Board game Review prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.7.15 |Anaconda, Inc.| (default, May  1 2018, 18:37:09) [MSC v.1500 64 bit (AMD64)]\n",
      "0.23.4\n",
      "2.2.3\n",
      "0.9.0\n",
      "0.19.2\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import pandas\n",
    "import matplotlib\n",
    "import seaborn\n",
    "import sklearn\n",
    "\n",
    "print(sys.version)\n",
    "print(pandas.__version__)\n",
    "print(matplotlib.__version__)\n",
    "print(seaborn.__version__)\n",
    "print(sklearn.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load the data\n",
    "games = pandas.read_csv(\"games.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([u'id', u'type', u'name', u'yearpublished', u'minplayers', u'maxplayers',\n",
      "       u'playingtime', u'minplaytime', u'maxplaytime', u'minage',\n",
      "       u'users_rated', u'average_rating', u'bayes_average_rating',\n",
      "       u'total_owners', u'total_traders', u'total_wanters', u'total_wishers',\n",
      "       u'total_comments', u'total_weights', u'average_weight'],\n",
      "      dtype='object')\n",
      "(81312, 20)\n"
     ]
    }
   ],
   "source": [
    "#Print the names of the columns in games\n",
    "print(games.columns)\n",
    "print games.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#make a histogram of all the ratings in the average_rating column\n",
    "plt.hist(games['average_rating'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id                             318\n",
      "type                     boardgame\n",
      "name                    Looney Leo\n",
      "yearpublished                    0\n",
      "minplayers                       0\n",
      "maxplayers                       0\n",
      "playingtime                      0\n",
      "minplaytime                      0\n",
      "maxplaytime                      0\n",
      "minage                           0\n",
      "users_rated                      0\n",
      "average_rating                   0\n",
      "bayes_average_rating             0\n",
      "total_owners                     0\n",
      "total_traders                    0\n",
      "total_wanters                    0\n",
      "total_wishers                    1\n",
      "total_comments                   0\n",
      "total_weights                    0\n",
      "average_weight                   0\n",
      "Name: 13048, dtype: object\n",
      "id                                  12333\n",
      "type                            boardgame\n",
      "name                    Twilight Struggle\n",
      "yearpublished                        2005\n",
      "minplayers                              2\n",
      "maxplayers                              2\n",
      "playingtime                           180\n",
      "minplaytime                           180\n",
      "maxplaytime                           180\n",
      "minage                                 13\n",
      "users_rated                         20113\n",
      "average_rating                    8.33774\n",
      "bayes_average_rating              8.22186\n",
      "total_owners                        26647\n",
      "total_traders                         372\n",
      "total_wanters                        1219\n",
      "total_wishers                        5865\n",
      "total_comments                       5347\n",
      "total_weights                        2562\n",
      "average_weight                     3.4785\n",
      "Name: 0, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#print the first row of all games with zero scores\n",
    "print(games[games[\"average_rating\"]==0].iloc[0])\n",
    "\n",
    "#print the first row of all games with greater than zero scores\n",
    "print(games[games[\"average_rating\"]>0].iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Remove any rows without user ratings\n",
    "games = games[games[\"users_rated\"]>0]\n",
    "\n",
    "#Remove any rows with missing values\n",
    "games = games.dropna(axis=0)\n",
    "\n",
    "#make histogram of all average rating\n",
    "plt.hist(games['average_rating'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr0AAAJwCAYAAACEUtmyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzs3XucXWV97/HPNyEQIAgKaL2goQgiN4MJQRQRLXq8UDgqFgWrUY5oFTnVUg9apRw8tVY9pSpqiRxAkaKiFUE5guUiglwSIJCEi3oAK8WiKCB3SOZ3/tgrM5txbgkzazJ7Pu/Xa79mXZ71/J61Bya/+c2zn5WqQpIkSeplMyZ7AJIkSdJEM+mVJElSzzPplSRJUs8z6ZUkSVLPM+mVJElSzzPplSRJUs8z6ZUkSdJ6Jcmrk9yc5OdJjh7i/LOTXJTk2iTXJ3ntqH26Tq8kSZLWF0lmAj8FXgncDiwB3lJVN3S1WQxcW1VfSrITcG5VzR2pXyu9kiRJWp8sBH5eVbdU1aPA14EDB7Up4EnN9ubAHaN1usG4DlHrvcfuuqW10v6eu76trVD8+pF7Wov1Rxs9ubVYAPevfri1WH0t/uVnRtJarAUbb9NarAvuvam1WABf3WjX1mK95u5LW4u1zWZbtRbr9vvuai3W/K22by3WzLRb17ru7ltbizVrxszWYvXR7l/E773//7X3w3EYbeQKG2693buBw7sOLa6qxc32M4Ffdp27HdhzUBfHAucneT+wKbDfaDFNeiVJktSqJsFdPMzpoRL/wYn4W4BTq+p/J9kLOC3JLlXVN1xMpzdIkiRpfXI70P0numfxh9MXDgO+CVBVlwOzgRH/RGTSK0mSpAF9qyf+NbIlwPZJtk2yIfBm4OxBbf4d+BOAJM+nk/T+ZqROTXolSZK03qiqVcARwHnAjcA3q2plkuOSHNA0+yvgXUmuA84AFtUoS5I5p1eSJEkDhp8W294Qqs4Fzh107Jiu7RuAl6xNn1Z6JUmS1POs9EqSJGlA3+RXeieCSa8kSZL6jbDq15Tm9AZJkiT1PCu9kiRJGtCj0xus9E5BSX4yzPFTkxzU9ngkSZLWd1Z6p6CqevFkj0GSJPWoHp3Ta9I7BSW5v6rmJAnweeAVwK0M/axqSZKkac/pDVPb64HnAbsC7wKGrAAnOTzJ0iRLT/rqGW2OT5IkTTWT/xjiCWGld2rbBzijqlYDdyS5cKhGVbUYWAzw2F23jPiIPkmSpF5k0jv1mcRKkqTx06Nzep3eMLVdArw5ycwkTwdePtkDkiRJWh9Z6Z3avkPnQ2zLgZ8CP5rc4UiSpCmvR9fpNemdgqpqTvO1gCMmeTiSJEnrPZNeSZIk9Svn9EqSJElTk5VeSZIkDejROb1WeiVJktTzrPRKkiRpgHN6JUmSpKnJSq8kSZIG9K2e7BFMCJPeaWbPXd/WWqwrl3+1tVht3lfb5sycPdlDmPI2zczWYu0455mtxQL42garWov1vo1f2lqsG1ff01qsnTd9Vmux2vRYy3+i3vMp27cWazXt3dvzNnhya7E0sUx6JUmSNKBH5/Sa9EqSJGmAS5ZJkiRJU5OVXkmSJA3o0ekNVnolSZLU86z0SpIkaYBzeiVJkqSpyUqvJEmS+lX15sMprPRKkiSp51nplSRJ0gBXb9Bwkuyb5HvDnLstyVbN9k9G6ef+JziO/liSJEkaYKV3kCQza4Ims1TViyeiX0mSpHHj6g3rnyQfT/Lfu/b/LsmRSf46yZIk1yf5n13nz0pydZKVSQ7vOn5/kuOSXAns1VRM/yHJVc3ruU27U5Mc1H1d13CelOQ7SW5I8s9J/uC9XdM+ydOTXJJkWZIVSV466B6uS3JFkqc1x7ZO8u3mnpYkeUlzfMsk5ye5NsmJQMbhbZUkSeo5UzrpBf4P8HaAJsl8M3AnsD2wEJgHzE+yT9P+nVU1H1gAHJlky+b4psCKqtqzqi5tjv2+qhYCJwD/NIaxLAT+CtgV2A54wwhtDwHOq6p5wAuAZV3juKKqXgBcAryrOf5Z4Piq2gN4I3BSc/xvgUuranfgbODZQwVLcniSpUmW3vXgf47hViRJ0rRVfRP/mgRTenpDVd2W5LdJdgeeBlwL7AG8qtkGmEMnCb6ETqL7+ub4Ns3x3wKrgW8P6v6Mrq/Hj2E4V1XVLQBJzgD2Br41TNslwMlJZgFnVdWapPdRYM3c4KuBVzbb+wE7Jf2F3Ccl2QzYhya5rqrvJ7l7qGBVtRhYDPDCp+9dY7gXSZKknjKlk97GScAi4I+Ak4E/Af6+qk7sbpRkXzrJ415V9WCSi4HZzemHh5jHW0Nsr6KpjqeTgW44TPuh9gdOVF3SVJ9fB5yW5NNV9VXgsapac91qBr4/M5pxPzTonkaMI0mStNb6XKd3ffUd4NV0KrznNa93JpkDkOSZSZ4KbA7c3SS8OwIvGqXfg7u+Xt5s3wbMb7YPBGZ1tV+YZNtmmsXBwKUMI8lzgF9X1ZfpTNF44ShjOR84ouv6ec3mJcChzbHXAE8epR9JkqRpacpXeqvq0SQXAfc01drzkzwfuLyphN4PvBX4AfCeJNcDNwNXjNL1Rs0H22YAb2mOfRn4bpKrgAuAB7raXw58ks6c3kvoJOPD2Rf46ySPNeN72yhjORL4QjP2DZr+3wP8T+CMJNcAPwL+fZR+JEmSRtaj6/Rm4K/pU1NTWb0GeFNV/Wyc+rwNWFBVd41Hf+uTNuf0Xrn8q22FYs9dR/u9YfzMcJGMKWf+Rn/UWqyfrrqntVgAz97gSa3F2uJxf9yaWDeubu993DAzW4vVpsdaTlxWtfjo2tW0d2/P26DdP6KeeNuZk/6PzMNXnTnhucLshW9q/T6ndKU3yU50Pvj1nfFKeCVJkqa1Hl2nd0onvVV1A/DHE9Dv3PHuU5IkSZNnSie9kiRJGmc9OqfXpFeSJEkDenR6Qy8sWSZJkiSNyEqvJEmSBljplSRJkqYmK73TzK8faW/tyzbXzm1zTWCAV817d2uxHm1x7cs21yDua/EJ2o+2uKbnw7WqtVgAl95/a2uxtpn9lNZiPdj3WGuxZrW4Tu99qx8avdE4Sctris+ZObu1WG3+/PjRg7e1Fmt9US3+u9MmK73SWmoz4ZUkSePDSq8kSZIGOKdXkiRJmpqs9EqSJGlAjz6cwkqvJEmSep6VXkmSJA1wTq8kSZI0NVnplSRJ0gDn9EqSJElTk5VeSZIkDXBO7/SU5IAkRz+B629LstV4jkmSJElrx0rvKKrqbODsyR7HcJLMrF59SLYkSWqfc3p7T5K5SW5KclKSFUlOT7JfksuS/CzJwiSLkpzQtD81yeeS/CTJLUkOao7vm+SSJN9JckOSf07yB+9tkrOSXJ1kZZLDm2OHJTm+q827kvxjs/3WJFclWZbkxCQzm+P3JzkuyZXAXkk+2cS9PslnWnjrJEmSppRpnfQ2ngt8FtgN2BE4BNgbOAr4yBDtn96c3x/4ZNfxhcBfAbsC2wFvGOLad1bVfGABcGSSLYGvAwckmdW0eQdwSpLnAwcDL6mqecBq4NCmzabAiqraE7gBeD2wc1XtBvyvwUGTHJ5kaZKlDzzyuzG8JZIkadrq65v41yQw6YVbq2p5VfUBK4ELqqqA5cDcIdqfVVV9VXUD8LSu41dV1S3NVIMz6CTGgx2Z5DrgCmAbYPuqegC4ENg/yY7ArKpaDvwJMB9YkmRZs//HTT+rgW83278HHgZOSvIG4MHBQatqcVUtqKoFm270lLG+L5IkST3DOb3wSNd2X9d+H0O/P93t07Vdg9o9bj/JvsB+wF5V9WCSi4HZzemT6FSVbwJO6er7K1X14SHG8PCaebxVtSrJQjpJ8ZuBI4BXDHGNJEnS6Fy9QaNYmGTbZi7vwcClg85vDtzdJLw7Ai9ac6KqrqRT+T2ETpUY4ALgoCRPBUjylCTPGRw0yRxg86o6F/hLYN4435ckSdKUZ6V3/FxOZ47vrsAlwHcGnf8B8J4k1wM305ni0O2bwLyquhugqm5I8lHg/CaRfgx4H/CLQddtBnw3yWw61eEPjN8tSZKkaadHV2+Y1klvVd0G7NK1v2iYc6cOPt/sz+nafbCqDh4ixtyu3deMMJy9geO7D1TVN4BvDNHnnK7tX9H5EJ0kSdIT5/QGTYQkWyT5KfBQVV0w2eORJEnqRdO60jtequpi4OJ1vPYeYIfxHI8kSdI669HpDVZ6JUmS1POs9EqSJGmAc3olSZKkqclKryRJkgY4p1eSJEmamqz0TjN/tNGTJ3sIE+JV897darzzl53YWqyFu/x5a7F61XV9d7YWq2rwE8kn1qYbbNRarLtXPdharBmPe8r7xHqMVa3FSov31eZ7CPDg6kdajdeWmZmG9UHn9EqCdhNeSZI0Pqz0SpIkaYCVXkmSJGlqstIrSZKkAS1/NqEtVnolSZLU86z0SpIkaYBzeiVJkqSpyUqvJEmSBljplSRJkqYmK72SJEkaUFZ6tZaSzE2yYrLHIUmSNN1Z6Z3CksysqtWTPQ5JktRDnNPbG5rq601JTkqyIsnpSfZLclmSnyVZ2Lx+kuTa5uvzmms/mOTkZnvX5vpNkhyb5LQkFzZ9vGuYuD9Ock3zenFz/LQkB3a1Oz3JAUlmJvl0kiVJrk/y7ub8vkkuSvIvwPIkmyb5fpLrmvEc3MobKUmSelPVxL8mwXSt9D4XeBNwOLAEOATYGzgA+AjwNmCfqlqVZD/gE8AbgX8CLk7yeuBvgHdX1YNJAHYDXgRsClyb5PuDYv4aeGVVPZxke+AMYAFwEvAB4LtJNgdeDLwdOAy4t6r2SLIRcFmS85u+FgK7VNWtSd4I3FFVrwNo+nicJIc398qzn/Rctt7kj57IeydJkjTlTNek99aqWg6QZCVwQVVVkuXAXGBz4CtNclrALICq6kuyCLgeOLGqLuvq87tV9RDwUJKL6CSmy7rOzwJOSDIPWA3s0PT5oyRfSPJU4A3At5tk+1XAbkkOaq7fHNgeeBS4qqpubY4vBz6T5B+A71XVjwffbFUtBhYDLHj6S3vz2YKSJGl8OL2hpzzStd3Xtd9H5xeBjwMXVdUuwJ8Cs7vabw/cDzxjUJ+Dk8nB+x8A7gReQKfCu2HXudOAQ4F3AKc0xwK8v6rmNa9tq2pNpfeB/iBVPwXm00l+/z7JMcPdtCRJ0nQ1XZPe0WwO/EezvWjNwWbqwGeBfYAtu6qwAAcmmZ1kS2BfOtMmBvf5q6rqA/4cmNl17lTgLwGqamVz7DzgL5LMamLvkGTTwQNN8gzgwar6GvAZ4IVre7OSJEn9+vom/jUJpuv0htF8is70hg8CF3YdPx74YlX9NMlhwEVJLmnOXQV8H3g28PGquiPJ3K5rvwh8O8mbgIt4fLX2ziQ3Amd1tT+JzlSLa9KZNPwb4L8OMdZdgU8n6QMeA/5i3W5ZkiSpd6Um6RN0vSTJscD9VfWZdbx+EzrTE15YVfeO59gG69U5vZvNnD16o3Fy/rITW4sFsHCXP281Xi9qPmzairZ/pq6mvYpJaO99nNFirDb1/cHMt4nTq+9h2x6tVa3GW3nnlZP+jXvopA9O+H+oG/+3f2z9Pp3eMMma1SFuAj4/0QmvJEnSdOX0hnFQVcc+gWv/jc6UCEmSpElXfT35R2ErvZIkSep9Jr2SJEkasB6s3pDk1UluTvLzJEcP0+bPktyQZGXzpNoROb1BkiRJ640kM4EvAK8EbgeWJDm7qm7oarM98GHgJVV1d/OQrxGZ9EqSJGlATfoT2RYCP6+qWwCSfB04ELihq827gC9U1d0AVfXr0Tp1eoMkSZJaleTwJEu7Xod3nX4m8Muu/dubY912AHZIclmSK5K8erSYVnolSZI0oIXVG6pqMbB4mNNDreE7eFAbANvTeQrus4AfJ9mlqu4ZLqZJ7zRz/+qHW4s1p8UHRjxaq1uL1fbDIq5acVqr8XrR/ru/r7VYv1v9wOiNxtHWMzdrLVabD1b4fYs/q2amvT96tvnAiDa/X9D+g1nasuOsrSd7CNPR7cA2XfvPAu4Yos0VVfUYcGuSm+kkwUuG69TpDZIkSRow+as3LAG2T7Jtkg2BNwNnD2pzFvBygCRb0ZnucMtInZr0SpIkab1RVauAI4DzgBuBb1bVyiTHJTmgaXYe8NskNwAXAX9dVb8dqV+nN0iSJGnAGNbRnWhVdS5w7qBjx3RtF/DB5jUmVnolSZLU86z0SpIkaUCPfijRpFeSJEkD1oPpDRPB6Q2SJEnqeVZ6JUmSNKCFh1NMBiu9kiRJ6nlWeiVJkjSgnNPbs5JcnGTBOl57bpIt1vHaeUle27V/QJKj16UvSZIkDc9K7xNUVa8dvdWw5gELaBZfrqqz+cPH7EmSJLXHOb1TX5K5SW5K8pUk1yf5VpJNBrX5UpKlSVYm+Z/NsT9J8p2uNq9M8q/N9m1Jtmr6vjHJl5trz0+ycdNmjybe5Uk+nWRF8yzp44CDkyxLcnCSRUlOaK45tRnLRUluSfKyJCc3MU7tGsurmn6vSXJmkjkT/kZKkiRNMdMq6W08D1hcVbsBvwfeO+j831TVAmA34GVJdgMuBJ6fZOumzTuAU4boe3vgC1W1M3AP8Mbm+CnAe6pqL2A1QFU9ChwDfKOq5lXVN4bo78nAK4APAOcAxwM7A7s2UyO2Aj4K7FdVLwSWMsTj+JIc3iTyS+956DejvT+SJGkaq76+CX9NhumY9P6yqi5rtr8G7D3o/J8luQa4lk6CuVPzfOfTgLc283f3Av7vEH3fWlXLmu2rgblN+82q6ifN8X9Zi7Ge08ReDtxZVcurqg9YCcwFXgTsBFyWZBnwduA5gzupqsVVtaCqFmyx8daDT0uSJPW86Tind/BElf79JNsCRwF7VNXdzTSC2c3pU+hUWx8GzqyqVUP0/UjX9mpgYyBPYKxr+usb1Hcfne/dauCHVfWWJxBDkiRpgHN6e8azk+zVbL8FuLTr3JOAB4B7kzwNeM2aE1V1B3AHnekEp441WFXdDdyX5EXNoTd3nb4P2Gxtb6DLFcBLkjwXIMkmSXZ4Av1JkiT1pOmY9N4IvD3J9cBTgC+tOVFV19GZ1rASOBm4bNC1p9OZHnHDWsY8DFic5HI6ld97m+MXATut+SDb2t5IVf0GWASc0dzPFcCOa9uPJElSv+qb+NckmI7TG/qq6j2Dju27ZqOqFo1w7d7Al7sPVNXcZvMuYJeu45/paray+eAczTq8S5s2vwP2GBTj1MHjqKrbBvXdfe7CIfqQJElSl+mY9K6TJFfTmfrwV+tw+euSfJjO+/0LOtVZSZKk9U+PzumdVknv4IrpWl47/wnE/QYw1JJkkiRJasG0SnolSZI0iklaR3eiTccPskmSJGmasdIrSZKkAT06p9dKryRJknqelV5JkiQNmKR1dCeaSe8001e9+ieLJ/K0Z2n8VMv/j63u0X+c6g+eGD9x2nwPq8WfVX0tvofQ7vesTat69L5G5PQGSZIkaWqy0itJkqR+5ZJlkiRJ0tRkpVeSJEkDnNMrSZIkTU1WeiVJkjTASq8kSZI0NVnplSRJ0oAeXf/bSq8kSZJ6npVeSZIkDXBOb+9KckCSo5/A9bcl2WodrtsiyXu79p+R5FvrOg5JkiQNzUovUFVnA2dPQugtgPcCX2zGcQdw0CSMQ5IkCYCy0js1JZmb5KYkJyVZkeT0JPsluSzJz5IsTLIoyQlN+1OTfC7JT5LckuSg5vi+SS5J8p0kNyT55yR/8P4lOSvJ1UlWJjm8OXZYkuO72rwryT8CnwS2S7Isyaebsa5o2ixq+jonya1JjkjywSTXJrkiyVOadtsl+UET88dJdpz4d1WSJGlq6fmkt/Fc4LPAbsCOwCHA3sBRwEeGaP/05vz+dBLTNRYCfwXsCmwHvGGIa99ZVfOBBcCRSbYEvg4ckGRW0+YdwCnA0cD/q6p5VfXXQ/S1SzPWhcDfAQ9W1e7A5cDbmjaLgfc3MY+iqRp3S3J4kqVJlt778G+GCCNJktToq4l/TYLpkvTeWlXLq6oPWAlcUFUFLAfmDtH+rKrqq6obgKd1Hb+qqm6pqtXAGXQS48GOTHIdcAWwDbB9VT0AXAjs31RiZ1XV8jGM+6Kquq+qfgPcC5zTHF8OzE0yB3gxcGaSZcCJdBL2x6mqxVW1oKoWbD576zGElSRJ6i3TZU7vI13bfV37fQz9HnS3T9f24F9NHrefZF9gP2CvqnowycXA7Ob0SXSqyjfRqfKOx7hnAPdU1bwx9idJkjSyPtfpFSxMsm0zl/dg4NJB5zcH7m4S3h2BF605UVVX0qn8HkKnSgxwH7DZug6mqn4P3JrkTQDpeMG69idJktSrTHrXzuV05viuAG4FvjPo/A+ADZJcD3yczhSHbt8ELququwGq6rfAZc0H7D69jmM6FDismVKxEjhwHfuRJEnq2Tm9PT+9oapuo/OBsDX7i4Y5d+rg883+nK7dB6vq4CFizO3afc0Iw9kbOL77QFUdMqjNLs3xU9eMaXCM7nNVdSvw6hFiSpIkTXs9n/SuD5JsAVwFXFdVF0z2eCRJkobVo+v0mvSOUVVdDFy8jtfeA+wwnuORJEnS2Jn0SpIkqV9nVdfeY9IrSZKkAT06vcHVGyRJktTzrPRKkiRpgJVeSZIkaWqy0jvNzEhGbzQF9f3BE6KlyZGW/x+bmfZqF23+fxbaex/bfA9ntHhfafnnYu9++Kk3/90cSVnplSRJkqYmK72SJEkaYKVXkiRJmpqs9EqSJGlA32QPYGJY6ZUkSVLPs9IrSZKkfq7eIEmSJE1RVnolSZI0wEqvJEmSNDVZ6ZUkSdIAV2/QUJLMTbJiHa+dl+S1XfsHJDl6/EYnSZIksNI72eYBC4BzAarqbODsSR2RJEma1ly9YQppqq83JTkpyYokpyfZL8llSX6WZGHz+kmSa5uvz2uu/WCSk5vtXZvrN0lybJLTklzY9PGuYeL+OMk1zevFzfHTkhzY1e70JAcAxwEHJ1mW5OAki5Kc0LQ5NcmXklyU5JYkL0tycpIbk5za1derklzexDszyZwJfXMlSZKmoJ5MehvPBT4L7AbsCBwC7A0cBXwEuAnYp6p2B44BPtFc90/Ac5O8HjgFeHdVPdic2w14HbAXcEySZwyK+WvglVX1QuBg4HPN8ZOAdwAk2Rx4MZ3q7jHAN6pqXlV9Y4h7eDLwCuADwDnA8cDOwK7N1IitgI8C+zUxlwIfHNxJksOTLE2y9J6HfjP6OydJkqavvhZek6CXpzfcWlXLAZKsBC6oqkqyHJgLbA58Jcn2QAGzAKqqL8ki4HrgxKq6rKvP71bVQ8BDSS4CFgLLus7PAk5IMg9YDezQ9PmjJF9I8lTgDcC3q2pVktHu4ZyuMd856H7mAs8CdgIua/raELh8cCdVtRhYDLDjU/fozb9ZSJIkjaCXk95Hurb7uvb76Nz3x4GLqur1SeYCF3e13x64HxhcyR2cMA7e/wBwJ/ACOlX0h7vOnQYcCrwZeOda3kP3+LvvYTXww6p6yxj7kyRJGpFzenvP5sB/NNuL1hxsph98FtgH2DLJQV3XHJhkdpItgX2BJUP0+auq6gP+HJjZde5U4C8Bqmplc+w+YLMncA9XAC9J8txm7Jsk2eEJ9CdJkqa7Hp3eMJ2T3k8Bf5/kMh6fnB4PfLGqfgocBnyymZYAcBXwfTrJ5ser6o5BfX4ReHuSK+hMbXhgzYmquhO4kc484TUuAnZa80G2tb2BqvoNnYT9jCTXN+PacW37kSRJ6nWp6s0S9nhLcixwf1V9Zh2v3wRYDrywqu4dz7GtjTbn9M6ZObutUGw0Y1ZrsR5c/cjojcbRVStOazVeL9p/9/e1Fut3qx8YvdE4evLMTVqL1fcHM7omzu9XPzx6o3EyM+3Vf2Yw6mc5xk2b3y+AXs0ntp21RavxzvjFWe39RzKM3/7pyyb8m7nlOT9q/T6nc6W3NUn2o7NaxOcnM+GVJEmarnr5g2zjqqqOfQLX/hvw7PEbjSRJ0gTxMcSSJEnS1GSlV5IkSf3KSq8kSZI0NVnplSRJ0gArvZIkSdLUZKV3mlmw8Tatxdo0M0dvNE4ebfHX0uv67mwtFrS7xmyv+t61X2gt1jvmH9VaLID51d46vc96rL11WM/bqL31sLegvXW+29Tmz0WA+1ndWqyHq71Yf/pYe/+PrS+c0ytJkiRNUVZ6JUmS1M9KryRJkjRFWemVJElSPyu9kiRJ0hRlpVeSJEkDKpM9gglhpVeSJEk9z0qvJEmS+jmnV5IkSZqirPRKkiSpX/X15pxek96WJDkA2KmqPjnZY5EkSRpOr05vMOltSVWdDZw92eOQJEmajpzTOw6SzE1yU5KTkqxIcnqS/ZJcluRnSRYmWZTkhKb9qUk+l+QnSW5JclBzfE6SC5Jck2R5kgO7YnysifHDJGckOao5vl2SHyS5OsmPk+w4Oe+CJEnqBVWZ8NdkMOkdP88FPgvsBuwIHALsDRwFfGSI9k9vzu8PrJny8DDw+qp6IfBy4H+nYwHwRmB34A3Agq5+FgPvr6r5TawvDg6U5PAkS5Ms/dn9tz7hG5UkSZpqnN4wfm6tquUASVYCF1RVJVkOzB2i/VlV1QfckORpzbEAn0iyD9AHPBN4Gp3k+LtV9VDT/znN1znAi4Ezk/7fmjYaHKiqFtNJjnnrc95Q43CvkiSpRzmnV6N5pGu7r2u/j6Hf5+72azLWQ4GtgflV9ViS24DZXecHmwHcU1Xz1nXQkiRJ04HTG9YvmwO/bhLelwPPaY5fCvxpktlNdfd1AFX1e+DWJG8CaKZCvGAyBi5JknpD9WXCX5PBpHf9cjqwIMlSOlXfmwCqagmdlR+uA/4VWArc21xzKHBYkuuAlcCBgzuVJEma7pzeMA6q6jZgl679RcOcO3Xw+WZ/TvP1LmCvYcJ8pqqOTbIJcAnwv5trbgVe/YRvQpIkCage/fSPSe/UsTjJTnTm+H6lqq6Z7AFJkiRNFSa9U0RVHTLZY5AkSb1vfXgMcZJX01kKdiZw0nBPtG2edXAmsEdVLR2pT+f0SpIkab2RZCbwBeA1wE7AW5q/dg9utxlwJHDlWPo16ZUkSVK/9WD1hoXAz6t8yt0rAAAgAElEQVTqlqp6FPg6Q39Q/+PAp+g83GtUJr2SJElqVffTYpvX4V2nnwn8smv/9uZY9/W7A9tU1ffGGtM5vZIkSerXxuoN3U+LHcJQpeD+USWZARwPLFqbmFZ6JUmStD65Hdima/9ZwB1d+5vRWQ724ubptS8Czk6yYKROrfROMxfce1NrsXac88zRG42Th2tVa7E2mjGLh1c/2lq8361+oLVY1eLijEl7nw5+x/yjWot1ytWfaS0WwLsXfKi1WPOrve/Zl5Z+qrVYbVp942WtxZr5/Je0FquX/Y8FH2k13ltajTa09WD1hiXA9km2Bf4DeDPQv4pVVd0LbLVmP8nFwFGu3iCNszYTXkmSppuqWgUcAZwH3Ah8s6pWJjkuyQHr2q+VXkmSJPWrFv+qM/wY6lzg3EHHjhmm7b5j6dNKryRJknqelV5JkiT1q77JHsHEMOmVJElSv771YHrDRHB6gyRJknqelV5JkiT1Wx8+yDYRrPRKkiSp51nplSRJUr/14OEUE8JKryRJknqelV5JkiT1a/GJ9K2y0rueSrIoyTPW8pq5SVZM1JgkSZKmKiu9T1CSDZpnRK/LtTOravUwpxcBK4A71nVskiRJa8s5vT1icDU0yVFJjk1yZJIbklyf5OvNuU2TnJxkSZJrkxzYHF+U5Mwk5wDnJ3l6kkuSLEuyIslLR4h/f5LjklwJ7JXkmKb/FUkWp+MgYAFwetPnxknmJ/lRkquTnJfk6U1/85Ncl+Ry4H3DxDw8ydIkSx989O5xey8lSZKmimmX9I7gaGD3qtoNeE9z7G+AC6tqD+DlwKeTbNqc2wt4e1W9AjgEOK+q5gEvAJaNEGdTYEVV7VlVlwInVNUeVbULsDGwf1V9C1gKHNr0uQr4PHBQVc0HTgb+runvFODIqtpruIBVtbiqFlTVgk02fPLavSuSJGla6atM+GsyOL1hwPV0KqtnAWc1x14FHJDkqGZ/NvDsZvuHVfW7ZnsJcHKSWcBZVTVS0rsa+HbX/suTfAjYBHgKsBI4Z9A1zwN2AX6YBGAm8KskmwNbVNWPmnanAa8Z6w1LkiRNF9Mx6V3F4yvcs5uvrwP2AQ4APpZkZyDAG6vq5u4OkuwJPLBmv6ouSbJP08dpST5dVV8dJv7Da+bxJpkNfBFYUFW/THJs13geFxJYObiam2QLoEc/YylJkiaDT2TrHXcCT02yZZKNgP3pvA/bVNVFwIeALYA5wHnA+9OUV5PsPlSHSZ4D/Lqqvgz8H+CFYxzLmgT3riRzgIO6zt0HbNZs3wxsnWSvJt6sJDtX1T3AvUn2btodOsa4kiRJ08q0q/RW1WNJjgOuBG4FbqIzXeBrzXSBAMdX1T1JPg78E3B9k/jeRidJHmxf4K+TPAbcD7xtjGO5J8mXgeVN30u6Tp8K/HOSh+jMHz4I+Fwzxg2aca0E3kFnasWDdJJ0SZKkddar6/ROu6QXoKo+B3xuDO0eAt49xPFT6SSla/a/AnxljLHnDNr/KPDRIdp9m8fP/V1GZ/rF4HZX0/nw3BrHjmUckiRJ08m0THolSZI0tMlaXWGimfROkGYd3o0GHf7zqlo+GeORJEmazkx6J0hV7TnZY5AkSVpbrt4gSZIkTVFWeiVJktSvV1dvsNIrSZKknmeld5r56ka7thbraxusai3Wpfff2losgE03GPwZxYmz9czNRm80TlZXX2uxZqa937nn1yatxXr3gg+1FgvgxKWfai3WyfOOaS3WtxZ8uLVYD9Hef/fbVXs/O+A87k979/YbHmst1qMtfs+O7Gvv37L1has3SALaTXglaV21mfCqt/hBNkmSJGmKstIrSZKkfr06vcFKryRJknqelV5JkiT169EVy6z0SpIkqfdZ6ZUkSVI/5/RKkiRJU5SVXkmSJPVznV5JkiRpirLSK0mSpH69+iw/K73riSQfGbT/k8kaiyRJUq+Z1klvkpnrUazHJb1V9eIJHI4kSdKQikz4azJMqaQ3yVlJrk6yMsnhSf4iyae6zi9K8vlm+61JrkqyLMmJa5LOJPcnOS7JlcBeSY5JsiTJiiSLk6Rpt0eS65NcnuTTSVY0x2c2+0ua8+8eYbz7Jrkoyb8Ay4e6h+bYJ4GNm7GevmacXX1cnORbSW5KcnrXGF/bHLs0yeeSfG/c33RJkqQeMKWSXuCdVTUfWAAcCfwr8Iau8wcD30jy/Gb7JVU1D1gNHNq02RRYUVV7VtWlwAlVtUdV7QJsDOzftDsFeE9V7dVcv8ZhwL1VtQewB/CuJNuOMOaFwN9U1U5D3UOSLavqaOChqppXVYcO0cfuwF8COwF/DLwkyWzgROA1VbU3sPVwA2h+QViaZOn3H/p/IwxVkiRNd3018a/JMNWS3iOTXAdcAWwDbAvckuRFSbYEngdcBvwJMB9YkmRZs//HTR+rgW939fnyJFcmWQ68Atg5yRbAZlW1Zl7tv3S1fxXwtqbfK4Etge1HGPNVVXXrCPcw0rXdfdxeVX3AMmAusCNwS1ffZwx3cVUtrqoFVbXgdRtvN4ZwkiRJvWXKrN6QZF9gP2CvqnowycXAbOAbwJ8BNwHfqapq/vz/lar68BBdPVxVq5s+ZwNfBBZU1S+THNv0OdJkkwDvr6rzxjj0B8ZwD6N5pGt7NZ3vW28uoidJkiZVX4+mGFOp0rs5cHeTLO4IvKg5/q/AfwXeQicBBrgAOCjJUwGSPCXJc4boc03CeVeSOcBBAFV1N3BfkjUx3tx1zXnAXySZ1fS9Q5JNn+A9ADy2ps8xugn44yRzm/2D1+JaSZKkaWXKVHqBHwDvSXI9cDOd6QFU1d1JbgB2qqqrmmM3JPkocH6SGcBjwPuAX3R3WFX3JPkynQ+Z3QYs6Tp9GPDlJA8AFwP3NsdPojO94JqmovwbOkn3Ot9DYzFwfZJrhpnX+zhV9VCS9wI/SHIXcNUYxyBJkjSsyVpdYaJNmaS3qh4BXjPMuf2HOPYNBiq/3cfnDNr/KPDRIbpdWVW7ASQ5GljatO+js7zYR4a4ZnCsi+kkzGO5h/8B/I/B4xyijyO6LruoqnZsku8vrBmjJEmSHm/KJL2T4HVJPkznPfoFsGhyhzOkdyV5O7AhcC2d1RwkSZLWWa8+kc2kdxjDVYqHkmRX4LRBhx+pqj3HfWBdqup44PiJjCFJkqYXpzdoWFW1HJg32eOQJEnS0Ex6JUmS1K9XpzdMpSXLJEmSpHVipVeSJEn9rPRKkiRJU5SV3mnmNXdf2lqs92380tZibTP7Ka3FunvVg63FAuijWo3Xljbv61mPtRdrfrX7qeeT5x3TWqx3LjuutVg/3+uI0RuNk6fu8MDojcbJo/e2V2u699cbtxYLoFr8UXXn78f6INQnbtv97mst1vqiV1dvsNIrSZKknmelV5IkSf36erPQa6VXkiRJvc9KryRJkvr1OadXkiRJmpqs9EqSJKlfb64ZZKVXkiRJ04CVXkmSJPXziWySJEnSFGWlV5IkSf36Mk1Xb0gyN8mKNgYznSWZl+S1XfsHJDl6MsckSZLUK6Z1pTfJBlW1aj2JNw9YAJwLUFVnA2e3NTZJkiRw9YYNknwlyfVJvpVkkyTHJFmSZEWSxenYLsk1ay5Ksn2Sq5vt+Ul+lOTqJOcleXpz/MgkNzR9f324ASRZmOQnSa5tvj6vOX5lkp272l3cxNo0ycnNGK9NcmBzflGSM5OcA5yfZE6SC5Jck2T5mnZN248luSnJD5OckeSo5vh2SX7Q3MuPk+w4wrhPTfKPSS4C/mGo+0iyIXAccHCSZUkObsZ5Qlcfn2va35LkoOb4jCRfTLIyyfeSnLvmnCRJkgaMtdL7POCwqrosycnAe4ETquo4gCSnAftX1TlJ7k0yr6qWAe8ATk0yC/g8cGBV/SbJwcDfAe8Ejga2rapHkmwxwhhuAvapqlVJ9gM+AbwR+DrwZ8DfNon0M6rq6iSfAC6sqnc2/V6V5N+avvYCdquq3yXZAHh9Vf0+yVbAFUnOBuY3/e/evE/XAFc31y8G3lNVP0uyJ/BF4BUjjH0HYL+qWp3kSYPvo6remOQYYEFVHdG8p4sG9fF0YG9gRzoV4G8BbwDmArsCTwVuBE4eHDzJ4cDhAJm5OTNmbDrCUCVJ0nTWq6s3jDXp/WVVXdZsfw04Erg1yYeATYCnACuBc4CTgHck+SBwMLCQTtK8C/DDdCZHzwR+1fR3PXB6krOAs0YYw+bAV5JsT6fyPqs5/k3gh8Df0kl+z2yOvwo4YE11FpgNPLvZ/mFV/a7ZDvCJJPvQ+T4/E3ganQTzu1X1EEBTGSbJHODFwJkZmOi90QjjBjizqlaPch+jOauq+oAbkjytObZ303cf8J9NNfkPVNViOok6G2z4zF79q4UkSRoHfb35ObYxJ72DE6WiU91cUFW/THIsnaQS4Nt0EtALgaur6rdJngGsrKq9huj7dcA+wAHAx5LsPMy8148DF1XV65PMBS4GqKr/SPLbJLvRSbLf3bQP8Maqurm7k6Yy+0DXoUOBrYH5VfVYktuaexnuWz4DuKeq5g1zfijd8Ya8jzF4pGs7g75KkiRpBGOd0/vsJGsS1rcAlzbbdzWVz/55pFX1MHAe8CXglObwzcDWa/pIMivJzklmANtU1UXAh4AtgDnDjGFz4D+a7UWDzn29uX7zqlreHDsPeH+acmyS3Ufo99dNwvty4DnN8UuBP00yu7nH1zX393s6Ve43Nf0myQuG6Xtt7uM+YLO16GfNGN/YzO19GrDvWl4vSZL0OH1kwl+TYaxJ743A25NcT2cqw5eALwPL6UxJWDKo/el0qsHnA1TVo3QS439Ich2wjM4UgZnA15IsB64Fjq+qe4YZw6eAv09yWXNdt28Bb6Yz1WGNj9OZOnB9OkuufXyYfk8HFiRZSqfqe1Mz5iV05s5eB/wrsBS4t7nmUOCw5l5WAgcO7nQEw93HRcBOaz7INsa+vg3cDqwATgSu7BqjJEmSGqka/ymezTzazavqY+PeeYuSzKmq+5NsAlwCHF5V14x2XZu6xrglcBXwkqr6z+Hatzmn933PeGlbobhu1V2txbp71YOtxQLYetba/gFg3U3Ez4PhpMXFz9+9aqvWYj2tHm0tFsCNG4z2kYLx885lx7UW6+d7HdFarKfu8MDojcbJo/e29yDUe3+9cWuxAFr88cGdv2/vA9nz9vtta7EAtjj9wkmfuvi1Z7x1wr+bb73ja63f57iv05vkO8B2jLyawVSxOMlOdOb4fmV9S3gb32tWp9gQ+PhICa8kSdJ0Ne5Jb1W9/olcn+QdwH8fdPiyqnrfE+l3XVTVIWNtm+RvgDcNOnxmVf3d+I7q8apq34nsX5IkTS/TffWG1lTVKQx8AG7KaJLbCU1wJUmStG7Wu6RXkiRJk6dXH07R3ox6SZIkaZJY6ZUkSVK/Xn10q5VeSZIk9TwrvdPMNpu1t17pjauHe87I+Huw77HWYs1o+Ukyv1/9cGuxqsXf79Pi+3jeRo+M3micfGnpp1qLBfCtBR9uLVaba+c+9/ITWovVplXX/Vtrsbbaqb210gGY1d6a0c9tLRL8/fx2HzlwTKvRhtarqzdY6ZUkSVLPs9IrSZKkfq7eIEmSJE1RVnolSZLUz0qvJEmSNEVZ6ZUkSVK/cvUGSZIkaWqy0itJkqR+vTqn16RXkiRJ/Xo16XV6gyRJknqelV5JkiT1a++B9O3q2Upvki2SvHeUNnOTHDKGvuYmWTF+o5MkSVKbejbpBbYARkx6gbnAqEnv+iqJlXpJkjSu+jLxr8nQy0nvJ4HtkixL8unmtSLJ8iQHd7V5adPmA01F98dJrmleLx5LoCSzk5zS9H1tkpc3x89NsluzfW2SY5rtjyf5b0n2TXJxkm8luSnJ6UnStJmf5EdJrk5yXpKnN8cvTvKJJD8C/nuSNzX3dV2SS4YZ3+FJliZZet/Dv30i76kkSdKU1MuVwqOBXapqXpI3Au8BXgBsBSxpEsSjgaOqan+AJJsAr6yqh5NsD5wBLBhDrPcBVNWuSXYEzk+yA3AJnaT6NmAV8JKm/d7A14CnA7sDOwN3AJcBL0lyJfB54MCq+k2TpP8d8M7m+i2q6mXNmJcD/6Wq/iPJFkMNrqoWA4sBtt3yBb06VUeSJI2DXl29oZeT3m57A2dU1WrgzqZKugfw+0HtZgEnJJkHrAZ2WIv+Pw9QVTcl+UVz7Y+BI4Fbge8Dr2wS67lVdXNTvb2qqm4HSLKMzpSLe4BdgB82hd+ZwK+64n2ja/sy4NQk3wT+dYzjlSRJmlamS9I71tkjHwDupFMRngE8/AT7X0KnUnwL8EM6VeZ3AVd3tXmka3s1ne9JgJVVtdcw/T6wZqOq3pNkT+B1wLIk86rKOQySJGmd9Gqlt5fn9N4HbNZsXwIcnGRmkq2BfYCrBrUB2Bz4VVX1AX9Op8I6FpcAhwI00xqeDdxcVY8CvwT+DLiCTuX3qObrSG4Gtk6yV9PnrCQ7D9UwyXZVdWVVHQPcBWwzxjFLkiStl5K8OsnNSX6e5Oghzn8wyQ1Jrk9yQZLnjNZnzya9TbXzsmapsb2A64HrgAuBD1XVfzbHVjUfAvsA8EXg7UmuoDM94YGhe/8DXwRmNvNrvwEsqqo1FdwfA3dW1YPN9rMYJeltkuWDgH9Ich2wDBjuQ3Wfbj5At4JO8n3dGMcsSZL0B6qF10iSzAS+ALwG2Al4S5KdBjW7FlhQVbsB3wI+Ndp99fT0hqoavBzZXw86/xjwJ4Pa7Na1/eGm3W105tgOF+dhYNEw5z4GfKzZvoOuqRBVdTFwcdf+EV3by+hUpAf3t++g/TcMNy5JkqQpaCHw86q6BSDJ14EDgRvWNKiqi7raXwG8dbROezrplSRJ0tppYx3dJIcDh3cdWtysNgXwTDrTQ9e4HdhzhO4OA/7vaDFNetdCkv8C/MOgw7dW1esnYzySJElTUfdyqkMYKu0eclZEkrfSWTTgZaPFNOldC1V1HnDeZI9DkiRpoqwHqzfczuM/mP8sOs8zeJwk+wF/A7ys67NUw+rZD7JJkiRpSloCbJ9k2yQbAm8Gzu5ukGR34ETggKr69Vg6tdIrSZKkfpP96NaqWpXkCDp/XZ8JnFxVK5McByytqrOBTwNzgDObB3n9e1UdMFK/Jr2SJElar1TVucC5g44d07W939r2adI7zdx+312txdp502e1FmtWxvockSfuMVa1FgtgZtqbhbS62pvJ1eZ9bcGs1mK17aEWZ989dYexLl2u4fT9+KLRG42TVb/6RWuxADZ49WGtxmvLU9tYymA90zfptd6J4ZxeSZIk9TwrvZIkSeq3HqzeMCGs9EqSJKnnWemVJElSv96c0WvSK0mSpC5Ob5AkSZKmKCu9kiRJ6terq7RZ6ZUkSVLPs9IrSZKkfj6cQpIkSZqirPRKkiSpX2/WeXu40ptkiyTvHaXN3CSHjKGvuUlWjHB+XpLXrss4B/VzapKDnmg/kiRJeryeTXqBLYARk15gLjBq0jsG84Ahk94kE1ZNn8i+JUnS9NTXwmsy9HLS9ElguyTLgB82x15Dp2r/v6rqG02b5zdtvgJ8BzgN2LRpf0RV/WSkIEk2BI4DNk6yN/D3wPOBZ9BJqu9K8pGh+k0S4PPAK4BbgXT1Ox/4R2AOcBewqKp+leRi4CfAS4Czk/w78LfAauDeqtpniDEeDhwOMGPm5syYsengJpIkST2tl5Peo4FdqmpekjcC7wFeAGwFLElySdPmqKraHyDJJsArq+rhJNsDZ/x/9u48Tq6qWvv470kIYyCADBcRCEYGESFAkCkgXED0MonMk4oMFxVUvOCFexUcLwJOgCIGBByQVxFUUCQgU8KcABlAQJSgIAiCDGEIkGS9f+xdnUqlx+Ts092V55tPfbq66tRZpzrV1av2WXttYEx3QSLiDUmnAmMi4ri8ny8CWwBjI+K1bva7D7AB8G5gdeCPwEWShpGS4b0j4p+SDgS+Bnwsh10xIt6bY00HdouIv0tasYtjHAeMAxi25JrtWqpjZmZmFWjX7g3tnPQ2GwtcFhFzgKcl3QJsCbzUst0w4LuSRpNGTtdfhJhXRcRrPex3h6bjelLSjfn2DYCNgevTYDBDgaea9v3zpuu3AZdI+gVw5SIcr5mZmVnbWlyS3t6uLXIC8DRpRHgIMGsRYr7Sy/129nFKwAMRsU1P+46IYyVtBewOTJE0OiKeW4TjNjMzs8VYe47ztvdEtpnA8vn6BOBASUMlrUoaYb27ZRuAEcBTETEXOJw0wtrXWJ3par8TgIPyca0B7JRvfxhYVdI2AJKGSXpXZzuWNCoi7oqIU0m1v2v18pjNzMzMFhttO9IbEc9Jui23Gvs9MA2YSvoA87mI+Iek54DZkqYClwDnAVdI2h+4iflHa7tzE3BynhB3eif3d7XfX5EmsU0H/gTcko/9jdy67BxJI0j/T98BHuhk32flOmEBN+TnaGZmZrZQ+qu7Qmltm/QCRERrO7KTWu5/E9i5ZZtNmq6fkrd7jFRj21Wcf5FqhLu6/5Eu9hvAcV08ZgppRLr19h1bvv9QV3HNzMzMLGnrpNfMzMzM+sbdGwxJuwFntNw8IyL26Y/jMTMzM7PecdLbBxExHhjf38dhZmZmVkp7jvO2d/cGMzMzMzPAI71mZmZm1sTdG8zMzMys7UWbFjg46V3MbLHKev19CEXMnPNazxtVRL1e4K8aQ2qMFzXGqvN51WnOg7fVGm9ULFVbrDderK8ibvbUP9QWa+7Em2qLteRxX6stVt3mPHJXbbHiX0/XFmv4nNpCWWFOes3MzMysQ7uWN3gim5mZmZm1PY/0mpmZmVmHdl2cwiO9ZmZmZtb2PNJrZmZmZh3ac5zXI71mZmZmthjwSK+ZmZmZdXBNr5mZmZnZIOWRXjMzMzPr4D69ZmZmZmaDlEd6zczMzKxDuKZ3YJO0oqRP9LDNSEmH9GJfIyXdX93R9UzSRyW9tc6YZmZmZouLtkl6gRWBbpNeYCTQY9LbTz4K9CnpleSRejMzM6vU3Bou/aGdkt6vA6MkTZF0Vr7cL2m6pAObttk+b3NCHtGdKOnefNm2N4EkXSNpk3z9Pkmn5utfkXSUpOGSbsj7nC5p73z/SEkPSrpA0gOSrpO0jKT9gDHApfnYlpG0haRbJN0jabykNfI+bpb0f5JuAT4taf/8PKdKmtDF8R4jabKkyc+8+uSi/IzNzMzMBqV2Gik8Gdg4IkZL2hc4FtgUWAWYlBPCk4ETI2IPAEnLArtGxCxJ6wGXkZLPnkwgJc+PAbOB7fLtY4GfArOAfSLiJUmrAHdKuipvsx5wcEQcLekXwL4R8VNJx+VjmyxpGHAusHdE/DMn7V8DPpb3sWJEvDc/h+nAbhHxd0krdnawETEOGAew9Vt3bM9CHTMzM6tEu9b0tlPS22wscFlEzAGezqOiWwIvtWw3DPiupNHAHGD9Xu5/IvApYAbwO2DXnECPjIiHc9L6f5J2II3irwmsnh87IyKm5Ov3kEouWm0AbAxcLwlgKPBU0/0/b7p+G3BJTqCv7OXxm5mZmS1W2jXpVS+3OwF4mjQiPIQ0Qtsbk0gjwo8C15NGk48mJbEAhwKrAltExJt5RHjpfN/rTfuZAyzTxfE/EBHbdBH/lcaViDhW0lbA7sAUSaMj4rlePg8zMzOz+bhP78A3E1g+X58AHChpqKRVgR2Au1u2ARgBPBURc4HDSSOqPYqIN4DHgQOAO0kjvyfmr439PpMT3p2Adfp4/A8Dq0raBkDSMEnv6uxBkkZFxF0RcSrwLLBWb56DmZmZ2eKkbUZ6I+I5SbflVmO/B6YBU4EAPhcR/5D0HDBb0lTgEuA84ApJ+wM30TSC2gsTgZ0j4lVJE4G3MS/pvRS4WtJkYArwUC/2dwlwvqTXgG2A/YBzJI0g/T99B3igk8edleuRBdyQn7OZmZnZQpkbrukd8CKitR3ZSS33vwns3LLNJk3XT8nbPUaqqe0u1heAL+TrT9JUUhERz5IS185s3LTdN5quXwFc0bTdFNIIdWvcHVu+/1B3x2lmZmbWF+2Z8rZXeYOZmZmZWafaaqS3apJ2A85ouXlGROzTH8djZmZmVtrcNh3rddLbjYgYD4zv7+MwMzMzs0XjpNfMzMzMOrTr4hSu6TUzMzOztueRXjMzMzPr0K6LUzjpXcwMVX2D+29Gfb826vUifNUYUmO8OicU1BlLNcZ6o8a38KHv3K62WAAv6/e1xXrxmc4WkCxjlY22ry3W7Kf+WlusdjZ05OjaYsXqM2uL9fzQybXFsrKc9Jr1UZ0Jr5mZWd3atXuDa3rNzMzMrO15pNfMzMzMOrh7g5mZmZnZIOWRXjMzMzPr0K7dGzzSa2ZmZmZtzyO9ZmZmZtYhwjW9ZmZmZmaDkkd6zczMzKyD+/SamZmZmQ1SHuk1MzMzsw7u3jBISFpR0id62GakpEN6sa+Rku5fhGN5q6Rfltq/mZmZmfVO2yW9wIpAt0kvMBLoMeldVBHxZETsV2r/kjxSb2ZmZpWKGv71h3ZMer8OjJI0RdJZ+XK/pOmSDmzaZvu8zQl5xHWipHvzZdveBJJ0jaRN8vX7JJ2ar39F0lHNI7mS3iXp7hxzmqT18m6GSrpA0gOSrpO0TN5+lKRrJd2Tj23DfPslkr4l6SbgDEnvzfucko9h+U6O8xhJkyVN/scrTy7Cj9bMzMza3Vyi+KU/tGPSezLwl4gYDdwJjAY2BXYBzpK0Rt5mYkSMjohvA88Au0bE5sCBwDm9jDWBlDyvAMwGtsu3jwUmtmx7LHB2Pq4xwBP59vWA70XEu4AXgH3z7eOA4yNiC+BE4Lymfa0P7BIR/5Xv+2Te7/bAa60HGRHjImJMRIz5t+Xe2sunZmZmZtY+2v30+FjgsoiYAzwt6RZgS+Cllu2GAd+VNBqYQ0oqe2Mi8ClgBvA7YFdJywIjI+JhSclZ00cAACAASURBVCObtr0D+F9JbwOujIhHJAHMiIgpeZt7gJGShgPbApfnbQCWatrX5fk5AdwGfEvSpXm/T2BmZma2kNp1cYp2T3rV8yYAnAA8TRoRHgLM6uXjJpFGbR8FrgdWAY4mJa/ziYifSboL2B0YL+mo/LjXmzabAyyTj+GFPHrbmVea9vt1Sb8D/gO4U9IuEfFQL4/fzMzMbLHQjuUNM4FGXesE4EBJQyWtCuwA3N2yDcAI4KmImAscDgztTaCIeAN4HDiAVEoxkVRu0FragKS3A49GxDnAVcAm3ez3JWCGpP3zYyVp0862lTQqIqZHxBnAZGDD3hy7mZmZWWfm1nDpD22X9EbEc8BteQLZNsA0YCpwI/C5iPhHvm22pKmSTiDVy35E0p2k0oZXOt97pyYCT0fEq/n62+gk6SXVCt8vaQopMf1xD/s9FDhS0lTgAWDvLrb7TJ6oN5VUz/v7Phy7mZmZ2WKhLcsbIqK1HdlJLfe/Cezcsk3zyOspebvHgI17iPUF4Av5+pM0lVQ0Pz4iTgdOb3n4v5r3HxHfaLo+A3h/J/E+2vL98d0dn5mZmVlf9FdLsdLabqTXzMzMzKxVW470Vk3SbsAZLTfPiIh9+uN4zMzMzErprz66pTnp7YWIGA+M7+/jMDMzM7OF46TXzMzMzDq0a59e1/SamZmZWdvzSK+ZmZmZdWjXml6P9JqZmZlZ2/NI72Jm6vMzaou11crr1RZr+NCla4v16pzXe96oQnXWVtXZm7HO5/Uyc2qLVbd/8mZtsWot8xu2VG2hlnj/kbXFmvPIXbXFGjqyq5XsC6nx/0w1xnp+SHuOenbHfXrNzMzMzAYpj/SamZmZWYe57t5gZmZmZjY4eaTXzMzMzDq05zivR3rNzMzMbDHgkV4zMzMz6+A+vWZmZmZmg5RHes3MzMysQ7uO9DrpNTMzM7MOdS4eVCeXN5iZmZlZ2xuwSa+kFSV9oodtRko6pBf7Ginp/uqOrv/05udiZmZmtrDmEsUv/WHAJr3AikBPyd1IoMekt8305udiZmZmZk0GctL7dWCUpCmSzsqX+yVNl3Rg0zbb521OyCO6EyXdmy/b9iaQpKGSvpH3PU3S8fn2nSXdl2+/SNJS+fbHJP2fpDskTZa0uaTxkv4i6di8zY6SbpH0C0l/kvR1SYdKujvvb1TeblVJV0ialC/b5du/mGPeLOlRSZ/q4ueyhqQJ+fv7JW1f2f+AmZmZLXaihn/9YSBPZDsZ2DgiRkvaFzgW2BRYBZgkaULe5sSI2ANA0rLArhExS9J6wGXAmF7EOgZYF9gsImZLWlnS0sAlwM4R8SdJPwY+DnwnP+bxiNhG0rfzdtsBSwMPAOfnbTYF3gn8C3gUuDAi3iPp08DxwGeAs4FvR8StktYGxufHAGwI7AQsDzws6fvNP5f8nP8LGB8RX5M0FFi29clJOiY/R5Yc9haGLbF8L34kZmZmZu1jICe9zcYCl0XEHOBpSbcAWwIvtWw3DPiupNHAHGD9Xu5/F+D8iJgNEBH/krQpMCMi/pS3+RHwSeYlvVflr9OB4RExE5gpaZakFfN9kyLiKQBJfwGua3rMTk2xN5LUOJYVJDWy0t9FxOvA65KeAVbv5NgnARdJGgb8OiKmtG4QEeOAcQDDl123PadkmpmZWSXcvaF/qedNADgBeJo0wjoGWLIP+2/9H+4p5uv569ym643vl2jZpnW75m2GANtExOh8WTMn0K2Pn0MnH1IiYgKwA/B34CeSPtzDcZuZmZkNaJLeL+lhSX+WdHIn9y8l6ef5/rskjexpnwM56Z1JOq0PMAE4MNferkpK8u5u2QZgBPBURMwFDgeG9jLWdcCxkpYAkLQy8BAwUtI78jaHA7cswvPpLvZxjW/yKHV35nvOktYBnomIC4AfApsXOEYzMzNbTPR394Zcrvk94APARsDBkjZq2exI4PmIeAfwbeCMnp7XgE16I+I54LbcamwbYBowFbgR+FxE/CPfNlvSVEknAOcBH5F0J6m04ZVehrsQ+BswTdJU4JCImAUcAVwuaTppdPb8bvaxsD4FjMkT6P5Iql3uUvPPRdJZwI7AFEn3AfuSaoTNzMzMBqv3AH+OiEcj4g3g/wF7t2yzN6n0FOCXwM5qqhXtzICu6Y2I1nZkJ7Xc/yawc8s2mzRdPyVv9xiwcTdxZgOfzZfm228ANutk+5FN1y8hTWRrve/mfGncvmPT9Y77IuJZoNGNojnGF1u+37jpeuvP5UeYmZmZVaCOmt7mSfbZuDwHCWBN4PGm+54AtmrZRcc2uQnBi8BbgGe7ijmgk14zMzMzaz/Nk+w70dmIbW/mXnWbrS9WSa+k3Viw5mNGROzTH8djZmZmNtD014ppTZ4A1mr6/m3Ak11s80SekzWC1CK2S4tV0hsR40l9cM3MzMxsYJoErCdpXVJ3qoNYcAXeq4CPAHcA+wE3Rg91GYtV0mtmZmZm3euvFdM64qca3eNIA5VDgYsi4gFJXwYmR8RVpI5VP5H0Z9II70E97ddJr5mZmZkNKBFxDXBNy22nNl2fBezfl3066TUzMzOzDnO9IpuZmZmZ2eDkkd7FzLAhvV2kbtHNYW5tsQbATFMbwGbFnP4+hGLeqPH37OmXlqst1jt63mRQin89XV+s1Wf2vFGFNGypWuPV5RXV9zs2UPR3TW8pHuk1MzMzs7bnkV4zMzMz69CuNb1Oes3MzMysg8sbzMzMzMwGKY/0mpmZmVmHdi1v8EivmZmZmbU9j/SamZmZWQfX9JqZmZmZDVIe6TUzMzOzDq7pNTMzMzMbpDzSa2ZmZmYdXNM7CEhaUdInethmpKRDerGvkZLur+7o5tv37b3Y5jFJq3Ry+46Sti1xXGZmZmbtqq2SXmBFoNukFxgJ9Jj0lhQRi5K07gg46TUzM7MiIuYWv/SHdkt6vw6MkjRF0ln5cr+k6ZIObNpm+7zNCXlEd6Kke/OlVwmlpGskbZKv3yfp1Hz9K5KOytdPkjRJ0jRJX2p67Mv56xBJ50l6QNJv8z73awpzfD6m6ZI2lDQSOBY4IR//9pL2z89xqqQJXRzrMZImS5r8+psv9eXnaWZmZtYW2q2m92Rg44gYLWlfUoK4KbAKMCknhScDJ0bEHgCSlgV2jYhZktYDLgPG9CLWBFLy/BgwG9gu3z4W+Kmk9wHrAe8BBFwlaYeIaE5MP0QaeX43sBrwIHBR0/3PRsTmuWTjxIg4StL5wMsR8Y18/NOB3SLi75JW7OxAI2IcMA5gpeHvaM9CHTMzM6vEXNf0DjpjgcsiYk5EPA3cAmzZyXbDgAty8ng5sFEv9z8R2CHH+R0wPCfQIyPiYeB9+XIfcC+wISkJbj3GyyNibkT8A7ip5f4r89d7SMlxZ24DLpF0NDC0l8duZmZmtlhpt5HeZurldicAT5NGhIcAs3r5uEmkEeFHgetJo8lHkxLURvzTI+IHi3CMr+evc+ji/yoijpW0FbA7MEXS6Ih4rndPwczMzGx+4T69g8JMYPl8fQJwoKShklYljcre3bINwAjgqUhV1YfTy9HSiHgDeBw4ALiTNPJ7Yv4KMB74mKThAJLWlLRay25uBfbNtb2rkyap9eU5ImlURNwVEacCzwJr9eb4zczMzBYnbTXSGxHPSbottxr7PTANmAoE8LmI+Iek54DZkqYClwDnAVdI2p9UXvBKH0JOBHaOiFclTQTelm8jIq6T9E7gDkkALwOHAc80Pf4KYGfgfuBPwF3Aiz3EvBr4paS9geNJk9rWI40a35Cfr5mZmdlCadea3rZKegEiorUd2Ukt979JSjSbbdJ0/ZS83WPAxj3E+gLwhXz9SVrKFSLibODsTh43PH+dK+nEiHhZ0ltII9HT830jm7afTB4Fjog/tRzvRMzMzMysW22X9A5Cv81dF5YEvpIntJmZmZn1i3at6XXS2wNJuwFntNw8IyL2qWL/EbFjFfsxMzMzs6456e1BRIwnTUozMzMza3tzPdJrZmZmZu0u2nQiW7u1LDMzMzMzW4BHes3MzMysQ7tOZFO7PjHr3Ijho2r7Dz9olc3rCsUtrz5WW6yhqvcEySZLr1FbrNk1ntJaoteLJi66vd5crrZYk5acU1ssgMPnvlZbrHW3n1lbrHNure91v9rc+l6Lw2t8eTxf88L0zw+p7/3jFc2tLdZXJ3+ttlgAw1Z5e30vyC6sPmLD4v+ZT7/4UO3P0yO9ZmZmZtahXRencE2vmZmZmbU9j/SamZmZWYd2LX31SK+ZmZmZtT2P9JqZmZlZh3ZdnMIjvWZmZmbW9jzSa2ZmZmYdXNNrZmZmZjZIeaTXzMzMzDq4T6+ZmZmZ2SDlkV4zMzMz6+CaXitK0jWSVuxhm5sljenk9tGS/qPc0ZmZmZkNbovVSK+koRExp7+PozMRsShJ62hgDHBNRYdjZmZmiyn36e0Hkn4t6R5JD0g6RtLHJZ3ZdP9HJZ2brx8m6W5JUyT9QNLQfPvLkr4s6S5gG0mnSpok6X5J4yQpb7elpGmS7pB0lqT78+1D8/eT8v3/2c3xnidpr3z9V5IuytePlPTVHo7zMUmr5OtfkPSQpOslXSbpxKYw++fH/0nS9pKWBL4MHJj3eWAnx3WMpMmSJr/x5ksL/x9iZmZmNkgN6KQX+FhEbEEaxfwUcCXwoab7DwR+Lumd+fp2ETEamAMcmrdZDrg/IraKiFuB70bElhGxMbAMsEfe7mLg2IjYJj++4UjgxYjYEtgSOFrSul0c7wRg+3x9TWCjfH0sMLGH4wQgly/sC2yWn2trOcMSEfEe4DPAaRHxBnAq8POIGB0RP289qIgYFxFjImLMksNW6OLQzczMzCBq+NcfBnp5w6ck7ZOvrwWsCzwqaWvgEWAD4Dbgk8AWwKQ8cLsM8Ex+3BzgiqZ97iTpc8CywMrAA5ImAstHxO15m58xLxl+H7CJpP3y9yOA9YAZnRzvROAzkjYC/gisJGkNYBtS0v6Rbo6zYSzwm4h4DUDS1S33X5m/3gOM7OQYzMzMzKzFgE16Je0I7AJsExGvSroZWBr4OXAA8BDwq4iIXKLwo4g4pZNdzWrU8UpaGjgPGBMRj0v6Yt6nujsU4PiIGN/TMUfE3yWtBLyfNOq7cj7WlyNiZg/H2RyvO6/nr3MYwP9/ZmZmNji5prd+I4Dnc8K7IbB1vv1K4IPAwaQEGOAGYD9JqwFIWlnSOp3sc+n89VlJw4H9ACLieWBmHkEGOKjpMeOBj0salve9vqTlujnuO0ilBxNII78n5q+9Pc5bgT0lLZ2PcfduYjXMBJbvxXZmZmZm3YqI4pf+MJCT3muBJSRNA74C3AkdCeofgXUi4u582x+BzwPX5e2vB9Zo3WFEvABcAEwHfg1Marr7SGCcpDtIo60v5tsvzPHuzZPbfkD3I6wTSXW3fwbuJY32TuztcUbEJOAqYCopwZ/cdCxduQnYqKuJbGZmZmaLO7VrA+K+kjQ8Il7O108G1oiIT/fnsUhaljRifExE3FvFvkcMH1Xbf/hBq2xeVyhuefWx2mINVb2fFTdZeoHPb8XMrnFywRI9VvJUZ683uzs5U61JS9bbFfHwua/VFmvd7WfWFuucW+t73a82t77X4vAaXx7PD60vFsDzQ+p7/3hFc2uL9dXJX6stFsCwVd5e3wuyC0stvVbx/8zXZz1e+/N0Teg8u0s6hfQz+Svw0X48lnF5MtzSpBrgShJeMzMzs8WVk94st/paoN1XZyS9G/hJy82vR8RWFR3LIVXsx8zMzKyv2rUKwEnvQoiI6aRV0MzMzMxsEHDSa2ZmZmYd2nWkdyB3bzAzMzMzq4RHes3MzMysQ3uO83qk18zMzMwWA+7Ta70i6ZiIGOdYgyeeYznWQInnWIMrVt3xHMvq4pFe661jHGvQxXMsxxoo8RxrcMWqO55jWS2c9JqZmZlZ23PSa2ZmZmZtz0mv9VaddUntGqvueI7lWAMlnmMNrlh1x3Msq4UnspmZmZlZ2/NIr5mZmZm1PSe9ZmZmZtb2nPSamZmZWdtz0mv9StLK3V36+/jMbHCRtJKkTRyrz/tfTtKQfH19SXtJGlYqXn+p4ee4f29us/7hiWy2AEkf6u7+iLiywlgzSMt8C1gbeD5fXxH4W0SsW1WspphnAl8FXgOuBTYFPhMRPy0Qa3/g2oiYKenzwObAVyPi3qpj5XjLAv8FrB0RR0taD9ggIn5bINb6wPeB1SNi4/yHZK+I+GqBWG33vCRNp5sl7iOi8j/Mkq7uJOaLwGTgBxExq8JYnb2PvAhMj4hnqoqTY90M7AUsAUwB/gncEhGfrTJOm8e6B9geWAm4k/SaeDUiDi0QaztgSkS8Iukw0vvi2RHx16pj5Xg3U9/P8d6I2Lyn26x/eKTXOrNnvhwJ/BA4NF8uBA6rMlBErBsRbwfGA3tGxCoR8RZgD6Cy5LrF+yLipRzjCWB94KRCsb6QE96xwG7Aj0gJVSkXA68D2+TvnyAl+CVcAJwCvAkQEdOAgwrFasfntQfp9+zafGn8nl0D/LJAPIBHgZdJz/EC4CXgadLvwAUVxzqS9J7ReF4XAJ8FbpN0eMWxRuTf6Q8BF0fEFsAuFcdo91iKiFdzrHMjYh9go0Kxvg+8KmlT4HPAX4EfF4oFNfwcJX1A0rnAmpLOabpcAsyuMpYtPCe9toCIOCIijiCNCG0UEftGxL7AuwqG3TIirmk6ht8D7y0Uq3HK7j+AyyLiX4XiAMzJX3cHvh8RvwGWLBhvVEScybyE7TXSyHkJy0bE3S23lXpzb7vnFRF/zSNb20XE5yJier6cTPqAVMJmEXFIRFydL4cB74mIT5JG26o0F3hn0/vHRqQPLlsB/11xrCUkrQEcAFQ++r+YxJKkbUgfUH7XiF8o1uxIp5n3Jo3wng0sXygW1PNzfJI0Oj4LuKfpchXlfp+tj0q9oK09jIyIp5q+b4wIlfBsPv3/U1KyfRjwXKFYV0l6iFTe8AlJq5LeqEr4u6QfkEYVzpC0FGU/bL4haRnyKWxJo0iJRgnP5v03Yu0HPNX9QxZauz4vgOUkjY2IW3O8bYHlCsVaVdLaEfG3HGttYJV83xsVxxoZEU83ff8MsH5E/EvSmxXH+hLpbNGtETFJ0tuBRyqO0e6xPk06w/GriHggx7qpUKyZkk4hvc/vIGko8wYjSij+c4yIqcBUST+LiKpf31YRJ73WnZsljQcuIyUAB1HuTfBg4DTgVznWhHxbpfJEjauBM4GXImKOpFdJIw4lHAC8H/hGRLyQRxtKlVJA+hleC6wl6VJgO+CjhWJ9krTi0IaS/g7MoOLylybt+rwglQFcJGkE6bX/IvCxQrH+C7hV0l9II+Xrkj74LUcqvanSREm/BS7P3+8LTMixXqgqSE6Y1mqugY6IR3O8SrV5rD0jYq+WWJ+qOlZ2IHAIcGRE/CN/+DqrUCyAp1p/jpK+VSjWeyR9EViHlGMphYy3F4pnfeCJbNatPBll+/zthIj4VeF4wyPi5cIx7oiIbXrecpHjDAGmRcTGpWO1xH0LsDXpzfbOiHi2cLzlgCERMbNwnLZ8Xk3xViC9J79YOM5SwIakn+NDVU5ea4kjUoK2XY51K3BFFPijI+mmiNip6v0uZrFujIh/rynWGRHx3z3dVmG82iaX5bOIJ5BKGxrlbUREqTOX1gdOem1AyKd0LwSGR8TaeYLDf0bEJwrE+hIwDbiyxB/glliXAqc0TifXIXcbGEnTmZyosONGU5wVgQ93EqvI6FCNz6uzGd0vAvdExJQC8VYH/g94a0R8QNJGwDYR8cOqY+V427Lgz7HkJKLiJH0NGAH8HHilcXsU6JLSxrG+CaxHGplvjlXid6yzJHRaVNyxJNcobwt8Bvh2010rAPtExKZVxssx74qIrarer1XDSa8tQNKtETFW0kzmb2/UOE2zQoGYdwH7AVdFxGb5tvtLjJLm57Uc6VN4Y0JUqed1I7AlcDfz/yHZq8sHLVq8i4BNgAdIE4lyuKj8dLmk20mtjaY3xSIiqj5NXvfz+hkwhlQGA2kS4iTS6OjleUJdlfF+T+pO8b8RsamkJYD7IuLdVcbJsX4CjCK1bWqMQkWJDyr5LNEZwGqk37GSv2edlV1FiZHLNo51cRexKvsdk/Rx4BPA24G/NN21PHB7VNweTdJ7gR2BY4Hzm+6aCVwdEZXV9UpqJPEHAENJ3Yc65h2U+KBifeek1waExqdjSfc1Jb1TS3wSr1N+011ARNxSKN4fI6JUm6HWWLX1nqz5eY0H9m2U2UgaTmohtg9ptLfS45A0KSK2bHntT4mI0VXGyft9kNSRpfgbv6Q/k+pEHywdywaHXLe+EnA6cHLTXTOjYBcdSetEoR7ATTG6m+9S5IOK9Z0nstlA8Xg+7RqSliRNoCjyxzLXGh4KrBsRX5G0FrBGLNimapFFxC2S1gHWi4g/KC2yMLTqOE3ukLRRRPyxYIyGn0g6mtQCqHlEo8Qfrzqf19rM38ngTWCdiHhNUomOEa/keuVGt4itSeUUJdwP/Btlu1E0PF1XwltniUgbxyq+KEuuV38RODhPnludlIcMz/M5SpWBLSVpHAuW9VSWiNZVe22LxiO9NiBIWgU4m9TaS8B1wKdLFP9L+j7pFPm/R8Q7Ja0EXBcRWxaIdTRwDLByRIxSWkns/IjYuepYOd4OpNPy/yAloo1TyiVW9/ok8DXSTPzGG0lEgVnKNT+vL5BGdX+Tb9qT1Gvzm8C4AqdgNwfOBTYmJaWrAvtHaoFUqTwaNZpUbtP8QaXychtJZ5MS7F+3xCpRI1pniUi7xrqF1FnmBzWUmB0HfJHUBrO5XKnI8sCSppLKG1onl91TIFatcwKsbzzSawNCpJn4lS932YWtImJzSffl2M/n0eUSPgm8B7grx3pE0mqFYgFcBBxOS51tIZ8F3hGFuyhktT2vPPr/e+Z1HTg2Iibnu0u8Rh8gLcSyQY73MOV6OX+x0H47swLwKvC+ptuCMistrhIRv1Dq/UpEzJY0p6cHOdZ8lo2Iu9OJsA6lFpv5DGkZ8bo6GsyOiJIrYTYbQ+dzAo6VVPmcAOsbJ702ICgtEHE0C55+KtGv9M18aq1xOnlVyiVSr0fEG40/JHmkpuTplb9FxFUF99/sAVJSU4c6nxcRMVnS34ClIS3iUPDU6x25NvqBxg2S7qX6FdKK1ZJ3EeuIumJRb4lIu8aqc1GWxyn3PDpIWjlfvVrSJ0h94EuXYr0F2LxpTsBppDkBO5BGmp309iMnvTZQ/AaYCPyBptNPhZxDevNbLbcE2g/4fKFYt0j6H2AZSbuSZi5f3cNjFsVDufvA1RQ+pUz6f5qST5k3xyrRsqy25yVpL1Ipw1tJq4itDTxExctwS/o3YE3Sa2Mz6FhWeQVg2Ypj1daRRdLnIuJMSefSyQe8Qq+Pz5JKUEZJuo1UIrJfgTgDIdb+hWLVuSjLo6TFj37H/L/PVS8YcQ/pNdj43WpeGChIXSSqVvecAOsDJ702UCwbhRqTt4qISyXdA+xMejP8YMEJNyeTVtyaDvwncA2pH3Epy5D+iNRxSvnX+VKHOp/XV0iLYPwhIjaTtBMFVgcEdiOtKvc2oPmP/Uzgf6oMFBFj89flq9xvFxq/S5O73apCEXFv7pTSUSIShZaCrTMWNZa+RFqBbRfVsyjL3/JlyXwpIiLWLbXvbvwMuFNS85yAy/LPtY6JuNYNT2SzAUHSV0l9Gq+pIdY3gIsj4oEeN170WHsA10RE6fpaq4ikyRExJk9+2Swi5kq6OyLeUyjevhFxRYl9dxLrJxFxeE+3VRRr/4i4vKfbKoxXdNENpb7DXSp01qHOlcSWIq2gN5L5f4ZfrjpWU8zlIuKVnrdc5Did/d+9CEyPiGcKxNsCGEv6oHJr05wA62ce6bV+1XS6VcD/5NM/b1KwkT3pVPW4XF97MXBZlFv69SDgbElXkBLtUm3YajulLOkXEXGApOldxKpsBnY/nSp/Qak37wTgUknPUG5CDxFxhaTdSeUTSzfdXiLZmK9EI/8ObFEgDsAppNW9erptkamLRTeAKlea2zN/XY20yteN+fudgJup8KxDnaUvTX5D7jJAU8lBCUorpf0QGA4UXYEzOxLYBmj00t2RtLDO+pK+HBE/WdQAklaIiJdyHfGMfGnct3Kh+mHrIye91q9qOt3aGvNC4EJJGwBHANNyvdwFEdFdg/GFiXWYpBVIp8cvlhTMS7SrPH1Y5ynlT+eve9QQq/ZT5cDewCzgBFK3hhFAydGu80mJzE6k0pf9SC3FqoxxCqlkYhlJLzVuJtUejqs41geA/wDWlHRO010rUO7DwxgKL7rRmJgn6bc51lP5+zWA71UcrrbSlyZvi4j3F9p3q++QnuNVABExNbclLGUu8M6IeBo6+h9/H9iK9OF2kZNeUlnDHsxfR9z8tUT9sPWRk14bECRtB0yJiFckHUaauf6dUjPmc/eGDfPlWWAq8FlJ/xkRB1UZK3/6v4JUl/oZUg/YkySdExHnVhSjMTnu1c5OKVcRoylWY0b3J1rrsCWdAVRWm13n82qK2Xy6tfIllTuxbURsImlaRHxJ0jepuFY5Ik4HTpd0ekScUuW+O/Ek6UPKXqQEoGEm6YNECXUuujGy6XcAUq/Z9asMEGkp7x/VWfoC3C7p3RExvY5gEfF4S3u0khOYRzYS3uwZYP2I+JekSuqxI2KP/LU/6oitl1zTawOCpGnApsAmpE/dPwQ+FBGdLuO7iLG+RfqDfAPww2haiU3SwxGxQYWx9gQ+Rjr1+hPgRxHxjNLKbA9GxDpVxcrx6qwB7CzWtCrLG3qIVep5fQg4g3QaW5QttaFRLyzpTuBDwHPA/RGxXqF4KwHrMX8pxYQCcYYVnODVGqvORTe+S/r5XUYawTsI+HNEHF8gVvE626YypSVIz+tRyi8A80vSCPZ3SZNGPwWMqXrAoSne8PoNrgAAIABJREFUeaSuCo0PzvsCT5C6Ofw2KlxNTVpgxc+1gX+LAit+Wt95pNcGitkREZL2Bs6OiB9K+kihWPcDn4+IznrMVj1ZaX/g261JRUS8KqmyHsR1nlKW9HFS67VR+cNKw/LAbRXH6o9T5WcCexbs6NHqakkrAmcB95ISkAtKBJJ0FKk85W2k+tetgTuAypZjbTJS0unARsyfYJc4zfvFAvvsVEQclz8YbZ9vGhcRvyoUro462zrKlFodS1qBc01S8nkdqWVaKZ8kJbqNBWd+DFyRy2GqXj74PPKKn6ROMDOBK4DKV/y0vvNIrw0ISktgXkuqsd0B+Cep3KHy5TZzvFpGu+qSJ4KMJtWentp010zgpoh4vsJYI4CVgNNJLdk6YlU9WaPO59UU87aI2K7q/XYRawiwdUTcnr9fCli61MTKPKq3JXBnRIyWtCHwpYg4sECsW4HTgG+TJoEdQfqbc1qBWB8DJkbEI1Xvuz+p0DLAXcQaBTwREa9L2pF01u3HEfFCHfHbReMMlKT7Yt5yzlMjYtP+PjbzSK8NHAcChwBHRsQ/8imhs0oEqnO0S2kFpXOBd5L6UQ4FXqn6VHlETAWmSvpZ6VPKOSF7UdKTwHIRUaz3ZNPz+hfwu6in9dtkST8n9SAuuhBGpHZo3yTNLCciXqfszPlZETFLEpKWioiH8oTOEpaJiBskKSL+CnxR0kRSIly1kcBhktYhjYpOJCXBU6oOVNfvdFZnne0VwBhJ7yCVl11Fmpz1H1UHkrQucDwLlm1UWo6iGhdlaVLnip/WR056bUCIiH/QNEs5T2Crst1Qs08zb7Rrp8ZoV6FY3yXV/F1OmmH+YeAdhWIB3Js7RDR7kTSx6KtR7Vr3DwIXqJ7WbwcC31Hh1m/ZCqTlletYCAPgOkn7AleW7D6QPZFLKX4NXC/pedLEsxJm5ZHsRyQdB/ydVCdduYg4FUDSMqTlzE8idQgYWiBcnb/TY4GPSppB4TpbYG5EzM6lG9+JiHMl3VcgDqTX3w9JKywWSwij3kVZGupc8dP6yOUN1q/645O4pEkRsaWkKcBW+XTelIgYXSBWY6GDjglekm6PiG2rjpX3fSZpFvTP8k0HkX6WLwJjI2LPrh67CDEbrd8OJtX0Vt76LcdptH47gvRaKdH6rXb5tb8cqUZ5FoUnzjXFfS+pHdu1EfFGT9svxP63JH0wWpFU27gCcFZE3Fkg1udJ9ZrDgfuAW0kjvZV3c6jzdzqPXC8gj5xXHesu0geF/yXVtM8oVV4h6a6I2Krq/fYQcyywXkRcLGkVYPmImNHT4xYy1obMW/HzhhrnB1gPPNJr/aqfPonXOdr1qqQlgSk5IX2KlOCUsl1LPer0Ro2qUiu4SqmNWr+pfxbC6PG1L+ldUcHqgXnUdVojiYmIWxZ1n93EGgocEBEnAS+TPqiU9CHSh4bfAbeQzuLMKhSrtt/pRnIraTWa5h8UcgRpgtnXcsK7LvDTQrHOlnQaaQJbcwnRvSWC5VhjSMs5X0wqS/kp6YNS1bG+TCqvuSRqWG3O+sYjvdavlFav6VLVE6M6iV96tGsdUk/IYaQepSOA8yLiz1XHyvGmAsdExF35+/eQRl43bZ5YUVGstmr9JmnPiLg6dw3pLOktVW7TLVXYmk3SpcApUaj/dUusG4GdayjZaMRbnlQOMBY4AHi68aG64jjrkHrzLknh32lJewHfBN5Keh9Zh/R6f1e3DxzgclePw4G/MK+8ISKiRBcR8lm9zYB7myaXlWqv+DHSa3Ab0oTbicCEiPhN1bGs75z0Wr/KtWqNVWtaRVTY3qi/E+w65FPKF5FO8wp4CTgKeADYPSJ+UWGsjwH/Lzpp/SZpRJX1vZJ+DFwYnXTYkLRzRNxQYawtSatejWTe2bBSdZS9OZ7KPqzkRHRLUj/bjlGoqicQ5VjfJHVIubwlVuW10ZI2JrUQey9pRO9xUnnDqd0+sO9xhpI+cFV+1qSLeFNJE2z/EBGbSdoJODgijqkwRm3LijfFfAjYpMRAQxfxGr2wG50VlgPuKPk7rbSU9AHAicBKNZ/NtC446bXFRs0Jdqd/QJqCFU2glNqKKQq3G1KbtX6DNEpNmgg1naZJNiXqKHt5PFWO9Ha62EuJUgdJF3ceKirrT90U63ek5WQnApOiYAcTSeNJNa/FE7am+uGpwGaRun3cHRGV9ROXtEZEPFVz/fDPgeMj4pmq991FvBNJ71O7klotfgz4WRVlUZ3EupDUm/pp0uvxVtIIc6m+4tYHrum1ASPPGh5LShYnRsSvq9x/1Ls8ZH80fF9gBSflZT6jwhWcmmK1Xeu37J8RcVWB/fa7npJbSXdExDYVxeq2jlfSKZGWR64i1u65znZ9YINcXlMq8X0MuE3SVcw/gv2tLh+x8F6QNJyU0F8q6RkqXpSlabLfztTX63h14CFJkyi8gl7e7zck7Uo687UBcGpEXF8iFvAW0vvTC8C/gGed8A4cTnptQFBaJvIdpKU9AY6VtGtEFFmlp4YEu2N0JJ/mek+ONSlSe7ZS6ljBqaFdW7+dlkdrbqBwn95equUUcFZ6slSz/Umjbossj2D/mJSQClhL0kcKnXV4Ml+GkFYhhG7O6iyivYHXSLXDh5Lqhyv/AJuNpKZex5Tp1dwlzVu85KTSsSJinxzzncBuwE2ShkbE20rHtp456bWB4r3Axo1JL5J+RDq9XLk6E+w8GnoqcCPpj/G5kr4cERdVHSt7W0S8v9C+W9W50AER8ef8x2MOcLGk2wuFOoLUjWIYTZNsKNSnV9J2pNUHX8kdNjYnLcX9V4CI2LpE3C7UWe/WWZnRwvoW8L6IeBhA0vqk3+8tKozR8MeIuLz5Bkn7F4gDqT91Y/T1R4ViAPX2Om6ccVBqQ1hHHjKS+hYv2YNUX74DaeXKG3M8GwCc9NpA8TCwNtAYIV0LmFYoVm0JNukPx2aRF4WQ9BbgdtJksxLqXMGpXVu/bRqFlr/uwveBTZWWXP4cqWn/j0mv03ZWZYI9rJHwAkTEnyQNq3D/zU4hnXHo6bYqjCQlayNJC8yUTNZaex2fSKFkTdIxpN7Nr5E+WIr0eqhsXkWzOhN64AOkcpSzI6LU+6EtJCe91q8kXU16sxsBPCjp7vz9VqTksIQ6E+wnSG1rGmaSZpaXUtsKTo3TeKTlZW8it36rOk52OOkP1HGkU71rkWqXS7hT0kZRcHnlFrMjIiTtTfpD+cPcNq0/VDn6WmesyZJ+SGpnB6kU4J4K94+kD5CW5F1T0jlNd61AxXW2DTUna3X2Oj4JeFdEPFto//OpM6Hv6YxhlXXz1ndOeq2/faMfYr6FeQk2pLrUO/LElKonU/wduEvSb0jJ/N7A3ZI+m2NVPfnlAxXvbwFdtH5rjCwPJ03eqFRTjfRrlKsbbhgLfKSODw7ZTEmnAIcBO+S2WKVGKRt9ZteLiD/kZGqJmLeq3eGl4naiypHRjwOfBD5F+v+aAJxX4f4hncWYTOpN3ZxQzyR9EKtczcna5prX63hX0hLjRXodk/rzLtDqsKA6E/qe1Fk3by3csswWO121bWqosn2T0kpA3cWqJIGTtEKkFcs67UVcZQ/idm/9Vmfrphzv34BDSJMcJ0paG9gxCiyGIelo4Bhg5YgYJWk94PyI2LlArPVJpRurR8TGkjYB9oqIr1YdqxfHckVEVHJmQNKw7jpDVBzrXmpK1lRTr+McazPSymh3Mf9k0SKrHuaYtSxe0ovjqKwFofWdk14bECTNZF5ysyRppKtUS6raSFq69Y+UpFWqPq0n6bcRsUcXCWmliWidukpAG/qrd25V8qju+IjYpaZ4U0idRO6KeStTTS9RwyzpFtJp7B80xbo/8jLIdVLFqxHWGauuZE2p1/EtpL6ypXsd353jtPbCLjJZr86EvhfH4qS3H7m8wQaEaFmtRtIHSX+cK5fblZ0BrEZKDhunr0sk2HdLOiYi7syx9yW1aVq/yiARsUf+Wmcv4nZu/VaLiJgj6VVVvIJdN16PiDeU+zdLWoJyHRuWjYi7G7Gy/upXWufoTmWxukrWqtp/s4jYvYdjqWwEm1TH/tmK9tUbZ5AS+nMonND3Qp1189bCSa8NSBHxa0knF9r9maQVlR4stP9mhwIXSboZeCupnrjI+vIAkn5J6gxxbUTM7Wn7RYzVzq3f6jQLmC7peuZf7KDEqd5bJP0PsIxSs/5PAFcXiAPwrKRR5CRQ0n6krhvWewMpWavybNFNuYPD1cxf3lBkKfiaE/qBVDdvLVzeYANCHjFsGEIa1XhviVmukm6LiO2q3m838T5ImlU+E9ghIv5cMNYupD6zW5MmCl0SEQ8VivUA87d+GwJMj4h3FYj1MLBta+u3iCjWF7guXXVqKHGqN/8fHQm8j/ThYTxwYRT4QyDp7cA4YFvgeWAGcFhEPFZ1rF4cy6Atb+ghVqXJWg+xqlwOe0YnN/dbGVaV/2d11s1b33mk1waKPZuuzyatrLR3oViTldZ+/zWFV9zKbZRGAZuQShqulvTdiPhe1bEAIuIPwB8kjQAOJvXPfRy4APhpxSNF7dz6rTYR8aM8GrR2c6/ZQrHmkl4LF5SMk2M9CuwiaTlgSNNIV3/47zaNNShr9esuw+qFKj/0fZJcNw8QEY9IWq3C/dsicNJrA0JEHFFjuBVI7XLe13wIlFlx637gqDySNkPS1qTVo4rJo6CHkU6j3QdcSm7DBexYYah2bv1WG0l7klr3LQmsK2k08OWKf36NWJ11w3iR1Irrq42R9Ipifbbl+0ase6KixRW66e4xX5u5iLhuMMXqg0G5gp7SwiEfJ61aBnAzacJjf5ZvVKXOunnrIye9NiDkU6Fnk07LB3AHcEIeLapUnQl2RHy75fsXSaeXi5B0JWkJ3Z+Q6pYbNZQ/lzS54nB1znz+S740/CZ/Xb6TbQebL5JGhm4GiIgpkkqNhP0emAP8LH9/UP76EnAJ859xWVRj8qVRM7w7MIlU+315RJxZQYw9KtjHQIw1EFU5gv19UoeeRi/lw/NtR1UYoy+qnFxWZ9289ZFrem1AkHQn8D3mTYo6CDg+IraqMMbnIuJMSefSySfvEhOHcj3X6cBGNDUlL1W7JunfI+LGEvvuT3W1fusPku6KiK2a6wolTYsyPYgXqGdv3FZ16zJJ44F9I+Ll/P1w4JfAPqTR3o2qirW4qqIWtbcj2FWSNDUiNu3ptrpIel9VI/R11s1b33mk1wYKRcRPmr7/qaTjKo7R6NYwmfpON10MnAZ8G9iJNMmsWMuaiLgxtzlqTbJLLHTQdq3f+sn9kg4BhuYPSZ+i3BLcwyVtFRF3AUh6D2m1L6i+ndjawBtN378JrBMRr0l6vYvHLJRcNnQu8E5SmchQCvX5rjNWL1Qx+tofI9hzJI2KiL9Ax5m+OVUH6Y+SlDrr5q3vPNJrA4KkrwMvAP+P9CZ1ILAUafS36hXFtgT+BxjJvA9+pUY07omILZpH0SRNjIjtq46V930aqW53I+Aa0rLEt0bEfgVi/ZmaWr9JejepFdvNzGv9dlREPFE6dmmSlgX+l/lHhr7SOrJdUawtST/H4TnWS6RTyg8Au0fELyqM9QXSqG6jFGVP4Crgm8C4iDi0wliTSWeHLieVVHwYeEdE/G9VMeqM1R+jr3WStDNpQOBR0nNaBzgiIm6qOE7ti9vUWTdvfeek1waELlrYNFTayia3vzqJBVcDKvEGeBupufwvST1m/w58vVSrrfyGuylwX0RsKml10qm1Kms1G7HatvVbf1FaoW25iHipcJwRpPf/FwrHGQNsR0psbo2IquvKG3EmR8SY5rIQSbdHxLaDMVY/JWu1jmBLWgrYgPTaeCgiKh397y+SzqTruvmxJd6Lrfdc3mD9LtdAHRYRt9UU8p8RcVVNsT4DLEs6Zf0VUonDhwvGey0i5kqaLWkF4BnKtTVq29ZvdZL0M+BY0h/Ke4ARkr4VEWcVirc78C5g6cYM84j4colYETFZ0t/IpTaS1o6IvxUI9aqkJYEpOel4CliuQJxaYpVIanvhu3Qygl0ikKRPApdGxLT8/UqSjoyI83p46MLGqzOh365lMGB6U938YQXiWR8M6e8DMMs1UN+oMeRpki6UdLCkDzUuhWIFaXTyKtIfkvUpW+s1WdKKOcY9wL3A3d0/ZKE1t37bM19K1QfeD+wUETMiYjypy0e7rF+/UR7Z/SCpJGVtCq3aJOl8UunQ8aQRtv1Jp5ZLxNpL0iOkRSluyV9/XyIW6ec1BDiOtKrdWkCp3+naYknaWtIkSS9LekPSHEnFzgLksydDI2JORFxM+pBewtHNZxki4nng6EKxICX0BwOPAMuQSnrOLRRruKSOCdiF6+atjzzSawPFdXly0pU1zHI9gtTWaxjzyhtK9em9lE5KKUqJiE/kq+dLuhZYoTGaUiBW27Z+q9mw3Lf0g8B3I+LNxghsAdtGxCb51PyXJH2TMq97SGc2tgb+EBGbSdqJlHiU8MGIOJu0pPOXACR9mtQGcTDHqm30lXpHy4dIUuO9Ppf1LFkoFpASeklDI2IOcLGkUpNFjyItPT9f3bzSIi2nF4ppveSk1waKz5LeYGdLmkXZTgCbVtmaqQe1lFJI6nLUU9LmEXFvhbHavvVbzc4njYJOAybkes4XC8VqTI57VdJbgeeAUj2B34yI5yQNkTQkIm6SdEahWB9hwaTzo53cNthi1ZmsNY9gn0DZ0fLxwC/ymYcglfdcWygW1JjQR8Qk4N1d1M1XNlHUFo6TXhsQIqLORQbulLRRRPyxhlinSboQuIGyda/fbLrenIgqf//vFcZq+9ZvNVuZeSUvXyAlHjcXinV1Ln85i1T6EpQrt3khj3ZNAC6V9AwVn96VdDBwCGklu+YPlyuQEvpBGatJnaOvdY5g/zdwDGlVNgHXARcWiNNQZ0Jfa9289Y27N9iAIWklYD3mH8mbUCDOg6RJUTNIiWjJJuw/JZVSPEBTKUVEfKzqWDneMqQVgMaSEpqJwPcLtr9qy9ZvdZL0X03fLk2qi36w6tdInjC6dUTcnr9fClg6l4pULp/OfY2UbBwKjCBNXqpyqeN1SCPVpwMnN901E5gWEZUl2XXGaon5NOnU/wmkn+H3Gv1tK451b0Rs3nLbIi9+sZDHckVE7Fvh/j6dE/pub6so1vmkycs7kRL5/YC7I6JdyrEGNSe9NiBIOgr4NPA2YAqpFvCOiKhyhLIRq9OJO6V6NtZYSoGkX5BqyC7NNx0MrBgRBxSI1bat3/pTTkaviojdCuz7jojYpur9dhJnKDA+InYpHasp5urAlvnbuyPimcEeq45krWkEeyzpQ3LDCsDsOv8Pm46p0mS7zoQ+18tv0vR1OGmuyvuqjmV95/IGGyg+TfojcmdE7CRpQ/IptqrV3A6ozlIKgA1i/qU8b5I0tVCsdm791p+WpVybuVomjEbEHEmvShpRaiS5maT9SR1gbiaduTlX0kkR8cvBHIt66odvJ5VNrML8ZVIzSXXm/aGS12Y/laTUWTdvfeSk1waKWRExSxKSloqIhyS1wyjeWOAjSotvFC2lyO6TtHXMW653K6BU/+O66pVhXuu3dUhdNyDVog7qlalggRWchgKrAqXq/xoTRudIeo2yE0ZnkXqUXk9q7QWUmegIfB7YsjHiKmlV4A+kMwODLladyVoeBPgrsE3LCPaDJUo2atYfCX2ddfPWR056baB4Ir9R/Bq4XtLzwJP9fExVeH/N8bYCPqy0IACknq8PNhKripPttm39VrPm3sazgadLJRs1Txj9Xb7UYUhLicFzlOtDX0es2pO1mkewezycKnZSd0Kf6+ZvyB0brpD0WwrWzVvfuabXBhxJ7yVN2Lg2It7o7+MZTLqqV26osrSjznplSbdGxNg6YrUzpankhwLrRsRXJK0FrBERRRYwyRMr146Ih0vsvynOmaTlty/LNx1Imlz234M5Vo5XV/3wVGDX1hHslnKpWkh6X0RcV+H+WhP67YFS5S+11M3bwvFIrw0YksYC60XExfkNd01ShwXrpTauV66zlKKdnUcaKf93Um30y8D3mJdUVUbSnqREY0nSafrRwJcjYq+qY5HOMPyAVE4kYBxpMmwJtcWqefS1+Ah2SynPfHfRdCaqyoQ3q7P8pc6FlqyPPNJrA4Kk00grDm0QEevnCQCXx/xrmNsA0s6t39pVYxZ788x1SVNLjOZJuoeUXN/cFKvI2YEuZudPK/RarDNWbaOvdYxg13kmqiXufK+7XIYwtdBrcSa5bp7Usq9k3bz1kUd6baDYB9iMVPhPRDwpqc76Q+u7OuuV61xFr529mduJNZZ/XZVyNdKzI+JFzb+kcqWjLJI+TupL/XZJzbWuy1PxBM46YzWps1a5+Ah2zWeimv1e0njmT+ivKRGo5rp56yMnvTZQvBERIanxx7jUqkNWkTYupWhn/7+9uw+xvK7iOP7+qKuZua5lWhaaqYlWa5aaWREWWZSBmc9ZQlBgpBWVBgqp2R8mBZvEVvjUA+JjmkRmpoaypbk+pLZqomWYFZWmpW35cPrje8emZdUm7u/+Zu7v/YJl5t5h9pxZlpkz33u+53wFuAjYPMkXaIPzj+so1m1JDgXWTVsjfRTtgtY4nQ1cyloWRlTVAws41oyJFWu0E+VjmHURNckJtO1pY5VkD+BUYEda+8u6wCMdnoZOsiVlon3zmhvbGzQvJPk0bRvb22k/VD4EnF1Vp/aamOaFSbZSTLvRDOy30f4Nr6iq25/lU/7fOM8FjgVmhvJfBpxUHWwHnFZJTgau4z/F2tW0rXrjbDl46gQbmL3pbWNgRVUdNq5Ys2KuBA4Gzqe1tX0Q2K6qjh13rFG8SbakLGfUN19VO6ZtGv1RVY29b15zZ9GreSHJkcAfgN1p39wvq6rL+81K88XT9QL2+HLpgpRkGXBujVYRdxxrl6q6qes402wSxVqSTYBNmeAJdpKVVbXr7K8lyU+ras8xx+mjoJ9Y37zmzvYGzRdb0Lay3QicQbtZKwEWt2N0I3BcklfQ2hzOraqVHcX6cpIX007zzqmqX3YUZ+pMsn94NEP2IdrK8kl5NMn6wM2jC3S/p13+Grc+WlIm2TevOfKkV/PGqBdqb9rSg12B84DTq+ruZ/xESXOS5PnA+2gvMW9VVdt3FOdFwIG0XtTFtCL7pC5iTZM+Tl8nafTKzR9p/byfpM1l/+o0fK9P8n7a//fXAt9k1DdfVef3mpgAi17NM0l2phW97wSuol02uLyqju41MWmKJNmd9oN5X2BVVb2n43ivBo4GDqqq9buMpfkvyceratmzPbdQTapvXnNn0at5IclRwOHAn4HTgIur6rHRPMW7qmrbXhOUpsDoYtR+tP7Gc4GLRitTu4i1I62w3p82ausc4MKuNopp4XiaXuWnemAXskn2zWvu7OnVfLEZsN+avZtV9WSSfXrKSZo2vwb2pF3s2QBYmoSqurqDWGfSRm3tXVX3d/D3a4FJcghwKG1D3yWzPrSY9ovRNJhk37zmyJNeSRqIJB+mzct9KXAzrX3oZ1X11l4T0yCMenm3YS29yrTtb4/3klgHJtU3r7nparOLJGn+OQrYDbi3qvaibUH8UxeBkmyf5IIkq5LcM/Oni1haGKrq3qr6SVW9AbiDNo1iY+C+aSp4R7ajrU5/Ge1r1Txg0StJw7F6ZjlEkg2q6g5gh45inQksBx4H9gK+BXy7o1haQJIcAPwcOIA23eO6JPv3m9V4JDk5yV3AicBtwOu6viiq/509vZI0HPclWQJcDFye5EGgq37bDavqiiQZ9eofn+Qa4HMdxdPCcRyw28ylxtEs2x8DF/Sa1XhMsm9ec2TRK0kDUVXvHb17fJKraPNRf9hRuNUz01eSfAz4HbB5R7G0sKyzxhSPvzA9rzw/AVzJGn3zgH3z84AX2SRJY5dkN+B2YAnwedoN/VOq6tpeE1PvRlvYdqZN94A22u6Wqjqmv6zGI8mttL75a6vqNaOZvSdU1UE9pyY86ZUkdaCqrh+9+3fawpn/kuTUqjpysllpnijg68CbaAscvkE7EZ0Gq6tqdZKn+uaTdNU3rznypFeSNHFrW1CgYXia5RS3VNXSvnIalyQX0X7J+wStpeFBYFFVvavXxARY9EqSemDROzxJjgA+SrvkdfesD20MrKiqw3pJrCNJ3sKob76q/tV3PrLolST1wKJ3eJJsAmzKWpZTVNUD/WSlIbHolSRNXJKbqmqXvvOQNBzTMiJEkjRPJVknyeI1nl7WSzKSBsuiV5I0dknOTrI4yUbAKuDOJJ+Z+XhVndVbcpIGyaJXktSFnarqYWBf4AfAVsAH+k1J0pBZ9EqSurAoySJa0fu9qnqMNp9Vknph0StJ6sLXgd8AGwFXJ9kaeLjXjCQNmtMbJEkTkWS9qnq87zwkDZMnvZKksUuyRZLTk1w6erwTcHjPaUkaMIteSVIXzgIuA7YcPf4VbTWrJPXColeS1IXNquo84EmAUVvDE/2mJGnILHolSV14JMkLGE1sSLIH8FC/KUkasvX6TkCSNJU+BVwCbJtkBfBCYP9+U5I0ZE5vkCR1Isl6wA5AgDtHs3olqRe2N0iSxi7JSuAjwP1VdZsFr6S+WfRKkrpwMPAS4Pok5yR5R5L0nZSk4bK9QZLUmSTrAPsAy2mTHM4AllXVA70mJmlwPOmVJHUiyVLgS8ApwIW0i2wPA1f2mZekYXJ6gyRp7JLcAPwVOB34bFX9c/Sh65K8sb/MJA2V7Q2SpLFL8vKquqfvPCRphkWvJKkTSd4NvBJ4zsxzVXVifxlJGjJ7eiVJY5fka8BBwJG0Ob0HAFv3mpSkQfOkV5I0dkluqaqls94+D/huVe3dd26ShsmTXklSF/4xevtoki2Bx4BtesxH0sA5vUGS1IXvJ1kCfBG4YfTcaT3mI2ngbG+QJI1dkg2BI4A3AwVcAyyvqtW9JiYMZ5t0AAAAmUlEQVRpsCx6JUljl+Q84G/Ad0ZPHQIsqaoD+8tK0pBZ9EqSxi7JL6pq52d7TpImxYtskqQu3JRkj5kHSV4PrOgxH0kD50mvJGlsktxK6+FdBOwA/Hb0eGtgVVW9qsf0JA2YRa8kaWySPOMCiqq6d1K5SNJsFr2SJEmaevb0SpIkaepZ9EqSJGnqWfRKkiRp6ln0SpIkaer9GzDj2ZdEfCy/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation matrix\n",
    "corrmat = games.corr()\n",
    "fig = plt.figure(figsize=(12,9))\n",
    "\n",
    "sns.heatmap(corrmat,vmax=.8,square=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get all the columns from the dataframe\n",
    "columns = games.columns.tolist()\n",
    "\n",
    "#Filter the columns to remove the data we do not want\n",
    "columns = [c for c in columns if c not in [\"bayes_average_rating\",\"average_rating\",\"type\",\"name\",\"id\"]]\n",
    "\n",
    "#Store the variable  we will be predicting\n",
    "target  = \"average_rating\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(45515, 20)\n",
      "(11379, 20)\n"
     ]
    }
   ],
   "source": [
    "#Generate training and test dataset\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "#Generate training set\n",
    "train = games.sample(frac=0.8,random_state=1)\n",
    "\n",
    "#Select anything not in the training set and put it in test\n",
    "test = games.loc[~games.index.isin(train.index)]\n",
    "\n",
    "#print shapes\n",
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import linear regression model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "#initialize the model class\n",
    "LR = LinearRegression()\n",
    "\n",
    "#Fit the model the training data\n",
    "LR.fit(train[columns],train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.078819032629326"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Generate predictions for the test data\n",
    "predictions = LR.predict(test[columns])\n",
    "\n",
    "#Compute error between our test prediction and actutal values\n",
    "mean_squared_error(predictions,test[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=10, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n",
       "           oob_score=False, random_state=1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import the Random forest model\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "#initialize the model\n",
    "RFR = RandomForestRegressor(n_estimators = 100 , min_samples_leaf = 10,random_state=1)\n",
    "\n",
    "#Fit to the data\n",
    "RFR.fit(train[columns],train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4458564605687358"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Generate predictions for the test data\n",
    "predictions = RFR.predict(test[columns])\n",
    "\n",
    "#Compute error between our test prediction and actutal values\n",
    "mean_squared_error(predictions,test[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "yearpublished      2011.0000\n",
       "minplayers            2.0000\n",
       "maxplayers            6.0000\n",
       "playingtime         200.0000\n",
       "minplaytime          60.0000\n",
       "maxplaytime         200.0000\n",
       "minage               14.0000\n",
       "users_rated       15709.0000\n",
       "total_owners      17611.0000\n",
       "total_traders       273.0000\n",
       "total_wanters      1108.0000\n",
       "total_wishers      5581.0000\n",
       "total_comments     3188.0000\n",
       "total_weights      1486.0000\n",
       "average_weight        3.6359\n",
       "Name: 9, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[columns].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8.12061283]\n",
      "[7.91373581]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#Make predictions with both models\n",
    "rating_LR = LR.predict(test[columns].iloc[0].values.reshape(1,-1))\n",
    "rating_RFR = RFR.predict(test[columns].iloc[0].values.reshape(1,-1))\n",
    "\n",
    "\n",
    "print(rating_LR)\n",
    "print(rating_RFR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.07933"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[target].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}