuracy')\\n\",\n    \"average_accuracy = np.mean(scores) * 100\\n\",\n    \"print(\\\"The average accuracy is {0:.1f}%\\\".format(average_accuracy))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"avg_scores = []\\n\",\n    \"all_scores = []\\n\",\n    \"parameter_values = list(range(1, 21))  # Including 20\\n\",\n    \"for n_neighbors in parameter_values:\\n\",\n    \"    estimator = KNeighborsClassifier(n_neighbors=n_neighbors)\\n\",\n    \"    scores = cross_val_score(estimator, X, y, scoring='accuracy')\\n\",\n    \"    avg_scores.append(np.mean(scores))\\n\",\n    \"    all_scores.append(scores)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"plt.plot?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[<matplotlib.lines.Line2D at 0x7f58cf956ac8>]\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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xzUk+Prq/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\\nPmwHyTY5FyAn5LS+EZI95qxvUT7rAwAA9bElzt/pj4dOBQBbiK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     \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f58cf990e80>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from matplotlib import pyplot as plt\\n\",\n    \"plt.figure(figsize=(32,20))\\n\",\n    \"plt.plot(parameter_values, avg_scores, '-o', linewidth=5, markersize=24)\\n\",\n    \"#plt.axis([0, max(parameter_values), 0, 1.0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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vAwHRbpbm8P08nriRmkE+xzPIyjolmBKtVGk97sjybN6SqTzf6Q5BSn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\\nERERERERERERERERERER2ck/ATDNxtpAl5PZAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f58cf4ba5c0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for parameter, scores in zip(parameter_values, all_scores):\\n\",\n    \"    n_scores = len(scores)\\n\",\n    \"    plt.plot([parameter] * n_scores, scores, '-o')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[<matplotlib.lines.Line2D at 0x7f58cfb47b38>,\\n\",\n       \" <matplotlib.lines.Line2D at 0x7f58cfb47eb8>,\\n\",\n       \" <matplotlib.lines.Line2D at 0x7f58cfb4b080>]\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f58d0422dd8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(parameter_values, all_scores, 'bx')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"ename\": \"KeyboardInterrupt\",\n     \"evalue\": \"\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[1;31mKeyboardInterrupt\\u001b[0m                         Traceback (most recent call last)\",\n      \"\\u001b[1;32m<ipython-input-20-53d2156a8105>\\u001b[0m in \\u001b[0;36m<module>\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m      5\\u001b[0m     \\u001b[1;32mfor\\u001b[0m \\u001b[0mi\\u001b[0m \\u001b[1;32min\\u001b[0m \\u001b[0mrange\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;36m100\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m      6\\u001b[0m         \\u001b[0mestimator\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mKNeighborsClassifier\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mn_neighbors\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mn_neighbors\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m----> 7\\u001b[1;33m         \\u001b[0mscores\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mcross_val_score\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mestimator\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mX\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0my\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mscoring\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[1;34m'accuracy'\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mcv\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[1;36m10\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m      8\\u001b[0m         \\u001b[0mall_scores\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[0mn_neighbors\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mappend\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mscores\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m      9\\u001b[0m \\u001b[1;32mfor\\u001b[0m \\u001b[0mparameter\\u001b[0m \\u001b[1;32min\\u001b[0m \\u001b[0mparameter_values\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32m/usr/local/lib/python3.4/dist-packages/sklearn/cross_validation.py\\u001b[0m in \\u001b[0;36mcross_val_score\\u001b[1;34m(estimator, X, y, scoring, cv, n_jobs, verbose, fit_params, score_func, pre_dispatch)\\u001b[0m\\n\\u001b[0;32m   1149\\u001b[0m                                               \\u001b[0mtrain\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mtest\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mverbose\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[1;32mNone\\u001b[0m\\u001b[1;33m,\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m   1150\\u001b[0m                                               fit_params)\\n\\u001b[1;32m-> 1151\\u001b[1;33m                       for train, test in cv)\\n\\u001b[0m\\u001b[0;32m   1152\\u001b[0m     \\u001b[1;32mreturn\\u001b[0m \\u001b[0mnp\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0marray\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mscores\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[1;36m0\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m   1153\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32m/usr/local/lib/python3.4/dist-packages/sklearn/externals/joblib/parallel.py\\u001b[0m in \\u001b[0;36m__call__\\u001b[1;34m(self, iterable)\\u001b[0m\\n\\u001b[0;32m    650\\u001b[0m                 \\u001b[0mos\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0menviron\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[0mJOBLIB_SPAWNED_PROCESS\\u001b[0m\\u001b[1;33m]\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[1;34m'1'\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    651\\u001b[0m             \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0m_iterating\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[1;32mTrue\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m--> 652\\u001b[1;33m             \\u001b[1;32mfor\\u001b[0m \\u001b[0mfunction\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0margs\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mkwargs\\u001b[0m \\u001b[1;32min\\u001b[0m \\u001b[0miterable\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m    653\\u001b[0m                 \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mdispatch\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mfunction\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0margs\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mkwargs\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    654\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32m/usr/local/lib/python3.4/dist-packages/sklearn/cross_validation.py\\u001b[0m in \\u001b[0;36m<genexpr>\\u001b[1;34m(.0)\\u001b[0m\\n\\u001b[0;32m   1149\\u001b[0m                                               \\u001b[0mtrain\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mtest\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mverbose\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[1;32mNone\\u001b[0m\\u001b[1;33m,\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m   1150\\u001b[0m                                               fit_params)\\n\\u001b[1;32m-> 1151\\u001b[1;33m                       for train, test in cv)\\n\\u001b[0m\\u001b[0;32m   1152\\u001b[0m     \\u001b[1;32mreturn\\u001b[0m \\u001b[0mnp\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0marray\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mscores\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[1;36m0\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m   1153\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32m/usr/local/lib/python3.4/dist-packages/sklearn/externals/joblib/parallel.py\\u001b[0m in \\u001b[0;36mdelayed\\u001b[1;34m(function, check_pickle)\\u001b[0m\\n\\u001b[0;32m    118\\u001b[0m     \\u001b[1;31m# using with multiprocessing:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    119\\u001b[0m     \\u001b[1;32mif\\u001b[0m \\u001b[0mcheck_pickle\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m--> 120\\u001b[1;33m         \\u001b[0mpickle\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mdumps\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mfunction\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m    121\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    122\\u001b[0m     \\u001b[1;32mdef\\u001b[0m \\u001b[0mdelayed_function\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;33m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[1;33m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;31mKeyboardInterrupt\\u001b[0m: \"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from collections import defaultdict\\n\",\n    \"all_scores = defaultdict(list)\\n\",\n    \"parameter_values = list(range(1, 21))  # Including 20\\n\",\n    \"for n_neighbors in parameter_values:\\n\",\n    \"    for i in range(100):\\n\",\n    \"        estimator = KNeighborsClassifier(n_neighbors=n_neighbors)\\n\",\n    \"        scores = cross_val_score(estimator, X, y, scoring='accuracy', cv=10)\\n\",\n    \"        all_scores[n_neighbors].append(scores)\\n\",\n    \"for parameter in parameter_values:\\n\",\n    \"    scores = all_scores[parameter]\\n\",\n    \"    n_scores = len(scores)\\n\",\n    \"    plt.plot([parameter] * n_scores, scores, '-o')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"plt.plot(parameter_values, avg_scores, '-o')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.preprocessing import MinMaxScaler\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "Chapter 3/Basketball Results.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"home_folder = os.path.expanduser(\\\"~\\\")\\n\",\n    \"data_folder = os.path.join(home_folder, \\\"Data\\\", \\\"basketball\\\")\\n\",\n    \"data_filename = os.path.join(data_folder, \\\"leagues_NBA_2014_games_games.csv\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Date</th>\\n\",\n       \"      <th>Unnamed: 1</th>\\n\",\n       \"      <th>Visitor/Neutral</th>\\n\",\n       \"      <th>PTS</th>\\n\",\n       \"      <th>Home/Neutral</th>\\n\",\n       \"      <th>PTS.1</th>\\n\",\n       \"      <th>Unnamed: 6</th>\\n\",\n       \"      <th>Notes</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>Tue Oct 29 2013</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Orlando Magic</td>\\n\",\n       \"      <td>87</td>\\n\",\n       \"      <td>Indiana Pacers</td>\\n\",\n       \"      <td>97</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>Tue Oct 29 2013</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Los Angeles Clippers</td>\\n\",\n       \"      <td>103</td>\\n\",\n       \"      <td>Los Angeles Lakers</td>\\n\",\n       \"      <td>116</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>Tue Oct 29 2013</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Chicago Bulls</td>\\n\",\n       \"      <td>95</td>\\n\",\n       \"      <td>Miami Heat</td>\\n\",\n       \"      <td>107</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>Wed Oct 30 2013</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Brooklyn Nets</td>\\n\",\n       \"      <td>94</td>\\n\",\n       \"      <td>Cleveland Cavaliers</td>\\n\",\n       \"      <td>98</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>Wed Oct 30 2013</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Atlanta Hawks</td>\\n\",\n       \"      <td>109</td>\\n\",\n       \"      <td>Dallas Mavericks</td>\\n\",\n       \"      <td>118</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>Wed Oct 30 2013</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Washington Wizards</td>\\n\",\n       \"      <td>102</td>\\n\",\n       \"      <td>Detroit Pistons</td>\\n\",\n       \"      <td>113</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"              Date Unnamed: 1       Visitor/Neutral  PTS         Home/Neutral  \\\\\\n\",\n       \"0  Tue Oct 29 2013  Box Score         Orlando Magic   87       Indiana Pacers   \\n\",\n       \"1  Tue Oct 29 2013  Box Score  Los Angeles Clippers  103   Los Angeles Lakers   \\n\",\n       \"2  Tue Oct 29 2013  Box Score         Chicago Bulls   95           Miami Heat   \\n\",\n       \"3  Wed Oct 30 2013  Box Score         Brooklyn Nets   94  Cleveland Cavaliers   \\n\",\n       \"4  Wed Oct 30 2013  Box Score         Atlanta Hawks  109     Dallas Mavericks   \\n\",\n       \"5  Wed Oct 30 2013  Box Score    Washington Wizards  102      Detroit Pistons   \\n\",\n       \"\\n\",\n       \"   PTS.1 Unnamed: 6 Notes  \\n\",\n       \"0     97        NaN   NaN  \\n\",\n       \"1    116        NaN   NaN  \\n\",\n       \"2    107        NaN   NaN  \\n\",\n       \"3     98        NaN   NaN  \\n\",\n       \"4    118        NaN   NaN  \\n\",\n       \"5    113        NaN   NaN  \"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"results = pd.read_csv(data_filename)\\n\",\n    \"results.ix[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Date</th>\\n\",\n       \"      <th>Score Type</th>\\n\",\n       \"      <th>Visitor Team</th>\\n\",\n       \"      <th>VisitorPts</th>\\n\",\n       \"      <th>Home Team</th>\\n\",\n       \"      <th>HomePts</th>\\n\",\n       \"      <th>OT?</th>\\n\",\n       \"      <th>Notes</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>2013-10-29</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Orlando Magic</td>\\n\",\n       \"      <td>87</td>\\n\",\n       \"      <td>Indiana Pacers</td>\\n\",\n       \"      <td>97</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2013-10-29</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Los Angeles Clippers</td>\\n\",\n       \"      <td>103</td>\\n\",\n       \"      <td>Los Angeles Lakers</td>\\n\",\n       \"      <td>116</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>2013-10-29</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Chicago Bulls</td>\\n\",\n       \"      <td>95</td>\\n\",\n       \"      <td>Miami Heat</td>\\n\",\n       \"      <td>107</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>2013-10-30</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Brooklyn Nets</td>\\n\",\n       \"      <td>94</td>\\n\",\n       \"      <td>Cleveland Cavaliers</td>\\n\",\n       \"      <td>98</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>2013-10-30</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Atlanta Hawks</td>\\n\",\n       \"      <td>109</td>\\n\",\n       \"      <td>Dallas Mavericks</td>\\n\",\n       \"      <td>118</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>2013-10-30</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Washington Wizards</td>\\n\",\n       \"      <td>102</td>\\n\",\n       \"      <td>Detroit Pistons</td>\\n\",\n       \"      <td>113</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"        Date Score Type          Visitor Team  VisitorPts  \\\\\\n\",\n       \"0 2013-10-29  Box Score         Orlando Magic          87   \\n\",\n       \"1 2013-10-29  Box Score  Los Angeles Clippers         103   \\n\",\n       \"2 2013-10-29  Box Score         Chicago Bulls          95   \\n\",\n       \"3 2013-10-30  Box Score         Brooklyn Nets          94   \\n\",\n       \"4 2013-10-30  Box Score         Atlanta Hawks         109   \\n\",\n       \"5 2013-10-30  Box Score    Washington Wizards         102   \\n\",\n       \"\\n\",\n       \"             Home Team  HomePts  OT? Notes  \\n\",\n       \"0       Indiana Pacers       97  NaN   NaN  \\n\",\n       \"1   Los Angeles Lakers      116  NaN   NaN  \\n\",\n       \"2           Miami Heat      107  NaN   NaN  \\n\",\n       \"3  Cleveland Cavaliers       98  NaN   NaN  \\n\",\n       \"4     Dallas Mavericks      118  NaN   NaN  \\n\",\n       \"5      Detroit Pistons      113  NaN   NaN  \"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Don't read the first row, as it is blank, and parse the date column as a date\\n\",\n    \"results = pd.read_csv(data_filename, parse_dates=[\\\"Date\\\"], skiprows=[0,])\\n\",\n    \"# Fix the name of the columns\\n\",\n    \"results.columns = [\\\"Date\\\", \\\"Score Type\\\", \\\"Visitor Team\\\", \\\"VisitorPts\\\", \\\"Home Team\\\", \\\"HomePts\\\", \\\"OT?\\\", \\\"Notes\\\"]\\n\",\n    \"\\n\",\n    \"results.ix[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Date</th>\\n\",\n       \"      <th>Score Type</th>\\n\",\n       \"      <th>Visitor Team</th>\\n\",\n       \"      <th>VisitorPts</th>\\n\",\n       \"      <th>Home Team</th>\\n\",\n       \"      <th>HomePts</th>\\n\",\n       \"      <th>OT?</th>\\n\",\n       \"      <th>Notes</th>\\n\",\n       \"      <th>HomeWin</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>2013-10-29</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Orlando Magic</td>\\n\",\n       \"      <td>87</td>\\n\",\n       \"      <td>Indiana Pacers</td>\\n\",\n       \"      <td>97</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2013-10-29</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Los Angeles Clippers</td>\\n\",\n       \"      <td>103</td>\\n\",\n       \"      <td>Los Angeles Lakers</td>\\n\",\n       \"      <td>116</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>2013-10-29</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Chicago Bulls</td>\\n\",\n       \"      <td>95</td>\\n\",\n       \"      <td>Miami Heat</td>\\n\",\n       \"      <td>107</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>2013-10-30</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Brooklyn Nets</td>\\n\",\n       \"      <td>94</td>\\n\",\n       \"      <td>Cleveland Cavaliers</td>\\n\",\n       \"      <td>98</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>2013-10-30</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Atlanta Hawks</td>\\n\",\n       \"      <td>109</td>\\n\",\n       \"      <td>Dallas Mavericks</td>\\n\",\n       \"      <td>118</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>2013-10-30</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Washington Wizards</td>\\n\",\n       \"      <td>102</td>\\n\",\n       \"      <td>Detroit Pistons</td>\\n\",\n       \"      <td>113</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"        Date Score Type          Visitor Team  VisitorPts  \\\\\\n\",\n       \"0 2013-10-29  Box Score         Orlando Magic          87   \\n\",\n       \"1 2013-10-29  Box Score  Los Angeles Clippers         103   \\n\",\n       \"2 2013-10-29  Box Score         Chicago Bulls          95   \\n\",\n       \"3 2013-10-30  Box Score         Brooklyn Nets          94   \\n\",\n       \"4 2013-10-30  Box Score         Atlanta Hawks         109   \\n\",\n       \"5 2013-10-30  Box Score    Washington Wizards         102   \\n\",\n       \"\\n\",\n       \"             Home Team  HomePts  OT? Notes HomeWin  \\n\",\n       \"0       Indiana Pacers       97  NaN   NaN    True  \\n\",\n       \"1   Los Angeles Lakers      116  NaN   NaN    True  \\n\",\n       \"2           Miami Heat      107  NaN   NaN    True  \\n\",\n       \"3  Cleveland Cavaliers       98  NaN   NaN    True  \\n\",\n       \"4     Dallas Mavericks      118  NaN   NaN    True  \\n\",\n       \"5      Detroit Pistons      113  NaN   NaN    True  \"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"results[\\\"HomeWin\\\"] = results[\\\"VisitorPts\\\"] < results[\\\"HomePts\\\"]\\n\",\n    \"# Our \\\"class values\\\"\\n\",\n    \"y_true = results[\\\"HomeWin\\\"].values\\n\",\n    \"results.ix[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Home Win percentage: 58.0%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Home Win percentage: {0:.1f}%\\\".format(100 * results[\\\"HomeWin\\\"].sum() / results[\\\"HomeWin\\\"].count()))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Date</th>\\n\",\n       \"      <th>Score Type</th>\\n\",\n       \"      <th>Visitor Team</th>\\n\",\n       \"      <th>VisitorPts</th>\\n\",\n       \"      <th>Home Team</th>\\n\",\n       \"      <th>HomePts</th>\\n\",\n       \"      <th>OT?</th>\\n\",\n       \"      <th>Notes</th>\\n\",\n       \"      <th>HomeWin</th>\\n\",\n       \"      <th>HomeLastWin</th>\\n\",\n       \"      <th>VisitorLastWin</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>2013-10-29</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Orlando Magic</td>\\n\",\n       \"      <td>87</td>\\n\",\n       \"      <td>Indiana Pacers</td>\\n\",\n       \"      <td>97</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2013-10-29</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Los Angeles Clippers</td>\\n\",\n       \"      <td>103</td>\\n\",\n       \"      <td>Los Angeles Lakers</td>\\n\",\n       \"      <td>116</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>2013-10-29</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Chicago Bulls</td>\\n\",\n       \"      <td>95</td>\\n\",\n       \"      <td>Miami Heat</td>\\n\",\n       \"      <td>107</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>2013-10-30</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Brooklyn Nets</td>\\n\",\n       \"      <td>94</td>\\n\",\n       \"      <td>Cleveland Cavaliers</td>\\n\",\n       \"      <td>98</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>2013-10-30</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Atlanta Hawks</td>\\n\",\n       \"      <td>109</td>\\n\",\n       \"      <td>Dallas Mavericks</td>\\n\",\n       \"      <td>118</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>2013-10-30</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Washington Wizards</td>\\n\",\n       \"      <td>102</td>\\n\",\n       \"      <td>Detroit Pistons</td>\\n\",\n       \"      <td>113</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"        Date Score Type          Visitor Team  VisitorPts  \\\\\\n\",\n       \"0 2013-10-29  Box Score         Orlando Magic          87   \\n\",\n       \"1 2013-10-29  Box Score  Los Angeles Clippers         103   \\n\",\n       \"2 2013-10-29  Box Score         Chicago Bulls          95   \\n\",\n       \"3 2013-10-30  Box Score         Brooklyn Nets          94   \\n\",\n       \"4 2013-10-30  Box Score         Atlanta Hawks         109   \\n\",\n       \"5 2013-10-30  Box Score    Washington Wizards         102   \\n\",\n       \"\\n\",\n       \"             Home Team  HomePts  OT? Notes HomeWin HomeLastWin VisitorLastWin  \\n\",\n       \"0       Indiana Pacers       97  NaN   NaN    True       False          False  \\n\",\n       \"1   Los Angeles Lakers      116  NaN   NaN    True       False          False  \\n\",\n       \"2           Miami Heat      107  NaN   NaN    True       False          False  \\n\",\n       \"3  Cleveland Cavaliers       98  NaN   NaN    True       False          False  \\n\",\n       \"4     Dallas Mavericks      118  NaN   NaN    True       False          False  \\n\",\n       \"5      Detroit Pistons      113  NaN   NaN    True       False          False  \"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"results[\\\"HomeLastWin\\\"] = False\\n\",\n    \"results[\\\"VisitorLastWin\\\"] = False\\n\",\n    \"# This creates two new columns, all set to False\\n\",\n    \"results.ix[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Date</th>\\n\",\n       \"      <th>Score Type</th>\\n\",\n       \"      <th>Visitor Team</th>\\n\",\n       \"      <th>VisitorPts</th>\\n\",\n       \"      <th>Home Team</th>\\n\",\n       \"      <th>HomePts</th>\\n\",\n       \"      <th>OT?</th>\\n\",\n       \"      <th>Notes</th>\\n\",\n       \"      <th>HomeWin</th>\\n\",\n       \"      <th>HomeLastWin</th>\\n\",\n       \"      <th>VisitorLastWin</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20</th>\\n\",\n       \"      <td>2013-11-01</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Milwaukee Bucks</td>\\n\",\n       \"      <td>105</td>\\n\",\n       \"      <td>Boston Celtics</td>\\n\",\n       \"      <td>98</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>21</th>\\n\",\n       \"      <td>2013-11-01</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Miami Heat</td>\\n\",\n       \"      <td>100</td>\\n\",\n       \"      <td>Brooklyn Nets</td>\\n\",\n       \"      <td>101</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>22</th>\\n\",\n       \"      <td>2013-11-01</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Cleveland Cavaliers</td>\\n\",\n       \"      <td>84</td>\\n\",\n       \"      <td>Charlotte Bobcats</td>\\n\",\n       \"      <td>90</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>23</th>\\n\",\n       \"      <td>2013-11-01</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Portland Trail Blazers</td>\\n\",\n       \"      <td>113</td>\\n\",\n       \"      <td>Denver Nuggets</td>\\n\",\n       \"      <td>98</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>24</th>\\n\",\n       \"      <td>2013-11-01</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Dallas Mavericks</td>\\n\",\n       \"      <td>105</td>\\n\",\n       \"      <td>Houston Rockets</td>\\n\",\n       \"      <td>113</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>25</th>\\n\",\n       \"      <td>2013-11-01</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>San Antonio Spurs</td>\\n\",\n       \"      <td>91</td>\\n\",\n       \"      <td>Los Angeles Lakers</td>\\n\",\n       \"      <td>85</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"         Date Score Type            Visitor Team  VisitorPts  \\\\\\n\",\n       \"20 2013-11-01  Box Score         Milwaukee Bucks         105   \\n\",\n       \"21 2013-11-01  Box Score              Miami Heat         100   \\n\",\n       \"22 2013-11-01  Box Score     Cleveland Cavaliers          84   \\n\",\n       \"23 2013-11-01  Box Score  Portland Trail Blazers         113   \\n\",\n       \"24 2013-11-01  Box Score        Dallas Mavericks         105   \\n\",\n       \"25 2013-11-01  Box Score       San Antonio Spurs          91   \\n\",\n       \"\\n\",\n       \"             Home Team  HomePts  OT? Notes HomeWin HomeLastWin VisitorLastWin  \\n\",\n       \"20      Boston Celtics       98  NaN   NaN   False       False          False  \\n\",\n       \"21       Brooklyn Nets      101  NaN   NaN    True       False          False  \\n\",\n       \"22   Charlotte Bobcats       90  NaN   NaN    True       False           True  \\n\",\n       \"23      Denver Nuggets       98  NaN   NaN   False       False          False  \\n\",\n       \"24     Houston Rockets      113  NaN   NaN    True        True           True  \\n\",\n       \"25  Los Angeles Lakers       85  NaN   NaN   False       False           True  \"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Now compute the actual values for these\\n\",\n    \"# Did the home and visitor teams win their last game?\\n\",\n    \"from collections import defaultdict\\n\",\n    \"won_last = defaultdict(int)\\n\",\n    \"\\n\",\n    \"for index, row in results.iterrows():  # Note that this is not efficient\\n\",\n    \"    home_team = row[\\\"Home Team\\\"]\\n\",\n    \"    visitor_team = row[\\\"Visitor Team\\\"]\\n\",\n    \"    row[\\\"HomeLastWin\\\"] = won_last[home_team]\\n\",\n    \"    row[\\\"VisitorLastWin\\\"] = won_last[visitor_team]\\n\",\n    \"    results.ix[index] = row    \\n\",\n    \"    # Set current win\\n\",\n    \"    won_last[home_team] = row[\\\"HomeWin\\\"]\\n\",\n    \"    won_last[visitor_team] = not row[\\\"HomeWin\\\"]\\n\",\n    \"results.ix[20:25]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.tree import DecisionTreeClassifier\\n\",\n    \"clf = DecisionTreeClassifier(random_state=14)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using just the last result from the home and visitor teams\\n\",\n      \"Accuracy: 57.8%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.cross_validation import cross_val_score\\n\",\n    \"\\n\",\n    \"# Create a dataset with just the neccessary information\\n\",\n    \"X_previouswins = results[[\\\"HomeLastWin\\\", \\\"VisitorLastWin\\\"]].values\\n\",\n    \"clf = DecisionTreeClassifier(random_state=14)\\n\",\n    \"scores = cross_val_score(clf, X_previouswins, y_true, scoring='accuracy')\\n\",\n    \"print(\\\"Using just the last result from the home and visitor teams\\\")\\n\",\n    \"print(\\\"Accuracy: {0:.1f}%\\\".format(np.mean(scores) * 100))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# What about win streaks?\\n\",\n    \"results[\\\"HomeWinStreak\\\"] = 0\\n\",\n    \"results[\\\"VisitorWinStreak\\\"] = 0\\n\",\n    \"# Did the home and visitor teams win their last game?\\n\",\n    \"from collections import defaultdict\\n\",\n    \"win_streak = defaultdict(int)\\n\",\n    \"\\n\",\n    \"for index, row in results.iterrows():  # Note that this is not efficient\\n\",\n    \"    home_team = row[\\\"Home Team\\\"]\\n\",\n    \"    visitor_team = row[\\\"Visitor Team\\\"]\\n\",\n    \"    row[\\\"HomeWinStreak\\\"] = win_streak[home_team]\\n\",\n    \"    row[\\\"VisitorWinStreak\\\"] = win_streak[visitor_team]\\n\",\n    \"    results.ix[index] = row    \\n\",\n    \"    # Set current win\\n\",\n    \"    if row[\\\"HomeWin\\\"]:\\n\",\n    \"        win_streak[home_team] += 1\\n\",\n    \"        win_streak[visitor_team] = 0\\n\",\n    \"    else:\\n\",\n    \"        win_streak[home_team] = 0\\n\",\n    \"        win_streak[visitor_team] += 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using whether the home team is ranked higher\\n\",\n      \"Accuracy: 56.1%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"clf = DecisionTreeClassifier(random_state=14)\\n\",\n    \"X_winstreak =  results[[\\\"HomeLastWin\\\", \\\"VisitorLastWin\\\", \\\"HomeWinStreak\\\", \\\"VisitorWinStreak\\\"]].values\\n\",\n    \"scores = cross_val_score(clf, X_winstreak, y_true, scoring='accuracy')\\n\",\n    \"print(\\\"Using whether the home team is ranked higher\\\")\\n\",\n    \"print(\\\"Accuracy: {0:.1f}%\\\".format(np.mean(scores) * 100))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Rk</th>\\n\",\n       \"      <th>Team</th>\\n\",\n       \"      <th>Overall</th>\\n\",\n       \"      <th>Home</th>\\n\",\n       \"      <th>Road</th>\\n\",\n       \"      <th>E</th>\\n\",\n       \"      <th>W</th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>SE</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>Post</th>\\n\",\n       \"      <th>≤3</th>\\n\",\n       \"      <th>≥10</th>\\n\",\n       \"      <th>Oct</th>\\n\",\n       \"      <th>Nov</th>\\n\",\n       \"      <th>Dec</th>\\n\",\n       \"      <th>Jan</th>\\n\",\n       \"      <th>Feb</th>\\n\",\n       \"      <th>Mar</th>\\n\",\n       \"      <th>Apr</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Miami Heat</td>\\n\",\n       \"      <td>66-16</td>\\n\",\n       \"      <td>37-4</td>\\n\",\n       \"      <td>29-12</td>\\n\",\n       \"      <td>41-11</td>\\n\",\n       \"      <td>25-5</td>\\n\",\n       \"      <td>14-4</td>\\n\",\n       \"      <td>12-6</td>\\n\",\n       \"      <td>15-1</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>30-2</td>\\n\",\n       \"      <td>9-3</td>\\n\",\n       \"      <td>39-8</td>\\n\",\n       \"      <td>1-0</td>\\n\",\n       \"      <td>10-3</td>\\n\",\n       \"      <td>10-5</td>\\n\",\n       \"      <td>8-5</td>\\n\",\n       \"      <td>12-1</td>\\n\",\n       \"      <td>17-1</td>\\n\",\n       \"      <td>8-1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Oklahoma City Thunder</td>\\n\",\n       \"      <td>60-22</td>\\n\",\n       \"      <td>34-7</td>\\n\",\n       \"      <td>26-15</td>\\n\",\n       \"      <td>21-9</td>\\n\",\n       \"      <td>39-13</td>\\n\",\n       \"      <td>7-3</td>\\n\",\n       \"      <td>8-2</td>\\n\",\n       \"      <td>6-4</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>21-8</td>\\n\",\n       \"      <td>3-6</td>\\n\",\n       \"      <td>44-6</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>13-4</td>\\n\",\n       \"      <td>11-2</td>\\n\",\n       \"      <td>11-5</td>\\n\",\n       \"      <td>7-4</td>\\n\",\n       \"      <td>12-5</td>\\n\",\n       \"      <td>6-2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>San Antonio Spurs</td>\\n\",\n       \"      <td>58-24</td>\\n\",\n       \"      <td>35-6</td>\\n\",\n       \"      <td>23-18</td>\\n\",\n       \"      <td>25-5</td>\\n\",\n       \"      <td>33-19</td>\\n\",\n       \"      <td>8-2</td>\\n\",\n       \"      <td>9-1</td>\\n\",\n       \"      <td>8-2</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>16-12</td>\\n\",\n       \"      <td>9-5</td>\\n\",\n       \"      <td>31-10</td>\\n\",\n       \"      <td>1-0</td>\\n\",\n       \"      <td>12-4</td>\\n\",\n       \"      <td>12-4</td>\\n\",\n       \"      <td>12-3</td>\\n\",\n       \"      <td>8-3</td>\\n\",\n       \"      <td>10-4</td>\\n\",\n       \"      <td>3-6</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>Denver Nuggets</td>\\n\",\n       \"      <td>57-25</td>\\n\",\n       \"      <td>38-3</td>\\n\",\n       \"      <td>19-22</td>\\n\",\n       \"      <td>19-11</td>\\n\",\n       \"      <td>38-14</td>\\n\",\n       \"      <td>5-5</td>\\n\",\n       \"      <td>10-0</td>\\n\",\n       \"      <td>4-6</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>24-4</td>\\n\",\n       \"      <td>11-7</td>\\n\",\n       \"      <td>28-8</td>\\n\",\n       \"      <td>0-1</td>\\n\",\n       \"      <td>8-8</td>\\n\",\n       \"      <td>9-6</td>\\n\",\n       \"      <td>12-3</td>\\n\",\n       \"      <td>8-4</td>\\n\",\n       \"      <td>13-2</td>\\n\",\n       \"      <td>7-1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>Los Angeles Clippers</td>\\n\",\n       \"      <td>56-26</td>\\n\",\n       \"      <td>32-9</td>\\n\",\n       \"      <td>24-17</td>\\n\",\n       \"      <td>21-9</td>\\n\",\n       \"      <td>35-17</td>\\n\",\n       \"      <td>7-3</td>\\n\",\n       \"      <td>8-2</td>\\n\",\n       \"      <td>6-4</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>17-9</td>\\n\",\n       \"      <td>3-5</td>\\n\",\n       \"      <td>38-12</td>\\n\",\n       \"      <td>1-0</td>\\n\",\n       \"      <td>8-6</td>\\n\",\n       \"      <td>16-0</td>\\n\",\n       \"      <td>9-7</td>\\n\",\n       \"      <td>8-5</td>\\n\",\n       \"      <td>7-7</td>\\n\",\n       \"      <td>7-1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>Memphis Grizzlies</td>\\n\",\n       \"      <td>56-26</td>\\n\",\n       \"      <td>32-9</td>\\n\",\n       \"      <td>24-17</td>\\n\",\n       \"      <td>22-8</td>\\n\",\n       \"      <td>34-18</td>\\n\",\n       \"      <td>8-2</td>\\n\",\n       \"      <td>8-2</td>\\n\",\n       \"      <td>6-4</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>23-8</td>\\n\",\n       \"      <td>6-4</td>\\n\",\n       \"      <td>28-9</td>\\n\",\n       \"      <td>0-1</td>\\n\",\n       \"      <td>12-1</td>\\n\",\n       \"      <td>7-7</td>\\n\",\n       \"      <td>10-7</td>\\n\",\n       \"      <td>9-2</td>\\n\",\n       \"      <td>11-6</td>\\n\",\n       \"      <td>7-2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>New York Knicks</td>\\n\",\n       \"      <td>54-28</td>\\n\",\n       \"      <td>31-10</td>\\n\",\n       \"      <td>23-18</td>\\n\",\n       \"      <td>37-15</td>\\n\",\n       \"      <td>17-13</td>\\n\",\n       \"      <td>10-6</td>\\n\",\n       \"      <td>12-6</td>\\n\",\n       \"      <td>15-3</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>22-10</td>\\n\",\n       \"      <td>7-5</td>\\n\",\n       \"      <td>31-12</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>11-4</td>\\n\",\n       \"      <td>10-5</td>\\n\",\n       \"      <td>7-6</td>\\n\",\n       \"      <td>6-5</td>\\n\",\n       \"      <td>12-6</td>\\n\",\n       \"      <td>8-2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>Brooklyn Nets</td>\\n\",\n       \"      <td>49-33</td>\\n\",\n       \"      <td>26-15</td>\\n\",\n       \"      <td>23-18</td>\\n\",\n       \"      <td>36-16</td>\\n\",\n       \"      <td>13-17</td>\\n\",\n       \"      <td>11-5</td>\\n\",\n       \"      <td>13-5</td>\\n\",\n       \"      <td>12-6</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>18-11</td>\\n\",\n       \"      <td>9-4</td>\\n\",\n       \"      <td>23-17</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>11-4</td>\\n\",\n       \"      <td>5-11</td>\\n\",\n       \"      <td>11-4</td>\\n\",\n       \"      <td>7-5</td>\\n\",\n       \"      <td>8-7</td>\\n\",\n       \"      <td>7-2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Indiana Pacers</td>\\n\",\n       \"      <td>49-32</td>\\n\",\n       \"      <td>30-11</td>\\n\",\n       \"      <td>19-21</td>\\n\",\n       \"      <td>31-20</td>\\n\",\n       \"      <td>18-12</td>\\n\",\n       \"      <td>6-11</td>\\n\",\n       \"      <td>13-3</td>\\n\",\n       \"      <td>12-6</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>17-11</td>\\n\",\n       \"      <td>4-9</td>\\n\",\n       \"      <td>27-14</td>\\n\",\n       \"      <td>1-0</td>\\n\",\n       \"      <td>7-8</td>\\n\",\n       \"      <td>10-5</td>\\n\",\n       \"      <td>9-6</td>\\n\",\n       \"      <td>9-3</td>\\n\",\n       \"      <td>11-5</td>\\n\",\n       \"      <td>2-5</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>Golden State Warriors</td>\\n\",\n       \"      <td>47-35</td>\\n\",\n       \"      <td>28-13</td>\\n\",\n       \"      <td>19-22</td>\\n\",\n       \"      <td>19-11</td>\\n\",\n       \"      <td>28-24</td>\\n\",\n       \"      <td>7-3</td>\\n\",\n       \"      <td>5-5</td>\\n\",\n       \"      <td>7-3</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>17-13</td>\\n\",\n       \"      <td>5-3</td>\\n\",\n       \"      <td>20-18</td>\\n\",\n       \"      <td>1-0</td>\\n\",\n       \"      <td>8-6</td>\\n\",\n       \"      <td>12-4</td>\\n\",\n       \"      <td>8-7</td>\\n\",\n       \"      <td>4-8</td>\\n\",\n       \"      <td>9-7</td>\\n\",\n       \"      <td>5-3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>10</th>\\n\",\n       \"      <td>11</td>\\n\",\n       \"      <td>Chicago Bulls</td>\\n\",\n       \"      <td>45-37</td>\\n\",\n       \"      <td>24-17</td>\\n\",\n       \"      <td>21-20</td>\\n\",\n       \"      <td>34-18</td>\\n\",\n       \"      <td>11-19</td>\\n\",\n       \"      <td>13-5</td>\\n\",\n       \"      <td>9-7</td>\\n\",\n       \"      <td>12-6</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>15-15</td>\\n\",\n       \"      <td>11-7</td>\\n\",\n       \"      <td>16-16</td>\\n\",\n       \"      <td>1-0</td>\\n\",\n       \"      <td>6-7</td>\\n\",\n       \"      <td>9-6</td>\\n\",\n       \"      <td>12-4</td>\\n\",\n       \"      <td>5-8</td>\\n\",\n       \"      <td>7-7</td>\\n\",\n       \"      <td>5-5</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>11</th>\\n\",\n       \"      <td>12</td>\\n\",\n       \"      <td>Houston Rockets</td>\\n\",\n       \"      <td>45-37</td>\\n\",\n       \"      <td>29-12</td>\\n\",\n       \"      <td>16-25</td>\\n\",\n       \"      <td>21-9</td>\\n\",\n       \"      <td>24-28</td>\\n\",\n       \"      <td>7-3</td>\\n\",\n       \"      <td>7-3</td>\\n\",\n       \"      <td>7-3</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>16-11</td>\\n\",\n       \"      <td>5-5</td>\\n\",\n       \"      <td>26-13</td>\\n\",\n       \"      <td>1-0</td>\\n\",\n       \"      <td>6-8</td>\\n\",\n       \"      <td>10-6</td>\\n\",\n       \"      <td>8-9</td>\\n\",\n       \"      <td>6-5</td>\\n\",\n       \"      <td>9-5</td>\\n\",\n       \"      <td>5-4</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>12</th>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>Los Angeles Lakers</td>\\n\",\n       \"      <td>45-37</td>\\n\",\n       \"      <td>29-12</td>\\n\",\n       \"      <td>16-25</td>\\n\",\n       \"      <td>17-13</td>\\n\",\n       \"      <td>28-24</td>\\n\",\n       \"      <td>6-4</td>\\n\",\n       \"      <td>6-4</td>\\n\",\n       \"      <td>5-5</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>20-8</td>\\n\",\n       \"      <td>8-5</td>\\n\",\n       \"      <td>18-17</td>\\n\",\n       \"      <td>0-2</td>\\n\",\n       \"      <td>8-6</td>\\n\",\n       \"      <td>7-7</td>\\n\",\n       \"      <td>5-11</td>\\n\",\n       \"      <td>9-4</td>\\n\",\n       \"      <td>9-6</td>\\n\",\n       \"      <td>7-1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>13</th>\\n\",\n       \"      <td>14</td>\\n\",\n       \"      <td>Atlanta Hawks</td>\\n\",\n       \"      <td>44-38</td>\\n\",\n       \"      <td>25-16</td>\\n\",\n       \"      <td>19-22</td>\\n\",\n       \"      <td>29-23</td>\\n\",\n       \"      <td>15-15</td>\\n\",\n       \"      <td>7-11</td>\\n\",\n       \"      <td>11-7</td>\\n\",\n       \"      <td>11-5</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>15-16</td>\\n\",\n       \"      <td>5-5</td>\\n\",\n       \"      <td>19-20</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>9-5</td>\\n\",\n       \"      <td>10-5</td>\\n\",\n       \"      <td>7-9</td>\\n\",\n       \"      <td>7-4</td>\\n\",\n       \"      <td>8-10</td>\\n\",\n       \"      <td>3-5</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>14</th>\\n\",\n       \"      <td>15</td>\\n\",\n       \"      <td>Utah Jazz</td>\\n\",\n       \"      <td>43-39</td>\\n\",\n       \"      <td>30-11</td>\\n\",\n       \"      <td>13-28</td>\\n\",\n       \"      <td>17-13</td>\\n\",\n       \"      <td>26-26</td>\\n\",\n       \"      <td>5-5</td>\\n\",\n       \"      <td>5-5</td>\\n\",\n       \"      <td>7-3</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>13-15</td>\\n\",\n       \"      <td>5-7</td>\\n\",\n       \"      <td>19-21</td>\\n\",\n       \"      <td>1-0</td>\\n\",\n       \"      <td>8-8</td>\\n\",\n       \"      <td>6-9</td>\\n\",\n       \"      <td>10-4</td>\\n\",\n       \"      <td>6-6</td>\\n\",\n       \"      <td>7-9</td>\\n\",\n       \"      <td>5-3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15</th>\\n\",\n       \"      <td>16</td>\\n\",\n       \"      <td>Boston Celtics</td>\\n\",\n       \"      <td>41-40</td>\\n\",\n       \"      <td>27-13</td>\\n\",\n       \"      <td>14-27</td>\\n\",\n       \"      <td>27-24</td>\\n\",\n       \"      <td>14-16</td>\\n\",\n       \"      <td>7-9</td>\\n\",\n       \"      <td>8-9</td>\\n\",\n       \"      <td>12-6</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>13-16</td>\\n\",\n       \"      <td>8-7</td>\\n\",\n       \"      <td>18-23</td>\\n\",\n       \"      <td>0-1</td>\\n\",\n       \"      <td>9-6</td>\\n\",\n       \"      <td>5-9</td>\\n\",\n       \"      <td>8-7</td>\\n\",\n       \"      <td>8-4</td>\\n\",\n       \"      <td>8-8</td>\\n\",\n       \"      <td>3-5</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16</th>\\n\",\n       \"      <td>17</td>\\n\",\n       \"      <td>Dallas Mavericks</td>\\n\",\n       \"      <td>41-41</td>\\n\",\n       \"      <td>24-17</td>\\n\",\n       \"      <td>17-24</td>\\n\",\n       \"      <td>17-13</td>\\n\",\n       \"      <td>24-28</td>\\n\",\n       \"      <td>5-5</td>\\n\",\n       \"      <td>6-4</td>\\n\",\n       \"      <td>6-4</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>18-12</td>\\n\",\n       \"      <td>5-8</td>\\n\",\n       \"      <td>17-19</td>\\n\",\n       \"      <td>1-1</td>\\n\",\n       \"      <td>6-8</td>\\n\",\n       \"      <td>5-10</td>\\n\",\n       \"      <td>7-8</td>\\n\",\n       \"      <td>6-5</td>\\n\",\n       \"      <td>11-5</td>\\n\",\n       \"      <td>5-4</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>17</th>\\n\",\n       \"      <td>18</td>\\n\",\n       \"      <td>Milwaukee Bucks</td>\\n\",\n       \"      <td>38-44</td>\\n\",\n       \"      <td>21-20</td>\\n\",\n       \"      <td>17-24</td>\\n\",\n       \"      <td>24-28</td>\\n\",\n       \"      <td>14-16</td>\\n\",\n       \"      <td>11-7</td>\\n\",\n       \"      <td>7-9</td>\\n\",\n       \"      <td>6-12</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>12-19</td>\\n\",\n       \"      <td>7-5</td>\\n\",\n       \"      <td>13-25</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>7-7</td>\\n\",\n       \"      <td>9-6</td>\\n\",\n       \"      <td>8-7</td>\\n\",\n       \"      <td>4-8</td>\\n\",\n       \"      <td>7-9</td>\\n\",\n       \"      <td>3-7</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>18</th>\\n\",\n       \"      <td>19</td>\\n\",\n       \"      <td>Philadelphia 76ers</td>\\n\",\n       \"      <td>34-48</td>\\n\",\n       \"      <td>23-18</td>\\n\",\n       \"      <td>11-30</td>\\n\",\n       \"      <td>22-30</td>\\n\",\n       \"      <td>12-18</td>\\n\",\n       \"      <td>7-9</td>\\n\",\n       \"      <td>7-11</td>\\n\",\n       \"      <td>8-10</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>12-19</td>\\n\",\n       \"      <td>4-5</td>\\n\",\n       \"      <td>13-24</td>\\n\",\n       \"      <td>1-0</td>\\n\",\n       \"      <td>9-6</td>\\n\",\n       \"      <td>4-11</td>\\n\",\n       \"      <td>5-9</td>\\n\",\n       \"      <td>3-8</td>\\n\",\n       \"      <td>8-9</td>\\n\",\n       \"      <td>4-5</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19</th>\\n\",\n       \"      <td>20</td>\\n\",\n       \"      <td>Toronto Raptors</td>\\n\",\n       \"      <td>34-48</td>\\n\",\n       \"      <td>21-20</td>\\n\",\n       \"      <td>13-28</td>\\n\",\n       \"      <td>22-30</td>\\n\",\n       \"      <td>12-18</td>\\n\",\n       \"      <td>5-11</td>\\n\",\n       \"      <td>8-10</td>\\n\",\n       \"      <td>9-9</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>13-16</td>\\n\",\n       \"      <td>8-8</td>\\n\",\n       \"      <td>16-22</td>\\n\",\n       \"      <td>0-1</td>\\n\",\n       \"      <td>4-12</td>\\n\",\n       \"      <td>7-7</td>\\n\",\n       \"      <td>5-10</td>\\n\",\n       \"      <td>7-5</td>\\n\",\n       \"      <td>4-11</td>\\n\",\n       \"      <td>7-2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20</th>\\n\",\n       \"      <td>21</td>\\n\",\n       \"      <td>Portland Trail Blazers</td>\\n\",\n       \"      <td>33-49</td>\\n\",\n       \"      <td>22-19</td>\\n\",\n       \"      <td>11-30</td>\\n\",\n       \"      <td>15-15</td>\\n\",\n       \"      <td>18-34</td>\\n\",\n       \"      <td>5-5</td>\\n\",\n       \"      <td>5-5</td>\\n\",\n       \"      <td>5-5</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>8-21</td>\\n\",\n       \"      <td>9-6</td>\\n\",\n       \"      <td>13-24</td>\\n\",\n       \"      <td>1-0</td>\\n\",\n       \"      <td>5-10</td>\\n\",\n       \"      <td>9-4</td>\\n\",\n       \"      <td>8-8</td>\\n\",\n       \"      <td>3-9</td>\\n\",\n       \"      <td>7-9</td>\\n\",\n       \"      <td>0-9</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>21</th>\\n\",\n       \"      <td>22</td>\\n\",\n       \"      <td>Minnesota Timberwolves</td>\\n\",\n       \"      <td>31-51</td>\\n\",\n       \"      <td>20-21</td>\\n\",\n       \"      <td>11-30</td>\\n\",\n       \"      <td>14-16</td>\\n\",\n       \"      <td>17-35</td>\\n\",\n       \"      <td>4-6</td>\\n\",\n       \"      <td>7-3</td>\\n\",\n       \"      <td>3-7</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>12-20</td>\\n\",\n       \"      <td>3-10</td>\\n\",\n       \"      <td>15-26</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>7-8</td>\\n\",\n       \"      <td>7-5</td>\\n\",\n       \"      <td>3-12</td>\\n\",\n       \"      <td>3-10</td>\\n\",\n       \"      <td>6-11</td>\\n\",\n       \"      <td>5-5</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>22</th>\\n\",\n       \"      <td>23</td>\\n\",\n       \"      <td>Detroit Pistons</td>\\n\",\n       \"      <td>29-53</td>\\n\",\n       \"      <td>18-23</td>\\n\",\n       \"      <td>11-30</td>\\n\",\n       \"      <td>25-27</td>\\n\",\n       \"      <td>4-26</td>\\n\",\n       \"      <td>6-12</td>\\n\",\n       \"      <td>8-8</td>\\n\",\n       \"      <td>11-7</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>8-20</td>\\n\",\n       \"      <td>6-10</td>\\n\",\n       \"      <td>15-29</td>\\n\",\n       \"      <td>0-1</td>\\n\",\n       \"      <td>5-11</td>\\n\",\n       \"      <td>6-10</td>\\n\",\n       \"      <td>6-7</td>\\n\",\n       \"      <td>6-8</td>\\n\",\n       \"      <td>1-13</td>\\n\",\n       \"      <td>5-3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>23</th>\\n\",\n       \"      <td>24</td>\\n\",\n       \"      <td>Washington Wizards</td>\\n\",\n       \"      <td>29-53</td>\\n\",\n       \"      <td>22-19</td>\\n\",\n       \"      <td>7-34</td>\\n\",\n       \"      <td>15-37</td>\\n\",\n       \"      <td>14-16</td>\\n\",\n       \"      <td>5-13</td>\\n\",\n       \"      <td>5-13</td>\\n\",\n       \"      <td>5-11</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>14-17</td>\\n\",\n       \"      <td>6-9</td>\\n\",\n       \"      <td>13-17</td>\\n\",\n       \"      <td>0-1</td>\\n\",\n       \"      <td>1-12</td>\\n\",\n       \"      <td>3-11</td>\\n\",\n       \"      <td>7-9</td>\\n\",\n       \"      <td>7-5</td>\\n\",\n       \"      <td>9-8</td>\\n\",\n       \"      <td>2-7</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>24</th>\\n\",\n       \"      <td>25</td>\\n\",\n       \"      <td>Sacramento Kings</td>\\n\",\n       \"      <td>28-54</td>\\n\",\n       \"      <td>20-21</td>\\n\",\n       \"      <td>8-33</td>\\n\",\n       \"      <td>14-16</td>\\n\",\n       \"      <td>14-38</td>\\n\",\n       \"      <td>4-6</td>\\n\",\n       \"      <td>4-6</td>\\n\",\n       \"      <td>6-4</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>9-19</td>\\n\",\n       \"      <td>7-3</td>\\n\",\n       \"      <td>12-31</td>\\n\",\n       \"      <td>0-1</td>\\n\",\n       \"      <td>4-10</td>\\n\",\n       \"      <td>7-8</td>\\n\",\n       \"      <td>6-11</td>\\n\",\n       \"      <td>3-9</td>\\n\",\n       \"      <td>7-8</td>\\n\",\n       \"      <td>1-7</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>25</th>\\n\",\n       \"      <td>26</td>\\n\",\n       \"      <td>New Orleans Hornets</td>\\n\",\n       \"      <td>27-55</td>\\n\",\n       \"      <td>16-25</td>\\n\",\n       \"      <td>11-30</td>\\n\",\n       \"      <td>12-18</td>\\n\",\n       \"      <td>15-37</td>\\n\",\n       \"      <td>3-7</td>\\n\",\n       \"      <td>5-5</td>\\n\",\n       \"      <td>4-6</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>8-21</td>\\n\",\n       \"      <td>7-5</td>\\n\",\n       \"      <td>10-27</td>\\n\",\n       \"      <td>0-1</td>\\n\",\n       \"      <td>4-9</td>\\n\",\n       \"      <td>3-13</td>\\n\",\n       \"      <td>8-8</td>\\n\",\n       \"      <td>5-8</td>\\n\",\n       \"      <td>6-9</td>\\n\",\n       \"      <td>1-7</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>26</th>\\n\",\n       \"      <td>27</td>\\n\",\n       \"      <td>Phoenix Suns</td>\\n\",\n       \"      <td>25-57</td>\\n\",\n       \"      <td>17-24</td>\\n\",\n       \"      <td>8-33</td>\\n\",\n       \"      <td>8-22</td>\\n\",\n       \"      <td>17-35</td>\\n\",\n       \"      <td>1-9</td>\\n\",\n       \"      <td>4-6</td>\\n\",\n       \"      <td>3-7</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>8-21</td>\\n\",\n       \"      <td>6-8</td>\\n\",\n       \"      <td>10-31</td>\\n\",\n       \"      <td>0-1</td>\\n\",\n       \"      <td>7-9</td>\\n\",\n       \"      <td>4-11</td>\\n\",\n       \"      <td>5-9</td>\\n\",\n       \"      <td>4-9</td>\\n\",\n       \"      <td>3-12</td>\\n\",\n       \"      <td>2-6</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>27</th>\\n\",\n       \"      <td>28</td>\\n\",\n       \"      <td>Cleveland Cavaliers</td>\\n\",\n       \"      <td>24-58</td>\\n\",\n       \"      <td>14-27</td>\\n\",\n       \"      <td>10-31</td>\\n\",\n       \"      <td>18-34</td>\\n\",\n       \"      <td>6-24</td>\\n\",\n       \"      <td>5-13</td>\\n\",\n       \"      <td>3-13</td>\\n\",\n       \"      <td>10-8</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>8-21</td>\\n\",\n       \"      <td>6-11</td>\\n\",\n       \"      <td>7-27</td>\\n\",\n       \"      <td>1-0</td>\\n\",\n       \"      <td>3-12</td>\\n\",\n       \"      <td>3-13</td>\\n\",\n       \"      <td>6-8</td>\\n\",\n       \"      <td>7-5</td>\\n\",\n       \"      <td>2-12</td>\\n\",\n       \"      <td>2-8</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>28</th>\\n\",\n       \"      <td>29</td>\\n\",\n       \"      <td>Charlotte Bobcats</td>\\n\",\n       \"      <td>21-61</td>\\n\",\n       \"      <td>15-26</td>\\n\",\n       \"      <td>6-35</td>\\n\",\n       \"      <td>18-34</td>\\n\",\n       \"      <td>3-27</td>\\n\",\n       \"      <td>6-12</td>\\n\",\n       \"      <td>6-12</td>\\n\",\n       \"      <td>6-10</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>9-21</td>\\n\",\n       \"      <td>6-6</td>\\n\",\n       \"      <td>6-37</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>7-8</td>\\n\",\n       \"      <td>1-15</td>\\n\",\n       \"      <td>3-11</td>\\n\",\n       \"      <td>2-10</td>\\n\",\n       \"      <td>4-12</td>\\n\",\n       \"      <td>4-5</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>29</th>\\n\",\n       \"      <td>30</td>\\n\",\n       \"      <td>Orlando Magic</td>\\n\",\n       \"      <td>20-62</td>\\n\",\n       \"      <td>12-29</td>\\n\",\n       \"      <td>8-33</td>\\n\",\n       \"      <td>10-42</td>\\n\",\n       \"      <td>10-20</td>\\n\",\n       \"      <td>2-16</td>\\n\",\n       \"      <td>5-13</td>\\n\",\n       \"      <td>3-13</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>5-25</td>\\n\",\n       \"      <td>2-9</td>\\n\",\n       \"      <td>8-30</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>5-10</td>\\n\",\n       \"      <td>7-9</td>\\n\",\n       \"      <td>2-12</td>\\n\",\n       \"      <td>2-11</td>\\n\",\n       \"      <td>3-13</td>\\n\",\n       \"      <td>1-7</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>30 rows × 24 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    Rk                    Team Overall   Home   Road      E      W     A  \\\\\\n\",\n       \"0    1              Miami Heat   66-16   37-4  29-12  41-11   25-5  14-4   \\n\",\n       \"1    2   Oklahoma City Thunder   60-22   34-7  26-15   21-9  39-13   7-3   \\n\",\n       \"2    3       San Antonio Spurs   58-24   35-6  23-18   25-5  33-19   8-2   \\n\",\n       \"3    4          Denver Nuggets   57-25   38-3  19-22  19-11  38-14   5-5   \\n\",\n       \"4    5    Los Angeles Clippers   56-26   32-9  24-17   21-9  35-17   7-3   \\n\",\n       \"5    6       Memphis Grizzlies   56-26   32-9  24-17   22-8  34-18   8-2   \\n\",\n       \"6    7         New York Knicks   54-28  31-10  23-18  37-15  17-13  10-6   \\n\",\n       \"7    8           Brooklyn Nets   49-33  26-15  23-18  36-16  13-17  11-5   \\n\",\n       \"8    9          Indiana Pacers   49-32  30-11  19-21  31-20  18-12  6-11   \\n\",\n       \"9   10   Golden State Warriors   47-35  28-13  19-22  19-11  28-24   7-3   \\n\",\n       \"10  11           Chicago Bulls   45-37  24-17  21-20  34-18  11-19  13-5   \\n\",\n       \"11  12         Houston Rockets   45-37  29-12  16-25   21-9  24-28   7-3   \\n\",\n       \"12  13      Los Angeles Lakers   45-37  29-12  16-25  17-13  28-24   6-4   \\n\",\n       \"13  14           Atlanta Hawks   44-38  25-16  19-22  29-23  15-15  7-11   \\n\",\n       \"14  15               Utah Jazz   43-39  30-11  13-28  17-13  26-26   5-5   \\n\",\n       \"15  16          Boston Celtics   41-40  27-13  14-27  27-24  14-16   7-9   \\n\",\n       \"16  17        Dallas Mavericks   41-41  24-17  17-24  17-13  24-28   5-5   \\n\",\n       \"17  18         Milwaukee Bucks   38-44  21-20  17-24  24-28  14-16  11-7   \\n\",\n       \"18  19      Philadelphia 76ers   34-48  23-18  11-30  22-30  12-18   7-9   \\n\",\n       \"19  20         Toronto Raptors   34-48  21-20  13-28  22-30  12-18  5-11   \\n\",\n       \"20  21  Portland Trail Blazers   33-49  22-19  11-30  15-15  18-34   5-5   \\n\",\n       \"21  22  Minnesota Timberwolves   31-51  20-21  11-30  14-16  17-35   4-6   \\n\",\n       \"22  23         Detroit Pistons   29-53  18-23  11-30  25-27   4-26  6-12   \\n\",\n       \"23  24      Washington Wizards   29-53  22-19   7-34  15-37  14-16  5-13   \\n\",\n       \"24  25        Sacramento Kings   28-54  20-21   8-33  14-16  14-38   4-6   \\n\",\n       \"25  26     New Orleans Hornets   27-55  16-25  11-30  12-18  15-37   3-7   \\n\",\n       \"26  27            Phoenix Suns   25-57  17-24   8-33   8-22  17-35   1-9   \\n\",\n       \"27  28     Cleveland Cavaliers   24-58  14-27  10-31  18-34   6-24  5-13   \\n\",\n       \"28  29       Charlotte Bobcats   21-61  15-26   6-35  18-34   3-27  6-12   \\n\",\n       \"29  30           Orlando Magic   20-62  12-29   8-33  10-42  10-20  2-16   \\n\",\n       \"\\n\",\n       \"       C    SE ...    Post    ≤3    ≥10  Oct   Nov   Dec   Jan   Feb   Mar  \\\\\\n\",\n       \"0   12-6  15-1 ...    30-2   9-3   39-8  1-0  10-3  10-5   8-5  12-1  17-1   \\n\",\n       \"1    8-2   6-4 ...    21-8   3-6   44-6  NaN  13-4  11-2  11-5   7-4  12-5   \\n\",\n       \"2    9-1   8-2 ...   16-12   9-5  31-10  1-0  12-4  12-4  12-3   8-3  10-4   \\n\",\n       \"3   10-0   4-6 ...    24-4  11-7   28-8  0-1   8-8   9-6  12-3   8-4  13-2   \\n\",\n       \"4    8-2   6-4 ...    17-9   3-5  38-12  1-0   8-6  16-0   9-7   8-5   7-7   \\n\",\n       \"5    8-2   6-4 ...    23-8   6-4   28-9  0-1  12-1   7-7  10-7   9-2  11-6   \\n\",\n       \"6   12-6  15-3 ...   22-10   7-5  31-12  NaN  11-4  10-5   7-6   6-5  12-6   \\n\",\n       \"7   13-5  12-6 ...   18-11   9-4  23-17  NaN  11-4  5-11  11-4   7-5   8-7   \\n\",\n       \"8   13-3  12-6 ...   17-11   4-9  27-14  1-0   7-8  10-5   9-6   9-3  11-5   \\n\",\n       \"9    5-5   7-3 ...   17-13   5-3  20-18  1-0   8-6  12-4   8-7   4-8   9-7   \\n\",\n       \"10   9-7  12-6 ...   15-15  11-7  16-16  1-0   6-7   9-6  12-4   5-8   7-7   \\n\",\n       \"11   7-3   7-3 ...   16-11   5-5  26-13  1-0   6-8  10-6   8-9   6-5   9-5   \\n\",\n       \"12   6-4   5-5 ...    20-8   8-5  18-17  0-2   8-6   7-7  5-11   9-4   9-6   \\n\",\n       \"13  11-7  11-5 ...   15-16   5-5  19-20  NaN   9-5  10-5   7-9   7-4  8-10   \\n\",\n       \"14   5-5   7-3 ...   13-15   5-7  19-21  1-0   8-8   6-9  10-4   6-6   7-9   \\n\",\n       \"15   8-9  12-6 ...   13-16   8-7  18-23  0-1   9-6   5-9   8-7   8-4   8-8   \\n\",\n       \"16   6-4   6-4 ...   18-12   5-8  17-19  1-1   6-8  5-10   7-8   6-5  11-5   \\n\",\n       \"17   7-9  6-12 ...   12-19   7-5  13-25  NaN   7-7   9-6   8-7   4-8   7-9   \\n\",\n       \"18  7-11  8-10 ...   12-19   4-5  13-24  1-0   9-6  4-11   5-9   3-8   8-9   \\n\",\n       \"19  8-10   9-9 ...   13-16   8-8  16-22  0-1  4-12   7-7  5-10   7-5  4-11   \\n\",\n       \"20   5-5   5-5 ...    8-21   9-6  13-24  1-0  5-10   9-4   8-8   3-9   7-9   \\n\",\n       \"21   7-3   3-7 ...   12-20  3-10  15-26  NaN   7-8   7-5  3-12  3-10  6-11   \\n\",\n       \"22   8-8  11-7 ...    8-20  6-10  15-29  0-1  5-11  6-10   6-7   6-8  1-13   \\n\",\n       \"23  5-13  5-11 ...   14-17   6-9  13-17  0-1  1-12  3-11   7-9   7-5   9-8   \\n\",\n       \"24   4-6   6-4 ...    9-19   7-3  12-31  0-1  4-10   7-8  6-11   3-9   7-8   \\n\",\n       \"25   5-5   4-6 ...    8-21   7-5  10-27  0-1   4-9  3-13   8-8   5-8   6-9   \\n\",\n       \"26   4-6   3-7 ...    8-21   6-8  10-31  0-1   7-9  4-11   5-9   4-9  3-12   \\n\",\n       \"27  3-13  10-8 ...    8-21  6-11   7-27  1-0  3-12  3-13   6-8   7-5  2-12   \\n\",\n       \"28  6-12  6-10 ...    9-21   6-6   6-37  NaN   7-8  1-15  3-11  2-10  4-12   \\n\",\n       \"29  5-13  3-13 ...    5-25   2-9   8-30  NaN  5-10   7-9  2-12  2-11  3-13   \\n\",\n       \"\\n\",\n       \"    Apr  \\n\",\n       \"0   8-1  \\n\",\n       \"1   6-2  \\n\",\n       \"2   3-6  \\n\",\n       \"3   7-1  \\n\",\n       \"4   7-1  \\n\",\n       \"5   7-2  \\n\",\n       \"6   8-2  \\n\",\n       \"7   7-2  \\n\",\n       \"8   2-5  \\n\",\n       \"9   5-3  \\n\",\n       \"10  5-5  \\n\",\n       \"11  5-4  \\n\",\n       \"12  7-1  \\n\",\n       \"13  3-5  \\n\",\n       \"14  5-3  \\n\",\n       \"15  3-5  \\n\",\n       \"16  5-4  \\n\",\n       \"17  3-7  \\n\",\n       \"18  4-5  \\n\",\n       \"19  7-2  \\n\",\n       \"20  0-9  \\n\",\n       \"21  5-5  \\n\",\n       \"22  5-3  \\n\",\n       \"23  2-7  \\n\",\n       \"24  1-7  \\n\",\n       \"25  1-7  \\n\",\n       \"26  2-6  \\n\",\n       \"27  2-8  \\n\",\n       \"28  4-5  \\n\",\n       \"29  1-7  \\n\",\n       \"\\n\",\n       \"[30 rows x 24 columns]\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Let's try see which team is better on the ladder. Using the previous year's ladder\\n\",\n    \"ladder_filename = os.path.join(data_folder, \\\"leagues_NBA_2013_standings_expanded-standings.csv\\\")\\n\",\n    \"ladder = pd.read_csv(ladder_filename, skiprows=[0,1])\\n\",\n    \"ladder\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Date</th>\\n\",\n       \"      <th>Score Type</th>\\n\",\n       \"      <th>Visitor Team</th>\\n\",\n       \"      <th>VisitorPts</th>\\n\",\n       \"      <th>Home Team</th>\\n\",\n       \"      <th>HomePts</th>\\n\",\n       \"      <th>OT?</th>\\n\",\n       \"      <th>Notes</th>\\n\",\n       \"      <th>HomeWin</th>\\n\",\n       \"      <th>HomeLastWin</th>\\n\",\n       \"      <th>VisitorLastWin</th>\\n\",\n       \"      <th>HomeWinStreak</th>\\n\",\n       \"      <th>VisitorWinStreak</th>\\n\",\n       \"      <th>HomeTeamRanksHigher</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>2013-10-29</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Orlando Magic</td>\\n\",\n       \"      <td>87</td>\\n\",\n       \"      <td>Indiana Pacers</td>\\n\",\n       \"      <td>97</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2013-10-29</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Los Angeles Clippers</td>\\n\",\n       \"      <td>103</td>\\n\",\n       \"      <td>Los Angeles Lakers</td>\\n\",\n       \"      <td>116</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>2013-10-29</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Chicago Bulls</td>\\n\",\n       \"      <td>95</td>\\n\",\n       \"      <td>Miami Heat</td>\\n\",\n       \"      <td>107</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>2013-10-30</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Brooklyn Nets</td>\\n\",\n       \"      <td>94</td>\\n\",\n       \"      <td>Cleveland Cavaliers</td>\\n\",\n       \"      <td>98</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>2013-10-30</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Atlanta Hawks</td>\\n\",\n       \"      <td>109</td>\\n\",\n       \"      <td>Dallas Mavericks</td>\\n\",\n       \"      <td>118</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"        Date Score Type          Visitor Team  VisitorPts  \\\\\\n\",\n       \"0 2013-10-29  Box Score         Orlando Magic          87   \\n\",\n       \"1 2013-10-29  Box Score  Los Angeles Clippers         103   \\n\",\n       \"2 2013-10-29  Box Score         Chicago Bulls          95   \\n\",\n       \"3 2013-10-30  Box Score         Brooklyn Nets          94   \\n\",\n       \"4 2013-10-30  Box Score         Atlanta Hawks         109   \\n\",\n       \"\\n\",\n       \"             Home Team  HomePts  OT? Notes HomeWin HomeLastWin VisitorLastWin  \\\\\\n\",\n       \"0       Indiana Pacers       97  NaN   NaN    True       False          False   \\n\",\n       \"1   Los Angeles Lakers      116  NaN   NaN    True       False          False   \\n\",\n       \"2           Miami Heat      107  NaN   NaN    True       False          False   \\n\",\n       \"3  Cleveland Cavaliers       98  NaN   NaN    True       False          False   \\n\",\n       \"4     Dallas Mavericks      118  NaN   NaN    True       False          False   \\n\",\n       \"\\n\",\n       \"   HomeWinStreak  VisitorWinStreak  HomeTeamRanksHigher  \\n\",\n       \"0              0                 0                    0  \\n\",\n       \"1              0                 0                    1  \\n\",\n       \"2              0                 0                    0  \\n\",\n       \"3              0                 0                    1  \\n\",\n       \"4              0                 0                    1  \"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# We can create a new feature -- HomeTeamRanksHigher\\\\\\n\",\n    \"results[\\\"HomeTeamRanksHigher\\\"] = 0\\n\",\n    \"for index, row in results.iterrows():\\n\",\n    \"    home_team = row[\\\"Home Team\\\"]\\n\",\n    \"    visitor_team = row[\\\"Visitor Team\\\"]\\n\",\n    \"    if home_team == \\\"New Orleans Pelicans\\\":\\n\",\n    \"        home_team = \\\"New Orleans Hornets\\\"\\n\",\n    \"    elif visitor_team == \\\"New Orleans Pelicans\\\":\\n\",\n    \"        visitor_team = \\\"New Orleans Hornets\\\"\\n\",\n    \"    home_rank = ladder[ladder[\\\"Team\\\"] == home_team][\\\"Rk\\\"].values[0]\\n\",\n    \"    visitor_rank = ladder[ladder[\\\"Team\\\"] == visitor_team][\\\"Rk\\\"].values[0]\\n\",\n    \"    row[\\\"HomeTeamRanksHigher\\\"] = int(home_rank > visitor_rank)\\n\",\n    \"    results.ix[index] = row\\n\",\n    \"results[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using whether the home team is ranked higher\\n\",\n      \"Accuracy: 60.3%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"X_homehigher =  results[[\\\"HomeLastWin\\\", \\\"VisitorLastWin\\\", \\\"HomeTeamRanksHigher\\\"]].values\\n\",\n    \"clf = DecisionTreeClassifier(random_state=14)\\n\",\n    \"scores = cross_val_score(clf, X_homehigher, y_true, scoring='accuracy')\\n\",\n    \"print(\\\"Using whether the home team is ranked higher\\\")\\n\",\n    \"print(\\\"Accuracy: {0:.1f}%\\\".format(np.mean(scores) * 100))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy: 60.6%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.grid_search import GridSearchCV\\n\",\n    \"\\n\",\n    \"parameter_space = {\\n\",\n    \"                   \\\"max_depth\\\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20],\\n\",\n    \"                   }\\n\",\n    \"clf = DecisionTreeClassifier(random_state=14)\\n\",\n    \"grid = GridSearchCV(clf, parameter_space)\\n\",\n    \"grid.fit(X_homehigher, y_true)\\n\",\n    \"print(\\\"Accuracy: {0:.1f}%\\\".format(grid.best_score_ * 100))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Date</th>\\n\",\n       \"      <th>Score Type</th>\\n\",\n       \"      <th>Visitor Team</th>\\n\",\n       \"      <th>VisitorPts</th>\\n\",\n       \"      <th>Home Team</th>\\n\",\n       \"      <th>HomePts</th>\\n\",\n       \"      <th>OT?</th>\\n\",\n       \"      <th>Notes</th>\\n\",\n       \"      <th>HomeWin</th>\\n\",\n       \"      <th>HomeLastWin</th>\\n\",\n       \"      <th>VisitorLastWin</th>\\n\",\n       \"      <th>HomeWinStreak</th>\\n\",\n       \"      <th>VisitorWinStreak</th>\\n\",\n       \"      <th>HomeTeamRanksHigher</th>\\n\",\n       \"      <th>HomeTeamWonLast</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>2013-10-29</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Orlando Magic</td>\\n\",\n       \"      <td>87</td>\\n\",\n       \"      <td>Indiana Pacers</td>\\n\",\n       \"      <td>97</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2013-10-29</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Los Angeles Clippers</td>\\n\",\n       \"      <td>103</td>\\n\",\n       \"      <td>Los Angeles Lakers</td>\\n\",\n       \"      <td>116</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>2013-10-29</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Chicago Bulls</td>\\n\",\n       \"      <td>95</td>\\n\",\n       \"      <td>Miami Heat</td>\\n\",\n       \"      <td>107</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>2013-10-30</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Brooklyn Nets</td>\\n\",\n       \"      <td>94</td>\\n\",\n       \"      <td>Cleveland Cavaliers</td>\\n\",\n       \"      <td>98</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>2013-10-30</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Atlanta Hawks</td>\\n\",\n       \"      <td>109</td>\\n\",\n       \"      <td>Dallas Mavericks</td>\\n\",\n       \"      <td>118</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>2013-10-30</td>\\n\",\n       \"      <td>Box Score</td>\\n\",\n       \"      <td>Washington Wizards</td>\\n\",\n       \"      <td>102</td>\\n\",\n       \"      <td>Detroit Pistons</td>\\n\",\n       \"      <td>113</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"        Date Score Type          Visitor Team  VisitorPts  \\\\\\n\",\n       \"0 2013-10-29  Box Score         Orlando Magic          87   \\n\",\n       \"1 2013-10-29  Box Score  Los Angeles Clippers         103   \\n\",\n       \"2 2013-10-29  Box Score         Chicago Bulls          95   \\n\",\n       \"3 2013-10-30  Box Score         Brooklyn Nets          94   \\n\",\n       \"4 2013-10-30  Box Score         Atlanta Hawks         109   \\n\",\n       \"5 2013-10-30  Box Score    Washington Wizards         102   \\n\",\n       \"\\n\",\n       \"             Home Team  HomePts  OT? Notes HomeWin HomeLastWin VisitorLastWin  \\\\\\n\",\n       \"0       Indiana Pacers       97  NaN   NaN    True       False          False   \\n\",\n       \"1   Los Angeles Lakers      116  NaN   NaN    True       False          False   \\n\",\n       \"2           Miami Heat      107  NaN   NaN    True       False          False   \\n\",\n       \"3  Cleveland Cavaliers       98  NaN   NaN    True       False          False   \\n\",\n       \"4     Dallas Mavericks      118  NaN   NaN    True       False          False   \\n\",\n       \"5      Detroit Pistons      113  NaN   NaN    True       False          False   \\n\",\n       \"\\n\",\n       \"   HomeWinStreak  VisitorWinStreak  HomeTeamRanksHigher  HomeTeamWonLast  \\n\",\n       \"0              0                 0                    0                0  \\n\",\n       \"1              0                 0                    1                0  \\n\",\n       \"2              0                 0                    0                0  \\n\",\n       \"3              0                 0                    1                0  \\n\",\n       \"4              0                 0                    1                0  \\n\",\n       \"5              0                 0                    0                0  \"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Who won the last match? We ignore home/visitor for this bit\\n\",\n    \"last_match_winner = defaultdict(int)\\n\",\n    \"results[\\\"HomeTeamWonLast\\\"] = 0\\n\",\n    \"\\n\",\n    \"for index, row in results.iterrows():\\n\",\n    \"    home_team = row[\\\"Home Team\\\"]\\n\",\n    \"    visitor_team = row[\\\"Visitor Team\\\"]\\n\",\n    \"    teams = tuple(sorted([home_team, visitor_team]))  # Sort for a consistent ordering\\n\",\n    \"    # Set in the row, who won the last encounter\\n\",\n    \"    row[\\\"HomeTeamWonLast\\\"] = 1 if last_match_winner[teams] == row[\\\"Home Team\\\"] else 0\\n\",\n    \"    results.ix[index] = row\\n\",\n    \"    # Who won this one?\\n\",\n    \"    winner = row[\\\"Home Team\\\"] if row[\\\"HomeWin\\\"] else row[\\\"Visitor Team\\\"]\\n\",\n    \"    last_match_winner[teams] = winner\\n\",\n    \"results.ix[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using whether the home team is ranked higher\\n\",\n      \"Accuracy: 60.6%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"X_home_higher =  results[[\\\"HomeTeamRanksHigher\\\", \\\"HomeTeamWonLast\\\"]].values\\n\",\n    \"clf = DecisionTreeClassifier(random_state=14)\\n\",\n    \"scores = cross_val_score(clf, X_home_higher, y_true, scoring='accuracy')\\n\",\n    \"print(\\\"Using whether the home team is ranked higher\\\")\\n\",\n    \"print(\\\"Accuracy: {0:.1f}%\\\".format(np.mean(scores) * 100))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy: 60.0%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.preprocessing import LabelEncoder, OneHotEncoder\\n\",\n    \"encoding = LabelEncoder()\\n\",\n    \"encoding.fit(results[\\\"Home Team\\\"].values)\\n\",\n    \"home_teams = encoding.transform(results[\\\"Home Team\\\"].values)\\n\",\n    \"visitor_teams = encoding.transform(results[\\\"Visitor Team\\\"].values)\\n\",\n    \"X_teams = np.vstack([home_teams, visitor_teams]).T\\n\",\n    \"\\n\",\n    \"onehot = OneHotEncoder()\\n\",\n    \"X_teams = onehot.fit_transform(X_teams).todense()\\n\",\n    \"\\n\",\n    \"clf = DecisionTreeClassifier(random_state=14)\\n\",\n    \"scores = cross_val_score(clf, X_teams, y_true, scoring='accuracy')\\n\",\n    \"print(\\\"Accuracy: {0:.1f}%\\\".format(np.mean(scores) * 100))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using full team labels is ranked higher\\n\",\n      \"Accuracy: 60.6%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.ensemble import RandomForestClassifier\\n\",\n    \"clf = RandomForestClassifier(random_state=14)\\n\",\n    \"scores = cross_val_score(clf, X_teams, y_true, scoring='accuracy')\\n\",\n    \"print(\\\"Using full team labels is ranked higher\\\")\\n\",\n    \"print(\\\"Accuracy: {0:.1f}%\\\".format(np.mean(scores) * 100))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(1230, 62)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"X_all = np.hstack([X_home_higher, X_teams])\\n\",\n    \"print(X_all.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using whether the home team is ranked higher\\n\",\n      \"Accuracy: 61.1%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"clf = RandomForestClassifier(random_state=14)\\n\",\n    \"scores = cross_val_score(clf, X_all, y_true, scoring='accuracy')\\n\",\n    \"print(\\\"Using whether the home team is ranked higher\\\")\\n\",\n    \"print(\\\"Accuracy: {0:.1f}%\\\".format(np.mean(scores) * 100))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy: 64.2%\\n\",\n      \"RandomForestClassifier(bootstrap=True, compute_importances=None,\\n\",\n      \"            criterion='entropy', max_depth=None, max_features=2,\\n\",\n      \"            max_leaf_nodes=None, min_density=None, min_samples_leaf=6,\\n\",\n      \"            min_samples_split=2, n_estimators=100, n_jobs=1,\\n\",\n      \"            oob_score=False, random_state=14, verbose=0)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#n_estimators=10, criterion='gini', max_depth=None, \\n\",\n    \"#min_samples_split=2, min_samples_leaf=1,\\n\",\n    \"#max_features='auto',\\n\",\n    \"#max_leaf_nodes=None, bootstrap=True,\\n\",\n    \"#oob_score=False, n_jobs=1,\\n\",\n    \"#random_state=None, verbose=0, min_density=None, compute_importances=None\\n\",\n    \"parameter_space = {\\n\",\n    \"                   \\\"max_features\\\": [2, 10, 'auto'],\\n\",\n    \"                   \\\"n_estimators\\\": [100,],\\n\",\n    \"                   \\\"criterion\\\": [\\\"gini\\\", \\\"entropy\\\"],\\n\",\n    \"                   \\\"min_samples_leaf\\\": [2, 4, 6],\\n\",\n    \"                   }\\n\",\n    \"clf = RandomForestClassifier(random_state=14)\\n\",\n    \"grid = GridSearchCV(clf, parameter_space)\\n\",\n    \"grid.fit(X_all, y_true)\\n\",\n    \"print(\\\"Accuracy: {0:.1f}%\\\".format(grid.best_score_ * 100))\\n\",\n    \"print(grid.best_estimator_)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "Chapter 4/ch4 Affinity Analysis.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:25d074ec46320302c17c06c6abf5418625ce7b1ae12f86ad84fdd3d25968e23e\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# data from http://grouplens.org/datasets/movielens/\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import os\\n\",\n      \"data_folder = os.path.join(os.path.expanduser(\\\"~\\\"), \\\"Data\\\", \\\"ml-100k\\\")\\n\",\n      \"ratings_filename = os.path.join(data_folder, \\\"u.data\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import pandas as pd\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 3\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"all_ratings = pd.read_csv(ratings_filename, delimiter=\\\"\\\\t\\\", header=None, names = [\\\"UserID\\\", \\\"MovieID\\\", \\\"Rating\\\", \\\"Datetime\\\"])\\n\",\n      \"all_ratings[\\\"Datetime\\\"] = pd.to_datetime(all_ratings['Datetime'],unit='s')\\n\",\n      \"all_ratings[:5]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>UserID</th>\\n\",\n        \"      <th>MovieID</th>\\n\",\n        \"      <th>Rating</th>\\n\",\n        \"      <th>Datetime</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0</th>\\n\",\n        \"      <td> 196</td>\\n\",\n        \"      <td> 242</td>\\n\",\n        \"      <td> 3</td>\\n\",\n        \"      <td>1997-12-04 15:55:49</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1</th>\\n\",\n        \"      <td> 186</td>\\n\",\n        \"      <td> 302</td>\\n\",\n        \"      <td> 3</td>\\n\",\n        \"      <td>1998-04-04 19:22:22</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>2</th>\\n\",\n        \"      <td>  22</td>\\n\",\n        \"      <td> 377</td>\\n\",\n        \"      <td> 1</td>\\n\",\n        \"      <td>1997-11-07 07:18:36</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>3</th>\\n\",\n        \"      <td> 244</td>\\n\",\n        \"      <td>  51</td>\\n\",\n        \"      <td> 2</td>\\n\",\n        \"      <td>1997-11-27 05:02:03</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>4</th>\\n\",\n        \"      <td> 166</td>\\n\",\n        \"      <td> 346</td>\\n\",\n        \"      <td> 1</td>\\n\",\n        \"      <td>1998-02-02 05:33:16</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 4,\n       \"text\": [\n        \"   UserID  MovieID  Rating            Datetime\\n\",\n        \"0     196      242       3 1997-12-04 15:55:49\\n\",\n        \"1     186      302       3 1998-04-04 19:22:22\\n\",\n        \"2      22      377       1 1997-11-07 07:18:36\\n\",\n        \"3     244       51       2 1997-11-27 05:02:03\\n\",\n        \"4     166      346       1 1998-02-02 05:33:16\"\n       ]\n      }\n     ],\n     \"prompt_number\": 4\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# As you can see, there are no review for most movies, such as #213\\n\",\n      \"all_ratings[all_ratings[\\\"UserID\\\"] == 675].sort(\\\"MovieID\\\")  \"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>UserID</th>\\n\",\n        \"      <th>MovieID</th>\\n\",\n        \"      <th>Rating</th>\\n\",\n        \"      <th>Datetime</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>81098</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>   86</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"      <td>1998-03-10 00:26:14</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>90696</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  223</td>\\n\",\n        \"      <td> 1</td>\\n\",\n        \"      <td>1998-03-10 00:35:51</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>92650</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  235</td>\\n\",\n        \"      <td> 1</td>\\n\",\n        \"      <td>1998-03-10 00:35:51</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>95459</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  242</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"      <td>1998-03-10 00:08:42</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>82845</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  244</td>\\n\",\n        \"      <td> 3</td>\\n\",\n        \"      <td>1998-03-10 00:29:35</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>53293</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  258</td>\\n\",\n        \"      <td> 3</td>\\n\",\n        \"      <td>1998-03-10 00:11:19</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>97286</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  269</td>\\n\",\n        \"      <td> 5</td>\\n\",\n        \"      <td>1998-03-10 00:08:07</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>93720</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  272</td>\\n\",\n        \"      <td> 3</td>\\n\",\n        \"      <td>1998-03-10 00:07:11</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>73389</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  286</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"      <td>1998-03-10 00:07:11</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>77524</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  303</td>\\n\",\n        \"      <td> 5</td>\\n\",\n        \"      <td>1998-03-10 00:08:42</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>47367</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  305</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"      <td>1998-03-10 00:09:08</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>44300</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  306</td>\\n\",\n        \"      <td> 5</td>\\n\",\n        \"      <td>1998-03-10 00:08:07</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>53730</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  311</td>\\n\",\n        \"      <td> 3</td>\\n\",\n        \"      <td>1998-03-10 00:10:47</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>54284</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  312</td>\\n\",\n        \"      <td> 2</td>\\n\",\n        \"      <td>1998-03-10 00:10:24</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>63291</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  318</td>\\n\",\n        \"      <td> 5</td>\\n\",\n        \"      <td>1998-03-10 00:21:13</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>87082</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  321</td>\\n\",\n        \"      <td> 2</td>\\n\",\n        \"      <td>1998-03-10 00:11:48</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>56108</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  344</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"      <td>1998-03-10 00:12:34</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>53046</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  347</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"      <td>1998-03-10 00:07:11</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>94617</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  427</td>\\n\",\n        \"      <td> 5</td>\\n\",\n        \"      <td>1998-03-10 00:28:11</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>69915</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  463</td>\\n\",\n        \"      <td> 5</td>\\n\",\n        \"      <td>1998-03-10 00:16:43</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>46744</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  509</td>\\n\",\n        \"      <td> 5</td>\\n\",\n        \"      <td>1998-03-10 00:24:25</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>46598</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  531</td>\\n\",\n        \"      <td> 5</td>\\n\",\n        \"      <td>1998-03-10 00:18:28</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>52962</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  650</td>\\n\",\n        \"      <td> 5</td>\\n\",\n        \"      <td>1998-03-10 00:32:51</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>94029</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  750</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"      <td>1998-03-10 00:08:07</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>53223</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  874</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"      <td>1998-03-10 00:11:19</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>62277</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  891</td>\\n\",\n        \"      <td> 2</td>\\n\",\n        \"      <td>1998-03-10 00:12:59</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>77274</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  896</td>\\n\",\n        \"      <td> 5</td>\\n\",\n        \"      <td>1998-03-10 00:09:35</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>66194</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  900</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"      <td>1998-03-10 00:10:24</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>54994</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td>  937</td>\\n\",\n        \"      <td> 1</td>\\n\",\n        \"      <td>1998-03-10 00:35:51</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>61742</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td> 1007</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"      <td>1998-03-10 00:25:22</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>49225</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td> 1101</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"      <td>1998-03-10 00:33:49</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>50692</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td> 1255</td>\\n\",\n        \"      <td> 1</td>\\n\",\n        \"      <td>1998-03-10 00:35:51</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>74202</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td> 1628</td>\\n\",\n        \"      <td> 5</td>\\n\",\n        \"      <td>1998-03-10 00:30:37</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>47866</th>\\n\",\n        \"      <td> 675</td>\\n\",\n        \"      <td> 1653</td>\\n\",\n        \"      <td> 5</td>\\n\",\n        \"      <td>1998-03-10 00:31:53</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 5,\n       \"text\": [\n        \"       UserID  MovieID  Rating            Datetime\\n\",\n        \"81098     675       86       4 1998-03-10 00:26:14\\n\",\n        \"90696     675      223       1 1998-03-10 00:35:51\\n\",\n        \"92650     675      235       1 1998-03-10 00:35:51\\n\",\n        \"95459     675      242       4 1998-03-10 00:08:42\\n\",\n        \"82845     675      244       3 1998-03-10 00:29:35\\n\",\n        \"53293     675      258       3 1998-03-10 00:11:19\\n\",\n        \"97286     675      269       5 1998-03-10 00:08:07\\n\",\n        \"93720     675      272       3 1998-03-10 00:07:11\\n\",\n        \"73389     675      286       4 1998-03-10 00:07:11\\n\",\n        \"77524     675      303       5 1998-03-10 00:08:42\\n\",\n        \"47367     675      305       4 1998-03-10 00:09:08\\n\",\n        \"44300     675      306       5 1998-03-10 00:08:07\\n\",\n        \"53730     675      311       3 1998-03-10 00:10:47\\n\",\n        \"54284     675      312       2 1998-03-10 00:10:24\\n\",\n        \"63291     675      318       5 1998-03-10 00:21:13\\n\",\n        \"87082     675      321       2 1998-03-10 00:11:48\\n\",\n        \"56108     675      344       4 1998-03-10 00:12:34\\n\",\n        \"53046     675      347       4 1998-03-10 00:07:11\\n\",\n        \"94617     675      427       5 1998-03-10 00:28:11\\n\",\n        \"69915     675      463       5 1998-03-10 00:16:43\\n\",\n        \"46744     675      509       5 1998-03-10 00:24:25\\n\",\n        \"46598     675      531       5 1998-03-10 00:18:28\\n\",\n        \"52962     675      650       5 1998-03-10 00:32:51\\n\",\n        \"94029     675      750       4 1998-03-10 00:08:07\\n\",\n        \"53223     675      874       4 1998-03-10 00:11:19\\n\",\n        \"62277     675      891       2 1998-03-10 00:12:59\\n\",\n        \"77274     675      896       5 1998-03-10 00:09:35\\n\",\n        \"66194     675      900       4 1998-03-10 00:10:24\\n\",\n        \"54994     675      937       1 1998-03-10 00:35:51\\n\",\n        \"61742     675     1007       4 1998-03-10 00:25:22\\n\",\n        \"49225     675     1101       4 1998-03-10 00:33:49\\n\",\n        \"50692     675     1255       1 1998-03-10 00:35:51\\n\",\n        \"74202     675     1628       5 1998-03-10 00:30:37\\n\",\n        \"47866     675     1653       5 1998-03-10 00:31:53\"\n       ]\n      }\n     ],\n     \"prompt_number\": 5\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Not all reviews are favourable! Our goal is \\\"other recommended books\\\", so we only want favourable reviews\\n\",\n      \"all_ratings[\\\"Favorable\\\"] = all_ratings[\\\"Rating\\\"] > 3\\n\",\n      \"all_ratings[10:15]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>UserID</th>\\n\",\n        \"      <th>MovieID</th>\\n\",\n        \"      <th>Rating</th>\\n\",\n        \"      <th>Datetime</th>\\n\",\n        \"      <th>Favorable</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>10</th>\\n\",\n        \"      <td>  62</td>\\n\",\n        \"      <td>  257</td>\\n\",\n        \"      <td> 2</td>\\n\",\n        \"      <td>1997-11-12 22:07:14</td>\\n\",\n        \"      <td> False</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>11</th>\\n\",\n        \"      <td> 286</td>\\n\",\n        \"      <td> 1014</td>\\n\",\n        \"      <td> 5</td>\\n\",\n        \"      <td>1997-11-17 15:38:45</td>\\n\",\n        \"      <td>  True</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>12</th>\\n\",\n        \"      <td> 200</td>\\n\",\n        \"      <td>  222</td>\\n\",\n        \"      <td> 5</td>\\n\",\n        \"      <td>1997-10-05 09:05:40</td>\\n\",\n        \"      <td>  True</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>13</th>\\n\",\n        \"      <td> 210</td>\\n\",\n        \"      <td>   40</td>\\n\",\n        \"      <td> 3</td>\\n\",\n        \"      <td>1998-03-27 21:59:54</td>\\n\",\n        \"      <td> False</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>14</th>\\n\",\n        \"      <td> 224</td>\\n\",\n        \"      <td>   29</td>\\n\",\n        \"      <td> 3</td>\\n\",\n        \"      <td>1998-02-21 23:40:57</td>\\n\",\n        \"      <td> False</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 6,\n       \"text\": [\n        \"    UserID  MovieID  Rating            Datetime Favorable\\n\",\n        \"10      62      257       2 1997-11-12 22:07:14     False\\n\",\n        \"11     286     1014       5 1997-11-17 15:38:45      True\\n\",\n        \"12     200      222       5 1997-10-05 09:05:40      True\\n\",\n        \"13     210       40       3 1998-03-27 21:59:54     False\\n\",\n        \"14     224       29       3 1998-02-21 23:40:57     False\"\n       ]\n      }\n     ],\n     \"prompt_number\": 6\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"all_ratings[all_ratings[\\\"UserID\\\"] == 1][:5]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>UserID</th>\\n\",\n        \"      <th>MovieID</th>\\n\",\n        \"      <th>Rating</th>\\n\",\n        \"      <th>Datetime</th>\\n\",\n        \"      <th>Favorable</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>202</th>\\n\",\n        \"      <td> 1</td>\\n\",\n        \"      <td>  61</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"      <td>1997-11-03 07:33:40</td>\\n\",\n        \"      <td>  True</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>305</th>\\n\",\n        \"      <td> 1</td>\\n\",\n        \"      <td> 189</td>\\n\",\n        \"      <td> 3</td>\\n\",\n        \"      <td>1998-03-01 06:15:28</td>\\n\",\n        \"      <td> False</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>333</th>\\n\",\n        \"      <td> 1</td>\\n\",\n        \"      <td>  33</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"      <td>1997-11-03 07:38:19</td>\\n\",\n        \"      <td>  True</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>334</th>\\n\",\n        \"      <td> 1</td>\\n\",\n        \"      <td> 160</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"      <td>1997-09-24 03:42:27</td>\\n\",\n        \"      <td>  True</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>478</th>\\n\",\n        \"      <td> 1</td>\\n\",\n        \"      <td>  20</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"      <td>1998-02-14 04:51:23</td>\\n\",\n        \"      <td>  True</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 7,\n       \"text\": [\n        \"     UserID  MovieID  Rating            Datetime Favorable\\n\",\n        \"202       1       61       4 1997-11-03 07:33:40      True\\n\",\n        \"305       1      189       3 1998-03-01 06:15:28     False\\n\",\n        \"333       1       33       4 1997-11-03 07:38:19      True\\n\",\n        \"334       1      160       4 1997-09-24 03:42:27      True\\n\",\n        \"478       1       20       4 1998-02-14 04:51:23      True\"\n       ]\n      }\n     ],\n     \"prompt_number\": 7\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Sample the dataset. You can try increasing the size of the sample, but the run time will be considerably longer\\n\",\n      \"ratings = all_ratings[all_ratings['UserID'].isin(range(200))]  # & ratings[\\\"UserID\\\"].isin(range(100))]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 8\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# We start by creating a dataset of each user's favourable reviews\\n\",\n      \"favorable_ratings = ratings[ratings[\\\"Favorable\\\"]]\\n\",\n      \"favorable_ratings[:5]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>UserID</th>\\n\",\n        \"      <th>MovieID</th>\\n\",\n        \"      <th>Rating</th>\\n\",\n        \"      <th>Datetime</th>\\n\",\n        \"      <th>Favorable</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>16</th>\\n\",\n        \"      <td> 122</td>\\n\",\n        \"      <td> 387</td>\\n\",\n        \"      <td> 5</td>\\n\",\n        \"      <td>1997-11-11 17:47:39</td>\\n\",\n        \"      <td> True</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>20</th>\\n\",\n        \"      <td> 119</td>\\n\",\n        \"      <td> 392</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"      <td>1998-01-30 16:13:34</td>\\n\",\n        \"      <td> True</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>21</th>\\n\",\n        \"      <td> 167</td>\\n\",\n        \"      <td> 486</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"      <td>1998-04-16 14:54:12</td>\\n\",\n        \"      <td> True</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>26</th>\\n\",\n        \"      <td>  38</td>\\n\",\n        \"      <td>  95</td>\\n\",\n        \"      <td> 5</td>\\n\",\n        \"      <td>1998-04-13 01:14:54</td>\\n\",\n        \"      <td> True</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>28</th>\\n\",\n        \"      <td>  63</td>\\n\",\n        \"      <td> 277</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"      <td>1997-10-01 23:10:01</td>\\n\",\n        \"      <td> True</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 9,\n       \"text\": [\n        \"    UserID  MovieID  Rating            Datetime Favorable\\n\",\n        \"16     122      387       5 1997-11-11 17:47:39      True\\n\",\n        \"20     119      392       4 1998-01-30 16:13:34      True\\n\",\n        \"21     167      486       4 1998-04-16 14:54:12      True\\n\",\n        \"26      38       95       5 1998-04-13 01:14:54      True\\n\",\n        \"28      63      277       4 1997-10-01 23:10:01      True\"\n       ]\n      }\n     ],\n     \"prompt_number\": 9\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# We are only interested in the reviewers who have more than one review\\n\",\n      \"favorable_reviews_by_users = dict((k, frozenset(v.values)) for k, v in favorable_ratings.groupby(\\\"UserID\\\")[\\\"MovieID\\\"])\\n\",\n      \"len(favorable_reviews_by_users)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 10,\n       \"text\": [\n        \"199\"\n       ]\n      }\n     ],\n     \"prompt_number\": 10\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Find out how many movies have favourable ratings\\n\",\n      \"num_favorable_by_movie = ratings[[\\\"MovieID\\\", \\\"Favorable\\\"]].groupby(\\\"MovieID\\\").sum()\\n\",\n      \"num_favorable_by_movie.sort(\\\"Favorable\\\", ascending=False)[:5]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>Favorable</th>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>MovieID</th>\\n\",\n        \"      <th></th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>50 </th>\\n\",\n        \"      <td> 100</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>100</th>\\n\",\n        \"      <td>  89</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>258</th>\\n\",\n        \"      <td>  83</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>181</th>\\n\",\n        \"      <td>  79</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>174</th>\\n\",\n        \"      <td>  74</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 11,\n       \"text\": [\n        \"         Favorable\\n\",\n        \"MovieID           \\n\",\n        \"50             100\\n\",\n        \"100             89\\n\",\n        \"258             83\\n\",\n        \"181             79\\n\",\n        \"174             74\"\n       ]\n      }\n     ],\n     \"prompt_number\": 11\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from collections import defaultdict\\n\",\n      \"\\n\",\n      \"def find_frequent_itemsets(favorable_reviews_by_users, k_1_itemsets, min_support):\\n\",\n      \"    counts = defaultdict(int)\\n\",\n      \"    for user, reviews in favorable_reviews_by_users.items():\\n\",\n      \"        for itemset in k_1_itemsets:\\n\",\n      \"            if itemset.issubset(reviews):\\n\",\n      \"                for other_reviewed_movie in reviews - itemset:\\n\",\n      \"                    current_superset = itemset | frozenset((other_reviewed_movie,))\\n\",\n      \"                    counts[current_superset] += 1\\n\",\n      \"    return dict([(itemset, frequency) for itemset, frequency in counts.items() if frequency >= min_support])\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 12\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import sys\\n\",\n      \"frequent_itemsets = {}  # itemsets are sorted by length\\n\",\n      \"min_support = 50\\n\",\n      \"\\n\",\n      \"# k=1 candidates are the isbns with more than min_support favourable reviews\\n\",\n      \"frequent_itemsets[1] = dict((frozenset((movie_id,)), row[\\\"Favorable\\\"])\\n\",\n      \"                                for movie_id, row in num_favorable_by_movie.iterrows()\\n\",\n      \"                                if row[\\\"Favorable\\\"] > min_support)\\n\",\n      \"\\n\",\n      \"print(\\\"There are {} movies with more than {} favorable reviews\\\".format(len(frequent_itemsets[1]), min_support))\\n\",\n      \"sys.stdout.flush()\\n\",\n      \"for k in range(2, 20):\\n\",\n      \"    # Generate candidates of length k, using the frequent itemsets of length k-1\\n\",\n      \"    # Only store the frequent itemsets\\n\",\n      \"    cur_frequent_itemsets = find_frequent_itemsets(favorable_reviews_by_users, frequent_itemsets[k-1],\\n\",\n      \"                                                   min_support)\\n\",\n      \"    if len(cur_frequent_itemsets) == 0:\\n\",\n      \"        print(\\\"Did not find any frequent itemsets of length {}\\\".format(k))\\n\",\n      \"        sys.stdout.flush()\\n\",\n      \"        break\\n\",\n      \"    else:\\n\",\n      \"        print(\\\"I found {} frequent itemsets of length {}\\\".format(len(cur_frequent_itemsets), k))\\n\",\n      \"        #print(cur_frequent_itemsets)\\n\",\n      \"        sys.stdout.flush()\\n\",\n      \"        frequent_itemsets[k] = cur_frequent_itemsets\\n\",\n      \"# We aren't interested in the itemsets of length 1, so remove those\\n\",\n      \"del frequent_itemsets[1]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"There are 16 movies with more than 50 favorable reviews\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"I found 93 frequent itemsets of length 2\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"I found 295 frequent itemsets of length 3\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"I found 593 frequent itemsets of length 4\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"I found 785 frequent itemsets of length 5\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"I found 677 frequent itemsets of length 6\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"I found 373 frequent itemsets of length 7\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"I found 126 frequent itemsets of length 8\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"I found 24 frequent itemsets of length 9\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"I found 2 frequent itemsets of length 10\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Did not find any frequent itemsets of length 11\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 13\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"print(\\\"Found a total of {0} frequent itemsets\\\".format(sum(len(itemsets) for itemsets in frequent_itemsets.values())))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Found a total of 2968 frequent itemsets\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 14\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Now we create the association rules. First, they are candidates until the confidence has been tested\\n\",\n      \"candidate_rules = []\\n\",\n      \"for itemset_length, itemset_counts in frequent_itemsets.items():\\n\",\n      \"    for itemset in itemset_counts.keys():\\n\",\n      \"        for conclusion in itemset:\\n\",\n      \"            premise = itemset - set((conclusion,))\\n\",\n      \"            candidate_rules.append((premise, conclusion))\\n\",\n      \"print(\\\"There are {} candidate rules\\\".format(len(candidate_rules)))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"There are 15285 candidate rules\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 15\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"print(candidate_rules[:5])\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"[(frozenset({79}), 258), (frozenset({258}), 79), (frozenset({50}), 64), (frozenset({64}), 50), (frozenset({127}), 181)]\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 16\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Now, we compute the confidence of each of these rules. This is very similar to what we did in chapter 1\\n\",\n      \"correct_counts = defaultdict(int)\\n\",\n      \"incorrect_counts = defaultdict(int)\\n\",\n      \"for user, reviews in favorable_reviews_by_users.items():\\n\",\n      \"    for candidate_rule in candidate_rules:\\n\",\n      \"        premise, conclusion = candidate_rule\\n\",\n      \"        if premise.issubset(reviews):\\n\",\n      \"            if conclusion in reviews:\\n\",\n      \"                correct_counts[candidate_rule] += 1\\n\",\n      \"            else:\\n\",\n      \"                incorrect_counts[candidate_rule] += 1\\n\",\n      \"rule_confidence = {candidate_rule: correct_counts[candidate_rule] / float(correct_counts[candidate_rule] + incorrect_counts[candidate_rule])\\n\",\n      \"              for candidate_rule in candidate_rules}\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 17\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Choose only rules above a minimum confidence level\\n\",\n      \"min_confidence = 0.9\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 18\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Filter out the rules with poor confidence\\n\",\n      \"rule_confidence = {rule: confidence for rule, confidence in rule_confidence.items() if confidence > min_confidence}\\n\",\n      \"print(len(rule_confidence))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"5152\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 19\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from operator import itemgetter\\n\",\n      \"sorted_confidence = sorted(rule_confidence.items(), key=itemgetter(1), reverse=True)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 20\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"for index in range(5):\\n\",\n      \"    print(\\\"Rule #{0}\\\".format(index + 1))\\n\",\n      \"    (premise, conclusion) = sorted_confidence[index][0]\\n\",\n      \"    print(\\\"Rule: If a person recommends {0} they will also recommend {1}\\\".format(premise, conclusion))\\n\",\n      \"    print(\\\" - Confidence: {0:.3f}\\\".format(rule_confidence[(premise, conclusion)]))\\n\",\n      \"    print(\\\"\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Rule #1\\n\",\n        \"Rule: If a person recommends frozenset({64, 56, 98, 50, 7}) they will also recommend 174\\n\",\n        \" - Confidence: 1.000\\n\",\n        \"\\n\",\n        \"Rule #2\\n\",\n        \"Rule: If a person recommends frozenset({98, 100, 172, 79, 50, 56}) they will also recommend 7\\n\",\n        \" - Confidence: 1.000\\n\",\n        \"\\n\",\n        \"Rule #3\\n\",\n        \"Rule: If a person recommends frozenset({98, 172, 181, 174, 7}) they will also recommend 50\\n\",\n        \" - Confidence: 1.000\\n\",\n        \"\\n\",\n        \"Rule #4\\n\",\n        \"Rule: If a person recommends frozenset({64, 98, 100, 7, 172, 50}) they will also recommend 174\\n\",\n        \" - Confidence: 1.000\\n\",\n        \"\\n\",\n        \"Rule #5\\n\",\n        \"Rule: If a person recommends frozenset({64, 1, 7, 172, 79, 50}) they will also recommend 181\\n\",\n        \" - Confidence: 1.000\\n\",\n        \"\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 21\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Even better, we can get the movie titles themselves from the dataset\\n\",\n      \"movie_name_filename = os.path.join(data_folder, \\\"u.item\\\")\\n\",\n      \"movie_name_data = pd.read_csv(movie_name_filename, delimiter=\\\"|\\\", header=None, encoding = \\\"mac-roman\\\")\\n\",\n      \"movie_name_data.columns = [\\\"MovieID\\\", \\\"Title\\\", \\\"Release Date\\\", \\\"Video Release\\\", \\\"IMDB\\\", \\\"<UNK>\\\", \\\"Action\\\", \\\"Adventure\\\",\\n\",\n      \"                           \\\"Animation\\\", \\\"Children's\\\", \\\"Comedy\\\", \\\"Crime\\\", \\\"Documentary\\\", \\\"Drama\\\", \\\"Fantasy\\\", \\\"Film-Noir\\\",\\n\",\n      \"                           \\\"Horror\\\", \\\"Musical\\\", \\\"Mystery\\\", \\\"Romance\\\", \\\"Sci-Fi\\\", \\\"Thriller\\\", \\\"War\\\", \\\"Western\\\"]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 22\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def get_movie_name(movie_id):\\n\",\n      \"    title_object = movie_name_data[movie_name_data[\\\"MovieID\\\"] == movie_id][\\\"Title\\\"]\\n\",\n      \"    title = title_object.values[0]\\n\",\n      \"    return title\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 23\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"get_movie_name(4)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 24,\n       \"text\": [\n        \"'Get Shorty (1995)'\"\n       ]\n      }\n     ],\n     \"prompt_number\": 24\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"for index in range(5):\\n\",\n      \"    print(\\\"Rule #{0}\\\".format(index + 1))\\n\",\n      \"    (premise, conclusion) = sorted_confidence[index][0]\\n\",\n      \"    premise_names = \\\", \\\".join(get_movie_name(idx) for idx in premise)\\n\",\n      \"    conclusion_name = get_movie_name(conclusion)\\n\",\n      \"    print(\\\"Rule: If a person recommends {0} they will also recommend {1}\\\".format(premise_names, conclusion_name))\\n\",\n      \"    print(\\\" - Confidence: {0:.3f}\\\".format(rule_confidence[(premise, conclusion)]))\\n\",\n      \"    print(\\\"\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Rule #1\\n\",\n        \"Rule: If a person recommends Shawshank Redemption, The (1994), Pulp Fiction (1994), Silence of the Lambs, The (1991), Star Wars (1977), Twelve Monkeys (1995) they will also recommend Raiders of the Lost Ark (1981)\\n\",\n        \" - Confidence: 1.000\\n\",\n        \"\\n\",\n        \"Rule #2\\n\",\n        \"Rule: If a person recommends Silence of the Lambs, The (1991), Fargo (1996), Empire Strikes Back, The (1980), Fugitive, The (1993), Star Wars (1977), Pulp Fiction (1994) they will also recommend Twelve Monkeys (1995)\\n\",\n        \" - Confidence: 1.000\\n\",\n        \"\\n\",\n        \"Rule #3\\n\",\n        \"Rule: If a person recommends Silence of the Lambs, The (1991), Empire Strikes Back, The (1980), Return of the Jedi (1983), Raiders of the Lost Ark (1981), Twelve Monkeys (1995) they will also recommend Star Wars (1977)\\n\",\n        \" - Confidence: 1.000\\n\",\n        \"\\n\",\n        \"Rule #4\\n\",\n        \"Rule: If a person recommends Shawshank Redemption, The (1994), Silence of the Lambs, The (1991), Fargo (1996), Twelve Monkeys (1995), Empire Strikes Back, The (1980), Star Wars (1977) they will also recommend Raiders of the Lost Ark (1981)\\n\",\n        \" - Confidence: 1.000\\n\",\n        \"\\n\",\n        \"Rule #5\\n\",\n        \"Rule: If a person recommends Shawshank Redemption, The (1994), Toy Story (1995), Twelve Monkeys (1995), Empire Strikes Back, The (1980), Fugitive, The (1993), Star Wars (1977) they will also recommend Return of the Jedi (1983)\\n\",\n        \" - Confidence: 1.000\\n\",\n        \"\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 25\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Evaluation using test data\\n\",\n      \"test_dataset = all_ratings[~all_ratings['UserID'].isin(range(200))]\\n\",\n      \"test_favorable = test_dataset[test_dataset[\\\"Favorable\\\"]]\\n\",\n      \"#test_not_favourable = test_dataset[~test_dataset[\\\"Favourable\\\"]]\\n\",\n      \"test_favorable_by_users = dict((k, frozenset(v.values)) for k, v in test_favorable.groupby(\\\"UserID\\\")[\\\"MovieID\\\"])\\n\",\n      \"#test_not_favourable_by_users = dict((k, frozenset(v.values)) for k, v in test_not_favourable.groupby(\\\"UserID\\\")[\\\"MovieID\\\"])\\n\",\n      \"#test_users = test_dataset[\\\"UserID\\\"].unique()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 26\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"test_dataset[:5]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>UserID</th>\\n\",\n        \"      <th>MovieID</th>\\n\",\n        \"      <th>Rating</th>\\n\",\n        \"      <th>Datetime</th>\\n\",\n        \"      <th>Favorable</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>3 </th>\\n\",\n        \"      <td> 244</td>\\n\",\n        \"      <td>   51</td>\\n\",\n        \"      <td> 2</td>\\n\",\n        \"      <td>1997-11-27 05:02:03</td>\\n\",\n        \"      <td> False</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>5 </th>\\n\",\n        \"      <td> 298</td>\\n\",\n        \"      <td>  474</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"      <td>1998-01-07 14:20:06</td>\\n\",\n        \"      <td>  True</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>7 </th>\\n\",\n        \"      <td> 253</td>\\n\",\n        \"      <td>  465</td>\\n\",\n        \"      <td> 5</td>\\n\",\n        \"      <td>1998-04-03 18:34:27</td>\\n\",\n        \"      <td>  True</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8 </th>\\n\",\n        \"      <td> 305</td>\\n\",\n        \"      <td>  451</td>\\n\",\n        \"      <td> 3</td>\\n\",\n        \"      <td>1998-02-01 09:20:17</td>\\n\",\n        \"      <td> False</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>11</th>\\n\",\n        \"      <td> 286</td>\\n\",\n        \"      <td> 1014</td>\\n\",\n        \"      <td> 5</td>\\n\",\n        \"      <td>1997-11-17 15:38:45</td>\\n\",\n        \"      <td>  True</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 27,\n       \"text\": [\n        \"    UserID  MovieID  Rating            Datetime Favorable\\n\",\n        \"3      244       51       2 1997-11-27 05:02:03     False\\n\",\n        \"5      298      474       4 1998-01-07 14:20:06      True\\n\",\n        \"7      253      465       5 1998-04-03 18:34:27      True\\n\",\n        \"8      305      451       3 1998-02-01 09:20:17     False\\n\",\n        \"11     286     1014       5 1997-11-17 15:38:45      True\"\n       ]\n      }\n     ],\n     \"prompt_number\": 27\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"correct_counts = defaultdict(int)\\n\",\n      \"incorrect_counts = defaultdict(int)\\n\",\n      \"for user, reviews in test_favorable_by_users.items():\\n\",\n      \"    for candidate_rule in candidate_rules:\\n\",\n      \"        premise, conclusion = candidate_rule\\n\",\n      \"        if premise.issubset(reviews):\\n\",\n      \"            if conclusion in reviews:\\n\",\n      \"                correct_counts[candidate_rule] += 1\\n\",\n      \"            else:\\n\",\n      \"                incorrect_counts[candidate_rule] += 1\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 28\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"test_confidence = {candidate_rule: correct_counts[candidate_rule] / float(correct_counts[candidate_rule] + incorrect_counts[candidate_rule])\\n\",\n      \"                   for candidate_rule in rule_confidence}\\n\",\n      \"print(len(test_confidence))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"5152\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 29\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"sorted_test_confidence = sorted(test_confidence.items(), key=itemgetter(1), reverse=True)\\n\",\n      \"print(sorted_test_confidence[:5])\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"[((frozenset({64, 1, 7, 172, 79, 50}), 174), 1.0), ((frozenset({64, 258, 98, 7, 174, 181}), 172), 1.0), ((frozenset({64, 1, 98, 7, 79, 181, 56}), 174), 1.0), ((frozenset({64, 1, 98, 7, 172, 79, 181}), 174), 1.0), ((frozenset({64, 258, 98, 7, 174, 50, 181}), 172), 1.0)]\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 30\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"for index in range(10):\\n\",\n      \"    print(\\\"Rule #{0}\\\".format(index + 1))\\n\",\n      \"    (premise, conclusion) = sorted_confidence[index][0]\\n\",\n      \"    premise_names = \\\", \\\".join(get_movie_name(idx) for idx in premise)\\n\",\n      \"    conclusion_name = get_movie_name(conclusion)\\n\",\n      \"    print(\\\"Rule: If a person recommends {0} they will also recommend {1}\\\".format(premise_names, conclusion_name))\\n\",\n      \"    print(\\\" - Train Confidence: {0:.3f}\\\".format(rule_confidence.get((premise, conclusion), -1)))\\n\",\n      \"    print(\\\" - Test Confidence: {0:.3f}\\\".format(test_confidence.get((premise, conclusion), -1)))\\n\",\n      \"    print(\\\"\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Rule #1\\n\",\n        \"Rule: If a person recommends Shawshank Redemption, The (1994), Pulp Fiction (1994), Silence of the Lambs, The (1991), Star Wars (1977), Twelve Monkeys (1995) they will also recommend Raiders of the Lost Ark (1981)\\n\",\n        \" - Train Confidence: 1.000\\n\",\n        \" - Test Confidence: 0.909\\n\",\n        \"\\n\",\n        \"Rule #2\\n\",\n        \"Rule: If a person recommends Silence of the Lambs, The (1991), Fargo (1996), Empire Strikes Back, The (1980), Fugitive, The (1993), Star Wars (1977), Pulp Fiction (1994) they will also recommend Twelve Monkeys (1995)\\n\",\n        \" - Train Confidence: 1.000\\n\",\n        \" - Test Confidence: 0.609\\n\",\n        \"\\n\",\n        \"Rule #3\\n\",\n        \"Rule: If a person recommends Silence of the Lambs, The (1991), Empire Strikes Back, The (1980), Return of the Jedi (1983), Raiders of the Lost Ark (1981), Twelve Monkeys (1995) they will also recommend Star Wars (1977)\\n\",\n        \" - Train Confidence: 1.000\\n\",\n        \" - Test Confidence: 0.946\\n\",\n        \"\\n\",\n        \"Rule #4\\n\",\n        \"Rule: If a person recommends Shawshank Redemption, The (1994), Silence of the Lambs, The (1991), Fargo (1996), Twelve Monkeys (1995), Empire Strikes Back, The (1980), Star Wars (1977) they will also recommend Raiders of the Lost Ark (1981)\\n\",\n        \" - Train Confidence: 1.000\\n\",\n        \" - Test Confidence: 0.971\\n\",\n        \"\\n\",\n        \"Rule #5\\n\",\n        \"Rule: If a person recommends Shawshank Redemption, The (1994), Toy Story (1995), Twelve Monkeys (1995), Empire Strikes Back, The (1980), Fugitive, The (1993), Star Wars (1977) they will also recommend Return of the Jedi (1983)\\n\",\n        \" - Train Confidence: 1.000\\n\",\n        \" - Test Confidence: 0.900\\n\",\n        \"\\n\",\n        \"Rule #6\\n\",\n        \"Rule: If a person recommends Toy Story (1995), Silence of the Lambs, The (1991), Fargo (1996), Raiders of the Lost Ark (1981), Godfather, The (1972) they will also recommend Pulp Fiction (1994)\\n\",\n        \" - Train Confidence: 1.000\\n\",\n        \" - Test Confidence: 0.750\\n\",\n        \"\\n\",\n        \"Rule #7\\n\",\n        \"Rule: If a person recommends Godfather, The (1972), Silence of the Lambs, The (1991), Empire Strikes Back, The (1980), Raiders of the Lost Ark (1981), Twelve Monkeys (1995) they will also recommend Shawshank Redemption, The (1994)\\n\",\n        \" - Train Confidence: 1.000\\n\",\n        \" - Test Confidence: 0.854\\n\",\n        \"\\n\",\n        \"Rule #8\\n\",\n        \"Rule: If a person recommends Pulp Fiction (1994), Toy Story (1995), Shawshank Redemption, The (1994), Godfather, The (1972) they will also recommend Silence of the Lambs, The (1991)\\n\",\n        \" - Train Confidence: 1.000\\n\",\n        \" - Test Confidence: 0.870\\n\",\n        \"\\n\",\n        \"Rule #9\\n\",\n        \"Rule: If a person recommends Shawshank Redemption, The (1994), Fargo (1996), Return of the Jedi (1983), Raiders of the Lost Ark (1981), Fugitive, The (1993) they will also recommend Pulp Fiction (1994)\\n\",\n        \" - Train Confidence: 1.000\\n\",\n        \" - Test Confidence: 0.756\\n\",\n        \"\\n\",\n        \"Rule #10\\n\",\n        \"Rule: If a person recommends Silence of the Lambs, The (1991), Fargo (1996), Empire Strikes Back, The (1980), Raiders of the Lost Ark (1981), Fugitive, The (1993), Star Wars (1977), Return of the Jedi (1983) they will also recommend Pulp Fiction (1994)\\n\",\n        \" - Train Confidence: 1.000\\n\",\n        \" - Test Confidence: 0.756\\n\",\n        \"\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 31\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 31\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}"
  },
  {
    "path": "Chapter 5/adult_tests.py",
    "content": "import numpy as np\nfrom numpy.testing import assert_array_equal\n\ndef test_meandiscrete():\n    X_test = np.array([[ 0,  2],\n                        [ 3,  5],\n                        [ 6,  8],\n                        [ 9, 11],\n                        [12, 14],\n                        [15, 17],\n                        [18, 20],\n                        [21, 23],\n                        [24, 26],\n                        [27, 29]])\n    mean_discrete = MeanDiscrete()\n    mean_discrete.fit(X_test)\n    assert_array_equal(mean_discrete.mean, np.array([13.5, 15.5]))\n    X_transformed = mean_discrete.transform(X_test)\n    X_expected = np.array([[ 0,  0],\n                            [ 0, 0],\n                            [ 0, 0],\n                            [ 0, 0],\n                            [ 0, 0],\n                            [ 1, 1],\n                            [ 1, 1],\n                            [ 1, 1],\n                            [ 1, 1],\n                            [ 1, 1]])\n    assert_array_equal(X_transformed, X_expected)"
  },
  {
    "path": "Chapter 5/ch5_adult.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/usr/local/lib/python3.4/dist-packages/pandas/io/excel.py:626: UserWarning: Installed openpyxl is not supported at this time. Use >=1.6.1 and <2.0.0.\\n\",\n      \"  .format(openpyxl_compat.start_ver, openpyxl_compat.stop_ver))\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"import pandas as pd\\n\",\n    \"data_folder = os.path.join(os.path.expanduser(\\\"~\\\"), \\\"Data\\\", \\\"Adult\\\")\\n\",\n    \"adult_filename = os.path.join(data_folder, \\\"adult.data\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"adult = pd.read_csv(adult_filename, header=None, names=[\\\"Age\\\", \\\"Work-Class\\\", \\\"fnlwgt\\\", \\\"Education\\\",\\n\",\n    \"                                                        \\\"Education-Num\\\", \\\"Marital-Status\\\", \\\"Occupation\\\",\\n\",\n    \"                                                        \\\"Relationship\\\", \\\"Race\\\", \\\"Sex\\\", \\\"Capital-gain\\\",\\n\",\n    \"                                                        \\\"Capital-loss\\\", \\\"Hours-per-week\\\", \\\"Native-Country\\\",\\n\",\n    \"                                                        \\\"Earnings-Raw\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"adult.dropna(how='all', inplace=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Index(['Age', 'Work-Class', 'fnlwgt', 'Education', 'Education-Num', 'Marital-Status', 'Occupation', 'Relationship', 'Race', 'Sex', 'Capital-gain', 'Capital-loss', 'Hours-per-week', 'Native-Country', 'Earnings-Raw'], dtype='object')\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"adult.columns\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"count    32561.000000\\n\",\n       \"mean        40.437456\\n\",\n       \"std         12.347429\\n\",\n       \"min          1.000000\\n\",\n       \"25%         40.000000\\n\",\n       \"50%         40.000000\\n\",\n       \"75%         45.000000\\n\",\n       \"max         99.000000\\n\",\n       \"dtype: float64\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"adult[\\\"Hours-per-week\\\"].describe()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"10.0\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"adult[\\\"Education-Num\\\"].median()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([' State-gov', ' Self-emp-not-inc', ' Private', ' Federal-gov',\\n\",\n       \"       ' Local-gov', ' ?', ' Self-emp-inc', ' Without-pay', ' Never-worked'], dtype=object)\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"adult[\\\"Work-Class\\\"].unique()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"X = np.arange(30).reshape((10, 3))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 0,  1,  2],\\n\",\n       \"       [ 3,  4,  5],\\n\",\n       \"       [ 6,  7,  8],\\n\",\n       \"       [ 9, 10, 11],\\n\",\n       \"       [12, 13, 14],\\n\",\n       \"       [15, 16, 17],\\n\",\n       \"       [18, 19, 20],\\n\",\n       \"       [21, 22, 23],\\n\",\n       \"       [24, 25, 26],\\n\",\n       \"       [27, 28, 29]])\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X[:,1] = 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 0,  1,  2],\\n\",\n       \"       [ 3,  1,  5],\\n\",\n       \"       [ 6,  1,  8],\\n\",\n       \"       [ 9,  1, 11],\\n\",\n       \"       [12,  1, 14],\\n\",\n       \"       [15,  1, 17],\\n\",\n       \"       [18,  1, 20],\\n\",\n       \"       [21,  1, 23],\\n\",\n       \"       [24,  1, 26],\\n\",\n       \"       [27,  1, 29]])\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.feature_selection import VarianceThreshold\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"vt = VarianceThreshold()\\n\",\n    \"Xt = vt.fit_transform(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 0,  2],\\n\",\n       \"       [ 3,  5],\\n\",\n       \"       [ 6,  8],\\n\",\n       \"       [ 9, 11],\\n\",\n       \"       [12, 14],\\n\",\n       \"       [15, 17],\\n\",\n       \"       [18, 20],\\n\",\n       \"       [21, 23],\\n\",\n       \"       [24, 26],\\n\",\n       \"       [27, 29]])\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"Xt\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 74.25   0.    74.25]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(vt.variances_)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X = adult[[\\\"Age\\\", \\\"Education-Num\\\", \\\"Capital-gain\\\", \\\"Capital-loss\\\", \\\"Hours-per-week\\\"]].values\\n\",\n    \"y = (adult[\\\"Earnings-Raw\\\"] == ' >50K').values\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.feature_selection import SelectKBest\\n\",\n    \"from sklearn.feature_selection import chi2\\n\",\n    \"transformer = SelectKBest(score_func=chi2, k=3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[  8.60061182e+03   2.40142178e+03   8.21924671e+07   1.37214589e+06\\n\",\n      \"   6.47640900e+03]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Xt_chi2 = transformer.fit_transform(X, y)\\n\",\n    \"print(transformer.scores_)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from scipy.stats import pearsonr\\n\",\n    \"\\n\",\n    \"def multivariate_pearsonr(X, y):\\n\",\n    \"    scores, pvalues = [], []\\n\",\n    \"    for column in range(X.shape[1]):\\n\",\n    \"        cur_score, cur_p = pearsonr(X[:,column], y)\\n\",\n    \"        scores.append(abs(cur_score))\\n\",\n    \"        pvalues.append(cur_p)\\n\",\n    \"    return (np.array(scores), np.array(pvalues))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 0.2340371   0.33515395  0.22332882  0.15052631  0.22968907]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"transformer = SelectKBest(score_func=multivariate_pearsonr, k=3)\\n\",\n    \"Xt_pearson = transformer.fit_transform(X, y)\\n\",\n    \"print(transformer.scores_)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.tree import DecisionTreeClassifier\\n\",\n    \"from sklearn.cross_validation import cross_val_score\\n\",\n    \"clf = DecisionTreeClassifier(random_state=14)\\n\",\n    \"scores_chi2 = cross_val_score(clf, Xt_chi2, y, scoring='accuracy')\\n\",\n    \"scores_pearson = cross_val_score(clf, Xt_pearson, y, scoring='accuracy')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Chi2 performance: 0.829\\n\",\n      \"Pearson performance: 0.771\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Chi2 performance: {0:.3f}\\\".format(scores_chi2.mean()))\\n\",\n    \"print(\\\"Pearson performance: {0:.3f}\\\".format(scores_pearson.mean()))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.base import TransformerMixin\\n\",\n    \"from sklearn.utils import as_float_array\\n\",\n    \"\\n\",\n    \"class MeanDiscrete(TransformerMixin):\\n\",\n    \"    def fit(self, X, y=None):\\n\",\n    \"        X = as_float_array(X)\\n\",\n    \"        self.mean = np.mean(X, axis=0)\\n\",\n    \"        return self\\n\",\n    \"\\n\",\n    \"    def transform(self, X):\\n\",\n    \"        X = as_float_array(X)\\n\",\n    \"        assert X.shape[1] == self.mean.shape[0]\\n\",\n    \"        return X > self.mean\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"mean_discrete = MeanDiscrete()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X_mean = mean_discrete.fit_transform(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Overwriting adult_tests.py\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%%file adult_tests.py\\n\",\n    \"import numpy as np\\n\",\n    \"from numpy.testing import assert_array_equal\\n\",\n    \"\\n\",\n    \"def test_meandiscrete():\\n\",\n    \"    X_test = np.array([[ 0,  2],\\n\",\n    \"                        [ 3,  5],\\n\",\n    \"                        [ 6,  8],\\n\",\n    \"                        [ 9, 11],\\n\",\n    \"                        [12, 14],\\n\",\n    \"                        [15, 17],\\n\",\n    \"                        [18, 20],\\n\",\n    \"                        [21, 23],\\n\",\n    \"                        [24, 26],\\n\",\n    \"                        [27, 29]])\\n\",\n    \"    mean_discrete = MeanDiscrete()\\n\",\n    \"    mean_discrete.fit(X_test)\\n\",\n    \"    assert_array_equal(mean_discrete.mean, np.array([13.5, 15.5]))\\n\",\n    \"    X_transformed = mean_discrete.transform(X_test)\\n\",\n    \"    X_expected = np.array([[ 0,  0],\\n\",\n    \"                            [ 0, 0],\\n\",\n    \"                            [ 0, 0],\\n\",\n    \"                            [ 0, 0],\\n\",\n    \"                            [ 0, 0],\\n\",\n    \"                            [ 1, 1],\\n\",\n    \"                            [ 1, 1],\\n\",\n    \"                            [ 1, 1],\\n\",\n    \"                            [ 1, 1],\\n\",\n    \"                            [ 1, 1]])\\n\",\n    \"    assert_array_equal(X_transformed, X_expected)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"ename\": \"NameError\",\n     \"evalue\": \"name 'test_meandiscrete' is not defined\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[1;31mNameError\\u001b[0m                                 Traceback (most recent call last)\",\n      \"\\u001b[1;32m<ipython-input-29-cbf134d89aa0>\\u001b[0m in \\u001b[0;36m<module>\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[1;33m \\u001b[0mtest_meandiscrete\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n      \"\\u001b[1;31mNameError\\u001b[0m: name 'test_meandiscrete' is not defined\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test_meandiscrete()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.pipeline import Pipeline\\n\",\n    \"\\n\",\n    \"pipeline = Pipeline([('mean_discrete', MeanDiscrete()),\\n\",\n    \"                     ('classifier', DecisionTreeClassifier(random_state=14))])\\n\",\n    \"scores_mean_discrete = cross_val_score(pipeline, X, y, scoring='accuracy')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print(\\\"Mean Discrete performance: {0:.3f}\\\".format(scores_mean_discrete.mean()))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "Chapter 5/ch5_advertisements.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/usr/local/lib/python3.4/dist-packages/pandas/io/excel.py:626: UserWarning: Installed openpyxl is not supported at this time. Use >=1.6.1 and <2.0.0.\\n\",\n      \"  .format(openpyxl_compat.start_ver, openpyxl_compat.stop_ver))\\n\",\n      \"/usr/local/lib/python3.4/dist-packages/pandas/io/parsers.py:1130: DtypeWarning: Columns (3) have mixed types. Specify dtype option on import or set low_memory=False.\\n\",\n      \"  data = self._reader.read(nrows)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>1549</th>\\n\",\n       \"      <th>1550</th>\\n\",\n       \"      <th>1551</th>\\n\",\n       \"      <th>1552</th>\\n\",\n       \"      <th>1553</th>\\n\",\n       \"      <th>1554</th>\\n\",\n       \"      <th>1555</th>\\n\",\n       \"      <th>1556</th>\\n\",\n       \"      <th>1557</th>\\n\",\n       \"      <th>1558</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>  125</td>\\n\",\n       \"      <td>  125</td>\\n\",\n       \"      <td>    1.0</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>   57</td>\\n\",\n       \"      <td>  468</td>\\n\",\n       \"      <td> 8.2105</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>   33</td>\\n\",\n       \"      <td>  230</td>\\n\",\n       \"      <td> 6.9696</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>   60</td>\\n\",\n       \"      <td>  468</td>\\n\",\n       \"      <td>    7.8</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>   60</td>\\n\",\n       \"      <td>  468</td>\\n\",\n       \"      <td>    7.8</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 0</td>\\n\",\n       \"      <td> 1</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 1559 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   0     1       2    3     4     5     6     7     8     9     ...   1549  \\\\\\n\",\n       \"0   125   125     1.0    1     0     0     0     0     0     0  ...      0   \\n\",\n       \"1    57   468  8.2105    1     0     0     0     0     0     0  ...      0   \\n\",\n       \"2    33   230  6.9696    1     0     0     0     0     0     0  ...      0   \\n\",\n       \"3    60   468     7.8    1     0     0     0     0     0     0  ...      0   \\n\",\n       \"4    60   468     7.8    1     0     0     0     0     0     0  ...      0   \\n\",\n       \"\\n\",\n       \"   1550  1551  1552  1553  1554  1555  1556  1557  1558  \\n\",\n       \"0     0     0     0     0     0     0     0     0     1  \\n\",\n       \"1     0     0     0     0     0     0     0     0     1  \\n\",\n       \"2     0     0     0     0     0     0     0     0     1  \\n\",\n       \"3     0     0     0     0     0     0     0     0     1  \\n\",\n       \"4     0     0     0     0     0     0     0     0     1  \\n\",\n       \"\\n\",\n       \"[5 rows x 1559 columns]\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"data_folder = os.path.join(os.path.expanduser(\\\"~\\\"), \\\"Data\\\")\\n\",\n    \"data_filename = os.path.join(data_folder, \\\"Ads\\\", \\\"ad.data\\\")\\n\",\n    \"\\n\",\n    \"def convert_number(x):\\n\",\n    \"    try:\\n\",\n    \"        return float(x)\\n\",\n    \"    except ValueError:\\n\",\n    \"        return np.nan\\n\",\n    \"\\n\",\n    \"from collections import defaultdict\\n\",\n    \"converters = defaultdict(convert_number)  #{i: convert_number for i in range(1558)}\\n\",\n    \"converters[1558] = lambda x: 1 if x.strip() == \\\"ad.\\\" else 0\\n\",\n    \"    \\n\",\n    \"ads = pd.read_csv(data_filename, header=None, converters=converters)\\n\",\n    \"#ads = ads.applymap(lambda x: np.nan if isinstance(x, str) and not x == \\\"ad.\\\" else x)\\n\",\n    \"#ads[[0, 1, 2]] = ads[[0, 1, 2]].astype(float)\\n\",\n    \"ads[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(3279,)\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"#ads = ads.astype(float).dropna()\\n\",\n    \"X = ads.drop(1558, axis=1).values\\n\",\n    \"y = ads[1558]\\n\",\n    \"X.shape\\n\",\n    \"y.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The average score is 0.9334\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.tree import DecisionTreeClassifier\\n\",\n    \"from sklearn.cross_validation import cross_val_score\\n\",\n    \"\\n\",\n    \"clf = DecisionTreeClassifier(random_state=14)\\n\",\n    \"scores = cross_val_score(clf, X, y, scoring='accuracy')\\n\",\n    \"print(\\\"The average score is {:.4f}\\\".format(np.mean(scores)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.decomposition import PCA\\n\",\n    \"pca = PCA(n_components=5)\\n\",\n    \"Xd = pca.fit_transform(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(2359, 5)\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"Xd.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 0.854,  0.145,  0.001,  0.   ,  0.   ])\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.set_printoptions(precision=3, suppress=True)\\n\",\n    \"pca.explained_variance_ratio_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([-0.092, -0.995, -0.024, ..., -0.   , -0.   , -0.   ])\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"\\n\",\n    \"pca.components_[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The average score from the reduced dataset is 0.9356\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"clf = DecisionTreeClassifier(random_state=14)\\n\",\n    \"scores_reduced = cross_val_score(clf, Xd, y, scoring='accuracy')\\n\",\n    \"print(\\\"The average score from the reduced dataset is {:.4f}\\\".format(np.mean(scores_reduced)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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S94hz0PzPGd9iavLoX29vaRcnNzM83NzaUS1TCqH1t3wwD6+vro6+sr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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10f34d0b8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"from matplotlib import pyplot as plt\\n\",\n    \"classes = set(y)\\n\",\n    \"colors = ['red', 'green']\\n\",\n    \"for cur_class, color in zip(classes, colors):\\n\",\n    \"    mask = (y == cur_class).values\\n\",\n    \"    plt.scatter(Xd[mask,0], Xd[mask,1], marker='o', color=color, label=int(cur_class))\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "Chapter 6/ch6_classify_twitter.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:0572f58810bcde678e56e4c78ad5181092637198a0b58fe081990b0b1540ee52\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Labelling the class values for the twitter dataset.\\n\",\n      \"import os\\n\",\n      \"input_filename = os.path.join(os.path.expanduser(\\\"~\\\"), \\\"Data\\\", \\\"twitter\\\", \\\"python_tweets.json\\\")\\n\",\n      \"classes_filename = os.path.join(os.path.expanduser(\\\"~\\\"), \\\"Data\\\", \\\"twitter\\\", \\\"python_classes.json\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 125\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import json\\n\",\n      \"tweets = []\\n\",\n      \"with open(input_filename) as inf:\\n\",\n      \"    for line in inf:\\n\",\n      \"        if len(line.strip()) == 0:\\n\",\n      \"            continue\\n\",\n      \"        tweets.append(json.loads(line)['text'])\\n\",\n      \"print(\\\"Loaded {} tweets\\\".format(len(tweets)))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Loaded 200 tweets\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 126\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"with open(classes_filename) as inf:\\n\",\n      \"    labels = json.load(inf)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 127\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"n_samples = min(len(tweets), len(labels))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 128\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"sample_tweets = [t.lower() for t in tweets[:n_samples]]\\n\",\n      \"labels = labels[:n_samples]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 129\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import numpy as np\\n\",\n      \"y_true = np.array(labels)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 130\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"print(\\\"{:.1f}% have class 1\\\".format(np.mean(y_true == 1) * 100))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"52.0% have class 1\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 131\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from sklearn.base import TransformerMixin\\n\",\n      \"from nltk import word_tokenize\\n\",\n      \"\\n\",\n      \"class NLTKBOW(TransformerMixin):\\n\",\n      \"    def fit(self, X, y=None):\\n\",\n      \"        return self\\n\",\n      \"\\n\",\n      \"    def transform(self, X):\\n\",\n      \"        return [{word: True for word in word_tokenize(document)}\\n\",\n      \"                 for document in X]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 132\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from sklearn.feature_extraction import DictVectorizer\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 134\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from sklearn.naive_bayes import BernoulliNB\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 138\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from sklearn.cross_validation import cross_val_score\\n\",\n      \"from sklearn.pipeline import Pipeline\\n\",\n      \"from sklearn.feature_extraction.text import CountVectorizer\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 140\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"pipeline = Pipeline([('bag-of-words', NLTKBOW()),\\n\",\n      \"                     ('vectorizer', DictVectorizer()),\\n\",\n      \"                     ('naive-bayes', BernoulliNB())\\n\",\n      \"                     ])\\n\",\n      \"scores = cross_val_score(pipeline, sample_tweets, y_true, cv=10, scoring='f1')\\n\",\n      \"print(\\\"Score: {:.3f}\\\".format(np.mean(scores)))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Score: 0.799\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 147\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"scores\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 148,\n       \"text\": [\n        \"array([ 0.77777778,  0.86956522,  0.77777778,  0.8       ,  0.82352941,\\n\",\n        \"        0.82352941,  0.82352941,  0.58823529,  0.75      ,  0.95238095])\"\n       ]\n      }\n     ],\n     \"prompt_number\": 148\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 142\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}"
  },
  {
    "path": "Chapter 6/ch6_get_twitter.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:cb93d043019583326c0d63bbbdc20c5e315beec9df3c4f2372243ab77d035675\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Getting Twitter data using the API\\n\",\n      \"import twitter\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 17\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"IMPORTANT: BEFORE THIS GOES LIVE, SET THE FOLLOWING KEYS AND SECRETS TO \\\"INSERT KEY HERE\\\"\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"consumer_key = \\\"52Nu7ubm2szT1JyJEOB7V2lGM\\\"\\n\",\n      \"consumer_secret = \\\"mqA94defqjioyWeMxdJsSduthxdMMGd2vfOUKvOFpm0n7JTqfY\\\"\\n\",\n      \"access_token = \\\"16065520-USf3DBbQAh6ZA8CnSAi6NAUlkorXdppRXpC4cQCKk\\\"\\n\",\n      \"access_token_secret = \\\"DowMQeXqh5ZsGvZGrmUmkI0iCmI34ShFzKF3iOdiilpX5\\\"\\n\",\n      \"authorization = twitter.OAuth(access_token, access_token_secret, consumer_key, consumer_secret)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 18\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import os\\n\",\n      \"output_filename = os.path.join(os.path.expanduser(\\\"~\\\"), \\\"Data\\\", \\\"twitter\\\", \\\"python_tweets.json\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import json\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"t = twitter.Twitter(auth=authorization)\\n\",\n      \"\\n\",\n      \"with open(output_filename, 'a') as output_file:\\n\",\n      \"    search_results = t.search.tweets(q=\\\"python\\\", count=100)['statuses']\\n\",\n      \"    for tweet in search_results:\\n\",\n      \"        if 'text' in tweet:\\n\",\n      \"            print(tweet['user']['screen_name'])\\n\",\n      \"            print(tweet['text'])\\n\",\n      \"            print()\\n\",\n      \"            output_file.write(json.dumps(tweet))\\n\",\n      \"            output_file.write(\\\"\\\\n\\\\n\\\")\\n\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"momomi_752\\n\",\n        \"RT @seira_923: [Python] Help with a tricky loop issue, how do you use continue to  http://t.co/H54k0nWDiN http://t.co/FydAl78nvH\\n\",\n        \"\\n\",\n        \"ikuho_379\\n\",\n        \"RT @otoka_380: Python/Flask app using apache/fastcgi producing 500 Error http://t.co/sn4d0S5aHD\\n\",\n        \"\\n\",\n        \"benignowl\\n\",\n        \"?? \\uc774\\uac74 visual studio \\uc5d0\\ub7ec\\uc778\\uac00 python \\ucef4\\ud30c\\uc77c\\ub7ec\\ub85c \\ub3cc\\ub9ac\\uba74 \\uc5d0\\ub7ec \\uc548\\ub098\\uace0 \\uc798 \\ub3cc\\uc544\\uac00\\ub294\\ub370 vs \\ub514\\ubc84\\uac70\\ub85c \\ub3cc\\ub9ac\\uba74 \\uc5d0\\ub7ec\\ub97c \\ubc49\\uc5b4\\ub0b4\\ub294\\uad70\\n\",\n        \"\\n\",\n        \"GsonMathias\\n\",\n        \"F\\u00f6rsta klassens \\\"tomte\\\" #moron http://t.co/yMt1PZU1Cp\\n\",\n        \"\\n\",\n        \"brewokr\\n\",\n        \"Python #private #class variables that aren't class variables: When trying to access\\u2026 http://t.co/4yIhHsIizx\\n\",\n        \"\\n\",\n        \"mima_tea\\n\",\n        \"\\u201cPython\\u30c7\\u30a3\\u30fc\\u30d7\\u30e9\\u30fc\\u30cb\\u30f3\\u30b0\\u30e9\\u30a4\\u30d6\\u30e9\\u30ea\\u306e\\u30c4\\u30fc\\u30c8\\u30c3\\u30d7\\u300cPylearn2\\u300d\\u300cCaffe\\u300d\\u3068\\u306f\\uff5ePyData Tokyo Meetup #1\\u30a4\\u30d9\\u30f3\\u30c8\\u30ec\\u30dd\\u30fc\\u30c8 \\uff081/3\\uff09\\uff1aCodeZine\\u201d http://t.co/M5qsyZZTnD\\n\",\n        \"\\n\",\n        \"elena_casanovas\\n\",\n        \"Four Beautiful Python, R, MATLAB, and Mathematica plots with LaTeX http://t.co/8krYerBJfB via @rbloggers\\n\",\n        \"\\n\",\n        \"io_python\\n\",\n        \"@0243Kyon http://t.co/mSyeNVcahe\\n\",\n        \"\\n\",\n        \"0243Kyon\\n\",\n        \"@io_python \\u3046\\u308f\\u3041...\\n\",\n        \"\\n\",\n        \"BlueWaterMarket\\n\",\n        \"#Freelancer Python Restless Example by homejinni http://t.co/QRDLJxlsIM #computer via @BlueWaterMarket\\n\",\n        \"\\n\",\n        \"BlueWaterMarket\\n\",\n        \"#Freelancer Python Crawler/Indexer/Spider/Bot by meetire http://t.co/CbzQTdVXdi #computer via @BlueWaterMarket\\n\",\n        \"\\n\",\n        \"io_python\\n\",\n        \"@0243Kyon \\u6ce3\\u3051\\u3088(\\u30b2\\u30b9\\u9854)\\n\",\n        \"\\n\",\n        \"CashBoards\\n\",\n        \"#Freelancer #seo Python Restless Example by homejinni http://t.co/RmZQVvMlz2 @CashBoards\\n\",\n        \"\\n\",\n        \"CashBoards\\n\",\n        \"#Freelancer #seo Python Crawler/Indexer/Spider/Bot by meetire http://t.co/GWf3vHkZ30 @CashBoards\\n\",\n        \"\\n\",\n        \"pypi_updates\\n\",\n        \"SchemePy 1.1.01: R5RS scheme interpreter, supporting hygenic macros and full call/cc http://t.co/cN4bfcStmm\\n\",\n        \"\\n\",\n        \"yaakov_h\\n\",\n        \"RT @shiftkey: current status: installing the other version of python\\n\",\n        \"\\n\",\n        \"yaakov_h\\n\",\n        \"RT @shiftkey: current status: installing python\\n\",\n        \"\\n\",\n        \"artwisanggeni\\n\",\n        \"#python floraconcierge-client 0.6.7: FloraExpress API python client library. http://t.co/0TfRRqsOR8 http://t.co/Ew6FJoY9Hx\\n\",\n        \"\\n\",\n        \"artwisanggeni\\n\",\n        \"#python printNest21 1.4.0: A simple printer of nested lists http://t.co/1OPTOogruI\\n\",\n        \"\\n\",\n        \"artwisanggeni\\n\",\n        \"#python pyupp 0.1: Read and write unity player preferences data. http://t.co/SF7gUilR8x\\n\",\n        \"\\n\",\n        \"artwisanggeni\\n\",\n        \"#python musa 3.3.13: Module for music tagging and library management http://t.co/Wg2qcbMeCS\\n\",\n        \"\\n\",\n        \"Music_Reddit\\n\",\n        \"Lady - Python [Hiphop?] https://t.co/nekBNpmHvL\\n\",\n        \"\\n\",\n        \"Python_Bob123\\n\",\n        \"https://t.co/WKdTIueR9S\\n\",\n        \"\\n\",\n        \"distantcam\\n\",\n        \"RT @shiftkey: current status: installing the other version of python\\n\",\n        \"\\n\",\n        \"distantcam\\n\",\n        \"RT @shiftkey: current status: installing python\\n\",\n        \"\\n\",\n        \"kfujii\\n\",\n        \"\\u201chttp://t.co/oAqSNLIRXP\\uff1a Python\\u8a00\\u8a9e\\u306b\\u3088\\u308b\\u30d7\\u30ed\\u30b0\\u30e9\\u30df\\u30f3\\u30b0\\u30a4\\u30f3\\u30c8\\u30ed\\u30c0\\u30af\\u30b7\\u30e7\\u30f3: \\u4e16\\u754c\\u6a19\\u6e96MIT\\u6559\\u79d1\\u66f8: \\u4e45\\u4fdd \\u5e79\\u96c4, John V. Guttag, \\u9ebb\\u751f \\u654f\\u6b63, \\u6728\\u6751 \\u6cf0\\u7d00, \\u5c0f\\u6797 \\u548c\\u535a, \\u95a2\\u53e3 \\u826f\\u884c\\u2026\\u201d http://t.co/qAmAQ0skir\\n\",\n        \"\\n\",\n        \"projectsuperior\\n\",\n        \"Top hourly 'python' htags [bm500.., #python, #cgyvsvan, #rdsch, #3dmu] 377 tweets http://t.co/l5IY5gFCyK http://t.co/CEOidrDhmv\\n\",\n        \"\\n\",\n        \"yutuki_355\\n\",\n        \"RT @yuhiro_274: Linking python as backed when using Django + Bower + Grunt http://t.co/MDMfhkRyKS\\n\",\n        \"\\n\",\n        \"1LuxuryLifeMag\\n\",\n        \"@Michael_Louis_ Python Accessories\\n\",\n        \"\\n\",\n        \"Available Now At -\\u2026 http://t.co/tvgQugZZT5\\n\",\n        \"\\n\",\n        \"tgmp10\\n\",\n        \"Black Hat Python: Python Programming for Hackers and Pentesters http://t.co/qBknn5EDOX 3325 http://t.co/JpLvvwjPOz\\n\",\n        \"\\n\",\n        \"No_not_that_one\\n\",\n        \"RT @SamuelHLowe: - Excuse me, do you have snake belts?\\n\",\n        \"- Not sure, but I can check in the back.\\n\",\n        \"- Please do, my python's pants keep falling\\u2026\\n\",\n        \"\\n\",\n        \"if1live\\n\",\n        \"perl oneliner, python oneliner \\ub4f1\\ub4f1\\uc758 \\uc5b8\\uc5b4\\ubcc4 oneliner\\uc911\\uc5d0\\uc11c \\uac00\\uc7a5 \\ubcc0\\ud0dc\\ub294 lisp \\uacc4\\uc5f4 \\uc544\\ub2d0\\uae4c?\\n\",\n        \"\\n\",\n        \".....)))))))))))\\n\",\n        \"\\n\",\n        \"pypi_updates\\n\",\n        \"floraconcierge-client 0.6.7: FloraExpress API python client library. http://t.co/dXyHxnjqcK http://t.co/wo4TTGYIHx\\n\",\n        \"\\n\",\n        \"irigo56\\n\",\n        \"RT @ElBaulDeKubrick: Still Flying, un tributo a los Monty Python http://t.co/PYb9h5Gssk \\u00bfAlguien los ha olvidado? http://t.co/TooNHtSiMm\\n\",\n        \"\\n\",\n        \"R_Programming\\n\",\n        \"Four Beautiful Python, R, MATLAB, and Mathematica plots with LaTeX http://t.co/Dgpmvoocdn #rstats\\n\",\n        \"\\n\",\n        \"ydfished\\n\",\n        \"@Bergg69 @Canada yep. Like Monty Python 'Dennis Moore' sketch. Rob the poor to give to the rich.\\n\",\n        \"\\n\",\n        \"sawa_453\\n\",\n        \"RT @urara_645: Keep This Python Cheat Sheet on Hand When Learning to Code http://t.co/R72YIj24j4 http://t.co/FtEYHLwj6H\\n\",\n        \"\\n\",\n        \"mirage_1205\\n\",\n        \"RT @mura_cin: Python\\u3067\\u5909\\u6570\\u540d\\u3092\\u3064\\u3051\\u308b\\u3068\\u304d\\u306b\\u306f\\u4e88\\u7d04\\u8a9e\\u3060\\u3051\\u3067\\u306a\\u304f\\u7d44\\u307f\\u8fbc\\u307f\\u95a2\\u6570\\u3068\\u306e\\u885d\\u7a81\\u3082\\u6c17\\u3092\\u4ed8\\u3051\\u307e\\u3057\\u3087\\u3046 on @Qiita http://t.co/1n5c7ut7Dl\\n\",\n        \"\\n\",\n        \"rinna_815\\n\",\n        \"RT @seira_923: [Python] Is this if-statement correct (conceptually)? http://t.co/dYy9pXaSkj http://t.co/p42JgUVIEV\\n\",\n        \"\\n\",\n        \"yuko_267\\n\",\n        \"Is there any reason I should witch to python 3 http://t.co/t17y7JI92b http://t.co/A0oxjbpOn2\\n\",\n        \"\\n\",\n        \"pypi_updates\\n\",\n        \"printNest21 1.4.0: A simple printer of nested lists http://t.co/h5iLQ3zTil\\n\",\n        \"\\n\",\n        \"yakmoose\\n\",\n        \"@shiftkey a python install for you! And a python install for you. Everyone gets a python install!\\n\",\n        \"\\n\",\n        \"sashiya_107\\n\",\n        \"RT @seino_988: Making GIFs from Video Files with Python http://t.co/xOelfi2SKU\\n\",\n        \"\\n\",\n        \"MAMI_nagisa_\\n\",\n        \"@python_octopus \\u304a\\u75b2\\u308c\\u3055\\u307e\\u3067\\u3057\\u305f\\uff01\\u3053\\u308c\\u3067\\u5fc3\\u7f6e\\u304d\\u306a\\u304f\\u3067\\u3059\\u304b\\u2026\\uff1f\\n\",\n        \"\\n\",\n        \"sinatrasapporo\\n\",\n        \"RT @kuroneko1988: \\u3054\\u3081\\u3093\\u306a\\u3055\\u3044\\uff0e\\u3054\\u3081\\u3093\\u306a\\u3055\\u3044\\uff0e\\u3084\\u3063\\u3064\\u3051\\u3067\\u3059/Sinatra\\u98a8\\u30de\\u30a4\\u30af\\u30ed\\u30d5\\u30ec\\u30fc\\u30e0\\u30ef\\u30fc\\u30af\\u3067\\u59cb\\u3081\\u308bPython http://t.co/0rCghxCNGh @SlideShare\\u3055\\u3093\\u304b\\u3089\\n\",\n        \"\\n\",\n        \"Python_Bob123\\n\",\n        \"https://t.co/basmBzXZNb\\n\",\n        \"\\n\",\n        \"yuno_730\\n\",\n        \"RT @seira_923: Python: Is it a good starting point? http://t.co/mvnIxwRn8V http://t.co/vTVQPN16XF\\n\",\n        \"\\n\",\n        \"python_octopus\\n\",\n        \"@halulan \\u87fb\\u3067\\u3059\\uff01\\uff01\\n\",\n        \"\\n\",\n        \"Python_Bob123\\n\",\n        \"Fuck work.\\n\",\n        \"\\n\",\n        \"halulan\\n\",\n        \"@python_octopus \\n\",\n        \"\\u4e59\\u3067\\u3059\\uff01\\uff01\\n\",\n        \"\\n\",\n        \"WomansIndex\\n\",\n        \"Vince Camuto Women's VC/5022RGBN Leather Rosegold-Tone Date Function Brown Python Strap Watch   http://t.co/imePT9i3gx\\n\",\n        \"\\n\",\n        \"ArchUpdates\\n\",\n        \"python-rpy2 2.5.4-1 x86_64 http://t.co/oawb8APara #Archlinux\\n\",\n        \"\\n\",\n        \"ArchUpdates\\n\",\n        \"python-rpy2 2.5.4-1 i686 http://t.co/TnMRb22DI2 #Archlinux\\n\",\n        \"\\n\",\n        \"shiftkey\\n\",\n        \"current status: installing the other version of python\\n\",\n        \"\\n\",\n        \"shiftkey\\n\",\n        \"current status: installing python\\n\",\n        \"\\n\",\n        \"miyoshi_107\\n\",\n        \"Using BOTH error bars and upper/lower limits in python http://t.co/X5FyAWHikJ\\n\",\n        \"\\n\",\n        \"DoubleDownDuck\\n\",\n        \"RT @NYDailyNews: A 15-foot Burmese python was found at a Florida real estate office. http://t.co/ry0yXBZ6Ek http://t.co/1CQVQuZVjj\\n\",\n        \"\\n\",\n        \"python_octopus\\n\",\n        \"@kazunaduck \\u304a\\u3064\\u3042\\u308a\\u3067\\u3059\\u3046\\u3047\\u3072\\u3072\\u3072\\u3072\\uff01\\uff01\\n\",\n        \"\\n\",\n        \"naoka_870\\n\",\n        \"Woman Finds Python In Secondhand S-s-s-s-sofa http://t.co/hvWepmNbD6 http://t.co/r2g2xHF0kE\\n\",\n        \"\\n\",\n        \"kazunaduck\\n\",\n        \"@python_octopus \\u304a\\u75b2\\u308c\\u3055\\u307e\\u3067\\u3059\\u3046\\u3047\\u3078\\u3078\\u3078\\u3078\\uff01\\uff01\\n\",\n        \"\\n\",\n        \"python_octopus\\n\",\n        \"@Escalator_Rider \\u300e\\u304a\\u3064\\u3042\\u308a\\u3055\\u307e\\u3063\\u3059\\u300f\\n\",\n        \"\\n\",\n        \"python_octopus\\n\",\n        \"@kobudashiOdashi \\u3042\\u3063\\u3063\\u3063\\u3063\\u3063\\u3063\\u3063\\u3063\\u3063\\u3063\\uff01\\uff01\\uff01\\uff01\\uff01\\uff01\\uff01\\uff01\\n\",\n        \"\\n\",\n        \"Data88Geek\\n\",\n        \"RT @Python_Quest: How to add assumptions in limit in Sympy? http://t.co/um3d45PzDu http://t.co/t3TXOZrjLV #Python via @dv_geek\\n\",\n        \"\\n\",\n        \"Data88Geek\\n\",\n        \"RT @Python_Quest: Dealing with list.index(x) error in Python 3 http://t.co/0jp5moJqwh http://t.co/Ntbf0XWdt2 #Python via @dv_geek\\n\",\n        \"\\n\",\n        \"Escalator_Rider\\n\",\n        \"@python_octopus \\u300e\\u304a\\u3064\\u304b\\u308c\\u3055\\u307e\\u3063\\u3059\\u300f\\n\",\n        \"\\n\",\n        \"python_octopus\\n\",\n        \"@mechashiorina \\u304a\\u3064\\u3042\\u308a\\u3067\\u3054\\u3056\\u3044\\u307e\\u3059\\uff01\\uff01\\n\",\n        \"\\n\",\n        \"Wiajbo__Vuuzqa\\n\",\n        \"Michael Kors Hamilton Python Embossed Leather Large Brown Mocha Tote Purse Bag - Full read by eBay: Price 339.0 USD\\u2026 http://t.co/IkkhhgJrrn\\n\",\n        \"\\n\",\n        \"JimmyMcH\\n\",\n        \"Monty Python - Always Look on the Bright Side of \\u2026: http://t.co/xYHUiKxp4K\\n\",\n        \"\\n\",\n        \"kobudashiOdashi\\n\",\n        \"@python_octopus \\u304a\\u3063\\u3063\\u3063\\u3063\\u3063\\u3063\\u3063\\u3063\\u3063\\u3063\\u3063\\u3063\\u3063\\u3063\\u3063\\u3063\\u3063\\u3063\\u3063\\u3063\\u3063\\uff54\\n\",\n        \"\\n\",\n        \"mechashiorina\\n\",\n        \"@python_octopus \\u304a\\u3064\\u304b\\u308c\\u3055\\u307e\\u3067\\u3059\\uff01\\uff01\\n\",\n        \"\\n\",\n        \"regis_alenda\\n\",\n        \"RT @GodDonut: Python *finira* par \\u00eatre un langage typ\\u00e9 https://t.co/USii3gwnWa . Mais forc\\u00e9ment, comme pour tout le reste, \\u00e7a sera mal fait.\\n\",\n        \"\\n\",\n        \"python_octopus\\n\",\n        \"@mkmnsh_ \\u304a\\u3064\\u3042\\u308a\\u3067\\u3054\\u3056\\u3044\\u307e\\u3059\\uff01\\uff01\\n\",\n        \"\\n\",\n        \"shkoga\\n\",\n        \"C++ \\u3067\\u66f8\\u304b\\u308c\\u305f\\u30b5\\u30f3\\u30d7\\u30eb\\u30b3\\u30fc\\u30c9\\u3092\\u3001C# \\u3068\\u3001\\u305d\\u308c\\u304b\\u3089 F# \\u3068 Python (IronPython) \\u3067\\u66f8\\u304d\\u76f4\\u3057\\u3066\\u307f\\u307e\\u3059\\u304b\\u306a\\u3002\\n\",\n        \"\\n\",\n        \"mkmnsh_\\n\",\n        \"@python_octopus \\u304a\\u3064\\u304b\\u308c\\u3055\\u307e\\u3067\\u3059\\uff01\\n\",\n        \"\\n\",\n        \"brewokr\\n\",\n        \"How to protect python class variables from an evil programmer?: How can I protect my\\u2026 http://t.co/n0AzC6hWWw\\n\",\n        \"\\n\",\n        \"python_jobs\\n\",\n        \"Object Initialization -- Patterns And Antipatterns - http://t.co/Pi5xu4NMkR\\n\",\n        \"\\n\",\n        \"python_octopus\\n\",\n        \"\\u7de0\\u5207\\u307e\\u30675\\u6642\\u9593\\u534a\\u3002\\u3044\\u3064\\u3082\\u304e\\u308a\\u304e\\u308a\\u3067\\u3059\\u306a\\u3041\\u3002\\n\",\n        \"\\n\",\n        \"mrsergani\\n\",\n        \"Python! http://t.co/JjdDGlGba1\\n\",\n        \"\\n\",\n        \"abadahenno\\n\",\n        \"How do I convert this piece of code written in Python 3.4 into Python 2.7 (gives error with end)? http://t.co/Yn5WvjVHZD #Question\\n\",\n        \"\\n\",\n        \"Python_Quest\\n\",\n        \"Dealing with list.index(x) error in Python 3 http://t.co/0jp5moJqwh http://t.co/Ntbf0XWdt2 #Python via @dv_geek\\n\",\n        \"\\n\",\n        \"marie_cali\\n\",\n        \"Create a Freelancer/Elance type site by seekdesign http://t.co/1haUCxuI2g\\n\",\n        \"\\n\",\n        \"Python_Quest\\n\",\n        \"How to add assumptions in limit in Sympy? http://t.co/um3d45PzDu http://t.co/t3TXOZrjLV #Python via @dv_geek\\n\",\n        \"\\n\",\n        \"python_octopus\\n\",\n        \"\\u3048\\u3078\\u3078 \\u5408\\u540c\\u8a8c\\u539f\\u7a3f\\u7d42\\u308f\\u3063\\u305f\\n\",\n        \"\\n\",\n        \"sebalinux\\n\",\n        \"I really want to thank @drchuck and @coursera for the great #python course #PR4E. A good starting point, now looking forward to learn more\\n\",\n        \"\\n\",\n        \"jvw1954\\n\",\n        \"RT @pythontrending: Bedfellows - Command-line tool for exploring the PAC donor-recipient relationship http://t.co/gZ5gFWEVTb #python\\n\",\n        \"\\n\",\n        \"natuha_306\\n\",\n        \"RT @yuhiro_274: Django, Python, and Class Variables http://t.co/LnHRC1rQQp\\n\",\n        \"\\n\",\n        \"MyNextWifey\\n\",\n        \"\\\"Doctor, I keep thinking I'm a python. Oh you can't get round me like that, you know.\\\"\\n\",\n        \"\\n\",\n        \"jvw1954\\n\",\n        \"RT @pythontrending: eatiht - A tool for extracting article text in html documents. http://t.co/Wu2jxwHpWE #python\\n\",\n        \"\\n\",\n        \"rssnest\\n\",\n        \"24.6 10.2 http://t.co/hYAym4DQUR pyupp added to PyPI\\n\",\n        \"\\n\",\n        \"IJInformatico\\n\",\n        \"#empleo #IT A/P   PYTHON   PHP  Y   JAVA - Madrid http://t.co/2HPny4tseI\\n\",\n        \"\\n\",\n        \"LearnEnglPhuket\\n\",\n        \"rt Monty Python's Silly Walk to PHUKET LEARN GOOD ENGLISH -  http://t.co/GiZnKicQMf\\n\",\n        \"\\n\",\n        \"NuphaUtsen\\n\",\n        \"rt Monty Python's Silly Walk to PHUKET LEARN GOOD ENGLISH -  http://t.co/xH9w7LcUW9\\n\",\n        \"\\n\",\n        \"IrishCause\\n\",\n        \"rt Monty Python's Silly Walk to PHUKET LEARN GOOD ENGLISH -  http://t.co/5jDQynuWnl\\n\",\n        \"\\n\",\n        \"thedamnwells\\n\",\n        \"Let us always carry with us into each generation the legacy of Monty Python.\\n\",\n        \"\\n\",\n        \"satoe_445\\n\",\n        \"RT @kinori51: Can't input (\\u010d \\u0107 \\u0161 \\u017e \\u0111 ) in python 2.7.x console [duplicate] http://t.co/WCev9si6nK\\n\",\n        \"\\n\",\n        \"python_octopus\\n\",\n        \"\\u3042\\u308a\\u304c\\u3068\\u3046\\u3054\\u3056\\u3044\\u307e\\u3059\\u3002\\n\",\n        \"\\n\",\n        \"pgdkg\\n\",\n        \"Python Data Visualization Cookbook http://t.co/W18vDQBzgE 1728 http://t.co/FeGqj442Gy\\n\",\n        \"\\n\",\n        \"patatostew\\n\",\n        \"monty python http://t.co/t6g9nDIlJL\\n\",\n        \"\\n\",\n        \"aussiecoley\\n\",\n        \"RT @NYDailyNews: A 15-foot Burmese python was found at a Florida real estate office. http://t.co/ry0yXBZ6Ek http://t.co/1CQVQuZVjj\\n\",\n        \"\\n\",\n        \"python_octopus\\n\",\n        \"\\u308a\\u3063\\u8336\\u5e97\\u306f\\u4f55\\u6642\\u307e\\u3067\\u3067\\u3059\\u304b\\uff1f\\n\",\n        \"\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 22\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 21\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}"
  },
  {
    "path": "Chapter 6/ch6_label_twitter.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:bedd2d93552b56f12dfc316010dc889b286a3ae6dad17066f6067cfe5a3cc22c\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Labelling the class values for the twitter dataset.\\n\",\n      \"import os\\n\",\n      \"input_filename = os.path.join(os.path.expanduser(\\\"~\\\"), \\\"Data\\\", \\\"twitter\\\", \\\"python_tweets.json\\\")\\n\",\n      \"classes_filename = os.path.join(os.path.expanduser(\\\"~\\\"), \\\"Data\\\", \\\"twitter\\\", \\\"python_classes.json\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 114\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import json\\n\",\n      \"tweets = []\\n\",\n      \"with open(input_filename) as inf:\\n\",\n      \"    for line in inf:\\n\",\n      \"        if len(line.strip()) == 0:\\n\",\n      \"            continue\\n\",\n      \"        tweets.append(json.loads(line))\\n\",\n      \"print(\\\"Loaded {} tweets\\\".format(len(tweets)))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Loaded 200 tweets\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 115\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"tweet_sample = tweets\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 116\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"labels = []\\n\",\n      \"if os.path.exists(classes_filename):\\n\",\n      \"    with open(classes_filename) as inf:\\n\",\n      \"        labels = json.load(inf)\\n\",\n      \"print(len(labels))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"0\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 118\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def get_next_tweet():\\n\",\n      \"    return tweet_sample[len(labels)]['text']\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 117\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"%%javascript\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"function set_label(label){\\n\",\n      \"    var kernel = IPython.notebook.kernel;\\n\",\n      \"    kernel.execute(\\\"labels.append(\\\" + label + \\\")\\\");\\n\",\n      \"    load_next_tweet();\\n\",\n      \"}\\n\",\n      \"\\n\",\n      \"function load_next_tweet(){\\n\",\n      \"   var code_input = \\\"get_tweet()\\\";\\n\",\n      \"   var kernel = IPython.notebook.kernel;\\n\",\n      \"   var callbacks = { 'iopub' : {'output' : handle_output}};\\n\",\n      \"   kernel.execute(code_input, callbacks, {silent:false});\\n\",\n      \"}\\n\",\n      \"\\n\",\n      \"function handle_output(out){\\n\",\n      \"   console.log(out);\\n\",\n      \"   var res = out.content.data[\\\"text/plain\\\"];\\n\",\n      \"   $(\\\"div#tweet_text\\\").html(res);\\n\",\n      \"}\\n\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"javascript\": [\n        \"function handle_output(out){\\n\",\n        \"   console.log(out);\\n\",\n        \"   var res = out.content.data[\\\"text/plain\\\"];\\n\",\n        \"   $(\\\"div#tweet_text\\\").html(res);\\n\",\n        \"}\\n\",\n        \"\\n\",\n        \"function set_useful(){\\n\",\n        \"    var kernel = IPython.notebook.kernel;\\n\",\n        \"    kernel.execute(\\\"classifications.append(1)\\\");\\n\",\n        \"    load_next_tweet();\\n\",\n        \"}\\n\",\n        \"\\n\",\n        \"function set_notuseful(){\\n\",\n        \"    var kernel = IPython.notebook.kernel;\\n\",\n        \"    kernel.execute(\\\"classifications.append(0)\\\");\\n\",\n        \"    load_next_tweet();\\n\",\n        \"}\\n\",\n        \"\\n\",\n        \"function load_next_tweet(){\\n\",\n        \"   var code_input = \\\"get_tweet()\\\";\\n\",\n        \"   var kernel = IPython.notebook.kernel;\\n\",\n        \"   var callbacks = { 'iopub' : {'output' : handle_output}};\\n\",\n        \"   var msg_id = kernel.execute(code_input, callbacks, {silent:false});\\n\",\n        \"}\\n\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"text\": [\n        \"<IPython.core.display.Javascript at 0x10562f438>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 119\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"%%html\\n\",\n      \"<div name=\\\"tweetbox\\\">\\n\",\n      \"    Instructions: Click in text box. Enter a 1 if the tweet is relevant, enter 0 otherwise.<br>\\n\",\n      \"    Tweet: <div id=\\\"tweet_text\\\" value=\\\"text\\\"></div><br>\\n\",\n      \"    <input type=text id=\\\"capture\\\"></input><br>\\n\",\n      \"</div>\\n\",\n      \"<script>\\n\",\n      \"$(\\\"input#capture\\\").keypress(function(e) {\\n\",\n      \"    console.log(e);\\n\",\n      \"  if(e.which == 48) {\\n\",\n      \"    // 0 pressed\\n\",\n      \"    set_label(0);\\n\",\n      \"    $(\\\"input#capture\\\").val(\\\"\\\");\\n\",\n      \"  }else if (e.which == 49){\\n\",\n      \"    // 1 pressed\\n\",\n      \"    set_label(1);  \\n\",\n      \"    $(\\\"input#capture\\\").val(\\\"\\\");\\n\",\n      \"  }\\n\",\n      \"});\\n\",\n      \"\\n\",\n      \"load_next_tweet();\\n\",\n      \"</script>\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div name=\\\"tweetbox\\\">\\n\",\n        \"    Instructions: Click in text box. Enter a 1 if the tweet is relevant, enter 0 otherwise.<br>\\n\",\n        \"    Tweet: <div id=\\\"tweet_text\\\" value=\\\"text\\\"></div><br>\\n\",\n        \"    <input type=text id=\\\"capture\\\"></input><br>\\n\",\n        \"</div>\\n\",\n        \"<script>\\n\",\n        \"$(\\\"input#capture\\\").keypress(function(e) {\\n\",\n        \"    console.log(e);\\n\",\n        \"  if(e.which == 48) {\\n\",\n        \"    // 0 pressed\\n\",\n        \"    set_notuseful();\\n\",\n        \"    $(\\\"input#capture\\\").val(\\\"\\\");\\n\",\n        \"  }else if (e.which == 49){\\n\",\n        \"    // 1 pressed\\n\",\n        \"    set_useful();  \\n\",\n        \"    $(\\\"input#capture\\\").val(\\\"\\\");\\n\",\n        \"  }\\n\",\n        \"});\\n\",\n        \"\\n\",\n        \"load_next_tweet();\\n\",\n        \"</script>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"text\": [\n        \"<IPython.core.display.HTML at 0x10562fcf8>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 120\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"print(len(labels))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"200\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 106\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"with open(classes_filename, 'w') as outf:\\n\",\n      \"    json.dump(labels, outf)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 107\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 93\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}"
  },
  {
    "path": "Chapter 6/replicable_dataset.json",
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  },
  {
    "path": "Chapter 7/ch7_collect_twitter_data.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:92626e6b8c220e06f64c1f7e8ed2f882c28441fc05cc1d6bd66a84c37e091595\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import twitter\\n\",\n      \"consumer_key = \\\"52Nu7ubm2szT1JyJEOB7V2lGM\\\"\\n\",\n      \"consumer_secret = \\\"mqA94defqjioyWeMxdJsSduthxdMMGd2vfOUKvOFpm0n7JTqfY\\\"\\n\",\n      \"access_token = \\\"16065520-USf3DBbQAh6ZA8CnSAi6NAUlkorXdppRXpC4cQCKk\\\"\\n\",\n      \"access_token_secret = \\\"DowMQeXqh5ZsGvZGrmUmkI0iCmI34ShFzKF3iOdiilpX5\\\"\\n\",\n      \"authorization = twitter.OAuth(access_token, access_token_secret, consumer_key, consumer_secret)\\n\",\n      \"t = twitter.Twitter(auth=authorization, retry=True)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import os\\n\",\n      \"data_folder = os.path.join(os.path.expanduser(\\\"~\\\"), \\\"Data\\\", \\\"twitter\\\")\\n\",\n      \"output_filename = os.path.join(data_folder, \\\"python_tweets.json\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import json\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 3\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"original_users = []\\n\",\n      \"tweets = []\\n\",\n      \"user_ids = {}\\n\",\n      \"\\n\",\n      \"search_results = t.search.tweets(q=\\\"python\\\", count=100)['statuses']\\n\",\n      \"for tweet in search_results:\\n\",\n      \"    if 'text' in tweet:\\n\",\n      \"        original_users.append(tweet['user']['screen_name'])\\n\",\n      \"        user_ids[tweet['user']['screen_name']] = tweet['user']['id']\\n\",\n      \"        tweets.append(tweet['text'])\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 4\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"len(tweets)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 5,\n       \"text\": [\n        \"100\"\n       ]\n      }\n     ],\n     \"prompt_number\": 5\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"model_filename = os.path.join(os.path.expanduser(\\\"~\\\"), \\\"Models\\\", \\\"twitter\\\", \\\"python_context.pkl\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 6\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from sklearn.base import TransformerMixin\\n\",\n      \"from nltk import word_tokenize\\n\",\n      \"\\n\",\n      \"class NLTKBOW(TransformerMixin):\\n\",\n      \"    def fit(self, X, y=None):\\n\",\n      \"        return self\\n\",\n      \"\\n\",\n      \"    def transform(self, X):\\n\",\n      \"        return [{word: True for word in word_tokenize(document)}\\n\",\n      \"                 for document in X]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 7\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from sklearn.externals import joblib\\n\",\n      \"context_classifier = joblib.load(model_filename)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 8\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"y_pred = context_classifier.predict(tweets)\\n\",\n      \"y_pred\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 9,\n       \"text\": [\n        \"array([0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0,\\n\",\n        \"       0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0,\\n\",\n        \"       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1,\\n\",\n        \"       0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0,\\n\",\n        \"       0, 0, 0, 0, 1, 0, 0, 0])\"\n       ]\n      }\n     ],\n     \"prompt_number\": 9\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"relevant_tweets = [tweets[i] for i in range(len(tweets)) if y_pred[i] == 1]\\n\",\n      \"relevant_users = [original_users[i] for i in range(len(tweets)) if y_pred[i] == 1]\\n\",\n      \"print(len(relevant_tweets))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"33\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 10\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import time\\n\",\n      \"import sys\\n\",\n      \"\\n\",\n      \"def get_friends(t, user_id):\\n\",\n      \"    friends = []\\n\",\n      \"    cursor = -1  # Start with the first page\\n\",\n      \"    while cursor != 0:  # If zero, that is the end:\\n\",\n      \"        try:\\n\",\n      \"            results = t.friends.ids(user_id=user_id, cursor=cursor, count=5000)\\n\",\n      \"            friends.extend([friends for friends in results['ids']])\\n\",\n      \"            cursor = results['next_cursor']\\n\",\n      \"            if len(friends) >= 10000:\\n\",\n      \"                break\\n\",\n      \"            if cursor != 0:\\n\",\n      \"                print(\\\"Collected {} friends so far, but there are more\\\".format(len(friends)))\\n\",\n      \"                sys.stdout.flush\\n\",\n      \"        except TypeError as e:\\n\",\n      \"            if results is None:\\n\",\n      \"                print(\\\"You probably reached your API limit, waiting for 5 minutes\\\")\\n\",\n      \"                sys.stdout.flush()\\n\",\n      \"                time.sleep(5*60) # 5 minute wait\\n\",\n      \"            else:\\n\",\n      \"                raise e\\n\",\n      \"        except twitter.TwitterHTTPError as e:\\n\",\n      \"            break\\n\",\n      \"        finally:\\n\",\n      \"            time.sleep(60)  # Wait 1 minute before continuing\\n\",\n      \"    return friends\\n\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"\\n\",\n      \"test_friends = get_friends(t, user_ids[relevant_users[0]])\\n\",\n      \"print(test_friends)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"friends = {}\\n\",\n      \"for screen_name in relevant_users:\\n\",\n      \"    print(\\\"Obtaining friends for user {}\\\".format(user))\\n\",\n      \"    sys.stdout.flush()\\n\",\n      \"    user_id = user_id[user]\\n\",\n      \"    friends[user_id] = get_friends(t, user_id)\\n\",\n      \"friends = {user_id:friends[user_id] for user_id in friends\\n\",\n      \"             if len(friends[user_id]) > 0}\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user ionelmc\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user lauren_leclaire\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user meguri_796\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user ticketsflying\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user AllTechBot\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user VTCVillage\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user findmjob\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user rapitup204\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user rapitup204\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user rapitup204\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user rapitup204\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user projectsuperior\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user jobely\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user python_job_feed\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user _TrabajoMadrid_\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user Empleo_enMadrid\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user ReptileInsider1\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user HeatherA789\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user AB_2412\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user OurNBASpurs\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user satoho_177\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user spantreellc\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user divideby0\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user honatu_956\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user MayaDelany\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user satolone\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user Valencia_job\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user artwisanggeni\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user artwisanggeni\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user artwisanggeni\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user artwisanggeni\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user pypi_updates\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Obtaining friends for user jsdc\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 18\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from collections import defaultdict\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 3\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def count_friends(friends):\\n\",\n      \"    friend_count = defaultdict(int)\\n\",\n      \"    for friend_list in friends.values():\\n\",\n      \"        for friend in friend_list:\\n\",\n      \"            friend_count[friend] += 1\\n\",\n      \"    return friend_count\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 4\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"#TODO: Remove before production\\n\",\n      \"import os\\n\",\n      \"import json\\n\",\n      \"\\n\",\n      \"data_folder = os.path.join(os.path.expanduser(\\\"~\\\"), \\\"Data\\\", \\\"twitter\\\")\\n\",\n      \"friends_filename = os.path.join(data_folder, \\\"python_friends.json\\\")\\n\",\n      \"with open(friends_filename) as inf:\\n\",\n      \"    friends = json.load(inf)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 5\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"friend_count = count_friends(friends)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 6\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from operator import itemgetter\\n\",\n      \"best_friends = sorted(friend_count.items(), key=itemgetter(1), reverse=True)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 7\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"best_friends[:10]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 8,\n       \"text\": [\n        \"[(1533877544, 21),\\n\",\n        \" (1437397166, 18),\\n\",\n        \" (264158124, 18),\\n\",\n        \" (838820264, 18),\\n\",\n        \" (2205707677, 17),\\n\",\n        \" (2222807107, 17),\\n\",\n        \" (1963872133, 17),\\n\",\n        \" (2294262750, 17),\\n\",\n        \" (174087579, 16),\\n\",\n        \" (1423236582, 16)]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 8\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"while len(friends) < 150:\\n\",\n      \"    # Get the best friend that isn't already in our list\\n\",\n      \"    for user_id, count in best_friends:\\n\",\n      \"        if user_id not in friends and str(user_id) != '467407284':\\n\",\n      \"            break\\n\",\n      \"    print(\\\"Getting friends of user {}\\\".format(user_id))\\n\",\n      \"    sys.stdout.flush()\\n\",\n      \"    friends[user_id] = get_friends(t, user_id)\\n\",\n      \"    print(\\\"Received {} friends\\\".format(len(friends[user_id])))\\n\",\n      \"    print(\\\"We now have the friends of {} users\\\".format(len(friends)))\\n\",\n      \"    sys.stdout.flush()\\n\",\n      \"    # Update friend_count\\n\",\n      \"    for friend in friends[user_id]:\\n\",\n      \"        friend_count[friend] += 1\\n\",\n      \"    # Update the best friends list\\n\",\n      \"    best_friends = sorted(friend_count.items(), key=itemgetter(1), reverse=True)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2540917573\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Collected 5000 friends so far, but there are more\\n\",\n        \"Received 10000 friends\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"We now have the friends of 98 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2715520412\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Collected 5000 friends so far, but there are more\\n\",\n        \"Received 10000 friends\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"We now have the friends of 99 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2323494632\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Collected 5000 friends so far, but there are more\\n\",\n        \"Received 10000 friends\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"We now have the friends of 100 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2279999317\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Received 3990 friends\\n\",\n        \"We now have the friends of 101 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2527724792\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Collected 5000 friends so far, but there are more\\n\",\n        \"Received 8171 friends\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"We now have the friends of 102 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 272305232\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Collected 5000 friends so far, but there are more\\n\",\n        \"Received 10000 friends\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"We now have the friends of 103 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2857719008\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Collected 5000 friends so far, but there are more\\n\",\n        \"Received 10000 friends\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"We now have the friends of 104 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2420119549\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Collected 5000 friends so far, but there are more\\n\",\n        \"Received 10000 friends\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"We now have the friends of 105 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 156882778\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Collected 5000 friends so far, but there are more\\n\",\n        \"Received 10000 friends\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"We now have the friends of 106 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2658970874\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Received 3508 friends\\n\",\n        \"We now have the friends of 107 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 144388857\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Collected 5000 friends so far, but there are more\\n\",\n        \"Received 10000 friends\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"We now have the friends of 108 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2495448150\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Collected 5000 friends so far, but there are more\\n\",\n        \"Received 10000 friends\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"We now have the friends of 109 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2581008416\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Collected 5000 friends so far, but there are more\\n\",\n        \"Received 10000 friends\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"We now have the friends of 110 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 101514223\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Collected 5000 friends so far, but there are more\\n\",\n        \"Received 10000 friends\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"We now have the friends of 111 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2503943929\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Collected 5000 friends so far, but there are more\\n\",\n        \"Received 10000 friends\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"We now have the friends of 112 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2793385537\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Collected 5000 friends so far, but there are more\\n\",\n        \"Received 10000 friends\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"We now have the friends of 113 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2958027876\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Received 4760 friends\\n\",\n        \"We now have the friends of 114 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2928508388\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Collected 5000 friends so far, but there are more\\n\",\n        \"Received 6864 friends\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"We now have the friends of 115 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 1732417652\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Collected 5000 friends so far, but there are more\\n\",\n        \"Received 8567 friends\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"We now have the friends of 116 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2931059779\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Received 2373 friends\\n\",\n        \"We now have the friends of 117 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2957938574\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Received 2587 friends\\n\",\n        \"We now have the friends of 118 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 975731222\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Collected 5000 friends so far, but there are more\\n\",\n        \"Received 10000 friends\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"We now have the friends of 119 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2823412374\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Collected 5000 friends so far, but there are more\\n\",\n        \"Received 10000 friends\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"We now have the friends of 120 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2971020739\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Received 2071 friends\\n\",\n        \"We now have the friends of 121 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 107697554\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Collected 5000 friends so far, but there are more\\n\",\n        \"Received 10000 friends\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"We now have the friends of 122 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2962181449\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Received 2168 friends\\n\",\n        \"We now have the friends of 123 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 776960353\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Collected 5000 friends so far, but there are more\\n\",\n        \"Received 10000 friends\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"We now have the friends of 124 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2688288828\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Collected 5000 friends so far, but there are more\\n\",\n        \"Received 10000 friends\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"We now have the friends of 125 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2957580608\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Received 2425 friends\\n\",\n        \"We now have the friends of 126 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 1226769313\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Collected 5000 friends so far, but there are more\\n\",\n        \"Received 10000 friends\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"We now have the friends of 127 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 1445807384\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Collected 5000 friends so far, but there are more\\n\",\n        \"Received 10000 friends\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"We now have the friends of 128 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2970632521\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Received 1891 friends\\n\",\n        \"We now have the friends of 129 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2962508989\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Received 2875 friends\\n\",\n        \"We now have the friends of 130 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2960182986\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Received 3469 friends\\n\",\n        \"We now have the friends of 131 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2965449895\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Received 1903 friends\\n\",\n        \"We now have the friends of 132 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 448402915\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Collected 5000 friends so far, but there are more\\n\",\n        \"Received 10000 friends\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"We now have the friends of 133 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2965694707\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Received 1985 friends\\n\",\n        \"We now have the friends of 134 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2977860883\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Received 1498 friends\\n\",\n        \"We now have the friends of 135 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2974210219\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Received 1977 friends\\n\",\n        \"We now have the friends of 136 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2668260272\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Collected 5000 friends so far, but there are more\\n\",\n        \"Received 10000 friends\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"We now have the friends of 137 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2965296720\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Received 1989 friends\\n\",\n        \"We now have the friends of 138 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 1438122422\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Collected 5000 friends so far, but there are more\\n\",\n        \"Received 10000 friends\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"We now have the friends of 139 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2922384588\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Received 4375 friends\\n\",\n        \"We now have the friends of 140 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2965659278\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Received 1637 friends\\n\",\n        \"We now have the friends of 141 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2974113402\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Received 1972 friends\\n\",\n        \"We now have the friends of 142 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2979433100\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Received 1807 friends\\n\",\n        \"We now have the friends of 143 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2975784277\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Received 2001 friends\\n\",\n        \"We now have the friends of 144 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 1220556188\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Collected 5000 friends so far, but there are more\\n\",\n        \"Received 10000 friends\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"We now have the friends of 145 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2910113112\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Received 4915 friends\\n\",\n        \"We now have the friends of 146 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2946815604\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Received 3660 friends\\n\",\n        \"We now have the friends of 147 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2975720264\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Received 1903 friends\\n\",\n        \"We now have the friends of 148 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2967186031\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Received 1999 friends\\n\",\n        \"We now have the friends of 149 users\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Getting friends of user 2946867715\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Received 4119 friends\\n\",\n        \"We now have the friends of 150 users\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 20\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import json\\n\",\n      \"friends_filename = os.path.join(data_folder, \\\"python_friends.json\\\")\\n\",\n      \"with open(friends_filename, 'w') as outf:\\n\",\n      \"    json.dump(friends, outf)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 21\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import networkx as nx\\n\",\n      \"G = nx.DiGraph()\\n\",\n      \"\\n\",\n      \"main_users = friends.keys()\\n\",\n      \"G.add_nodes_from(main_users)\\n\",\n      \"\\n\",\n      \"for user_id in friends:\\n\",\n      \"    for friend in friends[user_id]:\\n\",\n      \"        if friend in main_users:\\n\",\n      \"           G.add_edge(user_id, friend) \\n\",\n      \"G\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 22,\n       \"text\": [\n        \"<networkx.classes.digraph.DiGraph at 0x10c9f7ac8>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 22\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"%matplotlib inline\\n\",\n      \"nx.draw(G)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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ykpKRg/fjxSUlKwffv2Vl9rwJcP63a7MXLkSKxduzaibXz33XcgIvzt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\\nWhhufDkcDofDaWG48eVwOBwOp4X5f3e7IG0mVWuWAAAAAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x10c9f7128>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 23\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from matplotlib import pyplot as plt\\n\",\n      \"plt.figure(3,figsize=(10, 10))\\n\",\n      \"nx.draw(G)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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WsbVDNNfXK7aMECPD1pEtYXFWFzcTHGAVXmGmgAjAewxeHA+qIiPP3o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\\nIiIikgiGfyIiIiIiifh/+zxl6mF+pSQAAAAASUVORK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x10bcf79e8>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 24\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}"
  },
  {
    "path": "Chapter 7/ch7_part2_twitter.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import json\\n\",\n    \"\\n\",\n    \"data_folder = os.path.join(os.path.expanduser(\\\"~\\\"), \\\"Data\\\", \\\"twitter\\\")\\n\",\n    \"friends_filename = os.path.join(data_folder, \\\"python_friends.json\\\")\\n\",\n    \"with open(friends_filename) as inf:\\n\",\n    \"    friends = json.load(inf)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"friends = {user: set(friends[user]) for user in friends}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_similarity(friends1, friends2):\\n\",\n    \"    set_friends1 = set(friends1)\\n\",\n    \"    set_friends2 = set(friends2)\\n\",\n    \"    return len(set_friends1 & set_friends2) / len(set_friends1 | set_friends2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import networkx as nx\\n\",\n    \"def create_graph(friends, threshold=0):\\n\",\n    \"    G = nx.Graph()\\n\",\n    \"    weights = []\\n\",\n    \"    for user1 in friends.keys():\\n\",\n    \"        for user2 in friends.keys():\\n\",\n    \"            if user1 == user2:\\n\",\n    \"                continue\\n\",\n    \"            weight = compute_similarity(friends[user1], friends[user2])\\n\",\n    \"            weights.append(weight)\\n\",\n    \"            if weight >= threshold:\\n\",\n    \"                G.add_node(user1)\\n\",\n    \"                G.add_node(user2)\\n\",\n    \"                G.add_edge(user1, user2, weight=weight)\\n\",\n    \"    return G\\n\",\n    \"\\n\",\n    \"G = create_graph(friends, 0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.collections.LineCollection at 0x10c8dbdd8>\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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W1tbVKO99Tq1Tzi9+NMwrFcwMN+P0+tXp2EowmRXBJ0iWHn3Z07udLv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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x108071358>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"from matplotlib import pyplot as plt\\n\",\n    \"plt.figure(figsize=(10,10))\\n\",\n    \"pos = nx.spring_layout(G)\\n\",\n    \"nx.draw_networkx_nodes(G, pos, node_size=500)\\n\",\n    \"\\n\",\n    \"edgewidth = [ d['weight'] for (u,v,d) in G.edges(data=True)]\\n\",\n    \"nx.draw_networkx_edges(G, pos, width=edgewidth)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Subgraph 0 has 11 nodes\\n\",\n      \"Subgraph 1 has 62 nodes\\n\",\n      \"Subgraph 2 has 3 nodes\\n\",\n      \"Subgraph 3 has 2 nodes\\n\",\n      \"Subgraph 4 has 2 nodes\\n\",\n      \"Subgraph 5 has 3 nodes\\n\",\n      \"Subgraph 6 has 5 nodes\\n\",\n      \"Subgraph 7 has 2 nodes\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"G = create_graph(friends, 0.1)\\n\",\n    \"sub_graphs = nx.connected_component_subgraphs(G)\\n\",\n    \"\\n\",\n    \"for i, sub_graph in enumerate(sub_graphs):\\n\",\n    \"    n_nodes = len(sub_graph.nodes())\\n\",\n    \"    print(\\\"Subgraph {0} has {1} nodes\\\".format(i, n_nodes))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Subgraph 0 has 9 nodes\\n\",\n      \"Subgraph 1 has 19 nodes\\n\",\n      \"Subgraph 2 has 4 nodes\\n\",\n      \"Subgraph 3 has 2 nodes\\n\",\n      \"Subgraph 4 has 2 nodes\\n\",\n      \"Subgraph 5 has 5 nodes\\n\",\n      \"Subgraph 6 has 2 nodes\\n\",\n      \"Subgraph 7 has 2 nodes\\n\",\n      \"Subgraph 8 has 3 nodes\\n\",\n      \"Subgraph 9 has 3 nodes\\n\",\n      \"Subgraph 10 has 2 nodes\\n\",\n      \"Subgraph 11 has 5 nodes\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"G = create_graph(friends, 0.15)\\n\",\n    \"sub_graphs = nx.connected_component_subgraphs(G)\\n\",\n    \"\\n\",\n    \"for i, sub_graph in enumerate(sub_graphs):\\n\",\n    \"    n_nodes = len(sub_graph.nodes())\\n\",\n    \"    print(\\\"Subgraph {0} has {1} nodes\\\".format(i, n_nodes))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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G5uXqa+PSx08X1bLSw6dQgjhiqwaeu2Cl3wEBGJgeGfiGqso0ePYsnC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\\nYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x105ba0940>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sub_graphs = nx.connected_component_subgraphs(G)\\n\",\n    \"label_dict = {}\\n\",\n    \"for i, sub_graph in enumerate(sub_graphs):\\n\",\n    \"    for node in sub_graph.nodes():\\n\",\n    \"        label_dict[node] = i\\n\",\n    \"labels = [label_dict[node] for node in G.nodes()]\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(10,10))\\n\",\n    \"nx.draw(G,node_color=labels,cmap=plt.cm.Paired, node_size=500)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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RUg3M6OPUrhWLIkDuXLo9PpuHXrFmXKlOHBgweUzNy6p33mkf3evZkH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\\n6ZIkSWrApkuSJKmB/wOz+yc94wiLrwAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x105b4fb70>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sub_graphs = nx.connected_component_subgraphs(G)\\n\",\n    \"plt.figure(figsize=(10,10))\\n\",\n    \"pos = nx.spring_layout(G)\\n\",\n    \"for i, sub_graph in enumerate(sub_graphs):\\n\",\n    \"    nodes = sub_graph.nodes()\\n\",\n    \"    edges = sub_graph.edges()\\n\",\n    \"    nx.draw_networkx_nodes(G, pos, nodes,node_size=500)\\n\",\n    \"    nx.draw_networkx_edges(G, pos, edges)\\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/bob/anaconda/lib/python3.4/site-packages/matplotlib/axes/_subplots.py:69: MatplotlibDeprecationWarning: The use of 0 (which ends up being the _last_ sub-plot) is deprecated in 1.4 and will raise an error in 1.5\\n\",\n      \"  mplDeprecation)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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48Pt23YwHdmM2MpOaTh8rWxwHdmM53Wr6efjw8ZGRkAbNq0CV9fX4xG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n5uvm6CpJ9eeEHvTpni5isDAPD3rjeD+eyJGkn6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MGA/5ycOX/+vObPn3/T7yxevFgvv/yyNmzYoIcffrgY0wFA0VDUAACAG6Ko\\nMSd2MEA6f/686tatq5SUFNWrV++m31u+fLlGjx6tdevW6dFHHy3GhABQeDyjBgAAAECJ4uXlpZde\\nekkTJkwo8Hu9e/fWBx98oNDQUO3cubOY0gGA63CiBgAAN8aJGnNiBwP+4/Lly6pbt65Wrlx5y9My\\niYmJeuaZZ/Tpp5+qY8eOxZQQAAqHW5/gVJmZmdq5c6cO7t6ts6dOSZKq+vioaatWCgoKkp+fn8EJ\\nAQC3i6LGnNjBgP8xf/58xcfHKzk5WRZLwf+52rlzp3r16qUPP/xQoaGhxZQQAO4cRQ2cIiEhQdOj\\no/X10aPqYLWqWXa2ql//7LSkAzabku12NfT3198iI/nLEQBKAIoac2IHA/5HXl6e/P39NWvWLIWE\\nhNzy+3v37tUTTzyhuXPnqnfv3sWQEADuHEUNiiQrK0ujBg1S2vbtis7OVg9Jnjf5bq6kVZIibTY1\\nbtdOcxcskLe3d/GFBQDcEYoac2IHA35v+fLlmjRpkg4cOCCr9daP2fzqq6/UuXNnTZ06Vf379y+G\\nhABwZ3iYMArt22+/VYuAANVMStKh7Gz11c1LGl3/rK+kQ9nZ8klMVIuAAH333XfFExYAAAClUq9e\\nvVS2bFktXbr0tr7/8MMPKykpSa+++qref/99F6cDAOfiRA1uKisrSy0CAjQuK0uj7fZCzZhrtept\\nb2/tTUvjZA0AmBAnasyJHQz4oy1btmjYsGE6duyYPD0L+l+H/yMjI0MdO3bUuHHj9MILL7g4IQDc\\nPk7UoFBGDRqknufOFbqkkaTRdrt6nj+v0YMHOzEZAAAA3E1wcLD8/Pz03nvv3fbP1KlTR9u3b9fM\\nmTMVGxvrwnQA4DycqMENrVu3Ti8//bS+ys5WuSLOypHUxGbTtPh4HjAMACbDiRpzYgcDbuzQoUPq\\n0qWLMjIyVKFChdv+uTNnzqhDhw7q27evoqKibvn2KABwNbHEcBAAACAASURBVB4mjDsW3Ly5wvbv\\nV18nzYuX9F7z5kret89JEwEAzkBRY07sYMDNPf3002rYsKHCw8Pv6OfOnj2rTp066fHHH1dsbCxl\\nDQBDUdTgjmRkZCjoz3/WqZycAh8cfCdyJfmUL6+U1FT5+fk5aSoAoKgoasyJHQy4uczMTLVs2VLp\\n6emqXLnyHf3szz//rJCQELVs2VKzZs26rTdIAYAr8Iwa3JGUlBR1sFqdVtJI/3kbVLDVqpSUFCdO\\nBQAAgLvx8/NT3759NXny5Dv+2fvvv1/Jyck6dOiQhg8frvz8fBckBICioajBHxzcvVvNsrOdPrdZ\\ndrYO7Nrl9LkAAABwL+Hh4Vq4cKFOnjx5xz977733atOmTfrmm2/07LPP6tq1ay5ICACFR1GDPzh7\\n6pSqu2Bu9euzAQAAgKJ44IEHNGrUKEVGRhbq5ytUqKD169fr3Llz6tu3r3Jzc52cEAAKj6IGAAAA\\nQInz8ssva8OGDTpy5Eihfr58+fJavXq1HA6HevTooZycHCcnBIDCoajBH1T18dFpF8w9fX02AAAA\\nUFQVK1bU+PHj9frrrxd6xl133aVly5apYsWKCg0NVbYLbv8HgDtFUYM/aNqqlQ7YbE6fe8BmU7PW\\nrZ0+FwAAAO5p5MiROnz4cJFeWFG2bFktXrxYPj4+CgkJ0cWLF52YEADuHEUN/iAoKEjJdruceadu\\nrqQtdrsCAwOdOBUAAADurFy5coqOjtb48eNVlFfalylTRh988IEaN26sjh076ty5c05MCQB3hqIG\\nf+Dn56eG/v5a5cSZKyU9+NBDql27thOnAgAAwN0NGDBAFy5cUEJCQpHmWK1WzZ07V0FBQWrfvr1+\\n/PFHJyUEgDtDUYMbGhsRoUibTc54pFqOpPC77tKF/Hw98sgj+vzzz4v0fzwAAACA/ypTpowmT56s\\n1157Tfn5+UWaZbFYNG3aND355JNq27at/v3vfzspJQDcPooa3FC3bt0U0Latwj09izwr3NNTTTp2\\nVGZmpl5//XW9+uqratWqlTZv3kxhAwAAgCLr1q2b7rvvPi1evLjIsywWi2JiYvTss8+qTZs2Onny\\npBMSAsDtsxT0D2WLxeLgH9LuKysrSy0CAjQuK0uj7fZCzZhrtWp6lSrak5oqb29vSZLdbteyZcsU\\nFRUlb29vxcTEqH379s6MDgC4TRaLRQ6Hw2J0DvweOxhw51JSUvSXv/xFx48fV7ly5Zwyc+bMmZox\\nY4aSkpLk5+fnlJkAIBW8g3GiBjfl7e2t5N279ba3t17y9Lyj26ByJI3z9NT0KlWUtGvXbyWN9J/7\\nf/v166ejR49q+PDhGjZsmIKDg4v0tH4AAAC4t8DAQDVu3Fjvvvuu02a++OKLev3119WuXTsdO3bM\\naXMBoCAUNSiQr6+v9qal6VSnTmpisyleKvBtULmS4iU1sdl0+rHHtCc1Vb6+vjf8bpkyZTRgwACl\\np6erf//+GjBggEJCQrR3714X/CYAAAAo7SZPnqy33nrLqa/YHj58uCZPnqzg4GAdPnzYaXMB4Ga4\\n9Qm3LSEhQTNiYnT0yBEFW61qlp2t6tc/Oy3pgM2mLXa7/Bs10tiICIWGht7R/NzcXC1YsECTJk1S\\n48aNFR0drWbNmjn99wAA/A9ufTIndjCg8AYOHKiaNWsqJibGqXM/++wzPf/880pISFDz5s2dOhuA\\n+yloB6OowR3LzMxUSkqKDuzapbOnTkmSqvr4qFnr1goMDCzy/btXr17Ve++9pylTpqh58+aKjo7W\\nn//8Z2dEBwD8HxQ15sQOBhTeyZMn1bRpU3399deqWrWqU2evW7dOzz33nFauXKnAwECnzgbgXihq\\nUCLl5OQoLi5OsbGxCgwMVFRUlPz9/Y2OBQClCkWNObGDAUUzduxYXbt2TXPmzHH67MTERD3zzDOK\\nj49Xhw4dnD4fgHugqEGJlp2drblz52ratGnq2LGjIiMjVa9ePaNjAUCpQFFjTuxgQNH89NNPql+/\\nvvbu3avatWs7ff727dvVu3dvLVy4UF26dHH6fAClH299Qolms9n0yiuv6JtvvlGjRo0UGBiogQMH\\n6ptvvjE6GgAAAEyocuXKevHFFxUeHu6S+W3bttW6des0aNAgrVy50iXXAOC+KGpQYtxzzz16/fXX\\nlZmZqVq1aqlFixYaOnSoTp48aXQ0AAAAmMzYsWO1detWHTp0yCXzW7ZsqY0bN2rUqFFasmSJS64B\\nwD1R1KDEuffeexUZGakTJ07ogQceUNOmTTVy5EidPn3a6GgAAAAwiQoVKuiNN97Qa6+95rJrNG3a\\nVElJSXr55Zf14Ycfuuw6ANwLRQ1KrEqVKunNN9/U8ePHVbFiRTVu3FgvvPCCvv/+e6OjAQAAwASG\\nDRumjIwMbd261WXXaNSokbZu3aqoqCiXPLwYgPuhqEGJV7lyZcXGxurYsWPy8PCQv7+/xo0bpx9/\\n/NHoaAAAADCQp6enJk6cqPHjx8uVD+iuW7eutm/frunTp2vq1Kkuuw4A90BRg1KjatWqmj59uo4c\\nOaLc3Fw1aNBA48eP108//WR0NAAAABikX79+ys3NdflDf319fbVjxw69//77io6OdmkxBKB0o6hB\\nqfPggw9q9uzZ+uqrr3ThwgXVq1dP4eHhOn/+vNHRAAAAUMysVqumTJmiCRMmKC8vz6XXql69unbs\\n2KHly5frtddeo6wBUCgUNSi1atSooXnz5unAgQP697//rTp16igmJka//PKL0dEAAABQjEJCQlSt\\nWjUtWLDA5deqWrWqtm7dqsTERL344ouy2+0uvyaA0oWiBqXen/70J33wwQfas2ePvvnmG9WpU0dT\\npkzRpUuXjI4GAACAYmCxWPTWW28pOjpaly9fdvn1KleurOTkZH355ZcKCwtTfn6+y68JoPSgqIHb\\n8PPz08KFC7Vjxw6lpqaqdu3amjp1arH8ZQ0AAABjtWjRQi1atCi2NzPdd9992rx5szIzMzVw4ECX\\n33YFoPSwFHTfpMVicXBfJUqrI0eOKCoqSl988YXGjx+vsLAwlStXzuhYAFCsLBaLHA6Hxegc+D12\\nMMA10tPTFRQUpBMnTsjLy6tYrnn58mX17NlTFSpU0JIlS+Tp6Vks1wVgbgXtYJyogdtq1KiRli9f\\nrg0bNmjr1q3y8/PT3LlzdfXqVaOjAQAAwAXq16+v7t27KzY2ttiueffdd2vNmjXKy8tTz549deXK\\nlWK7NoCSiRM1wHX79+9XZGSk0tLS9MYbb2jw4MEqW7as0bEAwKU4UWNO7GCA65w5c0aNGzdWamqq\\nHnrooWK77rVr19S/f3+dO3dOq1evls1mK7ZrAzAfTtQAt+GRRx7R559/rqVLl2r58uWqV6+eFixY\\nwP3EAAAApchDDz2koUOHKjo6ulivW7ZsWS1ZskQPPfSQOnfurIsXLxbr9QGUHJyoAW5ix44dioiI\\n0JkzZxQZGamnn35aZcqUMToWADgVJ2rMiR0McK3z58+rbt26SklJUb169Yr12na7XaNHj9bBgwe1\\ncePGYntWDgBzKWgHo6gBbmHLli0KDw/XuXPnFBUVpT59+shq5TAagNKBosac2MEA14uNjdWXX36p\\n5cuXF/u1HQ6Hxo0bp61bt2rz5s3y9vYu9gwAjEVRAxSRw+HQ5s2bFRERocuXLys6Olo9evSQxcK/\\nbQCUbBQ15sQOBrje5cuXVbduXa1cuVKPPvposV/f4XAoPDxcq1atUlJSkqpVq1bsGQAYh6IGcBKH\\nw6H169crIiJCDodD0dHRCg0NpbABUGJR1JgTOxhQPObPn6/4+HglJycbts9NmjRJH330kZKTk+Xj\\n42NIBgDFj6IGcDKHw6E1a9YoIiJC5cqVU0xMjEJCQihsAJQ4FDXmxA4GFI+8vDz5+/tr1qxZCgkJ\\nMSzHjBkzNGvWLCUlJal27dqG5QBQfChqABex2+1asWKFIiMj5eXlpZiYGAUHB1PYACgxKGrMiR0M\\nKD7Lly/XpEmTdODAAUOfQzhv3jxNmjRJiYmJql+/vmE5ABQPXs8NuIjValWfPn2Ulpam0aNHa+TI\\nkWrXrp127NhhdDQAAADchl69eqls2bJaunSpoTlGjBihN998U8HBwUpNTTU0CwBjcaIGcKK8vDx9\\n8skniomJka+vryZOnKhWrVoZHQsAbooTNebEDgYUry1btmjYsGE6duyYPD09Dc2ydOlSvfjii0pI\\nSNAjjzxiaBYArsOJGqCYeHh4aODAgUpPT1e/fv3Ur18/de7cWV9++aXR0QAAAHATwcHB8vPz03vv\\nvWd0FPXt21dxcXHq0qWLvvjiC6PjADAAJ2oAF7p69ao+/PBDTZo0SU2bNlV0dLSaNGlidCwA+A0n\\nasyJHQwofocOHVKXLl2UkZGhChUqGB1HmzZtUv/+/bV06VIFBwcbHQeAk3GiBjDIXXfdpZEjRyoz\\nM1MdO3ZU165d1atXL6WlpRkdDQAAAP9LkyZN1K5dO73zzjtGR5EkhYSE6LPPPlPfvn21YcMGo+MA\\nKEacqAGK0eXLl/Xuu+9q6tSpateunaKioniqPwBDcaLGnNjBAGNkZmaqZcuWSk9PV+XKlY2OI0na\\nvXu3nnzyScXFxalHjx5GxwHgJJyoAUzi7rvv1rhx45SZmamHH35Ybdq00YABA5SRkWF0NAAAALfn\\n5+envn37avLkyUZH+U2rVq20YcMGjRw5UvHx8UbHAVAMOFEDGOjixYuaOXOmZs6cqSeeeELh4eHy\\n9fU1OhYAN8KJGnNiBwOM88MPP8jf318HDx5UzZo1jY7zm7S0NIWEhGjSpEkaPHiw0XEAFBEnagCT\\nqlixosLDw5WZmanq1avrkUceUVhYmE6dOmV0NAAAALf0wAMPaNSoUYqMjDQ6yu8EBARo69atioiI\\n0D/+8Q+j4wBwIYoawATuu+8+xcTE6MSJE6pUqZKaNGmi559/XmfOnDE6GgAAgNt5+eWXtWHDBh05\\ncsToKL9Tr149bd++XVOnTtXbb79tdBwALkJRA5jI/fffrylTpujYsWMqV66cAgICNGbMGP3www9G\\nRwMAAHAbFStW1Pjx4/X6668bHeUPatWqpR07diguLk4TJ04Ut0kCpQ9FDWBCVapU0bRp03T06FFJ\\nkr+/v1555RVlZWUZnAwAAMA9jBw5UocPH1ZKSorRUf6gRo0a2rFjh5YuXaoJEyZQ1gClDEUNYGLV\\nqlXTO++8o8OHDys7O1v169fXhAkTdO7cOaOjAQAAlGrlypVTdHS0xo8fb8oi5IEHHtC2bdu0YcMG\\njR071pQZARQORQ1QAlSvXl1z587VwYMHlZWVpbp16yoyMlIXLlwwOhoAAECpNWDAAF24cEEJCQlG\\nR7mhypUra8uWLdqzZ49GjBghu91udCQATkBRA5QgNWvW1Pz587Vv3z6dOnVKderU0Ztvvqlff/3V\\n6GgAAAClTpkyZTR58mS99tprys/PNzrODXl5eSkxMVHp6ekaNGiQ8vLyjI4EoIgoaoASqFatWlqw\\nYIG++OILpaenq3bt2oqNjVV2drbR0QAAAEqVbt266b777tPixYuNjnJT99xzjzZs2KAffvhBTz/9\\ntHJzc42OBKAIKGqAEqxu3bpavHixtm3bpoMHD6p27dqaPn26cnJyjI4GAABQKlgsFr311luKjIzU\\n1atXjY5zU3fffbfWrl2rq1evqnfv3rpy5YrRkQAUEkUNUAo0bNhQS5cu1ebNm5WSkiI/Pz/Nnj2b\\nv6ABAACcIDAwUAEBAXr33XeNjlKgcuXKafny5SpXrpyeeOIJXb582ehIAArBUtDTwS0Wi4OnhwMl\\nz8GDBxUZGamvvvpKEyZM0JAhQ+Tp6Wl0LAAmZLFY5HA4LEbnwO+xgwHmk5aWpo4dOyojI0MVK1Y0\\nOk6B8vLyNGTIEJ08eVIJCQm65557jI4E4P8oaAfjRA1QCjVt2lTr1q3TihUrtHr1atWtW1cffPCB\\nrl27ZnQ0AACAEikgIECPP/64pk2bZnSUW/Lw8NBHH32k+vXrq1OnTrwpFChhOFEDuIEvvvhCkZGR\\n+uc//6mIiAg988wz8vDwMDoWABPgRI05sYMB5nTy5Ek1bdpUX3/9tapWrWp0nFtyOBwaO3asduzY\\noc2bN6ty5cpGRwJwXUE7GEUN4Ea2b9+u8PBwnT17VpGRkerbt6/KlCljdCwABqKoMSd2MMC8xo4d\\nq2vXrmnOnDlGR7ktDodDEyZM0Nq1a5WYmKhq1aoZHQmAKGoA/C8Oh0PJyckKDw/XxYsXFR0drZ49\\ne8pq5U5IwB1R1JgTOxhgXj/99JPq16+vvXv3qnbt2kbHuW1vvvmmFi1apOTkZNWoUcPoOIDbo6gB\\n8AcOh0MbN25URESEcnNzFR0drSeffFIWC/9eA9wJRY05sYMB5jZx4kQdO3ZMS5YsMTrKHXn77bc1\\nZ84cJScnq1atWkbHAdwaRQ2Am3I4HFq3bp0iIiJUpkwZxcTEqEuXLhQ2gJugqDEndjDA3C5duqQ6\\ndepo/fr1atKkidFx7sg//vEPTZkyRUlJSapXr57RcQC3RVED4JbsdrtWrVqlyMhIVahQQTExMerU\\nqROFDVDKUdSYEzsYYH5z587VunXrtHHjRqOj3LEFCxZowoQJ2rRpkwICAoyOA7glihoAt81ut2vZ\\nsmWKioqSt7e3YmJi1L59e6NjAXARihpzYgcDzC83N1cNGjTQ+++/XyJ3pU8//VRjx47V+vXr1bRp\\nU6PjAG6HogbAHcvPz9eSJUsUHR0tHx8fxcTEKDAw0OhYAJyMosac2MGAkmHJkiWaOXOm9uzZUyJP\\nIa9evVrDhw/XmjVr1KpVK6PjAG6loB2M17wAuKEyZcpowIABSk9PV//+/TVgwACFhIRo7969RkcD\\nAAAwhX79+ik3N1crV640OkqhdO/eXYsWLdITTzyhbdu2GR0HwHUUNQAK5OHhoSFDhuj48ePq1auX\\n+vTpo9DQUB04cMDoaAAAAIayWq2aMmWKJkyYoLy8PKPjFMrjjz+upUuXqk+fPtq0aZPRcQCIogbA\\nbfL09NTw4cOVkZGhzp0764knnlD37t11+PBho6MBAAAYJiQkRNWqVdNHH31kdJRCCw4O1urVqzVg\\nwACtWbPG6DiA2+MZNQAKJScnR3FxcYqNjVVgYKCioqLk7+9vdCwAd4hn1JgTOxhQsuzdu1e9e/fW\\niRMnVL58eaPjFNr+/fsVGhqqmTNnqm/fvkbHAUo1nlEDwOnKly+vMWPGKDMzU48++qiCg4P1zDPP\\n6Pjx40ZHAwAAKFYtWrTQo48+qtmzZxsdpUgeeeQRbd68WWPHjtXChQuNjgO4LU7UAHCKX3/9VbNn\\nz9aMGTPUpUsXRUREqHbt2kbHAnALnKgxJ3YwoORJT09XUFCQTpw4IS8vL6PjFEl6ero6deqkCRMm\\naMSIEUbHAUolTtQAcLl77rlHr7/+ujIzM1WrVi21aNFCQ4cO1cmTJ42OBgAA4HL169dX9+7dFRsb\\na3SUIqtfv762bdum2NhYzZgxw+g4gNvhRA0Alzh//rzefvttvfvuu3rqqac0YcIEVa9e3ehYAP4P\\nTtSYEzsYUDKdOXNGjRs3Vmpqqh566CGj4xTZqVOn1KFDBw0aNEgTJkwwOg5QqnCiBkCx8/Ly0ptv\\nvqnjx4+rYsWKaty4sV544QV9//33RkcDAABwiYceekhDhw5VdHS00VGcwsfHRzt27NCSJUs0YcIE\\nUSADxYOiBoBLVa5cWbGxsTp27Jg8PDzk7++vcePG6ccffzQ6GgAAgNONHz9eq1atKjUvWKhWrZq2\\nbdumzz//XOPGjaOsAYoBRQ2AYlG1alVNnz5dR44cUW5urho0aKDx48frp59+MjoaAACA03h5eeml\\nl14qVbcKeXt7a+vWrfriiy80atQo2e12oyMBpRpFDYBi9eCDD2r27Nn66quvdOHCBdWrV0/h4eE6\\nf/680dEAAACc4q9//av27Nmjffv2GR3Faby8vJSYmKijR49qyJAhys/PNzoSUGpR1AAwRI0aNTRv\\n3jwdOHBA33//verUqaOYmBj98ssvRkcDAAAokrvvvlsREREaP358qbpVqGLFitqwYYPOnDmjv/zl\\nL7p27ZrRkYBSiaIGgKH+9Kc/6f3339eePXv0zTffqE6dOpoyZYouXbpkdDQAAIBCGzJkiM6cOaPN\\nmzcbHcWpbDab1q1bp0uXLql37966evWq0ZGAUoeiBoAp+Pn5aeHChdqxY4dSU1NVu3ZtTZ06VZcv\\nXzY6GgAAwB3z8PDQpEmTNH78+FL3TJdy5cpp5cqVKlu2rJ544gn2NcDJKGoAmEr9+vX16aefKjk5\\nWfv27ZOfn59mzpypK1euGB0NAADgjvTq1Utly5bVsmXLjI7idJ6enoqPj5e3t7e6du2qX3/91ehI\\nQKlBUQPAlBo1aqTPPvtM69ev19atW+Xn56e5c+dyvBYAAJQYFotFb731lt544w3l5uYaHcfpPDw8\\ntHDhQvn5+SkkJEQXLlwwOhJQKlDUADC1hx9+WKtXr9bq1au1fv161alTR/Pnz+fhdQAAoEQIDg5W\\n7dq19f777xsdxSXKlCmj+fPnq3nz5urQoYN+/vlnoyMBJZ6loKeQWywWR2l6SjmAkm/37t2KjIxU\\nZmamwsPDNWDAAHl4eBgdCyixLBaLHA6Hxegc+D12MKB0OXTokLp27aoTJ06oQoUKRsdxCYfDodde\\ne02ff/65EhMT9cADDxgdCTC1gnYwTtQAKFFatWqlzZs3a+HChVq0aJEaNGigxYsXKz8/3+hoAAAA\\nN9SkSRO1bdtW77zzjtFRXMZisWjKlCnq06eP2rZtq9OnTxsdCSixOFEDoETbsmWLwsPDde7cOUVF\\nRalPnz6yWumggdvFiRpzYgcDSp/MzEy1bNlS6enpqly5stFxXGrq1Kl69913lZycLF9fX6PjAKZU\\n0A5GUQOgxHM4HEpMTFR4eLguX76s6Oho9ejRQxYL//YEboWixpzYwYDSafTo0brrrrs0ffp0o6O4\\n3Jw5c/T3v/9dSUlJqlu3rtFxANOhqAHgFhwOh9avX6+IiAg5HA5FR0crNDSUwgYoAEWNObGDAaXT\\nDz/8IH9/fx08eFA1a9Y0Oo7LffjhhwoPD9emTZvUqFEjo+MApkJRA8CtOBwOrVmzRhERESpXrpxi\\nYmIUEhJCYQPcAEWNObGDAaVXeHi4/vWvf+mjjz4yOkqx+PTTT/W3v/1N69evV5MmTYyOA5gGRQ0A\\nt2S327VixQpFRkbKy8tLMTExCg4OprAB/heKGnNiBwNKr4sXL6pOnTpKTk5Wo0aNlJmZqZ07d+rg\\n7t06e+qUJKmqj4+atmqloKAg+fn5GZy46FauXKmRI0dqzZo1atmypdFxAFOgqAHg1vLz87V06VJF\\nRUWpWrVqmjhxotq0aWN0LMAUKGrMiR0MKN1mzJih+Ph42ex2fX30qDpYrWqWna3q1z8/LemAzaZk\\nu10N/f31t8hIhYaGGhm5yNavX6+BAwdq+fLlatu2rdFxAMNR1ACApLy8PH3yySeKiYmRr6+vJk6c\\nqFatWhkdCzAURY05sYMBpVdWVpbCBgzQ/s2bNdXhUA9Jnjf5bq6kVZIibTY1btdOcxcskLe3d/GF\\ndbLk5GT169dPn3zyiR577DGj4wCGKmgH4x22ANyGh4eHBg4cqPT0dPXr10/9+vVT586d9eWXXxod\\nDQAAuIFvv/1WLQICVGvrVh13ONRXNy9pdP2zvpIOZWfLJzFRLQIC9N133xVPWBfo0KGDVq1apf79\\n+2vdunVGxwFMixM1ANxWbm6uPvjgA02aNElNmzZVdHQ0D7mD2+FEjTmxgwGlT1ZWlloEBGhcVpZG\\n2+2FmjHXatXb3t7am5ZWok/WfPnllwoNDdWcOXPUp08fo+MAhuBEDQDcgKenp0aOHKnMzEx17NhR\\nXbt2Va9evZSWlmZ0NAAAUMqMGjRIPc+dK3RJI0mj7Xb1PH9eowcPdmKy4te8eXNt3rxZL7zwgj7+\\n+GOj4wCmw4kaALju8uXLevfddzV16lS1a9dOUVFRql+/fpFmusObHFCycaLGnNjBgNJl3bp1evnp\\np/VVdrbKFXFWjqQmNpumxceX+AcMHzt2TJ06dVJERISGDx9udBygWPEwYQC4A5cuXdKcOXM0ffp0\\nhYSEKCIiQnXq1LmjGQkJCZoeHe02b3JAyUVRY07sYEDpEty8ucL271dfJ82Ll/Re8+ZK3rfPSRON\\n89+TzWPGjNGYMWOMjgMUG4oaACiEixcvatasWZo5c6a6deum8PBw+fr6FvgzWVlZGjVokNK2b1d0\\ndrZbvckBJRNFjTmxgwGlR0ZGhoL+/Gedyskp8MHBdyJXkk/58kpJTS0Vp3NPnjypDh066LnnntNr\\nr71mdBygWPCMGgAohIoVK+qNN95QRkaGatSooebNmyssLEynrt/C9H/9900ONZOSdCg72+3e5AAA\\nAP4oJSVFHaxWp5U00n92iGCrVSkpKU6capyaNWtqx44dWrRokcLDw0VRDXdHUQMAt3DfffcpOjpa\\nx48f1/33368mTZro+eef15kzZ377TlZWljq2bq1xWVmalpur8ncwv7ykabm5GpeVpQ6tWikrK8vp\\nvwMAADDGwd271Sw72+lzm2Vn68CuXU6fa5QHH3xQ27dv/8/zfF5+mbIGbo2iBgBu0/3336/Jkyfr\\n2LFjKl++vAICAjRmzBj98MMPvMkBAADc0NlTp357Tp0zVb8+uzSpUqWKtmzZoh07duj555+XvQh7\\nFVCSUdQAwB2qUqWKpk6dqq+//lqSVKdOHR3YvFlvXrtW5NkTc3OVum2bEhISijwLAACgpKlUqZKS\\nkpKUmpqqoUOHKj8/3+hIQLGjqAGAQnrggQf0zjvv6M+1amlKXl6RX7cp/ec2qKjsbM2IiXHCNAAA\\nYLSqPj467YK5p6/PLo0qVqyojRs36uTJk+rfv7+u3qloDQAAIABJREFUOeF/hgElCUUNABRBRkaG\\nMjMy1MOJM3tKOnrkiDIzM504FQAAGKFpq1Y6YLM5fe4Bm03NWrd2+lyzsNlsSkhI0MWLF/XUU0/p\\n6tWrRkcCig1FDQAUAW9yAAAABQkKClKy3a5cJ87MlbQlP1+BgYFOnGo+5cuX16pVq2S1WtW9e3fl\\n5OQYHQkoFhQ1AFAEvMkBAAAUxM/PTw39/bXKiTNXSrp67ZpmzZqlo0ePOnGy+Xh6emrp0qWqVKmS\\nunbtqkuXLhkdCXA5ihoAKALe5AAAAG5lbESEIm02OeM8SI6kKJtNb8+fr3vvvVedOnVSUFCQFi9e\\nrCtXrjjhCubj4eGhRYsWqVatWgoJCdEvv/xidCTApShqAAAAAMCFunXrpoC2bRXuWfSbpcM9PdW4\\nXTsNGTJEEydO1MmTJzV27Fh9/PHHqlGjhsaNG6fjx487IbW5lClTRvPnz1fTpk3VoUMH/fzzz0ZH\\nAlyGogYAioA3OQAAgNvxj48+0kovL821Fv6fYHOtVq2qVElzFyz47c/Kli2rnj17atOmTdqzZ4/K\\nli2rNm3aqH379lq6dKlyc535dBxjWa1WzZo1S8HBwWrfvr3Onj1rdCTAJShqAKAIeJMDAAC4Hd7e\\n3krevVtve3vrJU/PO7oNKkfSOE9PTa9SRUm7dsnb2/uG36tdu7beeust/etf/9KIESMUFxenGjVq\\n6NVXX9U333zjlN/DaBaLRbGxserZs6fatm2rM2fOGB0JcDqKGgAoApe9ycFuL/VvcgAAwN34+vpq\\nb1qaTnXqpCY2m+KlAneIXEnxkprYbDr92GPak5oqX1/fW17H09NTffv21ZYtW7Rjxw7l5eWpZcuW\\neuyxx7RixQpdu3bNSb+RMSwWi6KiojR48GC1adNG//znP42OBDiVxeFw3PxDi8VR0OcAACm4eXOF\\n7d+vvk6aFy/pvebNlbxvn5MmAjdnsVjkcDgsRufA77GDAaVfQkKCZsTE6OiRIwq2WtUsO/u3FxSc\\n1n9O126x2+XfqJHGRkQoNDS0SNe7cuWKVqxYobi4OGVmZmrIkCEaNmyYatasWeTfxUizZ8/WtGnT\\nlJSUpDp16hgdB7htBe1gFDUAUETr1q3Ty08/rUPZ2SpfxFk5+s//NZsWH1/khQy4HRQ15sQOBriP\\nzMxMpaSk6MCuXb+98bGqj4+atW6twMBA+fn5Of2aR48e1fz587V48WK1bNlSYWFh6tKlizw8PJx+\\nreLw/vvvKyoqSps3b1bDhg2NjgPcFooaAHCxPl27qmZSkqYV8YF9L3l66lSnTlqWkOCkZEDBKGrM\\niR0MQHG4fPmyli1bpri4OJ0+fVpDhw7Vc889p+rVq9/6h03mk08+0UsvvaQNGzbo4YcfNjoOcEsU\\nNQDgYllZWWoREKBxWVkabbcXasZcq1XTq1TRntTUmz4kEHA2ihpzYgcDUNwOHz6suLg4xcfHq02b\\nNgoLC9Njjz2mMmXKGB3ttq1YsUKjRo3S2rVr1aJFC6PjAAWiqAGAYvDdd9+pQ6tW6nn+vCbm5t72\\nbVA5kt7w9NTqSpWUtGvXbT0kEHAWihpzYgcDYJRLly4pPj5e8+bN008//aRhw4ZpyJAhqlatmtHR\\nbsvnn3+uwYMHa/ny5WrTpo3RcYCbKmgH461PAOAkxfUmBwAAAFepUKGChg4dqv3792vFihU6efKk\\nGjZsqN69eysxMVH2Qp4cLi5du3bVkiVL1KtXLyUlJRkdBygUTtQAgAsU95scgMLiRI05sYMBMJOL\\nFy/qk08+0bx585Sdna3hw4dr8ODBpr5Ve+fOnerVq5c+/PBD9iyYErc+AYBBjHiTA3AnKGrMiR0M\\ngBk5HA7t3btXcXFxWrVqlTp37qywsDC1bdtWFov5/irZt2+funXrprlz56p3795GxwF+h6IGAADc\\nEEWNObGDATC78+fP6+OPP1ZcXJzy8/MVFhamgQMHqlKlSkZH+52vvvpKnTt31tSpU9W/f3+j4wC/\\noagBAAA3RFFjTuxgAEoKh8OhL774QvPmzVNCQoK6deumESNGqHXr1qY5ZfP111/rscceU1RUlIYO\\nHWp0HEASRQ0AALgJihpzYgcDUBL9/PPPWrhwoeLi4lS2bFmFhYVpwIABuu+++4yOpoyMDHXs2FHj\\nxo3TCy+8YHQcgKIGAADcGEWNObGDASjJHA6Htm3bpnnz5mnTpk3q2bOnwsLC9Oijjxp6yuaf//yn\\nOnTooOHDh+vVV181LAcgUdQAAICboKgxJ3YwAKXFjz/+qAULFmj+/PmqWLGiwsLC9Je//EX33HOP\\nIXnOnDmjjh076qmnnlJUVJRpbs+C+6GoAQAAN0RRY07sYABKG7vdrqSkJMXFxWnLli166qmnFBYW\\npqZNmxZ7lh9//FGdOnVSSEiIYmNjKWtgCIoaAABwQxQ15sQOBqA0+/e//60PP/xQ7733nqpWraqw\\nsDD169d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L27mA+9QkAgF5z7tln57QXXtjhSJMk53V25rQNG3Lepz/d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\\nENQAAAAAAABYCYIaAAAAAAAAK/G/cVILrzESFMAAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10c35abe0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sub_graphs = nx.connected_component_subgraphs(G)\\n\",\n    \"n_subgraphs = nx.number_connected_components(G)\\n\",\n    \"\\n\",\n    \"fig = plt.figure(figsize=(20, (n_subgraphs * 3)))\\n\",\n    \"for i, sub_graph in enumerate(sub_graphs):\\n\",\n    \"    ax = fig.add_subplot(int(n_subgraphs / 2), 2, i)\\n\",\n    \"    ax.get_xaxis().set_visible(False)\\n\",\n    \"    ax.get_yaxis().set_visible(False)\\n\",\n    \"    pos = nx.spring_layout(G)\\n\",\n    \"    nx.draw_networkx_nodes(G, pos, sub_graph.nodes(), ax=ax, node_size=500)\\n\",\n    \"    nx.draw_networkx_edges(G, pos, sub_graph.edges(), ax=ax)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(90,)\\n\",\n      \"(90, 90)\\n\",\n      \"0.0994250608578\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#from sklearn.metrics import silhouette_score\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"def compute_silhouette(threshold, friends):\\n\",\n    \"    G = create_graph(friends, threshold=threshold)\\n\",\n    \"    if len(G.nodes()) == 0:\\n\",\n    \"        return -99  # Invalid graph\\n\",\n    \"    sub_graphs = nx.connected_component_subgraphs(G)\\n\",\n    \"    if not (2 <= nx.number_connected_components(G) < len(G.nodes()) - 1):\\n\",\n    \"        return -99  # Invalid number of components, Silhouette not defined\\n\",\n    \"    label_dict = {}\\n\",\n    \"    for i, sub_graph in enumerate(sub_graphs):\\n\",\n    \"        for node in sub_graph.nodes():\\n\",\n    \"            label_dict[node] = i\\n\",\n    \"    labels = np.array([label_dict[node] for node in G.nodes()])\\n\",\n    \"    X = nx.to_scipy_sparse_matrix(G).todense()\\n\",\n    \"    X = 1 - X\\n\",\n    \"    return silhouette_score(X, labels, metric='precomputed')\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"print(compute_silhouette(0.1, friends))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(90,)\\n\",\n      \"(90, 90)\\n\",\n      \"(85,)\\n\",\n      \"(85, 85)\\n\",\n      \"(84,)\\n\",\n      \"(84, 84)\\n\",\n      \"(84,)\\n\",\n      \"(84, 84)\\n\",\n      \"(77,)\\n\",\n      \"(77, 77)\\n\",\n      \"(65,)\\n\",\n      \"(65, 65)\\n\",\n      \"Warning: Maximum number of iterations has been exceeded.\\n\",\n      \"  status: 2\\n\",\n      \" success: False\\n\",\n      \" message: 'Maximum number of iterations has been exceeded.'\\n\",\n      \"    nfev: 6\\n\",\n      \"       x: array([ 0.135])\\n\",\n      \"     fun: -0.19204945304474358\\n\",\n      \"     nit: 3\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from scipy.optimize import minimize #(fun, x0, args=(),\\n\",\n    \"\\n\",\n    \"def invert(func):\\n\",\n    \"    def inverted_function(*args, **kwds):\\n\",\n    \"        return -func(*args, **kwds)\\n\",\n    \"    return inverted_function\\n\",\n    \"\\n\",\n    \"result = minimize(invert(compute_silhouette), 0.1, method='nelder-mead', args=(friends,), options={'maxiter':10, })\\n\",\n    \"print(result)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Subgraph 0 has 10 nodes\\n\",\n      \"Subgraph 1 has 28 nodes\\n\",\n      \"Subgraph 2 has 2 nodes\\n\",\n      \"Subgraph 3 has 2 nodes\\n\",\n      \"Subgraph 4 has 7 nodes\\n\",\n      \"Subgraph 5 has 2 nodes\\n\",\n      \"Subgraph 6 has 2 nodes\\n\",\n      \"Subgraph 7 has 3 nodes\\n\",\n      \"Subgraph 8 has 5 nodes\\n\",\n      \"Subgraph 9 has 2 nodes\\n\",\n      \"Subgraph 10 has 2 nodes\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"G = create_graph(friends, threshold=0.135)\\n\",\n    \"sub_graphs = nx.connected_component_subgraphs(G)\\n\",\n    \"\\n\",\n    \"for i, sub_graph in enumerate(sub_graphs):\\n\",\n    \"    n_nodes = len(sub_graph.nodes())\\n\",\n    \"    print(\\\"Subgraph {0} has {1} nodes\\\".format(i, n_nodes))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 124,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 0, 1, 1, 1, 1, 3, 0, 1, 1, 1, 1,\\n\",\n       \"       1, 1, 4, 1, 1, 5, 0, 1, 1, 1, 1, 6, 7, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\\n\",\n       \"       0, 1, 1, 6, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 3, 0, 1, 5, 1, 1, 1,\\n\",\n       \"       1, 1, 1, 4, 5, 2, 1, 0, 1, 1, 4, 7, 0, 1, 1, 0, 1, 1, 2, 1, 0])\"\n      ]\n     },\n     \"execution_count\": 124,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"labels\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 125,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X = 1-nx.to_scipy_sparse_matrix(G).todense()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def silhouette_score(X, labels, metric='precomputed'):\\n\",\n    \"    labels = np.array(labels)\\n\",\n    \"    print(labels.shape)\\n\",\n    \"    return np.mean(silhouette_samples(X, labels, metric=metric))\\n\",\n    \"\\n\",\n    \"def silhouette_samples(X, labels, metric='precomputed'):\\n\",\n    \"    print(X.shape)\\n\",\n    \"    distances = X  #pairwise_distances(X, metric=metric, **kwds)\\n\",\n    \"    n = labels.shape[0]\\n\",\n    \"    A = np.array([_intra_cluster_distance(distances[i], labels, i)\\n\",\n    \"                  for i in range(n)])\\n\",\n    \"    B = np.array([_nearest_cluster_distance(distances[i], labels, i)\\n\",\n    \"                  for i in range(n)])\\n\",\n    \"    sil_samples = (B - A) / np.maximum(A, B)\\n\",\n    \"    # nan values are for clusters of size 1, and should be 0\\n\",\n    \"    return np.nan_to_num(sil_samples)\\n\",\n    \"\\n\",\n    \"def _intra_cluster_distance(distances_row, labels, i):\\n\",\n    \"    \\\"\\\"\\\"Calculate the mean intra-cluster distance for sample i.\\n\",\n    \"\\n\",\n    \"    Parameters\\n\",\n    \"    ----------\\n\",\n    \"    distances_row : array, shape = [n_samples]\\n\",\n    \"        Pairwise distance matrix between sample i and each sample.\\n\",\n    \"\\n\",\n    \"    labels : array, shape = [n_samples]\\n\",\n    \"        label values for each sample\\n\",\n    \"\\n\",\n    \"    i : int\\n\",\n    \"        Sample index being calculated. It is excluded from calculation and\\n\",\n    \"        used to determine the current label\\n\",\n    \"\\n\",\n    \"    Returns\\n\",\n    \"    -------\\n\",\n    \"    a : float\\n\",\n    \"        Mean intra-cluster distance for sample i\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    mask = (labels == labels[i])\\n\",\n    \"    mask[i] = False\\n\",\n    \"    mask = mask.reshape(distances_row.shape)\\n\",\n    \"    #print(\\\"Cluster {}\\\".format(i))\\n\",\n    \"    #print(mask)\\n\",\n    \"    #print(distances_row.flatten())\\n\",\n    \"    #print(distances_row.flatten()[mask])\\n\",\n    \"    a = np.mean(distances_row[mask])\\n\",\n    \"    return a\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def _nearest_cluster_distance(distances_row, labels, i):\\n\",\n    \"    \\\"\\\"\\\"Calculate the mean nearest-cluster distance for sample i.\\n\",\n    \"\\n\",\n    \"    Parameters\\n\",\n    \"    ----------\\n\",\n    \"    distances_row : array, shape = [n_samples]\\n\",\n    \"        Pairwise distance matrix between sample i and each sample.\\n\",\n    \"\\n\",\n    \"    labels : array, shape = [n_samples]\\n\",\n    \"        label values for each sample\\n\",\n    \"\\n\",\n    \"    i : int\\n\",\n    \"        Sample index being calculated. It is used to determine the current\\n\",\n    \"        label.\\n\",\n    \"\\n\",\n    \"    Returns\\n\",\n    \"    -------\\n\",\n    \"    b : float\\n\",\n    \"        Mean nearest-cluster distance for sample i\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    label = labels[i]\\n\",\n    \"    b = np.min([np.mean(distances_row[(labels == cur_label).reshape(distances_row.shape)])\\n\",\n    \"               for cur_label in set(labels) if not cur_label == label])\\n\",\n    \"    return b\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 127,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(90,)\\n\",\n      \"(90, 90)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.099425060857770378\"\n      ]\n     },\n     \"execution_count\": 127,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"silhouette_score(X, labels, metric='precomputed')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 127,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "Chapter 8/CH8 Rewrite.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from PIL import Image, ImageDraw, ImageFont\\n\",\n    \"from skimage import transform as tf\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def create_captcha(text, shear=0, size=(100,24)):\\n\",\n    \"    im = Image.new(\\\"L\\\", size, \\\"black\\\")\\n\",\n    \"    draw = ImageDraw.Draw(im)\\n\",\n    \"    font = ImageFont.truetype(r\\\"Coval.otf\\\", 22)\\n\",\n    \"    draw.text((2, 2), text, fill=1, font=font)\\n\",\n    \"    image = np.array(im)\\n\",\n    \"    affine_tf = tf.AffineTransform(shear=shear)\\n\",\n    \"    image = tf.warp(image, affine_tf)\\n\",\n    \"    return image / image.max()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.image.AxesImage at 0x7fbe01f3d198>\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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EuBhA/ucGwAr2KiwOyBTaVSUn5O9gG9KLIYDg8PcXR0hMPDQ7x8+RL7\\n+/s4ODhAPp9/LTF4GTjuZDKB0+kU1kG5XMbLly9xeHiI4+NjfPrpp8jlciiXy5fqjqjjBYNBJJNJ\\nJJNJmKaJ8XgslZwsi69Wq8IVprd3X+DceTpgSIoetMPhgNvtFo/15OQE+XwezWZThIwGgwHcbrcw\\nekKhkIRHyEW/7Br5WeAGnkgkZB5MAnPjKJfLqFQqsCxLNs9lioTqPYlGozKe3+/HeDwW7RGOV6vV\\n5F4t43lraNwAS4sQH7XBJhjiUGl8Xq9XJFYdDocY608++QQHBwdirA8PD1EoFGBZFnq93rUTXnwo\\n6UV3u13UajWcnZ3h8PAQuVwOJycnYqxbrZY8wBeNTaPk9/uRTCYRi8UQDoelijMcDsO2bdRqNSkX\\nZwL1vsWAOHefzycJ2HA4DIfDAZfLBZ/PB8uykMvl8OLFC5yenor3TFomY9/RaHSOOke+9XWukfdk\\n8bPgcrkkIVqtVmUN6Q1fppcyGo1EjyYWi4kYFMXFaKwty8JwOJyT4tXQWBFLDfZ1QiI/DeCPAygB\\n+Oz5z+IAfgHAM8yaGHwngOtRLNYIPshMhNGYlctlmKYpPGKn04l6vY6TkxN8+OGHyOfzKJfLKJVK\\naDabwhhxOp3iIV0FNXY4GAxQq9Xw8uVL4RyrYQAaA1X3ZNFoq0f+ZrMp11EqleB0OhEOhyVGn0gk\\nkE6npVS50+mseWVvhsW5W5YlczcMA36/X7jSpVIJ+/v7qNfrknBUPetoNCrJS1b5sYK02WxeKOK/\\n+FloNBqo1WoolUowTVMSj4FAQFg9TAyTLbQI9R6TulgqlZBMJuF2uxGJRETZj+MxMXuXyUeNp4vr\\neNg1zIz2nwLwE+c/+2EAXwXw3QA2AXwbZjKrKj5YzxQvBo/aHo8HPp8Pu7u7ePbsGfb29pBKpRAO\\nh+Vo3Ww2RWhoMBig1WqhXq8LzUstTriq6nHZPAg1ps5E4U3H9vv9CIfDc6cEcompZcJxqWVyk3DO\\nXYJe57K593o9MWik95GjTsMejUbFqyX9j/cxFApJMc5VPHTTNBEIBKTQhTKswWBw7rPATYG0xYvA\\n9+d4Ho9HCnIajYZUG7K837ZtbbQ1boOVQyLHAHwA/gxeGex/BuD7AbQBvADwIwD++cLffbDKLG8C\\nwzDEWPv9fjHYz549QzKZFI1pt9stDyjLmC3LQrFYRKPRmCt6uKmxJtT4qVoRucrYHo9nrtGCWqlH\\nPjYAqTK0LOvOKX7XBefO+C+NdSqVklAB16Df76NWqyGfz4uxTqfTcLvdS401T0ukAV5WYOP3+yWP\\n4Xa7YZqmhEjIJ2dpN73nywysz+cTPj853vF4HOl0WsZzOBwYDodzYSsNjRWxckhkGTIAWAlSPP/+\\njYMeNruEqILzfr9fHnzDMKRs3TAMlMtlBAKBOQNyGxEfUgUXk1bqWBcd5ZdhMBjAsixJZqbTaTx/\\n/hyTyQR+v18kTPv9Ps7Ozu5N+nMZyG13Op2SCObcydTg5tVoNFCtVnF0dIR4PI69vT04HA4pWWdh\\nTT6fl5fL5UK3272wEIlgAng6nYpMarfbFUGnjY0NuFwuNBoN5HK5KxUce70eqtWqfOY2NjbQbreF\\n4snPVqvVEjlXDY11Yx20vun5616gcqtVLYdlMc5Fw7wYT76NDsSqnvkyjEYjdDod1Go1uFwuqcJs\\nNpvCTKDHnUgkpGPNQ+j3x1Jz0irVubvdbrjdbkSjUXQ6HdFJabVa4mnTeKu/S0ZGIBCQytTT09PX\\ntDxUMFQEAKVSCbVaDY1GQ+L9wWAQhmEgmUwiHo9LVexFvR8ZRnM4HAiFQqhWq2g0GiKty42UUggc\\nT+39qaFxW6xqsIsANgAUAGQxS0i+cTDRxO4b5D+Tv8s4J3nYfNht28ZwOJQY60UP/X2B83U4HNJp\\nhBWHNDRMnsViMemmo6rd3afB5txdLtfc3Ml48fv9Ek/2+Xxwu91oNBo4PDyUEwWZJoxD89+lUgmF\\nQgHpdFoSrssU/lSdbXa7IR2SSWEqOcbjcSQSCViWNaffvXhd6njqdZHvzwQkx0skEg/inmi8PVjV\\nYP8ygO8F8KPnXz+/thndAExOMbTBhquLBpsVYrZtS3sq1WBzrIdisGn0GGe1LEvaawGQ5JtqHJLJ\\nJOr1ujAq7qtog5sN11Itb6dRCwaDcwabolkHBweoVCrY2trC3t6elLCzitXlcqFYLOLs7AypVEpo\\nmMsof0wmsu0Z51Gr1SQeHg6HZQ2TyaTEnal1roIGezQavWaw1Ri7Oh7b0V3VzEJD47q4jsH+eQDf\\nCiAJIAfg72OWZPxFAH8Rr2h9bxyLxRSdTgftdhvNZhPtdhuhUEg8G3riqmAOPS16SA8FauNbetns\\nS8iYPVXbmPjKZrPCXWYV4bqhhn1ULfHFuav8aRb6UOODyTsaOOYc2Njg+PhYGC+sJmVVIns2bmxs\\nIJvNisdLap46J56YuFGrVENuHKRJcg1Z+LKMJqnytVXqYqFQQCaTEf1v0i4zmYx43zT2dyGHq96X\\nu9RK13gYuI7B/tMX/Pxz65zIOtDpdFAul3F0dCT0K1WxT334yTZQu8I8RKiiRi9fvpRu6ywISSaT\\neP78OUajEYrFIpLJpDTOXfc8VKPFzbHdbl9oiFSlwpcvXwqrJxaLSXx6c3MTtm2jVCqJKiAbIbhc\\nLgwGA+zs7MDpdCIajcI0TVH4CwQCODs7QzAYRKlUkmYRi3NiIvT09BThcBgAJFHIIqXd3V3hfC/j\\nZKug2l8+n8eLFy8ksZlMJuH3+5FOp/E1X/M1MAxDytWLxeLaP2MqC6nf78u13zcvX+Pu8Oi1RFQs\\nKt5FIhFsbm6KkaPB5vGaxmeZp/hQoBo9yrnGYjEMBgNpLjAcDuHz+UT0KhaLrb3ykUaBLdIo9nRZ\\nE10KKBWLRWmOzLlTWXFzc1M2TTW/oN4fl8sl/OxwOIytra05AS6PxwO3241qtSoFOqrBHg6H0l7L\\n6/WKsVaThTs7O8LHzufzS6+HUA02Gz8nEgmMx2P4/X5kMhlMJhNR+GMzhXV/xtgdhwnRarX6IAqp\\nNO4Ob53BLpfLAGY8XB6b6dmRz0zvTW3R9VATQvT4CoWCiCttb2+LcFIymYTP55OYaTQaRTQaXbvB\\nHg6HkoxjxSHL8S+bO/VDptOpeMf9fh/RaFQMNtkkhmHAtm3hxrfbbfldyrUycRmJRGCaphToABBj\\nvTgn1cMGgFQqJTFyGluGkgqFAgKBwJVrQYM9Ho+RSCSwu7uL0WiEQCCAdDotejAsZzdNc+2fMbYy\\n433gemu8vXirDDbji4ZhiL5DvV5Hq9XCdDoVOhwfIJZ7X0Tlegigapxt2wAg19VoNKSTNzurMEnp\\n8/nWHsMm60KtTGSRCJNxi9WHTPYyJr04d8aS2eSW1Ylq01mv14vT01PkcjlkMhnJN6hNCVgcQ+U8\\n27Zfkx9tt9uSwKTwk23bcyEmijtRIlf9e9XYUhmwXq9jOp3OjcecSCAQkPg126Wt22BT+rbX60lS\\nl2yoi+au8bjxVhlstkuaTqfCCKCBAF5V4anCQBRRuuxof5+YTqdy5GWPSL4ikYjwldkHko1z1837\\nZSKRRpo0SsrAcg2XGezFuVuWhWg0CgBC61PvCbnxrVYLlUpFOoWTXcJXIBBAJpMRmiDDAbVaTcrP\\nGXsno8TlcskcarWaGFa2fFN7gJLit0jJU2mii/eEm4Lb7Rb5VrYWW7fhpPQB5V9Ho5Fw2tX78RA/\\n1xqr4a0y2IxHs+RZNdhqJSQfzHg8Lsfnh9rElEZPVQTki+2qGEdVO8ev2zioDARqXVOdkD9fFobh\\nPWGlIOeeTCbnYtDqPVETaMPhECcnJ9KtfHNzE5ubm1LNyth9MplEu91GrVaT0AfXTqX4TafTuTVU\\nY+GMNzPmTA+53+/PbUTcAEj/U8djOT2VAWms7+LzpbJiEomEbHD5fF7ohGTSaLwdeKsMtlr8oir3\\nlUoliSsuUrl4rL3pB3uxT6HaMOG6WEaTW1Ymr1bKqSp0sVgMhmHMJd7UcdUx1wmyU1gcQlrdYo/J\\nxbnznpRKJemgTq0QVfGOxpBUzFKpJOX3g8FAempSLyYUCsE0TZydneHs7Aynp6cS1yajhZvGIk2S\\nnenZ3o2hkY2NjbmGEYvXxfkx5EPqIqVXQ6GQjKveY5V+t06Mx2PRUI/H43K9l3XV0Xh8eKsMtgrq\\ncZyenspxNBAICPUqmUzi2bNn4j1dReVaBA00tZzZVfuqVmAEH1xSCmn0yJi4CGRoHB8fi3JdKBSS\\nxJmqEsjx1h0e6Xa7GAwG8Hq9SCQSEjqgkbzs79j70e/3SwybDQuomcJybtu25cWkK2PprVYLW1tb\\n4nGzGfL29jaazaYo67FTj8rVVmmSAIRiqFINqYfCk8FlHjJ7P7IVHNufXXSPFz32dYDa3n6/H6lU\\nSiRetcF+u7CqHvYHAP4SgPL59z8E4FfWPbnboN/vw7IsEeJhJxRSuWiwqbNcKt2sup69JCmMT+/9\\nuqI/qvdJBobD4bgy+bnY8JZJO+AVL5daJAwrrJuTTSoZDTYLSa4y2DR+uVxO5p5OpyUsQT1pFruQ\\nhcH2Y4xLN5tN1Ot1dDodGIYxV1izs7MDAGKsGYNut9tiPFutFgqFgoQtotGoXE8kEhEmC5N5V11X\\nu92W3p0saMpmswBeNddg4pP3ZN2bKBON/GyT5veQCsI0bo/rGOyfwUxO9d8oP5sC+EfnrwcJetge\\njweGYSCVSmF3d3fO0+axkTzhm0Dt1s6kVyQSuZISRix61TTWV5Uw02BTBIpGjt4cqYrkFKuCR+sC\\n+dFU1KvX6/D7/dc22JQE4NxpsNPpNACg1WoJx3k0GqHZbKLZbMLhcIixJueYzXHJ1QZmpfsqxY/J\\nN1aA0sNWqYbktdPDBiAb41VeKj1sXkc2m0W/33/tnnQ6HTQaDdTr9TvZROlhs8ze5/NpD/stw3UM\\n9m8A2Fvy8we9ddPLJDWMqnFkOJAWpqq1XUblWgQfRDIP6N0lk0k5Fl8WHqGHTcW5s7MzaStGHvIy\\nb5tHXVb+UTWO1EUAQvGr1Wo4OTlBtVpdK3WRHmu73Rbj2mg0rkyske3icrlQLpfnlPwmk4m0F2N7\\nNHZ0Ue8JKY6sIiyVSigWi4hEIqIPY5omNjc3MRwO4fV6kU6n59q1ORwOKbJSqYYqY0RVQiQD5TLq\\nIpUUy+WyjEfGEmmH/X4flUoFuVxOTg3ruidkSLVaLTlBkMWi8fbgNjHsvwbgzwH4LQB/G/fQIuwy\\nkOI1nU7hdDrnDLbX65V4JasDyb3t9XqiNXKVwebvuFwuxGIx7O7uYnd3Vxoq+Hy+C/9elYQdj8f4\\n5JNP4Ha75b3VeaigwaZ3SYqabduSeKQCoWVZODo6Qi6Xk/HWwRqgkWJopF6vX8trpFGZTCYIh8PC\\nk1cNNhOa6j0hL5ueKq+/XC5L2XcsFpM8QigUkrh2KpVCOp0Wvj0TjxyzWq3Csiw0Gg1hhoRCIbhc\\nLil6iUQiGAwGogeyGH8m/380Ggknm9el3hMa7IODA5RKJbkf6yhy4qmH94XXpA3224VVDfZPAPgH\\n5//+hwB+HDMhqAcDetg8mqqGLRqNSql0p9OZ86QcDodQuS4DM/1sdUWD/f7774sSXCgUunQMVaPb\\n7/cLI4Ke2TKPlUnJbrf7mtFjBSc9bBrsjz/+WDyvdVTCqQwUlZ99lYfNzabb7SIQCMjcbdsWmh6P\\n9Dz1cA3V/pncjCORCIrFIgqFghhXlp4z+TYajZBKpTAej0WwiRWbTODSuHW7XclHqE13I5GIbP7L\\nrpPUwU6nM9fgt9VqSYs6j8eDwWCASqWCly9f4vj4GK1WS+Ly67wnKj9bG+y3C6sabDVD968BfGEN\\nc1krVGPicrmEykXeMI/OavutbDYLt9stySp6Uss8bZV+pzZPcDgcIn3KzjDEsgQQf1YsFoX1wLgu\\n454XXZeqGpfP5yUcEwqFhGJoGMZckUu1Wl3L+q4Cde6tVuu1ubvdbukOH4/HkclkUC6XpZkxPWsa\\noXq9jmKxiMPDQ3g8nrl2XUwCU7tkc3MTW1tbKJVKqFQq4jFTI71YLCIajSKZTCIQCAj9M5lMYmNj\\nQ4z0shPKMjopQzXj8ViqMHlPmK9gYvCh9OPUePi4Tk9HAIhivqdjFgBdtT+PmeH/Dwt/88FtJ7cu\\nMGRBvrK7k8poAAAVLUlEQVTT6ZRmsdPpFM1mU8IP9K5pHIDlhlZFOBxGOByGz+eTbuls+grMe9LL\\nuts4HA6JQzN5pMYkLwIFrtgOjYwV0zSFTaGWjTPp9RDAubPnJsu3TdOUmDDvyUVzV8vR6a3yxfAM\\nu6+zXJ2bK9+D987r9UoSORgMIhQKoV6vzzU85vtc1azXNE3R+WYVaiQSkXvMUwI3ZK3/obEEKzfh\\n/XnMwh67AP4KgAaAv3w+4F8FEAHwfZg15FXxwYoTXTucTqdUylGHgl4cG/TSm6bXVavVrk29oooe\\nvSgankQicamxVotvGL5hMrPdbotQ/kXweDyiPEjP3jRNJJNJUXKjgWq322g0Gg+mWa/b7ZaNBpjp\\noVBLWm3CwNPBsrkzRMGTBo02DSA3BTbbnUwmc3obtVptrmmwy+USemAsFhNKJN+HZd9XbaJkzLCt\\nGXMlnU5HuODckBn/19BYwMpNeJfpYf/07ebyZkEOb7FYhMPhEJ7sYDCQgguKCFGbORAILGUGLAuP\\nMHmlFk2olDVCNdAqj5uVe4lEQsqdWd232MxXhaoaZxgGYrEYtre3MRqNhAWzvb2Nfr+PcrmM4+Pj\\nS8d7k6AmST6fh8PhmJs7xZhIt7to7kzalUoleDwelEolZDIZadAbCoWwsbEhpecbGxsAZtWirErk\\nPWczYxpWAHMyrGRe+Hy+S9eQsghMbJMTrUq5knd/nXusoaHiuiGRVfDBHY59I9A4MqFITy6VSsmR\\n2TAM8cDp7UUiEWF6sGIOeP3BokIb8Cqe2e120Wg0cHBwgMPDQxwcHMi/Dw8PcXJyIswCt9stnpzT\\n6cRgMJCyesuyLn1fVVqUralYjMI5U6CoUqlITFhtPnwfYJUoMFuzxblT5W4ymcjcS6WSxIEX5056\\nJf9WpdvR+wZmZfXcIEnr9Pv9EmsmrTAej0tYxe12i05JuVyWZrxcYxW8J9PpFIZhIB6PC1OFWiYu\\nlwv9fl9ajNXrdbnHD1WXXeONY+WQyKr44A7HvjGY6KG+ciqVQiaTQTAYFIlNMhVM0xTtEeCVJ8ew\\nxmLmnR4StSUovclyZdVo8+vZ2ZkYiXA4LGwTv98vbAImyIDlPSdpuMjnpsHb2NiQ+K7P5xMJUCbC\\n1PHuy2hzzTh3dhzPZDISoqA3y7kXCgW55mV6HCrDh8a6UqnAtm1Mp1MReKKxzmazoqjXaDQwmUxE\\nSIoJY5fLhUAggFarJbo0lLpdpoSn3hOHwyHGmt498wyk+BWLRREgW3aPNZ4sVg6JPHpQbJ8lz6VS\\nCZZlSTNexqA9Ho8UwHS7XVG9I03rogeKGX/GWqvVKg4PD+W4y5eKSCSCfr8vjRbYY9Dr9aLdbos+\\nNDcNJqlUI8VYd6/Xk+YN9CbJA6fBTiQS0i6NnuG6q+1uAhbfdLtdtNtt8TRJhePcHQ6H0C4X10LF\\n4j1uNBoS5nj+/Dmm0ykymYw0eOD9+OpXv4rhcIjj42PYti0x8E6nA5/PJ42C0+n0nGY3jfIi1M+C\\nw+EQFki73Ra6oc/ng23bQl1kdex16KQaTxtPxsNWhXioMheNRoXZQf4tqWUU0rdtW9gbAERB7qKx\\n6TGqEqFqVSBfw+FQmijQw6aGMr0vy7JEAY9l64tepeopszovEonMMRQWRfZ52lhWmPMmoXKH1cpC\\nde70sNW5s7Boce7qfaBR73a7UolKrj3XOR6Py2mITRkSiYRwpw3DgNfrRSgUEvpfvV6X09ay7urq\\nHFwulxRmMfm57B4zZs7xdCxbA089JKKC7aVU6lUwGJQGrfSIaWhJw6LRvEzZb9GTVtXaaEjYsZ26\\nyYyB0zhQ8IicYsbEWRG4DEw6UqWOeh2hUAhOp1PEkobDobAwuHHcN5h0pCSpOndy6NW5M9xxnblP\\np1MRmuLY3KAjkYgoDfb7faH0ARBpUtIzO52OUA0pJ8AN/SIweUo6Ke8xJRBYrEPa4FX3WONJQRts\\ngsdc1eMhlUvVt+71ehKGUBOJl/UxVKFWA6rGmp4hmw5Mp1Mx1vF4HIZhiLGmVrdt2yJ4tAwsqWb8\\nV+1h6fP50Ol05AhPnnm1Wr3XsAjhcDiEC817wrmzGnU4HMLhcMzRLq8KH6g6HjxJqUllVkKqCV9S\\nLC3LEmOdSCTmTiTqZ+GyQiTSBEldpLFOJBJSHs/TEY1/tVrVBlsD0Ab7FVgkMZlMhCvL0AQfFqfT\\nKd6tYRii/tZqtWDbtuhecCyVrQHMG+tlLyY6gVnsMhgMSsWlx+MR75GNaVk9d5HBJr+czAom1lKp\\nFEKhkBgll8slVYbFYvFBxEyXzZ1Vh+Fw+LW5s8LxKl0UGmwKcakyAKZpIhqNirdM1gr51tQWYTIU\\ngCQS2XWHFZIXFVZRUZFhHG5CmUwGbrdb7jFVCHmPtcHWgDbYr+ByuYTuxoeDHhSLaNxut3g/PEar\\nTA4K52ezWYmL0hO/Dq9WpbUNh0OhGmYyGfh8PqG1GYYBy7KEUXDR+CqtbTQaSVODdDqNcDgsf+f1\\neuc6rvT7/QfBA6YXys2LczdNUwyqx+OZmzuFnK6aO9dRZW8wwWfbtkjFqpWqXq8XW1tb0iCY7A+2\\n3iLVUDXYF1EN+b7chDKZzFxzBcMwZLxCoSASB1ddl8ZbjZVYIjuY6WCnMdPA/lcA/imAOIBfAPAM\\nwCGA78QDU+u7DP1+X9Tl1CNpvV7H8+fP8fz5c5HaZEyY3jCb+KrecqlUwvHxsRgBhh4u85RUNcFO\\np4OdnR1JMpqmKVWALL4gJ1ztnKKC12BZFnq9HjKZjIynFqOEQiGkUilEo1EEAgHx5pep0L1JMHnX\\n7/dFz5kxds49HA7j8PBQGCNer1e878vmzmpIJiup02LbNrLZLDKZDDKZjGy8Ho8HzWZTktLAbJMP\\nh8PCmU8mk5IHYXefRaohK0wnk1mX9WfPnkm5O5kwLMvneH6//1qfH42niasM9hDA3wTw2wBCAP43\\ngF/DTD/k1wD8GIAfBPB3z1+PApQFJQOExrpSqWA0GiEQCGBjY2NOY8I0TelIks1m5xKJh4eHogZH\\n+t9VDxsfZnZNKZVK8jCrUqM0sHyYWWK9SCmjwaZBymazYvRY+UiDlE6nxWB7vd4LKWpvCurc6/U6\\n0um00BOpac3X4mazjAu9CFLtWApOOdRSqYSv+7qvQzAYxPPnz0WiletOg804OBvqkmlE/RjgVWcZ\\nFSp10bIsoZNSWIwdb1hhSV0YKkze5z3ReJi4ymAXzl/ATOzpQwBbAL4DwLee//xnAfw3PCKDrep6\\n8FjM6rRwOCyNUxOJhFDvGG6g0VM9bACSFIvFYiLdeRPZzFQqBa/XK9QuHsvdbjdM00Qmk8GzZ8/g\\n9XphWdZSA6FeF9X5Tk9PJaEaj8fh9/tFCvbrv/7rEY/HhXJ41/FstRGB2m2HXvLi3M/OzqSLOdkW\\n3MB2dnZEQtayrEuTp+rYwKyjDe9hJBJBLBaTNmFklajCWsxRMHzBtmIbGxvY29sT6d5lzQhU7j67\\nmudyObjdbrkutlrb29vDZz/7WTkZUXjqLsEY/uI9eQjsIY3XcZPCmT0AvxvA/wKQAVA8/3nx/PtH\\nCWb8Gdf0+XyiHbGxsSHVd6ZpSpUaW1DRk+bDHAwGsbm5KQUYN2nsu7m5Kd1Vut3uXEyVfRs/85nP\\nyMZylWpcr9dDrVZDLpcTqiCTeZFIRPpZZrNZNBoN2La99lZiy+ZEGl273Uaz2ZQYsgrqcahzZyiK\\n+h7Pnj2TePRVVEsVLFm3bRuj0Qimacrppd/vi/FWNdOZ82BcmSeubDaLd955B16vV6iYl4Fdbo6O\\njiTn4ff7hTnyzjvvyCbEe3LXm6iqDc6E+nUpkxpvHtc12CHM5FP/BoDFJ2N6/nqUYCyZUpeU4iwU\\nCtjZ2cHe3h76/T42NzclBhyNRuc8NtXzq9Vq4nFdl/4HzDzsYDAowkBMPLJ0nQabx/urmgarDW95\\n9I7FYgCAWCyGvb09+P1+aZF1lQrdOkDFQLJeqJy3+L6c+8nJyWtzZzPl58+fS2HMTRQISZNklSi9\\naW6ELpcL6XQaqVRK2CVMOAOvEomRSASbm5tot9siLlYoFC59b1ajHh0dSSiE7dCSyaQwSXg/2I/y\\nLsGqULJeuD4aDxPXMdhuzIz1vwXw+fOfFQFsYBYuyWK+ocGjgiqqz9ghY6mqljSPiTSgBJkX8Xgc\\ngUAA8XhcjumXSaMugkafXdfpaTNW7vf750qZGaIBLlYQtCwLp6en8Pl8iMfj2NraEq8ulUpJowXO\\n9a4NNulyjUZDmtYyvqtKz7LF1enpqazt1taWNJlNJBLo9XpS0u73+4UFctF6EIwNM5xkWRYKhYIY\\nZ26OZNUsMnGAVx72xsaGFMAUCgWpjlwMwRBqx3s2gqZBZmcdcvG54d+18eT9aDQaCAaDmE6ncvq5\\nznpqvFlcZbAdAH4KwO8A+CfKz38ZwPcC+NHzr59//U8fH9Ry4+l0Kl4sdZBp1KgTwQIbp9MpjA7T\\nNOd0kK8Lr9c7p6XMJrrD4VC8X8bL+b6XGSn2WmS13vb2tiTxyAFnZxom2e7aOFDzpNPpIJ/PA4B4\\npmpOYDAYoNFoSMWjOnf24gRm3mEul5PiFLXzz03mZFmWeM6hUEgqE03TlKpYtaEyDXs6nYbT6UQ+\\nn0c0Gp3j9y8T1up2u3LqMk0TOzs74qFTQVCNk5ODfpfg/eh0OojFYiKEVavVVlpPjbvFVQb7WwB8\\nD4AvA/jS+c9+CMCPAPhFzPo4HmJG63srQD42E0itVku6c7daLal+5INlGAZM05SkHttLXafzugpq\\nY1N6k8L2jUZDYr5qBxxuFDRyi+/DMXq9HtxuN95555252CSpiizbjsViN2IlXFQsctn1qr0Gk8kk\\nms0mzs7O4Ha756hsnHu/34fT6USlUpG5U6ArGAyi0WhIXFvloN+EDkeDTX0QKja63W5R2KMHzzg2\\nDTYrGQ8ODoR2yX6ay4TCaLC73S5M00StVhPaJY00N24a69tovVznHlH7ZjgcIhgMol6vI5fLrbye\\nGneLqwz2/wBgXPB/n1vzXO4d9LBp1NRE1tnZ2Vw3dRZYOJ1OpNNp6fZN7Q4+4KuAFMNarYZ8Pj9X\\n3s6HZ9HDXgQlRoHZZkCjNxgM5DTg8XiubH92V4hEIjg9PcVHH30kRTMsGCFTgS3ByuWyzN3j8YhB\\ntSxLNkmOcVPjwnUi3Y9qemonnHg8Lh42151NEVjgoxrsi2idlOm1LAvRaBTVanXu5MB7cl8wDAPH\\nx8fChAI0F/yh4UnIq64Dw+EQlmUhl8tJuTpfpVJJjpHpdFq8bKrN3RT1el26b9dqtbljaz6fR7lc\\nntOhuAqDwQDVahUHBwf48pe/jFAoJJ3d1aP+mwRpcMFgEJlMRlQM1U0JgISEOHd2ow+FQqJzTf1y\\ntghjIdBNMRgM5B6TG95sNtFoNCR3QO46QSEtNj+gqBb57xeBXYVevHghwmO8rlU3+tti3eupsX5o\\ng31NDIdD6b1n2/ZcB+xEIiGlxZlMBolEQji2qxhs9i9UGSc04OzzyBL6ixJci3OnRncgEBB52UQi\\nMWd83iTIJQ8EAshkMnC5XMLYUTEajWTu9GjZFYY0unA4jFQqBcMwpFfiKgaGmwNpk0yS1mo1kUll\\nCIagYVcNNrC8u7oKtjbb39+Hw+GQpgmJREIkC9401r2eGuuHNtjXBD3sZrMpfQiJeDwu7bey2azo\\njAwGg5UMtm3bYrAty8LZ2Zm8VHnPy/pMqlA97NFoNDc/v99/4/mtAywVp4dNY73oXXKzOTg4wHg8\\nnps7k8NMAJKqtyroYbP/J4314n1Vk8mqwaa+dq/Xu7I7PQ221+sV2ujm5iaGw+G9nXoajcZa11Nj\\n/dAG+5pgMmxZIQM9XTJMqJ181UN7EdrttvT740ZQrVYljHBTsKijWCwKXVBVHbwP9Ho9EfBnIndZ\\nkpYcZyoVskONbdsYj8eoVCpoNBoyxm2SdOo9ZuMD/kwt9jFNc+7vKpUKqtWqaKdfJ1HHBspMuKr3\\n5L48bFZirms9NdYPbbDXAEpyMmFGfvDJycnc7xUKBWEeXAYWk7BakkyRVY+l9F5p8Jn4yufzklx6\\n0zg5OZFeieo1LhoIGullcyeLR12ndXVsUXXIabS56S2eSthWjJWbtm1fWSnIUIPD4RB9kWq1itPT\\n03uLYTP+fhfrqbEe3CVF4MncZZfLBY/HI1l+0rMWM/7lchmpVOrK8Uh/U7Ud6Pmt8vBQmlSdH7/e\\nl3EolUqIRCJLr1H1Thfnrq7vdDqVgib17xd7X64Ctmzje6v3dDFkwfdn8RXncJnRJsecutjxeFze\\n477Atmp3sZ4aN8ZS26wNtoaGhsbDw1LbfD/ulYaGhobGjXGXBvu/3+HYGhoaGm8rtO3U0NDQ0NDQ\\n0NDQ0NDQ0NDQeHP4dgAfAfgUs96PTwU7AP4rgP8H4KsA/vr5z+OY9cL8BMCvAri+/urjhxMzxccv\\nnH//VNciCuCXMGu39zsAvhlPdy1+CLNn5CsA/h0AL57uWtw7nAD2MWst5saske/79zmhN4gNAN94\\n/u8QgI8xu/YfA/AD5z//Qcxkap8K/haAn8NMSx14umvxswD+wvm/XQAieJprsQfgJWZGGgB+ATNt\\n/ae4Fg8Cvx/AryjfP6rO6mvG5zGTo/0Ir/pfbpx//xSwDeDXAfxhvPKwn+JaRDAzUot4imsRx8yR\\niWG2cX0BwLfhaa7FtXGXtL4tADnl+5Pznz017OEtbF58Q/xjAH8HgCqw8RTX4jmAMoCfAfB/APwk\\ngCCe5lrUAPw4gGMAZwDqmIVCnuJaXBt3abB1peNb3Lz4BvgTmPX8/BIurqx9KmvhAvB7APyL869t\\nvH7qfCpr8Q6A78fModnE7Fn5noXfeSprcW3cpcE+xSz5Ruxg5mU/FVzWvBh45M2Lb4A/AOA7ABwA\\n+HkAfwSzNXmKa3Fy/vri+fe/hJnhLuDprcXvBfA/AVQBjAD8R8zCqE9xLa6NuzTYvwXgazHbQT0A\\nvguvEk5vO65qXgy8Rc2Lr8Dfw2yzfg7guwH8FwB/Fk9zLQqYhQnfPf/+c5ixJL6Ap7cWHwH4fQD8\\nmD0vn8PseXmKa/Fg8McwSyzsY0bheSr4g5jFa38bs1DAlzCjOMYxS749VcrSt+LVpv1U1+J3YeZh\\n/1/MvMoInu5a/ABe0fp+FrNT6VNdCw0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0N\\nDQ0NDQ0NDQ0NDQ0NDQ2Np4v/D87IQfh22uh3AAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fbe020a4390>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"from matplotlib import pyplot as plt\\n\",\n    \"image = create_captcha(\\\"GENE\\\", shear=0.5)\\n\",\n    \"plt.imshow(image, cmap=\\\"gray\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from skimage.measure import label, regionprops\\n\",\n    \"\\n\",\n    \"def segment_image(image):\\n\",\n    \"    labeled_image = label(image > 0)\\n\",\n    \"    subimages = []\\n\",\n    \"    for region in regionprops(labeled_image):\\n\",\n    \"        start_x, start_y, end_x, end_y = region.bbox\\n\",\n    \"        subimages.append(image[start_x:end_x, start_y:end_y])\\n\",\n    \"    if len(subimages) == 0:\\n\",\n    \"        return [image,]\\n\",\n    \"    return subimages\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAksAAACRCAYAAADTh/xiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nSUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fbe01f945c0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"subimages = segment_image(image)\\n\",\n    \"f, axes = plt.subplots(1, len(subimages), figsize=(10, 3))\\n\",\n    \"for i in range(len(subimages)):\\n\",\n    \"    axes[i].imshow(subimages[i], cmap=\\\"gray\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.utils import check_random_state\\n\",\n    \"random_state = check_random_state(14)\\n\",\n    \"letters = list(\\\"ACBDEFGHIJKLMNOPQRSTUVWXYZ\\\")\\n\",\n    \"shear_values = np.arange(0, 0.5, 0.05)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def generate_sample(random_state=None):\\n\",\n    \"    random_state = check_random_state(random_state)\\n\",\n    \"    letter = random_state.choice(letters)\\n\",\n    \"    shear = random_state.choice(shear_values)\\n\",\n    \"    return create_captcha(letter, shear=shear, size=(20, 20)), letters.index(letter)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The target for this image is: 11\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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eDX8sCmpYxc+sjCWxGM34O3974FlgXteIRVSuzWNMOPo+J+elHQUZwP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\\nEEIIIYQQQuzK/wM0vIhTjxisGAAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fbe01e33d68>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"image, target = generate_sample(random_state)\\n\",\n    \"plt.imshow(image, cmap=\\\"gray\\\")\\n\",\n    \"print(\\\"The target for this image is: {0}\\\".format(target))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"dataset, targets = zip(*(generate_sample(random_state) for i in\\n\",\n    \"range(3000)))\\n\",\n    \"dataset = np.array(dataset, dtype='float')\\n\",\n    \"targets = np.array(targets)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.preprocessing import OneHotEncoder\\n\",\n    \"onehot = OneHotEncoder()\\n\",\n    \"y = onehot.fit_transform(targets.reshape(targets.shape[0],1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"y = y.todense()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from skimage.transform import resize\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"dataset = np.array([resize(segment_image(sample)[0], (20, 20)) for\\n\",\n    \"sample in dataset])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X = dataset.reshape((dataset.shape[0], dataset.shape[1] *\\n\",\n    \"dataset.shape[2]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.cross_validation import train_test_split\\n\",\n    \"X_train, X_test, y_train, y_test = \\\\\\n\",\n    \"train_test_split(X, y, train_size=0.9)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from pybrain.datasets import SupervisedDataSet\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"training = SupervisedDataSet(X.shape[1], y.shape[1])\\n\",\n    \"for i in range(X_train.shape[0]):\\n\",\n    \"    training.addSample(X_train[i], y_train[i])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"testing = SupervisedDataSet(X.shape[1], y.shape[1])\\n\",\n    \"for i in range(X_test.shape[0]):\\n\",\n    \"    testing.addSample(X_test[i], y_test[i])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from pybrain.tools.shortcuts import buildNetwork\\n\",\n    \"net = buildNetwork(X.shape[1], 100, y.shape[1], bias=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from pybrain.supervised.trainers import BackpropTrainer\\n\",\n    \"trainer = BackpropTrainer(net, training, learningrate=0.01,\\n\",\n    \"weightdecay=0.01)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"trainer.trainEpochs(epochs=20)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"predictions = trainer.testOnClassData(dataset=testing)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"F-score: 1.00\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.metrics import f1_score\\n\",\n    \"print(\\\"F-score: {0:.2f}\\\".format(f1_score(predictions, y_test.argmax(axis=1))))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"             precision    recall  f1-score   support\\n\",\n      \"\\n\",\n      \"          0       1.00      1.00      1.00        10\\n\",\n      \"          1       1.00      1.00      1.00        10\\n\",\n      \"          2       1.00      1.00      1.00        11\\n\",\n      \"          3       1.00      1.00      1.00         9\\n\",\n      \"          4       1.00      0.86      0.92         7\\n\",\n      \"          5       0.92      1.00      0.96        11\\n\",\n      \"          6       1.00      1.00      1.00        10\\n\",\n      \"          7       1.00      1.00      1.00        16\\n\",\n      \"          8       1.00      1.00      1.00        13\\n\",\n      \"          9       1.00      1.00      1.00         9\\n\",\n      \"         10       1.00      1.00      1.00        12\\n\",\n      \"         11       1.00      1.00      1.00        13\\n\",\n      \"         12       1.00      1.00      1.00        11\\n\",\n      \"         13       1.00      1.00      1.00        13\\n\",\n      \"         14       1.00      1.00      1.00        14\\n\",\n      \"         15       1.00      1.00      1.00        13\\n\",\n      \"         16       1.00      1.00      1.00        15\\n\",\n      \"         17       1.00      1.00      1.00        17\\n\",\n      \"         18       1.00      1.00      1.00        13\\n\",\n      \"         19       1.00      1.00      1.00        13\\n\",\n      \"         20       1.00      1.00      1.00         9\\n\",\n      \"         21       1.00      1.00      1.00        11\\n\",\n      \"         22       1.00      1.00      1.00         9\\n\",\n      \"         23       1.00      1.00      1.00        15\\n\",\n      \"         24       1.00      1.00      1.00         9\\n\",\n      \"         25       1.00      1.00      1.00         7\\n\",\n      \"\\n\",\n      \"avg / total       1.00      1.00      1.00       300\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.metrics import classification_report\\n\",\n    \"print(classification_report(y_test.argmax(axis=1), predictions))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def predict_captcha(captcha_image, neural_network):\\n\",\n    \"    subimages = segment_image(captcha_image)\\n\",\n    \"    predicted_word = \\\"\\\"\\n\",\n    \"    for subimage in subimages:\\n\",\n    \"        subimage = resize(subimage, (20, 20))\\n\",\n    \"        outputs = net.activate(subimage.flatten())\\n\",\n    \"        prediction = np.argmax(outputs)\\n\",\n    \"        predicted_word += letters[prediction]\\n\",\n    \"    return predicted_word\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"GENE\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"word = \\\"GENE\\\"\\n\",\n    \"captcha = create_captcha(word, shear=0.2)\\n\",\n    \"print(predict_captcha(captcha, net))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def test_prediction(word, net, shear=0.2):\\n\",\n    \"    captcha = create_captcha(word, shear=shear)\\n\",\n    \"    prediction = predict_captcha(captcha, net)\\n\",\n    \"    prediction = prediction[:4]\\n\",\n    \"    return word == prediction, word, prediction\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from nltk.corpus import words\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"valid_words = [word.upper() for word in words.words() if len(word) == 4]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"num_correct = 0\\n\",\n    \"num_incorrect = 0\\n\",\n    \"for word in valid_words:\\n\",\n    \"    correct, word, prediction = test_prediction(word, net, shear=0.2)\\n\",\n    \"    if correct:\\n\",\n    \"        num_correct += 1\\n\",\n    \"    else:\\n\",\n    \"        num_incorrect += 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Number correct is 3453\\n\",\n      \"Number incorrect is 2060\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Number correct is {0}\\\".format(num_correct))\\n\",\n    \"print(\\\"Number incorrect is {0}\\\".format(num_incorrect))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.metrics import confusion_matrix\\n\",\n    \"cm = confusion_matrix(np.argmax(y_test, axis=1), predictions)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.image.AxesImage at 0x7fbdfbd5e0f0>\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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WKfPgXO+3fzez//ZZ7KWVFOlQmeucxePCl1LN4/0KJHZrnWkD0ClNyRKhOzHjc/EzwYZVss1SKE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\\n/w/tatS1CjdN8AAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fbdfaf68128>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(20,20))\\n\",\n    \"plt.imshow(cm, cmap=\\\"Blues\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The number of steps needed is: 1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from nltk.metrics import edit_distance\\n\",\n    \"steps = edit_distance(\\\"STEP\\\", \\\"STOP\\\")\\n\",\n    \"print(\\\"The number of steps needed is: {0}\\\".format(steps))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_distance(prediction, word):\\n\",\n    \"    return len(prediction) - sum(prediction[i] == word[i] for i in range(len(prediction)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from operator import itemgetter\\n\",\n    \"def improved_prediction(word, net, dictionary, shear=0.2):\\n\",\n    \"    captcha = create_captcha(word, shear=shear)\\n\",\n    \"    prediction = predict_captcha(captcha, net)\\n\",\n    \"    prediction = prediction[:4]\\n\",\n    \"    if prediction not in dictionary:\\n\",\n    \"        distances = sorted([(word, compute_distance(prediction, word))\\n\",\n    \"                            for word in dictionary],\\n\",\n    \"                           key=itemgetter(1))\\n\",\n    \"        best_word = distances[0]\\n\",\n    \"        prediction = best_word[0]\\n\",\n    \"    return word == prediction, word, prediction\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"ename\": \"OSError\",\n     \"evalue\": \"cannot open resource\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[1;31mOSError\\u001b[0m                                   Traceback (most recent call last)\",\n      \"\\u001b[1;32m<ipython-input-42-fe20ee79ead7>\\u001b[0m in \\u001b[0;36m<module>\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m      2\\u001b[0m \\u001b[0mnum_incorrect\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[1;36m0\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m      3\\u001b[0m \\u001b[1;32mfor\\u001b[0m \\u001b[0mword\\u001b[0m \\u001b[1;32min\\u001b[0m \\u001b[0mvalid_words\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m----> 4\\u001b[1;33m     \\u001b[0mcorrect\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mword\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mprediction\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mimproved_prediction\\u001b[0m \\u001b[1;33m(\\u001b[0m\\u001b[0mword\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mnet\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mvalid_words\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mshear\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[1;36m0.2\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m      5\\u001b[0m     \\u001b[1;32mif\\u001b[0m \\u001b[0mcorrect\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m      6\\u001b[0m         \\u001b[0mnum_correct\\u001b[0m \\u001b[1;33m+=\\u001b[0m \\u001b[1;36m1\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32m<ipython-input-41-1e2803e9aaea>\\u001b[0m in \\u001b[0;36mimproved_prediction\\u001b[1;34m(word, net, dictionary, shear)\\u001b[0m\\n\\u001b[0;32m      1\\u001b[0m \\u001b[1;32mfrom\\u001b[0m \\u001b[0moperator\\u001b[0m \\u001b[1;32mimport\\u001b[0m \\u001b[0mitemgetter\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m      2\\u001b[0m \\u001b[1;32mdef\\u001b[0m \\u001b[0mimproved_prediction\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mword\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mnet\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mdictionary\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mshear\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[1;36m0.2\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m----> 3\\u001b[1;33m     \\u001b[0mcaptcha\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mcreate_captcha\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mword\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mshear\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mshear\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m      4\\u001b[0m     \\u001b[0mprediction\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mpredict_captcha\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mcaptcha\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mnet\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m      5\\u001b[0m     \\u001b[0mprediction\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mprediction\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;36m4\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32m<ipython-input-7-e98df31ba56b>\\u001b[0m in \\u001b[0;36mcreate_captcha\\u001b[1;34m(text, shear, size)\\u001b[0m\\n\\u001b[0;32m      2\\u001b[0m     \\u001b[0mim\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mImage\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mnew\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;34m\\\"L\\\"\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0msize\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[1;34m\\\"black\\\"\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m      3\\u001b[0m     \\u001b[0mdraw\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mImageDraw\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mDraw\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mim\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m----> 4\\u001b[1;33m     \\u001b[0mfont\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mImageFont\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mtruetype\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;34mr\\\"Coval.otf\\\"\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[1;36m22\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m      5\\u001b[0m     \\u001b[0mdraw\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mtext\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;36m2\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[1;36m2\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mtext\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mfill\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[1;36m1\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mfont\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mfont\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m      6\\u001b[0m     \\u001b[0mimage\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mnp\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0marray\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mim\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32m/usr/lib/python3/dist-packages/PIL/ImageFont.py\\u001b[0m in \\u001b[0;36mtruetype\\u001b[1;34m(font, size, index, encoding, filename)\\u001b[0m\\n\\u001b[0;32m    226\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    227\\u001b[0m     \\u001b[1;32mtry\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m--> 228\\u001b[1;33m         \\u001b[1;32mreturn\\u001b[0m \\u001b[0mFreeTypeFont\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mfont\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0msize\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mindex\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mencoding\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m    229\\u001b[0m     \\u001b[1;32mexcept\\u001b[0m \\u001b[0mIOError\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    230\\u001b[0m         \\u001b[1;32mif\\u001b[0m \\u001b[0msys\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mplatform\\u001b[0m \\u001b[1;33m==\\u001b[0m \\u001b[1;34m\\\"win32\\\"\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32m/usr/lib/python3/dist-packages/PIL/ImageFont.py\\u001b[0m in \\u001b[0;36m__init__\\u001b[1;34m(self, font, size, index, encoding, file)\\u001b[0m\\n\\u001b[0;32m    129\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    130\\u001b[0m         \\u001b[1;32mif\\u001b[0m \\u001b[0misPath\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mfont\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m--> 131\\u001b[1;33m             \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mfont\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mcore\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mgetfont\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mfont\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0msize\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mindex\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mencoding\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m    132\\u001b[0m         \\u001b[1;32melse\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    133\\u001b[0m             \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mfont_bytes\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mfont\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mread\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;31mOSError\\u001b[0m: cannot open resource\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"num_correct = 0\\n\",\n    \"num_incorrect = 0\\n\",\n    \"for word in valid_words:\\n\",\n    \"    correct, word, prediction = improved_prediction (word, net, valid_words, shear=0.2)\\n\",\n    \"    if correct:\\n\",\n    \"        num_correct += 1\\n\",\n    \"    else:\\n\",\n    \"        num_incorrect += 1\\n\",\n    \"print(\\\"Number correct is {0}\\\".format(num_correct))\\n\",\n    \"print(\\\"Number incorrect is {0}\\\".format(num_incorrect))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "Chapter 8/Sigmoid.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"x = np.arange(-6, 6, 0.001)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import math\\n\",\n    \"def sigmoid(x):\\n\",\n    \"    return 1 / (1 + math.exp(-x))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"y = np.array([sigmoid(xx) for xx in x])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from matplotlib import pyplot as plt\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[<matplotlib.lines.Line2D at 0x7f998578b128>]\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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ADAyekbk/746goAAAAAgBsZtcsdFQMAAAAAACfWb0h6POlFq0sAAAAAAG5k1C53VAwAAAAAAJxY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\\nAPBNRu1yR8UAAAAAALyz/rGkbyT9o6tLAAAAAADgm4za5Y6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  },
  {
    "path": "Chapter 9/Chapter 9 Authorship Analysis.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"data_folder = os.path.join(os.path.expanduser(\\\"~\\\"), \\\"Data\\\", \\\"books\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Downloading titles from gaboriau\\n\",\n      \" - Getting book with id 1748\\n\",\n      \"1748 1/7/4\\n\",\n      \" - http://www.gutenberg.myebook.bg/1/7/4/1748/1748.txt\\n\"\n     ]\n    },\n    {\n     \"ename\": \"KeyboardInterrupt\",\n     \"evalue\": \"\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[1;31mKeyboardInterrupt\\u001b[0m                         Traceback (most recent call last)\",\n      \"\\u001b[1;32m<ipython-input-2-9d8d6f295482>\\u001b[0m in \\u001b[0;36m<module>\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m     86\\u001b[0m         \\u001b[1;32melse\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m     87\\u001b[0m             \\u001b[0murllib\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mrequest\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0murlretrieve\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0murl\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mfilename\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m---> 88\\u001b[1;33m             \\u001b[0msleep\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;36m60\\u001b[0m\\u001b[1;33m*\\u001b[0m\\u001b[1;36m5\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m     89\\u001b[0m \\u001b[0mprint\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;34m\\\"Download complete\\\"\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;31mKeyboardInterrupt\\u001b[0m: \"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# %load getdata.py\\n\",\n    \"# Downloads the books and stores them in the below folder\\n\",\n    \"import os\\n\",\n    \"from time import sleep\\n\",\n    \"import urllib.request\\n\",\n    \"\\n\",\n    \"titles = {}\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"titles['burton'] = [4657, 2400, 5760, 6036, 7111, 8821,\\n\",\n    \"                    18506, 4658, 5761, 6886, 7113]\\n\",\n    \"titles['dickens'] = [24022, 1392, 1414, 1467, 2324, 580,\\n\",\n    \"                     786, 888, 963, 27924, 1394, 1415, 15618,\\n\",\n    \"                     25985, 588, 807, 914, 967, 30127, 1400,\\n\",\n    \"                     1421, 16023, 28198, 644, 809, 917, 968, 1023,\\n\",\n    \"                     1406, 1422, 17879, 30368, 675, 810, 924, 98,\\n\",\n    \"                     1289, 1413, 1423, 17880, 32241, 699, 821, 927]\\n\",\n    \"titles['doyle'] = [2349, 11656, 1644, 22357, 2347, 290, 34627, 5148,\\n\",\n    \"                   8394, 26153, 12555, 1661, 23059, 2348, 294, 355,\\n\",\n    \"                   5260, 8727, 10446, 126, 17398, 2343, 2350, 3070,\\n\",\n    \"                   356, 5317, 903, 10581, 13152, 2038, 2344, 244, 32536,\\n\",\n    \"                   423, 537, 108, 139, 2097, 2345, 24951, 32777, 4295,\\n\",\n    \"                   7964, 11413, 1638, 21768, 2346, 2845, 3289, 439, 834]\\n\",\n    \"titles['gaboriau'] = [1748, 1651, 2736, 3336, 4604, 4002, 2451,\\n\",\n    \"                      305, 3802, 547]\\n\",\n    \"titles['nesbit'] = [34219, 23661, 28804, 4378, 778, 20404, 28725,\\n\",\n    \"                    33028, 4513, 794]\\n\",\n    \"titles['tarkington'] = [1098, 15855, 1983, 297, 402, 5798,\\n\",\n    \"                        8740, 980, 1158, 1611, 2326, 30092,\\n\",\n    \"                        483, 5949, 8867, 13275, 18259, 2595,\\n\",\n    \"                        3428, 5756, 6401, 9659]\\n\",\n    \"titles['twain'] = [1044, 1213, 245, 30092, 3176, 3179, 3183, 3189, 74,\\n\",\n    \"                   86, 1086, 142, 2572, 3173, 3177, 3180, 3186, 3192,\\n\",\n    \"                   76, 91, 119, 1837, 2895, 3174, 3178, 3181, 3187, 3432,\\n\",\n    \"                   8525]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"assert len(titles) == 7\\n\",\n    \"\\n\",\n    \"assert len(titles['tarkington']) == 22\\n\",\n    \"assert len(titles['dickens']) == 44\\n\",\n    \"assert len(titles['nesbit']) == 10\\n\",\n    \"assert len(titles['doyle']) == 51\\n\",\n    \"assert len(titles['twain']) == 29\\n\",\n    \"assert len(titles['burton']) == 11\\n\",\n    \"assert len(titles['gaboriau']) == 10\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# https://www.gutenberg.org\\n\",\n    \"#url_base = \\\"http://gutenberg.pglaf.org/\\\"\\n\",\n    \"#url_base = \\\"http://gutenberg.readingroo.ms/\\\"\\n\",\n    \"url_base = \\\"http://www.gutenberg.myebook.bg/\\\"\\n\",\n    \"url_format = \\\"{url_base}{idstring}/{id}/{id}.txt\\\"\\n\",\n    \"\\n\",\n    \"fixes = {}\\n\",\n    \"fixes[1044] = url_base + \\\"1/0/4/1044/1044-0.txt\\\"\\n\",\n    \"fixes[5148] = url_base + \\\"5/1/4/5148/5148-0.txt\\\"\\n\",\n    \"fixes[4657] = \\\"https://archive.org/stream/personalnarrativ04657gut/pnpa110.txt\\\"\\n\",\n    \"fixes[1467] = \\\"https://archive.org/stream/somechristmassto01467gut/cdscs10p_djvu.txt\\\"\\n\",\n    \"\\n\",\n    \"# Make parent folder if not exists\\n\",\n    \"if not os.path.exists(data_folder):\\n\",\n    \"    os.makedirs(data_folder)\\n\",\n    \"\\n\",\n    \"for author in titles:\\n\",\n    \"    print(\\\"Downloading titles from {author}\\\".format(author=author))\\n\",\n    \"    # Make author's folder if not exists\\n\",\n    \"    author_folder = os.path.join(data_folder, author)\\n\",\n    \"    if not os.path.exists(author_folder):\\n\",\n    \"        os.makedirs(author_folder)\\n\",\n    \"    # Download each title to this folder\\n\",\n    \"    for bookid in titles[author]:\\n\",\n    \"        if bookid in fixes:\\n\",\n    \"            print(\\\" - Applying fix to book with id {id}\\\".format(id=bookid))\\n\",\n    \"            url =  fixes[bookid]\\n\",\n    \"        else:\\n\",\n    \"            print(\\\" - Getting book with id {id}\\\".format(id=bookid))\\n\",\n    \"            idstring = \\\"/\\\".join([str(bookid)[i] for i in range(len(str(bookid))-1)])\\n\",\n    \"            print(bookid, idstring)\\n\",\n    \"            url = url_format.format(url_base=url_base, idstring=idstring, id=bookid)\\n\",\n    \"        print(\\\" - \\\" + url)\\n\",\n    \"        filename = os.path.join(author_folder, \\\"{id}.txt\\\".format(id=bookid))\\n\",\n    \"        if os.path.exists(filename):\\n\",\n    \"            print(\\\" - File already exists, skipping\\\")\\n\",\n    \"        else:\\n\",\n    \"            urllib.request.urlretrieve(url, filename)\\n\",\n    \"            sleep(60*5)\\n\",\n    \"print(\\\"Download complete\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def clean_book(document):\\n\",\n    \"    lines = document.split(\\\"\\\\n\\\")\\n\",\n    \"    start= 0\\n\",\n    \"    end = len(lines)\\n\",\n    \"    for i in range(len(lines)):\\n\",\n    \"        line = lines[i]\\n\",\n    \"        if line.startswith(\\\"*** START OF THIS PROJECT GUTENBERG\\\"):\\n\",\n    \"            start = i + 1\\n\",\n    \"        elif line.startswith(\\\"*** END OF THIS PROJECT GUTENBERG\\\"):\\n\",\n    \"            end = i - 1\\n\",\n    \"    return \\\"\\\\n\\\".join(lines[start:end])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"def load_books_data(folder=data_folder):\\n\",\n    \"    documents = []\\n\",\n    \"    authors = []\\n\",\n    \"    subfolders = [subfolder for subfolder in os.listdir(folder)\\n\",\n    \"                  if os.path.isdir(os.path.join(folder, subfolder))]\\n\",\n    \"    for author_number, subfolder in enumerate(subfolders):\\n\",\n    \"        full_subfolder_path = os.path.join(folder, subfolder)\\n\",\n    \"        for document_name in os.listdir(full_subfolder_path):\\n\",\n    \"            with open(os.path.join(full_subfolder_path, document_name)) as inf:\\n\",\n    \"                documents.append(clean_book(inf.read()))\\n\",\n    \"                authors.append(author_number)\\n\",\n    \"    return documents, np.array(authors, dtype='int')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"documents, classes = load_books_data(data_folder)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"function_words = [\\\"a\\\", \\\"able\\\", \\\"aboard\\\", \\\"about\\\", \\\"above\\\", \\\"absent\\\",\\n\",\n    \"                  \\\"according\\\" , \\\"accordingly\\\", \\\"across\\\", \\\"after\\\", \\\"against\\\",\\n\",\n    \"                  \\\"ahead\\\", \\\"albeit\\\", \\\"all\\\", \\\"along\\\", \\\"alongside\\\", \\\"although\\\",\\n\",\n    \"                  \\\"am\\\", \\\"amid\\\", \\\"amidst\\\", \\\"among\\\", \\\"amongst\\\", \\\"amount\\\", \\\"an\\\",\\n\",\n    \"                    \\\"and\\\", \\\"another\\\", \\\"anti\\\", \\\"any\\\", \\\"anybody\\\", \\\"anyone\\\",\\n\",\n    \"                    \\\"anything\\\", \\\"are\\\", \\\"around\\\", \\\"as\\\", \\\"aside\\\", \\\"astraddle\\\",\\n\",\n    \"                    \\\"astride\\\", \\\"at\\\", \\\"away\\\", \\\"bar\\\", \\\"barring\\\", \\\"be\\\", \\\"because\\\",\\n\",\n    \"                    \\\"been\\\", \\\"before\\\", \\\"behind\\\", \\\"being\\\", \\\"below\\\", \\\"beneath\\\",\\n\",\n    \"                    \\\"beside\\\", \\\"besides\\\", \\\"better\\\", \\\"between\\\", \\\"beyond\\\", \\\"bit\\\",\\n\",\n    \"                    \\\"both\\\", \\\"but\\\", \\\"by\\\", \\\"can\\\", \\\"certain\\\", \\\"circa\\\", \\\"close\\\",\\n\",\n    \"                    \\\"concerning\\\", \\\"consequently\\\", \\\"considering\\\", \\\"could\\\",\\n\",\n    \"                    \\\"couple\\\", \\\"dare\\\", \\\"deal\\\", \\\"despite\\\", \\\"down\\\", \\\"due\\\", \\\"during\\\",\\n\",\n    \"                    \\\"each\\\", \\\"eight\\\", \\\"eighth\\\", \\\"either\\\", \\\"enough\\\", \\\"every\\\",\\n\",\n    \"                    \\\"everybody\\\", \\\"everyone\\\", \\\"everything\\\", \\\"except\\\", \\\"excepting\\\",\\n\",\n    \"                    \\\"excluding\\\", \\\"failing\\\", \\\"few\\\", \\\"fewer\\\", \\\"fifth\\\", \\\"first\\\",\\n\",\n    \"                    \\\"five\\\", \\\"following\\\", \\\"for\\\", \\\"four\\\", \\\"fourth\\\", \\\"from\\\", \\\"front\\\",\\n\",\n    \"                    \\\"given\\\", \\\"good\\\", \\\"great\\\", \\\"had\\\", \\\"half\\\", \\\"have\\\", \\\"he\\\",\\n\",\n    \"                    \\\"heaps\\\", \\\"hence\\\", \\\"her\\\", \\\"hers\\\", \\\"herself\\\", \\\"him\\\", \\\"himself\\\",\\n\",\n    \"                    \\\"his\\\", \\\"however\\\", \\\"i\\\", \\\"if\\\", \\\"in\\\", \\\"including\\\", \\\"inside\\\",\\n\",\n    \"                    \\\"instead\\\", \\\"into\\\", \\\"is\\\", \\\"it\\\", \\\"its\\\", \\\"itself\\\", \\\"keeping\\\",\\n\",\n    \"                    \\\"lack\\\", \\\"less\\\", \\\"like\\\", \\\"little\\\", \\\"loads\\\", \\\"lots\\\", \\\"majority\\\",\\n\",\n    \"                    \\\"many\\\", \\\"masses\\\", \\\"may\\\", \\\"me\\\", \\\"might\\\", \\\"mine\\\", \\\"minority\\\",\\n\",\n    \"                    \\\"minus\\\", \\\"more\\\", \\\"most\\\", \\\"much\\\", \\\"must\\\", \\\"my\\\", \\\"myself\\\",\\n\",\n    \"                    \\\"near\\\", \\\"need\\\", \\\"neither\\\", \\\"nevertheless\\\", \\\"next\\\", \\\"nine\\\",\\n\",\n    \"                    \\\"ninth\\\", \\\"no\\\", \\\"nobody\\\", \\\"none\\\", \\\"nor\\\", \\\"nothing\\\",\\n\",\n    \"                    \\\"notwithstanding\\\", \\\"number\\\", \\\"numbers\\\", \\\"of\\\", \\\"off\\\", \\\"on\\\",\\n\",\n    \"                    \\\"once\\\", \\\"one\\\", \\\"onto\\\", \\\"opposite\\\", \\\"or\\\", \\\"other\\\", \\\"ought\\\",\\n\",\n    \"                    \\\"our\\\", \\\"ours\\\", \\\"ourselves\\\", \\\"out\\\", \\\"outside\\\", \\\"over\\\", \\\"part\\\",\\n\",\n    \"                    \\\"past\\\", \\\"pending\\\", \\\"per\\\", \\\"pertaining\\\", \\\"place\\\", \\\"plenty\\\",\\n\",\n    \"                    \\\"plethora\\\", \\\"plus\\\", \\\"quantities\\\", \\\"quantity\\\", \\\"quarter\\\",\\n\",\n    \"                    \\\"regarding\\\", \\\"remainder\\\", \\\"respecting\\\", \\\"rest\\\", \\\"round\\\",\\n\",\n    \"                    \\\"save\\\", \\\"saving\\\", \\\"second\\\", \\\"seven\\\", \\\"seventh\\\", \\\"several\\\",\\n\",\n    \"                    \\\"shall\\\", \\\"she\\\", \\\"should\\\", \\\"similar\\\", \\\"since\\\", \\\"six\\\", \\\"sixth\\\",\\n\",\n    \"                    \\\"so\\\", \\\"some\\\", \\\"somebody\\\", \\\"someone\\\", \\\"something\\\", \\\"spite\\\",\\n\",\n    \"                    \\\"such\\\", \\\"ten\\\", \\\"tenth\\\", \\\"than\\\", \\\"thanks\\\", \\\"that\\\", \\\"the\\\",\\n\",\n    \"                    \\\"their\\\", \\\"theirs\\\", \\\"them\\\", \\\"themselves\\\", \\\"then\\\", \\\"thence\\\",\\n\",\n    \"                  \\\"therefore\\\", \\\"these\\\", \\\"they\\\", \\\"third\\\", \\\"this\\\", \\\"those\\\",\\n\",\n    \"\\\"though\\\", \\\"three\\\", \\\"through\\\", \\\"throughout\\\", \\\"thru\\\", \\\"thus\\\",\\n\",\n    \"\\\"till\\\", \\\"time\\\", \\\"to\\\", \\\"tons\\\", \\\"top\\\", \\\"toward\\\", \\\"towards\\\",\\n\",\n    \"\\\"two\\\", \\\"under\\\", \\\"underneath\\\", \\\"unless\\\", \\\"unlike\\\", \\\"until\\\",\\n\",\n    \"\\\"unto\\\", \\\"up\\\", \\\"upon\\\", \\\"us\\\", \\\"used\\\", \\\"various\\\", \\\"versus\\\",\\n\",\n    \"\\\"via\\\", \\\"view\\\", \\\"wanting\\\", \\\"was\\\", \\\"we\\\", \\\"were\\\", \\\"what\\\",\\n\",\n    \"\\\"whatever\\\", \\\"when\\\", \\\"whenever\\\", \\\"where\\\", \\\"whereas\\\",\\n\",\n    \"\\\"wherever\\\", \\\"whether\\\", \\\"which\\\", \\\"whichever\\\", \\\"while\\\",\\n\",\n    \"                  \\\"whilst\\\", \\\"who\\\", \\\"whoever\\\", \\\"whole\\\", \\\"whom\\\", \\\"whomever\\\",\\n\",\n    \"\\\"whose\\\", \\\"will\\\", \\\"with\\\", \\\"within\\\", \\\"without\\\", \\\"would\\\", \\\"yet\\\",\\n\",\n    \"\\\"you\\\", \\\"your\\\", \\\"yours\\\", \\\"yourself\\\", \\\"yourselves\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.feature_extraction.text import CountVectorizer\\n\",\n    \"extractor = CountVectorizer(vocabulary=function_words)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.svm import SVC\\n\",\n    \"from sklearn.cross_validation import cross_val_score\\n\",\n    \"from sklearn.pipeline import Pipeline\\n\",\n    \"from sklearn import grid_search\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}\\n\",\n    \"svr = SVC()\\n\",\n    \"grid = grid_search.GridSearchCV(svr, parameters)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"pipeline1 = Pipeline([('feature_extraction', extractor),\\n\",\n    \"                      ('clf', grid)\\n\",\n    \"                     ])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/usr/lib/python3/dist-packages/scipy/sparse/compressed.py:119: UserWarning: indptr array has non-integer dtype (float64)\\n\",\n      \"  % self.indptr.dtype.name)\\n\",\n      \"/usr/local/lib/python3.4/dist-packages/sklearn/metrics/metrics.py:1771: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\\n\",\n      \"  'precision', 'predicted', average, warn_for)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"scores = cross_val_score(pipeline1, documents, classes,\\n\",\n    \"scoring='f1')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0.812103970357\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(np.mean(scores))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Score: 0.818\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"pipeline = Pipeline([('feature_extraction',\\n\",\n    \"CountVectorizer(analyzer='char', ngram_range=(3, 3))),\\n\",\n    \"('classifier', grid)\\n\",\n    \"])\\n\",\n    \"scores = cross_val_score(pipeline, documents, classes,\\n\",\n    \"scoring='f1')\\n\",\n    \"print(\\\"Score: {:.3f}\\\".format(np.mean(scores)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"enron_data_folder = os.path.join(os.path.expanduser(\\\"~\\\"), \\\"Data\\\",\\n\",\n    \"\\\"enron_mail_20110402\\\", \\\"maildir\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from email.parser import Parser\\n\",\n    \"p = Parser()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.utils import check_random_state\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def get_enron_corpus(num_authors=10, data_folder=data_folder,\\n\",\n    \"                     min_docs_author=10, max_docs_author=100,\\n\",\n    \"                     random_state=None):\\n\",\n    \"    random_state = check_random_state(random_state)\\n\",\n    \"    email_addresses = sorted(os.listdir(data_folder))\\n\",\n    \"    random_state.shuffle(email_addresses)\\n\",\n    \"    documents = []\\n\",\n    \"    classes = []\\n\",\n    \"    author_num = 0\\n\",\n    \"    authors = {}\\n\",\n    \"    for user in email_addresses:\\n\",\n    \"        users_email_folder = os.path.join(data_folder, user)\\n\",\n    \"        mail_folders = [os.path.join(users_email_folder, subfolder)\\n\",\n    \"                        for subfolder in os.listdir(users_email_folder)\\n\",\n    \"                        if \\\"sent\\\" in subfolder]\\n\",\n    \"        try:\\n\",\n    \"            authored_emails = [open(os.path.join(mail_folder, email_filename), encoding='cp1252').read()\\n\",\n    \"                               for mail_folder in mail_folders\\n\",\n    \"                               for email_filename in os.listdir(mail_folder)]\\n\",\n    \"        except IsADirectoryError:\\n\",\n    \"            continue\\n\",\n    \"        if len(authored_emails) < min_docs_author:\\n\",\n    \"            continue\\n\",\n    \"        if len(authored_emails) > max_docs_author:\\n\",\n    \"            authored_emails = authored_emails[:max_docs_author]\\n\",\n    \"        contents = [p.parsestr(email)._payload for email in authored_emails]\\n\",\n    \"        documents.extend(contents)\\n\",\n    \"        classes.extend([author_num] * len(authored_emails))\\n\",\n    \"        authors[user] = author_num\\n\",\n    \"        author_num += 1\\n\",\n    \"        if author_num >= num_authors or author_num >= len(email_addresses):\\n\",\n    \"            break\\n\",\n    \"    return documents, np.array(classes), authors\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"documents, classes, authors = get_enron_corpus(data_folder=enron_data_folder, random_state=14)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'I am disappointed on the timing but I understand.  Thanks.  Mark\\\\n\\\\n -----Original Message-----\\\\nFrom: \\\\tGreenberg, Mark  \\\\nSent:\\\\tFriday, September 28, 2001 4:19 PM\\\\nTo:\\\\tHaedicke, Mark E.\\\\nSubject:\\\\tWeb Site\\\\n\\\\nMark -\\\\n\\\\nFYI - I have attached below a screen shot of the proposed new look and feel for the site.  We have a couple of tweaks to make, but I believe this is a much cleaner look than what we have now.\\\\n\\\\nUnfortunately, because of the virus we lost a good part of development time last week.  This has pushed back the completion schedule at this time by at least a week, which means that the site will not be updated and operational by the time the law conference comes along.  If you want information about the site disseminated, I can provide an overview of the new look and feel and new functions that we anticipate having.  I believe a new target release date is realistically Nov. 1.\\\\n\\\\nPlease let me know if you should need anything further.\\\\n\\\\nMark\\\\n\\\\n\\\\n << File: lg-0928-withmenu.gif >> '\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"documents[100]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import quotequail\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def remove_replies(email_contents):\\n\",\n    \"    r = quotequail.unwrap(email_contents)\\n\",\n    \"    if r is None:\\n\",\n    \"        return email_contents\\n\",\n    \"    if 'text_top' in r:\\n\",\n    \"        return r['text_top']\\n\",\n    \"    elif 'text' in r:\\n\",\n    \"        return r['text']\\n\",\n    \"    return email_contents\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"documents = [remove_replies(document) for document in documents]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Score: 0.523\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"scores = cross_val_score(pipeline, documents, classes, scoring='f1')\\n\",\n    \"print(\\\"Score: {:.3f}\\\".format(np.mean(scores)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.cross_validation import train_test_split\\n\",\n    \"training_documents, testing_documents, y_train, y_test = train_test_split(documents, classes, random_state=14)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"pipeline.fit(training_documents, y_train)\\n\",\n    \"y_pred = pipeline.predict(testing_documents)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'C': 1, 'kernel': 'linear'}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(pipeline.named_steps['classifier'].best_params_)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.metrics import confusion_matrix\\n\",\n    \"cm = confusion_matrix(y_pred, y_test)\\n\",\n    \"cm = cm / cm.astype(np.float).sum(axis=1)\\n\",\n    \"sorted_authors = sorted(authors.keys(), key=lambda x:authors[x])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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626uIy01c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\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pyrxtU85Y8k57rsmmvZqoAK0YB+mw4UfVrOTJahSaVKi0C4lKUbmXJc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\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5aVLK0sN99kyUmueYMlKQvXLirth9q6h2CpeWC36pBlL8UPrn6IjgJ+\\nSF1ER0E/ZpfBrPaxTasV7w/WuREgLFFclqiSkDjN02gvJm76GvFCKkNMRlfTYWxCfpUXZ1C++VMH\\nBdqR/FZpn990Rnqpg6ljs9nnkctr5mmQx5A/yAvR+skVkPbvrbdzaO0+f87mHOYKLbyM+/d5n1n2\\n+pQ4jCTpzsHq/ekS5+KcG0d/B7v1DYlnT2dwXoTZXmxSs9vmzoKueXKSKZFIJBI/CXclkjb5dddK\\nXIWLXlR8My6d+7Sf1g17ke+ZiUQi8V6U4GgRPCbC5NBoXJHXyJQhSaZEWCmqiR3pVIoq8/umFbHc\\n7KiM6JwjqRjkjc1KQY0ILKloI6nq+UPKstZGZKY8INLAH62JJQAfoL1ArhA8D57Ih+fT9sVDvHzM\\n55VC9KwJjTh6ag0Laf7jUfOaTKGn1KDqap8iV+1qNNPkcxehRTMRWi66ykRouTItAssRcKrTkXsP\\nJfBUl5NhIPyoz394mVAHlleGzuoW8jK2NyICZZxZnaQEIsU6kexjKGcAeR1ENfdpjbTrMrUOWxGS\\ncHJv8ekuLyxHRIXdTSlxCZKkS3wPTiJ1riKSrvgivl4nvBmeqvf9ONB7iUQikUh8LdyPq7vjW8I7\\n+lHOvwFnt0fUxkt9MEpLLOJI963plkQikUisRX8nXXztCATmyijB5suwRoE1LeQINucbE0SP9T4Q\\nuTymRgCacjwJcrUnEWMkUWfiIQMBWITgmPxrZuoBq/vUCEfJ6/xj9Q2j2qQ9mIgx6q0QFfEP2qe1\\nLWOEXmltRVBnDPYi0sg/jX6biaAz9noyE3VGhGdpkXSkRCn0fURQIZHUyCbRGZBhPTkl/Sv2fHQb\\nNY7IEFBIZIkuAl0B0YbEUryHnbMBQPuY4OtOoM+m6ejCcQadrfrIC8Q+tUux08gtNXrh6uwHrsym\\njxxx2BpYMCcvl48KTCd5orS3AAAgAElEQVSzUXeJ05EkXeJjwd3BSfouKHDaDe2QonWFVz4PEolE\\nIpFIXIwzOJhTI5xexfwV9+mPE9dgT4jc2rQ7Ymkc+7QbvRPv6ZajWGqiGzVPIpFIbMC2F5lOGggo\\nS35NmY3wafJcyaISRuTp8o8aYdWit+qnEl+WwCK2pJMQSm0pySpvSafKlYFdIYFazJonq5ioFAZi\\nQ2UIZNC+EEKGZxFiC4gsJLseJFFxoAsi7Z5Sd+Ip0gujhYQzeqrNGrjW7DOVGtE26RL7LSrPRdBJ\\nNJ5Ev0nkXYu4k88alccii/bFTx9RR+Si1IIIukclrlq0m0bQIQFnIuqYnCxZWYjOEx+iaD0ffUdE\\nLr/XbQg+r4uxLpaMU9LQR7Sx0yfjAslKJxvoltFmlhStFbJRd4qeRKzjqx2yZoJfqkA/R+9LPMow\\nCjYASLd26Ji6QnKNwVUCZPi8T4mzkSRd4iNx3n3ic+84V3m+9dcYl2C3A7fwPpFIJBKJTbgFp7HF\\niSTPEt+ArUzX2WP9w15Z93C4I3xY1ROJxE/FNv5uuRASeoF4X3JKEVJMUXBK3eoWgg8VCjFHmC5E\\ni4tQqxqFfCARBT0ycV/AuCcDSci5yQhxy7P+gCvjZTilzlVYIu0aSVPtSN2UcKxRb61ONiquiC2y\\ny24WIFGbryzy8Z50jH42MhVI1OYBkiJAbkqbs/+EiLGAuBJyyJBiHTk1I4vEVUdScedPJytDQHQ7\\nWyTnnpwyei3B1UfOQfuAD023ijQpHb8wkrkvj6V8gvELpDriaualxvu1osiyb6PSRS+9RTTZfg41\\nnFXN73svQZJ0iY8DdwfHNG1Ws6HAYRcHitbrba8qe8ycVoFNanbb3FbXJbun9V0ikUgkfjxe+r0G\\nv1nlF6pE4r64ivTzoW03DHXbWvUbVyWRSHwhupkFIJX6iOJKuwjbJEsu1nJ+uUciJL3YEFmjJRuR\\niAuXkQSyqVFsTIZQKkAWNYIMiaRoaUaooRBKjdji0gggvDujrDIzltjiUuiJxEsltZ4o2wiiUqPf\\nVLdEurWouAd1EXNeVlqHH5MfUzRetclTeb8HHcMec9I3hZn4OR0/nrocoNjkWh6j7Zipj6gD2QL9\\nIVFxD+4j6Eb7y9mIOmp5j7onHQWyNqqN+n3mTNRcL0ugx5QFEvHR+lHIO/CHLBnnyzKWbf6SLSvj\\nr7Mz5VtCUsQsESl2PHkJl5EjKGHcth63dSIV84dkUgPZ9VhfqC2/qikaO+ei7ZpmiEpNvA5J0iU+\\nCud9IVNNHxF7FYQkr/N52y31qrbYpHe3Ewe9h+K3Hw+JRCKRuCVe/kVmZDC/Uc0Cf1Ht93lpM17d\\ncZTfp0U6Od8sEq/AUuQfni8N7ZsN2aWq3dj1RCLxIqx5Wp+P2Erlo0xWLzmlLMrCicpWRgAjyEB2\\nIsL62/5EeUlCCQk/lW3e1bQ+Wq5Fm1FviysBictsqsZqtPrJxgdLGsJOb4MlPZVI0UX7BrJIZkJb\\ntYi7SLZG9GH9ufWBxNDZujXyR+oQRtTZsiRthqSU2FwksEjlgExr/FNYFgioxjX5stzJUtNJhMSZ\\nJ7/my/JC2SoqZWFkSVlN4k6/GYfuzLwjgB0PK8kovrFs59bMvOPFbzBAvHWWgkT5zcArXEtMSJLu\\nBcixfAEONSrPnJ1n89R+5/DwdC+uGqt5DSQSiUTim3D5pI+bLElM2E6obdVHTsdSfp8W6YztXIGl\\n9rCTbfI7WiQtEz8Ee4Z+hLkh98LhtMX1N7qZSCQcotuHHB99Dbrkyct1hrs9N1eQg0zEbYlGW7a7\\nH1XyR5ZzlLJC8LSIK9J7V6nskiWbipIhVMuKjdLbwbIiy41IgneGiLwS0qfakaOOhHJlJXJK94KT\\negWRc2WqZyO6oCwxtSg6U/Yx1c3uXweklNlXboq4a/vT4R501bEidgZlNVpvipJr0XdRRJ18kt3P\\nzkerLUfQ9WVMtJqUIR9RB/vWDc7jfeY0Ig6JNh/B1+1J5/wWUs2Th1yVoH029pUU8/5QoFP+sbrA\\nL7iSsKwMRvWLmmEp2wTc9ShA+1jWoy+38FZSs9eGKPRyBUj+KdPwcIbIy7elV+PxbgcSic3YfW/o\\nb51X3WZO1XuovgekT6rEZjWH6nvgVRzs5pxoIpFIJEa47BlRnPIf/DAq5j9pmrNYhW/GeoJR2hOm\\n7cI2X3c+T1ImvhijISev5T7/BsPDu1DoNq4lEl+JNbeC6FZy62uS10xbMPwblGWbGOmLyyKrxcEs\\nV1SWlbADRkxIiIGk8S0uq2QFt7K9Hx3JMUeGuHJC3Kwqy+p9XFZOHTlUGRyM5mr8ipMdlnXEkrTP\\nuOyIyPJ6FnRQr4PwnITcxLYatym2kyXNpGOQVOvHjY4TbUsRxHYx48b3ZyvSetHaMv5IGrtrYzy+\\nUZuvB2GuI+NQkLsCvZzXG9mPhKJycaLNnLuvzqX5xDXfuNac7ykzOv82HSNkJF3iszB7Y7pQ1Yl2\\nj2CbG2t/W3EtrvTC6r1HfROJRCLxHTh9cmitwlvPSq3HeWTNlzTI7TFH8C1/7b8DUbf46+PE67Bl\\nOK3Bi7p2jWs5yhI/FUfv7GfcCk7BDsM8KiYZTG7ZxynDl+OaYJaRZCZuy0KCLiYqdUnFSff0lCtt\\nzzpqQTH6BLTLPHK3pCVDJF2pKzZChJq6YHSWetai4ArVvensnndM/T521M5xvsbJVX+ErJHoMgZZ\\nJtmDTqP1UI6r/Wc94brp26NGrMn+dY9aI4zWE68k2u7BpNFuXGoEHdsIOtAtNX2Q7uT39PvZ1bKl\\nRdJN+9u1CDvxT/aCe2j02toIuj6ijqb9+oibXiGzHhHRR33UG0bSjfaz6wjGRtBpuTbWh+SkTadA\\njrCM97mOI0z3RKQ9R6LPkpmNiNRuCenCXsbqgI+e6GMsFaBV6Ly3Dj9jqldnPXf3H72/ULd8LpYf\\nna+ReYfOO+nwSJLuFcg3+TfCvmZg6uriG62dBp493VZ4q/SJFdmkaoPwzh5NJBKJRGIWp0wa4bee\\nL+OYSv+V7l2unI+5qvjq4nm+htwC68jBuY7UcwZ9Sf7dAL5r52aZXujKaDQlEp+CuUnbj3+6n3Rh\\nMnNbPlJ09u3jZrLrRLcn3hqBB0RbtBcb7vmGE+VigkgWoiv9fagqnT4qsdHINIKt1Lj6GC1jyUDw\\ngVwlDsRPITM60g3ObQtJ+6itRyF6CqExIPIsaadk2BPkHsyiepLhSsZVNuZhlqi0BItQaw+/3KUQ\\nbPWTH1Nb0mMiQZ/CoQTknfTJU+xUHc9Hbf9HHVdio/qORJhfvnIi2pQ4Q0KPyJ8T8YMHBB45G0p+\\neQLQEnKqg5wOcjoIddTKefJOiLJZwq+eE+Z1+nr9StixO9d0cgQfOX3U5NmdqzyBfjvW9Si+FhCq\\nf+/9SqlrOe/v7cuqgRqX+9Y+dxI7kSRd4stxgKCLiw9xn5vX0bfQc7y4GrZrzv1q/CFNkEgkEokT\\ncXgyas1P7D4Iloz7gsodnXmc+znkHn3oR754vBDrfufaJlLJLufZd5yfptW9/hIXIiLtom65uBtG\\nd8bs/cRdMXfpzMndFnPPdkk7VJlA8bqkxVyfukZHk6kHluSjnlBksuSg6PD55nwi6KxeqgTfZFBI\\njFJUs731KlnX/SQGSMS2L171C6P+2JCG6utkd0qPyEYkI3viU7gQbk94IVCqWCUnGeyqH1NbTk96\\nIXSK39MP9gPUPrPkpdFR06cmRfIqIKhIzpVMUrJJSaimq5JQtbsg35Jk9tySU+ZcuCSO/MN8HxUH\\neY5AwxGOZzqGfP6kCOvayCRzzu6c9B9PiHX5kT48Z3vufHdZYarPX4d9pSL0JN7g3dVcwGtuuHgc\\npS2VGeXvKXOFzrP8iJEkXeJLgTcFm7q6+LsQ2L7SnXdW9Qjs4y2/DicSiURiGy4h5T4IMQkXfYm4\\nYUWPEG7l9TUK31COknx7HclXpg1Ymrru8/u9/jy2f5FPwm8FRt0yd5u7oFmL+/TInkxcjbUk3Ej+\\nrdg6vxuVXUhbU181FRttqUA2xYQWURcNZ4gxrucFiDSxMQmXdibPlkpsDCLllMyy5BQbHY50MiQU\\nh5F11M5rBeuEfTuEJxU3K5iuN90oWu4hy2XWZImCE1LnUZvqaewynEfRdpMOWdZS8iX67lGt4zKY\\nLWKOSyN5xK6QMhIxx1xqBB3b5S9rPi59WeBvdE61b1vUHMMSlRJBV5eu5FEEHZTFSLyWV21M0Xh4\\nPrU5kSMJiVpbr4l2C9PB5rhMdEzjdLkOvO7ufC7yrsq3UcrtXOCpQpsPOSz9R+YAScT+3L4RnPl+\\nsETA9flwXu9VDHn+aP378Z4yo/wjfpyp8yw/YjxWSyYSH4WDBN2N3lbX36y339a7Eid/c3zNF9Hz\\nv2XfqPsTiUQicREOPTk+lOgo7T+i6OvXrZ+AfkaDaNHdaDLkHTX09kd/Xv4SR9Y4dLkjPxlrv8jr\\nVJC9bqmlJTZg7r7xgqaM7raJxNlYO7V4C0TPGrntrZ3ffSv2vQSOo4MIJvLZnRMc9HbZHXkJPyuG\\n/AASFuQIDM1X9qzPd0v9OX2NCIE6CfEjCrF870/kr48OYxs9xqPzFdFnFOknKB9Fn9n0Vt4RV+TI\\nJh9tZskpbAtLOtFcHTobrn6gy9bP6kIbxqb509GM6TZH/O/bsPUplMM+aMW7MxwPeK71stfO6LqA\\nBkIBaGc9VxuYZfXO2Ynhr3X9nCt1Euo9tbj7LZJyt7vt/gBkJF3iS8EzZ5uLnyV6MX7yLfRDZ0sT\\niUQi8VLsflJ+0MzXuq9UN3B+pwtbi92gpqvxBu4gfnt6pSP5+jZAfNM5QtS9YMrns7CGADipyZZM\\nZc8k5vBJz7FNzr7joXcymKzbTBjz5vOZcM86/awT/1003DSJL8tXylKOGC1nl2Sc5Px+crqEI9V8\\nJX902Uivi9o5V9u9Lh4u9agRhJXRqHJcavRb01s0Sq2jO/RHK/YeWRqRo3vScd0/Dvaxe0Bdiejx\\nqHvtPSZ/HnVfOW57xFEYEafnhZ7VWZtf6jnXCDuScD2qapV3hrL0LFRY98qTepu941oEXaWUIIKO\\niV1EHcX7z3VlyOqs8n4fuUdHNKqutZF0dq86MqRgIx5X6EK/LGlpzwnSDfEY2KDmjxJvlkxG0s8S\\njQR+DUk8Q7wps9d0BuhmsT15F0gtQa5hwuu/qhhG2JUCEbaQmbgcSdIlvhDdre1zwLOn2xW8EZs9\\n2e26fzXeqeIMVxKJRCJxW+x6UmBQy42/nPQ7ftwUO9rwxs3+FVhqXxz6l4yuEWPh028+tO+NqUHt\\nxDETLqSWCHD54Fd8yB088WLc/vmHZBPi5u9MW+CrsqVqVhbvuv28d2xHU83tiImoLkspZBdVPkzI\\nMb/8ZnFkmUTPCKHVngedLjJ73KGuiFS0xB8BKcBt3zixq3WklaRgr0uX5Sx1OMoTrbThKUQOGb+U\\nrRjtZ8criUc9p9ZejUBqPceN/FBdLat1SLe8oyGdlEiyxBJB2Tgaz5ZxBBeSX60cwzm3saFlVIZM\\nmTrWWSPc1EclEIlRX19G7VU/NLkny6IyIDsCD495kNdr4+5fqycuyfZjy4N/iywSbOOkllHadTdW\\nl7geSdIlvgzh7W9P0bPFNyvcrn9bCR6eJBKJRCLxPThEzh1Scg3iveQ0923YaPpGTZqYwVIE0B7g\\nvNSi0SPG8RL50e+6xX3ifWR0T+nvLz+OzFsa/Bc1Rw7Zn4fb/h5oDQl31v36hZhzz1TX1d3SbJit\\ndJMtW2e9G3mDmeRKB1bapLklvuw9AneaA60tCmZEfBFEyyjZVKrGRnwUq0vGBO5lp0QWK3HV2JNS\\nSR5Nx/3viAs9St1rTogrEuKi1L3oNN3vVYdEh+xBJ0SSRKjxc6rTs5FBU7Gns/EUHUx1r7lCjyev\\njqgTAq8UomfxeaKzPy8mks5FujFEtdHGCDqeiLNWRs4bSdZHvWHUmY+si/aOe7h03eeu1kUINklv\\nfcuBjBJkPRGJPrMrT32ZRjxS77/zgVw9LXk2NVC7HkQcaENW4YBw4+Bo7n2KZ87GwHfq0XFfRq5B\\nL1fj64AYJ4rqlrgCuSdd4ouw74b2E9G1zbtfpA911rudTyQSicRdsfkJUfYUuh4F/sPUt6K4v3PF\\nE18MnNtd+jvF0EuMfQPm7y/F/ffjccHYudEdPnExouHz9j6P7okfSMIdxp65CbafHGR158IxtbKW\\nDAhNILmA9oC0Ap6h5nEg29vDSKk4jw0ZASY1ikrIkJY3XjLQRmYpYbRsbxCpFRAx1h7atza0Duzy\\nBkRSkN6oG+Ygj8Iy7VPIJ9dmxo+ml8AO1JnJ/LV2XUlcRX2NYynsO/hXiCduRrAM1LFrXztGfd8S\\nltehFF8P4K897gVMn/kyAenG/gALmfKsNmKRHRjTentQZu7h/TMpX5JfiYykS3wwxrepTTewjXe7\\nM2+OkcJ9+g96dWKlNqvaYbsvcnqvJBKJROInYTas5z24zRKWPyhCbovvNxsuX4uXBtBtHQA3uUTf\\nhdHCbedOJX0IcOycWP0fPsS+Cj664RbIAbYBeNOHU+YpeoxscxZiivafk9USqdRkKV/TcJnJKdKt\\nyjJNy0wSES5nyZDH1Z7VyS26jMFLtMewrCU73zQab9KvS0VO6RKJQxQvI4nLSz4kQofItBZTaW1p\\nh6Lfe85FyxG3/d7Y5Fktk45Cz5ouZZ41UyPuSs2b7LXrdUtEXSGzvx0z9RFzZWqiR/1sZcQfINA0\\nQk7PicjtUafRcTYajjtdI5kuneoedcRuHzpLRI6i7eJ0ssQgyjSdQJ41ktARk42gU9JSiDp2dgnt\\nNj0i2BNySBziv4ZoNOQi2WNV2o4Z0hoZCeOrf2tacUOeExl8p62XY5c3H4WnKbd5bn05MpIu8cGI\\nbxMf9Y7p3vX2KdhW46vbZ1fUwiEcrNFHDZhEIpFIrMHm3/wdfh6fB41Swa9Jb3FktelPC0Y6I5Bq\\nTWDWJ7XJN2FtAN0lRig4/npIhWUix0bb/aioO3mWnFjlC1QmXojoVvFWR6KbYA4wwMLkAPdSXQn2\\nh9zJMfuUdR7Zif2ovBIOcopEhBYBMgIICEy35ILXB+mGwMBzKcBdHVr8kvMPZ7eYAkKkynbRZs4H\\nS8oQkDXsiBuUtaQSseapD5F+JJTI+YuRaj4Kzka6UaSzEUgQXdbKQDtIOoudKB2OjV/Y7gHx5Xy3\\ndoEc8/Ju2DXKjdGWHVe4lGVLhz6QtiDwvY2B6DrDc6is7yt7KUmb92nzYHvEXfK8Oh5nrbE5h1Hk\\nXJhc+tMSnUQv1dH779Yyo/w9Za7QeYYfM8hIusQHg2fOdqk4S3S3D5fZWGn/huperj+RSCQSPwhv\\nnpSan7x+gXMyr77C1CfN323y9YyKYRsGLypnt12+C52DPf2y2PZ+4nuNoYXx8znoK2qJuv5mE08y\\nfzCi/hfsrKpXo3Ro4o7AS/ktXH007n7UjwYOYO59aE0eM3Hxu3y6goEelJPJ/SkYjWvkXdH9oYjs\\nXlFVhljvskxUI9+IJBpP944TkqK0Y4m6KyAzlalRaS0iEPyBinQRdEREcM/vCJmaz0TEZdoLbtoH\\nTyLmCj1IIwCbnyRkjO4v92C11I6r+PMx1fHxmFxT3URSAyYyNjVib7JuI/h0z7oooo5rk5RCVB6k\\nEXQ1rU3QP1SmwPlUP41Cwyg6H/0mpNTDRLlpPkbHjSLomg4h1JouMjriPen8OUbDAdFIXgdp/tAO\\nWR2NAAViFvo5WtITiVYyOpC0YyD2YkK4IzircR3LQLK6UT6WAQnupccP+G1P/tG7Qpxe9J4BMu2e\\nIIkUHEdp0TNnbZlR/p4yV+g8y48BMpIu8aHgmbNdKj4M5vGyqdR8wjG8th9Ocj6/rCQSicTXYMUP\\n1G4BGy334hcS/8VpocE+p01X+Op/5XhWxfwXsSU7B+2OTHxCP3065tp8d/uPxk90/tHAqevp70fs\\nb9dmzY+r+vKW+mh4nv0tfeUvsbc58n3g6JhHEnPlOdBV/43m6eWAA/3KQXSaO4IgcBNtIAGierm3\\nwWqn/eujmsjKROQHAbEi9lVGjRmSpJUfRIF5Gay5I3QwOgsJnWbfEFNeBkgfbDtPUCEx1PzFKDQy\\nunvCi4wPZHS6qDWnw7TFWh00o0NkOx04jrBP+joSpIkOM95I+m0wJrfqoF4HYV2a27EOgmPukp1e\\nWtJh1PWH7DKWdAAWv/es+KLXvcuGRaKX1aXzPWVG59+mI0ZG0iU+EDxztkvF2eK7lG6zMfpdxHvx\\nWq/yG0cikUgkFKufCm96fMQT0S9wZqXZOz9V7963q3AyUUdE4UvXWU1wv7fM+2IvUbfYxnvGzEd0\\nXF+RJaKOP6NiMeYGyM5qoZoPbpmvwClE/RGje/ITq8BEbQ8xm8EkoU+YHR4zwT50TEx+nzlqE/tF\\notxwL7qqTe6RTBjlhvMvNfKMibhwH30nd9FSGgGCkWql7i/HrDLNPnOL1pOwvCl70iKRdPZehBF0\\nUm7SPZEiTTk9CtGToSGoVBKo1Eg2u/cctnK/J51wMbUs8xTtRlSj70oLuXs8pughjJZ7PqjtD1fI\\nRtRJ/QrVSD92EXVELTru+YB97Aq1fepMBF2zATUCQs3vR9ftH0cEe9LVunbkok9TEvHxqD0U5fty\\nBPniJ3GLvrPRcE4fKREWEpzUl2vkHqm/BMeN9Gx66hhsBKPaMOUauaY2iPRTCM/WLgQd32pg9YoI\\nsxFrOlySk+HwdMjZLUDG/2heFtM1Sg6urKL1kOOilxstv5iu+dJ59PzbdFhkJN1bEFy9i/l7yozy\\nv8GPsfQiNhbaZWOj0m02tnvEUakLKrZZ5Q37IpFIJBJfjhff998WJbLyR3vrf9v3WhRa6dtqwS9E\\nmfkjOtQmcyp/YlNfgVEbn6KU6Es6SqZk7T53XwGZSUt8LMzc5VcbTSjWNPxgTovxjEPR0ZS+JTpA\\nC0zTMRx0E/6Ble5fQyxYEoOEKGk6LUkS+W/kHQnSSBXRbXxG8sNGMUk7YPSaJ1saSdP0B6SQ/Nt0\\nwpwZ2MEoteanHJMjnKD9GrnE2g4op6QUklNEHcGFf+gnWzsEOrQ9gbRCX7F9kdRCMg77orWhNrKv\\n02h8tHywhWOZWI9Zi5vro9mCMYJjj2smjnM7nvHEfJgSo+uqSwuuTZsQ+DK0Za+fGEsC8/lL1FIJ\\nhPLV5H3ISLqXILpo5i/bOH9PmW/0Yyy1iI2FdtnYoHS//oOeXVCxzSovadxEIpFI/ESs+jLxhm8c\\ndj+my42dKfYyrO47Xit8HOYX6yQ7l3wYziBqXLXPiBj5wJZ8Kda262I7LvX/x3REX4Hi0j/y+iTq\\n+2hnNbCFPrQlPgajR9FljyavmGfyErPY/B4GHd0OXefjqcpwFwnXHzPJPm9cSxrvqg5iiGahqqOm\\nTURAaXvXUWeTda86iH4TT6eguEnHpHqyyWBzUjjpaNF5prYRGVHsfQj2uWpRO9V/iVLzOqYoOElV\\nO0wQDdcIJN3LDvenQx1oh6nuQcc10o2IuEbPPR8aEbcqoo4hWu5Z02uU3OMxRdBRPcYIOquDqDyh\\nqcmSdFFkHHONXKNpvz0k4/rIOz0WQksi6MIIu2qz5ZPNfzji0i/RKX6F+WEZIBEprisxdXKNeJ0y\\nq5/UymkZHbGGIOx8oaZTOodbj5DaZsjvhGeIPnZpS28uyNytfbDL4F/KBjkTOUdU7wd67ZnrM585\\nL0GSdImPxK4vIBsLXfIlxylduI9+N3ZU/pL2+tGdkEgkEp+Pu31neHmkx4K5u7UP0U5S9cKKjPps\\nLcn6sSTBEuaqvbPKF6j8kZibu9+lYJeSd6G4sy8g7IhO+U6SX2uuw0sDVEdG7vhAvzGueB8Teio6\\nWxBeLNUIOJPGVLrExvH1HnU6mLgKG5LZ6ZAFNblmFLyZFNShst76dCxER2kEAJMl/mS5zqEOQ1JO\\ntkc69MdUyn1aHVzLueVBG1FaaVIG4rJYfcRCXVh7RgdXSsMQsFA5IF2mJNHRKt36z0bW1b7C9Iik\\nioiw1v8bI+jQXSTKHEHX/CUgv6AOqKf52obVEvmFctzJtbIM7QxvADaqz/xDVhItgT5pV7LlvCpb\\npte8FZvLjgrUcdW/Eyy/JeB9gl164nokSZf4OGy6ce24Q17yxWagdL2tfV51pS6p3A4c7peTKnKX\\n9kgkEonEdXjRrGVphnC64VKDw+QXebAKuwi5C1Dg3/N1r9Hbf9X9eEJhC1ZU9QKVPwajtosmWjYp\\n+ahGZnOdf+T1hTPVB1R8YM1vi4hIuNzQnR7iNwbGeZTu4jmxARf6o3EsxR/jXnL9+1kj4JimSDUq\\nlYQS/sfuRdeIL3J7zhUlWIrs+Qb7yk2kHFc+aWqzdtyYqMmKEE2TH9WAsHmsFJW0OrnjVjuMoCvt\\nHyLSaDxPkzDVKDfxnZgeHETBUd0TrnrMzPR81r3dmIhrVJtE3U2RbLp/3ZPQsEbUPblGzDEvRtSV\\naqvtSVeoRsvVfe4emEazEXRtz7qKx0NJM4x6oygyzkW76d50mDaxSCZCLtIj0XAzkXaiX8hCa9/K\\n0kw+MaaJLmo9bSLi8E8kRD+xKW9IRRlj3EfpNWKt6ZVxWEdny5RjAv/Uz6aEVG8b0DC6m5g5XsKc\\nUJ839+w3nDvB5ejyu7xCEOWZeAWSpHsBcjAnPLaPiXt/3drs2eHqnPAN9hQ/EolEIvFOLE7BFPd5\\niQ/FnF1kZFP2O+b2VvfFpT7ceVaz9+2ovx9FQuyt6kwVt6j8oJY6FUu3wMV2mWvk2zVqcWe98x9z\\nzRz8qpNfcY4hGvanP11GCu/8GHsT5p6Vfd6RBlSSCcm0PhcIOJsIhVDXRKpVtoxIorbaqcsnjeSi\\nSsCVyrbJsUR9ES5RSURUiT9mXMiSYBYeyUAmiQSbODjRK/eP6oCLXEPSjqi0aDNPBTCpf0TipJBl\\nlSykMi1VSRChhms5LyAAACAASURBVHcvBl0mf7IlxBsEqtVlL0tdxpItKUdC5hV6Pietz8dUx8dD\\nl618VsXiq5CCz2rrWZVOBKASd4aIIzJl6DnZfRQ25J2BkGksJBmScZOPRINlMImIH5aQGhJyZO00\\nYm9IrMX+CHFldYE/QKCFpCDKNjkk0zjIr/SbEGjs7JD61ghFINoaxSZ6SO0Y36EvcQ89URbycTVB\\nJHRcwtsHW1n7ZuK0caB/L8y1HbwnwNqXRaJU63U/+ZJvFa9AknSJ78SO+8clt5zo7S6R3xoTiUQi\\ncRj+F8khLn4Ox4v1nGrgSPbLMOvHxU72v57/OegnJ+2k4FcgusR2VC0ahl/SQoeA7bK5PcxPr8/y\\n6CpMjn7cHpM72/ZjuuWnYtULzM+GXKuXv2edASHLKHhkQaKQaShgyyDxV0kpKKNxbNT/q2tXujIq\\nJQmOD2xEQKkz+LgUpSnDkwckx1Wu7Yln6s1tbz4h64pxs9aGuaaVaqcKESmpULTmE6fRMiabyi24\\nMjqOlFisH3UZzVJQJxkSFFkVJRVJoxVJyUqT38buVJJJiNPamLIMJykR1CImfRRaI5+UqArJKSkr\\nRBVrxJYlzKjJSh4BKUctn1SP9CGBb4S+6RiiJlvHAIEdaVJH9qif1Gw0kguFVK0hr9Re+0dJObQz\\n8AGousCelfMy/hlrJBhLBWCXd9IDe+uz38jPFL7p3ffrkCRdInE1ePZ0ReF9X7GuuOEv2nkp3mc5\\nkUgkEvfAu8ihc3+5HRo4kn0ZFu2+jJC7wPja4nZG7WYocLRUIXzH/ABirwyO57BQnS0j5sYtcxpG\\n7TFb9+I+Vxd8NYo70vNbj3vBxq+D2C0fULvb4NL2uvi14VOByzn6H90UJ/kSKMtE9mFfQrkgZ0pr\\n+aMoOltSynCVK04O80GVIc6EnTPkTyXLGilRKjFWj00Zse/JgxamJ0UsaRYRVLhkpdixRAi8fxhi\\nx16FUdRdqzxBJB24a6LfmOj5nMrb6DiVe1brzwd1y1zayDjVWZ6TV/1ymNNnuwdz1VmbYRRBp0sK\\nzkTBEdVIOSDPat6jtrWNwoOybJe7VJmACHQRdBjFZwi/yL/6qRF1dTyhjXqsS2eKLLs2oNA/odV6\\nHwkIPrKRcaBbyLUWYVf/MMpObBDoJqMbDsRX0G3eLMQGoHvOGOJy8BTimbwA4fMMiWo57dIsydzi\\nV/OZ9RI83u1AInE6drxZv+rLy3Y7JxB0oiaxCq8aC4lEIpFIdMBv9tuzL8Oi3QsdK/DfBcq3++7J\\notHf7dGzK2Xmv4/EiX3zcd17InbX+baN5af+b+mk4ubufQvm+ObTlCca/LMlXjr8uoY7opmHZ+OZ\\nBA4kuDvQ46FWzI8ySMmCRhg4OfZHQJIhV6D+Kctgoq2CurV8IWCCOjW55p9GTykRY+0r2cNaBtxr\\nXIvYamyNJWjUDgGB03JbWxjCJSCMWvRZI3LwD0gm0jIadab5SPB44gkJItWH+h1x5fwwaWTbxRBX\\npi8iv0Ff6w8ti21v+1bHihhA3T1xa/utdT/2BfRdB9cW0KumHGr2/4KrNrWz5/wLZUIXneC40LK6\\nFQZXolt2NfE2ZCRd4nuw4x513m1tXvF2Oyd7dlFFd6k9pZ9OqtDhfkokEonE7XDhT+F9BMYFyrdm\\nXYJFexc6dFkbD1SeZakbcmsVM8je+EVkTWQeE/yK/s6Yq8pK15db49Jb0VsQ1XlV/aIwk5sBo3lu\\njR2D6tvG4dmYiZU6rjgbnojw+cEtRc9vMDs840aXBQnM5JaVjPOnJPuwn5Z6pGl5x3bsbXG/ZGQl\\nIGTPKDUmQ072r3PPZAiDMVE0RKQhfFyD3twecE1QKqT9yKZPp/xG6LgIOm992gquxuyU2mDUR8gx\\nFXqCj0REj1qzcE+6GqnWRcqJxpmIOtmv7vGcIokk6k0i6uz+cjTVoehvUnDPukdt2lJln4W6aCTM\\nl+GD0XJ+r7cHezLPR5L1EWbjsqBjUFbT6551aJPwc7kskpPDshTVRcsK7db768lbKNvGR0wy9uTe\\n1F5K9Pmy1MrqsGP97MpSK0sg3/KYUMqKefU70AJmjZr4AeXvDSYI9w736h+AjKRLfD6YvuwF+NjL\\n6quaYped05y75gGRj51EIpH4DMzery+a8yntK/jJT9qZaJN3BKLM2rvAIYzVKmcamAl9uiIiahS0\\ntYnw3KXgLtCoiEJ9v37Ml/vdHdmriT6/Dbua55Zje7qv3zp6FGfPNuCiR+JX4HKC7nbj/HXoryVs\\nECR2boAT3WD4N85zB20yH6b72RcKqQA9CAgBJQC4M2xIB+euITNEDvWbqmEklAqZaDrxB4gNquSM\\nJGJkGtpTkUFUF9a9kT9YD1JSBcmopo+ajlH0m5I9jnwx/nCz0y0HSaAbjtUftrYduWVJqGrR+SPN\\n0BFl8h8c2/7R8WDsu7p5n7XP4BP9MGX9eOOwLI4vW9aNWegvq1XLoryvpxmz3q67jqCjO72eSGP3\\nGeUamVhwnD7O2IzSHcQCRu4mt+qfgIykS3w2dt6rzrvFzSu+zM4686934HIw5RMikUgkEkNcRs6x\\nSTlB6Z6syzC0eWl7euUHjN2sPT32+GBe4dYqkGZ9+/ufZx7lKKqIHQv8fufHWNMPgftLRN1tuu0g\\nRr27qdDbGqH3vsC/txmXOJhWurSjyNfjxKfPxUo/A/29PRpp8XPhs1Dv1u4VhomoMBOG1fVRcVpm\\nkkcZ1cul7gJVk/CNibnmFYIINLt3FLOjRQtSBIWIq/xgbzoRnXSqjRaJo/+Qej8dT7rUjkEplSwp\\nU/QgF+PXo2qQ2gi5M+1B598bmR6PQqUIjSZyE1qEHE9j81n1PquvcxF1GDFHXOhRuEblTe0rfEWL\\nmCvU70nH0z50KlPrjBF+TC2CTvaqI3IkmSfp5JgojLCjlq6Ray36jca6lqLgJO1hfNuqC9OmBGYd\\nPY0AdAScLycEYC2i8qzPaR7oakScfKKuptPpkn+lEh6oc6jLF+jVYLk5rH2Gm/sGUYuQRQ1yKRd2\\nsqRjWu4FW2wnjiEj6RKfi6+9S+z7CtWV+JL2sdU48RERP2MTiUQicXO8Z2rnNVZfXbfhjyMv+NWk\\n333mBIWzfn76Dz9L8LeqUFQY826H0p35+ItbRjWNsGMeOuq2b8HmutyqAXCa+jZOTdjxpSW/51hc\\n0h7CGPwgWEpI/m52vSA2/5pAJv6XVfcyjt7nWA8bAWfXJ9sCIMNGjycMcGx6nYwZJurIyuqxJTAw\\nfURYGFKmmbORVoZUMS65iDWwayO5nH0kdRxxw86+IW9QX7OP0WSoA+Qjgk20hIQWgX3u/UKd4GdM\\nTkn9wE9S+6Eu9MH4C/6Av2TaW9udXDtoH7u2x/IEcqIfxw2MVzOSoA/sOI98tbr8eG82GKVdHVGP\\nqSOMW3TR6dK8IG0JgS4l9ffh9l8JEhlJl/hZ2H872658v61LvTwFr/Twsl99BorzF6aJRCLxwbiA\\nVDpZ4Z6sV7nwovbbaeQGbXcHbK1re6fZ8638bS9EluXaQtTxXd7i1hJ1M+4u1fYmNV2FzXPiUaE3\\njkc/Bm8xzrB9VrqTv4afcPok5Q5i/lMxfx/+jMqv85KHkuMclRBan6kGlhFPkWugQfSoDLXIOtHA\\nXCUh0k1KNxtsI2Kmveg0Sq7tSec2z2NiKmz9mIuCk8g9CcZBP0jKsnivpkSKqIAMOFKEtJnknuoN\\nOlB9KvSoFh+tZlKOmn2z1xxPUW1+bzpjy0XUFbJ7yzERPbuou6lN/J50pdp8lmqXZd86G4mHEXTP\\nh22WFhX3AOLOE3yPgHwjR6y1zzkdPj0qv0N3lw5EGkHaiBwMdSgxaveUc8SZnHsyD8lCIA13EXzE\\nkO5LkTqEhT1mSb1xkcOAS6zAVdzuP6wieDXKLSRxLTKSLvF5aDez7cUuA8+eblCyv+QJDuyzdXFB\\n7s6uq9xP/9KaSCQSd8fryKzXfRP5NoLu9N2cAmXfGHF0BQ4F1PlCt29spuL+uz12dYwt+gG1DLEr\\nwu6t0G8JHzO+Bvhcz4/j9B9k/hDmsx/zPDj+KeDwcCQTi3Coxs7ZxxSAEhE2n8iRFXjE7hgIBk9I\\n9K6iDJAbSFJAWeunI2akNMOnq4sSMaKa9bOlc1+O+7q0tvJEjSF9lDQic2zbc7RHHJJN2BTRXnR2\\nrzVWXdVmR5hFdj2Z5uqgpJhtS/UFyhmSLCDhfPv4hsFOg/HSPhkEWMeDjgHoXxzP2HdmCLM9x0/p\\nV3PdQL979yjA6Bpw16u7UMEXNqK9j4P0SE+XHjpGRGe8U/Ua7D7TiVchI+neASb7Qhf9UtDn7ykz\\nyv90PzZiR5FDBi63N2/+9Q58IqJxlkgkEonPxV3v6TN+Xe3yq0i5SZ1XuNPAoNgrunerjU983drK\\nB7EX3lTw1RhPMEzoX/74Lr241DELbkbFblKzWURf+VYVwO+lL0PcOS1K5d14eXt8Hnby4WNl7M6/\\nDHYZyz43Pv4y1HvN9MGEG82Z6bHRHBbZzCliZdqbrUhUHDP5KDeiaTJf9pMrzGYPuWlfOrZ9xDUi\\nphRT1vjRwu2INNpO72KTTyCHm62JshrV13zgSZeGA0oDFJUn0EU8RaEVJjaRd/FN7FHr+6ieSiQd\\nRt5Nn1UHc42coxURdVM7P5+qQ/a6w6g6E/VWvZzKFnoWJi4anSdN/ShE5Vmj5x5Qlmt0HoMBadra\\nGkjIPWq74x5zREyP6WCwJ52e84OHefac6fHwtqxc8yfImz0n6vbII+l5JkfaCdmJUXBqn0jbhttx\\npFNkYViiLVIbnjzFPGOf1H6nE0lA+eSaTy6jk6yH7NJMpj3FqXn/KPKXYA+XIZdqpLO0f7QxE5ci\\nSbpXYOk6W5u/p8w3+nEn9PfLRISdDdMXO+kb6Bd/l0gkEonEfpwaHXFXwukkB35EW+0o903vgrvJ\\nn83syyuwROLpBMztsKMjRjEvd8bq32Qi2/LGyumvzN9M1OX3mkWc2kSnsn33wqnLVN8WZ9ZHprTx\\naHPRelgJMybkAmfk6zESZgTLYXIx8+tGD1HHvxXRVYm6wqX6UR0iBtawflQCoq3GSQRL5glhWMnE\\nUo0DAdjKQq2msk4XAblZdSG9KMt2io+N1PS6mIjNcqBCFgJBKWqI1ALowinR0i0tWvMbMYltrMuO\\nMksveV1AGJHwNkA6AflEhCQQkEco2/4jQzqprI8y455wAqLJyvYkVOgP+gUDsIt6q77F/jgbCDgf\\nyYK2QC6efDYlQp2d+UFCpNWJsjm7HJ6Mk+ujDt6X+pLokSRd4mtx+a2FZ0/3K9pbavOb4Qk2Lys0\\nUnJiBfPZk0gkEt8B/JZxGCcpClS84mtP6PmJ7VPg3xMUbc260uw+w2ZWZruaT3wVmatf0BzLhbDw\\nmzGOvLshfYeM1sLN5dMIuw1Vu0HluLohE/VvbuGVD5qfNA136ivCFwL3JPL3vY9vtZOrItRKT6ap\\ngZ5gAtKKLDGluoQwIkmhFtFW69GNYyanq34i+UTiiOhikqg9IbLEjonCa8XqyJjZq64RKaR3w4iM\\nkqg344tVRkRuPznSSDqGFmOqkWvMJgpOdUwlQl2sEXWFWD+Z6FkK0XOyY/a+Y917zuuSCLtCZHSV\\nMu1Lx8XWVfeo63VJGy7u+SafPp2cjD8XfUEeEZ8Slbe8R91cHWr9ydUhapMoj1wb1HEnfrVPkx6n\\ntfHMnkyTY1YBModwAJ+mLMG1NPfWEOTwMKeDIaflPJLxnwWeCxwIyzGtOF8js/b823QMkCRd4iux\\n5qZ1DwMnenrxt63dqnf6ZYtsuKutVf6Tvp0mEonEt+MU3uikyag3EFBDvWVJYK3+a9vm6mnAy8jL\\npfZdeM+Iin3yq0nUHKvrE/Fjb22MYo71TAfN2wkZos2NfvIb9aXAqq3y9y2VuxGhseGaucUl9kKc\\n0jM36uqzoARdVKEPreSpVWGKw9o0m8qZQwIUkiid0phgsr1or7Vrmat454jUAZ2NyMFJVKPDCKLi\\ncPIebR4jHrHKYR2gcko8QuSaROqZKDgc1fMRdRidx57UJCAlmz+O0sBlSk1loF0Yax/obtD3iiVC\\nzZJTARlF9nNxSUghkYBLMtFmmt3nmfOINKPmI7XzXra9UVV/rV/NUmstbXLIYyPS12nwOVcnFMA6\\n+PLk8jo/4pMBePaUaP75PcyDjOlw/i0glPH3l6XzPWVeofNOOhySpEt8HS77sjH4NvPqLzdDexc6\\nckj1Xb/93dWvRCKRSLwcp07gXaF7i8nT+LRTmM89WdeYfMcc5Fr9M99/VxS5NXbVZw35+TaGocBR\\n76CZjHwXvFuDCR6PO4+pTcTvLpb4KEr379vGwIpr40Ppl0047RbhFX144/n71odX51TydKgizHCJ\\nSPwQkFl14r+LeCNHHMm5I7UmIqnSdC1yTSPlCkbBTU7YMYvtQ+JQcXaBqJJ94qoOpsl5jLhrpGHj\\nE6td3yZSKVtr24xoh5RneVb/njwRBc/aGvo5tZPuSae6NZJuIaKuEES0TdFzbd+4Uu0T2sc96Wok\\nn5HTfeeo6tYou/rHLh3OiRzB5aPnqBJaHO1Vx7b8KALP641kRpF03EfbzUf2Dc5pKdpN2oLaeRf5\\n18rSYhQehUSgjjYZw5JHLQ98EhmwTeCTGcPcaV35PgCVgKQz0K5A5Ie7e8N0PH52fvzT4iOQJF3i\\na3D516DTCLoLPL2w8odU7yzcFzu5gjcgWxOJRCKxDdd+NQgmF26Oa8m/E9rjxaTlrN67d6snFTZO\\nsq8scmv4edbZuiCJh8P0Bg0g184tyBrByva5UTPOYvVYf0tF6gQ30XvI2g33urv381Gc+kS/+zNk\\nJUp3lQt18IHYEaGwFp5W2lxwtiTTmui8KNFEPbYuDAoI2zdja1rKzkfBwSdDuo9kIyFW5HkHe+Ix\\n2Yi3Uu+EnhhEO3Le7Dl/on3kqBKUhZWorH5MFcSFXKfKDIlRaQ9HPqofLjKvRfKR+kGOWAU/ZkdU\\nuySFzEHSakBkwbkSRhFZ1evQNCVrkAzDIURINjVyjGbszZFh1ITRTksTO8Gn2nNpgT3zYDPt0xNs\\nzZeumPVh3dOyl41KsT858UEcvRute1/S91a/Eq0S8WSXvkxchiTpEl+Bl9wvePZ0A06eRvnCm2Xf\\nQtdPPX365FYikUh8M4aTJAcnZewvyncqeyEZdVU7TCoOznjdgZS7pB0UL5lwL+5zDry7yG2xti6t\\nHmsv4ZdWvLiz2LFbEDiBCyvF3grvY+jf2ypSYBy/KapyA9F/t749A4cfBR/KW3mMn2cfVsHD73on\\nqut4loB4keRKDqGMklMa/TYky4QkcqRRWIkWvlU/8VyUEFNPrFEl0BwRRQGxxvZnCFI/qSrT5GSL\\nxiP3WUBOmmh0jzSMwYjo4hoON6U9CBcB1Yg6aY9pLzq/313/WZioPJXCNn8PolJk77mJMOyi5Yio\\nPAo9C2uUHWt0npGrQ6RglSCSTaK0tkSuXaGDav6eCDqvI0xboUPGGZk0NmRcH6VHlpBEHa3JrQ4i\\nS97N6SAap3V2IN9eA/3xUayZ2/RXlb6xyHXTXW2JFyBJusRH4/IbRWBgn017+zvFlRfcJQ+ZOFCY\\nZ85OwWn9mkgkEolXAL8k3F3zJ5FSVy1p+TKC8jRSbt04mG+vN0TP7GDlvoHIIxrXY+j75gLXw44n\\nbqkvJXVWsV0UenonbPqW9eJf5t35h4DHv6HeD+vv6DMK6KiS9wHjhxQfVplrXk3mZbY0kZ+5LkJF\\n2SluMwnOpKlR9BtqYCTLpCxBUBxod3u8LUaKmf3SqqYaqUa8QKwVISOCqDLSiX5J4bq0JhNPZF4l\\nDi1BYW0+q95pmUu7nKX9nMrjJy3JPMiSZk/Mn2zTSLbQ5BdxUBbllPw0XQXdHS6pCTfhraQYlonl\\nNxBoC7a49ssR4q9Fv00ZoNPbkPEhcjpiJI1Yz+eWuSTwwcvL+DZpzsYWHVNb0hB9hJ5969v6PN76\\nDEf5nqibUfxBj5BPRpJ0icRLcMHXn2/7RjXEa3678WOaM5FIJD4QtySoXkROXaX3U+r/mr4/w3Mb\\nPYN4SySNwLMrK1547k7IzGFzdaOuf0ul9X33reNnxWv3XcfHqm8MBT5f5jwuMvgioxuu9Tv14RmQ\\nqu8Czlh+8IRk/wOAD6nMQYL0lbXESyzk3IJKmDI0TfAjcRbLDsg/RjKuRq9Fe+IFvrZz195hGaZ+\\n+Ue3/CSRVtVE5LW983Cpylonv5ce1lKIPxNp6CMP5Vz9ojKSjeoAbVgboS1UHckS2eU2o2jBIsSO\\n6O3rH1M01QhRRzwRIfk0TyRJCU9WSRRY0yGkEjigusnYMnJiEUgpWlHGyxMBQQct4j+9HNqizp6v\\nDGiDfIb8oBeMy9HxGhj/m9OgqWuPMzHWOnze+5enwcvUhzxFPh5J0iU+Epd/mQgM7LOJrzknuXJc\\n5W0Rvt+2nEQikUgkboKvIqg2Fd6SfJ4JnDHapfO9Xy1j+1ih6fjyCfziPufA60Tv/Ia2VN2h75sL\\nnIneeDR+XjZW1OCi2F3Gwmqf7uj8WXgLGfl+HKruB89A9nvN2dzbYuTaBpdfVTucgpl7RMwRO7RI\\noFlZqpFnuoTlZKUjf+ryi8QiSy4aj/vz6vBE1gGJRvDplsi0S2ZiQ4BMWLugJX0d52Td5yhaDpe+\\nXIq6E43496DB0pRMw+UvzVKZXF0osvQmmeUsi5GxZZQkdRFlq6LfGIgyR0pJNN5Al5wTB1FxFPnQ\\n698VpUfBuZBrMs55nC9km5Jz1JGFjdAj4MiC/K7NmlBP9DFFtlkdcKM3Ovbg5hDInf3clkuYyETZ\\nBiLumwn4ebJLiRiPdzuQSGzF5TeHUw1c8M3oRV+2dpuQh8wOxFV7zeMgHzqJRCLx03Denf8TCLpp\\nDmA3yzUkJl82/bfDUHk7PTeH0h3rxM8NPMdZqA1i923vHpv9fumAH2F60X75+Fhh7u1Nc2tMz5uX\\n99uKx9y39dvu+sh32I9skA90/uCcxqW3441+ba7GyvkS7j4HMyUwud8m/BsR10ddMXvdg1imQQRQ\\n84YjmUpgiCYO9EakBwlZpGleJiJScL8yIzOQRbmo3XoZ125zf+KHJ66IAxl7To4A00/4k/9aO7Gp\\ns8mDP+kP41s3HrQPQtKstSmMva4OfT5hnaGfsH9UqB8fOGpw/FDg82jchuXxM/C5JUMewic3GzN1\\noo2fzHvK9SgbbpT2mwmQ8R/0aPl0ZCTdCzC+XBK3Q9BZx/rvgtIvGFD3GrM/7CegiUQikVjGIbLq\\nINN1nitrTdymvi8hI0+v60qFW+1e8mpS3Nm8U9GE3SVYahu2YivFb4Mlvzt/5yp4eeUKHPWOXDom\\noiCH7SIvxWp/Lv+5eNxvl1/D+JP4BbF399VRHHo+fdgE5Nt/xLEHL3w8nwKeDHNgX9KivAV1cR7H\\nES6jZRebwhmZIpwB6EQbwiFgPZjI3gz8/cNFv7W97Kgvg0tQ+qUhH9Qvymki0kLP+k8mGy0X5jHD\\nPnFTxOCjTL5bm/o5HU8++k+pahidx2Si5sR+qfZKbRcfWSfnMhZ85BrR/L5xJh/zgOyKZfsouJGN\\n/f5EOpBYkzo3Tqsj/sjneZ/r6EJSrCN1gTzrCNTqBJJiZv87bhbAMes7cZfVocnuwcEH9Nwzvl3m\\n0ftC0Yxy1InEKiRJl0gIbnbPCd15gY/vbAbuzj7wC0gikUgkTsX5T4Lzni93JujO3nPu/nXd+QYz\\nQ7zOavST+m+Y5b7N3ncbmZnL+ZCTscnfN1dOxsRLxsLKMX8nAuhOvrzsel1p5k5tsxWHCboP+tpp\\nl7b8AKcPunuPGjJ1SzwOiayuZPCkxmUlrdy6ZhqTV6IT9UTSRN3illNJYZpanbkukUmG6MM6zy2N\\nGe0Rh/nTMpwUpLFZqhPzxYbfow7bv9vzbqTTtQwsBhrAyqhs/YRlQ9sd3hGv4/Gjz25LZtXsiKwS\\nNSavJ6eGRBaQVS1SztlXcoqbDWMP/BF4X1t1JR2Eu8hFaRjnjyfREB1J5kQYDgxJBqwa90lGD4Ny\\n7kp4h5afpdgeq7HxAb30TF/zzP/k94JPRZJ0iQTR+EZ+lsIzSv7Yu+OPrXgikUgkTsbZZM5ZGKre\\nNaF1Hjn3GcRcaSl7HRmVXKuRt7hw4WvNR0TeORc2iN4CI39DXyPhl3WBGn9ZdF1gxl8ad+jTRV9e\\n5GyBf1/SR3do/AtwmKA7rOQ1mCcPbgjfpivb+C5dgW8XmuYpMP8JKZXQolLTGKRM2kQkIfm0RFYV\\nmiLDSEg1KiRRbqWSHhpRh0Rbr6NQqaSJ6hjpFL+JgaxjaqQUklbjNpo+JdJN734+bfqMou7mdBHo\\n9HvPacScRsVRtS2SD+hjpkJP86m6/J/Zk65q9XvODfe7KwSRdECO8caoN0jv9pdz0XVEg3SeIgyp\\npXsdk95QP/rV+Qs6GerqdBidZNNHPiOR19mGuspDUMtr/US/OMsyirx954dNb1pUlSSwyw/BLfPw\\nnV5uJYGuluYJZMgngtsRnpsXiv4OOf5pwJYyo/w9Za7QeZYfMZKkSyQO3wG/B4eb4tS2vMsreiKR\\nSCRuhcOPh13M1/lubLR3OVYSVyeoPmTAElH7ibkDLizqmX0d2iR8Ll6+PGLshHdglejdX9dX+/qG\\nSr0sum4FGXQnviiavNomcIYHicQy5Bre/fx7JXY/2++L9dOrPXnUR7wv61o11S33Jn9TbWSPEGlT\\nSYnwKkV8I7NE5ryOmgzyzYcS+xi1CELIAV3Ccxx1tzaiDglGWYqz1HNLSIqjIo+OxzqYCtESqTnT\\nEtgG8WNFCCRLVnVRb450EpLJE22E6Z50AvIpJKNcdBs7FgfE4kg5itLBluhuSrD+lgiz7aI69X2G\\ne/2M+dDeoMP3hI8G7CWMm7GMq8vwBYLDw4HVbWjX64KKWfKunkzXO6YuPX+i/D1lRvl7ylyh8yw/\\nYiRJl/i55sHMiAAAIABJREFUWLgJn6b0aOkXfYs9bOaAgr7o6HXuBFzS74lEIpH4WlxM0BV/svOh\\ntDuC7iJybqhnN0d6nFx91STgnJ3uDWdJ+GLE4+aF1N3KGb1RM93xHS5u0RWCF77+TubsZMtlvbyC\\niPTTme/CImn4EgfvOIq/HNKvEatwI2hk0u0d3ezijiK3RETLmHrJpH0BqYA4wug4T6h5eWPXz5/X\\n87m96SZxHi5R2ZFfENEXE1LLbRQiZAUsySkRcnMRdXEknUbBlfovRsl1clzoWXhq7hLvMdci5YDE\\nNFFxc3+g61EbxUfUCWyUmkafjaLgiJgeJp1MxFsXodZFvUGe2PBRbmCT3LnRheVqG1GgA8uY+jpd\\nJl2dte0gw4jheoA2Q11aBvyVws4HTxKC+QZJV5IQ0Moo8ajOUfcOxsMD/zlhdPXNpg/erzVqrt6H\\nULCO9xIpTZyOJOkSPxPBDeb4PeeYhrD0i26Eh80cVNA/K15H0CUSiUTiwyDfrTdiF4l18exRqP5L\\n6naWvbPqdpeJwOi3pbMTWB4veZexiyWKIy+h7qIGEjdmxBfE3o5FMgiFVgkf9+SlEXaBiTKffR9c\\n7iDuS3Whods39Itxl4fCAg4vYX0lLO+/q/jdsOsVsxZioj7CLdAb2RjaZSVyhCQoBPZCLUpO+aUo\\ncSlHiTLzEWF+GUwTlQZkI0YK+qi7Nr8/aJcuSq8SZc0X9Hkhok73pNPyUfRbtBxnq0WRtqIWMUiO\\n2PTP5hYFyKoRge2vBOigbOtUJXCUnHMEFQ2IqzBdzXpCS9PX6nK+RbrIp7uoPHnKgS4iTDfNNLRh\\n8wa6iI0u6DqX7gg215FsEjkoH0nP5frE+QfzaY/tAmO7y4p/uFVgYJ/mR2IWSdIlfh6Cu8vxG84x\\nDfHN+pDKj8IPqmoikUgkjuJVJNY5ptfp+RIC64x69XvN7XPgjpN/Iyz5at6T5oQveaEqcBSzhi95\\njxvV2xlfKfY2bOJdLyVp+37Fad3TsJKJW0VaX4hF++928Cys6IdPqd4n+boFSNneGqMfVKwo8naU\\n2dNlMFnirEyEDJI/8tkTY2QIPSbqlps0RFexWkk0hcSVCnXLWzqSKiLUtGxN8iSVg16DNuqOiDtS\\nCpqOnvVY9oibjtdFyq3Zow6fZoULPQq3CDbti37Puu6PFyLnajVVt63joyq3UXnqVwG/lImiLtoM\\nI+PW7kk3F7lmzgn2vXPpa/2Y9w/8AH9mfSevc/onsteRgIvpNa1VEJrek5LkogphPBuZ1nM6TjSB\\n26fo8hgk2+I+ceHxsOb5KFS4Z+6+5XXnU5EkXeLnILjDHL/pXKThk+6GB32NH1NEl3z1+tZvc4lE\\nIvGlOGtCB3/t+y24E0EX6jikeGPhoxNuN4f/wjx8nSlrhM4GLr41nb+IthNzihmzd572fku3hX5c\\nbH1lJ9z6df0yx6beP50kjczsz74V5HrZjBs/IDD26baOnvPqcStsGktN2BNDY51M1C07uegH2nGE\\nXEj+SYEWHVfVVLstom1Y54now2UwcY84rC+LnWAJz36JTntP98SkRNRh/bpIOVoXUdf5wRgVCA5U\\nsnToBzm9c8t/BoRJq29dHrQRIdg/M6OuW3IRCTAgsZS40n5WLgoIOmGhgKBqe7+xVqGdN52WtJPM\\nzg+CMp1OlVVn1Y+mFcs4nTTyg7QOpozTadPViK8zqO78QOAefUbEyYf6R8Lrk+ZzgxeouXeqLm9G\\nuMTDNXEykqRL/Azc9BvH2vv1y314IfrnwJ2nURKJRCLxVuyaHDo+o3TW95JOz5vqs9P0so4P7p+t\\nOt71llLcZwReI0R0ciUKHA0mnq5utbmZzwURJ/YWjLqtm0CJcJrzfe+d3m8riNV3fhuYtX3ZT8zt\\n9XPZtbLQsJ/0LeybCLqy4v75Vhz73cw9UcLDGEyNZMEffUU0S0tzJFAvAbHLjgQqddlH3EGNiEzA\\ny3DPOTQD9uPlJW2DtCUfJepuSCxVX4UQM4SVldMf7sTEl88/HlEH9a1tglFuvhf6aDjRu0JWmkei\\n46iPkhvuQRfoFL8jgm1MzvnotyCyzRFq48i5hWg5kZ0rS16H89vXD3W2dFdnVTSsN/oggusIyVE7\\nof+2rI4LcIhsG3dge6g6uReYKTvCkFuTyw7yp/tAIe+scs5aaLQ8ZuI6PN7tQCJxOYK7CsfJG5V+\\n/u3qUA1Oqn7/CnSi8nljV1pKJBKJxBfhjGfFXSatZGLgDD3zCUvly/bJyMD5o3UJ22NFI5V1Ym/B\\nat/WknknAfv8pRPRK019dH9e6Pyua/U02z8N09PmlkTNp+MjGMibObfzNzQ3q8VBbKjNWlGYSoqK\\ntDS2hEEkzXDA7FOB4JAzIEs6XaAjIiaQXPCRSyoLhEVQB0OGgI8+wqmRNJ4M8kQVyrGtQ6s6EDMt\\n39et6WBL7tT0jvRx58RBOviGdSfGutX/0HZA/LApY30lZ7+1actCHbYuvqxpJzcmKCpr7PRtz5CB\\nbWLGDfmy8Ziw4yWwE5VVlS1P21bbB4QcXJ3BphfrTtBOoHmELbK7UDZ8P6D7vht/KzKSLvGdmLmz\\n7b/pMZ312hlquPxufIG5g0peXOVEIpFIJDbjki8mOwitozbuQMxNRY7/JP8MYm5V4hpDvCz2zved\\nkW/sBRYFz8McUcdXtVZUv8Gr/Uj0DkCeIfxWciERUeDf0/opCm+YEXllPyzavaStCxy9eOnYD8Kh\\nZ8CNZhtvTcZu+BGHF7lxrVYA7q5unzcvYdIYoqEwX+brQZVEjhWx4faNwyi6WAeTib5jXZJS96ar\\nNphb1IyJjmt6q+O1AuFyk1yMz91edu1pVGxyaztpS3ay48+lvef6fNcfpORCi6Srbvi96DTSTtvw\\nWdhEvYU6ahN1e9LV9HD/Op72uSOfXrSsiSSbiVijRo65KDBXbhjVBvqa7tDGQHYgY/OrLfQTZU06\\n1LvzU3WTK9feSFp70NBPcn7KYGlEWiijsn20nvypbKs3oVIAa5IfuTzKmMUkvGumWm4/RCQRdAUU\\nTdd6vTo6ZjJxBZKkS/woHLutnPO6eYdb2x18iPH6V/rv+7VfIpFIJARnEELHfbjexrLRC1T+YKJx\\ntnzwUrFC5OVY7dOLnffj5FKSwk9Cz5h6F1E0Aroe+nOJwy8gkBZezN/13j5rN79MvBS7btk3ZI3i\\nZ+KbHT3+mL49lv2NJTjKcYmRjE0LyLdiZdoxJMrtxZNAeGKIP1FKShJ1ukmVGIKRKdhXTnUzlWB/\\nPdYJfrePHTuis+2pF5GGUk8hXpA0rHvMUaeba3HbkOijVh4IR09IAgmoy37WVLzHiyutTkwdAVm0\\nraQNWz607VR/0E1A+ITkGFsSyhFf0oGoo+n15BjBuZRzxFdPXpGWMzJKkqktNSz+sNcnaoHRMr5j\\nXWsB9jLiryHDAqKLTRXUT+97XwzqHz3oOTyMZNuym4H4rN6diF9tt7yw5MvNO5AkXeJ7MHP/2H9r\\nwVeiY1qGGl5434teHN+hpFfhX00vQKD2lPZIJBKJxNfi3c+Isuf942Ri6zg5t7vQXpPz6q7u1JF+\\n3iTycsw1i/Hrxc7biWzzW/4rjPUIzNyNdF3k4y56xdZ9mU5XPOvrzyDquKp9Dxl6d2z6Ducm2O8A\\nf1+7jWMrgZTEN8DWZVwzkyNci5eWyf4ChBTVfeYEnnyrBIIlypQ8KW2zKCWBWM4DUqrxWU03W1LK\\nkXCluOg5f70wXkZVptrn1gL1c2YfO08X2rIeU3oUUSdUZ0FHmUPC1BKetuwDdIhIi7Kr1TR7z4HO\\n0tInotGnPwrZCDnI83vVSYRd8z2IaLPRddQTbXMRbCNyrv6DtjqdYF/3dkOy0NutZdAG2XqsiqAj\\nJx+0S3VlSuvsujLUnDHlxUn0FctJ4aZDPIO6tmteTei5yGDi8OG7RXY+i4i6S8u/Cpo96ApDRF1f\\nhoh038lY2+B8jcza82/TESNJusTXY/kymMM532rvQNDN+vFiJb2Kiwm62Oinfz9NJBKJxAyU4Hrf\\nNNI55NCGgv0c0CEcIejuEDl3lGA8FSuYnZUc0cux6i1tkSk6xxOhhS4jiHqTioGpl1R9JWb76pLX\\n7Yv6YsHXF3xzGNod2jz1i8XFN6t3D9QD2PVUvxGbVMzobTTCe7HxNeO9b1bn42hd+vY40kJT2Ubs\\nOeKJRLMzwZA+kUZTT7U7dJ3/L4Fbwqf5OpglNfFeXx1pEWfgysSVcZvQb+/iVRcXlWeiuoRe1Wsc\\nEGKTgRwoLn26ntiEAopvUIe5KLzqjUQPMpRvpA2Qki1Cjtg8iPQHFcK4oT/qO3nCVQC6kIjqIuSA\\nBULSTMtpR9hIMSGfnIzRaQk+kvKGOFomvVo+6lTmrfeV+ug/E61HoBN8CeuPrmIZQ8SpVfso5F6B\\nf1h2+UG5EJEAj7O2qFmD095P/A1k6XxPmVfovJMOiyTpEp+P4GZz/P4DD9KrtLzhy9EpJk9Q8pbv\\nhR/8ZTSRSCQSL8DVBNGmshvfQW5Gbm0Sv6XvcaFLSIgRZKJoPvulmHPZ+HO50wWO/IT3yf0Um1WT\\nCyIDscsxSxyeyioWd3RyHyz4+g6y7lQubrXNCyLq3lGRH45b7UF34Ll+o1qswjG6bC5B0pV8weUs\\nLXcUkVyoVIijvgxDxJsQSYaEIr83HZkyQlShLrSrxBVWCu25Mk0XNozWKVpCEokyTGcieoY3okBH\\nK8P0rDIPAiIQu4SICkMdiOkpFJvpl0LP2o6lTPtxFbL71Em7lsJu7zkpM/lB1O9VJ1FyPkoySm++\\nAzm2Zi86T66NIto2Rd/BeBgtu0kjP41uS6bZuqHvzl5QBwrqoqShEmWjfeOkx20UHOq09ZU3GbEt\\nKX20HDV5U761EfYv6oQUHL72QgRL10Aj6kg55nqMZPan3fc/FUnSJT4Xg/vU/tuXf+s650YYannx\\nF6LTzL3Y70QikUj8XJzzZWCflmME2zFlOr2zomAg8i7fj+7/9wl+j2TbRNqZL0oLjNhqwuxFWMXL\\nXcpgvYG4W1mf879dbMMskXUZYXdBu89U5NVk3ZDfusCRSybGPnS27UPdJqLo+fGm2mx6PvYzJJ+C\\n7T7HNW2pLlsm5X20WxMF4k6WpWxlCNKJdGlKIaEawWbNtrl72BTNEGkt/KtY8kc4MppkJHqNu6Up\\na5+b+5jkaX3avm4N/oVO6qZlmQo9m9+26IMKPQPyUN6sor6MlsGUJ06pLkibP1sZ0iUqCZe97B+F\\nbVlK1/5C1vXp1JF1Jk+ar6h/U3rtQ2jzidRxBBySXuTzgNCK5DcRaFbelnNpaNdEqq0kDbs0a4/Y\\nHw/aBoizzq/WoLbdIFnTOtvaiehftdzq11JweIvtCGAPP9iLnARzOQMJ1yJCUZDtcQEFIXeYOB1J\\n0iU+E3P3u90491vVUEve3BKJRCKR2I4rZ4luMgN19Ff2pxFdu4nFrYbuQogeg+hBfZdFc02GxMhs\\ntvryPixyQJcyWBFx96K+mQxtzb4Ui990LiGYvjcCbNaNU318/QPqJk18DDd5rhOd96w5jB1u3MTz\\nTXibz8LumOfNhLnfdBRiooDsaxP4xWt0+oUAwqK1bIu4k1FYTTWfnOkp0qwSXrA0pSqt5B4XsAk/\\nVWrRgFW2+lu4ylh+0u2lB/qBhGNsRIY6BZF5kX4kJxmIRn0bKISsXqkF7T59VUcjesC23+OvdR04\\nXrQdsF0Mm9iauTFcjkhToikktMS3RlhZMskSXEA6jaLRRC+QY5bsErvIO8URf5YUYyhr3xCg6gM5\\nIMXgzNTHimndOhtOyJRVaz6rj3xzFeiy5p6mgc/tdN9TuHuVW/lAb+UaU89f8jLwWUiS7lXwT+et\\n59+kAy/yPWWcK8fvG+doQW3rEq/HKWbPbZ5AzQsaZ2b8JBKJROKH4yrS6GrCqISHm3GE6HLTN5sM\\nvc/n10zf9XaWv6rvMBLDGVkpdjmiFjGvmC9iF23fXEjZbSDsBiKXYDVxepJDSJWe1tqD7yYX8Ix7\\n3FiRuRVcVV5Aen4r5AZzK9zSKYN7ezfGEb9NrwRd1CW1KDlM4krizCgXzsGRUPhBoFreGiRyrUCG\\nIbmEQyLu9oITQgmXvUTSjApExsnkPCi1e61pHXy0XNurrYBz5invSK3gNqaRc75M1KQLkXRelpke\\nRcoQyEm5SeZZvH7t7kJEj1rwiTqkuYraLt4P0QHNK2WlW4ULjKLVPLFmos9qc/oItDkdXJ1a0uHl\\nvI4+6i7W4f0Z6eCqg1bo6OqAOsjqwPqRKUc9aUlWR6sX6DI6VIyoZXcJ2i5SL0IsPNe5OwiFxq8d\\nLkfG4UA+nLr/1IfDhyFJuleAB8d7zn+6jhUqtuOcu81xP26KSwm6kw2MjOYvQBKJROLzIffzFdhL\\nxJxKHF3p71UE3aaym9m8E2yuTYzEdrTxqe8OntKcBsglE+4r2Z/5abDrUdxn58Mim3SOF6UZkKML\\n+2RG9UuqO7AZ2juN8VJFp5JMMwP3lWTda4g6nUo+FQtteKevT5ufHXiDexP6pXff5Mzq5+Tn4Qyf\\nxzr6AbRqSDHZaLVWEEq7Q3LZQyKwsjvF6JD3CZh4b0Rg4Hj1TyL4mmyVmQi/etyKKclXagbaEGKi\\nVBmuMri/XlNOfcQbaQ2gnpNAtHQnUSUkWzv1dSCqxCZE0CGBWAjrPunhJtN6hSyRWXqOUfog2osP\\nZVy9UFYJOiWHuLabWdoRj0VlGxtYbkaH5G3RAXVG8k4+eh3Vrpx4HRToENE5HdAz/om4rKMlGR0I\\no4MYRGynz+mwxTnIGJ4OcM6T2Ow91xK9+vwp0LuRJF3iY3HsxuHeUq7CG+9up5m+rA7uxeVK5FMm\\nkUgkPhtXPS5uoPcdS0V25a7w9yRfw7Iv8HfRzuF3CyHtYiOnfkWOTLDNGlX1la9QM26+wMECR3Zi\\n/VV9MRJ7RR/M2juZrDt1v7oF315FNL2GqLsfcXZbLN3YfgJ2/C7lU7DJ11MrNpErc8tPxlMclbQS\\nl4RIgmMkbJiFiBN7lUhCfSzRcTTldctLqjuTUfVfiDZLeglvNBFbI7KKYAlItQEySFLBHnm2ccBw\\n95DnTo8cocTT23Kl5WnDtT4YQfeseQ/iPgqOkViskX1M9Cjad/FedWSIx0L2j5imKD2URX+hvywp\\nJyQWd3ktvRJRjYBx0XO49GTTS71uJOQosBcurdn5iXmR/4M89F1sDKL1FvNozs+WEdQvyrPvKp5c\\nxAcytg8UMH52hTpweMozInMI3xnadakyZJNaRuEmce67cGKIx7sdSCS2gunIlxP/AnH8RhNqefP9\\n6zTzJynq28i/rF2IwYt5PmISiUTig7DpC8nrpzGPzQGt9PUWBN1JNjeUK7MJc2XLNgLUmTCTK0uC\\nF0C8L7SBaNxmYJX/7564XXTx0n4o8O+FWNEH7+4HIjp/svtMzPj2qrYb2jn1i9m++9qCymHWnbDa\\nnxswmfr8kW/Bb7iC971a3Bqbfb26crwwo9TNekflkQCYmYwPJpxMNltzbCTQUSVTUI8QOVOeEhvG\\nL1DXR2lRL+uIFLHmyRoriz6OlytEAqpFoDF1vjWrYq8jsJSQ6ZZkZCBsSP9phJV8Yp3I5TW9YDeU\\nAWLI6Na6dXkBQafLPQZ5ps36NjY2oC/Qb5L6mjw35lqeb08dQ22JTRy3ngRsWl0/+bGvxe04NmMa\\nR6At6cevP1IZJm/Yj1nMsBoiG33CqMwc9tzmNv/Q4ZMeFB+OjKR7AfZcaImrcC4xNKvlTR3/GePt\\n/Xf5G3x/SyQSicQAR4ik1QXeTHq9OoLuGDm3ulJ7TcyXe3ObzuXxKoG9KHDUGzntV60L/l9WvQ0Y\\n+cBzQqc5Nx5Zl/YBz4tc1fazdsooY7sVq+qE2sx8zXvRTwMHv1qH4xPvB6e22SDrLt+VbkfOzJou\\n5uwNDmwWe/8383ms9vWyinCvnJmiTZrkmsFoOWJqkW4qwzpWsBDsCRfrmk786o0t1qVGurWI5WYX\\nlYgtanlt6Ue/vCTB/nZteclarvpil28U/UQYdddkTXsy4T50RV44umUjET7PytTWIYxC1B87oT3r\\nifwk51FTngQRfNJ0BXRjWVniszZFAeVdhF797PamE4WkxNkw6kx0C1k2INUaCdZIIxexFtijQI+R\\ng7xhWSQXwTb6s+yrrXNrTykT+krGL2nSpbYTPd5XIwe6lQTWTuuIxprXP0OhFPe5mjfMCY5jjJ7h\\nLd1cQnr9tVubu6zv/pz4FiRJl/hhWL6ZvU7LT8eLWjEwg6+IiUQikfg2vPEuv8nsSj9PIr724jSi\\nbW+5KypcwsOjquI3m9Mm6CPV1vtLlqJZYDdeRX6MMGv/wrYXpacu3eixou1f0e5DOyc68E27oNyJ\\n3FqFj3J2Bd76CuBvOvmN8yiuasF5vRv7rhOfKR9kCR3UE3tahEj2e/v/2XuT0H2a4D+o+h9wjYoB\\nL3owoAmiEDWCwSQmAUEhGkHxIIoxuKAgASEuF5EoggsqiFFEJKigAUVEPQUPColLPJhERRAU8aJH\\nxRhjiKY9TC9V1dV79cw8z7c+L+/vO9NdXVVds/X056meUIYIHnCBQAP82MjfjksVyXbphIOSDPQe\\nP9/Qd9vipD262fElOTF/B5AJvprOi2hh1JlnfjrE9wH+3hwkx33oX/p+nmP2IgHK/UMB8nHfQ52x\\nwAHl65dWyUnIOmvkJjAiSyDgLplBYo0RYlHYxZg4Shj1ySqZ6EuizJ+sH5LN0p9cF48+/je3j8pI\\ntJBM6jTtC6rielJbrJv5g5WyI8WdadZgH6qyTiztmeuiS9ihnTweuwrSVRmvs+QMv8c5oV4q67Wp\\n1a+0OaFTyw8ZRtIZfgA27mYz2nTNTEPNvHI/ygfZO15WHj5cBoPBYDiA1WXAVAilQSWfkkG36ufd\\n5NxKlt/dk35uWGDXvjxBfDTjxtWr7xxrde0fcdCjLfoirko4NXyXpgROAE+1iHFVOcWUztcGOfs4\\nsalq43uITTU8yJCWz6Ib33nXhxWvxbCfCx0aaiLMr8a7e/wXgN8C8Q83Qn0ktpKeTJ4VxFZqc/3j\\nQCa2sp1IRgU7QLPh8qVwlWVSiHU0ZchFMgraBBORgxwc6SnhAUrSCpCc8BcTWeyC/oXkUaLSUiyy\\nB0K2W4xXiC/9JlyWJ21CHBw4+Vt1UT7U+dDXlL1X2IHM55E26LBErghn06VyllmHynAbKhszz0Lv\\nyV8gpFrOaKNlnBgkdljZCJnIM+QkO9Se4AfuG4tX1Am4n+l8yGU4RS4efcefqhU7kJumnSyDD2T2\\nCTVDtnG86LleNiox8sgbIegksq5si+8d0l1Uql9pU6vf8UNTp5YfMoykMxgMFEdfbt7xWvDg+5vB\\nYDAYDGNQIOh6Outi88TXhPp2m5eSiDtocAaDAntWn8r2OtKtCXT5I0WCiSs9GvOO9VMWm9eOMll3\\nMm5HDvtHGK+gcdJ85DvTO143X4dPCctJcq7bLhIogqiDSlNcURVqyA21CSyO2IYpiLtx0j+ydYU8\\nKkzE0bXRXEqTEILZjg92xjLqIGe0IX8y8QgA3mc7QZcP5J0LDsbMQoeMeBfJuPAsDsRk8iv1lcUi\\nEGypw03SMtrjcqGjDnKnxaU7WR1lVClJ1SGhhuoRITREhqUzBW2lTUbGJYlEVeXtFqGFokEWiCTE\\nXfapJLzqdiRiDVA98RO1IZl6rvQTK6A+QRe1LLp2o+buEqRn+uhz/lOeIZ8OI+kMXwY+4tHTKGrT\\nMbEFNRcO9EVWeVPQWgNeg8FgMBgC7iSWVg28jvx6yD+PJ0cmDbxlSNCaixsT2LEtKz+Saeemqo+i\\na/sgm4hzJFRV88laJ1edQDNcSqyOGjnUIZFPnofnCa6Yz3Eu4C941f0I8EzaGw1/xUGaIuYOhXhb\\nLZ+G4ks1EtGrjhNGefnG+GOPS4ATXdeSk0FH1BkII+8x8QWZRHKFg/kv+RYdXyoy3mE8IrwAiFMF\\n2VSbQeMHkPnB6lLGGubKwOe4AM+k86lVyqRzl9+c6/TEHypXZtDF44LbxtDGJTuBfpMuHtt43FDb\\n6IvH8igkOONtJHuMyrqC4Kplt4nZb4L9qmxPV6iv64qxl3WKulAsRnTJ9fkvlQVRF/D6eM7gfwkR\\nWuoCJIv3KEHJN8o2O+g9LoqnmId0L1B2xdCAkXSGL8OZ19K33o/U/KqNozZV3mKohsYo+63H02Aw\\nGAzr2FlGctPwoNia0fsJsE1bxxXMEXQ78btzvPAckXTou2ofQNqJNo/F+VBunUd/KzG+fdyrYhgv\\nTnYOjxF1KobPk0Fv4ICmCJwHQH888oAT879ZeRVODAFWMKQWc0jVCrQdSKGsv6qANUVsEqvm/sYy\\n/lk0mSSkZbRtIOcAyuyzKB/k8rfxsp+UaIT+Up7MFkDMsnOJ7BO/H5faMlukzKHvZ+HAREfQjgcA\\nl79VRzP1ABIh6n1IksNto7+Q2haZi6lt6EuIRvqBBVkCFMU5klrdzLlcHuWK+l0iC2eqdUg5mTis\\n6Gr0izBdzDeXmtcz6GKbsr7UFTWVzzqHtmZGI5Kka9ZqQHxeVx7iuRgLhPOeNXlsLPlDYSSd4QvA\\nB0U6GvcEzkPdhRf0SR3f2CeDwWAwqOGVE1dKBNOMgjyN0mnwqG/jBlSIzYaeO4YXkm3XqiQCa5ak\\nWKuRJJz8YrMAx7ixBpoxlgRUHCujfCzGrPhEXJvHTYHhwdHailMjCI8RdYaPx+qPbxQMd0+qV45v\\nEN5GvuIZJblClsDT2wCQCSUiFMgYrCbySsB/KMMz7CAt84h14My5fHeMWV2e8T6YPEqNC0KLZ+4V\\nhFbsKf62XVje8VLlqS3pYc90ROoKZ8gBIs/EtqlVLZMulKWYRUonHhckFzKFYmg84R9zlpwHSHpz\\n23yc+PflcIYc0QtYF2obCSTgxJXLXFWVAKPlwNqJ350j5bme6iuJuF72m+Qbt9H3rexznaTc8wfb\\nK/wAYfyKAAAgAElEQVRMIrkeBHupKsoCkH8B1SONDGXJWKksUXC/iK8ukl8drSeXIb8PGY7jF552\\nwGBYhwN6J5FvUCtaq/cfPTNbeIELVbQfJDe9pr7kOBkMBoPhTgze+DdfMtYIoGFGatPOSpv5Z/Pr\\nCLo426EBL/zfEbkLXZsHnPEnpqH5TJVQfXdsse1m5aEYH1A6UnTapKLRswP7R+aeto26oOZ7Z86m\\nnk43v/vlpUabswdnMGDyza/CQ9F66iEwAldsNGTyLp7Qz6yA3JRM6Kd2iaFAzYXl+iQnXGmPZBq5\\n7CORjzawf5jYcFQcEzmcIMmmKBlT05uXeMT+OaQjEy5YLylzLhMuhNjJPuCQOt42OY1IqriNjk20\\nkctKkujyBW+jv9kMANJR9JvFlBJqkl+5/2npyeAHOFYPriDoAPmDjyGgux8+P/CZggkrIot8y+cO\\nOhfSad7P1ssOoZjkIn7mAkh7yB7xhws6IHbb/khyZT8Ft6r+asNXtmslhntgmXSGD4YuOQfQuU+e\\nvUcOQ92NA/0qVfpqzRHcxAUaDAaD4QYMvyfMvVCokGADSh75xtu0X40GmsThCXJuTnVdvqWgVueG\\nqo9AsulqFURgzVq9jwq9rHamWXUMXZtcQCUEpdXt2GKVQjxPxFEc8asYVfq6X+OV5OTrQ1X3llGP\\nthS+TffS96dhQucB3E6Qzg8nXoNDQ7n74ADA50vEozIigPYA0NKQKavrqsTfKnOhAH/L7Cr1wdZV\\n6JAJjx1K2TDoO3JoyUiXfECOI3vOM6ccAE6x8YD9cUFXlI1ll7yYUYflkvcxg5DfdFBZzPQDzNmS\\njjA9UnYdPjro+3Hhrkm6nrYvOZr8mO+yaTt0sVxqFHL2HAoT/l4dXs3Ux3bR41YmG6mP5w405Nv1\\nVzElp6rZahVZrqPrE9JX60tfX9uPlj4uz/0g9ZB9bWXJpd30l+lj1dJeu6YuC0Af39L29Vd+yKdS\\ndq4Catv3wKAFy6QzfBhcZVtX81zlfVB34yX9MhgMBoPhFmhO/hyaSLqLCJtXfE/zYVJTi6DzvGBS\\nUYffvHO+0UPH5gGH4mSY+mR1w9e749q1ecgh1bgKak7GsBmrN6Bxbt1sUsHo977QDfXsgXNq9Ruu\\nm0Y/Fu8m6ITjOHtoHfnTFabUEf3jSJmTlUbioeJKJB3Qn6wP/3GyParboX25vWQPZ25FwgTXcd1y\\nGc1QIoQNzgbD5A8vq+jG5FH6K5BjNONN0o0z9ngsHK3HMRN0kyw90k4mzmjmHCavxgi6ql/gknyO\\nXA6WCw1cPLbJZ9kmIJsEjKDDPuUwomtAzFijGXTxLyXUiFG05YpS2mfcF2oTn5NcdxE3yYATZCoe\\nr6J7O/X0rySfifEPfgB9GCyT7hE4oHw0P+Gl+pU2tfpP9iPu60HUhrv7Eqi7cqBvbZUvCqbBYDAY\\nvgp3/Jp9hQibzaK7g6C7fenNKXMdYS1ibkXBlHIEJ1efHhU1bao65NEWHc8fybJz9SpWfQTNN5KG\\nr/s2fVC5oVT4OfS5N6zGq9Tmz7JV4yE0f9kroMEw+Gx/H95NzkmQ56IcerpdIuX8FS3O9Tn3qrQT\\n7zNRMqZXuZCiRb9Nd31DjtRBzm6LGWw+fuvNA1yZbR7ynTIoC2Xp31gWOpGy8ZJ3rL8xy84D5Ay5\\n8JfsekiZeYC2000WCfuQmQb423Q4VjivEB8ROk8Z//U4HSgdthgfuLLl0tFBzxXngCYeRn8AYvZd\\nDMF1fK5jcn1zDm2nMF02PQpz9I1n1iVfEfmUtuMpAo7UR/lMKDmkAxKBRuURmSQQZUSG6xTIvnmd\\nuB7rLH1PvU+ytRj0M/J4HKsy8bRBf0sfWJ+Bktn0zJO3XSpyqaogMlmrLcRLEF+WsULaY5e+V3PE\\n0IKRdLdAOptrF2urfqXNN/qxj6Y2qZsP4YgLB5TKKtEd/e5gVsy94JAaDAaD4SncPAk0ZO52gm5O\\n6RYR9kYic9qnayyjQozw4gfGKncTd8eXcMxKxerT475uyNSGwSUReoqcArlqy1REoXeZDaPx0CYu\\nY/GJ86ca4y2D+XpTIcYF7U+9Q72GuwmgMb7Bu+Fn1vvQ9en2MdmCwTihLVWFqQ7C10GecydLVwY9\\n0Kkr6cHr33TfJ6RbpqwAfObZHGSiLRJ+5J4JrD21c03Oo44nvY6ScoEodGTWnxFnwtKZ+N/yziKX\\nSd5mK5hEAxSVsOylcHwiKYf4ytTdal2MA1JHdAEm5gIZh3rF62JcMVGHfeySYUJ5msYUZLDOixNy\\niRsaIbKG/enI1DLtXMduKo96EOGX7Zdte/3K2ZGdWAGSAYbUBu8AcMFKMdEtC0stZIhXUDzvpDrI\\n57FjGuLlnpa+9B4M52HLXRp+FLq3t/H733EcceWAUjbuQYg3+PcE1R4rBoPB8K04+6xZe36cfeqs\\nEXTvehLeQRquE3S5EdGjhRp5d8pew42qrQOO3Lk05p1xxDbvcGQ7hg8EpxqbLTj98yngVHjEp1X9\\nhWpY6wmC7tJsiHAHz7dvwjsjdHheQpq5L4qcUJvYBcBZR3y+nmhDMrVeyXcEV8jTnB9MpkD6h2Zs\\nRYfo0oOYgMHCmbTJhArvK84Yw21cpY0jfuelHmlGVu5r1sfIGdYnvPRjJmwcKg/to2z663J7pD9l\\nngltUjnWD7LuchvHAvcf98tlmWiviKMTs8EAt026GnFB51uPxMunCDmC6RgTGYlURCcdv1SapCLX\\nk53K/uBzN6H0DYrashz3y1Xa0iiM1xS+TaL1PpiI5XfexH8ELJPO8GPAb+Ji5UvwMne6qPv7wG8v\\nPy14BoPBYKhjiHyae5PYeu/Q9GeTfBpt/DZ/LpFhVvEl/rQpzuXJ8ZZZJ1efGubU+1ap3HTkliw7\\n1yw+hqo9ZUdwDFXOQSFe2rHCGQjtwjmN2xmGlVeWE28yzRhsaM16NzwWOvzA21yyqyOkg1vJublH\\n5GvQ9en2HwaUBmdccFDeD2uPw3gHyktWXoU8kwsAhCytUBBkSTYcXNt5CcugL7Thy16mniP9gPR7\\n78C5LEsWfQyyPqTeXGI+2c8ZdT6TFnzpS5KFl5xF0UcBiUtcJhHpbuMBkodR/1WesuWI3kib+BBX\\nbNGjSF09x/djjw9hWKbSIf34UMoZfOH3L/E4xn1Jhh5G0u0iS+zaQaQTFORXLZPMSTKC3oLAkvwo\\nSK6SiBrxrytD/MNtam2pXzOZgSD4J8cGSv24ju8X/mEZSnAnMFmpbgqdhze5avClm/p0e+64AYyk\\nM/wAdO9nKze8gzjiziN9xEORG02uVxsMBoPhbTjwKJlVeeRJdiMBNdTjVxFiDf2z8gf8qdvcJAlk\\npcXgZYvP0HPjiCNHluurkA5QFt8PRUdUlsEU9Z6Jk6h3y5jCw+JGou4UTmXTvRYPvG4+ZJSAT/o/\\njbf4EVF+r+wELt0jFhzk5SdZacfTIJNm0kFk/aS2hOhDbFAihSDf10qC0UFezjKavMg9z+oB3PV9\\nt+BEtBt1eWY3+5UtRycIAViziygz+tzzhV0XiUkQSEx0/EgEGVNBv1x3ydaoi0zlZRuSXYEaRDIS\\nQSSTSxJBlsggkQBzlDQSsuvKtjDgByeuBB/jkRJtMN/FPlQIL2j53iAg0V+Z4OSywtPV4VoqI8uK\\nNTMivcp56A/GDJswks7wlcDjmKrAy/BCl5oY8/fTemUwGAyGp/EmUmyeIBpihTb0K/uClJ4nwwYE\\ntY5T14z2WYYnaeRpNgXVtFhQeXLUVUwA1io2HVGNn2d/s8JWsTq6IfK1ihVbG2RdxY9TpKbu3FC8\\nwyh8o+6m15cTpr5lam3oDv1YZw9TUoOPyTcQY0OH4EZHy+eHnvFM6dR1SjISDcNrqQwmn3CM0Tfj\\nXCDaXCbIHMt284Ggciyzzftwj3Se6LiMUR0XEdfPqBsiozCzjMhAn5k7FqUonjPWMA3mhW0pWm0d\\ncQ9nxcnbUZYfFy9uhyy9UJj+CvKS7oJUcoj8KYirkpTqZrVJNiQ53r5pH5FVLlkRdTkk2CcWK7Eo\\n/Mc2Gv3k2XHZOnYESBXSDbifQPuCVVG4ctOhHcdkgNuXVVWBLsXqmEi6ebPL6Lo0r4xSgBwWw1kY\\nSWf4Wtg9JOAnBeJb3kwNBoPBcOENs1AR3+jLop63kZczDWd90RhXqCxLWCotfLuLdOK2CjvKrI56\\nlljFv1NkVM0F0Y6SEyeWfzwRnypRt2HoBFF32yvGtqEf9jJ003P5tqUux3/H8ho0ffq241MFzYpr\\nSvCMNjQpLmWtteov4gfTPVj82pLGBYUORPYhF4cz6gAyUUeJrEzUdXWEpSkTCZh6VWae4XJCuREd\\nOIbjRKSU6Vbzg8rEpw46aMF2iiE+mEQWIPcWMpEEDeKql7mG5OS6saUjh3xBkaiSYkg/sPYlwcUI\\nK64A94VXRd2SL9xXEgOpnrvj8CaFE3aKvtwHPhLgNPbsX4B0qRsOw0g6w9ege/t76fvKMbcOKW6r\\nfSjIA2ZfevgNBoPBwECnGxTwJvJnTuWi7OAkrZ/XXchrxGXx+BSyQ2bmfXG1JosDCxqPTdpJ8s3V\\nq0+Nhap2lB2gv5bfjh6fY0v+PRG3woYSUYynT5caC820aaATtNKnEHVVknLL9Y2+81k5HZeW3dgX\\n2sc9Syf2Vb9pfrTry43kXMgdOmBU0BmKUo0wmx0/9xbJIcI4xfYxoy3sR+LqqpeWhvT023XIGbq8\\npAub0vKTgAgpH3wsibqCLBvJqKs9TNE2zkoTZRCJ5lxb9rJCM+XowUF0mAPwHsmiuFzHwIH3EGIc\\nv3FHjz7eLusprUfq0XH0OWwkww7XR2OYTJvOqJPkXD4KOBtq5ptwyZ+WHcGfVTtULuwjOdkOayfF\\nTojFeKZhww6PidQOy0EpJ8NVtltyjXLyML92UsYcoOtGlDfcASPpDB+P7j3jxTeVI6490t94O3/o\\nLo5HUAaDwWD4aLxpIioBv6GvQqtjQ3rOzPbNEHQns+fuJAqrQwwVEoUvapVf4pfRGBPdNVzqxmzL\\nAY+2lGN2EynVckG0s+VAmITZIeoEx04QdYWZJ8mqqk79c0GXqNt8WL3kAazxmNOAEXQlhgi6w+G6\\nzOR723Gg/jiQvi/XbdaVKmXpuACPFgAqYWYkEF72EpOI/DtxvBxSe2BEHcjZfshDvhhlmVsHSDEi\\nCmuyqJeeRaLIpPM5OnEJ0CThAinHYuzJv5n09K5un27jI4LleR+BMnLBDl6KNHmFSKA2ocTqQoVM\\nMu1mzqF9yQ72qUrQAeoTJ7FcvU9Q9olq6PQJ+N8KwUbsMGB2k9c27HC5KlbrusjnKL5n4NOx08xw\\nM4ykM3wsmveMl99QXu7eZwE/aQwGg8FgUMIcIbWod1ZWww+/6ccc9/awH+PO9iSlejdUOYKSeOIT\\ndNNo+LTt7qILevHiduh05XYGleBTtS/KqHK/Ww548q9WVt0thNUmGa7yjb4b+q2HN9E5B/FN3fyQ\\nvkwRp0f5TE/2nkR+6jT2HFtKEqTbuZxnJt32vcsEVkEaJTvScpKQyadACsVMsovfyuXi8pPVjDrJ\\n41pP4iCkRlYFMq0gvyLlIX8vDgT5y2qO0EXAOWEJ0dynIvOtsIl7lOk9qZeBu0Skpg8kqGCHhbFH\\ngtUyxIo6QEQf0oPJLqIj2XBkG5N0hRyzRdplkeS8C3pE/YjtKmPA/WQ6pH5JOgR7og4cb5bxR+rw\\naEAg8upZg0SqomMNPCMuXq5DAxckR8915qfhGH7haQcMhhVUbw8fcN845uINfS9NOJAHZDdi4IHz\\nAaeFwWAwGAJm7tmnfkE9r1WRxdvScYMfXQ/ilIWuH+oEXXBzwNueCrn9dqwpcadyrjc6uxMHBfOK\\n8coTctv4unht0kuCzRNx0NV35i3gQ3iVj0X3qN32cocn/g/g3YxvwlvczOTN3d7IsyDkXzLrzsiG\\nVOXQdsUEIV1QdpGrtaXT/A6VSFGSfKBEDrLFCYngG5bn9UD0ZFKGLlGI+oqUYeIj9oNnOuF/sy+B\\njHLBFtoGvM3lUZ8AlQMpd6V80p1tgRN8AIdihvU4oofHle4jvZDjlexj30U/cx8A2cPHOh8Ol44D\\n7Qc7R9DBTceCHNuwH/yKNlyygXRgYB0FSSkQdKQR1ZHOL1IsEHSSDsG1XIZ10jg225aKOiUdRV0b\\nDUy8Q/n4jw16boNl0t2AnevHMAHpxvwi3OLWQSOy6s2ft2qh8+bwlhcLg8FgMPSh+h7gxc1lHXL1\\nOCE148MMKaWZMXbMhw+OwwgkfWe/Z4enCqYVlXBy1YnxU9VGw68V7Theatl1lzKx6tRYs2pj6Tdy\\nm19FrDij/XM9Ud9if69/fWi64OENv0Ws9nfjXrF1zj+M5v35th+bHDdC/zZEnoKvbFeFjvkx5Mm9\\ncACAsqFQUUv8mtT3wL4NF+qjUFCaks1YedE2/Y2ZYh5/Og4RCGh5SNQwlQE0M+qiDykDL+iMqWA4\\nM6/MtMNRwG0hkCn1zDiWLxhqsTyG3DbK19o6HB3Ul5h5l78pV8usy9bjX/LtOchtYyjJ9+gIx1OS\\nP3Eb8WPJ16sqK+hll7WXzMS2Sz1yG+qbpIdmp5Vtq34jP+vfuev1sSHbiCklMKlvgP/iXeIDlIIF\\nmYeVdJ7anUf6ypAhnb8eIH0LktxzsPBL7r1fDsukM3w+HCy/wJxG5f79QQZa6l1X4hZ0jv9LTw2D\\nwWAwqKBzl999CDz0PqJudlehBjm24Io6YXuIoBswWVYoOOLRf9uoOOvrVWpo6lcyfqlR6kVFzR3n\\nlRiODcNbMamcL+/E5sPgqReKxYB+MkHXxbd07Rv6sUEkj5u4gSVH6FoZcAPRKu1GBVkiTe/UDDry\\nr0MNMcEiEgnEECIpHGvmZFICZzJhwgNnXSWfmB+1jLaUbcXIIJKdl7LIgnyRgUZ14swwQH6mtrHP\\nOKsNcttuZh1pm7cB6yLbKKMNmEz8L2WvORqTIj68n4LfQjzKZSJZrMnxkI8JkDbsHEI6SZ/5eQGs\\nj7mEnLYS0UUIPnYOlgL0GqN95Krp9UJ9oEYdE6SqmB60XSgX/ai0qSoYR+v1ozbcOP0OYKCwTDrD\\n52LtvnQ7jrt52EBf/fsPxPs9NBgMBsMs/Mjs0OG3ilPZY6ONjmfQjasfEjjhw5PZc6OQ7Lram/Li\\noOVYlp0bqlJBU7+vVcxpP51dt+3mpHlXLZjR5UPTpcaFXc15e7FbS33Nx1+zn9Nu3AScdfJp+BGT\\ngeNDh8cw/Gw/5Gw5xnsiKg4g5E2lLLeWDMSMMFyDNhyAC9+FK3W5IJbtxe9IeeEbcx7putSVfhBF\\nHgBCZpjDaXYeUmaeh/wcSHpdzNpzMR2M9w7wN+Wu5kgmtkkdim2jXH07xoPGJmfVUV0yYtYczli7\\nvjfHdWUyJycMBdsOxMy6KBFb478eaMj4d+hA+IucZsQWECIu/oOJMcohlSQnaUc4LJwtlisw2YTr\\nZfuyLPa/5ZMDZlfyKelidokuIfNNso/bif7jdqkFIxfxISOKcYngv9BuCHXZ5pgrnmMAxXfpyKUp\\nKPLhn+j3G55LPwGWSWf4PDiYu589gFtcvCEGbRM3/HROAfYwMRgMhm9G4y6/+AB4+rmhan9B2RyR\\n1vl95dMEHXJv1j7+/wSqupUMq2XZNfw5GZ+mfiXDalmIl7LCp9PxiTa0jGou8afd9+p5sKRrw7PD\\nB1T3CHwpjh+DjznIxzBM0B2zf5gBnMHOdAcjTiS1hXpXbDDiQWjvaH2Z+eMKGVxOCA6H5rKEuoJN\\n4jY4ERSLAxmT86oyOZOz6WR7hByBqE/K2OL2kC7UPhFDrtQFks5CBhFNDkg2Hc3SYxlmWC+WKTLw\\neHZe7hvwetwPcMQXQpRhP4HqBSYbo1yTTcekIZvPNcfOB3Q1oCy7dEzw+YFkS4LOpWNbHOfiPEXX\\nRmqPJZBPSLZoR8WTgGCuALVAfZHUdhUuY+wJ5yNDZ7gdlkl3B05dX4bX4eihdnArLyabiaUvIOhi\\nPAbEDAaDwfCN6DwIUPUjrxkLBJWv7kiyAwTVhG0iO2S784Tdsd9p6HsCrHop/rHAleWaY4uab06q\\nXDS8+RUyrEj05WR8GmaPxGc78yiqeiA+RWwWDC2P8EUnXvHGIELzm22VruthOYgbHj144AZf8W7A\\nIU8aat/Q7+cJunegPEw4S65yEB1AzBiL2W2xjQtNfNRUZKqFOg8oMc1B+cE6CBldPs/yp+/X4awv\\n2uZiJqIDTK8DwN+685EESQ5RP2n/2TbpBAC9mfC7JRpkOawpt0mZcMDHnhVdxSGJ/YIQc5daesCx\\nZnUxRCRUV7+cVAfUf+9cO3MO9cAjPSQrLhFXOT5xoyCyUtdLMi1XUUKplq2XywT5qA+RYuAEXbgf\\nqMJhO70+4X5geWJbJudEUrASAynbjrRt9MkBquN9whD6lK0yeeIDDCAL8auAXzGuqLjO6XQbKD3P\\n39wc8sWwC8ukMxiUcPyeddPLkhPN4Fv7S163X+KGwWAwGJ7COEmzpHGXJFt3Y58k2+17F3hSpq5s\\nmSDcnSVE9qcJOg+0oaCEi5yAqF/FsFL+WMOPk7FphkDBsOo3/uardE0uxWMzAkJDrf42r4klfcsN\\nCxx9JVlS3rlHH7Gpg67XB32TSQBVA021T77adi+l0w89iPE/ZERRdXGcWgeO10myhEigPx9w5C8i\\nEQSCQPaLsDR1fwQCpMyGq2XBITKI/wVKqIAkw/kMiQzCdtJ2K1MNxGy2Qh+pA5StlkmfTPTQrDYx\\n4y3ZdIWfVCaQQMRn7nf2KfnO5bGf8fxI/qAYFv0uYxkPBfmWH5fHMYTsJ/cjCjiX5bNudn6g07JL\\n0JHzCbdD5082gU8q4jc+z3g7vs0hX2dl3bqO2kVabX3Bi5tNlGMqnsd8+KZvKGCZdAbDJhr3Xh3l\\nN5JRY2ZucqaHl7hhMBgMBj3MkGRLOpUazXyLTsmkYLvBkEy6MBr3E9/gUyVGd8nBIUEEVxZrD0+q\\n+rcNl9FcyiyqxEWqOhkbJ1VsGMyT9T6oUoiNk6s04yJ2fSke+LtKkx4K9jRfZ3R1bXyjTjieGn4V\\n/Vs8fte/n/Vdut3fZ7waDxD3o3gy7l716hENrCHc/vNTQBBxEDKg0Jfo8PyNxwXX9pXZBUC+FScZ\\nZ5l2KOEtm4kqHOTv02EnsB/YH/xxKiGjLrIL+DtsztFv1iWlHgCcT1l8uH1x8yrq8vYVGciRrGbi\\nlbqvrtXkULyS/tBGyqzjIQt9urIagx1Hv02X26B+hJhcMiibD+sP7aOedOhjvwhpRMmrInOLZYzV\\nyDCuLzctyTDCYfUy1hry2F852w2V4ba5C9T/Ql+9f9V4YX85eecE3bgtjzV1VPAFK6fbO3e90bEQ\\nuexwW2EcQy45jy5DQOeo4SiMpDMYFrBzM32jsTETt/ZaBZ/nscFgMBiegtq7x0Gi6sRE2ihJpU3Q\\nPUnOzdgeVxSKhMGH5nikyokpMGJSnLfJKScXs6ptiCYViMy8tUHWSf5U4qIVEz2ybqPvryeyYtP3\\nEVmaRKRhDMe/RfdSNHt98EQs460Y/0FVspgTa0rihsk5KEmY+NdBsTziZT/Te3EJSyyDl710HpFk\\nLg56wjJ1kcTzkYIKup0PdhnRFhSLdZHGIo7H/gKQ5S+B6suRApGsSwRWJL1SXbmddAMm71zl6GT6\\nEBNvNR2iErKUJSInAS+XCSKvSo9V2PfZDCfjOB/KSZQqCRaKRaJIIN1oe9SmphMTVxWdSDWQjLkg\\nhHgqUX+LSKvpl3SOknN9nW3yEevHt0InFDi6QfxBmktltZts7d4rlEu3arEMnWuEXI6EMtBrLRJ1\\nLXcMurDlLg2GSdx2c3rVXfBnvrQYDAaDwbCKpSenEgu3RFY15cZJsul+7xB0yODtBF3LgJeLTo2m\\nRN1KRreXf2z4cSomVb1viAf2ZbxY28x9qBz3Q6rvxeMOHMQ3962LA53vPMaewpDtLzwX2l0a67Cr\\n/JX2mjowKUIIgspPB5y8m3VhEgIRDBLRUKlLfoTKpBvJuyBH7BKdmJLgmUixPRTLMGISimY0UbJE\\nWnYyWcI64r7LOnm7Qge3K/iV2sVt7FPU4bDvWae4HKZjdcRHbo/aofW8X9EXphPFxYUN0g/mb4o/\\n0QlZJztP8PmQzhV8/jTINIfK0/lanGPIJxxzSWeWpjoBSD0Q/bR9bsCy7khLKLZJmZPqWXvHa05g\\n9qZ+8g3GwGGZdAZDA+dujM8b7Zt5pPd1OBj+lezLPDcYDAbDyzFD3Gj/2t4XG4t2V8kqJbujOGF3\\nxIUdu9vZZJeSVrEKqrq9VDirOytZzjqq+DE4vNsyR/T/sHgU+hYN8MXOphxw3aIlFHo2+rZ8nbvq\\n7gvg1vvG8L6+6eH4t+iqdp9D0/ZBx/wpA1pDlZT2JNUJilLZtRF3HYTlJ/1VGrPaUgPcjqfgYeV4\\n2UuAK8sL8JKKFzkRs9/I0pfMNs6a45lxV7/DPu5YrIu2kVx+IqA2hQ5IEcl1+E5Sq6vo4O3QtgMa\\nRgfQzqyLy3RCGf6YKOlRGCCdGjFfMd87iDxAypgD/jfUJV3F6UaJpKEMOdTAhco6uSXZIJoowYVJ\\nLaKX2whtyEOCZaEVeh1rz3Q44lVBxJbbzAbrs5Rxh7dRGEq9Yp/LuNZsFLqIXpBKyb7jxT00xibS\\nsxxffnEb3yIA8r7hLIykMxjegptvevUXrTg8OTVlsoiJN8Nvfok0GAwGgz5xNGdXmqWhdrXNP9vf\\nvt1pYnAXh+zW+vtJhIxk0nULZ/UqxuOmWET9hW7FeGwRIQ/EY83AIlV3J1G3hPeRWSIBOa14o0/M\\n3uPvVgeJI5em7w/gSSaugucIOkYA6SmuqhStLJtGRlpd6NaFM64hF7nCIE55PLyf5PBSl2FfJOoy\\nOTRKEqYuu2tJTtKHZAsqy24iH13wJcnFO5TPDAlfXjMbz3UgByEvI4n6i+PgXPjrkRygZUZDOzw8\\nB28AACAASURBVAh9wH0CJ8sBXsIzykEmQi9DqH8ABRGatunxT09axB5J5FydRAo6qiQS0VQnqBiJ\\nhZ8pPYLKMcFCR9N/TlC58k+x7Ug5jxv2jZeT/nNUfCsaC3uOb8mVzJkaFJ7E6NkuLX/Zbmg4DSPp\\nDIaAPKD5bqOyqdoI8fHXsSV8ptcGg8FgeBxD7x96BJ0vNhbgxc1tm6/M3DsU45nJWZVMO67CiUVq\\nEHUrGdwm7B6MhRuqmNGdZzk1yVwF19p6Fsi6JWKyQkQeGbdP98mHfxeO3csJyNvfbz8M/mSEXjin\\n+QRBV8ZYmaBTVhnRSqoLEtSwNKWSyuKzARI556SGIVPNAyKE8NOFs3fEPqoDYN9Roxl15KNpkIQQ\\nURAHXJFFKVhByD3AbfnN18s6sA0sCyN1wj7uArqTY+7xChnOrLv0pKy4IrMOfY8O0HfqWPjioSiI\\nU/QXAIrMuXTqSmEDSGSZSx2k3NMMWVbIIFvFMpAFwZZt1XWwdo5INfohE4sj/mJboh2ig9qqxovr\\nFn0sbRX8mrD0JhR/yueOa+yNovZEGyqP5yjQ79QBQOaWDUdhJJ3BEHDrC0zlQXzaZB18QHRbJMbw\\nMncMBoPB8DTYxATGJGn1FKbJrU6lZl+fyqDTnjzUJuhaOj4pi0rUrWAw0hzb2UhsKHoqFlW9W0Ph\\nOLV3bWtkZp3Am0b77yKiFrWU88VnsNTRxeiwJo+fM43H/j7wVP49Kp8Ynzw7JlK2PjZMGQQ9UMUe\\nyiYb1oNJGyj5rUIdnx/yrBJntAHWi7PGIJBK+GcUXiTqmplp2Ar+E0WQjYKQApRF5yAvvQlAM9eS\\nrwC9pTk9QCIMEsMl+E8z5uqZdfjA+OQxsoEPBM/sWyEzPV5eExBxlpwAsnwoIaAonYOz3zDZhdtd\\ncg5tIxnS3AFrJhBZXE+dnMv7FVtUtL6UJzHLlsxk/ea2cF+qS08ibx3fI7Zc3RZQW0RrJSPOVfZE\\naaWHbR6ROqG83GnNCr/5nfqbYCSd4cfjsZeNR99yJNzMGs5g4q3whd4bDAaDQR16rwrjBFKT6Tni\\nwCiBNCLlqzsTNhdIwW2i7Ambse3EoOJIhh3keSGqVwfcnKsWzmnksVjKtsIqXLVIBaficG3lqdIt\\nxw7EoBj5L7wKvCmjTnR/uk94Qb6F65cdpyPvJQvB0soQfBQHZgjpvUqZoGuofB1Bdzy26spXqnSA\\n0usciHwM4rqCRCqLLWjdVXQJZf4okF2S7iYJResBhOUl+XfoOAuXO5uMX8tUQibc4h0lr2OJ2sYb\\nYSTRBuoR2XWpo3boUqkQI498Rd/qA5wBl0kK/H25ejZcJQuvcoyTN9eBuLwOsvl45XGF6IPLusrs\\nOIe283EpiSuX91MdJ686pBOuQPu1pSn79i457PpwZh/ZL+0RraP20D8j9ljI5XiSfozZG/Gby7EN\\nqbIE5YALUUrI5TECz/6c/h6eYQu/8LQDBsMTcOj/7zaaTY9JfPYd+LO9NxgMBoMmXjcJtiB3BArG\\nR0lBFbJswiaR2yXo4l/SkTn48N8WJOJOLt6GqFfB2HYcboxBxdymMYXvXQkdPnYfWVC81L+7+vPj\\nofOG9N3vWff17s1jk9ejc+O/q5+usUen7+tnFs9KclzaVWRpIbFIiBsy6d+zg8kHShU5gRwh01rU\\nKPXDuewHIjlc8MM5l7YvOYcVkz4kPxzShcRJPTha3/ADGn5cthzR7YKeoDLoRm1xPbhSd9xP9Wgb\\nxyf2I9WX+rB/jvjnsl8pVtiPLFfYk3xKMc/C5DDheKNzLPqFY8ftpfMlnUbUHj7H+PHD5xI/X/A5\\ng88bfK7nStkerq9m0DFRUuxkAZ71KDc+g5F3JEHacCMsk87wo3Dwflc3+OCvEGWz0akx6Vfgxa4Z\\nDAaD4QBuei/w1Z2GXKVyiaB7wmZVZsyZ6UOzSpat9nHTHlfhmoVj2M6yqwzbeLHWcKnb/0Vj2lll\\nvJhVbYH/Zr/lw6g21W/WOXFXQ+Wy4qVXHtZI47VJIyZaOPYaOKVY54H64CvtDWMCRQMvm9ccGUvo\\n2jtwpnRUanUjz5LQ+RI8pePlBkVmVq4PJfFPkJPaRPGc6ObAJ+Gr0uU0vaRgPKMO0pKQFxHhqWFA\\nuiDkpSVZAJJVdxXQ+jRwiySEVO/Q8cQHNo8PrlKPjgVgj4J/eb+sR0tHJtnsN/kunYMQ40wExfrs\\nlQ/fAgTURj5ugLY9Chug417NsIuHNW2gkUI8hmgfUVeUVyrIKiSHDHQz1pIdRFAxBzB1VXBbot0y\\nqwyTVoVcxd+aXceU17P1qKJa32W7ZcyLJS57MS+FWYlr1GbwK0ja5sL48qPnYD2bLjkibUOjfqVN\\nrX6lzQmdWn5UYJl0hh8BB+2b23HjrzJ7ajrDYDAYDIbzOLqEUsPqnSqvPupNrKaNTZWjzX2xIck0\\nK++1J+lC+0XoxMI5fEp2WVXnhjGP/ttyqhIDTej3P035LfuE1NR2NVQuKF48pgf6ItqYVKzRFw08\\n8cR7Aj+hn3f3sWnvqDP3XQizlpY9q0yZzM+kcDKDEiWMgUiT5tUp+2ItOjmjzuVaYitnk2XZ2BZn\\no10ygi+sPhISOBOM+xIdwn1zDdlEvEi+YF0OtSXtXfbFRVsoIzBlnwm+oLiIGXAu95vqB6a/0j50\\nkOjiGXfYP6Ijl6X4c1usLe4/JF0hRjxDDmXb0eOAsvKS3XRSoKw5Gpd0xNFxxpmI+TCXZKRMlDG7\\n7N8aQccUkWK+LRODXBdFTd9Qg/FWm/Dllqf3R8/khMqayry90qZWv+OHpk4tPyqwTDrDV+KO29rb\\njDuo/GKCSABQOv+liJ2J2wPiBoPBYPgi4OfAHfiU2cHKu9KAeEOmPXqYIcwKIqtp87PtuWbhODxT\\nMKVCctqd+TlWVefmsHLjC2DUPvJh85CMmqEVm8d+ObvQ0d1FV2oq79Mh9OURP16q48fj2JjgvsHG\\npwwzdqDex/6j+wwGToumSKh0ACFrin5fjrQOQtd31KD4bpwPpIX36Pt04TmfP/EWK65dKaMOYnu4\\n/ImNffwGnPeByMjKsyxAmsEfzZoj35trtCdzVPgh4BNpdNXEfLgYvUpmXYrhVcPb58jnQF4xCtl1\\nqT0NK0mIxDLRXZe7CNJxSsceKhl2dDaPkk+UIXJIwNFCtk/b5rqyraONkDkpQw7Xkn8KMmzcPu0g\\nlujbH/MnlQj+YBOtbLyuP+UBSPtlbHID3gyKfWkUIZShk5Kcn0y6NS5Jden+Y7gTRtIZvg6P3UQe\\nvnv1CboPIOciJt5m6XDGYDAYDF+B5uSIqwscmLUZXSJxTFe/3Uj2hiaBNRLP6bBuEmbTWLE3SXi2\\nTDqpkFSMa92krMRB0YkRYKFzmxmjy1MtOyX0XXuMKOrcCjKePtx3ZrfP1WM7rHTxPL6DqJtWunBe\\nHjjpdGKxscyqoh81NJ5E6s/2Ixn5L2Ph7orlpfLeeD4Z6uI8xQxOKiq/Q8rb4TkMtiJm+1oIDfpf\\nOq0QdczdZNMBW4YzE4eJeUJLbcalODOR5cCzpTid90h3IB4h60vLUDogRGYm8/jSnReZBgCIUIs2\\nXCIao75MNIb2kUWLbEmozP5f+nj95fPFVhAyM3TGA9TJzNoSoYDkycFFWxXypyR7KCGEyShyvxbJ\\nKNSCCj9OznH/+ZKSOEa0K+2lMrlPXJZ1hNrsxQRtEd9ps+ZztPmM1R0i9+t8JpVPPfsNFEbSGT4e\\nj98sHnRg3vTj0TqC7+yVwWAwGD4FU6TZ6sTTnQQWaqfbNx3CbJvsXCUfe/4EFORGUTGik07cTPIK\\not1tHq1jSuz3pKFyYnNaQWF7mzsdM0MrFg7Y8jfrGn3e6es+Wbdo1FV3NVSeZZoqNo6YfKAfhvfj\\nZVzhPBrn3HN9Y6QYZrSwFC4uSLF4b48cTdDp810fM3WR9MLfp7sa+qTPFSQUQCujDlwgwhJzBuE7\\nVAA4Iy5/j0omqrJMJqp4fYwbEJ+YPnKgy2/MUdoj/2DCJ/n8tCwz67KFGvGZfh7joossWw9/uw5i\\n7B3pkki0OhCz8SBuC2GVnuPyUouuIK84+RaKaPwQAcWqCNlFqoYJMOokIac4sVb1K+hukGBVX4V4\\n9fySyDmZMKz4xf1gymg3JQeAd5434hUyWFX3kY0ErnuBQ/twkdTgWAYdvuIMp2EkneEj8ejtgT/1\\nH3aj7gIeNH3QG9akmx/UM4PBYDCMQnqbTvj4KagG+n2bIrFWZZRJrFFtd5KdJ8+i6jBxedASJwc3\\nl4R0zaJtiDo3DeWpgQUFNw0SRTPL/c509jeMb5eyt15HcC1kOgqi6v2YVrZgXZpDPITmPbk5JljF\\nEaUiXjNqOeCIehbd6cOipjs7GskirLzaDUTglOrk7LUkz4ggnFGXm7jEFEa/IpmXyD8oM9iyX3n5\\nS8kvh/yCSERBzoK7Jvovy5nACkSaj3YC8YUy63imnyOsF/Ur6gQPiKh0iXDEJGPM9rvsRObBJ5vX\\nLmLR4jM4EaXXEwBiLHMg0YHLOkjGHLNJiM2CuBTOE7bjinraaJoAY0od/UfgmBzabvgGUCHnkO6u\\nb1RI9I34Th1LUtxfR6SpbOFrxTeJOOSakMHSV6kVExJ2j6EyOEnFaTz7mifZV8NIuhtw17VluBEP\\nH1TH/sp4AZO4gsk32A/rncFgMBhGsPkeoEf29JkgPdJM2dad/bo7hpUGmtlzGhPq3JarFo5rxM23\\nssyC7S13xs0Ac3xa2/L3257sM66YNrJ4rJm95bDXVU73aYliVibqqgTykNJ8Dq4eBw0U7k7bWIji\\nHYQpDNyXj8wNKirtP3qO4+4YqpNzt8ZwwvvM2xTlEtlGs+mAkGqYEAuMGluSkstfpA+wjDrPMvRw\\nRh12IvJDcelKcJCIophRF795d5FWMasu6MgsXCaohG/VRaLuCi0mvHK8s85oAKLiq38Q+orbFPuX\\nnauGPlmiNG6Fs+tkHdR7oiN1P1qLx+UKGrVFM+yI9w4C4enId+sc3wYIhKJEbklkVe47oLIeOVWW\\n1QiurIdyVsJT0BFPkN/IJie+qKGwL9BV3O9eP8UywW9HtMo+so5yH6lPpY+FD02/B7Dw8M2EddgP\\n/6RzjqmOo514fZMx5F0Psx8OI+kMhlGceCNZQN8NPJJ8idOj+DB3DQaDwfAi3PnysGprhVxSxp22\\nmlAmA7VsYTkNgkPSS/RtDNk28+vEGfcTI8h9cqGjb7PxCeJB7POykYVMrjteBSb7NB2Cmwgiw8vx\\nkkdWFXhmX6h6BV7jSAUvIDlrxzAXVwRClZgd12gj1WSiyjO9RU2WD/8U36gTvgkHUH5Djqxk2SER\\nIbXFhGLuPCYRIdmlMpetvNQnILt0aUhGJIYPY+VsuUii5OU5yXf0APJ36xgbVmbXycuGYgKz+DYd\\nohTrGXY8ew7pYbRI9I+SPP1vq+XNdXKKEnGO7Y/r4SRXWRbPBSY1Qs6hjTlSkbSQfS/6WxJ0Umfa\\nS1zKx6o6ipFEl1BqGBo7DQi9/THyLTCSzmCQ8JI3wDg+qrtTG/i9pAMH8f09NBgMBsNxbL5xHCeY\\nvLh51JaWnU/LQvS1HVct2kKhT5qtG9aFJ302vmeG7G640zXjpMIpI+UR3yWwtI+vaGbZSPxtc1Yw\\nldHFjulq//Z5P5ZxMWrUVXcXPGDtp4nGhetL8aVlv/+LS8ca2nh49vJO8/7EST1e/CwSMUXvhz5t\\n0LmZq+hinmJ1b3lJTDYBIA4YcToOk03JbJlR5xlRB2iZyUyeQSLDEsuAlJRkGCWcrj+R5WBPCZGU\\nChVBL+0lFDKRQPFMt5hJ53yIXdQgZ+jh7DpMDOblPPOdMh5b/I25yPkRz1HYPNaL5F0Icsq2c8C5\\nPxapRdKJlKGYFZetTEbJ34FjnrF2IvnGBCmnVfahKC2IKykeRc/FPkjxKPuhFY9ahiPIMeJtSLva\\nvbZ+Dx4Z5/CixDE7ed9wH4ykMxg4XnQj6g+BXzl8NRgMBoPhMWg9GV+TcQaTfVolzu7CS8nA0qiw\\nr0R2tEw6XjhlhJI5y99xE+xqTstWu7ZhZPnbZxGOFmu+Doj91TbSc0Cxf6T9wnm6S3Q9TdTtQPve\\nYZjHXc/BFzxt1Z3IsVNS/Iog6QMveZmIHlwfy1AllpOWzIwbVX3hH/pNu0tZJOo42ZR9ZHl4Dkh2\\nG9TIRAi6I6Hg81KUAJAy0WqZdZkcu/SkzkAkByNRiIgSH3W70JcW4Rn8Sel5l1M4u84D0x37dRXk\\ncWQnww6TnkhxNJoDm543XAZSncClFc9MnsVFy3L5ypKN5a4TylBpg5wrXVIi54KNkpwrrA7FRSLo\\n2rEq3M165AqpoFmsOVKQxj35e4uSnUwoG1F3L4ykM/xcvPRmM+aW8MD/VCy4/sG9NRgMBsPHQJoK\\n0YceqbhRfxdx9qF2SmFIcy1Ai7ZR8EYbRtQy7Fy1aAtNcnLByEKeVunMob4KJu7t62mi68Pa7+BJ\\n2/uY9Pymjjaftvc8ig1DUDwY/d/XPIyyr5kMy+RSZtMojdb6Nh1m0dIWXgIy7F9tPMq4CgRTIrQC\\nlRQy6sBBziQrlpcEQjBdk+8tEiwTUeW36rJcTodDRBieq+JElDh/xR9QUVe9XY6Ly/6mrEGXTUNe\\nmNIhuRzLqDln2F3HJDxjHebifCIHs828nY+FC8dClivbVAgwRi7JBFHJXjn6j0Aa1crHyLLSNSeU\\nCfqICieU0YKS46qRcoX1QWIOnfdFW9JrcWP8GJXGsyrJKSZTEwgYHZNIRB1vmJag5WVthwyKMJLO\\n8DPxwhvMnEvxjvrCjhzGz+uxwWAwGCK0fgHfJ7T6s0dTpFiVS2qTTCN2iMysHdTmLf15JTnXVICK\\npBfzDYj83BKxE6eg+ITjoALBkfcSdkpLYSKbp/paTJZMG8E07ECjChG52ifSftr/SZpRcHaHLBMJ\\n08m4ryw5qoGRibZW6yd9b5mZq1i1o6iw/7g7DovbC8DS5qRv07lACFE5vuxlkCy+Ace/swZ0GzAp\\n5RFnVln+EoCwQj2yLjuCFEGktK6tKJhEijUc0eBMIOzo4p6xJPYj9om3QzITZWOknWdE36Ur9dnF\\nEPpA1EKyQZe3zNu5TRlOmvmYezlKhBV3dELyOKEMCUpkWLBXkmSVZwfR4YQy5M0gUZZlayQarRMz\\n44TmKSZTRFrRujgQgks0/mU1sSf0srnbQzlO8ACuzJuL1x05rh5Qlh0y/rE36c+CkXSG70flofcG\\n9N2hw4zRVh+BhW58Sc8NBoPB8KlYeUHZeKk5TjZNkFqlwXl5jfc7jcnCY++Z7O1Xe/gpEgsbBpa/\\nT6VM9kjqC10bRrb66bpFy9CLm5vro3Ce7viwQxpN2z5JGt2oW9XUN78gSa/Cb1F6xDcFvNGniDf7\\nxkAOb9qRD3pZWi+hU1OZqMPiZRYeXZASk1qSa5lICgRVYPPEmaWgPxF8UYcLFn1y4bJMvnEXpvcF\\nYhEQYZiz3YK/yAYgfTgLMBN+aMlK1LlrN/ucltBM7aIzQDIBo78AmDTLNuhSlqlRJjghfpsv609y\\nAJSYTGRHvEnjATGVK+7jEiHm8B/HZGVBqby2fKFIljnBXqHDVcrbNmfIOVrUtic0R01revv2BPPV\\n8ppsKSCV6zzUh8YZQUgax+HvJBrug5F0hu+E8JB7E9ru1Eb8L+vECvDodKGpwWAwGAy72CK19pue\\n0dFQdkfW2Tdl0PlK5XLWGcDRDDvHCyYN4JjuftNN82dlVV2LzNZyP4tgi0VbqJKvwwbiWTtJ1CH9\\nzxF1W4uUbhOdn0owElXaLPmX4o5ssLtwl3mvedJWbdyDNTvlvExrpsazjZjJlq/Ri1FKpBcizjDp\\ndbVG0U8ElqftQiOSUQfAvhsXSDJGfAXB5HP7G2uQmLWcjZdli4y5FJEIdJPy0S+POi7LUZ2cREEZ\\ncS72Wcqay88Xurwl6hrSl8tCDCFn2CXZdKzitpRlB6RdtE/bCSFgvaxxNtIyl0l0guQC0b6rlKNn\\ntTiuLX2iJl2lXKgjeuokWd+uEAfBkGCuaffaLWNBeiGWj9hlqAmhzWJMIOlqEHK1co+uwfzjAPk+\\nSK+9rG+2Ta1+pc0JnVp+1GAkneH7UH3IPY8x1zzbfnGHZvHwC43BYDAYDHdg7HG3Tzj1LexrGSHP\\n2g0P92UwXr66M2AD1b0t80zUtUlkvTXrTKOPV9P3ZtYVuiYNTPcN6X+W8FonGHexfgz3CcYvess7\\ngyPvjudeSB9/1VXlIX3aUlD2GJZNo1lVOsGamZ5cXkiIk7PFRG1gclKWHBMmS2di2SB3/bkEKfmU\\n8+6wjxLJFzPUfLIZssoQWQcAKbOOLtGYdTqAkEEX2kYCKtiJ5CQ4SASgD8rosp0oUy/odEHIB1mI\\nvgOyw5b8TG0DyUC/+xYZCawzkiqUObvqY+PYcZC/0xftMBtZD7KRwB7AqIjsSt9OowKNurwhiAJ5\\nigkPpSYZ1bXtKuUjtut1qTdiecO2gl/Sd/sKL2oPd/kAcIHFsUHZqjbOKMuvkmJ4JYwR+bZUJj09\\nRtvU6lfanNCp5UcNRtIZPhMvf6OJg6ExN/NDvSz7AuDuLXTriyJhMBgMhsN4C7GFlM3ZGCScRmSq\\n9X5Ahtc33W1r2bVxVe3Fa8tGVW5hap4N9bjFnTGPOIrkM4MThEnWNUn4MCc0R7fNPk4aWD6OxQzG\\nmT66bmFLR56SvRvFabY4/t+2exvsTeVzUFAkqjin+X7kHC6FXrUf30fRtTHjRCscgbBKAolgExph\\nwio1Q1Ra2PRhoj8tLxn0xow6ygG5/A0pD2TJxoK0AgCeVZem51Pby6erOoz0ArGWHY8+uJwtVmTW\\nQewMSBlzODMHP+gc2aNPNFqeCY04Hi2zfa7e0U8HekQyJt4txDrGHdEWziGyknBv5PuAaZ91mRKE\\n2Q+SaRf1AgPhx2qMlFBf8mrAyaPSVoXUSgXyd+rwzrD/4mbpNFVf9Kbwr17X9k+srcW3KuYadTDs\\nf31UsTbeqI2PyNCSjTNJG359G47CSLonQJ8u8hu69FI926ZW/+l+fAjGXP3Qzs1gsXtnX6MMBoPB\\n8FVQfWBUnkCDNm6deGoaUyLPVqRmybOmzJ6NnrF1UnaLrmuSPbsjwkLPxkmJJ772nNAd8VbJrGnl\\ni9lQh4fv+/2b+E6dMDlT2J7AKlE3TS4yvWpE3ZSivWw6QweqL4Tf8WYp9kKxa3cQdHdgyHSnm83q\\nUJlkGsKUIIKUWYZ1OY++OsemiBwg8UtBFhD8SFl17ro3keUvQ4WckQeJFMx2Q218PghkVSaqLllM\\nXPFvwF1tA5GGlv7E362LbSHE6vIj9wGCX5ys9KEyZQeG/l+kmURWhnJs63KMkJXUoXgceEBCX9Pz\\ngxyEJOtD/5MuRnxUaRyRH8qF1SeQazydQhDE+qRaqCW+uEYdqxf7EEpKMVQgakMFoidMp+xnr67Q\\nXhUtDwLZ7RJ0FbRdI+gPXTxQAp23oxo03yUM4zCS7g5IV4pbqF9p841+vBR9F2sjtw/o3ArY4HJV\\nhcFgMBgMd2GEsDlGCs2STk3TSgRdQ/B0dtsd37mrtp8cv1Bf43TXcGMKB4W2VRSqBVujWvjxWOqf\\nqxYtozmJMKFcK3tQs29RveMFQ8rzcXviO3WrRN2+ISU1J/0F+JExbqk/DbUM+cOxGDF/Vr9PWwrK\\nZopVoUZkovkMPrXRmtWhpF0YEzhO1IFArqG8sVCRv7tGl2y8CKFMRgEAIbocxPaZFPK+k1UHmRQj\\nzoaHmw9+0PbYEIoCIaAcu3Y8JdUAn3v8AgvxAMdinhW6tBefe7Q8jsm4Bfx8zO773Degxynuk6VF\\n03FCofJBrwMW2xyupJuHD0eg8rE6R4VkmQYLVPsGXdusS3/qt0DXqb+UV+uTiYpEq96RP1B44aTN\\notFkfdgTdeOCqlfFcawftr0HDzn3hedYqhcuQ+Pm7oWRdAbDBsZulWS4NtzqYxFHQ4svMV8eHYPB\\nYDA8iP6LRmXq5Y1vKLPkVmhzlAScJejmDQ/rGKvvHOuloVvWvDymQY01R4/rhA/HojeVl/8jfdtQ\\nvuwTa6h17EQ9E04uf4Nvzky/7aCyKX8VXxzW+zqRtQjLRlT1vsJfpL56v1Z69uYJ+k2FDw4R7hmG\\nNI/GOB4aMzWv4TQlMzGQqKIWJ1rejmZme5Kcg3pGHbhE1OWqOLFf/owmk1v4GUJJQA8AecnHUI7I\\nukSmRd8AxMy6REh5ZDOQE1d7n7mMSDy64HfsT1x2c4F4TP3B38RLvl2VicQLJ4kP8QCPdIf+pe/Y\\nxXi4GGWg36eLjibmA6JzkHbISRkNe6E9hSs2aEGvPm2JF4Tr1Ge5noyrEIm5vqdApKeE+opMwaPJ\\njFpBrI3Uy6pQQVUrKmj3HW8cGwtL9RXBtDaA1uDcMA0j6QyGSczfq34IORex2V17HhgMBoNhCoPE\\nkNRmomLIhi82eL0SubUysTTYxndkn86gG+lGy8aK/tUMtLLZEENROuJKv3aIk0KP5wV9LUt9k5xw\\nC+bH1DYK+5rwpOaSEwfJuqRrQvFuRt2gGVHVUaJO0dfY3ok7I06sGFEkQk+9RB1+OTtN0J3+zuyj\\nBJ1q19QCPlOsBunROW1YZNVkqs2Bu4gtRrIV36YDSh6RBDVMmiEdckZdsIp1JKKs/p26SLQBQF7+\\nESBke0G+ttGyjlzHpQY7jh7cZIBE2Ct070A3aEZQJbIQpOw6AGSIWOHLI1/cmA9mY7yj2/HrgIKO\\nEO+kI2ylJ5DDXfcQv09Hwsb24/M58XU8FGyfh0XgkMrKpkw0LLYcl3FiaaVqRKYiJ3dgTsZV4oDl\\nZO5NNNLU1Y09rWm7XrHUiVkXjec2uXRFmfBtRl+Lu+EkjKQzGAYwdm+SBnA/5K7Gu77Y7R8SLYPB\\nYDAYjuCxyc5Zgm7e8Lz+A/OyBfEzzd/QSaV1B/TmzUUSBXjhiJ7FbC1FwqKhdln51jFzcUIecQAA\\nIABJREFU3aJbkSZCb/biOFF3EhMH7RX+DuNT/DTcgW8h6MYgk2718jmRi/RpiBXzJpkFqhF1VCcl\\n6nLDTEPRz6g5kiWXHvHBbt6PbqDv1SUdiDwTMt1Idl5y2THCD0h2HTAbOLuOklg0wy6TWoFOI30T\\ndFRIQqzj0ukTaZHu5D6UI0LFJ+ICDUyDDewn/h4eYHngT2Hc2RK9TDVMCNXJJ7qxLFcp6PNJTK7j\\nwJRcMzyZUGuLdeLC5NquMXJRcEmq2eXkMMgp19A1MrzxSMhGDffASDqDISAOitYJOVz3Q/GDu24w\\nGAyGd6NP8OxNI/liY1D/LAHVFBQqlfSf/s6dr1UO+t9zRDVDL2Atw476opWBNmG+p5YWTrmHGy1+\\nl89V/FlAs1+TypfIugq5Omm6r3pYabzK7s9SWyUoh9u9wNcdI08QuKuk4tNk82M4zTQ9ZFZN/9sI\\nukbFlk8FyXbt02K8sCSQ78uljLfMOV1EVIWoS/4GviERaeHmz79Td6kvibS4jwks/H0w/L068iBO\\nDFvuzHUPIExaliEEFp/dF9qE8hw/fnehGXY5RvSZnO9LIQvICa7HOmQlPhWvzEhIx/Jy35Msu9B9\\nuMhNh/aFUKCHEOIGSVh4mGQIxFCDCCMlfTaO7Mrirrc5UilTUhX/ih53HjjxGhqCmxjHDZN3Waof\\n8lESr2O53bDxnKY1+Dytaph9DzNswUg6gwFh/IXDCDqCzbe1Hxo1g8FgMDyB2beMD3k5UV3Sa1S3\\namweIBg3/S+IgA1i6y2ZWhp9miaDeNPTpFbF1pgePbJOffw7oXR6+UsFZ4maCfbtEn3wbeEUK3VC\\n7zSraRjC2wg6JX++NoPukGHOz00J9hpXMurSN+OA1YW9YglNB/m7cUEKk08+sFcl9+agyKxLjeGy\\nA1kPJFLLE39daFN+hy7cx13oCyHDXEEu5naBcIt1sa/SN+liTIKjUjagQ0ry5+BSZwnJ5lIHUCei\\nsVTs6WflfCAxCSuX21yEXT5gOGuPoMnPtAmxqvQY2yQQNm3ZIfUjy282ZMU2riiptxmUnSPx8ka7\\n74PFI2TkBKaGGB5KTv3Y4MfQgpF0hh+DNAiAmVsNboX3fyiUwoEHlz88ogaDwWB4DYanX9SxT0K1\\nG+7q38GY7ycJunZDjS5XiS3gFS0dWMtkphOy4+nuMgo9i4qXvu5WIbUmTY+pXu7X9e8OWadxrHZI\\n1VWibmfaZrXtNPe0eaIU9gYd2Fny8u7psPmJSHFXwY87nrzPPd+/Hm8i6BSM8jOldea4SB3xb9MB\\nADgHzntBVya9AKDMqCsIrFCPlnfExNdF1F0F9Ft1jn1LDd1jPPpeHdbFM+uSYxC+ewdAl4tMxmIr\\nZITfKRC1mOxn/zIFicRdWZdjnHpf3Pb5TztyfSTnHI1x6JfzSFfqd7YDjh0vCEQf5uMiwSVwejjr\\nkeyzrjfBGozxboOk14B8IT7bgaB/SLpk5Cp7QwW0tqG75ctg1Ku+V1vO+DPRV5+uI1Iqt0lkNi38\\nnGW8vwNG0hm+HtJAqy8Zt7WmOr4ESuGw32QYDAaDoYrH5tHqRNGOClX5GUUfQ3LVNej4vh6bWVSH\\nSVPEgSf/apBAx4itKcULdJ0QUM0+FTqmBqjxCO1nC06bHlM52O7+JQ/zRPGMkglhNs/Lig7DLcf0\\nXkz4yUKv/R53imxRVVhhah4ZrigZVcmie2i8dqtZB5BS0aA8Fab2pfOoKHOJ9Ov5RcXKb9XRbDiW\\n8RY2UsYbGxe50Bh/bw4AQpYeJf4y4YXIP6zPhed+1FcQWfLynUFTyoyLXXGpT5BJS4Bmll3UlXx1\\naEwSY5HIDJ/5xtw47LMDg1g5DzmOPNsOUJ9S4NKDsIU5wmbont4hecY4oBkSrC8vVTf7Mqi/z331\\nbXSiM+RXs9UsYTgLD0KWXFuePvPvH0n9ZBhJZ/g6SLeOsqwy0u5q+qGI4doMiZIag8FgMBhUMUwG\\nteqqRFFb+z4RdW6ibWeZyGW/FchFNd2wR0QUOqYUZxIoN1kjt6Q5wRUU7i8GSpOAnDQtqiQ6FuYi\\nNI7RoulC5Uo/hpfwFI7B9pj+Bj9XUfRvqMOT92OFIH6Knx+L9uPkTpNKutVYvpnikyanjKr4N0qs\\noWUcCXfjI2WGyC7C49ClJAGgyKgD8CRr6/qTyS64miPCy6HMrUywOTR776M/QcQ7dL+ViKZoCmfX\\nRWfx/YKvx0kiR5+gFK0nfX4e8Ft+JhYjMUjN+5BeRGKLYsO/aXfJ+Nw35Fnrm3QFJ0f0leMo+dHl\\nxM0+ynb95iJTNmRrlpNrCnW7vOJng2qrtl0hFtfbrhONAwgn6PD7QPclxnAKRtIZPhb5ITx6r8It\\neCv7dUABHp6N0MRx7E9+vzMYDAZDG2noX/kdzclvrp198ah2qIs+maREAM7gBe9oPQJwqO1gvcYI\\nUdSxMChaGkcdGnwVfZqy48m/b/i+m0h0AC8c0bOfRbXTn7vfaFZ9XWtXuZces7eKT3nbWfNTs3fV\\no7l+qJn+FzywNKHWnc0AxwvqxvDeY6rVKVaHdtMmInv4mFYqI9oRUZdE2HfqAAAl9TEHAPOEgeoT\\nMusAgHyzDnF+iDb0hKTyqHH8nhomqUh2naTTpZqCqAJwwW+f9F0+hrrUHTlTzfvSrsNEGsq0S2Sa\\nc7QAxZBk7iFSo0VmUnYuNcj99I6IAGmbfeX31j7vtHo3dtKfpbaz7RzdnbPZKWpYm2w/TrCVkivk\\n3EA7dZCzuO3Blz1G3w4j6W7ApwzTPxXj8W3dguwoFcAjBYXwWIQNBoPBMIQDLwM7pI6i9XtUePKn\\nr3KGEJ3RXVWxrrt3HI/pdu1R5CgKHdxWVzH/vs2gJ8yOZ83UCMiF/lz/xmnFdbLuyPER7IzpmeyP\\nYHiXbEvD+AlFd2fUkXZDSvL5ctcyjSJ5O3id3vm9v7V2d7ZaMLKt4tyz9+RQoqpbNSabBJ2g4lNj\\nsgKJynOI3CpJORfK6hl1AB4tT5ntJIILkXViVl2oxPd9KbMu2Q4T9I5lyHms18X7LaBKx+7dHu3U\\niaqr3Ak31NhSIgxYnrrH45dSmhKZJFeRSea7NCcjXfA7E4+5YebucgRoFh6g5T5zR8mYy6MnGCIk\\nV7mbLhk1pGvhjj7ItvU183Ni2ZHFblQaDetaJeX6AkWNKLowbk7N6NPcuLh3wEg6w4eCPOlY2e5r\\n7Q8CHj0qheuASoPBYDD8JEizHw/iiCszpFG1ft2zHdJymPxbUHokHgrk3wmSS+SzJpkm+p2IiQwu\\nZuctBFeaNNvoy4LZptpbyTrBzk5f5kmwdQOrfhY+Diq5hSjawid8m27Nt1t6pDAOSKTJw37M4EVD\\nnymc8rup92Cw+GHPCW25JpFlvt6SzE6FBuSsRKRabfnLpAeRdQVxBA7xYZ5+fwrrZoSdy4WUgCIX\\nuSP3Zom0I5BIuySb7RPdyF9/ORydA44cP9Qnj7WW5Bwn764y1G9EtKWfWKCDR/QEppCW4a5n8k/i\\nKglP6XPZPhQGpkLDdV5oh/jb0NFSvq2uoWBKt0t3ivVQbnbGJmxfCyPpHgGnMaTXdF6/0qZW/w1+\\n8DfyWObA7jQTOHBztvu9wWAwGFaQnvbViZf6bNnOXE2P3Gm3XdA966yo4hwh1fK5398zupf0Kulu\\nNnoNKeTJvzvEwHv6stAPNuOhQeQUOu4a6Cr1hbQb9P395BLAcESEa3WLSBxWonH2ncaaj5o9qz7V\\ndx7qSQWfu1hS8g5YPDK0fKjp2QjR1dSB+H06pLvN7QUtiPghNWxdzNQUbRREIBkLIf0FARX890wa\\nEVaZ3PKZXMP6HaK/sH5E3OXkvawjl0WCyxPdkPwI+n3yIvMvPmkNZdmxdN9CpKIXCE+H2TWA/C06\\nrJ/wPbmfMUMRGUi6Y3OiPzuN9FMVlYJNOPJHRZeKmIJD/BjoKCxmpNdMuMbeUBN9hO8zvn648oNg\\nJJ3hAyHdQeyu0kSNb1VWb0fBYDAYDG/DDgkz1GSR41MnjtabqOA4QVet3yQVdwJWIetQ0a7KSaWZ\\n4MrThV1GhuI1xCM9trtZghr9IHomBr5L/cCGN88v8TgMEHVXm8HzZ+O8WT1Gq2TifUTdmoE736k+\\ng5B9CDc/TKvm1Ai6DWWL45wdnCJvV1WOZtOVpFm+niOJBQDgPFu+mpBVuQUn6zDRFLPOONFUEFmc\\nk/GYyAqCxQMm1ZBvtBGhJtkkTPwz0qq86dFnpTQ8qS36jWOOvoCXGkb1Pkunb9NJbnmWcQcAaJnR\\n3FEflEshcMQ5VC0dU7IhYfE+3W02p3f8Eqzo3X7cHHheDX7bb97ygq/Kx6sFj7fIfeeYScMAjKS7\\nBT1SabR+pc03+mEYRhwc8rc9pXDicYcdIYPBYDDsgLy8js7OzEzYVPVuYsbfkxgwd4rwukWvqGI9\\nxiME3vDYRmAZdsdGInGx4NS0H6yBFsm1S3DsZtZp9EPUP6lUI0NQBYM6HxnjDxudJDwf6MhldsDw\\nQ8f3aYKueh9WeV5vKqk0v/XJnn9vsaFCQcnNeMOQahRSZBPBVMuoQ1JF26AQ0YCpPHJlRIMDEDPr\\n0g7JfUvlRXZdYJloOebrYgacJ+W5CNlBtxWaCedC2xwXyuc5QqQhrjDZSfesVC4RlQ7ZRrScaAfA\\nISfzd/vovZFn3TEnSGcz55h9zbdkLwVXHV5Lr5N2Tz83NvSjQyLf9V7o+3CTed0qt82X3nu/FUbS\\nGQzfjIMvpAc4P4PBYDAY1MmYJ/T2ZIeIqRl/N/VWdQ/oLQ3o6D3l74xeaeKtCdbgyLfrcMGQwtrv\\nzgeNumrRFIr2Cwq3MutO9UOw0dcRJzknSSZkY4V0LNoMvScMEk2Cf7PxJSqGOoi/yjjHOK76d+yd\\nRyl+821emkm3+Wjefra3H7XqaOrdJug2lNwch6rup8+HACmbDuIulEVctCDQwCUiiXiYOTNEtmTS\\nSvpeHdbCxwnpiYMGQGV23bWXyzG7Fu4vEqlEvkGH6sUbemyT2DIUE/x8L0km52NfqK5MVOIyn8zE\\nDmFtibDznrUAIfstRM/lfhXjgNBHz23h+3kiDB0j7aAID8bqmesae71iHV/OPVdkP+Qen/MiUa41\\nF47Ya2H/Lrc7QjZowEg6g+HbcOB+isc9drs2GAwGw5vw1A/82kTQKa/k34U+iek4zBB/M3oV0NM7\\nPaGtMPHeVLkwOJsmiAQbu/0QySJmo69j4VgoD2ILlZM2NMi6bQz6vELmHCW1FPDVRNibA2+4Ge8b\\nN9TwDoLOQTMDTghnLcIpVw4RPwWfF4ikvERj6cpVjsi6OKYo/AjEAfM/tUQblNbLQqW9qzCThsxm\\nWjKSkmyEqyPklUfEIaCY5CU40x4eL6ZyTpbRjLvLF2QPDZYiT5ZJtEy0XGK5D16wF8uLhTgDmUqT\\n73wmN1lQsj3uEDGV+0Cgf3MfvUI0n2Nrl3VJuA7bixfpUhfqjWo1unfcOwg6SaMNJJ6AkXQGw6eB\\nM2Y33T/tFm0wGAyGV+HwnNOQ+hkfBsgpX2wMGNzUK0wLpYpe99p61w9Q+sGxWKcfg6rejtzKd9I0\\nsuuwyoKsmyBdribzfcATbE+SddPHotKHCZNVlXuE4/Xv6vfVJs1tk4vqDvX0ab6EvLmvmnizbz18\\nBp+khioxtXnsTmTRnTw0Jwi6HdSJN0hPTyDPIEyCYXlEv6EmRBQRODG7qyDPQiEmz6RlGqNN+sMa\\nz8ip3AcyhkAEGn1GYCeQbPIJiuUhswgnoWq/+PE0omK2HXGWknuCBZxrnf51IJCbIeZpTdHy8vMh\\nUM7T2rjLxwBFOa/jRtCYe+zS1yNuRm81c5djW2vt+tpFTSclcsdvrqs+rpN36+NALUjXmuE+GEln\\nMHwa+NP74MvXJ77XGQwGg8GwhYF3kjYZdOalpqX3FPE3hFNEpVi3GdtNgq7W7u5lC6vq8JzsJNG1\\nmpV2jKybVLibWbc7J75H1i2cf5sOrxB1K+fKKolI2hzy7Ruxclp8W5bkuWfwzTo3DJ56Vp7CKYJu\\nPQ6jFIJA1FWkLirIF0JFm7CmpqjLAUvyQ5RhfCYLDkTSLpJ12afQhrF3mPZinFSZaec88OceJqHw\\nvWU22w7rcp60RD7nLD/enyILjsUKN0j3NKaM8oyc/AyxwjHnRBCpy1+I5M56x8oXUCMr1/VQDfPP\\nljamycGBS7L2LNMk23ZuTQKP37DUh9at2ui498BIOoPhjcB370NvQPwB8dYXLYPBYDAYxKmPUz/D\\n3MaaY0td2Z6HqxN0O8TfCb07S2f6hlBLb8IgQRCP/VRW1wmybkHhW7LSVv2/mlwahpsoH4OoY5b8\\nyqKTRNMmWapBjo4rvwP3xe1YtzSVD+l54YNUxaUNJS8LxywyDfAZHTlB0HkNJdAg3QghJhB1lZn4\\nlFWXma7UBnAThwgrIbOOPicppVYSUPg+x0kmj/kw5Iyj90Y8iCruK47cSBNxxXwA1K9SQ/mlObxV\\nHoPiS31i6xS5xCciwpA1oLGm9rFCOtbxADymPExVsjHXD932KyFlqoawSp6NX1GYcBXaTtyiSGym\\nxoOSR2ttOURdnreLHayd83M2tdG09xmPjq+FkXQGw5PADyfpPn7gDTCaMXLOYDAYDJ+DASJlunld\\nwd77yabeSV+HcEJn1dRNBN2wP5M6peLhifM4wTORmXIXWTeh9MmstF2ycYGqU/W/2v4UWafs+4iC\\nuzKvCtJ2YBZz+tgvYta34ZjdfCxXrRyN8JMEnULzbajY3lCyOdzaNKVA0D1x8ASirixGdaGiWo8L\\nXBAVqMdEPkGoE9hBxCGJ37ALLBwfKxGCBTNVnlrK9dgXRoa5+Eyj7uGIOVbpuS+MScxkWEkeUkIM\\nO5dHaQ4Lpn6U5BL/Th299+WYZB5SoBQTT1goACEYMjbGiJzIHCbzhHOzb0tGSQRD0aduO26n0VBq\\nN3pnqJKLLUl8neHCg7ejFdVFmwHC1Di7e2EkncFwEhIJh+vwX759wA3JpMFgMBgMhoBTxF/xC8uK\\nqgX7tSY7L1WqZNoJgu4BnU4awzXtk+mnkQZ0l777T0N0d2J2R+ubdYPmeuqmFeJskp3MwF2yEQCd\\nO5N8yPAxYM7O0i4iMTpAOg35xuycJ+oOOrNtcuBc1CZdDXOoBPzHTFLe2NHXE3QDZG1YmVJskCbs\\nZd4s/evx4KLkmzLZF1sxdgLzULldfohhyXqWnROIsEwKluMfSsLlZ7Sv3LBkhilHDMcg9LNJ4Ixl\\n0tFe4LrsAA4n1RRaIkeqxE8iaDwZvGXiTmrEwtG40TfHcw3Zos2ALGnHAinL0wnPmF2ociXWh+rd\\nmEkqeuLyUrF9vc02PZ6OnXunbsHN98xGnA33w0g6g0Eb+A7LB/o3vmVxU/aCZzAYDIbPxcBsiYB2\\ni/foXMWQpUniT51MHBUfJdM2Da9/f26N7Mo2J/N7kEEtwmidrFv3XdidRtF+WOH+tahOktzEumyb\\n0fJTm3QaJBBv/8baR7Np884f7e72YzQT9PfbHsfrSaqkUx+f0XcH7ANwslSDqCMllXMr3qvS9+qA\\nyhVFkZ0I7FJ1ch8Td8EBRu0hcgmE8R+is1zysDDk0EbyBg0We4RFkXnnBC9YI48cJ5QYJyYd6UWW\\n5P6JxLyjxBwbQKVhFPHNibpkVq5QLDar3mtdIdrTVsp2zu2hroXKCg0qHDupdcW+UCESpY1+VPhR\\nWbZFDFfO+5G7TvOxwhSdeATNvLsZOfc8jKQzGLTwMCv2se+FBoPBYDBEVN8ONl4bbtI5tXziT9DJ\\nJ0taql6us6ZS+rZIqzUWf+K7b+tk3Sf7Tr3/NN8vcR/Eu+wU0TtLpIh+ahBi2n4NtZnPWpvFMaLq\\nrX4p4yxRs6io/ohQxStJqp/U92HxKzOoJ15+o44aoUSWbN+hdoScQJP3GGQJyrjF2ApK7DHpgtCS\\nctIAyUnZdkSDcLMuNSYSIv7jeH0kLesQl/7ksSi9S3spQ8zXbZG+enyOMRKKk2VIWdF7RgJ1SbIm\\nMdaxFWWEMu57bdw8RXBxmeLjfHl3dLzteEHFn8qnDsVmEnk4NJQXCOYk29DFdXbJuqbQPGr3m0Jm\\nVJfhOIykMxgkSE8PXPbgm0904xNevgwGg8Fg+CbcNmH1Vp0vd3aaoFOwV0wiDBMv1787365bHQtq\\nkEZPfrNOnLi5yfcd3OY7MrTtfleBm4/pm/DthNiQg/M9eHWfj+QifAoW+/6TCLrbIB8LByAugYkl\\nEF1Xnd0X5/QpW9gmWTBxh8jFIp6c4OPkHibHGGNRy3By9J+S2OG+czIpMCYlCVaSd9Ql2sKnvjFH\\nOeniGj+S8dSmTBjlDpBbMmd4mPkWSeWrtphMRwexI+grYskKqwQXcq5HlDWJxCgq+DZCkNHrBPXY\\nCeeanyQiBSMO6ud9UweWcW0do9Ai6Ly0MzJf7itlvTa1+pU2J3Rq+VGBkXSGn4naRdTar5Upgd/H\\nWqbe/WJkMBgMBsPzeMW0TeMFVJIb1dkSbxFV2zpH6zr6Wjp3s93k5ps6B80V8y3NAVuc2Mmjv6ks\\nLzdhqqMqtZ9QiCc7hmwz3ZP8VEsVLRwKYZzAnCdI1WPO9PfbT/oOgzxNS76pIJ/HM5lrsz6RNoPx\\nmiU1d85J6uCWyKSgQQ31x8RpM9uGtjMIb8CZft8A58AxIqYhmkCz6mhrTIq0xmzk/iUQUHGnJKtc\\nKQuBjKowGc0nOnlw+c7tKStzrhYzWhrv677DOsmESylRJXhY8BC9SD0rwy4Cy7bIMCzritIySKJJ\\nRlRKhBnf7RFdvG/1iLJ6NJdaJ53o+V4legZAzjdhDrenLtWTAAjXSHF8+tebGDMUWF8R6pF16dpZ\\nvMn12g69o82+/HlhWyrrtVmxc6dOLT8qMJLO8HPxwhcfx/4aDAaDwWC4CUqkTUXtQjv9GcMtnWLT\\nNYdak1J36jt1rOfJlwmiAzdx1d1pzBEzVPASnbDOyBoYNVVRRdpPBGKJrFOM+VGwwMz6unI+3EHU\\nfT7mr5fzcZq38O5j916i6pW4iZzUNrKcQXfT6VFmw8ipFAVhV/UPE1BhzNBhC0rCwdGHpW/J1hQ5\\nRu4IR8KJm2icLdw9GsttykSIJB3iwhq4YoPu4DFkQd0wMwUJ1vxwGl8kkyspGb7WvdV7oY6MpeQY\\nVlae7I/DvNDfkbb+Kmm1FeuK9zBP64W2AJRsFM+VWlsWz1ZbX+zk+sZVzZQs3HxkA1vocm/2CH0U\\nRtLdBX5Bz+5/kw7pTrij42Hw+yZ29UVuGgwGg8HwdVgnwHSxSn4dIwRHCbUBYnL6ha0yocJMjk8M\\ndvS1Gm9/1y5gdjy3TtZRn4e/9RV3K/MCo9j3+xoV73z3bYesW/E7i89nMi6YaqlaivcQ/6tF1GlB\\ny6eBxu98H3ufRwY4Mhl6B7ZcvomgOzbWuUtPIIRGM+pYs2yvw6q59I8gJbBmlLyg+jgZUap2dVuA\\nKJYm61FqLUCUUtKFqhb0+NJNTuBIjjnhWm4ds0TMMTan23WxII6bG8/oSsg8FIcx64PyfLrM4JhS\\ngVq7KokJ5WnmUGGXxCrq6lQ6OUTVsU/ZYTFGnTFk+e2+fEFI87hSf0mMpLF2JUaU5GNsLK8XrghP\\nNsbQem+rqWnxh+1rzqANI+nugKtsr+ybjscx0o1amcFgMBgMhjdAfntfJZe2PFAkl7T11V70qq/d\\nIwRdtans4zrhp6Sv9hI/gOJlfVLRFGGHDW4SSIWbw37HaYY4aTTFkm2TdVW/h5isSb9fQdYNLjq6\\nSYolVwb8Go7hbUTdIJ2pGaMZ5nTCxvK75UDj2SzIN2Lr22Q3zD2KJjbtavdZOwzafX78GAf2Jz7L\\nZ1USssQXX0ITNbpig0l7WaBF4InWfEXD0L2jprRwpKiqxlBMG8NFhG6RBQtypnTSVyQKDnOY9Ajx\\nEBm1dpET3BBJKfzsZ3ZKQkqww5i1KhmFC338IwcihZs8J1znGAtGq7JomVUpRpLeWCeQf8k0k8X+\\nOFSDr7VqjOIf4V2jIDNFdpM7yg7midtf9b3ovV/7/HYYSWf4kZDucbHsxe8eBoPBYDAYTmDj5edb\\nX2Ome9Vp0CbHNmYLJwi6ITVcTiBjVseKBbkAMKxsmkAqf7B7M4E0OPHPjSmQXkzV9EGbXnYUQP/8\\nmFI2d058w/vO6T68ghTTgpaZTz9pWhDGAN/5ZK/jFoLuRVj3z10z+35r6FiQQK1v2DVUiIRF2VS6\\neBm5JukZcKO4j6EHr8AtCe1rijFFIiEfg4ZzFfOc6msZKpmZGqlFSiRdhT+VJT5jva8dY4fDTPyq\\ncYPS9whjRlhBDQlkFLiohwaiRjJ6Hm2JEOQxbZJg9EyjZCQjXZFgjVBPHNmAbCIbUyPaN+lyc8zH\\nqjwhQCW7C2+ZEw0kkrmq4u039S+BkXSGj0KLXIvbM7pGygwGg8FgMLwM2r8m3CCEWnIrbq5kvbUm\\nU1r9XdOn7V89Spr+tUnCjkFc5+ov+yMo2rZne4T2g2SdoPcJsu6pbMCi7VKcJ2gU4fxQi3NX0T0Z\\ndTM+DZ2nWsThkD9DYXzXy6CWP2/r1xvxkyYfn+zrom2/cxIf6a9LD5bi+bJoUsr2wcoYDdHybO7H\\nHZXxkSMbSz1K+kulI5AoNVwbmZBpjxoCErFYpeGmjTXHfwKk1VGLK4E/Qwlp55GIy+MTRoYVfkWd\\nrIKG2ye+WtQRtCd7iBCsvpcwxlQoyqIsCGkMxK/FgogMJJRECHJZXzl9HaIFPY8jOm9ZvLGegqgT\\n5AoPnScxkCCV1+LtBSlfFv2ox+MbYCTdDbAxsS6MXDMYDAaDwTCELUZup8Vgm1vefGQjagTiqL5R\\ng6MvhZOEX1fNpH/8V9WrY9FZ8iOL7mV7od0prJFIUXSQYBQM7fpcTPQMKZqm6tp2V9tqkiw3EnW3\\n+jMgPXQsbyS0TmbTHenGDyL7Hnw0DzbV9VC7v0pDL20FBzTFWX06cGnOua+awAX4Sb34AAAgAElE\\nQVQFKbhhyXUua38JrYz+xgnDrKZNzUFZ22O6KrbkVsIduR+ceq1AztCCuvKCdHPxmSF44HE8cPuS\\n6OXH5CrzhZzkHl2BNJBwVWILEYTslJWIPZJVF865gqPE3zSsEGic2CyvRZfsZQIt6+XkmYPyOBJZ\\nZOAqoxZd9OmGh8owQVeUCwRdp73hHIykMxgMBoPBYDB8Fm564TkF6ZeVV5EuCba87ONMbKuTHW1z\\nd2XQrerrgs0OSJMFS6omFOH8KRiZ8D9F1k0qmqK+lHwWycVBRa8gRAcVxamWUX9PE3WzRNQyDzTU\\ncE7zWdLwSaXfisWBwUOTp3vQ6+ubCbqtDLqK3TOHW2AqwBXkh4QlfxzfnYxRY6AXI55qC5Kw48y0\\nH5IdiUJQtC8Rk0MHgvvVIRIXByrkrE9jifAs9VS8IN08aXaVFVxyQVllOZHYCrKuJKyk5RgpWYXL\\naOfKu5jvPPNy/32NXMbnNgpXSSLi2DlaRi5b312WE5N1OcaMzgwkq9g2yjmqv0SDNZwAbS4QdP3b\\nluEgjKQzGAwGg8FgMDyKNskjz7KsEEOrPtTbLJJgc0ZuI8Habmz0ddRgp6/1ZqcIv3uJpNh2nkia\\njJrg7+o025q/l/AEVde2u9NuUNFUFqCgV41r6cZ44FzY9G1O3kGXqNsIzhxpOH/O/STSsHucFMlC\\nfWLovdOJr+rr4R84aRJ0FzYc9s3dw3D0L/mlBd3sXVZH/ObMDq8CeRnPWXSbt8iYEbHC0Oa10dUy\\n6RdHobhgvBrtKFsqjb08I5SSHBo3FM9AkbRC9DgnBQvCimW8JTcz08P148NFySpHCFTPvv1G9VPd\\nSc6Fs5eQYeiu6eS2BTHtJbLx2uOZcRKpLZ9EDhofvit1VbF+XzSC7v0wks5gMBgMBoPBYOjhiTeW\\nm0mwKcKv8yLXJsEmjOPqJqGm5Zuv7s1+B26VrOMTB2VBrR2e0BhinvImElfzd4L8usQ7RA4xsh/f\\nFXJxd8nOVWIRqZlo0yBdtIhaDdKQOXCajLpf4eOGDDU8NRP5kvHEcTc2DGyRkY8SdDX0HrKyl53f\\nHRwCJYRWMHd32+wIZ3A2ceyuPDDoIKRalHS0aRFbFwg6NPAUiTFP2yXdnpM1joYUEV4yseWFssQI\\nyu0klhGDM4KA+k06R9umJTnJONERURpTL8dG4NcjYUfJRo8IPdTHaL8g8xzeASkTkSuTKbk+UTdy\\nVXGCTmoz/a5p2IKRdAaDwWAwGAyGR9F61XDyK4weRlVP/HBxxdvXZAJMuLHWT6HlKKk2badRPkr4\\nsbbDkzeMdICZtkzNStvpzC9mc3WSSpx30Z7xYg7ukF+uWTDRdlBYNbbd2dtzBNAn00tDS3AudnDu\\nGA36ooEBX+78Xt96HkBNX8zbmNSq7chh5PyWD3D6Ja5+QKQCehcXvgARa6INpeM255rijYXgJSdh\\ngbEfz5CKOE52ZfVFTGXyhz6HUFYcH5OmDVfc3n1grng7TiA6JEDJLo+IrEx2IZOoDf/+nM8kmi/D\\n4tFl4LBtAHDBQb7sp0c7jpBRDpFuuYKcNeyyw8U+sZB+6Ewr9AoEXyk4D5Fs44xcrwHkY2q4D0bS\\nGQwGg8FgMBgeRWv8v0xeKZFNU78g7MjUdN31Lbqhfg7q6ro04Vfbfr2y0DdK9i0QdJLUamZd2J1C\\nMakyqGQpUw1AJbNulqybygSsxHbV19R2WNHkoqhI7w53ySe8ekRd18cNv2YIqSHSmPkyE58kvxPc\\nnu5D8id9mdN9E2EI+nN+muMETd9EXdsGFhQ80c9FI9+XQad1zbrKtjIc+zuEt0SaY6kzt6Eg2xgB\\nl7LCgjB/znHCK47zMW3lnStWWCRkV0Fm+azTO3JkndDGI82UV2K9Ex9evlLe+VUTYuDK8WYgHFE7\\nD5CyBYOIQEiG8QnWyTIM69lxudKFl5ahNlGAveh0+blsFOgRqDfkn7HjfJ3UbPPTd4ZFfApJ958B\\nwK8BgD8XAH41APyuZ90R8ZsA4C8EgH/6aUcMBoPBYDAYDCXuyFZ7+p1mhVSryWl/125WV5tUnPNg\\nn6Crt3ts6cMDhETN5qqvrlnQajtBGCC9OyTGDOlE262RGyqHb8jPwfPzZfOY30JI9fAmXz4Rr8lC\\nPwzNLLr3R0ynn6dhVy0g5oaVARQ38Z8TL37ujp3P9R82BeKp9guo+IeQT6gNH4gVv07Kahw6llmf\\np3waIbKyLQBMSmXiLupK+8kFR8m0WpuClHSJ1ypyqIPPBSmGUvf4+izO9Yi3kqzDvBol3aLj7Hi3\\nToFam2Pw4mZFApW9/578DXjTfRLfymv4DQDw2+AixJ7GLwKA/29Azv+RP2Yns8FgMBgMBkMN0wRK\\n5Vd/RM2ILvbit+XXW3UFOYvXoC5JbuKNaXiyvTKnNQtX3em1W/Nz0ky73bALUx2bVd9TcyauTO92\\nTAcUdH3b8Gc2Xk1fNq4N8+OgH0zHzvVVfR4tTJu8OfOq0Ldo4K19FHVtubocIC0XCqhMmB6edX3T\\npC7Hkm+Rr7ipY2+MHx+Xe1bJrxUsz89/z5TxMbEsf9WUduuyXHchywRauksfWX9r17wgPx3Lpiwt\\nreomsfVtWaJblq/H/apVPaZEj/ZT0QAA8Kt/2S8BqNx6ns6k+6UA8LsB4L8EgL8MAP4dAPjrAOBP\\nBIB/HwB+e5D7vwDgFwPAPwUAfwEA/H4A+DcA4H8HgL8BAP4UAPhlAPDPAcCfBAB/CwD8UQD4jUHm\\nzwOA3wEAfxYA/N8A8HcDwP8KAH8QAH45APy/APCnA8AfCHp+qSD/PwDAvw4A/w8A/CUA8HsB4B9A\\nffktoQ+/dTMmBoPBYDAYDIYaOu8L9jrBsBgvzRcz3aXIDutCL7j1XzW3zAxmxTCdEyYKNat+XqId\\nYUHn6txZ0W5Q0U5WHYyZGNbZF13PqjufUTeu48b50aYfj8L8uBdf9vB+RXde4cQY7ljafBbTl92B\\n6/TpS7/+45r7mMfbY6D446RlpPu+L8Z5V4YbynjzOMPNU1mA4rtxKTPNxXFgloUkD9doBpNKLutO\\nS0QiWfDZTyyb/Eg+Xhs5Q84XGXBlNl32H8DXs9nCBv5OKU9U45lvzrN8PJQ+VMoCinCpG5BsVumG\\n/BDvZbXyBkTSrSJoBN378DRJBwDw5wPA3wYAfwYA/E0A8JcDwC8AwH8IAH8lAPweyOfOPwwXMRYz\\n6X4LAPxFcJFmfzIA/E8A8A8CwK8EgH8eAH4zAPwLAPCvAsDfAwD/IwD8KgD4lwHgrwKA/xQA/loA\\n+A8A4G8GgH8Pruy4mjwAwJ8NAH8FTJz7BoPBYDAYDIY62u8gC28oh6FLYM2VtwRqfs3q0vxGnigv\\nvBgWbSZItVO68LbDBV1eKwt2J3YUyLqqnwOK4vTPsJ9B5w6pSNpNxXSQ/lKM6duJWtKmQ+YMxXCR\\nqJvxY1jhoh9O3LnXjzk8PQ0fcZ8f+k/1940TCmy5p9O/4xFa5tl0CDqt/lWvBKUMr3rzRcWuuauO\\nYf0Ljuj7TjW6w8FZVo8u8UysAf0uHUBegrKQ9UHWibLkGe2obOSRxG/XBdlr35E6OizkV3HrwelZ\\n+ZhsrnaEmAy9J+MPLFuOtTzta4qTIy4kElOUzWSdq8kCoKU0a8yfBMLwZRtcxHOpEvwdyBcbdVne\\n6OVP2K/BG0i6/wUA/isA+GcB4K+GK0sOAOBPhYvA+z1Ill+yHgD+EwD4w+H//wMA/qNQ998CwK8I\\nen41APy7qN2fEP7+awDwD8FF0v0WAPi74MrYq8n7UD51fv4T//hvT9u/7tf/Bvh1v/43zDQ3GAwG\\ng8Fg+Gq0B1Y6rwWv+B5djbHStDEhPKtrhQybnUW7o38r3+0rJv8BhMkE2dJjmXVigdxqggJTIS5u\\n8VMB68dh3dOtOeBu4wHtGsTUAGE4E51TBNmp78HNkYXnjslJP3Yg3m63eKiFhgeHBJqq35hhJqp/\\nmKC7DdvE08YF5MRNNXR1HiHdBpU+3fcFw9N+SrPdnFWMg21Hb8fes++7QSSMPP1WXSCXHH6eoPQ1\\nqvMqpIThJVuShbJO6Vt0LjBdPijNpJhEKrqggxJr2Kcod+nwJQ8WCMiaTiLHdF4hkHVWWTMG/M07\\nF+lMSXz5GbjW0Fe2r31frTPM47/+fb8Xfv/v+71Dsm8g6f4w2v4n4cpim8EfRdt/HO3/cbj69wtw\\nLXn5lwpt/3MA+KVwfevuFwHAfw/Xspc1eezv3wcXqefhysar4h/5R397swMGg8FgMBgMBgMAjL0N\\nTbwx1YkqPXtTZNgiuv2YbSjI9EQLwmZwwnp4aUlB58qc+I6fdy4redxPJeKzpbMvPO/nREtZXpkg\\newyLhNAcMbWhWxEfc0wMHwtd4vBhHHRAvAoXL83la1popnl3cNWdWT3v69/jx0+jb2RMkAeomZxy\\ngqwXysI/uIotWXmJuNQsEXu5mC1t6QoCELuanpHOFfpKEizLQSAAq3KJWLsIwNRFgayT5CSdPhQm\\nQhHplOQiodiW8wP3Jxc7zMhDYEtnLrN2GcJpMSI73sgwgl/5q34t/Mpf9WvT/u/8Hf9MVfYX7nBo\\nEL8bAP4OuDLfAAD+HLi+CYfxhwDgT0P7rftdrPtDAPA/w7WUZiz/i5HcvwkA/xYA/M6w/38K8r9C\\n0PsvwUXk/UoA+N8afhgMBoPBYDAYGmi/wB59dX8fRtxU6Mod0dCddK78QrXvBNMi6Lnl1Fgz8iFn\\nrfmpjLN+ThCHT0IjCLM3CEXpmRCeIujmJuMHfFByU1Szdc7pTKa/EzqOHu/usgGdY6d1y9K79ekR\\nWJoo1C93+EMuoCU33fo9+aZn55QZ8quVqQbC3grWni13nmF3XgbNJtrnjxM3DS/GG0i6eBr+xwDw\\nbwPAfwEA/w1cy0r+YibzB+H6ZtwfAIC/P5TXsjRx3d8KAH9naPffQf6mHQSbfyYA/C5UxuX/+oqN\\nWl8MBoPBYDAYDINoD6DeN/VyFMqZdAdVDNg4b0Uzk+4s1ox8yFlrfirjrJ8fEgUNN1VuEKvSM3rP\\naPbVna70ssgI9Hu7oPFDLgMtR493d9nAtx67RScP901P/UcchEU3/S3j19sw3RXf2DvkgCDyEUfg\\n7bevmUw6wytgZOqVMfebAOBv39Tz2+AiFf8xVu7/yB+zy8FgMBgMBoOhBl9s4DrPC2qiTV2t75Bp\\n6er+kkurf5VKSdds35Z9EgR0Yy4X3hFzjtWlooZ/me2auxP25pVM/Xr8B/lZtHmhnzNx7Pq1+Otr\\nVR/e4sficZk9rz/tmOxMJFXvtUsTnnpkyNGfBC27qdM/zRmpx/tXHQPtQzyvl5Pi9LLpNCduV8cw\\npZ739e/x46fQN49OZI8GqPz8xmNcch0xWVEfK6f6fLV9S66wu6lPlAsVRSwkMmpCjuxvyXn5WEly\\nWZzWeSYnvKOIcpK+BbvXZv28M+zj1/zyXwJQuTW84Zt0T+JfBIC/BgB+46aevxcAfjMA/I3bHhkM\\nBoPBYDAYDAaDwWAwGAwGg8FgMBi+Hj+dpPutSnr+lfC/wWAwGAwGg8FgMBgMBoPBYDAYDAaDwdDF\\nG75JZzAYDAaDwWD4wWgvBXN0EZz3YcRNha7cEY3lpZBEyAuudC0wAXFprVtOjTUjH3LWmp/KOOvn\\ngPY3rG+kEYTZG4Si9EwIde+VWG9tpyu9LDICUc3WOaezLN07oePo8e4uG9A5dkeXKl2C3lKQmijU\\nL3f4Qy6gJTfd+j35pmfnlJnUlaVF3RWO9Nqz5c4z7M7LoNlE+/xZXCbb8ByMpDMYDAaDwWAwPIr2\\nO8n7pl6OYsRNha7cEY3lb+lM2dgVGJTZxpqRDzlrzU9lnPXzQ6Kg4abKDWJVekbvGc38e0ET0ssi\\nI9Dvrd53zd4HHUePd3fZwLceO51vCWpDT/1HHIRFN/0t49fbMN0V39g75IAg8hFH4O23L+nbeoZX\\nw0g6g8FgMBgMBoPB8HJoZRPYb0kNhi7sMtmGhXASFjDDBn7Ymgt6+HEdPoyb4vndazPcj9dE5jWO\\nGJ6CkXQGg8FgMBgMBoPBYDAYDAaDwWAwGAwGw80wks5gMBgMBoPBYDC8HFpLftmCLwZDF3aZbMNC\\nOAkLmGEDP2xhdD38uA4fxhu/SbfZ6ifgNZF5jSOGp2AkncFgMBgMBoPhUbRX9/hhixiNuKnQlTui\\nccfSkl0LN8XzlJEPOWvNT2Wc9fNDoqDhpsoNYlV6Ru8Zza6605VeFhmBfm8XNH7IZaC31PNhLBv4\\n1mO36OThvump/4iDsOim+66l0ae74hp7hxwQRD7iCLz99uXETcOLYSSdwWAwGAwGg+FRtH84+MN+\\nHz3ipkJX7oiGbtaa/HrZtcAExImXW06N7/7ds/mpi7N+Dmh/w2yORhBmbxCK0jMhPJXh66s7Xell\\nkRGIarbOuQXHPuVmoJZFfhjLBnSO3ft+zrUYkMMHqlC/3OEPuYCW3PTr9+Q3fpMudWW0lW/srWDt\\n2XLnGXbnZdBson3+eHHT8GIYSWcwGAwGg8FgMBgMBoPBYDAYDAaDwWAw3Awj6QwGg8FgMBgMj8KW\\nu0Sw5S4rkH8DOruCm/jraFvuchvmpy4eX+7yDT+5/vDlLuey7my5y71z7u1rju3AlrscafK+NRc+\\nZLnL5Q5/yAV093KXb/wmXerKaKuft9zlnZdBs4n2+WPLXX4cjKQzGAwGg8FgMDwKW+4SwZa7nLSx\\nKzAosw1b7vINMD/Pa1fDhy93OafXlru8XeOHXAa23KWmrTvxIctdvkDTUdy93OUbMd2Vn7fc5TLe\\nfvuy5S4/DkbSGQwGg8FgMBgMhpdDK5vAfktqMHRhl8k2LISTsIAZNvDD1lzQw4/r8GG88Zt0m61+\\nAl4Tmdc4YngKRtIZDAaDwWAwGAwGg8FgMBgMBoPBYDAYDDfDSDqDwWAwGAwGg8Hwcmgt+WULvhgM\\nXdhlsg0L4SQsYIYN/LCF0fXw4zp8GG/8Jt1mq5+A10TmNY4YnoKRdAaDwWAwGAyGR9Fe3eOHLWI0\\n4qZCV+6Ixh1LS3Yt3BTPU0Y+5Kw1P5Vx1s8PiYKGmyo3iFXpGb1nNP//7d15/DVXXdjxz80T1kBY\\nUlBeVvixCEVKLVKIEpFAkR0te4UitEgQpSjCq9ACEgqClFZflcUFJAFrCmirgAUDQkIWSALkSZ4k\\nbBbCUqWrIFsrSG7/+M597jz3mTt3ljNzz9z7eb9eeeXO7zlz5py5Z84s58z3ztYubEzdOUkT6Wvb\\nIceJHAbpQj0PrPMGdvW761jIgeuWLvtJfAkdiznbrdDorasyq1kaqAAVSSbxDeTefc0qPypjDtJJ\\nkiRpq+onDu7Z/OgmxUxQlTH2Rtq31qpvLzduYSVB5YOXUZrGbs97tpxpDVvOBrnn8DQnxU5o20Ek\\nTN1mFw71hu987cLG1J2TNFGZTa8216FgU+kMkr1FPrDOG0jz3eU3navjDhn4izou+84VnsgB1KmY\\n8+59co6/SXe0Kk3XmtcsddHt3DJmCxvzMKhdJXX7mVd+VMYcpJMkSZIkSZIkSZJG5iCdJEmSJEmS\\nJEmSNDIH6SRJkiRlLlXILwO+SBt5mPTmLmzJHaYe9iwwejp7V+GBjbQ/dzuA+viy2TPZFETb4iCd\\nJEmStqo+BH9+vzQyqCbFTFCVMfZG5e+/Jd9G3wQN0/TWbSMTabWWM7FhyzmRvZCimEk6iK6p2+Q7\\nTM6ztQsbU3dO0kT62nbIcSKHQaqCDl7dzhvY1e+uYyEHrlu67CfxJXQs5myU69fRtK7KrGZpoAJU\\nJJnEN5B79zWr/KiMOUgnSZKkraqfOLhn86ObFDNBVcbYG2nfWqu+vdy4hZUElQ9eRmkauz3v2XKm\\nNWw5G+Sew9OcFDuhbQeRMHWbXTjUG77ztQsbU3dO0kRlNr3aXIeCTaUzSPYW+cA6byDNd5ffdK6O\\nO2TgL+q47DtXeCIHUKdizrv3ySOdO1tt5mhVmq41r1nqotu5ZcwWNuZhULtK6vYzr/yojDlIJ0mS\\nJEmSJEmSJI3MQTpJkiRtleEuSwx3uUb1HNC2EdwqZ0cb7rI3y5nW1sNd5jDleuLhLtu9dWe4y35t\\nLveYY30Y7rLJKvnFXJhIuMvOFZ7IATR2uMscf5PuaFWarrV/4S7HPAxqV0ndfgx3OTkO0kmSJGmr\\nDHdZYrjLltvom6Bhmt4Md5kDyzl87slMPNxlu3wNdzl6jhM5DAx3mXJbY5pIuMsMchrU2OEuc9S6\\nKvsX7rKz3Lsvw11OjoN0kiRJkjKX6m0C55JKG3mY9OYubMkdph72LOZCOntX4YHl+Jt0PdfaB9ns\\nmWwKom1xkE6SJEmSJEmSJEkamYN0kiRJkjKXKuSXAV+kjTxMenMXtuQOUw97Fhg9nb2r8MBy/E26\\nnmvtg2z2TDYF0bY4SCdJkqStqo/usWdBjJoUM0FVxtgbY4SW3LiFkfbnUBuZSKu1nIkNW86J7IUU\\nxUzSQXRN3SbfYXKerV3YmLpzkibS17ZDjhM5DNKFeh5Y5w3s6nfXsZAD1y1d9pP4EjoWc7ZbodFb\\nV2VWszRQASqSTOIbyL37mlV+VMYcpJMkSdJW1U8c3LP50U2KmaAqY+yNtG+tVd9ebtzCSoLKBy+j\\nNI3dnvdsOdMatpwNcs/haU6KndC2g0iYus0uHOoN3/nahY2pOydpojKbXm2uQ8Gm0hkke4t8YJ03\\nkOa7y286V8cdMvAXdVz2nSs8kQOoUzHn3fvkHH+T7mhVmq41r1nqotu5ZcwWNuZhULtK6vYzr/yo\\njDlIJ0mSJEmSJEmSJI3MQTpJkiRtleEuSwx3uUb1HNC2EdwqZ0cb7rI3y5nW1sNd5jDleuLhLtu9\\ndWe4y35tLveYY30Y7rLJKvnFXJhIuMvOFZ7IATR2uMscf5PuaFWarrV/4S7HPAxqV0ndfgx3OTkO\\n0kmSJGmrDHdZYrjLltvom6Bhmt4Md5kDyzl87slMPNxlu3wNdzl6jhM5DAx3mXJbY5pIuMsMchrU\\n2OEuc9S6KvsX7rKz3Lsvw11OjoN0kiRJkjKX6m0C55JKG3mY9OYubMkdph72LOZCOntX4YHl+Jt0\\nPdfaB9nsmWwKom1xkE6SJEmSJEmSJEkamYN0kiRJkiRJkiRJ0sgcpJMkSdJW1Uf32LMgRk2KmaAq\\nY+yNtKElq39NYeMWVhJU/s7IKE1jt4MTWc60hi1ng9xz+PGSFDuhbQeRMHWbXThUGN7Z2oWNqTsn\\naaIym15trkPBptIZJAv1PLDOG0jz3eX368Udd8jAX9Rx2Xeu8EQOoE7FnHXvk0c6d7bazNGqNF1r\\nVrPURbdzy5gtbMzDoHaV1O1nVvlRGXOQTpIkSVtVf0+S36OXQTUpZoKqjLE3KgfEkm+jb4KGaXrr\\ntpGJtFrLmdiw5ZzIXkhRzCQdRNfUbfIdJuf52oWNqTsnaSJ9bTvkOJHDIFVBB69u5w3s6nfXsZAD\\n1y1d9pP4EjoWcz7K9etoWldlXrM0UAEqkkziG8i9+5pXflTGHKSTJEmSlLlUbxM4l1TayMOkN3dh\\nS+4w9bBnMRfS2bsKD2yk/bnbsRnGl82eyaYg2hYH6SRJkiRJkiRJkqSROUgnSZIkKXOpQn4Z8EXa\\nyMOkN3dhS+4w9bBngdHT2bsKDyzH36TrudY+yGbPZFMQbYuDdJIkSdqq+ugeexbEqEkxE1RljL0x\\nRmjJjVsYaX8OtZGJtFrLmdiw5ZzIXkhRzCQdRNfUbfIdJufZ2oWNqTsnaSJ9bTvkOJHDIF2o54F1\\n3sCufncdCzlw3dJlP4kvoWMxZ7sVGr11VWY1SwMVoCLJJL6B3LuvWeVHZcxBOkmSJG1V/cTBPZsf\\n3aSYCaoyxt5I+9Za9e3lxi2sJKh88DJK09jtec+WM61hy9kg9xye5qTYCW07iISp2+zCod7wna9d\\n2Ji6c5ImKrPp1eY6FGwqnUGyt8gH1nkDab67/KZzddwhA39Rx2XfucITOYA6FXPevU/O8Tfpjlal\\n6VrzmqUuup1bxmxhYx4Gtaukbj/zyo/KmIN0kiRJkiRJkiRJ0sgcpJMkSdJWGe6yxHCXa1TPAW0b\\nwa1ydrThLnuznGltPdxlDlOuJx7ust1bd4a77Nfmco851ofhLpuskl/MhYmEu+xc4YkcQGOHu8zx\\nN+mOVqXpWvsX7nLMw6B2ldTtx3CXk+MgnSRJkrbKcJclhrtsuY2+CRqm6c1wlzmwnMPnnszEw122\\ny9dwl6PnOJHDwHCXKbc1pomEu8wgp0GNHe4yR62rsn/hLjvLvfsy3OXkOEgnSZIkKXOp3iZwLqm0\\nkYdJb+7Cltxh6mHPYi6ks3cVHliOv0nXc619kM2eyaYg2hYH6SRJkiRJkiRJkqSROUgnSZIkKXOp\\nQn4Z8EXayMOkN3dhS+4w9bBngdHT2bsKDyzH36TrudY+yGbPZFMQbYuDdJIkSdqq+ugeexbEqEkx\\nE1RljL0xRmjJjVsYaX8OtZGJtFrLmdiw5ZzIXkhRzCQdRNfUbfIdJufZ2oWNqTsnaSJ9bTvkOJHD\\nIF2o54F13sCufncdCzlw3dJlP4kvoWMxZ7sVGr11VWY1SwMVoCLJJL6B3LuvWeVHZcxBOkmSJG1V\\n/cTBPZsf3aSYCaoyxt5I+9Za9e3lxi2sJKh88DJK09jtec+WM61hy9kg9xye5qTYCW07iISp2+zC\\nod7wna9d2Ji6c5ImKrPp1eY6FGwqnUGyt8gH1nkDab67/KZzddwhA39Rx2XfucITOYA6FXPevU/O\\n8Tfpjlal6VrzmqUuup1bxmxhYx4Gtaukbj/zyo/KmIN0kiRJkiRJkiRJ0sgcpJMkSdJWGe6yxHCX\\na1TPAW0bwa1ydrThLnuznGltPdxlDlOuJx7ust1bd4a77Nfmco851ofhLpuskl/MhYmEu+xc4Ykc\\nQGOHu8zxN+mOVqXpWvsX7nLMw6B2ldTtx3CXk+MgnSRJkrbKcJclhrtsuQTiNHMAABnoSURBVI2+\\nCRqm6c1wlzmwnMPnnszEw122y9dwl6PnOJHDwHCXKbc1pomEu8wgp0GNHe4yR62rsn/hLjvLvfsy\\n3OXkOEgnSZKkrfJNuhLfpGu5jb4JGqbpzTfpcmA5h889mYm/SdcuX9+kGz3HiRwGvkmXcltjmsib\\ndBnkNKix36TLUeuq7N+bdJ3l3n35Jt3kOEgnSZKkrfJNuhLfpFuj+vay7cshlQ9efJOuN8uZ1tbf\\npMvhac7E36RrFxrTN+n6tbncX2fowzfpmqyS33SuibxJ17nCEzmAxn6TbqRzZ6vNHK1K07X27026\\nMQ+D2lVStx/fpJscB+kkSZIkSZIkSZKkkTlIJ0mSpK0y3GWJ4S7XqJ4D2jaCW+XsaMNd9mY509p6\\nuMscplxPPNxlu7fuDHfZr83lHnOsD8NdNlklv5gLEwl32bnCEzmAxg53OdK5s9Vmjlal6Vr7F+5y\\nzMOgdpXU7cdwl5PjIJ0kSZK2ynCXJYa7bLmNvgkapunNcJc5sJzD557MxMNdtsvXcJej5ziRw8Bw\\nlym3NaaJhLvMIKdBjR3uMketq7J/4S47y737Mtzl5DhIJ0mSJClzqd4mcC6ptJGHSW/uwpbcYeph\\nz2IupLN3FR5Yjr9J13OtfZDNnsmmINoWB+kkSZIkSZIkSZKkkTlIJ0mSJClzqUJ+GfBF2sjDpDd3\\nYUvuMPWwZ4HR09m7Cg8sx9+k67nWPshmz2RTEG2Lg3SSJEnaqvroHnsWxKhJMRNUZYy9MUZoyY1b\\nGGl/DrWRibRay5nYsOWcyF5IUcwkHUTX1G3yHSbn2dqFjak7J2kifW075DiRwyBdqOeBdd7Arn53\\nHQs5cN3SZT+JL6FjMWe7FRq9dVVmNUsDFaAiySS+gdy7r1nlR2XMQTpJkiRtVf3EwT2bH92kmAmq\\nMsbeSPvWWvXt5cYtrCSofPAyStPY7XnPljOtYcvZIPccnuak2AltO4iEqdvswqHe8J2vXdiYunOS\\nJiqz6dXmOhRsKp1BsrfIB9Z5A2m+u/ymc3XcIQN/Ucdl37nCEzmAOhVz3r1PzvE36Y5Wpela85ql\\nLrqdW8ZsYWMeBrWrpG4/88qPypiDdJIkSZIkSZIkSdLIHKSTJEnSVhnussRwl2tUzwFtG8Gtcna0\\n4S57s5xpbT3cZQ5Trice7rLdW3eGu+zX5nKPOdaH4S6brJJfzIWJhLvsXOGJHEBjh7vM8Tfpjlal\\n6Vr7F+5yzMOgdpXU7cdwl5PjIJ0kSZK2ynCXJYa7bLmNvgkapunNcJc5sJzD557MxMNdtsvXcJej\\n5ziRw8Bwlym3NaaJhLvMIKdBjR3uMketq7J/4S47y737Mtzl5DhIJ0mSJClzqd4mcC6ptJGHSW/u\\nwpbcYephz2IupLN3FR5Yjr9J13OtfZDNnsmmINoWB+kkSZIkSZIkSZKkkTlIJ0mSJClzqUJ+GfBF\\n2sjDpDd3YUvuMPWwZ4HR09m7Cg8sx9+k67nWPshmz2RTEG2Lg3SSJEnaqvroHnsWxKhJMRNUZYy9\\nMUZoyY1bGGl/DrWRibRay5nYsOWcyF5IUcwkHUTX1G3yHSbn2dqFjak7J2kifW075DiRwyBdqOeB\\ndd7Arn53HQs5cN3SZT+JL6FjMWe7FRq9dVVmNUsDFaAiySS+gdy7r1nlR2XMQTpJkiRtVf3EwT2b\\nH92kmAmqMsbeGOOttY1bGGl/DrWRibRay5nYsOWcyF5IUcwkHUTX1G3yHSbn+dqFjak7J2kifW07\\n5DiRwyDdW+QD67yBXf3uOhZy4Lqly34SX0LHYs53K+pC66rMa5YGKkBFkkl8A7l3X/PKj8qYg3SS\\nJEmSMpfqbQLnkkobeZj05i5syR2mHvYs5kI6e1fhgY20P3c7NsP4stkz2RRE2+IgnSRJkiRJkiRJ\\nkjQyB+kkSZIkZS5VyC8DvkgbeZj05i5syR2mHvYsMHo6e1fhgY20P3c7gPr4stkz2RRE2+IgnSbl\\ngg+ev+0iSJVsm8qZ7VO5WrTN+ugeexbEqEkxE1RljL0xRmjJjVvouD8vufiCDqXpW5BUa43Pcqa1\\nqZwXX/jBAXPPRIpiJukguqZuk+8wOc/WLmxM3TlJk2vO9LXtkONEDoN0oZ4H1nkD4353F11wfveV\\nW+lYyIG/qHTZT+QA6lTM2VZCow92v966KrOapYEKUJFkEi0s91PPrPJja5dfelHvoqgZB+k0KT5o\\nVq5sm8qZ7VO5WrTN+omDezY/ukkxE1RljL2R9q216tvLjVtYSVD54KUik0s/lHqQbrfnPVvOtDaV\\n80MX9Rmka7AXcng6luLLattBJEzdZhcO9YbvfO3CxtSdkzS55qzMpleb67D/ptIZJHuLfGCdN5Dm\\nu2vafC7aMMEhXdfXcYcM/EUdl33nCk/kAOpUzHn3PrlHA2pzv95qM0er0nStec1SF93OLWO2sDEP\\ng9pVUl97zSs/tnbYQbrROEgnSZIkSZIkSZIkjcxBOkmSJG2V4S5LDHe5RvUc0LYR3CpnR4/SNAx3\\nmQPL2TD3HF6SmHi4y3Zv3e1OuMsmKrPp1eZyjznWh+Eum6ySX8yFiYS77FzhiRxAY4e7zPE36Y5W\\npela+xfucszDoHaV1O0nUbhLjcfvaXjnA/fbdiEkSZIkSZIkSZI0ug8Cp2+7EJIkSZIkSZIkSZIk\\nSZIkSZIkSZIkSZIkSZIkSZIkSZKUwJnAc4GXAv+wJt1TgdeMUB7thwPgqgHy/Rxwy+LzxRvSng/c\\nc4AyaJoOGKZNln194PzbOB1417YLoVEckKZtf45l/5oinSTl5IDmfeUzgCcn3v7peF5Wc13b4AHD\\nX+9KQzsf7+PVzpnEc8+uHgk8v+bfD2jWt2567qr9s3hueTvgJ7dZkBqb2v9eOHHbBdBemBf/f0nD\\ndFLOyu30tAZpbdcak+1NU9a0/drOtU2HgO/0WP9E4G8SlUW767cS5GFbU1eHSNMGpSHNiv8PcV3o\\ntabaatNmVq8lDxGTaFJMpNn03FW7qa4/XDy3vD3wROA/jlKieqvHQKr2P2knbLsA2lkvBD4FXAjc\\npfjbWcBjis/3IkbzrwAuAW7CslMBeDjwIeAU4HHEjJErgA8W/37DIr8jwOXEzFCIt/H+M/Ae4NPA\\nq9aU727ApcBh4ErgTh3qqPwdAn4buBo4l2g3TwcuI9rTHwA3KtLeqli+rPjvPsXfTwHeW+TxBo5t\\np+W3lp5PtMcrgFeslOME4GzgXxefX11s40rgjDVlPxt4PfBh4DNEG38z8HGi7WuaTgT+A/E9/j7R\\n/n6JaA9XcewDkfNZzuD8W8C1xecbA28HriH6u0uAHyyt93KiHX4YuHXxt7Np1p5eD3yEaO9nlv7+\\nuWL5Y0Q7vwvVHgJ8okj3qDVptJtW+9vvJ9rBwvcVyw8m2u/C6Wy+IK/rh7X7TgL+C9GvXQU8nugb\\nzwc+CvwJ8N1F2vOBXyX6sU8Q15t/SFwTvqxIcwB8kuP7Yjbk+2tFvs8u8j1CXEe+muXM5kNUn+NP\\nJ66J30H03at+qkh/BfCWzbtEO+AOxD3MvYn7lo8CF7A8v57Jckb+s4l2cyXLBysnAW8i7mcuB368\\n+PtTgXcC7wfeV7P9exXr3b5vRTRJLyb6wQuBc4i2dh7Lfu7niQe9iza4rs9bvae+Y/H3quvdBxD9\\n8cKPEdexq84krlEvIK4/Hw3822L778GJ5vvugHjO9GaiHf4O6+9dXkW0m0tZts2zgd8s1vkU8dxp\\nnScTbfsq4hiQVlU997wj1ef1s4m2dwnwb4h78PLyU1hGFvsuor+8ovjvh1a2u7iGqHrb82yWz121\\n2w44tj98Mct7kDNL6RbPLX8FuC/Rr/0C0eb+iLjPvhZ4FvA8om19GLhFsV5Vm74p8FmW5+STi+VD\\na9LDscfA6rP6p2JkPWkQ9yQuhm5IHLh/Rlzgn0VcZF+feEi8OKHchDiQFyelRxEH8s2Kfz8C3Kb4\\nfHLx/+cCbyw+3wX4PHAD4sD+TLHdGxAXZ99TUcZfJ2YQQHQqN+xUU+XsAPg28PeK5bcBT+LYEGkv\\nI05EEDeoixkmtyVuKiHayouKzw8Drivl8bXi/w8lBp0X7ejmxf/PA04lHqj8y+JvZxAXcxBt9CNF\\nWVedVZQJ4sHLV4kb4RlxsvuBinWUtwOi/fxwsfw7RF92i1KatwCPKD6fx3LwrTxI9zzgN4rPdyPa\\n+SLddSxvNl/Fsq2dTbP2tCjLoWL7f7dYvhb4ueLzM4mBklU3BL7A8ib4bcSDQu2+A6r72w+wbFuv\\nINrQIeKcvRgU+Q2W5+NrqQ5jWdcPa/c9hhgAXjiZOOeeUiw/gehPIfqtVxafnw38BfGg4/rAF4k+\\n7oDqvvhElhPEqvJ9bakMVxPnd4rtHSk+rzvHn07cIN+uon53I26wF236FhVptBsOiIcodyEegNyd\\nGExbTBY8tViGGCD5xeLznwPXKz4v7oVeQfSzENednyIm8TyVaOuLa9Gy04lJEfchzv1/u1dtNFX3\\nIh7QXZ+4D/80y0G6cj9XboPr+rzXcPw99QHVfSzE5IlFH3sO1QMkZxLPAg4R1xXfJCb4QAzq/USz\\nampHHRBvX9y7WK67d1ncfz+Z5YSws4F3F5/vRPSX16/YznksJ0/eF0O46njrnnv+KdXn9bOJe+PF\\nZMOzVpbLg3RvI65jISZ5n0z1NUSVxXNX7b4Dlv3hj7Hss04A/pjou2D53PJ+HDs59qlEuz2JeN70\\nVywnGP4qMWEH1l+rvonlOfkMYhJPXfqzObbNl5Xb/97yTToN4b7EBfT/IzqD8kPaGXFS+RLLGfZf\\nJzqWGTHD7l8QD+H+qvj3i4mZAT/NcpT+NGJ2HsRN6eeBOxOv9r6/2O5fEwMtBxVl/DDwr4ptHRRl\\n1e65luVN5MeI7/ruxEynI8TDje8v/v2BxI3pYWKm+02Jk9V9Wba1dwNfrtjOA4kT1KIdfaX4/4w4\\nUV7F8qHhg4gZ84eJGSS3ZP2bnIsT6NXAfydmUc+L/x+srbVy9kWi/4FoVz9C9HuXEm3yASzb5Dqn\\nAW8tPl/Dso0DfIt44wSWbR6i3TRpT08o1ruceHBcLstitvPlVLe/v0Mcc58p1c83nvZHVX/7RuCf\\nEtebjyceyH2HeEPpx4lz+sOIPrdOk35Yu+sIceP5K0SfeVviIdyfEufSF3LshKzFdefVxX//g+gb\\nPwt8b/FvVX3xXYh+b12+byv+f3PiwfalxfI5LPu6unP8ZcT16qoHEG+X/mWxbPvebbcmZi0/keg3\\n70O8aXSYmF383RXrHCHa2ZNYhgZ6EPCCYr3ziEHh2xLn9fexvBZddVfi2vQRwH/rXRtN0WlEG/wW\\ncR9efmD3tor0N2N9n/chqu+pq/pYgN8lBkxuTrwZ8p6K7c2Lv3+H6MNPIN7Qh7inOthUQe28zxPn\\nVKi/d1m8efxWloPGc5YRHf4rcW1w1zXbWax/ITFIcvKadNpPVc89b8j68/q8+Hs5HOHq8sL9WU7K\\nvY6YYAvHXkPUDRx7D74/Fv3hg4lrw8NEn3hnjn/OuNou5sQ15DeA/01cOy6uCRbn25NY36YX9/oQ\\nA35nEdcLbY4BlRgqQEOYU39SWHdAzomHu7cnHpQsBvGeScwMeHjxt8UbeOu28delz98h2vk/Yhmb\\n+WnEBdclxA3qu4kfxj6vpsyaptW2cCPixPETxEnnKcRsEoj2dCpxw7pq00XOujY/J25e7w/8u1J5\\nnsXxIYheTrTxOcu3ohZluW6lLtdh/z1V5f5vViy/jujX/pzopxZvZP4Ny8k0q2/7rmuT3y59Xm0n\\nde3pENH3Phf4B8QkibNWtrtYZ9GvQjw0uTXxtsjrGpZRu2m1v70h8J+INv0B4vy9GHx4K9EP/iXx\\nNsc3SuvOiDfufpo4Ph5e+rv2058B9yDawsuJ67VrWIalXrVoi3Xnzqq+eLYh32+s+ftq26w6x59e\\nWv97WQ4k/iabr5u1W75CPFC5LzEg8mWifVdZtIuHAz8KPJIYPF7Mnn80cXyUncqyrZ1KtDGI0Npf\\nJSZK3oC41nw32kd1fc43G6xfXrfqnvpaqvtYiGvLdxEPtd9O9MtV5/zyNWvdta3206KP23TvUlb3\\nUHhOTLi9B3E/9oiadNJCVV96AnGeX3deX+1j6/rcqn66fA3xyeJvTdqudlf5/uSVHBt9pInVe6Xy\\nfdSJRJted636IZYRQw4RL8mcXJO+XN6qc//e8006DeECYlBs8dr3I0v/NifefLsNcTFFkeYQcRL6\\nPPBYIuTbYhbUHYmZAS8B/hfxcONCliFe7kzMHP0k6284/ojoJO7B8vcXriVep30H618V1+65CfEW\\n0fWAf1L6+3tZhhSAZYi2C1iGcXko1WGo3kfMIFmEbyuneSNx0/p2op2fC/wsyxvMOxPhiV5EtM/y\\nb4tp99yWZUz5JwIXFZ//D9E2H1dK+zmW/eRjS3+/mHgrCaKfTNF/zYi++BvEQ7zvItr7Jg8m2u0Z\\nRN9+QMTIB/jJBOXSdM2Ii/xziZmgbyr92weJvu7pHP/D1YuB60V/+CWa9cPaXbchHuj+HvG7RPcm\\nQrIs+tLrsfkN5FWrffGFRB92q5p8F9eYXyFmTC9Cbf3jUpp15/iyL7K8Jv0tYhD7cSzDXRrKdbd9\\nixhc+yniYdq1LM/xM5Zhgyn97bbE7yK+gOVbTedy7HXrPUrpFy5l2dbeVfzbV4rtvpLlRDXtl4uJ\\n+/MbEG2p7qHujBj8WNfn3YHqe+qqPhbinP4XxH3P4jeRV8/5UlMnU3/v8oTS/z9UfJ4R59wZ8Zzp\\nDsRzpH9GtMNHlNIt1v8Rlud+aaHquec32XxeX6d8/n4/8bICxDOkxVuc5WuIxb32atvVfjqXaAsn\\nFcvfQ9zXlH2NaKsLdZMEF//2NY5v0+Wf3nkLcY+2uNf/akX68jGwyNdzfwUH6TSEw8TM0CuJwYnL\\nVv7928QFz2uIH0E9lzixzVkO4j2JeA32DsSPqB4h3ny6uMj39UT7PULMyH9Kke8ij7KqGU+PJ8Jn\\nHCbCIrylY12Vt6rv/peIhxYXEb+LsPBsYkDkSmIm/TOKv7+UmL18NfF7ieVQVYv8zyVmxX+UaFPP\\n5Vi/Vvz9d4lBu48Tg8VXEQ+v180Ina/5vK5uytuif/s5og3cjPj+30C0rz9hGUoI4mH0M4m2cgrL\\n7/z1xAXXNcTvKl7DMjzwaptZ14aq2tMRop1+krjQuohqVf0sxEP0M4hwmx8jQszZTvfHuj7qHGIm\\n3ntL/3YdESf/IcX/1+WxUNcPa/fdnegbDxM/iP5i4gHbq4jryMMsw1iVreuroLov/jZxQ7ku33Je\\nTyP67sPEINyiD153jq8ry8eBXyYGr68g+n7trjnxEO8RwC8Q9zFPI777q4lQwOW0h4jrxyNEu/r3\\nRHt7GTGQfKRY76WldeqilsyB/1ls/3XE75Npv3yUuG85QtyrX0W0qbr76HV93rp76qo+duEc4jeM\\nP1VTRu+BVGfRBq6k/t7lFkWafw48p7TuF4hnVIu3P6si6cyJe5vLiXuvp6UrvnZE1XPPOfEss+68\\nzprlch/880Q0piNEn33XUprFNcRz8K1PLb/r9xHn1w8T7eb3iYk45TRXEhFvriCuQTc9L1osr7bp\\n8os45xB9bXnibZtjoKoukiRJk3ACMfsZYgboZzH0j/L1PJYPj6UcHFD/Ox5NnFT6/AJiMo4kTcWi\\nD7sxEbL87zdMD/37vNey/A0baSjXUv1m+lnEm0jSrnonvimv8TwWeHOCfJ7L8ieqJEmSJuGmxAOV\\nK4jZUA/ebnGktf6QaKeG71NODogZpn08nphBfRURRvCUnvlJ0ph+j+jDPgE8v0H6VH3ex4jQrdfr\\nuL7U1GdxkE77503EG1WHtl0Q7YXXAJ8G7tQzn58hnmvdsXeJJEmSJEmSJEmSJEmSJEmSJEmSJEmS\\nJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEnSfvgOcBi4Cng7cKMeeZ0NPKb4/Abg\\nrjVp7wf8cIdtfA64ZYf1JEmSJCkbJ2y7AJIkSZKkrfsmcA/g7sC3gJ9Z+fcTW+Q1L/4DeDrwiZq0\\n9wfu0yLv8jYkSZIkadIcpJMkSZIklV0I3Il4y+1C4B3A1cT946uBy4ArgTOK9DPgtcAngfcBty7l\\ndT5wz+LzQ4CPAVcU6W4HPAN4DvEW32nArYA/KLZxGcsBvFOA9xbleEOxTUmSJEmSJEmSJGnSvlb8\\n/0RiUO4ZxCDd14nBNIhBuRcWn28AfAQ4AB5NDKDNgNsAXy7+BnAe8IPE4NsXSnndvPj/S4BfLJXj\\nHGKwDuC2wMeLz78OvKj4/DDgOgx3KUmSJGni2oQskSRJkiTtphsRb7MBXAC8iRgsuwz4fPH3BxHh\\nMB9bLJ8MfB9wX2JwbQ58CfjASt4z4IeKfBd5fWXl3xceyLG/YXdT4KRiG48q/vZuYiBQkiRJkibN\\nQTpJkiRJ0v8lfpNu1TdWlp9FhKosexibw082/Q25GXAq8bt4Vf8mSZIkSTvD36STJEmSJDVxLvCz\\nLCd73hm4MfGG3BOI+8vbAPdfWW8OXAL8KBEeE5ahKr9GvC238F7g2aXlHyj+fwHwxOLzQ4FbdK+G\\nJEmSJEmSJEmSlIevVvztfsA7S8sz4JeBI8BVwPtZDrC9BvgkMcj2xxz/m3QADwEuB64gBvwgwmVe\\nSYTaPA04BXhr8bdrgNcX6W5ZrHM18NvAtfibdJIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIk\\nSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIk\\nSZIkSZIkSZIkSVLe/j8HHCrWLAfAPAAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f66ea021d30>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"from matplotlib import pyplot as plt\\n\",\n    \"plt.figure(figsize=(30, 30))\\n\",\n    \"plt.imshow(cm, cmap='Blues')\\n\",\n    \"tick_marks = np.arange(len( sorted_authors ))\\n\",\n    \"plt.xticks(tick_marks, sorted_authors )\\n\",\n    \"plt.yticks(tick_marks, sorted_authors )\\n\",\n    \"plt.ylabel('Actual')\\n\",\n    \"plt.xlabel('Predicted')\\n\",\n    \"plt.show()\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "Chapter 9/getdata.py",
    "content": "# Downloads the books and stores them in the below folder\nimport os\nfrom time import sleep\nimport urllib.request\n\ntitles = {}\n\n\ntitles['burton'] = [4657, 2400, 5760, 6036, 7111, 8821,\n                    18506, 4658, 5761, 6886, 7113]\ntitles['dickens'] = [24022, 1392, 1414, 1467, 2324, 580,\n                     786, 888, 963, 27924, 1394, 1415, 15618,\n                     25985, 588, 807, 914, 967, 30127, 1400,\n                     1421, 16023, 28198, 644, 809, 917, 968, 1023,\n                     1406, 1422, 17879, 30368, 675, 810, 924, 98,\n                     1289, 1413, 1423, 17880, 32241, 699, 821, 927]\ntitles['doyle'] = [2349, 11656, 1644, 22357, 2347, 290, 34627, 5148,\n                   8394, 26153, 12555, 1661, 23059, 2348, 294, 355,\n                   5260, 8727, 10446, 126, 17398, 2343, 2350, 3070,\n                   356, 5317, 903, 10581, 13152, 2038, 2344, 244, 32536,\n                   423, 537, 108, 139, 2097, 2345, 24951, 32777, 4295,\n                   7964, 11413, 1638, 21768, 2346, 2845, 3289, 439, 834]\ntitles['gaboriau'] = [1748, 1651, 2736, 3336, 4604, 4002, 2451,\n                      305, 3802, 547]\ntitles['nesbit'] = [34219, 23661, 28804, 4378, 778, 20404, 28725,\n                    33028, 4513, 794]\ntitles['tarkington'] = [1098, 15855, 1983, 297, 402, 5798,\n                        8740, 980, 1158, 1611, 2326, 30092,\n                        483, 5949, 8867, 13275, 18259, 2595,\n                        3428, 5756, 6401, 9659]\ntitles['twain'] = [1044, 1213, 245, 30092, 3176, 3179, 3183, 3189, 74,\n                   86, 1086, 142, 2572, 3173, 3177, 3180, 3186, 3192,\n                   76, 91, 119, 1837, 2895, 3174, 3178, 3181, 3187, 3432,\n                   8525]\n\n\n\nassert len(titles) == 7\n\nassert len(titles['tarkington']) == 22\nassert len(titles['dickens']) == 44\nassert len(titles['nesbit']) == 10\nassert len(titles['doyle']) == 51\nassert len(titles['twain']) == 29\nassert len(titles['burton']) == 11\nassert len(titles['gaboriau']) == 10\n\n\n# https://www.gutenberg.org\n#url_base = \"http://gutenberg.pglaf.org/\"\n#url_base = \"http://gutenberg.readingroo.ms/\"\nurl_base = \"http://www.gutenberg.myebook.bg/\"\nurl_format = \"{url_base}{idstring}/{id}/{id}.txt\"\n\nfixes = {}\nfixes[1044] = url_base + \"1/0/4/1044/1044-0.txt\"\nfixes[5148] = url_base + \"5/1/4/5148/5148-0.txt\"\nfixes[4657] = \"https://archive.org/stream/personalnarrativ04657gut/pnpa110.txt\"\nfixes[1467] = \"https://archive.org/stream/somechristmassto01467gut/cdscs10p_djvu.txt\"\n\n# Make parent folder if not exists\nif not os.path.exists(data_folder):\n    os.makedirs(data_folder)\n\nfor author in titles:\n    print(\"Downloading titles from {author}\".format(author=author))\n    # Make author's folder if not exists\n    author_folder = os.path.join(data_folder, author)\n    if not os.path.exists(author_folder):\n        os.makedirs(author_folder)\n    # Download each title to this folder\n    for bookid in titles[author]:\n        if bookid in fixes:\n            print(\" - Applying fix to book with id {id}\".format(id=bookid))\n            url =  fixes[bookid]\n        else:\n            print(\" - Getting book with id {id}\".format(id=bookid))\n            idstring = \"/\".join([str(bookid)[i] for i in range(len(str(bookid))-1)])\n            print(bookid, idstring)\n            url = url_format.format(url_base=url_base, idstring=idstring, id=bookid)\n        print(\" - \" + url)\n        filename = os.path.join(author_folder, \"{id}.txt\".format(id=bookid))\n        if os.path.exists(filename):\n            print(\" - File already exists, skipping\")\n        else:\n            urllib.request.urlretrieve(url, filename)\n            sleep(60*5)\nprint(\"Download complete\")\n"
  },
  {
    "path": "LICENSE",
    "content": "The MIT License (MIT)\n\nCopyright (c) 2016 Taabish Khan\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
  },
  {
    "path": "README.md",
    "content": "This is the code repository for [Learning Data Mining With Python](https://www.packtpub.com/big-data-and-business-intelligence/learning-data-mining-python?utm_source=github&utm_medium=repository&utm_campaign=9781784396053), written by Robert Layton, and published by Packt Publishing.\n\n*Learning Data Mining With Python* is for programmers who want to get started in data mining in an application-focused manner.\nIf you haven't programmed before, it is strongly recommend that you learn at least the basics before you get started. This book doesn't introduce programming, nor does it give too much time to explain the actual implementation (in code) of how to type out the instructions. That said, once you go through the basics, you should be able to come back to this book fairly quickly---there is no need to be an expert programmer first!\nIt is highly recommend that you have some Python programming experience. If you \ndon't, feel free to jump in, but you might want to take a look at some Python code first, possibly focusing on tutorials using the IPython Notebook. Writing programs in the IPython Notebook works a little differently than other methods such as writing a Java program in a fully fledged IDE.\n\n## Related books from Packt:\n- [Learning Data Mining with R](https://www.packtpub.com/big-data-and-business-intelligence/learning-data-mining-r?utm_source=github&utm_medium=related&utm_campaign=9781784396053)\n- [Python Data Analysis](https://www.packtpub.com/big-data-and-business-intelligence/python-data-analysis?utm_source=github&utm_medium=related&utm_campaign=9781784396053)\n- [Python Data Visualization Cookbook - Second Edition](https://www.packtpub.com/big-data-and-business-intelligence/python-data-visualization-cookbook-second-edition?utm_source=github&utm_medium=related&utm_campaign=9781784396053)\n- [Python Data Science Cookbook](https://www.packtpub.com/big-data-and-business-intelligence/python-data-science-cookbook?utm_source=github&utm_medium=related&utm_campaign=9781784396053)\n- [Mastering Python Data Visualization](https://www.packtpub.com/big-data-and-business-intelligence/mastering-python-data-visualization?utm_source=github&utm_medium=related&utm_campaign=9781784396053)\n"
  }
]