9/ml_9.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import FunctionTransformer\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import hinge_loss, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.964300</td>\n",
       "      <td>4.5957</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2.275300</td>\n",
       "      <td>3.8589</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2.978100</td>\n",
       "      <td>4.5651</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2.932000</td>\n",
       "      <td>3.5519</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3.577200</td>\n",
       "      <td>2.8560</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>4.015000</td>\n",
       "      <td>3.1937</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>3.381400</td>\n",
       "      <td>3.4291</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>3.911300</td>\n",
       "      <td>4.1761</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2.782200</td>\n",
       "      <td>4.0431</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2.551800</td>\n",
       "      <td>4.6162</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>3.369800</td>\n",
       "      <td>3.9101</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>3.104800</td>\n",
       "      <td>3.0709</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.918200</td>\n",
       "      <td>4.0534</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>2.263800</td>\n",
       "      <td>4.3706</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>2.655500</td>\n",
       "      <td>3.5008</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>3.185500</td>\n",
       "      <td>4.2888</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>3.657900</td>\n",
       "      <td>3.8692</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>3.911300</td>\n",
       "      <td>3.4291</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>3.600200</td>\n",
       "      <td>3.1221</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>3.035700</td>\n",
       "      <td>3.3165</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.584100</td>\n",
       "      <td>3.3575</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>2.010300</td>\n",
       "      <td>3.2039</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.952700</td>\n",
       "      <td>2.7843</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>2.275300</td>\n",
       "      <td>2.7127</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>2.309900</td>\n",
       "      <td>2.9584</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>2.828300</td>\n",
       "      <td>2.6309</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>3.047300</td>\n",
       "      <td>2.2931</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>2.482700</td>\n",
       "      <td>2.0373</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>2.505700</td>\n",
       "      <td>2.3853</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1.872100</td>\n",
       "      <td>2.0577</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>2.010300</td>\n",
       "      <td>2.3546</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1.226900</td>\n",
       "      <td>2.3239</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>1.895100</td>\n",
       "      <td>2.9174</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>1.561000</td>\n",
       "      <td>3.0709</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>1.549500</td>\n",
       "      <td>2.6923</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>1.687800</td>\n",
       "      <td>2.4057</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>1.491900</td>\n",
       "      <td>2.0271</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.962000</td>\n",
       "      <td>2.6820</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1.169300</td>\n",
       "      <td>2.9276</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.812200</td>\n",
       "      <td>2.9992</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.973500</td>\n",
       "      <td>3.3881</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>3.1937</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>1.319100</td>\n",
       "      <td>3.5109</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>2.229200</td>\n",
       "      <td>2.2010</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>2.448200</td>\n",
       "      <td>2.6411</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>2.793800</td>\n",
       "      <td>1.9656</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>2.091000</td>\n",
       "      <td>1.6177</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>2.540300</td>\n",
       "      <td>2.8867</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.904400</td>\n",
       "      <td>3.0198</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.766150</td>\n",
       "      <td>2.5899</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>0.086405</td>\n",
       "      <td>4.1045</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0       1    2\n",
       "0   1.964300  4.5957  1.0\n",
       "1   2.275300  3.8589  1.0\n",
       "2   2.978100  4.5651  1.0\n",
       "3   2.932000  3.5519  1.0\n",
       "4   3.577200  2.8560  1.0\n",
       "5   4.015000  3.1937  1.0\n",
       "6   3.381400  3.4291  1.0\n",
       "7   3.911300  4.1761  1.0\n",
       "8   2.782200  4.0431  1.0\n",
       "9   2.551800  4.6162  1.0\n",
       "10  3.369800  3.9101  1.0\n",
       "11  3.104800  3.0709  1.0\n",
       "12  1.918200  4.0534  1.0\n",
       "13  2.263800  4.3706  1.0\n",
       "14  2.655500  3.5008  1.0\n",
       "15  3.185500  4.2888  1.0\n",
       "16  3.657900  3.8692  1.0\n",
       "17  3.911300  3.4291  1.0\n",
       "18  3.600200  3.1221  1.0\n",
       "19  3.035700  3.3165  1.0\n",
       "20  1.584100  3.3575  0.0\n",
       "21  2.010300  3.2039  0.0\n",
       "22  1.952700  2.7843  0.0\n",
       "23  2.275300  2.7127  0.0\n",
       "24  2.309900  2.9584  0.0\n",
       "25  2.828300  2.6309  0.0\n",
       "26  3.047300  2.2931  0.0\n",
       "27  2.482700  2.0373  0.0\n",
       "28  2.505700  2.3853  0.0\n",
       "29  1.872100  2.0577  0.0\n",
       "30  2.010300  2.3546  0.0\n",
       "31  1.226900  2.3239  0.0\n",
       "32  1.895100  2.9174  0.0\n",
       "33  1.561000  3.0709  0.0\n",
       "34  1.549500  2.6923  0.0\n",
       "35  1.687800  2.4057  0.0\n",
       "36  1.491900  2.0271  0.0\n",
       "37  0.962000  2.6820  0.0\n",
       "38  1.169300  2.9276  0.0\n",
       "39  0.812200  2.9992  0.0\n",
       "40  0.973500  3.3881  0.0\n",
       "41  1.250000  3.1937  0.0\n",
       "42  1.319100  3.5109  0.0\n",
       "43  2.229200  2.2010  0.0\n",
       "44  2.448200  2.6411  0.0\n",
       "45  2.793800  1.9656  0.0\n",
       "46  2.091000  1.6177  0.0\n",
       "47  2.540300  2.8867  0.0\n",
       "48  0.904400  3.0198  0.0\n",
       "49  0.766150  2.5899  0.0\n",
       "50  0.086405  4.1045  1.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_table('ex6data1.txt', sep=',', header=None, dtype=np.float32)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = data.iloc[:,[0,1]].values\n",
    "Y_train = data.iloc[:,[2]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe48e619d68>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.axis([np.min(X_train[:,0])-0.1, np.max(X_train[:,0])+0.1, np.min(X_train[:,1])-0.1, np.max(X_train[:,1])+0.1])\n",
    "pos = np.where(Y_train[:,0] == 1)[0]\n",
    "neg = np.where(Y_train[:,0] == 0)[0]\n",
    "ax.plot(X_train[pos,0], X_train[pos,1], marker='.', color='#0F00FF', markersize=10, linestyle='') \n",
    "ax.plot(X_train[neg,0], X_train[neg,1], marker='.', color='#FF00AE', markersize=10, linestyle='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LinearKernel():\n",
    "    def __init__(self, K):\n",
    "        self.K = K\n",
    "    def __call__(self, inputs):\n",
    "        l1 = self.K.shape[0]\n",
    "        l2 = inputs.shape[0]\n",
    "        result = np.zeros((l1,l2))\n",
    "        for i in range(l1):\n",
    "            for j in range(l2):\n",
    "                result[i,j] = np.dot(self.K[i,:],inputs[j,:].T)\n",
    "        return result.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('linear_kernel',\n",
       "                 FunctionTransformer(accept_sparse=False, check_inverse=True,\n",
       "                                     func=<__main__.LinearKernel object at 0x7fe48e685128>,\n",
       "                                     inv_kw_args=None, inverse_func=None,\n",
       "                                     kw_args=None, validate=False)),\n",
       "                ('svm',\n",
       "                 SVC(C=0.01, break_ties=False, cache_size=200,\n",
       "                     class_weight=None, coef0=0.0,\n",
       "                     decision_function_shape='ovr', degree=3, gamma='scale',\n",
       "                     kernel='linear', max_iter=10000, probability=False,\n",
       "                     random_state=None, shrinking=True, tol=0.001,\n",
       "                     verbose=False))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Pipeline([('linear_kernel', FunctionTransformer(LinearKernel(K=X_train))),\n",
    "                  ('svm', SVC(C=0.01, kernel='linear', max_iter=10000))])\n",
    "\n",
    "model.fit(X_train, Y_train.flatten())  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.35425725,  1.67841772,  5.11043316,  1.94169616,  1.00000885,\n",
       "        2.82673959,  2.38761585,  5.62844577,  3.1638733 ,  4.48836377,\n",
       "        3.83083851,  0.79252865,  1.61915617,  3.21528779,  1.28174385,\n",
       "        4.64757944,  4.23186101,  3.35423876,  1.85209449,  1.41419748,\n",
       "       -1.10893237, -0.79910332, -2.18162818, -1.81113737, -0.99999828,\n",
       "       -1.05141252, -1.68033731, -3.48903041, -2.38760493, -4.5407572 ,\n",
       "       -3.3847595 , -4.90727218, -1.88148359, -2.02361181, -3.19721998,\n",
       "       -3.81747958, -5.3274633 , -4.3002732 , -3.17440651, -3.60783086,\n",
       "       -2.12960564, -2.2170666 , -1.12531591, -3.45307891, -1.71372285,\n",
       "       -3.13982151, -5.48100826, -0.79799955, -3.3769278 , -4.93792917,\n",
       "       -1.56676274])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred = model.decision_function(X_train)\n",
    "Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06229672435000859"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hinge_loss(Y_train, Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred_label = model.predict(X_train)\n",
    "Y_pred_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
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       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [1.]], dtype=float32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9803921568627451"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(Y_train, Y_pred_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe48eb824e0>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.axis([np.min(X_train[:,0])-0.1, np.max(X_train[:,0])+0.1, np.min(X_train[:,1])-0.1, np.max(X_train[:,1])+0.1])\n",
    "u = np.linspace(np.min(X_train[:,0])-0.1, np.max(X_train[:,0])+0.1, 100)\n",
    "v = np.linspace(np.min(X_train[:,1])-0.1, np.max(X_train[:,1])+0.1, 100)\n",
    "z = []\n",
    "for i in range(100):\n",
    "    for j in range(100):\n",
    "        z.append([u[i], v[j]])\n",
    "z = np.array(z)\n",
    "w = model.predict(z)\n",
    "model_pos = np.where(w == 1)[0]\n",
    "model_neg = np.where(w == 0)[0]\n",
    "ax.plot(z[model_pos,0], z[model_pos,1], marker='+', color='#C7C3FF', markersize=10, linestyle='')\n",
    "ax.plot(z[model_neg,0], z[model_neg,1], marker='+', color='#FFECFB', markersize=10, linestyle='')\n",
    "pos = np.where(Y_train[:,0] == 1)[0]\n",
    "neg = np.where(Y_train[:,0] == 0)[0]\n",
    "ax.plot(X_train[pos,0], X_train[pos,1], marker='.', color='#0F00FF', markersize=10, linestyle='') \n",
    "ax.plot(X_train[neg,0], X_train[neg,1], marker='.', color='#FF00AE', markersize=10, linestyle='')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.107143</td>\n",
       "      <td>0.603070</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.093318</td>\n",
       "      <td>0.649854</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.097926</td>\n",
       "      <td>0.705409</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.155530</td>\n",
       "      <td>0.784357</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.210829</td>\n",
       "      <td>0.866228</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>858</td>\n",
       "      <td>0.994240</td>\n",
       "      <td>0.516667</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>859</td>\n",
       "      <td>0.964286</td>\n",
       "      <td>0.472807</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>860</td>\n",
       "      <td>0.975806</td>\n",
       "      <td>0.439474</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>861</td>\n",
       "      <td>0.989631</td>\n",
       "      <td>0.425439</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>862</td>\n",
       "      <td>0.996544</td>\n",
       "      <td>0.414912</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>863 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1    2\n",
       "0    0.107143  0.603070  1.0\n",
       "1    0.093318  0.649854  1.0\n",
       "2    0.097926  0.705409  1.0\n",
       "3    0.155530  0.784357  1.0\n",
       "4    0.210829  0.866228  1.0\n",
       "..        ...       ...  ...\n",
       "858  0.994240  0.516667  1.0\n",
       "859  0.964286  0.472807  1.0\n",
       "860  0.975806  0.439474  1.0\n",
       "861  0.989631  0.425439  1.0\n",
       "862  0.996544  0.414912  1.0\n",
       "\n",
       "[863 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_table('ex6data2.txt', sep=',', header=None, dtype=np.float32)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = data.iloc[:,[0,1]].values\n",
    "Y_train = data.iloc[:,[2]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe48ebf5a90>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.axis([np.min(X_train[:,0])-0.1, np.max(X_train[:,0])+0.1, np.min(X_train[:,1])-0.1, np.max(X_train[:,1])+0.1])\n",
    "pos = np.where(Y_train[:,0] == 1)[0]\n",
    "neg = np.where(Y_train[:,0] == 0)[0]\n",
    "ax.plot(X_train[pos,0], X_train[pos,1], marker='.', color='#0F00FF', markersize=10, linestyle='') \n",
    "ax.plot(X_train[neg,0], X_train[neg,1], marker='.', color='#FF00AE', markersize=10, linestyle='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GaussianKernel():\n",
    "    def __init__(self, K, sigma):\n",
    "        self.K = K\n",
    "        self.sigma = sigma\n",
    "    def __call__(self, inputs):\n",
    "        l1 = self.K.shape[0]\n",
    "        l2 = inputs.shape[0]\n",
    "        result = np.zeros((l1,l2))\n",
    "        for i in range(l1):\n",
    "            for j in range(l2):\n",
    "                z = np.sum(np.power(self.K[i,:] - inputs[j,:], 2))\n",
    "                result[i,j] = np.exp(-z/(2*np.power(self.sigma,2)))\n",
    "        return result.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('gaussian_kernel',\n",
       "                 FunctionTransformer(accept_sparse=False, check_inverse=True,\n",
       "                                     func=<__main__.GaussianKernel object at 0x7fe48ecbe6a0>,\n",
       "                                     inv_kw_args=None, inverse_func=None,\n",
       "                                     kw_args=None, validate=False)),\n",
       "                ('svm',\n",
       "                 SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None,\n",
       "                     coef0=0.0, decision_function_shape='ovr', degree=3,\n",
       "                     gamma='scale', kernel='linear', max_iter=10000,\n",
       "                     probability=False, random_state=None, shrinking=True,\n",
       "                     tol=0.001, verbose=False))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Pipeline([('gaussian_kernel', FunctionTransformer(GaussianKernel(K=X_train, sigma=0.1))),\n",
    "                  ('svm', SVC(C=1., kernel='linear', max_iter=10000))])\n",
    "\n",
    "model.fit(X_train, Y_train.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.0130405 ,  3.99081578,  3.74951797,  2.96348401,  3.29844675,\n",
       "        2.2401751 ,  2.54182168,  2.14988433,  7.20286809,  8.68477127,\n",
       "        7.98356924,  3.682588  ,  7.09617474,  7.69970167,  7.55769703,\n",
       "        6.71827657,  1.85480469,  0.99991903,  7.92197273,  8.63401382,\n",
       "        1.5681827 ,  2.47921263,  2.52219653,  1.8743242 ,  1.63544556,\n",
       "        2.58681828,  2.31521407,  2.38641763,  2.28694074,  2.06907796,\n",
       "        1.46624381,  2.54246395,  6.99172234,  6.77019872,  6.24708741,\n",
       "        7.07819049,  6.65990022,  5.67464287,  3.79329203,  4.00813819,\n",
       "        3.88966222,  3.47719396,  3.93914942,  3.96675244,  4.07435084,\n",
       "        5.20305843,  5.21383069,  3.86404469,  3.12297627,  3.94640888,\n",
       "        1.61658411,  1.90187389,  2.2604177 ,  5.21452855,  6.52828432,\n",
       "        6.74415781,  6.09273776,  6.91893767,  7.01395359,  5.74550375,\n",
       "        5.2234965 ,  6.92651143,  6.48826908,  3.58149878,  3.45263963,\n",
       "        3.42476556,  2.95426326,  2.86698664,  2.97229353,  3.63351553,\n",
       "        4.25079168,  5.17280732,  4.8416066 ,  5.08444437,  2.73547141,\n",
       "        2.53098821,  2.48774946,  2.44092947,  2.35852577,  2.39603263,\n",
       "        2.04831457,  1.75526314,  2.50101439,  2.47352284,  2.29245849,\n",
       "        2.17351906,  2.55896609,  2.4996096 ,  2.32203787,  2.27249201,\n",
       "        2.1745677 ,  1.65477491,  2.16422932,  2.24057635,  2.67519155,\n",
       "        3.64695946,  4.48786661,  3.89320266,  6.55391909,  7.48493988,\n",
       "        4.69601308,  5.48626481,  6.07203365,  4.50413174,  3.94258413,\n",
       "        5.71332836,  5.88861252,  4.90017446,  2.24948088,  0.74266977,\n",
       "        2.54285899,  6.05725111,  6.55098403,  5.54490216,  4.00067283,\n",
       "        2.0601577 ,  3.55123059,  6.5478398 ,  6.87107227,  6.75785048,\n",
       "        7.57708457,  7.87508286,  7.29437654,  7.41282199,  4.02026802,\n",
       "        1.98681359,  1.89785862,  2.38232206,  2.58972272,  2.2613683 ,\n",
       "        2.05408972,  1.95787759,  2.49391893,  2.13517156,  2.46688101,\n",
       "        2.52597809,  2.03584955,  1.44485875,  2.04082002,  2.02747723,\n",
       "        2.63551632,  2.3180039 ,  2.49327526,  2.12703988,  4.86996091,\n",
       "        6.41881521,  8.08599247,  8.69631443,  2.91525903,  3.54352397,\n",
       "        1.55064788,  1.27358217,  1.46884471,  1.705925  ,  2.09978538,\n",
       "        2.16443425,  2.38190147,  1.85509401,  1.27812487,  0.9999844 ,\n",
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       "       -2.73056874, -1.61772406, -1.64067796, -1.84252976, -4.02262735,\n",
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       "       -0.5922317 , -0.81346958, -0.99975449, -1.26834885, -2.75670071,\n",
       "       -4.74979381, -0.43708393, -0.01850316,  1.34694561,  0.07572902,\n",
       "        0.99940535,  1.68935886,  2.93925261,  2.5072749 ,  1.38000879,\n",
       "        1.38000879,  2.86805376,  3.4658359 ,  4.34910721,  5.22690881,\n",
       "        4.47142013,  3.25909644,  3.82876843,  4.45608041,  3.82004651,\n",
       "        3.53877022,  3.7582332 ,  3.29424466,  1.44616927,  1.0000706 ,\n",
       "        3.61630038,  4.32483439,  4.61401823,  4.97475131,  5.15627485,\n",
       "        4.84559986,  4.46008054,  5.11173246,  5.38519909,  5.40546622,\n",
       "        5.20751064,  4.48075154,  2.54201588,  0.78183322,  2.70927235,\n",
       "        3.91483802,  3.61737593,  2.37547702,  1.00034565,  1.7668869 ,\n",
       "        2.13387018,  2.17971487,  1.5081749 ,  4.46698431,  5.39677088,\n",
       "        5.29715039,  4.97921915,  4.64935815])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred = model.decision_function(X_train)\n",
    "Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.027063269353211364"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hinge_loss(Y_train, Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.], dtype=float32)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred_label = model.predict(X_train)\n",
    "Y_pred_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9918887601390498"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(Y_train, Y_pred_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe48f5f2c88>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.axis([np.min(X_train[:,0])-0.1, np.max(X_train[:,0])+0.1, np.min(X_train[:,1])-0.1, np.max(X_train[:,1])+0.1])\n",
    "u = np.linspace(np.min(X_train[:,0])-0.1, np.max(X_train[:,0])+0.1, 100)\n",
    "v = np.linspace(np.min(X_train[:,1])-0.1, np.max(X_train[:,1])+0.1, 100)\n",
    "z = []\n",
    "for i in range(100):\n",
    "    for j in range(100):\n",
    "        z.append([u[i], v[j]])\n",
    "z = np.array(z)\n",
    "w = model.predict(z)\n",
    "model_pos = np.where(w == 1)[0]\n",
    "model_neg = np.where(w == 0)[0]\n",
    "ax.plot(z[model_pos,0], z[model_pos,1], marker='+', color='#C7C3FF', markersize=10, linestyle='')\n",
    "ax.plot(z[model_neg,0], z[model_neg,1], marker='+', color='#FFECFB', markersize=10, linestyle='')\n",
    "pos = np.where(Y_train[:,0] == 1)[0]\n",
    "neg = np.where(Y_train[:,0] == 0)[0]\n",
    "ax.plot(X_train[pos,0], X_train[pos,1], marker='.', color='#0F00FF', markersize=10, linestyle='') \n",
    "ax.plot(X_train[neg,0], X_train[neg,1], marker='.', color='#FF00AE', markersize=10, linestyle='')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-0.158986</td>\n",
       "      <td>0.423977</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-0.347926</td>\n",
       "      <td>0.470760</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-0.504608</td>\n",
       "      <td>0.353801</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.596774</td>\n",
       "      <td>0.114035</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-0.518433</td>\n",
       "      <td>-0.172515</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>406</td>\n",
       "      <td>0.005203</td>\n",
       "      <td>-0.544449</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>407</td>\n",
       "      <td>0.176352</td>\n",
       "      <td>-0.572454</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>408</td>\n",
       "      <td>0.127651</td>\n",
       "      <td>-0.340938</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>409</td>\n",
       "      <td>0.248682</td>\n",
       "      <td>-0.497502</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>410</td>\n",
       "      <td>-0.316899</td>\n",
       "      <td>-0.429413</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>411 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1    2\n",
       "0   -0.158986  0.423977  1.0\n",
       "1   -0.347926  0.470760  1.0\n",
       "2   -0.504608  0.353801  1.0\n",
       "3   -0.596774  0.114035  1.0\n",
       "4   -0.518433 -0.172515  1.0\n",
       "..        ...       ...  ...\n",
       "406  0.005203 -0.544449  1.0\n",
       "407  0.176352 -0.572454  0.0\n",
       "408  0.127651 -0.340938  0.0\n",
       "409  0.248682 -0.497502  0.0\n",
       "410 -0.316899 -0.429413  0.0\n",
       "\n",
       "[411 rows x 3 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_table('ex6data3.txt', sep=',', header=None, dtype=np.float32)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = data.iloc[:,[0,1]].values\n",
    "Y_train = data.iloc[:,[2]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe48adcea20>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.axis([np.min(X_train[:,0])-0.1, np.max(X_train[:,0])+0.1, np.min(X_train[:,1])-0.1, np.max(X_train[:,1])+0.1])\n",
    "pos = np.where(Y_train[:,0] == 1)[0]\n",
    "neg = np.where(Y_train[:,0] == 0)[0]\n",
    "ax.plot(X_train[pos,0], X_train[pos,1], marker='.', color='#0F00FF', markersize=10, linestyle='') \n",
    "ax.plot(X_train[neg,0], X_train[neg,1], marker='.', color='#FF00AE', markersize=10, linestyle='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('gaussian_kernel',\n",
       "                 FunctionTransformer(accept_sparse=False, check_inverse=True,\n",
       "                                     func=<__main__.GaussianKernel object at 0x7fe48ae04828>,\n",
       "                                     inv_kw_args=None, inverse_func=None,\n",
       "                                     kw_args=None, validate=False)),\n",
       "                ('svm',\n",
       "                 SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None,\n",
       "                     coef0=0.0, decision_function_shape='ovr', degree=3,\n",
       "                     gamma='scale', kernel='linear', max_iter=10000,\n",
       "                     probability=False, random_state=None, shrinking=True,\n",
       "                     tol=0.001, verbose=False))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Pipeline([('gaussian_kernel', FunctionTransformer(GaussianKernel(K=X_train, sigma=0.1))),\n",
    "                  ('svm', SVC(C=1., kernel='linear', max_iter=10000))])\n",
    "\n",
    "model.fit(X_train, Y_train.flatten()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.96728236,  1.14587519,  1.05418783,  1.00003975,  0.99983229,\n",
       "        0.46437391,  1.00864221,  0.40802735,  1.86000395,  0.99986795,\n",
       "        1.6177361 ,  1.17607192,  1.01312164,  1.87950414,  1.30118776,\n",
       "        1.14170489,  1.56219068,  1.06839114,  1.83053588,  1.58359857,\n",
       "       -1.20205412, -1.89412143, -2.36037127, -2.35757266, -1.43970564,\n",
       "       -1.06747   , -1.00471702, -1.14912365, -1.14442803, -1.02154044,\n",
       "       -1.02762016, -1.88680352, -1.09231075, -1.00321477, -1.13918778,\n",
       "       -1.04857207, -1.79040299, -1.21376388, -1.06603527,  0.61540273,\n",
       "        0.34033632, -0.00865318, -1.39474139, -1.36536369,  1.43674032,\n",
       "       -0.04637711, -1.1003293 , -1.14334871, -1.07879786, -1.32209568,\n",
       "       -2.05416667, -2.85917055, -2.27044971, -2.33692743, -1.42857106,\n",
       "       -1.18741092, -1.01544827, -1.00007645, -0.99992515, -1.10355979,\n",
       "       -1.10351097, -1.05744878, -1.10387916, -1.00001612, -0.99997233,\n",
       "       -1.24548471, -1.07939736, -1.02045974, -1.00014153, -1.00023446,\n",
       "       -1.12197069, -1.18500321, -1.50695996, -1.75684133, -1.17948428,\n",
       "       -1.00011067, -1.08183815, -1.02058739, -1.07070749, -1.18801321,\n",
       "       -1.16063782,  1.65081568,  1.87771328,  1.90640591,  1.02234857,\n",
       "        0.99956071,  1.00020022,  1.12700231,  1.55685011,  1.61535367,\n",
       "        1.55105936,  1.05079713,  1.65368043,  1.56318651,  1.81984321,\n",
       "        2.35933066,  2.0795059 ,  2.26571686,  1.17788743,  1.09848601,\n",
       "        1.38201449,  1.43852296,  1.3278618 ,  1.12150959,  1.17338097,\n",
       "        1.28937949,  1.04138967,  0.99975195,  1.42604022,  1.44027485,\n",
       "        1.17820736,  1.00006509,  2.08966803,  1.90506885,  1.54886541,\n",
       "        0.61541105, -0.65396349, -1.47634012, -0.43876967, -1.52419737,\n",
       "       -2.24551618,  1.0001111 ,  1.2754445 ,  1.29736071,  1.18896582,\n",
       "        1.02969232,  1.25248575,  1.01159431,  0.9999167 ,  0.99959299,\n",
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       "        1.00002313, -1.02584627, -1.32663254,  1.1817679 ,  1.54654609,\n",
       "        1.10918414, -1.10928939, -1.48632981,  1.75982915, -1.13636954,\n",
       "        0.95268215, -1.52567179, -1.2383144 ,  1.00019038,  1.62039049,\n",
       "        1.21900309, -0.97881489, -1.39598152, -1.0161484 , -1.06908819,\n",
       "       -0.26912119])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "Y_pred = model.decision_function(X_train)\n",
    "Y_pred"
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  {
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   "execution_count": 29,
   "metadata": {},
   "outputs": [
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  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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   "source": [
    "Y_pred_label = model.predict(X_train)\n",
    "Y_pred_label"
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  {
   "cell_type": "code",
   "execution_count": 31,
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       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.]], dtype=float32)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9513381995133819"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(Y_train, Y_pred_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe48adcb3c8>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.axis([np.min(X_train[:,0])-0.1, np.max(X_train[:,0])+0.1, np.min(X_train[:,1])-0.1, np.max(X_train[:,1])+0.1])\n",
    "u = np.linspace(np.min(X_train[:,0])-0.1, np.max(X_train[:,0])+0.1, 100)\n",
    "v = np.linspace(np.min(X_train[:,1])-0.1, np.max(X_train[:,1])+0.1, 100)\n",
    "z = []\n",
    "for i in range(100):\n",
    "    for j in range(100):\n",
    "        z.append([u[i], v[j]])\n",
    "z = np.array(z)\n",
    "w = model.predict(z)\n",
    "model_pos = np.where(w == 1)[0]\n",
    "model_neg = np.where(w == 0)[0]\n",
    "ax.plot(z[model_pos,0], z[model_pos,1], marker='+', color='#C7C3FF', markersize=10, linestyle='')\n",
    "ax.plot(z[model_neg,0], z[model_neg,1], marker='+', color='#FFECFB', markersize=10, linestyle='')\n",
    "pos = np.where(Y_train[:,0] == 1)[0]\n",
    "neg = np.where(Y_train[:,0] == 0)[0]\n",
    "ax.plot(X_train[pos,0], X_train[pos,1], marker='.', color='#0F00FF', markersize=10, linestyle='') \n",
    "ax.plot(X_train[neg,0], X_train[neg,1], marker='.', color='#FF00AE', markersize=10, linestyle='')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}