[ { "path": ".gitignore", "content": ".idea/" }, { "path": "LICENSE", "content": "MIT License\n\nCopyright (c) 2018 刘建平(Pinard Liu)\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": "classic-machine-learning/birch_cluster.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C) 2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"用scikit-learn学习BIRCH聚类 https://www.cnblogs.com/pinard/p/6200579.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline\\n\",\n \"from sklearn.datasets.samples_generator import make_blobs\\n\",\n \"# X为样本特征，Y为样本簇类别， 共1000个样本，每个样本2个特征，共4个簇，簇中心在[-1,-1], [0,0],[1,1], [2,2]\\n\",\n \"X, y = make_blobs(n_samples=1000, n_features=2, centers=[[-1,-1], [0,0], [1,1], [2,2]], cluster_std=[0.4, 0.3, 0.4, 0.3], \\n\",\n \" random_state =9)\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], marker='o')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Calinski-Harabasz Score 2220.952539045443\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.cluster import Birch\\n\",\n \"y_pred = Birch(n_clusters = None).fit_predict(X)\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=y_pred)\\n\",\n \"plt.show()\\n\",\n \"from sklearn import metrics\\n\",\n \"print (\\\"Calinski-Harabasz Score\\\", metrics.calinski_harabaz_score(X, y_pred)) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Calinski-Harabasz Score 2816.407652684516\\n\"\n ]\n }\n ],\n \"source\": [\n \"y_pred = Birch(n_clusters = 4).fit_predict(X)\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=y_pred)\\n\",\n \"plt.show()\\n\",\n \"print (\\\"Calinski-Harabasz Score\\\", metrics.calinski_harabaz_score(X, y_pred) )\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Calinski-Harabasz Score 3295.634922726645\\n\"\n ]\n }\n ],\n \"source\": [\n \"y_pred = Birch(n_clusters = 4, threshold = 0.3).fit_predict(X)\\n\",\n \"print (\\\"Calinski-Harabasz Score\\\", metrics.calinski_harabaz_score(X, y_pred))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Calinski-Harabasz Score 2155.10021807852\\n\"\n ]\n }\n ],\n \"source\": [\n \"y_pred = Birch(n_clusters = 4, threshold = 0.1).fit_predict(X)\\n\",\n \"print (\\\"Calinski-Harabasz Score\\\", metrics.calinski_harabaz_score(X, y_pred)) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Calinski-Harabasz Score 3301.8023106358173\\n\"\n ]\n }\n ],\n \"source\": [\n \"y_pred = Birch(n_clusters = 4, threshold = 0.3, branching_factor = 20).fit_predict(X)\\n\",\n \"print (\\\"Calinski-Harabasz Score\\\", metrics.calinski_harabaz_score(X, y_pred)) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Calinski-Harabasz Score 2800.878409621567\\n\"\n ]\n }\n ],\n \"source\": [\n \"y_pred = Birch(n_clusters = 4, threshold = 0.3, branching_factor = 10).fit_predict(X)\\n\",\n \"print (\\\"Calinski-Harabasz Score\\\", metrics.calinski_harabaz_score(X, y_pred)) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\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\": \"3.6.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "classic-machine-learning/bpr.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C) 2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"用tensorflow学习贝叶斯个性化排序(BPR) https://www.cnblogs.com/pinard/p/9163481.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"max_u_id: 943\\n\",\n \"max_i_id: 1682\\n\"\n ]\n }\n ],\n \"source\": [\n \"import numpy\\n\",\n \"import tensorflow as tf\\n\",\n \"import os\\n\",\n \"import random\\n\",\n \"from collections import defaultdict\\n\",\n \"\\n\",\n \"def load_data(data_path):\\n\",\n \" user_ratings = defaultdict(set)\\n\",\n \" max_u_id = -1\\n\",\n \" max_i_id = -1\\n\",\n \" with open(data_path, 'r') as f:\\n\",\n \" for line in f.readlines():\\n\",\n \" u, i, _, _ = line.split(\\\"\\\\t\\\")\\n\",\n \" u = int(u)\\n\",\n \" i = int(i)\\n\",\n \" user_ratings[u].add(i)\\n\",\n \" max_u_id = max(u, max_u_id)\\n\",\n \" max_i_id = max(i, max_i_id)\\n\",\n \" print (\\\"max_u_id:\\\", max_u_id)\\n\",\n \" print (\\\"max_i_id:\\\", max_i_id)\\n\",\n \" return max_u_id, max_i_id, user_ratings\\n\",\n \" \\n\",\n \"\\n\",\n \"data_path = os.path.join('D:\\\\\\\\tmp\\\\\\\\ml-100k', 'u.data')\\n\",\n \"user_count, item_count, user_ratings = load_data(data_path)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"defaultdict(, {196: {257, 8, 393, 13, 269, 655, 1022, 663, 25, 153, 411, 285, 286, 287, 428, 173, 306, 692, 66, 67, 580, 70, 202, 845, 340, 1241, 94, 1118, 108, 238, 111, 1007, 110, 242, 116, 762, 251, 381, 382}, 186: {1033, 12, 1042, 1046, 540, 31, 546, 550, 38, 554, 44, 53, 566, 55, 568, 56, 1083, 71, 588, 77, 591, 79, 595, 596, 95, 98, 100, 106, 117, 118, 121, 147, 148, 159, 684, 689, 177, 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\"source\": [\n \"print (user_ratings)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def generate_test(user_ratings):\\n\",\n \" user_test = dict()\\n\",\n \" for u, i_list in user_ratings.items():\\n\",\n \" user_test[u] = random.sample(user_ratings[u], 1)[0]\\n\",\n \" return user_test\\n\",\n \"\\n\",\n \"user_ratings_test = generate_test(user_ratings)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{196: 153, 186: 588, 22: 290, 244: 596, 166: 984, 298: 196, 115: 234, 253: 192, 305: 89, 6: 64, 62: 228, 286: 1411, 200: 771, 210: 692, 224: 1401, 303: 577, 122: 70, 194: 402, 291: 941, 234: 16, 119: 168, 167: 288, 299: 198, 308: 411, 95: 183, 38: 94, 102: 667, 63: 475, 160: 117, 50: 15, 301: 334, 225: 603, 290: 484, 97: 175, 157: 250, 181: 1187, 278: 301, 276: 710, 7: 52, 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\"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"128\\n\"\n ]\n }\n ],\n \"source\": [\n \"print (random.sample(user_ratings[1], 1)[0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def generate_train_batch(user_ratings, user_ratings_test, item_count, batch_size=512):\\n\",\n \" t = []\\n\",\n \" for b in range(batch_size):\\n\",\n \" u = random.sample(user_ratings.keys(), 1)[0]\\n\",\n \" i = random.sample(user_ratings[u], 1)[0]\\n\",\n \" while i == user_ratings_test[u]:\\n\",\n \" i = random.sample(user_ratings[u], 1)[0]\\n\",\n \" \\n\",\n \" j = random.randint(1, item_count)\\n\",\n \" while j in user_ratings[u]:\\n\",\n \" j = random.randint(1, item_count)\\n\",\n \" t.append([u, i, j])\\n\",\n \" return numpy.asarray(t)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 181 681 46]\\n\",\n \" [ 133 258 568]\\n\",\n \" [ 679 223 101]\\n\",\n \" ...\\n\",\n \" [ 81 476 1566]\\n\",\n \" [ 602 358 1281]\\n\",\n \" [ 494 237 200]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print (generate_train_batch(user_ratings, user_ratings_test, item_count, batch_size=512))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def generate_test_batch(user_ratings, user_ratings_test, item_count):\\n\",\n \" for u in user_ratings.keys():\\n\",\n \" t = []\\n\",\n \" i = user_ratings_test[u]\\n\",\n \" for j in range(1, item_count+1):\\n\",\n \" if not (j in user_ratings[u]):\\n\",\n \" t.append([u, i, j])\\n\",\n \" yield numpy.asarray(t)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 196 153 1]\\n\",\n \" [ 196 153 2]\\n\",\n \" [ 196 153 3]\\n\",\n \" ...\\n\",\n \" [ 196 153 1680]\\n\",\n \" [ 196 153 1681]\\n\",\n \" [ 196 153 1682]]\\n\",\n \"[[ 186 588 1]\\n\",\n \" [ 186 588 2]\\n\",\n \" [ 186 588 3]\\n\",\n \" ...\\n\",\n \" [ 186 588 1680]\\n\",\n \" [ 186 588 1681]\\n\",\n \" [ 186 588 1682]]\\n\",\n \"[[ 22 290 1]\\n\",\n \" [ 22 290 3]\\n\",\n \" [ 22 290 5]\\n\",\n \" ...\\n\",\n \" [ 22 290 1680]\\n\",\n \" [ 22 290 1681]\\n\",\n \" [ 22 290 1682]]\\n\",\n \"[[ 244 596 2]\\n\",\n \" [ 244 596 4]\\n\",\n \" [ 244 596 5]\\n\",\n \" ...\\n\",\n \" [ 244 596 1680]\\n\",\n \" [ 244 596 1681]\\n\",\n \" [ 244 596 1682]]\\n\",\n \"[[ 166 984 1]\\n\",\n \" [ 166 984 2]\\n\",\n \" [ 166 984 3]\\n\",\n \" ...\\n\",\n \" [ 166 984 1680]\\n\",\n \" [ 166 984 1681]\\n\",\n \" [ 166 984 1682]]\\n\",\n \"[[ 298 196 2]\\n\",\n \" [ 298 196 3]\\n\",\n \" [ 298 196 4]\\n\",\n \" ...\\n\",\n \" [ 298 196 1680]\\n\",\n \" [ 298 196 1681]\\n\",\n \" [ 298 196 1682]]\\n\",\n \"[[ 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1682]]\\n\",\n \"[[ 928 9 1]\\n\",\n \" [ 928 9 2]\\n\",\n \" [ 928 9 3]\\n\",\n \" ...\\n\",\n \" [ 928 9 1680]\\n\",\n \" [ 928 9 1681]\\n\",\n \" [ 928 9 1682]]\\n\",\n \"[[ 927 380 2]\\n\",\n \" [ 927 380 3]\\n\",\n \" [ 927 380 4]\\n\",\n \" ...\\n\",\n \" [ 927 380 1680]\\n\",\n \" [ 927 380 1681]\\n\",\n \" [ 927 380 1682]]\\n\",\n \"[[ 924 7 3]\\n\",\n \" [ 924 7 4]\\n\",\n \" [ 924 7 5]\\n\",\n \" ...\\n\",\n \" [ 924 7 1680]\\n\",\n \" [ 924 7 1681]\\n\",\n \" [ 924 7 1682]]\\n\",\n \"[[ 929 136 2]\\n\",\n \" [ 929 136 3]\\n\",\n \" [ 929 136 4]\\n\",\n \" ...\\n\",\n \" [ 929 136 1680]\\n\",\n \" [ 929 136 1681]\\n\",\n \" [ 929 136 1682]]\\n\",\n \"[[ 931 347 1]\\n\",\n \" [ 931 347 2]\\n\",\n \" [ 931 347 3]\\n\",\n \" ...\\n\",\n \" [ 931 347 1680]\\n\",\n \" [ 931 347 1681]\\n\",\n \" [ 931 347 1682]]\\n\",\n \"[[ 917 121 2]\\n\",\n \" [ 917 121 4]\\n\",\n \" [ 917 121 5]\\n\",\n \" ...\\n\",\n \" [ 917 121 1680]\\n\",\n \" [ 917 121 1681]\\n\",\n \" [ 917 121 1682]]\\n\",\n \"[[ 932 435 2]\\n\",\n \" [ 932 435 3]\\n\",\n \" [ 932 435 4]\\n\",\n \" ...\\n\",\n \" [ 932 435 1680]\\n\",\n \" [ 932 435 1681]\\n\",\n \" [ 932 435 1682]]\\n\",\n \"[[ 909 531 1]\\n\",\n \" [ 909 531 2]\\n\",\n \" [ 909 531 3]\\n\",\n \" ...\\n\",\n \" [ 909 531 1680]\\n\",\n \" [ 909 531 1681]\\n\",\n \" [ 909 531 1682]]\\n\",\n \"[[ 934 481 3]\\n\",\n \" [ 934 481 5]\\n\",\n \" [ 934 481 6]\\n\",\n \" ...\\n\",\n \" [ 934 481 1680]\\n\",\n \" [ 934 481 1681]\\n\",\n \" [ 934 481 1682]]\\n\",\n \"[[ 933 202 2]\\n\",\n \" [ 933 202 3]\\n\",\n \" [ 933 202 5]\\n\",\n \" ...\\n\",\n \" [ 933 202 1680]\\n\",\n \" [ 933 202 1681]\\n\",\n \" [ 933 202 1682]]\\n\",\n \"[[ 935 846 2]\\n\",\n \" [ 935 846 3]\\n\",\n \" [ 935 846 4]\\n\",\n \" ...\\n\",\n \" [ 935 846 1680]\\n\",\n \" [ 935 846 1681]\\n\",\n \" [ 935 846 1682]]\\n\",\n \"[[ 938 255 2]\\n\",\n \" [ 938 255 3]\\n\",\n \" [ 938 255 4]\\n\",\n \" ...\\n\",\n \" [ 938 255 1680]\\n\",\n \" [ 938 255 1681]\\n\",\n \" [ 938 255 1682]]\\n\",\n \"[[ 940 610 1]\\n\",\n \" [ 940 610 2]\\n\",\n \" [ 940 610 3]\\n\",\n \" ...\\n\",\n \" [ 940 610 1680]\\n\",\n \" [ 940 610 1681]\\n\",\n \" [ 940 610 1682]]\\n\",\n \"[[ 888 153 1]\\n\",\n \" [ 888 153 2]\\n\",\n \" [ 888 153 3]\\n\",\n \" ...\\n\",\n \" [ 888 153 1680]\\n\",\n \" [ 888 153 1681]\\n\",\n \" [ 888 153 1682]]\\n\",\n \"[[ 925 218 1]\\n\",\n \" [ 925 218 2]\\n\",\n \" [ 925 218 3]\\n\",\n \" ...\\n\",\n \" [ 925 218 1680]\\n\",\n \" [ 925 218 1681]\\n\",\n \" [ 925 218 1682]]\\n\",\n \"[[ 942 131 1]\\n\",\n \" [ 942 131 2]\\n\",\n \" [ 942 131 3]\\n\",\n \" ...\\n\",\n \" [ 942 131 1680]\\n\",\n \" [ 942 131 1681]\\n\",\n \" [ 942 131 1682]]\\n\",\n \"[[ 937 326 1]\\n\",\n \" [ 937 326 2]\\n\",\n \" [ 937 326 3]\\n\",\n \" ...\\n\",\n \" [ 937 326 1680]\\n\",\n \" [ 937 326 1681]\\n\",\n \" [ 937 326 1682]]\\n\",\n \"[[ 926 269 1]\\n\",\n \" [ 926 269 2]\\n\",\n \" [ 926 269 3]\\n\",\n \" ...\\n\",\n \" [ 926 269 1680]\\n\",\n \" [ 926 269 1681]\\n\",\n \" [ 926 269 1682]]\\n\",\n \"[[ 943 282 1]\\n\",\n \" [ 943 282 3]\\n\",\n \" [ 943 282 4]\\n\",\n \" ...\\n\",\n \" [ 943 282 1680]\\n\",\n \" [ 943 282 1681]\\n\",\n \" [ 943 282 1682]]\\n\",\n \"[[ 939 934 1]\\n\",\n \" [ 939 934 2]\\n\",\n \" [ 939 934 3]\\n\",\n \" ...\\n\",\n \" [ 939 934 1680]\\n\",\n \" [ 939 934 1681]\\n\",\n \" [ 939 934 1682]]\\n\",\n \"[[ 936 129 2]\\n\",\n \" [ 936 129 4]\\n\",\n \" [ 936 129 5]\\n\",\n \" ...\\n\",\n \" [ 936 129 1680]\\n\",\n \" [ 936 129 1681]\\n\",\n \" [ 936 129 1682]]\\n\",\n \"[[ 930 286 2]\\n\",\n \" [ 930 286 3]\\n\",\n \" [ 930 286 4]\\n\",\n \" ...\\n\",\n \" [ 930 286 1680]\\n\",\n \" [ 930 286 1681]\\n\",\n \" [ 930 286 1682]]\\n\",\n \"[[ 920 333 1]\\n\",\n \" [ 920 333 2]\\n\",\n \" [ 920 333 3]\\n\",\n \" ...\\n\",\n \" [ 920 333 1680]\\n\",\n \" [ 920 333 1681]\\n\",\n \" [ 920 333 1682]]\\n\",\n \"[[ 941 1007 2]\\n\",\n \" [ 941 1007 3]\\n\",\n \" [ 941 1007 4]\\n\",\n \" ...\\n\",\n \" [ 941 1007 1680]\\n\",\n \" [ 941 1007 1681]\\n\",\n \" [ 941 1007 1682]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"for uij in generate_test_batch(user_ratings, user_ratings_test, item_count):\\n\",\n \" print (uij)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def bpr_mf(user_count, item_count, hidden_dim):\\n\",\n \" u = tf.placeholder(tf.int32, [None])\\n\",\n \" i = tf.placeholder(tf.int32, [None])\\n\",\n \" j = tf.placeholder(tf.int32, [None])\\n\",\n \"\\n\",\n \" with tf.device(\\\"/cpu:0\\\"):\\n\",\n \" user_emb_w = tf.get_variable(\\\"user_emb_w\\\", [user_count+1, hidden_dim], \\n\",\n \" initializer=tf.random_normal_initializer(0, 0.1))\\n\",\n \" item_emb_w = tf.get_variable(\\\"item_emb_w\\\", [item_count+1, hidden_dim], \\n\",\n \" initializer=tf.random_normal_initializer(0, 0.1))\\n\",\n \" \\n\",\n \" u_emb = tf.nn.embedding_lookup(user_emb_w, u)\\n\",\n \" i_emb = tf.nn.embedding_lookup(item_emb_w, i)\\n\",\n \" j_emb = tf.nn.embedding_lookup(item_emb_w, j)\\n\",\n \" \\n\",\n \" # MF predict: u_i > u_j\\n\",\n \" x = tf.reduce_sum(tf.multiply(u_emb, (i_emb - j_emb)), 1, keep_dims=True)\\n\",\n \" \\n\",\n \" # AUC for one user:\\n\",\n \" # reasonable iff all (u,i,j) pairs are from the same user\\n\",\n \" # \\n\",\n \" # average AUC = mean( auc for each user in test set)\\n\",\n \" mf_auc = tf.reduce_mean(tf.to_float(x > 0))\\n\",\n \" \\n\",\n \" l2_norm = tf.add_n([\\n\",\n \" tf.reduce_sum(tf.multiply(u_emb, u_emb)), \\n\",\n \" tf.reduce_sum(tf.multiply(i_emb, i_emb)),\\n\",\n \" tf.reduce_sum(tf.multiply(j_emb, j_emb))\\n\",\n \" ])\\n\",\n \" \\n\",\n \" regulation_rate = 0.0001\\n\",\n \" bprloss = regulation_rate * l2_norm - tf.reduce_mean(tf.log(tf.sigmoid(x)))\\n\",\n \" \\n\",\n \" train_op = tf.train.GradientDescentOptimizer(0.01).minimize(bprloss)\\n\",\n \" return u, i, j, mf_auc, bprloss, train_op\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[5 6]\\n\"\n ]\n }\n ],\n \"source\": [\n \"em = tf.constant([[1,2],[3,4],[5,6]])\\n\",\n \"ccc = tf.nn.embedding_lookup(em, 2)\\n\",\n \"session111 = tf.Session() \\n\",\n \"print (session111.run(ccc))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"epoch: 1\\n\",\n \"bpr_loss: 0.7236263042427249\\n\",\n \"_train_op\\n\",\n \"test_loss: 0.76150036 test_auc: 0.4852939894020929\\n\",\n \"\\n\",\n \"epoch: 2\\n\",\n \"bpr_loss: 0.7229681559433149\\n\",\n \"_train_op\\n\",\n \"test_loss: 0.76061743 test_auc: 0.48528061393838007\\n\",\n \"\\n\",\n \"epoch: 3\\n\",\n \"bpr_loss: 0.7223725006756341\\n\",\n \"_train_op\\n\",\n \"test_loss: 0.7597519 test_auc: 0.4852617720521252\\n\",\n \"\\n\",\n \"Variable: user_emb_w:0\\n\",\n \"Shape: (944, 20)\\n\",\n \"[[ 0.08105529 0.04270628 -0.12196594 ... 0.02729403 0.1556453\\n\",\n \" -0.07148876]\\n\",\n \" [ 0.0729574 0.01720054 -0.08198593 ... 0.05565814 -0.0372898\\n\",\n \" 0.11935959]\\n\",\n \" [ 0.03591165 -0.11786834 0.04123168 ... 0.06533947 0.11889934\\n\",\n \" -0.19697346]\\n\",\n \" ...\\n\",\n \" [-0.05796075 -0.00695129 0.07784595 ... -0.03869986 0.10723818\\n\",\n \" 0.01293885]\\n\",\n \" [ 0.13237114 -0.07055715 -0.05505611 ... 0.16433473 0.04535925\\n\",\n \" 0.0701588 ]\\n\",\n \" [-0.2069717 0.04607181 0.07822093 ... 0.03704183 0.07326393\\n\",\n \" 0.06110878]]\\n\",\n \"Variable: item_emb_w:0\\n\",\n \"Shape: (1683, 20)\\n\",\n \"[[ 0.09130769 -0.16516572 0.06490657 ... 0.03657753 -0.02265425\\n\",\n \" 0.1437734 ]\\n\",\n \" [ 0.02463264 0.13691436 -0.01713235 ... 0.02811887 0.00262074\\n\",\n \" 0.08854961]\\n\",\n \" [ 0.00643777 0.02678963 0.04300125 ... 0.03529688 -0.11161\\n\",\n \" 0.11927075]\\n\",\n \" ...\\n\",\n \" [ 0.05260892 -0.03204868 -0.06910443 ... 0.03732759 -0.03459863\\n\",\n \" -0.05798787]\\n\",\n \" [-0.07953933 -0.10924194 0.11368059 ... 0.06346208 -0.03269136\\n\",\n \" -0.03078123]\\n\",\n \" [ 0.03460099 -0.10591184 -0.1008586 ... -0.07162578 0.00252131\\n\",\n \" 0.06791534]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"with tf.Graph().as_default(), tf.Session() as session:\\n\",\n \" u, i, j, mf_auc, bprloss, train_op = bpr_mf(user_count, item_count, 20)\\n\",\n \" session.run(tf.initialize_all_variables())\\n\",\n \" for epoch in range(1, 4):\\n\",\n \" _batch_bprloss = 0\\n\",\n \" for k in range(1, 5000): # uniform samples from training set\\n\",\n \" uij = generate_train_batch(user_ratings, user_ratings_test, item_count)\\n\",\n \"\\n\",\n \" _bprloss, _train_op = session.run([bprloss, train_op], \\n\",\n \" feed_dict={u:uij[:,0], i:uij[:,1], j:uij[:,2]})\\n\",\n \" _batch_bprloss += _bprloss\\n\",\n \" \\n\",\n \" print (\\\"epoch: \\\", epoch)\\n\",\n \" print (\\\"bpr_loss: \\\", _batch_bprloss / k)\\n\",\n \" print (\\\"_train_op\\\")\\n\",\n \"\\n\",\n \" user_count = 0\\n\",\n \" _auc_sum = 0.0\\n\",\n \"\\n\",\n \" # each batch will return only one user's auc\\n\",\n \" for t_uij in generate_test_batch(user_ratings, user_ratings_test, item_count):\\n\",\n \"\\n\",\n \" _auc, _test_bprloss = session.run([mf_auc, bprloss],\\n\",\n \" feed_dict={u:t_uij[:,0], i:t_uij[:,1], j:t_uij[:,2]}\\n\",\n \" )\\n\",\n \" user_count += 1\\n\",\n \" _auc_sum += _auc\\n\",\n \" print (\\\"test_loss: \\\", _test_bprloss, \\\"test_auc: \\\", _auc_sum/user_count)\\n\",\n \" print (\\\"\\\")\\n\",\n \" variable_names = [v.name for v in tf.trainable_variables()]\\n\",\n \" values = session.run(variable_names)\\n\",\n \" for k,v in zip(variable_names, values):\\n\",\n \" print(\\\"Variable: \\\", k)\\n\",\n \" print(\\\"Shape: \\\", v.shape)\\n\",\n \" print(v)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(944, 20)\\n\",\n \"[[ 0.08105529 0.04270628 -0.12196594 ... 0.02729403 0.1556453\\n\",\n \" -0.07148876]\\n\",\n \" [ 0.0729574 0.01720054 -0.08198593 ... 0.05565814 -0.0372898\\n\",\n \" 0.11935959]\\n\",\n \" [ 0.03591165 -0.11786834 0.04123168 ... 0.06533947 0.11889934\\n\",\n \" -0.19697346]\\n\",\n \" ...\\n\",\n \" [-0.05796075 -0.00695129 0.07784595 ... -0.03869986 0.10723818\\n\",\n \" 0.01293885]\\n\",\n \" [ 0.13237114 -0.07055715 -0.05505611 ... 0.16433473 0.04535925\\n\",\n \" 0.0701588 ]\\n\",\n \" [-0.2069717 0.04607181 0.07822093 ... 0.03704183 0.07326393\\n\",\n \" 0.06110878]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print (values[0].shape)\\n\",\n \"print (values[0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(20,)\\n\",\n \"[ 0.08105529 0.04270628 -0.12196594 -0.0118052 0.00052658 0.03041512\\n\",\n \" 0.08178367 0.00329294 0.07350662 0.09376822 0.08431987 -0.0375998\\n\",\n \" 0.08964082 0.20457171 0.08042991 0.07238016 -0.00179652 0.02729403\\n\",\n \" 0.1556453 -0.07148876]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print (values[0][0].shape)\\n\",\n \"print (values[0][0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(1, 20)\\n\",\n \"[[ 0.08105529 0.04270628 -0.12196594 -0.0118052 0.00052658 0.03041512\\n\",\n \" 0.08178367 0.00329294 0.07350662 0.09376822 0.08431987 -0.0375998\\n\",\n \" 0.08964082 0.20457171 0.08042991 0.07238016 -0.00179652 0.02729403\\n\",\n \" 0.1556453 -0.07148876]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"session1 = tf.Session()\\n\",\n \"u1_dim = tf.expand_dims(values[0][0], 0)\\n\",\n \"print (u1_dim.shape)\\n\",\n \"print (session1.run(u1_dim))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(1, 20)\\n\",\n \"(1683, 20)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print (u1_dim.shape)\\n\",\n \"print (values[1].shape)\\n\",\n \"u0_all = tf.matmul(u1_dim, values[1],transpose_b=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(1, 1683)\\n\",\n \"[[-0.07065812 -0.02992807 -0.01091636 ... -0.03492806 -0.01390784\\n\",\n \" 0.04102187]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print (u0_all.shape)\\n\",\n \"print (session1.run(u0_all))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[-0.07065812 -0.02992807 -0.01091636 ... -0.03492806 -0.01390784\\n\",\n \" 0.04102187]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"session1 = tf.Session()\\n\",\n \"u1_dim = tf.expand_dims(values[0][0], 0)\\n\",\n \"u1_all = tf.matmul(u1_dim, values[1],transpose_b=True)\\n\",\n \"result_1 = session1.run(u0_all)\\n\",\n \"print (result_1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"以下是给用户0的推荐：\\n\",\n \"405 0.11510968\\n\",\n \"1117 0.12954654\\n\",\n \"1256 0.099157736\\n\",\n \"1260 0.12162529\\n\",\n \"1627 0.09925515\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"以下是给用户0的推荐：\\\")\\n\",\n \"p = numpy.squeeze(result_1)\\n\",\n \"p[numpy.argsort(p)[:-5]] = 0\\n\",\n \"for index in range(len(p)):\\n\",\n \" if p[index] != 0:\\n\",\n \" print (index, p[index])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\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\": \"3.6.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "classic-machine-learning/dbscan_cluster.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C) 2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"用scikit-learn学习DBSCAN聚类 https://www.cnblogs.com/pinard/p/6217852.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from sklearn import datasets\\n\",\n \"%matplotlib inline\\n\",\n \"X1, y1=datasets.make_circles(n_samples=5000, factor=.6,\\n\",\n \" noise=.05)\\n\",\n \"X2, y2 = datasets.make_blobs(n_samples=1000, n_features=2, centers=[[1.2,1.2]], cluster_std=[[.1]],\\n\",\n \" random_state=9)\\n\",\n \"\\n\",\n \"X = np.concatenate((X1, X2))\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], marker='o')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.cluster import KMeans\\n\",\n \"y_pred = KMeans(n_clusters=3, random_state=9).fit_predict(X)\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=y_pred)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.cluster import DBSCAN\\n\",\n \"y_pred = DBSCAN().fit_predict(X)\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=y_pred)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"y_pred = DBSCAN(eps = 0.1).fit_predict(X)\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=y_pred)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"y_pred = DBSCAN(eps = 0.1, min_samples = 10).fit_predict(X)\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=y_pred)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\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\": \"3.6.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "classic-machine-learning/decision_tree_classifier.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C)\\n\",\n \"2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"scikit-learn决策树算法类库使用小结 https://www.cnblogs.com/pinard/p/6056319.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sklearn.datasets import load_iris\\n\",\n \"from sklearn import tree\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"iris = load_iris()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"clf = tree.DecisionTreeClassifier()\\n\",\n \"clf = clf.fit(iris.data, iris.target)\\n\",\n \"with open(\\\"iris.dot\\\", 'w') as f:\\n\",\n \" f = tree.export_graphviz(clf, out_file=f)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from IPython.display import Image \\n\",\n \"import pydotplus \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"dot_data = tree.export_graphviz(clf, out_file=None, \\n\",\n \" feature_names=iris.feature_names, \\n\",\n \" class_names=iris.target_names, \\n\",\n \" filled=True, rounded=True, \\n\",\n \" special_characters=True) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"graph = pydotplus.graph_from_dot_data(dot_data) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"Image(graph.create_png()) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dot_data = tree.export_graphviz(clf, out_file=None)\\n\",\n \"graph = pydotplus.graph_from_dot_data(dot_data) \\n\",\n \"graph.write_pdf(\\\"iris.pdf\\\") \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\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\": \"3.6.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "classic-machine-learning/decision_tree_classifier_1.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C) 2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"scikit-learn决策树算法类库使用小结 https://www.cnblogs.com/pinard/p/6056319.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from itertools import product\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"from sklearn import datasets\\n\",\n \"from sklearn.tree import DecisionTreeClassifier\\n\",\n \"\\n\",\n \"\\n\",\n \"# 仍然使用自带的iris数据\\n\",\n \"iris = datasets.load_iris()\\n\",\n \"X = iris.data[:, [0, 2]]\\n\",\n \"y = iris.target\\n\",\n \"\\n\",\n \"# 训练模型，限制树的最大深度4\\n\",\n \"clf = DecisionTreeClassifier(max_depth=4)\\n\",\n \"#拟合模型\\n\",\n \"clf.fit(X, y)\\n\",\n \"\\n\",\n \"\\n\",\n \"# 画图\\n\",\n \"x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\\n\",\n \"y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\\n\",\n \"xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\\n\",\n \" np.arange(y_min, y_max, 0.1))\\n\",\n \"\\n\",\n \"Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\\n\",\n \"Z = Z.reshape(xx.shape)\\n\",\n \"\\n\",\n \"plt.contourf(xx, yy, Z, alpha=0.4)\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=y, alpha=0.8)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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fTTz8tafBEiLWFdTgcTqqysfrBrFy5ckhjLa+//rokZbV7FQAAAHLLZY72VhIAgFHlcrm0bds2rVmzxulQMlJRUTHgttDWryWrj0u695ZMx400r9ebcrlQeXm5qqurB4xRkurr61VSUpLR/GzGStL69etVU1Ojzs7OEem544S6ujrdeeedo/49AgAAOIUKGQBATlVWVioSicjj8UjqWcbU1tbmcFTZ27Jli0KhkH0fHo9HoVBIjzzySEbzi4uL1dzcbC858ng8ikQiKRMs2YyVpJqaGkkj0wAZAAAAo4MKGQAY58ZbhUw6LpcrbcUHJh8qZAAAwERHhQwAIGesLaVbWlrsY4lEQlVVVZKkW2+91anQAAAAgJw62+kAAACTR0NDg7xer5YuXdrvM4/HoxUrVjgQFQAAAJB7VMgAAHLG4/GosbHR3v5a6mlMG4lEVFtbK7fb7WB0AAAAQO5QIQMAyKnCwkIVFhaqsrLS6VAAAAAAx1AhAwAAAAAAkGMkZAAAk47VXHi8MwxjwPuw7jPVq69EIqFwOCyv1yuXyyWv16v6+nolEonRvAUAAIBJiyVLAACMQ7FYTF6vN+3nHR0dWZ3v61//umpqauz3hmHIMAx5PB41NDQMOU4AAACkRoUMAADjTEtLiwoKCjIaGwwGZZpmv1dvsVhMNTU18vv9isfjMk1T8Xhc5eXlMgxD7e3to3EbAAAAkxoJGQAAxpGqqiotXbpUkUhkwHH79++XJF133XWDnnPv3r2SpNLSUuXl5UmS8vLyVF5eLkl65ZVXhhMyAAAAUiAhAwAYsmg0qvXr19t9SSoqKhSLxfqNi8ViqqqqssdZ/Ul6693bxOqN4vV6ZRiGPaa+vt4eN9D8vuMy7YPS+368Xq+i0eiw7ruvgXq6ZNrXxufzqaGhQcXFxRndUyas5U2zZ89OOn7ppZdKkt58880RuxYAAAB+zwQAjGuSzG3btuX8ug0NDaaklK/GxsaMxkUikaT7kJRyfGtrq+n3+4c83+PxJMVuHe8t1fklmX6/f0j3nUq6eb1f2RhoTjAYtJ9dKBSyx4ZCIbO7uzvj8wwlrpGwbds2R64LAACQK1TIAACGxGooa/UcMU1Tzc3NkqTt27f3G9fc3GyPi8fjkqSSkpJ+5927d6+6u7tlmqYaGxslye6X0vd4qvnhcDipD4rf75dhGGmrXaSeipdAICC/329fo7u7W36/X4FAIKn6JdP7TsVM0cul72ukFRQUqKyszH5fVlam0tJSdk8CAABwGAkZAMCQeDweST1JiGg0qkQioSVLlsg0TVVXV9vjrETDvHnzFIvFZBiGwuFw2vNu2LBBbrdbklRYWGgf9/l8KY/3FQwGk/qgrFu3zo4znT179vS7htvtls/nkyTt3r076/t2mhV770SYaZqKRCIyDEO7du1yOEIAAIDJzWWOxn+OAwDkjMvl0rZt27RmzZqcXjcWiyXt9OPxeHT//fenTJZUVFQoEAikPI/1a8jqn9L311Kmx9ONy2RsJr1brLHZ3He6ODK5TiYGuufB5vXezjqbZ5crdXV1uvPOO3N+XQAAgFyhQgYAMCT5+fkyTVOtra0KBoMyDENFRUXyer1JS3zC4bACgYDKy8vV2Nio1tZWdXZ2Ohj58GR632Nd72bJfr9fkvotY7LeW58DAABg5JCQAQAMS35+vjZu3Kh4PK7GxkYZhpFUQWL1L6murlZhYaHy8/M1bdq0UYvH2jHI0t7eLmngpIK1vbPVPyaT3i6D3Xcquewh4/V65XK50iZZrHuWpGuuuUaS+iXKDh06JEn2EjAAAACMHBIyAIAhsbZ9bmlpkdTzR/v8+fPTjrcSI4lEQsFgcNTiCofDdlKmo6NDtbW1kqTly5ennbN69WpJPf1nurq67OPRaFQul0tVVVX2sWzv2ynWEra+vWKs99Y9S9KiRYskSbW1tUnP7umnn5YkLV68eNTjBQAAmGzOdjoAAMD4tHbtWtXU1Gjp0qX9PguFQvY/RyIRlZSUaOHChSnP097ergULFoxobHPmzEl67/f7B+zxUlhYaO+o1LfXjcfjUWlpqf0+0/t22ooVK+TxeFRSUtJvN6q+zyM/P18ejyfl/ZeXlys/Pz8nMQMAAEwmVMgAAIZkyZIlam1tTVoK5Pf71dDQYO9sJEnFxcVJiQq/36+2tja1trZKkpqamkY0rsrKSrsCx+PxqLGxUZWVlRnNi0QiSUt5QqGQtmzZolmzZtnHMr1vp7ndbtXW1ioSidg7Q1l9fFI9jy1btigUCtljPR6PQqGQHnnkkZzGDQAAMFmwyxIAjHNO7bI01ji1GxBGB7ssAQCAiY4KGQAAAAAAgBwjIQMAAAAAAJBjJGQAAAAAAAByjF2WAAATAr1GAAAAMJ5QIQMAAAAAAJBjJGQAAEPmcrns3Y3GEyvuvvH3PZ5qjCWRSCgcDsvr9crlcsnr9aq+vl6JRGLE4hrv1x/sHAAAAJMZ214DwDjn5LbX43Wr6b7JAdM01dHRoTlz5qSd0/ce169fr5qamn7jPB6PGhoaso5pIl4/1XPOFNteAwCAiY4KGQDApGWaZr8/+IPBoH2896u3WCymmpoa+f1+xeNxmaapeDyu8vJyGYah9vb2Icc0ka6fai4AAAB6kJABAEDS/v37JUnXXXfdoGP37t0rSSotLVVeXp4kKS8vT+Xl5ZKkV155hesDAABgQCRkAGAScblcWr9+fcrP1q9fL5fLZfcAicViqqqqsvt/WD1CBjt/ql4h6Y5Ho1H7ul6vV9FoNOP7GOw1mjo6OiRJs2fPTjp+6aWXSpLefPNNrg8AAIABkZABgEkkGAyqpqZGXV1dSce7urpUU1OjYDAot9stwzBUUFAgn89njzEMQyUlJYMmZTJVUVGhoqIiuw+JYRgqKipSRUXFiJw/W6+++qok6aKLLlI4HLYTO+FwuF+j3EAgIElyu91Jx2fNmpX0OdcHAABAOiRkAGAS+dM//VNJ6leJYr33eDySJK/XK0lqbm62+4DE43FJUklJybDjiEajCgQC8vv96u7ulmma6u7ult/vVyAQUCwWG3B+qh4nA/U8yUZBQYHKysrs92VlZSotLR3W7kVcHwAAAH2RkAGASSQ/P18ej0d1dXVJx+vq6lReXq4FCxZI+ijhMW/ePMViMRmGoXA4PGJx7NmzR5Lk8/nsKgu3221X5OzevXvErpUp69q9k1CmaSoSicgwDO3atYvrAwAAYMSw7TUAjHPZbnsdjUZVVFSktrY2LViwQO3t7Vq4cKEaGxtVWFhoj6uoqEi79MT61dF32+t022CnGzeQgX49jdT8TH8FulyulNs5p5o/GluBj/frDyUmtr0GAAATHRUyADDJXH/99ZKkpqYmSR/tiGMdl6RwOKxAIKDy8nI1NjaqtbVVnZ2duQ92DDEMw/5nv98vSf2W8Vjvrc+5PgAAANIhIQMAk4zb7VYoFFJZWZm6urpUUlKiUCiU1KDV6iFSXV2twsJC5efna9q0aUO6Xt8GwpLs7ZGt/jHZ9oAZjR4yXq83aZcpi/XeilmSrrnmGknql6Q6dOiQJNlbQXN9AAAApENCBgAmoVtvvVXSR9sWf/azn005rr29XVLPH+XBYHDQ81pNgVtaWux5mzdv7jdu9erVknp2feqdsIlGo3K5XKqqqsr0VkaMteSrb68U670VsyQtWrRIklRbW2tvAd3R0aGnn35akrR48WKuDwAAgIGZAIBxTZK5bdu2rOeVl5ebkszy8vJ+n0UiEVNS2ldbW5t97d6/SlLNCwaD/caZpmn6/f6U5/Z4PGZnZ2fW95ONVPF0d3ebHo8nZUx+v7/fOdKN7fs8U10rlYl6/Wxi6G3btm1ZzwEAABhP+DcdABjnhpqQaWxsNCWZzc3NKT8PhUJJf5C3tbWZra2tpiQzFArZ1+77R3MkErH/WB9onDXWSgxZ40c7GTNQPN3d3Unxl5eXm42NjSnP0dnZaYZCIXusx+MxQ6GQ2d3dndG1UpmI1882BgsJGQAAMNGxyxIAjHPZ7rKE0dmJaLDrOfnrdixcX2KXJQAAgN7oIQMAwChqaWlRKBSatNcHAABAaiRkAACTlsvlsqs3RssLL7ygdevWjeo1xur1c/F8AQAAxisSMgAAjKKNGzdO6usDAAAgtbOdDgAAgFyjL0lu8JwBAADSo0IGAAAAAAAgx0jIAABygn4i2evo6HA6BAAAAIwSEjIAAIxBVVVVmjNnjtNhAAAAYJSQkAEAYAzy+XxOhwAAAIBRREIGAAAAAAAgx0jIAACGLZFIqL6+Xl6vVy6XS+vXr1d7e/ug82KxmKqqquz+Ml6vV/X19f3GRaNRrV+/3h5XUVGhWCw25HF9WeMHeg0mm2v3Huv1ehWNRvvF0ze23no/63TPLJuYMv0eAAAAMIJMAMC4Jsnctm2bozF4PB5TUr9Xa2urPcY6ZmloaEg5R5IZiUQyGtfY2Jj1uFTSzev9Gkg21/b7/SnH+f3+AePJZv5IPbfe30Oubdu2bdDnDgAAMJ5RIQMAGBbDMGQYhvx+v7q7u2WapiKRiCSppqYm7Tyv1ytJam5ulmmaMk1T8XhcklRSUtJvXDwet8c1NzdLkrZv3571uFSs8QO9BpLptaPRqAKBQNKz6u7ult/vVyAQsKtXel+v9/V7z7euFY/H7fm9K22yfW6DfQ8AAAAYWSRkAADD8q//+q+SpA0bNsjtdkuSiouLZZqmqqur086z/vifN2+eYrGYDMNQOBzuN87j8UjqSSJEo1ElEgktWbKk3/kzHTcaMr32nj17JPU07LWeldvtthv47t69e8DrWImUdevWKS8vT5KUl5endevWJX2eTUyZfg8AAAAYWS5zsP/sBwAY08477zyFQiGVlpY6cn2rv8lgv05SjauoqFAgEEg53hoXi8VUUFBgH/d4PLr//vtVWFiYND7TcQPFNpCB7i/Ta2dznVTPa6Bn3fezbJ5HJt9Drm3dulV/8zd/o+PHjztyfQAAgNFGhQwAjHMXXnih3n//fafDyFo4HFYgEFB5ebkaGxvV2tqqzs7OfuPy8/NlmqZaW1sVDAZlGIaKiork9XqTGtRmOm40OHnt4caU6feQa0ePHtVFF13kdBgAAACjhgoZABjnbr/9duXl5WnLli2OXH/9+vWqqalRZ2enZs2alXZc3wqOVNUeiURC06dP73e8t46ODu3fv19FRUUjMm40pLu29ay6u7vtJUvppHo+1vx4PG4vWZKk9vZ2LVy4UOXl5WmXZ6WLaajfw2i755571NHRoX//93935PoAAACjjQoZABjnbrnlFr3wwguOXf/WW2+VJG3evFmJREJSz7bM1vbXg7G2x04kEgoGg/0+t7ZtbmlpkdTTM2X+/PlDHjcaMr326tWrJUnBYFBdXV328Wg0KpfLpaqqqn5zrGfae344HFZHR4eknkRLbW2tJGnlypVZx2QZ7HvItRdeeEG33HKL02EAAACMnhHYqQkA4KDXXnvNlGTu27fPsRiGsu11JBIZcJvptrY20zRNs7m5Oe2YUChkny/TcaMhm2un27ba4/GYnZ2d9rjez7S8vHzQ+X23vc40pky/h1zat2+fKcl87bXXcn5tAACAXKFCBgDGuWuvvVZLly5VKBRyLIba2tqk6/v9frW1tSk/Pz/tnOLi4pRzWltbJUlNTU2SpCVLlqi1tVV+vz9pbENDg727UDbjRkM2166srFQkElF5ebl9LBQKacuWLUlLviorK+0xhw8f7jff2kXJ4/EoEomosrJySDFl+j3kUigU0tKlS3Xttdfm/NoAAAC5Qg8ZAJgA9uzZoz//8z/Xvn37knqLAONNR0eHFi1apH/5l3/R8uXLnQ4HAABg1JCQAYAJ4i/+4i909tln66mnnnI6FGDIvvjFL+rUqVN65plnnA4FAABgVJGQAYAJ4uDBg7r66qtVU1OjtWvXOh0OkLUnnnhC5eXleuuttzR37lynwwEAABhV9JABgAli7ty5+sd//PwUgZQAACAASURBVEeVlZVpz549TocDZGXPnj0qKyvTP/7jP5KMAQAAkwIVMgAwwfzt3/6ttm/frl27dun66693OhxgUC+//LJWrFih1atX67HHHnM6HAAAgJygQgYAJpgf/OAHWrZsmW699Vbt2LHD6XCAAe3YsUO33nqrli1bph/84AdOhwMAAJAzJGQAYIKZMmWKnnzySZWVlemOO+7Qgw8+qOPHjzsdFpDk+PHjevDBB3XHHXeorKxMTz75pKZMmeJ0WAAAADnDkiUAmMBCoZA2btyoWbNmKRgMatWqVU6HBGjHjh3y+Xzq6upSVVWVysrKnA4JAAAg56iQAYAJrKysTO3t7brxxhv1l3/5l7rhhhv0ox/9SO+9957ToWGSee+991RTU6MbbrhBf/mXf6kbb7xR7e3tJGMAAMCkRYUMAEwSL7/8sjZv3qwdO3bo17/+tebMmaMrrrhC06dPHxdLRU6ePKmpU6c6HcaYMF6exenTp9Xd3a133nlH8XhcbrdbX/jCF7RhwwYaTgMAgEmPhAwATDK/+93v9OKLL+qll17SgQMH9MEHH+jMmTNOhzWgw4cP6+WXX9af/dmf6ZxzznE6HEf97ne/07/927/p+uuv12WXXeZ0OAM666yzNGPGDF1xxRW64YYbdNNNN0367w8AAMBCQgYAMKYdO3ZMixYtUlFRkR5//HGnwxkT7r77bjU2Nmrfvn06//zznQ4HAAAAQ0APGQDAmPbQQw/p+PHjevTRR50OZcx49NFHdfz4cT300ENOhwIAAIAhIiEDABizYrGYNm3apIcfflgzZ850OpwxY+bMmXr44Ye1adMmxWIxp8MBAADAELBkCQAwJpmmqc985jOSpF/84hdyuVwORzS28HwAAADGNypkAABjUjgc1t69e1VdXU2yIQWXy6Xq6mrt3btX4XDY6XAAAACQJSpkAABjzpEjR3TVVVfpy1/+soLBoNPhjGk+n08/+clP9Pbbb7OsCwAAYBwhIQMAGHPuvvtuRaNRvfXWW+wiNIhjx47p6quvVmFhYUa7UGVSbZTtvxpY5+RfKQAAADLHkiUAwJjS1NSkrVu3atOmTSRjMnD++edr06ZN2rp1q5qampwOBwAAABmiQgYAMGacPHlSBQUFmjdvngzDcDqcccXj8ejAgQNqbW3V1KlT044bjWoWKmQAAACyR4UMAGDMCAaDOnTokDZv3ux0KOPO5s2bdejQIXruAAAAjBMkZAAAY8LBgwcVCATk9/t1+eWXOx3OuHP55ZfL7/crEAjo4MGDI3beWCymqqoquVwuuVwueb1e1dfXDzovGo1q/fr19ryKigrFYrFBx3q9XkWj0RGLHwAAYKxiyRIAYEzIdMkN0stkyVc2y4sMw5DX6035WSQSUXFxccpzDjSvsbFRhYWF9vuKigoFAoF+4/x+vyorKweNEQAAYLyiQgYA4LhnnnlGO3fuVHV1NcmYYZg6daqqq6u1c+dOPfPMMwOOtSpX+r56s5Iqzc3NMk1TpmkqHo9LkkpKStKe25oXj8ftec3NzZKk7du32+Oi0ahdFdXd3S3TNNXd3W1X+qSrqAEAAJgISMgAABx17Ngx3Xfffbrrrru0bNkyp8MZ95YtW6a77rpL9913n44dOzasc1nJlHnz5ikWi8kwDIXD4UHneTweST3Jl2g0qkQioSVLlsg0TVVXV9vj9uzZI0ny+Xxyu92SJLfbLZ/PJ0navXv3sOIHAAAYy1iyBABwlM/n0+OPP659+/Zp5syZToczIRw5ckSLFi3S3Xff3a/Jb7Y7IqVbUtT7HH3PGYvFVFBQYI/zeDy6//77k5Yq9Z43EP41BQAATFRUyADAOJRuuclAy0/Golgspk2bNunhhx8mGTOCZs6cqe985zvatGnTsJb9hMNhBQIBlZeXq7GxUa2trers7Bx0Xn5+vkzTVGtrq4LBoAzDUFFRkbxeL8uQAAAAfo8KGQAYhzJNtozl/4s3TVOf+cxnJEm/+MUvxkUCaTxJ93yzqZBJNTaRSGj69OlJxwc7Z0dHh/bv36+ioqKkcevXr1dNTY26u7vtJUsAAACTBRUyADAOWb09rNdgx8eicDisvXv3qrq6mmTMKHC5XKqurtbevXsz6vsykPb2dkk9yZi+S6BSsbawbmlpkSTl5eVp/vz5/catXr1akhQMBtXV1WUfj0ajcrlcqqqqGlbcAAAAYxkVMgAwAWTbF8RpR44c0VVXXaUvf/nLGf2Bj6Hz+Xz6yU9+orffflszZ87M6melvr5+wN2U2tratGDBgn7nbGlp0dKlS1POCYVCWrdunf0+XY8aj8ejLVu2aNasWYPGCQAAMB6RkAGACWCwP7Ktz+PxuO69917l5+ersrIy7bx0x6PRqLZv366ampq0jVozcffddysajeqtt97S+eefn/V8ZO7YsWO6+uqrVVhYqMcff9zpcAAAAPB7LFkCgEkkHA7LMAxdc801Wc+tqKhQUVGRampqJMlu1FpRUZHVeZqamrR161Zt2rSJZEwOnH/++dq0aZO2bt2qpqYmp8MBAADA71EhAwATQKYVMpFIRMXFxYPO63s8Go2qqKhIfr9fPp9Pbrfb7icSCAQyXip18uRJFRQUaN68eTIMI4s7xHB5PB698847isVimjp1qtPhAAAATHpUyADAJDKU5UWStGfPHkmykzGS5Ha75fP5sjpPMBjUoUOHtHnz5iHFgaHbvHmz4vE4PXsAAADGCCpkAGACyLRCJtNeMX2PD7YLUia/Sg4ePKhPfvKT8vv9+sY3vjHoeIy873znOwoEAnrjjTc0d+5cp8MBAACY1EjIAMAEMB4SMh6PRwcOHFBraytLZhzCkjEAAICxgyVLAIAkXV1d/Y6Vl5dLkrq7u2WaZr/XYJ555hnt3LlT1dXVJGMcNHXqVFVXV2vnzp165plnnA4HAABgUiMhAwCTmMfjkSS1tLRIkhKJRMr+LqtXr5bU0wOmd8ImGo0OWj1z7Ngx3Xfffbrrrru0bNmykQodQ7Rs2TLddddduu+++3Ts2DGnwwEAAJi0SMgAwCS2Zs0aSdLSpUvlcrk0ffp0TZ8+vd+4wsJC+f1+BQIBzZ49Wy6XSy6XS0VFRXZSJ52HHnpIx48f16OPPjoq94DsPfroozp+/Lgeeughp0MBAACYtOghAwATwFB7yEhSfX296urqZBiGQqGQ1q1bl3Z8fX29mpqaVFNTI0kKhUL6/Oc/r1mzZqW8biwW0w033KDHHntMZWVlQ7s5jIpwOKyvfOUr+uUvf6n8/HynwwEAAJh0SMgAAEaFaZr6zGc+I0n6xS9+MejSJuQW3w8AAICzWLIEABgV4XBYe/fuVXV1NX/sj0Eul0vV1dXau3evwuGw0+EAAABMOlTIAABG3JEjR3TVVVfpy1/+soLBoNPhYAA+n08/+clP9Pbbb2vmzJlOhwMAADBpkJABAIy4u+++W9FoVG+99ZbOP/98p8PBAI4dO6arr75ahYWFevzxx+3jp06d0lNPPaWioiLNnj3buQABAAAmKJYsAQBGVFNTk7Zu3apNmzaRjBkHzj//fG3atElbt25VU1OTpJ6eMp/85Cd15513qrq62uEIAQAAJiYqZAAAI+bkyZMqKCjQvHnzZBiG0+EgCx6PR+3t7Vq6dKm2bt2qs846S2fOnNFtt92mZ5991unwAAAAJpyznQ4AADBxBINBHTp0SDt37nQ6FGTBNE3dcsst+vd//3cdPHhQpmnq9OnTkqSWlhaZpkljZgAAgBHGkiUAwIg4ePCgAoGA/H6/Lr/8cqfDQYbeeOMN3XTTTfrGN76h3/3udzp58mTS57/+9a/V3t7uUHQAAAATFwkZAMCI+OpXv6rLL79cPp/P6VCQoe9+97u69tpr9ctf/lJnzpxJOWbKlCnau3dvjiMDAACY+EjIAACG7ZlnntHOnTtVXV2tqVOnOh0OMvSzn/1MUs+OSumcddZZ+n//7//lKCIAAIDJg4QMAGBYjh07pvvuu0933XWXli1b5nQ4yMLOnTv1rW99Sy6XK22PmJMnT+r555/PcWQAAAATH7ssAQCGxefz6fHHH9e+ffs0c+ZMp8PBEOzYsUN33nmnTp48mbJaZsqUKfr1r3+tP/iDP3AgOgAAgImJChkAwJDFYjFt2rRJDz/8MMmYcWzVqlXau3evPv7xj6dccnb69Gn98pe/dCAyAACAiYuEDABgUI2NjXrggQfU1dVlHzNNU1/5yle0ePFirVu3zsHoMBI++clP6tVXX9VNN92kKVOmJH02depUtbS0OBQZAADAxERCBgAwqB07duj73/++rrjiCv3oRz/SmTNnFA6HtXfvXlVXV6ftP4Lx5cILL9Tu3bt17733Jh0/c+aMXnzxRYeiAgAAmJjoIQMAGNR1112n1tZWSZLL5VJBQYE6Ojp09913KxgMOhwdRsMTTzyhdevW6cyZMzp9+rQuvvhiHTlyxOmwAAAAJgwqZAAAAzpx4oTeeOMN+71pmnr99df1wQcf6Pjx4/rwww8djA6jZe3atXr++ed14YUXSpKOHj2qd9991+GoAAAAJg4qZAAAA3rxxRd18803p/xs6tSpmjFjhh577DHdcccdOY5sYvjd736nF198US+99JIOHDigDz74QGfOnHE6LNvx48fV1NSkDz/8UDfeeKPy8vKcDglpnHXWWZoxY4bmzZunT3/607rpppt0zjnnOB0WAABIgwoZAMCAWlpaUu68I0knT57U0aNHtXr1at111105jmx8e/nll7V27VrNnDlTy5cv12OPPab29vaU20476bzzztPtt9+ua665xq6Wwdh06tQptbe367HHHtPy5cs1c+ZMrV27Vi+//LLToQEAgBTOdjoAAMDY1tLSklHFxpIlS3IQzfj33//939q4caPq6+v1x3/8x3r00Ufl9Xp1ySWXOB0aJpD33ntPDQ0NCoVC+vSnP63i4mJVVVXp0ksvdTo0AADweyxZAgAM6LLLLtN//dd/pfzs7LPP1rnnnqunnnpKK1asyHFk408oFNLGjRs1a9YsBYNBrVq1yumQMAns2LFDPp9PXV1dqqqqUllZmdMhAQAAsWQJADCA9957L20yZurUqZozZ45efvllkjGDOH36tL72ta9p/fr1+trXvqY33niDZAxyZtWqVXrjjTeSfgZPnz7tdFgAAEx6LFkCAKTV3Nwsl8ulvsWUZ511loqKilRfXy+32+1QdOPD6dOn9aUvfUn/9m//pqeffppEDBxx3nnn6Vvf+pYKCgpUWlqqjo4OPfnkk5oyZYrToQEAMGlRIQMASKulpSVplxaXyyWXy6V/+Id/0M6dO0nGZOCrX/2qnn/+eTU1NZGMgeNWrVqlpqYmPf/88/rqV7/qdDgAAExqJGQAAGn94he/0G9/+1tJPf1izjnnHNXV1enhhx/WWWfxK2QwNTU12rJli5588kldf/31ToczLFYyzsnzphrb0dEx5PONtlgslvW9DfTKdPxgrr/+ej355JPasmWLampqhnRvAABg+Pi3aQBASqdPn7a3y506dapmzZql5uZmFRcXOxzZ+HDw4EE98MADCoVCWr58udPhTEhVVVWaM2eO02Gk1NXVpYKCghE7n8fjsf+5bxJqKJYvX65QKKQHHnhABw8eHPb5AABA9kjIAABSeuONN+zqmBtuuEGvvvqqrrvuOoejGj82btwoj8ejtWvXOh3KmGaaZr8eRZny+XwjHE1qsVhMVVVVWc158MEHsxpvPYe+r9bWVklSMBjsNycYDKack6m1a9fK4/Fo48aNWcUKAABGBk19AYxpR48e1Z49exSLxfTf//3f+vDDD50OadI4cOCAJGnu3Ln6+Mc/rnvvvdfhiD5ywQUX6NJLL1V+fr6WL1+uiy++2OmQkuzZs0fPPvus9u3b53QoGIaWlhY98cQT9rKeTBMXVVVVOnz48LCvb1XZhEIhLViwwD6+f/9+SRqRBGkwGNSiRYu0Z88eKrkAAMgxlznU/ywFAKPk1KlTevLJJxWq+aFeeLFFZ7mkBZf8oWZ+bIrOn+p0dJPHydOmEidO6eKPjb2HfuykdOQ3p9X+3q91xpQ+c/NSrfub9frSl76ks892/r81LF26VEuXLtX3vve9jMZHo1Ft377d/sPf7/frjjvuUH5+/oBjPR6P7r//fhUWFiaNsfqImKap+vp6lZSUSJIikYhWrFjRrxlzLBbT7t277YoTj8ejNWvWJC1P633OvlpaWrR06VL5/X5VVlbax9vb27Vw4UK1trYm3cv69etVU1Oj1tZWe1lP3/PW19errq5OhmEoEomouLg4KYZUvVJ6H+9976nuJ51EIqHnn39e4XBYhmGovLxcK1eu1I033qhZs2YNOj8ajaqoqGjAe8tURUWFYrGYGhoaUl6jsbGx33c/FF/72tfU0tKiF198cdjnAgAAmSMhA2BM+dnPfqZ7/3a92tv+QyuuvlCr8y/WzXP/UNPOZoUl+vvtqTN64eCvtT12VLve+pUWLLxS/99j1fqTP/kTx2J6/fXX9alPfUpvv/22Fi5cOOh4wzDk9XpTftb3D+6KigoFAoF+4/omQqykRENDQ79zezyepD/wB7q+lQjpfc5U/9qQSCQ0ffr0fp9bCZFQKKR169b1i69vAsViJWx6CwaDdsIok4RM7/Gp7qevjo4Ovfjii0kJnJtuukl5eXkpx6diJaBSJZCyNVDSpaqqSj6fT62trdq7d6/KysokSaFQSF/84hez3v3s7bff1qJFi/Taa6/p2muvzTpWAAAwNPyFA2BM+M1vfqM1JcVavny5Ljl9RD+791OqvmO+Cq+cTjIGaU07+ywVXjld1XfM18/u/ZQuOX1Ey5cv15qSYv3mN79xJKaf/vSnuuqqqzJKxkiykyHxeNzuAdLc3CxJ2r59uz0uGo0qEAjI7/eru7tbpmmqu7tbfr9fgUBAsVis37nD4bB93ng8Lr/fL8MwFI1G+12/ubnZvn48Hpcku7JmMG63W36/X1JPUsJSV1cnSXbCoPfnoVAo5bmi0ahqamrk9/uTYu/u7k4a1zvJkap3Snd3t/2crASUFU8qc+bMUUlJiSKRiBoaGlRcXJxVMiaRSMjn88nv949I4+vvf//78ng8A1bAFBQUJD3bsrIylZaWKpFIZHWtq666SldddZV++tOfDjleAACQPf7KAeC4w4cPa9lnblbjrgbV/o9F2rpmgS6/8Fynw8I4c/mF52rrmgWq/R+L1LirQcs+c/OI9PHI1s9//nPdfPPNGY+3ds/Zvn27otGoEomElixZItM0VV1dbY/bs2ePpJ5GtlYFhNvttqtAdu/e3e/cwWDQTirk5eXZVSq9Ez1WMmPevHmKxWIyDEPhcDibW5Ykfe5zn5MktbW1SepJvFjLjSTZCaN3331XkrR48eKU57Huc926dUmxl5aWZhXPhg0b7OdkPWPDMNKOj8fjikQiKikpkdfrVX19fVa7GQWDQRmGoQ0bNmQVZyotLS0yDCOpqqg36zvvnUQzTVORSESGYWjXrl1ZX/Pmm2/Wz3/+82HFDQAAssOSJQCO2r9/v5bdcrOmu07oiZL5usw9zemQMAEcTvxWayP71W2eq+d//oLmz5+fs2tfdtll+ru/+zvdf//9GY2PxWJJ2yMP1hdmINav9IGWyqT6LN1SqEzPKX20bMlaPmUtV7KWEVnLlqzlNunOm03sqcamm5/p8qGh9JCx7rW5uVlLlizJ+pp9WUu2uru7s15+5HK5+i1Ly8T3vvc9fe9737MTZgAAYPRRIQPAMYlEQiv/7LOa97FT+umXryIZgxFzmXuafvrlqzTvY6e08s8+m/USjuH41a9+pYsuuijj8fn5+fb2xlaVRVFRkbxeb8plSCMtHA4rEAiovLxcjY2Nam1tVWdnZ9bnsZYtWYmduro6e1lSKBSyl9b4fL6UWziPFW63205oWEvHvF6vZs+enXaOtbRr6dKlcrlc9svS9/1Aurq67CVb2SZjLANVAqVz8cUX6/333x/S9QAAwNCQkAHgiFOnTmnV5706+/j7+vEX5+v8aVOcDmlYLnuwWZc92Jyzedn68MRpbXu5U3fXva3LHmzW3XVv659fP6oPT5zOyXwnnD9tin78xfk6+/j7WvV5r06dOpWT6544cUJTpmT/85yfn6+NGzcqHo+rsbFRhmEkVc6Ul5dLkt0XJdWrr75Lbqz+LVa/F+mj/i7V1dUqLCxUfn6+pk0bWnLUWrZkGIYMw7CXJVmNYuvr6yVpwCVdqXrRpLqXXFiyZImqq6vtZFkuWNvNp1vSJfUkiFwuV79Eo/Xe+lnJxtlnn60TJ05kPQ8AAAwdCRkAjvjhDx/Ta6+8pH/64hW64NzxnYwZD/737rj+vuGAnmv7QJL0XNsH+srT/6ENz/xHTuY75YJzp+ifvniFXnvlJf3wh485HU5K69evl8vlUktLi6SefimpllitXr1aUk+vkq6uLvt4NBqVy+VSVVVVvznhcNhOZHR0dKi2tlaStHz58n5jrQRIIpEYcvJh0aJFkj5qFHz55ZcnHbcqSaz3qVix+Xy+pNgH6msz2hVQVrIsncESZOkSZqm8/vrrkjRgU+g1a9ZIUr9eMdZ762cFAACMbSRkAOTckSNH9E2/X99Z+UfKmzExlikd/tZSHf7W0pzNy8Zb7/1GtS916r5bP6G9X/tjHf7WUu392h+r9NOz9VzbBzrw/sD/VXy4852WN2OavrPyj/RNv19HjhxxOpx+1q5dKyl5ucucOXMkJe9EVFhYaC8Jmj17tj22qKhIHo8nbdPbOXPm2Oe0dmnq3Z/Garq7cOFCuVwuTZ8+PamfTN9KlYH03m2p95Ibt9ttV20MthTHuk/DMJJiT7XjkdWsd/r06Vq/fn3GcabSe6lRutdISnfOV155RZLsbcRTWbFihTwej0pKSpLiKykp6ff9AgCAsYuEDICcq/hf/1OfvORcfe7qzPtsYOhePXxMknRH/ky7T89l7mm664aenhiv/9exUZ0/Fnzu6ov0yUvOVcX/+p9Oh9LPkiVL1NramrSMyO/3q6Ghod8uO5WVlYpEIklLUkKhkLZs2ZKy4WxlZaVd7eLxeNTY2KjKysqkMcXFxUmJH7/fr7a2NrW2tkqSmpqasrofa9lS3yqclStXJn0+EOs+rYRLJBJJueNQZWWl/Syc2FFrNNTU1EhS2gbCUk+Cq7a2NukZWT2A+n6/AABg7GKXJQA5FY/HdcW8efrpX1+tP/7EBU6Hk5F/fv2odrx+VM+1faD7bv2E7sifqVt+8Kok2dUtVh+Yvu9jf3+D/m/siL79bFy3LZyhVdderM9fe7F97r7zUsmkx8xA878b/U9tanpXb39jcdLysKO/Oan87/5S9936Cf194R+N2vyx4uX//FCr/uktvXPggF2BMhpcLpe2bdtmLytxwlB398HkVVdXpzvvvJOfGQAAcogKGQA5tWXLFi245IJxk4z5bvQ/9ZWn/8PunbKp6V07GZMJ3z+/o28/G5f0Ud+Vf3796KjEms6mpp5tbPv26rn4Y1OTPh+t+WPF9X90gRZccoF+/OMfOx0KAAAAoLOdDgDA5PLT//u0Pnvl+EjGvHAwoU1N7+q+Wz+hO6+fpcvc03Q48Vtt/vlh1b6U2bbAV1/yMW3+iyt1wblT9MLBhL74+Fva8frRpCqZwYx2j5nJ5LNXXqAdT2/Xt7/9badDAQAAwCRHhQyAnPnggw/0xr63deOcP3Q6lIy8cPDXkmQnY6Se3illSz+e8Tn+6sZL7MqSm+f2NDK1qm2QezfO+UO9se/tUd+VBwAAABgMFTIAcuatt96SJC2cdZ7DkWTGWopjJWMs8y46N+NzWMt6hmO4PWTwkQUze3723nzzTd10000ORzN66AMCAAAw9lEhAyBn3n//fUnS9PPIBefSfbd+QpL04YnTScet99bnozV/LJnxBz0/e0eP5raPDwAAANAXfxUByJljx3q2R5529vjIBd936ye0qeldHU78NqlK5nDitzmNY7jVLwt/XxVy5Dcnkxrz/mf3CUnSZe5zRnX+WGL97Fk/ixgZQ93VKVe7QSUSCT311FMyDEOGYcjj8WjNmjVasWKF3G53xnGmQjUSAAAYqvHxVxEAOODmuT29bra93GUnYQ4nfqttL3c5GVbWrvx9QuXp2JGk+/iXt34lSbrusvNHdT7gtK9//esqKyuTYRiSJMMwVFJSotLS0kHndnR0jHZ4AABgkqJCBgDSuHmu266SGS9bO6dy9SUf020LZ6S8j9JPz9bVl3ws6ZjVs8aqzMl2PiafoVaJ5KK6JBaLqaamRn6/X+vWrVNeXp46Ojr0ne98RzU1NWpvb9eCBQsGPU8wGNTGjRtHPV4AADB5UCEDAAP4+8I/0g/vuFK3LZwhqWcZ08+/ep3DUWUv+Pkr9F3vPPs+bls4Q9/1ztP/+tM5OZkPOGXv3r2SpNLSUuXl5UmS8vLyVF5eLkl65ZVXBpy/f/9+SdJ1142//90DAICxjQoZABjE56+9WJ+/9uJ+x0s/Pdv+5759XtL1fcl03Ei7+GNTdef1s3Xn9bMHHZsqpmzmY2Kpr69XXV2dDMOQ3+9XaWmpFi5cKOmjCpe+vWCs952dnaqtrZXP57P7thQXF9vnzqSHzED9WywDzbeWHM2enfyze+mll0rq2XELAADACVTIAEAalz3YrMsebNYr735oH/vwxGn96MX/kiQtnfOHToUG5ERFRYVKSkrs3iuBQMBOxmTinnvukc/nk/RR35b6+vpRiTWdQCAgSf2a986aNSvp83ReffVVSdJFF12kcDgsl8sll8ulcDisRCIxChEDAIDJgoQMAKTx+JqrJEme8Bt2cuaq7+zVt5+N67aFM1R45QyHIwRGTzQaVSAQkN/vVzwel2maisfj9lKfTOTn56u7u1umaaqxsVGSVFdXQ+MinwAAIABJREFUl1UcpmkO+sqFgoIClZWV2e/LyspUWlpKUgYAAAwZCRkASOO2hTP01N1X675bP2EfK/30bP3wjiu1+S+uTNoCGpho9uzZI0l2I1ypp/fKAw88kPE5NmzYYFemFBYWSpJdbTNeWBU+zc3NSUmgSCQiwzC0a9cuhyMEAADjFT1kAGAAN8916+a5bv194R85HQqQU9ZSHisZY8lkRyKLtSxoOIbbQ2a40p27uLhYJSUlqqurS+qLAwAAkCkqZAAAwITl9/slqd/SIuu99flQjbeKHwAAMHZQIQMAY8RlDzZLyt3OSyPJij2Vvvfz4YnTanjzqJ5r+0DPtX2g2xbO0KprL1bhlTNYBjaG+P1+BQIBdXR0JFXJWLsW5cpwq1+uueYaST07PvVu7Hvo0CFJ/SuA+vJ6vTIMQ93d3UnzrYRONj11AAAAeqNCBgAwLIcTv81q/P/eHdffNxzQc20fSJKea/tAX3n6P7Thmf8YjfAwRMuXL5ckhcNhOwnT0dGhcDjsZFhZW7RokSSptrY26T6efvppSdLixYsHnL9mzRpJ6tcrxnq/evXqEY0XAABMHlTIAABGxDc/O0d/c9PHBxzz1nu/Ue1Lnbrv1k/ozutn6TL3NB1O/Fabf35YtS916sD7JzTvonNzFDEGUlhYaFfJDLY19FiWn58vj8eT8j7Ky8uVn5+fdMzqWWNV5qxYsUIej0clJSUqKSlJGuv3++1mxQAAANmiQgYAMCyHfnVCkvTJSz826NhXDx+TJN2RP1OXuadJki5zT9NdN8yWJL3+X8dGKUoMRWVlpSKRiDwej6SeBERbW5vDUWVvy5YtCoVC9n14PB6FQiE98sgjg851u92qra1Neg7l5eVqbGxUZWXlqMYNAAAmNipkAEw4LxxMyHjzfdW+1ClJuu/WT+jPr75QV1+SnDB4673f6OcHEvr2s3FJsnuZfP7ai+0xvfu6PNf2ge6ue1u3LZyhO6+frdsWzpAk/fPrR/WVp3uW2/zwjivTzu87LtOeKb3v57aFM7Ru6aW6ea57wHED3ff/z969x0Vd5f8Df42K4AURRC6CKJSioOJ9o8tiKJrVYLpqXhbtu7sYuLXZZur2Q12TsgzzUqsI2ZYoeFtvYxdFUVTCQBEQTMRAEBQQgREvKOn8/qDPR4aZgRluHwZez8ejx6PPZ87nnPf5TI8ezttz3qem2uq/CBqrrk2+8iEAoGcXE7X7NuYdAQAZN+83yjjUeGbMmKH1FKHqtVNq1nnRVfdF33aNzcbGBv7+/vD396+zrbaYLCwsdL4HIiIiovriChkialWiM0ox/ZuLYlICANbH5sFnUyrispVq7Xw2pYrJGOHe/D2ZOHChWGu/b0ReUvv3iwV3sTrmmphkAVDr8zXb6VMzZXXMNbX5CPNbHXOtXvNuCmk37gIALDt1wPZzhXBYHg+H5fHYfq4Q5RWP1Nquj80DAI1ElPXvCRrhc5KeTCaDTCbDmTNnxHtKpRJr1qwBAHh5eUkVGhEREVGrwBUyRNSqCEmThH8OF7fEJOWVQx6eBkX6LXFlidBO4T8Iwx3NAVQVpx39eRLm78lUW+UCVG21ufSv0TA3a4+4bCWmf3MRPptS8Y6Xo8Z9bc9vP1coxpSvfIDt54qwPjYPcdlKratdgKoVL+tj8/COlyMCn+0Fc7P2KK94hE0/Xcf62Dy11S/6zlubxlr94rMpVe1aKNz7xZR+PD3JCB08eBC+vr7w9NT870Mul2PixIkSREVERETUejAhQ0Stio+rJaIzSnEo/RYG2XfBEPuuGO5orpF0EK6L71biYsFd5CsfivVNtPnLH+zEpEL15IaQKKl5v6ZlE/qq1UyZPcIG62Pzak2WxGXf1hjD3Kw9Ap/thfWxeTiVpRQTMvrOuykIq4yqJ7eAJ1u5YjJLNRJU1PLJ5XIcO3YMx48fF4vhBgQEwMvLCxMnTlQ7ApqIiIiIDMeEDBG1Kou8eyM6o1StLoyumiurY67pvUXGukbNE4G+Kz9qnhwkJGciEgvxyasuWp8RYhuwKkHr5x8ezhFPNTJk3jU1tIaMrs8mDbbG/D2Z2HehmAkZI+Xt7Q1vb28WryUiIiJqAkzIEFGr4mbXBfkrPNUK9kZnlMLH1RKLvHuLK0q2nyvE+tg8+I2yhdy9Byw7dYCNeUd4rD4r8QzqR995SyE6o1T893e8HLE+Ng/lFY/UkllCrZl3vBybPT4iIiIiIikwIUNErZKbXRe42XXBq+49cLWkAtO/uYjojFJxNceig1kAoLY6pWYB2saUr3wgrooBgKxbVUdF15aA8Btli4jEQrFGjT7qmrfW2Bq4remNyEuIzijViFN4n36jbMV7rj07AQBu3q1Ua3utrOp9OFh0bFAsZBxkMhmA5jtlqakoFAr4+vrqnIdSqcSuXbugUCigUCggl8sxa9YsrVu+DGlLRERErQNPWSKiVmXJoSw4LI9HUl45gKqtQX2tzHS2FxIjQrHcprL9XBHylQ8AVCVn9qTcBAA859xN5zNy9x4AgE0/XUfx3Urxfly2Eg7L47G5WryGzrsxTf59O1JMZqnafeFamAcA9Ps9IbMn5aba+zh0sQQAMMyha5PHS9QYUlJS4OvrW2ubJUuWYN68eVAoFACqEjgzZ86En59fg9oSERFR68AVMkTUqkwf2hMRiYWQh6dpfLba98lqmI1T+2H+nky8sOG81n6yblVo1H1pqNGfJ6ldv+PlWGuNl+ecLcQtPjVr3fi4WuJPHj3Fa33n3RS8+1nCx9US8/dkqh3tDWjO0c2uC3xcLbXOyW+UraRbq4j0debMGa2nT1WXkpKC0NBQBAUFwd/fH05OTsjNzcWqVasQGhqKy5cvo3///ga3JSIiotaDK2SIqFUZ7miO6MAhaluB3vFyxDezBmD2iCdbZyYNtlZLVLzj5YhT/xiG6MAhAID4q8pGjWuRd28sm9AHQFUyZdcbbljk3Vuv5zZO7ae27We1rwtCJj2lVmhY33k3BXOz9vhiSj9snNoPPq6WAKqSK7rmGDLpKaz2dRHb+rhaYrWvC/7fuD5NGidRY1izZg08PT0RFRVVa7uEhKpi3H5+fnBycgIAODk5ISAgAACQlJRUr7ZERETUenCFDBG1OkIdlboSHrNH2GpNVlSvqaKrvoqh9wHgzWd7iaciGfLspMHWmDTYWudpTAJ9590UzM3ai3HWxbqLic53T4aJiYnB7t27ERoaCgAICgrC1KlT4eHhodYuJSUFR48excKFCwFArE8yY8YMsU31ui5CbRS5XA5/f3/I5XIAwI4dOzBz5kwAQFRUlM7na7bTtw5K9fnI5XIsWLAA3t7e9Z53TUKMtamrrs3ChQtx8OBByOVycY7a5ObmAgBsbdX/O7e3twcApKen16stERERtR4ylbFX1CMioxEZGYnZs2c3uIisMRGOlG5Lc27pHJbHY/v27Zg1a1aT9C+TyZq0f4GQNNHm2LFjYiKjtnbVkypCsuLgwYMa7ZOTk7Fnzx4EBwfX63m5XI6DBw+K19qK+i5dulSjf6Aq2VL92G19561NYyRktPWn7RlDPqtvP41J+P8z/1hIRETUfLhliYiIyAgJSYmcnByoVCqoVCrEx1clAHfv3q3RLj4+XmyXk5MDAFpXeCQkJKCsrAwqlQrHjh0DAAwdOhQANO5rez48PFyMKScnB0FBQVAoFIiJidE5l5iYGAQHByMoKEgco6ysDEFBQQgODkZKSorB89ZGaF/bP0RERETNhQkZIiIiIyRsI9q9ezdiYmKgVCrxzDPPQKVSYdOmTWI7IdHg4uKClJQUKBQKhIeH6+z37bffFrcXVV9tsnDhQq33awoJCVGrg+Lv7y/Gqcvx48c1xrCwsBC3WB09etTgeRMRERG1dNyyRETNpi1uWaKWp7VsWUpJSRFXrgCoteaKru1AQN3bZvS935AtOoZsJTJk3rri0GccfXDLEhERETUEV8gQEREZIQ8PD6hUKiQnJyMkJAQKhQJjx46Fr6+v2haf8PBwBAcHIyAgAMeOHUNycjIKCwsljLxh9J231IKCggAASqX6iW3CtfC5oW2JiIio9eApS0TUZhhrgV0hboEQf8372toIyise4WB6MaIzShGdUQofV0tMHmwN736WMDdrX6+4pOpT1/toqzw8PODh4YFp06bhypUrGDt2LBQKhbjSYd68eQCgtp2n5g//xpSbmytuWQKAy5cvA6g9qRAQEIDQ0FCUlZXpdRoTUPe8tWnO1R/u7u4AgMLCQrU5Xb16FQDU3pEhbYmIiKj14AoZIiIjlK98YFD7j47mYNHBLERnlAIAojNKMX9PJt7em1nvGIylz9YqMDAQMpkMZ86cAVD1o/3pp5/W2V5IjCiVSoSEhDRZXOHh4eIxzrm5uYiIiAAAvPjiizqfmTZtGoCq+jNFRUXi/ZiYGMhkMqxZs0a8Z+i8pTJw4EAAQEREhNr72LNnDwBg9OjR9WpLRERErQdXyBARGQltK0GWTeiDN5/tVetzFwvuIiKxEO94OWL2CBs4WJgiX/kAX5zKR0RiIbJuVcClh5lBsUjZpz4rhNqCuXPnIjQ0FJ6emv9dhIWFif8eFRWFmTNnwtXVVWs/ly9fRv/+/Rs1tj59+qhdBwUF1VrjxdvbWzxRqWatG7lcDj8/P/Fa33lLzcPDA3K5XOucAgIC4OHhUa+2RERE1HpwhQwRkRG6WlIBABhk36XOtufz7wAApnr0hIOFKQDAwcIUc0baAgAuXL9j8PjG0mdr9swzzyA5OVmjFsnBgwfFk40AYMaMGWqJiqCgIGRkZCA5ORkAEBsb26hxrVy5UlyBI5fLcezYMaxcuVKv56KiohAQECDeCwsLw1dffQUbGxvxnr7zbgm++uorhIWFiSdDyeVyhIWF4ZNPPmlQWyIiImoduEKGiFosh+Xx8Btli09eddH4bMmhLEQkFuLSv0bD3Kw9LhbcxaksJT48nAMAYu2RSYOta+0f0Fx5out+XLYSivRbiEgshI+rJfw97fGcc931LvRZydGUdVDylQ8BAD27mKjdtzHvCADIuHm/1fbZ2gl1VOpKePj7+2tNVlSvqaKrvoqh9wHgvffew3vvvafzc13PzpgxAzNmzKjz+Gp9593U6qpJY2Njo/PdN6QtERERtQ5cIUNELdayCX0QkViI4ruVaveL71YiIrEQyyb0gblZ+6rir5tSxWQM8KT2yIELxY0Sy+qYa5j+zUVEJBaK/U//5iJWx1xrlP4NlXbjLgDAslMHbD9XCIfl8VXHOZ8rRHnFI7W262PzAECj0K7174kP4XNDGEufREREREQtFVfIEFGL9YJL1eqTuCyl2kqXuKyqE2J8XK0AAG9EXgIAKPwHYbijOYCqorejP0/C/D2Zta6S0UdcthLrY/PwjpcjAp/tBXOz9iiveIRNP13H+tg8vOpmBTc73VuHmnL1i8+mVLVroSDuF1P61fukIyIiIiIianpMyBBRi+Vm1wU+rpbYd6FYLamy70Ix/EbZikVjhYRH8d1KXCy4i3zlQ7EeSWOIy74NAGIyBqhaxRH4bC+sj83DqSxlrQmZpiCsBqqehAKAAxeKMX9PJmIySxuciCIiIiIioqbDhAwRtWj+nvaY/s1F8YSdrFsViM4oxa433NTarY651mRbWoR+B6xK0Pr5h4dzaj3pqClqyOhqP2mwNebvydRIYhE1tbrqqRARERGROtaQIaIWbYh9VwBA/NWqbUrCSTvCfQDYfq4Q62Pz4DfKFrvecEN04BCkLBrZ/MG2INEZpeK/v+PlCAAatWWEa+FzQxhLn0RERERELRVXyBBRi2Zu1h6rfV2w6GAWJgywwvw9mVjt66JWH2XRwSwAUDuNqeaPen3VLCAMAH6jbNVOdDJUU9SQeSPyEqIzSjViEubtN8pWvOfasxMA4ObdSrW218qqjs52sOho8PjG0mdbJZPJABjfqhUhboEQv1KpxK5du6BQKKBQKCCXyzFr1ixMnDgRFhZ1n3Smz1jaxhUYw/i63h0RERG1XFwhQ0Qtnmffqh8cHqvPAgDGPN1da7usW1U/3IWCu3XxcbUEACTllYvPff1zgUY7uXsPAMCmn66rJWzispVwWB6PzXqM1dgm/74dKSazVO2+cC3EDAD9fk907Em5iXzlAwBVRY8PXSwBAAxz6ApDGUuf1DosWbIE8+bNg0KhAAAoFArMnDkTfn5+9eovNze3TY9PRERELQNXyBBRi+fSw0xcpeI3yhYOFqZqn2+c2g/z92TihQ3ntT4v1J+pafJga0RnlEIenibeWzahj0a755wt8I6XI9bH5mnUqfFxtcSfPHrWZ1oN4t3PEj6ulpi/JxPz92SqffaOlyOec37yt/ZCcWRt8fuNslUrSCzUu6lrVY/UfVLrVn11R0pKCkJDQxEUFAR/f384OTkhNzcXq1atQmhoKC5fvoz+/fvXa5yQkBC89957tbYxlvGFd1bbyhsiIiJqWbhChoiMgrDiY/pQzeTHpMHWWO37ZLvSO16OOPWPYYgOHALgSf0Zbc9tnNpPXCmz2tdFZ3HeRd69sXFqP7WtQKt9XRAy6SlYdzGp36QawNysPb6Y0k8tfqGGziLv3hrtQyY9hdW+LmJbH1dLrPZ1wf8bp5mA0pex9EnGLSGhqpi2n58fnJycAABOTk4ICAgAACQlJRnc55UrVwAAw4YNa5PjExERUcvAFTJEZBSec7aodYXF7BG2mD3CVuN+9We0PT9psLXGaUS1nWA0abC1Wq0aKZmbtdcavzbWXUx0vqPq8ld46nUqlNR9tjYymQwBAQHYtGmTxmeBgYEIDQ1FWVkZLCwskJKSgqNHj2LhwoUAINYTmTFjRq39A5p1RXTdj4mJwe7duxEaGgq5XI4FCxbA29tbr3nUxdDaJsL2Hltb9f8m7O3tAQDp6ekG9Weotj4+ERERNR2ukCEiIlFSXrnaaqOW2mdrExISgtDQUBQVFandLyoqQmhoKEJCQmBhYQGFQoGhQ4eKyRjgST2RHTt2NEosS5cuxdixYxEaGir2P3bsWCxdurRR+jdUcHAwAGgUz7WxsVH73BDnz1dtb+zRowfCw8Mhk8kgk8kQHh4OpVJ9RV1rHJ+IiIhaBiZkiIiMhMPyeL1XmtRXYm55o69Oaew+m+M9NLdx48YBqFqZUp1wLZfLAQC+vr4AgPj4eKhUKqhUKuTk5AAAZs6c2eA4YmJiEBwcjKCgIJSVlUGlUqGsrAxBQUEIDg5GSkpKrc8LMdX2T0sydOhQzJs3T7yeN28e/Pz8NJIirXV8IiIikhYTMkREJNJVQ6el9dnaeHh4QC6XIzIyUu1+ZGQkAgICxKKxQlLDxcUFKSkpUCgUCA8Pb7Q4jh8/DgBYuHChuCLDwsJCXJFz9OjRRhtLSsJ8qie2VCoVoqKioFAo8MMPP7Tq8YmIiKhlkKla2l9XEVGrFRkZidmzZ9d52g5RU3JYHo/t27dj1qxZTdK/TCarV/8xMTEYO3YsMjIy0L9/f1y+fBmurq44duyYWv2WpUuX6tymUvOkHV3X1WPV1q42tf2xobGer95GV+x1fVZfMpkMcrkcBw8eNMrx6xuT8P9n/rGQiIio+XCFDBERUQswYsQIAEBsbCyAJ6fnCPcBIDw8HMHBwQgICMCxY8eQnJyMwsLC5g+2GQUFBQGAxjYe4Vr4vDEpFIo2PT4RERE1DyZkiKjNaI21R5pavvKB1CG0GRYWFggLC8O8efNQVFSEmTNnIiwsTK2Yq1BvZNOmTfD29oaHhwdMTU3rNV7NAsIAxKOUhfoxhtaAaYoaMu7u7gCgkXi6evUqAIhHQRvC19cXMplMZ5JDeA+tdXwiIiJqGZiQISIirTb/dB2jP0+SOow2xcvLC8CTI44nTJigtd3ly5cBVP2ADwkJqbNfoSjwmTNnxOe++OILjXbTpk0DUHXqU/WETUxMDGQyGdasWaPvVBrNwIEDAQARERHiEdC5ubnYs2cPAGD06NEG9ylsJ6tZq0W4Ft5Dax2fiIiIWgYmZIiISKsPD+dIHUKb079/f3F1REBAgMbqh6ioKACAq6srZDIZunfvrlZPRkjU1CQkADw9PcXnunfvrtHO29tbPFHJ1tZWPI557NixkMvl8PPza5R5GkIoeBwcHIw+ffpAJpOhT58+4tYtDw8Psa0Qb10mTpwIuVyOmTNnis/IZDLMnDkTQUFBajV7jGl8IiIiMi5MyBAREbUgwuqIuXPnanw2Y8YMhIWFiddBQUHIyMhAcnIygCf1Z7Q9FxUVJa6UCQsLw3vvvae17cqVKxEVFaW2bSYsLAxfffUVbGxs6jepBvrqq68QFhYmxi+XyxEWFoZPPvmkXv1ZWFggIiJC7Z0IdXlWrlzZ6scnIiKiloGnLBFRs2nKU5bKKx4hJrMU+y4UIzqjFH6jbDHPsxdcepiJbYT6MdXHv1hwF6eylOJqEB9XS0webI1Jg63V+o/LVkKRfgsRiVV1HN7xcsSrblZws+tSr3Y16VPbpq73ZsjY1dv6uFrC39Mezzk/qVWiLZ7q4x+4UCy+a13vzJCY9P0eGkNLPWWJGufUIplMJulJQS1hfICnLBERERmDDlIHQETUGN7em4nojFLxOiKxEBGJhYgOHKIzGRKdUYo3Ii9p3BP6EZIB2tqtj83D+tg87HrDTUxk6NuuKRgy9uqYa1gfm6f2bHRGKd7xcsQi7951jqXr+Yyb99Web8h70/Y9ENXlzJkzaiuI2tr4REREZFyYkCEio1c9oRD4bC+Ym7XHgQvFmL8nE1vPFuKTV120PickART+gzDc0RxA1alCoz9Pwvw9mWIiQGiX8M/hcLCoOtEmKa8c8vA0KNJviYkFfdtp09BVQ/qOHZetxPrYPLV3VV7xCJt+uo71sXni6pX8FZ5aVxRVf372CBs4WJgiX/kA288VYX1sHp5z7mbw+9D3e6C2o76rPOLi4nRuxWoOUo6vT+0aIiIialmYkCEio3css2olxV/+YAdzs/YAqlZV1PVDXkg0FN+txMWCu8hXPsT5/Dsa7XxcLRGdUYpD6bcwyL4Lhth3xXBHc40kir7tmoK+Y8dl3wYAMRkDAOZm7RH4bC+sj83DqSxlrdurFOm3AEBMxgCAg4UpZo+wwfrYPLVEi74x6fs9ENVFymRMSxifiIiIjAsTMkTUbNq1a5o64kJ9EusuJgY/W3P7jTaLvHsjOqNUrb5JzZorhrTTpqE1ZPQdW5jrgFUJWvv58HAO3ny2l85xhHctJGPE+H+/jkh8siLJkPehz/fQmJrqv0VqGNYvqT++OyIiIuPDhAwRNRsLi6of4ncePEJX0/YSRwNsP1eI9bF58BtlC7l7D1h26gAb847wWH1WrZ2whad64VmhmO0i797iihJ92zUFKcduaEz6fg+N4c6DRwCg9chnIiIiIqLmxIQMETWbXr2qVl4Ulj9EV9NOjdav3yhbRCQWovhupUGrZBYdzAIAtRoz5RWPdLZ3s+sCN7sueNW9B66WVGD6NxcRnVGqsXJF33bVNda2prrGFt7VpX+NFrcsGUJ4Pl/5QG2VTNatCvFzQ2My9HtoiILyhwCe/LdorBrjNCLST3O8a36fREREbRPXbBNRsxk4cCBMO5rgYsG9Ru3Xs083AMDXPxeIP+QPXCiGw/J4LDmUVefzQjJBKG5b05JDWXBYHo+kvHIAVdtz+lqZ1btdU9B3bLl7DwDApp+uo/hupXg/LlsJh+Xx2Kxl/tWTI8Lz288VIV/5AEBVAd49KTcBAGP7WRock6Cu76Ex/FJwD6YdTTBw4MAm6Z+IiIiISF9cIUNEzaZjx4744x9fwMmsdMgH9Wi0ficNtsa+C8XikcrVzRmpuWJDsHFqP8zfk4kXNpzX+nnWrQq49DDD9KE9EZFYCHl4mkab1b5PVnXo264p6Dv2c84WeMfLUeu78nG1xJ88eqpdR2eUYsCqBPiNssUnr7rU+vw7Xo7wcX2SkNE3Jn2/h8ZwMkuJP/7xBZiYGF5viNomrlohIiKipsIVMkTUrF6fMQuHL9/Gw98eN2q/X0zpp/Yj/x0vR5z6x7Baa6dMGmyt9ZnowCEAgPirSgDAcEdzRAcOwTtejmptv5k1ALNHPEn46NuuKRgy9iLv3tg4tZ/a9qLVvi4ImfSU2pavRd69xTYFtx9qPC8kX3xcLbFxaj8s8u5dr5j0/R4a6uFvj3H48m3MmDm7UfojIiIiImoImYp/9UNEzejevXvo09sBH/yxB14fZiN1ONSG7DxfhI9P3kLOtXx07ty5ycaRyWTYvn07Zs2aVa/nlUolfvjhB0RGRkKhUCAgIADvvvsu+vfvrzYGoL56IyUlBUePHsXChQsBAHK5HLNmzcKMGTPU+o+JicHu3bsRGhoKAAgKCsLUqVPh4eFRr3ba5l8XbX/0UCqV6N69OwICArBp0yaNzwMDAxEaGoqysjKxQHj1GOVyORYsWABvb2+t8eTk5OCtt96Ch4cHVq5cqfcctb1rfb4jwY4dO8R2ur4TXTVkDHlW2/wMERkZidmzZ3NFEBERUTNiQoaIml1oaCg+/OB9xM53Q5eO0p+2RK3f3YeP4LXxIpZ9/BkCAgKadKyGJmR8fX2hUCg07icnJ4uJgpo/4BUKBXx9fbX2FxUVJf6Ir63dsWPHxGSGvu20qW9CBgDWrFmDhQsXorCwEDY2TxK2RUVFsLW1RUhICN577z0AwNKlSxEcHKzRR1BQkFpCQognKCgIwcHB4vvQd47akiX6fEf1ibH6GA2dn6GYkCEiImp+3LJERM3O398fNg5OWHeyaQq3EtW07uR12Dg4wd/fX+pQaqVQKKBQKBAUFISysjKoVCpERUUBgLiKQxshsRAfHw+VSgWVSoWcnBwAwMyZMzXa5eTkiO3i4+MBALt37za4nTZC+9r+0WXcuHEAqlauVCdcy+Vy8TqpSj/6AAAgAElEQVQ4OFjtPZWVlYlJiZSUFI2+3d3doVKpxGRFfeeo73dUPUZhjJycHDHGmnOsOV9Dn605PyIiImr5mJAhombXvn17fLExFKFx+fj+lxKpw6FW7vtfShAal48vNoaiffuWvSLr+++/BwC8/fbb4racGTNmQKVSad3GIxASCi4uLkhJSYFCoUB4eLhGOyGhsXv3bsTExECpVOKZZ57R6F/fdo3Nw8MDcrkckZGRavcjIyMREBAgbgk6fvw4AGDhwoXie7KwsBC3ax09elSj75qreuo7R32/IyGp4+/vDycnJwCAk9OTpGBtSZ/6PFvbqiUiIiJqmbhliYgk88knq/Dhv5dj95wBGObYVepwqBU6n3cH07ZewrJ/r8CSJf9qljEbsmVJVy0Rfdrp2uJSvV1KSgqGDh0q3tdVd0XfdrXFVpva5hcTE4OxY8ciIyMD/fv3x+XLl+Hq6qp1G5E+Y+h6p/rOsebzDfmO9O2zIc/WF7csERERNT+ukCEiySxZ8i/8aepUzN1xBQm55VKHQ61MQm455u64gj9NndpsyRgAMDc3R0VFRbONBwDh4eEIDg5GQEAAjh07huTkZBQWFmq08/DwgEqlQnJyMkJCQqBQKDB27Fj4+vqqbfPRt11TGDFiBAAgNjYWAJCUlKR2v7FIOceW6P79+zA3N5c6DCIiojaFCRkiktSWr7/Bi+MnYsbWX/C/lJtSh0OtxP9SbmLG1l/w4viJ2PL1N806dq9evXD9ev3qIwkFh4uKigx6bt68eQCATZs2wdvbGx4eHjA1NdXZ3sPDA++99x5ycnJw7NgxKBQKtdUihrarriE1ZICqrUdhYWGYN28eioqKMHPmTISFhYnbg4An70mo4WLoGA2Zo77fkdAuNzdX7f7ly5fVPm/sZ+srPz8fDg4Ojd4vERER6caEDBFJqmPHjti1ew8WLfkAC/b/in8eyEJR+UOpwyIjVVT+EP88kIUF+3/FoiUfYNfuPejYsWOzxuDh4YHk5OR6Pevl5QUA+OKLL6BUKgFUHX0sk8kQGBhY5/PCD3alUomQkBCNzwMDAyGTyXDmzBkAVXVJnn766Xq3ayrCe7C1tQUATJgwQe3zadOmAQBCQkLUEiMxMTGQyWRYs2ZNnWPUd476fkdCjOHh4WJiJTc3FxEREQCAl19+WecYDXm2vlJTUzFkyJBG75eIiIh0Yw0ZImox9u7di3ffeRult4qx4AV7+I2y5bHYpJe7Dx8hIrEQ607dgGUPa6xd/wWmTJkiSSxfffUVFi9ejKKionoVEa7Psdc7duxQO02pJqEey5kzZ+Dp6am1TVhYmFg0Vt92TSkwMBChoaEICAjQWmRXV80cuVyOr776Sjw2W1eNFX3naCzHXjfkj3OPHj2CjY0NPv30U/ztb3+rdz9ERERkGCZkiKhFuX//Pj7++GN8viYE7fEYLw/oDq+nLDDIvgtszDvC3JQJGgLKHzxCUflDpN24ixO/KvHDpTI8Qjv8872F+OCDD9CpUyfJYisuLoajoyP279+Pl156yeDnlUoldu3aJW5DCgoKgp+fn3jCEKD9R3h4eLjGM/fv38fQoUPVEgwpKSnYs2eP+GM/KCgIo0ePFk8dEujbrqkIxX3j4+PxzDPPaG2zY8cOxMbGisdNh4WFYdKkSWIyBqg9YaHPHLU9r893VD3GyMhIKBQKyOVyzJo1S+Noal0xNuRZQ/zwww+YPHky8vLyYG1tXe9+iIiIyDBMyBBRi1RWVobt27dj3949OHXqNB5W/iZ1SNQCdTTpgBdeeB6Tp0zF7Nmz0b17d6lDAgD85S9/QWFhIb777jupQyGq0yuvvAJbW1t8/fXXUodCRETUpjAhQ0Qt3sOHD/HLL7/gxo0bKC/naUxUdZKRvb09Bg4c2Ow1YvSRn5+P/v37Y9++fRg/frzU4RDpdOTIEUyePBmXL19mUV8iIqJmxoQMERFRE1i5ciUiIyORlJQk6RYqIl3u37+P4cOHY9asWVi6dKnU4RAREbU5TMgQERE1gYqKCri7u8PT0xMRERFirQ+ilkClUsHPzw/x8fFIT0+HmZmZ1CERERG1OTz2moiIqAmYmZlh//792LdvH1asWCF1OERqVqxYgX379mH//v1MxhAREUmkg9QBEBERtVaDBw9GVFQU/vSnP6F9+/YICgriShmSlEqlQnBwMD766CP873//w+DBg6UOiYiIqM3iliUiIqImtnXrVvj7+2P69On46quvYGpqKnVI1AY9ePAAf/vb37Br1y6EhYVh7ty5UodERETUpjEhQ0RE1AxiY2MxZcoUODk5YcOGDXjhhRekDonakFOnTuEf//gHcnNzsXfvXnh5eUkdEhERUZvHGjJERETNwMvLCwkJCbCzs4OXlxdmzZqFK1euSB0WtXJXrlzBrFmz4OXlBVtbWyQkJDAZQ0RE1EJwhQwREVEz27dvHxYvXoxff/0VL774IiZNmoQ//OEPeOqpp2BpaYl27fj3JWS4x48fo7S0FL/++it+/vlnHDhwAMePH8dTTz2FTz/9FJMnT5Y6RCIiIqqGCRkiIiIJPH78GN9//z127dqFH3/8ETdv3pQ6JGpFevbsiZdeegmvv/46Jk6cyCQfERFRC8SEDBERUQtw9epVZGVlobS0FI8fP5Y6HADAkSNH8N///herV69G7969pQ6nRbl27RoWLVqE//u//8P48eOlDgcA0K5dO1haWsLFxQV9+/aVOhwiIiKqAxMyREREpOHatWtwd3fHW2+9hY8//ljqcFqkDz74AF9++SXS09OZsCIiIiKDMSFDREREGl5++WVkZ2fj/PnzMDMzkzqcFqmiogLDhg2Ds7Mzvv/+e6nDISIiIiPDDcVERESkZtu2bTh8+DDCw8OZjKmFmZkZwsPDcfjwYWzbtk3qcIiIiMjIcIUMERERiYqKiuDm5oYZM2bgyy+/lDoco/DWW29hx44duHjxImxsbKQOh4iIiIwEEzJEREQkmjFjBuLj45Geno6uXbtKHY5RuHPnDtzd3eHp6YkdO3ZIHQ4REREZCW5ZIiIiIgDAgQMHsHPnToSFhTEZY4CuXbsiLCwMO3fuxIEDB6QOh4iIiIwEV8gQERERysrK4O7uDh8fH3zzzTdSh2OU3njjDURHRyM9PR3du3eXOhwiIiJq4bhChoiIiLBw4UI8evQIn3/+udShGK3PP/8cjx49wsKFC6UOhYiIiIwAEzJERERt3LFjx/D111/jyy+/hJWVldThGC0rKyt8+eWX+Prrr3Hs2DGpwyEiIqIWjluWiIiI2rC7d+/Cw8MDQ4YMwd69e6UOp1WYMmUKUlNTkZKSgi5dukgdDhEREbVQXCFDRETUhi1duhQlJSX4z3/+I3UorcZ//vMflJSUYOnSpVKHQkRERC0YEzJERERt1JkzZ7BhwwasWbMG9vb2UofTatjb22PNmjXYsGEDzpw5I3U4RERE1EJxyxIREVEb9ODBA4wYMQK9evXC4cOHIZPJpA6pVVGpVJgwYQKuX7+Oc+fOwdTUVOqQiIiIqIXhChkiIqI26OOPP0ZOTg42b97MZEwTkMlk2Lx5M3JycvDxxx9LHQ4RERG1QEzIEBERtTEXLlzAqlWrEBwcDGdnZ6nDabWcnZ0RHByMVatW4cKFC1KHQ0RERC0MtywRERG1IY8ePYKnpyc6dOiA06dPo107/t1MU3r8+DGef/55/Pbbb4iPj0f79u2lDomIiIhaCP4pjIiIqA1Zu3YtUlNTsWXLFiZjmkG7du2wZcsWpKamYu3atVKHQ0RERC0I/yRGRETURly5cgXLli3D0qVLMXDgQKnDaTMGDhyIpUuXYtmyZbhy5YrU4RAREVELwS1LREREbYBKpcKLL76IsrIyJCYmwsTEROqQ2pTKykqMGjUK3bt3x/Hjx1lImYiIiLhChoiIqC0ICwtDXFwctmzZwmSMBExMTLBlyxbExcUhLCxM6nCIiIioBeAKGSIiolYuLy8PgwYNwptvvolPP/1U6nDatMWLF2Pz5s1IS0uDo6Oj1OEQERGRhJiQISIiauXkcjkyMjKQmpoKMzMzqcNp0yoqKjBkyBC4urpCoVBIHQ4RERFJiFuWiIiIWrHIyEh899132LJlC5MxLYCZmRm2bNmC7777DpGRkVKHQ0RERBLiChkiIqJW6ubNm3Bzc8O0adOwceNGqcOhaubPn4/du3fj4sWL6Nmzp9ThEBERkQSYkCEiImqlZs2ahdOnTyM9PR3m5uZSh0PVlJeXw93dHc8//zxXyhAREbVR3LJERETUCikUCkRFRWHz5s1MxrRA5ubm2Lx5M6KiolhLhoiIqI3iChkiIqJWRqlUYtCgQRgzZgwiIiKkDodq4efnhxMnTiAtLQ0WFhZSh0NERETNiCtkiIiIWplFixbh4cOHWLdundShUB3WrVuHhw8fYtGiRVKHQkRERM2MCRkiIqJW5Pjx4wgPD8eGDRvQo0cPqcOhOvTo0QMbNmxAeHg4jh8/LnU4RERE1Iy4ZYmIiKiVuHfvHoYMGYJBgwZh//79UodDBnjttdeQlpaG1NRUdO7cWepwiIiIqBlwhQwREVErsWzZMty6dYtHXBuhjRs34tatW1i2bJnUoRAREVEzYUKGiIioFUhISMC6desQEhKCXr16SR0OGahXr14ICQnBunXrkJCQIHU4RERE1Ay4ZYmIiMiIbNmyBWfPnsVHH30EKysrAMDDhw8xcuRI2NjYIDo6GjKZTOIoqT5UKhV8fHxQVFSEs2fPomPHjgCAkpISLF26FBMnTsSrr74qcZRERETUWLhChoiIyIhs3LgRoaGh6NevH/bu3QsAWLVqFbKyshAWFsZkjBGTyWQICwtDVlYWVq1aBQDYu3cv+vXrh40bN2L58uUSR0hERESNiStkiIiIjMSjR49gbm6O+/fvo127dnj8+DHGjRuH2NhYfPrpp3j33XelDpEawdq1a7F48WJ4eXnh6NGj4nfdqVMnlJeXo3379lKHSERERI2AK2SIiIiMRFJSEu7fvw8AePz4MQAgNjYWHTp0QLdu3cC/YzF+KpUK3bp1Q4cOHRAbGwvgyXd9//59JCUlSRkeERERNSImZIiIiIxEbGwsTExM1O5VVlaioqIC/v7+GDduHK5evSpNcNRgV69exbhx4+Dv74+KigpUVlaqfW5iYiImaYiIiMj4MSFDRERkJE6cOIFHjx5p3FepVFCpVDh16hScnZ1x6NAhCaKjhvjxxx/h7OyMU6dOid9nTY8ePcKJEyeaPzgiIiJqEkzIEBERGYHHjx/j5MmT4vYVXW3IOCmVSgC1f4f6/DdARERExoMJGSIiIiOQlpaG8vJynZ+bmJjAxsYGiYmJPBrZCL3++utITEyEjY2Nxra06srLy5GWltaMkREREVFTYUKGiIjICAjFe7Vp164dxowZgwsXLmDkyJHNHBk1lpEjR+LChQsYM2YM2rXT/ke06sV+iYiIyLgxIUNERGQETpw4oVFXpF27dpDJZFi2bBl+/PFH9OjRQ6LoqLH06NEDP/74I5YtWwaZTKaRmFGpVKwjQ0RE1ErIVDwjk4iIqMWzsrJCaWmpeG1iYoLOnTtj586dmDBhgoSRUVM5fPgwXn/9ddy7d0/txCVLS0uUlJRIGBkRERE1Bq6QISIiauF++eUXtWRMhw4d4O7ujtTUVCZjWrEJEyYgNTUV7u7uatvVSktL8csvv0gYGRERETUGJmSIiIhauJMnT6ptXfnrX/+KM2fOwMnJScKoqDk4OTnhzJkz+Otf/yrea9euHU6ePClhVERERNQYmJAhIiJq4Xbu3InHjx/D1NQUW7duRWhoKExNTaUOi5qJqakpQkNDsXXrVpiamuLx48fYuXOn1GERERFRA7GGDBFRK3X9+nUcOnQI0UejcS75PAoLCnCv/K7UYVEr1dHMFBaW3THIfRBe/KMXJk6caLQnPp09exY//PADYk+cQlpaGsrKSvHgYYXUYZGR6tLFHHY2dhg2Yih8fMZBLpfD3t5e6rCIiKgFYEKGiKiVSU5OxrJ/L8N3h75DR/NO6PasMzoPtoOJjTk6dOWqCmNUeesu2ncxRTsz7cdetwSPH/yGytJ7uHe5CPd+ysXt7CIMHOSGoH/9P8ycORMymUzqEGulUqkQFRWFj4NXIf2XNNh2d4ZL9+dgZ94fnTtawqS9mdQhiiofVeDBb3fQ1dRa6lBIDxW/leN2RSHyb6fhSvFp3H94B6+88io+/PDfGDp0qNThERGRhJiQISJqJYqLi7Fs+XKEhW1GtyGOsAt8Fj3GD4DMpL3UoVEbdCftBm5siUfh7vMYMXoUQr/ciBEjRkgdllbnzp3D/MC3kHg2AaOdpuMFl7/CsftgqcOiVujR40qkFRzGiV83Irc0FW/Om4cVH66AtTWTa0REbRETMkRErUBCQgJemSTHPdUDOC4ZC9vpw4AWviKB2oY7aTeQs+wHlCZcxaeffIL3339f6pDUfPbZZ1iyeAme6vkHTHL/kIkYahYqqJCQsxM/ZKxCx84yKA4dwOjRo6UOi4iImhkTMkRERm737t3wmzsHFmOextPrJ6M9tyVRS6NS4fo3Cche/j3eeOMNhG7cBBMTE0lDqqysRGDgfHzz328wefBKPOfyBmRgEpOa14Pf7iDy/D+QcfM4tkZ8i2nTpkkdEhERNSMmZIiIjNj27dvh5+cHh8Dn4fzBeKAdf1BSy1UaewWZb+7CRJ8J2Lv7f5LVlVGpVPjTn6bh8PfRmDMyDANsxkgSBxEAqFSPcehiMGIyNyEiIgKzZ8+WOiQiImomTMgQERmpn3/+GX8c44Ve7/4Rvd/2kjocIr3cvViA9ClfY8H8t/HJqk8kiWHJkn/hi/Ub8fdn98HBwl2SGIhqOnp5A6IzP0fsyRP4wx/+IHU4RETUDJiQISIyQgUFBXAb7I7OE/vB5VO51OE02KleQQCAF64HN6hNc3uQXwZTh+7idUuK8e7FAiSN+9KgWG4eSEXR3lSURF+C/ZzRsJ8zGl3c7DTaCfPURp/xSo5dxi9vbMOOqB3NvkVj9+7dmDFjJv72h61wsxvbrGM3hQX7qr6fdZMLJOtXW9vSe/mw7OxQr/4amzC2NvrEU1F5G+fzDyLtxhGkFxyBu914jOg9BW623jAz6daoY+1OWYRLpT8g7WIq7Ox090VERK1DO6kDICIiwy1c/D7aPdUdzsEvSx1Km5UXehoJo0KkDkOryuK7SBr3pUHPpM/dhkuBu1ASfQkAcGNrApLGfYmbB1LV2j3IL2twfFZj+6PvspcQ+Pbfcfv27Qb3p6/bt2/j74FvY5L78laRjGmpjmduworDLeNErdJ7+Q3uQ5H+EXaeX4j0giMAgPSCI9iaGICIs281+lhTBn8Eq45PYdH7SxrcFxERtXwdpA6AiIgM8/PPP2Pnjh3wOPp3HmktoewPf2yWce5eLEDpyStwDHhe72dyQo4ZNMbNA1WrYpyXvQS7WSPRoZuZeP9S4C50G+mkthIIAJyXvWRQTDU5/NUTyn3p+DB4JUJWf1bvfgyxcmUwura3xx+f+luzjGfMGrKS5UDaikaMRLd8ZTouF53Ei/0C62w7adByvdppGyMu+1uMd30Xnn3/DMvODii9l4+jlzcgLvtb3LzzK3p2fapRxgKA9u1MMHVwCEJ2eGP+3wPwzDPP1KsfIiIyDlwhQ0RkRFQqFea/8xZ6/Z8nOj/dU+pwqAmVn7uGK0sOImnclwYlf/JCT+PBDcNWnRTtrVoFUz0ZAwCWL/YHAJSeuCLeu59dAgDoOqiXQWNoaCdD75UvYcOG9bhy5Urd7RvoypUr2LB+AyYN/AgyGf/4Y8yulpzD7uTF+CxmbJ3Jn+K72QBQ7+PMc0vPAwBGOU0Vt2BZdnbAs85zAADXyi402lgCW/On8YLLX/D2WwvAygJERK0b/0RCRGREYmNjkXzuPOwDntX7mbLTWbiy5CBO9QrCqV5ByFl9FHcvav/b7+pt0+duQ9npLI02Qj9A1QoK4frmgVT8drtCo/3diwXICz0ttkufu01jG0xDGBJzZfFdMZba4rh5IBXpc7eJ7+t+VrHavKvXUKl+v2Yfhs73t9sVKIm+hPS525As3wwAcP/2z3gm9V96PV92OgvZH/6IvovH6dVeIGxTqp6MqX5958J1g/rTl/mI3ug2zAlr161tkv6r+/zztejbYwT6Wum/lSbz5mnsTl6MBfvssGCfHb6/+Cnylel1tg2Pn4PMm6c12gj9AEBS3n7xOilvPyoqNZNo+cp0HM/cJLYLj5+DpLz9esd/teScGHd1N+/8igX77DTmIsSfr0xXi7W6pLz9CI+fI8atbY7a5luzD0PnU1F5G+kFRxAePwfrYl8BAPh7bkXwy2l6PV9fwjYkc1P1BLiFmS0AoOB2RpOM6/VUAM6fP4eTJ082Sf9ERNQycMsSEZER+e+336CHV390tDXXq73w47663HUnkLvuBAbv+gu6P+8i3s9ZfRS5606oPVsSfQlOC8agzyLNH/gl0ZdwKXCXeH0pcBesfAbA/ds/1zq+0C8A9Jw0RK956GJozJff2yeOrSuOmn0K78sQeaGnxVUt+sz3QX4Zbp/NFd+hzZQhePrjVzW2CdXmflYxLkz/GgM2TddaiLc2Vj4DUBJ9Cb/drlBLyggJthtbE/D0J74AgDtpVckZE6vOKNh+FpnvV/2g7vfZa7CWD9JI6tSlx+se2PrvCKxbuw4mJiYGPauvyspKbIvYDrnrh3o/I/z4r+5IxlocyViLvz+/B/16Ptmu9f3FT3EkY63as+kFRzDe9V287LZYa99bEwPE662JAXC3Gw9/z621ji/0CwDDHV+rcw525v3EuKvHIazqyC09r3bKVFz2twCg8+Sp3cmLxTZC3Mr7N+qMo7rjmZvEVS36zKf0Xj6ySxLFdzSi9xRM9VilVjC4Nnm/z7VzR0vEX92GnecXAgBeHxaCYQ6+GkV5axK+15rtuppai58L77ahY1VnYWaHgXZj8N//fgMvL56iR0TUWjEhQ0RkRA59dwhW7+q/OkZIhoxOXCj+uC8/dw3J8s0oPpQmJmTKTmchd90JOC0YA4eA59Ghmxl+u12B/NDTyF13AtavDtL4kX9j21mx3wf5ZSjYfha5606g7HSW2K8w/lDFmzAf0RtAVfIhYVQILgXualBCpj4xd3W3g+sXU9GhmxnKTmfhwvSvUbQ3VYyjep92s0eKc7v2xUnc2Jog9vPC9eBaT1R6dLsCnpeC0KGbmZiUqj5OTUJx4AGbptfrnfx2uwJZK36E04Ix9XreZsoQlERfQunxy+LzwrvUpWbR4Mz39+PWkUvi+9WX1VhXXH53L3766acm++EZFxeH8jtKuNnqX8hXSIYsn3BO/PF/teQc1sW+guR8hZiQybx5Gkcy1mK867vw7hcIM5NuqKi8jZjMTTiSsRYeDq9qJDh+yt4m9lt6Lx/xV7fhSMZaZN48LfYrjL/A6ztxVU/pvXysODwCWxMD9ErImJl0w3jXd3EkY61arZNz1/YCAHaeXwjPvlUJ1Jt3fgVQlTzQJvPmaa21VOKvqidc100uqPVEpfuVt/HJq5dhZtJNTDqdu7ZX53yE4sBzRoXqNWddPotR/+53nl+ItBtH4DfyS4MSJc051oCe4/Cd4vNGjY2IiFoWblkiIjIS2dnZKLl5C1099PubYaBq5QMA3FSkoex0Fn67XQHzEb3xwvVgccUDACh/qtrmIyQ2gKrtKg6/F20tPalZ48Nl+UtiksfUoTvsZo8EABQferKF4IXrwXjhejDM+ljh7sUClERfQsH2s4ZMW6f6xNzrL55iWyFpJKxeqd6nkIwR5uYwT/8kWM1xhO+g+jg1jU5ciAGbpuNS4C5xi5Mhpxnlh55GSfQl9PqLp0FxCixf7A8rnwG4FLhL3IIVP0D78dXCyp+hijfF7/eF68EYsGm6mNQxhIl1F3R17IGEhIS6G9dTYmIirLs5iqsa9OFuNx4AkJx/EJk3T6Oi8jb6Wo3AuskFmDb0yRagzJtxACAmY4CqRIj370VdLxdpbjl5bfBytXokQlIkOV8htlk3uQDrJhfAuksf5CvTkV5wRCP5oQ83u6qVYkW/J1xu3vkV6QVHMGdUKACI25bKfl/p4mQ5TGs/wjyFZIwQ+yinqQbF88en/iq+J+EdC6tktFk+4RzmjArF1sQAcYuTIacZCatxFnh9J77TdZMLMGdUKNILjuBiYYxB8TfnWE6WHiguKUJOTk6jxUhERC0LV8gQERmJX3+t+kHVybmH3s/0XTwOJdGXxB/RVj4D4OD/rNpWJQDilpzafoTXPFGnk4v6j1shgVF9ewuguQWosdQnZhPrLnr1WXOrUM251qWucWoydeiOng7dYflif9z++SpubDuLS4G7YD9nNKzG9of5sN46+7x5IBW5605gqOJNg8cVdOhmhv5rJuPW4V+Q+f5+cdtUz0lDNL47bSuCgKrtWJcCd9W6EkgXs75WyMrSrP3TWH799VdYd3Y26JmX3RYjveCI+CPb3W48xjw9T22rEvBkS8uSQ/219nMgbYXGiTs1T+UREhxx2d+qJXtqboWqD2HbUk7JebjbjRe3Kw13fA1bEwPEbUvCdhtd25WEOGpuFao5l7oYkhQTxrPs7AA3W2/8eusMfsrehq2JAXjOeS7c7Maij+XwWvvUdVqUMP/aVucYqrHH6tGlL4CqgtR9+vRpjBCJiKiFYUKGiMhIKJVKAEB7c1O9n+niZocXrgeLRydnf/gjSqIvwcpnAPouHmdwrRFDCduY7OeMhvWrg2Bi1RkdbcxxZsiqJh3XWHXoZgYrnwGw8hmA8nPXULj7vLjtS1ciRKjjIxQBrqm2rVXVmVh3gd3skeJKJwDiKh3nZS/pPYfaVgLp1K0jbt827GQoQ5SXl8O0vWHbUhws3LFucsIAFQ0AACAASURBVIF4tPKBtBVILzgCd7vxeNltsc7ERWMRtjE95zwXQx3k6NzREhZmtgj6fpBB/VTftvSy22Kcu7ZX3Jb0+rAQcdvSgbQVmDRoeVNMpVGYmXSDu914uNuNx9WSc0jM3SVu62rIEd21rc4BIL67isrbatuNhCLM413fbbSxaur0+3hlZfqvliMiIuPChAwRkZF48OABAEDW3vDdpl3c7NDFzQ495YNwP7sEF6Z/jZLoS+KPdPs5o3Fja4JY90SvePLL1FaS3M8qBgA4LRgj3hMKvlZfMaPtJKb6qE/MdXFaMAa5605ozM2Q7UONxXxEb5iP6A37OaO1br9qTOlzt6Ek+pLGuxSOuDa171ZnW+F7tZ8z2uDx25k2/R9HOrTTP5FZnYOFOxws3DHUwRfFd7Pxn9NTkV5wREwCPOc8F3HZ34p1UfRRei9fbaWJUL+l+o97oSBs9RUz2k5i0oeb3TgcyVgrFtEVitDadxsIAOJJR849dH932mrRCHNpbn2tqk7LetZ5jtYtYdWFx89BesERje9HeJfPOc+t9Xm7bq4AgPIHN9Wev3XvGgD1FUMNHaumdrL2AJ78v5+IiFof1pAhImrFhOOgy89V/XgwdeiOTs5WGu2sX636W/f80NOoLL4r3i87nYVTvYKQp6W4a8H2s2Ki4kF+GYr2JAMALJ510WgrJGvqKhRriPrEXBch9ppzq63uTWMlmHTp4mansfWquup1XKr/U/Pz2thMqdpiVKx4Uv/nflaxWA+o20gnjbY1a8UI18L3YuyEI6CvlpwDUPXD27qL5ranoQ5yAEBM5ibceVAs3s+8eRoL9tnheOYmjWfir24TExml9/KRmLsHANCv53MabYVkjVAouD6EbUvCipIenXur3RdOfBKutRFi239hhVrstdW1qW8CSV8OFu4a28FqGtF7CgBo1G8RroXvTxfb399JYu4etXmn5B8CoF5zp6FjERFR28MVMkRErZjttGG4sTVB63aWfp89qWXQ/XkXcXVIzZohVj4DYDtVe6FP4XQggdOCMWr1aYRCtWefX6f1+ftZxQbXZ2lozPXtsybhqOj4AcGwnzNabRWQoYRtRbWpK6lSn/GEPoWivpnv7xdXNQkGbJqutlqoegHg6seeA5rfvzEb5TQdcdnfYl3sKxqfVT+JqF/P58XVIzXrvbjbjccop2la+xdODxKMd31XrT6NUMj2o2jNJA0AjZUqtam+bWm867tqxYeFFT7V72tTfZ7Vt95oO5XJ3W480guOYMmh/njOea7aKh9DCSc21aa2LUtutt5wtxuPrYkBakeNA5rvvPp4Qp8OFu5wtxuv9ft9znmu2tY1Q8ciIiJiQoaIqBUzH9Ebw4++heJDaWKCwWnBGJgPcxRP/xH0WTQOnV1toIy/Kh7x3O+z19BjwkCtxWL7LBqH9t3MkP3hjzqLBfecNASP7jwUf+Q7LRgDm6lD8bjiNySN+xLK+Kv1TsjUJ2ZD+izam4qS6EtizDWTSn0Xj4OpfTfc2JqABzeadiVAU6tZ1Beo+q60HR3eoZsZXL+YitLjl8V3JNQIai3JGKBqW8z73seQkn9I/CE+3vVd9LEaJp4OJHjZbTHsurni1+J4xGV/C6AqUTHY/iWtBWdfdluMTibdcCBthc5iwcMdX8OD3+6IW5fGu76LUU5T8fBRBT6LGYsrxfEGFdQVti3VXIXjZjcWcdnfiqcx1UaY57lre8WTmoY7vibGWL1d9072iMv+Vjy9SSpmJt3gN/JLXCyMEeMW6vLomyCZOfxzXLjxI9JuHBHrCA2yH49hDupJ2MYYi4iI2haZSqVSSR0EERHVLTIyErNnz27UlRL1oW+R2NbmVK+gBq+EIe0y/r4b4zq5Y/v27U3S/+zZs5FxqgJ+Izc2Sf/6qrn6gqguC/bZYfv27Zg1a5bUoRARURPgChkiIqLfCcmmoYo3YT6iqs7Gb7crUBBZVUPGwrOvVKERERERUSvDhAwREdHv3L/9M9LnbtNac8fKZwAsX+wvQVRERERE1BoxIUNERPQ7K58BGLzrL1D+lCXW3LGfMxoWnn1h+WL/Rjtem4iIiIiICRkiIjJIa68d0/15F3R/3gV9FtVd5JTIEKwdQ0RERNW1kzoAIiIiIiIiIqK2hgkZIiKS3KleQWJB3eZ4zlC/3a5AwfazSJ+7Dad6BSF97jbcPJCK325XNPnzdy8W6Jyj0K/wHnJWH8X9rGKD5kbGY8E+O/GkpuZ4zlAVlbcRf3UbwuPnYME+O4THz0FS3n5UVBp+LHx6wZE6Y07K2y+OtTt5MfKV6Xr1na9Mb5b3QUREVBduWSIiIqrD1Y+P4MbWBPG6JPoSSqIvwcpnANy//XOTPV9ZfBdJ477U+XnG23tQEn1JvM5ddwK5605g+NG30MWNPzipeSnSP0Jc9rfidXrBEaQXHIG73Xj4e27Vu598ZTrC4+fU2iY8fg7SC46I13HZ3yIu+1vMGRWK4Y6v6XzuzoNifBYzVu9YiIiImhITMkREJLn61qVpjno2dy8W4MbWBDgtGAO72SNh6tAdD/LLcO2Lk7ixNQH3s4rRycW6SZ7PCTmms9+bB1JREn0J/T57DXazRwIAyk5n4cL0r3FjawKe/sS3YROnFqe+NWiao3ZNvjIdcdnfYrzru/Ds+2dYdnZA6b18HL28AXHZ3+LmnV/Rs+tTdfZzteQc1sW+UmubpLz9SC84gkmDlsOz72yYmXQT729NDICz1ShYdnbQ+uwPv3xm+OSIiIiaCLcsERER1aL8fB4AwGbqUJg6dAcAmDp0h/2c0QCAOxeuN8nzeaGn8eCG7q0eRXtTAQDW8kHive7PuwCA2mocouaQW3oeADDKaaqYDLHs7IBnnatWulwru1BnH8czN2Fd7CuYMyq01nbnru0FALVkDAC42XoDAC4VHdfZf9n9G3XGQURE1Fy4QoaIiJrUzQOpKNpbtZrDacEY2EwdirPPrwPwZIWLUCOl5vUzqf9C4Z7zyP7wR1j5DIDNlCHoOWmI2HfN57TRp8ZMbc8/yC8DAJhYd1W739HGHABwL6Oo1r7r83zZ6Sxkf/gjhh99S21LUnXatjoJbQdsml5rTNTyJOXtx7lre5FecATjXd/FKKep+Cj6OQBPVrgIdU9qXge/nIbE3N04kLYC7nbjMaL3FLVtOzWf00afmiq1PV96Lx8AYG7aU+2+hZktAKDgdkad/R9IWwF/z61wtxuPrYkBOtsJW5WqJ2OqX+dpSf5k3jyNA2kr8L73MbWtTkRERFJiQoaIiJpMzuqjyF13QrwWapzo6/J7+8Qkg1B3BYBaUqapCfF26Gamdt/Euov4eW1HZBv6/P2sYlyY/jUGbJqudx2YvNDTyP7wRwBVyZjmfD/UcN9f/BRHMtaK10cy1qpd1yUq6Z9ikkGo2wKg1loqjU2It2aSpKuptfj5y26La+1D361V7nbjkV5wBBWVt9XGE4oHx2V/i2n/n717D6uqTNsAfqMgBzlJoCiIoiIkaCiCImSkgqbiafCIYmOjYVnhjGZ9aabjdNKEKUvKxgLF8/mUgseEMBBBBQNRUBQhQUQEhQD5/qC1YsMG9oa9WRzu33XNdc3e613vetZeKezH531ex8/E93MKb+LrSB/4OQfDwsheoWsQERE1BSZkiIhILfIj05ARdLbW3imK0Lc3h+1XPtA01BH7o9zfd0WphENT9JlRlbKCYqStOg6rAA+l7lHfoRusPxyDR9G3kLxwF4CmTVpRw6XmRCI8JbDW3iuKsDCyx5zBG6CjZYjUnEh8HemDuDv7lErINEWfGVVx6j4FSdnhuPb7afEei0sLcDp1Y42xxaUFOHB1FbxsFzdpgoqIiEgR7CFDRERq8eiXNAAQkzFAZe8UiwXDFJ6j2zxXsbJE6I9S2xKe1iAzOBJ5EcnoNs9VqfOM3XvB0t8d9iGzYbN2EpIX7kJ+ZJqaoiRVSs2JAgAxGQNU9l7x6LNA4TmG935NrBSxMXMHgFa9LKdflxHisiZhS+/3jvSVO/Z06kYkZYdjeO/XmjhKIiKi+rFChoiI1EJYqiMkYwR17UhUnbCspzEa20OmqeQcvIKMoLNwPPx6o+7b1NsBqUsPIHPTL2ISi5ovYalP9V2BFNmRSCAsC2qMxvaQaUo6WoaYOWg9rmYdx874JTJ9c6ou9bp09wDCUwIR8NJRlXxGREREqsaEDBERUR2sAjyQEXQWZQXFMn1gygqKxeOqOF9YapTg/a3ceRRpYAz81aumNVcSUfPjZbsY4SmBtfZ18bJdrNLr6WubwrXnbLj2/Ku5tdBYeKLDSgAQGwPXto22Is2OiYiI1IkJGSIiUgshEVGSmS9TJSPsOtRUGlv9omfbGQBQmlsok1ApuVt5H9UrgFR9fm2S5m5FXkQyXJOXy8xbmlsEAOK22tS8CYmMh08yZapkhORCU2lsUsLc0BYA8LgkRyYh8+DJHQA1K4AaY1O0H5Kyw/Hp+Osy18otSgcAGOl2Vdm1iIiI1IkJGSIiUgujYb2AoLPIDrso09Q3O+yi1KEpRc+mMqFyf0+CzH3kHkkEABgMtFTJ+bUljmqrjOk8ZQDyIpKRezgR5r6DAVRW3fy+Jx4AYDreQel7paZnY+aG8JRARN/aKtPUN/rWVqlDU0oXAxsAQGzGHpn7uJx5BABg1Wmgyq4lNPWNzzwkVsjkFN5EQuZhAIC1iTOA2pNMrIwhIqLmggkZIiJSC2P3XmKVjDJbXTc3HfuZw8TTTu59dPVzqbE1dfUEirLnK8ps4gDc33cFqUsPIHXpAZljVgEe7B/TQtiYuYtVMspsdd3cWBjZw97cS+59uFnPrbHddGOSIkJT353xS7AzfonMMT/nYJVW4xAREakTEzJERKQ2Pd4dBT3bzri/7wryIpJhFeCBzj6OuOgeJHVoSun7xWQ8OPEbHoQnIy8iGSaednjOyw6m3opVoTT2/NrYh8xGzsEr4ufb1c8FpuMdmIxpYcb2WwZzQ1vE3dmHpOxweNkuhrOVD/4T4SZ1aEoRGu0mZoUjKTsc9uZecOjqhYEWE1R6nepNfYHKpV8vWIyvkfghIiJqzjQqKioqpA6CiIjqt23bNvj6+jaLHYEa63y35ejq54I+n6r2ixq1TClv7sYoXXuEhYWpZX5fX1+knC/GnMHfqGV+dQnYbw4367mY6viZ1KGQRAL2myMsLAyzZs2SOhQiIlIDVsgQEZFaCEt3HA+/DgOn7gAqe5xkb6vsIWPk2lOq0IiaDWHpTsBLR9HTxAlA5c5E0bcqk1O9TV0li42IiIjUiwkZIiJSC/uQ2Uiau1XuNs4mnnbo9HJfCaIial7mu4ZiU7Sf3K2Z7c290K/LCAmiIiIioqbAhAwREamFiacd+u+ah0e/pInNbLv6ucDItSc6vdxXZqtmorbK3twLb7rvQWpOlNgM1816LnqbuqJflxEy2zoTERFR68KEDBERqY2xey8Yu/dCj3dHSR0KUbNlY+YOGzN3jO23TOpQiIiIqAm1kzoAIiIiIiIiIqK2hhUyRETU5ggNh1vDjlVF17JxadQGufci3Kc8reHeSb2EhsNBk7MljkR5QuzyVL8fZcYSERGpEitkiIiIWqjS3CJcGrVB7rGSzPwmjoaoeXj4JFMtY4mIiFSNFTJEREQt1O11p+odY/3hGFj6uzdBNETNy0SHlXjZZqHKxxIREakKK2SIiIhaoLvBkSjJKqj1+NP0PACAvkO3pgqJqFnILUoHAFga91fpWCIiIlVjhQwRETVYfmQaco8kIis0BgBgFeAB0/EO6NhPtidD0bVsPPz5BtJXHwdQuSV25ykDYDZxgDimal+XvIhkJM3dChNPO3SdPRgmnnYAgJyDV5C8cBcAwG7jtFrPrz5O0W22q96PiacdLOYPg7F7rwbfd3V19XQRKNLbJT8yDemrj2PQyUXIi0iudzxJKzUnEgmZhxGVHgIA8LJdjBcsxsPCyF5mXOajJFy//zMOJq4CULkltlP3KRhkOUkcU7WvS1J2ODZF+8He3AvDrGfD3twLAHDp7gGExvoDAPycg2s9v/o4RbfZrno/9uZe8OizADZmNauwFL3v6urq6SJgbxciImoNmJAhIqIGEZImVWUEnUVG0Fn03zVPTGTIG5cXkSwmEqomVaqPF8YNOrkIuUcSkRF0VhwnJFzknS8cE8aZeNrBPmR2nfdz+/OTMvML17YK8JDZtlvR+1aXp2m5uDptM+w2TqszAVSYeA8AoGWih+ywi0hdegAAYLN2Eky9HRRKUFHjCUmTqsJTAhGeEog33feIiQx545Kyw5GUHQ4AMkmV6uOFcUtHnMLlzCMITwkUxwkJF3nnC8eEcfbmXpjvGlrn/Ry79pnM/MK1vWwXy2zbreh9q8Pd/KsAAL0OnRB9ayt2xi8BAEwfuA4DLSbIJJ2UGUtERKRqTMgQEVGDCEkJl9gl0LYwBgA8jruDBO9vkXskUUxMCOMcD78OA6fuACobzsY4r0Pywl01EiqP4+/CNXk5NA11kB+ZhqvTNuPSqA2wCvCo8b6887O2XhRjKsnMR3bYRWQEnUV+ZFqtyZL8yDRkBJ2FVYAHLPzdoWmog7KCYmQGRyIj6KxM9Yui9y1PY3c2KisoRtqq47AK8Khx37Wp3vQ3dekBPAhPhu1XPkzKNAEhKbFydBw66VkAAG7lxSHo3DgkZB4WExPCuICXjqKniROAyoazq044ITTWv0ZC5XZePD4dfx06WoZIzYnE15E+WHt6JLxsF9d4X975v6RvFWN6+CQT0be2IjwlEKk5kbUmS1JzIhGeEggv28UYYbMQOlqGKC4twOnUjQhPCZSpflH0vuVRVfXL2tMjZV7vjF+CxKxwzBm8oUaiRZmxREREqsKEDBERNYiJpx3yIpKRczgR+g7doD+gGwycutdIOgivS3OLUHQtGyWZ+Xgcf7fWebvNcxUTBVWTG0KipPr71fVaOUZMlGhbGMPcdzAygs7WmSx59EtajWtoGurAwt8dGUFn8fDnG2JCRtH7VofM4EjkRSSj7xeT6x0rLA+rmggD/lr29fDMdYWTOtRw9uZeSMoOR0LmIVga90d34wHoaeJUI+kgvC4syUXmoyTkP83E7bz4Wucd3vs1MVFQNbkhJEqqv1/dpP4rxURJJz0LuPacjfCUwDqTJak5UTWuoaNliBE2CxGeEojr938WEzKK3rc6CEu+qia3gL+Wcl37/bSYoFJmLBERkaoxIUNERA3Sc9ko5EUky/SFqa3nSvXlQHXRMu0o931Fqzl0e5nKvBaSM1mhMejz6QS55wixRdvJT6qkrz4u7lSkzH1X15geMjkHryAj6CwcD79e62ekyDxmEwcgeeEu3N93hQmZJjC23zIkZYfL9IWpredK9eVAddHXNpX7vqLVHGb6vWVeC8mZqPQQTHX8TO45QmzvHekr9/jBxFXiTkXK3Hd1je0hU9uxQZaTEBrrj7g7+8QkizJjiYiIVI0JGSIiapCO/czx4r01Mg178yKSYeJph57LRokVJcKSoa5+LjAd7wAtEz106GyACwM+kfgOGkbR+1Y1oS9Ogve3co9XbWqsCDYDbhoWRvYImpwt07A3KTsc9uZeGNtvmVhRIiwZcrOeC0cLb+h16AQjnS5YfsxB4jtoGEXvWwpCXx5VjyUiIlIWEzJERNQoHfuZo2M/c5h5O+Bpeh6uTtuMvIhkMTEgNJOtWp1SVlCstnhKMvPFqhigsgkuULkTUm26+rkgKzRG7FGjiPruW56mWNYkSJq7FXkRyTXuSfjsu/q5NFksVJmgsDCyh6PFBOQWpePrSB8kZYeLFRpCM9mq1SnFpbVva95YD59kilUxAJBTeBNA5U5ItXGznouo9BCxR40i6rtveRq7rGlTtB+SssNrxCl8nm7Wcxs0loiISNXaSR0AERG1TDfeO4Tz3ZbjcdwdAJVLg3StTWodLyRGhGa56pIddhElmfkAKpMz9/ckAACMhtW+pMh0fGUVQmZwJEpzi8T38yPTcL7bctytEq+y960qL95bI/d/1Y8LOk+pXI708Mx1mXmE18I9k3rtTliGgP3muJUXB6ByaZBpR+taxwuJEaFZrrpE39qKh08yAVQmZ2Iz9gAAbMzcaj3H0cIbAHA6dSMKS3LF91NzIhGw3xxnqsSr7H2rklP3KQCAa7+flnlfeC3ch7JjiYiIVI0VMkRE1CBdpg5EVmiM3CU0Nmv/6rlgt3EakhfuwkX3ILnzPE3LrdH3pbFinNfJvLYK8Kizx4uxey9YBXiI21dXZeJphy4+A8XXit631Dq93BcmnnZIXrhLZhtwoP7Pg1TH2WoaotJDEHRuXI1j0wf+9d+pn3MwQmP98Z8I+QmRnMKbNfq+NNaqE04yr71sF9fZ48XGzB1etovF7aursjf3grPVVPG1ovetDv26jIC9uRdCY/1ltvYGat6jMmOJiIhUjQkZIiJqEAOn7hh0chFyjySKSQyrAA8YDLSEiaedOM5s4gCUF/4hLl2yCvBAZx9HPCsuw6VRG/Ao+pZKEzI93h2F9oY6SF99XKmGuz3eHQU92854FH0LWaExACoTLM+Nfl6mia6i9y01TUMd2H7lg4dnruP+vivIi0gW+/gwGdN0epo4YemIU7iceURMYnjZLkYPk4GwN/cSxw2ynISSskJx6ZKX7WI4W/ngj/JirD09Ejdyo1WakBnbbxl0tQxxMHGVUg13x/ZbBnNDW9zMjUZUegiAygRL/65jZBoNK3rf6qCjZYg5gzfg2u+nEXdnH5Kyw8XePNXvUZmxREREqqZRUVFRIXUQRERUv23btsHX17dJ+5C0JMo2taXmI+XN3Rila4+wsDC1zO/r64uU88WYM/gbtczfkgg7GDXF9tPUeAH7zREWFoZZs2ZJHQoREakBe8gQERERERERETUxJmSIiIiIiIiIiJoYEzJERERERERERE2MTX2JiKhVYO8YovqxdwwREVHzwQoZIiIiIiIiIqImxgoZIiJSuZa645EQt0CIv6ygGLmHE/EgPBl5Eckw8bRD5ykD0OnlvtA01FHJtYuuZePSqA0KfWbKjFVEXkQykuZurTFfbZ8HqUZL3fFIiFsgxF9cWoD4zENIzApHUnY47M294NR9Cvp1GQEdLcMGXy+n8CZiM/aI22fL22a7MTIfJWHt6ZG1PodLdw/IbIk9zNoPFkb24vHaPg8iIqL6MCFDRERUj1sfhyMrNEZ8nRfxV2LGPmR2o+cvzS3CpVEbVD5WEUXXspE0d6vK5qO263DSfxCVHiK+Tsr+KzEz3zW0QXMKyZKqdsYvQWJWOOYM3tCoRA8AFJbk1pi/qk3RfkjKDhdfR6WHICo9BH7OwRhkOalR1yYiImJChoiIqJqqlSBF17KRFRoDqwAPmPsOhraFMUoy83Hnq5+RFRqDp2m50O3VuH+pv73ulFrG1udx3B0keH9b63Hhc6heKUMEyFaCZD5KQlR6CLxsF8O152x00rPAwyeZOHn9S0SlhyCn8CbM9HsrNX9xaQHWnh4Je3Mv+LzwCTrpWaC4tADRt8JwMHEVrv1+utFJkZ9+W1vrsUt3DyApOxwTHVbCtaevmPy5dPcAQmP9YW3ijE56FuLnUL1ShoiIqD7sIUNERFSHx/F3AQCdfRyhbWEMANC2MEZXPxcAQOHVe42a/25wJEqyClQ+VpG5Ery/hd3GaSqZj9q2jIfxAABnKx900rMAAHTSs8Awaz8AwJ38q0rPmf04FQDg1H2KOKeOliFce/oCAOLu7GtUzGdSNyL/aVatx4X5qyZjAKBflxEAgOT7Zxp1fSIiIlbIEBERzndbjq5+Lujz6YQax268dwhZoTFwTV4OTUMdFF3LxsOfbyB99XEAEPupmE0cUOf8QM0eJLW9nx+ZhtwjicgKjYGJpx0s5g+DsXsvhe6jPsr2QSnJzAcAaJnqy7zfobMBAOBJyn2l5qsqPzIN6auPY9DJRciLSFbZWEWkrz4O+5DZMPG0Q/LCXY2ery0I2G8ON+u5mOr4WY1juxOWISo9BJ+Ovw4dLUNkPkrC9fs/42DiKgAQ+6nUVdFRW0+Z2t5PzYlEQuZhRKWHwN7cCx59FsDGzF2h+6iPsn1QHj7JBAAYaJvJvG+k0wUAkF2QotR8AJD+oHKZoLWJs8z7OlqGje7TkpoTiYOJq7B0xCmZJUlVCe9XXxYlvL7bgCQTERFRVayQISIiWH84BlmhMSjNLZJ5vzS3CFmhMbD+cAw0DXWQF5GMS6M2iMkYoLKfSvLCXcg5eEUlsdz+/CSuTtss9mzJi0jG1WmbcfvzkyqZX1kZQWcBoEbzXi3TjjLHlfU0LRdXp22G3cZp6Niv7i/IyoxV1Iv31sDE004lc7UVEx1WIio9BIUluTLvF5bkIio9BBMdVkJHyxBJ2eFYe3qkmIwBKr/ch8b649LdAyqJ5di1z/B1pI/YsyUpOxxfR/rg2LWayaKmIDTcrZ68EBrvCseVcSM3GkBlpc2luwewKdoPAfvNcSZ1Y41noIycwpv4OtIHfs7BMs15q7M39wJQuXSqKuF11X45REREDcEKGSIiQqfhfZAOID/qpkylS37UTQDAc16VX9yF5q+Oh1+HgVN3AJUVJDHO65C8cFedVTKKyI9MQ0bQWVgFeMDC3x2ahjooKyhGZnAkMoLOwnS8Q50JiZayC1BZQTHSVh2HVYBHvZ+ZMmNJvfp2Hg4AuJ4TKVPpcj0nEgDg0LXyC/ym6MplOgEvHUVPEycAlRUkq044ITTWv9F9T1JzIhGeEggv28UYYbMQOlqGKC4twOnUjQhPCcQLFuPrTDS0lF2AhAqVY9c+k0noHExchRu50Q1q6ltcWoADV1fBy3Zxvc/BqfsUJGWHy/SqET5nIiIiVWBChoiI0LGfOUw87XB/3xWZL/33911BVz8XsWmtkPAozS1C0bVslGTmFzkQYgAAIABJREFUiz1WVOHRL2kAICZjgMrKFAt/d2QEncXDn2+orEJESpnBkciLSEbfLyardCypl4WRPezNvRB3Z5/Ml/m4O/vgZj1XbForJDwKS3KR+SgJ+U8zcTsvXmVxpOZEAYCYjAEqK1NG2CxEeEogrt//uc6ETEu0ZmyiWG0jNNVtSFPf06kbkZQdjpmD1tc7tl+XEbA390JorD9CY/3F971sFysXPBERUS2YkCEiIgCAxfxhuDpts7hr0NO0XORFJKP/rnky425/frLBy3TqI8wbbSe/0iV99XFY+tfeI0MdPWRULefgFWQEnYXj4dfFZU+qGEtNw6PPAnwd6SPuGpRTeBNJ2eF4032PzLjqVR2qJMz73pG+co8fTFyFl20W1nq+OnrIqFPVxBPwV1Pd6omx+ly6ewDhKYEIeOmomNypi46WIWYOWo+rWcexM36JTB8gdT1bIiJqW5iQISIiAID+gG4AgEfRt6Dby1TcPUh4HwCywy4iI+gsuvq5wHS8A7RM9NChswEuDPhEipCbhFWABzKCzqKsoFimj0xZQbF4XBlCA93atpuu2uhYmbHUNLobV1aQ3ciNhpl+b3H3IOF9AIi+tRXhKYFws54LRwtv6HXoBCOdLlh+zEGSmJuCl+1ihKcEori0QCZ5IvRbaUhViTBnbU11a2vGWxuhyiXo3Di5x+U1T9bXNoVrz9lw7TlbfE9oYDzRYaVS1yciIqqOCRkiIgJQuTTIZu0kpC49gOdGP4/khbtgs3aSTBIidWllQ9KquzEJiQllVW8gDABd/VxkdnRSljoSE3q2nQEApbmFMjGV3K3cfUnYCpvaBh0tQ0wfuA4745egf9cxCI31x/SB62SSBjvjlwCAzG5M1RvDKkpe81o367kyOzopSx3VL+aGtgCAxyU5MjE9eHIHAMRtqxsy58MnmTLnC5+lm/XcBseriE3RfkjKDq/xOecWpQMAjHS7qvX6RETU+jEhQ0REIiPXngAgVrx08ugjd5ywrElouFsfE0875EUk43HcHRg4dUdZQTHubY6uMc50vAOyQmOQGRyJbvNcxWU6+ZFpuDptM6w/HFPnkiV10LOpTMjc35MAc9/B0LYwRklmPnKPJAIADAZaKjVfbUkjedUuyoylptPH1BUAxIoXu84vyx0nLGtStBGsvbkXkrLDcSsvDj1NnFBcWoCfb/6vxjhHC29EpYfgdOpGDO/9mrj8JjUnEl9H+mCiw8o6lyypQxcDGwBAbMYeuPacjU56Fnj4JBOXM48AAKw6DVR6TmG76+hbW2WWLV37/TQAoJ/5SKXmqy0RVdu24kJT3/jMQ2KFTE7hTSRkHpaJj4iIqKGYkCEiIpFuL1OxSqWrn0uN6g+7jdOQvHAXLroHyT1fSNRU13nKAORFJMssvbH+cEyNccbuvcQlQtX71Jh42qGLj/Jf6hpLaHgsL6aufi4yTYalTpRIff22wky/t1il4mY9t0b1h59zMEJj/fGfCDe55wuJmuqEBEDVJTXylsXYmLmLy3mq9zKxN/eCs9XUhtxWowgNj+XF5GY9V6bJcG0JkOo66VmIn6W8OYVtqZWZUxlCU9+d8UvEqieBn3Nwg6p+iIiIqmJChoiIZAhVKl2m1kx+mE0cgPLCP8SlS1YBHujs44hnxWW4NGqD2H9G3nlA5a5NeRHJsFk7Cea+g5G++niNsT3eHQU92854FH0LWaExAACbtZPw3OjnJWts2/eLyXhw4jc8CE9GXkQyTDzt8JyXHUy9W29PEKqbUKXibDWtxrFBlpNQUlYofon3sl0MZysf/FFejLWnR4r9Z+SdB1Q2q03KDsf0gevg2nM2DiauqjF2bL9lMDe0xc3caESlhwAApg9ch/5dxyjUsFYdhAa4iVnhSMoOh725Fxy6emGgxYT6T67FIMtJMNHrjtiMXYhKD5FprKtu1Zv6ApXPsr5txYmIiBSlUVFRUSF1EEREVL9t27bB19eX1Q9qpIoKk/Pdlkv6jNRxfXVX3qS8uRujdO0RFhamlvl9fX2Rcr4YcwZ/o5b52xpVVKME7DdXeS8bdcyp7PUB1VbpBOw3R1hYGGbNmqWyOYmIqPloJ3UARERErcXjuDuwWav+f7lvrtcnUsStvDhMH7iu2c9JRESkblyyREREVE1DK0Iexd5u8qbD6ry+8DkQydPQipD0BzEqbzqsjjkVJXwOREREymKFDBERkYpImYxpDtcnUoQ6EidSJWOIiIgagxUyREREf2J/Hln8PEgeKfu0NEf8PIiIqKFYIUNERERERERE1MSYkCEiojqd77acvUSaSFN81nye0gjYb96me42o4/4bOmdbfxZERNR8MCFDRERERERERNTE2EOGiIiomWDPFmqt1NFnpaFzsucLERE1F6yQISIiIiIiIiJqYqyQISJqw8oKivHwzHXc33cFeRHJ6OrnAosFw6Dby7TO84quZePhzzeQvvo4AMDE0w6dpwyA2cQBMuPyI9OQeyQRWaExAACrAA+YjndAx37mDRpXnSK9UORVnZQVFCPabg26+rmgz6cTahy/8d4hZIXGwDV5OTQNdWrEaOJpB4v5w2Ds3ktuPC6xS3Dj/45A394cPd4dpfA9CudXjVmZZ5Rz8Io4rrZnUhtFzq3r/tqy4tICXPv9NOLu7ENSdjjcrOfCo88CmOn3rvO8zEdJuH7/ZxxMXAUAsDf3glP3KRhkOUlmXGpOJBIyDyMqPQQA4GW7GC9YjIeFkX2DxlWnSD8VeVUlxaUFeO9IX7hZz8VUx89qHN+dsAxR6SH4dPx1vHekr8w8wjVXjo7Dnsvvw8LIHmP7LRPPvXT3gPh5etkuhrOVD/4T4SZ3juqv14xNRGzGbhxMXCX3M61+nnAvijxDRZ8ZERGRIpiQISJqw1Le2oO8iGTxdVZoDLJCYzDo5KJakyF5EclImru1xnvCPMKXeHnjMoLOIiPoLPrvmicmMxQdp0qahjqw/nAM0lcfR48lI6Fl2lE8VppbhKzQGFh/OEZMxtz+/CQygs7WuF+rAA+5CYnssIvIi0hG5ynKfRbyKPqMaovxScr9epMmyp5b/f7aui0XFyEpO1x8HZUegqj0ECwdcarWZEhSdjg2RfvVeE+YR/iCL29ceEogwlMC8ab7HtiYuSs1TpV0tAwx0WElDiauwivPL4W+9l9JwsKSXESlh2Ciw0roaBnWOkf0ra1Iyg6HU/cp4nvHrn2G8JTAGvehqO2X/il+jvI+U3kUeYaKPjMiIiJFMSFDRNRGVU0qWPi7Q9NQBzkHryB54S5khcbIrRwBICYWHA+/DgOn7gCAksx8xDivQ/LCXWJCRhjnErsE2hbGAIDHcXeQ4P0tco8kikkIRcfJ05ieK52G90E6gPyomzKVIPlRNwEAz3nZVb6OTENG0FmZz6msoBiZwZHICDort5JHz7azTGwNvUdFn1HVGM19B0PbwhglmfnIDruIjKCzMBrWq9ZrNOTc6vfXlglfyL1sF2OEzULoaBni0t0DCI31xy/poXIrRwCIX+wDXjqKniZOAICHTzKx6oQTQmP9xS/3wriVo+PQSc8CAHArLw5B58YhIfOwmGhRdJw8jemp0rfzcADA9ZxImYTE9ZxIAIBDV686zzc3tJW5fmpOJMJTAuFluxiuPWejk54FHj7JxMnrX4qVP/WxMLLHnMEboKNliNScSHwd6YO4O/tqTZgo+gwVfWZERESKYkKGiKiNyjt1HQDQbZ6rWAliNrH+JS7CF/HS3CIUXctGSWY+HsffrTHOxNMOeRHJyDmcCH2HbtAf0A0GTt1rfJFXdJyqdexnDhNPO9zfd0Xmnu/vu4Kufi7ikqBHv6QBgJgQASorbCz83ZERdBYPf75RIyFj7Ca7zKGh96joM8o9kggAYkIFALQtjGHuOxgZQWfrTPo05Nzq99eWXcs+BQAY3vs1sRJkkOWker+cC0mIwpJcZD5KQv7TTNzOi68xzt7cC0nZ4UjIPARL4/7objwAPU2caiRRFB2nahZG9rA396qR8Ii7sw9u1nPrXbbVt1qiKDUnCgDEZAwAdNKzgEefBQonZKo+i6oVRLVR9Bkq+syIiIgUxYQMEVELoa2tDQCoKH8GjfaN78ku9DKpulxHUdWXuMjTc9ko5EUky/SZkdd3RdFx8jS0h4zAYv4wXJ22GU/TcqHbyxRP03KRF5GM/rvmiWOE+4y2kz9P+urjsPSX/VJZ/TNt6D0q+oyEcUJCRSC8rqviqSHnNuS/mbo8KykDdFU6ZQ1lz0rUMq+QJKi6XEdR1ZfmyDO23zIkZYfL9Czx6LOgRsWLouPkaWgPGYFHnwX4OtIHOYU3YabfGzmFN5GUHY433ffUO2/1z034PIRkjKC+xE5dc9ZHmWeoyDNTlWcV5QD++rufiIhaHyZkiIhaCCMjIwBA+eMSaBqr+dtrHYSlLF39XGA63gFaJnro0NkAFwZ8IjOuYz9zvHhvjUwDYKFhbM9lo8SqEkXHqYP+gG4AgEfRt6DbyxSFV+/JvK8qUt5ji1DwBwytau8z0lgGBgYoKb+jtvkbIvrWVoSnBMLNei4cLbyh16ETjHS6YPkxB5lxFkb2CJqcLdNMNik7HPbmXhjbb5nY30TRcerQ3biyYutGbjTM9HvjTv5VmfdbC0Wfmao8LS0AABgbG9czkoiIWiomZIiIWog+ffoAAJ6mP4DBQMtGz9fVzwVZoTEozS1SquIhdemByniqVE2UFRTXOr5jP3N07GcOM28HPE3Pw9Vpm5EXkVyjckXRcVU1dlmTpqEObNZOQurSA3hu9PNIXrgLNmsnicuDgL8+p6o7LjWUsveo6DMSxpVk5stUujxNyxWPq+NcVSm+lYdeE1TfvFnQu3dv7H1yVC1zu1nPRVR6CApLcpWqzNgZvwQAZHrMFP/5BVweCyN7WBjZw9FiAnKL0vF1pA+SssNrVK4oOq6qxi5r0tEyxPSB67Azfgn6dx2D0Fh/TB+4rs5mvrXxsl2M8JRAPHySKVMl8/BJZqNirIuiz1DZZ9ZYD4puAQBsbGzUdg0iIpJW42veiYioSfTs2RMmZs+h8LJqvpgYufYEANzbHC0mVHIOXsH5bstx471D9Z4vfGEXGtxWd+O9QzjfbTkex1VWJmhbGEPX2qTB49RF+ByECp9OHn1kjpuOr/zX78zgSJTmFonv50em4Xy35bgr596ra+g9KvqMhBizwy6iJDMfQGWj5ft7EgAAJiP71nqNxpyrCqW5RSi8+wAuLupL/Dg7OyO34C4KS3JVPndvU1cAwM83/yd+Ob909wAC9ptjd8Kyuk4FAOQUVjaRLi4twOnUjTWO705YhoD95riVFwegcimPaUfrBo9Tlz5/fg5CtYhd55cbNI+NWeXW1tG3topJmIdPMhF9a2tdpzWKss+wvmemKhkPL8PUpDOsrKzUdg0iIpIWK2SIiFqQ8ePG46dTMej66pBGz2U2cQDu77sibr9cVV1VEXYbpyF54S5cdA+Se1zox9Jl6kBkhcYgwfvbGmNs1v7VLFPRceqi28tUrBLp6udSo5eKsXsvWAV4yP2cTDzt0MVnYL3XaOg9KvqM6orRKsADJp52tV6jMeeqQt6pFOgbG2LYsGFqu4abmxsM9I1w7fdTcLGartK5B1lOQtydfXK3Zh5m7VfLWYCfczBCY/3xnwg3uceFfizOVtMQlR6CoHPjaoyZPnCd+P8VHacuZvq9xUoTN+u5NXrAKMrGzF2skmmqXi2KPkNFn5mqJOeexDjvsSqbj4iImh9WyBARtSB/n/sqHpy7jj9+f6yS+Wy/8pFJCFgFeGBwZECdPU3MJg6Qe86gk4sAVPZjAQADp+4YdHIRrAI8ZMbah8yGue9g8T1Fx6mTUCXSZar85EqPd0fBbuM0mSSIzdpJ6PvFZIWWezXmHhV9RkKMQgLFxNMOdhunoce7o+qNrzHnNtaDnZfh5zsbWlpaaruGlpYWZs/xxcXMnWqZf87gDTJJDy/bxfjAM6rOvi2DLCfJPWfpiModf27kRgMAepo4YemIU/CyXSwzdr5rKFx7zhbfU3ScOjlaeAOoTA41xth+y+DnHAx788ots4XPRp0UeYaKPjNVeFScjd+yzuLvf39VZXMSEVHzo1FRUVEhdRBERKSYiooKDHZ1Qa6jLnqsHC11OESN8jjuDpJ8NuNa4jWxR5K63LhxA/b9HPDGsH3oaeKk1muRegTsN4eb9VyZHi6t1aGkj1BgEI+Y2GhoaGhIHQ4REakJK2SIiFoQDQ0NfPPfDbj3QzSe3MiROhyihntWgTsrjuPtt99RezIGqGyK/fY7b+Pgbx+gouKZ2q9HDROw31ymFw5Q2avlzJ+9WoR+L63Z749v4HzaZny1IYjJGCKiVo4VMkRELdDsuXNwPOUXPL9rLjS02ksdDpHSMjf9gkffxCLt+g0YGqpvy+uqCgoK0KdXX7h3exMv9VnQJNck5SRlh2NTtPzeO/bmXpgzeEODdm9qKcqflSL4wlQM9bRF6JYfpQ6HiIjUjAkZIqIWKDs7G/3620PvFRv0+sxb6nCIlJJ36jp+e3UrdmzfgalTpzbptXfv3o0ZM2biH0NC0c98ZJNemxSTmhOJ1JwoscGum/Vc9DZ1Rb8uI1p1MgYAdl9+F8kPf0LitSswN6+9lxcREbUOTMgQEbVQv/76K4Z7vIRui4ej+1svSR0OkUKKrmUjacpmBLzxFj795FNJYnjvvffx1X+/wZvD9tfZeJeoKZ28/iUiUtfj3M9nMWRI43fSIyKi5o8JGSKiFiwsLAxz5syBxUJ3WP+fF9CO/Qao+Xp47gZSX9+FVzxHY9/uvZL1x6ioqMDf/jYVJ45FwG/wd7Dr7CFJHEQAUFHxDEeurcHp1I3YsmULfH19pQ6JiIiaCJv6EhG1YL6+vti5cydyfohFyvydKC8skTokopoqKnDvh19xbc4W+E6biV3bd0rarFRDQwM7d27HzNnTsCl6NiLTfkAF+O9T1PRKygrx48V/IOr2D9i5cyeTMUREbQwrZIiIWoGYmBiMm+iNJxUlsHxvJLpMGwhwdw5qBgoTs3D7w5/wMOYWPvv0UyxdulTqkGSsXbsW7y17D73NhmCi/WpYGveXOiRqAypQgZjbO/FTyifooKeBw0cOwsXFReqwiIioiTEhQ0TUSuTm5uLDlSvx3XffwnCAJcz9h+G50XbchYkkUXj1HrI2X8Dvu+Ph5OKM4A3fwMnJSeqw5IqLi8MbCxch9mIMXKym4cVerzExQ2pR/qwUiVkncDbtG2Q8vILXFyzAqtWrYGpqKnVoREQkASZkiIhamYSEBKxctRJHDh+Blr4OjNx6QdfBHB26GEBTX7v+CSoAsLiGlPSsuBSl+U/xJOU+nkRnoCD9Pp536Ifl73+AmTNnSrpESREVFRXYvn07Pl7zCZJ+S0QXY2v0MnaDuUFf6HUwhlZ7XalDpBan8i/T4rLHePQ0G/ceJyE19zxK/ijC+PHe+GjVSjg6OkodJBERSYgJGSKiViorKwtHjhxBxMkIxCXE4/fsbBQVFEodFrVSHXS0YWzSCfb97PHy8JcwduzYZlsRU5+4uDgcO3YM586eR1JSEh4+zEPJH8VSh0UtlH5HQ5h3McdAJ0d4eo7C+PHj0bVrV6nDIiKiZoAJGSIiAgCEhoZiwYIFGDVqFLZt2wZDQ0OpQ5LcG2+8gatXr+L8+fNSh0LULA0YMADjxo3DJ598InUozUpiYiKmTJmCx48fY9euXXjxxRelDomIiJoh7rJERNTGlZeXY+nSpZg7dy7eeecdHDx4kMmYP3Xo0EHqEIioBXJwcMDFixcxdOhQjBgxAoGBgVKHREREzRATMkREbdijR48wYcIEbNiwAaGhofjss8/Qvj2bAAt0dXVRWMhlXkSkPENDQ+zbtw///ve/sXTpUkyfPp1/nxARkQwmZIiI2qgbN27A1dUV8fHxOHPmDObMmSN1SM1Ou3btUF5eLnUYRNRCaWho4L333sOJEydw5swZuLi4IDk5WeqwiIiomWBChoioDTp58iSGDBkCPT09xMTEYOjQoVKH1CwZGBigpKRE6jCImjUjIyOpQ2j2Ro4cibi4OBgaGmLIkCHYu3ev1CEREVEzwIQMEVEbs2HDBrzyyivw9PTE+fPnYWlpKXVIzVpxMXfXIaLG6969O86dOwdfX19MnToVS5cuRVlZmdRhERGRhJiQISJqI0pLS/H666/j7bffxkcffYTt27dDV1dX6rCaNX19fS5ZIqrDo0ePpA6hRdHW1sY333yDH3/8EV9//TU8PT3x+++/Sx0WERFJhAkZIqI2IDc3V9zOet++ffjggw+goaEhdVjNXvv27dmEk4hUzs/PD7/88gsyMjLg5OSEX375ReqQiIhIAkzIEBG1clevXoWLiwsyMjIQFRWFSZMmSR1Si6GjoyN1CETUSjk6OiIuLg6Ojo7w8PDAhg0bpA6JiIiaGBMyRESt2MGDBzFs2DBYWloiJiYGAwYMkDqkFkVbWxuPHz+WOgwiaqWMjY1x+PBhrFixAu+88w5mz56NJ0+eSB0WERE1ESZkiIhaoYqKCnz88ceYMmUKZs6ciZMnT8LMzEzqsFqc9u3b49mzZ1KHQdSstWvHXycbQ0NDAytWrMCxY8dw/PhxDB06FKmpqVKHRURETYA/QYmIWpmnT5/C19cXK1euRGBgIL777jt06NBB6rBaJH19fQCVnykRyWdgYCB1CK3C6NGjcfHiRXTo0AHOzs44dOiQ1CEREZGaMSFDRNSKZGZmYvjw4Th+/DiOHTuGt99+W+qQWoU//vhD6hCImiVWkKlWz549ERkZCR8fH0yaNAkffPABd3ojImrFmJAhImolfv31V7i4uKCwsBC//vorPD09pQ6pxTM0NARQuWU4EdXEHkuqp6Ojg++//x6bNm3C+vXrMWbMGOTk5EgdFhERqQETMkRErUBYWBg8PDwwYMAAXLhwATY2NlKH1CoIW4OzySYRNbXXXnsNkZGRSE1NxeDBgxETEyN1SEREpGJMyBARtWDl5eVYtmwZZs+ejUWLFuHIkSMwMjKSOqxWQ09PT+oQiKgNc3JyQlxcHJ5//nkMHz4cwcHBUodEREQqxIQMEVELVVBQgIkTJ+K///0vQkJCsHbtWrRv317qsFoVLS0tAMCjR48kjoSI2qrnnnsOx44dw7vvvos33ngDf//731FcXCx1WEREpAJMyBARtUA3b96Eq6sr4uLicObMGfj5+UkdUqskJGSIqHaamppSh9DqtWvXDqtXr8bhw4dx4MABuLq6Ij09XeqwiIiokZiQISJqYU6fPg0XFxfo6OggNjYWrq6uUofUaglLloqKiiSOhKj56tixo9QhtBnjxo1DXFwcgMrlTEePHpU4IiIiagwmZIiIWpBvvvkGo0ePxqhRo3D+/HlYWlpKHVKrJjT1LSsrkzgSouaJO5A1vV69eiEqKgre3t6YMGECVq5cye3HiYhaKCZkiIhagNLSUvj7+2PRokX48MMPsWPHDjacbQLCttd//PGHxJEQNU/cgUwaenp6CAkJwYYNG/DZZ59h/PjxePDggdRhERGRkpiQISJq5nJzc+Hl5YWwsDDs2bMHK1asECs3qGk8ffpU6hCIiGpYuHAhzp07h8TERDg7O+PSpUtSh0REREpgQoaIqBlLTEzEkCFDkJ6ejsjISEyZMkXqkNoUoTcGlwMQUXM1ZMgQxMXFoVevXnBzc8PmzZulDomIiBTEhAwRUTN16NAhDBs2DN26dUNMTAxeeOEFqUNqc4TdYx4/fixxJEREtTMzM8OJEycQEBCAf/zjH5g/fz5KSkqkDouIiOrBhAwRUTNTUVGBTz/9FJMnT8a0adNw6tQpdO7cWeqw2ixtbW2pQyBq1jp06CB1CASgffv2+OSTT7Bv3z7s2rUL7u7uyMjIkDosIiKqAxMyRETNSHFxMWbPno3ly5dj/fr1+P777/llR2I6OjooLCyUOgyiZktXV1fqEKiKSZMm4eLFiyguLsagQYMQHh4udUhERFQLJmSIiJqJe/fuYfjw4fjpp59w7NgxvPPOO1KHRKj8V+fy8nKpwyBqdrgkpvmysbHBhQsX4OXlhbFjx2LNmjWoqKiQOiwiIqpGU+oAiIgIiI2NxaRJk2BgYIALFy6gb9++UodEf9LX10dxcbHUYRA1O/xz0bx17NgR27Ztw9ChQ7FkyRL8+uuv2LJlC4yNjaUOjYiI/sQKGSIiiW3btg3Dhw9H//79mYxpplgJQEQt1dtvv40zZ84gPj4ezs7OuHz5stQhERHRn5iQISKSyLNnz/D+++9j9uzZWLhwIY4ePcp/uWyGDAwMUFZWJnUYREQN5ubmhosXL8LCwgLDhg3Dli1bpA6JiIjAhAwRkSQeP36MyZMnIzAwEN9//z3Wr1+P9u3bSx0WydGuXTsUFRVJHQYRUaOYm5vj5MmTWLhwIebOnYs333yT1X9ERBJjDxkioiaWlpaGiRMn4v79+zh16hTc3NykDonqwB1kiOqmo6MjdQikIE1NTaxbtw5DhgzBvHnzcOnSJezevRuWlpZSh0ZE1CaxQoaIqAmdPXsWQ4YMgaamJmJjY5mMaQE6dOiAgoICqcMgara0tbWlDoGUNHXqVMTGxiI/Px+DBg3CmTNnpA6JiKhNYoUMEVET+fbbb/HWW29hwoQJCAkJQceOHaUOiRSgqanJ7WKJAHz55Zf48ssvxWqKZ8+eoVevXvj444+hr68PAHj48CHGjx+Pd955R8pQSQF2dnaIjY3FvHnz4OnpiY8//hhLly6FhoaG1KEREbUZGhX8LZOIqNGysrJw584duLi41DhWVlaGd955Bxs3bsSHH36IlStX8hfeFmT8+PEwNTXFjz/+KHUoRJLy8fHB3r176x1namqKnJycJoiIVKGiogKBgYFYtmwZvL298eOPP8LQ0FCKpf0TAAAgAElEQVTuuIKCAhgZGUkQJRFR68QlS0REKjBu3DgMGTIE+/btk3k/Ly8Po0ePRkhICHbu3ImPPvqIyZgWqLS0VOoQiCS3cOHCese0b98eH330kfqDIZXR0NDAP//5T5w8eRLR0dFwdnZGUlJSjXEfffQRjI2NcfLkSQmiJCJqnZiQISJqpL179yI+Ph4aGhqYNWsWrly5AgBISkqCi4sLUlNTcf78eUydOlXiSKkhjIyMmJAhAuDh4QETE5N6x02fPr0JoiFVe+mll3Dx4kWYmZlh6NCh2LFjh3gsPDwca9asAQDMmzePO88REakIEzJERI1QWFiIt956C+3atUNFRQXKy8sxevRobNu2Da6urjA3N8fFixcxcOBAqUOlRnjy5InUIRBJrn379pgxYwY6dOgg97impibGjBkDU1PTJo6MVMXCwgJnzpzBvHnzMHPmTAQEBCAtLU0myZaVlYWVK1dKGCURUevBhAwRUSOsWbMGOTk5ePbsGYDKfjEPHjzAypUr4ePjg1OnTqFz584SR0mN0bFjRzb1JfrTjBkz8Mcff8g9Vl5ejjlz5jRxRKRqWlpa+O9//4tt27bh+++/x8svv4yioiKZn3OBgYG4ePGixJESEbV8TMgQETXQb7/9hnXr1qGsrEzm/dLSUty6dQsAt4NtDTQ1NbntNdGf3Nzcak0y6+rqYuLEiU0cEanLzJkzMW7cONy7d6/Gss127drBz8+PyzmJiBqJCRkiogaoqKjA66+/jnbt5P81WlZWhh9//BFBQUFNHBmpWm3LM4jaonbt2mHWrFk1/lxoaWlh2rRp0NHRkSgyUrUffvgBu3btqvGPDkDlz7jr16/j888/lyAyIqLWgwkZIqIG2L59OyIjI+v818GKigosXrwYBw8ebMLISNV0dXVRWFgodRhEzcb06dNrLFsqLS3lcqVW5NKlS/D3969zTHl5OVatWoWUlJQmioqIqPVhQoaISEkFBQV4++236x0nVM+8//776g6J1Khdu3YoLy+XOgyiZmPIkCHo1q2bzHtdunSBh4eHNAGRyjk5OdXaK6i6V199lX22iIgaiAkZIiIlLV++HAUFBbX+AqqlpQUNDQ0MHToUP/zwAy5cuNDEEVJDlZaWwsPDA1ZWVujfvz969OiBb7/9Funp6TAwMICBgYH4fPfs2SN1uESS0NDQwOzZs8VlS1paWvDz86t1CSe1PFu3bsWAAQOgoaEBTU1NaGhoyB1XWlqKmJgYBAcHN3GEREStg0YFU9pERApLSEiAk5OTuNuEQEtLC6WlpejZsyfmzZuHuXPnwsrKSqIoqaFKSkoU7oHx008/YcyYMWqOiKh5iouLw+DBg8XXly9fxoABAySMiNTh7t272L17N7Zt24a4uDi0b98e5eXlNf5BQk9PDykpKbC0tJQoUiKilokJGSIiBVVUVGDo0KG4dOkSysrKoKmpifLychgYGMDX1xdz5syBq6ur1GFSIy1atAjfffddnf2BOnbsiLy8PDb8pTatR48eyMjIgJ2dHX777TepwyE1u337Nnbv3o2wsDAkJCSIPwMrKiqgpaWFUaNG4dixY1KHSUTUomhKHQBRc5Keno709HTk5eVxPTTVsHfvXsTExACo7CsycOBATJgwAfPnz0eXLl0kjo5U5e9//zu+/vrrWo9raWlh0qRJTMa0Efy5ULsXXngBGRkZcHJywu7du6UOp9nQ0NCAiYkJrK2tYW1tLXU4KmNkZIShQ4eib9++uHPnDn755RdERkYiIyMDpaWl+OmnnzBlyhTMnDlT6lCpBdPX10fXrl3Rr18//pylNoEVMtSmPXv2DMeOHcPOnTtx4sQJ5OTkSB0StVAODg4YO3YsXn31VTz//PNSh0ON9PzzzyMlJUXuF3Chf8yUKVMkiIzUTfy5sGMnjh8/gdwH/LlADWf6nBnGjBmN6TOmY+zYsS2uz861a9cQEhKCwweP4reUJKnDoTZES7MD3N3cMcVnMmbPng1jY2OpQyJSCyZkqM3av38/li1bhps3b2LEiBGYMGEChg4dil69eqFTp04t7pcmanrFxcXIy8tDUlISzp49i927dyM1NRUTJkzAF198gT59+kgdIjXQ+vXrsWzZMpSVldU4pq2tjby8POjp6UkQGanT/v378e67y5CWdhMuL3jgJZex6G/nAktzaxjoG6OdBn8uUP2eVTzD48J83M1Ox9XkGJyLOYaYy2fRq1dvfP75Z5g8ebLUIdbrxo0bWLz4Xzhy5BC6GPaGndE4WBsNQ5eOttDVNIZmO22pQ6RWqqS8EI//uI+swkTcyD+Da3nHgHbl+NeSf+L//u//oKurK3WIRCrFhAy1OTdv3sSiRYtw4sQJzJgxA6tXr+YXZ1KJiooKnDhxAv/617/+/GV2MVatWgVtbf7i2tLcv38f3bp1q7HdtaamJsaPH4/9+/dLFBmpw82bN/Hmm4sQHn4CY16aioW+y9G9a2+pw6JW5E7WTWwMW4Pj53bD09ML33zzNXr3bn7/jZWUlGDlypVY/0UgntPridFWK9Gnkwc0IH+XJSJ1+6O8CDFZoTiXGYTnTDvhv18FskKVWhUmZKhNOXfuHKZMmQIrKyt8+eWXePHFF6UOiVqhsrIyBAcHY/ny5ejfvz/2798PU1NTqcMiJXl7e+P48eMyVTLt2rXDli1bMGvWLAkjI1U6d+4cJk+egi4mlli6YB0G2btJHRK1YpeSorD2uyX4Pe8u9u/fh5deeknqkES5ubmY6D0ZCfFXMMJyGVy6zkE7DbabpObh8R+/4+TtTxH/+258sPwDrFq1qtbt2IlaEiZkqM0IDQ3F/PnzMW3aNHz//fesWiC1S01Nxbhx41BeXo4jR46wt0wLc/DgQUyePFmmj4yWlhZycnJgZGQkYWSkKqGhoZj/j/nwfHEKPnzrG3TQ4s8FUr8/Skuw+qs3EHF+H77b9B3mzp0rdUj47bff8MqYcXj6UAO+tlvwnG7raUZMrUvC/b04eHMJJkzwxrbtYWz8Sy0eEzLUJhw6dAh/+9vf8OGHH2L58uXMqFOTycvLw5QpU3D79m3ExMTAzMxM6pBIQaWlpejSpQsePnwIAGjfvj1GjhyJEydOSBwZqcKhQ4fwtyl/w4KZ7+Mf05fx5wI1qYqKCny/8zN8t/0T7N23FxMmTJAslpycHDgNckaHJ90ww/Z/0NVk81Rq3m4XxGDH9dfgPekVhG3bInU4RI3C7nTU6l29ehUzZ87EBx98gBUrVvCXbmpSJiYmOHbsGLp06QJvb28UFxdLHRIpSEtLC6+99hq0tLTE96ZNmyZhRKQqV69exYwZM/Ha9Hcxf8Z7/LlATU5DQwPzZ7yH16a/ixkzZuLq1auSxFFcXIyxr3ij3ZPnMPv5rUzGUIvQw9AFvrYh2LtnLz755FOpwyFqFFbIUKtWXFwMe3t7uLq6YsuWLa3ul27hflT9x1iZeeWNzcjIgJWVVYPmU7VHjx5h165dWLBgAQBg+fLlmDNnDvr27avwHDt27MC2bdtw+PBh+Pv7w9/fHy+88IJScdy9exeurq7w8fFBYGCgUueSdJKSkuDg4ACgsn9MdnY2q5xauOLiYvR73h79rF3w739+3+J/Lgwc3xEAEH+kqFFjmlp2zh2Ym3UXX0sd4+3MVBw9sx2bdnwGAFjx1gZ4DBkPE+P6/7wLsctT3/1UVFRgxfp/4Fp6DK79lgQdHR3lAm+kd95ejC2bd2G+/WEYandt0murw/Lz3QAAa16816gxTS2/JBPG2hbia6ljzH2ahoT7e3A2IwgAMMlmLZ5/bjQ6atXfD6+4rACJuYeR/CAcyXkRsDPxxIDOU9C308vQ0TSUGSvcpzyK3Pu13GPYkbwAZ8+dZV9IarFYIUOt2tq1a9GhQwds2rSpxf/S3VJ88cUX6NGjh9RhiObMmSMmYwBgzZo1sLW1xeXLlxU6f8KECZg5cyYOHz4MAAgODoajoyN27NihVByWlpbYu3cvvvnmGyQmJip1LknH3t4eAwcOBAAMGzaMyZhW4PPP10LjmRZWvLWBPxcksmX/l3jl73ZShyG6nn4Vk153FJMxAPDvrxZh9ZdvoLCooM5zs3PuNOraGhoalf8tPtPC55+vbdRcykpMTMTGjd9gus2mVpGMaaki7wZjXYyz1GGIsouuIeiiu5iMAYADqUux//q/UFxW958HAAi/9TEOpC5Fcl4EACA5LwK7khdiT8pbMuPySzIbHWs/07Fw7+6PN/zfqrErIlFLwdbp1GplZmbi008/xf79+6Grqyt1OC1KYypZlixZosJIanf58mWcPHkS//rXv2ods2PHDhw+fBjfffcd5s+fDwA4ffo0Ro4cieDgYGzcuLHOawjnr1u3Dv/4xz/ERq47duzAzJkzMWzYMJlKoPq4uLjAz88PAQEBOHnypMLnNaX8/HwkJiYiLy8PJSUlUofTLDg6OiI+Ph49evTA7t27pQ6nWdHQ0ICJiQmsra1hbd38m4AKPxe++L8d0O7AnwtSWf+/95vkOtfTr+LXhDOYM/ntWscUFhVg+ltD8ZLLWLy3cD3MzbqjsKgA+8N/xPr/vY+ouBMYPXxqvdf652uf1Hmdumh30MWS+Wvxr49n4LXX5sHCwqL+k1TgrTffwcAuU2FpMLBJrkfyHU9f3STXyS66hhsPf4a7pX+tY4rLCrDh0ijYmXhifJ+PYaxtgeKyAlzM3obj6atx/eEZDDCbWOc1YrJC4WEVgMHmvjDWtkB+SSZ+vvMVYrJCkfs0Daa6vWTOGWP9YZ0x1cfDcjG+urIfmzZtgr9/w+chkgoTMtRqrVixAh4eHvDy8pI6FFKhCxcuICQkBMHBwQBQZ0Jm27ZtAGT7fowYMQIAFErICOdXTcYAwCuvvAIAOHHihJjoUdTHH3+MHj164OjRoxg3bpxS56rLtWvXEBISgoNHDyMl6Tepw2m2wsLCEBYWJnUYzVYnUxOMHfMKZkyfgbFjx6Jdu+ZXhLt8+QoM7v8iXAeOlDoUUqOryTE4dCoMe376HgDqTJSk30kGALziMU1cQqXf0RCTvV7F+v+9j5/O7qozIZNxLw0AYNtLuWWs1bkOHInB/V/EiuUrsPmHzY2aSxFHjx5FdHQ0/ukUo/ZrkbTuPI5D/O+7EZMVCgB1Jj9ynqYCAAZ0niIuodLRNMRg81k4nr4aV+7vqzMhc/dxPADAsbOPeL6xtgVcuvohJisU9wqvigmZvKfpAIBu+g6Nur8O7Tvi5W7v4oP3V8DPzw96enqNmo+oqTEhQ61Sbm4uwsLCcPDgwQbPcfr0aezevVv84r98+XL4+PjI7R1Sday3tzcCAgLEL/6Cqn1UhAoLANi+fTteeeWVGtvoChUgQsWJt7c3Zs2ahRkzZigU/4ULF+Dq6orly5fj3//+t/j+9evXYWtri4SEBJl7WbhwIYKDg5GQkABHR0cx1qqq9lLZvn17jViqlv/X1jdGuHdl7ufRo0f4+eefsWnTJrGPy6FDhzBkyJA6zzt06FCN94SlR9u3b6/3usLY6s9GeH3p0qV656jOzMwMM2f+P3tnH5fzvf/xV2ZjJ3Qj3YiiTa4hMXYdDSs3zWqusFOtkLmL+DGd6YSzS3ucamMt+2UcIhuTu6UxXU6xSxFZdjFKOBWnlEqq5bqi3+qw0++P7/l8Xd/r/rY7n+fj4THf7/dz8/58Y7pevd+vdyi2b9/e4YLMnTt38Oe1f8bJjJPo+4o9+r3LwwjhG3h5uD16Wr+MHr3oPxEULfynDU9lv6Gl4iEeX61ClliCQ7MOYYibG7YkfIE5c+Z0dIQsDQ0NOHTwIP5XmKbzHElhLsR5x9gP9uEh6zB94hy4D/XQONab74+5s1aB7+nNGSPvk3L6/FGsT1gIANgcvQ8Tx81AH0uuvwLJ8CAZJd58f/j5BOuUsaHv+bTFnH3gLv5x9jC+/HqDxjhOnz+KrHNpyJVkIjxkHd6dEorZy8ew55b3W1HnG0PejT7nfdzchF9u5uH4qb3IlWQi0G8pkmKOwsNdcylIwT8vAQA8X5vAud/Hsl+7+9m87x+Bjz4LQcIXCbCz0+7VYQxb/3cbPOxmw/LF/jrPKZPm4UbDSfaDvY9LJEbZzYSj5QiNY3m2vnjTORxu1pM4Y+R9Uq7Xn0Ba8QoAQDBvp0q/EZLhQTJKiDeJJoFAH/SJecOE67j2IB2nymM1xnG9/gSu1x1DcaMYPi6RGGMfiKQrk9hzy3uoqPONIe9Gn/O2PG3C3aafceX+ARQ3isF3WoD5I7/FYC3ZUBWyywAAl37jOfd79+ynk6cLKUPqo+A10/clewBA3f+VaF3DEEYPmAPxvXgcOXIEixcvNsseFIq5oKa+lG7Jnj17sG7dOtTV1eGFF17Qe75IJFLbgjI7O5sjtmzcuBHx8fFK4xSFECJQZGRkKK0tEAg44oGm/eWFEE1muTKZDNbW1krPiSAiX8ajuJaqdYlgI09iYiIrGMnPk0f+vvx4VedRpLKyEj/99BNHwNG3TIiwZcsWdm9Ne8oTEBAAkUgEqVTKEWXUvVtd+fHHH+Hv74+6ujrY2trqPd9YWltb8cknn2DL/36Jl4f0x6AYX1j7vAJQPw2KCWi524jqL86i/kQRpr09Hcl/34lXXnmlo8PCnj178Je10cg+UIEePbT/u5AryURkrGohYNenmRzhYseBWI7/CCE8ZB1Wzo9hr4kAkRRzVGltb74/kmKelcRp2n9z9D5WpDDU1FffmL35/siVZKqNQ9OaBEVBRtX9j5ZsUippUtxHntr6eyj85yWOgOP52gSOYbAmImODkCvJZEUyIiZ9tGQT3p0SqtXUN/X4V/jy6w34btsl3Ci9grhtqwAwpsBvT/qTksimif/853dMm++KL7YkYOnSpTrP05fGxkYMGGCPBSMO4FUbb+0TwPiAHLj5gcpniz3SOMLFmYoEjv8IwcclEtNdo9lrIkDMH/mt0to8W1/MH/mtTvsH83ayIoWhpr76xsyz9WU9UlTFoWlNgqIgo+r+O0NjlEqaFPeRR9pajcqmKxwBx6XfeI5hsCYO3PwAxY1iViQjYtI7Q2Mw1iFQq6mvpvev+CyvKhmnymOx6vUzqHp0DT/c/gsAxkB4lJ1ASZDTxok7UbB6rR7i7B/1mkehdDSdL5+YQjEBYrEYU6ZMMUiMAcCKIRUVFWhra0NbWxvy8/MBgOMhkZOTg/j4eAiFQkilUrS1tUEqlUIoFCI+Pl6lcWxKSgq7bkVFBYRCIUQiEXJycpT2z8/PZ/evqKgAADazRhtWVlYQCoUAmKwYAinDkTe6Jc93796tcq2cnBwkJydDKBRyYpdKpZxx8uIEiVseqVTKviciQJF4VOHq6orQ0FAcPnwYGRkZCAkJMUiMAYCxY8ciMTERAoEAoaGhOpnyzp07FwCQlZXF3pPJZEhMTDQoBoK3tzd69OiB7Oxso9YxhIaGBvhMn4qkndswOMYXI8XLYT3lVSrGUExG7yG2eOXvf8KI7xdBUn0L4/jjkZub29FhQfyjGOM9vHUSYwCwYkjW3mJcO9mMayebsT/xLLNW3jF2nKQwFylHPkd4yDpc+O4+rp1sxoXv7iM8ZB1SjnyO0nLldsbHT+1l183aW4zwkHXIlWRCUvjsPZH99yeeZffP2suU15DMGkMxJGZ3Nw927K5PGWEm61yayjXlzxboxxUW5EUhci55HjVL2X2IQCW/jyJ+i3hYn7AQm6P3ISnmKGa8FaSzGAOAFZl2HIjF+oSF7PWXX2/QydSX8P7qCawYAzCmwMItS3SeDwA9eryA8R7eEP8o1j7YCLKzs2GBHhhi5aXzHCKGRPEvI35yDeIn12D5GCaL9EbDSXZcmTQP5yqT4OMSCaFXMSMueBXDxyUS5yqTUNt8S2ntK/cPsOtG8S/DxyUSxY1ilEnzlPZfPkbE7h/FZ7I5SGaNoRgSs2OfkezYxR7Mn8/rdcdUril/Nr7TAs468sIFOZc8Lb83sfsQgUp+H0USJW8grXgFgnk7MX/ktxg9YJbOYgwAVmQ6U5GAtOIV7PWp8lidTX31ZfvV6awYAzAGwuklq/Xey83qLZy/cB5PnjwxdYgUilmhggylW1JYWMh2RjEEgUAAgBFfcnJyIJPJMGHCBLS1tXF8R86eZb45j4qKYjMorKys2EwMVcatiYmJrKjg4uLCZqnICz1EzHBzc0NhYSFEIhFSUlL0PgcpiSkpYVJES0tL2XIjAKxgVFVVBYAxnVUFOWd4eDgn9rCwML3iWb16NfueyDsmZUGqqKiowOHDhxEaGoqAgAAcOXIElZWVeu1JmDp1KtauXYuMjAzs3r0boaGhHBFMFX5+fqyAY2FhAQsLCzYzxhh69eoFHo+nc6cnU/HPf/4T4/jjcaPmNkZkLoXjoj/Coif9Z4BiHvr90RWviRaj15QhmP62L7799lutc8xJQUGhXj4f3nx/AIA47zgkhbl43NwEDx4f10424+P/2cqOu1LEiCgL5kSy2RB9LPthwZxIAMDPBWeV1v7zks9Y0cBxwGC8N2PRf/d69kGLiBXOjkNRWl6EXEkmjp3eq8+R1WJIzCEzV7BjSXaQfMYMWfO9GYs4Z5s/exX0QX4f8jVQzMyRJ2tvMTZH78P6hIWIjA3C6fNHDe58lH3gLvveN0fvQ64kExd/Oa1xDsnmkRfO9JmvyLAho1BYeN2g+HWlsLAQjv2GoWePl3Sew7P1BQDcqBehTJqHlqdNGNx3HOIn1yDg1c3suDLZTwCASc4RbIZD7579MMmZ8S258/C80trvuH3C8RsZ7ziP2UtO6CFihW1vV9Q230JxoxhXak3j6WVIzF4DF7NjSXaQfMYMWZMY25Kzvem8DPogvw/5Gihm5sgTxb+MYN5OpBWvwIGbH+B6/QmDuxltmHCdfe/BvJ0obhSj9KHy/xsMhWT+yItsxuzlaDkC/37Sin/+k3rhUboW9DtxSrekpqYGTk6Gt3AkpUZRUVGYNm0awsLCVH54J6VK1tbW7Ad2+Q/tqjoOubu7c66JwKFYDrRx40Y4ODhgzJgxCAgIUFkWpY3XXnsNACCRMKZ9xPOElOuQ+9euMSZsqvxxgGfnVMxOUTyLNuzt7fUa7+LigpCQEEilUoSHh+PQoUNwdXXFihUrIBKJUFdXp9d6BGLym5SkPpUYYMS1PXv2sJlDAoEAhw8f5pSiGcrAgQNx//59o9fRlfr6erztNwOPHF/Aa6Il6D1Ud98ACsVQerzUE69snQ2nNZOxZOkSlb5O7UXN/RrY2TrqPH5lGFO28+XXG7D8Y38ItyzhZLAQSInO5PedMHamJftr8vtO7HxFXJ2Hca6JgEG8agg7DsRi2vwheH/1BETGBmksB9IHQ2LWVrpD1lTMTlE8qza07aOI44DBmPFWEC58dx9z3lmErHNp8FvEw6d/X4NcSSYapfU6rbNgTiRn74njZgDQnJ0DPBPOPHjcH2iQEitt8xWx7z8Q1TXGtwPWRE1NDfr01P3vAgBMH7IOAPMh+puiYKSXrOZksBBIiU58Pg/CCwPZX/H5PHa+Iopdd4iAQbxqCGcqErDp0mhsvzodB25+oLEcSB8MiVlb6Q5ZUzE7RfGs2tC2jyLWvZwxesAsCL2KMd5pPq7XHUOi5A1k3FmP4kYxmp806LTOJOcIzt7uNlMAaM7O0RciwAzuO45zn5Rj6btXv17Mn+maGu1eNxRKZ4IKMpRuyaNHj9C7d2+D53t6eqKtrQ0FBQVITEyESCTCtGnTEBAQ0C5ZDSkpKYiPj0dERASys7NRUFCABw8e6L0OKVsigsqhQ4dYcWH37t1s2VJUVJTRZTjmxMrKivXZIaVjAQEBcHBwMHg9QHN2DsHe3h7h4eFsmVVISAibpWPMO7O2tkZLS4vB8/WhpaUF78z0R3P/HnBPnYue1rTdL6UdsbDAoEhvDPxwMoJD30dRkXI5THvw+PEj9HpR938X3Id64NrJZny37RI+WrIJuZJMLP/YH5GxQSpLekzNsdN7kXLkcwT6LcWuTzPx3bZLyD5w1+z7dlX6WPZjfXhIaVlkbBCmzR+icV54yDp2vuJ6gObsHF3Qd36vl17G48ePjNpTG62trXjJoo9ecxwtRyB+cg1WvX4G7wyNQXGjGN8UBePAzQ9UlvSYmiu1B3GuMgl8pwVY7JGGVa+fwYYJ5s0k6sr07tmP9eEhpWUHbn6ATZdGa5zn4xLJzldcD9CcnSM/X7HciFyT57qgbS9Fer3A/JlWLKenUDo7tIUGhaIBT09PeHp6IigoCHfu3MG0adMgEolYb5SIiAgkJycrmb5qorKykpNpQvxbiN8L8MzfRb48SiaTGXSGd999F/Hx8RCJRBCJRGx2h4cH0yWEeKlMnDhR7RpE1CktLeVkxRhaPmQMEyZMwIQJExAREaGyJEwedaa8JLMmIkJ960dN8+/cuQMAcHbWvS5bEUP9jQwhesM6lFT9C7yMxejx8ovttm97ke/8CQDAq/pv7TJPX35/1IKGjJt4KC7BQ3EJbHyHw26OB2ymDsMLfbULBMbO7ywM+sgH/74rxbuzBCi9VWyUaN6euA/1gPtQD/hOmoPKmjIs/9ifNYEFgEC/pUjP2oML393X2cC1tv4eJ5OkopppNUvEAQCsH4l8eZQ+fiSaMCRmbRD/GcWzGVo+ZAwePD48eHwE+S9VWX4lzysuTCapYtzkXSt64ChCTIEV36Wu8zuKHhaG/RvkaDkCjpYjMGqAAI2/leObomDWBBYA295Y6FWssymrtLWak0nS8BvTSlz+wzvxGJEvjzKVn4khMWuD+M8ons3Q8iFjGNx3HAb3HQe+0wKV5Vfy2P9hOADlrwl514oeOOrmP37SwHmX0lamNF5+TWIgrPjedd1LHf/5z38MmkehdBQ0Q4ZCUcGKFStgYWGBS5eYdpguLi549dVXlcYFBTEpyYmJia5hKtYAACAASURBVJzymZycHFhYWGDLli1Kc1JSUlgho7KyEqmpqQCAKVOmKI0lYo0xRrKkbIkYBQ8ZMoRzn5gEk2tVkNiioqI4sWvytTFUQNIVT09PrF27VuMYYsqblvYsZVwmk7HvnHz99JlfWlrK+v28+eab+gfezty4cQM7d+yEW0oQXnIyzTeaFP2o+PQMyqIz8FDMeDk9FJfg9sp03F6tWzq2sfM7DRYWGPLFTDT1bEXCFwkdHY1WPv37GoydaYmiYqa003HAYLgMVC438J30HgBg//EkTnmMpDAXY2daIvX4V0pzjp3eywoVtfX38I+zjK/XeA/ljjdErHnc3IT9x01TomFIzNogsSueTZPvjakEJnW4D/VA2JwPNY4h7a6Pnd7LiYd4v0x6Y4bG+X4+wZzxivPJu+7qZNxZD+GFgbj36BcAzAdr25eHKo0bZTcTAJBXncwpjymT5kF4YSDyqpKV5lypPcgKFdLWahTUpQMA3KyU/40lYk3L0ybkVSuvZQiGxKwNErvi2TT53pjDMFceR8sRmDRI8w+iSLvrK7UHOfEQPxd322ka59v/gSlRLKhL55yb+AENkmu7Pdr+Pc7ainuRrwuF0t2hGTIUigo++OADJCcnw8tLuQOBfCeiqVOnstkjih4vAoFAremtq6sr51ooFHJaaRMj2+HDh6ucr5ipogn5siWhUMgxHyYZPvL3VSF/TvkyH1VdmQQCAUQiEaytrREREcHJ8tEXVW20FdHUdjokJASHDh3CsmXLOF2lAOV3Lr8fWZOY+qqaf/jwYYM7PrUnK9eshn3gGPQZY3g2T2fH0AwXc2fGAEDzrVo8SL2MQWu8YT9vHHo5W6G1WobqbRfwIPUyWsp+RW839X4+xs7vbPTo/SIG/u1tfLZ0E5YsXmJUlpm5CZg2D+lZe7AgSlks37h6O/t7vqc3mx2i6PHizffHu1NUd8bzW8TjXIeHrOO00iZGtbOXj1E5v6L6tt7+LMbGbOiaipAW2pPfd0Kg31JOFpC+qGqjrYimluCOAwaz71ox7kC/payxsOJ+ZM2J42bAm++P9QkLlbpfKX5NuzJjHYIgub8fuwoESs9mD/uC/b2b9SQ2O0TR44Vn64uxDoEq10+UvMG59nGJ5LTSJka1SVcmKU4FwAg1+vqzGBuzoWsqQlpox+fzwHdawMkC0hdVbbQV0dQS3LqXM/uuFePmOy1gjYUV9yNrOlqOAM/WV+W5+U4L4Gg5gr12t5kCnq0v0opXKHXKUvz6UyjdGZohQ6GoYMKECSgoKOCUEQmFQmRkZLBdkQhxcXE4fPgwp/xl9+7d2LNnj0oT27i4ODbbRSAQIDs7W8kkNiQkhCN2CIVClJSUoKCgAAD0biFLui0pZuH4+/tznmuCnJN0Rzp8+LDSuyDjyLuorm7/1FxFMjIyOHETXx5djHkVTX0B5mtRUFDAGiN3Zv7xj3/gUn4+Bq5X/kBJaR8eX2P+DgwI9EQvZ0b07OVsBYcFzE8hHxdpNh80dn5nxNr7FfTzGoK/bvy4o0PRiAePj++2XeKUEYWHrENSzFG2KxJh5fwYbI7exylP2bh6O2I+3KHSpHbl/Bh8tGQTAEac2PVpJlbOj+GMmfFWEEf4CQ9Zhx92FeC7bUzm5i83lA1V9UHfmPVZk4gYJGalcWEx7L71v3b8n+EZbwVhf+JZNiZvvj82R+/TSSjqY9kP8Wu/5pyb+P4ofk27MoP7jsOq189wyoh8XCIxf+S3bFckwnTXaATzdnJKTmYP+wJz3LeoNKmd7hqNd4Yy74pn64vFHmmY7hrNGTN6wCyO8OPjEonI8XlY9TpTunxXlm/U+fSNWZ81iYhBYlYaN2Qdu29Ta/uZ/atj9IBZWD5GxMbEs/VFMG+nzkLRHPctmD3sC/bcPFtfzB72Bd4e8lfOuN49+yFw+DbOOyIeQYpffwqlO2PRpunHyxRKF8XCwgIHDx5kS046A4rZF5Tnm3nzmG9gDx40TdtOVUx/523ctGnE0C0BZtvD3DScKELD8SI8FJdg0BpvDAj0xLXJTDkFyXBR9IIh1+MLo1H/fSEqYk+zvit2szzYtXXxkCFjNKFp/r2EHFRtzQW/eAPH7+VJQzOueCZg0BpvDI6earb5nRVpzm3cWZqGmqpq2NkZ9mFHXywsLPBZ1F62xKQjUMyueF4YO9PS6EyY54Wsc2n4a+Iis36vMG/ePNw88xuChv/dbHvogmJ2xfOC8MJAozNhKKoRXhjY6b7/p1C0QTNkKBQKpRvS2NiIs2eyYRMwsqNDMZh7CTm4vTKd9U6p2prLijG68K+oE6iIZXwciO9Kw4n27fBTtZXJZlM0333RzpLz3FzzOytW3q/ghT+8hB9++KGjQ6GYCNI6m3juAIxHDPGjGe9Byw8ozw+kdTbx3AH+63vzXz+aIVbKJfEUCuX5hHrIUCgUSjckOzsb6GGBfl6u2gd3QmQXy1G1NVetd4ouWI5wxLBt7+GFvr0hu1iOW8H70HC8iJMlo4328Jl5HrF4oQf6vOmKH8U/YunSztmFhqIfSTFHERkbpNJzx5vvj4njNJvjUijdifkjv8WBmx+o9Nzh2frC3YaWElMoFAYqyFAoFEo3pLCwEP2GOaLHS13zf/NNF8sBgBVjAMY7ZeAyL50FGcfFf2QzS6wmMt1ASLYNpeN5+TV7XBUp+4tQuibEC+dKUS5rjhvotxTjPSZh4rgZJmuvTaF0BYgXTpnsJ9bclu+0AEOsvOBuM8Vk7bUpFErXp2t+p06hdEGodwylPampqcELDtq7j3RWSCkOEWMI+nQUImU9xmCshwxFPS859sP9mufLO6K7e8fwPb3B9/TuVma2FPPR3b1j3Kwnwc16EjWopVAoGqEeMhQKhdINaW1tBfq+1NFhPPcMWsO0vP39UQvnPrkmz801vzPTo3dP/N+j7i1QUCgUCoVCoWiCZshQKF0cQ7s3tVfXJ5lMhrS0NIhEIohEIggEAsydOxd+fn6wsrLSOp/EqQqadaQZixe6ruY+aI03qrbmorVaxsmSaa2WtWscxma/vDycaR/8pL6ZY8zbck8KAHjJWfPfAWPnU7oPhnZoaq/OTo+bm/Bj3vc4/3MmciWZ8Ob7w88n2OBypdLyIry/eoJS3OQ8mpCfQ+KK27YKANOK+90poXB1HqZ3TJTOjaFdm9qr21PL0ybcaBCh+NcfUdwoBs/WF6Pt39O5hMmY+bXNt7D96nS1Z2z4rQwFdelsedXsYV/gtf4zDG75TaFQdKfrfrdOoVC6BOvXr8eyZcsgEokAACKRCKGhoQgLC9M6t7Ky0tzhUTop/f7r+VJ38BdWhGmtlqHu4C+apnU6Xh7GCCr16YWcczSevAUA6DPW2azzKZT2Yuu+jYjbtgq5kkwAQK4kE+sTFkK4ZYneazVK6/H+6gkGxeHN9+dcC7csYcUYAEg58jlmLx+D0vL27bhGofx49zP8cPsvKG4UAwCKG8VIK16B9JLVZp3f/KQB269OV/u8tvkWkq5MYsUYAPjh9l9wvHQtWp426RQbhUIxHJohQ6F0cQzNEmmP7JLCwkIkJydDKBQiPDwcLi4uqKysxKZNm5CcnIzS0lK4u7trXScxMRFr1641e7yUzoPVxKFslkxXbe0MMJ2ebHyHqzyHQ9gbsBzhyLlHPGtIZo6+8yndF0MzXNrDt6a0vAjpWXsQHrIO781YBMcBg1Fbfw9fpyUiPWsPKqpv65WRsvNgvNpn6s5DMmr+vOQz9t7p80eRK8nExtXb8d6MRQAASWEuln/sj6OZe/Dx/2zVOSZK58fQDJf28LKpbb4Fyf398HGJxHjHebDu5QxpazXO39sGyf39aPitDHYvu5llfnZFotp1W542YfvV6eDZ+mLmq5/BupczWp424UrtIZwqj0Xpw7MYPWCW0eenUCjqoRkyFArFbEgkEgBAWFgYXFxcAAAuLi6IiIgAAFy9elXj/Dt37gAAxo4da8YoKZ2VwdFTMWxHIGx8hwNgypjGXviwg6PSn1cSZ8EtIYA9h43vcLglBMD1Y/U/sTTlfArF3NwovQIAeHdKKBwHDAYAOA4YjCB/pqV58b9076aVevwr1P+q3wdkklGzcfV2jvCTdS4NAPD2pD+x9/iejO9SetYevfagUIyh6tE1AMAY+0BY92IyG617OYPvtAAAUPNYc8aWofPzqpLR1Hpf7br1v90GAIy2f49dt3fPfhjvOBcAcL3umPbDUSgUo6AZMhRKJ+bIkSM4dOgQRCIRhEIhwsLCMHw486GMZLgoesGQ6wcPHiA1NRVRUVGsb0tISAi7ti4eMpr8Wwia5pOSIwcHB859JycnAMDNmze1rk95vrGb5QG7WR5K9x3C3mB/r+jzos73RddxpuZFO0s4zBsHh3njtI5VFZM+8yldk9PnjyLrXBpyJZmsx8ns5WMAPMsIUfSCIdfZB+7iH2cP48uvN7C+LTPeCmLX1sVDRl9fFkVq6+8BAPpbc/9fb2fDZHD9q/KfWtcHmOyVL7/egO+2XWJLn3ThyMmd8Ob7s1kwhKSYo0pjybqbo/fpvD6l47lefwLX646huFEMH5dIjLEPRNKVSQCeZbgoesGQ6w0TruPag3ScKo9lfVfksz508ZAhYzShab60tRoA0EfBk6XvS/YAgLr/K9G4tiHzy6R5OFUei1Wvn2HLnBSpkF0GALj0G8+537tnv27fBYtC6SzQDBkKpZOyceNGhIaGst4r8fHxrBijC0uXLkVUVBSAZ74tR44cMUus6oiPZ9LOFc177e3tOc/Vce0a8xOh/v37IyUlBRYWFrCwsEBKSgpksvY1d6W0L/nOnyDf+RM8ulrF3vv9UQtqdv0EAOjn5dpRoVEoJmXHgVisT1jICgXE40RXYr9aiS+/3gDgmW/L6fPKQoQ5STnyOQAomffaWg/gPNdERfVtLP/YH5uj98F9qLIIqw5JYS5SjnyOubNWaRyXevwrjJ1picjYIGyO3scRrSidmzMVCUgrXsGKCucqk1gxRheOl67FqfJYAM98V67XnzBLrOog/iyK5rvENFfev8UU8xt+K8M3RcEI5u2Eo+UIteveleUDYLJtrtefwIGbH0B4YSDyqpLR/KRB27EoFIoJoBkyFEonJCcnB/Hx8Wq9V3TB09MTqampsLKyQk5ODqZNm4ZDhw5xsmS00Vm6GI0Zw/1wQkyCyfko3Q/evrkoXngINwQpSs9sfIfDZirtkELp+hAxQZ33ii64u3kgfu3X6GPZj/VHyTqXppfg0B4+M5p43NyE//36rwgPWae3UHLoxHZ48/3ZUiR1DHfzxEdLNuGXogtYn7AQAKgo0wUok+bhXGWSWu8UXXDsMxKBw7ehd89+KJPm4ZuiYFyvO6aXN0pXyhZpedqEU2V/g49LpNYzEpHrTEUCR9Q5VR6Lu7J89r1RKBTzQTNkKJROyNmzZwGAFWMAxnvlz3/+s85rrF69mhUrpk6dCgBstk1XgWT45Ofno62tjf11+PBhiEQiZGVldXCEFHNh4zscI9IWYtCaZx+yHMLewLAdgRi27T1OC2gKpatypYgxaiZiDMB4r8yfrTnbQ56QmSvYzBQiSuhT7tMZ2H88CbmSTITMXKHXvKJiCXIlmZjzziKtY/me3gib8yGSYo5i4+rtWJ+wEJLCrmsY/rxQJmOyIokYAzDZHG86L9N5Da+Bi1lRwc2ayaxRV8LTHcirTkZxoxheAxfrNW/DhOuIn1yD+Mk1CObtRHGjGKUPz5opSgqFQqAZMhRKJ4SU8hAxhqBLRyICKQsyBmM9ZIxF3dohISEIDQ3VO+OH0rWwmjgUVhOHYnD01I4OhUIxC6SUh4gxBH06EpGyIGMw1kPGGE6fP4qUI59jf+JZvc+SkX0QADBupO7lKwBj8hu3bRUOndiuNbOG0rGQrA0ixhA0dSRSxFLBd8UQjPWQaS+u15/AucokLB8j0uvck5wjOJkw7jZTmPX0zCSiUCj6QzNkKBSK2RAKhQCg5PdCrslzQ+lqGT8UCoXSHQkPWQeAKT2Sh1yT56og5UMLoqZg7ExL9hdB8ZrQKK1nW20retdog4zvaplElK6Lj0skAKacSB5yTZ4bOz+tmMky21UggPDCQPYXQfGazFMsSyLX3TmTiELpLNAMGQqlEyIUChEfH4/KykpOlgzpWtReGJv9MnLkSABMxyd5r5e7d+8CUM4AUiQgIAAikQhSqZQznwg6pH02hWIs+c6fAGi/zkvmpPlWLa777lR5lt8ftaAh4ybKojMAMK3EBwR6ordb//YOkwJGqEg58jlq6+9xsmRI16L2wtjsl1dcXgMA/Cp9wBFHauoqAChnAJmC6tpyAMBI9/Fqx0TGBiFXkokL393nxNUorQcABPotNXlcFNPi4xKJc5VJkLZWc7JkSNeh9sLY7Bf7PzBNGR4/aeCIH9JWxrheMQPI1PO1rav4fonQQ9pqUygU80EzZCiUTsiUKUyqaEpKCivCVFZWIiVF2eC0M/Paa8w36ampqZxzpKenAwD4fL7G+XPnzgUAJa8Ych0URA0ZKRR5njQ047rvTrXPb68+xooxAFC1NRfXJn+F5lu17REeRYHxHky5zLHTe1kRprb+Ho6d3tuRYenN0ME8AMA/zh7mnOPMxeMAgFEaRJNrJ5tV/lJ8rsjtipsAgCEayrv8fIIBAD/mfc/ee9zchH+cPQwA8J30nk7no3QcblZvAgCu1B5kRRhpazWu1B7syLD0xv4PzJ/Tgrp0zjluNJwEAAzqO9Yk84kHjOIvguI1aXd9pfYgJ/uGeMe4204z8MQUCkVXaIYMhdIJmTp1Kpslo601dGfG09MTAoFA5TkiIiLg6enJuUc8a0hmjp+fHwQCAUJDQxEaGsoZKxQKWbNiCoXCcC9RvQFjw4kiPBSXwC0hAA7zxgEAZBfLcSt4Hx7svwK3zTPbK0zKf+F7erNZMrq0hu6suA/1gDffX+U5Av2WKrWxJiVIxmTm/PNOAQCgr6W12jEz3gpC1rk0xG1bhbhtXKPk8JB11D+mC+BmPYnNktHWGroz42g5AjxbX5Xn4DstUGpNTcqKiHii73xdse7ljGDeTqQVr1C5Ls/W16B1KRSK7tAMGQqlkxIXF4fDhw9DIBAAYASIkpKSDo5Kf/bs2YPdu3ez5xAIBNi9ezc2b96sda6VlRVSU1M57yEiIgLZ2dmIi4sza9wUSlejZtdP+Hdtk9rnDceLAAB2ASPZe1YThwIAHqReNm9wFLWsnB+DzdH74M33B8AIBT/sKujgqPQn5sMd2Lh6O3sOb74/Nq7ejjULzfP/atIWXJsRcFLMUc77DfRbil2fZmLl/BizxEUxPdNdoxHM28mKAz4ukYgcn9fBUenPHPctmD3sC/YcPFtfzB72Bd4e8td2ma+O0QNmYfkYEVuexLP1RTBvJwJe1f59GoVCMR6LNnO2SKFQOggLCwscPHiQLXnpTlhYWCAiIgI7d6ovS6B0fubNmwcAOHjQPGnX8+bNw4+/3cCw7X8yy/rqkF0sx6+im+wH/EFrvGE7cwQsRzhyxjXfqoXsQhkqYk8DYNpc283xgN2sZz9Jl/d1eSguQfHCQ7DxHQ6HeeNg48vUvTecKMLtlUwJ3LAdgWrnK46zmTqM0zpbnYeM/HlsfIfDKdyLFTEMObciZF9N6OJrQzJdRotXsCVLuswj71Xx3bUHDcev4/aq783aqU0eCwsLfBa1ly1j6eyMnWmJQL+l+Ph/tnZ0KJR2JOtcGv6auMisfy/mzZuHm2d+Q9Dwv5ttj/ZAeGEg+E4LqHBAYRFeGNhtv/+ndF9oyRKF0gkhpTv5+fmYMGECAMbIds8e5ieC3t40zZrS+SAf7uWp2pqLqq25GJG2kBUyVI17KC7BQzGTAaYoDMiPJ+NGi1eg8eQtVG3NZccRwUXVfPKMjLPxHQ7ePs3fsN1LyOGsT/YetMab04pb13Obi5ayX3EreB+G7QjUKgARanb9xIphHSHGUBhI6c7+xLPw4DGeWo+bm3D8x30AgPEe+rVzplC6G6R0Z/kYEQb3ZUotW5424Uot8//cIVZeHRYbhUKhmAIqyFAonZCMjAwEBATAy0v5Gw2BQAA/P78OiIpC0QwRJV6XfIRezkxXrEdXq3BDkIJfRTdZYYKMGyUKR9/XBwEAWqtluMr/ErdXpiuJA4+vVYNfvAEv9O3NZoJc992JQWu8le6rmv/g4C9sTK3VMtQd/AVVW3Mhu1iuViyRXSxH1dZcDFrjjYEr3sQLfXvj90ctqNn5E6q25nKyX3Q9tyqM7er0+6MW3I09jUFrvPUSVSxHOcE1Zgaa8u+qFbIo5icp5igiY4OwIGqK0jNvvj8mjpvRAVFRKJ2H+SO/xYGbH2BXgUDpGc/WF+42yn93KBQKpStBBRkKpRMiEAiQnZ2Ns2fPsma4ERER8Pb2hp+fH6cFNIXSWbDxHY6H4hL8evImLEc5oc9oJ/R9fZCS6ECunzQ0o/lWLf5dLcPja+pbmDou/iNbXiQvbhChRPG+IkNiZrBCSS9nK9jPG4eqrbkaxZKmi+VKe7zQtzcGrniTEXMulLGCjK7nNgc1O3/CQ3EJXkmcpdc8q4lDYTVxKAYufxMPDv6C2yvT8aJdH7Nn81C4ePP9sevTTFwpymXNcAP9lmK8xyRMHDeD06qZQnke4dn6YrFHGspkP7Gms3ynBRhi5QV3mymcFtAUCoXSFaGCDIXSSZk6dSqmTp1KzWspXYbB0VPxUFzC8YVR57miWA6kiRftLFXel/eA0URvt/6cayLOPEi9rLazEIlNwtuk8nlF7GkMXM60Y9Xn3IoY4yHTcKIIVVtzMUoUrvYd6YJdwEiURWfgfko+FWQ6AL6nN/ie3tRklkJRg5v1JLhZT8J01+iODoVCoVBMDhVkKBQKhWISLEc4wqv6bxzD3ofiEtj4Dsfg6KlsRsmD/5YMOYS9gf6Ckehp8zJesu+LK54JHXwCw9D13KaGlBrdEKSofK7OqFgRImwRDx8KhUKhUCgUSvtABRkKhaISYizcFRuxyWQyZGVl4dChQxCJRBAIBBAIBJg1axbs7e07Orxuj+UIR1iOcET/mSPRcrcRt4L34aG4hBUGyqIzAICTnfL7oxazxdNaLWOzYgDGBBdgOiGpwyHsDTxIvcx61OiCtnOroj3KmgjFCw/hobhE6UxPGpoBMGemUAjEcPjayeYOjsQ4ciWZiIwNUnuOx81N+DHve8RtWwWAaTv+7pRQuDoPa88wKV0QYjgcP7mmgyMxntrmW9h+dbrKs7Q8bULpw7O4XncMxY1i8Gx9wev/Nl7rPwOWL9p1QLQUSveiR0cHQKFQKKZEJpMhLCwMoaGhEIlEAACRSIRly5Zh6dKlqKur6+AIuy9l608i3/kTPLpaBYApDeo9xFbteCKMELNcc1F38Be0VssAMOJMfXohAKCfhvKc/oKRABiPFiJYAIzZb77zJ6jZ9Sxefc9tKryq/6byl+Jzgt0cxrS3IeMme+/3Ry2o/555H+TMFEp3obS8CJGxQRrHCLcsYcUYAEg58jlmLx+D0vIic4dHoXQKmp80YPvV6SqftTxtQnrJaqQVr0BxoxgAUNwoxg+3/4LjpWvR/KShPUOlULolNEOGQqF0K7KysiASibB7924EBwfDysoKMpkMiYmJiI+PR2pqKtauXdvRYXZLBgSPwYPUyypLaNwSAtjfD9sRiNsr03Ft8lcq12kp+1XJ98VYrvK/5FwPWuOt0S/FauJQDFrjzbavlsfGdzgG/MmTvdb13B2N3SwPNBwvQll0BpulRND2PiiUrkZRsURl9yp5Tp8/ilxJJjau3o73ZiwCAEgKc7H8Y38czdyDj/9na3uESqF0KNkViWqflT48i+JGMWYP+wKj7ATo3bMfWp42Ia86Gecqk3DtQTomDYpox2gplO4HzZChUCjdikOHmBbE4eHhbDcqKysrREVFAQD7X4rp6fv6IIwWr+CUAg1a4w3evrlwmDeOvWc3y4MjVAxa442xFz7EaPEKAIAs/65J4xocPRWuMUz7YBvf4RiRthCDo6fqNG/YjkBOKY9bQgBeSZzFMdHV9dydAd6+uRi2IxA2vsMBMGVKur4PCqWrkHr8KyyImoLN0fs0jss6lwYAeHvSn9h7fE/m73F61h6zxUehdBbyqpLR1Hpf7fPrdccAAOMd57EdrXr37IdJzowIc6o81vxBUijdHJohQ6GYmZycHBw9ehTJyckAAKFQiMDAQHh6enLGFRYW4syZM6xgIBAIMHfuXISEhLBj5H1dRCIRAgICIBAIEB4eDoFAAAA4cuQIQkNDAQCHDx9WO19xnK7ttOXPIxAIEBkZialTlT/M6XpuRUiMmtDka5ORkaHyPm0V3j4QHxVtH/Ad5o1TKVYoltyoQt/7ADBw+ZtsVyR95trN8mAEJDXdmAi6nrs90OZLQ85EaR8khbkQ5x1jP+CHh6zD9Ilz4D6U+zUoLS/CzwVn8eXXGwAwLbH9fIIx461nJTfyvi7EG8Wb74857yyCN98fAJP1sT5hIQBgc/Q+tfMVx+naZlv+PN58f8ydtYoVMQw5tyIkRk1o87X58usNSIo5Cm++P3tGVSTFHFW6lyvJBACtYg7FdJRJ83Cj4SQk9/cDAHxcIjHKbiYcLUdwxtU238Kdh+dZEYBn64vR9u9h9IBZ7Bh5X5fiRjEO3PwAPFtfjHeaD56tLwDgev0JpBUzPwAI5u1UO19xnK5ttuXPw7P1xZvO4XCznmTwuRUhMWpCF1+bMmkeTpXHYtXrZ9hyJEXmj/xW5X3abpxCMR1UkKFQzAgRTeSJj49HfHw8srOzWSFD1TiRSMR6oMiLKorjybiCggKkp6cjPj6eHUcEF1XzyTMyTiAQqBUzCBs3buSsT/YWCoWc9ty6nrs9KS0tBcCITxQKhdIeENFEnpQjnyPlyOfY9WkmGU289QAAIABJREFUK2SoGpcryWTFAXlRRXE8Gffdtks4c/E4Uo58zo4jYoSq+fJCxfqEhfDm+6sUKOTZcSCWsz7ZOzxkHadtt67nNheGGBGnHv+KFcMUhSyK+SCiiTznKpNwrjIJiz3SWCFD1bjiRjErJMiLKorjybhVr5/BjYaTOFeZxI4jgouq+eQZGcez9VUrUBDOVCRw1id7+7hEctp263puc9HwWxm+KQpGMG+nVgFI3XyAEaooFIpx0JIlCsWMEFGioqICbW1taGtrQ35+PgDg6NGjSuPy8/PZcRUVFQDAEU4IEokEUqkUbW1tyM7OBgCMGTMGAJTuq5qfkpLCxlRRUQGhUAiRSIScnBy1Z8nJyUF8fDyEQiG7h1QqhVAoRHx8PAoLC/U+tyrIeE2/DCE1NRUCgQB+fn4GzadQKBR9IaJE1t5iXDvZjGsnm7E/8SwAQJx3TGnc/sSz7LisvcUAoDLD42bpFVz47j6unWzGrk8Z0eb91RMAQOm+qvnHT+1lY8raW4zwkHXIlWRCUpirNJYgKcxFypHPER6yjt3jwnf3ER6yDilHPueY4Op6blWQ8Zp+mYPhbp74aMkmNqvm9HnN/1ZRTAMRJaL4lxE/uQbxk2uwfAzzw6gbDSeVxi0fI2LHRfEvAwBHOCFUPboGoVcx4ifXYLEHU5pGjGsV76uaf+X+ATamKP5l+LhEorhRjDJpntqzlEnzcK4yCT4ukeweQq9i+LhE4lxlEmqbb+l9blWQ8Zp+aaLlaRNOlf0NPi6RSkKUrhTUpYNn6wt3G80+TRQKRTtUkKFQzAgpIzp69ChycnIgk8kwYcIEtLW1YefOZz9VIEKDm5sbCgsLIRKJkJKibBBKWL16NVuCI59tEhUVpfK+IomJiXBxcQEAuLi4IDw8nI1THWfPnlXaQ96b5cyZM3qfu70gmT1xcXG0dIlCobQbpIxInHccksJcPG5uggePj2snmzmGsURocHYcitLyIuRKMnHs9F6164bMXMGWF8lnmyyYE6nyviJ/XvIZHAcMBgA4DhjMGtpqEkuuFOUq7dHHsh8WzIkEAPxccFbvc3cm+J7eCJvzIZJijmLj6u1Yn7BQo0BFMQ2kjOhGvQhl0jy0PG3C4L7jED+5BgGvbmbHEaHBtrcraptvobhRjCu1B9Wu6zVwMVtWI59tMsk5QuV9Rd5x+wTWvZwBANa9nDHecR4TpwaxpEz2k9Ie8n4rdx6e1/vc5iCvOhnFjWJ4DVxs0HySBTR9yDpaukShmABaskShmJG4uDiIRCKOL4w6zxXFciBN2Nvbq7yvq9jg7u7OuSbiTHJyslrBhMRmbW2t8nlUVBTbvUifcytirIeMIuS9FhQUaPWvoXQvtHmpUCjmZmVYDHIlmRxfGHWeK4rlQJqwtR6g8r4uHjAA4Oo8jHNNxJn0LPWdhUhsk993Uvn8y683IGzOhwD0O7cipvCQMZa3J/0JcdtW4dCJ7WYvr3remT5kHYobxRxfGHWeK4rlQJqwfNFO5X1dBQS7l90410Sckdzfr1YwIbHF5/NUPj9VHst2JNLn3IoY4yFzvf4EzlUmYfkYkdp3pAnyNVj1+hmDSp0oFIoyVJChUMyIp6cn2traOIa9IpEIAoEAcXFxrECQkpKC+Ph4REREICgoCP3794eTkxMcHBw6+ASGoeu5zUldXR22bduGwsJClJSUKIlQFAqFYm7ch3rg2slmjmFvriQT3nx/rAyLYQ1uj53ei5QjnyPQbyl8J70H6362sLNxxLT5Qzr2AAai67k7K0TYIh4+FPPhaDkC8ZNrOIa9xY1i8Gx9MX3IOvZD/5XagzhXmQS+0wKMspuJP7xoi74v2WPTpdEdfALD0PXcpoaUZ+0qEKh8Lm9qLE/zkwbk13yD2sc3ETk+T0mwolAohkMFGQqlHfD09ISnpyeCgoJw584dTJs2DSKRiM30WLZsGQBwslNkMpnZ4qmsrGSzYoBnhrdCoVDtnIiICCQnJ0MqleqciaPt3Kow1CNGnsLCQmzcuBGenp7Ys2eP2owiSvuR7/wJgK6XtULiJpD4f3/UgoaMm3goLsFDcQlsfIfDbo4HbKYOwwt9e5tk7+Zbtbjuu1PlOyP7l0UzRtyD1nhjQKAnerv1N3g/Xc6k7n1QNOM+1APuQz3gO2kOKmvKsPxjf+RKMtlMj7htqwCAk53yuLnJbPHU1t9js2IAoKL6NgCmE5I6Av2WIj1rDy58d1/nTBxt51aFubNf5ImMDUKuJFPpTI3SegDMmSntg6PlCDhajsCoAQI0/laOb4qCUdwoZoWBH27/BQA42SktT833d0TaWs1mxQDPTGx9XCLVzuE7LYDk/n4IvYp1zsTRdm5V6NJByZTUNt/Cmbufw7HPSMxx32JQZg2FQlEP9ZChUMzIihUrYGFhgUuXLgFgSoNeffVVteOJMCKTyZCYmGi2uFJSUlBZWQmAEWdSU1MBAFOmqDdnCwpiTBoTExNRV1fH3s/JyYGFhQW2bNnC3tP33KaksrISY8aMgaenJ+Li4qgYQzELFZ+eQVl0Bh6KSwAAD8UluL0yHbdXazYs1ZUnDc247qveb+n26mOsGAMAVVtzcW3yV2i+VWvwnuY+0/PIp39fg7EzLVFULAHAlAa5DFT/k2UijDxubsL+47qVZhjCsdN7UVt/DwAjzvzjLNN9bryH+vIc30nvAQD2H09iBQuAMfsdO9MSqce/Yu/pe+6Ows8nGADwY9737L3HzU3s+yBnppiPjDvrIbwwEPce/QKAKQ2yfXmo2vFEGGl52oS86mSzxXWl9iCkrdUAGHGmoC4dAOBm9abaOaPsZgJgPFqanzSw98ukeRBeGIi8qmfx6ntuU6HNBFjxWtpaje1Xp8Oxz0hMd42mYgyFYgZohgyFYkY++OADJCcnw8vLS+nZ7t272d8fPnwYoaGhGD58uMp1SktLTV5y4+rqyrkWCoUaPV6mTp3KdlRS9LoRCAQICwtjr3U9tzk4ffo0AKiMk2CKLBzK84V8JkjzrVo8SL2MQWu8YT9vHHo5W6G1WobqbRfwIPUyWsp+NSpTBQDuJZ5V+6zhRBEeikvglhAAh3njAACyi+W4FbwPD/ZfgdvmmXrvp+uZyHtQzJShqCZg2jykZ+3BgihlsXvj6u3s7zdH78P6hIWYvXyMynUqqm8r+b4Yi98irs9FeMg6jX4pfE9vtqOSoteNN98f70551tFP13N3NDPeCkLWuTTEbVvFZikRtL0PimkY6xAEyf39KktoZg/7gv19MG8n0opXIOmKao+Vht/KTF5Gkyh5g3Pt4xKp0ePFzXoS21FJ0euGZ+uLsQ6B7LWu5+5o7jw8BwAqz0Ro74wdCqW7QTNkKBQzMmHCBBQUFHBKgYRCITIyMtjORgAQEhLCESqEQiFKSkpQUFAAAMjNNW2nh7i4ODYDRyAQIDs7G3FxcTrNO3z4MCIiIth7u3fvVioL0vXc5oCUf1Eo5uLxNeanpgMCPdHLmSnf6+VsBYcF45nnRcZ9c1qz6yf8u1Z9Kn7Dcaa9sF3ASPae1UTmJ6sPUi8btKe5z/S84sHj47ttlzilQOEh65AUc5TtbAQwwoC8UBEesg4/7CrAd9uYLMNfbqhvtWsIK+fH4KMlmwAwYsquTzOxcn6MTvM2R+/jlPJsXL0dMR/u4BgN63ruzkBSzFFsjt7HdoYK9Fuq8/ugGM/gvuOw6vUznFIgH5dIzB/5LdvZCABGD5jFESp8XCIROT4Pq15nOjzeleWbNK7prtF4ZyjzZ4Bn64vFHmmY7hqt07xg3k7wnRaw92YP+0Kp1EfXc3c0pFSMQqGYD4s2+qNiSjfEwsICBw8exNy5czs6lE4F6WBE/9p3PPPmMd9wHTyovm2nsev/+NsNDNv+J4Pm5zt/AoewN1RmW5StP4kHqZfBL96AF/r2RvOtWsgulKEilslOIt4jdrM8OOsBUMqwUPQgUXdfdrEcv4pu4kHqZdj4DodTuBcrQmg7hzY0+aCoiudeQg6qtuay5yc8aWjGFc8EDFrjjcHR2juKqYJkuowWr2BLlnTxaXkoLkHxwkMYtiOQ8951Rd8zmcITqOH4ddxe9X27/f/IwsICn0XtZctUnkdIB6P29Gmh6EfWuTT8NXGRWf9ezJs3DzfP/Iag4X832x5dFXWmtpSugfDCQPr9P6XLQTNkKBQKhaKEa8wMPEi9jCcN3A9uTxqa8SD1MlxjZuCFvr3xUFyC6747WTEGeOY90nCiyCSx3EvIYcpx/pv98VBcglvB+3AvIcck6+tL1VYmY03RvPdFO0vOc31pKfsVt4L3YdiOQFiOcNRpTs2un5Dv/IlRYgxgvjNRKBQKhUKhUNRDPWQoFAqFooTVZKYWX3axjPMhX3aRMVS09WX8jooXHgIAjBKFo+/rgwAArdUyXOV/idsr0w0WCJ7tV46qrbkYtMYbA1e8iRf69sbvj1pQs/MnVG3Nhe3MERrFi67SBej3Ry24G3sag9Z46/XOLEc5wTVmBpry7+L2SsZ00th3TqFQKBQKhUJpH6ggQ6FQKBQlLEc4wsZ3OBqOF3E+4DccL4JD2BusaS0RPJ40NKP5Vi3+XS1j/UhMQdPFcgBgxRiAyeIYuOJNVG3NhexCmc7ZJJ2Zmp0/4aG4BK8kztJrntXEobCaOBQDl7+JBwd/we2V6XjRro9O5VwUCoVCoVAolI6FCjIUynME9Y6h6INTuBduBe9jO+y0lP2Kh+ISjEhbyBlH/EfMAVlXwtuk8nlF7GkMXK6+DamxHjLtQcOJIlRtzcUoUThbImQIdgEjURadgfsp+VSQoaiEesdQKJqh3jEUCqW9oYIMhUKhUFTSZ7QTAECWfxe93fqznXbIfQB4cPAXVG3NhUPYG+gvGImeNi/jJfu+uOKZ0BEhtwuD1nijamsufn/UwvFc+f1RC/tcH0ip0Q1Bisrnuhroklgeikv02h8w/ZkoFAqFQqFQKNqhggyF0kF01Y5HJG4CiV8mkyEtLQ0ikQgikQgCgQBz586Fn58frKysjN5XJBIhICDApO9L3Zrqzvi88ULf3nBLCEBZdAZsZ/Bwe2U63BICOB/Yy6IzAIDTjYl8iNcXRQNhAHAIe4PT0UlfzJH98vJwpr3vk/pmTkwt96QAgJecjf/zronihYfwUFyisiMSwLwzfenoM1EYumoXJBI3gcT/uLkJP+Z9j/M/ZyJXkglvvj/8fIIxcdwM9LHsZ5K9VO2rL4+bm3Dxl9PIOpemNk51Z6SYj67a8YjETSDxtzxtwo0GEYp//RHFjWLwbH0x2v49uNtMQe+ehv19kKe4UYwDNz8w+fuqbb6F7Vena11X3f7q3geFQmGgggyFQjEJ69evR3JyMnstL8xkZGQYtXZhYSECAgKMDdHsa3ZHrLyGAACb8WLt86rKcaSsiRjuasPGdzgeikvw6GoV+r4+CL8/akHtNz8rjesvGIkHqZdRs/MnOC7+I1vSQ9pDu8bM0FiyZA5eHsaIF/XphbCfNw69nK3QWi1D48lbAIA+Y531Wk+daKQuM8ZujgceikvQkHETDvPGAWBEsPrvCwEw70xfTH0mCgUAtu7biPSsPex1ruSZMJMUc1Tv9Wrr75kyPABAo7QesV+tRK4kk70nH2fMhztgaz3A5PtSnj9+vPsZJPf3s9fFjWJWmJk/8luj1q5tvoUDNz8wNkQlmp80YPvV6R22P4XyPEAFGQqFYhDyWSOFhYVITk6GUChEeHg4XFxcUFlZiU2bNiE5ORmlpaVwd3c3aJ9Lly7By8vLVGHrtCY5m2KmzPNIb7f+bJaKQ9gb6KWQKTFsRyBur0zHtclfqZxPhBpFiKggX6bjGjNDaZzVxKFsOY2iT42N73AM+JOnIccyCmJ4rComh7A3OCbDupYb6YPdLA80HC9CWXQGm6FEGLTGm+Mfo+v++pyJQlGHfNZIaXkR0rP2IDxkHd6bsQiOAwajtv4evk5LRHrWHlRU34ar8zCD9vloySaEzfnQJDGf+/kkciWZ2By9DzPeCmLvnz5/FOsTFuLczyfx3oxF7Nk0ZehQKPLIZ4LUNt+C5P5++LhEYrzjPFj3coa0tRrn722D5P5+NPxWBruX3Qza596jX7CrQGCqsDlkVyQavT95D4qZMhQKhaFHRwdAoVC6PhKJBAAQFhYGFxcXAICLiwsiIiIAAFevXjVo3S1btsDLywuHDx82TaBmWrO7QzIuBgSPUXpmN8sDbgnPMo0GrfHG2AsfYrR4BQDGf0YVdrM8MGxHIGz+2z7bLSFAbabL4OipGLYjkFOK45YQgFcSZxllgmsMryTOgltCABu/je9wuCUEwPVj7T9JNAW8fXM5788h7A2MSFuIwdFTDV6zo89E6V7cKL0CAHh3SigcBwwGADgOGIwg/6UAgOJ/Fei9ZmVNGQBguJvphNi4basAgCPGyF+T5xSKMVQ9ugYAGGMfCOteTMahdS9n8J0WAABqHhcZtG5eVTJ2FQgQzNtpmkAV1m5qvd9h+1Mozws0Q4ZC0RELCwtERERg507lf3RWrFiB5ORkSKVSWFlZobCwEGfOnEFUVBQAsH4qISEhGtcHlP1K1N3PycnB0aNHkZycDIFAgMjISEydqv3DmC5ZH/p6plRWVgIAHBwcOPednBjz15s3b+q1HiEqKgoZGRkQCAQIDQ01aI32WLO7YzVxqMYMC4d549jSGXnk56iabzfLg9NSW904+bHyXjUdyYt2lmrPLY9X9d906vSkbq4mVL0/Y/bX9UyUZ4ydaYlAv6X4+H+2Kj379O9rkJ61Bxe+u48+lv1QWl6EnwvO4suvNwAA61OiKAQorg8o+5Wouy8pzIU47xjSs/bAm++PubNWge+p3ZBZl6wPfT1TSHlRf2vuvwt2Nky21b8q/6nXeubCm+/PKVdS9ZyiG8ILA8F3WoCAVzcrPcu4sx6S+/sh9CpG7579UNt8C3censep8lgAYP1URg+YpXF9QNmDRN39MmkebjSchOT+fvBsffGmczjcrCfpdA5t6OuDIm2tBgD0edGOc7/vS/YAgLr/09+MHQBOlcdi/shvwbP1RVrxCoPWUEWZNA+nymOx6vUzKG4Ut/v+FMrzBM2QoVB0JDExEcnJyairq+Pcr6urQ3JyMhITE2FlZQWRSIQxY8awYgzA+KmEhobiyJEjJoll48aNmDZtGuvZIhKJMG3aNGzcuNEk6+tLfHw8ACiZ99rb23Oe60tbWxsEAtOm4ZpjTQpFHY+uVnEyiJ63/bs7Hy3ZhPSsPWiU1nPuN0rrkZ61Bx8t2YQ+lv2QK8nE+6snsGIMwPiUrE9YiNPn9fdSUcWOA7FY/rE/69mSK8nE8o/9seNArEnW15eUI58DgJJ5L/FjIc/1oaSM8Umy7meLY6f3YuxMS4ydaYljp/ficXOTQXHOeWcRACh9Hcg1eU7RzjtDYyC5vx/NTxo495ufNEByfz/eGRqD3j37obhRjO1Xp7NiDMD4qaQVr8D1+hMmieVMRQK+KQpmPVuKG8X4pigYZyo6pgPgucokAFAy77X8r0BDnutL/OQa8Gx9jQtOgYbfyvBNUTCCeTvhaDmi3fenUJ43aIYMhaIj06czafs5OTmcTJecnBwAYD/kE6PY/Px8TJgwAQCTQeLq6orQ0FCNWTK6kJOTg/j4eAiFQkRFRcHKygoymQyJiYmIj49HYGAgPD3Vp3M/rx2DKBRjMNQL5tHlynY3HTbn/oZm+3RX/jhmCgDg8vVznEyXy9fPAQDe4vsBACJjmWf7E8/Cg8cHwGSQ+C3iYX3CQo1ZMrogKcxFypHPER6yDgvmRKKPZT88bm7C/uNJSDnyOaZPnAP3oeqzqbpax6D3V0/gXMdtW4XzP2cifu3Xendv8ub7Y9enmTh0YjvWJyxUuq9LhhGF4VWbt4By4F/Si5xMl39JLwIAeP3fBgDW/HX5GBEG92Uy8qSt1UiUvIG04hUas2R0oUyah3OVSfBxicQk5wj07tkPLU+bkFedjHOVSRhlN1Oj0PA8dwFqedqEU2V/g49LpNFfBwqFohs0Q4ZC0RFPT08IBAIcOnSIc//QoUOIiIhgTWvb2trQ1tYGNzc3FBYWQiQSISUlRdWSBnH27FkAYMUYgMlMIRk5Z86cMdleFArFODpSjOkM+3d33Id6wJvvj6xzaZz7WefSEOi3lDWtvXayGddONsPZcShKy4uQK8nEsdN7TRbHlSLGiJmIMQCTmbJgTiQA4OeCsybbqyMhGUb7E8+y7/TayWZsjt6HXEkmLv5y2qB1S8oKlcqWciWZqKotMzrm5wlHyxHg2friet0xzv3rdcfAd1rAmtbGT65B/OQa2PZ2RW3zLRQ3inGl9qDJ4iiTMZ3+iBgDMJkpk5wZX7s7D8+bbK/uRl51MoobxfAauLijQ6FQnhtohgyFogeRkZGYNm0a2zWotLQUIpEI2dnZnHEbN240uExHG2Rda2trlc+joqKwdu1atfPN4SFDoXRXTNkdqTtA34cyc2etwvKP/dmuQRXVt5ErycSuT7kf8HcciDWoTEcXyLqT33dS+fzLrzdo7EpkDg8Zc6AuhhlvBWF9wkJknUvTO9vo9Pmj+PLrDWq7LFm+3MfoDKbniTedw/FNUTDbNajhtzIUN4qx2IMrWp6pSDC4TEcbZN34fJ7K56fKYzFpUITa+ebwkOkKXK8/gXOVSVg+RsSWUlEoFPNDM2QoFD0YN45Jrc3NZX4aSboHkfsAkJKSgvj4eERERCA7OxsFBQV48OBB+wfbjgiFQgCATCbj3CfX5DmFQqF0N0a8OhYA8MuNPADPugeR+wBw7PRepBz5HIF+S7Hr00x8t+0Ssg/cbfdY25PwkHUAoOTtQq7Jc1OiyZxXHaRMSV2XJcXsJ4pmBvYZDQC4K8sH8Kx7ELkPAFdqD+JcZRL4Tguw2CMNq14/gw0Trrd/sO2IjwuTrdbylPv3gVyT5x0JMeXdVSCA8MJA9hdB8ZpCoZgGmiFDoeiBlZUVdu/ejWXLlmHWrFkIDQ3F7t27OWa2y5YtAwBONyZFoUJXFA2EASAiIoLT0UlfzJH9MnIk0xb5wYMHnJju3r0LAGwrbEr3wFA/FYr+tMe7pl9P4+hj2Q8bV29H3LZV8PnjTKxPWIiNq7dzvExI62T5bkyGmtAqGggDQKDfUk5HJ30xR/bLKy6vAQB+lT7gxFRTVwEAbCtsfYiMDUKuJFPpnORdBvotNSZklRgi8jzP9O7ZD7OHfYEfbv8Fr/WfgbTiFZg97AuOme0Pt/8CAJxuTIpCha4oGggDAN9pAaejk76YI/vF/g/DAQCPnzRwYpK2VgEA2wqbQqE8f9AMGQpFT7y9GYM/0uJ5xowZKseVlpYCAGu4qw1iCnzp0iV23rZt25TGBQUxP7VLTEzkCDY5OTmwsLDAli1bdD2KyXjtNeYb79TUVLYFdmVlJdLT0wEAfD6/3WOiUCiU9mLcKKaV7rT5QwAAb74+XeW4iurbAMAa7mqDtFwuKpaw846c3Kk0znfSewCA/ceTOIKNpDAXY2daIvX4VzqexHQMHcyUi/zj7GG2BXZt/T2cuXgcADDKfbzea/r5BAOAklcMuSbvQR8+WrIJAPOu5EUy0mWJPKfozhArLwDApktMVsyrNj4qxzX8xnj0EMNdbZBuPvce/cLOy6/5RmncKLuZABg/FHnBpkyaB+GFgcir0r6XqbH/A+MnVVCXzrbAlrb+P3v3HdbU2f4B/BtAQJE9BHGiIEURB1NFBLVOXEVE3FYUHFWrdaJt1dbtT0UEcbbuSRUr+qIoTgQnQ0CsiIqLjWwI/P5IkxISIECSE+D+XFev903ynHPukxMfnnOfZ6QgJu0yAKCNas8qt5UW7tw+lf+r/DkhRLyohwwhtWRiYsLrpeLp6SnQ++PkyZOYOHEiunTpInR77vwzlbm7uyMoKAh2dna894QlcpycnODt7Y0NGzYIzFPj7OyMKVOm1OW06oU74bGwmDw9PflWfeLOYSPOnjqS2CchsoB6rTQM7Q2Neb1UXIbNEuj9sWnZEazYMh1j5vQQuj13/pnKhg1wRVjEFUxd6sh7T1iCwNrCAR5uy7H/1GaBeWocrIdjhOPEupxWvXAnPBYWk8uwWXyrPnHnsKmpp07f3kPgYD0cK7ZM51sRCeAMgaq4IpKo+xzhOBGPo+9gzurhAp8x9d01dDrNjXi9VKwNpgr0/nA19cOZeC/sfNRP6Pbc+Wcq6643DvEZIdj3zJn33tCOawXKGWn0w4B2i3Dr7U6BeWpMtQajZyuXupxWvXAnPBYWk7XBVL5Vn7jDgsSZ/JDEPgkh4kE9ZAipA24vlWnTpgl85ubmhoCAAN5rb29vJCQk4NkzzrwC3PlnhG138uRJXk+ZgICAKifnXb9+PU6ePAlPz/8mpQsICMCBAwegp6dXt5OqpwMHDiAgIIAXv7OzMwICArBp06YatiSEkIaP2ztj1MBJAp8N6T8eaxbs4b32cFuOv/Y9w2kfTo9I7vwzwrbbtOwIr6fMmgV7qpycd+7ktdi07AjfsJ01C/Zg7Q97oaWhW7eTqqe1P+zFmgV7ePE7WA/HmgV7sHD6+jrtr6WKGjYsOcj3nXDn5Zk7WfDGXBRaGroC+3SwHo5Ny45gw5KDjH13DR23l0rPVoITInfXHY0xxlt5rwe0W4RFlncxvxdnlUju/DPCtnM19eP1lBljvLXKyXkHtV8GV1M/WBtM5b03xngrxppsZ2zC2rEm2zHGeCsvflOtwRhjvBXfdljFSDyEENnAKqdHyqQRYrFYOH78ONzd3ZkOpdERR28UFosl9t4sktonIJmeN5MmcW7ajh8X31Kflff/v4IYGO/5rlbbsb8WIjM0EWmB0cgMSUCrKVZoPdsOykbavDLC5hzJe/EJ2XdeI3kdZ+iA5uAu0BlrDp3R5nz7z76XhPSgWHw+GgkAaLPYb+fYAAAgAElEQVTQAVojzaBipl+ncpVxY6uOsF4n7K+FiDDdiFZTrGC0aaTA569XXMbno5Gwjl8JeVVlgRg1B3eBgYcd1Pt2FBpPr4gfkbT6b6iY6aPtMieRz1HYdy3KNeJKuxjNK1fVNalqDpnabCvs/GqSFhiFxPnnpdazjcVi4felh3nDXoj4iNobpaZ9iHsuG0ntE5DcqlPBt85g1bYZEv13MWnSJMReL8D4Lr4SO0ZTJo7eKN53Wou9N4sk9lnb4wOS7aXjfac1tf9Jg0NDlgghUhUeHs7Xg0hW99lUJS64gMyQBN7rz0cj8floJLqHeFWZDMkMSUD89BMC73H3w72JF1bu/a4wvN8VBrMz03nJDFHLiZO8qjLarx2C5HXX0HapI5rp/LcMcElaHj4fjUT7tUN4yZh3W0Lxftd/vd2459tmoYPQhMSX44+RGZIAnbG1+y6EEfUaVRVjQUJqjUmT2m5b+fwIqY3o+Ai+HkSyuk9CpOHd18d8PYhkdZ+EEPGghAwhpE7q2nvk3r17VQ7Fqitx75N7bk1NxaRCa68+kFdVRtrFaCTOPYfPfz4S2nMEAC+x0C3IA6q92gAAilKy8cR6BxLnnuMlZLjlekX8CCVDzmpcX5+8R4zzfqQHxfKSEKKWE6Y+c66o23PmLMi+95qvJ0j2Pc7Ek1qDu/z7Ognvd4XxfU/sr4X44Hcf73eFCe3J07yLLl9sdT1HUa9RxRj1JvWGkqE6ilKy8eX4Y7zfFQa1vh2rPEZdtq18fqRpqmvvkWdx4VUOxaorce+Te26EiKquPUKSsyOrHIpVV5LYp6hoqWxCqkcJGUKIVIk7GSOpfTZFmTc4K8Doz7Th9QTRGS04TKUy7o14SVoe8l58QnFKNnKfpgiU0xzcBZkhCUi/HAuVbgZo2d0Aqr3aCNzIi1pO3FTM9KE5uAvSAqP5zjktMBqtpljxhgTl3EsCAF5CBOD0sGnt1Qfvd4Uh+85rgYSMel/+CSrreo6iXqP0oFgA4CVUAEDJUB16k3rj/a6wapM+ddm28vkRUhviTsZIap+ESIMkEidMJWMIITWjhAwhpFaawrRTjeEc5eTkUJ5bXKttuHOZVByuI6rKQ1yEabvMCZkhCXzzzAibd0XUcsLUdQ4ZLgMPO7xwPYLC1+lQNtJG4et0ZIYkwOzMdF4Z7nlGmApfDjd53TW0ntOH773K32ldz1HUa8Qtx02ocHFffz4aWWWPp7psW5ffTDm7DIrKSrXejsgeSc2nIkuawjkS8aCVjPjR90FI9SghQwghjZCOjg7K4gulcqzP/w5laTXFCtrOXaGg2RyKeqp4ZLGFr5yKmT7sUn7lmwCYO2Fs22VOvF4lopaThJbdDQAA2Q/eQNlIG7nRH/jeFxcmz1FWlGbkQ0NLk+kwCCGEEEIYQwkZQmpBkqvuEH7S+K4b8/X85ptvkH+odhMdt5pihc9HI1GSllerHg+vl10CAL5eE+yvVSeDVMz0oWKmD+2RXVH4JgMvXI8gMyRBoOeKqOUqqu+wJnlVZRhtGYXXyy5Ba4gpEueeg9GWUbzhQcB/31PFFZfqqrbnKOo14pYrSsnm6+lS+Dqd97kktq2N/Jep6GbWVSz7kmWSXpVH1kni/Ou6z6Z+LWSZNFbgkWWSOP+67rOpXwtCpE2O6QAIIYSIX//+/VGUk4/8+M8ib6Nm1x4A8OnQQ15CJe1iNB4Y/ozXKy7XuD33hp07wW1lr1dcxgPDn/H1yXsAnCEwyh206lxOUtTtOgAAr4ePxoDOfJ9rO3OSCB/87qMk7b8bu+x7SXhg+DM+7BM898rqeo6iXiNujF+OP0ZRSjYAzkTLqeeeAwA0BxpXeYz6bFsbhY8+YEB/B7HsixBCCCGkIaIeMoQQmdQYe61Ik6mpKYxMOiPjajxamLYSaRud0eZIC4zmLb9cUaupllVuZ7zXBYlzz+Gp/W6hn3PnY9F17YHPRyMR47xfoIzRllG8/y9qOUlRNtLm9RJpNcVKYC4V9b4d0Wahg9DvSXNwF+h+Z1HjMep6jqJeo+pibLPQAZr/rhglTH22FVXBqzTkJH7CmDFj6r0vItsk0RulrvuknjFEVkmiN0pd90k9YwiRLuohQwghjdS8OV7IPPkM5ewykbcx9hnHlxBos9ABPe/8UO2cJjqjzYVu0z3ECwBnPhYAUO3VBt1DvNBmoQNfWdMj7mg1qTfvPVHLSRK3l4iuaw+hn7dd5gTjvS58w3eMtoxCp22jRRruVZ9zFPUacWPkJlA0B3eB8V4XtF3mVGN89dlWFF+OPYalrTXMzatfwYsQQgghpDFjldNjaNIIsVgsHD9+HO7u7iJvk52djeDgYJw4cQJBQUHw9PTE4sWLYWJiwrdfgL/3xvPnz3H9+nUsXboUAODs7Ax3d3e4ubnx7T80NBRnz56Fv78/AMDb2xsuLi6wsLCoUzlh51wTYf/cs7OzoaGhAU9PT/j5+Ql87uXlBX9/f2RlZUFdXV0gRmdnZyxatAhOTvw3atx4kpOTMX/+fFhYWGD9+vUin6Ow71qUa8R16tQpXrmqrklVc8jUZlth5yeKSZMmAQCOHz8u8ja1lZ+fj06mxmgxtxdaTRXPvB+E1FdRSjaiHXxx7e9gODo6Su24LBYLvy89jGEDXMW2z9y8HNx7fA3Bt84gLOIKXIbNwuQx89He8L9hXcLmLXmZFI2Hz25ix8GVAAAH6+EYNsAVQ/qP59t/xPMwhNy9gHPBBwAAHm7LMajvWJh0NK9Tucq4sVVHWK+S3Lwc2E8wgMuwWVg9b5fA57/5LsS54AO4c/oj7CcY8O2He8zgw/HY5PcjTIzMMXfyWt62126f5X2fHm7LMcJxIsbM6SF0H5Vf3zj2Bn/fPIkdB1cK/U6FXQtRriEg+jWrr+BbZ7Bq2wyJ9hKdNGkSYq8XYHwXX4kdo6LC0hy8zLyJqC8XEJ8RAmuDqehjOBs6zY14ZYTNW/Ip7wVeZd7G1aR1AABTrcHorjcO3XVH8+3/ddZdxKRdRsTHPwEAA9otQjedkdBXMatTucq4sVVHWK+SwtIcbHhgCmuDqRjVeZPA55derUDExz/hbRePDQ9M+fbDPeZS60hcfrUK+i27YlD7Zbxto1Iv8r7PAe0WoYeeC3Y+6id0H5Vfr7SNwtPP53A1aZ3Q71TYtRDlGgKiXzNJ8b7Tutbtf0KYRgkZ0ijVJSEzatQoBAUFCbz/7NkzXqKg8g18UFAQRo0SPrzg5MmTvJv46srduHGDl8wQtZwwdU3IAMD27duxdOlSfP78GXp6erz3v3z5glatWmHbtm1YsmQJAGDNmjXYsGGDwD68vb35EhLceLy9vbFhwwbe9yHqOQpLlohyjeoSY8Vj1Pf8RCWNhAwAnDhxArMWeKLb7blQ0Gwh0WMRIookz/OwlO+AoMCLUj2uJBIyi9aNR1jEFYH3T/uE85IhlZMAYRFXsGid8Jv4TcuO8G7wqyu377crsLZwqFU5YeqakAGAo4G7sePgStw49gZaGrq89zOyUjFwcgf8+P1GTBn7Q5XJEw+35dh/ajPfOe89tg77T22uMZaq9ulgPVzgelTcv7CEjCjXUNRrJg6NMSFzLHYa4jNCBN6f3+s6LxlSOQkQnxGCY7HThO7P1dSPd4NfXbmZ5mdgpNGvVuWEqWtCBgDuvvfH1aR1WGkbBZVmOrz380rSsDG8O4Z2XIt+bTyrTJ4MaLcIt97u5Dvn68lbcOvtzhpjqWqfplqDBa5Hxf0LS8iIcg1FvWaSRAkZ0hDRkCVCwEmEBAUFwdvbG1lZWSgvL8fJkycBgNeLQxhuYuHBgwcoLy9HeXk5kpOTAQATJ04UKJecnMwr9+DBAwDA2bNna11OGG756v6ryqBBgwBweq5UxH3t7OzMe71hwwa+7ykrK4uXlHj+/LnAvrt27Yry8nJesqKu5yjqNaoYI/cYycnJvBgrn2Pl863ttpXPT9ZMnDgR3c264c2SoFoNXSJEElLPPkN6SDx276j6ZqKhCIu4wuvFcef0Rzy9nIdNy44AAM5eOVDldtwb+z+33cTTy3l4ejkPwYfjAQArtkwXKBd8OJ5X7s9tNwEAIXcv1LqcMNzy1f1XFZsenN5NkVG3+N7nvu5vPazaY3dq9w2eXs7jJTMinodh/6nN8HBbzjuX4MPxcBk2q9r9VGRiZM67Fvt+4yRZgm+dqbK8qNdQ1GtGBMVnhPB6cXjbxWOD/Qe4mnJ643J7qgjDvbGf0yMIG+w/YIP9Byy1jgQAnIn3Eii31DqSV25OD86Dm5i0y7UuJwy3fHX/VaWzZn8AwD9Z9/je57421f622mPrteiCDfYfeMmM11l3cevtTgxot4h3LkutI2FtMLXa/VSk37Ir71rMNOf8+4j6UnVdIeo1FPWaEUL40aS+hAC4coXTcFuwYAFvWI6bm1uNN9ncJMeXL1/w/PlzvH37FhEREQLlnJ2dERQUhLNnz6Jnz57o3bs3bG1tBZIkopYTNwsLCzg7O+PEiRN853zixAl4enryhgTdvMlp5C9dupT3Pamrq2Pp0qXYsGEDrl+/LjC0qnKvnrqeo6jXiJvU8fDwQLt27QAA7dq1g4eHBzZs2ICzZ89W2dOoLttW12tJFrBYLPx58Ah6W1vi3W/X0W5t9Y0/QiQl+34SkpYFYc8uH3Ts2JHpcOrtbuQ1AIDbSC+0VFEDAAzpP77G3hLcJEdGVipeJkXjY+o7xL58JFCO29sj5G4guhhZwKxzT5ibWgskSUQtJ24mHc3hYD0cwbfO8J1z8K0zcBk2S2DIT2VW3QfwvX4UzZlAetyQGdDXbQsA0Ndti8lj5vOGYtWk4rWo2IOoKqJeQ1GvGRH0MuMGAMCu9UwoK3C+4+66o2vsLcFNcuSVpOFT3gtkFaXg/denAuW4vT1iUoPQumU3tG7ZHW1VewskSUQtJ276KmYw1RqMqC8X+M456ssFWBtMFRjyU1knjb58r19nc1bxs9SfBA0lQwCAhpIh+hjOrjbBVVHFa1GxB1FVRL2Gol4zQgg/SsiQRklZWRlsNlvk8tweFhWH64iqqiEuFa1fvx5BQUF888wIm3dF1HLC1GfIEgAsWrQIAwcOxMuXL2FiYoKXL18iKCgIN27c4JXhnqeGhobQfSxdupQ3tImr8nda13MU9Rpxy3ETKlzc1/7+/kLnyqnrtnX5zQDA169fq/wexc3ExAQXL/yFIcOGQkFXBa29+ta8ESFilBf1AUme5+ExywOenp6MxKCspIyyMtH/LtSEmySoOFxHVDUNzQGAuVPWIiziCt+cJe6j5wsMQRK1nDD1GbIEAO6j52PO6uFITklEe0NjJKckIiziCq93SnUqf2/c74ObjOGqKbFT3T5rUptrKMo1Ewd2WSmUlZQlegw5OTkUl0lnxSlukqDicB1R1TQ0BwAGdViO+IwQvjlL+hh6CAxBErWcMPUZsgQAfQw9cCjaFWkFr6HT3AhpBa8RnxHC651SncrfG/f74CZjuGpK7FS3z5rU5hqKcs0kpbSsCADQsmVLRo5PSF3RkCXSKGlpaSE9PV3ix9m/fz82bNgAT09P3LhxA8+ePcPnz58FyllYWKC8vBzPnj3Dtm3bEBQUhIEDB2LUqFF8w3xELScJvXtzVnYJC+M8pXzy5Anf++LC5DnKktTUVGhra0vteE5OTvDz3Yt3G68jacVllJfS8CUiHRnBcYj77giGDBiEPbt9GItDU1MLWV8l/3ehJheuHcb+U5vhMmwW9v12Bad9wnHj2BuBciYdzfH0ch5O+4Tjx+83IiziCuasHo5F68bjZVJ0rctJglnnngCAxzF3AQDx/zzje7+xEPWaiUNmThq0tCT7t0FHRwcFZRkSPUZ9Pfp0HLfe7oS1wVTMND+D+b2uY6VtlEA5fRUzbLD/gPm9rmNox7WIzwjBoWhXHIudhk95L2pdThJat+wOAHiTzRme/SE3mu/9xkLUayYp+aWZAABd3donyQlhEvWQIY1S165dERMTI3J5T09P+Pv748uXL7Xq8TB79mwA4Os1kZ2dXWV5CwsLWFhYYPz48Xj16hUGDhyIoKAggZ4roparqL7DmtTV1REQEIDZs2dj9OjRmDhxIgICAnjDg4D/vqeKKy7VVW3PUdRrxC339u1bvp4uL1++5H0uiW1rKy4uDjNmzBDb/kQxa9YsGBoawmXCeLx8k4kOW52h1FY6vXRI01NWWIKUPXeRsisMCxcuwratWyEvL89YPF27dcWrZPHdeLkMm4VzwQeQkZVaq54Z633mAwDf6kS5eTlVljfpaA6TjuYY3G8s3n54jTmrOUOUKvdcEbVcRfUd1tRSRQ1rFuzBep/5GGAzEiu2TMeaBXt4w39qgzvJ76fUd3y9ZD6lvqtXjNUR9RrW9prVx+u38TDrWv2qP/X1zTffICDvkESPwWVtMBURH/9EXklarXpm/JX4EwDwrU5UWFr1d66vYgZ9FTN003VGRkESDkW7Ij4jRKDniqjlKqrvsCZlBTWMMd6KvxJ/wjfaQ3Am3gtjjLfyhv/UBneS36yiFL5eMllFKfWKsTqiXsPaXjNx+5LHaauZmUn23w8h4kY9ZEijZG9vj3v37tVc8F8ODpyu3T4+PryEyqlTp8BiseDlVfNEZNwb9uzsbGzbtk3gcy8vL7BYLISHhwPgDIHp3LlznctJCvd7aNWqFQBgyJAhfJ+PH88ZV79t2zZ8+fKF935oaChYLBa2b99e4zHqeo6iXiNujPv378fbt28BAG/fvsXRo0cBAMOHD6/yGPXZtjaio6ORnZ2N/v37i2V/tTFs2DCE33sA7TR5RA3wxdvNN1BWUCL1OEjjlhEch1gnf6Tvj4C/nz/+b8cORpMxANC/vz2i4sPFtj9Lc85Qh1OX/Xg359dun0XPkSr4zXdhjdsnpyQC4NzY/xko2MX/N9+F6DlSBdHxnHnJ9HXbol1rwWEJopaTlN7dON/DwMkdAAB9eg2q034szTl1/IVrh3lJmE+p73Dh2uH6B1nlMWt3DWu6ZuIQFR+O/v3tJbJvrv79+yO/KAef8+IkehwA6KBuBwB48OEQ7+Y8KvUivO+0xqVXK2rcPq3gNQDOjf3dFMFFFi69WgHvO63x7utjAJyhPFrNBeeoErWcpHC/h43hnF4xnTUH1Gk/Rup9AHB6o3CTMFlFKXj0SXIrNtb2GtZ0zSQlOechzEy71fuBISHSRstek0YpOjoa3bt3R1xcHExNTUXapi7LXp86dYpvNaXKEhISYGJigvDwcNjZ2QktExAQAA8PDwAQuZwkeXl5wd/fH56enkLnWqlqzhxnZ2ccOHCA13tF2JLSgOjn2FCWva5LFbpu3TqcPHkScXGSbwxXpbi4GD4+Pvh1wzqUKgBaEy2gPbIrVLoZMBYTadhKvuQi41oc0k88Q050ClzdJuD/tu+AgYFs/Ka4fxcC/Z+iQxsTseyzLsteX7t9ttqVef7a9wztDY0RHR+BqUsdhZZZs2APxg3h9LATtZwk/ea7EOeCD8Bl2Cy+XiRA1UtUC+udU99lryvvU5RyolxDUa9Zfb15/xJjPXsiKioK5ubm9d5fdYw7dUF79mg4tlss0eMAdVv2Oir1YrUr8yyyvAud5kZ49/Ux9j1zFlpmjPFWWOpPAgCRy0nSpVcrEPHxT1gbTOXrRQJUvUS1sN459V32uvI+RSknyjUU9ZpJil/0QMxc4IJ169ZJ7BiESAINWSKNkrm5Oezs7BAQEIAdO3aItM3Ro0dx5swZ3jAkb29vTJkyhbfCkDBubm74+vWrwDYFBQXo0aMHwsLCYGJiAltbWzx79gznzp3j3ex7e3vD2tqat6Q0AJHLSdL48ePh7++PadOmCf18/fr16Nq1K8LCwniT4AYEBGD06NEiDfeqzzmKeo24MZ44cQJBQUFwdnaGu7u7SEtT12dbUbDZbBw8eBCLFi0Sy/7qSlFREUuWLMGUKVOwb98+7D90AFE+d6Ck3gIqJnpgaSgDSsz2aCANQFk5kFOMouRM5L5LR0t1VYwbMxY/HPpB7PNP1Ze5uTlsbexw/uohLJm1qeYNRLBhyUH87+553pAWD7flGOE4sdqb8yH9xyOvIFdgm6LiQkxYYIvHMXfR3tAY5qbWOO0Tjuv3AnlJCg+35ehqYgkH6/9664laTpIG9xuHc8EHMGpg/W5q505ei07tvkHwrTO85ahHOE7EmDk9xBSpIFGuoajXrL7OXz0EWxs7iSdjAMBr3hz8/vP/waHtD5BjSbaud+nig5i0IN6QlgHtFqGHnku1N+fddUejmJ0rsE1pWSH2PBmEN9kPoNPcCG1Ve2N+r+uISbvMS1IMaLcIbVR7wlRrMG9/opaTpG46IxHx8U/0bFX9Smw1GdR+GfRadEHUlwu85ah76Llg56OaJyiuK1GuoajXTBLe5jzGx5yX+P777yWyf0IkiXrIkEbr5s2bGDlyJOLi4gRWzSGEKX5+fti0aRPi4uLQokULpsPhEx8fjzt37iA2NhYZGRkoLCxkOiQi4+Tk5KCpqYlOnTrB0tISffr0gaKiItNhVenmzZsYMXwkLvg9EVjNh8iuniNVhPa+aUw+pb7DOK9e+PvKZTg6Cu/xJE75+fkw7mQKy5ZzYW0g/AEMaVi877QW2vumKfgjfjwGjO6GgP3SGyJFiLhQQoY0auPGjYOCggLOnKl5aUFCJC09PR1dunTB7t274e7uznQ4hDRJY8eMQ34mC5uXHWU6FFIBdzjRn9tuwtzUGgBnrpbA/x3BjoMrsWnZEQzpX7+eBbJs+ZYpaKFZjsC/LkjtmCdOnIDnrAVYYHEHLZppSu24pO64w4nm9AhCW1VOL8TC0hw8+nQCV5PWwdXUD911RzMZotTFpl3G5Xc/4Z/XibTCEmmQKCFDGrWkpCSYmZlVOwSHEGlgs9kYN24cMjMzERYWxpuDhhAiXUlJSfjmGzOsnrsbzvUcYkPEJyziChatE55wcbAejg1LDtZp9aaGIOjGcfy29wfExb1Ax47Sm2i2vLwcfe36IydJBRNMDkh86BKpv/iMEByLFd6eNdUaDJcuPnVavamhyix8i/0xI7Bu41osXPgD0+EQUifyv/zyyy9MB0GIpGhqakJHRwfz5s1Dv379pNrQIaSiZcuWITAwEEFBQdDREX3pT0KIeHH/Lqz89Qf0MOsDw1btmQ6JAOhgaIxeXfvBQK8NnsRwVkl0GTYLU8f9gOnf/dhokzGRUbexfMtU7Nq1E4MHS2cuEy4WiwX7/v2w1fdn5BVmorOmg1SPT2pPp3kndFC3gbqyId5kc1aNszaYir5tPGHfZm6TSsYUlubgaMJEdO3VCXv27IacHC0eTBom6iFDmoR58+bh7NmzCA4OlrmJJknjt3XrVnh7eyM4OBhOTk5Mh0MIATBv7jycOnkGe375C9907sl0OKQJinv1FPN/GQO3ia7w3evLWByhoaEYOmQYnNoug32buYzFQYioiti5OJkwA+XqX/D4SSQtdU0aNEolkiZh9+7d6N+/PxwcHBAYGMh0OKSJKC0thZeXF1auXAlfX19KxhAiQ3b77MYAJwfMWjkEoQ8uMR0OaWJCH1zCrJVDMMDJAbt9djMai5OTE/b6+SIkeSOC/lmBsvJSRuMhpDpZRSk4GDMaufKvcfXaFUrGkAaPhiyRJkFOTg7fffcdMjIysGTJEpSVlcHW1hbNmjVjOjTSSL158waurq64evUqLly4gAkTJjAdEiGkAjk5Obi4fIeMzAys37YMZWVl6G5qBQV5+rtAJKeouAD7T23GZv8fMW/+POzfHwB5eebnbunVqxesra3g++d6JGXdR3tVWzRXoBtdIlteZobiRMI0tO2kg1u3Q9GhQwemQyKk3mjIEmlyAgICsGTJEujp6WHbtm0YO3Ys0yGRRiQ/Px+///47duzYgQ4dOuD06dMwNzdnOixCSDUCAgLw449LoKWmi0Uzf4eT3SimQyKNUOiDS9h5aBUyclKxY8d2zJ49m+mQBERHR2P8dxOQlJSEPgZz4NDuBzSTa850WKSJSy94g+A3axCfdgNuEybiwMH9UFFRYTosQsSCEjKkSfr48SOWLFmCU6dOoVevXvDw8MDo0aOhr6/PdGikgXr69CnOnj2LgwcPori4GN7e3liwYAEUFRWZDo0QIoKPHz9iyY9LcOr0KXxj3ANjB8+Ao+1IaGu2Yjo00oClZ37GzfAgBIYcQVziM7hNcMP2HdthYGDAdGhVKi4uho+PD9b9ugEoVUBPnYnopuMMg5bdmA6NNCGlZUV4nXUXz1LPIjY9GF2MTeDrtwcDBgxgOjRCxIoSMqRJe/z4MXx8fBAYGIicnBy0b98enTp1goaGBuTl5VFWVkaztpMqFRYWIj09HS9evEBWVhaMjIwwffp0zJkzB3p6ekyHRwipg8ePH8Nntw8uBAbi69cctNZvhzb6RlBtoQ6wWJBj0d8EUr2yMja+5mfj/afX+PDpLdTU1DF2zBgs+GFBg1pY4MuXL9i3bx8O7D+Et+/eoIWSOlqpmEBZThPyUGI6PNIAlaMMrBqmMC1BLnJLP+NTTiLKUYa+dv3gOXc2XF1doaCgIKVICZEeSsgQAs7ToPv37yMyMhKvX79GZmYm8vLycO/ePRgZGcHIyIjpEGXK8+fPoa2tjTZt2jAdCqOUlZWhpaWFrl27wt7eHqampkyHRAgRk8p/F2JiYvDkyRMMGDCAuspXUFBQgKioKJibm6NFixZMhyMT5OTkoKmpiU6dOsHS0hJ9+vRp8L0l4+PjcefOHcTGxiIjIwOFhYVMhyTzoqKioKOjg9atWzMdikxITU1FZGQk7OzsoKmpWWU5VVVVGBgYwMLCAo6OjtDR0ZFilIRIHyVkCBHiwYMHGDduHLS1tXHx4kV06tSJ6ZBkio2NDezt7bFt2zamQyGEEIkqKyvDr7/+ivXr18PDwx99zksAACAASURBVAO7d++GkhL1DuBKSEiAqakpLylDCOEwNjbG999/jxUrVjAdikzIyMiAm5sb7t69i0OHDsHNzY3pkAiRCdTvlpBK/vzzTzg6OsLS0hIPHjygZIwQGhoayMrKYjoMQgiRqOzsbIwePRqbN2+Gv78/9u3bR8mYSpo350z4+vXrV4YjIUS2FBcXg8ViMR2GzNDS0kJwcDDmzJkDd3d3rFq1CmVlZUyHRQjjKCFDyL/YbDZ++uknTJs2DYsWLcLFixehqqrKdFgySVNTE5mZmUyHQQghEhMXFwdra2s8efIEoaGhMrkijizgzulAN1aE8CsqKoKamhrTYcgUeXl5/N///R8OHjyIHTt2YMyYMcjJyWE6LEIYRQkZQsB5Cjpq1Cjs2bMHx44dw6ZNm2gy32pQDxlCSGP2119/wcbGBjo6OoiMjESfPn2YDklmcefTycvLYzgSQmQLzbNTtRkzZuDmzZt49OgR7Ozs8OrVK6ZDIoQxdMdJmrzExETY2dnh2bNnuHXrFiZNmsR0SDKPEjKEkMaorKwMa9euxbhx4+Du7o7Q0FCakLMG3IcXpaWlDEdCiGwpKSmhntbVsLOzQ0REBFq0aAEbGxuEhIQwHRIhjKCEDGnSQkJCYGNjg5YtWyIyMhI2NjZMh9QgaGpqUkKGENKoZGVlYfTo0diyZQv27dsHf39/mi9GBNwbzoKCAoYjIUS25OfnU2/rGrRp0wa3b9/G0KFDMWzYMOzcuZPpkAiROlrMnTRZu3fvxpIlS+Dq6oqDBw9CWVmZ6ZAaDErIEEIakxcvXmDs2LHIzc3FzZs3YWdnx3RIDU5xcTHTIRAiM7hzKnGH9JGqNW/eHMePH0f37t2xdOlSREVFwc/PjxLipMmgtC1pcoqLizF79mwsXrwY69evx7FjxygZU0saGho0qS8hpFEIDAyEra0tdHR0ePMZkNpRV1enhAwhFXBXHeNOek1qtnz5cly8eBEXLlyAo6MjPn78yHRIhEgFJWRIk5KamopBgwbh1KlTCAwMxIoVK2hJwjrQ0NBAeXk59ZIhhDRYZWVlWLNmDb777ju4u7vj5s2bMDAwYDqsBouGLBEiiLssPBHNiBEjEB4ejvT0dFhbW+PRo0dMh0SIxFFChjQZUVFRsLa2RkpKCu7fv49Ro0YxHVKDpaGhAQCUkCGENEhZWVkYNWoUtm7dioCAAPj7+0NRUZHpsBosVVVVmtSXkAqys7MBgOqVOjA1NUV4eDi6du2K/v3748SJE0yHRIhEUUKGNAkXLlxA37590bFjRzx8+BDdunVjOqQGjRIyhJCG6sWLF7C2tuatrDdr1iymQ2rw5OTkaNlrQoSgeVDqRlNTE3///Te8vLwwadIkrFixAmw2m+mwCJEISsiQRq28vBzr16+Hi4sLpkyZgmvXrkFHR4fpsBo8TU1NAKB5ZAghDcqFCxdgY2MDPT09REZGwtbWlumQGgUVFRXeJKaEkP/mkKE5CutOXl4e27dvxx9//IFdu3Zh9OjRvJ5HhDQmlJAhjVZ+fj7c3Nywfv16+Pr6Yu/evWjWrBnTYTUK1EOGENKQlJWVwdvbGy4uLpg8eTJCQ0NpvhgxUlBQ4N2AEkL+W2WJJvWtv6lTp+LmzZt4+vQpbG1t8fLlS6ZDIkSsKCFDGqX379/D3t4eN27cwLVr1+Dl5cV0SI1Ks2bNoKKiQgkZQojMy8rKgrOzM7Zt24b9+/fDz8+P5nUQM5q4lBB+3CF8tOy1eNja2iIyMhJqamqwtbXFtWvXmA6JELGhhAxpdB48eAArKysUFRXh4cOHcHR0ZDqkRklDQ4MSMoQQmcadL+b58+cICwvD999/z3RIjZKioiINJSCkAu4k13JydKslLq1bt0ZYWBhGjBiBESNGYMeOHUyHRIhYUC1BGpU//vgDjo6OsLS0xIMHD9CpUyemQ2q0KCFDCJFl58+f580X8+jRI9jY2DAdUqNFE5cSwq+wsBAAZwUyIj7Kyso4evQoNm7ciGXLlmHatGm875qQhooSMqRRYLPZWLp0KWbMmIHFixfj4sWL9EdQwrS0tGhSX0KIzCkrK8Pq1asxfvx4TJkyBTdv3oS+vj7TYTVqysrKNIcMIRUUFRUxHUKj9tNPP+Hy5cu4dOkSHB0d8eHDB6ZDIqTOKCFDGrzs7GyMHDkSvr6+OHbsGDZu3EhdRKWAesgQQmRNVlYWRo4cie3bt2P//v00mbuUKCgo0CpLhFRQXFwMAFBXV2c4ksZr6NChCA8PR2ZmJqysrBAREcF0SITUCd21kgYtMTERtra2iIqKwu3bt+Hu7s50SE0GJWQIIbIkNjYWVlZWiIqKovlipExFRYU3iSkhBCgoKGA6hCahS5cuePjwISwsLODg4IBjx44xHRIhtUYJGdJghYSEwMbGBqqqqoiMjISVlRXTITUplJAhhMiK8+fPw9bWFvr6+nj8+DHNFyNlcnJyvElMCSGcSX1p6Lx0qKurIygoCAsWLMCUKVOwbNkysNlspsMiRGSUkCEN0u7duzF8+HAMGzYMt2/fRuvWrZkOqcnR0NCgOWQIIYxis9lYtWoVxo8fj6lTpyI0NBStWrViOqwmR1VVlXoEEFJBXl4eDZ+XInl5eWzZsgXHjh2Dj48PRo4cSQ8NSYNBNQVpUIqLizF79mwsXrwY69evx7Fjx6CsrMx0WE2SpqYmJWQIIYzJzMyEs7MzduzYgQMHDsDX15fmi2EQd84MQghncvEWLVowHUaTM2nSJNy+fRvR0dGwtbVFQkIC0yERUiNKyJAG48uXLxg4cCBOnTqFv/76CytWrACLxWI6rCaLhiwRQpgSExMDa2tr3vxhM2fOZDqkJk1dXZ1WlSGkgq9fv1KCmCHcCX41NTVhY2OD4OBgpkMipFqUkCENwvPnz2FtbY2PHz/iwYMHcHZ2ZjqkJk9DQwN5eXkoKSlhOhRCSBNy7tw52NnZwcDAAI8fP4a1tTXTIREAhYWFTIdAiEyhHtzMad26NW7duoUxY8bA2dkZW7duZTokQqpECRki8y5cuIB+/frByMgIDx8+RNeuXZkOiYCTkAFAvWQIIVLBZrOxcuVKuLq6Ytq0abhx4wbNFyMjVFVVaVJfQirIycmBkpIS02E0aUpKSjhy5Ai2bNmClStXYvLkyZQ4JjKJEjJEZpWXl2P9+vVwcXHBlClTcO3aNWhrazMdFvkXJWQIIdKSmZmJkSNHYufOnTh48CD27NlDwwFkiJycHC17TUgF5eXlUFRUZDoMAuDHH3/ElStXcOXKFfTv3x8pKSlMh0QIH0rIEJmUn58PNzc3rF+/Hr6+vti7dy81vmWMpqYmANDEvoQQiYqOjoaVlRViYmIQFhaGGTNmMB0SqURFRQVlZWVMh0GIzMjNzUXz5s2ZDoP869tvv8XDhw+Rm5sLa2trhIeHMx0SITyUkCEy5927d7C3t8eNGzdw7do1eHl5MR0SEYJ6yBBCJO3s2bPo06cPDA0N8ejRI5ovRkYpKCjg69evTIdBiMxgs9mQl5dnOgxSgbGxMR48eICePXtiwIAB+OOPP5gOiRAAlJAhMub+/fuwsrJCcXExIiIi4OjoyHRIpAoaGhpgsViUkCGEiB13vpgJEyZg+vTpuH79Os0XI8OoJwAh/PLz89GyZUumwyCVqKur49KlS1i0aBFmzJiBJUuWgM1mMx0WaeIoIUNkxpEjR+Dk5AQbGxvcv38fRkZGTIdEakBLXxNCxC0jI4M3X8yhQ4fg4+NDQ1ZlnKKiIrKzs5kOgxCZUVJSAhaLxXQYRAg5OTls2rQJx44dg5+fH4YPH07D7wmjKCFDGMdms7F06VLMnDkTixcvRmBgIFRVVZkOi4hAU1OTEjKEELGJioqCtbU1YmJicPv2bUyfPp3pkIgIaDUZQvgVFRVBTU2N6TBINdzd3XHnzh28ePECNjY2iIuLYzok0kRRQoYwKjs7GyNHjoSvry+OHTuGjRs3Qk6OfpYNhYaGBjIyMpgOgxDSCJw5cwZ9+/aFoaEhHj9+DCsrK6ZDIiJSVlYGAJpHhpB/0fLKDUPv3r0RGRkJXV1d2NnZ4e+//2Y6JNIEKTAdAGm6EhMTMWrUKOTk5OD27dvU+G4AMjMzkZqaiqysLGRlZaGoqAj379/H5s2bkZmZifT0dPzvf/9DbGwsjZ0mhIiEzWZj9erV2LJlC+bNm4cdO3bQECUZ9+HDB8TExPCGKUVGRqJFixbYv38/SkpKAADPnj3DlClTMHz4cCZDJUTiPn/+jF27dkFVVRVycnJQUlLCx48fUVRUhLNnzwLgzLOkoaGBfv36MRwtqUxfXx+hoaHw8vLCqFGj8Pvvv2PZsmU05IxIDau8vLyc6SBI41NWVgYWi1VlZRYSEoIJEyagc+fO+Ouvv9C6dWspR0hqKyoqChYWFnzvsVgsyMvL81YSKC4uRnl5OaKjo9GtWzcmwiSENCAZGRlwd3dHWFgY/P39MW3aNKZDIiJQU1Pj6w0jLy/P693K/d+ioiKMGDECly9fZiRGQqTlwoUL+O677yAvLw8FBQWUl5fz/istLeUr+/79exgaGjIUKanJrl27sHTpUri6uuLAgQM0YTmRChobQiRixYoVkJOTEzoec9euXRg2bBiGDx+O27dvUzKmgWjXrp3Ae9zGRlFREYqKilBeXg4dHR107dqVgQgJIbLm4sWLYLFYiIqKEvgsKioKVlZWePHiBe7cuUPJmAbEw8ODb0lfNpuNkpISlJSU8P4eyMnJYdKkSQxGSYh0fPvtt2jWrBnYbDaKiopQXFyMkpISgWSMtrY2tXll3MKFC3HlyhVcvXoV9vb2eP/+vUCZH374ASwWCx8+fGAgQtIYUUKGiF1iYiK2bdsGABg2bBhv5vLi4mLMmjULP/74I3777TccO3aMN+6cyD4NDQ3MmjULioqKVZZp1qwZhg8fTt08CSHIycnBzJkzAQDOzs58802dPn0affr0Qdu2bfHo0SNYWloyFSapg1mzZtW4VKy8vDwNVyJNQsuWLTF48GC+JGVlzZo1w6JFi6h91AAMHjwYERERKCgogJWVFe7fv8/77NSpU/Dx8QEALF68mKkQSSNDCRkidnPnzoWCAmd6og8fPmDs2LH4+PEjBg4ciDNnzuDixYtYvnw5w1GSuli4cCGKi4ur/JzNZmPYsGFSjIgQIqtWrVqFnJwcAMDHjx/h6uqK4uJiLF++HBMnTsTMmTMREhICPT09hiMltfXNN9/Aysqqykn45eXl4ejoCHV1dSlHRggzXFxcUN0sEKWlpZg6daoUIyL10alTJ4SHh8PKygpOTk44fPgwnjx5wrfy35kzZ3D9+nXmgiSNBs0hQ8Tq3LlzGD9+PN978vLyUFdXh6amJi5evEjDWRo4e3t7PHjwQOjTURaLhbS0NGhpaTEQGSFEVkRERMDOzg5lZWW89+Tl5dGjRw/ExsZi3759dHPSwO3fvx9eXl5C/xbIy8vD398fs2bNYiAyQqQvPT0drVq1qvLfQ//+/REaGspAZKQ+ysrKsGbNGmzcuBFqamrIy8vjDUWTl5dH27ZtER8fDyUlJYYjJQ0Z9ZAhYpObm4v58+cLPDFjs9nIyMjA/PnzKRnTCCxcuJDvJquinj17UjKGkCautLQUM2bMEPq34PHjx/jll18oGdMITJgwgdcbtrLy8nKMGjVKyhERwhxtbW3069dPaK+xsrIyzJ49m4GoSH3Jycnh559/homJCfLz8/nmBWKz2Xj37h02b97MYISkMaCEDBGbdevWIT09vcqb9Z9++gm3b9+WclRE3MaMGQN9fX2B9xUVFTFy5EgGIiKEyJKdO3ciPj5eYEJLgNOL7pdffkF0dDQDkRFxUlNTg6urq8AS5XJycrCzs6OhaKTJcXFxETpHTMuWLTFmzBgGIiLi4OXlhX/++QclJSUCn7HZbGzYsAH//PMPA5GRxoISMkQsYmNjsWPHDqENcK7y8nKMHj0ab968kV5gROwUFBT45gniKi4uxpAhQxiKihAiC968eYM1a9ZUmZjnrszm7OzMm/CdNFwzZ84UuEmRk5MTGLpMSFMwduxYgbqvWbNmmDx5Mi1i0UD5+Pjg8OHD1d7fAJz5MwmpK5pDhtRbeXk57O3t8fDhwxorLADQ09PD58+fpRAZkZS0tDQYGBjwXW9VVVVkZmZWu8oAIaRxGzp0KEJDQ4U+SaysXbt2SE5OlkJURFLKy8vRrl07vqVhWSwW3rx5g3bt2jEYGSHMsLS0xJMnT/gm+I2MjKSV5BqgV69ewdjYWOTyZ8+ehYuLiwQjIo0V9ZAh9Xb8+HHcv3+/ymQMi8WCvLw85OXlMWTIEFy8eFHKERJx09HRwcSJE3ld1eXl5fHtt99SMoaQJuzMmTO4du1atckYbp3BHZdPGjYWi4U5c+bwriuLxUL37t0pGUOarIpzK7FYLJiamlIypoFq3749Fi5cyBumX3l4ZkVycnKYN28ecnNzpRUeaUQoIUPqJSsrC4sWLRL6Gbfi6t69O3bu3IlPnz7h6tWrsLW1lWaIRELmz5/Pd+NFy10T0nRlZ2dj3rx5Qie05P4t0NDQwOzZs3H37l2UlpZi5syZ0g6TSMDUqVN5D2QUFBTg6urKcESEMGfMmDG8tpG8vDw8PDwYjojUVbNmzbBz506kpKTg1q1bmDx5MlRUVHgPmisqKytDRkYGPWggdUJDlki9zJ8/H/v27eM1xpo1a4aSkhIYGBhg6tSpmD59OkxNTRmOkkiKpaUlHj9+DAB4//49DA0NGY6IEMIELy8vHDhwgO/GnM1mQ1lZGd999x0mTZqEQYMGVbkqD2nYnJyccOvWLZSXlyMuLo7+7pMmzdTUFAkJCVBQUMCHDx+gq6vLdEhETAoKCnDp0iUcPnwY169fB4vFApvN5g1Rk5OTw9OnT9G9e3eGIyUNCSVkSJ2Fh4fDzs4OAKcCUlJSwvjx4zFlyhQ4OTkJfVJKGpc///wT06ZNg6amJjIyMpgOhxDCgJs3b8LJyQkA54mwnJwchg4dismTJ8PZ2RnNmzdnOEIiaadOncLEiROhoaFBkzWTJu+XX37Br7/+CmdnZ1y6dInpcIiEfPnyBadOncKhQ4fw/Plz3kNpNTU1ZGVlCV1xixBhxJKQSUtLw82bN/H8+XN8/PgRX79+FUdsRMbdunULqamp0NXVRceOHWFoaFirp5+qqqowMDCAhYUFHB0doaOjI8FoJa8p/jsoKyvD+fPnoaWlhYEDBzIdDmOUlJSgpaWFbt26wd7enp4OVxIfH487d+4gJiYGGRkZKCoqYjokIkbXrl1DTk4OdHV10b59exgaGkJRUREA1fNNBZvNxoULF6CjowNHR0emw5E5je3fQXWovucM5w8JCUHv3r1hZGTEdDhS09h+57Wp779+/Yrk5GTExcUBAOzt7Xlzz5DGRRK/8zonZEpLS3H69GnsDfBH+L37gBwLaib6kNdVAVpWPekRaTzKikpRXloGeRXFuu0gtwTs1DzkvPwElJXDrl9feHnM4ZsQTdZx/x347w3A/fB7YEEO+momUJHXRTO0ZDo8qSgpK4A8SxFyrKY7oS8bRSgqz8bnvATkF2WjXdsO+H7WDHh6ekJPT4/p8Bjx5csX+Pv749DBI0h+m4SWzdVhqG0KFUVNKLCUmA6PiFEJuxDsMjaUm6kIfFbI/oqcwi94n5qA8vIy9OnTD3M8PRpkPU/tneqVFZSApaQAlhw9FRbQCNo71aH6XlBhSS6UmzWNdiBXY6rv696uL0cROxdK8i0BUF3YGJUgF3nsVHzKeYlylKGvXT/M8arf77xOCZlbt27Ba/48JL5MgNYwM+iMt4Ba346QU2oY/9iIbCkrKkXOvSSknX2OjOAXMDbpAr89vhgwYADToVXr1q1bmOc1HwmJL2GmNQwWOuPRUa0vFOSaZuODcHzMi0Fs+mU8zzgJKJRi7c/eWLBgAa/HQGNXXFwMHx8frFu3AXLliuhn7A6bzmPQTqcb06ERBpWwixCXcgf3Xp7Go38uw8SkC3z3+jSIep7aO0RcGmp7pypU3xNhGnJ9T+16IqrSsiIk5dzD87SzeJERjC7GJvD121On33mtEjJ5eXn43mMWTp88Be2BXdBu3VAod9Cq9UEJqUrhmwy8XXsV6TcSMGGiGw7uPwAVFcGnrkzKy8vDrO89cOr0SXTRHoih7dZBS7kD02ERGVNSVoDb73cj/HMAOnbsiLPnT8Pc3JzpsCQqOjoa410m4E3SGwzp7gXn3ouhqEDzhxB+n7OTcOL+KjxLCoHbhIk4cHC/TNbz1N4hktQQ2jvVofqeiKKh1PfUrif1kVH4BlffrkVC+o06/c5FTsikpKRgxKiRePk+CR22O0PDybjOQRNSk6zQRLxZEgSTNh3x96XLMrN6T0pKCkaOGIWkl+/h3GE7jDWcmA6JyLisoncIevMTPhY+w9lzpxvt8uDBwcFwHT8BHbR7YobDTuiotmM6JCLjot5ex6GwhejQqQ2CLl+UqXqe2jtEWmS1vVMdqu9JbclyfU/teiIuiVmhCHqzBB1N2uDy35dE/p2LlJB59eoV+va3R6EGC53/mAglQ/V6B0xITYpSsvFq2kkoZ5Xj3u076Ny5M6PxvHr1CvZ9+4NVqIGJnf+AupJs/DEhsq+svBRX3njj8ZcT2LfPH7NmzWI6JLE6cOAAPOd4wsFsCib32wR5ORrOQUSTnvseO6+6o5iVhTt3w2Sinqf2DpE2WWvvVIfqe1JXsljfU7ueiFt2UQpOvpqGcuUs3Ll3W6TfeY0JmezsbPSy6o0sPaDzoQmQb0nj6Ij0sHOL8GrmaWh8AZ5EPoa6OjON4+zsbPTuZQVk6WFC50P/TtZFSO3c++CHmylbcPVaMG+Z4IYuNDQUQ4cOwzjLVRjecz7T4ZAGqLAkF7uuTkax4hc8ehzBaD1P7R3CFFlp71SH6ntSX7JU31O7nkhKETsXp1/NBDS+4PGTyBp/59UmZEpLSzHw20F4/ikRXQKnQV5VWewBM+mB4c8AALuUX6WyXW2xvxYi7VIsMkMSkBmSAM3BXaAz1hyaTsYiXYv6bi8r2F8LkTD2D1joG+PG/65Lfab20tJSDBr4LRKff8K0LoFQlleV6vHF4ecHnKz/r3YpUtmutgrZXxGbdgkJmSFIyAxBF83BMNcZC2NNJ5G+7/puL03/e7sOUdmn8OhxBExMTJgOp15evnwJS0tr9Os8CW5265gOp1rT9nKWJfxjbppUtqutguIcPHx1Ec/eXMXTN9fQs8MQ2Bq7wKL9QDRXVBN5+8O3FgMARlkuQV8TV+hrdBJa9nnyDYQnnuMdq0eHoejVcRjUmjOzTGlBcQ42XhqJjt+0Qsj1/zFSz1N7R3zb1Ra1dziYbu9Uh+p78aH6nvn6vqG36yuiNr5sKmR/xR8JY2FsoY/rN6r/nctVtyPfvXsRGfUEnQ65Nqg/aI1F8m/X8XrZJWSGJAAAMkMSkDj3HBIXXJDK9rJCXlUZnQ65IjLqCXz37pX68ff67sWTyCi4djok0//wG7Lryb/h0utlSMgMAQAkZIbgXOJcXEhcIJXtpWlQ29Vo29wW06d+jzosciczysvLMWPa9zDRs4Or7c9Mh9PgnXmwDodvLcbTN9cAAE/fXINfiAf2XfcSaft91714jXMAuPRoO5afsMHbtFi+cgXFOdh33Qt+IR58xzp8azEO3VyInALJ3ohUpbmiGhZ8exRPH0fD11f69Ty1d5hF7R0Opts7VaH6Xryovme2vqd2vXQ1pTZ+RcryqnDtdAhPIqOwt4bfeZU9ZFJTU2Fk3Amttw6H9ggziQRKqpb34hOiBvuhzUIH6E3qDSVDdRSlZCPF5w4+H41Ezzs/QNlIW2Lby6L0v1/gw09X8DrxH+jq6krlmKmpqehkZIzhrbfCTHuEVI7Z1HzKewG/qMFwaLMQvfUmQV3JENlFKbiT4oPIz0fxQ8870FY2ktj2TMgvzcTemP7wP+ADd3d3psOpkxMnTsBr9gJsnBCOlsq0+kx9vE2LxZozDhhluQQDzKZAu2UbpOe+x+XHOxEaewSb3R8KffLJFZ4YCL8QD8wY8H8YYDYFAPAi5Q42XxwLp67TMc1hm9CyNp1Ho7miGgqKc3DlmS8uPdoOtz7rMKzHXImfc1Ui/7mEP+//iFf/vJRqPU/tHeZQe0cQE+2d6lB9Lz5U3/+Hqfqe2vXS0xTb+JW9SP8bVz78hH9eJ1b5O6+yh8yqNauh3E2fGicMyX3K6T6m62LBm1RQyVAdraZacj6P/iDR7WWR9ggzKHfTx6o1q6V2zNWr1kBfuRtV2hKUkvsUAGCh68KbUE1dyRCWraYCAD7kRkt0eya0UNCEg8FP+GnJCuTn5zMdTq3l5+dj2U8rMLb3Smqci8HrL08AAH1NXKHdsg0AQLtlGzh2nQEAeJMaVe324YnnAAA2nUfz3jMztAcAhMYeEVp2gNkUXtf45opqGN5jHgDg1P219TmVerPqNArttMzhvXqN1I5J7R1mUXtHEBPtnapQfS9eVN//h4n6ntr10tUU2/iVmWmPgL5yN6xeVfXvXOhgpuTkZBw6cBBmf30vseAkLe1iNNICo5EZkoA2Cx2g62KBp/a7Afw3Frry2Gjua8vny5B6/jmS113jjUPWGW3O27coY6q5ZapT3fbFKdkAgGa6/GuYK+pxutYVJKRWu+/6bi+r9Jc74NDYg/BeuRrt27eX6LGSk5Nx8NABfG/2l0SPU1/RaRcRnRaIhMwQOLRZCAtdF+x+yvnjzB0XWnmcKPf1MsvneJ56HteS1/HGZJrr/PdHXpTxpdwy1alu++xizmcqzfizxqqKegCA1IKEavdd3+2Z0ltvEu7H+MLf3x8//vgj0+HUir+/P4rzyzGg61SmQwHAeQrIHR/PHUu//IQNgP/mAqg8NwD3tc+MeNxLh1ftqQAAIABJREFUOINT99fyxvHbGo/l7VuUOQW4ZapT3fbpue8BAOot+H/DGiqtAAApmfHV7nvR8OMC73G7p3sN3l9jWQAizVsgLWN7r8LvB0di1eqVUqnnqb1D7R1ZJM32TnWovudH9b14Sbu+bwjt+oqojd8w2/iVOegvx8FDY7HaW/jvXGgPmQMHDkDVRB+qvdpIPEBJeLclFIlzz/HGEr/fFcZrnIjin6UXkbyOU7lxxyGnXZRuBu79rjAAEBjL3kxHhe9zSW0vq1R7t4WqiT4OHjwo8WMdOHAA+qomaKPaS+LHqqvQd1twLnEub1xl2PtdvIpaFBf/WYpryZzJ+bhjMqPTLkok1qqEvd8FAALjeFWa6fB9LqntmSLHkoeFphv8fPcxHUqt+e3dB3uTyZBjyTMdCs5HbOQbH88dSy+qQzcX8p4ScsfxhycGSiTWqlx6tB2AYCOZO+Ei93NRBD/bi2l7dbDzyiR4Dd7Pd7NRnU9Z/wAQbNAzobO+FdrodpFaPU/tHWrvyCJptneqQ/W9eFF9z0/a9b2st+srojZ+w23jV9ZWtTf0VU2q/J0L7SFz7q/zUB1iLNHAJCX7XhLe7wqrciyxKFTM9GHsMw7yqsrIvpeEF65HkBYYzffUqCaSXpGgKVMdYoyzgeewbp1kZ/k/f+4vGKsOkegx6iMp+x7C3u+qclylKPRVzDDO2AfK8qpIyr6HIy9cEZ0WyJdBr4mkZ2dvzL7RGoabz7chPj4epqamTIcjkvj4eLz65yVmWA5nOhS8SLmDS4+2VzkWXxRtdbphziA/NFdU443DD088J3LDFpD8ihy10V7XHG591iHhwz34hXgAgEjncu/lGfTsMAQW7QdKOkSR9Gg7HOfPBUq8nqf2DrV3ZJm02jtVofpeENX34iet+l7W2/UVURu/8TFWHYJzZ4X/zgV6yGRmZiI+Jg5qNsx1j6yPnHtJAMBrnACcscStZ9uJvA/9mTa8Jy3qfTsCAO/pE2Gemk17xMfEITs7W2LHyMzMRFx8DNqrif7kRdqScu4BAK+iBjjjKu1azxZ5Hzb6M3lZ547qfQGAl4knkteqhSlaKKnh9u3bTIcistu3b0OluRraaDM/30Zcyl0A4DXOAc5Y/CEWoq1UAQCDzT14Tyq54/C5T18bIjNDewzrMReLhh/HjAH/B78QD7xIuVPtNucjNuLSo+0YZ71KZrqymxjY4kVcjMTreWrvUHtHlkmjvVMdqu9lG9X3omsI7fqKqI3f+LRXs0FcvPDfuUAPmRcvXgAAmnfRk3xkEsDtmsptnHDVZoZ9bjfX+qjvmGpSteYmnHGEsbGx6NOnj0SOwf13oNe8i0T2Lw7cbnrcipqrNrONc7v81Ud9x5c2dbotjBEXF8d0GCKLi4uDoZZs/Lvgdu3mNs65qluhojJuN/H6qO+cApJi03k0Dt9ajP899+fdfFTGbZyvdw1DO52uUo6watzfmDTqeWrv1A+1dyRHGu2d6lB9L4jqe/GTZn0vy+36iqiN3/joNjcBIPx3LtBDJj09HQCgoNFcCqGRqrRZ6AAAYH8t5Huf+5r7uaS2l2UKmi0AAGlpkvuDx/130FxBQ2LHIBwObRYCAArZX/ne577mfi6p7ZnWXE6L93trCNLS0tBSqWEtISvrRlkuAQAUFOfwvc99zf28trhPP4U9Ac4pSMP5iI14lxaDze4PZapxDoC3mos06nlq7zCL2jtVk0Z7pzpU34sf1feCpFnfU7teupp6G7+iFgqaAIT/zgV6yOTm5gIA5JSETi8j89osdMD7XWEoSsnme2pUlCLd7p71fRrUvAvnqUhJah7fRHWF77IAAIqVnoiJe3tZxv1tcn+rksDdt4KcksSOUV8ObRYi7P0uZBel8GXQs4ukm6mub2Zc99+nFXklqXyTdmUVvgMAqCtWn52v7/ZMU2S1BJvNZjoMkZWVlUFJvv5P1cVhlOUSXHq0Hem57/memnJXsZCW+j4NNdTkzB+UnZ/K14U8NYfzG678RLiynVcm4emba/Cf9Zpv+5wCTlxOXafzlX+bFosLEb+jrU43zHTcJZanxv/f3vlHRXWde/+bEPkhAo4MggwqonQQRETFqNSgmAlJqnCNlpsrJZK89Rpo89LELBLTN5qQNzWhMQlpC9dlm5BwceVaDAVtEt5prdQCRlA0yMiEOIAOyOjgOPxQwBDePybnOL85M8xPeD5rsZZzzt77PPucmed83Xs/z7Y10zw0vtcRfp70zsQgvWM/HKF3zEH+3hDy97bHkf7elXW9NqTxbVPflWC+e8a+50Z3WXJn/H+Igb5edpYVJcNdalwvO+tMsyzGJ1IjMG6UX9Dpx83jmiV3M+LNfwEnWp9wfRb4a+JBz14vYx20ergLZ68b3+bQVQny0STUvHCjXKcfkpvHAQCCGfF2rU+4L4sFPwYAnJSUsqK8d0COkxJuCe9chVCeZhlr7TdHdPrRIKsCAETMNr8jxOrIbQCAr769t3vCnZE+1EqPAAASFt1L4Nc7IMerR5Iwl78EW1ftcUlxTnCD9I5t6hPuAfl7DeTvpwak8W1T311wz2khMwQkLmBnjdx1q0NAs/MBTyQ02o/gzAT4RofoHGNiuJmZKkvrE+7HgoBEdgTdXbZ9M0aIbzSEPJHRfiQEZyLEVzeZIBPPyozaW1qfmDxEC9axs6aWbBXqaszjxyA+PMVoP5JjsgyWlzM5DJiZ2tWRmp1CPjr5PD46+bxO2dSVu3XyCTRf+QcAmL1nrrSLCGEa0jukd6YS5O/J308lSONPLY0/6QZkAGBuXjJ8hEFQVjRDJZYiLDcJQdvi0LTuA2ebZhEL30nDzepWqMRSqMRS8ERC8ERC8FO5xX5OtD7h+iTPzUOQjxDNygpIVWIkheUiLmgbPmgyntDNVUlb+A5ab1ZDqhJDqhJDyBNByBMhhp/qkPqE+7J11R4IeFE43VaOpo5qpK7cjcQfpeOlw+6xkwLDMxsKca79C5zv+BJNHdWID0/BsvBH8eAibttT/urxMpxuq2DvQ3JMFhIWpRkkd9QX8IR7Q3rHNvUJ94D8vQby91MD0vi2qe8O3Dc2NjamfeDw4cPIyMiYlBnx6wX7EJyZgIi3NjnbFGKC1Av2oaysDNu3b7dL+8zvwF0zh++rFyAhOBObIt5ytinEOBxt+yWWPOKDsjL3WIaakZGB9oZhPCs66GxTzLKjiI/kmCzsSHrH2aYQVrKjiO8QP096h3Bl7K13zEH+nnAUjvL37qrrtSGN777sqxcY/Z5Puhwy9YJ9qBfsQ/+5e0m+RvuH0H2wDgDgv2a+s0wjCJuyr16AffUCyPvPsceGRvtR160RTvP91zjLNIJwCDuK+NhRxMdlRSN77M5IH744XwQAEIYmOss0grA7pHeIqQT5e2IqQRp/ajHpQpaiSrajNeswLm4+ZHCOJxKClxzpBKsIwvZsjyrB4dYsHLq42eCckCdCJC/ZCVYRhOP41eNleP/zDOQffdTgXHx4CuLmb3SCVQThGEjvEFMJ8vfEVII0/tRi0g3I8ERCRB/JQl9tO5vcLTgzAf5r5oOXHKmzJSJBuDNCnghZ0UfQ3lfLJrpKCM7EfP81iOQl62wPRxCTkfjwFLyUVoFLXf9iExYmx2RBGJqIuPkbdbYEJYjJBukdYipB/p6YSpDGn1pMugEZQLPzQEDiAszNo9FDYnKzICARCwISkTw3z9mmEIRTiBasQ7RgHbau2uNsUwjC4ZDeIaYS5O+JqQRp/KnDpMshQxAEQRAEQRAEQRAE4epMyhUyzqZesA8A3H7nBpVYitasw27fD8J+7KsXAIDbZ62XqsQ43Jrl9v0gHM+OIj4A4OMcpZMtmRhNHdV4//MMk/24M9KHr76t1NmmdXXkNgoVmOK4s94Z7R+CsqoFsrwqAGC3DPeOCHSyZYS74c7vAcZ2Y7hjfwjrcGc9PzTajxZlFapkmpVEzPbggd4ROuWYPprDWf2nFTKEUQYlPWjNOuxsMwjC7vQMSnC4NcvZZhCE07iibMH7n2eYLXOkPh8fnXweTR3VADQDOMXinTj4t2xHmEgQNqftuc/YwRgAkBfWoGndBxiU9DjRKoJwHL0D8vELEYSL81nbc+xgDADUyAvxQdM69AxKLGpHyBPZ2jTO0AoZwoD+c3KjuzYQxGRD3n/OaAZ7gpgqXFY0Gt21RJsryhacaClB6srdWB+dicAZYegdkOP42fdxoqUEPbcuI2TmQgdZTBATR1nZDJVYioiCVARnrAAAqGvbIUkvgeKTRkS8tcnJFhKE43hybT4eW5bjbDMIwmKalZWQqsRIjSjAimDNxFK7uhYlknQ0Kj7Bpoi32LKmVr/0DEpQ/LUIKeF7HWKzMWiFDKFD98E6XNx8CJFF25xtCkHYlbrugzh0cTO2RRY52xSCcApfnC9C/tFHkS0yPwAvu34OAJD4o3QEzggDAATOCMOGmKcBAB03vravoQRhY5QVzQAAfmoMeywgcQEAQFHa4BSbCMLRKNTtAID5QbFOtoQgrKNZWQEAiOGnsscWBCQCABoUpePWH7yrRPHXIqRGFBiEODkSl14ho65tR++xFvblGJabhFmbouEbHaJTblDSA/UpGTrzNUupeSIh+FtiwU+752C045yZ3Cg8kRDBGSvAEwkBaGZM2nLKAQCRRdtM1tcvx3V7Se3+8ERCzNm5hhUA1vRbH8ZGc4wX592ZX42oku3giYRsHwn7066uRUvvMdZ5JIXlInrWJoT4RuuU6xmUQKY+herOfACa5XWx/C2I5aexZbTjQJncKEKeCCuCM9jleM3KSpS3aWZDtkUWmayvX47rVnva/RHyRFgzZyfrIK3ptz62iAOt7szH9qgSCHkito+E85B0nULDt5U40VICAEhduRsJEamYx4/RKXdF2YIWeQ0+rdPMZDC5TFZHbmHLaMfzM7lR4sNTkBT9FOLDUwAAp9sqUCzeCQDIFh0yWV+/HNecKdr9iQ9PwSNxzyJasM7qfutjLu6fYbz4/0/r9uJXj5chPjyF7aMxmGXtAdODdI7P9A0GAHSpWse1hTAP6R3H6p2oku0Gx1RiKQDQhJQTofeA+X7rY4v3AGFbSM87Vs9vjyoxOCZViQGA04TrVz0fsvfUmbjsgAwjIrSRF9ZAXliD6CNZ7IvdWDmVWMq+WLVFhn55ptxScTZuHpdAXljDlmMEiLH62gMVbTnl4ImERl/u2lwtOKHTPnPtsNwkne0qufbbXrhjYj53h3Gy2tTIC1EjL0RW9BHW8RkrJ1WJWcej7YT1yzPlspeKIbl5HDXyQrYc46CN1dceqChvy4GQJzLq/LQ5cbVAp33m2klhuTpb93Htt71wx8RlkxVGLGtT1XgAVY0H8FJaBStgjZVr6qhm85poi2n98ky5N9Jr0CCrQlXjAbYcI7SN1dceqCgW70R8eAp+9XiZ2f4cPbNfp33m2qkrd+ts18q13/aCq1Bn+qL/HxB/Hz57nrahtR7SO+b7bW+6D9axA1z6g1OE46D3gPl+24POG5qVYjO8ZuGkpBQfnXweAPD0+vfw4KI0SthuIaTnzffb3tR1H2QHuPQHp4zRrq5lbXQ2Ljsgw7ykl595AV6CAAD3cpv0HmthX9RMuSXHdsJvuWYp9XCXGudWvYu2nHKDF+tAUxdWte6Bh583Gy/8tagYYblJBseN1VeUnWVtGu5S43rZWcgLa6CubTcpHtS17ZAX1iAsNwmh2Wvh4eeN0f4hdBfXQV5YozMbxLXfxqDBFPeEcWIvLD+DAC/NSDGT26Sl9xjryJhyO5ccQ5jfcgCAergL755bhfK2HAPH0zXQhD2rWuHt4cfGUxZ/LUJSWK7BcWP1zyrKWJvUw104e70MNfJCtKtrTTpXxrklheVibWg2vD38MDTaj7ruYtTIC3VGy7n22xg0mDK5YMTou0+dZ0NimNwmDd9WsoKUKbd365dYGLwSgGb1xgufLEOxeKeBkJZdP4f/+rkMPp7+kHSdwtuVW/DqkSSkrtxtcNxY/RrJJ6xNvQNynJSUoqrxACRdp0yKZEnXKVQ1HkDqyt14fNkv4OPpjzsjffj8/B9Q1XhAZ9aTa7+NQbOekwfSO87VO75L5mD+3hT01XeYHJwi7A+9B5z3Hnj1SJLO549OPo/zHV9i18PFNChjAaTnnavn5/guQcr8vejoqzc5OKVN/bVDEPJEDhswMofLDsjwREKoxFL0Hm+B75I5mLF0DvyWhxm8hJnPd5WDGJT0YKRLjYEm0w835JkH2eW22i97RjjoH9cnfG8KKxy8BAGYnbEC8sIas+Khr7bd4Boeft4IzV6rETenZKxA4dpvYvIg5IkgVYnR0nscc3yXYM6MpQjzW27gpJjPg3eV6BmUQD3Sha6BJpPtPhjyDLscUdvZMI5V/7g+KeF7Wcca4CXAitkZqJEXmnWu7X21Btfw9vDD2tBs1MgLIVOfYh04134Tk5/48BQ0dVTjzLdVmB8UiwVBcVgYvNJAbDKf++4ocUXZgt4BOZvfxBii2J2smNQWtYxA1j+uz5Nr83VypqyPzkRV4wGzIvlS178MruHj6Y/Hl/0CVY0H0CKvYYU4134TkxvSO87VOwGJCxCQuAChu9ZCUXYWbTnlmMaf4bAVOoQGeg84/j3AhHxpD24B90K5LnT+3WCAijAN6Xnn6vkFAYlYEJCItaG7cFZRhvK2HMyYxjfaR3n/OUhV4nFXCTkKlx2QmZuXDJVYqhMnbSoGWX95rDmm8X2NHucSEw0A3hGBOp8ZsaIobTCZlZ+x7UzUfqPnO/OrEbprLQDL+q2PLXLIEI4neW4epCqxThypqRhN/eWD5vCdZjy2mEvMKACD5FaMM29QlOpkLdeGsW3/mSij56s787E2dBcAy/qtjy1yyBCuwxOrXkFTR7VOPgBTsfb6y8DNwYTU6MN1xk9/5yBGlJ9oKcGOpHeM1mFse/aPxpPDfVq3l93NwpJ+60O5AyYPpHdcR+/wU2Mgy6vCtUP1NCDjYOg94Pj3gKlzqyM1q4VOt5XTgIwFkJ53HT0fw09FlSwP9dcOGbXj/A1NmNJ8/9UWt20PXHZAxjc6BGu6XtdJYKcSS8ETCTE3L5mdYVH8sIQ2ODMBgZtj8ADPB56z/dAYV+DkHlgH134Tk4cQ32i8vqZLJ8GXVCWGkCdC8tw8dgT6rEKzxDAhOBMxgZvh8wAPfp6zUdAY5+QeWAfXfhOTn3n8GHyco9RJ1NjUUY348BQ8seoVdiaRWSqeHJOFhEVpmOE1CzN9g/HcR8YFg6vDtd/OJnXlblQ1HsCdkT6d/8TcGeljzxPWQ3rHdfQOM1jF5OUhHAe9B1zvPcDk5SG4QXredfQ8M1jF5OXRZvCuEg2KUiSF5XIe1LI3Ljsgw+AbHQLf6BAEborBUMdNSNJLoBJL2ZkPWV4VAOjM1oz2D9nNnuEuNTtLBABDsl4Amp0BTBGcmQBFaQMbs82F8fptDFr94t6E+EYjxDcaMYGbcHOoAyWSdEhVYnZkuEqmSaClPZo9NNpvN3vUw13sKDoA9A7JAGgyp5siITgTDYpSNqaVC+P12xi0+mVyMo8fg3n8GKxalAqFuh1vV25BU0c1O4vHJBzUnpVkBgXsQe+AnJ0NBYCeW5cBmB+ASI7JwomWEjY3ARfG67cxHLn6RcDT/EdHffuGTp9u9F0FAJ17RFgP6R3H6Z3WrMNQiaUGdt5VDrL9IJwDvQcc9x54//MMNHVUG9jJ3M/kmKwJtT9VIT3vOD1/uDULUpXYwM7Bu0q2H/qohq4AAAQz4id0bVtyv7MNMIXs5eOoF+xD/znNdpteggB4h88yWZ4RCkzyOHtxvewshrvUADRi5Ub5BQCAv5mlrYGbNaPb3cV17Mse0CS/qxfsQ/fBe/Za2m/C/Tkuexn76gWQ92tioAO8BJjlHW6yPONImeRa9uLs9TKohzWOUj3chQs3NMkOF/ibXoIYE7gZAFDXXcw6Q0CTHGxfvQB13QfZY5b2m5i8fFzzInYU8XFZ0QhA8x/84ADTPpURxEySRHtxUlLKbvncOyBH7TeaJa6LBT82WSdhkSaB3Ofn/4C+O/d+A5KuU9hRxMcX5+9tw2hpv51FKO9HAIDab47o3I8G2Q8DBLOXO822yQDpHcfrHf4WTdJeZVULe2y0fwg3jmr6yPSDcBz0HnD8e2B1pGaL9wudf9c5znxm+kFwg/S84/V8LF8TUteirGKPDY3248KNowDu9UMbxe1LAAC+z0KDc87CZVfIBKUvg6K0ARc3HzI4F1GQyv47smgb2nLK0bTuA6PtDMl6DeKgJ8q5Ve/qfA7LTTIbaxyQuABhuUnsdo7a8ERCBG29t0SNa7+JycOyoHQ0KEpx6KKh00iNuLcUfVtkEcrbcvBBk/G44t4hmUGc6ER599wqnc9JYblmY0IXBCQiKSyX3e5OGyFPhLigrexnrv0mJj8/jnoSJ1pKkH/0UYNzT69/j/13tugQisU78dLhB42203PrskG8/0R54ZNlOp9TV+42G9sfLVjHhvjo5ziID09BojCd/cy1385mHj8G8eEpRvuUHJPlMqFV7grpHcfrHX5aLJQVzZDlVbErjxjG6yNhH+g94Pj3QNz8jYgPT0GxeKfO1t7A+H0kDCE973g9H8tPQ7OyAlWyPHblEYOpPl4b1Gz37u3hOjuIueyAjN/yMCwVZ+PmcQn7Ug/LTcKMeAF4IiFbjp8Wi9GBEfaFGpabhKBtcRgduouvRcVQ13fYVKDMzUuGR4A3OvOrLUpANzcvGT7CIPTVd0JR2gBAIzhmpUTpJN7j2m9i8hDmtxzZS8WQ3DzOOr2ksFwIZsRDyBOx5WL5aRgZHWAdTlJYLuKCtuHu6BCKvxahQ11vUweePDcP3h4BqO7MtyhBV/LcPAT5CNHZV48GRSkAjUOOmpWik5iMa7+Jyc/C4JV4I70GDbIqVrymrtyNiNnLER+ewpZbHbkFQ3cH2CXrqSt3I/FH6Rj5bgivHklCa3edTYX41lV7MN0zAJ/W7bUo0eLWVXsg4EVB2l2LEy0lADTCevmCx3QSTHLttyvwzIZCnGv/Auc7vmTzGywLfxQP0gzqhCG94xy9E1WyHcrKZigrmqESS9ncPDQY4xzoPeD494CPpz92PVyMC51/x+m2cjR1VLO5eWgwxnJIzztHz2+PKkGzshLNygpIVWI2N4+pPjJ9MZUs2RncNzY2NqZ94PDhw8jIyKB8JHowGf3pvrgG9YJ9KCsrw/bt2+3SPvM7mIq5SpiM51Ox787gaNsvseQRH5SVlTnbFE5kZGSgvWEYz4oOjl/YTWF2rqBdipzLjiK+Q/w8vdd1Ib3jWthb75hjKvh7U9B7wLE4yt9PJW1Let712FcvMPo9d9kcMgRBEARBEARBEARBEJMVGpAhCIIgCIIgCIIgCIJwMDQgQxAEQRAEQRAEQRAE4WBcNqmvq0Gx1MRUgWJNiakO5QwgpjKkdwiC3gOE+0N63n2gFTIEQRAEQRAEQRAEQRAOxi1XyLjrDgCM3QyM/aP9Q1BWtUAllkIlloInEoK/JRa85Eh4+HlP+LoqsRStWYcnfL+42Gmqj4R1uGuGdMZuBsb+odF+tCirIFWJIVWJIeSJEMvfgkheMrw9/Ky6lj3a1EaqEuNwa5bBMzDVR8IxuOsOGIzdDIz9d0b68NW3lTrbSq+O3Ia4+Rvh4+lv1bXujPTpbGfKbFWtv+3qRNvUt9NUHwnLIb0zPkyb+tuBT2QLcNI7rgX5e8to6qjG+59n2Px+XVG24NUjSTrtkr/nBun58bFnm/pbjOtvK67fT2O8vqbLbtrfLQdkJhudb/4NitIG9rO2AIgqmdj2b4OSHrRmHZ6oiQDsaycxNfhb55toUJSyn7Wd7vaoEpdpk6FnUILDrVkTaoMguHCkPh8nWkrYz00d1axQ/9Xjlm+JfmekDwf/lo2mjmqDNs93fIlnNhRaPCjTd0eJD/+Ra7TN+PAUq9okphb20BFtz30GlVjKfpYX1kBeWIOl4mz4Roe4jJ0EwWBrf6/NFWUL3v88Y4IWGtJ3R4lXjyTZvF3CPXEXPf9Z23OQqsTs5xp5IWrkhcheKkaIbzTndoQ8kVXX5woNyDgB7VmUQUkPFKUNCMtNwuyMFfASBGC4S42u352CorQBQ7Jeq2d5+s/JcXHzIZvYzNVOpm/6M0fE1ER75LhnUIIGRSmSwnKxYnYGArwEUA934VTX79CgKEXvkMxgxHo87NEmg7z/HA5d3Dxu37iMqhOEPtqziFeULTjRUoLUlbuxPjoTgTPC0Dsgx/Gz7+NESwl6bl1GyMyFFrV/ofPvaOqoxtPr38ODi9Lg4+mPOyN9+Pz8H1DVeAC10iN4bFmORW2ea/8CTR3VyBYdwurILezx020VKBbvxLn2L7A+OpPtm/7MKTH1sLfeUVY2QyWWIqIgFcEZKwAA6tp2SNJLoPikERFvbbLYZtI7hK2xt79nuKxoRP7RR21ltg4VZ94yepz8/dTAHfV8s7ISUpUYqREFWBGsGaRsV9eiRJKORsUn2BRx7zttaqVLz6AExV+LkBK+V6ecrbU/5ZBxMgNNmgcbtC0OXoIAAICXIADBT63UnG/utqrd7oN1uLj5ECKLtrm0ncTUoWugCQAQF7QNAV4aRxbgJcDK4KcAAN0DzS7RJgDUdR/EoYubsS2yyKr6BGEJsuvnAACJP0pH4IwwAEDgjDBsiHkaANBx42uL2zzdVg4AWB+dyS6B9/H0x+PLfgEA+LRur8VtfnTyeQDQGYzR/sycJwhj2ENHKCs0Pp6fGsMeC0hcAAA6K1ycbSdBMNjD3wPAF+eLkH/0UWSLbDMRq9+2avCazdsl3BN30fPNygoAQAw/lT22ICARAHRW4phi8K4SxV+LkBpRYPXkLlccskKmXrAPwZkJRmcqZC8fh6K0Aata98DDzxs/aAM0AAAgAElEQVSDkh6oT8nQma9ZEs3E7fLTYs22DxjG75o6rq5tR++xFihKG8ATCTFn5xr2BT5eP8bD0hjikS41AGBakK/Occ/Zmli5O9IbFrXH0JlfjaiS7eCJhGjLKbeqDW3sZedkY1+9AAnBmTqjrgzHZS+jQVGKPata4e3hh55BCWTqU6juzAcANlYylp9mtn3AcCTX1PF2dS1aeo+hQVEKIU+ENXN2ss5ovH6Mh6Vxk+oRTXnfaUE6x/08ZwMAbtyRGtRxRpsAUN2Zj+1RJRDyRChvs2wVAWGcHUV8JMdkYUfSOwbnPq55ESdaSvBfP5fBx9MfV5QtaJHXsIMGTHy9/kCAfvuAYfy6qeOSrlNo+LYSJ1pKEB+egkfinkW0YB2nfoyHpTH0vQNyAEDAdN3v8UzfYABAl6rVovYAmFz2PpH8BPHhKTrhSsbOT3VI75jGHjrCWPgQE75k7YQU6Z2JQ/7eNPbw94BmkP1Xj5chPjwFxeKdVrVhDEnXKXxatxdvpNeY9f+TDdLzpnEXPW8szIkJX+Iy4fpVz4cQ8kTs6hp74pAVMvP3pkBR2oC7ykGd43eVg1CUNmD+3hR4+HlDJZbia1ExK04AzYu1LaccykrrZrr1uVpwQrOU9YeZE5VYCkl6Ca4WnLBJ+5YiL6wBAINkdtP4vjrnLWVN1+vgiYQTM04Le9k52UiZvxcNilIM3tV9QQ/eVaJBUYqU+Xvh7eEHqUqM4q9FrPMGNE6ivC0HzcpKm9hy4moBSiTp7CiwVCVGiSQdJ64W2KR9S6mRFwKAQWIu32l8nfPObhPQvJzsHS861XhyrSZmvu+O7m+j744SJ1pK8OTafPh4+qOpoxqvHknSWcHR1FGNYvFOnG6rsIktR8/sx9uVW9gY/qaOarxduQVHz+y3SfuWUtV4AIDhYAmTj4U5bwt6bl0GAKtmUZOiNTNV+s+B+cycn8qQ3jGNvXVE98E61Av2oTXrMCKLtpkd2HKmnVMB8vemsZe//zhHafNB8Z5bl/F25RZkiw5hHj9m/AqTCNLzpnEnPc9Q130Q++oFONyahW2RRWYHywDNAFiNvBBr5thucNMcDlkhE7BOs8xHXSvTeUGqa2UAgFk/DBwwyWeXHNsJv+WaZXzDXWqcW/Uu2nLKrX653rteO+SFNQjLTUJo9lp4+HljtH8I3cV1kBfWYNamaLMJ4CiDPjEeEQGaGReZulbnxy5T1wIAhLM0/8lnEsXuXHIMYX7LAQDq4S68e24VyttyxnUU48E4kqSwXKwNzYa3hx+GRvtR112MGnkhomdtMpvMyt2ywBOuT0yYJhmgRH5KZ+ZTIj8F4N7qCiYZ4d6tX2JhsCZEoHdAjhc+WYZi8U6zs6ZckHSdQlXjAaSu3I3Hl/3CILdKQkSqWeHp7jtI1H5zBPHhKYibv9HiuvHhKXgprQL/78J/6czAMse5zDhPdkjvOA/fJXMwf28K+uo72JXBE72PhHWQv3d/7oz04dO6vUhduXvCz8EdIT0/uZjjuwQp8/eio6+eXflu7tnUXzsEIU/EaRWSLXDIChnf6BDwREI21pdBWdGM4MwENonbmq7XsabrdXjP42FQ0gOVWIrrZWdtZkdfbTsAsOIE0MyAhGavBQCoT8lsdi1iahLiGw0hT8TGLTI0KyuQEJzJxiC+vqYLr6/pAs97HnoGJZCqxDh7fWKZ9bVp79O8MBjnDWhGndeGZgMAZOpTNrsWQXBhHj8G8eEpbG4ThtNt5UiOyWKTGH6co8THOUoE+YfjirIFTR3VOCkZP9aXK5e6/gUArDgHdHOrtMgn7+z30TP7UdV4AE+sesXq0KXOG80Gy9abOqpxXd1hAwvdH9I7ziMgcQFCd61FVMl2RBSkoi2nHOof7gPhWMjfuz+fn/8DmjqqIYp1zAoBV4P0/ORiQUAi1obuwvaoEqRGFKC8LQftPwyu6SPvPwepSuyQUCUGh+2yNGfnGkjSS9js9EOyXqjEUkQfydIpd7XghN2WgzLtnokyvkyxM78aobvWmqxvj5hqYvKxZs5OlEjS2YzgvUMySFViZEUf0Sl34mrBhJfgmYJpd/+ZKKPnqzvzsTZ0l8n69og5JYhH4p7F25Vb2F0kem5dRlNHNV5K0xU8zMCBPWDaffaPxhO0fVq31+zuQ/bIKeAImHv6RnqN1UvPT7dV4NO6vSZ3WfKeNmNKzqTqQ3rH+fBTYyDLq8K1Q/WccuYQtof8vftyuq0CVY0HsHfrl2wo1VSE9PzkJIafiipZHuqvHTK6Aub8Dc3zne+/2mE2OWyXpRlL5wAA1PUdAO5lqWeOA4Ci7CzkhTUIzkxA9JEsLBVnY+WFPEeZ6BTCcjXLOkf7h3SOM5+Z887GXex0BebMWAoA6FDXA7iXGZw5DgBnFWWokRciITgTWdFHkL1UjLyVFxxvrANJCssFAAyN9uscZz4z553dJmE/FgTFAQBau+sA3NtNgjkOACclpahqPIDkmCy8lFaBN9Jr8LunrUty6C6krtwNQLNEXBvmM3PeGvruKHH0zH5cVV7E29u/mlAeACZMydQuS/qz4VMV0jvGcaSOYFYFMQl+LYH0jm0gf28ce/p7W8H4+vyjj2JHEZ/9Y9D/PFkhPW8cd9fzzEojJsGvNkyOoKSwXIN8NvbEYStkPPy8EVGQClleFWalRKEtpxwRBak6SdNkeVUAoLM7gf4LkSv6CfUAIDgzQWeHA0uxx2yQj1CTTfrujUEdm4au3gIAeP6w5aKzcRc7XQFvDz+kRhSgSpaHqFkpKG/LQWpEgc4Pu0qmEd7a2dv1nRBX9BOOAUBCcKZOBnhLscdoeZCPJnfC4N0bOjbdGroKAAjwHH8U3xFtEvbDx9MfT69/Dx+dfB7LFzyGYvFOPL3+PZ3wGWbrZO3dOfSFK1f0E0oCQHJMls4OH5Zij9lQAU8z86W+fUPHpht9mu8xszWqpVxRtuCzM7/BXP4SPLOh0O4znVNpBw5zkN4xjj10RGvWYajEUoN+MvckODPBJeycipC/N469/D1he0jPG8dd9Pzh1ixIVWKDe8fc54TgTIM6qqErAADBjHiLrzcRHLZCBgAC1oQDABrjNFmhZ65fZLTckKwXANgEdOPB7CbUf07O1uv58CuDcoGbNTOD3cV1OgJGXduOesE+dB8c/1q2xidS8+K/UX4Bwz9stTjcpcbN4xIAwIx41/gPpbvY6SqEB6wBABQ0amaCFs1cb7Rc75Amjp9J0DUezM4/8v5zbL2vej40KBcTuBkAUNddrOPg29W12FcvQF33QY49sR1BPpEAgAs3yqEe1rwg1MNdkNw8DsA652ePNgn7EhWqCZN47iONKI2dt8FoOWY3ICYB43gwSSIvKxrZeuJmw52EEhZpkrh9fv4POgJe0nUKO4r4+OL8+Fsh2ppQ3o8AaBLuMlui9g7I0SD74T/ts5db3GbvgByvHknCXP4SbF21xyaDMU+u1ewiIek6pfOfJmY3FOY8QXrHGPbQEfwtmqS9yqoW9tho/xBuHNXMUDP3wdl2TlXI3xtiD39va5jcPvp/+uenAqTnDXEXPR/L16zebVFWsceGRvtx4cZRAPfurTaK25cAAHyfhRZfbyI4bIUMAHhHBLKzNsGZCfDSm2WILNqGtpxyNK37wGh9Jh5bH/6WWKjEUlzcfM8Zz99ruPVbQOIChOUmQV5YYxC3zRMJEbQ1zqCOvWESABqzKTgzQWcXBCam25YzV1zbtMROAgj0jmBHtROCMxHgpSvgtkUWobwtBx80Gd+VhIlX1SeWvwVSlRiHLt5zIinz9xqUWxCQiKSwXNTICw3iWoU8EeKCtlrTrQnBJEgzZlNCcKZOlngm5nW8kX17tEnYl5CZC9lZy+SYLIPZwGzRIRSLd+Klww8arc/kI9BndeQ2NHVUI//oo+wxYwME0YJ1SF25G1WNBwzyFsSHpyBRmG5NtyYEkwDTmE3JMVk6YUbMMvHxxHDzlX8AgNE2GZg2uLaZKEyHtLsWb1ca5olx1r1zVUjvGGIPvcNPi4WyohmyvCp21RFDWG6STv4Y0juOh/y9Ifbw95ZgjzYnM6TnDXEXPR/LT0OzsgJVsjx2JRNDUliu0fwx1wY1YWneHtZtfGAtDh2QATSzFYrSBgSlLzM4x0+LxejACPtSDctNQtC2OIwO3cXXomKo6zuMC5QftjVUVjRDJZYioiAVwRkr0JlvuHx6bl4yfIRB6KvvhKK0AQAQUZCKWSlRmMb3tWFPubPwnTTcrG6FSiyFSiwFTyQETyQEP9X6WH974C52ugoxgZvRoCjFsiDDF34sPw0jowOsg0gKy0Vc0DbcHR1C8dcidKjrTThwzWxPs7ICUpUYqREFWBGcgepOQyGSPDcPQT5CdPbVo0Gh2bUgNaIAUbNS4DvNObG/aQvfQevNakhVYkhVYgh5Igh5IsTwU12qTcK+JCxKw4mWEvw46kmDc6sjt2Do7gC7lD115W4k/igdI98N4dUjSWjtrjMh0O/lMWnqqMbT69/D+uhMfFpnKHC2rtoDAS8K0u5anGgpAQA8vf49LF/wmNMSGD6zoRDn2r/A+Y4v0dRRjfjwFCwLfxQPLrJuy0zm/tkSfx8+dj1cjAudf2fvc3x4ClZHbkPc/I1W79w0WSG9Y4g9dERUyXYoK5vZexKcmYDAzTETSuZLesd2kL83xNb+nrAvpOcNcRc9vz2qBM3KSvY+JwRnIiZws8ntrJn76+j7et/Y2NiY9oHDhw8jIyPDpbPnuyu2WOFSL9hn82djrzYB++3CUC/Yh7KyMmzfvt0u7TO/A1pNYR22WI2yr15g8/tvrzYB6/t6tO2XWPKID8rKbLdNoj3JyMhAe8MwnhU5fpnsZMAWs5M7ivg2n920V5uA9X3dUcR3iJ8nvWN7SO/Ytn17/g7MQf5+YpC/t6w+6XrXYyrpeUuvD1h+X/bVC4x+zx2aQ4aYGP3n5IgosO2svz3aJIiJIu8/h9SIApdvkyAczWVFI55e/57Lt0kQE4H0DkGQvyfcH9Lz3HB4yBJh/WxKf8MVhO5aa1NbbN0m0zeCAKwfQb7S34C1obtsaout22T6RhDWYO1s4jfXzuCxZTk2tcXWbU6F7VAJbpDeIQjy94T7M5n1vCXYS/vTChk3wtbixF5tEsREsYejdZbzJghbYmtxbq82CWIikN4hCPL3hPtDep4btELGgUyFOPWp0EdifKZCjO5U6CNhe6bCzhZToY+EeaaCFpgKfSQmxlTwhVOhj1MZ0rq62Ot+0AoZgiAIgiAIgiAIgiAIB+PQAZl6wT6KuXUQjrjX9DytY1+9YErnH7FH/61tc6o/C1djRxF/Ssej26P/1rY51Z/FRKH3o+MgveOeTHUfQ/5+cjLVdSVpfOugFTIEQRAEQRAEQRAEQRAOhnLITFIotplwVewRf2ltmxQbS7gS9ojFt7ZNygtAuAukdwh3hPw9MRkhjW8dtEKGIAiCIAiCIAiCIAjCwdhshcxo/xBUJ9qgrGiGSixFcGYCQv9zDbwjAs3WG5T0QH1Khs78agAATyQEf0ss+GmxOuXUte3oPdYCRWkDACAsNwmzNkXDNzrEqnL6cIkNNjYLM9o/hDNR+xGcmYCItzYZnJe9fByK0gasat0DDz9vAxt5IiHm7FyDgMQFRu1ZfuYFtP/6r/CNDsHcvGTOfWTqa9tsyTNSVjaz5Uw9E1NwqWuuf+7M0Gg/2lQn0KysgFQlRkJwJtaE/icCvSPM1usZlECmPoXqznwAgJAnQix/C2L5aTrl2tW1aOk9hgZFKQAgKSwX0bM2IcQ32qpy+nCJtTQ24jw02o/9Z6KQEJyJTRFvGZw/LnsZDYpS7FnViv1nonTaYa75wvIz+Gv7rxHiG43kuXls3WZlJXs/k8JyERe0DR80rTPahv7nvJUXcOHGUVR35hu9p/r1mL5weYZcnxmh4c5IHy50/h2n28rR1FGN5JgspMRlI2TmQrP1rihb0CKvwad1ewEA8eEpWB25Dasjt+iUk3SdQsO3lTjRUgIASF25GwkRqZjHj7GqnD5c4uuNzTLeGenDs3+MQHJMFnYkvWNYp+ZFnGgpwX/9XIZn/xih0w5zzXefOo/Sf76Eufwl2LpqD1v3dFsFez9TV+5G4o/S8dLhB422of/5d0+3olZ6BJ/W7TV6T/XrMX3h8gy5PjN3hfQO6R1L6k5WvWMO8vfk7yeLv9eGND5pfHtpfJsNyLQ99xlUYin7WVHaAEVpA5aKs02KA5VYitaswwbHmHaYl5qxcvLCGsgLaxB9JIt9uXMtZ0s8/Lwxf28KOvOrMffFDZjG92XP3VUOQlHagPl7U1hxcrXgBOSFNQb9DctNMvqCvl52FiqxFPwtlt0LY3B9RqZsvCO9Ma6IsLSufv/cnc/anoNUJWY/NyhK0aAoRfZSsUlHKVWJcbg1y+AY0w7z4zdWrkZeiBp5IbKij2BBQKJF5WyJt4cfUubvRXVnPjbMfRG+0+6JmcG7SjQoSpEyfy+8PfxMtnH2ehmkKjFi+fde4ieuFqBGXmjQD65UXn6RvY/G7qkxuDxDrs+MuMfBv2WjqaOa/XyipQQnWkrwRnqNSXHc1FGN9z/PMDjGtMMIPmPlqhoPoKrxAF5Kq0C0YJ1F5WyJj6c/nlybj0/r9mLLqpfh73Pvt9F3R4kTLSV4cm0+fDz9TbZxUlKKpo5qrI7cxh47emY/qhoPGPSDKx/+I5e9j8buqTG4PEOuz8ydIb1DeseaupNN75iD/D35+/Had0dI45PGH699a7HJgIz2SzY0ey08/LyhrGxGW045FJ80Gp1JAcC+aJcc2wm/5WEAgOEuNc6tehdtOeWsQGHKLT/zArwEAQCA/nNyXNx8CL3HWtiXMtdyxphIDHLAOs2omrpWpjMzoq6VAQBmiYQ/fG6HvLBG5z6N9g+hu7gO8sIaozNbPsIgHdus7SPXZ6Rt4+yMFfASBGC4S43rZWchL6yBf+ICk9ewpq5+/9wZ5seaFJaLtaHZ8PbwQ7OyEuVtOWhUfGJ0VBkA+6PfueQYwvyWAwDUw11499wqlLflsD98ptwLy88gwEsz6ivvP4dDFzejpfcY64S5ljPGROItIwI04kamrtVxVjJ1LQBAOEtktn6Qj1Dn+u3qWtTIC5EUlosVszMQ4CWAergLp7p+x84KjEeIbzSeiPwdvD380K6uRYkkHc3KCpPOlOsz5PrMCA2MQEtduRuPL/sFfDz9cbqtAsXinfhHy0dGZxIBsEJv79YvsTB4JQCgd0COFz5ZhmLxTlbsMeXefeo8Amdo3iWXFY3IP/ooGr6tZIU313LGmEiMfUxYEgBAIj+lI1Al8lMANDOK5hDwonSuL+k6harGA0hduRvrozMROCMMvQNyHD/7PjsTPB5z+Uuw6+Fi+Hj6Q9J1Cm9XbsHptnKTAprrM+T6zNwV0jukd6ytO5n0jjnI35O/nyz+XhvS+KTx7anxbTMg8/c2AEDIMw+yMyP8tPGXfDIvprvKQQxKejDSpcZAk+GXhScSQiWWovd4C3yXzMGMpXPgtzzM4MXGtZyt8Y0OAU8khLKiWafPyopmBGcmsEtk+2rbAYAVCIBmxik0ey3khTVQn5IZCJSARN0lVNb2kesz6j3WAgCswAAAL0EAZmesgLywxqwIsqaufv/cmTbV3wEAD4Y8w44Sx/LTxv3hMg5q8K4SPYMSqEe60DXQZFBOyBNBqhKjpfc45vguwZwZSxHmt9zAwXItZ2tCfKMh5IkMnGGzsgIJwZnjLumM0HuJtPdpnDzjqAEgwEuANaH/ydlZaz8L7dkFU3B9hlyfGaHh607NPRfF7mRnBldHbhlXrDGitO+OEleULegdkEN2/ZxBufjwFDR1VOPMt1WYHxSLBUFxWBi80kBUcy1na+bxYxAfnmIggE+3lSM5JmvcZfzRYbr/cbjU9S8AYMU5AATOCENKXDZnga79LLRnlE3B9RlyfWbuCukd0jvW1p1Mescc5O/J309GSOOTxrcnBgMy999veZ5fJrZXe/kqV/SXfBpjbl4yVGKpTty1sThkruWMYW1MNcOcnWsgSS/BkKwX3hGBGJL1QiWWIvpIFluG6eeZqP1G2+jMr0borrU6x/TvqbV95PqMmHKMwGBgPitKG0zOAFpT15rvDIM131V7ts04EO2lfFzRX7ZnjOS5eZCqxDrxjGvm7DQYDedazhjWxpcyrJmzEyWSdPQOyRDoHYHeIRmkKjGyoo+M267+fWPuB+OoGcZz+ubaHA9LniGXZ8aF78dGJ9yGo/l+7HuLyjOiUXv5Nlf0l2ob44lVr6Cpo1onhv2RuGcNZkC5ljOGtTkFGB6JexZvV25Bz63LCJm5ED23LqOpoxovpVWM267+fWPuByPOGcYT+ubaHA9LniGXZ8YVV/PzpHdI71hb11X1zniQvzcO+XsNk9nfa0ManzS+rTD2XTQYkAkI0LxMRgeG4THDyy6GMCh+WNoZnJmAwM0xeIDnA8/ZfmiMK9Ap5xsdgjVdr+skxGMSqM3NS2ZnWbiWswczls4BAKjrO+AdEYiB5m6d47bCmX10FUYHhgEAM2fOtNs1mN/B8OgAvDxm2O06AHBWUYYaeSESgjMRE7gZPg/w4Oc5GwWNcTrlQnyj8fqaLp1EU1KVGEKeCMlz89jYR67l7MGcGUsBAB3qegR6R6B7oFnn+GSB6zPjwgj64O1t+QveWXh5eWHoO4VDrnVSUoqqxgNIjslCwqI0zPCahZm+wXjuoyidcvP4Mfg4R6mTXLCpoxrx4Sl4YtUrbLw713L2YEGQ5rvR2l2HkJkL0XHja53jkwWuz2w8hu4OAHCMnye9YxmkdxyHI/SOOcjfWwf5e9f1947Q9dqQxnc/bKnxtRkeNf09NxiQCQ0NBQCMKPrhw1GgBGcmQFHagLvKQYtmAGR5VQCgM4sw2j9ksrxvdAh8o0MQuCkGQx03IUkvgUosNZjJ4VpOm4ku8/Xw80ZEQSpkeVWYlRKFtpxyRBSksstlgXv3SXsHAmuxtI9cnxFTbrhLrTPzMyTrZc/bo64ljPT0A7j3XbUHTNv9Iwp4+XBz3AnBmWhQlGLwrtKiUdsqmSbbuHb86dBov8nyIb7RCPGNRkzgJtwc6kCJJB1SldhgVJtrOW0muuTR28MPqREFqJLlIWpWCsrbcpAaUWA20ZcpksJyUSMvhHq4S2cEXT1sv2WZXJ+hpc/MHAOjCsyZY/skbPYiNDQUJ+58ZVGd5JgsnGgpQd8dpUUzdR+dfB4AdHIO3BnpM1l+Hj8G8/gxWLUoFQp1O96u3IKmjmqDmUyu5bSZ6DJ3H09/PL3+PXx08nksX/AYisU78fT698wmdzRF6srdqGo8gN4Buc6sae+AfEI2moPrM7T0mZlCNXANgGP8POkdyyC9M7n0jjnI31sH+XvLcKS/t0TXa0ManzT+ROkf6QFg/HtusGZm8eLFmObliduSHs4X8F8zHwDQ8+FXrMBQVjajXrAPspePj1ufeYExCd/0kb18HPWCfeg/p3E+XoIAeIfPsrqcvQhYEw4A7IzXzPWLdM4HbtaMxncX1+GucpA9rq5tR71gH7oPGvZdH2v7yPUZMTZeLzuL4S41AE3iwRvlFwAAvI2RJq8xkbqWcPtSD6Z5eWLx4sU2ac8Yixcvhuc0L/TclnCuM99/DQDgq54P2R9us7IS++oFOC57edz6vUOapIhDo/2o6y42OH9c9jL21Qsg79fE5wZ4CTDLO9zqcvYiPEBzH5iR5EUz11vVzgJ/zSDF2etlrINWD3fh7PWyiRtpAkuf4XjPbDy++34Eir5vERfnPrNmcXFx6FK24bvREc51hKGaZyluPsSKtdNtFdhRxMfHNS+OW7/n1mUAGqH3+fk/GJz/uOZF7Cji47KiEYBmaXdwgGFYA9dy9iIqVBOiwcwexs7bYFU7iwU/BqCZnWREee+AHCcl3OKurcHSZzjeMxuPq70t8PT0srufJ71jHaR3Jo/eMQf5e+shf88dR/l7S3W9NqTxNZDGt56e25fgOc3499xghYynpyfWPfQQWv4pQ+DmJZwuwE+LhbKimd2OUJvgp1aarBdZtA1tOeVoWveB0fNMfHJQ+jIoShtwcfMhgzIRBansv7mWsxfeEYHsrElwZoJBbHFA4gKE5SYZvU88kRBBW8f/T5m1feT6jMzZGJabBN4POygYYyJ1LUH9TxnWPfQQpk2bZpP2jOHp6YmH1j0EWcs/sSRwM6c6sfw0NCsrjG7btjL4KZP1tkUWobwtBx80GY9rZmI1lwWlo0FRikMXDe1Jjbi37J1rOXsR6B3BjkInBGcaxIdyZUFAIjuCbq84Tn24PkOuz2w8OvvqMYbvsXHjxokZ7kA2btyIsbHv0dpdiyVzuQnM1ZGaHR2MbdW5IeZpk/WyRYdQLN6Jlw4/aPQ8E5//46gncaKlBPlHHzUo8/T699h/cy1nL0JmLmRnHpNjsgxyAnAlWrCOnTW1Vez+eHB9hlyf2Xi0yGvw0Loku/t50jvWQXpn8ugdc5C/tx7y967n7y3V9dqQxtdAGp+7xtdHpv4nHlpn3J8bzXC0/d+fRF/1N/h+5DvOF4n83RM6L8iw3CTEn/rfZmN8+WmxRussFWcD0MQnA4Df8jAsFWcjLDdJp2xUyXYEZ6xgj3EtZ0+YWZOg9GVGz8/NS0Zk0Tad5awRBalY+E4ap+XPE+kj12fE2MgICp5IiMiibZiblzyufROpy4XvR75DX/U3yHjyP2zSnjme3P7v+KavGt99z31m6InI3+k4xKSwXPzv+FNmYzpj+WlG62Qv1WQK71DXAwDC/JYje6lmuzbtstujSrAiOIM9xrWcPYn54WW3LCh9Qu0kz83DtsgiCHma7fSYe2NPuDxDrs9sPC6qKrFxw8OYNctxM9sTZdasWdi48WGcufwXi+rterhYRwSnrp1TFagAAAlSSURBVNyNt7d/ZTaOf3XkFqN13kjX/AeotVszy74weCXeSK9B6srdOmV/9XgZ1kdnsse4lrMnCYs02fx/HPXkhNrZumoPskWH2C1UmXtjT7g8Q67PzBzfjY6g6crn+I/t/25D641Desd6SO9MHr1jCvL3E4P8vWv5e2t0vTak8TWQxuem8bX57vsRfNNXjf/IMO4L7hsbGxvTP3j79m0I5s9F4CsPYfa/x1t8UYKwF9f/pwm9v/knujqvYvr06Xa91u3btzFXMB8PBb6C+Nn2f1EQ3NlXL0BCcKZOfKe7MXi3F+9fWIXPKsrxk5/8xNnmWMRf//pXbH3ipziQcR5+PoHONofQYkcRH8kxWTox/e7GqdbDqGh6A1fkHQ7x86R3CFfEkXrHHOTvXRfy95ZBut49mAwaX5+m6/+Df/b+Ble7Oo1+z42ukJk+fTr2v/EmFL+tweigdaOIBGFrRgdHoPhtDfa/8aZDxMn06dPx5v43UKP4LUZGB8evQNiUffUCnThZgInjPAjgXiyou/KP7rewdvVatxuMAYCf/OQnWLNmDY42/F9nmzIl2VHE18mNAGhi9784XwTgXvy/OzJ0dxB/OfcW/u9v8h3m50nvEK6Go/WOOcjfOxfy97aDdL3rMNk1vjYjo4OoUfwWb+5/w+T33OgKGQAYHR3F0hXL0JfIw9xfP2xXQwmCC1ff/Bv8a1X4+ux5eHh4OOSao6OjWLZ0BXh9iXh47q8dck1Cg1QlxuHWLKPnhDwRnoj8nVWZ3V2BroHz+OjSFpxrOoslS7jlrnA1Ll68iOXxK/DKvx1HxOzlzjZnStHUUY33Pze+NDk+PAW7Hi62ajcPV+DI6ddxZfifaLpw1qF+nvQO4Uo4Q++Yg/y98yB/b1tI17sGk1nj6/O3q29C5V+L81+b/p57vPbaa68ZO3H//fdjaUwsPvjlG5geFQyfyCB72koQZrn5+SV0vPYljh75M8LDwx123fvvvx+xS2Pwxge/RPD0KAT52GbnBGJ8+D4LEe6/GgFeoejs08RKJwRnYm3os/ix4Bdu66j7Rq6hrG07/td/ZmHHDtOJ4Fyd2bNno7f3Jv702ZtIiEiDj6d7Pg93ZM7MRRCGrkGgXxik3ZpY5uSYLDy67Bf4yfLn3FacN8qO4/C//g/+XH7E4X6e9A7hKjhL75iD/L3zIH9vW0jXuwaTVePrc+nm5/iy4zX8+aj577nJFTIM+996C/vyX0PUn5/CjHjrMoQTxEQYaJKj9aef4PW9r2HPy+NvLWcP3tr/Fl7bl4+nov6MsBmUZ4Cwjrvf30HpN+kIWvAATtXWwNvb29kmTYihoSE8tG49bnV/h7xNf4HnAz7ONolwUy4rzqLg2Bbsfe1V7NnjHD9PeodwNq6gd0xB/p6wFa7g70nXE/ZGPtCET1p/itde34uXx/mejzsgAwDbM3+Gv3xRhYV/TIffqnk2M5QgxqP/zBVc/vkR/NtjqThc+t9OteVn2zNR9ZcvkL7wj5jnt8qpthDux53vbuHItz/HXd9unD3XgKCgyTELf+PGDaxckYAZCMMvHymBrxfP2SYRbsY3107j9+IsbEp9FP9dVupUW0jvEM7ClfSOKcjfExPFlfw96XrCXlzpP4Mjl3+O1H97DP99ePzvudGkvvqU/OlDPLbhEVx68hPcOHphwkYSBBduHL2AS09+gsc2PIKSP33obHPwYcmf8MhjG/DJpSdx4cZRZ5tDuBG9Q+3406XNGPXrQfX/+2LSDMYAQFBQEL6s/gK377+GNyoehUItc7ZJhBtR982fUXBsKx5+ZD0+/OhPzjaH9A7hFFxN75iC/D0xEVzN35OuJ+zBhRtH8cmlJ/HIYxvwYQm377nJHDI6hTw8kP7Tn2L07neo/PUhjMjV8I0XwMPXa6I2E4QBI9f70fnql5C/exK/3vMKiouK8MADDzjbLHh4eOCn6T/Fd6N3cajy11CPyCHwjYeXh6+zTSNclO/HvsMZxcf4TJaDmLhF+MfJv2P+/PnONsvmBAUF4Wc/y8CX4r/if/7xW3g94Iv5QUtx/32cxvyJKcit2wqU/esV/KWhAK/8eg+Kil3Hz5PeIRyFq+odc5C/JyzFlf096XrCVvSPXMeXna/ipPxdi7/nnAZkAOC+++7Dhg0bEBsbiy//eBQdxTXAA/dh+uIQ3O/p/AzwhPszOjiCng9P4/KucsxQjqHkTx8iJycH9913n7NNY9H+HRz98o+o6SjGfXgAIdMXw+N+T2ebR7gIYxjDt7f+gT9f/jku9lbiVy88h5KPP4K/v3sm3+PC9OnT8bOfZeD2nQEc+vN+NMj+gtl+4ZgdsAD3wXV+w4RzGbo7CHHzIRSJn8GQxw18+NGfXNrPk94h7IE76B1zkL8nuOBu/p50PWENI6ODON3zIcov78LYDCU+LLH8e84ph4w+d+7cwW9+8xu88+4BfO8BzHw8CgFJC+G7ZA48Z/vBw49mkojxGe0fxsj1fgxevAb1ycu49UUr7h8FXnxhN1555RX4+Lh2wjjmd3DgnXeB7z0QNfNxLAxIwhzfJfDznA2vSZIhnBif774fxu3vVLhx5xu0q+sg7fsrrvfLsGlTKt577wAWLVrkbBMdyrfffosXnt+NY8erEBq4EMvnb8ZiwY8hmCXEDO9ZmOZB74ipwp2Rfty6rUDnjWZclJ/A2fbjGMModr/4glv5edI7xERwd71jDvL3BMNk8fek6wlzDI/2o3/kOq4NXsRl9Um03voCuH+C3/OxCaBSqcZ+//vfj63fmDz2gOe0MQD0R38W/z3gOW1s/cbksd///vdjKpVqIl9Jp8D8DpLXbxyb9oCn0+8n/Tn3b7EwZiwvL29MIpE4+6vpdCQSyVheXt7Y4qgYpz8X+nPu37RpnmPJGx52ez9Peof+JvLn7nrHHOTv6Y/5myz+nnQ9/Zn7m/aA51jy+o02+Z5btULGGCMjI7h06RKuXbuG/v5+WzRJTHL8/PwwZ84cLF68GJ6ek2NZIP0OpiZeXl4IDAxETEwMZs6c6WxzXJJbt26hpaUFvb29GB4edrY5hIMgP08Qk/N3YA7y91OTyfg9J39P6GOP77nNBmQIgiAIgiAIgiAIgiAIblBKdIIgCIIgCIIgCIIgCAdDAzIEQRAEQRAEQRAEQRAOhgZkCIIgCIIgCIIgCIIgHMz/B6q7Z4b30PKJAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from IPython.display import Image \\n\",\n \"from sklearn import tree\\n\",\n \"import pydotplus \\n\",\n \"dot_data = tree.export_graphviz(clf, out_file=None, \\n\",\n \" feature_names=[\\\"sepal length\\\",\\\"sepal width\\\"], \\n\",\n \" class_names=iris.target_names, \\n\",\n \" filled=True, rounded=True, \\n\",\n \" special_characters=True) \\n\",\n \"graph = pydotplus.graph_from_dot_data(dot_data) \\n\",\n \"Image(graph.create_png()) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "classic-machine-learning/fp_tree_prefixspan.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C) 2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"用Spark学习FP Tree算法和PrefixSpan算法 https://www.cnblogs.com/pinard/p/6340162.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"import sys\\n\",\n \"\\n\",\n \"#下面这些目录都是你自己机器的Spark安装目录和Java安装目录\\n\",\n \"os.environ['SPARK_HOME'] = \\\"C:/Tools/spark-2.2.0-bin-hadoop2.6/\\\"\\n\",\n \"os.environ['PYSPARK_PYTHON'] = \\\"C:/Users/tata/AppData/Local/Programs/Python/Python36/python.exe\\\"\\n\",\n \"\\n\",\n \"sys.path.append(\\\"C:/Tools/spark-2.2.0-bin-hadoop2.6/bin\\\")\\n\",\n \"sys.path.append(\\\"C:/Tools/spark-2.2.0-bin-hadoop2.6/python\\\")\\n\",\n \"sys.path.append(\\\"C:/Tools/spark-2.2.0-bin-hadoop2.6/python/pyspark\\\")\\n\",\n \"sys.path.append(\\\"C:/Tools/spark-2.2.0-bin-hadoop2.6/python/lib\\\")\\n\",\n \"sys.path.append(\\\"C:/Tools/spark-2.2.0-bin-hadoop2.6/python/lib/pyspark.zip\\\")\\n\",\n \"sys.path.append(\\\"C:/Tools/spark-2.2.0-bin-hadoop2.6/python/lib/py4j-0.10.4-src.zip\\\")\\n\",\n \"sys.path.append(\\\"C:/Program Files/Java/jdk1.8.0_171\\\")\\n\",\n \"\\n\",\n \"from pyspark import SparkContext\\n\",\n \"from pyspark import SparkConf\\n\",\n \"\\n\",\n \"\\n\",\n \"sc = SparkContext(\\\"local\\\",\\\"testing\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print (sc)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"from pyspark.mllib.fpm import FPGrowth\\n\",\n \"data = [[\\\"A\\\", \\\"B\\\", \\\"C\\\", \\\"E\\\", \\\"F\\\",\\\"O\\\"], [\\\"A\\\", \\\"C\\\", \\\"G\\\"], [\\\"E\\\",\\\"I\\\"], [\\\"A\\\", \\\"C\\\",\\\"D\\\",\\\"E\\\",\\\"G\\\"], [\\\"A\\\", \\\"C\\\", \\\"E\\\",\\\"G\\\",\\\"L\\\"],\\n\",\n \" [\\\"E\\\",\\\"J\\\"],[\\\"A\\\",\\\"B\\\",\\\"C\\\",\\\"E\\\",\\\"F\\\",\\\"P\\\"],[\\\"A\\\",\\\"C\\\",\\\"D\\\"],[\\\"A\\\",\\\"C\\\",\\\"E\\\",\\\"G\\\",\\\"M\\\"],[\\\"A\\\",\\\"C\\\",\\\"E\\\",\\\"G\\\",\\\"N\\\"]]\\n\",\n \"rdd = sc.parallelize(data, 2)\\n\",\n \"#支持度阈值为20%\\n\",\n \"model = FPGrowth.train(rdd, 0.2, 2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[FreqItemset(items=['A'], freq=8),\\n\",\n \" FreqItemset(items=['B'], freq=2),\\n\",\n \" FreqItemset(items=['B', 'A'], freq=2),\\n\",\n \" FreqItemset(items=['B', 'C'], freq=2),\\n\",\n \" FreqItemset(items=['B', 'C', 'A'], freq=2),\\n\",\n \" FreqItemset(items=['B', 'E'], freq=2),\\n\",\n \" FreqItemset(items=['B', 'E', 'A'], freq=2),\\n\",\n \" FreqItemset(items=['B', 'E', 'C'], freq=2),\\n\",\n \" FreqItemset(items=['B', 'E', 'C', 'A'], freq=2),\\n\",\n \" FreqItemset(items=['C'], freq=8),\\n\",\n \" FreqItemset(items=['C', 'A'], freq=8),\\n\",\n \" FreqItemset(items=['D'], freq=2),\\n\",\n \" FreqItemset(items=['D', 'A'], freq=2),\\n\",\n \" FreqItemset(items=['D', 'C'], freq=2),\\n\",\n \" FreqItemset(items=['D', 'C', 'A'], freq=2),\\n\",\n \" FreqItemset(items=['E'], freq=8),\\n\",\n \" FreqItemset(items=['E', 'A'], freq=6),\\n\",\n \" FreqItemset(items=['E', 'C'], freq=6),\\n\",\n \" FreqItemset(items=['E', 'C', 'A'], freq=6),\\n\",\n \" FreqItemset(items=['F'], freq=2),\\n\",\n \" FreqItemset(items=['F', 'A'], freq=2),\\n\",\n \" FreqItemset(items=['F', 'B'], freq=2),\\n\",\n \" FreqItemset(items=['F', 'B', 'A'], freq=2),\\n\",\n \" FreqItemset(items=['F', 'B', 'C'], freq=2),\\n\",\n \" FreqItemset(items=['F', 'B', 'C', 'A'], freq=2),\\n\",\n \" FreqItemset(items=['F', 'B', 'E'], freq=2),\\n\",\n \" FreqItemset(items=['F', 'B', 'E', 'A'], freq=2),\\n\",\n \" FreqItemset(items=['F', 'B', 'E', 'C'], freq=2),\\n\",\n \" FreqItemset(items=['F', 'B', 'E', 'C', 'A'], freq=2),\\n\",\n \" FreqItemset(items=['F', 'C'], freq=2),\\n\",\n \" FreqItemset(items=['F', 'C', 'A'], freq=2),\\n\",\n \" FreqItemset(items=['F', 'E'], freq=2),\\n\",\n \" FreqItemset(items=['F', 'E', 'A'], freq=2),\\n\",\n \" FreqItemset(items=['F', 'E', 'C'], freq=2),\\n\",\n \" FreqItemset(items=['F', 'E', 'C', 'A'], freq=2),\\n\",\n \" FreqItemset(items=['G'], freq=5),\\n\",\n \" FreqItemset(items=['G', 'A'], freq=5),\\n\",\n \" FreqItemset(items=['G', 'C'], freq=5),\\n\",\n \" FreqItemset(items=['G', 'C', 'A'], freq=5),\\n\",\n \" FreqItemset(items=['G', 'E'], freq=4),\\n\",\n \" FreqItemset(items=['G', 'E', 'A'], freq=4),\\n\",\n \" FreqItemset(items=['G', 'E', 'C'], freq=4),\\n\",\n \" FreqItemset(items=['G', 'E', 'C', 'A'], freq=4)]\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sorted(model.freqItemsets().collect())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from pyspark.mllib.fpm import PrefixSpan\\n\",\n \"data = [\\n\",\n \" [['a'],[\\\"a\\\", \\\"b\\\", \\\"c\\\"], [\\\"a\\\",\\\"c\\\"],[\\\"d\\\"],[\\\"c\\\", \\\"f\\\"]],\\n\",\n \" [[\\\"a\\\",\\\"d\\\"], [\\\"c\\\"],[\\\"b\\\", \\\"c\\\"], [\\\"a\\\", \\\"e\\\"]],\\n\",\n \" [[\\\"e\\\", \\\"f\\\"], [\\\"a\\\", \\\"b\\\"], [\\\"d\\\",\\\"f\\\"],[\\\"c\\\"],[\\\"b\\\"]],\\n\",\n \" [[\\\"e\\\"], [\\\"g\\\"],[\\\"a\\\", \\\"f\\\"],[\\\"c\\\"],[\\\"b\\\"],[\\\"c\\\"]]\\n\",\n \" ]\\n\",\n \"rdd = sc.parallelize(data, 2)\\n\",\n \"model = PrefixSpan.train(rdd, 0.5,4)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[FreqSequence(sequence=[['a']], freq=4),\\n\",\n \" FreqSequence(sequence=[['a'], ['a']], freq=2),\\n\",\n \" FreqSequence(sequence=[['a'], ['b']], freq=4),\\n\",\n \" FreqSequence(sequence=[['a'], ['b'], ['a']], freq=2),\\n\",\n \" FreqSequence(sequence=[['a'], ['b'], ['c']], freq=2),\\n\",\n \" FreqSequence(sequence=[['a'], ['b', 'c']], freq=2),\\n\",\n \" FreqSequence(sequence=[['a'], ['b', 'c'], ['a']], freq=2),\\n\",\n \" FreqSequence(sequence=[['a'], ['c']], freq=4),\\n\",\n \" FreqSequence(sequence=[['a'], ['c'], ['a']], freq=2),\\n\",\n \" FreqSequence(sequence=[['a'], ['c'], ['b']], freq=3),\\n\",\n \" FreqSequence(sequence=[['a'], ['c'], ['c']], freq=3),\\n\",\n \" FreqSequence(sequence=[['a'], ['d']], freq=2),\\n\",\n \" FreqSequence(sequence=[['a'], ['d'], ['c']], freq=2),\\n\",\n \" FreqSequence(sequence=[['a'], ['f']], freq=2),\\n\",\n \" FreqSequence(sequence=[['b']], freq=4),\\n\",\n \" FreqSequence(sequence=[['b'], ['a']], freq=2),\\n\",\n \" FreqSequence(sequence=[['b'], ['c']], freq=3),\\n\",\n \" FreqSequence(sequence=[['b'], ['d']], freq=2),\\n\",\n \" FreqSequence(sequence=[['b'], ['d'], ['c']], freq=2),\\n\",\n \" FreqSequence(sequence=[['b'], ['f']], freq=2),\\n\",\n \" FreqSequence(sequence=[['b', 'a']], freq=2),\\n\",\n \" FreqSequence(sequence=[['b', 'a'], ['c']], freq=2),\\n\",\n \" FreqSequence(sequence=[['b', 'a'], ['d']], freq=2),\\n\",\n \" FreqSequence(sequence=[['b', 'a'], ['d'], ['c']], freq=2),\\n\",\n \" FreqSequence(sequence=[['b', 'a'], ['f']], freq=2),\\n\",\n \" FreqSequence(sequence=[['b', 'c']], freq=2),\\n\",\n \" FreqSequence(sequence=[['b', 'c'], ['a']], freq=2),\\n\",\n \" FreqSequence(sequence=[['c']], freq=4),\\n\",\n \" FreqSequence(sequence=[['c'], ['a']], freq=2),\\n\",\n \" FreqSequence(sequence=[['c'], ['b']], freq=3),\\n\",\n \" FreqSequence(sequence=[['c'], ['c']], freq=3),\\n\",\n \" FreqSequence(sequence=[['d']], freq=3),\\n\",\n \" FreqSequence(sequence=[['d'], ['b']], freq=2),\\n\",\n \" FreqSequence(sequence=[['d'], ['c']], freq=3),\\n\",\n \" FreqSequence(sequence=[['d'], ['c'], ['b']], freq=2),\\n\",\n \" FreqSequence(sequence=[['e']], freq=3),\\n\",\n \" FreqSequence(sequence=[['e'], ['a']], freq=2),\\n\",\n \" FreqSequence(sequence=[['e'], ['a'], ['b']], freq=2),\\n\",\n \" FreqSequence(sequence=[['e'], ['a'], ['c']], freq=2),\\n\",\n \" FreqSequence(sequence=[['e'], ['a'], ['c'], ['b']], freq=2),\\n\",\n \" FreqSequence(sequence=[['e'], ['b']], freq=2),\\n\",\n \" FreqSequence(sequence=[['e'], ['b'], ['c']], freq=2),\\n\",\n \" FreqSequence(sequence=[['e'], ['c']], freq=2),\\n\",\n \" FreqSequence(sequence=[['e'], ['c'], ['b']], freq=2),\\n\",\n \" FreqSequence(sequence=[['e'], ['f']], freq=2),\\n\",\n \" FreqSequence(sequence=[['e'], ['f'], ['b']], freq=2),\\n\",\n \" FreqSequence(sequence=[['e'], ['f'], ['c']], freq=2),\\n\",\n \" FreqSequence(sequence=[['e'], ['f'], ['c'], ['b']], freq=2),\\n\",\n \" FreqSequence(sequence=[['f']], freq=3),\\n\",\n \" FreqSequence(sequence=[['f'], ['b']], freq=2),\\n\",\n \" FreqSequence(sequence=[['f'], ['b'], ['c']], freq=2),\\n\",\n \" FreqSequence(sequence=[['f'], ['c']], freq=2),\\n\",\n \" FreqSequence(sequence=[['f'], ['c'], ['b']], freq=2)]\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sorted(model.freqSequences().collect())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\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\": \"3.6.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "classic-machine-learning/kmeans_cluster.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C) 2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"用scikit-learn学习K-Means聚类 https://www.cnblogs.com/pinard/p/6169370.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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52Jr8OK+x9hE4PeJ5m8hwsuuIB0+rsAPProE9gWBfuBFuAztLU95UuX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\\n5f8H0uT/1oW078MAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline\\n\",\n \"from sklearn.datasets.samples_generator import make_blobs\\n\",\n \"# X为样本特征，Y为样本簇类别， 共1000个样本，每个样本4个特征，共4个簇，簇中心在[-1,-1], [0,0],[1,1], [2,2]， 簇方差分别为[0.4, 0.2, 0.2]\\n\",\n \"X, y = make_blobs(n_samples=1000, n_features=2, centers=[[-1,-1], [0,0], [1,1], [2,2]], cluster_std=[0.4, 0.2, 0.2, 0.2], \\n\",\n \" random_state =9)\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], marker='o')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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wfZB9yCiO3PwK2IBTAbyS7ZgSxkNkdsji844WtHIVbJBvN8fYHK5lwf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\\nAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.cluster import KMeans\\n\",\n \"y_pred = KMeans(n_clusters=2, random_state=9).fit_predict(X)\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=y_pred)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3116.1706763322227\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn import metrics\\n\",\n \"metrics.calinski_harabaz_score(X, y_pred) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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kw8xt2z9wh5pxluTV2r3vu6FVtGbGbp0qWY1DDBf5AfsbtuY2RpROLp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\\n+l8SXUMsatLF4QAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.cluster import KMeans\\n\",\n \"y_pred = KMeans(n_clusters=3, random_state=9).fit_predict(X)\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=y_pred)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"2931.625030199556\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"metrics.calinski_harabaz_score(X, y_pred) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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RTwBAbBc+0kK6RVCcCQNL86wfDBCzB+AYTkBZtdKikP75WCnA69JLIu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\\nABcRCSkFuIhISCnARURC6v9YNXPQBX5DhAAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.cluster import KMeans\\n\",\n \"y_pred = KMeans(n_clusters=4, random_state=9).fit_predict(X)\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=y_pred)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"5924.050613480169\"\n ]\n },\n \"execution_count\": 36,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"metrics.calinski_harabaz_score(X, y_pred) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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wfZB9yCiO3PwK2IBTAbyS7ZgSxkNkdsji844WtHIVbJBvN8fYHK5lwf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\\nAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.cluster import MiniBatchKMeans\\n\",\n \"y_pred = MiniBatchKMeans(n_clusters=2, batch_size = 200, random_state=9).fit_predict(X)\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=y_pred)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3116.1706763322227\"\n ]\n },\n \"execution_count\": 38,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"metrics.calinski_harabaz_score(X, y_pred) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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aATQmyPAJ0RLoANiOiSC4iNzNoIN8dKfvVreyBcJScc9+sDhDr6+sHR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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.cluster import MiniBatchKMeans\\n\",\n \"y_pred = MiniBatchKMeans(n_clusters=3, batch_size = 200, random_state=9).fit_predict(X)\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=y_pred)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"2827.3884139215338\"\n ]\n },\n \"execution_count\": 40,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"metrics.calinski_harabaz_score(X, y_pred) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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juB51EugBWo6JLfUAuZ5VFujsWk+7V9Ua6Sg67+2gN5XG3NcJWbjrK0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\\ngg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.cluster import MiniBatchKMeans\\n\",\n \"y_pred = MiniBatchKMeans(n_clusters=4, batch_size = 200, random_state=9).fit_predict(X)\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=y_pred)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"5921.4549600014598\"\n ]\n },\n \"execution_count\": 42,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"metrics.calinski_harabaz_score(X, y_pred) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 75,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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UCiUmrYfzlHD9rr5Bl2ux2rlxdvuFwYkXiCLxJbKI64X64jNUpe7uMT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\\nrkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.subplots_adjust(left=.02, right=.98, bottom=.096, top=.96, wspace=.05,\\n\",\n \" hspace=.01)\\n\",\n \"plt.subplot(2,2,1)\\n\",\n \"y_pred = MiniBatchKMeans(n_clusters=2, batch_size = 200, random_state=9).fit_predict(X)\\n\",\n \"score2= metrics.calinski_harabaz_score(X, y_pred) \\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=y_pred)\\n\",\n \"plt.text(.99, .01, ('k=%d, score: %.2f' % (2,score2)),\\n\",\n \" transform=plt.gca().transAxes, size=10,\\n\",\n \" horizontalalignment='right')\\n\",\n \"\\n\",\n \"\\n\",\n \"plt.subplot(2,2,2)\\n\",\n \"y_pred = MiniBatchKMeans(n_clusters=3, batch_size = 200, random_state=9).fit_predict(X)\\n\",\n \"score3= metrics.calinski_harabaz_score(X, y_pred) \\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=y_pred)\\n\",\n \"plt.text(.99, .01, ('k=%d, score: %.2f' % (3,score3)),\\n\",\n \" transform=plt.gca().transAxes, size=10,\\n\",\n \" horizontalalignment='right')\\n\",\n \"\\n\",\n \"plt.subplot(2,2,3)\\n\",\n \"y_pred = MiniBatchKMeans(n_clusters=4, batch_size = 200, random_state=9).fit_predict(X)\\n\",\n \"score4= metrics.calinski_harabaz_score(X, y_pred) \\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=y_pred)\\n\",\n \"plt.text(.99, .01, ('k=%d, score: %.2f' % (4,score4)),\\n\",\n \" transform=plt.gca().transAxes, size=10,\\n\",\n \" horizontalalignment='right')\\n\",\n \"\\n\",\n \"plt.subplot(2,2,4)\\n\",\n \"y_pred = MiniBatchKMeans(n_clusters=5, batch_size = 200, random_state=9).fit_predict(X)\\n\",\n \"score5 = metrics.calinski_harabaz_score(X, y_pred) \\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=y_pred)\\n\",\n \"plt.text(.99, .01, ('k=%d, score: %.2f' % (5,score5)),\\n\",\n \" transform=plt.gca().transAxes, size=10,\\n\",\n \" horizontalalignment='right')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 87,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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pFFZSdEcR5EFGUuEmowILF6N6Lo6iJJNR8CdSwW7rLbuQh87+vLj6tW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\\nRYHEMoAWFhYWFgWS/wfC1ertYBFk7AAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.subplots_adjust(left=.02, right=.98, bottom=.096, top=.96, wspace=.1,\\n\",\n \" hspace=.1)\\n\",\n \"for index, k in enumerate((2,3,4,5)):\\n\",\n \" plt.subplot(2,2,index+1)\\n\",\n \" y_pred = MiniBatchKMeans(n_clusters=k, batch_size = 200, random_state=9).fit_predict(X)\\n\",\n \" score= metrics.calinski_harabaz_score(X, y_pred) \\n\",\n \" plt.scatter(X[:, 0], X[:, 1], c=y_pred)\\n\",\n \" plt.text(.99, .01, ('k=%d, score: %.2f' % (k,score)),\\n\",\n \" transform=plt.gca().transAxes, size=10,\\n\",\n \" horizontalalignment='right')\\n\",\n \"plt.show()\"\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\": \"3.6.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "classic-machine-learning/knn_classifier.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C)\\n\",\n \"2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"scikit-learn K近邻法类库使用小结 https://www.cnblogs.com/pinard/p/6065607.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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H7uuRKtqxYnjROXzl5CkiSsViu7d+9m4eKFCEnQtVNXOnbsWNKnQPYQ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\\nAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline\\n\",\n \"from sklearn.datasets.samples_generator import make_classification\\n\",\n \"# X为样本特征，Y为样本类别输出， 共1000个样本，每个样本2个特征，输出有3个类别，没有冗余特征，每个类别一个簇\\n\",\n \"X, Y = make_classification(n_samples=1000, n_features=2, n_redundant=0,\\n\",\n \" n_clusters_per_class=1, n_classes=3)\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], marker='o', c=Y)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\\n\",\n \" metric_params=None, n_jobs=1, n_neighbors=15, p=2,\\n\",\n \" weights='distance')\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn import neighbors\\n\",\n \"clf = neighbors.KNeighborsClassifier(n_neighbors = 15 , weights='distance')\\n\",\n \"clf.fit(X, Y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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8Sci6Fc43L0n9qffEXypavM0/tO82HjD4l7Ng7pK1nSfAkfzf+Iig9V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\\nUspmtHArpZTN/D8mSPRIBTkpjAAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from matplotlib.colors import ListedColormap\\n\",\n \"cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\\n\",\n \"cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\\n\",\n \"\\n\",\n \"#确认训练集的边界\\n\",\n \"x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\\n\",\n \"y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\\n\",\n \"#生成随机数据来做测试集，然后作预测\\n\",\n \"xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),\\n\",\n \" np.arange(y_min, y_max, 0.02))\\n\",\n \"Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\\n\",\n \"\\n\",\n \"# 画出测试集数据\\n\",\n \"Z = Z.reshape(xx.shape)\\n\",\n \"plt.figure()\\n\",\n \"plt.pcolormesh(xx, yy, Z, cmap=cmap_light)\\n\",\n \"\\n\",\n \"# 也画出所有的训练集数据\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=cmap_bold)\\n\",\n \"plt.xlim(xx.min(), xx.max())\\n\",\n \"plt.ylim(yy.min(), yy.max())\\n\",\n \"plt.title(\\\"3-Class classification (k = 15, weights = 'distance')\\\" )\"\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\": \"3.6.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "classic-machine-learning/lda.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C) 2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"用scikit-learn进行LDA降维 https://www.cnblogs.com/pinard/p/6249328.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from mpl_toolkits.mplot3d import Axes3D\\n\",\n \"%matplotlib inline\\n\",\n \"from sklearn.datasets.samples_generator import make_classification\\n\",\n \"X, y = make_classification(n_samples=1000, n_features=3, n_redundant=0, n_classes=3, n_informative=2,\\n\",\n \" n_clusters_per_class=1,class_sep =0.5, random_state =10)\\n\",\n \"fig = plt.figure()\\n\",\n \"ax = Axes3D(fig, rect=[0, 0, 1, 1], elev=30, azim=20)\\n\",\n \"ax.scatter(X[:, 0], X[:, 1], X[:, 2],marker='o',c=y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0.43377069 0.3716351 ]\\n\",\n \"[1.21083449 1.0373882 ]\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.decomposition import PCA\\n\",\n \"pca = PCA(n_components=2)\\n\",\n \"pca.fit(X)\\n\",\n \"print (pca.explained_variance_ratio_)\\n\",\n \"print (pca.explained_variance_)\\n\",\n \"X_new = pca.transform(X)\\n\",\n \"plt.scatter(X_new[:, 0], X_new[:, 1],marker='o',c=y)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\\n\",\n \"lda = LinearDiscriminantAnalysis(n_components=2)\\n\",\n \"lda.fit(X,y)\\n\",\n \"X_new = lda.transform(X)\\n\",\n \"plt.scatter(X_new[:, 0], X_new[:, 1],marker='o',c=y)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 95,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 0.43377069 0.3716351 0.1945942 ]\\n\",\n \"[ 1.20962365 1.03635081 0.5426502 ]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.decomposition import PCA\\n\",\n \"pca = PCA(n_components=3)\\n\",\n \"pca.fit(X)\\n\",\n \"print pca.explained_variance_ratio_\\n\",\n \"print pca.explained_variance_\"\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\": \"3.6.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "classic-machine-learning/linear-regression.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \" Copyright (C) \\n\",\n \" 2016 - 2019 Pinard Liu(liujianping-ok@163.com) \\n\",\n \" \\n\",\n \" https://www.cnblogs.com/pinard\\n\",\n \" \\n\",\n \" Permission given to modify the code as long as you keep this declaration at the top \\n\",\n \"\\n\",\n \"用scikit-learn和pandas学习线性回归\\n\",\n \"https://www.cnblogs.com/pinard/p/6016029.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"from sklearn import datasets, linear_model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# read_csv里面的参数是csv在你电脑上的路径，此处csv文件放在notebook运行目录下面的CCPP目录里\\n\",\n \"data = pd.read_csv('.\\\\CCPP\\\\ccpp.csv')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
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411.8040.661017.1397.20
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\"\n ],\n \"text/plain\": [\n \" AT V AP RH\\n\",\n \"0 8.34 40.77 1010.84 90.01\\n\",\n \"1 23.64 58.49 1011.40 74.20\\n\",\n \"2 29.74 56.90 1007.15 41.91\\n\",\n \"3 19.07 49.69 1007.22 76.79\\n\",\n \"4 11.80 40.66 1017.13 97.20\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X = data[['AT', 'V', 'AP', 'RH']]\\n\",\n \"X.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
PE
0480.48
1445.75
2438.76
3453.09
4464.43
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" PE\\n\",\n \"0 480.48\\n\",\n \"1 445.75\\n\",\n \"2 438.76\\n\",\n \"3 453.09\\n\",\n \"4 464.43\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"y = data[['PE']]\\n\",\n \"y.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\Python27\\\\lib\\\\site-packages\\\\sklearn\\\\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\\n\",\n \" \\\"This module will be removed in 0.20.\\\", DeprecationWarning)\\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=1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(7176, 4)\\n\",\n \"(7176, 1)\\n\",\n \"(2392, 4)\\n\",\n \"(2392, 1)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print X_train.shape\\n\",\n \"print y_train.shape\\n\",\n \"print X_test.shape\\n\",\n \"print y_test.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.linear_model import LinearRegression\\n\",\n \"\\n\",\n \"linreg = LinearRegression()\\n\",\n \"\\n\",\n \"linreg.fit(X_train, y_train)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 447.06297099]\\n\",\n \"[[-1.97376045 -0.23229086 0.0693515 -0.15806957]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print linreg.intercept_\\n\",\n \"print linreg.coef_\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"MSE: 20.0804012021\\n\",\n \"RMSE: 4.48111606657\\n\"\n ]\n }\n ],\n \"source\": [\n \"#模型拟合测试集\\n\",\n \"y_pred = linreg.predict(X_test)\\n\",\n \"from sklearn import metrics\\n\",\n \"# 用scikit-learn计算MSE\\n\",\n \"print \\\"MSE:\\\",metrics.mean_squared_error(y_test, y_pred)\\n\",\n \"# 用scikit-learn计算RMSE\\n\",\n \"print \\\"RMSE:\\\",np.sqrt(metrics.mean_squared_error(y_test, y_pred))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"MSE: 23.2089074701\\n\",\n \"RMSE: 4.81756239919\\n\"\n ]\n }\n ],\n \"source\": [\n \"X = data[['AT', 'V', 'AP']]\\n\",\n \"y = data[['PE']]\\n\",\n \"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\\n\",\n \"from sklearn.linear_model import LinearRegression\\n\",\n \"linreg = LinearRegression()\\n\",\n \"linreg.fit(X_train, y_train)\\n\",\n \"#模型拟合测试集\\n\",\n \"y_pred = linreg.predict(X_test)\\n\",\n \"from sklearn import metrics\\n\",\n \"# 用scikit-learn计算MSE\\n\",\n \"print \\\"MSE:\\\",metrics.mean_squared_error(y_test, y_pred)\\n\",\n \"# 用scikit-learn计算RMSE\\n\",\n \"print \\\"RMSE:\\\",np.sqrt(metrics.mean_squared_error(y_test, y_pred))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"MSE: 20.7927937888\\n\",\n \"RMSE: 4.55991159879\\n\"\n ]\n }\n ],\n \"source\": [\n \"X = data[['AT', 'V', 'AP', 'RH']]\\n\",\n \"y = data[['PE']]\\n\",\n \"from sklearn.model_selection import cross_val_predict\\n\",\n \"predicted = cross_val_predict(linreg, X, y, cv=9)\\n\",\n \"# 用scikit-learn计算MSE\\n\",\n \"print \\\"MSE:\\\",metrics.mean_squared_error(y, predicted)\\n\",\n \"# 用scikit-learn计算RMSE\\n\",\n \"print \\\"RMSE:\\\",np.sqrt(metrics.mean_squared_error(y, predicted))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\Python27\\\\lib\\\\site-packages\\\\matplotlib\\\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\\n\",\n \" if self._edgecolors == str('face'):\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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/sSfBbVlSvXsWfPHhvl9L/orvwm4tFFy1DT0mHikVCLUG2jCTUNnYCS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\\n4orCcRzHyYkrCsdxHCcnrigcx3GcnPw/BVOaDlMF3+MAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, ax = plt.subplots()\\n\",\n \"ax.scatter(y, predicted)\\n\",\n \"ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4)\\n\",\n \"ax.set_xlabel('Measured')\\n\",\n \"ax.set_ylabel('Predicted')\\n\",\n \"plt.show()\"\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\": \"3.6.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "classic-machine-learning/lle.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C) 2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"用scikit-learn研究局部线性嵌入(LLE) https://www.cnblogs.com/pinard/p/6273377.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from mpl_toolkits.mplot3d import Axes3D\\n\",\n \"%matplotlib inline\\n\",\n \"from sklearn import manifold, datasets\\n\",\n \"from sklearn.utils import check_random_state\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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Xa73djtduLi4s70IZ2G/IDpv2YmC4Fc9LmlEs+DqfbiP+5IVn3xdxv7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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"n_samples = 500\\n\",\n \"random_state = check_random_state(0)\\n\",\n \"p = random_state.rand(n_samples) * (2 * np.pi - 0.55)\\n\",\n \"t = random_state.rand(n_samples) * np.pi\\n\",\n \"\\n\",\n \"# 让球体不闭合，符合流形定义\\n\",\n \"indices = ((t < (np.pi - (np.pi / 8))) & (t > ((np.pi / 8))))\\n\",\n \"colors = p[indices]\\n\",\n \"x, y, z = np.sin(t[indices]) * np.cos(p[indices]), \\\\\\n\",\n \" np.sin(t[indices]) * np.sin(p[indices]), \\\\\\n\",\n \" np.cos(t[indices])\\n\",\n \"\\n\",\n \"# Plot our dataset.\\n\",\n \"fig = plt.figure()\\n\",\n \"ax = Axes3D(fig, elev=30, azim=-20)\\n\",\n \"ax.scatter(x, y, z, c=p[indices], marker='o', cmap=plt.cm.rainbow)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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Et0vHecMrWKCg9n+tzaQ18ep/PqWdQ7O+RqMy4ujk7urrR30tLOTkVH\\nJxNXrVpFTz9vGhxtWa1uLZ47d45BAT4c2EbFJWPBikE6jnrr9Xy/f9HR0XTzMHDJZgeu2ulI3zJG\\nfrpkccF+CUKhiQQhPFdfffUVjVotbTQaujk6smPHrlSpIgh8mpUgGlIms2fnzt25ZcsWxsfH56vd\\nLl36UKerSmAWdbp6bNiw9TP9Btq/ezc2d9TySEkwyh10NOh56tSp7NevXLnC4W+MZLdeA7h+/fpc\\n9Tdv3pwjQaTUBzVKOe10avYtCbZ0A4OM4IIwMLSM7yPjSEhIoFKvJy4+sCaIuFRqa9WlVqWgzs2e\\nDrH76WS5SN3UN6h3caKxX1v68jh9eZyl0g8TkkSz2ZyjzW79etN3bEdG8jtG8ju6d42g0kZPU5g3\\ny4xvxYCRzVm2Yihv3rzJMaPeZK/u7bnk08UFev+79WjHdz615Rl68gw9uTTagXXqVsx3faFoiAQh\\nPBf379/n0aNHadRo6AkNjdDQHWo62trS1bUEAQ8CpQm4E5hPCXJ2adeOly5dytXWzz//zKVLl2aP\\nVLp06RI1GkcCCQTSCSRSr/fhkSNHiv24LBYL3585kwYZ6K4AJzmC5gBwsLM6X53of/nrEtMbvgpu\\nKg8289DSw87Ar6uB7GDdOnmBNgYtf//998e2FdG0GVXN2lDRpiP1IeVotLPj7Pffo1anpqRUUDLo\\n6eZbkl27dqXC04U+t/dazyCi3qPWwS5XezUb1WPod5MYye9Y4/JyKmz19JvVmyHfTKSpcmmWGdeS\\nNm6OvHjxYoHfv/T0dMbExLBR43p8faopO0HMXm3PBo2qc+3atfzoo4/422+/FbhtoeCKIkGIxfqE\\nfDObzejXsyfWfvklaLEgw6JAItqCqIwk7ALubcf00aMxZcoapKa2BxCYVZOQb9iAGj/9hN9Pn86e\\nPDZz6lQsmDULlQAckSR0GzwYPfv1g0JhAqDLqquCXO6ApKSkYj++lStWYPmsafjZF1BKQPdYwE4O\\n3KAcoVptvtvR6/X4cf8vmDRmFKb/eggKbxtY/vgdl5OBfkfU8FRloIzRAtdWfREcHPzYtlYv+RRh\\nZf3R0isZVVyJ+Te1uJdwB2f+PIdTp07BxcUFQUFByMjIwO6QYFz2aQi5qyMsV29iQ9TaXO3VrloD\\nKxZugV3dYFyP+hGOLavAe1R7AIChnDcOhY+ALM1S4LkZSUlJiGhYF9eTb0ChU2LXe6l4cI9wdpNh\\nyawMeJe4h7kfDUDZEDPemQ7MnbMEXbp0LdA+hOegsBmmuDeIM4jnJiMjg/fv389+/P6sWQzS6fgh\\nwNcByuBKYG3WFkXAxO3bt9PW1pmS1I/ADKoQwppQcRvAcLWGUVFRJMnr16/TRq3m9wB/BbgToKNW\\ny1OnTtHXN4QKxWgCv1Mmm0E3N99c8wqKQ/smjbjaC2SwddvsDfqpwNJeHkxISChwe8uWLafOyZNo\\nMYVKkxPljl5Ej3lU1uhCuUbPDRs2PLGNVatWsXmgnnwT5Jtg3CBQp1HlecnHbDZz06ZN/OSTT7KH\\n9f5TWloa23frTKVGTZlCTveekaxz/0t6T+hMlYcjZTo1gytXKHBH/6QpkxjUqQJfN0/ncM5k9SkN\\n6Bfky1cG9+E777zD8KpG3klT8l6mij/9oqCTk7FA7QsFBzHMVSguc+d+BL3eCHt7J4SFVcb169ex\\nNyYGVZKToYH1+70MyQAys2qkQam0wN/fH/v3/4R69eIBTEcj/IGxsA6PTEpLw7p1X+OHH37AjBkz\\noJGk7AU1bAB4qFS4desWdu/eirp1/4SLS1vUrLkHe/dueyazjW0dHHE+8+//EufSAYO3L34+fBS2\\ntrYFbu+tcROQPPAboNlEZKQlw/z2XqDxMGS8ugaK0lWQnJz8xDbS09NhUv69koBRBZjNljwXAZTJ\\nZGjZsiUGDRqUPaw3rzJenl6wdXGGjasLrq/bg92uPXFt1S6YE1PgProLruuIidOmFOhY/7xwFu71\\nfSBlLVPi1dAPGp0OHy/6DA4ODggIIuRy66TeoHISEhKSsocjCy+wwmaY4t4gziCeuZiYGOp0jgRe\\nJzCJCkVt1qhRlyNee421VSouBLgAoANUlOBPoDu12jLs0qVXjnbq1axJf8g4AWBrKKiGA9UyBX30\\nevaVy1kaYHmAvwCcA9DJZMr3KKT4+Hi2bdeDfv4V2bpNN16/fr3Qx33mzBm62Jr4irOSrzkr6GQy\\nFKrvQ2MwEbNvEh9nEgoVsSyRWE1iNamr051L8zErOi4uji72Ji6IlLi3M9i8jJY9u3R46phGTxxP\\n+9qV6HNyIz1jllHSqem3dTYrcg/LXVhHhYs9/daMZ5mKoQVqd95H81iytj+HJE7hsMzpDO1bhX0H\\n9SVJHjt2jE5OOm7breD1B0q+9oaadetVyVe758+fZ5MW9Vk60JsdurThjRs3CnzM/1UQndRCcZg5\\ncyYVipoEJmdto6jR6Hnnzh16OTvTE2AZgA4AXRQKVgirxKVLl+YaMfPxxx9TrQikDlWpRDsC66gE\\n+CPAPwAeAegiSZRLEr2cnLh3797HxnXo0CF26NibzVt0podHKSpt3yAc9lNp8xb9SocyLS2t0Md+\\n+fJlzpo1izNmzODZs2cLXN9isXDJ4k9Yt0oFlnRzorJMLWLaGaJMBFG+OTH9MDHwMxrsnfLstM/L\\n8ePH2bxBHVYO9ufIEa8xNTW1wHH9pVRIWZb4ZQ39eZSlru2g3NbIityTvdm2qknXYW1ZpV7tArWb\\nmZnJHn17UmfS0ehgYs16tXj37t3s1zds2EA3NzsqdUrauNqwXpNInjlz5rFtPnjwgCVKurP3u6W5\\n4PcqbDOiJCuGh+T6OxPyJhKEUCxWrlxJvb40gYlZCaIrvbyswzE7tWnDxgBHAvwM4CSAFQMD82wn\\nNjaWRqNT1pnIUqrV4bSVy3kiK0H8AbCmjQ03btz4xJh+++036vSOhHYOoVtKSPaE7VeEGwlXC402\\ngS/E6JiPFy5ggKOO0TXB1VVAG7WcNo6uLBUUysYt27BEmXIMrx3JX3/99bnEF1qjGt2+nk1/HmXp\\njF8pM+ro/+MCVuQeBsdtpMLRhlpbU4EmLj7s1q1bvHr1anYfSXR0NDt2aMIunVuwUvXKLNejMjsc\\nGMqa7zenq5dbjjPG6OhoVq8RzLDyvpw+Ywp37NjBclXdsgbkRnLS5lDqjApqdSoGBftz9OjRPH/+\\nfJG8Ly8jkSCEYpGRkcE6dRrQYPCm0Vieer0tY2JiSJKvDxnC5goF1wJcC7A/wIa18/62efbsWbq5\\n+VImMxFQsVat+gzy8eHrMhl/BvghQGeTKV/zI3r3foXQziLsaN30mwhlnawEkU6doQSPHz+eXf55\\nfcusEhzAXXWQPZz1vRBwyMB+zyWWvGzbto16Jwc6jOtPU59WlNkYKLPRUxPoTaXJwAZNm2SvZVVY\\n33zzDd1ctVz+ETh7CihXKzgkYyZf43t8je/Rv1Fw9peDffv20clZx+Ubdfx2n54Vwo185ZX+9C7j\\nwE0Zdfnxyao0OSo5Y3sIa3Rwor2nliUr2VGtV/Dbb78tknhfNkWRIMQwVyEXhUKBHTu2YPv27bhz\\n5w6qV68Ob29vAMCoceNQZd063H/wAGqLBb8qldg+d26e7bRs2QXXr/cEOQhALH79tQ1WrZqHeTNn\\nYtmxY/Dx8MC3UVFwdnZ+YkyZmWYA6r+fkNSQLOfA5KXQYiOqhIcgMDAQZ8+eRYtWXXD65G9wdPbC\\n/6KWo27duk/9XpDE/AWLMP+T5VAoFHh7zHB06dL5keUVCgVSHup7TTFLUChUT73/ola/fn38tPUH\\nrN+4AbHmdFyLiIBGrUaL+o3QsGHD7N9zUfh40buYOy0FHVsBiYnA6BkWZCSlQ22jBUmk3U2GWm39\\nna77Kgr9XjejcSsNAGD6wky82ecH+JcMxozWp6EyZKJ8fTvcv5WJ65cy8e6ZplBp5Ni/9jJ6D+iO\\nW9fuFlncwt9EghDyJJfL0ahRo1zPu7u74/CJE1i3bh3S09OxuGVLlCxZMlc5i8WCU6d+A/l11jNe\\nsFgicfXqVfz4yy8FjmfQoJ5Yv74dktMcAMkEnfQGmrSpDplsL8qH1cabbw4HAETWb4FYyyAwdB9u\\nPtiFFq064vTJI/Dw8CjwPgHgk8VLMHb6QiRXXQyYU9B/SD8YjQY0b948z/LDRk/AgEF9MSklGQkZ\\nwPxYHXYNfjXf+7t9+za2bNkCSZLQtGnT7DkjRalChQqoUKFCkbf7TxaLBfKsQWEGA1CjqoSNEZ+i\\n7CtVcGN3LGwtxuzkrdHokHDn7xFkd+8QWo0W3236HgsWLsDW76MRe+cwrp1LgX8dZ6g0cgBAuYYu\\nWHa34H9PQv6IGwYJxcbVtSTi42fCem/oFOj1LRAVNQMtWrR4qva2b9+OqdPmIi0tHYNf6Y7evXvm\\neP3q1avw8y+PFP/47OdM8c2wctFAtGrV6qn2WbFaPfxmPxLwbmJ94o+laO38IzZ8ueqRdaKjo7F2\\nxVJodHoMfWMkQkJC8rWvS5cuoXa1SqhgnwILJPx+V4dtMXvh5ub2zG8qVBS+/uorDB/eCx9MSkZa\\nGvDWFA269xyMW/duw8fLByPfeCv7RlGXLl1Claph6NArDY4uxCfvy9CubR9otEoEBQaja9euaNws\\nEhfjjyEx0YxJB+vD6KTGdzNP4sDSW5g8/h0cOnwQpX3LYMirQ7LPTP7LxA2DhELLzMzkxImT6ecX\\nzPLlq+W5AujT2rVrF/V6B5pM9anXl2LHjj1zTPDasWMHa9ZsyooV6/HTT5cWer2lpKQkqtR6ouxF\\nojyJ0GTqbXy5b9++p26zRt0mROQqYjCJwaRU9V127zWgUHHm5cGDB2xYrw57h8iY/haYMRIMctdS\\nUqqoUGvYpnO3IhmlVZxu3LjBnTt35li3av369WzerDZbtazHbdu2Pbb++fPn+dbI4Xz11X5s2CiC\\nlaqZOGamjlVqmtilaxumpqZy+fLl9CzhRoVKRr2dijYOerbt0Jr+Vd3ZeW4FVmjmw3oN6zAzM7O4\\nD/eFhxehkxpAYwCnAJwBMPoRZT7Kev0ogPIPPX8RwO8ADgM4+Ii6xfLmCVajR4+jTudP4C0C/anR\\n2LBFkyZs26QJV61cWej2r1y5wk2bNnHfvn05EsC+ffuo1ToR+ILAZup0ZbhoUeFX/Jwzdz51Jk9q\\n3AfSYB/Cjp1755l44uLieObMmUd+kFy9epV9+g1mhSoRVGpsiPDpRKW3qbdx5LFjxwod58MuX75M\\nNx9fKnzLU+NZimEldJxYS0ZVcDXiu0QiOpnaak05ZsLbRbrforRjxw6anBzoUSuMBhcHvjVu9FO3\\ndfbsWTq76HgmyZ5X6MAzyfZ089Dz5MmT2WUuXLjANWvWcM2aNdQZtVxwvwPHH2jIoEhnagwKVqxU\\n4YnDpl92zz1BAJADOAvAB4ASwBEAgf8o0xRAdNbPVQDsf+i1CwDsn7CPYnnzBCsXF28CEwgsytoa\\nsywkvgGwhE7HuR9+WCz77dv3VQLvZ90JjgR2MiAgf5OnnuTnn3/mwoUL+e233+ZKDpmZmaxVJ5Iy\\npZYqrR39A8vnmmSXkJBAV89SVISNJOp9SY1HdQaUq8Chw97I1wgfs9nMme99wLCqdRjRqAUPHDjw\\n2PLN2nakvOtkYhOJDWaqq7eml5OOmPQVsYPWbcZmVqnX8LHtxMXF8bPPPuPq1atzLJHysDt37rBb\\np9b08XRk9crB/OWXX554PE9isVho7+rM0O0zGMEtrHH7S9r6uD/1B/SRI0foH2jDK3TI3sqG2GbH\\narFYOGjoIDp4ObB0hD+VOiUbvRVArY2SJmcVQxo5s/FwPxodtIxaG1Xo4/u3KooEUdhO6nAAZ0le\\nBABJktYCaAXg5ENlWgL4POuT/oAkSbaSJLmQ/OtCsbg7+nNkvVb795IPEhIRAiICgFdyMubMmgW5\\nUoWoqE2ws7PBjBkT831N/XEUCjkkKRXM7l5KQeK9mwgq4WVdFPDVIRg5bhwkqeB/HlWrVkXVqlXz\\nfK1R05bYfegSUGos0m/twp9XrqNP/6GI/nZddpktW7YgURuEzErvAQBSPRrgTJQLjh0+kK870U2Y\\nNBXzoqKR3GgGcOci6jVqhkM/70ZAQECe5c+cvwBzF2snO2QypFVojtgjOyH7/UdYarcDAChO7EVJ\\nL89H7vO+fT7bAAAgAElEQVT48eOoEVkf5vC6kBLvYezUaTi8by/s7e1zlOvcvgW8Zb9g25vp2H/m\\nFpo1roffjp586k58wLpQ34O792AXWR4AoLQ3wqZ6EM6cOYPq1asXuL2AgADAYsL8GXfRopMc0V9n\\nIjXZBmXLlgVg7Yv6Zvs36H2iH9RGNc5/fw5ft/0SRic1XErpMHJLTUiShGpdvPBGu9fRudOjR50J\\nj1fYtZg8AMQ+9PhK1nP5LUMA2yVJOiRJ0oBCxiI8hWnTJkCnWwVgFyRpPRQ4gAZZrykBPHjwAKNH\\nz8e+fZGIjvZAjRr1cPbs2ULvd+jQAdDp5gF4H8ASKOTtwRsX8FniFaxNuYY1783EovnzC72fhyUk\\nJGDXzl1AzQOA/0Sg6veAJRMHDx7IUY4kIMn/fiLrZ/6dzR5ryWcrkNzxc6BMfaBaf6RU6IUvv1z3\\nyPI+Xh6QtiwCzGYgNQm63V9g8ui34HHsBxhHR8I4tiEc967FB9OnPrKNIaPG4MErE5E0OwqJi6Nx\\nPaw23v1gdo4yycnJ+HHPfizqnQ4/V6B7LaB2APHjjz/m67geRa/Xw8nNBTe+/AkAkHr5BhJifn/i\\nSrWPolar8cP3u3F0T3l0jVTg0M4wbPthN7RZK+qeP38eHjU8oTZaO6J96pdEZmomKtZQwb2MIftL\\nhau/AXcT7hfq2P7rCnsGkd/hRY/6GliT5FVJkpwAbJMk6RTJ3f8sNHny5OyfIyIiEBERUdA4/7NI\\n4urVqzCbzfDy8sr1jbxnzx5wcnLE//63HhaLAd98pcDBlAw4A4jS6ZBOOZJTxgMoBRJISbmOqKgo\\nTJw4sVBxBQcHY+/e7XjvvQU49ttB3DqTihk6oqrS+voMSxLmrP4CQ4YNK9R+HpaYmAi5Sg+LMmvo\\nqEwJqJzg5paRo1yjRo2geWMMUo5MhdmhMnR/zkHrLj2gVCrztR+5QgFkpGQ/lmWmQKFwyLPskSNH\\ncOjH71FKJ+Fyj03IzMiAT2AQJowfhzffGIEdO3bAYrEgMjISJpMpzzYA4Fp8PFi2Yvbj9LIVcfl8\\nzsSnUqkgSRJu3APc7a0X9uISAIPBkK/jehRJkvDtVxvQsEUzXB39OVJv3cP06dMLNZTW29sbW6Lz\\nTlxhYWG48M553I+9B5OXDX5fdhglggzoOtIVr0WeRbUuXnAPNCHqzRNo2DjyqWP4t4mJiUFMTEzR\\nNlqY61MAqgLY+tDjsfhHRzWATwB0fujxKQAuebQ1CcCbeTxfhFfl/lvS0tLYqkkTmjQa2mm1rFOt\\nGh88ePDYOvPnz6dRLqceoJONDW1tXQisILCHwB7K5e05bdq0Io3Ty8GeHdTgDD1IZ+v2qRFsXT+y\\nSPdjNpvpH1Se8BtNRF4iQhZTUujzXPbi4sWLbN+pJ8NrNOCEt6cyPT093/uZ99EC6tz8iC6fUdb4\\nbdo4uvLy5ct5lh07ehQn1AAn1FZQpVFT6+hMuVZf4I7wV4e/QU39lsThRGL3VeoCQ7hixee5yk2f\\nNpllvPSc0QlsFa5hjSphRTI66sGDB6xfrxrtbFS0MarYrUvbIr03+D/NnjubGr2GOkctbV3VXHUi\\niHtYkW2HOlFjVFBnULNtx5Y51oP6r8EL0EmtAHAO1k5qFZ7cSV0VWZ3UsK4Ybcz6WQ9gL4CGeeyj\\nuN6/l960KVMYotVydtZqqVXVag4ZNOiR5a9cuUJ7nY4fZd2fYTRAB4OBWm0pAlMoSYNpNDoW+fo3\\nJRwd+K0N6CyBb2jB0VrQqJAzoIQXQ0r58P2ZM4vslqNxcXGsE9mMRlsX+gWEPbED+WlFRa1lq47d\\n2GfAYJ47d+6R5Sa9PZHtAmXUOzoTy64TX5N4dSlLBQYXaH8pKSls27U75SoVlVotx0x8m2azmUeO\\nHOH27dt569at7LIbNmzgyDdHcM6cOUxOTn7qY3zYa0MHsHtLNTOOgUm/gfVr6Pj+ezOLpO1HOXHi\\nBNV6NQ2OSo5cXIIjF5egzijjpEmTinW//xbPPUFYY0ATAKdhHc00Nuu5QQAGPVRmQdbrRwFUyHqu\\nVFZCOQLg+F9182i/uN6/l16rxo3ZC+BHWdsQgFXDwvIse/jwYbq4eDII4K6HNmedjrNmvcfIyBbs\\n0KE7//jjjyKPc+bUqQwx6rjQADZQgVqFnM5aFbe5gvvdwVAbHed+8EGR7/dFcPHiRRr1Girq9rQm\\nh69J/C+dkkz2VOtJZWRk0Gw202KxcEDf7vRy1rFWORu6OJqKLRlmZmayetWyjPkc5EnrtvJdsHPH\\nZsWyv4ct/GQhdSYdHb1NNNqrOHqMdXhtYmIie/ftyTJlS7NZyyb5vh/6y+SFSBDFvYkEkT8Wi4Vx\\ncXE5hmy+NWIEq6nVnAdwHsD6CgV7du6cq+79+/dpZ+dKoAdNUPO7rOTwBUCVJNHRaGSVcuV49OjR\\nYov9k4UL2SKiNru3bcN2TZtwkSPIUtZtpxsY5u/HsAq1aGfvwYh6zRkXF1cssTwPy5cvp8LFh1h5\\n15ogRn5N91KlC9Xmxo0bGVJKz8QlIFeBXw4Fg/y9iybgLOnp6RzQrzvVagWNeoljBkjkSdDyB9iz\\ntZpjx7xVpPt7lPPnz3Pbtm3Zy6dbLBaGhJWl2qSif7OSNHkY6OBmz6SkpGcSz4tCJAiBpPXbUv3a\\ntWnSaGhQq9m6WTOmpaXx3r17rBgcTB+jkd56PV0dHbl48eJct+88cOAATaYyBKKpQhPaQ8MakGiQ\\nJNaWJG4B+DZAVzs73r59u0hjP3r0KJcuXcqtW7dmX0YaOmAAJ9tL2QliiSOoUBopOS8mvC9S4TSO\\nZQIqFHrF1pSUFPbr0ZVGrZp2eg2bNG70TCdXRUdHMzw0gEG+ngyvVIEaexfalKtGk6ML9+/fX6i2\\nZ8+ezWGNVeQqa4JIXAKqVYoiitxq8tvj2KC6jvf2gMfWgbZGiZVD1KxQzsjwSmVzzMXYunUrG7dr\\nxSbtWz9xRnVhXbx4kXK1nAN+6cnJHM2x94fT6G7gRx99VKz7fdGIBCGQJF8fOpTlNRpOybo/Q1mt\\nllOyrsOmp6dz5cqVtNPrWUOvZ3mDgYGlSuW4x/K5c+eo0dgT+JJAdNZd5DS0U6t5KOue0b8CDDeZ\\n+P333xdZ3CtWrKRO50K9TS8ajOXYvoN1KY7Tp0/TyWTkaDuJ0+1AG7WaOruahB+tm88NmpQq2um1\\nDPIp8dTLgwx/dRCbe2l5oxF4oi7oolVSpbNjVNTaIjvGRzlw4ACdbXX8tjN4eCBYx0/HgX178ccf\\nf8z3XfUeZ/v27fR11zN+gTVBzO8pMbxCUBFE/rd6dSrw+0Ugj1i35VPBWjUqcteuXTk6vrds2UKj\\nqxN9V4ym7/JRNLg4FipJPHjwgBs3buTXX3+d573CT548SblazkmWUXwrfihH3RrG0k1LsVmz4r/k\\n9SIRCUIgSVavUIG9AL6TtXUC2Kx+/ezX69esyX6SxLUAowBGqlQcP3ZsjjaGDBlBvd6banUr6vUl\\nOHjwUBpUKu7ISg77Afro9YX+ZvuXjIwMqtUGQvcHYSRhSKHeGMDJkyczNKw2/UqXZ5OGDTli6BAu\\nXbqUeht/wjed8CP12koc5ATGh4Lf+YGOBt1T3f0tyMeTR+qAbGndPiwLqkq0pItHqSI5xscZO3oU\\nJ9UB+bZ1OzEY9PVyKdJ9TJ08gSaDmiXdDPT1duPp06eLtP0unVpy5jBZdoJ4q5eCQ1/tn6tcwzYt\\n6LdyDKtxJ6txJ30/G8lmHdo+1T5v3LjBMgHerF7PgXUaOdDbJ/cIMbPZTJ2Nlq7lXaix01BlUFFl\\nVFGrVRXZYId/g6JIEGK575eAX0AAzv/+O0pnZoIALqjVqBEYmP36ldhYhJGwwDoz0js9HXGXLuVo\\nY/782WjevCFOnTqFoKBX0bBhQxjUCryyZAkikpLwm16PsFq1EB4eXiQxJyYmwkIA8qw4JQ0sDMCM\\nmfOQrlsJSGpcvT4EHTp3Ra9ePbD2f99g36+NkMxaSE05hAVBgEICmtkCjVMl/PTTT/D19S1QDA4O\\nDjiZeAWhNtbHRx4okaH3RdLVfUVyjI+j0xsQl6IAkAkAiE8EdFkTwYrKxEnTMHjI60hISICPj0++\\n53Hk17TpH6JOrT349VQa0jMlHD9vwJ59f0/m+/nnn3Hy5EncvXsXeHj+jXWV0afb5zsTUaXBfUz8\\nyLq67bxJSRg7/g18sfLviYgymQzB5YJx1/ku+h7oDUuGBWubrUXs3qvIzMws8vfhpVbYDFPcG8QZ\\nxBNdv36dpb296Ws00sdoZGhgIO/evUuLxcIhAwdSI5PRCNAb4CyAvno9P/889xj5f7JYLFy/fj3H\\njx/P5cuXF+kKmRaLhSVLlaOk+ZAwmAndfsrlJsIww3qXODcSdt+wcrj1TCgjI4Offvop33prNHUq\\nBc+UA1kJNFcEqzkZuH79+gLHsGfPHtrrNexTAmzsqqZW70q1TzN26tanyI7zUa5evUpPVwcOraLg\\nu5Ggm52W/1tb/Je2itqNGze4YsUKrlq1KsflnrenTaHJ24PuPZtR6+xAjaMt/VaOod/nY2hwcXzq\\nS5Wt2zbg3LX2PENPnqEnP/vekRH1KuUqFxIewh47u3Iix3Eix7H1Fy3p6lO0Z2gvOohLTMJfkpOT\\nuWPHDsbExGTf1H716tUsrdfzs6xLSy0AagGOHTXqhTjVPnv2LP39y1MmU9BodGTNWpGEafbfCcJm\\nFWvWapqr3scLFtDLpOMYdxkbOOsYUaVyjolsGRkZPH78OM+ePfvE4zx9+jT79etHeyc32ti7sWvP\\n/s9stEtcXBwnjh/HEcOGcNeuXc9kn89CbGwsdfa2LBu/mWH8meVuRFNtNLB2kwZs2KZFofqx3nt/\\nJqtG2PLwfXceS/Zgvea2HDsu92ipTj06sdbYGpzIcZxgGcuQ7sEcMmxIYQ7rX0ckCOGR/vjjD1ap\\nWJGds+4dvRbgXIAeDg7PO7RcUlNTabFY+Ntvv1FvcCSMMwjjh9TqnLh9+/YcZdet+4puHqWp1duy\\nXHB5Lly4MDshkmR8fDz9A8vTYO9LrdGVzVp2KNYZvUJuhw4domNoAMP4c/bmVD6IBw8efGSdvyb1\\nHThwIMfv858yMzPZf0APqtUKajQKdu7SOs/ycXFx9CntQ7+afvSp5MPgisH/uVnVRZEgxB3lXkJH\\njhxBvZo14ZWUhHQAE2Cd8v6DJOFspUr48eDB5xzhox09ehQfzf8UmZlmDBzQAzVq1Mh+7cCBA6hb\\nvxVS3NYBqlLQ3ByO1vVNiFq9LLtM63bdsPmIGzJLvg9Y0qA71QLvjGyOESNefx6H85/04MEDePv7\\nwWbRCNi0roN7m37CvcEf4uLpM3muJ5WWlobGrVvg6J9/QKnXwkQldm/bCVdX10fuIyUlBRaL5bF3\\n2ktMTMS+ffsgl8tRs2bN/9xd5orijnIiQbyEOrVuDdWmTWgAYB6sS+kaFQqk2thgx549j1x2+kU3\\nZcpUTP0kBRa3mdYn0mNhigvHvYRr2WVKlQ7DBftlgClr4borH6OBz/d44/VXEB4enmv56/+yjIwM\\nbN26Fffv30edOnXg6Zl7OfGMjAzcvHkTTk5OBercPXjwIFp37oj42Dg4e7pj49ovUaVKlTzLzpz1\\nLhbu/Qb+68dAkstwaexKhMZK+Hr1/5762ISiSRCFXe5beAEl3r8PW1jv5jQc1jtC2wcGYu+hQxg9\\nehKcnLwQElIFhw4dei7xXbhwAR2bN0P14LJ4c+gQpKSkPLkSADs7W6h47u8n0s7BZLLNUSYosAwU\\ndzZYlyq1ZEB28ysc3bMV77/SCSEBpfHHH38U5aH8a6WmpqJaZH10nfIOXvnfBgSWr4ADB3Ku/rpt\\n2za4u9mjfIgf3N3ssWTJEvz222/IyMh4RKt/Cw8Px9XzF5H04AGuXbj0yOQAAL+f/gPGFhUhU8gh\\nSRLs2lTFH6dPP/WxHTlyBOMnjMPUaVNx5cqVp25HgOiDeBmt/Pxzeuh0fBvgZIBeOh2XLVvG0NAq\\nlKRGBJYReIsGg/0zX7Lizp07LOHsyBnOMv7kCbZ30LBtk8b5qnvv3j2WLFWWGpf2lLmNotbgnGv0\\nUlxcHL1LBdHoHEyVwZN2OgOTWoJsC35cXmLdqrlHvOTX3bt3+fHHH/ODDz7I153lXmQLFy6kNrIx\\nEZtMxKUSi1YxoFLl7Ndv375NR3s9YxaBibvAMoFaytycaSrjx7KVKxXJZL6/vPfB+3RvVJl1Uzew\\nnuVblhzWip16dX+qtmJiYmjvaGD38V5sM9SDLm72vHDhQpHF+m8C0UktPMqC+fMZ4O3NMiVKcP68\\nebx8+TIBOYFvs2ZLR1OSKnL16tXPNK6vvvqKTZyNZGmQpcFUX1CrVDxxGfK/3Lt3j/Pnz+fUqdMe\\n2emZkpLCgwcPslf3bpweZE0ObAueaQj6uDo+Vdx37tyhV6ky1JZtT1Xl16izcfzXjTxKS0vj6dOn\\nefPmTY4dN454c4I1OcSlEgdO087dI7vs/v37WbGsidwPvtlbQVPHxrRPv0T7zFgaB/Vgn8GDiyyu\\n9PR0NmvXmkZXR9qV8mS5SuV58+bNp2qrTmRVjl9TOmtKXjV2HePF10cMLbJY/02KIkGIiXIvqSFD\\nh2LI0KHZj9evX5/10z0A9gAsIG8hLS3tmcalVCqRZLFeAZIkIJWAhYRcLn9yZQAmkwlDHzquvGg0\\nGlSuXBmNmjbDrB82YnB6EmyVwCeXFahYseJj6z7KwoWLEG+shvQGywEA6W4RGDJiDE4c3v9U7T1r\\nf/75J5o2ioAl4wFu30tHi5ZtoP/5VyR16gW4ukO56ENUf2hAgKenJy7EpSM2Hvg9VgNL/1aQ/vod\\ntW6MY+8vKbLYlEolvl23HufPn0d6ejpKly4NhUIBs9mMufPnYcfuGHi4umNAr77Ys2cP5HI5Onbs\\nCDc3t1xtJSY+gLOXKvuxk5cCD47cK7JY/3MKm2GKe4M4g8glLS0t39+4SXL5Z59Rr1JRBTkBRwK9\\nCIQT0PLq1avFGGluSUlJDPbz5QBHFVe4gNXtdBzSv1+x7MtisfCt14fSoFHR2ahleEjZHKvdFsRr\\nr79B1HqXeIvWrfcJupYo3Iqrz1J4hSDOHyyR0WD8GtDXU8++AwdSodFQrlazWmRkroUY5839gC6O\\nWvqW0lPVLJL2aRdpn3GZpj6dOWBo8X8rf2XYELrXCGKV/w1lyd61qdCrGTgwggF9atHR3SXP+5K8\\nM2Mqy1V15LJjoZy3uyzdSpj43XffFXusLyKIS0z/LRaLhW+PH0+VQkG1QsGI6tV58eJFTpo0mX37\\nDmRUVFSuiWGnTp2ivVbLZQC3AewAUAYllUpHDhr05P/kFouFmzdv5sKFC7lv374iOY47d+5w9Bsj\\n2K11Ky6YN6/Qq7I+SUJCAq9cuVKoyYFbt26lzqEE0fMIMTiemoCW7JeP9+9FoVLKmbQeZLR1G9Za\\nxdmzZzMjI+OxEwNPnDjBtWvXskLNGjR4edDgU4Lla1Qv9jkFmZmZVKpVbHFnMdvxC3q0r8xKH3Zg\\nD37KHvyUYZNasPfAvnnWm/D2WJb0c2eZIB8uX/FZscb5IhMJ4j/mq6++ortOx9EApwKsrFTSoLej\\nSlWZQEvq9Z6cOHFSjjrr1q1jbZOJ27ISxDaAermcixcvfuIHpsViYbdu/ajXB1Kj6UGdzoMffDCn\\nGI/wxfbJJ5/SztmDWoMtu/box5SUlOcdUr4FlSnBL8dak0PiejDET89NmzblWTYpKSlX0jabzTx1\\n6hRPnDhRpEuuPEpGRgYVKiVb3l/CdvyCzpFBrLdlWHaCqLm6H5u1b5Wr3p49ezh16lQuWrToP3f/\\nh38SCeI/Zvhrr7HRQ6u2NgEoSSUIvEtgFoHxVCrVOf5zHzp0iK46Hb/OSg4LANrodPmaXXzw4EHq\\n9d4EzhO4TuAQVSpDgS5vCS+GgwcP0sXJxFphNvRy1XFgvx65viBcvHiRlcoHUq2W02TU8ItVK59T\\ntFY9+/ehV4Mw1tgykq5NQmkX4sk2F2aw1elpdAn25qdLP81R/vOVK+jsbmT3Me6s1cKFFSqXK7Jb\\nqv4bFUWCEJ3U/yJePj7YodGAqamQANwAIElG/D0XRguz2Qyz2QyZzDrFpWLFiujz6qsYvGgRSiqV\\nOJORgRWrV0OhePKv/saNG1AofGG9fTgAeEKhMCAhIQEGgyFfMV+5cgWjR0/GpUvX0KBBDYwfPypf\\n+y4OJPHjjz/i+vXrqFy5coFXf/03q1y5Mv44dQFHjx6Fg4MDgoODIUk551B17tAcbaqexsFFFpw4\\nb0b9119BueAQhIaGPpeYly5ajHfenYHts3eipksA7HxssS78A8jkMgx/7XX079s/R/mRo0bg3WhP\\n+JfXgSRGNo7DunXr0LNnz+cS/8tAzKT+F0lJSUFE9eq4fe4cjAAuWCxIMwOpqVUAlINGswf16nlh\\n8+aNueoeO3YMsbGxCA4OhpeXV772d+3aNfj7hyAxcT6AWpCkz+HhsQIXL57M16ijhIQEBARWwO17\\n3WC2hEOnmod2bXyxcuWnBTvwIkASHbv0xpadByGzKQdzfAzWrv4MLVq0KHBbcXFxePf9D3HjVgLa\\ntWyCjh07FEPEz1ZGRga0WjXSfiL++tX2m6lDlaZzMHDgwOcbXD5ptCp8E18WepP1AD589TrqBIzC\\nsGHDnnNkz0dRzKR+7peQnrRBXGLKIS0tjd999x0XLFhAV9cS1OtLUCazoVptYo8efXLdTrSwYmJi\\n6OzsTUmS0d+/fIFuOhMVFUWDTXNCS+umuU+5XJVj5dWCunbtGmNjYwvc4bxlyxbqncsRbVKI9iTq\\n/ky9yYF//vlngRbzi4+Pp6OrF+VV3yQaL6bOpTQ/mD33ifXMZjNXrFjBkW+O4NKlS5/JdfyCsFgs\\ndHY0cf9SkPvBtN1g+UADv/3222ceS0JCApu3b029rYmefj75jqF1u6Zs1tuVG6+GcM620rR3NPDY\\nsWPFHO2LC6IP4r+rfv2mlMvrE3iHwBRqtUGcPXt2se3vaUYarV27lgabZg8liHtPnSDS09PZsnVn\\nqrV21OidWLV6ZI57Hj/JkiVLqPPvbU0O7SxU+b9GjRwsYa9j2dI+2Te8f5J58+ZRHdaTGEvr1v84\\n7Zw9HlvHYrGwX6+urOKr44ymYE1/Hbu0b/VCLLn+sI0bN9LJQcdWETraOBvp4FOCk99555mvhtuk\\nTQv69q3HJvGfsMbOCTQ62fHo0aNPrHf37l127NKa9o5Glg4owejo6GcQ7YurKBKEWIvpXygqKgq7\\nf9oNs7lM1jNypKSUxPHjp4ptn3/1aRREo0aNoNf+ATnHAeaN0Clao1u33k91R6/3P5iD7fvuIC0w\\nDqkB13D4ggdGvDku3/UrV64MXt8C3D8JxK2He/xixHUBLrZJRmfbWPTv3jlf7aSnp8OiemhFUpUR\\nGRnpj61z6dIlfLNxPXb0S8bYSGBb32TsjtmGkydP5jv+Z6FVq1b4dvNO7DyhR2q/QUj6YCZmb/se\\nA54wMbGo7djyAwLmdIPa2QZOdcvCrWMV7Nq164n1bGxs8L81G3D75n38efISmjRp8gyifbmJBPEv\\nExUVhRH9+8MlPRUSfgVAAOnQ6f5EpUphAIDDhw+jWmgoPBwc0LZpU9y6deu5xGpra4tfD+1Gl/a3\\nUKvqMowd3QjLli18qrb27T+MZH0PQKYFJDnSTH1x4ODhJ9a7efMmrl69ipCQECya9x7Uu8Mh+6UL\\nupVMh73GOpu7r58ZR44dz1ccrVq1gur0WuDoZ0DsHui+74We3bs/tk5iYiLsDUros1ab1igBR6MC\\n9+/fh8Viydd+n5U///wTsmrhUE4YDUWThrBErcCq5cuRmZn5zGIw2tkg6cx1ANYrHCl/xsPW1roo\\n4zfffIMevTpg0OA+OF2IBf2EfCrsKUhxbxCXmHKoHR7O/gCnA3SGknLoKJNp2b59Z2ZmZjI+Pp5O\\nJhNHAlwJsK1SyWrly79wlzMKatTo8VS7dScqWohKpMJzLNu17/HI8pmZmezdtRNtdCo6GjSsWz2c\\nJ06cYPeObVmulCd9bBS82wNkf3BpLYlVw8rlO5aDBw+yRt3GDAitwnETJj/xEkxaWhoDfEtwWlM5\\nz44F32spo5ONlmqVnBq1gsNfG1zskwXza9WqVTQ1b0pD0k0akm5Sf+UMFWp1ofpMNm/ezIoVQ2lr\\nq2VgoOcT+xRWrf6CJlcHlnmrBUs0qcjQKhWZkpLCz1eu+D975x3X5PX98c+TnSchEBL2FlFQceHA\\nvUBwi3uPqnXX0bprXXWP1tm66q7btto6sK2zVVvEWmfdG7eobEI+vz9CqXwZIqC2P/N+ve7rleQ5\\n99zzRHlO7r3nnkM3Ty0nLLHnwIl6Gh10vHjxYr7t+v8OrHsQ7x51q1ZlN4DzAH4GMBRg80aNMq5v\\n27aNVV84GLcHoEomY+/efThz5kwmJycXqj337t3jV199xRUrVmRJ1UBaDjz17TuEWhsH2tu787PP\\n5uVrnGfPnjGwTDC1htK0caxMD6/iuaYJmfvZHNbyEBnXDjR1BDv5KWiwUfPjICkjw8GGnhIa1BJW\\n99bRzUHPP//8M1925ZXr16+zYUhNeroYWNLPg5V8VXw0HXw4DaxWXOScWTNIknv27GHPbh04sN/7\\nrxQQUFg8evSIjl6eVA8bTNXXK2hTrQp79s9/qc4Zc2ZT5WKk05DWNFQpyqBqShqNKkZHR+fa78iR\\nI5wyZQqXLFmScZahdNmiXLXPiefoxXP0Yo/hdhw5ani+bfv/jtVBvIN8//33tFer2RZgS4B2osij\\nR49mXN+7dy+LabXcnZFaQ0rAQKAjgRK0s3N96QnTlJQUXr169aURUZcvX6a9vRs1mjbUaFrQ0dGL\\nN2/ezCQzcuQ4itrahOY6IZ6iqCnK+fMXsGHtGizm7sLWjRvmOT9SSkoKDx48yJ9//vml99C1bSsu\\nC7KbSqwAACAASURBVAbZ2dKmlweDnQSyp2XWkNQdFBUyfvfdd3zy5EmuumJiYrhz505GRUUVykws\\nvE4V7ugNcoGlbe0JNgmrxc2bNtHVKHJ+R3BcM4GOBptMv5AfPXrESRMncPAH/V5rfqGbN2+yU8+e\\nrNW4MafOmJHv2UNSUhIVahUDb2xlEA+zfMp+2pdyZssICadOnfrK+gJKenHTb84ZDmLABDt+NGxI\\nvmx7F7A6iHeUyMhItmnWjO1btuTPP/+c6aGVmprK6hUqMBBgEJCe4nsDgb0EdhPwYLt2HXLU/fvv\\nv9Pe3pWi6EaVSsdx4yYwOLAUPQz2bN2oER8+fJghGxHRiRLJZFpys5JS6Sh27donk75ixSsS6sOE\\nDS1N8TntRTVneEh4piT4kZucQSX8Cz3sc/zYj9nOT8m0ThYH0bGIhEGOUpp7WBzE866gWiF7aSTU\\nvn37aLTTMDTAll5GkX16dC2wk+jaoTUnNpZmOIiPG0jZs1tHVirnz91DQa6wtFGNBQ770PIAjI2N\\nZfGiHuxeT8GZnUEfV5ELF2SejcXHx/+r6m8/fPiQKlsbljcfYhAPM4iH6dq0HCtWVHDRokWvrG/G\\nzKkMKGPL5XsdOX2NgQajhlFRUa/B8v8fWB3EO8ypU6fo4VGEUqmcNjZ6LliwgNu3b+fVq1e5cuVK\\nCoIdgQACKgJ70h3EXgKVGBBQNludJpOJRqM7gaXpqTUOEtBwshw8qwZ7i3LWqVSJly5d4q5du1i6\\ndM302hJMb5tYr15EJp3BVeoTqhUZDkKqaM0yOgVZAWQF0BwEuutEXr58+ZW/g2fPnnHXrl388ccf\\nsxSuj4uLY7UKZVnGRcvqXjr6uDnTv4gH3y8h4/o6YLAj6O3u9tLCN16uDtzdDuRoMO4jsISrhrt3\\n735lW1/k6tWrdHc2sHUlDVtU0NDDxcgbN26wTAkf/jrmHwcxrRU4aICl7sLixYsZUUUkN4PcDJ6e\\nAzoZdSQt5wbCQqpn7Gl8OnFcruO/qf0os9nMEkHl6DqmK8s82knf76ZRbqNksWIerxSi/KK+ufPm\\nsHrNsqwfXo0HDhx4DVb//+Ff4SAAhAM4D+AigBE5yMxLv34SQLlX7Ptavrz/MiaTiU5O7gQiCEwi\\n0IuAnKIYQLXall26dCFgT2BrupNoRWA9gdEERHbq1D1bvTExMVSpDOnOwdJkqMavlWCCBnwugiqp\\nhAa1iiEGW6plaioUNQg8JnCfohjM2bPnZtJ57NgxajRGysWBVGm70NbWkUVtRaaUtziI5+VAvVrJ\\nmJiYV/oObt68SRd3X+pca9LGqQIDSlXg06dPM8mkpKRw//79jIyM5LNnz/jTTz9RVKlo41KJ0jLj\\nKC/eg/UbRuQwguV7lkgEmkZaHARHg70qqvP16/d/uX//fsbezd+zspnTpzLQS819I8DN/UBHvZiR\\nQfezzz5j33BlhoN4sBy00ShJkh3bRbBXAwVTd4B31oL+3poslfZIyywkoml9KpUyOhp1XLVyxSvZ\\nnB/HcuvWLVYNqUOlVkO9qzMHDhyY5d/JyuvhrTsIWMoeXwLgDUAO4A8AAf8j0xDAzvTXlQEczWtf\\nWh1Etty8eZNqtT79kNzfzY9AXwIjKZHIKQhuBLYTWJVe+0FJQdDRw8M3281k0vJAVavt0pei7hI4\\nSwn0PKyyOIjLalAG8Lw9SEfwnB6US0XKZCrKZCr27z8022icyMhI9u3bl5MnT2ZMTAwb1a3NMEc1\\n57iDVYwie3V59fKSzSI6UFrkE6IOidpmKj268qNho/nrr7/y/T4DOeCDoTx37lymPrNmzaKi1AdE\\nZ1pa21gqVJpcxykTUJRfhAvkaPB6f9DdIPLIkSOvbO/LSExMZI2QcKp0dtTbyOloL3Lz5s0Z18+f\\nP0+jXuTGIeCp2WDTyip279KOJOnj6cALS5GRyntqN/CjoYOzjNG2VWO+10TBuL3gHytAV0eRhw8f\\nfqltf/75J31LB1IildK7RACPHz9eeDeeR5KSkjjm4+EMCavKHr0657uux7vEv8FBVAGw+4X3IwGM\\n/B+ZLwG0feH9eQDOeelLq4PIlvj4eCoUIoEh6c5hLAEtJSif/lpBGxsHCkJHAvMokUTQ3b0o9+7d\\n+9IU1du2baMoGmhrW4ei6EJ3Zw820Ko5Tg4WVavoqVKSjshopfU6/v777zmGaY756CM6atQMdrKl\\no60NDx8+zOTkZM79/HMO7P0+ly1blq8Qz5JlqhLlDlgcRB0SAasYXLUORRtHImA6hWJjqbV1yKgd\\nbTKZuGTJEoreoUQns8VBhP9Ko7NnruOcO3eOvp4udLMXqVUr+PnsWa9sa14YN2ES1ZWbEetSifVm\\nysP7s0O3nplkDh06xGqVStO/qBsH9uuZ8W9ZrXJprv7Q4hzMP4DNq6k4Z86cLGMY9BrGfAfysKWN\\n6CThp59+mqtdCQkJNHq4U7diFh2SLlD39TzqXZzf+CygVZsmrNtEzy++d+B7H9nTr7inNavwSygM\\nB1HQtJpuAG6+8P5W+izhZTJuAFzz0NdKNoiiiHnzPsPQoSORmuqO1NRrABLhiGg8xEkIEBDRtD5u\\n343F+fPzUbZsaSxffhhOTk4v1R0REYHz5yvg1KlT8PT0RNGiRbFs2TLcvH4dn5QujSF9++BoKhAs\\nBw6nAHdMaShatGi2J60PHz6Mr5d+gXM+ibCXJWLnM6B9i+a4ce8BPhg0qEDfQbXgCri8czGSdFUB\\nJkN8shK3kx8gwW8e4NYWBBAvUWDWnAUoXsQVEyZORFqaGXZaEThQFybb0pDe2IAvli/KdRx/f3+c\\nv3wDt2/fhl6vx/oNm1C9XiPobDT49JORKF++fIHu42+iT59DYlALQGr5k0yt3AYnd47KJJOamgob\\n9+LQuhINGreASqUCAMxd+BUahtfBd1HAnUeEoPFFnz59sozhYNTj9JV4OBssO0anr6nQpLoxV7su\\nXLgAk04LVTdLQkJV+2YwzfkKZ8+eRXBwcL7u9fr169i1axdUKhVatGgBnU6Xq/yTJ0+wa+ce/PLA\\nGUqVgNqNgNO/xeHgwYNo2LBhvmywkjcK6iDymma1QBkFx48fn/G6du3aqF27dkHU/b+gd+/3YTDY\\no2ObNugJohaAbQCOIg2pEgkqVamC/v3750u3h4dHpoyvL9aAttfr0ahdO9iYgDgC67ZszTjl+r/8\\n9ddfqKEB7NP/lzWwAWKuPUZSUlLGwy2/zJ41GecvtMGxow4wm01oHNES5y6k4qbCkCFDuQEXLuzH\\n4e2rcKF1KpzUQJ8jybioeYbmbbxQu/auPD3gZTIZvLy8MGv25xg3ZykSqk0G4u7gYN0w/H7kIAIC\\nAgp0LwBQrqQ/9u7+FknVOgASKRS/b0HpEv/o3b9/Pxq1bofE7lMBiRSHuvbAphVL0ahRIwQFBeHE\\nyfM4cOAAtFotwsLCoFAosowxZ+4SdOjcCi1rmnHpjhTP03xemgrbaDQi6c5dyB49gcSghzn2KZJu\\n3IbRmLtjyYno6GiEh9dCaCMg9omAKVPH4sivf8BgMOTY5++05JYfxemvzciSrvxdZ//+/di/f3/h\\nKi3I9ANAMDIvE43C/2w2w7LE1O6F9+cBOOWlL61LTLny5ZdfsrogcAPADQDXAZQA9DQYMiXES0xM\\n5K+//srjx48XSjhpfHw8L1269NJiLEePHqWHTuSdkiDLghu8QF83lwKP/zdms5n379/PiESaN28h\\nNQ6liKqHiMq7KNq6sXXLCE6pBLKPpV1sD3q7GPM1nnuRAKLH78RYEmNJodpIjhw1plDuJTExkdXr\\nhVHj7EWtZ3H6lwnKFFLcpHV7YugS4ida2pivWTO88SuPc/r0ac6bN4+rV6/Oc0W8j8aMpk1RH9r1\\n60Kdvx/7D83/2YOQ0GDOXabgY6r5mGp2663mmI9HvrRfuw4RrNVAz7lbHdj5A3sGlPB55yvGvQz8\\nC5aYogD4CYLgDeAOgLYA2v+PzHYAAwBsEAQhGEAsyXuCIDzKQ18rLxAXF4etW7ciISEBYWFhcHR0\\nxB2FAmnJyZACuA7LlHDe8uUZCfHu3LmDulWrQvL4MRJJ+JYuje9/+qlAv+BFUcxTsZ3KlStjwPBR\\nCPj0U7iICjwX5Nge+X2+x/1fBEGAg4NDxvsBA/oi1WTCF0sHQyFXYPzSz3H79i0cWLQLZBIEAfjt\\nPuDi7JzfAQGm/fMWZkiEzH9Cu3fvxsmTJ+Hr64tq1arh0/FjcOfWdVSrFYIhHw7PsY6GSqXCgcid\\nOHfuHEwmE0qUKJEpqaH5hV/Pf4+enyopJUuWRMmSJV+pz8xPJyOsdh2cOXMG/k07oH79+llk4uPj\\nsXPnTiQnJyM0NDTH5cwHD+6jZOl/7qNEoAmXTt3JVjY5ORmCIEChUGDVig2YNv1TRK48BG8vPxw8\\nMAWiKGbbz0ohUlAPA6ABgL9giUgalf5ZbwC9X5BZkH79JIDyufXNRv/rca//MZ48ecJiPj4M1GhY\\nRa2mnUbDw4cPM7xOHRZRKllDECgKAj8YODBTvzZNm7KbTMajAH8BWEet5uRJkzKum0wmnjp1iqdP\\nn87XZvHTp0/Zpk03Ojj4sFSpKtlG+Ny7d49//vknExISePz4cc6cOZPLli17I+Ug4+PjGVw+kNW9\\ntWxbQksHOy2PHTuWL11z5y2g6FyMiFhPIXQWtXqHTOkwRoz5hBrXYpTV+ZCiVxkabEUOCZFxcw+w\\nVoDI3j265vs+fvrpJ4pGJ2L4SmLUWqodXbl9+/Z86ytMHj16xCIlA+gQWpVOLUOpd3HKEkH2N0OG\\n9mN4Ew2vxap44oqSxQO03LhxYyaZ5ORktuvSgTKFnDKFnD379f7X1c/4L4C3HcX0JprVQViYNHEi\\nKyoUnA5wOsB2ACuXLUuTycRt27bxyy+/zDafUDk/Py4DeDS9jQLYuXVrkpa4+CplytBHo6GnRsO6\\nwcGvXHAoJKQZlcouBP4isJ4ajZHXrl3LVnbLlq1Uaxwptx1E0S6cJUpWzNZJmEymLAffCkJSUhK3\\nbdvG1atX88aNGwXStWrVGoY0asGW7bpk+r7v379PhUZHTLhPzCbRYS2rFhEyTks/nQkq5NIC3Vdk\\nZCRDmkSwbuPmb6WQT04MGz2KDr1aMoDRDGA0nT//iCFNs1/+SkxMZNdubalSyWhrq+bUaf/8WHny\\n5AkXLlzIuiF16Vrdn23iF7FV7Hy61yjBmXNeT/TY/2cKw0FY033/R7gXEwOHlH/qDrjAksr6+PHj\\n2Lz5W/z00yE8fvw4Sz8HFxfsgCWaIBnATgCu3t4AgFFDh8L7/HkciI/Hofh4aE6cQKe2bREZGZmn\\nNNSpqan4+eedSE5eAgjFAKEdgDD8/PPP2cr37fchElXbkCp+jgTVTly7Y8S6desyyUyeMB5aUQWd\\nVoNmYSF4/vx5nr6f3FAqlYiIiEDnzp3zXG41J7p06YS932/FlvWrEBgYmPF5bGws5Fp7QJu+5CVT\\nQCr9ZylFWgh/aaGhodi7fRt+2vENGjduXHCFhcSNmDuQVvxnQ11ZqSRuxWS/bKRSqbByxQYkJKTg\\nyZN4jBzxMQDg0aNHKFMpCLMPrMN1PwkenLmJR79dhcJWhFf/Gtj3y4E3ci9WMmN1EP8RQsPDcVwU\\n8RCWB/0+lQplypVDnTr1sX79PWze/AQNGjRHZGRkpn7PY2NxEkAEgOYAEgDE3LqFcuWqYe1XX6FR\\ncjIksJxabJScjH27j6JlyyEIC2uOtLQ05IZMJoNMpgBgyd0PEoJwG1qtNlv5588fAzJ/yxtBQIrZ\\nP5NT27JlC9bNn4nL1U14HpIG2wuHMaRf71f+rt4G3t7e0IsKSA5+BiTGAgmxiL4BfLJTil1ngFar\\n1GjfpiWUSuXbNhVPnz7Fnj17cODAgUKp8xBSvQaSv9wG04MnMCcmIX7WOtStXiPXPoIgZIpCWrBo\\nIeQ1vFBuYz+UXdQNQct64o/R2wAAsceuwdO1YI7dSj4p6BTkdTdYl5gymDNrFm3UaiplMraJiGCz\\nZq0INCAwPr21YI0aIRnyMTExdDE4sKqlphu3ARwK0KB3o1Tai0o0Z0vIeAPgdYBhkFOG3gSuUKOp\\nwA0bNrzUpmnTZlEUixKYSqWqFQMCKuQYHdOwUWsq7LoTTo8Iw69Ui478/fffM64P6teXs/xBNrS0\\n0zXAYh65Rz2lpqbyp59+4o4dO3I8If6muHTpEstUrEalqKVviTL84Ycf2LVDa4bWrMRxH48qUC3u\\nwuLixYv0dDeydmUdS/trWatGhQLvBSUlJTGscSNK5HJKFXI2bdv6lXUOHDKIpaa3Y0uuZUuuZejp\\naVQ62NAnrCy9/HysJ6fzAax7EO8mZrOZDx8+ZK1aoQSavOAg2jI4uBZJy5q4o6M7JZJwAl2pgA3b\\nAnRQqSgIUgJRBA5RDT86QUoDQDVKp+8l3KRC0YOzZuVt3Xf79u0cPPgjzpo1K9c9jNjYWDZo2IpK\\nlQ0dHL24adPmTNenTpnCtp5KmhtYHMSK0mCtSuVz1JeYmMg6VSuxjIuWob46ujnY8+zZs3my+V2l\\nccPanDVUQv4BpkWDESEqTp/26qm3/yYlJYXBdWrRWLcyHYd0oMbFkUuXLyNJRkVFMaRpIwbVqsbJ\\n06fmGgSxe/du2nk6sd6JyWwUs4CejYJYv1E4N23alJHYz2w289GjR9YN6zxidRDvIFFRUfTz9KQK\\noEYqJaAk0IZAe4qiA9etW0cyPe+Qol56wr6tBKZSLtNyy5YttLV1JLCWwEkCx6hW+9HDw5cSyUAC\\nNwgcpii65ilPT2Hy/PlzBpX0Zy03Ldv4aOloZ5Nr3p85s2ezSRE1TV1AdgMXVhEYUr3yG7H1zJkz\\n9CtZjlKZnF5+Jf4TaafT0tLo4qilrRbU68CPe4HzRoB93u+Wb51btmyhsWpZlkw7ylL8jUXPbKBo\\nq+P58+dpY7RnkcVDGbB3Jo2VS3HEx6Nz1bV46WI6uDtTq7dll57dMs1Ez549S+9iPlTbiNToNFy/\\n8eWz23cdq4N4x7h+/TptRZHNAPYBWBKgK0CpRGT58lW4Zs3aDNkJEyZQImn+goNYSL3emaTlj1qt\\nNlAUI6jVBrJu3Ua8fv06S5WqRJlMpEIhcv78hYVuf1JS0kszgiYmJnLLli1cvXo1b926lavsoP59\\nOauCxTmwG3imOVjMs/AO4v0vZrOZixcvZeMW7ajSGYiGs4jxCUTb9bRzcGFsbOxrG7swmDVzGsuX\\nkPLmHvD6LjAoAPTxkHPpkiX51rlkyRK6dGvKUvyNpfgbS6b8SolMxomTJtJ9cCtW4c+swp9Z9sJq\\nGrI5JJmamvrSGhZJSUk0uBpZ94vmHMyp7PjHB7R1sHsrFff+SxSGg7BuUv+HiIyMhE9aGsrBku2w\\nGYB7ALQKM7Zv34xOnToCsByOs7W1hVy+D8ARAFcgikvRtq0ln07Lli3x++8H8PnnDbB27SRERn4H\\nT09PnDp1DI8exSA+/ikGDOhXaHbfuXMH1YPKwkYjwk4rYvXKlRnXYmNjMX7sWPTp3hXr1q6FUqlE\\ny5Yt0blzZ7i5ueWqt0JwVay7rcHjZMBM4ItLclSoWKnQ7P5fho/6GEMmL8L3DEeSf1vg6BeAKRko\\n3Q609cKpU6de29iFwd4932JCnzS4OwGeLsDI9wCV2oj3evTIt85atWrh+feHEffjbzA9foqHH85F\\nlTq1oFKqwMR/ou7MicmQyv45JJiWlob3B/SFqNVArRHxXp9e2W6Yk0TT5uF4+iQWpftYUrU5lHGB\\nR60iiI6OzrfdVvJGQU9SW3mDiKKIRIkEf5+pTYAlDC3ZZEKFwECY0tJQv0ED7Pr+exSXSmErpCBZ\\ntwa2dnZo1aoZpk37NENXTidqX5Y4LT90atkctR6cxoEKZkTHJaH2+wMwe+5XqBgUiCP7fkCltBhU\\nEFMw/fstuHj+LMZ/OgWxsbHYsWMH0tLS0KBBg2xP5nbs2BF//H4MHosXQymToFTJkvhm8fJCtx+w\\nPKjmfv4ZUgdeAWycgbJdga+bAue3AyUikPLoWqZT3S/j22+/xfZdkXA02OPDIYNeqW9+MRidcPay\\ngMY1LWewz1yRoGq1OtkmWswrxYoVw9Z169GjXz9cv3cfVWvVxIavNyA5ORnTKs7GbccVkBdxwaOp\\nGzF2yIcZ/WZ9Nhs//HkIoXcXQZAIiIyYiykzpuGT0R9n0n/p0iX8cfJ3KBTAg5MxcCjjgpS4ZNyN\\nvgX3Qe75tttKHinoFOR1N1iXmDKIi4ujv68vS0ulDANoB1AhkdBFqeR4gFMAegCsB3A7wK0Ai2s0\\n3Lp162uz6WVLRmazmTKphAmVwLTKYLCNSKkYQRi3U2bbnRq5DZNDQIaBd2qDKrmMt2/fpoubL7Uu\\nTalxbUM7e5dclxOePn3Ku3fvvtZKaWlpaZTKFcSoWGICLc2/GWVF61LjVoK9+g58uZJ0Pps7j6Kb\\nL9FtHuVhfeni7fvSynaFwV9//UVnJ1t2a65i5yYqurroeeXKlXzrO3LkCDds2JDjqekrV66wZ78+\\njOjYjmu/XpfpWkjTBqy0dQibcz2bcz2DdwxjjfB6WXScOXOGnkVsOWOjM20clCzRqji1bjZs3jri\\njVXG+68C6xLTu4VGo8HR48fR6pNPYGzXDkMnTkR4aCjCk5PhBsABQCtYElsBlipMxVNTcf369UK3\\nZcuWrbCzc4FMpkDlyvVw9+7dbOUEQYCLvR5RccDlJODPRBXS7DcD6iYw2SxHMu1wMs4iayez5Bya\\nMHEaHqAp4ty+Q7zrRjzTDsXgDz/OVj9gmfU4OTm91uyeEokEbdp1gvrbdsC1AxCOfA7N3V/wcaea\\n2LRkJhYvnJtnXeMmTUbC0B1A+ECkdl+EWLcgbNy48bXZ/jfFihVD1PEzCKo3E5XDZuF49Fn4+Pjk\\nS9fAj4YirH1rDNm8DBVr1cDqtWuyyPj4+GDpwi+wbe16dGzfIdM1N2dXPIu6mvH+WdRVuDu7ZGuz\\no9ED0QfTMGauHoaEO3BU2WHDmvXWbK5vgoJ6mNfdYJ1B5ErvHj3YSCLhUoBLAXYE6A3wO4DLARpk\\ncvr6lmB4eLMcf+m9KqdOnaJa7UDgGIEkymQjWLFinRzlv//+exq1Ips5qymR2BPuqYQHCfc0CjIP\\njvIBo4LB1h4qtm7aiI2atiN81hAVaGnFfmKZ8jULxfaCkJyczI9GjGGpoGqs37hFvkNq1Ta2xOJ7\\nxAYSG0hFWN9sC/y8DdLS0jh5xnRWCqnLRm1aZxRcepGoqCjqPF0ZEPtzRuSSykab5+ywpKUUqYu3\\nB72bVKJPs2A6e7rx+vXr2co+evSI3d5rzwqVS7BTl9a8d+9evu/vXQLWKCYrV69epZNez1oKBevJ\\n5bQTRfq4ulKvUlEukVApcyYwjILQnjqdkbdv3y7wmF988QXV6h60lJ0hgRRKJLJc49PPnz/PZcuW\\n0T8giCp9O8L4LZX69+hXrCxDqwWzjJ8PB/Tqwfj4eC5Y8AVFQwWizD2i3FOqHcM4fMTYAtv9b6FL\\nz95UBzUgJv9O9F9Djd7ICxcuZCsbGxvLlStXcunSpS+N6ioMBg8fRl2VINruXEmbOWOpc3TI8uD+\\n5ptv6NKoZkbkUin+Ro2j4ZX/bz1+/Jjr1q3j2rVr3/ohx/+PWB2EFZLk7du3OXv2bE6fPp0XL15k\\nWloa79y5Q5lMSWA+gRUEVlAUq3NJAUIa/2br1q3UaoMJmNIdxHHa2DjkqW98fDwHDxnB6jUbsV+/\\nIdmWrkxJSWHtOmEUJAoKEjnbdejO5OTkAtudEyaTiRPHfczgsgEMq131tdScfpHk5GQOHDqMPiXK\\nsEKNujmOd//+fboWKUpNzaYUwzpS5+DE06dPv1bbNPZ6Gm4coSOv05HXaduzPT///PNMMlevXqXW\\naE/fqFUsxd/ovmYCnb09X3qA7dixY/QvF0idQc86DUJ5586d13kr7zxWB2ElR8xmM5VKkcCcFxxE\\nZS5fvrzAulNTU1mjRji12ipUq9+nWu3IDRs2vrxjHjCbzWzSrC1Fh7qEx1yqHEIZGtbstW5Ijvho\\nMKv7ijz4HrgyAjTaaXj27FmazWY+e/bsrW2GDv5wGOUt+hMHSBwghUHzWadhk2xlf/jhB06dOpWb\\nN28ukL02DkYaLh/6x0F0acX58+dnkdu6bSs1drZU6mzo4uPFP/74I1e9MTExtHM0sPyGwax/dwmL\\nj2rBwIrlcrU1Li6OCxYs4PgJ43no0KF839O7itVBWMmW58+fc+HChaxevTaVSi8CvSiTNaSjo8dL\\np/J3797lnDlzOG3aNJ4/fz5HudTUVG7evJkLFix46cPhVbh48SLVWmeifKJl/6F8MkWdJ0+dOlVo\\nY5CWNfCwOtXooNfSRiXhpcEgJ1rakGpS9undmw4unpQp1dQ7uHD//v2FOn5eaNWpKzF8WYaDwPxD\\n9A/KelJ89MgPWdxHw486yVi+hIbdu7bLt5MYO3ECdWVKUrd+Pm3GDaa9qwtjYmKylTWZTHz06FGe\\nxvrmm2/o3bASm3ATm3ATG5s3UtTreP/+/Wzl4+PjWbZ8AOs2M7DHKAOdXLVctXplvu7pXcXqIKxk\\nYfGXX1IukVAEqAGokkjp6e7L3r37v3RKf/PmTboZDGynVLKXTEaDRsOjR4++IcstnDp1ilp9USLI\\nnLFJbWMM5G+//VYo+uPi4rh79276erpxVA0J7wwBnbVgdN9/HMR7FRVUiTZEy2+IUSTa7qFW7/DG\\n18mXf7WCmmJliK23iZ1Pqa7akIOHjcgkc/fuXdraKPlwD8ijYPx+0MNF5MmTJ3PU++jRIx49ejTb\\nPQOz2cwvlixmWKsW7Px+L169erVQ7mXfvn10KOnDRqnr2YSbGHpnMRVqVY5J/b766ivWbGDkH2Y/\\nnmQxboj2pIOTLStWq0Cd3oYVqgYVWtDF/1esDsJKJk6ePEnb9DMRi9IjmrQARYA6jS3lchWrAxET\\noAAAIABJREFUV6+XJTPmhg0baDR6UCpV0xtqXgJ4F+DnAOtXrfrScR88eMCB7/di0zq1OWncJwXK\\nWpqSkkLfYqUpcx9BlDhBqftYevoEvFKETE7ExMTQs4g/tZ5VKNgF0M9R5ONh4KKGoKsO/LIJOLym\\nlM4GW2qc/SzOIb3pfCq9ltxUK1auot7ZjUqNDSPadcyU7NBsNnPU2HFUiBrKlEq269o9S8Gh8+fP\\ns4iHljyKjFatvC337duX7Xg7d+6kxmCgXfmyVOn1nLtgQaHfU3akpaUxpHE4XWuVZrHREbQv6s4J\\nkyflKP/ZZ5+xfX9HnmQxnmQxHo71pUwusOn8Ohxz/302W1iXbl6u1rrUuWB1EFYysXLlSlZSqbgo\\n3UEsBCgHOAOgHFICoymT1WHFitUz+hw9epSi6EBgJYEfKUUIw6Hi3fRQ2Ur+/rmOGR8fz5JFfDjQ\\nQcGtLmC4Qc0OLSIKdB8xMTFs3LQtPbxLMqxBy0KL3unQuQfl5YYRPUn0MFPh3439KiuYPAZ0sVOw\\naXg9DvmgP48fP06lxpYYcMviIAbdp0pn5OXLl/M17tOnT3n+/Pksv5b3799P0dGNmHOcWPOQylpt\\n2bZL9yz9zWYz09LSmJSUxG69+1JrdKDRw4vLln/F5ORk+vm6cc5gCR9HgqvHgS5Odnzy5EkWPYmJ\\nidQaDFT/+D218Q8onj1O0SHnCKrCJjU1lStWrOC48eO4c+fOXGVPnTpFg4OGX0a6MfKWDxt3tKfe\\nSc2pHJzRvMq4Z0oXbyUzVgdhJRP79++niyhyTrqD+DB9BrEGoB5KAh8RmEmJRJYRFTR58mRKpV0I\\nHE9ve6iCgr8BrCKKHD9mDM+cOcNdu3bx5s2bWcbctWsXqxltaC4K0g9M8AW1Cnm2D6jXxb1799i4\\nWVu6efmzVr1GOT7Ig6rWI8J3WxxETxJ1N9LbwYY1fDWMaByWKR31tBmzKdq7UVuuPUWjJ8eMnZAv\\n21avXElbGxV93bR0Mup48ODBjGujx3xMtP2E+I6WtuQq9c5uOerqO2gI1dUbEDtvEmujKLp4cM+e\\nPbx48SKrBZehjVbJcqX9eOLEiWz7X716lRo3N2rjH2Q0fUhd/vDDD/m6t9fNzp07WTzAiw6OOjZt\\nHk6tXuS4p305lYM5/lk/2jnZ8uLFi2/bzH8tVgdhJRNms5n93n+fRpWKRdKXloYBHARQDjWBqQSG\\nU6XSZmwsLlq0iGp17fT6EMcJLKFE0NBJp+PQAQM4YsRYqtVOtLWtTVE08ptvvsk05s6dO1nTaEP6\\nWRxEUrqDeBOpI0jLRmlAqQqUFR9K1DtFSeB0Orn68Pnz51lkBw0ZTlWxVkT3ZKJbAhVeoaxZuy5X\\nrFiRbYhmVFQUV69ene99mMuXL9Nop+bZWSDXg7tGgE5G2wznPGfOHKpqtP7HQXyyk0o7A5cvW5qt\\nPle/4sSGP4njtLRBM9h/0JA825OYmEit0Uj1nu8sM4jTv1N0MP4nHrK//vorNXZKOpfUs/bICnTw\\n17NClaC3bda/GquDsJItUVFRnDJlCg22tlTKZNTI5FSpPKlQ1KYoGjKFusbFxbF48bIUxVqUyztS\\nFI389ttvSZInTpygKLqmFxF6RGAvRdGOKSkpNJvNXLVqFXt26kQHnY6DjFL+4Ao2M6jZqlHDN3av\\nly9fpmjnTjQ3ExEkIkide5Vso47i4+NZL6wJlaIdFWodm7Zo91qrvO3YsYPhFWzJ9choDrayjJDN\\nZ8+e0bdEIJWVGhJh71OhFTm5K+jnKXLFV1nDkUtUDCZmbstwEPIWvThu/KvNbPbs2UOt0Ui70oFU\\n6/Vc+OWXee57584dzpgxg5M+nfTGCzMNHvoBu33qybFb/Nlloid7TPNmsRJeb9SG/xqF4SCs2Vz/\\nHxIUFISgoCCMHDkScXFxUCqV2LRpEyIjI6FQ+EKj0YAkBEGARqNBdPQvWLduHWJjYxES8iHKlSsH\\nALh69SpkstIAbgLYAECPtDQpHj58iLkzZ2DP8iV4Dwl4JiixxaTFGd9iqFi9Bho0a44DBw6gQoUK\\n0Gg0r+Uev167FuPHDMezuHgkJxBIiwdkWsBsgjnpEdRqdZY+oihi767v8ODBA0gkEhiNxtdi29/4\\n+vrij6upiHkCuOiB41eAhCQTWkU0wrGoP+Hl5YU/jv2K+iE14Ra3E+NmAKW8gTI+CZizchG6dX8v\\nk7750yajSeu2SDl5GLLHd6E/dwwDFh99JZvq16+Pmxcv4vLly3B3d882S2523LhxA0FVgyFrUBGC\\nTsT0mp8hcvv3qFKlyiuNn1/UKjVuPiZqtDSiRksgKvIJojbnXjPdSiFQUA/zuhusM4hCoXf37iyi\\n0bCRINBXo2HbFi24atUqbtmyJUtkzN9cuHCBCrmWWijZUypjsKCkjVTGhw8fUiWT8aEzSFfQ7AJW\\n0dtw27ZtrFc1mP52WlYy6ujnnnN+nYKwb98+utqp+UsT8Gpb0EWnocwQRAR+RrVHA9as2yDX8pZv\\nkulTJ9FWFFi9OGi0Ab8ZDg5pKuWY0SMzZHp278CZPUDutLT1I8DwkOyjx/78809OnTqVc+fOfaNh\\ntwOGDKLr8I4M4mEG8TC9V45h9bCQl3csJK5du0ZHZz3bDPfk+7N96OBiw02bN72x8f+LwDqDsJIX\\nrl69io3r12NSUhJUAJLi4zF82zbs2H0JEkkyvLwm49ixQ1l+7fv5+cFWCWwQklFBApAmNJeqsHnz\\nZkgEwDY9maYgAEYpsHXzJmgvnMRp50RIBWDi43gM7fM+tuzcncWmEydOoHPXfrh16zoqVKiEdWsW\\n5/nX7M4d29G/aCKqpotHhsSj3r6LaFb1Mkr4h6Ffv74FqnFQmAwf+TGWLF6ADjXvoWklwM0AnLmV\\nhieJCRkyfQd8iLCQ72BKi4daAUzZqsaar8dlqy8wMBCBgYFvyvwMHj2NhazSP9lWFUVc8ST2yRsb\\n38vLC8eORGPhF/ORcDUOG9a2Rd26dd/Y+O8q/46/IiuvldjYWNjK5VClv1cB0ECGhIS6iIvrhQsX\\nTJg/f362feNTkuH3giMoTjMSEhJQq2pV9ExU4o9UYGGCgONmGYTUVITLLM4BABqq03DpwoUsOu/f\\nv4/adRrgzP2+eKo7hgMniiEktNnfM8aXorPT42qCPOP99TjA080ZS76cj8GDB0GhUOT1q3kjdOve\\nGysPibj5EPj2GDBvt4hWbdojOjoa3v4lUaFiRSj0roh+1hRnTB2xedtu1K9f/22bnYmWjZrg6YyN\\nSIj+C0mXbuHx6KVo2bhpoY9z5MgRLFu2DAcPHsxyzdvbGzOnz8bC+YutzuFNUdApyOtusC4xFZiE\\nhAR6OjuznSBwFsB2ABUQ06OaZhNoSLU6+7QHzevXZxe1gteU4B4F6CiqGR0dzadPn7Jnxw4s5eXJ\\n8OrVeObMGc6fO5e17EXGFwfT/MH+jgp2ad0qi87vvvuOOsdwoigtzddMpVqfY9qF/+XBgwcs4u7M\\nzgFKjigroYNO5K5du3KUN5vNjIyM5OLFiwvtRParkJaWxsmTxrN8oC+rVy7NnTt38tmzZ9Q7uxIj\\n1hHfJxPD19DexS3TQbl/G/MXLaSzjycNbi4cMmLYS5PzvSqfTv2URk8jy3erSMciThw+enih6n/X\\ngDWKyUpeuXDhAquUL0+9VksnWzsCJQjMIDCWgJGAO52c3LIc5nry5AkjwsJoo1TS29GB27Zty3EM\\nk8nELm1aU69W0lWrZnCZQD58+DCL3L59+6jVBxK+qRYH4X2PMrn6lR6ODx8+5OzZszlxwgRGR0fn\\nKGc2m9mtR19qnAIolnqPop0r585bmOdxXhfHjh2jrng5Yg8zms6vNI8fP/62TXsr3L17l1o7Lfvf\\n+ZAjOJ4fPBpOW0c7Xrp06W2b9p+lMByEwDxO698WgiDw327jf42nT5/CaHSHyZQIyypjHQApkEpP\\n44svpqB9+/bQarX51n/nzh0kJyfDy8sr272AtLQ01KnXGMdPmZCA6hBNG+DpJODJw9twMhoxd8ly\\n1K5dO9/jv0hUVBRqh7dGfONTgFwLPL8GxfZAPH5w97VFWOWFq1evokRQJSQtvQDY6IFnj6DqVRx/\\nnYyGp6fnW7PrbXHq1CmEt22Azmd7ZXy2OXgNVs9ehWrVqr1Fy/67CIIAkgUqu5fvPQhBEOwFQdgr\\nCMIFQRAiBUGwy0EuXBCE84IgXBQEYcQLn48XBOGWIAgn0lt4fm2x8mrY2tqiZcvmAPwA9IYCv0OC\\n/RDSHqJ/794w2tmhZLFiWLlyJe7fv//K+l1dXeHj45PjRrFUKsVPe7fjs6mtMap3KgK8pKiZdhnH\\nyz3DJN0VtG7aCJcvXy7YTaZz48YNpKq9LM4BAGy8IVPa4PHjx4WiP7/4+PigT48e0HwYDOWCvtD0\\nLgpp8lOUDvTHiGGDYTab36p9bxpfX1+YnqXi7IZTIIlL3/+FJ1efoESJEm/btHeb/E49AMwAMDz9\\n9QgA07KRkQK4BMAblhLJfwAISL82DsDQPIxT2DMvK7SkBHdwcKcCYEOAfQA6AFwMsCxAD4BlpVI6\\n6HSvddkjNTWVcqmEyeEgG1paF1+RS5dmf5r4VTCbzaxSox4htyEa/Ej0SCOqLqSzexF+9dVK+gaU\\no2fRUpw2Y9Zbq/kQGRnJ1q1asFwxJW+sB29tAINLiZwze8Zbsedtcvz4cXr5eVMildDVy5W//PLL\\n2zbpPw0KYYmpIFFMTQGsSn+9CkDzbGQqAbhE8hrJVFhOWzV74bq16vhbQqvV4uzZE0gFUB/ASQC1\\nYfHgUgBLAcxMS0P3Z8/Qp2vXTH1NJhMePHiQ7a/cJUuWwWDwgEZjQJcuvZGcnJyrHVKpFCqFAjcS\\nLe9J4GqiBLa2tgW+x3v37iE6OhqotxnY3xlYLgd+H4FmjUIxYNgnuBw4BzcqL8fEuasxd97CAo+X\\nH0JDQ5GS+ARj2ifDwxFwcwBGt03A3t3fvhV73ibly5fHtQtXkRCfgNvXbqNq1apv26R3noI4CCeS\\n99Jf3wOQXRC7GyzHcP/mVvpnfzNQEISTgiAsz2mJysrrw2AwQG9jg78ARAM4C+A+gFKwOAkAKA3g\\n5q1bGX2++eYb2No6wMOjGFxcfCwP4HT27NmDIUMm4fHjHUhIOI0tW25j0KCMVcVsEQQB06bPQL2T\\nIsZeFNDklBppzr5o2rTgIZRSqRSkGdAVB+QawK0OYCiNFWs3IaHUAMCzNuBSCQlVZmHV+i3Z6rhz\\n5w5ad+iKssG10H/Qh4iPjy+wXf+LweiEM9f/+VM8c10CgzFvZ0IA4MqVK4iKinottr0NlErl2zbB\\nSjq5HpQTBGEvAOdsLo158Q1JCoKQ3U5ybrvLXwCYmP56EoDZAHpkJzh+/PiM17Vr1y60Dcx3HUEQ\\nsGzVKnRu3x5Oycl4CmA/LGuBjQDYAtgCoHT6wazr16+jU6deSEhYB6A07t//DmFhzRETcwUymQzb\\nt+9GQsIAQCgLAEhMnIodO1rjyy9zt6PfwIEoXqIEDh44gIbOznjvvfcK5SHh4OCAsLBw7IwMQZpP\\nc6DqLABAyi9DgQtbgYrDLILxd6ERxSz94+LiUKl6bdwr2hqmcl3x169LcK5FW/y0ewcEofAmv2M+\\nmYwa1fbg4p0kSCXEnmglDh6e/tJ+JNH7gw+wduMmyJ2coXgaiwO7d1nX7d9R9u/fj/379xeu0vyu\\nTQE4D8A5/bULgPPZyAQD2P3C+1EARmQj5w3gVA7jFOq6nJWsrFu3jo5KJfsCVAMsD1CRng1WFAR2\\n6dKNU6dO5caNG6nT1SVwM6OJojNv3LhBkhw3bgLl8h6EYImPA7awZMkqGeOYTCZOmjSNFSuHsEnT\\ndrmWNC0sUlJS6OlXmgj/luhLSwv/jlDaUag0nKg2gaLOmG1yv927d1NXvDoxm5Y2I4VKrR3v3buX\\nSc5sNvP69eu8dOlSvlN8xMTEcOHChVy4cGG2ld6y49tvv6W2RCkKF+9QcjeOklnzGVChwkv7/fXX\\nX5wwcQInfTopzxXjbt26xd27d/PMmTN5ks8P9+7dY6v2LVk80I9NWjbONr28lbyDt3kOApZN6hHp\\nr0ci+01qGYDL6Q5Agcyb1C4vyA0B8HUO47yeb89KBmlpaWwYGko/jYZFAaoAeisUFGUyKhR2BBpS\\nqSxPT0/f9Oyup9IdxE9UqXQZZycePnxIV9eiVKvbUC7/gKJozFTZbMCADynaVSOMOynoZ9LW1qnQ\\nigHlxifjJxHO1YmecUTPeMIjjBKHMgwJDeOgIR/luAn/448/0sa3IjHLbHEQU+OpEG0yne1ITk5m\\ng2YtqdI7UHRwY7ng6m+sFsakSZMo9OpvcQ534yj8dYsKjTbXPtHR0dQaDbQf2pX2H3SiraPDS0t3\\nfrf9OxqNGtaqp6eTs8hPxo3MVT4/pKamMrB8Kdb+MIiDotuxzrAK9Czi8a8+OPhv5207CHsAPwK4\\nACASgF36564AfnhBrgGAv2CJZhr1wuerAfwJy/7ot7DsaVgdxFsiNTWVK1eu5Pjx47ls2TLu37+f\\ncrmKwEgC0wlMo1Zbgg0bNqcoulOna0C12shVq1Zn0vP48WMuXLiQM2fOzPJrU6XWEa53CA8SHqTK\\nvisXLnz9h9ZSUlIoU9kRUiUhVRHFu1D0rcs1a9bk2i8pKYnFA8tTWbUH0XEdxZKhbNmuUyaZT6dM\\no7p8OLEyiViTRmW9nuzSo/frvJ0MWrVtS/j4Urhw2+IgZsylrbt7jvImk4khTRvTMH80fXmavjxN\\n45RBbP9etxz7pKSkUK8XufeYko+p5sUHKrq5a145su3EiRMcMXIYPx47hleuXMly/ezZs3Qu4sDp\\n5gEMHVeRKhs5tQYFvX3dee3atVcay4qFwnAQ+U7WR/IxgJBsPr8DyxL23+93AdiVjVyX/I5tpfCR\\nyWTo+kK0UmpqKsxmE4C/D8wJILVo2bIJxo8fjWvXrqF06c9QvHjxTHr0ej369euX7RgCBICmF96n\\nvpGkenK5HF8umIOBH41BYpFOUMedh49NHFq1apVrP6VSiaMHf8L4SVNw4cp3qN4pBMM/Gppxfe/e\\nvfh66zdIrNQPkFv2TJKrdEbU7pGv9X7+5mlqKlC0KFi1DOjoDDx6CBcP92xlt2zZgl69uiIpKRHy\\nE4eRUrcyFCV8IfF2w5PoGzmO8fjxY0gkZgRVsvw7GYwCygbJceXKFZQvXz5Pdh46dAjNWzRAyz5K\\nJMUBlYMX4JfDv8PPzy9DRqVSITk+BWe2X8Gp9eex4HId6ByU+HbqZXTq1haH9r1aWnMrhURBPczr\\nbrDOIN4aYWFNqFRWIDCUQFtqtfoCpe8ePuJjirblCcMGSvVjaW9w4927dwvR4tw5ePAgx4+fwEWL\\nFmVJKfKqjBjzCTVuRSkpGkwENSPWpBFrzZQ3GcZWHboUir1ms5mXLl3iuXPnss17NGz0aIotWlL2\\n+wnKftxPZddu7PL++1nkLl68SKNRzeP7QPMjcPEc0Mbbnm7Rm6nz9+XK1atIkrdv32a10LpUaTX0\\n8vfjgQMHaDKZ6OZm4OptCj6mmr/9paSDo/hK+0e16lZm0fIaSmUCNXZy1m5qw779e2a515btWtDo\\no2eTj3y5iU24iU24/GEYbWw1r/jNWSHf8hLTm2pWB/H2eP78OTt06EpnZy+WLVu5wInuzGYzFyz4\\ngiGhLdi5y/tvbelg86ZNLO3vzaKeThw1bChTU1Nfqf/du3ep1NoS8+8TS+KJYjUIgwc1RcrQu3hJ\\nxsTEFNjG5ORk1m/anGonF2o8vBlYOThL/Ye4uDhWrFWL2iK+tCnuz4CgCtnmvtq4cSNbNNHR/AgZ\\nTVSDjt6enPnZnIxDgqUrV6DnmE6s+GQ7/b+fQhujPa9fv85jx47R1VVPTy8tdToVv1qxjLGxsTx6\\n9Gie/g2NbvYs06ciByZPYpezQ6h1EhkWXieLXGpqKrt260qfsnquS2rITWzCD9aVY2C5gHx+i+82\\nVgdh5V/NrVu32LfvYLZo0YVr1379ts0haUkU6GIv8qc+4NEPQG+DlEXc9GwT0TDPDuvs2bPUuhUl\\nVtHSvkqlxieQCxcuZGJiYqHYOXnqVKrrNCTOJREXTZR36M123d7LImcymRgdHc2oqKgcy6f+8ssv\\n9PXR8Nl1i3M4eQi0sVFlkn/y5AmVWg2DzT+xCn9mFf5Mjxa1uWHDBpKW/ZiLFy/y2bNn/OWXX2jn\\naKBbkB+1BluOnfBJrveislHz/ftjOJhTOZhTGdi7EsuVK8e+fftyw4YNmQpWpaWlsXX7FnQtYs+y\\ndTzp4GzPqKio/HyF7zxWB2HlX8v9+/dpNHpQKv2IwDKKoj+nTp35ts3ikA/6c1oj0DwLrF8c7FQR\\n/GUoOKmJlD4eTnz69OlLdSQlJdHJ04dC10XEkjii73raObrw8ePHhWZn8w6diOlfEZfNlrbxIOVG\\nx3wtyZnNZvbt251Fi2jYurkNHYxqrlu3NpNMSkoKlaKa5a99zSr8mcGpe2ks7cfIyMgsupw8XFl9\\nx2C24Uo2vT+P9l7OPHLkSI7j+/gXYcSe7hzMqex+5SOqbGSs38nI8K5GylUCNbZKBpTx46IvF2WM\\n8dtvvzEyMjLbGZGVvGF1EFb+tcyfP58qVSdakmeQwDna2jq/bbP48ehR/KCmlPfGg3oRTJ0LcoGl\\n1Sqhy7WuxIucO3eOAWUrUqZQ0tu/VKH+yn327Bmr1axOoU5D4kIqcSmN6PkhhZJl2Kxdh3zpNJvN\\nPHjwIL/++uscw1rnzP2cWjdHOrSpTftyxRnSpGGWcx3x8fGUKeRsbV7BNlzJNlzJ4p1qcsWKFTmO\\nHRkZSZ3RlmW7B9Pey47th7vw/Wnu1NhK6VJESVEnZc/l5enmZ+CKVTnrsfJqFIaDsJYctfJasERB\\nvZgy3AYmU8pbs+dv+vYfgMpfLUFcSiySU9OQZAK0UosLi0tmnqvR+fv74+yJ3wrdvpSUFITWqwpf\\nlws4eVSBuDpFAa0OEAh+PAWnZmVfivRlCIKAGjVq5CpTplQg5InP4X71N9y4aUKlBo2zRJmp1Wo4\\nuDjhznfRcGsehMS7sbh/4DwCBgTkqDc0NBRLFizGB4P7ISn+OaQyNb5ddB/LzgXB4KLAz1/fx6oJ\\nf6H11EBsWLkG3bp0y9c9WnkNFNTDvO4G6wziP8nly5ep1ToQWEzgAAWhMsuXr5bvk8aFye3btzlx\\nwgQGlfZn9WIqLu8IdgpWslK5kkxOTi60cY4ePcqQOpVZrnQRjhw+JE+6Dx48yDIlbGi+AE4dBior\\nlCO++Zm4HEt536H5nkG8DLPZTCcnW279UcUH1PKvhxp6emv566+/ZpE9evQo7Z0d6BzoQ41ex4lT\\nJuWo12Qy8fTp0zQ46DhpW1GO+MqHNvZShnZ1ZCSrM5LVuTutGiVSgZ3nlWZE6yav5f7eRWBdYrLy\\nb+aLL76gRGJPoBSBHlSrK3DcuElcvnwF/YpVYFG/IC5c+OVbs89kMnHe3M/YpX0LfvLxKD5//rzQ\\ndF+8eJFGew1XjgWPLgPrV1Gzb+/uL+23b98+ViprQ14Ek8+A9eqqCb09NUWL0adkSa5Zs4ZHjhwp\\n9PTkz549o1ot4wNqM1rL9nYcOXIkW7ZuwOYt6nPHjh2Z5I8fP57rSfgff/yRBgdb6uyUVGulnHcw\\ngAdYiU16O9DeWc6tj4MZyeqcuKMEdUY59UYdjx37v/buPC6qcv8D+OdhZ4Zh30EExVzRCBVMxX1P\\nTInM3EVb3Het3FJTMaXFNn+VW3nVXK43rbzut2suqSlkNxRySctQQQRBEZjP749BxBwQGAik7/v1\\nOi/mzHmec57vPDrfOctzzpFyjevvTBKEqNKio0cSWEDgRv60h25uNanRBhC2ewnb/1CjrcOVK1dX\\nWhtTUlL47bff8syZM+W63tjYWL4caUUeBnkYvPwV6OhgS71eX+yXe2ZmJuvXq8mpL1hw72fggKet\\n2CK0CVeuXEk7N1c6d25Pu8Ba7N3/+XLdG9Pr9azp78FPvjDsQZy4oKGHpw3tHaw5aLSGL0/X0tNb\\nw61bt5ZofSkpKXR21fHdfY/xAEO4dEcgHd0t+E16CLf8/jittWbUOlqwdhMH2jnYsO9zzzIuLq7c\\n4hHlkyAqfhir+Nuyt9fAzOyPQu/8gaxbemTlzQcs2gEW4cjKW4RPPt1QYW04ffo0ovoNRpvOEXj/\\ng4/u/ugAYBjh61+nPnpGT0OT0NaYNPXVctuujY0Nrt80L5hPvQGAebDX2cLOzhqjRkYjNzf3gXoa\\njQZ79x3Gldt9MPujxnCuMQQ7dx/A7MUx0L+/GHnb1sLs+B7sSfgftmzZUm7tVUph86avMHuCBqF1\\nFNoE5cHVzQ9mFndwPjEP+77OhrP7Hbz3wcPvMgsACQkJ8KmlwRNtdQCA0C4OsNWYIWbEObzc8jQ6\\nzQhB59ebwU1XFxfO/ob16zagcePG5RaPKCemZpiKniB7EI+s8+fP09HRi+bmIwnMoEbjzhZPdiSs\\n3yF0NEzWy9mlS2SR69i3bx9ffHksJ0+ZXnDX2JK6cOEC7Z09qNosIiI2Uev7OGfNMRwv1+v1dPH0\\nJYZ+TSwiMSuF1o4erB/ow0B/T04aP7rIcQUlkZKSwoCaHhwdZcH3JoMeLpYMqmvJS/vBawfBdmEa\\nzn19RonXZ2lrS/triXTIvkyH7Mu0Gz2Cb75Z/pcNJyUlccqUKZw5cya9fR04/0N7nqUXE3M92bqz\\nFevWC3igzrFjxzhs+AAOGvpcwc0Zz58/T0dnDbf+FsQDDOGm841oozGnXzN3jtzTg7F5I9hiSENO\\nnDKh3GMQBpBDTKKqu3DhAl99dQbHjZvEw4cP8/jx49RqXams5xDW86jRuhZ5Df3GjZuosfciar9J\\n85oT6eji9UCSuHjxIt966y3GxsbetywnJ4eRkZE0axxNTKZhGnaaDq5eJMlbt27RzMLET0s+AAAV\\nJUlEQVSSWKg3JIiRh6nTmHPHTPCnd8COwbacOG4kSTIhIYGbN2/miRMnShX7H3/8wVemT+YLwwcy\\nvFUwP18M8mfDtOtTsG14cInXFdyqJTVvvEb7279Td+4E7QJqcu/evaVqz8OcO3eOLl7ufOyF9qw3\\nrgu1OjPuOePGs/TiWXpxykIdez391H11jh49SmdXO45Z4s1J7/nQ1cOu4FLhRYvfoLuXju2e9qGb\\np44LFs5jo+CG9G/iQ7+GXgwJCy7RuBNRNpIgxCMpPj6eY8ZM5KhRE4r90g2s+wTRZCfRjkQ70txv\\nLGfMuDdq9/Tp07R39qB1g+G0bjCc9s4eTEhIoF6vZ49eUbR0rkUEjbiXIKLP0MHl3lgM75qBRN/P\\niYk/EyGD+NozILcYpoRloL+vK1d8+jHdnG0ZEW5PHw8N570+86HxnTp1ikuXLuXy5cuZnp5Okhw1\\nMppTh5sXJIil0xSjIrs9dF1JSUl8rm9PhoY2oKO3FzUe7rTSajlu4kS2e6o76zzxOF8YO5qZmZkP\\nXdfDjBj5IhvN7FUwvsEzrAb7v6xlUp4nj1/z4GMNtQUjq+8aMqwfx8Z68xAf5yE+zrnrarJT11YF\\ny+Pi4rhx40aeOnWKpGGQ4cGDB3no0CGT9tDEw0mCENWaj189oumJggSBgHkcP2FKwfLIZwfSrNlC\\nYiiJoaRquoh9nh3IU6dOUeNSgxh8mtC4E+ExRK9/0sa7CT1r1KadoyubNGvJFStW0MzWgdB5ENY6\\n+nlrmLsR/PEtcGRX0MvDiTo7K57+HOR/wOStoIerbbHPT9i1axddXTQcM8iKT3fWsGGDAKalpfHS\\npUus6efOZ7pqOKCXhh7uDg99DsOVK1fo4+PM+bPMuGMr2Km9DaOiIvjLL7/QycuTbu++Qp8j6+gS\\n1ZU9nulj8ufd+/koNlsZXZAgntw8mi4eWjo62VCjseTUaRMeOMH+/MBITv3ItyBBLP4ygGEtm3DD\\nhg2lviW4KF+SIES19sqrs6lxb0GEfE8E/Yu2Ovf7Dke17vAU0X5LQYJA+y1s1b4Hv//+e9r7NiEm\\nkBj0P6Jef5rZeVDr4ErVfSkx/TJVxPu00rnSov0kwwOBFmVR+TVngxpmtNRoiPBhtGzYnsrWjnXr\\nOrD9k3Y8tQps29ThgdtPFBbcJJDbloNMNEz9eloXnCu4du0aP/nkEy5fvrxET4377LPP2DtCy5wb\\nYM4NMPUSaGVlzjVr1tC9d6eCZzrUuv0Dza2s7runUVl8tvZzutb1Zee4eeyWGEPvsLqcv2gBk5OT\\nmZGRwZSUFE6dPpkDBj/Lj5Z/SL1ez507d9LN044LNvlzyfYAevlpaOdgw7A+gXTzceScebNNapMo\\nu/JIEDKSWlRZ8+bOhJmZGdauGw6tVouYdSsQFhZWsPyZiK44HjMfWU6NAaWgSXgDkVOHoFGjRtCZ\\nZyHz6ALk1e4Dc6fa8Mg6jIw8K7Cl4XkODB2JnD2zwccHAEoBlrZgyEAk7v4FOSPXAkFdkPPJMADA\\n6ajXceb8cYSOnQ4rs2w0bNiwyDanXk9Dvdr35uvXykbKtSsAABcXF0RHG33sulHm5ubIzr737Ouc\\nHMPVRjY2NtDfuAmSUEpBn37TcBXS5s24fPkyQkND0apVqxJv564Bz/dH8pVkLImIRW5uLoYNHoJm\\nwSHo3rM9UlNTkZmZhRZPaVC/tRWWLd+DM4k/Y+mb72Dc6OmIHbMQebm5yLyZi1nfd4BvI0ekX7mN\\n2UGxeL5v//ue/SAeIaZmmIqeIHsQogh6vZ6vvDabOid36hzdOP3VWQWHQOLi4hgS1ppu3gHs0LUX\\njxw5Qht7V2LmDeINErNv0kzrTPMuswueN23bqBut7eyJ2F+JlXmEpTWxIo1YT8PUtBdHjx5dbJtG\\nRA9gSJAV/eva07uWPR0cbbh79+4yxZeWlsbatb04bpQF13wChjbTcPz4kczMzGRgUEO6DO1D149m\\n0TG4IQPq16V7WCP6jetDe18Pvvv+e2XaZmFxcXF0drVjzNYAjo31ZqMntTzAEO7ODObwed60tDLj\\nu+++SydXHSdtDWXsmY4MifBii/5++QepnmXDVn73PXZW/HUg4yDE35lSCgvmz0F6ajLSr1/Bwjde\\nh1IKX365DS1at0Pipeu4mZGGoQP6onnz5ujfry+0q9tA7ZoBzapwhAY3gtOPq6B7Lxja2HoI8zZD\\nt249YP3PV4GsG4AyA25nFmxPa040a9as2Da1Cu+Mk1fccP7Vbfh97je45eCLX86eK1N8Dg4O+O67\\nE8hTw7BtR1c8P+ANLF26DBqNBke/PYAXfBug89GLiO7QDenmuQj8djH83n4Jj327GFOmTEFOTk6Z\\ntnvXV199ha6DdAjv5QAXT0s4ulog+7Yeo9sl4sQJhZ6zgzBj7nQ80dsNTXt5wauOHV5aFYzjW34D\\nAJw5cBW/J6ShQYMGJrVDVB45xCSqlfT0dPQbOARZPb8GvEKBq6cw4uU2aNeuDT7+cBm6bNqEo0eP\\n4bOzaTiVbAnqfGCT/ivWrv4UHTt2RGZmJvoNGY6dk31hZm0NLuqMO90mwvLXk3C48j9ERKwudvtr\\n//Ul8ibGAE3DgZ9PwkKfjpdeHIHY2HlY+/kWhISEGK2XkZGBd5e9g8TEn3HufBLu3MlCyBNhWLTw\\nLSxbtvyB8o6Ojlgwdx4AYMOGDdh47hjMLA3/na39PQEzhczMTDg6Opb5s9RqtUj5UQ8AaNZRh7cn\\n/IbFL1yA3soaL28Kh1IKZhYKP//7UkGd1Eu3oJTCWOftMDezwLq1X8Dd3b3MbRCVS/YgRLVy4cIF\\nmNt5GJIDALg1gpVbPSQlJUEphaioKNzKyUOqfwdkjDuEm2O/w/UmA/DpmnUwMzODTqfD9s0bcCcr\\nE7dupOKD2ZPQJ+M/eLm+NU4eOQhHR0ekpqbizTffxGszZuLQoUP3bd9eowFSrgBZmbAZ3QGLFmbh\\nbLYzxs1JxVM9OyE9Pf2BNmdlZaF1eDMcPLkEX/97PRq0+BljFibjt7TN6B3Z7e6h1iKFhYXh+n/i\\ncf3fx5CbkYXfXv8cgfXqFpsc8vLyMH/BXLRoHYzuER3xww8/PFBm4MCBSDxqiYXDL2P7ihRQD8R9\\nlwWtqy2UMpwbaT28FpKOXMcHz5/ElvmnEdvjBN6OXYak0+dxNTkVXbp0eViXiarM1GNUFT1BzkGI\\nUrh+/TptdU7EoJMFYx9s7V148eLFgjKdIyKJIf8gXtpGRCwgus5gYFBIidafkpJC71qBtO4xiGrY\\nTGrcPLl58+aC5SdPnqTWxZWIHE6fOra8RJeCqfETTjx8+PAD6/ziiy/4ZHtnrtjhzMbNLZlIXybS\\nlz/n+NDZxbZEjzDds2cPfevUopWtDcPahd8XrzGTpo5nUEt3LtrbhGOW16Wzq46JiYkPlLt27Rrn\\nzpvL8HYtWbO+I5cebkqdqxVfWPckFyT2ZPjQx9ixa3suXryYU6ZNLvYKL/HXglzmKsSD1q//grY6\\nZzoEhNJW58z/+/jT+5b36z+QcAkgvBoQXSYb/tromJWVdV+506dPc8iIl/h0vwHcuHETSXLJkiW0\\n7j6QOEzD9P5e1qjb4L56P/30E4e/+BJ19haMv+rES3ThjylOdHWz5dmzZx9o76pVq9j5aWfWbmBB\\n/zoWPKP3YSJ9GZ/pTZ29Na9evVrOnxDp6u7AVedC+Q3b8hu2ZcSomsXeuiMvL4+Tpk6gg5Md7ew1\\nrFHLi77+nuw38FmmpaWVe/uE6cojQcg5CFHt9O0bhbZtw5GUlAR/f3/4+Pjctzywlj+QsRlYcgnQ\\nOgG95gBTaqDPM1H45qvtAIBz586hacvWyHxqFPQ+vtg5fgpSr19H2o103HH3u7cyz5rIvJlx3/ob\\nNGiA5R+8Dw83e/QK+xCtOprju715iI5+CQEBAQ+0Nzg4GNGjsuDYpwNyDhzFuOdS0a6HDdYvz0Tr\\n1q3h6upa3h8RLCwtcDtLXzCfnamHhUXRXwdmZmZYEhOLJTGx5d4WUXVJghDVkoeHBzw8PIwuCwwM\\nBGwdDMkBAKy1gFMN7N63r6DMylWrkdW2P/QDZwEAsvzqYf7SEdi4egVin+qFrKbtAfcasF02Eb16\\n9iyol5mZiehRo7B5/XqYW1ggsk8fNG/UHIOj6qNDhw5G2/P1N1/DNao9aq6chryMLBwftQRHpv8X\\nGo01Fs2ZVk6fyP0mT5yKRZEx6D3VDb8nZiN+1y2sWvhchWxLPLrkJLX42+nbty9wOx3Y9TZwMxU4\\nsApI/RXU3/tFnZObC7217b1K1hrk5uYiNDQUC2e+CovXIoG+dYH/HcaAqEjcuXMH/YYOhs7JERvW\\nroX5sL6w+WkvtsUdh0arLTI5AMDNzExY+Bj2Esx1GnjOHIZsvSV4xwbNmzevkM9g0sSpmD/zHfyx\\nLwiuN7vg8MHj8PT0rJBtiUeYqceoKnqCnIMQFaBr9x6E1pmwtiPcAghLG3bodu9OpfHx8dQ4uxJT\\nVhAxO6mpG8y58xcwOzub7jX9qZa+R3UpjWrNRurc3Dlm0gQ6dQ1njYxj9E05RKsWwdS8M5faj5fw\\nmUEDim3LkSNHaOfuwse+jmHQT6upaxVEn1q+D71XkxDFQTmcg1CG9VRdSilW9TaKRw9J9Ip8Btv+\\nvQfIy8UTwY9jzzfb77s09NChQ5j++htIv3kTA57pjYnjxiIxMREhnbsg68ipgnL2kd3gejMN6UvG\\nw7bjkwCAzH9sR/o/98PK0wPDrJ3wzpIlxbZn+/btmDx7BjLS09Gn19NYumARrKysKiZ48beglAJJ\\n9fCSxayjqn/5SoIQFSkvLw85OTmwsbEpUfnU1FR4BwTgzrfHoTy9wJsZsA1viuaNGyI+vCF0r7xg\\nKDf2DWTvOAg3Kvzw3UEZLCb+cpWaIJRSzgA2AKgJ4DyAZ0mmGSm3AkAPAFdIBpWhviQIUaXMj4nB\\novc/ANt2hNmRg3iuc0dMHz8eoW3CoZo3gv7WbTD+DOZMewWDBw+Gg4NDZTdZ/A1VdoJYDOAaycVK\\nqWkAnEhON1KuNYCbANb8KUGUtL4kCFHlHDhwAPHx8QgMDESnTp2glMK1a9ewY8cOmJubo3v37pIY\\nRKWq7ASRAKANyWSllCeA/STrFVHWH8C2PyWIEtWXBCGEEKVXHgnClMtcPUgm579OBmD8ovOKqy+E\\nEKICFTtQTim1C4Cxi6NfKzxDkkqpMv/MN7W+EEKI8ldsgiDZqahlSqlkpZQnyT+UUl4ArpRy2yWu\\nP2fOnILXbdu2Rdu2bUu5KSGEqN7279+P/fv3l+s6TT1JnUIyRik1HYCjsZPM+WX98eA5iBLVl3MQ\\nQghRepV9ktoZwBcA/FDoMlWllDeAj0n2yC+3DkAbAC4w7CXMIrmyqPpGtiMJQgghSkkGygkhhDCq\\nsq9iEkIIUY1JghBCCGGUJAghhBBGSYIQQghhlCQIIYQQRkmCEEIIYZQkCCGEEEZJghBCCGGUJAgh\\nhBBGSYIQQghhlCQIIYQQRkmCEEIIYZQkCCGEEEZJghBCCGGUJAghhBBGSYIQQghhlCQIIYQQRkmC\\nEEIIYZQkCCGEEEZJghBCCGGUJAghhBBGSYIQQghhlCQIIYQQRkmCEEIIYZQkCCGEEEZJghBCCGGU\\nJAghhBBGSYIQQghhVJkThFLKWSm1Syl1Rim1UynlWES5FUqpZKXUj396f45S6pJS6kT+1LWsbRFC\\nCFH+TNmDmA5gF8nHAOzJnzdmJQBjX/4EEEsyOH/aYUJbHln79++v7CZUqOocX3WODZD4hGkJIgLA\\n6vzXqwE8bawQyf8CuF7EOpQJ268Wqvs/0uocX3WODZD4hGkJwoNkcv7rZAAeZVjHGKVUnFLq06IO\\nUQkhhKgcxSaI/HMMPxqZIgqXI0kYDhmVxocAAgA8DuAygKWlrC+EEKICKcN3exkqKpUAoC3JP5RS\\nXgD2kaxXRFl/ANtIBpV2uVKqbA0UQoi/OZImHca3MKHulwAGA4jJ/7u1NJWVUl4kL+fP9gbwo7Fy\\npgYohBCibEzZg3AG8AUAPwDnATxLMk0p5Q3gY5I98sutA9AGgAuAKwBmkVyplFoDw+ElAjgH4MVC\\n5zSEEEJUsjInCCGEENVblRhJXZ0H3ZVDbCWqX1lKEV9XpVSCUipRKTWt0PtVsu+Kau+fyrybvzxO\\nKRVcmrqVzcT4ziul4vP76/u/rtUl97D4lFL1lFKHlFK3lVKTSlO3spkYW+n6jmSlTwAWA5ia/3oa\\ngEVFlGsNIBjAj396fzaAiZUdRwXFVqL6VTk+AOYAkgD4A7AEcBJA/arad8W1t1CZ7gC+zn8dCuBw\\nSetW9mRKfPnz5wA4V3YcJsbnBqApgPkAJpWm7qMaW1n6rkrsQaB6D7ozNbYS1a9EJWlfcwBJJM+T\\nzAGwHkCvQsurWt89rL1AobhJHgHgqJTyLGHdylbW+AqPdapqfVbYQ+MjeZXkMQA5pa1byUyJ7a4S\\n911VSRDVedCdqbGVx2dTkUrSPh8AFwvNX8p/766q1ncPa29xZbxLULeymRIfYLiwZLdS6phSakSF\\ntbLsShJfRdT9K5javlL1nSmXuZaKUmoXAE8ji14rPEOSZRj78CGAufmv58Ew6C661I0sowqOrdzq\\nl1U5xFdcmyu174pQ0s+4Kv+KLo6p8bUi+btSyg3ALqVUQv4ecFVhyv+Rqn7Vjqnta0nyckn77i9L\\nECQ7FbUs/+SsJ+8NurtSynUXlFdKfQJgW9lbWnoVGRsAU+ubrBzi+w1AjULzNWD45VPpfVeEIttb\\nTBnf/DKWJahb2coa328AQPL3/L9XlVL/hOGwR1VKECWJryLq/hVMah/zx56VtO+qyiGmu4PugDIO\\nuis0W+Sgu0piUmzlUL+ilaR9xwDUUUr5K6WsAPTNr1dV+67I9hbyJYBBAKCUCgOQln+orSR1K1uZ\\n41NKaZRSuvz3tQA6o2r0WWGl6YM/7yVV9f4rc2xl6rvKPiuff2bdGcBuAGcA7ATgmP++N4CvCpVb\\nB+B3ANkwHIcbmv/+GgDxAOJg+ILyqOyYyjE2o/WrylSK+LoBOA3DFRivFHq/SvadsfYCeBGGAZ13\\ny7yXvzwOwBMPi7UqTWWND0AtGK6cOQng1KMaHwyHTC8CuAHDxSG/ArB7FPqvrLGVpe9koJwQQgij\\nqsohJiGEEFWMJAghhBBGSYIQQghhlCQIIYQQRkmCEEIIYZQkCCGEEEZJghBCCGGUJAghhBBG/T/7\\nTqdaEGObbAAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"train_data = np.array([x, y, z]).T\\n\",\n \"trans_data = manifold.LocallyLinearEmbedding(n_neighbors =30, n_components = 2,\\n\",\n \" method='standard').fit_transform(train_data)\\n\",\n \"plt.scatter(trans_data[:, 0], trans_data[:, 1], marker='o', c=colors)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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D1d0pPnOGcyvlXTmUA/SesLv98FnMI3xJ4nqd7FkL+kmPf+v3naLcqa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\\nEAaDwWBwy/8DG2GtnkorbjQAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"for index, k in enumerate((10,20,30,40)):\\n\",\n \" plt.subplot(2,2,index+1)\\n\",\n \" trans_data = manifold.LocallyLinearEmbedding(n_neighbors = k, n_components = 2,\\n\",\n \" method='standard').fit_transform(train_data)\\n\",\n \" plt.scatter(trans_data[:, 0], trans_data[:, 1], marker='o', c=colors)\\n\",\n \" plt.text(.99, .01, ('LLE: k=%d' % (k)),\\n\",\n \" transform=plt.gca().transAxes, size=10,\\n\",\n \" horizontalalignment='right')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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45h924fT43LIrN5CG89fZg2PZx8tn4Jna50Mu6hcE4c9dG3TjYvzl9C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\\n/wFUztlB0tBfoAAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"for index, k in enumerate((10,20,30,40)):\\n\",\n \" plt.subplot(2,2,index+1)\\n\",\n \" trans_data = manifold.LocallyLinearEmbedding(n_neighbors = k, n_components = 2,\\n\",\n \" method='hessian').fit_transform(train_data)\\n\",\n \" plt.scatter(trans_data[:, 0], trans_data[:, 1], marker='o', c=colors)\\n\",\n \" plt.text(.99, .01, ('HLLE: k=%d' % (k)),\\n\",\n \" transform=plt.gca().transAxes, size=10,\\n\",\n \" horizontalalignment='right')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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D5w2yQTL00vYfd6+Px2qJBYSLO7YcUOg4+WgtNr4eJbE+lzcxKb/n2U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\\nneYqWGazAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"for index, k in enumerate((10,20,30,40)):\\n\",\n \" plt.subplot(2,2,index+1)\\n\",\n \" trans_data = manifold.LocallyLinearEmbedding(n_neighbors = k, n_components = 2,\\n\",\n \" method='modified').fit_transform(train_data)\\n\",\n \" plt.scatter(trans_data[:, 0], trans_data[:, 1], marker='o', c=colors)\\n\",\n \" plt.text(.99, .01, ('MLLE: k=%d' % (k)),\\n\",\n \" transform=plt.gca().transAxes, size=10,\\n\",\n \" horizontalalignment='right')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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lQhlXT+cAlEAXDq1Cn27dOHL3XsyMWLFzMjI4NdOnViU4AdABYDOM1Z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\\nvIG6J6AAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"for index, k in enumerate((10,20,30,40)):\\n\",\n \" plt.subplot(2,2,index+1)\\n\",\n \" trans_data = manifold.LocallyLinearEmbedding(n_neighbors = k, n_components = 2,\\n\",\n \" method='ltsa').fit_transform(train_data)\\n\",\n \" plt.scatter(trans_data[:, 0], trans_data[:, 1], marker='o', c=colors)\\n\",\n \" plt.text(.99, .01, ('LSTA: k=%d' % (k)),\\n\",\n \" transform=plt.gca().transAxes, size=10,\\n\",\n \" horizontalalignment='right')\\n\",\n \"plt.show()\"\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\": \"3.6.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "classic-machine-learning/matrix_factorization.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"Copyright (C)\\n\",\n \"2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"矩阵分解在协同过滤推荐算法中的应用 https://www.cnblogs.com/pinard/p/6351319.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"import sys\\n\",\n \"\\n\",\n \"#下面这些目录都是你自己机器的Spark安装目录和Java安装目录\\n\",\n \"os.environ['SPARK_HOME'] = \\\"C:/Tools/spark-2.2.0-bin-hadoop2.6/\\\"\\n\",\n \"os.environ['PYSPARK_PYTHON'] = \\\"C:/Users/tata/AppData/Local/Programs/Python/Python36/python.exe\\\"\\n\",\n \"os.environ['HADOOP_HOME'] = \\\"C:/Tools/hadoop-2.6.0\\\"\\n\",\n \"\\n\",\n \"sys.path.append(\\\"C:/Tools/spark-2.2.0-bin-hadoop2.6/bin\\\")\\n\",\n \"sys.path.append(\\\"C:/Tools/spark-2.2.0-bin-hadoop2.6/python\\\")\\n\",\n \"sys.path.append(\\\"C:/Tools/spark-2.2.0-bin-hadoop2.6/python/pyspark\\\")\\n\",\n \"sys.path.append(\\\"C:/Tools/spark-2.2.0-bin-hadoop2.6/python/lib\\\")\\n\",\n \"sys.path.append(\\\"C:/Tools/spark-2.2.0-bin-hadoop2.6/python/lib/pyspark.zip\\\")\\n\",\n \"sys.path.append(\\\"C:/Tools/spark-2.2.0-bin-hadoop2.6/python/lib/py4j-0.10.4-src.zip\\\")\\n\",\n \"sys.path.append(\\\"C:/Program Files/Java/jdk1.8.0_171\\\")\\n\",\n \"\\n\",\n \"from pyspark import SparkContext\\n\",\n \"from pyspark import SparkConf\\n\",\n \"\\n\",\n \"\\n\",\n \"sc = SparkContext(\\\"local\\\",\\\"testing\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print (sc)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'196\\\\t242\\\\t3\\\\t881250949'\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"user_data = sc.textFile(\\\"C:/Temp/ml-100k/u.data\\\")\\n\",\n \"user_data.first()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['196', '242', '3']\\n\"\n ]\n }\n ],\n \"source\": [\n \"rates = user_data.map(lambda x: x.split(\\\"\\\\t\\\")[0:3])\\n\",\n \"print (rates.first())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Rating(user=196, product=242, rating=3.0)\\n\"\n ]\n }\n ],\n \"source\": [\n \"from pyspark.mllib.recommendation import Rating\\n\",\n \"rates_data = rates.map(lambda x: Rating(int(x[0]),int(x[1]),int(x[2])))\\n\",\n \"print (rates_data.first())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from pyspark.mllib.recommendation import ALS\\n\",\n \"from pyspark.mllib.recommendation import MatrixFactorizationModel\\n\",\n \"sc.setCheckpointDir('checkpoint/')\\n\",\n \"ALS.checkpointInterval = 2\\n\",\n \"model = ALS.train(ratings=rates_data, rank=20, iterations=5, lambda_=0.02)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1.0247527279555144\\n\"\n ]\n }\n ],\n \"source\": [\n \"print (model.predict(38,20))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[Rating(user=38, product=966, rating=7.105754614983208), Rating(user=38, product=704, rating=6.602574630322047), Rating(user=38, product=143, rating=6.553191616139379), Rating(user=38, product=255, rating=6.362197879632264), Rating(user=38, product=595, rating=6.185609885051051), Rating(user=38, product=1164, rating=6.1297225859932105), Rating(user=38, product=313, rating=6.071157532420378), Rating(user=38, product=451, rating=5.970718466267092), Rating(user=38, product=692, rating=5.903949492186043), Rating(user=38, product=562, rating=5.836255993242025)]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print (model.recommendProducts(38,10))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[Rating(user=98, product=20, rating=7.11448886612415), Rating(user=531, product=20, rating=7.033999654677902), Rating(user=148, product=20, rating=6.483862698355137), Rating(user=448, product=20, rating=6.3606279513508515), Rating(user=612, product=20, rating=6.3502915572316505), Rating(user=358, product=20, rating=6.283901393579333), Rating(user=46, product=20, rating=6.09776516011452), Rating(user=772, product=20, rating=6.06177184817216), Rating(user=169, product=20, rating=5.977540934545959), Rating(user=122, product=20, rating=5.783316875752751)]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print (model.recommendUsers(20,10))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[(451, (Rating(user=451, product=219, rating=6.833222529976895), Rating(user=451, product=763, rating=6.2160154442342055), Rating(user=451, product=834, rating=5.998552156157251))), (454, (Rating(user=454, product=1311, rating=5.07194724500117), Rating(user=454, product=880, rating=5.059066387308917), Rating(user=454, product=1278, rating=4.574046818547554))), (147, (Rating(user=147, product=1160, rating=6.381795458855278), Rating(user=147, product=1176, rating=6.199439823579881), Rating(user=147, product=1105, rating=5.917712166737264))), (155, (Rating(user=155, product=502, rating=5.775465626969604), Rating(user=155, product=694, rating=5.6728294385816405), Rating(user=155, product=344, rating=5.6069731464245045))), (772, (Rating(user=772, product=1021, rating=6.260758762886597), Rating(user=772, product=1194, rating=6.148211950871701), Rating(user=772, product=20, rating=6.06177184817216))), (752, (Rating(user=752, product=616, rating=6.3088545647632195), Rating(user=752, product=695, rating=6.120397505448311), Rating(user=752, product=1205, rating=6.039847014851175))), (586, (Rating(user=586, product=388, rating=4.987674336387565), Rating(user=586, product=1446, rating=4.949808837084644), Rating(user=586, product=624, rating=4.865066690899953))), (667, (Rating(user=667, product=320, rating=7.5670917175115555), Rating(user=667, product=543, rating=6.861153645614058), Rating(user=667, product=844, rating=6.5365238550694595))), (428, (Rating(user=428, product=454, rating=6.357982147962503), Rating(user=428, product=703, rating=5.894259761527545), Rating(user=428, product=904, rating=5.616480944657443))), (464, (Rating(user=464, product=1159, rating=5.846468163555558), Rating(user=464, product=1066, rating=5.793021245436287), Rating(user=464, product=1005, rating=5.752894191764675))), (11, (Rating(user=11, product=1449, rating=5.23194447490978), Rating(user=11, product=6, rating=5.193686956908072), Rating(user=11, product=1167, rating=5.045039374993298))), (14, (Rating(user=14, product=962, rating=7.292605739982915), Rating(user=14, product=253, rating=6.578196398970585), Rating(user=14, product=502, rating=6.432486261612122))), (466, (Rating(user=466, product=244, rating=6.079199883616624), Rating(user=466, product=253, rating=5.6178208666560385), Rating(user=466, product=1368, rating=5.496758659952307))), (912, (Rating(user=912, product=1172, rating=6.519078987021415), Rating(user=912, product=57, rating=5.706296565525048), Rating(user=912, product=165, rating=5.247726748459797))), (302, (Rating(user=302, product=1069, rating=6.367380328330142), Rating(user=302, product=334, rating=5.652077765689108), Rating(user=302, product=60, rating=5.652020939635012))), (140, (Rating(user=140, product=904, rating=6.084299343666348), Rating(user=140, product=262, rating=5.766785355590104), Rating(user=140, product=502, rating=5.444774650756214))), (204, (Rating(user=204, product=60, rating=7.451303808327542), Rating(user=204, product=958, rating=7.383652658062177), Rating(user=204, product=906, rating=7.124410529285549))), (195, (Rating(user=195, product=187, rating=5.267381564386821), Rating(user=195, product=1643, rating=5.261035104061932), Rating(user=195, product=713, rating=5.228970859544242))), (215, (Rating(user=215, product=57, rating=5.226593438149547), Rating(user=215, product=1063, rating=5.105991610789014), Rating(user=215, product=253, rating=5.072346129778808))), (1, (Rating(user=1, product=1129, rating=6.09646745150211), Rating(user=1, product=647, rating=5.990307600539187), Rating(user=1, product=1067, rating=5.737934859212475))), (226, (Rating(user=226, product=1166, rating=6.29951716913482), Rating(user=226, product=1012, rating=6.133261522373109), Rating(user=226, product=6, rating=5.858890944286139))), (291, (Rating(user=291, product=1159, rating=5.37620589400203), Rating(user=291, product=433, rating=5.321883988968924), Rating(user=291, product=223, rating=5.275004827797661))), (318, (Rating(user=318, product=960, rating=5.622402650320851), Rating(user=318, product=632, rating=5.596311911664357), Rating(user=318, product=339, rating=5.589019126164642))), (931, (Rating(user=931, product=914, rating=5.523805297386416), Rating(user=931, product=278, rating=5.144013456693656), Rating(user=931, product=339, rating=5.139109213014599))), (443, (Rating(user=443, product=888, rating=7.229532606438902), Rating(user=443, product=224, rating=6.2965377460461145), Rating(user=443, product=272, rating=6.271611870994028))), (755, (Rating(user=755, product=904, rating=7.468214213209096), Rating(user=755, product=1643, rating=6.581478908493831), Rating(user=755, product=1176, rating=6.015277617814491))), (228, (Rating(user=228, product=1262, rating=7.063353776194306), Rating(user=228, product=344, rating=6.53111488709828), Rating(user=228, product=1134, rating=6.485130103372802))), (94, (Rating(user=94, product=1512, rating=5.479490129273689), Rating(user=94, product=1449, rating=5.473415212371289), Rating(user=94, product=474, rating=5.367299199504915))), (192, (Rating(user=192, product=253, rating=7.996904210165267), Rating(user=192, product=113, rating=6.65472164922934), Rating(user=192, product=904, rating=6.5587330126423975))), (450, (Rating(user=450, product=344, rating=5.4982079288618575), Rating(user=450, product=947, rating=5.216935608345309), Rating(user=450, product=615, rating=5.144285549711002))), (160, (Rating(user=160, product=56, rating=5.554753615514364), Rating(user=160, product=1065, rating=5.43584626582697), Rating(user=160, product=1098, rating=5.428997534955827))), (492, (Rating(user=492, product=1160, rating=6.398170114715048), Rating(user=492, product=1073, rating=6.2041792474629), Rating(user=492, product=697, rating=5.930183866475677))), (21, (Rating(user=21, product=467, rating=5.812254158774702), Rating(user=21, product=238, rating=5.741327793108013), Rating(user=21, product=100, rating=5.57868941977712))), (321, (Rating(user=321, product=947, rating=4.993269534856391), Rating(user=321, product=516, rating=4.98498192138408), Rating(user=321, product=497, rating=4.831754133824129))), (577, (Rating(user=577, product=716, rating=5.429013758420336), Rating(user=577, product=1159, rating=5.367492220306736), Rating(user=577, product=909, rating=5.246490362182611))), (715, (Rating(user=715, product=64, rating=5.2147460881344925), Rating(user=715, product=12, rating=4.9774137013198505), Rating(user=715, product=176, rating=4.815373713801645))), (403, (Rating(user=403, product=1159, rating=7.5768056492974205), Rating(user=403, product=500, rating=7.06135760534475), Rating(user=403, product=253, rating=6.700006857004542))), (355, (Rating(user=355, product=1401, rating=6.156139936883587), Rating(user=355, product=11, rating=6.0865267194828006), Rating(user=355, product=723, rating=6.032622156190095))), (801, (Rating(user=801, product=557, rating=7.060273752907461), Rating(user=801, product=211, rating=6.704931591056148), Rating(user=801, product=516, rating=6.67230346708675))), (460, (Rating(user=460, product=1103, rating=5.978477685796581), Rating(user=460, product=753, rating=5.892239585753291), Rating(user=460, product=887, rating=5.384793892739577))), (131, (Rating(user=131, product=680, rating=7.752026543356796), Rating(user=131, product=1021, rating=7.362781824355434), Rating(user=131, product=1176, rating=7.177540687874962))), (815, (Rating(user=815, product=530, rating=5.500185802002656), Rating(user=815, product=735, rating=5.4577867317557835), Rating(user=815, product=318, rating=5.259190735274869))), (347, (Rating(user=347, product=624, rating=6.578980988549827), Rating(user=347, product=697, rating=5.993208061732304), Rating(user=347, product=640, rating=5.783849221124757))), (799, (Rating(user=799, product=904, rating=6.907614974810732), Rating(user=799, product=611, rating=6.68348310715242), Rating(user=799, product=6, rating=6.564521251820443))), (548, (Rating(user=548, product=1019, rating=6.829077796309135), Rating(user=548, product=616, rating=5.9283024853226225), Rating(user=548, product=1199, rating=5.819053839362241))), (637, (Rating(user=637, product=727, rating=5.0609427214043095), Rating(user=637, product=22, rating=5.004745435132544), Rating(user=637, product=1233, rating=4.9682776292646675))), (646, (Rating(user=646, product=1278, rating=4.574492271822261), Rating(user=646, product=313, rating=4.303793454142348), Rating(user=646, product=316, rating=4.275438755488965))), (293, (Rating(user=293, product=1126, rating=4.820422733591884), Rating(user=293, product=641, rating=4.750918724766876), Rating(user=293, product=136, rating=4.730899843775969))), (93, (Rating(user=93, product=1184, rating=7.749417217100374), Rating(user=93, product=267, rating=7.006035826590082), Rating(user=93, product=937, rating=6.635847768526715))), (489, (Rating(user=489, product=253, rating=6.009060867620909), Rating(user=489, product=741, rating=5.91353264371835), Rating(user=489, product=1217, rating=5.890333995796379))), (758, (Rating(user=758, product=160, rating=5.809213316833922), Rating(user=758, product=962, rating=5.792076357788061), Rating(user=758, product=1005, rating=5.68084652799816))), (528, (Rating(user=528, product=1286, rating=6.707272268414105), Rating(user=528, product=551, rating=5.916243979110599), Rating(user=528, product=630, rating=5.908027447444513))), (696, (Rating(user=696, product=45, rating=6.673498517940471), Rating(user=696, product=337, rating=6.639101027714513), Rating(user=696, product=1103, rating=6.470956932387978))), (558, (Rating(user=558, product=753, rating=6.9288982510269985), Rating(user=558, product=960, rating=6.17714720461199), Rating(user=558, product=639, rating=6.0195706753981355))), (453, (Rating(user=453, product=320, rating=5.083962213224081), Rating(user=453, product=1449, rating=4.896702799195447), Rating(user=453, product=272, rating=4.778292182062639))), (58, (Rating(user=58, product=184, rating=6.450669550116911), Rating(user=58, product=1466, rating=6.268491520689757), Rating(user=58, product=718, rating=5.969574549945667))), (599, (Rating(user=599, product=64, rating=6.06957796255665), Rating(user=599, product=1101, rating=5.902572516724796), Rating(user=599, product=630, rating=5.869234847428939))), (855, (Rating(user=855, product=624, rating=5.2411549136403135), Rating(user=855, product=81, rating=5.202297849453514), Rating(user=855, product=445, rating=5.18394751695434))), (878, (Rating(user=878, product=543, rating=5.0530920342798264), Rating(user=878, product=1021, rating=4.815173969380936), Rating(user=878, product=1426, rating=4.805063023933007))), (196, (Rating(user=196, product=962, rating=6.57447692032245), Rating(user=196, product=57, rating=6.2784874502747385), Rating(user=196, product=906, rating=5.953158829676656))), (929, (Rating(user=929, product=1176, rating=5.5196436589118), Rating(user=929, product=127, rating=5.27344910792832), Rating(user=929, product=601, rating=5.218877325149097))), (338, (Rating(user=338, product=543, rating=6.2851345427286125), Rating(user=338, product=1021, rating=6.234505577159058), Rating(user=338, product=961, rating=5.766834220682345))), (784, (Rating(user=784, product=320, rating=5.848951001254236), Rating(user=784, product=836, rating=5.312548904533052), Rating(user=784, product=458, rating=5.147382650509591))), (848, (Rating(user=848, product=904, rating=7.772520763569307), Rating(user=848, product=543, rating=7.305823816873008), Rating(user=848, product=341, rating=6.912858822721003))), (387, (Rating(user=387, product=646, rating=5.81976784831528), Rating(user=387, product=589, rating=5.812161519918922), Rating(user=387, product=179, rating=5.618248083330544))), (918, (Rating(user=918, product=1120, rating=5.639330991799617), Rating(user=918, product=137, rating=5.250888848745309), Rating(user=918, product=1199, rating=5.23806876997893))), (292, (Rating(user=292, product=603, rating=5.373617766907569), Rating(user=292, product=182, rating=5.35526622876904), Rating(user=292, product=489, rating=5.324207700620751))), (79, (Rating(user=79, product=543, rating=6.853412912276214), Rating(user=79, product=1449, rating=6.247675982038363), Rating(user=79, product=1126, rating=6.144991175925867))), (375, (Rating(user=375, product=50, rating=5.913812673534363), Rating(user=375, product=900, rating=5.87980324606742), Rating(user=375, product=1063, rating=5.862184740689664))), (835, (Rating(user=835, product=1512, rating=5.478119185975928), Rating(user=835, product=316, rating=5.320323990622268), Rating(user=835, product=516, rating=5.3085060710587655))), (30, (Rating(user=30, product=615, rating=6.882509826169991), Rating(user=30, product=502, rating=6.383037716423782), Rating(user=30, product=904, rating=6.290878970961722))), (127, (Rating(user=127, product=630, rating=7.109840149633671), Rating(user=127, product=1217, rating=6.772644435086435), Rating(user=127, product=454, 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Rating(user=541, product=205, rating=5.452885691460156), Rating(user=541, product=630, rating=5.427237032082164))), (834, (Rating(user=834, product=960, rating=5.710966405195989), Rating(user=834, product=197, rating=5.59858788749461), Rating(user=834, product=458, rating=5.597468038076537))), (741, (Rating(user=741, product=320, rating=5.700891140645842), Rating(user=741, product=127, rating=5.452086511962046), Rating(user=741, product=1126, rating=5.317248822407843))), (664, (Rating(user=664, product=320, rating=5.602356577525176), Rating(user=664, product=1137, rating=5.55636162417381), Rating(user=664, product=641, rating=5.485543270770507))), (252, (Rating(user=252, product=1065, rating=6.627086607053475), Rating(user=252, product=180, rating=6.492967908920953), Rating(user=252, product=959, rating=6.253915471382207))), (827, (Rating(user=827, product=320, rating=4.97911075552083), Rating(user=827, product=543, rating=4.634531336360405), Rating(user=827, product=1449, 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Rating(user=465, product=1639, rating=4.805040133388748), Rating(user=465, product=1137, rating=4.719646210753842))), (323, (Rating(user=323, product=1512, rating=5.420699861509471), Rating(user=323, product=136, rating=5.282655740169359), Rating(user=323, product=187, rating=5.245587490430841))), (407, (Rating(user=407, product=50, rating=4.7196804530963705), Rating(user=407, product=1449, rating=4.632709009915878), Rating(user=407, product=963, rating=4.627421353551769))), (55, (Rating(user=55, product=1245, rating=7.232651318502443), Rating(user=55, product=641, rating=7.017478844266899), Rating(user=55, product=616, rating=6.889795269946107))), (802, (Rating(user=802, product=1019, rating=6.110920789876452), Rating(user=802, product=250, rating=5.616973569870052), Rating(user=802, product=787, rating=5.307447854033179))), (927, (Rating(user=927, product=670, rating=6.163562820831607), Rating(user=927, product=1278, rating=6.05757377086262), Rating(user=927, product=1046, rating=5.923243918502546))), (648, (Rating(user=648, product=1199, rating=5.533558816279059), Rating(user=648, product=1093, rating=5.449604636441275), Rating(user=648, product=945, rating=5.416890116102011))), (107, (Rating(user=107, product=652, rating=6.252101267480796), Rating(user=107, product=664, rating=5.808708947567773), Rating(user=107, product=854, rating=5.7973214101136135))), (610, (Rating(user=610, product=998, rating=5.917466730935206), Rating(user=610, product=1065, rating=5.829951813101465), Rating(user=610, product=416, rating=5.7022055960346965))), (765, (Rating(user=765, product=753, rating=7.380918507588374), Rating(user=765, product=1266, rating=6.728322228840712), Rating(user=765, product=865, rating=6.717240703652079))), (792, (Rating(user=792, product=136, rating=5.4960977153633195), Rating(user=792, product=705, rating=5.357707996137708), Rating(user=792, product=1120, rating=5.30481305699466))), (50, (Rating(user=50, product=320, rating=7.279986308373255), Rating(user=50, product=1282, rating=7.2248073819150065), Rating(user=50, product=372, rating=7.187569447496413))), (179, (Rating(user=179, product=426, rating=7.335606458615404), Rating(user=179, product=267, rating=7.215437767336809), Rating(user=179, product=11, rating=6.71819918656024))), (133, (Rating(user=133, product=1022, rating=6.5658770479078985), Rating(user=133, product=863, rating=6.208906812574317), Rating(user=133, product=390, rating=6.033016595037667))), (205, (Rating(user=205, product=1066, rating=5.70248419301841), Rating(user=205, product=745, rating=5.559285901512188), Rating(user=205, product=613, rating=5.404753098499132))), (154, (Rating(user=154, product=646, rating=5.385787045481502), Rating(user=154, product=135, rating=5.297135949648377), Rating(user=154, product=175, rating=5.134594315850375))), (416, (Rating(user=416, product=64, rating=5.691145557891338), Rating(user=416, product=272, rating=5.557833915563618), Rating(user=416, product=496, rating=5.553690579393305))), (680, (Rating(user=680, product=81, rating=6.121510217168254), Rating(user=680, product=1137, rating=5.886729799262616), Rating(user=680, product=1005, rating=5.679332082016214))), (873, (Rating(user=873, product=916, rating=6.5413769394385195), Rating(user=873, product=539, rating=6.242601339287547), Rating(user=873, product=366, rating=5.989641736093627))), (183, (Rating(user=183, product=1286, rating=7.520998608338765), Rating(user=183, product=1022, rating=7.333821195085505), Rating(user=183, product=543, rating=6.715803162201277))), (422, (Rating(user=422, product=1065, rating=5.829022775586629), Rating(user=422, product=42, rating=5.095524492959704), Rating(user=422, product=56, rating=5.0711034387813765))), (596, (Rating(user=596, product=800, rating=6.0697432404355105), Rating(user=596, product=812, rating=5.973178939392113), Rating(user=596, product=101, rating=5.969425587601621))), (788, (Rating(user=788, product=1194, rating=5.355671546830838), Rating(user=788, product=887, rating=5.236161793321241), Rating(user=788, product=1063, rating=5.042163777692169))), (564, (Rating(user=564, product=900, rating=5.409741873502128), Rating(user=564, product=904, rating=4.9076232980016075), Rating(user=564, product=1157, rating=4.733712200125077))), (687, (Rating(user=687, product=1069, rating=6.381362684001112), Rating(user=687, product=1159, rating=5.697853810048102), Rating(user=687, product=806, rating=5.448130030695299))), (47, (Rating(user=47, product=960, rating=6.4024196438444685), Rating(user=47, product=60, rating=5.924263626469767), Rating(user=47, product=1021, rating=5.918907439916488))), (724, (Rating(user=724, product=219, rating=6.702335880324683), Rating(user=724, product=478, rating=6.032772877484815), Rating(user=724, product=136, rating=6.027703827062746))), (485, (Rating(user=485, product=921, rating=5.537140211912489), Rating(user=485, product=1203, rating=5.457174883078132), Rating(user=485, product=874, rating=5.449072045587727))), (276, (Rating(user=276, product=853, rating=5.117785208026142), Rating(user=276, product=603, rating=5.113073097395506), Rating(user=276, product=12, rating=5.1011785236479925))), (374, (Rating(user=374, product=1278, rating=6.698979537441865), Rating(user=374, product=951, rating=6.506872292765173), Rating(user=374, product=827, rating=6.064861148041117))), (207, (Rating(user=207, product=1242, rating=4.7067925701496955), Rating(user=207, product=496, rating=4.543877821543206), Rating(user=207, product=1066, rating=4.5404158897946445))), (521, (Rating(user=521, product=253, rating=4.529342824871663), Rating(user=521, product=1449, rating=4.501323739494637), Rating(user=521, product=774, rating=4.401310497242705))), (739, (Rating(user=739, product=634, rating=10.47919915585312), Rating(user=739, product=1174, rating=8.25026541816126), Rating(user=739, product=671, rating=7.785490361536553))), (233, (Rating(user=233, product=543, rating=6.111384075312228), Rating(user=233, product=1063, rating=6.021332187108449), Rating(user=233, product=514, rating=5.6167956018998675))), (537, (Rating(user=537, product=543, rating=4.465117279243018), Rating(user=537, product=1449, rating=4.35543261808364), Rating(user=537, product=514, rating=4.3438818692823))), (880, (Rating(user=880, product=320, rating=5.80424375794367), Rating(user=880, product=127, rating=5.571826749431251), Rating(user=880, product=1286, rating=5.464240014910492))), (604, (Rating(user=604, product=703, rating=6.616559694266031), Rating(user=604, product=962, rating=6.590542697606157), Rating(user=604, product=874, rating=6.5389985639367785))), (377, (Rating(user=377, product=64, rating=5.789686797068116), Rating(user=377, product=429, rating=5.68663824856894), Rating(user=377, product=1063, rating=5.683723983170156))), (384, (Rating(user=384, product=896, rating=5.78161306504484), Rating(user=384, product=745, rating=5.4805487490929305), Rating(user=384, product=613, rating=5.360412982241984))), (522, (Rating(user=522, product=1021, rating=6.299062453256895), Rating(user=522, product=81, rating=6.23309083429986), Rating(user=522, product=512, rating=6.216741354585814))), (315, (Rating(user=315, product=1137, rating=6.491032081921193), Rating(user=315, product=543, rating=5.842002856339769), Rating(user=315, product=1449, rating=5.790044376798785)))]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print (model.recommendProductsForUsers(3).collect())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[(1084, (Rating(user=165, product=1084, rating=6.550013747986581), Rating(user=270, product=1084, rating=6.317922205103175), Rating(user=335, product=1084, rating=6.313206032234378))), (1410, (Rating(user=137, product=1410, rating=5.502019134716544), Rating(user=487, product=1410, rating=5.009688289298317), Rating(user=619, product=1410, rating=4.854230003447392))), (667, (Rating(user=739, product=667, rating=6.047339602247056), Rating(user=260, product=667, rating=5.604183247472806), Rating(user=366, product=667, rating=5.400251349042385))), (1053, (Rating(user=223, product=1053, rating=6.911688342256087), Rating(user=731, product=1053, rating=6.897913251644746), Rating(user=824, product=1053, rating=5.94038406344269))), (466, (Rating(user=408, product=466, rating=6.752961767800702), Rating(user=898, product=466, rating=6.6617798312894845), Rating(user=810, product=466, rating=6.227354731197914))), (1040, (Rating(user=859, product=1040, rating=5.373581272438935), Rating(user=258, product=1040, rating=5.225013233355378), Rating(user=705, product=1040, rating=5.069606119561671))), (912, (Rating(user=159, product=912, rating=5.10953045201341), Rating(user=519, product=912, rating=5.08144840203197), Rating(user=811, product=912, rating=5.061685755734658))), (1325, (Rating(user=166, product=1325, rating=1.8395132743506089), Rating(user=39, product=1325, rating=1.6855158870987696), Rating(user=462, product=1325, rating=1.6415581728884774))), (140, (Rating(user=211, product=140, rating=5.326679908520493), Rating(user=134, product=140, rating=5.301830496089494), Rating(user=431, product=140, rating=5.278730848184977))), (204, (Rating(user=688, product=204, rating=6.485334020164283), Rating(user=801, product=204, rating=6.054466842576136), Rating(user=513, product=204, rating=5.771738757287627))), (956, (Rating(user=353, product=956, rating=6.472839824999802), Rating(user=511, product=956, rating=6.310808264488376), Rating(user=75, product=956, rating=6.122772345625705))), (291, (Rating(user=217, product=291, rating=6.00283413497496), Rating(user=471, product=291, rating=5.5351972868465396), Rating(user=98, product=291, rating=5.373799399021432))), (1, (Rating(user=282, product=1, rating=5.970247850394842), Rating(user=688, product=1, rating=5.638047129752698), Rating(user=801, product=1, rating=5.577080990498603))), (931, (Rating(user=475, product=931, rating=5.241067454404927), Rating(user=681, product=931, rating=5.170170409944573), Rating(user=98, product=931, rating=4.878584994379669))), (1466, (Rating(user=306, product=1466, rating=6.306135947054474), Rating(user=58, product=1466, rating=6.268491520689757), Rating(user=516, product=1466, rating=6.1144387730645375))), (755, (Rating(user=166, product=755, rating=5.696373098711048), Rating(user=777, product=755, rating=5.5079242176534455), Rating(user=127, product=755, rating=5.4761120161512515))), (1393, (Rating(user=689, product=1393, rating=4.897633843607563), Rating(user=89, product=1393, rating=4.441538531873183), Rating(user=202, product=1393, rating=4.296400590454725))), (450, (Rating(user=366, product=450, rating=7.546881043228528), Rating(user=202, product=450, rating=7.514111409814123), Rating(user=180, product=450, rating=7.4139787285130785))), (492, (Rating(user=818, product=492, rating=7.300225188693744), Rating(user=219, product=492, rating=6.782009612451925), Rating(user=337, product=492, rating=6.268245064757858))), (160, (Rating(user=408, product=160, rating=7.360482022624984), Rating(user=818, product=160, rating=6.835084229298612), Rating(user=898, product=160, rating=6.5107835094353765))), (1419, (Rating(user=97, product=1419, rating=6.132820721504625), Rating(user=96, product=1419, rating=6.08968005543899), Rating(user=166, product=1419, rating=6.015692109292848))), (1148, (Rating(user=475, product=1148, rating=5.897940199075459), Rating(user=98, product=1148, rating=5.481389505698242), Rating(user=366, product=1148, rating=5.328063958574628))), (1168, (Rating(user=811, product=1168, rating=6.381261610346599), Rating(user=588, product=1168, rating=6.224512003430776), Rating(user=935, product=1168, rating=6.1167676711235055))), (1596, (Rating(user=425, product=1596, rating=2.0704914184384857), Rating(user=777, product=1596, rating=2.063766280273635), Rating(user=714, product=1596, rating=2.025436626710082))), (355, (Rating(user=153, product=355, rating=6.827131461335215), Rating(user=281, product=355, rating=5.962478365940836), Rating(user=818, product=355, rating=5.826936834897343))), (801, (Rating(user=212, product=801, rating=5.345912893645925), Rating(user=636, product=801, rating=5.219255594896247), Rating(user=219, product=801, rating=4.845842705882594))), (1500, (Rating(user=689, product=1500, rating=5.660977522728015), Rating(user=604, product=1500, rating=5.5036390699287185), Rating(user=562, product=1500, rating=5.242734475828101))), (460, (Rating(user=819, product=460, rating=6.297840185274095), Rating(user=111, product=460, rating=6.185323850863586), Rating(user=98, product=460, rating=6.116899360976609))), (131, (Rating(user=98, product=131, rating=6.870284121306037), Rating(user=366, product=131, rating=6.470699205731633), Rating(user=46, product=131, rating=5.914810104212577))), (347, (Rating(user=86, product=347, rating=6.034370115112175), Rating(user=517, product=347, rating=5.984828570163043), Rating(user=111, product=347, rating=5.941067894886429))), (1390, (Rating(user=636, product=1390, rating=4.151467684654692), Rating(user=677, product=1390, rating=3.9360852460568956), Rating(user=202, product=1390, rating=3.792520118646177))), (548, (Rating(user=310, product=548, rating=5.400315733240733), Rating(user=415, product=548, rating=5.14486697653224), Rating(user=562, product=548, rating=5.10560027645666))), (1630, (Rating(user=519, product=1630, rating=4.725876398131033), Rating(user=628, product=1630, rating=4.32897135969395), Rating(user=89, product=1630, rating=4.19201216191856))), (293, (Rating(user=818, product=293, rating=5.44441533493913), Rating(user=444, product=293, rating=5.396569842619689), Rating(user=241, product=293, rating=5.362139027047301))), (93, (Rating(user=475, product=93, rating=5.845508148471851), Rating(user=626, product=93, rating=5.7398806945126974), Rating(user=526, product=93, rating=5.524804170626556))), (528, (Rating(user=86, product=528, rating=6.439901033071045), Rating(user=98, product=528, rating=6.045335076727886), Rating(user=46, product=528, rating=6.028204339926864))), (1550, (Rating(user=97, product=1550, rating=3.804993994695141), Rating(user=818, product=1550, rating=3.780580338587016), Rating(user=405, product=1550, rating=3.329259164591126))), (1182, (Rating(user=597, product=1182, rating=5.0522643339408795), Rating(user=777, product=1182, rating=4.9002718655226785), Rating(user=444, product=1182, rating=4.140069456830622))), (1632, (Rating(user=519, product=1632, rating=4.725876398131033), Rating(user=628, product=1632, rating=4.32897135969395), Rating(user=89, product=1632, rating=4.19201216191856))), (558, (Rating(user=418, product=558, rating=6.578168667509551), Rating(user=123, product=558, rating=5.984073022186173), Rating(user=611, product=558, rating=5.982168422481482))), (1150, (Rating(user=362, product=1150, rating=5.660471025200166), Rating(user=939, product=1150, rating=5.542082008332736), Rating(user=810, product=1150, rating=5.472780848470784))), (453, (Rating(user=340, product=453, rating=4.69873288168716), Rating(user=304, product=453, rating=4.611136601519915), Rating(user=777, product=453, rating=4.2089948947512426))), (944, (Rating(user=850, product=944, rating=6.31500367873087), Rating(user=706, product=944, rating=6.009088547511161), Rating(user=14, product=944, rating=5.873700328586384))), (1046, (Rating(user=927, product=1046, rating=5.923243918502546), Rating(user=282, product=1046, rating=5.4439759880346115), Rating(user=688, product=1046, rating=5.40985569959912))), (1280, (Rating(user=517, product=1280, rating=7.829363616403402), Rating(user=86, product=1280, rating=7.066718470573156), Rating(user=839, product=1280, rating=6.211988269421553))), (1031, (Rating(user=562, product=1031, rating=5.230112518403541), Rating(user=38, product=1031, 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(989, (Rating(user=219, product=989, rating=7.350245743098014), Rating(user=440, product=989, rating=6.838760672066284), Rating(user=928, product=989, rating=6.40980816476845))), (276, (Rating(user=362, product=276, rating=6.393349659907915), Rating(user=111, product=276, rating=6.156782015237797), Rating(user=98, product=276, rating=5.998003317504096))), (1103, (Rating(user=696, product=1103, rating=6.470956932387978), Rating(user=225, product=1103, rating=6.341516099792317), Rating(user=219, product=1103, rating=6.326290135275642))), (1536, (Rating(user=385, product=1536, rating=5.155367373338038), Rating(user=448, product=1536, rating=5.1352740585823256), Rating(user=180, product=1536, rating=4.724537877840638))), (207, (Rating(user=390, product=207, rating=6.502355719576242), Rating(user=225, product=207, rating=6.303064632054686), Rating(user=78, product=207, rating=6.283510658593953))), (1460, (Rating(user=193, product=1460, rating=3.659373494144236), Rating(user=755, product=1460, rating=3.642784367003351), Rating(user=105, product=1460, rating=3.620725772140422))), (521, (Rating(user=55, product=521, rating=6.130003514444512), Rating(user=444, product=521, rating=6.072207631639295), Rating(user=762, product=521, rating=6.005415949351253))), (739, (Rating(user=688, product=739, rating=5.675160844724664), Rating(user=801, product=739, rating=5.485524802486696), Rating(user=462, product=739, rating=5.270867742347932))), (1490, (Rating(user=689, product=1490, rating=4.58780386526624), Rating(user=89, product=1490, rating=4.574494039982861), Rating(user=180, product=1490, rating=4.533632411663378))), (991, (Rating(user=50, product=991, rating=6.512364030455609), Rating(user=150, product=991, rating=5.778350388970245), Rating(user=584, product=991, rating=5.691975536595785))), (880, (Rating(user=440, product=880, rating=6.26062311995672), Rating(user=225, product=880, rating=6.092032047660133), Rating(user=8, product=880, rating=6.001869044373712))), (604, (Rating(user=86, product=604, rating=7.008775721063351), Rating(user=928, product=604, rating=6.160935987769628), Rating(user=702, product=604, rating=6.082188135985447))), (377, (Rating(user=706, product=377, rating=4.533572343108061), Rating(user=242, product=377, rating=4.249195525465314), Rating(user=718, product=377, rating=4.235568041484277))), (384, (Rating(user=434, product=384, rating=5.20246207844421), Rating(user=839, product=384, rating=5.112448670701104), Rating(user=507, product=384, rating=5.1010957867288385))), (155, (Rating(user=935, product=155, rating=5.902111105543759), Rating(user=217, product=155, rating=5.84944305753772), Rating(user=688, product=155, rating=5.631944952768034))), (522, (Rating(user=219, product=522, rating=7.0777190491099455), Rating(user=818, product=522, rating=6.882696087093414), Rating(user=743, product=522, rating=6.704620041212051)))]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print (model.recommendUsersForProducts(3).collect())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\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\": \"3.6.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "classic-machine-learning/native_bayes.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C)\\n\",\n \"2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"scikit-learn 朴素贝叶斯类库使用小结 https://www.cnblogs.com/pinard/p/6074222.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"==Predict result by predict==\\n\",\n \"[1]\\n\",\n \"==Predict result by predict_proba==\\n\",\n \"[[ 9.99999949e-01 5.05653254e-08]]\\n\",\n \"==Predict result by predict_log_proba==\\n\",\n \"[[ -5.05653266e-08 -1.67999998e+01]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])\\n\",\n \"Y = np.array([1, 1, 1, 2, 2, 2])\\n\",\n \"from sklearn.naive_bayes import GaussianNB\\n\",\n \"clf = GaussianNB()\\n\",\n \"#拟合数据\\n\",\n \"clf.fit(X, Y)\\n\",\n \"print \\\"==Predict result by predict==\\\"\\n\",\n \"print(clf.predict([[-0.8, -1]]))\\n\",\n \"print \\\"==Predict result by predict_proba==\\\"\\n\",\n \"print(clf.predict_proba([[-0.8, -1]]))\\n\",\n \"print \\\"==Predict result by predict_log_proba==\\\"\\n\",\n \"print(clf.predict_log_proba([[-0.8, -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\": \"3.6.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "classic-machine-learning/pca.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C) 2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"用scikit-learn学习主成分分析(PCA) https://www.cnblogs.com/pinard/p/6243025.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"d:\\\\users\\\\pinard.liu\\\\appdata\\\\local\\\\programs\\\\python\\\\python36\\\\lib\\\\site-packages\\\\matplotlib\\\\collections.py:902: RuntimeWarning: invalid value encountered in sqrt\\n\",\n \" scale = np.sqrt(self._sizes) * dpi / 72.0 * self._factor\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 1,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from mpl_toolkits.mplot3d import Axes3D\\n\",\n \"%matplotlib inline\\n\",\n \"from sklearn.datasets.samples_generator import make_blobs\\n\",\n \"# X为样本特征，Y为样本簇类别， 共1000个样本，每个样本3个特征，共4个簇\\n\",\n \"X, y = make_blobs(n_samples=10000, n_features=3, centers=[[3,3, 3], [0,0,0], [1,1,1], [2,2,2]], cluster_std=[0.2, 0.1, 0.2, 0.2], \\n\",\n \" random_state =9)\\n\",\n \"fig = plt.figure()\\n\",\n \"ax = Axes3D(fig, rect=[0, 0, 1, 1], elev=30, azim=20)\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], X[:, 2],marker='o')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0.98318212 0.00850037 0.00831751]\\n\",\n \"[3.78521638 0.03272613 0.03202212]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.decomposition import PCA\\n\",\n \"pca = PCA(n_components=3)\\n\",\n \"pca.fit(X)\\n\",\n \"print (pca.explained_variance_ratio_)\\n\",\n \"print (pca.explained_variance_)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0.98318212 0.00850037]\\n\",\n \"[3.78521638 0.03272613]\\n\"\n ]\n }\n ],\n \"source\": [\n \"pca = PCA(n_components=2)\\n\",\n \"pca.fit(X)\\n\",\n \"print (pca.explained_variance_ratio_)\\n\",\n \"print (pca.explained_variance_)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"X_new = pca.transform(X)\\n\",\n \"plt.scatter(X_new[:, 0], X_new[:, 1],marker='o')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0.98318212]\\n\",\n \"[3.78521638]\\n\",\n \"1\\n\"\n ]\n }\n ],\n \"source\": [\n \"pca = PCA(n_components=0.95)\\n\",\n \"pca.fit(X)\\n\",\n \"print (pca.explained_variance_ratio_)\\n\",\n \"print (pca.explained_variance_)\\n\",\n \"print (pca.n_components_)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0.98318212 0.00850037]\\n\",\n \"[3.78521638 0.03272613]\\n\",\n \"2\\n\"\n ]\n }\n ],\n \"source\": [\n \"pca = PCA(n_components=0.99)\\n\",\n \"pca.fit(X)\\n\",\n \"print (pca.explained_variance_ratio_)\\n\",\n \"print (pca.explained_variance_)\\n\",\n \"print (pca.n_components_)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0.98318212]\\n\",\n \"[3.78521638]\\n\",\n \"1\\n\"\n ]\n }\n ],\n \"source\": [\n \"pca = PCA(n_components='mle', svd_solver='full')\\n\",\n \"pca.fit(X)\\n\",\n \"print (pca.explained_variance_ratio_)\\n\",\n \"print (pca.explained_variance_)\\n\",\n \"print (pca.n_components_)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\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\": \"3.6.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "classic-machine-learning/regression_production_example.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"

可用于产品的线性回归算法建流程

\\n\",\n \"

数据准备阶段

\\n\",\n \"\\n\",\n \"1.载入数据集\\n\",\n \"\\n\",\n \"2.数据预览\\n\",\n \"\\n\",\n \"3.获取特征数据和输出数据\\n\",\n \"\\n\",\n \"

特征工程阶段

\\n\",\n \"\\n\",\n \"4.缺失值处理，异常值处理，高级特征生成（若有）\\n\",\n \"\\n\",\n \"5.划分数据集为训练集和验证集\\n\",\n \"\\n\",\n \"6.训练集数据的标准化\\n\",\n \"\\n\",\n \"

模型训练阶段

\\n\",\n \"\\n\",\n \"7.使用不同的参数在训练集上训练出模型(使用训练集交叉验证是可选的)\\n\",\n \"\\n\",\n \"8.观察训练集的MSE和RMSE\\n\",\n \"\\n\",\n \"

模型验证阶段

\\n\",\n \"\\n\",\n \"9.验证集数据的标准化\\n\",\n \"\\n\",\n \"10.使用训练出的模型在验证集上做预测，并观察验证集的MSE和RMSE\\n\",\n \"\\n\",\n \"11.画图观察(可选)\\n\",\n \"\\n\",\n \"12.重复步骤7-11，选择验证集上MSE或RMSE最小的模型作为模型输出\\n\",\n \"\\n\",\n \"

算法上线阶段

\\n\",\n \"\\n\",\n \"13.保存模型，加载模型做预测,共3种方法\\n\",\n \" * 使用python pickle API 保存\\n\",\n \" * 使用python 加载pickle文件预测\\n\",\n \" * 使用sklearn joblib API 保存\\n\",\n \" * 使用sklearn joblib API 加载sklearn模型预测\\n\",\n \" * 保存为PMML文件\\n\",\n \" * 使用PMML Java API加载模型预测\\n\",\n \"\\n\",\n \"
by Pinard Liu @ 20190131
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"

1. 载入数据集

\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#载入必要的库，matplotlib用于画图，numpy用于数组操作，pandans用于数据处理\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# read_csv里面的参数是csv在你电脑上的路径，此处csv文件放在notebook运行目录下面的CCPP目录里\\n\",\n \"data = pd.read_csv('.\\\\ccpp.csv')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"

2. 数据预览

\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
ATVAPRHPE
08.3440.771010.8490.01480.48
123.6458.491011.4074.20445.75
229.7456.901007.1541.91438.76
319.0749.691007.2276.79453.09
411.8040.661017.1397.20464.43
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" AT V AP RH PE\\n\",\n \"0 8.34 40.77 1010.84 90.01 480.48\\n\",\n \"1 23.64 58.49 1011.40 74.20 445.75\\n\",\n \"2 29.74 56.90 1007.15 41.91 438.76\\n\",\n \"3 19.07 49.69 1007.22 76.79 453.09\\n\",\n \"4 11.80 40.66 1017.13 97.20 464.43\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(9568, 5)\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"

3. 获取特征数据和输出数据

\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
ATVAPRH
08.3440.771010.8490.01
123.6458.491011.4074.20
229.7456.901007.1541.91
319.0749.691007.2276.79
411.8040.661017.1397.20
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" AT V AP RH\\n\",\n \"0 8.34 40.77 1010.84 90.01\\n\",\n \"1 23.64 58.49 1011.40 74.20\\n\",\n \"2 29.74 56.90 1007.15 41.91\\n\",\n \"3 19.07 49.69 1007.22 76.79\\n\",\n \"4 11.80 40.66 1017.13 97.20\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"#获取特征数据\\n\",\n \"X = data[['AT', 'V', 'AP', 'RH']]\\n\",\n \"X.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
PE
0480.48
1445.75
2438.76
3453.09
4464.43
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" PE\\n\",\n \"0 480.48\\n\",\n \"1 445.75\\n\",\n \"2 438.76\\n\",\n \"3 453.09\\n\",\n \"4 464.43\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"#获取输出数据\\n\",\n \"y = data[['PE']]\\n\",\n \"y.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"

4. 缺失值处理，异常值处理（若有）

\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"

5. 划分数据集为训练集和验证集

\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(7176, 4)\\n\",\n \"(7176, 1)\\n\",\n \"(2392, 4)\\n\",\n \"(2392, 1)\\n\"\n ]\n }\n ],\n \"source\": [\n \"#将数据划分为训练集和验证集\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\\n\",\n \"print (X_train.shape)\\n\",\n \"print (y_train.shape)\\n\",\n \"print (X_test.shape)\\n\",\n \"print (y_test.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"

6. 训练集数据的标准化

\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"AT 19.708303\\n\",\n \"V 54.403151\\n\",\n \"AP 1013.189242\\n\",\n \"RH 73.220410\\n\",\n \"dtype: float64\\n\"\n ]\n }\n ],\n \"source\": [\n \"#对训练集数据特征进行归一化，保存均值向量和标准差向量\\n\",\n \"norm_data_mean = X_train.mean()\\n\",\n \"print (norm_data_mean)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"AT 7.441593\\n\",\n \"V 12.747985\\n\",\n \"AP 5.898733\\n\",\n \"RH 14.625541\\n\",\n \"dtype: float64\\n\"\n ]\n }\n ],\n \"source\": [\n \"norm_data_std = X_train.std()\\n\",\n \"print (norm_data_std)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
ATVAPRH
91030.4410250.6712781.086878-0.558679
62811.0753421.564031-2.551444-0.093706
62011.3804051.701317-0.9442080.162712
2646-1.362478-1.144037-0.0117391.089238
35681.5107631.463616-0.7339790.112112
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" AT V AP RH\\n\",\n \"9103 0.441025 0.671278 1.086878 -0.558679\\n\",\n \"6281 1.075342 1.564031 -2.551444 -0.093706\\n\",\n \"6201 1.380405 1.701317 -0.944208 0.162712\\n\",\n \"2646 -1.362478 -1.144037 -0.011739 1.089238\\n\",\n \"3568 1.510763 1.463616 -0.733979 0.112112\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from scipy.stats import zscore\\n\",\n \"X_train_norm = X_train.apply(zscore)\\n\",\n \"X_train_norm.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"

7. 使用不同的参数在训练集上训练出模型(使用训练集交叉验证是可选的)

\\n\",\n \" * 不考虑正则化\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"#训练模型，不考虑正则化\\n\",\n \"from sklearn.linear_model import LinearRegression\\n\",\n \"linreg = LinearRegression()\\n\",\n \"linreg.fit(X_train_norm, y_train)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[454.21842252]\\n\",\n \"[[-14.68689837 -2.96103407 0.40905751 -2.31169185]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print (linreg.intercept_)\\n\",\n \"print (linreg.coef_)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"

8. 观察训练集的MSE和RMSE

\\n\",\n \" * 不考虑正则化\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train MSE: 20.99974553734626\\n\",\n \"Train RMSE: 4.582547930720011\\n\"\n ]\n }\n ],\n \"source\": [\n \"y_train_pred = linreg.predict(X_train_norm)\\n\",\n \"from sklearn import metrics\\n\",\n \"# 用scikit-learn计算MSE\\n\",\n \"print (\\\"Train MSE:\\\",metrics.mean_squared_error(y_train, y_train_pred))\\n\",\n \"# 用scikit-learn计算RMSE\\n\",\n \"print (\\\"Train RMSE:\\\",np.sqrt(metrics.mean_squared_error(y_train, y_train_pred)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"

9. 验证集数据的标准化

\\n\",\n \" * 只需要做一次即可\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
ATVAPRH
5014-0.256437-2.871289-0.7593570.360984
69471.329245-1.945653-0.293155-1.403737
9230-1.162157-3.4000001.4377250.493629
42901.391059-1.9095690.752833-1.342201
64770.023073-2.708126-0.1558371.283343
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" AT V AP RH\\n\",\n \"5014 -0.256437 -2.871289 -0.759357 0.360984\\n\",\n \"6947 1.329245 -1.945653 -0.293155 -1.403737\\n\",\n \"9230 -1.162157 -3.400000 1.437725 0.493629\\n\",\n \"4290 1.391059 -1.909569 0.752833 -1.342201\\n\",\n \"6477 0.023073 -2.708126 -0.155837 1.283343\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"#验证模型\\n\",\n \"#测试集基于训练集的均值和方差进行标准化\\n\",\n \"X_test_norm = X_test.loc[:,['AT','V','AP','RH']]\\n\",\n \"X_test_norm['AT'] = (X_test['AT'] - 19.708303)/7.441593\\n\",\n \"X_test_norm['V'] = (X_test['AT'] - 54.403151)/12.747985\\n\",\n \"X_test_norm['AP'] = (X_test['AP'] - 1013.189242)/5.898733\\n\",\n \"X_test_norm['RH'] = (X_test['RH'] - 73.220410)/14.625541\\n\",\n \"X_test_norm.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"

10. 使用训练出的模型在验证集上做预测，并观察验证集的MSE和RMSE

\\n\",\n \" * 不考虑正则化\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train MSE: 86.35903350105353\\n\",\n \"Train RMSE: 9.292956122841296\\n\"\n ]\n }\n ],\n \"source\": [\n \"#模型测试集验证，确认最终表现\\n\",\n \"y_test_pred = linreg.predict(X_test_norm)\\n\",\n \"\\n\",\n \"# 用scikit-learn计算MSE\\n\",\n \"print (\\\"Train MSE:\\\",metrics.mean_squared_error(y_test, y_test_pred))\\n\",\n \"# 用scikit-learn计算RMSE\\n\",\n \"print (\\\"Train RMSE:\\\",np.sqrt(metrics.mean_squared_error(y_test, y_test_pred)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"

11. 画图观察(可选)

\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"#画图观察\\n\",\n \"fig, ax = plt.subplots()\\n\",\n \"ax.scatter(y_test, y_test_pred)\\n\",\n \"ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=4)\\n\",\n \"ax.set_xlabel('Measured')\\n\",\n \"ax.set_ylabel('Predicted')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"

12. 重复步骤7-11，选择验证集上MSE或RMSE最小的模型作为模型输出

\\n\",\n \"

7. 使用不同的参数在训练集上训练出模型(使用训练集交叉验证是可选的)

\\n\",\n \" * 考虑L2正则化\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Ridge(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=None,\\n\",\n \" normalize=False, random_state=None, solver='auto', tol=0.001)\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"#加入L2正则化\\n\",\n \"from sklearn.linear_model import Ridge\\n\",\n \"ridge = Ridge(alpha = 0.1)\\n\",\n \"ridge.fit(X_train_norm, y_train)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[454.21842252]\\n\",\n \"[[-14.6857877 -2.96170729 0.40929435 -2.31128706]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print (ridge.intercept_)\\n\",\n \"print (ridge.coef_)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"

8. 观察训练集的MSE和RMSE

\\n\",\n \" * 考虑L2正则化\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train MSE: 20.999745748547557\\n\",\n \"Train RMSE: 4.582547953764102\\n\"\n ]\n }\n ],\n \"source\": [\n \"y_train_pred_ridge = ridge.predict(X_train_norm)\\n\",\n \"from sklearn import metrics\\n\",\n \"# 用scikit-learn计算MSE\\n\",\n \"print (\\\"Train MSE:\\\",metrics.mean_squared_error(y_train, y_train_pred_ridge))\\n\",\n \"# 用scikit-learn计算RMSE\\n\",\n \"print (\\\"Train RMSE:\\\",np.sqrt(metrics.mean_squared_error(y_train, y_train_pred_ridge)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"

9. 验证集数据的标准化(之前做过，此处忽略)

\\n\",\n \"

10. 使用训练出的模型在验证集上做预测，并观察验证集的MSE和RMSE

\\n\",\n \" * 考虑L2正则化\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train MSE: 86.3891373545495\\n\",\n \"Train RMSE: 9.294575695240182\\n\"\n ]\n }\n ],\n \"source\": [\n \"#模型测试集L2正则化验证，比较最终表现\\n\",\n \"y_test_pred_ridge = ridge.predict(X_test_norm)\\n\",\n \"\\n\",\n \"# 用scikit-learn计算MSE\\n\",\n \"print (\\\"Train MSE:\\\",metrics.mean_squared_error(y_test, y_test_pred_ridge))\\n\",\n \"# 用scikit-learn计算RMSE\\n\",\n \"print (\\\"Train RMSE:\\\",np.sqrt(metrics.mean_squared_error(y_test, y_test_pred_ridge)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"

11. 画图观察(可选)

\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"#画图观察\\n\",\n \"fig, ax = plt.subplots()\\n\",\n \"ax.scatter(y_test, y_test_pred_ridge)\\n\",\n \"ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=4)\\n\",\n \"ax.set_xlabel('Measured')\\n\",\n \"ax.set_ylabel('Predicted')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"

12. 重复步骤7-11，选择验证集上MSE或RMSE最小的模型作为模型输出

\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"

13. 保存模型，加载模型做预测,第一种方法

\\n\",\n \" * 使用python pickle API 保存\\n\",\n \" * 使用python 加载pickle文件预测\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#保存模型方法1\\n\",\n \"\\n\",\n \"# 保存成python支持的文件格式pickle, 在当前目录下可以看到regression.pickle\\n\",\n \"import pickle\\n\",\n \"with open('regression.pickle', 'wb') as fw:\\n\",\n \" pickle.dump(ridge, fw)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[444.72624337]\\n\",\n \" [442.43179818]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"#加载并预测\\n\",\n \"\\n\",\n \"# 加载regression.pickle\\n\",\n \"with open('regression.pickle', 'rb') as fr:\\n\",\n \" new_ridge = pickle.load(fr)\\n\",\n \" test_data = np.array([\\n\",\n \" [(1- 19.708303)/7.441593, (1- 54.403151)/12.747985, (1- 1013.189242)/5.898733,(1- 73.220410)/14.625541],\\n\",\n \" [(2- 19.708303)/7.441593, (2- 54.403151)/12.747985, (2- 1013.189242)/5.898733,(2- 73.220410)/14.625541]\\n\",\n \" ])\\n\",\n \" result = new_ridge.predict(test_data)\\n\",\n \" print (result)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"

13. 保存模型，加载模型做预测,第二种方法

\\n\",\n \" * 使用sklearn joblib API 保存\\n\",\n \" * 使用sklearn joblib API 加载sklearn模型预测\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['regression.pkl']\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"#保存模型方法2\\n\",\n \"\\n\",\n \"#保存成sklearn自带的文件格式\\n\",\n \"from sklearn.externals import joblib\\n\",\n \"joblib.dump(ridge, 'regression.pkl')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[444.72624337]\\n\",\n \" [442.43179818]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"new_ridge2 = joblib.load('regression.pkl')\\n\",\n \"result2 = new_ridge2.predict(test_data)\\n\",\n \"print (result2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"

13. 保存模型，加载模型做预测,第三种方法

\\n\",\n \" * 保存为PMML文件\\n\",\n \" * 使用PMML Java API加载模型预测 \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"PMMLPipeline(steps=[('regresser', Ridge(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=None,\\n\",\n \" normalize=False, random_state=None, solver='auto', tol=0.001))])\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"#保存模型方法3,跨平台用，可以用于线上Java调用\\n\",\n \"from sklearn2pmml.pipeline import PMMLPipeline\\n\",\n \"from sklearn2pmml import sklearn2pmml\\n\",\n \"regression_pipeline = PMMLPipeline([(\\\"regresser\\\", Ridge(alpha = 0.1))])\\n\",\n \"regression_pipeline.fit(X_train_norm, y_train)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[444.72624337]\\n\",\n \" [442.43179818]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"result3 = regression_pipeline.predict(test_data)\\n\",\n \"print (result3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"使用PMML Java API加载模型预测的实例参见：\\n\",\n \"\\n\",\n \"[用PMML实现机器学习模型的跨平台上线](https://www.cnblogs.com/pinard/p/9220199.html)\\n\",\n \"\\n\",\n \"[PMML跨平台上线相关实例代码](https://github.com/ljpzzz/machinelearning/blob/master/model-in-product/sklearn-jpmml)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\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\": \"3.6.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "classic-machine-learning/ridge_regression.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \" Copyright (C) \\n\",\n \" 2016 - 2019 Pinard Liu(liujianping-ok@163.com) \\n\",\n \" \\n\",\n \" https://www.cnblogs.com/pinard\\n\",\n \" \\n\",\n \" Permission given to modify the code as long as you keep this declaration at the top \\n\",\n \"\\n\",\n \"用scikit-learn和pandas学习Ridge回归\\n\",\n \"https://www.cnblogs.com/pinard/p/6023000.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from sklearn import linear_model\\n\",\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 1 2 3 4 5 6 7 8 9 10]\\n\",\n \"[[0]\\n\",\n \" [1]\\n\",\n \" [2]\\n\",\n \" [3]\\n\",\n \" [4]\\n\",\n \" [5]\\n\",\n \" [6]\\n\",\n \" [7]\\n\",\n \" [8]\\n\",\n \" [9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print np.arange(1, 11)\\n\",\n \"print np.arange(0, 10)[:, np.newaxis] \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 1 2 3 4 5 6 7 8 9 10]\\n\",\n \" [ 2 3 4 5 6 7 8 9 10 11]\\n\",\n \" [ 3 4 5 6 7 8 9 10 11 12]\\n\",\n \" [ 4 5 6 7 8 9 10 11 12 13]\\n\",\n \" [ 5 6 7 8 9 10 11 12 13 14]\\n\",\n \" [ 6 7 8 9 10 11 12 13 14 15]\\n\",\n \" [ 7 8 9 10 11 12 13 14 15 16]\\n\",\n \" [ 8 9 10 11 12 13 14 15 16 17]\\n\",\n \" [ 9 10 11 12 13 14 15 16 17 18]\\n\",\n \" [10 11 12 13 14 15 16 17 18 19]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print np.arange(1, 11) + np.arange(0, 10)[:, np.newaxis]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# X is a 10x10 matrix\\n\",\n \"X = 1. / (np.arange(1, 11) + np.arange(0, 10)[:, np.newaxis])\\n\",\n \"# y is a 10 x 1 vector\\n\",\n \"y = np.ones(10)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 1.00000000e-10 1.09698580e-10 1.20337784e-10 1.32008840e-10\\n\",\n \" 1.44811823e-10 1.58856513e-10 1.74263339e-10 1.91164408e-10\\n\",\n \" 2.09704640e-10 2.30043012e-10 2.52353917e-10 2.76828663e-10\\n\",\n \" 3.03677112e-10 3.33129479e-10 3.65438307e-10 4.00880633e-10\\n\",\n \" 4.39760361e-10 4.82410870e-10 5.29197874e-10 5.80522552e-10\\n\",\n \" 6.36824994e-10 6.98587975e-10 7.66341087e-10 8.40665289e-10\\n\",\n \" 9.22197882e-10 1.01163798e-09 1.10975250e-09 1.21738273e-09\\n\",\n \" 1.33545156e-09 1.46497140e-09 1.60705282e-09 1.76291412e-09\\n\",\n \" 1.93389175e-09 2.12145178e-09 2.32720248e-09 2.55290807e-09\\n\",\n \" 2.80050389e-09 3.07211300e-09 3.37006433e-09 3.69691271e-09\\n\",\n \" 4.05546074e-09 4.44878283e-09 4.88025158e-09 5.35356668e-09\\n\",\n \" 5.87278661e-09 6.44236351e-09 7.06718127e-09 7.75259749e-09\\n\",\n \" 8.50448934e-09 9.32930403e-09 1.02341140e-08 1.12266777e-08\\n\",\n \" 1.23155060e-08 1.35099352e-08 1.48202071e-08 1.62575567e-08\\n\",\n \" 1.78343088e-08 1.95639834e-08 2.14614120e-08 2.35428641e-08\\n\",\n \" 2.58261876e-08 2.83309610e-08 3.10786619e-08 3.40928507e-08\\n\",\n \" 3.73993730e-08 4.10265811e-08 4.50055768e-08 4.93704785e-08\\n\",\n \" 5.41587138e-08 5.94113398e-08 6.51733960e-08 7.14942899e-08\\n\",\n \" 7.84282206e-08 8.60346442e-08 9.43787828e-08 1.03532184e-07\\n\",\n \" 1.13573336e-07 1.24588336e-07 1.36671636e-07 1.49926843e-07\\n\",\n \" 1.64467618e-07 1.80418641e-07 1.97916687e-07 2.17111795e-07\\n\",\n \" 2.38168555e-07 2.61267523e-07 2.86606762e-07 3.14403547e-07\\n\",\n \" 3.44896226e-07 3.78346262e-07 4.15040476e-07 4.55293507e-07\\n\",\n \" 4.99450512e-07 5.47890118e-07 6.01027678e-07 6.59318827e-07\\n\",\n \" 7.23263390e-07 7.93409667e-07 8.70359136e-07 9.54771611e-07\\n\",\n \" 1.04737090e-06 1.14895100e-06 1.26038293e-06 1.38262217e-06\\n\",\n \" 1.51671689e-06 1.66381689e-06 1.82518349e-06 2.00220037e-06\\n\",\n \" 2.19638537e-06 2.40940356e-06 2.64308149e-06 2.89942285e-06\\n\",\n \" 3.18062569e-06 3.48910121e-06 3.82749448e-06 4.19870708e-06\\n\",\n \" 4.60592204e-06 5.05263107e-06 5.54266452e-06 6.08022426e-06\\n\",\n \" 6.66991966e-06 7.31680714e-06 8.02643352e-06 8.80488358e-06\\n\",\n \" 9.65883224e-06 1.05956018e-05 1.16232247e-05 1.27505124e-05\\n\",\n \" 1.39871310e-05 1.53436841e-05 1.68318035e-05 1.84642494e-05\\n\",\n \" 2.02550194e-05 2.22194686e-05 2.43744415e-05 2.67384162e-05\\n\",\n \" 2.93316628e-05 3.21764175e-05 3.52970730e-05 3.87203878e-05\\n\",\n \" 4.24757155e-05 4.65952567e-05 5.11143348e-05 5.60716994e-05\\n\",\n \" 6.15098579e-05 6.74754405e-05 7.40196000e-05 8.11984499e-05\\n\",\n \" 8.90735464e-05 9.77124154e-05 1.07189132e-04 1.17584955e-04\\n\",\n \" 1.28989026e-04 1.41499130e-04 1.55222536e-04 1.70276917e-04\\n\",\n \" 1.86791360e-04 2.04907469e-04 2.24780583e-04 2.46581108e-04\\n\",\n \" 2.70495973e-04 2.96730241e-04 3.25508860e-04 3.57078596e-04\\n\",\n \" 3.91710149e-04 4.29700470e-04 4.71375313e-04 5.17092024e-04\\n\",\n \" 5.67242607e-04 6.22257084e-04 6.82607183e-04 7.48810386e-04\\n\",\n \" 8.21434358e-04 9.01101825e-04 9.88495905e-04 1.08436597e-03\\n\",\n \" 1.18953407e-03 1.30490198e-03 1.43145894e-03 1.57029012e-03\\n\",\n \" 1.72258597e-03 1.88965234e-03 2.07292178e-03 2.27396575e-03\\n\",\n \" 2.49450814e-03 2.73644000e-03 3.00183581e-03 3.29297126e-03\\n\",\n \" 3.61234270e-03 3.96268864e-03 4.34701316e-03 4.76861170e-03\\n\",\n \" 5.23109931e-03 5.73844165e-03 6.29498899e-03 6.90551352e-03\\n\",\n \" 7.57525026e-03 8.30994195e-03 9.11588830e-03 1.00000000e-02]\\n\"\n ]\n }\n ],\n \"source\": [\n \"n_alphas = 200\\n\",\n \"# alphas count is 200, 都在10的-10次方和10的-2次方之间\\n\",\n \"alphas = np.logspace(-10, -2, n_alphas)\\n\",\n \"print alphas\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"clf = linear_model.Ridge(fit_intercept=False)\\n\",\n \"coefs = []\\n\",\n \"# 循环200次\\n\",\n \"for a in alphas:\\n\",\n \" #设置本次循环的超参数\\n\",\n \" clf.set_params(alpha=a)\\n\",\n \" #针对每个alpha做ridge回归\\n\",\n \" clf.fit(X, y)\\n\",\n \" # 把每一个超参数alpha对应的theta存下来\\n\",\n \" coefs.append(clf.coef_)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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UbTM+65B667rkNRSTW6xKLRaDRDnbIymDkTNm+G0aM73CWVJRYtLBqN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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ax = plt.gca()\\n\",\n \"\\n\",\n \"ax.plot(alphas, coefs)\\n\",\n \"#将alpha的值取对数便于画图\\n\",\n \"ax.set_xscale('log')\\n\",\n \"#翻转x轴的大小方向，让alpha从大到小显示\\n\",\n \"ax.set_xlim(ax.get_xlim()[::-1]) \\n\",\n \"plt.xlabel('alpha')\\n\",\n \"plt.ylabel('weights')\\n\",\n \"plt.title('Ridge coefficients as a function of the regularization')\\n\",\n \"plt.axis('tight')\\n\",\n \"plt.show()\"\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\": \"3.6.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "classic-machine-learning/ridge_regression_1.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C)\\n\",\n \"2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"用scikit-learn和pandas学习Ridge回归 https://www.cnblogs.com/pinard/p/6023000.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"from sklearn import datasets, linear_model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# read_csv里面的参数是csv在你电脑上的路径，此处csv文件放在notebook运行目录下面的CCPP目录里\\n\",\n \"data = pd.read_csv('.\\\\CCPP\\\\ccpp.csv')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\Python27\\\\lib\\\\site-packages\\\\sklearn\\\\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\\n\",\n \" \\\"This module will be removed in 0.20.\\\", DeprecationWarning)\\n\"\n ]\n }\n ],\n \"source\": [\n \"X = data[['AT', 'V', 'AP', 'RH']]\\n\",\n \"y = data[['PE']]\\n\",\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=1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.927643364697\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.linear_model import Ridge\\n\",\n \"ridge = Ridge(alpha=1)\\n\",\n \"ridge.fit(X_train, y_train)\\n\",\n \"print ridge.score(X_train, y_train)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[-1.97373209 -0.2323016 0.06935852 -0.15806479]]\\n\",\n \"[ 447.05552892]\\n\",\n \"MSE: 20.0804091931\\n\",\n \"RMSE: 4.4811169582\\n\"\n ]\n }\n ],\n \"source\": [\n \"print ridge.coef_\\n\",\n \"print ridge.intercept_\\n\",\n \"y_pred = ridge.predict(X_test)\\n\",\n \"from sklearn import metrics\\n\",\n \"# 用scikit-learn计算MSE\\n\",\n \"print \\\"MSE:\\\",metrics.mean_squared_error(y_test, y_pred)\\n\",\n \"# 用scikit-learn计算RMSE\\n\",\n \"print \\\"RMSE:\\\",np.sqrt(metrics.mean_squared_error(y_test, y_pred))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"7.0\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.linear_model import RidgeCV\\n\",\n \"ridgecv = RidgeCV(alphas=[0.01, 0.1, 0.5, 1, 3, 5, 7, 10, 20, 100])\\n\",\n \"ridgecv.fit(X_train, y_train)\\n\",\n \"ridgecv.alpha_ \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"MSE: 20.0804574476\\n\",\n \"RMSE: 4.48112234241\\n\"\n ]\n }\n ],\n \"source\": [\n \"y_pred = ridgecv.predict(X_test)\\n\",\n \"from sklearn import metrics\\n\",\n \"# 用scikit-learn计算MSE\\n\",\n \"print \\\"MSE:\\\",metrics.mean_squared_error(y_test, y_pred)\\n\",\n \"# 用scikit-learn计算RMSE\\n\",\n \"print \\\"RMSE:\\\",np.sqrt(metrics.mean_squared_error(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\": \"3.6.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "classic-machine-learning/spectral_cluster.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C) 2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"用scikit-learn学习谱聚类 https://www.cnblogs.com/pinard/p/6235920.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"from sklearn import datasets\\n\",\n \"X, y = datasets.make_blobs(n_samples=500, n_features=6, centers=5, cluster_std=[0.4, 0.3, 0.4, 0.3, 0.4], random_state=11)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Calinski-Harabasz Score 14910.411255265064\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.cluster import SpectralClustering\\n\",\n \"y_pred = SpectralClustering().fit_predict(X)\\n\",\n \"from sklearn import metrics\\n\",\n \"print (\\\"Calinski-Harabasz Score\\\", metrics.calinski_harabaz_score(X, y_pred)) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Calinski-Harabasz Score with gamma= 0.01 n_clusters= 3 score: 1979.7709609161868\\n\",\n \"Calinski-Harabasz Score with gamma= 0.01 n_clusters= 4 score: 3154.0184121901607\\n\",\n \"Calinski-Harabasz Score with gamma= 0.01 n_clusters= 5 score: 23410.638949991386\\n\",\n \"Calinski-Harabasz Score with gamma= 0.01 n_clusters= 6 score: 19296.861797427086\\n\",\n \"Calinski-Harabasz Score with gamma= 0.1 n_clusters= 3 score: 1979.7709609161868\\n\",\n \"Calinski-Harabasz Score with gamma= 0.1 n_clusters= 4 score: 3154.0184121901607\\n\",\n \"Calinski-Harabasz Score with gamma= 0.1 n_clusters= 5 score: 23410.638949991386\\n\",\n \"Calinski-Harabasz Score with gamma= 0.1 n_clusters= 6 score: 19427.961894359105\\n\",\n \"Calinski-Harabasz Score with gamma= 1 n_clusters= 3 score: 260.64607765944106\\n\",\n \"Calinski-Harabasz Score with gamma= 1 n_clusters= 4 score: 2274.7475744437675\\n\",\n \"Calinski-Harabasz Score with gamma= 1 n_clusters= 5 score: 23410.638949991386\\n\",\n \"Calinski-Harabasz Score with gamma= 1 n_clusters= 6 score: 1575.424687869317\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"d:\\\\users\\\\pinard.liu\\\\appdata\\\\local\\\\programs\\\\python\\\\python36\\\\lib\\\\site-packages\\\\sklearn\\\\manifold\\\\spectral_embedding_.py:234: UserWarning: Graph is not fully connected, spectral embedding may not work as expected.\\n\",\n \" warnings.warn(\\\"Graph is not fully connected, spectral embedding\\\"\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Calinski-Harabasz Score with gamma= 10 n_clusters= 3 score: 26.939459595237867\\n\",\n \"Calinski-Harabasz Score with gamma= 10 n_clusters= 4 score: 37.011003925097825\\n\",\n \"Calinski-Harabasz Score with gamma= 10 n_clusters= 5 score: 31.922080624500378\\n\",\n \"Calinski-Harabasz Score with gamma= 10 n_clusters= 6 score: 28.434790532434917\\n\"\n ]\n }\n ],\n \"source\": [\n \"for index, gamma in enumerate((0.01,0.1,1,10)):\\n\",\n \" for index, k in enumerate((3,4,5,6)):\\n\",\n \" y_pred = SpectralClustering(n_clusters=k, gamma=gamma).fit_predict(X)\\n\",\n \" print (\\\"Calinski-Harabasz Score with gamma=\\\", gamma, \\\"n_clusters=\\\", k,\\\"score:\\\", metrics.calinski_harabaz_score(X, y_pred)) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Calinski-Harabasz Score 14950.49397167252\\n\"\n ]\n }\n ],\n \"source\": [\n \"y_pred = SpectralClustering(gamma=0.1).fit_predict(X)\\n\",\n \"print (\\\"Calinski-Harabasz Score\\\", metrics.calinski_harabaz_score(X, y_pred))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\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\": \"3.6.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "classic-machine-learning/svm_classifier.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C)\\n\",\n \"2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"支持向量机高斯核调参小结 https://www.cnblogs.com/pinard/p/6126077.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from sklearn import datasets, svm\\n\",\n \"from sklearn.svm import SVC\\n\",\n \"from sklearn.datasets import make_moons, make_circles, make_classification\\n\",\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X, y = make_circles(noise=0.2, factor=0.5, random_state=1);\\n\",\n \"from sklearn.preprocessing import StandardScaler\\n\",\n \"X = StandardScaler().fit_transform(X)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from matplotlib.colors import ListedColormap\\n\",\n \"cm = plt.cm.RdBu\\n\",\n \"cm_bright = ListedColormap(['#FF0000', '#0000FF'])\\n\",\n \"ax = plt.subplot()\\n\",\n \"\\n\",\n \"ax.set_title(\\\"Input data\\\")\\n\",\n \"# Plot the training points\\n\",\n \"ax.scatter(X[:, 0], X[:, 1], c=y, cmap=cm_bright)\\n\",\n \"ax.set_xticks(())\\n\",\n \"ax.set_yticks(())\\n\",\n \"plt.tight_layout()\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The best parameters are {'C': 10, 'gamma': 0.1} with a score of 0.91\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.model_selection import GridSearchCV\\n\",\n \"grid = GridSearchCV(SVC(), param_grid={\\\"C\\\":[0.1, 1, 10], \\\"gamma\\\": [1, 0.1, 0.01]}, cv=4)\\n\",\n \"grid.fit(X, y)\\n\",\n \"print(\\\"The best parameters are %s with a score of %0.2f\\\"\\n\",\n \" % (grid.best_params_, grid.best_score_))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\\n\",\n 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\\n\",\n 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\\n\",\n 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\\n\",\n 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\\n\",\n 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\\n\",\n 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\\n\",\n \"y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\\n\",\n \"xx, yy = np.meshgrid(np.arange(x_min, x_max,0.02),\\n\",\n \" np.arange(y_min, y_max, 0.02))\\n\",\n \"\\n\",\n \"for i, C in enumerate((0.1, 1, 10)):\\n\",\n \" for j, gamma in enumerate((1, 0.1, 0.01)):\\n\",\n \" plt.subplot() \\n\",\n \" clf = SVC(C=C, gamma=gamma)\\n\",\n \" clf.fit(X,y)\\n\",\n \" Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\\n\",\n \"\\n\",\n \" # Put the result into a color plot\\n\",\n \" Z = Z.reshape(xx.shape)\\n\",\n \" plt.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.8)\\n\",\n \"\\n\",\n \" # Plot also the training points\\n\",\n \" plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.coolwarm)\\n\",\n \"\\n\",\n \" plt.xlim(xx.min(), xx.max())\\n\",\n \" plt.ylim(yy.min(), yy.max())\\n\",\n \" plt.xticks(())\\n\",\n \" plt.yticks(())\\n\",\n \" plt.xlabel(\\\" gamma=\\\" + str(gamma) + \\\" C=\\\" + str(C))\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\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\": \"3.6.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "data/nlp_test0.txt", "content": "ɳ̾ѧϰػǽɽİи¶￵Ĵܴѧϰֻ˷ֿǰһһȾƻѧϰְصس·º̣￵ǸöԲңԲ·ͺѧϰһ·5Ǯ·ԼŲ5飬ʼº̡û뵽·Ȼ÷ˮɳˣʱڻĭܲס" }, { "path": "data/nlp_test2.txt", "content": "ɳë櫴ǼھݵıëЦ˵·ҵг֮Ҫŷݼ˾ĹȨûҪ·ھݵۺ԰ױ￵ѧϰҪЩӶ·£ŷݼȥסë櫲ȥ÷̫˷ѣԼסþͺ̤ʵ" }, { "path": "ensemble-learning/adaboost-classifier.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C)\\n\",\n \"2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"scikit-learn Adaboost类库使用小结 https://www.cnblogs.com/pinard/p/6136914.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline\\n\",\n \"from sklearn.ensemble import AdaBoostClassifier\\n\",\n \"from sklearn.tree import DecisionTreeClassifier\\n\",\n \"from sklearn.datasets import make_gaussian_quantiles\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# 生成2维正态分布，生成的数据按分位数分为两类，500个样本,2个样本特征，协方差系数为2\\n\",\n \"X1, y1 = make_gaussian_quantiles(cov=2.0,n_samples=500, n_features=2,n_classes=2, random_state=1)\\n\",\n \"# 生成2维正态分布，生成的数据按分位数分为两类，400个样本,2个样本特征均值都为3，协方差系数为2\\n\",\n \"X2, y2 = make_gaussian_quantiles(mean=(3, 3), cov=1.5,n_samples=400, n_features=2, n_classes=2, random_state=1)\\n\",\n \"#讲两组数据合成一组数据\\n\",\n \"X = np.concatenate((X1, X2))\\n\",\n \"y = np.concatenate((y1, - y2 + 1))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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oSP4HR+wzXXdAcgNzeXCRPGkZy8gHr1GmC3/2T2Mgr+2mx23O5kIiI+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\\ntkT8Ee3amlUAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.scatter(X[:, 0], X[:, 1], marker='o', c=y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"AdaBoostClassifier(algorithm='SAMME',\\n\",\n \" base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=2,\\n\",\n \" max_features=None, max_leaf_nodes=None,\\n\",\n \" min_impurity_split=1e-07, min_samples_leaf=5,\\n\",\n \" min_samples_split=20, min_weight_fraction_leaf=0.0,\\n\",\n \" presort=False, random_state=None, splitter='best'),\\n\",\n \" learning_rate=0.8, n_estimators=200, random_state=None)\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"bdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2, min_samples_split=20, min_samples_leaf=5),\\n\",\n \" algorithm=\\\"SAMME\\\",\\n\",\n \" n_estimators=200, learning_rate=0.8)\\n\",\n \"bdt.fit(X, y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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yE9fhqSbrqgwOGBaNKPPZ/FZt+tUPbJOCSPlCPvvrpn/zHT7ivMfjqY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\\naYZA3AEBAQFphkDcAQEBAWmG/we4F9g4gQTF3AAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\\n\",\n \"y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\\n\",\n \"xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),\\n\",\n \" np.arange(y_min, y_max, 0.02))\\n\",\n \"\\n\",\n \"Z = bdt.predict(np.c_[xx.ravel(), yy.ravel()])\\n\",\n \"Z = Z.reshape(xx.shape)\\n\",\n \"cs = plt.contourf(xx, yy, Z)\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], marker='o', c=y)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Score: 0.913333333333\\n\"\n ]\n }\n ],\n \"source\": [\n \"print \\\"Score:\\\", bdt.score(X,y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Score: 0.962222222222\\n\"\n ]\n }\n ],\n \"source\": [\n \"bdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2, min_samples_split=20, min_samples_leaf=5),\\n\",\n \" algorithm=\\\"SAMME\\\",\\n\",\n \" n_estimators=300, learning_rate=0.8)\\n\",\n \"bdt.fit(X, y)\\n\",\n \"print \\\"Score:\\\", bdt.score(X,y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Score: 0.894444444444\\n\"\n ]\n }\n ],\n \"source\": [\n \"bdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2, min_samples_split=20, min_samples_leaf=5),\\n\",\n \" algorithm=\\\"SAMME\\\",\\n\",\n \" n_estimators=300, learning_rate=0.5)\\n\",\n \"bdt.fit(X, y)\\n\",\n \"print \\\"Score:\\\", bdt.score(X,y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Score: 0.961111111111\\n\"\n ]\n }\n ],\n \"source\": [\n \"bdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2, min_samples_split=20, min_samples_leaf=5),\\n\",\n \" algorithm=\\\"SAMME\\\",\\n\",\n \" n_estimators=600, learning_rate=0.7)\\n\",\n \"bdt.fit(X, y)\\n\",\n \"print \\\"Score:\\\", bdt.score(X,y)\"\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\": \"3.6.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "ensemble-learning/gbdt_classifier.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C) 2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"scikit-learn 梯度提升树(GBDT)调参小结 https://www.cnblogs.com/pinard/p/6143927.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\Python27\\\\lib\\\\site-packages\\\\sklearn\\\\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\\n\",\n \" \\\"This module will be removed in 0.20.\\\", DeprecationWarning)\\n\",\n \"C:\\\\Python27\\\\lib\\\\site-packages\\\\sklearn\\\\grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\\n\",\n \" DeprecationWarning)\\n\"\n ]\n }\n ],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"from sklearn.ensemble import GradientBoostingClassifier\\n\",\n \"from sklearn import cross_validation, metrics\\n\",\n \"from sklearn.grid_search import GridSearchCV\\n\",\n \"\\n\",\n \"import matplotlib.pylab as plt\\n\",\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 19680\\n\",\n \"1 320\\n\",\n \"Name: Disbursed, dtype: int64\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"train = pd.read_csv('train_modified.csv')\\n\",\n \"target='Disbursed' # Disbursed的值就是二元分类的输出\\n\",\n \"IDcol = 'ID'\\n\",\n \"train['Disbursed'].value_counts() \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"x_columns = [x for x in train.columns if x not in [target, IDcol]]\\n\",\n \"X = train[x_columns]\\n\",\n \"y = train['Disbursed']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Accuracy : 0.9852\\n\",\n \"AUC Score (Train): 0.900531\\n\"\n ]\n }\n ],\n \"source\": [\n \"gbm0 = GradientBoostingClassifier(random_state=10)\\n\",\n \"gbm0.fit(X,y)\\n\",\n \"y_pred = gbm0.predict(X)\\n\",\n \"y_predprob = gbm0.predict_proba(X)[:,1]\\n\",\n \"print \\\"Accuracy : %.4g\\\" % metrics.accuracy_score(y.values, y_pred)\\n\",\n \"print \\\"AUC Score (Train): %f\\\" % metrics.roc_auc_score(y, y_predprob)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"([mean: 0.81285, std: 0.01967, params: {'n_estimators': 20},\\n\",\n \" mean: 0.81438, std: 0.01947, params: {'n_estimators': 30},\\n\",\n \" mean: 0.81451, std: 0.01933, params: {'n_estimators': 40},\\n\",\n \" mean: 0.81618, std: 0.01848, params: {'n_estimators': 50},\\n\",\n \" mean: 0.81751, std: 0.01736, params: {'n_estimators': 60},\\n\",\n \" mean: 0.81547, std: 0.01900, params: {'n_estimators': 70},\\n\",\n \" mean: 0.81299, std: 0.01860, params: {'n_estimators': 80}],\\n\",\n \" {'n_estimators': 60},\\n\",\n \" 0.8175146087398375)\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"param_test1 = {'n_estimators':range(20,81,10)}\\n\",\n \"gsearch1 = GridSearchCV(estimator = GradientBoostingClassifier(learning_rate=0.1, min_samples_split=300,\\n\",\n \" min_samples_leaf=20,max_depth=8,max_features='sqrt', subsample=0.8,random_state=10), \\n\",\n \" param_grid = param_test1, scoring='roc_auc',iid=False,cv=5)\\n\",\n \"gsearch1.fit(X,y)\\n\",\n \"gsearch1.grid_scores_, gsearch1.best_params_, gsearch1.best_score_\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"([mean: 0.81199, std: 0.02073, params: {'min_samples_split': 100, 'max_depth': 3},\\n\",\n \" mean: 0.81267, std: 0.01985, params: {'min_samples_split': 300, 'max_depth': 3},\\n\",\n \" mean: 0.81238, std: 0.01937, params: {'min_samples_split': 500, 'max_depth': 3},\\n\",\n \" mean: 0.80925, std: 0.02051, params: {'min_samples_split': 700, 'max_depth': 3},\\n\",\n \" mean: 0.81846, std: 0.01843, params: {'min_samples_split': 100, 'max_depth': 5},\\n\",\n \" mean: 0.81630, std: 0.01810, params: {'min_samples_split': 300, 'max_depth': 5},\\n\",\n \" mean: 0.81315, std: 0.01898, params: {'min_samples_split': 500, 'max_depth': 5},\\n\",\n \" mean: 0.81262, std: 0.02090, params: {'min_samples_split': 700, 'max_depth': 5},\\n\",\n \" mean: 0.81807, std: 0.02004, params: {'min_samples_split': 100, 'max_depth': 7},\\n\",\n \" mean: 0.82137, std: 0.01733, params: {'min_samples_split': 300, 'max_depth': 7},\\n\",\n \" mean: 0.81703, std: 0.01773, params: {'min_samples_split': 500, 'max_depth': 7},\\n\",\n \" mean: 0.81383, std: 0.02327, params: {'min_samples_split': 700, 'max_depth': 7},\\n\",\n \" mean: 0.81107, std: 0.02178, params: {'min_samples_split': 100, 'max_depth': 9},\\n\",\n \" mean: 0.80944, std: 0.02612, params: {'min_samples_split': 300, 'max_depth': 9},\\n\",\n \" mean: 0.81476, std: 0.01973, params: {'min_samples_split': 500, 'max_depth': 9},\\n\",\n \" mean: 0.81601, std: 0.02576, params: {'min_samples_split': 700, 'max_depth': 9},\\n\",\n \" mean: 0.81091, std: 0.02227, params: {'min_samples_split': 100, 'max_depth': 11},\\n\",\n \" mean: 0.81309, std: 0.02696, params: {'min_samples_split': 300, 'max_depth': 11},\\n\",\n \" mean: 0.81713, std: 0.02379, params: {'min_samples_split': 500, 'max_depth': 11},\\n\",\n \" mean: 0.81347, std: 0.02702, params: {'min_samples_split': 700, 'max_depth': 11},\\n\",\n \" mean: 0.81444, std: 0.01813, params: {'min_samples_split': 100, 'max_depth': 13},\\n\",\n \" mean: 0.80825, std: 0.02291, params: {'min_samples_split': 300, 'max_depth': 13},\\n\",\n \" mean: 0.81923, std: 0.01693, params: {'min_samples_split': 500, 'max_depth': 13},\\n\",\n \" mean: 0.81382, std: 0.02258, params: {'min_samples_split': 700, 'max_depth': 13}],\\n\",\n \" {'max_depth': 7, 'min_samples_split': 300},\\n\",\n \" 0.8213724275914632)\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"param_test2 = {'max_depth':range(3,14,2), 'min_samples_split':range(100,801,200)}\\n\",\n \"gsearch2 = GridSearchCV(estimator = GradientBoostingClassifier(learning_rate=0.1, n_estimators=60, min_samples_leaf=20, \\n\",\n \" max_features='sqrt', subsample=0.8, random_state=10), \\n\",\n \" param_grid = param_test2, scoring='roc_auc',iid=False, cv=5)\\n\",\n \"gsearch2.fit(X,y)\\n\",\n \"gsearch2.grid_scores_, gsearch2.best_params_, gsearch2.best_score_\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"([mean: 0.81828, std: 0.02251, params: {'min_samples_split': 800, 'min_samples_leaf': 60},\\n\",\n \" mean: 0.81731, std: 0.02344, params: {'min_samples_split': 1000, 'min_samples_leaf': 60},\\n\",\n \" mean: 0.82220, std: 0.02250, params: {'min_samples_split': 1200, 'min_samples_leaf': 60},\\n\",\n \" mean: 0.81447, std: 0.02125, params: {'min_samples_split': 1400, 'min_samples_leaf': 60},\\n\",\n \" mean: 0.81495, std: 0.01626, params: {'min_samples_split': 1600, 'min_samples_leaf': 60},\\n\",\n \" mean: 0.81528, std: 0.02140, params: {'min_samples_split': 1800, 'min_samples_leaf': 60},\\n\",\n \" mean: 0.81590, std: 0.02517, params: {'min_samples_split': 800, 'min_samples_leaf': 70},\\n\",\n \" mean: 0.81573, std: 0.02207, params: {'min_samples_split': 1000, 'min_samples_leaf': 70},\\n\",\n \" mean: 0.82021, std: 0.02521, params: {'min_samples_split': 1200, 'min_samples_leaf': 70},\\n\",\n \" mean: 0.81512, std: 0.01995, params: {'min_samples_split': 1400, 'min_samples_leaf': 70},\\n\",\n \" mean: 0.81395, std: 0.02081, params: {'min_samples_split': 1600, 'min_samples_leaf': 70},\\n\",\n \" mean: 0.81587, std: 0.02082, params: {'min_samples_split': 1800, 'min_samples_leaf': 70},\\n\",\n \" mean: 0.82064, std: 0.02698, params: {'min_samples_split': 800, 'min_samples_leaf': 80},\\n\",\n \" mean: 0.81490, std: 0.02475, params: {'min_samples_split': 1000, 'min_samples_leaf': 80},\\n\",\n \" mean: 0.82009, std: 0.02568, params: {'min_samples_split': 1200, 'min_samples_leaf': 80},\\n\",\n \" mean: 0.81850, std: 0.02226, params: {'min_samples_split': 1400, 'min_samples_leaf': 80},\\n\",\n \" mean: 0.81855, std: 0.02099, params: {'min_samples_split': 1600, 'min_samples_leaf': 80},\\n\",\n \" mean: 0.81666, std: 0.02249, params: {'min_samples_split': 1800, 'min_samples_leaf': 80},\\n\",\n \" mean: 0.81960, std: 0.02437, params: {'min_samples_split': 800, 'min_samples_leaf': 90},\\n\",\n \" mean: 0.81560, std: 0.02235, params: {'min_samples_split': 1000, 'min_samples_leaf': 90},\\n\",\n \" mean: 0.81936, std: 0.02542, params: {'min_samples_split': 1200, 'min_samples_leaf': 90},\\n\",\n \" mean: 0.81362, std: 0.02254, params: {'min_samples_split': 1400, 'min_samples_leaf': 90},\\n\",\n \" mean: 0.81429, std: 0.02417, params: {'min_samples_split': 1600, 'min_samples_leaf': 90},\\n\",\n \" mean: 0.81299, std: 0.02262, params: {'min_samples_split': 1800, 'min_samples_leaf': 90},\\n\",\n \" mean: 0.82000, std: 0.02511, params: {'min_samples_split': 800, 'min_samples_leaf': 100},\\n\",\n \" mean: 0.82209, std: 0.01816, params: {'min_samples_split': 1000, 'min_samples_leaf': 100},\\n\",\n \" mean: 0.81821, std: 0.02337, params: {'min_samples_split': 1200, 'min_samples_leaf': 100},\\n\",\n \" mean: 0.81922, std: 0.02377, params: {'min_samples_split': 1400, 'min_samples_leaf': 100},\\n\",\n \" mean: 0.81545, std: 0.02221, params: {'min_samples_split': 1600, 'min_samples_leaf': 100},\\n\",\n \" mean: 0.81704, std: 0.02509, params: {'min_samples_split': 1800, 'min_samples_leaf': 100}],\\n\",\n \" {'min_samples_leaf': 60, 'min_samples_split': 1200},\\n\",\n \" 0.8222032996697154)\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"param_test3 = {'min_samples_split':range(800,1900,200), 'min_samples_leaf':range(60,101,10)}\\n\",\n \"gsearch3 = GridSearchCV(estimator = GradientBoostingClassifier(learning_rate=0.1, n_estimators=60,max_depth=7,\\n\",\n \" max_features='sqrt', subsample=0.8, random_state=10), \\n\",\n \" param_grid = param_test3, scoring='roc_auc',iid=False, cv=5)\\n\",\n \"gsearch3.fit(X,y)\\n\",\n \"gsearch3.grid_scores_, gsearch3.best_params_, gsearch3.best_score_\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Accuracy : 0.984\\n\",\n \"AUC Score (Train): 0.908099\\n\"\n ]\n }\n ],\n \"source\": [\n \"gbm1 = GradientBoostingClassifier(learning_rate=0.1, n_estimators=60,max_depth=7, min_samples_leaf =60, \\n\",\n \" min_samples_split =1200, max_features='sqrt', subsample=0.8, random_state=10)\\n\",\n \"gbm1.fit(X,y)\\n\",\n \"y_pred = gbm1.predict(X)\\n\",\n \"y_predprob = gbm1.predict_proba(X)[:,1]\\n\",\n \"print \\\"Accuracy : %.4g\\\" % metrics.accuracy_score(y.values, y_pred)\\n\",\n \"print \\\"AUC Score (Train): %f\\\" % metrics.roc_auc_score(y, y_predprob)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"([mean: 0.82220, std: 0.02250, params: {'max_features': 7},\\n\",\n \" mean: 0.82241, std: 0.02421, params: {'max_features': 9},\\n\",\n \" mean: 0.82108, std: 0.02302, params: {'max_features': 11},\\n\",\n \" mean: 0.82064, std: 0.01900, params: {'max_features': 13},\\n\",\n \" mean: 0.82198, std: 0.01514, params: {'max_features': 15},\\n\",\n \" mean: 0.81355, std: 0.02053, params: {'max_features': 17},\\n\",\n \" mean: 0.81877, std: 0.01863, params: {'max_features': 19}],\\n\",\n \" {'max_features': 9},\\n\",\n \" 0.822412506351626)\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"param_test4 = {'max_features':range(7,20,2)}\\n\",\n \"gsearch4 = GridSearchCV(estimator = GradientBoostingClassifier(learning_rate=0.1, n_estimators=60,max_depth=7, min_samples_leaf =60, \\n\",\n \" min_samples_split =1200, subsample=0.8, random_state=10), \\n\",\n \" param_grid = param_test4, scoring='roc_auc',iid=False, cv=5)\\n\",\n \"gsearch4.fit(X,y)\\n\",\n \"gsearch4.grid_scores_, gsearch4.best_params_, gsearch4.best_score_\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"([mean: 0.81828, std: 0.02392, params: {'subsample': 0.6},\\n\",\n \" mean: 0.82344, std: 0.02708, params: {'subsample': 0.7},\\n\",\n \" mean: 0.81673, std: 0.02196, params: {'subsample': 0.75},\\n\",\n \" mean: 0.82241, std: 0.02421, params: {'subsample': 0.8},\\n\",\n \" mean: 0.82285, std: 0.02446, params: {'subsample': 0.85},\\n\",\n \" mean: 0.81738, std: 0.02236, params: {'subsample': 0.9}],\\n\",\n \" {'subsample': 0.7},\\n\",\n \" 0.8234378969766262)\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"param_test5 = {'subsample':[0.6,0.7,0.75,0.8,0.85,0.9]}\\n\",\n \"gsearch5 = GridSearchCV(estimator = GradientBoostingClassifier(learning_rate=0.1, n_estimators=60,max_depth=7, min_samples_leaf =60, \\n\",\n \" min_samples_split =1200, max_features=9, random_state=10), \\n\",\n \" param_grid = param_test5, scoring='roc_auc',iid=False, cv=5)\\n\",\n \"gsearch5.fit(X,y)\\n\",\n \"gsearch5.grid_scores_, gsearch5.best_params_, gsearch5.best_score_\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Accuracy : 0.984\\n\",\n \"AUC Score (Train): 0.905324\\n\"\n ]\n }\n ],\n \"source\": [\n \"gbm2 = GradientBoostingClassifier(learning_rate=0.05, n_estimators=120,max_depth=7, min_samples_leaf =60, \\n\",\n \" min_samples_split =1200, max_features=9, subsample=0.7, random_state=10)\\n\",\n \"gbm2.fit(X,y)\\n\",\n \"y_pred = gbm2.predict(X)\\n\",\n \"y_predprob = gbm2.predict_proba(X)[:,1]\\n\",\n \"print \\\"Accuracy : %.4g\\\" % metrics.accuracy_score(y.values, y_pred)\\n\",\n \"print \\\"AUC Score (Train): %f\\\" % metrics.roc_auc_score(y, y_predprob)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Accuracy : 0.984\\n\",\n \"AUC Score (Train): 0.908581\\n\"\n ]\n }\n ],\n \"source\": [\n \"gbm3 = GradientBoostingClassifier(learning_rate=0.01, n_estimators=600,max_depth=7, min_samples_leaf =60, \\n\",\n \" min_samples_split =1200, max_features=9, subsample=0.7, random_state=10)\\n\",\n \"gbm3.fit(X,y)\\n\",\n \"y_pred = gbm3.predict(X)\\n\",\n \"y_predprob = gbm3.predict_proba(X)[:,1]\\n\",\n \"print \\\"Accuracy : %.4g\\\" % metrics.accuracy_score(y.values, y_pred)\\n\",\n \"print \\\"AUC Score (Train): %f\\\" % metrics.roc_auc_score(y, y_predprob)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Accuracy : 0.984\\n\",\n \"AUC Score (Train): 0.908232\\n\"\n ]\n }\n ],\n \"source\": [\n \"gbm4 = GradientBoostingClassifier(learning_rate=0.005, n_estimators=1200,max_depth=7, min_samples_leaf =60, \\n\",\n \" min_samples_split =1200, max_features=9, subsample=0.7, random_state=10)\\n\",\n \"gbm4.fit(X,y)\\n\",\n \"y_pred = gbm4.predict(X)\\n\",\n \"y_predprob = gbm4.predict_proba(X)[:,1]\\n\",\n \"print \\\"Accuracy : %.4g\\\" % metrics.accuracy_score(y.values, y_pred)\\n\",\n \"print \\\"AUC Score (Train): %f\\\" % metrics.roc_auc_score(y, y_predprob)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Accuracy : 0.984\\n\",\n \"AUC Score (Train): 0.899583\\n\"\n ]\n }\n ],\n \"source\": [\n \"gbm11 = GradientBoostingClassifier(learning_rate=0.1, n_estimators=60,max_depth=7, min_samples_leaf =60, \\n\",\n \" min_samples_split =1200, max_features=9, subsample=0.7, random_state=10)\\n\",\n \"gbm11.fit(X,y)\\n\",\n \"y_pred = gbm11.predict(X)\\n\",\n \"y_predprob = gbm11.predict_proba(X)[:,1]\\n\",\n \"print \\\"Accuracy : %.4g\\\" % metrics.accuracy_score(y.values, y_pred)\\n\",\n \"print \\\"AUC Score (Train): %f\\\" % metrics.roc_auc_score(y, y_predprob)\"\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\": \"3.6.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "ensemble-learning/random_forest_classifier.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C) 2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"scikit-learn随机森林调参小结 https://www.cnblogs.com/pinard/p/6160412.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"d:\\\\users\\\\pinard.liu\\\\appdata\\\\local\\\\programs\\\\python\\\\python36\\\\lib\\\\site-packages\\\\sklearn\\\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\\n\",\n \" \\\"This module will be removed in 0.20.\\\", DeprecationWarning)\\n\",\n \"d:\\\\users\\\\pinard.liu\\\\appdata\\\\local\\\\programs\\\\python\\\\python36\\\\lib\\\\site-packages\\\\sklearn\\\\grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\\n\",\n \" DeprecationWarning)\\n\"\n ]\n }\n ],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"from sklearn.ensemble import RandomForestClassifier\\n\",\n \"from sklearn.grid_search import GridSearchCV\\n\",\n \"from sklearn import cross_validation, metrics\\n\",\n \"\\n\",\n \"import matplotlib.pylab as plt\\n\",\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 19680\\n\",\n \"1 320\\n\",\n \"Name: Disbursed, dtype: int64\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"train = pd.read_csv('train_modified.csv')\\n\",\n \"target='Disbursed' # Disbursed的值就是二元分类的输出\\n\",\n \"IDcol = 'ID'\\n\",\n \"train['Disbursed'].value_counts() \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"x_columns = [x for x in train.columns if x not in [target, IDcol]]\\n\",\n \"X = train[x_columns]\\n\",\n \"y = train['Disbursed']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.98005\\n\",\n \"AUC Score (Train): 0.999833\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"d:\\\\users\\\\pinard.liu\\\\appdata\\\\local\\\\programs\\\\python\\\\python36\\\\lib\\\\site-packages\\\\sklearn\\\\ensemble\\\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\\n\",\n \" warn(\\\"Some inputs do not have OOB scores. \\\"\\n\",\n \"d:\\\\users\\\\pinard.liu\\\\appdata\\\\local\\\\programs\\\\python\\\\python36\\\\lib\\\\site-packages\\\\sklearn\\\\ensemble\\\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\\n\",\n \" predictions[k].sum(axis=1)[:, np.newaxis])\\n\"\n ]\n }\n ],\n \"source\": [\n \"rf0 = RandomForestClassifier(oob_score=True, random_state=10)\\n\",\n \"rf0.fit(X,y)\\n\",\n \"print (rf0.oob_score_)\\n\",\n \"y_predprob = rf0.predict_proba(X)[:,1]\\n\",\n \"print (\\\"AUC Score (Train): %f\\\" % metrics.roc_auc_score(y, y_predprob))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"([mean: 0.80681, std: 0.02236, params: {'n_estimators': 10},\\n\",\n \" mean: 0.81600, std: 0.03275, params: {'n_estimators': 20},\\n\",\n \" mean: 0.81818, std: 0.03136, params: {'n_estimators': 30},\\n\",\n \" mean: 0.81838, std: 0.03118, params: {'n_estimators': 40},\\n\",\n \" mean: 0.82034, std: 0.03001, params: {'n_estimators': 50},\\n\",\n \" mean: 0.82113, std: 0.02966, params: {'n_estimators': 60},\\n\",\n \" mean: 0.81992, std: 0.02836, params: {'n_estimators': 70}],\\n\",\n \" {'n_estimators': 60},\\n\",\n \" 0.8211334476626017)\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"param_test1 = {'n_estimators':[10,20,30,40,50,60,70]}\\n\",\n \"gsearch1 = GridSearchCV(estimator = RandomForestClassifier(min_samples_split=100,\\n\",\n \" min_samples_leaf=20,max_depth=8,max_features='sqrt' ,random_state=10), \\n\",\n \" param_grid = param_test1, scoring='roc_auc',cv=5)\\n\",\n \"gsearch1.fit(X,y)\\n\",\n \"gsearch1.grid_scores_, gsearch1.best_params_, gsearch1.best_score_\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"([mean: 0.79379, std: 0.02347, params: {'max_depth': 3, 'min_samples_split': 50},\\n\",\n \" mean: 0.79339, std: 0.02410, params: {'max_depth': 3, 'min_samples_split': 70},\\n\",\n \" mean: 0.79350, std: 0.02462, params: {'max_depth': 3, 'min_samples_split': 90},\\n\",\n \" mean: 0.79367, std: 0.02493, params: {'max_depth': 3, 'min_samples_split': 110},\\n\",\n \" mean: 0.79387, std: 0.02521, params: {'max_depth': 3, 'min_samples_split': 130},\\n\",\n \" mean: 0.79373, std: 0.02524, params: {'max_depth': 3, 'min_samples_split': 150},\\n\",\n \" mean: 0.79378, std: 0.02532, params: {'max_depth': 3, 'min_samples_split': 170},\\n\",\n \" mean: 0.79349, std: 0.02542, params: {'max_depth': 3, 'min_samples_split': 190},\\n\",\n \" mean: 0.80960, std: 0.02602, params: {'max_depth': 5, 'min_samples_split': 50},\\n\",\n \" mean: 0.80920, std: 0.02629, params: {'max_depth': 5, 'min_samples_split': 70},\\n\",\n \" mean: 0.80888, std: 0.02522, params: {'max_depth': 5, 'min_samples_split': 90},\\n\",\n \" mean: 0.80923, std: 0.02777, params: {'max_depth': 5, 'min_samples_split': 110},\\n\",\n \" mean: 0.80823, std: 0.02634, params: {'max_depth': 5, 'min_samples_split': 130},\\n\",\n \" mean: 0.80801, std: 0.02637, params: {'max_depth': 5, 'min_samples_split': 150},\\n\",\n \" mean: 0.80792, std: 0.02685, params: {'max_depth': 5, 'min_samples_split': 170},\\n\",\n \" mean: 0.80771, std: 0.02587, params: {'max_depth': 5, 'min_samples_split': 190},\\n\",\n \" mean: 0.81688, std: 0.02996, params: {'max_depth': 7, 'min_samples_split': 50},\\n\",\n \" mean: 0.81872, std: 0.02584, params: {'max_depth': 7, 'min_samples_split': 70},\\n\",\n \" mean: 0.81501, std: 0.02857, params: {'max_depth': 7, 'min_samples_split': 90},\\n\",\n \" mean: 0.81476, std: 0.02552, params: {'max_depth': 7, 'min_samples_split': 110},\\n\",\n \" mean: 0.81557, std: 0.02791, params: {'max_depth': 7, 'min_samples_split': 130},\\n\",\n \" mean: 0.81459, std: 0.02905, params: {'max_depth': 7, 'min_samples_split': 150},\\n\",\n \" mean: 0.81601, std: 0.02808, params: {'max_depth': 7, 'min_samples_split': 170},\\n\",\n \" mean: 0.81704, std: 0.02757, params: {'max_depth': 7, 'min_samples_split': 190},\\n\",\n \" mean: 0.82090, std: 0.02665, params: {'max_depth': 9, 'min_samples_split': 50},\\n\",\n \" mean: 0.81908, std: 0.02527, params: {'max_depth': 9, 'min_samples_split': 70},\\n\",\n \" mean: 0.82036, std: 0.02422, params: {'max_depth': 9, 'min_samples_split': 90},\\n\",\n \" mean: 0.81889, std: 0.02927, params: {'max_depth': 9, 'min_samples_split': 110},\\n\",\n \" mean: 0.81991, std: 0.02868, params: {'max_depth': 9, 'min_samples_split': 130},\\n\",\n \" mean: 0.81788, std: 0.02436, params: {'max_depth': 9, 'min_samples_split': 150},\\n\",\n \" mean: 0.81898, std: 0.02588, params: {'max_depth': 9, 'min_samples_split': 170},\\n\",\n \" mean: 0.81746, std: 0.02716, params: {'max_depth': 9, 'min_samples_split': 190},\\n\",\n \" mean: 0.82395, std: 0.02454, params: {'max_depth': 11, 'min_samples_split': 50},\\n\",\n \" mean: 0.82380, std: 0.02258, params: {'max_depth': 11, 'min_samples_split': 70},\\n\",\n \" mean: 0.81953, std: 0.02552, params: {'max_depth': 11, 'min_samples_split': 90},\\n\",\n \" mean: 0.82254, std: 0.02366, params: {'max_depth': 11, 'min_samples_split': 110},\\n\",\n \" mean: 0.81950, std: 0.02768, params: {'max_depth': 11, 'min_samples_split': 130},\\n\",\n \" mean: 0.81887, std: 0.02636, params: {'max_depth': 11, 'min_samples_split': 150},\\n\",\n \" mean: 0.81910, std: 0.02734, params: {'max_depth': 11, 'min_samples_split': 170},\\n\",\n \" mean: 0.81564, std: 0.02622, params: {'max_depth': 11, 'min_samples_split': 190},\\n\",\n \" mean: 0.82291, std: 0.02092, params: {'max_depth': 13, 'min_samples_split': 50},\\n\",\n \" mean: 0.82177, std: 0.02513, params: {'max_depth': 13, 'min_samples_split': 70},\\n\",\n \" mean: 0.82415, std: 0.02480, params: {'max_depth': 13, 'min_samples_split': 90},\\n\",\n \" mean: 0.82420, std: 0.02417, params: {'max_depth': 13, 'min_samples_split': 110},\\n\",\n \" mean: 0.82209, std: 0.02481, params: {'max_depth': 13, 'min_samples_split': 130},\\n\",\n \" mean: 0.81852, std: 0.02227, params: {'max_depth': 13, 'min_samples_split': 150},\\n\",\n \" mean: 0.81955, std: 0.02885, params: {'max_depth': 13, 'min_samples_split': 170},\\n\",\n \" mean: 0.82092, std: 0.02600, params: {'max_depth': 13, 'min_samples_split': 190}],\\n\",\n \" {'max_depth': 13, 'min_samples_split': 110},\\n\",\n \" 0.8242016800050813)\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"param_test2 = {'max_depth':[3,5,7,9,11,13], 'min_samples_split':[50,70,90,110,130,150,170,190]}\\n\",\n \"gsearch2 = GridSearchCV(estimator = RandomForestClassifier(n_estimators= 60, \\n\",\n \" min_samples_leaf=20,max_features='sqrt' ,oob_score=True, random_state=10),\\n\",\n \" param_grid = param_test2, scoring='roc_auc',iid=False, cv=5)\\n\",\n \"gsearch2.fit(X,y)\\n\",\n \"gsearch2.grid_scores_, gsearch2.best_params_, gsearch2.best_score_\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.984\\n\"\n ]\n }\n ],\n \"source\": [\n \"rf1 = RandomForestClassifier(n_estimators= 60, max_depth=13, min_samples_split=110,\\n\",\n \" min_samples_leaf=20,max_features='sqrt' ,oob_score=True, random_state=10)\\n\",\n \"rf1.fit(X,y)\\n\",\n \"print (rf1.oob_score_)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"([mean: 0.82093, std: 0.02287, params: {'min_samples_leaf': 10, 'min_samples_split': 80},\\n\",\n \" mean: 0.81913, std: 0.02141, params: {'min_samples_leaf': 10, 'min_samples_split': 100},\\n\",\n \" mean: 0.82048, std: 0.02328, params: {'min_samples_leaf': 10, 'min_samples_split': 120},\\n\",\n \" mean: 0.81798, std: 0.02099, params: {'min_samples_leaf': 10, 'min_samples_split': 140},\\n\",\n \" mean: 0.82094, std: 0.02535, params: {'min_samples_leaf': 20, 'min_samples_split': 80},\\n\",\n \" mean: 0.82097, std: 0.02327, params: {'min_samples_leaf': 20, 'min_samples_split': 100},\\n\",\n \" mean: 0.82487, std: 0.02110, params: {'min_samples_leaf': 20, 'min_samples_split': 120},\\n\",\n \" mean: 0.82169, std: 0.02406, params: {'min_samples_leaf': 20, 'min_samples_split': 140},\\n\",\n \" mean: 0.82352, std: 0.02271, params: {'min_samples_leaf': 30, 'min_samples_split': 80},\\n\",\n \" mean: 0.82164, std: 0.02381, params: {'min_samples_leaf': 30, 'min_samples_split': 100},\\n\",\n \" mean: 0.82070, std: 0.02528, params: {'min_samples_leaf': 30, 'min_samples_split': 120},\\n\",\n \" mean: 0.82141, std: 0.02508, params: {'min_samples_leaf': 30, 'min_samples_split': 140},\\n\",\n \" mean: 0.82278, std: 0.02294, params: {'min_samples_leaf': 40, 'min_samples_split': 80},\\n\",\n \" mean: 0.82141, std: 0.02547, params: {'min_samples_leaf': 40, 'min_samples_split': 100},\\n\",\n \" mean: 0.82043, std: 0.02724, params: {'min_samples_leaf': 40, 'min_samples_split': 120},\\n\",\n \" mean: 0.82162, std: 0.02348, params: {'min_samples_leaf': 40, 'min_samples_split': 140},\\n\",\n \" mean: 0.82225, std: 0.02431, params: {'min_samples_leaf': 50, 'min_samples_split': 80},\\n\",\n \" mean: 0.82225, std: 0.02431, params: {'min_samples_leaf': 50, 'min_samples_split': 100},\\n\",\n \" mean: 0.81890, std: 0.02458, params: {'min_samples_leaf': 50, 'min_samples_split': 120},\\n\",\n \" mean: 0.81917, std: 0.02528, params: {'min_samples_leaf': 50, 'min_samples_split': 140}],\\n\",\n \" {'min_samples_leaf': 20, 'min_samples_split': 120},\\n\",\n \" 0.8248650279471544)\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"param_test3 = {'min_samples_split':[80,100,120,140], 'min_samples_leaf':[10,20,30,40,50]}\\n\",\n \"gsearch3 = GridSearchCV(estimator = RandomForestClassifier(n_estimators= 60, max_depth=13,\\n\",\n \" max_features='sqrt' ,oob_score=True, random_state=10),\\n\",\n \" param_grid = param_test3, scoring='roc_auc',iid=False, cv=5)\\n\",\n \"gsearch3.fit(X,y)\\n\",\n \"gsearch3.grid_scores_, gsearch3.best_params_, gsearch3.best_score_\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"([mean: 0.81981, std: 0.02586, params: {'max_features': 3},\\n\",\n \" mean: 0.81639, std: 0.02533, params: {'max_features': 5},\\n\",\n \" mean: 0.82487, std: 0.02110, params: {'max_features': 7},\\n\",\n \" mean: 0.81704, std: 0.02209, params: {'max_features': 9}],\\n\",\n \" {'max_features': 7},\\n\",\n \" 0.8248650279471544)\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"param_test4 = {'max_features':[3,5,7,9]}\\n\",\n \"gsearch4 = GridSearchCV(estimator = RandomForestClassifier(n_estimators= 60, max_depth=13, min_samples_split=120,\\n\",\n \" min_samples_leaf=20 ,oob_score=True, random_state=10),\\n\",\n \" param_grid = param_test4, scoring='roc_auc',iid=False, cv=5)\\n\",\n \"gsearch4.fit(X,y)\\n\",\n \"gsearch4.grid_scores_, gsearch4.best_params_, gsearch4.best_score_\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.984\\n\"\n ]\n }\n ],\n \"source\": [\n \"rf2 = RandomForestClassifier(n_estimators= 60, max_depth=13, min_samples_split=120,\\n\",\n \" min_samples_leaf=20,max_features=7 ,oob_score=True, random_state=10)\\n\",\n \"rf2.fit(X,y)\\n\",\n \"print (rf2.oob_score_)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\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\": \"3.6.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "ensemble-learning/xgboost-example.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"import xgboost as xgb\\n\",\n \"import matplotlib.pylab as plt\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"from sklearn.model_selection import GridSearchCV\\n\",\n \"from sklearn.model_selection import train_test_split\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sklearn.datasets.samples_generator import make_classification\\n\",\n \"# X为样本特征，y为样本类别输出， 共10000个样本，每个样本20个特征，输出有2个类别，没有冗余特征，每个类别一个簇\\n\",\n \"X, y = make_classification(n_samples=10000, n_features=20, n_redundant=0,\\n\",\n \" n_clusters_per_class=1, n_classes=2, flip_y=0.1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(7500, 20)\\n\",\n \"(7500,)\\n\",\n \"(2500, 20)\\n\",\n \"(2500,)\\n\"\n ]\n }\n ],\n \"source\": [\n \"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\\n\",\n \"print (X_train.shape)\\n\",\n \"print (y_train.shape)\\n\",\n \"print (X_test.shape)\\n\",\n \"print (y_test.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"

XGBoost 使用原生API

\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"dtrain = xgb.DMatrix(X_train,y_train)\\n\",\n \"dtest = xgb.DMatrix(X_test,y_test)\\n\",\n \"param = {'max_depth':5, 'eta':0.5, 'verbosity':1, 'objective':'binary:logistic'}\\n\",\n \"raw_model = xgb.train(param, dtrain, num_boost_round=20)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.9664\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.metrics import accuracy_score\\n\",\n \"pred_train_raw = raw_model.predict(dtrain)\\n\",\n \"for i in range(len(pred_train_raw)):\\n\",\n \" if pred_train_raw[i] > 0.5:\\n\",\n \" pred_train_raw[i]=1\\n\",\n \" else:\\n\",\n \" pred_train_raw[i]=0 \\n\",\n \"print (accuracy_score(dtrain.get_label(), pred_train_raw))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.9408\\n\"\n ]\n }\n ],\n \"source\": [\n \"pred_test_raw = raw_model.predict(dtest)\\n\",\n \"for i in range(len(pred_test_raw)):\\n\",\n \" if pred_test_raw[i] > 0.5:\\n\",\n \" pred_test_raw[i]=1\\n\",\n \" else:\\n\",\n \" pred_test_raw[i]=0 \\n\",\n \"print (accuracy_score(dtest.get_label(), pred_test_raw))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"

XGBoost 使用sklearn wrapper，仍然使用原始API的参数

\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0]\\tvalidation_0-error:0.0636\\n\",\n \"Will train until validation_0-error hasn't improved in 10 rounds.\\n\",\n \"[1]\\tvalidation_0-error:0.062\\n\",\n \"[2]\\tvalidation_0-error:0.0624\\n\",\n \"[3]\\tvalidation_0-error:0.062\\n\",\n \"[4]\\tvalidation_0-error:0.062\\n\",\n \"[5]\\tvalidation_0-error:0.062\\n\",\n \"[6]\\tvalidation_0-error:0.062\\n\",\n \"[7]\\tvalidation_0-error:0.062\\n\",\n \"[8]\\tvalidation_0-error:0.062\\n\",\n \"[9]\\tvalidation_0-error:0.0608\\n\",\n \"[10]\\tvalidation_0-error:0.0608\\n\",\n \"[11]\\tvalidation_0-error:0.0608\\n\",\n \"[12]\\tvalidation_0-error:0.0608\\n\",\n \"[13]\\tvalidation_0-error:0.0604\\n\",\n \"[14]\\tvalidation_0-error:0.0604\\n\",\n \"[15]\\tvalidation_0-error:0.0604\\n\",\n \"[16]\\tvalidation_0-error:0.0604\\n\",\n \"[17]\\tvalidation_0-error:0.0604\\n\",\n \"[18]\\tvalidation_0-error:0.0608\\n\",\n \"[19]\\tvalidation_0-error:0.0608\\n\",\n \"[20]\\tvalidation_0-error:0.06\\n\",\n \"[21]\\tvalidation_0-error:0.06\\n\",\n \"[22]\\tvalidation_0-error:0.06\\n\",\n \"[23]\\tvalidation_0-error:0.0592\\n\",\n \"[24]\\tvalidation_0-error:0.0588\\n\",\n \"[25]\\tvalidation_0-error:0.0588\\n\",\n \"[26]\\tvalidation_0-error:0.0584\\n\",\n \"[27]\\tvalidation_0-error:0.0584\\n\",\n \"[28]\\tvalidation_0-error:0.0584\\n\",\n \"[29]\\tvalidation_0-error:0.0584\\n\",\n \"[30]\\tvalidation_0-error:0.0584\\n\",\n \"[31]\\tvalidation_0-error:0.0584\\n\",\n \"[32]\\tvalidation_0-error:0.0584\\n\",\n \"[33]\\tvalidation_0-error:0.0584\\n\",\n \"[34]\\tvalidation_0-error:0.0584\\n\",\n \"[35]\\tvalidation_0-error:0.0584\\n\",\n \"[36]\\tvalidation_0-error:0.0584\\n\",\n \"Stopping. Best iteration:\\n\",\n \"[26]\\tvalidation_0-error:0.0584\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\\n\",\n \" colsample_bynode=1, colsample_bytree=1, eta=0.5, gamma=0,\\n\",\n \" learning_rate=0.1, max_delta_step=0, max_depth=5,\\n\",\n \" min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,\\n\",\n \" nthread=None, objective='binary:logistic', random_state=0,\\n\",\n \" reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\\n\",\n \" silent=None, subsample=1, verbosity=1)\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sklearn_model_raw = xgb.XGBClassifier(**param)\\n\",\n \"sklearn_model_raw.fit(X_train, y_train, early_stopping_rounds=10, eval_metric=\\\"error\\\",\\n\",\n \" eval_set=[(X_test, y_test)])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"

XGBoost 使用sklearn wrapper，使用sklearn风格的参数(推荐)

\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sklearn_model_new = xgb.XGBClassifier(max_depth=5,learning_rate= 0.5, verbosity=1, objective='binary:logistic',random_state=1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0]\\tvalidation_0-error:0.0636\\n\",\n \"Will train until validation_0-error hasn't improved in 10 rounds.\\n\",\n \"[1]\\tvalidation_0-error:0.0624\\n\",\n \"[2]\\tvalidation_0-error:0.0604\\n\",\n \"[3]\\tvalidation_0-error:0.0592\\n\",\n \"[4]\\tvalidation_0-error:0.0592\\n\",\n \"[5]\\tvalidation_0-error:0.0584\\n\",\n \"[6]\\tvalidation_0-error:0.058\\n\",\n \"[7]\\tvalidation_0-error:0.0588\\n\",\n \"[8]\\tvalidation_0-error:0.0588\\n\",\n \"[9]\\tvalidation_0-error:0.0588\\n\",\n \"[10]\\tvalidation_0-error:0.0588\\n\",\n \"[11]\\tvalidation_0-error:0.0588\\n\",\n \"[12]\\tvalidation_0-error:0.0588\\n\",\n \"[13]\\tvalidation_0-error:0.058\\n\",\n \"[14]\\tvalidation_0-error:0.0584\\n\",\n \"[15]\\tvalidation_0-error:0.0584\\n\",\n \"[16]\\tvalidation_0-error:0.0584\\n\",\n \"Stopping. Best iteration:\\n\",\n \"[6]\\tvalidation_0-error:0.058\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\\n\",\n \" colsample_bynode=1, colsample_bytree=1, gamma=0, learning_rate=0.5,\\n\",\n \" max_delta_step=0, max_depth=5, min_child_weight=1, missing=None,\\n\",\n \" n_estimators=100, n_jobs=1, nthread=None,\\n\",\n \" objective='binary:logistic', random_state=1, reg_alpha=0,\\n\",\n \" reg_lambda=1, scale_pos_weight=1, seed=None, silent=None,\\n\",\n \" subsample=1, verbosity=1)\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sklearn_model_new.fit(X_train, y_train, early_stopping_rounds=10, eval_metric=\\\"error\\\",\\n\",\n \" eval_set=[(X_test, y_test)])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"

使用sklearn网格搜索调参

\\n\",\n \"\\n\",\n \"一般固定步长，先调好框架参数n_estimators，再调弱学习器参数max_depth，min_child_weight,gamma等，接着调正则化相关参数subsample，colsample_byXXX, reg_alpha以及reg_lambda,最后固定前面调好的参数，来调步长learning_rate\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"GridSearchCV(cv=None, error_score='raise',\\n\",\n \" estimator=XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\\n\",\n \" colsample_bynode=1, colsample_bytree=1, gamma=0, learning_rate=0.5,\\n\",\n \" max_delta_step=0, max_depth=5, min_child_weight=1, missing=None,\\n\",\n \" n_estimators=100, n_jobs=1, nthread=None,\\n\",\n \" objective='binary:logistic', random_state=1, reg_alpha=0,\\n\",\n \" reg_lambda=1, scale_pos_weight=1, seed=None, silent=None,\\n\",\n \" subsample=1, verbosity=1),\\n\",\n \" fit_params=None, iid=True, n_jobs=1,\\n\",\n \" param_grid={'max_depth': [4, 5, 6], 'n_estimators': [5, 10, 20]},\\n\",\n \" pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\\n\",\n \" scoring=None, verbose=0)\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"gsCv = GridSearchCV(sklearn_model_new,\\n\",\n \" {'max_depth': [4,5,6],\\n\",\n \" 'n_estimators': [5,10,20]})\\n\",\n \"gsCv.fit(X_train,y_train)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.9533333333333334\\n\",\n \"{'max_depth': 4, 'n_estimators': 10}\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(gsCv.best_score_)\\n\",\n \"print(gsCv.best_params_)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"GridSearchCV(cv=None, error_score='raise',\\n\",\n \" estimator=XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\\n\",\n \" colsample_bynode=1, colsample_bytree=1, gamma=0, learning_rate=0.1,\\n\",\n \" max_delta_step=0, max_depth=4, min_child_weight=1, missing=None,\\n\",\n \" n_estimators=10, n_jobs=1, nthread=None,\\n\",\n \" objective='binary:logistic', random_state=1, reg_alpha=0,\\n\",\n \" reg_lambda=1, scale_pos_weight=1, seed=None, silent=None,\\n\",\n \" subsample=1, verbosity=1),\\n\",\n \" fit_params=None, iid=True, n_jobs=1,\\n\",\n \" param_grid={'learning_rate ': [0.3, 0.5, 0.7]},\\n\",\n \" pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\\n\",\n \" scoring=None, verbose=0)\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sklearn_model_new2 = xgb.XGBClassifier(max_depth=4,n_estimators=10,verbosity=1, objective='binary:logistic',random_state=1)\\n\",\n \"gsCv2 = GridSearchCV(sklearn_model_new2, \\n\",\n \" {'learning_rate ': [0.3,0.5,0.7]})\\n\",\n \"gsCv2.fit(X_train,y_train)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.9516\\n\",\n \"{'learning_rate ': 0.3}\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(gsCv2.best_score_)\\n\",\n \"print(gsCv2.best_params_)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0]\\tvalidation_0-error:0.062\\n\",\n \"Will train until validation_0-error hasn't improved in 10 rounds.\\n\",\n \"[1]\\tvalidation_0-error:0.0592\\n\",\n \"[2]\\tvalidation_0-error:0.0608\\n\",\n \"[3]\\tvalidation_0-error:0.0608\\n\",\n \"[4]\\tvalidation_0-error:0.0608\\n\",\n \"[5]\\tvalidation_0-error:0.0604\\n\",\n \"[6]\\tvalidation_0-error:0.0592\\n\",\n \"[7]\\tvalidation_0-error:0.0588\\n\",\n \"[8]\\tvalidation_0-error:0.0588\\n\",\n \"[9]\\tvalidation_0-error:0.0588\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\\n\",\n \" colsample_bynode=1, colsample_bytree=1, gamma=0, learning_rate=0.3,\\n\",\n \" max_delta_step=0, max_depth=4, min_child_weight=1, missing=None,\\n\",\n \" n_estimators=10, n_jobs=1, nthread=None,\\n\",\n \" objective='binary:logistic', random_state=0, reg_alpha=0,\\n\",\n \" reg_lambda=1, scale_pos_weight=1, seed=None, silent=None,\\n\",\n \" subsample=1, verbosity=1)\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sklearn_model_new2 = xgb.XGBClassifier(max_depth=4,learning_rate= 0.3, verbosity=1, objective='binary:logistic',n_estimators=10)\\n\",\n \"sklearn_model_new2.fit(X_train, y_train, early_stopping_rounds=10, eval_metric=\\\"error\\\",\\n\",\n \" eval_set=[(X_test, y_test)])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.9412\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n\n ]\n }\n ],\n \"source\": [\n \"pred_test_new = sklearn_model_new2.predict(X_test)\\n\",\n \"print (accuracy_score(dtest.get_label(), pred_test_new))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\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\": \"3.6.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "mathematics/mcmc_2.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C) 2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"MCMC(二)马尔科夫链 https://www.cnblogs.com/pinard/p/6632399.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"matrix = np.matrix([[0.9,0.075,0.025],[0.15,0.8,0.05],[0.25,0.25,0.5]], dtype=float)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 0.9 0.075 0.025]\\n\",\n \" [ 0.15 0.8 0.05 ]\\n\",\n \" [ 0.25 0.25 0.5 ]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print matrix\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Current round: 1\\n\",\n \"[[ 0.8275 0.13375 0.03875]\\n\",\n \" [ 0.2675 0.66375 0.06875]\\n\",\n \" [ 0.3875 0.34375 0.26875]]\\n\",\n \"Current round: 2\\n\",\n \"[[ 0.73555 0.212775 0.051675]\\n\",\n \" [ 0.42555 0.499975 0.074475]\\n\",\n \" [ 0.51675 0.372375 0.110875]]\\n\",\n \"Current round: 3\\n\",\n \"[[ 0.65828326 0.28213131 0.05958543]\\n\",\n \" [ 0.56426262 0.36825403 0.06748335]\\n\",\n \" [ 0.5958543 0.33741675 0.06672895]]\\n\",\n \"Current round: 4\\n\",\n \"[[ 0.62803724 0.30972343 0.06223933]\\n\",\n \" [ 0.61944687 0.3175772 0.06297594]\\n\",\n \" [ 0.6223933 0.3148797 0.062727 ]]\\n\",\n \"Current round: 5\\n\",\n \"[[ 0.62502532 0.31247685 0.06249783]\\n\",\n \" [ 0.6249537 0.31254233 0.06250397]\\n\",\n \" [ 0.62497828 0.31251986 0.06250186]]\\n\",\n \"Current round: 6\\n\",\n \"[[ 0.625 0.3125 0.0625]\\n\",\n \" [ 0.625 0.3125 0.0625]\\n\",\n \" [ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 7\\n\",\n \"[[ 0.625 0.3125 0.0625]\\n\",\n \" [ 0.625 0.3125 0.0625]\\n\",\n \" [ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 8\\n\",\n \"[[ 0.625 0.3125 0.0625]\\n\",\n \" [ 0.625 0.3125 0.0625]\\n\",\n \" [ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 9\\n\",\n \"[[ 0.625 0.3125 0.0625]\\n\",\n \" [ 0.625 0.3125 0.0625]\\n\",\n \" [ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 10\\n\",\n \"[[ 0.625 0.3125 0.0625]\\n\",\n \" [ 0.625 0.3125 0.0625]\\n\",\n \" [ 0.625 0.3125 0.0625]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"matrix = np.matrix([[0.9,0.075,0.025],[0.15,0.8,0.05],[0.25,0.25,0.5]], dtype=float)\\n\",\n \"for i in range(10):\\n\",\n \" matrix = matrix*matrix\\n\",\n \" print \\\"Current round:\\\" , i+1\\n\",\n \" print matrix\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Current round: 1\\n\",\n \"[[ 0.405 0.4175 0.1775]]\\n\",\n \"Current round: 2\\n\",\n \"[[ 0.4715 0.40875 0.11975]]\\n\",\n \"Current round: 3\\n\",\n \"[[ 0.5156 0.3923 0.0921]]\\n\",\n \"Current round: 4\\n\",\n \"[[ 0.54591 0.375535 0.078555]]\\n\",\n \"Current round: 5\\n\",\n \"[[ 0.567288 0.36101 0.071702]]\\n\",\n \"Current round: 6\\n\",\n \"[[ 0.5826362 0.3492801 0.0680837]]\\n\",\n \"Current round: 7\\n\",\n \"[[ 0.59378552 0.34014272 0.06607176]]\\n\",\n \"Current round: 8\\n\",\n \"[[ 0.60194632 0.33316603 0.06488765]]\\n\",\n \"Current round: 9\\n\",\n \"[[ 0.6079485 0.32790071 0.06415079]]\\n\",\n \"Current round: 10\\n\",\n \"[[ 0.61237646 0.3239544 0.06366914]]\\n\",\n \"Current round: 11\\n\",\n \"[[ 0.61564926 0.32100904 0.0633417 ]]\\n\",\n \"Current round: 12\\n\",\n \"[[ 0.61807111 0.31881635 0.06311253]]\\n\",\n \"Current round: 13\\n\",\n \"[[ 0.61986459 0.31718655 0.06294886]]\\n\",\n \"Current round: 14\\n\",\n \"[[ 0.62119333 0.3159763 0.06283037]]\\n\",\n \"Current round: 15\\n\",\n \"[[ 0.62217803 0.31507813 0.06274383]]\\n\",\n \"Current round: 16\\n\",\n \"[[ 0.62290791 0.31441182 0.06268027]]\\n\",\n \"Current round: 17\\n\",\n \"[[ 0.62344896 0.31391762 0.06263343]]\\n\",\n \"Current round: 18\\n\",\n \"[[ 0.62385006 0.31355112 0.06259882]]\\n\",\n \"Current round: 19\\n\",\n \"[[ 0.62414743 0.31327936 0.06257322]]\\n\",\n \"Current round: 20\\n\",\n \"[[ 0.62436789 0.31307785 0.06255426]]\\n\",\n \"Current round: 21\\n\",\n \"[[ 0.62453135 0.31292843 0.06254022]]\\n\",\n \"Current round: 22\\n\",\n \"[[ 0.62465253 0.31281765 0.06252982]]\\n\",\n \"Current round: 23\\n\",\n \"[[ 0.62474238 0.31273552 0.0625221 ]]\\n\",\n \"Current round: 24\\n\",\n \"[[ 0.624809 0.31267462 0.06251639]]\\n\",\n \"Current round: 25\\n\",\n \"[[ 0.62485839 0.31262947 0.06251215]]\\n\",\n \"Current round: 26\\n\",\n \"[[ 0.624895 0.31259599 0.06250901]]\\n\",\n \"Current round: 27\\n\",\n \"[[ 0.62492215 0.31257117 0.06250668]]\\n\",\n \"Current round: 28\\n\",\n \"[[ 0.62494228 0.31255277 0.06250495]]\\n\",\n \"Current round: 29\\n\",\n \"[[ 0.62495721 0.31253912 0.06250367]]\\n\",\n \"Current round: 30\\n\",\n \"[[ 0.62496827 0.31252901 0.06250272]]\\n\",\n \"Current round: 31\\n\",\n \"[[ 0.62497648 0.31252151 0.06250202]]\\n\",\n \"Current round: 32\\n\",\n \"[[ 0.62498256 0.31251594 0.0625015 ]]\\n\",\n \"Current round: 33\\n\",\n \"[[ 0.62498707 0.31251182 0.06250111]]\\n\",\n \"Current round: 34\\n\",\n \"[[ 0.62499041 0.31250876 0.06250082]]\\n\",\n \"Current round: 35\\n\",\n \"[[ 0.62499289 0.3125065 0.06250061]]\\n\",\n \"Current round: 36\\n\",\n \"[[ 0.62499473 0.31250482 0.06250045]]\\n\",\n \"Current round: 37\\n\",\n \"[[ 0.62499609 0.31250357 0.06250034]]\\n\",\n \"Current round: 38\\n\",\n \"[[ 0.6249971 0.31250265 0.06250025]]\\n\",\n \"Current round: 39\\n\",\n \"[[ 0.62499785 0.31250196 0.06250018]]\\n\",\n \"Current round: 40\\n\",\n \"[[ 0.62499841 0.31250146 0.06250014]]\\n\",\n \"Current round: 41\\n\",\n \"[[ 0.62499882 0.31250108 0.0625001 ]]\\n\",\n \"Current round: 42\\n\",\n \"[[ 0.62499912 0.3125008 0.06250008]]\\n\",\n \"Current round: 43\\n\",\n \"[[ 0.62499935 0.31250059 0.06250006]]\\n\",\n \"Current round: 44\\n\",\n \"[[ 0.62499952 0.31250044 0.06250004]]\\n\",\n \"Current round: 45\\n\",\n \"[[ 0.62499964 0.31250033 0.06250003]]\\n\",\n \"Current round: 46\\n\",\n \"[[ 0.62499974 0.31250024 0.06250002]]\\n\",\n \"Current round: 47\\n\",\n \"[[ 0.6249998 0.31250018 0.06250002]]\\n\",\n \"Current round: 48\\n\",\n \"[[ 0.62499985 0.31250013 0.06250001]]\\n\",\n \"Current round: 49\\n\",\n \"[[ 0.62499989 0.3125001 0.06250001]]\\n\",\n \"Current round: 50\\n\",\n \"[[ 0.62499992 0.31250007 0.06250001]]\\n\",\n \"Current round: 51\\n\",\n \"[[ 0.62499994 0.31250005 0.06250001]]\\n\",\n \"Current round: 52\\n\",\n \"[[ 0.62499996 0.31250004 0.0625 ]]\\n\",\n \"Current round: 53\\n\",\n \"[[ 0.62499997 0.31250003 0.0625 ]]\\n\",\n \"Current round: 54\\n\",\n \"[[ 0.62499998 0.31250002 0.0625 ]]\\n\",\n \"Current round: 55\\n\",\n \"[[ 0.62499998 0.31250002 0.0625 ]]\\n\",\n \"Current round: 56\\n\",\n \"[[ 0.62499999 0.31250001 0.0625 ]]\\n\",\n \"Current round: 57\\n\",\n \"[[ 0.62499999 0.31250001 0.0625 ]]\\n\",\n \"Current round: 58\\n\",\n \"[[ 0.62499999 0.31250001 0.0625 ]]\\n\",\n \"Current round: 59\\n\",\n \"[[ 0.62499999 0.3125 0.0625 ]]\\n\",\n \"Current round: 60\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 61\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 62\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 63\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 64\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 65\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 66\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 67\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 68\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 69\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 70\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 71\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 72\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 73\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 74\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 75\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 76\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 77\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 78\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 79\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 80\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 81\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 82\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 83\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 84\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 85\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 86\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 87\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 88\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 89\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 90\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 91\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 92\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 93\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 94\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 95\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 96\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 97\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 98\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 99\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 100\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"matrix = np.matrix([[0.9,0.075,0.025],[0.15,0.8,0.05],[0.25,0.25,0.5]], dtype=float)\\n\",\n \"vector1 = np.matrix([[0.3,0.4,0.3]], dtype=float)\\n\",\n \"for i in range(100):\\n\",\n \" vector1 = vector1*matrix\\n\",\n \" print \\\"Current round:\\\" , i+1\\n\",\n \" print vector1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Current round: 1\\n\",\n \"[[ 0.695 0.1825 0.1225]]\\n\",\n \"Current round: 2\\n\",\n \"[[ 0.6835 0.22875 0.08775]]\\n\",\n \"Current round: 3\\n\",\n \"[[ 0.6714 0.2562 0.0724]]\\n\",\n \"Current round: 4\\n\",\n \"[[ 0.66079 0.273415 0.065795]]\\n\",\n \"Current round: 5\\n\",\n \"[[ 0.652172 0.28474 0.063088]]\\n\",\n \"Current round: 6\\n\",\n \"[[ 0.6454378 0.2924769 0.0620853]]\\n\",\n \"Current round: 7\\n\",\n \"[[ 0.64028688 0.29791068 0.06180244]]\\n\",\n \"Current round: 8\\n\",\n \"[[ 0.6363954 0.30180067 0.06180393]]\\n\",\n \"Current round: 9\\n\",\n \"[[ 0.63347695 0.30462117 0.06190188]]\\n\",\n \"Current round: 10\\n\",\n \"[[ 0.6312979 0.30668318 0.06201892]]\\n\",\n \"Current round: 11\\n\",\n \"[[ 0.62967532 0.30819862 0.06212607]]\\n\",\n \"Current round: 12\\n\",\n \"[[ 0.62846909 0.30931606 0.06221485]]\\n\",\n \"Current round: 13\\n\",\n \"[[ 0.6275733 0.31014174 0.06228495]]\\n\",\n \"Current round: 14\\n\",\n \"[[ 0.62690847 0.31075263 0.0623389 ]]\\n\",\n \"Current round: 15\\n\",\n \"[[ 0.62641525 0.31120496 0.06237979]]\\n\",\n \"Current round: 16\\n\",\n \"[[ 0.62604941 0.31154006 0.06241053]]\\n\",\n \"Current round: 17\\n\",\n \"[[ 0.62577811 0.31178839 0.0624335 ]]\\n\",\n \"Current round: 18\\n\",\n \"[[ 0.62557693 0.31197244 0.06245062]]\\n\",\n \"Current round: 19\\n\",\n \"[[ 0.62542776 0.31210888 0.06246336]]\\n\",\n \"Current round: 20\\n\",\n \"[[ 0.62531716 0.31221003 0.06247282]]\\n\",\n \"Current round: 21\\n\",\n \"[[ 0.62523515 0.31228501 0.06247984]]\\n\",\n \"Current round: 22\\n\",\n \"[[ 0.62517435 0.31234061 0.06248505]]\\n\",\n \"Current round: 23\\n\",\n \"[[ 0.62512926 0.31238182 0.06248891]]\\n\",\n \"Current round: 24\\n\",\n \"[[ 0.62509584 0.31241238 0.06249178]]\\n\",\n \"Current round: 25\\n\",\n \"[[ 0.62507106 0.31243504 0.0624939 ]]\\n\",\n \"Current round: 26\\n\",\n \"[[ 0.62505268 0.31245184 0.06249548]]\\n\",\n \"Current round: 27\\n\",\n \"[[ 0.62503906 0.31246429 0.06249665]]\\n\",\n \"Current round: 28\\n\",\n \"[[ 0.62502896 0.31247352 0.06249752]]\\n\",\n \"Current round: 29\\n\",\n \"[[ 0.62502147 0.31248037 0.06249816]]\\n\",\n \"Current round: 30\\n\",\n \"[[ 0.62501592 0.31248545 0.06249863]]\\n\",\n \"Current round: 31\\n\",\n \"[[ 0.6250118 0.31248921 0.06249899]]\\n\",\n \"Current round: 32\\n\",\n \"[[ 0.62500875 0.312492 0.06249925]]\\n\",\n \"Current round: 33\\n\",\n \"[[ 0.62500649 0.31249407 0.06249944]]\\n\",\n \"Current round: 34\\n\",\n \"[[ 0.62500481 0.3124956 0.06249959]]\\n\",\n \"Current round: 35\\n\",\n \"[[ 0.62500357 0.31249674 0.06249969]]\\n\",\n \"Current round: 36\\n\",\n \"[[ 0.62500264 0.31249758 0.06249977]]\\n\",\n \"Current round: 37\\n\",\n \"[[ 0.62500196 0.31249821 0.06249983]]\\n\",\n \"Current round: 38\\n\",\n \"[[ 0.62500145 0.31249867 0.06249988]]\\n\",\n \"Current round: 39\\n\",\n \"[[ 0.62500108 0.31249901 0.06249991]]\\n\",\n \"Current round: 40\\n\",\n \"[[ 0.6250008 0.31249927 0.06249993]]\\n\",\n \"Current round: 41\\n\",\n \"[[ 0.62500059 0.31249946 0.06249995]]\\n\",\n \"Current round: 42\\n\",\n \"[[ 0.62500044 0.3124996 0.06249996]]\\n\",\n \"Current round: 43\\n\",\n \"[[ 0.62500033 0.3124997 0.06249997]]\\n\",\n \"Current round: 44\\n\",\n \"[[ 0.62500024 0.31249978 0.06249998]]\\n\",\n \"Current round: 45\\n\",\n \"[[ 0.62500018 0.31249984 0.06249998]]\\n\",\n \"Current round: 46\\n\",\n \"[[ 0.62500013 0.31249988 0.06249999]]\\n\",\n \"Current round: 47\\n\",\n \"[[ 0.6250001 0.31249991 0.06249999]]\\n\",\n \"Current round: 48\\n\",\n \"[[ 0.62500007 0.31249993 0.06249999]]\\n\",\n \"Current round: 49\\n\",\n \"[[ 0.62500005 0.31249995 0.0625 ]]\\n\",\n \"Current round: 50\\n\",\n \"[[ 0.62500004 0.31249996 0.0625 ]]\\n\",\n \"Current round: 51\\n\",\n \"[[ 0.62500003 0.31249997 0.0625 ]]\\n\",\n \"Current round: 52\\n\",\n \"[[ 0.62500002 0.31249998 0.0625 ]]\\n\",\n \"Current round: 53\\n\",\n \"[[ 0.62500002 0.31249999 0.0625 ]]\\n\",\n \"Current round: 54\\n\",\n \"[[ 0.62500001 0.31249999 0.0625 ]]\\n\",\n \"Current round: 55\\n\",\n \"[[ 0.62500001 0.31249999 0.0625 ]]\\n\",\n \"Current round: 56\\n\",\n \"[[ 0.62500001 0.31249999 0.0625 ]]\\n\",\n \"Current round: 57\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 58\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 59\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 60\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 61\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 62\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 63\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 64\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 65\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 66\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 67\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 68\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 69\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 70\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 71\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 72\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 73\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 74\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 75\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 76\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 77\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 78\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 79\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 80\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 81\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 82\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 83\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 84\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 85\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 86\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 87\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 88\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 89\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 90\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 91\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 92\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 93\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 94\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 95\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 96\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 97\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 98\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 99\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\",\n \"Current round: 100\\n\",\n \"[[ 0.625 0.3125 0.0625]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"matrix = np.matrix([[0.9,0.075,0.025],[0.15,0.8,0.05],[0.25,0.25,0.5]], dtype=float)\\n\",\n \"vector1 = np.matrix([[0.7,0.1,0.2]], dtype=float)\\n\",\n \"for i in range(100):\\n\",\n \" vector1 = vector1*matrix\\n\",\n \" print \\\"Current round:\\\" , i+1\\n\",\n \" print vector1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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vRW8lkG/P7J706slR+iZ8dD1SKrAaymlLLpegQcxu7CXffflWTkkKgD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\\nA2AlhAq62mmRDiPfDqANEV1H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X6kattsSTel99ynWXJPgwE2pz/cpiTtDhwFXAAUfaRXoeOTtANwWERc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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import random\\n\",\n \"import math\\n\",\n \"from scipy.stats import norm\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"def bivexp(theta1, theta2):\\n\",\n \" lam1 = 0.5\\n\",\n \" lam2 = 0.1\\n\",\n \" lam = 0.01\\n\",\n \" maxval = 8\\n\",\n \" y = math.exp(-(lam1 + lam) * theta1 - (lam2 + lam) * theta2 - lam * maxval)\\n\",\n \" return y\\n\",\n \"\\n\",\n \"T = 5000\\n\",\n \"sigma = 1\\n\",\n \"thetamin = 0\\n\",\n \"thetamax = 8\\n\",\n \"theta_1 = [0.0] * (T + 1)\\n\",\n \"theta_2 = [0.0] * (T + 1)\\n\",\n \"theta_1[0] = random.uniform(thetamin, thetamax)\\n\",\n \"theta_2[0] = random.uniform(thetamin, thetamax)\\n\",\n \"\\n\",\n \"t = 0\\n\",\n \"while t < T:\\n\",\n \" t = t + 1\\n\",\n \" theta_star_0 = random.uniform(thetamin, thetamax)\\n\",\n \" theta_star_1 = random.uniform(thetamin, thetamax)\\n\",\n \" # print theta_star\\n\",\n \" alpha = min(1, (bivexp(theta_star_0, theta_star_1) / bivexp(theta_1[t - 1], theta_2[t - 1])))\\n\",\n \"\\n\",\n \" u = random.uniform(0, 1)\\n\",\n \" if u <= alpha:\\n\",\n \" theta_1[t] = theta_star_0\\n\",\n \" theta_2[t] = theta_star_1\\n\",\n \" else:\\n\",\n \" theta_1[t] = theta_1[t - 1]\\n\",\n \" theta_2[t] = theta_2[t - 1]\\n\",\n \"plt.figure(1)\\n\",\n \"ax1 = plt.subplot(211)\\n\",\n \"ax2 = plt.subplot(212) \\n\",\n \"plt.ylim(thetamin, thetamax)\\n\",\n \"plt.sca(ax1)\\n\",\n \"plt.plot(range(T + 1), theta_1, 'g-', label=\\\"0\\\")\\n\",\n \"plt.sca(ax2)\\n\",\n \"plt.plot(range(T + 1), theta_2, 'r-', label=\\\"1\\\")\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"plt.figure(2)\\n\",\n \"ax1 = plt.subplot(211)\\n\",\n \"ax2 = plt.subplot(212) \\n\",\n \"num_bins = 50\\n\",\n \"plt.sca(ax1)\\n\",\n \"plt.hist(theta_1, num_bins, normed=1, facecolor='green', alpha=0.5)\\n\",\n \"plt.title('Histogram')\\n\",\n \"plt.sca(ax2)\\n\",\n \"plt.hist(theta_2, num_bins, normed=1, facecolor='red', alpha=0.5)\\n\",\n \"plt.title('Histogram')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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/wslflCZEXGbVJCH2aySMKKIibIMc+6zXtFRb0eKuJKkjZ+mFW8zlhj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\\ndf9JFAAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from numpy.linalg import cholesky \\n\",\n \"mu = np.array([[3, 5]]) \\n\",\n \"Sigma = np.array([[3, 0.5], [1.5, 3]]) \\n\",\n \"R = cholesky(Sigma) \\n\",\n \"s = np.dot(np.random.randn(1000, 2), R) + mu \\n\",\n \"# 注意绘制的是散点图，而不是直方图 \\n\",\n \"plt.plot(s[:,0],s[:,1],'+') \\n\",\n \"plt.show() \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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v+Zp1KJDA8FAxkQn845/WNzBX7tAhgrTUSZLSCV2no2vmYkMnaoA1kG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\\nYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"def norm_dist_prob(theta):\\n\",\n \" y = norm.pdf(theta, loc=3, scale=2)\\n\",\n \" return y\\n\",\n \"\\n\",\n \"T = 5000\\n\",\n \"\\n\",\n \"t = 0\\n\",\n \"while t < T:\\n\",\n \" t = t + 1\\n\",\n \" theta_star = norm.rvs(loc=theta[t - 1], scale=sigma, size=1, random_state=None)\\n\",\n \" #print theta_star\\n\",\n \" alpha = min(1, (norm_dist_prob(theta_star[0]) / norm_dist_prob(theta[t - 1])))\\n\",\n \"\\n\",\n \" u = random.uniform(0, 1)\\n\",\n \" if u < alpha:\\n\",\n \" theta[t] = theta_star[0]\\n\",\n \" else:\\n\",\n \" theta[t] = theta[t - 1]\\n\",\n \"\\n\",\n \"\\n\",\n \"plt.scatter(theta, norm.pdf(theta, loc=3, scale=2))\\n\",\n \"num_bins = 50\\n\",\n \"plt.hist(theta, num_bins, normed=1, facecolor='red', alpha=0.7)\\n\",\n \"plt.show()\"\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\": \"3.6.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "mathematics/mcmc_3_4.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C) 2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"MCMC(三)MCMC采样和M-H采样 https://www.cnblogs.com/pinard/p/6638955.html\\n\",\n \"MCMC(四)Gibbs采样 https://www.cnblogs.com/pinard/p/6645766.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import random\\n\",\n \"import math\\n\",\n \"from scipy.stats import norm\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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atmNpJQb/BLM/sD8Dtgjbs/kUo8kh2WLLkLkq5e1gK0HF0gZrALI6pb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\\nCEoGIiKCkoGIiAD/Hw9Z0g2yGzJQAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"def norm_dist_prob(theta):\\n\",\n \" y = norm.pdf(theta, loc=3, scale=2)\\n\",\n \" return y\\n\",\n \"\\n\",\n \"T = 5000\\n\",\n \"pi = [0 for i in range(T)]\\n\",\n \"sigma = 1\\n\",\n \"t = 0\\n\",\n \"while t < T-1:\\n\",\n \" t = t + 1\\n\",\n \" pi_star = norm.rvs(loc=theta[t - 1], scale=sigma, size=1, random_state=None)\\n\",\n \" #print theta_star\\n\",\n \" alpha = min(1, (norm_dist_prob(pi_star[0]) / norm_dist_prob(pi[t - 1])))\\n\",\n \"\\n\",\n \" u = random.uniform(0, 1)\\n\",\n \" if u < alpha:\\n\",\n \" pi[t] = pi_star[0]\\n\",\n \" else:\\n\",\n \" pi[t] = pi[t - 1]\\n\",\n \"\\n\",\n \"\\n\",\n \"plt.scatter(pi, norm.pdf(pi, loc=3, scale=2))\\n\",\n \"num_bins = 50\\n\",\n \"plt.hist(pi, num_bins, normed=1, facecolor='red', alpha=0.7)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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tZ5RSPrfMVikWKxOOv6WWG/E1gsqQ84BCwHVqQLSHo38BjwmYjYV0/d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\\n9mZmOeCwNzPLAYe9mVkO/H+JfIbMePMDYgAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from mpl_toolkits.mplot3d import Axes3D\\n\",\n \"from scipy.stats import multivariate_normal\\n\",\n \"samplesource = multivariate_normal(mean=[5,-1], cov=[[1,0.5],[0.5,2]])\\n\",\n \"\\n\",\n \"def p_ygivenx(x, m1, m2, s1, s2):\\n\",\n \" return (random.normalvariate(m2 + rho * s2 / s1 * (x - m1), math.sqrt(1 - rho ** 2) * s2))\\n\",\n \"\\n\",\n \"def p_xgiveny(y, m1, m2, s1, s2):\\n\",\n \" return (random.normalvariate(m1 + rho * s1 / s2 * (y - m2), math.sqrt(1 - rho ** 2) * s1))\\n\",\n \"\\n\",\n \"N = 5000\\n\",\n \"K = 20\\n\",\n \"x_res = []\\n\",\n \"y_res = []\\n\",\n \"z_res = []\\n\",\n \"m1 = 5\\n\",\n \"m2 = -1\\n\",\n \"s1 = 1\\n\",\n \"s2 = 2\\n\",\n \"\\n\",\n \"rho = 0.5\\n\",\n \"y = m2\\n\",\n \"\\n\",\n \"for i in xrange(N):\\n\",\n \" for j in xrange(K):\\n\",\n \" x = p_xgiveny(y, m1, m2, s1, s2)\\n\",\n \" y = p_ygivenx(x, m1, m2, s1, s2)\\n\",\n \" z = samplesource.pdf([x,y])\\n\",\n \" x_res.append(x)\\n\",\n \" y_res.append(y)\\n\",\n \" z_res.append(z)\\n\",\n \"\\n\",\n \"num_bins = 50\\n\",\n \"plt.hist(x_res, num_bins, normed=1, facecolor='green', alpha=0.5)\\n\",\n \"plt.hist(y_res, num_bins, normed=1, facecolor='red', alpha=0.5)\\n\",\n \"plt.title('Histogram')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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XNiwg45Dg0N4ff7CyZzaFnVO2jiK4QmvlGAVdpUF3symawqPbpd9K0u\\nPq8L3q3F7nZLFapBSZ1qtqC136H6HHoxaz4EAoGKN1X1knNstIiv+AZTPdbM0MTnMbLZLNFolFwu\\nl5c2K7kqFWppPqsK0GuRNktdzHYcoW5ydapuMRwO097eXtC4N5VKFSxm2WwWIL9oWSe2Fy9mGhMb\\n9WLIaTTiU9AR3zA08XkEZSiJx+OA3L06cVW6kfmUtKkWAi9LIUbDEWptXO33+/NyrHUxy2QytLe3\\nF7yvVAsmu6NfNDk2Brwik7EgRxVBNQoBNspxjiU08XkAwzCIRqMFExXsSJu17M/ay1OR7Ghs2yvZ\\n1OoGjUQipFKpsq8rhlUKLfeeUouZE3IMBAITTlJtdjmrHGolR6VY9Pb2NkTOUU9mGAlNfDVCSZux\\nWKyg16aTTiZgP+JTObdcLpeXNtX7ajHHqG046RNq95itebtIJFIgjxabW9xALS5OyFEtXplMxpUE\\n1iik0iiLW719n5XIMZVKkUwm6ejoqIucYzVo4hsJTXwuYZU21YmrTvrRGrpqlTats/O82I8qg/B6\\nqoKSTFV3l/GCXXK0s5CBfC4l2dbjXX4jotG+s3rJOVaDljpHQhOfC+RyOeLxOKlUKn/SK2nNrbRZ\\nLYqwU05QywmuHKFOyancMVcroi/Xq3M87/ztLmRDQ0P519lZyEoZcsZqITruuONYtWoVGzdupLOz\\nc0z2OdHh5DqrB3LUxDcSmvgcIpPJ5G34xa7NcDg8Kvk8O+UEbk5stW3DMFyRXiXXW7VBtLUc93hC\\nLS6BQKDsLDPDMPJS6niZcWSwcBBoBdqYNm0h0EsikXC9zdFEIy3OXh+rE3K0DpTNZrOkUqn8/4GS\\n55F6jzrvvFSKGhWa+GxCkYQiuUAgUBCF1WouKRXtWHNjdvKFTqIla6mCl9Kc0yL6iVjA7vP5KpqC\\nvDDjVCJHIb1uwAD2AHqAHUCUSCRSt+TXKBiPc9QuORbfcGUymfwQ2r6+PkDIMRQKNe0QWtDEZwuq\\n2Nrq2lQjX1QUpu643KK4Js6aGwsGg1UJxAlxFZcquF0Ii4kqk8m4lkybCW7MOOpuv5oZZ/LkyUiU\\ntyvwGrAFWA98CjgIuKMuya+RIj6oz2ip3A1XPB7HMAza2tpG5KqbFZr4qkC1HTMMo0DaLI7CvIpW\\nnDSYLvXeas+PVqmCk0G0EyWyG024MeM8//zzQAdwBHA68EdgOdAO/BL4EHAm8GsikUi+TZg24zhD\\no5G09XjVDZJSFJoVmvjKQEmb8Xg8b1CwSpvFUViti7mK+JwOdbW+v9rnKZd3q+XY1XYBx8dcjEYh\\nxHo5zmL565hjjgFmAD8FpgNnAfsDS83HTwbmAAEgk+8wVCpyLDbkNPMiWYxGJD43zS0mMjTxlYAd\\nadNrWNt4uS2FKLcYu2lebQdKmrV2YXGKRltE6hUykaMFSCKENwRcAXQBGfP36cDlQA5o56tf/So3\\n3nhjXZhxGu08aKRj1X06R0ITXxGKpU1lAgkEAlV7bbrpW2mVCZUz1A3KHZedMgg3EYzaLuA4Sa6O\\ntfiCLPe4RnV0dXUBncCNSGT3CvBBhAwXAXHgXiAGhIEUt956KzfeeOO4m3EaDY12fpY63maXtzXx\\nmSiWNv1+f94EYtes4ZQ8rM7KlpaWsm287MK6f7tlEG72Yc3nqcR5M19E9YMO4FQgDcxDHJ3PAgcj\\nrs44QoYHATcBb9vaqlszjiJHFU2q86SUlNpI/S8b4RitaLTjHQto4kOkwKGhoXyUB+TbgrW1tdnS\\nx52eWMXOSnXRu4V1/1ZCtZt3c9J6zE0O0i5U9KkvVPu46aabAD8Szf0TWIyQ3KuIrLnOfOWuwADw\\nFHAZcKFnDs9aO+MADAwM1EWLr4kGfT2NRNMT39DQEJlMhng8nrf7lmoLVg125cJKzspatXfDMFxN\\nVbBz7F7nCX0+H5lMZsTE7VKFthqVceGFFwJtwCzgI8DewHNACon6Npn/nglciZQ4fAXJ/Y0dytWi\\nGYZBb28v3d3dwMhpHE66mKhIcjTPnUYjknJSZzOjqYnPMAxOOukkli5dCpBfiEerDq1aR5Naoeq8\\nvJ4IoebklSqBcBuhKSlW3VwoWQxgcHAwv81y8/fG+86//gwCYeCbwFYggpQwJIE1iLElikR5i4F9\\ngH9DzC/eG7XcQv0tnXQxsRZqj1W+URNf46Opia8Y6XTatrRZjGpRk92IyS2JqO4Mbo6/nDGnOE/o\\nBVGrHCFIh5GWlpaC7fb19TFp0qR8rZHTO/+xsOHX06Ih32Un4AO+DewLrELKFrYCIeAdpIvLICJ1\\nGkjU927gHzzxxBMcddRRY3/wLlGti4k24xRCuzpHoqmJz+fz0dXVxZlnnslNN92UX3DdbqvUCWW3\\naNztBaYIVR23V1HkaOTzDMPI1ylC5c9st0VT8Z1/s83gk8bTk5C2ZMvN388D70Mkz58DvwOuBb4B\\nfA7YBvwZ+DjwDCeccMK4dnIZjd6XtZhxKp0/1sbk46062EE5kqvnYx4LNC3xGYbB1VdfzSOPPMLP\\nf/5zz/rWWS9ip9KmU9nQWqoAuO4XWkzaiky9nBxfHPGqSfVut6de6+bOP5VKjdlImLGBH9gNyAJ9\\nwJ7AVKR12fuR3F8bsARYC0xBSDGFOECbD7WYcdR1rcqe6vn8UetJfZ+/Y4+mJb4f/vCH3H333Rx8\\n8MEcf/zxnpg1rBitonEoXargxQT20WppZqeWcDRQq9NQ/RsouOPPZrP4fL6CeXzjt7BEzN9vAG8i\\n5PZHhNAGgS8jZDcbeAg4FHgYkUGfHOuDbSiUu7FKpVJ5daja+WPn5kopD6OBcjfSzU6ETUt8n//8\\n5/nKV77COeecw8DAANOmTatZ+1a5slwu52qhtxPtVJIgazl+dcy5XM7zfJ7dHp4w9u3A7EiqxQub\\nyqfG4/GKkqo13zgad9133303Yk7pBK4BPoNEfb3AlxCJ82fAZ4HDgZXAM8hl/12EHG8ENnD55Zfz\\nzW9+09Pjs4tGNos4keTLNRsfzXyjJr7SqLsGbg888ACLFi1iwYIF/OhHPxrx/Jo1azjiiCOIRCJc\\neeWVI57PZrMccMABnHrqqRX3093dTTgcpquri4GBAU8WXLXQK5OMm+im0jGosotgMEgkEik4eWs5\\nka01hE5Jr1JuM5FI5GshixcG9b5GSLT7/f585B6JRAiFQoTDYTo7O+nu7qanp4euri7a29vzZh11\\nIxGNRunv76e3t5e+vj4GBgYYGhoiFouRSCTyNwZuuv6cddZZyCWcQ4rSnwDuQRydLUgZw1mI2/OT\\nwC8QYvwRcCFCjhcCYS677LKav6dmgJvzVZGjGgXU1tZGR0dH2fNHNcNPp9PE43EGBwfp7e2lt7eX\\n/v5+BgcHiUajxONxkslkvklAuetQYyTqKuLLZrNceOGFLFu2jNmzZ3PIIYdw2mmnsXjx4vxrpkyZ\\nwrXXXmve7Y7ENddcw1577cXg4KCtfXZ1deXnVNUC68LlpP7PikpOTyVBVipVcHOSq7o/1T3GiztB\\n1Wx7NGTeekAx2dcqqSpzjru7/k7EpHImcAKwDEgAf0LKGVqQYnYD6ewCUuT+NPBbpNzBOel6iUaL\\n+MDbiGk0zTjqfZlMZsK4VL1AXRHfU089xR577MHuu+8OyB3tPffcU0B8U6dOZerUqdx3330j3r9x\\n40aWLl3Kt771LX7605/a2md3d3fNEZ/KYfl8Pluz88qh1DHYNci4KYGw5vNSqVRNdU0Ktc7kG2up\\nc6wwGhZ8qd1TbcgOR+ryNgHnAg8ACxCpcx4wF/g/wAeA/0Yiw0uRPp4vI2UOGtUwXiTthByL6xsN\\nwyiYxNHsQ2ihzohv06ZN7Lrrrvn/z5kzhxUrVth+/yWXXMJPfvITBgbsX8RWqdOp3FRsMqmFPErB\\nzTRzO1DHrfJ5tcDqYFVEqmfyOYfThe3tt99GLt99gdOQMoadSLR3O9KfcyVS3/d/EZdnHCG4TqSG\\n7xSGI8FLR+mTTSzUc3Ra6hxSN+QdHRLtq/On2VFXOb5aTqg///nPTJs2jQMOOMDRgtrT00N/fz/g\\nTCpUcp4qGlfOrFr7bar3K30/HA7bkiDtfnfquGE4n1frcauo1ImJpRw0IZaHWtRCoRALFy5EjC2v\\nInV8xwIHAicB5yMEtwMhR9X8/HbgNuBXiNTZh5Q8iDM0EomUzRWNJuqZTIrRSMcKpSegeN0xqhFR\\nVxHf7Nmz2bBhQ/7/GzZsYM6cObbe++STT3LvvfeydOlSEokEAwMDfOITn+DWW2+t+L7Ozk7Wrl3r\\n6GR20w/TLhSJZLNZV11YKl2Y6rjD4XBNkmwx1NzCYsONxmjDQBycpyG9Ny8EbkVKFT6DTGrYHylf\\nUB1e3kaixG7gTvPf/4VqXTY4OFggqZZrGadrwxoD2tVZGnVF/QcffDCvvvoq69evJ5VKcccdd3Da\\naaeVfG3xXenll1/Ohg0beOONN7j99ts57rjjqpIeOMvxKYkwmUwSiURGyI9eRCtuW49VOpGV21Qd\\ndzmydnrsmUwmnzR3aowp5+ps9gvSGWYiEd4kpC1ZK9KizIc4OzuAf0XIsR/YiOT6BpG6v18gRPkF\\nYHeAvMuws7Mz70z2+Xxks9mqLtVqDsNyaKQoqpGOFRrveMcKdRXxBYNBrrvuOk466SSy2Sznnnsu\\nixcv5oYbbgDg/PPPZ8uWLRxyyCEMDAzg9/u55pprWL16dV7DVrD7x+7u7qa/v78qadlp4VUL8WUy\\nmXxBtJeRkx1zTC3GGGXY8DrqrXeM/zG2IGT2BlKI/pz5//9EZM9fIkaXXyIkeDRS0jADiQgHkIjw\\nMsQJuia/ZSe1aW7bxY1/4b87NBqRqOJ56/81wFfli5jw39KGDRu4+OKLufnmm4nH47S3t494jV1p\\nM5VKYRiGI8eUlUSCwSCGYRCJRKq/sQSi0WgBuTkpKxgaGqK9vd2WgUYZYyKRSD5CdeoSU0n3SCRS\\n0CEmGo3mZdN6hZKiS50rY4ElS5awbt1OhNROAlYDHzP/fRfSoHoRQnAPI8Nod0WiwtMQ2TOGRIIB\\nxB2aA1Ls2PHGiJtINyjnUrUSpXVRVudQvUuqSjmZNGnSeB+KLQwNDRW4ONV6X8/Xl4coe+LUVcQ3\\nHujq6iob8Vk7j9hp4eXUGVocjVlH87iF+gxOywrU56+0yKio1+/3u65VLN5eMpkklUrlc0lqsdQo\\nj3Xr1iEy57HIuKE9kXKGvRG35t8Q+fI+pKh9FkJwM5GWZUcikue3kIbVNwBnA3522WWXMR9MqyRS\\nJamWa/dVKd84VmjEiK+Rjnes0PTE197eXtAwWZ0o6oKsVj9nhROps1SpQq0Lvtp/Mpn0xGFZ6ni9\\nNPSoXFAoFCrojak+Q7n2X/UYCYw9EsCjCIltRaaq72U+HkXEmhVI/u9S4GtI4fqDCNm9gkR9EeBm\\nYDLw1ph+AqukWq60pjhidCKpWofSepk6aKTzrtGOd6zQ9MSnCM16coxmg2kYLngvlvm8KCtQtYRO\\nRwlV2ne1QbROCVstXn6/f0RrN9X/UkXAigyrNf4tFQ1M3Au+DfGlfRqJ3NYjBPYwkuvrNH/vCXwf\\nqeP7M0KGPcBhwH8Af0VyfK2Iu7MXIc6xRaXFebhYv/x7rZKqklKtvTCLz5VmcqmW+m4n4ud0iqYn\\nPgW16DuRNotRjbisBe9uB96Wg4qUvDTHFBfoe3G8Kl+qiKoUCruTlD+24sWuUiRQjhgbcxEIIjm7\\n9yFSph9QDasHkenqncDl5us2Ik2s25HG1J9D0h+fAX6NEOdBSHTYP3YfwwPU2i7OOoGjnJRqPV8a\\nLYLSxFcaTU981s4jQF4idLvIlyM+a37Ma1eotWWaWxmyeN92XKxOjrm436h1wSn1WjvHq2SyUjlM\\naySgiLHe80d2IINnuxHJMoF0aPkekq+bjMiYQSTaAyG9TQjp/Yf5ukeQ+r5ZCBFuRKLFOUiEOLHg\\nZbs4tT3DMBpCfi8mvkYj7tFC0xMfSPeWk046id/97nd0d3e7Jr1qheNeF7xbzTetra2k02lP7Mpe\\nS73FLdLU9IJS8Oq7sUYC5SJ3J5Z86yKnpFf1/FguJKlUConsfo7k8uYjrcp+iPTs3GD+fhaRMmMI\\nsX0WmcDehnRu+TFS0rAbw6UQs4EQ7e3tRKPRMftM470Y240aDcMoGD5bT3P3Kh2zJrqRaGriMwyD\\n66+/nodkXl9QAAAgAElEQVQffpgrr7ySjo6Omk6S4ujHSkyVpiqUe38llIrIanWEQvn8o1sYhsyt\\n8/l8njhBvYTdSMBKjNZZav39/fnFrpJM5v1nVn/n/YGrEdlyNrDKfO5IxPjyX0h0eDxS+jATIchZ\\nSN6v13y9DyHJ64GU7uVYAoocfT5ffixYMUpFjWM1d6/asWsUoqmJ76abbuL666/nxBNP5N3vfrcn\\nnVdgWKqzM1Wh3PsrnazlIrJaj1/dvTpxg1baZ6XIsdz7vPobeIFykYCy33d0dIwgRrtRY20S2YeA\\nT5n/fhkZONuHlDJsQobN9gP7mf9+HZEwfwksBB5D6vYeN7dxN/B5ZIxR0uG3VDsaKSqpdKxeSqpe\\nnC+6XVl5NDXxnX322Zx99tl89atfpb+/n9mzZ9e06KoTSrV3cioV2nldJYcluOvMYK2dc+oGLYda\\nxxM1ClQEUA7VjBVKIrNjrJC/USuSrwsg5NWLRHQ/QrqybATeRKTQOcBVSGeWlxFyXIXIoYczfPmf\\njJhfTkBk0LF3djYDvDDilJNUS+UbNfGVR10S3wMPPMDFF19MNpvlvPPO4+tf/3rB82vWrOHTn/40\\nK1eu5Ac/+AGXXiojVTZs2MAnPvEJtm3bhs/n47Of/SwXXXRR2f20tbUBw23LvEIikXAtFZY7Ye04\\nLN1ESyoqU3erXuTznIwnKtWrs14iPi9QLQoA+7VqM2fORHpx/g64H5iO5Pf8yOSFPqTx9IsIuWUR\\n88uziJx5KPBFxL3pAz4KdAG3IGT3mPn4ZM+/h4mC0Y5O7Zwvdh3N6mYpGo0WEGI4HB61428U1B3x\\n1TKFPRQKcdVVV7H//vszNDTEQQcdxIknnljw3lKwzuRzu+gqYgI8y48p2HVYOoU1KrO61pzA+p2V\\nMrHYeV+zw27phmAO8AdgGhKdXWs+/h6kf+ckpMzhLiSSexyRPR9GyPA+hDiTiDQ6GSG9LLIcTAW2\\nc8455/DrX//auw9ZAcX9JOsZ9SDL2nU0p1IpEolEvkGAMr+pG/5mRt2dbdYp7KFQKD+F3YqpU6dy\\n8MEHj/ijz5gxg/333x+Ajo4OFi9ebA7srIxKbcvsIJcbnnFXq8uv+Biy2Wy+32a1+jwnpQWq52Br\\na2veaVoLEVm/A69q/jQEaqETnIAYWVqQAvY0cCbwTYT0PgqsRUocTkM6tsxEpjR83Xwui0ilN5j/\\n/jwynT2NdHlp5c477xyDT9Z4aISbteL61UgkQltbGx0dHTUb+CYK6m51KjWFfdOmTY63s379elau\\nXMlhhx1W9bU9PT2uI75MJpMnJtVk1yuDjLpja2lp8ayDjOoP6lVLM/WdWb8Dp8dp/b4aIRIc32N8\\nEZEj/Uhe7jBkIsMngN8wTGbTGZ7BtwQhxalIe7KvIiT3HaSc4UHgAXMb9yNOUOmi43bUkBPUQxTl\\nBI1yrOVSJhp1KHV6cVINDQ1xxhlncM0119jqNK8iPidwWqpgF0qXtzos7UZP1RZkaxG9V91dMpkM\\ngKtON/oidIoZwC7IeKE9gP9FjC0ZJIJTsuUA0rtzEVLH95r5uoeQri0bkEt/AJFDv4z06fwaYpbZ\\nAQzna609VOupTm080Egk3UjHOtaoO+KrZQo7iOvxwx/+MB//+Mf50Ic+ZOs9XV1dDA4OOpIKy5Uq\\neBENqGkFbureyu272vT1Ydeg/f0o4gdctXcr97gmxJGQurFZCOmlEIJrBW4D/ohEdT9DenFOQgbM\\nvoVIohchPT3/jER970civ16kNEJFhovMx+VvWZwLquQ4tBb0VyPGRl6MG4lMtKuzPOqO+KxT2GfN\\nmsUdd9zBbbfdVvK1xQukYRice+657LXXXlx88cW292l3GC3Y62riduHOZDJks1kCgYArybDcsVhb\\nhXkRmSriB8nnqW4Wbi8ofSHaxSAyNf0LiBz5I6R7yzVIR5b9kY4sfwR+C3wJ6dX5KYQEL0aivw8h\\nNX6nI5LnFiQPGEOkzzbgdXK53IibOrd1aooYS0WNmUwm/7sZosaxQiOR9Fij7oivlinszz//PL/5\\nzW/Yd999OeCAAwD44Q9/yPvf//6K++zu7mZgYKDiawzDIJPJVKyhA3eLuJWcvJxoPhouS6tc6oac\\ni6G+U2vTarVA6ou2GB1IecJ/IGT1cYTYBoEjkLq+JJLjy6JyddKSLIuUQgSR6G4KEEfKG/4VKYVo\\nQbq9/AzYxsyZM9m6davto3Nbp6bO/1QqVfdRYyOdl8VuWa2kDKPuiA/g5JNP5uSTTy547Pzzz8//\\ne8aMGQVyqMJRRx3lSK5TUMRXbvEfTZt+8bZraTtm3beVoLxqFWaVS70oSlefPRKJ5Bc/lUvq7e3N\\nRxjNmk8aiRSwBpEqf4aYUXJIbi6CRG3XI/07X0eK1l9GJNIdwP8A/4LU6/0YGUW0EokCT0FcoE8i\\nk9rjnta2KpSKGlXNZygUKhs1KlKs1hPTOoPPazQacZQi6UaXmr1CXRLfWCMcDufn2EHhCTMaBKJQ\\natte5LjcDI2ttt9qM/mc3Amr/CCIVGqNHLPZLIODg3R1dZXMJ1kXwFKRgZUoR/MiH/tF0I/IkuuQ\\nSeunAl9BcnyXIPLnImQY7YOI8SWAuD4vM987FWlh9pK5jS2IMWYFcBywFPgBEv3NMH+PLWrpblLt\\n3CgmxokuzTdSdDrW0MQHIwhP/XYzVcGuSaSa2cTt51D5Nzcuy1Kw0zHG6fZUfhAYsT3r9+8kn1Rt\\nAKmXY4fGejE58MADEWlyFtJv04/IlFcgdX2DSDH6ZxA5M4sUqd8HzEOixMuA8833T0PIbgtChtMR\\nibPPfG43ZD7f2E5ktws3uUZrc/FKUWPxjVPxdhuJSBrteMcSmvgYuZCpxd5NqUK1yKma2cSpu7L4\\nmAHXJpZSZiFFUtU6xtjND6p6v3A4TDQazS9Sbow8diODSmOHxkMyc4rVq1cjZpTJyCXbgciaewIv\\nIK3IXkDKFdqQNmaTgN2RfN5cYDsygf3HwB1I3V4SGVd0KRIdvo4YYr6ETH1oHYNP5/0C7SZqVMRY\\nKWpUx6jMOPUuG5aTOjU08ZWEGurqdQeSSmUQCm6kTmtLMxgZRdlB8QVRTFLVOsZUQ6X8oPX9XpYz\\n2IkM3Ehm1ohi7Ba/BEJiPwK+CzyNyJYHAn9BiO5XiPx5OXAB0s7sCww3qL4SMbAMAO9C8nthpCA+\\naD6XQmTR14A4P//5z/n85z8/Bp9vbOEmasxkMhiGQTQaddQserygia88NPGZiEQivPXWW0yZMqWm\\nAu9yC7cTInGC4vIKLwaIem1iqTZRYrzgVk5Vg2it8/i8llNHIopEYI8Am5Hm0v+JkFwGKUUAkTJv\\nRiK4/4dMaZgF/DdwFFLSsBWRMc80H5+CtDJ7zNzHZYhk2sGXv/zlUSe+epTkSkWNqtyoq6sLKB01\\nlhsxNF55aOu26/F7Hi/Uzyo0jjAMg5aWFo4//ngeffRRpk+fXlPiu5j4nIzocRLxlBoa69RoUrzf\\nVCrluOavkhvWOiG+XD6vFOrhIi0nmaXTaeLxOJ2dnRhG9Xl8tcup05D2Yqcgzaf/gJQ13IZMXvgp\\nQoCfQCTRt5H8Xitihvk2Io/2I5Hf383tbkcI86dIk+svIQR4OyKDjt0U9kZA8TnpdR7a66ixHq6h\\nekXTE188HueCCy5g+fLl3HLLLUyZMsUzqa144feqrdlobFd95nIk5WZ7TvKD6vlGu1B9Pnfz+CrJ\\nqdaFT4wtIWSA7CBSfP4yUs4wCSlj6ECiuiMQt2YnIoVOB4aA68zn+hGjy5nAauB5JP+XA84xt7U3\\n8CoyBeL3aPJzD6e5xkojhqoRYymfgkZ5ND3xLVu2jFQqxVlnnZXvXF7LSaPe72ThL/X+cqi23Vpy\\nhCCSb62k53WRe73ByXfsJiqwSmavvPIKQmA3IuS3BcnpRZH2ZXciUmcvQlx+hnN1WeBE4ABkWsOQ\\nuQ0DcYDejpBcyHxvNxL9vWT+wFj0sW+UyGQ0jtN6flQaMVQstVcaTGs915Ti0Kg3lqOFpie+U089\\nlVNOOYXvf//7+YJdL+6WYrHYqOfzvNiutebPWsvoBFYicFpDWOq7divXNiLsRAUiWf4DeAUpQN8H\\nKUeYhXRv+S4Szf0CKUpfixShv2w+tw9Sz+cz3/cL899HA8eY+/gSw5Mc7kLI9MvA8qb5W1TDeHwP\\n1vOjnLJQyr2czWYBGBwczEeNgUCAyZP1kGGow7FEVjzwwAMsWrSIBQsW8KMf/WjE82vWrOGII44g\\nEolw5ZVXOnqvFT6fr2AYbS1QTZvD4bDrfpulyECNP6q2Xac5wng8XjD2qBbST6fTjsYo6cXUDlqQ\\nMoZWJLr7N8TUshi4CnFuvoPU3a0zf3JIBHgE4t7cDmxDav16EDOLIsFDkWjy08jUhn9heOzRBwG/\\nrQkntUATa21QpBYOh/Oz91QKpLu7m56eHrq6usxG5xpQxxFfLZPY7by3GNZhtG7r6KzTCmrNu1mL\\n6Ucjn+flNg3DyCfsvWiE7WVJQ+OjCzgPidLmIHm+PoZ7dM5CSO4HyJT1XyGS5gBSz/dPhDhPQojv\\nB0hT60MR9+eFCBHuhdT2vYJEiAOIcYZ8G7lmJ6dG+g6K8+bVctHNhrqN+GqZxG7nvcWo1q+zElSp\\nQi6Xc5TPK4Vi+3Eikch3TbFDKHZzhE62WQnW/FRbW5tnBh4N2GOPPZBcXRKRMH+ARGUPIc7MaxES\\n+wRiRGlHzCl/AVaZ/34ZqclbgUxp/y5icDkHyRPOQOr2ssDhSPT3JWSiw6MIaUJvby99fX0MDAww\\nNDRELBYjkUiQSqVqGlLbSDc4jUZ8pdAoxz/aqNtbgFKT2FesWDFq77UzoaEUSuXdvDDIZLNZkskk\\ngUDAs6GxVtNJqW06PW6rKcZN2zUd2VXGxo0bkQLzKxD35RNIo+nJwMeQobSvIYaVnyGdV/4V6d4C\\n8HmE0NKI1PkcYmDZZD6+znzNB5CyhheRwbR3Auci98XXA0F6enpGuFOttny37kOFRliQG+1cbYTv\\ndLxQt8TnVdRkF9Ycn5McWanC7FoXdGu/TacF5OX27aZxdSVYt1frglD8fk2IVsxDLtMeRN5cjzgw\\nVyA5vB6EwLYAbyA1f8cgpQ7PMZzj+wcwG5FD04iTM4yQ4P8gJLobEuXdhuQUOxA51Fnv1Gruw1L9\\nMLPZ7Lh3OrGDej8+hXLRaaMc/2ijbomvlknsbt7rZBit142brdtVY4m86poC9jun2CUcVZCvCudT\\nqZRrosrlcgwODuYXVr/fn69lAupy/NDYEnMSKUA/GunacjOS61uJdGmZgRBeBonQ/oFIn6pn55mI\\ny/NhhOhuRIbUfgT4dyS6+5X5/oOQnOAspJzhaWScUZDTTz+du+66q+xR2nUfFhf7q5z4wMCAq8bR\\nYwl1fI2ARpJlxwN1S3y1TGJ38l4Fq9RZaVGz9sUsl89zszAqMrVaj93Auu9qnVOcQhGzmp/mRX4w\\nlUrlc4NqMQRIpVIF34dbCa1RIX/DABKxnYgQ2RnAr4H7gUOQTi5BpD/npQhJ3YCQ3BWIoeVuxMhy\\nu7m94xDyPAORR9uBn5jv+z1inDnMfPwApBPMyyxdurTmz1TKYJHL5ejv76enp6dksX+lxtGlWsSN\\nZQuwekYjHet4oG6Jr5ZJ7B0dHSXfWwmdnZ35yANKnzh25UK3uTK/X+byqUbWtcBNAX21FmLlhvE6\\ndcIqAs3lcvnv0u5g0uICbyWhlVoEvZi9Nl7o6elBIrQ4YlSZh7g4tyGDaFcgPTtPQ4wuaxCZ81Uk\\n/zeATGHYieT+PmNu63OI1HkOIoeei0x0X4JElxuQmsBbzOcNxPm5eXQ/MM6K/SvJqdWGFzfaueAG\\npfp0agzDV+ULaZpvyzAMjjrqKB544AGGhoZob28vIEEV6dhptKymO4TD4ar7LUWm8XicUCjkyn6c\\nTqfzpgOnhe6JRKJkBwlrlFvKFKMmp9upEyqObIPB4IgSiMHBwXwtoB0URwnFxbxeN5JWw3K7u7tt\\nv8cp5LtUY4j2R6K2ZxByOhbJy3Uh5LgTycn9FLgJKW/YgESF6xByOwWpz7sWuNV87K9IvnALYnr5\\nNhLpXQJ8FCmOjwH/F/hdwRxFr+D1d1nqXLCeD7XIqU7Py/FENBrNG+NgmPiarJav7EVdtxHfeMDa\\nMcT6u1ykU207laBq37w2x6iL3I0xphS87BZjjUJbW1tryg1aoRasSvstJkMVJSgrfn2Ol+lByGcS\\nsAyJ1OYj09UPRCTLIaSQ/V6ktAEkOkwj5pbJiGlFfT+7ISOONpiv3w58FXGDLkHI9GvI1IaLzOdX\\njd5H9Bh2zwUrIdqVU5Wq0QgyYiMc43hCE5+JUidJsQTp5ESqVktXiUzd5ghVVOr3+12RXvF+7Y4n\\nsnO8lcYyjbYMY0dCszNexiqdqvzk6A2sbUeIaDLiwPwS8H+QtmJrEYfmvyOX8OEIIa5Gis8XI4QY\\nRwrSLzUfa0cMKwHEsRlBitaDCDF2IJJmDJFUTzAfXwSs4hvf+AZXXHGFp59yrBfoWuTUbDbL0NAQ\\nQN3LqZr4KkMTnwXWSC+TyZBOp13Z/yu91iobOiXTcrASaUtLS94pV8v2Kk2JdwoVNdp1qo51OYMb\\nYlTRq13ThRNj0UMPPYQQzqvAkYgM+RtE0tyJRGX7IvV3Q8B/IfP1DkRGEj2O5PISSNH6u4API3V6\\nSxGCW48Uxz+BGGG+gxAsyPT1heZjG5Hht21cffXVnhNfvaGSO7W/v5/29vZ89Gf9UcQ42qOG7KIU\\n8WkiHIYmPgva29sZHBzMW/Stc+6coNZaOicLfzGRqgvPDZRJxam0C+WjtmqlFI2QdC8mRvUddXZ2\\nAtVNF9lsdoQztVReSZ0Pp556KhKNKedmF9KzcwUyqSEDnI0YWAYQU0sGGTDrQ1yZHUh0GEMmr/cg\\npQ0bgQfN7d+G5AzDyDy/vyJdYToRgv03RDYNmPv3Ho3w91dQZOJGTq00amg03Kma+CpDE58FPT09\\nfO973+PSSy9l+vTpnva2KzU0tlZ4Pa1B1Vk5lXZLvc5O1FiO4ButgL1aDVuxM9WaWypV3C3wIxHb\\n983/74sYWDYjZhc/kq/rRyLCSxCp8xokkpuM9OA0kHxfN8PNp49CnJ59iKR5MSKbJsz3/hwh26uQ\\n0om5SL1grzdfWBEm2oJsV04tnqZgvVGC2uTUUq7OifY91wJNfCbWrVvHX//6V4466ija2tqqv6EC\\nKtXS2e23Wa08oNxUd7ekYS0mrrVFmhtD0ESGlRjLwboQDkuOzyD5NZCi8xbzZxpiSNkHkTbnIHKm\\nHyG5O83H25FJDRcBX0SIcR0wE4kUFyMy59+QwvY4Im/ORCLI/ZGOLl8zX7exti+iweEVedg5H0o5\\nU53IqSqq1CgNTXzAihUrOPXUU9l3330577zzaG9vr7nXprqrczOMthK8LiKHYROL2pbT4ywm+ng8\\nXnMOs5EiPi+gSjvkbj+MRHcHIzV2LUgebynSePqDCKl9E+m5eQEicz6OEOAHgeUIoQWRsUWXILLl\\nLsBjiNS5CYnujmU4qluKdInpQ5yi/44Uxh8O3Mluu+3Gm2++6dnnbqRIZCyPtVY5FSQnaY0QR3u8\\nVCNBEx+waNEi7r33Xh5++GHXExqKYRiG62G05fZvJ5JyeuzWHJyKOtyiknOzFFRkW3y8jbIQjgba\\n29uR/NxkxLjyHcTNeRoSAZ6C5OFORCKy5QgBzkKivO8gUd5eyCijx4A/IuR3M1IWkUJGFC02X/ss\\nQpx7IJ1enjCPYbb5sxppZRZk69ato/jp6xf1diNWSU41DIPe3l66uroKcs7NfF0VQxMf0qD68MMP\\n55lnnnE1oaEYijzc9tssRV5OSyuq3Z2WameWyWRcX+Aq0vOyx+hERDQaZcuWLfj9fmbPnl2mGHou\\n4qScgZDO5xGim2U+tx6Yiowc2gMxqByKlDnsikRyaSRqDCMdX24B3kTamP0JyeGdixDfVuApRE5d\\nhIwr2ob0AL0IMcbkEPnTWzRSxAeNcVNWyoSTy+WaPuVgRd1/E3YmqV900UUsWLCA/fbbj5UrV+Yf\\nv+qqq1iyZAn77LMPH/vYx0gmkxX3VctMPhiOyFKpFICnJhYVSVWb6m7nwrRa8dva2mq+IBTRe1U0\\nX6/mlp07d/LCC6t4+eW1xONxx+/v7e3lf//3SR5+OMmDDw7wpz89Ueac3Achm10QokojRetnA/cg\\nxHUh0qbMj8iWk5Hc3E8RifLH5nOHIC3MliPGl4uQPp2LEPLbBakXDJqPX0VhfeDBSLeXy5Bi+m4G\\nBweJRqPE4/H8zVMtbuJGQCMRdCMd63ihrolPTVJ/4IEHWL16Nbfddhsvv/xywWuWLl3KunXrePXV\\nV7nxxhu54IILAJnJd+211/Lss8/y4osvks1muf322yvuzzqF3U0BuXXAay2w7j+dThOPx/Otkmo9\\noZUcWar9mNPPXSvR1yvBlcKWLVv43/99jhUrIjz2mMHSpU/ZJj/DMHjzzTf54x+XsX59B9DDpEnz\\neOedmaxb90b+dXID0YFEbkHz368iDapTSMlBH1KWsB2RKt9C3Jo3Iw7M5xADSwsyqeERcztTkKjN\\nh5RExIAnkRKG7UgJw3cQ9+cngd2RgbazzOe7kKiyg8ceeyzfVDyZTBKNRunv7y8YVquIMZlM5nNP\\njfK3LoVGIpNGOtbxQl1LndZJ6kB+krq14fS9997LJz/5SQAOO+ww+vr68nmITCZDLBYjEAgQi8WY\\nPXt2xf25HUaryMQ6NNZaDO8GilScuEEVyu3by5l8xfnGWCzm+vMqx5q1hslJ0+vRgJIkAXbddVee\\nfvpV2toOoKtrGoZh8OqrMd588y0WLVpYdVuPPvoUL76Y5dFHt/P22yHmzg3T0xNi9uwkicRw70TJ\\n772L4U4thyLEdiYSiX0aeC9SdvAikre7ECG1lUiR+2cQKbQViRTfhXRuuch8zwJE+rwPcYduB/6M\\nmGQ+am4vZe6jByHSAxEyXQeE+PCHP1yyb2e51nCVnIjq8UaZx9cIKHcd6u92GHVNfHYmqZd6zaZN\\nmzjwwAO59NJLmTt3Lq2trZx00kmccMIJFffnZhhtubZeXkQzSor04oS1M5PP7jF75dxU21I3J9bi\\nXvXceHS96Ovr4w9/+Dux2BwMI8O0aesACAaHi7j9/jDpdHUj0DvvvMNLL0WZPPlw/P6ddHTsQl9f\\niilTZvPkkzdx5JFHsmPHDnbZZRfzHRkkytqE5NmOQ9qMJZFo74dIPm5Xhufrnc5wnd4k8/c3kfFE\\nA0hk9z2kJnA9Io0ejRDZPYiMeTRiYPkWw6UR8xBCvMo8pufM/5eGmw44ihQHBwfzeahKTaTHa/Fu\\npCiqkY51vFDXxGf3j1dqse7t7eXee+9l/fr1dHV1ceaZZ/Lb3/6Ws88+u+x2enp6HA2j9bKtl4KK\\nHoGq+bxyKFdH6MVxOnVuVoJa9FpbW/PysCLVbDZLKBQqaCitftx217eLp59eTTa7hDlz5gGwceM/\\nmT17PZs3/5NMZjHvvLOJoaHnmTXrg/n3DA4O8sorr5FKZchkkmzcGCMc9rNgwVQCgVay2Sw9PQvo\\n7IQXX/w9TzwRJp3ezi23rGWfffo48UTVIWU/4LsIAf4akTA/g8if/20+/37gPCSquxnJ3x2HmFJ+\\nD5yKDJLNmD9rEGJrtTz2KNLfczISWYaAPRHifAQ4y3ydgUisEcQd+mPX32spYozH4xiGQVtb2whi\\nVORYqoF0uTFUo7XgNxKZ6IivOuqa+OxMUi9+zcaNG5k9ezbLli1j3rx5TJkyBYDTTz+dJ598siLx\\n2Y34RqOsAAqjRzXaqBaovKNhGLYLySsdc6Wem06kXSsZK1lTSZ1qYcvlciWJtdTiWKq7fvHC6IQY\\no9EULS2T8v8PhycxffoM5s4NcOutNxOLTWHy5ACPPfYsH/zge1mx4lluv/1xgsF3kcvleP75l9ln\\nn2OYPr2H119fR3t7lkRiFuFwP2++2cfgYAvJ5B50dITYvLkT2MFdd/0GieAOYlju/CySt3sJMZq8\\njkRiXUiR+vuQYbFPI9PYZyJS6FqEwOLIFIZHkTKGNuAIpCbwcnMfjyDdXyYjJNeHmGjeMbf7cUTy\\nfAuRSduAIE899RSHHnpo1b+1E3jR8aS4NVwzDC0uRiOR9HihronPziT10047jeuuu46zzjqL5cuX\\n093dzfTp05k7dy7Lly8nHo8TiURYtmxZ1Qu1vb29wLBQ6gSqZWJDOVijRyVFqpE9biM+ZTzw+/22\\nO7FUeo0dqdQOrDcNiuCtRbfq+w0Gg/lOMtbFqtim7RUxWhfJ+fOns2zZaiKRQ8nlMsTjr7Lbbgt5\\n6aU32G23D9DXF+XZZ5/i+ec3cN99jxIILGTHjhMYGFjP2rV3Yhgf4e23N9PdvY0ZM/r43Od2xTBe\\np7//LZ5++l5isW6SybeJx09k06YgTz+9nGRyKWIw+TvwCYTEHkKkxWlIu7DZwEfMxwIImW1GyG8y\\n0lnlo+b7HkDMLIMIGa4GXgM+hczfex2p9TsMKZfYC5E6j0NyekuQyPOPCOHNQsohBoF23vOe94zK\\nfL5KqNbxpLg1nJdDixuJTBrpWMcLdU18dqawf+ADH2Dp0qXssccetLe3c8sttwBidDnjjDM48MAD\\nCQaDHHjggXz2s5+tuL9yi6mCisjsmEOc5Mu8bu+ltqkiM6cXgfXCKVXvV8txqdygmiLh9/vJZrNE\\no9H869R3l06n8wtdpTv1SsSoHrMeQ6moQRFjNBrFMNLMnbuTTZvuIRwOctxxezB9+nQefPBZXnhh\\niOefX0M8vg+53BDbtr3FbrsNEo/vZPPmOPF4O5nMRpJJP1u37uTllzewcuXtHHLIfuzYsY1//jND\\nLrcBITHl3lQGFwNpDn2h+e+k+fw6JOKaj3RRiSJEeCdwDEJsP0XI0odEgvci5NVmvmcWImXuQNyb\\n08OZ5HAAACAASURBVM19hJCShtcQolyIyKn7IB1gHjVf34nkC1vw0gzu5SLtthWY9abIasCxEqQq\\n2WkEUinVp1OjEHoCuwWGYXD00UezdOlSYrFYfqEvFZFVg50p7FYiKI7KYrEYLS0tjnNyqoenmmzu\\nFNbp81ZSjkQiFReUasdrzQ2q1lyBQIBgMIjf78+XbYRCIQKBQMnIzUqCVunKKTEWv079f8eOHfzm\\nN39laGgXUql+5s/PcMopxxGJRFi5ciVXX30rzz6bJJncD/gIicQastn7aWmJEY1GyeXmYxhPIUSy\\nEImmdgBb8flWYxjtCAH5kOjpGERSNBgmowRCRlmEyAzElbkWkT2/geTe7keMKV1IRGYgs/fmIx1f\\nBoHjEQJ7DSGrBQybZlqQ0UX9SF3gbYjMeSiSP/QB/w8hu7fNb2oxIp9uBNaTSPSV/L6dIBaL5U1S\\n9YBSzlSlGihVolJ+uR6cqXr6eh5l/xB1HfGNNcpJHKPRcHk0Jisocq6UJ3GyvXg87omka80Nqrvn\\nYDCYXzAU6bW2tpYtgC+WsbwmxnQ6zeWX/4I1axbQ2hoilergscdWceed36ajo5UXX0yTzR5Lb+/f\\nSaU20dq6k0BgI4nEYpLJHfh8+2EYzyCE8i4kxxZByKwdw9iOTFN4yHw8gXReOQQhHR+SSzsb6cZy\\nO+LeXIDImscgObe/Inm71xBZ8gBzWx1IRNdl/jyAlC+EEbLLIQaXKEJeM4EXkFyfgUilMSTCW2u+\\nrweJTN8EfmIew1vA14G3OProo3n88cernwAVoCKsekG5PKOq0W1ra6vYI9OOlD7axNgIUel4QxNf\\nEQKBQIHpIpVKubLtV5I6y01WsPv+YhSTczqddi1v+Hw+stksyWTSE1JWn1VFtLlcjlAolI+ak8kk\\nyWSS9vb2imTtNL/jlBgfeugJVq+GqVNP4pVXNhKPS9F2KnUQ//jHXSSTBxEOb8bnW0Iu9zLx+PfI\\n5WIIMYUxjM1I3kxNNldTFFYjebi3kWL0MxAyCSImkyhCfEsQifKjCGlOQeTMLebvN819nYOUMCww\\nt/NeJCpTxeYJc/97IJHdrubv1xHiTCDNrBcB15vvv9Q8vtvNn21I4+qvI2USH0dKGyaZvxcDz/L0\\n00+X/XtNNCgycVqyUcmZWs6dWuuNQCni00RYCE18Rejs7GRgYIDW1tYCB6PTE6dUEbY1KvNqsoKX\\nNXVqe9VIuRRKEXUqlcp/VkVGVikzkUiQyWTo6Oio+WJ3S4yZTIbHHlvONdfcR3//PAYHHyedXkgu\\nN4m+vuVs2zZELNYNdJFIzMcw/gSsIpdTPTITiKQ4HSGxvyNS4ybELNLLcOT0CiKD+sz37YbUxrWY\\n730OcWf+GxKFpRDSfBRxdu6HNKpeiTSrnotEZ3sgObjXkKgvjUR9RyLNrd+NzNzbYu57M1LP14G4\\nPDsRstwXmc6wDWlVFkRk13lIs+s9kRzkc+Zn6tfRRRHsOlOtcmotQ4vL7UP/TSpDE18Rurq6+Mtf\\n/sLJJ59MJBKpmKNzAqeSqZ2Ir5xc6qaUAsS5Ce6baytYDTEtLS35GwAldaqidcMw6OjoGJOLtBwx\\nPvPMSp54Is2MGe+hvX0/Nm36K9u338fQ0FYymRhSE7c/hrHRzN8dhhDeAiSamq62hEiFgwjZtSEF\\n5B1Ifm0HEplFkcjsLURqfNv8/xlIF5WHgTsYnoL+EYRwZiLEth4ZSfRnhNDWIM7LTeZ7PgKsQtqV\\nHYXkCp83X2sgJOlDpjCkELn1PYgUus48bh9wF/AB87NOAm5AosoN5mNJIMD111+fbxPoBo2ySHt1\\nnNbz0IuhxaUI0toEwrpfjWFoc4sF6XSaY445hi1btnD33XczZ84c1wSQyWQKoh1VAmG3KD2RSBAI\\nBMruv1zHGCBfCtHS0lLyvcWwEhXgqtDdalxRNvdwOJxvRRUKhfL1ebFYzNNykFpw223389Zb++L3\\nB1mx4lHWr9/Jhg0v4/fvSjzuJ51uR8iuB1n8ZyIR1wIkP/cSQkzPIuaQXiTayiLENQ8hkpD5moz5\\n+jcQeXMDEomdiJDmvyCR4tXI7L1zEbn0HWQM0RJE9rwMIdVJSIS2BIkgNyBE9woSwanc4N6IRHkm\\nMuNPTVz4CzLpIcXwuKIVSH5xGkLsk5A849NI7u99yOiit4CBmsoahoaGCIVCts/V8UKxYWQ8UexK\\nthJkudZwbW1tdf8djwLKLi71k1UeZ+zYsYOTTjqJrVu3csUVVzBr1qyabcDqBLU7WcGKclGbIqlk\\nMkkkEilJjG7ygypx75aI1D6V7KpIT5G3tWwhGAzWBekBdHe3Eo/voLt7NkcffTLJ5EvMmHEYu+wS\\nIZfbhpg+1iGmktXAg4gTUjWCPhwZ5xNF5MHPIlJiJ0IafvPxJUjjZ9Ufc6b58xZCOE8BX0HI6UiE\\noNqAKxGTyhpzfypy7EFIcZb5eAiJwl4w9/9RJD/3P+ZjO83XvYZIlScAH0ImMaxBiO1YhAQ/hLhS\\nBxkmv1XmZ/qUuf/55jHAT35yKy+9tMrV998oEV89wefz5VWeSCRCW1sbHR0ddHV10dMjf5P29nZa\\nW1sJBoO6nKEEAt/73vcqPV/xyYmETZs20d/fz2GHHUZHRwcLFizAMAzXxdqGYeSjPkVQTi5wdedm\\n3b+VpCrlCEu9t9zrEolEQTmFqq1zmnNLp9NkMhmCwSChUGhEuYJqGN7S0mK7oH4sMGPGFNas+Tvr\\n169n9eq/sGXLSsLhXdi2zSCTmUcu9yQSka1ECG8mEumlzf8PIYT4bvO5XZCIrct837uRHOAkJFp6\\n1HxfCpFGD0Civ41IJPcOw47LAEJmcYZHEG02t/8OEvEFzP3HEaJbBfwA+FeEyNYj5LrK/Awg8qYP\\nKVo3kJFHCxHSO8c8lm5EUl2NRI9+JE/5trntAxCy38Tf/tbG0NAgS5ZMZfLkyY6+/1QqlXf41jNS\\nqVT+fK53xOPxvFlMqTBeOL0bEN8v94QmPhNTpkzh2GOPZdWqVcTjcZYsWQK4m6mnojKVz3OzDVUw\\nq96rzCBA1RyhijQrybTWujprJJrJZPK5AyfHqhYGRXrWvKO1XMGrnKlX8Pl8TJvWzvLlD7NjxyQG\\nBzvZuPExYrEA2ewcJH+WQiK/E5BI522ku0kv0j2lBZE+pyIR1dOIpDkJidpaGTavbEYiwFUIqa1D\\njC6fNrf9KJKfW4+4KbuQsoUgYjx5HYkAdyDS6j+RaG8BQn5RpA4PhED7zOMbMo/pNSSS24n0+fw9\\nQnCvI5HcBkSmzSLNrb+AGGCeQoj6YIal3/nA34BH2bz5aDKZ55g9ezpTpkzhlVde4eab/8Rf/vIP\\ngsE0u+023EjeikYhPuVyrvfjVOuEVVGxdqtpMpQlvvq/fUGG0V588cVks1nOO+88vv71r494zUUX\\nXcT9999PW1sbv/zlLznggAMA6bR/3nnnsWrVKnw+HzfffDOHH3542X11d3ezfv36kq5MO7D2xwRc\\nXyjW/XvZGBoq5wedGmNUKzPVILhcuUJbW9u43y3v3LmTF19cRS5nsGTJYtLpND/84c089dTrrF//\\nDj7fHAzjOBIJH7ncHUiEtB8yDLYLyX2diRDfIBIJ9SOR1z8RovAj5DLEcK1eDMn1rUB6cb6KRIYx\\nREo8FHFfbkcI5TIkWvsr4qZULtAlCAktNY9DzZfc13zd48DPgF8hEuZrCHF/ztxnGCGtQxHTzutI\\nJPkr87OuM/fVaX6ei8z95RAC/ZP5eeciEeFmhDRb2LFjLTfe+AKrVqU4/PAenntuiLfe2gefbxb3\\n3XcP3/72Tk455eQRrsRGkTobRS60ll1Y0Qjf8Vii7olPDaNdtmwZs2fP5pBDDuG0004rmMlnHUa7\\nYsUKLrjgApYvXw7Al770JT7wgQ/whz/8gUwmU9AaqxS6u7vp6+tz5YwsNrHEYjHnH7gIlUiqHCod\\nuyKqWic1FE+nSKVSeSOLajqtZrF5Ua5QK3bs2MGVV97BwMD++HwB7rvvTmKxzaxc2QVcQDK5AcPI\\nEAhAMDiNVOq9SEQzgJDXMUjE9Diy4C9FSO5dwBeR6O7viCmlBYmYdkOIYy1SUJ5GpMNdkMjsKwiJ\\nZpGoMmS+V0VeCxDSfQkRX25GWpF9BIk+5wHnI1Gnz9zP6Uid3/sQsv53JLfYi+Tq3kKIMo7InAFE\\nNs0ihPZ+pLbv8wgJT0Uco5OR2r8ngT8gBp0/AnMQwv4dsdhuLFv2PI88spmOjg+z66770dk5i23b\\nwlxxxfUsWbKYnTt3EggEmDdvXj4PrNzO1fpljjfq8ZiK0Sg3EuONuie+WobRRiIRHn/8cX71q18B\\nIht2dXVV3F9XVxeDg4OAs7u84iGvCrU0mlZNdmttDK2Ow07PTTuEby3NUOUK1vyHIjy1nWg0OqJw\\nfKy75T/++DNEo4ez++6HAbBhQxvPP/8DwuGLCYcX4fdvJ53el2z2WqSm7XSETBYjJBBCyOJvSKQ0\\nBYneuhHJMoWQ4EyEsPZFiPPPSC5tEiJfno4QyCNIVHUmoshkEBK6B5FGv4pEWrsBXzbf+16EcHsQ\\nMutAyG1XJCf4Q2SE0UyEyBJIJHgVQqYBJJ+4nOGavt+az30OydvdZn6e4xESDyOkeQvSPFs1u15u\\nfqb5CEkbwCQM43jS6a309qbo7X0Svz9GZ+ccgsEOPvWp77Nw4fsIBmGvvV7kvPM+kv/7lOqXWW7C\\nxngQY6MQSqMc53ij7onP7TDajRs3EggEmDp1Kp/+9Kd54YUXOOigg7jmmmtoa2sruz/raCK78Gpy\\ngYKKpgxD5pQ5jZaKycsqv9Y62Naaa2xpaclHeapGT5UrKOcmjGwMXKomqRQhernIxeNpwuH2/P9b\\nWzsJhfwkk5vo7DyK1laDVOoFfL5eDGMKQijbEbkxghBFkOHm0h9GFv67EKKajBBRK1JcvhWJyBYh\\nObshJC84gMiGuzFMokcB1yEkc6q5T5+5v14kOpyPRJRHIR1XZiET14Pmsf4XUtbwFyQHmEQI8miE\\nuH+P1Ad+GYn8voWQeS9C4FMQkv8+kiPciZDntxB5M2x+hvcBNyER56fMz7sEIdDXkFzgCUhk2kMu\\nF6Cv7wY6O3dn+/a9mT9/Fu9614m8+OJdvPDCC+y9995EIpERJq5iu346nSaRSFRsJK3+PxrE2CiE\\nUu44G+HYxxJ1T3x2/2DFUYpyKD733HNcd911HHLIIVx88cVcccUVXHbZZWW343QYbaUoSm3DyUln\\nJalKnUjswiq/1uqmtG6rlHNTTTdQDbLVvpy2eFLEqIp5SxGj04bABx20J0888Th9fZ34/UEGBh7h\\nkkvO4tpr/8Tata+RSsXw+Z7FMFQD5ymIzLcDKVk4GpH2VF/L3ZG6vv0RKXIHw/VusxDSeYxhmTCG\\n5NOmImS3ASl6f838/w7ExLIBieD+E5FJnzUfuxHJ0/0nQl4fR4jr3eZrTkNI8zTgk0jU+UGGJz10\\nInLrDQgpLjK3dy7SyHoZQvSHm8d5GlKaEQKuQcj3l4i5ZU8kN6nk0nnmNg9ESH6W+fyTiBQ6nYGB\\nHPH4Gtas2cyCBccTCs1iy5btLF48/De3Epb1nCn+G5dqJK1UhvEixnpBoxD0eKPuia+WYbSGYTBn\\nzhwOOeQQAM444wyuuOKKivvr7u62PYxWRT7loiineUIrsYTDYdeFwWq/xfKrnQui3DFbtxUIBEaQ\\nnqrRa2lpcVQo66b3oTVihOFu+a+//jqrV79GR0eEI488jO7u7vxnXrhwIZ/7XJIHH3yIbDbHqafu\\ny/7778Mzz6zD74+RTG4385R7IRHRo0iUczRicJmERER/QBb5HyMEcD5CTL9AIrkjke4rByER2CZE\\novw3pCH0PxDSXINcfpsQkkwgMuIkJCrMIRHefyAy514I2W1BoskcYmTZAyGzKxCS60LKIjoRs0oO\\nyS2+gcixPoSMjkZyjd9HCO9IpEH2/ebnPhEh8cVI+cL9COmdghDmQ8DHEHn31+YxzzL39XtE6j0U\\nIeU24DnS6b144YV/kExuY/78PZk587h8nZlSOaLRKB0dHQXng7Ujkfq3teRmLIixUQhFR3z2UPfE\\nV8swWoBdd92VV155hT333JNly5ax9957V9xfOBzOt+6C8sNovXRZwsgcoRcusng87rjnZilYDTYq\\nslP1eoCt6QpuUYkY1YJpGAYrV67kZz97lEDgSNLpPpYtu4GvfOUcJk2alI8MFyxYwMKFC/OL3Lp1\\n69i8eRf22+9MNmy4hXfe2YFEegFk4W9BFu8oUqJwMzADOAuRO59Gat7iiCy4zXy+FyGtRxBJdKH5\\nmk7zuXZEFu1GZM0OpDZwLUKCVyBR4DWIkeZiJN92PRI5XmD+vgqJ1h5EjDZXmscxDyG/R5FWaDPM\\nf3/D3MfvEEfqFES+nIMQoyrYn4FEo/OQ3OOzDNcWfgUh5w7g2+ZfYrr5/P7m53sbiWRB3K9ZpC3a\\nLkA3a9Zs5O23H2P+fLmGFixYwNq1a/mf/7mfWCxEd3eOL3zhdObNm1eQFy53TViJUf12Q4zqsWKV\\nQfWWtZb61CuRNApBjzfqnvhqGUYLcO2113L22WeTSqWYP39+wXOlUO2kceKytBvxlcsRuiU/a+sx\\npznH4jIOqwsUyJOe2m4qlSKRSIxLuYL1zvzuu5czZcrH6OycC8D69Tlee+01jjrqqJKNqbPZLEND\\nQ2Qyadav/wvbtt2LYaSQaG4fJII5CcmZbWO4MfO/Iwt5K0JKVyMEMQNZ/NcikdGr5uu2mz/PIpLo\\nDQjxbQC+iRDrlxEieQAhyzeQUoWjkcjqN0hEeDiSP/sXhFDeBfxfpOj8QoQw4+ZjCcSMshS5zL+B\\nRGYfQAwwv0YIbC8k33g7Qp7fRQj2CSRXGDc/6yKE+CLmY3Ek97cYIfSN5vbOQCLeLQj57Y7cOGQR\\n80wcuIOBgflcd92LLF8e5+tfP5F77nmO1tbzmDp1Djt3vsKll/4Xs2bNoLW1hdNPP4IDDti/oM9l\\ncS54NIjRSo4gN5Kl8tKlemaOF/kUfweNUoYx1qh74gM4+eSTOfnkkwseO//88wv+f91115V87377\\n7edofIr1hLXm6Irt+3ZKAezIpSpHWG6bTu7gintu1lquYM1fqoJ663SFZDJJOp2uOlJoLJBMZggG\\nh01Lfn876XTpLjSqtdq8efNIp3/JU0914vN9FZEJv4Ms+vshkVEOIa3dkdzbEoQIVyL5rwfN3yci\\nkuDvkJKBDEJULyFS5b5IRHQHUo4wHyHKqUg+bKv52D2Is/IKc5/bgUvMY3oZqe/LMNxGN4jk8vxI\\nFHeq+Z52hIx3R2TMWxFyPBWJ6mYjHV7+A5Eij0JIrd/cxxeRvN9DCCGvMffbiZhljjS/g9+br9kD\\naY+2u7mNmPmdqKkQD5jfy3GoZte5XIJnn03yxS/+gNmzj2Ly5Bi53FpyuTTr1rUyd+4n8Pk6uOmm\\nW/nWt6Yxb968/HlWLH+rxsx2iLH4uqxEjOpmLpFI5B3hpYixeCbfeBGjljrtoSGIbzyhpDSvh9FW\\n26bTE7XYuammHzjdjor4rJPXFelZpyuou9/29vZxr9EDOPbYvbnttruZOvX9JBJ9hEJPs9deHyeV\\nSrFu3ToA5s+fTzgcztdX9vT0MG3aHCZN2hO/v5tw2Ecs9kGy2RuQhfoAhGD6kGimHbgWafj8DhIR\\n/QtCJH9HnIwJxO0YQfJiXcCXEElxV/Ox+xAS6DW3sd183/NIJNhlbnMDIkPujnR6eQdxVHYhUdh/\\nI9HUV81jyyFlBy8iUejfkaLzBUguchdzu5PM7R5kfr43kcjvafNnAcM1iv+KEFUbQnwLERPL7khk\\n/Fvz+3gvQo5/R6LRFqQmMIVEy9uQiFHlIP1Akmx2iK1bj2fz5hW0tb1BZ+c8+vuforV1C8lkH5Mn\\n78nbb+/B1752Ne3tU5k5s51LLjmbuXPn/n/2zjs8inJt47/ZTSeNUBJ6QgcLglJUFI9SPCqKqHgs\\nB1RA9PjJUVEUOxYQURFRVFQQwQLKEVEiCCqxAKJipSgQEiAkoaaXze7O98cz72YTNskm2ZQNc19X\\nLmXLzDu7O3PP/ZT7KfP9u0818JQXrg0xKsWnzoPyxFiZYiw/dsgTMXoix5oSlVKj7jBJ70SYxOcB\\nwcHBLg9Ld+NlXw2jdS9i8YVZsy8rN9VJq2lamXYFZTSt6zr5+flomkazZs0azUl18cUXYbVuYPPm\\nj2nbNojRo68iIiKCxx57ib17ZTRQu3aruPfecURFRaFpGgcPHqSkJI/8/C/Iz2+PzXYQXd+H1ToM\\nhyMXUWslxt8+5KL/PXLaZCDFIJ2REGgkUuCiph5kIUqvwPivAyn+yERyYuON/38aIa4oREE9bWzn\\nE0RVbUUIcQbSxH7IeE0w0nZwPkLGR5CWhgIk1Pk5oiQjEdK9GCHNdkgo8mOELE9HFO0HiCL9FlF6\\nAUgO8COEpO5HyE9VhdqRUG+28RxIGFaFbPcjhTybkZBtAkLCLxvbsSA5wmwcjrXABeTnz6OwsANQ\\niKb1YsuW9XTvnsy2bavp0uUmOna8iKNH/+SZZ5YwZ869ZYqo3EnNE2pKjCDuQ+7jtcqjNqHUuiDG\\nxnJONmaYxOcBUVFRZGdnExkZ6So4qasilqp+wFWptoq2V92KUhACVRPnKxop5KldoTHAYrEwYsSF\\njBhxoeuxZctWkpzcgw4dRgM6KSmfkJj4NUOGDOCZZ5aQlxfJ1q1b0fVrcDj64XAkAa0JChqMwxGI\\nkMwKRKnFIWQSjeT+diLKqAghgJaICgsH5iMuKPcgCiwTCYfuN7Z5vbG9VkiOrBei/nYhRBWIFNK8\\njpDoTIRUXkPyZv2NNfQxnr/U2I/yBz0PIbTfEeLph4RVdxr7Hozk/FYjRHc7knv70HguBSmcUb2A\\nVyNWZ7FIn95sY9+/ITcFecYxtDP2fw5Cpt8gSlIZdl9qvCffOJ4XkZaJA6ihu05nK+AyLJZAMjKK\\nOXDgBSwWnc6dD5Cfn05ISEt+/nkPkyfPpE+fBG688UoiIyMr/mEYqAkxKkLSNI3i4mLXuVGTHGP5\\nFIqviVGRozKTKL9/E2VhEp8HREVFsXHjRi666KJql+e7ozz52O12ioqKvC46qYq8amJnVhEUgapW\\nhcraFXx1E1DXSE/PIjR0gCs/Gx7ejcOHv2b27KU4HP+mTZseWCwHCQnpRUjIUXJydEpK+hgDaPsg\\nF+wwxK7MgYT9liO5tkBEJZ2GXOj/Ml73J3KRT0DIZCSisFR7hAOpejyEkFYBEn7cYPz7TaRp/HRE\\nXT5trGEzQjL9keby14xtX0xpw7qGEOh3SGHLxQi5PEBpTjAYKW5Zh4Qc/4Eox3yEtJYiBGczXp9n\\nHI9u/F1trOVDY1/qcxhLKdFeZWyvM6JW9xifS6Zx/M2R/N8hhLCPA/9E8qfbgRjy89MJDd2DpvVH\\n10/lu+/eZ/v2r7DZMgkIOI8ePa7jq6/+YP/+13nqqXtqnWMuT4wqsqFG/6jHahNKrWrf7v+uDjGq\\ngi2Vk1dV1kolKiMJE6UwpzOUg9PpZP78+XzwwQdcccUVxMTE1PikUj9Kq9Xqqo6szrSGkpISF/F4\\nek5VW3raXnXGC7kTsqZprmZi9V7lmhESElKtmYINjZyco2za9CchIb2wWi0cO5bI4MExbN2aTmzs\\nv9A0jUOHfqWwMJQuXeLJyjpMQcGX2O1qVt565AK+n1Lz6dOQEN4uxHQ6BQn3dUJsvn5AFEw7ZK5d\\nV+P57xFCSzferyEhwd8Q1dgPuQ/dieQIjyFtFUcQ8kpFCCgFIaBrEAKcjZBKGtIa8QGSM+yP9Bnm\\nIY3kpyA9eH8Yr++PEPaPxmt6GPtsZqyzI6LanjP++7ux7zeN14w2PoN8pF/wB+PYeiAhzWLk5uEb\\nY+0jEbLrhuQMdxmfZxxC/ruNz+8XIBRd343TmU6zZjfgcIRjt19CUdG32O2dCAsbQefOCRw71oxt\\n2xKJjw+ia9cuHn8DDoeDzMxMbDZbpY5N7lCk556KcCc1dUOoIkHq5jgoKKjMBAd3taYKwcr3E5Z3\\nWHJXnu7VyO5/QBmV576OkpISwsLCXNcZTdPKmMafZJhe0RMn5adREfLz8xk3bhw7d+5kxowZdOzY\\nsdblwLUpjPGk+Lz13PQW7oQMcrIqclOhT/dy7uLiYo9hlsbY2zRkyGB2797Hhg3TsVisDB3anVGj\\nLmX9+l/Jzt5FVFQ3evW6gk2b7sdq7UN+/rfY7WmIEnkXaTXoiYQuY5FClh5IYUc60msXjxBVGEJM\\nBZTmzl5CwpnbEaL7HVF7lyLqrR9ywW9B6QT3aKRSdK2xrQOIqtpmrOc4EvqMQhSkBSHm9UgeMsBY\\ny/8Qkgqi1FNzmbGP7kh+8giiyu5Hil2WImrwK6RCM8f4ux8J+b6OqNMYpGcwAVFq2xFltw1RuRcb\\n+1xm7CMAyW+GGO9RedGhxnt/Q0LFTxmf3Xyk8KUfOTlL0fUCoDtOpxWns4SiIgubNv2GrnfEZgvm\\nxRe/IiQklCFDzivz/WdlZfHEE6+wd68dp7OISy/tzcSJN1b6O9V1nYKCgmrn332RYwQ8nlPVUYxK\\neboTY0NXXDdGmMTnhrS0NGJjY5k8eTJQszxZeaiQoS+KWLxxi1GoTiuFe7tCUFBQmXYFwOWkUZWL\\nSmVhnvomRnWXfeut/+aWW+zouu4i93vvvYGZMxdx8GAMcJQXX7yNDz5YhdU6lKCgTEpK/g9d/x4J\\n2yUhF20LcsEfhuT6AihtTH8SuXCrQa9xyAX/eiR8uRfJdS1FCPE2RLUp1bia0knnhxDCehYhid3G\\nv/cijfOvGusIM/YdjhBVDKIcz0GU1FzE5mw7UnH5PVLFGWusKR0hzxCEjJ9ElOKzxnYHIWT5KaL0\\nrkfygbsQBRuHtEjEIorxBoT4pyFq0YqEhEMR9bfX+LwOI+QbZHxGXRDSVGOUlhvv6Qa0QNcfBf6S\\nDQAAIABJREFURcKzz2O1NsNq7U5e3kICAy8iLGwb8fF9adXqHyxfvugE4nvjjWXs3XsmbdqMxOm0\\n8emncznttM2cffbZeIIqZAN8cr66w1tirIgUvSFGm83m2pZqaXrvvfcICwvjxhtv9NmxNAWYxOeG\\n7t2788orr7Bw4UKysrJqtS2lloAaF4K4k5d75WZtw43uKrQ67QqaVrWLiqekvHulaGVq0VcXGtVU\\n7z6F2h3yPd9PZmYm0dHR7NjxF0lJeygsHIndvgUxj74ACQOei1y0DyIVluEIEexEwph/I+ShXE9+\\nRy7m3xp/OcZeQxAVFIwouU6IYvzbeOy/xvtnIyqzPRJu7Y1UeXZFwpQXIuHHK401vI30+fVESPFr\\nY1+PG9tfZaztIFJIotZ8ChKKfBshud+QCtE/jH3PQ8h5LzIkd6OxbhDFmI6oXQuiXjsgeU6H8X6n\\n8f5kJIf4KkJ8mZROe89GwqvFSM7yckTdHjVeMwIJ4dqA9jgcP+F0FqFpm4iODqVLl2to1+4fFBSk\\n43CceJO3e3cG0dHXGL/bYKzWM0hJScMT76mbSvWbr+/ohfvvvyqXosqIUdM0PvjgA37++WeCg4PZ\\nsGEDCxYsIDs7u8rJNCcTGr4By0usWbOGnj170q1bN2bNmuXxNZMnT6Zbt2706dOHX375pcxzDoeD\\nvn37MnLkyCr3pUYT1VTx2e12l6WZL5SOw+HwOC29MlS0dve7WtWuoKo4FempHsDq9OgpUlM2biEh\\nIYSFhREeHk5ERASRkZGEh4eXyUmq3GJeXh45OTnk5uaSn59PQUEBRUVFLkWqwkNVQV283EmvIoSF\\nhZGQkEB0dDRz5nxATMxFOJ1/ImFIgCmI4tuBVDqegoQSv0YKOwYhebxrECXWHAlnRiFqZTaSYjgH\\nucAXIfmwHohjy91INWUcQgxrETL4F0I2BQih/IFUa/ZGyEGFFScgocErkIKTkcb2IhD11NfYn5oQ\\nH4eQ7VuIs8xaSnsSr6K0364YUVxhxl9XhOxPR1RcT+NzyEGU6CGk0vM3433zjM/nDuOzikJaIlIQ\\nElMm4Lca6zsHUcuTkfCsxVjTT4iCboGEP48Ce9F1G05nR9LSNvHjj/PYsGEyBw++wqWXDjjhO05I\\naE129q8AOJ127PY/6dAh7oTXgbQsOByORtWi4w73/GJgYCDBwcGEhobSrFkzlzoNCwujWbNmdO3a\\nlYiICDZt2kSbNm2YMGEC7du358ILL6x6RycJ/ELx1XYYLcDcuXPp3bu3a9ZeZajOhIbysNlsLncX\\nVShSU2ia5hrU6QvPTXeP0YCAgHptV/Am1FP+DtZdLZa3iSr/B+KuYbfbqzX4tri4mJwcGwUFZyP9\\nbz8AcQQE/BO7fQlSoHIJcuF9EwnHhSGFKd2R0F8gQhBTEaJajZBBPEIy8ZSOB1qBkOYGhETuQEKD\\nqYjryyAkZ3c/EmLdh6ipZZSaPh9HiLEFou4sSPhwFaLOLjGOLhIJHZ6CqM9ARJGdi4RHr0TIfQGi\\nLs83jms3orYeRMgwECGxaUju7zUkn/eS8ecw1nIdEs79AiHCZyj1I12CVL0qB5prkZCqyot+jZDp\\nY0hIdrWxrlTkpuGo8dlYgHbo+kMEBobidO4gLOxbLr10+Anf7YQJ17B//8ukp/+M05nHsGEdPIY5\\ni4qKXA5EjZH0KoPD4aCgoKCMZWCbNm3YsGEDH330EZ06dQJKc5cmBH5BfLUZRhsbG8uBAwdITEzk\\noYce4oUXXqhyf+4z+bwlPpUzczgcrqKT8lVb1YWq/goNDa12grr82lW7gsrhKcPdxtKuUFkYFbyb\\n0gASJvJUgFPR8YSEhBAeXkJOzh5CQs6muLgDDse3SG6rGRZLF5zOPZS2HeQhJPYWckF2IhfsaYiq\\naYeoukNI/ms70jLQAyGl2QhxtkOqGO1IGDIUCV1upnS00UFjf2uN5zcjaikQaVH4EKnitCKK7ksk\\nfNkVISOlDm9C8mnTjbWei1SKzkAUYx+EqNcipKZcZyYiaqsAUX1hxjo6GOt+2thGOqIUz0fCw38g\\nCjGWUqeXr5Cm+VcQhbcDsYfrjYSVX0RCoysodYg5E2lxOI6o6YsR79EcSko0srJyCAw8nX37EsnM\\nzOSzz9axb99RTj21E6NHj6Rly5bMmfMgaWlpBAcH06ZNmxN+B+62e7UtFKtvqBtV9yjKsWPHuOmm\\nm3jjjTdcpAe4DCdMCPyC+Go6jFYVq9x9993Mnj2bnJwcvIEaTeQt3ItOfFXEoqop3cuja4ry7QoO\\nh6PMdAW73U5BQQEhISEEBQXVal91hYqI0T00GxwcXIYgKyu8cf+75JJz+O2378jPP4zdnkZk5E3k\\n579CQMDpaNrPOJ2jEBJKQloCuiBK7g5EnX2MKMGuyMVbVSp+jSiUBxAy+QsJX16D5LzmIGrseuO1\\nWQjJvIBUPmqI6kpDyK698Xw+QnqFxvaOGI9HI8TRDSGpRQgBzUII8EEk3FmEFNO8TenA3f8iLQ4D\\njXUMQ9TiQ4iCS0SUYAESkjzVWNd+xNfzEEKsHRFSO4gozjDjdcEIefZAiLItQrgHjHXeYKwlHake\\nvQ1Ref9EVOdq41gB/kbXbZSUtCcj4xjHjmUycOAoHI7LaNWqP9999xfJya8ybdpkgoKCSEhIwBNs\\nNhvFxcXVihA0FqiWC3WjCqJcx44dy5NPPskZZ5zRwCts3PAL4vOWSDyV/n/22We0bt2avn37smHD\\nBq+2Ex0d7XWo0z18WF4pqf+vrtG0ItGgoKAKbZKqgns7QklJCaGhoS5SaCzTFWoL936ryqpcPRUF\\nuJNi37596Nz5N0JCniYvbxf7979NeHguLVuOYPfuTZSUTKN0Cvn3CMHsRi7qo5ECjXnIdPIMxKvy\\nTIS4/kJUUQlChlbEuLk3UkmZi+TqzkGIaSrSK2hBTs+eyIV/LJLLK0aIdBRCLM8hxBaAqMPmSFhR\\nN9bbByHRUIRsDhnrshrvOYqo1g4IYR1BiE+NaBqBKFyVE2yJKL0iY/tXIvnGHISYHzbWmIGEjgcj\\nSjUBCSOvp3Ss0X6EKAORHOVfyPDddUjIsw8Sgt2H9B7+jeT+QhFl2hOn8zeKinRKSjrRsuXDHDmS\\nQlhYL7788hluuimdjz5KZOfO/XTpEseECde7XF5Ub6o/Kj31u1fXHJCIzq233spNN93EsGHDGniF\\njR9+caWrzTDaFStWsGrVKhITEykqKiInJ4exY8fyzjvvVLi/yMhI8vLyqiQ+9/ChL+bQla/crE1+\\nUNd1V6jU3Wew/HQFm83WKKYrVBfVyUdWlV/s27cvTz5ZwhtvvEtYWBFjxgylT58eTJ++lO7d+7N9\\n+wGczgyjveMQkotLRfw21STy05BQ6HpEpdyEqMBfENLsgRDmQYQE70cu/kGIikpH8n4WRM1dg4T4\\n0pFc4mUImbVEyCbL2F+AsZ6xCElNoTQnqQbbLka8P1UOriNCpjORkUYHkKb6rgihdjfW/X8ImaYb\\n+56JkO3flBpkByHEGW18FuGIamyHhIsPIiT/NaI+3zS2sd1YYy5yA7ENIXmnsd12xvEPMLZZAjxO\\nQMDpQAfsdgtCljcCa3E6V2K3awQGdiIj4w86dnTwyCPPsXNnbyIiruPPP39k+/aneOWVJ7FYLBQW\\nFhIWFuZ3v3t372D1u9d1nYceeoh+/frx73//u6GX6BfwC+Kr6TDauLg4ZsyYwYwZMwBISkriueee\\nq5T0QGYAuistT4qtohl65eGN3yZ49tysaVWp6uPRdZ2QkBDXsZRvV3A4HH4Z5lH5SOVWUdvQsqZp\\nDBw4kIEDB5Z5/M03O/Dnn3/yzTct+PbbAaSmtuDo0cUImdyFqKt3EQLMQAgiCBk71AHJjw1CiCIc\\nCZVakbCfUll/IUrrF0SZvYSQ4nKETEYjebPdSFi0AClUiUNI6iyEkJ5HyKGn8dq/EYW3BiGfLkjf\\nXQaitM5GFN1TxvPtkdaDYcY2DiDE/R4S5iwx1rnbOKZ4hPz/ZzwXg4xTijXWo+zabkduFvogyjQZ\\nyT++hBBgmvF5vooQ9mZj3xON48w1PrMsoBd2+x4slpYIyccgarsFun6cY8dewWrtRUTEagYO7MAP\\nPxymbdtb0TSN8PBe7N49hZ07d7pqBQoLC/3GjEGhfMuFruu8/PLLlJSU8MADDzTadTc2+AXx1XYY\\nrTt8lX/z1jnFG/KqrodnZVCqUe1b5bfcpyuo6q7w8HC/O1EU6dVHPrJ9+/a0b9+eESNGsH79V7z/\\n/krWrRtMVlY6TucE5AL+K3Jx1hGCykcIw0JpFeU2hASfRYhuLjLJ/BaECD5BQnfnICHGXkhY9GaE\\n+Hoh4c12CMloCFHejqiiKCTUeLXx7wlI393rCEEdNY4oGKmsLDRe0w4ZV/QVUkF5GRKi7Y0UqryD\\nhE3TkPBiElJdGoQQ7gOIup2JhCTDkByiatuIRwi6t/E5RBvvSzYeD0Tyi6EIqf6AkNxQ49+XGNso\\nMo7lEkTdfYnVquFwtEHTeqLrScAF6Prb2O1WwMEZZ9zLli0ZaJqOxWJF00DTSs0kAgICKg1/qwhB\\nRbnhhjhviouLXVXLav8ff/wxmzdv5sMPP/S7G9iGhF8QH9RuGK3CkCFDGDJkSJX7Kp+nU4qtOs4p\\n3sI9B+fJVb06is8936hpmqtARoVhVemzr5xk6huqCCc0NNQnoWVvoWkaw4ZdhNNpZ9euFHbsyMJm\\nK8DpbIGuX4jk6loZ/92IKJ/BSLP2GkR5vYpc4PsiBJllvD4HIZJlCDn8ieSxnkOKPrYZ2zmIKMR7\\nEfL6DQl5Xo0QwvNIaPELpJCmJZIb+wIJg15qrPEFY/+ZxvY6IsR4qrGPHQih9kLUoZqaPsU4rqNI\\n+DMQUVzrEEJV2Iy0bXRGqjmzEOLbhViR9UeIXjX7t0HClQEI+aca712OKGflmHM2Qoq3AXtxOn/E\\nYkkGOhAU1JuSkuuAY4SEjCEuLp4FC55h8OBTSUqaTXDwuRQXb6Ffv3C6du1a5obJWzOGhnYpci/E\\nUdv+7rvvePPNN0lMTPS7/HxDw/y0KkB5wqupc0pljeTl2x9qA0/tCuquVoVH1Hrcj6Wh72K9hXKc\\nr8sinKKiIpYsWcb27al07dqWm266rkwJeJ8+fQgL+5CIiN4cOTINyVn9gKiwIcgFvjuifN5HLvIq\\nhxeIXLgPIGS3BcmJhSAKJx0hwkOIwslAcm5pxnNnGNu+gFIyCAfGISHL5xDnkwOI6opDCOowQiQW\\npEn9bIT05hjbtCAkGou0Y+QiBTbdjXUdQRTlaISc1yDFLt8jBHoe0gCfipDiTcZjexElOA0Jh45B\\nDKs/Mo6/hXGc9yDKsxsSOp6NhFdzjM9rgPFZbELCn2lAAbr+b3R9NRbLTeh6HJpmQ9MsBAS0Iyam\\nK4WFIfznP2Pp0+cH/vrrR9q2bc7VV9/smrRQGarju1kfLkUqIuReiLNjxw4eeeQRPv30U6/NtyvD\\nmjVruOuuu3A4HEyYMIH777+/6jf5MUziqwDNmjUjNzeXwMBAV3WkNzP0yqMio2lv2h+8VXzu+cbK\\n2hVUPtLbu9i6shSrLuqj8lTXdaZNm8n330cQFHQJ33//A7/88hivvfaMa59xcXHMmzeV2bPfYs2a\\nHcAB8vMtFBfnIxdnO0IcvZF83KOI6nkcyQNeiBBEIdICoJSfTmlFZjZCfs8iyqknEuYrAj5DiK8d\\noo5KkArKNoiaVJMVbkBI7HMkf/Ydotw2InP+spCQ6Fbj6FWRzFCkOCcXIZ5gpGHeiYQ9dYTwViI5\\nw55I2NWJhHCjEPXWnFLVdtxYz0ZEyfZFCHeJ8d4s47MKQG4QzkAKYboi4dckhET3GO9XPql7sVha\\noutvAWHo+k6CgkbStWtnbLafiY0NoHXr1owefXmZfLAvUBNidD/P1A21N+eae4O6Uqfp6encdttt\\nvPfee7Ru3brWx+ONQUhTg0l8FSAyMpKcnBxiYmIoLi72Sf4Nque56Y3RtBp1oopYdF2vsl3B2/BO\\n+ZO1opO0LomxuLiY4uLiOq88zcjIYNOmVFq1WoqmWdH1c9i27Vb27NlDjx49XK/r3r07b7wxi6ys\\nLDZt2sSrry4kKWkgdvsxhOhUv95NSK7LgZCSAyGP4UjbQmtEGW40/tRnF4MQ442UjieKQVRYD4TY\\nIozXKI/PWLfXpSCtABFIvi6B0okQbY337TOetyFE9DNCdD8jajEXKY55FCGgYsSxpRBpzVAVl68j\\nbjEOhNw+RZRtsbGGwwjBdUVCr12RvGYoojQ3IBPZs433fIWEWVch6vEh47haI835PxifQRJCwN0I\\nCOjMaaf9xn33TeOVVz4iL+8OWreO5Nln78disZCXl+ey+Kov+IoYVXQmICCA3bt3c/jwYVq2bMmd\\nd97JvHnz6Natm0/W641BSFODSXwVICoqig8++ICxY8cSHR1dY9JzJ6/qTF+vCu5G075oV6iJpVh5\\n55TKLMWqe6xKFVfXgqw2OPEeo+I1R0dH889//pM1azaxefMfBAc/QGHhbpzOjUgocR+iUj5FlNI9\\nSB7uRoSwDiEX8myEJAoQNXa98e9mCNFEUNoYv9PYexBCDGEISY1HiOQFRDlGGtvbgTiz6EiLwSRE\\ncS5CSHKJsc5XkNzZBER9fYSQYCvEGcaOtEEsQcKWVxrrSEWa4O9D1GcEomTtSBj4/xBCfQFRq88j\\nRBuMVIr+jpDrbiSsaTdeE4EUwjxgHPtu45hHGp/LXuACnM5eOJ3PcPrpAxg5ciSXXnqpKxwOuNpd\\n6pP0vIE355pSesrAYuvWrbz++uskJyeTm5vLv//9b+Lj4+nduzdz586t1Xq8MQhpajCJzwMKCwv5\\n6aef0HWda6+9tlYXXUV8tancLN8O4R4qdSc993YFX5OGt5MZauKcUl4tVjQdoi4RFxfHoEEd2LRp\\nNsHBF1BcvJlTTw2kSxfPA04VLrvsfL7+ejeHD88hOLgAh2MfzZrlcvz4a4gSUsbVh5BQ5/PIBfww\\nUhH6BJJPa4uQ1iLktFyG5MF6IwUtcYg6CkRCp4EIud2OtDVYkdBpGkJSIDmxOYhK0pCqTSsSfk1D\\nyO90pEL0NIRwi5Ac3WBEmYGELvsg/YA/IuSXgJD4AIRUVyCFNpvc1hVmHMu5CGkvpXTq/FcIuZ2B\\nqNF84/P4HiHSTkjP32cIkY9Gql6dSAP9y8bnkkti4gJ27NhBr169aNasmaty2WKx+Nxztr6gbpDV\\n+v/1r3+RlJTE+PHjGTduHKmpqaSkpHDs2LFa78sfP5/awiS+csjMzOSKK67A4XAwdepUYmNja+W3\\nqe7eVPtDdcJ1nn6Q7qFSlX9s6HYFX4R23ElQjUmqz8pTTdOYNesh3n77fbZtW0nXrm255ZbpVd6k\\nDB16EU88UcjixYnY7eGMG3czERFh3H77AkpKOmG1BlFQ8Av5+TOQ0v+/EKU3AKmctCEEeRxRb8XI\\nBT4NyfPFIeG+hxCSciLq7ClKVV8b4/FsRI01QwjuOqTf8FtK2xOKkMkI/RAy/BNRVaHG/tVwW9Ws\\nf9BY36uUTp24Ewl5hhjr3o4ovpUImRUg+cOzKZ05eBQh983G9kqQsOw3SNtGGkKO4yh1qLkeUb3t\\nEfKzG8dbgnK20bR88vK688YbC3n++WcBXL9/f6xcVjd97qSt6zpPPfUUnTp1YtKkSWiaRs+ePenZ\\ns6dP9umNQUhTg0l85WCxWLj22msJCAigqKioxk3kUNY9JSwsrEbKxb261D1UarVaTyA9p9NZo+nR\\ndQ1vw6jq+NTrleoD34ZRK0JISAi33XZztd6jaRqjRo1k1KiRrpuOXbt20bx5GNHRs7Dbj3Pw4KsU\\nFLyNrq+j1FMzH2kkn4lc7AOQiklVkTkRIYUlxp4OIOTmQCotle3YfERRqurMDsbjxQiRnIK0RIw1\\nHnMirjAfG9schjTh5xrb6o0UpRQZj11NaQFLBKL2xiN5SgtCjMcRUlPDcR9BlOY6499pSG5zHUL2\\nOxEiHowot3eQkOhxRAl/iqhU1Rd4p7HNFkhBzWzUxHZdX01h4an873+/0a7dK9x++y2A79qN6hMV\\nzQRcuHAh6enpLFq0qE6OyRuDkKYGk/jKoVWrVtx9990sXbqUzMxM4EQPUG+gfsS6rmO1WmsdrrPb\\n7RQXF5/QrqC27Ws3k/qEIrri4uIy4R04US2q/29s1ajuvqF9+vRh3LjzeOut6zhw4AA222Xo+uWI\\n0XI/hPQikQv6A0hf23Ck9N+BFLIMQPJgFuSC/xai/CKQcn87EoLMQ/JwgQgBvkvpcNsfjdeAEFkC\\nQpp7kdxeG0oVXxSlPYRhCIFqSPjzRWReno6EYtONbW4ztnsdkodzIES41DieFES1HUXaOz5EWh+u\\nQsKUoYiKnWmsqwNSyKKKY3Tj/a2RMUbPGmv9xVhva+AudP0loqNnsHjxc1x//VXExcX51e9fQRlj\\nuEdqVq9ezeeff87KlSvrLORfkUFIU4ZfEZ83vSaTJ0/m888/JywsjLfffpu+ffuyf/9+xo4dy6FD\\nh9A0jVtvvZXJkydXuq+oqCh27dpVoxPIPRwZFBRU65l8JSUl2O12Vw+SP05XqAwqka+q78obCPgq\\njFpXatGd9JTSnjz5VlJTU1i5cgSFhSPJyXkIXX8YaRloiRSafEpk5HCKilrhcJyG0/k0uh6MqJ9H\\njK3PRy72vyIX/lSkWGQ0Ev78GSGYdIQsX0HybUsQAgoxtnUvEmYMQEgoDCFLFc4MRcjFabyvPWJN\\n9gNCbCMQwrzb2JYysT6GKMdRiHrMRio330aIdqfx/gVI9Wkk0jTvMNbQz9hOCVIZegOlDfIvIGHb\\nX5E2hhLjOAciNw0dgCis1kByc3+nWbPgBm27qQ08Naj/9NNPzJ07l8TExDo/rz0ZhDRl+A3x1WYY\\nbWBgIHPmzOGMM84gLy+PM888k2HDhlV6V6NGE1U31Fm+ctPb6eGeoC7qKj+ocl/u7Qqqsbu+3Ux8\\nBfc5gDWpvmvoalQVXrZarScUUoSGRhIW1hYIIjs7GXE/CUOqPjsDS2nV6i0yMl7GZvsKCft1QtN+\\nQ9cnIWqvDUICFyIE9Rpi43XQ2N5wpEAkBwkZBlA6wHYTQhitjMfVKCMNCWH+FyGqO5F+uiSEWDoj\\nTi6bETIroDTM6kBCscHGNpXh9DOU2qNdgDTX/x8SEn0PIchPjeP/BGmJ6IQU+2xH+gJjkFCnjpDm\\nYCR8+wUSHh6GTIlQuS0NCEDX8ykq+okzz2xFTEyMKwrgLwToqUF9z549TJkyhU8++cQ1UcKE7+A3\\nxFebYbRxcXHExcUBUvDRq1cvDh48WCnxRUVFVXsKuy89N1U7gq7rBAYGukjPvV1B3SX643QFqB+l\\n6m01ak2a+lVOr6IJEZdddgGrVj1PeHgcFksETmcS8B80LQCL5W90PQ6rNYrY2P8jOfkZNK2d0bvV\\nC7m4/47kyu5Ewn9fIsUrjyBTFdIpdWQpRsglDiGEPcbfk4g/5lGk0CUGIbjrEeXZCim6eRoh2EBj\\nOx8hJKf8NDMRZfa28Xw3JGQ7ACGy3xDiO4IUtPwLUW6jEIV43FhLJyQUO9VYdyjSqK/IcwZCcu8j\\nxTGDjOPuiSjA05Aw52w07QZgH4GBSxk2bDBPPTWN4uJiCgsLgfrJC9cWnhrUDx8+zPjx41m8eDFt\\n27Zt4BU2TfgN8dV0GO2BAweIjY11PZaSksIvv/xyghN/eTRv3tzrYbTujeTlKzdrUhzj3q6gLrDl\\nKzfru8fN16gPC7KqUJswqroRcS86Kn9xHTRoEC+8MIn58xcQExPEH3+8jM22CovFTrNmrQgPd1Bc\\nfC9Op53AwGPoehF2uxNRV8VIaDALUWdhiOI5hCiipQiZOBCCzEXI7B9Ini4FOb27IqouASHALCRk\\nuAnJr9kR+zQQRajCrNmIKrvaWE86Ej5NRsgr1diXKqyZhhTMJCNkerqxtr+MbanWjmZIccxQJDe5\\nBym2CULaIyYjxTnRCDHPJzj4PIKCppKX9yu6XoDVugqrNQBNe4zTTkvgzTc/OqGZuzo3NJ5ubuqD\\nGNVoLfcb5fz8fMaOHcvs2bPp3bt3na/hZIXfEJ+3P8TyJOP+vry8PK6++mrmzp1LeHh4pdvxVvG5\\nN5J78tysidF0UVERVquVgIAAV8Lb3QNQ5Qz9cYgm+M/w24qIUV2w3MPZFV1cBw4cyNlnn43FYiE3\\nN5cXX5xPcnImp57alTvvfNW1j9dfX8iTT15FKVFNQPrm/s/492xEYbVESOc2ZMqDmmK+FQlvrkPI\\nqwNSNarmAx5FeuTCKW1P+ApRb8lIeLQrQqC3Ir2C+YjiHIIQ1D6kAX2YsZ0JiBKMRwbQfoUouENI\\nbrLY2H8A0ru41tjmgwjhSnGK2L0NQ8hyIOLccgmQTFhYAS1blgCJtGjxJf/976N88cW3bN0aha6f\\ny8GDiSxevJyJE29k7969dOzYkc6dO1db6del16YneJqgbrfbmTBhAnfccYdXZvomao7Ge9Uph9oM\\nowVRGFdddRU33ngjo0aNqnJ/zZo1cw18rIi4vPXcVK+t6mRxzw8GBAS4KjdV7svhcGCz2Vyvz83N\\nbRQVjdVBfVmQ1RU85SS9vbiGh4fz4IP3ery4/t//TeKss/owceI9HD48mpKSfohauhcJa0YhpAZS\\nSXk3oox2A3cgZPcAUjgSjSi6KUh15KuI0rMgubm5iGPKH8Z2ByC5vzyEjJTf6AJj+9OQnGIQkk/M\\nQNReMKLiOiNEqBxkViIq9HdjPesRFfg+Es48iKhWB0LIhUgLxS5jv0HAO0RGBhAf352wsDQuvrgL\\nQ4bci9Vq5fnn1xIe/o4R8r+SRYv6sWzZFwQGno7dvp377x/H+PFjK/wOfVUwVdG5V9V55z5BXf2G\\nnE4nU6ZM4YILLuCaa66p8L3VQU2K+k4W+A3x1XQYrWpAHz9+PL179+auu+7yan/lT4qswlGQAAAg\\nAElEQVTyxOWuzIKCgir8oXtLPO75QaXqyrcrqOkLqvKxMVQ0eoumEJ6tzizAmlxcBwwYwGefvc/U\\nqTNISroch6M1wcHDcTiWYrNlImHAMUhIczyiivYh5NgOCW9eghDSq8aeLAihxCFqUeXN/otYpKUa\\nfwFIq0K48fg84/+7IVWaTyNFLuuNdbyDFKQUGq89C1GnR43Hn0CcYjIQ8rzKeP8RJL93EZK7S0Zc\\nXjogpLoVyEfTmqFp3Th27FkOHfqDpUvnMW7cOP7++280LQKr1WqEmQvJzS0iPPx9AgM7o2mZzJp1\\nBRdffJHrpre6qG3BVGXnnepPVQ3qansvvPAC4eHh3HXXXT47L2tS1HeywG+IrzbDaL///nuWLl3K\\n6aefTt++fQGYOXMmF198cY3WUl3PTfcmdE9Q0xVCQ8UiShnTVtWuUNsTtL7UYkNYkPkavh6A6/7d\\nFRUVUVhYSHR0ND169OCTTxZz/PhxXnnlLVJTM9i4MZB9+1qgaQ/hdB5B1y1IO4MVafTui6Z1R9cX\\nIYbX+UiPnUwukOKRiUiu7iOkmf0SSnOF+UhebypCUAMQtdkLuURsQsKWY5FWiTcQElPKJABRnccQ\\nFReOEPFkpEXiDmRs0mAk3DnRWN9ZCBl3MY5lDTJ9/k10/QBRUfMIDOxCcPCpFBZuZvPmzdhsNgoK\\nfuL48TlERw/FZltAUFAcISGdZSUBsTid8WRkZNSY+KpCZWFUqPi8czgcruiRpmnMmTOH9PR07HY7\\nO3bsYP78+RQVFbmuA7VFTYr6ThZoVeSfau7V1QQwePBgEhMTXcNPLRZLjSo38/PzPeb/VGVm+XYF\\nVbkJddeuUFFFo6/VoruFmj+6aUDdDsB94YV5zJ37JhDIaad1Z/HiebRo0aLMa6644ma++uoXLJb1\\nOJ2xOJ39kX49Ne38X4SFDcZu/xKn86AxhXwtEjZU/p9bkOKV/yEh0UIkz6ZaFJojxKghKm8nQnyB\\nSK4wAWm3uBQhy2KEtFTFaRrisRmJ9BumI8U5ucY21xrbtyB9fZOR3OQ+JJQaBvyKpj1EcPABwsIg\\nImILmhaDxaJRXHwTo0fHsGzZFmy2aykoWIWm7WX06KFs3vwLNtuLhIaeQ3Hxr1gst/HttytO+Bwb\\nGso0PiwsDF3X+frrr1m/fj3ff/89LVq0IDU1lX379jFs2DA+/fRTn+47JSWFIUOGsG3btirrG5oQ\\nKrzY+I3iawgohxQpMZe7toqmpVcGT3lC96KYkJAQj6RXl/mw+uh/89TY7W+oS9Jbv3498+atITDw\\nOyyWFvz++1Pce+/jLFo0r8zr7r9/Ij/9dBvZ2aOQQhWAW7BYLkTXfyMgII3zz09h8+ZuhISsJiPj\\nQiAbTQtB1/MRctkITEeUWgukleBHRMWtRUhKN57/BSlWGYCQ4xsIAYIU1UxCwqW/IaHLaYip9qMI\\nieYjbRN5iAoMBn4lICAQuz0cIVKbscZOgAVNy0HTItC0VN58czZr167n3XcvQ9dvwGL5g1NOSSUp\\nKQ1Nm0Nk5AAiIyeTl/cUp54axKRJNzJx4r0UFFgJCrIxf/6TjY703BvU1TkXGxvLzz//zPr164mJ\\niQEk2uNtNbm3qE5R38kCk/gqgZrJFxIS4qr4qum0dHficzeiVdMVGlu7Qk2r4so37CsSLCkpadRF\\nN55Q1y0XW7f+SnHxFUREtAIgMPBmfvrpxMKGc845h88/X8rrr7/F0aPb2L8/jH37ulBSEkBQUBA3\\n33w7KSnpWK0jsVo7YLVG43Dcha6PQ9PSgQ/R9SBEccUg/p0RiFLLR5Tdh4hay0N65d5AVKCODIZt\\njjSSt0dU2/+QAhcHUs3ZB8kDDjf20QWZ8tAdq/V/OBwTsNtDEaUZgLjODEHXf8Zq7UWrVr0oKnqJ\\nK6+8gv79+zFlylM0azYep/MwDoeTgoJ8QkIi0LRmrs9F18Ox2fLp378/P/64liNHjtCiRYtG517k\\nqUF9//793HnnnXz44Ycu0gM5X6Kjo3227+oW9Z0sMImvEkRGRpKdnU1wcLCL9GpywS5fFFNYWOhy\\nX3E4HFgslhOmK+i6Xm/TFaqLqtSiyocFBAS4jkmRYn2aTtcG9dFn2L59W4KCNqDrDjTNSknJFtq1\\ni/P42tNPP51XXpG5azabjWXLlrF370HOOOM/jBw5khkznuPrrzcAl9Gy5XIOH74UTXuamJjmvPba\\nAj77bA3vvLMQh0O1ETyN+Hr+jOTjdIQEP0Z67S5ACmT2I2pwPPAsFksEFksOdvs/kRxdGPAS0sKg\\ncnyFSNhyIEFBaRQX/4CmdULXb0Ssz5YjvqVfERgYQlzcQwQGBnLVVUN44olpfPPNN1gsZxIR8V/X\\nTVR2dl+uu+4iXn/9AYqLH8ThyCQ0dAmXXfYGIJGSNm3a+Oib8R08NagfP36ccePGsWDBApchR12g\\nJkV9JwvMHF8l+M9//sN3333HW2+9RefOnWt8J6mqP1Uhg7vRtOrXs1ga73SF6sAbC7LKcouNwXS6\\nvprrbTYbN9xwG1u3ZmGxtCEw8DeWL3+NU089tdrbys3NZcyYCfz9dyGaFkibNsUsXDiHrl27uj4v\\nm81GdnY2CxYsZsWKtaSnZ2KxOMjOtqPrlyC+mU8gjeqnIsruTkJC1nPKKX05ciQTqzWIM87owbp1\\nm8jNvRtRhpciziovo2mHadVqBzbbeM48MxubzcEvv5SQn+9A179E0i5HEEeXAqKj8/jmm/+VmXv4\\n+++/c/nlk7FY1mKxROJw7ELTrmD79k0sWfIBK1asIyKiGVOnTqR///61+AbqFk6nk7y8vDIFUUVF\\nRYwZM4b77ruPESNG1On+v/vuO84//3xOP/1012+gNkV9fogKLxQm8VWAzZs3M3z4cCZOnMgDDzyA\\nxWKpFfEBLqNp5fbh3q6gmqIrsr/yB/jCgqy+im4qgmqur68+Q7vdzpYtW8jNzaVfv360atWqxtsq\\nKSnhjz/+oKioiO7duxMTE1NlmPyjjz7inntWU1R0FjbbJkpKvgG+QtO6ACHo+kP06fMVGzduKPO+\\nLVu2MHz4jZSUtEaqOGVKfGBgEc2aXUR4+FzWrfuQ3Nxchg+/nqysMKSBPhBpeh8OZHDzzdcwZ85z\\nJ+SGH3zwSd5/fwNW62k4nVuYM2cao0ZdXuPPpr6hzmfVfgS4qtEvvfRSl7WiiTqFSXzVwfbt2xky\\nZAiXXXYZw4YNY/jw4WiaVqOLuXspv3u7gtVqPaFdoaZGzY0B9WWWXZdqsb5Jz9dQBVMlJSVet43k\\n5+czdOho9u3rg93em5KSZykpOQ1dfxTYh8VyJytXLuDCCy884b033HAbX399OkFBU9D1EvLy/kVc\\n3G769OnD9On3umzEFi1awt13P0pJSU8k1JmIOMHk8tFHiznvvPPK5IbVd7Rt2zYyMzPp1asX3bp1\\n85vcsCrqslqtrnNe13UefvhhWrRowUMPPeQXx9EEYBJfdaDrOqmpqaxduxan08mYMWPQdb3apOTe\\nruBeKFIf7Qr1icZiQVYbtah6KU8m0lPIzs5m8eIlHDp0jEGD+pGUtIVVq9YRFhbKjBn3VTiuJi0t\\njcsvv5FjxyJxOnMYMCCed9993eMN4sGDBxk0aAhHjwYi6nAP3bpFsmVLkuv1yhTC4XAQFBR0wnfZ\\nmAwZKoLK0Wua5kpX6LrOa6+9xt9//82rr77ql32sfgqT+GqCZcuWkZKSwsSJE3E4HC6nBW/gbmem\\n2iECAwMJCgpy/fBVu0JDE0Zt4E8WZBWRontjsQo9+4P9mzuKiopqRHq1RUFBAdu3byc4OJhTTjml\\n0n3b7XYeffRRfv31T/r0OZVHHnmYsLAwoPR8cTgcZaaPu6Omal89XtffX0XHsHLlSpYtW8aKFSv8\\n9jz3UzSdPr6aDqP19r3uiI6OJjs7u9prVHeuqlpTVTSWlJTgcDhcRKjCn42dMDzBXWH4iwVZ+RYN\\n915KdaHyB/u38mgo0gMxJTjrrLO8em1AQAAzZszw+FxxcTF2u71C0oPqt9iU//7qumjK0wT1jRs3\\n8vrrr5OYmGiSXiOCX30TtRlG6817yyMqKsplBO3thAVP7QqqYEUdg5qzp6o8G0MlY3XQFCzI3Hsl\\n3Y+hpg39DfX9NSTp+Qo1DdG6w1tDBvfv0Zc3Np4mqO/cuZMHH3yQzz77jGbNmlX6/urA4XBw1lln\\n0b59e587vJws8Cviq+kw2oyMDPbu3Vvle8ujusNo3Y2kPbUruDeuu9t3ud+tOhwOlzqsqO/NPRxX\\n36To3mdY2d15Y0b5kJQ3F9vaqo26UIsNSXo5OTksWbKErKxshg69qMr5lhXBk6NJXUB9fxWhNjc2\\n6rx3/x4yMjKYNGkS7777Lq1bt/bpscydO5fevXuTm5vr0+2eTPAr4qvpMNq0tDQOHjxY5XvLozrD\\naFVxhJqcUH66QmXtCu53q+XDIY1pAoOqVtM0za9Jz12t+uIYfGUWXlmOqjx8oZJqipycHAYNuojM\\nzFMoLu7KnDnX89prz3L11VdVazslJSUnEEZDoaY3NspqEGDHjh289NJLtG/fnsTERO68806ioqK8\\nGknmLQ4cOEBiYiIPPfQQL7zwgk+2eTLCr4ivOsbIvkB0dDQ5OTmVKj7lSlJSUuIKZzqdZacreNPU\\nXREaywQG1VxvtVr9ts+wLkjPG9TW/q38d+hwOHA4HA1m+v3ee++RmdmToqLlABQWXsp9942tFvHZ\\n7XYKCwv9oiiqonPQvUHdarXSokUL+vbty/r164mKimLBggVMmzaNwsJCPvnkE4YOHVrrtdx9993M\\nnj3b536eJxv8ivhqOoy2ffv2lJSUVPne8ggKCsJms1VIfO7FEfU9XUGhPkJwSq0GBga6FK2/wX1K\\nRGNSq9W9sXEvkMrPzwfq3/4tNzeXkpLObo90Jj/f+wux6lt1t/HyN6hzwn2CemxsLL///jsjR47k\\nP//5j+u1OTk5Pjn3P/vsM1q3bk3fvn3ZsGFDrbd3MsOviK82w2hbtGhR5XvLwz0Hp/7r/phqVwgO\\nDnZNcXBvV2jo/jZfqEVV6eg+ELcxFtxUBn8ejeR+Y6OKoiIiIlzfaUMU3QwdOpRZs0ZTWPhPoBvB\\nwXd7bYOlvCtDQ0P9tspR/Z7cJ6jrus6MGTNo164dt99+e5nXR0ZG+mS/GzduZNWqVSQmJlJUVERO\\nTg5jx47lnXfe8cn2Tyb41S+vNsNoK3pvZVAXhfIXh/LtCp6Mpt1zMI31rrYqtajCUeq4/KW83x1N\\nYTQSlPZLli8CaYiim759+7J48cvcffcd5OVlM2zYMF599fkqj8F9mK+/mjUo0rNYLGX6ehcvXsz+\\n/ftZvHhxnf3GZsyY4WoHSUpK4rnnnjNJr4YwG9irQPlhtErpqXCmw+HwWLnpdDoJCwtr8KR9TVFV\\niLaxG02rNTZl0qst6vM7dM+HNbaxQd6iogb1tWvXsmDBAj755JN6O7akpCSef/55Vq1aVS/781M0\\nnQb2+kZwcDBFRUWuSs2SkpJK2xX8veoRvAvRVkdpuBdreGrPcG/R8BUpNoViHKg70oP6U4ue8mH+\\nCE8N6j///DPPPfcciYmJ9XpsQ4YMYciQIfW2v6YGk/iqgOrli4iIwGazuUxnVbuCUkOVtSv4E3xh\\nQVZZbtGbKsbahlCbynehetwaotzfly0aKipitVrL5I79CZ4a1JOTk7nnnntYuXIlUVFRDbxCE9WB\\nSXxVIDIykg8++IAbbrjB9eN2OsV3U6kh93aFoKAgvzupof4syOq6PUOpbn+uQIXGPynCG7WoGrvV\\nd61civwlP6zgqd/wyJEjjB8/nkWLFtGuXbsGXqGJ6sIkvkpgs9n4448/+PXXX7nmmmtcF15P7QpN\\nIXdR3r6rIVDb8BvgCjvbbLZGfUGtCI2d9KqC+pxtNptrNI/7Z99Y7d88weFwuIYSq++ioKCAsWPH\\nMmvWrBoNDTbR8DCJrwJkZWUxevRo8vPzeeyxx2jevLmrelO5+SvfTX+eruBejOMexmmMqEwtKtWt\\nlHhjv6BWBH8nPSjbPuKpqKgx2r95ggqZu7de2O12Jk6cyG233cYFF1zgs32ZqF/459W6HpCfn8/5\\n559PeHi4q4pThdJsNhtpaWnExcUBuPw3/SFs447G2tRdXbiXyVekuhvTBbUiNBXSKywsBGrWM9mY\\nnIpU+sI9jz916lQGDx7MtddeW63jqgxZWVlMmDCBbdu2oWkaCxcuZNCgQT7bvokTYbYzVAJdl6nJ\\nK1asoG3btsTHx9OxY0d++uknCgsLWbp0KREREZWWhTdWlaGqHv291N8b0vMGDd2e0VRIr6qZenW9\\n/6oGEXvzPao8cfkJ6i+++CKHDh3ixRdf9OmxjRs3jiFDhnDLLbdgt9vJz883i2V8g6Y7iPbYsWNc\\ne+21pKamEh8fz/Lly4mOjj7hdRXN4rvvvvv47LPPCAoKokuXLixatKjMj06V3xcVFfH7779z2223\\n4XQ6Offcc9m7dy95eXlomkbbtm1JSEggISGBzp0706VLF1q1agWceFFVExiAMqX89UWK6m7W3wtA\\nlPVVXU+v99Se4f59Qu3aM5oC6UHptIjGGjKv6uYGyppWBAQEkJSUREJCAj///DNr167lgw8+8Ol3\\nlJ2dTd++fUlOTvbZNk240HSJb+rUqbRs2ZKpU6cya9Ysjh8/zjPPPFPmNQ6Hgx49epSZxff+++/T\\nq1cv1q1bx0UXXYTFYuGBBx4AOOH9Co899hiZmZm8/PLLrpi/cjjZv38/e/bsYffu3ezZs4fk5GQy\\nMzPRdZ0WLVrQuXNnFykmJCTQoUMHrFZrhSdjXYXeamOY3ZhQX6RXFbxRGZV9j/5k1lwZiouLsdls\\nDV4cVVOo71BVngYGBpKbm8vYsWNJSUkhLS2NNm3a0KVLFzp37szVV1/NJZdcUuv9/vrrr0yaNIne\\nvXvz22+/ceaZZzJ37lzXZHoTtULTJb6ePXuSlJREbGwsGRkZXHDBBezcubPMazZt2sT06dNZs2YN\\nUEpsiugUPv74Y1asWMHSpUs97ks1rFfXseLw4cMuQlR/+/fvx+FwEBIS4lKK7sQYFhZWoxBqZetT\\nZOHPFajQeEjPG3ijMhpC9fsSSrHW9Uy9uoYnxfrnn3/y3//+l5UrV1JQUEBycjLJycl0797dJw3k\\nP/30E2effTYbN26kf//+3HXXXURGRvLEE0/UetsmmrBzS2ZmJrGxsYC4o2dmZp7wGm/m+AEsXLiQ\\n6667rsJ91aRy02KxEBsbS2xsLOeee26Z53RdJy8vj+TkZHbt2sWePXv4+uuv2bt3r8t1pEOHDmVI\\nsXPnzsTExLjMo8s3gFdEik6nE5vN5vekp9pH/KWStqIKRndLOPfvsrEV3FQF9zCtP5OezWbDZrOV\\nIb20tDTuuOMOli9f7rrGJCQkcNFFF/lsv+3bt6d9+/b0798fgKuvvrrCiJMJ36HxXzmAYcOGkZGR\\nccLjTz/9dJl/V3SH7M0F4umnnyYoKIjrr7++5gutJjRNIyIigj59+tCnT58yz6k5f6mpqa4Q6qpV\\nq0hOTubo0aPouk7r1q1PUIrt2rVz9bGpC+iff/5Jly5dAKlALS4ubtQX04rgb6RXEdRxVBXebOz9\\nbu6N3f4cpvXUoJ6VlcXYsWN57bXXSEhIqLN9x8XF0aFDB/7++2+6d+/O+vXrOeWUU+psfyYEfnH1\\nWLduXYXPqRBnXFwc6enptG7d+oTXVDXH7+233yYxMZEvv/zStwuvBTRNRhx169aNbt26lXlO5ZXS\\n09NdIdSNGzeyZMkS0tLSXD15nTt3pqCggHXr1vHBBx9wyimnEBwcXGlZf/mLqXsIriFJsakUgLiT\\nd1XH0Zj73VRu0p9n6oHnBvXi4mLGjRvHo48+yplnnlnna5g3bx433HADNpvNVWBnom7h9zm+qVOn\\n0qJFC+6//36eeeYZsrKyTggV2O12evTowZdffknbtm0ZMGCAq7hlzZo1TJkyhaSkJFq2bNlAR+Fb\\n6LpOVlYW999/P59++ikTJ04kLS2NlJQUiouLCQgIoGPHjieEUFU1bPmqxYZWGE2R9OpasdZle4b7\\nIFl/Vt5Op0yMcM8VOxwOJk6cyIgRI7j55psbeIUmaommW9xy7NgxxowZw759+8q0Mxw8eJCJEyey\\nevVqAD7//HNXO8P48eOZNm0aAN26dcNmsxETEwPA2Wefzfz58xvseHyFnTt3cvPNN7Ny5UpXfgJK\\nPTlTUlLYvXt3mSrUrKwsANq0aVMmfNq5c2fi4uJcPU71WYXqC9PsxoDGFKatyCi8svYMpf5V/6c/\\nFBZVBtXSExQUVGaY7KOPPkpUVBSPPPJIow77m/AKTZf4TFQM94nx3r7e6XSSlpZWpjVjz549ZGRk\\noOs6kZGRdO7cmfj4eBcxxsfHExgY6JEUlb2bUhgVVS96gr+XyCs0JtKrCpW1ZzgcDqA0BOtvOWIF\\n1aCupneoxxYsWMC2bdtYsGCBX//eTLhgEp+J2kPXdY4fP86uXbvKkGJqaiolJSUEBwfTqVOnMkqx\\nc+fOhIeHV6s1Q42ycTgcfp9DamphweDg4DJeqI0lHO4tlE2fpmllHItWrVrFe++9x4oVK/xayZoo\\nA5P4TNQtlEdjcnJyGVJMTk6ulruN3W5n27ZtdO/e3bXtxl7SXxGaEukppx+lkDyhts38df1dVmSp\\npvp8V69eTURERJ2uwUS9wiQ+Ew0H5W5z4MAB9uzZ4+pZLO9uEx8fz969e0lPT+ftt9+mY8eOJ7jb\\nuOeiGvpCWhmaCum5+1bWdqhvQ/uhempQ/+uvv5g0aRKrVq1ymc77AnPmzOGtt95C0zROO+00Fi1a\\n5NdOSX4Kk/hMNF44nU4yMjKYNGkSf/31F2PGjCElJaXO3G3qOuxmkl7N9lWXatGTu0xGRgb/+te/\\nWLJkCT169PDZsaSlpXHeeeexY8cOgoODufbaa7nkkksYN26cz/Zhwis0XeeWusKHH37I448/zs6d\\nO/nxxx/p168fACkpKfTq1YuePXsCTacKtCFhsVj466+/KCgoYOvWrYSHh7ue88bdpn379mVyip7c\\nbbztc1OWbzW9yDcl0lPTO+qa9KBuxxGpSfDuRVJ5eXmMGzeOOXPm+JT0FNTvwGq1UlBQYE5pb2Qw\\nFV8F2LlzJxaLhUmTJvH888+XIb6RI0fyxx9/NPAKmx6cTme1quk8uduoEGpl7jbt27fHYrG4hgn7\\nSl00JdIrLCxE1/UazdSrb3jTnqFpGhkZGbz11lvEx8ezatUqrr32Wm699dY6+a7mzp3Lww8/TGho\\nKCNGjGDJkiU+34eJKmEqvupCKToT9YfqlpB7426TkZHh6lcs727TrFmzE6ZmJCQkVNvdRpFocXFx\\nmWnd/ghFeurzaeykBxWrRfdKVBWujY6OZs2aNRw6dIhnn32Wu+++m/bt2/PUU09V6tNbHRw/fpxV\\nq1aRkpJCVFQU11xzDe+++y433HCDT7Zvovbw3zO0AbF371769u1LVFQUTz31FIMHD27oJZkoB3Ux\\nbNu2LW3btuX8888v87yu62RnZ7tU4q5du1izZo3X7jbupLh3715iY2PRNM0VHnQPnTamcv7KoKoe\\n/Yn0KoKqRHUfv6WM6vv168cXX3yBpmnYbDZSU1N9Ws25fv16EhISaNGiBQCjR49m48aNJvE1IpzU\\nxFeR+fWMGTMYOXKkx/e0bduW/fv307x5c7Zu3cqoUaPYtm2bWQbtZ9A0jejoaM466yzOOuusMs+5\\nu9uoEOqHH37ocrfRNI24uDgSEhIIDQ1l/vz5vPfee5x99tk1yis2hipUEMMAu93u96Sn8pNq0LLC\\nkiVL2Lt3L++8847r+FTEwJfo1KkTmzdvprCwkJCQENavX8+AAQN8ug8TtYOZ46sC//jHP8rk+Kr7\\nvImmBXd3m8TERKZOncpVV11FXl5ejd1tGroKFZqOS05FDepffPEFr776KqtWraqXtoLHH3+cZcuW\\nERAQQL9+/XjzzTfNxvj6h9nOUFP84x//4LnnnnO5tB85coTmzZtjtVpJTk7m/PPP588//3SFwBoC\\nFVWgAsycOZOFCxditVp56aWXGD58eIOtsynB4XDQr18/nnzySS6//HLX4+7uNqqJf/fu3Se426gQ\\nqprorSpZq/LPrIuBtU1lkKx7qNa9KOeXX37hvvvuIzExsUHPUxP1DpP4qouPP/6YyZMnc+TIEaKi\\noujbty+ff/45K1as4LHHHiMwMBCLxcITTzzBpZde2qBrragCdfv27Vx//fX8+OOPpKWlMXToUP7+\\n+2+/vrg1JhQVFVXqZFIevnK38WWPm7ezAf0BnhrU9+7dy0033cTHH39cZhSZiZMCJvGdDCgfdp05\\ncyYWi4X7778fgIsvvpjHH3+cQYMGNeQyTXhAeXcb99YMFUJt0aLFCa0ZnTp1OsHdxtsQqiLipkB6\\nnlTr0aNHueaaa3jjjTc47bTTGniFJhoAZjvDyYiDBw+WIbn27duTlpbWgCsyURE0TSMwMNBFbEOH\\nDi3zvNPp5MiRI64Q6tatW/nwww+rdLdRRVfuodOSkhJyc3MJDQ0FoLCwsNGaSnsDTxPUCwsLGTdu\\nHDNmzDBJz8QJMInPT1CTClRP8IcLmYkTYbFYaN26Na1bt+bcc88t85yyFnNXihW52yQkJBAQEMDD\\nDz9MUlISsbGxfluFCp4nqDscDm699VYmTJjAhRde2MArNNEYYRKfn2DdunXVfk+7du3Yv3+/698H\\nDhwwrZOaIDRNIzw8nD59+tCnT58yz5V3t9m4cSMvvPACF110kcs7slWrVpW623jbyF++Z7GuoXr1\\n3E0DdF3ngQceYODAgT5rSDfR9GDm+JoQylegquKWLVu2uIpbdu/e3Sju1BUef/xx3nzzTVcBx8yZ\\nM7n44osbeFVNEzabjVNOOYVp06Zxyy23nOBu416F6sndxp0YPbnb1GdrhiK98owkUswAAAaESURB\\nVBPUX3rpJQ4ePMhLL71Uq33ccsstrF69mtatW7vsCY8dO8a1115Lamoq8fHxLF++3KwSbdwwi1ua\\nMiqqQAUJhS5cuJCAgADmzp3LiBEjGni1ZTF9+nQiIiK45557GnopJwVSU1Pp1KmTV6/VdZ2cnByX\\n5ZsiRuVuExgYSIcOHbxyt/HlpAVPE9RB2no+/fRTli1bVutinW+//Zbw8HDGjh3rIr6pU6fSsmVL\\npk6dyqxZszh+/DjPPPNMrfZjok5hEp+Jxonp06cTHh7OlClTGnopJqoBT+42qgr1+PHjZdxt3JVi\\n27Zt0TStQlNpXdddatFTzyLgsUH9m2++4dlnn2X16tWuop3aorwhfc+ePV150YyMDC644AJ27tzp\\nk32ZqBOYxGeicWL69OksWrSIqKgozjrrLJ5//nkzfOTncHe3cSfFPXv21NrdRiEgIIDVq1e7einn\\nzZvHmjVrXCFzX6A88TVv3pzjx4+7jjEmJsb1bxONEibxmWg4VFSR+vTTTzNo0CDXxeqRRx4hPT2d\\nt956q76XaKIe4cndZs+ePaSmpmKz2U5wt1Eh1Hnz5tGqVStXfnLx4sVs2LCBrVu3kpubi81mczX9\\nL1++vNYWYZURH0BMTAzHjh2r1T5M1CnMPj4TDQdvK1InTJhQrdYME/4JTdOIiYlh4MCBDBw4sMxz\\nntxtNm3axLfffkt6ejp9+/blhx9+ID4+njZt2pCamspHH31E//79yc7OdhFoXfhiqhBnXFwc6enp\\ntG7d2uf7MFE/ML2rTDQo0tPTXf//8ccfN+pm4zVr1tCzZ0+6devGrFmzGno5TRKaphEWFsapp57K\\nqFGjmDJlCldccQWHDx/mu+++IzExkZkzZzJ06FDy8vIYO3Ys/fv3ByAqKop+/fpx5ZVX1snaLr/8\\nchYvXgzA4sWLGTVqVJ3sx0Tdwwx1mmhQjB07ll9//RVN00hISOD1118nNja2oZd1AhwOBz169GD9\\n+vW0a9eO/v378/7779OrV6+GXlqTx549ezhy5MgJ6rAucd1115GUlMSRI0eIjY3liSee4IorrmDM\\nmDHs27fPbGfwD5g5PhMmaoNNmzYxffp01qxZA+AqY3/ggQcaclkmTJioGBUSnxnqNGHCC6Slpbkm\\neIPpe2rChD/DJD4TJrxAY3K7MWHCRO1gEp8JE16gvO/p/v37zfluJkz4KcwcnwkTXsBut9OjRw++\\n/PJL2rZty4ABA/yiuCU+Pp7IyEisViuBgYFs2bKloZdkwkR9wezjM2GiNggICODll19mxIgROBwO\\nxo8f3+hJDyREu2HDBmJiYhp6KSZMNBqYis+EiSaMhIQEfvrpJ1q0aNHQS2kweJq0cN999/HZZ58R\\nFBREly5dXLZ5JpoUzKpOEyZORmiaxtChQznrrLN44403Gno5DYKbb77Z1YaiMHz4cLZt28Zvv/1G\\n9+7dmTlzZgOtzkRDwAx1mjDRhPH999/Tpk0bDh8+zLBhw+jZsyfnnXdeQy+rXnHeeeeRkpJS5rFh\\nw4a5/n/gwIGsWLGinldloiFhKj4TJpow2rRpA8iU9SuvvNIsbvGAhQsXcskllzT0MkzUI0ziM2Gi\\niaKgoIDc3FwA8vPz+eKLLxq1F2pD4OmnnyYoKIjrr7++oZdioh5hEp8JE00UmZmZnHfeeZxxxhkM\\nHDiQyy67jOHDhzf0sk7ALbfcQmxsbBlSPnbsGMOGDaN79+4MHz6crKwsn+/37bffJjExkXfffdfn\\n2zbRuGFWdZowYaJB8e233xIeHs7YsWNdVZdTp06lZcuWTJ06lVmzZnH8+HGXP2pNUH623po1a5gy\\nZQpJSUm0bNnSJ8dhotHBNKk2YcJE40V5YurZsydJSUmuGXgXXHABO3furNG2y09amD59OjNnzsRm\\ns7n6G88++2zmz5/vs+Mx0ShgEp8JEyYaLyqbdq7rOjExMWWmn5sw4QXMPj4TJkz4JzRNM03CTfgU\\nJvGZMGGi0UGFOAHS09Np3bp1A6/IRFOCSXwmTJhodLj88stZvHgxAIsXL2bUqFENvCITTQlmjs+E\\nCRMNivLFJ0888QRXXHEFY8aMYd++fcTHx7N8+XKio6Mbeqkm/AtmcYsJEyZMmDipUOOxRGZG2YQJ\\nEyZMNCmYOT4TJkyYMHFSwSQ+EyZMmDBxUsEkPhMmTJgwcVLBJD4TJkyYMHFSwSQ+EyZMmDBxUsEk\\nPhMmTJgw8f8bUQAAJQuPgH3ohFMAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = plt.figure()\\n\",\n \"ax = Axes3D(fig, rect=[0, 0, 1, 1], elev=30, azim=20)\\n\",\n \"ax.scatter(x_res, y_res, z_res,marker='o')\\n\",\n \"plt.show()\"\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\": \"3.6.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "mathematics/random_data_generation.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C)\\n\",\n \"2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"机器学习算法的随机数据生成 https://www.cnblogs.com/pinard/p/6047802.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[[0.20011634, 0.64204013],\\n\",\n \" [0.20115805, 0.81735394]],\\n\",\n \"\\n\",\n \" [[0.60662038, 0.89004787],\\n\",\n \" [0.61624492, 0.87006243]],\\n\",\n \"\\n\",\n \" [[0.98481706, 0.15648228],\\n\",\n \" [0.05232036, 0.58822938]]])\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.random.rand(3,2,2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[-1.38052653, -1.52454151],\\n\",\n \" [-1.52251769, 2.01995676],\\n\",\n \" [-0.05587328, -0.25584719]])\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.random.randn(3,2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[-0.57894445, 1.20365505],\\n\",\n \" [ 1.52348525, -0.96396235],\\n\",\n \" [ 1.60472535, -1.87940798]])\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"2*np.random.randn(3,2) + 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[[0, 0, 2, 0],\\n\",\n \" [1, 0, 0, 0],\\n\",\n \" [0, 2, 0, 0]],\\n\",\n \"\\n\",\n \" [[1, 0, 1, 0],\\n\",\n \" [0, 0, 0, 0],\\n\",\n \" [0, 2, 1, 1]]])\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.random.randint(3, size=[2,3,4])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[5, 4, 3],\\n\",\n \" [4, 3, 5]])\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.random.randint(3, 6, size=[2,3])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([2.29466296, 2.75012827, 4.12039746])\"\n ]\n },\n \"execution_count\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.datasets.samples_generator import make_regression\\n\",\n \"# X为样本特征，y为样本输出， coef为回归系数，共1000个样本，每个样本1个特征\\n\",\n \"X, y, coef =make_regression(n_samples=1000, n_features=1,noise=10, coef=True)\\n\",\n \"# 画图\\n\",\n \"plt.scatter(X, y, color='black')\\n\",\n \"plt.plot(X, X*coef, color='blue',\\n\",\n \" linewidth=3)\\n\",\n \"\\n\",\n \"plt.xticks(())\\n\",\n \"plt.yticks(())\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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crWlINJySI6E50hYVS7Ecxj35xN10HdcLhshOT4MYd5+LfL/+TDkM6cfsP30EtO3KbptV6fi+1jcgtK484VuD18O3GDewsKYn65dQ1jelrMiLXkZK7fppJgddTeazI5+Oqrz5nV2lETTrF4UDMP4HqoaoOcJ6A0Oof/XU0okwxRzHJ6Ym8/Nvj5GzNpSS/lHY9WuNwObh/1o8VpX9rrvNiE4K605Fq55T24dEy/126mBcWzMWmafgNI9pLOCHTZEdpccTx1Xm5ZBYURGTLBg2Tj1et4M7jhh+gtIqDjXCPRoYywfMxCAfIANj7IBKfOdSi/eVRil1Bs3bpNGuXXvn5i/VrCdRQTjjGbifO7uD6gYN5ZM4vDUpkCpvHZuf6gUMqPy/L3sVLC+fhN4xaSxm7bTYMU/LArJ/onpbGqC7diHM4yCouQtciTS5B0yCzIL/yc05ZKQDN4tSO76+OEAKRcA8ybjwEN4DeHGGLDHVVRKIUuyKCYA2KVQCfj7mUjimpzN2+bb/mduo6J7Ztz2OnnEaye99r9qTVK+sMg7RrGgHD4LuK6Be3zc7Lv8/n80vG0bVJGkEj8g3Dpdvo16wFG/L3cNN3X1cWN2ubmMQrZ4/kmNQm+3UfioOH0FLAOaTugYpKlI1dEcHxbdpGOE41ITiudRs6pzZBE4K+zZo3qJ78Xr4ddwVvjjwvzMkJVvRMbXt/t81G87h4JFSGNHpDQfI9Hh6c9SMdklM4qV07XFV6xGpCEOuwM6prN8ZOn8LGgvzKN4KNBfmMnT6lMk5eoTiSUIpdEcHDJ55KotNVqSRdNhsJDiePnXxa5ZgEp5M7hx4fNXKlaQ39WXUh2F4Ubh9flbubyz6bxq9bt9QqU/ukZHaWlkSUUZDA7G1bKfH7eOWskfx7wGCaxcaR4HQysnMXvhh7GfO2byNghmelSiBgWrt/heJIQ5liFBG0Tkzkl79fyfQ1GWTk5dKtSRoXd+8Z0enoX/0H0jO9Kc/O/42s4mI6Jidz7/CTuPPHmeyuEp2yF02IsAJla/JyGTt9ctREpOoMbtmKzIICDBndTPT+8mXcNHgoNwwawg2Dwl/bc8rK8EbZmXuDQbJLS+tcW6E43FCKXRGVBKeLK4/tX+e4oa3b8Pkl4yo/SynZWUN4Ycg0Wbcnj0d+nUXQNAlWlAmoC4emccOgoWwqLGBODbb9n7ds4qbBQynx+/jv0sV8vymTeKeTf/Q5lj5Nm+G22yPMLm67nT7NordgK/B6mLYmgw35e+iV3pTR3XrUu2qmQnGoUYpd0agYUuKvRVk/8dvsOpW5xr6+Op1TUplw9rnomqB5LZEsTWJiKA8EGDX5f+SUlVVG9azNy+PSnr3oktqENXm5lRE3Tt1Gl9QmDGsdGWWRWZDPRdMmETAMfKEQMzM38Pri35lxyTjV8UlxWKAUu6JRsWkaHVNSyCwoiHq+Pjt0u25j4VXX4LLZcNpslAUCjPjkQ3LLy6KOd2ga/+zbj4lLFpFbRalb6wX5aNUKfrjsH8xYt5bP1lnJTaO79uBf/QdEdQDf+/MPlPr3OXO9oRB+w+CJ337ltXP2vz6PQnGwUIpd0eg8cuKpXPXl5/iMcCVen4h3l83GmGr2/MmrV5LnKa8xvj1kSm6e+S3Ffl/Urk9SSn7bvpWbBg/lpsFDyfd4eG3R75zxv/eJc1jmmou790QIQcAwWJaTHSGrKWWdDl6F4q+CiopRNDpDW7fhtA4do0bM1Hlty9bcNPi4sGOzt22pNcbdRFLo89bYyi9omjw2ZzY/b9lEid/PuZM/4uNVy9lRUlJp899bqEwXosYaOCFp8vuOrP1OylIoDhaNotiFEO8KIXKFEKsbYz7F4c/6/D371apj9rYtDH3nTcZ9NpXdZZbpJcZmP2B5AobB7T98x6RVKyjy+cJKD3hDIaatWU12aSlZJcUkRSkytneOyz6fxhUzpteYxKVQ/BVoLFPM+8AE4MNGmk9xmNM8Pp6NVVL564vE2mEv2JHF8Pfeom1iUmW26IFimpIft2yKuvt3aDoLd2Tx2JxfKPb7apzDkJKFO7KYkrGKy6o0NinyeXlszmy+y9yAKSWndejIQyecQlpsbI1zKRR/Fo2yY5dSzgGie8sURyXX9h+E2xa+b2homd+QlGwqKiTUSKaPkGnSMj4+qhwmkhW7s/EZoTrfNAwpmbpm1b7PpsnF0ybz9YZ1+EIhAobBD5kbuWDKx7VGCCkUfxYHzcYuhBgvhFgshFicl5d3sJZVHCKGtGrNIyedSrzDQYzdjkPXaZWQuF9294ZS2xrr8vIibPE2IWidkEiep7xebfuAME/wr9u2klNWGmbeCUlJsd/HzE0bGyC5QtE4HLSoGCnlW8BbAAMGDFDep6OAi7r3ZFSXbmwvLiLZ5WZXWSmXTJ9cf+XZQOyaRou4eLaXRJb1BfAZITYWhr9Y6kLQv0VLXjlrJFPXrGLWls21VpcE68Expkevys+ZFTVoqlMeDLIhf0/Db0ShOEBUVIziT8Wh63RKSSU1JoZe6U25tGdvHFFa2x0odk3Dpmnkesob5LTVgMEtW5MWG8vYHr1x2ure6/Rp2oxLqij2DsnJOPXIVn4xdjudU1IbII1C0Tgoxa44qDxwwsm8O+pCbFHs3Pv7ZbQJQcg08YZC9UqAqkpQSt5eughfKEhqTAyfXvw34h2OqGObuGN46cxz+HTMpdirKPKT2nWgSUys1VWqAk0I4h0OzurUef9uSqE4ABor3HESsADoIoTYIYS4qjHmVRxZmFLywoK5jP/6C6uJAtaOPs7hwGWz8cxpZ4WV3a1KtAfBXkJS1rpLd2h6nbb9XRXFwJrGxVMWCEQdU+L3c16XbhE9VG2axrSL/8YZHTph0zR0ITi5XQc+GzMOVyOEaioUDaVRbOxSyr81xjyKI5sXF8zlveVLI3bVl/fqy3UDBxPncFAeDPD03F/xVbFZu2w2Ut0xNRYXqwldCBKcTtolJrGhoIDyYHSF7Q+FaBobV/n/NT0kAqZBkc9Lkqt6H06rVs2Ec86tTF5SDbQVhxJlilEcFIKGwfvLl0Uo9YBhMGvrZuIqzB9X9DmWp047kw7JycTaHQxo3pKPLriI/s1bNLixhyElhT4fK3N3EzBqNtHYNJ3YivVTY2Jq3d1Pyag5B6/Y56PA61VKXXHIUbViFAeFskCAkIzeHLt6TfTzunTjvC7dwo4lOFz8uDmzATZ0WfEjMKSl5Gsi1hFuLunTtBnLd+dEHbsiJzvi2M7SEm6d+S0rdmcDgg7Jybx45jl0a5JWT1kVisZF7dgVB4VEl4tYe3SnZJcmdfcd7ZyayqTRl9To2IxEYBOS45vu4PimWYD1Za+enOTQdc7v2h2g0oxy//CTou7a7ZpGm8REQlXj1U2TMdMmsyxnl1Vj3jRYn7+HsdOnUOyrOYNVofgzUYpdcVDQhODuYcMjslFdNht3DzuhXnP0btqMMT16hUWf1EZI6izMbcmrQ3+kc0J+ZY13gfXFd9usmuxu3Ub3116m46sv0nXCS0xavYIeaekR64RMk3eXL6XvxAk8O28Ohmkye+tmSvz+iDeCoGnw+bo19ZJToWhslClGcdAY06MXiS4XLy+cT3ZpKV2bpHHXsOH0a96i1uu+3rCOlxbOJ7uslLaJSdiERojoZp3qmICumdg0a/xeBawJQaLLRe+0pkxcuqjyeMA0+WzdWlrExXNGh078uDmzcocusZR7yDT5YMUyAoZJ68QEQmZkcpIvFIpa42bF7hx+2pyJU7cx8pgutEtKrtd9KBQNQSl2xUHlzI6dOb3FJij7EIydoDXF9NyCFnNB1PFTVq/ikTmzKrNV1+fvwaHrdEltwsaCfNw2G5f06MWU1SspDwWpWlBAw2RAkxy2lSWyqSRcgRpSUuzzM3nNqqj295zyMvq3aMk1/Qfy4OyfWVnN5u4Nhfhk9QreGXUButCAcOUea7eHPbCklDzy6yymrlmNPxRC1zReX/w7D55wMn/r2bvevz8pJVszsggFQnTo0xa9Sjy9lBL8PyA904AQwn0euM5FCPVnfrSh/sUVBxXp+wGK7wIq7M9mNpQ8hImBFnNR2FhTSp6b/1tECYKAYeCy2ci88bbKY79s3cK2onxcegiPYcetB3HqBk8O+JW5u1tiykiruTcUrNEWaUrJE7/NJsZurzGuXQBtEpLo3awZy7J3VZYVcOg6TePiOLPjvuSkJdm7mLYmo/Je9u78H/11Fqd36ESTmJhafmsWW1Zt4z/nP0tRbjFCEzhcDu775Bb6nWplwcqS+8H7DeC1PgeWWZ+T327USJ2SglIyl24huWki7XtFthZUHHqUYlccVGTpC1Qq9Up8UPYSVFPsZYEApQF/1Hkyq5UEvrRXb56fP5dTW2wizh6ga1I+o9pmEqMHSXWmYshIFe7QdUK11IUxpaxRqQPomkZ6bCzvjbqQiUsWMXXNKkKmyYjOXbh58FAcVXbT32xcjy8UjDrH7K2buah7zxrXAQj4AtxxyiOU5O+LIPKW+vjPec/w/oZXSE3bA96vCf/deiG4GALzwTms1vnry0ePTmPy059jc9gwQiatOjfnye/uI6WZMin9lVCKXXFwMXZEP27uQcogQuwLPYy123HabASjKNfqTaWv6H0sc7dv4+edggvbreK4prsIGjrf7GrDC6sGIaPEuZhSMqpLV2asX1d57KTm25iX04qgjKz9UhW3zcbNg4ZWlhbY23avJnRhIpARcggZQPN+xI7dvXh6SRyzt23Hoetc3L0ntw45rjJzdeHXSwgGIkM9TcPkxw9nM/bGMojmd5AepH8uohEU+/wvFjH1uS8I+IIEfNZDamvGdh6+8Dlemf8kAMFAkBmvfsd378zCNExOu2w4F90+CleM84DXV9QfpdgVBxe9JRhbI49rqWFKHazd7DX9B/L6ot/D4tfdNhu3DQ1XVHZd591RF7I8J5vfdw7lzJlza2yVtxcpJQt2ZHFZrz58vGoF13ZbyjmtN/FHXnOCoUjF7tB17JpOemwsNwwczAXdetT7tke1mMknq9LwGeF/coY0GJj0AxfMiKfQ78REwxMM8uGKZazO3c3HF44BoCi3BCNKDH/QHyR/ZwFoaSBsIKs/BB2gpdRbztr49KWv8ZWHv0EZIZNNK7axe1se6W2a8OC5T7N63jr8HkuOSU/NYP6Xi3l14ZNh/gDFn4tS7IqDioi/HVl0J+EmAzfE3RJ1/L8HDEZDMHHJIsqDQVLcbu4eNjzMfl05txAc27wFxzZvwU9bNrEse1etNWQMKSny+vhhUyZpbpPruy3Fa9gwzOj26NPad2TCOefWen+mlPy4OZMv16/DoWtc1L0nx7WIo2fcTK7r2oPX1/YDQBPW7v35QbP4cWc7PCEbZhWLv98wWLRrJ71efwWfEaJPMI5ozyl3nIt+p/UBZ3fgkSgSaZR5T2b6U5+waOZymrRM4aLbzqXPSfV/KO2lOD96SQebTae0oIw9OwvImL++UqmDZULasX4Xf3y7jKHnDmjwmor9Qyl2xUFFuM5EJoag7HkwdoGWDnE3RzhOK8cLwXUDB3PNgEH4QiHcNlu9HIHPnX4WF02dhC8UrDVb1W8a7PGUc2yT3eR4Yxg3exRmNXu8homJRqLThTT2IL2TIbgW7D0RMZeASAb/bEzvl1z/aypzd8XiCVlmkR82reHSTju4ubvO9T2WMaptJr9kt8GhGZzeciupLh+3LTwFrxFZLGyvgxVgqa2UFr2SSMoorlSczhgH7Xu1YfDIfghNh+R3kIXXAX4s167AIx7jX32fpbSglKA/ROayLSybtZprX/w7I8efXufvsSrHjRrIrszdBP3hvgKhCdr2aMWXr31PKBj5u/aW+ciYt04p9oOIUuyKg47mHgHuEUgp6x2toQlBjL3+lRLbJyUz5x9X88Rvs/l0bUZYd6PqmMAeXwwT1/Ul3+cmVM2+btcM/KZGdmkOcs+ZFeYOP/jnIMv+C85BEFjAgpxk5u48E49RtVG2xv82tGB0WyfpLhObFuKKzhlh8x+TWIBTD+E3av9zzBvXiT45buy/7SToD3HaZcMZMf70ShNHiJ4EnD/gdm9ECBPsfZh0z1RK80vD7PN+j5+Jd3x5VFbrAAAgAElEQVTI6ZefgNNdf9v3Rbedy0//m0NxXikBXwAhBA63nRsmXIXdYSe1RTJ2p51QINwh7YxxkNa67uxiReOhFLvikPFnF8uKdTjokd6UGevX1qjYBVbi0bayRIoDjgilDhAwbdiERp/E5SDL2NcXz2/9+H8BTH7Z1QdPFOUshGRRXgvGdlxDnAQpoeqtX9x+HRPX9SVgaMhaksGDSHKPTWLak9eFHfd7/Uy48V1+/vg3TNOkaZsm3PzGePqd5mDRzGWVSj02wWDY2cW4Yg0yFqWxNWMHXQZ0rPkXWI2E1HjeWvECX77xPYtnLietdSoX3jyCLgM7AXDceQOZcOO7+Mp8YWYj3aZz8t8aJypHUT+UYlcc9kizFOn5CHyzQEtBxP69MgpkaKvWlTVgqhPvcDK2Zy/+t3IF3lCQkkD03atE4NA1Lm3/E0Sx2ktpIgTE2/3YhBnxcNCEJM4eQLesIxGkunxMOeUL7l9yFivyE9EqGodURxeC7mnpEcefvPT/WPz98koTya5Nu/nP+c/wyvwnSWmWxJZV2+l3YikPvbMFaYJmA8jGsH0EPBz1nmsiLimWS++9kEvvvTDinMPl4MU5j/LYmBfZlZmNEILUlincP+kWElLiG7SO4sBQil1xWCPNMmT+eWDkYe2gQQZ/R8bdjBZ7JR2SU7ikRy+mr8nAUxFH7rLZ6JmWziejL0EXAk8wyPQ1qwmagqgeSuDTM34j1eWNei5kCuy65Px2G3lr/bGEooTGn9piW633cUxiIdNOmYTfsJEf7MR1c3qSURTeVk8CVx3bP+xY3o58Fn+/vDL8cC9BX5Apz37B6NvOZeOSNTz49lZcMdXv7XNk4ByEY1CtsjWENl1b8vbKF8jN2oMRMmjWLr1R38y2rcmiJL+MTv3a4451Ndq8Rxqipt3Mn8mAAQPk4sWLD/q6iiMPs+wtKHuVvUp9H05E+gKEFoeUkl+2bmFKxkr8hsH5Xbox8piuYUW+thcXMW/7dp6c+2tYQw4BPDw0lnGtXycysQqMkE4QcNksbf7V9o7cu+hEbGJvww3Jm8O+Z3B6ZLnf2igP2jnr+4vJ9uzb6QosX4PTZuOCrt24d3AShZlzeOfBX5n3jQOzWjRP537teX3xs8yf9jS9+r5PbHz1twAB7gvREp/CNE2+eG0mM179Dk+pl8Hn9OMfj15Ck5Z/jZ6teTvyuX/Ek+zatBvdpmGETMY/dzmjrjvzUIt2UBFCLJFS1umFVopdcVhj5v8NgksiT4g4RNLrCOeQBs0XNAw+XLGM7zI30DwugRsHDaGTuA2CCyPGSsAIwpTNXbig46bKMElPyMa9i07kimMyGJq+E6dev4JlVZm6+Rj+s+SEqDZ/hxbi3RNm0jc1F6eu4S0LUlKoc/v5ndiTbZU11u06I8afxo2vXo30fY9ZdA+C8siFXKPQkp7n+ateZ/aU+fg91gNSt2nEp8TzzpqXajSjlBWVs+q3tbjjXPQa3g3d9ufEqUspGd/ndrav3YlZxTHtjHHw1HcP0Gt4t1quPrKor2JXphjFXwYpA8iyCeCZCvjBMRyRcA9Cr6X6o5bGPhdo1ckM0Bqe5m4TXq7sncSVfUchNKtdnlmUAsFoa4Cmw1ltt3H8l+MYmJ6NITUW5LbkgrbrGZyWHaHUqztOo5HtieWRpcOjKnWAa7stp09KDi7dekuIiQOHy+TOV7Zz98WdEAKcbgdj7jjPusBxHIIoIZ8ihlLPSbxz95v88P7sMF+EETLxlHj4euKPUe3pX77xPRNv/xCbQwcJDreDp2beT6e+7Wu/uf1g6+rt5GzJDVPqAAFvgM/+75ujSrHXl8ZqZn2WEGK9ECJTCHFPY8ypOPqQRddD+fsgC0CWW5UK91yANItrvEbEXgFUd3rqYGsNtmPqv7Y0MUueROYOReZfjMwdan2WJiLm8ihrCBACTbOcn1NP/YItJUnMzm6D39CxlYAzSiPr+pibZ+7oUOv5i9uvIyQ1vs3qwDdZHSgJOLDZoOcgD01a2Bk8oj+vLnyKpm2tDk5Ci4fEJwEXYLdkx40vdDz/7D2d79/7JaqDOeAL8vFj07m84/W89+BkFn2/nI1LN7N+8SbeuvNDAr4AnhIvnlIvRbnF3HPm41Hj2A+UkvwytChZq1JC4e7I0siKRtixCyF04DXgdGAHsEgI8aWUUnUZUNQbGcoE/++E27FNkF6kZyoi7l9RrxOOAcj4+6DsKUAHGQJbO0TyWw1y2snyt8AzBfCDrLDXe6YgtRS0uGuR8XdC6bMg7JZcIg4hy60HENApsYgfzpkKwMoF8ZjxL6NRs7mxtp17yNQwolSj3MvcnJY8sux49Ao7fkhqPDvwF0a0yeKT7a8jtKSIazT3uUj7sUjfVyDLEc6TmTB+PuXFcyN2wlUJ+ILkbMnlkyc+RdOtipJC0yKctWA5bFfMzqD/6X1qnC8ahmGw6DvrodG8Q1OGjx4cFl/fqV97jCgPDIfbwZCRKukpGo1hihkEZEopNwMIISYD5wFKsSvqT3AdCD1KNKEPgitqvVSLHYuMOQ+CGaAlIWydGr5++bvsLXe7Dy+Uvwdx16LFXo50X2DJoiUg9Y6QF1n0y+cVuJtcSJfhwzG9L0DxjUQrzlXbM+eUFtt5OWNI1OgagAej2N7vXnQy/ZstoplI5K0li3hn6WKK/T56pKXz4Aknc2zzFghbK0Tcvhj4pT9NrFWpV8c0ZEStmOqUF3tqPGcYBqZhYq/SY7a8xMNtJ/yH7M278Zb7cMe6mHjHB7w893FadmoOQGxCDFc++TfevX9ypQ/A4bKT2jyZc687o97yH000himmJZBV5fOOimNhCCHGCyEWCyEW5+XlNcKyiiMKW1uI2uzaUS+TihBuhGPA/il1AFmDuUfue9UXWhzCOQxh74WmxUD8o1TdG0nA5XbQeeg4ADT36ZD4gnUP1aetIWbBb+iUBB3EO9w4dB0N64/Uqeu0jI9HYO3Qq2NK+C73Sp787Vde+X0+e7weQt4gmz5fwQ3nP87L931IfnZh2DUJqY0bWx4MhOh9YveI455SL8/+YwIjYy9jRMw4bhh8L5nLtwDw0SNTyVq/E2+ZD6RVfqB4TynP/mNC2BwX3jySx7+6h6GjBtB1cGcu/8/FvLH0WWIT6q5jfzTSGDv2aHuPiK+tlPIt4C2womIaYV3FkYStJ9g6QWgdUOU1X9iteiwVSBkAM7+iGmR9G1tbph7p/QqkD+E6Hez9w001tq4QWhtFrq5V5thhrW3rjNBiELYWSHSocExaswWh8Dpkk28RQiBc5yCDS8EzlZBp4qvchUtcuoFNkxX3BaVBB+9u6MXb6/viNzyc1nIH13TfidumE5NwEW3TRzNxySJeWjgvIpM2JO0UBRP4eNVi/IaBVhak1Qur0MtDaAGTb5d/w6wJP/Lczw/RZWAndmzYxfCLhrBrU05Y0S6bQ6dl5+bkbMkNO14dTRfoNhtBf7CitICDyx+6mKS0xIix9494kvWLMglVZMCuX5TJbSc+xLtrXmLWJ3MJ+sPNLNKUbFi0ifIST5ji7ntyT/qeXHvdeoVFYyj2HUDrKp9bAbsaYV7FUYQQAlLeQ5b8B3w/ABJs3RCJjyP0pkgpkeWvQ/nb1s5eCGTMlYi4m+q0pZvlH0Pp01gK2ER6JoN7BCQ8UXmtSHgAWXAVVjy8xFLTTuu4WYQs/DcEV1k2dmkg42+FwBIi4+dNMHdBaD3Yu1rKPeFBZMxlTF70Okt2l/LTznY0cXm5tecfHNd0J8UBN+9v7MukTZ3Z+3h4vP+vnNsmk1i7pfSCRgZ/rJ1Obvm/0KLcryklWcXFlRmrsasK0EuCaEbFgyNo4g36ePJvL2N32cnZmoum65imxGbXccY4CPpD9Dy+G+Ofv4J/97+z1t+ppmmMved8MpduITYphpPHDqN5h6YE/EEczn2mlk0rtrJx6ZYI5R0KBPnqjR9qXUOx/zSGYl8EdBZCtAd2AmOBSxthXsVRhtASEEkvI2UQMBBiX2ah9HwEZW9RaQeXQPm7SBGLiLu6xjmlsadCqVdVwF7wfQOu88A52FrbMRBSP7HCLUMbrV153A0Ie0/Mgisq7PzBfY7V0pes2vJRsYEZbvYQtvas957Bt1lWj9Wscju3/X4aYNWXFzJI54RC8nwxNI8p47y2mbht+5ShXfPTI2ElLy7/CVNGlhUA+HLDvoYhZf2aYLptNH1/Y9gr9a7NuxGaQJr7Xpqdbgc3vfYvuh/XhWbt0rnv7CcQ0UJIq/5egWnPf0lcUiytu7bk4Qufx2bXEULwzyfGcv4N5wCQtX4nmh75IAr6Q2xeuY1TLj2eL1//PkzxC03QZWAnZWY5AA5YsUspQ0KIG4DvAR14V0qZUcdlCkWNWA03qoUKlk8kunPzbahFsROYE90pK31I33eICsUOIOw9Eclvhg8zciCwjDDz0N61jSwsC3g134AMgr1XhCiX9+7L5+vWhDXM0IWgaYzBF6e+i0Rg1ww2lSRhE5H+BqcWYnD6NhbtaVKH2gXp1PF0S8LXPh73ltLwc2b4laFgiPWLNzF01AC+eO07lvy0sk6nqhE0MIIGvnI/e3YWAFbTD9Ou8frT00hulULOhhw+fvxTy35eDYfLTtfBnbngpnNYNms12Zt24/P4ccU6cbqd3PXBDbWur6idRklQklJ+C3zbGHMpFFExC6Ifl4V1lP91EN0NJKA+NnqzoIbrIdIMU4HzZBCxEYe7NknjxdPP5p5ZP2CYJoYp6Zik88bQjytNLgCdEwqjRs0ETZ3SoCVznU4qU+LMKieY4sC9pfahRshk6+osxrW9joA/1KBImb1IDfac35bSIekg4eqNc2jyUzZxUZS60ATOGCcjrzmdmHg3ry9+hsXfryBz2Raat09n2AWDGlROWBGJyjxVHB7YOkJoQ+RxvX3tNnbniTVG2wj3eXUuK2U50WrE1Ip/NtIzGRH7t4hTZ3U+hlM7dGRjQT5xDgetgpdDKLwxt12XUaNmJPBNVt1ldh3ZHpq/sRbhNxDmPo9BTbhinWzNyKK0MErJgXqSf25bSgenIx1WGKYB5J7eHJnvJX5ZlfsTMGRkf6594e8kNrH61uq6zuBz+jH4nH77vb4inEbJPFUo/mxE/H1YmZNVcSES7qv9Oi0ekfRyxbUxFf91QNwNCHs92sOVPrcf0nqh/JUaz9p1ne5p6bRJTAJzT3S5hR1EDD7DRWnQQXnQzk0LTmePLwaHptMsNi6qExVT0vz1teglQXS/iRY0w8w2MQluXLH7dsMOl53UFimUFpRGzlVPpC4oGZaOdIbH1kunTuEZ4X4I3abz6Iy7adGx2X6vp6gbtWNXHBYI53FW1EzZyxDKBL0DIv7m+pWcdQyEuButhhhaE4i7Hs3epX4LB/czz84sQMoQQtT8JyaNPKutHvmRJ0U8pP2C5l3IawvnMX2jm6C047ZBt7Q0HJpOridyhx2zqRQRMCJ36MJqDn7lk5eSlJbIF699h6fEy4kXD2XYBYO4rt9d+3efgOnSkTW8NRmJ4eau1sfUUvdH0Wgoxa44bBCO/oiUjxp0jTR2IfNHg/SA9AIuCCxEpk5D2NrWPYGWDObuhgurpdWu1IOrkAVXWI7WCFyQ8ACa5sYVezL3nXoy5/XazcaCfFICGvnLdvLw8rmYbWNB26dQBSA80Wu1CAnNOjZl1HVnIoTgxIvDs2abtW/K9rU7wm9B1xBCYNSUArt3nCeE7g1h2Kv5LCrs/Htxuh2Mf+7yWudSNA7KFKNoNKRZhFn6AmbeOZj545C+nw6dLFJa1SJLHrVCD+XeiBofyBIrXr4+xF4FuKsd1K0fEUd056wb4m6vXb6iuyrqzFRLAtKaIZLfRnOPDDvcPS2dHa8v4Jkhj/LOje/R5M01tHliObaCfQ5cCfg7JSCMSAO96dAYd//oGv0RD0y+hbjkWJwx+5SzaZgYRu1KHayHRuqMbYgqvU4FVrZsnw0hYhLcdBnUiUe/vIeBZx1b53yKA0ft2BWNgjRLkHvOszIzCYABsmg1Mu5qtLgbD6ospudzKHuhwn4dzXFqQuD3ipZ2te9tRMzfrVh4z4cVYZMhcI+GuOsRobVW2WBjJ7L0eTC2g94C4m6JUMxVkWaBNTYqobAQzL38Mmku37/3S2XxLQ0QAT9N31nPzjt7A2DXNI5p14Kcc/JImJmFCFj2ddOhEds+lZMuOS7qisU+Hymdm/LJtjd44V9vMu+z3wkFK5R0PXPE45fmo3tCFJ7VilATFx2cCTw37gJ63di8fhP8Bfj9myW8eceH7NyYTXLTJMY9MJpzrz3jT+/N+2egFLuiUZCeSRWhgVV3oF4om4iMuTxqxcHKa80ypHc6+OeA3hwRcznC3rXG8bXK4ZsJJQ9RdySLbiVCSQOhRSbCyOAGMLaArSNawh3IuOvAzLZ21BV12tGtsrjYuyFcVrJRwBfgp//9xtzPnySxSTznXnsG3YdWt+dHlvPdR/QwvxkTvosowCWkFQFj31WO0TKOGLudCWeP5H6ni4wOCcT+loPwBJHdUmmx3stjY17k0vsupOugzgBsKSrktu+/ZU1eLgDdmqTBDyv2KfUGErOumJh1Vs0dW4yDWZk2er0xfr/mOtgs+XEFj415Eb/X+v4WZBfy1p0f4ff4ufj2UYdYuoajOigpGgUzfxwEF0WeEHGIpFcrm0tXR5olyPwLKnqW+rD2og5IfAbNfXbD5cg701LItWK3dtpmHmBaWaaJTyKNbCj7EEIrsGLUNUBYdWVS/osQtcdWB/xBbjn+Abav3Ynf46+ooWLn6sf6c96/3GDrDM6TEMKGWfB3CPyBFRi4FxfEXYdWpQLjXq7ueSvb1uyIOA5gJjvp+fZF3HnaybRKsGq1rM3LZe7y9Uy56SO0zGKkYTXctjltDDtvEC26tuANLYv8RFG5KRchk/Z3/oGoQyVUjq99GA6XnUlZE0lIjWfdHxv54KEpbF65nVbHNOeKh8fQ58S6o5J8Hj/5uwpIaZ78p/Y4vX7QPWxYvCnieGxSDJ/mvYsepR78oUB1UFIcXPRmEIyWhWlYkSg1IMvfB2M3+3b6JuCDkgeRrtMqslAbgFFbmSI3ljoywcxlb/EuQuuQ+ZFdgiqVbvB35O5eSK0ZuC8CswjwIVxnWl2eKsw5P300h6x1OytLy0op8XsCvH3/PE4dsZbYRCdoacik/wORyL4I8wqbvXMoIvaqqJKfcPFQJj/9eUTNFQCXTzJ0I7S6cF8Brm5p6Xz44tuI9YWVilhKCPpCzJ4yH6FrJGgQGNmakhMtc4m0aZhJTvTCmkvzSiCQ7sJW5EcP1P4EsDvt7MzMYduaHdx79uOVRcUKsgu5/5wneWDKbQwZ2T/qtVJK3n9wMp++9DVC15CGyajrz+Lqp8ehaY3vGty5MXpP2oA3QHmRp9ErYf7ZKOepolEQsX8nsjytDra2iNpCC/0/EOFABMCwarY0lJra6IlEiL8fKuPe96PTj5kD5RPA+z/wTkcW3Wz9VLz1/vbZwqj1ym12ScYit+UsNbIg/2Lw/4j1EKtQ7s5T0JIn1lixcvStI0lKS4h6LuANsODLyLelNQvW13gr0jARQZPUr7ajF/mx5ftwry+i4NTm6M5aonnsGkWntKioJVM7AX+Q5h3SefP29yMqRfq9AV6/5b0ar/30pa/59OVv8HsD+Mp8+L0Bvnz9e6Y8M6POdfeHlp2ix9U7XA5ikw6/mjVKsSsaBWHvDQmPW/HXIhZwgr0XIvmdOi6MrqyQIWuuGpBSIgPLkZ6Pkf5fkdJAmoUVO/GIRSD+HrTYMQgRB7WEITYI6YHAbxCYC0BCSnzUUgBSQmz8XpOLgfUgq2qCCYL/Z6QRTXaL2IQYHvr0TmyO6LILIdi9LbzPQWKTeuwyBTSfuI7WT62g6XsbaTJjGy37tqZ9rza44lwkN0vC4bKj6RqxzRIo+GcXyoak428Rg1mLdULTNTr1bQfAllXRHcU5W3JrbKU39bkvKt989uL3+Jn8zAxeumYid5/5GFOf+4Ly4v3Plq3KP5+4FKc7/KHqinFy6f2j/zJmmIagbOyKRkXKgJVApCUiaqx+WGW873tk8V1VwhHB2ul3RWvyefjY4BqkZxqYxVZZXGMHIK1oFS0ZnCPA8wGRjlMHInUywt4TGdqM3HN+lDEHgHsMWuLjZMxfz91nPBq2OxVCkto8yEd/rKVWC4KIRyS9hHCeUOMQKSXj+9zO9rU7I+q5uGKdmIbJoLP7ce8nN+Nw2vl64g+8efuHEQoybE5NIKVEq6IGnG4HY+46jyseGgOAaZoE/UGmbFjDU3Pn4DdC6AU+2jy9sjLyJlrZAt1uFV+zu+z4otSMiUlwM6Pwg6hRJ2c5LsEIRa9Zs7c6pdPtID41jjeWPBu1DnxDWfj1Et684wN2bcwhKT2Ryx4czbkVcf9/FeprY1c7dkWjIoQDYe9eL6UOgPMMcF8OOKy4cBEDeltE8hthw8zyj5D5Y8E7Cfxfg7ERq9qjr8LEkQ3e6URV2MIOIcsxJmwdwDmMyPIE+4tmyQz0OK4LVz7xNxwuOzEJbtxxkNosyFOTNldR6oKobkcZDDMjSSMXs+x1zOK7kZ7pSOlDCMET39xHm24tccU6sVfZvfvK/QR8Qf6YuYx37/sEgBHjT+eCm87G4XKElRGoijDDlTpQafYAKMor5q27PuLafncx75rJuFdaJRASFuSBKSvvJJrqM4IGRsiwlHq1AfYEF93vPpUZ69aSW14Wdq4gp5C45Lio8sK+6pR+b4Ci3BImN5J5ZsjI/ry/7hW+D01havbbjPr3WX8ppd4Q1I5dYZkySp+H0BbQm1fEYY84uDIYeyC4EvQmYOsV9gclzUJk7nCi2+KromGFElbboQo3IuV/iIpSulIGkGVvgncySB+IpIoImdr7eUbHhUidGhaeWVpYxpr564mJ89Otyz1ootR6+IhYy7xU4Xzdhx3svdFSJ1nyBVYgC/9umaMIAG7QmyBSP0VoSUgp2ZqRxU1D74tq03fHuZie+w66TUe36ZSXeMjevJv5XyyqtFELTWCEzMquRtWxO21M3vEW/+p9OyX5pZXjhMtG8SktcC7Nw5lTvYxyzQghELrA4bRT3jqGXeO74nDaMZGETJNbBh/HtQMGkZ9dyDV97qCsqLzOjNe9tOjYjA82vlpvWQ5nVFSMol5I/xxk4Y1UKhpjGxTfiyl9aDGjG389KSG0CkLbwdYFYbdiqoXeBPRTol8UWFjRuaguxS6wHLgB9gXlOaxOTPZeyOBaZMljEFxq7bLdYxHxtwA2Kw7f85GldG0dQG9tNcc2NlaZqyo2QIf4OyJi7uOT4xg8on/F/f5kdYQytlSEO54KgT+QxfdVxP2b4DwRkfg0AGt/38i7dz3E5tXtado6wOV35DD4tFIwcpBlryMS7kMIQfuebQj6o5UjsPqGnht/ObpN48RLhnHjq1fRqW97OvVtz5n/OJkFXy3GZreRtX4nn738TdQ5mndoyowJ31FaWBam/KUvRPJPu/AmNUx1SClxupy8uvQZRv34KYFggEBw37/nK38sYEir1sx76jvKiiOVuqYLhKZhRImxPxydm382SrEf5cjS54k0X/ig7AWk+8JGfRWVZgmy4Eor2kVoVos5xyBE8ut19C91UXfUNIAGyW9A+evWwwAHOPqD6wxM/xIouroijR+QZeD5H9LIQkt+FRE7DmLHWT4C/y8Q2oq0dUKWTUBUa/DhLdPIyR1JhyH3ILSUWiUSwgHVs1CdwyBttuXoFbGVCU9rFm7grtMewe+xAYKSQhv/+Xt70poHufSW3Zx12Uy0KtUsuw/twqrfovRpxSoHYBomv06ZR962PJ7/5REAmrZN4/wbrPyA9x74hJo6dpx48VCW/LiSoC/y4eFyO0iyO8lvoJ9CAJv06Lt8fyjEg29Oxfzv71GVtzPGSavOzdm8cnuY0nfFOrnw5oP7dnk4oGzsRzuhGpJ5zALqMk1IKZH++ZhFd2MW3YH0/0Ztpj1Z8nBFw2hvhYL1Wan9ZTWXuAUqbOL1+aoaUPYSWsr7kDLFaqQRXA6lz0DhZdUctFjr+2cjjZ2WfEYOMu90ZPE9VhXJstcoyBX4vPseKn6fYMcWJ3dfkItp7r/DzjJNNN2XxQr89+7/VTheqzzEpCBvl4M3H2rBi7eGZ+9e/8qVuONd2OwVURvRmnP4Q6xblMnWjKyw47s25TBjwsyoSl3TNUZeewbpbZpEfbD7PH4Ksovqfa8ANrvOCRcPxR8KIaMsKoFtW3IqSyZUxwga3P7u9bTpbvkXYhPd2J12zrn6VE4dN7xBshwNqB370Y7eEozNkcdFPDWlt+9Flj5uOSwrFKb0/QjukYjEJyLHyhD4vieyxZwfPNMg/o4a1xHCAclvIwuvBio6RxAtzM2E4CpMIxcKx4MsqVV+a3JHhW+hJbL4wYpwyb07Qg8v3dGeM8bk0/+EMgxD8MOUZD58rhm63WT9oszKcgEZ89cz871ZBH1BTrpkGIPOObbBiTSZy7fWeM7v1fn1CzuXbcqprGXesU873l75Ip++9DUbl24md/secrdH1ne32XR2ZebQrse+nvOPjXkRT0n03bOma8QlxzH6lhEs+HJRWJSPZtMwQ2atD/DqOGMcpLVKZfyzl2PE2CobbldF+A3ilkUpXwzYHDZ6Ht+Njr3bMnHZ82xavpU9Ows4ZkAHUpol11uO+rBnVwFTnpnBsp9X0aRVKmPuPI9+p0a2OfyroxT7UY6Iv8WqNBj2Wu2GuH+HOzCD6yvqweQinCcjbV0thRx2nRe8XyNjxlY6KvdhEB67XZU63gz8s5Hl74HWDPRWINxWL1NZFmW0gMAi6na07p08AHo768ETmBsh463PZhETb+COlfh9gnMuK2DuN0ls3RDL3qD1Dx+ZytTnviTgDSClZN6MPxg8oqLE/k8AACAASURBVB/3T7q1QaasJi1TyFq3s8bzut3Buj8yw5pUNG2bxr9f/icAHzw8hSnPfBFhew8GQrTruU+p79mZz7a10csTgPUgKMwpouugztz29rW8cv1/rUqPQYOUFin8f3vnHR5F1cXh98z23SQQEkA6ijTFDlIEC4oiKiCKXVFRrChYsGDvvYEF22cBxYaiomJDEOwFEAURpQsECCFt+9zvj9mUzc4mGxISEud9Hp/H3Z29c2aZnLn33HN+Z8u6rUk3XRNstmtcPmk0g84+DJvdWFlcs19vnpk8E7UtQGD3NPy7p+NZkY/3920J33e6HezVtys3vTEeMFY6ex6wO3sesLvp+bb8m8vcN76huMDPwcceQNdee6ZkJxi/y0X7X0txfjGRcJTVf6xjyfxlXPrYuQy54KiUx9kVqJFjF5GRwG1Ad+BgpZSV6tLAEPdgVEYACh8y1BAlw3Dq3lGlx+j+WbD9BgxnqaOCC0DzkTj7BggaYl4VHLuIC2Xf29g4jUMDZ/KltF74LBQ9WRZGiZZUoyZxmLbmgDeWUVIVLmPj0t7WEAQzIbN5BC0W6XC5FaCY8MQaxg3fja69OrFp9WbeuP+9uBBCoCjI97N+YeGcJRwwMPXZ3jm3juSh0U8nzztXhvNPxtBLBzNz8idEwpHSlEDRhKw2zQiXc8SRcNS8+1IJApktjTDTwNMHcOjJfVm7bD3pzdL47JV5vHzrGyldj9PtpPdxB9DloE4s/W45xQV+np0wldW/r6WpJqArNLediGboyFe0KC3TxzO/PEjLDs0TxvYX+lmy4E88Phfd+3bBZrMx/93vue+sJ1BKEQ5FeOOBmRx+Sl+ufuHSlB6wr9/7LkXbi+Ni+MHiIFOueYWjzj4Mp6ua8hb1SE1n7EuAEcCUWrDFop7QvMNRnmEYjtoRP1NXIci/iYSZuW7uCMFemtcNsdS9widjXY/aGLNtVVJ96TZSETOuNx1J6QVQOAnzGb1ZKMAB6bdB/p1JvuM0csWjq2NZMaci6VcBRis65ewDoW8pr3ejmRQdZrcKc9fM0dhsNn7+dBGimcShi4J8M/PHajn2w089hPytBbxww2sUF8SHSTSbRuZuTenRP7nqpcvj5KrnLmbmk5+waM7vxh6Irti0KofLel3Hja+No9/QXrTs0JxmrTLZ8E9iAxGbw8aIccdRsK2IGY+9yZL5S2nbpTUnX3U82W2y6HPCQUy76+0qUxHtThuHDO/F8p//4cpDJhIOhuMLjmIPHj0QwW7TwKbFFV25PE5GjDvO1KnPfPJjnr32VexOO0opPGkebn3nGu4/e1KpOiMYTnnuW99y6Mh+HHxsvA78j7MX8t6kj8jfWkj/EQcz9JJj+PmzxabXpZRi/fJ/2X2fFBqz7CLUyLErpZYCDTaJ36IM49/QJDMlvATz2XEkyfsCbiPrQgW/QW27mNKHgr4ecIH7BCOM4tgP8Y5EtCSbkOHfjRi4SjW/XIPtt4Nam+TjNkj2LMBmes9Kk7tRW0fGui0VkSxlxOG20/Xg7oCRlaFpGpqmOGBAAVm7RVj6s5d/V/nwplds0FE1Qy8dzHFjBrHgvR94atz/KMorJhrV2fPA3bn5jatM4/ZKKV67Zwav3zMDm8OWMOPXo4YY2UPnP8VbG5/HZrdx42tXMmHQnYSD4dKwimbTOOumkxh45gAu3OcqAoUBwqEIy35Ywdy3vuG2GRPoefR+nHjFEN6d9BGhQLh0ZVAep9vBqDtP5f0nZ5OzZovpMfH26XjS3UTDURwuB6FAmENH9uWMG+KF2VYvXcedpzzC6thGcMkqyV8Q4IbBd6HZE3+bQFGQz6fOi3Ps0+5+h+n3vVtaA/DP4tV88uIcMls0MRUDi4SiNEmi07OrUmcxdhEZA4wBaN++fV2d9j+LUmEIfgmRf4y8bNfA6islQkz3JcnszN4doqsoraZUEWjyIGIzYsAq/07iZ/rKeB35Ey17RgrXoJLE0ZMRTO7UATR3pe3qxNYamn8BgU9RkZWGUmTgY+KvwYY49i1Nc+xzQk9a3PU4d726FF9GFE0DTVN8MzuTLkf0r+Ta/BD4kmBxDvM/0PnpyyL22Lcjg88/goxm6Rx6cl+at8vimWteYd2y9aBgzdJ1NG+blTDWvLe+Zfp97xqz1UpqhiLhCKt+X0un/TrS7eDOvLJiEp+9MpdNqzfT45Bu9B/RG7vDzj1nPkZRXhF6yaw6qhMsDvHoRc8w9Z+nGH3vmfQf0Zt3J33EV298k5CeGAqE+fj5L8nfUlClUy+hRbts7p51I//+vZF23dqQ3To+5FSUX8z4ATdTkGt+P0TD0aTRuQ3/bGLbpjwyWzYlf2sB0+56J24fIuQPsXntFvY/Ym9W/LqSQLkHo8NpZ/8jetT6Ju3OpkrHLiKfA2bSZxOVUjNTPZFS6lngWTAqT1O20KLaqOhWVO4pRsqiKjbCDlomNHsDsSUubSvF3sXYtIyuIn726kHSrgBnHwh9Y3zm7IdoPsMGpUM0Ud8agMiyKk+r60HIuxTzkMuOUvU8RsQNnqHGXF1FUKoQgl8DYuTea82Qpo+WHu9N9/DYrAJczjDltaIGHF+IPfNboF3FU6DCS1C5o9D1KCro55DDILStKU/e1InX753BE9/cTf7Wwjjdmfyty7n1xAeY8NLlHHpyfL/S6Q+8Z1qBWpFoRI9bRTRt3sS0icQvny0uderl2bZxO3k528ls2ZS2XVrha+JN0KwpQUSSCnyZkd02i5YdmpuGXgC+mr4gaUEWgNgEPYm2zD+LVnHW7pdy8SOjaNG+OQ6XI2GsYHGIDf/kcN49p/O/ia+jaRrhcIR9B3Rn4utXpnwduwpV3ulKqYa1HWyBKrjb0E4pkaZVRRANovLvQDKrV3otIpD5bKzxcj7GzDwE3nPAdYTxudvsFhEjZVIVJH5USTelUgruxTylsdz4SStCzRyKIL7qNVIWsSOZTxrdlMJLDM15Zx9AUHo+iBv0zXg9iRrwdnsI/NPAd0rc+0rpRnhKFaAJuGKSNYcN3c5PczYzf1Ymj1/yHOFgOFHqtjjEM1e/zICT+sSFkrbnVJ3Wqdk02ndrQ6s9WlZ5bFpmGtu3mPy7oXCnuSku8HPJQdexZf1W0xm52+di+NjBTLqsCmXPckRMdObLs37FxkofXtGIztXPXcyjF01BKUXIX25GHgvZPDXuJfbYrwOBwsRljWhCs92aMuKK4xhywVGsXbaezJZNyG6TuEJqCFgFSo2RwKckOreIIQ27A9pAYu+ANJ+DZE5BmtyLNP8SLePqSvdWRAS8o0hsBG3I+arit43N0WQEZ1dhlY3EPQEB0sG+V+Lhjp7g3rEWZ+LognhHIK5+EJqP2nIkKqcPatNBqPz7SfpnpEwqMyNLyqpfy+Hx6Qw5aytKKRbP+4O/F60yHTJ3wzYCRfHj7j+wB5rN3AZPmhtPmpvdOjbnthnXVnaZpZw0/jhc3vgaBpvDRvvubfnx41/54OnZbN2wzbTph8Nlp/+IPhx19mEpnauEZq0rf9h37dkJT1py4bbeQw7kyDMPZdqqp+l/Yu+yoq1yREIRlv/4t+lqxOl2MCxWkev2uuh84B4N1qlDDR27iJwoIuuAvsAsEanqr9GigSKiGVonej4EZqMiiRrbSilU+C9U5G+UUkjaZeAdiVHo5MW43aIQnI/KvxO1eQAq9IP5CZOkH5ahg7Mv2DpgSA64DO2Z7OnQbAY4BlB2e2sQXozyv7VjF19q0hLUtstjcsERjNTOLzFvmO0Ct0mpu0q26QwOp+FwbHZb0piu0+NMcLqjbj8Vb4bHkMnFSK93eZ2MnTyaa/93Gfd8PJGXlk9KGuaoyHFjBnHs6IE4YiqVIoLSdVb/sY6HRj/FSzdPJ+RPVicg/PDRLyyY8X1K5yph/rs/cMfIhxnW5BwGu07j+mPuZN3yspVQv+G9yGrTzDQDCWDhnCVEI1GaZGfQvU+XpA+6ijjdDtxpbsZOvoCuPTtVy+ZdGUvdsRGibxsX60xUfkZlj3XpmbxDY6rAl6i8cYbXUDFHljYGLW2s4dADH0PBXcZsVGGoETadjDi6x5pVz4CCh0nY3ZMmSItvEjZ29bxrIPB+5UY5eiLNpkF0PYi9bNM2+B0q7yITCQEX0mJulfouydC3jY39rhX/ZhwYK4goEI7tabQ2VB+1ePlZXQ9CTi8q6vP4izSeubU1X7zdkkNP6ceBR+7DE5c9H5fh4vK6GHn1CYy6/dQE2zav28qbD8xk0dzf2a1jC06ZMIwehxipkbkbt/HP4jXs1rE5bbsk6TBlwrac7fzvptf5fOo8U82YynC47KYz+kopF10TEXxNvLy47HEyWxhZUwXbCjmz46X4CxJDKe40N0/9eB/turZhw8pNXLD3+KTyBOXp0b8b982+CZen8irrXQVL3fE/jGTcjMpdDPq2ss1TaYJk3FLp95QKxmbjfyP2zuA+GhGn4ZjzxmNon5f7QuFz6FobKHwc9AppYtG1qNyzocXXiJaGCn2DecpGBEK/gKt3/DWkjUMFv4rF6M0mHy5w9TdCPva28dcR+Mg8DILN2Aj1DKv0d0hKdKW5LeKCJg8ZcXh9PeLsC+4h5sJmBbdScYavR2HFbz7mf9SKjvu05fInzsfXxMv2LflMvfPt0vzvI88cwMaVOZy1+6Vkt23G6defWKoi2bxtFpc9cX78uLrOpMue59OXv8LhchAJRejWuzO3vzcBX0a8ImI0GmXJ/GUU5/vZZ0B30pr6yGzRhMVzf6+2UzfGM9/IrJRyP61SilAgxKwpn3LWzSMBQzWzw15tWfZ9YsvEaDhKejPjIdpq95acedNJvHb3DMLBsDHxSKKJ07572wbj1KuD5dgbIWLLguzZEPwCIitQWguIrEHljUXZOiK+cxFH97jvqOjGWA53AahilHih8GFo9haEvo+pMVY8UxDyb8a8AhUgYsT7PcPKZvmJ1mKWTin2tpA9y5ASCLxfJnELgMPo0OQ9I8mYDkw3V0XYkVteRdahCicZ/UpNDwgjzp6IO4nscOk4/4B/FglyB+JAd53CXR8OZ6++XUr3LkZePZQTrxhC7sY8/IUBruw3EX9hAD2qs2n1Zu489VHGPHAWQy8dbHq+95+azWevziMUCJfOXv/45k8eufAZbn7jqtLjVv62musH342/MICIEYu+8P6zGD52SNIuRnaHDbvTjh7VTWfGyTJUSr4bMVFwrEgoEGb5z/E6RqdcO4wHRk2K20h1OO0ceNQ+cV2UzrjxJA4eciBzXptPKBRhzmtfk7+1IM7BO1x2Trj46CrtaIhYm6eNFBEH4h4MnpONEEjxSxD+FQIzUVtPRQXmxB2v8m81mk2o4tgbxRDdZGTYEMV0yoPCPL5c8nHIkCkAxDPMqDo1w2m+shRbS7SM65HmC5Amj4Bjf7B1BO/ZSNZMJEl2jXiHYVpspaLgqt6mnvHAGw6BmSahHQAPeM9CtBQKWEI/Y/Ynp2lh9uu3nb37dU3YkLY77LRol80b979X6tRLCBYHeeGG1wglSQN89/FZCcVK4VCEb2b+WJqrHY1Gue6Yu8jdsA1/gZ/ifD+hQJjnb5jG0u//4sgz+uN0J9Y/tOrUknc2v8h1r1yBw6TU3uF24EjSFNtmtyXt3Rp/nEagKBinTDlgRG/OuHEELo8Tb4YHp9tB54P2YODpAxL0b/bcf3cufOBsLnvsPB6ZdyctOjTHk+bGm+HB5XVx+aTRSTVnGjrWjH0XRUVzQN8Itj0S4rTVGqfw8ViaYskMSQcCqPybwTUPEc3IlAnOI9FJRyDwOWTcgnmRUrLUwhIcKJyovGsNp2rvZuSwKz/GhqogTR+pQos9lmHjGYJ4hqRyyYhjX1TaGCicQmn+udKRpo9X+7dUhc/FHnYmDzCtDfguRLynpzaYlpVk5eMwagUq4bevl5rmjCu9mH9/6EWz3Zry2cxBrPkrm649O3HEGf0p3F5sOpYIBIoCuL0ulny9DK+vgDPvXUfPw/PJ2+Lg7aebs+DjTD585lMun3Q+3374Mxv+3oS/MIDL6zQqV6eNw+l2cujJffj5s4V8+dr8+HREpbDZbIRN7o/Ssn8xsnaCxSHTa4tGdBbN/Z3LD76eoZcdw5gHzgHg9BtGMOzyY1mxcBXT7niLJQuW8filzxINR9mrXxduf+86PD4jg0bXdb6f9QsL3vuBviccRPfeXcjcrSndencuPaYxYm2e7mIo5UflXQPBubFy+jD4zkPSqqcUWIKe0z8mRVsRN9L8E8TW2ohBbtobcyftRtttMXrxWzENlggQNXK4HQdD+MeyWX4cTtBaxGbsJfFujzE7t++N2JqC+3jE1qLa15QqKrIWFfzUWG14RqDZqr9pqm85wWicXRFJQ5q9hDj2Td0eFUZtPjQWVqpQ7NX8Y6PyNQnjD7uFJSZNNRxOnQffWcFNZ+1BOKQR9Gu4fS7Smvro0mtPvnv/x4T0vuw2zejWe0+WfvcXLdoJtz//Nd6MCI7YxNtfJLwxuSWrVh7HHe9dRzQS5dsPfuKPb5fTskNzBp7Rn/RyPUmVUkwe+wIfPPNpXF67w2XH7rDjN2lkDYBAs1aZbM/JT6kNnjfdw5AxR3HenafhdDt57vqpvDfp47gMHYfbwaCzD2X8lIvRdZ1bhj/Aojm/EygKoGmCw+XgwgfOYthlx1Z5vl0Rq5l1A0VtvzU2ew7FyumDUPSykVWyIyTTYUEHMf44RcRo2ZawgLOD24jfat6RSPaH4LvMmKVmvgRNp4C9B/GNocWQIfBeEOsjWkE8LPwT4j4S8Z2/Q05dhZejggtQeqLEa9xxeiGq4D4oeBQKn4Ctx6P7P6v2+UjWlFuFjQdXNRBxIM2mxlI0PcbvJJlI5pOVOnWA068/EZc3fmXjdOn0OXo7z93ZmqJ8G0G/8eccKAqybVMeStfxNfWVhkQ0m4bT7SB/awEL3vuRrf9uo9+gJXjSypw6gMenOP3KTRw+cj/jJ7Db6H9ib8Y8cDbHXzyIFb+uZMF7P5C/tSB2XcI37/+YUKwUDkZo1iqzNA0zAQW5/25LubdpcYGf95/8hFuGPwDAR899npB2GQ6E+eyVeSil+Pb9n1g0Z0lp3r+uK4L+EFOufbXU9saKFYrZhTD0Qz4iUUvcD0XPwY70IPWeF5tpl48PCzgOiIsLS8atqNw/IFreGUcg+i9Kz0W0Zoi9PZJ+WflRUM1eRBVPg5IHj+ckY1OzeCrKLHyhAqjgV4hzP1NzVXgxKjAbsCPu40t7oqroFqPRRmQliB1UCOUbjaRdabqSUXlj43XZ9QBsvxpln2aiFZ8c8V2ICn5D/APKCc7epemV1UHsnYyN7eg/hriZvSsiSRxfOQ4+9gAuefRcnpswlWhUJxry0+fo7Vxx33pO2XdvlIr/DaIRnYVfLuHlvybx3qSPWfz1Utp1bU3Omi38/Nmi0gXDBy9l0TQrwqBTtlH+Z9SjNgYMj1/hrPxtNRMG3UFhXnGpcFjLDs256Y3xbF2fa2r3xpU5aDaNaFIt/uoRCoRZMn8pK39bnVCZW0IkFEHXdea9/a1ptaqK6twy/H4Gn38kA08/BKe78lBgQ8Ry7LsSeiUl9FXMUJMhnpNQwTkQLD9bVRBehAr/jjj2No6zZaOaPAm5FR4e4V9QuRci2e+Yjy9OcB8dawDdEbEb0qZKvBi3V8WNPUfSOLeefy8Uv44huauhil5EpY9D8402HHVkORApi2IU/Q8c3cF9TNw4KrIOQj+R+IAMogqfRzIfNz2/6fU5D0I1ucd4OKoAEAXX4aXNp3cEI0Wz+sUwx104iKNHHc7GlTmka2PJSF9DJAzJInR2p53Mlk05766yPYCRu10QFwXKWedi8o1tyFnv4KyrykJ2bp8NzdWq9HU0GuX6wXeTV0G+YNPqzVx9xG34mvoo3JZ4/7Zon0Xv4w7ivUkfV/t6k6HZNFb+tob9D9+bnz5dlFBN3a33nthsNjxpHkSThJVEJBzl9wV/8vfCVbz10Eye+PaehPTPho4VitmV0LKS6Kho4Oy1Q0OKSBLRrQAq/974t/xTMd1AjaxAhRPjzEqF0fPGozYPRuVdhdpyAnruaGPl4T4G8/xzzbQiU4WXxJx6IPa9KBCEgsfQQ4ti8sEV9wD8qKKXEk+hbzT2JxLPkjxlsRI0z/FGEVX2B0iLBWiZk2u0oV0THE4H7bq2oUn78YAbuwN6H7Udmz3+383hcjDIpKzfbJMy6Lfx5pMtCRSXuAMH4tgfsZcJmP2+4E+K8803Y0OBEO27tUmoiHV5XYy6/TQufngU/Yanfv/anXZsDlvS6lGlK9p0ac2lj5+HN8NTmpVjd9rxpHu44qkLARh8/hGmGT0lBIqCbFyZwzuPfpiybQ0Fy7HvQogIknE7Rsy6ZBpmA/Ei6Vfv0JhK+Q35WTPCFboZRddgmv0iNsNZVhy78GkIfIEhl1uA0Zz6B1T+3YiWaQiOideI5YvPuK4mD5iGMIzwi9nSOgy5F5A0+8ZsJWPvnETD3QHOg83HKbFDRVDB+Sj/LFS0rH+oiM0IRSXds6hbxDUAmjwMtrZc+eA6Wu8ewZNmw+Vx4va56HzQ7px712kJ30uWZqhpig1rMjDCTH2RChXKm9duSV5JqoxZ8Pl3n0Z6szQ0m0bTFk247PHzOPLMAdjsNm6fMYGX/5rEqROGscd+yRtWuH0u7vvkJqb+8yRn3zIyIZXS4bKze4/2dDloD9p2ac2LSx/jlGuH0vOY/Tlp3HG88Puj7Lm/kcLY7eDOnH3rKTjcjoT9iRJCgTBz3/wmqT1Vkb+1gKeveokzO17C+XtdyXuTPyYarZ2wU02wsmJ2QVRoEapoiuFoHQcivjFGwc6OjKWiqJwDzXOwtdZoLb4qfakXPgWFT5PYfchpiIBVkPzVN/UBZRZbdSItF8dSKf0Q/BaIxsn6VkQveByKplB5+mRFHOAbhZY+wWS8R6DoZcr2FjSQdCT7Q8RmrnCowstQuedSev0qDGmXoqVdWg2b6h5DXlhj4ZzfWf/XBjrt14HufbqY7j3cOORufvxkYcL7DpeD11ffSEZ26wTJhU2rN3PJgRMo2GauhS6aMPj8gVz17MUoZWxQujzOpFlc4VCYk1uMTmim7fQ4ueC+MzlxbFla68ola3jsoiks/f4vbHYbh43sy9jJo3F6nPz4yULycvLZ99DulUol5G7cxpzp3/D89VNNe7V26dmJMQ+ezY+fLCQ908fAMwaY6t5XxF8U4MIeV7F1w7bScV1eF/2G9eLGaTtH6jfVrBjLsTcSVPgPVP7dEF4EWjp4z0V8FyBiKxe7LrcBKB5IuxbNd1bZGHoeassQ0PModbDiAc/JaBk3J5xT37gPSRtRZ3+GZk+9lZiK/I3aciIVNVSS4zS00bPfM9V+UUpB4H1U0fPG9Tj7IelXIEmyXJSKxlIRN1f4xGOoWrr6pHwtuwLr/trAu4/PYtXva9mrbxeGjx1CVqtMlsxfyvWD74rbeHR6nBx6Uh+ue2Ws6Vj3nvU4X01fYKqKCMYs++lfHqRt51amn5ux4Z9NjO17I8UFfvSIjlKK3Tq24NYZ17CHSQu6SDiCZtPQNI1Vv6/lmoG3EQqE0aM6Stc58swBjH/24kpTgi864BpW/rYmLubu8rpo370Na5etJ1AUxOG0IzaNG6ddySHDK1/dffD0bKZc+2pCEZjT42TKrw9WS5cnVax0x/8QKrISlXuGkVNOCPStUPgkKv8OACT9GvAcD7jKQiLeUYj3zLhxRGuKZM0Ez6mgtTIyNtJvQdJvij+fUqjoRrBXksNd9Eq1rkHsnSD9asNGUigckTTIej/OqavIKvSCB9DzrjZkCNzHomV/gNbia7Sm9yd16oBRlWuaj+9H5d+Fvv0WlP9DowfsLs6SBcu45MBrmfXsZyye+wfvPPohF+w9nvUrNtCjf3euf/UKslpn4nDZcbodDDrnUMY/d3HS8X76dFFSp95p/4489OVt1XLqAK32aMmrf0+mRbssRJOYTEIOV/S50TQ0YnfY0TSjmO6WYfexfXM+/gI/weIgoUCYOdMXMGf6goTvFRf4+faDn/jp00XcNH08zdtm4Uk3pIydbgfde3dmzdJ1pdkz4VCEkD/E/edMIuivvHnJorl/mDYet9k1lv+UpMlMHWFlxTQCVNGzJqJXAfC/g0q/0khVbHIPKv06iG4EW9ukIRGxNUea3Arcan6u0C+o7ddCNIdKwyZhY0Wmohsgsgbse1TZvUnzjUK5jzHkDgruIelqAED5EX0z2IzNZhX4IiZUFgEiqOAXUPQiZE1HkkkZVBgvaW+16F/gX47yvw9FzxidqJL8frsCj100JS7NLxyMEAlHeW7CVG6bcS39T+zNIcMPJn9rAZ50D04TSYDy+DI85Js03tBsGsMuO5as1jvWNu6zV+axZX1ZGCMa0YlGQjx84dP0HdoTp9tJNBrluw9+5rsPfiI9K40e/buzbdP2hLECRUE+nPIpA08va0f4+bR5PDZmCjaHDT2qIyKlQmmaprH3IV2Zcs0rpmmTogmL5y2l1zH7J7W/daeW2J32xPCOMjpC1SeWY2+gqOhGVMGDEJwTc0omOePiMpyq05jVitakkoKlVM65AbXtvCSaKXEnBq0t+rZLDTXFWENq5RmKZNxZad622HZDfKej6xuNdMZkoRmxUdIPVakwavt18ceqYoisRBW9hqSNrvriHAfEtNLNKJmtFkNkNaroRSTdPGxR3/gL/XE65iUoXfHLF2Wb5SJCk+zUGjQPHzuEFye+njg7FXh6/P+YdHmUUycMM5UTrow50+ebznhFhGU/rGCvvl24YfDdLPvhLwJFQWx2Wyxt0vwBXL5r0rrl//LomClGAVO52/Wh85/C7rSR1aoZN795FTZ78nuxss/A0K1/94mP4hy7ZtPIap3JPgO6V/LNnY8VimmAKL0AFdUPnQAAIABJREFUtXWEUcykCknabFoFwbZjm66mwxW/WYnzK4/LSK4Ofk1ZxkwI/B+iip5L6VySNjYmr5vsj0uBI9YpKbIUczGyAARmpXY+LS2mieOm8j+LIAQ+SGnM+sDutKPZzH+z8v1Oq8Pwscdy5Bn9Y403yvK99YiOvzBAOBjm7Uc+iHtwlGfLv7lMuvx5ztnzcq48ZCIL3vuhUnuUrnD7XMyZvqDUqQNEI1HCwYhp71OX18nAM8pm65+9MjehyXYJkVCUTas3M+GoOzh0ZF/cvkTZXhFhnwHdkvwiBi07NOfuWTfSskNznB4nDpedvfp15cEvb9sh+Y/axHLsDRDlnxErZqosrcoN7sGILbv2ThxdhblErw1j8ecyyuybPGxo3SSEUgJQ/GpKpxKxozW5E7K/jD2cSuLumvH/6bchUvIH6TFExkwHSr3wRPOejGS9AZ7TwXk4SRe0suvqdzucDg4/tV9CmqDL62T45ebyvlWhaRrjn72YV1ZM5sybTjJNHQwUBbn/nCf4s0JsOXfjNi7e/xpmPfs5G/7ZxB/fLue+s59g+v3vcsIlx5g61YysdDofuAdzpi8wrRx1eQ0n6oilbnrS3Ozeoz3HXzSo9JiCvKIqpQqi0SgFWws4etThuDxOHC4HnjQ3bp+LM24cwfPXTWXa3W+Ts6bihnoZ+x66F6/+8yT/W/oYr615hkfn3kF26x1r5FKbWKGYhkh4IeZNK2KzBHGD57Qdzn1PiqM3BL40Obcdsl5HtOagNQfCqO1JNNor63NqgmZvhcqehSp+1wg72Vog3jMRR7m+pvY9wdYSoquJL4pyg605esFDiOtwcBxU5UxKHN1jewygbz4OoisqjOkBT2J++K7E2Mmj2bphG0vmL8PhtBMOhhlwcl9GXrtjPV9LyG7djN33aZ80RJG7IY+rD7+Fu2fdyH6HGRXNbz38AUX5/jgnGygKMvWOt3ljw3MMvfQY3n3iY+wOGyKC0+Pk7lk3ICJ4TJw+GCGSa1+8jNVL17H131x6Hr0//Yb1irOr7/EH8ckLX5rO7ksIFofI3ZjH2MkXMPSywfz86SI8aW7mTF/Aq3e+TaAwgMNp5/V73mXi9PH0PSGJvLQILdqn1nawrrAce0PE3hkje6RizNMDTZ9FnL1qfSmoVAgiK0iMebvBdTiao0e591woeyeIVOx0I0m11ytDxIP4zgCfeWMNEYHMZ4yOTaoYUIYWPCEIzAaiqKJXwX0UNHkopd9GRdZBdBMJ1bPOAxDvru3YPWke7p99M+v+2sCGfzbRce92KeVlp0KPQ7ombb4BhrN8atz/mPLrQwD8+sVvprnjNqed1X+s48L7z2b42CH8Nu8P0rPSOfDIfUod9HFjBvHDx78mzNqdLgf9T+rNYbZ+Se046Oj96NG/G78mCQ+BMdPfq19XADp0b0uH7m357NW5LP1ueVyWDMB9Zz3BWzkvVLnRvKtQ02bWD4rIMhFZLCLvikjlrcYtagXxjDSEsOJwgK39Djt1pcJGI+qomcQvRsaJ/w3iHZ2A73yk6SOJNmbcDngoi5E7YhW0N1TbtlQQ+x5I87mQdh04SzoZ6RhZMgrwGx2lgl+lNJ4quAcwKchRgZREu3YF2nZuRa9j9q81pw7GQ2Psk6NxeZILZ638razReYt25qHASDBMVisjm6Z52ywGnjGAXsfsHzfrPvCofTnxiiFGw2mfC2+6h/RMH3fNuhFbkn2EEjRN495PJnLI8F5oJg2wHW4HnfbvyEGD4lN2v5j2tblwGLDg3e9TVqI0Y8M/m1i5ZE2dVKbWNMb+GdBDKbUvsBzYOX+1FnGIrTnS7LWYZG4svu06Amn28o5pthfPROX0QeWegtp8JHruqDhZXBVZG5MSrnjD20EVIwkPGYxWcdkzwHOike/uOhzSb4CdpL+ulI7afqORJhn8BNNUTFWMSnXjMzgf0w3Z8EKUqn4P0MbEMaOOYPIP9yXVYSmv1X7KtUMTYvJ2p529+nalZYeqwxfn330G//vzCcZOvoDrXhnL9H+fo2vP1ATUbDYbt82YwODzB8Y5dxGh1R4tuXf2TWhavAt0JJFc8Bf4efC8pzi5xWg+ev7zlM5fwr9/b2TMfldz4T5XcUW/iZzW5iJDYXMnUiPHrpT6VKnSNInvgNpLwbCoFHF0R8uegbT4GWm5MCZMVf18YhX6xehbqgpAFQFBCP2E2laujD66MomoVhjCfyS30d4J8Z4D+r8Q+gYK7kPl9EcvernadlZJ4EMIzsYIFSXL3BGMfqgpkLSrk43kmToNE13XExQSq6Lj3u046+aTTYW/Tr7q+NLXPfp3Z9wzY/A19eJJc+NwOTjgiB7c8nbq+z8t2mVz9KjD6TesV7VDIdtytvP51HlxBVZKKXJWb+HXzxPDNMeOPtJ0QxcgHAxTmFfEU+Ne4vtZP6d0/mg0ytVH3Mqq39cS9IcIFAbIy9nOrSc+yMZV5qvj2qA2s2LOB2pPm9MiAaWCKD037o9QNG9cazmlouhFU9E3D0HPGYhe8ABKTyzoKD2+6EUSZ+JhCC9BRVYbL20dYzHritjLUg5N7Y2itp1vdFFSRWWNQwoeRoVqd8aiiqenkF/vRjwnpjag52SMfYzyOME9BJHGkUy27Ie/uOzg6xjsOI1hTc5hyjWvEA6lvho59brhnHDJ0TjdTjzpbpxuJ0MvPZpTrxsed9xRZx3G25teYNJ39zBt1VPc8/HEuFn9zuTXzxebbvYGigJ8PeO7hPf7Du3JoHMOw+lxJl2RBIuDTL3LXMY64fxfLKF4uz9BOjgaifDRc9Wb+VeHKjdPReRzwKyjwESl1MzYMRMxpknTKhlnDDAGoH379tU2VCllVDOGl4KtHbgGmIYAGiNK+VH5t8U63CvQsqHJ7UamR8Vjt18Tn7lS9Aoq8Blkf4CISal+dAOm8rrijOmmdEDs7VGu/rHwRDDuGPGNSm546MckzjaI8r+etNnGjlGZQ3IAGnhPT1nzRdLHoSLLIPRLrBhKB3tnJOOWWrG2vlm3/F+uPfL20niyvzDAB0/PZvP6rdz0+viUxtA0jYsePIdzbh1JztqttGiXhSfNPDfd7rDTYS9DBnjL+q3MfukrtqzP5cAj90nIaKlN3D43YhJj1zQxzaMXEa548kKGjx3C3DcX8Pq975qqWm5euyXhPTO2bcwzXQ1FQlFyUhxjR6jSMyqljqrscxEZBRwPHKkqWc8ppZ4FngVDBKw6RirlR+WOMhotqAiIA6SpkWK3A11sGhoq7+pYsU9JN6ANqG1XQNZrSLlsFBX522g+HTcDDxnl//4PwXty4uCuQ2I9PSvMyFXIaD4dQ5o+jip4CPxvGc7acQCScXPl+iuqEPMqQQWVrCJ2CPcwCP9JYtaOB9LHIa7DEXvqHelF3Eizl1DhZUZ2j70D2Pep98KT2uKthz9ISAUM+kN8O/NHtqzfSnab1DdcPWkeOnRPLQr765e/ccuw+0uLjT6fOo/23drwyNzbcXlqvz6g5zH7mf6bOdwOBp830OQbBu27teH0G0bwzqOzEhy7aEL3Pl1SOn/3vl1MN1zdPhcHHVWbE5t4apoVMxi4DhiqlKmCUq2gCicbM3VVjNELtAj0jajtjX+vVkU3lVVwxhFEFVao4gwvNmaXCfhRocRlJ4D4zjXUIOOe8R5IuzSumYSIEy3jRrSWvyItl6JlvR6fS26Gs6chfZuAB6nQ9agEFVmJKpqK8r+H0hOzUlR4OXruuegb90XPOQS9cApKRRHvKeDYGygpSHIa58l8Cs13XrWcennE0Q3xnIA49m00Th3g74UrTdMWHS4H61ckau/vKP5CP6/fO4OLD7iWK/tP5LYRDxIoCpY6y0BhgNW/r+X9p2ZXOVY0GmXhnCV8/c535G5MraOY0+3krg+ux9fEizfdgzfdg9PtYPS9Z7LnAZXfE3aHndH3nhG3jyAiuLwuzr0jNfmEtp1bccTp/ePi9k63g5YdW3DYqcnTNWtKTWMZkzECkZ/FbvrvlFLJZeJ2FP+7JDq2KIS+Ryl/aiJPDZXov8YKJaFxhDI2NcujtcR8huwwwlcmiNYMst5HFT1jPEC0LKPRtHuQ6fFAlQ5O6UWgbwJbK0OxseARjH8/BXjA0Q3cQ+K/o5SRYlg8PfaODeQ2yHwWiTXHUJG1qNxTY5u8GH1MC59ERdeiNbkLmk2F4FyjR6ktG/EM/0+s6HaEzgfuwYpfVyXMJkOBcK3JzYYCIa7oO5F//95IKFBJoZA/xJevzWfk1ckLqNb+uZ4Jg+6gaLsfEUPYbOS1QznvjqprCnr0786bG57jp08XESwOccCRPWjaPDXNpBMuPoas1s2Ydtc7bF63lb36dOHcO08rDSulwlXPXcy+h+7F+0/NJlAc4IjTDmHElcft1Jz4Gjl2pdSetWVI5SeqRJ9ERZOK8jUK7J2SzHrt4Dgw/i1nH9AyIRrrzVmC2I0ZbRLE1hwx0VuvLkpFUQX3Gc65JC7tOw8yX4bAdNC3I+7BsQ3IClknoQXgf5O4B7gCte0SaPEtIk5U0QsmD7gA+N9DpY1HbFngHoi4ky+xLQxGXjOUL6Z9jb+w7D5xeZwMOKlPaX55TZkzfQEbV+VU6tRLqCiBUB6lFBOPv5et63MpH+yd8eiH7N23Kwcfe0CV4zvdTvoN3bH2kv2G9trh74KxF3H0qMM5etThOzxGtc9ZZ2eqCe7BJD6DBBx71VvvybpCtAzwnm1UlZa9C+JGfBfEHysa0mwaOPbBCEW4QWuNZD6P2Kqnl70jqMInoTjmnFUxhjbMSxBZjNbkfrTMZ4xZtEkqofK/nWSjVRmbsGCEmsxSGcUF0X9q70L+A7TutBuPzL2DHv27YbPbSG+WxslXn8DVL1xSa+f4afZC02Kfirh9rjidl4r8vXAV2zZtp+IOXqAoyPtPfVJTMxslDSKtRNLHo0ILjAYSqjjm5Jw16hTfkJD0a1G29lD8QqwbUC8k/RrTdnlia4VkvYmKbjY02m1t6yQ2rJQynHhFHRnlh6LnoLLsGUiyKikh9pmjM0T+IKFwSIWShposkrPnAbvz6Lw7d9r4zdtlYXPYElUWBVxuJwgoBYec2Jujzj406TjFBX7T6lGAwrydtrWXlLzN2wkHI2S3abbL7rs0DMeuZUL2xxCYjQr/BrYOiGcooqXXt2l1goggvtPAl7pGSVVNLWqfaFnsuyJmDacrIJ6hqOB8EgXGouDsbRzjuxDl/6TCMS5wHW7F0ndBhlw4iPefmh3n2EUTslpnculj57F9cwE9+nej496VP5S79upk2sHJ5XVy2Cl9a93uZOSs3cI9ZzzG8h//jl1HM65/dSx79e1aZzakitXz9D+I0gtRxS9D4DPQMhDvKMR9ZI3H1TcfHZP2rYBjX7Sstyu3SemovLEQmh/rBmUHbNDkATRPmdysCv2I2n4bRP8GnOA5Gcm4rpyEr8WuxHcf/swD504mEo6gR3V269iCO2ZeR+tO1XsQfz5tHo9dNIVw0BjH7XPRpnMrHl9w105Jk6yIruuM6jyWnDVb0KNlK0ZPmpsXlz1eZ1K9VjNrC1OUXoTaOtxokVe6UekB33lo6eNqNnbwa9S2yyjLJRfAhTT7H+I8qOrvK2VkOgXnGg8cz9CEPHmlF0FwjlFN6zoMzSQcZbFrEY1EWfnbGtw+V40ybv5ZvJoPp3xK7sY8+hzfk4GnH4LTnVyMrDb55fPF3HbSg/gL4uskHC4HZ0wcwVk3mdSI7ARSdewNIhRjUXso/9sxOdrym1p+KHoB5Tsnrjl06Xciq4yN0fAvYGuH+C42reAU1wBo9rJRdxBZAY7uSNpYxLF3SraJCLj6JK0OVcHvUXkXYQRndSi4Dz1tDFrartmmzsLAZrdVmTOeCnvs24ErnrywFiyqPjlrtybIAoChH/NvLeb91xaWY/8PoPR8VPHrRp56dBWmfUTFAaFF4D4i/ruRFaitI8v6qkbXGg2tm9yD5jk+cRjnAUizF2r/GlQQlXdJLNumHIXPo5z9TFcEKroBQj+D1hScff4zEhQW8SilWPbDCv745k+yWmcaYmLVnOl3O3hPU8fu9rnY97DUJi51iXWnN2BU+C9UwQMQjjkv7wWI9/S4nXqlb0NtGQ56LolFXuUHC6CKpqCiaxDPSaVppKrg0bLmFaUEIP8uVF0KYgW/SfJBAOV/O86xG8VO90PxVAwdeIwWec1eQeypSb5a1D25G7cx7+3vCBQF6T3kAHbfp0ONx4yEI9w6/AEWz/uDaDiK3Wln0tgXeOSr26tVZNRx73b0OvYAfvzkV4LFhvyGw2mn2W5NOeK0nVdBuqM0jDx2iwRUZA0q9xQIzTM0WaLroPB+VMGD8ccVPW+kiVbm1AGIGKGWgodRW4ei9Dzj7fAvmIqEqaKYSFhdETK3A5VYtBT8Avyvx75TFLN1C2rbmGrL01rUDfPf/Z6zO13Oc9dN5aWbpzO2z408eeWLNf73+uDp2Sya+4chYxCK4C8MULC1gDtGPlztsW6aPp7z7jyNtl1a06J9NkMvG8zkH+6rk83b6mLN2BsoqmhKLHuk3I2v/FD8KirtkrJU0MCXJAh8leImMSwTgGgOqugFo2eqlh17MCRYAFpGTS8jdZx9zSuQxYu4j4t7SxVPMyl2UoZ8cGQZOLrvPDstqk1xgZ/7zn6CkL/sPo1Gonzy4pccMvxg9j+iRyXfrpyPnv+CYHH8g18p2LRqMxtWbqLV7i1THstmt3HS+BM4afwJO2xPXWHN2BsqoYXEyQaUII74lEMtWbdCF6RPwHDuCYND4FNjON9FGC3uKnzXc0KdavSIlgEZt2HYG5uPiBech4Arfl8gIQ5fii0FzXaLuubnTxeZtroLFgf5Ytq8Go1dPjUxDpFKe7c2dCzH3lCx74GpSI4Kg1YmHyC+c0l0zDZDjsF9FObhDSAWYxfP8ZB2uVHtKz6MZhNHIxm31fgSqovmPQnJfhd8F4DnTKTpk0jTyYlxfvcQzB9YChw7PvuzqFuUIkFGoLocedahOE36s2a2yKDNno23qM0KxTRQJG2Mke8dF0pxgftIxFaugbDraPD9AUUvxtq9RYzK3aaTEVtzlKMbhH8jvkzfY7S0i6GlXYjynQ2RtWBrjiRdBex8xN4JSb+q8mO8p6L870HkH4wqVbvxX8a9pjo1FvXLgYP2NW3w7Pa5OPLMATUa+6Rxx/HtzB9Z/cc6/IUBXB4nml1j4vTxu6wcQG1gFSg1YFRwntFZKboRsIHnRCRjomkVptJzIfw7aM0RR1kDDRXdiMo9F/SNgGbornhPRdJvatA3vlIhCHyMCn5lXLP3VCsjZhfm63e+4/5zJqGUIhKO4nDZOfrcIxg7aXSN78NoNMoPH/3Kb18vpUW7bAae0Z+MrIYpR2JVnv5HUEoZjajFnTAbVSoK0fWgpZkWHsWNEV5saKg79rV0VyzqhS3/5jLvzW/xFwXofdyB7Ll/zYuaGhtW5el/BBEBScxOUYEvUfkTQS8GoihnL6Tpw6YOXkSgVvuPWlhUn+zWzRgx7riqD7SoEmvztBGiwstQeeNiaYp+IGRosOSOqW/Tdioqsho97yr0nP7oW05EBaput2Zh0RixZuyNEFX8Eom56xGI/IUK/4U4OteDVTsXFVmD2npiLNVRBz0HlTcBlb4OzTe6vs2zsKhTLMfeGImsI6EZBRjt6vRNQJljV3qBEV/XmqJseyDBT1GRVcYGq2sgIrXXl1FFt6L8MyC6DnH2MtImaylLRRU9XaZnU4ofCp9Aec9ExCz90cKicWI59saIqw+EF5EgI6BCYC+rutSLXoKCh2PNsiNACIUL8KPEazTHznqjVtIbVWgRatu5sfMEUYGZUPgUZL1ZO+0NQz9hWrCFBpE14OhS83NYWDQQahRjF5E7RWSxiCwUkU9FpHbam1vUCPGeCVo68c9tD3jPNBo+Ayr0AxQ8itGftBAjH16ntDuRKjaUHCtoz+wISinU9qtiHZaC5cZfY0gj1AYVdNvLTh6G8nn9Fhb/AWq6efqgUmpfpdT+wIfALbVgk0UNES0TyZoJnlNBaw327kiT25H060uPUUWvktiGriIRCNRCs2B9A0RzTD4IQfGHNR+fEumDiuEWlxFOqiTV08KiMVKjUIxSKr/cSx9J69Mt6hqxNUea3Arcan6AqbCX6Ui1YI2dpLeG2oSKbq5xj1Zx9UVl3AkFd8fUHqPgHoQ0ubtG41pYNERqHGMXkbuBc4DtwBGVHDcGGAPQvn37mp7Woqa4joLwEkybbpTiAHfN84rF1gJl7wKRJSaf6qjt19dKcw7NOwzlOR6iG0BrWjuxewuLBkiVlaci8jlgVoo4USk1s9xxNwBupVSSKWIZVuVp/WP0Ph1hOME45+4EoiBusLVGmk0vkwCuyfkia1FbBmGarYMdafETonlrfB4Li8ZMrVWeKqWOSvGcrwGzSLr2t9iVEM0HWTNQ/rcg8DloWeA9HVF5EFkFjm7gHIBIopzqDp3P3g4lGaDykhzReCVULSzqmhqFYkSks1Lqr9jLocCymptkUVeI5jNkfX3n1s0J3YPA/y5QoWGGvasVNrGwqEVqGmO/T0S6Yky3VgMX19ykxokK/4UqfAzCC40Qh+9SxJ10S6JRIulXo4ILQM8DigE3iANpcl99m2Zh0aioaVbMSbVlSGNGhZcb/UmVH6NF22ZU3jhUxkQ07yn1bV6dIVozaP4x+D9ChReCrSPiHYFomfVtmoVFo8KqPK0DVOGjZU69FD8UPIjyjEDkv/PPIOIB70kI1pzAwmJnYak71gXhRZjmcasg6Jvr3BwLC4vGjeXY6wItWSd0BdKkTk2xsLBo/FiOvQ6QtMtIbCjtBs8wK3fbwsKi1rEcex0g7qMg/TqQdBAP4ALPCUiGJa1jYWFR+/x3du3qGc13Bso7EvQckKZGgZCFhYXFTsBy7HWIiCO5vKyFhYVFLWGFYiwsLCwaGZZjt7CwsGhkWI7dwsLCopFhOXYLCwuLRobl2C0sLCwaGZZjt7CwsGhkWI7dwsLCopFhOXYLCwuLRobl2C0sLCwaGZZjt7CwsGhkWI7dwsLCopFhOXYLCwuLRkatOHYRuUZElIhk18Z4FhYWFhY7To0du4i0AwYBa2puzs5FqShK6fVthoWFhcVOpTZm7I8CEzBt6rlroCJr0HNHoTbtjdrUA33bWJSeW99mWVhYWOwUauTYRWQosF4ptSiFY8eIyE8i8tPmzXXXwFnpRaitp0Doe0AHIhD8ArX1dGv2bmFh0SipstGGiHwO7Gby0UTgRuDoVE6klHoWeBagZ8+edTe7D3wAyo/h1EuIGJ2MQgvANaDOTLGwsLCoC6p07Eqpo8zeF5F9gN2BRSIC0Bb4RUQOVkptrFUra4CKrAD8Jh+EIbLScuwWFhaNjh1ujaeU+g1oUfJaRFYBPZVSW2rBrlpD7HuhxAuquOIHYO9SP0ZZWFhY7EQafx67ZwhIBmAr96YTbB3B2buejLKwsLDYedSaY1dKddzVZusAIm4k621wDwbcID7wjECaTSUWQrKwsLBoVOxwKKYhIbYWSNNHd9r4SilU8atQ9Czo28DeFcm4AXH22mnntLCwsEhG4w/F1AGq8AkoeNjItCEMkSWo3NGo8OL6Ns3CwuI/iOXYa4hSASh+kcTMmwCq4In6MMnCwuI/juXYa0o0B0gSq4/8WaemWFhYWIDl2GuOrTkkq2C1d6pbWywsLCywHHuNEfGA90zAU+ETN5I2tj5MsrCw+I9jOfZaQNKvhbQxIOmAgK0jkjkZcR5U36ZZWFj8B/lPpDvubEQ0JO0ylO9SIIqI9bNaWFjUH5YHqkWMgifrJ7WwsKhfrFCMhYWFRSPDcuwWFhYWjQzLsVtYWFg0MizHbmFhYdHIsBy7hYWFRSNDlKr7HtQishlYXc2vZQO7nCxwCjRUu6Hh2t5Q7YaGa3tDtRsalu0dlFLNqzqoXhz7jiAiPymleta3HdWlodoNDdf2hmo3NFzbG6rd0LBtT4YVirGwsLBoZFiO3cLCwqKR0ZAc+7P1bcAO0lDthoZre0O1Gxqu7Q3VbmjYtpvSYGLsFhYWFhap0ZBm7BYWFhYWKWA5dgsLC4tGRoN07CJyjYgoEcmub1tSQUTuFJHFIrJQRD4Vkdb1bVOqiMiDIrIsZv+7ItK0vm1KBREZKSK/i4guIrt8KpuIDBaRP0VkhYhcX9/2pIqIvCgiOSKypL5tqQ4i0k5E5ojI0th9cmV921SbNDjHLiLtgEHAmvq2pRo8qJTaVym1P/AhcEt9G1QNPgN6KKX2BZYDN9SzPamyBBgBzKtvQ6pCRGzAk8CxwF7A6SKyV/1alTIvAYPr24gdIAJcrZTqDvQBLmtAv3mVNDjHDjwKTAAazK6vUiq/3EsfDcv2T5VSkdjL74C29WlPqiilliqlGko38YOBFUqpf5RSIWA6MKyebUoJpdQ8ILe+7aguSqkNSqlfYv9fACwF2tSvVbVHg+oKISJDgfVKqUVGU4uGg4jcDZwDbAeOqGdzdpTzgTfq24hGSBtgbbnX64De9WTLfw4R6QgcAHxfv5bUHrucYxeRz4HdTD6aCNwIHF23FqVGZXYrpWYqpSYCE0XkBuBy4NY6NbASqrI9dsxEjOXrtLq0rTJSsbuBYDZLaTCruoaMiKQB7wDjKqysGzS7nGNXSh1l9r6I7APsDpTM1tsCv4jIwUqpjXVooinJ7DbhNWAWu5Bjr8p2ERkFHA8cqXahwodq/Oa7OuuAduVetwX+rSdb/jOIiAPDqU9TSs2ob3tqk13OsSdDKfUb0KLktYisAnoqpXZ5VTYR6ayU+iv2ciiwrD7tqQ4iMhi4DjhMKVVc3/Y0Un4EOovI7sB64DTgjPo1qXEjxuzwBWCpUuqR+rantmmIm6cNkftEZImILMYIJTWk1KrJQDrwWSxd85n6NihD9x3DAAAAi0lEQVQVROREEVkH9AVmicjs+rYpGbHN6cuB2RibeG8qpX6vX6tSQ0ReB74FuorIOhEZXd82pcghwNnAwNh9vVBEhtS3UbWFJSlgYWFh0ciwZuwWFhYWjQzLsVtYWFg0MizHbmFhYdHIsBy7hYWFRSPDcuwWFhYWjQzLsVtYWFg0MizHbmFhYdHI+D+FSKBuZ7i7LQAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline\\n\",\n \"from sklearn.datasets.samples_generator import make_classification\\n\",\n \"# X1为样本特征，Y1为样本类别输出， 共400个样本，每个样本2个特征，输出有3个类别，没有冗余特征，每个类别一个簇\\n\",\n \"X1, Y1 = make_classification(n_samples=400, n_features=2, n_redundant=0,\\n\",\n \" n_clusters_per_class=1, n_classes=3)\\n\",\n \"plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline\\n\",\n \"from sklearn.datasets.samples_generator import make_blobs\\n\",\n \"# X为样本特征，Y为样本簇类别， 共1000个样本，每个样本2个特征，共3个簇，簇中心在[-1,-1], [1,1], [2,2]， 簇方差分别为[0.4, 0.5, 0.2]\\n\",\n \"X, y = make_blobs(n_samples=1000, n_features=2, centers=[[-1,-1], [1,1], [2,2]], cluster_std=[0.4, 0.5, 0.2])\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], marker='o', c=y)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline\\n\",\n \"from sklearn.datasets import make_gaussian_quantiles\\n\",\n \"#生成2维正态分布，生成的数据按分位数分成3组，1000个样本,2个样本特征均值为1和2，协方差系数为2\\n\",\n \"X1, Y1 = make_gaussian_quantiles(n_samples=1000, n_features=2, n_classes=3, mean=[1,2],cov=2)\\n\",\n \"plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\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\": \"3.6.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "model-in-product/sklearn-jpmml/PMML_Example.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C) 2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"用PMML实现机器学习模型的跨平台上线 https://www.cnblogs.com/pinard/p/9220199.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline\\n\",\n \"import pandas as pd\\n\",\n \"from sklearn import tree\\n\",\n \"from sklearn2pmml.pipeline import PMMLPipeline\\n\",\n \"from sklearn2pmml import sklearn2pmml\\n\",\n \"\\n\",\n \"import os\\n\",\n \"os.environ[\\\"PATH\\\"] += os.pathsep + 'C:/Program Files/Java/jdk1.8.0_171/bin'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"PMMLPipeline(steps=[('classifier', DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\\n\",\n \" max_features=None, max_leaf_nodes=None,\\n\",\n \" min_impurity_decrease=0.0, min_impurity_split=None,\\n\",\n \" min_samples_leaf=1, min_samples_split=2,\\n\",\n \" min_weight_fraction_leaf=0.0, presort=False, random_state=9,\\n\",\n \" splitter='best'))])\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X=[[1,2,3,1],[2,4,1,5],[7,8,3,6],[4,8,4,7],[2,5,6,9]]\\n\",\n \"y=[0,1,0,2,1]\\n\",\n \"pipeline = PMMLPipeline([(\\\"classifier\\\", tree.DecisionTreeClassifier(random_state=9))]);\\n\",\n \"pipeline.fit(X,y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sklearn2pmml(pipeline, \\\".\\\\demo.pmml\\\", with_repr = True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\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\": \"3.6.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "model-in-product/sklearn-jpmml/demo.pmml", "content": "\n\n\t
\n\t\t\n\t\t2018-06-24T05:47:17Z\n\t
\n\t\n\t\tPMMLPipeline(steps=[('classifier', DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n max_features=None, max_leaf_nodes=None,\n min_impurity_decrease=0.0, min_impurity_split=None,\n min_samples_leaf=1, min_samples_split=2,\n min_weight_fraction_leaf=0.0, presort=False, random_state=9,\n splitter='best'))])\n\t\n\t\n\t\t\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\n\t\t\n\t\t\n\t\n\t\n\t\t\n\t\t\t\n\t\t\n\t\t\n\t\t\t\n\t\t\n\t\n\t\n\t\t\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\n\t\t\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\n\t\t\n\t\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\n\t\t\n\t\n\n" }, { "path": "model-in-product/sklearn-jpmml/pmml_demo/pom.xml", "content": "\n\n 4.0.0\n\n test\n test-demo\n 1.0\n\n \n org.jpmml\n pmml-evaluator\n 1.4.1\n \n \n org.jpmml\n pmml-evaluator-extension\n 1.4.1\n \n\n" }, { "path": "model-in-product/sklearn-jpmml/pmml_demo/src/main/java/PMMLDemo.java", "content": "/*\r\n\tCopyright (C) 2016 - 2019 Pinard Liu(liujianping-ok@163.com)\r\n\r\n\thttps://www.cnblogs.com/pinard\r\n\r\n\tPermission given to modify the code as long as you keep this declaration at the top\r\n\r\n\t用PMML实现机器学习模型的跨平台上线 https://www.cnblogs.com/pinard/p/9220199.html\r\n*/\r\n\r\nimport org.dmg.pmml.FieldName;\r\nimport org.dmg.pmml.PMML;\r\nimport org.jpmml.evaluator.*;\r\nimport org.xml.sax.SAXException;\r\n\r\nimport javax.xml.bind.JAXBException;\r\nimport java.io.FileInputStream;\r\nimport java.io.IOException;\r\nimport java.io.InputStream;\r\nimport java.util.HashMap;\r\nimport java.util.LinkedHashMap;\r\nimport java.util.List;\r\nimport java.util.Map;\r\n\r\n/**\r\n * Created by 刘建平Pinard on 2018/6/24.\r\n */\r\npublic class PMMLDemo {\r\n private Evaluator loadPmml(){\r\n PMML pmml = new PMML();\r\n InputStream inputStream = null;\r\n try {\r\n inputStream = new FileInputStream(\"D:/demo.pmml\");\r\n } catch (IOException e) {\r\n e.printStackTrace();\r\n }\r\n if(inputStream == null){\r\n return null;\r\n }\r\n InputStream is = inputStream;\r\n try {\r\n pmml = org.jpmml.model.PMMLUtil.unmarshal(is);\r\n } catch (SAXException e1) {\r\n e1.printStackTrace();\r\n } catch (JAXBException e1) {\r\n e1.printStackTrace();\r\n }finally {\r\n //关闭输入流\r\n try {\r\n is.close();\r\n } catch (IOException e) {\r\n e.printStackTrace();\r\n }\r\n }\r\n ModelEvaluatorFactory modelEvaluatorFactory = ModelEvaluatorFactory.newInstance();\r\n Evaluator evaluator = modelEvaluatorFactory.newModelEvaluator(pmml);\r\n pmml = null;\r\n return evaluator;\r\n }\r\n private int predict(Evaluator evaluator,int a, int b, int c, int d) {\r\n Map data = new HashMap();\r\n data.put(\"x1\", a);\r\n data.put(\"x2\", b);\r\n data.put(\"x3\", c);\r\n data.put(\"x4\", d);\r\n List inputFields = evaluator.getInputFields();\r\n //过模型的原始特征，从画像中获取数据，作为模型输入\r\n Map arguments = new LinkedHashMap();\r\n for (InputField inputField : inputFields) {\r\n FieldName inputFieldName = inputField.getName();\r\n Object rawValue = data.get(inputFieldName.getValue());\r\n FieldValue inputFieldValue = inputField.prepare(rawValue);\r\n arguments.put(inputFieldName, inputFieldValue);\r\n }\r\n\r\n Map results = evaluator.evaluate(arguments);\r\n List targetFields = evaluator.getTargetFields();\r\n\r\n TargetField targetField = targetFields.get(0);\r\n FieldName targetFieldName = targetField.getName();\r\n\r\n Object targetFieldValue = results.get(targetFieldName);\r\n System.out.println(\"target: \" + targetFieldName.getValue() + \" value: \" + targetFieldValue);\r\n int primitiveValue = -1;\r\n if (targetFieldValue instanceof Computable) {\r\n Computable computable = (Computable) targetFieldValue;\r\n primitiveValue = (Integer)computable.getResult();\r\n }\r\n System.out.println(a + \" \" + b + \" \" + c + \" \" + d + \":\" + primitiveValue);\r\n return primitiveValue;\r\n }\r\n public static void main(String args[]){\r\n PMMLDemo demo = new PMMLDemo();\r\n Evaluator model = demo.loadPmml();\r\n demo.predict(model,1,8,99,1);\r\n demo.predict(model,111,89,9,11);\r\n\r\n }\r\n}\r\n" }, { "path": "model-in-product/tensorflow-java/TFDemoJava/pom.xml", "content": "\n\n 4.0.0\n\n tfDemo\n tfDemo\n 1.0-SNAPSHOT\n \n \n org.tensorflow\n tensorflow\n 1.7.0\n \n \n\n" }, { "path": "model-in-product/tensorflow-java/TFDemoJava/src/main/java/TFjavaDemo.java", "content": "/*\r\n\tCopyright (C) 2016 - 2019 Pinard Liu(liujianping-ok@163.com)\r\n\r\n\thttps://www.cnblogs.com/pinard\r\n\r\n\tPermission given to modify the code as long as you keep this declaration at the top\r\n\r\n\ttensorflow机器学习模型的跨平台上线 https://www.cnblogs.com/pinard/p/9251296.html\r\n*/\r\n\r\nimport org.tensorflow.*;\r\nimport org.tensorflow.Graph;\r\n\r\nimport java.io.IOException;\r\nimport java.nio.file.Files;\r\nimport java.nio.file.Paths;\r\n\r\n\r\n/**\r\n * Created by 刘建平pinard on 2018/7/1.\r\n */\r\npublic class TFjavaDemo {\r\n public static void main(String args[]){\r\n byte[] graphDef = loadTensorflowModel(\"D:/rf.pb\");\r\n float inputs[][] = new float[4][6];\r\n for(int i = 0; i< 4; i++){\r\n for(int j =0; j< 6;j++){\r\n if(i<2) {\r\n inputs[i][j] = 2 * i - 5 * j - 6;\r\n }\r\n else{\r\n inputs[i][j] = 2 * i + 5 * j - 6;\r\n }\r\n }\r\n }\r\n Tensor input = covertArrayToTensor(inputs);\r\n Graph g = new Graph();\r\n g.importGraphDef(graphDef);\r\n Session s = new Session(g);\r\n Tensor result = s.runner().feed(\"input\", input).fetch(\"output\").run().get(0);\r\n\r\n long[] rshape = result.shape();\r\n int rs = (int) rshape[0];\r\n long realResult[] = new long[rs];\r\n result.copyTo(realResult);\r\n\r\n for(long a: realResult ) {\r\n System.out.println(a);\r\n }\r\n }\r\n static private byte[] loadTensorflowModel(String path){\r\n try {\r\n return Files.readAllBytes(Paths.get(path));\r\n } catch (IOException e) {\r\n e.printStackTrace();\r\n }\r\n return null;\r\n }\r\n\r\n static private Tensor covertArrayToTensor(float inputs[][]){\r\n return Tensors.create(inputs);\r\n }\r\n}\r\n" }, { "path": "model-in-product/tensorflow-java/tensorflow_model.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C) 2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"tensorflow机器学习模型的跨平台上线 https://www.cnblogs.com/pinard/p/9251296.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline\\n\",\n \"from sklearn.datasets.samples_generator import make_classification\\n\",\n \"import tensorflow as tf\\n\",\n \"X1, y1 = make_classification(n_samples=4000, n_features=6, n_redundant=0,\\n\",\n \" n_clusters_per_class=1, n_classes=3)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(4000, 6)\\n\",\n \"(4000,)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print (X1.shape)\\n\",\n \"print (y1.shape)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"learning_rate = 0.01\\n\",\n \"training_epochs = 600\\n\",\n \"batch_size = 100\\n\",\n \"\\n\",\n \"x = tf.placeholder(tf.float32, [None, 6],name='input') # 6 features\\n\",\n \"y = tf.placeholder(tf.float32, [None, 3]) # 3 classes\\n\",\n \"\\n\",\n \"W = tf.Variable(tf.zeros([6, 3]))\\n\",\n \"b = tf.Variable(tf.zeros([3]))\\n\",\n \"\\n\",\n \"# softmax回归\\n\",\n \"pred = tf.nn.softmax(tf.matmul(x, W) + b, name=\\\"softmax\\\") \\n\",\n \"cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))\\n\",\n \"optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\\n\",\n \"\\n\",\n \"prediction_labels = tf.argmax(pred, axis=1, name=\\\"output\\\")\\n\",\n \"\\n\",\n \"init = tf.global_variables_initializer()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(4000, 3)\\n\"\n ]\n }\n ],\n \"source\": [\n \"sess = tf.Session()\\n\",\n \"sess.run(init)\\n\",\n \"y2 = tf.one_hot(y1, 3)\\n\",\n \"y2 = sess.run(y2)\\n\",\n \"print (y2.shape)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch: 0010 cost= 1.050110102\\n\",\n \"Epoch: 0020 cost= 1.001712561\\n\",\n \"Epoch: 0030 cost= 0.958428741\\n\",\n \"Epoch: 0040 cost= 0.919634283\\n\",\n \"Epoch: 0050 cost= 0.884774268\\n\",\n \"Epoch: 0060 cost= 0.853360713\\n\",\n \"Epoch: 0070 cost= 0.824967980\\n\",\n \"Epoch: 0080 cost= 0.799225986\\n\",\n \"Epoch: 0090 cost= 0.775815248\\n\",\n \"Epoch: 0100 cost= 0.754458249\\n\",\n \"Epoch: 0110 cost= 0.734916270\\n\",\n \"Epoch: 0120 cost= 0.716981947\\n\",\n \"Epoch: 0130 cost= 0.700476527\\n\",\n \"Epoch: 0140 cost= 0.685244024\\n\",\n \"Epoch: 0150 cost= 0.671149671\\n\",\n \"Epoch: 0160 cost= 0.658075333\\n\",\n \"Epoch: 0170 cost= 0.645918190\\n\",\n \"Epoch: 0180 cost= 0.634587705\\n\",\n \"Epoch: 0190 cost= 0.624004900\\n\",\n \"Epoch: 0200 cost= 0.614099383\\n\",\n \"Epoch: 0210 cost= 0.604809642\\n\",\n \"Epoch: 0220 cost= 0.596081078\\n\",\n \"Epoch: 0230 cost= 0.587864757\\n\",\n \"Epoch: 0240 cost= 0.580117583\\n\",\n \"Epoch: 0250 cost= 0.572800457\\n\",\n \"Epoch: 0260 cost= 0.565879166\\n\",\n \"Epoch: 0270 cost= 0.559322178\\n\",\n \"Epoch: 0280 cost= 0.553101718\\n\",\n \"Epoch: 0290 cost= 0.547192395\\n\",\n \"Epoch: 0300 cost= 0.541571438\\n\",\n \"Epoch: 0310 cost= 0.536218166\\n\",\n \"Epoch: 0320 cost= 0.531113505\\n\",\n \"Epoch: 0330 cost= 0.526240945\\n\",\n \"Epoch: 0340 cost= 0.521584213\\n\",\n \"Epoch: 0350 cost= 0.517129600\\n\",\n \"Epoch: 0360 cost= 0.512863755\\n\",\n \"Epoch: 0370 cost= 0.508774936\\n\",\n \"Epoch: 0380 cost= 0.504852176\\n\",\n \"Epoch: 0390 cost= 0.501085103\\n\",\n \"Epoch: 0400 cost= 0.497464716\\n\",\n \"Epoch: 0410 cost= 0.493982226\\n\",\n \"Epoch: 0420 cost= 0.490630001\\n\",\n \"Epoch: 0430 cost= 0.487400532\\n\",\n \"Epoch: 0440 cost= 0.484286994\\n\",\n \"Epoch: 0450 cost= 0.481283128\\n\",\n \"Epoch: 0460 cost= 0.478383124\\n\",\n \"Epoch: 0470 cost= 0.475581378\\n\",\n \"Epoch: 0480 cost= 0.472873092\\n\",\n \"Epoch: 0490 cost= 0.470253319\\n\",\n \"Epoch: 0500 cost= 0.467717826\\n\",\n \"Epoch: 0510 cost= 0.465262204\\n\",\n \"Epoch: 0520 cost= 0.462882727\\n\",\n \"Epoch: 0530 cost= 0.460575819\\n\",\n \"Epoch: 0540 cost= 0.458337963\\n\",\n \"Epoch: 0550 cost= 0.456166118\\n\",\n \"Epoch: 0560 cost= 0.454057366\\n\",\n \"Epoch: 0570 cost= 0.452008665\\n\",\n \"Epoch: 0580 cost= 0.450017571\\n\",\n \"Epoch: 0590 cost= 0.448081255\\n\",\n \"Epoch: 0600 cost= 0.446197867\\n\",\n \"优化完毕!\\n\",\n \"0.8675\\n\"\n ]\n }\n ],\n \"source\": [\n \"for epoch in range(training_epochs):\\n\",\n \"\\n\",\n \" _, c = sess.run([optimizer, cost], feed_dict={x: X1, y: y2})\\n\",\n \" if (epoch+1) % 10 == 0:\\n\",\n \" print (\\\"Epoch:\\\", '%04d' % (epoch+1), \\\"cost=\\\", \\\"{:.9f}\\\".format(c))\\n\",\n \" \\n\",\n \"print (\\\"优化完毕!\\\")\\n\",\n \"correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y2, 1))\\n\",\n \"accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\\n\",\n \"acc = sess.run(accuracy, feed_dict={x: X1, y: y2})\\n\",\n \"print (acc)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"INFO:tensorflow:Froze 2 variables.\\n\",\n \"Converted 2 variables to const ops.\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"'.\\\\\\\\rf.pb'\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"graph = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, [\\\"output\\\"])\\n\",\n \"tf.train.write_graph(graph, '.', 'rf.pb', as_text=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\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\": \"3.6.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "natural-language-processing/chinese_digging.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C) 2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"中文文本挖掘预处理流程总结 https://www.cnblogs.com/pinard/p/6744056.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# -*- coding: utf-8 -*-\\n\",\n \"\\n\",\n \"import jieba\\n\",\n \"\\n\",\n \"with open('./nlp_test0.txt') as f:\\n\",\n \" document = f.read()\\n\",\n \" \\n\",\n \" document_decode = document.decode('GBK')\\n\",\n \" document_cut = jieba.cut(document_decode)\\n\",\n \" #print ' '.join(jieba_cut) //如果打印结果，则分词效果消失，后面的result无法显示\\n\",\n \" result = ' '.join(document_cut)\\n\",\n \" result = result.encode('utf-8')\\n\",\n \" with open('./nlp_test1.txt', 'w') as f2:\\n\",\n \" f2.write(result)\\n\",\n \"f.close()\\n\",\n \"f2.close()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"4\"\n ]\n },\n \"execution_count\": 45,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"jieba.suggest_freq('沙瑞金', True)\\n\",\n \"jieba.suggest_freq('易学习', True)\\n\",\n \"jieba.suggest_freq('王大路', True)\\n\",\n \"jieba.suggest_freq('京州', True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"with open('./nlp_test0.txt') as f:\\n\",\n \" document = f.read()\\n\",\n \" \\n\",\n \" document_decode = document.decode('GBK')\\n\",\n \" document_cut = jieba.cut(document_decode)\\n\",\n \" #print ' '.join(jieba_cut)\\n\",\n \" result = ' '.join(document_cut)\\n\",\n \" result = result.encode('utf-8')\\n\",\n \" with open('./nlp_test1.txt', 'w') as f2:\\n\",\n \" f2.write(result)\\n\",\n \"f.close()\\n\",\n \"f2.close() \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"#从文件导入停用词表\\n\",\n \"stpwrdpath = \\\"stop_words.txt\\\"\\n\",\n \"stpwrd_dic = open(stpwrdpath, 'rb')\\n\",\n \"stpwrd_content = stpwrd_dic.read()\\n\",\n \"#将停用词表转换为list \\n\",\n \"stpwrdlst = stpwrd_content.splitlines()\\n\",\n \"stpwrd_dic.close()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"沙瑞金 赞叹 易学习 的 胸怀 ， 是 金山 的 百姓 有福 ， 可是 这件 事对 李达康 的 触动 很大 。 易学习 又 回忆起 他们 三人 分开 的 前一晚 ， 大家 一起 喝酒 话别 ， 易学习 被 降职 到 道口 县当 县长 ， 王大路 下海经商 ， 李达康 连连 赔礼道歉 ， 觉得 对不起 大家 ， 他 最 对不起 的 是 王大路 ， 就 和 易学习 一起 给 王大路 凑 了 5 万块 钱 ， 王大路 自己 东挪西撮 了 5 万块 ， 开始 下海经商 。 没想到 后来 王大路 竟然 做 得 风生水 起 。 沙瑞金 觉得 他们 三人 ， 在 困难 时期 还 能 以沫 相助 ， 很 不 容易 。\\n\"\n ]\n }\n ],\n \"source\": [\n \"with open('./nlp_test1.txt') as f3:\\n\",\n \" res1 = f3.read()\\n\",\n \"print res1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"with open('./nlp_test2.txt') as f:\\n\",\n \" document2 = f.read()\\n\",\n \" \\n\",\n \" document2_decode = document2.decode('GBK')\\n\",\n \" document2_cut = jieba.cut(document2_decode)\\n\",\n \" #print ' '.join(jieba_cut)\\n\",\n \" result = ' '.join(document2_cut)\\n\",\n \" result = result.encode('utf-8')\\n\",\n \" with open('./nlp_test3.txt', 'w') as f2:\\n\",\n \" f2.write(result)\\n\",\n \"f.close()\\n\",\n \"f2.close() \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"沙瑞金 向 毛娅 打听 他们 家 在 京州 的 别墅 ， 毛娅 笑 着 说 ， 王大路 事业有成 之后 ， 要 给 欧阳 菁 和 她 公司 的 股权 ， 她们 没有 要 ， 王大路 就 在 京州 帝豪园 买 了 三套 别墅 ， 可是 李达康 和 易学习 都 不要 ， 这些 房子 都 在 王大路 的 名下 ， 欧阳 菁 好像 去 住 过 ， 毛娅 不想 去 ， 她 觉得 房子 太大 很 浪费 ， 自己 家住 得 就 很 踏实 。\\n\"\n ]\n }\n ],\n \"source\": [\n \"with open('./nlp_test3.txt') as f4:\\n\",\n \" res2 = f4.read()\\n\",\n \"print res2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"jieba.suggest_freq('桓温', True)\\n\",\n \"with open('./nlp_test4.txt') as f:\\n\",\n \" document3 = f.read()\\n\",\n \" \\n\",\n \" document3_decode = document3.decode('GBK')\\n\",\n \" document3_cut = jieba.cut(document3_decode)\\n\",\n \" #print ' '.join(jieba_cut)\\n\",\n \" result = ' '.join(document3_cut)\\n\",\n \" result = result.encode('utf-8')\\n\",\n \" with open('./nlp_test5.txt', 'w') as f3:\\n\",\n \" f3.write(result)\\n\",\n \"f.close()\\n\",\n \"f3.close() \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" (0, 44)\\t0.154467434933\\n\",\n \" (0, 59)\\t0.108549295069\\n\",\n \" (0, 39)\\t0.308934869866\\n\",\n \" (0, 53)\\t0.108549295069\\n\",\n \" (0, 65)\\t0.108549295069\\n\",\n \" (0, 49)\\t0.108549295069\\n\",\n \" (0, 40)\\t0.108549295069\\n\",\n \" (0, 20)\\t0.0772337174664\\n\",\n \" (0, 62)\\t0.108549295069\\n\",\n \" (0, 10)\\t0.108549295069\\n\",\n \" (0, 41)\\t0.154467434933\\n\",\n \" (0, 56)\\t0.108549295069\\n\",\n \" (0, 35)\\t0.108549295069\\n\",\n \" (0, 24)\\t0.108549295069\\n\",\n \" (0, 12)\\t0.154467434933\\n\",\n \" (0, 2)\\t0.217098590137\\n\",\n \" (0, 15)\\t0.108549295069\\n\",\n \" (0, 17)\\t0.108549295069\\n\",\n \" (0, 26)\\t0.217098590137\\n\",\n \" (0, 0)\\t0.217098590137\\n\",\n \" (0, 23)\\t0.108549295069\\n\",\n \" (0, 57)\\t0.108549295069\\n\",\n \" (0, 66)\\t0.108549295069\\n\",\n \" (0, 64)\\t0.108549295069\\n\",\n \" (0, 18)\\t0.108549295069\\n\",\n \" :\\t:\\n\",\n \" (1, 55)\\t0.0995336411066\\n\",\n \" (1, 54)\\t0.0995336411066\\n\",\n \" (1, 43)\\t0.419673178975\\n\",\n \" (1, 37)\\t0.139891059658\\n\",\n \" (1, 11)\\t0.279782119316\\n\",\n \" (1, 16)\\t0.279782119316\\n\",\n \" (1, 9)\\t0.139891059658\\n\",\n \" (1, 8)\\t0.139891059658\\n\",\n \" (1, 42)\\t0.279782119316\\n\",\n \" (1, 14)\\t0.139891059658\\n\",\n \" (1, 52)\\t0.139891059658\\n\",\n \" (1, 28)\\t0.139891059658\\n\",\n \" (1, 46)\\t0.139891059658\\n\",\n \" (1, 33)\\t0.139891059658\\n\",\n \" (1, 3)\\t0.139891059658\\n\",\n \" (1, 6)\\t0.139891059658\\n\",\n \" (1, 61)\\t0.139891059658\\n\",\n \" (1, 36)\\t0.279782119316\\n\",\n \" (1, 21)\\t0.139891059658\\n\",\n \" (1, 29)\\t0.139891059658\\n\",\n \" (1, 5)\\t0.139891059658\\n\",\n \" (1, 27)\\t0.139891059658\\n\",\n \" (1, 47)\\t0.139891059658\\n\",\n \" (1, 30)\\t0.139891059658\\n\",\n \" (1, 60)\\t0.139891059658\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.feature_extraction.text import TfidfVectorizer\\n\",\n \"corpus = [res1,res2]\\n\",\n \"vector = TfidfVectorizer(stop_words=stpwrdlst)\\n\",\n \"tfidf = vector.fit_transform(corpus)\\n\",\n \"print tfidf\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"-------第 0 段文本的词语tf-idf权重------\\n\",\n \"一起 0.217098590137\\n\",\n \"万块 0.217098590137\\n\",\n \"三人 0.217098590137\\n\",\n \"三套 0.0\\n\",\n \"下海经商 0.217098590137\\n\",\n \"不想 0.0\\n\",\n \"不要 0.0\\n\",\n \"东挪西撮 0.108549295069\\n\",\n \"之后 0.0\\n\",\n \"事业有成 0.0\\n\",\n \"事对 0.108549295069\\n\",\n \"京州 0.0\\n\",\n \"他们 0.154467434933\\n\",\n \"以沫 0.108549295069\\n\",\n \"公司 0.0\\n\",\n \"分开 0.108549295069\\n\",\n \"别墅 0.0\\n\",\n \"前一晚 0.108549295069\\n\",\n \"县当 0.108549295069\\n\",\n \"县长 0.108549295069\\n\",\n \"可是 0.0772337174664\\n\",\n \"名下 0.0\\n\",\n \"后来 0.108549295069\\n\",\n \"喝酒 0.108549295069\\n\",\n \"回忆起 0.108549295069\\n\",\n \"困难 0.108549295069\\n\",\n \"大家 0.217098590137\\n\",\n \"太大 0.0\\n\",\n \"她们 0.0\\n\",\n \"好像 0.0\\n\",\n \"家住 0.0\\n\",\n \"容易 0.108549295069\\n\",\n \"对不起 0.217098590137\\n\",\n \"帝豪园 0.0\\n\",\n \"开始 0.108549295069\\n\",\n \"很大 0.108549295069\\n\",\n \"房子 0.0\\n\",\n \"打听 0.0\\n\",\n \"时期 0.108549295069\\n\",\n \"易学习 0.308934869866\\n\",\n \"有福 0.108549295069\\n\",\n \"李达康 0.154467434933\\n\",\n \"欧阳 0.0\\n\",\n \"毛娅 0.0\\n\",\n \"沙瑞金 0.154467434933\\n\",\n \"没想到 0.108549295069\\n\",\n \"没有 0.0\\n\",\n \"浪费 0.0\\n\",\n \"王大路 0.386168587332\\n\",\n \"百姓 0.108549295069\\n\",\n \"相助 0.108549295069\\n\",\n \"竟然 0.108549295069\\n\",\n \"股权 0.0\\n\",\n \"胸怀 0.108549295069\\n\",\n \"自己 0.0772337174664\\n\",\n \"觉得 0.154467434933\\n\",\n \"触动 0.108549295069\\n\",\n \"话别 0.108549295069\\n\",\n \"赔礼道歉 0.108549295069\\n\",\n \"赞叹 0.108549295069\\n\",\n \"踏实 0.0\\n\",\n \"这些 0.0\\n\",\n \"这件 0.108549295069\\n\",\n \"连连 0.108549295069\\n\",\n \"道口 0.108549295069\\n\",\n \"金山 0.108549295069\\n\",\n \"降职 0.108549295069\\n\",\n \"风生水 0.108549295069\\n\",\n \"-------第 1 段文本的词语tf-idf权重------\\n\",\n \"一起 0.0\\n\",\n \"万块 0.0\\n\",\n \"三人 0.0\\n\",\n \"三套 0.139891059658\\n\",\n \"下海经商 0.0\\n\",\n \"不想 0.139891059658\\n\",\n \"不要 0.139891059658\\n\",\n \"东挪西撮 0.0\\n\",\n \"之后 0.139891059658\\n\",\n \"事业有成 0.139891059658\\n\",\n \"事对 0.0\\n\",\n \"京州 0.279782119316\\n\",\n \"他们 0.0995336411066\\n\",\n \"以沫 0.0\\n\",\n \"公司 0.139891059658\\n\",\n \"分开 0.0\\n\",\n \"别墅 0.279782119316\\n\",\n \"前一晚 0.0\\n\",\n \"县当 0.0\\n\",\n \"县长 0.0\\n\",\n \"可是 0.0995336411066\\n\",\n \"名下 0.139891059658\\n\",\n \"后来 0.0\\n\",\n \"喝酒 0.0\\n\",\n \"回忆起 0.0\\n\",\n \"困难 0.0\\n\",\n \"大家 0.0\\n\",\n \"太大 0.139891059658\\n\",\n \"她们 0.139891059658\\n\",\n \"好像 0.139891059658\\n\",\n \"家住 0.139891059658\\n\",\n \"容易 0.0\\n\",\n \"对不起 0.0\\n\",\n \"帝豪园 0.139891059658\\n\",\n \"开始 0.0\\n\",\n \"很大 0.0\\n\",\n \"房子 0.279782119316\\n\",\n \"打听 0.139891059658\\n\",\n \"时期 0.0\\n\",\n \"易学习 0.0995336411066\\n\",\n \"有福 0.0\\n\",\n \"李达康 0.0995336411066\\n\",\n \"欧阳 0.279782119316\\n\",\n \"毛娅 0.419673178975\\n\",\n \"沙瑞金 0.0995336411066\\n\",\n \"没想到 0.0\\n\",\n \"没有 0.139891059658\\n\",\n \"浪费 0.139891059658\\n\",\n \"王大路 0.29860092332\\n\",\n \"百姓 0.0\\n\",\n \"相助 0.0\\n\",\n \"竟然 0.0\\n\",\n \"股权 0.139891059658\\n\",\n \"胸怀 0.0\\n\",\n \"自己 0.0995336411066\\n\",\n \"觉得 0.0995336411066\\n\",\n \"触动 0.0\\n\",\n \"话别 0.0\\n\",\n \"赔礼道歉 0.0\\n\",\n \"赞叹 0.0\\n\",\n \"踏实 0.139891059658\\n\",\n \"这些 0.139891059658\\n\",\n \"这件 0.0\\n\",\n \"连连 0.0\\n\",\n \"道口 0.0\\n\",\n \"金山 0.0\\n\",\n \"降职 0.0\\n\",\n \"风生水 0.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"wordlist = vector.get_feature_names()#获取词袋模型中的所有词 \\n\",\n \"# tf-idf矩阵 元素a[i][j]表示j词在i类文本中的tf-idf权重\\n\",\n \"weightlist = tfidf.toarray() \\n\",\n \"#打印每类文本的tf-idf词语权重，第一个for遍历所有文本，第二个for便利某一类文本下的词语权重\\n\",\n \"for i in range(len(weightlist)): \\n\",\n \" print \\\"-------第\\\",i,\\\"段文本的词语tf-idf权重------\\\" \\n\",\n \" for j in range(len(wordlist)): \\n\",\n \" print wordlist[j],weightlist[i][j] \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\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\": \"3.6.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "natural-language-processing/english_digging.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C) 2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"英文文本挖掘预处理流程总结 https://www.cnblogs.com/pinard/p/6756534.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"showing info https://raw.githubusercontent.com/nltk/nltk_data/gh-pages/index.xml\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 1,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import nltk\\n\",\n \"nltk.download()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"imaging\\n\"\n ]\n }\n ],\n \"source\": [\n \"from nltk.stem import WordNetLemmatizer\\n\",\n \"wnl = WordNetLemmatizer()\\n\",\n \"print(wnl.lemmatize('imaging')) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"arabic danish dutch english finnish french german hungarian italian norwegian porter portuguese romanian russian spanish swedish\\n\"\n ]\n }\n ],\n \"source\": [\n \"from nltk.stem import SnowballStemmer\\n\",\n \"print(\\\" \\\".join(SnowballStemmer.languages)) # See which languages are suppo\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'countri'\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"stemmer = SnowballStemmer(\\\"english\\\") # Choose a language\\n\",\n \"stemmer.stem(\\\"countries\\\") # Stem a word\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"country\\n\"\n ]\n }\n ],\n \"source\": [\n \"from nltk.stem import WordNetLemmatizer\\n\",\n \"wnl = WordNetLemmatizer()\\n\",\n \"print(wnl.lemmatize('countries')) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"ERROR: peope\\n\",\n \"ERROR: likee\\n\"\n ]\n }\n ],\n \"source\": [\n \"from enchant.checker import SpellChecker\\n\",\n \"chkr = SpellChecker(\\\"en_US\\\")\\n\",\n \"chkr.set_text(\\\"Many peope likee to watch In the Name of People.\\\")\\n\",\n \"for err in chkr:\\n\",\n \" print \\\"ERROR:\\\", err.word\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\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\": \"3.6.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "natural-language-processing/hash_trick.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C) 2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"文本挖掘预处理之向量化与Hash Trick https://www.cnblogs.com/pinard/p/6688348.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" (0, 16)\\t1\\n\",\n \" (0, 3)\\t1\\n\",\n \" (0, 15)\\t2\\n\",\n \" (0, 4)\\t1\\n\",\n \" (1, 5)\\t1\\n\",\n \" (1, 9)\\t1\\n\",\n \" (1, 2)\\t1\\n\",\n \" (1, 6)\\t1\\n\",\n \" (1, 14)\\t1\\n\",\n \" (1, 3)\\t1\\n\",\n \" (2, 1)\\t1\\n\",\n \" (2, 0)\\t1\\n\",\n \" (2, 12)\\t1\\n\",\n \" (2, 7)\\t1\\n\",\n \" (3, 10)\\t1\\n\",\n \" (3, 8)\\t1\\n\",\n \" (3, 11)\\t1\\n\",\n \" (3, 18)\\t1\\n\",\n \" (3, 17)\\t1\\n\",\n \" (3, 13)\\t1\\n\",\n \" (3, 5)\\t1\\n\",\n \" (3, 6)\\t1\\n\",\n \" (3, 15)\\t1\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.feature_extraction.text import CountVectorizer \\n\",\n \"vectorizer=CountVectorizer()\\n\",\n \"corpus=[\\\"I come to China to travel\\\", \\n\",\n \" \\\"This is a car polupar in China\\\", \\n\",\n \" \\\"I love tea and Apple \\\", \\n\",\n \" \\\"The work is to write some papers in science\\\"] \\n\",\n \"print (vectorizer.fit_transform(corpus))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 2 1 0 0]\\n\",\n \" [0 0 1 1 0 1 1 0 0 1 0 0 0 0 1 0 0 0 0]\\n\",\n \" [1 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]\\n\",\n \" [0 0 0 0 0 1 1 0 1 0 1 1 0 1 0 1 0 1 1]]\\n\",\n \"['and', 'apple', 'car', 'china', 'come', 'in', 'is', 'love', 'papers', 'polupar', 'science', 'some', 'tea', 'the', 'this', 'to', 'travel', 'work', 'write']\\n\"\n ]\n }\n ],\n \"source\": [\n \"print (vectorizer.fit_transform(corpus).toarray())\\n\",\n \"print (vectorizer.get_feature_names())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" (0, 1)\\t2.0\\n\",\n \" (0, 2)\\t-1.0\\n\",\n \" (0, 4)\\t1.0\\n\",\n \" (0, 5)\\t-1.0\\n\",\n \" (1, 0)\\t1.0\\n\",\n \" (1, 1)\\t1.0\\n\",\n \" (1, 2)\\t-1.0\\n\",\n \" (1, 5)\\t-1.0\\n\",\n \" (2, 0)\\t2.0\\n\",\n \" (2, 5)\\t-2.0\\n\",\n \" (3, 0)\\t0.0\\n\",\n \" (3, 1)\\t4.0\\n\",\n \" (3, 2)\\t-1.0\\n\",\n \" (3, 3)\\t1.0\\n\",\n \" (3, 5)\\t-1.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.feature_extraction.text import HashingVectorizer \\n\",\n \"vectorizer2=HashingVectorizer(n_features = 6,norm = None)\\n\",\n \"print (vectorizer2.fit_transform(corpus))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\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\": \"3.6.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "natural-language-processing/hmm.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C) 2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"用hmmlearn学习隐马尔科夫模型HMM https://www.cnblogs.com/pinard/p/7001397.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"('The ball picked:', 'red, white, red')\\n\",\n \"('The hidden box', 'box3, box3, box3')\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\Python27\\\\lib\\\\site-packages\\\\IPython\\\\kernel\\\\__main__.py:31: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future\\n\"\n ]\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"from hmmlearn import hmm\\n\",\n \"\\n\",\n \"states = [\\\"box 1\\\", \\\"box 2\\\", \\\"box3\\\"]\\n\",\n \"n_states = len(states)\\n\",\n \"\\n\",\n \"observations = [\\\"red\\\", \\\"white\\\"]\\n\",\n \"n_observations = len(observations)\\n\",\n \"\\n\",\n \"start_probability = np.array([0.2, 0.4, 0.4])\\n\",\n \"\\n\",\n \"transition_probability = np.array([\\n\",\n \" [0.5, 0.2, 0.3],\\n\",\n \" [0.3, 0.5, 0.2],\\n\",\n \" [0.2, 0.3, 0.5]\\n\",\n \"])\\n\",\n \"\\n\",\n \"emission_probability = np.array([\\n\",\n \" [0.5, 0.5],\\n\",\n \" [0.4, 0.6],\\n\",\n \" [0.7, 0.3]\\n\",\n \"])\\n\",\n \"\\n\",\n \"model = hmm.MultinomialHMM(n_components=n_states)\\n\",\n \"model.startprob_=start_probability\\n\",\n \"model.transmat_=transition_probability\\n\",\n \"model.emissionprob_=emission_probability\\n\",\n \"\\n\",\n \"seen = np.array([[0,1,0]]).T\\n\",\n \"logprob, box = model.decode(seen, algorithm=\\\"viterbi\\\")\\n\",\n \"print(\\\"The ball picked:\\\", \\\", \\\".join(map(lambda x: observations[x], seen)))\\n\",\n \"print(\\\"The hidden box\\\", \\\", \\\".join(map(lambda x: states[x], box)))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"('The ball picked:', 'red, white, red')\\n\",\n \"('The hidden box', 'box3, box3, box3')\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\Python27\\\\lib\\\\site-packages\\\\IPython\\\\kernel\\\\__main__.py:2: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future\\n\",\n \" from IPython.kernel.zmq import kernelapp as app\\n\"\n ]\n }\n ],\n \"source\": [\n \"box2 = model.predict(seen)\\n\",\n \"print(\\\"The ball picked:\\\", \\\", \\\".join(map(lambda x: observations[x], seen)))\\n\",\n \"print(\\\"The hidden box\\\", \\\", \\\".join(map(lambda x: states[x], box2)))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"-2.03854530992\\n\"\n ]\n }\n ],\n \"source\": [\n \"print model.score(seen)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 1.31697183e-12 3.07279825e-15 1.00000000e+00]\\n\",\n \"[[ 1.48161608e-01 1.50331031e-01 7.01507361e-01]\\n\",\n \" [ 2.19355550e-01 2.31744543e-01 5.48899907e-01]\\n\",\n \" [ 4.55650613e-01 5.44338342e-01 1.10450680e-05]]\\n\",\n \"[[ 2.19032398e-01 7.80967602e-01]\\n\",\n \" [ 1.15445252e-01 8.84554748e-01]\\n\",\n \" [ 9.99192283e-01 8.07717131e-04]]\\n\",\n \"-6.51866416797\\n\",\n \"[ 9.99999997e-01 3.19111963e-09 2.84005362e-23]\\n\",\n \"[[ 8.42932318e-09 4.12422449e-02 9.58757747e-01]\\n\",\n \" [ 1.35449734e-01 5.71715372e-01 2.92834894e-01]\\n\",\n \" [ 5.77139011e-01 9.57292050e-02 3.27131784e-01]]\\n\",\n \"[[ 9.99987996e-01 1.20035621e-05]\\n\",\n \" [ 5.22916524e-01 4.77083476e-01]\\n\",\n \" [ 1.51107630e-01 8.48892370e-01]]\\n\",\n \"-6.61149770824\\n\",\n \"[ 1.00000000e+00 1.30722027e-10 3.08257576e-12]\\n\",\n \"[[ 1.11200351e-05 4.22408201e-01 5.77580679e-01]\\n\",\n \" [ 6.44472673e-01 1.69915233e-01 1.85612094e-01]\\n\",\n \" [ 5.92638365e-01 1.91056314e-01 2.16305321e-01]]\\n\",\n \"[[ 9.99515188e-01 4.84812380e-04]\\n\",\n \" [ 1.87229564e-01 8.12770436e-01]\\n\",\n \" [ 1.49192055e-01 8.50807945e-01]]\\n\",\n \"-6.54281680533\\n\"\n ]\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"from hmmlearn import hmm\\n\",\n \"\\n\",\n \"states = [\\\"box 1\\\", \\\"box 2\\\", \\\"box3\\\"]\\n\",\n \"n_states = len(states)\\n\",\n \"\\n\",\n \"observations = [\\\"red\\\", \\\"white\\\"]\\n\",\n \"n_observations = len(observations)\\n\",\n \"model2 = hmm.MultinomialHMM(n_components=n_states, n_iter=20, tol=0.01)\\n\",\n \"X2 = np.array([[0,1,0,1],[0,0,0,1],[1,0,1,1]])\\n\",\n \"model2.fit(X2)\\n\",\n \"print model2.startprob_\\n\",\n \"print model2.transmat_\\n\",\n \"print model2.emissionprob_\\n\",\n \"print model2.score(X2)\\n\",\n \"model2.fit(X2)\\n\",\n \"print model2.startprob_\\n\",\n \"print model2.transmat_\\n\",\n \"print model2.emissionprob_\\n\",\n \"print model2.score(X2)\\n\",\n \"model2.fit(X2)\\n\",\n \"print model2.startprob_\\n\",\n \"print model2.transmat_\\n\",\n \"print model2.emissionprob_\\n\",\n \"print model2.score(X2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"startprob = np.array([0.6, 0.3, 0.1, 0.0])\\n\",\n \"# The transition matrix, note that there are no transitions possible\\n\",\n \"# between component 1 and 3\\n\",\n \"transmat = np.array([[0.7, 0.2, 0.0, 0.1],\\n\",\n \" [0.3, 0.5, 0.2, 0.0],\\n\",\n \" [0.0, 0.3, 0.5, 0.2],\\n\",\n \" [0.2, 0.0, 0.2, 0.6]])\\n\",\n \"# The means of each component\\n\",\n \"means = np.array([[0.0, 0.0],\\n\",\n \" [0.0, 11.0],\\n\",\n \" [9.0, 10.0],\\n\",\n \" [11.0, -1.0]])\\n\",\n \"# The covariance of each component\\n\",\n \"covars = .5 * np.tile(np.identity(2), (4, 1, 1))\\n\",\n \"\\n\",\n \"# Build an HMM instance and set parameters\\n\",\n \"model3 = hmm.GaussianHMM(n_components=4, covariance_type=\\\"full\\\")\\n\",\n \"\\n\",\n \"# Instead of fitting it from the data, we directly set the estimated\\n\",\n \"# parameters, the means and covariance of the components\\n\",\n \"model3.startprob_ = startprob\\n\",\n \"model3.transmat_ = transmat\\n\",\n \"model3.means_ = means\\n\",\n \"model3.covars_ = covars\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\Python27\\\\lib\\\\site-packages\\\\sklearn\\\\utils\\\\deprecation.py:70: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\\n\",\n \" warnings.warn(msg, category=DeprecationWarning)\\n\"\n ]\n }\n ],\n \"source\": [\n \"seen = np.array([[1.1,2.0],[-1,2.0],[3,7]])\\n\",\n \"logprob, state = model.decode(seen, algorithm=\\\"viterbi\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0 0 1]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print state\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"-41.1211281377\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\Python27\\\\lib\\\\site-packages\\\\sklearn\\\\utils\\\\deprecation.py:70: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\\n\",\n \" warnings.warn(msg, category=DeprecationWarning)\\n\",\n \"C:\\\\Python27\\\\lib\\\\site-packages\\\\hmmlearn\\\\base.py:459: RuntimeWarning: divide by zero encountered in log\\n\",\n \" np.log(self.startprob_),\\n\",\n \"C:\\\\Python27\\\\lib\\\\site-packages\\\\hmmlearn\\\\base.py:460: RuntimeWarning: divide by zero encountered in log\\n\",\n \" np.log(self.transmat_),\\n\"\n ]\n }\n ],\n \"source\": [\n \"print model3.score(seen)\"\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\": \"3.6.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "natural-language-processing/lda.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C) 2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"用scikit-learn学习LDA主题模型 https://www.cnblogs.com/pinard/p/6908150.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# -*- coding: utf-8 -*-\\n\",\n \"\\n\",\n \"import jieba\\n\",\n \"\\n\",\n \"with open('./nlp_test0.txt') as f:\\n\",\n \" document = f.read()\\n\",\n \" \\n\",\n \" document_decode = document.decode('GBK')\\n\",\n \" document_cut = jieba.cut(document_decode)\\n\",\n \" #print ' '.join(jieba_cut) //如果打印结果，则分词效果消失，后面的result无法显示\\n\",\n \" result = ' '.join(document_cut)\\n\",\n \" result = result.encode('utf-8')\\n\",\n \" with open('./nlp_test1.txt', 'w') as f2:\\n\",\n \" f2.write(result)\\n\",\n \"f.close()\\n\",\n \"f2.close()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"4\"\n ]\n },\n \"execution_count\": 45,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"jieba.suggest_freq('沙瑞金', True)\\n\",\n \"jieba.suggest_freq('易学习', True)\\n\",\n \"jieba.suggest_freq('王大路', True)\\n\",\n \"jieba.suggest_freq('京州', True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"with open('./nlp_test0.txt') as f:\\n\",\n \" document = f.read()\\n\",\n \" \\n\",\n \" document_decode = document.decode('GBK')\\n\",\n \" document_cut = jieba.cut(document_decode)\\n\",\n \" #print ' '.join(jieba_cut)\\n\",\n \" result = ' '.join(document_cut)\\n\",\n \" result = result.encode('utf-8')\\n\",\n \" with open('./nlp_test1.txt', 'w') as f2:\\n\",\n \" f2.write(result)\\n\",\n \"f.close()\\n\",\n \"f2.close() \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"#从文件导入停用词表\\n\",\n \"stpwrdpath = \\\"stop_words.txt\\\"\\n\",\n \"stpwrd_dic = open(stpwrdpath, 'rb')\\n\",\n \"stpwrd_content = stpwrd_dic.read()\\n\",\n \"#将停用词表转换为list \\n\",\n \"stpwrdlst = stpwrd_content.splitlines()\\n\",\n \"stpwrd_dic.close()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"沙瑞金 赞叹 易学习 的 胸怀 ， 是 金山 的 百姓 有福 ， 可是 这件 事对 李达康 的 触动 很大 。 易学习 又 回忆起 他们 三人 分开 的 前一晚 ， 大家 一起 喝酒 话别 ， 易学习 被 降职 到 道口 县当 县长 ， 王大路 下海经商 ， 李达康 连连 赔礼道歉 ， 觉得 对不起 大家 ， 他 最 对不起 的 是 王大路 ， 就 和 易学习 一起 给 王大路 凑 了 5 万块 钱 ， 王大路 自己 东挪西撮 了 5 万块 ， 开始 下海经商 。 没想到 后来 王大路 竟然 做 得 风生水 起 。 沙瑞金 觉得 他们 三人 ， 在 困难 时期 还 能 以沫 相助 ， 很 不 容易 。\\n\"\n ]\n }\n ],\n \"source\": [\n \"with open('./nlp_test1.txt') as f3:\\n\",\n \" res1 = f3.read()\\n\",\n \"print res1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"with open('./nlp_test2.txt') as f:\\n\",\n \" document2 = f.read()\\n\",\n \" \\n\",\n \" document2_decode = document2.decode('GBK')\\n\",\n \" document2_cut = jieba.cut(document2_decode)\\n\",\n \" #print ' '.join(jieba_cut)\\n\",\n \" result = ' '.join(document2_cut)\\n\",\n \" result = result.encode('utf-8')\\n\",\n \" with open('./nlp_test3.txt', 'w') as f2:\\n\",\n \" f2.write(result)\\n\",\n \"f.close()\\n\",\n \"f2.close() \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"沙瑞金 向 毛娅 打听 他们 家 在 京州 的 别墅 ， 毛娅 笑 着 说 ， 王大路 事业有成 之后 ， 要 给 欧阳 菁 和 她 公司 的 股权 ， 她们 没有 要 ， 王大路 就 在 京州 帝豪园 买 了 三套 别墅 ， 可是 李达康 和 易学习 都 不要 ， 这些 房子 都 在 王大路 的 名下 ， 欧阳 菁 好像 去 住 过 ， 毛娅 不想 去 ， 她 觉得 房子 太大 很 浪费 ， 自己 家住 得 就 很 踏实 。\\n\"\n ]\n }\n ],\n \"source\": [\n \"with open('./nlp_test3.txt') as f4:\\n\",\n \" res2 = f4.read()\\n\",\n \"print res2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"jieba.suggest_freq('桓温', True)\\n\",\n \"with open('./nlp_test4.txt') as f:\\n\",\n \" document3 = f.read()\\n\",\n \" \\n\",\n \" document3_decode = document3.decode('GBK')\\n\",\n \" document3_cut = jieba.cut(document3_decode)\\n\",\n \" #print ' '.join(jieba_cut)\\n\",\n \" result = ' '.join(document3_cut)\\n\",\n \" result = result.encode('utf-8')\\n\",\n \" with open('./nlp_test5.txt', 'w') as f3:\\n\",\n \" f3.write(result)\\n\",\n \"f.close()\\n\",\n \"f3.close() \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"347 年 （ 永和 三年 ） 三月 ， 桓温 兵至 彭模 （ 今 四川 彭山 东南 ） ， 留下 参军 周楚 、 孙盛 看守 辎重 ， 自己 亲率 步兵 直攻 成都 。 同月 ， 成汉 将领 李福 袭击 彭模 ， 结果 被 孙盛 等 人 击退 ； 而 桓温 三 战三胜 ， 一直 逼近 成都 。\\n\"\n ]\n }\n ],\n \"source\": [\n \"with open('./nlp_test5.txt') as f5:\\n\",\n \" res3 = f5.read()\\n\",\n \"print res3\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" (0, 44)\\t0.154467434933\\n\",\n \" (0, 59)\\t0.108549295069\\n\",\n \" (0, 39)\\t0.308934869866\\n\",\n \" (0, 53)\\t0.108549295069\\n\",\n \" (0, 65)\\t0.108549295069\\n\",\n \" (0, 49)\\t0.108549295069\\n\",\n \" (0, 40)\\t0.108549295069\\n\",\n \" (0, 20)\\t0.0772337174664\\n\",\n \" (0, 62)\\t0.108549295069\\n\",\n \" (0, 10)\\t0.108549295069\\n\",\n \" (0, 41)\\t0.154467434933\\n\",\n \" (0, 56)\\t0.108549295069\\n\",\n \" (0, 35)\\t0.108549295069\\n\",\n \" (0, 24)\\t0.108549295069\\n\",\n \" (0, 12)\\t0.154467434933\\n\",\n \" (0, 2)\\t0.217098590137\\n\",\n \" (0, 15)\\t0.108549295069\\n\",\n \" (0, 17)\\t0.108549295069\\n\",\n \" (0, 26)\\t0.217098590137\\n\",\n \" (0, 0)\\t0.217098590137\\n\",\n \" (0, 23)\\t0.108549295069\\n\",\n \" (0, 57)\\t0.108549295069\\n\",\n \" (0, 66)\\t0.108549295069\\n\",\n \" (0, 64)\\t0.108549295069\\n\",\n \" (0, 18)\\t0.108549295069\\n\",\n \" :\\t:\\n\",\n \" (1, 55)\\t0.0995336411066\\n\",\n \" (1, 54)\\t0.0995336411066\\n\",\n \" (1, 43)\\t0.419673178975\\n\",\n \" (1, 37)\\t0.139891059658\\n\",\n \" (1, 11)\\t0.279782119316\\n\",\n \" (1, 16)\\t0.279782119316\\n\",\n \" (1, 9)\\t0.139891059658\\n\",\n \" (1, 8)\\t0.139891059658\\n\",\n \" (1, 42)\\t0.279782119316\\n\",\n \" (1, 14)\\t0.139891059658\\n\",\n \" (1, 52)\\t0.139891059658\\n\",\n \" (1, 28)\\t0.139891059658\\n\",\n \" (1, 46)\\t0.139891059658\\n\",\n \" (1, 33)\\t0.139891059658\\n\",\n \" (1, 3)\\t0.139891059658\\n\",\n \" (1, 6)\\t0.139891059658\\n\",\n \" (1, 61)\\t0.139891059658\\n\",\n \" (1, 36)\\t0.279782119316\\n\",\n \" (1, 21)\\t0.139891059658\\n\",\n \" (1, 29)\\t0.139891059658\\n\",\n \" (1, 5)\\t0.139891059658\\n\",\n \" (1, 27)\\t0.139891059658\\n\",\n \" (1, 47)\\t0.139891059658\\n\",\n \" (1, 30)\\t0.139891059658\\n\",\n \" (1, 60)\\t0.139891059658\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.feature_extraction.text import TfidfVectorizer\\n\",\n \"corpus = [res1,res2]\\n\",\n \"vector = TfidfVectorizer(stop_words=stpwrdlst)\\n\",\n \"tfidf = vector.fit_transform(corpus)\\n\",\n \"print tfidf\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"-------第 0 段文本的词语tf-idf权重------\\n\",\n \"一起 0.217098590137\\n\",\n \"万块 0.217098590137\\n\",\n \"三人 0.217098590137\\n\",\n \"三套 0.0\\n\",\n \"下海经商 0.217098590137\\n\",\n \"不想 0.0\\n\",\n \"不要 0.0\\n\",\n \"东挪西撮 0.108549295069\\n\",\n \"之后 0.0\\n\",\n \"事业有成 0.0\\n\",\n \"事对 0.108549295069\\n\",\n \"京州 0.0\\n\",\n \"他们 0.154467434933\\n\",\n \"以沫 0.108549295069\\n\",\n \"公司 0.0\\n\",\n \"分开 0.108549295069\\n\",\n \"别墅 0.0\\n\",\n \"前一晚 0.108549295069\\n\",\n \"县当 0.108549295069\\n\",\n \"县长 0.108549295069\\n\",\n \"可是 0.0772337174664\\n\",\n \"名下 0.0\\n\",\n \"后来 0.108549295069\\n\",\n \"喝酒 0.108549295069\\n\",\n \"回忆起 0.108549295069\\n\",\n \"困难 0.108549295069\\n\",\n \"大家 0.217098590137\\n\",\n \"太大 0.0\\n\",\n \"她们 0.0\\n\",\n \"好像 0.0\\n\",\n \"家住 0.0\\n\",\n \"容易 0.108549295069\\n\",\n \"对不起 0.217098590137\\n\",\n \"帝豪园 0.0\\n\",\n \"开始 0.108549295069\\n\",\n \"很大 0.108549295069\\n\",\n \"房子 0.0\\n\",\n \"打听 0.0\\n\",\n \"时期 0.108549295069\\n\",\n \"易学习 0.308934869866\\n\",\n \"有福 0.108549295069\\n\",\n \"李达康 0.154467434933\\n\",\n \"欧阳 0.0\\n\",\n \"毛娅 0.0\\n\",\n \"沙瑞金 0.154467434933\\n\",\n \"没想到 0.108549295069\\n\",\n \"没有 0.0\\n\",\n \"浪费 0.0\\n\",\n \"王大路 0.386168587332\\n\",\n \"百姓 0.108549295069\\n\",\n \"相助 0.108549295069\\n\",\n \"竟然 0.108549295069\\n\",\n \"股权 0.0\\n\",\n \"胸怀 0.108549295069\\n\",\n \"自己 0.0772337174664\\n\",\n \"觉得 0.154467434933\\n\",\n \"触动 0.108549295069\\n\",\n \"话别 0.108549295069\\n\",\n \"赔礼道歉 0.108549295069\\n\",\n \"赞叹 0.108549295069\\n\",\n \"踏实 0.0\\n\",\n \"这些 0.0\\n\",\n \"这件 0.108549295069\\n\",\n \"连连 0.108549295069\\n\",\n \"道口 0.108549295069\\n\",\n \"金山 0.108549295069\\n\",\n \"降职 0.108549295069\\n\",\n \"风生水 0.108549295069\\n\",\n \"-------第 1 段文本的词语tf-idf权重------\\n\",\n \"一起 0.0\\n\",\n \"万块 0.0\\n\",\n \"三人 0.0\\n\",\n \"三套 0.139891059658\\n\",\n \"下海经商 0.0\\n\",\n \"不想 0.139891059658\\n\",\n \"不要 0.139891059658\\n\",\n \"东挪西撮 0.0\\n\",\n \"之后 0.139891059658\\n\",\n \"事业有成 0.139891059658\\n\",\n \"事对 0.0\\n\",\n \"京州 0.279782119316\\n\",\n \"他们 0.0995336411066\\n\",\n \"以沫 0.0\\n\",\n \"公司 0.139891059658\\n\",\n \"分开 0.0\\n\",\n \"别墅 0.279782119316\\n\",\n \"前一晚 0.0\\n\",\n \"县当 0.0\\n\",\n \"县长 0.0\\n\",\n \"可是 0.0995336411066\\n\",\n \"名下 0.139891059658\\n\",\n \"后来 0.0\\n\",\n \"喝酒 0.0\\n\",\n \"回忆起 0.0\\n\",\n \"困难 0.0\\n\",\n \"大家 0.0\\n\",\n \"太大 0.139891059658\\n\",\n \"她们 0.139891059658\\n\",\n \"好像 0.139891059658\\n\",\n \"家住 0.139891059658\\n\",\n \"容易 0.0\\n\",\n \"对不起 0.0\\n\",\n \"帝豪园 0.139891059658\\n\",\n \"开始 0.0\\n\",\n \"很大 0.0\\n\",\n \"房子 0.279782119316\\n\",\n \"打听 0.139891059658\\n\",\n \"时期 0.0\\n\",\n \"易学习 0.0995336411066\\n\",\n \"有福 0.0\\n\",\n \"李达康 0.0995336411066\\n\",\n \"欧阳 0.279782119316\\n\",\n \"毛娅 0.419673178975\\n\",\n \"沙瑞金 0.0995336411066\\n\",\n \"没想到 0.0\\n\",\n \"没有 0.139891059658\\n\",\n \"浪费 0.139891059658\\n\",\n \"王大路 0.29860092332\\n\",\n \"百姓 0.0\\n\",\n \"相助 0.0\\n\",\n \"竟然 0.0\\n\",\n \"股权 0.139891059658\\n\",\n \"胸怀 0.0\\n\",\n \"自己 0.0995336411066\\n\",\n \"觉得 0.0995336411066\\n\",\n \"触动 0.0\\n\",\n \"话别 0.0\\n\",\n \"赔礼道歉 0.0\\n\",\n \"赞叹 0.0\\n\",\n \"踏实 0.139891059658\\n\",\n \"这些 0.139891059658\\n\",\n \"这件 0.0\\n\",\n \"连连 0.0\\n\",\n \"道口 0.0\\n\",\n \"金山 0.0\\n\",\n \"降职 0.0\\n\",\n \"风生水 0.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"wordlist = vector.get_feature_names()#获取词袋模型中的所有词 \\n\",\n \"# tf-idf矩阵 元素a[i][j]表示j词在i类文本中的tf-idf权重\\n\",\n \"weightlist = tfidf.toarray() \\n\",\n \"#打印每类文本的tf-idf词语权重，第一个for遍历所有文本，第二个for便利某一类文本下的词语权重\\n\",\n \"for i in range(len(weightlist)): \\n\",\n \" print \\\"-------第\\\",i,\\\"段文本的词语tf-idf权重------\\\" \\n\",\n \" for j in range(len(wordlist)): \\n\",\n \" print wordlist[j],weightlist[i][j] \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" (0, 44)\\t1\\n\",\n \" (0, 75)\\t1\\n\",\n \" (0, 19)\\t1\\n\",\n \" (0, 57)\\t1\\n\",\n \" (0, 37)\\t1\\n\",\n \" (0, 97)\\t1\\n\",\n \" (0, 77)\\t1\\n\",\n \" (0, 32)\\t1\\n\",\n \" (0, 68)\\t1\\n\",\n \" (0, 48)\\t1\\n\",\n \" (0, 12)\\t1\\n\",\n \" (0, 81)\\t1\\n\",\n \" (0, 3)\\t2\\n\",\n \" (0, 45)\\t2\\n\",\n \" (0, 83)\\t2\\n\",\n \" (0, 86)\\t1\\n\",\n \" (0, 92)\\t1\\n\",\n \" (0, 8)\\t2\\n\",\n \" (0, 71)\\t5\\n\",\n \" (0, 27)\\t1\\n\",\n \" (0, 26)\\t1\\n\",\n \" (0, 94)\\t1\\n\",\n \" (0, 96)\\t1\\n\",\n \" (0, 85)\\t1\\n\",\n \" (0, 34)\\t1\\n\",\n \" :\\t:\\n\",\n \" (2, 60)\\t1\\n\",\n \" (2, 46)\\t1\\n\",\n \" (2, 52)\\t1\\n\",\n \" (2, 30)\\t1\\n\",\n \" (2, 53)\\t2\\n\",\n \" (2, 74)\\t1\\n\",\n \" (2, 64)\\t1\\n\",\n \" (2, 17)\\t1\\n\",\n \" (2, 89)\\t1\\n\",\n \" (2, 76)\\t1\\n\",\n \" (2, 42)\\t2\\n\",\n \" (2, 33)\\t1\\n\",\n \" (2, 28)\\t1\\n\",\n \" (2, 72)\\t1\\n\",\n \" (2, 11)\\t1\\n\",\n \" (2, 49)\\t1\\n\",\n \" (2, 35)\\t1\\n\",\n \" (2, 50)\\t2\\n\",\n \" (2, 21)\\t1\\n\",\n \" (2, 62)\\t2\\n\",\n \" (2, 7)\\t1\\n\",\n \" (2, 6)\\t1\\n\",\n \" (2, 66)\\t1\\n\",\n \" (2, 0)\\t1\\n\",\n \" (2, 81)\\t1\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.feature_extraction.text import CountVectorizer\\n\",\n \"from sklearn.decomposition import LatentDirichletAllocation\\n\",\n \"corpus = [res1,res2,res3]\\n\",\n \"cntVector = CountVectorizer(stop_words=stpwrdlst)\\n\",\n \"cntTf = cntVector.fit_transform(corpus)\\n\",\n \"print cntTf\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 61,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"lda = LatentDirichletAllocation(n_topics=2, max_iter=5,\\n\",\n \" learning_method='online',\\n\",\n \" learning_offset=50.,\\n\",\n \" random_state=0)\\n\",\n \"docres = lda.fit_transform(cntTf)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 62,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 1.26769883 1.16102982 1.04461812 0.82518913 0.88206482 1.03114126\\n\",\n \" 1.1885517 1.13996381 0.93194211 1.00688455 1.10354905 1.11630692\\n\",\n \" 0.69928055 1.02435199 1.18093584 0.77370588 1.22974701 1.2431948\\n\",\n \" 0.9286154 0.73965004 1.08269025 1.2223472 1.05647687 0.7751034\\n\",\n \" 1.07969089 0.84507667 0.75950728 0.86078486 1.06789865 1.01899967\\n\",\n \" 1.12893216 1.01505045 0.83309427 1.10521024 0.81853645 1.011645\\n\",\n \" 0.76359532 0.90990692 0.94738 0.98640661 1.01621653 0.92993322\\n\",\n \" 1.378293 0.97699963 0.79215505 0.96517309 1.16519283 0.85555997\\n\",\n \" 0.97114453 1.11765233 1.37373229 0.97109894 1.15837182 1.45512821\\n\",\n \" 1.14055357 1.14159898 0.8654311 0.95003605 0.98285046 0.82237984\\n\",\n \" 1.27665706 1.05877617 1.38353298 1.15332271 1.17732506 1.29439329\\n\",\n \" 1.15922322 0.91667578 0.8950295 1.10344976 0.94601797 1.43439026\\n\",\n \" 1.0765302 0.91651801 1.08569577 0.92926558 1.16104387 0.80059138\\n\",\n \" 1.14290502 1.03589518 0.81076569 1.3542147 1.12633107 1.15258967\\n\",\n \" 0.86923617 0.8283271 0.79376796 0.84607238 0.95302084 1.28105551\\n\",\n \" 0.89191116 0.9391752 0.80316179 1.02468113 0.95102785 0.89952071\\n\",\n \" 0.80035877 0.92917501]\\n\",\n \" [ 0.80628204 0.81469692 1.34342695 1.28066323 1.42620876 0.8842101\\n\",\n \" 0.7860628 0.86783501 1.28049162 1.03224191 1.04392107 0.85173653\\n\",\n \" 1.29455526 1.12041733 0.96502295 1.10392067 1.23918461 0.91224492\\n\",\n \" 1.55098581 1.06912838 1.02578471 0.8563807 0.93331311 1.14520856\\n\",\n \" 1.08720029 1.08950784 1.30125854 1.11601687 0.83124037 1.31431715\\n\",\n \" 0.87558798 1.02854077 1.01873743 0.82883489 1.08067986 0.89608485\\n\",\n \" 1.0392044 1.17738815 1.40753717 1.06829726 0.99890332 0.9953815\\n\",\n \" 0.9555837 1.1033081 1.16024664 1.33063176 0.91549849 1.08542582\\n\",\n \" 1.00409694 0.90968059 0.82458022 1.11248849 0.73151164 0.88051846\\n\",\n \" 0.84155207 1.16447331 1.19269311 1.146285 2.11638152 1.1115924\\n\",\n \" 0.72504866 1.7127765 0.81771361 1.292976 0.79013008 1.40962934\\n\",\n \" 0.81899839 1.51606321 1.30203704 0.9180146 1.02517725 2.66645525\\n\",\n \" 0.83572024 1.14556117 0.85584502 1.067851 0.77076768 1.02499129\\n\",\n \" 0.91583485 0.99475649 1.11061671 1.35448141 0.83637158 1.52942333\\n\",\n \" 1.11436122 1.11207849 1.14147815 1.05518863 0.92600769 0.84070595\\n\",\n \" 0.96819235 1.1894113 1.1794093 0.78181593 0.99302282 1.1521424\\n\",\n \" 1.20263811 1.08414725]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print lda.components_\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 0.01054041 0.98945959]\\n\",\n \" [ 0.0270532 0.9729468 ]\\n\",\n \" [ 0.98245239 0.01754761]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print docres\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\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\": \"3.6.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "natural-language-processing/nmf.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C) 2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"文本主题模型之非负矩阵分解(NMF) https://www.cnblogs.com/pinard/p/6812011.html\"\n ]\n },\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.array([[1,1,5,2,3], [0,6,2,1,1], [3, 4,0,3,1], [4, 1,5,6,3]])\\n\",\n \"from sklearn.decomposition import NMF\\n\",\n \"model = NMF(n_components=2, alpha=0.01)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 1.67371185 0.02013017]\\n\",\n \" [ 0.40564826 2.17004352]\\n\",\n \" [ 0.77627836 1.5179425 ]\\n\",\n \" [ 2.66991709 0.00940262]]\\n\",\n \"[[ 1.32014421 0.40901559 2.10322743 1.99087019 1.29852389]\\n\",\n \" [ 0.25859086 2.59911791 0.00488947 0.37089193 0.14622829]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"W = model.fit_transform(X)\\n\",\n \"H = model.components_\\n\",\n \"print W\\n\",\n \"print H\"\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\": \"3.6.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "natural-language-processing/tf-idf.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C) 2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"文本挖掘预处理之TF-IDF https://www.cnblogs.com/pinard/p/6693230.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" (0, 4)\\t0.4424621378947393\\n\",\n \" (0, 15)\\t0.697684463383976\\n\",\n \" (0, 3)\\t0.348842231691988\\n\",\n \" (0, 16)\\t0.4424621378947393\\n\",\n \" (1, 3)\\t0.3574550433419527\\n\",\n \" (1, 14)\\t0.45338639737285463\\n\",\n \" (1, 6)\\t0.3574550433419527\\n\",\n \" (1, 2)\\t0.45338639737285463\\n\",\n \" (1, 9)\\t0.45338639737285463\\n\",\n \" (1, 5)\\t0.3574550433419527\\n\",\n \" (2, 7)\\t0.5\\n\",\n \" (2, 12)\\t0.5\\n\",\n \" (2, 0)\\t0.5\\n\",\n \" (2, 1)\\t0.5\\n\",\n \" (3, 15)\\t0.2811316284405006\\n\",\n \" (3, 6)\\t0.2811316284405006\\n\",\n \" (3, 5)\\t0.2811316284405006\\n\",\n \" (3, 13)\\t0.3565798233381452\\n\",\n \" (3, 17)\\t0.3565798233381452\\n\",\n \" (3, 18)\\t0.3565798233381452\\n\",\n \" (3, 11)\\t0.3565798233381452\\n\",\n \" (3, 8)\\t0.3565798233381452\\n\",\n \" (3, 10)\\t0.3565798233381452\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.feature_extraction.text import TfidfTransformer \\n\",\n \"from sklearn.feature_extraction.text import CountVectorizer \\n\",\n \"\\n\",\n \"corpus=[\\\"I come to China to travel\\\", \\n\",\n \" \\\"This is a car polupar in China\\\", \\n\",\n \" \\\"I love tea and Apple \\\", \\n\",\n \" \\\"The work is to write some papers in science\\\"] \\n\",\n \"\\n\",\n \"vectorizer=CountVectorizer()\\n\",\n \"\\n\",\n \"transformer = TfidfTransformer()\\n\",\n \"tfidf = transformer.fit_transform(vectorizer.fit_transform(corpus)) \\n\",\n \"print (tfidf)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" (0, 4)\\t0.4424621378947393\\n\",\n \" (0, 15)\\t0.697684463383976\\n\",\n \" (0, 3)\\t0.348842231691988\\n\",\n \" (0, 16)\\t0.4424621378947393\\n\",\n \" (1, 3)\\t0.3574550433419527\\n\",\n \" (1, 14)\\t0.45338639737285463\\n\",\n \" (1, 6)\\t0.3574550433419527\\n\",\n \" (1, 2)\\t0.45338639737285463\\n\",\n \" (1, 9)\\t0.45338639737285463\\n\",\n \" (1, 5)\\t0.3574550433419527\\n\",\n \" (2, 7)\\t0.5\\n\",\n \" (2, 12)\\t0.5\\n\",\n \" (2, 0)\\t0.5\\n\",\n \" (2, 1)\\t0.5\\n\",\n \" (3, 15)\\t0.2811316284405006\\n\",\n \" (3, 6)\\t0.2811316284405006\\n\",\n \" (3, 5)\\t0.2811316284405006\\n\",\n \" (3, 13)\\t0.3565798233381452\\n\",\n \" (3, 17)\\t0.3565798233381452\\n\",\n \" (3, 18)\\t0.3565798233381452\\n\",\n \" (3, 11)\\t0.3565798233381452\\n\",\n \" (3, 8)\\t0.3565798233381452\\n\",\n \" (3, 10)\\t0.3565798233381452\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.feature_extraction.text import TfidfVectorizer\\n\",\n \"tfidf2 = TfidfVectorizer()\\n\",\n \"re = tfidf2.fit_transform(corpus)\\n\",\n \"print (re)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\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\": \"3.6.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "natural-language-processing/word2vec.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright (C) 2016 - 2019 Pinard Liu(liujianping-ok@163.com)\\n\",\n \"\\n\",\n \"https://www.cnblogs.com/pinard\\n\",\n \"\\n\",\n \"Permission given to modify the code as long as you keep this declaration at the top\\n\",\n \"\\n\",\n \"用gensim学习word2vec https://www.cnblogs.com/pinard/p/7278324.html\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# -*- coding: utf-8 -*-\\n\",\n \"\\n\",\n \"import jieba\\n\",\n \"import jieba.analyse\\n\",\n \"\\n\",\n \"jieba.suggest_freq('沙瑞金', True)\\n\",\n \"jieba.suggest_freq('田国富', True)\\n\",\n \"jieba.suggest_freq('高育良', True)\\n\",\n \"jieba.suggest_freq('侯亮平', True)\\n\",\n \"jieba.suggest_freq('钟小艾', True)\\n\",\n \"jieba.suggest_freq('陈岩石', True)\\n\",\n \"jieba.suggest_freq('欧阳菁', True)\\n\",\n \"jieba.suggest_freq('易学习', True)\\n\",\n \"jieba.suggest_freq('王大路', True)\\n\",\n \"jieba.suggest_freq('蔡成功', True)\\n\",\n \"jieba.suggest_freq('孙连城', True)\\n\",\n \"jieba.suggest_freq('季昌明', True)\\n\",\n \"jieba.suggest_freq('丁义珍', True)\\n\",\n \"jieba.suggest_freq('郑西坡', True)\\n\",\n \"jieba.suggest_freq('赵东来', True)\\n\",\n \"jieba.suggest_freq('高小琴', True)\\n\",\n \"jieba.suggest_freq('赵瑞龙', True)\\n\",\n \"jieba.suggest_freq('林华华', True)\\n\",\n \"jieba.suggest_freq('陆亦可', True)\\n\",\n \"jieba.suggest_freq('刘新建', True)\\n\",\n \"jieba.suggest_freq('刘庆祝', True)\\n\",\n \"\\n\",\n \"with open('./in_the_name_of_people.txt') as f:\\n\",\n \" document = f.read()\\n\",\n \" \\n\",\n \" #document_decode = document.decode('GBK')\\n\",\n \" \\n\",\n \" document_cut = jieba.cut(document)\\n\",\n \" #print ' '.join(jieba_cut) //如果打印结果，则分词效果消失，后面的result无法显示\\n\",\n \" result = ' '.join(document_cut)\\n\",\n \" result = result.encode('utf-8')\\n\",\n \" with open('./in_the_name_of_people_segment.txt', 'w') as f2:\\n\",\n \" f2.write(result)\\n\",\n \"f.close()\\n\",\n \"f2.close()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 64,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# import modules & set up logging\\n\",\n \"import logging\\n\",\n \"import os\\n\",\n \"from gensim.models import word2vec\\n\",\n \"\\n\",\n \"logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\\n\",\n \"\\n\",\n \"sentences = word2vec.LineSentence('./in_the_name_of_people_segment.txt') \\n\",\n \"\\n\",\n \"model = word2vec.Word2Vec(sentences, hs=1,min_count=1,window=3,size=100) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 65,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"祁同伟 0.977946519852\\n\",\n \"赵东来 0.969956457615\\n\",\n \"侯亮平 0.96396112442\\n\",\n \"沙瑞金 0.959288835526\\n\",\n \"易学习 0.95690870285\\n\"\n ]\n }\n ],\n \"source\": [\n \"req_count = 5\\n\",\n \"for key in model.wv.similar_by_word('李达康'.decode('utf-8'), topn =100):\\n\",\n \" if len(key[0])==3:\\n\",\n \" req_count -= 1\\n\",\n \" print key[0], key[1]\\n\",\n \" if req_count == 0:\\n\",\n \" break;\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 66,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"李达康 0.969956457615\\n\",\n \"陆亦可 0.969528734684\\n\",\n \"赵瑞龙 0.966745913029\\n\",\n \"祁同伟 0.965694904327\\n\",\n \"蔡成功 0.961919903755\\n\"\n ]\n }\n ],\n \"source\": [\n \"req_count = 5\\n\",\n \"for key in model.wv.similar_by_word('赵东来'.decode('utf-8'), topn =100):\\n\",\n \" if len(key[0])==3:\\n\",\n \" req_count -= 1\\n\",\n \" print key[0], key[1]\\n\",\n \" if req_count == 0:\\n\",\n \" break;\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 67,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"沙瑞金 0.961137533188\\n\",\n \"开玩笑 0.935248732567\\n\",\n \"祁同伟 0.931046843529\\n\",\n \"李达康 0.921290516853\\n\",\n \"打电话 0.916399776936\\n\"\n ]\n }\n ],\n \"source\": [\n \"req_count = 5\\n\",\n \"for key in model.wv.similar_by_word('高育良'.decode('utf-8'), topn =100):\\n\",\n \" if len(key[0])==3:\\n\",\n \" req_count -= 1\\n\",\n \" print key[0], key[1]\\n\",\n \" if req_count == 0:\\n\",\n \" break;\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"高育良 0.967257142067\\n\",\n \"李达康 0.959131598473\\n\",\n \"田国富 0.953414440155\\n\",\n \"易学习 0.943500876427\\n\",\n \"祁同伟 0.942932963371\\n\"\n ]\n }\n ],\n \"source\": [\n \"req_count = 5\\n\",\n \"for key in model.wv.similar_by_word('沙瑞金'.decode('utf-8'), topn =100):\\n\",\n \" if len(key[0])==3:\\n\",\n \" req_count -= 1\\n\",\n \" print key[0], key[1]\\n\",\n \" if req_count == 0:\\n\",\n \" break;\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 77,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.961137455325\\n\",\n \"0.935589365706\\n\"\n ]\n }\n ],\n \"source\": [\n \"print model.wv.similarity('沙瑞金'.decode('utf-8'), '高育良'.decode('utf-8'))\\n\",\n \"print model.wv.similarity('李达康'.decode('utf-8'), '王大路'.decode('utf-8'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 76,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"刘庆祝\\n\"\n ]\n }\n ],\n \"source\": [\n \"print model.wv.doesnt_match(u\\\"沙瑞金 高育良 李达康 刘庆祝\\\".split())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \" \"\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\": \"3.6.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "readme.md", "content": "# 刘建平Pinard的博客配套代码\n\nhttp://www.cnblogs.com/pinard 刘建平Pinard\n\n之前不少朋友反应我博客中的代码都是连续的片段，不好学习，因此这里把文章和代码做一个整理。\n代码有部分来源于网络，已加上相关方版权信息。部分为自己原创，已加上我的版权信息。\n\n## 目录\n\n* [机器学习基础与回归算法](#2)\n\n* [机器学习分类算法](#3)\n\n* [机器学习聚类算法](#4)\n\n* [机器学习降维算法](#5)\n\n* [机器学习集成学习算法](#6)\n\n* [数学统计学](#7)\n\n* [机器学习关联算法](#8)\n\n* [机器学习推荐算法](#9)\n\n* [深度学习算法](#10)\n\n* [自然语言处理算法](#11)\n\n* [强化学习算法](#1)\n\n* [特征工程与算法落地](#12)\n\n## 注意\n\n2016-2017年写的博客使用的python版本是2.7， 2018年因为TensorFlow对Python3的一些要求，所以写博客使用的Python版本是3.6。少部分2016，2017年的博客代码无法找到，重新用Python3.6跑过上传，因此可能会出现和博客中代码稍有不一致的地方，主要涉及到print的语法和range的用法，若遇到问题，稍微修改即可跑通。\n\n## [赞助我](#13)\n\n

强化学习文章与代码：:

\n\n|文章 | 代码|\n---|---\n[强化学习（一）模型基础](https://www.cnblogs.com/pinard/p/9385570.html)| [代码](https://github.com/ljpzzz/machinelearning/blob/master/reinforcement-learning/introduction.py)\n[强化学习（二）马尔科夫决策过程(MDP)](https://www.cnblogs.com/pinard/p/9426283.html) | 无\n[强化学习（三）用动态规划（DP）求解](https://www.cnblogs.com/pinard/p/9463815.html) | 无\n[强化学习（四）用蒙特卡罗法（MC）求解](https://www.cnblogs.com/pinard/p/9492980.html) | 无\n[强化学习（五）用时序差分法（TD）求解](https://www.cnblogs.com/pinard/p/9529828.html) | 无\n[强化学习（六）时序差分在线控制算法SARSA](https://www.cnblogs.com/pinard/p/9614290.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/reinforcement-learning/sarsa_windy_world.py)\n[强化学习（七）时序差分离线控制算法Q-Learning](https://www.cnblogs.com/pinard/p/9669263.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/reinforcement-learning/q_learning_windy_world.py)\n[强化学习（八）价值函数的近似表示与Deep Q-Learning](https://www.cnblogs.com/pinard/p/9714655.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/reinforcement-learning/dqn.py)\n[强化学习（九）Deep Q-Learning进阶之Nature DQN](https://www.cnblogs.com/pinard/p/9756075.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/reinforcement-learning/nature_dqn.py)\n[强化学习（十）Double DQN (DDQN)](https://www.cnblogs.com/pinard/p/9778063.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/reinforcement-learning/ddqn.py)\n[强化学习(十一) Prioritized Replay DQN](https://www.cnblogs.com/pinard/p/9797695.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/reinforcement-learning/ddqn_prioritised_replay.py)\n[强化学习(十二) Dueling DQN](https://www.cnblogs.com/pinard/p/9923859.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/reinforcement-learning/duel_dqn.py)\n[强化学习(十三) 策略梯度(Policy Gradient)](https://www.cnblogs.com/pinard/p/10137696.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/reinforcement-learning/policy_gradient.py)\n[强化学习(十四) Actor-Critic](https://www.cnblogs.com/pinard/p/10272023.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/reinforcement-learning/actor_critic.py)\n[强化学习(十五) A3C](https://www.cnblogs.com/pinard/p/10334127.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/reinforcement-learning/a3c.py)\n[强化学习(十六) 深度确定性策略梯度(DDPG)](https://www.cnblogs.com/pinard/p/10345762.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/reinforcement-learning/ddpg.py)\n[强化学习(十七) 基于模型的强化学习与Dyna算法框架](https://www.cnblogs.com/pinard/p/10384424.html) | 无\n[强化学习(十八) 基于模拟的搜索与蒙特卡罗树搜索(MCTS)](https://www.cnblogs.com/pinard/p/10470571.html) | 无\n[强化学习(十九) AlphaGo Zero强化学习原理](https://www.cnblogs.com/pinard/p/10609228.html) | 无\n\n\n

机器学习基础与回归算法文章与代码：

\n\n|文章 | 代码|\n---|---\n[梯度下降（Gradient Descent）小结](https://www.cnblogs.com/pinard/p/5970503.html) | 无\n[最小二乘法小结](https://www.cnblogs.com/pinard/p/5976811.html) |无\n[交叉验证(Cross Validation)原理小结](https://www.cnblogs.com/pinard/p/5992719.html) | 无\n[精确率与召回率，RoC曲线与PR曲线](https://www.cnblogs.com/pinard/p/5993450.html) |无\n[线性回归原理小结](https://www.cnblogs.com/pinard/p/6004041.html) |无\n[机器学习研究与开发平台的选择](https://www.cnblogs.com/pinard/p/6007200.html) | 无\n[scikit-learn 和pandas 基于windows单机机器学习环境的搭建](https://www.cnblogs.com/pinard/p/6013484.html) |无\n[用scikit-learn和pandas学习线性回归](https://www.cnblogs.com/pinard/p/6016029.html) |[代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/linear-regression.ipynb)\n[Lasso回归算法： 坐标轴下降法与最小角回归法小结](https://www.cnblogs.com/pinard/p/6018889.html) | 无\n[用scikit-learn和pandas学习Ridge回归](https://www.cnblogs.com/pinard/p/6023000.html) | [代码1](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/ridge_regression_1.ipynb) [代码2](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/ridge_regression.ipynb)\n[scikit-learn 线性回归算法库小结](https://www.cnblogs.com/pinard/p/6026343.html)|无\n[异常点检测算法小结](https://www.cnblogs.com/pinard/p/9314198.html)|无\n\n

机器学习分类算法文章与代码：

\n\n|文章 | 代码|\n---|---\n[逻辑回归原理小结](https://www.cnblogs.com/pinard/p/6029432.html) |无\n[scikit-learn 逻辑回归类库使用小结](https://www.cnblogs.com/pinard/p/6035872.html) |无\n[感知机原理小结](https://www.cnblogs.com/pinard/p/6042320.html) |无\n[决策树算法原理(上)](https://www.cnblogs.com/pinard/p/6050306.html) |无\n[决策树算法原理(下)](https://www.cnblogs.com/pinard/p/6053344.html)|无\n[scikit-learn决策树算法类库使用小结](https://www.cnblogs.com/pinard/p/6056319.html) |[代码1](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/decision_tree_classifier.ipynb) [代码2](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/decision_tree_classifier_1.ipynb)\n[K近邻法(KNN)原理小结](https://www.cnblogs.com/pinard/p/6061661.html) |无\n[scikit-learn K近邻法类库使用小结](https://www.cnblogs.com/pinard/p/6065607.html) |[代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/knn_classifier.ipynb)\n[朴素贝叶斯算法原理小结](https://www.cnblogs.com/pinard/p/6069267.html) |无\n[scikit-learn 朴素贝叶斯类库使用小结](https://www.cnblogs.com/pinard/p/6074222.html)| [代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/native_bayes.ipynb)\n[最大熵模型原理小结](https://www.cnblogs.com/pinard/p/6093948.html)|无\n[支持向量机原理(一) 线性支持向量机](https://www.cnblogs.com/pinard/p/6097604.html)|无\n[支持向量机原理(二) 线性支持向量机的软间隔最大化模型](https://www.cnblogs.com/pinard/p/6100722.html)|无\n[支持向量机原理(三)线性不可分支持向量机与核函数](https://www.cnblogs.com/pinard/p/6103615.html)|无\n[支持向量机原理(四)SMO算法原理](https://www.cnblogs.com/pinard/p/6111471.html)|无\n[支持向量机原理(五)线性支持回归](https://www.cnblogs.com/pinard/p/6113120.html)|无\n[scikit-learn 支持向量机算法库使用小结](https://www.cnblogs.com/pinard/p/6117515.html)|无\n[支持向量机高斯核调参小结](https://www.cnblogs.com/pinard/p/6126077.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/svm_classifier.ipynb)\n\n

数学统计学文章与代码：

\n\n|文章 | 代码|\n---|---\n[机器学习算法的随机数据生成](https://www.cnblogs.com/pinard/p/6047802.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/mathematics/random_data_generation.ipynb)\n[MCMC(一)蒙特卡罗方法](https://www.cnblogs.com/pinard/p/6625739.html)|无\n[MCMC(二)马尔科夫链](https://www.cnblogs.com/pinard/p/6632399.html)| [代码](https://github.com/ljpzzz/machinelearning/blob/master/mathematics/mcmc_2.ipynb)\n[MCMC(三)MCMC采样和M-H采样](https://www.cnblogs.com/pinard/p/6638955.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/mathematics/mcmc_3_4.ipynb)\n[MCMC(四)Gibbs采样](https://www.cnblogs.com/pinard/p/6645766.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/mathematics/mcmc_3_4.ipynb)\n[机器学习中的矩阵向量求导(一) 求导定义与求导布局](https://www.cnblogs.com/pinard/p/10750718.html)|无\n[机器学习中的矩阵向量求导(二) 矩阵向量求导之定义法](https://www.cnblogs.com/pinard/p/10773942.html)|无\n[机器学习中的矩阵向量求导(三) 矩阵向量求导之微分法](https://www.cnblogs.com/pinard/p/10791506.html)|无\n[机器学习中的矩阵向量求导(四) 矩阵向量求导链式法则](https://www.cnblogs.com/pinard/p/10825264.html)|无\n[机器学习中的矩阵向量求导(五) 矩阵对矩阵的求导](https://www.cnblogs.com/pinard/p/10930902.html)|无\n\n\n

机器学习集成学习文章与代码：

\n\n|文章 | 代码|\n---|---\n[集成学习原理小结](https://www.cnblogs.com/pinard/p/6131423.html) | 无\n[集成学习之Adaboost算法原理小结](https://www.cnblogs.com/pinard/p/6133937.html) | 无\n[scikit-learn Adaboost类库使用小结](https://www.cnblogs.com/pinard/p/6136914.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/ensemble-learning/adaboost-classifier.ipynb)\n[梯度提升树(GBDT)原理小结](https://www.cnblogs.com/pinard/p/6140514.html) | 无\n[scikit-learn 梯度提升树(GBDT)调参小结](https://www.cnblogs.com/pinard/p/6143927.html)| [代码](https://github.com/ljpzzz/machinelearning/blob/master/ensemble-learning/gbdt_classifier.ipynb)\n[Bagging与随机森林算法原理小结](https://www.cnblogs.com/pinard/p/6156009.html) | 无\n[scikit-learn随机森林调参小结](https://www.cnblogs.com/pinard/p/6160412.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/ensemble-learning/random_forest_classifier.ipynb)\n[XGBoost算法原理小结](https://www.cnblogs.com/pinard/p/10979808.html) | 无\n[XGBoost类库使用小结](https://www.cnblogs.com/pinard/p/11114748.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/ensemble-learning/xgboost-example.ipynb)\n\n\n

机器学习聚类算法文章与代码：

\n\n|文章 | 代码|\n---|---\n[K-Means聚类算法原理](https://www.cnblogs.com/pinard/p/6164214.html)|无\n[用scikit-learn学习K-Means聚类](https://www.cnblogs.com/pinard/p/6169370.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/kmeans_cluster.ipynb)\n[BIRCH聚类算法原理](https://www.cnblogs.com/pinard/p/6179132.html)|无\n[用scikit-learn学习BIRCH聚类](https://www.cnblogs.com/pinard/p/6200579.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/birch_cluster.ipynb)\n[DBSCAN密度聚类算法](https://www.cnblogs.com/pinard/p/6208966.html)|无\n[用scikit-learn学习DBSCAN聚类](https://www.cnblogs.com/pinard/p/6217852.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/dbscan_cluster.ipynb)\n[谱聚类（spectral clustering）原理总结](https://www.cnblogs.com/pinard/p/6221564.html) |无\n[用scikit-learn学习谱聚类](https://www.cnblogs.com/pinard/p/6235920.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/spectral_cluster.ipynb)\n\n

机器学习降维算法文章与代码：

\n\n|文章 | 代码|\n---|---\n[主成分分析（PCA）原理总结](https://www.cnblogs.com/pinard/p/6239403.html)|无\n[用scikit-learn学习主成分分析(PCA)](https://www.cnblogs.com/pinard/p/6243025.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/pca.ipynb)\n[线性判别分析LDA原理总结](https://www.cnblogs.com/pinard/p/6244265.html)|无\n[用scikit-learn进行LDA降维](https://www.cnblogs.com/pinard/p/6249328.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/lda.ipynb)\n[奇异值分解(SVD)原理与在降维中的应用](https://www.cnblogs.com/pinard/p/6251584.html)|无\n[局部线性嵌入(LLE)原理总结](https://www.cnblogs.com/pinard/p/6266408.html)|无\n[用scikit-learn研究局部线性嵌入(LLE)](https://www.cnblogs.com/pinard/p/6273377.html) |[代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/lle.ipynb)\n\n

机器学习关联算法文章与代码：

\n\n|文章 | 代码|\n---|---\n[典型关联分析(CCA)原理总结](https://www.cnblogs.com/pinard/p/6288716.html)|无\n[Apriori算法原理总结](https://www.cnblogs.com/pinard/p/6293298.html)|无\n[FP Tree算法原理总结](https://www.cnblogs.com/pinard/p/6307064.html)|无\n[PrefixSpan算法原理总结](https://www.cnblogs.com/pinard/p/6323182.html)|无\n[用Spark学习FP Tree算法和PrefixSpan算法](https://www.cnblogs.com/pinard/p/6340162.html)| [代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/fp_tree_prefixspan.ipynb)\n[日志和告警数据挖掘经验谈](https://www.cnblogs.com/pinard/p/6039099.html) | 无\n\n

机器学习推荐算法文章与代码：

\n\n|文章 | 代码|\n---|---\n[协同过滤推荐算法总结](https://www.cnblogs.com/pinard/p/6349233.html)|无\n[矩阵分解在协同过滤推荐算法中的应用](https://www.cnblogs.com/pinard/p/6351319.html)|无\n[SimRank协同过滤推荐算法](https://www.cnblogs.com/pinard/p/6362647.html)|无\n[用Spark学习矩阵分解推荐算法](https://www.cnblogs.com/pinard/p/6364932.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/matrix_factorization.ipynb)\n[分解机(Factorization Machines)推荐算法原理](https://www.cnblogs.com/pinard/p/6370127.html)|无\n[贝叶斯个性化排序(BPR)算法小结](https://www.cnblogs.com/pinard/p/9128682.html)|无\n[用tensorflow学习贝叶斯个性化排序(BPR)](https://www.cnblogs.com/pinard/p/9163481.html)| [代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/bpr.ipynb)\n\n

深度学习算法文章与代码：

\n\n|文章 | 代码|\n---|---\n[深度神经网络（DNN）模型与前向传播算法](https://www.cnblogs.com/pinard/p/6418668.html)|无\n[深度神经网络（DNN）反向传播算法(BP)](https://www.cnblogs.com/pinard/p/6422831.html)|无\n[深度神经网络（DNN）损失函数和激活函数的选择](https://www.cnblogs.com/pinard/p/6437495.html)|无\n[深度神经网络（DNN）的正则化](https://www.cnblogs.com/pinard/p/6472666.html)|无\n[卷积神经网络(CNN)模型结构](https://www.cnblogs.com/pinard/p/6483207.html)|无\n[卷积神经网络(CNN)前向传播算法](https://www.cnblogs.com/pinard/p/6489633.html)|无\n[卷积神经网络(CNN)反向传播算法](https://www.cnblogs.com/pinard/p/6494810.html)|无\n[循环神经网络(RNN)模型与前向反向传播算法](https://www.cnblogs.com/pinard/p/6509630.html)|无\n[LSTM模型与前向反向传播算法](https://www.cnblogs.com/pinard/p/6519110.html)|无\n[受限玻尔兹曼机（RBM）原理总结](https://www.cnblogs.com/pinard/p/6530523.html)|无\n\n

自然语言处理文章与代码：

\n\n|文章 | 代码|\n---|---\n[文本挖掘的分词原理](https://www.cnblogs.com/pinard/p/6677078.html)|无\n[文本挖掘预处理之向量化与Hash Trick](https://www.cnblogs.com/pinard/p/6688348.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/natural-language-processing/hash_trick.ipynb)\n[文本挖掘预处理之TF-IDF](https://www.cnblogs.com/pinard/p/6693230.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/natural-language-processing/tf-idf.ipynb)\n[中文文本挖掘预处理流程总结](https://www.cnblogs.com/pinard/p/6744056.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/natural-language-processing/chinese_digging.ipynb)\n[英文文本挖掘预处理流程总结](https://www.cnblogs.com/pinard/p/6756534.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/natural-language-processing/english_digging.ipynb)\n[文本主题模型之潜在语义索引(LSI)](https://www.cnblogs.com/pinard/p/6805861.html)|无\n[文本主题模型之非负矩阵分解(NMF)](https://www.cnblogs.com/pinard/p/6812011.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/natural-language-processing/nmf.ipynb)\n[文本主题模型之LDA(一) LDA基础](https://www.cnblogs.com/pinard/p/6831308.html)|无\n[文本主题模型之LDA(二) LDA求解之Gibbs采样算法](https://www.cnblogs.com/pinard/p/6867828.html)|无\n[文本主题模型之LDA(三) LDA求解之变分推断EM算法](https://www.cnblogs.com/pinard/p/6873703.html)|无\n[用scikit-learn学习LDA主题模型](https://www.cnblogs.com/pinard/p/6908150.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/natural-language-processing/lda.ipynb)\n[EM算法原理总结](https://www.cnblogs.com/pinard/p/6912636.html)|无\n[隐马尔科夫模型HMM（一）HMM模型](https://www.cnblogs.com/pinard/p/6945257.html)|无\n[隐马尔科夫模型HMM（二）前向后向算法评估观察序列概率](https://www.cnblogs.com/pinard/p/6955871.html)|无\n[隐马尔科夫模型HMM（三）鲍姆-韦尔奇算法求解HMM参数](https://www.cnblogs.com/pinard/p/6972299.html)|无\n[隐马尔科夫模型HMM（四）维特比算法解码隐藏状态序列](https://www.cnblogs.com/pinard/p/6991852.html)|无\n[用hmmlearn学习隐马尔科夫模型HMM](https://www.cnblogs.com/pinard/p/7001397.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/natural-language-processing/hmm.ipynb)\n[条件随机场CRF(一)从随机场到线性链条件随机场](https://www.cnblogs.com/pinard/p/7048333.html)|无\n[条件随机场CRF(二) 前向后向算法评估标记序列概率](https://www.cnblogs.com/pinard/p/7055072.html)|无\n[条件随机场CRF(三) 模型学习与维特比算法解码](https://www.cnblogs.com/pinard/p/7068574.html)|无\n[word2vec原理(一) CBOW与Skip-Gram模型基础](https://www.cnblogs.com/pinard/p/7160330.html)|无\n[word2vec原理(二) 基于Hierarchical Softmax的模型](https://www.cnblogs.com/pinard/p/7243513.html)|无\n[word2vec原理(三) 基于Negative Sampling的模型](https://www.cnblogs.com/pinard/p/7249903.html)|无\n[用gensim学习word2vec](https://www.cnblogs.com/pinard/p/7278324.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/natural-language-processing/word2vec.ipynb)\n\n

特征工程与算法落地文章与代码：

\n\n|文章 | 代码|\n---|---\n[特征工程之特征选择](https://www.cnblogs.com/pinard/p/9032759.html)|无\n[特征工程之特征表达](https://www.cnblogs.com/pinard/p/9061549.html)|无\n[特征工程之特征预处理](https://www.cnblogs.com/pinard/p/9093890.html)|无\n[用PMML实现机器学习模型的跨平台上线](https://www.cnblogs.com/pinard/p/9220199.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/model-in-product/sklearn-jpmml)\n[tensorflow机器学习模型的跨平台上线](https://www.cnblogs.com/pinard/p/9251296.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/model-in-product/tensorflow-java)\n\n

赞助我

\n\n你的支持是我写作的动力(1.微信/2.支付宝)：\n\n![微信赞助](./assert/invoice.bmp)\n\n\n![支付宝赞助](./assert/invoice_ali.bmp)\n\nLicense MIT.\n" }, { "path": "reinforcement-learning/a3c.py", "content": "#######################################################################\n# Copyright (C) #\n# 2016 - 2019 Pinard Liu(liujianping-ok@163.com) #\n# https://www.cnblogs.com/pinard #\n# Permission given to modify the code as long as you keep this #\n# declaration at the top #\n#######################################################################\n## reference from MorvanZhou's A3C code on Github, minor update:##\n##https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/blob/master/contents/10_A3C/A3C_discrete_action.py ##\n\n## https://www.cnblogs.com/pinard/p/10334127.html ##\n## 强化学习(十五) A3C ##\n\nimport threading\nimport tensorflow as tf\nimport numpy as np\nimport gym\nimport os\nimport shutil\nimport matplotlib.pyplot as plt\n\n\nGAME = 'CartPole-v0'\nOUTPUT_GRAPH = True\nLOG_DIR = './log'\nN_WORKERS = 3\nMAX_GLOBAL_EP = 3000\nGLOBAL_NET_SCOPE = 'Global_Net'\nUPDATE_GLOBAL_ITER = 100\nGAMMA = 0.9\nENTROPY_BETA = 0.001\nLR_A = 0.001 # learning rate for actor\nLR_C = 0.001 # learning rate for critic\nGLOBAL_RUNNING_R = []\nGLOBAL_EP = 0\nSTEP = 3000 # Step limitation in an episode\nTEST = 10 # The number of experiment test every 100 episode\n\nenv = gym.make(GAME)\nN_S = env.observation_space.shape[0]\nN_A = env.action_space.n\n\n\nclass ACNet(object):\n def __init__(self, scope, globalAC=None):\n\n if scope == GLOBAL_NET_SCOPE: # get global network\n with tf.variable_scope(scope):\n self.s = tf.placeholder(tf.float32, [None, N_S], 'S')\n self.a_params, self.c_params = self._build_net(scope)[-2:]\n else: # local net, calculate losses\n with tf.variable_scope(scope):\n self.s = tf.placeholder(tf.float32, [None, N_S], 'S')\n self.a_his = tf.placeholder(tf.int32, [None, ], 'A')\n self.v_target = tf.placeholder(tf.float32, [None, 1], 'Vtarget')\n\n self.a_prob, self.v, self.a_params, self.c_params = self._build_net(scope)\n\n td = tf.subtract(self.v_target, self.v, name='TD_error')\n with tf.name_scope('c_loss'):\n self.c_loss = tf.reduce_mean(tf.square(td))\n\n with tf.name_scope('a_loss'):\n log_prob = tf.reduce_sum(tf.log(self.a_prob + 1e-5) * tf.one_hot(self.a_his, N_A, dtype=tf.float32), axis=1, keep_dims=True)\n exp_v = log_prob * tf.stop_gradient(td)\n entropy = -tf.reduce_sum(self.a_prob * tf.log(self.a_prob + 1e-5),\n axis=1, keep_dims=True) # encourage exploration\n self.exp_v = ENTROPY_BETA * entropy + exp_v\n self.a_loss = tf.reduce_mean(-self.exp_v)\n\n with tf.name_scope('local_grad'):\n self.a_grads = tf.gradients(self.a_loss, self.a_params)\n self.c_grads = tf.gradients(self.c_loss, self.c_params)\n\n with tf.name_scope('sync'):\n with tf.name_scope('pull'):\n self.pull_a_params_op = [l_p.assign(g_p) for l_p, g_p in zip(self.a_params, globalAC.a_params)]\n self.pull_c_params_op = [l_p.assign(g_p) for l_p, g_p in zip(self.c_params, globalAC.c_params)]\n with tf.name_scope('push'):\n self.update_a_op = OPT_A.apply_gradients(zip(self.a_grads, globalAC.a_params))\n self.update_c_op = OPT_C.apply_gradients(zip(self.c_grads, globalAC.c_params))\n\n def _build_net(self, scope):\n w_init = tf.random_normal_initializer(0., .1)\n with tf.variable_scope('actor'):\n l_a = tf.layers.dense(self.s, 200, tf.nn.relu6, kernel_initializer=w_init, name='la')\n a_prob = tf.layers.dense(l_a, N_A, tf.nn.softmax, kernel_initializer=w_init, name='ap')\n with tf.variable_scope('critic'):\n l_c = tf.layers.dense(self.s, 100, tf.nn.relu6, kernel_initializer=w_init, name='lc')\n v = tf.layers.dense(l_c, 1, kernel_initializer=w_init, name='v') # state value\n a_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=scope + '/actor')\n c_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=scope + '/critic')\n return a_prob, v, a_params, c_params\n\n def update_global(self, feed_dict): # run by a local\n SESS.run([self.update_a_op, self.update_c_op], feed_dict) # local grads applies to global net\n\n def pull_global(self): # run by a local\n SESS.run([self.pull_a_params_op, self.pull_c_params_op])\n\n def choose_action(self, s): # run by a local\n prob_weights = SESS.run(self.a_prob, feed_dict={self.s: s[np.newaxis, :]})\n action = np.random.choice(range(prob_weights.shape[1]),\n p=prob_weights.ravel()) # select action w.r.t the actions prob\n return action\n\n\nclass Worker(object):\n def __init__(self, name, globalAC):\n self.env = gym.make(GAME).unwrapped\n self.name = name\n self.AC = ACNet(name, globalAC)\n\n def work(self):\n global GLOBAL_RUNNING_R, GLOBAL_EP\n total_step = 1\n buffer_s, buffer_a, buffer_r = [], [], []\n while not COORD.should_stop() and GLOBAL_EP < MAX_GLOBAL_EP:\n s = self.env.reset()\n ep_r = 0\n while True:\n # if self.name == 'W_0':\n # self.env.render()\n a = self.AC.choose_action(s)\n s_, r, done, info = self.env.step(a)\n if done: r = -5\n ep_r += r\n buffer_s.append(s)\n buffer_a.append(a)\n buffer_r.append(r)\n\n if total_step % UPDATE_GLOBAL_ITER == 0 or done: # update global and assign to local net\n if done:\n v_s_ = 0 # terminal\n else:\n v_s_ = SESS.run(self.AC.v, {self.AC.s: s_[np.newaxis, :]})[0, 0]\n buffer_v_target = []\n for r in buffer_r[::-1]: # reverse buffer r\n v_s_ = r + GAMMA * v_s_\n buffer_v_target.append(v_s_)\n buffer_v_target.reverse()\n\n buffer_s, buffer_a, buffer_v_target = np.vstack(buffer_s), np.array(buffer_a), np.vstack(buffer_v_target)\n feed_dict = {\n self.AC.s: buffer_s,\n self.AC.a_his: buffer_a,\n self.AC.v_target: buffer_v_target,\n }\n self.AC.update_global(feed_dict)\n\n buffer_s, buffer_a, buffer_r = [], [], []\n self.AC.pull_global()\n\n s = s_\n total_step += 1\n if done:\n if len(GLOBAL_RUNNING_R) == 0: # record running episode reward\n GLOBAL_RUNNING_R.append(ep_r)\n else:\n GLOBAL_RUNNING_R.append(0.99 * GLOBAL_RUNNING_R[-1] + 0.01 * ep_r)\n print(\n self.name,\n \"Ep:\", GLOBAL_EP,\n \"| Ep_r: %i\" % GLOBAL_RUNNING_R[-1],\n )\n GLOBAL_EP += 1\n break\n\nif __name__ == \"__main__\":\n SESS = tf.Session()\n\n with tf.device(\"/cpu:0\"):\n OPT_A = tf.train.RMSPropOptimizer(LR_A, name='RMSPropA')\n OPT_C = tf.train.RMSPropOptimizer(LR_C, name='RMSPropC')\n GLOBAL_AC = ACNet(GLOBAL_NET_SCOPE) # we only need its params\n workers = []\n # Create worker\n for i in range(N_WORKERS):\n i_name = 'W_%i' % i # worker name\n workers.append(Worker(i_name, GLOBAL_AC))\n\n COORD = tf.train.Coordinator()\n SESS.run(tf.global_variables_initializer())\n\n if OUTPUT_GRAPH:\n if os.path.exists(LOG_DIR):\n shutil.rmtree(LOG_DIR)\n tf.summary.FileWriter(LOG_DIR, SESS.graph)\n\n worker_threads = []\n for worker in workers:\n job = lambda: worker.work()\n t = threading.Thread(target=job)\n t.start()\n worker_threads.append(t)\n COORD.join(worker_threads)\n\n testWorker = Worker(\"test\", GLOBAL_AC)\n testWorker.AC.pull_global()\n\n total_reward = 0\n for i in range(TEST):\n state = env.reset()\n for j in range(STEP):\n env.render()\n action = testWorker.AC.choose_action(state) # direct action for test\n state, reward, done, _ = env.step(action)\n total_reward += reward\n if done:\n break\n ave_reward = total_reward / TEST\n print('episode: ', GLOBAL_EP, 'Evaluation Average Reward:', ave_reward)\n\n plt.plot(np.arange(len(GLOBAL_RUNNING_R)), GLOBAL_RUNNING_R)\n plt.xlabel('step')\n plt.ylabel('Total moving reward')\n plt.show()" }, { "path": "reinforcement-learning/actor_critic.py", "content": "#######################################################################\n# Copyright (C) #\n# 2016 - 2019 Pinard Liu(liujianping-ok@163.com) #\n# https://www.cnblogs.com/pinard #\n# Permission given to modify the code as long as you keep this #\n# declaration at the top #\n#######################################################################\n## https://www.cnblogs.com/pinard/p/10272023.html ##\n## 强化学习(十四) Actor-Critic ##\n\nimport gym\nimport tensorflow as tf\nimport numpy as np\nimport random\nfrom collections import deque\n\n# Hyper Parameters\nGAMMA = 0.95 # discount factor\nLEARNING_RATE=0.01\n\nclass Actor():\n def __init__(self, env, sess):\n # init some parameters\n self.time_step = 0\n self.state_dim = env.observation_space.shape[0]\n self.action_dim = env.action_space.n\n self.create_softmax_network()\n\n # Init session\n self.session = sess\n self.session.run(tf.global_variables_initializer())\n\n def create_softmax_network(self):\n # network weights\n W1 = self.weight_variable([self.state_dim, 20])\n b1 = self.bias_variable([20])\n W2 = self.weight_variable([20, self.action_dim])\n b2 = self.bias_variable([self.action_dim])\n # input layer\n self.state_input = tf.placeholder(\"float\", [None, self.state_dim])\n self.tf_acts = tf.placeholder(tf.int32, [None,2], name=\"actions_num\")\n self.td_error = tf.placeholder(tf.float32, None, \"td_error\") # TD_error\n # hidden layers\n h_layer = tf.nn.relu(tf.matmul(self.state_input, W1) + b1)\n # softmax layer\n self.softmax_input = tf.matmul(h_layer, W2) + b2\n # softmax output\n self.all_act_prob = tf.nn.softmax(self.softmax_input, name='act_prob')\n\n self.neg_log_prob = tf.nn.softmax_cross_entropy_with_logits(logits=self.softmax_input,\n labels=self.tf_acts)\n self.exp = tf.reduce_mean(self.neg_log_prob * self.td_error)\n\n #这里需要最大化当前策略的价值，因此需要最大化self.exp,即最小化-self.exp\n self.train_op = tf.train.AdamOptimizer(LEARNING_RATE).minimize(-self.exp)\n\n def weight_variable(self, shape):\n initial = tf.truncated_normal(shape)\n return tf.Variable(initial)\n\n def bias_variable(self, shape):\n initial = tf.constant(0.01, shape=shape)\n return tf.Variable(initial)\n\n def choose_action(self, observation):\n prob_weights = self.session.run(self.all_act_prob, feed_dict={self.state_input: observation[np.newaxis, :]})\n action = np.random.choice(range(prob_weights.shape[1]), p=prob_weights.ravel()) # select action w.r.t the actions prob\n return action\n\n\n def learn(self, state, action, td_error):\n s = state[np.newaxis, :]\n one_hot_action = np.zeros(self.action_dim)\n one_hot_action[action] = 1\n a = one_hot_action[np.newaxis, :]\n # train on episode\n self.session.run(self.train_op, feed_dict={\n self.state_input: s,\n self.tf_acts: a,\n self.td_error: td_error,\n })\n\nEPSILON = 0.01 # final value of epsilon\nREPLAY_SIZE = 10000 # experience replay buffer size\nBATCH_SIZE = 32 # size of minibatch\nREPLACE_TARGET_FREQ = 10 # frequency to update target Q network\n\nclass Critic():\n def __init__(self, env, sess):\n # init some parameters\n self.time_step = 0\n self.epsilon = EPSILON\n self.state_dim = env.observation_space.shape[0]\n self.action_dim = env.action_space.n\n\n self.create_Q_network()\n self.create_training_method()\n\n # Init session\n self.session = sess\n self.session.run(tf.global_variables_initializer())\n\n def create_Q_network(self):\n # network weights\n W1q = self.weight_variable([self.state_dim, 20])\n b1q = self.bias_variable([20])\n W2q = self.weight_variable([20, 1])\n b2q = self.bias_variable([1])\n self.state_input = tf.placeholder(tf.float32, [1, self.state_dim], \"state\")\n # hidden layers\n h_layerq = tf.nn.relu(tf.matmul(self.state_input, W1q) + b1q)\n # Q Value layer\n self.Q_value = tf.matmul(h_layerq, W2q) + b2q\n\n def create_training_method(self):\n self.next_value = tf.placeholder(tf.float32, [1,1], \"v_next\")\n self.reward = tf.placeholder(tf.float32, None, 'reward')\n\n with tf.variable_scope('squared_TD_error'):\n self.td_error = self.reward + GAMMA * self.next_value - self.Q_value\n self.loss = tf.square(self.td_error)\n with tf.variable_scope('train'):\n self.train_op = tf.train.AdamOptimizer(self.epsilon).minimize(self.loss)\n\n def train_Q_network(self, state, reward, next_state):\n s, s_ = state[np.newaxis, :], next_state[np.newaxis, :]\n v_ = self.session.run(self.Q_value, {self.state_input: s_})\n td_error, _ = self.session.run([self.td_error, self.train_op],\n {self.state_input: s, self.next_value: v_, self.reward: reward})\n return td_error\n\n def weight_variable(self,shape):\n initial = tf.truncated_normal(shape)\n return tf.Variable(initial)\n\n def bias_variable(self,shape):\n initial = tf.constant(0.01, shape = shape)\n return tf.Variable(initial)\n\n# Hyper Parameters\nENV_NAME = 'CartPole-v0'\nEPISODE = 3000 # Episode limitation\nSTEP = 3000 # Step limitation in an episode\nTEST = 10 # The number of experiment test every 100 episode\n\ndef main():\n # initialize OpenAI Gym env and dqn agent\n sess = tf.InteractiveSession()\n env = gym.make(ENV_NAME)\n actor = Actor(env, sess)\n critic = Critic(env, sess)\n\n for episode in range(EPISODE):\n # initialize task\n state = env.reset()\n # Train\n for step in range(STEP):\n action = actor.choose_action(state) # e-greedy action for train\n next_state,reward,done,_ = env.step(action)\n td_error = critic.train_Q_network(state, reward, next_state) # gradient = grad[r + gamma * V(s_) - V(s)]\n actor.learn(state, action, td_error) # true_gradient = grad[logPi(s,a) * td_error]\n state = next_state\n if done:\n break\n\n # Test every 100 episodes\n if episode % 100 == 0:\n total_reward = 0\n for i in range(TEST):\n state = env.reset()\n for j in range(STEP):\n env.render()\n action = actor.choose_action(state) # direct action for test\n state,reward,done,_ = env.step(action)\n total_reward += reward\n if done:\n break\n ave_reward = total_reward/TEST\n print ('episode: ',episode,'Evaluation Average Reward:',ave_reward)\n\nif __name__ == '__main__':\n main()" }, { "path": "reinforcement-learning/ddpg.py", "content": "\"\"\"\nDeep Deterministic Policy Gradient (DDPG), Reinforcement Learning.\nDDPG is Actor Critic based algorithm.\nPendulum example.\nView more on my tutorial page: https://morvanzhou.github.io/tutorials/\nUsing:\ntensorflow 1.0\ngym 0.8.0\n\"\"\"\n#######################################################################\n# Copyright (C) #\n# 2016 - 2019 Pinard Liu(liujianping-ok@163.com) #\n# https://www.cnblogs.com/pinard #\n# Permission given to modify the code as long as you keep this #\n# declaration at the top #\n#######################################################################\n\n## https://www.cnblogs.com/pinard/p/10345762.html.html ##\n## 强化学习(十六) 深度确定性策略梯度(DDPG) ##\n\n\nimport tensorflow as tf\nimport numpy as np\nimport gym\nimport time\n\n\n##################### hyper parameters ####################\n\nMAX_EPISODES = 2000\nMAX_EP_STEPS = 200\nLR_A = 0.001 # learning rate for actor\nLR_C = 0.002 # learning rate for critic\nGAMMA = 0.9 # reward discount\nTAU = 0.01 # soft replacement\nMEMORY_CAPACITY = 10000\nBATCH_SIZE = 32\n\nRENDER = False\nENV_NAME = 'Pendulum-v0'\n\n############################### DDPG ####################################\n\nclass DDPG(object):\n def __init__(self, a_dim, s_dim, a_bound,):\n self.memory = np.zeros((MEMORY_CAPACITY, s_dim * 2 + a_dim + 1), dtype=np.float32)\n self.pointer = 0\n self.sess = tf.Session()\n\n self.a_dim, self.s_dim, self.a_bound = a_dim, s_dim, a_bound,\n self.S = tf.placeholder(tf.float32, [None, s_dim], 's')\n self.S_ = tf.placeholder(tf.float32, [None, s_dim], 's_')\n self.R = tf.placeholder(tf.float32, [None, 1], 'r')\n\n with tf.variable_scope('Actor'):\n self.a = self._build_a(self.S, scope='eval', trainable=True)\n a_ = self._build_a(self.S_, scope='target', trainable=False)\n with tf.variable_scope('Critic'):\n # assign self.a = a in memory when calculating q for td_error,\n # otherwise the self.a is from Actor when updating Actor\n q = self._build_c(self.S, self.a, scope='eval', trainable=True)\n q_ = self._build_c(self.S_, a_, scope='target', trainable=False)\n\n # networks parameters\n self.ae_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='Actor/eval')\n self.at_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='Actor/target')\n self.ce_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='Critic/eval')\n self.ct_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='Critic/target')\n\n # target net replacement\n self.soft_replace = [tf.assign(t, (1 - TAU) * t + TAU * e)\n for t, e in zip(self.at_params + self.ct_params, self.ae_params + self.ce_params)]\n\n q_target = self.R + GAMMA * q_\n # in the feed_dic for the td_error, the self.a should change to actions in memory\n td_error = tf.losses.mean_squared_error(labels=q_target, predictions=q)\n self.ctrain = tf.train.AdamOptimizer(LR_C).minimize(td_error, var_list=self.ce_params)\n\n a_loss = - tf.reduce_mean(q) # maximize the q\n self.atrain = tf.train.AdamOptimizer(LR_A).minimize(a_loss, var_list=self.ae_params)\n\n self.sess.run(tf.global_variables_initializer())\n\n def choose_action(self, s):\n return self.sess.run(self.a, {self.S: s[np.newaxis, :]})[0]\n\n def learn(self):\n # soft target replacement\n self.sess.run(self.soft_replace)\n\n indices = np.random.choice(MEMORY_CAPACITY, size=BATCH_SIZE)\n bt = self.memory[indices, :]\n bs = bt[:, :self.s_dim]\n ba = bt[:, self.s_dim: self.s_dim + self.a_dim]\n br = bt[:, -self.s_dim - 1: -self.s_dim]\n bs_ = bt[:, -self.s_dim:]\n\n self.sess.run(self.atrain, {self.S: bs})\n self.sess.run(self.ctrain, {self.S: bs, self.a: ba, self.R: br, self.S_: bs_})\n\n def store_transition(self, s, a, r, s_):\n transition = np.hstack((s, a, [r], s_))\n index = self.pointer % MEMORY_CAPACITY # replace the old memory with new memory\n self.memory[index, :] = transition\n self.pointer += 1\n\n def _build_a(self, s, scope, trainable):\n with tf.variable_scope(scope):\n net = tf.layers.dense(s, 30, activation=tf.nn.relu, name='l1', trainable=trainable)\n a = tf.layers.dense(net, self.a_dim, activation=tf.nn.tanh, name='a', trainable=trainable)\n return tf.multiply(a, self.a_bound, name='scaled_a')\n\n def _build_c(self, s, a, scope, trainable):\n with tf.variable_scope(scope):\n n_l1 = 30\n w1_s = tf.get_variable('w1_s', [self.s_dim, n_l1], trainable=trainable)\n w1_a = tf.get_variable('w1_a', [self.a_dim, n_l1], trainable=trainable)\n b1 = tf.get_variable('b1', [1, n_l1], trainable=trainable)\n net = tf.nn.relu(tf.matmul(s, w1_s) + tf.matmul(a, w1_a) + b1)\n return tf.layers.dense(net, 1, trainable=trainable) # Q(s,a)\n\n############################### training ####################################\n\nenv = gym.make(ENV_NAME)\nenv = env.unwrapped\nenv.seed(1)\n\ns_dim = env.observation_space.shape[0]\na_dim = env.action_space.shape[0]\na_bound = env.action_space.high\n\nddpg = DDPG(a_dim, s_dim, a_bound)\n\nvar = 3 # control exploration\nt1 = time.time()\nfor episode in range(MAX_EPISODES):\n s = env.reset()\n ep_reward = 0\n for j in range(MAX_EP_STEPS):\n if RENDER:\n env.render()\n\n # Add exploration noise\n a = ddpg.choose_action(s)\n a = np.clip(np.random.normal(a, var), -2, 2) # add randomness to action selection for exploration\n s_, r, done, info = env.step(a)\n\n ddpg.store_transition(s, a, r / 10, s_)\n\n if ddpg.pointer > MEMORY_CAPACITY:\n var *= .9995 # decay the action randomness\n ddpg.learn()\n\n s = s_\n ep_reward += r\n if j == MAX_EP_STEPS-1:\n print('Episode:', episode, ' Reward: %i' % int(ep_reward), 'Explore: %.2f' % var, )\n # if ep_reward > -300:RENDER = True\n break\n if episode % 100 == 0:\n total_reward = 0\n for i in range(10):\n state = env.reset()\n for j in range(MAX_EP_STEPS):\n env.render()\n action = ddpg.choose_action(state) # direct action for test\n state,reward,done,_ = env.step(action)\n total_reward += reward\n if done:\n break\n ave_reward = total_reward/300\n print ('episode: ',episode,'Evaluation Average Reward:',ave_reward)\nprint('Running time: ', time.time() - t1)" }, { "path": "reinforcement-learning/ddqn.py", "content": "#######################################################################\n# Copyright (C) #\n# 2016 - 2019 Pinard Liu(liujianping-ok@163.com) #\n# https://www.cnblogs.com/pinard #\n# Permission given to modify the code as long as you keep this #\n# declaration at the top #\n#######################################################################\n## https://www.cnblogs.com/pinard/p/9778063.html ##\n## 强化学习（十）Double DQN (DDQN) ##\n\nimport gym\nimport tensorflow as tf\nimport numpy as np\nimport random\nfrom collections import deque\n\n# Hyper Parameters for DQN\nGAMMA = 0.9 # discount factor for target Q\nINITIAL_EPSILON = 0.5 # starting value of epsilon\nFINAL_EPSILON = 0.01 # final value of epsilon\nREPLAY_SIZE = 10000 # experience replay buffer size\nBATCH_SIZE = 32 # size of minibatch\nREPLACE_TARGET_FREQ = 10 # frequency to update target Q network\n\nclass DQN():\n # DQN Agent\n def __init__(self, env):\n # init experience replay\n self.replay_buffer = deque()\n # init some parameters\n self.time_step = 0\n self.epsilon = INITIAL_EPSILON\n self.state_dim = env.observation_space.shape[0]\n self.action_dim = env.action_space.n\n\n self.create_Q_network()\n self.create_training_method()\n\n # Init session\n self.session = tf.InteractiveSession()\n self.session.run(tf.global_variables_initializer())\n\n def create_Q_network(self):\n # input layer\n self.state_input = tf.placeholder(\"float\", [None, self.state_dim])\n # network weights\n with tf.variable_scope('current_net'):\n W1 = self.weight_variable([self.state_dim,20])\n b1 = self.bias_variable([20])\n W2 = self.weight_variable([20,self.action_dim])\n b2 = self.bias_variable([self.action_dim])\n\n # hidden layers\n h_layer = tf.nn.relu(tf.matmul(self.state_input,W1) + b1)\n # Q Value layer\n self.Q_value = tf.matmul(h_layer,W2) + b2\n\n with tf.variable_scope('target_net'):\n W1t = self.weight_variable([self.state_dim,20])\n b1t = self.bias_variable([20])\n W2t = self.weight_variable([20,self.action_dim])\n b2t = self.bias_variable([self.action_dim])\n\n # hidden layers\n h_layer_t = tf.nn.relu(tf.matmul(self.state_input,W1t) + b1t)\n # Q Value layer\n self.target_Q_value = tf.matmul(h_layer_t,W2t) + b2t\n\n t_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='target_net')\n e_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='current_net')\n\n with tf.variable_scope('soft_replacement'):\n self.target_replace_op = [tf.assign(t, e) for t, e in zip(t_params, e_params)]\n\n def create_training_method(self):\n self.action_input = tf.placeholder(\"float\",[None,self.action_dim]) # one hot presentation\n self.y_input = tf.placeholder(\"float\",[None])\n Q_action = tf.reduce_sum(tf.multiply(self.Q_value,self.action_input),reduction_indices = 1)\n self.cost = tf.reduce_mean(tf.square(self.y_input - Q_action))\n self.optimizer = tf.train.AdamOptimizer(0.0001).minimize(self.cost)\n\n def perceive(self,state,action,reward,next_state,done):\n one_hot_action = np.zeros(self.action_dim)\n one_hot_action[action] = 1\n self.replay_buffer.append((state,one_hot_action,reward,next_state,done))\n if len(self.replay_buffer) > REPLAY_SIZE:\n self.replay_buffer.popleft()\n\n if len(self.replay_buffer) > BATCH_SIZE:\n self.train_Q_network()\n\n def train_Q_network(self):\n self.time_step += 1\n # Step 1: obtain random minibatch from replay memory\n minibatch = random.sample(self.replay_buffer,BATCH_SIZE)\n state_batch = [data[0] for data in minibatch]\n action_batch = [data[1] for data in minibatch]\n reward_batch = [data[2] for data in minibatch]\n next_state_batch = [data[3] for data in minibatch]\n\n # Step 2: calculate y\n y_batch = []\n current_Q_batch = self.Q_value.eval(feed_dict={self.state_input: next_state_batch})\n max_action_next = np.argmax(current_Q_batch, axis=1)\n target_Q_batch = self.target_Q_value.eval(feed_dict={self.state_input: next_state_batch})\n\n for i in range(0,BATCH_SIZE):\n done = minibatch[i][4]\n if done:\n y_batch.append(reward_batch[i])\n else :\n target_Q_value = target_Q_batch[i, max_action_next[i]]\n y_batch.append(reward_batch[i] + GAMMA * target_Q_value)\n\n self.optimizer.run(feed_dict={\n self.y_input:y_batch,\n self.action_input:action_batch,\n self.state_input:state_batch\n })\n\n def egreedy_action(self,state):\n Q_value = self.Q_value.eval(feed_dict = {\n self.state_input:[state]\n })[0]\n if random.random() <= self.epsilon:\n self.epsilon -= (INITIAL_EPSILON - FINAL_EPSILON) / 10000\n return random.randint(0,self.action_dim - 1)\n else:\n self.epsilon -= (INITIAL_EPSILON - FINAL_EPSILON) / 10000\n return np.argmax(Q_value)\n\n def action(self,state):\n return np.argmax(self.Q_value.eval(feed_dict = {\n self.state_input:[state]\n })[0])\n\n def update_target_q_network(self, episode):\n # update target Q netowrk\n if episode % REPLACE_TARGET_FREQ == 0:\n self.session.run(self.target_replace_op)\n #print('episode '+str(episode) +', target Q network params replaced!')\n\n def weight_variable(self,shape):\n initial = tf.truncated_normal(shape)\n return tf.Variable(initial)\n\n def bias_variable(self,shape):\n initial = tf.constant(0.01, shape = shape)\n return tf.Variable(initial)\n# ---------------------------------------------------------\n# Hyper Parameters\nENV_NAME = 'CartPole-v0'\nEPISODE = 3000 # Episode limitation\nSTEP = 300 # Step limitation in an episode\nTEST = 5 # The number of experiment test every 100 episode\n\ndef main():\n # initialize OpenAI Gym env and dqn agent\n env = gym.make(ENV_NAME)\n agent = DQN(env)\n\n for episode in range(EPISODE):\n # initialize task\n state = env.reset()\n # Train\n for step in range(STEP):\n action = agent.egreedy_action(state) # e-greedy action for train\n next_state,reward,done,_ = env.step(action)\n # Define reward for agent\n reward = -1 if done else 0.1\n agent.perceive(state,action,reward,next_state,done)\n state = next_state\n if done:\n break\n # Test every 100 episodes\n if episode % 100 == 0:\n total_reward = 0\n for i in range(TEST):\n state = env.reset()\n for j in range(STEP):\n env.render()\n action = agent.action(state) # direct action for test\n state,reward,done,_ = env.step(action)\n total_reward += reward\n if done:\n break\n ave_reward = total_reward/TEST\n print ('episode: ',episode,'Evaluation Average Reward:',ave_reward)\n agent.update_target_q_network(episode)\n\nif __name__ == '__main__':\n main()" }, { "path": "reinforcement-learning/ddqn_prioritised_replay.py", "content": "#######################################################################\n# Copyright (C) #\n# 2016 - 2019 Pinard Liu(liujianping-ok@163.com) #\n# https://www.cnblogs.com/pinard #\n# Permission given to modify the code as long as you keep this #\n# declaration at the top #\n#######################################################################\n# SumTree and Memory class are referred from https://github.com/MorvanZhou #\n\n## https://www.cnblogs.com/pinard/p/9797695.html ##\n## 强化学习(十一) Prioritized Replay DQN ##\n\nimport gym\nimport tensorflow as tf\nimport numpy as np\nimport random\nfrom collections import deque\n\n# Hyper Parameters for DQN\nGAMMA = 0.9 # discount factor for target Q\nINITIAL_EPSILON = 0.5 # starting value of epsilon\nFINAL_EPSILON = 0.01 # final value of epsilon\nREPLAY_SIZE = 10000 # experience replay buffer size\nBATCH_SIZE = 128 # size of minibatch\nREPLACE_TARGET_FREQ = 10 # frequency to update target Q network\n\nclass SumTree(object):\n \"\"\"\n This SumTree code is a modified version and the original code is from:\n https://github.com/jaara/AI-blog/blob/master/SumTree.py\n Story data with its priority in the tree.\n \"\"\"\n data_pointer = 0\n\n def __init__(self, capacity):\n self.capacity = capacity # for all priority values\n self.tree = np.zeros(2 * capacity - 1)\n # [--------------Parent nodes-------------][-------leaves to recode priority-------]\n # size: capacity - 1 size: capacity\n self.data = np.zeros(capacity, dtype=object) # for all transitions\n # [--------------data frame-------------]\n # size: capacity\n\n def add(self, p, data):\n tree_idx = self.data_pointer + self.capacity - 1\n self.data[self.data_pointer] = data # update data_frame\n self.update(tree_idx, p) # update tree_frame\n\n self.data_pointer += 1\n if self.data_pointer >= self.capacity: # replace when exceed the capacity\n self.data_pointer = 0\n\n def update(self, tree_idx, p):\n change = p - self.tree[tree_idx]\n self.tree[tree_idx] = p\n # then propagate the change through tree\n while tree_idx != 0: # this method is faster than the recursive loop in the reference code\n tree_idx = (tree_idx - 1) // 2\n self.tree[tree_idx] += change\n\n def get_leaf(self, v):\n \"\"\"\n Tree structure and array storage:\n Tree index:\n 0 -> storing priority sum\n / \\\n 1 2\n / \\ / \\\n 3 4 5 6 -> storing priority for transitions\n Array type for storing:\n [0,1,2,3,4,5,6]\n \"\"\"\n parent_idx = 0\n while True: # the while loop is faster than the method in the reference code\n cl_idx = 2 * parent_idx + 1 # this leaf's left and right kids\n cr_idx = cl_idx + 1\n if cl_idx >= len(self.tree): # reach bottom, end search\n leaf_idx = parent_idx\n break\n else: # downward search, always search for a higher priority node\n if v <= self.tree[cl_idx]:\n parent_idx = cl_idx\n else:\n v -= self.tree[cl_idx]\n parent_idx = cr_idx\n\n data_idx = leaf_idx - self.capacity + 1\n return leaf_idx, self.tree[leaf_idx], self.data[data_idx]\n\n @property\n def total_p(self):\n return self.tree[0] # the root\n\n\nclass Memory(object): # stored as ( s, a, r, s_ ) in SumTree\n \"\"\"\n This Memory class is modified based on the original code from:\n https://github.com/jaara/AI-blog/blob/master/Seaquest-DDQN-PER.py\n \"\"\"\n epsilon = 0.01 # small amount to avoid zero priority\n alpha = 0.6 # [0~1] convert the importance of TD error to priority\n beta = 0.4 # importance-sampling, from initial value increasing to 1\n beta_increment_per_sampling = 0.001\n abs_err_upper = 1. # clipped abs error\n\n def __init__(self, capacity):\n self.tree = SumTree(capacity)\n\n def store(self, transition):\n max_p = np.max(self.tree.tree[-self.tree.capacity:])\n if max_p == 0:\n max_p = self.abs_err_upper\n self.tree.add(max_p, transition) # set the max p for new p\n\n def sample(self, n):\n b_idx, b_memory, ISWeights = np.empty((n,), dtype=np.int32), np.empty((n, self.tree.data[0].size)), np.empty((n, 1))\n pri_seg = self.tree.total_p / n # priority segment\n self.beta = np.min([1., self.beta + self.beta_increment_per_sampling]) # max = 1\n\n min_prob = np.min(self.tree.tree[-self.tree.capacity:]) / self.tree.total_p # for later calculate ISweight\n if min_prob == 0:\n min_prob = 0.00001\n for i in range(n):\n a, b = pri_seg * i, pri_seg * (i + 1)\n v = np.random.uniform(a, b)\n idx, p, data = self.tree.get_leaf(v)\n prob = p / self.tree.total_p\n ISWeights[i, 0] = np.power(prob/min_prob, -self.beta)\n b_idx[i], b_memory[i, :] = idx, data\n return b_idx, b_memory, ISWeights\n\n def batch_update(self, tree_idx, abs_errors):\n abs_errors += self.epsilon # convert to abs and avoid 0\n clipped_errors = np.minimum(abs_errors, self.abs_err_upper)\n ps = np.power(clipped_errors, self.alpha)\n for ti, p in zip(tree_idx, ps):\n self.tree.update(ti, p)\n\nclass DQN():\n # DQN Agent\n def __init__(self, env):\n # init experience replay\n self.replay_total = 0\n # init some parameters\n self.time_step = 0\n self.epsilon = INITIAL_EPSILON\n self.state_dim = env.observation_space.shape[0]\n self.action_dim = env.action_space.n\n self.memory = Memory(capacity=REPLAY_SIZE)\n\n self.create_Q_network()\n self.create_training_method()\n\n # Init session\n self.session = tf.InteractiveSession()\n self.session.run(tf.global_variables_initializer())\n\n def create_Q_network(self):\n # input layer\n self.state_input = tf.placeholder(\"float\", [None, self.state_dim])\n self.ISWeights = tf.placeholder(tf.float32, [None, 1])\n # network weights\n with tf.variable_scope('current_net'):\n W1 = self.weight_variable([self.state_dim,20])\n b1 = self.bias_variable([20])\n W2 = self.weight_variable([20,self.action_dim])\n b2 = self.bias_variable([self.action_dim])\n\n # hidden layers\n h_layer = tf.nn.relu(tf.matmul(self.state_input,W1) + b1)\n # Q Value layer\n self.Q_value = tf.matmul(h_layer,W2) + b2\n\n with tf.variable_scope('target_net'):\n W1t = self.weight_variable([self.state_dim,20])\n b1t = self.bias_variable([20])\n W2t = self.weight_variable([20,self.action_dim])\n b2t = self.bias_variable([self.action_dim])\n\n # hidden layers\n h_layer_t = tf.nn.relu(tf.matmul(self.state_input,W1t) + b1t)\n # Q Value layer\n self.target_Q_value = tf.matmul(h_layer_t,W2t) + b2t\n\n t_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='target_net')\n e_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='current_net')\n\n with tf.variable_scope('soft_replacement'):\n self.target_replace_op = [tf.assign(t, e) for t, e in zip(t_params, e_params)]\n\n def create_training_method(self):\n self.action_input = tf.placeholder(\"float\",[None,self.action_dim]) # one hot presentation\n self.y_input = tf.placeholder(\"float\",[None])\n Q_action = tf.reduce_sum(tf.multiply(self.Q_value,self.action_input),reduction_indices = 1)\n self.cost = tf.reduce_mean(self.ISWeights *(tf.square(self.y_input - Q_action)))\n self.abs_errors =tf.abs(self.y_input - Q_action)\n self.optimizer = tf.train.AdamOptimizer(0.0001).minimize(self.cost)\n\n def store_transition(self, s, a, r, s_, done):\n transition = np.hstack((s, a, r, s_, done))\n self.memory.store(transition) # have high priority for newly arrived transition\n\n def perceive(self,state,action,reward,next_state,done):\n one_hot_action = np.zeros(self.action_dim)\n one_hot_action[action] = 1\n #print(state,one_hot_action,reward,next_state,done)\n self.store_transition(state,one_hot_action,reward,next_state,done)\n self.replay_total += 1\n if self.replay_total > BATCH_SIZE:\n self.train_Q_network()\n\n def train_Q_network(self):\n self.time_step += 1\n # Step 1: obtain random minibatch from replay memory\n tree_idx, minibatch, ISWeights = self.memory.sample(BATCH_SIZE)\n state_batch = minibatch[:,0:4]\n action_batch = minibatch[:,4:6]\n reward_batch = [data[6] for data in minibatch]\n next_state_batch = minibatch[:,7:11]\n # Step 2: calculate y\n y_batch = []\n current_Q_batch = self.Q_value.eval(feed_dict={self.state_input: next_state_batch})\n max_action_next = np.argmax(current_Q_batch, axis=1)\n target_Q_batch = self.target_Q_value.eval(feed_dict={self.state_input: next_state_batch})\n\n for i in range(0,BATCH_SIZE):\n done = minibatch[i][11]\n if done:\n y_batch.append(reward_batch[i])\n else :\n target_Q_value = target_Q_batch[i, max_action_next[i]]\n y_batch.append(reward_batch[i] + GAMMA * target_Q_value)\n\n self.optimizer.run(feed_dict={\n self.y_input:y_batch,\n self.action_input:action_batch,\n self.state_input:state_batch,\n self.ISWeights: ISWeights\n })\n _, abs_errors, _ = self.session.run([self.optimizer, self.abs_errors, self.cost], feed_dict={\n self.y_input: y_batch,\n self.action_input: action_batch,\n self.state_input: state_batch,\n self.ISWeights: ISWeights\n })\n self.memory.batch_update(tree_idx, abs_errors) # update priority\n\n def egreedy_action(self,state):\n Q_value = self.Q_value.eval(feed_dict = {\n self.state_input:[state]\n })[0]\n if random.random() <= self.epsilon:\n self.epsilon -= (INITIAL_EPSILON - FINAL_EPSILON) / 10000\n return random.randint(0,self.action_dim - 1)\n else:\n self.epsilon -= (INITIAL_EPSILON - FINAL_EPSILON) / 10000\n return np.argmax(Q_value)\n\n def action(self,state):\n return np.argmax(self.Q_value.eval(feed_dict = {\n self.state_input:[state]\n })[0])\n\n def update_target_q_network(self, episode):\n # update target Q netowrk\n if episode % REPLACE_TARGET_FREQ == 0:\n self.session.run(self.target_replace_op)\n #print('episode '+str(episode) +', target Q network params replaced!')\n\n def weight_variable(self,shape):\n initial = tf.truncated_normal(shape)\n return tf.Variable(initial)\n\n def bias_variable(self,shape):\n initial = tf.constant(0.01, shape = shape)\n return tf.Variable(initial)\n# ---------------------------------------------------------\n# Hyper Parameters\nENV_NAME = 'CartPole-v0'\nEPISODE = 3000 # Episode limitation\nSTEP = 300 # Step limitation in an episode\nTEST = 5 # The number of experiment test every 100 episode\n\ndef main():\n # initialize OpenAI Gym env and dqn agent\n env = gym.make(ENV_NAME)\n agent = DQN(env)\n\n for episode in range(EPISODE):\n # initialize task\n state = env.reset()\n # Train\n for step in range(STEP):\n action = agent.egreedy_action(state) # e-greedy action for train\n next_state,reward,done,_ = env.step(action)\n # Define reward for agent\n reward = -1 if done else 0.1\n agent.perceive(state,action,reward,next_state,done)\n state = next_state\n if done:\n break\n # Test every 100 episodes\n if episode % 100 == 0:\n total_reward = 0\n for i in range(TEST):\n state = env.reset()\n for j in range(STEP):\n env.render()\n action = agent.action(state) # direct action for test\n state,reward,done,_ = env.step(action)\n total_reward += reward\n if done:\n break\n ave_reward = total_reward/TEST\n print ('episode: ',episode,'Evaluation Average Reward:',ave_reward)\n agent.update_target_q_network(episode)\n\nif __name__ == '__main__':\n main()" }, { "path": "reinforcement-learning/dqn.py", "content": "#######################################################################\n# Copyright (C) #\n# 2016 - 2019 Pinard Liu(liujianping-ok@163.com) #\n# https://www.cnblogs.com/pinard #\n# Permission given to modify the code as long as you keep this #\n# declaration at the top #\n#######################################################################\n##https://www.cnblogs.com/pinard/p/9714655.html ##\n## 强化学习（八）价值函数的近似表示与Deep Q-Learning ##\n\nimport gym\nimport tensorflow as tf\nimport numpy as np\nimport random\nfrom collections import deque\n\n# Hyper Parameters for DQN\nGAMMA = 0.9 # discount factor for target Q\nINITIAL_EPSILON = 0.5 # starting value of epsilon\nFINAL_EPSILON = 0.01 # final value of epsilon\nREPLAY_SIZE = 10000 # experience replay buffer size\nBATCH_SIZE = 32 # size of minibatch\n\nclass DQN():\n # DQN Agent\n def __init__(self, env):\n # init experience replay\n self.replay_buffer = deque()\n # init some parameters\n self.time_step = 0\n self.epsilon = INITIAL_EPSILON\n self.state_dim = env.observation_space.shape[0]\n self.action_dim = env.action_space.n\n\n self.create_Q_network()\n self.create_training_method()\n\n # Init session\n self.session = tf.InteractiveSession()\n self.session.run(tf.global_variables_initializer())\n\n def create_Q_network(self):\n # network weights\n W1 = self.weight_variable([self.state_dim,20])\n b1 = self.bias_variable([20])\n W2 = self.weight_variable([20,self.action_dim])\n b2 = self.bias_variable([self.action_dim])\n # input layer\n self.state_input = tf.placeholder(\"float\",[None,self.state_dim])\n # hidden layers\n h_layer = tf.nn.relu(tf.matmul(self.state_input,W1) + b1)\n # Q Value layer\n self.Q_value = tf.matmul(h_layer,W2) + b2\n\n def create_training_method(self):\n self.action_input = tf.placeholder(\"float\",[None,self.action_dim]) # one hot presentation\n self.y_input = tf.placeholder(\"float\",[None])\n Q_action = tf.reduce_sum(tf.multiply(self.Q_value,self.action_input),reduction_indices = 1)\n self.cost = tf.reduce_mean(tf.square(self.y_input - Q_action))\n self.optimizer = tf.train.AdamOptimizer(0.0001).minimize(self.cost)\n\n def perceive(self,state,action,reward,next_state,done):\n one_hot_action = np.zeros(self.action_dim)\n one_hot_action[action] = 1\n self.replay_buffer.append((state,one_hot_action,reward,next_state,done))\n if len(self.replay_buffer) > REPLAY_SIZE:\n self.replay_buffer.popleft()\n\n if len(self.replay_buffer) > BATCH_SIZE:\n self.train_Q_network()\n\n def train_Q_network(self):\n self.time_step += 1\n # Step 1: obtain random minibatch from replay memory\n minibatch = random.sample(self.replay_buffer,BATCH_SIZE)\n state_batch = [data[0] for data in minibatch]\n action_batch = [data[1] for data in minibatch]\n reward_batch = [data[2] for data in minibatch]\n next_state_batch = [data[3] for data in minibatch]\n\n # Step 2: calculate y\n y_batch = []\n Q_value_batch = self.Q_value.eval(feed_dict={self.state_input:next_state_batch})\n for i in range(0,BATCH_SIZE):\n done = minibatch[i][4]\n if done:\n y_batch.append(reward_batch[i])\n else :\n y_batch.append(reward_batch[i] + GAMMA * np.max(Q_value_batch[i]))\n\n self.optimizer.run(feed_dict={\n self.y_input:y_batch,\n self.action_input:action_batch,\n self.state_input:state_batch\n })\n\n def egreedy_action(self,state):\n Q_value = self.Q_value.eval(feed_dict = {\n self.state_input:[state]\n })[0]\n if random.random() <= self.epsilon:\n self.epsilon -= (INITIAL_EPSILON - FINAL_EPSILON) / 10000\n return random.randint(0,self.action_dim - 1)\n else:\n self.epsilon -= (INITIAL_EPSILON - FINAL_EPSILON) / 10000\n return np.argmax(Q_value)\n\n def action(self,state):\n return np.argmax(self.Q_value.eval(feed_dict = {\n self.state_input:[state]\n })[0])\n\n def weight_variable(self,shape):\n initial = tf.truncated_normal(shape)\n return tf.Variable(initial)\n\n def bias_variable(self,shape):\n initial = tf.constant(0.01, shape = shape)\n return tf.Variable(initial)\n# ---------------------------------------------------------\n# Hyper Parameters\nENV_NAME = 'CartPole-v0'\nEPISODE = 3000 # Episode limitation\nSTEP = 300 # Step limitation in an episode\nTEST = 10 # The number of experiment test every 100 episode\n\ndef main():\n # initialize OpenAI Gym env and dqn agent\n env = gym.make(ENV_NAME)\n agent = DQN(env)\n\n for episode in range(EPISODE):\n # initialize task\n state = env.reset()\n # Train\n for step in range(STEP):\n action = agent.egreedy_action(state) # e-greedy action for train\n next_state,reward,done,_ = env.step(action)\n # Define reward for agent\n reward = -1 if done else 0.1\n agent.perceive(state,action,reward,next_state,done)\n state = next_state\n if done:\n break\n # Test every 100 episodes\n if episode % 100 == 0:\n total_reward = 0\n for i in range(TEST):\n state = env.reset()\n for j in range(STEP):\n env.render()\n action = agent.action(state) # direct action for test\n state,reward,done,_ = env.step(action)\n total_reward += reward\n if done:\n break\n ave_reward = total_reward/TEST\n print ('episode: ',episode,'Evaluation Average Reward:',ave_reward)\n\nif __name__ == '__main__':\n main()" }, { "path": "reinforcement-learning/duel_dqn.py", "content": "#######################################################################\n# Copyright (C) #\n# 2016 - 2019 Pinard Liu(liujianping-ok@163.com) #\n# https://www.cnblogs.com/pinard #\n# Permission given to modify the code as long as you keep this #\n# declaration at the top #\n#######################################################################\n## https://www.cnblogs.com/pinard/p/9923859.html ##\n## 强化学习(十二) Dueling DQN ##\n\nimport gym\nimport tensorflow as tf\nimport numpy as np\nimport random\nfrom collections import deque\n\n# Hyper Parameters for DQN\nGAMMA = 0.9 # discount factor for target Q\nINITIAL_EPSILON = 0.5 # starting value of epsilon\nFINAL_EPSILON = 0.01 # final value of epsilon\nREPLAY_SIZE = 10000 # experience replay buffer size\nBATCH_SIZE = 128 # size of minibatch\nREPLACE_TARGET_FREQ = 10 # frequency to update target Q network\n\nclass DQN():\n # DQN Agent\n def __init__(self, env):\n # init experience replay\n self.replay_buffer = deque()\n # init some parameters\n self.time_step = 0\n self.epsilon = INITIAL_EPSILON\n self.state_dim = env.observation_space.shape[0]\n self.action_dim = env.action_space.n\n\n self.create_Q_network()\n self.create_training_method()\n\n # Init session\n self.session = tf.InteractiveSession()\n self.session.run(tf.global_variables_initializer())\n\n def create_Q_network(self):\n # input layer\n self.state_input = tf.placeholder(\"float\", [None, self.state_dim])\n # network weights\n with tf.variable_scope('current_net'):\n W1 = self.weight_variable([self.state_dim,20])\n b1 = self.bias_variable([20])\n\n # hidden layer 1\n h_layer_1 = tf.nn.relu(tf.matmul(self.state_input,W1) + b1)\n\n # hidden layer for state value\n with tf.variable_scope('Value'):\n W21= self.weight_variable([20,1])\n b21 = self.bias_variable([1])\n self.V = tf.matmul(h_layer_1, W21) + b21\n\n # hidden layer for action value\n with tf.variable_scope('Advantage'):\n W22 = self.weight_variable([20,self.action_dim])\n b22 = self.bias_variable([self.action_dim])\n self.A = tf.matmul(h_layer_1, W22) + b22\n\n # Q Value layer\n self.Q_value = self.V + (self.A - tf.reduce_mean(self.A, axis=1, keep_dims=True))\n\n with tf.variable_scope('target_net'):\n W1t = self.weight_variable([self.state_dim,20])\n b1t = self.bias_variable([20])\n\n # hidden layer 1\n h_layer_1t = tf.nn.relu(tf.matmul(self.state_input,W1t) + b1t)\n\n # hidden layer for state value\n with tf.variable_scope('Value'):\n W2v = self.weight_variable([20,1])\n b2v = self.bias_variable([1])\n self.VT = tf.matmul(h_layer_1t, W2v) + b2v\n\n # hidden layer for action value\n with tf.variable_scope('Advantage'):\n W2a = self.weight_variable([20,self.action_dim])\n b2a = self.bias_variable([self.action_dim])\n self.AT = tf.matmul(h_layer_1t, W2a) + b2a\n\n # Q Value layer\n self.target_Q_value = self.VT + (self.AT - tf.reduce_mean(self.AT, axis=1, keep_dims=True))\n\n t_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='target_net')\n e_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='current_net')\n\n with tf.variable_scope('soft_replacement'):\n self.target_replace_op = [tf.assign(t, e) for t, e in zip(t_params, e_params)]\n\n def create_training_method(self):\n self.action_input = tf.placeholder(\"float\",[None,self.action_dim]) # one hot presentation\n self.y_input = tf.placeholder(\"float\",[None])\n Q_action = tf.reduce_sum(tf.multiply(self.Q_value,self.action_input),reduction_indices = 1)\n self.cost = tf.reduce_mean(tf.square(self.y_input - Q_action))\n self.optimizer = tf.train.AdamOptimizer(0.0001).minimize(self.cost)\n\n def perceive(self,state,action,reward,next_state,done):\n one_hot_action = np.zeros(self.action_dim)\n one_hot_action[action] = 1\n self.replay_buffer.append((state,one_hot_action,reward,next_state,done))\n if len(self.replay_buffer) > REPLAY_SIZE:\n self.replay_buffer.popleft()\n\n if len(self.replay_buffer) > BATCH_SIZE:\n self.train_Q_network()\n\n def train_Q_network(self):\n self.time_step += 1\n # Step 1: obtain random minibatch from replay memory\n minibatch = random.sample(self.replay_buffer,BATCH_SIZE)\n state_batch = [data[0] for data in minibatch]\n action_batch = [data[1] for data in minibatch]\n reward_batch = [data[2] for data in minibatch]\n next_state_batch = [data[3] for data in minibatch]\n\n # Step 2: calculate y\n y_batch = []\n Q_value_batch = self.target_Q_value.eval(feed_dict={self.state_input:next_state_batch})\n for i in range(0,BATCH_SIZE):\n done = minibatch[i][4]\n if done:\n y_batch.append(reward_batch[i])\n else :\n y_batch.append(reward_batch[i] + GAMMA * np.max(Q_value_batch[i]))\n\n self.optimizer.run(feed_dict={\n self.y_input:y_batch,\n self.action_input:action_batch,\n self.state_input:state_batch\n })\n\n def egreedy_action(self,state):\n Q_value = self.Q_value.eval(feed_dict = {\n self.state_input:[state]\n })[0]\n if random.random() <= self.epsilon:\n self.epsilon -= (INITIAL_EPSILON - FINAL_EPSILON) / 10000\n return random.randint(0,self.action_dim - 1)\n else:\n self.epsilon -= (INITIAL_EPSILON - FINAL_EPSILON) / 10000\n return np.argmax(Q_value)\n\n def action(self,state):\n return np.argmax(self.Q_value.eval(feed_dict = {\n self.state_input:[state]\n })[0])\n\n def update_target_q_network(self, episode):\n # update target Q netowrk\n if episode % REPLACE_TARGET_FREQ == 0:\n self.session.run(self.target_replace_op)\n #print('episode '+str(episode) +', target Q network params replaced!')\n\n def weight_variable(self,shape):\n initial = tf.truncated_normal(shape)\n return tf.Variable(initial)\n\n def bias_variable(self,shape):\n initial = tf.constant(0.01, shape = shape)\n return tf.Variable(initial)\n# ---------------------------------------------------------\n# Hyper Parameters\nENV_NAME = 'CartPole-v0'\nEPISODE = 3000 # Episode limitation\nSTEP = 300 # Step limitation in an episode\nTEST = 5 # The number of experiment test every 100 episode\n\ndef main():\n # initialize OpenAI Gym env and dqn agent\n env = gym.make(ENV_NAME)\n agent = DQN(env)\n\n for episode in range(EPISODE):\n # initialize task\n state = env.reset()\n # Train\n for step in range(STEP):\n action = agent.egreedy_action(state) # e-greedy action for train\n next_state,reward,done,_ = env.step(action)\n # Define reward for agent\n reward = -1 if done else 0.1\n agent.perceive(state,action,reward,next_state,done)\n state = next_state\n if done:\n break\n # Test every 100 episodes\n if episode % 100 == 0:\n total_reward = 0\n for i in range(TEST):\n state = env.reset()\n for j in range(STEP):\n env.render()\n action = agent.action(state) # direct action for test\n state,reward,done,_ = env.step(action)\n total_reward += reward\n if done:\n break\n ave_reward = total_reward/TEST\n print ('episode: ',episode,'Evaluation Average Reward:',ave_reward)\n agent.update_target_q_network(episode)\n\nif __name__ == '__main__':\n main()" }, { "path": "reinforcement-learning/introduction.py", "content": "#######################################################################\n# Copyright (C) #\n# 2016 - 2018 Shangtong Zhang(zhangshangtong.cpp@gmail.com) #\n# 2016 Jan Hakenberg(jan.hakenberg@gmail.com) #\n# 2016 Tian Jun(tianjun.cpp@gmail.com) #\n# 2016 Kenta Shimada(hyperkentakun@gmail.com) #\n# Permission given to modify the code as long as you keep this #\n# declaration at the top #\n#######################################################################\n##https://www.cnblogs.com/pinard/p/9385570.html ##\n## ǿѧϰһģͻ ##\n\nimport numpy as np\nimport pickle\n\nBOARD_ROWS = 3\nBOARD_COLS = 3\nBOARD_SIZE = BOARD_ROWS * BOARD_COLS\n\nclass State:\n def __init__(self):\n # the board is represented by an n * n array,\n # 1 represents a chessman of the player who moves first,\n # -1 represents a chessman of another player\n # 0 represents an empty position\n self.data = np.zeros((BOARD_ROWS, BOARD_COLS))\n self.winner = None\n self.hash_val = None\n self.end = None\n\n # compute the hash value for one state, it's unique\n def hash(self):\n if self.hash_val is None:\n self.hash_val = 0\n for i in self.data.reshape(BOARD_ROWS * BOARD_COLS):\n if i == -1:\n i = 2\n self.hash_val = self.hash_val * 3 + i\n return int(self.hash_val)\n\n # check whether a player has won the game, or it's a tie\n def is_end(self):\n if self.end is not None:\n return self.end\n results = []\n # check row\n for i in range(0, BOARD_ROWS):\n results.append(np.sum(self.data[i, :]))\n # check columns\n for i in range(0, BOARD_COLS):\n results.append(np.sum(self.data[:, i]))\n\n # check diagonals\n results.append(0)\n for i in range(0, BOARD_ROWS):\n results[-1] += self.data[i, i]\n results.append(0)\n for i in range(0, BOARD_ROWS):\n results[-1] += self.data[i, BOARD_ROWS - 1 - i]\n\n for result in results:\n if result == 3:\n self.winner = 1\n self.end = True\n return self.end\n if result == -3:\n self.winner = -1\n self.end = True\n return self.end\n\n # whether it's a tie\n sum = np.sum(np.abs(self.data))\n if sum == BOARD_ROWS * BOARD_COLS:\n self.winner = 0\n self.end = True\n return self.end\n\n # game is still going on\n self.end = False\n return self.end\n\n # @symbol: 1 or -1\n # put chessman symbol in position (i, j)\n def next_state(self, i, j, symbol):\n new_state = State()\n new_state.data = np.copy(self.data)\n new_state.data[i, j] = symbol\n return new_state\n\n # print the board\n def print(self):\n for i in range(0, BOARD_ROWS):\n print('-------------')\n out = '| '\n for j in range(0, BOARD_COLS):\n if self.data[i, j] == 1:\n token = '*'\n if self.data[i, j] == 0:\n token = '0'\n if self.data[i, j] == -1:\n token = 'x'\n out += token + ' | '\n print(out)\n print('-------------')\n\ndef get_all_states_impl(current_state, current_symbol, all_states):\n for i in range(0, BOARD_ROWS):\n for j in range(0, BOARD_COLS):\n if current_state.data[i][j] == 0:\n newState = current_state.next_state(i, j, current_symbol)\n newHash = newState.hash()\n if newHash not in all_states.keys():\n isEnd = newState.is_end()\n all_states[newHash] = (newState, isEnd)\n if not isEnd:\n get_all_states_impl(newState, -current_symbol, all_states)\n\ndef get_all_states():\n current_symbol = 1\n current_state = State()\n all_states = dict()\n all_states[current_state.hash()] = (current_state, current_state.is_end())\n get_all_states_impl(current_state, current_symbol, all_states)\n return all_states\n\n# all possible board configurations\nall_states = get_all_states()\n\nclass Judger:\n # @player1: the player who will move first, its chessman will be 1\n # @player2: another player with a chessman -1\n # @feedback: if True, both players will receive rewards when game is end\n def __init__(self, player1, player2):\n self.p1 = player1\n self.p2 = player2\n self.current_player = None\n self.p1_symbol = 1\n self.p2_symbol = -1\n self.p1.set_symbol(self.p1_symbol)\n self.p2.set_symbol(self.p2_symbol)\n self.current_state = State()\n\n def reset(self):\n self.p1.reset()\n self.p2.reset()\n\n def alternate(self):\n while True:\n yield self.p1\n yield self.p2\n\n # @print: if True, print each board during the game\n def play(self, print=False):\n alternator = self.alternate()\n self.reset()\n current_state = State()\n self.p1.set_state(current_state)\n self.p2.set_state(current_state)\n while True:\n player = next(alternator)\n if print:\n current_state.print()\n [i, j, symbol] = player.act()\n next_state_hash = current_state.next_state(i, j, symbol).hash()\n current_state, is_end = all_states[next_state_hash]\n self.p1.set_state(current_state)\n self.p2.set_state(current_state)\n if is_end:\n if print:\n current_state.print()\n return current_state.winner\n\n# AI player\nclass Player:\n # @step_size: the step size to update estimations\n # @epsilon: the probability to explore\n def __init__(self, step_size=0.1, epsilon=0.1):\n self.estimations = dict()\n self.step_size = step_size\n self.epsilon = epsilon\n self.states = []\n self.greedy = []\n\n def reset(self):\n self.states = []\n self.greedy = []\n\n def set_state(self, state):\n self.states.append(state)\n self.greedy.append(True)\n\n def set_symbol(self, symbol):\n self.symbol = symbol\n for hash_val in all_states.keys():\n (state, is_end) = all_states[hash_val]\n if is_end:\n if state.winner == self.symbol:\n self.estimations[hash_val] = 1.0\n elif state.winner == 0:\n # we need to distinguish between a tie and a lose\n self.estimations[hash_val] = 0.5\n else:\n self.estimations[hash_val] = 0\n else:\n self.estimations[hash_val] = 0.5\n\n # update value estimation\n def backup(self):\n # for debug\n # print('player trajectory')\n # for state in self.states:\n # state.print()\n\n self.states = [state.hash() for state in self.states]\n\n for i in reversed(range(len(self.states) - 1)):\n state = self.states[i]\n td_error = self.greedy[i] * (self.estimations[self.states[i + 1]] - self.estimations[state])\n self.estimations[state] += self.step_size * td_error\n\n # choose an action based on the state\n def act(self):\n state = self.states[-1]\n next_states = []\n next_positions = []\n for i in range(BOARD_ROWS):\n for j in range(BOARD_COLS):\n if state.data[i, j] == 0:\n next_positions.append([i, j])\n next_states.append(state.next_state(i, j, self.symbol).hash())\n\n if np.random.rand() < self.epsilon:\n action = next_positions[np.random.randint(len(next_positions))]\n action.append(self.symbol)\n self.greedy[-1] = False\n return action\n\n values = []\n for hash, pos in zip(next_states, next_positions):\n values.append((self.estimations[hash], pos))\n np.random.shuffle(values)\n values.sort(key=lambda x: x[0], reverse=True)\n action = values[0][1]\n action.append(self.symbol)\n return action\n\n def save_policy(self):\n with open('policy_%s.bin' % ('first' if self.symbol == 1 else 'second'), 'wb') as f:\n pickle.dump(self.estimations, f)\n\n def load_policy(self):\n with open('policy_%s.bin' % ('first' if self.symbol == 1 else 'second'), 'rb') as f:\n self.estimations = pickle.load(f)\n\n# human interface\n# input a number to put a chessman\n# | q | w | e |\n# | a | s | d |\n# | z | x | c |\nclass HumanPlayer:\n def __init__(self, **kwargs):\n self.symbol = None\n self.keys = ['q', 'w', 'e', 'a', 's', 'd', 'z', 'x', 'c']\n self.state = None\n return\n\n def reset(self):\n return\n\n def set_state(self, state):\n self.state = state\n\n def set_symbol(self, symbol):\n self.symbol = symbol\n return\n\n def backup(self, _):\n return\n\n def act(self):\n self.state.print()\n key = input(\"Input your position:\")\n data = self.keys.index(key)\n i = data // int(BOARD_COLS)\n j = data % BOARD_COLS\n return (i, j, self.symbol)\n\ndef train(epochs):\n player1 = Player(epsilon=0.01)\n player2 = Player(epsilon=0.01)\n judger = Judger(player1, player2)\n player1_win = 0.0\n player2_win = 0.0\n for i in range(1, epochs + 1):\n winner = judger.play(print=False)\n if winner == 1:\n player1_win += 1\n if winner == -1:\n player2_win += 1\n print('Epoch %d, player 1 win %.02f, player 2 win %.02f' % (i, player1_win / i, player2_win / i))\n player1.backup()\n player2.backup()\n judger.reset()\n player1.save_policy()\n player2.save_policy()\n\ndef compete(turns):\n player1 = Player(epsilon=0)\n player2 = Player(epsilon=0)\n judger = Judger(player1, player2)\n player1.load_policy()\n player2.load_policy()\n player1_win = 0.0\n player2_win = 0.0\n for i in range(0, turns):\n winner = judger.play()\n if winner == 1:\n player1_win += 1\n if winner == -1:\n player2_win += 1\n judger.reset()\n print('%d turns, player 1 win %.02f, player 2 win %.02f' % (turns, player1_win / turns, player2_win / turns))\n\n# The game is a zero sum game. If both players are playing with an optimal strategy, every game will end in a tie.\n# So we test whether the AI can guarantee at least a tie if it goes second.\ndef play():\n while True:\n player1 = HumanPlayer()\n player2 = Player(epsilon=0)\n judger = Judger(player1, player2)\n player2.load_policy()\n winner = judger.play()\n if winner == player2.symbol:\n print(\"You lose!\")\n elif winner == player1.symbol:\n print(\"You win!\")\n else:\n print(\"It is a tie!\")\n\nif __name__ == '__main__':\n train(int(1e5))\n compete(int(1e3))\n play()\n" }, { "path": "reinforcement-learning/nature_dqn.py", "content": "#######################################################################\n# Copyright (C) #\n# 2016 - 2019 Pinard Liu(liujianping-ok@163.com) #\n# https://www.cnblogs.com/pinard #\n# Permission given to modify the code as long as you keep this #\n# declaration at the top #\n#######################################################################\n##https://www.cnblogs.com/pinard/p/9756075.html##\n##强化学习（九）Deep Q-Learning进阶之Nature DQN##\n\nimport gym\nimport tensorflow as tf\nimport numpy as np\nimport random\nfrom collections import deque\n\n# Hyper Parameters for DQN\nGAMMA = 0.9 # discount factor for target Q\nINITIAL_EPSILON = 0.5 # starting value of epsilon\nFINAL_EPSILON = 0.01 # final value of epsilon\nREPLAY_SIZE = 10000 # experience replay buffer size\nBATCH_SIZE = 32 # size of minibatch\nREPLACE_TARGET_FREQ = 10 # frequency to update target Q network\n\nclass DQN():\n # DQN Agent\n def __init__(self, env):\n # init experience replay\n self.replay_buffer = deque()\n # init some parameters\n self.time_step = 0\n self.epsilon = INITIAL_EPSILON\n self.state_dim = env.observation_space.shape[0]\n self.action_dim = env.action_space.n\n\n self.create_Q_network()\n self.create_training_method()\n\n # Init session\n self.session = tf.InteractiveSession()\n self.session.run(tf.global_variables_initializer())\n\n def create_Q_network(self):\n # input layer\n self.state_input = tf.placeholder(\"float\", [None, self.state_dim])\n # network weights\n with tf.variable_scope('current_net'):\n W1 = self.weight_variable([self.state_dim,20])\n b1 = self.bias_variable([20])\n W2 = self.weight_variable([20,self.action_dim])\n b2 = self.bias_variable([self.action_dim])\n\n # hidden layers\n h_layer = tf.nn.relu(tf.matmul(self.state_input,W1) + b1)\n # Q Value layer\n self.Q_value = tf.matmul(h_layer,W2) + b2\n\n with tf.variable_scope('target_net'):\n W1t = self.weight_variable([self.state_dim,20])\n b1t = self.bias_variable([20])\n W2t = self.weight_variable([20,self.action_dim])\n b2t = self.bias_variable([self.action_dim])\n\n # hidden layers\n h_layer_t = tf.nn.relu(tf.matmul(self.state_input,W1t) + b1t)\n # Q Value layer\n self.target_Q_value = tf.matmul(h_layer_t,W2t) + b2t\n\n t_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='target_net')\n e_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='current_net')\n\n with tf.variable_scope('soft_replacement'):\n self.target_replace_op = [tf.assign(t, e) for t, e in zip(t_params, e_params)]\n\n def create_training_method(self):\n self.action_input = tf.placeholder(\"float\",[None,self.action_dim]) # one hot presentation\n self.y_input = tf.placeholder(\"float\",[None])\n Q_action = tf.reduce_sum(tf.multiply(self.Q_value,self.action_input),reduction_indices = 1)\n self.cost = tf.reduce_mean(tf.square(self.y_input - Q_action))\n self.optimizer = tf.train.AdamOptimizer(0.0001).minimize(self.cost)\n\n def perceive(self,state,action,reward,next_state,done):\n one_hot_action = np.zeros(self.action_dim)\n one_hot_action[action] = 1\n self.replay_buffer.append((state,one_hot_action,reward,next_state,done))\n if len(self.replay_buffer) > REPLAY_SIZE:\n self.replay_buffer.popleft()\n\n if len(self.replay_buffer) > BATCH_SIZE:\n self.train_Q_network()\n\n def train_Q_network(self):\n self.time_step += 1\n # Step 1: obtain random minibatch from replay memory\n minibatch = random.sample(self.replay_buffer,BATCH_SIZE)\n state_batch = [data[0] for data in minibatch]\n action_batch = [data[1] for data in minibatch]\n reward_batch = [data[2] for data in minibatch]\n next_state_batch = [data[3] for data in minibatch]\n\n # Step 2: calculate y\n y_batch = []\n Q_value_batch = self.target_Q_value.eval(feed_dict={self.state_input:next_state_batch})\n for i in range(0,BATCH_SIZE):\n done = minibatch[i][4]\n if done:\n y_batch.append(reward_batch[i])\n else :\n y_batch.append(reward_batch[i] + GAMMA * np.max(Q_value_batch[i]))\n\n self.optimizer.run(feed_dict={\n self.y_input:y_batch,\n self.action_input:action_batch,\n self.state_input:state_batch\n })\n\n def egreedy_action(self,state):\n Q_value = self.Q_value.eval(feed_dict = {\n self.state_input:[state]\n })[0]\n if random.random() <= self.epsilon:\n self.epsilon -= (INITIAL_EPSILON - FINAL_EPSILON) / 10000\n return random.randint(0,self.action_dim - 1)\n else:\n self.epsilon -= (INITIAL_EPSILON - FINAL_EPSILON) / 10000\n return np.argmax(Q_value)\n\n def action(self,state):\n return np.argmax(self.Q_value.eval(feed_dict = {\n self.state_input:[state]\n })[0])\n\n def update_target_q_network(self, episode):\n # update target Q netowrk\n if episode % REPLACE_TARGET_FREQ == 0:\n self.session.run(self.target_replace_op)\n #print('episode '+str(episode) +', target Q network params replaced!')\n\n def weight_variable(self,shape):\n initial = tf.truncated_normal(shape)\n return tf.Variable(initial)\n\n def bias_variable(self,shape):\n initial = tf.constant(0.01, shape = shape)\n return tf.Variable(initial)\n# ---------------------------------------------------------\n# Hyper Parameters\nENV_NAME = 'CartPole-v0'\nEPISODE = 3000 # Episode limitation\nSTEP = 300 # Step limitation in an episode\nTEST = 5 # The number of experiment test every 100 episode\n\ndef main():\n # initialize OpenAI Gym env and dqn agent\n env = gym.make(ENV_NAME)\n agent = DQN(env)\n\n for episode in range(EPISODE):\n # initialize task\n state = env.reset()\n # Train\n for step in range(STEP):\n action = agent.egreedy_action(state) # e-greedy action for train\n next_state,reward,done,_ = env.step(action)\n # Define reward for agent\n reward = -1 if done else 0.1\n agent.perceive(state,action,reward,next_state,done)\n state = next_state\n if done:\n break\n # Test every 100 episodes\n if episode % 100 == 0:\n total_reward = 0\n for i in range(TEST):\n state = env.reset()\n for j in range(STEP):\n env.render()\n action = agent.action(state) # direct action for test\n state,reward,done,_ = env.step(action)\n total_reward += reward\n if done:\n break\n ave_reward = total_reward/TEST\n print ('episode: ',episode,'Evaluation Average Reward:',ave_reward)\n agent.update_target_q_network(episode)\n\nif __name__ == '__main__':\n main()" }, { "path": "reinforcement-learning/policy_gradient.py", "content": "#######################################################################\n# Copyright (C) #\n# 2016 - 2019 Pinard Liu(liujianping-ok@163.com) #\n# https://www.cnblogs.com/pinard #\n# Permission given to modify the code as long as you keep this #\n# declaration at the top #\n#######################################################################\n## https://www.cnblogs.com/pinard/p/10137696.html ##\n## 强化学习(十三) 策略梯度(Policy Gradient) ##\n\nimport gym\nimport tensorflow as tf\nimport numpy as np\nimport random\nfrom collections import deque\n\n# Hyper Parameters\nGAMMA = 0.95 # discount factor\nLEARNING_RATE=0.01\n\nclass Policy_Gradient():\n def __init__(self, env):\n # init some parameters\n self.time_step = 0\n self.state_dim = env.observation_space.shape[0]\n self.action_dim = env.action_space.n\n self.ep_obs, self.ep_as, self.ep_rs = [], [], []\n self.create_softmax_network()\n\n # Init session\n self.session = tf.InteractiveSession()\n self.session.run(tf.global_variables_initializer())\n\n def create_softmax_network(self):\n # network weights\n W1 = self.weight_variable([self.state_dim, 20])\n b1 = self.bias_variable([20])\n W2 = self.weight_variable([20, self.action_dim])\n b2 = self.bias_variable([self.action_dim])\n # input layer\n self.state_input = tf.placeholder(\"float\", [None, self.state_dim])\n self.tf_acts = tf.placeholder(tf.int32, [None, ], name=\"actions_num\")\n self.tf_vt = tf.placeholder(tf.float32, [None, ], name=\"actions_value\")\n # hidden layers\n h_layer = tf.nn.relu(tf.matmul(self.state_input, W1) + b1)\n # softmax layer\n self.softmax_input = tf.matmul(h_layer, W2) + b2\n #softmax output\n self.all_act_prob = tf.nn.softmax(self.softmax_input, name='act_prob')\n self.neg_log_prob = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=self.softmax_input,\n labels=self.tf_acts)\n self.loss = tf.reduce_mean(self.neg_log_prob * self.tf_vt) # reward guided loss\n\n self.train_op = tf.train.AdamOptimizer(LEARNING_RATE).minimize(self.loss)\n\n def weight_variable(self, shape):\n initial = tf.truncated_normal(shape)\n return tf.Variable(initial)\n\n def bias_variable(self, shape):\n initial = tf.constant(0.01, shape=shape)\n return tf.Variable(initial)\n\n def choose_action(self, observation):\n prob_weights = self.session.run(self.all_act_prob, feed_dict={self.state_input: observation[np.newaxis, :]})\n action = np.random.choice(range(prob_weights.shape[1]), p=prob_weights.ravel()) # select action w.r.t the actions prob\n return action\n\n def store_transition(self, s, a, r):\n self.ep_obs.append(s)\n self.ep_as.append(a)\n self.ep_rs.append(r)\n\n def learn(self):\n\n discounted_ep_rs = np.zeros_like(self.ep_rs)\n running_add = 0\n for t in reversed(range(0, len(self.ep_rs))):\n running_add = running_add * GAMMA + self.ep_rs[t]\n discounted_ep_rs[t] = running_add\n\n discounted_ep_rs -= np.mean(discounted_ep_rs)\n discounted_ep_rs /= np.std(discounted_ep_rs)\n\n # train on episode\n self.session.run(self.train_op, feed_dict={\n self.state_input: np.vstack(self.ep_obs),\n self.tf_acts: np.array(self.ep_as),\n self.tf_vt: discounted_ep_rs,\n })\n\n self.ep_obs, self.ep_as, self.ep_rs = [], [], [] # empty episode data\n# Hyper Parameters\nENV_NAME = 'CartPole-v0'\nEPISODE = 3000 # Episode limitation\nSTEP = 3000 # Step limitation in an episode\nTEST = 10 # The number of experiment test every 100 episode\n\ndef main():\n # initialize OpenAI Gym env and dqn agent\n env = gym.make(ENV_NAME)\n agent = Policy_Gradient(env)\n\n for episode in range(EPISODE):\n # initialize task\n state = env.reset()\n # Train\n for step in range(STEP):\n action = agent.choose_action(state) # e-greedy action for train\n next_state,reward,done,_ = env.step(action)\n agent.store_transition(state, action, reward)\n state = next_state\n if done:\n #print(\"stick for \",step, \" steps\")\n agent.learn()\n break\n\n # Test every 100 episodes\n if episode % 100 == 0:\n total_reward = 0\n for i in range(TEST):\n state = env.reset()\n for j in range(STEP):\n env.render()\n action = agent.choose_action(state) # direct action for test\n state,reward,done,_ = env.step(action)\n total_reward += reward\n if done:\n break\n ave_reward = total_reward/TEST\n print ('episode: ',episode,'Evaluation Average Reward:',ave_reward)\n\nif __name__ == '__main__':\n main()" }, { "path": "reinforcement-learning/q_learning_windy_world.py", "content": "#######################################################################\n# Copyright (C) #\n# 2016-2018 Shangtong Zhang(zhangshangtong.cpp@gmail.com) #\n# 2016 Kenta Shimada(hyperkentakun@gmail.com) #\n# Permission given to modify the code as long as you keep this #\n# declaration at the top #\n#######################################################################\n##https://www.cnblogs.com/pinard/p/9669263.html ##\n## 强化学习（七）时序差分离线控制算法Q-Learning ##\n\nimport numpy as np\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\n# world height\nWORLD_HEIGHT = 7\n\n# world width\nWORLD_WIDTH = 10\n\n# wind strength for each column\nWIND = [0, 0, 0, 1, 1, 1, 2, 2, 1, 0]\n\n# possible actions\nACTION_UP = 0\nACTION_DOWN = 1\nACTION_LEFT = 2\nACTION_RIGHT = 3\n\n# probability for exploration\nEPSILON = 0.1\n\n# Sarsa step size\nALPHA = 0.5\n\n# reward for each step\nREWARD = -1.0\n\nSTART = [3, 0]\nGOAL = [3, 7]\nACTIONS = [ACTION_UP, ACTION_DOWN, ACTION_LEFT, ACTION_RIGHT]\n\ndef step(state, action):\n i, j = state\n if action == ACTION_UP:\n return [max(i - 1 - WIND[j], 0), j]\n elif action == ACTION_DOWN:\n return [max(min(i + 1 - WIND[j], WORLD_HEIGHT - 1), 0), j]\n elif action == ACTION_LEFT:\n return [max(i - WIND[j], 0), max(j - 1, 0)]\n elif action == ACTION_RIGHT:\n return [max(i - WIND[j], 0), min(j + 1, WORLD_WIDTH - 1)]\n else:\n assert False\n\n# play for an episode\ndef episode(q_value):\n # track the total time steps in this episode\n time = 0\n\n # initialize state\n state = START\n\n while state != GOAL:\n # choose an action based on epsilon-greedy algorithm\n if np.random.binomial(1, EPSILON) == 1:\n action = np.random.choice(ACTIONS)\n else:\n values_ = q_value[state[0], state[1], :]\n action = np.random.choice([action_ for action_, value_ in enumerate(values_) if value_ == np.max(values_)])\n\n # keep going until get to the goal state\n\n next_state = step(state, action)\n #if np.random.binomial(1, EPSILON) == 1:\n # next_action = np.random.choice(ACTIONS)\n #else:\n values_ = q_value[next_state[0], next_state[1], :]\n next_action = np.random.choice([action_ for action_, value_ in enumerate(values_) if value_ == np.max(values_)])\n\n # Sarsa update\n q_value[state[0], state[1], action] += \\\n ALPHA * (REWARD + q_value[next_state[0], next_state[1], next_action] -\n q_value[state[0], state[1], action])\n state = next_state\n #action = next_action\n time += 1\n return time\n\ndef q_learning():\n q_value = np.zeros((WORLD_HEIGHT, WORLD_WIDTH, 4))\n episode_limit = 500\n\n steps = []\n ep = 0\n while ep < episode_limit:\n steps.append(episode(q_value))\n # time = episode(q_value)\n # episodes.extend([ep] * time)\n ep += 1\n\n steps = np.add.accumulate(steps)\n\n plt.plot(steps, np.arange(1, len(steps) + 1))\n plt.xlabel('Time steps')\n plt.ylabel('Episodes')\n\n plt.savefig('./q-learning.png')\n plt.close()\n\n # display the optimal policy\n optimal_policy = []\n for i in range(0, WORLD_HEIGHT):\n optimal_policy.append([])\n for j in range(0, WORLD_WIDTH):\n if [i, j] == GOAL:\n optimal_policy[-1].append('G')\n continue\n bestAction = np.argmax(q_value[i, j, :])\n if bestAction == ACTION_UP:\n optimal_policy[-1].append('U')\n elif bestAction == ACTION_DOWN:\n optimal_policy[-1].append('D')\n elif bestAction == ACTION_LEFT:\n optimal_policy[-1].append('L')\n elif bestAction == ACTION_RIGHT:\n optimal_policy[-1].append('R')\n print('Optimal policy is:')\n for row in optimal_policy:\n print(row)\n print('Wind strength for each column:\\n{}'.format([str(w) for w in WIND]))\n\nif __name__ == '__main__':\n q_learning()" }, { "path": "reinforcement-learning/sarsa_windy_world.py", "content": "#######################################################################\n# Copyright (C) #\n# 2016-2018 Shangtong Zhang(zhangshangtong.cpp@gmail.com) #\n# 2016 Kenta Shimada(hyperkentakun@gmail.com) #\n# Permission given to modify the code as long as you keep this #\n# declaration at the top #\n#######################################################################\n##https://www.cnblogs.com/pinard/p/9614290.html ##\n## 强化学习（六）时序差分在线控制算法SARSA ##\n\nimport numpy as np\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\n# world height\nWORLD_HEIGHT = 7\n\n# world width\nWORLD_WIDTH = 10\n\n# wind strength for each column\nWIND = [0, 0, 0, 1, 1, 1, 2, 2, 1, 0]\n\n# possible actions\nACTION_UP = 0\nACTION_DOWN = 1\nACTION_LEFT = 2\nACTION_RIGHT = 3\n\n# probability for exploration\nEPSILON = 0.1\n\n# Sarsa step size\nALPHA = 0.5\n\n# reward for each step\nREWARD = -1.0\n\nSTART = [3, 0]\nGOAL = [3, 7]\nACTIONS = [ACTION_UP, ACTION_DOWN, ACTION_LEFT, ACTION_RIGHT]\n\ndef step(state, action):\n i, j = state\n if action == ACTION_UP:\n return [max(i - 1 - WIND[j], 0), j]\n elif action == ACTION_DOWN:\n return [max(min(i + 1 - WIND[j], WORLD_HEIGHT - 1), 0), j]\n elif action == ACTION_LEFT:\n return [max(i - WIND[j], 0), max(j - 1, 0)]\n elif action == ACTION_RIGHT:\n return [max(i - WIND[j], 0), min(j + 1, WORLD_WIDTH - 1)]\n else:\n assert False\n\n# play for an episode\ndef episode(q_value):\n # track the total time steps in this episode\n time = 0\n\n # initialize state\n state = START\n\n # choose an action based on epsilon-greedy algorithm\n if np.random.binomial(1, EPSILON) == 1:\n action = np.random.choice(ACTIONS)\n else:\n values_ = q_value[state[0], state[1], :]\n action = np.random.choice([action_ for action_, value_ in enumerate(values_) if value_ == np.max(values_)])\n\n # keep going until get to the goal state\n while state != GOAL:\n next_state = step(state, action)\n if np.random.binomial(1, EPSILON) == 1:\n next_action = np.random.choice(ACTIONS)\n else:\n values_ = q_value[next_state[0], next_state[1], :]\n next_action = np.random.choice([action_ for action_, value_ in enumerate(values_) if value_ == np.max(values_)])\n\n # Sarsa update\n q_value[state[0], state[1], action] += \\\n ALPHA * (REWARD + q_value[next_state[0], next_state[1], next_action] -\n q_value[state[0], state[1], action])\n state = next_state\n action = next_action\n time += 1\n return time\n\ndef sarsa():\n q_value = np.zeros((WORLD_HEIGHT, WORLD_WIDTH, 4))\n episode_limit = 500\n\n steps = []\n ep = 0\n while ep < episode_limit:\n steps.append(episode(q_value))\n # time = episode(q_value)\n # episodes.extend([ep] * time)\n ep += 1\n\n steps = np.add.accumulate(steps)\n\n plt.plot(steps, np.arange(1, len(steps) + 1))\n plt.xlabel('Time steps')\n plt.ylabel('Episodes')\n\n plt.savefig('./sarsa.png')\n plt.close()\n\n # display the optimal policy\n optimal_policy = []\n for i in range(0, WORLD_HEIGHT):\n optimal_policy.append([])\n for j in range(0, WORLD_WIDTH):\n if [i, j] == GOAL:\n optimal_policy[-1].append('G')\n continue\n bestAction = np.argmax(q_value[i, j, :])\n if bestAction == ACTION_UP:\n optimal_policy[-1].append('U')\n elif bestAction == ACTION_DOWN:\n optimal_policy[-1].append('D')\n elif bestAction == ACTION_LEFT:\n optimal_policy[-1].append('L')\n elif bestAction == ACTION_RIGHT:\n optimal_policy[-1].append('R')\n print('Optimal policy is:')\n for row in optimal_policy:\n print(row)\n print('Wind strength for each column:\\n{}'.format([str(w) for w in WIND]))\n\nif __name__ == '__main__':\n sarsa()" } ]