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1 {
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2 "cells": [
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3 {
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4 "cell_type": "code",
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5 "execution_count": 13,
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6 "metadata": {
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7 "collapsed": false
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8 },
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9 "outputs": [
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10 {
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11 "name": "stdout",
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12 "output_type": "stream",
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13 "text": [
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14 "The autoreload extension is already loaded. To reload it, use:\n",
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15 " %reload_ext autoreload\n"
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16 ]
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17 }
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18 ],
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19 "source": [
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m@8
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20 "import numpy as np\n",
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21 "import pickle\n",
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22 "from scipy.stats import pearsonr\n",
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23 "from scipy.stats import skew\n",
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24 "import sys\n",
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25 "from sklearn.metrics.pairwise import pairwise_distances\n",
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26 "%matplotlib inline\n",
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27 "import matplotlib.pyplot as plt\n",
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28 "\n",
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29 "%load_ext autoreload\n",
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30 "%autoreload 2\n",
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31 "\n",
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32 "sys.path.append('../')\n",
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33 "import scripts.results as results\n",
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34 "import scripts.utils_spatial as utils_spatial"
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35 ]
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36 },
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37 {
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38 "cell_type": "code",
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39 "execution_count": 2,
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40 "metadata": {
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41 "collapsed": false
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42 },
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43 "outputs": [
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44 {
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45 "name": "stdout",
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46 "output_type": "stream",
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47 "text": [
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48 "WARNING: there are 21 disconnected observations\n",
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49 "Island ids: [3, 6, 26, 35, 39, 45, 52, 61, 62, 66, 77, 85, 94, 97, 98, 102, 103, 107, 110, 120, 121]\n",
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50 "Antigua and Barbuda\n",
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51 "Australia\n",
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52 "Cuba\n",
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53 "Fiji\n",
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54 "French Polynesia\n",
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55 "Grenada\n",
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56 "Iceland\n",
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57 "Jamaica\n",
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58 "Japan\n",
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59 "Kiribati\n",
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60 "Malta\n",
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61 "New Zealand\n",
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62 "Philippines\n",
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63 "Puerto Rico\n",
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64 "Republic of Serbia\n",
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65 "Saint Lucia\n",
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66 "Samoa\n",
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67 "Solomon Islands\n",
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68 "South Korea\n",
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69 "The Bahamas\n",
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70 "Trinidad and Tobago\n"
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71 ]
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72 }
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73 ],
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74 "source": [
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75 "X_list, Y, Yaudio = pickle.load(open('../data/lda_data_melodia_8.pickle','rb'))\n",
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m@8
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76 "ddf = results.load_metadata(Yaudio, metadata_file='../data/metadata.csv')\n",
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77 "w, data_countries = utils_spatial.get_neighbors_for_countries_in_dataset(Y)\n",
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78 "w_dict = utils_spatial.from_weights_to_dict(w, data_countries)\n",
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m@8
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79 "Xrhy, Xmel, Xmfc, Xchr = X_list\n",
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80 "X = np.concatenate((Xrhy, Xmel, Xmfc, Xchr), axis=1)\n",
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81 "\n",
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82 "# global outliers\n",
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83 "df_global, threshold, MD = results.get_outliers_df(X, Y, chi2thr=0.999)"
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84 ]
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85 },
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86 {
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87 "cell_type": "code",
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88 "execution_count": 3,
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89 "metadata": {
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90 "collapsed": false
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91 },
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92 "outputs": [
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93 {
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94 "data": {
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95 "text/plain": [
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96 "(8200, 380)"
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97 ]
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98 },
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99 "execution_count": 3,
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100 "metadata": {},
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101 "output_type": "execute_result"
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102 }
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103 ],
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104 "source": [
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105 "X.shape"
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106 ]
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107 },
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108 {
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109 "cell_type": "code",
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110 "execution_count": null,
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111 "metadata": {
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112 "collapsed": false
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113 },
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114 "outputs": [],
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115 "source": [
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116 "D = pairwise_distances(X, metric='mahalanobis')"
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117 ]
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118 },
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119 {
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120 "cell_type": "code",
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121 "execution_count": 6,
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122 "metadata": {
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123 "collapsed": false
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124 },
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125 "outputs": [
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126 {
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127 "data": {
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128 "image/png": 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vueeeBW2/2OmjJ4E7m+U7gSc67TuSXJTkWmAzcLCqTgA/SLKt2fF8B/C1WR7rM4x2XEuS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\n",
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129 "text/plain": [
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130 "<matplotlib.figure.Figure at 0x1174100d0>"
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131 ]
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132 },
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133 "metadata": {},
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134 "output_type": "display_data"
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135 }
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136 ],
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137 "source": [
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138 "plt.hist(D.ravel(), bins=100);"
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139 ]
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140 },
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141 {
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142 "cell_type": "code",
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143 "execution_count": 16,
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144 "metadata": {
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145 "collapsed": true
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146 },
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147 "outputs": [],
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148 "source": [
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149 "def n_occurrence_from_D(D, k=10, n_items=None):\n",
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m@8
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150 " if n_items is None:\n",
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m@8
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151 " n_items = len(D)\n",
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152 " sort_idx = np.argsort(D, axis=1)\n",
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153 " D_k = sort_idx[:, 1:(k+1)] # nearest neighbour is the item itself\n",
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154 " N_k = np.bincount(D_k.astype(int).ravel(), minlength=n_items)\n",
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155 " return N_k"
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156 ]
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157 },
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158 {
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159 "cell_type": "code",
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160 "execution_count": 18,
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161 "metadata": {
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162 "collapsed": false
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163 },
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164 "outputs": [
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165 {
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166 "name": "stdout",
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167 "output_type": "stream",
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168 "text": [
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169 "2.71175956476\n"
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170 ]
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171 },
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172 {
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173 "data": {
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174 "image/png": 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aqqrzwDlg92i6KkkahaVejW+pqsmuPAls6crbgImBdhPA9iW+hiRpBSx7GqaqCqj5miz3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\nY/4f5A0BHDAleMkAAAAASUVORK5CYII=\n",
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m@8
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175 "text/plain": [
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m@8
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176 "<matplotlib.figure.Figure at 0x1e926a590>"
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177 ]
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178 },
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m@8
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179 "metadata": {},
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m@8
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180 "output_type": "display_data"
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181 }
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182 ],
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m@8
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183 "source": [
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184 "N_k = n_occurrence_from_D(D, k=100)\n",
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185 "print skew(N_k)\n",
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186 "plt.hist(N_k, bins=100);"
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187 ]
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188 },
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m@8
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189 {
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m@8
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190 "cell_type": "code",
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191 "execution_count": 9,
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192 "metadata": {
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193 "collapsed": true
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194 },
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195 "outputs": [],
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196 "source": [
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197 "N_k"
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198 ]
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199 },
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200 {
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201 "cell_type": "code",
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m@8
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202 "execution_count": null,
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203 "metadata": {
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204 "collapsed": true
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205 },
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206 "outputs": [],
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207 "source": []
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208 }
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209 ],
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m@8
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210 "metadata": {
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211 "kernelspec": {
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m@8
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212 "display_name": "Python 2",
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m@8
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213 "language": "python",
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m@8
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214 "name": "python2"
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215 },
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m@8
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216 "language_info": {
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217 "codemirror_mode": {
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m@8
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218 "name": "ipython",
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m@8
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219 "version": 2
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220 },
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m@8
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221 "file_extension": ".py",
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m@8
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222 "mimetype": "text/x-python",
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m@8
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223 "name": "python",
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m@8
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224 "nbconvert_exporter": "python",
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m@8
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225 "pygments_lexer": "ipython2",
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m@8
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226 "version": "2.7.12"
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m@8
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227 }
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228 },
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m@8
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229 "nbformat": 4,
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230 "nbformat_minor": 0
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231 }
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