Comparing_ML_algorithms.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python: 2.7.13 | packaged by conda-forge | (default, May  2 2017, 12:48:11) \n",
      "[GCC 4.8.2 20140120 (Red Hat 4.8.2-15)]\n",
      "scipy: 0.19.1\n",
      "numpy: 1.12.1\n",
      "matplotlib: 2.0.2\n",
      "pandas: 0.19.2\n",
      "sklearn: 0.18.2\n"
     ]
    }
   ],
   "source": [
    "#####\n",
    "# reference\n",
    "# http://machinelearningmastery.com/machine-learning-in-python-step-by-step/\n",
    "#####\n",
    "#Check the versions of libraries\n",
    " \n",
    "# Python version\n",
    "import sys\n",
    "print('Python: {}'.format(sys.version))\n",
    "# scipy\n",
    "import scipy\n",
    "print('scipy: {}'.format(scipy.__version__))\n",
    "# numpy\n",
    "import numpy\n",
    "print('numpy: {}'.format(numpy.__version__))\n",
    "# matplotlib\n",
    "import matplotlib\n",
    "print('matplotlib: {}'.format(matplotlib.__version__))\n",
    "# pandas\n",
    "import pandas\n",
    "print('pandas: {}'.format(pandas.__version__))\n",
    "# scikit-learn\n",
    "import sklearn\n",
    "print('sklearn: {}'.format(sklearn.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load libraries\n",
    "import pandas\n",
    "from pandas.tools.plotting import scatter_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import model_selection\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load dataset\n",
    "dataset = pandas.read_csv(\"datasets/datasets/iris.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 6)\n"
     ]
    }
   ],
   "source": [
    "# shape\n",
    "print(dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<bound method DataFrame.info of       sepal-length sepal-width petal-length petal-width      class\n",
      "1.0            5.1         3.5          1.4         0.2     setosa\n",
      "2.0            4.9           3          1.4         0.2     setosa\n",
      "3.0            4.7         3.2          1.3         0.2     setosa\n",
      "4.0            4.6         3.1          1.5         0.2     setosa\n",
      "5.0              5         3.6          1.4         0.2     setosa\n",
      "6.0            5.4         3.9          1.7         0.4     setosa\n",
      "7.0            4.6         3.4          1.4         0.3     setosa\n",
      "8.0              5         3.4          1.5         0.2     setosa\n",
      "9.0            4.4         2.9          1.4         0.2     setosa\n",
      "10.0           4.9         3.1          1.5         0.1     setosa\n",
      "11.0           5.4         3.7          1.5         0.2     setosa\n",
      "12.0           4.8         3.4          1.6         0.2     setosa\n",
      "13.0           4.8           3          1.4         0.1     setosa\n",
      "14.0           4.3           3          1.1         0.1     setosa\n",
      "15.0           5.8           4          1.2         0.2     setosa\n",
      "16.0           5.7         4.4          1.5         0.4     setosa\n",
      "17.0           5.4         3.9          1.3         0.4     setosa\n",
      "18.0           5.1         3.5          1.4         0.3     setosa\n",
      "19.0           5.7         3.8          1.7         0.3     setosa\n",
      "20.0           5.1         3.8          1.5         0.3     setosa\n",
      "21.0           5.4         3.4          1.7         0.2     setosa\n",
      "22.0           5.1         3.7          1.5         0.4     setosa\n",
      "23.0           4.6         3.6            1         0.2     setosa\n",
      "24.0           5.1         3.3          1.7         0.5     setosa\n",
      "25.0           4.8         3.4          1.9         0.2     setosa\n",
      "26.0             5           3          1.6         0.2     setosa\n",
      "27.0             5         3.4          1.6         0.4     setosa\n",
      "28.0           5.2         3.5          1.5         0.2     setosa\n",
      "29.0           5.2         3.4          1.4         0.2     setosa\n",
      "30.0           4.7         3.2          1.6         0.2     setosa\n",
      "...            ...         ...          ...         ...        ...\n",
      "121.0          6.9         3.2          5.7         2.3  virginica\n",
      "122.0          5.6         2.8          4.9           2  virginica\n",
      "123.0          7.7         2.8          6.7           2  virginica\n",
      "124.0          6.3         2.7          4.9         1.8  virginica\n",
      "125.0          6.7         3.3          5.7         2.1  virginica\n",
      "126.0          7.2         3.2            6         1.8  virginica\n",
      "127.0          6.2         2.8          4.8         1.8  virginica\n",
      "128.0          6.1           3          4.9         1.8  virginica\n",
      "129.0          6.4         2.8          5.6         2.1  virginica\n",
      "130.0          7.2           3          5.8         1.6  virginica\n",
      "131.0          7.4         2.8          6.1         1.9  virginica\n",
      "132.0          7.9         3.8          6.4           2  virginica\n",
      "133.0          6.4         2.8          5.6         2.2  virginica\n",
      "134.0          6.3         2.8          5.1         1.5  virginica\n",
      "135.0          6.1         2.6          5.6         1.4  virginica\n",
      "136.0          7.7           3          6.1         2.3  virginica\n",
      "137.0          6.3         3.4          5.6         2.4  virginica\n",
      "138.0          6.4         3.1          5.5         1.8  virginica\n",
      "139.0            6           3          4.8         1.8  virginica\n",
      "140.0          6.9         3.1          5.4         2.1  virginica\n",
      "141.0          6.7         3.1          5.6         2.4  virginica\n",
      "142.0          6.9         3.1          5.1         2.3  virginica\n",
      "143.0          5.8         2.7          5.1         1.9  virginica\n",
      "144.0          6.8         3.2          5.9         2.3  virginica\n",
      "145.0          6.7         3.3          5.7         2.5  virginica\n",
      "146.0          6.7           3          5.2         2.3  virginica\n",
      "147.0          6.3         2.5            5         1.9  virginica\n",
      "148.0          6.5           3          5.2           2  virginica\n",
      "149.0          6.2         3.4          5.4         2.3  virginica\n",
      "150.0          5.9           3          5.1         1.8  virginica\n",
      "\n",
      "[150 rows x 5 columns]>\n"
     ]
    }
   ],
   "source": [
    "print(dataset.info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'Unnamed: 0', u'Sepal.Length', u'Sepal.Width', u'Petal.Length',\n",
       "       u'Petal.Width', u'Species'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Sepal.Length</th>\n",
       "      <th>Sepal.Width</th>\n",
       "      <th>Petal.Length</th>\n",
       "      <th>Petal.Width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>43.445368</td>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38.250000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>112.750000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  Sepal.Length  Sepal.Width  Petal.Length  Petal.Width\n",
       "count  150.000000    150.000000   150.000000    150.000000   150.000000\n",
       "mean    75.500000      5.843333     3.057333      3.758000     1.199333\n",
       "std     43.445368      0.828066     0.435866      1.765298     0.762238\n",
       "min      1.000000      4.300000     2.000000      1.000000     0.100000\n",
       "25%     38.250000      5.100000     2.800000      1.600000     0.300000\n",
       "50%     75.500000      5.800000     3.000000      4.350000     1.300000\n",
       "75%    112.750000      6.400000     3.300000      5.100000     1.800000\n",
       "max    150.000000      7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# descriptions\n",
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Species\n",
      "setosa        50\n",
      "versicolor    50\n",
      "virginica     50\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(dataset.groupby('Species').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tfCLZF01vpwBnp/XvlPTiALu4LyI2AKSeciuwaoD61dbTQ4esR7+YLPFOJrv/BWRJ/Pl+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\noQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cb0509290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# box and whisker plots\n",
    "%matplotlib inline\n",
    "dataset.plot(kind='box', subplots=True,  layout=(4,4), sharex=False, sharey=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ujdDs2gfrgUnZ5WJmVkWTosvFzGwymNAul1mzZsXAwMCI8s2bNzNt2qij9krF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\nnNDNzCrCCd3MrCKc0M3MKsIJ3cysIv4/BK55jlwsT6kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cb057c750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histograms\n",
    "dataset.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TrnnV034zHE0VVE3v79KVWNJFQUFXBKYjA9bexuy01gyum4l2BMIL9Wrhb9Ls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\nZBHww2Y1x2UgGqNQtfjX41OLqOfr7b99PWinJ9LqnWwMqf2ea9trOU6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WMqLiSVA9iaL4\nBmCuauG4koSuYtguhu0umieqohDVRMv8nu1KeuoVyQXDCdtnVi13wwrPGt+lfzk2waW5ayqkXn2t\nAlAFuNK/95LpAJKDI508+/q1rpAS+MrZOQ5t6wqPeWw8X1er1SjVHDwJpZpDQlfpTUcpGBbv3NnN\nAzu7b8gDapf89bKQUr7SrmMFhWkFwyYZ1cnGNQ5s6UBTBHNlPzZ/93B2Sbz74EgHjx8Y5EP3DpOJ\nR0LKZDqmLfqMrgq+cGwSw3IQwi9iUxV/GuUqNqbt4ngS15W4UvruWUTFrlsbibpejCclMU1Ben65\nvBSAlMSjKlHNv/6Fqh/GKVQtXrw4T7XmMF00mchVKZk2uhAIAcmYxl1DHWzrSuJ4EkdKXM8Lx2K+\nYiHqOi3gJ9kDnaS7h7PEI6ovaqYK0nGNSs1eYjk0j9fWrgQSSa3OaDAsB9v1E3v5qgUIopp/Pqdu\nzbmuR65iYVgucc0/ZyO7JaKpqKqgYFj0pGP86vv388G3D6OpChXLF9ebL/uW7K7eFEXD4qlT06Gq\nZcWy62ylqh922MwzLIvgeVYsO/QEgsWwNx1rq8JpKxlmXRWYjkurPafxV64n6561/67ZriQV1UhE\nNRzPQxUKmZiGogiqtouHJKIqjM5VcF2PXb0popqg5kjOThfJVe1Fnk88ohLV1bBM2+8BAem4dsMW\n9GoI6LU+e3FxvZAj/bwBNLwbAtIxjUxMZyJXJR5ZvCQr9RBfwbD5ytkZkhF/PAzbrXt7LumoVs+J\nOkR1lX0D6Ru+/naxjL7A4me9CFLK72rHeRrL8nVVhG7Ut+zo5u56XiBgyqxFTRSucd+ni2bYMKdo\nOmzvTtQ3Hg2l/tDOzZYxLZ8uFlFV7tqS5Wfeu5upggnAYDbG187P8ddfG6VqOaiaIK6p9KWj5A2H\nfQMZVCHIGRaaIjAtl0988RSu5zFfqeG6vtVQtap0pSKMdCX4vkPDvHdvH7A4H/Hr/3itPeA/vTKB\n7XlLFBsPbe3kjz9yiOfOzdGVjHBnAz23WSSucVymiyZ/+40x8oaF5UqqloeiwI6eFO/Y0cXpqSL5\nqsVC1Z/wnoSK7XJp3g9zBdXOEdW3ZO7oT3FopJPTU0XUulXYfN4gj/LkiSmmCkaYxA90qKYKJpbj\n89M3G6WvjMZx/eCh4Xpi1aY3HeXH37OzrVTTVvTqv3r+EobloisCx/PDU4l6HU2pQVfJk6ACCEky\noqKpgl98dA/5qsUfPnUWT8BkweTn3rubmitRBLw6lqdcsymZNlMFg0w8wvv29vGZl8eJamJRODQR\nUfnD7z/Ic+fm+PKpq1Qsl4FMjF/+jrdtaC7Kn5/+PeWNa+1gNAGpqEbFcvCkRBUKj981yPe+fRjw\n16J3bO/i1z5/Asf19zFPSv70mfOcrIelVUXwtr4MecOiUnNJxyJ0JCP8yLu2M1kwGcrGbriAD9oX\nMvqDNh1nVTSyAQL541YPdjnWQPPvg5+fOjWN60lGupKMzpXZ2pXkhx/YznSpRsmwOX4lT1c8Qg6L\ndExnIBPj4EjHEu5+fyZGvmrzwoU5dvUmmSnVworoqYK5KGz1lddnMCyXZEzF9UAoAk2Ch0QRgnRU\n545+f7efyBmLXuRfb7iXgyMdy47Doa2dy9JJl5PWmMgZvG0gzfhClcvzFQT+RrVQqfHtdw7wnXcP\n8t++epGJXBVP4lc+qwqpqObfT1SjarkcGukgqiv8wP1b6UlFWahaS1z1xvMegpAuDIRhANuVfvOj\nhH5TNGbeDGgc1zu3ZDeMUtlsTDx9ZoZTU0U0RdCViiCA+7Z18cCubr5wbJILM2VyVQtP+tYv+BuD\nrioIAa9Pl+hIROiuJ1QLVYtYROMn37Wdly4tcGmuwl1bspydLob08ImcQecycyOY/x+6d3hDxqD5\nHZrIGTieR28qxuh8ZdE99mVixKMqasX31BYqFvMVKxzH4Lru3dbFbLnGdKHGbLnGVN7A9SQ7elJc\nmiuDAvdv7+al0XlGuhIhxfzROwd46dLCurSY2sUyekMk6dtJFbt7OIuqCMYXKlQsP1b5/Pm5sIhk\nMm/4XG4h0BXh68R8/TKaAlFd5eOP7WO6VOOJE1NEVEHJtHl1LI+qKpydLvHgHT08e3Y2TI4alh9a\nqlgOZcvGqxct2vUsrO16aPXzrKVXwPWOw0ptPM9Olzg/U/bzFN41128ib/LsmWnmqjaG7VJzPTwP\nBBJPSgzL70BXqcc2QTKQjYdey1o0YgK6cND8pvGzgSuejmmbIaPrwEZSKhsXxOmiyV88e4GZokHN\n9Rf8mKaQN/xFLxXViOoKmiawHbkopJSry7G8dGmegYyvKDBfrhFtaOna6I0EKsfBfV2r7Pdb6jZ3\nP9yIMQjo4UE/hV95fB+6Kjg6nsewXKR3rfYBoGDY2J5HRFMpGBYVy+HibJlPHj4Xvn+6KrhaNMlX\nbUo1h8qsn/voTER8JQFNoTcVDcOsQZOjQI+tsXHQG55UFkLcAfwusB9f6RQAKeXOdp5nI3Boayd/\n+P0H+cKxSS7NVbh7uCNMPAcL2r7BbEj5/MxLY5RrNsmIhus5fPLwOWbLNUqmw5aOuF9iL+DerZ0U\nDDts+RgotRZNv1o6G9fZ3pNEAHdtyVKuObx2pUAi4scFpwrmhqgvLiet8cnD57haMPCk5M6hDAvn\nZkPWB8ALF+fZ2ZtiR0+S01dLRFWfDtuZ0InqGiev5NnSmSCuK0uKbtaqVNmqAnwyb4Su+GbI6NZA\ns1GxpSPu57d0lZrroiqCzoTOuZky6qmrpGM6P/1tuwF47UqBI5cXMC2XybxJRBNYrkRTFHRNMJSN\n40hJbyoatnRdThkg+P3R8TxPnpji8Olpnj07u+x1twtHx/McD/WIqmFIt1C1QQhc12NrVxwPmMqb\nJKMauir48Du2slCxuDhb5p6RzkXvte1K9g6kOTddplBnJ1qOxwO7url/exd3D2fDyEijBE4j02o9\nWkztZhn9NfAbwB8B7wV+DG6f9/fQ1s6Qstqo1XN2ukjNkewbSJNNRJgr1+hNRbiS8xM5cV1DSokm\nBKriF79l4hpRTfV1hBSlHvM0mS/XKJoO2bjOjp4kV4s1oprKQDbGj717BxM5I6TBBu0hA4XXmnNj\ndMFWtMC5cg2rrkkUWBJhRWVUw5UwVu8419hrdv9gBkfChdky0Trja6pgMF2qUSiZ6JrCUEdskQXX\neP61asQ0W3QTOYOIpnCw6QW6nbGSuN7of/rOm3glq6MV17/ZqOhKRnBciWl5KPiMFcP2SERUdvWm\nmCr4oY9H9/fz8N4+fueJ07w2mfcJCJ4fJo1HVGqOpC8TDUOGR8fzLUObjQjCNRFNWfTu3CzUbJev\nvD6DKgQIQUJXKUtJRFNRFL8GKB3TsD1JZyLCzt4UM6XaotxLYOGrikKkTtowLBdNFTy4u4cdvakw\nP9I4Bs1hovVoMbV7Q4hLKQ8LIYSU8jLwm0KI5/A3idsCrRKsn3r6PJ7n8TtPng61d1IxnYMjWYqm\nw3fs7+e/f+MyecPGw5/MnfWCs4Lh86v/4ZUJzLqY3K7eJKPzFWK6wt1bsjzWJAXczJAazMb41NPn\nFyXN1opWUtqBNSG5pkk01OHf65mrJT+xbtikozEkAl3xaYF39KX4mYf9RjpHx/M8cWKKgmFjOZKr\neTNMer17dy8P7+1bVEew3oYjt3MTndtdVXW5Z9j8TO4cyrC1O0HeKKBrfk7p39w7zJW80ZIokK9Y\nXC3U0FVBRFP4kQe28dDe/tDiDUJAT56YCll1K82f5uvZaBwc6eDAliwTuSpF0+H5c7OAIB31l9Wu\nRITeVBTb8+p5kSjpmMaLF+eJaEqoCdZYS2E5Xr3Xi0ZXQicZ1elLR3n+/BwvjS60HIN2vhvt3hBM\nIYQCnBNC/CxwBehr8zk2FK2SRJ0JHYHg4lyFZFRHSsmOniQLFYtv2dlNNhHh6ES+XhPgJ5Evz1dC\nfaF8tV5jICWm7bGlM0FHQl+kmRSglVvcKmm2Vlyz+nWKhhWGrlppEtmuZCATY7Zcw3IkQx1JKjWX\nrd1JpIQfedf2RW56kMx+8eI852ZKjHQlQitwOSvyRi375cIFm9h4rKTcG4QndFXw3Lk5KpYTymIM\nZOM8vK8/rImBa0SB4xMFSjWHqKqgRxRURdCXveZBBmGRuXKNw6en1zR/mudIIAC5URjqiPPd9wzx\nl89fIqYpZOMRCobFB+4ZYqQrgaoIXh3LMdyZoCvpJ8F709FF99OT8nWZmiXlRzqTTBVM7t/RxWyp\nRtG0l/WO2/lutHtD+AUgAfw88FvAw8CPtPkcG4ZWllCw+wYaRJWaje1IPvvyOELAM6/P8PHH9pGJ\nR3Bcj+3xFA/e0cOVvBGGnTRF4fxsGceToVR3c1KsEc0u4XosgEYdFVURfPDQMKemWh+rULU4ccWn\nt9mupGhYRHWVzoSv/trchDy4Tl0V/POrE0wVjLAWpB3Xvtq4bOLmYDnl3kav8y+fv8TRsRzzdc2w\nrmSE3nQ0XKCaiQJ3D2d57uws52fL2LZLdzKyaN4Ez3oyb/Ds2dk1z5+bOUdeHcvxO0+epmT4vcor\nlouuCt65oyts6HPiSmFREhxoeT9hsrwepm5MGqdjGoKVtb7add/tbqH5MkDdS/h5KWWpncdvF5Zr\nT9lK1fQdO7oWceVtV/LixXnOzpQYzMaZKhicvlrioT29gF+LYLtyUWIHCDWKgr9fb9en1SyA5fRc\ngiRVMqIzVzaZLJjLJp0mCybJiEosolKpuRza2skH7hla9XoPbe3k44/t4+sX5/mWnd2LaK63gmV/\nu4ds1oN2qHg2P8NG9c7A6/Q7f+nEdBUEvH1rJx95x9ZFOaTmefeJ77mLp8/MsFCxFtUgTnVFAAAg\nAElEQVQQrXTudsyfdimbBsqkyahK0YSRTj+Mlk1EwmsP9L++ZWf3iuSKVvVRb0S/kHazjO7DTyyn\n6/8uAD/ezmrl9aKVFwAsUTXVFGUJfS1I6DRaxFJKjk/kuTxfWbHj2vUs4q2wkgWwUpx+uDNOJh6h\naFhcXqjy4sU5Tk0VW9NXszEqlkup5qAIsWRxD9DYwKM/EwvzCVFN4ctnZrhzy2KBuk3L/o1BOxvG\nNz7DINfU6HWmYxqj8y6u60ttX56v8FfPX+LH30MYHw9aZwYL3FBHnIf39jGRM1bMi7Vz/rR1TLIx\n8oZTv2dBTypCfzYe3l/gQbie5JnXZ+hJRzm0tZPposnpqWLY+6TVPS4XOdhoXNeGIIT4IPCf8fMC\ngXiglFJm6h/5K+DfSimfq3/+PfgbxN1tu+J1Yrl4aEAJBZ9eena6xOHT0zx5YmrJIh9QVI9PFNAU\nwTfrccJmfaSV4p3tnJgrxekDC6s5htvq2rKJCPdt60Tiy3IHlk4jGht4SAl7+tM4Te093wwMoDcD\n2pW/aUbodUZ1KjWbbCLCxx/fx9HxPK+O5XjixBRFw+FqscBz5+aWbZ0JtO0dWCvaOSa2J8nENGS9\nmPRdu3v50L3D4fGO10X0gkjC8QlfvPJWbSkL1+8h/B7wASnl6WX+Xgo2AwAp5fNCiFsqbLRcTLux\n4GV3X2pRUrjVIh9UQE7mDY4HccJ6/G8t8c52TszV4vQrFXs1H6c/Gw9f0FafaZzko3MV5so17t3W\nyeh8NWzv+UYxgN7KoaFW2ChmVuB1Oq4X6iI1Wrj/s258gJ9LuJI3uDDrN+za1ZsKjQZgQzas1a69\nnWMS1VUSukrVdtndl1p0/UGxa2NurdUmcTtvCNMrbAYALwkh/gL4DH6B3g8AXxFCvB1ASvnN5i8I\nId6JX7fgAkeklP/+Oq/purBcTLI5hhckflZb5JuPB2uL9bU72bpanLVdn2mc5BFNoScVpWDYLemz\nm3hjsVH5m5WOG1Ax/ZyCxsN7+3h4b19YNFYw7JaG2M2iErdzTA6OdHD3lmxYqdxMumiMJDTK2zRv\nErcShGzuvt3qQ36oCOAhYAD4HFAL/i6l/Kf6555Z4TBSSvlwi2MPAHkppSmE+H+B/ySlPNHqAPfd\nd588cuTIqte7XkzmjTAJ/P+z9+Zhcl13nffn3KXWrqquXtWbpJZkWbYsWfIWO44htkkg5mULIc7M\nBGaACfDMEDwDb96HhHmZzDCEl+F5ySRm3mECIQQIWchC4uAkCNs4XmNZlizZ2i21ulu9d9dedffz\n/nHrlqpbvUrVUkvq7/PoUVfde6vOPXXuOee3fb/Bj3wleFBWgrvuuouV9kWjgmlzYwhXOxU06Ivr\nyUK41MK0SxkXl4OFEjTmGxMrObcRuBJ9Uf8sLHenfynXXC6EEAeklHctdd5yLYSfqPu7DLy77rUE\nvg4gpXxw2S0MLpZyrO6lA7gLnXslMNe3H+x4V2Oyu5LB1kbGLOYS5q1bBI3HShe3q1HZvNCYWmhc\nz/f+tZxwMJKt1ALmgZzlcu5lMcLJq41lLQhSyl8EEELcL6V8of6YEOL+ur87gU8A3VLK9wghbgXu\nk1J+dqnvEELsBtqklEfnvP8rwK8AbNy4cTnNvSysViDuauN6va91XD3c6GPqerz/ZbmMaicL8ZqU\n8o6F3hNCfAc/q+h3pJS3CyE04KCUctcSn9uC74Z6/xyLYRba2trk5s2bl93etQrb9RjNGXierySe\njoWIhdSayM1yMDAwwPXQF8uF7Xqcz1ZwXImmCnqao7X+uty+sF2P8bxJIEHYmQyjqwp2lUogpCkr\n+m0utQ2N+K4bbVwshuupL+YbH8F7QoiaQmP9+K3HgQMHpJRyyYG1LAtBCHEf8HagXQjxm3WHklQ1\nLqpok1J+RQjxUQAppSOEWNQFVF00/gb4yGKLAcDmzZuvqH+0UZjrJ33yyCh/+N3j6IpgNG9wW3eK\nzW3xFblxrrSv+GpiJFvhcy+c5Qsvn0NXFRwp+ch7buGD924CLr8vXjk7w5f3D9Z2eo/evZHedHRB\nevBG+7wb6c67kcbFUrhe+mI+PrLRnFGr/cmU7Vkyoo/evfEicjshxEUJPfNhuTGEENBUPb9eny0P\nvK/udUkI0UqVAlwIcS+QW+Kzfw64G/hD4cvLfVRK+dIy27XmsVAhHPgrvJTQ2hSqyQ/ClalIvFYQ\n9N/h4Qym6xHWVaQjmSlZtcn5cjFfxtdi9OCNzpufW/nbCNfDQjGItcaiuo6lUT8+xnMVHn/6NG5d\n7U/ZcjAd2ZBsreXGEJ4FnhVC/GWVxXQh/CbwLWCrEOIFoJ3ZC8Z8n/1F/DTV6xLBxJKK6rw1WeTQ\nULaWrjZRMGrC4/NxxFyJQp21hnkVqFyPXT3NnJvyKY1b4yFu2ZCo9dXlYqFUxLn8PfNRmzTi95nL\nN3UpFOfruH4w9xmoHx+2K7m5s4mt7U2zan8uRwOhHiutQ/gTIcTcoEMOmAA+LaV8TQjxw8CvAj8L\n/CMwfMmtuw7Qm45iOR7fe3MMx5N87cAwe/qa+egjt1zEV3JoKMtYzmBre5zRnM+ttBAB3vWGINU3\noDq2HI/37OqiKxVBUxUc1+OOjWm601HedUsnqViotoNvBOajDvjA3X0cHs7RXaUnzlcs3jifY6Zk\n0ZG4tAK8+VxOcyt/7fnU6ddxQ6DeoxA8A0BtfEwXTYQQF9X+ABdZy/VjbblY6YJwBn/XH+zoHwXG\n8RlNe6uv3w78DvBhYA/wGZawEq5XBD/Ijg0J9g9kSIQ1TlethEd2dc3KxT40lOVrB4YZmC5xeqKA\n4rvPFuQdWmu4HN96IEU4OFNmqmhy/7Y2Dp/PkTec2u4n8Jm6nuSfjk/wgbv7ajv41bqfIKXw2ZO+\nj7YrFeXIcA6jylk19/y5C/zcflhMV2Bu5e86bkwE7iGB4PBwlrzhkIxoqIqClJKO5MXWwEJu6U88\neaxWIIiq68v5/pUuCHullD9U9/oJIcT3gRHgtup7jwKfkVJ+DfiaEOLQCr/jukC93mrRcNAUX0jc\n9tyLzgtkKwemy7ytv4UTYwUiurqgYtRaw3KDogsVJn31wDAHBzNEdJVsxebIeb8ocGt73NehdSVt\nTWHCdWpYtitrbp7VEPSujyGULV+t7q3JIkBNKjJwGQW/9UTBYCRb4baeFMlo6KJ+WChNcS0wwq5j\nbSBwD5VMh5LlsiEZxvEkD9/SSVtTeBYbQoD5YlBTRZMjNXlPFyUUiS/n+1e6ILQLITZKKQcBhBAb\ngTbAA6zqOQ9TrRu4xO+4LhDoreqKIFuxaY2HCOkKPekLovMB5fZE3tdbNW2XE+MF2prCREMqw5ky\nluPxtQPDOJ5XE/JeaxPGYgIqvekob57Pse/YOCNZn9UyYL0MlKIGpopMly1aoiESYY13bGtnNGcs\nSXNwuUVNi1k1cwXdP3B3H2+O5Dk+eoqX3pomqqs1X39A6ma7kmzZYiBSpjPhXhRjWIyu5Fou0FrH\npWEh9+Hm1jgVy2VgpsxY3mRDKlIrkK3faOqKwnvv7EVXBG+cz9elpfrj0nE9ylLieMt3Qa50sv4t\n4HkhxFv4TKf9wL/DzxL6BSHEN4EKELCdbmPpLKPrFo7rMVOyMR0Px/W4eUOCqO5n6Qa76vFchVfP\nZYiHVHKGTURXcF3J/dtaaz7lbx4apSmsMuD51sJamTjq3SS5ss3piQlCqmDfm2OcnSqRiumM5wwO\nDGZwXIknJQ/e3MGpySIFw0ZVFMKaYFNrnCPDOcq2S1NY45FdXfNSYjRyFz1fKl+9Gd7dPJvLvjMZ\nqUqGWqiKguG4vHB6CtuVnJ0skqnYeJ7EdCWnx4sMzZQ5W7Uo6j9z3RK4MTGfrvl8VrWuCk5PFLEc\nD0WBd9/ayYNVOVrwNx+vDWZAwlTZ5HyuQkxXsV3Pnzs8yYunp8hWbBxX4ri+KJd0HWuJJgIrXBCk\nlE8KIW4CduAvCMellAbwD0KI7wJdwD/KC9VuCn4s4YbDnr5mOpMRpksWqhBYjkdrUxjH8/jqAT/O\nnq9YtDVFSEY0UrEQuYrNeN5k0Klwatx3G7mexJGSiq0Q1VVOTxRnaTRcLQQDOl+xGM+bnM9UsFyP\nXMXm2GgBx/O4b0sbZ6eLmI5HSFWwHcmBcxnCukI8pFXT5RTKeYOWeIjdfc1I6WdSrDbNQb1Vc3I8\nz+NPnyYd02sP53jeqHHZ7zs6zs2dCc7nyliuJCwkrpR8/bVh9h0bx3MlibCGafuc/x3JMI4r+euX\nz9HaFJpl2a1bAjce5pv8F3IfjuYMKravK6GqCul4aNZ4mSlZTBdNHE/ieDBTtJiSEteThDUF0/H4\n/EvnAEnRcHjbllYiusILiGVVO16KO+dOYHP12t1CCKSUfyWlfHnuiVLKk5fw+dcFupujvPeOXkae\nPk1YE0yVrCpdtMWBcxk8CSFVYXtnE2FdJawpUA0kI33T0fYcpISIrqAAnoRjozk+9dTVXxACv+Xg\nTIWRbIWK5ZKK6ngSDMfF8eDAuRkURSAAx/MQQFhXyBs2h4YyhHWVj73nFmxP1opsrhTrZb37xnQk\nYU3UFod9R8fJVexZNN+TRZPuVJTjo0UM2w8qn88YhIuW/2C3xPAiEsNxkVIigamiietJBqbXlmW3\njiuL+Sb/hdyHZyaLZMp29Xl3eO7U5CwG4VzZwnZ9LRIA1/Mr9x1PkjccXE8S1RW6mqOcGi8yU7bY\nsSGJdG1zgebNwkoFcv4a2Aoc4gIJnQT+apFrNgM/AI4BlpTy3XXHuvGrlCPA70op/2kl7bnaWCqz\nZmd3ki3tcVzPoy8dY6JgUjYdDMdFVxW8kModm1p47Ee2M5oz+MLL5zg5XsDxLBzXN8EU4f/TVYXm\nqIZhe8yUilf0Pve9OcZLZ6a5uTNBf3sTuiqYKppkKw4Fw6Y5qmPYLpmyb5U61dKAsK7S1xKlZLpM\nFy08KbljY5oD52boSERojumkYiHu6W9hT1/zFXWl1LtvdFXwpf1DnBzP89q5LEdH86QiOqbtcXKs\nQEhTaG8KM1k00QSEdBXbcfGkh0DBlRLb8UhGdXZ2J/mh7R2oAr5y4IbOuJ4XN2LB3Hya1MOZCj+y\no4ORnMHu3gsKg54EXQEhBBVb8vTxcSbyJh+4u4+RnMFQpkJYU3y9ZcslHlFpawozMFnCAxQhcDwY\nzRokoxrvv7OPB3d08N9d215OW1dqIdwF3FrnElou9kkpPzjP+78N/CfgMPBt4JpYEII00fpd7dyM\nkiBtMRXVMB3JnRub+dtXBrFdf/fsSQ8pJfmKTWcywt6Nafb0NfP08Qm+vH+Qwekyhu2vuY4ryZQt\nJosWpydLRDSFxmTfL40vvzLIx594E09KXM/XQ5gsmHQ3R9AVP+snrCmULBfTcrFdB10Fy4Vs2aZk\nOWxrT9CRDJMt28yUTEqWy0TBIGfYtQDYarhSllqwAynD3b0pHnv4Jh5/6hRTJROlJBiSFeIhFU31\nH75H7+7jmRMTHB7OYVv+76ILMBwPpKBgOtUsKV8oZU9fM8fGCrW0v7lc+eu4cTB38/HZ588yWTCr\nGWlJjo7mAd8r0JkIoygKluOPsUzJ4vlTExwYzBCu8hMlInrt/w89sJXBmRKHhnJVq0KyqTVGIqLT\n3hSeFX9YDla6ILyBr4cwusLrHhRCPAd8XUr5ybr3dwOPSSmlEKIghEhIKa+Kwtpy8ujrF4KCYS8q\nGxmYiUHqaK3WSBHgSlTh7wJefGuK1wYzPHpXH/3tTbTEQ2zvTHD35hZeOjNF2XIxLI+p4gWLLxHV\nV50jPLjXP332LSzXQxMCT/qUEdMlC0WA40ke2dWFlPDGSB5VFQgBAoEiJD3pKLmKzVCmzIZkBEXA\nxtY4QgjCmooQrFoR1lKpsAcHM/zGFw/WMjM+/S/24krfFFcU38pxPElvOkKuYjGSM3A8iRDU3HfN\n8RA96Sgl0yFvOLO+v7s5yseqxYfrAeR1BBueJ4+M8uq5GUzbo2K7CAT5ilWLYWXKNlva4gxlyuQq\nDlIKbE/iGDYypGG5Hj+1p5tbulI1PYVPP3UKXRWEVYWK4xLWFd55c8clVdOvdEFoA44KIV5htkDO\nTy5yzSiwvXr+N4UQT0kpD1ePqXXWRg5IA7MWhCtBf31wMMPjVV+/pigXVf/1pqOM5w3+6Hsn/J1t\nxebeKnnUW5MlkhGNqaK/4gedP9dMfOCmNl4+M41pe+iKi1JdEIYzFSzX5T9/K88tXQla4n7Kaa5i\n05OKkalYnJ0sIfHXEvB3AauJC7URBkXTQcEflOBnTkkpMWyPbMXiO0dG8SR+BWX1+l29KUayBp6U\n6KpfUFO2XJwqNcNwpoIQICWcHC+syoS5FDXxc6emmC5ZxHSV6ZLFc6emuHNjmq+9Noxdnfhtx+PU\nRAFNUYhoCobtVvVzfVQsh9GsgaoIOhNhSrZLR1Oo1oc3cgD5ehIrWinm21wGbtdM0WQybyLxNxVv\njuboaY6Rimr0pmOcz0xxYryAW90oKQJU4Z9btlw8oDcd41+/fXPt+x64qY2/e3UIy/GIhzV6UrFL\n5jVa6YLw8RWej5TSpLp4CCG+jV/AFiwI9RvdJJCd5/rP4Fc7c9dddzV8JhzJ+mRRpycKqIqgaDgM\nZcrEQhqxkEoqqmM5HudzFY6N5GuEdC+fnWF3TzMP7ujg5TPTPHVsnO8cGZ0lIzk3xfAjP3ozjz99\nGs/zsFzJVNFkOFNGV3yXy0jOYKZs86EHttASD/HkkVE6tDAAluOhKgJFEfzs3l4++Y1G98QFBJPp\n1vY4A9Ml2pvCVGyXRERDEX6AvGjamI70J/7qdZriT/J7N6b51R9qYd+xcTRF8MyJCYqmTcV2OTaa\nx5OS7R0JTo4X2Hd0jCPncw2vxl5KorQlHqpaKB5C+K/725u4Z3MLFctlumgynDVAgut5fPaFs3jV\nYLEqAAmqohDRFcqWy/msAUhGsxW+sn+wJiR/oy4INyrmS2d+4fQUf/LMaaSUuI6s+vr98x3HI6ar\nqIo/VscL5iyrOR7W6ElHGcsZKMK3wLe0N836zr0b03z6X+xtiIrhStNOnxVCbAJuklL+kxAixmz6\n64swxw10P/B43eHDVWrtw0BSSplfSXsageFMhbAmUIRgeKaMlFCx3Vps4O7NaY4M5zBtF6p7YFX4\nGUIP7uhAAG9NFonpCoMzFSYKJs/WUVkHOcevnJ2hNx3lv/30bbMKtn73W29SsRyEEEQ0vytb4qG6\nytwmYqHyrErF7uYon1z4li7Cct1hgY9zqmiSq9hMFS360lHu6W+lJR7i4GAGgfArhT0JeNRTywkB\nuiIomg5f2j/E8bE8ZctFABtSEaaLFt2pKJmyzUzJQlOVWaLrjZw867mIAt3a4Dfobo7y0I4Ovntk\nlNG8QVcyws7uJKM5g9Zqv0+XLN8qw7cIBqdLtRoSie9WCkwiy5UoQDKqU7ZcJALH9dZ8hfk6Go+5\nzKS/9w9HOTNZwrA9NMXfAUuouZDT8RC6Jrh3SyuuJxnNVTg7VUb1Pcvc3JXk//rRm/ns82cXjUc1\nSsVwpVlGH8J337TgZxv1AH+KX528EB4QQvwevpXwvJTyB0KIx6WUHwb+O36GUhT4z5fQ/stGbzqK\npijkKjaO509qFculKaLhun4Ouu1Kf0cowJH+6p4pWXzz4DDnZspMFS286vtNEYOortSI6eBiyuSA\nq7y7OUpbIsxzp6Z45ew0qiJmiXXX73AvVbx+ObQS9TUFx8cKbG6Nc3qiQGs8zHTJJBHR0BTFT6Us\nGGQrTs09FEDgZ0K5nmT/wDTTRRvH8/A8WUvTtF2PsuWwqyfFvVta+cGZ6YuqkRuFei6iVwdmkDAr\nAQD8GIBazfr4i+fPEtIUBPDwLZ2ENYW/fWWotuBZjsRy/DiBJ6v/PA/D9tAVKFoORsHFdiWW42I5\n3qJJB+u4PlHPTFoy/Sy8any4ln1Xj7cmizV6llRURyCIaP5zFNIEv/T2zezdmOZjj1wZ7fKVuoz+\nPXAPfhopUspTQoiOxS6QUj4JPDnnvQ9X/x8GHlphGxqK7uYo79nVxenJIo4rcaWH40paYiEqloth\nu8RCCqbj4km/wwTQnggzU7axHUkspFK2/FTSmYLFZMFksmiyf2CGR3Z1LerLDlb2kWzvqlTmLkfm\nLzgnHtJxPT+w6nq+e6RiuYxkDdIxnR+5dQMvnp6iOabTHNP9VDcJmurXGvQ0RzmfqVAxPcqmA0Kg\nVemcb+5MENEVHtzRWWNwfWhHx6oN8vr7PjSUAQRb+5pnUWuENYWtfc0cGspiuzZ7+tKcHM8zWTC5\ntTtFW9M4rutRcVxMW15EaJeM6Ny3tZ2hTBnTcUlFQ5i2w4/d1kV7IsxTx8avK3nFdSwN25Vsaokh\nEZwYy2PVOcWDTVT9OIrqGt3N0WqVvJ87+OsPbWOmZHHfllbetXMDcOWoTVa6IJhSSqsqZBOonV3z\nXL17+prpSUWZKpggFDa3xC7sDKs0tBJJVNdIRjVmSjaG47EtHaNsOZRL/s5RICmaDlLAibECozmD\nHRsSi/qyA6xWZe5SvvT6c/IVC8eVDEwVmSnZFAyHgumSrfjsIzMli5Z4iLLlgvT9m56EZFQjW/HT\nnIUiiIZU0vEQqahONKQyXbRIx3WS0dAsOu/VHOS96Sim47ttNEVBCL/sPxHRan0QHNdVAVLw8pkp\nhmYqzJRsEhGNza0xMtXU2Ym8iagLKAO0NoWRSNoTYQQQ0hQ0NVKzDJ89OdkQ0ZJ1rF0cHMzU3JJ7\nN6bRVcFbkyUsxyNvzE7911UIqwoF68Ioak+E6UhEkFAbK++9o/eqbR5WuiA8K4T4GBAVQrwLn8fo\nicY368ojElLZ0t6Eogh29yT55qFRwppACEFTWMOopnN5Em7uSvCze3u5tTvJC6en+Pprw0wWTL/k\nHBDS9/8VDIcX35rmIz96c0PEKy4Fi/Hn1McWHnv4Jp4+PsGhoSwzZRvL9ZDVPU1g6h4dyfNjuzaQ\niGhEdIWKBYqi4HiSvX3N/NhtXTxzfALHkyQiGr/8jn5sVy5KCb2a8Fvv68z62xY5y9VVsVyyZYum\nsEY0pJI3HKZLFpbjEdy9pvh0ALoq8DzwPIkmQFUV7ulv5d07N8xioFwt7qV1rD0cHMzwm185VLWq\nJb/2w9sAsD0Pqllq9WiOhfiF+zbznSOjZMo2TWGVf/m2TbUNxFoYKytdEH4b+GXgCL4IzpNSyj9r\neKuuMIar1X/3bmnl5HieF05Pkzdsf/KQfgqiJyUh1Wcr/ddv70dXBL/37aNIKSlZLsmIjiclnu35\n8QRgQzJCKqphu/IijdMrifl24vPFFlxP4nl+9ownXZw5NQKOhCPDOSK6ys6uJG+M5LitJ0XJdHj0\n7o08sqtrVd1AK8FwpkJIU9jTl+bQUBaQ7OlL8/pQhj9/7gyt8RCnJ4vEdJXT+SKbW+M0VzPKMp6F\n50EyorGtI87QTImOZJyy6TCSNYiGVDxgc1t81u+6mtxL61h7OFxNNomFNYZmyvzFi2eJaSr5sl3V\n/p59vml7nBgr0N/exI5qbGmuxXy1sdIsIw/4s+o/AIQQX5ZSPtrohl1JzOW1aWsKYToeUwUTG39z\nGVJVPCmJh3V0RfC733rDD4gKQTikYXkShKA1HqIpotEU1mrCJ2vRXTBfbCFSza7xqjUHEV3BsD1c\nCariZxDdt7WNoungen4arJSSDakLlN5rZRKs/00TEQ0BvD6U4dVzmSo9iJ8EENNVNEWgKoJs2UII\niGoqlusihGA0VyGsq/zy/f2MF0yeOzmBVdVneGjHouGzdVzn6E5FyBsOU0WfX8gwXXJlm4iukoho\nPhOBeSGIcFtPkpCmXJQxuJbQCK2C+xrwGVcVc0vL/+L5s8TDNrmKQn97jNGcyU0dMaIhjQ8/tI3D\nwzk8zy+6shyPjYkwH7jHL5priYcuKmpbKz96vYtovtjCsdE8rXEdIQTjeROBoDmmEwtphHWFTS1x\nfqOaobOUOtjVvLf5qKYB/vy5M5wcL9DX4u/6W+JhWptCbOto4idv7+YHZ2eYLvp8TGktxK8/uA3D\n8ehORfin4xM4rseGVHRWrck6GoPFCtnWKs9RKuazCoxkK9U0agFCYUMqQlNYY1tHgvOZMuMFAyn9\ntOTLyRi8ErghxWvmQzCJHBzMMF2yqpTNgj19aTqTFe7d0lYz76YKPh+Pz+Ap+Pl7N/FodUGon5iu\nppuoHvUSnfVCO3N93Lt7U8TCGmXTJaor7OpJMZQp090cJR7W+PWHtq0p8zaA7XrzptbWWysj2Qpb\n25uQwMBUiZDmn5eKhWrkdo7rcVtPitv7mnngprZaXvcrZ2dmWVNtTeE1df/ruDrIlS2OjuZxXZ9+\nOh7W2NLeVIudTRVNnjo2zo+muzg5np81h6xVLGtBEELcsdAhYFlandcCgqrl4UyZSLUI6a3JEhtS\nkVk/ZCoW4q5NaSQCgaS/Wjm4XCnJK4mgTQNTRd4YydOVjNSEdup1ncFPgf3j9+/huVNTHB7KYns+\nJ/vO7mQtV3otwqoKEC2U4llfZyEQtMR1epqj7OzxWSbrJ3woc++W1llFPsvJ1FrHjYdXBmZ8BgGf\nwIst7XE+VqdoOJKt1DLN5mbYrVUs10L4fxc5drwRDVkLCKqWExGdgmGzsTXOo3f3XWTi9aajdKai\ntYm/PsukXnQlKE672sFVx/VojYeRVZ0Fx/NqQjtz23ahLuICkd9qFY81CqFqgG6hCbtWZxHW0VTB\nzu4UElmrJNZVsej160pn65gP0g8bBnyVIGeXa16L42ZZC4KU8sHVbshaQBAE3tgCphPhww9tm7VT\nDLDQDx3sJE+O5zk+5rN1HB3NX1VLIWiTxKE1HiId15kuWjWhnYXaFrhbrrROwaVAr1OhWkwfOV+x\nqjzyNpqizKokniuhORdrJVi+jrWDH9/dxZNHRimZDorrUbIcPvXUqVnP1LU2bgKnJVYAACAASURB\nVC47hiCE2CClHGtEY642VrKiL1RI9tjDN7Hv6DhAjfr6alapzg2YHx7O8fKZqWW37VoZ0Iu1c24f\n1Pt3AzfT1U4NXse1h70b0/zPf3UHT7w+wtmpErt7m6/68365aERQ+bPAgmkAQoi3AZ/E53V6VUr5\nH+uOfRz4GSADfEtK+ccNaM9l4XInwO7mKO+6tZOjo/k143Ouv6fOZGRNte1KYe7vWu/fvZH64UZU\nLFtN7N2YpjMZ4VNPnbouxtJlLwhSyqVG0jngISmlIYT4ghBil5TySN3x37rWpDOXwlr2Ha7ltl1J\nrPfDOhqF62ksLTfLaFFbWko5s8ixeneSAxeJff2hECID/J9SykPLac+1gLXsalnLbbuSWO+HawPX\nglVzvYyl5VoIB6iRwlwECWxZ6gOEELuBNinl0bq3Py2l/LgQ4ibgL4AH5rlu1RXT1rGOdaxjHcvP\nMuq/nC+pWhh/Arx/zufOVP8/FTCozvPdq6qYto51rGMd6/Cx4hiCECIN3AREgveklN9f5HwN+Bvg\nI3OzkYQQSSllXgjRdiltWQwrUQm71v1+i2G+e7wR7nstYCX9fC3+JteSbvJC/Xst9vtqYqWKaf8W\neAzoBQ4B9wIvsbjIzc8Bd+PHCgA+CvzLqkjOHwkhbsMnB/3tlbRloYnu0FCWmZLFP5+YwHZ9Gub6\n6sH669daVXGjMd89AnziyWM1Ob6gb+byutd/xvoDs3zUS5HWyx7ONwbrrwl+E10VvPeO3jXNd3Ot\nob5/59KyB7/Rer/7WOmu/DH8yf1lKeWDQogdwH9Z7AIp5ReBL855+6XqsV9d4fcD8wtZj+YMvnZg\nmNOTRUqmQ9GwSUVDWK7Hp586xQ9tb5/1Yx8ayjIwVaI1HkLiXNO5w/NhJFth39Fxzk0VCesaAsnX\nXxvmwLkMrw9nSYQ1HM+v1h3PGzVed1UR/PH799Sqla/3RXOlqF8ggVl/HxrK8p0jo4Q0hZGcwenx\nQk0Y/enjE2zvTMy7sB4aynJwMAMSZioWBcPm2bam9f5uEA4NZXnu5CSW46GpgpmSRVcqwkjO4Oxk\nEVURZEoX+n2pIsXrGStdEIxq+ihCiLCU8rgQ4uZVadkCODiY4YuvDHJoKIOuKkwVTL5/csIXRi9a\nRHQV03YxHIlZMJHAV/YP8c3Xh9mQ8IXXTVfy3MkJ3hzJY3seYU1lU0v8mh0A9ZPUm+dz7Ds2zki2\ngutJXhnwJxoP+P7JKSR+FkCmZKMo8Offf4t0U5hCxSYe1ihUbJ54fYTOZOSCYHhYJ1+xrrtFcyUI\nrM/PPX+GmYpNS9Sn8Ar+TkVDTBZNxvIGd25MM1UwmKwypwJ85tm3CGkK8ZDGw7d28sBNbUwVTF46\nM41hu2RKFq4Ex/WI6RqO683q73rLY+5kNXeRWscFfPmVQf7HUyfJGb6qIQ4cPZ9lIh/BsB2mixYS\niSchFtLIVywef/o06Zh+Q26CVrogDAshmoG/B/ZV00VHGt+s+VG2XH7ji68xljOw5xGsBijU8Y8H\nEWgPqFiSs9Nl/p/vniCsCeyqeoUL2K7L514c4PRkkf/6U7ddUwNgJFvhd//+Dc7nypi2x2jOwKtO\nLO2JMF5dGL6+yyTgenBwKIeoHpsq+ZJ/z5+aYrJg8pO3d9cEw9WqNvK1istxfQWW0pGhLMfGCggB\nZ6r9qimCM54kHlbRVYV8xeaVs9MUzdnZ1YOZCgK/3wemi3zh5XO+CJMA1wWJ38cSGMqWaaqT+qwn\n5zs+VmDHhgTJaKjmAqy34m4kLJWOmilZ/M7fv4Hjzc5FyRouWaN00XXnM2V6W+KENXHDamGvVCDn\nZ6p/flwI8QyQAr4THBdCbAc+Amyq/2wp5WIxhmWjYrmULZcF1oIaBL6gizPPiRIw5kgZCXyCqsmi\nec0NgKePT/DiW1PYrsT2JArQHNOZKXtMFc0VfZaugO3BTNni8PkcN3Um2LEhgUAwXbIYzRnsXZ3b\nWFVcrusrIMezPM/PvZYXNhuaKrA9SdF0UfDHZmcqgjldwZgzAGujTgpKloPjSjqSYaYKFrqq+L9b\nyUJXFCQwnjcYzlSYKpo+OV9Ir9Is6zULApjF9LqOCyiYDollSL4r+Bui1kSYDz+0jS/tH7ouqo4v\nBSsNKv+1lPLnAaSUzwbvAT9fPeXvgD/FV1SbW4B22YiGVFTF17ZdCmFNISYUXOlRsha/oMpeS3tV\nxehawUi2wuHhLK70FzRF+AyMecNGEbCjK8Gx0QLuMvpLEaAoAqrWAEBrPISqKBw5nwPgO0dGr8mg\n23zqcCu5h4AcL6Rc2IH7esvgSdCEP6Eo+P1vOR5b2uK8Xu23uTBcl0TYn9wzZQtVFdzSlajRi9+3\ntZXRXKXmurAcr2rR+Sp1JdOepcRXz9S6GriWsonqkQgvb3oTCqjA/VvbalQUN2oixUpdRjvrXwgh\nVODOurccKeX/uuxWLYBYSOX3f3oXn3rqFJbjYnuS3lSE01MliobjZw4oAqEItrbHaYmHeXBHB9my\nxUtnpnl9KINle1i+BjZhTbCpNc4dG9Pc1pPiwR0d18wACERhJvIGigAPQVgTRDSVeFjFdiV96Rgb\nElGEgHPTJUZyBrqiYLmuL4jTnULVFFpiIY6czzFTspguWWxujdOe8PsuHQ9RMGy2tjeRq9jXnAUF\nl69nEFATPH18gv+x7ySG4xIPafzS/ZsZL5hYtsfXDg2jIHCkxw9v7+CX3tHPE4fO88yJSbZ3NPG9\no+MUTAfH9ehribGpJc67b+3kxHiB+7a0srMnVQtK5yo2piNnuS4C2cX5Ygj1tAlP/cFq9OC1iaaI\nxva+Zl4fzmLVbU91AZqmENFU+lqilCyXDckI772jF7h+qo4vBculrvgo8DEgKoTIc6Fi2QI+U0dt\n8YQQ4t8B3wBq/orFqC1Winft3MDOnlTtARjOVPjcC2cYnKmQKZk0hXV60tEa8+D2zgT39Lfw3jt6\n+YMnjzFRMChbLrd1p665RaAegSjM7X1+imhHMkIqqlfvOXmRQtNItsIfPHmMvGHXFNPmy8eeb8J5\n9uTkmtdEWAyN4Jrpbo6yvTPB3f1p4iGdkmWzd1ML9/S3MJKtMJo3mCqatDWF+aV39NPdHOVX37mN\nX33nNgB+5s5ennh9hDOTRW7vSzOcKdPf3lRT2gu+I6AbD1TcgkVsMcvsRp7AFoPleGxsjdMU1nnm\n5CSK8K25f/OOfnqaY+zuTd3Q1sB8EFIuv/hXCPEHUsqPzvP+WRahtpBSLkltsRy0tbXJzZs3N+Kj\nVh226zGeN6GawQC+WwYEnckw+jLN+/rPqb92YGCAtdIXC7VxqWPLuX45WCt9UbZchjLl2pPQl44R\nC6m145d7n8vBWumLy8XcvmqJh5gqmlSqW/1oSKUrFVm0/66XvmgEDhw4IKWUSw62lbqMfkcI8UGg\nX0r5e0KIPqAroLYQQkSklEb9BUKIyHwfdCnYvHkzr776aqM+blXxytkZvrx/kN50jENDGUBUd38X\nzP/lVFGfGi/wj0fH2doeJ1exefTujdzT38Jdd911xfpiqQydV87O8LkXztR2zr94/5aatkDtWFin\nZM4+Vn990FfDmXLtHpeL1eqLpe577vHPvzjAZ58/Q1cqymiuwvvu7OPeLa2145d7n8vBlRwXjcJ8\n/fyFl8/xlQND7OhM4HiS3b3NPH18nIm873joSEb49w9uW7T/rsW+WC0IIV5bznkrXRD+J3787CHg\n94Bi9b27q8dfBObqL8/33nWPer91MqIjgeFMGcvxZil1zZfxUp9m+Mb5HFLCwHSJXT2pK+6yWU6G\njq6KBdNTFzsWYC1qFi913/Md392bQlUEo7kKUsLrQ1nOTZdqx9fifV5tzNeP43mDP332NDMlm9MT\nRe7b0sru3hSvDswwMO1nUiXq0nKXwrXAlrpWsNIF4W1SyjuEEAcBpJQZIURICLEB6MGPMezlguso\nCcQa19xrB3P91kAthfCJ189TsSQjuSJfPTDM++7snTXZBAVhZcvDcjy6mqMUTZumsMahoewVvY+l\nMnT8TKccm1pitCUiTBdNDg/n6ExG6G6OYruSTS0xJALTdjg8nGOqYDKSM2o0Gcv18S9Er9EIzN2l\nLnTfQRtURfhFeyG/aO8brw0zXbL4wF19REIaqiJ44fQkAlEr6runv+W64c1vFIKxLhCMZIt87oWz\n5Co2rifZ1BpjNGvQ3xanMxnhPbu6uHlDAk/CAze1LWm1rWPlWOmCYFcziySAEKId32L4UeDf4HMc\n1aueFfCD0Tck5gb7ggnljfM5pooWlisZyxucHC/M4roJdtVl02GqaJGt2Niu5HzG4PlTk+xp8GS4\nGBbb1dZbMudm/J3buZkyL5+ZqmlJ66rg3EwZ03bJGw6W63FyvEgyohHW1RpNxlKB0YODmXnpNRqB\n+Xap8913fRscVyKR6KqCaXv884nJquC64JPv30NbIsxnnz9zkWW0HgCeDV0VF54Hx+PEeIGmiEbR\ncJgp26iK4NhInk88eQzX82qFeeezldqmI8Dc33EdK8dKF4RP42cQdQghfh94H/CfpJR/B3xeCPGz\nUsqvNbqR1zLm7jxtV9LdHKVie2A6qEJQMC5wKdXvuCWCw1WLwBAuQghURaEQlOFfASy2ew920ds7\nkwB0pfxj8ZDOeM7nUmpPhNmxIUHF8jg5nsfzwJOSVCxEyfQthuVM7IeHc5i2SyoWIle2ln3dcjCf\nNXBPfwsfuLuvZpF0N0fZd3S81obJvEF7MsLO7hT7B6bJVqAtHiJTTXH+sdu62LEhUYudBDUGC+FG\nJREMngfLkdXKbUFMV4mHNCzH445NzUwWTIYyZXqao35hXkifl9pj39Fx8hWrphe+jpVjpZXKXxBC\nHAAexncL/bSU8pgQ4jeDc+r/rrvuqmslXw0cHMzw+NOn8TwPRVH48EPb0FWB40mKho3t+eX1rudX\nFe97c4wv7h/C8zzOzZTpTITRNb9q1ec3kLieRyLSUKbwJbHQrnZWnCQa4r4trew7Os4b53OUbT8b\nJBHRsV1JxXYBgeV6SAmTeZNoSKU7FeGVszNLBtgLhk224pCt2ChC0J1qWK7CvNbASLZSY8LcPzDD\nxx6J0J2KMFOymSiYqEKhv01lsmDS0RRmsmCSKfvcRa3xELoqSEZDOK43q4hsofu7UUkEe9NROhIR\nzmcqIEBKSbbiIKVEAC+enkZTBYoQjGQr6IpCyZpdmFf/nAWWajIauop3de1iuXUIEeDXgG3AEeB/\nSynrt6mJ6v834weYv1V9/RPAgloJ1c9+G/BJ/MrmV6WU/3HZrV/DGMn6labHR3OULY9YSOGPvneC\nlngITRE0RXS2dzRRtl08CU+8fp5DQ1kEkI6H6UyEsVzJrV0JLFdy/9ZWUrEQLfEQe/qa+ck1sMTO\ntR4ODWWRUqKqClgusZCG43mULRefrUcSUhUSEY3WpjCt8RDfen2E0DIC7GO5ComIRn9rnIiukIo1\n7oGfzwp68sgoR87niOkqA9Muh4aynJksYjoeAnDwyBkOsbDGhlSU993Zx4HBDCPZCmemSgxmKstm\nzbzcSuprGd3NUT76yC0cGspydrLId98cY2CqRMl0ay64WFilJe5blF3NEX7i9p5aXUbwnJ2eKJCI\n6GxqidXqb9aL9FaO5W41Pw/YwHPAe4BbgP8QHJRS/hcAIcQ/AndIKQvV1x/Hp7NYDOeAh6osql8Q\nQuySUh5Z0V2sQQxnKoQ1QUhTyVZsmjWdsumQKVtsSESIaAq6pqI6HtmyRURTfE4lxWdwFYTpa4nW\nzN+7+1trhXhXAosVqtWj3no4NJRFVRUiuiBXtnhzJEdbIkxHIkw8rDOaM0jFQsyULXZ2p5gsmOQN\nmz3VQq35JsJgstza3sTpiSIV26U9Ea7t4i+nP+a6aeZ+t+l4GJaLqFJ5DM6UEcIntLNdiWl7tbYn\nojo7u1MUDLs2sduuXFZaaW86iuV4HBrKkIzoN0xAtF6/BMCtMo4mo7pvYUo/xoCEkunQnojQkQjT\n1hSuLQb7jo7jeR4RXWWyYJCK6rVizHWsHMtdEG6VUu4CEEJ8FnhlgfM24lcvB7CAzYt98BwVNYdV\n4EC6GuhNR0lGQ2xujWE6Hl2pCAPTJXJlm3PTZVJRjR0bmvjW6yPkKw5np0skwzqe9Hk1UlEdVbng\nxtBVccUCZouxay72oO3pa2Z3T4qhTBkhBCXTxXYNmqMhwA8QiiqrZ8m0SUQ0BCyahhm4c0ZzFRQh\niOhqjfjtS/uHcJZD1LTIPS7kptGrWUS1oLAiuHNjmq+9dh7HkwgBHYkQw5kypuPxnSOjONWgJ7Ck\nm2gu/AiDWAYV2/WBQLTm0GCG6bKFkJCM6ihC4EnfkvSkJBnV2dbRhCchFdVq/Vo/Rt+aLGG7nh9/\nqCsEXMfKsdwFwQ7+kFI6C+kfA38NvCKE+Ab+GP8Z4K+W8wVCiN1Am5Ty6Jz3fwX4FYCNGzfOd+ma\nRHdztBaU/Fdv28RIzuAbr/l8NwjoTceqqk0KN3U2MZKtcFtviqmiWSvGqeevee7UFANTJXZ2J8lV\n7KUbcBkIduXxKgGbYXmMZPM8fXyCD967ad5rgt32T97ezTcOnmc0a7C5Lc5orsLtfc3cu6W1Zm3U\nWx3B9y0mbfjYwzex7+g4QM1iOjycq7lZLvUe69NGA+tk35tjvHRmmorlkghrREIqhuUykjO4pSvJ\nPZvTVGyXqK7y8/dtpq0pzFTR5Klj42xJNwHMogxZblvCmsLWauHijeAyGs5UKBgOUoLrekgpquyv\nIe7f2samtnjVfeq7SINrdFXMYoDd3plkpmRj2C53bkqvSb6ta6kOYrkLwu1VDiPwg8n1nEZSSpnE\n/+P3hRDfAR6onvuLUsqDS314lQvpT4D3zz0mpfwM8BmAu+6666ptoFaaBTKSrfCl/UPkKxamI7ml\ns4mB6RKG4yEEbEhFuLkzwb6jYwzNlLEdj1NjeSwPsmWbbe1NzJQsZkoWzxyf4PhYnmzF5tx0if72\nplW912BXnq9Y2K7HD85OgxA8/tQpBqZKvK2/hVTMD5yO5gxmShbffWOUmZLFVNGirSlE3rB5a6JA\nNKRxywY/xDQ3TTBAYP6/cnam9plzi/fedWsnR0fzNWtid2+q9vpS4Kc75rEcj5DmW2D73hzjsS8f\n9NlhA0qXioOqgGE5vHxmmmzJJm/YdDdH6EpFsF1JVypCtmxzemKCtqbwil0WN2LB2tnJIsdG/XRT\nn9pFMlWyUBQ4NupPNZva4rMWg1zZ4ov7hwhrAsvxExXKlkN7IoyAa5pva61gWQuClHJRO0wIkZRS\n5qsT+0D1X3CsZTFyOyGEBvwN8JE57qM1g0vJAgl2oIMzFaYKfv2ArzQnCasqmqLw/OkpNiQjHB8r\nUDJdSpZLNKTS2hIjW7H4s+fOYNgunidJRXU0RZA3HDxvddfF+iDrP745xhOvj5CMapyZKvN3rw7x\npf2D7KoSDHpSUrE9chWfx99wPKSUSCmJRzT60rFlB44DF1VHIsxY3uThHR21Hd98RV0BMdmzl3CP\nozkDKSURXcH1JKM5g38+MYHpeIRUxWcb1RWSUR3H8fjs82exXY9MxUEAo3mDT/zDUTa2+pQiR0dz\neB7MlCzG88aKFoRGkO9dS9j35hgff+JNP/W6CgXfpdAc1XltMMP+wQyaIvjukVGa4yFcz6slXcTD\nOhXLoScdxXQkH36of52krkFolDP6b6v/HwBerfsXvF4MP4efmfSHQoh/FkLc16A2NQz1WSCO6w/M\nV87OMJJdOKDZWx2sBcP2d0BSEtIEUvqZEzu7k+QNG8uVxEJqVc/Ad6XYrkRT/Hxsn9NdUDRdDMcj\nHlLZ2Z1cUfuD3fdi7Z2L7uYo9/S38OO7u4iFVaYKFiBJx0L+ImC5vkatImo6AIGuhOX4abY7u1Ko\nisJEwUCICxW7C/Vv4KJqjYcBeGuyNGvHF7QpeOCD15eHC+7PTS0xwHddICCk+tTgHuBKWTtVU/xA\n5/mcQW86xmTRxPUkm9viCOHXTKwUc+/tekQwDvcdG8dx5azJRwKKAiXLwQViuoomBKN5g4LhEA/p\nCCCkqeQrNo4n2dmdIh3Ta7UM13v/XQk0JKFdSvl/VP/vv4Rrvwh8sRHtWC3Um/TL4SIC/wH/8EPb\n+KPvneDUeAEXge14NIU0bqnGAZIRHU1RGMsbSCHQFEhGNH75Hf08f3qKsbw/sezu9Wm6AV4+M72i\nGMLl5rjv3Zjmj9+/hyePjPIPh0exXBdFCKIhlZCm4HgSTVMIaYJItWaipyVKoeJUK3kFZyYrjOaM\nJbmM8hWrKiMp2d2T4j27ulZNkKcrFUEIqNgOYU2hKxVhT18zL52ZZjRv0BLV/ToCz6O1KcS56RL5\nst/vtufnyLfF/aBye1OYmaLJaK6Cqgh296Ya3t5rHfXjcKw6FmwvCKVDLKxy64YkP3xzO3/7g0Gy\nFV9etCsZIRHRKFk2YV1lU0sM05XEdXXdRbQKaMiCIIT4e+AFfCK7/VJKa4lL1izmixXUm/RBALE3\nHePkeJ6vHhhmW0fTvBPX3o1pPnjvJr68f4hERGVopsLP3dXHQzs6ZvGtBDnuuYrN5tY4D2xv54Ht\n7TXeovrPDq5drpukETnuezem2bsxzSO7ujg8nMOwHMYLJj+9p4dEVWy+ULE5MV7g5s4E/e1NtcBx\nwN0UMKHOV7FbH4B/795eUrHQqpv+tiu5rSc1q13dzVF+690316qT690Q43mDJ14f4Znj45iOJB3T\n+bV3bqux1o7nDQ4P5+iuxhUCa2zdjeGjFsQP64Q0hZ+5o4cTYwWmSybb2puYKlr81N4ePnjvJu7f\n1sZzp6ZoiYd4aEdHrW/rx0bwmet921g0quT1z4G3A78P7BZCHOfCAvGilHK8Qd+zqlhsNx3kqY9k\nKzx7cpKT43neOJ/jyHAOVVXY1ZOaxUcUYE9fM8+enMRxPXZ0JXmoKsgzl+Mo+O7XBjMcPp/jsYdv\n4pFdXRe1caVcOI0MWAZUEQGfzzMnJvjj9++hMxmp9dtrQ1ke2N4+i1IguP+FUjGDALzjehwdvTKV\nukFacH27ZrfD52IKXFJBe545MYGmQtl26UpFan0SxDSCfghkL5eyJG8UBPxcAafVXZvS9DRHScdC\nnJ4sAr71+9COjtoGBBYfGzdyf64WGuUy+jbwbajJau4F3gn8EdCPL1m6pnFwMMMXXxnk1HiBvRvT\njFa5eHb3phjN+RIPQVbJj+zo4KUz04zlfP9mxXJ4a7LIoaHsRYN0sYBhvTVSv5M/OZ5n39HxhhTY\nLDdgOdcyms9SGslWeOL1Ed+nG1Ypmw7PnZoiFdU5N1UkrGuYtlPrtyC1dC4n0HKZRS8Xi7Gj1lsl\nQbteOTszqx1PH5/A9WTNWjg8nCMe0jBsl2RU5+hIfpY1Uc+lE2hg3EippHNR/zsHKdZTBRPbdTFs\nD8P2Y0sCSX9bE67nXdRPN3IV99VAw0hxhBBt+FbC24F7gQjwT8BLjfqO1cK+N8f4nb9/g1zFwvF8\nxk5NURjPG3z6qVNIKaupiQptTSEGZ8psao0xljfIlCyfk6hs87c/OAdwkftovl39XGvkA3f3oalK\nzfKYKVm8OjBzkdTlpWAhq6K+GjnYhQVtqX/92MM3AfCpp05xcizPdNFipuQHkv/mpQGa4zpnJsuo\nisD1JKbj8ZcvnmVTSwzLlSgCVEXhuVOTvPPmDn5wZpqQpmA6Ho/s6qIrFWl42uVS7KhzrQGAqaJJ\ntmwzVczieh7/3zOna9dvaY8znjc4NVFC4AfQz04WCWkqqiLob4vjeBdcRfUaGDeSn3u+MZUr2+Qr\nFifHi7XzXj47gwA0BRwPxvIm7dWam7mfc6Ol5F5NNCqGcArIAV8Dvgf8NyllcfGr1gZGshX+17Nv\nkatYgEBX/fzmiArHR32qBEXxM05CisJU0cRyPczxIptaYn5g1ZFoquD0RIkv7x/i2ZOTS7oI5u58\nbFfy2MM38dUDwxwZzpGvOIzlc/NaHY2672BBypRtwpqYt+gr2JUBNfdKSFOIaAo5w2GyaDFd8oPB\nzTGdfMXPqjJtl4HpMgXDpmS5dCbCTJUszmcrlC2Xt/W3cOR8joJhsyEVXTbvz3JxeDiH68maetlc\ndtS5FtnjT58mrAlOjhfobo6QKdtkyxaxkEa2YlOyLqT7VpPGyBkOLTGldrw7FcWTklu6Urzvzt7a\n99wofu75xlQiovPS2WlM+2ICAgl40s8uiupqjQ14vs1SI8fGOhZGoyyEv8C3Cn4W2AXcJoR4CTgo\npVzTVBTDmQrJiFZlUfQn/0RIRVMFBfNC013Pw/DAkx6q8HfCQgi2tSd4YyRHtuhnPCQjKifG8nzi\nyaPct6WNB6sxg7mYz7ff3RxlW0eTTw53Be47mBDLVh7TkQxnymTLNkMzZU6NFzg0lEVXBF9Vhrhz\nYxpNVRDVzCGnOjmqCn4hlyexHT+v3HY8HFfien5GEoBheyBhQzLCmakSJ6oUD1vbm8hV9R4aKSdZ\nr16mKn4G1OdfHKi5j+r5g2xXkopq1bRXj4iuoSkOtivJVWwkYDoO9hzWcYGv/Sugdp+aqrCto+mG\n8nMHu/mgeth0XN4c8TcVjuvzQcVDKnnz4qnAlX7ue1hXahxV822WGi01uo750agYQo1XUAixHd9t\n9CHgASHEpJTyhxvxPauB3nSUREQnGlIxHY+wpmC6fjWx50FI9dMgo7qGEJA3PHQFEmGt5kr57a9n\nsR2PiuXyvTfHcFw4Mpzj6WMTPHNigv/6U7ctO7awp6+ZXT0pCoZDIqLVKjVX477rqas/cHcfb47k\n+d/PvsXrQ1mmSxYKPrHU2akSTx4Z5f/+8Vvpv6OXXNniidfP8+0jY1TXAGIhlZamEIWKQyKqEw+r\nnJvx+YcUAa1Nup+aqvsB+Hu3tPKDagrtargCgnTZw8M5IprCn37/rVnuCp6U6QAAIABJREFUo85k\npMYfFNUVVEVhumiSNxxOjfsLpFNnEThzKJPCquDmDQmEECQiGhFNxfY8khF91X6ztYj63bzpeIzl\nKrw2mGVu7eR8i4GuQF9LjHfd2sntfelZrtYb2U20ENUFrD7dRUOJ9YUQW4B7gLfhWwztwJlGfkej\n0d0c5T27uhicKZOK6tiuR8l06UlHGcv58n05w3cn2dUFIxJS+dADW9nZk+LPnzsDSOJhjaJl+zse\nhWpVsmCyaC4YCJvPt9/dHOVjj9yy6q6G+RYk383i4XoSCaiqwHX9v03b47lTUzx6j88nlYqFODaW\nZzRnoisKIU2hpznGWadIX7VYq79N0JeOM1U0eHBH56xAc3dzdFb67WrcZ5Ct8vkXBy5yH93SJWfx\nB/W3xTk6kmdza5y2pjCnJwp+cWBYJV9x/P4Q/o42HlLZ1ZPkF97eX0s7hRvLPRRg7m5e1xTmY+hT\ngJAmaI2HMB0PhOBHbungNx7evqJEjHWsLhoVQ/gG/gKQww8ivwA8Ppeobq1iT18zG1tiHD6fw/Ek\n0ZBKOqaTN2zScZ32RJiZksWh4SxS+tWSnYkwn3rqFOO5CiXLwbIl9ZtIgURTJO11E8ZysdLU0kvF\nRemvqYgvc1ndDjvVmgGr+v9LZ6Y4OJhh78Y0uiowbF/sxnR8yo0ak6nlM5kmIxoSSWcqOm/G1JW6\nz7nuoyArKNiF5so2X94/hOW4TJdsWuN+5bErJUXzgp8oKKHQFMF4wZyVdhrcz42GuUWbJcNhIf5Z\nCZiupOJ4pKM6xXmshgBXamysYzYaZSF8DviQlHJqpRcKIbrxU1ZvBZrmCO9cEdSLdMCF9NJ6ds7n\nTk3VAocSybGxAmM5g63tTWTLNqM5ww82Oy5hVSWkC+7e1MovP9C/5gb2QkR9qViIXT0pKrZLvmIT\n0VXGq9QBYU1BVUQtOBsUdgkhmC6a/MTtPWzvTCyLyfRKo959FBSOAbX04ZCqcHKiQEtTmJxhsyEV\nIaKrdBo2uqoQDalsaYtzcCiL40l29zYvSxbzesJCY6Z+N39qvMDpySItMZ2S5auepaIhypbL9g1N\nNEdDtCfCnJsu11h719NI1xYaFUP41kLHhBAbliCtm8GX5PxGI9pyqagvPAtEO1riIXRF1GQtcxXb\nT5FUFJ47OcHpyRInxvLs7E7R2hTm+Fge03YRCqhCwZUeX9o/tCDL55VC8DDnyhbHxgocHs76ZHlz\nCqYCauGK5VC2XXb3pJguKlV+IklTxN9dj2T9AKKqKIQ1hc1tTbWCu7m4Uve9UN1E8F5nMkJ7wuAv\nXxygaDqEVMFY3kT8/+2deZhcVZ33P99aekk6G9kghC3soEhCBIOAgIii4ygO6rxuL6/6Oo7bqI/z\nPG6vo+OMODLOOM4rA24PjsM7jsqAioAsIbIkEEISEgJhSwLZu9Pp9FrVtf3eP865ldvd1fut7mpy\nP89TT906Vfecc0+de8/y24QXgBs9vb0IIcGMhhQLZtSXnfJ98vLDqrcjCYv5SqKSwWZgPdyQcg4N\nG1IJbt2wi+0tXeS9qvH0BhfLYF9HlqNnNpRlVb94fGfsdqJGmYjgvD8BBpWEmFkWyA4RY2HC2HMo\nw3V3PsP6l9s4lMkzoz5FqeRUSudMry+H5+vK5ln5bDMJIJMvsvT42bxr2WJWbm3mtg276MkV6MwU\nOGZW46TPgoKbeX97hnUvtVHvb+A/efUxAwKV723PUjIjmUhgViwL0tOpBEmJk+c5t9vBw0HAG89c\nWDV/QyMlXywNeGCF6xlYDe9q62Hz7g6mpZ0NxPT6FKctnMHOg93MmpYmmYBEl7Mubkwn+fBFJw1Q\ndzwS97b7ywke2NrMjx/eRk9vkdbuHLMbU3TnijSkkySTzoFj0cdERuLLV53Zxx1J7Jm0dqm6fqOZ\njUssLuljktZJWtfS0hJVtSqyqy1DRzZPKiFSch275GUGndk8iUSCN521kKI5PfT6VNJHeHIz4dMW\nzuDEudNZsWQeyWRigLfOySC4mQ0XiWrO9Dow2Lq/s2LdUj7mcSIh9nVmMJyq6KLZjSST6mOjUJdK\nlMMZTia5QqmPN9pdbZk+D7GObJ7ObIGGdBKwsuaTGextz2CIRbMaOHvRbBrSSY6bM526VKKs7th/\ni+RI86rZX0W6tdtFknPR64ykNyZLJUTBaRfTmE4yd3oddUkX/7qSl9ojqQ2nClEJlYdUEh4qHsJw\nTGSAnMVzGpnZkKZQMgrmwiQ21iU5ce40EokEn778FBbNbuTiU+fxq3U7yRVKzJ1ex8Wnziufn0om\naM/kq+6tczTXFNgPJCR6egscNT3Ne847boCNRFjl9cR507n09AWseraZ55u7yJeKzGxI9wlMM9mD\nXUCwrdO/TmW1Wm81nOh1g3g66eIcfOqyU8gWSiya1cB9W5vL3la7c/kjaktoOPpr/ezvyLqVcG8R\n4TTRkglxyoIm8oUSvcUSB7tz5EvGjIZU3I5TiKi2jJ7ATQwq7fsYsCSicqpKWLgcyBACAXN4ebv0\n+Dl8/38sHeAnpxbV5cJ1au/Jsac9W9G3T/Db/iqvl5+xYIDX1Vpb8qf9NlH/OoXTwK2W3nd+5TY4\n2wf8CQvFa+HaaoWw1s+i2Y19bDyCQTXsibSSp96Y2icqofKo4yAESEoDdwGvAf4g6ctm9lgU9RoL\nI1V3C3tkHMv5E8lo6lTJE+tkqYuOhpHUc6g61+I11TKD9f+AuC0nlqjiNkcuVJY0BzgV59wOADN7\ncLDfm1keuCLqesTExMTEjI6oLZU/CvwVsBjYiDNWWwNcHmU5UTCYXvUrmSPxmmuFWmj7WqhDTG0T\n9Qrhr3DxkR81s8sknQF8I+Iyxs14w0pORY7Ea64VaqHta6EOMbVP1GqnWW9XgKR6M9sKnB5xGSNi\nqMDyYZXEQE3xlU4fNcxMjnuf3l+xbWLGR6V+Vwv9LajDrMY0+9qzZaFvTEyYqFcIuyTNBm4H7pXU\nBuyJuIxhGW42FGVYyalCcM3P7e9gq3c9HYSJjGeK0TBYv6uF/rZ4TiO9hRL3b20G4K7Ne2MNoCnI\nUJ5QoyDSAcHMrvaHX5f0ADALp0EUOZVCPgaznrbuHPvasyycWc++9gwrtzZz2sIZ5d8GqphH0ixp\n0WwXhOZ3T+5hX3uWnlyJTC5TtlS+d8s+1mxrZcWSubzp7KPL58X7ziOnv0VvOARnJbXYcNsG5w+n\nGlyJ/vlU+m57SxetXb0UiyVOXTiDzmw+sjCtU5WoNHOGymuqEbVQ+edm9kEAM/tjkAZ8MMpyAhcT\nHdk8MxvSfPiik/jpw9tZu+MgPb0FhKivS7LupQIz6lNsa+nmVcfOZGZjXdkPy0PPH2Dt9laSCXHX\n5r2RhKqcTMIhB8N69OH0nz68nW0tXTyzr5OkRCop3n9Bjnu37ONzv9xIyYxfrtvJP7/nXN509tED\n2nkqt1FUA9tQ+YRXAu09+XIIzrpUomwEl06q/L+EXWu09eTY355l16EMc6alqU8nB4T9HKw+4VVJ\n/+++dvtTbNrVRnNXvpy+rz1LOpVAUrxKHCWvlAf/YES9ZXR2+IOkJHBexGWwcechNu1uZ1o6yY7W\nHh56/gA723po7eotBzKZnYDGVIIT501nz6EM0+vTFIolVm5t5icPb6Mjk+dQpsDi2Y0gqhaqciII\nHgodmRxb93VyxtEz+jgSKxRL7G3P8lJrNz25og9eYhSLxtrtBymaObcW0+po68mxZlsrbzr76AHt\nPFXbKCqB6nD5hI0A792yj9XbWpmWTtLSmeW6u7YyszFVDtCTL1p5NfHgc808s68TDLKFEgtSCYol\nGxD2sxL9VyVhVm5t5qEXWugt9DXwL3mXLI11yQH+rGImjlocXKJyXfEl4MtAo6QODlss5/BuJ6Ji\nw8tt3Lp+F61dvXQmE+SLJTbtbGN/e9aFcvR09xZIpxLsPthNe7bIEy+1AuKpXYc40NlLU32SUsk4\n0JUlIfHgc81Tdk/1tvW7WPfSQRY01dOZKfDc/i4a0wl+lMnz8sEeegtFDmXydGYK5H0jmffH9MTL\nBznr6JkUS0ZzRy+SkckV2PByWzn/fLFENl+krTs3WZc4Lvo/NMfyANxzKMO9T+/n+f2dlMxoTCe5\nbf0uWrtzrFgyl20tXTzwbAvnHT+bN5yxkFnT0uTyRTK5IgKSiSK9+QTZQpEfPbiNq5cey6GePC80\nN9ORzWNmpBJuht/WlWPujHoWzWpg7faDg24zVZJPhH/z+PbWAYMBUI5XsONAN0vmNx0RMrSYkRFl\nCM3rJF1nZl+KIs9K9OSKfOKWJ9jX3uuDMrkAGyu3tpDsF6gpX4J8rkRPrheAjmzfMAtBvOTunLs9\nfrNxDwe6chXDXdYy927Zxw9WvUBvocS25m4MONidowS82NJFJl/Z/VOQunlXB8/s7cQMEgkjX4SV\nz7bw6PaDfPmqMzl1QRObd7eTTohHt7UOGiO6lhmvUDdYGTy3r4ONO9sJHPM+8dJBUskEP1+zg7x/\nyj66/SB/fP4A2XyR4FlsuH7W7fviI88fYMPONoolN3MqlnxAopLRkErwttcs4vLTF3Df1uZBPbiG\nVyhh+cTdf1cqe7d9+PnBw5MUfPS/yfcxHFNLRL1l9BVJHwBOMrNvSjoOOMbM1kaReSZXpC5XJJUQ\n+VIQEdfNeAQ01SfJF0skJOfy2Bg0epNwbo5zxRIJuSDpQ4W7rEX2HMpw24bdmMG8pnoOdPaWPU12\n54q4qxzGH6CgVDLMYM70Og505WhIJSmWjD3tWd61bDH5YomT5zdNuivvsTJeH1PBCiOIE1yXdO6z\niyWY2ZCiNedWTqmEi73c1pOjy09Awv9A4PcnkYSe3iKJhDht4Qx2HOjmuKOmsXj2NCS4euligD6r\nmo07D9HS2UtHJsdpC2f2WemE3W4Enl+zhRJDxe9JJsXJC2ZgZlPyP42pDlEPCD/APYMvB74JdPm0\n10aReWNdksa6ZJ/ZftDniyXo7i0yd3odErQWckM+CgU0pN2A4LZPxhbucrIIYgAc7O4lXzQ6MnlS\nSSFEoeR80SeGmP4l5PaRiyWjiHvIFYuGENlCkWl1yXKoyT8+1zLlA5qMx1dRsMJwbsTdPj+49mvL\nHO5ngfxqzrQ6Zjakad/XWf4unYCSuU89vUWUEDPTafa2Z6hLJTjhqOnMnpau6K01Vyhx5+a9FEul\nssrwYN5Y61JusNpxoHvI/j+tLkl3b+zVNaYvUQ8IF5jZMkkbAMysTVJdVJlPq0tyw/vP487Ne+nK\nFujOFVjzYisd2QKpBKQSCa5edixLj5/DtpYu2jN59ndkeWZvBzMbUuw6lKFUguPmNFKXSnDhKfNJ\nCNozeU6cO31KbYcEM8ELljjX20dNr+fqpccCsGZbKwtn1LO/s5c7N7utsGRCCFg4q4Hlx80hUyjx\n8sFumurT9BaKXHHmQpoa0uVZbFjtsdY8uE40wQrjxw9tY097lrqUyPaWmN2UYnZjPY3pBEvmN7Gt\npbssQ1g8p5Fb1uzggedauOy0+Zx7/Bxu27Cblw/2MK+pHskFFwrUUit5kA3a/UBXL/c/s5/Fc1yA\notctmTeoumg6meCtrz6Gzmye5SfO4aHnWmjP9o1dvPyE2XzlbWfFXl0HoRaFvROFzKILMSDpMeBC\n4HE/MMwH7jGzpRHl3wJ0A25zNJlOp2bOP17phhkAWKlQaN+/zXKZw+oWyXQ61TR3EUKUd3/NMKzQ\n1bqHYj5P9ZhXrmvkaFlq1sJWhAa9lgrtkz+4u41ifnefdhlbW0R1bVHkswxYH1Feg6K6xmmpWQtO\nCvqRFXIZrFQccfuNrc3nkUy3j+K8ZSTTm8u/VyKpVP10JRIpACsV84X2/S/2uUcGK7eKbTlBZQX9\notpMVFuNp5wTzGz+cD+KeoXwfVxs5AWS/h64BvhqVJmb2XxJ68xseVR5VpNarGtUdaqlfII8arG9\nx4ukdVbITfg1TWRbTvX/baLqPxHlRG2pfIukJ4A34rbp32lmz0RZRkxMTExMdYjKDqEB+DhwCrAZ\nuMnMCkOfFRMTExNTS0Tl7fRnwHLcYHAV8I8R5VuJSA3dqkwt1jWqOtVSPj/s9/5KYrKuaSLLner/\n20TVv+rlRCJUlrTZzF7tj1PAWjNbNu6MY2JiYmImjKhWCGWNh3irKCYmJmZqEtUKoYhTBwUnTG4E\nevyxmdnMcRcSExMTE1NVIrVDqAaSzsPFZp4DHMKF51w3ubU6spB0jJntlSTgHcCZwHbg1xO9IpSU\nBt4CtJrZau8qZRZwi5m94gJcSPqkmf1gsusRMzSSXmtmj092PcZLTQ8Ikv4ZqAfuA9qBmcAVQNHM\nPjOZdRuMWh3AJL0KeBXw4mg7rqSVZna5pH8BMsBK4FxguZm9ZxT5/Clwn5kNZxQ1VB63AY8Ds4FL\ngD8CTwPvM7M3S7rAzB4ba/6TiaSHOOyNJXA8cjbwlJldMjm1qg6SpuPvETPrmuz6jBRJlbbZBdxt\nZm+KuKyzcc+6raG0qvbvWh8QHqx0IwyWPtnU2gAm6W4ze4ukz+JsQ34PvB7YbWZfHEU+95nZFcF7\nKP0BM7tsFPnsAV4C9uMMGH9rZm1DnzUgjwfM7DJJ3wU+ggvXOheYY2YXBYPXaPKsFSR9HjgHuNnM\nVvm0u8zsqiqX+1kz+56k1wD/ihuUUsAXzeyhiMu6HPg/QId/zQRmAN8ys/uiLKsaSOoBHqWv30IB\n55jZ3AjL+S6wECjg+veHzayl2v07akvlqFkn6UbcAzboPG9kYszRx8J5FQaq2yQ9OCm1gcCP1NXA\nZWZWAm6U9PAo8/mZpB8DOyX9B25Wfg4w2pXPs/5hfhLwLlzb9AK/MbMbRphHt6Sv4qzgH8epOk8D\nPicpEieKk4WZ/ZP3/fVRSR8H/t8EFf2nwPeA63EPnhckzQN+g5tARMnfAleGV4l+tXAP7j6vdZ4B\nrjaz9nCipHsjLme5mb3B530O8CtJfx1xGQOo6QHBzD4vaSmwAjgNtwXzQzPbMLk1G5RaG8DOkvTv\nwMm4lUvGpzeMJhMz+7mk+4E342YtKeDHZvbkWCplZtuB7wLflbQQJ5cYKe/GyRAOAn8GvA+nwLAU\nuIF+UfumGmaWA26Q9ENc6NkxtfEoOcrP3I8ysxd8PQ5Iqsb2QS/waiC87fFqIFuFsqrBn3D4PgoT\n9SouJanOzHJmtknS1cB/UOX+XdNbRlOR0AA2i8MyhEkZwCSdEPq4x8zykpqAi83srkmoz5vN7A8R\n5XU+sMPMmkNpSeDdZvaLKMo4UpD0N6GP/2JmhyTNAK43s49HXNYxwBdxK8wELsrVJl/W7ijLmspM\nVv+OB4SI8ULlFTiBWRs1IlSOiYmJGY6oDNNiKAuVPwzsAVYDu4H/Jen7k1qxmJgx4LXKJqqs+B6p\nAeIVQoRMNa2omJiAQVQcX2dmj1axzDGrQsdUh3iFEC3rJN0o6RpJV/r3f6PKQmVJX5G0RdImSRsl\nXRBh3pdKuqNC+omSnoqqnEHK/qykaaHPI9ZXn6Q2uU3SO0Ofn/UaUcHnWyW9S9LywWbEknZImidp\ntqRPDFdmFHgVxy8BX5T0O7nAVgDfqkJZd/v3zwLX4exJPiPpuqjLGqIORd8nnpL0q3AfG+T3Xx5h\nvju8dlY47R2Sbg99/pKkF0Kf3y7pt/74TkmzK+T7dUlf8MfXSlo0VJnjIR4QIsTMPg/cBCzAeX9d\ngPNQeEu1ypS0Aqf5sMzMzsHZPeysVnkTzGdxKqWjYhLbZDUuYiCS5uJiiq8Ifb8CWG1m60ZglzIb\n+MQwv4mK5Wb2ATO7FvgKTsWxWiq8YVXod5jZjWb2QeDiKpVXiYyZnWtmrwJyONf9QzGiAWEQVjOw\nD3RIWuA/Xwg8AmBmbx2Btf21wKJhfjNm4gEhQuSsGJ8EbgS+7d83UYWZVohjgANm1gtOXdDM9kg6\nT9IfJT0h6Q9euwNJqyR9T9JqP0M636ef79M2+PfTx1IZSSdLutuX+5CkM3z6zZK+7/PeJukan56Q\ndIOfzd/hZ0nXSPoMruM/IOmBUP5/L+lJSY/KqazWUps8gh8Q/PsdwHw5TsI9iPaFZ/uS5kq6x5dx\nE4ctlL8NnOxnstf7tCZJv5a0VdItUhASdtyk5GOfm9km3MP661RHxbG/KnTAqFShI+QhXBwXJH1A\n0lrf5jdJSkr6NtDo027xv7vd96Etkj42VOZm1gK0SzrFJx0L3ErffrLa51ue7cutcJ+VdB9wuk+7\nBjfRvMXXJwiG/WlJ6yVtDu63MWNm8SuiF04ffmW/1wM4vzvVKrMJ2Ag8h9PDfwOQ9p1svv/Ne4Gf\n+uNVwI/88SU4twjgbCZS/vgK4FZ/fClwR4VyTwzO7Zd+P3CqP74AWOmPbwZ+hZuEnAW84NOvAe70\n6UfjNLOu8d/tAOaF8jbg7f74O8BXa6xN6nGqxnW47ZC3AD/31/t+4N/7n48LO/s1f/w2f43z+rev\nP6cdWOzbag1wUUR96HxgQb+0JPDnVeivJ4Re6dD/dVW1789QHbr8ewpnfPeXOP9cvwvV6QbgQ+Hf\nh84/yr83Ak8Bcyv119DvbwY+hHuw/wJnm/QdX34b0BA+HziPwwaXM4EXgC+E+uryUN47gE/740/g\n7IPG3DY1bZg2BZkoK8YyZtYlp+p6MXAZ8F/A3+GEdff6SWQS2Bs67T/9uQ9Kmun3LWfgLJJPxT2U\n0qOti5yNw4W4LYcgOTwLvN2ctfTTodn9RcCvfPq+8GqgAjncrBvgCaCi75jJahMz65W0BRfc/XW4\nm34Jrk2W4meC/bgEZ7WNmf1e0lCuPNaa2S4ASRtxg8Zorc4r1XtthbQi7uEVKWb2UoW0LmAi7WIa\nffuBWyH8BPgY7kH8uO8fjUBz5dP5jJyhGMBxwKlA6xDlBSvHJG4gXwt8DdcnnjWz/kZ5FwO3mbfm\nlpcxDMF/+/cn8H1prMQDQrRMlBVjH/zNuwpYJWkz8Elgi5mtGOyUCp+/CTxgZldLOtHnN1oSOGdl\n5w7yfW/oWP3eR0Le/FQIZ9A0aP+dxDZZjXvIzzCzNkmPAp/C3fw3jrDswQi335DXHzMkmf591G+/\n/czMvjTUiZIuxa0WV5hZj6RVDL/dtRr4NG5A+JGZdcqFHb4ULz+owGjUP4N+Me4+EcsQIsTM9ppz\nPdA/vWouoiWd7mewAefiVirz5YSrSErLqRUGvNenXwS0+xXNLJzdBDjB1agxsw5gu6R3+/wl5zBt\nKB4G/szLEhbibpKATtwsfVRMcps8AvwFh11ObMKtFo4HtlT4/YO47SQkXYUzaIQxXnvMmLkfuEZe\n2CvpKB229M/LuV0H1yfa/GBwBu6/HY6ncfKwi4HAa8FGnDC70qrxQeBqSY1yFuNvD31X1X4RDwhT\nnybctsbTkjbh9qu/htub/wdJT+I634Whc9okrcbNWD/i074DXCfpEdxMZgBy6pI/DiWdLmlX6PVu\n3MPtI77cLQzvp+hWYBduL/YmnI+bYMvth8Bdw2wjVWIy22Q1bptoDZQnA83AOr8t1p9vAJdIWg9c\nCbzsz2sFHpETcl9f4bxh0QSqV/r0VZKWj6WuIyz3UkkXhj7f7AWt48bMnga+Ctzj+8y9OOUEcP1w\nkxcq340Twm/CrSAr2mnIKUcs8nkbrl8fMLMguuQaXD8ZMCCY2XrcNudG3P0R9jh7M85BZVioHBmx\nYdoRhl/ifsFqyJ2GpCa/7z8Xt7/6ejPbN4Hlr6LG2iQKJHWZWZM/vgV4wsz+aSS/HybfHTjB5oF+\n6auoYjtK+jpOwPuP/vPNOOH8r6tR3pHIlF0hqIJhlEIGHLXKSOsob8Aip3r25omo2yRyhxfyPQR8\ncyIHgyOIqqpXDobP+3pJj8sZCf6FT7/UrygGqNFKeqtPe1hOVfkOORnOx3FuzjdKCuwWLlE/VeaY\nsRMLpWoQSWcBf47TA18E3CfpNC8oHRdmdul484iaya7TZJdfbSSlcIoNd0s6Eycveb0577c3AO83\nsy9K+lQ/YeuHzeyg35p4XNKtfitrNHwEJ5N5raR63DbYPf67pbg+vgcne3m9pHW4rcNLzGy7pED7\na4eca/nwCuEjuG2di4AzgN8C8WphHEzZFcJw+NnHP/iZ0HPBjELO9Pu/5Yynnpf0ndA5/yZpnZ8R\nfSOUvkPStySt8d8vkzNselEukEnwu78OzYTC5w8wMhmGdwC/MLNec7EDXsDpisfEjIZAvXIdTjbx\nE5wOfKBeudF/XjLI+Z+Rk7c8ymH1ytFyJfAhX9ZjuOhfQT5rzWyXl60EarRnANt8vwevDjwEt5tZ\nycsABjNUjBkhr/QVQsrMzpf0VuBvcOpi4LROluLUtZ6V9K9mthP4ip8RJYH7JZ1jznITYKeZrZDz\naHozLpJUA05weqOkK3Ed/XycKuVvJV0CdONm+0tx7b0epy9MMJiYWX91xGPpK6za5dNiYkbDRKtX\nVswKZzjVJw6Gz7+SGu1ora8rqTLHjJGpvEIYTBoeTg8bbJwYSr/fzNq9QcjTOKtJgPfIaXtswC1l\nzwqdExiHbAYeM7NOc2bpWTkjpiv9awPuoX8GboAoG5l4tcyykYk5Py6VdNMrdexY+h8TBdVUr6zE\nH4C/DPKVdJpcyMzB2Aos8TID8OrAnlgVt8pM5RVCK4d1tgOOAraHPg9msDFgZiLna+YLwGu9QdHN\n9J0RBeeU+p1f4vDM5jozuylcITmvjqN9mO/CLdEDFuP2WWNixoWZPS3ngfUeOd9beZzR3kscVq9c\nj4vr8XE59cpnGUK9EviomQX98/eSwqqV78VNxtb71UkL8M4BGR2uX0bOy+vdkg7gtM4Cfgf8WtI7\ncIZeMVEzHr8Xk/3C7Y2+0R8fhfNdc7L18/mB8w+ywx9fC/zfUB534IyhXoMzJkrg9iL3A9daPx8l\nFc7f4fO/ErdH2uTTj8V5O12GM05qxM1unsf7JRnius72dakHTgInt973AAAAx0lEQVS2AcnJbu/4\nFb8m4hW6h4TzKfS5ya7TkfKayisEcA6jfiDnzx3gG2b24lgyMrMnJW3AyQS2MbhJ+WDn3+M1ONZ4\n7bku4ANmtl5SYGTyEiEjk8FkCGa2RdIvcdtZBeCTFoGGUUzMFOF/S/qfOCeBG3BaRzETQGyYFhMT\nExMDTG2hckxMTExMhMQDQkxMTEwMEA8IMTExMTGeeECIiYmJiQHiASEmJiYmxhMPCDExMTExQDwg\nxMTExMR44gEhJiYmJgaA/w8k2pvBHL/2SwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cb05b8e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot matrix\n",
    "scatter_matrix(dataset)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Split-out validation dataset\n",
    "array = dataset.values\n",
    "X = array[:,0:4]\n",
    "Y = array[:,4]\n",
    "validation_size = 0.20\n",
    "seed = 7\n",
    "X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Test options and evaluation metric\n",
    "seed = 7\n",
    "scoring = 'accuracy'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Unknown label type: 'unknown'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mValueError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-39-129701fcdca3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m  \u001b[0mkfold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_selection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKFold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m  \u001b[0mcv_results\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_selection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkfold\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscoring\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m  \u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcv_results\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m  \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python2/lib/python2.7/site-packages/sklearn/model_selection/_validation.pyc\u001b[0m in \u001b[0;36mcross_val_score\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch)\u001b[0m\n\u001b[1;32m    138\u001b[0m                                               \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m                                               fit_params)\n\u001b[0;32m--> 140\u001b[0;31m                       for train, test in cv_iter)\n\u001b[0m\u001b[1;32m    141\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    756\u001b[0m             \u001b[0;31m# was dispatched. In particular this covers the edge\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    757\u001b[0m             \u001b[0;31m# case of Parallel used with an exhausted iterator.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 758\u001b[0;31m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    759\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    760\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    606\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 608\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    609\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    610\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    569\u001b[0m         \u001b[0mdispatch_timestamp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    570\u001b[0m         \u001b[0mcb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBatchCompletionCallBack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdispatch_timestamp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 571\u001b[0;31m         \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    572\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    573\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python2/lib/python2.7/site-packages/sklearn/externals/joblib/_parallel_backends.pyc\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    107\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 109\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    110\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python2/lib/python2.7/site-packages/sklearn/externals/joblib/_parallel_backends.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    324\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    325\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 326\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    327\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    328\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python2/lib/python2.7/site-packages/sklearn/model_selection/_validation.pyc\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, error_score)\u001b[0m\n\u001b[1;32m    236\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python2/lib/python2.7/site-packages/sklearn/linear_model/logistic.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m   1172\u001b[0m         X, y = check_X_y(X, y, accept_sparse='csr', dtype=np.float64,\n\u001b[1;32m   1173\u001b[0m                          order=\"C\")\n\u001b[0;32m-> 1174\u001b[0;31m         \u001b[0mcheck_classification_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1175\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1176\u001b[0m         \u001b[0mn_samples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_features\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python2/lib/python2.7/site-packages/sklearn/utils/multiclass.pyc\u001b[0m in \u001b[0;36mcheck_classification_targets\u001b[0;34m(y)\u001b[0m\n\u001b[1;32m    170\u001b[0m     if y_type not in ['binary', 'multiclass', 'multiclass-multioutput',\n\u001b[1;32m    171\u001b[0m             'multilabel-indicator', 'multilabel-sequences']:\n\u001b[0;32m--> 172\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Unknown label type: %r\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    173\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Unknown label type: 'unknown'"
     ]
    }
   ],
   "source": [
    "# Spot Check Algorithms\n",
    "models = []\n",
    "models.append(('LR', LogisticRegression()))\n",
    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('CART', DecisionTreeClassifier()))\n",
    "models.append(('NB', GaussianNB()))\n",
    "models.append(('SVM', SVC()))\n",
    "# evaluate each model in turn\n",
    "results = []\n",
    "names = []\n",
    "for name, model in models:\n",
    " kfold = model_selection.KFold(n_splits=10, random_state=seed)\n",
    " cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)\n",
    " results.append(cv_results)\n",
    " names.append(name)\n",
    " msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
    " print(msg)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-40-cab732b7538d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuptitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Algorithm Comparison'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m111\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xticklabels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python2/lib/python2.7/site-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mboxplot\u001b[0;34m(x, notch, sym, vert, whis, positions, widths, patch_artist, bootstrap, usermedians, conf_intervals, meanline, showmeans, showcaps, showbox, showfliers, boxprops, labels, flierprops, medianprops, meanprops, capprops, whiskerprops, manage_xticks, autorange, zorder, hold, data)\u001b[0m\n\u001b[1;32m   2783\u001b[0m                          \u001b[0mwhiskerprops\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwhiskerprops\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2784\u001b[0m                          \u001b[0mmanage_xticks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmanage_xticks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mautorange\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mautorange\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2785\u001b[0;31m                          zorder=zorder, data=data)\n\u001b[0m\u001b[1;32m   2786\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2787\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python2/lib/python2.7/site-packages/matplotlib/__init__.pyc\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1896\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m   1897\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1898\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1899\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1900\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python2/lib/python2.7/site-packages/matplotlib/axes/_axes.pyc\u001b[0m in \u001b[0;36mboxplot\u001b[0;34m(self, x, notch, sym, vert, whis, positions, widths, patch_artist, bootstrap, usermedians, conf_intervals, meanline, showmeans, showcaps, showbox, showfliers, boxprops, labels, flierprops, medianprops, meanprops, capprops, whiskerprops, manage_xticks, autorange, zorder)\u001b[0m\n\u001b[1;32m   3272\u001b[0m             \u001b[0mbootstrap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrcParams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'boxplot.bootstrap'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3273\u001b[0m         bxpstats = cbook.boxplot_stats(x, whis=whis, bootstrap=bootstrap,\n\u001b[0;32m-> 3274\u001b[0;31m                                        labels=labels, autorange=autorange)\n\u001b[0m\u001b[1;32m   3275\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnotch\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3276\u001b[0m             \u001b[0mnotch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrcParams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'boxplot.notch'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python2/lib/python2.7/site-packages/matplotlib/cbook.pyc\u001b[0m in \u001b[0;36mboxplot_stats\u001b[0;34m(X, whis, bootstrap, labels, autorange)\u001b[0m\n\u001b[1;32m   1992\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1993\u001b[0m     \u001b[0;31m# convert X to a list of lists\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1994\u001b[0;31m     \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_reshape_2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1995\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1996\u001b[0m     \u001b[0mncols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python2/lib/python2.7/site-packages/matplotlib/cbook.pyc\u001b[0m in \u001b[0;36m_reshape_2D\u001b[0;34m(X)\u001b[0m\n\u001b[1;32m   2257\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"input `X` must have 2 or fewer dimensions\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2259\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__len__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2260\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2261\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cb0a88950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare Algorithms\n",
    "fig = plt.figure()\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(results)\n",
    "ax.set_xticklabels(names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Unknown label type: 'unknown'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mValueError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-41-9cd1d5512918>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Make predictions on validation dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mknn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKNeighborsClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mknn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mknn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_validation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY_validation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python2/lib/python2.7/site-packages/sklearn/neighbors/base.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    773\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs_2d_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    774\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 775\u001b[0;31m         \u001b[0mcheck_classification_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    776\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    777\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python2/lib/python2.7/site-packages/sklearn/utils/multiclass.pyc\u001b[0m in \u001b[0;36mcheck_classification_targets\u001b[0;34m(y)\u001b[0m\n\u001b[1;32m    170\u001b[0m     if y_type not in ['binary', 'multiclass', 'multiclass-multioutput',\n\u001b[1;32m    171\u001b[0m             'multilabel-indicator', 'multilabel-sequences']:\n\u001b[0;32m--> 172\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Unknown label type: %r\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    173\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Unknown label type: 'unknown'"
     ]
    }
   ],
   "source": [
    "# Make predictions on validation dataset\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train, Y_train)\n",
    "predictions = knn.predict(X_validation)\n",
    "print(accuracy_score(Y_validation, predictions))\n",
    "print(confusion_matrix(Y_validation, predictions))\n",
    "print(classification_report(Y_validation, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}