SVM/04-SVM-in-scikit-learn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## scikit-learn中的SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "\n",
    "# 只取前两个特征\n",
    "X = X[y<2,:2]\n",
    "y = y[y<2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[y==0,0], X[y==0,1], color='red')\n",
    "plt.scatter(X[y==1,0], X[y==1,1], color='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "standardScaler = StandardScaler()\n",
    "standardScaler.fit(X)\n",
    "X_standard = standardScaler.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1000000000.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "svc = LinearSVC(C=1e9)\n",
    "svc.fit(X_standard, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_decision_boundary(model, axis):\n",
    "    \n",
    "    x0, x1 = np.meshgrid(\n",
    "        np.linspace(axis[0], axis[1], int((axis[1]-axis[0])*100)).reshape(-1, 1),\n",
    "        np.linspace(axis[2], axis[3], int((axis[3]-axis[2])*100)).reshape(-1, 1),\n",
    "    )\n",
    "    X_new = np.c_[x0.ravel(), x1.ravel()]\n",
    "\n",
    "    y_predict = model.predict(X_new)\n",
    "    zz = y_predict.reshape(x0.shape)\n",
    "\n",
    "    from matplotlib.colors import ListedColormap\n",
    "    custom_cmap = ListedColormap(['#EF9A9A','#FFF59D','#90CAF9'])\n",
    "    \n",
    "    plt.contourf(x0, x1, zz, linewidth=5, cmap=custom_cmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\MachineLearning\\lib\\site-packages\\matplotlib\\contour.py:1000: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(svc, axis=[-3, 3, -3, 3])\n",
    "plt.scatter(X_standard[y==0,0], X_standard[y==0,1])\n",
    "plt.scatter(X_standard[y==1,0], X_standard[y==1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=0.01, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc2 = LinearSVC(C=0.01)\n",
    "svc2.fit(X_standard, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\MachineLearning\\lib\\site-packages\\matplotlib\\contour.py:1000: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
      "image/png": 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vp1k3RquIW1Tczup5LMbxLdYXZ76+rSkPLOwZihNDdKnNbVRrX1fX06pnulXELSpuZ/U8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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(svc2, axis=[-3, 3, -3, 3])\n",
    "plt.scatter(X_standard[y==0,0], X_standard[y==0,1])\n",
    "plt.scatter(X_standard[y==1,0], X_standard[y==1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4.03243224, -2.49297251]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.953661])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_svc_decision_boundary(model, axis):\n",
    "    \n",
    "    x0, x1 = np.meshgrid(\n",
    "        np.linspace(axis[0], axis[1], int((axis[1]-axis[0])*100)).reshape(-1, 1),\n",
    "        np.linspace(axis[2], axis[3], int((axis[3]-axis[2])*100)).reshape(-1, 1),\n",
    "    )\n",
    "    X_new = np.c_[x0.ravel(), x1.ravel()]\n",
    "\n",
    "    y_predict = model.predict(X_new)\n",
    "    zz = y_predict.reshape(x0.shape)\n",
    "\n",
    "    from matplotlib.colors import ListedColormap\n",
    "    custom_cmap = ListedColormap(['#EF9A9A','#FFF59D','#90CAF9'])\n",
    "    \n",
    "    plt.contourf(x0, x1, zz, linewidth=5, cmap=custom_cmap)\n",
    "    \n",
    "    w = model.coef_[0]\n",
    "    b = model.intercept_[0]\n",
    "    \n",
    "    # w0*x0 + w1*x1 + b = 0\n",
    "    # => x1 = -w0/w1 * x0 - b/w1\n",
    "    plot_x = np.linspace(axis[0], axis[1], 200)\n",
    "    up_y = -w[0]/w[1] * plot_x - b/w[1] + 1/w[1]\n",
    "    down_y = -w[0]/w[1] * plot_x - b/w[1] - 1/w[1]\n",
    "    \n",
    "    up_index = (up_y >= axis[2]) & (up_y <= axis[3])\n",
    "    down_index = (down_y >= axis[2]) & (down_y <= axis[3])\n",
    "    plt.plot(plot_x[up_index], up_y[up_index], color='black')\n",
    "    plt.plot(plot_x[down_index], down_y[down_index], color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\MachineLearning\\lib\\site-packages\\matplotlib\\contour.py:1000: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
      "image/png": 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C/cmtGCFsdvvSuzolmfNLhhP3xy8Mb9eONk89ZXguq5tPjw5oQHJyEr0jllOk1AOm5xDu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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_svc_decision_boundary(svc, axis=[-3, 3, -3, 3])\n",
    "plt.scatter(X_standard[y==0,0], X_standard[y==0,1])\n",
    "plt.scatter(X_standard[y==1,0], X_standard[y==1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\MachineLearning\\lib\\site-packages\\matplotlib\\contour.py:1000: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
      "image/png": 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TTZanMZrqYlvpl5vYhpX1mtgnE/tjh9IcpTfe+za3bR5IhZy896BqZiq3bR4IIOHuMQf2\nfMfrwx9iz9YNtLizK/c9+RKVzr+gTNuSi6cuc+TECXolJ/PQlCk0qlWLz59+modaty7ViF1TXWwr\n/XIT27CyXhP7ZGJ/oq20p15a7Uw6E+oFKuRk0mpnkumliSjRWrPqndcY160lB1N2E580h24jk8sc\n6iBH7K6yatcuek+fzr7Dh0ns0IEnylhjNNXFttIvN7ENK+s1sU8m9ieaynKBNC5zb6keF+5y7NB/\nmZ/Ui60rF9O45S10GTaVC2vVi3i7EuwucCo7m6RFi5i4bBkNa9RgSWIi10dwU2lTXWwr/XIT27Cy\nXhP7ZGJ/oiGSDxsdq1SPqpmpRT4u3G3Llx8wf3QvsjKO8qeBz3Njp76UK2fmp9Hpn+nA275vH7eN\nGcOEjz6ie6tWfPH00xGFOpjrYlvpl5vYhpX1mtgnE/tjWqSfIF11xVBOx1Q667HTMZVYdYW0ztzq\n5InjvDGmD1MH3UfVGnUZNHMNrR/obyzUQY7YHZObm8trn37K8AULOL9iRaPTGE11sa30y01sw8p6\nTeyTif0xxdRIgIILpNKK8YbQGuOtPZ7gzoRhlD8v1vjrSLA7YN/hw/SfOZNPtm0rdY3RKlMz0IfW\nOhpx8DWrdJqVmTn8nB1D7fI5NKt0ukzbMbFPJvYnUqbnvOyqd78tQS61yrIzVWO0SoLdZqE1xhe6\nduWhUtQYvciuKqNXeHV4l9Qqy+7Anu+YPaInP21ZH3GN0SoJdpscOXGCIW+8wfw1a7iuYUMmu3Qa\no2kl1RRDT6+Ee47XeTXQC5RUq5RgL5rWmjXvTuPdl56k/HmxxCfNodnt9rxXEuw2WP3dd/RKTmZv\nWlpENUYvsqvK6GZeD3WQWmVpHUv7Ja/G+OWHRmuMVgUjXRxyKjubMe+9x/iPPjpTY2x56aVOL8tW\ndlUZ3cgPgV5AapXWbf3yQ+Yl9SLr+BHufXwcN/2ln9HGixVSd4ySHfv2cfuYMby8dOmZGmPQQh3s\nqzK6jZ9CHaRWaUVBjfG1QX+mavU6DJq5hjadH7U91EGO2I0rqDGOeOcdqsTGuuqm0k6wq8roFn6d\nxii1ypL9uGU9s4c/yKHUH7il20Da9RoRlRqjVRLsBu1PT6ffjBlnaowTevSg9gXRvfrtBVZqiqbq\nmU7y+8x0u2qVXpKTnc3y6WNZlvysLTVGqyTYDVm0aROPz55N1qlTEdcYTYyntbINu0bYmhoh7FbR\nPEr3Wnfca+sNp6T9OZCym9nDe/LTlnU0b9uZ+558mcpxFzq84jwS7BE6mpnJ4PnzjdUYTXS6rWzD\nrhG2fu+oR/Mo3Wvdca+tN5zi9kdrzayNR1n40pOUr3AePZJmc93tHR1e7dnk4mkEVn/3HTeMGsWb\na9fyZPv2LE1MjLibbmI8rZVt2DXC1tQIYTeK9gVSr43k9dp6wylqfw4fzeCVob1449k+NLzm9yTO\n3eS6UAc5Yi+TaNYYTXS6rWzDrhG2fuyo23WB1Gvdca+tN5zC6/5w12l6vpfFkSztWI3RKgn2UtqR\nf1Ppb1NS6H7DDSR16kScwZtKm+h0W9mGXSNs/dZRt/MCqde6415bbzgF+5NxSjNoWRaTN53m2trl\nWPS3S1jX+VGnl1cid/5140Jaa6Z88gk3JyWx9/Bh5vTpw4QePYyGOpjpdFvZhl0jbP3SUb+wa4zt\n3XSvdce9tt5wVl0xlNX7y9NscgZTNp3myf93HqseqcHh1s84vbSw5Ijdgv3p6fSfOZOPt27l9quv\nZmJ8fNRqjCY63Va2YdcIWz901J2qMXqtO+619ZYkJzubCYt3sGxaOhfFlePj+Eo0v/JivvTI/iit\nCx+1RV+zhg31p0O98bd4aI1xdKdOvp/GKM7mt0+QivAK1xjvTxwf9WmMVg1oGbtJa90i3POMHLEr\npdoC44EYYKrWeqyJ7TrpaGYmQ+bPZ96aNTS7+GImP/wwl9ep4/SyLDPVHQ+3nYTUaqw/+esn7FrG\nnmRK/TQj++AkCXR3a705kWtTZqF0DlrF8G2DHnx+zT9KtY3CHfWVjZ86U2OMKV+BHqNf57o7OkVp\nD6Ir4mBXSsUAk4DbgVRgg1LqPa31tki37ZTV331H7+RkUtPSeLJ9e55s395T0xhNdcfDbefXUP/1\nXzDrT8aSkFrN0+Euoe5urTcn0mTP9DM/dUrn0GTPdADL4V64o551cA//mvEwH+w4SePrb86bxli7\nfjSWbwsTadUS2K21/gFAKTUfuAfwXLCH1hgvrl7ds9MYTc03D7edwqGeR511BO8lEujecG3KrCJ+\n6vIetxrsoR310Brjs+1rU/Hpxa6tMVplItjrASkhX6cCvy/8JKVUApAAUL9aNQMva9bO/ftJmDo1\najVGO5nqjvuxg14cCXXvULroqmxxjxclLnPvOTXGj3tU5qpaWUzweKiDmWAv6kriOVdktdZTgCmQ\nd/HUwOsaobU+c1PpKrGxzO7dm/bNmjm9rIiY6o77rYNeHAl1b9EqpsgQ18r6AcdnB6vxyLyf2J2W\nyxN/OI/Rt8QSW15x1KOd+8JM/NWUCjQI+bo+sM/AdqNuf3o6HSdMYPD8+dxwxRWsGj7c86EO5rrj\n4bbTMvYkRXXh8x53v5lN4yXUPejbBj2K+KnLezycnOxslk5N4rZ//URWDnwSX5lxd1QktrzydOe+\nMBNH7BuAy5VSlwB7gQeALga2G1XvbdrEgPwa4/NdutCzdWvf1BhNdcfDbWdK/TTPtmIk0L2r4Dx6\naVsxhWuMg7vdzHWpL6M93rkvSsTBrrXOVkr1Az4ir+6YrLV27VRqp2uMVmqIJqqKpuab/zuzAv/N\njkED/82O4d+ZFc7a7r0XZJKSVv7MWu+94NzXdNPY3jU1q9BqZxKPfvhkRH+Yw9XtTI2vNbEdr43S\ntbLe/dVa0ujAcuIy93K8Yl32V2tZ7Pa01qxdNJ2FLz1xVo1xHzC9sT//gjfS4dNaLwYWm9hWNK3J\nv6l0aloag9q1Y3CHDrbWGK3UEN005jbcaF8v7c+FXWNYsqyikbGy4ep2psbXmtiO10bpWllvafbp\n+OEDzE/qxZYvPuDyFm3oOnyap2uMVnn/8q8Fp7KzGbVwIR2efx6lFIsTE/m/e++1vZtuZYStm8bc\nhhvt65X9KZjGaGqsbEl1OzA3vtbEdrw2StfKeq3u09aVixnb+Tp2rF3OvQP+Qe9XlgQi1CEAs2Lc\nVGO0Uh90U8Uw3GhfL+xP6JwXU2Nlw9XtTL2Oie14bZSulfWGe87JzAwWvZzI6oVTueiya+g7aSl1\nL73K/GJdzLdH7AXTGNuMHs3ew4eZ3bt3VKYxlkZxNcHQx608xy7F/XAUPO7m/SlqGmNx42NLO1a2\nuFpdweOmXsfEdkytxS5W1lvSc37auoHnu7VkzbvTuLnb4wycsSpwoQ4+Dfb96encP36862qMVmqI\nbhpzG260r1v3Z2bT+CJvhGFqrGy4up2p1zGxHa+N0rWy3qKek0lF+n59JeP/2prTp07SZ9JS7nl0\nLOXP8+anoCPlu1Mx73/1FY+9/jpZp04xrksXHnZRjdFKDdFNY27DjfZ12/6Eu7ORqbGy4ep2pl7H\nxHa8NkrXynoLP+ebEzXpvAh27FrguptKO8U3Y3uPZmby9zfeYO7q1TS9+GKmeGwao4iMXberE+5R\nuMbYcfBEz05jtMrWsb1OW/Pdd/SePp2UQ4d4on17Ej02jbEwN/W+vcCJG2GE61rb2R03sRav9e7z\naoy92fLF+1zeog1dhk/lN7UbFLPF4PFu+pFXYxz7/vuMX7qU31avzuLERH7vwWmModzS+/YCp47S\nw/Wo7eyOm1iL13r3W1cuZv7oXpw4dph7HnuO1p0f9fw0RtM8+27s3L+fO8aO5aUlS+j8hz/wxbBh\nng91cEfv2wuKu0Bqh3A9aju74ybW4pXe/cnMDN4c24/XBv6JuGq1GDRzDTd3HSChXgTPHbFrrZn6\n2WcMe/ttKp93Hq/37k0HFzReTHG69+12bpjxEq5HbWd33MRavNC737H7J56f1JKDqd9zc7fHad9r\nZGAbL1Z4Kth/Tk+nX/5NpW+7+mom9uhBnQv9dfU7KKNyy8INoQ55femqmalFPm7l1922FlPrNbGd\nwtvIztWM+fIUI784SVzNvBrj5S3alGpdQeSZf8O8/9VXtBo5ktW7djGuSxfe7N/fd6EO7uqxu4lb\nQh3Cd63t7I6bWItbe/ffp+Vy4/QTDPvsJDe0+gOJczdKqFvk+iP2wjXGyT170rhuXaeXFTVu6rG7\ngZsCvUC4rrWd3XETa3Fb715rzfezn+Tv7++nfEw5Egf25aIHXizVWoLO1T32tbt30ys5mZRDh3j8\nzjttn8YonOXGUBfRdVaNsXkbuoyQGmMoT/fYT2Vn89z77/Nyfo3xwyef5H8vu8zpZQmb+CHQw81r\nD+paSuqob1u1hHnPPGKkxui1GfSmuS7Yd+3fT8K0aXyzZw/dWrXi2b/8xbM3lRal55dQL2lee1DX\nUlxHPSsri+ffWsvqd16j7mVX03vih1x0+TXGXwfcOYM+Glxz8bTgptJtRo8m5dAhXu/dm4nx8RLq\nAeKHUIfw89qDupaiOupf7zlOYv/erFk4lZu7DmDg9FURhXpxr+PmGfTR4Ioj9tAa461XXcUr8fG+\nbLyIovkl0AuEm9duJzetJbSjfqbG+PlJLopTRmuMXptBHw2OB/v7X33FgNdf58SpU/yjc2f+2qaN\na6Yxiujy6+AurWKKDM7i5rgHZS0FHfXv03LpvjCTNak5dL66PGP+fCnvGqwx2vk5Ardy7FTM0cxM\n+s2YQY9XX+W3NWrw+f/9H3+7+WYJ9YBwciRAtIWb1x7Utaxs/BSvfQNNJx9n24Ec5vy5EjM7VWdb\ns2FGX8drM+ijwZEj9oysLG4cNcqxm0oLZ/nt1Eth4ea1B3Etxw8fYMj4N9n8+VFubFSZ2XfHcGGd\nBqyIQlvFazPoo8GRHrtSSl9cowav9uwpNcYAifTUi5sqbCbWYqqG6Kb3pSihNcYOfZ6RaYwRcHWP\nvVqVKnw5bJg0XgIk0pnpbqqwmViLqRqim96Xwk5lneC9CUNY+fZkIzVGYZ0jf23+tkYNCfWAKOqm\n0mXhpgqbibWYqiG66X0JtWfbRsZ1a8nKtyfTpstjRmqMwjo5sS2ixuSdjdxUYTOxFlM1RDe9LwA5\n2dl8PHMcS6eOpmr1OvSZtJTG19/syFqCTIJdRIXpC6RuqrCZWIupGqKb3peDqd8ze3hPfty8luvu\n+Av3J46nctXf2L4O4aJPngp/mNk0PiqtFzdV2EysxVQN0Q3vi9aate/NYFy3lvz8n+10HzWTHqNn\nSag7KKIjdqVUR2AEcCXQUmu90cSihDdFs8bopgqbibWYqiE6/b4cP3yAN57tw+bP3+Oy5q3pOnwq\nv6nzW1teWxQvorqjUupKIBeYDDxhNditju0V3uD3Xroo2rbVS5n/zCNkHE2jQ59RtO78mNQYo8yW\nuqPWejsgnxYNMD+GuoleuJVtuL1/XpzCNcZeEz6QxovL2HbxVCmVACQA1K9Wza6XFVHk11CPtBdu\nZRtu7p+XZM+2Tcwe/iC//LSL1p0fpUOfZ6gQK9Vltwn77yal1Aql1JYi/runNC+ktZ6itW6htW5R\nIy6u7CsWjovWBVI3MNELt7INt/bPi5OTnc2yaWN4+eGbOJV5gj6TlvKnx8dJqLtU2CN2rfVtdixE\neINfA72AiV64lW24rX9ekoN7f2D2sIf4cfNamt3RiY6JE6Tx4nLSYxeW+D3QC5johVvZhpv658XR\nWrPu/ZksfHEQqlwM3UfNpHnbB5xelrAgokvYSqk/KaVSgT8AHyqlPjKzLOEWpkYCeIWJXriVbbih\nf16S44cPkJzYifmjH6HBlc0ZPHejhLqHRNqKWQgsNLQW4TJ+vRFGSUz0wq1sw+n+eUlCa4x3PzqG\nNl0GSI3RYxwZ2ys9dvcL0lG6yHNWjfHSq+g2cgb1Gl/r9LJECFeP7RXu5dRRups63X7uoBfHSo3R\nb/vsZxLs4gyT0xhLw02dbj930IuSk53Nx7OeZ+lrzxBXvTZ9XllC45a3nPM8P+1zEMiJM+H4BVI3\ndbr92EEvzsG9P/BKr9tY/Opwmtz6ZwbP3VRkqIN/9jko5Ig94Jw6Sg/lpk633zroRSlcY+w2agYt\n2nYu8Xu8vs9BI0fsAeaWC6RfKRaCAAAMJUlEQVTFdbedmrUe7nE3rbe0jqcfPKfGGC7Uwdv7HEQS\n7AHk9KmXwtzU6fZDB70421Yv5R+dm7Nt9VLufnQMfSYttTxi16v7HFRyKiZg3HDqpTA3dbq93kEv\nSmiNsU6j/+GR8e+XusbotX0OOgn2gIjWEbqpCtyueve7JiTqpq3n/Kz9gOb8rP3UTVt/ztrctN6S\npGz/iteHxRuZxuiVfRYS7IEQzVD3WwWu9eZEmuyZTsEdBpTOocme6QClvruRk3Jzclgxc1zYGqPw\nJznH7nPRPJfuxwrctSmzKHzbGJX/uFcc3PsDEx+5Na/GeMufSqwxCn+SI3afsuPiqB8rcErnlOpx\nN9Fas/6DWbzzwsAzNcbmf3xA7nAWQBLsPmRX48ULo2dLS6uYIkNcqxgHVmPd8fSDvDmmL99++q7c\nVFrIqRg/sfvORn6swH3boAeFx+Lp/Mfdavuaj/hH5+ZsXbm41DVG4U9yxO4TTvTS/ViBK7hAem3K\nLJTOQasYvm3Qw5UXTk9lneC9iX9n5VuvlrnGKPxJxvb6gJs+bCTsYbLGKLxDxvb62OJjlZiYFsf+\n7PLEVapIq2r7ufK3dZ1eVrG8Nu7Vzet1ssbo5vdFnE2C3WMWH6vEM4cvIDM77/LIscwsVny1DcCV\n4e61rrub13to73+YPaIn//lmNc1u78j9iROockE1W17bze+LOJdcPPWYMcfqknn67N+27JxcVm3Z\n7dCKSua1rrsb11swjfEfXVuwf/cWuo2cTo/Rr9sW6uDO90UUT47YPWRm03iOfb+8yF87lpll82qs\n8VrX3W3rDa0xXnrdTXQdPpVqdS+2fR1ue19EyeSI3QNCpzHGVSr6AllxjzvNa+Ne3bTeMzXGLz/k\n7v7P0nfSUkdCHdz1vojwJNhdbmbT+LPuQdrq6ssoH3P2b1v5mHK0uvoyu5dmide67m5Y76msEywY\nN4DJj91N5QuqMXDGam7pPohyMc59SMoN74uwTk7FuFRxN5UuuEC6astujmVm5bVirr7MlRdOwXtd\nd6fXm7L9K14f/iC//LiT1g/0p0Pf0a6oMTr9vojSkR67C0kvPXhyc3L4eNbzLJkyirjqteky7DWu\naHmrpe81UUOUKqM3SI/doyTUgye0xtj0tvvpOHii5caLiRqiVBn9R86xu4Tbblcnok9rzboPZp1V\nY4xPml2qGqOJGqJUGf1HjthdwI23qxPRZarGaKKGKFVG/4ko2JVS44C7gFPA98BDWut0EwsLAjlC\nD6bta5Yx75kEMtIPcle/JG7u+niZGy8mRif7cfxy0EV6KmY5cLXW+lpgF/D3yJcUDBLqwfNrjfEu\nKlf9DY/PWMWtPZ6IqMZoooYoVUb/ieiIXWu9LOTLtYBcaQmjuBqj8LeUHf/Om8ZouMZoooYoVUb/\nMXmOvSfwhsHt+Y6cSw+e3JwcPn79BZZMHklc9dr0fmWx5RqjVbvq3R9xCJvYhnCPsMGulFoB1Cni\nl4ZqrRflP2cokA3MKWE7CUACQP1q9g0vcgs59RI8h/b+hzkjHuaHb1aVusYoRCTCBrvW+raSfl0p\nFQ90AG7VJXzaSWs9BZgCeR9QKuU6PUsCPXi01mz4cDYLXngchaLbyOk0b9tZbiotbBNpK6YtMBho\nrbU+YWZJ/iDn0oMpI/0Qb47tyzefLOTSZjfSdcQ0xwZ3ieCK9Bz7K0AssDz/aGSt1rpXxKvyAQn1\n4DFZYxQiEpG2Ytw5UtBBcuoleE5lZfL+K0/x5Zv/pM4lV5Lw8iLqN27i9LJEgMknTw2RUy/BlLLj\n38we/iD//c8ObnqgHx36jOa8ipXCf6MQUSTBboDUGIMnNyeHT2a/yOJXRxBXrRa9J37IFb8vsWcg\nhG0k2CMgR+nBdFaN8db76DjkFakxCleRYC8jCXXr/DLru3CNseuIZFrc2UVqjMJ1JNjLQE69WOeX\nWd95NcZ+fPPJOzRqegNdR0yj+kUNnV6WEEWSYC8FOUovvZJmfXsl2HesXc7cUX8jI/0gHfomcUs3\nqTEKd5Ngt0iO0svGy7O+z6kxvvQu9a9o6vSyhAhLgj0M6aVHxquzvlN3fs3rw+Klxig8SW6NVwIJ\n9ch5bdZ3bk4OK2aO46WHbiDr+FF6T/yQPw98QUJdeIocsRdDQt0ML836PrTvx7wa49cr82qMgydS\n5cLqTi9LiFKTYC9EAt08t8/6Llxj7DJ8Gte36yo1RuFZEuwhJNSDJ3QaY6OmN9BtZLJMYxSeJ8GO\nBHpQSY1R+FXgg11CPXikxij8LtDBLqEePGdNY/xLXzr0TZLGi/CdQAa7BHrwFNxUeumUUVS5sAa9\nJnzA7/73dqeXJURUBCrYZSRAMB3a9yNzhvfkh29W0eSWP9NpyCtSYxS+Fphgl5EAwRNaYwSkxigC\nIxDBLqdegicj/RBvPdefrz9eINMYReD4Otjl1EswSY1RBJ1vg11OvQRPaI2x9iW/kxqjCCzfBbsc\npQdT6s6vmT3sQX7+z3Zu7NSHu/o9KzVGEVi+CnY5Sg+evJtKv8SSySOkxihEPt8Eu1wgDZ7QaYxS\nYxTiV54Pdjn1EjxaazYumcuCcQPQaLoMm8r17btJjVGIfJ4Odjn1EjwZR9J4a2y/vBpjk1Z0HZks\nNUYhCvFksMtpl2DauW4Fc0f9jeOHD9Ch72hu6TZQaoxCFMFzwS6hHjynsjL5YNJQvnhjErUv+R1/\nfeEdGvyumdPLEsK1Igp2pdQzwD1ALvAL8KDWep+JhRVFQj14Und9w+yn46XGKEQpRHrEPk5r/TSA\nUupRYBjQK+JVFSKBHkw/blnPxIRbpMYoRClFFOxa66MhX1YBdGTLOZeEenD99srm3P7QEG7s2Ftq\njEKUgtI6sixWSiUBPYAjwM1a6wPFPC8BSMj/8mpgS0Qv7G41gINOLyKK/Lx/ft43kP3zuiu01nHh\nnhQ22JVSK4A6RfzSUK31opDn/R2oqLUeHvZFldqotW4R7nleJfvnXX7eN5D98zqr+xf2VIzW+jaL\nrzkX+BAIG+xCCCGip1wk36yUujzky7uBHZEtRwghRKQibcWMVUpdQV7d8SesN2KmRPi6bif7511+\n3jeQ/fM6S/sX8cVTIYQQ7hLRqRghhBDuI8EuhBA+41iwK6WeUUp9q5T6Wim1TCl1kVNrMU0pNU4p\ntSN//xYqpS50ek0mKaU6KqW2KqVylVK+qZYppdoqpXYqpXYrpYY4vR6TlFLJSqlflFK+/PyIUqqB\nUupTpdT2/J/Nx5xekylKqYpKqfVKqW/y921k2O9x6hy7UqpqwSdX88cR/I/W2vg4Aicope4APtFa\nZyulngPQWg92eFnGKKWuJO+C+WTgCa31RoeXFDGlVAywC7gdSAU2AJ211tscXZghSqmbgOPALK31\n1U6vxzSlVF2grtb6K6VUHLAJuNcPv38q70YDVbTWx5VSFYCVwGNa67XFfY9jR+x2jCNwitZ6mdY6\nO//LtUB9J9djmtZ6u9Z6p9PrMKwlsFtr/YPW+hQwn7wBd76gtf4CSHN6HdGitd6vtf4q//+PAduB\nes6uygyd53j+lxXy/ysxLx09x66USlJKpQBdyRsg5kc9gSVOL0KEVQ9ICfk6FZ8EQ9AopRoCzYB1\nzq7EHKVUjFLqa/Km6C7XWpe4b1ENdqXUCqXUliL+uwdAaz1Ua90AmAP0i+ZaTAu3b/nPGQpkk7d/\nnmJl/3ymqPvq+eZfkUGhlDofWAAMKHRWwNO01jla66bk/eu/pVKqxNNpUb3Rhp/HEYTbN6VUPNAB\nuFV78MMCpfi984tUoEHI1/WBqN1bQJiXf/55ATBHa/2O0+uJBq11ulLqM6AtJQxSdLIV49txBEqp\ntsBg4G6t9Qmn1yMs2QBcrpS6RCl1HvAA8J7DaxIW5V9gnAZs11q/6PR6TFJK1Sxo1imlKgG3ESYv\nnWzFLADOGkegtd7ryGIMU0rtBmKBQ/kPrfVL4wdAKfUnYCJQE0gHvtZa/9HZVUVOKdUOeBmIAZK1\n1kkOL8kYpdQ8oA15Y23/CwzXWk9zdFEGKaVuAL4ENpOXKQBPaa0XO7cqM5RS1wIzyfu5LAe8qbUe\nVeL3ePAsgRBCiBLIJ0+FEMJnJNiFEMJnJNiFEMJnJNiFEMJnJNiFEMJnJNiFEMJnJNiFEMJn/j/V\nvlzlzEbKXQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_svc_decision_boundary(svc2, axis=[-3, 3, -3, 3])\n",
    "plt.scatter(X_standard[y==0,0], X_standard[y==0,1])\n",
    "plt.scatter(X_standard[y==1,0], X_standard[y==1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": []
  }
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