ml-ex6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support vector machines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This exercise is described in [ex6.pdf](data/ml-ex6/ex6.pdf)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.io as sio\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from oct2py import octave\n",
    "from sklearn.model_selection import GridSearchCV, PredefinedSplit\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM with linear kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load dataset 1\n",
    "data = sio.loadmat('data/ml-ex6/ex6data1.mat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Examples with features x1 and x2\n",
    "X = data['X']\n",
    "# Examples target class (0=neg, 1=pos)\n",
    "y = data['y'].ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_data(X, y):\n",
    "    '''Plots examples X and their class y in a 2D scatter plot.\n",
    "    \n",
    "    X must be an array of shape (m,2)\n",
    "    y must be an array of shape (m,)\n",
    "    '''\n",
    "    \n",
    "    plt.plot(X[y == 0,0], X[y == 0,1], 'yo', label='neg')\n",
    "    plt.plot(X[y == 1,0], X[y == 1,1], 'b+', label='pos')\n",
    "    plt.xlabel('x1')\n",
    "    plt.ylabel('x2')\n",
    "    plt.legend(loc='lower left')\n",
    "    \n",
    "def plot_boundary(X, clf, level=0.0):\n",
    "    '''Plots a decision boundary using trained classifier clf.\n",
    "    \n",
    "    The decision boundary is drawn at given level (default=0.0)\n",
    "    in the range of X. X must be an array of shape (m,2).\n",
    "    '''\n",
    "    \n",
    "    x1_min, x1_max = X[:, 0].min(), X[:, 0].max()\n",
    "    x2_min, x2_max = X[:, 1].min(), X[:, 1].max()\n",
    "\n",
    "    h = 0.01 # grid step size\n",
    "    grid_x1, grid_x2 = np.meshgrid(np.arange(x1_min, x1_max, h),\n",
    "                                   np.arange(x2_min, x2_max, h))\n",
    "    \n",
    "    grid_y = clf.predict(np.c_[grid_x1.ravel(), grid_x2.ravel()])\n",
    "    grid_y = grid_y.reshape(grid_x1.shape)\n",
    "\n",
    "    x1_extra = (x1_max - x1_min) / 50\n",
    "    x2_extra = (x2_max - x2_min) / 50\n",
    "    \n",
    "    plt.xlim(x1_min - x1_extra, x1_max + x1_extra)\n",
    "    plt.ylim(x2_min - x2_extra, x2_max + x2_extra)\n",
    "    \n",
    "    plt.contour(grid_x1, grid_x2, grid_y, levels=[level])\n",
    "    \n",
    "def plot_support_vectors(clf):\n",
    "    '''Plot the support vectors from trained support vector classifier clf.\n",
    "    \n",
    "    Only the support vectors of the first two classes are drawn.\n",
    "    '''\n",
    "    \n",
    "    # Obtain support vectors for first two classes\n",
    "    sv_neg, sv_pos = np.vsplit(clf.support_vectors_, np.cumsum(clf.n_support_)[0:1])\n",
    "\n",
    "    # Plot support vectors\n",
    "    plt.plot(sv_neg[:,0], sv_neg[:,1], 'r+', label='neg SVs', alpha=0.5)\n",
    "    plt.plot(sv_pos[:,0], sv_pos[:,1], 'rx', label='pos SVs', alpha=0.5)\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107da4cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot dataset 1\n",
    "plot_data(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train a support vector classifier with C=1 using a linear kernel.\n",
    "# SVC uses libsvm. For scaling to a large number of examples consider\n",
    "# using LinearSVC which uses liblinear instead (see docs for details).\n",
    "clf = SVC(C=1, kernel='linear') \n",
    "clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "C=1 misclassifies the outlier as shown in the following figure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x107ffa470>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107e4cb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data(X, y)\n",
    "plot_boundary(X, clf)\n",
    "plot_support_vectors(clf)\n",
    "plt.title('C=1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train a support vector classifier with C=100 using a linear kernel.\n",
    "clf = SVC(C=100, kernel='linear')\n",
    "clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "C=100 correctly classifies the outlier as shown in the following figure (less regularization = higher variance, lower bias):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x108719a58>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ni9uMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107e622b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data(X, y)\n",
    "plot_boundary(X, clf)\n",
    "plot_support_vectors(clf)\n",
    "plt.title('C=100')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### SVM with Gaussian kernel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An [RBF kernel](http://scikit-learn.org/stable/modules/metrics.html#rbf-kernel) with gamma=1/sigma<sup>2</sup> is know as Gaussian kernel of variance sigma<sup>2</sup>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load dataset 2\n",
    "data = sio.loadmat('data/ml-ex6/ex6data2.mat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Examples with features x1 and x2\n",
    "X = data['X']\n",
    "# Examples target class (0=neg, 1=pos)\n",
    "y = data['y'].ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1085b1240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot dataset 2\n",
    "plot_data(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma=100, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train a support vector classifier with C=1 using an RBF kernel.\n",
    "# In ex6.m (original Octave exercise code), sigma=0.1, hence we\n",
    "# set gamma to 100 (=1/sigma**2)\n",
    "clf = SVC(C=1, kernel='rbf', gamma=100)\n",
    "clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x108de3710>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsXXd4FVXa/53cQOggAQIIhGICsaJiQVFiQZFddVV0VURs\ny4qrH2KhlwSk2ZBVUVlFFFAXO7uigEoQFizY0QBBEKQFCL0F7s35/phMMnfuqVPuTZnf8+RJMnPm\nzJmZc97+vodQShEgQIAAAQIAQFKiBxAgQIAAASoOAqYQIECAAAHKEDCFAAECBAhQhoApBAgQIECA\nMgRMIUCAAAEClCFgCgECBAgQoAwBUwgQIECAAGUImEKAuIMQcishZCUh5CAhZBsh5GNCSDfNPk4l\nhCwghOwihATJNjYQQqYTQtYQQkoIIXcwzg8ihGwnhOwnhMwghKRYzrUlhCwmhBwmhKwmhFwe18EH\nSCgCphAgriCEPATgGQATAKQBaANgGoBrNbs6DmAugLs9HWDVwY8A7gPwnf0EIeRKAEMBXAYgHUB7\nALmWJm8C+B5AKoARAN4hhDT1e8ABKggopcFP8BOXHwANARwEcKOHfZ5kTGPt6wYD2AZgK4B7AFAA\nJ5We+xMMorgfwB8AcizXtS1te2fpuT0A7gVwDoCfAOwF8Jyl/R0A/gdgSum59QAuKD3+B4AdAPpZ\n2nPv7fD9LANwh+3YGwAmWP6/DMD20r8zARQDqG85vxTAvYmeP8FPfH4CTSFAPNEVQC0A7/MalJqW\n9gp+2rgdBCGkJ4CHAFwOg6lk25ocAnA7gEYwiPQAQshfbG3OA5AB4K8wNJ8Rpf2dAuAmQkh3W9uf\nYEjebwB4CwYTOQnAbQCeI4TUU7m35N0MVXwFp8DQJEz8CCCNEJJaem49pfSA7fwpin0HqOQImEKA\neCIVwC5KaZjXgFL6BqW0keBnkwfjuAnAq5TSXyilhwHk2MaQRyn9mVJaQin9CYY5pbutj3GU0qOU\n0oUwCPmblNIdlNItMCTrMy1tN1BKX6WURgD8G0BrAGMppcWl1x+DwSCk95a8m0mKz18PwD7L/+bf\n9RnnzPP1FfsOUMkRMIUA8UQRgCaEkOQEj6MlDNOMCevfIIScV+po3UkI2QfDPNTE1keh5e8jjP/r\nCdqCUspsr3hvtzgIoIHlf/PvA4xz5vkDCFAtEDCFAPHEChj2arsppgyEkD6lUUm8H9fmIxi+hFaW\n/1vbzr8BYB6A1pTShgBeBEA8uK8KhPeWvJvhivf4BcAZlv/PAFBIKS0qPdeeEFLfdv4XNw8VoPIg\nYAoB4gZK6T4AowE8Twj5CyGkDiGkBiHkKkLI46Vt5lBK6wl+NgEAMVALQM3S/2vZwipnEkJmcoYy\nF8CdhJAsQkgdAKNs5+sD2E0pPUoIORfArV6+BwmE95a8mwlmO0JIzdL3QwDUKH0/5np/HcDdhJCT\nCSGNAIwEMLO0/7UAfgAwpvSa6wCcDuBdn587QAVBwBQCxBWU0qdgOHlHAtgJw3RzP4APNLtKh2F2\nMSXYIwDWWM63hhH1wxrDxwD+CWAxgHUAviw9VVz6+z4AYwkhB2AwsbmaY3MDr+69EMY7uQDA9NK/\nLwYASuknAB6H8fybAGwEMMZy7c0AusCIrJoEoDeldKfDcQSoZCCUBnk/AaoWCCE1YUTMnE4pPa7Q\nPgvAKgApIid4gADVAQFTCFAtUWoWmQ+gDoDXAJRQSrm+jgABqgt8Mx+Vps7vIISs4pwnhJB/EkLW\nEUJ+IoSc5ddYAgRg4O8wEsd+AxABMCCxwwkQoGLAN02BEHIxjPC21ymlpzLO9wLwAIBeMJJ7plJK\nz/NlMAECBAgQQAm+aQqU0i8A7BY0uRYGw6CU0i8BNCKEtPBrPAECBAgQQI5EJhGdiOikoc2lx7bZ\nGxJC+gPoDwB169Y9u1OnTnEZYIAAAQJUFXz77be7KKXSwoaJzixVAqV0OoywOnTp0oWuXLkywSMK\nECBAgMoFQshGlXaJzFPYguhM0lalxwJUM+TkJHoEAXQRfLOqi0QyhXkAbi+NQjofwD5KaYzpKEDV\nR26uvE2AioXgm1Vd+BmS+iaMWjcdCSGbCSF3E0LuJYTcW9pkPoza8usA/AtGJmeAAAEqKQLtoWrA\nz+ijWyilLSilNSilrSilr1BKX6SUvlh6nlJK/0Ep7UApPY1SGjgKqhFycgBCjB+g/O+AsFRcyL5Z\noD1UDVS6jObA0Vz1QAhQyaZhtQfrmwXfsWKDEPItpbSLrF1QEC9AtUagmbhDoPFVPQRMIUDCMWaM\nvA0PbolPYPIoh867NL9ZTo6hHZgagvl3wBQqLwLzUYAKgZwcZ4TEbrLQ7ScweZTD7bsI3mXFRmA+\nClCp4JXErtJPYPLwB240vgAVBwFTCOA5VIirGwLslqjH2+RRkZmNlwyyIj9nAA1QSivVz9lnn00D\nVGyYAcdjxojbjBljkuPoH9F1bvqx/m/+XRYc7RPMsXnVl5+oLOMM4AwAVlIFGptwIq/7EzCF+MLJ\nAjeJi4jI2M/pECQRE1C9pwrjUhmHDObY3CBeDMzav8qz8dr4Pc4AzhAwhQCeQHWBq0jrTom5zvh0\nmYIbiPrwQguy30eXcerCeo3KvXhtAqZQMaHKFAKfQjVAPGy9pp3e7mzMzS23UYts+V45Ke398Gzm\n1r/N9+PVe8rJYTu8x4zRv4fZXtfmH+9QW6e+CR0/UIA4QYVzVKSfQFPQh67k5lbKdWI+cgpd/4Po\n/ipj0n03Ts1HXn0DHbDup6PtObm3attA+3APBOajACbcLCinBE12bSKckV4xBZ32TkxG9vaq5iMv\nGUm8zEcBU4gfAqZQzeGVTdvNYnRL+L1mHKzoIyeSP+tvlXuqgucncaoVicB7fjdMQTZOnWgxr/wy\nAQKmEKAUTs0XJlQWuF/w27Hq5H7WNn49u30crPt0767Xh+59rUyTB945P5hXoCm4R8AUAlBK1ZiC\nG+Lm52L1wwzhpA+RxOoVY3CjsfD604WKSU01DNfJPWV5LQHcIWAK1RgyAsOzWdv7UIGOmUK1nROT\ngd3E4jYkU3Yf3v+J0FjscDoG1nWy52XBqYZX0XxQVQ0BUwhAKWUvNJWFLluguoTbrTlDdxxuJUtV\nCd0J0ZSB10f37uzntZuSvHh23ffqVXZ6AP8QMIUqArcSEo/gyxaxrq1XVZKXwa2mYP/tFCLixxuj\nSbR5UPXPuDXReElcVeaKm/sHzuT4IWAKVQReSbwq0p8bDcDah9PIHl7fKs/Iuo8bAqPDFFWfU9an\nzrdmma38IK5+m49E1wWMwVsETKGKwLpQvFrgbs1HVtg1C5MYqfbnNgzRLl2znpF1Le+YU6Zo/u1E\ngndS20gUfcTqx62fQWeMTiPWnDCgAOoImEIlhpvYcRF4TEHFwchqrzNOXn9uzSA8QmIn1rp9q75r\nnp1fVVvyQ8J3w+RlffrpSNfVrJygOmsfAVOoIpARNxmcEh0dp7GIOcjs5F4zBdb9/GQK9mucONt5\nzJoFFaLG076cwgvHvRUygcMPM5jKvas6qiVTqIpSgIzY6vbl1Zh4x1Q0Gp2MVqfXy/wMsnepkrTl\nxmfg1qGuArcE1ksC7cQs6AcBD5hCNWMKrA/uBaNIJLNRjeNWgRfSokwTUGEKXo1J9XodTUEn8oen\npfAg05bcRh2JrnHznt1eT6mzCCZdBujVvasqAqYgOKaLiiJdeLEwvYBM/deVRP0cC6+NzJSjck83\nREuHUDkhahVJS1Dtzy4A6dxL9VtUlLWcCFQbpuBVvL0IFWUiVRTJxqv34cYUwfqb1T+vja6Pw23Y\nq+id6UjFidIUVMxpOv3xjjtlRAFTkKPaMIXohzZ+eyHlBConH4l2+nlNAJyYNnT6l7WtDEzBzXnV\n9tbjKn061aCqKyoEUwDQE8AaAOsADGWcTwfwGYCfAOQBaCXrszqbj1hQlZpl18YLXjBbmQbA619W\nWdTaN6tfexvZvPAjHDURgo2Xzm97f16FX1fkNVpRkHCmACAE4DcA7QHUBPAjgJNtbd4G0K/070sB\nzJL1qxp95IX5yEsTlF/Qla541+ogEZqCEwJn71/lfjIp1q+oIb/nmNf9+51f4eVcrs7agRUVgSl0\nBbDA8v8wAMNsbX4B0Lr0bwJgv6xf1TwFkTQpAksirMiTyouF5JVNWBduzUe6hFbVJKHSl2p/qm39\nINp+9u9H327mspP8kOqGisAUegN42fJ/XwDP2dq8AWBg6d/XA6AAUhl99QewEsDKNm3aKL4Apy/O\nfR92uGEqOiYSFYlNloGrgkQyBd2oHdXKok7u5dZ8ZG/rJexj8/OeKvdSgRdzU2V81RWVhSm0BPAe\ngO8BTAWwGUAjUb8y85EXBM9NH6x2bialjoSpax5Rlbgp9dY+7bYfM+w1XuYy3rWJYKRe3s8voUfG\nJFSjidwKU34wmMqMisAUpOYjW/t6ADbL+tU1H7GgGjPuZNFYr/HCJ+EXU3CzaBJtPjKvTTRTcHq9\nX4TJaRSV23vK+pX9z4LT9WeCZQau7qgITCEZwHoA7SyO5lNsbZoASCr9ezyAsbJ+dZiCk8WgS2R5\n17shuk7NDry+ef2ZJpR4Elev+nEi9amYjHgQvVvRNV4JG7rg3cMrTc1+H1G/TpiCE02Qd4+AKRhI\nOFMwxoBeANaWRiGNKD02FsA1pX/3BlBQ2uZlACmyPnWYAm8yqDIFHZORaPHrEAL7gvN6QrP6072H\nV5JuIkxPXkP13cWbSPlpPpI9i5UZyH5UtXY3wRDV2WRkhSpTIEbbyoMuXbrQlStXStsRYvw2Hy8n\nB8jNjW03ZoxxzkROTvT/IrDamvdlQeVVE1Lezv4MXsDavwmdZ64oYD1HRR4Hb17Y558IhRt3YuWC\nH5Tazlt2Ol56PS3qmP07O32Hsmex9yv7X3QfnfGprvHqCkLIt5TSLtKGKpyjIv04cTTbJQ0/zR92\nKUpX6nFyjQmv2lRk+Jk74lYzVE06czL2P9ZsoTc2v5teTnor/7z/7PyoPuz39SIRjvUsMsez1eTk\nVyipVUsPYAAVwXzkx4/f5iMdyBaEqskgyOpUh5Wg+NV3Rbtm++876MibLqUfzq1LFy8mdNnSVrQg\n/yW6c0sR+2fzLjrmusn0ctKbfvLq567GKhu/qn+A9b9sjjuJWrKPqzqsCVUETME24RIRUulEOnRj\ne64OC8DPZ4wXU1CZc9u3z6bLl6fTxYsJ/XheCl20MIkuXoyynyVL6tDt22cLr5v/QUN6Xvvpnsx7\nK7yy1+sKPqptK6LvqSJAlSlUWZ+CaV9kPZ4TWyXLJqnTj6rd3tqn9Rre9X7YUY8dPYYpf38JBd+u\nd9aBAmrXr40HnrsbmWd3kLb101bstm+v/THfLPgBn749EufcuADJKWFh25SUdHTt+nvZ/4WFc7Bm\nTX+UlBwuOxY+loyPnuqCA1vOxyu/TMHdpwyK6qNRs4Z4ZMZ9aN62mfIY3Twz730D8ncuWm9erNGq\nDlWfQpVjCrxJ1707sGRJ7HGVxc+bWE4nnGhRuZncXiyA8PEwxt30NJZ/+A26XtMFyTWTXfXX9KRv\n0e78j5FSfy+KDzTChi+vws51ZyN/xVocO3ocT+Xlou0prZX782qR84IEKHX2fbzAj0t+wfCrxmPA\nrP+iXuoBhSsIsrNLyv5bsaItios3xrQ6dqgxvnxtOMa8/TByb3wq6tz3n/6MBqn18PQX45Da4gS3\nj6AFlUAKVaYtWqOBo9lAtWUKVqgQc5VFzuvHvJbVh6hfFeIjG4P9WpFmpIqSkhK8nNMPzU5+Dw3T\njqBWrTZo33480tL6OOqPJbkmJdVBx47TETl4GQZdPBoAMOWLsWjZoblSn14xBVY/5jHRPfySPFd/\nXYDBl49FszZNcNtzUwHIb2LXFPLykjjXGcyDNd/yvyrA4Mtz0bxtMzyVl4sGqfUdP4Muw9SNrnPy\nXbxYF1UFqkwhKR6DqciwSiF2IktI+cQ1/7a3sffB6lfl3iyIxmC/NjfXkIhUUFg4BytWtEVeXhKW\nLG6JLz+fhO8++xkzJ9yNthe8iUbND4MQiuLijVizpj8KC+eodWzD+vUjohgCAJSUHMb69SPQskNz\nTF44CuFjYQzpMRZff/w9vvvsZ3z32c/YvHYrt0/RM7qVBmXvzy9pc8PPGzH8qvFo1KwhJi0chZSU\nNtJrkpLqoH378VHHeNeZx3Nyor/9ihVt0bjt1xg3bygatPoSS79ohbzF5XNixx+7tJ5DZc5bMWaM\n+pxlQWeNBlBHldYURHZ487g9L8CJmUgkdVrv6cZ2bZdincZ+s6T3Y0dD+OjJs3HJPavQqPlhxlUh\nACVISdHTHGSSKwCs/fY3PHpZLg7vP1J2NimUhFFzH0K3685Tuk9ZrxINTPQNAGfn3BKdzQXb8NDF\no5AUSsKUpePQol0a8xsBNZCc3ADh8G7udxBpZmlpfbjnmzfvh61bXgXI0bLjx46GsOi583DnyFdx\n8vmZSs/ihxal4ldj3TvIWYhFYD4SQOTsUmUKoj7ssE5Ep5PVtI2q3FPUF8/unIQWKMF2yMwWViIj\nA+9eptmjsHAO1q8fgeLiTUhCc9Qmg5BC/oSXh87Guu82YOy8oehyxRnS+5hQMf/Y2+qcs5o7vPAt\n7Ni0E4MuHo3iw8V4aslYpGe1KjtnfTc6zFh0He97GEw/EnP04K56eKX/DXhycQ46nNGWeT+/ia+q\nqTURJr/KhoApKEIl09TJhFSZpDJfhfV/2cJTnfgi6T0lpQ2HaETDbsvmQSS5AuCeq1PzWjxyaQ62\nrN2GSQtG4tRuWdx7uGGyPL+S/ZyOAKGKPYV7Meji0di7Yx+e/DwHJ53ZznlniuB/ex4Ipt/eH8eP\nhfH0kly07niiuLXiHHTiS5P17bUPrzJm+csQMAVFqJiPdPoQHTNhTjgn5ipd89FvP/6ONya8h2NH\njwEAzus3AfUax0a2pKSko3378QyzBXMUUVEvIvAkV5kWsWfHPjzcfTSKtu3BE5+NkYau6jIHngnR\nfs4KaySLG+kzEo7gvnOGYGvBdkxaOAqnXNDRWUea0NUUUlLS0arx//DQxaNRI6UGpiwdh7T0ptz+\nvSpf4bX2IVtvTsZYGRE4mhXhxtFlont3tXZWzcPtve3XsvramL8ZQ3qMxfef/Yxdm3dj1+bdWPVJ\nd9CSlKh2ptMyLa0POnacjpSUdBgb4YWY91ZxhJpIS+uDrl1/R3Z2Cbp2/b3MlFFcvInZ3jx+QqnT\ntf4J9TCs53hs/PUP4X3Md2ouZDNlSRRaKuuLhdxcsWNTBTs3F2H9jxtx5/hbcMoFHWOcvzLHvm57\nE+3bj0dSUh3bUQKDIUSrzOacaN3xRExaOApHDh7F4MtzUbRtD7d/L9YSYLxPSmP7M9+97rfT/T5V\nTUPQhkqGW0X6Uc1odgKnGY+sTEvVY9Z7q2Rhqo5x6/rt9OZW/emNze+mf6zdGnXOmvW6fHk6MzPW\nbLdkSZ2oTNrFi2vQpUtT6eLFhC5dmkq/+CJV2g8Lxv0R87N8eXpUu80FW+lNLe6hN7X8G92ybpuw\nT526SKL3rVIm201m9bYNhWUlKFjvmJetTCn7m4jas64vf/fE9v5J2Tew9/fL8tX0z/X60HtOHUT3\n7dovvY9X1U/N96z6vu3tdCsQVOVMaFT3jGY3UIlassJqsxSpvX5Vh7Rj15YiDLp4NA7tPYSn8nLR\n7rR0x31ZzT+hUGOUlBwApceYbXWc0LJIGSs2rNqEh7PHoG6D2nj6i3Fo2ipV2LeTeHkVU5zoGh1s\n/30H+rb/Bx6ZcR8adLyTadJJTk5FKFRP2+xmR2HhHBQUDEQ4XAQACIVSkZk5tfSb8kxJ7Eiz7z77\nGSP/PBHtTmuDxz8djboN7FpHObxy/Oqaftze17qGKxlplCIwH7kAz7Foz2mwx0ibuQJWE4YZMeTW\n5KCKfbv2Y8gV47Bv535M+HiEK4YARJt/kpPrcRkCUJ6DoNqv1VSVkpLOZSjtTm2DiZ+MxL5dBzDt\nwVelfcdD/XdjKsn/sgAAUCOlBteMFg4XlRLt6HwRmdnNisLCOVi9+q4yhgAAkUgR8vPZjKi0Rcw9\nTZx12WkYNfchrPt+A+aMe0fpWd3CamoVmQJl+Qoq9zHXsAk367Qym6ACpuAQpt1TZsNmtZM5zJwS\nnEP7DmFYz8fQKP1rPDLvM2w/3FHL5iyzVfMIkqwNr1+ev4GFjl06ILNLB+zbtV/pWVRgJzg6hMXp\nov920Y944o7n0Om8DHS9pouyf8ZkuLIENSvWrx/BYeLHwfMXse5pRderu6DJiY2xryg2WMEL4syD\nKArMvsYA9hqTJT7arxf5pGTQTeSrSAiYQil4Ezo729lEF01AlUxmXRw5dBQj/jwRKalLcfWj36ME\n28CT+ExYifWyZU0sEiT7OhUCZm9jmolE/SYK5ncwTRQqTN4NVv1vNXKuewKtO52ICfOHo3bdWhzn\nLxvFxZuY7VnZzWZ7PiJK91URBEyI3qGfDIMF1hpT1TTM6yuztO8G1YYpyD4wb0Ln5cVKIXZiwWIA\nrPt5FZ0BABt//QMvPfI6Xhg0Ey8MmomhV4xD/oq1uHbwhqjMVIAt8dmJtWFiOC68TkbAWMSJV+oi\nP7+fI8ZASyqnoXfjr39gxJ8moEmrxpi0YCTqn1APANuMFgqxfSYpKW20zG4iJm5e5zTSTPc7OGW6\nqszEbGeFKtOxj82pliAaa2ViMNXG0czLShY5jlnHAWcOKC9jrzet3oKHu4/Gwb2HULN2TQBAzZQa\n+NvjfVEz/VLwktNUKmrKrrM6npOTG4NSIBLhl14QJUzpOKYB4Jl7p2P+vz7F8DcGIvuvFypdY4fK\nd/BjEc/KfRuv587FnI0voFnrJsK2Ok546zX2fBAAWL36LoYJqQaysl6N6kvnnkOuHIdflq3GxE9G\n4rSL2ImFKslkuohHfoNX+QlOy9D4iSB5zQJzstgf1cuMYh24mSDbNhTioYtHIxKO4OkvxqFVRouo\n86rRKarZrTrZy6wktaVLmyASKeJex9oTgFem4ejhYgy/ajx+XbEWY959BF2vlm83K0I8F+rrOXMx\na+zbWFTytlJ7nTIXssxxVvQRqy/Ve5YlFm7dg5xPzsRRTNUqx+GU6epEIAHOhTcvBAK3TME+juxs\nw2rhbkxB9FFMRIGqHVPFWeyFvVn3+l1bd2NIj3EoPlyMSQtGxTAEgG3i0amoKbuOBZHfgFdGxITV\nZi3zP9Sqk4Jx/xmKDp3bYtxNT+P7z3+Wjs0K3WgUP9qqQscJL6pGm5bWBxkZU8vMRMnJ9bTvaQ8U\nOEb/i8mLRuOMXluxa9+j2v4iu8ClClXzq9vKq15c5yR4wQq7EMraC8YvVHmmYPcF6ISI+pUJKaq8\nKcLk25/F3h37MH7+CLQ/nR1qqmJzLiycg0jkYMy1hNQstWeLbdV2iIhSOLxbeK2VOYn6MVG3QR1M\n/HgETjypOcb85XEc2i8ryVEO+/t2EwzgtC0PTrKUzWt4ZsDi4k2uHf2860tqfIIr7luDGrWiy2Po\nhCUD/kTpJMJ+b3+OeAUv+IEqyxRYEQVWtVLlQ4lKKVvvI2rPG5sTbN+wA1c/RLC3pIeQeIikTHOR\nW2PXAcOs0KnTDFx00S4l6dQKUey8SCOxayKqMfgNUuuj98NX48jBo9jPCI1URbw1PR50CHc58yDI\nz+8r8QsloaBgoJTRiiBk+JEtzGt0IpZ04Gb/kcoA3vhZEZF+okozBS8iClj9WqEj6bgNzWt/3lq0\n7/aWq/BO1iIHgOTkemU193UlVlHsPC9iKTk5NUYT0YnBJzK7VCl03rduW6+SElU0JMDOPAC5TygS\nw/xNqBJuJwxfZpr0i3jHWzLXeQ4VkxZv/KyISD9RZZmCHaofysmEVW1vxj47nbhdrv8SoWRx2KgM\nokWuKrHaGUdqai+uH4NlzsrKmo1u3XbFaCI6Mfiq0Hnfum3NNrK2MqhqSDyG7gSqSXMyhl8SqRl1\nXOV76b7niir9O5kvlQHVginwuLRTwsAyTZnwSzpZ8vYK1Gsc6wcAYomHSNoXLXJRToHZ19q198Uw\nju3bX0Pz5v24fgxVp6lODL5buHEo2omU0/7M78ST+O3fyjuzDEFqai/mWOxzRsSo09L6oHj7g9i7\nvQ4o9ed7ORWinDibKxLhto9ftRKzF6gWTEGUyei0P9ZE5bXlSTqqE/er+d9h0m1TcWRfA+Z5K/GQ\nSfuiRc4nOuX1cLZufZHJOIqK5itHy4igE3WjC+v7lpn9RIKEbtkSFmJNQdFwFjHGNqkRUtd2jmL7\n9tfK5oRozqSl9UHz5v1QntwWQvPm/cq+yxV/nYCf3huLCZfdgD2rXtb+Xl4mdFrhZG27cXqrPofq\nuOzt/PYjWOErUyCE9CSErCGErCOEDGWcb0MIWUwI+Z4Q8hMhpBerH7/glDDotBdJOioT5Mclv2Bs\n7yfR7rQ2OKXzFKl5hSftFxQMxIoVbZGffxtKSsr3Q7ba9tVMCmzup6OtqJy3w94+qc6nCmONRkUK\nSRWZgngSN38/BOOali3vZc6PUKgW7N/NanYU+TQKC+dg+/bXUL4JTySKoYSSQxg2+/9wTs/OeObe\n6fj8zWVqL6AUTkNSE2micXNvp4wnnrWUfGMKhJAQgOcBXAXgZAC3EEJOtjUbCWAupfRMADcDmObX\neJxA5UPLJqobrPlmHUZdPQnN2zXDhI9HIL3dXejYcXpUGYSkpNpR18irbgJWAmFlEDp1eOzQ0VZ0\nwyRZ7UOpT+LkS/XMKbr2aS9NFPZCfjJTUH5+3xhmyfbPzEJ2NkXXrr8jM3Ma0/zGCws254PIp6Hi\nBK9RswZGv/MITr2oEybf/iwKvlsvfDankEX6OSGcTnwWlbnYnQr81BTOBbCOUrqeGnn2bwG41taG\nAjBtIg0BbPVxPAC8d1z5kXwDABt+3ohhPR9Dw6YNMHnhKDRq2rDsHKXlhDwcLooiqDq7ogHRC9wk\nOnJEmylUtRUVyZQFVnuSVIxL7lmlMNZy6NqnvSpcuOj1JZg3bQEu6n1+2bFQqDG3vYhZysxrrPP8\nOUFQWDhlyIanAAAgAElEQVRH6GdSdYLXqpOCh6bfi5JICTb87E9Iqh+IR8SSU5qTKCe7n0zhRADW\nPRQ3lx6zIgfAbYSQzQDmA3iA1REhpD8hZCUhZOXOnTtdDcqLSeDUvqd6j80F2zDkinGoWbsmHv90\nNJqcWK4ZiMxDgDNp37rADSLCTowDDAbQsuW9QmewjJDo7AkgOt6wmTeROH5i6Xtf4cm7nseZl52G\noa+XT2/FiFrt6DIWDIbNuiHF+vUjhH4mnbDTUA15OW47dM10vDh+vwmnGwLtlOYkKvkt0Y7mWwDM\npJS2AtALwCxCSMyYKKXTKaVdKKVdmjblbxweL3idcm79yDv+2IUhPcaiJFKCyYtGo0W7tKi2IvOQ\n6RhUrbppwr7AZbkFmZnThNKqjJDoxrdzJV0C/La5i6NqqyJHsldEZuXCHzHhlinodF4Gct9/FDVr\nlYdvyjK9rXAbdWR8H74vSBT15UeYsBW6eT52Imn+9oJwiiJ8Kmt2shP4yRS2AGht+b9V6TEr7gYw\nFwAopSsA1AIgLiHpIfyKfLBCZdJYF8a/J3+AvTv2YeInI5Ge1Sqmrcg8ZDUDWYl2ZuZUrvZgX+Bm\nUTRDGwmV3pOfW8CCjJDoEhoekyIECEc2O9qfQeRHsO+e53TxvzDoVbQ8qTnGfzQctetF+350zHyi\ntqoOe572Z/bNM0vFM0zYKbxi4vGoL+SU5sSDVpnwkyl8AyCDENKOEFIThiN5nq3NJgCXAQAhJAsG\nU3BnH9KArsnIScq5rlOq+PAxNGzaABlntWeeF0loIoky2iFdHrFiXeCxYZKRqJh0VcgIiS6hiW4f\nCy9MLFZ45UgsPnwMHc89CfUa1Y05x2J0xjKpEXVMxCxVHPbR9ZH4viARc/E6TNitNma2i2eekBUV\noeCen/CNKVBKwwDuB7AAQD6MKKNfCCFjCSHXlDZ7GMDfCCE/AngTwB20gtbytm+24zblnLcwFn5/\njvC6tLQ+wk1Y7GDVOkpKqo2srNkxC1zXAWy/j5WoANB2iIpgtufF4vtRb8dP6YzFGDt1moGsrFeV\nmaXse7HLYsQKBPHeHc+tKUYnT0jUh/lbl0FVRZORFb76FCil8ymlmZTSDpTS8aXHRlNK55X+/Sul\n9EJK6RmU0s6U0oV+jideUJlovIl9xZnfSPtnmYN4EiWPcKxdOzCmra6j14STEFPd+komnNbbkYH1\nzRKxJaMOs5R9L3YuBC3bw8LsW5anoPutio+w9oX2HzpMvDKHlVZ1R3NC4TQJheWQEhF8QF+llanz\nquYXHuGIRIpiFriI4IrGo6NhuJVK/XJ8xtuR6OY9qJbHUGXy/HYbtcaY2rIxWrRPw6sj38SGVWqa\nm1ttzG2ekOy7J0IrkN3Tb4ZWrZmC0wQYlsnIyxT5DucXSBejqkSp4pg2wSO4qam9hOPR0TDcmKiA\ncoZIw2mgJUByqFWFc3yyYGeqa9c6K2ldWDgH+fl3KpXHUNWq+HMkpDXGmik1MHnhKNSsVQNDrxiH\nzQXbBE9iwC3R9SKk1etENbfPlGgtplozBSv8ims2ISvDbMW5N37tinBaoeOY5mkgRUXzhePRMemI\npFJV80RaWh8c3/IWJlzeGx1arawUDMHOVHlblMpMdYbZ7zjznF1jVNWqeO3KS1uoj7FF+zRMXjgK\nkXAEQ3qMxY5NcYsbkUKmFXjlQ/Jr46B4JbJVK6Zg1htivVwntfFFH8qJOeLIoaNY98MG1EtVq4aq\ngrS0PkhOFjumrVKsmcxk1UBkmoCOSUekufjt4EwU9EpeUyFzFO13bdcYZcXsrO1YwgAv2is5mZ+N\nDQDpJ7fGxE9G4uCeQ3jhodeEbe1IpBPXjfPZi3uL7hlP82aVZQqslyXaz8D823osnhmHx44eQ851\nj2PDTxtBSpoz2/Cii2SOwIwMvmNapU4Rb5pYY9xVtgAVbR1pwuvwUh7s36htW2chxyrQZeZeMUdZ\nMTsrWObI9u3Hl4bJRiMc3i8dW8ZZ7dHhzLY4sJst4PAgkrK9JIAircDJuvYizLaiJMdVWabA2jOV\nBa9q44sgU0vDx8MYf8sz+O7Tn/HQywOQdeqTSpK3qrNSRLRlkSdr1vQHy4xgH4/KFqAyhmDCr+0c\nrbDPj40bvQ05toKnHSUnp2rnXvC0PtZxL/w3SUn1GWeOx4Vx2+GlWcYPc3G8iLrfiWxVlimYMDm4\nOaHs+xl4URtfZQwiTL13OpZ/+A3+MfUuXHnHJcrRRSpx6iKzEKBfIdNASMu5q7tjmNvw0ooASilK\nIiUA+Oa1jIypktyLWCaakTE1RnonpCYyMqYyrncWYmxFJMKrsKrWh/kOREhU4TcVxDOTWPWeQUiq\nBnix5taXLNvPwI8XLuqzsHAOTrriUQz/7B2kdRlQJuWrRBd5sbWmkwqZQKRMk1CBDhHysq6O/b2L\nCqpZ/09nC+9aeHPi+9i5uQjpWa2lTF5WxdSKtLQ+6NRpRlRfLVrcjfXrR8SYEL3I6XDTR3pWK6xa\nthqfzVkqbMeraSTyAcaLYTi5j1tGkmhmWOWYgnVymR/HqnbyJpSTeGfVCctTe03C3TDtMAjRtyU7\n2VpTNQxVVCET0Bur6o5hXtfVYZkQWSq+/f/ff3d33w+e/RivjnwTl/W5CDc+cjUAMZOXVTEVIRw+\niG3bXmEyf92cDpZ/yk1eyL1P98Pp3U/G43c8h+UfypMyWahItnZVVOSxqaBKMQU7WEyCN6Gsx3j+\nCJbkyZuwKhPDrc2XtxNXcfFGrv1eNQyVVyHTyVhlO4ZZN4qp6OGlMiyYuRjPD5yBC649B4/MuA9J\nSfIlJqtiaoVdA4xEimBsV1IO87vINBQrE1i2rIkl/6GcuQBQimBiIaV2CsZ+OASZZ7fHY399Gt8u\n+lHY3l7TSCeMO4B3qLJMgbc1phOYTELH0WWGuIomuFubb2yhOAIecTHBktrVKmSyoTJW2Y5hThhB\nUsiYupvXRO/LpKq92bPSvdhBb3PBNjx9zws4q8fpGPHWICTXSFa+VlbF1ISqf8b8Lrxva2cuRm2s\n6PwHsxyKagQTC3Xq18b4+cPRqmNL5Fz/BA7t549dJ48g0QleVRlVlinYF7aK88aN7ZLVv0zt9cLm\nay56g6iIGYI1DFW1nk10/87H6nWlzXN7nYnWHVvisZunRG3/qGpusJdJFmmKqijashslJRS3DL0O\nNVNqyC+wQNVMoyowyMpt5+f3U2IukUiR60TKBo3r4/qBf8LRQ8U4uOeQ8nV2BNpBfFBlmYIdshhj\nmT9CxixETjEevKzjIyYW5aYDAFwHNItZqJZejjcaNK6PSQtHof4J9TD0ysew8dc/5BfFCUT00TlQ\njThTYcIq5bZ52cqq0A4b1nwnPE2/okYpVSVUG6YgAksy5EVEiBxdPCmVp6WYhGBfYR1Q6m4DE77W\nkR4lnYuqptqZxerVd9nq7FDwnMI62oebKqlWNGvdBJMXjUJyjRCGXDEOhRujSyrY37ssAz3RBMfU\nprKyZgEA8vP7xrwftn8mhPKlLLb5FxTE1lxyAut88+p7WqGzvgKm4C0CpsCA17HJokmbltYH0/r8\nCRs/f8OVWcWt+YFlJjAcmPY6O7Gll3Uqfnpdu//Ek1pg0sJRKNq6B4tej7YJ6QYGuCE4x48dx9tP\nzQMhBCc0b+ToWQD5+7FrFEbSWhIAMx+Ab/MvLJwTta+GGPz9lu2b86h8zyYnNsbJl27Cj6s6eco8\nAniPassUZFKjFSaT8Nsv4QZemh9ksDMWnSgqtxFXLLQ71XimSNidScQpIpEIJt/+LL766Ds88Pw9\naNPpRMd98d5PQcFAZiJiKFQPLAcx792rg590Zp1Xqt+z1emrcO3QH1Cz7m54IQwkIqmsuqDaMgUd\n8EJSWe28VG911XIVZy5Po+CVT2AjumibThSVF1m2XoBHVMxMd1VQSvHM36djydwV+Nvk23D1vVe4\nGhfvPYTDRUxp3It3zwKv6F1KSnrUvFK9/4YNI5CUzA6ddYLAZOQfqi1TUCHgiZx4OvvvipgGa5tM\nlkbBKpongnU8OlFUfu2cBgB7d+xTbsv7tjq7rVFK8eJDr+GTGZ+jz8gbcNOj1zLb6TB31fdgElSd\n9ymrbmpFOLyfUQyPxJQ4V70/j3kcPboRfdoOQM4NT+Do4WLl8QXwD9WWKajAy41zdCErVGckG90W\nwzTWrr2vjAAtXdoEq1ffxUxIsmsULPOTDOZ4dKKo/No57dJbu+G/Ly3Cx6985qofHbyeMxfvTf0I\nf3ngKvTL/Suzja4PRZYwaEVx8SZu+0jkYMw9rDsBynEcSUn1mTkwTrKmeczj2KETcGq3Tljx4TfI\nuf4JHCtm7xURIH4ImALKCbiXmoHbvmRbJLIchiUlh7F164tK2a4s2M1PKoyhuHiT1vagOm118PAr\n96HLlWdgSv+XkPfv/2ld68QX9PaT8zB73DvoeeclGDDlDm4Yqq4PhZ2QyEZKSpuy9qFQtPkvHC6K\nYT684nY8RCK7uTkwqlnTJnjMo/M5z2LY7IEYNP1efLvwR0zsMzVhvqEABgjVEx8Sji5dutCVK1f6\n0jchBoNgaQh+VE81cWXyTbhl2PW4Y9zNZcf4ew+E4DbGHDBsw8XFm5CS0gbt249nEmUjyakvRElx\nZiRSRcDRw8UYftV4/LpiLXLffxTn/els7T4IkUvUH01fhGfunY6Lb+yK4W8MRCjEj9TJy0sC7/3J\nvoFo/4mkpDpRxJfX1vp9VPazYF3LfwaC7Gx5FVQThYVzsH79CO4zvzf1I7wwaCYuueVCZN90IbOP\ntLZN0eGMtsr3tGN/0QGsWrba8fUVGWdfcTpSaqdwzxNCvqWUdpH1o56HX01gjT5SIRB+oX378Viz\npn+UlJmUVMeTGHPTNgwgyqTE2pFr377/YevWF8EiCqpmHxkx8Aq16qRg3LwhePTyscjt/RQmzB+O\nzpec6uk9Pn9jKaYO+BfO7XUmhs56QMgQALPaLIsQy7+ByDFs5iKUv1t5rSvWnBLButczm+GwN33i\nfWvTTMnD9QP/hCMHjmLm6Lew+E22tkcIwbA5A3HJzWymIcKuLUUYdPFobN+wQ/vayoA5G19As9Z8\npqCKas8UcnJiq6gC+j4B1SJ4qrCG/FkXmIgAqNQ+YrWxmgLsyMychoYNL7Tc19BUUlLSlYi7aVM3\nCZGICXmBug3rYuLHI/Bw9hiMvnYyJi8ajazzMpSvF3335R9+g8n9nsNpF2dh9NsPo0ZNeSkLNiFW\n+wZ8hgIUFc2PebcshEKNSzUEYw41b94PRUXzUVy8CcnJjREO744ZS/kY+c8g2vTJzbfuM/IGZN98\nAY4cOBpzjlKKFx6aicm3P4tadVPQ9Wqp0FuGvTv3YXCPcdi/6wDGzRuKJieqO90rC05Ia+hJP4H5\nyAK7ZqBD6N1oFSzzEQ88QhAKpSIt7SZs3/6a7VwNJCc3QDi8W0hkACA72/u5oGLW8AO7tu7GQxeP\nxsE9B/Hk4ly0P13uHxHhu09/wsg/T0SHzm0xedFo1KlfW/lau/QsYuqmOaawcA7Wrh0o2JOZSL8n\nUAOEkCi/kml2AiBlKFYTlYq2F49vfWj/YQzpMRbrf9qE8R8Nw5mXnia95uDeQ3j0slz8sXoLJnw8\nAqdffLInY6lsUDUfBUzBAjeEPZ5MoaBgYJmjORRKRWbm1KjsYt7iNXwEt3F6DiE7Oyy9t64ZyCt7\ntBNs/30HBl00CsVHjqHDGe6Ywuqv1qFFhzQ8uTgHDRqztqhUh4x4qmgApj9C5K+IRA4yAxJMJ7aK\nf0GHoMfrW+8vOoCHLxmD7et3oNN5J0nbF27chZ1/7MLYD4fgnJ5nejaOygZVplCtoo9kUr+uM5kX\nteLFZu8smMTCutApPRLVRlYqmQ+x89ppeQo/8xJkaN62GSYvGo2Tu2YiEi5x9dOlZ2dMWjDSMUOw\n5iqEwwdjcgCs5hiV8tipqb2k75BX0qK4eJNyIpuZl5CXR5CXl4y8PIJly5pg6dImvuz0poIGqfUx\neeEodOnZWenbNWvTBKPfeaRaMwQd+KopEEJ6ApgKwxD9MqV0ku38FACXlP5bB0AzSqmwcIwbTUEm\nzZv+BSevxNq3rtagqim4Uc9lkSeyPpzemyX1qpol4uWg9htsyT/arGd9NlHEkgnTpyPWKNg+Jh1N\nQcVPRUhNdOo0AwDLJGVcn5KSjtTUXmX+jMr8PSsrEh59RAgJAXgeQA8AmwF8QwiZRyn91WxDKR1k\naf8AgISycl611IqSUi/KXcjLSxIuNJFkyIsishJl1Z3B7OA5zE2GwHNMAsDq1XeV2cPNqq3WPisL\n2JL/cYRC9dCt266Y9nJfQXl+iNk/u71Z1bb821m/tTwSSSVwwSicWFAwsOxZyscTnfC2desLlvH7\nG3AQwDmE5iNCSANCSAfG8dMV+j4XwDpK6XpqrOy3ALDrABi4BcCbCv1KYS9VIUtMMtuYsLZRzWru\n3t3/YnhiNVxs0uFfG2ImG9nNRc7GZIBn0hIldhUUDIxJvDOJT2WDbr0nlaxm872b75af5EaZiWWs\naquE1C27yqiFpa7umqYqnU2f3BZCDOAPuEyBEHITgNUA3iWE/EIIOcdyeqZC3ycCsO58srn0GOte\n6QDaAfhcoV8prIRcVuOIR/h1/Qt5ec6K4encQ4VY8BYaL6M0K+s1pqSmYtd2U57CKObGj63n2cPV\nSz9XHPAYJ68WkSyrWaeMhGneYxVJtO7fUFJyBJSW74oWDu+VPZYQOj4LtyW0ZbWl/NjvoSpDpCkM\nB3A2pbQzgDsBzCKEXFd6Tn9rKTFuBvAOpZTp7SSE9CeErCSErNy5cyeriWPYmQZQntUcjxLYOvWV\nTGIhqnUPsBekbnkJ2aJWLU9RviDLHZVmTSZ+3/47oa3wm2i0bz+eUVzOKDrHu1e5dkWRlTXLcRkJ\nFabNFgD0subtZTZ0iu+5KaEtC4BQDZAIGEc5REwhRCndBgCU0q9hOIRHEkL+D2p65RYArS3/tyo9\nxsLNEJiOKKXTKaVdKKVdmjZtymyjYiZSSUgzNQQ3JbCdFMOLRNRC9gxiIG6bktKGOclV90k2FoRo\nahClDYGiFyRgEhpWTSYTJiGzExkTvONO4fWmPyykpfVBUhIraul4WYFDFkEyjxulRoCsrFnc967C\n9O33MYsn6pS+YKMGMjOnRt3HSIpTA0+7VSHUstpSKrWn4jEHKhO40UeEkOUA+lJKf7Mcqw/gAwDd\nKKXCfGpCSDKAtQAug8EMvgFwK6X0F1u7TgA+AdCOKoRCqUQfmTWMdCV7llOZF0nkxgHNM1nd338P\nnn3pBOn1spo4zZv3i0lis9fK4UE1Rl4ldt0JwcnKml3mhM7PvxPRG8jUQFbWq546Jp1EVXmbrxFb\nvsTtN2SPNdrx6xRm1JP1+a1RRaFQY5SUHOAyfT6icxkKC+dEBRoA5VFO1ueX5Uao5E4kKsEy3vAi\nT2EAgCRCSFn6H6X0AICeAO6RdUwpDQO4H8ACAPkA5lJKfyGEjCWEXGNpejOAt1QYgg6clL1mEXme\n1O+mrLZdEyncuBO3pg/Atvcfxsb8zdLreb6F5ORUdOw4HUVF85nSUX7+bVLVWOZLEEUq2aU63Y1z\nrBu4pKX1QVbWq1GSr9cMAdB3ApvEyr6XtdN8DSDE/FZbt053vUNdrKbmdomRMgZoapzt24/H9u2v\nCSvzRoNt+rS/H9VAA1luhEruREXZ+KmigMsUKKU/UkoLAMwlhAwhBmoDeBrAfSqdU0rnU0ozKaUd\nKKXjS4+NppTOs7TJoZQOdfkcUfByq754hKM2a9MUkxeNRlIoCUN6jMW2DYXC9ixTQVbWbHTrtgtp\naX2Ek1mmGstyGVQilcx76NiVWcxG1dzlBroJVzxilZ9/u9DMwWbkBHzbPfu4DqFSCRZgwYg8std1\nImjZ8t6Yb6Bzj6SkOmjZsr+S70M10EDmS1HxtfC+dSjkrj5SZfVTqGQ0nwfDN7AchgloKwD9EoVx\ngOlXMKV4rx3EfuzBbDKwVhktMHnhKBw7cgyTbvun9DoRwZQ5akU2XF4MgalKq0YqlZQcBqUQREvV\nKCU+6nsqeL3IDNv3wZjjIgctP/qpBCJ7dFpaHzRv3g/R75eC9775wQRU+dmdSLopKeno1m0XQ0ub\nhczMaS7uYYQ+Z2ZO83Q/DZkvRcXXYnzr2OKGJSUHfHOAV2RIM5qJETYxHkYSWj0AIymlb8VhbEyo\nZjT7XfZaJTvaCbN44q7n8f1nP+ONjS86HZqSX4BVj4bvAyDIyprFXbgiu21W1ixHFVbtdvvU1F5K\nNnZVez/vHSUnpyIjY6rgWVUD70IASqLGIHq/9vdn5AwcF5hi5P4VXZ+OE5/F0qVNBEX7ymGv0eWm\nX9UKvSLY58mxY7uiQnKt93LiVxC9ey/G7wRe1j76BsARAOcAuAjALYSQt12OL6Fwmjugoyk49TkQ\nD6J9Y+PcY8HSJvhSHxVOYJEJxhpamZ0dRnY2lZqCWFLW1q0vehpFwjN7hEL1hGNTj36KxIxB9H4N\nrclyhB4CpbHHy3Eca9eKE/n4JiuDMLVsOcCVxF5YOAclJQcYZ5IQCtWLOhKJFCE//04sWxZbM4kF\nI5qJXZrcbVgpa56wGIJxL2d+BTcm3ERDhSncXeoHOE4p3UYpvRbAPOlVCYbIryAj2LzzbkNV44ny\nxKTZyvHrogQoHpyYYGRgE2xxmY21a+9Dfv5tys5ZmXORR2BExIoHcwwiZzMbx2OIqxUyCZ3te5pV\nxpgzM6e58tmsXz+CqckkJ5/AYWbHS81vcnNKdKBBLPwQCFhwmjPj1IRbESBlCpTSGFsNpXSWP8Px\nDvEi1LolNeIJU0U2FoBBeEQSoW4ClLkQ7cQpOTkVzZv3w/r1IxzZ/3Wks1CoMdauvS+qro5KfyLt\nRkRg7FFRoVAqMzGNNQZ+RnpEWNHUDbx01tsZJc88Eg7vVjJbyQijrISH9d3o7IWt+k7dCDYq1Qcq\nanRTtSmdLSPYugTd1ER0SmrEE6zEsaSkOkhN7cUl1rpZzzyJi1JEhSkWF29Efn5f5OURJQahI50R\nAmzdOl27PxEDlBEYK6G96KJd6NRpRtk7E4Vcqmak26/jmZD4pqVYuHXSsxglPyihDVSfUYUweh1W\nyi87kmrRTEJl39yJmcepCbcioFoxBVkNJB2CnkiTkZtMz61bXxSq2DqSJW8hRiJFXPOPij1VRcoy\nYWTO8ksy8KQ9EQPUjVu31hAKhWIrv1vHoJKRbr8uI2NqjDZCSE1kZEzlXBkNLyJh+CY9Xl0mtTIZ\nKmHLbsJKdQSCjIyplnPG+N3Y/52YcCsCqg1T8BJeldRggRCgTedfsXx5GybRV13gIqemFW5sm04l\nHRWzgZ1g8xy8MqlUpOnwGKCTzWJEpjT7GNTeW3n12rS0PlHaSEpKekxmrwg6phUeRPOJxVhFErIV\n4XCRVHNR0WBTU3sxr2UdF/XnxbtiISmpfPvWUCh2TlQkVEumwCLYOgRdRauwaxKqmsWFt+7DpQOW\n49ixP8Ai+qqTVodgl++upWda4ElcKmYNXXtq/fqdwZNKW7Zk7yhHSF3k5/fVNpfwNJVw+CC3H51o\nJjVNqIRZ0dSJb8CLjF3dKqw62p6KeVH2/EVF85l979gxl3mc15/X2c0quyVWNFRLpsALIbWakryG\naogqrfMSataKVr2tRF910orCEWNBHJkWeBJXRsZUBYKQJCx1bNeG9u79DNFaDkHz5v2QltYHmZnT\n0LLlAJRrDEkAkkrDDPXNJeZz2ZlbJFIUU4HTZKaiMuC8/uNlb/Zim0x2glcNrgnEPjfkPgZ18yIL\nvHURDhdp9eX1lqJ+aR5+wred1yoD7AlmVsagCi9LagDi3dUA/q5c9knL2vGMlQDGSpwyJ62KNGqa\nN1gQF2KLttnaxywPGaRR0mFm5rSyjNtly5rERPPoPJM5lvXrR3D7AVR2LuMTE/O9GZFTL4K7O9o3\n67D9d/1y8c3bNkXHc4xN7VnbdjqxaRNCohI2CeEJGQasc0MtodKA7rcCxLvV6fTl1bsy4aXmwUro\n9GN7U1/3aPYDbvZotoM3p60VVv2ohiqq4CrLKgZiiZFOJqp9YvFDB2Mznp3umRy9rWcSWE5I0wxh\nVEe9TdqnaIz862PbiyDK1FbZMlP2XdiE0qgxlJk5DQtfy8MTdz6vPF47Bs+8Hz1u7152Lzf7Xbut\nJFpYOAcFBQM1NknS+1ZOvzvrvQDs7WOdwKsKrCpMVTbfVDOaqz1TMDfUscMk3LrlMnhMRLUfY3L3\nBW/DdZNwuiXO5nW8/X3tk5Y1KZ2URZCVxFCVJlljBOTlBUQL0f5+wuGDzASxlJR0iPatNpmG7LuI\nCMbxLW9i/M1TcMYlp2LAlDukUrkVlFK8MGgmfly8CiPnPoyLrj9P+VoeVEpQ86CjJZhwUl6CVxqD\n15dXc1oEr+6hWrJE9N4CpsCBbN9l++vQZQq89jr98Ovr6ElPVvAmp2rdfq8kHlE/gLhKqxW8hSXa\nt8Dcq4EF1vshpCaM9VG+p4N5X1VmKgJ3rJRgcs+bkHnOSZi0YCRq162l1J8VRw4ewdArH8Palb9h\n7LyhOOfKzlrX2xlkJHKQKeWzhAdVwYMH67fVEYB0CXC89lFwq6UB4nkdDT6N8LL2UZUCL3LI6hvw\nsxqqCngOSJmzS5S/wHN4FRXNV0pY88o2KgodFPXFqtMDIOZ5+WWQU4ULkfV+KD2G5OQGzHfjZvtL\nE7yx7ttRG+mntMb4/w5zxBAAoHa92hj/0XCkn9IaOdc9jgcvGln2M+exdyESBlmO/nB4f0yuhP15\neeHSOgzB+o518yt0ky/jtY8CL9JJJ6FQ1dHtRYBCtdEUVHZV09l5zd63ru9ABLbUWhudOv3LsZTk\nRv0HEqspsO7hVvOxw8n7cSsBFhbOwS+r7kJSqLx+UPhYDfy29Cb89f+eR6OmDZX74mHPjn2Y9uCr\n2PsboEoAACAASURBVLdzPwDg8P7DWPPNb7h1+PW487FbmNfwvlEolIrk5Hrc5+WbOIzquCrIzi7/\nBn5L8onccU1Xq2HvRBiLli0HMEucA4GmEAMe0bbCyfad5m8vy1vYJZ79O+ri85cuRCjck3uNLPTN\nbaidF5IxIJbOdO7hVvOxQ+f9mBKeyt7JInw+ow4+nNgZxw41LhvraWe8ir+Pm+0JQwCAE5o1xIg3\nHsTji0bj8UWj8eyXE9HrnsvwxoT38NbkD5jX8DPVdwtzBfgSdgT8cGgrSJS07Lck79WcdgLdUNW0\ntD5ITm4g7ZeXr6GDah+SKoOs2qpf5S6s4XxrVv6GVz7KRY9F32Dxr12ZBEO2gNyG2rFCXJ3YRkUh\ntTr3ED2vKEwWYEv4qu/HLuGxQmpZiEQi2PDzJpREDK3j+89WYcaIN3Dprbfisp73IxRSr4fkBoQQ\n/N8Lf8ORQ0fxyrA5qFO/Nq6578qoNqphz6zzfFORWRLDKAfOjkKiUeGjTsehCq/mtBON0QnDM0q6\niOEFw6zSmoIXvgE/8hZEfbLOdezSAY/9dxh+2NUTQ698DAf3sjYDEUu6sQlTsQW/ZDZO3axaVn8y\n6Uz1Hk41H56dGoCShuEkGen4seMYfe1kDDhrMP5xzlD845yheHnobHS9pgseffUfcWMIJkKhEAbP\nvB/nX302nr3/ZSyatSTqvFMJWp7FbJTE6NZtF7eFlajxxpGa2suzHfjcVpF1WlfKyfxVYYZeMMwq\nzxRYZh23fcqqrcogin6yV101cdpFWQCAjb/8gRF/nogjh45GXaeykKMdpNHJY2vX3ufp9oFuia8M\nTgmXiKirEAhdCS8SjmDibf/E1/O/x52P3YKxHw7B2A+HYOInIzH67YeRXMOdsu5UU02ukYxR/34I\nnS89FU/eNQ3L3v+q7Jyuw5Z9HRvme1IJpmCNw/QZVZRtLp1mLDuZvzKm65Xpq9o4mq0OY6+26nTa\nj5kfIctnEOVRXNBuCRb/2hU1a5VHhKiosbrOQJ6T1+l9vHTiOVHb4+lwLykpwVP3vICFM/Mw4Ok7\ncP2Df5L2z0N2NpCXF3vc7Vw+cvAIhlwxDuu+24Cx84aiyxVnOO/MAtl7chq/H2/nsGyOuc3f0J2/\n1mtCocYgxDArqVwf5CnYkGimIIpQAvjaQ3RZAeN/M9O16zVdcO/zLbFx0yjliaUe71x216jJrbqY\n3RJf815e23tFCWmqmbkqz08pxbSBr+KD5z7G7WNuQt8xN2qN2w63+S+izPwDew7ikUtzsGXtNlzU\n+/yyRLmMs9rjLw9cxU2cE30flfeUCKauA5VnSGQEky4CpgB5ohrgPGzU7N/JteYa4y1yFkyNwbzm\nw+c/wefvj8E1Q75HqEZschVvcelqCvYwRFWi6kVZBLeZoLoJaYCa01GFmL068k28MeE93DDoz/j7\nk7drZSSzYCX+TkKgZcxjz459mHDrM9i+vhAAED4ewa4tu9FnxA24Y9zNMe39IvoyxJMIq9wrHlnR\nXiFgCjb4oSnoQmUxi8ZpZ0KfLmiG5BR2sbSUlHTmItSJ7wdqlBZBi92HNxaGpFZOCGIL4eksFtXF\nLyI8vD6Sk1MRCkXH2wPuakpZ8dbkD/DKsDnodc9lePClvztmCNnZwJIlsce7dy83JanOZd05TynF\nlP4v4eNXPsM9k27DXwdfG3U+URJyPImwqlbiB/PzA6pMoVqHpMYbJlE3mQNrkYqil+xSYHKKKIqD\nHSopCsNr2PBCpdIGLFj3Ni5fsOVhiDwmZYdRNXQ6eMlOVoeuLDyUX055d0wEzIoVbYUOaFXMm7YA\nrwybg0tuuRD/98LfXGkIVh+CE0HGLoSYQ1HRjgkhGPiiEbr68tDZqF2vVlToaryyge3wKoxUBToV\niVXuf/RwMcLHwp6Nz446DWojKcl97FC1YQpWYut1uWtdiMxaOpv9yCp18ogabxLbjxuSkhyivY0N\nxhBCcfGmqD2OWTAYwgvCe1kXpCySSCfO3Qsit2n1Fjx7/8s4/+qzMXimOPfATfVdK2RCBEsDVUUo\nFMKQ1+7H0UNH8ez9L6N2/Vro0deouqqbQ+ClNK1KhN3CyzLaC2YuxjN/fwnh42qZ3U4wZ+MLaNa6\niet+qg1TEO2KJrvOjwQ1t/kMAHvS2uFGcuMtfJb5JS2tT1mGbyz4eydYYWgIfNgXpJuEPTuRCoUa\nc3wl6nHfe7bvBQD0HnS1NNRUN/Gxe3f2cb/3CjdDV0f8eSKevGsaaterhW7XnadFMJ0m/CUaXmkl\nX7yzAk/f8wJOu/hkXHDNOX4MFQBQr1FdT/rxlSkQQnoCmArDi/kypXQSo81NAHJgiJQ/Ukpv9XNM\nuvAra9mLPqMnLa9ctPNkFt7Cb9bsJmY6vcoeA2KTDF+KYpmfZNIqb1EDiCFSRrG3GrA7oCvK5uqs\ncFQW/BBiataqibEfDMaQK8Zhwi3PlIauqhNMmUZXkeFWK/n64+8xsc9UZHXNxLj/DHVc4DCe8M3R\nTAgJAVgLoAeAzQC+AXALpfRXS5sMAHMBXEop3UMIaUYp3SHq18v9FFSQKKe0LvxywLF2e+IVnAPU\ndiPjhQ/m5SWDzRhCyM6OtcV6HetergFthBmNpeoLAYAfFq/Co5fl4snPc3BG9ikx570unMiC2ygl\nEQ7sOYhHLsnBloJteOLzHGSdl6F0XUUJT/YblFJ8OvsLbN9gkLDwsTDeefo/aJPVCk98NsYzSd4p\nKkJBvHMBrKOUrqdG+MpbAK61tfkbgOcppXsAQMYQvIJsQTgtj2E/r7LwvCIGZvZnzZptQCmwr7AO\nQoeHuV449izfoqL5QqlPZV9envbSsmV/reNOM29FDmhexrcsY/bY0WOYM/5dJIWSkHpiY2YbXoY9\nSwvwYu7ICjXqzr36J9TDpAUjEaoRwvx/fap8HV9bTVLKRHZaSiLemDHiTTze7zm8njMXr+fMxRsT\n3kObrFaY+MmIhDMEHfipKfQG0JNSek/p/30BnEcpvd/S5gMY2sSFMChIDqX0E0Zf/QH0B4A2bdqc\nvXGjen129tjUpX83bVWu9UMTObTvEB69fCx+X/UHJswfjs6XnOpZ3zpSnxNJPjr6KISWLfuXlQJW\nlRZl7US5GsnJjZQ2k7EifDyM3N5P4sv/fIvBr91f5owVQRYirTN3eBqBLHTV6dzr03YAOl96Kh6d\n8Q+l9qKd1ypiFrMTvDnxfcwY8Qb+9LfL8cC0e8qizgghURFofvkoVVARNAUVJAPIAJAN4BYA/yKE\nNLI3opROp5R2oZR2adq0aZyHKIcXH9nLiVK3YV1M+mQkTjypOUZfOxn5XxV41rdOIS8nknxm5jRk\nZ4eRnU2RnR2OYggq0qJKO34NmQg3BJdfTjqCx+94Dl/+51s88Nw9SgwB8DYCjqcRWHMc7JtIxRPm\nPGBpjip1ghIV/qqKD577uLTibTc8MO0ehEIhJCUlISkpKSYkWZZMWxHgJ1PYAqC15f9Wpces2Axg\nHqX0OKV0AwytQc1QqQkVkxBrsfAWr1UFz82N7Vd2H3sbrx3aDVLrY9LCUTghrSEeungUrm9yJ65v\nciduTLsbc5/40HG/uoW83FahNKFaeEylnYhI8cBiepRS/HPAv7D4zf/h7ol9YkpQi5CXx54nTuaO\njmmTN1915h4hBLu27Bbu3gZEV8k13r8894QFNxVxdaup6l6z8LU8PP9/M3DBteckpOKtH/DTfJQM\ng8hfBoMZfAPgVkrpL5Y2PWE4n/sRQpoA+B5AZ0opN2PKC0ezF/sos9R/N+YjWVunaueOTTvxwbMf\n49hRI6rmjzVb8N2nP2PAlDtw/UBnBdp45hmZ2caNs1DVbKVj3lKtA8WrbfTSI6/j3Sn/xS3DrsNd\n450Hzbk1H1nBy4JmZc07NR/NGvs2Xs+Zixsfvhp/e7wvM0GPbTKKznA3ISu6mJzcGOHwfrBKkzjd\nidCLa5a++yUe++vT6HzpqRg3b2hUcUor4hFgoIKEm48opWEA9wNYACAfwFxK6S+EkLGEkGtKmy0A\nUEQI+RXAYgCPihhCRYNdYgP4H5l13LpIrf2x2jpVO5u1aYr+T9yO+5+9G/c/ezcmzB+BC687Fy8M\nmolPZnzuqE+W9C8z24jOq0hnqtKijlQp2s9ZZvKaPe4dvDvlv/jL/Vdxt7VMBPLy+OXidearCLeN\n6o1r7rsSbz/1H8x57F1mG34iYzQDEW1kZM6VcLgIhBCEQqlQNUM6KWmtc803C37AhFufQafzM5Hz\n/mAuQwDc7cyoG9ziBXz1KVBK51NKMymlHSil40uPjaaUziv9m1JKH6KUnkwpPY1S+paf4zFhTQTS\nUcftba2gNJbzW01PLKKek2O08WobTxWEkkMY/saDOKvH6ZjS/0Usmbvck35lC4p3vqBgoJKvQNVs\npWPe4rXNzJwqNHm998xHeD1nLq64IxsDnrlDq5QF69uyEtPMuSOaCzp+CRZhciqpEkLwj3/ehR63\nd8drY/6N9575KKYN3yREHW1kROkxJCfXUzZD8v0QG7mCh6rvYtWyfORe/wTST2mN8f8d5mvugUwY\n9MNHUW0K4lnhl/nIyT1NsxDrvJ9q55FDRzH8qvHI/7IAuR8Mxnm9znLVn8xso1uy2+k+DjrtdNua\n6FXrljKTQShZz4asa7JxE53GMzl6FfEWCUfw2M1TsOy9r/DQv+7FVXdfVnbOTcSQF3kN/AgzEzWQ\nnNwgai8CXhKofcxDrhiLP1ZvxfMrJ+OEZnp7aeuagWXfSo9mVY7oo0oF3scUOaNlWohJ9Fl9uFE7\nZahdtxYe+89QtD+9Dcb2fhI/5v0iv0gAmdlGN7OaJbXxzFZ205OOc9uJI/z4sTA6nZuhzBASFYLo\n931DySEMmzMQ5/TsjCn9X0Lev/9Xds7pzniAc8eyFfKtQY+XRpqVa6epqb2Uxny8OIwTM5prMwRV\nyOiG02ADVVQbpqDyImXquFVqt6r3Ij+CKlFPBOGo27AuJn4yEi3ap2HUNZOw+mvnoasyIsA7b9iJ\nY6FCACpLUpMZ7aO6iP1e9F6iZkoNjH7nEZzarRMm9X0WX/73WwDOEwsBdwzFhMrWoFaUlBxGUdF8\nT7aKFUFk7rESfVnSoV/CIlDNmILsReoWygOc2fScLHqdkto6aNikASYtHIVGzRpi+FXjseFnkcrN\nh4wI8M5nZk51TACc7o/rBof2y0p4sGFV8WWL2Mmi9yJLPztb3AcPteqkYNx/hqJD57YYe+NT2LDK\n0PKchiO7YSj2frp2/V2ZMRQXb5KOmVKKwweOaI1DFSoMIx6oNkzBKbyS2uzJQ14uerfOpiYtG2Py\nolGoWbsmhlwxDpsLtjnqx7qgTButilknKal2WR+hUCqaN+8Xcy0L8U5qOnq4GKOunoRQcghnXX6a\nsC1r3vgJ2RxQmXOsUFZV1G1QB4Nn/gPHi4+j4Nv1zjsqhVf5LYCKKcmATDullOKlh1/Dbz/8jnN6\nnql8fzc0RLZdrx/bAFRLpuBF1IZuAlBFVP2taNEuDZMXjUZJpASDL8/Fjk3sHd1UoJt9bM0ijkT2\nY9u2V5RMQl7YnlVxrPg4cm94AquWrcbQWQ/g1G5ZwvbmvGHNNVWCYEanOUEi5lvN2vywzETCrnmE\nQqkwquKWQ0U7nT32Hbz7zEf4ywNX4cZHruG2Y9VA4zFkFf+BCL58Z0pppfo5++yzaaJQFkQrOaaD\nMWOcX1c+vcp/nPZnouC79fTaRrfT2zPup0XbdjvqY/nydLp4MWJ+li9PV2qnci2llG7fPpsuWVIn\nqt2SJXXo9u2zHY2bh/DxMM3t/QS9nPSm81/+VPt6c47ozhVZe9EcEF1rPde9O7uP7t31xkoppVvX\nb6eXk950wczFzPPbt88u/eaELl+e7vl30oHuWN5+ah69nPSmT9z5PI1EIsK2qu9eds7rNQ5gJVWg\nsdVSU3AKP1Q1p5zeL2fTSWe2w/iPhmP31j0YeuVj2L/7gHYfqmYdHTMPLxrJb8cgAKz4z0osffcr\n9H+8b1TYpSq8njdOHI48iTQ7m92H6v4NVtSqkwIAWLnwB5SUsIsjOg0KcFKyQgQd89SeHfsw/dFZ\nuPC6czHoX3/X2vLS7qdRnQvmN/RjjcsQMAUN6NRGqihwMoFOuaAjcj8YjM1rtmLEnyZqO9bcZh+r\n9hmvGvuH9xvP3+2G8xxdb34D1d32VMOYda71O2IFAE5Ia4Tbc27C4jf/hxcenAlq3gz8oID8/Nuk\nRL6wcA7y8++MYij5+XdqMQY3TOXowaOglOLCa8/l1jbivXu7n0Y1mCQ3N3HF8wKm4BIVwVcgIjZO\nJ9ZZl5+Okf9+CGtX/oYxf5mM4iPFyte6yT4GaijZeytLOKoVqn4EJ8TbC+GEt+WnDm4b1Ru9H7oa\nHzz3MWaOKi9QINIKZd9u7dqBsNY9MnC89Lgc8ZgrvO+m2wfvWDyFz2rJFLwk5E7CWL2GX/1ecO05\nGPLa/fgx71dM6vus8nUis469cmbz5v2i2mVlvYpOnWY4KoXgVzhq+Fjsrm9uofvNVBySqgyFR2Cc\nmIzsIISg/xN90euey/DGhPfw4fPG9igyrVD07Vh7Z7OO87QBt3PluIPvzzPVicAqo2MNaIkXgjIX\nGmAlqjktjeEnvC6P8fzAGfjg2Y+xIPxvLXuqFYWFc7B27cCYhex0y1AvSiGoYMu6bXjo4tEgSQSv\nFTyLlNopnvSrMhfclKmI11zjIRKJYMBZg9EgtT6e/DxHuNFONEiMKTAvj08Vs7ONhxRVOM3P7wun\nc+XIoaMY1vMxrP5qHZ77eiJO6txOMv7o7+b0O1gZgVffMShz4QPiYeNTJdqy8Fcv7ccNUus7u7AU\n5oJlSXwqEhtLAoxHOOqOP3ZhSI9xCB+PYPLCUZ4xBBXo1sixI9G+rlAohHonlG9BqZ5hHGveSU5m\nZ71bj4u0AadzxQhDfhL5K9Zi2Oz/U2IIgHvNnSV4xjOjvdowBS9LB+j0pXtfVcZTGXZwMsEuo1wO\nkb2ZZw9WrVMj6lfkeNxTuBdDeozFgT0HMWnBSKSf3JrTkzp05oLo+6o6rCsazIifrKzZ0mQyq7CQ\nkTE1xs9ESE1kZEwt+4684nfFxZsclc2IhCOYcOsz+Hbhjxg0/V50v+kClUeMQbpaMnUU7Pkp8Yo6\nMlGtmIIT6Zm1kHNz2SWvvbyvV7ATkEQQC6c7awF8CbCwcG5MJrSqGUrmeDyw5yCG9nwMuzbvxviP\nhiPjrPbSPu3gSfpezAU33zAhyVA22H1OPJjzJi2tT4yfqVOnGQBg+Y5spKS0cRS6/NH0T/G/97/G\ngCl3oOddlzp5TACA0+3kE8nUqw1TcAodou5UeleVIJ1oO/ZzidAwRERfJrHx90YuisqEppQfNmvX\nCgoKBnJNDYcPHMHwXhPwR/4WjHnvUZx6YSduvyKwQgpVSlH4XQgvEfX5WbDmCfBMStZ5w8orkGmg\nhNTUKqJnxe7te0AIcbw7oVOwnM3xLoZYLZmCl/ZWnb54G6eoMh5WO1Z/FQ282jPJyamleyWDa8pR\n9RHwfBMsrcDKTKwoLt6EMX+ZjLUrf8PwNx9ElyvOULq3E8SrVLpqGGwi4bQqqkwDTUqqr7QroNdw\nw9wTbVkAqilTcPqCeQxAx78AOK+s6hTxkEBFYKnvWVmz0a3bLuzb9z/k5/flLljVYmYAe1ctmTRp\nxZH9DfHD4l/w6Kv/QLfr9BPVeFIe692L+pDdQ+e75ebKv79uaW/dMcsgM+9YNb2lS5tg2bImpdFn\nYvIViewGEJ/wZes78CtnIV6olkzBS3jN2XmMx8pIrOFu5m/RIo6X9CFy3vI2yNm69UXYwwWtC5ZF\nMHjRKEBsIpRqKQ1CauOTf2aiz4gbcPltF+s9eCns7xnQ10pFmy6Z53WFCpXv74aIeWFy4pWdsEv5\n5WZDCiAi7NPUMuNRTVflHei8J9NvmQgETEEDbia/jt9Apa9Eq5h2OFHRDcLPpj7WBWsnGBkZsXsw\nWGFlKjzzUyiUGsVoWqQ+jV8/b4Pm7dOkz6oDp1Kj35ohr4313hUhwk1H07PCan7imyCpJ3WU/EKi\n1nPAFDyEbCMcVb+B9W+vzT5+SR9OatuIJDWRL0El5t3sm2evzsycGsVoGta/gduXE4wZo15+RCVb\nmWeW4pkpeXPN6tditTHLwssQL5OkqjSflTWba34SmSDd+BdU34Ff4et+IWAKEujmJLiFnVjYF649\nK1mXyPs1wZzUtuETfiJ0MloL4QHsAmVm36rhiIUbjf0jkpIEBn8OeHPBfly0l7dIYGCZpfwKZzVL\nXahotNYxmSHaXs8v1UADUdVTmRDh1L8g0gJ5viPRdxPNg3gGDARMQQI/TDVupHW7FJfoyBETTmrb\n8CQ4QuogP78vU8Owm6lYdmV75IqsTPKm1Vsw7san0LR1Ks7p2VnypLHQMbPEWxJUmWumoEFIbFVP\nVWLvl6lJJdBA5GMyYc4BXl6El/4FEbNw+q1VfRZezKWAKbiA3dTj5DrzfxmxSHTZAhlUFq914ZnS\nvmFyMqR9Y3HXAKWHwPNL8G3MIagmJlmx/fcdGNJjLEgSweOfjsEJaY2UrnMC03wjEjBUibjOPVXa\nmITMhCoRs5qk/IBMyjczmwG18th+lUdR/W6qzNPpO/WCOQdMQQP2D2X9AG6d0IDYfKC7/We8oWLn\nNxdetLQPABELQ4kukWzXMPgSXYn2fr67t+/B4MvHovhwMSYtGIVWGS2UrgOc239VEthk9zLnglff\nn+ezkJXSNuel+Ux+zctyTY/G+A46dZqhlYvgNCdChTmyYN+bXRUmo9YNGDDbufkGAVPQQKKIcEWM\nNmJBVNvGuvB4TmlRUpkJLyW9Ba/mYdv6QoyfPwLtT+czMxbMb2Iuet1v4nafcC+/P88/ICulLRJm\n8r8qQMG369GgibtiinbwTIGquQhOd+tzU63AqQCh8t15mpobU1LAFDQhCuVzkvRjv948XpkhW3i6\n9lsrwXcq6bFg7pPQ6dyTtK814TRSxyv7rx9QIVaiebv+p40Y0Ws8TkhrhPueudOnUUabi0QF8ezQ\n2YrTC8SLqZtw27+vTIEQ0pMQsoYQso4QMpRx/g5CyE5CyA+lP/f4OR4vIHIi6X4MJ5OlovsWTIgW\nnih3gEfwTQKQn98XSUm1EQqlwokPwQ/E85v4fS/VCqy8qLjNa7di6JXjkFInBZMXjUKTlo19GWds\nwAEbvG1cVbbm/K3kRl8DA6zmYRm89jWJ4BtTIISEADwP4CoAJwO4hRByMqPpvymlnUt/XvZrPJUB\n8Qw7SyREuQMsDQNAFAEIh4tA6RFkZc1yJekd3HvI0XW6Nn4WETWPO7m3n3Daf06OEdY7uMdY0BKK\nyYtGo0U7bxMBrVBJanO7jWuHpLd9jTw0tUxVbVOljReMwU9N4VwA6yil6ymlxwC8BeBaH+8Xd1g/\ngNuPoROZUNkhMi+pVsN0W7vmi3dW4INn5+P8P58NIipGxICXRL6yw5z3u7fvweAeY3F4/xFMXDAS\nbTqd6Ot9xSbIirGNqx2q+QZu7+EWfjKFEwH8Yfl/c+kxO24ghPxECHmHEMLcyYQQ0p8QspIQsnLn\nzp1+jNURVEJSdUxJ1QlW4t++/XisXz+Cq857Xbvm64+/x8Q+U5HVNRPD33xQ+3qnviDzfEWOItNF\nTg6wf/cBDLliHHZv24MJ84cr71DmBvyAg3Shr8DpXPJCAucVJzR/y7TNeMG3PZoJIb0B9KSU3lP6\nf18A51FK77e0SQVwkFJaTAj5O4C/UkqFO1qw9mg+fvw4Nm/ejKNHj3r+HFbs3Qs00gxj37hRvPvS\n3r3Avn2xxxs2BJo3r4VWrVqhRo0aejf1GLPGvo3Xc+a62qOZB9Heuuai5u2slZKSXpqQpI4fl/yC\n4VeNR/rJrfDEZ2NQt2Fd+UUcmCGZdqjshU2IXtE5GXZt3Y0tBdu861AVFPjXkFlY/9MmPPbfYTjr\nstPiclv2ns8EAEVKSnrUHs9WqM6lmaPfwhvj38PCyFzPxmz/5ub/KnPBi/miukdzsrvbCLEFgFXy\nb1V6rAyUUmsM4ssAHndyo82bN6N+/fpo27attilABytXAllZetccOqR+zcqVQJfST0YpRVFRETZv\n3ox27fyXvBIFfs2kfgAMjaJ9+/FMxqEbcbTmm3UYfc1ktGifhomfjHTMEEwzgJ0pWBdtPEwFJlZ/\nXYDBl4/FkYP+CkU8JIWSkPPeo3FjCACiQlENIm8wBKC8rIq1nQmVuXT0cDG+/+xn1K5Xy/U47XPE\nJE8VOWDET6bwDYAMQkg7GMzgZgC3WhsQQlpQSk3x5hoA+U5udPToUd8Zgg62bjV+TJiKTcuWxo8K\nCCFITU2F1VwWT0ITL/DV9kjMwjbrHaWktOFKgjxsWLUJw64aj4ZNG2DSwlFo2KSB4zFbw0n/v71z\nD4+iuhvwe5Jgwh1BQe6gEAkSRImVPoqFagHjtU/BG1gQ1A9QS1H8CCJWrooK2FLQWovSikUptVLF\nK58pVEAMtYAY7g1IUSRBwiWQy+Z8f+zOZnczMzszO7O7Ied9Hh72MnP25MzM+Z3zuxrZgvRcTs0m\nCKfX9T/b9vPY9bNp0bo501Y8QoNzvHyk9Tm/Yyvad7Me+OcWmg1Kb/Wv2Qki75Fo91JlRSUzhs2j\ncONupiybEFP/QhcOZjsCs5xYbt8vVvBMfQQghMgFnsefg2CJlHK2EGIGUCClXCWEeAq/MKgCjgLj\npJQ7zNrUUx8VFhaSZXcJb5HICV7D6gQfuvq38lta2xqFhYW88UZWUBfp4eUyxEv1kVnRdXCmIork\n4O5vePiaaaSkprBg3cyYvWKEMBYGoXmEzK6VG9dS+7tS01KZv3aGp94+yYy/4I7eYAoGDKi23I6v\nysfsu55n3V82MvGlseTee21M/YpUD8VyzeOpPvI0TkFKuVpKmSmlvEhKOTvw2RNSylWB11Ok/RJK\nywAAFulJREFUlJdIKS+VUg6MJhDOdtq10xdAifZKqiyvjH6QQ6LlTIo1UdnJY6eY/JMZVPuqY3KT\n1HNDhfCIZk1QeGVIXvfXz5g+9Lngv0kDf6X7dxn54Vv1z69L+P8GfQ2B3Sj3Fx9eyrq/bGTsvJEx\nCwQ9p4LQz5OZ+O81k4DQ1MvRVBGhOwI7q/7Q82Ph2DH//5E3l9dbSI0vP93BW79ezcVXXOSJek4b\nd78NoXbG08gH2861A9i7pYjvDhTzxF8m0Tmrg+N+hqruIldtmoAwOyZW/rFiA3PuXECrdi1p3MIv\nRNt0ac1DC8eEuX9GGmDLy/dTWDiCXbvG4vOVo+WWCtW7g746xe5Yx5vDh5exY8doQG830MC2zenT\ntzdx1a1X8LOJNzruUyzOB2ZtxtMGUe+Egt5DY2SUcgMrQsHIBgG1PZPiJQwAdv9rH1NvmEOr9i2Z\nuSrPsVCINrlor6MZAGO5dk3Pde5lFA2rUcBOr9tnq//F0yP8LrRPvf84DRvrG0APH15mKFx9vpO1\nPquuLmPXrglIebrWmJaWfsq33y6N23PihH37puIPgapNWlozR/1s0qJJTH3SWxjEulvUbBLxot7l\nPooleMVsgi8qKiIrK4v77ruPSy65hEGDBnH69Gn27t3LwIFD6Nu3L/3792fHDr+GbO/evfTr14/s\n7GwWL36cAQOaBHchOTk1/zR31ngHSO0vPMiUIbNo0qIxz3w0zXFKaasRpFYSlSUy8CiUSCGgd030\nMuo6SYy25R/bmTH0Obpmd2L2O1NMBYJ/0javWxyJz1eiO6aHDr2UFGMNxmovM9ViVdXReHUvKlZX\n+cmiWqp3QiGWQKhoq/7du3fzwAMPsH37dlq0aMHKlSu5//77eeihhWzevJnnnnuO8ePHAzBhwgQm\nTJjAtm3b6NDBmlojnlvI5+5ZhEhJYe5HT9C60/mO27EzkUdLVObk2u36fC8AaTF45eh5Edk9R/vM\nTtqEHZt2M+2mp7mga2vmvDfV1IXWaS1jY/SFS3n5fvLzBfn5aeTnC89tE2aLCjObQXp6p4TbULRd\nvdXJ3k6JVi+pd0LBqyIbAF27dqVPH3/lrr59+1JUVMT69evJyxtGnz59GD36f/jmG78H7oYNGxg2\nbBgAd91V46mrJ3hC6+rGi2NHjpMz+FJbNQb0cDMa2e61W7NsHb+f/BpX3nA5WVd2t/17Gk5z08Ty\nYGuups3Pb8bcD6fR4vzmpsc7Gc+UlEYmVcuiTQ2+wO86r3FsBbNFxYUXzkaIc3TOakCrVrmWcxx5\nhZF9Idmpd0LBzdTLkaSnpwdfnzqVSmHhURo3bsHrr/+bl1/+N0uX/ps1a8xDMfSEQrJsK53gphC2\nc+3Wv/05z4z6LZcO6Mm0Nx8mNU2/lrNb6D38ZjsDs13fwd3fMHnQTM5peA7PfPQE57WPXm7SyngK\ncU6t7LLdu/+61pjqT7TGeKlWMltUtGkznB49loQJttTUVmRlvUJJyeqkUX+ZYZaKP1E1VOqdUHBa\nZMMuzZpB9+7NyMzsyscfryAnxx+lfOTIFgD69evHypUrAVi+fLmrv51MuCmErV67rWu/Ytbt88nM\nuYjpf5tMesN0/QZNiGWVH8sx331dXONC++E02l5ozYVW37W3QWDCrKlS1r9/cVA9V1r6KYWFI8Mm\nz/T0zqSkNEXfo8cYN2sch5Kaqp96W/u8TZvhXH11MQMGSAYMkPTvX0ybNsNdz5dlBzv3jte1FpxQ\n74QCeFNk4/Dh8PfHj/s9iiZPXsbbb/+BzMxLuf32S1i8+G0KCmDKlOeZP38+vXv3Zs+ePTRvbq4e\nqKtYncit6n+tXLuPluaT3iid2e9OoVHThsHP7TxoVgqXGD38Ri6J0fj+8DEm/2QGJ4+d4qn3H6dz\nT938kLrojXNW1iuBCbM6GACojfG6dU05dOgFIm0HrVrl4vPZN9K6oX7Vw8jhLZojnJdq4mho947T\nqnyRRLt33BYg9c4l1StSU7vw5ZdfBt/PmDEp+Lp9+/fJyQmPcygra8/GjRsRQrB8+XJ27twZ7y7H\nDS0dQSihbqqpqS2prj4RdC+M1f2xWkoaNWtIs5bhpSDdrnZmFJegN2FF+90T358kb/Asig8e5akP\nHqf75RcC9uIy9MZZI9KdV89FFeDQoZdIT+9kEGVek18oFLs7Pzt/k5EXUTTvIrfyZcWCXXuCWbqL\naL/j5n1dL3cKycDmzZvp06cPvXv3ZvHixcybNy/se73I5rOFSI8Sn6+klr95pP43np4kkTsA0N/+\nR753qm4qO3Gax3Ln8PWO//LkW4/S66oeQHR3XjtjYt07yWeo8mvXbmxgJxJKKhdcMNKy8Db6m3bt\nGq/7tzhd8cdLTRwNu7W4kwElFGLg0CH/6l8LNtNeR07omvE41Ijcv39/tmzZwtatW1m7di3dunWr\n1XaiOH70BKeOnUKkeJNg0OoEpel/7VTLqq6u5nDREVICfXdiG7Cq5w1dCWrF7u3qhivOVPCrW+ey\nq2AvU5dPpO9PLg1+Z+Z5Y2dMwI4uPdVwQs3MXKwjMHx8++1SS0JaC67Tj4t4UfdvicUmZUdNXFp8\nnLLjp2O+5/XSoXjhSuqly6pSH+lw6JC1SGSrKTD0hEKyUnbiNFNz53CmrILrR8eW/8UIqxOUtho0\nmxxDH3QpJb998A9syd/O/c/cDXibeiIUJw9jVWUVM2+bz5b8r/jfpQ9y1a0/CPvezFhqdUw0jFVC\n4bRrV6O202vH7u9qRA+uC78wWpuaLcTLdBunjpcx5frZVJZXMviegbbOjYxD0F57ncDSy/ta7RR0\nsLNKd3NFr7fz2L8/ftvK8tPlTLv5aXZt3se0Nx8mu783mWetGPtCV4NWPEmklLyct4y/v/ghd0y+\nlWGTbnbcP7NVmJUVmp7KIPIa+nw+5o78LRvf2cxDi+7luhHX1DrHaJzS0lra9q7RW3ELkUZNMrlU\n2rUbR2bmYt3zo7VvJuiNdgjR0NrUamr4BduB4E7JKaFqt/XrO/PiY8PZt2U/T6x4JKi6s4pR2nQv\n8Xo+UEIhRvTSXTulXbua9BZQk+YiHkLBV+VjxrB5bFtbyOQ/PsQPb7KZ+c8GVtwnQ/W/VvTKb8z9\nG28++zY3jRvM6Dl36R5vJ92AkSrIimpJ73pFThQLx79M/vJPuW/uCG4aO0i3H0bBWVVVx0lL03fV\nNBorTSXkj1Pwk5ranKysPwXcOauiCgSz9o0+t5Z+wzzLqV1VmRmRbVVUHCD7xr+Tt6IHV97Q13Z7\nZniVgSDyXnL7d5RQCGDVPmBEXVANmbGzYC+bVn/BfXNH8OM7r3a17UiDKBDVfVLL0llTbyF84ojU\nK//xyTfpd1NfHlw42jBxn1sxBrGeV3zoKO/+/mNueWAItz16i+FxbdoMD8QMRFKJlDjStUt5Ovi6\nqqrE9uRqV8cf3X6USrt2Y03bdDPnlV5b52T4SG/9quU2jHaLkZ9F8wqykz4lnkboei0UQgdab5We\nk6M/2ccqQLQ2zIi3kKmqqAKg2+VdXW3XaJUHmBoBw88Dv87Z/8TpeZJUVlTRrU/XmIsA2VmFhX5n\nFNGsN3k8/Yw/mK573wsN29YEos9Xovu9z3fUtneNG5OrXa8eM7VSSkojsrKWkpm52LRNNwPR3GjL\naLdo19Eg8p4xUztpBms9taXb6qp6bWh26t8ba40FiG7Mrus7Dw2rhslI33Wf76TOClO6UonNDrGs\n3oyMgd8WneLu3xifp1+UPpz09E6mcQl6uDW52vldYwN3atjEbxbLol9VzVkgmlF/4hHUFiuhcTB6\n9Tzcol7vFIxwMiEXFRXRo0cPhg8fTlZWFkOHDqWsrIw1a9bQq9dlZGdnM3r0aMrLywFYuDCPnj17\n0rt3byZNmhTW1tkUo2BlItLbTVRV6a+QvUhT4NS9z+l5J47qB45pRFO5OA3CSkSUr5G6KStrqSVv\nJSOPKadjoG/PynAc1Ka3kzTbXZqpnkLfmzk1mLXjhpqp3gkFK4NpRyiEHrtz507Gjx9PYWEhzZo1\nY/78+YwaNYrp099g27ZtHD9exZQpL/DxxyXk57/F0qXbWbJkK2PGPB7W5tkkFKxMRHbSPnsxgTnN\nP2PnPG2iKD50lJm3zafpuY3p/aOeuu2aCb5Y0oR4mQzSCKdBZOb3hLAVMGfYHyk49m0jGjPdsYur\nUT4js+ONVE+h782cGrSU3F7lTKqXQsHNwQwVCh07duSqq64CYMSIEaxZs4auXbvSuXMmAOPGjaSo\naC0DBjQnPT2DF14Yw4EDf6VhQ+MaxXUVO0ZiO6v/Vq1y3epiLawIgVjaLi0+Tt6gmRz7rpQ57001\nrBdtLEg7W7C/xFbIyAuc5BozvyckJSWrbfUhVGhqabebyy0suiuX9BTr5TeTIepYuaTWIUK9XoqL\nAVpwMqApKCiAnTvhzBlIS0vj1Vc3MXToUN555x2GDRtiaLzWajTXJewaie2s/u1OBnaYPt08xsBM\ndxvNLfBU6SmmXD+bb/YdZuaqPHr8wLi+gxsePk4LGSUL0e4JOwsJI6FZwbu2++Wm/j7yntGr6BdL\nvIxT6rVQcNu/98CBA2zYsAGADz98neuuy6G4uIivv95DTg5s2vQnBg/+ESdPnqRJk1Jyc3NZsGAB\nu3dvMfR+auGsCmZC0d/6S8OVrr6eVx9/5S/v8h9ZiTGwel4o7770Mbs37+Oxty6hLP0G0+plbnn4\nxCNNtFXs5q6Kdk/YWUgYCc3S088ChGXSjSfRXFKdxMu4Qb0WCm5vwy6++GIWLVpEVlYW33//PRMn\nTuSVV14hL28Y2dnZpKSkMHbsWE6cOMHIkTfSvXtv+va9ml/+cr4jt9ZkxXiS0jca6k2CxhXBwI1K\nWpHpCIxWXKHeQ3rfW+XMqXJ6/vgAvoZzQsahpnpZYeE9/POf54XFclhd0ScyTbQVnASfafeE3n1g\n1w5idD82aHiUn/4il4v6dDE938tVeeTvWPnMDDd2MvVaKLhNWloar732GoWFhaxcuZJGjRpx7bXX\n8sknX7Bt2zaWLFlCeno6bdu25YsvNrF791b27NnGjTeODIuLqOvuqMaTkbBcJ0GvIlgkdn3sQx8w\n7eExW3Hp1UbQEt85mRAG3vulifG0MuBxZV/gJcKAbAen8RFaAZ2srNdisoMY3Y8VZS0Zt2CUYbCj\nRrwK4ZilzLAaL+MGSijEAaNJ3mhXUNeFgn8y0nvQpOVJPHL3YER5+QEqzlQYfh+KnQAzqD0RaMc7\npXlr67l/7Ai8ZEkTbUSs6q1Y7SB6QtNX2YDefZ+PKhA03BIAXkXMu7mTUULBJbp0CS+yYwU38yYl\nE/6HVl/BaUfPHToZ1M7j7yc9vSOz7lgAQM8fZkZtUy8Vgbb6B2tupU4p/c6el5nTsUo2A3Ki1Vuh\nQlNKOH6kMT16vkzbtndbbsPKit1OOxpWU2aYTfJu72Q8FQpCiCFCiJ1CiD1CiDyT434mhJBCCMdZ\n2KRXOWodYNcuYCQUkulvsovxJO5sIjBSkWx77xo2rCrgwYVjuGLIZbXO0yuYo2H14dEmglhXi5+8\n3MuyQR2SxyYQK8mg3tKE5qZXf8OKvJG0b/9zR+14YUdwI2WGm3gmFIQQqcAi4HqgJ3CnEKJWtI4Q\noikwAfjM6W9lZGRQUlKSNJOomVCwmjdJSklJSQkZGRneddRD3J4IaqtIOrHvn7ezYtYZxsy5i1se\nGKJ7nh0vDaflEK3y1f914uKLX7J0bDLZBGIl2dVbRrhlYI6XoRrcsS94mfvoB8AeKeU+ACHEcuAW\n4KuI42YCc4FHnf5Qhw4dOHjwIEeOHHHahKsUF0NhofH3jRv7/9+/358aG6C01P8vlIyMDDp06OBN\nJyM4t01zBo8aSMu257rSnvbAu1kcJTQ/TkV5JX+f9Sx35HXhjryf2m7LSs0DN+l2WVcGjxpImzbD\nA2Oinw8Iqj0pJJNo7OZp8ors/j1p2caan7dbhWystmM3ZYbRb8WK8Gp1LYQYCgyRUt4beH83cKWU\n8sGQYy4HpkopfyaEyAcmSSkLzNrNycmRBQWmhyQEo2yFWki6Hl5WZqoPVFVWkZqWastYmAwRqXoJ\n71JSGtWJ1XN9xa1nNZHPvBBis5Qyqoo+YYZmIUQKMB94xMKx9wshCoQQBcmyG4jEibHHqyIc9YW0\nBmmWBQIkh0CAuqtOqc+49azWhWfey53CD4EnpZSDA++nAEgpnwq8bw7sBbSUkRcAR4GbzXYLybpT\nCEXtABQKRbKRDDuFz4HuQoiuwl9T8A5glfallLJUSnmelLKLlLILsJEoAqGuUBdWAwqFQqGHZzsF\nACFELvA8fgvaEinlbCHEDKBASrkq4th8LNgUhBBHAP18CWcf5wHFie5EkqDGIhw1HjWosQjHaDw6\nSynPj3ayp0JBERtCiAIr2736gBqLcNR41KDGIpxYx0NFNCsUCoUiiBIKCoVCoQiihEJyYy38tX6g\nxiIcNR41qLEIJ6bxUDYFhUKhUARROwWFQqFQBFFCQaFQKBRBlFBIAqKlGBdCPCyE+EoIsVUIsUYI\noZ+X+iwgnunW6wJWxkMIcVvg/tguhHg93n2MFxaek05CiE+EEF8EnpXcRPQzHgghlgghvhNC6BZx\nEX5+ExirrYE8c9aQUqp/CfyHP7BvL3AhcA6wBegZccxAoFHg9TjgjUT3O1FjETiuKbAWfxR8TqL7\nneB7ozvwBXBu4H3rRPc7gWPxEjAu8LonUJTofns4HtcAlwNfGnyfC7yHv2xhP+Azq22rnULiCaYY\nl1JWAFqK8SBSyk+klFpKzY1AfPJpx5+oYxFAS7d+Jp6dSwBWxuM+YJGU8nsAKeV3ce5jvLAyFhJo\nFnjdHLBZ7qruIKVciz9XnBG3AH+UfjYCLYQQba20rYRC4mkPfB3y/mDgMyPG4F8BnI1EHYvANrij\nlPLdeHYsQVi5NzKBTCHEp0KIjUII/WpDdR8rY/EkMEIIcRBYDTwUn64lJXbnlSBeFtlRuIwQYgSQ\nA/wo0X1JBCHp1kcluCvJRBp+FdIA/DvItUKIbCnlsYT2KjHcCbwqpZwXyNL8JyFELylldaI7VpdQ\nO4XE81+gY8j7DoHPwhBCXAdMxZ9JtjxOfYs30caiKdALyBdCFOHXla46i43NVu6Ng8AqKWWllPI/\nwC78QuJsw8pYjAHeBJBSbgAy8CeHq49Ymlf0UEIh8ZimGAcQQlwG/A6/QDhbdcZQj9OtGxD13gD+\nhn+XgBDiPPzqpH3x7GScsDIWB4BrAYQQWfiFQnJW5fKeVcDPA15I/YBSKeU3Vk5U6qMEI6WsEkI8\nCHxATYrx7REpxp8FmgArApXGDkgpb05Ypz3C4ljUGyyOxwfAICHEV4APeFRKWZK4XnuDxbF4BPi9\nEGIifqPzKBlwxTnbEEL8Gf9i4LyADeVXQAMAKeWL+G0qucAeoAy4x3LbZ+mYKRQKhcIBSn2kUCgU\niiBKKCgUCoUiiBIKCoVCoQiihIJCoVAogiihoFAoFIogSigoFC4ihHhfCHFMCPFOovuiUDhBCQWF\nwl2eBe5OdCcUCqcooaBQOEAIcUUgT32GEKJxoJZBLynlGuBEovunUDhFRTQrFA6QUn4uhFgFzAIa\nAq9JKXULnigUdQklFBQK58zAn5PnDPCLBPdFoXAFpT5SKJzTCn9Oqqb4k68pFHUeJRQUCuf8DpgG\nLMNfCU6hqPMo9ZFC4QAhxM+BSinl60KIVGC9EOLHwHSgB9AkkL1yjJTyg0T2VaGwg8qSqlAoFIog\nSn2kUCgUiiBKKCgUCoUiiBIKCoVCoQiihIJCoVAogiihoFAoFIogSigoFAqFIogSCgqFQqEI8v+y\nXC8gVQ+1GQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108d9a4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data(X, y)\n",
    "plot_boundary(X, clf)\n",
    "plt.title('C=1, gamma=100')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load dataset 3\n",
    "data = sio.loadmat('data/ml-ex6/ex6data3.mat')\n",
    "\n",
    "# Training data \n",
    "X_train_0 = data['X']\n",
    "y_train_0 = data['y'].ravel()\n",
    "\n",
    "# Validation data\n",
    "X_cv_0 = data['Xval']\n",
    "y_cv_0 = data['yval'].ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x108f56f60>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEWCAYAAACaBstRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXvQXVWV4H8rCSTNBIiETARCSNImGBAE88lAIQXVYHek\nbGhrAEXQoCCjFjWUrT2dbrQJKNUo1UBPy4ziAxGCPMSRtLbaEKVthyCE4qGAISEG/BRiCIGGQSIx\na/6490vud3Mf57Ff55z1q/rqu499z1ln33PX2nuttdcWVcUwDMMwsjAhtgCGYRhGdTCjYRiGYWTG\njIZhGIaRGTMahmEYRmbMaBiGYRiZMaNhGIZhZMaMhmH0QUQmisjLIjLbZVsHcp0kIht8n8cwemFG\nw6gNbaU99rddRH7X8fysvMdT1T+o6lRVfdpl25CIyHkicndsOYz6MCm2AIbhClWdOva4PRI/T1Xv\n6tdeRCap6rYQshlGXbCZhtEYROQzInKLiHxDRF4CzhaRY0TkXhF5QUSeEZH/KSK7tdtPEhEVkTnt\n5ze23/+eiLwkIqtEZG7etu333yEiT4jIiyLyTyLyf0XknD5y7yEiN4jIFhF5FFjU9f4nRWR9+zyP\nisgp7dcPAz4PHNeebT3Xfv0UEXlIRP5DRJ4WkU857Gaj5pjRMJrGu4CbgL2BW4BtwIXAvsCxwGLg\nvw34/HuBTwH7AE8Dn87bVkT+M3Ar8Fft8/4SOGrAcS4FDgTmAScDS7ref6It+97AZcBNIjJTVX8G\nXAD8e9t1tm+7/cvAWcA04M+BC0XknQPObxg7MKNhNI2fqOo/q+p2Vf2dqt6vqj9V1W2quh64Fjh+\nwOe/qaqrVfU1YDlwRIG27wQeUtU72u9dBTw34DhnAJ9R1S2q+hSt2cMOVPVWVX2mfU03ARuAkX4H\nU9Ufquqj7fYPAzcPuWbD2IEZDaNp/KrziYi8UUS+KyLPish/0BrV79v7owA82/H4FWBqv4YD2u7f\nKYe2qoaODjjOfl1yP9X5poicIyIPt11sLwBvZMA1tF1yd4vIJhF5EThvUHvD6MSMhtE0uss6fxH4\nOfAGVd0L+DtAPMvwDDBr7ImICHDAgPbP0nJPjbEjrVdE5gH/G/gIMF1VpwG/YOc19CpjfTNwO3Cg\nqu4NfBn/12zUBDMaRtPZE3gR+H8ispDB8QxXfAd4i4j8uYhMohVTmTGg/a3A34rItPY6kAs63ptK\nyzBsomV/PkRrpjHGRmDWWHC/zZ7A86r6qogcDbyn/CUZTcGMhtF0Pk4rsPwSrVnHLb5PqKobgXcD\nVwKbgT8GHgS29vnIxbRmJxuA7wFf7zjWI8A/Afe12xwM/LTjs3cCa4GNIjLmLvsI8PftDLK/pWWU\nDCMTYpswGUZcRGQi8BvgNFX999jyGMYgbKZhGBEQkcVtd9NkWmm5r9GaLRhG0pjRMIw4vA1YTysW\n8WfAu1S1n3vKMJLB3FOGYRhGZmymYRiGYWSmdgUL9913X50zZ05sMQzDMCrFAw888JyqDkr9Bmpo\nNObMmcPq1atji2EYhlEpROSp4a3MPWUYhmHkIKrRaKcdrhGRdSKytE+bM0TksXbJ55tCy2gYhmHs\nJJp7qr2g6Rrg7bSKtd0vIitU9bGONvOBvwGOVdUt7ZLShmEYRiRizjSOAtap6npV/T2tImqndrX5\nEHCNqm4BUNXfBpbRMAzD6CCm0TiA8eWeR9m10ucCYEF7V7N7RWRxrwOJyPkislpEVm/atMmTuIZh\nGEbqgfBJwHzgBOBM4EsiMq27kapeq6ojqjoyY8bQjDHDaDTLlsWWwKgyMY3Grxm/R8Cs9mudjAIr\nVPU1Vf0lrW0t5weSzzBqySWXxJbAqDIxjcb9wHwRmSsiu9Oq6b+iq823ac0yEJF9abmr1ocU0jAM\nw9hJNKOhqttobSbzA+Bx4FZVfVRELhWRU9rNfgBsFpHHgB8Bf6Wqm+NIbBjVZdkyEGn9wc7H5qoy\n8lK7goUjIyNqK8KNFFm2rJiSLvq5fohAqj9719dqZEdEHlDVkaHtzGgYRhiKKmvXSj5lo5GybHUn\nq9FIPXvKMAzHXHxxbAmMKmNGwzA8UjSW4DMGkZr7x+It1cLcU4YRiFTcUynTpGtNDXNPGYYRFZsp\n1BMzGoYRiKKxhKrGIIosIqzqtTYJMxqGEYiiI++sn8t7/BRnAinKZIzHjIZh1IS8I3sf5UQsqF1/\nzGgYhieaqCiXLWsFsseC2WOPm9gXdcWMhmF4IkRhwLwje5sJGGWxlFvD8ETo9NG85/Mtn5UEqRaW\ncmsYEbCR/E6aeM1NwIyG4ZWmKY6YPv286aqW3moUwdxThleavMK3ydduVA9zTxlGZGwkb9QRMxqG\nc8yv36Jp12s0AzMahnOqlKufmkypydOLKsho+MNiGoZXUvfrpyZfavL0ogoyGvmxmIaRBObXN4x6\nYUbD8EqKrozQMZeYGy65ogoyGmEw95QRlNRWCYdwteQ5RxF5XPRpnmOYe6qeZHVPmdEwgpKawqmD\n0XBxDb5lNNLHYhqGkQFfMZei7pwqxICqIKPhj6hGQ0QWi8gaEVknIksHtPuvIqIiMtQKGumRsj/c\nZxyjSNpxng2XyvZp0WO4ugajmkRzT4nIROAJ4O3AKHA/cKaqPtbVbk/gu8DuwAWqOtD3ZO6ptGmi\na8P3NYd2T4U8Vlk2blzO+vUXsXXr00yePJt58y5j5syzYouVJFVwTx0FrFPV9ar6e+Bm4NQe7T4N\nfBZ4NaRwhuEKc+fEYePG5axZcz5btz4FKFu3PsWaNeezcePy2KJVmphG4wDgVx3PR9uv7UBE3gIc\nqKrfHXQgETlfRFaLyOpNmza5l9RwRhMVqG93jYs+LXuMFF2Q69dfxPbtr4x7bfv2V1i//qJIEtWD\nZAPhIjIBuBL4+LC2qnqtqo6o6siMGTP8C2cUJmV/d0zZypzbhdwuUnZTKx2zdevTuV43shHTaPwa\nOLDj+az2a2PsCbwJuFtENgBHAyssGG74IsT2rCmeu65Mnjw71+tGNmIajfuB+SIyV0R2B94DrBh7\nU1VfVNV9VXWOqs4B7gVOGRYIN9Ig5RmF4ZdUXJDz5l3GhAl7jHttwoQ9mDfvskgS1YNoRkNVtwEX\nAD8AHgduVdVHReRSETklllwxqap7pBdVGTnH9MWnGAdwgUv5N25czqpVc7j77gmsWjUnVxB75syz\nOPjga5k8+SBAmDz5IA4++FrLniqJrQhPiJipiq7P7fp4IcqP1Kn/85JaeRfYmf3UGcyeMGEPU/ye\nqELKrVEzfI6cqzJzqSop9q9lP6WJGY3I1Mk9kmIGTR5i+uJTiQOkhGU/pYkZjcjEVLSpK/nUSpj7\nxOe5+x079ZiKZT+licU0EqJOPnXXPvLYPv8xUvT9DyNL36XSv51YTCMsFtOoIHVyj1RNsWYlRd9/\nXSma/VQm48oYjhmNhKire8QF5vPPR17XU6r9O3PmWRxzzAZOOGE7xxyzIZPBsHpTfjGjYVSCQUbN\nt8FL3fffi7zxqpSvJQ+WceUfMxpG5fHtMiqSMOBKCddFmftmzCXVmmHsimVcucOMhhGVVJViWblc\nGTIXx0nV9eSK8S6p3ljGlTvMaBhRKaoUfbuM+slVRQWcqmF2RS+XVCdWb8otZjSMShJrjckwl5QL\nQ1bFGEpMBrmerN6Ue8xoGMFJVSmWlcuVIYsZQ6ki/RcBHpQp48rIhy3uM6LiYlGZjwV3ZeVytVgu\n63F6taviQsQi2CJAN9jiPqM0VVE4Kcp5/PFujlMmhtKUhYhWAj0sZjSMvpRVOlmUeaqB5bJy/du/\nuZEjRAxl2Hm6SXHFdd5FgEZxzD1l9CUVF00VCX3tY+dbtqy3sb/44sGGIau8ZV1BGzcuZ/36i9i6\n9WkmT57NvHmXmYJPBHNPGYVINUhdBVLoO99ZZWVWXFuJj3pgRsMYR1mlk4LijEXMUvNF3GlFvqsy\ne1xYiY96YEbDcEqqe3TEPr9vel3fMENS5Lsqs8eFbapUD8xoGH1JNUhdhNCZRCn0nQ9DOW/eZUyY\nsMe417KuuLZNleqBGY0ESHUUXFauFBRnLIos6ItJ1u+qTHprGYNjpINlTyVAk7OMfFI0kygGee+B\nqi7cs+ypdMmaPWVGIwHMaPgn9T7OK1/q19MLMxhpU4mUWxFZLCJrRGSdiCzt8f5fishjIvKIiKwU\nkYNiyOmDlLKMqjhi9UXo9NhU7gHfWLptfYhmNERkInAN8A7gEOBMETmkq9mDwIiqHg58E/hcWCn9\nkVKWURXKTYSKr2TtCxffU5Hd9apqZIqm2xZZfe5qxXqKK99TIJp7SkSOAZap6p+1n/8NgKr+fZ/2\nRwKfV9VjBx3X3FPVO38WQslYpkBgiPP6Or9v7r57AtBLYOGEE7b3/EyR1eeuihc2sQhiFdxTBwC/\n6ng+2n6tH+cC3+v1hoicLyKrRWT1pk2bHIoYhhhZRlUetbrGd19kGbGmkmnma3RdJN22yOzE1QJC\nW4jYn0qk3IrI2cAIcEWv91X1WlUdUdWRGTNmhBXOAbHiGKm4x/oRyrBl7Ysi8mT15ee9Jh9Gxmfc\noUi6bZHFgK4WENpCxP7ENBq/Bg7seD6r/do4ROQk4CLgFFXdGkg2IwHGFGkqhq2IofU1YvXRBz5H\n10XWdxSZnWT9zLAZlS1E7E9Mo3E/MF9E5orI7sB7gBWdDdpxjC/SMhi/jSBjreinaEK4RlKawQzC\ndV9UacTqW9a85cuLzE6yfCbLjMoWIvYnmtFQ1W3ABcAPgMeBW1X1URG5VEROaTe7ApgK3CYiD4nI\nij6HMzLQLzMohELPk6HVyw009rpvsp4jq3Gp0oi1qKy+4iBFZidZPpNlRmUbO/XHFvc1iJgZN0XP\nXbUsoW6qlIUTM1spJEUyuZpAFbKnjADEzJJKLUMrxnlTGrEOu/4islYxy6hKs78UsZlGg6jiTMNl\njaWqz1oGkaWffFx/FUftVZwdhcBmGoY3QpfaMIYTa1V/FUftKc3+qogZjQbhKjOoiIKKtXit6S4y\n39df1SyjvJlcxk7MPWXkpqpunhTkdilD3tLvvq7fZ/Vaq4wbDiuNXpCq7lPgmyrtTdGPuhmNvMdN\n4frzYLGHsFhMoyBVqPgagyqUHRlG011kqdS3ykoVM7OagBkNo9Z0KuYqlR/JSxaDUCUDD9VaTd8k\nzGiQzkiwKlRpxNqUmWMd79UqZmY1ATMa1MP1EhLrl10pW37E+nRX8mRm2YZJ4TCjYdSOGDPHsjv+\nNWVGlIes6ylsK9mwmNHoItaGSCE+0xRs5tibKo7Gs6ynsIB5WMxodFGVPbptZBqfIjOa7sB8qBlR\nnUfjFjAPixkNo9Z0zxxdKuQiM5pOYx9yRpRnNO76/L5nOHkD5lWccaWEGY1IFB2lWpZXPrpH9lWb\nobn6bvOMxl32UYgZTt6AeV1nXKEwoxGJIqNM89WXw6fBGBQLy2Ls+33elcyx0ldDxBvyFCC0+Ed5\nzGgYQfFp4Pode0xpj+FjhlbW2Ps2/MNG475msaHiDVkLEFr8ozxmNBKgSMZWlRbYdeJztN/r2FWs\nmeVDgQ8bjfuaxaa2QC81eaqIFSw0clG2oKPPonnDjj2mhGPe8nn7L0aRQZfnTK3oYGrypIQVLDS8\nUGSm4DOAn/fYeWdormcjqc5uOnE5i01tw6PU5KkiNtMwclF2FBpzplFklhS7nHjdSvXb/hjpYjON\nyNTph16XVN+qyQv5ZE59/YGlu9YDMxqeSGk9QFll6TJI6jOA7+rYVTSSVVDIlu5aD6K6p0RkMfCP\nwETgy6p6edf7k4GvA4uAzcC7VXXDoGOm4p6K7dboxKUsKV1XJ77cOKlebzerVs1pG4zxTJ58EMcc\ns8HZecq4l+6+ewLQqzOFE07Y7kxGoxjJu6dEZCJwDfAO4BDgTBE5pKvZucAWVX0DcBXw2bBS5qOK\nI9S8pJrqm9LMLgYh1h+Unc3ESHet028vFWK6p44C1qnqelX9PXAzcGpXm1OB69uPvwmcKNK5TCst\nUlqx7cuANe1H6NLl5ZMQCrmseylPuQ9XNH0w4YOYRuMA4Fcdz0fbr/Vso6rbgBeB6d0HEpHzRWS1\niKzetGmTJ3GrRUoGzBchZnaujuVbefVTyNOnn+wsOF52NmPprvWgFoFwVb1WVUdUdWTGjBmxxQHS\ndePUiSYYxqz0Usivf/0Snn32emfBcRezmazlPspQdjCRehZabGIajV8DB3Y8n9V+rWcbEZkE7E0r\nIJ48KSmulA1YSv3kmtAxrm6FvHnzvzjNVorhXipCmcFEFbLQYhPTaNwPzBeRuSKyO/AeYEVXmxXA\nkvbj04Afat1WI/agSauQXbltshjG0P0aeybkOjjeBPeSpQUPZ2DKrYjsBcxQ1Se7Xj9cVR8pfXKR\nk4GraaXcflVVLxORS4HVqrpCRKYANwBHAs8D71HV9YOOmUrKbRmqkubpgpDX6vpcWY83VjQx9Hca\nKg03ZfKmYjc5Lbh0yq2InAH8ArhdRB4Vkbd2vP218iKCqv6Lqi5Q1T9W1cvar/2dqq5oP35VVU9X\n1Teo6lHDDIZRDZqQmtzJJZf0ngnFLofeBPL2sY8stDwxkirEUwa5p/4WWKSqRwAfAG4QkXe130s2\n7bWqNEmRhnTbuO7Xosfr9b7vjCoX7qQqKDGXuDa0eWIkVYmn9HVPicjPVPWwjuf7Ad+htW7iHFV9\nSxgR82HuqWpRV/dUln08Uv+em1pG3GVRxTwuwtjuRBcrwl8SkT8ee6KqzwAn0Fpwd2hpCY2kiDWj\nSTmzqwz9ZlNQnRlllYLCLmdELtOC8yQjVGVXwUFG4yPAhM7SHqr6ErAYOM+3YE0mhiKNtXI2pLJ0\n3a9Fjhc7oyoPIZVYGaWfslsnT4ykKrsK9jUaqvqwqq4FbhWRv5YWfwRcCXw0mIQNJEUFUgeKxB1c\ntK/qbCqUEiur9FOeEeWJkVQlcSHLOo3/QmuB3T201lb8BjjWp1BGGOoWfC8rt6/ZVpldBMuOwMu4\nbEIpsSxKf9C1hJoRFenPPMkIVVkHM7Q0envh3WXA24GpwCdV9eYAshWiDoHwGORZc5CqUUl5V8Ei\nlAlEuwpih9hpb9jaiGHXEiKA3ISkAJel0e8Hfge8FTiOVgnz20rKZ1SUGLEPn0Yq5dlWGbeLK5dN\niFpRw9xgw64lxIwoZRdYaLIYjXPbC+5eU9VnVPVUdi33YVSclP3ugwxVWaWfcmC6jNulKpk4MFzp\nD7uWEG6dKvWnb4YaDVXdxdejqjf4EceIxSAlmfJoPGWlX5YygeiqZOLAcKWf5Vp8z4iq1J++qUVp\ndMMvMRRzDEOV2myrjNulKpk4YwxS+ilcSwoypIIZjYSow+jYFUUMVV6l350N85GPxM/r76SM2yVU\nJk6IezaFrKIUZEiFodlTVaPK2VOpZe/0Ikb2lI9+8Z0NUybrKOUMtW6qcM8a2XCZPWUYO3C9QC4L\nH//4I86L5vnMhim7WM32tTZSxoxGZIb57qsy4hzDtcLbuHE5p5xyjPMSET6zYeqenplyYoThHzMa\nkRnmu2/6qNOXAvaZDVPEIFVJEdc5Y80YjhkNozQ+FZ6vGYHPbJgiBskUcXyatndIUcxoJMRY9k+o\nUaerH4lPhedrRuAzG6ZJ6ZmppSkXJeVKualh2VOJ4ys7xVf2kGt5q1rzJ2b2lKt6USHqTqVC1vpV\nde6TrNlTk0IIY+xK7LTKQbGCMj8C1yPPMVmq9kOdOfOswjKWNRidRnZsxDwmU+jjVIUsbtCYfZKS\nsbKZRiRiV5UdVlnUKEbsH7eriq+xtx4NTZbrjdUnoWbbtk6jJviajaRYS8fXtYaa0aXgF3eVONC0\nAn1Z4lCx+iS1FG4zGgFJKa0yxWCtr/TiUGnLKfy4XQ0GUhxU+CRLYkSsPknNgEcxGiKyj4jcKSJr\n2/9f16PNESKySkQeFZFHROTdMWR1SUpplVZLxz0p/LhdDQaKHqfKaavDKuW66Nsi/ZOaAY8101gK\nrFTV+cDK9vNuXgHer6qHAouBq0VkWkAZa0+IDXaG4Wv2FWNWl8KP29VgoMhxUnDP+aRs3xbtn9S8\nAlEC4SKyBjhBVZ8Rkf2Au1X14CGfeRg4TVXXDmpXlUB47Owp3xS5vmHJAUX7LFRRvaqmB7uiacHz\nvJRJ6wX/GYRZA+GxjMYLqjqt/ViALWPP+7Q/CrgeOFRVd0ntEZHzgfMBZs+eveipp3b9YoywFFHU\nwz5TVPmHrMQaO3sqJq4y8urah1n6J+bAI/o6DRG5C3h9j7fGRQVVVUWk70+6PRO5AVjSy2C0j3Et\ncC20ZhqFhTai4mt1cchVy2XWZ8TApYKePHl2n5F0dvec67UQIQxQ1nNk6R9f66dc4i2moaonqeqb\nevzdAWxsG4Mxo/DbXscQkb2A7wIXqeq9vmQ13OAjjuDimGXdgFUO7g7CdQzChe/dZQZaiBhLnnOk\nnNabh1juqSuAzap6uYgsBfZR1f/R1WZ34HvAP6vq1VmPXZWYRt1JyT1VhlDughgxLh8xiLIje5eL\nTkPEWPKeY1j/xIwLpR7TmA7cCswGngLOUNXnRWQE+LCqniciZwPXAY92fPQcVX1o0LHNaKRBXYxG\nqB9xjGtLsSqAy/4OcX2uz1GFmEaUlFtV3ayqJ6rq/LYb6/n266tV9bz24xtVdTdVPaLjb6DBMNIh\naxwhj/spRkXVKrgLipJCinA3LtNLQ1yf63NUYf2UrQg3vJDV1ZJnwWOMFGWfiid2hYDU8v/BrdIM\ncX0+zpHC+qlBWMFCIxl8uGjK+tjDFYsL756C+qa3jpFS9lTqJB3T8IkZjeriOhjsSuGHUAqxjIYr\n6qI4m4wZDaPxVGmFcpUrBNR5JXyTjGHSgXDDCEGVgthVNRiQRnVfH9S9llZRzGgYtSXF7KA6UiXj\nnIe6GsOymNEwSpPqKDnF7KA6UlfjXFdjWBYzGkZpQm1ylJcQOe8plxgJJVtdjXOVjGHI+9BbwULD\nSAGfBQRdF9frd44igdgQso0xdry6BYznzbusZ4A/JWO4ceNy1q69kG3bNu94zed3DTbTMAoSe2Fa\nCvj2eZcJxIb2x6e+IK0Iqa/OHrs/Og3GGD6/a0u5NUrTa41BnhFyrLTGlIrr9aJMynCKdaXqTuj7\nuN/9sZN837Wl3BrRyDNCjpXW6OK8vn3eZQKxVfLH5yXFOFKM+3jYfeDruzajYZSmu5BgHtdIrLTG\nMucdU1otBSHj3nPp8y6j+OsanE517USM+3jQfeDzuzajYZSmO46RZ4QcK62x6HnHKy1ouYBahsO1\nz7uM4k/dH1+UVNdOxLiPe90fABMnTvf6XVv2lOGcPNt+utgitAhFz9tLaYF6KU1SNisp9tazPnz8\nqa6diHEfx8paM6NhOCdPqmKstMai5w2ttGIr/qL4SvmNNcgYRqz7OMb9Ye4pYyBFUmjzuEZiuVGK\nnrfOAWaX+HIjpRqrqas7sBeWcmsMpOolu11T54quLvGZ8lvE7dWkarVFyZpya+4pw8hBXVc/u8an\nGymvSybk6vgmYO4pYxdstfdg6rj62TUpuZFiZ1yluK6kDGY0GkIehZ9n324jHFVSPin5+GNmXKW6\nrqQMFtNoCEVjE6nFNJrqmw4VS6lj/8bcwbFKu0daGRHDCd2rvWNSx1FbVkK4WOravzFdZamuKylD\nFKMhIvuIyJ0isrb9/3UD2u4lIqMi8vmQMtYBF7GJoi4pH66s2L7pmLhQPsPcW3Xt35iusjqmaEdx\nT4nI54DnVfVyEVkKvE5V/7pP238EZrTbXzDs2Oae6k1oN5OP8zW5cmtZN0cW91aT+9cXVUrRTt09\ndSpwffvx9cBf9GokIouAmcC/BpLLSJiqj9rKBLLLuliyzCKq3r8p4mKWk1oCRCyjMVNVn2k/fpaW\nYRiHiEwA/gH4REjB6kqI2ITvVN2U0jjzUjZeUFb5ZHFvhe7f1JShL8qkaKcYZ/LmnhKRu4DX93jr\nIuB6VZ3W0XaLqo6La4jIBcAeqvo5ETkHGOnnnhKR84HzAWbPnr3oqacGbUxihMCXO8xXdo/vrKHY\nWTRZz9/ZDxMn7oMIbNv2vPM+qZLbJiYh75us7qlYMY01wAmq+oyI7AfcraoHd7VZDhwHbAemArsD\n/0tVlw46tsU00iC1VN1e7FSQY/ti7BTYtQKLHS/Iq6R9K/XQRrSqqcQh75vUYxorgCXtx0uAO7ob\nqOpZqjpbVefQclF9fZjBMNIhrzss9MLB3vti7MR11lDseEFe95bvTKqQqagpuniyEvu+6UUso3E5\n8HYRWQuc1H6OiIyIyJcjyWTkZJCiz2sELrmkjCT56b0vxnhcKrAU4jF5fOu+lXpIZVjlVOIU7ptu\nohgNVd2sqieq6nxVPUlVn2+/vlpVz+vR/mtZ0m2NsIRW9C4ps892EVIqq5EF30o9pDKs8gK7FO8b\nq3JrRGPZsvGGZyzr6uKLx89UfPij+1VhHcOHAqvShkq+NxUKWS041Y2bspLafWNlRIxcuEyrzVIY\n0Zc/uvf+yn72+q4iIUa4oaoFx3Lx1DWl2AoWGjtYtiyf8neZIdXvWD6zbKqaUeODYX1R9b4KLX8V\nU4qTTrn1iRmN4uQ1Ai6NRj+DFTtVtQkMU3BVVICxib0upwipp9waNcDlKvN+M5wUUw7r5nYYll1U\n5eyjWFQ5+D4MMxoNp0yMIsTaitRSDquS85/HsA1TcP0SBuqgAH2R4mDHFWY0Gk7qu/SllnJYhVF3\nXsM2SMG1PiO5PmekN9hxiaXcGsmTUsphFdwOgwxbr34clF7bMoa9Y0p1UIC+CJlSHJpGGI3XXnuN\n0dFRXn311diilOKFF2DatJ3Pp0yZwqxZs9htt92cHD+lXfpSJWbOf9YMoLyGbZCCe/zx9/WRRmuh\nAH2S0mDHJY0wGqOjo+y5557MmTMHkd5T7SqwejUsXNh6rKps3ryZ0dFR5s6d6+T4qbikUsb1ores\nhqA7g2nM5QTs0r6IYeun4AYtgly1ak5PeVNNz01VrqrRiJjGq6++yvTp0yttMLoREaZPnx589tR0\nw+IyxpLjiplnAAAMXklEQVQn9pAnluLSn957EWSLXvKWTRTwlZlWlQSGKtCIdRqPP/44C8eG6BXj\nN79p/XWz//6tv9DXVnRtho3ydiVPLn/e9Sou+3t8Cfld6ZS3zPoEn+tBqrhuIjS2TqMm7L8/jIy0\n/mDn4/33jytXHmyU15s8sYe8KZydJTrGAtpFR+9jx+qXRdUpb5lEAZ+ZaVVIYKgKZjR6ULfFW2Up\nW2+qCmmq3YS4B/IYgqIuJ5cGO4u8ZdYn+FTsdV43ERozGl2kPCqONbsou5ajaqO8UPdAHkNQNJbi\n0mBnkbdMPMWnYq/zuonQmNHowteoeMOGDSxcuJAPfehDHHroofzpn/4pv/vd73jyySdZvHgxixYt\n4rjjjuMXv/gFAE8++SRHH300hx12GJ/85CeZOnVqpVxSnfhQBj5nAqFmRnkNQZGqsC4NdhZ5yyQK\n+FTsqS0SrTKNSLnNg89R8dq1a/nGN77Bl770Jc444wxuv/12rrvuOr7whS8wf/58fvrTn/LRj36U\nH/7wh1x44YVceOGFnHnmmXzhC18ofW5XFFnL4SNNNWv6aRFCzozK5vIPC3i7XleSRd6i1+R7QVxd\n102ExoxGFz4Xb82dO5cjjjgCgEWLFrFhwwbuueceTj/99B1ttm7dCsCqVav49re/DcB73/tePvGJ\nT5Q+vwuKpNy6VgZ5VzznpSqb9mQxnr43U+onV9Hv2hR7+pjR6MLnj2zy5Mk7Hk+cOJGNGzcybdo0\nHnroodLHTh2XysD3TCCGoi1CP+O5du2FO/o6dDkL37NAIz4W0+gipO9zr732Yu7cudx2221Aa5X3\nww8/DMDRRx/N7bffDsDNN9/s/NxVxncmTFX83/2M5LZtm8fFeELtkAfVzJQz8mFGowchf2TLly/n\nK1/5Cm9+85s59NBDueOOOwC4+uqrufLKKzn88MNZt24de++9tzcZqkaITJiQ90BRBhnJWEq6aply\nRn7MPRWIOXPm8POf/3zH884Yxfe///1d2h9wwAHce++9iAg333wza9asCSJnFfDtcqnK6vV58y7j\n8cfP7vleLCVdlXiQURwzGonywAMPcMEFF6CqTJs2ja9+9auxRUoKXwHTKvnkZ848iyeeuJA//GHz\nLu/FUtJViQcZxYliNERkH+AWYA6wAThDVbf0aDcb+DJwIK3COyer6oZggkbkuOOO2xHfMMLhOzPL\nNQsW/GNSSrrO+0gYLWLNNJYCK1X1chFZ2n7+1z3afR24TFXvFJGpwK6V2QzDIaF98mVdYSkqaUub\nrTexjMapwAntx9cDd9NlNETkEGCSqt4JoKovB5TP8ETq8YKQPnlXrjBT0kZIYmVPzVTVZ9qPnwVm\n9mizAHhBRL4lIg+KyBUiMjGciIZrUq7rNUbIGkWWnmpUEW9GQ0TuEpGf9/g7tbOdtjb06LVRwCTg\nOOATwFuBecA5fc51voisFpHVmzZtcnshDcb1hktVUJIh12hYeqpRRbwZDVU9SVXf1OPvDmCjiOwH\n0P7/2x6HGAUeUtX1qroN+Dbwlj7nulZVR1R1ZMaMGc6uoem71F1yidvjVUVJhlqjYeW6jSoSyz21\nAljSfrwEuKNHm/uBaSIyZgX+BHgsgGw7cK00m44pyfFYuW6jisQyGpcDbxeRtcBJ7eeIyIiIfBlA\nVf9AyzW1UkR+RmvbsC9Fkrc0GzZs4I1vfCNnnXUWCxcu5LTTTuOVV15h5cqVHHnkkRx22GF88IMf\n3FGwcOnSpRxyyCEcfvjhQYsVlt1waRCmJMdTlXIlhjEOVa3V36JFi7Sbxx57bJfX+nHxxWNbDI3/\nu/jizIfoyS9/+UsF9Cc/+Ymqqn7gAx/QT3/60zpr1ixds2aNqqq+733v06uuukqfe+45XbBggW7f\nvl1VVbds2dL3uHmuLS+tiJNbnn32Rr3nnoP0Rz8Sveeeg/TZZ290fxKjL1Xo/yrIWEeA1ZpBx1rt\nqS7K7lI3iAMPPJBjjz0WgLPPPpuVK1cyd+5cFixYAMCSJUv48Y9/zN57782UKVM499xz+da3vsUe\ne+wx6LCVogo1nepKFbLXqiBj0zGjERAZ8/m0mTZtWs92kyZN4r777uO0007jO9/5DosXLw4h3i4U\n2XDJSBdf2Wsud1GsQoZd0zGjMQDXSvPpp59m1apVANx0002MjIywYcMG1q1bB8ANN9zA8ccfz8sv\nv8yLL77IySefzFVXXRWtnEjTs8fqho/sNdczg6pk2DUZMxoDcK00Dz74YK655hoWLlzIli1b+NjH\nPsZ1113H6aefzmGHHcaECRP48Ic/zEsvvcQ73/lODj/8cN72trdx5ZVXuhXEaCQ+stdczwwswy59\nrMptQCZNmsSNN9447rUTTzyRBx98cNxr++23H/fdd19I0YwG4KMCreuZgVXJTR+baRhGQ/CR4ut6\nZmBpyOljM41AdG/CZBgxcF3c0MfMwAowpk1jZhqqvcpbVZs6XpNRLWxm0DwaMdOYMmUKmzdvZvr0\n6bukvVYVVWXz5s1MmTIltihGw7GZQbNohNGYNWsWo6Oj1K0C7pQpU5g1a1ZsMQzDaBCNMBq77bYb\nc+fOjS2GYRhG5WlMTMMwDMMojxkNwzAMIzNmNAzDMIzMSN3SNkVkE/BUwY/vCzznUJyqY/0xHuuP\n8Vh/jKfq/XGQqg7d+rR2RqMMIrJaVUdiy5EK1h/jsf4Yj/XHeJrSH+aeMgzDMDJjRsMwDMPIjBmN\n8VwbW4DEsP4Yj/XHeKw/xtOI/rCYhmEYhpEZm2kYhmEYmTGjYRiGYWSm0UZDRPYRkTtFZG37/+v6\ntJstIv8qIo+LyGMiMiespGHI2h/ttnuJyKiIfD6kjCHJ0h8icoSIrBKRR0XkERF5dwxZfSIii0Vk\njYisE5GlPd6fLCK3tN//aV1/H2Nk6I+/bOuJR0RkpYgcFENOXzTaaABLgZWqOh9Y2X7ei68DV6jq\nQuAo4LeB5AtN1v4A+DTw4yBSxSNLf7wCvF9VDwUWA1eLyLSAMnpFRCYC1wDvAA4BzhSRQ7qanQts\nUdU3AFcBnw0rZTgy9seDwIiqHg58E/hcWCn90nSjcSpwffvx9cBfdDdo3xCTVPVOAFV9WVVf6W5X\nE4b2B4CILAJmAv8aSK5YDO0PVX1CVde2H/+G1oBi6KraCnEUsE5V16vq74GbafVLJ5399E3gRKnL\nxjW7MrQ/VPVHHTriXqBW+xc03WjMVNVn2o+fpaUIu1kAvCAi3xKRB0XkivZoo44M7Q8RmQD8A/CJ\nkIJFIsv9sQMROQrYHXjSt2ABOQD4Vcfz0fZrPduo6jbgRWB6EOnCk6U/OjkX+J5XiQJT+/00ROQu\n4PU93rqo84mqqoj0yj+eBBwHHAk8DdwCnAN8xa2kYXDQHx8F/kVVR+swmHTQH2PH2Q+4AViiqtvd\nSmlUERE5GxgBjo8ti0tqbzRU9aR+74nIRhHZT1Wfaf/oe8UqRoGHVHV9+zPfBo6mokbDQX8cAxwn\nIh8FpgK7i8jLqjoo/pEsDvoDEdkL+C5wkare60nUWPwaOLDj+az2a73ajIrIJGBvYHMY8YKTpT8Q\nkZNoDTyOV9WtgWQLQtPdUyuAJe3HS4A7erS5H5gmImN+6j8BHgsgWwyG9oeqnqWqs1V1Di0X1der\najAyMLQ/RGR34P/Q6odvBpQtFPcD80Vkbvta30OrXzrp7KfTgB9qfVcND+0PETkS+CJwiqrWL2lG\nVRv7R8vvuhJYC9wF7NN+fQT4cke7twOPAD8DvgbsHlv2mP3R0f4c4POx5Y7ZH8DZwGvAQx1/R8SW\n3XE/nAw8QStWc1H7tUtpKUWAKcBtwDrgPmBebJkj98ddwMaO+2FFbJld/lkZEcMwDCMzTXdPGYZh\nGDkwo2EYhmFkxoyGYRiGkRkzGoZhGEZmzGgYhmEYmTGjYRgBEZHvi8gLIvKd2LIYRhHMaBhGWK4A\n3hdbCMMoihkNw/CAiLy1vZ/CFBH5T+39Nt6kqiuBl2LLZxhFqX3tKcOIgareLyIrgM8AfwTcqKo/\njyyWYZTGjIZh+ONSWrWKXgX+e2RZDMMJ5p4yDH9Mp1UJeE9a9ZkMo/KY0TAMf3wR+BSwnBpvgWo0\nC3NPGYYHROT9wGuqelN7p8d7RORPgEuANwJTRWQUOFdVfxBTVsPIg1W5NQzDMDJj7inDMAwjM2Y0\nDMMwjMyY0TAMwzAyY0bDMAzDyIwZDcMwDCMzZjQMwzCMzJjRMAzDMDLz/wHt7eDt0EUbjwAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108ebaa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data(X_train_0, y_train_0)\n",
    "plt.title('Training data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x108ffc0b8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEWCAYAAACaBstRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwzBiY0rDMAzDiI0pDcMwDCM2/x9p/023a5agPAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108fc4518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data(X_cv_0, y_cv_0)\n",
    "plt.title('Validation data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Concatenate training and validation data\n",
    "X = np.concatenate([X_train_0, X_cv_0])\n",
    "y = np.concatenate([y_train_0, y_cv_0])\n",
    "\n",
    "num_train = X_train_0.shape[0]\n",
    "num_cv = X_cv_0.shape[0]\n",
    "\n",
    "# Create a cross validator that selects the pre-defined \n",
    "# validation dataset from the concatenated dataset.\n",
    "cv_fold = np.empty(num_train + num_cv, dtype='int8')\n",
    "cv_fold[:num_train] = -1\n",
    "cv_fold[num_train:] = 0\n",
    "cv = PredefinedSplit(cv_fold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Values for grid search (see description in ex6.pdf)\n",
    "grid = np.array([0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30])\n",
    "\n",
    "# Grid values for C\n",
    "grid_C = grid\n",
    "# Grid values for gamma\n",
    "grid_gamma = 1 / (grid ** 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=PredefinedSplit(test_fold=array([-1, -1, ...,  0,  0])),\n",
       "       error_score='raise',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, 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={}, iid=True, n_jobs=1,\n",
       "       param_grid={'C': array([  1.00000e-02,   3.00000e-02,   1.00000e-01,   3.00000e-01,\n",
       "         1.00000e+00,   3.00000e+00,   1.00000e+01,   3.00000e+01]), 'gamma': array([  1.00000e+04,   1.11111e+03,   1.00000e+02,   1.11111e+01,\n",
       "         1.00000e+00,   1.11111e-01,   1.00000e-02,   1.11111e-03])},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Classifier used for grid search\n",
    "clf = SVC(kernel='rbf')\n",
    "\n",
    "# Grid search to find the best C and gamma values using the predefined\n",
    "# training and validation set.\n",
    "gs = GridSearchCV(clf, param_grid={'C':grid_C, 'gamma':grid_gamma}, cv=cv)\n",
    "gs.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>param_C</th>\n",
       "      <th>param_gamma</th>\n",
       "      <th>params</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.003872</td>\n",
       "      <td>0.001880</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.01</td>\n",
       "      <td>10000</td>\n",
       "      <td>{'C': 0.01, 'gamma': 10000.0}</td>\n",
       "      <td>42</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.003072</td>\n",
       "      <td>0.001746</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1111.11</td>\n",
       "      <td>{'C': 0.01, 'gamma': 1111.11111111}</td>\n",
       "      <td>42</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.003044</td>\n",
       "      <td>0.001766</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>{'C': 0.01, 'gamma': 100.0}</td>\n",
       "      <td>42</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.003155</td>\n",
       "      <td>0.001521</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.01</td>\n",
       "      <td>11.1111</td>\n",
       "      <td>{'C': 0.01, 'gamma': 11.1111111111}</td>\n",
       "      <td>42</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.001952</td>\n",
       "      <td>0.001278</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1</td>\n",
       "      <td>{'C': 0.01, 'gamma': 1.0}</td>\n",
       "      <td>42</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.002496</td>\n",
       "      <td>0.001414</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>{'C': 0.01, 'gamma': 0.111111111111}</td>\n",
       "      <td>42</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.001969</td>\n",
       "      <td>0.001275</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>{'C': 0.01, 'gamma': 0.01}</td>\n",
       "      <td>42</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.001987</td>\n",
       "      <td>0.001479</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00111111</td>\n",
       "      <td>{'C': 0.01, 'gamma': 0.00111111111111}</td>\n",
       "      <td>42</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.002463</td>\n",
       "      <td>0.001579</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.03</td>\n",
       "      <td>10000</td>\n",
       "      <td>{'C': 0.03, 'gamma': 10000.0}</td>\n",
       "      <td>42</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.002587</td>\n",
       "      <td>0.001685</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1111.11</td>\n",
       "      <td>{'C': 0.03, 'gamma': 1111.11111111}</td>\n",
       "      <td>42</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.002254</td>\n",
       "      <td>0.001318</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.03</td>\n",
       "      <td>100</td>\n",
       "      <td>{'C': 0.03, 'gamma': 100.0}</td>\n",
       "      <td>42</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.002467</td>\n",
       "      <td>0.001426</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.876777</td>\n",
       "      <td>0.03</td>\n",
       "      <td>11.1111</td>\n",
       "      <td>{'C': 0.03, 'gamma': 11.1111111111}</td>\n",
       "      <td>21</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.876777</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.002076</td>\n",
       "      <td>0.001698</td>\n",
       "      <td>0.795</td>\n",
       "      <td>0.819905</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1</td>\n",
       "      <td>{'C': 0.03, 'gamma': 1.0}</td>\n",
       "      <td>33</td>\n",
       "      <td>0.795</td>\n",
       "      <td>0.819905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.002307</td>\n",
       "      <td>0.001430</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>{'C': 0.03, 'gamma': 0.111111111111}</td>\n",
       "      <td>42</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.002448</td>\n",
       "      <td>0.001467</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>{'C': 0.03, 'gamma': 0.01}</td>\n",
       "      <td>42</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.002211</td>\n",
       "      <td>0.001579</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00111111</td>\n",
       "      <td>{'C': 0.03, 'gamma': 0.00111111111111}</td>\n",
       "      <td>42</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.002431</td>\n",
       "      <td>0.001407</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.1</td>\n",
       "      <td>10000</td>\n",
       "      <td>{'C': 0.1, 'gamma': 10000.0}</td>\n",
       "      <td>42</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.002848</td>\n",
       "      <td>0.001708</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1111.11</td>\n",
       "      <td>{'C': 0.1, 'gamma': 1111.11111111}</td>\n",
       "      <td>42</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.002570</td>\n",
       "      <td>0.001626</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.919431</td>\n",
       "      <td>0.1</td>\n",
       "      <td>100</td>\n",
       "      <td>{'C': 0.1, 'gamma': 100.0}</td>\n",
       "      <td>28</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.919431</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.001493</td>\n",
       "      <td>0.000929</td>\n",
       "      <td>0.930</td>\n",
       "      <td>0.900474</td>\n",
       "      <td>0.1</td>\n",
       "      <td>11.1111</td>\n",
       "      <td>{'C': 0.1, 'gamma': 11.1111111111}</td>\n",
       "      <td>13</td>\n",
       "      <td>0.930</td>\n",
       "      <td>0.900474</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.001622</td>\n",
       "      <td>0.001073</td>\n",
       "      <td>0.845</td>\n",
       "      <td>0.862559</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>{'C': 0.1, 'gamma': 1.0}</td>\n",
       "      <td>29</td>\n",
       "      <td>0.845</td>\n",
       "      <td>0.862559</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.001955</td>\n",
       "      <td>0.001278</td>\n",
       "      <td>0.535</td>\n",
       "      <td>0.511848</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>{'C': 0.1, 'gamma': 0.111111111111}</td>\n",
       "      <td>39</td>\n",
       "      <td>0.535</td>\n",
       "      <td>0.511848</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.001963</td>\n",
       "      <td>0.001276</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>{'C': 0.1, 'gamma': 0.01}</td>\n",
       "      <td>42</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.002036</td>\n",
       "      <td>0.001344</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.00111111</td>\n",
       "      <td>{'C': 0.1, 'gamma': 0.00111111111111}</td>\n",
       "      <td>42</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.002089</td>\n",
       "      <td>0.001361</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.3</td>\n",
       "      <td>10000</td>\n",
       "      <td>{'C': 0.3, 'gamma': 10000.0}</td>\n",
       "      <td>42</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.002789</td>\n",
       "      <td>0.001848</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.668246</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1111.11</td>\n",
       "      <td>{'C': 0.3, 'gamma': 1111.11111111}</td>\n",
       "      <td>41</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.668246</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.003104</td>\n",
       "      <td>0.001402</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0.952607</td>\n",
       "      <td>0.3</td>\n",
       "      <td>100</td>\n",
       "      <td>{'C': 0.3, 'gamma': 100.0}</td>\n",
       "      <td>1</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0.952607</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.001438</td>\n",
       "      <td>0.000941</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.933649</td>\n",
       "      <td>0.3</td>\n",
       "      <td>11.1111</td>\n",
       "      <td>{'C': 0.3, 'gamma': 11.1111111111}</td>\n",
       "      <td>6</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.933649</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.001585</td>\n",
       "      <td>0.001066</td>\n",
       "      <td>0.910</td>\n",
       "      <td>0.876777</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "      <td>{'C': 0.3, 'gamma': 1.0}</td>\n",
       "      <td>20</td>\n",
       "      <td>0.910</td>\n",
       "      <td>0.876777</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.002141</td>\n",
       "      <td>0.001394</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.819905</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>{'C': 0.3, 'gamma': 0.111111111111}</td>\n",
       "      <td>30</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.819905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.002750</td>\n",
       "      <td>0.001006</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0.943128</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>{'C': 1.0, 'gamma': 100.0}</td>\n",
       "      <td>1</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0.943128</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.001175</td>\n",
       "      <td>0.000744</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.938389</td>\n",
       "      <td>1</td>\n",
       "      <td>11.1111</td>\n",
       "      <td>{'C': 1.0, 'gamma': 11.1111111111}</td>\n",
       "      <td>6</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.938389</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.001229</td>\n",
       "      <td>0.000823</td>\n",
       "      <td>0.940</td>\n",
       "      <td>0.919431</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>{'C': 1.0, 'gamma': 1.0}</td>\n",
       "      <td>10</td>\n",
       "      <td>0.940</td>\n",
       "      <td>0.919431</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.001528</td>\n",
       "      <td>0.001029</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.867299</td>\n",
       "      <td>1</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>{'C': 1.0, 'gamma': 0.111111111111}</td>\n",
       "      <td>26</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.867299</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.001968</td>\n",
       "      <td>0.001257</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>{'C': 1.0, 'gamma': 0.01}</td>\n",
       "      <td>40</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.001896</td>\n",
       "      <td>0.001238</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00111111</td>\n",
       "      <td>{'C': 1.0, 'gamma': 0.00111111111111}</td>\n",
       "      <td>42</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.002543</td>\n",
       "      <td>0.001232</td>\n",
       "      <td>0.555</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>10000</td>\n",
       "      <td>{'C': 3.0, 'gamma': 10000.0}</td>\n",
       "      <td>35</td>\n",
       "      <td>0.555</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.003045</td>\n",
       "      <td>0.001328</td>\n",
       "      <td>0.880</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>1111.11</td>\n",
       "      <td>{'C': 3.0, 'gamma': 1111.11111111}</td>\n",
       "      <td>23</td>\n",
       "      <td>0.880</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.002206</td>\n",
       "      <td>0.000842</td>\n",
       "      <td>0.945</td>\n",
       "      <td>0.957346</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>{'C': 3.0, 'gamma': 100.0}</td>\n",
       "      <td>8</td>\n",
       "      <td>0.945</td>\n",
       "      <td>0.957346</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.001106</td>\n",
       "      <td>0.000660</td>\n",
       "      <td>0.955</td>\n",
       "      <td>0.938389</td>\n",
       "      <td>3</td>\n",
       "      <td>11.1111</td>\n",
       "      <td>{'C': 3.0, 'gamma': 11.1111111111}</td>\n",
       "      <td>5</td>\n",
       "      <td>0.955</td>\n",
       "      <td>0.938389</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.001100</td>\n",
       "      <td>0.000694</td>\n",
       "      <td>0.925</td>\n",
       "      <td>0.924171</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>{'C': 3.0, 'gamma': 1.0}</td>\n",
       "      <td>15</td>\n",
       "      <td>0.925</td>\n",
       "      <td>0.924171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.001211</td>\n",
       "      <td>0.000815</td>\n",
       "      <td>0.915</td>\n",
       "      <td>0.876777</td>\n",
       "      <td>3</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>{'C': 3.0, 'gamma': 0.111111111111}</td>\n",
       "      <td>18</td>\n",
       "      <td>0.915</td>\n",
       "      <td>0.876777</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.001664</td>\n",
       "      <td>0.001094</td>\n",
       "      <td>0.820</td>\n",
       "      <td>0.824645</td>\n",
       "      <td>3</td>\n",
       "      <td>0.01</td>\n",
       "      <td>{'C': 3.0, 'gamma': 0.01}</td>\n",
       "      <td>31</td>\n",
       "      <td>0.820</td>\n",
       "      <td>0.824645</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.001753</td>\n",
       "      <td>0.001149</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00111111</td>\n",
       "      <td>{'C': 3.0, 'gamma': 0.00111111111111}</td>\n",
       "      <td>42</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.002299</td>\n",
       "      <td>0.001073</td>\n",
       "      <td>0.555</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>10000</td>\n",
       "      <td>{'C': 10.0, 'gamma': 10000.0}</td>\n",
       "      <td>35</td>\n",
       "      <td>0.555</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.002759</td>\n",
       "      <td>0.001222</td>\n",
       "      <td>0.880</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>1111.11</td>\n",
       "      <td>{'C': 10.0, 'gamma': 1111.11111111}</td>\n",
       "      <td>23</td>\n",
       "      <td>0.880</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.001894</td>\n",
       "      <td>0.000710</td>\n",
       "      <td>0.945</td>\n",
       "      <td>0.962085</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>{'C': 10.0, 'gamma': 100.0}</td>\n",
       "      <td>8</td>\n",
       "      <td>0.945</td>\n",
       "      <td>0.962085</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.001242</td>\n",
       "      <td>0.000555</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.943128</td>\n",
       "      <td>10</td>\n",
       "      <td>11.1111</td>\n",
       "      <td>{'C': 10.0, 'gamma': 11.1111111111}</td>\n",
       "      <td>3</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.943128</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0.000968</td>\n",
       "      <td>0.000581</td>\n",
       "      <td>0.925</td>\n",
       "      <td>0.928910</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>{'C': 10.0, 'gamma': 1.0}</td>\n",
       "      <td>15</td>\n",
       "      <td>0.925</td>\n",
       "      <td>0.928910</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0.000985</td>\n",
       "      <td>0.000659</td>\n",
       "      <td>0.930</td>\n",
       "      <td>0.924171</td>\n",
       "      <td>10</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>{'C': 10.0, 'gamma': 0.111111111111}</td>\n",
       "      <td>13</td>\n",
       "      <td>0.930</td>\n",
       "      <td>0.924171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>0.001307</td>\n",
       "      <td>0.000852</td>\n",
       "      <td>0.865</td>\n",
       "      <td>0.867299</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>{'C': 10.0, 'gamma': 0.01}</td>\n",
       "      <td>27</td>\n",
       "      <td>0.865</td>\n",
       "      <td>0.867299</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0.001613</td>\n",
       "      <td>0.001072</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.530806</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00111111</td>\n",
       "      <td>{'C': 10.0, 'gamma': 0.00111111111111}</td>\n",
       "      <td>34</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.530806</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0.002117</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.555</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30</td>\n",
       "      <td>10000</td>\n",
       "      <td>{'C': 30.0, 'gamma': 10000.0}</td>\n",
       "      <td>35</td>\n",
       "      <td>0.555</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.002576</td>\n",
       "      <td>0.001141</td>\n",
       "      <td>0.880</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30</td>\n",
       "      <td>1111.11</td>\n",
       "      <td>{'C': 30.0, 'gamma': 1111.11111111}</td>\n",
       "      <td>23</td>\n",
       "      <td>0.880</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>0.001867</td>\n",
       "      <td>0.000697</td>\n",
       "      <td>0.920</td>\n",
       "      <td>0.985782</td>\n",
       "      <td>30</td>\n",
       "      <td>100</td>\n",
       "      <td>{'C': 30.0, 'gamma': 100.0}</td>\n",
       "      <td>17</td>\n",
       "      <td>0.920</td>\n",
       "      <td>0.985782</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0.001539</td>\n",
       "      <td>0.000694</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.943128</td>\n",
       "      <td>30</td>\n",
       "      <td>11.1111</td>\n",
       "      <td>{'C': 30.0, 'gamma': 11.1111111111}</td>\n",
       "      <td>3</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.943128</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0.001156</td>\n",
       "      <td>0.000616</td>\n",
       "      <td>0.935</td>\n",
       "      <td>0.928910</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>{'C': 30.0, 'gamma': 1.0}</td>\n",
       "      <td>11</td>\n",
       "      <td>0.935</td>\n",
       "      <td>0.928910</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>0.001067</td>\n",
       "      <td>0.000627</td>\n",
       "      <td>0.935</td>\n",
       "      <td>0.924171</td>\n",
       "      <td>30</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>{'C': 30.0, 'gamma': 0.111111111111}</td>\n",
       "      <td>11</td>\n",
       "      <td>0.935</td>\n",
       "      <td>0.924171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>0.001079</td>\n",
       "      <td>0.000731</td>\n",
       "      <td>0.915</td>\n",
       "      <td>0.876777</td>\n",
       "      <td>30</td>\n",
       "      <td>0.01</td>\n",
       "      <td>{'C': 30.0, 'gamma': 0.01}</td>\n",
       "      <td>18</td>\n",
       "      <td>0.915</td>\n",
       "      <td>0.876777</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0.001427</td>\n",
       "      <td>0.000939</td>\n",
       "      <td>0.820</td>\n",
       "      <td>0.819905</td>\n",
       "      <td>30</td>\n",
       "      <td>0.00111111</td>\n",
       "      <td>{'C': 30.0, 'gamma': 0.00111111111111}</td>\n",
       "      <td>31</td>\n",
       "      <td>0.820</td>\n",
       "      <td>0.819905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  mean_score_time  mean_test_score  mean_train_score param_C  \\\n",
       "0        0.003872         0.001880            0.435          0.502370    0.01   \n",
       "1        0.003072         0.001746            0.435          0.502370    0.01   \n",
       "2        0.003044         0.001766            0.435          0.502370    0.01   \n",
       "3        0.003155         0.001521            0.435          0.502370    0.01   \n",
       "4        0.001952         0.001278            0.435          0.502370    0.01   \n",
       "5        0.002496         0.001414            0.435          0.502370    0.01   \n",
       "6        0.001969         0.001275            0.435          0.502370    0.01   \n",
       "7        0.001987         0.001479            0.435          0.502370    0.01   \n",
       "8        0.002463         0.001579            0.435          0.502370    0.03   \n",
       "9        0.002587         0.001685            0.435          0.502370    0.03   \n",
       "10       0.002254         0.001318            0.435          0.502370    0.03   \n",
       "11       0.002467         0.001426            0.900          0.876777    0.03   \n",
       "12       0.002076         0.001698            0.795          0.819905    0.03   \n",
       "13       0.002307         0.001430            0.435          0.502370    0.03   \n",
       "14       0.002448         0.001467            0.435          0.502370    0.03   \n",
       "15       0.002211         0.001579            0.435          0.502370    0.03   \n",
       "16       0.002431         0.001407            0.435          0.502370     0.1   \n",
       "17       0.002848         0.001708            0.435          0.502370     0.1   \n",
       "18       0.002570         0.001626            0.860          0.919431     0.1   \n",
       "19       0.001493         0.000929            0.930          0.900474     0.1   \n",
       "20       0.001622         0.001073            0.845          0.862559     0.1   \n",
       "21       0.001955         0.001278            0.535          0.511848     0.1   \n",
       "22       0.001963         0.001276            0.435          0.502370     0.1   \n",
       "23       0.002036         0.001344            0.435          0.502370     0.1   \n",
       "24       0.002089         0.001361            0.435          0.502370     0.3   \n",
       "25       0.002789         0.001848            0.465          0.668246     0.3   \n",
       "26       0.003104         0.001402            0.965          0.952607     0.3   \n",
       "27       0.001438         0.000941            0.950          0.933649     0.3   \n",
       "28       0.001585         0.001066            0.910          0.876777     0.3   \n",
       "29       0.002141         0.001394            0.825          0.819905     0.3   \n",
       "..            ...              ...              ...               ...     ...   \n",
       "34       0.002750         0.001006            0.965          0.943128       1   \n",
       "35       0.001175         0.000744            0.950          0.938389       1   \n",
       "36       0.001229         0.000823            0.940          0.919431       1   \n",
       "37       0.001528         0.001029            0.870          0.867299       1   \n",
       "38       0.001968         0.001257            0.500          0.502370       1   \n",
       "39       0.001896         0.001238            0.435          0.502370       1   \n",
       "40       0.002543         0.001232            0.555          1.000000       3   \n",
       "41       0.003045         0.001328            0.880          1.000000       3   \n",
       "42       0.002206         0.000842            0.945          0.957346       3   \n",
       "43       0.001106         0.000660            0.955          0.938389       3   \n",
       "44       0.001100         0.000694            0.925          0.924171       3   \n",
       "45       0.001211         0.000815            0.915          0.876777       3   \n",
       "46       0.001664         0.001094            0.820          0.824645       3   \n",
       "47       0.001753         0.001149            0.435          0.502370       3   \n",
       "48       0.002299         0.001073            0.555          1.000000      10   \n",
       "49       0.002759         0.001222            0.880          1.000000      10   \n",
       "50       0.001894         0.000710            0.945          0.962085      10   \n",
       "51       0.001242         0.000555            0.960          0.943128      10   \n",
       "52       0.000968         0.000581            0.925          0.928910      10   \n",
       "53       0.000985         0.000659            0.930          0.924171      10   \n",
       "54       0.001307         0.000852            0.865          0.867299      10   \n",
       "55       0.001613         0.001072            0.590          0.530806      10   \n",
       "56       0.002117         0.001000            0.555          1.000000      30   \n",
       "57       0.002576         0.001141            0.880          1.000000      30   \n",
       "58       0.001867         0.000697            0.920          0.985782      30   \n",
       "59       0.001539         0.000694            0.960          0.943128      30   \n",
       "60       0.001156         0.000616            0.935          0.928910      30   \n",
       "61       0.001067         0.000627            0.935          0.924171      30   \n",
       "62       0.001079         0.000731            0.915          0.876777      30   \n",
       "63       0.001427         0.000939            0.820          0.819905      30   \n",
       "\n",
       "   param_gamma                                  params  rank_test_score  \\\n",
       "0        10000           {'C': 0.01, 'gamma': 10000.0}               42   \n",
       "1      1111.11     {'C': 0.01, 'gamma': 1111.11111111}               42   \n",
       "2          100             {'C': 0.01, 'gamma': 100.0}               42   \n",
       "3      11.1111     {'C': 0.01, 'gamma': 11.1111111111}               42   \n",
       "4            1               {'C': 0.01, 'gamma': 1.0}               42   \n",
       "5     0.111111    {'C': 0.01, 'gamma': 0.111111111111}               42   \n",
       "6         0.01              {'C': 0.01, 'gamma': 0.01}               42   \n",
       "7   0.00111111  {'C': 0.01, 'gamma': 0.00111111111111}               42   \n",
       "8        10000           {'C': 0.03, 'gamma': 10000.0}               42   \n",
       "9      1111.11     {'C': 0.03, 'gamma': 1111.11111111}               42   \n",
       "10         100             {'C': 0.03, 'gamma': 100.0}               42   \n",
       "11     11.1111     {'C': 0.03, 'gamma': 11.1111111111}               21   \n",
       "12           1               {'C': 0.03, 'gamma': 1.0}               33   \n",
       "13    0.111111    {'C': 0.03, 'gamma': 0.111111111111}               42   \n",
       "14        0.01              {'C': 0.03, 'gamma': 0.01}               42   \n",
       "15  0.00111111  {'C': 0.03, 'gamma': 0.00111111111111}               42   \n",
       "16       10000            {'C': 0.1, 'gamma': 10000.0}               42   \n",
       "17     1111.11      {'C': 0.1, 'gamma': 1111.11111111}               42   \n",
       "18         100              {'C': 0.1, 'gamma': 100.0}               28   \n",
       "19     11.1111      {'C': 0.1, 'gamma': 11.1111111111}               13   \n",
       "20           1                {'C': 0.1, 'gamma': 1.0}               29   \n",
       "21    0.111111     {'C': 0.1, 'gamma': 0.111111111111}               39   \n",
       "22        0.01               {'C': 0.1, 'gamma': 0.01}               42   \n",
       "23  0.00111111   {'C': 0.1, 'gamma': 0.00111111111111}               42   \n",
       "24       10000            {'C': 0.3, 'gamma': 10000.0}               42   \n",
       "25     1111.11      {'C': 0.3, 'gamma': 1111.11111111}               41   \n",
       "26         100              {'C': 0.3, 'gamma': 100.0}                1   \n",
       "27     11.1111      {'C': 0.3, 'gamma': 11.1111111111}                6   \n",
       "28           1                {'C': 0.3, 'gamma': 1.0}               20   \n",
       "29    0.111111     {'C': 0.3, 'gamma': 0.111111111111}               30   \n",
       "..         ...                                     ...              ...   \n",
       "34         100              {'C': 1.0, 'gamma': 100.0}                1   \n",
       "35     11.1111      {'C': 1.0, 'gamma': 11.1111111111}                6   \n",
       "36           1                {'C': 1.0, 'gamma': 1.0}               10   \n",
       "37    0.111111     {'C': 1.0, 'gamma': 0.111111111111}               26   \n",
       "38        0.01               {'C': 1.0, 'gamma': 0.01}               40   \n",
       "39  0.00111111   {'C': 1.0, 'gamma': 0.00111111111111}               42   \n",
       "40       10000            {'C': 3.0, 'gamma': 10000.0}               35   \n",
       "41     1111.11      {'C': 3.0, 'gamma': 1111.11111111}               23   \n",
       "42         100              {'C': 3.0, 'gamma': 100.0}                8   \n",
       "43     11.1111      {'C': 3.0, 'gamma': 11.1111111111}                5   \n",
       "44           1                {'C': 3.0, 'gamma': 1.0}               15   \n",
       "45    0.111111     {'C': 3.0, 'gamma': 0.111111111111}               18   \n",
       "46        0.01               {'C': 3.0, 'gamma': 0.01}               31   \n",
       "47  0.00111111   {'C': 3.0, 'gamma': 0.00111111111111}               42   \n",
       "48       10000           {'C': 10.0, 'gamma': 10000.0}               35   \n",
       "49     1111.11     {'C': 10.0, 'gamma': 1111.11111111}               23   \n",
       "50         100             {'C': 10.0, 'gamma': 100.0}                8   \n",
       "51     11.1111     {'C': 10.0, 'gamma': 11.1111111111}                3   \n",
       "52           1               {'C': 10.0, 'gamma': 1.0}               15   \n",
       "53    0.111111    {'C': 10.0, 'gamma': 0.111111111111}               13   \n",
       "54        0.01              {'C': 10.0, 'gamma': 0.01}               27   \n",
       "55  0.00111111  {'C': 10.0, 'gamma': 0.00111111111111}               34   \n",
       "56       10000           {'C': 30.0, 'gamma': 10000.0}               35   \n",
       "57     1111.11     {'C': 30.0, 'gamma': 1111.11111111}               23   \n",
       "58         100             {'C': 30.0, 'gamma': 100.0}               17   \n",
       "59     11.1111     {'C': 30.0, 'gamma': 11.1111111111}                3   \n",
       "60           1               {'C': 30.0, 'gamma': 1.0}               11   \n",
       "61    0.111111    {'C': 30.0, 'gamma': 0.111111111111}               11   \n",
       "62        0.01              {'C': 30.0, 'gamma': 0.01}               18   \n",
       "63  0.00111111  {'C': 30.0, 'gamma': 0.00111111111111}               31   \n",
       "\n",
       "    split0_test_score  split0_train_score  std_fit_time  std_score_time  \\\n",
       "0               0.435            0.502370           0.0             0.0   \n",
       "1               0.435            0.502370           0.0             0.0   \n",
       "2               0.435            0.502370           0.0             0.0   \n",
       "3               0.435            0.502370           0.0             0.0   \n",
       "4               0.435            0.502370           0.0             0.0   \n",
       "5               0.435            0.502370           0.0             0.0   \n",
       "6               0.435            0.502370           0.0             0.0   \n",
       "7               0.435            0.502370           0.0             0.0   \n",
       "8               0.435            0.502370           0.0             0.0   \n",
       "9               0.435            0.502370           0.0             0.0   \n",
       "10              0.435            0.502370           0.0             0.0   \n",
       "11              0.900            0.876777           0.0             0.0   \n",
       "12              0.795            0.819905           0.0             0.0   \n",
       "13              0.435            0.502370           0.0             0.0   \n",
       "14              0.435            0.502370           0.0             0.0   \n",
       "15              0.435            0.502370           0.0             0.0   \n",
       "16              0.435            0.502370           0.0             0.0   \n",
       "17              0.435            0.502370           0.0             0.0   \n",
       "18              0.860            0.919431           0.0             0.0   \n",
       "19              0.930            0.900474           0.0             0.0   \n",
       "20              0.845            0.862559           0.0             0.0   \n",
       "21              0.535            0.511848           0.0             0.0   \n",
       "22              0.435            0.502370           0.0             0.0   \n",
       "23              0.435            0.502370           0.0             0.0   \n",
       "24              0.435            0.502370           0.0             0.0   \n",
       "25              0.465            0.668246           0.0             0.0   \n",
       "26              0.965            0.952607           0.0             0.0   \n",
       "27              0.950            0.933649           0.0             0.0   \n",
       "28              0.910            0.876777           0.0             0.0   \n",
       "29              0.825            0.819905           0.0             0.0   \n",
       "..                ...                 ...           ...             ...   \n",
       "34              0.965            0.943128           0.0             0.0   \n",
       "35              0.950            0.938389           0.0             0.0   \n",
       "36              0.940            0.919431           0.0             0.0   \n",
       "37              0.870            0.867299           0.0             0.0   \n",
       "38              0.500            0.502370           0.0             0.0   \n",
       "39              0.435            0.502370           0.0             0.0   \n",
       "40              0.555            1.000000           0.0             0.0   \n",
       "41              0.880            1.000000           0.0             0.0   \n",
       "42              0.945            0.957346           0.0             0.0   \n",
       "43              0.955            0.938389           0.0             0.0   \n",
       "44              0.925            0.924171           0.0             0.0   \n",
       "45              0.915            0.876777           0.0             0.0   \n",
       "46              0.820            0.824645           0.0             0.0   \n",
       "47              0.435            0.502370           0.0             0.0   \n",
       "48              0.555            1.000000           0.0             0.0   \n",
       "49              0.880            1.000000           0.0             0.0   \n",
       "50              0.945            0.962085           0.0             0.0   \n",
       "51              0.960            0.943128           0.0             0.0   \n",
       "52              0.925            0.928910           0.0             0.0   \n",
       "53              0.930            0.924171           0.0             0.0   \n",
       "54              0.865            0.867299           0.0             0.0   \n",
       "55              0.590            0.530806           0.0             0.0   \n",
       "56              0.555            1.000000           0.0             0.0   \n",
       "57              0.880            1.000000           0.0             0.0   \n",
       "58              0.920            0.985782           0.0             0.0   \n",
       "59              0.960            0.943128           0.0             0.0   \n",
       "60              0.935            0.928910           0.0             0.0   \n",
       "61              0.935            0.924171           0.0             0.0   \n",
       "62              0.915            0.876777           0.0             0.0   \n",
       "63              0.820            0.819905           0.0             0.0   \n",
       "\n",
       "    std_test_score  std_train_score  \n",
       "0              0.0              0.0  \n",
       "1              0.0              0.0  \n",
       "2              0.0              0.0  \n",
       "3              0.0              0.0  \n",
       "4              0.0              0.0  \n",
       "5              0.0              0.0  \n",
       "6              0.0              0.0  \n",
       "7              0.0              0.0  \n",
       "8              0.0              0.0  \n",
       "9              0.0              0.0  \n",
       "10             0.0              0.0  \n",
       "11             0.0              0.0  \n",
       "12             0.0              0.0  \n",
       "13             0.0              0.0  \n",
       "14             0.0              0.0  \n",
       "15             0.0              0.0  \n",
       "16             0.0              0.0  \n",
       "17             0.0              0.0  \n",
       "18             0.0              0.0  \n",
       "19             0.0              0.0  \n",
       "20             0.0              0.0  \n",
       "21             0.0              0.0  \n",
       "22             0.0              0.0  \n",
       "23             0.0              0.0  \n",
       "24             0.0              0.0  \n",
       "25             0.0              0.0  \n",
       "26             0.0              0.0  \n",
       "27             0.0              0.0  \n",
       "28             0.0              0.0  \n",
       "29             0.0              0.0  \n",
       "..             ...              ...  \n",
       "34             0.0              0.0  \n",
       "35             0.0              0.0  \n",
       "36             0.0              0.0  \n",
       "37             0.0              0.0  \n",
       "38             0.0              0.0  \n",
       "39             0.0              0.0  \n",
       "40             0.0              0.0  \n",
       "41             0.0              0.0  \n",
       "42             0.0              0.0  \n",
       "43             0.0              0.0  \n",
       "44             0.0              0.0  \n",
       "45             0.0              0.0  \n",
       "46             0.0              0.0  \n",
       "47             0.0              0.0  \n",
       "48             0.0              0.0  \n",
       "49             0.0              0.0  \n",
       "50             0.0              0.0  \n",
       "51             0.0              0.0  \n",
       "52             0.0              0.0  \n",
       "53             0.0              0.0  \n",
       "54             0.0              0.0  \n",
       "55             0.0              0.0  \n",
       "56             0.0              0.0  \n",
       "57             0.0              0.0  \n",
       "58             0.0              0.0  \n",
       "59             0.0              0.0  \n",
       "60             0.0              0.0  \n",
       "61             0.0              0.0  \n",
       "62             0.0              0.0  \n",
       "63             0.0              0.0  \n",
       "\n",
       "[64 rows x 14 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display grid search results in a pandas DataFrame\n",
    "pd.DataFrame(gs.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Obtain best classifier from grid search\n",
    "clf_best = gs.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best C value = 0.3\n"
     ]
    }
   ],
   "source": [
    "print('best C value =', clf_best.C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best gamma value = 100.0\n"
     ]
    }
   ],
   "source": [
    "print('best gamma value =', clf_best.gamma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x108d4c6a0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEWCAYAAACaBstRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108749fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data(X_train_0, y_train_0)\n",
    "plot_boundary(X_train_0, clf_best)\n",
    "plt.title(f'C={clf_best.C:.4}, gamma={clf_best.gamma:.4}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spam classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load spam classification training data\n",
    "data_train = sio.loadmat('data/ml-ex6/spamTrain.mat')\n",
    "# Load spam classification test data\n",
    "data_test = sio.loadmat('data/ml-ex6/spamTest.mat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Example feature vectors\n",
    "X_train = data_train['X']\n",
    "X_test = data_test['Xtest']\n",
    "\n",
    "# Example classes (0=non-spam, 1=spam)\n",
    "y_train = data_train['y'].ravel()\n",
    "y_test = data_test['ytest'].ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=0.1, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train a linear SVC (running liblinear) using the \n",
    "# same value for C as in in the original exercise \n",
    "# (see ex6_spam.m)\n",
    "clf = LinearSVC(C=0.1)\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99975000000000003"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training score\n",
    "clf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99199999999999999"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test score\n",
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vocab = {}\n",
    "\n",
    "# Read provided vocabulary from vocab.txt file and add \n",
    "# content to vocab, converting 1-based to 0-based index\n",
    "with open('data/ml-ex6/vocab.txt') as vocab_file:\n",
    "    for row in csv.reader(vocab_file, delimiter='\\t'):\n",
    "        word = row[1]\n",
    "        index = int(row[0]) - 1\n",
    "        vocab[word] = index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def read_file(file):\n",
    "    with open(file) as f:\n",
    "        return f.read()\n",
    "\n",
    "# Read non-spam email samples\n",
    "email_1 = read_file('data/ml-ex6/emailSample1.txt')\n",
    "email_2 = read_file('data/ml-ex6/emailSample2.txt')\n",
    "\n",
    "# Read spam email samples\n",
    "spam_1 = read_file('data/ml-ex6/spamSample1.txt')\n",
    "spam_2 = read_file('data/ml-ex6/spamSample2.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> Anyone knows how much it costs to host a web portal ?\n",
      ">\n",
      "Well, it depends on how many visitors you're expecting.\n",
      "This can be anywhere from less than 10 bucks a month to a couple of $100. \n",
      "You should  ...\n"
     ]
    }
   ],
   "source": [
    "# Print first 200 characters of a non-spam sample\n",
    "print(email_1[:200], '...')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Do You Want To Make $1000 Or More Per Week?\n",
      "\n",
      " \n",
      "\n",
      "If you are a motivated and qualified individual - I \n",
      "will personally demonstrate to you a system that will \n",
      "make you $1,000 per week or more! This is NO ...\n"
     ]
    }
   ],
   "source": [
    "# Print first 200 characters of a spam sample\n",
    "print(spam_1[:200], '...')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Email pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following, we reuse email pre-processing logic from the original course exercise and use [oct2py](http://blink1073.github.io/oct2py/index.html) for calling Octave functions from Python. The called function for email pre-processing is `processEmail` which is located in directory `func/ml-ex6/`. It is a modified version of the original function and returns pre-processed words instead of their index in the dictionary (to avoid sharing Octave code that must be written during assignments). For a more detailed description of `processEmail`, see [ex6.pdf](data/ml-ex6/ex6.pdf), section 2.1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Add directory func/ml-ex6 to Octave path.\n",
    "# It contains file/function processEmail.m\n",
    "octave.addpath('./func/ml-ex6');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "warning: implicit conversion from numeric to char\n",
      "warning: implicit conversion from numeric to char\n",
      "warning: implicit conversion from numeric to char\n",
      "warning: implicit conversion from numeric to char\n"
     ]
    }
   ],
   "source": [
    "# Pre-process emails with provided Octave code.\n",
    "proc_email_1 = octave.processEmail(email_1)\n",
    "proc_email_2 = octave.processEmail(email_2)\n",
    "proc_spam_1 = octave.processEmail(spam_1)\n",
    "proc_spam_1 = octave.processEmail(spam_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['anyon     ', 'know      ', 'how       ', 'much      ',\n",
       "       'it        ', 'cost      ', 'to        ', 'host      ',\n",
       "       'a         ', 'web       ', 'portal    ', 'well      ',\n",
       "       'it        ', 'depend    ', 'on        ', 'how       ',\n",
       "       'mani      ', 'visitor   ', 'you       ', 're        ',\n",
       "       'expect    ', 'thi       ', 'can       ', 'be        ',\n",
       "       'anywher   ', 'from      ', 'less      ', 'than      ',\n",
       "       'number    ', 'buck      ', 'a         ', 'month     ',\n",
       "       'to        ', 'a         ', 'coupl     ', 'of        ',\n",
       "       'dollarnumb', 'you       ', 'should    ', 'checkout  ',\n",
       "       'httpaddr  ', 'or        ', 'perhap    ', 'amazon    ',\n",
       "       'ecnumb    ', 'if        ', 'your      ', 'run       ',\n",
       "       'someth    ', 'big       ', 'to        ', 'unsubscrib',\n",
       "       'yourself  ', 'from      ', 'thi       ', 'mail      ',\n",
       "       'list      ', 'send      ', 'an        ', 'email     ',\n",
       "       'to        ', 'emailaddr '],\n",
       "      dtype='<U10')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pre-processed non-spam sample\n",
    "proc_email_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def words_to_features(words, vocab):\n",
    "    '''Creates a feature vector from a list of words.\n",
    "    \n",
    "    The length of the returned feature vector is equal to \n",
    "    size of the vocabulary. The feature vector element i is \n",
    "    1 if the i-th word in the vocabulary is in the words list, \n",
    "    0 otherwise.\n",
    "    '''\n",
    "    \n",
    "    features = np.zeros(len(vocab))\n",
    "    for word in words:\n",
    "        idx = vocab.get(word.strip())\n",
    "        if idx:\n",
    "            features[idx] = 1\n",
    "    return features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convert pre-processed samples to feature vectors\n",
    "X_samples = np.vstack([words_to_features(proc_email_1, vocab),\n",
    "                       words_to_features(proc_email_2, vocab),\n",
    "                       words_to_features(proc_spam_1, vocab),\n",
    "                       words_to_features(proc_spam_1, vocab)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 1], dtype=uint8)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict the classes of samples (should be \n",
    "# [0, 0, 1, 1] where 0=non-spam and 1=spam)\n",
    "clf.predict(X_samples)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}