6-Sol.StatisticalLearning-PolynomialRegressionOverfit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](https://pythonawesome.com/content/images/2018/05/scikit-learn.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries we will need in the following\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.io as sio\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In statistics, several methods to search for relationships among variables goes under the name of __regression__."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Polynomial Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Further than a simple linear relation between two quantities, one can look for a regression law relating several features to a continuous value, or even looking for something more complex than a linear fit.\n",
    "The simplest way to do so is to make use of [_polynomials_](https://en.wikipedia.org/wiki/Polynomial)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, we want to study the trend of water level of a bathub as a function of the water falling off from a dam. We load data from a file and collect them into a known and loved pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = sio.loadmat('Data/PolReg/dataset.mat') # load a .mat file. Recall the function loadmat returns a dictonary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Give names to variables\n",
    "\n",
    "X = dataset['X']\n",
    "X_test = dataset['Xtest']\n",
    "X_val = dataset['Xval']\n",
    "y = dataset['y']\n",
    "y_test = dataset['ytest']\n",
    "y_val = dataset['yval']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why all these variables? \n",
    "We need only `X` and `y`, don't we?\n",
    "\n",
    "Exploring the role of the other quantities is the aim of this lecture.\n",
    "\n",
    "For the moment being, let's focus on the pandas DataFrame containing values stores in `X` and `y`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "measures = pd.DataFrame({'Change in water level' : X.flatten(), 'Water flowing out of the dam': y.flatten()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "measures = measures.sort_values(by = ['Change in water level']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__questions:__ Why do we sort values? Is it strictly necessary?\n",
    "\n",
    "Try to ignore the executution the cell above, and see whether something changes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot data, with trend polynomial of several degrees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# complete the function\n",
    "def plot_poly(x, y, xlabel, ylabel, max_degree = 10):\n",
    "    \"\"\"\n",
    "        :return: plots of data in measures, with axis legends given by xlabel and ylabel, and polynomial trend curves\n",
    "        from degree 0 to max_degree (default = 10).\n",
    "    \"\"\"\n",
    "    for k in range(max_degree + 1):\n",
    "        measures.plot(kind='scatter', x = xlabel, y = ylabel, color='blue', figsize=(7,5))\n",
    "        z = np.polyfit(x, y, k)\n",
    "        p = np.poly1d(z)\n",
    "        plt.plot(x, p(x), \"r--\", label='polynomial of degree %s'%k)\n",
    "        pst = plt.legend(loc='lower right', frameon=True)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = measures['Change in water level']\n",
    "y = measures['Water flowing out of the dam']\n",
    "xlabel = 'Change in water level'\n",
    "ylabel = 'Water flowing out of the dam'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call the function defined above to plot curve trends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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a8C+VeXSvEBZeNqAW0AyYB7Qtd4QiIuny7bfw0EOhmfKrr8JCy6efDnXrhptUeamsjNK++HMz60TouxMRideLL8I114SFlo85Bp59Fo44Iu6opIJJaWWU4qIdww/LRDAiImVyh82bYZddwjy4+vXhn/+Ek0/WpqeyXWUmOjP7bbGn1YBOwBcZi0hEpCRvvBG2yznjDLjuurCqSa9emgcnpUrlp6NesduuhD670zIZlIjINqZOhW7d4Gc/g0WLvh9BWa2akpyUKZU+uoGwdadw3H1NpoMSEdlq0KCw6HLDhmHpriuuCGtTiqQolabLdsBjQMPo+TLgAnefleHYRKSqWrwYdt0VcnLg+OPDyMprr4U99og7MqmEUqnzDwd+6+5N3b0pcG1UJiKSXsuWwe9+BwceCAMHhrIjjgibnyrJSTmlMuqyjrtP3PLE3d8wszoZjElEqprVq+Gvf4U//zlM/D7vvJDwRNIglUS30MxuITRfAvwKWJi5kESkyrnuOnjwwTCactCgsLqJSJqk0nTZB8gBngWeAfaKykREymfz5rAW5ZZtcwYMgPffDxO+leQkzcpMdO6+wt2vcvdO7t7Z3a9x9xXZCE5EEsY9JLP27eGii2DkyFDetCkcemi8sUliaQKKiGTHG2/A4YfDmWeGhPf00/CXv8QdlVQBO7wEmIhIuYwfD19+CY88Egab7KJfP5IdqtGJSGbMmQM9e8LLL4fnN94I8+aFJssKlOQKC2Hy5HAvyZTKhPHB2yleBeS7+wvpD0lEKrVFi+D22+Hvf4c6deAXvwjldSrerKSxY6FvX6hZM8xJHzEiLJ0pyZJKja4W0BGYH906AI2BvmZ2XwZjE5HK5s47oWXLkEH694eFC0MmqYAKC0No69fDqlXhvm9f1eySKJX2gw7Ake6+GcDMhgFvAUcBMzMYm4hUBt98A7VqhWrRvvvC+efDrbdCkyZxR1aqgoIQ8vr135fVqBHKc3LiikoyIZUaXQOg+Da9dYCGUeLbmJGoRKTiW78+jJps3jzs8A3Qp094XMGTHEBubmiuLG7TplAuyZJKovsTMN3MRprZKGAacE+0DNiETAYnIhVQURE8/DC0aBGW6crLq5S7eufkhD652rVh993D/YgRqs0lkbl72QeZNQK2zOac7O5Z3Xg1Ly/P8/Pzs/mRIlKS//kfePLJMCfuj3+E446LO6KdUlgYmitzc5XkKhMzm+Lueakcm+oY32pAYXT8gWZ2oLtPKm+AIlKJuMO4caHmtuee8JvfQO/e0KMHmMUd3U7LyVGCS7pUphfcDfwPMBv4Lip2QIlOJOnefTesQ/nmm2GrnFtugaOOijsqkR2SSo3udKCVu2vgiUhVMXMm3HQTvPQS7LMP3H8/XHJJ3FGJlEtK2/QANdAIS5Gq47bbYNKksGXO1VdD3bplv0ekgkol0a0jjLp8nWLJzt2vylhUIpJdX34Jf/gDXHkltGoFgwfDbrtBw4ZxRyay01JJdC9GNxFJmpUr4U9/gr/9LUwq69gxJLrGjeOOTCRtykx07v5oNgIRkSy7774wwGTFirDA4x13wIEHxh2VSNqVmOjM7El3P8fMZhJGWW7D3TtkNDIRSb+iou93Dli0CLp0CU2WHTvGG5dIBpVWo7s6uu+ejUBEJIO++w4efzysQTl8OBx/PPz5z1C9etyRiWRciUuAufuS6OGJQE13X1T8lp3wRGSnuMMrr0CnTmGSd926YSVjUJKTKiOVwSj7Aw+aWS4whTBR/C13n57BuEQkHc46C559Fg44AMaMCct3VdN+y1K1pDIY5TYAM6sNXAJcB9wH6M9BkYpo5kxo0yb0xfXoASedFHYVqFEj7shEYlHmn3ZmdrOZ/RMYBxwI/I6w8aqIVCQLFsAvfwkdOsA//hHKLrwQfv1rJTmp0lJpuuwJFAGvAG8C72o5MJEK5IsvwtSAESNC/9uNN8Lpp8cdlUiFkUrTZScz2x04EugKDDezpe6ulV1FKoJTT4UZM6Bfv7A+5b77xh2RSIWSyu4F7YCjgWOBPOAz4K0MxyUiJVmzBoYODYlt991h2DDYay9o1izuyEQqpFSaLu8iJLbBhE1XN2U2JBHZro0bwxy4QYNg6VLYb78wZeCQQ+KOTKRCS6XpsruZ1QRaAq3MbJ6SnUgWucNjj4UdBQoKwo7ezz8fVjURkTKl0nR5LPB3oAAwoImZXaAdxkWyxAxGjw47CTz4IHTtmoidvUWyJZWZo/cCJ7n7se5+DHAy8NfMhiVSxU2cCMceG9ajhLB81+TJYU6ckpzIDkkl0dVw93lbnrj7x4SNWEUk3aZMgZNPDmtRLlwYmioBGjTQiiYi5ZTK/5x8M3vYzI6Lbg8B+WW9ycweMbOlZjarWFlDMxtvZvOj+wY7E7xIYriHyd55eSHZ/eUvMH9+qNWJyE5JJdFdBswBropuc6KysowCuv2g7AbgdXdvAbwePRepupYvD/dm0KhR2F1g4UL47W+hVq14YxNJCHP/0VZz6Tt5WAj6ZXdvFz2fBxzn7kvMrBHwhru3Kus8eXl5np9fZiVSpPJYtgz++EcYMgTeeAMOPzzuiEQqFTOb4u55qRxb2sar291wdYtybry6T7Htf74E9inl8y8FLgXYf//9y/FRIhXQ6tVw772haXLtWrjggjAfTkQyprTpBWcD6zP1we7uZlZaIh0ODIdQo8tUHCJZs3lz2BduwQLo2TNM/G7TJu6oRBKvtEQ3Jlrn8jF3Py9Nn/eVmTUq1nS5NE3nFamYiorguefgzDPDRqeDBkHz5lrNRCSLSkt0Nc3sl8ARZtbzhy+6+7Pl+LwXgQsIy4pdALxQjnOIVHzu8MwzcPPNMG9e2OX75z8PG5+KSFaVluj6Ab2B+kCPH7zmQKmJzszGAscBe5nZYuA2QoJ70sz6AouAc8oXtkgF5Q4TJoStcvLzQ9Pks8/CKafEHZlIlVVionP3t4G3zSzf3Ufs6IndvVcJL52wo+cSqTQ2bw67ChQVwciRcN55oclSRGJT5jy68iQ5kSpl9mzo0wfWrYNddgnNlB9/HHb3VpITiZ3WFBIpr4KCMD2gffvQHzd9eihv3Rp23TXW0ETke6UmOguaZCsYkUph40a46ipo2RKeeAKuvTasZnLEEXFHJiLbUWqi87BsyqtZikWkYisqCvc1a8LMmaFpcsECuOce2HPPWEMTkZKl0nQ51cw06UeqrvXrQzJr1gyWLAnrUo4fH3b7btw47uhEpAxlbrwKHAb0NrNFwFrC5qteziXARCqPTZvCyMmBA+GLL8L2OevWhdd2SeW/johUBKn8bz0541GIVDTr1oXluubNgy5dYMwYbZmTRoWFYSxPbi7k5MQdjSRdKtMLFgFNgOOjx+tSeZ9IpeP+/cjJ3XYLq5i88AK8846SXBqNHQtNm0LXruF+7Ni4I5KkK3ObHjO7DcgDWrl7SzP7CfCUux+ZjQBB2/RIFrzzDgwYAG+9BR9+CB3UMp8JhYUhua0vtlx87dqwaJFqdrJjdmSbnlRqZmcApxL653D3L4B65Q9PpAKZMQN69ICjjgqTvIcMCfPgJCMKCsKg1eJq1AjlIpmSSh/dt8W31DGzOhmOSSQ7Vq+GI48Mq5fceWeYG1dHP96ZlJsL3367bdmmTaFcJFNSqdE9aWYPAvXN7BJgAvBwZsMSyZAlS+BPfwr9cfXqwdNPh8neAwYoyWVBTg6MGBGaK3ffPdyPGKFmS8msMvvoAMysK3ASYWrBa+4+PtOBFac+OtlpK1bA3XfD4MGhCjF1ali6S2KhUZeys3akj67Mpkszu9vd/xcYv50ykYpt/Xq4775Qi1u1Cnr1gjvugAMOiDuyKi0nRwlOsieVpsuu2ynT5lpSeQwZEvripk2D0aOV5ESqmBJrdGZ2GXA50NzMZhR7qR7wTqYDEymXzZvDxKxRo+DVV0Mn0Icfai1KkSqstKbLMcA/gT8CNxQrX+3uX2c0KpEd5Q4vvww33RQWXP7pT+Hzz8P6lEpyIlVaiU2X7r7K3QvcvVe0Isp6wIG6ZrZ/1iIUKcuyZWEe3Kmnhj65sWPDYJNmzeKOTEQqgDL76Mysh5nNBz4B3gQKCDU9kXgtXx7uGzYMtbYHH4Q5c+Dcc6GaVqkTkSCV3waDgMOBj929GXAC8F5GoxIpzfz5IZkdcEBIdtWqwYsvwqWXhmU2RESKSSXRbXL35UA1M6vm7hMJa1+KZNfixSGZtWkDL70EV16pxCYiZUplCbCVZlYXmASMNrOlROteimTNF19Ay5Zhl+/LLw+DTvbZJ+6oRKQSSKVGdxphIEp/4F/Af4EemQxKBIA1a0LNDeAnP4G77gr7ww0erCQnIikrbR7dNcB/gKnuvjkqfjQrUUnVtnFjGFgyaBB8/XXYw2W//cKiyyIiO6i0Gl1j4D5gqZm9aWZ3mll3M2uYpdikqtm8GR59FFq1gquvhrZt4e23Q5ITESmnEmt07v47ADOrSRh8cgRwETDczFa6+0HZCVGqjC++CINN2reH4cPDFtRmcUclIpVcKoNRagO7A3tEty+AmZkMSqqQf/87rGhy773QpAlMnhwSnRKciKRJiU2XZjbczN4BngC6EPrrznb3PHe/KFsBSkJNnhxqbCecAE89BUuXhvIOHZTkRCStSuuj2x/YFfgS+BxYDKzMRlCSYEuWwFlnwaGHwvTpoSY3fz7svXfckYlIQpXWR9fNzAxoS+ifuxZoZ2ZfA++6+21ZijF22iQyDYqKYJddoG7dkOBuvx369w/bTEuFpJ97SYpS59F5MAt4lbC+5TvAAcDVWYitQhg7Fpo2Da1sTZuG57IDCgtDQjvkkJDs6tWDjz6C225TkqvA9HMvSVJaH91VZva4mX1KWMy5O/AR0BOoElMMCguhb9+wIP6qVeG+b99QLmX45ptQa2vePEzw7tQJ1q0Lr+2SyhgoiYt+7iVpSvuNkws8BfR39yXZCadiKSiAmjXDf/QtatQI5WrKKcXcuXD00WHB5bPOgt//Hlq3jjsqSZF+7iVpSuuj+202A6mIcnPh22+3Ldu0KZTLDxQVhSbJdu3CmpRnnhnmxHXuHHdksoP0cy9JU2U27SosDCPad6T5JScHRoyA2rVDd1Lt2uG5/qot5rvvwvSAdu3guONg9WqoXj0s4aUkVynp516Spkokup3pWO/VKyy1OGFCuO/VK3Nxlld5kvhOc4dx48I0gXPOCcltxIgwqlIqvcrwcy+SKnP3uGMoU15enufn55frvYWFIbkV72+oXTv8503CX6hjx4aBAjVrhuamESOy9EtpyhTIywvtWQMHQu/eIdmJiGSBmU1x95T2Rk18jW5Lx3pxWzrWK7usj46bNQtGjgyPO3eGZ54J/XLnn68kJyIVVuITXZI71rOWxD/5JCSzDh3ghhu+nybQsyfsumuaPyx74mjyjaWZWaSKS3yiS3LHesaTeGEh/OY3Yducp56C664LUwd22y1NHxCfOCZEaxK2SDwS30e3RVKXM9rSR1ejRkhyae2jmz8/1OLOPx9uvTUx+8LF0W+b9L5ikWzbkT66KrNERU5OMn+h9OoFJ56YpiS+bh3cf3+otY0cCS1awOLFsOeeaYq2YohjQrQmYYvEp8okuiTb6SS+aRM88gjccUfY/PTnP4eNG0P/W8KSHMTTb5vkvmKRii7xfXRShmnT4KCDoF8/aNYMJk2CV16p1INMyhJHv22S+4pFKroq00cnxbjD11+H2trXX8MZZ8D114ea3A82PU1q3ybE892SfD1Fskl9dFKyt9+GAQNg7VrIz4eGDeHNN7d7aGyT0bMkjn7bpPYVi1RkarqsKj78ELp3D7sKLFgAl1wS1qksgbZqEZGkUI2uKhg3Drp1gz32gLvuCnPjypgLp1GCIpIUSnRJ9cUXoeZ2zDFhV4E//CEMOGnQIKW3a5SgiCSFmi6TZsWKsEzXgQeGid6bN4eq2YABKSc50ChBEUkO1eiSYu1aGDwY7r4bvvkm7CYwcOBOLbac1snoIiIxiaVGZ2bdzGyemS0wsxviiCFxJk2CG28MTZUffgiPPQbNm+/0aXNy4JBDlOREpPLKeo3OzKoDQ4CuwGJgspm96O5zsh1LpbZ5M4wZE+bBXX3jhlGoAAAS8UlEQVR1GGwybRp07Bh3ZCIiFUocNbpDgQXuvtDdvwUeB06LIY7KyR1efDEktPPPh6efDtMEzJTkRES2I45Etx/wWbHni6OybZjZpWaWb2b5hZq8FUyfDkceCaedFtaifOKJMNm7msYUiYiUpML+hnT34e6e5+55OVW9g2jz5nBfrVrYTWD4cJg9G845R0lORKQMcYy6/BxoUux546hMfmjePLjlljBTe/TosDfcwoWwiwbLioikKo7qwGSghZk1M7OawLnAizHEUXEtXhyW6GrbFl59NewLt2XxbSU5EZEdkvXfmu5eZGZXAq8B1YFH3H12tuOosJ5/Hs49NyS2K66Am26CvfeOOyoRkUorluqBu78KvBrHZ1dIq1eH1ZKbN4cuXeC88+Dmm6Fp07gjExGp9DSSIU4bN8Lf/gYHHBCmCrjDPvvAQw8pyYmIpIkSXRw2b4ZRo6BlS7jmmjDI5N57f7TpqYiI7DwlujiMGAEXXRT63saPhwkT4NBD445KRCSRNIQvW15/Pexz061b6IPbe+8w8Vu1OBGRjFKNLtM++CBsAXDiiWHTUwh73px+upKciEgWKNFlykcfQc+ecNhhYTeB++6D116LOyoRkSpHTZeZMmtW6HsbOBD694d69eKOSESkSlKiS5elS+HOO2G//eC66+DMM+FnP4M994w7MhGRKk1Nlztr1Sq49dYw2fv+++HLL0O5mZKciEgFoBrdznjmGbj00rD56dlnw+9/D61axR2ViIgUoxrdjioqgm++CY+bNIFDDoH8fHjySSU5EZEKSDW6VH33XdjN+5Zb4Oij4eGHwyTvf/0r7shEKoRNmzaxePFiNmzYEHcokiC1atWicePG1KhRo9znUKIrizuMGwcDBsC0aWHrnNNOizsqkQpn8eLF1KtXj9zcXExzRCUN3J3ly5ezePFimjVrVu7zqOmyLH/6U1jNZMUK+Pvfw5y4Hj3ijkqkwtmwYQN77rmnkpykjZmx55577nQrgWp02zNzJlSvDgcdBL16QZ06YdBJzZpxRyZSoSnJSbql42dKNbriFi6EX/0KfvrTsOEpwP77w5VXKsmJiFRSSnQAS5aE3bxbtQpTBq67LuwwICKJdtxxx5Gfnx/LZx9xxBFlHlO3bt0dOufgwYNp06YNvXv3LvW4OL/3D9100000adJkh7/rjlCigzCC8sEHoW9fWLAA7r4bGjaMOyoRSbD//Oc/aT/n0KFDGT9+PKNHj077ubenqKhop8/Ro0cPPvjggzREU7KqmejWrQs7Cbz8cnh+zTVhEeYHHghLeInIzjvuuB/fhg4Nr61bt/3XR40Kry9b9uPXylBQUEDr1q3p3bs3bdq04ayzzmLdunUAvP766xx88MG0b9+ePn36sHHjxm3e+8gjj3DNNddsff7QQw/Rv39/CgoKaNOmDZdccglt27blpJNOYv369QBMnz6dww8/nA4dOnDGGWewYsWK6GsfR//+/cnLy6NNmzZMnjyZnj170qJFC26++eatn7GlBrNmzRpOOOEEOnXqRPv27XnhhRfK/K733nsv7dq1o127dtx3330A9OvXj4ULF3LKKafw17/+dZvj169fz7nnnkubNm0444wztn4HgHHjxtGlSxc6derE2WefzZo1awB49dVXad26NZ07d+aqq66ie/fuANx+++2cd955HHnkkZx33nls3ryZ6667jkMOOYQOHTrw4IMPbj33Pffcs7X8tttu2+53Ofzww2nUqFGZ33lnVK1Et2kTDBsGBx4YpguMGxfK69ULZSJSqc2bN4/LL7+cuXPnsvvuuzN06FA2bNjAhRdeyBNPPMHMmTMpKipi2LBh27zvnHPO4aWXXmLTpk0AjBw5kj59+gAwf/58rrjiCmbPnk39+vV55plnADj//PO5++67mTFjBu3bt2fgwIFbz1ezZk3y8/Pp168fp512GkOGDGHWrFmMGjWK5cuXb/PZtWrV4rnnnmPq1KlMnDiRa6+9Fncv8TtOmTKFkSNH8v777/Pee+/x0EMPMW3aNB544AF+8pOfMHHiRPr377/Ne4YNG8Zuu+3G3LlzGThwIFOmTAFg2bJlDBo0iAkTJjB16lTy8vK499572bBhA7/+9a/55z//yZQpUygsLNzmfHPmzGHChAmMHTuWESNGsMceezB58mQmT57MQw89xCeffMK4ceOYP38+H3zwAdOnT2fKlClMmjRpR/4506bqjLp86aVQc1u4EI46KqxkctRRcUclklxvvFHya7vtVvrre+1V+uslaNKkCUceeSQAv/rVrxg8eDBdu3alWbNmtGzZEoALLriAIUOGbFODq1u3Lscffzwvv/wybdq0YdOmTbRv356CggKaNWtGx44dAejcuTMFBQWsWrWKlStXcuyxx24959lnn731fKeeeioA7du3p23btltrLM2bN+ezzz5jz2Lr4Lo7N954I5MmTaJatWp8/vnnfPXVV+y7777b/Y5vv/02Z5xxBnXq1AGgZ8+evPXWWxx88MElXpdJkyZx1VVXAdChQwc6dOgAwHvvvcecOXO2XrNvv/2WLl268NFHH9G8efOtc9d69erF8OHDt/l+tWvXBkKNcMaMGTz99NMArFq1ivnz5zNu3DjGjRu3Na41a9Ywf/58jjnmmBLjzJSqk+i+/jrU3F55BU45RZueiiTQD4ei78jQ9Isvvpg777yT1q1bc9FFF20t33XXXbc+rl69+jbNfiXZ8p5q1apt8/5q1ar9qF9r9OjRFBYWMmXKFGrUqEFubm7WVpdxd7p27crYsWO3KZ8+fXqp79uSZLec4//+7/84+eSTtznmtddeY8CAAfz6179OX8DlVHWaLs87D6ZOhZ//XElOJKE+/fRT3n33XQDGjBnDUUcdRatWrSgoKGDBggUAPPbYY1trYsUddthhfPbZZ4wZM4ZevXqV+jl77LEHDRo04K233ir1nKlYtWoVe++9NzVq1GDixIksWrSo1OOPPvponn/+edatW8fatWt57rnnOProo0t9zzHHHMOYMWMAmDVrFjNmzABC/9g777yz9dqsXbuWjz/+mFatWrFw4UIKCgoAeOKJJ0o898knn8ywYcO2Nvt+/PHHrF27lpNPPplHHnlka5/f559/ztKlS8u+IBlQdWp01apOThepqlq1asWQIUPo06cPBx10EJdddhm1atVi5MiRnH322RQVFXHIIYfQr1+/7b7/nHPOYfr06TRo0KDMz3r00Ufp168f69ato3nz5owcObJcMffu3ZsePXrQvn178vLyaN26danHd+rUiQsvvJBDDz0UCDXR0potAS677DIuuugi2rRpQ5s2bejcuTMAOTk5jBo1il69em0doDNo0CBatmzJ0KFD6datG3Xq1OGQQw4p8dwXX3wxBQUFdOrUCXcnJyeH559/npNOOom5c+fSpUsXIDQP/+Mf/2Dvvffe5v3XX389Y8aMYd26dTRu3JiLL76Y22+/vdTvs6OstE7PiiIvL88rypwPEdm+uXPn0qZNm9g+v6CggO7duzNr1qxyn6N79+7079+fE044IY2RVU5r1qyhbt26uDtXXHEFLVq0+NEgl2zZ3s+WmU1x97xU3q9qjohUeStXrqRly5bUrl1bSS7y0EMP0bFjR9q2bcuqVasqRF9bealGJyJpEXeNTpJLNToRqTAqwx/OUrmk42dKiU5E0qJWrVosX75cyU7SZst+dLVq1dqp81SdUZciklGNGzdm8eLFP1pFQ2RnbNlhfGco0YlIWtSoUWOndoEWyRQ1XYqISKIp0YmISKIp0YmISKJVinl0ZlYIlL4AXOWxF7As7iASQtcyfXQt00fXMn1Ku5ZN3T0nlZNUikSXJGaWn+okRymdrmX66Fqmj65l+qTrWqrpUkREEk2JTkREEk2JLvuGl32IpEjXMn10LdNH1zJ90nIt1UcnIiKJphqdiIgkmhKdiIgkmhJdlpnZtWbmZrZX9NzMbLCZLTCzGWbWKe4YKzozu8fMPoqu13NmVr/YawOiaznPzE6OM87Kwsy6RddrgZndEHc8lYWZNTGziWY2x8xmm9nVUXlDMxtvZvOj+wZxx1pZmFl1M5tmZi9Hz5uZ2fvRz+YTZlazPOdVossiM2sCnAR8Wqz4FKBFdLsUGBZDaJXNeKCdu3cAPgYGAJjZQcC5QFugGzDUzKrHFmUlEF2fIYSfw4OAXtF1lLIVAde6+0HA4cAV0bW7AXjd3VsAr0fPJTVXA3OLPb8b+Ku7HwisAPqW56RKdNn1V+B6oPgIoNOAv3vwHlDfzBrFEl0l4e7j3L0oevoesGUPj9OAx919o7t/AiwADo0jxkrkUGCBuy9092+BxwnXUcrg7kvcfWr0eDXhF/R+hOv3aHTYo8Dp8URYuZhZY+AXwMPRcwOOB56ODin3tVSiyxIzOw343N0//MFL+wGfFXu+OCqT1PQB/hk91rXccbpmaWBmucDBwPvAPu6+JHrpS2CfmMKqbO4jVAS+i57vCaws9kdtuX82tR9dGpnZBGDf7bx0E3AjodlSUlDatXT3F6JjbiI0H43OZmwixZlZXeAZ4Bp3/yZURAJ3dzPTHK4ymFl3YKm7TzGz49J9fiW6NHL3E7dXbmbtgWbAh9F/gsbAVDM7FPgcaFLs8MZRWZVW0rXcwswuBLoDJ/j3k0F1LXecrtlOMLMahCQ32t2fjYq/MrNG7r4k6oZYGl+ElcaRwKlm9nOgFrA78DdCV84uUa2u3D+barrMAnef6e57u3uuu+cSquCd3P1L4EXg/Gj05eHAqmLNHrIdZtaN0MRxqruvK/bSi8C5ZrarmTUjDPD5II4YK5HJQItodFtNwmCeF2OOqVKI+pBGAHPd/d5iL70IXBA9vgB4IduxVTbuPsDdG0e/H88F/u3uvYGJwFnRYeW+lqrRxe9V4OeEgRPrgIviDadSuB/YFRgf1ZDfc/d+7j7bzJ4E5hCaNK9w980xxlnhuXuRmV0JvAZUBx5x99kxh1VZHAmcB8w0s+lR2Y3AXcCTZtaXsL3YOTHFlwT/CzxuZoOAaYQ/LHaYlgATEZFEU9OliIgkmhKdiIgkmhKdiIgkmhKdiIgkmhKdiIgkmhKdJJaZ7Wtmj5vZf81sipm9amYtzey4Laujx83M7jCzUifHp+lz6pvZ5Wk4zxtmlpeOmDJ5TpHiNI9OEimazPsc8Ki7nxuV/ZQKtu6gu9+apY+qD1wODE31DdE1NHf/rsyDRSow1egkqX4GbHL3B7YUuPuH7v5W9LSumT0d7Ws3OvqljpndamaTzWyWmQ0vVv6Gmd1tZh+Y2cdmdnRUvpuZPRntSfZctHdWXvTaSWb2rplNNbOnojURt2Fmo8zsrOhxgZkNjI6faWatt3P8K2bWIXo8zcxujR7fYWaXmFldM3u92Dm27ERwF3CAmU03s3ui91wXfdcZZjYwKsu1sDfd34FZbLs82A9j+dH3s7C33VPFjtlae07leohkghKdJFU7YEoprx8MXEPYg605YZULgPvd/RB3bwfUJqynucUu7n5o9L7borLLgRXRnmS3AJ0BLGysezNwort3AvKB36YQ97Lo+GHA77bz+lvA0Wa2B2H1ly1xHw1MAjYAZ0Tn+BnwlyhZ3wD81907uvt1ZnYSYYm0Q4GOQGczOyY6VwtgqLu3dfdF2wuylO83ATjMzOpEh/4PYWWL8l4PkZ2mpkupqj5w98UA0fJNucDbwM/M7HpgN6AhMBt4KXrPlkV7p0THAxxFWHwWd59lZjOi8sMJSfSdqFJYE3g3hbiKf0bP7bz+FnAV8AnwCtDVzHYDmrn7PAuLDN8ZJa3vCNuabK+59qToNi16XpeQ4D4FFkV7I5Zmu98vWlLsX0APM3uasL/Y9cCx2zu+jM8QSQslOkmq2Xy/GOz2bCz2eDOwi5nVIvRh5bn7Z2Z2O2El9R++ZzNl/98xYLy799qhqMv+jMlAHrCQsNP6XsAlfF977Q3kAJ3dfZOZFfzgOxSP74/u/uA2hWFftbUpxFna93scuBL4Gsh399VRrbI810Nkp6npUpLq38CuZnbplgIz67Clb60EWxLCsqj/qLREucU7RIv2mtlBQPuo/D3gSDM7MHqtjpm13MHv8CPRLuCfAWcTakRvEZo4J0WH7EHY12uTmf0MaBqVrwbqFTvVa0CfLf1kZrafme29A6GU9v3eBDoREvDjKRwvklFKdJJI0R51ZwAnWpheMBv4I2HH55LesxJ4iDAI4zVC7aksQ4EcM5sDDCLUJFe5eyFwITA2as58F/jR4JJyeouQzNZHjxtH9xA2oc0zs5nA+cBHAO6+nNBsOMvM7nH3ccAY4N3o2KfZNhGWqrTvF+0Y8TJwSnRf6vEimabdC0R2gplVB2q4+wYzO4AwGKNVVPMSkQpAfXQiO2c3YGI0CMSAy5XkRCoW1ehERCTR1EcnIiKJpkQnIiKJpkQnIiKJpkQnIiKJpkQnIiKJ9v829ETV53JbeAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Tb8UiOr87lLENjawsJblkKrXXpZldCIwEto62kdFirCIiUh6XXw4ff8yW+3Rm112V5JItkfXoBgI93X0ZgJkNBj4F7ktmYCIiaccdfvsNttoKevaMO5oaI5FxdAasKfR6DUUvqCoiIiUZPRp22AG++SbuSGqURGp0jwGfm9mL0eu+wIjkhSQikoaWLg1NljvuGJbhkZRJpDPKPWb2PrBnVNTf3b9MalQiIunm1lvh55/hueegdu24o6lREqnR4e6TgElJjkVEJD3NnAl33x3Wmtttt7ijqXE016WISLK9/DLUrw+DB8cdSY2kRCcikmyXXQbTp8O228YdSY2UyDi6Tf4JUlSZiIhsZNUq+Pbb8Lx583hjqcESqdH1KaLs4MoOREQk7QwZArvsEu7RSWyK7YxiZucA5wJtzKzwlF9NgI+THZiISLU2bx7ccgscfjjstFPc0dRoJfW6HAX8F7gduKpQ+RJ3/y2pUYmIVHeXXw5r1oTelhKrYpsu3X2xu+cAVxKW5SnYGpvZ9qWd2Mxamtl7ZvatmX1TsIBrtJ7dGDObGT1uUTlfRUSkivjoozALyhVXQOvWcUdT4yUyju51QoIzoD7QGpgOdCzlffnApe4+ycyaABPNbAxwGvCOu99hZlcRaotXljN+EZGqZ+rU0Fx5pf60VQWldkZx987uvkv0uBPQgzCpc2nv+yUaaI67LwGmAdsBRwBPRIc9QZhSTEQkfZxzTkh2DRvGHYlQjnF0UfIq07TbZpYFdAU+B7Zx91+iXf8DtinmPWea2QQzm5Cbm1vWMEVEUm/BAnjzzfC8bt14Y5F1Sm26NLNLCr2sBXQDfk70A8ysMfA8cLG7/2G2fuEDd3cz86Le5+7DgeEA2dnZRR4jIlKlXHstPPII/PADtGoVdzQSSaRG16TQVo9wz+6IRE5uZhmEJDfS3V+Iin81s+bR/ubA/LIGLSJS5UyaBMOHw/nnK8lVMYmsXnAjrKuZ4e5LEzmxharbCGCau99TaNcrwKnAHdHjy2WMWUSkanGHCy6AZs3ghhvijkY2kkjTZSfgSWDL6PUC4FR3n1rKW/cATga+NrOvorJrCAnuWTMbAMwBjitn7CIiVcPIkfDJJzBiBGy+edzRyEYSGV4wHLjE3d8DMLN9o7LdS3qTu4+j+JXI9ytDjCIiVVvt2nDIIXDaaXFHIkVI5B5do4IkB+Du7wONkhaRiEh1068fvP461NKCMFVRIv9VZpnZP8wsK9quBWYlOzARkSpv+nR44IEw1ZdUWYkkutOBTOAFQg/KZlGZiEjN5Q4XXQRXXQULF8YdjZQgkV6XvwMXpiAWEZHq49VX4a234J57YOut445GSqAGZRGRslq5EgYNgg4dwrg5qdIS6XUpIiKF3XUXzJoFY8dCRkbc0UgpVKMTESmr3XYL683tp5FS1UEiA8aHFlG8GJjg7prVRERqnv32U5KrRhKp0dUHugAzo20XoAUwwMzuTWJsIiJVy/vvh5rcsmVxRyJlkMg9ul2APdx9DYCZPQB8BOwJfJ3E2EREqo78/DCf5dKlcNNNcUcjZZBIotsCaExoroQwK8qW7r7GzFYlLTIRkark/vvDYqovvAANGsQdjZRBIonun8BXZvY+Ye7KvYHbzKwRMDaJsYmIVA25uXDdddCnD/TtG3c0UkaJDBgfYWZvAD2iomvcvWDh1cuTFpmISFVx7bXhvtzQoWDFzVUvVVWi4+hqAbnR8Tua2Y7u/mHywhIRqUIuvhh69oR27eKORMohkeEFg4G/Ad8Aa6NiB5ToRCS9uYcaXPv2YZNqKZEaXV+grbur44mI1CxPPBGW33n0UWjSJO5opJwSWqYH0Bw3IlKzLF4MV14Jc+dCIy3BWZ0lUqNbTuh1+Q6wrlbn7lrRQETS1003hd6Wb7yhBVWruUQS3SvRJiJSM0ybFnpYDhwI3bvHHY1UUCLDC55IRSAiIlXGP/4BjRvDrbfGHYlUgmITnZk96+7HmdnXhF6WG3D3XZIamYhIXB58EKZMgczMuCORSlBSje6i6PGwVAQiIhK71auhTh1o1gx69447Gqkkxd5hdfdfoqf7A3XdfU7hLTXhiYik0O23Q69esHx53JFIJUqkK9H2wENmNsvM/mNmF5hZl2QHJiKSUjk5cMcd0KYNNGwYdzRSiUpNdO5+vbv3BjoSlue5HJiY7MBERFLq0kvDMII774w7EqlkpSY6M7vWzP4LvA3sCFxGWHi1tPc9ambzzWxqobIbzGyemX0VbYdUJHgRkUoxdmxYfueaa6Bly7ijkUqWSNPlUcBWhCV5XgBeLnT/riSPAwcVUT7E3btE2xsJRyoikixDh4Ymy0svjTsSSYJExtF1M7OmwB5AH2C4mc139z1Led+HZpZVKVGKiCTTf/4Dc+ZA/fpxRyJJkMjqBZ2AvYB9gGzgJ8K9uvI638xOASYAl7r77xU4l4hI+eXmsmBpfWYvaEJW1s5o1Fx6SqTp8g6gKTAUaO/uf3H368r5eQ8AOwBdgF+Au4s70MzONLMJZjYhNze3nB8nIlIMd+YefAYLdujBQfvn06oVjB4dd1CSDIn0ujwMGAL8AbQ1s3KvZODuv7r7GndfCzzM+lXLizp2uLtnu3t2pmYnEJFKtvjR52gx8WUe8QH89kcdVqyAAQPCPM6SXhLpdbkPMBMYBtwPzDCzvcvzYWbWvNDLI4GpxR0rIpI0CxfS8IrzmVQ7m3u5eF1xRkYYTifpJZHVC+4BDnD36QBmtjMwGihxSm8zGw3sCzQzs7nA9cC+0WBzB3KAs8oduYhIeV1yCXX++I2z64xhzZr1fwbz8iArK76wJDkSSXQZBUkOwN1nJNJ86e79iigeUZbgREQq3apVMHcudtVVDOqwCwMGhJpcXh6MGKF5nNNRIolugpk9AjwVvT6R0GNSRKT6qVcvDBDPz6dfBuy/f2iuzMpSkktXifS6PAf4Frgw2r6NykREqpcRI2DePDAL1ThCctt1VyW5dJZIr8tV7n6Pux8VbUPcfVUqghMRqTQffwxnnAFDhsQdiaRYSQuvFrngagEtvCoi1cbKlTBwIGy/PdxwQ9zRSIqVdI/uWGBFqgIREUmaW26B776DN9+Exo3jjkZSrKRENyqa5/JJdz85ZRGJiFSmyZNh8GA49VQ48MC4o5EYlJTo6prZCcDuZnbUxjvd/YXkhSUiUklatICzz4Ybb4w7EolJSYnubMJQgs2Bv260zwlL9oiIVG1bbQX33Rd3FBKjYhOdu48DxpnZBHfXQG8RqV5mzoTTT4dHHoG2beOORmKUyPACJTkRqV7Wrg1DCb7+Gpo0iTsaiVkiM6OIiFQvDz8MH3wQanN/+lPc0UjMSqzRWdAyVcGIiFTY3Llw+eXQu3doupQar8RE5+4OvJGiWEREKm7wYMjPD7U6s7ijkSogkbkuJ5nZrkmPRESkMtx1V5i0uU2buCORKiKRe3Q9gRPNbA6wDDBCZU9TgIlI1fH771CnTuh8svvucUcjVUgiiU5TCYhI1XfBBfD55zB1aliKRySSyPCCOUBLoHf0fHki7xMRSZk33oCRI+HEE5XkZBOlJiwzux64Erg6Kspg/SKsIiLx+uOPMMVXx45w9dWlHy81TiJNl0cCXYFJAO7+s5lpBKaIVA1XXx2GFHzyiWpzUqREmiBXR8MMHMDMGiU3JBGRBOXlwYwZcNFF0KtX3NFIFZVIje5ZM3sI2NzMzgBOBx5JblgiIgnIyIC33w4JT6QYiXRGuQt4DngeaAtc5+5Dkx2YiEiJRo4MTZZmULdu3NFIFZZIZ5TB7j7G3S9398vcfYyZDU5FcCIiRfryy7CQ6m23xR2JVAOJ3KPrU0TZwZUdiIhIQvLzYcAAyMyEW2+NOxqpBoq9R2dm5wDnAm3MbEqhXU2Aj5MdmIhIke6+O9Tonn8ettgi7mikGiipM8oo4L/A7cBVhcqXuPtvSY1KRKQoM2bA9dfD0UfDUUfFHY1UE8U2Xbr7YnfPcfd+0YwoKwhDDBqb2falndjMHjWz+WY2tVDZlmY2xsxmRo/655iIJK5Zs9Bs+a9/xR2JVCOJdEb5q5nNBGYDHwA5hJpeaR4HDtqo7CrgHXffCXiHDWuKIiIl23JLGDYMtt027kikGkmkM8otQC9ghru3BvYDPivtTe7+IbBxE+cRwBPR8yeAvomHKiI11k8/wb77wrRpcUci1VAiiS7P3RcCtcyslru/B2SX8/O2cfdfouf/A7Yp7kAzO9PMJpjZhNzc3HJ+nIhUe+5wzjkwfjzUrx93NFINJTIzyiIzawx8CIw0s/mEdekqxN3dzLyE/cOB4QDZ2dnFHiciaW70aHj9dRgyBFq3jjsaqYYSqdEdQeiIMgh4E/gB+Gs5P+9XM2sOED3OL+d5RKQmyM2FCy+Enj3DenMi5VBsojOzi82sB7DK3de4e767P+HuQ6OmzPJ4BTg1en4q8HI5zyMiNcGdd4ZleEaMgNq1445GqqmSanQtgHuB+Wb2gZndZmaHmdmWiZzYzEYDnwJtzWyumQ0A7gD6RL04949ei4gU7eabw6TNHTvGHYlUYxZW4CnhALO6hM4nuwO7Rdsid++Q/PCC7OxsnzBhQqo+TkTitmRJ6ITStGnckUgVZmYT3b3UzpGJ3KNrADQFNou2n4HPKxaeiEgJrrgC/vxnWFbhfm8iJc51ORzoCCwhJLZPgHvc/fcUxSYiNdEHH8CDD8Ill0AjrfMsFVdSjW57oB5hvNs8YC6wKBVBiUgNtWIFDBwIbdrATTfFHY2kiWJrdO5+kJkZoVa3O3Ap0MnMfgM+dffrUxSjiNQUN9wA338P77yj2pxUmhIHjHvoqTLVzBYBi6PtMKAHoEQnIpVnzRqYPDnU6Hr3jjsaSSMl3aO7kFCT2x3II9yj+wR4FPg6JdGJSM1Ruzb897+walXckUiaKalGlwX8BxhUaH5KEZHK99xzYfaTli01n6VUupLWo7vE3Z9XkhORpPruOzjxRLj22rgjkTSVyKTOIiKVKjcXcnIga/u1ZA4cGDqe/POfcYclaUqJTkRSavTosEh43brQf/kDDMn7GJ54ArYpdtUukQpJZGYUEZFKkZsbktyKFdB08Y/clHcVY2odSO5BJ8cdmqQxJToRSZmcnFCTA1hCE0ZyIoMaPkTOHIs1LklvaroUkZTJyoLVq6EeK1nEFpzDgzRYE8pFkkU1OhFJmcxM+PC4fzHZutCm8XwaNAhLzWVmxh2ZpDPV6EQkdYYMIfuJS1h10BE884/NabWTkpwknxKdiKTG4MFw1VVwzDHUGzWK7IyMuCOSGkJNlyKSfMOHhyR3/PFhfIGSnKSQanQiknxHHgk//QTXXw919GdHUks1OhFJDnd48snQzTIzE26+WUlOYqFEJyKVzx0uvxxOOSXMeiISI/3zSkQqlztcfDEMHQrnnRfWlxOJkWp0IlJ51q4NyW3o0JDs7rsPTLOeSLyU6ESk8uTkwMiRcMUVcM89SnJSJajpUkQqzj0ktTZt4OuvwwKqSnJSRahGJyIVk58PJ5+8fj257bdXkpMqRYlORMovLw9OOik0V65ZE3c0IkWKpenSzHKAJcAaIN/ds+OIQ0QqYPVq6NcPXngB7roLLr007ohEihTnPbq/uPuCGD9fRMrLHY47Dl5+Gf7v/+DCC+OOSKRY6owiImVnBoccAgccAOeeG3c0IiWKK9E58LaZOfCQuw/f+AAzOxM4E2D77bdPcXgiUqTly2HqVOjRA848M+5oRBISV2eUPd29G3AwcJ6Z7b3xAe4+3N2z3T07UwtWicRv2TI47DDYbz/IzY07GpGExZLo3H1e9DgfeBHoEUccIpKgJUvg4IPhgw/gwQe1WqpUKylPdGbWyMyaFDwHDgCmpjoOEUnQH3/AQQfBJ5/AqFFw4olxRyRSJnHco9sGeNHCgNI6wCh3fzOGOEQkEcOGwRdfwDPPwNFHxx2NSJmlPNG5+yzgz6n+XBEppyuugD59IFvDXaV60swopZk7lxUnn8Fvff7GHw+NDs04IukqLw/GjIFzziGvWw8mv/YTub/VVpKTak2JrhQ/Hj0Ie+pJVr/zIU3PPoE1W2XCoEFxhyVSub7/Hk49FbbeGg44gLzHnuQNKgIpAAAX1klEQVTVya0YdPwvtGoFo0fHHaBI+SnRbcwdnn8efviB3Fw4YPKdtGcaf/J57ME4hvl5LNlmx3DsihVw+OEwfDjMnx9v3CJl8ccfIXt9+GF4XacOvPoqHHEEi//9MttYLkev/Q/vLevBihUwYIBGFEj1pURX2JQpYYzQMcfAffeRkwP/q59FDq1xavEJe/CPRvfw3X7nheNzcmDaNDjrLGjeHPbdNyw4qb8IUhXl5sKIEXDooWF4wAknhNcAWVnhH2uPP86Mdoeztl6DDd6akRF+7iLVkRIdwIIFYRqjrl1h8mS4/3646y6yssK8tYXl5YW/CQC0bw8zZoT3/P3v4Q/JRRet/4swaxbMnp267yGyscWL1z/ff38YOBC+/RbOPx/GjYNHH12/v07om1bq716kunH3Kr91797dk+qyy9xr13a/4AL3hQs32DVqlHuDBu5Nm4bHUaNKOdd337mvXRuen3mmO7h36+Z+661hn0iyzZjhfscd7j16hB/uypWhfOxY90mT1v8+S1Dm371IDIAJnkAOsXBs1Zadne0TJkyo0Dlyc0NFKysrmtRhzBho2hR69oTff4eff4aOHRN7b6Jmzw73+55/Hj77LJTttx+MHVuh7yJSpDFjwlI5X38dXmdnw1FHhdpbkyZlPl25f/ciKWJmEz2BZd5qxOoFo0eHm+l160KLld/zZqdLaTHxFTjyyLCW1hZbhK0YmZnl/B+9dWu47LKwzZ0LL74Ia9eGfWvXhqaknj3DINzu3cu9KrP+INVAa9eGQdwvvAB9+8Luu8Nmm8GWW4Zlc/r2DSt9V0C5f/ciVU0i1b64t4o0Xc6fH5peGvOH386VvpK6/geNfcm1d6xv0onDggXuffqEJlNwb9XKfdAg92nTynSagiamzTZTE1PaW7PG/Z133M87z/1Pfwq/m4wM96FD445MJBYk2HSZ9p1RcnJCTe4U/s1VDGYUJ9C98QymHX4l1KsXX2BbbQVvvx16uj32GHTqFKZamjkz7J8zB959F/Lziz1Fbm6oqa5YEfocqBt4Glq5MnQegTD05fjjQweSXr3gqafC7+eCC+KNUaSKS/umy4IeZMM5k8/oxSS602BNFepBtuWWcNppYfvjD6hfP5Q/9hjceCM0awZHHBGGPPTuHbJ2pCCJr1ix/nQF3cDV5FSNLVkC//1vaJZ8/fXwG8nJgdq14a23oG1baNgw7ig3kZeXx9y5c1m5cmXcoUiaqV+/Pi1atCAjI6Nc70/7RJeZGYYKDRiQwfcZ3WmQF15XyUTQtOn651dcAbvsAs89B88+G4Ju3hx+/DF0A3cnK8vUDbwC4ri3Wepn3nsvXHUVrFoVZik54YTQocQ93MPt2jU1gZbD3LlzadKkCVlZWVg57zeLbMzdWbhwIXPnzqV169blOkfaJzqAfv1Cv49q1WGjYcPwB+6oo0Lz1dixoRdnNNaJ3r3J3GYb3j7jaI4cfjCr6zYmryon8SqmcAel1avDdevXL7WfOequn+nLS6Hmdu+9ofm6Uyc455zw33333UMtrppYuXKlkpxUOjNjq622IrcC92RqRKKDat6DrH79sLJzgfz80Hz14ovsOf8Z5tevzx/t9sTOOIOm/Y4L1bqpU0Ovz803jy/uqmTtWqgVbkkvnDCb+/r/TvdVy6i7IlSJh/bfkv337xp+Ix9/vGF7MIQfz5+jRTc++GDTEdXNm4ckBeEfJQW9ayO/NWrJgAHtWbliLeetuJsjeZHdz/s07Gzbdv0UcvvvH7ZqSklOkqGiv6sak+jSSp06YZXnYcNg3Djs+efZ7JNPgGhlhdmzoVu38HyLLaBNm5D0LrgA9t4bli+HX34J3c/L2eadVCtXhvtUy5bB0qVhW7MG9tgj7H/1VZg+ff2+ZcvC97zttrB/4ED4/PP1+5cuhS5d4NOQWBocfzifrNpwrd/38vcnJ2dMSHQnn7zpjDYFQ1Eg3C9dsGDD/SefDP/+d3h+2GGh6bGQvGPOpW7dYeStWMOdXMEkunJzvZvp+++j6Xxc+4peMREpgRJddVa7NuyzT9gK23bbMEh99uwwDdmsWWEez0WLwv5PPw21hlq1oGXLkATbtAmDjTt0CElm5crQEaakf0nl5W2YjHbaKcT0zTehRlk40SxdGhKRWUjSr7yy4T4I06kB9O8PTz+94Wdtsw3873/h+cMPh2QHoYm3ceMQd4GttgqxNG68fmvTZt3u1bfeyWmnrGLh6sasJnTuWVlnc97Iig545plNEhVbbbX++WuvbdobtnBzwXvvhXtqhdTJ2JbVr8MaatOKHH6kFQ1qwdl/Kf7ySvLtu+++3HXXXWTHsAzR7rvvzieffFLiMY0bN2Zpwf8fCRg6dCgPPPAA3bp1Y+TIkcUeF+f3LmzJkiXstdde617PnTuXk046iXvvvbdSP0eJLh01bRru8RSnffvQq7NwInzjDTj77LD/+edDsmnUKCSIZs1CQnvxRfjTn+Duu8Pcnhsng9zccOzIkXD77Rvuq1MHrrsOGjQIM9EsWBDOv912IREV7ohzyimw556hvFGj8LjZZuv3//vfIaE2bFj0PazBg0u8PJv/7SCOXBvul2VksOm9zV13LfH99OxZ8v7ddtukaCsKOkXVYlFGq6rdKUpSorQkVx73338/Y8eOpUWLFpV+7qLk5+dTp07500iTJk346quv1r3u3r07R5X0t6u8EhlsF/eW9LkuZUPTprnfe6/7RRf5ygP/6n/8eU9f9ZcD3X/6Kex/9133K690v+km93vucR8+PIxUX7487J83z/2bb9znzAlzh65aFd93KcH8+e5ffBEe0/kzU+Xbb7/dsGCffTbdhg0L+5YtK3r/Y4+F/bm5m+4rxezZs71t27Z+wgkneLt27fzoo4/2ZcuWubv72LFjvUuXLt6pUyfv37+/r4wmi9hnn318/PjxPmLECL/ooovWnWv48OF+8cUX++zZs71du3Y+cOBA79Chg/fp08eXR7/zL7/80nv27OmdO3f2vn37+m+//bbunBdffLF3797d27Vr51988YUfeeSRvuOOO/rf//73dZ/RqFEjd3dfsmSJ9+7d27t27eqdOnXyl156aZNjNnb33Xd7x44dvWPHjj5kyBB3dz/rrLM8IyPDO3Xq5Pfcc88Gxy9fvtz/9re/ebt27bxv377eo0cPHz9+vLu7v/XWW96rVy/v2rWrH3PMMb5kyRJ3d3/99de9bdu23q1bN7/gggv80EMPdXf366+/3k866STffffd/fjjj/f8/Hy/7LLLPDs72zt37uwPPvjgus/95z//ua78uuuuK/G/3/Tp071Fixa+tpi5WDf5fXniA8ZjT2KJbEp08dCsK1IWVSHRAT5u3Dh3d+/fv7/feeedvmLFCm/RooVPnz7d3d1PPvnkdcmhINEtWbLE27Rp46tXr3Z39912282nTJnis2fP9tq1a/uXX37p7u7HHnusP/nkk+7u3rlzZ3///ffd3f0f//jHukS5zz77+BVXXOHu7vfee683b97cf/75Z1+5cqVvt912vmDBAndfn8Ty8vJ88eLF0dfO9R122GHdH/uiEt2ECRO8U6dOvnTpUl+yZIl36NDBJ02a5O7urVq18tzc3E3ec/fdd3v//v3d3X3y5Mleu3ZtHz9+vOfm5vpee+3lS5cudXf3O+64w2+88cZ112zWrFnu7n788cdvkOi6deu2LuE/9NBDfvPNN7u7+8qVK7179+4+a9Ysf+utt/yMM87wtWvX+po1a/zQQw/1Dz74oNj/fjfeeKNfeumlxe6vSKJT06UUqfCsKwUdEAcMCLf21NwmCXn//eL3NWxY8v5mzUreX4yWLVuyR9Rp6aSTTmLo0KH06dOH1q1bs/POOwNw6qmnMmzYMC6++OJ172vcuDG9e/fmtddeo3379uTl5dG5c2dycnJo3bo1Xbp0AULTWk5ODosXL2bRokXsE90fP/XUUzn22GPXne/www8HoHPnznTs2JHmzZsD0KZNG3766Se2KnTP19255ppr+PDDD6lVqxbz5s3j119/Zdttty3yO44bN44jjzySRo0aAXDUUUfx0Ucf0bWEMZYffvghF154IQC77LILu+yyCwCfffYZ33777bprtnr1anbbbTe+++472rRps27cWr9+/Rg+fPgG369Bg7Bm4dtvv82UKVN47rnnAFi8eDEzZ87k7bff5u23314X19KlS5k5cyZ77713kTE+/fTTPPnkk8V+h4pQopMiadYVqY427oZelm7pAwcO5LbbbqNdu3b0799/XXm9QlMF1q5dmxUbDz0pQsF7atWqtcH7a9WqRf5GHZlGjhxJbm4uEydOJCMjg6ysrJTNLuPu9OnTh9GjR29QXvi+WVEKkmzBOe677z4OPPDADY556623uPrqqznrrLNKjWPy5Mnk5+fTvXv3MkSfuLSf61LKR4tvSnX0448/8mk0jGTUqFHsueeetG3blpycHL7//nsAnnzyyXU1scJ69uzJTz/9xKhRo+hXyuwBm222GVtssQUfffRRiedMxOLFi9l6663JyMjgvffeY86cOSUev9dee/HSSy+xfPlyli1bxosvvrhBz8Wi7L333owaNQqAqVOnMmXKFAB69erFxx9/vO7aLFu2jBkzZtC2bVtmzZpFTrSI9DPPPFPsuQ888EAeeOAB8vLyAJgxYwbLli3jwAMP5NFHH13Xa3TevHnMLxgvupHRo0eXes0rQjU6KdL6qdOK6ZkoUgW1bduWYcOGcfrpp9OhQwfOOecc6tevz2OPPcaxxx5Lfn4+u+66K2cX9DDeyHHHHcdXX33FFiUs21XgiSee4Oyzz2b58uW0adOGxx57rFwxn3jiifz1r3+lc+fOZGdn065duxKP79atG6eddho9evQAQk20pGZLgHPOOYf+/fvTvn172rdvv67mlJmZyeOPP06/fv1YFfWivuWWW9h55525//77Oeigg2jUqBG7ltATeeDAgeTk5NCtWzfcnczMTF566SUOOOAApk2bxm5RL+TGjRvz1FNPsfXWW29yjmeffZY33nijxO9QETVm4VUpH611J4maNm0a7dvHN/g9JyeHww47jKlTp5Z+cDEOO+wwBg0axH777VeJkVVPS5cupXHjxrg75513HjvttBODBg2KLZ6ifl+JLryqpkspUWZmGFamJCfpbNGiRey88840aNBASS7y8MMP06VLFzp27MjixYsTutdWVcVSozOzg4D/A2oDj7j7HSUdrxqdSNUXd41O0lu1qtGZWW1gGHAw0AHoZ2YdSn6XiFQH1eFWiFQ/Ff1dxdF02QP43t1nuftq4GngiBjiEJFKVL9+fRYuXKhkJ5XKo/Xo6hcsSl0OcfS63A74qdDruUApkweKSFXXokUL5s6dW6F1w0SKUrDCeHlV2eEFZnYmcCbA9ttvH3M0IlKajIyMcq8ALZJMcTRdzgNaFnrdIirbgLsPd/dsd8/OVJc/EREppzgS3XhgJzNrbWZ1geOBV2KIQ0REaoCUN126e76ZnQ+8RRhe8Ki7f5PqOEREpGaoFjOjmFkuUPIEcNVHM2BB3EGkCV3LyqNrWXl0LStPadeylbuXem+rWiS6dGJmExIZ4Cil07WsPLqWlUfXsvJU1rXUFGAiIpLWlOhERCStKdGl3vDSD5EE6VpWHl3LyqNrWXkq5VrqHp2IiKQ11ehERCStKdGJiEhaU6JLMTO71MzczJpFr83MhprZ92Y2xcy6xR1jVWdmd5rZd9H1etHMNi+07+roWk43swPjjLO6MLODouv1vZldFXc81YmZtTSz98zsWzP7xswuisq3NLMxZjYzetwi7lirAzOrbWZfmtlr0evWZvZ59Nt8JppNq8yU6FLIzFoCBwA/Fio+GNgp2s4EHoghtOpmDNDJ3XcBZgBXA0TrGh4PdAQOAu6P1j+UYmh9yArLBy519w5AL+C86PpdBbzj7jsB70SvpXQXAdMKvR4MDHH3HYHfgQHlOakSXWoNAa4ACvcAOgL4twefAZubWfNYoqsm3P1td8+PXn5GmBgcwrV82t1Xufts4HvC+odSPK0PWQHu/ou7T4qeLyH8kd6OcA2fiA57AugbT4TVh5m1AA4FHoleG9AbeC46pNzXUYkuRczsCGCeu0/eaFdR6/Ntl7LAqr/Tgf9Gz3Uty07XrJKYWRbQFfgc2Mbdf4l2/Q/YJqawqpN7CRWBtdHrrYBFhf5RW+7fZpVdj646MrOxwLZF7Po7cA2h2VISUNK1dPeXo2P+Tmg6GpnK2EQ2ZmaNgeeBi939j1AZCdzdzUzjuEpgZocB8919opntW9nnV6KrRO6+f1HlZtYZaA1Mjv4HaAFMMrMeJLg+X01T3LUsYGanAYcB+/n6waC6lmWna1ZBZpZBSHIj3f2FqPhXM2vu7r9EtyLmxxdhtbAHcLiZHQLUB5oC/0e4lVMnqtWV+7eppssUcPev3X1rd89y9yxCFbybu/+PsBbfKVHvy17A4kJNHlIEMzuI0MRxuLsvL7TrFeB4M6tnZq0JHXy+iCPGakTrQ1ZAdB9pBDDN3e8ptOsV4NTo+anAy6mOrTpx96vdvUX09/F44F13PxF4DzgmOqzc11E1uvi9ARxC6DixHOgfbzjVwr+AesCYqIb8mbuf7e7fmNmzwLeEJs3z3H1NjHFWeVofssL2AE4Gvjazr6Kya4A7gGfNbABhibHjYoqvursSeNrMbgG+JPyjosw0BZiIiKQ1NV2KiEhaU6ITEZG0pkQnIiJpTYlORETSmhKdiIikNSU6SVtmtq2ZPW1mP5jZRDN7w8x2NrN9C2ZHj5uZ3WRmJQ6Or6TP2dzMzq2E87xvZtmVEVMyzylSmMbRSVqKBvK+CDzh7sdHZX+mis056O7XpeijNgfOBe5P9A3RNTR3X1vqwSJVmGp0kq7+AuS5+4MFBe4+2d0/il42NrPnonXtRkZ/1DGz68xsvJlNNbPhhcrfN7PBZvaFmc0ws72i8oZm9my0HtmL0dpZ2dG+A8zsUzObZGb/ieZD3ICZPW5mx0TPc8zsxuj4r82sXRHHv25mu0TPvzSz66LnN5nZGWbW2MzeKXSOgpUI7gB2MLOvzOzO6D2XR991ipndGJVlWVib7t/AVDacHmzjWDb5fhbWtvtPoWPW1Z4TuR4iyaBEJ+mqEzCxhP1dgYsJa7C1IcxwAfAvd9/V3TsBDQjzaRao4+49ovddH5WdC/werUf2D6A7gIWFda8F9nf3bsAE4JIE4l4QHf8AcFkR+z8C9jKzzQizvxTEvRfwIbASODI6x1+Au6NkfRXwg7t3cffLzewAwhRpPYAuQHcz2zs6107A/e7e0d3nFBVkCd9vLNDTzBpFh/6NMLNFea+HSIWp6VJqqi/cfS5ANHVTFjAO+IuZXQE0BLYEvgFejd5TMGHvxOh4gD0Jk8/i7lPNbEpU3ouQRD+OKoV1gU8TiKvwZxxVxP6PgAuB2cDrQB8zawi0dvfpFiYYvi1KWmsJy5oU1Vx7QLR9Gb1uTEhwPwJzorURS1Lk94umFHsT+KuZPUdYX+wKYJ+iji/lM0QqhRKdpKtvWD8ZbFFWFXq+BqhjZvUJ97Cy3f0nM7uBMJP6xu9ZQ+n/7xgwxt37lSnq0j9jPJANzCKstN4MOIP1tdcTgUygu7vnmVnORt+hcHy3u/tDGxSGNdWWJRBnSd/vaeB84DdggrsviWqV5bkeIhWmpktJV+8C9czszIICM9ul4N5aMQoSwoLo/lFJibLAx0QT9ppZB6BzVP4ZsIeZ7Rjta2RmO5fxO2wiWgX8J+BYQo3oI0IT54fRIZsR1vXKM7O/AK2i8iVAk0Knegs4veA+mZltZ2ZblyGUkr7fB0A3QgJ+OoHjRZJKiU7SUrRG3ZHA/haGF3wD3E5Y7bm49ywCHiZ0wniLUHsqzf1Appl9C9xCqEkudvdc4DRgdNSc+SmwSeeScvqIkMxWRM9bRI8QFqHNNrOvgVOA7wDcfSGh2XCqmd3p7m8Do4BPo2OfY8NEWKKSvl+0YsRrwMHRY4nHiySbVi8QqQAzqw1kuPtKM9uB0BmjbVTzEpEqQPfoRCqmIfBe1AnEgHOV5ESqFtXoREQkrekenYiIpDUlOhERSWtKdCIiktaU6EREJK0p0YmISFr7f4ZRKCxiOzzqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2bGjdGho0SDqSGiVOPzoD1hf6vJ6iJ1QVEZHirF0LRx0Fv/td0pHUOHFKdI8AH5rZ2Ojz8cCI1IUkIpKGHnwQZs0KgzdLpYrTGOUeM3sbODBaNdDdP0lpVCIi6WTJErj5Zjj88FCqk0oVp0SHu08BpqQ4FhGR9HTrrZCXB0OHatDmBGisSxGRVFq/Ht5/P4zx1bVr0tHUSLFKdCIiUk61a4dEt2pV0pHUWKWW6KLuBKWuExGRLXz5JSxeDLVqqUtBguJUXfYpYt1vKjoQEZG0smEDnHYaHHYYuCcdTY1WbNWlmV0A/AFoZ2aFh/xqBLyX6sBERKq1xx+HKVPgiSfUACVhJT2jGw28BtwOXFNo/TJ3X5LSqEREqrMVK+C666BXL+jfP+loarxiqy7dPc/dc4CrCdPyFCwNzax1aSc2s53N7C0z+9zMPiuYwDWaz268mc2KXptWzFcREakihg6FhQvhnnvC8zlJVJxWl68QEpwBdYG2wFdAp1KOWwdc7u5TzKwRMNnMxgNnAW+6+x1mdg2htHh1OeMXEal6Zs+Gk06CAw5IOhIh3sgoXQp/NrMehGd3pR33HfBd9H6ZmX0BtAR+C/w62m0k8DZKdCKSTh57LIxtKVVCmcvU0Sgp+5TlGDNrA3QHPgR2jJIgwPfAjsUcc66ZZZtZdm5ublnDFBGpfF9+CV99Fd7XqZNsLLJRnPnoLiv0sRbQA/g27gXMrCHwH2CIu/9shVofububWZHtbt19ODAcICsrS21zRaRqc4cLLgiJLidHia4KifOMrlGh9+sIz+z+E+fkZpYR7fuEuxdM3PqDmbVw9+/MrAWwqCwBi4hUSS++CG+/DcOGKclVMeYxOzJGJTPcfXnM/Y3wDG6Juw8ptP4uYHGhxijbu/tVJZ0rKyvLs7OzY8UpIlLp1q6Fzp1hm21g2rTwKilnZpPdPau0/eJUXXYGRgHbR59/BM509xmlHHoAMACYbmZTo3XXAXcAT5vZIGAecHJpMYiIVGkFc8298oqSXBUU519kOHCZu78FYGa/jtbtX9JB7j6J4mciP6wMMYqIVG1Ll8LRR8NvNDpiVRQn0TUoSHIA7v62mWl0UhGRAjfeGMa21FBfVVKc7gVzzOxPZtYmWm4A5qQ6MBGRKm/2bHjzzfBeI6BUWXH+Zc4GMoHnCC0om0frRERqtiuvhOOPh59+SjoSKUGckVF+Ai6uhFhERKqPt9+G55+Hv/wFmmrI3qpMZW0RkbLasAEuuwx23hkuvTTpaKQUagcrIlJWo0bBJ5+Euebq1Us6GimFSnQiImVVqxYcdZTmmqsm4nQYv6+I1XlAtru/UPEhiYhUcQMGhEWqhTglurpAN2BWtHQFWgGDzOxvKYxNRKRqWbgQRoyA9euTjkTKIM4zuq7AAe6+HsDMHgDeBQ4EpqcwNhGRquWGG2D0aDj8cNhll6SjkZjilOiaAg0LfW5AGIh5PbAmJVGJiFQ1U6bAyJEwZIiSXDUTp0T3V2Cqmb1NGLvyYOC2aBiwCSmMTUSkanCHyy+HZs3guuuSjkbKKE6H8RFm9irQK1p1nbsXTLx6ZcoiExGpKgrmmrv/fmjSJOlopIzidi+oBeQCPwG7mtnBqQtJRKSKadw4DPV1zjlJRyLlEKd7wZ3AKcBnwIZotQMTUxiXiEjVceihYZFqKc4zuuOBDu6uhiciUrMsWQL/+EcY7qtRo6SjkXKKNU0PkJHqQEREqpw//zksOTlJRyJbIU6JbiWh1eWbFOpO4O6a0UBE0tfMmTBsGAweDF26JB2NbIU4ie7FaBERqTmuvhrq1g0lOqnW4nQvGFkZgYiIVBkFc83ddhvsuGPS0chWKjbRmdnT7n6ymU0ntLLcjLt3TWlkIiJJ2XHHMGjzkCFJRyIVoKQS3SXR6zGVEYiISJWxxx7w2GNJRyEVpNhWl+7+XfT2cKCOu88rvFROeCIilWjFCrjwQpg/P+lIpALF6V7QGnjIzOaY2TNm9kcz65bqwEREKt1dd4VhvpTo0kqpic7db3L33kAnwvQ8VwKTUx2YiEilWrgQ/vpXOPlk2H//pKORClRqojOzG8zsNWAcsCtwBWHi1dKOe9jMFpnZjELrbjazhWY2NVqO2prgRUQqzA03hAlV77gj6UikgsWpujwBaEaYkuc54IVCz+9K8ijQt4j197p7t2h5NXakIiKpUniuubZtk45GKlicqssehAYpHwF9gOlmNinGcROBJVsdoYhIqrVsCRdfrLnm0lScqsvOwGnAmYRZDBYC/92Ka15kZtOiqs2mW3EeEZGtlpsLH3+zI7nX/01zzaWpOFWXdwCNgfuAPdz9UHe/sZzXewBoD3QDvgOGFrejmZ1rZtlmlp2bm1vOy4mIFO+pkat5s8VpXNF7CrvsAmPGJB2RpEKcqstjgHuBn4EOZlbumQzc/Qd3X+/uG4B/sWnW8qL2He7uWe6elZmZWd5LiogUKTcXFg6+kf7rR1Nn+WJWrYJBg8J6SS9xqi4PAWYBw4D7gZnlnWHczFoU+tgPmFHcviIiqfTjC+8xZN3dPMS5TKAPABkZmpEnHcWZveAe4Ah3/wrAzHYHxgA9SzrIzMYAvwaam9kC4Cbg11FncwdygPPKHbmISHmtWMHufzmTb2wXrvC7N67Oz4c2bZILS1IjTqLLKEhyAO4+M071pbufWsTqEWUJTkQkJf7+d2rnfM3XN7zF+qGNaJwRktyIEaAnJeknTqLLNrN/A49Hn08DslMXkohIil1+OXTtymHH/Jp5F4fqyjZtlOTSVZxEdwFwIVAwo/i7hGd1IiLVy7JlsGFD6EZwTJiYJTNTCS7dxZl4dQ3hOd09qQ9HRCSFLr0U3nwTPvsM6tdPOhqpJCVNvFrkhKsFNPGqiFQrr74aHsJdc42SXA1TUonuJGBVZQUiIpIyS5bA4MHQuTPcfHPS0UglKynRjXb3HmY2yt0HVFpEIiIV7eKLQ0/wl1+GbbdNOhqpZCUlujpm9ntgfzM7YcuN7v5c6sISEakgq1fDDz+EaXh69Eg6GklASYnufEJXgu2AY7fY5oQpe0REqra6dWHcuDDXnNRIxSY6d58ETDKzbHdXR28RqV7cwySqAwZAq1awTZzeVJKO4gzqrCQnItXP6NFhfrmnnko6EklYnGl6RESql2+/hYsugv32C7OGS41WYqKzYOfKCkZEZKu5h64Ea9bAyJFQu3bSEUnCSkx07u7Aq5UUi4jI1hs1Cl57De68E3bbLelopAqIU3U5xcz2TnkkIiIV4bjjQiOUCy9MOhKpIuI0Q9oHOM3M5gErACMU9jQEmIhUHRs2wLp1sN12cPXVSUcjVUicRHdkyqMQEdla998fxrKcMAGaNUs6GqlC4nQvmAfsDPSO3q+Mc5yISKWZNSuU4lq0gO23TzoaqWJKTVhmdhNwNXBttCqDTZOwiogka/16OOssqFMH/vUvMEs6Iqli4lRd9gO6A1MA3P1bM2uU0qhEROK69154//3Q2rJly6SjkSooThXk2qibgQOYWYPUhiQiEtP69fDMM9CvH5x2WtLRSBUVp0T3tJk9BGxnZucAZwP/Tm1YIiIx1K4N774LK1eqylKKVWqic/e7zawP8DPQAbjR3cenPDIRkZK8/DIcdBA0aRKez4kUI05jlDvdfby7X+nuV7j7eDO7szKCExEp0tSpobryT39KOhKpBuI8o+tTxLrfVHQgIiKxrFkDZ5wBzZvDzTcnHY1UA8VWXZrZBcAfgHZmNq3QpkbAe6kOTESkSH/+M0yfHqou1WdOYijpGd1o4DXgduCaQuuXufuSlEYlIlKUDz8M41iefTYcfXTS0Ug1UWzVpbvnuXuOu58ajYiyitDFoKGZtS7txGb2sJktMrMZhdZtb2bjzWxW9Nq0Qr6FiNQMLVqEbgT33JN0JFKNxGmMcqyZzQLmAu8AOYSSXmkeBfpuse4a4E133w14k81LiiIiJWvdGh57LLS0FIkpTmOUW4F9gZnu3hY4DPigtIPcfSKwZRXnb4GR0fuRwPHxQxWRGuudd+Coo2DRoqQjkWooTqLLd/fFQC0zq+XubwFZ5bzeju7+XfT+e2DH4nY0s3PNLNvMsnNzc8t5ORGp9pYvh4EDYeZMqF8/6WikGoozMspSM2sITASeMLNFhHnptoq7u5l5CduHA8MBsrKyit1PRNLclVdCTg5MnAgNGyYdjVRDcUp0vyU0RLkUeB34Gji2nNf7wcxaAESvqocQkeKNGwcPPgiXXQYHHph0NFJNFZvozGyImfUC1rj7endf5+4j3f2+qCqzPF4Ezozenwm8UM7ziEi6cw8dwjt2hP/7v6SjkWqspKrLVsDfgI5mNp3QSfx94P04/ejMbAzwa6C5mS0AbgLuIAwSPQiYB5y8deGLSNoyg1dfhR9+gHr1ko5GqrFiE527XwFgZnUIjU/2BwYCw81sqbvvWdKJ3f3UYjYdVs5YRaSm+Pxz2HVX2G67sIhshTjP6OoBjYEm0fIt8GEqgxKRGmzxYujdGwYNSjoSSRMljXU5HOgELCMktveBe9z9p0qKTURqogsvhCVL4Iorko5E0kRJJbrWwLaE/m4LgQXA0soISkRqqKefhqeegptugr32SjoaSRMlPaPra2ZGKNXtD1wOdDazJcD/3P2mSopRRGqC77+HP/wB9t4brr466WgkjZTYYdzdHZhhZkuBvGg5BuhFaEUpIlIx8vKgfXt49FHYJs5YFiLxlPSM7mJCSW5/IJ+oawHwMDC9UqITkZqjQwf44IPQrUCkApX0jK4N8Aywj7u3d/cB7v6Au3/q7hsqJzwRSXvz54cGKHl5SnKSEiXNR3eZu/+n0CDMIiIVyx0GDw7VlT/+mHQ0kqZUES4ilS43N4zT3PGd4TQaNw6GDQvP50RSQIlORCrVmDGhL/hu28zhvWWX833nw9np/POTDkvSWJyRUUREKkRubkhyq1bBrcsuYT21OXjWCHIX61eRpI5KdCJSaXJyoE6dkOguZBh78jk/bNuanBzIzEw6OklX+jNKRCpNmzZw0KpxGBuYT2veoC/5+WG9SKoo0YlIpcl8ehgvrT2SP2Y8ROPGYfadESNUmpPUUtWliFSOhx+Giy6C3/6WG+4fzOkLQ0lOSU5STYlORFJv9OjQX+7II+Gpp8jcNoPMXyUdlNQUqroUkdRavBjOOw8OOQSeew623TbpiKSGUYlORFKrWTOYMAH23BPq1086GqmBVKITkdR4883wXA5gn32gUaNk45EaS4lORCrepElw3HHw97/D2rVJRyM1nBKdiFSsjz6Co46CnXeGceNCD3GRBCnRiUjFmTo1tKzMzAxVlzvumHREIkp0IlKB3nknPIt7801o2TLpaEQAJToRqQgbormYL7kEpk/XmF5SpSjRicjWycmB7t3hww/D5yZNEg1HZEuJ9KMzsxxgGbAeWOfuWUnEISJbaeFCOOwwWLJEHcGlykqyw/ih7v5jgtcXka3xww8hyeXmhg7h3bolHZFIkTQyioiU3U8/QZ8+MH8+vP469OqVdEQixUrqGZ0D48xsspmdm1AMIlJe9etD587wwgtw0EFJRyNSoqRKdAe6+0Iz2wEYb2ZfuvvEwjtECfBcgNatWycRo4hsaflyWLMmjF85enTS0YjEkkiJzt0XRq+LgLHAL+o93H24u2e5e1amJqwSSd6qVWFYr8MOg3Xrko5GJLZKT3Rm1sDMGhW8B44AZlR2HCJSBmvWwAknwNtvw5VXwjZ6vC/VRxI/rTsCY82s4Pqj3f31BOIQkTjy86F//9Do5N//htNOSzoikTKp9ETn7nOAvSr7uiJSTtddB88/D/fdB4MGJR2NSJmp/iGG3Nww+EObNmGsWpGaoODnvt0Zl9KsY0clOam2NARYKcaMgV12CV2GdtklfBZJa6tX884VLzHsV3+h7+Hr2HmfXzGmvpKcVF/m7knHUKqsrCzPzs6u9Ovm5obk1nTVQhqxjFnsxrb1ajNvnkp2kmaWLYOXXoKxY/FXX8NWriCPxgxgFC9xHPXqoZ97qXLMbHKcISRVoiuOOzk5Yc7IYVzIl+zBMhoxcc0++HnnhYfyItXZd9/BggXh/fTpoZHJpEnkHnk6J9R/nUxyeYnjAMjmZNWNAAAYLUlEQVTICNWYItWREt2WliyBe+6Bjh1p51+zdi1cy+0M5GEe5HyW04Dm/30a/vnPTccMHBhapd1+O7z2WvgFUg1KylIDzZ4Nd90F++8f5ou7886wft994f33YeFC7KEHed2PJJ9NM4Pn52vmHam+1BilwOTJcP/9YbSH1avhgANotk0eI0bAoEF78G3GHjyVDyNGQK3+DkuXbn78Rx/BU09t+nzyyZs+v/gi7LYb7L471K5ded9JpLDeveGtt8L77t3hllvgxBPD51q1YL/9gFA9GX7uQ0kuP/q5V7WlVFd6RgehFNeiRegEe/rp8Ic/wF6bekDEbnW5dClMmwZTp8KvfhV+iaxYEWZcdod69aBLlzDKe//+cOihqftOUnOtXw+TJsHYsfDpp/Df/4IZ3H13+Bk//vhYxTO1NpaqLu4zuhpTotvsP+2yOfDQQzBzZvhlsP32odS1775FThqZmRnzP/p228HBB4elQN26IfEVXp55Bjp1Colu7lzo2zckv8LLTjuFX04icU2ZAsOGhZ/lH38M88P16RMamjRuDFdcUabTxf65F6niakSiGzMGzjl7PUfVep2zV9/Pkf4aVqtW+Mt29eqQjI48MjUXr10bunYNyxlnhHXum8YKXLMmJL2PP4ann9503HPPQb9+MGdOmLm5W7diqz71l3cNlZcHr7wSqhzbtg1/ND37LBxzTPjZ7ts31CaI1HTuXuWXnj17enktWuRer577QEa4gy+khd+6zU3+46cLyn3OlFm61H3iRPf77nNfuDCs++c/3UNqDF+kVy/3c891//57d3cf/cQGr1fPvUmTsHn06ATjl9T77jv3Bx90P/JI94yM8HNx111h29q17mvWJBufSCUCsj1GDkn7Z3Qffxxqbzbk/UxfXmcs/ajfOIMJE2DvvSs40FTIz4cvv9y86vPTT2HuXHJXN+Lfv7qR3617kokczH1czOx6XdXfKd2sWAENGsDKlWF6nNWroX37UOLv1y9UuddSA2qpefSMLtKmDaxdC6tozDOcDFSzptIZGaEBS5cuMGBAWOcOZuR8CTMzOjFjXWf68ySDGcF/1/ZhyVNXknlRn2TjlvJzD3/QjB0blkaNQtP/+vVD/82uXcOkpwk9w83Pz2fBggWsXr06ketLzVO3bl1atWpFRkZGuY5P+0SXlk2lo19wbdrAU5zCo5zCdvzEeTzEJevvY/vXH4SCRLd+vbo0FCOJZ5ulXvOhh0J/zHnzQintwAPhd7/b+MdNVZg5YMGCBTRq1Ig2bdpgajAlKebuLF68mAULFtC2bdtynSPtEx3AqafC4YenX4ONwkl8Q0ZT7su/hu4PXsopR0Z9/L76Knzxiy6C884LrUJrIvdQ/dewYfg8dChzn5/KD+9/TQNW8hWN+eG4g+k89taw/e67Q2OhJk1Ca8UmTUJjj06dwvalS8O5yjgn25gx4d+qTp1Qy/DIA6s5pdmEUGq7447wD1q7dii9/+lPYZLTKvjDunr1aiU5qTRmRrNmzcjNzS3/OdL9GV1NUGwpYfp0uPxyGD8+/GIeNAiGDKlG9bZlUFDigdBi9cMP4euvw0ggc+ZAx46hUz+Qn7Uv30/5ltnenmU0ojE/8+k2Pfn9t0PD/fvVr8LoNoWddho8/nh437DhpudmTZqEZcAAuPbaEMe554YEWZAkmzRhSZsetDp6L9asWs/JPE0/xvIbXqMRy8N+zz9fbfpVfvHFF+yxxx5JhyE1TFE/d3pGV4MU29+pSxcYNy40Xhk6NPSxGjECvv8+/JKurj76KDyz+vrrTcuaNZsGY3ziCXj55VAKa98eDjkkPNeKTP3ne/TpW5u8vE2nbFwf9s+J7uPChaHBR14e/PxzeG3cOOzoDrfeuvm2vLzQSARg1Sp49dWwbsWKjedfffYN1KmzFw1XLWYMv+c7duKZjNM44O5+dDj/0FDME5HUiNM0M+lla7oXSCHz57v/5z+bPl90kfuLL7qvX59cTEWZO9f9ySfd//IX97PPdj/4YPedd3ZfvTpsv/ji0Ky+cWP37t3dTzzR/ZprNn2PvDz3deuKPX1Bl5OCXhsFPTcWLarg75Gf775kifvcuZ77+SKvV8+9Hit8H/7nxvrUXLMSfP7550mHUGaHHHKIf/zxx4lce7/99it1nwYNGpTpnH//+9+9Y8eO/vvf/77E/ZL83lu67rrrvFWrVr/4rqtXr/aTTz7Z27dv77169fK5c+cWeXxRP3fE7F6gEl1N0qpVWAAWLQojaPzzn9ChA1x2Wah+q1cv9XEUDJVWUBqbPTu8PvtsmBfp2WfhyivDvjvtFEplhx4aSkjbbgvXXx+eYTVrVnTLw4LSVzEqrYHSNttA06bQtCnNKbhmfb7I2Je66dAoSmJ5//33K/yc999/PxMmTKBVwf/nFFu3bh3blPGZ9JaOPfZYLrroInbbbbfN1o8YMYKmTZsye/ZsnnzySa6++mqeKjxucEWIkw2TXlSiS5H8fPcxY9x79gzFmsxM948+2myXRYvCqjKVPPLz3WfOdH/ttdDh/dJL3Y87zj07O2x/8slNRanatd3btXPv08f9iy/C9oUL3adNc1++vGK+ZzHK9d2q4TUr2i/+sj7kkF8uw4aFbStWFL39kUfC9tzcX24rxdy5c71Dhw7++9//3jt27Oi/+93vfMWKFe7uPmHCBO/WrZt37tzZBw4c6KujWoCCks2IESP8kksu2Xiu4cOH+5AhQ3zu3LnesWNHHzx4sO+5557ep08fX7lypbu7f/LJJ77PPvt4ly5d/Pjjj/clS5ZsPOeQIUO8Z8+e3rFjR//oo4+8X79+vuuuu/r111+/8RoFJZhly5Z57969vXv37t65c2d//vnnf7HPloYOHeqdOnXyTp06+b333uvu7uedd55nZGR4586d/Z577tls/5UrV/opp5ziHTt29OOPP9579eq1sUT3xhtv+L777uvdu3f3E0880ZctW+bu7q+88op36NDBe/To4X/84x/96KOPdnf3m266yU8//XTff//9vX///r5u3Tq/4oorPCsry7t06eIPPvjgxuv+9a9/3bj+xhtvLPHfb8vvesQRR/j777/v7u75+fnerFkz37Bhwy+O25oSXeJJLM6iRJdiGza4v/22e//+4ReTu/s77/hLd39Z/Kgry5a5T50aqkL/+lf3885zf/PNsO29935ZL9ipk/v48WH799+7v/66+6xZYTQPqVaqQqIDfNKkSe7uPnDgQL/rrrt81apV3qpVK//qq6/c3X3AgAEbk0NBolu2bJm3a9fO10Y/d/vtt59PmzbN586d67Vr1/ZPPvnE3d1POukkHzVqlLu7d+nSxd9++213d//Tn/60MVEecsghftVVV7m7+9/+9jdv0aKFf/vtt7569Wpv2bKl//jjj+6+6Rd7fn6+5+XlRV8719u3b7/xF3pRiS47O9s7d+7sy5cv92XLlvmee+7pU6ZMcXf3XXbZxXNzc39xzNChQ33gwIHu7v7pp5967dq1/eOPP/bc3Fw/6KCDfHn0x+Mdd9zht9xyy8Z7NmfOHHd379+//2aJrkePHhsT/kMPPeT/93//5+6hurFnz54+Z84cf+ONN/ycc87xDRs2+Pr16/3oo4/2d955p9h/vy2/a6dOnXz+/PkbP7dr167I76aqS9k6ZqHBxiGHhM/urDv/Io76YgZjOJYXVx1HKxbwzFlZHH740WSuXbipCrRAs2bQs2eYCqZTJ3jkkVDl2L59mBmicBXjjjumbmxRqXxvv138tvr1S97evHnJ24ux8847c8ABBwBw+umnc99999GnTx/atm3L7rvvDsCZZ57JsGHDGDJkyMbjGjZsSO/evXn55ZfZY489yM/Pp0uXLuTk5NC2bVu6desGQM+ePcnJySEvL4+lS5dySPR/48wzz+Skk07aeL7jjgsT03bp0oVOnTrRokULANq1a8f8+fNpVtBIiVCouO6665g4cSK1atVi4cKF/PDDD+y0005FfsdJkybRr18/GkQNx0444QTeffddunfvXux9mThxIhdffDEAXbt2pWvUCOuDDz7g888/33jP1q5dy3777ceXX35Ju3btNvZPO/XUUxk+fPhm369e9Dhj3LhxTJs2jWeffRaAvLw8Zs2axbhx4xg3btzGuJYvX86sWbM4uPDg9glTopNfMmP6vRMY99thDFozjN/yIhsw/sEV5OQcTWbPFnDbbZsSWfv2m/fRa9IEzjorsfAl/W3Zh68sffoGDx7MbbfdRseOHRk4cODG9dtuu+3G97Vr12bVqlWlnqvgmFq1am12fK1atVhXMHB75IknniA3N5fJkyeTkZFBmzZtKm10GXenT58+jBkzZrP1U6dOLfG4BoVaZ7s7//jHPzhyiz9S33jjDa699lrOO++8csXWsmVL5s+fT6tWrVi3bh15eXmb/YFQETRAnhSpVY8duKXWLezMfHbnK+qzkmtr/zV0watVK/QZO/nkUIqrqR3RJTHffPMN//vf/wAYPXo0Bx54IB06dCAnJ4fZs2cDMGrUqI0lscL22Wcf5s+fz+jRozn11FNLvE6TJk1o2rQp7777bonnjCMvL48ddtiBjIwM3nrrLebNm1fi/gcddBDPP/88K1euZMWKFYwdO5aDDjqoxGMOPvhgRo8eDcCMGTOYNm0aAPvuuy/vvffexnuzYsUKZs6cSYcOHZgzZw45UdeckhqBHHnkkTzwwAPk5+cDMHPmTFasWMGRRx7Jww8/zPLlywFYuHAhixYtKv2GRI477jhGjhwJwLPPPkvv3r0rfDACleikSJtaJtbjh4zdqaVWglKFdOjQgWHDhnH22Wez5557csEFF1C3bl0eeeQRTjrpJNatW8fee+/N+eefX+TxJ598MlOnTqVp06alXmvkyJGcf/75rFy5knbt2vHII4+UK+bTTjuNY489li5dupCVlUXHjh1L3L9Hjx6cddZZ9OrVCwgl0ZKqLQEuuOACBg4cyB577MEee+xBz549AcjMzOTRRx/l1FNPZc2aNQDceuut7L777tx///307duXBg0asHcJI90PHjyYnJwcevTogbuTmZnJ888/zxFHHMEXX3zBftEM9Q0bNuTxxx9nhx122Oz4q666itGjR7Ny5UpatWrF4MGDufnmmxk0aBADBgxg1113Zfvtt+fJJ58s+UaWg0ZGkRJprjvZUtIjo+Tk5HDMMccwY8aMcp/jmGOO4dJLL+Wwww6rwMiqp+XLl9OwYUPcnQsvvJDddtuNSy+9NOmwfmFrRkZR1aWUKDMzTGekJCfpYOnSpey+++7Uq1dPSS7yr3/9i27dutGpUyfy8vLK/aytKkukRGdmfYG/A7WBf7v7HSXtrxKdSNWRdIlOaqZqVaIzs9rAMOA3wJ7AqWa2Z2XHISLlVx0eeUj62NqftySqLnsBs919jruvBZ4EfptAHCJSDnXr1mXx4sVKdlIp3MN8dHXr1i33OZJoddkSmF/o8wJgny13MrNzgXMBWrduXTmRiUipWrVqxYIFC7ZqfjCRsiiYYby8qmz3AncfDgyH8Iwu4XBEJJKRkVHumZ5FkpBE1eVCYOdCn1tF60RERCpcEonuY2A3M2trZnWA/sCLCcQhIiI1QKVXXbr7OjO7CHiD0L3gYXf/rLLjEBGRmqFajIxiZrlAyQPDVR/NgR+TDiJN6F5WHN3LiqN7WXFKu5e7uHupw1lUi0SXTswsO04HRymd7mXF0b2sOLqXFaei7qWGABMRkbSmRCciImlNia7yDS99F4lJ97Li6F5WHN3LilMh91LP6EREJK2pRCciImlNiU5ERNKaEl0lM7PLzczNrHn02czsPjObbWbTzKxH0jFWdWZ2l5l9Gd2vsWa2XaFt10b38iszOzLJOKsLM+sb3a/ZZnZN0vFUJ2a2s5m9ZWafm9lnZnZJtH57MxtvZrOi16ZJx1odmFltM/vEzF6OPrc1sw+jn82notG0ykyJrhKZ2c7AEcA3hVb/BtgtWs4FHkggtOpmPNDZ3bsCM4FrAaJ5DfsDnYC+wP3R/IdSDM0PudXWAZe7+57AvsCF0f27BnjT3XcD3ow+S+kuAb4o9PlO4F533xX4CRhUnpMq0VWue4GrgMItgH4LPObBB8B2ZtYikeiqCXcf5+7roo8fEAYGh3Avn3T3Ne4+F5hNmP9Qiqf5IbeCu3/n7lOi98sIv6RbEu7hyGi3kcDxyURYfZhZK+Bo4N/RZwN6A89Gu5T7PirRVRIz+y2w0N0/3WJTUfPztay0wKq/s4HXove6l2Wne1ZBzKwN0B34ENjR3b+LNn0P7JhQWNXJ3wgFgQ3R52bA0kJ/1Jb7Z7PKzkdXHZnZBGCnIjZdD1xHqLaUGEq6l+7+QrTP9YSqoycqMzaRLZlZQ+A/wBB3/zkURgJ3dzNTP64SmNkxwCJ3n2xmv67o8yvRVSB3P7yo9WbWBWgLfBr9B2gFTDGzXmh+viIVdy8LmNlZwDHAYb6pM6juZdnpnm0lM8sgJLkn3P25aPUPZtbC3b+LHkUsSi7CauEA4DgzOwqoCzQG/k54lLNNVKor98+mqi4rgbtPd/cd3L2Nu7chFMF7uPv3hLn4zohaX+4L5BWq8pAimFlfQhXHce6+stCmF4H+ZratmbUlNPD5KIkYqxHND7kVoudII4Av3P2eQpteBM6M3p8JvFDZsVUn7n6tu7eKfj/2B/7r7qcBbwEnRruV+z6qRJe8V4GjCA0nVgIDkw2nWvgnsC0wPiohf+Du57v7Z2b2NPA5oUrzQndfn2CcVZ7mh9xqBwADgOlmNjVadx1wB/C0mQ0iTDF2ckLxVXdXA0+a2a3AJ4Q/KspMQ4CJiEhaU9WliIikNSU6ERFJa0p0IiKS1pToREQkrSnRiYhIWlOik7RlZjuZ2ZNm9rWZTTazV81sdzP7dcHo6Ekzsz+bWYmd4yvoOtuZ2R8q4Dxvm1lWRcSUynOKFKZ+dJKWoo68Y4GR7t4/WrcXVWzMQXe/sZIutR3wB+D+uAdE99DcfUOpO4tUYSrRSbo6FMh39wcLVrj7p+7+bvSxoZk9G81r90T0Sx0zu9HMPjazGWY2vND6t83sTjP7yMxmmtlB0fr6ZvZ0NB/Z2GjurKxo2xFm9j8zm2Jmz0TjIW7GzB41sxOj9zlmdku0/3Qz61jE/q+YWdfo/SdmdmP0/s9mdo6ZNTSzNwudo2AmgjuA9mY21czuio65Mvqu08zslmhdGwtz0z0GzGDz4cG2jOUX38/C3HbPFNpnY+k5zv0QSQUlOklXnYHJJWzvDgwhzMHWjjDCBcA/3X1vd+8M1COMp1lgG3fvFR13U7TuD8BP0XxkfwJ6AliYWPcG4HB37wFkA5fFiPvHaP8HgCuK2P4ucJCZNSGM/lIQ90HARGA10C86x6HA0ChZXwN87e7d3P1KMzuCMERaL6Ab0NPMDo7OtRtwv7t3cvd5RQVZwvebAOxjZg2iXU8hjGxR3vshstVUdSk11UfuvgAgGrqpDTAJONTMrgLqA9sDnwEvRccUDNg7Odof4EDC4LO4+wwzmxat35eQRN+LCoV1gP/FiKvwNU4oYvu7wMXAXOAVoI+Z1QfauvtXFgYYvi1KWhsI05oUVV17RLR8En1uSEhw3wDzorkRS1Lk94uGFHsdONbMniXML3YVcEhR+5dyDZEKoUQn6eozNg0GW5Q1hd6vB7Yxs7qEZ1hZ7j7fzG4mjKS+5THrKf3/jgHj3f3UMkVd+jU+BrKAOYSZ1psD57Cp9HoakAn0dPd8M8vZ4jsUju92d39os5VhTrUVMeIs6fs9CVwELAGy3X1ZVKosz/0Q2WqqupR09V9gWzM7t2CFmXUteLZWjIKE8GP0/KikRFngPaIBe81sT6BLtP4D4AAz2zXa1sDMdi/jd/iFaBbw+cBJhBLRu4QqzonRLk0I83rlm9mhwC7R+mVAo0KnegM4u+A5mZm1NLMdyhBKSd/vHaAHIQE/GWN/kZRSopO0FM1R1w843EL3gs+A2wmzPRd3zFLgX4RGGG8QSk+luR/INLPPgVsJJck8d88FzgLGRNWZ/wN+0biknN4lJLNV0ftW0SuESWizzGw6cAbwJYC7LyZUG84ws7vcfRwwGvhftO+zbJ4IS1TS94tmjHgZ+E30WuL+Iqmm2QtEtoKZ1QYy3H21mbUnNMboEJW8RKQK0DM6ka1TH3gragRiwB+U5ESqFpXoREQkrekZnYiIpDUlOhERSWtKdCIiktaU6EREJK0p0YmISFr7f/7k4WnzJAUrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_poly(x, y, xlabel, ylabel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do we determine which one is the best fit?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before digging into the previous question, let's make the following observation.\n",
    "\n",
    "The hypothesis function for a polynomial fit has the structure\n",
    "$$ h_\\beta (x) = p(x) = \\beta_0 + \\beta_1 x + \\beta_2 x^2 + \\ldots $$\n",
    "\n",
    "by making the substitution $x^{(k)} = x^k$ we can use the multivariable _linear_ regression and write down\n",
    "\n",
    "$$h_\\beta(x) = X \\cdot \\beta = \\begin{pmatrix} 1   & x^{(1)}_1 & x^{(2)}_1 & \\\\\n",
    "                                                1   & x^{(1)}_2 & x^{(2)}_2 & \\vdots\\\\\n",
    "                                                    & \\ldots & \\ldots& \\end{pmatrix} \\begin{pmatrix} \\beta_0 \\\\\n",
    "                                                \\beta_1 \\\\\n",
    "                                                \\vdots \\end{pmatrix} \\, . $$\n",
    "\n",
    "Thus, thanks to this observation, from the computational point of view, polynomial regression is just a multivariable linear regression."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Complete the following"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write down the code to store the matrix $X$ of the dataset previously loaded, ready to be fed to a polynomial regression of degree $3$.\n",
    "\n",
    "_Hint_: The function `np.power(a, n)` gives the element-wise $n$-th power of the array $a$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# complete\n",
    "X = np.ones((len(x), 4))\n",
    "X[:,1] = x\n",
    "X[:,2] = np.power(x,2)\n",
    "X[:,3] = np.power(x,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.00000000e+00, -4.80588295e+01,  2.30965109e+03,\n",
       "        -1.10999128e+05],\n",
       "       [ 1.00000000e+00, -4.43837599e+01,  1.96991814e+03,\n",
       "        -8.74323736e+04],\n",
       "       [ 1.00000000e+00, -3.47062658e+01,  1.20452489e+03,\n",
       "        -4.18045609e+04],\n",
       "       [ 1.00000000e+00, -2.91529792e+01,  8.49896197e+02,\n",
       "        -2.47770062e+04],\n",
       "       [ 1.00000000e+00, -1.59367581e+01,  2.53980260e+02,\n",
       "        -4.04762197e+03],\n",
       "       [ 1.00000000e+00, -8.94145794e+00,  7.99496701e+01,\n",
       "        -7.14866612e+02],\n",
       "       [ 1.00000000e+00,  1.38915437e+00,  1.92974986e+00,\n",
       "         2.68072045e+00],\n",
       "       [ 1.00000000e+00,  7.01350208e+00,  4.91892115e+01,\n",
       "         3.44988637e+02],\n",
       "       [ 1.00000000e+00,  1.53077929e+01,  2.34328523e+02,\n",
       "         3.58705250e+03],\n",
       "       [ 1.00000000e+00,  2.27627489e+01,  5.18142738e+02,\n",
       "         1.17943531e+04],\n",
       "       [ 1.00000000e+00,  3.61895486e+01,  1.30968343e+03,\n",
       "         4.73968522e+04],\n",
       "       [ 1.00000000e+00,  3.74921873e+01,  1.40566411e+03,\n",
       "         5.27014222e+04]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If everything is completed properly, you should get this\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And use `sklearn.linear_model.LinearRegression` to predict the value of the flow, if the observed water level is `level_test = 25`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your predicted flow: 22.3186877411\n",
      "You should get approximatively: 22.32\n"
     ]
    }
   ],
   "source": [
    "# complete\n",
    "\n",
    "level_test = 25\n",
    "model = LinearRegression()\n",
    "model.fit(X, y)\n",
    "\n",
    "Xts = np.array([1, level_test, level_test**2, level_test**3]).reshape(1, -1)\n",
    "\n",
    "prediction = model.predict(Xts)\n",
    "\n",
    "\n",
    "print('Your predicted flow: %.10f' %prediction)\n",
    "print('You should get approximatively: 22.32')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Overfit problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To start our discussion, let's plot again the trend curves as above. This can be done, by a new execution of the function `plot_poly`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SHECzZnD33fC738USZ6YoN9GZ2fnAQ8D20fZQtBiriIhU0oABsHAhTJ4cHgcM2OyEK64AjR9OikTWozsd2MvdfwUws78AU4G/pTIwEZFMl5NTQikO4LXX4M9/hiZN1NMyCRIZR2fA+mKv11PyOnMiIlJV7jByJLRoEQaJS5UlUqK7H3jPzJ6KXh8HjEtdSCIitdiTT8J//xsa7erXjzuajFDuXJcAZtYV2C96OcXd309pVJvRXJciUiusWwcdO0KdOmGVgi0SKYvUXonOdZnQXXT3mcDMKkclIiKlW7cOTjkFunRRkksi3UkRkeqifv3Q21KSSpM6i4hUB/ffD489FjqjSFIlMo7uL4nsExGRSvrpJ7joIhg/Hkyd2pMtkRJdnxL2HZ7sQEREaq2bbw6zO990U9yRZKRS2+jM7GzgHKCNmc0udmgr4O1UByYiUit8/TXcfnvohNKpU9zRZKSyOqNMAF4AbgJGFtu/zN1/TGlUIiK1xbXXQmEhXHdd3JFkrFITnbsvBZaa2R83O9TIzBq5+5dlXdjMdgL+CexAWNZnrLv/X7SW3SNALpAPnOTuP1X+K4iI1GC9e8Muu5SwdIEkSyLDC54jJCoD6gOtgU+ADuW8bx1wsbvPNLOtgBlm9jJwGvCKu99sZiMJpcXNk6mISO3wm9mcJdkSWaank7t3jh7bAj0IkzqX977F0UBz3H0ZMA9oDhwLjI9OG0+YUkxEpHZ57rnQCaWwMO5IMl6Fx9FFyWuvirzHzHKBLsB7wA7uvjg6tIRQtSkiUnssXgynnQYTJ/52BVZJunKrLs3somIv6wBdgW8S/QAzawQ8AVzo7r9YsTEi7u5mVuLoSDM7EzgToGXLlol+nIhI9VZYCKeeCr/+Cg8/rImb0yCREt1WxbYtCW12xyZycTPLIiS5h9z9yWj3t2bWLDreDPiupPe6+1h3z3P3vJwSF2wSEamBbr01rLY6Zgy0bx93NLVCuSU6d78WNpTMcPfliVzYQtFtHDDP3W8rdugZYDBwc/T4dAVjFhGpmb77Dq66Ck44Ac44I+5oao1Eqi47Ag8C20avvwcGu/vcct66LzAImGNms6J9lxMS3KNmNgxYCJxUydhFRGqW7beHV16Bdu001VcaJTK8YCxwkbu/BmBmB0b79inrTe7+FqWvRH5wBWIUEan55swJM5/sU+Y/nZICibTRNSxKcgDu/jrQMGURiYhkmocfhs6d4cUX446kVkqkRLfAzK4kVF8CnAIsSF1IIiIZJD8fzjoL9t4bDjkk7mhqpURKdEOBHOBJQg/KptE+EREpy7p18Pvfh+cTJmjV8Jgk0uvyJ+D8NMQiIpJZrrsOpk4NA8M1l2VstMK4iEiqtGgBZ58N/fvHHUmtpnK0iEiqnHlm3BEIKtGJiCSXOwwZEtrkpFpIZMD47SXsXgpMd3fNaiIiUty998IDD8Duu8cdiUQSKdHVB/YE5kdbZ6AFMMzMxqQwNhGRmmXePLjwwjCM4OKL445GIom00XUG9nX39QBm9ndgCrAfMCeFsYmI1ByrVoVFVBs2hH/+E+qoZai6SCTRbQM0IlRXQpgVZVt3X29mq1MWmYhITfLMM/DBB/Cf/0CzZnFHI8Ukkuj+Cswys9cJc1fuD9xoZg2BySmMTUSk5jjpJNh1V9hzz7gjkc2Ye4nrnm56Ulg3rkf0cpq7J7zwajLk5eX59OnT0/mRIiKJWbIkbEpwaWdmM9w9r7zzEq1ErgMUAD8Bu5jZ/lUJTkQkIxQWwuDBcNBB8MsvcUcjpUhkeMFfgJOBD4HCaLcDb6YwLhGR6m/0aHjpJbj7bmjcOO5opBSJtNEdB+zm7up4IiJSZMYMGDUKjj9eM6BUc4lUXS4AslIdiIhIjbF8eViVYPvtwwBxrRZerSVSoltB6HX5CrChVOfuWtFARGqnLbeEE0+EPn1gu+3ijkbKkUiieybaRESksBCysuDPf447EklQIuvRjU9HICIi1V5+Phx1FIwbB3vtFXc0kqBSE52ZPeruJ5nZHEIvy024e+eURiYiUp2sWwcDB8JXX4W2OakxyirRXRA9HpWOQEREqrUbboB33gnL77RuHXc0UgGlJjp3Xxw9PQR4093npyckEZFqZsoUuP56OPXUMHGz1CiJdEZpCdxjZrnADMJA8SnuPiuFcYmIVB///Ce0aQN33BF3JFIJiXRGuRrAzLKBM4BLgTFA3dSGJiJSTdxzD3z7LWy1VdyRSCWUO2DczK4wsxeAl4BdgEsIC6+KiGS2556DL78Ma8tp6Z0aK5GZUU4AtiMsyfMk8HSx9rtSmdl9Zvadmc0ttu8aM1tkZrOi7YhKRy4ikkoffxyW3rnoorgjkSoqN9G5e1dCh5T/An2AOWb2VgLXfgDoW8L+0e6+Z7Q9X5FgRUTSYvXq0OkkOxtuvz3uaKSKElm9oCPQCzgAyAO+AqaU9z53fzPqwCIiUm0VFIRx4Lm5kJMT7Rw1CmbNCquG/+53MUYnyZBI1eXNQGPgdqC9ux/k7ldV4TPPM7PZUdXmNqWdZGZnmtl0M5teUFBQhY8TESnZxInQqlWYsrJVq/CayZPD8jvnnQdHHx13iJIEia4wXg/YNXr5ibuvTejioUT3rLt3jF7vAHxPmGnleqCZuw8t7zpaYVxEkq2gICS3lSs37svOhi/n/kLT+/4KV1wB9evHF6CUK2krjJvZAcB84E7gLuDTyq4w7u7fuvt6dy8E7gV6VOY6IiJVlZ8P9eptfG0U0miLVXzxQ+MwC4qSXMZIpOryNuBQdz/A3fcHDgNGV+bDzKx4/9zjgbmlnSsikkq5ubBmzcbXFzKGV5b3oHXjH2KLSVIjkUSX5e6fFL1w909JYCFWM5sITAV2M7OvzWwY8Fczm2Nms4GDgBGVjFtEpEpycsIiBNnZ0KvhTG5mJNt0bUPTXbeNOzRJsnLb6MzsPqAQ+Fe0ayBQN5G2tWRRG52IpEpB/q80OqAr9dYsp+6cD6Bp07hDkgQl2kaXyFyXZwPnAkUrik8htNWJiNR4OTdcAF/Nh1deUZLLUInMdbma0E53W+rDERFJoxUr4P33w7i5gw6KOxpJkbIWXi1xwdUiWnhVRGo0d2jQAF5/HRo2jDsaSaGySnT9gJVlHBcRqZkefBAeeQQefVQrEtQCZfW6nODuC4Eb3H3h5lu6AhQRSaqnnoIhQ8JI8TqJdDyXmq6sEl09M/s9sI+ZnbD5QXd/MnVhiYikwMsvQ//+0L07PP20BoXXEmUluuGEoQRNgM0nfHPCkj0iIjXDO+/AccdBu3bw/PPQqFHcEUmalJro3P0t4C0zm+7u49IYk4hI8mVnQ5cu8MQTsE2p88lLBkpkeIGSnIjUXD/+CNtuG5LclClgFndEkmZqiRWRzJWfD507w803h9dKcrVSmYnOgp3SFYyISNIsXgyHHAK//gpHHBF3NBKjMhOdh4kwn09TLCIiyfHjj3DoobBkCbzwQijVSa2VSNXlTDPrnvJIRESSYf16OPJImD8fnnkGevaMOyKJWSKTOu8FDDSzhcCvgBEKe/oTSUSqn7p14fzzw/CB3r3jjkaqgUQS3WEpj0JEpKrWrg0TNPfoAQMGxB2NVCPlVl1G033tBPSOnq9I5H0iImmzfj0MGgS9eoWeliLFlFuiM7OrgTxgN+B+wuri/wL2TW1oIiIJcIfhw8MkzX/9K+Tmxh2RVDOJlMyOB44htM/h7t8Amu5bROLnDpdcAv/4B1xxBVx6adwRSTWUSKJbEw0zcAAz08JNIlI9PPss3HYb/OEPcN11cUcj1VQinVEeNbN7gCZmdgYwFPhHasMSEUnAUUfBww9Dv36a9URKlchcl/9rZn2AXwjtdFe5+8spj0xEpDQPPwx5ebDLLnDyyXFHI9VcuVWXZvYXd3/Z3S9190vc/WUz+0s6ghMR+Y3HHoOBA1VVKQlLpI2uTwn7Dk92ICIi5Xr++ZDk9tkH7r477mikhii16tLMzgbOAdqY2exih7YC3k51YCIim3jjDfif/4FOnUInlAYN4o5Iaoiy2ugmAC8ANwEji+1f5u4/pjQqEZHN3XQTtG4NkybB1lvHHY3UIGWtML4UWAoMADCz7YH6QCMza+TuX5Z1YTO7DzgK+M7dO0b7tgUeAXKBfOAkd/+p6l9DRDLe44/DsmXQtGnckUgNk0hnlKPNbD7wBfAGIUG9kMC1HwD6brZvJPCKu7cFXmHTkqKIyKYWLAhtcsuWhUmamzWLOyKpgRLpjHID0BP41N1bAwcD75b3Jnd/E9i8ivNYYHz0fDxwXOKhikitsmgRHHxwqKr85pu4o5EaLJFEt9bdfwDqmFkdd3+NMPdlZezg7ouj50uAHUo70czONLPpZja9oKCgkh8nIjVSQUFYHfyHH0Ki2223uCOSGiyRmVF+NrNGwJvAQ2b2HdG8l1Xh7m5mXsbxscBYgLy8vFLPE5EMs3QpHHZYWIVg0iTo1i3uiKSGS6REdyywEhgBvAh8Dhxdyc/71syaAUSP31XyOiKSqb79Fn78EZ56CvbfP+5oJAOUNY7uQuAdYKa7r492jy/t/AQ9AwwGbo4en67i9UQkU6xbF1YH33VX+OQT2HLLuCOSDFFWia4FMAb4zszeMLMbzeyoaIhAucxsIjAV2M3MvjazYYQE1yfqxXlI9FpEart168KclZdcEl4ryUkSlTWO7hIAM6tH6HyyDzAEGGtmP7v77mVd2N1LW8v+4ErGKiKZqLAQTj8dnnwSxoyJOxrJQIl0RskGGgNbR9s3wJxUBiUitYQ7XHABjB8P114bnoskWVltdGOBDsAy4D1Ce91tmslERJLm6qvhjjvg4ovhyivjjkYyVFltdC2BLQnj3RYBXwM/pyMoEaklunSBc8+FW27RwqmSMuZe+hA1MzNCqW6faOtImO1kqrtfnZYICePopk+fnq6PE5FUcofp06F797gjkRrOzGa4e7kTmJQ5js6DucDzhPkt3wZ2BlSRLiIVt3x56F3Zsyd88EHc0UgtUVYb3flsLMmtJbTRvQPchzqjiEhFff45HH88fPgh3HwzdO4cd0RSS5TV6zIXeAwYUWx+ShGRips0CQZEI45efBH69Ik3HqlVyhpHd1E6AxGRDDZrFuy0U5jWq02buKORWiaRuS5FRCpu+XKYMSM8v+wyePfdDUmuoACmTQuPIqmmRCciyff557D33tC3b1g01QyyswGYOBFatQq1l61ahdciqaREJyLJ9eKLkJcXFk6dMAG22mrDoYICGDYMVq4Mq/GsXBleq2QnqaREJyLJ4R56Ux5xBLRsGcbKbdbpJD8f6tXb9G1ZWWG/SKoo0YlI8syZE8bJvfNOiZ1OcnNhzZpN961dG/aLpEoikzqLiJTu88/DCgRt28L994ciWinTeeXkwLhxoboyKyskuXHjwn6RVFGiE5HKe/HFMD6uY0d4883f1kuWYMAAOOSQUF2Zm6skJ6mnqksRqbjN2+PGj6/QpMw5OWGqSyU5SQeV6ESkYn79FYYMgcceC+1x48ZBw4ZxRyVSKpXoRKRi6tSBL7+Ev/41DIJTkpNqTiU6EUnMq69Ct26w9dbw1luwhf75kJpBJToRKVtRe9whh8C114Z9SnJSg+jXKiKlW74chg4N7XH9+8P118cdkUiFKdGJSMm++AKOOQY++ghuuQUuvrhCPStFqgslOhHZlHtIaPXqhYHgWj9OajglOhEJVqyA//s/mDwZXn4ZmjcPU3rVUVO+1Gz6BYvUdmvXwt13wy67wOWXh+ECv/wSjinJSQaIpURnZvnAMmA9sM7d8+KIQ6TW++yzMLvJ/Pmw777w6KOw335xRyWSVHFWXR7k7t/H+PkitZM7fPst7LhjmL6rXTu49VY46ih1NpGMpDY6kdpk2jQYORLmzQuluQYN4Jln4o5KJKXiqoB34CUzm2FmZ8YUg0jt8ckn0K8f9OgBs2fDH/+oQd9Sa8T1S9/P3ReZ2fbAy2b2sbu/WfyEKAGeCdCyZcs4YhTJDHPmQJcukJ0NV18dxsNttVXcUYmkTSwlOndfFD1+BzwF9CjhnLHunufueTlay0OkYn76CSZNCs87dgwDvj//HK65RklOap20Jzoza2hmWxU9Bw4F5qY7DpGJMqsgAAAYsUlEQVSMtGIF/OUv0KYNnHhiGCZgBiNGwPbbxx2dSCziKNHtALxlZh8A/wWec/cXY4hDJHOsWwdjx0LbtqGzyb77wttvQ+PGcUcmEru0t9G5+wJgj3R/rkhG+/hjGD4cevaEhx+GXr3ijkik2tC0BwkoKAi9sgsK4o5EpJhXXoHrrgvPO3YMP9K3305aktPvXjJF7Ul0P/0EDz0Ey5ZV6G0TJ0KrVmFO21atwmuRWM2YEX6QhxwC99238TfdrVvSBnzrdy+ZpPYkuv/8B045JTTIn3hiWF9rxYoy31JQAMOGwcqVsHRpeBw2TH/hSky+/hpOPhny8uD99+G220KVZZJ7Uep3L5mm9iS6U06BKVPg9NPhrbfgpJNC0ivj/978/LBSSXFZWWG/SNq4h8esLHjjDbjySliwIPSkrF8/6R+n371kmtozNUKdOmGy2v32gzFjwj8Yb78NRWP0zjgjzOJ+8smhSigri9xcWLNm08usXQu5uekOXmqln3+Gv/4Vpk8PY+J22AEWLoQtt0zpx+p3L5mm9pToiqtbF3r3Dn8ZF6lXD/797zCTe7NmcOaZ5Hw2lXHjwoQSjRuHx3HjNuZGkZRYuTIM8G7TBm66KfzgiqrZU5zkIHycfveSScyLqkWqsby8PJ8+fXrqP2j16vCX8yOPwNNPw4UXwg03ULBoDT88/RbbHb0POTslv6pIZIPZs8MfW4sWweGHw403wp57xhJKQUGorszNVZKT6snMZiSyzFutSXQV/p92xYqQ+LbZBp5/Ho48MrSH7LdfqNo8+OAwf2DdulWKSwT3kNhatAilud//PvyRdcABcUcmUq0lmuhqRdVlpbpKN2gQkhzA/vuHXpvDh8OSJWHmie7dYdascPzzz+HTTzd2GkgzjXeqwV59FfbaK4x9W7Mm1BM+9ZSSnEgSZXyiS0pX6UaNwqKUo0eHmeAXL4YJEzZWKd16K+y2W8iiQ4aE8XpLlqTk+2xO451qqJkz4bDDQs3AkiVw1VWqHRBJkYyvupw2LSSBpUs37mvcGCZPDoWypFiwAF56KVz0tdfgxx+heXP46qswgHfWLGjdGrbeOkkfGBQUhOS2cuXGfdnZoWOe2lSqsalTYZ99YNtt4fLL4dxzUzJMQCTTJVp1mfHDC9LSVbpNm1CtOXw4rF8fEtuSJSHJuYfS4JIlYdHLgw8ObXw9e1a5B13ReKfiia5ovJMSXfnS0tmioCD8ETRpErRoQcGIG8mvsxftbrydrc45Nel//MRp7dq1fP3116xatSruUCTD1K9fnxYtWpCVlVW5C7h7td+6devmVTFhgnt2tnvjxuFxwoQqXa5iCgvdX33V/U9/ct9rL/c6ddzB/ZxzwvH1691nzgyPFfTdd+H7hGwatuzssF/KVvSb2HrrFP0mRo9279bN3Sz8h9luO593xEWp/cyYLViwwAsKCrywsDDuUCSDFBYWekFBgS9YsOA3x4DpnkAOyfiqyyLVpqv0zz+HweotW4ZemzNnhjkKt9sujO0r6tHZpk1C8xZOnBjaHLOyQkl13DgYMCAN36MGS3qVb35+KLFNnQr33x/+u519NsydG9rhDjuMgp260qpN3YyuZp43bx7t2rXDkjTfpkgRd+fjjz+mffv2m+xX1eVmcnKqyT8oTZrAscdufN26Nfzzn2Em+smTwxycAC+/HJLed9+F16UsmjlgQDitWiTxGiIpVb5z58I//gEvvgiffBL2tWwZOir97ndw112b/KGSP612VDMryUkqVPV3lfG9Lqu9bbaBQYPggQdC55WPP4Y77ghteBD+wdxhB9hjD7joojCmb/nyTS6RkxM61mTSP5ipVOF2W3f48MPQu/ajj8K+L76Ae+4Jf6iMHh325+eHJAe/KY1rWi2R+CjRVSdmYZjCueeGIQ0A/fqF2TFyckLSO/LIMLB43bpw/Msvw7+YkrCEprhavTqUrk8/PZTUOnaESy4JJW8IVZI//ggvvBAGd7dvX2ZVs6bVqp4OPPBA0jLrUgn22Wefcs9pVPTvQIJuv/122rdvz8CBA8s8L87vvbm+ffuyxx570KFDB4YPH8769euT/hm1puqyxurQIWyjRoV6r3feCQ07W0T/6Y47DubPD4OO27WDXXcNxbu994437mruN1W+266H/86AX3+Fgw6CwkI49dTQM7ZPnw1tbey0U7jA5tP7V+YzleRqtXfeeSfp17zrrruYPHkyLVq0SPq1S7Ju3Tq22KJqaeTRRx+lcePGuDsnnngijz32GP37909ShIFKdDVJdnboqDJ06MZ9V10Vqj5/+QUefBAuuAD+9reNx3v1gv/5n5AoH3ggJMqffkr4IzNi1hX3UEJbunRjmyeQs/Qzun/4ADl/6B/aQPfaCy67LBzMzg4LnH7//caSXVGSq4JaVc184IG/3e66KxxbsaLk4w88EI5///1vj5UjPz+fdu3aMXDgQNq3b8+JJ57Iimgy7FdeeYUuXbrQqVMnhg4dyurVqzd573333ceFF1644fW9997LiBEjyM/Pp3379pxxxhl06NCBQw89lJVRQ+usWbPo2bMnnTt35vjjj+en6P+rAw88kBEjRpCXl0f79u2ZNm0aJ5xwAm3btuWKK67Y8BlFpbXly5dz8MEH07VrVzp16sTTTz9d7ne97bbb6NixIx07dmTMmDEADB8+nAULFnD44YczevToTc5fuXIl/fv3p3379hx//PEbvgPASy+9xN57703Xrl3p168fy6Omkeeff5527drRrVs3zj//fI466igArrnmGgYNGsS+++7LoEGDWL9+PZdeeindu3enc+fO3HPPPRuufcstt2zYf/XVV5f4XRo3bgyEpLlmzZrUtPMm0jUz7q2qwwtqjcJC9yVL3BcuDK9XrXI/5hj3du3cs7I2jkG49NJwfPly99NOc7/xRvcnnnCfM8d95coNl0t5F/zly93z893nzQtDLN5+233y5BC3e9h3zz3uY8a433ST+1VXhdhXrw7Hx493P/5497593Q84wL1HD/c99wz3wd39/PPdGzTYOKQDwhiTIiefHPbtuKP74MHuEye6FxQk+UvWHh999NGmOw444LfbnXeGY7/+WvLx++8PxwsKfnusHF988YUD/tZbb7m7+5AhQ/yWW27xlStXeosWLfyTTz5xd/dBgwb56NGjoxAP8GnTpvmyZcu8TZs2vmbNGnd333vvvX327Nn+xRdfeN26df399993d/d+/fr5gw8+6O7unTp18tdff93d3a+88kq/4IILNlzzsssuc3f3MWPGeLNmzfybb77xVatWefPmzf377793d/eGDRu6u/vatWt96dKl0dcu8J133nnDEI2ic4qbPn26d+zY0ZcvX+7Lli3z3Xff3WfOnOnu7q1atfKCEn7Dt956qw8ZMsTd3T/44AOvW7euT5s2zQsKCrxXr16+fPlyd3e/+eab/dprr91wz4q69Pfv39+PPPJId3e/+uqrvWvXrr5ixQp3d7/nnnv8+uuvd3f3VatWebdu3XzBggU+adIkP+OMM7ywsNDXr1/vRx55pL/xxhsl/rc79NBDvUmTJj5gwABft25dief85vfliQ8vUNVlJjELHVeKbLllWIUBQptefn7oIdiyZdi3ZEkYzFz0V3TRNf7xDwqOHsrlQ5cwZNUTLF7ZjPqs4s3BKzlq4Uq2+v0x4RoffBC6069cuelWNCXak0+GmT+K9q9aFR5nzw5tWmPHhg42m/vyy1B6evbZUGItrn79UDqtVw++/TbMMZqdHbYmTcLj+vWhanfffUPXxqLj2dkb2z4hzFk6ahR07pzQUA6poNdfL/1YgwZlH2/atOzjpdhpp53Yd999ATjllFO4/fbb6dOnD61bt2bXXXcFYPDgwdx5552blOAaNWpE7969efbZZ2nfvj1r166lU6dO5Ofn07p1a/aMpvvr1q0b+fn5LF26lJ9//pkDojlJBw8eTL9+/TZc75hjjgGgU6dOdOjQgWbNmgHQpk0bvvrqK7bbbrsN57o7l19+OW+++SZ16tRh0aJFfPvtt+y4444lfse33nqL448/noYNGwJwwgknMGXKFLp06VLqfXnzzTc5//zzAejcuTOdO3cG4N133+Wjjz7acM/WrFnD3nvvzccff0ybNm1o3bo1AAMGDGDs2LGbfL/s7GwglAhnz57N448/DsDSpUuZP38+L730Ei+99NKGuJYvX878+fPZf//9fxPfpEmTWLVqFQMHDuTVV1+lT58+pX6XylCiqy222AJ22SVsRXbeOcyav2xZaOf75JOQOLp1Iz8futeZwZ2ct/H8tcAoYI+dQ6JbuDAkuuKJpH79jX3ot9029BatX3/Tc4omy+7TZ2MPjeLvL6rX+8MfQjVt0bEttwwL6Ba59NKwleakk8JWmpiWv5HU2bzaqyLVYKeffjo33ngj7dq1Y8iQIRv2b1lsBqO6detuUu1XmqL31KlTZ5P316lTh3VFHckiDz30EAUFBcyYMYOsrCxyc3PTNruMu9OnTx8mbjZJ7qyiCetLUZRki67xt7/9jcMOO2yTcyZNmsSoUaM466yzEoqlfv36HHvssTz99NNKdJICW20FXbuGLZJbAM8WHkEzvmF7vmMl2fiW2UydlU3TnaNpq445ZtNJRDdXXttKx45hK02TJmETSdCXX37J1KlT2XvvvZkwYQL77bcfu+22G/n5+Xz22WfssssuPPjggxtKYsXttddefPXVV8ycOZPZs2eX+Tlbb70122yzDVOmTKFXr16lXjMRS5cuZfvttycrK4vXXnuNhQsXlnl+r169OO200xg5ciTuzlNPPcWDDz5Y5nv2339/JkyYQO/evZk7d+6G79ezZ0/OPffcDffm119/ZdGiRey2224sWLCA/Px8cnNzeeSRR0q99mGHHcbf//53evfuTVZWFp9++inNmzfnsMMO48orr2TgwIE0atSIRYsWkZWVxfbFxgQvX76cZcuW0axZM9atW8dzzz1Hr169KnD3EqNEJyXKyYFx9xnDhjVjRVazDbOuNG0Xd2Qipdttt9248847GTp0KLvvvjtnn3029evX5/7776dfv36sW7eO7t27M3z48BLff9JJJzFr1iy2Kap1KMP48eMZPnw4K1asoE2bNtx///2VinngwIEcffTRdOrUiby8PNq1K/t/sq5du3LaaafRo0cPIJREy6q2BDj77LMZMmQI7du3p3379nTr1g2AnJwcHnjgAQYMGLChg84NN9zArrvuyl133UXfvn1p2LAh3cuYAf/0008nPz+frl274u7k5OTw73//m0MPPZR58+axd9QDvFGjRvzrX//aJNH9+uuvHHPMMaxevZrCwkIOOuigUv/bVEWtmQJMKqfaTJ0m1d68efN+M0VTOuXn53PUUUcxd+7cSl/jqKOOYsSIERx88MFJjKxmWr58OY0aNcLdOffcc2nbti0jRoyILZ6Sfl9aeFWSolZ1h5da6+eff2bXXXclOztbSS5y7733sueee9KhQweWLl2acFtbdRRLic7M+gL/B9QF/uHuN5d1vkp0ItVf3CU6yWw1qkRnZnWBO4HDgd2BAWa2e7rjEJHkqwlNIVLzVPV3FUfVZQ/gM3df4O5rgIeBY8t5j4hUc/Xr1+eHH35QspOkcnd++OEH6tevX+lrxNHrsjnwVbHXXwN7bX6SmZ0JnAnQsmiAs4hUWy1atODrr7+moEbPFyfVUdEK45VVbYcXuPtYYCyENrqYwxGRcmRlZW2YSUOkOomj6nIRUHx23BbRPhERkaSLI9FNA9qaWWszqwf0B56JIQ4REakF0l516e7rzOw8YBJheMF97v5huuMQEZHaoUbMjGJmBUDZE8DVHE2B7+MOIkPoXiaP7mXy6F4mT3n3spW7lzudRY1IdJnEzKYnMsBRyqd7mTy6l8mje5k8ybqXmgJMREQymhKdiIhkNCW69Btb/imSIN3L5NG9TB7dy+RJyr1UG52IiGQ0lehERCSjKdGJiEhGU6JLMzO72MzczJpGr83Mbjezz8xstpl1jTvG6s7MbjGzj6P79ZSZNSl2bFR0Lz8xs8PijLOmMLO+0f36zMxGxh1PTWJmO5nZa2b2kZl9aGYXRPu3NbOXzWx+9LhN3LHWBGZW18zeN7Nno9etzey96Lf5SDSbVoUp0aWRme0EHAp8WWz34UDbaDsT+HsModU0LwMd3b0z8CkwCiBa17A/0AHoC9wVrX8opdD6kFW2DrjY3XcHegLnRvdvJPCKu7cFXoleS/kuAOYVe/0XYLS77wL8BAyrzEWV6NJrNHAZULwH0LHAPz14F2hiZs1iia6GcPeX3H1d9PJdwsTgEO7lw+6+2t2/AD4jrH8opdP6kFXg7ovdfWb0fBnhH+nmhHs4PjptPHBcPBHWHGbWAjgS+Ef02oDewOPRKZW+j0p0aWJmxwKL3P2DzQ6VtD5f87QFVvMNBV6InuteVpzuWZKYWS7QBXgP2MHdF0eHlgA7xBRWTTKGUBAojF5vB/xc7I/aSv82q+16dDWRmU0Gdizh0J+AywnVlpKAsu6luz8dnfMnQtXRQ+mMTWRzZtYIeAK40N1/CYWRwN3dzDSOqwxmdhTwnbvPMLMDk319JbokcvdDStpvZp2A1sAH0f8ALYCZZtYDrc9XotLuZREzOw04CjjYNw4G1b2sON2zKjKzLEKSe8jdn4x2f2tmzdx9cdQU8V18EdYI+wLHmNkRQH2gMfB/hKacLaJSXaV/m6q6TAN3n+Pu27t7rrvnEorgXd19CWEtvlOj3pc9gaXFqjykBGbWl1DFcYy7ryh26Bmgv5ltaWatCR18/htHjDWI1oesgqgdaRwwz91vK3boGWBw9Hww8HS6Y6tJ3H2Uu7eI/n3sD7zq7gOB14ATo9MqfR9Voovf88ARhI4TK4Ah8YZTI9wBbAm8HJWQ33X34e7+oZk9CnxEqNI8193Xxxhntaf1IatsX2AQMMfMZkX7LgduBh41s2GEJcZOiim+mu6PwMNmdgPwPuGPigrTFGAiIpLRVHUpIiIZTYlOREQymhKdiIhkNCU6ERHJaEp0IiKS0ZToJGOZ2Y5m9rCZfW5mM8zseTPb1cwOLJodPW5mdp2ZlTk4Pkmf08TMzknCdV43s7xkxJTKa4oUp3F0kpGigbxPAePdvX+0bw+q2ZyD7n5Vmj6qCXAOcFeib4juobl7Ybkni1RjKtFJpjoIWOvudxftcPcP3H1K9LKRmT0erWv3UPSPOmZ2lZlNM7O5Zja22P7XzewvZvZfM/vUzHpF+xuY2aPRemRPRWtn5UXHDjWzqWY208wei+ZD3ISZPWBmJ0bP883s2uj8OWbWroTznzOzztHz983squj5dWZ2hpk1MrNXil2jaCWCm4GdzWyWmd0SvefS6LvONrNro325Ftam+ycwl02nB9s8lt98Pwtr2z1W7JwNpedE7odIKijRSabqCMwo43gX4ELCGmxtCDNcANzh7t3dvSOQTZhPs8gW7t4jet/V0b5zgJ+i9ciuBLoBWFhY9wrgEHfvCkwHLkog7u+j8/8OXFLC8SlALzPbmjD7S1HcvYA3gVXA8dE1DgJujZL1SOBzd9/T3S81s0MJU6T1APYEupnZ/tG12gJ3uXsHd19YUpBlfL/JwF5m1jA69WTCzBaVvR8iVaaqS6mt/uvuXwNEUzflAm8BB5nZZUADYFvgQ+A/0XuKJuydEZ0PsB9h8lncfa6ZzY729yQk0bejQmE9YGoCcRX/jBNKOD4FOB/4AngO6GNmDYDW7v6JhQmGb4ySViFhWZOSqmsPjbb3o9eNCAnuS2BhtDZiWUr8ftGUYi8CR5vZ44T1xS4DDijp/HI+QyQplOgkU33IxslgS7K62PP1wBZmVp/QhpXn7l+Z2TWEmdQ3f896yv9/x4CX3X1AhaIu/zOmAXnAAsJK602BM9hYeh0I5ADd3H2tmeVv9h2Kx3eTu9+zyc6wptqvCcRZ1vd7GDgP+BGY7u7LolJlZe6HSJWp6lIy1avAlmZ2ZtEOM+tc1LZWiqKE8H3UflRWoizyNtGEvWa2O9Ap2v8usK+Z7RIda2hmu1bwO/xGtAr4V0A/QoloCqGK883olK0J63qtNbODgFbR/mXAVsUuNQkYWtROZmbNzWz7CoRS1vd7A+hKSMAPJ3C+SEop0UlGitaoOx44xMLwgg+BmwirPZf2np+BewmdMCYRSk/luQvIMbOPgBsIJcml7l4AnAZMjKozpwK/6VxSSVMIyWxl9LxF9AhhEdo8M5sDnAp8DODuPxCqDeea2S3u/hIwAZganfs4mybCMpX1/aIVI54FDo8eyzxfJNW0eoFIFZhZXSDL3VeZ2c6Ezhi7RSUvEakG1EYnUjUNgNeiTiAGnKMkJ1K9qEQnIiIZTW10IiKS0ZToREQkoynRiYhIRlOiExGRjKZEJyIiGe3/AWIlZ4LwTFssAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SzC4GJgA7RNuEaDFWERGpqMcfh7VroV49srKgXz8luWRLZHjBucA+7n6tu18L7Aucl9ywRETS0KuvwqmnwoMPxh1JnZJIojOgsNjrQkpeUFVEREpTWAjDh0OnTnDhhXFHU6ckMtfMw8A7ZvZs9Pp4YEzyQhIRSUNjx8K8eWHy5kaN4o6mTjF3L/8gsz7AAdHL6e7+flKj2kZ2drbn5OSk8i1FRKrPqlWw665hmz4dTI1i1cHMZrt7uUs9JDR7qLu/B7xX5ahEROqi5cthzz3hlluU5GKgabJFRJKtUyd45ZW4o6izNKmziEgy3XcffPll3FHUaYmMo7slkTIREdnGG2/AH/8IjzwSdyR1WiI1uoEllB1V3YGIiKSVwkIYNgw6dIBLL407mjqt1Ht0ZnYBcCHQ1cyKT/nVDHgz2YGJiNRq48bB+++HmVAaN447mjqtrM4ojwMvAjcBVxQrX+3uPyY1KhGR2mz1arjqKth3XzjllLijqfPKWr1gpbvnAn8hLMtTtDU1s47lXdjMOpjZa2a20MwWFC3gGq1nN9XMFkWPrarno4iI1BD5+fDrX8Pdd2s4QQ1Q7oBxM/uAkOAMaAR0AT529z3KOa8t0Nbd3zOzZoQ17Y4HzgJ+dPebzewKoJW7/6Wsa2nAuIiIbCvRAePldkZx917u3jt63BXoD8xM4Lyvo4HmuPtq4EOgHTAIeDQ67FFC8hMRSQ8jR4L+MK9RKjxgPKqh7VORc8ysM7A38A6wo7t/He36BtixlHOGAkMBOnYst6VURCR+b74J11wDBQWQXW5FQ1Kk3ERnZsX7xdYD+gBfJfoGZtYUeAYY5u6rrFh7tbu7mZXYduruo4HREJouE30/EZFYbNoUhhO0awd//nPc0UgxidTomhV7XgD8l5C4ymVmGdGxE9z931Hxt2bW1t2/ju7jfVeRgEVEaqTHHgtNluPHQ5MmcUcjxZSb6Nx9BGyumeHuaxK5sIWq2xjgQ3e/s9iu54AzgZujx8kVjFlEpGZZuxauvBL694ff/S7uaGQbiTRd7gmMB7aPXn8PnOnu88s5dX/gdOADM5sTlV1FSHBPmdkQYClwciVjFxGpGerVgwsugMMOC8+lRkmk6XI0cKm7vwZgZgdHZb8s6yR3n0HpK5EfVoEYRURqtsxMuPrquKOQUiTyp0eToiQH4O7TADVAi4gAXHEFTNYdmJoskUS3xMyuMbPO0XY1sCTZgYmI1Hhvvx0WU9W4uRotkUR3DpAF/JvQg7JNVCYiUne5h+EEbdvCX8qc3Elilkivy+XAxSmIRUSk9pg4Ed55J6w117Rp3NFIGdQ9SESkotatC7W4vn3h9NPjjkbKUeEpwERE6rxGjeDmm6FrVw0nqAWU6EREKqpePTj11LijkAQlMmD8nhKKVwI57q4+tSJSt/zxj7DrrnCxui7UFonUuRsBewGLoq030B4YYmZ3JzE2EZGa5d134b774Ouvyz9WaoxEmi57A/u7eyGAmY0CpgMHAB8kMTYRkZrDHYYPhx13DPNaSq2RSKJrBTQlNFdCmBVle3cvNLONSYtMRKQmeeopeOst+Oc/oXnzuKORCkgk0d0KzDGzaYS5Kw8EbjSzJsDLSYxNRKRmyM8Pwwn22gvOOivuaKSCEhkwPsbMXgD6R0VXuXvRwqtaXVBE0l9GRlhvrmFDqF8/7mikghIdXlAPyIuO38XMdnH3N5IXlohIDeEOZnDAAXFHIpWUyPCCW4DfAguATVGxA0p0IpL+hg6FZs3gzjvLP1ZqpERqdMcD3d1dHU9EpG6ZPRvGjIHLLos7EqmChJbpATKSHYiISI1SNJygTRv461/jjkaqIJEa3TpCr8tXgM21OnfXtAAikr6eeQamT4d//ANatIg7GqmCRBLdc9EmIlI3uMM110CvXjBkSNzRSBUlMrzg0VQEIiJSY5jBCy/AihXQQHPf13al/gua2VPufrKZfUDoZbkVd++d1MhEROLw009hvFyXLnFHItWkrD9VLokej0lFICIiNcJFF8F338Gzz2qtuTRR6r+iuxdNz/0roKG7Ly2+pSY8EZEUmjMnDCfo1k1JLo0k0vjcEXjQzDoDswkDxae7+5wkxiUiklpFwwm23z50RJG0kUhnlOsAzCwTOI8wv+XdgCZ8E5H0MXkyTJsG998PrVrFHY1Uo3Lr5mZ2tZm9CEwBdgEuIyy8Wt55Y83sOzObX6zsejNbZmZzou3XVQleRKTa3Hor7L57mPJL0koiTZcnAgXAf4HXgZkJTgf2CHAfMG6b8rvc/faKBCkiknQvvgjLlmk4QRoqt0bn7n0IHVLeBQYCH5jZjATOewP4scoRiogk05o1UFAQZj/Zffe4o5EkSKTpck/gVOBMwioGy4BXq/CefzCzeVHTphrCRSRW6y+6jLV79CPvq/y4Q5EkSaT/7M1Ac+AeoKe7H+Lu11by/UYB3YC9gK+BO0o70MyGmlmOmeXk5eVV8u1EREr30shZNBz3EONzD6TTLhlMnBh3RJIM5v6zSU9+fpBZQ2C36OXH7p7Qnz7RkITn3X3PiuzbVnZ2tufk5CTyliIiCclb9hPfdMhme/+B3VnIKlqQmQlLl0JWVtzRSSLMbLa7Z5d3XCILrx5E6FCSCxjQwczOrMwK42bWtthA9BOA+WUdLyKSLBtvuJVe/gHH8hyrCKsTZGRAbq4SXbpJpHvRncDh7v4xgJntBkwE+pZ1kplNBA4G2pjZl8B1wMFmthdh7sxc4PeVjlxEpLIKC9lx1n94qv4pPF947Obi/Hzo3Dm+sCQ5Ekl0GUVJDsDdPzGzchdidffBJRSPqUhwIiJJUb8+GW/PoN64dWT+MdTk8vPD7F+qzaWfRBJdjpn9E3gsen0qoBtmIlI7vfIK9O0LLVvymyEtOOi40FzZubOSXLpKJNFdAFwEFK0oPh14IGkRiYgky9KlMGgQnHACjB8PhOSmBJfeEpnrciPhPt2dyQ9HRCRJ3OH3vw+Lqo4cGXc0kkJlLbxa4oKrRbTwqojUKuPHw0svwb33QqdOcUcjKVRWje4kYH2qAhERSZpvv4Vhw2D//eHCC+OORlKsrET3uLv3MbPx7n56yiISEaluhYVw4IFw881aULUOKivRNTSz3wG/NLMTt93p7v9OXlgiItVo551h0qS4o5CYlPWnzfnAAKAlcOw22zHJD01EpIpWrIDTTgvjB6TOKrVG5+4zgBlmluPuGugtIrXP5ZfDxIkwfLimPKnDElmPTklORGqf116Dhx6CP/0pDBCXOkt3ZUUk/axbB+edB7vsAtdfH3c0ErMyB4ybmQHt3f2LFMUjIlJ1t90GixeHWl3jxnFHIzErM9G5u5vZC0CvFMUjIlJ1w4bBrrvCwQfHHYnUAIk0Xb5nZv2SHomISFXl58NPP0GLFvC738UdjdQQiSS6fYCZZrbYzOaZ2QdmNi/ZgYmIVNgtt4SOJytXxh2J1CCJrF5wRNKjEBGpqoUL4YYb4PjjQ41OJJLI8IKlQAfg0Oj5ukTOExFJmcJCOPdcaNoU7rkn7mikhim3Rmdm1wHZQHfgYSCDsAjr/skNTUQkQQ88ADNnwrhxsOOOcUcjNUwiNbMTgOOAtQDu/hXQLJlBiYgkzB2eegqOOCJM9yWyjUTu0f0UDTNwADNrkuSYREQSZwavvgqrVoXnIttIpEb3lJk9CLQ0s/OAl4F/JjcsEZEEvPkmLF8OGRnQunXc0UgNVW6Nzt1vN7OBwCrCfbpr3X1q0iMTESnLt9/CccfBQQfBv7VqmJQukc4ot7j7X4CpJZSJiMTj4othzRr4+9/jjkRquESaLgeWUHZUdQciIpKwyZNDB5Rrr4WePeOORmq4Umt0ZnYBcCHQdZuZUJoBbyY7MBGREq1YARdeCL17h/XmRMpRVtPl48CLwE3AFcXKV7v7j0mNSkSkNBs2hGm+rr02dEIRKUepTZfuvtLdc919cDQjynrAgaZm1rG8C5vZWDP7zszmFyvb3symmtmi6LFVtXwKEak7dtoJnnsOsrPjjkRqiXLv0ZnZsWa2CPgMeB3IJdT0yvMIcOQ2ZVcAr7j7rsArbF1TFBEp3bp1MGQIfPZZ3JFILZNIZ5SRwL7AJ+7eBTgMeLu8k9z9DWDbJs5BwKPR80eB4xMPVUTqtOuvh7FjITc37kiklkkk0eW7+w9APTOr5+6vEea+rIwd3f3r6Pk3QKmT0pnZUDPLMbOcvLy8Sr6diKSFnBy44w447zw45JC4o5FaJpFEt8LMmgJvABPM7P+I5r2sCnd3wj2/0vaPdvdsd8/Oysqq6tuJSG2Vnx+aLHfaCW69Ne5opBZKJNENInREGQ78D1gMHFvJ9/vWzNoCRI/fVfI6IlJX3HsvzJsHo0ZBy5ZxRyO1UFnj6IYBbwHvuXthVPxoaccn6DngTODm6HFyFa8nIunu3HPDQqrHHRd3JFJLlTWOrj1wN9DDzD4gDBJ/C3grkXF0ZjYROBhoY2ZfAtcREtxTZjYEWAqcXLXwRSRtbdoEBQXQvHlouhSppFITnbtfBmBmDQmdT34JnA2MNrMV7r57WRd298Gl7DqskrGKSF3ywAPwj3/AtGnQpk3c0Ugtlsg9ukygOdAi2r4C3klmUCJSxy1dCldeCe3ba/kdqbKy7tGNBvYAVhMS21vAne6+PEWxiUhd5A7nnx8eH3xQi6lKlZV1j64jsB2wCFgGfAmsSEVQIlKHPfYY/O9/cM890KlT3NFIGijrHt2RZmaEWt0vgT8Be5rZj8BMd78uRTGKSF3hDuPGwX77hRUKRKpBmQuvRoO655vZCmBltB0D9Cf0ohQRqT5m8MIL8OOPUL9+3NFImii1M4qZXWxmT5jZ54TJnI8BPgJOBLZPUXwiUlfk5MDy5WHpnR1LnR1QpMLKqtF1Bv4FDC82P6WISPVbuRIGDYI99oApU+KORtJMWffoLk1lICJSh11+OXzzDUyaFHckkobKvEcnIpIMeXlhtZ3OnSFrwTQYPRouuwz69Ys5MklHSnQiklITJ4YZvRo2hPob15Hb4jyadesGI0bEHZqkqURmRhERqRZ5eSHJrV8fbsvZhnVMz+vBiltHQ+PGcYcnaUqJTkRSJjc31OSK/EAbBjf9D4s6HBpbTJL+lOhEJGU6d4affoIG5HM/F9KFJeTnh3KRZFGiE5GUycqCsQ8VMrr+hVzIKLIbfsCYMaFcJFnUGUVEUmfjRk6ZdBoUPs1XZ13F/bcOUpKTpFOiE5HUWL0ajj8eXn0V7ryTnYcPjzsiqSOU6EQkNQoKwhRf48bB6afHHY3UIUp0IpJcX34ZVghv1QrefRca6NeOpJY6o4hI8ixYAPvsExZSBSU5iYUSnYgkx8yZMGBAWGPuT3+KOxqpw5ToRKT6vfgiHHYYtG4Nb74JvXrFHZHUYUp0IlK91q2Ds8+GHj1CkuvSJe6IpI5Tg7mIVK/GjeGll0KCa9487mhElOhEpBq4w7XXhtXBr70WfvGLuCMS2UxNlyJSNYWFcMEFMHJkGErgHndEIltRohORytu4EU45BR58EK68MjyaxR2VyFZiabo0s1xgNVAIFLh7dhxxiEgVuMOgQeF+3J13gqb0khoqznt0h7j79zG+v4hUhVmozZ16qqb0khpNnVFEpGKWLoWPPoIjjoCzzoo7GpFyxZXoHJhiZg486O6jtz3AzIYCQwE6duyY4vBEpEQLFsDhh4dmy8WLITMz7ohEyhVXZ5QD3L0PcBRwkZkduO0B7j7a3bPdPTtLC1aJxK/4lF4vvaQkJ7VGLInO3ZdFj98BzwL944hDRBL04ovwq19pSi+plVKe6MysiZk1K3oOHA7MT3UcIlIBL70E3bvDjBma0ktqnTju0e0IPGthrE0D4HF3/18McYhIeZYvD+vI3XlnmMOyadO4IxKpsJQnOndfAmh+IJGaqrAQ3norrAT+4oswaxa0baskJ7WWhhckIC8PcnOhc2dQvxhJWx9+CLfeCs8/D99/jzdsyHeDhlKPLPRjL7WZpgArx6T7viRnp2PI3+9AHtx5BFNHvAUFBXGHJVJ1eXkwdiy8+254vWEDPPssHH44My5+kp3q5dF9yr106taAiRPjDVWkKsxrwQSs2dnZnpOTk7o3dIe8PPI19vCIAAAYEUlEQVRsB7p3XM+rG/Yjnwz6Mpt6OJuaNafehMfg2GNDM0+9eprfT2qHTz6ByZPD9tZb4Wf90kvhjjvC84IC8lZk0KkTrF+/5bTMzDBOXC0aUpOY2exEppBUja44d5g6FQ48EPbZh6Wf5rNpu0z25n36M4ss8jgj8198/6tTwqKSABMnQqdOMGRIeJ6XF+9nEClu06aQoSD8fB96KFx+OaxdG5bTef99uP32sN8MMjLIzYWGDbe+TEZGaL4XqY10jw7CL4AXX4S//Q3eeQfat4e//IVOHZ2ffgIItbUfac3T/IY7HvwNm29atGsH/fvDv/8dmoEA9toL3ngDmjUL11ZtT1JpwwZ49dVQa3vuuVC2bFloeXjssTA8oFOnUk/v3Jno536L/PxQLlIbKdFBqMUdfXT4z/+Pf4T5+7bbjixgzJhQWcvICP/Zx4zZpvnmkEPCVlgIs2eHa334YUhyEK711VcwcGDYfvGL8AtHJBkefBAuuwzWrAm9JI88Eo4/fksT+8EHl3uJrKwEfu5FapE6c49uq56TrTeFm+7Ll8O554bmnX/9C048MfzPLuvciv5nHzEiXHvBgvC6TRsYOhT+/vcqfR4Rli7dcr/t1luhb1+YNg2eeCIsn3PoobDddpW+vHobS02X6D26OpHoJk4Mf502yijk6PVPc++ON9DyywWwzz5h/r5UNC1+/TW8/HKo8fXsGRapzM+HffeF/fYLtb2DD4YWLSp8af1CqkNWrIC774ZJk2Du3FC2++6hbODAeGMTSTElukheXmiR7L9+GqO4gJ58xIfWk3ajrqH5uSdD/frVHG0FfPNNyMCvvx46B9SvH+73jRwZ/hpPQFESb9gw3FcZMwYGD05y3JI6+fnh5yM/H446KnSF3HHHcB940KCw7bJL3FGKxCLRRJf29+iKepCtW9+YfDI4iaeY2vT/MbVPPfrFmOMA2Gkn+O9/Q4aaOXNLja+oy9trr4Wpl4ru7/XosVXtMy8vJLn167d0BR8yJMy9q5pdLbZqVegcNXkyvPACrFwZav5HHRX6+X/1lWYpEamAtE90RT3IZtGfXzAXMDILalgPsoYN4aCDwnbDDVvKf/wxdGx5/vnwun37kMXuugtattycxIuPdyrqBq5EV8vk5W35RzvzzNA02aZNuG88aNDWzZI1NMnl5+fz5ZdfsmHDhrhDkTTTqFEj2rdvT0YJfSgSkfZNl7Clea94D7Ja1bz32Wehpjd1KsyZE1Z3rl+fNdfeyj9v+o4XCwYynQGsp7EG9lZAHPc2N79nJyfruwVbOpPk5MDnn4c/Zt59N/x1tt9+8TatV9Bnn31Gs2bNaN26NaYhNVJN3J0ffviB1atX02WblTN0j24badNho/i4vHPOoXD8BOoX/EQ+DfjAfkGD3xxP76eujjfGWiAl9zbdw73XH36AH37g2bfbcuplbfllvbcZve5UuvqScNw++4Ra27nn1uofzg8//JAePXooyUm1c3c++ugjevbsuVW57tFtIyurVv8O2aL4L5GxY6l/332s+M8brPvfdPZc/DYNt/8y7HOH3r1DT5x99w1b//7QvHk8cdcglbq3uWlTaA7YbrvwOGVKSGDff785kXHEEXDCCeEeWr9+oWzjxs2XmJlxJ+vzhzOfznxEd+7KuJzrZx9L6147J/9Dp4iSnCRDVX+u6kyiS1uNG9Pyt0fS8rdHbl2+YUNIbG+/HTq8QEiSf//7lqENH38chjrUouaxSnMPg6YbNCA3F3ZtsARjFS1YSWt+oL3/wPLH25N1yVHh+BNPhG+/3ZLEfvwRLrwQ7r03XOuYY7Zcu3592H77LQuStmgRBmq3bh3us7VuzaIfW/PSDb+AfPiWnTiaF2ieCWdsgNYp/zJE6hYlunSVmRna4yCMvZo1KyS9/fcPZXPnhlpHs2YhIRaN5xswIPW1PveQmDduhJYtQ9knn8B334XFPou2zMxQY4Iwg82iRVvv79IlDJwGOO44mD9/6/0DB8KLL9K5Mzy/5mA68MWWGDbAxiknQFGi+/FHaNQo1Ipbtw5b0XfXsGG4j9aqVUhkzZtvPdtNkyZbvvtIyzxYdN3WH1vTasXv4IMP5vbbbyc7u9zWr2r3y1/+krfeeqvMY5o2bcqaNWsSvuY999zDqFGj6NOnDxMmTCj1uDg/97YmTpzIjTfeiJmx884789hjj9GmTZtqfQ8lurqgZcstQxSKdOkSFtZ8++0wtOHmm0ON55VXwhi+uXNhxoyQAHfbLSSideugQ4fwS33RIli8OJStXRseN2yASy4J1584MczSUXx//fpbapcXXADPPBP2rV8fkl2HDqFDBoTr/G+bhee7d9+S6J58MnTgaNx4y5aZufWxLVtuvT+aiDsrC9649D7+fG8h6xs047vC1lxxexsGDSn2n2vatLK/0379KvRPoGm1ZFvlJbnKeOCBB3j55Zdp3759tV+7JAUFBTRoUPk0UlBQwCWXXMLChQtp06YNl19+Offddx/XX3999QWJEl3d1bo1nH562CAknNmzwzRSEMZxXXnlz89buTLUYEaP3jLrfXEXXQQNGoQk9NxzW5JMkyZbamsAffqEptTi+1sXa8QbMSIsH1O0r3HjrbvVv/Za2Z/vttvK3H3g7cfR8y+p7aA0eHC4D5gWnaISUdK8miefHJqA162DX//65/vPOits338Pv/nN1vvK+eMjNzeXI488kr59+/Lee++xxx57MG7cOBo3bswrr7zCZZddRkFBAf369WPUqFFsV2x6tLFjxzJv3jzuvvtuAB566CEWLlzIJZdcwlFHHcUBBxzAW2+9Rbt27Zg8eTKZmZnMmTOH888/n3Xr1tGtWzfGjh1Lq1atOPjgg9l7772ZPn06a9euZdy4cdx000188MEH/Pa3v2XkyJHAltramjVrGDRoEMuXLyc/P5+RI0cyaNCgMj/rnXfeydhoEvlzzz2XYcOGcf7557NkyRKOOuoozjnnHIYPH775+PXr13P22Wczd+5cevTowfpiY5KmTJnCddddx8aNG+nWrRsPP/wwTZs25YUXXuDSSy+lSZMm7L///ixZsoTnn3+e66+/nsWLF7NkyRI6duzIY489xhVXXMG0adPYuHEjF110Eb///e8BuO2223jqqafYuHEjJ5xwAiNGjNjqc7g77s7atWtp3bo1q1atYpdkTIBQ9EY1eevbt69Lim3a5JPuWeqnZjzpVze61Ydl3Oczh451X78+7F+82H3mTPe5c90XLXJftsx9+XL3TZvijVtis3Dhwq0LDjro59v994d9a9eWvP/hh8P+vLyf7yvHZ5995oDPmDHD3d3PPvtsv+2223z9+vXevn17//jjj93d/fTTT/e77rorCvEgnzVrlq9evdq7du3qP/30k7u777fffj5v3jz/7LPPvH79+v7++++7u/tJJ53k48ePd3f3Xr16+bRp09zd/ZprrvFLLrlk8zUvv/xyd3e/++67vW3btv7VV1/5hg0bvF27dv7999+7u3uTJk3c3T0/P99XrlwZfew879atm2+K/h8VHVNcTk6O77nnnr5mzRpfvXq177777v7ee++5u3unTp08Ly/vZ+fccccdfvbZZ7u7+9y5c71+/fo+a9Ysz8vL8wEDBviaNWvc3f3mm2/2ESNGbP7OlixZ4u7up5xyih999NHu7n7dddd5nz59fN26de7u/uCDD/oNN9zg7u4bNmzwvn37+pIlS/yll17y8847zzdt2uSFhYV+9NFH++uvv/6z2P71r395s2bNfKeddvIBAwZ4QUFBif++P/v5cncgxxPIIarRSYnyvjcG/6Uj6/M7Qn4oe3A8LB0JWY2Arl3DJlKasmpgjRuXvb9Nm/Kbj0vQoUMH9o/upZ522mncc889DBw4kC5durDbbrsBcOaZZ3L//fczbNiwzec1bdqUQw89lOeff56ePXuSn59Pr169yM3NpUuXLuy1114A9O3bl9zcXFauXMmKFSs46KCDNl/zpJNO2ny94447DoBevXqxxx570LZtWwC6du3KF198QetirRfuzlVXXcUbb7xBvXr1WLZsGd9++y077bRTiZ9xxowZnHDCCTRp0gSAE088kenTp7P33nuX+r288cYbXHzxxQD07t2b3r17A/D222+zcOHCzd/ZTz/9xH777cdHH31E165dN49bGzx4MKNHj97q82VGtwqmTJnCvHnzePrppwFYuXIlixYtYsqUKUyZMmVzXGvWrGHRokUceOCBm6+Tn5/PqFGjeP/99+natSt//OMfuemmm7j66uodIqVEJyXSrCtSG23bDb0i3dLPPfdcbrzxRnr06MHZZ5+9ubx4E2f9+vW3avYrTdE59erV2+r8evXqUVBQsNWxEyZMIC8vj9mzZ5ORkUHnzp1TNruMuzNw4EAmTpy4VfmcOXPKPK8oyRZd49577+WII47Y6piXXnqJK6+8cnMzZkmK3qdbt24AnHzyydx8880V+gyJ0MJoUiItvim10eeff87MmTMBePzxxznggAPo3r07ubm5fPrppwCMHz9+c02suH322YcvvviCxx9/nMHlzB7QokULWrVqxfTp08u8ZiJWrlzJDjvsQEZGBq+99hpLi1aEL8WAAQOYNGkS69atY+3atTz77LMMGDCgzHMOPPBAHn/8cQDmz5/PvHnzANh333158803N383a9eu5ZNPPqF79+4sWbKE3GhZ+SeffLLUax9xxBGMGjWK/PzQ9PPJJ5+wdu1ajjjiCMaOHbu51+iyZcv47rvvtjq3Xbt2LFy4kLy8PACmTp36s0Hh1UE1OimReglKbdS9e3fuv/9+zjnnHHbffXcuuOACGjVqxMMPP8xJJ520uTPK+eefX+L5J598MnPmzKFVq1blvtejjz66uTNK165defjhhysV86mnnsqxxx5Lr169yM7OpkfUO7g0ffr04ayzzqJ///5AqImW1WwJcMEFF3D22WfTs2dPevbsSd+o01lWVhaPPPIIgwcPZmM0ucHIkSPZbbfdeOCBBzjyyCNp0qQJ/croZXzuueeSm5tLnz59cHeysrKYNGkShx9+OB9++CH77bcfEJqHH3vsMXbYYYfN5+68885cd911HHjggWRkZNCpUyceeeSRcr+ziqozU4BJ5aTN1GmSdB9++GFS/hpPVG5uLscccwzz58+v9DWOOeYYhg8fzmGHHVaNkdVOa9asoWnTprg7F110EbvuuutWPTlTraSfr0SnAFPTpZQpKysMGVOSk3S2YsUKdtttNzIzM5XkIg899BB77bUXe+yxBytXrizzXltNF0uNzsyOBP4PqA/8093LvPuoGp1IzRd3jU7SW62q0ZlZfeB+4Chgd2Cwme2e6jhEpPrVhlshUvtU9ecqjqbL/sCn7r7E3X8CngDKngZARGq8Ro0a8cMPPyjZSbXyaD26Ro0aVfoacfS6bAfFZ9PlS2CfGOIQkWrUvn17vvzyy81dxUWqS9EK45VVY4cXmNlQYChAx44dY45GRMqTkZHxsxWgRWqCOJoulwEdir1uH5Vtxd1Hu3u2u2dnqcufiIhUUhyJbhawq5l1MbOGwCnAczHEISIidUDKmy7dvcDM/gC8RBheMNbdF6Q6DhERqRtqxcwoZpYHlD0BXO3RBvg+7iDShL7L6qPvsvrou6w+5X2Xndy93HtbtSLRpRMzy0lkgKOUT99l9dF3WX30XVaf6vouNQWYiIikNSU6ERFJa0p0qTe6/EMkQfouq4++y+qj77L6VMt3qXt0IiKS1lSjExGRtKZEJyIiaU2JLsXM7E9m5mbWJnptZnaPmX1qZvPMrE/cMdZ0ZnabmX0UfV/PmlnLYvuujL7Lj83siDjjrC3M7Mjo+/rUzK6IO57axMw6mNlrZrbQzBaY2SVR+fZmNtXMFkWPreKOtTYws/pm9r6ZPR+97mJm70Q/m09Gs2lVmBJdCplZB+Bw4PNixUcBu0bbUGBUDKHVNlOBPd29N/AJcCVAtK7hKcAewJHAA9H6h1IKrQ9ZZQXAn9x9d2Bf4KLo+7sCeMXddwVeiV5L+S4BPiz2+hbgLnffBVgODKnMRZXoUusu4HKgeA+gQcA4D94GWppZ21iiqyXcfYq7F0Qv3yZMDA7hu3zC3Te6+2fAp4T1D6V0Wh+yCtz9a3d/L3q+mvBLuh3hO3w0OuxR4Ph4Iqw9zKw9cDTwz+i1AYcCT0eHVPp7VKJLETMbBCxz97nb7Cppfb52KQus9jsHeDF6ru+y4vSdVRMz6wzsDbwD7OjuX0e7vgF2jCms2uRuQkVgU/S6NbCi2B+1lf7ZrLHr0dVGZvYysFMJu/4KXEVotpQElPVduvvk6Ji/EpqOJqQyNpFtmVlT4BlgmLuvCpWRwN3dzDSOqwxmdgzwnbvPNrODq/v6SnTVyN1/VVK5mfUCugBzo/8A7YH3zKw/Ca7PV9eU9l0WMbOzgGOAw3zLYFB9lxWn76yKzCyDkOQmuPu/o+Jvzaytu38d3Yr4Lr4Ia4X9gePM7NdAI6A58H+EWzkNolpdpX821XSZAu7+gbvv4O6d3b0zoQrex92/IazFd0bU+3JfYGWxJg8pgZkdSWjiOM7d1xXb9RxwipltZ2ZdCB183o0jxlpE60NWQXQfaQzwobvfWWzXc8CZ0fMzgcmpjq02cfcr3b199PvxFOBVdz8VeA34TXRYpb9H1eji9wLwa0LHiXXA2fGGUyvcB2wHTI1qyG+7+/nuvsDMngIWEpo0L3L3whjjrPG0PmSV7Q+cDnxgZnOisquAm4GnzGwIYYmxk2OKr7b7C/CEmY0E3if8UVFhmgJMRETSmpouRUQkrSnRiYhIWlOiExGRtKZEJyIiaU2JTkRE0poSnaQtM9vJzJ4ws8VmNtvMXjCz3czs4KLZ0eNmZn8zszIHx1fT+7Q0swur4TrTzCy7OmJK5jVFitM4OklL0UDeZ4FH3f2UqOwX1LA5B9392hS9VUvgQuCBRE+IvkNz903lHixSg6lGJ+nqECDf3f9RVODuc919evSyqZk9Ha1rNyH6pY6ZXWtms8xsvpmNLlY+zcxuMbN3zewTMxsQlTc2s6ei9ciejdbOyo72HW5mM83sPTP7VzQf4lbM7BEz+030PNfMRkTHf2BmPUo4/r9m1jt6/r6ZXRs9/5uZnWdmTc3slWLXKFqJ4Gagm5nNMbPbonP+HH3WeWY2IirrbGFtunHAfLaeHmzbWH72+SysbfevYsdsrj0n8n2IJIMSnaSrPYHZZezfGxhGWIOtK2GGC4D73L2fu+8JZBLm0yzSwN37R+ddF5VdCCyP1iO7BugLYGFh3auBX7l7HyAHuDSBuL+Pjh8FXFbC/unAADNrQZj9pSjuAcAbwAbghOgahwB3RMn6CmCxu+/l7n82s8MJU6T1B/YC+prZgdG1dgUecPc93H1pSUGW8fleBvYxsybRob8lzGxR2e9DpMrUdCl11bvu/iVANHVTZ2AGcIiZXQ40BrYHFgD/ic4pmrB3dnQ8wAGEyWdx9/lmNi8q35eQRN+MKoUNgZkJxFX8PU4sYf904GLgM+C/wEAzawx0cfePLUwwfGOUtDYRljUpqbn28Gh7P3rdlJDgPgeWRmsjlqXEzxdNKfY/4Fgze5qwvtjlwEElHV/Oe4hUCyU6SVcL2DIZbEk2FnteCDQws0aEe1jZ7v6FmV1PmEl923MKKf//jgFT3X1whaIu/z1mAdnAEsJK622A89hSez0VyAL6unu+meVu8xmKx3eTuz+4VWFYU21tAnGW9fmeAP4A/AjkuPvqqFZZme9DpMrUdCnp6lVgOzMbWlRgZr2L7q2VoighfB/dPyorURZ5k2jCXjPbHegVlb8N7G9mu0T7mpjZbhX8DD8TrQL+BXASoUY0ndDE+UZ0SAvCul75ZnYI0CkqXw00K3apl4Bziu6TmVk7M9uhAqGU9fleB/oQEvATCRwvklRKdJKWojXqTgB+ZWF4wQLgJsJqz6WdswJ4iNAJ4yVC7ak8DwBZZrYQGEmoSa509zzgLGBi1Jw5E/hZ55JKmk5IZuuj5+2jRwiL0Gab2QfAGcBHAO7+A6HZcL6Z3ebuU4DHgZnRsU+zdSIsU1mfL1ox4nngqOixzONFkk2rF4hUgZnVBzLcfYOZdSN0xuge1bxEpAbQPTqRqmkMvBZ1AjHgQiU5kZpFNToREUlrukcnIiJpTYlORETSmhKdiIikNSU6ERFJa0p0IiKS1v4/wz5QtJyYhi0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_poly(x, y, xlabel , ylabel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of this section is to answer the following question:\n",
    "* How do we determine which one is the _best_ fit?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A naive answer might be the polynomial of order $10$ since it perfectly pass through all the points of the dataset.\n",
    "\n",
    "However, one can immediately show that this poorly performs on predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now use `PolynomialFeatures` from `sklearn.preprocessing` to prepare data and `LinearRegression` to make predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly = PolynomialFeatures(10)\n",
    "X_ = poly.fit_transform(x.reshape(-1,1))\n",
    "level_test_array = poly.fit_transform(np.array(level_test).reshape(1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your predicted flow: 23.6271271029\n",
      "You should get approximatively: 22.32\n"
     ]
    }
   ],
   "source": [
    "prediction = model.predict(level_test_array)\n",
    "\n",
    "print('Your predicted flow: %.10f' %prediction)\n",
    "print('You should get approximatively: 22.32')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not really satisfying.\n",
    "\n",
    "The situation gets even worst when the test point falls out of the initial data range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "level_test2 = -60\n",
    "level_test2_array = poly.fit_transform(np.array(level_test2).reshape(1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your predicted flow: -418.5795052571\n",
      "You should get approximatively: 6.78\n"
     ]
    }
   ],
   "source": [
    "prediction2 = model.predict(level_test2_array)\n",
    "\n",
    "print('Your predicted flow: %.10f' %prediction2)\n",
    "print('You should get approximatively: 6.78')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the answer is completely wrong!\n",
    "\n",
    "Let's explore the reasons a bit more.\n",
    "First of all, we can plot in a bit wider range the ten-degree polynomial and the three-degree one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "measures.plot(kind='scatter', x = xlabel, y = ylabel, marker = 'x', color='purple', figsize=(15,10))\n",
    "xx = np.linspace(start=-100, stop=100, num=200)\n",
    "z10 = np.polyfit(x, y, 10)\n",
    "p = np.poly1d(z10)\n",
    "plt.plot(xx, p(xx), \"r--\", label = 'polynomial of order 10')\n",
    "z3 = np.polyfit(x, y, 3)\n",
    "p = np.poly1d(z3)\n",
    "plt.plot(xx, p(xx), \"b--\", label = 'polynomial of order 3')\n",
    "plt.xlim(-60, 50)\n",
    "plt.ylim(0,50)\n",
    "pst = plt.legend(loc='lower right', frameon=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the red line perfectly pass trough all the points, however, it loses predictivity on new data points.\n",
    "This issue is something typical in statistical procedures and when one handles a dataset describing complex situations (_i.e._ with a lot of features) and it is commonly known under the name of __overfitting__. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To give a more precise definition of overfitting, one can say\n",
    "\n",
    "> __Overfitting__ is the production of an analysis that corresponds too closely or exactly to a particular set of data, and may therefore fail to fit further additional data or predict future observations reliably.\n",
    "\n",
    "> An __overfitted model__ is a statistical model that contains more parameters than can be justified by the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](https://upload.wikimedia.org/wikipedia/commons/thumb/1/19/Overfitting.svg/600px-Overfitting.svg.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In other words, we have more (or a comparable number) coefficients to estimate than data points.\n",
    "\n",
    "In the case above, we had to predict $11$ coefficients (for a ten-degree polynomial) with $12$ datapoints.\n",
    "Indeed, if we try a polynomial of order $12$ for instance, python will give us a warning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py:1: RankWarning: Polyfit may be poorly conditioned\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "z = np.polyfit(x, y, 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do we face this issue?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have several possibilities:\n",
    "\n",
    "1. Reduce the number of parameters, _i.e._ the number of features\n",
    "2. Use a bigger dataset\n",
    "3. Use a different fit function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The point 1. and 3. can be implemented easily, the point 2. is not always easy to adopt. Furthermore, it might be not always a good idea to take new data, even ignoring all the issues related to data access, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even when we know how to fix this issue, we still ignore how to dectect it.\n",
    "Just to rephrase, we do not know which one is the _right_ polynomial fit to use, if one exists.\n",
    "\n",
    "We need a measure of the wellness of the fit, and this cannot be the cost function on the training dataset, since using this will lead to the overfit and to a loss of predictivity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Underfitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In contrast to overfitting, when a model is underfitted, it means that the model does not fit the training data and therefore misses the trends in the data. It also means the model cannot be generalized to new data. As you probably guessed (or figured out!), this is usually the result of a very simple model (not enough predictors/independent variables). It could also happen when, for example, we fit a linear model (like linear regression) to data that is not linear. It almost goes without saying that this model will have poor predictive ability (on training data and cannot be generalized to other data)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](https://miro.medium.com/max/1110/1*tBErXYVvTw2jSUYK7thU2A.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Machine Learning literature, an underfitted model is also known as affected of _high bias_, while the overfitting also goes under the name of _variance_."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Excercise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Determine (giving a numerical answer) which one is the best fit for the data above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test and Cross validation sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To avoid such issues, we follow such strategy.\n",
    "\n",
    "We split the database into three pieces.\n",
    "\n",
    "* __Training Set__: this data set is used to adjust the weights of the model.\n",
    "\n",
    "* __Cross Validation Set__: this data set is used to minimize overfitting. You're not adjusting the weights of the model with this data set, you are just verifying that any increase in accuracy over the training data set actually yields an increase in accuracy over a data set that has not been shown to the algorithm before, or at least it has not trained on it (i.e. validation data set). If the accuracy over the training data set increases, but the accuracy over the validation data set stays the same or decreases, then you are overfitting your model and you should stop training.\n",
    "\n",
    "* __Testing Set__: this data set is used only for testing the final solution in order to confirm the actual predictive power of the network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](https://www.researchgate.net/publication/307087929/figure/fig6/AS:399685689856008@1472303902570/For-cross-validation-and-cross-testing-data-are-divided-into-two-separate-sets-only.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can describe the simplest version of our strategy as follows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the full dataset into training set, test set and cross validation set. Typically, the splitting is $70\\%$-$10\\%$-$20\\%$.\n",
    "\n",
    "Train the algorithm on the training set, getting weights from this procedure.\n",
    "Hence, adjust other parameters of the model on the cross-validation set (_i.e._ determine thresholds, number of classes, etc.)\n",
    "Finally, compute on the test set error the accuracy of your model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to give more details about validation procedures later. Now we prefer rather to focus on an example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine = datasets.load_wine()\n",
    "df_wine = pd.DataFrame(wine.data, columns=wine.feature_names)\n",
    "df_wine['label'] = wine.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.640000</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.380000</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.680000</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.320000</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.20</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.45</td>\n",
       "      <td>15.2</td>\n",
       "      <td>112.0</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>1.05</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1450.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14.39</td>\n",
       "      <td>1.87</td>\n",
       "      <td>2.45</td>\n",
       "      <td>14.6</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.52</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.98</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>1.02</td>\n",
       "      <td>3.58</td>\n",
       "      <td>1290.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14.06</td>\n",
       "      <td>2.15</td>\n",
       "      <td>2.61</td>\n",
       "      <td>17.6</td>\n",
       "      <td>121.0</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.51</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.050000</td>\n",
       "      <td>1.06</td>\n",
       "      <td>3.58</td>\n",
       "      <td>1295.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14.83</td>\n",
       "      <td>1.64</td>\n",
       "      <td>2.17</td>\n",
       "      <td>14.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.98</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.98</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>1.08</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1045.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13.86</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.27</td>\n",
       "      <td>16.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.98</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.85</td>\n",
       "      <td>7.220000</td>\n",
       "      <td>1.01</td>\n",
       "      <td>3.55</td>\n",
       "      <td>1045.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14.10</td>\n",
       "      <td>2.16</td>\n",
       "      <td>2.30</td>\n",
       "      <td>18.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>2.95</td>\n",
       "      <td>3.32</td>\n",
       "      <td>0.22</td>\n",
       "      <td>2.38</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1510.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14.12</td>\n",
       "      <td>1.48</td>\n",
       "      <td>2.32</td>\n",
       "      <td>16.8</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.43</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.57</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.17</td>\n",
       "      <td>2.82</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.75</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.41</td>\n",
       "      <td>16.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.81</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>1.15</td>\n",
       "      <td>2.90</td>\n",
       "      <td>1320.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14.75</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.39</td>\n",
       "      <td>11.4</td>\n",
       "      <td>91.0</td>\n",
       "      <td>3.10</td>\n",
       "      <td>3.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>2.73</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14.38</td>\n",
       "      <td>1.87</td>\n",
       "      <td>2.38</td>\n",
       "      <td>12.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>3.64</td>\n",
       "      <td>0.29</td>\n",
       "      <td>2.96</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>1.20</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1547.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>13.63</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.70</td>\n",
       "      <td>17.2</td>\n",
       "      <td>112.0</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.91</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.46</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>1.28</td>\n",
       "      <td>2.88</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>14.30</td>\n",
       "      <td>1.92</td>\n",
       "      <td>2.72</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.14</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.07</td>\n",
       "      <td>2.65</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>13.83</td>\n",
       "      <td>1.57</td>\n",
       "      <td>2.62</td>\n",
       "      <td>20.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>2.95</td>\n",
       "      <td>3.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.72</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>1.13</td>\n",
       "      <td>2.57</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>14.19</td>\n",
       "      <td>1.59</td>\n",
       "      <td>2.48</td>\n",
       "      <td>16.5</td>\n",
       "      <td>108.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>3.93</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1.86</td>\n",
       "      <td>8.700000</td>\n",
       "      <td>1.23</td>\n",
       "      <td>2.82</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>13.64</td>\n",
       "      <td>3.10</td>\n",
       "      <td>2.56</td>\n",
       "      <td>15.2</td>\n",
       "      <td>116.0</td>\n",
       "      <td>2.70</td>\n",
       "      <td>3.03</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1.66</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>0.96</td>\n",
       "      <td>3.36</td>\n",
       "      <td>845.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>14.06</td>\n",
       "      <td>1.63</td>\n",
       "      <td>2.28</td>\n",
       "      <td>16.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.17</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.10</td>\n",
       "      <td>5.650000</td>\n",
       "      <td>1.09</td>\n",
       "      <td>3.71</td>\n",
       "      <td>780.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>12.93</td>\n",
       "      <td>3.80</td>\n",
       "      <td>2.65</td>\n",
       "      <td>18.6</td>\n",
       "      <td>102.0</td>\n",
       "      <td>2.41</td>\n",
       "      <td>2.41</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.98</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.52</td>\n",
       "      <td>770.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>13.71</td>\n",
       "      <td>1.86</td>\n",
       "      <td>2.36</td>\n",
       "      <td>16.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.61</td>\n",
       "      <td>2.88</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.69</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>1.11</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1035.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>12.85</td>\n",
       "      <td>1.60</td>\n",
       "      <td>2.52</td>\n",
       "      <td>17.8</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2.48</td>\n",
       "      <td>2.37</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.46</td>\n",
       "      <td>3.930000</td>\n",
       "      <td>1.09</td>\n",
       "      <td>3.63</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>13.50</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.61</td>\n",
       "      <td>20.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.53</td>\n",
       "      <td>2.61</td>\n",
       "      <td>0.28</td>\n",
       "      <td>1.66</td>\n",
       "      <td>3.520000</td>\n",
       "      <td>1.12</td>\n",
       "      <td>3.82</td>\n",
       "      <td>845.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>13.05</td>\n",
       "      <td>2.05</td>\n",
       "      <td>3.22</td>\n",
       "      <td>25.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>2.63</td>\n",
       "      <td>2.68</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.92</td>\n",
       "      <td>3.580000</td>\n",
       "      <td>1.13</td>\n",
       "      <td>3.20</td>\n",
       "      <td>830.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>13.39</td>\n",
       "      <td>1.77</td>\n",
       "      <td>2.62</td>\n",
       "      <td>16.1</td>\n",
       "      <td>93.0</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.94</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.45</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>0.92</td>\n",
       "      <td>3.22</td>\n",
       "      <td>1195.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>13.30</td>\n",
       "      <td>1.72</td>\n",
       "      <td>2.14</td>\n",
       "      <td>17.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.19</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.35</td>\n",
       "      <td>3.950000</td>\n",
       "      <td>1.02</td>\n",
       "      <td>2.77</td>\n",
       "      <td>1285.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>13.87</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.80</td>\n",
       "      <td>19.4</td>\n",
       "      <td>107.0</td>\n",
       "      <td>2.95</td>\n",
       "      <td>2.97</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1.76</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>3.40</td>\n",
       "      <td>915.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>14.02</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2.21</td>\n",
       "      <td>16.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.33</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.98</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.59</td>\n",
       "      <td>1035.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>13.32</td>\n",
       "      <td>3.24</td>\n",
       "      <td>2.38</td>\n",
       "      <td>21.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1.93</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.25</td>\n",
       "      <td>8.420000</td>\n",
       "      <td>0.55</td>\n",
       "      <td>1.62</td>\n",
       "      <td>650.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>13.08</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.36</td>\n",
       "      <td>21.5</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1.41</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.14</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.33</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>13.50</td>\n",
       "      <td>3.12</td>\n",
       "      <td>2.62</td>\n",
       "      <td>24.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.57</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.25</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.30</td>\n",
       "      <td>500.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>12.79</td>\n",
       "      <td>2.67</td>\n",
       "      <td>2.48</td>\n",
       "      <td>22.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1.26</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.47</td>\n",
       "      <td>480.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>13.11</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>116.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.56</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.33</td>\n",
       "      <td>425.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>13.23</td>\n",
       "      <td>3.30</td>\n",
       "      <td>2.28</td>\n",
       "      <td>18.5</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.87</td>\n",
       "      <td>10.520000</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.51</td>\n",
       "      <td>675.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>12.58</td>\n",
       "      <td>1.29</td>\n",
       "      <td>2.10</td>\n",
       "      <td>20.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.40</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.55</td>\n",
       "      <td>640.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>13.17</td>\n",
       "      <td>5.19</td>\n",
       "      <td>2.32</td>\n",
       "      <td>22.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>1.74</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.55</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.48</td>\n",
       "      <td>725.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>13.84</td>\n",
       "      <td>4.12</td>\n",
       "      <td>2.38</td>\n",
       "      <td>19.5</td>\n",
       "      <td>89.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.56</td>\n",
       "      <td>9.010000</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.64</td>\n",
       "      <td>480.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>12.45</td>\n",
       "      <td>3.03</td>\n",
       "      <td>2.64</td>\n",
       "      <td>27.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1.90</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.14</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.73</td>\n",
       "      <td>880.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>14.34</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2.70</td>\n",
       "      <td>25.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>1.31</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2.70</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.96</td>\n",
       "      <td>660.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>13.48</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2.64</td>\n",
       "      <td>22.5</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2.60</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2.29</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.78</td>\n",
       "      <td>620.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>12.36</td>\n",
       "      <td>3.83</td>\n",
       "      <td>2.38</td>\n",
       "      <td>21.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.04</td>\n",
       "      <td>7.650000</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.58</td>\n",
       "      <td>520.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>13.69</td>\n",
       "      <td>3.26</td>\n",
       "      <td>2.54</td>\n",
       "      <td>20.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>1.83</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.80</td>\n",
       "      <td>5.880000</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.82</td>\n",
       "      <td>680.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>12.85</td>\n",
       "      <td>3.27</td>\n",
       "      <td>2.58</td>\n",
       "      <td>22.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.96</td>\n",
       "      <td>5.580000</td>\n",
       "      <td>0.87</td>\n",
       "      <td>2.11</td>\n",
       "      <td>570.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>12.96</td>\n",
       "      <td>3.45</td>\n",
       "      <td>2.35</td>\n",
       "      <td>18.5</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.94</td>\n",
       "      <td>5.280000</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.75</td>\n",
       "      <td>675.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>13.78</td>\n",
       "      <td>2.76</td>\n",
       "      <td>2.30</td>\n",
       "      <td>22.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.41</td>\n",
       "      <td>1.03</td>\n",
       "      <td>9.580000</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.68</td>\n",
       "      <td>615.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>13.73</td>\n",
       "      <td>4.36</td>\n",
       "      <td>2.26</td>\n",
       "      <td>22.5</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.15</td>\n",
       "      <td>6.620000</td>\n",
       "      <td>0.78</td>\n",
       "      <td>1.75</td>\n",
       "      <td>520.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>13.45</td>\n",
       "      <td>3.70</td>\n",
       "      <td>2.60</td>\n",
       "      <td>23.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.46</td>\n",
       "      <td>10.680000</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.56</td>\n",
       "      <td>695.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>12.82</td>\n",
       "      <td>3.37</td>\n",
       "      <td>2.30</td>\n",
       "      <td>19.5</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.97</td>\n",
       "      <td>10.260000</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1.75</td>\n",
       "      <td>685.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>13.58</td>\n",
       "      <td>2.58</td>\n",
       "      <td>2.69</td>\n",
       "      <td>24.5</td>\n",
       "      <td>105.0</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.54</td>\n",
       "      <td>8.660000</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1.80</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>13.40</td>\n",
       "      <td>4.60</td>\n",
       "      <td>2.86</td>\n",
       "      <td>25.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.98</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.11</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.92</td>\n",
       "      <td>630.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>12.20</td>\n",
       "      <td>3.03</td>\n",
       "      <td>2.32</td>\n",
       "      <td>19.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.73</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.83</td>\n",
       "      <td>510.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>12.77</td>\n",
       "      <td>2.39</td>\n",
       "      <td>2.28</td>\n",
       "      <td>19.5</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.64</td>\n",
       "      <td>9.899999</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.63</td>\n",
       "      <td>470.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>14.16</td>\n",
       "      <td>2.51</td>\n",
       "      <td>2.48</td>\n",
       "      <td>20.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.24</td>\n",
       "      <td>9.700000</td>\n",
       "      <td>0.62</td>\n",
       "      <td>1.71</td>\n",
       "      <td>660.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.74</td>\n",
       "      <td>740.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.56</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.56</td>\n",
       "      <td>835.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.300000</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.62</td>\n",
       "      <td>840.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.60</td>\n",
       "      <td>560.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
       "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
       "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
       "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
       "5      14.20        1.76  2.45               15.2      112.0           3.27   \n",
       "6      14.39        1.87  2.45               14.6       96.0           2.50   \n",
       "7      14.06        2.15  2.61               17.6      121.0           2.60   \n",
       "8      14.83        1.64  2.17               14.0       97.0           2.80   \n",
       "9      13.86        1.35  2.27               16.0       98.0           2.98   \n",
       "10     14.10        2.16  2.30               18.0      105.0           2.95   \n",
       "11     14.12        1.48  2.32               16.8       95.0           2.20   \n",
       "12     13.75        1.73  2.41               16.0       89.0           2.60   \n",
       "13     14.75        1.73  2.39               11.4       91.0           3.10   \n",
       "14     14.38        1.87  2.38               12.0      102.0           3.30   \n",
       "15     13.63        1.81  2.70               17.2      112.0           2.85   \n",
       "16     14.30        1.92  2.72               20.0      120.0           2.80   \n",
       "17     13.83        1.57  2.62               20.0      115.0           2.95   \n",
       "18     14.19        1.59  2.48               16.5      108.0           3.30   \n",
       "19     13.64        3.10  2.56               15.2      116.0           2.70   \n",
       "20     14.06        1.63  2.28               16.0      126.0           3.00   \n",
       "21     12.93        3.80  2.65               18.6      102.0           2.41   \n",
       "22     13.71        1.86  2.36               16.6      101.0           2.61   \n",
       "23     12.85        1.60  2.52               17.8       95.0           2.48   \n",
       "24     13.50        1.81  2.61               20.0       96.0           2.53   \n",
       "25     13.05        2.05  3.22               25.0      124.0           2.63   \n",
       "26     13.39        1.77  2.62               16.1       93.0           2.85   \n",
       "27     13.30        1.72  2.14               17.0       94.0           2.40   \n",
       "28     13.87        1.90  2.80               19.4      107.0           2.95   \n",
       "29     14.02        1.68  2.21               16.0       96.0           2.65   \n",
       "..       ...         ...   ...                ...        ...            ...   \n",
       "148    13.32        3.24  2.38               21.5       92.0           1.93   \n",
       "149    13.08        3.90  2.36               21.5      113.0           1.41   \n",
       "150    13.50        3.12  2.62               24.0      123.0           1.40   \n",
       "151    12.79        2.67  2.48               22.0      112.0           1.48   \n",
       "152    13.11        1.90  2.75               25.5      116.0           2.20   \n",
       "153    13.23        3.30  2.28               18.5       98.0           1.80   \n",
       "154    12.58        1.29  2.10               20.0      103.0           1.48   \n",
       "155    13.17        5.19  2.32               22.0       93.0           1.74   \n",
       "156    13.84        4.12  2.38               19.5       89.0           1.80   \n",
       "157    12.45        3.03  2.64               27.0       97.0           1.90   \n",
       "158    14.34        1.68  2.70               25.0       98.0           2.80   \n",
       "159    13.48        1.67  2.64               22.5       89.0           2.60   \n",
       "160    12.36        3.83  2.38               21.0       88.0           2.30   \n",
       "161    13.69        3.26  2.54               20.0      107.0           1.83   \n",
       "162    12.85        3.27  2.58               22.0      106.0           1.65   \n",
       "163    12.96        3.45  2.35               18.5      106.0           1.39   \n",
       "164    13.78        2.76  2.30               22.0       90.0           1.35   \n",
       "165    13.73        4.36  2.26               22.5       88.0           1.28   \n",
       "166    13.45        3.70  2.60               23.0      111.0           1.70   \n",
       "167    12.82        3.37  2.30               19.5       88.0           1.48   \n",
       "168    13.58        2.58  2.69               24.5      105.0           1.55   \n",
       "169    13.40        4.60  2.86               25.0      112.0           1.98   \n",
       "170    12.20        3.03  2.32               19.0       96.0           1.25   \n",
       "171    12.77        2.39  2.28               19.5       86.0           1.39   \n",
       "172    14.16        2.51  2.48               20.0       91.0           1.68   \n",
       "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
       "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
       "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
       "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
       "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
       "\n",
       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "0          3.06                  0.28             2.29         5.640000  1.04   \n",
       "1          2.76                  0.26             1.28         4.380000  1.05   \n",
       "2          3.24                  0.30             2.81         5.680000  1.03   \n",
       "3          3.49                  0.24             2.18         7.800000  0.86   \n",
       "4          2.69                  0.39             1.82         4.320000  1.04   \n",
       "5          3.39                  0.34             1.97         6.750000  1.05   \n",
       "6          2.52                  0.30             1.98         5.250000  1.02   \n",
       "7          2.51                  0.31             1.25         5.050000  1.06   \n",
       "8          2.98                  0.29             1.98         5.200000  1.08   \n",
       "9          3.15                  0.22             1.85         7.220000  1.01   \n",
       "10         3.32                  0.22             2.38         5.750000  1.25   \n",
       "11         2.43                  0.26             1.57         5.000000  1.17   \n",
       "12         2.76                  0.29             1.81         5.600000  1.15   \n",
       "13         3.69                  0.43             2.81         5.400000  1.25   \n",
       "14         3.64                  0.29             2.96         7.500000  1.20   \n",
       "15         2.91                  0.30             1.46         7.300000  1.28   \n",
       "16         3.14                  0.33             1.97         6.200000  1.07   \n",
       "17         3.40                  0.40             1.72         6.600000  1.13   \n",
       "18         3.93                  0.32             1.86         8.700000  1.23   \n",
       "19         3.03                  0.17             1.66         5.100000  0.96   \n",
       "20         3.17                  0.24             2.10         5.650000  1.09   \n",
       "21         2.41                  0.25             1.98         4.500000  1.03   \n",
       "22         2.88                  0.27             1.69         3.800000  1.11   \n",
       "23         2.37                  0.26             1.46         3.930000  1.09   \n",
       "24         2.61                  0.28             1.66         3.520000  1.12   \n",
       "25         2.68                  0.47             1.92         3.580000  1.13   \n",
       "26         2.94                  0.34             1.45         4.800000  0.92   \n",
       "27         2.19                  0.27             1.35         3.950000  1.02   \n",
       "28         2.97                  0.37             1.76         4.500000  1.25   \n",
       "29         2.33                  0.26             1.98         4.700000  1.04   \n",
       "..          ...                   ...              ...              ...   ...   \n",
       "148        0.76                  0.45             1.25         8.420000  0.55   \n",
       "149        1.39                  0.34             1.14         9.400000  0.57   \n",
       "150        1.57                  0.22             1.25         8.600000  0.59   \n",
       "151        1.36                  0.24             1.26        10.800000  0.48   \n",
       "152        1.28                  0.26             1.56         7.100000  0.61   \n",
       "153        0.83                  0.61             1.87        10.520000  0.56   \n",
       "154        0.58                  0.53             1.40         7.600000  0.58   \n",
       "155        0.63                  0.61             1.55         7.900000  0.60   \n",
       "156        0.83                  0.48             1.56         9.010000  0.57   \n",
       "157        0.58                  0.63             1.14         7.500000  0.67   \n",
       "158        1.31                  0.53             2.70        13.000000  0.57   \n",
       "159        1.10                  0.52             2.29        11.750000  0.57   \n",
       "160        0.92                  0.50             1.04         7.650000  0.56   \n",
       "161        0.56                  0.50             0.80         5.880000  0.96   \n",
       "162        0.60                  0.60             0.96         5.580000  0.87   \n",
       "163        0.70                  0.40             0.94         5.280000  0.68   \n",
       "164        0.68                  0.41             1.03         9.580000  0.70   \n",
       "165        0.47                  0.52             1.15         6.620000  0.78   \n",
       "166        0.92                  0.43             1.46        10.680000  0.85   \n",
       "167        0.66                  0.40             0.97        10.260000  0.72   \n",
       "168        0.84                  0.39             1.54         8.660000  0.74   \n",
       "169        0.96                  0.27             1.11         8.500000  0.67   \n",
       "170        0.49                  0.40             0.73         5.500000  0.66   \n",
       "171        0.51                  0.48             0.64         9.899999  0.57   \n",
       "172        0.70                  0.44             1.24         9.700000  0.62   \n",
       "173        0.61                  0.52             1.06         7.700000  0.64   \n",
       "174        0.75                  0.43             1.41         7.300000  0.70   \n",
       "175        0.69                  0.43             1.35        10.200000  0.59   \n",
       "176        0.68                  0.53             1.46         9.300000  0.60   \n",
       "177        0.76                  0.56             1.35         9.200000  0.61   \n",
       "\n",
       "     od280/od315_of_diluted_wines  proline  label  \n",
       "0                            3.92   1065.0      0  \n",
       "1                            3.40   1050.0      0  \n",
       "2                            3.17   1185.0      0  \n",
       "3                            3.45   1480.0      0  \n",
       "4                            2.93    735.0      0  \n",
       "5                            2.85   1450.0      0  \n",
       "6                            3.58   1290.0      0  \n",
       "7                            3.58   1295.0      0  \n",
       "8                            2.85   1045.0      0  \n",
       "9                            3.55   1045.0      0  \n",
       "10                           3.17   1510.0      0  \n",
       "11                           2.82   1280.0      0  \n",
       "12                           2.90   1320.0      0  \n",
       "13                           2.73   1150.0      0  \n",
       "14                           3.00   1547.0      0  \n",
       "15                           2.88   1310.0      0  \n",
       "16                           2.65   1280.0      0  \n",
       "17                           2.57   1130.0      0  \n",
       "18                           2.82   1680.0      0  \n",
       "19                           3.36    845.0      0  \n",
       "20                           3.71    780.0      0  \n",
       "21                           3.52    770.0      0  \n",
       "22                           4.00   1035.0      0  \n",
       "23                           3.63   1015.0      0  \n",
       "24                           3.82    845.0      0  \n",
       "25                           3.20    830.0      0  \n",
       "26                           3.22   1195.0      0  \n",
       "27                           2.77   1285.0      0  \n",
       "28                           3.40    915.0      0  \n",
       "29                           3.59   1035.0      0  \n",
       "..                            ...      ...    ...  \n",
       "148                          1.62    650.0      2  \n",
       "149                          1.33    550.0      2  \n",
       "150                          1.30    500.0      2  \n",
       "151                          1.47    480.0      2  \n",
       "152                          1.33    425.0      2  \n",
       "153                          1.51    675.0      2  \n",
       "154                          1.55    640.0      2  \n",
       "155                          1.48    725.0      2  \n",
       "156                          1.64    480.0      2  \n",
       "157                          1.73    880.0      2  \n",
       "158                          1.96    660.0      2  \n",
       "159                          1.78    620.0      2  \n",
       "160                          1.58    520.0      2  \n",
       "161                          1.82    680.0      2  \n",
       "162                          2.11    570.0      2  \n",
       "163                          1.75    675.0      2  \n",
       "164                          1.68    615.0      2  \n",
       "165                          1.75    520.0      2  \n",
       "166                          1.56    695.0      2  \n",
       "167                          1.75    685.0      2  \n",
       "168                          1.80    750.0      2  \n",
       "169                          1.92    630.0      2  \n",
       "170                          1.83    510.0      2  \n",
       "171                          1.63    470.0      2  \n",
       "172                          1.71    660.0      2  \n",
       "173                          1.74    740.0      2  \n",
       "174                          1.56    750.0      2  \n",
       "175                          1.56    835.0      2  \n",
       "176                          1.62    840.0      2  \n",
       "177                          1.60    560.0      2  \n",
       "\n",
       "[178 rows x 14 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset is a composed of data about wines. It is a sort of tabular evaluation of different wines, compiled by a very conscientious (and expert on chemistry) critic.\n",
    "\n",
    "Finally, we have a column grading the wine quality."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several possibilities, for instance, one may think to apply a classification algorithm and train a model to give a scoreto a wine based on wine features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To begin, we split the full dataset into training and test set (we leave the validation one apart for the moment being). `sklearn` will help us in this task.\n",
    "\n",
    "Then, we can train, for example, a logistic regression classifier on our training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df_wine.label # Define which one is the target/label variable.\n",
    "X = df_wine.loc[:, df_wine.columns[:-1]]\n",
    "# create training and testing sets, ratio 70/30\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='multinomial', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='newton-cg', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define and train the model\n",
    "lg = LogisticRegression(multi_class='multinomial', solver='newton-cg')\n",
    "lg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this stage, we can make a check on how many good results the algorithm predicts, using the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error: 3.704 %\n"
     ]
    }
   ],
   "source": [
    "y_pred = lg.predict(X_test)\n",
    "\n",
    "print('Error: %.3f %%' %((y_pred!=y_test).mean()*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have about a $5.5\\%$ of error. However, since the dataset is not big, one may be more accurate by giving the error as an absolute value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count        54\n",
       "unique        2\n",
       "top       False\n",
       "freq         52\n",
       "Name: label, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y_pred!=y_test).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We missed the predition $3$ times. This gives us a measure of how well the algorithm performs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making use of the breast cancer dataset, compute the error on a test set which is the $20\\%$ of the full dataset.\n",
    "\n",
    "Study how such error changes by changing the dimension of the test set.\n",
    "Plot your answer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer = datasets.load_breast_cancer()\n",
    "df_cancer = pd.DataFrame(cancer.data, columns = cancer.feature_names)\n",
    "df_cancer['target'] = cancer.target\n",
    "X = df_cancer.loc[:, cancer.feature_names]\n",
    "y = df_cancer.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.990</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.300100</td>\n",
       "      <td>0.147100</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.16220</td>\n",
       "      <td>0.66560</td>\n",
       "      <td>0.71190</td>\n",
       "      <td>0.26540</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.570</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.086900</td>\n",
       "      <td>0.070170</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.12380</td>\n",
       "      <td>0.18660</td>\n",
       "      <td>0.24160</td>\n",
       "      <td>0.18600</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.690</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.197400</td>\n",
       "      <td>0.127900</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.14440</td>\n",
       "      <td>0.42450</td>\n",
       "      <td>0.45040</td>\n",
       "      <td>0.24300</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.420</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.241400</td>\n",
       "      <td>0.105200</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.20980</td>\n",
       "      <td>0.86630</td>\n",
       "      <td>0.68690</td>\n",
       "      <td>0.25750</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.290</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.198000</td>\n",
       "      <td>0.104300</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.13740</td>\n",
       "      <td>0.20500</td>\n",
       "      <td>0.40000</td>\n",
       "      <td>0.16250</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12.450</td>\n",
       "      <td>15.70</td>\n",
       "      <td>82.57</td>\n",
       "      <td>477.1</td>\n",
       "      <td>0.12780</td>\n",
       "      <td>0.17000</td>\n",
       "      <td>0.157800</td>\n",
       "      <td>0.080890</td>\n",
       "      <td>0.2087</td>\n",
       "      <td>0.07613</td>\n",
       "      <td>...</td>\n",
       "      <td>23.75</td>\n",
       "      <td>103.40</td>\n",
       "      <td>741.6</td>\n",
       "      <td>0.17910</td>\n",
       "      <td>0.52490</td>\n",
       "      <td>0.53550</td>\n",
       "      <td>0.17410</td>\n",
       "      <td>0.3985</td>\n",
       "      <td>0.12440</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18.250</td>\n",
       "      <td>19.98</td>\n",
       "      <td>119.60</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>0.09463</td>\n",
       "      <td>0.10900</td>\n",
       "      <td>0.112700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.1794</td>\n",
       "      <td>0.05742</td>\n",
       "      <td>...</td>\n",
       "      <td>27.66</td>\n",
       "      <td>153.20</td>\n",
       "      <td>1606.0</td>\n",
       "      <td>0.14420</td>\n",
       "      <td>0.25760</td>\n",
       "      <td>0.37840</td>\n",
       "      <td>0.19320</td>\n",
       "      <td>0.3063</td>\n",
       "      <td>0.08368</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13.710</td>\n",
       "      <td>20.83</td>\n",
       "      <td>90.20</td>\n",
       "      <td>577.9</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0.16450</td>\n",
       "      <td>0.093660</td>\n",
       "      <td>0.059850</td>\n",
       "      <td>0.2196</td>\n",
       "      <td>0.07451</td>\n",
       "      <td>...</td>\n",
       "      <td>28.14</td>\n",
       "      <td>110.60</td>\n",
       "      <td>897.0</td>\n",
       "      <td>0.16540</td>\n",
       "      <td>0.36820</td>\n",
       "      <td>0.26780</td>\n",
       "      <td>0.15560</td>\n",
       "      <td>0.3196</td>\n",
       "      <td>0.11510</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13.000</td>\n",
       "      <td>21.82</td>\n",
       "      <td>87.50</td>\n",
       "      <td>519.8</td>\n",
       "      <td>0.12730</td>\n",
       "      <td>0.19320</td>\n",
       "      <td>0.185900</td>\n",
       "      <td>0.093530</td>\n",
       "      <td>0.2350</td>\n",
       "      <td>0.07389</td>\n",
       "      <td>...</td>\n",
       "      <td>30.73</td>\n",
       "      <td>106.20</td>\n",
       "      <td>739.3</td>\n",
       "      <td>0.17030</td>\n",
       "      <td>0.54010</td>\n",
       "      <td>0.53900</td>\n",
       "      <td>0.20600</td>\n",
       "      <td>0.4378</td>\n",
       "      <td>0.10720</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12.460</td>\n",
       "      <td>24.04</td>\n",
       "      <td>83.97</td>\n",
       "      <td>475.9</td>\n",
       "      <td>0.11860</td>\n",
       "      <td>0.23960</td>\n",
       "      <td>0.227300</td>\n",
       "      <td>0.085430</td>\n",
       "      <td>0.2030</td>\n",
       "      <td>0.08243</td>\n",
       "      <td>...</td>\n",
       "      <td>40.68</td>\n",
       "      <td>97.65</td>\n",
       "      <td>711.4</td>\n",
       "      <td>0.18530</td>\n",
       "      <td>1.05800</td>\n",
       "      <td>1.10500</td>\n",
       "      <td>0.22100</td>\n",
       "      <td>0.4366</td>\n",
       "      <td>0.20750</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>16.020</td>\n",
       "      <td>23.24</td>\n",
       "      <td>102.70</td>\n",
       "      <td>797.8</td>\n",
       "      <td>0.08206</td>\n",
       "      <td>0.06669</td>\n",
       "      <td>0.032990</td>\n",
       "      <td>0.033230</td>\n",
       "      <td>0.1528</td>\n",
       "      <td>0.05697</td>\n",
       "      <td>...</td>\n",
       "      <td>33.88</td>\n",
       "      <td>123.80</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>0.11810</td>\n",
       "      <td>0.15510</td>\n",
       "      <td>0.14590</td>\n",
       "      <td>0.09975</td>\n",
       "      <td>0.2948</td>\n",
       "      <td>0.08452</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15.780</td>\n",
       "      <td>17.89</td>\n",
       "      <td>103.60</td>\n",
       "      <td>781.0</td>\n",
       "      <td>0.09710</td>\n",
       "      <td>0.12920</td>\n",
       "      <td>0.099540</td>\n",
       "      <td>0.066060</td>\n",
       "      <td>0.1842</td>\n",
       "      <td>0.06082</td>\n",
       "      <td>...</td>\n",
       "      <td>27.28</td>\n",
       "      <td>136.50</td>\n",
       "      <td>1299.0</td>\n",
       "      <td>0.13960</td>\n",
       "      <td>0.56090</td>\n",
       "      <td>0.39650</td>\n",
       "      <td>0.18100</td>\n",
       "      <td>0.3792</td>\n",
       "      <td>0.10480</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19.170</td>\n",
       "      <td>24.80</td>\n",
       "      <td>132.40</td>\n",
       "      <td>1123.0</td>\n",
       "      <td>0.09740</td>\n",
       "      <td>0.24580</td>\n",
       "      <td>0.206500</td>\n",
       "      <td>0.111800</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>0.07800</td>\n",
       "      <td>...</td>\n",
       "      <td>29.94</td>\n",
       "      <td>151.70</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>0.10370</td>\n",
       "      <td>0.39030</td>\n",
       "      <td>0.36390</td>\n",
       "      <td>0.17670</td>\n",
       "      <td>0.3176</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15.850</td>\n",
       "      <td>23.95</td>\n",
       "      <td>103.70</td>\n",
       "      <td>782.7</td>\n",
       "      <td>0.08401</td>\n",
       "      <td>0.10020</td>\n",
       "      <td>0.099380</td>\n",
       "      <td>0.053640</td>\n",
       "      <td>0.1847</td>\n",
       "      <td>0.05338</td>\n",
       "      <td>...</td>\n",
       "      <td>27.66</td>\n",
       "      <td>112.00</td>\n",
       "      <td>876.5</td>\n",
       "      <td>0.11310</td>\n",
       "      <td>0.19240</td>\n",
       "      <td>0.23220</td>\n",
       "      <td>0.11190</td>\n",
       "      <td>0.2809</td>\n",
       "      <td>0.06287</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>13.730</td>\n",
       "      <td>22.61</td>\n",
       "      <td>93.60</td>\n",
       "      <td>578.3</td>\n",
       "      <td>0.11310</td>\n",
       "      <td>0.22930</td>\n",
       "      <td>0.212800</td>\n",
       "      <td>0.080250</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.07682</td>\n",
       "      <td>...</td>\n",
       "      <td>32.01</td>\n",
       "      <td>108.80</td>\n",
       "      <td>697.7</td>\n",
       "      <td>0.16510</td>\n",
       "      <td>0.77250</td>\n",
       "      <td>0.69430</td>\n",
       "      <td>0.22080</td>\n",
       "      <td>0.3596</td>\n",
       "      <td>0.14310</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>14.540</td>\n",
       "      <td>27.54</td>\n",
       "      <td>96.73</td>\n",
       "      <td>658.8</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.15950</td>\n",
       "      <td>0.163900</td>\n",
       "      <td>0.073640</td>\n",
       "      <td>0.2303</td>\n",
       "      <td>0.07077</td>\n",
       "      <td>...</td>\n",
       "      <td>37.13</td>\n",
       "      <td>124.10</td>\n",
       "      <td>943.2</td>\n",
       "      <td>0.16780</td>\n",
       "      <td>0.65770</td>\n",
       "      <td>0.70260</td>\n",
       "      <td>0.17120</td>\n",
       "      <td>0.4218</td>\n",
       "      <td>0.13410</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>14.680</td>\n",
       "      <td>20.13</td>\n",
       "      <td>94.74</td>\n",
       "      <td>684.5</td>\n",
       "      <td>0.09867</td>\n",
       "      <td>0.07200</td>\n",
       "      <td>0.073950</td>\n",
       "      <td>0.052590</td>\n",
       "      <td>0.1586</td>\n",
       "      <td>0.05922</td>\n",
       "      <td>...</td>\n",
       "      <td>30.88</td>\n",
       "      <td>123.40</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>0.14640</td>\n",
       "      <td>0.18710</td>\n",
       "      <td>0.29140</td>\n",
       "      <td>0.16090</td>\n",
       "      <td>0.3029</td>\n",
       "      <td>0.08216</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16.130</td>\n",
       "      <td>20.68</td>\n",
       "      <td>108.10</td>\n",
       "      <td>798.8</td>\n",
       "      <td>0.11700</td>\n",
       "      <td>0.20220</td>\n",
       "      <td>0.172200</td>\n",
       "      <td>0.102800</td>\n",
       "      <td>0.2164</td>\n",
       "      <td>0.07356</td>\n",
       "      <td>...</td>\n",
       "      <td>31.48</td>\n",
       "      <td>136.80</td>\n",
       "      <td>1315.0</td>\n",
       "      <td>0.17890</td>\n",
       "      <td>0.42330</td>\n",
       "      <td>0.47840</td>\n",
       "      <td>0.20730</td>\n",
       "      <td>0.3706</td>\n",
       "      <td>0.11420</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19.810</td>\n",
       "      <td>22.15</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1260.0</td>\n",
       "      <td>0.09831</td>\n",
       "      <td>0.10270</td>\n",
       "      <td>0.147900</td>\n",
       "      <td>0.094980</td>\n",
       "      <td>0.1582</td>\n",
       "      <td>0.05395</td>\n",
       "      <td>...</td>\n",
       "      <td>30.88</td>\n",
       "      <td>186.80</td>\n",
       "      <td>2398.0</td>\n",
       "      <td>0.15120</td>\n",
       "      <td>0.31500</td>\n",
       "      <td>0.53720</td>\n",
       "      <td>0.23880</td>\n",
       "      <td>0.2768</td>\n",
       "      <td>0.07615</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>13.540</td>\n",
       "      <td>14.36</td>\n",
       "      <td>87.46</td>\n",
       "      <td>566.3</td>\n",
       "      <td>0.09779</td>\n",
       "      <td>0.08129</td>\n",
       "      <td>0.066640</td>\n",
       "      <td>0.047810</td>\n",
       "      <td>0.1885</td>\n",
       "      <td>0.05766</td>\n",
       "      <td>...</td>\n",
       "      <td>19.26</td>\n",
       "      <td>99.70</td>\n",
       "      <td>711.2</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.17730</td>\n",
       "      <td>0.23900</td>\n",
       "      <td>0.12880</td>\n",
       "      <td>0.2977</td>\n",
       "      <td>0.07259</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>13.080</td>\n",
       "      <td>15.71</td>\n",
       "      <td>85.63</td>\n",
       "      <td>520.0</td>\n",
       "      <td>0.10750</td>\n",
       "      <td>0.12700</td>\n",
       "      <td>0.045680</td>\n",
       "      <td>0.031100</td>\n",
       "      <td>0.1967</td>\n",
       "      <td>0.06811</td>\n",
       "      <td>...</td>\n",
       "      <td>20.49</td>\n",
       "      <td>96.09</td>\n",
       "      <td>630.5</td>\n",
       "      <td>0.13120</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.18900</td>\n",
       "      <td>0.07283</td>\n",
       "      <td>0.3184</td>\n",
       "      <td>0.08183</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>9.504</td>\n",
       "      <td>12.44</td>\n",
       "      <td>60.34</td>\n",
       "      <td>273.9</td>\n",
       "      <td>0.10240</td>\n",
       "      <td>0.06492</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020760</td>\n",
       "      <td>0.1815</td>\n",
       "      <td>0.06905</td>\n",
       "      <td>...</td>\n",
       "      <td>15.66</td>\n",
       "      <td>65.13</td>\n",
       "      <td>314.9</td>\n",
       "      <td>0.13240</td>\n",
       "      <td>0.11480</td>\n",
       "      <td>0.08867</td>\n",
       "      <td>0.06227</td>\n",
       "      <td>0.2450</td>\n",
       "      <td>0.07773</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>15.340</td>\n",
       "      <td>14.26</td>\n",
       "      <td>102.50</td>\n",
       "      <td>704.4</td>\n",
       "      <td>0.10730</td>\n",
       "      <td>0.21350</td>\n",
       "      <td>0.207700</td>\n",
       "      <td>0.097560</td>\n",
       "      <td>0.2521</td>\n",
       "      <td>0.07032</td>\n",
       "      <td>...</td>\n",
       "      <td>19.08</td>\n",
       "      <td>125.10</td>\n",
       "      <td>980.9</td>\n",
       "      <td>0.13900</td>\n",
       "      <td>0.59540</td>\n",
       "      <td>0.63050</td>\n",
       "      <td>0.23930</td>\n",
       "      <td>0.4667</td>\n",
       "      <td>0.09946</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>21.160</td>\n",
       "      <td>23.04</td>\n",
       "      <td>137.20</td>\n",
       "      <td>1404.0</td>\n",
       "      <td>0.09428</td>\n",
       "      <td>0.10220</td>\n",
       "      <td>0.109700</td>\n",
       "      <td>0.086320</td>\n",
       "      <td>0.1769</td>\n",
       "      <td>0.05278</td>\n",
       "      <td>...</td>\n",
       "      <td>35.59</td>\n",
       "      <td>188.00</td>\n",
       "      <td>2615.0</td>\n",
       "      <td>0.14010</td>\n",
       "      <td>0.26000</td>\n",
       "      <td>0.31550</td>\n",
       "      <td>0.20090</td>\n",
       "      <td>0.2822</td>\n",
       "      <td>0.07526</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>16.650</td>\n",
       "      <td>21.38</td>\n",
       "      <td>110.00</td>\n",
       "      <td>904.6</td>\n",
       "      <td>0.11210</td>\n",
       "      <td>0.14570</td>\n",
       "      <td>0.152500</td>\n",
       "      <td>0.091700</td>\n",
       "      <td>0.1995</td>\n",
       "      <td>0.06330</td>\n",
       "      <td>...</td>\n",
       "      <td>31.56</td>\n",
       "      <td>177.00</td>\n",
       "      <td>2215.0</td>\n",
       "      <td>0.18050</td>\n",
       "      <td>0.35780</td>\n",
       "      <td>0.46950</td>\n",
       "      <td>0.20950</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.09564</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>17.140</td>\n",
       "      <td>16.40</td>\n",
       "      <td>116.00</td>\n",
       "      <td>912.7</td>\n",
       "      <td>0.11860</td>\n",
       "      <td>0.22760</td>\n",
       "      <td>0.222900</td>\n",
       "      <td>0.140100</td>\n",
       "      <td>0.3040</td>\n",
       "      <td>0.07413</td>\n",
       "      <td>...</td>\n",
       "      <td>21.40</td>\n",
       "      <td>152.40</td>\n",
       "      <td>1461.0</td>\n",
       "      <td>0.15450</td>\n",
       "      <td>0.39490</td>\n",
       "      <td>0.38530</td>\n",
       "      <td>0.25500</td>\n",
       "      <td>0.4066</td>\n",
       "      <td>0.10590</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>14.580</td>\n",
       "      <td>21.53</td>\n",
       "      <td>97.41</td>\n",
       "      <td>644.8</td>\n",
       "      <td>0.10540</td>\n",
       "      <td>0.18680</td>\n",
       "      <td>0.142500</td>\n",
       "      <td>0.087830</td>\n",
       "      <td>0.2252</td>\n",
       "      <td>0.06924</td>\n",
       "      <td>...</td>\n",
       "      <td>33.21</td>\n",
       "      <td>122.40</td>\n",
       "      <td>896.9</td>\n",
       "      <td>0.15250</td>\n",
       "      <td>0.66430</td>\n",
       "      <td>0.55390</td>\n",
       "      <td>0.27010</td>\n",
       "      <td>0.4264</td>\n",
       "      <td>0.12750</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>18.610</td>\n",
       "      <td>20.25</td>\n",
       "      <td>122.10</td>\n",
       "      <td>1094.0</td>\n",
       "      <td>0.09440</td>\n",
       "      <td>0.10660</td>\n",
       "      <td>0.149000</td>\n",
       "      <td>0.077310</td>\n",
       "      <td>0.1697</td>\n",
       "      <td>0.05699</td>\n",
       "      <td>...</td>\n",
       "      <td>27.26</td>\n",
       "      <td>139.90</td>\n",
       "      <td>1403.0</td>\n",
       "      <td>0.13380</td>\n",
       "      <td>0.21170</td>\n",
       "      <td>0.34460</td>\n",
       "      <td>0.14900</td>\n",
       "      <td>0.2341</td>\n",
       "      <td>0.07421</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>15.300</td>\n",
       "      <td>25.27</td>\n",
       "      <td>102.40</td>\n",
       "      <td>732.4</td>\n",
       "      <td>0.10820</td>\n",
       "      <td>0.16970</td>\n",
       "      <td>0.168300</td>\n",
       "      <td>0.087510</td>\n",
       "      <td>0.1926</td>\n",
       "      <td>0.06540</td>\n",
       "      <td>...</td>\n",
       "      <td>36.71</td>\n",
       "      <td>149.30</td>\n",
       "      <td>1269.0</td>\n",
       "      <td>0.16410</td>\n",
       "      <td>0.61100</td>\n",
       "      <td>0.63350</td>\n",
       "      <td>0.20240</td>\n",
       "      <td>0.4027</td>\n",
       "      <td>0.09876</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>17.570</td>\n",
       "      <td>15.05</td>\n",
       "      <td>115.00</td>\n",
       "      <td>955.1</td>\n",
       "      <td>0.09847</td>\n",
       "      <td>0.11570</td>\n",
       "      <td>0.098750</td>\n",
       "      <td>0.079530</td>\n",
       "      <td>0.1739</td>\n",
       "      <td>0.06149</td>\n",
       "      <td>...</td>\n",
       "      <td>19.52</td>\n",
       "      <td>134.90</td>\n",
       "      <td>1227.0</td>\n",
       "      <td>0.12550</td>\n",
       "      <td>0.28120</td>\n",
       "      <td>0.24890</td>\n",
       "      <td>0.14560</td>\n",
       "      <td>0.2756</td>\n",
       "      <td>0.07919</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>7.691</td>\n",
       "      <td>25.44</td>\n",
       "      <td>48.34</td>\n",
       "      <td>170.4</td>\n",
       "      <td>0.08668</td>\n",
       "      <td>0.11990</td>\n",
       "      <td>0.092520</td>\n",
       "      <td>0.013640</td>\n",
       "      <td>0.2037</td>\n",
       "      <td>0.07751</td>\n",
       "      <td>...</td>\n",
       "      <td>31.89</td>\n",
       "      <td>54.49</td>\n",
       "      <td>223.6</td>\n",
       "      <td>0.15960</td>\n",
       "      <td>0.30640</td>\n",
       "      <td>0.33930</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.2790</td>\n",
       "      <td>0.10660</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>11.540</td>\n",
       "      <td>14.44</td>\n",
       "      <td>74.65</td>\n",
       "      <td>402.9</td>\n",
       "      <td>0.09984</td>\n",
       "      <td>0.11200</td>\n",
       "      <td>0.067370</td>\n",
       "      <td>0.025940</td>\n",
       "      <td>0.1818</td>\n",
       "      <td>0.06782</td>\n",
       "      <td>...</td>\n",
       "      <td>19.68</td>\n",
       "      <td>78.78</td>\n",
       "      <td>457.8</td>\n",
       "      <td>0.13450</td>\n",
       "      <td>0.21180</td>\n",
       "      <td>0.17970</td>\n",
       "      <td>0.06918</td>\n",
       "      <td>0.2329</td>\n",
       "      <td>0.08134</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>14.470</td>\n",
       "      <td>24.99</td>\n",
       "      <td>95.81</td>\n",
       "      <td>656.4</td>\n",
       "      <td>0.08837</td>\n",
       "      <td>0.12300</td>\n",
       "      <td>0.100900</td>\n",
       "      <td>0.038900</td>\n",
       "      <td>0.1872</td>\n",
       "      <td>0.06341</td>\n",
       "      <td>...</td>\n",
       "      <td>31.73</td>\n",
       "      <td>113.50</td>\n",
       "      <td>808.9</td>\n",
       "      <td>0.13400</td>\n",
       "      <td>0.42020</td>\n",
       "      <td>0.40400</td>\n",
       "      <td>0.12050</td>\n",
       "      <td>0.3187</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>14.740</td>\n",
       "      <td>25.42</td>\n",
       "      <td>94.70</td>\n",
       "      <td>668.6</td>\n",
       "      <td>0.08275</td>\n",
       "      <td>0.07214</td>\n",
       "      <td>0.041050</td>\n",
       "      <td>0.030270</td>\n",
       "      <td>0.1840</td>\n",
       "      <td>0.05680</td>\n",
       "      <td>...</td>\n",
       "      <td>32.29</td>\n",
       "      <td>107.40</td>\n",
       "      <td>826.4</td>\n",
       "      <td>0.10600</td>\n",
       "      <td>0.13760</td>\n",
       "      <td>0.16110</td>\n",
       "      <td>0.10950</td>\n",
       "      <td>0.2722</td>\n",
       "      <td>0.06956</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>13.210</td>\n",
       "      <td>28.06</td>\n",
       "      <td>84.88</td>\n",
       "      <td>538.4</td>\n",
       "      <td>0.08671</td>\n",
       "      <td>0.06877</td>\n",
       "      <td>0.029870</td>\n",
       "      <td>0.032750</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.05781</td>\n",
       "      <td>...</td>\n",
       "      <td>37.17</td>\n",
       "      <td>92.48</td>\n",
       "      <td>629.6</td>\n",
       "      <td>0.10720</td>\n",
       "      <td>0.13810</td>\n",
       "      <td>0.10620</td>\n",
       "      <td>0.07958</td>\n",
       "      <td>0.2473</td>\n",
       "      <td>0.06443</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>13.870</td>\n",
       "      <td>20.70</td>\n",
       "      <td>89.77</td>\n",
       "      <td>584.8</td>\n",
       "      <td>0.09578</td>\n",
       "      <td>0.10180</td>\n",
       "      <td>0.036880</td>\n",
       "      <td>0.023690</td>\n",
       "      <td>0.1620</td>\n",
       "      <td>0.06688</td>\n",
       "      <td>...</td>\n",
       "      <td>24.75</td>\n",
       "      <td>99.17</td>\n",
       "      <td>688.6</td>\n",
       "      <td>0.12640</td>\n",
       "      <td>0.20370</td>\n",
       "      <td>0.13770</td>\n",
       "      <td>0.06845</td>\n",
       "      <td>0.2249</td>\n",
       "      <td>0.08492</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>13.620</td>\n",
       "      <td>23.23</td>\n",
       "      <td>87.19</td>\n",
       "      <td>573.2</td>\n",
       "      <td>0.09246</td>\n",
       "      <td>0.06747</td>\n",
       "      <td>0.029740</td>\n",
       "      <td>0.024430</td>\n",
       "      <td>0.1664</td>\n",
       "      <td>0.05801</td>\n",
       "      <td>...</td>\n",
       "      <td>29.09</td>\n",
       "      <td>97.58</td>\n",
       "      <td>729.8</td>\n",
       "      <td>0.12160</td>\n",
       "      <td>0.15170</td>\n",
       "      <td>0.10490</td>\n",
       "      <td>0.07174</td>\n",
       "      <td>0.2642</td>\n",
       "      <td>0.06953</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>10.320</td>\n",
       "      <td>16.35</td>\n",
       "      <td>65.31</td>\n",
       "      <td>324.9</td>\n",
       "      <td>0.09434</td>\n",
       "      <td>0.04994</td>\n",
       "      <td>0.010120</td>\n",
       "      <td>0.005495</td>\n",
       "      <td>0.1885</td>\n",
       "      <td>0.06201</td>\n",
       "      <td>...</td>\n",
       "      <td>21.77</td>\n",
       "      <td>71.12</td>\n",
       "      <td>384.9</td>\n",
       "      <td>0.12850</td>\n",
       "      <td>0.08842</td>\n",
       "      <td>0.04384</td>\n",
       "      <td>0.02381</td>\n",
       "      <td>0.2681</td>\n",
       "      <td>0.07399</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>10.260</td>\n",
       "      <td>16.58</td>\n",
       "      <td>65.85</td>\n",
       "      <td>320.8</td>\n",
       "      <td>0.08877</td>\n",
       "      <td>0.08066</td>\n",
       "      <td>0.043580</td>\n",
       "      <td>0.024380</td>\n",
       "      <td>0.1669</td>\n",
       "      <td>0.06714</td>\n",
       "      <td>...</td>\n",
       "      <td>22.04</td>\n",
       "      <td>71.08</td>\n",
       "      <td>357.4</td>\n",
       "      <td>0.14610</td>\n",
       "      <td>0.22460</td>\n",
       "      <td>0.17830</td>\n",
       "      <td>0.08333</td>\n",
       "      <td>0.2691</td>\n",
       "      <td>0.09479</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>9.683</td>\n",
       "      <td>19.34</td>\n",
       "      <td>61.05</td>\n",
       "      <td>285.7</td>\n",
       "      <td>0.08491</td>\n",
       "      <td>0.05030</td>\n",
       "      <td>0.023370</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>0.1580</td>\n",
       "      <td>0.06235</td>\n",
       "      <td>...</td>\n",
       "      <td>25.59</td>\n",
       "      <td>69.10</td>\n",
       "      <td>364.2</td>\n",
       "      <td>0.11990</td>\n",
       "      <td>0.09546</td>\n",
       "      <td>0.09350</td>\n",
       "      <td>0.03846</td>\n",
       "      <td>0.2552</td>\n",
       "      <td>0.07920</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>10.820</td>\n",
       "      <td>24.21</td>\n",
       "      <td>68.89</td>\n",
       "      <td>361.6</td>\n",
       "      <td>0.08192</td>\n",
       "      <td>0.06602</td>\n",
       "      <td>0.015480</td>\n",
       "      <td>0.008160</td>\n",
       "      <td>0.1976</td>\n",
       "      <td>0.06328</td>\n",
       "      <td>...</td>\n",
       "      <td>31.45</td>\n",
       "      <td>83.90</td>\n",
       "      <td>505.6</td>\n",
       "      <td>0.12040</td>\n",
       "      <td>0.16330</td>\n",
       "      <td>0.06194</td>\n",
       "      <td>0.03264</td>\n",
       "      <td>0.3059</td>\n",
       "      <td>0.07626</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>10.860</td>\n",
       "      <td>21.48</td>\n",
       "      <td>68.51</td>\n",
       "      <td>360.5</td>\n",
       "      <td>0.07431</td>\n",
       "      <td>0.04227</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1661</td>\n",
       "      <td>0.05948</td>\n",
       "      <td>...</td>\n",
       "      <td>24.77</td>\n",
       "      <td>74.08</td>\n",
       "      <td>412.3</td>\n",
       "      <td>0.10010</td>\n",
       "      <td>0.07348</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.2458</td>\n",
       "      <td>0.06592</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>11.130</td>\n",
       "      <td>22.44</td>\n",
       "      <td>71.49</td>\n",
       "      <td>378.4</td>\n",
       "      <td>0.09566</td>\n",
       "      <td>0.08194</td>\n",
       "      <td>0.048240</td>\n",
       "      <td>0.022570</td>\n",
       "      <td>0.2030</td>\n",
       "      <td>0.06552</td>\n",
       "      <td>...</td>\n",
       "      <td>28.26</td>\n",
       "      <td>77.80</td>\n",
       "      <td>436.6</td>\n",
       "      <td>0.10870</td>\n",
       "      <td>0.17820</td>\n",
       "      <td>0.15640</td>\n",
       "      <td>0.06413</td>\n",
       "      <td>0.3169</td>\n",
       "      <td>0.08032</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>12.770</td>\n",
       "      <td>29.43</td>\n",
       "      <td>81.35</td>\n",
       "      <td>507.9</td>\n",
       "      <td>0.08276</td>\n",
       "      <td>0.04234</td>\n",
       "      <td>0.019970</td>\n",
       "      <td>0.014990</td>\n",
       "      <td>0.1539</td>\n",
       "      <td>0.05637</td>\n",
       "      <td>...</td>\n",
       "      <td>36.00</td>\n",
       "      <td>88.10</td>\n",
       "      <td>594.7</td>\n",
       "      <td>0.12340</td>\n",
       "      <td>0.10640</td>\n",
       "      <td>0.08653</td>\n",
       "      <td>0.06498</td>\n",
       "      <td>0.2407</td>\n",
       "      <td>0.06484</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>9.333</td>\n",
       "      <td>21.94</td>\n",
       "      <td>59.01</td>\n",
       "      <td>264.0</td>\n",
       "      <td>0.09240</td>\n",
       "      <td>0.05605</td>\n",
       "      <td>0.039960</td>\n",
       "      <td>0.012820</td>\n",
       "      <td>0.1692</td>\n",
       "      <td>0.06576</td>\n",
       "      <td>...</td>\n",
       "      <td>25.05</td>\n",
       "      <td>62.86</td>\n",
       "      <td>295.8</td>\n",
       "      <td>0.11030</td>\n",
       "      <td>0.08298</td>\n",
       "      <td>0.07993</td>\n",
       "      <td>0.02564</td>\n",
       "      <td>0.2435</td>\n",
       "      <td>0.07393</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>12.880</td>\n",
       "      <td>28.92</td>\n",
       "      <td>82.50</td>\n",
       "      <td>514.3</td>\n",
       "      <td>0.08123</td>\n",
       "      <td>0.05824</td>\n",
       "      <td>0.061950</td>\n",
       "      <td>0.023430</td>\n",
       "      <td>0.1566</td>\n",
       "      <td>0.05708</td>\n",
       "      <td>...</td>\n",
       "      <td>35.74</td>\n",
       "      <td>88.84</td>\n",
       "      <td>595.7</td>\n",
       "      <td>0.12270</td>\n",
       "      <td>0.16200</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.06493</td>\n",
       "      <td>0.2372</td>\n",
       "      <td>0.07242</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>10.290</td>\n",
       "      <td>27.61</td>\n",
       "      <td>65.67</td>\n",
       "      <td>321.4</td>\n",
       "      <td>0.09030</td>\n",
       "      <td>0.07658</td>\n",
       "      <td>0.059990</td>\n",
       "      <td>0.027380</td>\n",
       "      <td>0.1593</td>\n",
       "      <td>0.06127</td>\n",
       "      <td>...</td>\n",
       "      <td>34.91</td>\n",
       "      <td>69.57</td>\n",
       "      <td>357.6</td>\n",
       "      <td>0.13840</td>\n",
       "      <td>0.17100</td>\n",
       "      <td>0.20000</td>\n",
       "      <td>0.09127</td>\n",
       "      <td>0.2226</td>\n",
       "      <td>0.08283</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>10.160</td>\n",
       "      <td>19.59</td>\n",
       "      <td>64.73</td>\n",
       "      <td>311.7</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.07504</td>\n",
       "      <td>0.005025</td>\n",
       "      <td>0.011160</td>\n",
       "      <td>0.1791</td>\n",
       "      <td>0.06331</td>\n",
       "      <td>...</td>\n",
       "      <td>22.88</td>\n",
       "      <td>67.88</td>\n",
       "      <td>347.3</td>\n",
       "      <td>0.12650</td>\n",
       "      <td>0.12000</td>\n",
       "      <td>0.01005</td>\n",
       "      <td>0.02232</td>\n",
       "      <td>0.2262</td>\n",
       "      <td>0.06742</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>9.423</td>\n",
       "      <td>27.88</td>\n",
       "      <td>59.26</td>\n",
       "      <td>271.3</td>\n",
       "      <td>0.08123</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1742</td>\n",
       "      <td>0.06059</td>\n",
       "      <td>...</td>\n",
       "      <td>34.24</td>\n",
       "      <td>66.50</td>\n",
       "      <td>330.6</td>\n",
       "      <td>0.10730</td>\n",
       "      <td>0.07158</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.2475</td>\n",
       "      <td>0.06969</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>14.590</td>\n",
       "      <td>22.68</td>\n",
       "      <td>96.39</td>\n",
       "      <td>657.1</td>\n",
       "      <td>0.08473</td>\n",
       "      <td>0.13300</td>\n",
       "      <td>0.102900</td>\n",
       "      <td>0.037360</td>\n",
       "      <td>0.1454</td>\n",
       "      <td>0.06147</td>\n",
       "      <td>...</td>\n",
       "      <td>27.27</td>\n",
       "      <td>105.90</td>\n",
       "      <td>733.5</td>\n",
       "      <td>0.10260</td>\n",
       "      <td>0.31710</td>\n",
       "      <td>0.36620</td>\n",
       "      <td>0.11050</td>\n",
       "      <td>0.2258</td>\n",
       "      <td>0.08004</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>11.510</td>\n",
       "      <td>23.93</td>\n",
       "      <td>74.52</td>\n",
       "      <td>403.5</td>\n",
       "      <td>0.09261</td>\n",
       "      <td>0.10210</td>\n",
       "      <td>0.111200</td>\n",
       "      <td>0.041050</td>\n",
       "      <td>0.1388</td>\n",
       "      <td>0.06570</td>\n",
       "      <td>...</td>\n",
       "      <td>37.16</td>\n",
       "      <td>82.28</td>\n",
       "      <td>474.2</td>\n",
       "      <td>0.12980</td>\n",
       "      <td>0.25170</td>\n",
       "      <td>0.36300</td>\n",
       "      <td>0.09653</td>\n",
       "      <td>0.2112</td>\n",
       "      <td>0.08732</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>14.050</td>\n",
       "      <td>27.15</td>\n",
       "      <td>91.38</td>\n",
       "      <td>600.4</td>\n",
       "      <td>0.09929</td>\n",
       "      <td>0.11260</td>\n",
       "      <td>0.044620</td>\n",
       "      <td>0.043040</td>\n",
       "      <td>0.1537</td>\n",
       "      <td>0.06171</td>\n",
       "      <td>...</td>\n",
       "      <td>33.17</td>\n",
       "      <td>100.20</td>\n",
       "      <td>706.7</td>\n",
       "      <td>0.12410</td>\n",
       "      <td>0.22640</td>\n",
       "      <td>0.13260</td>\n",
       "      <td>0.10480</td>\n",
       "      <td>0.2250</td>\n",
       "      <td>0.08321</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>11.200</td>\n",
       "      <td>29.37</td>\n",
       "      <td>70.67</td>\n",
       "      <td>386.0</td>\n",
       "      <td>0.07449</td>\n",
       "      <td>0.03558</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1060</td>\n",
       "      <td>0.05502</td>\n",
       "      <td>...</td>\n",
       "      <td>38.30</td>\n",
       "      <td>75.19</td>\n",
       "      <td>439.6</td>\n",
       "      <td>0.09267</td>\n",
       "      <td>0.05494</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1566</td>\n",
       "      <td>0.05905</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>15.220</td>\n",
       "      <td>30.62</td>\n",
       "      <td>103.40</td>\n",
       "      <td>716.9</td>\n",
       "      <td>0.10480</td>\n",
       "      <td>0.20870</td>\n",
       "      <td>0.255000</td>\n",
       "      <td>0.094290</td>\n",
       "      <td>0.2128</td>\n",
       "      <td>0.07152</td>\n",
       "      <td>...</td>\n",
       "      <td>42.79</td>\n",
       "      <td>128.70</td>\n",
       "      <td>915.0</td>\n",
       "      <td>0.14170</td>\n",
       "      <td>0.79170</td>\n",
       "      <td>1.17000</td>\n",
       "      <td>0.23560</td>\n",
       "      <td>0.4089</td>\n",
       "      <td>0.14090</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>20.920</td>\n",
       "      <td>25.09</td>\n",
       "      <td>143.00</td>\n",
       "      <td>1347.0</td>\n",
       "      <td>0.10990</td>\n",
       "      <td>0.22360</td>\n",
       "      <td>0.317400</td>\n",
       "      <td>0.147400</td>\n",
       "      <td>0.2149</td>\n",
       "      <td>0.06879</td>\n",
       "      <td>...</td>\n",
       "      <td>29.41</td>\n",
       "      <td>179.10</td>\n",
       "      <td>1819.0</td>\n",
       "      <td>0.14070</td>\n",
       "      <td>0.41860</td>\n",
       "      <td>0.65990</td>\n",
       "      <td>0.25420</td>\n",
       "      <td>0.2929</td>\n",
       "      <td>0.09873</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>21.560</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.243900</td>\n",
       "      <td>0.138900</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.05623</td>\n",
       "      <td>...</td>\n",
       "      <td>26.40</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.41070</td>\n",
       "      <td>0.22160</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>20.130</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.144000</td>\n",
       "      <td>0.097910</td>\n",
       "      <td>0.1752</td>\n",
       "      <td>0.05533</td>\n",
       "      <td>...</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.32150</td>\n",
       "      <td>0.16280</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>16.600</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.092510</td>\n",
       "      <td>0.053020</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.05648</td>\n",
       "      <td>...</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.34030</td>\n",
       "      <td>0.14180</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>20.600</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.351400</td>\n",
       "      <td>0.152000</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>0.07016</td>\n",
       "      <td>...</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.93870</td>\n",
       "      <td>0.26500</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>7.760</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1587</td>\n",
       "      <td>0.05884</td>\n",
       "      <td>...</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0         17.990         10.38          122.80     1001.0          0.11840   \n",
       "1         20.570         17.77          132.90     1326.0          0.08474   \n",
       "2         19.690         21.25          130.00     1203.0          0.10960   \n",
       "3         11.420         20.38           77.58      386.1          0.14250   \n",
       "4         20.290         14.34          135.10     1297.0          0.10030   \n",
       "5         12.450         15.70           82.57      477.1          0.12780   \n",
       "6         18.250         19.98          119.60     1040.0          0.09463   \n",
       "7         13.710         20.83           90.20      577.9          0.11890   \n",
       "8         13.000         21.82           87.50      519.8          0.12730   \n",
       "9         12.460         24.04           83.97      475.9          0.11860   \n",
       "10        16.020         23.24          102.70      797.8          0.08206   \n",
       "11        15.780         17.89          103.60      781.0          0.09710   \n",
       "12        19.170         24.80          132.40     1123.0          0.09740   \n",
       "13        15.850         23.95          103.70      782.7          0.08401   \n",
       "14        13.730         22.61           93.60      578.3          0.11310   \n",
       "15        14.540         27.54           96.73      658.8          0.11390   \n",
       "16        14.680         20.13           94.74      684.5          0.09867   \n",
       "17        16.130         20.68          108.10      798.8          0.11700   \n",
       "18        19.810         22.15          130.00     1260.0          0.09831   \n",
       "19        13.540         14.36           87.46      566.3          0.09779   \n",
       "20        13.080         15.71           85.63      520.0          0.10750   \n",
       "21         9.504         12.44           60.34      273.9          0.10240   \n",
       "22        15.340         14.26          102.50      704.4          0.10730   \n",
       "23        21.160         23.04          137.20     1404.0          0.09428   \n",
       "24        16.650         21.38          110.00      904.6          0.11210   \n",
       "25        17.140         16.40          116.00      912.7          0.11860   \n",
       "26        14.580         21.53           97.41      644.8          0.10540   \n",
       "27        18.610         20.25          122.10     1094.0          0.09440   \n",
       "28        15.300         25.27          102.40      732.4          0.10820   \n",
       "29        17.570         15.05          115.00      955.1          0.09847   \n",
       "..           ...           ...             ...        ...              ...   \n",
       "539        7.691         25.44           48.34      170.4          0.08668   \n",
       "540       11.540         14.44           74.65      402.9          0.09984   \n",
       "541       14.470         24.99           95.81      656.4          0.08837   \n",
       "542       14.740         25.42           94.70      668.6          0.08275   \n",
       "543       13.210         28.06           84.88      538.4          0.08671   \n",
       "544       13.870         20.70           89.77      584.8          0.09578   \n",
       "545       13.620         23.23           87.19      573.2          0.09246   \n",
       "546       10.320         16.35           65.31      324.9          0.09434   \n",
       "547       10.260         16.58           65.85      320.8          0.08877   \n",
       "548        9.683         19.34           61.05      285.7          0.08491   \n",
       "549       10.820         24.21           68.89      361.6          0.08192   \n",
       "550       10.860         21.48           68.51      360.5          0.07431   \n",
       "551       11.130         22.44           71.49      378.4          0.09566   \n",
       "552       12.770         29.43           81.35      507.9          0.08276   \n",
       "553        9.333         21.94           59.01      264.0          0.09240   \n",
       "554       12.880         28.92           82.50      514.3          0.08123   \n",
       "555       10.290         27.61           65.67      321.4          0.09030   \n",
       "556       10.160         19.59           64.73      311.7          0.10030   \n",
       "557        9.423         27.88           59.26      271.3          0.08123   \n",
       "558       14.590         22.68           96.39      657.1          0.08473   \n",
       "559       11.510         23.93           74.52      403.5          0.09261   \n",
       "560       14.050         27.15           91.38      600.4          0.09929   \n",
       "561       11.200         29.37           70.67      386.0          0.07449   \n",
       "562       15.220         30.62          103.40      716.9          0.10480   \n",
       "563       20.920         25.09          143.00     1347.0          0.10990   \n",
       "564       21.560         22.39          142.00     1479.0          0.11100   \n",
       "565       20.130         28.25          131.20     1261.0          0.09780   \n",
       "566       16.600         28.08          108.30      858.1          0.08455   \n",
       "567       20.600         29.33          140.10     1265.0          0.11780   \n",
       "568        7.760         24.54           47.92      181.0          0.05263   \n",
       "\n",
       "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0             0.27760        0.300100             0.147100         0.2419   \n",
       "1             0.07864        0.086900             0.070170         0.1812   \n",
       "2             0.15990        0.197400             0.127900         0.2069   \n",
       "3             0.28390        0.241400             0.105200         0.2597   \n",
       "4             0.13280        0.198000             0.104300         0.1809   \n",
       "5             0.17000        0.157800             0.080890         0.2087   \n",
       "6             0.10900        0.112700             0.074000         0.1794   \n",
       "7             0.16450        0.093660             0.059850         0.2196   \n",
       "8             0.19320        0.185900             0.093530         0.2350   \n",
       "9             0.23960        0.227300             0.085430         0.2030   \n",
       "10            0.06669        0.032990             0.033230         0.1528   \n",
       "11            0.12920        0.099540             0.066060         0.1842   \n",
       "12            0.24580        0.206500             0.111800         0.2397   \n",
       "13            0.10020        0.099380             0.053640         0.1847   \n",
       "14            0.22930        0.212800             0.080250         0.2069   \n",
       "15            0.15950        0.163900             0.073640         0.2303   \n",
       "16            0.07200        0.073950             0.052590         0.1586   \n",
       "17            0.20220        0.172200             0.102800         0.2164   \n",
       "18            0.10270        0.147900             0.094980         0.1582   \n",
       "19            0.08129        0.066640             0.047810         0.1885   \n",
       "20            0.12700        0.045680             0.031100         0.1967   \n",
       "21            0.06492        0.029560             0.020760         0.1815   \n",
       "22            0.21350        0.207700             0.097560         0.2521   \n",
       "23            0.10220        0.109700             0.086320         0.1769   \n",
       "24            0.14570        0.152500             0.091700         0.1995   \n",
       "25            0.22760        0.222900             0.140100         0.3040   \n",
       "26            0.18680        0.142500             0.087830         0.2252   \n",
       "27            0.10660        0.149000             0.077310         0.1697   \n",
       "28            0.16970        0.168300             0.087510         0.1926   \n",
       "29            0.11570        0.098750             0.079530         0.1739   \n",
       "..                ...             ...                  ...            ...   \n",
       "539           0.11990        0.092520             0.013640         0.2037   \n",
       "540           0.11200        0.067370             0.025940         0.1818   \n",
       "541           0.12300        0.100900             0.038900         0.1872   \n",
       "542           0.07214        0.041050             0.030270         0.1840   \n",
       "543           0.06877        0.029870             0.032750         0.1628   \n",
       "544           0.10180        0.036880             0.023690         0.1620   \n",
       "545           0.06747        0.029740             0.024430         0.1664   \n",
       "546           0.04994        0.010120             0.005495         0.1885   \n",
       "547           0.08066        0.043580             0.024380         0.1669   \n",
       "548           0.05030        0.023370             0.009615         0.1580   \n",
       "549           0.06602        0.015480             0.008160         0.1976   \n",
       "550           0.04227        0.000000             0.000000         0.1661   \n",
       "551           0.08194        0.048240             0.022570         0.2030   \n",
       "552           0.04234        0.019970             0.014990         0.1539   \n",
       "553           0.05605        0.039960             0.012820         0.1692   \n",
       "554           0.05824        0.061950             0.023430         0.1566   \n",
       "555           0.07658        0.059990             0.027380         0.1593   \n",
       "556           0.07504        0.005025             0.011160         0.1791   \n",
       "557           0.04971        0.000000             0.000000         0.1742   \n",
       "558           0.13300        0.102900             0.037360         0.1454   \n",
       "559           0.10210        0.111200             0.041050         0.1388   \n",
       "560           0.11260        0.044620             0.043040         0.1537   \n",
       "561           0.03558        0.000000             0.000000         0.1060   \n",
       "562           0.20870        0.255000             0.094290         0.2128   \n",
       "563           0.22360        0.317400             0.147400         0.2149   \n",
       "564           0.11590        0.243900             0.138900         0.1726   \n",
       "565           0.10340        0.144000             0.097910         0.1752   \n",
       "566           0.10230        0.092510             0.053020         0.1590   \n",
       "567           0.27700        0.351400             0.152000         0.2397   \n",
       "568           0.04362        0.000000             0.000000         0.1587   \n",
       "\n",
       "     mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
       "0                   0.07871  ...          17.33           184.60      2019.0   \n",
       "1                   0.05667  ...          23.41           158.80      1956.0   \n",
       "2                   0.05999  ...          25.53           152.50      1709.0   \n",
       "3                   0.09744  ...          26.50            98.87       567.7   \n",
       "4                   0.05883  ...          16.67           152.20      1575.0   \n",
       "5                   0.07613  ...          23.75           103.40       741.6   \n",
       "6                   0.05742  ...          27.66           153.20      1606.0   \n",
       "7                   0.07451  ...          28.14           110.60       897.0   \n",
       "8                   0.07389  ...          30.73           106.20       739.3   \n",
       "9                   0.08243  ...          40.68            97.65       711.4   \n",
       "10                  0.05697  ...          33.88           123.80      1150.0   \n",
       "11                  0.06082  ...          27.28           136.50      1299.0   \n",
       "12                  0.07800  ...          29.94           151.70      1332.0   \n",
       "13                  0.05338  ...          27.66           112.00       876.5   \n",
       "14                  0.07682  ...          32.01           108.80       697.7   \n",
       "15                  0.07077  ...          37.13           124.10       943.2   \n",
       "16                  0.05922  ...          30.88           123.40      1138.0   \n",
       "17                  0.07356  ...          31.48           136.80      1315.0   \n",
       "18                  0.05395  ...          30.88           186.80      2398.0   \n",
       "19                  0.05766  ...          19.26            99.70       711.2   \n",
       "20                  0.06811  ...          20.49            96.09       630.5   \n",
       "21                  0.06905  ...          15.66            65.13       314.9   \n",
       "22                  0.07032  ...          19.08           125.10       980.9   \n",
       "23                  0.05278  ...          35.59           188.00      2615.0   \n",
       "24                  0.06330  ...          31.56           177.00      2215.0   \n",
       "25                  0.07413  ...          21.40           152.40      1461.0   \n",
       "26                  0.06924  ...          33.21           122.40       896.9   \n",
       "27                  0.05699  ...          27.26           139.90      1403.0   \n",
       "28                  0.06540  ...          36.71           149.30      1269.0   \n",
       "29                  0.06149  ...          19.52           134.90      1227.0   \n",
       "..                      ...  ...            ...              ...         ...   \n",
       "539                 0.07751  ...          31.89            54.49       223.6   \n",
       "540                 0.06782  ...          19.68            78.78       457.8   \n",
       "541                 0.06341  ...          31.73           113.50       808.9   \n",
       "542                 0.05680  ...          32.29           107.40       826.4   \n",
       "543                 0.05781  ...          37.17            92.48       629.6   \n",
       "544                 0.06688  ...          24.75            99.17       688.6   \n",
       "545                 0.05801  ...          29.09            97.58       729.8   \n",
       "546                 0.06201  ...          21.77            71.12       384.9   \n",
       "547                 0.06714  ...          22.04            71.08       357.4   \n",
       "548                 0.06235  ...          25.59            69.10       364.2   \n",
       "549                 0.06328  ...          31.45            83.90       505.6   \n",
       "550                 0.05948  ...          24.77            74.08       412.3   \n",
       "551                 0.06552  ...          28.26            77.80       436.6   \n",
       "552                 0.05637  ...          36.00            88.10       594.7   \n",
       "553                 0.06576  ...          25.05            62.86       295.8   \n",
       "554                 0.05708  ...          35.74            88.84       595.7   \n",
       "555                 0.06127  ...          34.91            69.57       357.6   \n",
       "556                 0.06331  ...          22.88            67.88       347.3   \n",
       "557                 0.06059  ...          34.24            66.50       330.6   \n",
       "558                 0.06147  ...          27.27           105.90       733.5   \n",
       "559                 0.06570  ...          37.16            82.28       474.2   \n",
       "560                 0.06171  ...          33.17           100.20       706.7   \n",
       "561                 0.05502  ...          38.30            75.19       439.6   \n",
       "562                 0.07152  ...          42.79           128.70       915.0   \n",
       "563                 0.06879  ...          29.41           179.10      1819.0   \n",
       "564                 0.05623  ...          26.40           166.10      2027.0   \n",
       "565                 0.05533  ...          38.25           155.00      1731.0   \n",
       "566                 0.05648  ...          34.12           126.70      1124.0   \n",
       "567                 0.07016  ...          39.42           184.60      1821.0   \n",
       "568                 0.05884  ...          30.37            59.16       268.6   \n",
       "\n",
       "     worst smoothness  worst compactness  worst concavity  \\\n",
       "0             0.16220            0.66560          0.71190   \n",
       "1             0.12380            0.18660          0.24160   \n",
       "2             0.14440            0.42450          0.45040   \n",
       "3             0.20980            0.86630          0.68690   \n",
       "4             0.13740            0.20500          0.40000   \n",
       "5             0.17910            0.52490          0.53550   \n",
       "6             0.14420            0.25760          0.37840   \n",
       "7             0.16540            0.36820          0.26780   \n",
       "8             0.17030            0.54010          0.53900   \n",
       "9             0.18530            1.05800          1.10500   \n",
       "10            0.11810            0.15510          0.14590   \n",
       "11            0.13960            0.56090          0.39650   \n",
       "12            0.10370            0.39030          0.36390   \n",
       "13            0.11310            0.19240          0.23220   \n",
       "14            0.16510            0.77250          0.69430   \n",
       "15            0.16780            0.65770          0.70260   \n",
       "16            0.14640            0.18710          0.29140   \n",
       "17            0.17890            0.42330          0.47840   \n",
       "18            0.15120            0.31500          0.53720   \n",
       "19            0.14400            0.17730          0.23900   \n",
       "20            0.13120            0.27760          0.18900   \n",
       "21            0.13240            0.11480          0.08867   \n",
       "22            0.13900            0.59540          0.63050   \n",
       "23            0.14010            0.26000          0.31550   \n",
       "24            0.18050            0.35780          0.46950   \n",
       "25            0.15450            0.39490          0.38530   \n",
       "26            0.15250            0.66430          0.55390   \n",
       "27            0.13380            0.21170          0.34460   \n",
       "28            0.16410            0.61100          0.63350   \n",
       "29            0.12550            0.28120          0.24890   \n",
       "..                ...                ...              ...   \n",
       "539           0.15960            0.30640          0.33930   \n",
       "540           0.13450            0.21180          0.17970   \n",
       "541           0.13400            0.42020          0.40400   \n",
       "542           0.10600            0.13760          0.16110   \n",
       "543           0.10720            0.13810          0.10620   \n",
       "544           0.12640            0.20370          0.13770   \n",
       "545           0.12160            0.15170          0.10490   \n",
       "546           0.12850            0.08842          0.04384   \n",
       "547           0.14610            0.22460          0.17830   \n",
       "548           0.11990            0.09546          0.09350   \n",
       "549           0.12040            0.16330          0.06194   \n",
       "550           0.10010            0.07348          0.00000   \n",
       "551           0.10870            0.17820          0.15640   \n",
       "552           0.12340            0.10640          0.08653   \n",
       "553           0.11030            0.08298          0.07993   \n",
       "554           0.12270            0.16200          0.24390   \n",
       "555           0.13840            0.17100          0.20000   \n",
       "556           0.12650            0.12000          0.01005   \n",
       "557           0.10730            0.07158          0.00000   \n",
       "558           0.10260            0.31710          0.36620   \n",
       "559           0.12980            0.25170          0.36300   \n",
       "560           0.12410            0.22640          0.13260   \n",
       "561           0.09267            0.05494          0.00000   \n",
       "562           0.14170            0.79170          1.17000   \n",
       "563           0.14070            0.41860          0.65990   \n",
       "564           0.14100            0.21130          0.41070   \n",
       "565           0.11660            0.19220          0.32150   \n",
       "566           0.11390            0.30940          0.34030   \n",
       "567           0.16500            0.86810          0.93870   \n",
       "568           0.08996            0.06444          0.00000   \n",
       "\n",
       "     worst concave points  worst symmetry  worst fractal dimension  target  \n",
       "0                 0.26540          0.4601                  0.11890       0  \n",
       "1                 0.18600          0.2750                  0.08902       0  \n",
       "2                 0.24300          0.3613                  0.08758       0  \n",
       "3                 0.25750          0.6638                  0.17300       0  \n",
       "4                 0.16250          0.2364                  0.07678       0  \n",
       "5                 0.17410          0.3985                  0.12440       0  \n",
       "6                 0.19320          0.3063                  0.08368       0  \n",
       "7                 0.15560          0.3196                  0.11510       0  \n",
       "8                 0.20600          0.4378                  0.10720       0  \n",
       "9                 0.22100          0.4366                  0.20750       0  \n",
       "10                0.09975          0.2948                  0.08452       0  \n",
       "11                0.18100          0.3792                  0.10480       0  \n",
       "12                0.17670          0.3176                  0.10230       0  \n",
       "13                0.11190          0.2809                  0.06287       0  \n",
       "14                0.22080          0.3596                  0.14310       0  \n",
       "15                0.17120          0.4218                  0.13410       0  \n",
       "16                0.16090          0.3029                  0.08216       0  \n",
       "17                0.20730          0.3706                  0.11420       0  \n",
       "18                0.23880          0.2768                  0.07615       0  \n",
       "19                0.12880          0.2977                  0.07259       1  \n",
       "20                0.07283          0.3184                  0.08183       1  \n",
       "21                0.06227          0.2450                  0.07773       1  \n",
       "22                0.23930          0.4667                  0.09946       0  \n",
       "23                0.20090          0.2822                  0.07526       0  \n",
       "24                0.20950          0.3613                  0.09564       0  \n",
       "25                0.25500          0.4066                  0.10590       0  \n",
       "26                0.27010          0.4264                  0.12750       0  \n",
       "27                0.14900          0.2341                  0.07421       0  \n",
       "28                0.20240          0.4027                  0.09876       0  \n",
       "29                0.14560          0.2756                  0.07919       0  \n",
       "..                    ...             ...                      ...     ...  \n",
       "539               0.05000          0.2790                  0.10660       1  \n",
       "540               0.06918          0.2329                  0.08134       1  \n",
       "541               0.12050          0.3187                  0.10230       1  \n",
       "542               0.10950          0.2722                  0.06956       1  \n",
       "543               0.07958          0.2473                  0.06443       1  \n",
       "544               0.06845          0.2249                  0.08492       1  \n",
       "545               0.07174          0.2642                  0.06953       1  \n",
       "546               0.02381          0.2681                  0.07399       1  \n",
       "547               0.08333          0.2691                  0.09479       1  \n",
       "548               0.03846          0.2552                  0.07920       1  \n",
       "549               0.03264          0.3059                  0.07626       1  \n",
       "550               0.00000          0.2458                  0.06592       1  \n",
       "551               0.06413          0.3169                  0.08032       1  \n",
       "552               0.06498          0.2407                  0.06484       1  \n",
       "553               0.02564          0.2435                  0.07393       1  \n",
       "554               0.06493          0.2372                  0.07242       1  \n",
       "555               0.09127          0.2226                  0.08283       1  \n",
       "556               0.02232          0.2262                  0.06742       1  \n",
       "557               0.00000          0.2475                  0.06969       1  \n",
       "558               0.11050          0.2258                  0.08004       1  \n",
       "559               0.09653          0.2112                  0.08732       1  \n",
       "560               0.10480          0.2250                  0.08321       1  \n",
       "561               0.00000          0.1566                  0.05905       1  \n",
       "562               0.23560          0.4089                  0.14090       0  \n",
       "563               0.25420          0.2929                  0.09873       0  \n",
       "564               0.22160          0.2060                  0.07115       0  \n",
       "565               0.16280          0.2572                  0.06637       0  \n",
       "566               0.14180          0.2218                  0.07820       0  \n",
       "567               0.26500          0.4087                  0.12400       0  \n",
       "568               0.00000          0.2871                  0.07039       1  \n",
       "\n",
       "[569 rows x 31 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create training and testing sets, ratio 80/20\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='newton-cg', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression(solver = 'newton-cg')\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error: 3.51 %\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print('Error: %.2f %%' %((y_pred!= y_test).mean()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9649122807017544"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This error can be calculated also by an attribute of the model, `score`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 96.5 %\n"
     ]
    }
   ],
   "source": [
    "print('Score: %.1f %%'%(model.score(X_test, y_test)*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learning rates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test multiple learning rates, and plot the cost for each learning rate per the iterations.\n",
    "\n",
    "Which of the three sets would you use to complete this task?"
   ]
  }
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