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amirziai/sklearn logistic regression probabilities.ipynb

Created March 16, 2017 04:19
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sklearn logistic regression probabilities.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x = [1, 2, 3]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def sigmoid(x):\n",
" x_ = np.array(x)\n",
" return 1 / (1 + np.exp(-x_))\n",
"\n",
"def softmax(x):\n",
" return np.exp(x) / np.sum(np.exp(x))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.73105858, 0.88079708, 0.95257413])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sigmoid(x)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.09003057, 0.24472847, 0.66524096])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"softmax(x)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Automatically created module for IPython interactive environment\n"
]
},
{
"data": {
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7zhzZj5evH/lZmfgEBBISFcup/bsRJBLe/mUlTZpG1ap9q9XKm1Pv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\n/IQ3MjTY4W7C9LPZ1Lrufv7QcsGEVwjBluncTTP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"text/plain": [
"<matplotlib.figure.Figure at 0x10f8a3ef0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print(__doc__)\n",
"\n",
"\n",
"# Code source: Gaël Varoquaux\n",
"# Modified for documentation by Jaques Grobler\n",
"# License: BSD 3 clause\n",
"\n",
"\n",
"import matplotlib.pyplot as plt\n",
"from sklearn import linear_model, datasets\n",
"\n",
"# import some data to play with\n",
"iris = datasets.load_iris()\n",
"X = iris.data[:, :2] # we only take the first two features.\n",
"Y = iris.target\n",
"\n",
"h = .02 # step size in the mesh\n",
"\n",
"logreg = linear_model.LogisticRegression(C=1e5)\n",
"\n",
"# we create an instance of Neighbours Classifier and fit the data.\n",
"logreg.fit(X, Y)\n",
"\n",
"# Plot the decision boundary. For that, we will assign a color to each\n",
"# point in the mesh [x_min, x_max]x[y_min, y_max].\n",
"x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
"y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
"xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
"Z = logreg.predict(np.c_[xx.ravel(), yy.ravel()])\n",
"\n",
"# Put the result into a color plot\n",
"Z = Z.reshape(xx.shape)\n",
"plt.figure(1, figsize=(4, 3))\n",
"plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)\n",
"\n",
"# Plot also the training points\n",
"plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors='k', cmap=plt.cm.Paired)\n",
"plt.xlabel('Sepal length')\n",
"plt.ylabel('Sepal width')\n",
"\n",
"plt.xlim(xx.min(), xx.max())\n",
"plt.ylim(yy.min(), yy.max())\n",
"plt.xticks(())\n",
"plt.yticks(())\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>p1</th>\n",
" <th>p2</th>\n",
" <th>p3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9.058239e-01</td>\n",
" <td>0.068167</td>\n",
" <td>0.026009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>7.646318e-01</td>\n",
" <td>0.216377</td>\n",
" <td>0.018992</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>8.469082e-01</td>\n",
" <td>0.142190</td>\n",
" <td>0.010902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>8.156549e-01</td>\n",
" <td>0.175609</td>\n",
" <td>0.008736</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>9.296250e-01</td>\n",
" <td>0.051118</td>\n",
" <td>0.019257</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>9.357262e-01</td>\n",
" <td>0.021457</td>\n",
" <td>0.042817</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>9.063752e-01</td>\n",
" <td>0.085841</td>\n",
" <td>0.007784</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8.900042e-01</td>\n",
" <td>0.088675</td>\n",
" <td>0.021321</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>7.440552e-01</td>\n",
" <td>0.250433</td>\n",
" <td>0.005512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>8.028268e-01</td>\n",
" <td>0.178629</td>\n",
" <td>0.018544</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>9.128814e-01</td>\n",
" <td>0.039003</td>\n",
" <td>0.048116</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>8.996732e-01</td>\n",
" <td>0.087397</td>\n",
" <td>0.012930</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>7.695945e-01</td>\n",
" <td>0.215588</td>\n",
" <td>0.014817</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>7.864683e-01</td>\n",
" <td>0.209354</td>\n",
" <td>0.004177</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>8.851955e-01</td>\n",
" <td>0.015653</td>\n",
" <td>0.099151</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>9.326347e-01</td>\n",
" <td>0.004555</td>\n",
" <td>0.062810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>9.357262e-01</td>\n",
" <td>0.021457</td>\n",
" <td>0.042817</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>9.058239e-01</td>\n",
" <td>0.068167</td>\n",
" <td>0.026009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>8.817755e-01</td>\n",
" <td>0.028805</td>\n",
" <td>0.089420</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>9.494866e-01</td>\n",
" <td>0.028575</td>\n",
" <td>0.021938</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>8.545522e-01</td>\n",
" <td>0.089754</td>\n",
" <td>0.055693</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>9.381926e-01</td>\n",
" <td>0.038499</td>\n",
" <td>0.023309</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>9.437955e-01</td>\n",
" <td>0.049211</td>\n",
" <td>0.006994</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>8.567617e-01</td>\n",
" <td>0.114825</td>\n",
" <td>0.028413</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>8.996732e-01</td>\n",
" <td>0.087397</td>\n",
" <td>0.012930</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>7.587059e-01</td>\n",
" <td>0.217009</td>\n",
" <td>0.024286</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>8.900042e-01</td>\n",
" <td>0.088675</td>\n",
" <td>0.021321</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>8.983317e-01</td>\n",
" <td>0.068486</td>\n",
" <td>0.033182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>8.757658e-01</td>\n",
" <td>0.089488</td>\n",
" <td>0.034746</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>8.469082e-01</td>\n",
" <td>0.142190</td>\n",
" <td>0.010902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120</th>\n",
" <td>1.939029e-20</td>\n",
" <td>0.212185</td>\n",
" <td>0.787815</td>\n",
" </tr>\n",
" <tr>\n",
" <th>121</th>\n",
" <td>1.027614e-07</td>\n",
" <td>0.745542</td>\n",
" <td>0.254458</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122</th>\n",
" <td>4.934945e-36</td>\n",
" <td>0.350088</td>\n",
" <td>0.649912</td>\n",
" </tr>\n",
" <tr>\n",
" <th>123</th>\n",
" <td>1.764316e-18</td>\n",
" <td>0.503684</td>\n",
" <td>0.496316</td>\n",
" </tr>\n",
" <tr>\n",
" <th>124</th>\n",
" <td>1.661735e-16</td>\n",
" <td>0.190750</td>\n",
" <td>0.809250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>125</th>\n",
" <td>1.802399e-24</td>\n",
" <td>0.198855</td>\n",
" <td>0.801145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126</th>\n",
" <td>6.979944e-16</td>\n",
" <td>0.504279</td>\n",
" <td>0.495721</td>\n",
" </tr>\n",
" <tr>\n",
" <th>127</th>\n",
" <td>5.055272e-12</td>\n",
" <td>0.464632</td>\n",
" <td>0.535368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>128</th>\n",
" <td>1.335517e-18</td>\n",
" <td>0.447050</td>\n",
" <td>0.552950</td>\n",
" </tr>\n",
" <tr>\n",
" <th>129</th>\n",
" <td>6.468090e-27</td>\n",
" <td>0.279286</td>\n",
" <td>0.720714</td>\n",
" </tr>\n",
" <tr>\n",
" <th>130</th>\n",
" <td>4.932393e-32</td>\n",
" <td>0.351498</td>\n",
" <td>0.648502</td>\n",
" </tr>\n",
" <tr>\n",
" <th>131</th>\n",
" <td>1.475019e-26</td>\n",
" <td>0.043265</td>\n",
" <td>0.956735</td>\n",
" </tr>\n",
" <tr>\n",
" <th>132</th>\n",
" <td>1.335517e-18</td>\n",
" <td>0.447050</td>\n",
" <td>0.552950</td>\n",
" </tr>\n",
" <tr>\n",
" <th>133</th>\n",
" <td>3.044658e-17</td>\n",
" <td>0.473491</td>\n",
" <td>0.526509</td>\n",
" </tr>\n",
" <tr>\n",
" <th>134</th>\n",
" <td>5.318616e-17</td>\n",
" <td>0.589058</td>\n",
" <td>0.410942</td>\n",
" </tr>\n",
" <tr>\n",
" <th>135</th>\n",
" <td>1.365864e-33</td>\n",
" <td>0.274761</td>\n",
" <td>0.725239</td>\n",
" </tr>\n",
" <tr>\n",
" <th>136</th>\n",
" <td>8.623369e-10</td>\n",
" <td>0.218205</td>\n",
" <td>0.781795</td>\n",
" </tr>\n",
" <tr>\n",
" <th>137</th>\n",
" <td>6.966578e-15</td>\n",
" <td>0.325818</td>\n",
" <td>0.674182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>138</th>\n",
" <td>1.186082e-10</td>\n",
" <td>0.505308</td>\n",
" <td>0.494692</td>\n",
" </tr>\n",
" <tr>\n",
" <th>139</th>\n",
" <td>1.153010e-21</td>\n",
" <td>0.252870</td>\n",
" <td>0.747130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>140</th>\n",
" <td>5.786181e-19</td>\n",
" <td>0.272310</td>\n",
" <td>0.727690</td>\n",
" </tr>\n",
" <tr>\n",
" <th>141</th>\n",
" <td>1.153010e-21</td>\n",
" <td>0.252870</td>\n",
" <td>0.747130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>142</th>\n",
" <td>1.102296e-11</td>\n",
" <td>0.684442</td>\n",
" <td>0.315558</td>\n",
" </tr>\n",
" <tr>\n",
" <th>143</th>\n",
" <td>4.345244e-19</td>\n",
" <td>0.220088</td>\n",
" <td>0.779912</td>\n",
" </tr>\n",
" <tr>\n",
" <th>144</th>\n",
" <td>1.661735e-16</td>\n",
" <td>0.190750</td>\n",
" <td>0.809250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145</th>\n",
" <td>3.397438e-20</td>\n",
" <td>0.314530</td>\n",
" <td>0.685470</td>\n",
" </tr>\n",
" <tr>\n",
" <th>146</th>\n",
" <td>6.119041e-21</td>\n",
" <td>0.546353</td>\n",
" <td>0.453647</td>\n",
" </tr>\n",
" <tr>\n",
" <th>147</th>\n",
" <td>1.745193e-17</td>\n",
" <td>0.347308</td>\n",
" <td>0.652692</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>2.088411e-08</td>\n",
" <td>0.244264</td>\n",
" <td>0.755736</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149</th>\n",
" <td>2.782616e-09</td>\n",
" <td>0.549481</td>\n",
" <td>0.450519</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>150 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" p1 p2 p3\n",
"0 9.058239e-01 0.068167 0.026009\n",
"1 7.646318e-01 0.216377 0.018992\n",
"2 8.469082e-01 0.142190 0.010902\n",
"3 8.156549e-01 0.175609 0.008736\n",
"4 9.296250e-01 0.051118 0.019257\n",
"5 9.357262e-01 0.021457 0.042817\n",
"6 9.063752e-01 0.085841 0.007784\n",
"7 8.900042e-01 0.088675 0.021321\n",
"8 7.440552e-01 0.250433 0.005512\n",
"9 8.028268e-01 0.178629 0.018544\n",
"10 9.128814e-01 0.039003 0.048116\n",
"11 8.996732e-01 0.087397 0.012930\n",
"12 7.695945e-01 0.215588 0.014817\n",
"13 7.864683e-01 0.209354 0.004177\n",
"14 8.851955e-01 0.015653 0.099151\n",
"15 9.326347e-01 0.004555 0.062810\n",
"16 9.357262e-01 0.021457 0.042817\n",
"17 9.058239e-01 0.068167 0.026009\n",
"18 8.817755e-01 0.028805 0.089420\n",
"19 9.494866e-01 0.028575 0.021938\n",
"20 8.545522e-01 0.089754 0.055693\n",
"21 9.381926e-01 0.038499 0.023309\n",
"22 9.437955e-01 0.049211 0.006994\n",
"23 8.567617e-01 0.114825 0.028413\n",
"24 8.996732e-01 0.087397 0.012930\n",
"25 7.587059e-01 0.217009 0.024286\n",
"26 8.900042e-01 0.088675 0.021321\n",
"27 8.983317e-01 0.068486 0.033182\n",
"28 8.757658e-01 0.089488 0.034746\n",
"29 8.469082e-01 0.142190 0.010902\n",
".. ... ... ...\n",
"120 1.939029e-20 0.212185 0.787815\n",
"121 1.027614e-07 0.745542 0.254458\n",
"122 4.934945e-36 0.350088 0.649912\n",
"123 1.764316e-18 0.503684 0.496316\n",
"124 1.661735e-16 0.190750 0.809250\n",
"125 1.802399e-24 0.198855 0.801145\n",
"126 6.979944e-16 0.504279 0.495721\n",
"127 5.055272e-12 0.464632 0.535368\n",
"128 1.335517e-18 0.447050 0.552950\n",
"129 6.468090e-27 0.279286 0.720714\n",
"130 4.932393e-32 0.351498 0.648502\n",
"131 1.475019e-26 0.043265 0.956735\n",
"132 1.335517e-18 0.447050 0.552950\n",
"133 3.044658e-17 0.473491 0.526509\n",
"134 5.318616e-17 0.589058 0.410942\n",
"135 1.365864e-33 0.274761 0.725239\n",
"136 8.623369e-10 0.218205 0.781795\n",
"137 6.966578e-15 0.325818 0.674182\n",
"138 1.186082e-10 0.505308 0.494692\n",
"139 1.153010e-21 0.252870 0.747130\n",
"140 5.786181e-19 0.272310 0.727690\n",
"141 1.153010e-21 0.252870 0.747130\n",
"142 1.102296e-11 0.684442 0.315558\n",
"143 4.345244e-19 0.220088 0.779912\n",
"144 1.661735e-16 0.190750 0.809250\n",
"145 3.397438e-20 0.314530 0.685470\n",
"146 6.119041e-21 0.546353 0.453647\n",
"147 1.745193e-17 0.347308 0.652692\n",
"148 2.088411e-08 0.244264 0.755736\n",
"149 2.782616e-09 0.549481 0.450519\n",
"\n",
"[150 rows x 3 columns]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(logreg.predict_proba(X), columns=['p1', 'p2', 'p3'])"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"decision = logreg.decision_function(X)\n",
"dot = X.dot(logreg.coef_.T) + logreg.intercept_\n",
"np.testing.assert_equal(decision, dot) "
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 9.05823905e-01, 6.81672013e-02, 2.60088939e-02],\n",
" [ 7.64631786e-01, 2.16376590e-01, 1.89916235e-02],\n",
" [ 8.46908157e-01, 1.42190177e-01, 1.09016662e-02],\n",
" [ 8.15654921e-01, 1.75608861e-01, 8.73621791e-03],\n",
" [ 9.29624966e-01, 5.11184180e-02, 1.92566160e-02],\n",
" [ 9.35726243e-01, 2.14566456e-02, 4.28171113e-02],\n",
" [ 9.06375214e-01, 8.58410376e-02, 7.78374790e-03],\n",
" [ 8.90004223e-01, 8.86750246e-02, 2.13207520e-02],\n",
" [ 7.44055159e-01, 2.50433077e-01, 5.51176369e-03],\n",
" [ 8.02826805e-01, 1.78629207e-01, 1.85439881e-02],\n",
" [ 9.12881392e-01, 3.90028926e-02, 4.81157159e-02],\n",
" [ 8.99673219e-01, 8.73972775e-02, 1.29295031e-02],\n",
" [ 7.69594524e-01, 2.15588498e-01, 1.48169774e-02],\n",
" [ 7.86468274e-01, 2.09354239e-01, 4.17748690e-03],\n",
" [ 8.85195466e-01, 1.56532847e-02, 9.91512491e-02],\n",
" [ 9.32634691e-01, 4.55482910e-03, 6.28104799e-02],\n",
" [ 9.35726243e-01, 2.14566456e-02, 4.28171113e-02],\n",
" [ 9.05823905e-01, 6.81672013e-02, 2.60088939e-02],\n",
" [ 8.81775462e-01, 2.88045719e-02, 8.94199658e-02],\n",
" [ 9.49486589e-01, 2.85752663e-02, 2.19381451e-02],\n",
" [ 8.54552240e-01, 8.97543407e-02, 5.56934189e-02],\n",
" [ 9.38192573e-01, 3.84987122e-02, 2.33087152e-02],\n",
" [ 9.43795525e-01, 4.92109497e-02, 6.99352520e-03],\n",
" [ 8.56761685e-01, 1.14824908e-01, 2.84134063e-02],\n",
" [ 8.99673219e-01, 8.73972775e-02, 1.29295031e-02],\n",
" [ 7.58705901e-01, 2.17008582e-01, 2.42855166e-02],\n",
" [ 8.90004223e-01, 8.86750246e-02, 2.13207520e-02],\n",
" [ 8.98331697e-01, 6.84864517e-02, 3.31818511e-02],\n",
" [ 8.75765839e-01, 8.94880130e-02, 3.47461479e-02],\n",
" [ 8.46908157e-01, 1.42190177e-01, 1.09016662e-02],\n",
" [ 8.07768520e-01, 1.77773939e-01, 1.44575408e-02],\n",
" [ 8.54552240e-01, 8.97543407e-02, 5.56934189e-02],\n",
" [ 9.65419251e-01, 1.14464175e-02, 2.31343318e-02],\n",
" [ 9.46602696e-01, 8.51320169e-03, 4.48841019e-02],\n",
" [ 8.02826805e-01, 1.78629207e-01, 1.85439881e-02],\n",
" [ 8.32257392e-01, 1.44697291e-01, 2.30453165e-02],\n",
" [ 8.64274530e-01, 6.87165294e-02, 6.70089405e-02],\n",
" [ 8.02826805e-01, 1.78629207e-01, 1.85439881e-02],\n",
" [ 7.83804870e-01, 2.10802074e-01, 5.39305589e-03],\n",
" [ 8.83578139e-01, 8.91540136e-02, 2.72678469e-02],\n",
" [ 9.11942320e-01, 6.77418370e-02, 2.03158428e-02],\n",
" [ 5.48424664e-01, 4.43106994e-01, 8.46834185e-03],\n",
" [ 8.56145337e-01, 1.38772958e-01, 5.08170440e-03],\n",
" [ 9.11942320e-01, 6.77418370e-02, 2.03158428e-02],\n",
" [ 9.49486589e-01, 2.85752663e-02, 2.19381451e-02],\n",
" [ 7.69594524e-01, 2.15588498e-01, 1.48169774e-02],\n",
" [ 9.49486589e-01, 2.85752663e-02, 2.19381451e-02],\n",
" [ 8.50418487e-01, 1.41119103e-01, 8.46240963e-03],\n",
" [ 9.23125527e-01, 3.89138100e-02, 3.79606633e-02],\n",
" [ 8.63440513e-01, 1.14319744e-01, 2.22397430e-02],\n",
" [ 8.72446141e-22, 2.06253776e-01, 7.93746224e-01],\n",
" [ 1.20934054e-13, 2.81057482e-01, 7.18942518e-01],\n",
" [ 1.15300958e-21, 2.52869938e-01, 7.47130062e-01],\n",
" [ 1.42866352e-12, 8.25492553e-01, 1.74507447e-01],\n",
" [ 5.89709250e-20, 4.24870776e-01, 5.75129224e-01],\n",
" [ 4.50765015e-09, 7.04165933e-01, 2.95834062e-01],\n",
" [ 4.96959832e-11, 2.61017276e-01, 7.38982724e-01],\n",
" [ 2.69053427e-03, 9.47686199e-01, 4.96232672e-02],\n",
" [ 4.47639838e-20, 3.69771096e-01, 6.30228904e-01],\n",
" [ 1.41418505e-03, 8.87461330e-01, 1.11124485e-01],\n",
" [ 1.63611250e-09, 9.33261505e-01, 6.67384936e-02],\n",
" [ 2.78261580e-09, 5.49481304e-01, 4.50518694e-01],\n",
" [ 1.52052633e-20, 6.57200887e-01, 3.42799113e-01],\n",
" [ 2.83933702e-13, 5.04830477e-01, 4.95169523e-01],\n",
" [ 1.88638798e-06, 7.21065096e-01, 2.78933018e-01],\n",
" [ 5.78618140e-19, 2.72309750e-01, 7.27690250e-01],\n",
" [ 3.51070102e-05, 6.90184309e-01, 3.09780583e-01],\n",
" [ 1.10229617e-11, 6.84441718e-01, 3.15558282e-01],\n",
" [ 3.02272560e-23, 5.98870805e-01, 4.01129195e-01],\n",
" [ 1.84558320e-11, 7.86340742e-01, 2.13659258e-01],\n",
" [ 9.27201624e-07, 4.57288512e-01, 5.42710561e-01],\n",
" [ 1.60661034e-14, 5.39203926e-01, 4.60796074e-01],\n",
" [ 6.11904144e-21, 5.46353327e-01, 4.53646673e-01],\n",
" [ 1.60661034e-14, 5.39203926e-01, 4.60796074e-01],\n",
" [ 2.30781136e-17, 4.10616104e-01, 5.89383896e-01],\n",
" [ 7.66684770e-19, 3.29091849e-01, 6.70908151e-01],\n",
" [ 5.29672579e-24, 3.80394515e-01, 6.19605485e-01],\n",
" [ 3.39743786e-20, 3.14530076e-01, 6.85469924e-01],\n",
" [ 6.60137150e-12, 5.44610769e-01, 4.55389231e-01],\n",
" [ 1.42309095e-11, 7.40170024e-01, 2.59829976e-01],\n",
" [ 2.40955232e-11, 8.24053957e-01, 1.75946043e-01],\n",
" [ 2.40955232e-11, 8.24053957e-01, 1.75946043e-01],\n",
" [ 1.10229617e-11, 6.84441718e-01, 3.15558282e-01],\n",
" [ 2.10446283e-14, 6.04311053e-01, 3.95688947e-01],\n",
" [ 1.79354147e-02, 7.62565871e-01, 2.19498715e-01],\n",
" [ 1.24863700e-05, 3.12007700e-01, 6.87979814e-01],\n",
" [ 5.78618140e-19, 2.72309750e-01, 7.27690250e-01],\n",
" [ 2.21823877e-23, 5.68910927e-01, 4.31089073e-01],\n",
" [ 3.51070102e-05, 6.90184309e-01, 3.09780583e-01],\n",
" [ 4.12997927e-10, 8.19852479e-01, 1.80147520e-01],\n",
" [ 7.20885062e-09, 8.12294073e-01, 1.87705920e-01],\n",
" [ 5.05527210e-12, 4.64632073e-01, 5.35367927e-01],\n",
" [ 6.27749695e-13, 7.01424111e-01, 2.98575889e-01],\n",
" [ 7.33159320e-06, 9.37593390e-01, 6.23992788e-02],\n",
" [ 5.69236336e-09, 7.64125371e-01, 2.35874623e-01],\n",
" [ 1.51678040e-06, 6.43404602e-01, 3.56593881e-01],\n",
" [ 8.21751509e-08, 6.77081726e-01, 3.22918191e-01],\n",
" [ 1.22391545e-14, 4.68960435e-01, 5.31039565e-01],\n",
" [ 9.83269276e-05, 9.19490903e-01, 8.04107700e-02],\n",
" [ 4.50765015e-09, 7.04165933e-01, 2.95834062e-01],\n",
" [ 4.96959832e-11, 2.61017276e-01, 7.38982724e-01],\n",
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" [ 8.07768520e-01, 1.77773939e-01, 1.44575408e-02],\n",
" [ 8.54552240e-01, 8.97543407e-02, 5.56934189e-02],\n",
" [ 9.65419251e-01, 1.14464175e-02, 2.31343318e-02],\n",
" [ 9.46602696e-01, 8.51320169e-03, 4.48841019e-02],\n",
" [ 8.02826805e-01, 1.78629207e-01, 1.85439881e-02],\n",
" [ 8.32257392e-01, 1.44697291e-01, 2.30453165e-02],\n",
" [ 8.64274530e-01, 6.87165294e-02, 6.70089405e-02],\n",
" [ 8.02826805e-01, 1.78629207e-01, 1.85439881e-02],\n",
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" [ 9.11942320e-01, 6.77418370e-02, 2.03158428e-02],\n",
" [ 5.48424664e-01, 4.43106994e-01, 8.46834185e-03],\n",
" [ 8.56145337e-01, 1.38772958e-01, 5.08170440e-03],\n",
" [ 9.11942320e-01, 6.77418370e-02, 2.03158428e-02],\n",
" [ 9.49486589e-01, 2.85752663e-02, 2.19381451e-02],\n",
" [ 7.69594524e-01, 2.15588498e-01, 1.48169774e-02],\n",
" [ 9.49486589e-01, 2.85752663e-02, 2.19381451e-02],\n",
" [ 8.50418487e-01, 1.41119103e-01, 8.46240963e-03],\n",
" [ 9.23125527e-01, 3.89138100e-02, 3.79606633e-02],\n",
" [ 8.63440513e-01, 1.14319744e-01, 2.22397430e-02],\n",
" [ 8.72446141e-22, 2.06253776e-01, 7.93746224e-01],\n",
" [ 1.20934054e-13, 2.81057482e-01, 7.18942518e-01],\n",
" [ 1.15300958e-21, 2.52869938e-01, 7.47130062e-01],\n",
" [ 1.42866352e-12, 8.25492553e-01, 1.74507447e-01],\n",
" [ 5.89709250e-20, 4.24870776e-01, 5.75129224e-01],\n",
" [ 4.50765015e-09, 7.04165933e-01, 2.95834062e-01],\n",
" [ 4.96959832e-11, 2.61017276e-01, 7.38982724e-01],\n",
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" [ 4.47639838e-20, 3.69771096e-01, 6.30228904e-01],\n",
" [ 1.41418505e-03, 8.87461330e-01, 1.11124485e-01],\n",
" [ 1.63611250e-09, 9.33261505e-01, 6.67384936e-02],\n",
" [ 2.78261580e-09, 5.49481304e-01, 4.50518694e-01],\n",
" [ 1.52052633e-20, 6.57200887e-01, 3.42799113e-01],\n",
" [ 2.83933702e-13, 5.04830477e-01, 4.95169523e-01],\n",
" [ 1.88638798e-06, 7.21065096e-01, 2.78933018e-01],\n",
" [ 5.78618140e-19, 2.72309750e-01, 7.27690250e-01],\n",
" [ 3.51070102e-05, 6.90184309e-01, 3.09780583e-01],\n",
" [ 1.10229617e-11, 6.84441718e-01, 3.15558282e-01],\n",
" [ 3.02272560e-23, 5.98870805e-01, 4.01129195e-01],\n",
" [ 1.84558320e-11, 7.86340742e-01, 2.13659258e-01],\n",
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" [ 1.60661034e-14, 5.39203926e-01, 4.60796074e-01],\n",
" [ 6.11904144e-21, 5.46353327e-01, 4.53646673e-01],\n",
" [ 1.60661034e-14, 5.39203926e-01, 4.60796074e-01],\n",
" [ 2.30781136e-17, 4.10616104e-01, 5.89383896e-01],\n",
" [ 7.66684770e-19, 3.29091849e-01, 6.70908151e-01],\n",
" [ 5.29672579e-24, 3.80394515e-01, 6.19605485e-01],\n",
" [ 3.39743786e-20, 3.14530076e-01, 6.85469924e-01],\n",
" [ 6.60137150e-12, 5.44610769e-01, 4.55389231e-01],\n",
" [ 1.42309095e-11, 7.40170024e-01, 2.59829976e-01],\n",
" [ 2.40955232e-11, 8.24053957e-01, 1.75946043e-01],\n",
" [ 2.40955232e-11, 8.24053957e-01, 1.75946043e-01],\n",
" [ 1.10229617e-11, 6.84441718e-01, 3.15558282e-01],\n",
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" [ 1.79354147e-02, 7.62565871e-01, 2.19498715e-01],\n",
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" [ 5.78618140e-19, 2.72309750e-01, 7.27690250e-01],\n",
" [ 2.21823877e-23, 5.68910927e-01, 4.31089073e-01],\n",
" [ 3.51070102e-05, 6.90184309e-01, 3.09780583e-01],\n",
" [ 4.12997927e-10, 8.19852479e-01, 1.80147520e-01],\n",
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" [ 5.05527210e-12, 4.64632073e-01, 5.35367927e-01],\n",
" [ 6.27749695e-13, 7.01424111e-01, 2.98575889e-01],\n",
" [ 7.33159320e-06, 9.37593390e-01, 6.23992788e-02],\n",
" [ 5.69236336e-09, 7.64125371e-01, 2.35874623e-01],\n",
" [ 1.51678040e-06, 6.43404602e-01, 3.56593881e-01],\n",
" [ 8.21751509e-08, 6.77081726e-01, 3.22918191e-01],\n",
" [ 1.22391545e-14, 4.68960435e-01, 5.31039565e-01],\n",
" [ 9.83269276e-05, 9.19490903e-01, 8.04107700e-02],\n",
" [ 4.50765015e-09, 7.04165933e-01, 2.95834062e-01],\n",
" [ 4.96959832e-11, 2.61017276e-01, 7.38982724e-01],\n",
" [ 1.10229617e-11, 6.84441718e-01, 3.15558282e-01],\n",
" [ 1.41207875e-25, 2.82760602e-01, 7.17239398e-01],\n",
" [ 5.29862838e-16, 4.37505938e-01, 5.62494062e-01],\n",
" [ 1.74519274e-17, 3.47307593e-01, 6.52692407e-01],\n",
" [ 2.94210680e-32, 2.74531043e-01, 7.25468957e-01],\n",
" [ 4.33321990e-02, 9.07896923e-01, 4.87708776e-02],\n",
" [ 1.77959112e-29, 3.16490053e-01, 6.83509947e-01],\n",
" [ 2.49807853e-26, 4.72571286e-01, 5.27428714e-01],\n",
" [ 1.31170531e-19, 7.81040222e-02, 9.21895978e-01],\n",
" [ 5.18426688e-15, 2.60338725e-01, 7.39661275e-01],\n",
" [ 7.78730076e-20, 4.77984800e-01, 5.22015200e-01],\n",
" [ 1.51803800e-21, 3.03099024e-01, 6.96900976e-01],\n",
" [ 8.20655756e-13, 7.50427933e-01, 2.49572067e-01],\n",
" [ 1.96667857e-10, 6.61484294e-01, 3.38515706e-01],\n",
" [ 1.20934054e-13, 2.81057482e-01, 7.18942518e-01],\n",
" [ 1.74519274e-17, 3.47307593e-01, 6.52692407e-01],\n",
" [ 6.86592081e-24, 4.29391007e-02, 9.57060899e-01],\n",
" [ 1.80977068e-38, 4.09142590e-01, 5.90857410e-01],\n",
" [ 1.52052633e-20, 6.57200887e-01, 3.42799113e-01],\n",
" [ 1.93902883e-20, 2.12184620e-01, 7.87815380e-01],\n",
" [ 1.02761402e-07, 7.45542262e-01, 2.54457635e-01],\n",
" [ 4.93494528e-36, 3.50087883e-01, 6.49912117e-01],\n",
" [ 1.76431563e-18, 5.03684018e-01, 4.96315982e-01],\n",
" [ 1.66173494e-16, 1.90749933e-01, 8.09250067e-01],\n",
" [ 1.80239853e-24, 1.98855452e-01, 8.01144548e-01],\n",
" [ 6.97994415e-16, 5.04278647e-01, 4.95721353e-01],\n",
" [ 5.05527210e-12, 4.64632073e-01, 5.35367927e-01],\n",
" [ 1.33551699e-18, 4.47050183e-01, 5.52949817e-01],\n",
" [ 6.46809022e-27, 2.79286483e-01, 7.20713517e-01],\n",
" [ 4.93239346e-32, 3.51497540e-01, 6.48502460e-01],\n",
" [ 1.47501942e-26, 4.32647066e-02, 9.56735293e-01],\n",
" [ 1.33551699e-18, 4.47050183e-01, 5.52949817e-01],\n",
" [ 3.04465797e-17, 4.73491230e-01, 5.26508770e-01],\n",
" [ 5.31861649e-17, 5.89057807e-01, 4.10942193e-01],\n",
" [ 1.36586407e-33, 2.74761475e-01, 7.25238525e-01],\n",
" [ 8.62336949e-10, 2.18205082e-01, 7.81794917e-01],\n",
" [ 6.96657826e-15, 3.25817855e-01, 6.74182145e-01],\n",
" [ 1.18608184e-10, 5.05307868e-01, 4.94692132e-01],\n",
" [ 1.15300958e-21, 2.52869938e-01, 7.47130062e-01],\n",
" [ 5.78618140e-19, 2.72309750e-01, 7.27690250e-01],\n",
" [ 1.15300958e-21, 2.52869938e-01, 7.47130062e-01],\n",
" [ 1.10229617e-11, 6.84441718e-01, 3.15558282e-01],\n",
" [ 4.34524375e-19, 2.20087641e-01, 7.79912359e-01],\n",
" [ 1.66173494e-16, 1.90749933e-01, 8.09250067e-01],\n",
" [ 3.39743786e-20, 3.14530076e-01, 6.85469924e-01],\n",
" [ 6.11904144e-21, 5.46353327e-01, 4.53646673e-01],\n",
" [ 1.74519274e-17, 3.47307593e-01, 6.52692407e-01],\n",
" [ 2.08841089e-08, 2.44263730e-01, 7.55736249e-01],\n",
" [ 2.78261580e-09, 5.49481304e-01, 4.50518694e-01]])"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# https://github.com/scikit-learn/scikit-learn/blob/14031f6/sklearn/linear_model/base.py#L343-L360\n",
"a / a.sum(axis=1).reshape((150, -1))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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