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._\n",
    "\n",
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml/blob/master/05_support_vector_machines.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: this is the code for the 1st edition of the book. Please visit https://github.com/ageron/handson-ml2 for the 2nd edition code, with up-to-date notebooks using the latest library versions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To support both python 2 and python 3\n",
    "from __future__ import division, print_function, unicode_literals\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": [
    "# Large margin 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:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=inf, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='linear', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "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 864x194.4 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",
    "plt.figure(figsize=(12,2.7))\n",
    "\n",
    "plt.subplot(121)\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.subplot(122)\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": [
    "# 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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\n",
      "text/plain": [
       "<Figure size 864x230.4 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=(12,3.2))\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.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": [
    "# 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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\n",
      "text/plain": [
       "<Figure size 864x194.4 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",
    "plt.figure(figsize=(12,2.7))\n",
    "\n",
    "plt.subplot(121)\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.subplot(122)\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": [
    "# Large margin *vs* margin violations"
   ]
  },
  {
   "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(memory=None,\n",
       "     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('linear_svc', LinearSVC(C=1, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='hinge', max_iter=1000, multi_class='ovr',\n",
       "     penalty='l2', random_state=42, tol=0.0001, verbose=0))])"
      ]
     },
     "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": [
    "Now let's generate the graph comparing different regularization settings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ageron/.virtualenvs/ml/lib/python3.6/site-packages/sklearn/svm/base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('linear_svc', LinearSVC(C=100, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='hinge', max_iter=1000, multi_class='ovr',\n",
       "     penalty='l2', random_state=42, tol=0.0001, verbose=0))])"
      ]
     },
     "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 864x230.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,3.2))\n",
    "plt.subplot(121)\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, 6)\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, 6, 0.8, 2.8])\n",
    "\n",
    "plt.subplot(122)\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, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.title(\"$C = {}$\".format(svm_clf2.C), fontsize=16)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n",
    "\n",
    "save_fig(\"regularization_plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Non-linear classification"
   ]
  },
  {
   "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 792x288 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=(11, 4))\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": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('poly_features', PolynomialFeatures(degree=3, include_bias=True, interaction_only=False)), ('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', LinearSVC(C=10, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='hinge', max_iter=1000, multi_class='ovr',\n",
       "     penalty='l2', random_state=42, tol=0.0001, verbose=0))])"
      ]
     },
     "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": "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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\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": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', SVC(C=5, cache_size=200, class_weight=None, coef0=1,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='poly', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False))])"
      ]
     },
     "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": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', SVC(C=5, cache_size=200, class_weight=None, coef0=100,\n",
       "  decision_function_shape='ovr', degree=10, gamma='auto_deprecated',\n",
       "  kernel='poly', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False))])"
      ]
     },
     "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 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plot_predictions(poly_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title(r\"$d=3, r=1, C=5$\", fontsize=18)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_predictions(poly100_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title(r\"$d=10, r=100, C=5$\", fontsize=18)\n",
    "\n",
    "save_fig(\"moons_kernelized_polynomial_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "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 792x288 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=(11, 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": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=5, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False))])"
      ]
     },
     "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": "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 792x504 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",
    "plt.figure(figsize=(11, 7))\n",
    "\n",
    "for i, svm_clf in enumerate(svm_clfs):\n",
    "    plt.subplot(221 + i)\n",
    "    plot_predictions(svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "    plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "    gamma, C = hyperparams[i]\n",
    "    plt.title(r\"$\\gamma = {}, C = {}$\".format(gamma, C), fontsize=16)\n",
    "\n",
    "save_fig(\"moons_rbf_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression\n"
   ]
  },
  {
   "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": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVR(C=1.0, dual=True, epsilon=1.5, fit_intercept=True,\n",
       "     intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=1000,\n",
       "     random_state=42, tol=0.0001, verbose=0)"
      ]
     },
     "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": "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",
    "plt.figure(figsize=(9, 4))\n",
    "plt.subplot(121)\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.subplot(122)\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": [
    "**Warning**: the default value of `gamma` will change from `'auto'` to `'scale'` in version 0.22 to better account for unscaled features. To preserve the same results as in the book, we explicitly set it to `'auto'`, but you should probably just use the default in your own code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=100, cache_size=200, coef0=0.0, degree=2, epsilon=0.1, gamma='auto',\n",
       "  kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "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=\"auto\")\n",
    "svm_poly_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=0.01, cache_size=200, coef0=0.0, degree=2, epsilon=0.1, gamma='auto',\n",
       "  kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "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=\"auto\")\n",
    "svm_poly_reg2 = SVR(kernel=\"poly\", degree=2, C=0.01, epsilon=0.1, gamma=\"auto\")\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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cIuAoRePaxcW1K8/uleHn59x+++0A7Nixo8RnI0aMIDIykvfee8/bzVIoPIOUsGULHD0KhYXayxzjtppVq9b2TQMV7kZpnCKgcELj1IhfgNOpUyegpCiuWrWK//3vf4wbN46oqChfNE2hcD8HDsDZsyXF0IryCqOi4qA0ThFQOKlx5SGgfPwqaqJPKWWZ6zZv3pwaNWpYiGJ+fj6vvvoqrVu3ZtCgQe5ookLhewoKrveCjSRtiiFhSRvSzoURWzOb8b1T6NvllA8b6Tsqo76B0jhFAOGkxpWXgDL8KiNCCDp16sSWLVuQUiKEYOrUqRw5coR169ah1+t93USFwj2csjTokjbFMHBOPNl5moylZoYzcE68L1qm8CBK4xQBg5MaV6d6cLkOE1DTIVLKCvkqL506deLixYscPnyYs2fP8v7779OzZ0/uvfdeN1w1haKCkJ5u0RNOWNKmWBBNZOcZSFjSxtstqxD4Wsc8pW+gNE4RIDipcaezqpTrMGrErxJg7vz8888/k5uby6RJk3zcKoXCzeTlWfybdi7MZrG0c2FER3qjQQpvoTROERA4qXF5BeUbs/M7w+/ChQu+bkKFo2PHjuh0OubNm8eWLVt4/fXXadKkia+bpVC4l2DL6Y3YmtmkZoaXKFYj4ry3WqTwEkrjFAGBkxoXbCgq12H8bqr32LFjHD582NfNqFBERkZy0003sWnTJmrXrk1CQoKvm6RQuJ/69cHMn2t87xTCggssigQb8jh3eZi3W6bwMErjFAGBExoXFlxAgxrXynUYvzP8dDodr776qq+bUeHo2LEjAB9++CFVq1b1cWsUCg9gtSJH3y6nSByUTKPoqwghaRR9lfgmH9I+7hcfNVDhSZTGKSo9Tmhc4qBkakTk2dmBc/jdVG/9+vVZvXo1q1ev5sEHH/R1cyoE+fn5/PTTT8THx/Pss8/6ujkKhWcwGKBZM4t0B327nLJI3yJlS85dfpu/T5jgq1YqPIDSOEVA4ITGAUxeVb7D+N2IX+3atbnxxhsZMWIEeXnls3orC5988gknTpxg2rRpFTaXl0LhFlq3htq1LaZDANKzsjidlYUQgujISKSU5XOCUVQolMYpAgY7GudO/G7ETwjB5MmTSUxM5PLly9SsWdPXTfIJWVlZrF27lv379/Pxxx/z6quvFme4VygqLUJA585advujR7VthYW88sUX/HTwIKlz5lAlOJiMS5fO+rahivKiNE4RkNjRuGIMBorK2bF1u+EnhKgBzAe6AZnAaCnlv2yUCwGmAv8HBAFbgMFSytOlHaN79+50797dre32N9auXUufPn2oXbs2I0aMYOLEib5ukkLhHYSANm2gZUs4dYofV6zgP9u28X7//lS57TaIieHUY4+VqiNlO7Tn9U2hoTROEbBYaRzp6ZCfD0FBUL8++06e3Fee3XtixG8GkAfUAdoDq4QQ+6SUv1qVexm4HWgLXAQSgWnAo84e6MiRI6xcuTIggz169+5N7969fd0MhcJ3GAzkN2zIS3Pm0KRJE0ZOmwahoZ4+qtf0LdBRGqcIeAwGaNxYe5lR3hE/t/r4CSHCgceAd6SUV6SUm4EVwNM2ijcG1kop/5JS5gD/Blq5crxFixbx2muvsWnTpvI2XaFQ+CHTp0/nt99+Y8qUKYR62Ojztr4pFIoAoqAATpyALVtgwwbt/cQJbbubcXdwR3OgQEp5xGzbPmwL3nygsxCivhAiDOgL/M/WToUQA4UQyUKI5IyMjOLtb775JjExMbz00ksUeODiKBSKik1qair/+Mc/eOihh7xxOK/qm0KhCACkhJQUWLEC9uzRpnUzM7X3PXu07SkpWjk34W7DLwK4ZLXtImAr6dJR4BRw2linJTDO1k6llIlSyngpZXytWrWKt4eHhzN58mT27dvHzJkz3dF+hULhR0yZMoXly5d7K9LTq/qmUCgqOVJqI3um9C3mQRxwfdvRo1o5Nxl/7jb8rgDWq2RGApdtlJ0BhAA1gXDgG+z0iB3x6KOP0q1bN9555x3+/PNPV6srFAo/ZMeOHezatQsAg8FryQm8rm8KhaISc+AAnD1b0uCzprBQK3fggFsO627D7whgEEI0M9vWDrB2fAbNMfoLKWWWlDIXzfG5oxAi2pUDCiGYNm0aL7zwAmFhthc0VigUlYOkJGjUSHLbbfF06lSXRYu8mq7P6/qmUCgqKQUFFomaAZI2xRA39EF0vR4nbuiDJG0yW8nDNPLnBrc2txp+UsqraD3bcUKIcCFEZ6AHsMhG8Z3AM0KIakKIIGAokC6lzHT1uM2bN+fTTz8lMvJ6Z1y6cT48EFDXS1HRSUqCgQMhLU0AOgoKGjB4sI6kJO8c31f6Zqct7thNwKCul6LCccpyNY6kTTEMnBNPamY4UgpSM8MZOCfe0vizUa8seGLljqFAFeAssAQYIqX8VQjRRQhxxazcSCAHzRcmA3gQLedVmdm6dSuPPfYYOp1OBXu4SH5+PnoPZgpXKMpLQgJkZ1tuy87WtnsRn+mbCb1eT35+vjt2FTAUFBR40yVAoSid9HSL0b6EJW3IzrO8R7PzDCQsaXN9Q2GhVq+cuP1JkFJmAT1tbN+E5hxt+v8cWqSb28jKyuKbb76hX79+1K5dm6ioKHfuvlJz6dIltfC5okKTlubadk/gS30zUbVqVS5dukR0tJo1dpbLly97PN2PQuESVkvOpp2z7apWYrsbOn1+t1avIx566CEeffRRRowYQXp6OtnZ2WqI3wFSSvLy8sjMzOT8+fPUqFHD101SKOwSE2P7WY6N9XJDfEyNGjU4f/48mZmZ5OXlKY1zgJSS7OxsMjMzURHTigpFcLDFv7E1s20WK7E9KKjch650Y99Tp06lZcuWJCYmMnz4cPKsrGqFJXq9nqpVqxIbG0tISIivm6PwJwoKri8nlJenCVn9+hATo2WcdzMTJggGDrSc7g0Lg/Hj3X6oCk1ISAixsbFkZWVx8uRJCkuLCAxwQkJCqFOnjhrxU7iGp/Wtfn3466/i6d7xvVMYOCfeYro3LLiA8b1TrtfR67V65aTSGX4NGzbk/fffZ8SIEXTv3p2///3vvm6SQlG5kNL+AuJ//aUlHW3WDFq31tacdAMff/wxzZo1IzGxJwkJ2vRubKxm9PX1yIRqxSYkJIR69epRr149XzdFoahceEvfYmK0fRnp20UL2khY0oa0c2HE1sxmfO+U4u0W9cqJ3xl+zjg1Dxs2jKioKO6//34vtEihCCBMCUft5Z4ybTt6FC5ehM6dy2387dmzh9GjR/P8888zd27PUg291NTUch1PoVAEKN7UN4NBMyDNUrr07XKqpKFnQq/XyrthtNHvfPycEXWDwcCzzz6LXq/n2rVrXmiVQhEgeDnhaGFhIQMHDiQ6Opp//vOfpZbPzs6ma9eu5TqmQqEIULydULl1a6hdWzPqHKHXa+Vaty7f8Yz4neF38eJF1qxZ41TZ5ORk4uLi2LJli4dbpVAEAD5IODp9+nSSk5OZOnWqU1H6n3zyCWneDPNVKBSVA18kVBZCGzVs1kwz7qwNQIPh+kifG2ZPig/rbxFhQgjZokUL9u/fT1Ap0S1XrlyhVatWREREsHv3bhW8oFCUhxMnNJ8UozCaEo5aOyMnDkq+Pl2h10OHDtC4scuH+/PPP7nhhhvo2rUrq1atKnU93rS0NFq0aGEa5d8lpYx3+aA+Jj4+XiYnJ/u6GQpF4OFlfSuBeTBJfr4WvWsnmEQIUS5987sRv5CQEA4dOsSMGTNKLRsREcGsWbM4ePAgEydO9ELrFIpKjJcTjtapU4dZs2Yxc+bMUo0+gFGjRnHt2jWeeOKJMh1PoVAEMD5MqAxoxl3jxtrI3l13ae+NG3skQ4Lfjfg1a9ZM/v7771SrVo2jR486lZupb9++/Oc//2HPnj20atXKfkEvp6dQKPyKDRsg8/qKY7pejyNlSYNMCEnRv7+6vqFWLU3IXHi+8vPzSx3RN2fTpk107dqV0NBQDh06RFxcnBrxs0bpm0JhHy/qW3kp74if3z3t1apV44EHHmDt2rW89dZbzJ07t9Q6U6ZM4fvvv+ff//4348aNK1nAB+kpFAq/w0bC0dTM8BLFSiQcNRggJcXp5ysjI4Nbb72VSZMm8dhjj5XarMLCQoYPHw7AG2+8QaNGjVw7r8qO0jeFonQ8rW8tWsAff2iGYW6uZigKoU0Xh4R4tRPmd1O9oBlyBoOB+fPns3PnzlLL16pVi71799o3+rZsue7UaR3NY9p29KhWzs9GSBUKt1G/voXz8fjeKYQFWzo220w4mp3t0vM1fPhw0tPTadGihVPNSkxMZO/evcTExDBq1Kgyn16lROmbQuEcntS3Q4fgu+80IzA9Hc6d09LBXLig/Z2eDrt3w4oVmhHp4efQLw2/Fi1aMGLECKSUDBs2jKKiolLrNGjQAIDDhw9z4sSJ6x94O3xbofBXrBKH9u1yisRByTSKvooQkkbRVy0dn0F7bi5edPr5+m7aNJYuXUpCQoJjtwwj586d4+233wZg8uTJhIXZXu8yYFH6plA4hyf1DTRjzlG5oiKvdcL8zsfP5ANz+fJlbrzxRs6cOcOCBQt4/vnnS62bk5NDXFwcN954Ixs2bEBXVKRZ2Fbh2w4zZ+v18MgjyidGEZiYpjTKuEyYo+cr68oVWr36KnViY9mxcyfBVlMvthg8eDBz5szh3nvv5YcffigOAimvD4yvcKuPX0GB0jeFwhU8qG8uYUrh0qaNzY8DLqrXRNWqVfnkk08ALZrvwoULpdYJDQ1l/Pjx/Pzzz8yaNUtzxDTDFL6dmhmOlILUzHAGzom3zN0DJeopFAGDswlHbVDa87Vy1y7OXnyIs2d+ISQkGINBc4GJi4OkpJL72717N4mJiRgMBj777DOnIn8DCqVvCoVreFDfTGVMeQGjX3iE6H4Pey5HoAP81vAD6N27N126dCEjI4N33nnHqTr9+vWjW7dujBo1ihPJyb4N31Yo/I3SEo46oLTnS697ihDDAs5kaNO1pkczNRUGDrQ0/oqKihg6dCjS6BN40003le+8KiO+Tk+hUPgbHtQ3a8Pw3OUQzl0J9UknzK8NPyEEM2bMQK/XM3PmTHbv3u1Unblz56LT6eg3YYKFf2DaOdv+QSW2O7FesEJRaRFCm4J45BEteWnduiWK2Mp4b/f5ygxjx++/k7CkDdfybadwyc6GhITr/y9YsIDt27dTr149xowZ45bTqnAUFGhJZbdsgfXr4Ycf4Pvvtb+3bNE+czQikJdn8a/SN4XCCdytb8bttgxDc7zZCfN7R442bdowfPhwJk+ezNChQ9m6dSs6nWN7NjY2lilTppCydi0FRUUEG8s7Hb7tQn4xhaLSYko4CpCRYTfjvak3WyM8l3NXQkvsJizkL7qOGUNe/psOD2daie3cuXO8+aZW9tNPPyUyMtJNJ1RBME+/IqXm9G0LU6qIpk2halU4c8Yyf5iVn57SN4XCBdykb6bny55haI63OmF+PeJnYuzYsdSrV4/t27ezYMECp+r069ePyRMnEmy2jJvT4dv167ul3QpFpcDJKUWEKPF8hRjyuJr7Km/26EFstJUBYkWNGtr76NGjOXfuHPfccw+9evVyzzlUJMzTrzjKWGBKFXHkCOzapX0PmZna+549mmFo5veo9E2hKAPl0Dfz56tEB8sGNnMEeoBKYfhFRkby6aefAlqgR6ZZ9m2HxMSw9dAhnp42jcKiIufCt431FAqFESenFLOuBFs8Xw1rXCbYMIQOjbeR8OijmmESYn/q8tIleO+9o8ybN4+goCBmzJhR+QI6rlzRRu7KGFVYTGGhNlpolrVB6ZtCUQbKqG/Wz5etjpclkgdvtpraNRuYcid+PdWblKT5/aSlQUxML+rXDyU9vT21atWkUSMYPx769nWwA4OBEzodizdtolVMDG/27EnfLqfsh1+bQqxVqgOF4jouZLw3PV9SSh6bNIlVu3fz5YsTCTIY6HtXOtRPJ2F2LKmpJQ+Tnw/jx4cjpeS1115zOsGzX2E1tWOeHqJGeC4IQdaV4DKnilD6plC4SBn0zRam7QlL2pCaGQZYd1oFq3fXB/Ze35SVpfnxuvmZ9NsRv6QkLdJVfGW/AAAgAElEQVQvNVXr1KalCdLTewBxgLCIBExK0lJC6HQlU0P0eeMNnrj7bt5dtoy9J0/aP6Ber4V5t27tydNSKPyPMmS8l1Jya9OmfNS3L61jY4ufr76jYjh50v7KYfn5dYmLi3M6it+fKREFeCWUc5dDbEYB2nI2dwmlbwqFbcqyoocd+nY5xcmZq+3qW4nRxEuXPLKah98afgkJWqSfJZZXMzsbXn7Z0kC0Tg0hdDpmLVtGdFQUT02bRo71FIvBcL0n3LmzWstSobCmDBnvdTodsdHDmbJ6pmasDHuIpNTrz1dsrL2DpTFjxoyAWKHD2ShAp/Pz6XQl01MofVMoHFOWFT1w3Bmz5+9nc7sHVvPwu5U7QkLiZX5+crnPv1EjMB/gW7NmDd27d2fa2LEMu+8+bcolKMirCycrFH6LkxnvC4uK6D1lCrH1XmPW6v8jO/e6IRIWBomJmnuGaUTfsnN3lY4d57F9+8ulNsdfV+5oUqelLCpKJu1cmFHjHBtiQki7U0+Noq9ycubq6xvq1oWGDTVndaVvCoXzuLiih3XkL2ijgiYDsbTPbWK2mkd59c3vnnYrP0snkNgST1NqCBN///vf+f7777nnnnvKlLVboQhoWrfW1qwsZV3YT1au5D/btlGz5t8tjD64nquvb9/rvrmaD69EylRCQsbx9NOfEBenPb+xsU748foZqRlhFMmSRpw9YmtmO5+fr7BQS09hSlGhUCicw0l9M+EombO5H6D18m4AcUMftL3km2nkr2XLcp+O3071OsdVDIZLNj+xNZV0//33o9frOXPmDOfOnfNw2xSKSkRpGe8NBnadPMnbS5fy5JNPkpVl27gx75D17Qv79l2kXr2GQGN69erFqFE17LptmCgsb0SsDymSzk+1mvyKnJ42Uvn5FIqy4eKKHs50xkz+fkX//qp4ZN5bSypWCsNPCG3qdsgQ7V0IaNiwkPDwERQUDCHY2hEzTBspsMW1a9e49dZbGTBgAP42Da5Q+BTrjPf160OtWlC/PlebN6fPvHnUrVuX2bNnExtr28Cx7pAlJCSQnp7Obbfdxk8/dSvh12u9oscff/zBDTfc4OYT8zUSISQ1I3KoWTW3hF+Rys+nUHgBB/pG9eoWRV3y4TPizSUV/W6q1xprX73r6Fm4sDPPPfccQUER1K49i9On9aVOD1WpUoVXXnmF119/nfnz59O/f38Ptl6hqISYMt6bTSkmJSZy9OhR1q9fT1RUFOPHl/Ths+6Q/fLLL8ycORODwUBiYiLt29s2Fs1HCUeNGsWZM2fcfUY+pVF0tqWvnhX2po1Ufj6FwgPY0DdOnNCSphtnG8b3TrHpw+co8tebSyr69Yifo5E7gGeeeYZ7772Xq1fncuedz1JUpBmJpfkEvfrqq9x3330MHz6c3377za1tVigCkQEDBrBjxw7uuusuQHsGExOvj9A3anQ9sAMgNzeX/v37I6Vk5MiRtG3b1m6kr/n2IUOGMG3aNM+ejAfRCctZBlfTRJimjSyMPpWfT6HwLGWM/DXHmy4bfhfVGxx8i8zP30lExHlmz65ZqhF37Ngx2rRpw7Vr11i1ahUPPvigU8c5c+YM7dq1K14KLjS05Bp8CoXCMampqeTm5tK8eXOX6o0dO5b33nuPZs2asW/fPqpUqWIz0tc8EticyhDVW2qSZiFKT+9gys+nUrUoFJ7FlchfvV6bJnawBjDYiPTV66FDB0STJuXSN78b8WvbVjB8+Ahyc+txzz2lT+k0bdqU999/H4DBgwdz+fJlp45Tr149Fi5cSPPmzcnNzS1XmxWKQCQ/P5/evXtzzz33uPQMHThwgAkTJgAwb948qlSpAjgeJVy4cCEvvvgiOTk5HjkXb1EjIs/+yB1oSzhFR2t+RTffDM2b2w2mUfn5FAov0rq11skqLSuIqTPWqZPFZm8uqej2ET8hRA1gPtANyARGSyn/ZafszcAU4GbgKjBBSjnV0f7j4+PlDz/8wKlTp2jbtq22nMmpU5rDY16etryKVW6qgoICbr/9dpKTkxk6dCgzZsxw4xkrFApbjB49mokTJ7J06VJ69erlVJ3CwkI6d+7M9u3bGTx4MLNmzSq1zoULF2jevDnNmjVj8+bNCCE8NuLncX1r2lQmT5xo+8PgYKhZExo0sMy9Z66BKj+fQuE7pIQDB7SRP7Ac/TMYtM+bNdOMRCFcHyWswHn8ZgB5QB2gPbBKCLFPSvmreSEhRDSwBhgBfAUEAw2dOUBUVBRR1atDSgrn9+whKiLC8sL99ZfmaGm8wAaDgfnz53PLLbcwc+ZMevXqRdeuXZ0+oePHjzNw4EDmzp1LY5UDS6EolTVr1jBx4kQGDBjgtNEHMHXqVLZv306DBg346KOPnKozZswYMjMzWbt2LcLzI1ue1TdH7c/LgzNntFxiZvpm09lcoVB4H1Pkb8uWznXGnM0P6OYlFd064ieECAfOA62llEeM2xYBp6WUb1qVnQDESCmfduUY8fHxMnnnTtiyhfdnzGDW2rUcnjKFqsbpIAus/FvGjBnDuHHjaNq0Kfv373d62afjx49z880307x5czZv3kyw1aLNCoXiOqdPn6Z9+/bF/rFVbD2bNjhy5Ajt2rUjJyeHlStX8o9//KPUOikpKXTo0IGBAwcyc+bM4u2eGPHzir61aSOTZ82Cc+e0HwxH+qz89xQK/8fVUULKr2/u9vFrDhSYRNHIPqCVjbKdgCwhxFYhxFkhxH+FEDbj9oQQA4UQyUKI5IyMDO0inT1LtzZtOHP+PO9//bXt1hQWapb0gQOAlhOsTZs2HDt2jATz5F+l0KRJExYsWMDOnTsZNWqU0/UUikCkZs2a9OnTh2XLljlt9BUWFtKvXz9ycnJ45plnnDL6AF5//XWqV6/OBx98UJ4mO4vn9e3SJahWTdOu0jrlVvqmUCj8EEf5Adu317a3aePWzp27R/y6AP+RUtY12zYA6CulvMuq7BGgNnA/kAL8E7hFStnZ0THib7lFJickFFvFL8yaxec/1adu9Zn8eaGq7Ug4vV67eAYDu3bt4rbbbqOoqIhNmzbRubPDw1nw8ssv89lnn/HVV1/x2GOPOV1PofArnPCbtUdeXl6ZRsSnTp3KK6+8Qt26dfn111+pUaOGU/WOHz/O0aNHeeCBByy2e2jEz+v6Blq0X6k5+kzr8Cq/PoWidMqhcRWB8uqbuw2/DsAWKWWY2bbXgLuklA9bld0H7JZSPm/8vyaas3R1KeVFe8eIb9NGJr/3XrEwzv4+miHzOgLXl4CyFwJt8oFJSEhgwoQJNGvWjL179zo95ZuXl0fXrl3R6/XFTuQKRaXB0ZSDKVLNasrBnO+++46RI0fy/fffu+QLe/ToUdq1a8e1a9dYvnw5PXr0KLVOfn4+BoPB7jPoIcPP6/rm0mLupvQuVapoKwlYB4EoFIFOOTWuolDRpnqPAAYhRDOzbe2AX22U3Q+YW53OWaD5+RZf1sTllkYflL7Mybvvvkvr1q05evQoo0ePduqwAMHBwSxfvpwffvjBdaOvoEDL7r1lC2zYoL2fOKFtVyh8jZTaPWmKMLN2NDZtO3pUK2fVYTx27BjPPvss1atXp74LS4MVFhby3HPPce3aNfr27euU0QfaFO8jjzzi7XV5va5vTi3jVHwE4yGuXdOCQPbsgRUrtMhBT+drVfqmqOiUU+MqE241/KSUV4FvgHFCiHAhRGegB7DIRvHPgf8TQrQXQgQB7wCbHfWGjQex+Lcsy5yEhISwcOFCDAYDn332GT/99JPDQ5pTt25dwsLCuHLlCrNmzSp9PV8pNeFdsUIT4vR0yMzU3r0pzAqFI4x+s6WmFbDhV5adnc1jjz2GTqfjP//5DyEhIU4fdvLkyWzdupV69eo5veLGvn37mDZtGg0bNkRfWs4sN1Kh9c0W3vghU/qm8BfKoXGVDacMPyHEbCGEFEKU6MoLIW4UQuQJIT4zbhoKVAHOAkuAIVLKX4UQXYQQV0z1pJTrgbeAVcayNwB9nGiMxb/2ljmpEZFH3NAH0fV6nLihD5K0oZ7F5zfffHNxgMfzzz/vdGJnEwsXLiw9J6DqYSj8gYKCErmkkjbFWD4/m8yShpru2YICpJQMHjyY/fv3s3jxYuLi4pw+7MGDB3n77bcBLVFzVFRUqXWKiop48cUXqVGjBuMdrdfoOSqEvsXWzHb8HZnjqR8ypW8Kf6EcGudwn346yu3siN8vxveONj6bDFwCxgBIKbOklD2llOFSylhTclMp5SYpZYR5RSnlLCllAylllJTyYSml/YXsTAQFWWTGHt87hbBgywsdpC/gUraB1MxwpBSkZoYz8ONmDB0KcXGg02nvTZq8TYcOHTh58iSvvvqqk5dCY8iQITz88MOMGDGCzZs32y6kehgKf+CU5WNn8iuzeH7mxJc0LE6dIjs7m+PHj/Pee+85vRwiaP6yTz/9NLm5ubzwwgtO1/3888/ZsmUL//znP50OAHEnnta3a4WF5JjphS19Cwsu4MGb00t8R8/PvJXoFx4p+w+Zqyh9U/gL5dC4Evh4lHv37t3l3oezht8247uF4SeE+AfQHXhXSnm+3K1xBquIQYtlTpDoRBpFRZfIL7ScAsq+pmP2bEhN1b6P1FQYMsRAr17fERISwrx58/jvf//rdDN0Oh2LFi2icePGPPHEE5w+fdqygCd6GAqFJ0hPd92vzOg3Gx4ezoYNG1xKjwTw/vvvs3v3buLi4vj0009Lr1BQQNGxY3z6wQd0bduW55o185vetSscTU2l0eDBjPvqKzIuXbK7jNPq3fVLfEf5hXrOXQ5x/YesLCh9U/gT5dA4C3w0yl1UVMSKFSu46667uOWWW8q9P2cNvyNAFmaGn9Fv5VPgADCn3C1xFiG0qBuzUb++XU5p61su+4ovh31Coaxus6r1d5CdDbNmxRSvC9q/f3/Onj3rdFOqVavGt99+y+XLlxk0aJDlh+7sYSgUniQvz+JfZ/zK0jIz+X/vvMO5c+cICgpCp3PeXfiXX35hwoQJCCH48ssviYyMtF/YrHet27ePLWPG8OWgQQhvBy94icaNGxPfti1jli0jdsgQBsyezW3NdpZYv9cZHz+nfsjKitI3hT9RBo0DLGIDAJ+Mcv/111/ceOON9OjRgxMnTjBp0qRy79MptZZaBMM2IF5cD2d9GS2h6StSSq+G1jlaDLnPHXcQGvSn07tKS4NXXnmFu+++m7NnzzJw4MDSAzbMaNWqFd9++y3Tp0+3/MBdPQxF4OItHxKrUXRHfmUA2bm5/N/HH/O/HTvIyMhwuOukJEv3igULtATNRUVFvP7663Tp0sV+ZbPedeqff5Kfm0v18HAa1aqlfV4JfciqVq3Kqo0bObhoEc/efTdJmzeTarzGV3JyKCwqAux/R9aU+kNWVpS+KcqLN33kXNS4YoKCrv/txVHuY8eO8c033wBQu3ZtunTpwtKlSzl27JjLbmm2cCWqdxtQDbhRCFEbLUptuZTyx3K3wlWE0JYpMo38mRmAQgg+6HMAIbJLVLFFbKw2bfvFF18QGRnJd999x/z5811qzv33309cXBxFRUVs22acFXdXD0MReHjbh6R+/VL9ZsOCCxjfOwUpJQPmzGHPyZMkTZlCixYt7O42KQkGDrR0rxg4UPD777fStm1bxo0b57hdxt51bk4OD4wfzxP2poQrmw+ZELTs25fZn3zCH4mJ3NOuHQDvLF3KDS+9xMcrVjC6584S35EtHP6QlQelb4qy4gsfORc0rhi9XqtnwsOj3EVFRaxZs4aHH36YZs2a0b9/f3JzcxFCsGDBAnr16oXBTTk5XTH8zAM8JgAhwGtuaUVZcLDMyWtjmvHl58E0aqQVa9QIBg8G6zzNYWFgCgyMjY1l1qxZgLZCx5EjR3CVyZMn07lzZ9asWeOeHoYi8PCFD0mMpVDZ8yvr2+UUH377Lf/avJkPevfmoeeec7jbhATNncKy+SHAhyQlJTlO+2LWu/7w2285nJ7OjfVfDxwfMqO+1ejbF3HzzVCvHve0bk1sdDRvLF7MKwu7Ed90InWrX0QISc2IHIINlveKwx+y8o62KH1TlAVfRYK7oHF263lwlPvHH3+kfv2RdO/egpUrvyMy8hzjxh1zKTWWKzi9cocQIhJtgfItQGfgY+uFyb1BfHy8TE5OdqpsTk4OU6dO5cUXXyQiIoKkJO3HKC1NG+kbPx769rWs89RTT5GUlMQtt9zC1q1bXVp+6sqVK9xxxx2cOHGCbV99RcvLl13LwG+1wogiAElJKTGdYBe9Xhv1bmMjma8HjnslJ4fWr71G5xYtWDx9OqJtW4e71Ons6bZEylISoJ84AXv28FtqKu1ef534pu+w72SC08+PJ1bu8AYO9c34HaWcOMGMNWtYvGkTT9x+O58PHQrA5+vr8d5XHewv7abXw8MPw6FD5V+5wPj9KH1TuISv9M0dx96wQRuZNKLr9bhNHRNCUvTvr65vqFUL7rrLooyUku3bt1OtWjVatmzJhx+m8fbbtSgqur62eVgYJCaWtFG0Y3hp5Q4p5SXgINAFLS+VT5JoucLevXt58803GTNmDKBdwJMnoahIe7d1QWfMmEGjRo3YtWtXcT1niYiIYMWKFVSpUoWHBg/mnFluwDL1MBSBhS8jJR34zZqICA1lx0cfMX/MGIQTYhwb69p2C9LTKcrPZ2BiIhGhoZzKfFX5kBm/ozaNGzN74EBOz5nDhN69Adh1/DgvL+zGgzc/yq6JHxcHgRSj18MNN8D27e4ZbXHHCIoisPB1JLgTGgdon9eurZU3xw2j3BcuXGDGjBm0b9+e22+/nY8//hiAOXNiLYw+0GZLXEyW4DSurtyxw/g+WkrpWsZjH9CpUycGDRrElClTSE5OLuFonpRUsk61atVYvHgxOp2Ojz76iB9/dM2FMTY2lm+//ZbTp0/z/2bPRppFOxZHH5tF5xVj6mGodTUDF19GSjrwmz2dlUXCv/9NAVA7Pp7Qe+5xah3L8eNLuldUqVLEhAlOLHeYl0fGpUtczM7mk6ef5vT5CJvFAsqHzOo7qhYRQT1j0uuI0FDaxr7JrO8TuXnUSEL6/I1+My9wLS/v+g8ZuC8i0WCwn11B6ZvCFr6OBHegcYB2b5ru086dS2pcOf0EX331VerVq8ewYcPQ6/XMnj2bqVOnAtospC3sbS8vTht+xvQtdwHJwELPNMf9fPTRR9StW5dHH/2KgQOllaO5bePvjjvu4N1330XK/0e3bs3R6aRdQ9EWt99+O4sXL+adiRMRdeqUvYehCCx8HSlpw2/2cng4D02axGdr1nC8VSvtcyfXqe7bF95443cgFSiiVq1s5s7V2RxpL0FwMHWqVyd54kSev/tu5UNmwvw7Mvr+UaUKycc7sefEW0AjQEdeQQM+/6kPS7c2hmbNON2wIUVHjrh3tKW8IyiKwMKX+mbyad26VZuurVVLG32uW7c4NoD27bXnyp7GuTjK/ce5c0xZuZKiBg0AiIyM5LnnniM5OZndu3czaNAgqlatCpRzdqQMuNL9Ggk0BvpKV/Kd+Jhq1aoxc+ZMevZsB1h+maahVFs/RE2avINOl0dRUShw3VAE2+Wtefzxx7U/pOTAt9/S2iSO5r1tg0GzQp3xqVFUfipKpKTBAI0bUxATw//r0YOU48dZuXIlzW+6yaXdXLx4kYULuwEnGDFihHOJmtH8X2b9+CN9GzemWqj2/I3vnWLTh8xhFF5lxvgdmfzlEl6UZOdZ60c47628neemQ7cbb+TK+fM807Urz9x5Jzt+72hxPU2jLYDlSN2pU/Z98kwjKAcO2PYZVPqmMMcX+ial/fvTFZ9WuD7KbTZd3bfLKYvn5WpODkmbdrJw40bWpWhZEP42eDAdO3Zk7Nixdnc9frxmX5gHw5kHn7obhyN+QogaQojeQogPgfeBT6WU2xzVqYj06NEDrSdcktRU7fs0+kcX8847umKjz0RZ5txXrV5Nm8ce4/OsrBLRx6X2MBSBRQWKlJRSMnz4cFavXs2MGTP4+9//7nL9QYMGceLECTp06MCHH37odN1ly5bx4pgxLNq4sXib8iFzTNop2/qRmiYICoIG1RbRskEDxn/7Lc1ffpl+s2LcM9riILuC0jeFBd7WN09EEDsY5U5JS6PuwIE8NW0aR86c4Z1nn+X3o0fp2NHWSreW9O2rBXKYZyKxF9jhDhxG9QohegP/Qgvm+BJ40+vJmq1wJarXnLg4zchzxJAhMHOm9re9iEQhtOAQZ8nLy+Ohhx5i/fr1rFy50uUfUEUAUYEiJX///XfatWvHsGHD+Oijj1yuP2fOHAYPHkxERAS7du2iefPmTtXLzMzkpptuIi4ujq1z5mA4caJMUXiVMqrXAaXrm2RIt99JeHQ9/9q8mTcWL8RWv9+ZiESFokx4W988FUEsJTIlhV1r1rBk82bqVavGyEceoaCwkFcXLeKxTp3o8sgj6Nq29ViHx6NRvVLKJVJKIaWsI6V83ddGX3nQHM0dW/SJidf/tje3LqVr/n7BwcF89dVXtGnThscff5xdu3Y5V1ERePg4UtI8+Om++25g3LjfXRqpM7F3715efvllABITE502+gBeeuklLly4wPz58zG0b698yJzEViCNJYLEdU1pUKMGrz/yCLHR12yWkhJq97+PLzbU0zZUdp9Jhffwpr55KIL44MGDvDtmDDc+/ji3jhrFtDVrOH7lCtSqhSEmhs+mT+fO0aPRtWtXoUe5AybEyjRkOmDAWa5dq4W1vx9YdgxszblrCJf9/SIjI1m9ejW33347Dz74IIcOHSLKGI2nUBTjhA9JCUzLl5UT0yobpvs9NRXefbcedes6vsetc2O+8841PvroSXJzcxk4cCC9jelGnGH58uUsXbqUcePG0cbU81Y+ZE5h+o4SEuyP/BUWXb8+E2z4TGoIMi5F8fysDkih4/mR9TzTYEXg4U19sxNB7KpPq5SSSZPOMH16fdLSoEqVWmRnH+Oee2J44403ePTRR6lRo0bZ2uhDnE7gXFEo61SIidOnT9OwYR1s2bx6vaXBb/pRsyekjRpp+QCd5dChQ2zatIkBAwa41GZFAGHyS3Em7QZow3NClNv4sTdV6OgetzYWAfT6HAoL+9G27a9s27aNKlWq2K5sg9TUVD7++GMmT55MkPVIU0GBJsrp6Zqzd1CQ5kcWE2MzRUigTfWaYzDYvnX0uiIKln5d/H/SphgSlrQhNTMMWx3hRtFXOXkmhJ6PP06VKlV49NFH6d69OxERtlPrKBSl4i1927LFwj81buiDpGaGlyjWKPoqJ2euvr6hfn3k3/7Gnj17+Oabb1iwIIczZ94DrtetUqXI+ewEHqK8+hZwhh/APfccZMOGlliLnbmPnznu8vczZ9euXTRp0kSN/ClK4igSzR6m6U5b+aecQKezvZqGo3vcnrEoRBqHDuW4NMUrpUS4ccQukA2/oUPBuPqkBUOezGRmr59L3E+OViAoLIShQ4fy9ddfk5GRQUhICPfffz9DhgzhwQcfLFc7FQGKN/StjKts7D5/nv8bP560tDT0ej0Gwx/k5tYtUc/VQR9347WVOyoTP/7YkpiYlUABINHr7Rt9YN/fr0GDsmUUv3TpEvfffz8PPfQQV69eLdM+FJUY80hJZ6c6nEm6a4fjx4+j0522+ZmjPFL2k4vGuGT0LV++nO7du3Pu3Dmn6yjsM3Ompmcm18hifVta06bPpN3oyhgQQjBr1izOnDnDxo0bGTx4MAcOHODQoUMAnD9/ngkTJrB//378bRBB4SO8oW9ORhDXjDhP7ylTmPX99wA0iY2lQ4cOLFiwgD///JO8vJJGH3gusbK3CEjDTwjB9u3xnDr1J1IKCgrsG31gz3H6KhERH1JQhuVkIiMjmTt3Ltu2baNnz57k5OS4vA9FgJCRYfGvJ5Y4Wr9+PaGh4wgNtRzaKy2PlP2ko86P3GVkZDBo0CDOnj1bnMxUUX5mztRuAym5rm92Vi6wuQJBmGS82Qorer2erl27MmXKFI4fP85LL70EwLZt20hISKBdu3Y0atSIwYMH891335Fd0jlaoSiJp/TNiVU24CqZl4fx44EDXMnJAb2e6jfeyPLly3n++eeJjo72emJlbxGQhh9AvXr1aNiwIUVFRezZs8dhWescOw0aFBAZ+TqHDr3LqFGjynT8xx57jAULFrBu3Tp69epFfmVeakpRNjy8xFFSkhahPnBgf6KiZvH88zqX8kjZipQPC5NOJx2VUjJ06FAuXLjAwoULCbbqpSs8gI28e30fzSHxzWM0qp+vRVc2gsREYfe7F0IU+2B2796d06dPM2/ePOLj40lKSqJnz56cPq2NIKekpLBnzx6KyuoTo6i8eFLfjJHAs9bWpNYL9/LUtI5UCS6gZtVcoIgg/R/0jE9k6wetOJOYyOuPPGJRz4StQR9PJlb2GlJKv3rdcsst0p289957MiQkRKakpLhUb/PmzdJgMEhALlmypMzHnz59ugTktGnTyrwPRSVl82Yply0rfjWKviK1MRzLV6PoKxbl5ObNpe56zpzLUqe7ZrGfsDApFy92rYnduy+ScEJCoWzQIN+l+v/6178kID/88EPXDuoEQLKsAHrl6ismJkZeuXLF7dfDW+Tm5spNmzYV/9+nTx8JyOjoaNmrVy85d+5cefz4cR+2UFFh8JC+JScny5EjR8rYeq9JsNxnWHC+/GLoFsv9LVsm5ddfS7l/v839LV4sZaNGUgqhvbuqke6kqKhIbt68udz6FpDBHeacPXuW1q1b06BBA7Zv3+7SqMOMGTMYNmwYYWFh/PLLL7Rt27ZMbVi1ahUPPPAABrWAecXGPLI0L0/zI3EQWVpuyuigXFrS3QsXLlCnzjXy8kqm6nDFaXnJkiX06dMHg8HATz/9ROfOnZ2rCBQVFdGuXTuqVq3Kzz//7PZ732T9XpEAACAASURBVF+DO4QQMioqikGDBvHSSy9R38+XoDtz5gw//PAD69atY926dZw5c4aWLVty8OBBAH788UeaNm1KXFycbxuq8Et9u3r1Klu3bmXjxo0MHz6c2rVrM2XKFEaNGoUQqbYDM6wjecsZGOcNCgoK+Oabb5g0aRI7duwAUFG9JXDxBv7uu+/o2bMnb731FuNdGMOVUvLcc8/x5Zdf0rhxY3bu3EnNmjVdPaVizpw5w8yZMxk7diz60pLWKryHoyg0V9d7dIVypCTAjhF28eJFunXrxo4dv2DP02Px4tLzU+7evZs77riDa9euMW3aNIYNG1bq6Vhz/vx5rly5QowHllnzV8MvIiJCmgK+DAYDvXr14pVXXiE+3u9OpQRSSn777TcyMjK48847KSwspGbNmly8eJGYmBi6dOlCly5duO+++7jhhht83dzAwc/0LU2nY9q2bWzatIldu3ZRUFCAXq8vHkC5dOkSer2eqlXDsW3eSOSyr/wiD+iFCxeYO3cu06ZN45RxirtGjRpkZWWpqN5ipNSWaVmxQlsaJj1d61Gkp2v/r1ihfW51N/To0YN+/foxceJEtm7d6vThhBDMnj2b+Ph4Tpw4wZNPPlmmYA8TX3/9NR988AH9+vWj0JkQd4XnkR5Y79FZnHBQDgsuYHzvlOsb9Hqtng1M0eR79uyhVi37AUUDBzpemebs2bP07NmTa9eu0a9fP1588UXnzsfIL7/8Ql5eHlFRUR4x+vyZFi1asHXrVh5//HGKiopISkri1ltv5Y477mDZsmV+7QsshOCmm27izjvvBECn07Fp0yamTZtGp06dWL9+PUOGDGH+/PkAZGdn895777F27VrOnz/vy6ZXXiq4voUG5XNPmyU8N2MGy7ZuBb2e/Jo1+eyzzzAYDIwcOZI1a9Zw/vx5HnjgAUALngwPD7cbgCGApANtK/Ra0r/99htDhw6lQYMGvPHGG5w6dYpmzZoxc+bMYgOwPFSeET/TDVxaYkg7w7qXL1/m4Ycf5oMPPuCOO+5wqU1//PEH8fHx/PXXXwwfPpypU6e6VN+c999/n3fffZc+ffqwcOFCNf3razy13qMzFBRonRWrZYcSlrQh7VwYsTWzGd87xTLzvF6viZmN+6agoIB+/frxxBNPcOnSw3ZWptGwN+Wbl5fHfffdx6ZNm+jUqRM//fQTISEhTp/SwYMHueWWWxgyZAiffvqp0/VcxV9H/Mz17eTJk0yfPp158+Zx8eJFABo0aMDgwYMZMGAAderU8WVT3Y6UkmPHjhEaGkrDhg1JTk6mY8eOmH6jmjVrRseOHRk5ciTt27f3cWsrCRVE34qKiriYnc3qPS15a0kb0jKrIDiFZDSwhFqRkYzu2ZMRPXogH36Y3MJCQkNDHe4+KQmeftq2rerrPHy2KCwsZOXKlUyfPp1169YVb7/33nsZMWIE3bt3R6fTxupUAmcTvryBgS1btnD33XeTn5/P3Llz6d+/f4ky1stbjR9ve0rtww8/5K233uLJJ59k8eLFJVcxUHgHNxteZcIN93VGRgb5+fkl/MWSkuCpp2zvSghYtMj6fpVs3DiIuXPnUr9+fZKTk6lXz/klvXJzc7nttttIT09n//791K1rO0eWO6gMhp+JK1eusGjRIj777LPi/HlBQUE8/vjjDB06lM6dO7s1+XVZcVbfXOHSpUvs3LmTHTt2FL+WLVtG586d+eabb0hISKBDhw506NCBtm3b0q5dO4/eV5UKH+rb/v372bVrF3vXrWPPnj3sPXmSO1q0YPXo0QD0mTqV2tWqcWvTptzevDmNa9dGmJZ8c+F3295jYUpM74l71lXOnj3L/PnzmTNnDqnGjPhhYWE888wzDBs2jFatWtlovzL83HoD5+fn884773DzzTfz5JNPutS2BQsW8MILL2AwGPjhhx+4y8zB3tbyVmFh9lNmTJo0iS+++IKff/5Zre7hK06c0FwEjPeV9XqPoE21WiwsrtdraTLM1nssF+UcyT59+jT33Xcf4eHh7Nixo7jHaMLe6hs1a8K1a5b3a1BQPvn5zxIa+i0///wzt956q0unMnLkSCZNmsR///tfHnroIZfqukplMvxMSClZv34906dPZ/nyMGA8EEtQ0J/06XOAqVNvo1q1al5trwlX9a08SKmt8rJ+/XqmTp3Knj17LKa/Tpw4QVxcHD/99BOHDx/mpptuomXLlkRHR7u3If6Oh/UtNzeX33//nYMHD/Lrr79y+fJlJk2aBMBdd93Fxo0bCQ8Pp13jxnRo2JAuLVrQ629/s72zMgZgOFqKcvx4792z1kgp2bhxI3PmzOHrr78uduG44YYbePHFF3nuueeoXr263frK8AO33sD5+fl06dKFw4cPs2/fPmJdzNRo+nGrUaMGO3bsoGnTpkDZ1kLNyckhNDSU3Nxc8vPz1RqZ3sYDwRVlwpHztQMH5RMnTnDvvfeSmZnJypUr6dq1a4ld2/vBrlIFbC+kcZKlS7fTq1cvl05h3bp13H///QwdOpQZM2a4VLcsVEbDz0RSEgwYUMS1a+ZG/FWCgobx9NM6BgwYwG233ebVUcCy6Js7ycrKYv/+/aSkpPDiiy+i0+kYPHgwc+bMKS4THR1Nq1atWL9+PTqdjn379gHQtGnTwNRWN+hbYWEhp06d4tixYxw7dowBAwYghOC1115jypQpxfkbhRC0bNmSlJQUdDode/fuJTw8nKZNm6ITokz65gyOOiQJCd6/ZzMyMli0aBGJiYkcPnwY0HxdH3roIYYMGUK3bt1KdM5toQw/cPsP9LFjx2jfvj0dOnRgw4YNLkXYFhYW0qNHD1atWlXsqB0VFVWu9X579erFyZMnWbVqleq1ehMPpVMpM+bR6vn5EBRkN1o9JSWFBx54gJycHNauXetwdM7WdIc93xiwvaZvaaSkpDB27FgWLVpEWMllcNxOZTb87BlZcBLQOrKtW7emf//+PPXUU+XKNOAsnljPvLwUFRVx6tQpDh48yMGDBzl8+DCXLl1i6dKlgJZ8es2aNQDUrl2bxo0bc9tttxX7aO/cuZOQkBAaNmxIVFRUhZhOdyvO6huSDWPHcSozkx633krV2Fi+OHmS8ePHk5qaahFw9Oeff1KnTh2++eYb9uzZQ4sWLbjpppto0aIFVapUcdweF/TNFexN53rrni0sLGT9+vXMmzePb7/9tvh61atXj/79+9O/f3+XB5iU4Qce+YFevHgxTz/9NGPHjmXMmDE2y9i7oS5dusQdd9xBSkoKd999N2vWrKF58+Ay9y6+++47evXqRePGjVm7dq3LN4mijFSUET8XkVJy99138/vvv7N27VqbPiKlYc+4iI2VpKY6/wNompbzNpXZ8LP/gyUZOXIUX3zxBRnGpbCCg4Pp0aMHzz//PPfff79LwWKu+D/5esSvLBw4cICDBw9y7Ngxjh8/zsmTJ6lduzZJxpD2Nm3+P3tnHt5E1f3x7033Ugp0AVpoC4Uiq7K+8KqAgLIJKItsRRAUBAXR/lA2UVCKoIIKiMCrLFJkExCQVWVHRGRfBAq0hS4UutDSvUnO749p0uydSSZpWu7nefJAk8nkZjL55sy953xPC1wu6Q3r6emJ4OBg9O7dG0uWLAEALF++HAqFAoGBgQgMDIS/vz9q165dIVJzHj16hBubNyPt9m3cz87Gg+xszNnyBbLza5rYOh6aC4p/5s9Hm2efxa+ZmVi3bh3Cw8PRoEEDhIeHIyIiAnXq1BE1Y+UM2PucvX37NtauXYs1a9bgTklzX4VCgZ49e+KNN95Anz59rM7f54EfYLcf6JEjR2Lr1q2Ii4vDb7/V1BPB3r2BtWvN5wfcvXsX7du3R0pKCkaOHInu3ddg3DhmdT7B0aNH0bdvX/j6+mLv3r1o3rx52U/i2IYz5PhJRBNopaSkoKioCGFhYVbtR1hOJOTnlwZt3t5ksZWXKb7++mtcvHgR3333naTqX1upzIFfWT9YRUVF2LlzJ1atWoX9+/drl9uCg4MxYsQIjBw50uTFgG6g5+cHZGcLEy8aLOmVI3P8HMU///yDuLg4JCUlITExESkpKWjcuDFmzZoFQPBTM7SZGTFiBNatWwcAaNSoEdzc3ODr64uqVavCx8cH/fr1w2uvvQaVSoWPP/4Ynp6e8PDwgIeHB9zc3NC2bVu0a9cOBQUF+Pnnn7VBFBFBrVajdevWaNasGR4+fIiYmBgUFRWhsLAQhYWFyM/PR//+/dGhQwfcuHEDUVFRePToEbKzs5Gdna31hBswYAB+++03dO/eXW/sLmwEXF1+QKGytImBm0sBRnfZgEEdbiM0IAD1a9eG+3/+U276Jif2OGezs7OxdetWrF27FkeOHNHeX69ePYwePRqjR4+WxcLKZn2zpe1HedxMtmy7fVtouVLSfiVm0knydi82atUSM+mkfouWMloHZWdn04ULFygmRmhnpbs/xoxbywBELi6lrV0+/fQWeQvNTOnjjz+2ufXL+fPnKSgoiMLDw6moqEjakznSKS7WO68051ZYQA4xpqawgBz9c0pzXhUXl8twly5dSv3796diGV6/uLiYWrX6QtuOrW5dpeTz9cyZM+Tm5kYvvfQSqdVqm8ckBVTQlm1iWlKa0iNz7fbu3r1L0dHRFBERQQC0t9atW9NXX31FKSkpZvdp6ubvb17DnKm1lSPIz8+nO3fu0JkzZ2jfvn30008/0dGjR4mISKVS0ejRo2ngwIH0wgsvUPv27alZs2YUHR1NRMJvi0Kh0PtMANCsWbOIiCglJcXoMQD0+eefExFRbGys0WMeHh60cuVKIiK6du0atW7dmjp16kR9+/alyMhIevvtt+n06dNERPTgwQPasW0bHY+Oputff00Zq1aRetMmp9Y3eyDHOVtUVER79uyh4cOHk5eXl/bz8PLyohEjRtAff/xBKpVK1nHbqm+yz/gxxvwA/ACgO4A0ANOJ6CcL27sDuACgKhHVLWv/9q7qNYX5nBrLeHsD48efwddf/wdqtdqszYsU7ty5g5SUFLRv396m/QBwfIueikg52wSJQa1WY/r06fj888/Rt29fbNq0qex8GgsQEcaPH4+VK1fCz88Pf/75J5544glJ+8jKykKbNm1QWFiI8+fPOyTPTBd7zfiVi76ZQKoNBRHh5MmTWLNmDTZv3qz1BVQoFOjWrRvOnt2G9HTpBQ4VfVavPCEiKJVK7YydUqmEl5cXfH19oVQqERcXB93fZxcXF/j7+6N69epQqVTIyMiAu7s7PDw84O7ubt0SawXQN2dE833asGEDNm3apE2tAIBOnTph5MiReOWVV+Dr62uX13e6pV7G2AYIHUFeB9ASwG4ATxPRFTPbzwTQA0C4TcJoxxNYobAuoR0Qll+mTVuOCRMmwMXFBTt37kTv3r2t2pchc+fORXFxMWbPni0+j0qpFH4trl0DcnOFTFbdc8CeLXoqImSbnYq9yc3NxciRI7Ft2zZMmDBB62hvC3PnzsWsWbPg6emJP/74A0+bs1gwAxFhyJAh2LZtG44cOSKph69c2DHwKx99k5GCggL8+uuviImJwZ49e0qSzVWwtpGTM+fxccrAyfXNmSAinD9/Hps2bcKmTZsQr3PSN27cGJGRkRgxYoRD+k47VeDHGKsCIBNAcyK6UXLfOgBJRDTNxPb1AewBEAXgfzYJox1P4LAwwp07popFyu5go6kQ+vDDDxEdHQ1vb28cPHjQ5hk7IsLrr7+O1atXY8iQIVi9erXlWR4qsQS5cUNcyRL/opeiOXZ2sBuwlRdffBF79+7FokWLMHnyZJsLKTRelIwxbN26Ff3795e8j9u3b6NVq1aYMWMGpk6datN4rMUegV+56pudyMjIwM8//4x33ulnsqG9GMqzcpcjA06sb+WNJtj7+eefsWXLFsRqjhGELjpDhw7F8OHD0apVK4cWsTlb4NcKwAki8ta5bwqAzkTU18T2v0JYNskEEGNOGBlj4wCMA4DQ0NA2CebWXe10AptOdAdGjQL27BEm0BQK0/Gm5mqYiDBmzBisWbMGfn5+OH78OJo0aSJ6DKYgInzxxReYNm0a2rRpg+3bt6NuXROHUGxQbAif2tfHTnYDUjBc4hs9OhZt2lyXxRD5l19+wcCBA6FWq7F06VLJPXh1SUxMRHBwcLlV+Nkp8CtffbMjpr0BCwFkA/CHl1caFApf5OYat8niM36VBCfUt/LopKFWq/H3339j27Zt2LZtG27duqV9rGbNmhg0aBCGDBmCZ599tuLqmy0JgoY3AB0B3DO4byyAwya27Q9gb8n/nwOQKOY1xCQ/U3GxULhx/DjRoUPCv7dv25SUGhNDVLt2AQEq8vS8RzExaqPHy0q4Lioqoj59+hAAqlu3LiUkJFg9Hl127NhBVatWpbp161Jubq7xBhcvGhUpVIRiBY4+UpL6zT3fXCLzoUOHyMPDgwDQRx99ZNX4MjIyaMWKFQ4v5DAF7FDc4TT6Zid0z486dYpp+PBfqUOHDjoFBMMIyLH6/CsvHreik4qKPfWtLAoLC2nfvn00fvx4CgoK0iuaqVWrFk2YMIH++OMPUiqV1rw12bFV3+QWxlYA8gzu+z8AuwzuqwIgFkAEVSBhJCL64YcfaMOGDSYfE3Pi5eXl0bPPPksA6IknnqDU1FRZxnXlyhVav3698QMWKlMBNTGoba5+5jiG0FD9z0pzCwsr+7mWRPWff/4hX19fAkBvvfWWVYGbSqWiPn36kJubG/3777/S35zM2Cnwq/T6Zoo7d+7Q4sWL6bnnniNguLbSG4ijevVm0OzZs+mff/6RvXLRWnR12N+fyN3d+mCC4zjCwoy1TQ59M8f9+/dp7dq1NGjQIPLx8dEL9kJCQmjy5Ml0+PBhpwn2dHG2wK8KgCKN4JXc9yOA+QbbtQRQDOBeyS0DQnbxPQD1LL2G7MKoOzt48KCk2cG8vDyLj5sLBDMzMykkZCrZYpVhia1bt9Lw4cMpJydHlNWN0RctIEd/1u/4cfkGx7GKhISEkh9b48+LsbKfb05Ug4IKyd/fnwDQ4MGDrRa5efPmEQBasmSJVc+XGzsFfhVP32Tm/v37tHr1anrppZdKrCuGaXVMobhDnTuvoC1bttDKlTnlMssm1pZGTDDBcSzmLNJs0Tfdz1mlUtHff/9Nc+bMofbt2xNjTC/Ye/LJJ2nWrFl05swZp1i1sIRTBX7CeLARwIYSkXwGQBaAZgbbuAKorXMbACC55P8ulvYvmzCq1aVLoIbLoJr7Ll4kKioyGRhu2biRgoODKS4uzuTuLV2BxMQQeXnp/4h7eallE8dFixaRQqGgpk2b0uUff9R7b8JMn2VRZEytfzwOHZJnYOWFDcG9M5Cfn09169YlxhJMfl7+/qXbmrvYMCeqQjAJevHFF6mwsNCq8e3fv58UCgUNHTrUaQTTHoEfVSR9cwCrVhWSh4fhRWQOAUvIeElYPn2zhLkAwJpgguNYzH12uvpGZFrjzAeNalq7di1FRkZSYGCgXqDn4eFBPXr0oKVLl1J8fHw5vGPrsVXf7OXjtwrACwDSAUwjop8YYx0h5LwYmUUxxp6DheRnXWSpeiMSX+zAmHHlhosLYpOT0W7GDDSIiMDx48eNKmotuesDph9zcSGo1UyWpNY//vgDkZGRyH74EN+MGoU3unUDY8xsOzu9MTpZCzKrITJf7FMBbGuUSqXWmuXnn39GQsIzmDEjCEVF+tu5uQGrVwv/l9qQHIhHly5jsHv3bqu8/3Jzc1G/fn3UqlULf/31F6pUMe6YUx7Y2cfPufXNQZj3N1VCiH31cXfPho8PQ2amD0JCgHnzpHWBEYO5dnaG8IIU52P9emDMGJjVt8hI8902vLyA9HRTe42Hpt0cAISFhaFXr17o1asXunXr5jR6JRWnqup1BLIIoxTPPwv8eu4c+n72GUaNGoXVq1frlXNbagANlC1Ochij3rt3D6/27Yvf//kHR2bPRqemTc22s9O+rpO1ILMascG9k9rWXL9+HSNGjMB7772H4cOHa+8PCDAtcJYuKMLChAsJQ8EEctGgwec4d24KqlatavVYDxw4gPDwcDRs2NDqfchNRW3ZFhgYSO+//z7GjRuH6tWrl/dwLCI2yCqFAJR+x1xcCvHaaycwbVooGjRoIIsdhhizfW467bxY0rf4ePOfr5dXLgoLFVCrdS9ec+HmNhEvvHAfPXr0wAsvvIDGjRuXS+9wOSAi/P7771i0aBH27dtnk75VjG7KcqJUGgV964+FoN5bvaEYMgj13uqN9cfE9dLr06oVPh48GGvXrsXSpUv1HgsNNf0cIkEwyyIvT5ilsYXatWtj/8aN+GXqVHRq2hQA8H6/v+DtrtTbjpXMfocF5OoHfRpk6C3ocC5fFjejq1IJ25U0Yy9viAjLly9Hixaf4ezZbRgxYhjq1ROudAEgI8P08+7cEW6mSEgAXn1VuCquXl0FQA0gHvXrz8eZM1FWBX1Egr8VAHTv3t2pgr6KTGFhIaZOnYqQkBBMnjxZz0rCFtavF340FQronU+2YE7jzKP/g6tSeeCHH8IRERGBOnXqYMiQIVi6dCnOnTsHpVJpZh+WiY4WAjtd3NwAf3/hui4sjAd9zoC589GSvgn/mr7SyM/3hlqdA+ABADWqVcvEhx/eQU7OCuzevRvvvPMOmjRpUiGDvsLCQqxatQpPPfUUunfvrtVdm7Blnbg8bjbnwFjR19eS5Ylq40bq164dffThh2VWk0m9lZWHIqp8Xaeq99+vviJvDw964cn5FOL/yLyFi26u48WLth3v8qCC9djVcPfuXerZsycBw0ihyDeZH2opiVlMfhNjuQQMo9atW1NGRobVY124cCExxujEiRNyvX1ZQQXu1Xv27FkaMWIEubm5EWOMNm3aZNOxKKviUYoNhtwaB6iMcq8AkI+PDzVtGk3VqmUQY2pJBXCPq31LRXnfls5Hcxrm7X2f6tevT0IhkfnzSc5c+fJG03M9IyODvL29qUWLFrR69WoqKCiwWd/KXeik3mwO/I4fF1XsoKlsFRMYKjdvppivUo1OZjc3QRzNnaQuLsKX1MXF9OMhIebtESSVr5cUseSuW0eTe/cmxhiF16pFh2fPNh3waQKhY8eEIpiKhhXBvTPY1mzdupW8vb2pRo0ss8FdWUVDYioa3dySKC0tzepxaoo5Bg4c6DQWHoZU5MBPQ1JSEs2aNUv7We3Zs4dWrFghVOtLwNLFghQdMbVtWRqneR1z24SFEanVarp69SqtWLGCRowYQeHh4WTKM5CxXOrQYTF98cUXdOjQIXr48KGk41CZsdUDz5FYchh4880j5OpaaPBYTsn5APLwGG10UWzqnKqoqNVqOnnyJA0fPpzat2+vLZa7ceOGXuEcD/ykcvCgXoDDmGlvNE1la1mBoTaArGX6ZNRcgZl+DWFIpn+wc6hJk08pOzvb5NuQ5HmkVgtBXEkwdGT2bAqvVYsA0PgXXiD1pk2l72XLltKZvooY9BFJDu7L07YmNjZWzxcyJSVF1Pli7spe9zFzwsiY9Z/rjRs3qEaNGtSiRQt69OiR1fuxN5Uh8DPktddeIwBUrVo1eu+99+jGjRuijoWl80mKjlja1hqNsxSY1K1rznIqjnRnBhs0aECvvPIKRUdH0+7duykxMdFpKssdiS0eeI7G3G+uxmFA1yLI1TWRnn12GS1btozOnTtHSqXS4syg7jlXkcjLy6NVq1ZRmzZtCAD5+vrSu+++SwUFBSa354GfVCQGBWUFhmUHkOK+lLo/2MHBRVSt2gQCQB06dDC5JCfZ88jAvibnxx9pSt++NKVvXyHY27yZ1Lt3E926Ve5LnjYjMbgvD9uagoICmjdvHnl6elLNmjX1Oq7IIeKWLA6s/TF49OgRNWrUiAICAui2k5t6V8bAT61W07Fjx2jo0KHk6upKAGjixIllHgtbAjZdbA0gpSxFmr9wUdP48eOpbdu25O7uToZLxADIz8+POnXqRG+99RZ9++23dOjQIUpNTa3UAaEtHnj2Ij8/ny5cuEAbNmygDz/8kPr3708NGzYkc8u1Li53acCAARQdHU0HDhywmIpiD30rDzTn5Jo1awgANW3alJYtW2Z2wkcDD/ykInEZUI4ZP2um4WNjYyk0NJQAUMuWLY06fFgdHBi0s1MfO0Z0+zYd/uMP6tq1K12+fNm24+sMOPmM3/79+6lRo0YEgAYMGECJiYl6j8uxbGPu/GDM+uUftVpNCxcupKNHj1q3AwdSGQM/XZKTk+mTTz6hn376iYiIsrKy6OOPPzbZBtKanCqpM35yLzWKGVdhYSGdP3+eVq9eTZMnT6bnnnuOatSoYTIYBEDVq1enDh060KhRo2ju3Lm0efNmOnPmTKVYMi6vGb/i4mK6efMm7d+/n5YuXUqTJ0+mnj17Uv369Y0MkjU3xkYQY3l645Sam2cPfXMUhYWFtGnTJuratSt99dVXRESUm5tLBw8eFH1xwgM/qUhM/BebH2Yqx8/dvdiqBGoNd+7c0QYIjRo10jOZlFtot2zZQjVq1CBXV1eaNGmSTTlg5Y6jcvysMIaOi4sjhUJBDRs2pL1795rdztZEbfPLKdL2QyQEfElJSdKfWI5U9sDPkF27dhFjjBQKBfXu3Zu2bdtGRUVF2sfNnU+25vhZWyRSFtbqm1qtpsTERNq3bx998cUXNGbMGOrQoYO2JaG5m7+/P7Vt25YGDRpEU6ZMoSVLltCOHTvo7NmzdP/+faefLbRXjl9ubi7duHGDDh48SGvWrKE5c+bQmDFjqGvXrlS/fn3tzLOpm4uLCzVq1IhefvllmjFjBsXExNCFCxeooKBABn0zrW3W6JujuHbtGk2ZMkVbzBQWFkbff/+9VfuyVd+4j58I1h8LwcwNLXAn3Ruh/nmIHnZJ3/LExQXo1w/rN7li5kyh5NzD4z6efHIj/vrrl1wiaAAAIABJREFUHZtKyFNTU9GjRw9cuHABwcHBOHDgAJo1ayaMaz1KXg+ymD6npaVh1qxZWLlyJXx9ffH5559j7Nix1u+wvFAqgZ07jSx7xHyGcDU2njWCSJIxdFpaGvbs2YORI0cCAPbu3YuuXbvCw8PD1ndqkh07duDll1sCCDN6zBrj2q+++gqzZ8/GqVOn0LhxY1nGaG8qqo+fLfoWHx+P77//HqtXr0ZycjJq1aqFK1euwN/f3+LzpOiI3Joj17jKgoiQmpqKf//9F7Gxsbhx4wZiY2Nx69Yt3L59G/n5+Raf7+7ujuDgYAQFBSEoKAi1a9dGrVq1EBgYqL35+/vD398ffn5+dvtuW0LM8VKpVMjKykJGRgbS09ORlpaGBw8e4MGDB0hNTUVqairu3buH5ORkpKSkIDMz0+JrMsZQp04dNGjQAA0bNkSjRo0QERGBxo0bo0GDBnB3d7fLe7XUIMGZjLmLioq0x+D555/HkSNH0LdvX4wdOxbdu3eHi+b3QiLcwNkaiMR37igLFxfhR75FC727c3Jy4OXlZfUHq0tWVhb69euHo0ePokaNGti1axeesWMnjStXruD//u//0LNnT7z77rtQqVQgIm0XiQqBlODezGdoErHnjosLHvn4YNGff2LhokXIy8vDzZs3Ua9ePdFvwRp++OEHjBs3Dmr1EDC2FkRu2sd0HfDFsn37dgwcOBADBgzA5s2boRBjQukEPI6BnwalUom9e/fi+PHjWLBgAQDgk08+Qa1atTBkyBCnN4Z2NESEe/fuIS4uDnFxcUhISMCdO3eQkJCApKQkJCYmlhkAGeLp6Ynq1aujWrVqqFatGqpWrQofHx/4+PjA29sb3t7e8PLygqenJzw8PODh4QE3Nze4ubnB1dUVCoUCLi4u2kkDIoJarYZarYZKpUJxcTGKi4tRVFSEwsJCFBYWIj8/H3l5ecjLy0Nubi5ycnKQk5ODrKwsZGdn4+HDh8jOzoaU33x3d3fUqVMHderUQWhoqPZWv3591K9fH2FhYfD09JR0bORg/Xpg9GiguLj0Pmv0zR6o1WocO3YMa9euxfbt23HlyhUEBwfj6tWr8PPzQ+3atW1+DVv1rQL9kssIY0KnBkuzNiqVsJ2lL4mm60Pz5kYP+fgInZsSExMxadIkrFixAjVr1rRquNWqVcO+ffswbNgw7NixA926dUNMTAwGDRpk1f50MX2V2Az79u2DWq0GAPz444+YN28eZs6ciREjRlSMALB5cyArS3znDhOfoUlEGEPnFBRg8Z49WLR7N9IfPcLAgQPx6aef2jXoIyJ8/PHH+PTTTwEA/fsPwO7drnrtj6ROPJ8+fRqRkZFo164dfvzxxwoT9D3uuLq6om/fvujbty8A4Ydo165d+OeffzB58mT069cPI0eORI8ePeDm5lbG3io+Zc2EMca0M3lPP/20yX3k5eUhJSUFSUlJuHfvnnZmTDNb9uDBA+0sWkZGBgoKCnDv3j3cu3fPQe9SPL6+vtqZyYCAAO2MZa1atbS3OnXqIDg4GH5+fk77vTfUs/L2Zr5//z6WLl2KdevWIT4+HlWrVsXgwYNRXBKdNi1pouAMPJ4zfroolcDdu0BysnD54OYm9KatWxe4ds10YOjqKgSEIvq8nj59Gp07d0azZs1w6NAhbUBo3VCVeOedd/Ddd9+BMYYvv/wS7733ntVLyeb6Hho62//222+YOnUqzp07h/DwcEyZMgWvvfaaVb1dHYqlJVkJn6GWMpaQQ/zzMG/YJfRseRX13n4bnZs2xcdDhqBdVJS4JWQrKSoqwtixY7XB2XfffYd588bZtBSSkJCAdu3aoUqVKvjrr79Qq1Yt2cdtTx7nGT9TEBHOnj2LNWvWYOPGjUhLS8PHH3+M2bNnQ61WgzFWIbsalIVYjZMTIkJ+fj4yMzORnZ2N7OxsZGVlaWfhcnNztbNzmtm6wsJC7SyeUqnUzuzpopkFVCgU2tlBd3d37Yyhl5cXvLy84O3trZ1d9PHx0c441qhRA76+vpJWoRy5tC8FZ1nqvX//PjIyMtC4cWMkJiaifv366NKlC0aOHIn+/fvbrRewzfpmS4JgedxsLu6QikEVrJgEfkN27typTbouttEqRa1W0/z587UJtOPHj9dL4paClEowtVpNO3fupHbt2hEAev755216Hw5Fhs+QiCQVjaSsXOkQY+i0tDTq3LkzASBvb2/69ddfich2e4fCwkJ6++236dq1a3Ybuz3BY1bcIYWioiLasWMH3bp1i4iIfv31VwoPD6cZM2bQpUuX7P76jqQi+ds5G85sCl2e9jUPHz6kNWvWUPfu3cnFxYV69uypfcxRRZG26lu5C53Um8MDP5lYsWIFAaAxY8bIUiG2YcMG8vDw0AZhmZmZkvdhzZdHrVbTkSNH6FCJ5116ejqNHDmSjh075tyVb1ZU4BqhYxNT9NNPFFA13fSPioNsYq5du1biiwUKCgqi06dPax+z9gcvJyenYld0l8ADP/EcOXKEunfvTgqFggBQs2bNaM6cOZSXl+fwsciNM/rbVRScOWgur7FNmTJF6x9Zr149mj59erlcLNmqb865eF8JGTduHGbNmoVTp04hKysLgG2N04cOHYrDhw+jZs2a+P3339G+fXtcu3ZN0pjMNVm31HydMYZOnTrhueeeAwBcuHABv/zyCzp27IgmTZrg888/R1JSkqRx2BUiodBj507g3DlhST8tTfj33Dnh/kuXLOdyaigqgqok73HzyZNIe2Q6Sf5OukGXeN0MZAOsPQf27duH9u3b4+bNm2jVqhX+/vtvtG1bOvNvqlm9t7dwvzmKioowcOBAdOzYEYWFheIGwqnwdOrUCfv370dKSgqWLl0KPz8//PDDD9rK1N27d+Py5csgMd8RHWzRN7mwRuM4AnfuSLvfFPY6B6zRN6k8fPhQm0ufm5sLAGjYsCHeeustnDx5Erdv38a8efPQXGx+uDNhS9RYHreKOuNHJMyWadpcxcSoZZlGj4+PpyeffJIAoc3Lrl27RD9Xrqn8R48e0apVq+iZZ54hAKRQKCglJYWIqHx7uRq0qrO2L/HNmzdp/vz59FTDhvTFiBFEmzdT7rp1FOibYdOMnzXHX61W04IFC7TmqP379zfbOm3ChNI+0C4uwt/mUKlUNGyY0A/TWm8pZwJ8xs8mNJ1k1Go1BQcHEwCKiIigqVOn0smTJ8v8XjvLMqGzjKMiYuusmr2PfUyMfg9of3/b952enk4rVqygnj17kpubGwGgOnXq0Pnz5+UZtEzYqm/lLnRSb84ijLaQn59PXl6psk1V5+Tk0KBBgwgAMcZowICfKTRULcocU07TVSJh+XHFihXavwcMGECdO3emhQsX0tWrVx27HKxpUWcp6NMN/i5e1Hv63LlzqUWLFtp8yg6tWtGmqCit4TegJgZ9o2QpxtBldUIw/Fyys7Np8ODB2vHMmTPH7A+wFNFVq9U0ceJEAkDz58+36ZA7Czzwk4+UlBRavnw5de/eXWvY+9577xGRcMFgaknYnktxUjVLbo2r7Oh2dDFcKpcSuJV1Dtj6ucgRWKrVarp06ZI2l/ncuXMECD2g33//ffrzzz/Ld/LCDDzwq4AUFBSQ0JDa+Ethbe6JWq2muXPnEjCcgBybvgxyEh0dTc2bN9cGK6GhoY4JLiR0aFFu3EgXvviCFr/+On0wZYp2F927d6eOHTvSokWLhN60xcUUM/mUUUGHEPwZd33RBn5m8ggtuc8bCpqnp4qCgv6PAJCPjw9t377d4tuX8sP79ddfEwCKiopy7jxNCfDAzz5kZGTQ+vXr6cyZM0REdPr0afLy8qI+ffrQd999R3fu3CEi++XW8Rk8y9gjmNJ8llL3Z+kcsGdbyrIuLvLz82nPnj00ceJEql+/PgFC7j2R8Dt6+fJlp9dBW/WN27mUE6Ghaty9a5xiaWs5eq1a+bh/39hmpbwdzRMSErB//37s27cPHTp0wAcffIC8vDxERESgdevWaNOmDVq0aIEWLVogPDzcdq/AuDghh6/EEmH9sRCMW9EWeUWl+3V3LUKDWh/ibvoS5BQUAADCQ0Pxb2ws3N3doVQqjcZRL7gICSnGbvRhAbmIX7ZH/84yjKHNWRJobCSNiUfTpi9i27ZteOKJJ8y+dUDIqTH11WYMKElT1HL//n18//33mD59eqWx9OB2Lo4hNjYWixcvxu7duxEXFwcAaNasGTIzzyE52dgj0FYdchYbD2dEDusaOY+vpX0Btr+OWI0jIqSlpSEwMBCA4Kf377//wsvLC926dUPfvn3Rp08fBAcHi3thJ4B37qigrF8PjB1LyM8v/aGVw1/K/JeBoFY71496amoqpk2bhlOnTuHatWvQnIuLFy/GpEmTEBcXh88++wx16tRBUFAQ/Pz84Ofnh5YtW8LPzw+5ublITU0FILQiys/PR35+Ppo2bYqqFy/i/J9/YsvJk0jKyMDGP39CYbHxF9vdNQlju72O/zRsiE5NmqBey5aCubcZFAoCkfFxZIyg3vRz6R0aY+hnnjHrEWhOqHX/1keNR4/yRHlBihHwffv2oVu3bpXSxJcHfo6FiHDt2jXs2bMHv//+OwYP3oGJE931zmVPTzW+/54hMtJ6HZJyQfO4IUfQJufxtRSIvvqq7a9j6f2eOZOOQ4cO4bfffsOBAwe0BtwKhQKbNm2Cr68vunTpUi5dR+SA+/hVYGJiiOrWVRKgotq1C2RZrjA3/e3peY+SkpJsfwE7kZubS6dPn6ZVq1Zp8y2OHz9ONWvW1C4Ta26aApZffvnF6DEAdPToUaKDB2n9O++Qi0JBdf39LSytq/WXZktsasxhdnlBU9CxbVtpvqCI5QJTSzPmXiM0VPzyQ1lLKRp7oQULFojeZ0UCfKm33ImJIapRI6vkuxdHwDCqVasWjRs3zup9OrPFSHkjx/K63MfX3NKzHK9jTuOGDt2pLX6rWrUqvfTSS/Ttt99SYWGhdW/CCbFV38pd6KTeKpMwatDNJ9BUw1qLqS8DkEvAMPLz86OtW7faOlyHU1hYSImJiXTx4kU6fPgwPXjwgIiIEhISaO3atbR27Vpat24dbd26lfbs2SP40B0/TkU//UTKjRuJNm8uKcYwITQSPfdMio2HkmJmXrHeGFoHpVJJr7zyC8mRp2lOdFetWkUAqHfv3lRQUGD1WJ0ZHvg5FwkJCbRq1SoaPny4Np+KSMijHTZsGC1fvpyuXLlSZm4Vz/Ezjz2DKbmPr62vk56eTjt27KDevdeTu3uy3uTJ8ePHac6cOfTnn39a3dzA2eGBX3khhyGwAd999x35+vrSyZMnbRqa7g++vz9R9epKvavuESNGUHp6uk2v4fRI6LIhpgJXF3tVCV6/fp2efvrpkpnLYVS1arpQiCLja6xevZoYY9S9e3fKz8+XZ6dOCA/8ZMAOGqe/+2IaOnQo1a5dWztb7+/vT4sWLSIi4YI4NzfXaBwxH12jsOAiYkxN/v6CxvGKXfmCNkdVQYt9HbVaTTdu3NAWDv3111/a88XNzY06duxIH374obYTzeOArfrGc/ykQmS+/6umB6KU/q863L17F126dMH9+/exb98+sw3DxWIqxwIQEigUiiS8++59LFzYRtR+nLFfo0XK6Ksb6p+H6GGXENnxbulzXFyAfv3s2lfXFCqVCkuWLMH06dNRUFCAoKAgrFq1Cj179pT1dTIyMtCgQQO0a9cOO3bscP5eyzbAc/xswI4aZ/rlCDdv3sSxY8dw7Ngx9OrVC4MHD8btW7fwROPGeCosDO0jIvCf8HD8p2FDPBEcDIWbG9YfrSsUbBWUFskxJgw/LEyaTlVIjTOBpPeh26e+qAhwdxf61IeEOFwDdVGpVPjtt9/w999/49SpUzh16hTS09Mxffp0zJs3D/n5+Vi4cCE6duyI//znP5Vax8zBizscCRFw4gRw/765sksBEYn95khMTETXrl2RkpKCPXv2oGPHjlYP11zyaym56NBhFbZvfwW1a9c2uUV5NDk3fH2rBfnSJeHHy9JnpaGMClx7cfHiRYwbNw6nTp0CALz66qv4+uuv4efnZ5fXu3TpEiIiIipsUrNYeOBnJQ7QOLHjSPrlFyyLicHJ69dx+tYtbeX9z1FRGNihA+q82QPJmb5mdyFWp0xqnJcaK9+PReRzyXYNiMol4HRwYG+J1NRUnDt3DufOnUP16tUxYcIEqNVq+Pn5ITs7G40bN8Z///tfdOjQAV26dEHDhg3tOp6KAg/8HImDAonk5GR07doVSUlJuHXrFmrWrGnFYM1XaOkTj+rVW2HBggV44403oFDoW8zIbZ8gRehsDjqd5UfMBLm5uZg7dy6+/PJLKJVKBAcH47vvvkO/fv1kf62vvvoKxcXF+OCDD2Tft7PCAz8rcZaLJYNxqNRqXE9Oxt83b6J3q1aoWa0a2OBBACx/X0NDCQkJlrcxq3G6Fk0iAyKH6ps1lJMmFhQUICkpCQ0aNAAAjB8/Hjt27MC9e/e027z44ov49ddfAQDnz59HeHg4fH3NB/aPM7bqG+/VKxal0kgQ1x8LQb23ekMxZBDqvdUb64+FlG6vUgnbK5WSXyo4OBhHjhzB//73P9FBn6meiOL6UYbi4cOHePPNN9GhQwecPn1a71E5+jXqjnHcOEFkiYR/X31V0BVTfRxnzjS2NsnLE+4XBWOCcEVECEKmEW8Nrq6lP14OCvqICD///DOaNGmC+fPnQ6VS4e2338a///4re9BHRPjkk08QFRWF06dPQ/24+11wLONAjZM6jo0nwtB73jiM+W4p/jN9GNYfC0FogFnfIy137hA6deqEN998E19//TX27t1r9D0wq3G6PbdVqtL3e+KEyStqh+ubNVy+XHbQBwiP378vbG8Fv//+O6ZNm4aXX34ZERERqFKlil7qkp+fH3r06IFFixbh0KFDyMzM1AZ9ANCyZUse9NkRPuMnFhGGwN7uSqx885/SvDEXF6BVK6B+fZte+vfff0dqaioizVwGmrtyHDUKWLvWki+ccEX8+eebERUVheTkZDDG8Nprr2Hu3LkIDg52iKGn7ph1r3Zl9ezSzWcpLgbc3MpcvpF7GebChQuIiorCwYMHAQCtW7fGt99+iw4dOli/UzOo1Wp88MEHWLhwIUaNGoXvv//edlPsCgSf8bOCctQ4a8Yx6rk4rD1cX+9+Q3x80tGy5cu4evUqMjIyUKNGDaSnp4MxhqlTp+LKlSs4evRHPHpknFph0pQdMDvTWa76JgYZ855jY2Nx4sQJxMXF4ebNm7h16xZu3bqFu3fvwtPTE1FRUVi6dCkiIiLQtGlTNG3aFM2aNcOgQYOMVpU40uE+fo7i+HE92w+57EHE0KdPHwJAX3/9tcnHxfR81fg5mav4evToEX3wwQfaxtRVqlShOXPm0A8/5JusFJswQXrll6UWZaasB8rTs0tOW4PExER6/fXXtd5SNWrUoGXLlpFSqZR/4CRUwY0ZM4YA0Ntvv+2UvSbtDXhVr3TKUeOsHYfFvtkG39cHDx7QuXPntH9PnTqVnnzySXJ3f83IPsndVUnuLpkEqMjH8x4N6rCYfhg/nk58+qnWAUBl4AvnEH2zpdJagtPB9a+/pq9GjaIp/frR0D596JlnnqGQkBCKjY0lIqKFCxcSAFIoFFSvXj3q+t//0psvvUTpv/xCdPw4ZV+8SMWV2DWgvLFV3/iMn1gOHQLS0rR/KoYMgqgODoGBwHPPGe9PQkVVQUEBhg8fju3bt2PGjBmYO3euXmstsVeOYmawYmNjMXXqVGzfvh0AULNmTfTosRZHjvTA3bsMoaFA797GM4liclPKLjbRH3N5FpbIMdOZnp6OBQsWYMmSJSgoKICrqysmTpyIWbNm2a14Q8OKFStw7949fPTRR5WmDZsU+IyfFcipcbZUjFo5jvUXmmFmTFPJM/R0+zaWz7+DT7a0QupDX/hVLUJ2niuKVbqpIbkAxuLF1tfx67RpgIsLwiZNQl5REQIDAxEYGIjTp7cgP99yao6mgxIRYc2aYkyc6Ia8PBHdm8h8QQYpFFCqVMgPCYF7y5bw9PJCTk4OLl26hEePHiE7OxvZ2dl4eO4cXiypij57+zaemfUqCkx0MwoLyMUXr87C4K++gqebG+rWqoU6DRogNDQUs2fPRnh4OO7fv4/srCyEPnoEd40glmORyOOGrfr2+Kz92Iq7fn/WUP88JKRVMdos1N9gXdWwHZaFLzBSU4UlDoMvi6enJ7Zs2YIJEyZg3rx5SEpK0lu6Cw01HaQY5vhFRpYthBEREdi2bRuOHDmCqVOn4tSpU1i3rhdCQkLw7bfTMWbMGDzxhIfZ3BRL+4+ONmUvY37Mmn2Vh82CLbmN6enp+Oqrr7BkyRJkZ2cDAAYOHIh58+ahUaNGMo5SnwcPHuDq1avo3Lkz3nzzTbu9DqeSIofGWaFvco0jslcmIuea3qUlWEoKJrzwABNeOAAAqPdWb6Q/8jDYqgrq1FiOb15bK/ypUuGN3r2R7OqKBw8e4MGDB/Dz+xKpqXOhVBr38tbg45MJwA8FBQUYM8YbwDAAnwEIgUKRiBdfPIfIyJeQlpaGNm3aaJdFqaAAaqUSM15+GeO7d8fNe/fQZupUFCmVKFQqoZnAWfH++xi3YAGuX79u0g6s5sSJeKKkJ21BsWknhzvp3nixdWukr1qFGlWqgNWsaRTY1wwMRM0bN8znC2rui40FsrIcWjjHKRvZAz/GmB+AHwB0B5AGYDoR/WRiu/cBjAIQVrLdMiL6Qu7xyEZwsCBcJSd09LBLJvNOooddKn2Oi4vwPA1URkWVhS+Li4sLVqxYgbp16yI+Ph4uOoUKpgIqb2/hfmvp3LkzTp48iZ07d2LmzJm4cuUK3nrrLcydOxfJyYkwVU1XVlCkG8glJJR6blkas5hg1R6IDaZ1SUxMxDfffIPly5cjJycHAPDCCy9g3rx5aNvWvpNPN2/eRK9evZCZmYn4+HhR/Xw50qm0+gbYrnE26Jus45BKYaHen3pFHTokP6yKBjq2V7MiI40CIs2qSkIClehb6fvz8FBi2rRsAH5QKBT47LPPUFBQgMLC5SgsLIRSqUSPHj0AAK6urujSpYsQ0GVmAo8ewQVAWGAgAKC6tzdGd+kCd1dXeLi6wsPNDV7u7uhQuzZw+TIiIiKwd+9e+Pj4oFq1avD19UX1a9dQ9eFDAEDr8HCEBeSbDai9PTzg7VES/GoCe91Z3MxMID+/7GOrWyTiYKssjnnsMeP3LYAiALUAtASwmzF2gYiuGGzHAIwEcBFAAwAHGGN3iWijHcZkOyEhwtVqCZoEWIuJsZrnabCmokrny8IYw0cffSSs0TOGGzduwNPTE5GRQjQi98wYYwwvvfQS+vbti23btuHTTz/FxYsXASQAqGe0vZgqYt1Azl4eVnLsV0owfe7cOXzzzTf46aefUFxcDADo2bMnZs2apb3qtqdf1/Hjx9G/f38QEXbt2sWDPvtSOfUNsF3jbNQ32cYhBSKg5CJNg9WrOdDVN2biO++KyMh6AAAPDw9MmzbN7LCqV6+ONWvWlFGQMcb0sYiNhW+TJsYG8Gq1XtGM6IA6KKjUXgcw+flaLBLRVEM3aVKuxtCcUmTN8WOMVQGQCaA5Ed0ouW8dgCQiMn+WC9stLhnPJEvbVVgfP5k7SRAR2rVrh8TEROzYsQPt27e39d2ViVqtxu7du/H+++dw/fr/ASgVRw8PFb7/nmHEiPKt2JIzL9BSsFZQUIDt27fj22+/xYkTJwAACoUCAwcOxPvvv4927drZZUyGrFu3Dm+88QbCwsKwe/duRERE2LbDSoI9cvwqvb4B1muc3J1yHOUneOkScOOGXjJ0uVUzm0LOSmtrPiOFQvDze/DA7GfhVMfrMcGpDJwZY60AnCAib537pgDoTER9LTyPATgLYAURLTfx+DgA4wAgNDS0TUJZFQL2oqylDA2mzC/tYJVw9epV9OnTBykpKVi1ahWGDRsmy9s0h24g5OtbjLy8fBQX+wC4A2AG6tf/C6NHj8bIkSMRFhZm17GYQ27DaV2ICOfPn8eaNWuwbt06ZGZmAgB8fX3x2muvYfLkyQgPD3fomCZMmIBr165h69atdi8YqUjYKfCr3PoGWK9xcuub1HG0bw8kJkorJrEQCCWkecNFQVCpGcICyrG944kTwnsqod5bvU3ORhpZzwQHC5+NIVID6mrVhGV5C9vbPCaOZJzNwNkHQLbBfVkAqpbxvNklY1lt6kEiWklEbYmobWBJjkO5YIshcHKy3pdn5oYWRv5TeUWumLlB56pVpdL70hvStGlTnDp1Cu3atcPw4cMxdepUqMR8oa3A0Jw0K8sNbm6+WLo0C3Pm/IjQUMHT6aOPPkK9evXw7LPPYtmyZUhJSbHLeMwhp+G0hmvXrmHu3Llo1qwZWrdujcWLFyMzMxOtWrXCsmXLkJSUhG+++QYnT4YbmWjbY0xZWVm4fv06AGDx4sXYv38/D/ocQ+XWN8B6jZNb36SMw9cX2LVLCDyTk4WK4ORk4e+dO4Vgx9QEx139pWJNsCoEMQwqtQLe7irjoI8x4XUdsWxZVKT3p7n8Q6P7DZ6npXlzIVA2PJ6GMAYEBBgFfaYMvUWPqSQNhlP+yH3m5gAwtNv2BfDI3BMYYxMh5MJ0JKJCc9s5DYwJSwpNmkgzBLb2C1zGlyUwMBC///473n33XRw7dgxKpVKv8EMuzLnMf/FFDcTHf4SZM2fijz/+wOrVq7Fjxw6cOHECJ06cwMSJE9GhQwe89NJL6NWrF1q0aGFXexFrijIMKS4uxl9//YU9e/bgl19+wbVr17SPBQQEYOjQoRgzZgxatWqlvd9wOTchQfhbrjFpuHbtGl5++WWo1WpcuXIFbibyjDh2o/LrG2CdxtlD38oaR926wKlT1heTSAhW9QI/d3chgDJhfnzOAAAaXElEQVSFLTY2prC20rqkr7ERmoBaU3mtVpvv63n/vt6fhrO4CWlVMG5FW/hVKUR6jnHvbzE5kZzyQe7A7wYAV8ZYBBGVZILiKQCGic8AAMbYGADTAHQiokSZx2JfXF2FJQqxOQty2cGY3LU7li1bhry8PHh4eCAjIwPx8fFo3bq1uLGJoKxZKxcXF3Tv3h3du3dHTk4OduzYgU2bNuHAgQM4efIkTp48iWnTpiE4OBhdu3ZF586d0blzZzRs2FDWQDA6GhgzRv93yN3dcoWzUqnEhQsXcPToURw5cgSHDh3S2rAAQI0aNdC3b18MHToUzz//vMlgy1L7Jbmqrrds2YIxY8bAy8sLW7Zs4UGf43l89A2QpnF21Dez47h0ybZiEmuD1apVjauR5bCxMYWICmcGQu/WBjOnublCEGoq2GRMGMfDh8K+TWEiGDQXGHt5qODtrrRf1TVHdmRd6iWiXADbAHzCGKvCGHsGwEsA1hluyxiLBDAPwAtEdFvOcTglwcF60+vRwy7B212/x6WtXxZvb0Gg3n//fTz99NP43//+B90cTlP9fMVibnbK1P0+Pj6IjIzEzp07kZaWhi1btmD06NEICgpCcnIyYmJiMHbsWDRq1AgBAQHo1asXZs2ahc2bN+Pff//VVsZai6Fm6f6dl5eHM2fOYM2aNYiKikLHjh3h6+uLtm3bIioqCjt27EB2djaaNGmC9957T9sub+3atejVq5fZYMtSYBwZKRRyhIUJmhsWJq2wo7i4GFFRURg8eDBatGiBs2fPonPnzuKezJENrm8WcIC+6SFHX2ETwaopjO43eJ42H1EzHsNAVESfX7OEhOhtH9nxLkY9FweG0vsIDGsP19d/v4wZLWXrcfmyULAhYSzmAuOMHHesfPMfhAXkgjFCWECufi6n7nvhOAWyd+4o8blaBeAFAOkAphHRT4yxjgD2EpFPyXZxAOoC0F3+iCGi8Zb2X+5Vb9Yid9WbBdLS0hAZGYkDBw5g6NChWLFiBXbt8rWpslSOylQiwuXLl3H48GEcOXIEx48fR6qJK04XFxeEhYWhQYMGCAkJQZ06dVC7dm34+/vDz88PPj4+8PLygpeXl9bgVKlUIj8/H717N0VqqvGyg4fHPQQEtEVSUpLJsTVo0AAdO3ZE586d0aVLF6PilLLsWOxZwKFSqdCtWzc8+eST+PLLL+Fu+MPDMcJenTu4vpnBgfoGQJ5iErkKUuxdgbxnjzCDV4LoYoratYXlcMNl56AgYZ9SPispryvHe+ZYxKmqeh1BhRVGwHEWBRCsV+bPn68ttsjLu4qUFOOAIaxWAeJPpojKQZHLi650P4TgYBUGDPgHVavuwqVLl3Dp0iUkJCTA+vNSBdMT2WoALnBzc0PDhg3RokULtGjRAm3btkXbtm0REBBgcbxlBb32sGzZunUrnnnmGdSuXRuFhYXw8DDsJsAxB2/ZVg44UN9kqXaVI1h1RMB78CCQnq79U3QrPc1rGbZSI5JuXyNhO6PXN3S44NgMb9lWkWjeXEgyFmtRYC6BWAQKhQIzZsxAp06dEBUVhdu3zSxR3vcQrnpF5KDI0UVDP0BiSEpyxQ8/dMDKlR20OW8FBQWIj4/HrVu3kJiYiKSkJNy7dw+ZmZnIyMhATk4O8vPzUVBQoA0QFQoFvLy88O+/qSgqCjJ63Zo1C/HXX7cREhKibXUnFkv5e5rjIWd7uezsbEyaNAk//vgjoqKisHDhQh70cZwfB+qbLNWurq6C5ukEq5Ed71oOYgyrec1UBhsWQGj2rfc8sfnhBt990fmTgOllZwPEFrWINtIGhGNExHv1Oil8xs/RWEoCttOXhdRq1A8uQoKJJVC9K2IHXJ3Zc0kUsM/Mm0JhOhWGMb0LZ1k4evQoRo0ahTt37mDWrFmYOXMmL+KwAj7jV044St+snfHz8AD69i19bVu8WW0ZhxRPO2uWpCUgaQaxLLy8gBo1bKtk5pSJs/n4ccpCY1HQr5+QKxIcDAQGCv+2bCnc36KFrIEXu3IF0UMvwtNNv2jCKNlat/rNTtjDZ08XKYUUYotdpBS22ML69evRuXNnKBQKHD9+HLNnz+ZBH6di4Sh9s6aYBBDsYHT1TYxfoEIhvAcAOHxYCPbi4oRlXjvZdOlhosBDVDEFyih4KUF0UYslNK3dXnxROJ716/Ogz4nhn0x5IdUOxlpKqt8in1EBajWmrm+GpIwqANTIK3LBBzFNAcBhfRXl9LQzh5glaUu+e4bPlcuOxRz5+fnw8vJCr169MH36dMycORNVqhjPGnA4FQZ765uFfr6arht5RS5aw2itvqnVxvpmzi9QM0P54IFxyzKNRYun/iqKLDY2hl6ABQVGSwsWl6RLELvsLKpnLyAEwIw5ZJWKY1/4jF9l565+jsaCyCvwdldDiPkZkjN9MW5FW+MrQUtWADYQHS0ETbrIGUSJxVLeniG22rGYIz09HaNHj0bHjh2hVCrh5+eHefPm8aCPwykLTX6eTqAR2fFuycyfCiq1AgDTBjui9E0TrD7zDKCxS9IEfOYsWvLz9e62ycaGSCiQ2blTvwtJTk5ZR8MkorqnQOQMoouLMJvnoFUqjn3hM36VHZHu9NN/ao7a1fegW4sWpa2U7HC1LmcRhC1IXXKWo7BFg1qtxqpVqzBt2jRkZWXhgw8+gEqlklx0wuE81jRvLiy5FpY65ogqVBCjb5cvizOHNjETpxmHxQIIQ087sbmGEhC97AyRRS2eno5ZpeLYHT7jV9kRmYNyN90bz3/6KV5ZtAgJDx4A2dn6ZqcWkGoMHRkpFHKo1cK/cgVUUsbhqLw9Q5KTk/Hf//4XY8eORdOmTXH27FlER0fzql0ORyqMCV00dBAd7FjSN6nm0AZEdryL+GV7oN70M+KX7TGeOTPV51dEoCllDICMuXu2VmBznA4e+FVWlErhaviRfhtRc1/6EP88fDpkCHafPYsn3n0XM1auRPbGjeYbnJegyZVLSBA20+TKSekKIhVTAZ7UcTh6yVnTjSQgIABeXl6IiYnBkSNH0IKbmnI41qFUGhVJiA52cnKEJVVT+mbGoiUhrQqIzCwfM1b2Uqe5IEpEoPnW9y3FjUFR+pMuatlZM25TRS2aIJV78FU6uJ1LZcOSnQLKtgK4m5aGmRs3Yt3Ro3i1Uyf8OHmyRYsXe9uzGGLOrsXLS8/jVNQ45DKktkR6ejrmz5+Pbdu24cKFC/Dx8ZH3BTgm4XYulRhdjVOr9QI3yVYnpmxarLVo8fEpzfmTUgAhwq6FgUAw1l+jMTBmdDzKNJPu3RtISSktanFz43YsTg7v3MEpRWSeiJj2PKdv3oSvtzeeCA7GzdRUHImNxcg+feDm7a0nCo70uAPMB5rmsNc4yuLhw4dYvHgxFi5ciEePHuHVV1/Fl19+iUCNLQTHrvDAr5IiQuPE6JseCoVw5ejlJbQ1y87WK6gQ7XMXGAg8+6x+ZbCYIEpkoGkKozF4eQnpPY7onsIpN3jnDk4pIhOSTSXyWhLLNYcOIXrbNny6YQNm9O+PUV27wqOk00doSHMk3DEWRXvlykn1+7N5HIbWCpp+lxaEPCEhAU899RSysrLw8ssvY+7cuWjWrJmNA+FwOGI0Tqq+Qa0WeuFq+uEazMhJsmixxsZGZB62KYzGUL268K9YQ+rGjYUZRwn6xqn48By/yoINCcll5bB8OmQIdk+bhlrVquHNlStRb/x4fLNrFxAbi+hR1+HtrT/lZ89cOXOBnL+/zDl75qwVkpOFvw3yg+Lj47Fly5aSMYbi7bffxtmzZ7F9+3Ye9HE4cmClxonK0dPFYAnDJosWMbjr91A3l6fIYKCzpsZQp07ZhtSamT5fX2DXLlH6xqlc8MCvsmBNQnIJZfk9McbQu3Vr/BUdjQMffogWoaG4nZoKqFQY/tQVzBl/VnaPO3OYK8r45hsZvfY0y0maHxkzHl504wYOLVmCgQMHokGDBnjjjTeQl5cHxhiio6PRqlUrq98nh8MxwEqNE+tnZw7RnTIMLVrEIrILyfjuN8WNoazuKX37Cj2Vb9607FEYGyvoIA/+Kh18LreyINKvz7DxNlC2BYL+MsmLiB72IoY+IyTaHb18Ge8vGoROHTvik0/ewKBBg+BtGJnJSFk+gLIEnCKWk45cvYoJ//sf/k1Kgn/16pgyZQomTZpk1/fO4TzWWKlx0vTNdE6gKJ87a5dGQ0KAM2f0Xgsw5wV4XvwYzC07X7okzi9Qt4UnzwOsVPAZv8qCtT0jYdkCwdxV9cYTYQCA5iEhmD9iBJLv3MGoUaMQFBSE119/HemmSmxlIjJSCPZCQ4Xgb+bMEtsWjYXNiRPAoUP6PTXFYmY5KXRCLygGD0LQuO5YfywEgb6+qObtjVUTJuDusmVYEB2NunXryv9mORyOgJUaZ42+leWRp0UunztbZtUYE2b0xIxB6nK5ZuZPioZynB4e+FUWROaJmLrfUg5LWcsk/lWrYmq/frgRE4PDhw/j5ZdfxsGDB+Hr6wsA2L59Ow4cOIAiA9G2BZOefW+osX7KWdvzVXSWkx7l52PSqgKM+vYp3E33AYHh3sNqGLeiLc7F/Rcno6MxuksXeHl42K3FHYfDKcFKjbNF38wip8/d3bt6/ntWBaMPHggzc2VpnLUpQVzfKhU88KssiMwTMWq8Dcs5LGKvqplSic6dO2Pt2rW4desW3EqakM+ZMwc9evRAQEAABgwYgBUrViAuLs54hxJm60z22S1QYOb6Zjblq6hUKsSfOaPdR9tp07B0X0eo1F76r2X4w6BpAcXhcOyHlRonh77Bx8f2HrXmNC4xUc9zSnIwSiQ+J0/CcrkWrm+VDp7jV1kICRFmt0oQ3TNSZ3tTj0myMihBoXP1evLkSfz222/YvXs39u7di+3bt2PEiBFYt24diAjfLVuG1tWq4SkXF2HmTDdwS00V3pOB8anZPruWbBBM5KtkZWXh9OnT+Pvvv3Hy5EkcO3YMbgDur1wJxhg+Gz4cgxaGwZSMGr2WQQcBDocjMzZonM365usrzOxZgyVT/ZIiOV2kpOnoISYnz9qUIK5vlQoe+FUWXF2FAEknf8NiQrLGtLSgwGKSb/SwSyad8MVaGXh5eaFfv37o168fiAjXrl3TPpYQH4+3J04UhsMYIoKC0CI0FG/36IHnmjVDQX4+UrOyEFRcDPesLO2SSmioaRNnTc6O7g/Bx4PO4dkmZ3E3PR03793DzR9/xMdr16JKtWqYN28ePv/8cwBAo0aN8Morr6BzUBCUKhXcXF0xoH17hAZID3w5HI4dkKpxgFEnC0Ns1bcyKctw2sR9ZQWjFotRNDN/TZqYLjYxsVzO9e3xgwd+lYnmzYUyfbHmnU8/DVy5Yra9GyDhqlqElQFjDE2aNNH+XS8nB4krV+LvGzdwLi4Ol+7cwbm4ONzPygIAnLl9G89+9BEAIMDXF341asAvKAijRq3Bl18+obfc66LIB9FujFzaF2oSlmYT0qpgzPJWAJYB2AAA8HBzw6t//okWvXrh1VdfxfPPP4+2bduiRo0awo4M2ifZ/YeBw+GIR4rGBQYC1aoJtiWA3fXNJCJN9XWxpDmG7dw0OXm67wWAkJNnykQ6OFhvlpHr2+MJb9lW2bC0rGCuZ6Rud4riYqHfZG6uuGIIa9v+KJVC0YVBdZmuAL/f7y94uG1BUkYGUrOykJGbiwwvL3w6dy5u3myPqEnZuJ/pA1dFEgJ9FyDt0TQUq4wra/190rE5ajEa1q6Nun5+UNSta37ZRsS4TPa77NePO907EbxlWyVGqsaVh74B1mlJGduJ7hscHGxa47i+VQp4r16OaQzFTkrjbZE9f002OBeLiMbkRs3VXVwEQ1LNleyhQ0L1bgmSemo+95z5sV28CNy4Id5i4YkngCefFLctxyHwwO8xwFqNc4S+AdZpXBnIonFc3yo8vFcvxzTW9IzUwJggdlJnDqVgjRmrprpM857sma9SwS6IOJzHDms1zhH6Bthkqm8O2TSO69tjDQ/8OKbRtP1p0sT6mUNLyFFdZo98FaWyNCeohDKXQm7eBJo25UshHE5Fwd76Bthkqg8XF5OzkTZrHNc3DnjgxykLW2YOLSHHbJ219g6WErXNGJxanUzN4XCcF3vpG2C9xtWuDdStKwSjRUVAerp2hs5mjeP6xgEP/DjlhRyzdVLtHcT01JRjCZrD4XCs1bi6dfWD0UuX5NM4rm8c8M4dnPLC4IrUkru+peeheXMhAVvH0d8kYntqcoNTDocjB86ocVzfOOAzfpzyQq7ZOrkTtbnBKYfDkQNn1DiubxzwwI9Tnkg1nDZ3JStnojY3OOVwOHLhbBrH9Y0DHvhxyhO5Z+vkSNS2R8EIh8N5PHE2jeP6xgE3cOY4C7YYTsuNQTK1RWxx9ufYDW7gzHE6nEXjuL5VeJzOwJkx5gfgBwDdAaQBmE5EP5nYjgGYD+CNkru+BzCNKlokypEHe9oqSEWu5RlOpYPrG8dqnEXjuL499tijqvdbAEUAagGIBPAdY6yZie3GAXgZwFMAngTQF8CbdhgPhyMNzfJMRIQgfobVdK6upVfC1rZz4lRUuL5xKjZc3x57ZJ3xY4xVATAQQHMiygFwnDG2E8CrAKYZbD4KwEIiSix57kIAYwEsl3NMHI5VOMLZn1Oh4PrGqTRwfXuskfuTbQRASUQ3dO67AKCziW2blTymu52pK2cwxsZBuIIGgELG2GUZxlrRCYCw1MThx0IDPw6lPGGHfXJ9cyz8fBbgx6EUfiwEbNI3uQM/HwDZBvdlAahqZtssg+18GGPMMA+GiFYCWAkAjLF/KmLSttzw41AKPxYC/DiUwhizR4UE1zcHwo+FAD8OpfBjIWCrvsmd45cDwNfgPl8Aj0Rs6wsghyc/czgcJ4XrG4fDqfDIHfjdAODKGIvQue8pAFdMbHul5LGytuNwOBxngOsbh8Op8Mga+BFRLoBtAD5hjFVhjD0D4CUA60xs/iOAKMZYHcZYMID/A7BGxMuslGu8FRx+HErhx0KAH4dSZD8WXN8cDj8WAvw4lMKPhYBNx0F2A+cSn6tVAF4AkA7Bu+onxlhHAHuJyKdkOwZgAfR9rqbypRAOh+OscH3jcDgVnQrXuYPD4XA4HA6HYx32MHDmcDgcDofD4TghPPDjcDgcDofDeUxw+sCPMTaRMfYPY6yQMbZGxPbvMcbuMcayGWOrGGMeDhim3WGM+THGtjPGchljCYyx4Ra2nc0YK2aM5ejcwh05XjkR+96ZwALGWHrJbUFJrlWlQcKxqFTngCFSdMGZNYHrWylc47jGcX0TsLe+OX3gByAZwFwICdUWYYz1gNA6qRuAMADhAObYdXSOQ2yPUA2biMhH53bbIaO0D7w/ailSzoPKdA4YIkoXKoAmcH0rhWsc1ziubwJ21TenD/yIaBsR/QKhgq4sRgH4gYiuEFEmgE8BvGbP8TkCVtojdBYR5RDRcQCaHqGVGonvXdsflYiSACxEJfj8NTzO54EhEnTBqTWB65vA43xuc40TeJzPAUPsrW9OH/hJxFR/zFqMMf9yGo9cmOsRaulquC9jLIMxdoUxNsG+w7MrUt676P6oFRSp50FlOQdsoTJpQmV6L4ZwjeMax/VNOlZpQmUL/Ez1xwRM99KsSEjpEQoAmwE0ARAIYCyAjxhjw+w3PLsiS39UO43N0Ug5FpXpHLCFyqQJlem9GMI1Tp/HUeO4vknHKk0o18CPMXaYMUZmbset2KWp/piA6V6aToOI4yClRyiI6CoRJRORioj+BPANgEH2fRd2g/dHLUX0sahk54AtlJsmcH0rhWucRbjGCXB9k45VmlCugR8RPUdEzMztWSt2aao/ZioRicmfKTdEHAcpPUJNvgSAinpFyPujlmLLeVCRzwFbKDdN4PpWCtc4i3CNE+D6Jh2rNMHpl3oZY66MMU8ALgBcGGOejDFXM5v/COB1xlhTxlh1AB9CXH9Mp0Zij1Awxl5ijNUoKf3/D4B3AOxw3Ijlw0H9USsEUo5FZToHTCFBF5xaE7i+CXCN4xrH9a0Uu+sbETn1DcBsCNG87m12yWOhEKY6Q3W2jwKQCiFXYDUAj/J+DzIdBz8AvwDIBXAHwHCdxzpCmO7X/L0BQjVQDoBrAN4p7/Hb472beN8MwOcAMkpun6OkLWFluUk4FpXqHDBxHEzqQkXTBK5veseCa9xjrnFc37Tvz676xnv1cjgcDofD4TwmOP1SL4fD4XA4HA5HHnjgx+FwOBwOh/OYwAM/DofD4XA4nP9v7+5Zm4rjKI5/Dyo+vAA3pTgKok4OTi6CINjFBxQnnQT7BhQKOhRcdOiouHZyKQgubq6CkyB0sIiTIOgQiujPoRFKsUtuuTfp//uBDMnNcIZwOLlJbhrh8JMkSWqEw0+SJKkRDj9JkqRGOPwkSZIa4fCTJElqhMNPkiSpEQ4/Tb0kh5N8SbKe5OC2Y8+T/E5yY6h8kjQp+019c/hp6lXVCFgEjgH3/j2eZAm4A9yvqpWB4knSxOw39c3/6tVMSLIP+AAcBU4Ad4GnwGJVPRoymyR1Yb+pTw4/zYwkl4FV4C1wAViuqoVhU0lSd/ab+uLw00xJ8h44C6wAN2vbCzjJNWABOAN8q6q53kNK0gTsN/XB7/hpZiS5Dpwe3/25vRTHvgPLwIPegklSR/ab+uIZP82EJBfZ/BhkFfgFXAVOVdXHHZ4/DzzzHbGkaWe/qU+e8dPUS3IOeAW8A24BD4E/wNKQuSSpK/tNfXP4aaolOQm8Bj4B81W1UVVrwAvgSpLzgwaUpAnZbxqCw09TK8lx4A2b32u5VFU/thx+DIyAJ0Nkk6Qu7DcNZf/QAaSdVNU6mxc1/d+xr8CRfhNJ0u6w3zQUh5/2lPGFUA+Mb0lyCKiq2hg2mSR1Y79pNzj8tNfcBl5uuT8CPgNzg6SRpN1jv6kzL+ciSZLUCH/cIUmS1AiHnyRJUiMcfpIkSY1w+EmSJDXC4SdJktQIh58kSVIjHH6SJEmN+AvqXhOFfPLTaQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 648x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 4))\n",
    "plt.subplot(121)\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.subplot(122)\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"
   ]
  },
  {
   "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": [
    {
     "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=15)\n",
    "    ax.set_xlabel(r\"Petal length\", fontsize=15)\n",
    "    ax.set_ylabel(r\"Petal width\", fontsize=15)\n",
    "    ax.set_zlabel(r\"$h = \\mathbf{w}^T \\mathbf{x} + b$\", fontsize=18)\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": [
    "# Small weight vector results in a large 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 864x230.4 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",
    "plt.figure(figsize=(12, 3.2))\n",
    "plt.subplot(121)\n",
    "plot_2D_decision_function(1, 0)\n",
    "plt.subplot(122)\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": [
    "# Hinge loss"
   ]
  },
  {
   "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 0x12d8542e8>]"
      ]
     },
     "execution_count": 35,
     "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": [
    "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 0.8537578582763672\n",
      "[LibSVM]1 0.01 0.7340240478515625\n",
      "[LibSVM]2 0.001 0.7935328483581543\n",
      "[LibSVM]3 0.0001 1.4816207885742188\n",
      "[LibSVM]4 1e-05 2.543147087097168\n",
      "[LibSVM]5 1.0000000000000002e-06 2.138460159301758\n",
      "[LibSVM]6 1.0000000000000002e-07 2.3709118366241455\n",
      "[LibSVM]7 1.0000000000000002e-08 2.3185129165649414\n",
      "[LibSVM]8 1.0000000000000003e-09 2.213902235031128\n",
      "[LibSVM]9 1.0000000000000003e-10 2.2198519706726074\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12d840898>]"
      ]
     },
     "execution_count": 36,
     "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": [
    "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)"
   ]
  },
  {
   "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 t==0, +1 if t==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 = -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, 60000, 0, 100]"
      ]
     },
     "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, 6, 0.8, 2.8]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x230.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yr = y.ravel()\n",
    "plt.figure(figsize=(12,3.2))\n",
    "plt.subplot(121)\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",
    "\n",
    "plt.subplot(122)\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": [
      "[-14.06195929   2.24179316   1.79750198]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[4, 6, 0.8, 2.8]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\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 = 50, tol=-np.infty, random_state=42)\n",
    "sgd_clf.fit(X, y.ravel())\n",
    "\n",
    "m = len(X)\n",
    "t = y * 2 - 1  # -1 if t==0, +1 if t==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": {
    "collapsed": true
   },
   "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:                    [0.28474532] [[1.05364923 1.09903601]]\n",
      "SVC:                          [0.31896852] [[1.1203284  1.02625193]]\n",
      "SGDClassifier(alpha=0.00200): [0.319] [[1.12072936 1.02666842]]\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=100000, tol=-np.infty, 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": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    from sklearn.datasets import fetch_openml\n",
    "    mnist = fetch_openml('mnist_784', version=1, cache=True, as_frame=False)\n",
    "except ImportError:\n",
    "    from sklearn.datasets import fetch_mldata\n",
    "    mnist = fetch_mldata('MNIST original')\n",
    "\n",
    "X = mnist[\"data\"]\n",
    "y = mnist[\"target\"]\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:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "rnd_idx = np.random.permutation(60000)\n",
    "X_train = X_train[rnd_idx]\n",
    "y_train = y_train[rnd_idx]"
   ]
  },
  {
   "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!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ageron/.virtualenvs/ml/lib/python3.6/site-packages/sklearn/svm/base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 49,
     "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": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.86275"
      ]
     },
     "execution_count": 50,
     "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": [
    "Wow, 86% accuracy on MNIST is a really bad performance. This linear model is certainly too simple for MNIST, but perhaps we just needed to scale the data first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ageron/.virtualenvs/ml/lib/python3.6/site-packages/sklearn/svm/base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 52,
     "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": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.923"
      ]
     },
     "execution_count": 53,
     "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 in two), 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).\n",
    "\n",
    "**Warning**: if you are using Scikit-Learn ≤ 0.19, the `SVC` class will use the One-vs-One (OvO) strategy by default, so you must explicitly set `decision_function_shape=\"ovr\"` if you want to use the OvR strategy instead (OvR is the default since 0.19)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf = SVC(decision_function_shape=\"ovr\", gamma=\"auto\")\n",
    "svm_clf.fit(X_train_scaled[:10000], y_train[:10000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9475833333333333"
      ]
     },
     "execution_count": 55,
     "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": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "[CV] C=8.852316058423087, gamma=0.001766074650481071 .................\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=8.852316058423087, gamma=0.001766074650481071, total=   1.0s\n",
      "[CV] C=8.852316058423087, gamma=0.001766074650481071 .................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.6s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .. C=8.852316058423087, gamma=0.001766074650481071, total=   1.0s\n",
      "[CV] C=8.852316058423087, gamma=0.001766074650481071 .................\n",
      "[CV] .. C=8.852316058423087, gamma=0.001766074650481071, total=   1.0s\n",
      "[CV] C=1.8271960104746645, gamma=0.006364737055453384 ................\n",
      "[CV] . C=1.8271960104746645, gamma=0.006364737055453384, total=   1.2s\n",
      "[CV] C=1.8271960104746645, gamma=0.006364737055453384 ................\n",
      "[CV] . C=1.8271960104746645, gamma=0.006364737055453384, total=   1.3s\n",
      "[CV] C=1.8271960104746645, gamma=0.006364737055453384 ................\n",
      "[CV] . C=1.8271960104746645, gamma=0.006364737055453384, total=   1.2s\n",
      "[CV] C=9.875199193765326, gamma=0.051349833451870636 .................\n",
      "[CV] .. C=9.875199193765326, gamma=0.051349833451870636, total=   1.3s\n",
      "[CV] C=9.875199193765326, gamma=0.051349833451870636 .................\n",
      "[CV] .. C=9.875199193765326, gamma=0.051349833451870636, total=   1.3s\n",
      "[CV] C=9.875199193765326, gamma=0.051349833451870636 .................\n",
      "[CV] .. C=9.875199193765326, gamma=0.051349833451870636, total=   1.3s\n",
      "[CV] C=6.59992909281409, gamma=0.05991666578466177 ...................\n",
      "[CV] .... C=6.59992909281409, gamma=0.05991666578466177, total=   1.3s\n",
      "[CV] C=6.59992909281409, gamma=0.05991666578466177 ...................\n",
      "[CV] .... C=6.59992909281409, gamma=0.05991666578466177, total=   1.3s\n",
      "[CV] C=6.59992909281409, gamma=0.05991666578466177 ...................\n",
      "[CV] .... C=6.59992909281409, gamma=0.05991666578466177, total=   1.3s\n",
      "[CV] C=9.053435975487119, gamma=0.003596490522533181 .................\n",
      "[CV] .. C=9.053435975487119, gamma=0.003596490522533181, total=   1.2s\n",
      "[CV] C=9.053435975487119, gamma=0.003596490522533181 .................\n",
      "[CV] .. C=9.053435975487119, gamma=0.003596490522533181, total=   1.1s\n",
      "[CV] C=9.053435975487119, gamma=0.003596490522533181 .................\n",
      "[CV] .. C=9.053435975487119, gamma=0.003596490522533181, total=   1.2s\n",
      "[CV] C=2.701062804458301, gamma=0.004002330992905356 .................\n",
      "[CV] .. C=2.701062804458301, gamma=0.004002330992905356, total=   1.1s\n",
      "[CV] C=2.701062804458301, gamma=0.004002330992905356 .................\n",
      "[CV] .. C=2.701062804458301, gamma=0.004002330992905356, total=   1.2s\n",
      "[CV] C=2.701062804458301, gamma=0.004002330992905356 .................\n",
      "[CV] .. C=2.701062804458301, gamma=0.004002330992905356, total=   1.2s\n",
      "[CV] C=3.2711787843881437, gamma=0.017596957507461645 ................\n",
      "[CV] . C=3.2711787843881437, gamma=0.017596957507461645, total=   1.2s\n",
      "[CV] C=3.2711787843881437, gamma=0.017596957507461645 ................\n",
      "[CV] . C=3.2711787843881437, gamma=0.017596957507461645, total=   1.2s\n",
      "[CV] C=3.2711787843881437, gamma=0.017596957507461645 ................\n",
      "[CV] . C=3.2711787843881437, gamma=0.017596957507461645, total=   1.2s\n",
      "[CV] C=6.848991127746501, gamma=0.01573529056426603 ..................\n",
      "[CV] ... C=6.848991127746501, gamma=0.01573529056426603, total=   1.2s\n",
      "[CV] C=6.848991127746501, gamma=0.01573529056426603 ..................\n",
      "[CV] ... C=6.848991127746501, gamma=0.01573529056426603, total=   1.2s\n",
      "[CV] C=6.848991127746501, gamma=0.01573529056426603 ..................\n",
      "[CV] ... C=6.848991127746501, gamma=0.01573529056426603, total=   1.2s\n",
      "[CV] C=2.893035364914488, gamma=0.03834647526105027 ..................\n",
      "[CV] ... C=2.893035364914488, gamma=0.03834647526105027, total=   1.2s\n",
      "[CV] C=2.893035364914488, gamma=0.03834647526105027 ..................\n",
      "[CV] ... C=2.893035364914488, gamma=0.03834647526105027, total=   1.2s\n",
      "[CV] C=2.893035364914488, gamma=0.03834647526105027 ..................\n",
      "[CV] ... C=2.893035364914488, gamma=0.03834647526105027, total=   1.3s\n",
      "[CV] C=5.336260835426313, gamma=0.008808538172595842 .................\n",
      "[CV] .. C=5.336260835426313, gamma=0.008808538172595842, total=   1.2s\n",
      "[CV] C=5.336260835426313, gamma=0.008808538172595842 .................\n",
      "[CV] .. C=5.336260835426313, gamma=0.008808538172595842, total=   1.2s\n",
      "[CV] C=5.336260835426313, gamma=0.008808538172595842 .................\n",
      "[CV] .. C=5.336260835426313, gamma=0.008808538172595842, total=   1.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:   55.2s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, error_score='raise-deprecating',\n",
       "          estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "          fit_params=None, iid='warn', n_iter=10, n_jobs=None,\n",
       "          param_distributions={'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10f38c828>, 'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10f38c320>},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score='warn', scoring=None, verbose=2)"
      ]
     },
     "execution_count": 56,
     "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": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=8.852316058423087, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.001766074650481071,\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.865"
      ]
     },
     "execution_count": 58,
     "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 (run this at night, it will take hours):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=8.852316058423087, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.001766074650481071,\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99965"
      ]
     },
     "execution_count": 60,
     "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": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.971"
      ]
     },
     "execution_count": 61,
     "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": 62,
   "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": 63,
   "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": 64,
   "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": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ageron/.virtualenvs/ml/lib/python3.6/site-packages/sklearn/svm/base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearSVR(C=1.0, dual=True, epsilon=0.0, fit_intercept=True,\n",
       "     intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=1000,\n",
       "     random_state=42, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 65,
     "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": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.954517044073374"
      ]
     },
     "execution_count": 66,
     "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": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.976993881287582"
      ]
     },
     "execution_count": 67,
     "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": 68,
   "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=   5.4s\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:    7.3s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ... C=4.745401188473625, gamma=0.07969454818643928, total=   5.5s\n",
      "[CV] C=4.745401188473625, gamma=0.07969454818643928 ..................\n",
      "[CV] ... C=4.745401188473625, gamma=0.07969454818643928, total=   5.5s\n",
      "[CV] C=8.31993941811405, gamma=0.015751320499779724 ..................\n",
      "[CV] ... C=8.31993941811405, gamma=0.015751320499779724, total=   5.3s\n",
      "[CV] C=8.31993941811405, gamma=0.015751320499779724 ..................\n",
      "[CV] ... C=8.31993941811405, gamma=0.015751320499779724, total=   5.2s\n",
      "[CV] C=8.31993941811405, gamma=0.015751320499779724 ..................\n",
      "[CV] ... C=8.31993941811405, gamma=0.015751320499779724, total=   5.1s\n",
      "[CV] C=2.560186404424365, gamma=0.002051110418843397 .................\n",
      "[CV] .. C=2.560186404424365, gamma=0.002051110418843397, total=   4.6s\n",
      "[CV] C=2.560186404424365, gamma=0.002051110418843397 .................\n",
      "[CV] .. C=2.560186404424365, gamma=0.002051110418843397, total=   4.6s\n",
      "[CV] C=2.560186404424365, gamma=0.002051110418843397 .................\n",
      "[CV] .. C=2.560186404424365, gamma=0.002051110418843397, total=   4.6s\n",
      "[CV] C=1.5808361216819946, gamma=0.05399484409787431 .................\n",
      "[CV] .. C=1.5808361216819946, gamma=0.05399484409787431, total=   4.7s\n",
      "[CV] C=1.5808361216819946, gamma=0.05399484409787431 .................\n",
      "[CV] .. C=1.5808361216819946, gamma=0.05399484409787431, total=   4.8s\n",
      "[CV] C=1.5808361216819946, gamma=0.05399484409787431 .................\n",
      "[CV] .. C=1.5808361216819946, gamma=0.05399484409787431, total=   4.8s\n",
      "[CV] C=7.011150117432088, gamma=0.026070247583707663 .................\n",
      "[CV] .. C=7.011150117432088, gamma=0.026070247583707663, total=   5.4s\n",
      "[CV] C=7.011150117432088, gamma=0.026070247583707663 .................\n",
      "[CV] .. C=7.011150117432088, gamma=0.026070247583707663, total=   5.2s\n",
      "[CV] C=7.011150117432088, gamma=0.026070247583707663 .................\n",
      "[CV] .. C=7.011150117432088, gamma=0.026070247583707663, total=   5.4s\n",
      "[CV] C=1.2058449429580245, gamma=0.0870602087830485 ..................\n",
      "[CV] ... C=1.2058449429580245, gamma=0.0870602087830485, total=   4.8s\n",
      "[CV] C=1.2058449429580245, gamma=0.0870602087830485 ..................\n",
      "[CV] ... C=1.2058449429580245, gamma=0.0870602087830485, total=   4.8s\n",
      "[CV] C=1.2058449429580245, gamma=0.0870602087830485 ..................\n",
      "[CV] ... C=1.2058449429580245, gamma=0.0870602087830485, total=   4.9s\n",
      "[CV] C=9.324426408004218, gamma=0.0026587543983272693 ................\n",
      "[CV] . C=9.324426408004218, gamma=0.0026587543983272693, total=   5.0s\n",
      "[CV] C=9.324426408004218, gamma=0.0026587543983272693 ................\n",
      "[CV] . C=9.324426408004218, gamma=0.0026587543983272693, total=   4.9s\n",
      "[CV] C=9.324426408004218, gamma=0.0026587543983272693 ................\n",
      "[CV] . C=9.324426408004218, gamma=0.0026587543983272693, total=   5.0s\n",
      "[CV] C=2.818249672071006, gamma=0.0023270677083837795 ................\n",
      "[CV] . C=2.818249672071006, gamma=0.0023270677083837795, total=   4.9s\n",
      "[CV] C=2.818249672071006, gamma=0.0023270677083837795 ................\n",
      "[CV] . C=2.818249672071006, gamma=0.0023270677083837795, total=   4.9s\n",
      "[CV] C=2.818249672071006, gamma=0.0023270677083837795 ................\n",
      "[CV] . C=2.818249672071006, gamma=0.0023270677083837795, total=   4.8s\n",
      "[CV] C=4.042422429595377, gamma=0.011207606211860567 .................\n",
      "[CV] .. C=4.042422429595377, gamma=0.011207606211860567, total=   4.9s\n",
      "[CV] C=4.042422429595377, gamma=0.011207606211860567 .................\n",
      "[CV] .. C=4.042422429595377, gamma=0.011207606211860567, total=   5.1s\n",
      "[CV] C=4.042422429595377, gamma=0.011207606211860567 .................\n",
      "[CV] .. C=4.042422429595377, gamma=0.011207606211860567, total=   4.8s\n",
      "[CV] C=5.319450186421157, gamma=0.003823475224675185 .................\n",
      "[CV] .. C=5.319450186421157, gamma=0.003823475224675185, total=   4.8s\n",
      "[CV] C=5.319450186421157, gamma=0.003823475224675185 .................\n",
      "[CV] .. C=5.319450186421157, gamma=0.003823475224675185, total=   4.7s\n",
      "[CV] C=5.319450186421157, gamma=0.003823475224675185 .................\n",
      "[CV] .. C=5.319450186421157, gamma=0.003823475224675185, total=   5.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:  3.5min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, error_score='raise-deprecating',\n",
       "          estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
       "  gamma='auto_deprecated', kernel='rbf', max_iter=-1, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "          fit_params=None, iid='warn', n_iter=10, n_jobs=None,\n",
       "          param_distributions={'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x12fc2e390>, 'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x12fc2eac8>},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score='warn', scoring=None, verbose=2)"
      ]
     },
     "execution_count": 68,
     "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": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=4.745401188473625, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
       "  gamma=0.07969454818643928, kernel='rbf', max_iter=-1, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 69,
     "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": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5727524770785359"
      ]
     },
     "execution_count": 70,
     "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": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5929168385528734"
      ]
     },
     "execution_count": 71,
     "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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