05_support_vector_machines.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Chapter 5 – Support Vector Machines**\n",
    "\n",
    "_This notebook contains all the sample code and solutions to the exercises in chapter 5._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/05_support_vector_machines.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/master/05_support_vector_machines.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python ≥3.5 is required\n",
    "import sys\n",
    "assert sys.version_info >= (3, 5)\n",
    "\n",
    "# Scikit-Learn ≥0.20 is required\n",
    "import sklearn\n",
    "assert sklearn.__version__ >= \"0.20\"\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"svm\"\n",
    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear SVM Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next few code cells generate the first figures in chapter 5. The first actual code sample comes after.\n",
    "\n",
    "**Code to generate Figure 5–1. Large margin classification**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=inf, kernel='linear')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = iris[\"target\"]\n",
    "\n",
    "setosa_or_versicolor = (y == 0) | (y == 1)\n",
    "X = X[setosa_or_versicolor]\n",
    "y = y[setosa_or_versicolor]\n",
    "\n",
    "# SVM Classifier model\n",
    "svm_clf = SVC(kernel=\"linear\", C=float(\"inf\"))\n",
    "svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure large_margin_classification_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x194.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bad models\n",
    "x0 = np.linspace(0, 5.5, 200)\n",
    "pred_1 = 5*x0 - 20\n",
    "pred_2 = x0 - 1.8\n",
    "pred_3 = 0.1 * x0 + 0.5\n",
    "\n",
    "def plot_svc_decision_boundary(svm_clf, xmin, xmax):\n",
    "    w = svm_clf.coef_[0]\n",
    "    b = svm_clf.intercept_[0]\n",
    "\n",
    "    # At the decision boundary, w0*x0 + w1*x1 + b = 0\n",
    "    # => x1 = -w0/w1 * x0 - b/w1\n",
    "    x0 = np.linspace(xmin, xmax, 200)\n",
    "    decision_boundary = -w[0]/w[1] * x0 - b/w[1]\n",
    "\n",
    "    margin = 1/w[1]\n",
    "    gutter_up = decision_boundary + margin\n",
    "    gutter_down = decision_boundary - margin\n",
    "\n",
    "    svs = svm_clf.support_vectors_\n",
    "    plt.scatter(svs[:, 0], svs[:, 1], s=180, facecolors='#FFAAAA')\n",
    "    plt.plot(x0, decision_boundary, \"k-\", linewidth=2)\n",
    "    plt.plot(x0, gutter_up, \"k--\", linewidth=2)\n",
    "    plt.plot(x0, gutter_down, \"k--\", linewidth=2)\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(10,2.7), sharey=True)\n",
    "\n",
    "plt.sca(axes[0])\n",
    "plt.plot(x0, pred_1, \"g--\", linewidth=2)\n",
    "plt.plot(x0, pred_2, \"m-\", linewidth=2)\n",
    "plt.plot(x0, pred_3, \"r-\", linewidth=2)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", label=\"Iris versicolor\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", label=\"Iris setosa\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"upper left\", fontsize=14)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.sca(axes[1])\n",
    "plot_svc_decision_boundary(svm_clf, 0, 5.5)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "save_fig(\"large_margin_classification_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code to generate Figure 5–2. Sensitivity to feature scales**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure sensitivity_to_feature_scales_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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LlAAoIqOAUQBxcXHdU1NTvR7n9OnTrFixwg6EK1as4EyB1YgbN27sCIQdO3bU0BFgI0eOZObMmRw5csSxvXr16jz11FO88MILQaqZCgcaANEAqFSki5QA6Kkk7V5OTg4//fSTHQiTk5M5evSoo0zNmjVJSEiwA2H37t11YkkA5OfnF5pYsmfPHt544w2eeuopAJYsWcKLL75of2/69OmjYzyVBkDQAKhUqcnNhb174cAByM6GChWgUSNo2hTKhdpqUucFKwCKyI3ATmPM1lI6nl8CYEH5+fls3brVETr27t3rKFOpUqVCE0tiY2Mv6v1UyezZs4dKlSpRt25dAMaOHcu4cePs/VFRUXTt2tX+3tx22216OT8CaQBEA6BSl8wY2LQJUlKs53l55/e5F7lt3Rri4617WIWYYARAEYkBjgGfGmMevcRjlcNar3UM0AT4PZBrjMn19ZrSbvdSU1MdgXDrVmemjY6OLjSxxB1QlH8VNcazWbNm7N692y47bdo0unXrRosWLTQUlnEaANEAqNQlMQaWLoW0NGfwKyg6GurVg4SEkAuBQQqAVwM/AFcZY5Iu8VhjscKfp3HGmLG+XuPvdi8jI6NQ6Mgr8PPRrl07xzjCZs2aaegIAM8xnpUqVeLpp58GID093Z7t3ahRI8f3Jj4+Xsd4ljEaAIGYmBgTFxdHdHR0ocfbb79N3759Afj000/5z3/+Y++Lioqy/79u3bp89NFH9jH/+Mc/cvLkScex3OWHDBnC4MGDAdi6dSuTJ0/2+t5RUVE89NBDVKlSBYC5c+eyb98+r2UbN25Mnz59ADh37hyLFy8uVEf3o02bNtSoUQOwfuGPHDni9ZzKly/v+IR+5swZRMRRVhtrxcaNVs9fUeHPLTra6gns1Mn/9SqBIAXAl4DbjDEdA/m+boH+4Hvq1KlCE0vOnj3rKNOkSRNH6OjQoYOGjgDasWMHTz75pNcxnjVq1GD+/PlcccUVQaqdKm3FbfdCd/BOKcjOzmbHjh1e9508ef5uczt37uTHH3/0Wq5JkyaO519++SXp6eley9apU8cOgNu2bWPChAk+63b33XfbAXDixInMnTvXa7mbb76ZmTNnAlaou+6663we87vvvmPIkCEAvPPOOz7fv3Hjxuzbt89+3rx580Ln5A6E48aN47nnngNgzpw5/O53v/MZalesWEGdOnUAeOyxx1i2bJnXsomJiYwfPx6wehNGjhzpM9Q+8cQTdO7c2T6/efPmeQ21NWvW5Mknn7Tr/95773Hu3DmvQb1Hjx5069YNgH379rF06VKf59SvXz/7Nk5bt27lxIkTXutZrVo1e5HX3NxcDh8+7POcYmJiirxHaEjIzS0U/iYlNeX5KZ3Yc6QycbXP8PKIjdyV6BoflpdnlW/fPqTHBAbINcA/g12JQImNjWXAgAEMGDAAsNrdghNL9u3bx5QpU5gyZQpgTSzxXN7k8ssv14klftSqVStmzpzpdYznvn37aN26tV32/vvvZ9euXbp4eAQo0y11fHw806ZNIy8vr9DD8wf+gQce4OqrryYvL4/8/HxHuYL3cHznnXc4c+aMvd+zfO/eve1y7du3Z9y4cYXe112+cuXKdtnBgwfTqFEjr/X0/FRWoUIFBg4c6LWeeXl5du8fQO3atWnTpo3XehZc+LVixYrExMTY+40xGGPIzc3Fs4f43LlzhT49evIsm5KSws8//+y1nOdq96dOneLbb7/1eczhw4fbAXDZsmX8/e9/91quadOmjgA4ZsyYQksouL300kt2AFy1ahXDhw/3+f6HDx+2v16PP/448+bN81rulltu4ZtvvgGsFf+bNWvm85hz5szh+uuvB+DFF1/k1Vdf9Rpq4+LiWLdunf267t27c/z4ca+h9rHHHmPkyJHA+ZmBvkLtv//9b6pXrw7AG2+8waZNmwoH1dOn6VqlCiNd9z798L81ePRfM8nNnwNEkZoRzf3vC7PWHqRHyxMM69WLFg0bwt69rEpPZ926dV7PqWrVqtxwww32OS1cuJDc3Fyv5xQXF2eH6hMnTrB3716f59SgQQO7R+nMmTPk5+dTvnx5n98DfxGRKkA8cGPA3zxEVKhQgV69etGrVy9Gjx5Nfn4+W7ZsKRQ6Zs2axaxZswBrYkmvXr3s0NGrVy+dWOIHUVFRdOzYkY4dO/KHP/wBsNorz78dCxYsYP/+/SxevBiwxni6J5bcfvvtJCQkBKPqyg/KdACMiYmhTZs2FyzXsmVLWrZsWaxjFhUWPLVr146//OUvxSr75z//uVjl6tevz/z584t9zOIed8+ePY7nxhg7MHpeprnxxhtJT0/3GWpr1apll33vvffIzMz0GmrdvYQAdevW5ZtvvvEZajt5XFK84YYbqFevntdQW/AT6qOPPsrJkye9lnWHP7B6Q++44w6f51SxYkW7bNu2bTl27JjXc/K8xZOI0LBhQ5/n5BlMcnNz7e0FFbxP6O7du30G8MOHD9v/n5aWxpIlS7yWc7+n24IFC1iwYIHXcrddeaUdAMd/3Zjc/Ncc+3Py4P+WWY9OcXG0qFcPDhzgm9mzee2117wdkri4ODzXqLvzzjt9ntOECRN4/vnn7XrefvvtPs8pPT3d/rm69dZbmT9/Pl27dvVZviREZD4wEBhmjJnusV2AT4F7gdeNMc8AVwHfGGOOlcqblwFRUVHEx8cTHx/Pww8/jDGm0MSSbdu28eOPP9pXYqKjo7n88ssdE0s82w1Veho1auR4vm7dukJjPNeuXcvatWupV6+eHQB/+eUXVq5cqYuHh7FijwEsYSMYEnQSiAp1BQOi53OwenLdUlNTycnJ8Vq2YcOGNGzYELDC0ObNm32G2htuuMG+3LZgwQIOHDhQONRu20bLKlW4sXt3AOTOAcB7QB7WrWnzXI9cnrhxG6MGDKBto0ZQty7Tjx7l+++/91rP2rVr8/7779vndMcddxT6oOAu//DDD3PvvfcCMG/ePB5//HGf57R161a7F+Pmm29m4cKFdO7cmRUrVlzyGEAR6QL8BPwCdDLG5Lm2/xV4AvjIGDPKte054EdjzPJLec9LEY7tXnp6uiN0/Pzzz4U+FLVv377QxBLlf55jPIcNG2ZfkXnllVfsD2i6eHhoKfVJICVpBENFODaESoWEpUutNf9cmj8yhNSMKoWKNatzmt3vzzm/oVEjazZwiCitSSAi8hnWh9z7jTGfuYLey8BXwAhjTP6lvkdpKQvt3qlTp1i+fLljYsm5c+ccZZo2beoIHe3bt9fQEUDTp0/ns88+Izk5mWPHnB3e8fHxbNy40X6em5tLudIYGxym65EGml9mAYdTIwhloyFUKih27YKff7YngUxKasqof/bgTPb5RrZyhVw+fGjN+Ykg0dHQrRu0aBGMGntVigGwCZACHAbeBP4OzANuNsZkX+rxS1NZbPeys7NZu3atHQiXLl1aKHTUqlWr0MSSYIwDjTTexngmJCTw5ZdfAnD8+HGaNGnCFVdccfGLh4f5eqSB5q8AGDaNIICIGM/lTbwNNL/QNt1evO36ybuMyc2Fb78t/ixgsBrim28OqU/ipbkMjIi8CriHuCwDBhpjzhTxEvfrrgKeAroDjXB9gC6NOnlTFgNgQfn5+WzevNkROvbv3+8oU7ly5UITS9wrLyj/McZw7tw5KlWqBMAPP/zAtdde6ygTHX1+8fDRo0fbw1d8HDDs1yMNNL+tA1icRjDQDZ4vIlJ2FzkMQaEUSMvq9oCu0ajrABY81hPAX11P2xtjthXzdUOAvlhDaD4HHtEAWLqMMezevdsRCH/55RdHmXLlyhWaWOI5xlb5T1FjPNPS0ux1aT/55BPKly/PVVdddX6MZxlohwLNnwHwgo1goBs8X3r06GFWrlzpc+D4hbb5bXtuLnn79pF3+DD5xljP8/OtB5CXn09+jRrk1aplbQtWPUuwXQVOwYDo1+B5/DhROTlEixAdFeV4RLm3RUcTHRtLdOPGRJcrF1LBOT4+vrQuAY8AJmFd/WgA/MMY8/BFHOcU8JgGQP9LS0srFDry852jlDp06OAYRxgXFxek2kYW9xjPzZs3O1araN68ub1KQNOmTembkEBi1aoktm1LhyZNiIqKCssrEYHmr0vAJW4EA9Hg+RKSDWEZ7c4uajarbi+97WX5zj1+UhqzgIcAM7AmwF0DLAFaYU2GK1YvoMexNAAGycmTJx0TS1auXFloYklcXFyhiSW6vElg5OXl8de//hX34uHHjx937H95+HCa1f2TayxyFlABKB8WY5EDzR+zgC+qEdQAWIB2Z6tLYIwJTiDNziYvLY28I0fIy84mPyqKvGrVrAeETEAu+Ni6deslBUAR6QvMBw4BCcaYgyJyO/A1MNMYc0sJj6cBMERkZWUVmlhSMHTUrl3bMbGkW7duOrEkAOwxnp99RtKKFSRt28bnjz3GA+//P9dqBG9g3SK7F5BIvWpX8Ou7x6jiXrc1xFYjCLRSDYCX0giWpMETkUVY31H3SrX7jTFtXfuuxVqILA5YCdxnjEn1dhy3kGsICwysX/r72uRkFp48Ub56Pgkfue5iod3ZSl20SxkD6Fr6ajFwFuhrjNnpsW810AO4yhiTVIJjagAMUfn5+WzatMkxjvCAx1JIYE0s6d27t2NiieddnVQp+/FHyMiwr3xED78DYwR4GPiHo2h0VBSXt2jBzT168MIf/gD9+we8uqGi1O4F7GoEZwOZWBM+DgIYY6aKyBpgqIgklqQRvIDHjDH/KlCHOsB04EFgFvAS8H9YYdGnrKwsdu3a5XW8UNWqVe1Pcu5La9HR0f7t7t+71/HUW/jzun3v3ojuzlYq0ESkFdYKBwYY7Bn+XJ4FFmB1RRTZDqnwEBUVRefOnencuTOPPvooxhh27drlCITbt29n4cKFLFy4ELAmlnTv3t0OhAkJCTqxpDS5Fqx3/12Oq33G1QP4ATAOSAaSqFBuMbl561i9cydNatcG19/2kydPMnr0aLsXVxcPdyqyB9DVCCYDMUA/Y8yGAvsHYDWCK40xXhvBi+gB/MJLAByF1ePXx/W8CpABdLvA5WefJzd79mz7nqR/+ctfeOmll9yvcQwmb9KkCdu3b7df16VLFzIyMrwOPv/jH//Iww9bQyKXLFnC6NGjncHz1Cmic3KIEmHyn/7EhgesUDeJSaSQQjTRRLn+a9w/m+ioKLq1aMEj998PCQlkZmYyfvx4nwPehw8fTqtWrQBYvnw5q1ev9jpAvmrVqgwbNsw+pzlz5pCTk+P1nFq2bGn/0hw/fpydO3f6HIAfFxdnL/Z5/PjxQvd4LVheKX8rzVnApUF7AMPb4cOHHRNL1q1bV2hiSceOHR3jCJs2bRqk2pYBJViP9OYe21m+fTuxlSvTZ8QIaNGC+fPnM3jwYLtspCweXio9gMaYHViTPXzt/y9Q2l1mr4rIa1hjDZ83xiwCOgLrPd73tIjsdG13BEBXWBwF1qezxo0bex0bFBMT43jTqKgo8vPzHWOsAM6ePesod+DAATIyMrxW/MiRI/b/Z2RksGrVKp8nmesxBnA961nNameBRdY/Q3v04JGcHAAyMzP529/+5vOY3bt3twPgd999x8svv+y1XJMmTRwB8L777iM9Pd1r2fHjx/Piiy8C8OOPP3Lbbbf5fP9Dhw5Rv359AEaMGMHcuXO9lhs6dCgzZswAYN++fbRu3dpnqP3iiy+45pprAJg4cSIffPCB11DbsGFDZs6cab/HHXfcwYkTJ7yG2uHDh9vn/9NPP/Hee+/5DKljxoyx7zM8adIkfv31V6/1bNWqlf2B4vTp03z11Vc+z6l3797212nnzp2kpqZ6PaeKFSsSHx9vn9Ovv/5q91QXLB8bG2uvu5WXl+cI3zqIPbhEJBZrvDRAFBAnIl2Bo8aYPT5feJFyXO2FKn3169dn2LBhdvtx4sSJQhNLNm/ezObNm/nHP6xLlM2aNXOEjnbt2unvZHE1bWoFQBf3RA/vs4ArMahLF2vYlCt0t2nThtdee80e47l3714mT57M5MmTAdi/f799L+Tdu3fTuHHjiBrj6ZeBZZfQ4D0NbAGygeHALNfrYoGCCSUTqFrwAMaYD4EPofifhMePH8/48eMxxhQaZF+wh3Tjxo3k5uZ6HXzuXssIoH///ixfvtwZPNevJz8jg7z8fGp4LEh6F3dxHdeR7/Ff6z9kkpefT7M6dezu7OrVq/PGG2/4HPR+2WWX2cfs1asXjz32mNd61qxZ03FO1113HcePH/datnnz5na56tWr061bN58D8D1/capWrUqtWrW8hm/PWwLl5uYWmonnyfOP2eHDhwut7eVWcPmGH374gaNHj3ot27VrV/v/d+3axSeffOLz/Z9++mk7AH7++efMnz/fa7lbb73VDoAZGRk88MADPo85Z84crr/+esBa9+qVV17xWq5p06bs2XP+16Vnz54+P3xMmDDBvi/nzJkzHQHfczH0qKgoUlNT7Z/V4cOHs3jxYq9BdfDgwbzzzjuAFe5vuukmn6H2lVdeoUcP6wPn5MmTmT59utdQW7duXV5//XW7bi+88AJnz571GmqvueYaEhMTAUhJSWH27Nk+g/rw4cOp6BoAnpycTFpaGrVq1fL5PQiwHsCPHs/HuR7/Bu4r7TfbsGEDLVq0cISOtm3baujwg2rVqjF48GC7lykrK4s1a9Y4JpakpqaSmprKF198AUCdOnUKTSwplduklUXlylkTIT0mTt6VuNe57Isn98RJ19ezefPmPP300zz99NOFxnh6hj+AQYMGsX//fscYz549e5bpxcNLvA5gsQ4q0h9ng+f2b2PMfSU4zlzgO6wwWd4Y84jHvo3AWGPMNF+vD7lLIQW6sxfdWddn0f5fufJuGZ/Snp+fz7lz53yG2tq1a9t/2NPT08nIyPAaVN1jcdx+/PFHsrKyvJaNj4+nQ4cOgBUAFy5c6DPUPvbYY3bP2ueff05KSorXUNu5c2cefPBBu56jR4/2eU5jx46lW7duAPzrX/9i0qRJXuvZoEEDvvvuO/ucrrjiCo4cOeK17PPPP8/jjz8OWPfoHD58uF2moIyMDHuc0sCBA/nvf//r9XszbNgwpk6dan+dWrZs6fP7OG/ePAYNGgTAM8884wh5npo1a8bu3bvt57Vr1/YZ1F9++WWee+45AKZOncodd9zh8/09z2nQoEEsWLCArl27sm7dupC6BFxSIlIL+BgYhDXs5VljzOSiXhMdHW0Kft/r1q1L3759+etf/0oLf7Qleo9Wr/Ly8gpNLDl48KCjTJUqVQqFDp1Y4iEAS6edPn2a7t27+1w8fOzYsfaH9nDgt4WgA0lEvge+B84B9xpjElzbq2D1CF5e1BjAkAuAOgtYBZgxxg6K7n8rV65s9wYdO3bMDuAFH1WqVLHHL2VlZbF+/XqfobZ79+7UqVMHsHrJt23b5jWoVq5cmREjRtj1e//99zlz5ozX8D1gwACuuuoqANatW8dnn33mM6i///779if1cePGsW7dOlq0aMFbb70V7gFwCtZVlJFAV6wPxH2MMZt9vaZHjx7mo48+stdTS0pK4tChQ4AzKL/++utkZ2fbocP9QadE9B6tJWKM4ddff3UEwhT3186lfPnyhSaWhFBvdnAU9XNWrpy1vxR+znyN8fz++++57rrrAJgyZQqLFy8O6cXDwy4AikgNoCfWsgu5wG+wLuVeDhwFdgAPYDWA47AmpRQ5+y7kAiDoOoBKBVCoTQIpCdcH3WNAvDFmu2vbf7CWx3rG1+sKtnvGGHbu3Mm6deu4/fbb7e1xcXHsda1MUL58eXr06OG4TVqNGjWKrmAZXdQ+0A4dOuQIHevXry/Ucx8fH++4pN+kSZMg1TbIPHuac3Ks4VF+7Gk+ceIEK1asoE+fPsTGxgJw++23M23a+QuPnouH9+/fn7Zt25Z6PUoqHANgXWAO0A7Iw5rc8aIxZoFr/wDgXaAZ59cB3F3UMUMyAGqjqVTAhHkA7AYsM8ZU8tj2FNaH35t8va447V5+fj7Tpk0jKSmJJUuWsGHDBsd4Z8/JX+np6WRnZ9O4cWPnQfTDrF+cOHGCZcuW2YFw1apVZGVlOco0b95cx3gGyerVq1m4cKE9xjMzM9Ped8MNNzB79mwAzp07x4YNG4KyeHjYBUB/CMkACAHrzlYq0oV5AEwEvjbGNPDY9nvgLmNM/wJl7dUP4uLiurvvp1pcx48ft0NHcnIyr776Kn379gWsS8XPPPOMc2JJ79602bYNcfVU6XAW/8nKymL16tWOiSUnTpxwlHGP8XR/f7p27aoTSwKg4BjPa6+9llGjRgGwaNEirr76aqpUqUKvXr0Cuni4BkBCOAC6Bbg7W6lIE+YBsBuw1BhT2WPbk0D/S+0BLIkxY8bw1ltvcfLkScf2etWrc+sVV/CPUaN0QlsA5eXlsXHjRsc4QvcYT7fY2NhCE0suaoynumizZs3iySefLDTG0z1hcf78+VSrVs0v760BkDAIgEopvwrzAOgeA9jRGJPi2vY5cKAkYwBLQ15eHuvXrz8fOn74gbRjx7i9Vy++fuIJFt1Zl9OcZhzj6OT6rz3tiSHmfACEiL9Hqz+4x3h6BsIdO3Y4yhQc45mQkFBoOTDlH97GeDZu3JjU1FT7sv2wYcOoV69eqS0ergEQDYBKRbpwDoAAIvIl1u3oHsSaBTyHYswC9ne7Z374gR2bNpGTl0eHJk1YdGddVrGKp3naLlOe8rShDTcObUVi+/ZcEx9PpcaNI/oerYFy6NAhRyBcv369Y4yniBSaWFJojKfyixMnTrB79246d+4MWDeQcK+g4Oa5ePiNN97oWK+wODQAogFQqUhXBgJgLeATYCBwBHjmQusABqTdW7rUGrrisujOupzgBGtZywbXf7vYheH835dDH35I/Q4dICGBVatW0aRJkxL/YVMXJzMzs9DEkuzsbEeZgouHt2nTRieWBEBOTg4rV670OcZz5syZ3HzzzQCsX7+enJycC47x1ACIBkClIl24B8CLEZB2rxiL2p/iFJvYROYtK9mdlsaUJ5+0xwBedtll/Prrr7Rs2dIROlq3bq2hIwDOnTvnmFiybNmyQhNL6tWr55hY0qVLF51YEgAFx3h+8MEH9tqdv/3tb5kyZYo9xtP9/Sm4eLgGQDQAKhXpNAD6ySUsan8uN5dbbrmFZcuWFZpYUr9+fd5++22GDx/u3/orh7y8PDZs2OC4bHz48GFHmdjYWPr06WMHwiuvvFInlgTYs88+y9SpU72O8Xz00Ud56623AA2AgAZApSKdBkA/usR1AHNzc50TS5KSSE9PZ/78+QwcOBCADz/8kOnTpztCh/vWkMp/jDHs2LHD8b3ZuXOno0z58uW54oorHBNLLrh4uCoVBw8eLDSxZPz48bzwwguABkBAA6BSkU4DoB+V8qL2xhi2b99OXFyc3bN022238c0339hlKlSoYIeOgQMHcs0115Ta6aiiHTx40BEICy4eLiJ06tTJcUlfx3gGRmZmJnl5efYtAzUAogFQqUinAdDP/Lyo/f79+x2hY9OmTXbouPnmm5k5cyYAZ86cYfbs2SQmJtKwYcNLPi11YZ6LhyclJbF69epCE0t0jGdwaABEA6BSkU4DYIAEaFH7Y8eO2aGja9eu9ljBH374gWuvvRaAyy67zBE6WrVqpaEjAM6dO8eqVascE0sKLR7usdade2JJdHR0kGpcdmkARAOgUpFOA2BkWLJkCRMmTGD58uWcOnXKsa9BgwZs3bpVx6cFWKHFw5OSSEtLc5SpWrVqoYklOsbz0mkAJDIbQqXUeRoAI0tubi7r1q2zA0dycjJVqlRh165ddplevXpRs2ZNO3RcccUVGjoCwBhDSkqKIxD++uuvjjKeYzwTExPp06ePBveLoAGQyG4IlVIaACOdMYa0tDTq168PeL/rQkxMjB067rnnHtq1axeMqkakAwcOOALhxo0bC00s6dy5s+OysY7xvDANgGhDqFSk0wCoCtq3b5+jh3Djxo32vtmzZ3PDDTcAkJSUxKFDh0hMTKRBgwbBqm5EOX78OEuXLnVMLMnJyXGU0TGeF6YBEG0IlYp0GgDVhRw7dswOHc888ww1a9YE4De/+Q1fffUVAK1atXKEjssuu0xDRwCcPXu20MSSgmM869ev7/jedO7cOeInlmgARBtCpSKdBkB1sd59911mzpzJ8uXLOX36tGPfiBEjmDzZuiVznmvpm0gPHYHga/FwT9WqVXNMLInEMZ4aANGGUKlIpwFQXarc3Fx+/vlnx2Xjxx9/nOeeew6wLhXfdNNNhUJHTExMkGte9rkXD/cMhJ4TfsCaWHLllVc6JpZUr149SDUODA2AaEOoVKTTAKhKmzGG7OxsO+BNnDiRP//5z44yMTExduj4y1/+omEwgIpaPBysiSVdunRxXDYua2M8NQCiDaFSkU4DoAqEvXv3OkLH5s2bAWjYsCH79++3xwu++uqrtGnThr59+9ozk5V/eY7xTEpKYs2aNYUmlpS1MZ5hFwBFJAZ4HxgA1AJ2AM8ZY74XkebALsBzIMbrxpiXijqmNoRKRTYNgCoYjhw5wrJly8jMzOTuu+8GrCBSu3ZtuzeqTZs2JCYm0rdvXxITE2nZsmVYh45wcfbsWVauXGkHQl+Lh3sGwk6dOoXVGM9wDIBVgNHAZ8AeYAgwBejkKrILKG+MyS3uMbUhVCqyaQBUoeLIkSO8++67dug4c+aMY/+3337LTTfdBFjLoVSrVo2oqKhgVDWieFs83NvEkoSEhLAZ4xl2AdAbEdkAjAPWogFQKVVCGgBVKMrJybEnliQnJ5OcnMyWLVuoW7cuAL/73e+YNWuWI3T06NEjpENHWWGM4ZdffnFc0t+9e7ejjOcYT/fEkmrVqgWnwl6EfQAUkfpAKtAVOIcVAA8ABlgAjDbGZBR1DG0IlYps4RoAReQx4D6sKyBTjDH3Ffe12u6FH2OM4/JvYmIiycnJjjIVK1bkyiuv5IEHHuDee+8NdBUjmufi4e6JJZ6ioqIKTSwJ5hjPsA6AIlIe+B7YaYx5SERigXbAOqA28B5Q1Rgz2MtrRwGjAOLi4rqnpqYGrN5KqdASxgHwNiAfGAxU0gAYefbs2eMIHVu2bAFg7NixjBkzBoCNGzfy8ccf26GjXr16waxyxDh69GihiSW5uc6Lk61bt3YEwkCO8QzbACgiUcBkoBow1BiT46VMA+AgUN0Yc8LXsbQhVCqyhWsAdBORCUATDYDqyJEjJCcn0759e9q0aQPAm2++yejRo+0y7okl7keLFi10YkkAnDlzptDEkoKLhzds2NDxvYmPj/fbxJKwDIBi/aR+AjQHhhhjzvooVx84BNQwxmT6Op42hEpFNg2Aqixbt24dM2bMICkpiRUrVjgmltSpU4e0tDQ7AKakpHDZZZfpxJIA8LZ4eEaGc8Ra9erV/TbGM1wD4D+wxvwNMMac8tjeEzgOpAA1sZaLqWeMubqo42lDqFTpWNpgKTmHC3XGU75+eRIOJQShRsUTKQFQh76onJwcfvrpJztw1KlTh3/9618AnDp1iho1alC1atVCoaNChQpBrnnZZ4xh27Ztjkv6BX9HY2Ji6Nmzp/296d2790VPLAm7ACgizYDdQBbgeTH9IayxMK8A9YATWJNA/p8x5lBRx9QAqFTpWCSLfO7rb/oHrB4lFYoBUEQWAf187F5qjOnrUVZ7ANUl27RpE0OGDGHv3r2O7RUrVqRnz568++67xMfHB6l2kcnX4uFuUVFRdO3a1Q6EJVk8POwCoD9oQ6hU6dAAGBwaAFVpSk1NdYSOrVu3Atbt0xo1agTAK6+8Qnp6uh083EvTKP86cuSIY2LJ2rVrC00sKe4YTw2AaEOoVGnRABhYIlIOKAeMAZoAvwdyi7MOqrZ7qrgyMjJYvXo1119/vb2tTZs2pKSk2M/btm1rB45rrrmGJk2aBKOqEef06dOFJpYUXDy8UaNGhSaWREVFaQAEbQiVKi0aAANLRMZihT9P44wxYy/0Wm331KVYuHAhS5YssSeWnD17fi7mc889x8svvwzA4cOHSUtLo2PHjjqxJAA8Fw93j/M8cuSIo0yNGjVISEjgu+++0wCoDaFSpUMDYPjQdk+VluzsbHtiSVJSEo8//jhXX23NvZw4cSJ//vOfqVmzpmNiSffu3XViSQDk5+cXmliyZ88e924NgNoQKlU6dBZw+NB2TwXCxIkTefPNN9m3b59je6VKlbjuuuuYPn166b9pbi7s3QsHDkB2NlSoAI0aQdOmUK5c6b9fmHEvHn733XdrANSGUKnIpgFQKf8xxhSaWLJt2zZuuOEGZs+eDcDZs2e55ppr6N27tz2btcQTS4yBTZvAPTYxL+/8Pvdiyq1bQ3w86MLXOgYQtCFUKtJpAFQqsNLT0zl+/DitW7cGYNGiRfZlY7d27drZl4yHDh1a9Hp3xsDSpZCW5gx+BUVHQ716kJAQ8SGwuO2e9pkqpZRSqlTUrVvX0cN35ZVXMn/+fLuHcMWKFWzbto1t27bx0UcfsXv3bjsALlmyhFq1atGhQ4fzE0s2bbpw+ANrf1qaVb5TJ3+dXpmiAVAppZRSflG5cmUGDhzIwIEDAWtiydq1a0lKSmLLli00a9bMLvvII4+wefNmatWqZU0sSUgg0Ri6N2tG+XLlWPr72uRkFp5xXL56PgkfHbFCYEoKtG+vYwKLQb9CSimllAqIChUq0Lt3b3r37u3YnpeXR5cuXTh+/Dj79+9n1qxZzJo1C4BKFSrwt3vuoV3mXV6PWSgU7t0LLVr4pf5liQZApZRSSgVVdHQ0kyZNwhjD7t27rUvGU6eStHYtvxw4QKNateyys5nNd3xHJzrRmc50wuOSb16eNUtYA+AFaQBUSimlVEgQEVq0aEGLFi24p2lTyMggLTOTqpUqsdJVZh3r2Ob672u+BqD9441JbN+egZ07c/tNNwXvBMKIBkCllFJKhR7XgtL1qld3bH6CJxjMYDaykQ1sYCtb2bp/P1v372dXWhq333YbYI03/OSTT0hMTKR9+/Z6x5ICNAAqpZRSKvQ0agSHDxeaAVyZylzh+g8gm2xiX1pF0tatNG/QwHodsGbNGh5++GEAatWqRd++fe3lZy6//HLKly8f2PMJMRoAlVJKKRV6mjaFn3+2n5avnu91FnCV6uXo07Ytfdq2tdYDbNoUgIoVK/Kb3/yGpKQkDhw4wLfffsu3334LWLOTt2/fTuPGjQFrEkq0e1HpCKEBUCmllFKhp1w56w4fKSmQl2ct9VKU6GirvGsJmMsvv5wvv/wSYwy7du1y3LHkxIkTNHL1FAL07t0bEbF7CPv27Uvt2rX9eXZBp3cCUUqVWXonEKXCnJ/uBHLq1CliY2MBOH36NDVr1iQnx3m/8w4dOpCYmMiDDz5Ijx7h04wUt93TEZFKKaWUCk0iVqhr3doKeQUv05Yrd77nrwS3gXOHP4AqVapw5MgR5s2bxwsvvEC/fv2oWLEiW7Zs4Z///Ce7d++2yy5atIh//vOfbNmyhXDvQNNLwEoppZQKXSLW7d3at7cWeT5wAHJyoHx5a8JH06aXfOePqlWrMmjQIAYNGgRAVlaWfceSfv362eU+/fRTPv/8cwBq167tmFjSrVu3sJpYogFQKaWUUqGvXDlrgecALPIcExNDnz596NOnj2P7ddddR1ZWlj2xZObMmcycOROAwYMHM3fuXMCaVHLu3DmqVKni97peLA2ASimllFLFMGLECEaMGFFoYsmSJUvo2bOnXe6nn36iT58+dO/e3TGxpJbHHU2CLWwCoIjUAj4GBgEZwLPGmMnBrZVSSpUuEYkB3gcGALWAHcBzxpjvg1oxpZRNRGjZsiUtW7bk3nvvBSA/P9/ev3XrVvLz81m5ciUrV67kzTffBKBjx44kJibyxhtvOMYhBkM4TQJ5D8gG6gN3AR+ISMfgVkkppUpdOWAv0A+oDrwIfCUizYNZKaVU0TzvNHLPPfdw7Ngx5s6dy/PPP89VV11FTEwMmzdv5quvvqJy5cp22TFjxgRlYklY9ACKSBVgGBBvjDkFJIvIt8DvgGeCWjmllCpFxpjTwFiPTbNFZBfQHdgdjDoppUquWrVqDB48mMGDBwPWxJI1a9Zw8OBBOyyePn2aV155hdzcXADq1KlTaGJJuUuc4OJLWARAoA2QZ4zZ7rFtPdYnZKWUKrNEpD5WG7g52HVRSl28mJgYEhISHNvy8/N566237LGEBw8eZMaMGcyYMQOAyZMnM2LECADS0tKIjY119B5eirBYCFpEEoGvjTENPLb9HrjLGNO/QNlRwCjX03hgU6DqeYnqYI1tDAfhUtdwqSdoXf2lrTGmarArcbFEpDzwPbDTGPNQEeXCtd3zh3D6+fSHSD9/0K9Bsdq9cOkBPAVUK7CtGnCyYEFjzIfAhwAisiZc7gKgdS194VJP0Lr6i4iE3C0xRGQRvq9eLDXG9HWViwL+gzX2+bGijhmu7Z4/6PlH9vmDfg2K2+6FSwDcDpQTkdbGmBTXti7oJRGlVJgpeNXCGxERrFUP6gNDjDE5F3iJUkqVSFjMAnYNip4OjBeRKiKSAAzF+nSslFJlzQdAe+AmY8zZYFdGKVX2hEUAdHkEqASkAVOAh40xF+oB/NDvtSo9WtfSFy71BK2rv4RTXQEQkWbAQ0BX4JCInHI97irmIcLunEuZnr+K9K9Bsc4/LCaBKKWUUkqp0hNOPYBKKaWUUqoUaABUSimllIowZTIAikgtEflGRE6LSKqI/DbYdfJGRB4TkTUikiUinwW7PkURkRgR+dj19TwpIj+LyPXBrpc3IvKFiBwUkRMisl1EHgx2nS5ERFqLyDkR+SLYdfFFRBa56ugek/ZLsOtUFBEZLiJbXe3ATtd6omVeOP2u+lM4ta+lIVz+7vlLpH2/C7qY3/twWQampDzvG9wV+E5E1hdj0kigHQAmAIOxJriEMs/7k+4BhmDdn7STMWZ3MCvmxavASGNMloi0AxaJyM/GmLXBrlgR3gNWB7sSxfCYMeZfwa7EhYjIQOB14DfAKqBhcGsUUOH0u+pP4dS+loZw+bvnL5H2/S6oxL/3Za4H0OO+wS8aY04ZY5IB932DQ4oxZroxZgZwJNh1uRBjzGljzFhjzG5jTL4xZjbgvj9pSDHGbDbGZLmfuh6XBbFKRRKR4cBxYGGQq1KWjAPGG2NWuH5e9xtj9ge7UoEQTr+r/hRO7eulCqe/e/4SSd9vby7m977MBUB83ze4Y5DqUyaF+v1JReR9ETkDbAMOAnOCXCWvRKQaMB54Mth1KaZXRSRDRJaKSP9gV8YbEYkGegB1RWSHiOwTkXdFJBJ7BUL+d1WVCv27pxyK83tfFgNgLJBZYFsmELb3Aw01rvuTTgL+bYzZFuz6eGOMeQTre56ItYh4VtGvCJqXgI+NMXuDXZFieBpoCTTGWmdqloiEYs9qfaA8cDvW978r0A14IYh1Copw+F1VpUL/7ilbcX/vy2IALPZ9g1XJleT+pMFmjMlzXQppAjwc7PoUJCJdgQHAW0GuSrEYY1YaY04aY7KMMf8GlmKNMwk17jtn/N0Yc9AYkwH8jdCsa4m5JuMYH49kj3Jh87taUsX9GkQQ/bungJL93pfFSSB632A/CeP7k5YjNMcA9geaA3usLy2xQLSIdDDGXB7EehWXASTYlSjIGHNMRPZh1a/M0XsJF+9rEGH0754q8e99mesBDKf7BotIORGpCERj/eGvKCKhHMpD/v6kIlLPtfxHrIhEi8hgYATwQ7Dr5sWHWMG0q+vxD+A7rFlsIUVEaojIYPfPqFi3JbsKmBfsuvnwKfA/rp+HmsCfgdnBrVJAhfzvqr+FYft60cLp756/RNL3uwgl+703xpS5B1ALmAGcxpoO/dtg18lHPcdyfpaq+zE22PXyUddmrvqdw7rc4H7cFey6FahnXWAx1qzaE8BG4PfBrlcJfh6+CHY9ivi6rsa6pHQcWAEMDHa9iqhveeB9V10PAe8AFYNdrwCde1j8rgbg6xA27WspnW9Y/N3T77ffzr/Ev/d6L2CllFJKqQhT5i4BK6WUUkqpomkAVEoppZSKMBoAlVJKKaUijAZApZRSSqkIowFQKaWUUirCaABUSimllIowGgCVUkqpMkZEbhSR9sGuhwpdGgCVUkqpMkREYoCvKGP3gFalSwOgClsiMt918/fbCmwXEfnMte+1YNVPKaWCpA9QCfgy2BVRoUsDoApno4F8YIKIRHtsfxO4F/jIGPNMUGqmlFLBcw2wxRiTFOyKqNClAVCFLWPMeqybnbcHfgcgIs8BT2Bd/vhD8GqnlFJBcw3wz2BXQoU2vRewCmsi0gRIAQ5j9fz9HZgH3GyMyQ5m3ZRSKtBEpApwAGhujDkW7Pqo0KU9gCqsGWP2AW8DzbDC3zLgNm/hT0QeEZFdInJORNaKSGJga6uUUiVXwvHOVwHfaPhTF6IBUJUF6R7/P9IYc6ZgARH5DTAReAXohhUUvxeRuMBUUSmlLlpJxjt3Qy//qmLQS8AqrInICGAS1iXgBsA/jDEPeym3EthgjPm9x7YUYKox5tlA1VcppS6GiHyGFfbuN8Z85hrv/DLWeOcRxpj8YNZPhR8NgCpsicgQYAbwC9ag5yVAK6CTMWabR7kKwBmsRvJrj+3vAfHGmH6BrLdSSpWUjndWpU0vAauwJCJ9ganAPmCQMSYdeBEoBxRc+68OEI3VcHpy9xoqpVRIK8l4Z290DLQqSAOgCjsi0gWYDWQCA40xBwGMMVOBNcBQH41bwe5u8bJNKaVC1QXHO3ujY6CVNxoAVVgRkVZYlz0MMNgYs7NAEfd4vjc8tmUAeRTu7atH4V5BpZQKOa7xzm8Ch1yb/lSClz8BfGaM+cgYs9UY8z/AQaDQeGkVOTQAqrBijNlhjGlgjKlpjNngZf9/jTFijOnlsS0bWAsMLFB8INYnYaWUClmu8c7/BjYDnYFtwIMi0q4Yr60AdAfmF9g1H+uWcSpCaQBUkeJvwH0i8qCItBeRiUAj4B9BrpdSSvlUwvHO3ugYaOVVuWBXQKlAMMb8n4jUBl4AGgKbgCHGmNTg1kwppbwraryziNjjnYt5z18dA60ctAdQRQxjzPvGmObGmBhjTHdjzJJg10kppby5yPHO3ugYaOWVrgOolFJKlWGuhfDXG2NGeWzbDkzThfAjl14CVkoppcq2vwH/EZFVwFLgD+gY6IinAVAppZQqw3QMtPJGLwErpZRSSkUYnQSilFJKKRVhNAAqpZRSSkUYDYBKKaWUUhFGA6BSSimlVITRAKiUUkopFWE0ACqllFJKRRgNgEoppZRSEUYDoFJKKaVUhNEAqJRSSikVYTQAKqWUUkpFmP8PU02DC5lPzCIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x194.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xs = np.array([[1, 50], [5, 20], [3, 80], [5, 60]]).astype(np.float64)\n",
    "ys = np.array([0, 0, 1, 1])\n",
    "svm_clf = SVC(kernel=\"linear\", C=100)\n",
    "svm_clf.fit(Xs, ys)\n",
    "\n",
    "plt.figure(figsize=(9,2.7))\n",
    "plt.subplot(121)\n",
    "plt.plot(Xs[:, 0][ys==1], Xs[:, 1][ys==1], \"bo\")\n",
    "plt.plot(Xs[:, 0][ys==0], Xs[:, 1][ys==0], \"ms\")\n",
    "plot_svc_decision_boundary(svm_clf, 0, 6)\n",
    "plt.xlabel(\"$x_0$\", fontsize=20)\n",
    "plt.ylabel(\"$x_1$    \", fontsize=20, rotation=0)\n",
    "plt.title(\"Unscaled\", fontsize=16)\n",
    "plt.axis([0, 6, 0, 90])\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(Xs)\n",
    "svm_clf.fit(X_scaled, ys)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X_scaled[:, 0][ys==1], X_scaled[:, 1][ys==1], \"bo\")\n",
    "plt.plot(X_scaled[:, 0][ys==0], X_scaled[:, 1][ys==0], \"ms\")\n",
    "plot_svc_decision_boundary(svm_clf, -2, 2)\n",
    "plt.xlabel(\"$x'_0$\", fontsize=20)\n",
    "plt.ylabel(\"$x'_1$  \", fontsize=20, rotation=0)\n",
    "plt.title(\"Scaled\", fontsize=16)\n",
    "plt.axis([-2, 2, -2, 2])\n",
    "\n",
    "save_fig(\"sensitivity_to_feature_scales_plot\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Soft Margin Classification\n",
    "**Code to generate Figure 5–3. Hard margin sensitivity to outliers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure sensitivity_to_outliers_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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u5+7du6xduxaz2cyqVass6w6KFi1K7969MZlMNG/e3K41ADExMbz11lssWLAgydzkRx99lMDAQHr06EHhwoVtrk8IYV1ujZ3SQRZCiEyQW4O8LXIqdh48eBCz2czChQstGSqUUowaNYopU6bYXd+1a9dYsmQJZrOZnTt3Ws5XqlSJwYMHM3jw4DRzqScXGxvL77//jtlsZuXKlZYFwgsWLGDAgAF2t08IkVJujZ0yB1kIITKBvSu3BdSqVYv//e9/nD9/3rL4ztnZmYceeshS5sSJE2zevNmmucAlS5Zk2LBh7Nixw7L4zsfHh5MnTzJ+/HgqVqzIo48+SlBQEDdv3ky3PkdHR9q3b8+iRYsIDw9n9uzZtG3blu7du1vKjB8/nnHjxvH3339n7EMQooDLrbFTRpCFEPmOl1fqK6A9PVNfSW1v+axsS0blxRHk1Fy7dg03Nzc8PDwAePHFF5kxYwYBAQGWUeCKFSvaXF9sbCzr16/HbDazYsUKyyiwh4cHTz75JIGBgbRq1SpJSkZbRUVF4eXlxb///gtA8+bNMZlM9O7dm2LFitldnxA5qSDGTZApFkLkGbt37+aBBx6gePHiOd2UPMver+yy8iu+7Pr6ML90kJObPHkyn3/+eZIMFY888ggmk4mePXtSpEgRm+uKiIhg2bJlmM1mtm7dajnv5+fHoEGDMJlMVK5c2eb64uLi2LRpE2azme+//57IyEgA3Nzc6NatG+PGjaNatWo21ydETiqIcdN4L+kgC5Hr/fbbb3Tu3Jly5cqxdetWfHx8crpJeVJBDPT5tYMMxijwhg0bLKPACXnLR44cyWeffZahOk+ePMm8efMwm82cOXPGcr5Zs2aWUWB7fkm9efMmK1asICgoiODgYAAOHDhArVq1LK/Lwj6RmxXEuGm8l3SQhcjVdu7cSevWrYmMjMTR0ZHSpUuzZcsWu0a0hKEgBvr83EFO7N9//7WMAk+dOtWyIc/333/Pvn37MJlMVKlSxeb64uLi2Lx5M2azmWXLlllGgV1dXenWrRsmk4m2bdvi5ORkc51nzpxhzZo1Sbbgbty4MXFxcQQGBtK3b19KlSplc31CZIeCGDeN95IOshC51smTJ6lXr55lLiMYq/mLFi1KcHAwdevWzcHW5T0FMdAXlA6yNS1btmTLli0ANGnShMDAQPr06WPXKHBkZGSSUeCEn4/e3t4MHDgQk8lE9erV7W7blStXqFy5MhEREQA4OzvTuXNnAgMDefzxx3F2dra7TiEyW0GMm8Z73UcWC6WUm1JqjFLqN6XUfqXUwcRH5jZViIInIiKC2NjYJOe01kRERNCiRYtUdwAUQtzz3nvv8dRTT1G4cGH++OMPnn/+eby8vOjTpw8hISE21VGoUCEGDRrE+vXrCQ0N5b333qNy5cpcvHiRjz/+mBo1atCgQQO++OILrl69anPbypQpw8WLF1m8eDHt27cnNjaWFStW0KVLF3x9fdm1a1dGb1sIkUVsXbY7ExgLhAI/AMuTHTZRSr2olNqtlIpWSgWlUS5QKRWrlLqZ6Ghl6/sIkdfUq1ePjRs3UqxYsRQbGkRGRtKxY0dWrFiRQ63LPl5exshB8sPLK/PeI7X6rUktsYGjY+p1ODqmXkduTWGU37Rs2ZJvvvmG8PBw5s2bx2OPPcadO3dYunRpki2u7969a1N9fn5+vPXWWxw7doxt27bx7LPPUqxYMfbs2cOIESPw9vamR48e/PjjjzbV6e7uTt++ffn11185d+4ckydPplq1akRGRiZJa7d+/XpLTmghbJXVsfN+4ybYFztzRdxMbXu95AdwDWhjS9l06ukBdAO+BILSKBcIbLW3ftlqWuR1f/31ly5Tpox2dHS0bJ+bcLi7u+tZs2YlKX/58mXdvHlzffz48RxqcebKrC1H06rH3iOr2pjZyMVbTeeUM2fO6Pfff19fv37dcm7kyJG6Xr16+rPPPtNXrlyxq75bt27pJUuW6A4dOmgHBwfL/83SpUvrl156Se/du9euLbPj4uKSbGEdFRWlixcvrh0dHXWnTp30smXL9O3bt+1qoyiYMiMuZWXczKw2ZgVrsdPWju15oKotZW2s7z3pIAuRuvPnz+sKFSpoZ2fnFJ1kDw8PPXHiRB0XF6f//fdf/dBDD2lHR0f90ksv5XSzM4V0kDNOOsjpi4uL01WqVLH8f3JyctLdunXTK1eu1NHR0XbVdeHCBT1lyhRdvXr1JP9Ha9asqT/55BN98eJFu9t3/vx53aVLF+3k5GSpr0SJEnrYsGF6x44ddnW+RcEiHeSMsxY7bVqkp5QaCVQHXtBax2VoqDppfe8BvlrrQCuvBwIzgCiM0ev5wIda65hUyj4LPAvg5+dXP3G6HiHyqmvXrtGqVSuOHz9OdHR0ktc8PDwIDAxk79697Nu3j+joaIoVK8aVK1fy/GKfzFqYkVY99kr+vjm9Lao19i7SK6ix8/bt26xatQqz2cyvv/5KXJzxI6106dLMmDGD3r1721Wf1pq9e/diNptZtGiRZW6yo6Mjjz/+OCaTic6dO+Pm5mZznZcvX2bRokWYzWb2799vOX/w4EFq1qxpV/tEwZAZcSkr42Z69efG2Gm1g6yUWpXsVEsgAjgKJJlwpbXuYmdj0usgB2D89nwGo2P+HTBfa/1hWvXmh5XYQiSIjIykQ4cO7N6925L3NYGHhwdaa8v5IkWKsHDhQjp37pwTTc000kHOuIKexSIjLl68yMKFCzGbzRw+fJiQkBCaNGkCwJEjRyhZsiTe3t4213fnzh1Wr16N2Wxm9erVxMQYYzrFixenb9++mEwmGjdunGKdQVoOHjyI2Wzm0KFDrF271nLta6+9Rv369enWrZtl10FRcEkHOeMy0kH+1tbKtdZP2dmYNDvIqZTvC4zWWtdPq1xBDfIi/7pz5w69e/dm3bp13Lp1K82ybdq0Yd26ddnUsqwhHeSMkw5yxmmtOXjwILVq1bJ0QFu3bs2mTZto3749gYGBdOnSxa5R4CtXrrB48WKCgoLYt2+f5XzVqlUxmUwMGjQIX1/fDLX3xIkTllzPRYoUoXfv3gQGBtK8eXO7Ot8i/5AOcsZZjZ2pzbvI6oN05iCnUr4PsDe9cgVlHp0oWGJjY/XQoUO1h4dHijnJiQ9XV1cdHh6e0829L56eqc9P8/S0rx5758s5ONj+vtbKOjhkzmeQUcgc5Exz9+5d3aNHjyRzgYsXL66fe+45HRISYvdc4IMHD+rXXntNe3p6WupTSuk2bdroBQsW6MjISLvqu3Hjhp45c6Zu1KhRkhhQqVIl/e677yZZlCgKhsyInVkZN7XOe7HT1g7qBqB4KueLAhtsqSO+vBPgBnyIMa/YDXBKpVwHwDP+8YPAYWB8evVLkBf5VVxcnG7Tpk2qC/cSDnd3dz158uQca2NmdW7tYW/AtdZGe46svJ/7IR3kzHf58mX92Wef6Xr16iX5vxYUFJSh+u7evat//vln3atXL+3i4mKpr0iRIvrpp5/WmzZtsrvzffToUT127Fhdrlw5SxyIiIiwvB4TE5Ohtorsk9tjZ2bEzbwYO21dpBcHeGmtLyc7Xxa4oLW2aWWQUmoCMD7Z6XeBbzDmNlfTWp9VSn0CDAIKA5eABcAkrXWaySYL+teEIv+aP38+zz33XIq5yMn5+fkRGhqaI1+z5sTXZ5m585M9sup+7odMschahw8ftmxHvXfvXkqWLAnA119/jZubGz169KBQoUI213f9+nW+++47zGYzf/zxh+V8QEAAgwcPZvDgwVSsWNHm+mJjY9mwYQMnTpxg2LBhAERHR1OlShUeeeQRTCYTjz76KA7WktSKHJPbY2dWT73IaRnaalopVS/+4W6gHUZGiQSOQHvgGa21f+Y1NeMkyIv86JdffqFnz57pdo7B2Als/fr1NG7cOBtallRuD/LplbdHXgrytpDYaTutteUX0JiYGHx9fbl06RKFCxemV69emEwmWrRoYVdH9NixY5jNZubPn8/58+ct51u2bInJZKJXr14UKVLE7rYGBwfTunVry/Py5cszaNAgTCaTZQ6zyHm5PXZKBzn1i+IwvgICSO0jigJGaK2/yZRW3icJ8iI/qlWrFidOnODu3bsptqNOzsHBgYEDB2I2m7Opdffk9iCfXnl75KUgbwuJnRkTHR1NUFAQZrM5yXbW/v7+DB48mGeffZZy5crZXF9sbCzBwcEEBQWxYsUKyy/FHh4e9OjRwzIK7Ght28ZUnDx5knnz5jFv3jxCQ0Mt55s2bcrPP/9sGQkXOSe3x07pIKd+UQWMjvEpoBFwJdHLd4DLWuu0f2JnIwnyIj+KiYkhJCSEVatWsWLFCi5cuICTkxORkZGplnd3d+eff/7J9tRPuT3Ip1feHnkpyNtCYuf9O3bsmKUjmjAKvGXLFh5++GEg6cizLf7991++//57zGYzmzdvtpz39fVl8ODBdo8Cx8XFsWXLFss0kfLly3PkyBFLm0JCQmjYsCFOTk421ykyR26PndJBzgckyIuC4OLFi/z6668sXbqUTZs24eTkxK1btyyjy4ULF2bmzJkMGjQoW9uV24N8euXtkRvDpnSQc4eEUeDVq1czdepUSwe0a9euFClSBJPJROvWre0aBT516pSl83369GnL+SZNmmAymejTpw8lSpSwub7IyEjOnDlDtWrVADh9+jQBAQF4eXkxcOBATCYTNWrUsLk+cX9ye+yUDnLKCwbbWrnWet59tC3TSJAXBU1qo8txcXHUqlWL1atf4dSpt4iOPourqx8BAe/j6Tkg1Xq8vODSpZTnPT0hPNy2tlirI7+x5zPJTtJBzr2uXLmCl5eXZdc+X19fy1zgqlWr2lxPXFwcW7duxWw2s3TpUm7evAmAq6srXbt2xWQy0a5dO7tHgbds2cKQIUM4ceKE5Vz9+vUxmUz069eP0qVL21VfQZEZcTOtevKbvBY70+og/5fslAvgDCRsNe2AsaNetNa6aCa2NcMkyIuCLiwsjDVr1nD8+BqeeGINcXH3NhdxcPCgatXZqXaSs3IEI6/sW5AbRzbsIR3k3O306dPMnz8fs9nMqVOnLOcbN27M119/bfcW0pGRkaxcuRKz2cz69esTUqTi5eXFgAEDMJlMdtWpteaPP/7AbDazZMkSIiIiAGMjkkuXLuHu7m5X+woCiZt5P27CfU6xUEo9AUwAXgZ2xJ9uDEzFSL/2c6a19D5IkBfCEBLiT3T0mRTnXV0r0LRpaIrzEujzfqCXDnLeoLVm69atBAUFsWzZMm7fvk1YWJhllPbYsWNUqlTJrlHgc+fOsWDBAoKCgjh+/LjlfL169SyjwGXKlLG5vtu3b/Pjjz9iNpspWbIkCxYsAODu3bu8/fbb9OnTh7p16xb4Xfskbub9uAn330H+E3haax2S7HxTjB3xbP+OKAtJkBfCsHGjA/cS0CSmaNUqLuVZCfR5PtBLBznvuXXrFjt37qRVq1aAMX/Zz8+P2NhYyyhwrVq1bK5Pa82OHTsso8A3btwAwMnJiSeeeILAwEA6duyIi4uLzXXGxcVZUtb99NNPdOnSBYCaNWtiMpkYMGAAXl5eNteXn0jczPtxE6zHTlsTNfoDqS2ZvwX43Ue7REESFGT8r4//YZCEv7/x2saN9tc7YYJxbWDg/bQuX3F1Tf2/pbXzQojs5+HhYekcA5w/f56iRYty6dIlpk6dSu3atalXrx6fffYZV65csV5RPKUUTZo04csvv+TixYssXbqUjh07orXmxx9/pHv37vj4+DBy5Ej27NmDLQNkifM5V6lShREjRlCqVCkOHTrEqFGj8PX15YknnmDp0qXppqEUIi+xtYO8A5iulLIkdIx/PA34w+pVeV1goPUOnSgYWrXKk53vgID3cXBImubNwcGDgID3c6hFQoj0VKhQgaNHj/LHH3/wwgsvULx4cfbt28fLL7+Mj48Phw4dsrkuNzc3evXqxerVqzl//jyffPIJNWrU4OrVq3z++ec0aNCAWrVq8cknn3Dx4kWb6qxatSrTp08nLCyMFStW0LVrV5RS/PLLL7z99tuyS5/IV2z91zwEKAWEKqVClVKhQChQFhiaNU0T+U6xYlC1KvjJKGZW8/QcQNWqs3F1NVKZu7pWsLpAzyhvrZ6sa2NWsvZz2tr5vHqfIv9RStG4cWNmzpxpGQV+4okn8Pf3p3r16pZyM2fOZPfu3TaNAnt5efHaa69x8OBB9uzZw8iRIyldujSHDx9m9OjR+Pr60rFjR7777jtu376dbn0uLi50796dH374gbCwMD777DPeeusty5zks2fPUq1aNT744APOnTuX8Q8jl8tvcdOaAhs3tdY2HRgbhrQDRgIvAW2Jn8OcW4769evrTGUyaQ1aP/JI5tYrUqpQwfisg4Ptv3b8eONakylz26S18XefVXXnM56exkeV/HBwSP28p6ftdaRWNiPl7W27vfXkJGC3zi2xU2S627dvWx5fuHBBOzg4aEBXq1ZNT548WV+4cMGu+qKjo/UPP/ygu3fvrp2dnTXGogVdrFgx/dxzz+nt27fruLi4DLV1ypQplvqUUrpNmzZ6/vz5+ubNmxmqL7+zFn+yMhZmRszLD3FTa+ux0+bvQ+Lr+U1rPV1r/ZnWel18xUIIYTWPZ1zKNYFWy1urI7POW5NZ9QiRVVxdXZM8TxgFPnr0KGPGjKF8+fJ06NCBJUuW2DwK3LVrV1asWEFYWBjTp0+nfv36RERE8NVXX9GsWTMefPBB3n//fc6ePWtXW1955RVWr15Nr169cHZ25vfff2fQoEF4eXnxwgsv2DTqXZDYE2eyOkZmZVvyGqsdZKXUq0opt0SPrR7Z19xcImFealAQ/PsvvP46VKoE7u4QEADvvAOJA9T69dC+PZQuDYUKQcuWsGVL6nUnXnAWFwfTpkHt2sZ1pUpBly6wc2fa7fv3X6Oe2rWhcGHjqFULxo+H+NyWqdq0CXr2BF9fcHExpkRUrgzdusFXX6Xs6fz3H0yaBPXrQ5EixjU+PtCgAYweDYcPJy2f1iK9xM6ehWeegfLlwc0NKlaEUaPSbnt6tm6Fvn2Ne3N1NT7LNm1g8eL8sQxXCFFg+Pj4MG3aNMLCwiyL7xwdHfn1118ZOHCgJYexrUqXLs2IESPYvXu3ZfGdl5cXx48f5+2338bf3582bdowf/58q1vcJ+bk5ETHjh1ZunQp4eHhfPnllzRp0oSbN29y5coVy1SMmJiYJDsD2iQmBk6fhm3bIDjY+PP0aeO8EJkptWHl+N/uTgOlEj22dpyyVkd2H9k2xSLha/epU7V+8EHjcaFCWjs73/uOoXNno+yMGVorZXzPXLTovdddXLTeujXleyZMFxg8WOsePYzHTk5aFyt271pHR62XLEm9zSdO3JuuAFp7eBhHwnM/P62PH0953VdfJf2OxMPDuKfE56Ki7pW/cUPratXuvebgoHWJEkm/Tx8zJul7fPtt6p+n1vfa/PXXWpcpYzwuXFhrN7d79T3wgNZhYdY/M2vTIF5/Pel9FCmStJ19+2odG5v6tTLFwma2fkWY+LCnDnvfM7PanlcgUywKtCtXrujPP/9cjx492nIuNjZWP/roo3rSpEn6zJkzdtV39+5d/csvv+g+ffpoV1dXy5SJwoUL66eeekpv3LhRx1qLm1b89ddf+siRI5bnq1ev1oBu2bKlnjt3ro6IiLB+cVyc1gcPar18uXEsXXrvSDh38KBRLo+537iZXh2ZUT6r6sgNrMVOqyPIWuuKWuuriR5bOwKyrPee2737rvFvYcsWuHnTOL7+Gpyc4KefjNHVl1+GsWPh6lVjBDQ0FJo2hTt34JVXrNf944/GMXWqMSJ84wb8/Te0bQuxsfDUU3DyZNJr7tyBJ5+EM2eM0dfffrvXrt9/NxbHnT0L3btDdPS9627dgtdeMx4//bRRJjLSuO7qVVizBvr1SzpT/7PP4OhRKFMGfv7ZqO/aNWPk/Phx+OgjY1TdXqNGGSPXW7YYI9SRkfDDD8bo+99/g8lkX32ffQZTphjtnDkTrl83Ps/ISFi6FLy9YckSmDzZ/rYKIUQuUbp0aV588UWmTJliObdp0yaCg4MZN24c/v7+PPbYY8ybN8/mUeCEKRvh4eHMmjWLpk2bcvPmTb799ltatWpFpUqVGD9+PCeT/yyyomrVqlSrVs3y/PTp07i7u7N582aGDBmCl5cXAwcOZN26dUlTxmltjBSfOGH8/EueTi7h3IkTRjmtbWqPEGlKrdec/AAcbSmX00e2jyA7ORkjtsk9/fS9X6Oeeirl66GhxqgyaJ38t/qE0VDQ+r33Ul4bFaV11arG60OGJH1t3rx77Tp0KOW1hw/fG+WeO/fe+R07tGUUPCYm5XWp6dDBuOajj2wrr7VtI8hubql/phs23PtctmxJ+pq1EeTr141RaCcn4x5TExJi/F2UKKF1dHTK11u1MuoODEzv7go8GUHOWcgIskjm7t27es2aNbpv375JRoELFSqkTSaTvn79ut11Hjt2TL/11lu6fPnylvoA3aJFCz1nzpy0R4FTERERoefOnatbtmyZpL6WLVveK5Qwcpx41NjakTCSnIfICHLOshY7bV2kF6GUWquUekMp1VQp5ZjpPfW8qFcveOCBlOfbtLn3+I03Ur5eocK965LP003g4WGMPifn5nZvtHf58qS/KX//vfFnt25Qo0bKa6tXN+YYgzF6mqBoUePPu3eNEWNbJFxjY/5Mm/Xunfpn+uij0KyZ8TjhPtOzfLkxCv7ww9CoUeplmjQx5o1fvw579qR83cMj6Z+ZyMvLmJKd/Mjtm1JZa7e9UksRZG/apMxKs1RQ0jWJgsXJyYnHH3+cxYsXEx4ebll8FxkZyfr16ylSpIilbHh4uE11VqlShffee4/Q0FDL4jsPDw+2bNnCM888g5eXFwMGDEg5CmxF0aJFefrpp9m0aRMnT55kwoQJVKxYkdatWxsFYmII27GDWWvWcP3mTbyGdkb17pXi8Bra2SifMJKcC+ckZ0bszOoYaU/My+9x09YOcndgF/AEsBG4kbjDnFWNy/Vq1kz9fNmyxp9ubql39uDev6Dr11N/vUEDY2Feah55xPjzxg1jcUKCvXuNPx991GqTSQg6CWXBWIhXubIxRaNpU2Nh4F9/pf01VceOxp/Tp8OgQcY0jP/+s17eVmkt4Eu478RtT8v27cafO3YYkcnakbBCO7V8ncWKGX8m/EKQifLqCuCMtC+1MYbUfh6Hh9teNiPlrcmseoTIrYoXL86zzz7Ltm3bOH78ON988w2OjsZY16VLlyhfvjwPP/wwc+bMsWmRn4ODg2XKRnh4ON9++y2PPPIIUVFRLFq0iHbt2lGhQgXeeOMN/vrrL5vaGBAQwPjx4/n7778ZM2aMcfLcOeZv2sQLc+bg/dxzXIoYDKwGknaAL0W4Ja0sF+ZfzozYmVmxMDNiXn6PmzZ1kLWR0u1trfXDQHGMDnMYMBHYmnXNy+W8vVM/Hx908PS0/uthQpm7d1N/vVy51M8nfy3x9qMJj9O61tfX+PPq1XsdYEdHWLTIuO7UKXj1VXjoIWPeb69esGpVys7y4MHw7LPG+QULjA5z8eJQt66RxSOjI8u23LcNW64C99oQFWVEJmtHwt/BrVsp68jCDrIQQuSEypUr07ZtW8vzffv24eLiwrZt2xg6dCheXl7079+f3377zaZR4CJFihAYGMjGjRs5deoU7777LgEBAVy4cIGPPvqIhx56iMaNG/Pll19y7dq1dOtzcHDAzS2+wxsWRk1fX9rUrMmdmBhgGdAJ8AVeA1LZXTA2FsLCbPkohLDK5jzISilPpVQfYCowA+gLbMPoJIvslNbILiRdgGerBg2Mr6UWLDA6vwEBxqK777+Hrl3hiSdSLoz46itjisg77xgjv66usH+/sTixcmVYt87+dqQlvftOLiEt3Suv2Da1K7XtpKWDLITI5x5//HHLKHCrVq24ffs2ixcvpn379gQEBBAVFWVzXRUrVuSdd97h77//tiy+K1KkCDt37mTYsGF4e3vTq1cvfv75Z2JsmQZx5w4d69Vj3bhxnJkxA/gAqApcwuiOjEv9OmuDTwkkXZxIh00dZKXUEeAU8DwQDjwHFNdat9Jav5uF7Su40vrtN/HobJkyKR+fOWP92vPnjT9LlUo5uu3uDgMGgNlsZMg4dcqYQ62UMYVi1qyU9VWvbmTzCA42pnz89JMx9SQy0sg4kV6QSs6W+058z2lJmMZy9Kh9bUjso4+MzvPw4RmvQwghcrmEUeDg4GBOnz7NxIkTqVSpEtWqVcPd3R2AuLg45s6dy1Ub1qoopWjRogVz5swhPDychQsX0q5dO+7evcv3339P586dKVeunGULbKtcXCwPy5cuDbwB/An8AbyA0R0xrD90iG5TprBy507uWKtPazh0yPhmdN8+42fOP/8Yf+7bZ5w/dMj+ARmR79g6glwMiAVuAZHAf2D935/IBLt2pf6VPxgbeoAxpaFixXvn69Uz/gwOtl7vhg1Jy6alYkX44APo0yfp+1rj4gKdOsGyZcbzixeNUWl7pPUeCa/Z0nYw5lMnXGfr4kMhhCjg/P39GTduHCdOnGDhwoWW8wkL8by9venZsyc//fQTd20YBPHw8KB///6sXbuWs2fP8uGHH/Lggw9y+fJlpk6dSu3atalbty6ffvoply9fTnqxj8+9KYkWCmgMzAQ6WM5+GxzMj7t30+OTT/Dp0YMRI0awZ88edEJnV2tJFydsZuscZF+gHrASqAP8AFxTSq1SSqWRzDcppdSLSqndSqlopVRQOmVfUUqFK6UilFLfKKVc0yqf79y6ZeTwTS462siNDEZGisSjwAkZKtasMX4TTu7IkXsZIHr3vnf+Tjq/68SPHiSZupHWNQnlk19ji+++M0auk9u82QhYYMyLtkWvXsZCx9u3jZ390mJtsWQWyqsrgK21z8FKNMnt9yOESJ1SipIlS1qeu7q60r59e2JjY1m+fDldunTB19eXV199lQMHDthUp6+vL2PHjuXo0aPs2LGDYcOGUaJECfbv388rr7xCuXLlLFtg37lzx8jpn4hnsdS30fYsdptPBg/mk0GDqOnnx9Xr1/niiy9o0KABNWvW5JtvvjGmBF6+nLJjnFxsrFHOWpapDJLYmbfYPAdZa/231noOYAJ6Y3SSOwCf2PF+YcB7wDdpFVJKtQfGAo8B/kAAULCmchQrBuPGGZ3khPlfp04Z84H//NPIkDF2bNJr+vQxtpQGI9Xb77/f+w14/XpjId3du8a0iAED7l33yy/GaOvXXyednnHrlnEuYQShfft7r7VpAyNHGh3XxPPTjhy5N5fX29t6pg9rXFygQ4d7GSji4oxpGwmd/7ZtoXlz2+oqVQo+/NB4/O23xi8FiQPe7dvGFtTDh1uvM2Fb8dTmJ9+nzFgB7OiYetqgFAMuGSifVkqi1NodG5v6ebD+nnkxzZ0QBVWTJk349ddfOXv2rGXx3eXLl5k2bRodO3a0aUFfAqUUjRo1YsaMGVy8eJFly5bRqVMntNasWrWKJ598Em9vb0a88gq779xBx/ciw7/+Cb10WYoj/Ouf8CpenNe6dePATz+xd+9eXnrpJUqXLs2RI0c4ffKkZeQ48vZtHHp3SjVdnGOf+J81idLF2RtnMyN2enoaa8glbuYcJ1sKKaUaAo/GH80BV2Af8D8gje/zk9Jar4ivrwHGElRrTMBcrfWR+PKTgIUYneaCoWtXI23ayy8bo5+FChlzfMH4H/Lttyl3qnNxMXL/tmljdHTbtr2XvzdhuoafH6xYYSyoS+yPP4wDjBFgNzfj/RJ6OB07GlkrEvz7L3z+uXE4OBgd+qgoo9MJxvvOn2/sKmiPTz6BN980OqyFCxuRI6ED/sADxvxoe4wYYexg+M47xtSPZcuMtrm6GucTFvL5+9tXby6R0PysOJ9ZaeislbfWltye5k6Igq5cuXKMGTOG119/nd27dxMUFES5cuUsaeP++ecfnnnmGQYOHEjnzp1xTf7zJhlXV1d69uxJz549uXTpEgsXLsRsNnPw4EG++OILvvjiC6r5+2Nq0YKBzZvjk2hUOwlHRyhbFlWzJnWVom7dukyZMoU1a9ZQp1QpY1QYmLVuHZoRQB+M7kZTjGkbEKeTrc05d464uIqkxt4YZk9sk7iZ82wdQd6GkdrtAMbocUmtdROt9Vit9dosaFf1+PdKcADwVEqVyoL3yp2UMjpzU6caKdfu3IESJYw5vtu3Q9++qV/3wANw4IDRIUy8WUiNGsaI9MGDUKVK0mtatzY6syaTMeLr4WF0zkuVMjrbZrMxipu4sztnjrE479FHjU53Qif2wQfhxReNkdrHHrP/vh94AHbvNra8LlbM6CD7+xubo+zebT21Xlreftv4TJ591siuobWxiNDb2xit/vJLI1eyEEIImymlaNiwITNmzODNN9+0nF+8eDE//vgjvXr1wtvbm+HDh7Nr1657c4HT4OnpaZmysW/fPl5++WXKlCnD0dBQxsyfT/lhw3j8gw9Ysm0bUQlT/ZycjM5x5crG4EqiqYcuLi507dqVCkpZplbsDw0FIoDZGGN+VTG+3D6btDGSLq5AU7b8g1VKFdJap795u61vqtR7gK/WOtDK6yeB4VrrX+OfO2MsCqyotQ5NVvZZ4FkAPz+/+mfSyuCQF0yYYHQ8TSYICsrp1ohcLq1dmFL7r21PeXvrtiYju+zJ2hj7KaX2aK0b2FE+f8VOkWtcvnyZRYsWERQUlGRu8kMPPcTTTz/Na6+9hrIjMNy9e5c1a9ZgNpuTLAwsVqgQvVu3xjRoEM26dUM5O1uvJDjYyFYRT/WuBpiBBUBCZigFvIleWvvedWXKoB5tZbXa+42z1kjczD7WYqeti/QyrXNso5tA4sSzCY9TbNWmtZ6ttW6gtW5Qxtb0X0IIUcBJ7BRZpWzZsrz88svs37/fsviuTJky/Pnnn6xdu9bSOdZa25Rj2dnZmS5durB8+XIuXrxoWXwXERnJ1z/9xMO9e1O1enXee+89rP6ilyhdnKE6MAVj1HgNxnQLF+Be5/jw2bNsPHQIsDKvQeRrNi/Sy2ZHSPyv1Hh8SWstubqEEEKIPKJ27dpMnTqVCxcusGrVKsaNu7exx/bt2/Hy8mLo0KFs3brVpikYpUqVskzZOHLkCK+//jo+Pj6cOHGCcePG4e/vT+vWrTGbzdy8efPehammiwNjKdbjwBKMbR66Wl75+OefeXTkSKASMB44maHPQORN2dpBVko5KaXcAEfAUSnlppRKbRXXPGCIUqqaUqoE8DYQlI1NFSJPsJYeKDPOZ1YaOkltJIRwdnamc+fOtGzZ0nIuODiYf//9lzlz5tCiRQsqV67MxIkTCQ0NtanOatWqMXnyZM6ePcuvv/5K3759cXNzIzg4mMDAQLy8vCybn8SVK5fkWgeVWme8OA7q3jSNB8uVo3z58kAoxqbBDwAtgDlAhN0xzJ7YJnEz59k0BznT3kypCRi/hiX2Lkbat6NANa312fiyrwJjAHdgOfC81jrNpLoNGjTQu3fvzuxmZy+ZgyyEyAB75yAnli9ip8iT/vzzT8xmM/Pnzycs0YK4Pn36sGTJErvri4iIYOnSpZjNZrYl5M4HKlSowODHH2dwzZo8ULZs+hXFL/qLq16djRs3EhQUxPLly7kVnxHqjTfe4IMPPrC7fSL3sRY7s7WDnNUkyAshCirpIIu8LDY2lt9//x2z2czKlSsZNmwY//vf/wC4fv06+/bto1WrVjhYG0JNxYkTJ5g3bx7z5s3j7Nl7GSqaP/ggppYt6d2sGcUSUqEmFp8uLnlGjP/++4/ly5djNpv58ssvefDBBwEwm838+eefmEwmHnrooQx+AiKn2N1Bjh/BtYnWeup9tC3TSJAXQhRU0kEW+UVERAR37twhYfHol19+ybBhw/Dz82PQoEGYTCYqV65sW2UxMcSdOcOmX37BvHo132/eTGT8wkA3Fxe6N2yI6ZFHaFOrFo4uLkYqiMqVjdSoNqaSqFu3Lvv37wegUaNGmEwm+vbtm2QXQpF7ZaSDfNrGurXWOuB+GpdZJMgLIQoq6SCL/Oqbb75h4sSJSTJUNGvWDJPJRO/evSlevHjKi7Q28vGfOGE8j8+BfPP2bZbv3Il540aCE+2s6lO6NAN79MA0fDjVEnaktYHWmm3btmE2m/nuu+/47z8j2ZaLiwudO3dm1KhRNGnSxP6bFtlGplgIIUQ+Jh1kkZ/FxcWxefNmgoKC+P7774mMNLLPtmvXjrVrk+1XpjVs22bsnJfG9tehV68yf/duzBs3cvLkvQwVDRs2tIwClypl+/5kt27d4ocffsBsNrNu3Tq01ixatIh+/foBEB0dne6ugiL7SQdZCCHyMekgi4Li5s2brFixArPZjMlkYvDgwQDs3buXJUuWYGralOpap9k5tnB0RD/wANv/+88yCvzvv/8C9zJvBAYG8vjjj+Oc1kYkyVy4cIFFixbx4osv4u7uDsCQIUPYs2cPgYGB9O/fn7K2LBYUWe6+O8hKqZIYyQL9MLJpW2itJ2ZGI++XBHkhREElHWRR0L3wwgvMmjULgPoBAQS2akW/5s2JKb2fU4UWE+1wFde4UgRE9sMzusW9Cx0doUsXcHIiKiqKH3/8kaCgINatW0dcnLFJSJkyZRgwYAAmk4k6derY3ba4uDgCAgIs00ScnJzo0KEDJpOJTp06ychyDrqvDrJSqgmwGogGygAXAO/456Faa9sn7GQhCfJCiIJKOsiioNu5cydzP/2U7378kYj4dGzOTg40aaLp3EXTsKFRzkG7UPW/5+51kh0doW5dqFgxSX1hYWEsWLAAs9nM0aNHLedr166NyWSif//+eNqRgDg6OpqffvoJs9nMmjVriI0f4S5ZsiRz5syhe/fu93H3IqPua6tp4GNgIVAOuA20xhhJ3g1MzqxGCiGEEEJkRKNGjfhq+HAuzp7N4pde4vE6dYiNi2PLVs0ff9wrF6PvcNJj0b0TsbGQKAdzAh8fH15//XUOHz7Mzp07GT58OCVLluTAgQO8+uqrlCtXjs6dO7N8+XKio9PcpgEAV1dXevbsyU8//cSFCxeYOnUqtWvX5tq1a0mycuzdu5cLFy7c12ch7p+tI8gRQEOt9XGl1A2gqdb6T6VUQ2CR1trGfCtZS0ZBhBAFlYwgCwEEB8M//1iefk9vfl8PjRvfGyD++WdYuRKGNR/EgBYt8CpeHMqUgVat0q0+Ojqa1atXExQUxJo1a4iJiQGgRIkS9OvXD5PJRMOGDVE2pogDY7OUxPmTGzVqxJ49e2jbti0mk4lu3bpZ5jGLzHe/I8h3Ej2+BFSIf3wT8LnPtgkhhBBC3D+XJEukKFeyNH37Jp09sXUrnDoFo+bPx/f55+n00Ucs27KF27dvp1u9q6srPXr0YNWqVVy4cIFp06ZRp04drl+/zsyZM2ncuDHVqlXjo48+snkUOHHn+Pbt25QvXx5HR0fWrl1L//798fLyYujQoWzdupX8lFght7N1BHktME9rvVAp9RVQH/gcGAgU1lo3zdpm2kZGQYQQBZWMIAsBnD4N+/ZZMlhcct3CsSJfEXXnDi4uxt4fsXecORvclh9/v8zqffuIiS9bvHhxJkyYwEsvvWT32x44cACz2czChQu5fPkyAA4ODrRp08YyCuyR2q59Vly9epUlS5ZgNpvZtWuX5fzSpUvp1auX3e0T1t3vIr0GQBGtdbBSqgwwD2gOHAee1lofzOwGZ4QEeSFEQSUdZCGAmBhYtSpJirdwl800e+lL2raLJbBP6SRZLC5HRLB4+3bM+/axb/9+5syZw5AhQwA4f/48AL6+vja//d27d1m7di1ms5lVq1Zx547xBXzRokXp3bs3JpOJ5s2b2zUF4+jRo8ybN48ffviBPXv2UKhQIQCmT59OsWLFePLJJylcuLDN9YmkJA+yEELkY9JBFiLeoUPGDnrxneRVu3fTZ9o0XJycCPvqKwq5ud0r6+hobC1dsyYHDx6kYsWKFClSBIARI0YwY8YMyyhw9+7d7RoFvnbtmmUUeOfOnZbzlSpVYvDgwQwePBh/f3+b69NaWzrWUVFReHt7ExERQaFChXjyyScxmUy0atUKBwdbZ88KuM85yEqpDUqp4qmcL6qU2pAJ7RNCCCGEuH81akDZssYmIFozat48bt+9S0xsLNPXrLlXztHRKFejBgC1atWydI4BYmJicHZ2Zt26dQwcOBAvLy+eeeYZtmzZYtNc4JIlSzJs2DB27NjB0aNHGTt2LD4+Ppw8eZLx48dTsWJFHn30UYKCgrh582a69SUfdf74449p3rw5kZGRzJs3j8cee4yKFSsybtw4wlLJyiHsY+sUizjAS2t9Odn5ssAFrbXt28tkIRkFEUIUVDKCLEQiWsPhw6xatIgBn37KzfgFeEXd3QmbO5dCrq7GyHGNGsbEZCuuXbvGd999h9lsZseOHZbzb775Ju+//77dzYqNjWX9+vWYzWZWrFhhWRjo4eHBk08+SWBgoN2jwH///Tfz5s1j3rx5lo1I9u/fT+3ateM/Cm3XlI6CJkNTLJRS9eIf7gbaAdcSvewItAee0Vr7Z15TM06CvBCioJIOshBJaa2pWqUKJ/7+23LOw9WVt0eO5I0PPgAnJ7vq++uvvzCbzcyfP58VK1bQqFEjAH799VfCwsLo1atXkhHo9ERERLBs2TLMZjNbt261nPfz82PQoEGYTKYk+ZHTExcXx6ZNm1i/fj3vvfee5Xzr1q3x9PTEZDLRtm1bHB0dba6zIMhoBzkOSCiQ2q8fUcAIrfU3mdLK+yRBXghRUEkHWYikVq1axYABA1JMXyhatChhYWGWxW72io2NxcHBwTIq26pVKzZt2oSHhwc9evTAZDLRunVru0aBT548aRkFDg0NtZxv1qwZJpOJ3r17U7x4cbvbev78efz8/CxTQry9vRk4cCAmk4nq1avbXV9+lNE5yBWBShid40bxzxOOckDR3NI5FkIIIYQAY/R41KhRqc7tjYmJYfr06Rmu29HR0dI51lpjMplo0aIFt27dYsGCBbRt2xZ/f3/eeustTp06ZVOdlSpV4t133+XkyZMEBwcTGBhIoUKF2L59O8899xxeXl707ds3yeYktvD19eX06dNMmjSJBx54gIsXL/Lxxx9To0YNGjZsyF9//ZWhz6AgkCwWIkMuXVrIqVNvER19FldXPwIC3sfTc0BON0uIAktGkPMGiZ3Zw9rocYIiRYpw8eLFDI8ip+bUqVPMmzcPs9lsGQWeNWsWzz33XIbqi4yMZMWKFQQFBREcHHzfo8Baa7Zv347ZbOa7774jJiaG8PBwy7SQw4cPU7VqVZydc8Wysmxz32nelFIdgOFAANBea31OKfUMcFprvT5TW5tBEuSzx6VLCzl27Fni4m5Zzjk4eFC16mwJ9ELkEOkg534SO7OH1pqqVaty4sQJq2U8PDx4++23eeONNzL9/ePi4tiyZQvz58/n448/pkSJEgC88cYbnDp1CpPJRLt27XCyYw702bNnmT9/PmazOcl91a9fH5PJRL9+/ShdurTN9UVFRXHgwAGaNGkCGFtoe3t74+TkRP/+/QkMDKROnTo215eX3e9GIQOAWcAc4Hmgutb6lFLqOaCH1rp9Zjc4IyTIZ4+QEH+io8+kOO/qWoGmTUOzv0FCCOkg5wESO7NHeqPHCbJiFNmauLg4ypUrR3h4OABeXl6WUeAa8WnmbKG15o8//sBsNrNkyRIiIiIAcHZ2plOnTphMJjp27Gj3KPCJEyfo1q0bR48etZyrVasWJpOJAQMG4OnpaVd9ecl95UEGXgeGaq1fARJPfvkDqHP/zRN5SXT0WbvOCyGEkNiZHdKae5xcbGzsfc1FtoeDgwM7d+7k/fffp0qVKoSHh/PJJ59Qs2ZN6tevz4YNtm0poZSiadOmzJo1i4sXL7JkyRI6dOhAbGwsK1eupFu3bvj4+PDyyy+zb98+m/I1A1SuXJnDhw+za9cuXnzxRUqWLMnBgwd57bXXKFeuHH8nygRSUNjaQa4MhKRy/iZQNPOaI/ICV1c/u84LIYSQ2JkdfvrpJ86dO2dT2Vu3bvHhhx8SGRmZxa0ylC9fnjfffJO//vqLkJAQnnvuOYoVK8bevXtxS7S73/nz5y1bVKfF3d2dPn368Msvv3Du3DmmTJlC9erV+eeff/jss8+oV68etWvX5n//+59l5DotSikaNGjA559/TlhYGMuXL6dLly7UrFmTSpUqWcp98skn7Nixw+bOd15lawc5DKiSyvmWwMnMa47ICwIC3sfBIel2mw4OHgQE2J80XQghCgqJnVnvwIEDFCtWDE9PTzw9PXFxcUnyuru7e5LXixUrxsmT2duNUUrRpEkTZs2aRXh4OD/88ANNmza1vB4YGIiPjw8jR45kz549NnVEfXx8GD16NIcOHWL37t2MGDGCUqVKcejQIUaNGoWvry+dOnVi2bJlls1J0uLq6kqPHj348ccf2blzpyVrx8mTJxk9ejRNmjShWrVqfPTRR5w/fz7jH0ZuprVO98CYYvEn0Bz4D3gEMAFXgOG21BFfT0lgJRAJnAH6WykXCMRijFAnHK3Sq79+/fpaZI/w8AV6+/YKOjhY6e3bK+jw8AVpnhdCZC1gt7YxFic/JHZmH4md2att27YaYz8HDeiiRYvqZcuW5XSzrIqKitK1atVK0uYaNWrojz/+WIeFhdlVV3R0tF6xYoXu2rWrdnJystRXvHhx/fzzz+uQkBAdFxdnV53nzp3Tr776qi5btqylPqWUbtu2rV6wYIGOioqyq77cwFrstCeLxfvAK0DC9wDRwCda63G2dsaVUosxRq2HYMxdXg0001ofSVYuEGOHvodtrRtkoUlOkxXaQuQcWaSXd0nszDrt2rVj3bp1ludFixZl7ty59OzZMwdblTatNfv27cNsNrNw4UKuXr0KGPOYV65cSZcuXeyu88qVKyxevJigoCD27dtnOV+1alVMJhODBg3C19fX5vru3r3L2rVrMZvNrFq1ijt37uDm5kZ4eDjFihWzu3056X4X6aG1fgsojbFhSBOgjJ2d40LAk8A4rfVNrfVWYBUwyNY6RO526tRbSQI8QFzcLU6deiuHWiSEELmfxE6RmFKKevXq8dlnnxEWFmZZfOfu7k7z5s0t5VasWEFISIhNUzDKlCnDyJEj2bt3r2XxnaenJ8eOHePNN9/Ez8+Ptm3bsnDhQm7dupVufQlZM5YtW8bFixeZOXMmY8eOtXSO79y5Q5MmTZg4cWKSnQHzkjQ7yEopD6XUDKXUBaXUZYw0b6Fa651a6/SXiCZVBYjVWh9PdO4AYC3LdV2l1D9KqeNKqXFKqVQTBiqlnlVK7VZK7b5y5YqdTRKZSVZoC5F3SOzMPSR2CmtcXFzo1q0bK1euJDw8nFKlSgHGboDDhw+nWbNmPPjgg3zwwQc2L06sWbMmn3zyCefPn+fnn3+mV69eODs78/vvvzNw4EC8vLwYMmQImzdvtqnzXbJkSV544QXGjx9vObd27Vp27NjB+PHjqVixIq1ateLbb7/lv//+y9gHkQPSG0F+F2M+8GpgCdAW+DKD71UYiEh2LgIokkrZzUANoCzGqHM/YHRqlWqtZ2utG2itG5QpUyaDTROZQVZoC5F3SOzMPSR2ClsULlzY8jgqKsrSmT1+/DhvvfUWFSpUoE2bNixYsMCmzBxOTk488cQTLF26lPDwcL788kuaNGnCf//9xzfffMMjjzxi2QL79OnTdrW1Y8eO/Pbbb/Tv3x93d3c2bdrE008/jZeXF4MGDbIpDV9OS6+D3AMYorV+Vms9EngC6KaUcszAe6WWEq4oxqK/JLTWp7TWp7XWcVrrQ8BEIPdOGBKArNAWQoiMkNgp7FWkSBE+/vhjzp07x+rVq+nduzcuLi6sX7+eQYMGsXfvXrvqK1GiBM8//zwhISH89ddfvPHGG/j6+nL69GkmTJhAQEAAjzzyCN98841No8COjo6WKRvh4eHMmTOHFi1acOvWLXbv3p1kc5bcmgUjvQ5yeWBLwhOt9U6MjUJ8MvBexwEnpVTlROdqA0eslE9MAyoD7ymykafnAKpWnY2rawVA4epaQRaZCCFEOiR2ioxycnKiY8eOfPfdd1y8eJFZs2bRs2dPHn74Xo6DoUOHMn78eJvT2VWtWpUPPviA0NBQ1q1bx8CBA3F3d2fz5s0MGTLEMgr8+++/Exsbm259RYsWtUzZ+Pvvv5k9e7YlbVxoaCh+fn40a9aMr776ihs3bmToc8gK6XWQHYHk2apjANs3EI+ntY4EVgATlVKFlFLNga7A/ORllVIdlFKe8Y8fBMYBP9r7niJ1ly4tJCTEn40bHQgJ8efSpYVWy+7f34aNG5Xl2L+/jd11ZFZbhBAip9gbq7IydkrcFKkpUaIEzz33HMuWLbN0QMPCwvjmm2+YOHEiDzzwAC1btmTu3Ln8+++/6dbn6OhImzZtmD9/PuHh4cydO5eWLVty69YtFixYQNu2bfH39+fNN9/k2LFjNrWxUqVKtGjRwvL84MGDFCpUiJCQEJ5//nm8vLzo06cPa9asISYmJo2asl6aad6UUnHAOoyUbgk6AJsAyzJHrbVNOUeUUiWBbzDmMl8FxmqtFyml/ICjQDWt9Vml1CcY2S0KA5eABcAkrfXdtOqXVEXpsyed0P79bbhxY32KOtzdqxEdHZqiDi8vE+HhZptTFUlqIyEyj6R5yzr2xqqsjJ0SN+2TF9O8Zaa4uDiCg4Mxm80sX77ckqHC3d2dHj168N577+Hv729XnadOnWLevHnMmzcvydzkJk2aYDKZ6NOnDyVKlLC5vsjISFasWIHZbGbDhg2WhYH+/v4cP34cZ2dnu9pnL2uxM70O8re2VK61fuo+2pZpJMinLyTEn+joMynOu7pWoGnT0CTnNm60d1aLI8b+LunXbW9bhBBpkw5y1rE3VmVl7JS4aZ+C3kFO7L///uP777/HbDazadMmnJycCAsLI2GR7vXr1+3q2MbFxbF161bMZjNLly61LLxzdXWla9eumEwm2rVrh5OT7ZMOzp49y/z58zGbzdSpU4elS5cCRt7lOXPm0KtXL0qXLm3HXacvQ3mQtdZP2XJkaktFlsradEKpz0Wy9z0ltZEQIjfJ+lhle+yUuCkyqkiRIjz11FNs3LiRkydPMm/ePEvnODY2llq1atG4cWO+/PJLrl27lm59Dg4Olikb4eHhzJ8/nzZt2nDnzh2WLl3KE088Qfny5Rk1ahSHDh2yqY1+fn689dZbHDt2jDlz5ljOr127lmHDhuHj40P37t354YcfuHMn+QzgzGXzRiEif8jadEKpJzex9z0ltZEQIjfJ+lhle+yUuCkyQ0BAAP369bM8P378OBEREezcuZNhw4bh7e1Nr169WL16tU1zgQsVKsTAgQNZt24dZ86c4YMPPqBq1aqEh4fzv//9j1q1alGvXj2mT5+OLXnXlVIULXov8VmJEiXo0KEDsbGx/PDDD3Tv3p1y5crx0ksvsXfvXpvyNdtLOsgFjD3phIoXfyzVOtzdq6Vah4/Ps3alKpLURkKIvMDeWJWVsVPi5v3J6lHHvOqhhx4iPDzcsvju7t27fP/993Tq1AlfX19OnTplc13ly5fnjTfe4M8//7QsvitevDj79u3jpZdewsfHh27dutk1Cty8eXN++eUXzp8/z8cff0yNGjX4559/mD59Or17987obadJOsgFjKfnAIoWbZrkXNGiTfH0HJBi1TUYAT0xd/dqNG58JNWURFWqzMTLy8S90RBHvLxMVheOSGojIUReYE/c3L+/DXXq/J5lsVPipn369etHly5dLEeHDh2oXbt2TjcrV/Lw8GDAgAH89ttvnD17lg8//JCqVavi6uqaZCHfypUruXz5crr1KaVo0qQJX375JRcvXmTp0qV07NgRrTU//vgj3bt3x8fHh5EjR7Jnzx6bRoG9vb0ZNWoUBw8eZM+ePYwYMYKRI0dasnacP3+eTp06sXTpUm7fvp3hzwLSWaSX18hCk/QdPz6MsLCUmyE6O/tw925YKlcojDTUBslKIUTuJIv0so69cdNatgqJnSKv0VoTHh6Ot7c3AOHh4fj6+qKUomPHjphMJjp16oSLi4vNdYaHh7Nw4ULMZnOSucnVq1cnMDCQAQMGWN7PXh9++CFvvvkmAMWLF6dPnz4EBgbSuHFjSyc6uQxlschrJMinb+NGJ6wtCLGVZKUQIveRDnLWyYy4CRI7Rd537NgxRo0axZo1ayybhJQsWZL+/ftjMpmoX7++1Y5oclpr9u/fT1BQEIsWLeKff/4BjMV/7du3x2Qy0bVrV9zc3Gxu35UrV1i8eDFmsznJboJVqlRh6NChjBo1KsU1GcpiIfKj+w/ykpVCCFGw3H/cBImdIu+rWrUqP/30ExcuXOB///sfNWvW5Nq1a3zxxRc0adLEpgV4CZRS1K1bl88++4wLFy5YFt85OjqyZs0a+vbti5eXl2ULbFsGdMuUKWOZsnHw4EFee+01PD09OX78ONu3b7eUi42NJTIyMs26pINc4KS+WtoekpVCCFGw3H/cBImdIv/w9PTk1Vdf5cCBA+zdu5eXXnqJgQMHUrZsWcDIkTx48GCWLFlCVFRUuvW5uLjQtWtXVqxYQVhYGNOnT6d+/fpERETw1Vdf0axZMx588EHef/99zp617RfHmjVr8sknn3D+/HlWr17N2LFjLa+tW7cOLy8vXn31VavXSwe5gPHxeTbV887OPlauSPpViWSlEEIUNPbGTWvZKiR2ivwmYRT4008/JSgoyHJ+48aNzJ8/n379+uHt7c1zzz3H9u3bbRoFLl26NCNGjGD37t0cPnyY0aNH4+XlxfHjx3n77bfx9/e3bIGd3igwgJOTEx07dqRRo0aWc9u2bePmzZtpXi8d5AKmSpWZ+Pi8QOLV0j4+L9C8+YUUqYmKF3+Mhx6ab/NqaVldLYTIj+yNm9ayVUjsTNtvv/1Ghw4dKFWqFG5ublSpUoUxY8Zw/fr1DNX36aefsmLFihTnJ0yYkGKerFKKCRMmZOh9REp16tThiy++oEGDBkRERDB79myaN29O1apVee+992waVQZj4d6UKVM4d+4cv/zyC3369MHFxYX169czePBgvLy8eOqpp9i0aRNxcXE2t2/SpEn89ddfjBkzxmoZ6SDnIZcuLSQkxJ+NGx0ICfHn0qWFaZY/fnwYGzc6xacfcuL48WEAXL++iXtz6mLjn8ONG5uTXH/jxmb+/HNo/OIRTXT0Gf78cygA27aVS5LaaNu2ctlyT0IIYa/MiJ32xE0gS2NnfoybH3zwAe3bt8fNzY05c+awdu1ann/+eYKCgmjYsCHnzp2zu05rHeTUhISE8Mwzz9j9HiJ1JUuWZPjw4ezatcsyCuzt7c2JEyeYNWtWkqwXtuRCdnJyokOHDixZsoTw8HBmzZpF06ZNuXnzJkFBQbRq1YpKlSoxfvx4Tp48aVMbq1atSkBAgNXXJYtFHmFvGiBraYkcHIoTF3cjS9ro4FAcuCOpjYTIAZLFInWZFTuzkj2xMz/GzeDgYB577DFeeuklpk2bluS106dPU79+fWrXrk1wcLBd9fr7+/Pwww+zYMGCJOcnTJjAu+++myW7rwFER0fj6uqaJXXnZTExMfz+++/cuHGDvn37AkbWiapVq9KlSxdMJhOPPPIIDg62j90eP36cefPmMW/evCS/RD388MMEBgbSq1evJDvypUayWORxp069lSQgAsTF3eLUqbdSLR8WNjvV81nVOU6o25422ntPQghhr8yKnVnJntiZH+PmlClTKFmyJB9++GGK1ypWrMjYsWPZuHEjO3bsIDQ0FKVUkvmuYMx5VUqxceNGwOgcnzlzhoULF6KUQilFYGCg1TakNsXiwIEDdOnShRIlSuDu7k7z5s3ZsmVLkjKBgYH4+voSEhJCs2bNcHd35/XXX8/Ix5DvOTk58fjjj1s6xwC///47169fx2w207p1awICAnjnnXf4+++/baqzSpUqvPfee4SGhrJ+/XoGDRqEh4cHW7du5ZlnnsHLy8uy+UlCWjpbSQc5j7A/DVDmpCXKDJLaSAiRU/Jb7MxvcTMmJoZNmzbRtm1bq/luu3TpAsCGDRtsrnflypV4eXnRvn17QkJCCAkJYdy4cTZfv3fvXpo1a8a1a9f4+uuvWb58OaVKlaJNmzbs2bMnSdmIiAj69u1Lv379WLNmDf3797f5fQq6fv36WRbf+fn5cebMGSZNmkTlypV55JFHuHv3rk31ODg40Lp1a+bNm0d4eDjffvstjzzyCFFRUSxatIj27dtToUIF3njjDf766y/b6ryfGxPZx/40QJmTligzSGojIUROyW+xM7/FzatXrxIVFZVkK+PkEl6zZx5y3bp1cXV1pXTp0jRp0oQmTZpQqVIlm68fPXo0fn5+bNiwgZ49e9KxY0dWrlxJQEAAkyZNSlL25s2bTJ8+nREjRtCqVSsaN25s8/sIqFy5MpMmTeL06dOWxXceHh64ubnh7OwMGGnj1q9fb9MocJEiRQgMDGTjxo2cOnWKd999l4CAAC5cuMBHH33EQw89ROPGjfnyyy+5du2a1Xqkg5xH2JsGyFpaImOuW9ZwcCguqY2EELlKZsXOrGRP7MxvcTM3roOKiopi06ZN9OrVCwcHB2JiYoiJiUFrTZs2bdi8OenCTCcnJzp16pRDrc0/EkaBzWYzly5dYtasWZbXtm3bRps2bfDz82PMmDEcPXrUpjorVqxombKxefNmhgwZQpEiRdi5cyfDhg1Lc0tr6SDnEWmlAUptRbO1tEQtW17H3b1akrrd3avRqpW1IJX8n4gDrVrpFPk/nZ19aNnyuqQ2EkLkKpkVO+2Jm8b5rImd+S1uli5dGnd3d0JDQ62WSXitfPny2dKma9euERsby6RJk3B2dk5yfPHFF1y/fj1JSrGyZcvi6Jh7vnnIDwoXLkzFihUtzyMiIqhUqRJhYWFMmTKF6tWr06hRI2bMmJHmKHACpRQtWrRgzpw5hIeHs3DhQtq1a5fmFA6nTLkTkS08PQeku6I5OvoMx44ZIyBVqsykSpWZKepp3PhIinMJKeBSSp5XMI7jx4fRvPkFm9uYFnvLCyGEvTIrdiZnLW7u2FGdrIyd+SluOjk50bJlS9atW8ft27dTnYe8atUqAFq3bm15PXlqsKtXr2Zam4oXL46DgwPDhw9n8ODBqZZJnGkheU5lkfk6derEE088wfbt2zGbzXz33Xfs2rWLXbt28fHHH3P69Gmb/x48PDzo378//fv35/z581Z/8ZIR5Dwus1Y027NyOydWeQshRGbKjNhpLRZGRaX+9a/EztSNHj2aq1ev8uabb6Z47fTp00yePJmWLVvSuHFjPD09cXV15fDhw0nKrV69OsW1rq6uNm9IkVihQoVo0aIFBw4coF69ejRo0CDFIbKfUormzZsze/ZswsPDWbx4Me3bt6dfv36WzvHVq1d59dVX2b9/v011+vr6Wn1NRpDzuMxb0WzPyu3cs8pbCCEyInNip72xUGJnah577DEmTpzIO++8Q2hoKIMHD6ZEiRLs3buXjz76iGLFijF//nzA6CT16dOHuXPnUqVKFapWrcrq1ast6d0Sq1atGlu2bOHnn3/Gy8uL0qVLp7kYMLGpU6fSsmVL2rdvz5AhQ/D29uaff/5h7969xMbG8tFHH2XiJyDs5e7uTt++fenbt2+SeexLlixh2rRpTJs2jdq1a2MymRgwYABly5a1+z1kBDmPy7wVzfbMn5K5VkKIvC1zYqe9sVBipzXjxo1jzZo1REZG8tRTT9GuXTtmzpzJ4MGD2b17N35+9/5ePvvsM3r06MGECRPo06cPt2/f5vPPP09R54cffkjVqlXp3bs3DRs2tGsr6Xr16rFr1y5KlSrFyJEjadeuHS+99BKHDh2iZcuWmXHLIpMknlrRsmVLhg8fTsmSJTlw4ACvvvoqPj4+dOnShZUrV9pXb25cQZpR+Xk3KGsya1cle3aP8vF5wab5eUKI7CM76dknM2Kntbjp7l4t1WkWEjuFyB7R0dGsXr2aoKAg1qxZQ0xMDK1bt2b9+vXAvewp8ZvI5PxOekqpkkqplUqpSKXUGaWU1WzaSqlXlFLhSqkIpdQ3SinZtzEVmbWi2drK7dTOSYAXQuR1mRE7rcXNxo2PSOwUIge5urrSo0cPVq1axYULF5g2bRqvvfaa5fU//viDatWq8emnn1qtI1tHkJVSizE65UOAOsBqoJnW+kiycu2BeUBrIAxYCfyhtR6bVv0FcRRECCFARpCFEMJWr7zyCp9++il9+/ZlyZIlOTuCrJQqBDwJjNNa39RabwVWAYNSKW4C5mqtj2itrwOTgMDsaqsQQgghhMifpkyZwqpVqxg9erTVMtmZxaIKEKu1Pp7o3AHgkVTKVgd+TFbOUylVSmudeckOhRBCCCFEgeLs7Eznzp3TLJOdHeTCQESycxFAERvKJjwuAiTpICulngUS9gaNVkolTY6YP5UG/snpRmSTgnKvcp/5S07cZwV7CkvszNfkPvOXgnKfkItiZ3Z2kG8CRZOdKwr8Z0PZhMcpymqtZwOzAZRSuzM6By8vKSj3CQXnXuU+85e8cJ8SO/Mvuc/8paDcJ+Sue83OLBbHASelVOVE52oDKfc9Ns7VTlbukkyvEEIIIYQQWS3bOsha60hgBTBRKVVIKdUc6ArMT6X4PGCIUqqaUqoE8DYQlF1tFUIIIYQQBVd276Q3DHAHLgOLgRe01keUUn5KqZtKKT8ArfWvwBQgGDgTf4y3of6CstF9QblPKDj3KveZv+S1+8xr7c0ouc/8Re4z/8k195qvdtITQgghhBDifmX3CLIQQgghhBC5mnSQhRBCCCGESCRfdJCVUiWVUiuVUpFKqTNKqf453aasoJR6USm1WykVrZQKyun2ZBWllKtSam783+V/Sql9SqkOOd2urKCUWqCUuqiU+lcpdVwp9UxOtykrKaUqK6VuK6UW5HRbsoJSamP8/d2MP47ldJvSUhBip8TN/EfiZv6TG2NnvuggAzOAO4AnMAD4UilVPWeblCXCgPeAb3K6IVnMCTiHsctiMWAcsFQp5Z+TjcoiHwL+WuuiQBfgPaVU/RxuU1aaAezK6UZksRe11oXjj6o53Zh0FITYKXEz/5G4mT/lqtiZ5zvISqlCwJPAOK31Ta31VmAVMChnW5b5tNYrtNY/kGw3wfxGax2ptZ6gtQ7VWsdprX8GTgP5LgBqrY9oraMTnsYflXKwSVlGKdUXuAGsz+GmCApO7JS4KXEzL5O4mXPyfAcZqALEaq2PJzp3AMhvoyAFllLKE+PvObVNZfI8pdRMpdQt4C/gIvBLDjcp0ymligITgddyui3Z4EOl1D9KqW1KqVY53Zg0SOzMxyRu5n0FLG5CLoud+aGDXBiISHYuAiiSA20RmUwp5QwsBMxa679yuj1ZQWs9DOPfawuMzXSi074iT5oEzNVan8vphmSxMUAAUA4jn+dPSqncOrIlsTOfkriZbxSUuAm5MHbmhw7yTaBosnNFgf9yoC0iEymlHDB2WrwDvJjDzclSWuvY+K+4fYEXcro9mUkpVQdoA0zL4aZkOa31Dq31f1rraK21GdgGdMzpdlkhsTMfkriZPxSkuAm5M3Y65eSbZ5LjgJNSqrLW+kT8udrk06+VCgqllALmYiwe6qi1vpvDTcouTuS/uXStAH/grPHXSmHAUSlVTWtdLwfblR00oHK6EVZI7MxnJG7mK60ouHETckHszPMjyFrrSIyvVyYqpQoppZoDXTF+g85XlFJOSik3wBHjP4qbUio//JKTmi+Bh4DOWuuonG5MVlBKlVVK9VVKFVZKOSql2gP9gA053bZMNhvjh1ed+GMWsBpon3NNynxKqeJKqfYJ/y+VUgOAlsDanG5bagpK7JS4mb9I3MxfcRNyb+zM8x3keMMAd+AysBh4QWudH0dB3gaigLHAwPjHb+doi7KAUqoC8BxGUAhPlBdxQM62LNNpjK8FzwPXgU+Al7XWP+ZoqzKZ1vqW1jo84cD4av+21vpKTrctkzljpBO7AvwDjAC6aa1zPJ9nGgpC7JS4mb9I3Mx/cmXsVFrrnHx/IYQQQgghcpX8MoIshBBCCCFEppAOshBCCCGEEIlIB1kIIYQQQohEpIMshBBCCCFEItJBFkIIIYQQIhHpIAshhBBCCJGIdJCFSIdSKlQpNSqN1wOVUjezs01pUUoFKaV+zul2CCEKNomdIi+TDrLIE+IDl44/7iqlTimlPlFKFbLxev/4axtkdVuzS368JyFE5pLYmVJ+vCeR+fLrdpsif/odGISx604LYA5QCGNXJSGEEKmT2CmEnWQEWeQl0fHbbp7TWi8CFgLdAJThdaXUSaVUlFLqkFJqYKJrT8f/uSt+5GBj/HUNlVK/KaX+UUr9q5TaqpRqer8NVUp1VkrtUUrdVkqdVkq9r5RySfR6qFLqbaXUV/Hve14pNTpZHVWUUpvi6zimlOoYv3VsYFr3lOj6l5RSF5RS15VS3yqlPO73voQQeZLETomdwk7SQRZ5WRTGiAgY+7gPAYYD1YAPga+UUk/Ev94o/s/HAW+gR/zzIsB8jFGVRsB+4BelVOmMNkop1R7jB9AXQHXgaaAn8EGyoq8Ah4B6wGRgSsIPGKWUA7ASiAGaAIHAeMA10fXW7on4+6kBtAH6AN2BlzJ6T0KIfEViZ+r3BBI7RQKttRxy5PoDCAJ+TvS8EfAP8B3GV4VRQItk13wK/BL/2B/QQIN03kcBF4GBic6FAqPSuCYQuJno+WZgXLIy3YCbgEpU5+JkZU4Ab8c/bo8R4Msler1Z/D0EpnVP8Z/VOcAp0bmvgd9z+u9RDjnkyN5DYqfETjkydsgcZJGXPB6/4tkJY/TjR2AExqiHG/CrUkonKu+MEUytUkqVBSYBjwKegCPgDvjdRzvrA42UUmMSnXOIr9cL44cIwMFk14UBZeMfPwiEaa0vJHp9FxBnYxuOaq1jktXd2MZrhRD5i8ROiZ3CTtJBFnnJZuBZ4C5GALwLoJSqGP96Z+BssmvuplOnGSO4v4LxAyEaWA+4pHFNehyAd4Flqbx2JY22ae5Ne1LxzzMqrbqFEAWLxE7bSewUgHSQRd5yS2v9dyrnj2IE5wpa6w1Wrr0T/6djsvMPAyO11qsBlFKeGHPS7sde4EErbbXVn0A5pZSP1jos/lwDkgZqa/ckhBCJSeyU2CnsJB1kkedprf9TSn0CfKKUUhijJYUxFmjEaa1nA5cx5tq1V0qFAre11hHAcWCgUmoHxny8KdwLnhk1EfhZKXUGWIoxH64G0Ehr/bqNdawDjgFmZSTadwemxteVMDpi7Z6EECJdEjsldgrr5GsDkV+MAyYAo4AjGEHySeLT+cTPKRsJPIMxp+zH+OuexviBsAdYAnxDOnPv0qO1Xgs8gTE3b2f8MZaUX2GmVUccxupp1/jrzcD7GAH+djr3JIQQtpLYKbFTpCJhVagQIpdTStXGSKXUQGu9J4ebI4QQeYLETpER0kEWIpdSSnUHIjFSGPljfE2ogLpa/uMKIUSqJHaKzCBzkIXIvYpgJMEvD1wHNgKvSIAXQog0SewU901GkIUQQgghhEhEFukJIYQQQgiRiHSQhRBCCCGESEQ6yEIIIYQQQiQiHWQhhBBCCCESkQ6yEEIIIYQQiUgHWQghhBBCiET+DwoCHjgT+mFUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x194.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_outliers = np.array([[3.4, 1.3], [3.2, 0.8]])\n",
    "y_outliers = np.array([0, 0])\n",
    "Xo1 = np.concatenate([X, X_outliers[:1]], axis=0)\n",
    "yo1 = np.concatenate([y, y_outliers[:1]], axis=0)\n",
    "Xo2 = np.concatenate([X, X_outliers[1:]], axis=0)\n",
    "yo2 = np.concatenate([y, y_outliers[1:]], axis=0)\n",
    "\n",
    "svm_clf2 = SVC(kernel=\"linear\", C=10**9)\n",
    "svm_clf2.fit(Xo2, yo2)\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(10,2.7), sharey=True)\n",
    "\n",
    "plt.sca(axes[0])\n",
    "plt.plot(Xo1[:, 0][yo1==1], Xo1[:, 1][yo1==1], \"bs\")\n",
    "plt.plot(Xo1[:, 0][yo1==0], Xo1[:, 1][yo1==0], \"yo\")\n",
    "plt.text(0.3, 1.0, \"Impossible!\", fontsize=24, color=\"red\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.annotate(\"Outlier\",\n",
    "             xy=(X_outliers[0][0], X_outliers[0][1]),\n",
    "             xytext=(2.5, 1.7),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=16,\n",
    "            )\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.sca(axes[1])\n",
    "plt.plot(Xo2[:, 0][yo2==1], Xo2[:, 1][yo2==1], \"bs\")\n",
    "plt.plot(Xo2[:, 0][yo2==0], Xo2[:, 1][yo2==0], \"yo\")\n",
    "plot_svc_decision_boundary(svm_clf2, 0, 5.5)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.annotate(\"Outlier\",\n",
    "             xy=(X_outliers[1][0], X_outliers[1][1]),\n",
    "             xytext=(3.2, 0.08),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=16,\n",
    "            )\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "save_fig(\"sensitivity_to_outliers_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This is the first code example in chapter 5:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('linear_svc', LinearSVC(C=1, loss='hinge', random_state=42))])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = (iris[\"target\"] == 2).astype(np.float64)  # Iris virginica\n",
    "\n",
    "svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"linear_svc\", LinearSVC(C=1, loss=\"hinge\", random_state=42)),\n",
    "    ])\n",
    "\n",
    "svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.predict([[5.5, 1.7]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code to generate Figure 5–4. Large margin versus fewer margin violations**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ageron/miniconda3/envs/tf2/lib/python3.7/site-packages/sklearn/svm/_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('linear_svc',\n",
       "                 LinearSVC(C=100, loss='hinge', random_state=42))])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "svm_clf1 = LinearSVC(C=1, loss=\"hinge\", random_state=42)\n",
    "svm_clf2 = LinearSVC(C=100, loss=\"hinge\", random_state=42)\n",
    "\n",
    "scaled_svm_clf1 = Pipeline([\n",
    "        (\"scaler\", scaler),\n",
    "        (\"linear_svc\", svm_clf1),\n",
    "    ])\n",
    "scaled_svm_clf2 = Pipeline([\n",
    "        (\"scaler\", scaler),\n",
    "        (\"linear_svc\", svm_clf2),\n",
    "    ])\n",
    "\n",
    "scaled_svm_clf1.fit(X, y)\n",
    "scaled_svm_clf2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to unscaled parameters\n",
    "b1 = svm_clf1.decision_function([-scaler.mean_ / scaler.scale_])\n",
    "b2 = svm_clf2.decision_function([-scaler.mean_ / scaler.scale_])\n",
    "w1 = svm_clf1.coef_[0] / scaler.scale_\n",
    "w2 = svm_clf2.coef_[0] / scaler.scale_\n",
    "svm_clf1.intercept_ = np.array([b1])\n",
    "svm_clf2.intercept_ = np.array([b2])\n",
    "svm_clf1.coef_ = np.array([w1])\n",
    "svm_clf2.coef_ = np.array([w2])\n",
    "\n",
    "# Find support vectors (LinearSVC does not do this automatically)\n",
    "t = y * 2 - 1\n",
    "support_vectors_idx1 = (t * (X.dot(w1) + b1) < 1).ravel()\n",
    "support_vectors_idx2 = (t * (X.dot(w2) + b2) < 1).ravel()\n",
    "svm_clf1.support_vectors_ = X[support_vectors_idx1]\n",
    "svm_clf2.support_vectors_ = X[support_vectors_idx2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure regularization_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x194.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(10,2.7), sharey=True)\n",
    "\n",
    "plt.sca(axes[0])\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\", label=\"Iris virginica\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\", label=\"Iris versicolor\")\n",
    "plot_svc_decision_boundary(svm_clf1, 4, 5.9)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"upper left\", fontsize=14)\n",
    "plt.title(\"$C = {}$\".format(svm_clf1.C), fontsize=16)\n",
    "plt.axis([4, 5.9, 0.8, 2.8])\n",
    "\n",
    "plt.sca(axes[1])\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
    "plot_svc_decision_boundary(svm_clf2, 4, 5.99)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.title(\"$C = {}$\".format(svm_clf2.C), fontsize=16)\n",
    "plt.axis([4, 5.9, 0.8, 2.8])\n",
    "\n",
    "save_fig(\"regularization_plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nonlinear SVM Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code to generate Figure 5–5. Adding features to make a dataset linearly separable**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure higher_dimensions_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X1D = np.linspace(-4, 4, 9).reshape(-1, 1)\n",
    "X2D = np.c_[X1D, X1D**2]\n",
    "y = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n",
    "\n",
    "plt.figure(figsize=(10, 3))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.plot(X1D[:, 0][y==0], np.zeros(4), \"bs\")\n",
    "plt.plot(X1D[:, 0][y==1], np.zeros(5), \"g^\")\n",
    "plt.gca().get_yaxis().set_ticks([])\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.axis([-4.5, 4.5, -0.2, 0.2])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.plot(X2D[:, 0][y==0], X2D[:, 1][y==0], \"bs\")\n",
    "plt.plot(X2D[:, 0][y==1], X2D[:, 1][y==1], \"g^\")\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.ylabel(r\"$x_2$  \", fontsize=20, rotation=0)\n",
    "plt.gca().get_yaxis().set_ticks([0, 4, 8, 12, 16])\n",
    "plt.plot([-4.5, 4.5], [6.5, 6.5], \"r--\", linewidth=3)\n",
    "plt.axis([-4.5, 4.5, -1, 17])\n",
    "\n",
    "plt.subplots_adjust(right=1)\n",
    "\n",
    "save_fig(\"higher_dimensions_plot\", tight_layout=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "X, y = make_moons(n_samples=100, noise=0.15, random_state=42)\n",
    "\n",
    "def plot_dataset(X, y, axes):\n",
    "    plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
    "    plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")\n",
    "    plt.axis(axes)\n",
    "    plt.grid(True, which='both')\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "    plt.ylabel(r\"$x_2$\", fontsize=20, rotation=0)\n",
    "\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here is second code example in the chapter:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ageron/miniconda3/envs/tf2/lib/python3.7/site-packages/sklearn/svm/_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('poly_features', PolynomialFeatures(degree=3)),\n",
       "                ('scaler', StandardScaler()),\n",
       "                ('svm_clf', LinearSVC(C=10, loss='hinge', random_state=42))])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "polynomial_svm_clf = Pipeline([\n",
    "        (\"poly_features\", PolynomialFeatures(degree=3)),\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", LinearSVC(C=10, loss=\"hinge\", random_state=42))\n",
    "    ])\n",
    "\n",
    "polynomial_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code to generate Figure 5–6. Linear SVM classifier using polynomial features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure moons_polynomial_svc_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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kQqWPY53vOQhAVW/irKmG3a8wtOEDeaeyK6XyC01ACeJ2CaGRWpt0pH0XDe0tNNywgpaOX3K7LFuMj2NlZO34WNX2bQzt+FUNKqVKEJqAUvZLtZqqmndyfsUwjbMXMueWO90uyxWpsapY88sc6U6suYp03+lqt5+e76T8JtR78SlrxbuPc9WcYwx+oJ55v/w7vgmn9F2lrdxhenZTB3M/9Anq1iwnsvwYI8efYGjDxqwLkJ2gx3cov/FMQInITBHZKCIXReSoiKzLcd09IjImIgNpH6ucrVZlUzkUBWC0pQ5zZZO7xRQhfVfp9D9bZX7HauruWkv8A1X0dPyUmi2PEuncbdnPVyqovNTF900gBrQBNwH/ISK7jDF7s1z7qjFmhZPFlSos3Sqj5/uZfnI7r93ew7TMARkPy9xV2hgz/mcr90ab3dQBH+rgzLku+ppfdmzBr1J+5okWlIjMAD4CPGiMGTDGvAw8D3zc3crKF/RulWhXH8NPrCcev8Cp23t8N3U8fV+0kbGR8S1oUnujWS3V7TewejY9HT9lOPK0q91+SnmZV1pQ1wNjxpgDaZ/bBazMcf1yETkL9AJPAY8YY0YzLxKRe4F7AVpbWzkW229t1QXJfa5OvnpiZphjsf2su3slfeemT/p6c9MlNjz5kiUVlmr0fD8VdRfp+8hcqG6kYvY6KsZqOLYv5mpd6XpjvTxy4Ct87voHmFmdmEUXGzYc2xejN9bLxgObGUk+deJpi7lH4qNs7NrMh+p/d/z7LNW0BnPTILFFg5werqBi7AAXLjRQVTP5jUvquVC+0p6LxbKuXmdYXe/or9Tzeo1hRJYxcqKaS6et+/8h9dwNC68EVD1wPuNz54GGLNf+HFgKHAWWAE8Do8AjmRcaYx4HHge4/rrrjRPbxRQjXz2p7W2yhRNA37npjmx/k01q8W2s9hX6VtbSuOhmLp1uZd6iyfvYuW399mfY2/82zw/8YHyn6GP7YsxbVM367c9gJJ5zkxFDfML3Wa+aaNchZr15grrTi+huvz7rVHSrtjrKt5WRlc8lp7ZmsorV9fb+11ZuXDzKyen76Lne2l1RUs/dsPBKQA0Amds8NAL9mRcaYw6l/XW3iDwMPECWgAqbXONd6coZ+0odDFhzbAs9S95Bbl3CgmR33jEL3yVaJXN8KXNMKduu0umc2GF6rDXxHswMZb4/s16QxjxVOHgloA4AVSLSYYzpSn5uGZBtgkQmA7oKFwob1yp37Ouq9irqq6D31iWeH2ua6tRSr+wqfeSmOszgdlq3HCPSfScV7XN1Ua9SeGSShDHmIvAs8LCIzBCRDwB3kRhfmkBE1opIW/LPi4AHgeecrLcYuXaC9u0O0QNRtysoSK5TS61Y32Sl2U0d49PQ+z54juoLT1L9ylM6DV0pimhBichPgTuAjxhjnk37vABPAHcD/88Y89kSa/lT4NvAGSAKfMoYs1dE2oG3gcXGmG5gNbBeROqBHuA7wF+V+Ji2C0q3SrSrjxkvP0e0aQ/vrqxF8PbO3flOLb1n5r0uVZVbahr6idkvsHR/AwcuTP09SgVdMV18DwBvAF8WkeeMMakmwFdIhNM/lxFOGGN6gd/O8vluEpMoUn+/H7i/1MfxG7fP6Ekfdzq9dOK4k5flPbXUmqVNthhtqcMMThp6VSqUCg4oY8wuEXmKRBh9nEQr5i+APwe+D3zSnhLDze0WWLz7OAs4zODNw8Ru8s+mr/nGl/wwTTe1K0fY+H1he++mrUzr28nhcwOcXBCjjlwnQqtCFDsG9XlgGHhIRO4D/hL4CfBxYzL6U5TjCmlVldLyam5PzDRLzThT9pKGOrdLcI1fF7ZHu/oY2rCR4ejz9NxyhFO/0syCFet8f6RMPlbuW5lLUbP4jDHHReTvgc8C/whsBz5sjJnwllREPgd8GFgIXAJ+AXzOGLPHiqKDJNc7xuamK9n2vdNF/Sy73mHGB/pgdnhfNJ3WwylqLzbRu2krM39rldvlqCmkdvGvrz9FdHEVjbetsiyYIoO93L/1Eb666nOWbbtllfR9K+1aK1jKNPNI2p//2BgzmOWaVcA/ATtITAF/GHhRRBYnx5pUUq53hrkW6Kpgm9+xmqN0MtKwjbrtlQw/cYTRj/wy+GBt5uU3WxN3rPBL91w5ZrVV0rh4Pt0zBixtNTkRAqWYao2hVYoKKBH5QxKTIk4DVwKfBj6VeZ0xZk3G932cxM4QHwBeKLVY5bywjoW4KXVS72DLDi69tIeKwRuI9vSVvTZqqvGdcsd//No951XZQiBtvljO73GixTXVGkOrFDwGJSK/CTxJYvHse4F9wJ8k1yJNpSH5WLojpg/JFZmbfCi7zW7qoG7pzTRdP5uqSmtmbE4VIBow3pItBAr5HquPi8nk5BrDggJKRFYAzwDHgV83xkRILJCtAv66gB/xD8CbwKullancNHjuCIdre9wuI5yawjX2F7iF7SXKFQK9sdzv8TNbXHZNXsi3xtBqU3bxicgyYBOJLro7jDGnAIwxz4jIa8BdIvKrxphtOb7/a8AKYEXa2inlcamFuRfr97GjYYhpbR2BnpHkdRUDZ4Dgb38U9LGqQmULgTETZ8Pxp1n23j+b8nvs7HbLu8bQYnkDSkSuIzGN3ABrjDEHMy75HLAZ+Fvglizf/3fAHwC3Z2zyqpJyLcRtbrrkQjUJkc7d1BzbwuH5u6i96Woal96q4eSmCu1i84N4/zlG261p8WYLgdH4KO/078t6fa4Wlx2TF5zcwzJvQBlj3iUxGSLX118kx0atIvIPJMJplTEm+11VOd8xJs6ncf7IgmhXHws4TOOa93BhxjBzbrnT8RrUZYdre7gk52na0Umk905Pn77r9q4nQZIKgchgL7/xzCe4NBZjemU1X7rhC1mvz9ft5qXZf8WyZTdzEfkmid0mfhvoE5FUyA0YYwbseEylppKa4fTnc+9nnsf3EoTk/nwrOji46yynbu+hbvsTDG34VQZvXlXSjL6pAqTcgEm92fLbeVBeltltl6uLz8luNyfZddzGnyb/25nx+S8CD9n0mMpC8f5z4L3zB8uSmuG0gdz9+F5UPb2R6StXE5OXubrvHAe6j0MJATXV+I6O/3hLtm67zZFOHhj82KRuO68cHWM1W47bMMZIjo+H7Hg8ZY/RluDMIEuf4bQ50um5YzemMrupA5k9SzeS9Sg7Dpx0cracV3nlwMLA8/smmH7n1AwnOyXeMOiWl15V0dyAlUs9s06UMP7vtiuGBpRDdBGkezK7SkaNfTOclLJKtm67Y/tizFvkg32vLKIBpQIvSDOcpKGOypP+3n4qaL0JiTVqcDrW7XIlwaMBpQIvqDOc/CoovQmpwzzrDr7AW7f3MG1BB/N9cJinn2hAqcDL7CoJQjeJnOvBNHl/qnxQRTp3U9/zLsPNO+n74DCNt63Wxew2sGUWn1LKerWt7exccBSp3szQxv8k2qV7L7vp6o5ztNyykNoblmk42URbUA7RVfaqXKmFu0fbOjm//b+ofP1d6na8r+SFu8oaetK0fTSgHOLHwV/lTanzoto276PudCU6NO+OxJq0FrfLCDQNqBAJ2uwpZR0nnxtB6k0wLdp6spMGVIgUO3uqKjoIPu5ad+p0UTdZtYOBkzPr/P5mKNK5m4bu14lc28PJygpqaXe7pMDSgHKJ11szFQ1NwAm3yyhL+umiflvvVJCQHWbotmhXH3U7tjJc8ToXl0aRW5fotHKb6Sw+lwRlLYhXOXW6qJsO1/ZwafAkM15+Tmf02Ww8nJp3Iu+PUXfXWg0nB2hAqUDKtvdekMxu6qBu6c0c/LUBDs/fRvUrTxHp3O12WYE2q62Sme31Oq3cQRpQapKzPWMMHz/jdhkly3W6aNBaUbObOliwYh2N719IdMk71BzboiFlo9R4nxemlUcGe7n7Rw8E7jmdSQPKw1aum8vStfMnfaxcN7ekn5drllT651s6mhlou46TXU3Ud57hyMsbOHOuq6THc0vYjimYc8udTFt4De1LSn/hLOS5ESaR2Bnufvv3OLC/i6ENG6l+5SnebtvOkZvqPNF6Sh9fDTKdJFEEpyc2WD1OVWiNiWPFb2RoUzOtz2znVLSTocXd1La2e+J/zqmEde+9cs6K8sLEHC957MTXeePCDr7Tdz+faV7GxY5h6m5b64nnf+b4apB35deAKoKVgeGHtSAzf2sVvZtgZeUoJ4/so6fV7YoKE9TTRfNJnRVVOeTvnc69IBI7ww8jP8CI4Ucz9/K7Hct573W/g2lqcrs0IBhnmxVKA8ol+o5VWU0a6kAP3C3bYye+TpxUABi+G9nBjSt+3+WqEnpj2cdXg9qK0oBSKiB2NOymLvo2w08c4eKKuwK1P59T3eup1tOIGQEgJmP89MJ+7h/s9UQAbDj+9KTx1bH4WGBbUTpJQk3JnL/gdgm+4dbsqvkdq2lcuZrYnTM4PH8bNVseDdSMPqfWDf7df32B+NjE8UsDnpmM8E7//izHwI8FdnzVMwElIjNFZKOIXBSRoyKyLs+1nxGR0yJyXkS+LSLTnazVKV6YWTVWq5thFqPc2VXlBFxq2nndmuVUL7zI/KE3dQFvgaJdfQxt2Mge8wtGKib+/+WlCTbfXPb37PnEf7Ll97/L9MrEmWbTK6t57I4vuVyZPbzUxfdNIAa0ATcB/yEiu4wxe9MvEpE1wGeBDwIngY3AF5Ofs5XTExt0nMpfrJhdZcX2TLWt7VRfB+xMHUcenK4+O4xeGKL6lac40r6Lf7r6DqpvW+GJ2Xr5hGWihCcCSkRmAB8BlhpjBoCXReR54ONMDp67gW+lgktEvgR8N8t1ltPAUPmU+6Jh5fThw7U9XFsRp/bNV+kDmpcvLOnnBF2kczdyQz/RJe/QuHAhc2650+2SppRrIXoQJ0p4IqCA64ExY8yBtM/tAlZmuXYJ8FzGdW0i0mKMmTDHVkTuBe4FaG1t5Vhsv7VV2yhmhj1R7+iyMd6obmfEtDByooZLp2NZr4sNG47ty/41r7Gj1t5YLxsPbGbEXH7R2Ni1mQ/V/y4zqwtrwXzj0FOMxRMBNxaP87dbv8N9136yhHrnE29u48CqQcYujVAzeJpzkX6qrnBmBwR7nrvzc36lnMcaXTaGqavDNP8BlTOaPP8cjg0bvrH18vMkJf35EiReCah6IPPcgPNAtv+jMq9N/bkBmBBQxpjHgccBrr/uejOv2j/vIo/F9uOFeiO7drO8vY8TsR303LEoZ9fHsX0x5i2qdri60thR6/rtz2AknhhRTzLEeX7gBwW1oiKDvbz43z9jNBlwo2aUF8928sCqj0F3fQn1VgONHO3qZOTtLuperGRm/L2OzO6z47mbr3u91Mfq27kfc+IcwyumMdK6m3k+2Pz12L4YB0cOjD9PUkbNKAdH9vvm/8FCeSWgBoDGjM81kn1VR+a1qT/rChBVkN5YL5//0VctPSeq3N0r8m3PdM/Me0uuK3X67mDLDg4c/AXv2XKS3v23MvO3VpX8M91Qbvd6ajdyALmUeE9bPXKS2IKLDFb/Go2thZ3pZMcZY8X+zDAtRPdKQB0AqkSkwxiT2vhtGbA3y7V7k1/7ftp1PZnde8p6lZF+aHK7ivJtOP605edElfuikTfgynwdnN3UASs6ONrWSeQ9PTS/tCmQa6VyiXTupubYFo6076KhPTErdXRmYuKvtF3F9LGZzG66sqCfZccZY4E/t6wMnggoY8xFEXkWeFhE/oTELL67gFuzXP6vwHoR+S5wCvg8sN6hUkOnon0uXTveZbh5gP7Blxmgz9fn4EQGe9l85mclT0Sw65TefAFn1bjI/I7V0AFH2zoZeXsnV205yfDLVwc2qFKtppGRbVxcMpRzEkSh99eOPfDCtK9eKTyzDgr4U6AWOAN8D/iUMWaviLSLyICItAMYY34M/A2wBTia/PiCSzUHXktHM7XrfofR6t+ldec8zm/f48sdzlMee3ND2jY2xe9wHoRdpFOLeiMfHRpf1Nu7aavbZVkq0rmbmi2P0tPxU+IfqKLurrVlz9Cz44yxoJ9bVi5PtKAAjDG9wG9n+Xw3iYkR6Z/7GvA1ZypTkNjhvG9mNbec+G/OVR7hVFs3eHytSKbUu9VRU9r03CC9203v9js1r4u67c8z/MQRBlsXAJcXaCd2tveuaFcf8e7L41OVQ1HqIkeobtpD3wcrLNmBPDLYy6c7H2Zf3yFLp3aHabp4qbzUglIeF6+fTWzONbRxldullKTcc6LseLfr9sFz6VskHV+xk/qrt9HQsJmmqn+j+sKTDG3Y6NndKCKdu6l+5SmqLzxJU9W/0VT1bzQ0bOb4ip2c+70FzP3QJyxZcPvYmxt46+x+RjO2QErtgVfOzw3TuWWl8EwLSim7lTPTzq53u14YIE+1ps6c6+Jc2udH9u3j9//4f3Luqcn/vuaGEX74lxPnMFk5jjVVKM54+TlGKt7i4pIhpi28hkuLFo1/rREs2wki9XsHiKevIaD8PfDCem5ZMTSgVNFM/yB+bHynJiKUsg4q37vdUoPFa12Gk17Ub+ng3ED2evr6pzF75+NQk1iqeK53lEj37ZZ0CaZm3TXNTL48DU9eQbJ/xUmqWhqpW7rK1m2J0n/v0yqqWHvNSn5yZBuXxmJl74EXpunipdKAUsWpb4EQbm5ux7tdv++nduiPZlAVHQBgZP9hqt8+wvAT72X0d5bTt/fy7g7x+tk5f0Zir8AEc+LU+PjR+RUVXJg9C4DRlhmTvq+xdbXt++VlazVvOriFiorEmzM//s78JjQBZTKa5yo8rJgabvW73SAMkKemrQOcWdTF4Ht2cDy6k+kj76Hl1Evj18nAUM6fYeprx/8cre7h+IoBpi3u8MRShqytZuLEk9sM+fF35jehCSgVXlaO81i1DsqOLkM3pY9j9R+Fw3eldwFPbgFldxWNre2e2Uk8W6s5k58PC7RrTZ+VNKA8ZOKpoZc3x7T61FArVEUHx989e1m2cZ6MVQtFsSrsgjpAPrupg0unY77Y124qma3mjz73v9nXe2jC5/x8WKAXJuhMJVQBFYteoLolc8s/73Dq1NBySV0DMOx2GQXJNs5T6t52Vk5q8MsAecusS0TPTj4PtGXWJReqcVfqdxYZ7OU3nvmEJRMl3GrFODFBJ3ah/N3n/DcVq2TidgHKYbnGeXpjpa3rCeOq/5de38Keoz+e9PHS61vcLm0Sp9aUWfk8cGtnEr88l0MUUCpsco3zbDj+dNE/K1fYubXAVk3mxIu9lc+DzFaMU88lJ57LVrSeQANKFelU9yjde/sZ2X+YE794we1y8so1zvNO/76if5au+vc2p17srXweuNWKsfu5nAonM6ul7J8VqjEo09JKLBrx9DiUl7V0NENHM5FOaNkLx/t3MTLaT93Sm8l34qlbco3zlLI7eFAnNaT4YUZXPk6tKbPqeeDmMgMnnstWhBOELKC8Lt+poV7SuvpGol1zadv/Xmb94i0OsYN4cxuJU1yDyS+TGkrlhxlduTj5Ym/V88DNZQZ2Ppet6tpL0YDykPSp5F458j2Xlo5m+gauouZCH9cMVXAweMcJhYbXtlwqlh/XlAWxRW5l115KKAPK69PNlXKS37dc8uOLfdBa5HaEE4QwoExLKxKNuF2GCgi/j90EYculoL3Y+5XV4QQ6i0+VIV4/G/oH3C7DVX4/YVdnJ6pyWT3ulE4DSqkSubWOxUp+7B5T3mFX115K6Lr4UnQcSpXL72M3EOzuMb93v3qd3eEEIW1BmZZWt0tQPqc7S3if37tfvcyJcIKQBpRS5dKxG28LQverVzkVTqABpVRJdOzG2/yyGarfOBlOEOIxKNBxKFW6II/dOMmOcaIgTJ33IqfDCULcgtJxKBVmTh1NMRU7xom0+9V6boQThDiglAqzYoPBjkCza5xIu1+t5VY4Qci7+JQKslzdZ6XsvWfHZrJ2TdPX7lfruBlOEPIWVOL4jQtul6ECzM2utFytpGInENjR0vHLNH2vdIW6we1wgpAHlFLFKvYFy621OLlCpZRgsGNGnF/GicK6lsoL4QQaUEoVpZgXLDfX4uQKlWKDwa6WTjnjRE61asK6lsor4QQaUEoVrNgXLLfW4uQLlWKDwa6WzjN3fZM9n/jPSR+FjB851aoJ41oqL4UT6CQJPQa+DBUDZ9wuwVGFDupHBnv5dOfD7Os75MpanHyhUuwEAq/NiCt0gke566vCuJbKa+EEGlCqXA31wHm3q7BdMS9Yj725gbfO7qcCmfB5pzaUtTJUvDYjrtA3CeXOOvTjKb2l8mIwpWhAKVWAQl+wUkEGEMdMuN6plofXQsUqhb5JsOIIe6+1HO3i5XACDSilClLoC1Z6kE2rqOLDHWsC947bLYW+SbBifVVmyKd3GQaF18MJPDBJQkRmishGEbkoIkdFZF2ea+8RkTERGUj7WFVuDboeqnjRrj5q33yVE5de43Btj9vl2K6QQX2/rO3xq0LeJNj1O7BiYoZX1lTFLkR9EU7gjRbUN4EY0AbcBPyHiOwyxuzNcf2rxpgVThWnJot07qbu4Ascvu4gtTddTd3Sm7l0usbtslwXpnELNxQ6w8/q34EVXYap2qzejaNYfgmmFFdbUCIyA/gI8KAxZsAY8zLwPPBxN+tS2UW7+oh07ubKi9vo++A56tYsZ8GKdcxu6nC7NE8Iy7iFl9nxO7BiurkX1lT5LZwAxBgz9VV2PbjIcmC7MaY27XP3AyuNMXdmuf4eEi2uIaAXeAp4xBgzmnlt8vp7gXsBWltb37f+X3I/sWR0BKmqLP0fY7GYGaZavNUqGR0eYxojVIxdZKhmhOoZTeNfiw0bqmsk9zd7iJ9qhWDU2xvr5ZEDX+Fz1z/AzOpmlyrLLt/97Y318ok3/hcxExv/XHVFNU8sf7yof8c3Dj3KT868yKgZpUqqWDP7Du679pOW1pqLSQa2qXK+w+x/3HHX68aY95f6/W538WWbo3weaMhx/c+BpcBRYAnwNDAKPJLtYmPM48DjAB3XXW+urF6asxDpjwB4Zj3Usdh+5lUvdLuMCaJH+2jjNA3n3mDPwghzFl1+D3FsX4x5i6pdrK5wfqoVyqvXjvOWprLrrdN87dBXJzzm+u3PsLf/bZ4f+IHnujvz3d/125/BSJz0CZmGeFH/jshgLy/+988YTb6PHjWjvHi2kwdWfazo30mxzwU/tprS2drFJyJbRcTk+HgZGAAyE6ER6M/284wxh4wxh40xcWPMbuBh4KNW1KrnQxXODGb99SgPcmMvuQ3Hn57wmF7o3iqVFV2Gbuw7mJoIYWa1+DacwOYWlDFmVb6vJ8egqkSkwxjTlfz0MiDXBIlJDwH4p+8jQEZb6twuwXecbs1YNbhf7GNuPvOzCY9p17EaTrBiTZmTY5OpFhP4t9WUztUuPmPMRRF5FnhYRP6ExCy+u4Bbs10vImuBN4wxPSKyCHgQ+IFT9SpVDqdncbkRDI+9uYE4lx/z7157gh8f+XmotgzK5NTCab9352Xj+joo4E+BWuAM8D3gU6kp5iLSnlzr1J68djXwlohcBH4EPAv8lQs1h9dAdOpr1CROd3O5sSYr9ZipsZaR+CibDv2MsfjYhOvCsvGqU4LSnZeN6wFljOk1xvy2MWaGMabdGLMh7Wvdxph6Y0x38u/3G2Paktdea4z5v8aYEctq0QW7BZEG7d4rltM7Y7sx7pHtMcdMnFEzMaB06r01/LTgtlRuz+JTKvDc2Bk717jH8wc7bXvcbI8JsGjmtYHdH9ANQRtnykcDShVM9u/CDP4XO5afZBq6OLdQbuwwkS0QvrT9G3x//49se9zUY/ptGr9fxC5EMfHEktGgB1OK6118yvuiXX0MbdjIcPR59i8/ybTFHczvWO12Wb7hhR0m/DzVW6VNgKiqCk04gbag1BQinbup73mXqjmvEZ9TReNtq3RroyJ5oXvLz1O9s3FjAbIbJo0xnY7luTp4tAWlptRxcxPVs66g+rYVGk4+FMRd1t1YgOyU1OSHoM7MK4YGVAadyaeCxo0ZfXYKandl5qy8MAdTigaUmlJ8oA+adGq5X3lhDMxKTk/Zt5sGU246BqVUwHlhDMwqbkzZt0uYpouXSltQKq/KId05QnmH37sr08eXQFtMU9EWlJqSXNEInHS7DKV8210ZxtbSwHD5b241oFROvZu2Uhc5wuFzxzi5IEYd7VN/k1I28lN3ZXooQXiCCawJJ9CAUllEu/qo27GV4ZFt9K4YY9riDhbowlylChLG1lJKejBVX1H+v10DSk2QCqf6qgNEf1kX5ipViDCHUkoqnKwIphQNqBxi0QueOf7dabPaKqmfN5dDN8zQcFIqhzB34aWzutWUTgMqC9PSikQjbpfhGjN0Hp3gqdRkGkoT2dFqSqcBpbKqaG4A+twuQynXaShNZncwpWhAqQkqBs5AQ73bZSjlKg2l7OzszstGA0qNi3Tupu7gC/xieTeNla3Utd7sdklKOUZDKT+nWk3pNKDU+My9kZFtnLp9jCsWL9XznlTgje/mEK8ldqFfAykHN4IpRQNKEe8+Tn3VAeqWXUnPykU6c08FVrZWkjkd03DKwunuvGw0oBQADW21nL9xgdtlKGUp7bYrnheCKUUDKuQinbtp6H6dyLU9nKysYH6Tdu0p/8oMJNBQKpSXgilFAyqkxrczqnidi0ujyK1LdNxJ+Y4GUvm8GEwpGlAhNB5OzTuRjhh1t63VcSflCxpI1vFyMKVoQIXUrLZKBq+sJ37DQlo0nJRHaSBZzw/BlKIBFXJjrQ1ul6AUoGFkNz8FU4oGVEgl9ttTyh3Zwgg0kOzgx2BK0YAKs6Y6tytQIaBh5A4/B1OKBlTITNjOqLZVT8lVlopdiI7vzJBOw8g5QQimFA2okEjfziiyfIgrbtXtjFR5craMqqo0kBwWpFBKpwEVEvHu41zdcY5oc5VOK1dFK6qb7nTM5mpUSlCDKUUDKkTMYD9jt7ZrOKm8dMzI+4IeTCkaUFlINBKo497TtzOCq9wuR3mIhpF/DAxHicdrGRjuD3QopdOACjDdzkilaBD5U3pLCUAqq0ITTuCBgBKR+4B7gBuB7xlj7pni+s8A/weoBf4d+JQx5pLNZfqObmcUXhpG/pe7C88/43u98ezPw2K4HlDASeDLwBoSoZOTiKwBPgt8MPl9G4EvJj+nMuh2RsGnYRQcma0lv7aUrAimFNcDyhjzLICIvB+YO8XldwPfMsbsTX7Pl4DvogGVl25nFBy6HVDwBGnCQyqcZtRY8+9wPaCKtAR4Lu3vu4A2EWkxxkz6P1dE7gXuTf710vvWXrXHgRqtMgs463YRRfBTvX6qFbReu/mpXj/VCrCwnG/2W0DVA+mbyKX+3ABMCihjzOPA4wAi8pox5v22V2gRrdc+fqoVtF67+aleP9UKiXrL+f4KqwrJRkS2iojJ8fFyCT9yAEif/536c3+Wa5VSSvmYrS0oY8wqi3/kXmAZ8P3k35cBPdm695RSSvmbrS2oQohIlYjUAJVApYjUiEiu4PxX4I9FZLGINAOfB9YX+FCPl1+to7Re+/ipVtB67eanev1UK5RZrxhjrCqktAJEHgK+kPHpLxpjHhKRduBtYLExpjt5/Z8zcR3UJ3UdlFJKBY/rAaWUUkpl43oXn1JKKZWNBpRSSilPCmxAich9IvKaiFwSkfVTXHuPiIyJyEDaxypHCr1cQ8H1Jq//jIicFpHzIvJtEZnuQJmpx54pIhtF5KKIHBWRdXmudeXeFlmja/eymFq98DxN1lHM/1tu39uCavXQvZ0uIt9KPg/6RWSniKzNc72brwMF11rq/Q1sQHF5j79vF3j9q8aY+rSPrfaVllXB9crlPQlXAwuAa0nsSeiUb5LYtbIN+CPgURFZkud6N+5tQTV64F5CcffT7ecpFPhc9ci9LeZ1wAv3tgo4BqwErgAeBL4vIgsyL/TA/S241qSi729gA8oY86wx5odk2WHCi4qsd3xPQmNMH/AlEjvC205EZgAfAR40xgwYY14Gngc+7sTjF6LIGl27lyXU6glFPFddvbfgy9eBi8aYh4wxR4wxcWPMJuAw8L4sl7t6f4ustSSBDagSLBeRsyJyQEQezLMWywuWkNiHMGV8T0IHHvt6YMwYcyDj8fO1oJy+t8XU6Oa9hOLvpz5P7eO5eysibSSeI3uzfNlT93eKWqGE++v6L8Ajfg4sBY6S+KU/DYwCj7hZVB5F7Ulo82OnHj/Xlulu3NtianTzXmZ7/FQN2WrV56l9PHdvRWQaidManjTG7MtyiWfubwG1lnR/fdmCEov3+DPGHDLGHE42U3cDDwMf9Wq92LgnYQG1Zj526vGzPrbd9zaHYmp0e3/Hgmt16V6Ww+17WzCv3VsRqQCeIjE2eV+OyzxxfwuptdT768uAMsasMsZIjo8VVjwEIBb8nMQPs77e1J6EKZbtSVhArQeAKhFJPwFxGbmb9ZMeAgvvbQ7F1GjbvSxQOffTiXtZDrfvbTlcu7ciIsC3SEya+YgxZiTHpa7f3yJqzVTQ/fVlQBVCitjjT0TWJvtPEZFFJGajPJftWrsUUy/l7UlYFmPMReBZ4GERmSEiHwDuIvEOahI37m2RNbp2L4ut1QvP0+RjF/pcdfXeQuG1euXeJj0K3ADcaYwZynOd6/eXAmst+f4aYwL5ATxEIqXTPx5Kfq2dRPO4Pfn3rwA9wEXgEInm5zSv1pv83J8na74APAFMd7DWmcAPk/erG1iX9jVP3NtcNXrtXhZTqxeep/meqx69twXV6qF7Oz9Z43CyvtTHH3nt/hZTa6n3V/fiU0op5UmB7eJTSinlbxpQSimlPEkDSimllCdpQCmllPIkDSillFKepAGllFLKkzSglFJKeZIGlFJKKU/SgFLKZSLy0+RmvB/O+LyIyPrk1/7arfqUcovuJKGUy0RkGfAGsB+40Rgzlvz8V0lsZfPPxph7XSxRKVdoC0oplxljdpHYHPYGkifpishfkAin7wOfdK86pdyjLSilPEBE5gJdJDbU/Arwj8BPgA8ZY2Ju1qaUW7QFpZQHGGOOA39PYofofwS2Ax/ODCcRuU1EnheRE8mxqXscL1Yph2hAKeUdkbQ//7ExZjDLNfXAHuDTQL6zgpTyPQ0opTxARP6QRNfe6eSnPp3tOmPMj4wxf2GMeQaIO1WfUm7QgFLKZSLym8CTJI7wfi+wD/iT5MmjSoWWBpRSLhKRFcAzwHHg140xERLHYVcBuvZJhZoGlFIuSa5/2gScB+4wxpwCSHbfvQbcJSK/6mKJSrlKA0opF4jIdSSmkRtgjTHmYMYln0v+928dLUwpD6lyuwClwsgY8y5wZZ6vvwiIcxUp5T0aUEr5iIjUA9cl/1oBtIvITUCvMabbtcKUsoHuJKGUj4jIKmBLli89aYy5x9FilLKZBpRSSilP0kkSSimlPEkDSimllCdpQCmllPIkDSillFKepAGllFLKkzSglFJKeZIGlFJKKU/SgFJKKeVJ/x8+usDVSR3kxQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_predictions(clf, axes):\n",
    "    x0s = np.linspace(axes[0], axes[1], 100)\n",
    "    x1s = np.linspace(axes[2], axes[3], 100)\n",
    "    x0, x1 = np.meshgrid(x0s, x1s)\n",
    "    X = np.c_[x0.ravel(), x1.ravel()]\n",
    "    y_pred = clf.predict(X).reshape(x0.shape)\n",
    "    y_decision = clf.decision_function(X).reshape(x0.shape)\n",
    "    plt.contourf(x0, x1, y_pred, cmap=plt.cm.brg, alpha=0.2)\n",
    "    plt.contourf(x0, x1, y_decision, cmap=plt.cm.brg, alpha=0.1)\n",
    "\n",
    "plot_predictions(polynomial_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "\n",
    "save_fig(\"moons_polynomial_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Polynomial Kernel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Next code example:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('svm_clf', SVC(C=5, coef0=1, kernel='poly'))])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "poly_kernel_svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"poly\", degree=3, coef0=1, C=5))\n",
    "    ])\n",
    "poly_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code to generate Figure 5–7. SVM classifiers with a polynomial kernel**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('svm_clf', SVC(C=5, coef0=100, degree=10, kernel='poly'))])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly100_kernel_svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"poly\", degree=10, coef0=100, C=5))\n",
    "    ])\n",
    "poly100_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure moons_kernelized_polynomial_svc_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 756x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(10.5, 4), sharey=True)\n",
    "\n",
    "plt.sca(axes[0])\n",
    "plot_predictions(poly_kernel_svm_clf, [-1.5, 2.45, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.4, -1, 1.5])\n",
    "plt.title(r\"$d=3, r=1, C=5$\", fontsize=18)\n",
    "\n",
    "plt.sca(axes[1])\n",
    "plot_predictions(poly100_kernel_svm_clf, [-1.5, 2.45, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.4, -1, 1.5])\n",
    "plt.title(r\"$d=10, r=100, C=5$\", fontsize=18)\n",
    "plt.ylabel(\"\")\n",
    "\n",
    "save_fig(\"moons_kernelized_polynomial_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Similarity Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code to generate Figure 5–8. Similarity features using the Gaussian RBF**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure kernel_method_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 756x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def gaussian_rbf(x, landmark, gamma):\n",
    "    return np.exp(-gamma * np.linalg.norm(x - landmark, axis=1)**2)\n",
    "\n",
    "gamma = 0.3\n",
    "\n",
    "x1s = np.linspace(-4.5, 4.5, 200).reshape(-1, 1)\n",
    "x2s = gaussian_rbf(x1s, -2, gamma)\n",
    "x3s = gaussian_rbf(x1s, 1, gamma)\n",
    "\n",
    "XK = np.c_[gaussian_rbf(X1D, -2, gamma), gaussian_rbf(X1D, 1, gamma)]\n",
    "yk = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n",
    "\n",
    "plt.figure(figsize=(10.5, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.scatter(x=[-2, 1], y=[0, 0], s=150, alpha=0.5, c=\"red\")\n",
    "plt.plot(X1D[:, 0][yk==0], np.zeros(4), \"bs\")\n",
    "plt.plot(X1D[:, 0][yk==1], np.zeros(5), \"g^\")\n",
    "plt.plot(x1s, x2s, \"g--\")\n",
    "plt.plot(x1s, x3s, \"b:\")\n",
    "plt.gca().get_yaxis().set_ticks([0, 0.25, 0.5, 0.75, 1])\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.ylabel(r\"Similarity\", fontsize=14)\n",
    "plt.annotate(r'$\\mathbf{x}$',\n",
    "             xy=(X1D[3, 0], 0),\n",
    "             xytext=(-0.5, 0.20),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=18,\n",
    "            )\n",
    "plt.text(-2, 0.9, \"$x_2$\", ha=\"center\", fontsize=20)\n",
    "plt.text(1, 0.9, \"$x_3$\", ha=\"center\", fontsize=20)\n",
    "plt.axis([-4.5, 4.5, -0.1, 1.1])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.plot(XK[:, 0][yk==0], XK[:, 1][yk==0], \"bs\")\n",
    "plt.plot(XK[:, 0][yk==1], XK[:, 1][yk==1], \"g^\")\n",
    "plt.xlabel(r\"$x_2$\", fontsize=20)\n",
    "plt.ylabel(r\"$x_3$  \", fontsize=20, rotation=0)\n",
    "plt.annotate(r'$\\phi\\left(\\mathbf{x}\\right)$',\n",
    "             xy=(XK[3, 0], XK[3, 1]),\n",
    "             xytext=(0.65, 0.50),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=18,\n",
    "            )\n",
    "plt.plot([-0.1, 1.1], [0.57, -0.1], \"r--\", linewidth=3)\n",
    "plt.axis([-0.1, 1.1, -0.1, 1.1])\n",
    "    \n",
    "plt.subplots_adjust(right=1)\n",
    "\n",
    "save_fig(\"kernel_method_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phi(-1.0, -2) = [0.74081822]\n",
      "Phi(-1.0, 1) = [0.30119421]\n"
     ]
    }
   ],
   "source": [
    "x1_example = X1D[3, 0]\n",
    "for landmark in (-2, 1):\n",
    "    k = gaussian_rbf(np.array([[x1_example]]), np.array([[landmark]]), gamma)\n",
    "    print(\"Phi({}, {}) = {}\".format(x1_example, landmark, k))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gaussian RBF Kernel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Next code example:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('svm_clf', SVC(C=0.001, gamma=5))])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rbf_kernel_svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"rbf\", gamma=5, C=0.001))\n",
    "    ])\n",
    "rbf_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code to generate Figure 5–9. SVM classifiers using an RBF kernel**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure moons_rbf_svc_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 756x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "gamma1, gamma2 = 0.1, 5\n",
    "C1, C2 = 0.001, 1000\n",
    "hyperparams = (gamma1, C1), (gamma1, C2), (gamma2, C1), (gamma2, C2)\n",
    "\n",
    "svm_clfs = []\n",
    "for gamma, C in hyperparams:\n",
    "    rbf_kernel_svm_clf = Pipeline([\n",
    "            (\"scaler\", StandardScaler()),\n",
    "            (\"svm_clf\", SVC(kernel=\"rbf\", gamma=gamma, C=C))\n",
    "        ])\n",
    "    rbf_kernel_svm_clf.fit(X, y)\n",
    "    svm_clfs.append(rbf_kernel_svm_clf)\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10.5, 7), sharex=True, sharey=True)\n",
    "\n",
    "for i, svm_clf in enumerate(svm_clfs):\n",
    "    plt.sca(axes[i // 2, i % 2])\n",
    "    plot_predictions(svm_clf, [-1.5, 2.45, -1, 1.5])\n",
    "    plot_dataset(X, y, [-1.5, 2.45, -1, 1.5])\n",
    "    gamma, C = hyperparams[i]\n",
    "    plt.title(r\"$\\gamma = {}, C = {}$\".format(gamma, C), fontsize=16)\n",
    "    if i in (0, 1):\n",
    "        plt.xlabel(\"\")\n",
    "    if i in (1, 3):\n",
    "        plt.ylabel(\"\")\n",
    "\n",
    "save_fig(\"moons_rbf_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "m = 50\n",
    "X = 2 * np.random.rand(m, 1)\n",
    "y = (4 + 3 * X + np.random.randn(m, 1)).ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Next code example:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVR(epsilon=1.5, random_state=42)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVR\n",
    "\n",
    "svm_reg = LinearSVR(epsilon=1.5, random_state=42)\n",
    "svm_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code to generate Figure 5–10. SVM Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_reg1 = LinearSVR(epsilon=1.5, random_state=42)\n",
    "svm_reg2 = LinearSVR(epsilon=0.5, random_state=42)\n",
    "svm_reg1.fit(X, y)\n",
    "svm_reg2.fit(X, y)\n",
    "\n",
    "def find_support_vectors(svm_reg, X, y):\n",
    "    y_pred = svm_reg.predict(X)\n",
    "    off_margin = (np.abs(y - y_pred) >= svm_reg.epsilon)\n",
    "    return np.argwhere(off_margin)\n",
    "\n",
    "svm_reg1.support_ = find_support_vectors(svm_reg1, X, y)\n",
    "svm_reg2.support_ = find_support_vectors(svm_reg2, X, y)\n",
    "\n",
    "eps_x1 = 1\n",
    "eps_y_pred = svm_reg1.predict([[eps_x1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure svm_regression_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_svm_regression(svm_reg, X, y, axes):\n",
    "    x1s = np.linspace(axes[0], axes[1], 100).reshape(100, 1)\n",
    "    y_pred = svm_reg.predict(x1s)\n",
    "    plt.plot(x1s, y_pred, \"k-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "    plt.plot(x1s, y_pred + svm_reg.epsilon, \"k--\")\n",
    "    plt.plot(x1s, y_pred - svm_reg.epsilon, \"k--\")\n",
    "    plt.scatter(X[svm_reg.support_], y[svm_reg.support_], s=180, facecolors='#FFAAAA')\n",
    "    plt.plot(X, y, \"bo\")\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=18)\n",
    "    plt.legend(loc=\"upper left\", fontsize=18)\n",
    "    plt.axis(axes)\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(9, 4), sharey=True)\n",
    "plt.sca(axes[0])\n",
    "plot_svm_regression(svm_reg1, X, y, [0, 2, 3, 11])\n",
    "plt.title(r\"$\\epsilon = {}$\".format(svm_reg1.epsilon), fontsize=18)\n",
    "plt.ylabel(r\"$y$\", fontsize=18, rotation=0)\n",
    "#plt.plot([eps_x1, eps_x1], [eps_y_pred, eps_y_pred - svm_reg1.epsilon], \"k-\", linewidth=2)\n",
    "plt.annotate(\n",
    "        '', xy=(eps_x1, eps_y_pred), xycoords='data',\n",
    "        xytext=(eps_x1, eps_y_pred - svm_reg1.epsilon),\n",
    "        textcoords='data', arrowprops={'arrowstyle': '<->', 'linewidth': 1.5}\n",
    "    )\n",
    "plt.text(0.91, 5.6, r\"$\\epsilon$\", fontsize=20)\n",
    "plt.sca(axes[1])\n",
    "plot_svm_regression(svm_reg2, X, y, [0, 2, 3, 11])\n",
    "plt.title(r\"$\\epsilon = {}$\".format(svm_reg2.epsilon), fontsize=18)\n",
    "save_fig(\"svm_regression_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "m = 100\n",
    "X = 2 * np.random.rand(m, 1) - 1\n",
    "y = (0.2 + 0.1 * X + 0.5 * X**2 + np.random.randn(m, 1)/10).ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: to be future-proof, we set `gamma=\"scale\"`, as this will be the default value in Scikit-Learn 0.22."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Next code example:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=100, degree=2, kernel='poly')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "svm_poly_reg = SVR(kernel=\"poly\", degree=2, C=100, epsilon=0.1, gamma=\"scale\")\n",
    "svm_poly_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code to generate Figure 5–11. SVM Regression using a second-degree polynomial kernel**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=0.01, degree=2, kernel='poly')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "svm_poly_reg1 = SVR(kernel=\"poly\", degree=2, C=100, epsilon=0.1, gamma=\"scale\")\n",
    "svm_poly_reg2 = SVR(kernel=\"poly\", degree=2, C=0.01, epsilon=0.1, gamma=\"scale\")\n",
    "svm_poly_reg1.fit(X, y)\n",
    "svm_poly_reg2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure svm_with_polynomial_kernel_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(9, 4), sharey=True)\n",
    "plt.sca(axes[0])\n",
    "plot_svm_regression(svm_poly_reg1, X, y, [-1, 1, 0, 1])\n",
    "plt.title(r\"$degree={}, C={}, \\epsilon = {}$\".format(svm_poly_reg1.degree, svm_poly_reg1.C, svm_poly_reg1.epsilon), fontsize=18)\n",
    "plt.ylabel(r\"$y$\", fontsize=18, rotation=0)\n",
    "plt.sca(axes[1])\n",
    "plot_svm_regression(svm_poly_reg2, X, y, [-1, 1, 0, 1])\n",
    "plt.title(r\"$degree={}, C={}, \\epsilon = {}$\".format(svm_poly_reg2.degree, svm_poly_reg2.C, svm_poly_reg2.epsilon), fontsize=18)\n",
    "save_fig(\"svm_with_polynomial_kernel_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Under the Hood\n",
    "## Decision Function and Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code to generate Figure 5–12. Decision function for the iris dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = (iris[\"target\"] == 2).astype(np.float64)  # Iris virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure iris_3D_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "def plot_3D_decision_function(ax, w, b, x1_lim=[4, 6], x2_lim=[0.8, 2.8]):\n",
    "    x1_in_bounds = (X[:, 0] > x1_lim[0]) & (X[:, 0] < x1_lim[1])\n",
    "    X_crop = X[x1_in_bounds]\n",
    "    y_crop = y[x1_in_bounds]\n",
    "    x1s = np.linspace(x1_lim[0], x1_lim[1], 20)\n",
    "    x2s = np.linspace(x2_lim[0], x2_lim[1], 20)\n",
    "    x1, x2 = np.meshgrid(x1s, x2s)\n",
    "    xs = np.c_[x1.ravel(), x2.ravel()]\n",
    "    df = (xs.dot(w) + b).reshape(x1.shape)\n",
    "    m = 1 / np.linalg.norm(w)\n",
    "    boundary_x2s = -x1s*(w[0]/w[1])-b/w[1]\n",
    "    margin_x2s_1 = -x1s*(w[0]/w[1])-(b-1)/w[1]\n",
    "    margin_x2s_2 = -x1s*(w[0]/w[1])-(b+1)/w[1]\n",
    "    ax.plot_surface(x1s, x2, np.zeros_like(x1),\n",
    "                    color=\"b\", alpha=0.2, cstride=100, rstride=100)\n",
    "    ax.plot(x1s, boundary_x2s, 0, \"k-\", linewidth=2, label=r\"$h=0$\")\n",
    "    ax.plot(x1s, margin_x2s_1, 0, \"k--\", linewidth=2, label=r\"$h=\\pm 1$\")\n",
    "    ax.plot(x1s, margin_x2s_2, 0, \"k--\", linewidth=2)\n",
    "    ax.plot(X_crop[:, 0][y_crop==1], X_crop[:, 1][y_crop==1], 0, \"g^\")\n",
    "    ax.plot_wireframe(x1, x2, df, alpha=0.3, color=\"k\")\n",
    "    ax.plot(X_crop[:, 0][y_crop==0], X_crop[:, 1][y_crop==0], 0, \"bs\")\n",
    "    ax.axis(x1_lim + x2_lim)\n",
    "    ax.text(4.5, 2.5, 3.8, \"Decision function $h$\", fontsize=16)\n",
    "    ax.set_xlabel(r\"Petal length\", fontsize=16, labelpad=10)\n",
    "    ax.set_ylabel(r\"Petal width\", fontsize=16, labelpad=10)\n",
    "    ax.set_zlabel(r\"$h = \\mathbf{w}^T \\mathbf{x} + b$\", fontsize=18, labelpad=5)\n",
    "    ax.legend(loc=\"upper left\", fontsize=16)\n",
    "\n",
    "fig = plt.figure(figsize=(11, 6))\n",
    "ax1 = fig.add_subplot(111, projection='3d')\n",
    "plot_3D_decision_function(ax1, w=svm_clf2.coef_[0], b=svm_clf2.intercept_[0])\n",
    "\n",
    "save_fig(\"iris_3D_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code to generate Figure 5–13. A smaller weight vector results in a larger margin**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure small_w_large_margin_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x230.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_2D_decision_function(w, b, ylabel=True, x1_lim=[-3, 3]):\n",
    "    x1 = np.linspace(x1_lim[0], x1_lim[1], 200)\n",
    "    y = w * x1 + b\n",
    "    m = 1 / w\n",
    "\n",
    "    plt.plot(x1, y)\n",
    "    plt.plot(x1_lim, [1, 1], \"k:\")\n",
    "    plt.plot(x1_lim, [-1, -1], \"k:\")\n",
    "    plt.axhline(y=0, color='k')\n",
    "    plt.axvline(x=0, color='k')\n",
    "    plt.plot([m, m], [0, 1], \"k--\")\n",
    "    plt.plot([-m, -m], [0, -1], \"k--\")\n",
    "    plt.plot([-m, m], [0, 0], \"k-o\", linewidth=3)\n",
    "    plt.axis(x1_lim + [-2, 2])\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=16)\n",
    "    if ylabel:\n",
    "        plt.ylabel(r\"$w_1 x_1$  \", rotation=0, fontsize=16)\n",
    "    plt.title(r\"$w_1 = {}$\".format(w), fontsize=16)\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(9, 3.2), sharey=True)\n",
    "plt.sca(axes[0])\n",
    "plot_2D_decision_function(1, 0)\n",
    "plt.sca(axes[1])\n",
    "plot_2D_decision_function(0.5, 0, ylabel=False)\n",
    "save_fig(\"small_w_large_margin_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)] # petal length, petal width\n",
    "y = (iris[\"target\"] == 2).astype(np.float64) # Iris virginica\n",
    "\n",
    "svm_clf = SVC(kernel=\"linear\", C=1)\n",
    "svm_clf.fit(X, y)\n",
    "svm_clf.predict([[5.3, 1.3]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code to generate the Hinge Loss figure:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure hinge_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x201.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.linspace(-2, 4, 200)\n",
    "h = np.where(1 - t < 0, 0, 1 - t)  # max(0, 1-t)\n",
    "\n",
    "plt.figure(figsize=(5,2.8))\n",
    "plt.plot(t, h, \"b-\", linewidth=2, label=\"$max(0, 1 - t)$\")\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.yticks(np.arange(-1, 2.5, 1))\n",
    "plt.xlabel(\"$t$\", fontsize=16)\n",
    "plt.axis([-2, 4, -1, 2.5])\n",
    "plt.legend(loc=\"upper right\", fontsize=16)\n",
    "save_fig(\"hinge_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extra material"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f936084ecd0>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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avy3jPY7jHpYRW4mv6V2D1kRrwWeVnlEXa2aqp/7JTacEKi2Ua63EhH2eUWoQeSpm+tjCQwngD/cDCwZzZh03OqE83Qb889rQoZmRBKvOAWyfq07AR4gaKtcqzm1qMv22TJ+veSKFxMIiK6lWEJ2DuJI9iK1xC3NmzYH1ZSuymame+ic3nRKoNaEMlK6YbF+EH7GcZ4gqnKrgMxZi+93Hc4L2mm3efUQIV4wFOzLmX9YAWc3j4BbwlR5fzKjOSlyrv4fVXqXaqA7ijpYORxhXqjdxKZE95fJRlIOmUwK1iE4xHTudK3kQZskeRbCb/B6hiFhaQFsI7sP9MyUYEhnvamDjEPCNY8Cvl1cmMavTAj533Yxyc+MW8CHDKashSMP4tFQzRdusGWd6qRMiUzx8KXHy1XSs1ktkTxyIEqhR7FmIqdxxVMJ2+jISwRZuVDLqKgDIrwYei54JHKc9uy8FXPJTIOma3asmqAgKqJoryyC/Q7HOyjCC3DRrLiXJKk7HalRlVC+RPXEgSkChVpy4BUv2Hr2grThxZNCqqwCbRLZwNRBkciqlM5gb+1gE7yognximzpBLqaFUbYiA9362OGdlkCA3zZpLTbKKMtYgIR9FGYVVQPWQDRyGOv5Zx081/QXqUv7iO1I4dy73AJw9F17QllVZxWELv3yHN9qHAFz5w5m/AzKBw3QGU6+D7zVRj+VyPuvst1GL4ZV6P+KelJy9ILqzMowgN82aS5lN+zlWdcLXJOStcQvXfec6bBkKr4zCKqB6yAYOgyiBGsFZykes6KgKhFKbsaiKzyNo8rbwxIMhYu8N9Lz3UsOB8z/DoHMP4ZOwlbYtKIZ/ZXjgy1A5033NSk0UM2UcHz9eZK0rxZ8R1lkZJMhNs+aR0ZGSzDlDq4ewpncN2pJtAIC2ZBvW9q7F0Oohj/D1U1SpwRR++pufIp1NG8/BTZjInrh8BrWwmhAlUGtEMLvYpQl0DsFSMa2KMpng75oYWp13+k65SgK0vZMTvn7nHlFo24Kif1c/Wu/R+BBiLhAX94rRFv5h8wvKUQAxjFnENGte+fTKkuLkh61hbHx5o1G5qMLXraj6f9SPvq19GBkdweahzQDgfB5GGYWJ7InLZ1ALqwlRAgaKrTZacpVSH7OLuyyFrQDiiEevmN/DT/j6mZwiCO1haxgb9m1AlrMY2D+A6Ytf9DrYyxzqWWq5j6gKpRw5CWHMIqZZ82snXyspTv7OZ+4MVC62wHcrqoFXBvDikRex8umVmMp4C9GV6uiNy2ldKxFIuibwRUNE5wPYBOD/AHACwDpm/nvNdnfntzurvP3HzPzjOMdTCsVmeJacGepjXjmOGaFirwLiePhVk0Uyqbd9JxLhVgHd3foxOUrGJHyXPAr87a+AMwZtuXppaKF95zN3gpE7qUw2AxDyPQXuB87My43lB0NlLclR6r6Jqh+vH8YsUo64d2vcwsGxg57305k0Do4ddO5tOpPGo/sf1WYVA8CBsQPa/ZeatOWnHB/62ENF7aeY78dFrEoAwEMA0gC6AfQA2EFEI8ysuxt7mPmDMR+/aOKaUVeKsGO1BbzfjDTMbNVWDH4rGlsx+dGtE74fWwss25ib1e80PAQhfA/d3blVQMHD74SjTjv7L/Y+Bym4rkUWTvTdDjz1JIDS7TPV/j1WK7EpNZhCa7IV6Uwabck2fH7p5/HQxx7C2h1rsWlok0cx6cpO61D3FYQ1buH279+OJ29+EvM6C+9lHNnAptXE/X33e45XbmIzBxHReQBuAnA/M59h5pcAPAvg03Edo5xU+4Gr9qwvLH7XKYwJY+hVq9BGHzLixw/VPHbnM3fqN2qZKmr/fvV/bPOcfd4n3h9j+GoZ6e7WmC1nW6DP9IFmj1alyZKNn6nFVFiOQLC+bOHYl45pS1DbRBHUfrb6OLKBa6m2UJw+gSsAZJj5kPLeCIDFhu2XEtEJIjpERPcTkXZVQkT3ENE+Ito3NjYW43BLJ06HbCPVnjdhjVtY9siyQht9jA3OrXHLaAKIsn/1PvgpPfUza7x0ZVYptGZEV/5FuSdFdujm9ZuuD+VoTu1OFQhfNXKoNdmK1O6U9rt2VFEUQV0JW30t1RaKUwl0Ajjleu8UgNmabQcBXA3gQuRWD3cA+Ipup8z8CDP3MnNvV1dXjMMtnVIeFJ0Cibq/oNlapZVKkBO8f1c/rDNWzqbbswXD1gjar/eJ+ImYHZwaTCHh95MO6fgtJsonNRifMgtFZ2Fo4dyFJWRSF7kaK7UkxE9/81PsfWNvKEezLjzTvVoYPDIYi2CtRLZwLdUWilMJnAEwx/XeHADj7g2Z+XVmPszMWWZ+BcCDAG6OcSxlo1jB6v5eHDOt48f9E4qqVRRPPTdH2c228Njw4877Z89Na8MIW2dNA/dcOxMyGsK8YiufR/5xEFlovNpjV5S1BpEtlOLMOQikr9Bcccvf5iqD2rWAou6rGAVWSkkIO3QTADYPb3YUSdTwTJsMZ9C3oK9kwVpPfQDiIk4lcAhACxFdrry3BIDP+tyB4c0lrRmKyR6uVNaxX5Zz3F3JitmPoxBu6M8JGJuWKRx486DeyTfbym0fcXaaeX25dyXAALKtoVcVxSh5NcPboZyrgU4LWDpjrtDFzkfaVxFJc6WWhFBDN/06humoVCtJG6kdFBJmngDwNIAHieg8Ivo9AJ8AsM29LRF9lIi68/9/H4D7AfzQvV0lqZW6QXFSasOSsP0GdNimAnSO5gTNkse9aj7TUmCvdRLJCMA1A9Fnp/M1KwECcOHBXH0iw6qiFGVNhMqXl+5LoW3WjLnCHTuf2p0K/3suMmmulL67m4c2F9ynLGcLVgNBlGpK8TNj1ZKtvlLEHSK6FsBmAG8CeAvAGmY+QETzARwE8H5mPgrgBgBbiagTufD3AQB/GfNYIhH3TN09a66meSYqpZQ6sLFNBVieAogLVwE2LVOFD5cqkBKZme/Ys9PB+815BABwdDkS837h7U+bac0pFXtVMXg/AAZuvh1zf+wfzmkKCy2gXGWudeRn7qq5QnWGpzNpfHvvFmDifjjn1Wk55zp6OPeec14GBdayMLieUDHhje5VgDruSsXJq2Ys9/Fqsd5/uYk1Y5iZ32bmTzLzecw8304UY+ajzNyZVwBg5j9n5u78douY+T8zc7hg3xoiyiqhnNEWJfUIKAed+dkeZ4Glm4HL/klv7LN6sH9t7qFzomtMbSWDZqd54ahrUI6W9Ez1Uns/fSlgwUu49b9HWOYXUca6FIVa8Puy+x6sXqpXqAW4rlW+k9uJJTPVWh0z4gb9rHrqv88IQ/fMWWcymc5Oh1oN7Hljj9Zvk+VsqNm2OpZiHNO1kqVbS0jZiBLQ2ePDECaqx7eYm7IdUJlwvkjCry+Fyam8Tk+mATDgvjZTHcDjzztJaAXRNTqCzCs6swYDeOXWGRNTfj/t129G+3VbAAoWBAXXtogy1kTmEtRBkwj197XmiRRw6U+B2cdzOQ9+qNfK1cmNZo9GzgHo39WPwSODWLdrHaxxC4+NPKb15ew+stsolO2JyvC9QwVNg7ofjmbOcWpC/agfyx5ZhhePvBjJXt9MfQLCIkqgCkQpPWHKZK6keSk16C/8CpRgpwX0bJ6pz5/IAnMPBc7s/+55jVkCAKyecJE9pl7B7/uhRzmkM+nAqpK20HIoIanNXYbDnWQWhDuapr2lXdv31vGtqNdK08ktyqTBGrfw+Cu5qK5t+7dh3QvrcHb6LNb2ri1IzkpQAou7FhujhUzKNOpY7Fn8tv3bnHDjsDP6Zoz8CYMogRrFjrf3q0VUrNknkQjvCHfCHw3CT+toTIaw7Llm9tMPDc30+dU0lQ+aNfOGIRz70jFvdFDLpEc5ZJENrCqpTaaKKQ8g6r0LG02jVVyeTm6avs4+9O/qd2rxZDiDbSPbHFPKuhfWOdcxy1l89+B3g80sJShTdRavmpTCzuibMfInDKIEKkhUoV0uM082G76BTv+ufkxOTwIA2tq9PWk9s9n5g94uXeo5T3XM9A5WZ/Zu4XDhSIEJyi+j2n6/f1e/x96coIQza12+YDlWLVnlZJraBAqCKGGUMbW/tM8pSjSN5/ei7eSWWw2EqXSrrgKcY+fHMZ2dxsD+AW0ZB9/rGaBM/XoVq7N4lbAz+maM/AmDKIGYqeeQUje2EFCrNgY+bEeX57p0mTDNot3C4aaVjsnAtqkPvWrhur+7Htd953pY46MeRfTcoec8u80ii91Hdju25B2HdkQXBFHCKGNof6ma+oKiaXwxdXK7wnudgJlmNU6Hu7v6c1VYNUxlp5wVgm5sm362yVMSwqRM/TqE2UpBXXXoCDOjDwotrYUGL9VAlEDMuGfYFSfkTDRMrwPVFGAT+LDpbPMqLjNQIqGJDGpJAxcecFYFvzk1E5Wy9zd7PaUGgNw+Tk+e9hyuLdmG3ot7HVvyxNSE1p5ufXXIHFllCKN09yA2mjpM98Twvmouevi5EqJpTOG0pw0d3uBaTeiUSEgms5Pe+2RQpn4dwmylsP3Qdu0qwCaOGX0tNHgxUU4FJUqg0ShiJmoyO+14dYfnvaCHrfsHQzP2fLWL2HTHjPDdMFTQqcw3MohyjUOu/8712PSzTc7bm4c2e2aQuplpOpPGoz/Z7mT0mpSYr+lNacuIB9gxLT2/8vmCzTo+YjB1mO5J0L3qtIDJOTPmM+XV/XCIaJqjy70+liilM3yURVhsYd7dDaMytX9Png5hu/odpfDO1DuwvmyhZ16P9jg983pKivGv9dDRciooUQIRKblzWEQimZdiKMtsY41bmJiaKHivoyUnyP0eNndYY1t77qFum2VeQex5wxDZAwAtaQzsH8De3+x1InoA4FzmHNbtWle4Dx3HFwNtE87+S40I6e7WP5DWuAXW+Q0uHDGvDpz3NwOfu17fAtOgJHyVlp1ToBxXNZ+FxqX83C+TQFaxle7oqDknYWj1kLad5MD+AcccZaokavt8Sk3yquXQ0XIrKFECESm5c5iGoCJwoR/cEiJY3MpNVw8nanmAsOF4phLBznGhtz1vG9lWUHhsTa8muujocs8q4+y5aA85M3DstIXlW/rwj8P6Oj26yBPHt2FaHdjvJ9PAJXsL71cEhe6JCupLAZf8NJ+jgQKTS5yo981Uyz+s0jW1k7Qbxrj3E1YwhjGj1HroaLkVlCgBlHd2H2YmHzZSx5ciC4HZeJSYZuketSlH2HA89UE1NQ7RkUHGSWBatnEZNuzb4BWaASaIsNizf12dHlMCFVrSQNfBgnvS8QFNCe1ENrfyWbp5ZtwhFLq2BLmdp0GYidJqSWPDyxuw//h+AAFRVkVGN2mVYJ4gwWVqJ+m3n7CCMYwZpZZDRyuhoEQJoDyze5swM/lYlFCRhcCMKKaAYop0RQnHUx9U9+wySUnf42z9yXO4+I4Ufmb9DGxfaPW8DSaNYsoLZzmLA2MHPA+kmkClXqs1vWvQ1urtf6sroQ0ASE7mymh3j+QEeTEK3ZCnkc1mseRrKzBvnv+ko+MjxUU3+SnvoMmD3U4SmGkCozM12fsJKxjDrhZqOXS0EgqKOLStofr09vbyvn37Yt+vX+y++/JE2Tbs9/xw5FrQ91cvBS4a9r5v9QQ6AkMVSXNtH1e2sjVuYdG3FuHc9Dl0tHTg9fted4qQrXpmFR7b/5j/Dqbac8rPPduf6gD+5nVjhEzYc5670ELr/7kMb519Syvk2pJtSE9lcvH3Ux3A3+0B/ug/5voMr/yo9p60t7Tj3PQ5/QEZwIkrgQteLcy3mG4DfvZ5cw9mIDeLv28R0GrYd5aAbx7zXBP7furuxUWzzTORUkWHNW7hU09+CkOjQ5jMTDrvd7R04Nnbn8WNj9+ILGc9vwtdr2Fd/2B1uyj9hWuJpRuXYnh02PN+VEc4Eb3MzL26z+KuIipUi5ARH6YHt9xJbKbG3anBlOP8s4uQ2Q+qLjrJQzKtjyyyVwMGoel3Duo1uuW7/xFP/cIybpvOpJWM3LwPoOsXuWMb7okjoj+2Fli6qVCBEYC5v/SGZoYpTa1bDWbzi/1ENtdXQXNN7GuhJgbOlKR+yFi2RMUat/CnT/4piAjP3PZMYDVRu9WodcbyZHlnOINbn7rVY+6xfxeldB+rRiP3UqhEVVMxB0Wk2L4DlepXUIpdt5yYImq2DG9xnH9T2SmnSUrf1r5wD6ttT3cTQz1/a9zCU794yvO+bbJwnKFKJJOa3xB4/f1yKmxFpDq6gxS9bn+JbIFvwDQuU2Lg8K9GQ/mrTO0iTditRgF48iDSmTROnjtZ8Ldqziml+1iQT0r3d6MjSiAixTpxY3H+GvDsL4as1biwxi1c/53rndLS7ogad0bqdDbXdvKloy8Z2wXyVzlXZ4jhrU4KIIFc2GD3D0qLG+/Z2KP9zJ51+jlDQ5W+zucAHPuSkk8B5JSKrVjsEFI/hWIr/cefL/R9/Msab/a2YVwXf9qbHRzW9uzXLtK0vVqOgkBYtWSVc28Xdy32fOfcdGFIcBDF+qR0fzc6ogRQH13FohR8iytXICo6B/fFd6Sw9429TukDVbDseWOPswqwmcpO4eDYQd9uU8PWMLBsQ6GwVMgil01bbKlvIDdTfXPizYL3Ekjgukuuc3IltM5QdVXgd/0VRR1YRjuZ9lcobqVvK4X5g+E7nl3hzQ5OZ9LYfWS3+bh5oraLdGeiMxgD+wecHgG6SCEGa8uCmAjbfcztPC6pVWedIkoA5Z2lhyGMgA87xgKB4pr1lUOpqYLfYztWwhXtJb+6tN+5Yqc2tpzy0sgkTO585k6A8hLdLdingxPagtAVTgNy56CaO2xBE3XW7c4BGDyiEdYqiSywwCCMdUlndivNI32F2dv5Gv4es1KnBbSdKXgriSQIhN6LetG2ug80ezR3n2dboM/0OX0J/Arc2WY9VZAOW8NaZ7+dIaxGCrl5Z+qd2IWyO9RUFwLc6IgSqAGiKCFTOKn9gD68Z4snLt3OFgXiyYdQlYnRwdppAfcs04Yr2g+XyZxiCxTdamDYGs61U1TLIyv4ZSaHYe7CnMPSVBwNgGeG6FcSQTvrduUAHNjRN6NIsppHkgk4pg3s0Cedqa001byDBQYToSasNMMZMBiPDg9gat6L2hIYx4+bC9ydmz6Hm757E1488iKu3Xitc73ufOZO/XkA2HFoh2+oadxCWec8docAu8uTuL/fCL6DWJUAEZ1PRM8Q0QQRHSGiFT7b/hkRjRLRKSLaTESz4hxLJSlXspluv76ROZrokLPnMrjotlRgbwK/1UhRK6QP9wOzLW9ZaczYZsMkhrlXA35CRN03EPEh7cxlBP+H//c+x2Fpwi2MRkdhLrGgm3XrcgC6lfISbohz5hrNmAsSBG0nubuVpr0dZdHxAY2Jyq/8N2WABHvH2LsBuHC/sV0kALx28jUwGNYZK+cIDkgKm5iawPMrn3dyLAhUEDlUTKKU32/A16ejHLOURLR6IO6VwEMA0gC6AawE8DARebw8RHQjgH7kGs5fBmARgK/FPJaKEdT4pVhlEDkU02c2GrQvv9VIZCXnbmZiw8jV8vnGMcyZNQfPr3zeWG7AJstZxy7tJ0R65vV47L6RHtK+FF488iKeOqiPBlKT1tKZNL69Z8uMiYQQOiKr4yMpJFpdM2fKAJ9a6VHgbcm2mZVB2xl9bSFteGz+X1vB3LDO2S7DGeCG/sKx+pX/VpWBOkbKAp+73hHadnJfW0K/n4H9A1j3wjrH1EMaZ46agb1leAsY7FEwUVcDfr+BMJMQuxS5m1ovOBeF2JQAEZ0H4CYA9zPzGWZ+CcCzAD6t2XwVgE3MfICZTwJIAbg7rrHUGkECOLZG8WFnoz7oZk6RM6r7Ut5mJkBOoHQdBG5Y5zyYQbOx1kQr+hb0AYDWXmyHa275+Ba8+6/e7ZRGMD2k2hVPpwUszQkd9jgZckLfYx5y2/t9IrJUpbroPwx6Z84taeD817RlOpwZus45HFS22x7nNQMFBfSwZACYr5h4wuzHDn+1tyMAbe/gvufvczZJDaYKivypZDiDgZGZJjSm6xwUdaXLBzDN9IMEtcl5rNavaku2Ob8/lVouOBeVOFcCVwDIMPMh5b0RAN54r9x7I67tuonoAveGRHQPEe0jon1jY2MxDrd2KHej+CiUvMR1TA+Gz7MtwJIB58EcPDLoOxubyk45D71f2N+dz9yJU5OnsOL7K5zz0D2k7hVPdzdyAlxTpM6umqqtlulu5h4yImv5guUzBfKm22ZyAP6fs55y1bOSioVUFyqaV/q6onsF40xquosleMZftEGZPFiaczVBwHcPfteJ6lFLfevQFQJMUrKgv8POFTu1HcRsZa/LBzD9XosR1GFKUtR6wbmoxKkEOgGccr13CsDsENva//dsy8yPMHMvM/d2dXXFMlDBi+NY3httiavtMewX7tgylZudIvdg9i1wOUX/eW2+D0FOALa3tDt1+4dWDzm1/FXBseXjW3LOYgAHxg7ghddfCF1b5vgfX5e3q+sd2P27+jFn1hzneNoVVsjqrZ4WiT5hpKnBlCd8Fsm015QDs1mjZ16Pb7lnrWC0V5M6ZWBQ7Ot2rfNdBfjhHoNpFeAnjLOcLSiQp34WVVCHSTKr5YJzxRCnEjgDYI7rvTkAxkNsa/9ft60QE4Ehon0pANFmTp4CeSbTgtUz02QmL0zsB9NTc/+GdU60itsxp5v5uZ3Ft3zvllAPaWrQVXLZRTqTxvZD2/1XRiZHb6fX/GQsN33PtR5FMHhk0LttIgtc+azH7GRSjkOrh3zLPauC0fPbeHyno4iD2LZ/Wy7MNQSLuxZ7fECqcI4SHVTQeJ6zzirQ/ZmNXZbEjzBJZrVccK4Y4lQChwC0ENHlyntLABzQbHsg/5m63XFmfivG8ZQNtw2/0qgRO1G+o5pCPLgiTYpe4rr9EnZ8+uPPa1cJGXbX3J/O2awT3jBRnY3XCRlVOHnupPYh3X1kt2M/tvdVUHLZTSaJt06fQZaz+Pbe/LVwO4A14ZVt7RmsfSKFY6ctXPlXff4CriWdi6JyrR6WL1iO1oQrXj5LQPvpArOTLbxLLZnsNpOteULjwDaQ4QzePvs2Vi1ZVWBLt0trLF+wHMOrh7F8wXL0Xtzrq6BtpeVXRRTQN54/MHbAWQ3orrVqWjQRJsksbCJavRCbEmDmCQBPA3iQiM4jot8D8AkA2zSbPwbgc0T0fiJ6D4D1ALbGNZZyE9WGbz+osTmAoWkkEkBgWKdBQJe8xO1LIbHwJax9IoWeP9H3KCisuT9jLlK3cTuR7bHd9tRt2sMu7lrseUiXL1iOl46+hP4f9edyAewyCdNtwNgV3sSzREZZJeSOV1BqudMCrtnqUSLpTBrffu4nuPiOFAYPv4SLbkuha5GFObPmYHj18EyJiKn2mZWRyyyky6ZGgmeuTd7sZFf/tJXjw/seLjCLqISdweoSwIKwzlgY2D9QYH7ZPLwZ9/3DfRg8Mohbn7rV2CtY5+xVTXD2KkZt6WkyG9mrAVtQq6ufjpYOT0tQAfGWkiai8wFsBvCHAN4C0M/Mf09E8wEcBPB+Zj6a3/ZLAP4CQAeA7wO4l5kn9XvOUa5S0lGpdDnpOAgsiW0oRd0zrwfD95pnOL6lrpXSxmo54LU71mLjyxtx77J7wWA8vHdTYHTKlRdciSOnjhSUYG5PtiOdSWuFVXtLO87+p7PO32qZ5CSSXiclw+zMVo4H5FpbdrR04NbFt+LRkUeRoASynM2Vld7zeWBwPXDbp4B5Q0DrZK7E9M9vReLabbhq7lU4YL2aO1+1wqddKnpwPXDz7Rj+L9/CdZuuy52vPTb3GKc6YPW/jgd3P4jv/Ow7jtJY3LUYP1/7c/+T8WHtjrXYuG9joBJIIIFPX/NpPHnwSW1p7AQSnn24y0Ibj5//fdiVQ93vmUosEwjHvnxMW3aaQLhryV3Y+smtAVeg8fArJd3Q/QRMCVKl1sMvhxLQfa/iSuNja4FlG7H2399bUHc9zLi111oplWzXc1+/fH1BzfpF71nkMee4aUu24fLzL8erb79aMIskEBKUQIYzIBCG7x3GNd3XaPehq0FfeCIIVAIEAhE5An86M+0VlFkCDtwMXP09gBN5Ad+ar+ZpzkIG4CgL9GzD4guvwi9P/BLTPG3efroNq5bdgScPuIQwA3h4BHinC7j5dsz98ZMYOxw+WcUkYHVc0HEBxtPjobvBBdX11/U0YGZtz4mgvgLqvlRG7h0x/k4aFT8l0NBlI8rZMazhUMIci/EFuG3Kx05bBS0U1S5cqknHXSnUZAt+7eRrHkHDYCd+n8G49Xu3asemsx97IOSc17YfY/I8zyYMdsaezqT1sezEwNVPFfoaNCYu/RgySF47AFCug5mvAgCAlpzj2l39EwBw04p87sKLOPGpaz1RNdd/53pcv+l6bcSUbYoJaiQ/vHoYE1MTBdc1SUmvL0MhyNekM/mZQj2DzFsmk5Hpd9KsNPRKoNgZe6n79SvREGU85V4JqMf068JUzHU0zdIy2UxB4lUY80DQflV2fXoXHhx8sKB5TdB37l5yN7Z8cguA/LnmV0TYdy/w8heAe5fqzTEmwm5XLEzAf8t1COvZYJi1M+VMTK2TAAOrelY5ZpC1O9bi4X0P5/7fu9aZNd/+/dux8N0LsW3/NsfsYjd/GT0z6knwWty12LM6C4NpNaCbube3tIOZPZ3Hwvxm/FY01petumouUypNuxIoF0FVP2tlpRG2amhQTHWYKqfuzE3TLM2deRvF+RxmRn/Td2/yRMkElQfYtn+bU/Hy/Ktc4ao33z6zoZ9gV+VjkAJQk8ScHAkCsv79lJ1jZHIdwrq7gZ0rdur7MDPNOLUJBaWa1dr/tiM5NZgrmzGwf6Ag+io1mIJ1xtJm+P7q7V9FVgCA2Rm97JFlnpl7OpP2FKgL+5uxQ2cLku7y9O/qjzzuRkWUQBHEXXq6XP0MwiqjoOSXMOfrDlHUhdEFhf0VM043pyZPeZLdTCWr1XO1G9lc9MWVaGvPHaN11jTQ5Wr1yMhFErkxCX7dSqkljZ6P/6TQbJbgYJ+BWhOodwOO835cfJe3GQyAvA9i5uCZbAaXfaHfk4TGzFiSugUP78mVzbD3ZSfKbRneYhzOvzv/3wXeXzd21JY7nNJWNm6lkuWstvNYlN+MTlHZSlEQJVBxoigPt/AtGUORs1KTX8IW0yo1vjpMwS8bVYmpvXNNHBg7gCxnC0oJe0I0beb8m7ngmhtCrmieExbaAXzDKihy56fYeub16MtCUDZn97/c2wwGgFf5EDB5xQA2/WxT4fEIwNxDufwM+2/k7v/A/gG9gslzcOxgwb1Wla1dciNMHR4nZwO5CKwLz7sQBHLKRJgS4cLwT4f/SbuKqUSGb72Umm5oJVAPHcMqilLkTL0GYYWzKc/hvZ+tTDEtdZxBs07VpLXj1R1aQVAUBKDtrDHLuADb0Xx0uRLfP+0khunMW7bwVO+BsYNZ18GZZjD26TGAd96jVwyUMZd2aPE6oTOcMStCAK3JVmMGb4Yz6P9Rf6jSDer3JjOTeHPiTTC4wCSl608dRsC2JFqMn5U7w7deSk03tBKodsewmkKJ/un4QK6BeFS05qVOC2evrHwxraHVQ4GKwBZEE1MTJR/vos6LcP6TwzPJXRohe+UFVxYoUWwcUjKx88K0ZQpYmrs+uhWKTomqym9Nr9rBrNDu7/w763TOx+DGZLIq0omdzqTx6MijBRnY6u/g0f2PBvYtdn9PVdbT2WnHJKXrTx0kYHXZ5KqSLWeGbz2Vmm5oJVAtyrUCKen7SkZwrLP1cmUaG1BngEErg3Qmje2vbg/0I7ixSx7MfXSmAbw1buHtP7jNtziedtbZl/KGh9IUrt14LZ479JxnhWIyw9lhnZuHlDpFLru/g+pbcDmhj33pmN6R7ELnTAVyeQGqQjo7fda3JLh7JeE+Pz9z2FR2qsAk5e43ECRgdQ2IKlXorZ5KTYsSKAPlWoGY9huoHOKqC6RDUzCunMW0TDNAd6G01mPLgYeH8dbpicgRLPb4TyzpB5L5mToBmPtL38zm106+5n3zkj3eCqUt07DOWDg9eRqA3gRkYyu9dS+sw97f7NW2cvRAmFEOroJ2qcGUtzeCc7AeR1l8dulntfH+dp9ftyA2lQQnkFM3SDcDD/LzqCYpU66JTsCaGhBVotBbvZWabug8gWbFk6egZO7atCXbkBz5PM4+5c3cNGVUlyvvIiy6bFJdrPfaHWvx8D9vBMauAi541Si4/bJXrXELF//XSwtr8bvi/4OyX+fNA45PzJTOiLIfNW7/sZHHnMzoophuA165A63dh3HFkrf0GdpWj1Mae+5CC2c+5820VcfKYKdURWuiFV+49gsFJR7UnJPLz78cvzjxi4IyEG7U3AU/WhOtyHI2MNckKJu4nFTz2CYkT6ABiNLi0b1iMBVuO3uBfkZUK3kObsIssZ1Ik0S2sDCdBr9ZYf+ufm/IpksJBs0qR0dz1TjtkFO//bhnimrcvpoZ3ZZsw6olq2ZCXqfaZ0o+T3XkIpHctKRxwQe2I/M7LxVkaK/pXYMEJXJROBuGnN/LLX/r39nrH1/7R2wZ3uLM0KeyU874Tc3bg0w3e97YY7yOKlPZqVC5JtUs91xvpaZlJVAnlGMW7rfP7u7wyqCYWkz2TFfN7A3a3p1NGjgDnG7NlXkmAJkk8P+9AZyZF+p6zf3ruXjrrKGyeTbhqa/kPq9vfeRbuHf7vdj/5n68M/VO4PHC1LyxSVISCUrkhLCuCN3OwnEdOx2+Hg9gzrSdlZyFycwk3tP+HpxJnymw99urAQYbs7ODZsNhZtCmsfXM66nbUs6VQFYCQnjyuQTHJ8xS/dhpC8u39OVaExbp64ga9qdzIJ6bPleQ+ent3KXYzxOZXLMaDe7jWuOWf0RRItfjQFd7xz6vlU+vxN7f7MU7U++EcsSqM8X3fjaFc+fMpp+C0M1EVqlR5O1U1t2d9wPknat2YxW/VdXQ6iHcdc1dAIBVS1bl7Pj3DDmlG06eO+lx+Nq1+v1s/EG28TAz6Ear5V8LiBKoEUpNLIliLvLFp2G6Tanxz6boDr/96gQEg/HsL58tGJe3c5fy7zXbjK0c1eNq9+NaPaSn09j7xl6PEts8tNlJOrMJsuX3zOtxBJk1buHslZu17S7DYDe0sU07Q69aHtPN5uHN2Dy0ubD2/9DmAiX4+CuPA5jJrHVH2pDLpmXX6neHs7qT3PwiZUTAVwdRAjVCqYI1loqpYRqmd5Ye/6ybhQaF/bmjf+zZ9enJ06FaEwIAEhm0/3HhakB3XGNyloJdymDz0MyKIDWYChe9gxnBr2uc7u5UFgV3BzV1FWAzOT3pGafaxvO+5+9zFFeGM7jnuXs8zmR3aGut2eWF8IgSKJLYZt7ICSK7wfu392wBzR4taX9h8W0Sb2qY3lda/LMpfC5M2J9N/67+AiG1bldOsKuK4oKOC7xfJGDqqm0eB6z7uEOrhzzlDuxWie4aROlMbkXQv6s/dDcuvw5Xg0cGze0uA7DHaXdQS+1OaTuUMdgzziyy2H1kN6xxC0/94qmCz5479FzgsdXEMRuZ2dcHogSKJM5eBanBmQbvbuFbzkgdNYrIXf+/wL5s1xzqzlXYLCX+2VSszt2a0LRf1VRhs23/Ns+2l77rUu3xVQVjUkgjoyNec8nwZvRs6MF0prC8gi1M1fEHMZ2dxrUbr/WM2Rq38PbZt3MO7SKwzTpbhmZWNls/sdXjk0hS0ljX577n7wtdYqO9pV2bOCbUF6IEqowT0qgTvhVEb0/PKyTbT/AppSF8nqirgVJLTKurAHVbezVgs3PFTmPWq23/NimklU+v9Ga6Tqfx5jtvGhu9ZDgTWnhOZadgnbE859e/qx/WGatofwCQu5Z2faAMZ3DbU7dpr9c9z96jVYDPHnrWs08AmNsx1zOjt9t3qr4QuzR1MT6uoO/US0G2eiMWJUBE5xPRM0Q0QURHiGiFz7Z3E1GGiM4orw/FMY56xFf4VhCtHbwljcUf250vdZwFLvTG3aczaTzyvL+N11TqoZgS0zte3aE9httk4Weft+3fJoX0q7d/5bknUZquq1xx/hUF2bK3XHWL85m96ujb2oeR0RHPCsdDCB2TRbag+9kv3/qldrvn/nUHzp3zKkC1eYvKJe+6xHhMtTw1g7Hi+yuK8nEFfcf9uSiFeIglT4CI/gdyCuVzAHoA7ADwAWb2pCYS0d0APs/MH4x6nFrKE4grbt/Y/UjJ4GQuvptZmBh+v+9+apMad6+PQ7fHYELXOLxY/OLEd67Y6cToO03aDdix8X/xo7/Atv3bwOCCbFg143XhuxcahWkYCIQ1vWuwfvl6/M43f8dZMbQmWnHFBVfgFyd+kWtAb+i1bMfn+52Lu+l9ICG7n4XpCbzwbxZ6xteWaEM6mw7dBSwoG1z3+YO7H4ztd9XolLXRPBGdB+AkgKuZ+VD+vW0AfsPMnvY9ogSK218pQr7oY3daaO93JS1NdQB/8zpwpvChNp1z2FIPJsIklblbI14196rAtodtyTbccfUduQJlirmkPZlz/J7LmBVIMXS0dODDCz+M514NdrKaxmFqsq5r26liJ1LRHyvlQ1SF/rG1SPz7jTi//XycOHvC+H0V9zU3KR63EjHdT7/2prrP77j6Djx54Mmif1fNRrmTxa4AkLEVQJ4RAJr8dYelRHSCiA4R0f1EZCz6TUT3ENE+Ito3NjYWw3DjIe5KoUH7i6MonSmiyUhf6aaqUqsphjEruFsjHhw7GOikTWfSePaXz3oEp2pTj5Oz02dDKwDTOOzrN2wN4+F9DxfY89WyEnYzFjUaxxovLCLo+J4u2wUs24AsZzExNVFQyM5OEjt88jD2H99fMBb7vjz7y2d9Vx5uJ78pSdCv4Jruc111UaE44lACnQBOud47BWC2YftBAFcDuBDATQDuAPAV086Z+RFm7mXm3q6urhiGGw9xVwqtRO+DyJFGl+j9BLg0WsexYqOJwpQMtp2San2d1mSrIwjt+jgqdr0c9/tAoU29mujGYV+/279/u+Fb/o1bPGWwKQPceitAuWWcTpje+cydODV5Ciu+n3PzqSWts5zF6cnTnsQxN0G5IEHtTU2fu6uLim+gOAKVABH9mIjY8HoJwBkAc1xfmwNgXLc/Zn6dmQ8zc5aZXwHwIICbSz0RoQxsnHHiOk3RH2DHVxFE0MMd5fum7+l6yKpCYfDIoNfJy1m8cPgFT2kId0lnXV5AJUhSUlvGGciFlwb5J0yJW55iei1poONkQUtJVZiqTVkOjB1wGtKrJa3DREXZOQRqLsi56XNORJfJQb/h5Q144fUX8NjIY4ErO1kNFE+gEmDmDzEzGV4fBHAIQAsRXa58bQkAvZdLcwgU3duo/okz6axamExZpWSMhllFqKsANxnO4NqN16L34l5P6YK2ZBtaEi2BCkqnxNqSbVjcZbZ02pnAYZqum/Br6+jX7tFGd42HVg/lmuT8ejnwDSunzDUVR9Vr4C4Vcev3bs01tUFhtFRHSwdWLVnlSa5TcwjemXqnIJeCwU5+hy5ibHHXYmQ5i1u+dwvOTp8t6Dd8Xut5oc5ZCEdc0UFPICfMP49cdNBOmKODPgrgZ8x8nIjeB+ApAN9j5q8FHaeWHMNxUcka/b72fw2q07nSvQTCVJS0o478zDdzO+ZqnZ3tLe3a6KGeeT2wvjqUM52tXgpcNBzpu+6GMKZqoB0tHXj29mfxhwN/qB232wm88eWN+PTvfhpPHHjCE4nT3tKOw/cdDnSMqlFa7kgl93ls+fgWLH1kqe/+bFoTrR7lpKtWquPuJXdjyye3FLw3bA17ji0RQaXh5xg2d2GOxloAmwG8CeAtAGtsBUBE8wEcBPB+Zj4K4AYAW4moE8BxAAMA/jKmcQhFUmsVxcOsInSmHiAXm3/09FGcmz7nODvd4YZ+EUd0b/4/brNXp4Xlfxu+/LVf68Tp7DRufepW43ftGfn65esd+/vAKwPQTdrsvAc/oei2x09MTaA12eq5xklK4vmVz+PDj3048PxsdKsTe/wM9lXSupIUtz11m3Z//T/qx5MHn0SWs9jw8gas7l2Na7qvCT1OQU8sSoCZ3wbwScNnR5FzHtt//zmAP4/juEI0TD0CwkQ0lfLdYghTX2b5guWeUNC2ZBtak60eX4IqIO0Ilf5d/Tj828PhhHqnBdyzDIOHR3HRbamCXAlTmK5fQbup7BROnjtpPJyt8NRELFMIaJazgaYQt39l+6Ht2rHZ2dfaVpku2pJtuGPxHXhs/2PaXsm7j+zGaydf87Xn2+0q7es/bA3j0NuHPNulM2kMvDKAZCJXAiPLWaz4/gr8fO3PA8cp+CNlI5qIUiKQKhG9FBXTakENETWFG2Y5i4H9A3jxyIvhHIo39AOzrVzvXldZD1s5ujNYh1YP6QvZBWA7qHeu2OmsAmzaEm247pLrvKGcPkpT5195Z+odWF+2nL4BKtv2b8Ph+w6Hyure/up2tCZzTmy3L2D5guWBkVZuP4xuFaBuq95v21ktlIYoAaFslDutX+dQXNO7xhFKNqZwQzuyJTC8sNMCljyu9CaY0uZK6GLgTYXs/LDHq64CbNLZXNVSXQE6E6Yorf5d/dpSFRnO4L6d93kUmvtaH/vSMUxMTRgVbmBpb3hNfGFWICp26KpQPNJessqUMxO42sRZLiIsQWUldE5Kd6N0jxP8E6uAnscKY9gYwNYXgCN/AEDfwjFsBqupdeb8d833DQf9+BUfx28nf1tgztL5O0zX5IKOC4wtNGclZ2EqOxXYHN7tvCcQ7lpyF7Z+cmvQaXvwayH60cc/qj0HAuHYl49JtnAA0l6yhqlFM0schEn0Kgd+NexNjlq1UTrg8nO4VwEqt82kt5SSGW2aqbckWjzhrSrPHnrWY87SrUb8ZvEmJqcn8/0tNoM+fz26Fnrvn6nb245D+iJ/Qfjllbh7PNi0JlslP6BERAnUObWaZ1BquYhy4GeeUMeoKuY1T6Ry/YndEHKJVhfuBzotT/8BW6mEMYmZfBtBTlUABeasKIrXL3KpgGQauGQvTrzfe//c3d7sxLqJqYmilH5QRFg5OpVJJVJRAnVPnM1t4iJMLZhqPHi20NIle5mEyZ439vjv9KYV6PiI13av2vWDah+5haldevrsfzqrnf26CdM8XndevgrGXvkksrn/L90cWqkUq/R3rtjpKBI1e9t2epejU1mpbV0bAVECQuyEqQVTzQdv+YLlTv0gXaE1t0NUV38IQE44dh/Axb///2vr/Ow+stt3Zq5Thu5rE8a5OpWdcjqKha3TZBKo+Jc1uQqjbpJp4/0qtUaUTaVXj9UyWdYaogSE2PFbtlf7wQs6vk5BmZLSbN4++7a2ZIIaIqlrKek+1rA1jA37NhSMLbBvch6/qqORuERTYwgAEub7VWqNKCA+RRKFWjRZVgNRAkLshHXOVuPB8zu+SUEsX7Dc1yRz8txJbZ9iVajZLSX7d/Ubj3XnM3c6SVfuXsh9W/t8I2BMVUcj28s3DuVWA1mvaDDdrzhs9XEokihUQ+nUKqIEhIpR7Qcv6PgmBWEyycztmIu7rrnLU0p5OjuNlU+v1K4eBvYPePobZziD+56/r6CzmDo2e8XQt6CvIB9CZ9Iq1V4+d2G+70DCO3aTYI/DVl8Op68flVY6tYzkCdQ59ZRnEKYoXDlZ9cwqp5Wk+/jrl683xqj7tTlsb2nXln8wFZkDgFVLVjldsWwI5Cm9YOqgpRZli7urVrXvUaXwyycpxdFcq1SigJxQJWpN0PtR6dmemx2v7tDWuLFr9Jhmhu66Q6qd//TkaQCF7SBVwaxLgHp05FFPvwBdNc90Jo3th7Z7VidqUTbdGKPgTi6r9j2qFI0o6ItFlIBQMar54FnjlpMcpZs9L924NFD4uc1JalhoOpN2wirtkgyHf3sYC9+90Jig5oe6ClDNV3Y9f7dJ6/6++4taDajO6Yc+9pDnHg1bw/jQox/Co598NPK+K4mqzJg5sC+1MIP4BISaoZz5A0EO6TB2bb8EK9Uxa/fAffHIi9hxaIfWn0AgJw7eWJxNWQWo7/tFAUW5hnZTnixnsXlInwfgbi9Zq6jKrNohyPWGKAGhZijXwxuXQzpMzL6NXZxObd6uJn6p5Q5MCujSd13qOV5QFFCUa5gaTDltIu2eBCq69pK1iBppZedLNHvsfxTEMSyUTFCTlrD7qCdnp8mx6Caq47lYrHELf/rkn2JkdMTjlzBtv/BvFhZ0KWtPtuPw/zXToezqb19dELG0uGtxbPX74/jN2Kj3N4EEQLl+A43o0C4WKSAnlJU4ZvDlzB8otdexzrximr27m9Pbqw61ybpN0HlGMe2kBlP46W9+6piKgvatrgLUsaqJa6oCAKKtBoLGHteqz73Kc5vlZDUQjCgBoSTiyAAud/5AKXHsUYWVKcpI18krSBGFPbZt2wcQWgAOHhksaBYP5ATo7iO7AXibzNuE9Q34jT3OrPH+Xf2YnJ40ft6ssf9RiEUJENEXiWgfEU0S0dYQ2/8ZEY0S0Ski2kxEs+IYh1B54pjB12riTjHCyrTquPRdlwYqInX2HLUiqHtWD/hfQ10WdFuyDX0L+mCNWzg4dlD7vTBNX8KU5ohr1acL+1VpxPDWuIkrRPQYgK8DuBFAh9+GRHQjgH4Af5D/3jMAvpZ/T6gjTDP4qOGKtRqbrhNWQfbloNWFny1cnT2HzQVwBC7CZ/gC/tc8NZgCEWHNsjVF2dP9rltcvxl7X35hv0I4YnUME9HXAVzCzHf7bPP3AH7NzP93/u8bADzOzIF3TxzDtUUjZ5f6dbkqRdCYuq2px2tvaQczFzhtTceO+x6U6qAPum5xjlfdV6P87spFrTmGFwMYUf4eAdBNRNoSiUR0T97UtG9sbKwiAxTCUasz+Dgoh4nKz0yiHi+dSXvMO+Us3qZSqqkm6LrFNd5q16FqJKqRMdwJ4JTyt/3/2QA8DU+Z+REAjwC5lUDZRyeEppFT78uh4ExmEk+EiyYhza94W1zEYaoJum5Dq4dimcGHLfMhBBOoBIjoxwD6DB//L2b+YMRjngEwR/nb/v94xP0INUacsd/VJm4F5ydgdQKtGuaNOARrGJ9II/uR6pFAcxAzf4iZyfCKqgAA4ACAJcrfSwAcZ2bPKkCoLyRd34yfgC1GoJWjxEYlBGtcZrZytJpsVmIxBxFRS35fSQBJImoHMM3M05rNHwOwlYgeB2ABWA9gaxzjEKqH295dbEGzalLOlYyfgC1GcLkLv8WBO1y1HNdCZvA1CDOX/ALwAAB2vR7IfzYfORPQfGX7LwE4DuA0gC0AZoU5zrJly1ioTdZsX8NtqTbGA+C2VBuv3b622kOKzJrtazjxtUTNj/3Y6WPc/vV2xgPgjq93sDVuxX6MerkWQjgA7GODXI0lOoiZH2CvqeiB/GdHmbmTmY8q23+TmbuZeQ4zf4aZzSl/Qs3TCJEa1e59rI4jyMxT7hadtXIthMogZSOEkqnVjN8oVLv3sToOt19Fl0lcToVbK9dCqAyiBISSqXc7b62sZEwzcHet/HIq3Fq5FkLlkM5iQsnUe0RGrcSc62bg65evL1AMi96zqKwKt1auhVA5RAkITU8trGRMM/CJqYkCxdC3oC+2mv46auFaCJVFmsoIgoFKJr+ZaupkshlkOOO8J4XShGKotdpBglAy5exHbFPJ5DfTDFxVAIA4aoX4ESUg1CXlFtCVDpPUZcCaGtCLaUaIE1ECQt1RCQFdC2GS9VwaoRIrNSEeRAkIdUelkqUkTLJ4pI5U/SBKQKgrKp0sZSO2+PBIxnF9IUpAqCsqIaAlTLI0asGUJoRH8gSEuqISAroebO61Spw9hIXKIEpAqCtEQNc2knFcf4g5SBCE2BBTWv0hKwFBEGJDVmr1h6wEBEEQmhhRAoLQwEjSlhCEKAFBaGAkaUsIIhYlQERfJKJ9RDRJRFsDtr2biDJEdEZ5fSiOcQiCMIMkbQlhiGslcAzA1wFsDrn9nnzfYfv145jGIQhCnrBJW2Iyam7iajT/NDP/AMBbcexPEITSiFJeQ0xGzU21fAJLiegEER0iovuJyBiqSkT35E1N+8bGxio5RkGoW8KW1xCTkVANJTAI4GoAFwK4CcAdAL5i2piZH2HmXmbu7erqqtAQBaG+CZu0JXV+hMD2kkT0YwB9ho//FzN/UNn26wAuYea7Qw+A6HYAX2HmZUHbSntJQYgPa9zCom8twrnpc8570r6yMSmpvSQzf4iZyfD6YND3Q8AAKIb9CIIQASmZLQDxhYi2EFE7gCSAJBG1m+z8RPRRIurO//99AO4H8MM4xiEIQnikzo8AxFc7aD2Aryp/3wngawAeIKL5AA4CeD8zHwVwA4CtRNQJ4DiAAQB/GdM4BEEIidT5EYAQPoFaQnwCgiAI0SnJJyAIgiA0LqIEBEEQmhhRAoIgCE2MKAFBEIQmpq4cw0Q0BuBIwGZzAZyowHBqBTnfxqfZzlnON34WMLO25EJdKYEwENE+kxe8EZHzbXya7ZzlfCuLmIMEQRCaGFECgiAITUwjKoFHqj2ACiPn2/g02znL+VaQhvMJCIIgCOFpxJWAIAiCEBJRAoIgCE2MKAFBEIQmpuGUABHNIqJNRHSEiMaJaIiIPlrtcZUbIvpivhfzJBFtrfZ44oaIzieiZ4hoIn9vV1R7TOWk0e+nm2Z8bologIgsIjqd77f++WqMI65+ArVEC4B/Q64l5lEAfwTgu0T0u8z862oOrMwcA/B1ADcC6KjyWMrBQwDSALoB9ADYQUQjzHygqqMqH41+P90043P7XwB8jpkn8w22fkxEQ8z8ciUH0XArAWaeYOYHmPnXzJxl5u0ADgMI7GFczzDz08z8AwBvVXsscUNE5wG4CcD9zHyGmV8C8CyAT1d3ZOWjke+njmZ8bpn5ADNP2n/mX++t9DgaTgm4ybeyvAJAo84Ym4ErAGSY+ZDy3giAxVUaj1BmmuW5JaJvE9E7AP4VgAVgZ6XH0NBKgIhaATwO4FFm/tdqj0comk4Ap1zvnQIwuwpjEcpMMz23zLwWud/x7wN4GsCk/zfip+6UABH9mIjY8HpJ2S4BYBtyduQvVm3AMRD2nBuYMwDmuN6bA2C8CmMRykgjPbdhYeZM3sR5CYA1lT5+3TmGmflDQdsQEQHYhJwT8Y+Yearc4yonYc65wTkEoIWILmfmV/PvLUGDmwqajUZ7bougBeITiI2HAVwF4E+Y+Wy1B1MJiKiFiNoBJAEkiaidiOpOyetg5gnklsoPEtF5RPR7AD6B3IyxIWnk++lD0zy3RHQhEd1ORJ1ElCSiGwHcAeCfKj4YZm6oF4AFyHnZzyFnRrBfK6s9tjKf9wOYiTCwXw9Ue1wxnt/5AH4AYAK5EMIV1R6T3M9Yz7epnlsAXQB2A/gtgNMAXgHwhWqMRQrICYIgNDGNag4SBEEQQiBKQBAEoYkRJSAIgtDEiBIQBEFoYkQJCIIgNDGiBARBEJoYUQKCIAhNjCgBQRCEJuZ/A292a+mug/mOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = make_moons(n_samples=1000, noise=0.4, random_state=42)\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibSVM]0 0.1 0.2017989158630371\n",
      "[LibSVM]1 0.01 0.19569611549377441\n",
      "[LibSVM]2 0.001 0.23690319061279297\n",
      "[LibSVM]3 0.0001 0.41855812072753906\n",
      "[LibSVM]4 1e-05 0.7902979850769043\n",
      "[LibSVM]5 1.0000000000000002e-06 0.6455130577087402\n",
      "[LibSVM]6 1.0000000000000002e-07 0.7135508060455322\n",
      "[LibSVM]7 1.0000000000000002e-08 0.7550830841064453\n",
      "[LibSVM]8 1.0000000000000003e-09 0.8036937713623047\n",
      "[LibSVM]9 1.0000000000000003e-10 0.7757120132446289\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "tol = 0.1\n",
    "tols = []\n",
    "times = []\n",
    "for i in range(10):\n",
    "    svm_clf = SVC(kernel=\"poly\", gamma=3, C=10, tol=tol, verbose=1)\n",
    "    t1 = time.time()\n",
    "    svm_clf.fit(X, y)\n",
    "    t2 = time.time()\n",
    "    times.append(t2-t1)\n",
    "    tols.append(tol)\n",
    "    print(i, tol, t2-t1)\n",
    "    tol /= 10\n",
    "plt.semilogx(tols, times, \"bo-\")\n",
    "plt.xlabel(\"Tolerance\", fontsize=16)\n",
    "plt.ylabel(\"Time (seconds)\", fontsize=16)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear SVM classifier implementation using Batch Gradient Descent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training set\n",
    "X = iris[\"data\"][:, (2, 3)] # petal length, petal width\n",
    "y = (iris[\"target\"] == 2).astype(np.float64).reshape(-1, 1) # Iris virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.],\n",
       "       [0.]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.base import BaseEstimator\n",
    "\n",
    "class MyLinearSVC(BaseEstimator):\n",
    "    def __init__(self, C=1, eta0=1, eta_d=10000, n_epochs=1000, random_state=None):\n",
    "        self.C = C\n",
    "        self.eta0 = eta0\n",
    "        self.n_epochs = n_epochs\n",
    "        self.random_state = random_state\n",
    "        self.eta_d = eta_d\n",
    "\n",
    "    def eta(self, epoch):\n",
    "        return self.eta0 / (epoch + self.eta_d)\n",
    "        \n",
    "    def fit(self, X, y):\n",
    "        # Random initialization\n",
    "        if self.random_state:\n",
    "            np.random.seed(self.random_state)\n",
    "        w = np.random.randn(X.shape[1], 1) # n feature weights\n",
    "        b = 0\n",
    "\n",
    "        m = len(X)\n",
    "        t = y * 2 - 1  # -1 if y==0, +1 if y==1\n",
    "        X_t = X * t\n",
    "        self.Js=[]\n",
    "\n",
    "        # Training\n",
    "        for epoch in range(self.n_epochs):\n",
    "            support_vectors_idx = (X_t.dot(w) + t * b < 1).ravel()\n",
    "            X_t_sv = X_t[support_vectors_idx]\n",
    "            t_sv = t[support_vectors_idx]\n",
    "\n",
    "            J = 1/2 * np.sum(w * w) + self.C * (np.sum(1 - X_t_sv.dot(w)) - b * np.sum(t_sv))\n",
    "            self.Js.append(J)\n",
    "\n",
    "            w_gradient_vector = w - self.C * np.sum(X_t_sv, axis=0).reshape(-1, 1)\n",
    "            b_derivative = -self.C * np.sum(t_sv)\n",
    "                \n",
    "            w = w - self.eta(epoch) * w_gradient_vector\n",
    "            b = b - self.eta(epoch) * b_derivative\n",
    "            \n",
    "\n",
    "        self.intercept_ = np.array([b])\n",
    "        self.coef_ = np.array([w])\n",
    "        support_vectors_idx = (X_t.dot(w) + t * b < 1).ravel()\n",
    "        self.support_vectors_ = X[support_vectors_idx]\n",
    "        return self\n",
    "\n",
    "    def decision_function(self, X):\n",
    "        return X.dot(self.coef_[0]) + self.intercept_[0]\n",
    "\n",
    "    def predict(self, X):\n",
    "        return (self.decision_function(X) >= 0).astype(np.float64)\n",
    "\n",
    "C=2\n",
    "svm_clf = MyLinearSVC(C=C, eta0 = 10, eta_d = 1000, n_epochs=60000, random_state=2)\n",
    "svm_clf.fit(X, y)\n",
    "svm_clf.predict(np.array([[5, 2], [4, 1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 60000.0, 0.0, 100.0)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(svm_clf.n_epochs), svm_clf.Js)\n",
    "plt.axis([0, svm_clf.n_epochs, 0, 100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-15.56761653] [[[2.28120287]\n",
      "  [2.71621742]]]\n"
     ]
    }
   ],
   "source": [
    "print(svm_clf.intercept_, svm_clf.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-15.51721253] [[2.27128546 2.71287145]]\n"
     ]
    }
   ],
   "source": [
    "svm_clf2 = SVC(kernel=\"linear\", C=C)\n",
    "svm_clf2.fit(X, y.ravel())\n",
    "print(svm_clf2.intercept_, svm_clf2.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.0, 6.0, 0.8, 2.8)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x230.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yr = y.ravel()\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(11, 3.2), sharey=True)\n",
    "plt.sca(axes[0])\n",
    "plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\", label=\"Iris virginica\")\n",
    "plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\", label=\"Not Iris virginica\")\n",
    "plot_svc_decision_boundary(svm_clf, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.title(\"MyLinearSVC\", fontsize=14)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n",
    "plt.legend(loc=\"upper left\")\n",
    "\n",
    "plt.sca(axes[1])\n",
    "plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\")\n",
    "plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\")\n",
    "plot_svc_decision_boundary(svm_clf2, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.title(\"SVC\", fontsize=14)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-12.52988101   1.94162342   1.84544824]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(4.0, 6.0, 0.8, 2.8)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ktnhew7fffktMTAxvvPEGjRo1YsOGDWaL5Ba8NYDHTHos65HuXM/qc45W9A5ibZKoVKkS27Zt44EHHqBXr16cPXvWbPE8nhEjRrB8+XKqVKnCrl27aNWqFS+88AKxsbFmi5ZrpM7mU13nEpIT9Kw+G6xn86noWX3O0Yo+B6SaJPbt28fIkSPJnz8/8+bNIzg4OM+ZJBxFROjcuTN79+5lzJgxBAYGsmDBAkJCQnx2wdZbA3jMJONsPhU9q88ZWtE7QeHChZkwYQJ79uyhQ4cOXLlyJc+ZJHJKwYIFGTduHJGRkXTq1ImbN28SEuLSnVk9Bm8N4DGTQ5cOOXRdkzVu3+smN3FkrxtXo5RixYoVDB48mKNHjwLQo0cPwsPDKVeunCkyeRP79u2jZs2agDGWH3zwAT169KBixYomS6bRuB8R+Ucp1dhV7ekZvYuwNkm888476UwSU6dOJTHR3m198iapSh5g+fLljBgxgtDQUMLDw33GHdMZP3pnffDN7NsszJTb48bMldFXZh+uiox1BYcOHVJPPPGEwtj2QdWqVUv9/vvvZovlFRw9elQ9/fTTaWMXEhKifv31V7PFcpr+q/orv3F+6rVVr7m1rtl9m4WZcjvbN3oLBO9Q9Kn8+OOPqlq1amlKq1u3burEiRNmi+UV/PLLLyo4ODht7Lp06aKOHTtmtlg5IuZqjCrwXgHFWFTB9wqq2Guxbqlrdt9mYabcrujb1Ypem25ymccee4w9e/bw3nvvUbBgQRYvXkxoaCgffPCBz5gkcot27dqxe/duJk2aROHChVm6dClz5swxW6wc4YwfvbM++Gb2bRZmyu2JY6YXY93IsWPHGDJkCN9//z1Amjtmu3btTJbM8zl58iQTJ07kww8/pFChQgCcOXPGKxKeOJMIw9kkGmb2bRZmyu2qvvVirBdzzz33sHTpUn755RdCQkKIjo6mffv2dOnShePHj5stnkdTsWJFZs6cmabkr169Sv369XniiSc4fPiwydJljTN+9M764JvZt1mYKbenjplW9CbQrl07du3alWaS+P777wkNDWXChAnEx8ebLZ5XsH37dm7cuMHKlSupVasWY8eO5datTHPZmIozfvTO+uCb2bdZmCm3x46ZKw3+Zh+euBibHSdOnFDdunVLW3CsXr26+vHHH80WyyuIiYlRPXr0SBu7qlWrqh9++EGlpKSYLZpG4xToxVjfomLFiixatIjffvuNWrVqcfDgQR5//HGvMEmYTbly5fjmm29Yv349devW5ciRI3Tu3JnXX3/dbNE0Go9CK3oP4aGHHmLHjh1MmTKFokWLeoVJwlNo2bIl//zzD5988gnFixenU6dOZoukcRFmBR55XMCTs9gz7QcKAG8BvwI7gF3Whyt/YjhzeKPpxhYZTRJVqlRRy5cv1yYJO7h8+XK68zFjxqglS5bosfNSzAp6MjtIDBebbuxyrxSRL4GngCVAjEUBWX9ZjHPVF48zeLp7paNs2LCBAQMGsHv3bgA6dOjAtGnTqFGjhsmSeQc7d+6kQYMGKKVo27Ytn3zySbqtFjSejbWrojvdOs3q1xqz3CufBP5PKdVXKTVWKTXO+nCVMJr0tGzZkm3btqWZJP73v/8RFhbGv//9b27cuGG2eB5PWFgYM2fOpGTJkqxZs4a6desyfPhwrl27ZrZoGjswK/DIEwOenMVeRX8TOJGbgmhsExAQwOuvv05UVBQ9e/YkISGB999/n5o1a7J06VLs+UWWV/H396d///5ER0fzyiuvkJyczIcffkhoaCiLFi0yWzxNFpiVrMVXk8TYq+jDgSEiohdvTSIoKIh58+bx559/0qBBA06cOEHXrl1p3749+/fvN1s8j6ZMmTLMnj2bzZs3c9999xETE8OPP/5otliaLDAr8MhTA56cJVPFLSIrUg+gLdANOCIi/7O+Z7mvcRPNmjXj77//5tNPP6VkyZKsXr2aOnXqaJOEHTRp0oTNmzczZ84cwsPD065HRUVx5coVEyXTZMSswCOPDXhykkwXY0Vknr2NKKVedplETuBri7HZcf78eUaOHMmcOXNQSlG+fHmmTJlCt27dEBGzxfMKkpKSaNiwIWfPnuXDDz+kR48eeuw0puPqxVjTXSJdefiKe6WjbNmyRd13331p7pitW7dWu3fvNlssryA2NlY1a9YsbexatGihduzYYbZYmjwOZkTGisjvIlLCxvViIvK7y751NDnivvvuY/PmzXzxxReULl2atWvXUr9+fYYMGaJNEtlQtmxZNm7cyLx587j77rvZuHEjDRs25PXXX+fy5ctmi+cR6MxYjuNxAVf2fBsAKcDdNq7fDSS68pvHmSOvzuituXDhgnrttdeUn5+fAlRQUJD6+uuvdcCQHVy6dEm98cYbaWNXs2ZNlZycbLZYpqMzYzmOp2WYyjJgSkQaWj5uBdoBF61u+wPtgT5KqSou/v7JEXnNRp8V27dvZ8CAAWzatAmAFi1aMGPGDOrVq2eyZJ7Prl27GDhwID169KBv375mi2MqzgQPORt4ZGbfzuCKvt0dMLUV+BvDfvmr5Tz1+AsYAbzrKmE0rqNBgwZs3LiRr776SpskHKRu3bqsW7eOPn36pF0LDw/n1Vdf5cKFCyZK5n50ZizH8cSAq+xm9PcAAhwGmgDnrG4nAGeVUsm5KqED6Bm9bS5fvsw777zDjBkzSElJ4a677mLSpEn07NkTP788EhqRlAQnTkBMDCQkQP78UL48VKoEAQFZVr1+/ToVK1bkypUrlCpWjIl9+tC7c2f8K1Wyq763ojNjOY5XZphSSh1TSh1VSvkppbZazlOPWE9S8prMKVGiBNOmTWP79u20bNmSc+fO0bt3b5o3b84///xjtni5i1KwezesWAHbtxuK/vx549/t243ru3cb5TKpX+TIEf4cN442YWFcvHqVflOn0rRXL/5avDj7+l6MzozlOJ4acJVVwNSL9h7uFFiTc1JNEgsWLKBs2bJpkaL9+/f3TZOEUvDHH3DgACQnG4c1qdcOHDDKZVTWVvVrlS/Pb6NHs3jwYCqUKsXWQ4doOmIEfWbO5NaePbbrezk6M5bjeGrAVVYBUxnDLPMD+TA8cMD4kkgE4pVSxXJNQgeoVKmSevfdd3nppZfyjkkih1y9epV3332XadOmkZSURKlSpZg4cSK9e/fG39/fbPFcw+7dt5V8dvj7Q40aUKdOtvWvx8Ux4fvvmbJyJU2Dg1k3diwSEHBnfY0mh7jNdKOUKpp6AM9i7D3fEmNv+gKWzzuA5+zpSEQCRWSuiBwTkWsisl1EOmRR/k0ROS0iV0TkSxEJzK6PU6dO0atXL5o3b862bdvsESvPUqxYMSZPnszOnTt56KGHuHjxIv369aNp06b89ddfZovnPElJNpV0bOIlHjz6DqeTLqcvnzqzT0rKtv7jpycy6JnH2D1lCl/062dE0iYnc2jDBv5cvz5TkbzVJ1zj/dg77Z0MvKGU+kMplWQ5/gAGA1PsbCMAYwfMB4HiwGjgOxGpkrGgiLQH3gYeBqoA9wLZbodcpUqVNJNE48aN6d+/PxcvXsyuWp6mVq1arFmzhsWLF1OhQgW2bt1K06ZN6dOnD+fOncu+AU/lhO3NVsefX8rGm/sZf+6/Wdezo35I+fKElC8PGPEor8+ZQ/MHH6Rnz56cOXPmzrrrx7Px+MYc2WudqavR2KvoqwC2NkC/CVS2pwGl1A1l7GV/VCmVopRaBRwBGtko/hIwVykVqZS6BIwHembXR6lSpYiKimLIkCH4+fkxa9YsgoOD+eKLL0hJScmuep5FRHjmmWfYv38/b7/9Nvny5WPu3LkEBwczc+ZMku0xfXgaMTE2Z+PzLkeQgmLe5bW2Z/UxMTmqn6IUjapWJX++fMyfP5/g4OA0sxjc3v42RaU4vO2tM3U1GrBf0f8FfCIiFVIvWD5/BGzOScciEgQEA5E2btcGdlqd7wSCRKS0jXb6ishWEdl67tw5ihUrxpQpU9i5cydt2rThwoULLF68WG9UZQdFihRh4sSJ7N69m3bt2nH58mUGDhxI48aN+eOPP8wWzzESEu64NP78UlIsa1LJKsX2rD4xMUf1/f38GP/ss0TOm8djjz3G1atXGTx4MA0aNGDdunVe6xOu8Q3sVfS9gdLAURE5KiJHgaMYWyC84minIpIP+BaYr5SytZl6EcB6k5bUz0UzFlRKzVZKNVZKNb7rrrvSrteuXZvffvuNxYsXM3369DRFf+zYMe82SbiBkJAQfv75Z77//nsqV67Mjh07aNGiBS+99JJNk4RHkj9/utPU2XgCxgw7gSTbs/p8+ZyqX71KFVatWsWKFSuoWrUqe/bsoV27dny54cscJbPw1UQYGvdil6JXSh0C6gKPA1MxZvKPAXWUUgcd6dCSvOQbjICrgZkUuw5Ye/KkfnZow/VUk0RqnlClFL179/Zuk4SbEBGeeuop9u3bx6hRo8ifPz9ff/31HSYJj6V8ecOTxoL1bDyVO2b1/v5GPSfriwidOnUiMjKScePGUa9LHVQhi+lQAcmQnJzE+N/fyfYxPNUvW+Nd2O2DaNlr51el1CdKqWlKqdUqq7BaG4gxrZ4LBAFdlFKJmRSNBKw3ZakHnFFKOeXsff36dfz9/dOZJP7807sTCuQ2hQoVYvz48URGRvL444/fYZLwWCpVSne66WZ02mw8lQSS+PNmtO16ztYHCgYGMqZ5cxLrXiAh9VXfDXwGCYcS+XPvr/Dbb5DF+pGn+mVrvIus/OiHAJ8qpeIsnzNFKTXVrs5EZgH1gbZKqetZlHsU+Ap4CIgFlgJblFJvZ9W+PVsgKKVYtmwZb775JsePHwfgxRdfJDw8nKCgIHseI0+zcuVKBg0axJEjRwDo3r07kydPpnzqTNiTcNaPftcuiIqyv7+QEKhb1/ickgKrVkF8fLoibcaNY22ksSz1f02bMuXFF6lUoQJ07Ag69kNjwdV+9Fkp+iNAY6XUBcvnzFBKqXuz7cjYN+coEA/ppkb9gA3AXqCWUuq4pfwQ4C2gIIaif1Uplf5/TQYc2evm5s2bTJw4kfDwcBISEggKCuLIkSMULFjQrvp5mVu3bvHhhx8yceJE4uLiKFKkCO+88w6DBg0iX6qN2xNIjWw9ezZrZe/vD3ffDc2bg/Wi/a5dEB1tX8SrCAQH31b0v/0GNlx74xMTmbpqFe99/z034+MpFBjI6C5deLNHDwI7ZBpWosljuE3ReyM52dTs4MGDDB48mMaNGzN27NjcEcxHOXLkCEOGDGH58uUA1KxZk+nTp/Pwww+bK5g1SsGePcbMHtIr/IAA436NGhAWll7JJyUZ+9jYcLF89tTHLK74JmUDSqTvy98fnnjCqLty5R2i7Lh1lNbH3mF9lXcpcb0w//r6a/672XBaq1GuHD/++is1wsJc8dR3EHstlmeXPsvirovdtl2vJue4e5vi1E59JCb+TqpXr86qVasYPXp02rX58+fz3HPPEZPqU62xSdWqVVm2bBn/+9//qF69Ovv27aNt27Y888wznMgk4MjtiBjmmCeegAYNjEXWu+4y/q1f37hep056JQ/OBVzt3m3zVo9Tn3Al5RbPnZxG5TJlWDJkCKtHjSK0QgWUUlTOxWxgOuAqb2PXjF5ErgN/AGstxxZP3LnSFdsUJyUlUb16dY4dO+a5JgkPJD4+nqlTp/Lee+9x8+ZNChUqxKhRoxgyZAiBgdnuXuF5/PHH7eApC7GJl7j34EDiVCIFJT+Ha8y4c1ZfvjxcuHCHbX7HraM0ODI87XznvR9St8A9ACQkJXH8/Hmq33MPPPEEFy9eZPbs2QwePJgCBQo4/ShmJuHQ5AxTZvTAUxgJSB7HUPSXReQXERkhIs1cJYwnEBAQQEREBJ07d+b69esMGzaMevXq8dtvv5ktmkcTGBjIiBEj2LdvH127duXmzZuMHDmSOnXq8PPPP5stnuM4E3BlYz2gx6lP0p0/d3Ja2uf8AQFUL1s2zftm1KhRjBgxgtq1a7Nq1SpnnsKQWwdc5Xns9aNfrZQapZRqAZTAUPwxGNmlNuaeeOZQtWpVli9fzk8//XSHSULvnZM1lStXZsmSJaxevZrQ0FAOHDhAhw4deOqppzh69KjZ4tmPMwFXGXb/3HHrKJEJJ9Ndi0w4ya64Y+nrWrxuunXrRlhYGIcPH6ZTp0506tSJQ4cO5egxdMCVBhzwoxeRIBHphhEwNRNjR8s/8OFUgh06dGDPnj1MmDCBggULsnv3booUKWK2WF5B27Zt2blzJ+Hh4RQuXJjly5dTs2ZN3n33XeLi4rJvwBbXr8Pvv8PSpbBkifHv778b17MjLg7+/ttYYF22zPj377+N67ZwJuCqXLl05TLO5lOxntUDafUefPBBtm3bxkcffUSxYsVYtWoVtWvXZsyYMdy8eTP7Z7VCB1xpwH4bfSTGxmZbMEw364BN2bk7upvcTCV4/Phxzp8/T8OGRr70c+fOsX37dtq1a5cr/fkSp06dYtiwYfznP/8B4N5772XatGl07NjRvgaSk+GnnzJXygAFCsBjj90xmyYlBSIibLo6plGqFLRpk96PPYPXTYNDw9kRf/SOqvUDq7C9WrhxkonXTcF9zxNnIzawgOTjVs1vb1/o1Ml4DitOnz7N8OHD+eabbwBYv349LVu2zPxZMtDg8wbsOL3jTrnL1md7v+12t6NxL6a4V4rISYxtCDYAEZZjm6ORsbmNO3PG9unTh7lz5/LUU08xdepUqlSp4pZ+vZm1a9cycOBAIi0BQx07duTjjz+mWrVqmVdKTobly7OMHk3Dzw+efPK2ss8kaMkmgYF3Bi05E3C1Zg1cupR9vVRKloS2bTO9vXHjRlavXs24cbd36z5z5owO8vNRTFmMVUpVBBoCyzAiW5cDF0VkhYi86SphvInQ0FAKFy7MsmXLqFmzJuPHj8+5SSKP0Lp1a7Zv3+6YSeKnn+xT8mCU++mn2+cREfYpeTDKRUSkvxYWZgRSZZdxKzXgytoH/u677evXzvItWrRIp+Q3btxI5cqVGTFiBDdu2NpBXKO5jSN73RxUSs3B2Cv+GQxl3wEjKUmeY+jQoURFRdG9e3fi4uIYM2aMy7wkfJl8+fIxePBgoqKiePHFF4mPj2f8+PHUqlWLZcuWke5H4vXr6cw1ZV/phDzzf3ccZV/pdLtOXNztejbMNTtuHaXE/pfuXAgFo7z1l7WIES1bo4ahzDMq/ICA2zN566japCQ4eOdef5lmtwKjvAMbxa1fv56EhAQmTZpEaGgoS5YswcN+YAOwI3YHJSaVYNeZXTmqb1ZmLTMzesVei4UyhLiyTXsDpu4TkeEi8j/gEoadviZGdqnHXCmQN1GhQgUWLlxIREQEtWvXTvOS2Lt3r9mieTxly5Zl/vz5bNy4kXr16nHs2DGefvppOnToQHS0ZaOwLVvS1TlzxbZP+R3Xt2yxK2jJJhnr5STgytnsVnYwcuRINm/eTKNGjTh58iTPPPMMjzzyCPv27bO7DXfQY1kPrsRf4bmldmUcvQOzAr3MDDAbv3485MOlXh/22ugTgH8wFmHXAhuUUh73e9GdNvqMJCYmMnPmTI4cOcK0adPSXdfBVlmTlJTE559/zqhRo7h8+TL58uXjX//6F6PCwihs5eYoz/xfpm2o75bcPvHzM9wcHQhaSiMw0FDezuBMsFXz5g51lZyczJw5cxg5ciQXL14kICCARYsW0aVLF+eewQXsiN1Bg9kN0s53vrqTukF17a5vVqCXmQFmaX3PjEPFKJdlS7LXdFNSKdVMKfW2UupnT1TyZpNqkrBW8hEREQQHB7N8+XKP/FntKQQEBDBgwACioqLo1asXiYmJhkli0CC++/NPx8dOKYeDltJwRcpJZ7NbOYC/vz/9+vUjOjqafv36UaJECR588EGH28kNeizrke7c0Vm9WYFeZgaY2XKHdQX2LsZqxZ4DZs+ezdGjR3nqqad47LHHbpskNDa5++67mTt37m2TxIULdPv4Yx557z32nTyZfQOpiDgdtOQUzma3ygGlS5dm1qxZHDhwgDJlyhj9JCTQs2dPdmdixspNdsTuIPJc+iyhkeci7bbVmxXoZWaAWca+XYneADsX+eabb5gxYwYlSpTg559/JiwszDO8JJKS4MgRw8QQEWH8e+SIQ4uBucn999/PX3/9xazBgylVpAi/7d5N3WHDgGHYlWSsZEmng5buwJExcza7lROUKFEi7fPMmTOZP38+DRo0YPDgwVzJxU3TMpJxNp+KvbN6swK9zAwwy63ZPGhFn6tkapIIDcWUtQSljMXGFStg+3bDjnz+vPHv9u3G9d277dt/PZfx9/en3/jxRE+bRr+2bUlOScFw8AoBFmLk5LNQOMNsq0mT9AlEgEOJtnPd3nE9Q70cjZkLslO5gp49ezJw4ECUUkybNo3g4GDmz59PiivMU9lw6JLtLRsyu54RszJrmZnRy1bfriLP70fvTjZv3syAAQM4fvw40dHRlCxZ0n2dO5uEwyxWroS4OLYeOsTAL7/kL8u+8q1q1mRG797UqVw5ffkCBYwIU8g0+UemlCoF1nvpOzNmzma3ciE7duxg4MCB/PHHHwA88MADfPrpp9SrVy+bmhqzMGv3So0LaNq0KVu2bGHDhg1pSj4uLo4JEyZw7ZpDec8dZ8+e7BUWGPfPnjXKewKPPQZ+fjSuVo0/x49n7quvUqZoUdbv20eD4cMZ/NVXXE41hfn5GeVTadPG8KKxh8BAo7w1zoyZM8FWLqZ+/fps2LCBr7/+mqCgIP7880927tyZa/1pPI/scsbahb05Y3MbT5/R22L8+PGMGTOGcuXKMXnyZLp37464eibtTLakgADXypITMux1c+n6dcZ89x2f/vILKUpxd/HihL/8Mi+Eh+OXcUHTnr1uSpeG1q2z3Oum7CudbPrxBxWP4/QXln1tMo5ZTrNb5SJXrlxh3rx5DBo0KO0927ZtG/Xr18dP56z1GNydM9Ye7MoZ6w68UdFv3bqVgQMH8tdffwHQqlUrZsyYQR1X/ow/csSwJ2dQ9K/FzuHzS6t5teQjzCzXJ30df38jQKhqVdfJ4SzXrxvBUJcugVLsOHqUgfPn84dlJv3AAw8wY8YMGjRocGfduDjDnBIbayh/Pz9j4bVOnTs2EgPuGDO7fPgzG7OkJCMYKibGcKHMl89YeK1UyfQv0kOHDlG7dm3q1avHzJkzadzYZbpF4wQ6Z2wWeKOiB0hJSeGrr77irbfe4vz58/j7+zNw4EDGjRtH8eLFne/AjQE87kYpxYIFCxg2bBhnzpzBz8+PV199lffee8+5NZAMY2Z3sJYXjJk1a9eu5fnnnycmJgYRoU+fPrz//vtpLpoac9A2eh/Ez8+PXr16ER0dzYABA9K8JH755RfXdODGAB53IyK88MILREVFMXjwYESETz/9lODgYObMmZNzDxMbY2YXXjBm1rRu3Zr9+/czbNgw/P39+eKLLwgODuazzz4j2Z6FZI1XYPeMXkRKAY8ClYF0ESFKKY9IPuKtM/qM7Nixg6+//popU6ak2VHPnj3L3Y7uiJhKhtmp9Ww+FZuz+tyYnVqbMRISjOAiF5ox9uzZw8CBA1m3bh0ATZo0yZlJwpUz+lx+Zlexf/9+Xn/9ddasWQPAwIEDmT59uslS5U1MmdGLSFPgAIYj83igF/BvYCjQ1VXCaAzq16/P1KlT05R8VFQU99xzDwMGDOCSI3ucp2JiAE8abvLhDwsLIyIigoULF1K+fHm2bNlCkyZN6Nu3L+fPn7e/oQxjZhcZx8yL4hbA2Hr7119/ZcmSJVSrVo0BAwaYLZLGRdhruvkQ+BaoAMQBD2HM7LcCH+SOaJpUNmzYQGJiYppJYu7cuY6ZJMwO4En1R0/1K89oEki9duCAUc5JxScidO/e/Q6TREhICLNmzbLPJJHh2YOK2841cMf11HpufmZXISJ07dqVqKgoQkNDAWMdpEuXLkyfPp0kD4me1jiGvbtXXgHuU0pFi8hloJlSap+I3AcsVErVyGU57cJXTDe2cNok4WzwkDOYHDyU0STRsGFDZsyYQbNmzbKu6IzcHhQw5SwRERE89NBDANStW5eZM2fSokULk6XybcxKJXgOaG5R9FHAIKXUzyJSE9iqlCrsKoGcwZcVPRgzq0WLFjF06NA0L4lPP/2UV199NeuKSUnwww9pOzPa5RPu5wedOztvQ3aFP7oLUEqxdOlShgwZwgnLvu8vv/wykyZNynztI6eRsR7yzK5CKcWKFSsYPHgwR48eBaBHjx6Eh4dTLrO9gbyc2GuxPLv0WRZ3Xey2LYqtMcvrZhtwn+XzWuA9EXkJ+ATIWeoYjcNkNEkULlyYRx55JPuKJ06kC8ixK4GHiEOJMLLsO7M+srruir6tSDVJ7Nu3j5EjR5I/f37mzZtHcHBw5iaJnGaY8pBndhUiQufOndm7dy9jxowhMDCQBQsWEBISwsyZM80WL1cwM/FIbmCvov83kOqCMAo4B0wHSgL9ckEuTRYULVqU8PBwTpw4kZZYWylF//792bx5850VYmLsMyFYk5x8h+99jjCzbxsULlyYCRMmsGfPHh599FGuXLnCG2+8QaNGjdiwYcOdFXKSYcrDntlVFCxYkHHjxhEZGUmnTp24du0aFx0xB3oJqdsFp6gUt21RnNvYux/9VqVUhOXzOaVUB6VUMaVUY6WUntGbhPWWtIsXL2bWrFk0a9aMXr16cfbs2dsFzfQJ91B/9Bo1avDTTz+xfPlyqlSpwq5du2jVqhUvvPACsbGxd1YICDAiXps3N7ZLaN7cOLdlavHQZ3YV1apVY8WKFfz8888MGzYs7XpERASnTp0yUTLXYGbikdzCXvfK30WkhI3rxUTkd5dLpXGYTp06pTNJhISEMGPGDMMkkSERht24IgWimX1nQ1YmialTp5KYU8Xrwc/sStq3b08By/YRly5dolu3boSEhBAeHk5CTr/sTMbMxCO5ib2mm9ZkCJKyUABo6TJpNDkmo0ni8uXLvP766zRu3Jg/YmKc9wlPxdGkJa7wR3cFcXHw99/GIumyZca/f/8NcXFpJom9e/emmST+9a9/0aBBA9auXWvUdyLxiF3kxjO7kfj4eFq0aMGNGzd46623qFu3LqtXrzZbLIcxM/FIbpKloheRhiLS0HJaN/XcctwH9AW8/7eaD5HRJLFz5042pO6eaMFhn3DIefCPs/7ozpKSYriWrlwJR48aCcOTkox/jx41rv/2G6SkcO+997JixQpWrVpFtWrViIyMpE2bNnTv0IGT8+blOPGI25/ZBMqWLcv333/Pzz//THBwMFFRUbRr146uXbty/Phxs8WzGzMTj+QmWbpXikgKt1P52NpH9RbwulLqy1yQzWF83b3SUW7dusVnn33GgAEDCIyOhgMH2HX4MDUrVCBfVm58Gf26nU1asmsXREXZL3hICNSta3/5zEhJgVWrDKWeHYGB0LFj2lbFcXFxTP7wQ96fMIFb8fEUDgxkTNeuDH78cfJnHDtbz23WM3sACQkJfPTRR4wfP54bN27QuHFjtmzZ4vrtt30Yd7tXVgWqYSj5Jpbz1KMCUMwRJS8iA0Vkq4jEi8hXWZTrKSLJInLd6mhtbz8ag4IFCzJkyBACAwMhLIzz+fPTetw4Grz1FmsjI21XspUIwxVJS+z9T+5KZRARYZ+SB6NcRETaaYECBRj15JPs+/hjnm7ShBvx8bz17bfUHTqU1bsy+B9k9txmPLMHkD9/ft566y3279/PM888wwcffJCm5HO87qFxiiyjM5RSxywfXbXLZQzwHtAeKJhN2U1KKY8OvytbFs7YSEUaFASnPW3tRoQT5ctTskQJIk+coM24ccCzGLtbVLQUUqhde9InwkhKShfhmW3wT2pYf82ahkdKUhIcPJhm2si2vlJG+Vq1nAseiotLFwns360rKepOheoniuTFlj1+Ll406hUokPbc95QuzdKhQ/l1505e//JLomJiaPfeezzepCGf9uxD5dTtfK2fG9I9cyqZJnpx1TN7GBUrVmTx4sXprvXr148LFy7w8ccfU9WTch34OHYrcBHpICKrRGSviFSyXOsjInbHySulvldKLQcuOC6q52FLyWd13WwaNGxI5IEDwLsY6+iLgFAgHEgA5E6fcGeDf8wKHtq9O92pLSVv83pqvQz9t6tXj91TptC0Uw3IBz9u2Ubo4MFM+P574q1nqSdOZCr7+PNL2Xhzv+0toW306WucO3eOpUuXsmLFCmrVqsXYsWO5deuW2WLlCex1r3we+A5jB8uqQKofmD8wPHdEo4GInBeRaBEZLSK+M9UxEcMdbjSwD3gKuAG8BTxvu4KzwT9mBQ/Z8oV3pJ4NuS+oa+xofBQGgn9tP24lJDBq0SLC/vUvftq27bbcNurGJl5i3uUIUlDMu7yW00mX0/frBQFTznLXXXexf/9+nn/+eeLi4hg3bhy1a9dmxYoV+FICJE/E3hn9cOAVpdSbkG7bw81AfVcLBawHwoC7gS5Ad2CYrYIi0tdi99967ty5XBDFV6kCfA/8DIQAg2wXczb4x6zgoZwmzUjdFTSrZC3Fwf///HhiQCNqVazIwdOneXzSJDqHh3Pk+HGfTvTiLOXKlWPBggWsW7eOOnXqcOTIETp37kynTp30zpi5iL2Kvgawycb160Ax14ljoJQ6rJQ6opRKUUrtxrA12Nz3Xik12xKh2/iuu+5ytSh5gPZAJHB7OaRv375MmDCB+Ph454N/zAoectSPPZXUBNkZ5E6dkadu75xAEqvv3s3P749kyosvUrRgQVZs3Uqtnj0Z+8033LJS9rbq2pzVe1nAlDO0atWKbdu2MW3aNIoVK0ZQUBABPrQ+4WnYq+hjgGAb11sBh1wnTqYobLt3alzCbaW4d+9evvjiC0aNGkVYWBj/i452LvjHlcFDjgQt5XRXxdR6diZrmXRpOUM6diTq4495vlUr4hISGDdvHrWHDGHF1q0opcxJ9OIFBAQE8MYbbxAdHU14eHja9Q0bNrD0u+9Qhw/bH5inyRJ7Ff1s4BMRSc2RVsmye2U48Jm9nYlIgIgUwNAs/iJSwJbt3bLwG2T5HIphVP7B3n7cRVCQY9e9gVq1arFmzRpq1qzJwYMHeaxXLzpPmsQRy945Dgf/uCJ4KCfBWhn2dvcT2zbgO66n1nMwWUu5kiVZMGgQ637/nTphYRw5e5bO4eE8PmkSa49HujfRi5cRFBRE6dKlAUiIj6dfz5507daN9k8/TdTWrR6dlctbcCRn7ATgTQx3DYB4YLJSarTdnYmMBd7JcHkc8CWwF6illDouIpOBF4AiwBlgATBeKZWlEVMHTLmOxMREPvnkE8aOHcv169cpkD8/I598klFdumQd+GIriYYzwUPOBGs5m2wlh8lDkpKS+HTUKEZPn87VmzfJHxDAsCeeYMSTT1K4gA2vIw9PPOI2lCJ5/Xpmf/st/164kEs3bpDP358hHTsyqksXiqSOXWaBeT6EKYlHrDovBNTC+CWwVyl13VWCuAKt6F1PTEwMw4cP59tvv+XVdu34rE+fzAtn9h9wzRpwJNdtyZLQtq3x2ZlMTU5ExgLOfckoxZmVK3n744/5yhKIVal0aT566SWevv/+21+WeUBp2Y3V3/r81auM/M9/mPP77yilqFCqFFNefJFnmjUzxs7Hvxzdqugtiv1D4EkMl8o1wBtKKQeyLLuPvKTo/f1vO4hY4+eXc4eTrFi/bh21/fyo/cR9nLlcANgClAKqAxBUIo7T6w+kD7YCIwBp5crbctsTuATQqZMRPORspqaUFMPGm9XMvnRpY+thPxuWTKWMiNfU/YKsBzcgwLhfo8adz21V98+VKxk4Zw7bjxwB4JG6dfmkTx9CK1TIvG5eI0NWrlR+2r+NZ7/4mGsn4rirWDEOfvIJxQoVMm56cFYuZ3G1os9uhMYBPTESg8dhuDl+BvyfqwTQ5IzMcoM7kjPcEVo9+CAAZy6DYbXrARzD8HodyZnLhWzPrpwJXEqNOrXgULBVatSln59hjomLM9qMjTUGyc/PWHitU8eIhM2M1MQjNWsa7cbEGG6Q+fIZi6eVKmWuaCx1H6hZk7+feYbZs2bx79mzWb1rF3X/9S/eHDSI0e3aUSSvK3nINFhsVfFtXH85jtZRtehX9pE0JR+XkEB8SgrFrf/WmkzJbjH2aaC3UqqvUuoN4HHgSRHJoe+axjeIAx7AiKadANQEltoOenEmcMmVwVYFCsB99xkzwCefNP69776slbw1jiQesVHXv3p1+k+eTPThw7zyyiskJSURPnkyoaGhLFq0SAcMZRFkpvzgr1oHaX1/7bR7H/zwAyGvv843X36px84OslP0lYC0/GpKqS0YAVN5yw9Mk4HiwFfAH0AD4DjQlfbt2xOVcdHVmcAlH8zUVKZMGWbPns3mzZu57777OHXqFN27d+fhhx8mMrON5vICDgSZKaVYv28fZ65c4cX33qNVq1bs3LnTreJ6G9kpen+MaZs1SWRv8tHkCR4A/gZmAiVYvXo17du3Tx/h6Ezgkg9namrSpAmbN29m9uzZlC5dmoiICOrVq8eQIUO4evWq2eK5HzsC1FKDzESE1aNG8dVrr3F3yZJs3LiRhg0b8sYbb3D58mUThPd8slP0AiwQkRWpB4Z75RcZrmnyLP7Aa0A0r7zyCu+//35ahGNiYiKqbNmcNVuunM9navLz8+OVV14hOjqa/v37k5KSwkcffURISAgLFizIWyYJOwPUUmf1fn5+vPTww0RFRPDGG28AMH36dIKDgzl27Bia9GSn6OdjRMVesDoWACcyXNO4GVsOIllddxWZB4ndxezZs3nuuefSro0dO5aHhw8n0mqhzaHApTySqalUqVJ8+umnbN26lWbNmnH69GleeOGFvGWScDBALZUStWszbdo0tm/fTsuWLalTpw6VK1fOdXG9DYf86D2dvORe6encunWL6tWrExMTQ4C/P2906MA7Xbvedo3LCuvAJWf86L2QlJQUvv76a4YPH865c+fw8/NjwIABvPvuu5QoUcJs8XIXJ//WSimuXLmSNk779u1j+vTpjB8/Pi3y1ltwd4Ypr+KffwyPttTDHvz909dJPeyxGJhVF4ykJ7bq22MpcUfdggULsmvXLl599VWSU1KYumoVIYMHs2D9+qxNEoGB0KbN7fOwMCOgKLuBsZUZywvx8/OjZ8+eREdH32GSmDdvHim55T/rCTj5txaRdF+GQ4YM4bPPPiMkJITZs2eTnBsBJl6CT83oRRoruD2jt+fRsvpCyK6+WXXN7Dsndbdt28aAAQPYvHkzAC1r1uTHt9+maMEMScYyC1xyJmjJy9m1axcDBw5kwwbD+a1p06bMmDGDRo0amSxZLuHCv3VkZCSvv/46EZbI5MaNGzNz5kyaNGmSW9K7DFO3QPB0tKLP/b5zWjclJYX58+fz1ltv0bhGDX4cPhxRyv7AJTCiJx0NWvIBlFIsXLiQoUOHcvr0aUSEfv36MWHCBEqVKmW2eLmDi/7WSimWLFnCkCFDOHXqFCJC7969mThxImUyBOR5Eq5W9CilfOaARspQN8ZhD9blMx6eWteb5b506ZI6efJk2vmuXbvUl19+qZKTk+1rIA9z5coVNWTIEOXv768AVbp0afX555+rpKQks0XzeK5du6beeustlS9fPhUYGKgOHTpktkhZAmxVrtSNrmzM7EMres+X25qUlBTVsmVLBahmzZqpf/75x/FG8iB79uxRbdq0URh5GlTjxo3VX3/9ZbZYXsH+/fvVt99+m3aenJystm3bZqJEttGKXit6r5bbmpSUFLVgwQJVtmxZBSgRUf3791cXLlxwvLE8RkpKilq8eLGqUKFCmsLv3bu3Onv2rNmieRVfffWVAtRLL72kTp8+bbY4aWhF72JF7+dnW2n5+XluXaWUCgqyXT8oyHPrZoYtk8Ts2bO1OccOrE0SgCpRooSaMWOGNufYyZQpU1T+/PkVoIoVK6Y+/vhjlZiYaLZYWtFndTRq1MiZsdWYjLVJomDBgurEiRNmi+Q17N+/X7Vr1y5tdl+/fn31xx9/mC2WV3DgwAH12GOPpY1dWFiYWrdunakyaUWvFb1Pk5KSohYtWqQ+/fTTtGtJSUnq3LlzJkrlHaSkpKilS5eqypUrpyktTzNJeCopKSlqxYoVqmrVqmljt2LFCtPk0Yo+q4exMt04Y0qwl9wwY3hD386QE7k///xzbZJwgBs3bqhRo0Z5pEnC07l586YaN26catCggYqPjzdNDq3o7VT0OVkcdBRXLkx6U9/OkBO5u3fvrk0SOcATTRLegvWE4uzZs6p58+ZqzZo1butfK3qt6E3v2xlyIrc2SeQcWyaJ5557Tp06dcps0byGt99+O23s/u///k8dP3481/vUil4retP7dgZn5L5x44b697//nc4ksX379lyX2RdINUkUKFBAAapIkSLqww8/VAkJCWaL5vHExcWpCRMmqIIFCypAFSpUSL3//vsqLi4u1/rUil4retP7dgZXyJ1qkggLC9N2Zwc5fPiw6ty5c9oMtWbNmm41SXgzx44dU127dk0buxo1aqj169fnSl+uVvQ+tXulJm9QvXp1Vq1axfr169OSnMTGxtK7d29ibOWL1aRRtWpVli9fzk8//UT16tXZt28fbdu25ZlnnuFEJgm6NQaVK1dmyZIl/Prrr4SGhnLgwAH8c5pBzd248lvD7EN73fim1409vPzyy2kmicmTJ2uThB3YMklMnDgxV00SvkJ8fLxatWpVumv//e9/1a1bt1zSPtp0k/mh/ejzLrZMEr/99pvZYnkFtkwSP//8s9lieRUREREKUPfee69auXKl0+25WtFr043GJ7Blknj44Yfp1q0bJ0+eNFs8j8baJBESEsKBAwd49NFHeeqppzh69KjZ4nkFgYGB1KpVi8OHD9OpUyc6derEoUOHzBbrNq781jD70DN6jVLpTRIiorZs2WK2SF5DfHy8Cg8PV4ULF1aAKlCggHr33XddZpLwZRISEtTUqVNV0aJFFaACAwPV6NGj1Y0bNxxuC2260YpeYx/Hjh1Ts2fPTndNb4VsHydPnkwXqOYqk0ReIDY2Vr3wwgtpYzdy5EiH29CKXit6TQ755ZdfFKCefPJJdeTIEbPF8QoiIiJU7dq105RWx44d1cGDB80WyyvYsGGDevjhh9Ntu22vk4CrFb220WvyDLGxsRQuXJjly5dTs2ZNxo8fT1xcnNlieTStW7dm+/btTJ06laJFi7Jq1Spq167NmDFjuHnzptnieTQtWrRgzZo1aeke4+LiqF+/PiNHjuTGjRvuFcaV3xpmH3pGr8kObZLIORlNEvfcc49atmyZSklJMVs0r2DFihVpY1exYkX13XffZTp2aNONVvQa58lokshoy9dkzoYNG1S9evXSxq59+/YqKirKbLG8gk2bNqmGDRumjd3DDz+s9u7de0c5rei1ote4iISEBPXRRx+p4OBgdfnyZbPF8SoSExPV9OnTVfHixRWg8uXLp95++211/fp1s0XzeJKSktSsWbNUqVKlFKACAgLUpEmT0pVxtaLXNnpNniVfvnwMHjyYvXv3Urx4cQBu3rxJ69atWbZsmTET0tgkICCAgQMHEh0dTa9evUhMTGTSpEmEhoayZMkSPXZZ4O/vT79+/YiOjqZv374kJydzzz335G6nrvzWyO4ABgJbgXjgq2zKvgmcBq4AXwKB2bfv+BYI3rqVgCZ3mDZtmjZJ5AB7TRKaO9mzZ086W/2XX37p3aYb4GngSeCzrBQ90B44A9QGSgJrgUnZt+/47pXeugukJnfQJomcY8skMXToUHX16lWzRfMa9u/fn5ro3XsVfVqn8F42in4h8L7V+cPA6ezb1Ype4xrOnDmjevXqlc5LIuMmVhrbnD9/XvXt21eJiAJUuXLl1Lfffqu9c+zg4sWLas6cOXnGRl8b2Gl1vhMIEpHSGQuKSF8R2SoiW90mncbnufvuu5k7dy6bNm2iYcOGnDx5kvj4eLPF8gpKly7N559/zpYtW7j//vuJjY3l+eefp3Xr1uzevdts8TyakiVL0rt3b5e366mKvgiGbT6V1M9FMxZUSs1WSjVWSjV2i2SaPEXTpk3ZsmULy5cv56mnnkq7vmrVKq5du2aiZJ5P48aN+fPPP5k7dy5lypRh/fr1NGjQgMGDB3PlypXsG9C4DE9V9NeBYlbnqZ/1/yyN2/H396dz586ICACRkZE8+eSThIaG8p///CfVvKixgZ+fH7169SI6OpoBAwaglGLatGkEBwczf/58UlJSzBYxT+Cpij4SqGd1Xg84o5S6YG8DQUHOlbO3vibvoZSiUaNGxMTE8Nxzz9GmTRv27NljtlgeTcmSJZkxYwb//PMPzZs35+zZs/Ts2ZOWLVuyY8cOs8Xzedyq6EUkQEQKAP6Av4gUEJEAG0W/BnqLSC0RKQmMAr7Krv1GjW4vpZ4+bZ9Mp0/bXoq1t74m7xEWFsamTZuYM2cOZcqUYd26ddSvX1+bJOygfv36bNiwgfnz5xMUFMSff/5Jo0aNGDBgAJcuXTJbPN/FlSu72R3AWCxeDFbHWKAyhrmmslXZIRgulleBedjhR68jYzXu5sKFC2rAgAHKz89PAerll182WySv4fLly2rw4MHK399fAapMmTJqzpw5Kjk52WzRTAcXe92I8iH7YuPGjdXWrdr5RuN+duzYwbBhw5gzZ05alGNiYiL58uUzWTLPZ/fu3QwcOJD169cD0KRJE2bOnEnjxnnXv0JE/lEudDDxVBu9RuNV1K9fn9WrV6cp+ZSUFFq3bs3AgQO1SSIb6tSpw9q1a1m4cCHly5dny5YtNGnShH79+nH+/HmzxfMJtKLXaHKBv//+m7/++ouZM2cSHBzM3LlztYdJFogI3bt3Z//+/QwbNgx/f39mz55NSEgIs2bNIjk52WwRvRqt6DWaXOD+++9n+/bttGrVivPnz9OnTx+aNWuGNi1mTdGiRQkPD2fXrl08/PDDXLx4kf79+9OkSRM2b95stnhei1b0Gk0uYW2SKFeuXJpJYujQoWaL5vHUrFmT1atXs2TJEipVqsS2bdto1qwZvXr14uzZs2aL53VoRa/R5CKpJomoqCiGDh2Kv78/5cuXN1ssr0BE6Nq1K/v27WPEiBHky5ePefPmERwczPTp00lKSjJbRK9Be91oNG4kKiqKe++9N80bJ3XG2rRpU5Ml83yio6MZNGgQP//8MwB169Zl5syZtGjRwmTJXI/2utFovJiQkJA0JX/mzJk0233v3r21SSIbgoOD+emnn1i2bBn33HMPu3btomXLlrzwwgvExsaaLZ5HoxW9RmMSRYoUYcCAAeTLl48vv/ySkJAQZsyYoU0SWSAiPPnkk+zdu5cxY8YQGBjIggULCAkJ4aOPPiIxMdFsET0TV0ZfmX3oyFiNNxIVFaXat2+fFi1er149tWHDBrPF8goOHjyoOnXqlDZ2tWvXVhEREWaL5TTkkf3oNZo8Q3BwMP/73//STBI7d+7kzTffTN0KRJMF1apVY8WKFaxatYp7772XyMhI2rRpQ/fu3Tl16pTZ4nkMWtFrNB6AtUli9OjRzJgxI21b5MuXL2uTRDY8/vjjREZGMn78eAoWLMiiRYsICQkhPDychIQEs8UzHZ/yuhGRa0CU2XJkQhnAE+O5tVyO46myabkcx1NlC1FK3ZFoKafY2iLYm4lSHpppSkS2eqJsWi7H8VTZtFyO46myuTo1qjbdaDQajY+jFb1Go9H4OL6m6GebLUAWeKpsWi7H8VTZtFyO46myuVQun1qM1Wg0Gs2d+NqMXqPRaDQZ0Ipeo9FofByvUPQiUkNE4kRkQRZl3hSR0yJyRUS+FJFAq3ulRGSZiNwQkWMi8py7ZBORl0TkHxG5KiInRSRcRAKs7q+11L9uOVwSB2CHXD1FJNmq3+si0trqvpljNiuDXPGWGInU+y4dM0fac/d7Zq9s7n7PHJDLre+ZA3K59R2zavdZEdlned5DItIyk3Kufc9cuZ9Cbh3Ar8AGYEEm99sDZ4DaQElgLTDJ6v5/gMVAEaAFcAWo7SbZ+gMtgfxABeAf4G2r+2uBPiaMWU9gYxb1TRszG+W/Ar7MrTGztz0z3jMHZHPre+aAXG59z3L6nLn9jlnafAQ4BjTFmGRXACq44z3z+Bm9iDwLXAZ+y6LYS8BcpVSkUuoSMB7jBUNECgNdgNFKqetKqY3ACuAFd8imlPpMKbVBKZWglDoFfAs0d7ZvZ+XKpr6pY5aJLPOd7dsFmPKe2YMZ75mzmD1mNuTI7XdsHPCuUmqzUipFKXXK8rfKiMvfM49W9CJSDHgX+Fc2RWsDO63OdwJBIlIaCAaSlVLRGe7XdpNsGWkFRGa4NlFEzovIH9Y/a90gVwNLv9EiMtrqp74njVkX4BywPsN1l42ZA+25/T1zQLaM5Op75mB7bn3PHJArlVx/x0TEH2gM3CUiBy3mtRkiUtBGcZe/Zx6t6DG+yeYqpU5kU64Ixs+XVFI/F7VxL/W+s/tI2CtbGiLyMsYfe7LV5beAezF+xs0GVopINTfItR4IA+7GeNG7A8Ms9zxmzDBmN18ry29WC64eM3vbM+M9c/hZ3fSe2dueu9+znDynO96xICAf0BXDxFYfaACMslHW5e+Zxyp6EakPtAU+sqP4daCY1Xnq52s27qXev0YOcVC21DpPApOADkqptE2UlFJ/KaWuKaXilVLzgT+Ax3JbLqXUYaXUEctPyN0YM+2ultueMmaVgAeBr62vu3LMHGzPre+Zg7IB7nnPHGnP3e9ZDsbLLe8YcMvy73SlVKzlbzM1kzZd/p558qZmrYEqwHExtmstAviLSC2lVMMMZSOBesB3lvN6wBml1AURiQMCRKSGUuqA1f2MP2tzSzZE5FHgC+Bxy8ueFQoQd8iVRb/RmDxmFl4E/lRKHXZAdleQWXvufs8ckc2d75kz7eX2e+aoXG55x5RSl0TkpKWd7HD9e+bIqrE7D6AQUNbqmAz8F7jLRtlHgdNALYxV6t9Jv0q9CGOlujDGApWzK/uOyPYQcAFoZeNeCYwV9gIYX7rPAzcwtijNbbk6AEGWz6HAHuAdTxgzqzpRQK9cHjO72zPhPXNENne+Z47I5bb3LCfP6Y53zKrdd4G/McxYJTE8z8a74z3LsdDuPoCxWNzxgMoYP2EqW90fguGSdBWYBwRa3SsFLLf8sY4Dz7lLNiACSLJcSz3+Z7l3l+UPfw3DE2Uz8Iib5JpsGa8bwGHLS5jPE8bMcq2Zpe+iGeq5dMyyas/s98wR2dz5njkol9vesxz8Ld3yjlm1mw/41NLmaeATjC+TXH/P9F43Go1G4+N47GKsRqPRaFyDVvQajUbj42hFr9FoND6OVvQajUbj42hFr9FoND6OVvQajUbj42hFr9FYEJGjIjI0i/s9ReS6O2XKChH5SkRWmS2HxvPRil7jUViUl7IciSJyWEQmW7Zntad+FUvdxrktq7vwxWfSuBdP3utGk3dZg7G/dj6Mnf7mYIR79zdTKI3GW9Ezeo0nEq+UOq2UOqGUWoiRRONJADEYLkYatlsisltEeljVPWL592/LLHitpd59IvKrZX/xqyKyUUSaOSuoiHQSI4VfnIgcEZEJIpLf6v5RERklIp/L7TR/wzK0ESwi6yxtRInIY2Kkr+uZ1TNZ1R8kIqdE5JKIzBORQs4+l8a30Ipe4w3cwpjdA7wH9AYGYGz6NBH4XEQet9xvYvn3UaAc8LTlvCjwDcYvhCbADuAnESmTU6FEpD3Gl9AMjMQPvTC24H0/Q9E3gd1AQ+ADIDz1S0ZE/IBlGPvUNMXIJPQOEGhVP7NnwvI8YRhbQHcDngIG5fSZND6Ksxv16EMfrjwwcneusjpvApzHyJFZGEPpt8xQ52PgJ8vnKhhbwTbOph8BYoEeVteOAkOzqNMTuG51vh4jpZt1mScxNqgSqzb/k6HMAWCU5XN7DCVfwer+A5Zn6JnVM1nG6gQQYHXtC2CN2X9HfXjWoW30Gk/kUYt3SwDGTP4H4HWMGXwB4GcRsd6NLx+GQs0UEbkbI8NVG4xsP/5AQYydA3NKI6CJiLxldc3P0m5ZjC8SgF0Z6sVgbFULxta9MSp97tC/gRQ7ZdirlErK0Pb9dtbV5BG0otd4IuuBvkAihhJMBBCRqpb7nTC2Z7UmMZs252Mo+DcxvhTiMRKU58+iTnb4YSR8XmLj3rksZFPcNpsK9iWjyIys2tZoAK3oNZ7JTaXUQRvX92Io6HuUUr9nUjfB8q9/hustgDeUUj8CiEgQhr3bGbYBoZnIai/7gAoiUl4pFWO51pj0yjqzZ9Jo7EIreo3XoJS6JiKTgcli5CNcj5GSsCmQopSaDZzFsOO3F5GjQJxS6gpG2roeIvIXhq0/nNsKNKe8C6wSkWMYad+SMBZGmyilhtvZxmqMLEfzLcFaBTFyiSZxe6af2TNpNHahf+JpvI3RGNmphmLkyVwNdMHigmixV78B9MGwV/9gqdcL40vhH4xUbF+SjV0/O5RSvwCPY9j9t1iOt7nTrJRVGykYnjKBlvrzgQkYSj4um2fSaOxCZ5jSaDwMEamH4f7ZWCn1j8niaHwAreg1GpMRkacw8n8ewHClnIqxSNtA6f+gGhegbfQajfkUxQikqgRcAtYCb2olr3EVekav0Wg0Po5ejNVoNBofRyt6jUaj8XG0otdoNBofRyt6jUaj8XG0otdoNBofRyt6jUaj8XH+H71NWTULZV1TAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 396x230.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_clf = SGDClassifier(loss=\"hinge\", alpha=0.017, max_iter=1000, tol=1e-3, random_state=42)\n",
    "sgd_clf.fit(X, y.ravel())\n",
    "\n",
    "m = len(X)\n",
    "t = y * 2 - 1  # -1 if y==0, +1 if y==1\n",
    "X_b = np.c_[np.ones((m, 1)), X]  # Add bias input x0=1\n",
    "X_b_t = X_b * t\n",
    "sgd_theta = np.r_[sgd_clf.intercept_[0], sgd_clf.coef_[0]]\n",
    "print(sgd_theta)\n",
    "support_vectors_idx = (X_b_t.dot(sgd_theta) < 1).ravel()\n",
    "sgd_clf.support_vectors_ = X[support_vectors_idx]\n",
    "sgd_clf.C = C\n",
    "\n",
    "plt.figure(figsize=(5.5,3.2))\n",
    "plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\")\n",
    "plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\")\n",
    "plot_svc_decision_boundary(sgd_clf, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.title(\"SGDClassifier\", fontsize=14)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. to 7."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See appendix A."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Exercise: train a `LinearSVC` on a linearly separable dataset. Then train an `SVC` and a `SGDClassifier` on the same dataset. See if you can get them to produce roughly the same model._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the Iris dataset: the Iris Setosa and Iris Versicolor classes are linearly separable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = iris[\"target\"]\n",
    "\n",
    "setosa_or_versicolor = (y == 0) | (y == 1)\n",
    "X = X[setosa_or_versicolor]\n",
    "y = y[setosa_or_versicolor]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearSVC:                    [0.28475098] [[1.05364854 1.09903804]]\n",
      "SVC:                          [0.31896852] [[1.1203284  1.02625193]]\n",
      "SGDClassifier(alpha=0.00200): [0.117] [[0.77714169 0.72981762]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "C = 5\n",
    "alpha = 1 / (C * len(X))\n",
    "\n",
    "lin_clf = LinearSVC(loss=\"hinge\", C=C, random_state=42)\n",
    "svm_clf = SVC(kernel=\"linear\", C=C)\n",
    "sgd_clf = SGDClassifier(loss=\"hinge\", learning_rate=\"constant\", eta0=0.001, alpha=alpha,\n",
    "                        max_iter=1000, tol=1e-3, random_state=42)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "lin_clf.fit(X_scaled, y)\n",
    "svm_clf.fit(X_scaled, y)\n",
    "sgd_clf.fit(X_scaled, y)\n",
    "\n",
    "print(\"LinearSVC:                   \", lin_clf.intercept_, lin_clf.coef_)\n",
    "print(\"SVC:                         \", svm_clf.intercept_, svm_clf.coef_)\n",
    "print(\"SGDClassifier(alpha={:.5f}):\".format(sgd_clf.alpha), sgd_clf.intercept_, sgd_clf.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the decision boundaries of these three models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the slope and bias of each decision boundary\n",
    "w1 = -lin_clf.coef_[0, 0]/lin_clf.coef_[0, 1]\n",
    "b1 = -lin_clf.intercept_[0]/lin_clf.coef_[0, 1]\n",
    "w2 = -svm_clf.coef_[0, 0]/svm_clf.coef_[0, 1]\n",
    "b2 = -svm_clf.intercept_[0]/svm_clf.coef_[0, 1]\n",
    "w3 = -sgd_clf.coef_[0, 0]/sgd_clf.coef_[0, 1]\n",
    "b3 = -sgd_clf.intercept_[0]/sgd_clf.coef_[0, 1]\n",
    "\n",
    "# Transform the decision boundary lines back to the original scale\n",
    "line1 = scaler.inverse_transform([[-10, -10 * w1 + b1], [10, 10 * w1 + b1]])\n",
    "line2 = scaler.inverse_transform([[-10, -10 * w2 + b2], [10, 10 * w2 + b2]])\n",
    "line3 = scaler.inverse_transform([[-10, -10 * w3 + b3], [10, 10 * w3 + b3]])\n",
    "\n",
    "# Plot all three decision boundaries\n",
    "plt.figure(figsize=(11, 4))\n",
    "plt.plot(line1[:, 0], line1[:, 1], \"k:\", label=\"LinearSVC\")\n",
    "plt.plot(line2[:, 0], line2[:, 1], \"b--\", linewidth=2, label=\"SVC\")\n",
    "plt.plot(line3[:, 0], line3[:, 1], \"r-\", label=\"SGDClassifier\")\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\") # label=\"Iris versicolor\"\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\") # label=\"Iris setosa\"\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"upper center\", fontsize=14)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Close enough!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Exercise: train an SVM classifier on the MNIST dataset. Since SVM classifiers are binary classifiers, you will need to use one-versus-all to classify all 10 digits. You may want to tune the hyperparameters using small validation sets to speed up the process. What accuracy can you reach?_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's load the dataset and split it into a training set and a test set. We could use `train_test_split()` but people usually just take the first 60,000 instances for the training set, and the last 10,000 instances for the test set (this makes it possible to compare your model's performance with others): "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning:** since Scikit-Learn 0.24, `fetch_openml()` returns a Pandas `DataFrame` by default. To avoid this, we use `as_frame=False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "mnist = fetch_openml('mnist_784', version=1, cache=True, as_frame=False)\n",
    "\n",
    "X = mnist[\"data\"]\n",
    "y = mnist[\"target\"].astype(np.uint8)\n",
    "\n",
    "X_train = X[:60000]\n",
    "y_train = y[:60000]\n",
    "X_test = X[60000:]\n",
    "y_test = y[60000:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many training algorithms are sensitive to the order of the training instances, so it's generally good practice to shuffle them first. However, the dataset is already shuffled, so we do not need to do it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start simple, with a linear SVM classifier. It will automatically use the One-vs-All (also called One-vs-the-Rest, OvR) strategy, so there's nothing special we need to do. Easy!\n",
    "\n",
    "**Warning**: this may take a few minutes depending on your hardware."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ageron/miniconda3/envs/tf2/lib/python3.7/site-packages/sklearn/svm/_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearSVC(random_state=42)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_clf = LinearSVC(random_state=42)\n",
    "lin_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make predictions on the training set and measure the accuracy (we don't want to measure it on the test set yet, since we have not selected and trained the final model yet):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8348666666666666"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "y_pred = lin_clf.predict(X_train)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, 83.5% accuracy on MNIST is pretty bad. This linear model is certainly too simple for MNIST, but perhaps we just needed to scale the data first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train.astype(np.float32))\n",
    "X_test_scaled = scaler.transform(X_test.astype(np.float32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: this may take a few minutes depending on your hardware."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ageron/miniconda3/envs/tf2/lib/python3.7/site-packages/sklearn/svm/_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearSVC(random_state=42)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_clf = LinearSVC(random_state=42)\n",
    "lin_clf.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9217333333333333"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = lin_clf.predict(X_train_scaled)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's much better (we cut the error rate by about 53%), but still not great at all for MNIST. If we want to use an SVM, we will have to use a kernel. Let's try an `SVC` with an RBF kernel (the default)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: to be future-proof we set `gamma=\"scale\"` since it will be the default value in Scikit-Learn 0.22."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf = SVC(gamma=\"scale\")\n",
    "svm_clf.fit(X_train_scaled[:10000], y_train[:10000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9455333333333333"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = svm_clf.predict(X_train_scaled)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's promising, we get better performance even though we trained the model on 6 times less data. Let's tune the hyperparameters by doing a randomized search with cross validation. We will do this on a small dataset just to speed up the process:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "[CV] C=5.847490967837556, gamma=0.004375955271336425 .................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .. C=5.847490967837556, gamma=0.004375955271336425, total=   0.8s\n",
      "[CV] C=5.847490967837556, gamma=0.004375955271336425 .................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.8s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .. C=5.847490967837556, gamma=0.004375955271336425, total=   0.8s\n",
      "[CV] C=5.847490967837556, gamma=0.004375955271336425 .................\n",
      "[CV] .. C=5.847490967837556, gamma=0.004375955271336425, total=   0.8s\n",
      "[CV] C=2.544266730893301, gamma=0.024987648190235304 .................\n",
      "[CV] .. C=2.544266730893301, gamma=0.024987648190235304, total=   0.9s\n",
      "[CV] C=2.544266730893301, gamma=0.024987648190235304 .................\n",
      "[CV] .. C=2.544266730893301, gamma=0.024987648190235304, total=   0.9s\n",
      "[CV] C=2.544266730893301, gamma=0.024987648190235304 .................\n",
      "[CV] .. C=2.544266730893301, gamma=0.024987648190235304, total=   0.9s\n",
      "[CV] C=2.199505425963898, gamma=0.009340106304825553 .................\n",
      "[CV] .. C=2.199505425963898, gamma=0.009340106304825553, total=   0.8s\n",
      "[CV] C=2.199505425963898, gamma=0.009340106304825553 .................\n",
      "[CV] .. C=2.199505425963898, gamma=0.009340106304825553, total=   0.8s\n",
      "[CV] C=2.199505425963898, gamma=0.009340106304825553 .................\n",
      "[CV] .. C=2.199505425963898, gamma=0.009340106304825553, total=   0.9s\n",
      "[CV] C=7.327377306009368, gamma=0.04329656504133618 ..................\n",
      "[CV] ... C=7.327377306009368, gamma=0.04329656504133618, total=   0.9s\n",
      "[CV] C=7.327377306009368, gamma=0.04329656504133618 ..................\n",
      "[CV] ... C=7.327377306009368, gamma=0.04329656504133618, total=   0.9s\n",
      "[CV] C=7.327377306009368, gamma=0.04329656504133618 ..................\n",
      "[CV] ... C=7.327377306009368, gamma=0.04329656504133618, total=   0.9s\n",
      "[CV] C=7.830259944094713, gamma=0.009933958471354695 .................\n",
      "[CV] .. C=7.830259944094713, gamma=0.009933958471354695, total=   0.9s\n",
      "[CV] C=7.830259944094713, gamma=0.009933958471354695 .................\n",
      "[CV] .. C=7.830259944094713, gamma=0.009933958471354695, total=   0.9s\n",
      "[CV] C=7.830259944094713, gamma=0.009933958471354695 .................\n",
      "[CV] .. C=7.830259944094713, gamma=0.009933958471354695, total=   0.9s\n",
      "[CV] C=6.867969780001033, gamma=0.027511132256566175 .................\n",
      "[CV] .. C=6.867969780001033, gamma=0.027511132256566175, total=   0.9s\n",
      "[CV] C=6.867969780001033, gamma=0.027511132256566175 .................\n",
      "[CV] .. C=6.867969780001033, gamma=0.027511132256566175, total=   0.9s\n",
      "[CV] C=6.867969780001033, gamma=0.027511132256566175 .................\n",
      "[CV] .. C=6.867969780001033, gamma=0.027511132256566175, total=   0.9s\n",
      "[CV] C=3.584980864373988, gamma=0.01237128009623357 ..................\n",
      "[CV] ... C=3.584980864373988, gamma=0.01237128009623357, total=   0.9s\n",
      "[CV] C=3.584980864373988, gamma=0.01237128009623357 ..................\n",
      "[CV] ... C=3.584980864373988, gamma=0.01237128009623357, total=   0.9s\n",
      "[CV] C=3.584980864373988, gamma=0.01237128009623357 ..................\n",
      "[CV] ... C=3.584980864373988, gamma=0.01237128009623357, total=   0.9s\n",
      "[CV] C=5.073078322899452, gamma=0.002259275783824143 .................\n",
      "[CV] .. C=5.073078322899452, gamma=0.002259275783824143, total=   0.7s\n",
      "[CV] C=5.073078322899452, gamma=0.002259275783824143 .................\n",
      "[CV] .. C=5.073078322899452, gamma=0.002259275783824143, total=   0.7s\n",
      "[CV] C=5.073078322899452, gamma=0.002259275783824143 .................\n",
      "[CV] .. C=5.073078322899452, gamma=0.002259275783824143, total=   0.7s\n",
      "[CV] C=10.696324058267928, gamma=0.0039267813006514255 ...............\n",
      "[CV]  C=10.696324058267928, gamma=0.0039267813006514255, total=   0.8s\n",
      "[CV] C=10.696324058267928, gamma=0.0039267813006514255 ...............\n",
      "[CV]  C=10.696324058267928, gamma=0.0039267813006514255, total=   0.8s\n",
      "[CV] C=10.696324058267928, gamma=0.0039267813006514255 ...............\n",
      "[CV]  C=10.696324058267928, gamma=0.0039267813006514255, total=   0.8s\n",
      "[CV] C=3.8786881587000437, gamma=0.0017076019229344522 ...............\n",
      "[CV]  C=3.8786881587000437, gamma=0.0017076019229344522, total=   0.7s\n",
      "[CV] C=3.8786881587000437, gamma=0.0017076019229344522 ...............\n",
      "[CV]  C=3.8786881587000437, gamma=0.0017076019229344522, total=   0.7s\n",
      "[CV] C=3.8786881587000437, gamma=0.0017076019229344522 ...............\n",
      "[CV]  C=3.8786881587000437, gamma=0.0017076019229344522, total=   0.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:   24.8s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, estimator=SVC(),\n",
       "                   param_distributions={'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f93b0a61990>,\n",
       "                                        'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f93b0a61850>},\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import reciprocal, uniform\n",
    "\n",
    "param_distributions = {\"gamma\": reciprocal(0.001, 0.1), \"C\": uniform(1, 10)}\n",
    "rnd_search_cv = RandomizedSearchCV(svm_clf, param_distributions, n_iter=10, verbose=2, cv=3)\n",
    "rnd_search_cv.fit(X_train_scaled[:1000], y_train[:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=3.8786881587000437, gamma=0.0017076019229344522)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8599947252641863"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks pretty low but remember we only trained the model on 1,000 instances. Let's retrain the best estimator on the whole training set:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: the following cell may take hours to run, depending on your hardware."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=3.8786881587000437, gamma=0.0017076019229344522)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9978166666666667"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_train_scaled)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ah, this looks good! Let's select this model. Now we can test it on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9717"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_test_scaled)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not too bad, but apparently the model is overfitting slightly. It's tempting to tweak the hyperparameters a bit more (e.g. decreasing `C` and/or `gamma`), but we would run the risk of overfitting the test set. Other people have found that the hyperparameters `C=5` and `gamma=0.005` yield even better performance (over 98% accuracy). By running the randomized search for longer and on a larger part of the training set, you may be able to find this as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Exercise: train an SVM regressor on the California housing dataset._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load the dataset using Scikit-Learn's `fetch_california_housing()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "\n",
    "housing = fetch_california_housing()\n",
    "X = housing[\"data\"]\n",
    "y = housing[\"target\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split it into a training set and a test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't forget to scale the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's train a simple `LinearSVR` first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ageron/miniconda3/envs/tf2/lib/python3.7/site-packages/sklearn/svm/_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearSVR(random_state=42)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVR\n",
    "\n",
    "lin_svr = LinearSVR(random_state=42)\n",
    "lin_svr.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how it performs on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9641780189948642"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "y_pred = lin_svr.predict(X_train_scaled)\n",
    "mse = mean_squared_error(y_train, y_pred)\n",
    "mse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the RMSE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9819256687727764"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this training set, the targets are tens of thousands of dollars. The RMSE gives a rough idea of the kind of error you should expect (with a higher weight for large errors): so with this model we can expect errors somewhere around $10,000. Not great. Let's see if we can do better with an RBF Kernel. We will use randomized search with cross validation to find the appropriate hyperparameter values for `C` and `gamma`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "[CV] C=4.745401188473625, gamma=0.07969454818643928 ..................\n",
      "[CV] ... C=4.745401188473625, gamma=0.07969454818643928, total=   4.7s\n",
      "[CV] C=4.745401188473625, gamma=0.07969454818643928 ..................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    4.7s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ... C=4.745401188473625, gamma=0.07969454818643928, total=   4.6s\n",
      "[CV] C=4.745401188473625, gamma=0.07969454818643928 ..................\n",
      "[CV] ... C=4.745401188473625, gamma=0.07969454818643928, total=   4.7s\n",
      "[CV] C=8.31993941811405, gamma=0.015751320499779724 ..................\n",
      "[CV] ... C=8.31993941811405, gamma=0.015751320499779724, total=   4.3s\n",
      "[CV] C=8.31993941811405, gamma=0.015751320499779724 ..................\n",
      "[CV] ... C=8.31993941811405, gamma=0.015751320499779724, total=   4.2s\n",
      "[CV] C=8.31993941811405, gamma=0.015751320499779724 ..................\n",
      "[CV] ... C=8.31993941811405, gamma=0.015751320499779724, total=   4.3s\n",
      "[CV] C=2.560186404424365, gamma=0.002051110418843397 .................\n",
      "[CV] .. C=2.560186404424365, gamma=0.002051110418843397, total=   3.8s\n",
      "[CV] C=2.560186404424365, gamma=0.002051110418843397 .................\n",
      "[CV] .. C=2.560186404424365, gamma=0.002051110418843397, total=   3.8s\n",
      "[CV] C=2.560186404424365, gamma=0.002051110418843397 .................\n",
      "[CV] .. C=2.560186404424365, gamma=0.002051110418843397, total=   3.9s\n",
      "[CV] C=1.5808361216819946, gamma=0.05399484409787431 .................\n",
      "[CV] .. C=1.5808361216819946, gamma=0.05399484409787431, total=   3.9s\n",
      "[CV] C=1.5808361216819946, gamma=0.05399484409787431 .................\n",
      "[CV] .. C=1.5808361216819946, gamma=0.05399484409787431, total=   3.8s\n",
      "[CV] C=1.5808361216819946, gamma=0.05399484409787431 .................\n",
      "[CV] .. C=1.5808361216819946, gamma=0.05399484409787431, total=   3.9s\n",
      "[CV] C=7.011150117432088, gamma=0.026070247583707663 .................\n",
      "[CV] .. C=7.011150117432088, gamma=0.026070247583707663, total=   4.3s\n",
      "[CV] C=7.011150117432088, gamma=0.026070247583707663 .................\n",
      "[CV] .. C=7.011150117432088, gamma=0.026070247583707663, total=   4.4s\n",
      "[CV] C=7.011150117432088, gamma=0.026070247583707663 .................\n",
      "[CV] .. C=7.011150117432088, gamma=0.026070247583707663, total=   4.4s\n",
      "[CV] C=1.2058449429580245, gamma=0.0870602087830485 ..................\n",
      "[CV] ... C=1.2058449429580245, gamma=0.0870602087830485, total=   3.8s\n",
      "[CV] C=1.2058449429580245, gamma=0.0870602087830485 ..................\n",
      "[CV] ... C=1.2058449429580245, gamma=0.0870602087830485, total=   3.9s\n",
      "[CV] C=1.2058449429580245, gamma=0.0870602087830485 ..................\n",
      "[CV] ... C=1.2058449429580245, gamma=0.0870602087830485, total=   3.9s\n",
      "[CV] C=9.324426408004218, gamma=0.0026587543983272693 ................\n",
      "[CV] . C=9.324426408004218, gamma=0.0026587543983272693, total=   4.0s\n",
      "[CV] C=9.324426408004218, gamma=0.0026587543983272693 ................\n",
      "[CV] . C=9.324426408004218, gamma=0.0026587543983272693, total=   4.0s\n",
      "[CV] C=9.324426408004218, gamma=0.0026587543983272693 ................\n",
      "[CV] . C=9.324426408004218, gamma=0.0026587543983272693, total=   3.9s\n",
      "[CV] C=2.818249672071006, gamma=0.0023270677083837795 ................\n",
      "[CV] . C=2.818249672071006, gamma=0.0023270677083837795, total=   3.8s\n",
      "[CV] C=2.818249672071006, gamma=0.0023270677083837795 ................\n",
      "[CV] . C=2.818249672071006, gamma=0.0023270677083837795, total=   3.8s\n",
      "[CV] C=2.818249672071006, gamma=0.0023270677083837795 ................\n",
      "[CV] . C=2.818249672071006, gamma=0.0023270677083837795, total=   3.8s\n",
      "[CV] C=4.042422429595377, gamma=0.011207606211860567 .................\n",
      "[CV] .. C=4.042422429595377, gamma=0.011207606211860567, total=   3.8s\n",
      "[CV] C=4.042422429595377, gamma=0.011207606211860567 .................\n",
      "[CV] .. C=4.042422429595377, gamma=0.011207606211860567, total=   3.9s\n",
      "[CV] C=4.042422429595377, gamma=0.011207606211860567 .................\n",
      "[CV] .. C=4.042422429595377, gamma=0.011207606211860567, total=   3.9s\n",
      "[CV] C=5.319450186421157, gamma=0.003823475224675185 .................\n",
      "[CV] .. C=5.319450186421157, gamma=0.003823475224675185, total=   3.8s\n",
      "[CV] C=5.319450186421157, gamma=0.003823475224675185 .................\n",
      "[CV] .. C=5.319450186421157, gamma=0.003823475224675185, total=   3.9s\n",
      "[CV] C=5.319450186421157, gamma=0.003823475224675185 .................\n",
      "[CV] .. C=5.319450186421157, gamma=0.003823475224675185, total=   3.9s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:  2.0min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, estimator=SVR(),\n",
       "                   param_distributions={'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f9370860850>,\n",
       "                                        'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f9380503890>},\n",
       "                   random_state=42, verbose=2)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import reciprocal, uniform\n",
    "\n",
    "param_distributions = {\"gamma\": reciprocal(0.001, 0.1), \"C\": uniform(1, 10)}\n",
    "rnd_search_cv = RandomizedSearchCV(SVR(), param_distributions, n_iter=10, verbose=2, cv=3, random_state=42)\n",
    "rnd_search_cv.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=4.745401188473625, gamma=0.07969454818643928)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's measure the RMSE on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5727524770785359"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_train_scaled)\n",
    "mse = mean_squared_error(y_train, y_pred)\n",
    "np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks much better than the linear model. Let's select this model and evaluate it on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5929168385528734"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_test_scaled)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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