{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<div align='left' style=\"width:400px;height:120px;overflow:hidden;\">\n",
    "<a href='http://www.uff.br'>\n",
    "<img align='left' style='display: block;height: 92%' src='imgs/UFF.png' alt='UFF logo' title='UFF logo'/>\n",
    "</a>\n",
    "<a href='http://www.ic.uff.br'>\n",
    "<img align='left' style='display: block;height: 100%' src='imgs/logo-ic.png' alt='IC logo' title='IC logo'/>\n",
    "</a>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Machine Learning\n",
    "# Assessment and Parameter Optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [Luis Martí](http://lmarti.com)\n",
    "#### [Instituto de Computação](http://www.ic.uff)\n",
    "#### [Universidade Federal Fluminense](http://www.uff.br)\n",
    "$\\newcommand{\\vec}[1]{\\boldsymbol{#1}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import random, itertools, math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy\n",
    "import sklearn\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import seaborn\n",
    "seaborn.set(style='whitegrid')\n",
    "seaborn.set_context('paper')\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# fixing a seed for reproducibility, do not do this in real life. \n",
    "random.seed(a=42)\n",
    "np.random.seed(seed=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### About the notebook/slides\n",
    "\n",
    "* The slides are _programmed_ as a [Jupyter](http://jupyter.org)/[IPython](https://ipython.org/) notebook.\n",
    "* **Feel free to try them and experiment on your own by launching the notebooks.**\n",
    "\n",
    "* You can run the notebook online: [![Binder](http://mybinder.org/badge.svg)](http://mybinder.org/repo/lmarti/machine-learning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "If you are using [nbviewer](http://nbviewer.jupyter.org) you can change to slides mode by clicking on the icon:\n",
    "\n",
    "<div class=\"container-fluid\">\n",
    "  <div class=\"row\">\n",
    "      <div class=\"col-md-3\"><span/></div>\n",
    "      <div class=\"col-md-6\">\n",
    "      <div class='well well-sm'>\n",
    "              <img src='imgs/view-as-slides.png'/>\n",
    "      </div>\n",
    "      </div>\n",
    "      <div class=\"col-md-3\" align='center'><span/></div>\n",
    "  </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Measuring model error\n",
    "\n",
    "* The primary goal should be to make a model that most accurately predicts the desired target value for new data. \n",
    "* The measure of model error that is used should be one that achieves this goal. \n",
    "* However, many modelers instead report a measure of model error that is based not on the error for new data but instead on the error the very same data that was used to train the model. \n",
    "* The use of this incorrect error measure can lead to the selection of an inferior and inaccurate model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Model quality metrics\n",
    "\n",
    "Along the course we have mentioned several quality metrics:\n",
    "\n",
    "* Regression: mean squared error, mean absolute error, explained variance and R2 score.\n",
    "* Classification: accuracy, recall, precision, and many more.\n",
    "\n",
    "But we need more than that to be able to produce valid results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Classification metrics: Precision an recall\n",
    "\n",
    "<div class=\"container-fluid\">\n",
    "  <div class=\"row\">\n",
    "      <div class=\"col-md-6\"> \n",
    "      <ul>\n",
    "      <li>precision (also positive predictive value): fraction of relevant instances among the retrieved instances, \n",
    "      <li>recall (a.k.a. sensitivity) is the fraction of relevant instances that have been retrieved over the total amount of relevant instances.\n",
    "      </ul></div>\n",
    "      <div class=\"col-md-6\">\n",
    "          <img height='83%' src='https://upload.wikimedia.org/wikipedia/commons/2/26/Precisionrecall.svg'/>    \n",
    "      </div>\n",
    "  </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Bias and Variance\n",
    "\n",
    "* The bias is error from erroneous assumptions in the learning algorithm. \n",
    "    * High bias can cause an algorithm to miss the relevant relations between features and target outputs (underfitting).\n",
    "* The variance is error from sensitivity to small fluctuations in the training set. \n",
    "    * High variance can cause an algorithm to model the random noise in the training data, rather than the intended outputs (overfitting)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Training bias\n",
    "\n",
    "It is helpful to illustrate this fact with an equation:\n",
    "* a relationship between how well a model predicts on new data (its true prediction error and the thing we really care about) and \n",
    "* how well it predicts on the training data (which is what many modelers in fact measure).\n",
    "\n",
    "$$\n",
    "\\text{True Prediction Error}=\\text{Training Error}+\\text{Training Optimism}\\,.\n",
    "$$\n",
    "where,\n",
    "* *Training Optimism* is a measure of how much worse our model does on new data compared to the training data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "It turns out that the optimism is a function of model complexity: **complexity increases so does optimism**. \n",
    "\n",
    "Thus we have a our relationship above for true prediction error becomes something like this:\n",
    "\n",
    "$$\n",
    "\\text{True Prediction Error}=\\text{Training Error}+f(\\text{Model Complexity})\\,.\n",
    "$$\n",
    "\n",
    "As model complexity increases (for instance by adding parameters terms in a linear regression) the model will always do a better job fitting the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(size):\n",
    "    'Returns a noiseless sample with `size` instances.'\n",
    "    x = np.linspace(0, 4.5, size)\n",
    "    y = 2 * np.sin(x * 1.5)\n",
    "    return x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample(size):\n",
    "    'Returns a noisy sample with `size` instances.'\n",
    "    x, y = f(size)\n",
    "    return x, y + np.random.randn(x.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x113f73160>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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RaZibm6uct6ysrDIPAXRxcUF6erpKofv48WMMHToUf/zxB4BXf9vk6uqKa9euqSxClpOT\ng6NHjwrnTBfoXDEvk8lsZTLZ5zKZrNpLb0Xj+QOw1iKkRURERK9hZWWFwMBA3Lp1CxEREQCAzz//\nHBkZGRgyZAiOHj2KEydOYNiwYTh79qwwftvJyQl79+5FZGQkzpw5g2+++QZbt24F8P/juV/H1dUV\nUqkU0dHRQszCwgLOzs5Yv349jhw5gqNHjyIwMBDVq1d/o885efJkZGVlYf78+aXeZ/jw4bh37x6G\nDh2KX3/9Ffv378fQoUPx9ttvo2fPnsJ27u7uOH78uDA2HgBatGiB6OhotGvXrlzDiKKjo2Fubl6m\nGwEPDw/s2bMH+/btw++//16uVVk/+OADNGnSBOPHj8e+fftw4sQJjBo1CjVr1hQeVq1evTqioqIQ\nGxtbZP/evXujdu3aGDp0KPbv349ff/0VQ4cOxf379zF8+PAy56OpdK6YB2AJYD0Av5finQHc/d8P\nERERaaBPP/0Ub7/9NlasWIGHDx+idu3a2LZtG0xMTDBhwgSMGzcO+fn52LBhgzCkbO7cuWjQoAEm\nT56McePG4fr164iIiICpqWmppyw2NzdHmzZthB7fQnPnzkXdunURFBSEOXPmoG/fvnBzc3ujz/j2\n228jMDAQe/fuxcWLF0u1j6OjIzZt2oTc3FyMHTsWs2fPRosWLfD999+rzM1fOPT0xVlnWrVqpfJe\nWf3xxx/o0KEDpNLSD26YPHky3NzcEBwcLPy5V69eZWpXKpVi3bp1aN26NebMmYMvv/wS5ubm2Lhx\nI6pVe95nO3r0aJw7dw5Dhw5Fbm6uyv7m5ubYunUrnJ2d8c0332D8+PHQ09PD1q1byz1rkSaSlPbr\nJ20ik8l2AegIYDKARAC9AQwHMCghIWFDaY8TFRVVAOCVi2tUBo57pVfh9UEl4bWhmwrHzJc01rY0\nNG3MvKY6d+6cMBNMcYtX6aLXXRsPHjxA+/bt8cMPP+hUAaxur/t7XDiUyNXVtczTVulizzwAfAJg\nDZ4X8wcAvA/AvyyFPBEREVUtbm5ucHV1xbZt28RORWNs3rwZnp6eLOQ1mE7OZpOQkPAUwKT//RAR\nERGVyqxZszBw4ED06dNHmG+8qrp79y7279+PXbt2iZ0KvYJOFvNERERE5VG7dm0cP35c7DQ0Qs2a\nNfHrr7+KnQa9hq4OsyEiIiIi0nks5omIiIiItBSLeSIiIiIiLcVinoiIiIhIS7GYJyIiIiLSUizm\niYiIiIi0FIt5IiIiIqJXKCgoEDuFErGYJyIiIrU7c+YMBg8ejJYtW8LJyQne3t5YvHgxnjx5InZq\nAIBz585BJpPh0qVLb3Sc8PBwuLi4vHa7p0+fwsvLCzdv3nyj9nTRy+dwzJgx2LdvX7mO1bFjR3zz\nzTdl2mfp0qUavSowi3kiIiJSqxMnTmDQoEGwtbXF/PnzsXr1avTp0wfbt2/HkCFDkJeXJ3aKardo\n0SK0a9cO7777rtipaLygoCDMnz8f9+/fV0t74eHhyM7OVktb5cEVYImIiKqglJQUxMTEQC6Xw9bW\nVq1tr127Fm3btsXs2bOFWOvWrVG/fn0EBgbi1KlTaN++vVpzEtO///6L7du345dffhE7Fa1Qt25d\ntGrVCitXrsTXX38tdjqiY888ERFRFRMWFgZ7e3t06dIF9vb2CAsLU2v7Dx8+LHYMctu2bTFu3DjU\nqlVLiP31118YOnQoWrRoAUdHR3h5eWH79u3C+3v27IGbmxt+//13dO/eHU5OTvDz88P169fxyy+/\nwMvLCy4uLggMDMSDBw8AALdv34ZMJsPhw4cxcOBANGvWDD4+Pjh06NAr846Li8Onn34KZ2dnvP/+\n+5g1axaysrJUtlm3bh0++OADyOVyTJgwoVQ9ups2bUKzZs1gZ2cnxGQyGXbt2oUxY8ZALpejXbt2\n2LZtG1JTUzFs2DA4OzvDy8sLJ06cUDnWH3/8AX9/fzRr1gweHh5YsmSJyjcdSqUSYWFh8PLygqOj\nI9zd3TF+/HgkJycL2yQmJmLIkCFo0aIFmjdvjsGDByM+Pl54v7ihKrNnz0bHjh1V8l+5ciW6du0K\nNzc3REZGVug59PHxwe7du/H48eMSz+u9e/egUCjg6uoKd3f3YofmJCYmQqFQ4P3334ejoyM6duyI\nZcuWCdenTCYDAMyfP1/4fAUFBdi0aZNwvbm4uODzzz9HQkJCiblUJhbzREREVUhycjKCgoKgVCoB\nPC/ugoKCkJKSorYcPDw8cOrUKQwfPhwHDx7EvXv3AABSqRTDhw9H48aNAQB37tzBJ598AlNTUyxZ\nsgTLli1DvXr1EBwcrFJcZmZmYsaMGRg2bBgWL16MlJQU4c/jx4/HV199hdOnT2PJkiUqeUybNg2N\nGzfG0qVL0bRpU4wfPx6nTp0qNudr165h4MCBkEgkCA0NRVBQEA4dOoQvvvhC2GbdunUICQlBr169\nEBYWBqVSiU2bNr3yXBQUFODQoUPo3Llzkffmzp2LunXrYsWKFXBxccGsWbPw2WefoXnz5liyZAnM\nzc0xYcIEoRg+c+YMhg4dijp16mDp0qUYPHgwNmzYgG+//VblmFu2bMHQoUOxfv16jB49GufPn8ec\nOXOEbUaNGoW8vDwsXrwYixcvxqNHjxAYGFjm4U9Lly7FgAEDMGPGDLRo0aJCz2H79u2Rn5+PX3/9\ntdi28/LyMHjwYMTFxWHWrFn46quvEBYWhtTUVGGbzMxMfPLJJ0hLS8N3332HVatWwc3NDWFhYcJx\nd+zYAQAICAjA0qVLAQDr16/HwoUL4efnh3Xr1mHatGm4du0aJk+eXKbzU1E4zIaIiKgKiY2NFQr5\nQkqlEjExMfD29lZLDuPGjUNaWhr27dsnFE3169eHl5cXPv/8c1hYWAAA/vnnH8jlcixcuBBSqRQA\n4OzsDDc3N1y8eFEo+gtvSHx8fITPuHr1amzZsgUtW7YEAFy8eBGxsbEqebi7uwvDNDw8PHDjxg2s\nWrUK7dq1K5Lz8uXLYW1tjdWrV8PQ0BAA8O6772LAgAG4cOECXF1dsWbNGvj7+0OhUAjH/+ijj/Dv\nv/+WeC6uXbuGBw8eoEmTJkXec3FxQVBQEACgVq1a+PnnnyGXyzF8+HAAgJGRET777DPcvHkTDg4O\nCA0NhbOzMxYvXix8JgsLC0yePBmDBw9GnTp18PDhQ0ycOBF+fn4AACcnJ9y8eROHDx8G8Pxbk8TE\nRIwaNQru7u4AADs7Oxw4cABPnz5FtWrVSvwsL2vbti369+8vvJ4zZ06FnUMjIyM0aNAA586dQ8+e\nPYu0/dtvvyEhIQE7duyAXC4X2urdu7ewzY0bN2Bvb4/Q0FBYWVkBeD7c6+jRo7hw4QI6duwo7Gtn\nZyf8jpKTkzFy5Eh8+umnAIBWrVohPT0dc+fORWZmJszMzEp9jioCi3kiIqIqRC6XQyqVqhT0UqlU\nKFrUwdDQEHPnzsXYsWNx/PhxnD59GufPn8eKFSuwe/dubNu2De+88w7at2+P9u3b49mzZ4iPj8fN\nmzeF2WVycnJUjunk5CT82draGgDg6OgoxCwtLZGRkaGyT9euXVVed+zYEcuXL0d+fn6RnM+dOwdP\nT0/o6ekhNzcXwPNzaW5ujjNnzsDKygqPHj2Ch4eHsI9EIkHnzp2xbt26Es/Ff//9BwAqQ2wKNWvW\nTPjzW2+9VexnAoD09HRkZWXhr7/+wrhx44T8gOcFfX5+Ps6dO4c6deogNDQUAJCamorExETEx8cj\nJiZGOJ+WlpZ49913MW3aNJw+fRrt27dHu3btMH78+BI/Q0kaNGig8rqiz2Ht2rWF8/eyP//8ExYW\nFirXddOmTfH2228Lrx0dHbFt2zYolUpcu3YNN2/exOXLl5Gbm1vk+npR4Q1g4Y1PYmIijh8/DuD5\ndclinoiIiCqNra0tFi5cKAy1kUqlCAkJUftDsIW59O/fH/3790dubi5+/PFHBAcHY+nSpfjuu++Q\nl5eHefPmYceOHVAqlbC3t0eLFi0AFJ33u7gCysTE5JXt29jYqLy2srKCUqnE06dPi2yblpaGHTt2\nCMMuXnTv3j1h7HaNGjVU3isswktSeINhbGxc5L2yfKb09HTk5+cjJCQEISEhxeYIPC9yZ8yYgYSE\nBFSrVg0ymQxGRkbCdnp6eti4cSPCw8Nx7Ngx7N69G8bGxhg8eDDGjBkDiUTyys/zosKbqkIVfQ6N\njY1x586dYt9LT08vchyg6O985cqVWLt2LTIyMvD222/DxcUFBgYGr5xX/vr165g2bRqioqJgYmKC\nxo0bC78rMeajZzFPRERUxSgUCvTp00eU2WxiYmIwcuRIrFixAs7OzkLcwMAAvr6+OH78OK5fvw4A\nWLFiBX744Qd89913aN++PUxNTZGVlYVdu3ZVSC5paWkqrx88eAAjI6Nii2hzc3N4enqiX79+Rd6r\nUaOG0JP78OHDV7bxssLe9YyMjCKFZlkU5jxixAh4enoWeb9mzZrIyMjA8OHD0bx5c4SHh6Nu3brI\nysrC4sWLcfXqVWFbOzs7zJkzB/n5+YiJicHOnTuxbNkyNGzYUBjK9PK3F8XdAL2sos9henq6cP5e\nZmlpKTzwXNKx9u3bh9DQUAQHB6Nbt27CEKLWrVuX+Bny8/MxYsQIWFpa4qeffkLDhg2hp6eHrVu3\nlvi8RWXjA7BERERVkK2tLby9vdXeI//uu+8iMzMTmzdvLvJeXl4e/v33XzRq1AjA88Lf0dERXbp0\ngampKQDg5MmTACqmB/TlhyePHTuGVq1aFdv77OrqisTERDg6OsLJyQlOTk6ws7NDSEgI/vnnH9Sr\nVw81a9bEzz//rLLf77///socCofXvPhgZnmYm5ujcePG+Pfff4X8nJycIJVKsWjRIqSkpCAxMRGP\nHz/Gp59+irp16wJ4XpyePXtWOJ/x8fFo164d/v77b+jp6aF58+b49ttvYWBgIPSCm5ub4+7du0Lb\n+fn5iI6Ofm2OFX0OU1NTix2eBABubm7IyMjAmTNnhNiNGzeQlJQkvI6OjoatrS369esnFPJ///13\nkdmW9PT+v1x++PAhbt26hT59+uC9994T3iu8LsXAnnkiIiJSG0tLS4wbNw5z585FWloaevXqBVtb\nW9y9exfbt29HamqqMGuIk5MT1qxZgy1btuC9997DpUuXsGzZMkgkkgpZxGfnzp2wsrKCi4sL9u3b\nh4SEBGzZsqXYbUeOHIm+ffti7Nix8PX1RU5ODpYvX47k5GQ0adIEEokECoUC06ZNg7W1Ndq2bYvD\nhw8jLi4O+vr6JebQoEED1KxZE9HR0a/sES4NhUKBUaNGwdzcHJ06dcKjR48QGhoKPT09vPfee8jN\nzYWZmZnwXEB2djYiIiJw9epVSCQSFBQUoGHDhjAzM8OkSZMwevRoWFhYYN++fZBIJOjQoQOA5+Pw\nN2zYgIiICDRs2BDbt2/HgwcPXjtWvCLPYWZmJv755x8EBgYW21bbtm3RsmVLTJgwAUFBQTA1NUVo\naKjwIDXw/Pravn07li5dilatWuH69evFXl/Vq1dHVFQUWrRoAWdnZ9SuXRubNm3CW2+9BT09Pezb\ntw+//fYbABSZZlMd2DNPREREavXZZ59h5cqVAIBvv/0Wn376KebMmQM7Ozvs2rUL9vb2AIBhw4ah\nZ8+eWLp0KQIDA3HgwAFMmzYNbdu2LVVP8Ot88cUXOHXqFEaNGoVbt25h7dq1cHFxKXZbR0dHbNq0\nCY8ePYJCocDUqVNRq1YtRERECPPi+/v7Y9asWTh69ChGjhyJBw8eCDPPlEQikeDDDz/EH3/88caf\nx9PTE8uXL0dcXBxGjBiBOXPmQC6XY/PmzTAxMUG1atUQHh6O9PR0jBgxAt988w0sLS2xYMEC5Ofn\nIzY2FgYGBlizZg3q1q2LGTNmIDAwEImJiVi1ahUaNmwIABg+fDi6deuGxYsXY+zYsbCxsSmxqK6s\nc3jmzBlIpVJhxp2XSSQSrFixAu7u7pg9ezaCg4PRq1cvYQYkAOjduzeGDBmC7du3Y9iwYdiyZQsG\nDx4MPz8/xMTECNuNHj0a586dw9ChQ5Gbm4vw8HCYmZnhiy++wJQpU5CVlYUNGzYAgMp+6iIRY6C+\ntoiKiioAnn8tpE5XrlwBADg4OKi1XdIOvD6oJLw2dFPhWOTCYSblUdhb+LoHQquK27dvw9PTE0uW\nLFHbdJyvkpSUhC5duuDgwYM6rWIdAAAgAElEQVR499131dq2tl4bw4cPxzvvvIOpU6eKnUqpvO7v\ncVRUFADA1dW19E8Y/w975omIiIhEZG9vD19fX6F3l17t+vXriI6OxtChQ8VORSOwmCeqYCkpKYiM\njFTraopERKTdJk6ciNOnT+PGjRtip6LxFi1ahAkTJqBmzZpip6IR+AAsVSkpKSmVOhVbWFiYytzN\nCxcuFFaxIyIizVCnTh0kJCSInYYKc3Nz/PLLL2KnoRWWLVsmdgoahT3zVGWEhYXB3t4eXbp0gb29\nPcLCwir0+MnJyUIhD/z/8uLsoSciIqLKwmKeqgR1FNqxsbEqy6MXtiPGk+1EpDv09PSQl5cndhpE\n9Aby8vJeOUXpm2AxT1WCOgptuVyuMn8tAEilUsjl8gprg4iqHkNDQ2RmZoqyTDwRvbmCggJkZmYW\nqREqCsfMU5VQWGi/WNBXdKFta2uLhQsXqoyZDwkJUfvqikSkW/T09GBtbY3U1FSYmZmVq3evcAEc\n3hDQy3htVK68vDxkZmbC2tpaZSXZisSeeSo1bZ6lpbDQLrwrrqxCW6FQICkpCYcPH0ZSUhLGjBlT\noccnoqpJKpWiZs2aMDQ0LNf+ycnJSE5OruCsSBfw2qhchoaGqFmzZqX1ygPsmadS0oVZWhQKBfr0\n6VOps9kAz28cNGEREiLSLXp6ejAyMir3vsCbLTxFuonXhvZjzzy9li7N0lJYaHPoCxEREekCFvP0\nWpylhYiIiEgzsZin1+IsLURERESaicU8vZa6Hh6lyqPNDy8TERFRyVjMU6lwlhbtVdkr31LZ8eaK\niIgqCot5KjU+PKp9dOnhZV3BmysiIqpILOaJdBgfXtYsvLkiIqKKxmKeSIfx4WXNwpsrIiKqaCzm\niXQYH17WLLy5IiKiisZinkjH8eFlzcGbKyIiqmgGYidARJWv8OFlEp9CoUCfPn0QExMDuVzOQp6I\niN4Ii3kiIjXjzRUREVUUDrMhIiIiItJSLOaJiIiIiLQUi3kiIiIiIi2lk2PmZTKZPoCxAIYCsAdw\nC8ByAMsSEhIKxMyNiIiIiKii6GQxD2AagK8AzAJwFoA7gFAApgDmi5gXEREREVGF0bliXiaT6QEY\nD2BBQkLC7P+Fj8lkMhsAQWAxTzogLy8Pz549g6GhISQSidjpEBERkUh0rpgHYAFgM4A9L8UTANjI\nZDKzhISETPWnRVR2T548QXx8POLj43HlyhVcuXIFsbGxSEpKQm5uLu7fvw9ra+si+82ZMwfLly+H\ngYEBDAwMIJVKYWJiAgcHB7i4uAg/VlZWInwqIiIiqiiSgoKqMYRcJpP9AqBxQkLCO6XdJyoqqgAA\nTE1NKy2v4mRlZQEATExM1Nouief+/ftITExEYmIirl+/jhs3biAxMREpKSmv3O/cuXOoVq1akfiC\nBQuwYcOG17Zbu3ZtODg4CD9NmjRBzZo12duvpfhvB5WE1waVhNeGZnj69CkAwNXVtcz/Aetiz3wR\nMplsCIAPASjEzoXoRZmZmejTpw9u3LhRrv0NDIr/K5ybm1uq/e/cuYM7d+7g2LFjQmz+/Pno1q1b\nufIhIiIi9dL5Yl4mkw0AsBLALgBLy3MMBweHCs3pda5cuSJKuySOGjVqlKuYNzQ0hKOjIwwNDYu8\nV7169XLn4+3tXey1l5+fD4lEwl57DcZ/O6gkvDaoJLw2NENUVFS599XpYl4mk40DEAJgP4ABnJaS\n1K2goADR0dE4deoUFIrivxjy9/fHn3/+Wex7+vr6aNCgARwcHNC4cWM4ODjAyMgI9evXR6tWrUps\nd/bs2fjqq6+Qm5uL3NxcKJVK3Lt3DzExMYiOjkZ0dDT+/vvvIj34RkZGaNy4cbHH3Lt3LyZPnoyB\nAwdi4MCBqF+/finPAhEREVUWnS3mZTLZHACT8fxh2MEJCQmlG3dA9IYKCgpw8eJF7Nq1C7t27UJi\nYiIAoHv37qhXr16R7f38/DB9+nQ0a9ZMpWhv3LgxGjZsWKTnvbAX5VUsLCxgYWGhEnNwcICHh4fw\n+tmzZ4iLixOK++joaBgaGkIqlRZ7zM2bN+Off/5BcHAwgoOD0a5dOwQEBMDf3x81atR4bU5ERERU\n8XSymJfJZGPxvJBfAmAce+RJHZ49e4YNGzZg4cKFuH79epH3d+/ejaCgoCLxhg0b4v79+280NKY8\njIyM4OrqCldXVyFW0gPx9+/fx6FDh1Rip06dwqlTpzBmzBh0794dAQEB6NKlS7HDfoiIiKhy6Imd\nQEWTyWR2AL4DcAnAdgBuMpns/Rd+dPIGhsTz7NkzrFixAo0aNcKIESOKLeQBYOfOnSUeQ92FfElK\nGg9//vx56OkV/89FTk4Odu/ejZ49e6J27dqYMWMG0tPTKzNNIiIi+h+dK+YBeAEwAuAE4EwxP5bi\npUa6JDs7G8uWLUPDhg0xcuRI/Pvvv8VuZ2RkhI8++ghjx44tsedb0/n4+CA5ORkrVqxAmzZtStzu\nwYMHmDlzJurXr49FixYhOztbjVkSERFVPTrXS52QkLARwEaR0yAdlp2djbVr12LevHn477//it3G\n0NAQXbt2hb+/P7p27aoxPe9vwsrKCsOHD8fw4cNx/fp1bNmyBREREcV+E/HgwQN8+eWXWLx4MRYv\nXgw/Pz8RMiYiItJ9OlfME1Wm/Px8tGjRAn///Xex7xsZGSEwMBCTJk1C7dq11Zyd+jRo0ADBwcGY\nPn06zp49i82bN2P79u1IS0tT2e727dvsnSciIqpEujjMhqjS6OnpoU+fPkXixsbGGDt2LBITE7Fk\nyRKdLuRfJJFI0Lp1a6xYsQK3bt1CcHAwzM3NhfcdHR3Rr18/ETMkIiLSbSzmicpIoVAI0z6amJhg\n3LhxSExMRGhoaJUp4otTvXp1zJgxA4mJiRg3bhyMjIwwZ84c6OvrF7v93bt31ZwhERGR7mExT1SM\nf/75p8RZaSwtLTFlyhSMHz8eiYmJWLRoEezs7NScoeaysbHBokWLcOPGDXTr1q3YbdLS0tC4cWN8\n9NFHiIuLq7RcUlJSEBkZiZSUlEprg4iISEws5olekJubiwULFqBZs2b47LPPkJ+fX+x2EydOREhI\nCGxtbdWcofaws7MrcarLBQsW4NGjR9i/fz/kcjmCg4OhVCortP2wsDDY29ujS5cusLe3R1hYWIUe\nn4iISBOwmCf6n0uXLqF169aYOHEisrOzcerUKaxYsULstHROSkoKQkNDhdd5eXn45ptv8P7775f4\nYHFZJScnIygoSLhBUCqVCAoKYg89ERHpHBbzVOXl5ORg5syZcHV1xcWLF1XemzRpEpKTk0XKTDc9\nevQIzZo1KxL/888/0bx5cyxYsAB5eXlv1EZsbGyRnn6lUomYmJg3Oi4REZGmYTFPVdrFixfRokUL\nzJgxo0jxV6NGDSxfvpxDaSqYg4MDTp8+jd27dxc5tzk5OZg4cSI6dOhQ4jMLpSGXyyGVSlViUqkU\ncrm83MckIiLSRCzmqUrKysrCpEmT4ObmhkuXLhV539fXF5cvX8Ynn3xS4rhvKj+JRILevXsjLi6u\n2Kk+T506hWbNmmHFihXlWjXX1tYWCxcuFAp6qVTKZxyIiEgnsZinKufUqVNwdnbG/PnzizzgWrNm\nTezcuRO7du1i4acG1tbW2LFjB7Zv3w4rKyuV954+fYqRI0fC29sbt2/fLvOxFQoFkpKScPjwYSQl\nJWHMmDEVlTYREZHGYDFPVUZWVhYUCgU8PDzwzz//FHk/ICAAly9fhp+fnwjZVW0ff/wx4uLi4OPj\nU+S9n3/+GY6OjtiyZUuZj2trawtvb2/emBERkc5iMU9VQkpKCtq2bYvw8PAiwzbq1KmDgwcPYvPm\nzbC2thYpQ7Kzs8OBAwewZs0alVVkAeDx48c4ceKESJkRERFpLhbzVCVYWloWeSASAAIDA/H3338X\n2yNM6ieRSDBkyBD89ddfaN++vRCvW7cuQkJCRMyMiIhIM7GYpyrB2NgYu3fvRs2aNQEA9evXx/Hj\nx7Fy5UpUr15d5OzoZfXq1cPx48exaNEimJiYYOPGjfw9ERERFYPFPFUZderUwa5du+Dv74/o6Gh8\n8MEHYqdEr6Cnp4dx48bh1q1b6NChQ4nblWe2GyIiIl3BYp50TnZ2donvubu744cffmAvrxaxsbEp\n8b2ff/4ZHh4eXNiLiIiqLBbzpFOOHj2K+vXr48yZM2KnUqnu3buHyMhIpKSkiJ2KaK5evYqPP/4Y\np06dQsuWLREVFSV2SkRERGrHYp50QkFBARYuXAgvLy8kJyfD19dXZ3trIyIi4OnpiS5dusDe3h5h\nYWFip6R2aWlp6N69O9LS0gAA//33H9zd3bFjxw6RMyMiIlIvFvOk9TIzM9G/f39MmDBBWAQqOTkZ\n/v7+yMnJETm70klJSSlVT3tycjIWLFiA3NxcAIBSqURQUFCV7KGvV6+eyuusrCz07dsX06dPL7IY\nGBERka5iMU9aLTExEW3atMH27dtV4hKJBD4+PsVOR6lpwsLCYG9vX6qe9tjYWKGQL6RUKhETE1PZ\naWoUS0tLHDhwAOPGjSvy3qxZs+Dn54cnT56IkBkREZF6sZgnrXX06FG0aNECf/31l0rc0tISBw8e\nxJQpUyCRSETKrnSSk5MRFBQEpVIJ4PU97XK5HAYGBioxqVQKuVxe6blqGgMDAyxatAjr1q0rctO2\nd+9etG3bFrdu3RIpOyIiIvVgMU9aafv27ejSpQsePXqkEnd0dMSFCxfQpUsXkTIrm9jYWKGQL/Sq\nnnZbW1tMmDBBKOilUilCQkJga2tb6blqqkGDBuH48eNFZr3566+/0LJlS5w6dUqkzIiIqCKVdkhq\nVcNinrTOypUr0b9//yLDTfz9/XHmzBk0bNhQpMzKTi6XF+lVfl1Pe0BAAI4dO4bDhw8jKSkJY8aM\nqew0NV67du1w4cIFODs7q8Tv3buHjh07Yt26dSJlRkREFaEsQ1KrGhbzpDUKCgowZ84cjBgxoshC\nQfPmzcOOHTtgbm4uUnblY2tri4ULFwoFfWl72m1sbODt7V2le+RfVrduXZw6dQq9e/dWiSuVSgwZ\nMgRz584VKTMiInoTZR2SWtWwmCetUFBQgKCgIEydOlUlbmBggK1bt2LSpEkaPz6+JAqFAklJSexp\nrwDm5ubYuXMnpk+fXuS9KVOmYOvWrSJkRUREb6KsQ1KrGhbzpBXCw8OxaNEilZixsTH27duH/v37\ni5RVxbG1tWVPewXR09PDzJkz8cMPP8DExESIe3l5wc/PT8TMiIioPMozJLUqYTFPWmHIkCFo06aN\n8NrCwgK//PILunbtKmJWpMn8/f1x5MgRVKtWDe7u7tizZw+MjIzETouIiMqovENSqwqD129CJD5T\nU1McOHAAHTp0QEpKCo4cOcI7cnotd3d3nDx5EvXq1YOpqanY6RARUTkpFAr06dMHMTExkMvlLORf\nwGKetEaNGjVw5MgRZGRkoFGjRmprNyUlhf94aLGXZ7h5WUFBgdY+b0FEVJUUDkklVRxmQxonLy+v\nxPdsbW3VWshzKizdduzYMXTr1o2rxRIRkdZiMU8a5erVq3BycsLp06fFToVTYem4P/74Az169MCh\nQ4fQuXNnpKWliZ0SERFRmbGYJ43x559/ol27drhy5Qq6du2KS5cuiZoPp8LSXbGxsfDx8cHTp08B\nAGfOnMEHH3yAe/fuiZwZERFR2bCYJ41w7tw5lWIqLS0NnTt3xn///SdaTpwKS3fVqVOnyHCtmJgY\neHh4iHrNERERlRWLeRJddHQ0vL29kZ6erhL38fFBrVq1RMqKU2HpMmtraxw7dgzt2rVTicfHx8Pd\n3R03btwQKTMiIqKyYTFPovr777/RqVOnIuOVg4KCsHbtWhgYiDvhEldn1V0WFhaIjIxE586dVeI3\nbtyAu7s7EhISRMqMiIio9FjMV1EpKSmIjIwU9WHOq1evwtPTEw8ePFCJf/3115g/f77GTBfI1Vkr\nj9jXoZmZGfbv34+ePXuqxP/77z94enqyh56IiDQei/kqSBOmW7x58yY8PT2RmpqqEg8KCsI333yj\nMYU8VR5NuA4BwMjICD/88AMGDBigEv/vv//w4Ycf4s6dO6LkRUREVBos5qsYTZhu8fbt2+jYsSNu\n376tEh81apRG9chT5dGE6/BFUqkUmzdvxpAhQ1TiiYmJ6NSpE+7fvy9KXkRERK/DYr6KEXu6xdTU\n1GKHLwwaNAhhYWEs5KsIsa/D4ujp6WHVqlXo37+/Svzy5cvw9vbG48ePRcqMiIioZCzmqxgxp1t8\n8OABPvzwQ1y9elUl3r9/f6xevRp6erwcqwpNnfZTT08PGzduRI8ePVTiUVFRmDFjhjhJERERvQKr\npypGzOkWJ06ciLi4OJVY7969sWnTJujr61d6+6Q5NHnaT6lUih07dsDT01OIde7cGd9++62IWRER\nERVP3Hn/SBQKhQJ9+vRBTEwM5HK52gqoRYsWIT4+HqdPnwbwfB7577//XvTpJ0kcYl2HpWFsbIx9\n+/ahc+fOsLW1xffffw8jIyOx0yIiIiqCVVQVVTjdojpZWFjgyJEj6N69O/T19bF7924YGhqqNQfS\nLGJch6Vlbm6OyMhImJqa8oaTiIg0Fv+HIrUyNzfHoUOHkJ+fD2NjY7HTIXql6tWri50CERHRK+n8\nmHmZTNZDJpNliJ0H/T8TExOYmZkViYu9gBBRWZw5c4bj6ImISHQ6XczLZLI2ALYA4HyHapSfn49R\no0aVaZpBTVlAiKg0jh07hk6dOmHatGmYN2+e2OkQEVEVppPFvEwmM5LJZBMB/AogV+x8qpqJEydi\n+fLl+OCDD3D+/PnXbq9pCwgRvcqBAwfg4+ODzMxMAMDkyZOxfPlykbMiIqKqSieLeQBdAEwGMAFA\nuMi5VClLlixBSEgIACAtLQ0ffvihMHtNSTRxASGiktSqVavIzDajRo3C5s2bRcqIiIhKUhWG8Orq\nA7AXANRLSEhIk8lkM970YFeuXHnzjMogKytLlHbf1M8//4xx48apxAwNDZGZmfnKz1I4W0hu7v9/\niWJgYABTU1OtOwfqoK3Xh64wNzfHsmXLMHToUDx79kyIDxo0CFlZWfDw8BAtN14bVBJeG1QSXb42\nIiIisGDBAuTm5sLAwAATJkxAQECA2GlVOJ3smU9ISPgvISEhTew8qpKoqChMnDgRBQUFQszExASr\nVq1CnTp1XrmvjY0NJkyYIEz/Z2BggIkTJ8LGxqZScyYqrxYtWiAsLExlysq8vDyMHz9eJ/9DJCLS\nNvfu3RMKeQDIzc3FggULcO/ePZEzq3i62jNfoRwcHNTaXmExoO52yys+Ph4KhQI5OTlCTF9fH3v2\n7Cn1HOJz5syBQqHQyAWENI22XR+6ysHBAVZWVujTp49wE/v06VOMGTMGZ8+exTvvvKP2nHhtUEl4\nbVBJdPXauHXrlso3/sDzgv7p06ca+VmjoqLKva9O9syT+qSkpMDb2xuPHj1Sia9Zs6bMiwEVLiDE\nQp60hZ+fH0JDQ1Vid+7cgY+PDx4/fixSVkREJJfLIZVKVWJSqRRyuVykjCoPi3kqt4yMDPj4+ODW\nrVsq8ZkzZ+Lzzz8XKSsi9VIoFPjiiy9UYnFxcfD19VX5toqIiNTH1tYWCxcuFAp6qVSKkJAQneww\nZDFP5aJUKuHv74/o6GiV+JAhQzBt2jSRsiISx8KFC9GrVy+V2LFjxzBs2DCV50iIiEh9FAoFkpKS\ncPjwYSQlJWHMmDGv3P7FSQ20CYt5KrOCggIMGzYMR44cUYn7+PhgxYoVkEi4RhdVLfr6+tiyZQvc\n3NxU4ps2bcI333wjUlZERFTaIbxXr15Fo0aN8OOPP6ops4rDYp7KbMaMGdi4caNKzNXVFTt27FCZ\n3YOoKjE1NcVPP/2EBg0aqMR/+eUXDrchItJgDx48QLdu3fDvv/+iV69eCAkJ0apvVXW+mE9ISJiR\nkJBgLnYeuiI7OxsHDhxQidWrVw8HDx6EuTlPM1VtNjY2OHToEKysrAAA/v7+OHr0KAwNDUXOjIiI\nipOTkwNfX1/8888/AJ6PPggKCsKhQ4dEzqz0dL6Yp4plbGyM3377DV5eXgAAa2trREZGolatWkW2\nrQqrrhG97L333sP+/fsxefJkbN++HcbGxmKnRERExSgcNnzixAmV+CeffAIfHx+Rsio7FvNUZtWq\nVcNPP/2EESNGYP/+/XjvvfeKbBMWFgZ7e3t06dIF9vb2CAsLEyFTInG0bdsWc+bMgZ4e/4klItJU\n8+bNw6ZNm1Ri7u7uWL16tVY9/8f/aahcpFIpli9fjjZt2hR5Lzk5GUFBQVAqlQCez3wTFBTEHnoi\nIiLSCLt27cKUKVNUYg0bNsTevXthZGQkUlblw2KeKlxsbKxQyBdSKpWIiYkRKSMizXHy5En079+/\nyN8RIiJSj/PnzyMgIEAlZmlpiQMHDsDa2lqkrMqPxTyVKD8/H6NGjUJcXFyZ9qtKq64RlcWOHTvw\n4Ycf4vvvv8fIkSO1arYEIiJdcOvWLfTo0QPZ2dlCzMDAAHv27IFMJhMxs/JjMU8lmjJlCpYvX47W\nrVsXmcHmVarSqmtEpRUREYG+ffsK01SuXbsW8+fPFzkrIqKqIz09Hd27d0dqaqpKfNWqVfjggw9E\nyurNsZinYm3atAnfffcdAODJkyfo0aMHIiIiSr1/WVddI9J1nTp1wrvvvqsSmzx5Mn766SdxEiIi\nqkJyc3PRt29fXLp0SSU+adIkDBo0SKSsKgaLeSri9OnTGDZsmEqsVq1aZb5rLe2qa0RVga2tLQ4f\nPowaNWoIsYKCAvTv37/MQ9mIiKhsgoODcfjwYZVY7969MWfOHJEyqjgs5knFrVu30LNnT5UVK42N\njfHjjz+iTp06ImZGpP0aN26MnTt3Ql9fX4gVfvN1//59ETMjItJtQ4YMQZMmTYTXLVq0QEREhE5M\nIaz9n4AqTGFRce/ePZX4+vXr0apVK5GyItItnp6eCA0NVYnduHED/v7+nOGGiKiS1KtXD6dPn0an\nTp1Qp04d7N+/H6ampmKnVSFYzBOA5zPXDBw4EH/99ZdK/Ouvv0a/fv1EyopIN40aNarIULbffvsN\nCoVCpIyIiHSfhYUFDh48iJMnT8LOzk7sdCoMi3kCAEydOhU//vijSszX1xczZ84UKSMi3SWRSBAe\nHg4PDw+V+MqVK7F8+XKRsiIi0n1SqbTIZATajsU8ISIiAvPmzVOJubi4YNOmTToxlox0R0pKCiIj\nI3ViNWFDQ0Ps3r27yH8qCoUCx48fFycpIiIdkJOTg7S0NLHTUBtWalXcmTNnMGTIEJVYrVq18OOP\nP8LMzEykrIiKCgsLg729Pbp06QJ7e3uEhYWJndIbe+utt7B//36Ym5sLsby8PPj5+eHatWsiZkZE\npL0UCgVatmyJy5cvi52KWrCYr8KSkpKKzFxjZGSEH3/8Ee+8846ImRGpSk5ORlBQkPCAqFKpRFBQ\nkE700Ds5OWHr1q2QSCRC7NGjR1xQioioHFauXIlVq1bh2rVrcHNzw/79+8VOqdKxmK/CZs6cibt3\n76rE1q9fDzc3N5EyIipebGxskZlelEolYmJiRMqoYvXo0QOzZ88WXg8fPhzLli0TMSMiIu1z8uRJ\nlUUqnzx5gvHjx+PZs2ciZlX5DMROgMSzdOlSZGVl4fvvvwcATJkyBf379xc5K6Ki5HI5pFKpSkEv\nlUohl8tFzKpiffXVV4iPj4ebmxtGjhwpdjpERFolKSkJvr6+yM3NFWJmZmbYu3cvjIyMRMys8rGY\nr8JMTEywdetWNG3aFNHR0Zg1a5bYKREVy9bWFgsXLhSG2kilUoSEhOjU6sISiQQbN25UGW5DRESv\n9/TpU/Tq1avIOjmbNm2Ck5OTSFmpD4t5DXTv3j3Ex8ejRo0alV6sSCQSTJ06Ffn5+Zy5hjSaQqFA\nnz59EBMTA7lcrlOFfCEW8kREZVNQUIChQ4fizz//VIlPmzYNvr6+ImWlXqzeNExYWBg8PT0RGBio\n1hk7WMiTNrC1tYW3t7dOFvKv8+uvv+L27dtip0FEpFEWLlyIbdu2qcR69OiBGTNmiJOQCFjBaZDC\nGTsKx3tV5Iwdjx49Qmxs7Bsfh4jUq6CgAMuWLUOnTp3Qs2dPZGVliZ0SEZFGiIyMxKRJk1RiDg4O\niIiIqFKdlFXnk2qBypqxIy8vDwMGDEDr1q2L3L0SkeZSKpUYMWIERo8ejby8PERFRWH48OEoKCgQ\nOzUiIlFdvXoVffv2Vfn30NLSEj/++COqV68uYmbqx2JegxTO2PGiipixY/r06Th8+DCysrIwYMAA\nfPnllypPexORZtLX18edO3dUYps3b+a0lURUpaWnp6Nnz554/PixENPT08P27dvRqFEjETMTB4t5\nDVI4Y4eBwfPnkitixo7du3djzpw5KrHTp08jLy/vjXIlosqnp6eHLVu2QCaTqcTHjRuH33//XaSs\niIjEk5+fj4EDB+LKlSsq8e+++w5eXl4iZSUuzmajYRQKBeRyOeLj49GjR483KuTj4uLw6aefqsRs\nbW2xe/dunZ9zlUhXVK9eHfv27UOrVq2QkZEBAMjNzYW/vz+ioqJQp04dkTMkIlKftLQ0/Pfffyqx\nwlEHVRV75jWQjY0N3N3d36iQf/ToEXr27InMzEwhJpVKsXv3btSuXbsi0iQiNWncuDEiIiJUYnfv\n3oWvry+ys7NFyoqISP2srKxw8uRJ9OvXDwDQvHlzrFmzpkpP7ctiXgfl5eWhf//+uH79uko8PDwc\nbdq0ESkrInoTH330EaZPn64SO3/+PEaNGsUHYomoSjE1NcXWrVuxZMkS7Nu3DyYmJmKnJCoW8zpo\n2rRpiIyMVIkNHToUgYGBImVERBUhODgY3bp1U4mtX78eq1atEikjIiJxSCQSKBQKvPPOO2KnIjoW\n8zpm165dmDt3rkqsdQv6G4oAACAASURBVOvWCA8PFykjIu2SkpKCyMjIClnfoaLp6ekhIiKiyGwN\nCoUCf/zxh0hZERGRmFjM65C4uDh89tlnKjFbW1vs2rWLD7wSlUJYWBjs7e3RpUsXta7AXBaWlpbY\nu3cvzMzMhJhSqYSfnx/u3r0rYmZERBUvLCwM0dHRYqeh0VjM6wg+8Er0ZgpXYC5cuK0iV2CuaE2b\nNsWmTZtUYikpKfjiiy+Qk5MjvNbUbxiIiEpj//79GDt2LNq0aYMtW7aInY7GYjGvA/Ly8tCvXz8+\n8Er0BiprBebK4uvri8mTJ6vEXFxcoKenpxXfMBARvcrVq1cREBAAAMjOzkZAQACCg4NFzkozsZjX\nAXl5ebC3t1eJ8YFXorKprBWYK0JJveyzZs2Cl5cXTExM8P3332PChAl49OiR1nzDQERUnCdPnqBX\nr15IT08XYvr6+vD09BQxK83FYl4HGBoaYvXq1Vi5ciWkUikfeCUqh8IVmAsL+opYgbkivKqXXV9f\nH9u2bcPZs2fRt29fAEB8fLxWfcNARPSigoICDBo0CJcvX1aJh4SEwMPDQ6SsNBtXgNUhgYGBcHJy\nwrvvvssHXonKQaFQoE+fPoiJiYFcLhe9kC9pHH+fPn2E3KysrGBlZSXs07hxY0ilUpWCXlO+YSAi\nep2QkBDs3LlTJda/f38oFAqRMtJ87JnXMW3atOEDr0RvwNbWFt7e3qIX8kD5xvHb2Nho5DcMRESv\nc/z4cUyaNEkl1qxZM6xevbpKr/D6OuyZJyLSUIXj+Mvay65QKODv74/p06dDX18fY8aMqexUiYje\nSFJSEj7++GPk5+cLMUtLS+zZs0dlKl4qij3zWig0NBRr164VOw0iqmTlHcefkZGBsWPHYu3atVi1\nahW2bdumjnSJiMolOzsbvr6+uH//vhCTSCTYtm0bGjRoIGJm2oE981rm+PHj+PLLL5Gfn49z584h\nPDwcxsbGYqdFhJSUFI0Za65LyjOO/+OPP8bhw4eF10OGDIGjoyOaNWtWmakSEZVZQUEBRo0ahYsX\nL6rEZ86ciS5duoiUlXZhz7wW+ffff1W+glq7di169uwJgAvEkLg4r3nlKus4/lmzZqk8BJ+VlYXe\nvXsjLS2tslIkIiqXNWvWYP369SqxHj16YOrUqSJlpH1YzGuJZ8+ewc/Pr8hXUF988QULKRKVNq2c\nWlW4urpi+fLlKrHr168jICBAZTwqEZGYoqOjMXr0aJVYo0aNsHnzZujpsUQtLZ4pLaFQKHD+/HmV\n2MyZM+Hs7MxCikSlbSunVhWDBg3CsGHDVGIHDhzA7NmzRcqIiEhVkyZN8NlnnwmvzczMsHfvXlhY\nWIiXlBZiMa8F1q9fj9WrV6vEunfvjqlTp7KQItFp8sqpVV1YWBhatmypEgsODlYZT09EJBYjIyOs\nXr0aq1evhpGRETZs2ICmTZuKnZbWYTGv4S5evIiRI0eqxBo2bCh8BcVCiv6vvTsPj6q6/zj+CWQE\nwt5KiQsBBD1irQxSQUCwgkKwgAgYBQsuVaoIUTQgVrFVFlGDQlqVCigCiqAFoSqhgvzcaFGiiRST\ng8gSQAJowYUlJGR+f2QpQxLMMpk7d+b9eh6eJN+ZufeL3MRPzpx7jtNCdedUFP6P8u9//7tOP/30\nkprP59ONN96orVu3OtgZAPzP7bffri1btui6665zuhVXIsyHsG+++UaDBw9Wbm5uSS0mJkZLly5V\nkyZNJBGkEBoSExOVnZ2tlStXKjs7m3XNQ0iLFi20ePFiv/mnBw4c0ODBg3XkyBEHO0N5WNAAkejs\ns892ugXXCtswb4y53RjzpTHmiDHmX8aYLk73VBnHjx/X0KFDlZ2d7VefM2eOfvWrX/nVCFKoCZUN\nFKG0cyr89ezZU4899phfLT09XXfeead8Pp9DXaEsLGiAcJafn+90C2EpLMO8MWaEpFmSFkoaLOmg\npFXGmNaONlYJKSkpWr16tV/t7rvv1tChQ8t8PkEKgUSgCD/jxo3ToEGD/GovvfSSZs2a5VBHOBkr\nQyGcfffdd7r44ov1/PPPM4gQYGEX5o0xUZIelfS8tfYRa+3bkgZI+kbSWEebq6DVq1dr9uzZfrXu\n3bvrySefdKgjRBICRXiKiorSiy++qPPPP9+vfs8992jXrl0OdYUTsaABwlVBQYFuuukmbdy4UX/4\nwx/0+9//nml+ARR2YV5SW0ktJa0oLlhr8yS9JSneqaYqat++fXrggQf8ameccYaWLFlS6kZXoCYQ\nKMJXo0aNtHTpUjVo0KDk6yVLljBXNUSwoAHC1eOPP67ly5eXfP3iiy8qOTnZwY7CS7TTDdSA84o+\nbjmpvlVSG2NMbWvt8cocMDMzMyCNVdT48eM1depU5ebmKjo6WsnJyTpw4IAOHDgQ1D4QmopHM2rq\nuoyJiVF0dLTf3Mbo6GjFxMQE/XsBlVPRa2PSpEl69tlnNXPmTLVq1Yp/1xCSlJSkJ598Uvn5+YqO\njta4ceMC8vO/pn9uwL1q+tpYt26dHnroIb/aL3/5S/Xv35/rMUDCMcw3Kvr4w0n1H1T4TkR9Sd8H\ntaNK6tevn84991yNHz9eN910kzp06OB0S4ggzZo107hx4/wCxfjx49WsWTOnW0OA9OnTRz179uTd\nvhA0fPhwxcfHKysrS+effz7fd3C13bt3KykpyW/n6caNG2vGjBmqU6eOg52Fl3AM81FFH0++u6K4\nXum9zNu1a1ethiorMzNTXq9XX3zxhRo0aKCoqKiffhEiRvFIRk1el1OnTlViYqLS09Pl9Xq5sdol\ngnFtoOa1a9dOPXr0COgxuTZQnpq6No4ePaoRI0bo4MGDJbWoqCgtWbJEV155ZUDPFQ7S0tKq/Npw\nnDP/XdHHhifVG6gwyB8KbjtV17BhQ4I8HMMKSZGpoKCAm50BVNuYMWO0YcMGv9rkyZPVu3dvhzoK\nX+EY5r8s+njOSfVzJFlrLeshAUAZDh48qGuuuUY9evTQd99999MvAIAyzJkzR3PmzPGrDRgwQBMm\nTHCoo/AWrmF+p6SBxQVjjEfSbyWtcaopAAhlGzdu1K9//Wu9+eab+vLLL3XTTTf5zXMFgIrYsGGD\nRo8e7Vdr27at5s+f77cTNQIn7P6rFo28T5N0hzFmijHmaknLJZ0u6WlHmwOAEPXcc8/pq6++Kvl6\n+fLlmjZtmoMdAXCbb775RoMHD1Zubm5JLSYmRsuWLVPjxo0d7Cy8hV2YlyRr7bOSxkkaLul1SU0k\n9bHWbnW0MQAIUdOnT1fHjh39ag899JBWrVrlUEcA3OT48eMaOnSosrOz/epz5szRhRde6FBXkSEc\nV7ORJFlrp0ua7nQfAOAG9erV09///nd17NhR3377rSTJ5/Np2LBh2rBhg1q3bu1whwBC2cqVK7V6\n9Wq/2t13362hQ4c61FHkCMuReQBA5bVs2VKLFi3ym9f63//+V4MGDWLrdQCn1K9fP7388suqV6+e\nJOmyyy7Tk08+6XBXkSHgYd4Yc5ox5gJjzOXGmB5Fn58W6PMAAALvqquu0pQpU/xq6enpuuOOO+Tz\nsRgYgPINGzZM//73v9W9e3ctWbKEjemCJCDTbIwxjSWNkHSdpEsknRzec40xn0h6TdICay1rngFA\niLr//vv18ccfa9myZSW1+fPnq3Pnzho1apSDnQEIdRdddJHef/99p9uIKNUK88aYJpIeknSnpLqS\nvpD0sqStkr5V4ch/U0ltJHWWNFPS48aYZyVNsdYeLOu4AADnREVFad68ecrMzFRWVlZJ/Z577lGH\nDh3UpUsXB7sDAJyouiPzW1W44+ojkhZZa3ee6snGmBaSbpT0B0m3Svp5Nc8PAKgBjRo10tKlS9Wp\nUyf9+OOPkqS8vDwNGTJEaWlp7AwMRLj169erU6dO7FQfAqo7Z368pHOttU/8VJCXJGvtTmvtNEnn\nFr0WABCi2rVrp3nz5vnVvv76ayUkJCgvL8+ZpgA4LjU1VV26dNHQoUN16NAhp9uJeNUK89baOdba\n/OKvjTG/quDr8q21c6tzbgBAzRs8eLDGj/cfe9m0aZM2b97sUEcAnLR161YNGzZMPp9Pixcv1qWX\nXqotW7Y43VZEC/RqNu8bY3oE+JgAAAdNmTJFvXr1kiRdfPHFSktL0y9/+UuHuwIQbIcOHdK1116r\nAwcOlNS++OILbd++3bmmEPAwv1FSqjFmUFkPGmOaF938CgBwiejoaC1atEhJSUn68MMP1apVK6db\nAhBkPp9Pt99+uz7//HO/+rRp03TllVc61BWkwIf5KyW9LWmxMebO4qIxprExZqqkr1R48ysAwEWa\nNWumJ598smRDGACRZcaMGVq0aJFfLSEhQUlJSVU+Zk5OjlJTU5WTk1Pd9iJaQMO8tfaYCteaf07S\nX40xk40x41W46s0ESR9KujSQ5wQAAEDNWbt2rcaNG+dXu/DCCzV37twqr2aTkpKiuLg49e3bV3Fx\ncUpJSQlEqxEp4DvAWmt9ku6R9K6kByQ9JildUndrbby19pNAnxMA4Jx9+/Zpzpw5TrcBoAbs3LlT\n119/vY4fP15Sa9y4sZYtW6YGDRpU6Zh79uxRUlJSyapYeXl5SkpKYoS+igIe5o0x10vaJKmXpF2S\noiRZSesCfS4AgLPS0tLUsWNH3X777Vq8eLHT7QAIoKNHj2rQoEHav3+/X/3ll19W27Ztq3zcjIyM\nUsvb5uXlKT09vcrHjGQBDfPGmAxJr6hwN9hbJLWSNErSSEmvG2PqBPJ8AOC0SJ7z+dprr+myyy7T\nrl27JEm33HKLMjIyHO4KQCD4fD7ddddd2rBhg1/9z3/+s377299W69her1cej8ev5vF45PV6q3Xc\nSBXokflYSfdKMtbal6y1PmvtLBXOo+8r6R1jTJMAnxMAHBHpcz5btGihgoKCkq+PHDmigQMH6ptv\nvnGwKwCB8Le//U0vvPCCX61///6aOHFitY8dGxur5OTkkkDv8Xg0ffp0dpauokCH+TbW2plFN8KW\nsNYuk9RH0i9VeBMsALgacz6lSy+9VM8884xfbfv27br++uuVn59fzqsAhLp//etfSkxM9Kude+65\nWrBggWrVCkx0TExMVHZ2tlauXKns7GyNGTMmIMeNRIFezebHUzz2gaQekhoF8pwA4ATmfBa67bbb\nNGrUKL/au+++W2rXWADu8eOPPyomJqbk6/r162vZsmVq3LhxQM8TGxur+Ph4RuSrKeA3wJ6KtXaT\npK7BPCcA1ATmfP7PjBkz1KOH/+bfTz/9tBYsWOBXi+T7CwA3ueqqq7R+/XoZYyRJ8+bNY9fnEFat\nMG+Muaiyr7HW7ip6bfvqnBsAnMScz//xeDx67bXXdPbZZ/vVb7/99pKb5yL9/gLAbYwx+ve//62X\nXnpJQ4YMcbodnEJ1R+Y/M8b83Rjzm4q+wBhzhTFmmaS0ap4bABzFnM//+cUvfqE33nhDdevWLanl\n5ubq2muv1eeffx7x9xcAbtSkSRONGDHC6TbwE6Kr+fqukpIlvWuM2S3pn5I+lvSVpP+qcI35ppLa\nSuokqbekM1R4EyzTbQC4XvGcT0gdO3bU888/7/c//127dmnYsGHl3l/AfzsAqJ7qhvkzJQ2Q5JX0\nB0nXq3B9ed9Jz4uS9KOk5ZL+Zq1lRRsACEPDhw/XZ599pqeffrqktmnTJtWqVctvGctIvb8ACDXb\ntm3TzJkz9eqrr6pp06ZOt4MqqG6Yf13S76y1iyStNcZcKKmJCkfim6kw1O+X9IWkT621x8s9EgAg\nLDzxxBP6/PPPtWbNmpJaQUGBateurePHj0f0/QVAKDl48KDuuusubd++XZ06ddKKFSvUrl07p9tC\nJVU3zP+owt1ei2VIGm6tnVfN4wIAXCo6OlqLFy/Wr3/9a23fvl2S1K1bNz377LP6+uuv5fV6CfKA\nw44fP66hQ4eWfI9u2bJFnTt3Vnp6us455xxnm0OlVPcGWCvpOmNMcaCPqubxAABh4Oc//7neeOMN\nxcTE6I477tC7776riy66iDWlgRAxYcIEpaam+tUGDRqk1q1bO9QRqqq6I/OPqXCqzX5jzMcqnFbT\nzRhjJW08eSdYAEDkaN++vTZu3MgoHxBiFixYoOTkZL/apZdeqlmzZikqinFZt6lWmLfWLjPGdJM0\nUoWr00RJulPSHZKOG2OyJKWf+Mda+9/qtQwAcAuCPBBaPv74Y91+++1+tebNm2vp0qV+S8vCPao7\nMi9r7b8l/VuSjDEFkv4kKVuFK9x0kNRf0u9UtMKNMWaXtbZldc8LAHC3AwcOsHoGwk5OTo7S09ND\n8t6Qr7/+WgMHDlRubm5J7bTTTlNKSorOOOMMBztDdVR3zvzJnpC02lr7krV2rLX2N9bappLaSLpO\n0lRJnwf4nAAAl1m7dq3atm2rV155xelWgIAJ5Z2Ojx49qmuvvVZ79uzxq0+aNEm/+tWvHOoKgVDt\nkfkTWWsnlFPfJmmbpKWBPB8AwH1mz56tUaNGKT8/X7feeqtat26tLl26ON0WUC179uwpc6fjhIQE\nx0fofT6fRo4cqY8//tivPn78ePXv39+hrhAogR6ZBwCgXNu3b9fo0aOVn58vScrNzdXAgQO1Y8eO\ngBw/JydHqampysnJCcjxgIrKyMgod6djpz311FNasGCBX+3qq6/W1KlTHeoIgUSYBwAETatWrTRn\nzhy/2r59+9S/f3/98MMP1Tp2KE9xQPjzer3yeDx+tVDY6fitt97S+PHj/WrGGL3yyiuqXbu2Q10h\nkAjzAICgGj58uB544AG/2saNGzVs2DAdP161jcLLm+LACD2CJTY2VsnJySWBPhR2Oj5+/LjGjRun\ngoKCklrjxo21YsUKNW7c2LG+EFiEeQBA0E2ePFmDBg3yq7355pu6//77q3S8UJ7igMiRmJio7Oxs\nrVy5UtnZ2RozZoyj/dSuXVurV69Whw4dJEm1atXSq6++qvPOO8/RvhBYhHkAQNDVqlVL8+fP18UX\nX+xXnz59eqlpOBURqlMcEHliY2NDaqfjM888U++//7769++vlJQUxcfHO90SAowwDwBwRP369bVi\nxQqdeeaZfvU777xTa9eurdSxQnGKAxAqGjRooDfeeEN33XWX062gBhDmAQCOOeuss7RixQrVq1ev\npJafn6/Bgwfryy+/rNSxQm2KAxBKatUi8oUr/mUBAI7q2LGj5s+f71c7cOCA+vXrpwMHDlTqWKE2\nxQEIpvnz5ys7O9vpNhBkhHkAgOOGDBmiKVOm+NU2b96s6667rtSNrQBKW7p0qW6++WZ17txZaWlp\nTreDICLMAwAqrSY2Z3rggQc0fPhwv1r9+vV17NixgJ0DCEcff/yxfve738nn8yknJ0c9evTQP/7x\nD6fbciU3bjxHmAcAVEpNbc4UFRWl2bNnq2vXrpKkcePGaenSpapfv35Ajg+Eo+3bt6t///46cuRI\nSS03N1d169YNah9uDMEnc+vGc4R5AECF1fTmTHXq1NGyZcv08ssv64knnmCHSuAUDh48qKuvvlr7\n9u3zq8+aNUtXXXVV0Ppwawg+kZs3niPMAwAqLBibM/3iF7/QsGHDAnY8IBwdO3ZMgwcPVmZmpl99\nwoQJuu2224LWh5tD8IncvPEcYR4AUGGhsDnT4cOHg3YuIBT5fD7dcccdevfdd/3qCQkJpW4kr2lu\nDsEnCoWfbVUV1mHeGNPQGLPDGDPE6V4AIBw4vTnTokWLdO6552rz5s1BOR8Qih577DG9+OKLfrUu\nXbpo3rx5QV9P3s0h+ERO/2yrjrAN88aYhpKWS4pzuhcACCdObc709NNPa9iwYfr666/Vp08f172N\nDwTCokWL9OCDD/rVzjnnHC1fvtxv87VgcXMIPplbN56LdrqBmmCMuVzSLEnNne4FAMJR8eZMwZKa\nmqp777235Ovt27erb9++eu+999SoUaOg9QE4KTU1VSNGjPCrNW3aVG+//baaNWvmUFeFITghIUHp\n6enyer2uDPLFgv2zLRDCdWT+DUkbJbnrXwMAUKY+ffro1ltv9aulp6dr0KBBys3NdagrIDAqsqzj\nRx99pEGDBik/P7+k5vF4tGzZMhljqn386mL3ZeeEa5jvbq1NkLTvJ58JAAh5UVFRmjVrlq6++mq/\n+po1a3TzzTeroKDAoc6A6qnoso6PPvqo31ryUVFReumll3T55ZcH5Phwryifz+d0DxVmjPFIanOK\np+y11h444fmtJG2TdJ219vXKni8tLc0nSTExMZV9abUUf7M6MfcNoY/rA+WJhGvj8OHDuuWWW7Rx\n40a/+k033aT777/foa5CXyRcG260f/9+9erVy2+0PTo6WmvWrCk1bebQoUMaPXq01q9fL0maOHGi\nhg4dWu3jc22EhuJVujp27BhV2de6bWT+LEmZp/hzk3OtAQBqWkxMjGbNmqVWrVr51V966aVSq3sA\noS4rK8svaEtSfn6+srKySj23fv36mjVrlq688kolJib+ZJCv7PHhXq4ama+sQI3Md+zYMcCdnVrx\nBhDt2rUL6nnhDlwfKE8kXRvbtm1T165dS80BXrhwoW688UaHugpdkXRtuElOTo7i4uL81mn3eDzK\nzs4ud+758ePHtW/fPmVkZPzkzaYVOT7XRmhIS0uTFBkj8wAAqHXr1lq5cqUaNmzoV7/55pv1zjvv\nONQVUDlVWdbxmWeeUcuWLSs0Bz6clo1E+QjzAABX8nq9WrZsmd+GNfn5+Ro0aJA++eQTBzsDKu7k\ntc1HjhypG264QRs2bCj13D179igpKalkpD0vL09JSUmnXKXGrWuno+II8wAA1+rVq5fmz5/vV/vx\nxx/Vu3dv7dq1y6GugMopXtbx9NNP17Bhw7R48WJdccUVWrt2rd/zMjIy/KbMSIWBPj09vULHZ0Q+\nPBHmAQCudsMNN+ipp57yq40aNUpnnXWWQx0Blefz+fSHP/xBS5culVT4S2nfvn21Zs2akud4vV6/\nd6KkwqkzXq83qL0itIR1mLfWbrfWRlXl5lcAgHuMHTtWf/zjHyVJf/rTnzR58mRFRVX6PjLAET6f\nT+PGjdMLL7zgV2/durXat29f8jVz4FGWaKcbAAAgECZPnqyePXuqV69eTrcSlnJycpSenv6TK6ig\n8qZNm6bp06f71eLi4vTPf/5Tp59+ul89MTFRCQkJ/FugRFiPzAMAIkdUVBRBvoawi2jNmTVrVsm7\nSsWaNWumd955Ry1atCjzNcyBx4kI8wCAiDB37lxt2bLF6TZcpyorqKBiZs+erVGjRvnVGjVqpFWr\nVum8885zqCu4DWEeABD2nn76ad12223q2bOntm3b5nQ7rlLVFVRwas8884xGjhypEzfvrFu3rv7x\nj3+oQ4cODnYGtyHMAwDC2hNPPKF7771XkrRz505dccUV2rFjh8NduQcrqATeU089pdGjR/vVoqOj\n9dprr6lHjx4OdQW3IswDAMKWz+fTF1984VfbsWOHevbsqZ07dzrUlbuwgkpgPfbYY7rvvvv8aqed\ndpqWLl2qfv36OdQV3IwwDwAIW1FRUZo7d65uvPFGv/rWrVvVs2dP7d6926HO3IVdRANjyZIlpW52\nrVu3rlasWKH+/fs71BXcjjAPAAhrtWvX1rx583T99df71bds2aKePXtyI2cFsYJK9Q0cONAvtMfE\nxOitt95Snz59HOwKbkeYBwCEvejoaC1cuFCDBw/2q2/evFk9e/bUvn37HOoMkeS0007Ta6+9pj59\n+qhBgwZKTU1Vz549nW4LLkeYBwBEhOjoaC1atEjXXHONXz0zM1O/+c1vmEOPoKhTp46WLVumDz/8\nUN27d3e6HYQBwjwAIGJ4PB4tXrxYv/3tb/3qmZmZ6tKlizZt2uRQZ4gk9erVU/v27Z1uA2GCMA8A\niCh16tTR66+/rvj4eL/67t27ddlll+nDDz90qDOEi+PHj2vkyJFatWqV060gAhDmAQARp27dulq2\nbJmGDBniVz948KB69+6tPXv2ONQZQk1OTo5SU1MrfKN0Xl6efve732n27NkaOHCg3n333RruEJGO\nMA8AiEh169bVq6++qlGjRvnVH3vsMZ1xxhkOdYVQkpKSori4OPXt21dxcXFKSUk55fOPHTumG264\nQa+++qok6ejRo+rfvz/v9qBGEeYBABGrdu3a+utf/6pJkyZJkiZMmKC7777b4a4QCvbs2aOkpCTl\n5eVJKhxxT0pKKneE/sCBA4qPj9fSpUv96qeffjq/HKJGEeYBABEtKipKDz30kNauXaupU6c63Q5C\nREZGRkmQL5aXl6f09PRSz92yZYu6dOmitWvX+tVbt26t9957T23atKnRXhHZCPMAAEj6zW9+o6io\nqDIfKygoUG5ubpA7gpO8Xq88Ho9fzePxyOv1+tU++OADde7cWdZav/q5556r9957T61atarpVhHh\nCPMAgErf5BdJfD6f7r33XsXHx+u7775zuh0ESWxsrJKTk0sCvcfj0fTp0/12wF2wYIF69eql//73\nv36v7datmz766CO1aNEiqD0jMhHmASDCVfYmv0jzxBNPaObMmfq///s/XX755ax0E0ESExOVnZ2t\nlStXKjs7W2PGjJFU+E7NxIkTNWLEiFJTcYYNG6bVq1erWbNmTrSMCESYB4AIVtmb/CLN66+/rgkT\nJpR8nZGRoa5du2rz5s0OdoVgio2NVXx8fMmI/JEjRzRs2DBNnjy51HMfeeQRLVy4UHXr1g12m4hg\nhHkAiGCVuckvEvXs2VNdunTxq23fvl1du3bVmjVrHOoKTjl69Kh69uypxYsX+9Xr1KmjV155RQ8/\n/HC5910ANYUwDwARrKI3+UWqn/3sZ1q9erX69evnV//222/Vu3dvTZkyRQUFBQ51h2CrW7euunXr\n5ldr1qyZ3n33XQ0dOtShrhDpCPMAEMEqcpNfpIuJidGyZct0yy23+NULCgr00EMPacCAATpw4IBD\n3SHYHn/8cQ0YTiQUNAAAGZ5JREFUMECS1K5dO61fv15du3Z1uCtEMsI8AES48m7yw/9ER0dr7ty5\nevTRR0tNo3jrrbd08cUXKy0tzaHuEEy1a9fWyy+/rMTERK1bt06tW7d2uiVEOMI8AKDUTX4oLSoq\nShMnTlRqaqp+/vOf+z22fft2devWTbNnz5bP53OoQwTSkSNHdOTIkTIfa9CggWbOnKkmTZoEuSug\nNMI8AACV0Lt3b3366afq3LmzXz03N1cjR47ULbfcosOHDzvUHQJh48aN6tSpk8aNG+d0K8BPIswD\nAFBJcXFxev/99zV69OhSjy1fvlx79+51oCtUV0FBgWbOnKlLLrlE//nPf/TMM8/ozTffdLot4JQI\n8wAAVMFpp52mv/zlL3rllVcUExNTUp8/fz7zqF1oz549uvrqq3XPPfcoNze3pH7rrbey7wJCWrTT\nDQAA4GZDhw5V+/btNXjwYA0cOFD9+/d3uiVU0ooVK/T73/9e33zzTanH4uPj/X5ZA0INYR4AgGq6\n4IIL9Mknn5xy58+jR4+yM2iIOXz4sO677z7NmjWr1GONGzfWc889x/rxCHlMswEAIAAaNGig6Oiy\nx8i2bt2qli1bKiUlRcePHw9yZyjLZ599po4dO5YZ5Lt3766MjAyCPFyBMA8AQA3y+XwaNWqU9u3b\np7vvvludO3dmTXoHFRQUKDk5WZ07d1ZWVpbfY9HR0ZoyZYrWrl2rli1bOtQhUDmEeQAAatCSJUu0\natWqkq/T0tLUqVMnjR07Vj/88IODnYWenJwcpaam1tgNp7t371bv3r01btw45eXl+T3Wtm1bffTR\nR/rjH/+o2rVr18j5gZpAmAcAoAYdO3ZMjRo18qsVFBRoxowZuuCCC/TGG2841FloSUlJUVxcnPr2\n7au4uDilpKQE/Bx//OMftWbNmlL1W2+9VZ999pk6deoU8HMCNY0wDwBADRo+fLgyMzOVkJBQ6rFd\nu3bp2muv1cCBA7Vz504HugsNe/bsUVJSUsloeV5enpKSkgI+Qv/www/L4/GUfN20aVO99tprmjt3\nrho0aBDQcwHBQpgHAKCGnXnmmVq8eLHefvtttWrVqtTjy5cvV7t27TRjxgzl5+cHv0GHZWRklJr2\nkpeXp/T09ICep02bNiUbfV1xxRX6/PPPNWTIkICeAwg2wjwAAJVQnXndffv21aZNm3T//feXmpd9\n6NAhjR07Vp07d9aGDRsC1a4reL1evxFzSfJ4PPJ6vZU+1o4dOzRz5sxyH584caJmzpyp1atX6+yz\nz6708YFQQ5gHAKCCAjGvOyYmRtOmTdOnn36qSy+9tNTjn376qTp37qxnn302EC27QmxsrJKTk0sC\nvcfj0fTp0xUbG1vhY3z//fd64IEHZIzRPffcow8++KDM5zVt2lSJiYmqVYsIhPDAlQwAQAUEel73\nRRddpI8++kjPPfecGjdu7PdYQUGB2rdvX+2e3SQxMVHZ2dlauXKlsrOzNWbMmAq9Lj8/X88995za\ntm2radOmKTc3V5J07733qqCgoCZbBkICYR4AgAqoiXndtWrV0h133KGsrCzdcMMNJfVu3bqpW7du\nVT6uW8XGxio+Pr5CI/I+n09vvfWWLrroIo0aNUr79+/3e3zDhg1asWJFTbUKhAzCPAAAFRDIed0n\ni42N1aJFi5Samqo2bdro/vvvL/e5jzzyiJYsWRKxO8keOHBAKSkpuuiii9SvXz9lZmaWes4ZZ5yh\nuXPnqn///g50CARX2ftOAwAAP8Xzuoun2lRlXvdP6dOnjzIzM8vdtGjbtm169NFHVVBQoDZt2igp\nKUk33XST6tWrF7AeQpHP59OHH36o559/Xq+//rqOHj1a5vNiYmI0btw4JSUlsdQkIgYj8wCAiFXZ\nlWmqOq+7MjweT7k3Z06fPr1kHvhXX32lO++8U61atdLUqVN18ODBgPcSCt5//31dcMEF6tGjhxYu\nXFhmkI+KitLNN9+szZs3689//jNBPgBqejdeBA5hHgAQkaq6Mk1l5nUH0g8//KB58+aVqu/bt08P\nPvigfvGLX+jKK6/Uc889F1YbUMXGxiorK6vMx2rXrq1rrrlGn376qV588UWdddZZQe4uPAVjN14E\nDmEeABBxgrXjaCA1bNhQn3zyiW655ZZSc/elwr/DmjVrNGrUKMXFxcnr9WrixIlav359yK/q4vP5\ntHv37jIfO++883T55Zf71Vq1aqXJkycrOztbb7zxRkDuW0AhN35vRLqwDPPGmK7GmLXGmIPGmK+N\nMfONMc2d7gsAEBqCteNooLVr104vvPCCtm7dqvvuu08xMTHlPjcjI0OTJ0/WpZdeqr59+4ZUoD9y\n5Ig++OADPf7447rmmmvUvHlztW3bttS/SbGRI0cqOjpaQ4YM0apVq/TVV1/pwQcf1JlnnhnkzsOf\nW783IlnY3QBrjGknaY2kdyQNldRU0iRJq4wxl1hry/5JAQCIGMUr05wYWgK1Mk0wnH322UpOTlbn\nzp2VkJDwk883xpQ5D//YsWPaunWrWrZsWaM30e7atUvr1q3TunXr9K9//Uuffvqp8vPzSz0vPT1d\nl1xySan64MGD1atXLzVvzrhcTXP790YkCrswL2m0pD2SBhcHd2PMl5I+lnSVpLcd7A0AUEE5OTlK\nT0+X1+sN+Pz0YKxMEwzdu3cvFbyioqIkFU5dKXbyNJViWVlZJZtTNW/eXK1atVKrVq3UsmXLks+L\nvz7xXYDc3FzVqlWrzOk+27Zt04oVK/T9999r06ZNWrduXYXn8K9bt67MMF+nTh2CfJCEy/dGJAnH\nML9J0hcnjcDboo+tHegHAFBJKSkpfmEiOTlZiYmJAT1HYmKiEhISauwXhmAoL3gNHTpUK1eu1Jtv\nvql//vOf6tGjR5mv37FjR8nne/fu1d69e7V+/foyn3v66afL5/Pphx9+0LFjx7R8+XINGDCg1PMy\nMzN1zz33VPrvUr9+fX3//feVfh0CLxy+NyJJ1Im/uYcrY8yNkhZKutJau6air0tLS/NJOuWcxJpw\n5MgRSQr7dYNRNVwfKE+4XBv79+9Xr169/KZhREdHa82aNWrWrJmDnYWu/fv3KysrS+eff36p/0b5\n+fklI/cnXxsLFy7U1KlTq3TOxx9/vMxNmTZs2KARI0b85OtbtGih9u3bq0OHDvJ6vTr33HMVHR2O\nY4yhLVx+brjd4cOHJUkdO3aMquxrXfVdY4zxSGpziqfstdYeOOk1LSQlS9og6d0abA8AEABZWVml\n5lPn5+crKyuLMF+OZs2alfvfJjo6utwbS2vVqqUzzzxTOTk5lb5B9tChQ2XW69evX6rm8Xh04YUX\nyuv1qkOHDmrfvj3/lkCAuCrMSzpLUul9m/9nrKQZxV8UBfk1Kly15wZrbZXehmjXrl1VXlZlxVtT\nB/u8cAeuD5QnXK6Npk2bavTo0aVuwBswYABv91dRedfGpEmTNGnSJOXl5Wn37t3avn17yZ8dO3aU\nfL5z504dP3685HW1a9fWz372szKvtZ/97GcaPXq0GjZsqObNm6tTp066+OKLVadOnZr9S6JKwuXn\nhtulpaVV+bWuCvPW2u2SKvT2gzHmQkkrJXkkXWWt/aoGWwMABAg34AWfx+Mpudm1LPn5+dq7d688\nHo8aNmyounXrltxoe7LmzZvrL3/5Sw12C+BErgrzFWWM6azCIP+9pJ7W2i8dbgkAUAncgBdaoqOj\n2V0VCFFhF+aNMa1UGOT3Suplrf3a2Y4AAFURGxur+Ph4p9sAgJAWdmFe0kxJjSTdJSnOGBN3wmM7\nrLV7nGkLAAAACKywCvNFq91cLam2pFfKeMo4Fa5sAwAAALheWIX5oo2iSm9HBwAAAIShWk43AAAA\nAKBqCPMAAACASxHmAQAAAJcizAMAAAAuRZgHAAAAXIowDwBADcrJyVFqaqpycnKcbgVAGCLMAwBQ\nQ1JSUhQXF6e+ffsqLi5OKSkpTrcEIMwQ5gEAqAF79uxRUlKS8vLyJEl5eXlKSkpihB5AQBHmAQCo\nARkZGSVBvlheXp7S09Md6ghAOCLMAwBQA7xerzwe/03JPR6PvF6vQx0BCEeEeQAAakBsbKySk5NL\nAr3H49H06dMVGxvrcGcAwkm00w0AABCuEhMTlZCQoPT0dHm9XoI8gIAjzAMAUINiY2MVHx/vdBsA\nwhTTbAAAAACXIswDAAAALkWYBwAAAFyKMA8AAAC4FGEeAAAAcCnCPAAAAOBShHkAAADApQjzAAAA\ngEsR5gEAAACXIswDAAAALkWYBwAAAFyKMA8AAAC4FGEeAAAAcCnCPAAAAOBShHkAAADApQjzAAAA\ngEsR5gEAAACXIswDAAAALkWYBwAAAFyKMA8AAAC4FGEeAAAAcCnCPAAAAOBShHkAAADApQjzAAAA\ngEsR5gEAAACXIswDAAAALkWYBwAAAFyKMA8AAAC4FGEeAAAAcCnCPAAAAOBShHkAAADApQjzAAAA\ngEtFO91ATTDG9JE0WdIFkr6WlCLpr9Zan6ONAQAAAAEUdiPzxpgukt6U9B9J10iaLekpSfc42RcA\nAAAQaOE4Mj9W0iZJtxaNxK82xrSTdJekpx3tDAAAAAigcAzz90lqcNKUmmOS6jjUDwAAAFAjony+\n8J1GboxpImmApL9JmmytnVKZ16elpfkkKSYmpga6K9+RI0ckSfXq1QvqeeEOXB8oD9cGynPytbF/\n/35lZWXp/PPPV7NmzZxsDQ7j50ZoOHz4sCSpY8eOUZV9ratG5o0xHkltTvGUvdbaA0XPbSlpe1F9\ng6TnarY7AABC34IFC/Tkk08qPz9f0dHRGjdunIYPH+50W0HBLzEIR64amTfGtJK07RRPGWutnVH0\n3KaSvJJiJU2SdFxSB2vt4Yqer3hkvmPHjlVtuUoyMzMlSe3atQvqeeEOXB8oD9cGylN8bTRp0kQt\nW7ZUXl5eyWMej0fZ2dmKjY11qr2gSElJUVJSkvLy8uTxeJScnKzExESn23IcPzdCQ1pamqQIGJm3\n1m6XVKG/ZNEI/VpJMsb8R9LnkgZLWlBT/QEAEMoyMjL8grwk5eXlKT09XfHx8Q51VfP27NlTEuSl\nwr9zUlKSEhISwv6XGIS/cFyacqAx5pKTyv+RlCfpLAdaAgAgJHi9Xnk8Hr+ax+OR1+t1qKPgONUv\nMYDbhV2YlzRBUvJJtSskeSRtDH47AACEhtjYWCUnJ5cEeo/Ho+nTp4f96HSk/hKDyBCOYX6KpB7G\nmL8ZY3oZY+6UtFjS/0l629HOAABwWGJiorKzs7Vy5UplZ2drzJgxTrdU4yL1lxhEBlfNma8Ia+0/\njDHXSJooabikgyqcJ//QSWvPAwAQkWJjY8N6jnxZEhMTlZCQoPT0dHm9XoI8wkbYhXlJstaukLTC\n6T4AAEDoiMRfYhD+wnGaDQAAABARCPMAACDgcnJylJqaqpycHKdbAcIaYR4AAARUSkqK4uLi1Ldv\nX8XFxSklJcXploCwRZgHAAABU94GTYzQAzWDMA8AAAKGDZqA4CLMAwCAgGGDJiC4CPMAACBg2KAJ\nCK6wXGceAAA4hw2agOAhzAMAgIBjgyYgOJhmAwAAALgUYR4AAABwKcI8AAAA4FKEeQAAAMClCPMA\nAACASxHmAQAAAJcizAMAAAAuRZgHAAAAXIowDwAAALgUYR4AAABwKcI8AAAA4FKEeQAAAMClCPMA\nAACASxHmAQAAAJcizAMAAAAuRZgHAAAAXIowDwAAALgUYR4AAABwKcI8AAAA4FKEeQAAAMClCPMA\nAACASxHmAQAAAJcizAMAAAAuRZgHAAAAXIowDwAAALgUYR4AAABwKcI8AAAA4FKEeQAA4Eo5OTlK\nTU1VTk6O060AjiHMAwAA10lJSVFcXJz69u2ruLg4paSkON0S4AjCPAAAcJU9e/YoKSlJeXl5kqS8\nvDwlJSUxQo+IRJgHAACukpGRURLki+Xl5Sk9Pd2hjgDnEOYBAICreL1eeTwev5rH45HX63WoI8A5\nhHkAAOAqsbGxSk5OLgn0Ho9H06dPV2xsrMOdAcEX7XQDAAAAlZWYmKiEhASlp6fL6/US5BGxCPMA\nAMCVYmNjFR8f73QbgKPCfpqNMebPxhif030AAAAAgRbWYd4Yc6GkB5zuAwAAAKgJYRvmjTG1Jc2V\ntN/pXgAAAICaELZhXtJYSY0k/cXpRgAAAICaEOXzhd90cmNMW0npkuIl/VrS09baqMoeJy0tzSdJ\nMTExgW3wJxw5ckSSVK9evaCeF+7A9YHycG2gPFwbKA/XRmg4fPiwJKljx46VzquuWs3GGOOR1OYU\nT9kr6aCkOZIWWGs/NMb8OijNAQAAAEHmqjAv6SxJmad4fKyko5LaShoQqJO2a9cuUIeqkMzMTEfO\nC3fg+kB5uDZQHq4NlIdrIzSkpaVV+bWuCvPW2u2Syn37wRjTQtImSbdIOmyMiVbRfQFFnxdYawuC\n0CoAAABQ48LtBthekhpKel1SXtGf6UWP5Ul62KG+AAAAgIBz1ch8BfxD0iUn1YZKureo/nXQOwIA\nAABqSFiFeWvtt5K+PbFmjLms6LENjjQFAAAA1JBwm2YDAAAARIywGpkvi7V2hqQZTvcBAAAABFpY\nbhoVKMWbRgEAAAA1rSqbRjHNBgAAAHApRuYBAAAAl2JkHgAAAHApwjwAAADgUoR5AAAAwKUI8wAA\nAIBLEeYBAAAAlyLMAwAAAC5FmAcAAABcijAPAAAAuBRhHgAAAHApwjwAAADgUtFONwB/xpjbJY2X\ndLakdEn3Wmv/5WxXCDXGmAGSXrbWNnS6FzjPGFNb0t2SbpcUJ2mHpGclPWOt9TnZG5xljDlN0sOS\nhks6XdJ6SUnW2k8dbQwhxRhTR4WZY7219maH20ElMTIfQowxIyTNkrRQ0mBJByWtMsa0drQxhBRj\nTFcVXiNRTveCkDFR0lQVXhcDJC2RNEPSOCebQkh4WlKipGmSrpV0WNJaY0xLR7tCqPmTpPOdbgJV\nw8h8iDDGREl6VNLz1tpHimrvSLKSxqrwhzEiWNHIyd2SJkk6JOk0ZztCKDDG1JJ0r6QnrbVTispr\njDHNJCVJesKx5uAoY0xjFb5bM8Fa+1xR7QNJ36pwpH6yg+0hRBhjOqgwY3zjdC+oGkbmQ0dbSS0l\nrSguWGvzJL0lKd6pphBS+kp6QIWjrX9xuBeEjsaS5ktaelLdSmpmjKkf/JYQIg5J6izpxRNqeZJ8\nkuo40hFCijEmWtILkp6UtNvhdlBFjMyHjvOKPm45qb5VUhtjTG1r7fEg94TQ8omk1tbag8aYPzvd\nDEKDtfaApNFlPNRf0i5r7aEgt4QQYa3Nl/SZVPIOTktJj6gwzC90sDWEjvtV+C7vYyqchgUXIsyH\njkZFH384qf6DCt9BqS/p+6B2hJBirWXUBBVijLlN0pVieh7+Z6KkPxd9/rC11jrYC0KAMeZ8SQ9K\n6mWtPWaMcbolVBHTbEJH8c2MJ688UVwvCGIvAFzKGHOjCm+kf13SXx1uB6FjmaTfqHBk/mFjzCRn\n24GTit6pmStpLivmuR8j86Hju6KPDSXtPaHeQIVBnrfKAZySMWaspOkqvPfmRpalRDFr7edFn75n\njGkoaZwx5tGie7MQecaocNpVv6J588WijDHRRVO04BKMzIeOL4s+nnNS/RxJlv8pAzgVY8xUSU9J\nWiBpiLX2mMMtwWHGmFhjzC1F4f1En6nwBtifO9AWQsO1ks6S9F8V3hSdJ6m9pBGS8owxrZxrDZXF\nyHzo+FLSTkkDJf1TkowxHkm/VeGKNgBQJmPM3Spc6WimpLH88o8iTVS4Uonkv6JNb0n7iv4gMv1B\nhTMBTvSypM0qnIr1ddA7QpUR5kOEtdZnjJkm6a/GmAOSPlLhChWnq3DTDwAoxRhzhqTHJW2U9Kqk\nzifdyLaBt8wjk7U2yxjzd0nTi3aC3SppkArXmL/VWsu9WBGqrBugjTFHJH1rrd3gQEuoBsJ8CLHW\nPmuMqafCjYHGqnBr5T7W2q3OdgYghPVR4ZSJX0kq60a2ZmIzmEg2QoW7ez4g6QxJX0i6zlr7uqNd\nAQiYKJ+Pd2MBAAAAN+IGWAAAAMClCPMAAACASxHmAQAAAJcizAMAAAAuRZgHAAAAXIowDwAAALgU\nYR4AAABwKcI8AAAA4FKEeQAAAMClCPMAAACASxHmAQAAAJcizAMAAAAuRZgHAAAAXIowDwAAALhU\ntNMNAADczRjTUNJXkvZKam+tLSiq15H0jqT2knpYazOc6xIAwhMj8wCAarHW/iBpqqQLJV0vScaY\nWpJeltRJ0kCCPADUjCifz+d0DwAAlysahbeSciVdIOlpSXdJusFa+5qTvQFAOCPMAwACwhhzs6QX\nJa2R1EvSGGvtXx1tCgDCHNNsAACBMl/SThUG+WkEeQCoeYR5AECgjJTUoujz75xsBAAiBdNsAADV\nZoy5VtLrkmZLMpK8ks6x1h5wtDEACHOMzAMAqsUY013SK5L+ocKbXidKaiLpfif7AoBIwMg8AKDK\njDG/lPSBpExJV1prjxTVV0nqLqmNtXaPgy0CQFhjZB4AUCXGmBaSUlW4WVT/4iBfZKKkepL+5ERv\nABApGJkHAAAAXIqReQAAAMClCPMAAACASxHmAQAAAJcizAMAAAAuRZgHAAAAXIowDwAAALgUYR4A\nAABwKcI8AAAA4FKEeQAAAMClCPMAAACASxHmAQAAAJcizAMAAAAuRZgHAAAAXIowDwAAALgUYR4A\nAABwKcI8AAAA4FKEeQAAAMCl/h9xul0yMyoRsAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1153be278>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 257,
       "width": 377
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f_x, f_y = f(50)\n",
    "plt.plot(f_x, f_y, 'k--', linewidth=2, label='Real (unknown) function')\n",
    "x, y = sample(50)\n",
    "plt.plot(x, y, 'k.', label='Sampled (measured) data')\n",
    "plt.xlabel('$x$')\n",
    "plt.ylabel('$f(x)$')\n",
    "plt.legend(frameon=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Linear model fitting\n",
    "\n",
    "We will use least square regression (LSR) to fit a polynomial to the data. \n",
    "Actually, we will use **multivariate linear regression**, over a dataset built in the following way:\n",
    "\n",
    "For each sample $x_{i}$ we build a vector $(1 , x_{i} , x_{i}^{2} , \\ldots , x_{i}^{n})$  and we use LSR to fit a function $g:\\mathbb{R}^{n+1}\\rightarrow\\mathbb{R}$ to the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  1  1  1]\n",
      " [ 8  4  2  1]\n",
      " [27  9  3  1]]\n"
     ]
    }
   ],
   "source": [
    "# This illustrates how vander function works:\n",
    "x1 = np.array([1,2,3])\n",
    "print(np.vander(x1, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_polynomial(x, y, degree):\n",
    "    '''\n",
    "    Fits a polynomial to the input sample.\n",
    "    (x,y): input sample\n",
    "    degree: polynomial degree\n",
    "    '''\n",
    "    model = LinearRegression()\n",
    "    model.fit(np.vander(x, degree + 1), y)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "def apply_polynomial(model, x):\n",
    "    '''\n",
    "    Evaluates a linear regression model in an input sample\n",
    "    model: linear regression model\n",
    "    x: input sample\n",
    "    '''\n",
    "    degree = model.coef_.size - 1\n",
    "    y = model.predict(np.vander(x, degree + 1))\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1181f7748>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lMWLECBo2bAjkJadHjx5VrmdYWBidO3fmzz//RK1W4+Pjw7vvvouJiYnSC25t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\nxZRhXpIkSYqp/w+jo3iO6r9NKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117a7a048>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 257,
       "width": 377
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = fit_polynomial(x, y, 3)\n",
    "p_y = apply_polynomial(model, x)\n",
    "plt.plot(f_x, f_y, '--', linewidth=2, label='Real (unknown) function')\n",
    "plt.plot(x, y, '.', label='Sampled (measured) data')\n",
    "plt.plot(x, p_y, label='Model predictions')\n",
    "plt.xlabel('$x$')\n",
    "plt.ylabel('$f(x)$')\n",
    "plt.legend(frameon=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Model averaging\n",
    "---------------\n",
    "\n",
    "The following code generates a set of samples of the same size and fits a poynomial to each sample. Then the average model is calculated. All the models, including the average model, are plotted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1141367b8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ra6DNmUBepVbWqH0uoIO3Ix8+M4hufi4A6PQGPlqyh4W/H0TfABNjVSoL7now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\nndkIpfzBGQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113f4df60>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 257,
       "width": 377
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "degree, n_samples, n_models = 4, 20, 5\n",
    "for i in range(n_models):\n",
    "    x, y = sample(n_samples)\n",
    "    model = fit_polynomial(x, y, degree)\n",
    "    p_y = apply_polynomial(model, x)\n",
    "    plt.plot(x, p_y)\n",
    "plt.plot(f_x, f_y, 'k--', linewidth=2, label='Real (unknown) function')\n",
    "plt.xlabel('$x$')\n",
    "plt.ylabel('$f(x)$')\n",
    "plt.legend(frameon=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x114512208>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1Uq+8iIhUKRqLX0E98/XCbDYTExNTr/uDprnojfwynR8iIiK1dyQKi19BPfMi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JBZs2axIivFRha+rosU8+OElqjOR+1xTNPkpQfvZk6R2xblQghefs/giw+dN9dpp1k/VzsKX9e9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\nsm9Fpqvs6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pBvzd+bH18TY9u5umMfjz4Kl56vsWd5psB1SVfuAldFUdij3G83wjMFvNoc\npTkqBb1EMhg9SYNn14RpiGTuv1lFXvarDAzag2ZJVjFqlTZ0VP6BBx7gjjvuGLD0ROAHXHQRFBXl\nPq+Ps5yptin04nNtGNlpCmHN4mUL+YoAFYGN1112UyLF/BgjGnVOP59zpjOSXh9O2RXpU0PuYSPt\n/ZaUYEXmJRKJZH3YusDDlna7dQVTWFPX1TO25fL/e5Hzb7uXoqIi5pT42L3Kx89ujlA9wxnlu/lm\nuOdGNzuXZGYd31sbcwQbBqIqCntNDthNZUwBrzZFc24jkWyO1PeleLY+bDdjcquWkJ1dPLiPe3tc\nt73m89wqk5TB76mVK1eyMLvtPABbAzdRUwPnnJP7vNpiui2uvZoyos6yXwVTCN5sibEm7BTylTks\nuycaUsyPMS64QKFiSgrNJTjxdIMFC5w34vo0ikoYpt22OM+t5ixukUgkksFQFIVdy/yUpmcBdWHl\n1CsIqmdsS31Y55OOBIpifVkftLWfC/8eoWiy07nm4ovhrUczBa4CeK05Qk9y6Ol9VVHYuyJAZVrQ\nGwJeaYqwNiYFvURiCsGH7TFeb4mip6N8+R6Vg2tCVIeG1gdLuzKzYlaTqHXHJJNJjjvuOHp7e7OW\neoAHgBBXXgm+HHWsQghHOt2sIm9O28uvihCCt1pj1KfralQF9p68eQl5kGJ+zLHFFvD8U8t55+06\n7rpVc9xkkZTpeJservnT2ljmS3JzyBmTSCQbBlVRmDc506k1pgvyPZpdyPpZV4IV6bSZqXkejt4l\nwK//ESWQ75xpPO00hbXv+2wHrpQJLzdGiOewrOw/dv82hrC2aZOCXrIZEzdMXmqMsCRLmE8JuTm4\nJkS+Z2htEEmZrEkLX4+qDNkk6uKLL+bdd98dsPRPwI7Mng0/+EHu82uK6najppBbdbhUjRZCWB76\n/XWEClizeTleZCYqUsyPQVQVAoF13R5WD4jKD5diIy0pJRLJaOFLd2rtL2TtSZqUZKXuvZuVNhNt\nbWDRgz/hgpu6cHsyzzLDgGO+p+BrCFCQtr6M6FZjl1yWlZqqMK8iQHnW7MDLTRE64lLQSzY/OuI6\nz9SH7WZIADuV+Nhrsn/Y6Hddd8JO1Z1R6Bm04eQTTzzBn/70pwFLDwfOBqwGUVqOWKIQgo+ycuVn\nF3tHvVmlEIJ31sZsD/1+IV+zGQp5kGJ+XLEqnHkDHy7FBpxifrhCWYlEIhmOSV6NPbI6tXbEdUp8\n1rOlP23m3w89wk477cS/7rqThtcv4vwb4ihKRqhHIvDtbylMiQXxpd8M2uMGb7fGhrSsBHCpStor\n2jpeyoQXGyN0xodO05FIJhorepI83xAhms6r8WoKB1QF2XaSd9gAX8oUrOjN+L1vPUi0fPXq1Zxw\nwgkDllYD/wAUDjgADj449zmu6kvRk7Rm2yZ5VaaOssAWQvDu2pjtkgOw52Q/U0agiyYqUsyPE3qS\nht38YZJXpSDHNBpANGXaxTDFPm3QanaJRCJZX6aE3Gxf1F/IptCVMJiUjrK/+Mj9HP+9o+ju7gbg\n9r/dxszCxznnCqdzTVsbHHGYwnaugB3prw+n+KRzaFcNyAj6/heIfkHflZCCXjKxMdIC9p21Mdtu\nutinsaAmNOKZ9xU9mSZRU0Nu/IM4y7S2tuL1ZheqasB9QDEA11zDoDn22ef5SVau/I5DFOF+VYQQ\nvNcWZ8UAIT91GJvuiY5UeOMEZ4rN8BdtS1Y+abl0sZFIJKPIrCKvbRtpCIibgjyXwpxvHEr5lOmO\nsacvPI2zj1nDCWc7bSVXrFD44VEas0OZSP9nnQlW9g5tWQngVhX2rwpSnBb0SVPwYmOEbinoJROU\nqG7yQkPEYee6Zb6bb1QFCY7Q2MIUgqXdmZflbYdwlpk7dy4fffQR++13QHrJVcDeABx7LMyZk/s4\nX3QniaRnDcr92qim+AoheL8t7vg77FHuH7bfzuaAFPPjACGE08VmBFNWrTJfXiKRbCAsH/gAk9Jd\npGK6wKUpFBXkc84Nd+JyZ75c+/r6OPaYY7j1GoPDj3EK7vffV/jZSS5mFmRsMd5dG3M8vwbDrSrs\nXxm0TQAShuCFxtzOOBLJeKQ1qvN0fdg2v1AVmFvmZ7fyANp6uMOsCafs1JzJAReFOQw0ysvLmXfg\n08A9wC8AcLsFv/td7mOkTMFnWf71O5aMXlReCMGi9jhfZAn53cv9TB+igHdzQ4r5cUBnwrA7tZX6\ntWHfxIUQdmReVbCnpCUSiWS0cKkK+1QE8abzZLoSJkVeF1vN3IEfXHilY+yiRYu44ILzeehujXkH\nOJ1rnn1G4Y+/dLNFOt/VFPBac5TeYYS5R1PYvyrzQpEwBC80RIbdTiIZDwghWNqV4MXGCAnDEuF+\nl8I3qoNstZ7OMEIIlnZnRPA2hbm37+qC669xA8fTLxPPOENhiy1yH2dJV8I+15qQi2Lf6AQShRB8\n2B6nLuvfMLfMP6QTz+aIFPPjgNXrGZXvTZnE0m/gpT5t0Gp1iUQi+boE3Sr7VATof8Q0RXWm5Hk4\n8PjT2PWgbznG3njjjTzxxCM8+ajK9js4C13/fbfKwzf4bLeapCl4uSma07ISwKup7F8VpDCdsx9P\nR+j7UlLQS8Yvuil4szXGova47TxT5tc4pCZEyVcQyO1xw+45k+9RbZvXofjtlSZ9PRndkJcn+PWv\ncx8jrpssTUflFWCH4hwm9OtBv1999svIrmW+9X6hmehIMT/GEUKwOpyxXpoyAjEvLSklEsnGotTv\nYtdSv/15VV+KrQo8nPb7GymtnuIYe/LJJ9Pe/iXPPaMwdZpT0F97tco7/+cjPy3MwylzWMtKAJ+m\nckBV0N4uplsR+v7ZTIlkPNGdMHhuTdgRxNum0MMBVUF8gxSsjoQlWbny2xQ6XW/+8pe/sHbtWvvz\nmjVw21+dAcALL1QoLc19jE87E3bjqi3y3Tm97keKEIJPOhIOL/1dSn3MKNgwnWTHM1LMj3FaY4Yd\nZZ8ccI3oZpb58hKJZGOyZYGH2sJMZ9c1YZ2ZVSWcc92daO5MAKKnp4djjz2WoqIkzz2rUFzsFOq/\n/rnGyle9dupOe9zgzdZoTstKsDzw51cFyUunIEZ1qyg2IgW9ZJxgCMHHHXGerg/TnbZ11BTLqWXn\nUv9X9mnvSxk0hC1N4NUUpmfZN953332ce+65HHnkkbz55psAnH+RSTKROVZlpeCnPx3mGEnDLkrV\nFNh+lKLyn3YmHDn4c0p9bF0ohfxgSDE/xlndl3kjnTYCD1VTCFrT+fJulWG7xEokEslosFOJzw4e\nJE1BZ9xk7z3mctx5lzrGvfvuu1xwwQVsvTU8+aSC358R6sJUOO80N+2LPbZlZUNYZ1FWA5qh8LtU\n5lcHCbkzkf0XGyNEh0nVkUg2NW0xq8j1s85MQ6c8t8rBNaGv7dSSnWe+dYHHLppdunQpp512GgCd\nnZ2cdtppnHTSTdz/L6csvOwyhUCAnHySdd61hV4CX3EGIZtPO+IszrKq3bnER60U8kMixfwYxhSC\nNek3ak1hRC2KOxOG7SNb7neNqr+rRCKRDIWqKOw12W9Hx3tTJgI4buHZ7HzAAsfY66+/ngcffJDd\ndoMHH1RQ1YygTyUUfnGCh+7VLjsiX9edtPNxcxFIR+iDLuu515eyLP1iUtBLxiApU/D+2hjPN0To\nTUfjFWDbSR4OmRLK6TgzEhKGycqeTJOoGek882g0ytFHH00kErHHulxu7r9/D8f2224n+NGPch+j\nM27YKUEeVWG7ISwv14fPOuN82ul0xdlmFPY7kZFifgzTFNFJpvNFK4OuYds0g0yxkUgkmw6vprJP\nZYB+w62WqEFJwMXP/nQLxZXVjrEnn3wyS5cu5bDD4LbbnM+2SI/KxSf4aWtRSBiWyFnUHqc+7PSq\nH4ygW2V+dYhAtqBvjAxbTCuRbEyaIimeXN3HsiyrxUleKxq/U4l/VIwrVvSk7Dz26XluO0337LPP\nZvHixY6x+QXXEo9nTOQ9HsE9dyu4hpERH2U1iNquyItH++rnbbnWxPi4IyPkdyj2jsoLwkRHivkx\nzOqsL66RTrVlF7+WSzEvkUg2MgUejb0mZ+bll/ek2GHKZH5x4z8d/vMHH3wwlZWVAJx6Klx6qXM/\nHU0qVx0forlBoSdpYArBWy1R2mK5PegBQm4r5cafFvS9STNt8ScFvWTTEjdM3myJ8nJT1PZ9VxVL\ntB5cE6JolKykTSGo68kqfE0L4rvuuos777zTMbai4mg62s9yLLvpJmXYBlEtUd3WHAGXwtZfw2FG\nNwVvtMQcxa6zi73MLBqd/PuJjhTzYxRDQENazLtVqByBMNdNQVvafsrvUsgfYWc4iUQiGU0qg252\nKsl8CX/WleCI/ffihF9fjappfP+CK7j89n+Tn59vj7nkEkin8NqsXaNx3Y/yaW9Q6YgbRHSTV0bg\nQQ+Q59aYXxXEl44UdktBL9mEWM0fkzy5OuxoAlnq1zh0SoiZRb6vXOQ6GPV9Kds8ozLgosCjsXjx\nYs4880zHuLKyWpqb/4GV4GNx/AmCU08d/t/zcVZUfvsi31eeTYgb1r2ZPfO2c4mPWVLIjxip9sYo\nnaabdO8FakLuEXV6a4vr9Lu4TZb58hKJZBOyTaHHds4whWWP9/OzfszV/32Tb55yDh93JKjPEjWK\nAjffDEce6dxPe6PK9T/Kp7NBoydpsjam80LDyNJm8j0a86udja1eboqSNHK740gko0kkZfJKU5Q3\nW2J2UyW3avmlf6MqOCo2jtkIIZx2lJO8hMNhvvvd7xKLxezlXq+fjo4HgVBm7CyT225RGE4+rAnr\nDu/66fnD1/QNRm/S4Pk1EbvDrabAvIqAzJFfT6SYH6O0mZkbY+oIXGwAWqOZaJXMl5dIJJsSRVGY\nW+a3O1DHdEFD1GD+LtvbY95qjdKelTbjcsH998PRRzv31dmicuNJ+XSsUkkYgvpwiv+u7iM1gih7\ngceK0Pfn8nbEDV5uipAaxr9eIvm6CCFY1p3gyfo+mrJSYKuCLr45NY8ZBd4NEnRbGzPoSlj3RqFH\npcyncvrpp1NXV+cY5/ffjGHMtj8H8wSPPzK8e405ICq/Q/FXm1VYG9N5bk2EvrRrh1ezOtzWjMDs\nQ+JEivkxSEoodAvrYvZqCuX+kQlzmS8vkUjGEpqqMK8iYOeud8QNYrrJlJD1fDIEvNIctRs8CSFw\nu+Hee+H733fuq2utyk2nFLB2hYYJNER07l/RS2QEKTeF3rSgVzP+9VLQSzYkPUmD5xsivN8Wp38S\nyatZjk/7VARGxb5xKJYOiMrfcsst3HvvvY4xFRUn0d39I8eyv98pmDFjeFG+sjdlC/ASn0Z1cP31\nxuq+JC82RmyTj3yPVfxb/BU63EqkmB+TdJhu27N1asg9ojfehGHSmchMeW3IB4VEIpGMFL9LZZ+K\ngO0bvzqsU+jR7Ih9whC81Bjh3gce4Jvf/CapVAqXC+6+G0480bmvnnaVW07Lp3mZtW173OC+FT0s\n704M21hqklfjgKqg7bTTFjN4pSkybIdZiWR9MIVgcafV/Kk/dQQsN5nDpoaYmufZoCmwvUmDxogV\n2PNpCms+foefDuj6VFo6i+bmmxzLjjupi2OOGl436Kbg005nVH59/j1CCD7vTPBGS8xOCy7zaxyY\n1SNCsv7Iv9wYpP0rpNisjWWl2Iwwki+RSCQbg2Kfi93K/fbnTzoTbFXgJs+toqdS3HLZhfzg2GN5\n+umnOf/88wHQNPjHP1inEK+nQ+VvpxfQtMQS9H0pwcvNUV5qykT4h6LIp7F/lqBfGzN4tTmKIQW9\nZBToiOs8Ux/mk46ELVSDLoX9KwPsMTmAV9vwkmtpVpOowmgbxxx9NLqembX3+0O0tT0EZHJpttsl\nzoU/bRnR/uu6E47C2vXJAjCF4N21MYed5bQ8N/tXBTfK32YiI/96YwxTCHqEdXMEXYodvRoOmWIj\nkUjGMtPyPMzMKmp7vy3OTiVeHrnhdzx918328v6GUgCqCrfdBmec4dxXT6fC304voOVz61nXlzJZ\n1ZfkqdV9LBsmSl/ic7FfZZB05g8tUd0S9MNE9iWSodBNwaK2GM+uidCdzLxQ1hZ6OHRqHhXBjZMD\nHjdMvuy1xLymwMyKSeyyyy6OMYpyN7CN/bmw1OSGP63BPYJTTBgmn2c1b9uhZORuMylT8EpTlBW9\nmaL3WUVe9ij3o0mzjq+NFPNjDFVRCClWlH1W0cinrxxiXkbmJRLJGGR2sdfOr9VNWNSe4PJfnU9x\nxboNpT7//HPAEvR//Suce65zX709CjcvzKfjczcqlpd8VBe83xbnf40R+nLk0pf6XexbGbRTf5qj\nOq83RzGloJesJy1Rnafqw46IeIFH5aDqIHNK/SNq9jhaLO9J2i54W+R7KC8u4vHHH+c3v/kNAOXl\nFxONHmGPVzXBX+/SqSobvncDwOddCbvD/LQ8N5NG2KE2mjJ5fk2Y5rROUYDdy/3MXs8UHcnQSDE/\nBtneFWZXdy9bjrABQyRl2sUoxT7ta3Vgk0gkkg2FoijsMTlAgcf66gmnTNpcedz+7/scDaUikQhH\nHXUUfX196e3guuvgvPOc+wv3KVx3Wh7tiz34NKu5lG4K2mKGJbC6ho7Slwecgr4xovN6ixT0kpGR\nMEzebo3yYmPETu9SFdi+yMshU0KUbOSgmiEEy7JeKGoLrftJVVUuv/xyDjvsVVpbL3Nsc+qvkhx3\nyMhmDSIpk7r0/lUFZhePLCrflTB4tiFsz1i4Vdi/KsgW+V+9wZRkXaSYH4OoCniUkX+htGZZu8l8\neYlEMpZxqwr7VmasIltjBtUz5/Crq691jFu6dCknn3yyLcYVBa65Bn71K+f+omGFP50WovEjD/ke\nlXDKxBQCQ8Ci9jjPNUToGSJKPzngYp+KAP3B04awzpstMSnoJTmpD6d4cnWYlVkpIyU+jQU1Ibb/\nijaNX5fVfSni6bB8VdDl8K6/80544ol5QGbZ3INS/PHXI+9H82ln3K4D2KrAM6Ji1aZIiucbwnaO\nfcClcGB1SFpnbwCkmJ8AyHx5iUQyngi5VeZNDtg9J7/oSXL8Sady6Pd+4Bj38MMPc91119mfFQV+\n9zv47W+d+4tHFa45NcgX77gp8KgIsF8COuIGT9eH+awzPqhIrwi6mZcl6OvDKd5ujQ3rjiPZ/Ijp\nJq82R3i9OWoLZ02BOaU+DqwOUjjCtJPRRgjhmIXapjBTm/LRRzCg6SuTpxnc9DeT/BGeb0/SsF9c\nXCrMGkFDp+U9SV5pitq2nJO8lvXkpvobTXSkmB/nCCFsMa8pUDrCglmJRCLZlJQHXOxSlpmq9Q5K\ntgAAIABJREFU/6A9wY1//Stbbre9Y9z555/Pq6++an9WFLj0UrjySuf+knGFa08P8slrbjRFYZJX\nI5iucjUFfNyR4Nk1YboS60bpq4Ju9s56uVjVl+Kt1pgsipUA1vfs8p4kT6zuoyGcNRMesJo/1RZu\nmOZPI6UlptMejvPH077Hslefocxv6YDubjjqKIhnzGPw+AS//VuCXaaMPM3l4/bMDrYt9OLLYX0t\nhOCj9jjvro3ZFttVQRffqA7hl5bZGwz5lx3n9KZMO0JQ6nehbcRiG4lEIvk6zCjwMiNdGySAD3sV\nHnroYUL5hfYYwzA4+nvfY82aNY5tL74Y/vAH5/6SCYU/nxFg0UsuupMmkwMuti7I5AR3Jcy0deC6\nUfrqkJu9Bgj6FxoixPThu8xKJi59SYMXGyO8uzZmF396VIU9yv3sXxkYE97oS7uS3PP7X/Hxq89z\n2anHcvnll6PrJieeCCtXOseeemWMY/cZ+ctHW0ynIe1b79UUtskRlTdMwRstMYfjzYwCD/tUBDZq\nIfDmyKa/CiVfC6eLjYzKSySS8cWcUp8dSUwYguZgBXf+85+OMWtbWzns8G8TiUQcy88/H/78Z+f+\n9JTC9WcHeP95Fyt6UwTdVkOavLToEsDizgRP14fpiDtdPKbkudlzst9OuWlPp+i0xUbm9iGZOJhC\nsKQrwVP1YVqz+rhMCVnNn6bnb9jmTyOlO2Fw3z//wf/u/bu97NJLL+XQQ3/P4487x84/LsEZJ6kj\nTnURQjg84WcVeYcU5XHD5IXGCPXhTB3BziU+dimVjjUbAynmxznZYl4WlUgkkvGGqijsXZGJcHYn\nTcrmzudXF1/sGPfJRx/ygx+egGk6I+U/+xnc5GxmiZFS+Mu5Ad552sWH7XGiuskhU0JsOymTWtCT\nNHl2TYSP2uOOplFT8zx8oyqIP52iEzcELzRGWN6TRLJ50JUweG5NhA/b47bVo9+lsE9FgL0rAjnT\nTDY2j7z4Bnde6rR5qqiYzv/+50yU32KWzjmXJdluBPnu/TRHddrSLzIht2rPog2kN2nw/JqI3fFW\nU2BeRYBtJm3a9KPNibFzRUrWG1MI1qYjRm4VimRhiUQiGYf4NJV9KgL0a6SGiM7R517EYYcf7hj3\n2H8e4T9PPLXO9medZTWXysbQFW76eYA3n3DzVmuMzoTBTiV+Dq4J2taYYHlnPzUg+l7id7GgJmQ3\n7TMFvLs2xjutsrnUREWkv09fb47yTH2Yzqzaiq0KPHxzSh7VoY3T/GmkrG5s5pcnHYeeyrxo+v0B\nEolHEaLIXhYqNDn3xijzagIjdtrpz33vZ3aRd9Bt22I6zzVEbHtsr6YwvypIzRj7W010pJgfx3TG\nDTuHrzwwcospiUQiGWsUejX2LM+0mF/SneLSG//OrFlWQazmcnHKFTfg2n4fIql189gXLoR//MMq\nkO3HNBRuPs/PK4+4ebUpSm/SoNjnYsGUELOKvHZ+fF/K5PmGCB+0xdDTUXq/S+Ub1UFHNHJFr8yj\nn2gYQrCyN8kza8L8r8FKE+l/Xctzq8yvCjK3zD/m+rekUimO/O7RdLY2OZbX1Pydzs7Z9mdFEZz5\npxj7zvJQtB4GGav6UrY3fKFXZWreuuJ8dV+SFxojJNPTF3luy7FmY3vsS6SYH9e0SH95iUQygagO\nudkxqxlNXcLNTff+H9tsuy2X3/M4BxxzIhFd8GLj4IL6pJPg7rutrrH9CFPhtgv9PPeAi5ebosR1\nE01RmF3sY8GUEJO8mcF13Umeqg/b6YuqorBrmZ/dymQe/UQjppt80hHn0S/7eLs1Rlcicz15NIVZ\n6eZPY9Xu+Wc//zmL3n7DsWzOnPNYtuxYx7Ijzkow7xsms4pGnl5jCMEnWbnyOw7o1CqE4POuBG+0\nxGzv+VK/xkE1wTFRELw5Mjav0lGitrY2D1gM/KKuru7hTX0+o43Ml5dIJBONbSd5SJgmS7qs1IFG\nXxn/feMDKkJe/tcQJqIL+lJWsd03qoLr5C8ffzy4XNZPI50pIYTC7RcH0PUY3pOizK8K4lIt+8qD\na0Is6UrwaWcCU1hdaV9sjDA1z82MAg+lPo0tCzwUeFRea4kS04WdRz+n1MeMgpGLJMmmpyOuU9ed\npD6cwhyQMVXoUakt9DI1z41rDLuv3HXXXfx1QKHI9tvP54MPrnIsmz0vxRFnJditLLheTndf9CSJ\npBs9lfk1KrL0hSkE77fFHTUkU/Pc7F7uR5PZAZuMCasA00L+MWDKpj6XDYFuCrvYJOBSbKcGiUQi\nGc8oisKOxT4ME5alBcN77Un2druYXx3ifw1horqgN2kJ+vmDCPpjjwW32/qpZwXQ7/ytHyMVw3dG\nlH0qAiiKgqoozCzyUR1y83ZrjI70c3V1X4rVfSnyPSpb5nuYnudmQU2I15qjtMcNTAHvrY3TGTfY\npUwKmbGMKQT14RTLupP292Y21UEXtYVeyvzamE9XfeWVV1i4cKFj2eSKqaxceT/Zkq64wuTMP8bY\ntthD6XrM3KdMwWedGWvJHUsyUfmUKXi9OUpzViBxZpGX2UWy0HVTMyEVYG1t7b7Au8COm/pcNhRt\ncd2OKpT7Zb68RCKZOCiKwpxSH1vkW3m6Anijxcp5n5/lNNOdMLj6noeJ6+sKtKOOgocftkR9Nndf\n6ef2G1Xeb4s7urwWeDQOqg6yU4mP7NhIb9Lkw/Y4j67q44O2ODOLPGyVn9mpzKMfu8R1k8WdcR5b\n1cebLTGHkHersE2hh8On5bFPZXBc1J3V1dVxxBFHkEpl7B89Xh8B/3+IRErsZZpbcO5folSUK+yQ\nlbY2HEII3l8bI5HOga8OuSjxWS8CUd3k+YawLeQVYLcyPzsUS+vJscCEFPPAo8CnwIJNfSIbCpli\nI5FIJjKKorBbmd8uvDMFvNYcJaoLDqgKohkpbr/4HC47+RhOv/Ayuwgvm29/Gx59FLwDMmH+fbWf\nP/8RlnQ77SYVRWHbSV6+Mz2f3cr8tptN//HrwyleaYrREjMoD2iIdKmkzKMfW3QlDN5ujfLoqj4+\n6UgQ0zPXRp5bZZdSH9+Zns/Opf5xk+Pd1tbGoYceSldXl2P59jv+nZUrd3IsO+HiOFvuYDC3zL9e\n6UJf9CT5ss96UdAU7PqV7oTBs2vCdKfrCtwq7FcVYMshrColGx9FTECbrdra2ll1dXWLa2trpwFf\nAkd/lZz5Dz74QAAEAoHhho4qsVgMAL/fP+SYj1IhIsL6otnV3YtHmXj/j5LBGcn1Idk8mYjXhimg\nTg/QKSxRrwKVvfVc9LNz+PCD9+1xP7v2Nk5aMA/XINrljTeCnH12NYmEU7h9+8xefn7GWkq11Lob\npYmYKq2mhzbTg45z57oAXahoisCFQFVgCy3GZG3sedJPxGsjGyGgU7hpMjz0inUDXIWKTqWWoFDR\nGY+B5HvuuYerrnLmxO+076/58JUrHMt2OzTKKb/rZrKWZCtXbET7jsVihHHzhVZsO/nM0KKUaSm6\nTBd1egAjfe17MNnOFSGoypmo0SYajQIwZ86c9b5Cx1VIt7a21g1smWNIa11dXVddXd3ijXVOm4KU\nUGwh78eUQl4ikUxYVAVqXVGW6EG6hQsT+LjHYMXy5Y5xf734p5RVPcxh209noIvgXntFuOWWNZx1\nVg2xWEbQP3ZzPoaucNE5zRRq66bqAARVky3UONNEnA7TTavpoSctFl0KqJhEhUYMBQ8my3Q/YaGx\nhRZjDNdQThhSQmGt6aHZ8JAYkGygIihTU1RqCfzK+Bafxx9/PABXX301Qghm73Mcn7x+mWNM5ZYp\nfvibHryKYJo2MiEPkEJlOQW2kJ+sJinTUrQYHlYYmZe/oGKwnSvy/+zdeXxU1fnH8c+9d/bJvhLC\nvo0oSBQQEMEFFMWKdalLbau1tT+ttdalttqqdWut1qq0VWurba1rxQV33KqACypuaHVA9oQsbFlm\nn7n3/P64k2VIwIBIMuF5v5omuXMnOQnj5DvnPuc5kjl6oawK80Al8NkObr8IuG13f9PRo0fv7i+5\nQ5999tkOv++6liS+OvsV3KgCF6NLB+yxsYme92WPD7H36suPjYCl+O+GMBujJr7AGC6+436uP/Ob\npNIrXBOxKH+46P8Y/sxrnFA1rFN5wejRMHw4zJ4N4XD78WfuzsWhufjH7Y5ub3PfnDBZ1ZxkVXOC\nmKnwK7vDTtRUJIF1moeEp5CjB+bgd/aOzfz62mOjKWESbEywujmBqcAAWq+h5zh1RuW7GJbn6nX9\n4b+KK6+9nsSgMbz0wP1UL78X02x/8eL1Ky76S4zCYh+H9vdR6a/s1te0lGLesvVYyoHP56fEYzCj\n0scnWxLUbo23/U77+x1M7efDKa9QvzZLly7d5ftmVZgPBoNrgL3+kST95YUQexuHrnFYfz+v1oTZ\nHDMZOfEQfvCbP/DXX/+s7ZzNtdVc8YPTyZ33PEcOLerUjm/6dFiwAI45Blpa2o8/+Vc3VirOw3dq\neLtRQ53nMqgqMdi/2E1NOMXKpgQbIimcKYuWpEXCUqwLpbj380bGFXuoKvGQ5+odoT6bKaXYELFb\nS3ZcN9aq3GsQKHBT6e/9i1l3xfLGBFWHHcuC+09mS11mvfo5v4vQf5jFkFwnlf7u7776waZYW1mS\nx9CYXO7l7YYYa1vaS89G5rsYX+rp9u6xYs/LjpUfIkN9hyexMgnzQoi9hFPXOKy/j4L0Rk/TTzmL\nY886N+OcLz58lxsuOZ+FtWGsLtaETZ0KL70E+fmZx5+6x81pP0qR7GIh7fbomsbAHCeHVfo5fkgu\nB5V5qfA52v6wphS8vynGf1Y281J1iNXNibYdZkX3JS1FsDHOM2tDvL4hkhHkDQ2G5zmZPSiHGQNy\nGJDj7JNBPmUpVjQlePzPbj55IzPIH/P9OJOOTuE27C5Q3bWmJUGwwyLwfQpd/LcmnBHkDyjxMEGC\nfK8nYT7LhJP2zA9AscfoU5cQhRDiy7gNnSP6+8lz2X++Tr3seqqmHZFxzuL5j/CXm37L4tpIl4F+\n0iR45RUoLMw8/tQ/XZz8/RSpnQj0rfxOnbHFHk4ZnsecIe07yyqgJWmxsinBm3URnljdzLsNUbbG\nu67RF+1akiZLN0Z5cnUzSzfG2v72gb2/SlWxh28OzWVSua/bJVLZ4LPPPuOuu+7KOLaqOcE7/zV4\n8o7M1kz7TEhx2qX2bq0TSj24je7Fusa4yZJ6u67eUuDB5MNN8bbNogwNDunnY3Sh9JDPBjKtm2Wk\nJaUQYm/ncegcUenn5eowIRz8+NZ7ueaUmdSsal8U+9jc31FQUo7+w3M4uJ+308zi+PHw6qswcyZs\n3tx+/Kl/OzkxleLx+wwcXbXG+RKapjE0z83gXBdv10f5dEucqGnX06eURb5LZ0VTghVNCYo9BsPz\nXAzOdUotclrctNgcM1nRlKAm3LmUptRrMCrfxcAcZ5+cLa6vr2f27NmsWbOGzz//nFtuuQVd11n4\nSZI7f+5DqfafubBE8ZNbIzicdk/4QTndK6+JmxYLayOYCmKmRRKdqAJ/+vYSj8Gkci/5UhqWNSQN\nZpmO9fLlUmIjhNhL+doCfQjyCrjozof5zSkzCTU1tp1z728uJrewCP3kk5hS7u00w1hVBa+9BjNm\nQEND+/GnH3Jwomny+AMGjl18mtU1jYP7+Sj3OngnvRFP1LTYGjfJc9pXVTfHTDbHory/McrgXCfD\n810Uu3v/LqS7Q8pSNCVMGuOW/T5hv+/YE76VrsHgHCeBAjdFnr4bMKPRKMcffzxr1qwB4Pbbb2fV\nqlXceNeD/O48D6HG9ll3XVf8+NYwheUKl64xsbTz47srSineqovSnDBpTlqYlsKDhabZ3ZnGlXgY\nle/aKx6DfUmfToN9bcGsUqqtXt7QoLQPP6kJIcSXyXG2z9BXDB3BxXc+xI3fP4FE3C47UJbFXy45\nh5zCIvSZhzOprHPgGTPGDvRHHAF1de3Hn/6PwZRqi/mP6vTvv+tjHJ7vIt+ts6g2giulYSlF3FRo\n2OUNmqaRUvZOsiubkxS4dIbnu6jwOfA59J3a9Kc3stKdfhrj6cAeN2lKWBklM9vjMTRG5rsYme/C\n4+jbVcGWZXHmmWeyZMmSjOPl5eVcdpmLVZ9kxrVvXxpn30l2qdaBpR683fz9LNscY2Vzor1c120Q\nj9l9+I8ZnIs/SzbREpn6dJjva5oSFrF0LWep19GpU4MQQuxt8lxGW6APTJjCBbf/g1vP/w6WaQed\nZCLO7RecyZBXPkSjiIO6CPSjR8Prr9uBvqam/fh7b+qMHad45CGNmTN3fYwlHgdHD8xhUW2ETTET\nr0NDoajwOfA4NNaHUrSW6TcmLJZujLXd16nbVyHsNy3jY2/6Y6dOj8+kKqUIp1pn29uDe3PSortr\nfp065LsM8l065T67bKQvltJ05Ve/+hWPPvpoxrGZM2ey/4Q/8fdzMxe8TpuV4ugfxAGo8DkYmtu9\n8poVTXEW1UVJpP9BClw6XofOICNCqZ6UIJ/FJMxnEWlJKYQQnRW47UD/Sk2IA484hh9efzt3X/4T\nAHx+Pz/+w914c3JZ2ZzE0OyOH9uG31GjWgO9Yt269tu2bNI46ijFVVdpXHklGLt4QdTr0Jk5wM/S\njTFWNCUAjbqoSbHH4OhBOWyMmnzRlGDLNgtjk5Y9kdOU2P5MtmObwO/tIvgrxW7b+TSWstrKYhrT\nM+1NCZNuTLYDdtlMnkunwGVQ4DLId9sf+xxaj78o6Ql///vfufHGGzOO7bvvvlxzzTyOmJG54HXI\nMMV3bwjbZTE6Xb443ZZSio82x1i4IULro8vv0BmZ72ZCqYfVK2p2eH/R+0kizCL1svhVCCG6VOQx\n2vrQH3rSd2jevInn75nLC88/R9noA3g73bljeVMCXbNb7m0bgoYPh3ff1TjjDMXLL7ffppTGNdfA\n4sXwwANQXr5rY9Q1jYllXorcBu9ujGIp2BwzeaU6zLQKH0cPymFr3GRtS5JQ0iKSst+iKcWOJrdT\nFjQnLJp3EPijyXzcWNRUh7Y70+82MsN0ctu69vSMe3wnuv3kOvW2sJ7v0ilwG+Q69b1mxv3LzJ8/\nn3PPzWyvWl5ezkMPPctJJ+URj7X/njwexU//FMafZ39eVez50tn0poTJW3URVjQlOgR5jaMH+hmU\n69rhfUX2kESYJSylaEjPzLt0ra3tmRBCCFup18Gh/f28tiHMN865kOknfhtzcCVDc51YCt5psAP9\n540JDE1j/+LObffKyuCFFzSuv15xzTVkdA955RU44AB46CE49NBdH2fHOvpoShEzFa/UhBlf6mFE\nnovCksxe4UrZ59jhXrWF/NaPo+n3OypnUUAMnYaoCXTdFlPXwGvYM/vRlNXWprA7vA4tI7AXuAzy\nXNlf8/91eumllzjllFMwzfZ/D6/Xy/z5T3HNNUP44ovM83/++yTlI+1zy7wGI/O3H8Ytpfjf1jjL\nNsdoTFi0/lMWuHROHpZHrnSq6VMkzGeJzbH2S5jlvr2j24EQQuysfj4H0yp8LKqNkF9SxtqWJA7N\nLkewlOK9dD36p1vj6BqMLe68yY5hwNVXaxxyCJxyumLLxvbn29pau7b++uvhF78AfRfnVbato7cU\nvNsQY0vMZEKZF6PDc7ymaXjT5TPF2/l6Kr2wtmPYj3b4uDZikfiSrWUsBeGUIpzafg98p05bWG8N\n7vkuvdv9zYVt0aJFHH/88SQS7Zs2aZrG/fffz+LFB/H445nnn36WyT7H2C9GDY0uF3O32hIzebs+\nQmPCfsEXMxWGBgUugzlDciXI90ES5rNEx/7y0pJSCCG2r9Lv5OByH4vrIoDdKcbQNcaXeOwZ+toW\nXnv0PqzTvo+uwX5FXe+aOWMGLPtQ4/hTLN57oz2sWhZccQUsWgT33QclJbs2zs519PZYGxMW0yp8\n+Haig4umaXgcGh4HFNE5rH3Wsh6lYPiwfh1CfudZ/kjKImXZgbGtrj0d2AtcBt69tK59d3rvvfc4\n9thjiUajGcf//ve/U1JyIqecknn+qLEmJ/4yQmvsH1fs6TKQpyzFsi0xPttqn5kw7U5CPodGjkPn\nkIq+tbmWaCepMEvUR6VeXgghumtQrpMpystbrbXyjQmcmsYIr8k5Pz2Dxa+8SM3KINqvf4+uaYwu\ndHf5dfr3h7de0/nxZSn+dmvmc+/zz9tlN488AgcfvGvj3F4d/bNrW9i30E2gwL3bSlU0zd5B122w\nw1CXtBQOrec75PRFn3zyCbNmzaKlpSXj+O23347ffzbHHAMdqm7w5Smu+1uchN6+83ugoHN5TX0k\nxTsN0baWk6ZShFMWRW4Dp66xT4GLwVIj32fJdbEskLIUm2L2f90+h0autI8SQogvNTTPxcSy9ln3\nt1duYMr0w1j8yosAvPjvu5l/1y18sClGsDG+3a/jcMDdf3Rw1yNJcgoyF5lWV9v183/8I6jul5h3\nMjzfxZED/HjTu84mLfhoc5yn1rSwvDGO9VW++E5y6jL7/nX56KOPaGxszDh27bU3sGbNTzntNIhE\nMs+/9NYoqaIkYK9p2La8Jmkp3mmI8kpNuL13v1IYmkaBW8epa5R5DapKur76JPoGSYVZYGM01baw\nqZ/PIU+yQgjRTSPz3RyYDjKxaJjq6sw2fI/eej3//c996VKX7Qd6gP87xcn8hUlGHpDKOJ5KwSWX\nwAknwNatuz7WYo+DYwbmMKRD3/CYadf5P7M2xOrmBGoPhnqx+51xxhk8+OCDONJbC19wweW8/PIV\n3Hpr53NPuiDKfocm2z4fU+TOuKJSE07y7NoWvmhqr7svchsMyLEfPxr2WotD+vmke1AfJ2E+C0h/\neSGE2HX7FLrZv9hNaeUgfnHvY/jy8jNuv+eqn/Hui0/zbkOMlR2CUVeOGOvmsRdMZp/dOfjPnw8H\nHgjvvbfrY/U4dA7u52P2oBwq/e3P96GkxVv1UZ5bF6I6lJRQn8VOPfVUnnjiCU4//RLmzbuBhQsz\nb3c4FT+8PsKc8+OkWjd4cuvsmy4Fi6Us3qiL8PqGCJF0mxoj3W51RL6T6rCdGXQNpvXz9fndc4WE\n+ayQsfhV6uWFEGKn7VfoZt9CNwNH7culdz2M091edqAsiz9ddDbvvfQMSxqirG7ecaAfW+bmllvg\nojvC+PIyQ/WaNTB1Kvz5z1+t7KbAbXBofz9HDvBT6m2fjW1KWCysjfBSdThj7xGRPZSCtWu/wbx5\nf6C2NnPGvKTC4qqHwhxycoKkpbDn12FymQ8NWNOS4Nl1Ida2tM/Yl3kNZg/KodzraOvWBDC+1EOJ\nTADuFSTM93Ix02Jr3K6Dy09vvSyEEGLnaJrGuGI3owpcBCZM4YLb7kXr0FfSTCaZe+FZLHn+Sd6q\nj7K2ZceBfp8CN/93uoMbnmhh2NjMUJ1IwAUXwKmnQnPzVxt3qdfBzEo/h/X3Zewvsilm8kpNmP/W\nhNkS234rSdFzIpFIpysokQiceSb85CeQTGaeXzU1xXVPhBg2NkXcVHjS6yf2LXTjMTQW1kZ4sy7a\ntmmXM70D7IxKPy5DY1FtuK0kd1iekxF5suB1byHJsJerl1l5IYTYLTTNbk85PM/J+BmzOeeGuRm3\nm6kUf7robN54+lHeqIvy6ZbYDstZAgVuZh/o5qqHwhz13c5lN48+CuPHw4cffvVx9/c7OXpgDlP7\neTOaINRGUrywPsTi2gjNCQn1vUU4HGbWrFmce+65bZtCrVpldz369787n/+dnyS5+G9h8ooUKaXs\nFqBo5Dl1vA6NZ9e1UBNuzwOVfgfHDsplRHrjqDfqom2bfBW5DSaWbr8Pveh7JB32cvXR9idnqZcX\nQoivRtM0DirzklJw6EnfAeBvV1zQFtqVZXHnpT/CTCbhxG/TmLCYVObdbnvIUQVudE3DeWWUfSak\nuPsKH7Fw+7lffAGTJ8Of/gQ//KHdHvKrjH1wrouBOU5WNSdZtiVGNB3g1oWSrA8lGZbnZGyRB590\nPesxsViME044gcWLF7N48WLC4TCnnfZPvvc9R6cF0jk5cP1fkpROsdvYmEqhaxqGpmEqhUJllM64\nDY3xpR4G5zjbwvrHm+Nt5bguQ2NahQ9Ddt7dq0g67OVa/wPVkJl5IYTYHTRNY0q5F9NSHHrSdzAc\nTu76xXkoyy5pVEpx9+Xnk0omOeLUM2lOmEyv8OPfTkBunR3lmCiDR4eYe6GPtZ+117nH4/CjH8Hr\nr8Ndd9kB7qvQNY0R+S6G5DpZ0ZTg0y1xEpZCYW86tbolyah8F/sWufHIzqx7VDKZ5LTTTuOll15q\nO/bAAw/wwANDgesyzt1nH7jz/iQb8uwgn7QUGmDoEElZKKVo6fDqb0iukwNLPRn/putDST7d2n5V\n6JB+vu0+TkXfJf/ivVgoaRFKtm8U4ZRX2kIIsVvomsbUCh8VPgeHHH8qP/njPehGewBXSnHPlRfy\n6iP/Ymvc4oX1IRqi219wOiLfxaQyL/2GWPzmkRBHnNq57OaBB2DiRPj0093zMzh0e7OrOUNyGVPk\nJl1ijaXg88YET69pYdnmWHohpfi6RaNRTj75ZObPn7/NLQcAl2QcOflkeHGRSUOBHeQTpiJpKVKW\nYkvMJJZSbTsA+xwah/b3cXA/X0aQb0qYvF3f3pi+qtgjm0rupSTM92JSLy+EEF8fQ7NLEkq9BpNn\nn8BPb/8nhrO9x3t+QQGjx1UBEDcVr9aEM3p6b2t4vovJ5V5cHvjBdTF+/IcIXl9mkP78czvQ/+tf\nu+/ncBka+xd7mDMkl1EFLlrnfZIWLNtibzy1wXQhmf7r09TUxNFHH81TTz21zS2jgQVAAQC6Djff\nDPc9aPF+S5iUpYikLBoTJqGkRVPSQktv+KSlr8AcOziXSr8z46smLcWi2git+0QNyHEwulAWvO6t\nJMz3YtJfXgghvl4OXeOw/n6KPQYTjzqOn/3pPhwuF7m5uby4YAHnH3NIW2tIS8E7DVEc0A6LAAAg\nAElEQVTebYhud0fWYXkuppR7AZg6J8m1j4UYGsjcNTYahbPOgh/8oPOOn1+Fx6EzodTLcYNzGZbX\nHv7ipmK16WVpMpeVTYk9upvs3qC+vp7DDjuMhds2jGcY8DJQCkBpKbz8Mlx8ieL12jANMZOGaIqt\ncROlQAEuXaPApZPvMphR6eegMm+nq/JKKd6uj9CcsB9XeS6dKeU+WfC6F5Mw30sp1V4vb2hQ0qHP\nsBBCiN3HqWttrR8PPOIYLr7jQX55z2MMHXsgHofOjEp/e108sKIpwas1YWKm1eXXG5rn4uB+dqDv\nP9zi14+0MPvUziU6995rL44NBnfvz+N36kwu93Hs4BwG5LRPBCXQWdJgbzy1Xjae2i1WrVrF1KlT\n+bBTy6L9gIVAfwAOOgjefU8xfEKCR75o4rPGBM0Jk4RpXyFCA4+usX+xm5kDcvjG4JztXpH/rDHB\n+pD9eHLoMK3CJ2W4ezkJ871UROltvWRLvQ77P3YhhBBfC7ehc3h/P3kunXHTZzKk6iBerg4TbIyj\nYffznljmofWZuCFqsmBdiMZ41+0gh+TagV4DPD4447owl98ax+vNDNDLlsGECfDQQ7v/Z8p3GUyv\n8DNroJ98rf3FRHPCYlFthBerwxmbEoqd8/HHHzN16lRWrly5zS0HYwf5SgDO+qHF7Y9FeS/VYv/O\noyaWUpiWfWXIZWgMyXVy1j4FTKvw08/n2O4se10kxYeb2rvbTCn3ke+Syb69nYT5XqpJtb8ilwUt\nQgjx9WudhS/x2OFIAUs3xnirPkrKUozMd3NQnslrD9+DUopwSrFgfYh1oWSXX69joAcYc2yMO5+J\nEQhkBvpQCL79bTjvPIjFOn+dr6rY42CMM8x+jjBF7vbgtzlm8mpNmFeqQ2yOSajfGYsXL2b69OnU\n1dVtc8sxwItAES634qKbYxx5WQurovZMfEvSagvyPqdGicegqtjD8UNyv3RTyHDSYnFde13WvoVu\nBuY4d3APsbeQMN9LNVrt/4FKvbwQQuwZXofOzAF+RnUoq1nTkuTF9SFqtzZx5klz+NtVlzDvpl+j\nlMJUsLg2wsebu95ganCui6kVvrZA7xyYYO7TUU47vfO5d91lbyrUaaJ3NynQU8wa6GdahY88V/uf\n//qoyYL1YRbWhmmSjae+1HPPPceRRx5JU1PTNrd8G5gP+CmptLjqoRATjre7GqUsRXPCagtd+W6d\nIreDkfn2Cz79S66+m+kFr4n0Fft+Pgf7F7t3688lspeE+V7IUtCk7NkTl65lbOEthBDi66VrGhPK\nvEwp92KkM1ZdYwszZs3mjTfeAODJe/7CvZf+kHjUnin9ZEucRXWRLttADspxckiHQL+JJOf9IcId\ndypc2zQg+eADOPBAmDfPXju1u2maxsAcJ7MH5TCpzIvP0R4iq0Mpnl0b4u36CI1xU2rqtyOny40C\nfgr8G3AyZmqS6x8LMXSMvaYix6mha+B1aFiA19DJdeoMz3MyufzLg7xSinc3RtmSLunyObRuvQAQ\new9Jib1QSBlY6af9cp8hK9SFEKIHDM1zcdTAHHKcOps2rGfd8v9l3P7q049xy5nfYEvdBsAOwy+u\nD7XtD9LRwBwn0yp8bW0jq8Mp9psT4Y03FcOGZZ7b3Azf+hZMnQpPPglW1+tsvxJd0xie7+K4Ibkc\nWOrBbbT/nVnVnOS5dSEeW9XCazVhPt0Soz6SIiW9LQGorJxOefnDQGvJ0nXAbYDO8efG+MXfIxSV\nKEbmuzhygA+/QyeassuyHJrdfWZUgZuDyrzd+vu+sjnJqma7lEvX7AWvshmY6EgeDb1Qo9TLCyFE\nr1DoNpg10M9B48bwq/ueJrewOOP2Tz98n+tOmcHaTz8AoClh8cK6UJcLSwfkODmkX2agD1dEeHep\n4qSTOn/vt96CE06A0aPh73//eurpDU1jnwJ746mxRW46bh6asBQbIik+2hznlZowj65s5oV1Id7b\nGGVtS4JIFy9a+jKlFA88ZlJ1oGLt2uOBe4A7gV/j8cNFfwnz01+bHFLp5YSheUws87KmOcnK5iSh\nlF1iU+AyGFPkYXyJp1tBflMsxXsbo22fTyz1UuyRXCAySZjvhZqsDmFe6uWFEKJHuQ2dQyt8fGPq\nBK6d9woDRo7OuL2hrpZrTz+GDxc8Cdgh+L81diecbUtVBuQ4md5hhr4mnOLjSISH/6O4/XZwdrGe\ncflyOOccGDoUbrwRGht3/8/o1DXGFns4bkgu44rd9Pc5MoI92AuCt8RNljcmeKMuypNrWnhydTNv\n1EUINsbZEjP7ZA/7aMrik01xvv2zBN852SDU3BrCzwTOZeBIk0deTnDdOV6OqPQzJNeFQ9dY3hjj\n/c0xwin7RU++y6CqxMO4Yne3gnwsZXcdar0gMiLfxfB82RhKdCZhvpexlKIlPTPvc2jkbPtsKoQQ\nYo/TNDvsnjJpX67/zwKqDpuVcXssFuPmC85iwZ2/RynV1gnnnYYo5jYBt78/M9BvCKdYXBvh/J8o\n3n4bZs/uegx1dXD55TBwIFx6KVRX7/6f02Po7Ffk4bBKPycPy2P2oBwOKvMyLM9Jbhd/jyIpxdqW\nJEs3xnhhfYhHVzbzSnWIjzbH2BBOEt9OL/7ezlKKT2o28b2f/ZJ7397Ed07SeXhu5wWnc06y+HSp\nzpzJbvwdfj+14ST/rYkQSdn/9nlOnYPKvYwt7t6MvKUUi+siRNP3L/YYjC/17KafTvQ1Mu3by2iA\ngSKFxpBcl9TLCyFEL9Lf7+Sk/Sopuvdh7rr+Sp67988Zt9936++oXbWc06/9E26vj5XNSZoSFtMq\nfBmtB/v7nRza38fCDRFMBRsiKRbWRphW5ePZZzWWLYM//AEefBBS21TshEJwyy1w++1wxhnw85/D\nfvvt/p9V0zQK3AYFbqNt06xYymJTzGRjLMWmmMnmmEnHUnpT2d1x6qPtXXHyXTolHoNSr4MSj0Gu\nU+/xv22WUiRMRcxUxE1FzLSIpz+OpCwWL/2Qm3/8XerXruKRv35GIvZYxv0NQ3HTTRoXXaSz7Y/S\nnEjxzNoQkXTnGa+hcUiFj0BB97vPfLgpRkP6d+g2NKb188l+M2K7JMz3Mpqmsb8zRFgZ7F9c0dPD\nEUIIsY0cp84xg/MpueH3VI4IcO/VF2Mm23vNvzT/MerWreHcuf+moLw/m2ImL6wPMb3Cl1HvXOFz\ncmh/P69vCGMqqE0H+ukVPsaO1fjXv+D66+G22+Duu+0Q31EqBf/6l/127LHwi1/AIYfQKVzuTh6H\nzoAcnQHp/uaWUmyJmW0Bf2PUJGZmXoloSlg0JSxWphdxug0tI9wXuw2Mr7iDqakU8VQ6nFtWOqCn\ng3rKIm61fmy/T+xgMe/Cxx/kH7+5hETMrlVPxJ4CfoRdI69RVgaPPKJx2GGd7xs3LeataiGULq1x\n6RpHVPoZtRNBfm1Lgs8bE4A9wXdIPx8+uUovdkDCfC/k1Sy8miVtp4QQopdy6BqTy70Un3cO/YcM\n54/nf5eWrZvbbl/2wVKu/dYMfnnv45SNGE00pXi5OsykMi9D8trrnvv5HBmBvi6S4vUNYQ7t78eh\nawwcaM/C//rXdh/622+H+vrO43n2Wftt8mS47DKYMweMPbAxqK5plHgdlHgd7IO7bTOtjdFUW8Bv\njGeW2sRNRU04RU04lf4aUOQ2KPEYlHgdlHoMHLqWnim3Osye25/HMz63j+2OtbiJeIx/XXsZrz16\nXxe3fgRsYfLkYh59FAYM6HyGaVn854tmmhL2YBwaHDlg54J8XSTFkvr2Ba8HlHgol0YY4kvII0QI\nIYTYBZqmMarAzfnfPJJ+la9yww9Po3rFZ2235+fl8q0JI/kkZrA5ZmIqeLM+ytaESVWH2ul+PgeH\n9ffzWjrQ10dN/rshzNTy9hnZwkK7Xv6ii+Df/4abb4YVKzqP6e234cQTYdQou67+u98Fzx4stdY0\njRynRo7TxdA8+1jSUmyKpdgUNdkYM9kcS2WEb0vBpvTsPukZ6a+LrtlXBjyGhtvQ2z5uWL+GS7//\nbf738Ydd3OscYC7nnefh1lvB3UU2Ny3FvFUtbEr3gteBGTsR5C2lWLY5zqdb423HBuU4CRTIglfx\n5STMCyGEEF9BqdfBWQePoWL+y/zmx2fz4WsL8OXlc/U9/6GsuIiZwDsNUVa32GUmn21N0BS3OLif\nD5fRuqeIg8Mr/bxWEyalYGPU5Nl1LUws8zIktz3QeTx2Z5uzz4b58+H3v4d33uk8puXL4Uc/giuv\nhAsvhMMP18nP75nFqE5do8LnpMJnl+YopWhMWG2z95tiZpe9+bvDoduLdt2GhlvX7HDuSAd1vfVj\nre0ch0ZGvb5pwk03Pc0113yPeHzbNkFe4E48njO56y4488yux5CyFE+taWFDuh2pBhza38e+hd17\nFRVKWrxZF7FfzKSVeQ0mlXevD70QEuaFEEKIr8jj0Dlunwr6PzSPa6/6NWOnHgHlQ3hxfYhpFT4m\nl3spcBt8sMluFr8hkmLB+hCH9veR57LrYcq8dqBfVBshZiqSFrxZF6UmnGJCqQd3h42CDMOegT/h\nBFi0CG66yS6z2VZ9PVxxBfh8I/jWtxq57jq7G05P0jSNQrdBodtgVPpYtHVhbTTF5riJUqRnz9uD\n+LYz6m5Dw7ELtfaxGLz8Mjz2WIpHHrmSaPTGLs4aCcxjyJD9efxxOOCArr9W0lI8v66FdSH7hZoG\nTCrzUlXi7dZY1rYkeKchmnGlYmyRmzFF3WtfKQRImBdCCCF2C13TmNgvhz/fchNL6qOklL34c8H6\nEFPKfYwudFPg0vnXS28wcN8qWpL2bVP7+ejvt2etS70OZg/O4Z2GKNUhe6Z3bUuShmiKyeXettnt\nVpoG06fbb598YnfAeeCBzh1wIhGDf/2rmAcegG9/2+6AM2bMHvm1dIvXoTMwR2dgTheN9neD5mZ4\n7jl44gn7fShUB5wOvNbF2ScC9zJrVj4PPADFxV2cAiRMxUvVIVY3J1HYQX5skZvJ5V8e5FOWYunG\naNuiYLDbUR/cz0eZ7C8jdpIsjxZCCCF2o8G5Lo4amNPWlz1pwcLaCB9tirFi6VtcceIR/On8M2jc\nWE/Sgtc2RPjf1vYNpjyGzrR+PqaUe9s2boqmFP+tifDexiip7XRiGTMG/vlPWLUKLrkEcnI6n5NK\nwX33wdix8I1vwMKF0Af3eQKgoQH+9je7b39pKZx+OvznPxAKLQIOpHOQN3A4buH44+fx8MP5PPvs\n9oN8zLR4qTrEquYEFnaQH5bn5PBK/5fOqG+N292NOgb5gTkOjhmUI0Fe7BIJ80IIIcRuVuA2mDUw\nhwH+9nC2tGYLp3/v+yilWPLyc1x+7BTeeuYxlFJ8uCnGm/XtQV3TNIbmuThmUC6l3va2NMsbE7yw\nPsSWDvXV2xo40J6hX78efvc7KC/v+rxnn4VDD4UpU+Dxx+368Wy3Zg3ceitMmwb9+tnrBp5/HhJt\n62oVcBVQm3E/r7c/N9zwGo2NF/Pkkxqnnrr9bkDRlMUr1WHWhpKklB2k+vscHDUgZ4dd6JRSBBvj\nLFgfojnd8cbQYGKZh0P6+TLKqITYGfLIEUIIIb4GLkNjWoWPccV2R5NHb72ODWtXtd3e3LiFP1/8\nA+ZeeBbNWzaxtiXJy9VhIh0KqHOcOjMr/RxQ4mnbMbY5XbrzyZYY1g6m1QsK4Je/tAPuNdfUMmRI\nvMvzliyBk06C0aPtfvax2Ff/2fcUpWDZMrj2WruufehQuPhiWLx4e1ccNOB+dL2s7cjhhx/B6tXv\nc8UVh+D37/j7RZIWL1eHqQmniJsKHSjxGhw1MAePY/uRKmZaLKyNsHRjrG2TrXyXzqyBOYzMl/p4\n8dVImBdCCCG+JpqmsV+RhyMq/Rzz3XMYUTWx0znvvDCfX8yezLsLnmJLugSjIZrK+BqjC90cPTCH\nApf9Z1sBH2+O83J1mJbEjqfUPR741rcaefrpVTz+OEya1PV5K1bA//0fDBkCP/uZPcP9yCP2AtuV\nKyES2dXfwu5lWfDWW3Y//ZEjYf/94eqr4cOuukp2MGKEfZ+33qpkwYKHMAyDK664gpdeepHy7V2+\n6CCUtHi5JszGWIpwykIHCt0Gh1b4KXBvv6l/fSTF8+tCbX31AUbmu5g1MGeH9xOiu6Q4SwghhPia\n9fM5+MH0KoY//hL333k78267gVSyvad685ZN3HbB95jyjZM588qbeNlUDMpxsn+xu63bTYHbYNag\nHD7eHOOzrfZ9N8VMnlsXYnypl+F5zh3O8BqG3f3mm9+0Z65vugmeeabzefX19uZUXcnPh/79298q\nKjI/bz22u3vbJxLw2mv2Atb586G29svuYQHzqao6nhNP1DnhBNhvv4674x7B8uXLGTZsWLe+f3PC\n5NWaME0Ji+aEhaFBgctgQqm3bTfcTiNQimVb4ny6pf2KiEvXmFTu/doW+oq9k4R5IYQQYg/wO3WO\nHpxHySWXcsDhs/jrZeex6pMPMs5565l5fLZkET+47jY44hjWh5IMy3MytsiDz6ljaBoHlHip9Dt5\nqy5COKUwld3HvjqcZFKZF+8Oyj3ADrTTptlvn37a3gEnmdzh3QBoarLfPvtsx+cVFnYd8jt+3q9f\n1xswtQqHYcECO8A//bT9fb+MpkFV1Qds2fJj1q59mwsv/AdnnXVWl+d2N8g3JUxeqQ4TTlk0Jkx0\nDQpdBsPzXYwu7HpTp3DS4o1teseXeg0OLvfhd0pRhNi9NNVXl7HvBkuXLlUA48eP36Pf97P0s+To\n0aP36PcV2UEeH2J75LGRPb5oSvD2hmbm330bT/zlJswukvTk2Sdy8oVXUDF0BIYGowpc7Fvoblso\nmUy3N1zVoSuKy9CYVNZ55vfLHhvV1fZs/F//Ci0tu+un/HLFxZ1Df2GhXdqzYEH36vddLpg5E2bN\nauSjj67kn/+8A8uy1x2UlpYSDAYpLCzcpfFtjdsz8rGUxda4hUJR4DYo8zqYWenH6KLP/bqWJEsa\nIp16x+9X5N7hAtmeIs8bvcPSpUsBGD9+/E4/SGRmXgghhNjDRuS7KHQXkPPTyzjw8KO56xfnse7z\nTzLOefu5x1nywpP86Ld/ZvqJ3+azrQm+aEowutDNPgVunLrG5HIflf4kSxqiJExFwlQsqo0wLM/J\n+FIvzm5uqjRgANx8M/zqV3b3l9WrYcOGzLfa2s7967+qzZvtt2XLdu5+OTlw7LF22dDRRyvmz/83\nP//5z2loaMg4b+PGjVx99dXMnTt358cWS/FqTZikpWhO2kG+0G2Q49SZXuHrFOSld7zoKfLoEkII\nIXpAscfB0QNzyHVWUTnvVZ6882bm3/VHrA49Ip0OB8cdPZMWDSxl96z/eHOc5Y0JxhS5GZHvYmCO\nkxKPwZL6KBsidtpe1ZykPpJiyk4GyYICux97VyzLDt7bhvzWoN/6cV3d19PmsrQU5syxA/yMGXZd\n/rJlyzjuuPNZtGhRl/f55je/ycUXX7zT36shmuL1DWGSFoSTCtOyF7s6dY3pFf5OpUyNcZPFdZG2\nlpMAA3IcTCrzSstJ8bWTMC+EEEL0EI9D54hKPx95HDgu/BXjZxzL3Zefz7rgpwCc/cMfcuy4kYSS\nFss2x1jdYs/6xkzFextjfN6YYP8iN4NznRza38cXzQne3xjDVBBOKV6uDjO60IVLQTcn6bdL1+1A\nXVoK48Zt/zzThI0buw76Hd8aGuwXCDsyeLAd3k84AaZObe/9vmTJEubOncsjjzyC2cUrh2HDhjF3\n7lyOPfbYnf456yJ2kDcVxE2LuKUocOvomsbkci9FnvYONEopVjQleH9Te8tJXYMDSzyMzHdJy0mx\nR0iYF0IIIXqQpmlUlXgo9hg49SpumL+IJc8/yTN/u439Tzuf9xqiBApcTOnnY3ShyUebY1SHkjx1\n1x+ZcuxJhJJD+N/WOONKPIzIc1HudfBWfZTN6cWXn21NQCqHUcae6S1pGPbi1n794MADt39eKmUH\n+q5Cfv/+9g61BxzQ3oEmHo/z0EOPMnfuXN59990uv6bb7ebyyy/nsssuw+v17tS4Q0mL5Y1xljcl\nsJRdNhNNKQpcdpAfW+RmcG77gte4abGkPkp1h5aTeS6dQ/r5pOWk2KMkzAshhBC9wMAcJ/muHJZu\njDHl2BOZPPsENE1jeVOC5U0JBvgdBArcTK/w8dhzL/KfW69j3tzfMu2E0/nmeZfSmBhCicegqsTD\nkQP8fLolzidb4iggogw+SuXi3xpnn4LeMWPscLQvev0ySikmTpzIsh0U18+ePZu5c+cyfPjwbo9B\nKcXGmMnnjXGqQ+2h3FKKmGmR79LRNI1BOU7GFLW33qmPpHirPkIk1d5EZES+iwNLPDi+6iUQIXaS\nhHkhhBCil8hzGRxe6acpYfL51jirW5Jt5RvV4RTV4RQFLo0br78WAMs0eX3e/Sx+8mGmzjmFg4/7\nFnUHHcKgAi9VxR76+x28WRclgr3R1AebYtSEk0zJshaJmqYxZ86cLsP8qFGjuOmmm5gzZ063X6RY\nSrEulOTzrQm2xDPLdDTs35XfYQf5QrfO5HIvmqZhKcUn6RdJrZw6TCr3MUh6x4seImFeCCGE6GXy\nXQaTyn2MK7ZY0ZRgRVOCmGmn+jcXLWTp229mnG+mUix8/EEWPv4g/vwCDjziGCYedRzfOHoW0yry\neaNlE7WWPbPcEDV5bl0LE0q9DMnd8UZTe1o8HkfTNFyuzv3bzzvvPG688ca2GvnZs2dzwQUXcNRR\nR6Hr3XthEjctvkhf6YimMltzuwyNkXlOmhJWW+mMx9A4tMKPQ9cIJy3erI+wMdoe/ks8BlP7ZdcL\nI9H3SJgXQggheimPQ2dssYd9i9ysbUny+dY4gfFTOO/mv/LEX26ibs3KTvcJNzWy6ImHWPTEQ9zh\nz6Fq+pFMP/wIDpt+CLG8YURTiqQFb9XbG01NLPPi6cGOK/X19SxcuJDXX3+dRx99lN/97necffbZ\nnc6rrKzk7LPPxufzcf755zNy5Mhuf4/mhMnnjQlWNycwt9leJ8+lMzTHSQrF2pYUoXSDeF2D6f19\n+Jw660JJltRn9o4fU+RmTC/tHS/2Ln1y06hAIHAwcANwABABXgZ+HgwG63fm68imUaI3kseH2B55\nbPR9Sinqo3aN9/qmGG8+/ShP3HEz9WtXfel9HS43J5/5A869+iZqIu314R7D7tLS379nykSqq6t5\n/fXX2wJ8MBjMuL2qqor333//K18xaPtdbY23tezsqJ/PoNBt0BgzqY127ohzcLmXATlO3t8U44um\nRNtxr0Pj4HIf5b6+MR8qzxu9g2wa1UEgEBgNvAK8BJwOFALXAQsCgcDEYDDYjQ2rhRBCiN5H0zT6\n+Rz08zloKvEQOOtMps35Fh8uepV3XnyKpS8/R6hxS5f3TSXixF1+NsZMKv0O6iNJUkojZiqe/rSa\nwkQjxxwQINfv223jVUqxevXqjPC+evXqHd7nww8/ZPHixUybNm2XvqdpKda0JAk2xmlMZPa+NDTo\n53Pg0KAualIX6Rziy7wG+xS4yXHqLFgfoqlj73i/g0nl0jte9C59LswDPwFqgZNag3sgEFgBvAMc\nCTzXg2MTQgghdot8l8FBZV7GFbsZV3YcU2bOIhRL8vl7b/Lugqd576Vn2NpQm3GfCUceR8JS1IRT\nOHUNt2b3rH//v89z9y/PB6CifyUjhg9j+PDhDBvW/n7o0KEANDU10dzcTHNzM01NTcyePRu3291p\nfPfeey9XXXUVNTU1O/Vz5eXlsWrVqp0O87GUxfL0+oL4NrU0bkOj0GUQMy1qwp1n6b0OjWG5Lobl\nOclx6qxoSvBGXaStJEd6x4verC+G+U+B/20zA996DW9oD4xHCCGE+Nq4DZ39ijyMLrTr6kv8h7Hf\n5Ol878rfs/Ljpbz74tO888J8dBQHHVhFQ8yeabbrvxUK2LCmvUyndkMNtRtqtrur6raqq6uprKzs\ndHzDhg3dCvKGYTB+/HimT5/O9OnTOfzww8nJyenW9wZ799XPG+Os6dD5x6bwGDpuQ6MlYVIXzQz4\nGlCZ42B4nosKn4OEpdgYNXl/Uywj8EvveNHb9bkwHwwG7+ji8HHp95/vytdsrSfbU6LRaI98X5Ed\n5PEhtkceGwJgsIJCZVBjuqkctS+Vo/blG+deSsuWTazZ0oIfExONqNLbNmTqTs399nzwwQc0Nzd3\nOh6JdL1JlcPhYP/992fChAlMnDiRqqoq/H5/2+3r16//0u+pFGxVDjaYbppUZpSxFLg0haUgjMa2\nE+leLMqNBPlakuhmgw82OXhNOYiqzqUz5XqCIckotavsS/59kTxvZL+sCvOBQMAJ7Gg3iPpgMLh1\nm/sMBP4AvAe8+jUOTwghhOhxmgb5mkm+HiGqdDaYbmo1g7ziUgDC2DPMTs2elU8pjZy8fIr6VbK1\nfgM72xgjFAp1ebx1dt3tdlNVVcWECROYMGEC48aNw+Px7NLPZiposFxsMN3EaA/fSoGFhgMLhUYK\njfT/ANBRFGgpvJpJUunUmS7WsP0xGChGGFFKDFlmJ3q/rArzQCWwo5eOFwG3tX6SDvKvADpwWjAY\n3KXWPXt6hbesLBc7Io8PsT3y2BBdORD4+H+fU2+5CPlLtumvrnApOPvaWwGwkknqq9dSu241tevW\n0LBuDZuq17B5/Rq21FXjcLrw5uTiy8nFm36ryx/MgILBlHkNyryOtl1TCwoKmDFjBlVVVV3W1O+M\ncNJieVOcL5oSJC37j7oPSFkKSyk0TcPQaGsTqZTCVHYtvM+hk7AU0ZQilv56rffvqNCtU+p1UJ5+\ncxl7R228PG/0Dq3dbHZFVoX5YDC4hvYX2jsUCATGAM8DTuDIYDDYuRmvEEIIsRdwaooBRpzAkNxt\ndj61y1A0FHFToXQH5UNGUDmsvYe7pRRJy+5Nn7Dsj3XAoWs4dHBoGiubE6xtsTxdxeAAAArcSURB\nVLvtuAyNMo9BmbeIkePKcLp2vfPL5liKzxsTrGtJ0voSRCnVtoGWpoFbt2OBqSBmWigFDh0MTSNp\nkdGNppUGFHkMyrwGpR4HpV5DOtSIrJVVYb67AoHAJOwg3wwcEQwGV/TwkIQQQogep2saQ3JdDM5x\nsjFmLxytDqUADbehodKh3jQVKQuSSmFZtJWs6IBbB0tppCxFwgRTtYdlQwND16gLp3DocQxNw21A\nkdtBiUenxOugyGPg0vX0TLodug2Nti4xllKsDyUJNibYFGttHWm/mIilLEgHeF3TSFiKpoRF0lI4\ndQ2vQ8Pl0NC2mffTNShyG5R7HZR5DUq8Dpz63jHzLvq+PhfmA4HAEOwgXw/MCAaDG3p2REIIIUTv\nomkaZV4HZV4HLUmTYGOCVc0JUpaGJ11e0tq8xVKKlLJLWlKWIqU0UsqutycdxpVqPy+ZUukeOenv\nBdRHTTRIXwXQcOngMnRcuoZTt8djB3v7Pq07rVrpWfi4aaGnQ3/KgqaUhYV9vtfQyXPpGTuxGhoU\ne4y2n7HEY+CQ8C76qD4X5oHbgTzgfGBQIBAY1OG2tcFgsK8uSBdCCCF2Wq7TYEKpl/2LPKxstkN9\nzLRr0c10Yndp4MoIw/ZtdsCHlGp9n75V2U0vLZVenNpWI2P/X9KCSMpMh3u7ZMdt2C8knLpG0lKE\nk5ZdH6/Zob51sl3D3rXW49CxK3g0HBqUpGfdy7wOij0GhvSDF3uJPhXm091uZgMG8GAXp/wcu7ON\nEEIIITpwGRqjC92MLsxcrNq6mNRUdjhv/di00u9V+/ukZRFKKFqSJi1Ji1DSIpy0iKYUqfQ5KaXo\n2DDHwq7FT1iKlmSHDjTpoG+hgWaHfG868LsMjdL0rHuZ16DIbWTMzAuxN+lTYT69UZSzp8chhBBC\n9BWaZs98O7rXf6JLcdOiKWHRGDdpjJtsjJlsjJrETIuEpTC302tOBzwOjTynQaXf0VY2U+jWZSdW\nIdL6VJgXQgghRO/jNnTKvDpl3vbYoZQiklI0JkwaIik2RFJsitkz+i4dKv1ORuS5KPe1t7sUQnQm\nYV4IIYQQe5ymafidGn6nTqXfyQHp4yrdN14I0T3SVFUIIYQQvYYEeSF2joR5IYQQQgghspSEeSGE\nEEIIIbKUhHkhhBBCCCGylIR5IYQQQgghspSEeSGEEEIIIbKUhHkhhBBCCCGylIR5IYQQQgghspSE\neSGEEEIIIbKUhHkhhBBCCCGylIR5IYQQQgghspSEeSGEEEIIIbKUhHkhhBBCCCGylIR5IYQQQggh\nspSEeSGEEEIIIbKUhHkhhBBCCCGylKaU6ukx9FpLly6VX44QQgghhNgjxo8fr+3sfWRmXgghhBBC\niCwlM/NCCCGEEEJkKZmZF0IIIYQQIktJmBdCCCGEECJLSZgXQgghhBAiS0mYF0IIIYQQIktJmBdC\nCCGEECJLSZgXQgghhBAiS0mYF0IIIYQQIktJmBdCCCGEECJLSZgXQgghhBAiS0mYF0IIIYQQIks5\nenoAIlMgEDgHuAwYAHwIXBwMBt/q2VGJ3iYQCMwBHggGg7k9PRbR8wKBgAFcCJwDDALWAncAfwkG\ng6onxyZ6ViAQcAFXAd8FSoAlwKXBYPD9Hh2Y6FUCgYAbO3MsCQaDZ/XwcMROkpn5XiQQCHwPuAu4\nHzgJaAQWBAKBoT06MNGrBAKBg7EfI1pPj0X0GlcCv8V+XMwB/gPcBvy8JwcleoVbgZ8CNwInABHg\nv4FAYHCPjkr0NlcD+/T0IMSukZn5XiIQCGjAtcDdwWDwmvSxl4AgcBH2k7HYi6VnTi4ErgPCgKtn\nRyR6g0AgoAMXAzcHg8Eb0odfCQQCpcClwE09NjjRowKBQD721ZpfBoPBO9PHFgGbsWfqr+/B4Yle\nIhAIHICdMTb19FjErpGZ+d5jBDAYeKr1QDAYTALPAkf31KBEr3IMcDn2bOufengsovfIB+4DHt/m\neBAoDQQC/j0/JNFLhIFJwD86HEsCCnD3yIhErxIIBBzAvcDNQE0PD0fsIpmZ7z1Gpd9/sc3xVcDw\nQCBgBINBcw+PSfQu7wJDg8FgYyAQ+E1PD0b0DsFgcCvwky5uOg6oDgaD4T08JNFLBIPBFPABtF3B\nGQxcgx3m7+/BoYne4xfYV3l/h12GJbKQhPneIy/9vmWb4y3YV1D8QPMeHZHoVYLBoMyaiG4JBAI/\nBGYi5Xmi3ZXAb9IfXxUMBoM9OBbRCwQCgX2AXwEzgsFgIhAI9PSQxC6SMpveo3Ux47adJ1qPW3tw\nLEKILBUIBM7AXkg/D/hzDw9H9B5PAIdhz8xfFQgEruvZ4YielL5Scw9wj3TMy34yM997NKXf5wL1\nHY7nYAd5uVQuhNihQCBwEXAL9tqbM6QtpWgVDAY/Tn/4eiAQyAV+HggErk2vzRJ7nwuwy66+ka6b\nb6UFAgFHukRLZAmZme89VqTfD9vm+DAgKH+UhRA7EggEfgv8Efg3cHIwGEz08JBEDwsEAv0CgcD3\n0+G9ow+wF8AW98CwRO9wAlAJbMFeFJ0ExgHfA5KBQGBIzw1N7CyZme89VgDrgW8CLwIEAgEncCx2\nRxshhOhSIBC4ELvT0e3ARfLiX6QVYHcqgcyONkcBDek3sXf6P+xKgI4eAJZjl2Jt2OMjErtMwnwv\nEQwGVSAQuBH4cyAQ2Aq8gd2hogR70w8hhOgkEAhUAL8HlgEPA5O2Wcj2nlwy3zsFg8HPA4HAY8At\n6Z1gVwEnYveYPzsYDMparL1UVwugA4FAFNgcDAbf64Ehia9AwnwvEgwG7wgEAl7sjYEuwt5aeVYw\nGFzVsyMTQvRis7BLJsYCXS1kK0U2g9mbfQ97d8/LgQrgf8C3gsHgvB4dlRBit9GUkquxQgghhBBC\nZCNZACuEEEIIIUSWkjAvhBBCCCFElpIwL4QQQgghRJaSMC+EEEIIIUSWkjAvhBBCCCFElpIwL4QQ\nQgghRJaSMC+EEEIIIUSWkjAvhBBCCCFElpIwL4QQQgghRJaSMC+EEEIIIUSWkjAvhBBCCCFElpIw\nL4QQQgghRJaSMC+EEEIIIUSWkjAvhBBCCCFElnL09ACEEEJkt0AgkAusBOqBccFg0EofdwMvAeOA\n6cFg8KOeG6UQQvRNMjMvhBDiKwkGgy3Ab4ExwKkAgUBABx4ADgK+KUFeCCG+HppSqqfHIIQQIsul\nZ+GDQBzYF7gVOB84LRgMPtqTYxNCiL5MwrwQQojdIhAInAX8A3gFmAFcEAwG/9yjgxJCiD5OymyE\nEELsLvcB67GD/I0S5IUQ4usnYV4IIcTu8iNgYPrjpp4ciBBC7C2kzEYIIcRXFggETgDmAX8DAkAV\nMCwYDG7t0YEJIUQfJzPzQgghvpJAIDANeBB4GnvR65VAAfCLnhyXEELsDWRmXgghxC4LBAL7AYuA\nz4CZwWAwmj6+AJgGDA8Gg7U9OEQhhOjTZGZeCCHELgkEAgOBF7A3izquNcinXQl4gat7YmxCCLG3\nkJl5IYQQQgghspTMzAshhBBCCJGlJMwLIYQQQgiRpSTMCyGEEEIIkaUkzAshhBBCCJGlJMwLIYQQ\nQgiRpSTMCyGEEEIIkaUkzAshhBBCCJGlJMwLIYQQQgiRpSTMC/H/7dYBCQAAAICg/6/bEegKAQCm\nZB4AAKZkHgAApmQeAACmZB4AAKZkHgAApmQeAACmZB4AAKZkHgAApgIuA219LyvGlAAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114136320>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 257,
       "width": 377
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "degree, n_samples, n_models = 4, 20, 5\n",
    "avg_y = np.zeros(n_samples)\n",
    "for i in range(n_models):\n",
    "    (x,y) = sample(n_samples)\n",
    "    model = fit_polynomial(x, y, degree)\n",
    "    p_y = apply_polynomial(model, x)\n",
    "    avg_y = avg_y + p_y\n",
    "    plt.plot(x, p_y, c='skyblue', alpha=0.75)\n",
    "avg_y = avg_y / n_models\n",
    "plt.plot(x, avg_y, 'b-', linewidth=2, label='Average model')\n",
    "plt.plot(f_x, f_y, 'k--', linewidth=2, label='Real (unknown) function')\n",
    "plt.xlabel('$x$')\n",
    "plt.ylabel('$f(x)$')\n",
    "plt.legend(frameon=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Calculating bias and variance\n",
    "-----------------------------\n",
    "\n",
    "Same as previous example, we generate several samples and fit a polynomial to each one. We calculate bias an variance among models for different polynomial degrees. Bias, variance and error are plotted against different degree values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_samples = 20\n",
    "f_x, f_y = f(n_samples)\n",
    "n_models = 100\n",
    "max_degree = 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "var_vals = []\n",
    "bias_vals = []\n",
    "error_vals = []\n",
    "for degree in range(1, max_degree):\n",
    "    avg_y = np.zeros(n_samples)\n",
    "    models = []\n",
    "    for i in range(n_models):\n",
    "        x, y = sample(n_samples)\n",
    "        model = fit_polynomial(x, y, degree)\n",
    "        p_y = apply_polynomial(model, x)\n",
    "        avg_y = avg_y + p_y\n",
    "        models.append(p_y)\n",
    "    avg_y = avg_y / n_models\n",
    "    bias_2 = np.linalg.norm(avg_y - f_y) / f_y.size\n",
    "    bias_vals.append(bias_2)\n",
    "    variance = 0\n",
    "    for p_y in models:\n",
    "        variance += np.linalg.norm(avg_y - p_y)\n",
    "    variance /= f_y.size * n_models\n",
    "    var_vals.append(variance)\n",
    "    error_vals.append(variance + bias_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1147089b0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Du2fD3qz49ta336ajREMzDsSO+y7jo3ueHLrvyaH7Pvl0z5MjHe97urT4z4i9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\npAgo8BcRERERKQL/A6wcNpomApaHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114151240>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 257,
       "width": 383
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1, max_degree), bias_vals, label='bias')\n",
    "plt.plot(range(1, max_degree), var_vals, label='variance')\n",
    "plt.plot(range(1, max_degree), error_vals, label='error')\n",
    "plt.xlabel('Polynomial degree')\n",
    "plt.ylabel('Metric value')\n",
    "plt.legend(frameon=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<div class=\"container-fluid\">\n",
    "  <div class=\"row\">\n",
    "      <div class=\"col-md-3\"><span/></div>\n",
    "      <div class=\"col-md-6\">\n",
    "              <img src='http://scott.fortmann-roe.com/docs/docs/MeasuringError/ModelError.png'/>\n",
    "      </div>\n",
    "      <div class=\"col-md-3\" align='center'><span/></div>\n",
    "  </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Model assessment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "The simplest of these techniques is the holdout set method.\n",
    "\n",
    "* Split data into two groups. \n",
    "* one group will be used to train the model; the second group will be used to measure the resulting model's error. For instance, if we had 1000 observations, we might use 700 to build the model and the remaining 300 samples to measure that model's error."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "<div class=\"container-fluid\">\n",
    "  <div class=\"row\">\n",
    "      <div class=\"col-md-3\"><span/></div>\n",
    "      <div class=\"col-md-6\">\n",
    "              <img src='http://scott.fortmann-roe.com/docs/docs/MeasuringError/holdout.png'/>\n",
    "      </div>\n",
    "      <div class=\"col-md-3\" align='center'><span/></div>\n",
    "  </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Using the digits dataset for in a holdout case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import datasets\n",
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1078, 64), (1078,))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((719, 64), (719,))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Using a support vector classifier to deal with the digits problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "clf = svm.SVC(kernel='linear', C=0.01).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97496522948539643"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "When evaluating different settings (hyper-parameters) for estimators, such as the $C$ setting that must be manually set for an SVM, there is still a risk of **overfitting** on the test set because the parameters can be tweaked until the estimator performs optimally. \n",
    "\n",
    "This way, knowledge about the test set can \"leak\" into the model and evaluation metrics no longer report on generalization performance. \n",
    "\n",
    "To solve this problem, yet another part of the dataset can be held out as a so-called **validation set**: training proceeds on the training set, after which evaluation is done on the validation set, and when the experiment seems to be successful, final evaluation can be done on the test set.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Cross-validation\n",
    "\n",
    "* A solution to this problem is a procedure called cross-validation. \n",
    "* A test set should still be held out for final evaluation, but the validation set is no longer needed when doing CV. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "In the basic approach, called $k$-fold cross-validation:\n",
    "* the training set is split into $k$ train/test sets.\n",
    "* model is trained using $k-1$ of the folds as training data;\n",
    "* the resulting model is validated on the remaining part of the data (i.e., it is used as a test set to compute a performance measure such as accuracy)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<div class=\"container-fluid\">\n",
    "  <div class=\"row\">\n",
    "      <div class=\"col-md-3\"><span/></div>\n",
    "      <div class=\"col-md-6\">\n",
    "              <img src='http://scott.fortmann-roe.com/docs/docs/MeasuringError/crossvalidation.png'/>\n",
    "      </div>\n",
    "      <div class=\"col-md-3\" align='center'><span/></div>\n",
    "  </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's apply cross-validation to our previous experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = svm.SVC(kernel='linear', C=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "scores = cross_val_score(clf, digits.data, digits.target, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.96428571,  0.92265193,  0.96657382,  0.9719888 ,  0.92957746])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Accuracy: 0.951016 (± 0.041203)'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Accuracy: {0:2f} (± {1:2f})'.format(scores.mean(), scores.std() * 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "By default, the score computed at each CV iteration is the score method of the estimator. It is possible to change this by using the scoring parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = cross_val_score(clf, digits.data, digits.target, cv=5, scoring='f1_macro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.96439803,  0.9219161 ,  0.96592476,  0.97173493,  0.92962078])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# What happens when we want to compare different models?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "clf_lin = svm.SVC(kernel='linear', C=0.005).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "clf_rbf = svm.SVC(kernel='rbf', C=0.005).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "clf_poly = svm.SVC(kernel='poly', C=0.005).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "scores_lin = cross_val_score(clf_lin, digits.data, digits.target, scoring='accuracy', cv=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "scores_rbf = cross_val_score(clf_rbf, digits.data, digits.target, scoring='accuracy', cv=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "scores_poly = cross_val_score(clf_poly, digits.data, digits.target, scoring='accuracy', cv=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.92631579,  0.97849462,  0.98901099,  1.        ,  0.97777778,\n",
       "         0.96666667,  0.94444444,  0.97777778,  0.94444444,  0.96666667,\n",
       "         1.        ,  1.        ,  0.95555556,  0.97777778,  0.98876404,\n",
       "         1.        ,  0.98876404,  0.92045455,  0.97701149,  0.97674419]),\n",
       " array([ 0.10526316,  0.10752688,  0.16483516,  0.1       ,  0.1       ,\n",
       "         0.1       ,  0.1       ,  0.1       ,  0.1       ,  0.1       ,\n",
       "         0.1       ,  0.1       ,  0.1       ,  0.1       ,  0.1011236 ,\n",
       "         0.1011236 ,  0.1011236 ,  0.10227273,  0.10344828,  0.10465116]),\n",
       " array([ 0.94736842,  0.98924731,  1.        ,  1.        ,  0.98888889,\n",
       "         0.97777778,  0.98888889,  1.        ,  1.        ,  0.97777778,\n",
       "         1.        ,  1.        ,  0.97777778,  0.98888889,  1.        ,\n",
       "         0.98876404,  1.        ,  0.94318182,  0.97701149,  0.97674419]))"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_lin, scores_rbf, scores_poly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Representing results in a more readable way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(data=np.vstack((scores_lin, scores_rbf, scores_poly))).T\n",
    "data.columns = ('Linear', 'Radial', 'Polynomial')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>Linear</th>\n",
       "      <th>Radial</th>\n",
       "      <th>Polynomial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.926316</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.947368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.978495</td>\n",
       "      <td>0.107527</td>\n",
       "      <td>0.989247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.989011</td>\n",
       "      <td>0.164835</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.977778</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.988889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Linear    Radial  Polynomial\n",
       "0  0.926316  0.105263    0.947368\n",
       "1  0.978495  0.107527    0.989247\n",
       "2  0.989011  0.164835    1.000000\n",
       "3  1.000000  0.100000    1.000000\n",
       "4  0.977778  0.100000    0.988889"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Analyzing the results: Tabular form."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "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>Linear</th>\n",
       "      <th>Radial</th>\n",
       "      <th>Polynomial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.972834</td>\n",
       "      <td>0.104568</td>\n",
       "      <td>0.986116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.023790</td>\n",
       "      <td>0.014351</td>\n",
       "      <td>0.016711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.920455</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.943182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.963889</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.977778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.977778</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.988889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.988826</td>\n",
       "      <td>0.102567</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.164835</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Linear     Radial  Polynomial\n",
       "count  20.000000  20.000000   20.000000\n",
       "mean    0.972834   0.104568    0.986116\n",
       "std     0.023790   0.014351    0.016711\n",
       "min     0.920455   0.100000    0.943182\n",
       "25%     0.963889   0.100000    0.977778\n",
       "50%     0.977778   0.100000    0.988889\n",
       "75%     0.988826   0.102567    1.000000\n",
       "max     1.000000   0.164835    1.000000"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Analyzing the results: Graphical form."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QWbNmsX79er7xjW/w2muv5TqsY5ZKpSjL9NUYM2EGzz95f8+2ffv25TAySZIk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\nJEmSpG6DYjmmGGM6hPC3wD+GEPYBTwE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      "text/plain": [
       "<matplotlib.figure.Figure at 0x114edaac8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 282,
       "width": 784
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(11,4))\n",
    "plt.subplot(121, title='Violin plots')\n",
    "seaborn.violinplot(data=data, palette='Set2')\n",
    "plt.ylabel('Accuracy'); plt.xlabel('Kernel')\n",
    "plt.subplot(122, title='Box plots')\n",
    "seaborn.boxplot(data=data, palette='Set2')\n",
    "plt.ylabel('Accuracy'); plt.xlabel('Kernel')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "* Choosing the correct statistical test is essential to properly report the results.\n",
    "* [Nonparametric statistics](http://en.wikipedia.org/wiki/Nonparametric_statistics) can lend a helping hand.\n",
    "* [Parametric statistics](http://en.wikipedia.org/wiki/Parametric_statistics) could be a better choice in some cases. \n",
    "* Parametric statistics require that *all* data follow a known distribution (frequently a normal one).\n",
    "* Some tests -like the [normality test](http://en.wikipedia.org/wiki/Normality_test)- can be apply to verify that data meet the parametric stats requirements.\n",
    "* In my experience that is very unlikely that all your result meet those characteristics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The [Kruskal-Wallis H-test](http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.kruskal.html) tests the null hypothesis that the population median of all of the groups are equal.\n",
    "\n",
    "* It is a non-parametric version of [ANOVA](http://en.wikipedia.org/wiki/Analysis_of_variance). \n",
    "* The test works on 2 or more independent samples, which may have different sizes. \n",
    "* Note that rejecting the null hypothesis does not indicate which of the groups differs. \n",
    "* Post-hoc comparisons between groups are required to determine which groups are different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KruskalResult(statistic=41.713198148618517, pvalue=8.7517340303375917e-10)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.kruskal(*[data[col] for col in data.columns])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We now can assert that the results are not the same but...\n",
    "* ...which ones are different or similar to the others the others?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "In case that the null hypothesis of the Kruskal-Wallis is rejected the Conover–Inman procedure (Conover, 1999, pp. 288-290) can be applied in a pairwise manner in order to determine if the results of one algorithm were significantly better than those of the other.\n",
    "\n",
    "* Conover, W. J. (1999). *Practical Nonparametric Statistics*. John Wiley & Sons, New York, 3rd edition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def conover_inman_procedure(data, alpha=0.05):\n",
    "    num_runs = len(data)\n",
    "    num_algos = len(data.columns)\n",
    "    N = num_runs*num_algos\n",
    "\n",
    "    _,p_value = stats.kruskal(*[data[col] for col in data.columns])\n",
    "    \n",
    "    ranked =  stats.rankdata(np.concatenate([data[col] for col in data.columns]))\n",
    "    \n",
    "    ranksums = []\n",
    "    for i in range(num_algos):\n",
    "        ranksums.append(np.sum(ranked[num_runs*i:num_runs*(i+1)]))\n",
    "\n",
    "    S_sq = (np.sum(ranked**2) - N*((N+1)**2)/4)/(N-1)\n",
    "\n",
    "    right_side = stats.t.cdf(1-(alpha/2), N-num_algos) * \\\n",
    "                 math.sqrt((S_sq*((N-1-p_value)/(N-1)))*2/num_runs)\n",
    "    \n",
    "    res = pd.DataFrame(columns=data.columns, index=data.columns)\n",
    "\n",
    "    for i,j in itertools.combinations(np.arange(num_algos),2):\n",
    "        res[res.columns[i]].iloc[j] = abs(ranksums[i] - ranksums[j]/num_runs) > right_side\n",
    "        res[res.columns[j]].iloc[i] = abs(ranksums[i] - ranksums[j]/num_runs) > right_side\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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>Linear</th>\n",
       "      <th>Radial</th>\n",
       "      <th>Polynomial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear</th>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radial</th>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Polynomial</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Linear Radial Polynomial\n",
       "Linear        NaN   True       True\n",
       "Radial       True    NaN       True\n",
       "Polynomial   True   True        NaN"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conover_inman_procedure(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## But... how to properly set the hyper-parameters\n",
    "\n",
    "* Hyper-parameters are parameters that are not directly learned within estimators.\n",
    "* Typical examples include $C$, kernel and $\\gamma$ for support vector classifiers, learning rate for neural networks, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "In order to do this search we need:\n",
    "* an estimator (regressor or classifier such as `sklearn.svm.SVC()`);\n",
    "* a parameter space that specifies what set of values the hyper-parameters take;\n",
    "* a method for searching or sampling candidates;\n",
    "* a cross-validation scheme; and\n",
    "* a score function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Grid search\n",
    "\n",
    "The traditional way of performing hyper-parameter optimization has been *grid search* , or *parameter sweep*.\n",
    "* An exhaustive searching through a manually specified subset of the hyper-parameter space of a learning algorithm. \n",
    "* Must be guided by some performance metric, typically measured by cross-validation on the training set or evaluation on a held-out validation set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's program an example of using grid search. Using SVCs again, but with a more complex problem: digits.\n",
    "\n",
    "Loading and preparing the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()\n",
    "n_samples = len(digits.images)\n",
    "X = digits.images.reshape((n_samples, -1))\n",
    "y = digits.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We must start by defining our search space:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = [\n",
    "  {'C': [0.1, 1, 10, 100, 1000], 'kernel': ['linear']},\n",
    "  {'C': [0.1, 1, 10, 100, 1000], 'gamma': [0.001, 0.0001], 'kernel': ['rbf']},\n",
    "  {'C': [0.1, 1, 10, 100, 1000], 'kernel': ['poly']},\n",
    " ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Split the dataset in two equal parts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tuning hyper-parameters for **accuracy**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import classification_report\n",
    "from multiprocessing import cpu_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Preparing the grid search. Note how simple is to make it run in parallel (`n_jobs`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "folds = 30\n",
    "clf = GridSearchCV(svm.SVC(), param_grid, cv=folds, scoring='accuracy', n_jobs=cpu_count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=30, error_score='raise',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params=None, iid=True, n_jobs=4,\n",
       "       param_grid=[{'C': [0.1, 1, 10, 100, 1000], 'kernel': ['linear']}, {'C': [0.1, 1, 10, 100, 1000], 'gamma': [0.001, 0.0001], 'kernel': ['rbf']}, {'C': [0.1, 1, 10, 100, 1000], 'kernel': ['poly']}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "means = clf.cv_results_['mean_test_score']\n",
    "stds = clf.cv_results_['std_test_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.976 (+/-0.043) for {'C': 0.1, 'kernel': 'linear'}\n",
      "0.976 (+/-0.043) for {'C': 1, 'kernel': 'linear'}\n",
      "0.976 (+/-0.043) for {'C': 10, 'kernel': 'linear'}\n",
      "0.976 (+/-0.043) for {'C': 100, 'kernel': 'linear'}\n",
      "0.976 (+/-0.043) for {'C': 1000, 'kernel': 'linear'}\n",
      "0.964 (+/-0.068) for {'C': 0.1, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.907 (+/-0.099) for {'C': 0.1, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.988 (+/-0.026) for {'C': 1, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.968 (+/-0.056) for {'C': 1, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.989 (+/-0.025) for {'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.983 (+/-0.033) for {'C': 10, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.989 (+/-0.025) for {'C': 100, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.984 (+/-0.035) for {'C': 100, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.989 (+/-0.025) for {'C': 1000, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.984 (+/-0.035) for {'C': 1000, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.987 (+/-0.031) for {'C': 0.1, 'kernel': 'poly'}\n",
      "0.987 (+/-0.031) for {'C': 1, 'kernel': 'poly'}\n",
      "0.987 (+/-0.031) for {'C': 10, 'kernel': 'poly'}\n",
      "0.987 (+/-0.031) for {'C': 100, 'kernel': 'poly'}\n",
      "0.987 (+/-0.031) for {'C': 1000, 'kernel': 'poly'}\n"
     ]
    }
   ],
   "source": [
    "for mean, std, params in zip(means, stds, clf.cv_results_['params']):\n",
    "    print(\"%0.3f (+/-%0.03f) for %r\" % (mean, std * 2, params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "scores = [clf.cv_results_['split{0}_test_score'.format(i)] for i in range(folds)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(data=scores)\n",
    "data.columns = ['Case {}'.format(i) for i in range(len(data.columns))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style>\n",
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       "        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>Case 0</th>\n",
       "      <th>Case 1</th>\n",
       "      <th>Case 2</th>\n",
       "      <th>Case 3</th>\n",
       "      <th>Case 4</th>\n",
       "      <th>Case 5</th>\n",
       "      <th>Case 6</th>\n",
       "      <th>Case 7</th>\n",
       "      <th>Case 8</th>\n",
       "      <th>Case 9</th>\n",
       "      <th>Case 10</th>\n",
       "      <th>Case 11</th>\n",
       "      <th>Case 12</th>\n",
       "      <th>Case 13</th>\n",
       "      <th>Case 14</th>\n",
       "      <th>Case 15</th>\n",
       "      <th>Case 16</th>\n",
       "      <th>Case 17</th>\n",
       "      <th>Case 18</th>\n",
       "      <th>Case 19</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.938462</td>\n",
       "      <td>0.938462</td>\n",
       "      <td>0.938462</td>\n",
       "      <td>0.938462</td>\n",
       "      <td>0.938462</td>\n",
       "      <td>0.953846</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.969231</td>\n",
       "      <td>0.938462</td>\n",
       "      <td>0.969231</td>\n",
       "      <td>0.953846</td>\n",
       "      <td>0.969231</td>\n",
       "      <td>0.938462</td>\n",
       "      <td>0.969231</td>\n",
       "      <td>0.938462</td>\n",
       "      <td>0.953846</td>\n",
       "      <td>0.953846</td>\n",
       "      <td>0.953846</td>\n",
       "      <td>0.953846</td>\n",
       "      <td>0.953846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.984127</td>\n",
       "      <td>0.984127</td>\n",
       "      <td>0.984127</td>\n",
       "      <td>0.984127</td>\n",
       "      <td>0.984127</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.841270</td>\n",
       "      <td>0.984127</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.984127</td>\n",
       "      <td>0.984127</td>\n",
       "      <td>0.984127</td>\n",
       "      <td>0.984127</td>\n",
       "      <td>0.984127</td>\n",
       "      <td>0.984127</td>\n",
       "      <td>0.984127</td>\n",
       "      <td>0.984127</td>\n",
       "      <td>0.984127</td>\n",
       "      <td>0.984127</td>\n",
       "      <td>0.984127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.983607</td>\n",
       "      <td>0.983607</td>\n",
       "      <td>0.983607</td>\n",
       "      <td>0.983607</td>\n",
       "      <td>0.983607</td>\n",
       "      <td>0.983607</td>\n",
       "      <td>0.885246</td>\n",
       "      <td>0.983607</td>\n",
       "      <td>0.983607</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.983607</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.983607</td>\n",
       "      <td>0.983607</td>\n",
       "      <td>0.983607</td>\n",
       "      <td>0.983607</td>\n",
       "      <td>0.983607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.983333</td>\n",
       "      <td>0.983333</td>\n",
       "      <td>0.983333</td>\n",
       "      <td>0.983333</td>\n",
       "      <td>0.983333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.983333</td>\n",
       "      <td>0.983333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.983333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Case 0    Case 1    Case 2    Case 3    Case 4    Case 5    Case 6  \\\n",
       "0  0.938462  0.938462  0.938462  0.938462  0.938462  0.953846  0.923077   \n",
       "1  0.984127  0.984127  0.984127  0.984127  0.984127  0.888889  0.841270   \n",
       "2  0.983607  0.983607  0.983607  0.983607  0.983607  0.983607  0.885246   \n",
       "3  0.983333  0.983333  0.983333  0.983333  0.983333  1.000000  0.916667   \n",
       "4  1.000000  1.000000  1.000000  1.000000  1.000000  0.983333  0.983333   \n",
       "\n",
       "     Case 7    Case 8    Case 9   Case 10   Case 11   Case 12   Case 13  \\\n",
       "0  0.969231  0.938462  0.969231  0.953846  0.969231  0.938462  0.969231   \n",
       "1  0.984127  0.952381  0.984127  0.984127  0.984127  0.984127  0.984127   \n",
       "2  0.983607  0.983607  1.000000  0.983607  1.000000  1.000000  1.000000   \n",
       "3  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "4  1.000000  0.983333  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "\n",
       "    Case 14   Case 15   Case 16   Case 17   Case 18   Case 19  \n",
       "0  0.938462  0.953846  0.953846  0.953846  0.953846  0.953846  \n",
       "1  0.984127  0.984127  0.984127  0.984127  0.984127  0.984127  \n",
       "2  1.000000  0.983607  0.983607  0.983607  0.983607  0.983607  \n",
       "3  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  \n",
       "4  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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iB2PMh4C7gUAvy40AngJWAOcBxwNXAB24SQeMMTOBx4GHgV8A/wT8yRiz31q7ILbMR4EF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W0zFZ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x114e60cf8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 280,
       "width": 784
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(11,4))\n",
    "plt.subplot(121, title='Violin plots')\n",
    "seaborn.violinplot(data=data, palette='Set2')\n",
    "plt.ylabel('Accuracy'); plt.xlabel('Configuration'); plt.xticks(rotation=90)\n",
    "plt.subplot(122, title='Box plots')\n",
    "seaborn.boxplot(data=data, palette='Set2')\n",
    "plt.ylabel('Accuracy'); plt.xlabel('Configuration'); plt.xticks(rotation=90)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# This approach can be improved!\n",
    "\n",
    "* How to prevent doing exhaustive search? \n",
    "* In many dimensions exhaustive search is impossible (NP-hard).\n",
    "* We may know what areas of the search space are good or bad as the search process goes by.\n",
    "* Metaheuristic approaches like evolutionary algorithms are the solution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<hr/>\n",
    "<div class=\"container-fluid\">\n",
    "  <div class='well'>\n",
    "      <div class=\"row\">\n",
    "          <div class=\"col-md-3\" align='center'>\n",
    "              <img align='center'alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\"/>\n",
    "          </div>\n",
    "          <div class=\"col-md-9\">\n",
    "              This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/).\n",
    "          </div>\n",
    "      </div>\n",
    "  </div>\n",
    "</div>\n",
    "* Part of this notebook uses some materials of the [scikit learn tutorial](https://github.com/jakevdp/sklearn_tutorial/), Copyright (c) 2015, [Jake Vanderplas](http://www.vanderplas.com)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
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       ".reveal h1 {\n",
       "font-family: 'Titillium Web', sans-serif;\n",
       "/*font-size: 24pt;*/\n",
       "font-weight: bold;\n",
       "margin-bottom: 0.1em;\n",
       "margin-top: 0.5em;\n",
       "color:#4a4a4a;\n",
       "}\n",
       ".text_cell_render h2 {\n",
       "/*font-size: 21pt;*/\n",
       "    font-family: 'Titillium Web', sans-serif;\n",
       "margin-bottom: 0.1em;\n",
       "margin-top: 0.3em;\n",
       "color:#595959;\n",
       "}\n",
       ".text_cell_render h3 {\n",
       "    font-family: 'Titillium Web', sans-serif;\n",
       "/*font-size: 19pt;*/\n",
       "margin-bottom: 0.1em;\n",
       "margin-top: 0.3em;\n",
       "color:#595959;\n",
       "}\n",
       ".text_cell_render h4 {\n",
       "    font-family: 'Titillium Web', sans-serif;\n",
       "/*font-size: 17pt;*/\n",
       "margin-bottom: 0.1em;\n",
       "margin-top: 0.3em;\n",
       "color:#595959;\n",
       "}\n",
       ".text_cell_render h5 {\n",
       "    font-family: 'Titillium Web', sans-serif;\n",
       "/*font-size: 15pt;*/\n",
       "margin-bottom: 0.1em;\n",
       "margin-top: 0.3em;\n",
       "color:#595959;\n",
       "}\n",
       "div.text_cell_render{\n",
       "line-height: 120%;\n",
       "font-size: 100%;\n",
       "font-weight: 400;\n",
       "text-align: justify;\n",
       "margin-left:0em;\n",
       "margin-right:0em;\n",
       "}\n",
       ".reveal div.text_cell_render{\n",
       "line-height: 120%;\n",
       "font-size: 74%;\n",
       "font-weight: 400;\n",
       "text-align: justify;\n",
       "margin-left:0em;\n",
       "margin-right:0em;\n",
       "}\n",
       "\n",
       ".reveal h2 {\n",
       "font-family: 'Titillium Web', sans-serif;\n",
       "/*font-size: 24pt;*/\n",
       "font-weight: bold;\n",
       "margin-bottom: 0.1em;\n",
       "margin-top: 0.5em;\n",
       "color:#595959;\n",
       "}\n",
       ".reveal h3 {\n",
       "font-family: 'Titillium Web', sans-serif;\n",
       "/*font-size: 24pt;*/\n",
       "font-weight: bold;\n",
       "margin-bottom: 0.1em;\n",
       "margin-top: 0.5em;\n",
       "color:#595959;\n",
       "}\n",
       ".reveal h4 {\n",
       "font-family: 'Titillium Web', sans-serif;\n",
       "font-weight: bold;\n",
       "margin-bottom: 0.1em;\n",
       "margin-top: 0.5em;\n",
       "color:#595959;\n",
       "}\n",
       ".reveal .code_cell {\n",
       "    font-size: 92%;\n",
       "}\n",
       ".reveal code {\n",
       "font-family: 'Inconsolata', monospace;\n",
       "}\n",
       ".reveal pre {\n",
       "font-family: 'Inconsolata', monospace;\n",
       "}\n",
       "code {\n",
       "font-family: 'Inconsolata', monospace;\n",
       "}\n",
       "pre {\n",
       "font-family: 'Inconsolata', monospace;\n",
       "}\n",
       ".CodeMirror{\n",
       "font-family: \"Inconsolata\", monospace;\n",
       "}\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this code is here for cosmetic reasons\n",
    "from IPython.core.display import HTML\n",
    "from urllib.request import urlopen\n",
    "HTML(urlopen('https://raw.githubusercontent.com/lmarti/jupyter_custom/master/custom.include').read().decode('utf-8'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "--- "
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "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": "3.6.3"
  },
  "widgets": {
   "state": {
    "144e2dcabe02443984dd739ea34aa816": {
     "views": [
      {
       "cell_index": 43
      }
     ]
    },
    "69b948d56538456db1c9c5e53da1a41f": {
     "views": [
      {
       "cell_index": 53
      }
     ]
    },
    "aa39474074874247a5d79b941e4e9df4": {
     "views": [
      {
       "cell_index": 74
      }
     ]
    }
   },
   "version": "1.2.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}