Chapter 8.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8aba51d5",
   "metadata": {},
   "source": [
    "# Chapter 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "dde0c153",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib.pyplot import subplots\n",
    "from statsmodels.datasets import get_rdataset\n",
    "import sklearn.model_selection as skm\n",
    "from ISLP import load_data, confusion_table\n",
    "from ISLP.models import (ModelSpec as MS,summarize)\n",
    "from sklearn.tree import (DecisionTreeClassifier as DTC,DecisionTreeRegressor as DTR,\n",
    "                          plot_tree,\n",
    "                          export_text)\n",
    "from sklearn.metrics import (accuracy_score,log_loss)\n",
    "from sklearn.ensemble import (RandomForestRegressor as RF,GradientBoostingRegressor as GBR,\n",
    "                             GradientBoostingClassifier as GBC,\n",
    "                             BaggingClassifier as BC)\n",
    "from sklearn.linear_model import LinearRegression, Ridge, LogisticRegression \n",
    "import statsmodels.api as sm\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dff87a0",
   "metadata": {},
   "source": [
    "## Exercise 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "71fb3b51",
   "metadata": {},
   "outputs": [],
   "source": [
    "Boston=load_data(\"Boston\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "43610687",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MS(Boston.columns.drop('medv'), intercept=False)\n",
    "D = model.fit_transform(Boston)\n",
    "feature_names = list(D.columns)\n",
    "X = np.asarray(D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "1c82edd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We preserve the same dimensions of the test set as the lab\n",
    "(X_train, X_test, y_train, y_test) = skm.train_test_split(X,Boston['medv'],test_size=0.5,\n",
    "                                                          random_state=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "e6bb57a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We should vary the number of trees from 1 to 100 as the figure 8.10 did in the book\n",
    "k=0\n",
    "MSE=np.zeros((30,3))\n",
    "for t in range(10,600,20):\n",
    "    RF_Boston1=RF(max_features=1.0,n_estimators=t,random_state=30).fit(X_train,y_train)\n",
    "    RF_Boston2=RF(max_features=0.5,n_estimators=t,random_state=30).fit(X_train,y_train)\n",
    "    RF_Boston3=RF(max_features='sqrt',n_estimators=t,random_state=30).fit(X_train,y_train)\n",
    "    ypred1=RF_Boston1.predict(X_test)\n",
    "    ypred2=RF_Boston2.predict(X_test)\n",
    "    ypred3=RF_Boston3.predict(X_test)\n",
    "    MSE[k,0]=np.mean((y_test - ypred1)**2)\n",
    "    MSE[k,1]=np.mean((y_test - ypred2)**2)\n",
    "    MSE[k,2]=np.mean((y_test - ypred3)**2)\n",
    "    k+=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "f9284b96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_idx = np.arange(10,600,20)\n",
    "ax = subplots(figsize=(8,8))[1]\n",
    "ax.plot(plot_idx,MSE[:,0],'b',label='m=p')\n",
    "ax.plot(plot_idx,MSE[:,1],'r',label='m=p/2')\n",
    "ax.plot(plot_idx,MSE[:,2],'g',label='m=$\\sqrt{p}$')\n",
    "ax.set_xlabel('Number of trees')\n",
    "ax.set_ylabel('Test MSE')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26748c71",
   "metadata": {},
   "source": [
    "We notice that the test MSE is improved as the number of trees increases. Moreover, better results are obtained when the number of selected features is close to $\\sqrt{p}$ and $p/2$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54967a2b",
   "metadata": {},
   "source": [
    "## Exercise 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "19e17af3",
   "metadata": {},
   "outputs": [],
   "source": [
    "Carseats=load_data('Carseats')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "945da3f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MS(Carseats.columns.drop('Sales'), intercept=False)\n",
    "D = model.fit_transform(Carseats)\n",
    "feature_names = list(D.columns)\n",
    "X = np.asarray(D)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7b1cfc5",
   "metadata": {},
   "source": [
    "### (a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f911ef8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, X_test, y_train, y_test) = skm.train_test_split(X,Carseats['Sales'],test_size=0.5,\n",
    "                                                          random_state=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "554e1a9c",
   "metadata": {},
   "source": [
    "### (b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "149d3d7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA70AAAOwCAYAAADyQxNyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd1hTZ+M+8Ju9RFFwAuJWrEpVsOJeiAiIEBQMjmqtrWLtsFZfrdXaYYedVq3WDgcR1ICKoIi4UHHgHrgHw4nKlpnz+8Mf+WrrYCQ5Sbg/1/Verw3JOTckHHLnPOd5DARBEEBERERERESkhwzFDkBERERERESkLiy9REREREREpLdYeomIiIiIiEhvsfQSERERERGR3mLpJSIiIiIiIr3F0ktERERERER6i6WXiIiIiIiI9BZLLxEREREREektll4iIiIiIiLSWyy9REREREREpLdYeomIiIiIiEhvsfQSERERERGR3mLpJSIiIiIiIr3F0ktERERERER6i6WXiIiIiIiI9BZLLxEREREREektll4iIiIiIiLSWyy9REREREREpLdYeomIiIiIiEhvsfQSERERERGR3mLpJSIiIiIiIr3F0ktERERERER6i6WXiIiIiIiI9BZLLxEREREREektll4iIiIiIiLSWyy9REREREREpLdYeomIiIiIiEhvsfQSERERERGR3mLpJSIiIiIiIr3F0ktERERERER6i6WXiIiIiIiI9BZLLxEREREREektll4iIiIiIiLSWyy9REREREREpLdYeomIiIiIiEhvsfQSERERERGR3mLpJSIiIiIiIr1lLHYAIiIiItIPqampyMzMrPZ27Ozs0LRpUxUkIiJi6SUiIiIiFUhNTYWzszMKCgqqvS1LS0ukpKSw+BKRSrD0EhEREVG1ZWZmoqCgAGvXroWzs3OVt5OSkoLRo0cjMzOTpZeIVILX9BIRERGRyly9ehX5+fnIzc3FgQMHUFRUBAMDA+Tm5ir/Z2FhARMTE9StWxe2trawsrLCvXv3UFJSAltbW7G/BSLSMyy9RERERKQyjRs3xvXr11G/fn306dMH9+7dw7Vr11BcXPzM/QwNDZGdnY27d+8CACwsLHD27FlYW1uLEZuI9BiHNxMRERGRynTt2hVdunRR/reLiwsKCwtRXFyM2rVrv/Bxbdu2Rd++fXH8+HFNxCSiGoSll4iIiIjUYu/evQCAR48eoaioCHfv3oWLiwvs7e1hZmaGS5cu4f79+2jWrBm6d+8ucloi0lcsvURERERULenp6Vi9ejWAJxNRlbt06RIuXboEJycnFBcXo2XLlsjPz8eBAwcAPJnx2draGm3atFGe4S1/fGFhoYa/CyLSVwaCIAhihyAiIiIi3fLw4UPI5XLIZDLs3bsXJiYmEAQBJSUl1d62oaEhLC0tERgYCKlUiv79+8PYmOdqiKhqWHqJiIiIqEIKCgqwdetWhIWFYdu2bSgrK8OgQYMglUrh7++PrKwsZGZmVns/OTk52Lt3L8LCwnD58mU0bNgQQUFBCAkJgZubGwwMDFTw3RBRTcHSS0REREQvVFpaip07d0ImkyEqKgp5eXl44403IJVKMXLkSDRq1Eht+xYEAceOHYNMJkN4eDhu376Nli1bQiqVQiqVol27dmrbNxHpD5ZeIiIiInqGIAg4dOgQZDIZIiIicP/+fbRt2xYhISEYNWoUWrVqpfFMZWVlyrO/crkc2dnZ6Ny5M0JCQhAcHAx7e3uNZyIi3cDSS0REREQAgPPnzyMsLAzr1q3D9evXYW9vj1GjRkEqleL111/XmmHFhYWF2LZtG2QyGaKjo1FcXIy+fftCKpUiMDAQdevWFTsiEWkRll4iIiKiGiw1NRXh4eGQyWQ4deoUbGxsMGLECEilUvTu3RtGRkZiR3yp7OxsREVFQSaTISEhAUZGRhg6dCikUil8fHxgaWkpdkQiEhlLLxEREVEN8+DBA2zcuBFhYWFITEyEubk5hg0bBqlUiiFDhsDMzEzsiFVy584dREREQCaT4ciRI6hVqxYCAgIglUoxcOBAzgBNVEOx9BIRERHVAPn5+YiOjkZYWBi2b98OQRDg4eEBqVSK4cOHw9raWuyIKnXlyhWsW7cOYWFhuHjxIho0aICRI0dCKpWie/fuWjNUm4jUj6WXiIiISE+VlJQgPj4eMpkMmzZtQn5+Ptzd3SGVSjFixAg0bNhQ7IhqJwgCTpw4AZlMhnXr1uHWrVto3ry5cgbo9u3bix2RiNSMpZeIiIhIjygUCiQlJUEmk2H9+vXIzMyEs7OzcublFi1aiB1RNGVlZdi3bx9kMhk2btyIrKwsuLi4KGeAdnR0FDsiEakBSy8RERGRHjh79qxy5uWbN2/C0dFROfNyp06dOJz3X4qKirB9+3bIZDJs2bIFhYWF6NOnj3IGaFtbW7EjEpGKsPQSERER6aibN29i3bp1kMlkOHPmDOrVq6eceblXr14wNDQUO6JOyMnJwaZNmyCTyRAfHw8jIyMMGTIEUqkUvr6+sLKyEjsiEVUDSy8RERGRDsnMzMSGDRsQFhaGAwcOwMLCAn5+fpBKpfD09ISpqanYEXXa3bt3sX79eshkMhw6dAhWVlYYPnw4QkJCMGjQIJiYmIgdkYgqiaWXiIiISMvl5eVh8+bNkMlk2LFjBwRBgKenJ6RSKfz8/FCrVi2xI+qla9euKWeATklJgZ2dnXIGaHd3d55JJ9IRLL1EREREWqi4uBg7duyATCbD5s2bUVBQgJ49eypnXq5fv77YEWsMQRBw6tQp5QzQ6enpcHJyUs4A3aFDB7EjEtFLsPQSERERaQmFQoEDBw4gLCwMGzZswMOHD/Haa68pZ15u1qyZ2BFrPIVCgf379yufo0ePHqFjx47KGaCdnJzEjkhE/8LSS0RERCQiQRBw5swZ5czLaWlpaNq0qfIsYseOHcWOSC9QXFyMuLg45dn4x48fo1evXsqz8XZ2dmJHJCKw9BIRERGJ4vr168qZl8+dOwdbW1vl9aI9evTg9aI6Ji8vTzkD9I4dO2BgYKC87nrYsGG87ppIRCy9RERERBpy79495czLSUlJsLS0VM4M7OHhwZmB9cT9+/eVz/PBgwdhaWn5zAzbfJ6JNIull4iIiEiNcnNzn1kD1sDAQLkG7LBhw7gGrJ67fv06wsPDERYWpjyjX76Wcs+ePXlGn0gDWHqJiIiIVKy4uBjbt2+HTCbDli1b8PjxY/Tu3Vt5raetra3YEUkET1+7nZqaiqZNm2LUqFHKa7cNDAzEjkikl1h6iYiIiFRAoVAgMTERMplMOatvp06dlLP6Nm3aVOyIpCUUCgUOHjyIsLAwrF+/XjlLt1QqxahRo9C8eXOxIxLpFZZeIiIioioSBAEnT55Urt+akZGBZs2aKcsL12+lVykuLkZ8fDxkMhk2bdqEgoIC9OjRQzkqoEGDBmJHJNJ5LL1ERERElXT16lXlzMspKSmws7NDUFAQQkJC0L17dw5TpSrJz8/H5s2bIZPJEBcXB0EQ4OHhgZCQEPj5+cHa2lrsiEQ6iaWXiIiIqALu3r2L9evXIywsDIcPH4aVlRX8/f0REhKCgQMHckZeUqnMzExs3LgRYWFh2L9/PywsLDBs2DBIpVIMGTIEpqamYkck0hksvUREREQvkJOTg6ioKMhkMuzcuRNGRkbw8vKCVCqFr68vLC0txY5INcDNmzcRHh4OmUyG06dPo27dusoZoHv37s0ZoIlegaWXiIiI6ClFRUXYtm0bZDIZoqOjUVhYiL59+0IqlSIwMBD16tUTOyLVYGfPnoVMJoNMJsPNmzfh4OCA4OBghISEwMXFhUPriZ6DpZeIiIhqvLKyMuzbtw8ymQwbN25EVlYWXn/9dYSEhCAoKAiOjo5iRyR6hiAISEpKgkwmQ0REBDIzM+Hs7KycRK1ly5ZiRyTSGiy9REREVCMJgoDjx49DJpMhPDwct27dQosWLZSloX379mJHJKqQkpIS7Ny5EzKZDFFRUcjPz8cbb7yBkJAQjBw5Eg0bNhQ7IpGoWHqJiIioRrl8+bJy5uWLFy+iQYMGypmXu3XrxuGhpNMKCgoQHR2NsLAwbNu2DQqFAoMGDUJISAiGDx+O2rVrix2RSONYeomIiEjv3b59Wznz8tGjR2FtbY2AgABIpVIMGDAAxsbGYkckUrkHDx5ALpcjLCwM+/btg7m5OXx9fSGVSuHl5QUzMzOxIxJpBEsvERER6aXs7GxERkZCJpNh165dMDY2xtChQyGVSuHj4wMLCwuxIxJpTFpamnIG6JMnT8LGxgaBgYGQSqXo06cPjIyMxI5IpDYsvURERKQ3CgsLERsbC5lMhq1bt6K4uBj9+vWDVCqFRCJB3bp1xY5IJLrz588rZ4C+fv06mjRpopwBunPnzhziT3qHpZeIiIh0WllZGfbs2YOwsDDI5XLk5OSgS5cuypmX7e3txY5IpJUEQcDhw4eVM0Dfu3cPbdu2VU7m1rp1a7EjEqkESy8RERHpHEEQkJycrJx5+c6dO2jZsiVCQkIwatQotGvXTuyIRDqltLQUCQkJkMlkiIyMRF5eHtzc3JQfHjVq1EjsiERVxtJLREREOuPSpUvKYZmXL19Gw4YNERwcDKlUCjc3Nw7LJFKBgoICbN26FTKZDLGxsSgrK8OAAQMglUoREBCAOnXqiB2RqFJYeomIiEir3bp1SzkBz7Fjx2BtbQ2JRIKQkBD069ePMy8TqdGjR4+UM0Dv3bsXpqam8PHxgVQqxdChQ2Fubi52RKJXYuklIiIirZOVlQW5XA6ZTIbdu3fDxMTkmTfanHmZSPPS09MREREBmUyG48ePo06dOpBIJJBKpejXrx9ngCatxdJLREREWuHx48eIiYmBTCZDTEwMSkpKnhlSaWNjI3ZEIvr/Lly4oLzU4OrVq2jUqJFyBuiuXbvyUgPSKiy9REREJJrS0lLs2rVLOXlObm4uXF1dERISgpEjR6JJkyZiRySilxAEAUePHkVYWBgiIiJw9+5dtG7dGlKpFFKpFG3atBE7IhFLLxEREWmWIAg4cuSIcpmU8jfJ5TMv800ykW4qLS3F7t27IZPJIJfLlR9iSaVSBAUF8UMsEg1LLxEREWnEv4dDNm7cWDnzModDEumX512u0L9/f0ilUkgkEl6uQBrF0ktERERqk56erpx5+cSJE8qJb0JCQtC3b19OfENUA2RlZSEyMhJhYWHKiem8vb0hlUrh7e3NielI7Vh6iYiISKUePnyonHm5fIkTX19fSKVSeHl5cYkTohrs1q1byhmgk5OTUbt2bQQEBEAqlaJ///5cgozUgqWXiIiIqq2goABbt25FWFgYtm3bhrKyMgwcOBBSqRT+/v6oU6eO2BGJSMtcvHgR69atQ1hYGK5cuYKGDRsiKCgIISEhcHNz4yUPpDIsvURERFQlpaWl2LlzJ2QyGaKiopCXl4du3bopZ15u1KiR2BGJSAcIgoBjx44hLCwM4eHhuHPnDlq2bKmcAbpdu3ZiRyQdx9JLREREFSYIAg4dOqScefn+/fto27atcublVq1aiR2RiHRYWVkZ9uzZA5lMho0bNyInJwedO3dGSEgIgoODYW9vL3ZE0kEsvURERPRK58+fR1hYGNatW4fr16+jSZMmGDVqFKRSKTp37sxhiESkcoWFhYiNjYVMJsPWrVtRXFyMvn37QiqVIjAwEHXr1hU7IukIll4iIiJ6rtTUVOXMy6dOnYKNjQ0CAwMhlUrRp08fzrxMRBqTnZ2NyMhIyGQy7Nq1C0ZGRhg6dCikUil8fHxgaWkpdkTSYiy9REREpPTgwQNs3LgRYWFhSExMhLm5OYYNGwapVIohQ4bAzMxM7IhEVMPdvn0b69evh0wmw5EjR1CrVi3lDNADBw7kDND0Hyy9RERENVx+fj6io6MRFhaG7du3Q6FQwMPDA1KpFMOHD0ft2rXFjkhE9FyXL19WzgB96dIlNGjQACNHjoRUKkX37t156QUBYOklIiKqkUpKShAfHw+ZTIZNmzYhPz8f3bt3h1QqxciRI9GwYUOxIxIRVZggCDhx4oRyBuhbt26hefPmyhmg27dvL3ZEEhFLLxERUQ2hUCiQlJQEmUyG9evXIzMzE87OzsqZl1u0aCF2RCKiaisrK8O+ffuUM0BnZWXBxcVFOQO0o6Oj2BFJw1h6iYiI9NzZs2eVMy/fvHkTDg4OypmXXVxcOPyPiPRWUVERtm3bBplMhujoaBQWFqJPnz6QSqUYMWIE6tWrJ3ZE0gCWXiIiIj108+ZNrFu3DjKZDGfOnEHdunUxYsQIhISEoFevXjA0NBQ7IhGRRuXk5GDTpk0ICwvDzp07YWRkhCFDhkAqlcLX1xdWVlZiRyQ1YeklIiLSE5mZmdiwYQPCwsJw4MABWFhYwM/PD1KpFJ6enjA1NRU7IhGRVrh79y7Wr1+PsLAwHD58GFZWVhg+fDhCQkIwaNAgmJiYiB2RVIill4iISIfl5eVh8+bNkMlk2LFjBwRBwODBgyGVSuHn5wdra2uxIxIRabWrV68qZ4C+cOEC7OzsMHLkSISEhMDd3Z2XgOgBll4iIiId9O233+Kbb75BcXExCgoK0KNHD+U1ag0aNBA7HhGRzhEEASdPnoRMJsO6deuQkZEBe3t75OTk4PDhw3B2dhY7IlURSy8REZEOatq0KdLS0jBv3jyMGzcOzZs3FzsSEZHeUCgUSExMxOzZs3Hw4EF8/PHH+P7778WORVXE0ktERKRmqampyMzMrNY27Ozs0LRpU+V/KxQKFBUVwcLCorrxiIjoJfLz8/8zyZUqjuvAf4/tpB4svURERGqUmpoKZ2dnFBQUVGs7lpaWSElJ4ZsjIiKRqeq4DvDYrinGYgcgIiLSZ5mZmSgoKMDatWurfD1YSkoKRo8ejczMTL4xIiISmSqO6wCP7ZrERfqIiIg04OrVqzA3N4eRkRHS0tJw/fp1GBoaIjc3F7m5ubCwsICTkxMMDQ2RlpYGW1tbFBUVITc3F/b29mLHJyKifykoKMDZs2crfGzPyMiAlZUVLCwskJuby6KrQSy9REREGuDj44P27dsjKysLNjY2KCsrw4kTJ3Dq1CkAgImJCQoKCpCfnw8AuHPnDtzd3dG3b1/Url1bzOhERPQcXbt2xdixYyt0bL9z5w4KCgrQtm1bODs7o2/fvv+5TpjUh8ObiYiINKhv374v/bqjo6OGkhARkarw2K7dWHqJiIjU4NKlS5DL5VizZg2AJ9dulTt27BguXboEJycnFBcXo2XLljAxMUFKSgrs7e1RUFCA4uJidOvW7ZnHLlmyBKGhoejcuTMMDAw0/00REZFSRY7rd+7cQU5ODmrXro2CggIUFRWhf//+/3k8qRdnbyYiIlIBQRBw5swZREZGQi6X4+zZs7C0tET//v2RkJCAwsLCam3fxMQElpaWyM7ORrNmzSCRSBAQEIDu3bvD0JBXKxERaQpnb9Y9LL1ERERVJAgCkpOTIZfLIZfLceXKFdSpUwe+vr6QSCTw9PSEhYWFytbpbdy4Mfbu3Qu5XI6oqCjcvXsXTZo0gb+/PyQSCXr37g1jYw7iIiJSt38f11evXo1ffvkFCxcuxODBg5+575kzZzBx4kT4+Pjg008/fWakDtfp1QyWXiIiokooKytDUlIS5HI5IiMjkZqaCltbWwwfPhwSiQQDBw6EqampRnIcPHhQmSMtLQ12dnbKHAMGDNBIDiKimi4uLg5Dhw7FJ598goULFz73Pn///TcmTJiApUuXYvLkyRpOSCy9REREr1BSUqI8w7pp0ybcuXMHjRs3RkBAgFacYa3oGWciIlKtK1euwM3NDe7u7oiOjoaRkdEL7ztt2jQsW7YMCQkJ6NOnjwZTEksvERHRcxQVFWHnzp2Qy+XYvHkzHj58CCcnJ0gkEkgkEq29lrb82uLyAnzu3DlYWVlh6NChkEgkGDp0KKytrcWOSUSk83Jzc+Hu7o6SkhIcPnwYNjY2L71/SUkJPD09cfbsWSQnJ3NYswax9BIREf1/+fn52L59O+RyObZu3Yrc3Fy0bdtWWXR1cdbkixcvKifXOnbsGMzMzDB48GBIJBIMGzYMdevWFTsiEZHOUSgUCAwMxM6dO3H48GE4OztX6HGZmZlwdXWFra0t9u/fz1E4GsLSS0RENVpOTg62bt0KuVyObdu24fHjx+jUqZOy6LZv317niu6L3LhxQ1mADx48CGNjYwwYMAASiQTDhw9HgwYNxI5IRKQTFixYgHnz5mHz5s0YNmxYpR578uRJ9OjRAwEBAVizZo3e/I3RZiy9RERU4zx48ACbN2+GXC7Hzp07lWvili8D1KpVK7Ejqt2tW7ewadMmyOVy7NmzBwDQq1cv5c/AwcFB3IBERFpq8+bNGD58OBYsWIC5c+dWaRsREREIDg7GokWLMH36dBUnpH9j6SUiohrhzp07iIqKUpY8hULxTMlzdHQUO6JoMjMzn/kQoKSkBG+88YbybHeLFi3EjkhEpBXOnz+PN954A4MHD8aGDRuqNbfDrFmz8P3332Pbtm3/WeaIVIull4iI9NbNmzcRGRmJyMhIHDhwAIaGhujfv79yOG+jRo3Ejqh1srKylMO9t2/fjsLCQrz++uvKAlzR69aIiPRNVlYW3NzcYG5ujqSkJNSqVata2ysrK4Ovry8OHTqEo0ePomXLlipKSv/G0ktERHrl8uXLypmLk5OTYWpqqpy4ydfXF7a2tmJH1Bl5eXnPTOyVl5eHdu3aKQvw66+/zmvRiKhGKCsrg4+PDw4fPqzSgpqVlYVu3brB1NQUhw4dqnaRpudj6SUiIp0mCALOnj2rnKDpzJkzsLS0hJeXFyQSCby9vVG7dm2xY+q8wsJCxMfHQy6XY8uWLXj06BFatGihXKu4W7duWrmEExGRKpQPRd6+fTs8PDxUuu2UlBS88cYb8PDwqPaQaXo+ll4iItI5giDg2LFjyjO6ly9fRu3ateHr6wuJRAJPT09YWlqKHVNvlZSUYPfu3YiMjERUVBTu3bsHe3t7+Pv7QyKRoHfv3jAyMhI7JhGRSoSHh2PUqFFqnXRqy5Yt8PPzq9bkWPRiLL1ERKQTFAoFDh48qLxG9+bNm7C1tYWfnx8kEgkGDhwIMzMzsWPWOGVlZThw4ADkcjkiIyORnp6O+vXrY/jw4ZBIJOjfvz9MTU3FjklEVCXlywtJJBKsXr1arZd0VGcZJHo5ll4iItJapaWl2Lt3L+RyOaKionDnzh00atQIAQEBCAgIQN++fWFsbCx2TPr/FAoFjh49qhxqfvXqVdjY2GDYsGEICAjA4MGDYWFhIXZMIqIKuX//Ptzc3GBnZ4fExES1H78UCgUCAwOxc+dOHD58mBMHqhBLLxERaZWioiIkJCRALpdj8+bNePDgAZo2baqcPMnd3Z3XO+kAQRBw+vRp5RD08+fPw8rKCt7e3pBIJBg6dCgnbCEirVVSUoLBgwfj3LlzOHbsmMaWtcvNzYW7uzuKi4tx5MgR2NjYaGS/+o6ll4iIRFdQUPDMLME5OTlo3bq1suh27dqVswTruAsXLiiHQB8/fhzm5ubw9PSERCKBj48P6tatK3ZEIiKladOmYdmyZdi1axd69+6t0X1fvXoVbm5u6N69O6KjozlHggqw9BIRkShycnIQExMDuVyObdu2oaCgAB07dlQW3ddee41FV09dv35dOQQ6KSkJxsbGGDhwoHL95Pr164sdkYhqsL///hsTJkzA0qVLMXnyZFEy7NixA15eXvjkk0+wcOFCUTLoE5ZeIiLSmAcPHmDLli2Qy+WIj49HcXExXF1dlUW3devWYkckDcvIyEBUVBTkcjn27dsHAOjTpw8kEgn8/f1hb28vckIiqkkOHz6MPn36YNy4cVi+fLmoH74uWrQIM2bMQHh4OIKCgkTLoQ9YeomISK3u3LmDTZs2QS6XY/fu3VAoFOjZsyckEgkCAgLQtGlTsSOSlrh//z42b94MuVyOhIQElJSUoHv37soPRZo3by52RCLSY7dv34arqyuaNWuG3bt3iz7zvCAIGDNmDCIjI5GUlAQXFxdR8+gyll4iIlK51NRU5dJC+/fvh6GhIfr166ccvtq4cWOxI5KWy8rKQnR0NORyOeLi4lBYWIjOnTsrC3C7du3EjkhEeqSoqAj9+vVDamoqjh07hkaNGokdCQDw+PFj9OrVCw8ePEBycjLs7OzEjqSTWHqJiEglrly5opyp9+jRozA1NYWHhwckEgmGDRsGW1tbsSOSjsrLy8O2bdsgl8sRExODvLw8ODs7Kwuwi4sLr/8moioTBAFvv/021q5di3379qFbt25iR3pGamoqXF1d0aFDB8TFxcHExETsSDqHpZeIiKpEEAScO3dOOSHR6dOnYWFhAS8vL0gkEnh7e6NOnTpixyQ9U1hYiB07dkAul2PLli3IyspCixYtlAXYzc2NS1oRUaUsXboUoaGh+OeffzBu3Dix4zzXvn37MHDgQEyZMgW//PKL2HF0DksvERFVmCAIOH78uPKM7qVLl2BtbQ1fX19IJBJ4enrCyspK7JhUQxQXF2PPnj2Qy+WIiorC/fv34eDgAH9/f0gkEvTq1YtLfRDRS5WXydDQUPz8889ix3mp8nL+119/Yfz48WLH0SksvURE9FIKhQJJSUnKa3Rv3LiBevXqwc/PDxKJBIMGDYKZmZnYMamGKysrw/79+5VrAWdkZKBBgwYYPnw4JBIJ+vfvzyGBRPSM8mHDHTt2RFxcHIyNjcWO9FKCIGDSpElYvXo19u3bhzfeeEPsSDqDpZeIiP6jtLQU+/btU55Bu337Nho1agR/f38EBASgb9++LBCktRQKBY4cOaIcen/t2jXUrVsXw4YNQ0BAAAYPHgxzc3OxYxKRiAoKCtCrVy88evQIR48e1ZkJooqKitC/f3/cvHkTycnJnBiyglh6iYgIwJOhogkJCZDL5di8eTMyMzPRtGlTBAQEQCKRwN3dnUNFSecIgoBTp04ph+SnpKSgVq1a8Pb2hkQigZeXF2rVqiV2TCLSIEEQMHr0aGzatAkHDx7UuaWAypdWcnJywu7duznaqgJYeomIarCCggLExcVBLpcjOjoaOTk5aNWqlXJSIFdXV86KS3olJSVFOQT6xIkTMDc3x5AhQxAQEABfX1/Y2NiIHZGI1GzRokWYMWMGIiIiMHLkSLHjVMmRI0fQu3dvjB07FitWrODf6ldg6SUiqmFyc3MRExMDuVyO2NhYFBQUoEOHDsqi26FDB/7xpBrh2rVryiHQhw4dgomJCQYOHAiJRAI/Pz/Ur19f7IhEpGJxcXEYOnQoZs6cia+//lrsONXyzz//YPz48Vi6dCkmT54sdhytxtJLRFQDPHz4EFu2bIFcLkd8fDyKiorQtWtXZdFt06aN2BGJRJWeno6oqChERkZi3759AIC+fftCIpHA398fTZo0ETkhEVXXlStX4Obmhh49emDLli16ccnO+++/j6VLlyIhIQF9+vQRO47WYuklItJTd+/exaZNmyCXy7F7926UlZWhR48ekEgkCAgIgJOTk9gRibTSvXv3sHnzZsjlciQkJKC0tBTu7u7KD4maNWsmdkQiqqTc3Fx0794dpaWlOHz4sN5cylBSUgJPT0+cPXsWycnJaNq0qdiRtBJLLxGRHklPT1cO10xMTIShoeEzZ6s4yyNR5Tx69AjR0dGQy+WIi4tDUVERunTpoizAbdu2FTsiEb2CQqGARCJBQkICDh8+DGdnZ7EjqVRmZibc3NxQr149JCYmwtLSUuxIWoell4hIx129elU5M+2RI0dgYmICDw8PSCQSDBs2TGeWYSDSdnl5eYiNjYVcLkdMTAzy8/Px2muvKUdPdOrUidfDE2mhBQsWYP78+di8eTN8fX3FjqMWp06dgru7O/z9/bF27Voei/6FpZeISAedP39eWXRPnToFCwsLDBkyBBKJBD4+PqhTp47YEYn02uPHj7Fjxw7I5XJs2bIF2dnZaNWqlXKJLzc3N77pJNICmzdvxvDhw/HFF1/g008/FTuOWkVERCA4OBiLFi3C9OnTxY6jVVh6iYh0gCAIOHHihHKplQsXLsDa2ho+Pj6QSCQYMmQIrKysxI5JVCMVFxdj9+7dkMvl2LRpE+7fvw9HR0dlAe7Ro4deTJhDpGvOnz+PN954A56entiwYUON+CDqf//7H7777jts27YNgwcPFjuO1mDpJSLSUgqFAocPH1YW3evXr6Nu3brw8/ODRCLBoEGDYG5uLnZMInpKaWkp9u/fr/y9vXXrFho2bIjhw4dDIpGgX79+MDExETsmkd579OgRunXrBnNzcyQlJaFWrVpiR9KIsrIy+Pr64tChQzh69ChatmwpdiStwNJLRKRFSktLkZiYCLlcjqioKOUbZn9/fwQEBPANM5EOKf/gqnxyuevXr6NevXoYNmwYJBIJPDw8YGZmJnZMIr1TVlYGHx8fHD58uEYWv6ysLLzxxhswMTFBUlISrK2txY4kOpZeIiKRFRcXY9euXcqhkZmZmRwaSaRnBEHAyZMnldfil1+i4O3tDYlEAi8vL16iQKQis2bNwvfff4+4uDgMGjRI7DiiSElJwRtvvIFBgwZh48aNMDQ0FDuSqFh6iYhE8PjxY8TFxSEyMlI5CU7Lli2Vy6BwEhwi/VY+GV1kZCROnjzJyeiIVCQ8PByjRo3CDz/8gI8++kjsOKLasmUL/Pz8sGDBAsydO1fsOKJi6SUi0pDc3FzlciexsbHPLHcikUjQsWNHFl2iGujq1avKIdCHDx+GiYkJBg0aBIlEAj8/Py47RlRBJ06cQM+ePSGRSLB69Wr+TQXwxRdf4LPPPsPmzZsxbNgwseOIhqWXiEiNHj16hOjoaMjlcsTFxaGoqAhdu3ZVDl1u27at2BGJSIukpaUhKioKcrkciYmJMDQ0RN++fSGRSODv74/GjRuLHZFIK92/fx+urq6oX78+EhMTYWFhIXYkraBQKDBixAjEx8fj8OHDcHZ2FjuSKFh6iYhU7N69e9i0aRPkcjl27dqF0tJS9OjRAxKJBAEBAWjWrJnYEYlIB9y9exebNm1CZGQkdu3ahbKyMvTo0UP5oZmTk5PYEYm0QklJCQYPHozz588jOTkZjo6OYkfSKnl5eXB3d0dRURGOHDkCGxsbsSNpHEsvEZEKpKenP3N2BsAzZ2eaNGkickIi0mUPHz5UjhrZsWOHctRI+eURbdq0ETsikWimTZuGZcuWYdeuXejdu7fYcbTS1atX4ebmhu7duyM6OrrGTZDJ0ktEVEXXrl1TzsTK6/CISFNyc3MRExODyMhIxMTEoKCgAB06dFCOJuH8AFST/P3335gwYQKWLVuGd999V+w4Wi0+Ph5DhgzBJ598goULF4odR6NYeomIKiElJUVZdE+ePAlzc/NnZlytiUOGiEg85TPBy+VyREdHIzs7G61atVKeAXZ1dWUBJr116NAh9O3bF+PGjcPy5cv5Wq+AH374AR9//DHCw8MRFBQkdhyNYeklInqJ562tWatWLfj4+EAikWDIkCGoVauW2DGJiFBcXIyEhATlmt8PHjxA06ZNldcAu7u717ghjaS/bt26BVdXVzRv3hy7d++Gqamp2JF0giAIGDt2LORyOQ4ePIjXX39d7EgawdJLRPQvCoUCR44cUa6hee3aNdStWxfDhg2DRCKBh4cHzM3NxY5JRPRCpaWlSExMVB7Hbt++jUaNGmH48OGQSCTo27cvTExMxI5JVCVFRUXo168f0tLSkJycjEaNGokdSac8fvwYvXr1woMHD3D06FHUr19f7Ehqx9JLRASgrKwM+/fvV75BzMjIQIMGDeDv74+AgAD079+fbxCJSCcpFAocOnRIOWLl5s2bqFevHvz8/CCRSDBo0CCYmZmJHZOoQgRBwMSJExEWFobExES4ubmJHUknpaamwtXVFR06dEBcXJzev8dh6SWiGqukpAS7du1SDgW8f/8+HBwclEMBe/bsyaGARKRXBEHAiRMnlAX44sWLsLa2fuaSDSsrK7FjEr3QkiVLMHXqVPzzzz8YN26c2HF0WmJiIgYMGIApU6bgl19+ETuOWrH0ElGNUlhYiB07dkAul2PLli3IyspCy5YtlbOeurm5wdDQUOyYRERqJwgCzp8/ryzAp0+fhoWFBby8vJST89WuXVvsmERKe/fuxaBBgxAaGoqff/5Z7Dh6YdmyZZgyZQr++usvjB8/Xuw4asPSS0R6Ly8vD7GxsZDL5YiNjUVeXh7at2+vnN20U6dOnPGRiGq8K1euIDIyEnK5HEeOHIGpqekzy7DZ2tqKHZFqsJs3b8LV1RWdOnVCXFwcjI2NxY6kFwRBwDvvvINVq1Zh3759eOONN8SOpBYsvUSkl7KyshAdHQ25XI64uDgUFhaiS5cuyqHL7dq1EzsiEZHWSktLUxbg/fv3w9DQEP369YNEIoG/vz8nDiKNKigoQK9evfDo0SMcPXoUdnZ2YkfSK0VFRRgwYABu3LiB5ORkNG7cWOxIKsfSS0R64/79+9i0aRMiIyORkJCAkpISuLu7K4cuN2/eXOyIREQ6586dO9i8eTPkcjl27doFhUKBnj17IiAgAAEBAXBychI7IukxQRAQEhKCzZs34+DBg3BxcRE7kl66ffs2XF1d4eTkhN27d+vd5HYsvUSk0zIyMhAVFQW5XI59+/YBAPr06aM8G2Fvby9yQiIi/fHw4UNs2bIFcrkcO3bsQHFxMVxdXZWXi7Ru3VrsiKRnvv/+e3zyySeIiIjAyJEjxY6j144cOYI+ffpgzJgxWLFihV5d+sXSS0Q65/r168phd0lJSTAxMcHAgQMREBAAPz8/NGjQQOyIRER6LycnBzExMYiMjERsbCwKCgrQsWNH5eiaDh066NWbZtK8b775BrNnz8asWbPw9ddfix2nRli1ahXefPNNzJw5EwsXLtSb32GWXiLSCRcuXFDOMHrixAmYm5vD09MTEokEvr6+sLGxETsiEVGNVVBQgLi4OMjlckRHRyMnJwetW7dWngHu2rWr3rx5Js24ceMGmjdvDnNzc+Tl5XEJQQ0aOHAgdu3ahcWLF2Pq1Klix1EJll4i0kqCIODUqVPKM7rnz5+HlZUVvL29IZFIMHToUNSqVUvsmERE9C9FRUVISEiAXC7H5s2b8eDBAzg5OSmvAe7RoweXhqNXKiwsxKhRo/Dpp5+ia9euYsepUVJTUxEUFITvvvsOvXv3FjuOSrD0EpHWyMnJwV9//YVbt24hMjISV69ehY2NDYYNGwaJRAIPDw9YWFiIHZOIiCqotLQUe/fuRWRkJCIjI3Hnzh00atQI/v7+aNOmDQIDA+Hg4CB2TCLScyy9RKQ1XFxccPr0adSrV085JK5///4wNTUVOxoREVWTQqFAUlIS5HI5Nm7ciLS0NNja2iIzM1PsaESk51h6iahKUlNTq/1Gxc7ODk2bNlX+944dO7B161YsXLgQVlZW1Y1IRERaShAEzJkzB+3bt8fo0aOf+Zo6/r6QuPic6g59fa5Yeomo0lJTU+Hs7IyCgoJqbcfS0hIpKSlad2AkIiJx8O+L/uFzqjv0+bkyFjsAEemezMxMFBQUYO3atXB2dgYAnD9/HteuXUPt2rVhb2+PoqIi1KtXD5cuXQLwZFhbv379lNtISUnB6NGjkZmZqVUHRSIiEs/z/r5UFv++aJfKvmdQKBQAwPcMItDn3z9OnUdEVVZQUICzZ8/C3NwcHTt2hIuLCxwdHdG6dWs4OjrCysoKnp6eGDNmDAYMGICMjAxYWVnB2Ni42p8iEhGR/rp69SrMzc1hZGSEtLQ0XL9+HYaGhsjNzUVubi4sLCzg5OQEQ0ND5OTkwNbWFrm5uSguLkazZs3Ejk/PUdH3DOPGjcOAAQPw6NEj2NvbQxAElJaWih2/RqnM79+9e/eUv3/GxsZo3ry52PGfi2d6iajKunbtii5duij/28XFBYWFhSguLkbt2rWfua+trS1ef/115X/zDxgREb2Ij48P2rdvDwDIyspCcXExzp8/DyMjI9y9excuLi4wMTGBra0tHj58iNOnT8PX1xcAcPz4cTGj0wtU9j1DuYYNG3KNZw2rzO/fgwcPcObMGfj4+ADQ3t8/ll4iqra9e/cCAB49eoSioiLlAdHe3h5mZmZITU0FAJSVlaFPnz5iRiUiIh3Tt2/fl37d0dFRQ0lIVSryviEvLw+enp4iJyV9+f1j6SWiSispKQHw5LoNALC2tsaxY8dw6dIlODk5obS0FFlZWcjPz8f9+/cBPJkcoW7duqhVq9YzjyUiIvq38r8RT/9tKS4uRsuWLWFiYoJLly6hbt26ePz4MQoKCv5z/Sdpn5c9p61atcLVq1dx5swZWFpaori4GMD/nTXkc6pZFfn9a9SoES5evAgzMzMMHjz4P4/VNpy9mYgqLCMjA8uXL8eyZcvw8OFD5WQTVWVoaAg/Pz988skneOONNzh8iYiohtPn2WNrKj6nukOfnyuWXiJ6KUEQsGfPHixZsgSbNm2ChYUFxo4dC39/f9SrV6/K283KykJCQgLWrVuH69evo0uXLggNDcWoUaNgYWGhwu+AiIh0Sfk6oQqFAnPnzsXOnTvx008/oUePHv+5b2JiIqZPn44hQ4Zg/vz5MDR8MkerNq4TWpO9aO3XpKQkTJ06FeHh4fj7779x8+ZNhIWFPXcbfE4143nPVV5eHgYPHoy3334b48ePxwcffICHDx9i9erVz92GNj5XLL1E9Fy5ublYs2YNlixZgvPnz8PZ2RmhoaEYM2bMfyacqI6ysjJs374dS5Yswfbt21G3bl1MmDABkydPRosWLVS2HyIi0h2CIGDq1KlYtmwZwsPDMXLkyBfed926dQgJCcF7772Hn3/+maOGdMj777+PqKgo3Lx5EzKZDKNHj8atW7fQuHFjsaPRU/7++2+89dZbSE1NhYODAzZu3IgRI0YgJSUF7dq1EztehXDJIiJ6xvnz5zF16lTY29tj2rRpaNeuHRISEnDu3DmEhoaqtPACgJGREby9vREbG4vLly9j/Pjx+PPPP9GqVSvl7dUdRk1ERLrls88+w9KlS7F8+fKXFl4AGDVqFJYsWYJff/0VX3zxhYYSkirExsbC29sbBgYGGDJkCAwMDBAbGyt2LPqX1atXY+DAgXBwcAAA+Pr6wsbGBmvWrBE5WcWx9BIRSktLIZfLMWDAALz22mvYsGED3n//fdy4cUN5uyY+OW/ZsiUWLVqE9PR0rFy5Enfu3IG3tzdat26NRYsW4eHDh2rPQERE4vrxxx/x5Zdf4ttvv8Xbb79docdMnjwZX331FebNm4fFixerOSGpwqVLl3DlyhUMHToUwJNlirp3787Sq2Vu3LiBPXv2YOzYscrbzMzMEBwcjDVr1ujMiQmWXqIa7O7du/jyyy/RvHlzBAYGoqioCDKZDGlpafjiiy+Un+hpmqWlJSZMmIDk5GQkJSXB3d0dc+bMgb29Pd566y2tXQOOiIiq5++//8b06dMxc+ZMfPLJJ5V67P/+9z9Mnz4d06ZN06kzUDVVTEwMzMzMMGDAAOVt3t7eiI+PV87eTOJbu3YtrKys4O/v/8ztY8eORVpaGvbs2SNOsEriNb1ENYwgCDh48CCWLFmCjRs3wtjYGCEhIQgNDcXrr78udrwXunfvHlauXInff/8daWlp6N69O0JDQzFixAiYmZmJHY+IiKopMjISI0aMwMSJE/H7779XaYSRIAiYOHEiVq1ahcjISAwbNkwNSUkVBg0aBBMTE2zbtk1528mTJ9G5c2fs3LkTAwcOFDEdAU9+n9q2bYsePXrgn3/+qfDXtBHP9BLVEPn5+fjjjz/QuXNn9OrVC0ePHsW3336LjIwM/PHHH1pdeAGgQYMGmD17Nq5du4aoqCjUqlULY8aMgaOjI2bPno3U1FSxIxIRURXt3LkTo0aNQmBgIJYuXVrlS2oMDAywYsUKDB8+HCNHjtSZs1A1TW5uLvbt26cc2lzOxcUFTZo04RBnLXHo0CFcvnz5maHN5QwMDDB27Fhs3LgR+fn5IqSrHJZeIj13+fJlfPTRR3BwcMA777wDR0dHbN++HRcvXsSHH36IunXrih2xUoyNjTF8+HDEx8cjJSUFwcHBWLJkCZo3bw5/f3/s3LkTHMBCRKQ7Dh8+jOHDh2PAgAFYs2YNjIyMqrU9IyMjhIWFoXfv3vD19UVycrKKkpKq7Ny5EyUlJfD29n7mdgMDAwwdOhQxMTEiJaOnrV69Go6OjujXr99zvz569Gjk5+cjMjJSs8GqgMObifRQWVkZYmNjsWTJEsTFxcHW1hZvvfUW3n33XTRv3lzseCqXl5eHtWvXYsmSJTh79izatm2LKVOmYNy4cahTp47Y8YiI6AXOnj2LPn36oH379tixYwcsLS1Vtu28vDx4eHjg8uXLSExMhLOzs8q2TdUzceJEHDhwACkpKf/52qZNm+Dv748rV66gZcuWIqQjACgqKkKjRo0wefJkfP311y+8X79+/WBiYoL4+HgNpqs8nukl0iOZmZn49ttv0apVKwwbNgwPHz7EP//8g/T0dHz77bd6WXgBoFatWnj33Xdx+vRp7N27Fy4uLpg+fTrs7e3x7rvv4syZM2JHJCKif7l27RoGDx6Mpk2bYuvWrSotvMCTvw0xMTFo3LgxPDw8cPPmTZVun6pGEATExsb+Z2hzufJrfTnEWVzR0dHIysp67tDmp40bNw4JCQlIT0/XULKq4ZleIj1w9OhRLFmyBOHh4QCAoKAghIaGolu3biInE8+tW7ewYsUKrFixArdv30afPn0QGhoKf39/mJiYiB2PiKhGu337Nnr16gVDQ0Ps378fDRs2VPu+jIyMkJiYqNZ90audOHECXbp0QUJCwjMzNz/Nw8MDRkZG2L59u4bTUblhw4bh7t27OHz48Evvl5OTg0aNGmHevHmYOXOmhtJVHs/0EumowsJCrFq1Ct26dUO3bt2wZ88efP7550hPT1feXpM1adIE8+fPx82bNxEREQHgyYcBTk5OmD9/Pm7duiVyQiKimunhw4cYPHgwioqKEB8fr/YS2rhxY8THxyMvLw+enp7IyspS6/7o5WJiYmBtbY1evXq98D7e3t7Ys2ePTkyQpI/u3buHbdu2vfIsLwDUrl0b/v7+WLVqlVbPqcLSS6Rjbty4gZkzZ8LBwQFvvvkm6tWrhy1btuDq1auYOXMm7OzsxI6oVUxMTDBy5Ejs3bsXp0+fhp+fHxYtWgQnJyeMHDkS+/bt0+qDNBGRPsnPz4e3tzdu376NHTt2oFmzZhrZb4sWLbBjxw6kpqbCx8cHBQUFGtkv/VdsbCw8PDxgamr6wvt4e3ujqKgIu3bt0mAyKrdu3ToYGBggODi4QvcfO3YsUlJScOzYMTUnqzqWXiIdoFAoEBcXB19fX7Ro0QLLly/HmDFjcPHiRWzfvh2+vr7Vnu2yJujYsSOWLVuGjIwM/PDDDzh9+jT69u2LTp064ffff0deXp7YEYmI9FZRURH8/f1x9uxZbNu2De3bt9fo/jt06IDY2FicPHkSgYGBKC4u1uj+6cncI4cOHfrPrM3/1rp1a7Rq1YqzOItk9erV8PHxga2tbYXuP2jQIDRu3BirV69Wc7KqY+kl0mKPHj3CTz/9hLZt22LIkCFIS0vD8uXLkZGRgZ9++glt2rQRO6JOqlOnDqZNm4aUlBTEx8ejVatWCA0Nhb29PaZNm4YLFy6IHZGISK+UlZUhJCQE+/btw5YtW+Dm5iZKju7du2PTpk1ISEjA2LFjUVZWJkqOmmr79u0QBAFeXl6vvK+3tzdiYmI4GkvDzp49i+PHj1doaHM5IyMjjB49GjKZTGs/TGLpJdJCJ0+exNtvvw17e3vMnDkTbm5u2L9/P06cOIG3334bVlZWYkfUCwYGBhg0aBCioqJw/fp1TJ06FeHh4XB2dsagQYOwadMmlJaWih2TiEinCYKAd955B5s2bUJERAT69+8vap5BgwZh3bp12LBhA0JDQ1mqNCgmJgZdunRB48aNX3lfb29vpKen4+zZsxpIRuXWrFkDW1vbF86u/SJjx47FgwcPsG3bNjUlqx6WXiItUVxcjHXr1qFnz57o3Lkztm3bhv/9739ITU2FTCZDz549YWBgIHZMvdW0aVN89dVXSEtLw5o1a5Cfnw9/f3+0aNECX3/9Ne7duyd2RCIinSMIAj755BP8+eef+Ouvv+Dn5yd2JABAQEAA/vjjDyxfvhxz5swRO06NUFpairi4uFcObS7Xp08fWFlZcYizBpWVlWHt2rUYNWrUS6+5fp4OHTqgS5cuWjvEmaWXSGTp6emYO3cumjZtCqlUCjMzM2zcuBE3btzA3Llz0ahRI7Ej1ihmZmYYPXo0kpKScOzYMXh4eOCLL76Ao6Oj8naeFSAiqphvvvkGixYtwi+//FKp4ZKaMGHCBPzwww9YuHAhvv/+e7Hj6L1Dhw7h0aNHFS69ZmZmGDRoEEuvBiUkJODWrVtV/l0dO3YsoqOj8fDhQxUnqz6WXiIRCIKAXbt2QSKRoFmzZvj5558RGBiIc+fOKW83NjYWO2aN16VLF/z555/IyMjA119/jaSkJPTo0QNdu3bFX3/9xdk/iYhe4vfff8fs2bMxf/58TJs2Tew4z/XRRx9hzpw5+OSTT7By5Uqx4+i1mJgY2NnZwdXVtcKP8fb2xsGDB/Ho0SM1JqNyq1evRrt27Sr1HD1t1KhRUCgUCA8PV3Gy6mPpJdKgnJwc/Pbbb3jttdcwcOBApKSk4JdffkFGRgZ+++03jc9kSRVTr149TJ8+HZcvX0ZMTAwaN26MiRMnwsHBAR9//DGuXr0qdkQiIq0SHh6OKVOmYNq0afjss8/EjvNSX3zxBaZMmYJJkyZhw4YNYsfRW7GxsfDy8qrUahNeXl7KFSxIvXJychAZGYmxY8dW+XK6Bg0awMvLSyuHOLP0EmnA+fPnlbMDf/DBB2jfvj127dqFc+fOITQ0FLVr1xY7IlWAoaEhhg4dipiYGFy5cgVvvfUW/vrrL7Ru3Vp5u0KhEDsmEZGoYmNjMWbMGIwZMwY//fST1s9HYWBggMWLF2PUqFEICQnBjh07xI6kd9LS0nD69OlKT47k4OAAFxcXDnHWALlcjsLCQowePbpa2xk3bhwOHz6MixcvqiiZarD0EqlJSUkJNm7ciP79++O1116DXC7Hhx9+iBs3bihv1/Y3AvRiLVq0wPfff4+MjAz8+eefuHv3Lnx8fNC6dWssWrRIK69nISJSt8TEREgkEgwdOhR//vknDA11462moaEh/vnnHwwePBj+/v44ePCg2JH0SmxsLIyMjODp6Vnpx3p7e2P79u1cXkrNVq9ejQEDBsDR0bFa2/Hx8YGNjY3Wne3VjSMRkQ65c+cOFixYgGbNmmHEiBEoKSmBTCZDamoqFixYAAcHB7EjkgpZWFhg/PjxSE5OVl7zO2fOHNjb22PChAk4duyY2BGJiDTixIkT8PHxgbu7OyIiInRubgoTExOsX78eXbt2hbe3N06fPi12JL0RGxuLHj16oG7dupV+7NChQ5GZmYmjR4+qIRkBwI0bN7Bnzx6VTDZnbm6OoKAgrFmzRqtGv7H0EqmAIAjYv38/Ro0ahaZNm+Lbb7+Ft7c3Tp48qby9slO/k24xMDBA9+7dsWbNGqSlpeGzzz5DQkICXF1dlbcXFRWJHZOISC0uXboET09PtG3bFps3b4a5ubnYkarE0tIS0dHRaN68OQYPHowrV66IHUnnFRYWYufOnZUe2lyue/fuqFevHoc4q9HatWthZWWFgIAAlWxv7NixSEtLw969e1WyPVUwELj2BlGV5efnIywsDEuWLMHp06fRunVrTJkyBW+++SZsbGzEjkciKysrw9atW7FkyRLEx8ejfv36mDhxIt599100bdpU7HhERCqRlpaGXr16wcrKCvv27YOdnZ3Ykart3r176N27N4qLi7F//37Y29uLHUlnxcXFYciQITh9+jQ6duxYpW2EhITgwoULHD2lBoIgoG3btnB3d8eqVatUts02bdqgV69e+Pvvv1WyzerimV6iKrh06RI++OAD2Nvb491334WTkxO2b9+OCxcu4IMPPmDhJQCAkZER/Pz8sGPHDly4cAGjRo3CkiVL0Lx5cwwfPhzx8fFaNfSHiKiy7t+/j8GDB8PAwAA7duzQi8ILPJmFNj4+HmVlZRg8eDAePHggdiSdFRMTA0dHR3To0KHK2/D29sbx48dx+/ZtFSYj4Mn6yZcvX1bpOtoGBgYYO3YsNm7ciPz8fJVttzpYeokqqKysDFu2bFEO31q7di3effddXLt2TXm7rkzYQZrXtm1b5fJUS5cuxbVr1zB48GA4Ozvjl19+QXZ2ttgRiYgqJScnB15eXnj06BHi4+P1bs6Kpk2bIj4+Hvfu3YOXlxdyc3PFjqRzBEFAbGwshg4dWq3JO8vfY8XGxqowHQFPJrBycHBAv379VLrdMWPGIC8vD5GRkSrdblXxHTrRK9y/fx/ffPMNWrZsCT8/Pzx69AirVq1Ceno6vvnmGzRr1kzsiKRDatWqhXfeeQenTp3Cvn370LlzZ3z88cfKUQNnzpwROyIR0Ss9fvwYw4YNw5UrVxAXF4fWrVuLHUkt2rZti7i4OFy4cAHDhw9HYWGh2JF0yqVLl3D16lV4e3tXazu2trbo3r07S6+KFRUVITw8HGPGjKnU+skV0axZM/Tt2xdr1qxR6XariqWX6AWOHDmCsWPHwtHREfPnz0e/fv1w5MgR5e26OkkHaQcDAwP07t0b4eHhSE1NxYwZM7BlyxZ06tQJffr0QUREBIqLi8WOSUT0HyUlJQgKCsKRI0cQExMDFxcXsSOpVZcuXRAdHY2DBw9i1KhRKC0tFTuSzoiJiYGZmRkGDBhQ7W15e3sjPj6efxtVaOvWrcjKysKYMWPUsv2xY8di586dSE9PV8v2K4Oll+gpjx8/xj///AM3Nze88cYbSExMxIIFC5Cenq68nUjVGjdujHnz5uHmzZtYv349DA0NERwcDCcnJ8ybNw8ZGRliRyQiAgAoFApMmDAB27dvR2RkJHr27Cl2JI3o27cvNmzYgK1bt+Ltt9/mfAwVFBsbi/79+8PKyqra2xo6dChyc3ORmJiogmQEAKtWrYKbmxucnZ3Vsv3AwECYmZkhLCxMLduvDJZeIgDXr1/HJ598AgcHB4wfPx52dnaIjo7GlStX8Mknn+jNxByk3UxMTDBixAjs2bMHZ86cgb+/P3744Qc4OTlhxIgR2Lt3LzjhPhGJRRAEfPDBBwgLC8PatWsxZMgQsSNplI+PD1atWoVVq1Zh+vTpPB6/Qm5uLvbt21flpYr+zcXFBfb29ly6SEXu3buHbdu2Ydy4cWrbR+3ateHv74/Vq1eL/vvC0ks1lkKhwPbt2+Hj44OWLVvijz/+wLhx43Dp0iVs27YNPj4+Kr++gaiiOnTogKVLlyIjIwM//fQTzp49i379+qFjx45YtmwZJ1QhIo2bP38+Fi9ejN9//x0jR44UO44opFIpFi9ejJ9//hlfffWV2HG0Wnx8PEpKSqp9PW85AwMDDB06lNf1qkh4eDgMDAwQFBSk1v2MGzcO58+fx/Hjx9W6n1dh6aUa59GjR/jxxx/Rtm1beHl5ISMjAytWrEBGRgZ+/PFHvZ2Mg3RTnTp18N577+H8+fPYuXMn2rRpg6lTp8Le3h7vvfceUlJSxI5IRDXAzz//jAULFuCbb77BpEmTxI4jqtDQUHzxxReYO3cufvvtN7HjaK3Y2Fi0a9cOLVq0UNk2hw4diosXL+Lq1asq22ZNtWrVKnh7e6t9NOPAgQPRuHFjla0BXFUsvVRjnDhxAhMnToS9vT1mzZqFbt264cCBAzh+/DgmTpwIS0tLsSMSvZCBgQEGDhyIyMhI3LhxA9OmTcP69evRvn17DBw4EFFRUZxchYjUYtWqVfjwww8xc+ZMzJw5U+w4WmHOnDn46KOP8N5772nF9Yra5umlilRp0KBBMDU15RDnajp79iyOHz+u0rV5X8TY2BghISFYt26dqJOQsfSSXisqKoJMJkOPHj3QpUsXxMXFYc6cOUhLS0NYWBh69OhRrXXjiMTg6OiIL7/8EqmpqQgLC8Pjx48REBCAFi1a4KuvvsLdu3fFjkhEemLTpk1466238Pbbb2PhwoVix9EaBgYGWLRoEcaPH49x48YhOjpa7Eha5cSJE7h9+7bKhjaXq1WrFvr27cshztW0Zs0a1KtXT+XPz4uMGzcOmZmZ2L59u0b29zwsvaSX0tLS8Omnn6Jp06YICQmBhYUF5HI5rl+/jjlz5qBhw4ZiRySqNjMzM0ilUhw8eBDHjx/H4MGD8dVXX8HR0REhISE4ePCg6BNHEJHuSkhIQFBQEAICArBs2TJ+SPwvBgYGWLFiBYYNG4aRI0di7969YkfSGrGxsbC2tkavXr1Uvu2hQ4diz549yM/PV/m2a4KysjKsXbsWo0aNgqmpqUb22aFDB3Tu3FnUIc4svaQ3BEFAQkICAgIC0KxZM/z6668YOXIkzp8/r7zd2NhY7JhEatG5c2esXLkSGRkZ+Oabb3D48GH07NkTXbp0wZ9//omCggKxIxKRDjly5Aj8/PzQv39/rF27lhM7voCxsTFkMhl69uwJX19fHDt2TOxIWiEmJgaDBw9WS6ny9vZGUVEREhISVL7tmiAhIQG3bt3SyNDmp40dOxbR0dF4+PChRvdbjqWXdF5OTg5+++03tG/fHoMGDcLFixfx22+/ISMjA4sXL1bb2mNE2qhu3br46KOPlLOQ29vb4+2334aDgwOmT5+OK1euiB2RiLTcuXPn4OXlBRcXF8jlco2dDdJV5ubm2LRpE9q3b48hQ4bgwoULYkcS1f3793H48GGVX89brnXr1mjdujWv662i1atXo127dnBzc9PofkeNGgWFQoGIiAiN7rccSy/prHPnzmHKlCmwt7fHBx98gA4dOmD37t04e/YsJk+eDGtra7EjEonG0NAQQ4YMwdatW3HlyhVMnDgR//zzD1q3bg0vLy9s3boVZWVlYsckIi1z/fp1DB48GA4ODti6dSusrKzEjqQTatWqhZiYGDRs2BAeHh5ITU0VO5Jo4uLiIAiC2kovAOXSRbyEp3Jyc3MRGRmJsWPHavxyhYYNG8LLy0u0Ic4svaRTSkpKsGHDBvTr1w8dOnRAVFQUPvroI9y8eVN5O685InpWixYt8N133yE9PR1///03MjMz4evri9atW+O7777DgwcPxI5IRFrgzp078PDwgKWlJeLi4lC3bl2xI+kUW1tb7NixAyYmJvDw8MC9e/fEjiSKmJgYdO3aFY0aNVLbPry9vZGeno4zZ86obR/6SC6Xo7CwEKNHjxZl/2PHjsXhw4dx8eJFje+bpZd0wu3bt/H555+jWbNmGDlyJBQKBcLDw3Hz5k18/vnnsLe3FzsikdazsLDAm2++iaNHj+Lw4cPo3bs35s6dC3t7e4wfPx7JycliRyQikTx69AiDBw/G48ePER8fr9bCos+aNGmC+Ph45OTkwNPTE9nZ2WJH0qjS0lJs375drWd5AaBPnz6wsrLiEOdKWrVqFQYMGABHR0dR9u/r64s6depgzZo1Gt83Sy9pLUEQkJiYiODgYDRt2hTfffcdfHx8cOrUKezbtw9BQUG8zoioirp164ZVq1YhPT0d8+fPx65du+Dm5oY33ngDq1evRmFhodgRiUhD8vPz4e3tjYyMDMTHx6NZs2ZiR9JpLVu2xI4dO3Djxg34+vrWqIkEDx06hKysLLUvhWNmZoZBgwZx6aJKuHnzJvbs2aPxCayeZm5ujqCgIKxZswYKhUKj+2bpJa2Tl5eH5cuXw8XFBX369MGJEyewaNEiZGRkYPny5ejUqZPYEYn0Rv369TFr1ixcu3YNmzdvho2NDcaNGwdHR0fMmjULN27cEDsiEalRUVERAgICcObMGWzbtg3t27cXO5Je6NixI2JjY3Hs2DGMGDECxcXFYkfSiJiYGNSvX18jkyR5e3vj4MGDos0GrGvWrl0LKysrBAQEiJpj3LhxSE1N1fgSXyy9pDUuXryI999/H/b29pgyZQpatGiBHTt2ICUlBe+//z5sbGzEjkikt4yMjDBs2DDExcXh4sWLGD16NH7//Xe0bNkSfn5+2LFjh8Y/lSUi9SorK8OYMWOwd+9ebN68Gd26dRM7kl5xd3fHpk2bEB8fj3HjxtWIyQNjYmLg5eUFQ0P1VwwvLy8oFArExcWpfV+6ThAErFq1ChKJBLVq1RI1i7u7O1q2bInVq1drdL8svSSqsrIybN68GYMHD0a7du0gk8kwZcoUXLt2DZs2bYKHh4dGDpxE9H/atGmDn376CRkZGVi2bBlu3LgBT09PtGvXDj///DOysrLEjkhE1SQIAt59911ERkYiIiICAwYMEDuSXvLw8IBMJsP69evx3nvv6fVsw6mpqThz5ozar+ct5+DgABcXF17XWwGHDx/G5cuXRR3aXM7AwABjx47Fxo0bkZ+fr7H9sk2QKO7fv4+FCxeiRYsWGD58OHJycrB69WqkpaVh4cKFcHJyEjsiUY1nZWWFSZMm4eTJk0hMTETXrl0xY8YM2NvbY9KkSTh16pTYEYmoimbNmoWVK1fizz//hJ+fn9hx9FpgYCBWrFiBZcuWYe7cuWLHUZtt27bByMgInp6eGtunt7c3tm/fXiPOolfH6tWr4eDggH79+okdBQAwZswY5OXlISoqSmP7NBD0+SMn0iqCIODIkSP47bffsH79ehgaGmLUqFEIDQ1F165dxY5HRBVw584d/PHHH/j9999x69Yt9OrVC6GhoQgICODEckQ64ptvvsH//vc//Pzzz3j//ffFjlNjLFq0CDNmzMCiRYswffp0seOo3LBhw5Cdna3RazUPHDiAXr164eDBg3B3d9fYfnVJUVERGjdujHfeeQcLFy4UO45S3759YWZmhh07dmhkfzzTS2r3+PFj/P3333Bzc0P37t1x4MABfPnll0hPT8dff/3FwkukQxo1aoS5c+fixo0b2LBhA4yNjTFq1Cg4OTnhs88+Q0ZGhtgRieglli9fjv/973+YN28eC6+Gffzxx5g9ezY+/vhj/Pnnn2LHUanCwkIkJCRobGhzue7du6NevXoc4vwSW7duxaNHj7RiaPPTxo4di507d2rsfQNLL6nNtWvXMGPGDDg4OGDChAlo0KABtm7disuXL2PGjBmwtbUVOyIRVZGJiQkCAwOxe/dunD17FgEBAfjpp5/g5OSkvJ0DiYi0S0REBCZPnoz33nsP8+bNEztOjfTll1/i3XffxaRJkyCXy8WOozJ79+5FQUGB2pcq+jcjIyMMGTKESxe9xOrVq+Hm5gZnZ2exozwjMDAQZmZmWLt2rUb2x9JLKqVQKBAbGwtvb2+0atUKK1euxJtvvonLly8rbzcyMhI7JhGp0GuvvYYlS5YgIyMDv/zyC86fP48BAwagQ4cOWLp0KXJzc8WOSFTjbdu2DaNHj0ZISAh+/vlnGBgYiB2pRjIwMMBvv/2GkSNHQiqVIj4+XuxIKhETE4OmTZvitdde0/i+hw4dihMnTuDWrVsa37e2u3//PmJjY7XuLC8A1KlTB/7+/li9erVGPiRn6SWVePjwIX744Qe0bt0a3t7euH37Nv744w9kZGTghx9+QKtWrcSOSERqVrt2bYSGhuLcuXNISEhAu3btMG3aNNjb22Pq1KlISUkROyJRjbR//35IJBJ4eXnhr7/+4qoIIjMyMsKqVaswcOBADB8+HElJSWJHqhZBEBATEwNvb29RPkwZMmQIDA0Nebb3OcLDw2FgYIDg4GCxozzX2LFjcf78eRw/flzt++JEVlQtx48fx5IlSyCTyVBWVoaRI0ciNDQU3bt356fIRIT09HQsX74cK1aswL1799C/f3+EhobCz88PxsbGYscj0nsnT55Ev3790LlzZ2zbtg3m5uZiR6L/r6CgAJ6enjh37hz27t2Ljh07ih2pSi5evIh27dohOjoaPj4+omTo2bMnGjRooNHZgHWBq6srHB0dtfbnUlpaCkdHR4wYMQK//vqrWvfFj/qo0oqKirB27Vq4u7uja9eu2LFjBz799FOkpaUpb2fhJSLgyTqKX3zxBdLS0iCTyVBUVITAwEA0b94cX375Je7evSt2RCK9dfnyZXh6eqJVq1bYvHkzC6+WsbS0RHR0NJycnDB48GBcu3ZN7EhVUv7aEnOt5/r162PTpk04c+aMaBm0zcmTJ3Hs2DGtHNpcztjYGCEhIZDJZHj8+LFa98XSSxWWmpqKOXPmwNHREWPGjIGVlRUiIyNx/fp1zJkzBw0bNhQ7IhFpKVNTU4waNQoHDhzAiRMnMGTIEHz99ddwdHSEVCrFgQMHOPEVkQodP34cAwcORL169bB9+3bUrl1b7Ej0HDY2Nti+fTusra0xaNAgXL9+XexIlSIIAmbOnIni4mJYWlqKlsPT0xOGhob8O/KU8jV5PTw8xA3yCr169cKDBw8wZswYte6Hw5vppQRBQEJCApYsWYItW7bAysoKb775JqZMmYJ27dqJHY+IdNijR4/wzz//YOnSpbhy5Qpef/11TJkyBVKpFFZWVmLHI9JZDx48gJ2dHUxNTXH16lU4ODiIHYle4ebNm+jQoQPy8vJw5coVtGzZUuxIFdauXTt4eHhg8eLFouYQBIEjDZ/i5+eHvLw8JCQkiB3lpfLy8tCpUye8/fbb+N///qe2/bD00nMdOHAA69evR1xcHC5evIjXXnsNoaGhGD16NKytrcWOR0R6RKFQID4+Hr/99htiYmJQp04djBgxAi4uLggNDRU7HpHOefToEdzc3PDee+9xLV4d8u233+Lrr79GYmIiOnXqJHYcIr3C0kvPZWRkBIVCgREjRiA0NBR9+vThp2dEpHbXr1/H77//jh9++AFlZWXYvHkzhg0bJnYsIiIi0mEsvfRcf/75J6ysrLR2inMi0m/nzp3DunXr8Omnn3LyHSIiUkpNTUVmZma1t2NnZ4emTZuqIBHpApZePcBffiKqKVRxvOOxjnRJdV/zfL3rBm18L6eNx9vU1FQ4OzujoKCg2tuytLRESkqKTv1+aOPrpLLEel1xkUQdV9N/+Ymo5lDV8Y7HOtIVqnjN8/Wu/bTxvZy2Hm8zMzNRUFCAtWvXwtnZucrbSUlJwejRo5GZmakzvxva+DqpLDFfVyy9Oq4m//ITUc2iiuMdj3WkS8pf8wsWLICJiQns7OxgYGAAExMTFBUV4caNGzAzM0P37t1x69YtmJmZITc3F3Z2dhAEAYWFhXy96wBtfC+n7cdbZ2dn3Lp1C82bNwfwZELEnJwcKBQKKBQKNGjQAHl5eTA1NYW9vT2SkpKgUCjg5+en0hya9PRzIggC0tPTK3RMyMrKQsOGDSEIApo3by7q38Gqfg+PHz9GcXExGjRogOvXr+Pjjz+udH6WXj1RUFCAs2fPwtXVFSUlJbhx4wZKS0vRsmVLZGdnAwAaNGiABg0aIC0tDWlpaejWrRtu3LgBc3Nz5UGDiEjbXb16FZ07d67QsS4rK0v55ignJ4dv/EknCYKAYcOGPfOa79SpE9q0aQPgyWu+a9euSEtLw507d9C5c2dcuHABeXl5Iienyqjse7k7d+6gefPmyMnJwePHj9G4cWOVZ6rM8TYzMxOtW7fGjRs3AECtx9tjx47BzMwMLVq0UGa7f/8+XF1dkZ2djZSUFDg7Oyuz1atXD61atcLp06fx6NEjnV7Pt6CgAGZmZpU6Jjg7OyM7OxuHDx9G+/btRf4OKv895Ofno1WrVrhw4UKVJ9Zl6dUTXbt2RZcuXQAAe/fuhY2NDe7evYsTJ04gNzcXLi4uMDExQUFBAR4+fAiFQoGGDRuiYcOGAJ4sYk9EpAt8fHzQvn37Vx7rbty4gSZNmvBYRzqv/DUPAC4uLigsLERxcTFq1679zP1sbW2V/27YsCFf7zqmMu/lioqKMGTIkGcer47n++nXXlZWFoqLi3H+/HkYGRnh7t27yky2tra4ceMGnJyc4OTkpLY85Z7+WQGV+71QdzZ1q+r3DgCdOnXSiu+9Os9fVfOz9Oqhvn37vvTrjo6OGkpCRKQ+PNZRTbN3714AT9bhLSoqUpYOe3t7mJmZ4dKlSzAwMMCAAQNETkrVpY3HN23MVO5Vvxu3bt3CG2+8IVo+danIMcHc3Bw9e/YUOemLvep7uHz5skqOaSy9eiIlJUX572PHjuHSpUtwcnJCcXExWrZsCRMTE5w+fRq1atVCcXExbGxs8Prrrz/38URE2qz8ePWiY11KSgrMzMx4rCO9Uf66vXTp0n9e8/n5+di6dSuMjY1RXFwMS0tL2NjYPPM40g0VeS937do12NnZ4cqVK7CwsED//v2f+3hNZrpz5w4KCgpgbGyMwsJC9OnTR215KpqrVatWuHr1Ki5dugQjIyPk5ORg//79yp+XLv9uPJ39RceEjRs3Kt/zW1pawsLC4rmPF0tFvod9+/ahrKwMubm5OH36dLVfVyy9Os7Ozg6WlpYYPXp0tbdlaWkJOzs7FaQiIlI9VR3veKwjXaGK1zxf79pPG9/LaevxVht/VpqiD9+7mK8rrtOrB8rXuxIEAT/++CNkMhlmzpyJkSNH/ue+giBgyZIl+Pvvv/HBBx9gzJgxyq9xLT8i0napqam4ceMGZsyYgePHj2PBggXw9PR87n3Lysrw3XffYePGjXj77bfxzjvvwMDAgMc60ilPr2m5fft2zJkzB5988gmCgoKeud+uXbswY8YMTJs2DePGjVPezte7bnjZ2qXvv/8+ysrKcPPmTfTr1w/Tp09/4XY0tU7v1KlTcfnyZTRv3hy///67RvJUJNe5c+cwduxYAMAff/zxzHWjmsimbi/63stnZF6yZAmcnJzg4+ODL774AkOHDn3udrR1nd4jR45g8uTJ6N27N37++ecXbqNK+QXSCwqFQvjggw8EAMJvv/32yvvOnj1bACB8//33GkpIRFR96enpQqdOnYQ6deoIu3fvfuX9FQqF8M033wgAhDfffFMoLi5Wf0giNTh9+rRgaWkpjB49WlAoFM+9z6xZswRDQ0MhISFBw+lIXRQKhdCwYUPh008/FUaOHCn07t1b7EiCIAhC+/bthc6dOwutW7cWO8ozNm7cKAAQAAhr1qwRO47GfPzxx0L9+vWFkpISQRAEoUePHoKvr6/IqSpPLpcLAIRBgwapfNuGlavIpI0EQcCHH36In3/+GUuWLEFoaOhL729gYIAvv/wSc+bMwYwZM/D9999rKCkRUdWdO3cO7u7uePToEQ4cOIB+/fq98jEGBgaYOXMm1q5di7CwMPj4+CA3N1f9YYlUKDs7GwEBAWjVqhWWL1/+wiU7vvzySwwYMADBwcFIT0/XcEpSh4yMDNy9exdubm5wdXXF8ePHUVZWJmomQRCQmpqKpk2bIjU1VauW/0lNTYWlpSVsbW2RmpoqdhyNUCgUiIiIQGBgIIyNn1y5GhQUhO3bt+PRo0cip6uc8qWw8vPzVb5tll4dV154f/nlFyxZsgRTpkyp0OMMDAzwxRdfKIdJsfgSkTbbs2cPevbsibp16yIpKQmvvfZapR4fEhKC7du349ChQ+jTpw9u376tpqREqqVQKDBu3Djcv38fcrkclpaWL7yvkZERZDIZzM3NERgYiKKiIg0mJXU4evQoAMDV1RWurq7Iz8/HhQsXRM2UlZWFvLw8tG7dGkVFRbh//76oeZ5WXsbLC3lNkJSUhLS0NAQHBytvGzFiBEpLSxEVFSVissrLzs6GsbExcnJyVL5tll4d9nThXbp0aYULb7ny4vvpp5/ik08+wXfffaempEREVRceHg5PT0+4ubkhMTER9vb2VdrOgAEDsH//fty/fx/du3fXihksiV7lm2++webNm7F27Vq0atXqlfevX78+5HI5Tpw4gQ8//FADCUmdkpOT0bhxYzRp0gRdu3ZV3iam8jLZoUMHAEBaWpqYcZ6RlpYGR0dHODo6alUudYqIiECTJk3Qq1cv5W2NGzdGv379EB4eLmKyysvOzoa5ubnyjK8qsfTqKEEQ8MEHH+CXX37BsmXLMHny5Cptx8DAAAsWLMDcuXMxc+ZMfPvttypOSkRUNYIgYNGiRRg1ahSCgoIQExPzn0XrK6tjx444dOgQateujR49eiAxMVFFaYlUb8eOHfj0008xd+5c+Pj4VPhxbm5u+O2337Bs2TKsWrVKjQlJ3ZKTk+Hq6goAqF27Ntq2bSt66S0vk+WTRGnTGdWadqa3rKwM69evR1BQEAwNn611wcHB2LVrF+7duydSusrLzs6GhYUFSy89IQgC3n//ffz6669YtmwZ3n333Wptz8DAAJ9//jnmzp2LWbNmsfgSkejKysrw/vvvY8aMGZg9ezZWrVoFU1NTlWzbwcEBiYmJ6Ny5Mzw8PLBhwwaVbJdIlW7evAmpVIrBgwdj3rx5lX78xIkTMWHCBLz77rs4efKk6gOS2gmCgOTkZLi5uSlvc3V1VQ55FktqaipMTEzQvn17mJuba1W5rGmld+/evbh79+5/ZnMHgICAAACAXC7XdKwqy87ORq1atZCbm6vya9dZenVMeeFdvHgxfv/992oX3nLlxfezzz7DrFmz8M0336hku0RElfX48WOMHDkSS5YswbJly/DVV1+9cOKeqrKxscG2bdsgkUgQFBSEn376SaXbJ6qOwsJCBAYGwtraGmFhYTAyMqr0NgwMDPDbb7+hffv2CAgIwMOHD9WQlNTp+vXrePjwofJML/Ck9J48eRIlJSWi5UpNTYWDgwOMjIy0qlwWFhbi7t27ytKbk5OjljOG2iQ8PBzNmjVDt27d/vM1Ozs7eHh46NQQ5+zsbFhbWwOAyiedZOnVIYIgYNq0aVi8eDGWL1+Od955R6XbLy++8+bNw//+9z8sXLhQpdsnInqVBw8eYNCgQdi2bRs2bdqksg/2nsfMzAxr1qzBzJkz8dFHH+HDDz+EQqFQ2/6IKuq9997DmTNnIJfLYWtrW+XtWFhYQC6XIzs7G6NHj+brW8eUD2Muv5YXeDJ0vaioCOfOnRMrlvJsKgCtKr3lM5aXl15Au4Zeq1pJSQnkcjmCg4Nf+MFwcHAwEhMTkZGRoeF0VZOdnY06deoo/61KLL06QhAEvPfee/jtt9+wfPlyTJo0SW37mj9/PubNm4fZs2fj66+/Vtt+iIiedv36dfTo0QOXL1/Gnj174Ovrq/Z9GhoaYuHChViyZAl+/fVXBAUFobCwUO37JXqRlStXYuXKlVi2bJnymsnqaNasGWQyGbZv344vvvhCBQlJU5KTk9G0aVM0aNBAedvrr78OQ0NDUa/r1dbSW56jppTenTt34uHDh8/M2vxvw4cPh4mJic5cxpOdnY26desq/61KLL06QBAETJ06FUuWLFF74S1XXnznzJnD4ktEapecnIzu3btDoVDg4MGDzx2qpU5TpkxBZGQkYmJi4OHhwaGgJIrk5GRMnToVkyZNwvjx41W2XU9PTyxYsACff/45YmNjVbZdUq+jR48+cz0vAFhZWaF9+/aiXter7aXXwcEBjRo1grGxsdZkU4fw8HC0a9cOnTp1euF96tSpAy8vL50Z4pydnY169eop/61KLL1arrzwLl26FCtWrNBI4S03f/58zJ8/H3PmzMFXX32lsf0SUc0SGxuLfv36oXnz5jh48GCFlmVRBz8/P+zatQsXLlxAz549cePGDVFyUM2UmZkJiUSCTp064ddff1X59mfPng0fHx+EhITg2rVrKt8+qZZCocCxY8eeuZ63nJubm2hnektLS5GRkfFM6b1z545WrAmdlpaGhg0bwtzcHEZGRnBwcNDbZYsKCwsRFRWFoKCgV855ERwcjMOHD+P69esaSld12dnZqF+/vvLfqsTSq8UUCgVCQ0OxdOlS/PHHH3j77bc1nmHevHn4/PPP8emnn+LLL7/U+P6JSL+tXLkSw4YNw8CBA7Fr1y7lHzuxdO/eHQcPHkRxcTHc3d1x4sQJUfNQzVBWVgapVIqCggJs3LgRZmZmKt+HoaEhVq9eDVtbWwQEBKCgoEDl+yDVuXz5MnJzc59bel1dXXHmzBlRLsW4desWFAqFsvQ6OjoCgFZcM5qamqrMAzzJpq9nerdv347c3Nznztr8b76+vrC0tERERIQGklVPdna2cjg/S28NoVAoMHXqVCxbtgwrV67ExIkTRcvy2WefKZc0YvElIlUQBAHz5s3D22+/jUmTJiEyMhKWlpZixwIAtG7dGklJSXB0dESfPn0QFxcndiTSc/PmzUNCQgLCw8OVZUIdbGxsEBkZiUuXLmHy5MkQBEFt+6Lqed4kVuVcXV1RUlKCM2fOaDrWM9fNPv3/2lAunx52DWjX0GtVCw8Ph4uLC5ydnV95XysrK/j6+mp96S0uLkZhYSHs7OxgZGTE0lsTlJ/h/f3337Fy5Uq89dZbYkfCZ599hgULFmDu3LmcCIOIqqWkpAQTJkzAggUL8M0332DJkiVVWpJFnRo0aIDdu3ejX79+8Pb2xt9//y12JNJTW7ZswVdffYWvvvoKAwcOVPv+OnXqhBUrVmD16tX4/fff1b4/qpqjR4+iVatWykl9nubi4gITExNRrustHy5cfka1/P+1oVzWlNKbn5+P6Ojol05g9W9BQUE4efIkLly4oMZk1VNecm1sbFCnTh2Vl15jlW6Nqk2hUGDKlClYsWIFVq5ciQkTJogdSWnu3LkwMDDA3LlzIQgCPvvsM7EjEZGOyc3NxYgRI7Br1y6sWbMGo0ePFjvSC1lZWSEqKgqhoaGYMGEC0tLSlMdBIlW4fPkyxowZg+HDh2PmzJka2+/o0aNx+PBhvP/+++jcuTO6d++usX1TxSQnJz93aDPwZLm1jh07inJdb2pqKmxsbJRrqVpYWKB+/fqil0tBEJ5betPT01FWVqZ1H6xWR3R0NAoKCio0tLmcl5cXrK2tERERgXnz5qkxXdWVl9w6deqopfTyTK8WUSgUmDx5slYW3nKffvopvvjiC+W1vkREFXX79m307dsXSUlJ2LZtm1YX3nLGxsb4/fff8dVXXymHY5eUlIgdi/RAfn4+JBIJGjVqhH/++UfjH6b88MMPcHNzQ2BgIO7du6fRfdPLlZaW4sSJEy8svcCTIc5ild5/D8HXhjOqDx8+REFBwX9Kb1lZGW7fvi1iMtWLiIhAt27d0Lx58wo/xtzcHP7+/ggPD9fayxrKS27t2rVRp04d5OTkqHT7LL1aorzw/vHHH/jzzz+1svCWK5/Uav78+Sy+RFQhKSkpcHd3x71795CYmKiRYZyqYmBggNmzZ2PVqlVYtWoV/Pz8kJeXJ3Ys0mGCIGDSpEm4evUqIiMjUadOHY1nMDU1xYYNG1BaWorg4GCUlpZqPAM934ULF1BQUPCf5Yqe5urqinPnziE/P1+DybS39P77WuOn/y12NlXKzs5GbGxspYY2lwsKCsKFCxdEuRa8InimtwZQKBR499138ccff+Cvv/5S6dp86lK+jFH5skZERC+SmJiInj17wtraGocOHXrpmoLabOzYsYiNjcX+/fvRt29f3LlzR+xIpKN+++03yGQy/Pnnn3jttddEy9GkSRNERERg3759mD17tmg56FlHjx6FgYEBOnfu/ML7uLm5QaFQ4OTJk5oLBpZesW3atAklJSUYOXJkpR87aNAg1KtXT2vX7GXp1XMKhQLvvPMOVq5cib/++gtvvvmm2JEqbPbs2fjqq6/w+eefs/gS0XNt2LABHh4eeP3115GYmAgHBwexI1WLh4cHEhMTcefOHbi7u+PixYtiRyIdc+DAAXz00Uf44IMPqnS2RtX69u2L7777Dt9//z3kcrnYcQhPrudt166d8rrZ53nttddgZmam8SHOLyq9aWlpog6bTUtLg5mZ2TPL3pUPk9WntXrDw8PRu3dv2NvbV/qxpqamkEgkWjvEuXw4M0uvHiovvH/++Sf+/vtvnSq85WbPno2vv/4an3/+OebNm6eVv0REJI6ffvoJQUFBkEgk2LZtG2xsbMSOpBIuLi5ISkqCpaUlevTogYMHD4odiXTEnTt3MGLECLi7u+O7774TO47Shx9+iJEjR+LNN9/U6tlda4qXTWJVzsTEBK+//rpGS29OTg6ysrL+U3odHR2Rl5eHrKwsjWX5t9TUVDg4OMDQ8Nlqo09r9WZmZmLnzp2VmsDq34KDg3H9+nVRZv5+lezsbJibm8PU1JSlV58oFApMmjRJWXjHjRsndqQq+9///oeFCxdiwYIFmD9/PosvUQ2nUCjw4Ycf4qOPPsLMmTOxZs0amJmZiR1LpZo2bYr9+/ejY8eOGDhwICIjI8WORFru6SGJ69evh4mJiciJ/o+BgQH+/PNPODo6wt/fH7m5uWJHqrGKi4tx6tSpl17PW87NzU2j5aX8jOnzzvQC4g4jft4ZaEA7hl6rSmRkJBQKBQIDA6u8jb59+6Jhw4ZauWZvdna2cn4Dll49UV54//rrL/zzzz86XXjLzZo1S1l8ecaXqOYqLCxEUFAQfv31VyxZsgQLFy78zyfv+qJu3bqIi4uDn58fAgMDsXjxYrEjkRabOXMmkpKSsGHDBjRq1EjsOP9Rq1YtREVFISMjAxMmTODfcZGcPXsWRUVFrzzTCzyZzOrixYsqn+X2RZ533ezT/83Sq17h4eEYOHAgGjRoUOVtGBkZYcSIEYiIiIBCoVBhuupj6dUzCoUCb7/9trLwjh07VuxIKjNr1ix88803+OKLL/DZZ5/xDyZRDfPw4UN4eHhg69atiIyMxJQpU8SOpHZmZmaQyWSYPn06pk2bhhkzZmjdGwkSX0REBH766Sf8+OOP6Nmzp9hxXqht27ZYtWoVNm7ciB9//FHsODVScnIyjIyM4OLi8sr7lhfj48ePqzsWgCdneo2MjNC4ceNnbm/YsCFMTExYetXo9u3b2LNnj0rmAQgODkZGRgYOHDiggmSq8+/Sm5OTo9K/p8Yq2xK9kkKhwMSJE/HPP/9g1apVGDNmjNiRVG7mzJkwMDDAzJkzAQALFizQ+NqDRKR5N27cgJeXF+7fv49du3bB3d1d7EgaY2hoiO+//x6Ojo744IMPkJaWhlWrVundkG6qmnPnzuGtt96CVCrF1KlTxY7zSv7+/pg5cyZmzpyJrl27ol+/fmJHqlGSk5Px2muvwdLS8pX3bdeuHaysrJCcnKyR5yk1NRVNmjSBsfGz9cHQ0FDUa2dLSkpw69atF5behw8fIi8vD7Vq1RIhnWps3LgRxsbG8Pf3r/a23N3d4ejoqJwUS1v8u/QKgoC8vDzUrl1bJdvnmV4NKS+8q1atwurVq/Wy8Jb75JNP8N133+HLL7/E3LlzecaXSM+dOHEC7u7uKC4uxsGDB2tU4X3atGnTsHHjRmzevBmenp549OiR2JFIZNnZ2QgICEDz5s2xYsUKnfkQ+Msvv0Tfvn0RFBSE9PR0sePUKMnJyRW6nhd4MlS1S5cuGruu90VnUwFxz6hmZGRAEIQXll4AOj+Dc3h4ODw9PVG3bt1qb8vQ0BAjR47Exo0btWp97n+X3vLbVIWlVwPKysrw1ltvYdWqVVi1ahVGjx4tdiS1mzFjBr777jt89dVX+PTTT1l8ifRUXFwc+vTpA0dHRyQlJaFNmzZiRxJVQEAAdu7ciTNnzqBXr156MayOqkYQBIwfPx537txBZGQkrKysxI5UYcbGxli3bh1MTU0xYsQIFBcXix2pRigsLMSZM2fQtWvXCj/G1dVVo6XX0dHxuV8Ts/SWF9rnZdOG642rKzU1FQcPHqzWrM3/FhwcjHv37mH37t0q22Z1sfTquPLCu3r1aqxevbpGFN5yM2bMwPfff4+vv/4ac+bMYfEl0jN///03vL290a9fP+zevbtak2vok549e+LgwYMoKChA9+7dcerUKbEjkQi+++47REVFYc2aNWjdurXYcSqtQYMGkMvlOH78OD766COx49QIp06dQmlpaYXP9AJPSu/169fx8OFDNSZ7IjU1FU5OTs/9mqOjo2hnU180wRYANGnSBAYGBjp9pjciIgLm5ubw8/NT2Ta7du2Kli1batUsziy9Oqy88K5ZswZr1qxBSEiI2JE07uOPP8aiRYuwcOFCFl8iPSEIAhYsWIAJEybgrbfeQlRUlE6dxdKEtm3bIikpCU2aNEHv3r2xc+dOsSORBiUkJGD27NmYM2cOhg0bJnacKuvWrZtyJvY1a9aIHUfvJScnw9TUFB07dqzwY8oL8rFjx9QVC8CT97Tp6ekvHd6ckZEhynDZ1NRU1KtX77l/h0xMTNCkSROdPtMbEREBHx8fWFtbq2ybBgYGCA4Ohlwu15qRHCy9OqqsrAwTJkxQFl6pVCp2JNFMnz5dWXxnz57N4kukw0pLSzFp0iTMmzcPX331FX7//ff/TGpCTzRq1Ah79uxBr1694OXlxdJQQ6SmpiI4OBgDBw7E559/Lnacaps0aRLefPNNvPPOOxy1oGZHjx5Fp06dKjUJXsuWLVGnTh21D3G+e/cuSkpKXlp6FQoFbt26pdYcz/Oya40B3Z7B+fLlyzh27JhKhzaXCwoKQlZWFuLj41W+7apg6dVBZWVlGD9+PNauXVvjC2+56dOn44cffsA333yD//3vfyy+RDooLy8Pfn5+yhnoZ8+erTMT84ilVq1a2Lx5M8aNG4exY8fi66+/5vFPjxUVFSEwMBCWlpaQyWQwMjISO1K1GRgYYOnSpWjXrh0CAgI4QZsaJScnV2h93qcZGhqia9euSE5OVlOqJ142hPjp28Uol/pceiMiIlCrVi0MHTpU5dvu0KED2rdvj/DwcJVvu7JKSkpQUFCgLLtWVlYwNDRk6dVm5YU3LCwMa9euZeF9ykcffYQff/wR3377LWbNmsU3fkQ65O7du+jXrx8SExMRGxurV2uMq5uJiQn++OMPfP7555gzZw4mT56sVTNmkuq8//77OH36NORyOezs7MSOozIWFhaQy+V49OgRxowZw7Wo1SAvLw8pKSmVLr3Ak+t61V16y6+JfVG5LJ9EiqVXtcLDw+Hn51ehJawqq3yI86ZNm/D48WOVb78ycnJyAPzfGV4DAwPUrl2bpVdblZWV4c0330RYWBjCwsIwatQosSNpnQ8//BA//vgjvvvuOxZfIh1x8eJFuLu749atW9i3bx88PDzEjqRzDAwM8Nlnn+Gvv/7CypUr4e/vj/z8fLFjkQr9/fffWL58OZYsWVKl4qLtmjdvjrCwMMTGxuLLL78UO47eOXHiBBQKRaUmsSrn5uaGtLQ03L17Vw3JnkhNTUWtWrWUpeTfrK2tUbduXa0tvWlpaTr3Yc3Zs2dx7tw5BAcHq20fQUFByMvLQ2xsrNr2URHl5fbp11edOnVYerVReeGVyWSQyWRqfYHqug8//BA//fQTvvvuO8ycOZPFl0iLHTx4ED169ICFhQUOHTqE119/XexIOm38+PGIiYnBnj170L9/f9y7d0/sSKQCx48fx+TJkzFx4kS89dZbYsdRGy8vL8yfPx/z58/Htm3bxI6jV5KTk2Fubo727dtX+rHlH7Ko82xvebF82SUtYpxRzc7ORk5OzitLb3Fxsc4dbyMiImBjY4PBgwerbR9t2rRB586dRZ/Fubzc1q5dW3lbnTp1lGeAVYGlVwXKysowbtw4rFu3DjKZTC0Xm+ubDz74AD/99BO+//57fPLJJyy+RFooMjISAwcORIcOHbB///6XvqmgivP09MTevXuRlpYGd3d3XL58WexIVA0PHjyARCJBhw4dsHjxYrHjqN2nn36KoUOHIiQkBNevXxc7jt5ITk5G586dqzQxoJOTE2xtbTVSel+m/IyqJr1q2PXTX9OlIc6CICA8PBwBAQEwNTVV676Cg4OxdetW5ObmqnU/L8MzvTqgtLQUY8eORXh4OAtvJX3wwQf4+eefsWjRIsyYMYPFl0iLLF68GIGBgfDz88OOHTtQt25dsSPplS5duiApKQmmpqZwd3fHoUOHxI5EVVBWVoaQkBDk5uZCLpfD3Nxc7EhqZ2hoiDVr1qBu3bqQSCSiXwuoL5KTk6s0tBl4cvmEq6urWmdwrkjpdXR01HixLN9f+TXFzyPm9cZVdfz4cVy5ckUjI0dHjhyJx48fIzo6Wu37epF/X9Nb/m+WXi1RWlqKcePGISIiAuvWrcPIkSPFjqRz3n//ffzyyy/44YcfWHyJtIBCocCMGTMwbdo0TJ8+HTKZrFLLZ1DFNWvWDAcOHICzszMGDBiAzZs3ix2JKmnBggXYsWMH1q1bBycnJ7HjaEzdunURGRmJlJQUTJkyhX+7qykrKwuXLl2q1rXgbm5uSE5OVttzUdEzvWKUXiMjIzRu3PiF96lXrx4sLS11qvSGh4ejfv366N+/v9r31axZM7i7u4s6izPP9Gqx8jO85YV3xIgRYkfSWdOmTcOvv/6KH374AR9//DH/eBKJpKioCCEhIfjhhx/wyy+/4Pvvv4ehIf9MqFO9evUQHx8Pb29vBAQEYOnSpWJHograunUrFixYgC+//LJGTu7m4uKCFStW4J9//sGKFSvEjqPTjh8/DgDVKr2urq64e/cuMjIyVBVLqaCgAJmZmRUqvVlZWSq9DvNVUlNT4eDg8NLlwQwMDEQZel1VCoUC69evR2BgYJWGu1dFUFAQtm/fLtqSZNnZ2TA1NX1mtAxLrxYoLS3FmDFjsH79ehZeFXnvvffw66+/4scff8T06dNZfIk0LCsrC56enti0aRM2btyIadOmiR2pxjA3N0dERASmTZuG0NBQzJo1S+dmGa1prl69ijFjxmDYsGGYNWuW2HFEM2bMGISGhuK9997D4cOHxY6js5KTk1GrVi20adOmyttQ52RW6enpAF5+3ezTX9dkuazIGWhAt5YtOnToEFJTUzU6Ke6IESNQWlqKTZs2aWyfT8vOzv7PzOAsvSIrL7wbNmxAeHg4C68Kvffee1i8eDF++uknfPTRRyy+RBqSmpqKXr164cyZM9i5cycCAgLEjlTjGBoa4qefflIu6TZmzBgUFRWJHYueo6CgAAEBAbCzs8OqVatq/GiIH3/8EV27dkVgYCDu378vdhyddPToUXTt2vWlZytfpUmTJmjUqJFaruutyHWzgDgTRulj6Q0PD0eTJk3Qq1cvje2zSZMm6Nu3r2hDnFl6tUxpaSlGjx6NjRs3IiIiAoGBgWJH0jtTp07Fb7/9hp9//pnFl0gDTp06BXd3d+Tn5+PgwYPo2bOn2JFqtA8//BARERGQy+Xw8vJCVlaW2JHoKYIg4J133sGVK1cQGRkJGxsbsSOJztTUFBs2bEBxcTGCg4NRWloqdiSdk5ycXO21nQ0MDJTX9apaamoqDAwMYG9v/9L7NW7cGEZGRiy91VBWVoYNGzZg5MiRGv9ALTg4GAkJCaJ8ePWi0puTk6OyLsDSW0HlhVculyM8PBwSiUTsSHorNDRUWXw//PBDFl8iNdm5cyd69+6Nxo0bIykpCW3bthU7EuHJMLP4+HicPHkSvXv31pnr0GqCZcuWYe3atVi5ciU6duwodhyt4eDggIiICOzduxeffvqp2HF0SmZmJm7cuFHt0gs8GeKsjsmsUlNT0ahRo1dOamhsbAx7e3uNlcuysjKkp6dXuPTeu3dP62cb37dvH+7cuaPRoc3lyruNXC7X+L5fVHrLysqQn5+vkn2w9FZAaWkpQkJCIJfLERERwcKrAaGhoViyZAl++eUXFl8iNVizZg28vLzQs2dP7NmzB40aNRI7Ej2ld+/eOHDgAHJzc+Hu7o4zZ86IHanGS0pKwgcffIBp06Zh1KhRYsfROv369cO3336Lb7/9FpGRkWLH0RnHjh0DgCovV/Q0V1dXPHz4UOXrJ1f0bCrwZAi0pj6ou3PnDsrKyl457Br4v6HZ5dcna6vw8HA0a9YM3bp10/i+7ezsMGjQIFGGOL+o9JZ/TRVYel+hpKQEUqkUkZGRWL9+Pa9106ApU6Zg6dKl+OWXX/DBBx+w+BKpgCAI+PrrrzF27FiMGzcOW7ZsQa1atcSORc/h7OyMpKQkNGjQAL169cKuXbvEjlRj3b17F4GBgejWrRu+//57seNorY8++giBgYF48803cfHiRbHj6ISjR4/CxsYGLVq0qPa21DWZVWVKryaHEZfvp6Jnep9+jDYqKSnBxo0bERwcDAMDA1EyBAcHY9++fWqZBfxlWHpFVlJSgpCQEERFRWH9+vXw9/cXO1KNM3nyZCxbtgy//vor3n//fRZfomooLS3F5MmTMWfOHHz++ef4448/YGJiInYseonGjRtj7969cHd3x5AhQyCTycSOVOOUlpYiKChIuYyIqamp2JG0loGBAf766y/Y29sjICAAeXl5YkfSeuXX86qi5DRo0ABNmzZl6X0OBweHZx6jjXbu3ImHDx8iKChItAzDhw+HiYkJNmzYoNH9svSKqPwMb1RUFDZs2MDCK6J3330Xy5Ytw+LFi1l8iaooPz8f/v7+WLlyJf766y989tlnon2STJVjbW2N6OhohISEICQkBN9++y2Pgxr0v//9D/v378f69evRpEkTseNoPWtra0RFRSE1NRVvvfUWX6uvkJycrJKhzeXKr+tVFUEQkJaWVqnSm56ejrKyMpVleJHU1FTUrl37P2XpeczNzdGwYUOtLr0RERFo27YtXFxcRMtgY2MDLy8vREREaHS/zyu9tWvXVn5NFVh6n6OkpASjRo3Cpk2bsGHDBgwfPlzsSDXeu+++i99//x2LFy/GtGnT+EeUqBLu3buHAQMGYM+ePYiJicH48ePFjkSVZGJiovywYtasWZg6dapG3lTWdBs3bsSiRYuwaNEi9O7dW+w4OqNdu3b4559/sH79evz8889ix9Fat2/fRkZGhkomsSrn6uqKY8eOqWyt78zMTBQWFlboulngSektKSnB3bt3VbL/l6nMGWhAu2dwLiwsRFRUlKhDm8sFBQXh0KFDKr82/GV4plcE5YV3y5Yt2LhxIwuvFnnnnXewfPly/Pbbb3jvvfdYfIkq4PLly+jRowdSU1Oxd+9eeHp6ih2JqsjAwEA5LH358uWQSCQoKCgQO5beSklJwfjx4xEUFIT3339f7Dg6RyKRYMaMGZgxYwb27t0rdhytVH5GVtWlNycnB5cvX1bJ9iozhPjp+2miXFal9GrrbPjbt29HTk6OqEOby/n6+sLCwgLr16/XyP7KysqQl5f3n9JrbW0NAwMDll51eLrwbtiwAX5+fmJHon+ZNGkSli9fjiVLlrD4Er3CoUOH0KNHD5iYmCApKQldunQROxKpwMSJE7FlyxbEx8djwIABoqypqO9ycnLg7+8PJycnrFy5UvQzL7rq66+/Ru/evTFy5EiNT4yjC5KTk1G/fv0Kn0WtCFVPZqVvpVdbz/SGh4ejU6dOcHZ2FjsKatWqBV9fX43N4pyTkwPg/4YzlzM0NIS1tbXy69XF0vv/lZSUIDg4WHmGl4VXez1dfKdOncriS/QcmzdvxoABA9CuXTscOHAAzZo1EzsSqdDQoUOxd+9eXL9+HT169MDVq1fFjqQ3BEHAhAkTcOvWLURGRnJ282owNjZGREQETExMMHLkSBQXF4sdSauUX8+ryg9V6tati5YtW+Lo0aMq2V5qairMzc1hZ2dXofvXqVMH1tbWGjmjWplrjYH/K73a9r4xPz8f0dHRoqzN+yLBwcE4efKkRmZhLz+T+7xrs+vUqcMzvapUXFyMoKAgREdHQy6XY9iwYWJHoleYNGkSVqxYgaVLlyI0NFRl164Q6YOlS5ciICAA3t7eiI+PR7169cSORGrg6uqKpKQkGBoawt3dHUeOHBE7kl744YcfIJfLsWrVKrRp00bsODqvQYMG2LhxI44ePYqPP/5Y7DhaQxAEHD16VKVDm8upcjKr8rOpFS3mBgYGcHR0VPsZ1fz8fDx48KBSZ8kdHR3x+PFjPHjwQI3JKm/r1q0oKCjQiqHN5by8vGBtba2RCa3Kz+Sy9KpZcXExgoODsXXrVsjlcvj6+oodiSro7bffxh9//IFly5Zh6tSpLL5U4ykUCsyaNQuhoaGYNm0aIiIiYG5uLnYsUqMWLVrg4MGDaN26Nfr374+tW7eKHUmn7d69GzNnzsSsWbO4aoMKde/eHb/88gsWL16MtWvXih1HK6SlpeH+/ftqK70nTpxAaWlptbdV2SHEgGaGEZefSa7smV5A+5YtCg8Ph5ubm0rWalYVc3NzDB8+HOvWrVP7mXGe6dWA8jO8W7duRWRkJAuvDpo4cSJWrlyJZcuW8Ywv1WjFxcUYO3YsvvvuO/z444/46aefYGhYow/xNYatrS127twJT09P+Pn5Yfny5WJH0knp6ekICgpC//798cUXX4gdR++8++67GDt2LCZNmoTTp0+LHUd0cXFxAFQ7iVU5Nzc3FBQUVPvnLAgCzpw5U+GhzeWaNGmCy5cvq7UsXbp0CUDVSq8mhuxWVGZmJmJiYrRqaHO54OBgXLhwQe2/rzdv3gSA515KYmVlpbp5K4QaqqioSPDz8xNMTU2F6OhoseNQNa1cuVIwMDAQ3nnnHaGsrEzsOEQalZWVJQwYMEAwMzMT1q9fL3YcEklpaanw3nvvCQCEOXPmCAqFQuxIOuPy5ctCmzZtBAcHB+HevXtix9FbBQUFwuuvvy40bdpUOHHihNhxRGVraysAEHJyclS+7WPHjgkABHd392ptJzs7WwAg2NraVupxDg4OAgDhzp071dr/y3Tr1k0AIJw6darCj7l69aoAQGjXrp3aclVWnz59BADCgQMHxI7yH/n5+QIAwcbGRq37GThwoABA2Ldv33++BkBQVV2tkaW3/Ek0NDQUtm7dKnYcUpE///xT+bzyzR7VFOvXrxdMTU0FGxub5/7BoJpFoVAIixYtUr5RyM7OFjuSTih/A71u3Tqxo+i9CxcuCAAEKysrsaOI6scffxTGjx+vlm0XFxcLLi4uwpIlS6q9rZCQEGHHjh2Vesz69euF7t27q/W9WHh4uODu7l6pEx0KhULo27ev8Pfff6stV2V9+umngrOzs1BYWCh2lOfq3Lmz0LNnT7XuIzk5WRg+fPhzn8uPP/5Y8Pf3V8l+DARBy6Yw04Dr16+jdevWGDp0KLZs2SJ2HFKhzp074/z588jNzYWpqanYcYjUrlu3bjh69Ciio6Ph4+MjdhzSEh4eHti5cyf27NmDvn37ih1H60VGRuLMmTP47LPPuDyRBnz++ed48OABfv31V7GjEFENUSNLLxGRvsjPz0dBQQHq168vdhTSMgqFgtd1ExERgaWXiIiIiIiI9Jix2AGqIzU1FZmZmdXahp2dXaWnYiftpIrXA8DXBGkej2WkajX5eMjfJ+1VU54bTX6f1dnX7du3kZWVBRsbGzRu3LhKjwdQpceWe9H3Keb39bJcFaWLr3V1Za7Idsufs5d50fNZ0Z+Tzpbe1NRUODs7o6CgoFrbsbS0REpKitYfQOnlVPV6APiaIM3isYxUrSYfD/n7pL1qynOjye+zuvsyNDSs1lKP1X088PzvU+zv60W5KkoXX+vqylzR7VbnOavoz0lnS29mZiYKCgqwdu1aODs7V2kbKSkpGD16NDIzM7X24EkVo4rXA8DXBGkej2Wkak+/pgRBQHp6Ouzs7GBgYAATExMUFRXhxo0bMDMzQ/fu3XHr1i2YmZnh7t27aN26NfLy8tC6dWudfF2Vf+/Tp0+HmZkZmjVrhtq1a+PChQto0qQJ6tWrB3Nzc2RkZEAQBLRu3Rr37t1DQUEBGjZsiPv378PExETnvm9dUFOOdVX9/bt9+zbatm2LvLw8lJaWVuj7rM7PtPxnWdHfFScnJ+Tm5iIvLw8NGjRAcnIy5s+fr5bnUxXfl5ivs+flP3/+PK5du4batWvD3t4eRUVFqFevHu7fv4/i4mLk5uaiX79+KsugisyV9bzMFflZXLx4EXPnzq3W812Rn5POz3BRUFCAs2fPwtzcHEZGRkhLS8P169dhaGiI3Nxc5ObmwsLCAk5OTjA0NMSjR49ga2uLx48fq+RTcNIulX093Lt3D1ZWVsoDebNmzcT+FqiGqupr18zMDBcvXhQ7PmmhgoICKBQKDBs2DG5ubrCzs4OVlRXc3NwwePBg9O3bF46OjvD19YWzszPatWuHVq1aoXHjxsjNzYW9vb3Y30KV1a5dGyEhIejWrRvMzc3RoUMH5c+gVq1a6N27NwIDA1GvXj3Ur18fPXv2hLW1NZo3b442bdqIHV+vVeV9m5WVFUpLS1FUVCR2/Aqr7O/fG2+8AVdXVzRq1AiWlpaV2tfVq1cr/PN8+PAhbG1tYWz85LxX27Zt0bZtW/Ts2RPt2rXD66+/jnr16qFly5aoVasW2rZtiz59+sDNzQ2tW7eGo6MjXF1d4eTkVOV95+bm4ujR/8fem8dFceT//6+ZYRgGGG5BRUDlEFQ8wSOgRjSaW12NB4oRsyZmN9kj2d1cm2uTbJLdJN/9JLvRXOIV1JiouYMxGhVUBPGMKJc4yCUgDMcAc/XvD349GWCOPqdnoJ+PRx6R6a6qd3VXd9Wr613vKkBYWBin9YqOjoZerzdffzrt7NatW/Dx8YFSqcS1a9cY3HH79qtUKowbNw633347QkNDERERgVGjRuHee+9FcnIypk+fjvDwcLS1tUGv1wv2HmLyfIaHh6O9vR3BwcGU8k1MTMTEiRMRERGB2NhYcztg046p4LYzvSRTp07FlClTAABHjx5FQEAA6uvrcfbsWbS1tWHixImQy+XQarXo6OhAZ2cnoqKiEBUVBaVSKbD1IlxDpz3U1dVBpVKZX/gAUFRUJKT5IoMYOm331q1bAGBut4sXL8Yrr7wimO0irollmwKAiRMnoqurCzqdDn5+fr3OtRysDIT34b333ouxY8cCGFz1dgfovOuamprQ2dlpvjd0BrhCw/T5CwsLo90GLdt7S0sLdDodLl++DJlMhvr6evM1DQ4ORl1dHaKiotDU1MTKzgkTJjAuOyoqCnPmzHFYT7p5+/v7m+2i26fOnz/fXO7dd9+N559/ntY9cGQ/nfsPCPceoqurJBIJwsLCHLZbe+2MdGsmrxeVcsn7RbZjKrjP24MCjvYijIiIcJIlIq6A2B5E3BWx7YpwzdGjRwEAzc3N6O7uNg8Yw8PDoVAoUFJSApVKhWnTpglsKfc4qrtarYZcLh+QdXd1Bsu7zlEbLC0tRVpaGidlsbmmjuz85ZdfcOedd/JStiPY5O0K7czRtT137hzuu+8+3u2gA1/Xre+1KCws7HfO+fPnMWbMGCgUCsyaNcv8rm5vb4fRaMTXX39N+3q5vegtLi42//vMmTMoKSlBVFQUdDodoqOjIZfLUVxcDD8/PzQ0NMDb2xuzZ8/ul1ZkYEClPZSUlCAwMBDNzc3w8vLqNdAR24SIUFBtux4eHmhvb4ePjw9SUlL6pRURIbFsFyUlJf3aVEdHB/Ly8qDT6aDRaBAUFNRrJs2d2xVpu7VnKSYmBsXFxbh48SJ0Oh0A4PbbbzfPUrhzvd0BquO2kJAQ1NbWIjQ0FJMmTeqX1tWh8vwdO3YMMpkMOp0OAQEBVtPSLcvR2Gfs2LGQyWTmNFTtNBqNkMlk2Lx5M6ZNm9Yvva1y6+rqzK7eWq0WCxYsoFxPKnm3trbCz88POp0OBoMBgYGBlK9JXV0djEYj2tvbIZfLcdtttzG6/kzsj4mJQWVlJSorK9Hc3AyTydRrplSotk53HE1qqr5p7eXbt52REZnJc1QqFVJTU83nt7a2AgCUSmUvL92ioiJa18lt9+l1x8hoIvwxmKOVirg34rtMhGsG8/tQfJ5cl8Fyb8TozfQQozfzYwNdBkP0ZrcVvYDtfZ/+9Kc/obGxETt37sSJEyfw+OOPY9u2bRg/fny/c91hvzcRavRtD59//jlef/11PP/881i8eHGvc8vKyvDggw/itttuw7/+9S9IJBLzMbFNiDgbe3vYHTp0CE899RTuv/9+vPjiizbzENutiCW22tQHH3yA7du3IycnBz4+Pli5ciVGjBiBt99+22o+7tiubNW9sLAQjzzyCJ544gm88847+O6772wG0nHHersDtu7NCy+8gEuXLuGLL77Ali1bsGXLFhw6dMhq7BV3uDf23unr1q1DbW0tJkyYgH//+9828+Bin95//etf2LNnD/7xj3/gnnvu6Xfc3n62lZWVWLp0KWJiYrBnzx6r+dvbpzc9PR1Xr17FZ599hujoaJv2M9mn94knnsDRo0fx3//+FzNnzqRVr6KiImzYsAHTpk3Dpk2baNtFFXv233HHHQgLC4PBYMDu3bt5s4Eu9mz+3e9+B61Wi4sXL+Krr76yGeiQ7j69dXV1uOeeezB06FC89dZbdu1ju08viAFGbW0tIZPJiP/9738EQRCEwWAgwsPDiY0bNwpsmYgzOXbsGOHh4UE89thjNs/Zt28fAYB45ZVXnGiZiAg9Pv74YwIAsWrVKqFNEXFz9Ho9ER4eTjz88MPm395//31CJpMRVVVVAlrmHF5//XXC19eXUKvVBABi7969QpskQhCEVqslVCoV8fLLLxMEQRDl5eUEAGLXrl0CW8YPw4cPJyZPnkwkJyfzXtb69esJiURCvPfee7TT5ufnEwCImJgYRmXHx8cTAIi8vDxG6e2xcOFCAgDx2Wef0U779ddfEwCI1NRUzu2igkajIQAQ6enphLe3N2EymQSxgy4jRowgfve73xEAiG+//ZazfC9evEgAIIYPH85ZnrZw+y2L+rJz5054eHhg1apVAACZTIa1a9di165d6OzsFNg6EWdQVVWFZcuWITU1Fe+8847N85YsWYIXX3wRzz//PL7++msnWigiQh2NRgMPDw/zmhYREaZ88803qK6uxsaNG82/rV69Gl5eXvj4448FtMw55OfnIzk5GREREYiIiEB+fr7QJokA+Pbbb9HW1mYet40ePRozZsxAdna2wJZxj06nQ21tLSIjI6FWq3kvj+w/NBoNo7QAGLu7kumYlO2Ijo4OxnmTacg8nE1paSmAnmjGWq0WNTU1gthBh7a2Nty4cQPTpk0zuxJzBdt2RocBJXoJgkBWVhYWL15sXsgO9LiSaDQaHDhwQDjjRJyCVqvF4sWLoVQq8dlnn0Eul9s9/4UXXsDixYuxevVqtwqQITJ40Gg08PT05GXgIDK42LRpE6ZPn47Jkyebf/Pz88OaNWvw0UcfwWAwCGgdvxAEgfz8fEyfPh0AMH36dFH0ugjZ2dlISkpCbGys+bdVq1bhhx9+MG/PNlCorq4GQRCIiYlBfX097/sOazQaKBQKVuKwra2NUdnt7e298uGS1tZWyOVyxvWSSCSM68UWUvSSgZrIv12ZK1euAADGjRuH+Ph4XkRve3s7CJ5X3A4o0VtQUIDLly8jMzOz1+9xcXFISUlBVlaWQJaJOAOCILBhwwYUFxfjwIEDGDJkiMM0UqkU27dvR0REBBYtWoSWlhb+DRURoYFGo4GXl5coekVYUV5ejoMHD+LRRx/td2zjxo2oqakZ0B4vN27cQG1tbS/Re+bMmQEt9N2BlpYWfPfdd0hPT+/1+/Lly2E0GvHFF18IZBk/kLO748aNA9DTLvmETf9hKUboBhgiCMIsLvnou9jWS8g+tbS0FCEhIZg0aRKkUqlbiF5S5MbHxyMhIYEX0WswGHj3yB1QojcrKwsjRozotcE0SWZmJg4dOuQUdxIRYXj77beRnZ2NrKws8/YGVFCpVPjyyy/R0NCA9PR0GI1G/owUEaGJRqOBt7e3KHpFWPHBBx8gMDAQy5cv73ds0qRJmDFjBjZv3iyAZc6BnNW1FL1arRaXLl0S0qxBz/79+6HT6bBixYpevw8dOhTz5s0bcC7O5Bh0ypQpvf7mCzb9BzlLTBAE7VnRzs5OGI1GKJVK3kQvm3oJ2aeWlJQgLi4Onp6eGDlyJEpKSgSxgw7FxcWIiIiAr6+vWfRyNSur0WgglUrN/+aTASN6Ozs7sWvXLqxduxYymazf8eXLl0OpVGL79u0CWCfCNzk5OXjqqafw9NNP9+s8qRATE4Pdu3cjJycHzz33HA8WiogwQ6PRwMfHRxS9Iozp7u7Gli1bsG7dOqvRcIGe2d6DBw+irKzMydY5h/z8fERERJgjf06dOhUymUx0cRaY7Oxs3H777Rg+fHi/Y6tWrcLRo0dRXV0tgGX8UFVVheDgYMTFxQFwjuhl2n9oNBr4+vqa/003LQD4+vpy3ncRBIHW1lbW9dLpdOjq6uLUNiqUlpaaXfljY2PdZqY3ISEBAJCQkIDm5mbcvHmTk7zJjxDkv/lkwIjeAwcOQKPRYN26dVaPq1QqPPDAA9i6dSvvPuMizqWsrAwrV67EwoUL8eqrrzLOZ+HChXjzzTfx5ptv2g0hLyLiTDQaDVQqFVpbW1nvOygyOPn888/R1NSERx55xOY5y5cvR2BgID788EMnWuY8LNfzAj37OiYmJoqiV0Dq6upw+PBhcwCrvvzmN7+Bp6enze1y3BG1Wo3IyEgolUqEhoY6RfSqVCrG4lClUpn/TTctAMZl24N0t/bz83N6vbhgIIhe8jcu0Gg08PPzM/+bTwaM6M3KykJqamqvQAh9yczMRHl5OY4fP+5Ey0T4pK2tDYsWLUJoaCiys7OtzvLT4cknn8Tq1auxfv16nD17liMrRUSYo9Fo4O/vD4IgzIFBRETosHnzZqSlpWHMmDE2z1EqlcjMzMSWLVsEmf3gE4PBgDNnzvQSvYAYzEpoPvvsM8hkMixdutTqcX9/f9x9990DysWZFL0AeI/gTM5k+vv7MxaH/v7+5n/TTQuAcdl85s2mXmxpamrCrVu3zDP9cXFxKCsrc+lldd3d3SgvL8fYsWMB9HhGenh4cCp6nXU/BoToVavVOHToUL8AVn2ZPXs2Ro8eLQa0GiCYTCZkZGTgxo0b+PLLLxEQEMA6T4lEgo8++ggJCQlYvHgxGhoa2BsqIsICjUZjjkYvujiL0OXixYvIzc3ttU2RLR555BE0NTXh888/d4JlzuPSpUvQarVWRW9xcbG4HZhA7Nq1C3fddReCgoJsnpOeno4zZ864xbpHKjhT9JL9RUBAAKM2zqbvIc8PDAzkTfQGBAQwFr1C9ankrK7lTK9Op0NVVZVT7aBDaWkpjEajeYZXLpcjJiaGU9HrrPsxIETvtm3boFQq8cADD9g9TyKRYN26ddi7d684YzIA+Mc//oGvvvoKn376KeLj4znLV6lUYv/+/ejq6sKyZcug1+s5y1tEhC4ajcY8KBRFrwhdNm/ejKFDh2Lx4sUOz42Li8O8efMGXECr/Px8yGQyTJ06tdfv06dPB0EQKCgoEMiywUtFRQVOnTpl07WZ5J577oFKpcKuXbucZBl/EASB69evm0VvREQEr2KH7C+Cg4MZi8Pg4OBeedEtOygoiDfRy6ZeQvWp5MebmJgYAL+KX1f+qEOKW1L0AsDYsWM5Fb3Ouh9uL3pNJhO2bt2K5cuXm3307fHggw9Cq9Vi7969TrBOhC/279+Pl19+Ga+88gruvfdezvOPjIzE559/jhMnTuDPf/4z5/mLiFBFo9EgJCTE/G8REaq0t7djx44d+O1vf+twz3KSjRs3Ii8vDxcvXuTZOueRn5+PxMREc7AUkvj4ePj5+YkuzgKwa9cu+Pj44L777rN7nlKpxJIlS5Cdne328Vg0Gg3a29sREREB4NeZXr7qRfYXISEhaGtro+1CS4oRmUzGWPSGhITwJnqZ5t3a2ipYn1paWorhw4ebA4RFRUVBLpe79Lre4uJihISEmK8Z0COAL1++zEn+Go0GAQEB8PX15d3rxu1F7/Hjx1FRUeHQtZkkMjIS8+bNE12c3ZhLly4hIyMDy5Ytw7PPPstbObNmzcJ7772H//3vf/jkk094K0dExBZdXV3Q6XQIDQ0FIIpeEXpkZ2ejo6MDGzZsoJxm0aJFGDp06ICa7e0bxIpEKpUiOTlZFL1OhiAIZGdnY9GiRfDx8XF4fnp6OkpKStw+zgbpymzp3tzR0YHm5mZeyiP7C7L/oLvtEClGmKydJYNFMXVBdpQ30FMvjUZD+6OBRqPBkCFDeuXlLCyDWAGAh4cHRo8e7fKi13KWF+gRvTU1NZxcP3JNLx/rv/vi9qI3KysL0dHRmDVrFuU0mZmZOH78+IDdmmEgc+vWLSxatAjR0dHIysqCRCLhtbyNGzfi4YcfxqOPPooTJ07wWpaISF/IDiAsLKzX3yIijiAIAps3b8Y999xjHmRTQS6X47e//S127NgxIJYBtba2ori42KroBX4NZuXus4juxMWLF3H58mWkp6dTOn/evHkYMmSI2we0siZ6LX/nGrb9BxlVl6no5UvIkPu6DhkyBHq9nlbgPZPJhNbWVgQFBQmyV29JSUm/gLuxsbEu795sTfQCwJUrV1jnz6ad0cWtRW9bWxv27t2LdevW0RI/S5Ysgb+/P7Zu3cqfcSKcYzAYsHLlSmg0Ghw4cMDsHsI37733HqZNm4alS5cOqP0CRVwfsgMYOnQopFKpKHpFKHP69GmcPXuWUgCrvmzYsAEdHR1uLzIAoKCgAARB2BW99fX1vG8dI/Ir2dnZCA4OxoIFCyid7+HhgeXLl2P37t1uvW2bWq2Gh4cHhg4dCoB/0Uu6ipJ7IDtTuFqmbW1t5fSjEimSyOCldGxrb28HQRBOm1m0hCAIlJaWmiM3k8TFxbnsTK/RaMTVq1f7iV5yJwAu1vWKM70U2bt3Lzo7O/Hggw/SSqdUKrFy5Ups3brVpcOEi/Tm6aefxuHDh/HZZ59h1KhRTivX09MTX3zxBTw8PLBkyZIBt52HiOtiGaWS6Z6EIoOTzZs3Y+TIkVi4cCHttJGRkbjnnnuwadMmt58Bzc/Ph5+fn81gh6QYFl2cnYPJZMLu3buxbNkyyuvMgR4X5+rqarfecrKqqgojRowwb60YGhoKT09PXmd6vby8GK1fJZfWcCF6jUYjOjo6aKWnmjf5N520AAQRvfX19Whvb7c603vt2jWXDJp6/fp1dHV19RO9Pj4+iIqKYi16CYJAa2urKHqpkJWVhfnz55uDAtBh/fr1qK6uxqFDh3iwTIRrduzYgbfffhtvv/020tLSnF5+WFgY9u/fj4sXL2Ljxo1uPxAUcQ/6dtDi1ioiVLh16xZ2796Nhx9+mPHe5Y8++ijOnTuH06dPc2ydczl16hSSk5MhlVof7oSFhSEqKgqnTp1ysmWDk5MnT+L69euUXZtJZs6ciZEjR7q194HldkVAz5ryiIgIXkUvU3FI9jX+/v6MPriSQsbPz4922Y6wdIe1tJVqWkCYPpV0YbYmeo1GI65du+Y0W6hiLXIzSUJCAmvR29HRAZPJJIpeR5SWliI3N5dyAKu+JCcnY+zYsWJAKzegsLAQGzZswLp16/CHP/xBMDuSkpLw0UcfYdu2bXj33XcFs0Nk8CDkV2kR92X79u0wGo146KGHGOexYMECjBw5Eps2beLQMudCEATy8/MxY8YMu+fNmDFDnOl1Ert27cKIESOQmppKK51EIsHKlSuxd+9e6HQ6nqzjl76iF+B32yIhZ0TZlM1n3kL2qaWlpZBIJIiOju71O+nu7IouzsXFxfDx8bE6uciF6HX2/XBb0bt161b4+/tT2nvQGhKJBJmZmThw4ABvkfNE2FNfX48lS5Zg4sSJ2LRpE++BqxyxZs0aPPHEE3jyySfx008/CWqLyMCH7ACcFeRBxP0hA1gtXbrUHLWVCTKZDI888gj27NmDW7ducWih87h+/Tpu3rxpcz0vyfTp01FUVOSS7oUDCb1ej88++wwrV660OfNuj/T0dDQ3N+PgwYM8WMc/arW6n3ggty3iA1IcKpVKeHh4iKIXwoveyMhIeHl59fo9PDwcXl5eLit64+PjrY69ExISUFFRwWrJnyh6KWA0GrFt2zasWrUKSqWScT5r1qyBwWAYEJueD0R0Oh2WLl0Kg8GA/fv393tRCMWbb76JtLQ0LF++HBUVFUKbIzKAaW1thY+PDzw8PETRK0KJI0eO4OrVq4wCWPVl/fr15v7WHSFnb6mI3q6uLly4cMEZZg1afvrpJzQ0NNB2bSZJTEzEuHHj3NLF2WAwoLq6ut9MrzNEr0Qioe2izIXoZeqC7Ag2rtNCil5rkZuBHjf3mJgYl4zgXFxcjLFjx1o9lpCQAJPJxEqsi6KXAj/++COqq6sZuzaTDB06FHfffbfo4uyiPP744ygoKMC+ffvM0QddAQ8PD+zevRsBAQFYvHjxgNjWQ8Q1IQcOAETRK0KJzZs3IyEhAbNnz2adV2hoKJYuXYrNmze7ZRyD/Px8jBw50uGM9+TJk+Hh4SG6OPPMrl27MGbMGEyaNIlxHunp6fjyyy85DYzkDGpra2E0Gq2K3pqaGl68DEjRC4D2+lW2a1+dMdPr4eEBHx8f2qJXJpPBx8dHkJnevpGbSVwxgjNBELh8+bLV9bzAr+t82bg4i6KXAllZWRg7diySk5NZ55WZmYnCwkJcunSJA8tEuGLz5s348MMPsWnTJsycOVNoc/oRFBSEL7/8EhUVFVi3bp1bDghFXJ++gxZR9IrYo7a2Fvv378fGjRs5Wwry6KOPoqSkBEeOHOEkP2eSn5/vcJYX6NnRYeLEiaLo5ZHOzk7s27cP6enprNrmqlWroNVq8dVXX3FoHf/03aOXJDIyEiaTCTU1NZyXyab/6Lu0hu62Q2TZKpUKEomEF9ELMKuXn58fJBKJU/tUk8mEsrIyqzO9QE8wK1cTvXV1ddBoNDZFb3BwMIYMGcKp6NXpdLzukOJ2ovfWrVs4cOAAMjMzOenU77nnHoSEhIizvS7E8ePH8fjjj+Oxxx7D+vXrhTbHJuPHj8eOHTvwxRdf4LXXXhPaHJEBiCh6ReiwZcsWyOVyrF27lrM8Z82ahbFjx2Lz5s2c5ekM9Ho9ioqKKIleoMfFWRS9/PHNN9+gvb0dq1atYpXPqFGjMGPGDLdzcbYnei2Pcwlbcejt7Q25XA5/f3+YTCbKXm2W2x1JpVKoVCqXEr1C9Kk3btxAV1eXXdGrVqtdaktMe5GbSdgGs9JoNJBIJPD19eXFK6Avbid6d+3aBaPRiDVr1nCSn6enJ9asWYOdO3eKQSxcALVajaVLlyI1NRXvvPOO0OY4ZMmSJXjxxRfx/PPP4+uvvxbaHJEBhih6RahiNBrx4YcfYtWqVQgICOAsX4lEgo0bN2L//v2ora3lLF++uXDhArq6umiJ3qtXr4qBLXli165dSEpKsjnop0N6ejp++OEHNDU1cWCZc1Cr1b3WoZKQga1cUfRapiV/o5rWMh2XfRdBEJzM9JJpSYHON+Qsrj33ZoIgUF5ezrstVCkuLoZcLu8XbdqSsWPHsha9fn5+kEqlvGxv1Re3E71ZWVm4++67MXToUM7yzMzMxM2bN/Hdd99xlqcIfbRaLZYsWQJvb2989tlntDauF5IXXngBixcvxurVq1mHbxcRscTamizRlV7EGt9//z3UajUeffRRzvPOyMiAp6cntmzZwnnefJGfnw8PDw9MnjyZ0vmkOC4oKODTrEFJS0sLvv32W8YBrPqyfPlymEwmfPHFF5zk5wyqqqqsbvvi6+uLwMBAXrYtGoiiV6vVwmg0Ms6bDIJlaZ8zPiaXlpZCJpNh5MiRVo+TH4NcycW5uLgYsbGx8PDwsHlOQkICrl69CqPRyKgMa+2Mz72T3Ur0Xrx4EWfOnGEdwKovEyZMwJQpU0QXZwEhCAIbNmxAcXExDhw4gCFDhghtEmWkUim2b9+OiIgILFq0CC0tLUKbJDJA6NshGI1GtwvgIuIcNm3ahKSkJCQlJXGed0BAAFatWoUPP/yQ8eDG2eTn52PixImUd3iIjY1FQECA6OLMA/v374der8eKFSs4yS8sLAzz5s1zKxdna3v0kvARwVmv10Or1Q440cs2bzb1YkNJSQlGjx5tczInLCwMKpXKpSI4FxcX23VtBnpEb3d3N65du8aoDGffD7cSvVlZWQgJCcE999zDed7r16/Ht99+i/r6es7zFnHM22+/jezsbGRlZbGK7CgUKpUKX375pXk7BncZGIq4NkJ10CLuRWVlJb7//ntOtimyxcaNG6FWq93GI4pqECsSqVSKadOmiaKXB7Kzs3H77bdzugtDeno6jh07hhs3bnCWJ59Y26OXJDIyEtevX+e0PHK2jGn0f1H0cktpaald136JROJywayoil7yXCaIotcGer0eO3fuNLtZcc2qVasglUrx6aefcp63iH1ycnLw1FNP4emnn+bsS7AQxMTEYM+ePcjJycFzzz0ntDkiAwBR9IpQ4cMPP4Sfnx9WrlzJWxnkLLI7BLRqbm7G1atXaYle4NdgVuISAu6oq6vD4cOHOXNtJlmyZAk8PT2xZ88eTvPlC7VajaioKKvHIiMjOXdvFlIciqK3P45EL+BaEZw1Gg1qa2sdit7w8HCoVCpORK+4pteCb7/9Fg0NDZy7NpMEBQVh8eLF2LJli9jhOZGysjKsXLkSCxcuxKuvviq0OaxZsGAB3nzzTbz55pvYvXu30OaIuDmi6BVxhE6nwyeffIK1a9fCx8eH17I2btyI77//nrErm7Mg1+UyEb2NjY0uXz934rPPPoNMJsPSpUs5zdff3x/33HMPdu3axWm+fNDW1obm5manujdbE4etra0wmUyU05NpfX19IZVKaYtePvaYd0fRazAYUF5ebjOIFUlcXJzLuDdTidwM9MxQx8fHcyJ65XI5vL29RdEL9GzFMHXqVCQmJvJWRmZmJn755RcUFhbyVobIr7S1tWHRokUIDQ1FdnY2ZDKZ0CZxwpNPPonVq1dj/fr1OHv2rNDmiLgp5H51ougVscf+/ftx8+ZNXl2bSVauXAk/Pz989NFHvJfFhvz8fAQEBNCOFDxt2jQAwKlTp/gwa1CSnZ2Nu+66C4GBgZznnZ6ejjNnzuDq1auc580l5CyuPdGr0Wg439YH6C0OCYKgvO2QpRiRSCTw8/OjJXp9fHzMAZAGu+i9fv06DAYDpZne2tpayveIT4qLiyGRSDBmzBiH57LZtsjyfgD871LhFqK3rq4O3333HW+zvCR33HEHwsPDxYBWTsBkMiEjIwM3btzAl19+yekWG0IjkUjw0UcfISEhAYsXL0ZDQ4PQJom4IdY6d8vfRUSAngBWs2fPxtixY3kvy8fHB2vXrsUnn3zilG0+mEKu55VK6Q1xhgwZgujoaHFdL0eUl5cjPz+f9d68trj77ruhUqlcfrbX1h69JOTvXLo4s+0/2IgRPoUMua+rSqUy593d3Y3u7m6HaQmC6BW9WS6XQ6lU8t6nki7LVEQv0OMBKTTFxcUYOXIkvL29HZ5Lil4mXrKi6LXCzp07IZPJeHtxkshkMqxduxa7du1yqQ2iByL/+Mc/8NVXX+HTTz9FfHy80OZwjlKpxP79+9HV1YVly5aJe0CL0KbvoMXX1xcSiUQUvSJmiouLcfToUV62KbLFxo0bcfPmTezfv99pZdKBIAicOnWKtmszCbmuV4Q9u3fvho+PD+6//35e8lcqlfjNb36DXbt2ufSytKqqKkilUpuBvEjRy6WLMxei13JPYbqi11paLu6RRqOBSqUyf9Cisw60o6Oj13ZHZHq++9SSkhIoFAqbgcxISPdnV3Bxvnz5skPXZpKEhAS0trYy2sedTTtjgsuLXoIgkJWVhcWLFyMoKIj38tatW4eWlhYcOHCA97IGK/v27cPLL7+MV155Bffee6/Q5vBGZGQkPv/8c5w4cQJ//vOfhTZHxM3oO2iRSqVQqVSi6BUxs3nzZgwZMgRLlixxWpljx47FnDlzsGnTJqeVSYeKigo0NTWxEr1nz56lNHMkYhuCIJCdnY3FixdTmi1iSnp6OkpKSlBUVMRbGWxRq9UYPny4ze1qhg0bBplMxqnobW1thaenJ7y8vADQE719l9aQ6dnM9BoMBnR2dtKpAuW8yd+ppLVMQ/7bGTO90dHRDpfwBQUFISgoyCWCWVGJ3EzCNIIzQRDiTG9fCgoKcPnyZd5dm0ni4uKQkpIiujjzxKVLl7B27Vo88MADePbZZ4U2h3dmzZqF9957D//73//wySefCG2OiBvRNxgI8GswEhERrVaLbdu24aGHHoJCoXBq2Rs3bsTRo0dx+fJlp5ZLBXKWllyfS5fp06dDp9Ph/PnzXJo16Lh48SIuX77Mu4deWlqaOS6Iq2JvuyKgx8swPDycc/dmpuKQ7GOYzohauhCTaamW7QhrM4OWNjtKa5mG/DfffSqVyM0krhDBubOzE9euXaMsekePHg1PT0/aorezs7PfzPugF71ZWVkIDw/HHXfc4bQyMzMz8eOPP3IeQn6wc+vWLSxatAjR0dHIysqCRCIR2iSnsHHjRjz88MN49NFHceLECaHNEXEThPoqLeIe7N69G62trXj44YedXvZvfvMbDBkyBB988IHTy3ZEfn4+oqOjERISwij9pEmT4OnpKbo4syQ7OxvBwcFYsGABr+V4eHhg+fLl2L17N4xGI69lMUWtVttcz0vCdQTnvqKXjvBkKw7ZCG4+8xaqTy0pKXEYuZnEFSI4l5SUgCAIyqLXw8MDcXFxtEWvEPfDpUVvZ2cndu3ahbVr1zo1su/y5cuhVCqxfft2p5U50DEYDFi5ciU0Gg0OHDjA+9YarsZ7772HadOmYenSpaiurhbaHBE3wNrXdlH0ipBs3rwZd955J0aNGuX0sj09PfHQQw9h27Zt6OjocHr59iCDWDFFoVBg0qRJouhlgclkwq5du/DAAw/YdOnlklWrVqGmpgbHjx/nvSwmuILopbPtEFsxYkuYcjGj2ncW2dVFr06nw/Xr191qppfqdkWWMIngLIrePhw4cAAajQbr1q1zarkqlQrLli3D1q1bXTo4gjvx1FNP4fDhw/jss88EGaQJjaenJ7744gt4eHhgyZIlYqA0EYdoNBp4eXnB09PT/JsoekUA4MyZMygoKHBqAKu+PPLII2htbcWePXsEs6Ev3d3dOHv2LCvRC4jBrNhy8uRJqNVq3l2bSWbOnImRI0e6ZBRnk8mEqqoqwUUvnW2H+BK9g3Gmt6KiAiaTiZbobWxsRHNzM282OaK4uBhhYWG0thlLSEigvdxFFL19yMrKQkpKCmW3AC7JzMxEWVkZcnNznV72QGPHjh1455138M477yAtLU1ocwQjLCwM+/fvx8WLF7Fx40bxg4qIXfp27oAoekV62Lx5MyIiInD33XcLZsPIkSNx1113uVRAq/Pnz0On03EiesvKytDU1MSRZYOL7OxsjBgxAqmpqU4pTyKRYNWqVdi7d6/LbaVVX18PvV5PSfTeuHGDMxdtNv2HO4leOtsOkdsd+fr69rKNzz6VdFWm494MQNDZXjpBrEgSEhJQX19PS6yLotcCtVqNQ4cOYf369YKUP3v2bIwePVoMaMWSwsJCbNiwAevWrcPjjz8utDmCk5SUhI8++gjbtm3D//3f/wltjogLI4peEWtoNBpkZ2fj4YcfduqyH2ts3LgRhYWFKCwsFNQOkvz8fHh6emLSpEms8iFF8+nTpzmwanCh1+uxd+9erFy5kvY+yWxYtWoVmpubkZOT47QyqUDGhqEieg0GA+rq6jgply/RS+VjPZv1xHTztrSNSlo/P79e7ZLvPrW0tBQ+Pj4YNmwYpfPJGWF3FL1kWqrYamddXV28fbxyWdG7bds2KJVKPPDAA4KUL5VKsW7dOnz22Wdob28XxAZ3p66uDosXL8bEiROxadOmQRO4yhFr1qzBk08+ib/85S/46aefhDZHxEURRa+INbZv3w6dToeHHnpIaFNw9913IyIiAps3bxbaFAA9onfy5Mmso1lHR0cjODhYdHFmwE8//YSGhgakp6c7tdzExESMHz/e5VycSZdlKqLX8ny2sBWH1pbWGI1GaLVau2mtbXckk8ng6+vrEqLX2X1qaWkpYmJiKI9/VSoVwsLCBBO9BoMBJSUltEVvXFwcJBIJI9GrUqnMv3HpFWANlxS9JpMJW7duxQMPPNDrYjibBx98EFqtFnv37hXMBndFp9Nh2bJlMJlM2L9/v3mvOJEe3njjDaSlpWH58uWoqKgQ2hwRF0QUvSJ9IQgCmzdvxuLFiynPHPCJTCbDww8/jOzsbLS0tAhtDusgViQSiQTTp0/HqVOnOLBqcJGdnY34+HjWs+1MWLVqFb788kuXmqhQq9Xw8fFxuD7S1USvtbTkMUdpLc+nW7Y9rO3rSifvvtsdkWm1Wi30ej0r22xBJ3IziZARnK9duwadTkdb9CqVSowaNYq26FWpVL08lrj0CrCGS4re48ePo6Kiwml789oiMjIS8+bNE12cGfD444+joKAA+/btw/Dhw4U2x+Xw8PDA7t27ERAQgMWLF7tUJy3iGtjr3MX14IOT48eP4/Lly9i4caPQpph56KGHoNfrsWPHDkHtaGpqQllZGSeiF+hxcT59+rT4rNGgs7MT+/fvx6pVqwTx7Fq1ahW0Wi2++uorp5dtCzJys6Pr4e/vDz8/P95FL9X9bF1R9HZ1dUGv1/Mi5vnaq5fOHr0kQkZwZhK5mYRuBGc27YwpLil6s7KyMHr0aMyePVtoU5CZmYnjx4+jrKxMaFPchs2bN+PDDz/Epk2bMGPGDKHNcVmCgoLw5ZdfoqKiAuvWrRMHVyK9sNUhGAwGdHZ2CmSViJBs3rwZcXFxLhUQcNiwYVi8eDE2bdok6DuMXH/Lpehtbm4WfPsQd+Kbb75Be3u706I292XUqFGYOXOmS7k4q9VqREREUDo3MjLSvAaYDUajEe3t7QNuppdt3n23O7LMiw+RpdVqcePGDcaiV4j3aXFxMVQqFaPJKi5FL18fIVxO9La1tWHv3r1Yt26dS6wBXbJkCfz9/bF161ahTXELjh07hscffxyPPfaYYEHI3Inx48djx44d+OKLL/Daa68JbY6ICyHEV1AR1+XmzZv4/PPP8cgjj7hE32jJo48+iuLiYkH3Sc3Pz0dISAhGjx7NSX7Tpk0z5ytCjezsbCQlJdEe5HPJqlWr8MMPP7hM5G0qe/SSREREcDLTa22Pd/JvpqKXqtupK4teZ/ep5GQZE/dmjUaDhoYGzm1yRHFxMcaOHcuoj0lISEBlZSXlj/LiTC+AvXv3orOzEw8++KDQpgDo8VNfuXIltm3bxlko+YGKWq3GsmXLkJqainfeeUdoc9yGJUuW4MUXX8Tzzz+Pr7/+WmhzRFwEUfSKWJKVlQWZTOb0feupMHfuXMTFxQm6fVF+fj6mTZvG2QeBwMBAxMXFiaKXIi0tLfjuu++cHsCqL8uXL4fJZMIXX3whqB0kdEQvV3v18ikOHc3AiaL3V0gvESYzvZbpnQmTyM0kCQkJIAgCV69epXS+KHrR07HPmzeP8kvCGWRmZuLGjRtipF07aLVaLFmyBN7e3ti7dy/kcrnQJrkVL7zwAhYvXozVq1fTcg8RGbiw+douMrAwmUz44IMPsGLFCgQFBQltTj8kEgk2btyIL774AvX19U4vnyAInD59mjPXZpLp06eLopci+/btg16vx4oVKwS1IywsDPPnz0d2dragdgA9a5wbGhpcSvS2trY6dJu1FvCJDCpLdabXWsAovkSvn58f7zPYTCgtLYW/vz9CQkJopYuOjjandyYEQbAWvQD1bYustTNPT094eXkNDtFbWlqK3NxcwQNY9WXatGlISEgQA1rZgCAIbNiwAcXFxThw4ADtB1ykZ4us7du3IyIiAosWLXKJSKgiwqHX66HVasWZXhEAwMGDB3Ht2jWXCmDVlwcffBAymUyQfrKsrAy3bt3iRfSeP38eXV1dnOY7ENm1axduv/12lwhcuWrVKhw7dgw3btwQ1A6yfDqit6mpCR0dHazKtSd6jUajw/ytiUOZTAaVSkVJ9Pbd7ogse7DN9JaUlCA2Npa294m3tzdGjBjh9AjO1dXVaGtrYyx6AwICMGzYMFy+fJnS+dbuB8DvLhUuJXq3bt0Kf39/LFmyRGhTeiGRSJCZmYn9+/ejublZaHNcjrfeegvZ2dnIysoSZJuCgYJKpcKXX36JhoYGrFq1SnSnH8TYW5MFiKJ3sLFp0yZMmjSJc1HHJUFBQVixYgU++OADp7+7yNlYch0uV0yfPh16vR5nz57lNN+BRm1tLQ4fPiy4azPJkiVL4OnpiT179ghqB9U9eknI89gGs7InDi2P20vPVIzwKWTszSJ3dnba3XbI1nZHpEDna6aX7npekri4OKfP9LKJ3ExCJ5jVoBa9RqMR27Ztw8qVK6FUKoU2px8ZGRkwGAzYvXu30Ka4FDk5OXj66afxzDPPCO7WNBCIiYnBnj17cPDgQTz33HNCmyMiELY6d/JvviIbirgeVVVV+Oabb7Bx40aXC2DVl0cffRSVlZU4ePCgU8s9deoU4uLiHO6FSpcJEyZAoVCILs4O+OyzzyCTybB06VKhTQHQM2i+5557BHdxJkXviBEjKJ3P1V69bEWvtSjHZHqmaam6INtDo9HAx8en176upF1k2bbo7OyEwWCwWS8++lQm2xWRCLFtUXFxMRQKBUaNGsU4Dzqil007Y4rLiN4ff/wR1dXVLufaTDJ06FDcdddd2LJli9CmuAxlZWVYuXIl7rzzTrzyyitCmzNgWLBgAd588028+eabLrX1gojzsDVokclk8PX1FWd6BxEfffQRfHx8XGYWzR7Tpk3D5MmTnR7QKj8/n5ft8Tw9PTF16lRR9Dpg165duOuuuzj/6MCG9PR0FBUVUQ6qwwdqtRpDhw6FQqGgdH54eDgkEgknolcul8PLy6vX71REr8FgQEdHBy8zvTqdjtVSAXt5k8ftpbU8t296rvvU1tZW1NfXsxa9zty2qLi4GHFxcf0+KtAhISEBpaWlMBgMDs8d1DO9WVlZGDt2LOfuSVyyfv16FBYW4tKlS0KbIjitra24//77ERoaiuzsbFYPiUh/nnzySaxevRoPPfSQ6Fo3CLHl3kz+JorewYFer8fHH3+MNWvWmAPJuDJkQKtvv/2Wk4A8VOjq6sL58+d5c/2ePn06Tp06xUveA4Hy8nLk5+e73EeZu+++G35+foJ+OKYTuRkA5HI5hg8fzono9ff37+cZQkUcsu17HAlTNjOq9mYGybLt2WV5bt/0XPep5CwtG/dmrVaLmpoaLs2yC5sgViQJCQnQ6/UoLy+3e15XVxd0Ot3gFL23bt3CgQMHkJmZ6dLuW/fccw9CQkIGfUArk8mEjIwMVFdX48svv7TaaEXYIZFI8NFHHyEhIQGLFy/GzZs3hTZJxIk4u4MWcU2++uor1NbWunQAq76kp6fDx8cHH330kVPKO3v2LPR6Pa+it7KyUnwH22DXrl3w8fHBfffdJ7QpvVAqlViyZAmys7OdOltmSWVlJWXXZhIuIjjfunXL6kcyZ4hDNrOxjnCnmV6m2xWRCLFtEVeil8zLHkKNcVxC9N55553Q6XQuvybU09MTKSkpeOeddwR1mRESo9GI5557Dl9//TWys7MRHx8vtEkDFqVSiQMHDqCrqwsPPPCA3SANIgOLa9euAei/phcAfH19xQH4IGHTpk247bbbMGHCBKFNoYyvry8yMjLw8ccfO+Wd9e2330KhUPB2jUgx/d133/GSvztDEAQ+/fRTLF68GN7e3kKb04+0tDSUlpbihRdecHrZnZ2dOHz4MI4dO0YrXXBwMM6fP8+q7P/7v/8z9yGW+Pr6AgAaGhpspr116xYA630P4Hi9cWNjo9W2QP7GJqL2tWvXrHoVksKpsbHRZlqyz7RWLx8fH7vXhAlFRUUICgpCQEAAo/SjR4+GRCJxmqff9evXcfPmTcYz0yRDhw6FSqXC8ePH7Z5HBgW2dj+8vLw4vx9mCBdg9OjRhFQqJYxGo9CmOOT1118nABA5OTlCmyIIa9euJQAQTzzxhNCmDBqOHTtGeHh4ECtXriQ6OzuFNkfECSQmJhIAiNLS0n7HABAu8uoW4ZH//Oc/BADi1VdfFdoU2hw7dowAQNx11128l8X389Dc3EwAIMLCwngrw115+eWXCQDE22+/LbQpVqmoqCAAEA8//LDTyzaZTMTIkSOJZ555hlY6pVJJACC6u7sZl52RkUHcdttt/X6vqakhABCjRo2ymfb3v/89AYB4//33+x3z9fUlABBdXV1W0+p0OgIA4e3t3e/Yhx9+yPpe2HrWCwoKCABEamqqzbR33303AYD44YcfKOfLBrZ5dnV1EQAIX19fDq2yzd/+9jcCAPH666+zzotK3Z999lkCAPHPf/6z37HQ0FACAHHr1i3WtvTFgx8pTY9Lly5BJpNBKnWJiWe7PP3003j00UcHrUtvUFAQRo4cieeff15oUwYNs2bNwp///Gf8+9//hlQqxaeffiq0SSI883//93/48MMPzZvUW7JhwwbodDoBrBJxJu3t7ZBIJC63hR8Vpk6dCqVS6ZTlSn/4wx94nWUMCAhARkYGJk6cyFsZ7kpISAi8vb1dto2OGjUK3d3dkMvlTi9bIpFYnW11xO7du/Hll1/22+eWDtu3b7f6+9ChQzFz5kxkZGTYTLt+/XocPHgQy5Yt63csOzsbOTk5NgNzyeVyzJ07F3feeWe/YxkZGdi3bx+eeeYZirXoz3PPPYehQ4f2+33ixImYMGEC/vSnP9lM+/TTT6OxsRFz587td+zZZ5/FhQsXGNtljYcffhgmk4lxeoVCgSVLlmDcuHEcWmWbRx55BAUFBXjsscdY5/XUU085fObWrFmDvXv3Ys2aNf2Ovf3229i+fTsvgfEk/78qFxERcWG0Wi0WLVqE5cuXY8OGDUKbIyIi4gQIgnDpOBf2cGfbRUREREQGHqLoFRERERERERERERERERmw8OberFar7S4qp0JISAitcO98MFDqYQkXdbLE1eonIiIiIiIiMnhw5rjGFcZQbGwQx2wigxVeRK9arUZCQgK0Wi2rfLy9vVFcXCzYwzlQ6mEJV3WyxJXqNxAZiB9eBjtU7mltbS1aWlpsHg8ICMCwYcOsHhPvt2vC1WDZ1e4vX/Vy9rtPfNf24G7XwZnjGj7K8vLywueff27zfd6X2tpaPPDAA+js7GRUnkKhwJtvvomQkJB+x8j7bu0YiU6ns7nm2FF6e/1WbW0tANg9bq9PdFS2Pbsd2WbZnrl+37HNz9GzJtT72d79amxsRFtbG1QqFaO2Ys0eKvAiehsbG6HVarFz507Gez4VFxdjzZo1aGxsFKwDGSj1sISLOlniavUbaAzEDy+DHar3VCqVMg6EId5v14PLwbIr3V++6uXsd5/4ru3BHa+DM8c1bMuyJli7urpw77330s7Lmg1UBLFer7cZ9IlKv2PvHDb9lqO0fB+3B9meAXD6vjt06BDmz5/PKj97z5qQ72e+2ok1e6jCa/TmhIQE1NTUYNSoUQAAk8mE1tZWmEwmmEwmhIaGor29HZ6enggPD8fJkydhMpmwaNEiPs2izUCphyXl5eWIjIyEyWTChQsXkJSUBC8vL0ilUly9ehURERGQSqU4d+4cgoKCMH78eKt15KKDEbHNQPzwMtixdk8vX76MiooK+Pn5ITw8HFevXsXzzz/P6L6L99s14Wpg7mr3l696WeZbWVkJgiAQFhYGiUQCT09PaDQaSCQSREVFQSqVoru7Gy0tLWhtbYWHhwcmT56MyspKyteKLO/JJ5+EQqHAyJEj4efnhytXrmD48OEICgqCl5cXqqurQRAEYmNjUVVVBZ1Oh+HDh6OxsREeHh4udW+Y4M59jjPHNUzLamtrQ2dnp/n6fv/995TbdnNzM8LDw9HY2IjHH3/cPDYlCAKRkZGorKzEzZs3e+XfF/LeWDtOHrP3DNy4cQN/+tOf7Ka317d1d3cjKCgIarUaXV1dkMvlmDZtGkpKSuym7e7uxtNPP82oXlSO28OyPQPg9H1XUVHBKj9Hz5rl89zY2IibN286fLeNHj0aVVVVkEgkCA0NRUNDA+Ryuc33M937xeZeUK23LXgVvWfOnIFCocDo0aOh1+tRWVmJhoYGJCUlQaPRoLi4GAkJCQgNDUVVVRWCgoIQExODM2fOYP/+/XyaRgum9Th69ChKS0uFNt8q9957L9ra2nD9+nXMmzfPar0AYPbs2QgNDUVRURGMRiMSEhJQW1uL0NBQjB071mXrN9AoLy/H5MmTzffJYDAgOjrafJ9CQ0PN7a+jowMhISGoq6uDl5cXwsPDBbZexBparRaXLl1CUlISEhMToVKpzPeVdAmic98bGxsRGxsLDw+X2IlOxAaW953KfW1qasKIESOg1+vR1NTkss8z3Xp1dHRgxIgRuH79OgiCQFxcnNV8y8vLsWzZMlrPQEtLCzo6Oii7i1oyZswYKBQKcz28vLx6lTlmzBhzmUFBQb3KPH36NPML6GLQffdERERAo9Ggs7NTsDbKZFwjlUoRExOD0tJS+Pn5ISkpCVevXuWlLIlEguDgYAC/Pi9LliyhfI3nzZsHjUaD/Px8AMA333yDZcuWobu7G+fOnYOvr695OyFbzyO5Nai14zU1NQDsPwO3bt0CYL19WMu7b99G1m3OnDm96hYQENAvX5VKhXHjxiE6OhoFBQWU6mWr3ZIzklz1qXTfd42NjUhMTMSVK1esjsuYPG91dXWUt2vTarUIDAzEHXfcYS5j/PjxvcqYNWuWuQxvb2/ExMTgxo0b8PX1tTmuYHK/yHbG5BrGxsZCo9HAYDBQqndfeB0dTZ06FVOmTDH/PXHiRHR1dUGn08HPz6/XueSLAID5i9drr73Gp3mUYVMPlUrlNDvpMmfOnF5/26vXHXfc4UzTRPpw7733YuzYsQCAlpYW6HQ6XL58GTKZDPX19Zg4cSLkcjmCg4PR0dGB0tJSs7tUUVGRkKaL2MDee4V0+yHv+9GjRxEQEID6+nqcPXsWbW1t5nuu1WrR0dGB+fPnAwCampoEqY8INSzvO5X7Sg52SVz1ee7bnh29p6qqqpCSkoKoqCgAtutl+e6j2vc6ypNOPeiUaTQaaZfnqtDtcxobG5GSkgJA2DbKdFxDthm+yyKvDdN2BsA84Le8R5MnT0ZXV5f5w4ut9wwp6K0dJ9sv077JWt5U60ZeF1vPO1muo3rZ6jNLSkrsHqfbp9J5j3d3d5vzCgsL61VfEjrPW1NTE8aMGYMxY8ZQftbY6Bhr9lqzm+r9stbOHF3DpqYm8zUE4Jqi15KjR48CAJqbm9Hd3W2+ieHh4VAoFKipqcH06dOdZQ5jHNXjxIkTWLFihcBW0sdRvcgF667ssj1Y6NvR9iUiIsJJlohwRd/nr7CwsNex8+fPm7++T5kyxfxMVldXw2g0oqGhQSjTRVgwkJ9lPurmqJ8qKSnp9YGALVT6xebmZkbrMd0Jd22nVO6fwWBwWD8uynLUNtmkt0x75coVq+eQfUhfgWPZv1gL8kSnb5o8ebLL1KvvPS0qKsJbb71lNa1CocCsWbPMbaK9vR2+vr44fPgw0tLSrJZrr6y+0H0+nPG8Obov586dw3333cc4z773y1E7c9Y7hlfRSy76BoCSkhKUlJQgKioKOp0O0dHR6OjoQF5eHnQ6HVpaWiCXy62mFRoq9fjmm28wdOhQtLW1YfPmzZg2bVq/tK6EpV1nzpzpV6eYmBiUl5fj7NmzMJlMCA4ORm1tbb+G56r1G2g4ul9yuRwXLlzAsGHDUF1dDW9vb8yePbtfWhHXwd57hXTLJJdOpKamms9tbW0FACiVSvNvAQEB5i+x4v12bag8y8XFxQgPD0dNTQ28vb3N/Unf9K4ElXqVlJQgMDAQarUaKpXK/I7qm95avrb6qZMnTyI4OBharRYEQSAwMNBhnlTrYau/P3ToEAICAlBSUgJfX98B+exRuZ9qtdo8E6VUKjF37tx+aYWyGbA/7gR6ZvCkUmk/jzwq9lMt69ChQ1AqlWhubkZgYKA5HdX0P/zwA1QqlTm9ZVry/5Zpb9682S9/lUpl7kPImTvyuL1j1uyy1zf1tcve2PLixYvQ6XSQSqVWr0vftKRQolIva+Vev37dZr2B3v0q2bcqlUoUFRVZbQ9Uno+GhgazvlEqlbjtttuspqeaX0lJCXx8fCCTyTBp0iSr+diCzphfLpdDJpPhv//9r9lmR+9nwH47tNfObNW3rq4Ora2tCAoKMrvVsx7XEjxw/fp1wtvbmwDA6j9vb2/i+vXrfJg4qOphCVd1ctX6DTQGYhsc7FC9p1KpVLzfAwgu372udH/5qpez333iu7YHd7wOzhzXcFEWm3c7lTwc5c8mLRfp+bCbi+NU2gPX77sTJ06wzs/esybk+5mvdkKl3raQEARBgAfs7d/0/PPP49KlS1Cr1fjxxx8RFBRk9TxX2O/OXj3+9a9/4ejRo6irq8O+fftsrglxhXpYYq9OGzZsgEwmw+bNm3Hw4EE888wz+Pzzz82Rq63havUbaNi6X1988QXeeOMN/PTTT1i6dCnuu+8+/OEPf7Cah3iPXIu+9/T777/H3//+d7z00ktml6La2lo0NjbipZdeglarxWuvvdZvdlfcp9e9sPfu/fDDD7F7925MmzYNN2/exJYtW2zm42r31169fv75Zzz55JOQyWT461//igceeMBmPnT2gczIyMDly5eRnZ2NMWPGUM6TST3Onj2L3/72t9i2bRt+//vfIz09HY888gjr8lwVW9fh6tWrSE9Px1tvvYW//OUveP3117FgwQKreTj7OthrKwaDAbNnz8bGjRuxZ88ezJs3D0888YTd/OzZ72h/0osXL2LdunWYPHkyPv74437H++5f2vddfv/996O6uhrfffedeVbMWh5A/z1tP//8c7z++uu4//77sXz5cqtpme6zW1RUhHfeeQfJycn44x//SDv9hg0b0NnZifz8fKuBkezt0/vhhx/igw8+wNq1a222OVtlazQa/P73v0dgYCDee+89q2kBbvbp3bdvnzke0dGjR+Hr62uzPCr79KalpUGj0SA3N7dX/2/LNms4aq8LFy5EY2MjDh8+DH9/f4f2Usn3sccew8mTJ/Hcc89ZjdBsb5/eX375Ba+//jqSk5OxefNmyvZQgu4XNS6YMWMGcd999xEAiNzcXCFM4IQ777yTuOOOOwgAxDfffCO0OawpLy8nABA7duwgCIIgOjs7icDAQOKpp54S2DIRayxZsoRISUkhCIIgMjIyiEmTJglskQgTysvLCZVKRaSnpxMmk6nf8dLSUsLX15dYu3atANaJOIv77ruPWLhwIfHWW28R3t7ehF6vF9okTvj3v/9N+Pr6ErGxscSf//xnzvJNTEwkABA///wzZ3na4uWXXyYCAgIIg8FALFq0iJg9ezbvZboi7733HiGXywmtVkvExsYSjz32mNAmUSI/P58AQJw8eZJYvXo1MW3aNF7Ly8nJIQAQycnJjNIPGzaMAEBcunSJdto33niDkEqlxOOPP86obHvs2LGDAEAsWrSIUXpPT08CANHQ0EA77RNPPEHIZDLilVdeoZ326tWrBABiyJAhtNPS5bnnniNCQ0MZ37++kLOpNTU1HFhnnYCAAAIAUVFRwVmec+bMIQAQBw4coJ32iy++IAAQ8+fP58weEik9icwNJSUl5ohdZEQ1d6SkpAQTJkyAUql063qQbN26FX5+fvjNb34DAPDy8kJ6ejq2b9/OOFKaCD/o9Xr89NNPWLhwIYCeL3Xnzp1DfX29wJaJ0EGv1yM9PR0hISHYtGkTJBJJv3NiYmLw/vvvY/v27di5c6cAVorwDUEQKCgoQFJSEpKSkqDVagfM+tCSkhLExsYiLi6O036yo6MDAMxbW/DJkSNHMGfOHMhkMqSlpeHUqVPo7OzkvVxXIzc3F0lJSVAqlUhNTUVubq7QJlEiNzcXXl5emDJlClJTU1FUVGRuP3xAtkmmZbBp2xqNBnK5nJfngtw/uL29nXZavV4PnU5nzodJ2R4eHozTAjBvW8QnarW614wwG0wmk9lmPt9zfJTBtg1b5sElThe9t27dwq1btzB27FhERES47T6vOp0OlZWViIuLM+/x5s6YTCZs27YNK1as6LXvV2ZmJmpra3Hw4EEBrRPpS35+PlpbW82il9wOQbxP7sWLL76IM2fOYNeuXf2iT1qSkZGBNWvW4NFHH0V5ebkTLRRxBjU1Nairq0NSUhKmTJkCiUTSK0qqO1NaWoq4uDjExsZy2k+SA2++RW9nZydOnjxpDtI0d+5c6HQ6nDhxgtdyXQ2CIJCbm2veliglJQUXLlwwBwByZfLy8jBt2jR4enoiJSUFBoOB1z2VyTbJRBwSBMGqbWs0Gnh6evImepnmbdlOmNZLoVCwFlF8byumVqsxevRoyGQy1qLXsv3w9Z7T6XSsPkbYoq2tDTKZjNUHDj7eLU4XvWSnFxsby3kn6EyuXbsGk8nk9vUgOXz4MNRqNTIzM3v9PmXKFCQmJiIrK0sgy0SskZOTg6CgIEydOhVAzwbeU6ZMQU5OjsCWiVDlp59+whtvvIFXXnmF0nZt//vf/xAWFoZVq1aZOymRgQEpcJOSkqBSqRAfHz+gRC/ZT1ZUVHDmNdTa2sp4UEWHkydPoru72yx6x40bhyFDhuDw4cO8lutqkFukkRFYU1NTYTKZcOrUKYEtsw8p1km7x40bB39/f3MEZz5gI9C0Wq15j1OmgsHLy4s30cs0b8s0zq6XZZq2tjba6emgVqsxcuRIDB8+HFVVVazyYnvNhCyD7f3iqw07XfSS7k18uDs5E9LuuLg4t64HSVZWFsaMGYMZM2b0+l0ikWD9+vX48ssv7S6EF3EuOTk5uOOOOyCTycy/LVy4EAcPHjR3mCKuS0NDAzIyMpCWloa//e1vlNL4+flh165dOHv2LF544QWeLRRxJoWFhRg6dCjCw8MB9IjfgSB6Ozo6UF1dbe7vDQYDKisrWeer0+nQ1dUFb29v3kXvkSNHEBISgvHjxwMApFIpbr/9dhw5coTXcl0N0pWZ3MIkLi4OISEhLu/iXFZWhps3b5pFr1QqRUpKCq92azQaKJVKtLa20u6PyfYslUoZCwa+ngs2eXMheoUqmyomkwk3btxAZGSkOUAVG9xd9LK5X3y1YUFmeocNGwZfX1/ExsairKwMBD8BpHmltLQU3t7eGD58OGJjY1FVVYWuri6hzWJES0sL9u3bh8zMTKtrClevXg2CIJCdnS2AdSJ9aWxsRGFhodm1mWThwoVoaGjAuXPnhDFMhBIEQWD9+vXQ6/XYvn07pFLqr+Hk5GS89tprePPNN/Hjjz/yaKWIMyHX85Lv36SkJJw7d87tZ/TLysoAwOzeDIATryhyMOTr6+sU0Xv77bf3ek7nzp2LgoIC3meNXInc3FzEx8djyJAhAHo+iLvDut7c3FxIJBLMnDnT/FtKSgpOnDjBm6urRqOBr69vL1dlOmkBsBIMfD0XbPLmQvT6+PiwmjlkWjZV6uvrodfr3VL0crkOXK/Xo7Ozk9X98vX1RVtbG+fPqCCil+z8YmNjodVqUVNT42wzWFNaWoqYmBhIJBLExsaCIAi3XWu3Z88e6HQ6ZGRkWD0+ZMgQ3HfffaKLs4tw6NAhEATRL2z/zJkz4evrK7o4uzj//e9/8c0332Dr1q0YPnw47fR/+ctfMH/+fKxdu9a8AbyI+0IQBAoLC5GUlGT+LSkpCTqdDpcuXRLQMvZYLmeKiIiAQqHgVPSqVCpeB7Ht7e3Iz883uzaTpKWlwWAwuLzg45K8vDzzel6SlJQU5OfnQ6/XC2SVY/Ly8jB+/HgEBASYf0tNTUVbWxsuXrzIS5kajQYqlcr8b7ppgR7PHqaCga/ngsy7u7sb3d3dtNMCYOXyyrRebO4HHUiRy7Xo5fPjHh/vUq7aMMC9O7og7s1xcXEAYP6/O7oGD5R6AD2uzXfddZfdAXhmZibOnTsnziK6ADk5ORg/frzZFZLE09MTaWlpouh1Yc6fP4+//OUv+MMf/oB77rmHUR5SqRTbt2+H0WhEZmamW3rKiPzK9evX0dTU1Ev0Tpo0CTKZzO1dnEtKShAYGIjg4GBIpVLExMRw0k+SAyl/f39eB7F5eXkwGAxIS0vr9XtcXByGDRs2aFycm5ubcenSJbOLMElqaiq0Wq1Ljwss1/OSJCcnQy6X87auV6PRmPc7FUL0ks8F130DF/Vi+sxa1otpWks7+KCv6L1x4warmUpnvOf4KIOLe00G9uS63k4VvQRB9JrpHTVqFKRSqVsGgbKsR2hoKFQqlVvW4/Lly8jPz+8XwKovd911F8LCwsTZXoEhCAIHDx7s59pMsnDhQuTl5Q0qtzt3oaOjAytXrkRCQgLefPNNVnkNGzYMW7duxXfffYd3332XIwtFhMAyiBWJt7c3xo0b5/ail4zcTMJV0EcyqmdAQACvg9jDhw9j6NChGDNmTK/fJRIJ5s6dO2hE78mTJ0EQRD/xOGXKFHh5ebnsjHdDQwOuXr3az26lUomkpCTe7G5tbUVgYCAA5uIwMDCQUfRajUaDwMBAGAwGzrfVIvO2tJMqra2t8PT0ZCSESHdZtteE/DdfqNVq+Pr6IiAgAJGRkdDr9ay2kdRoNJDJZLy+5yzbG9eil6ndfN4vp4remzdvoq2tzSwWPT09MXLkSLcTi1qtFlVVVeZ6kC7O7lYPoGeWNzg4GPfdd5/d8zw8PJCRkYFPP/3U7deZuTOXLl1CTU2NXdFrMBgGzWDMnfjzn/8MtVqN3bt3m9cXseHuu+/Gn/70J/ztb3/D2bNnObBQRAgKCgoQERGBsLCwXr8nJSWhoKBAIKu4gdyjl4SrfpIcCAUHB/M6iD1y5Ajmzp1rNdZFWloaioqK0NLSwlv5rkJubi7CwsIQHR3d63dPT09Mnz7dZUUvOZPbV/QCPa7Zx48f58VTRqPRIDg42PxvumklEgkjEUIQBFpbWxEUFMSobCq2Mc2bnG1lInrJ84OCgtDe3k579pQUUXxHe1er1YiIiIBEIuFkr15yxpPvmV4vLy9OhTXb9zObduYIp4pey4jHJO4Y+Zhcu+vu9dDr9dixYwdWr14NT09Ph+dnZmaiqakJX3/9tROsE7FGTk4OlEolZs2aZfV4dHQ0oqOjRRdnF2Pv3r346KOP8O677yI+Pp6zfN944w2MHTsWK1euZLQnpIjw9F3PS5KcnIxLly5xPlvjTCw9ooCefvL69eu01wP2hRwIhYSE8DoYPHPmTD/XZpK5c+fCZDLh2LFjvJTvSpDrea2J/5SUFOTl5bnkMou8vDyMGDHCLEAsSU1NRXV1Net1l9bQaDQICQkx/5tuWpVKxUiEtLe3w2QymYON8SF6mebNheglrynd2V6NRoOAgAD4+/vzuq+0Wq02tzXy/2y2LWJzzajS2trKeRnkNWb6fmbTzhzhVNFbWloKiUTS62uhO86QWgbnIHHHevzwww+or6936NpMMnbsWEybNk10cRaQnJwczJkzx+5M4cKFC0XR60Jcv34dGzZswPLly7F+/XpO81YoFNi9ezdu3LiBP/7xj5zmLcI/JpMJZ86csSp6k5KSYDAYcOHCBQEsY09LSwsaGhr6uTebTCZUVFSwypucnQgKCuJtMHjs2DGYTKZ+QaxIRo0ahcjIyAG/X293dzdOnz5tdbYU6BGP9fX1LhnI09p6XhJy6yU+ZqlJ0ctk2yEuxGFoaGivv7nAaDSira2Ncd5C1ssZ4hHoLXr9/f3h6+vLeqaXb7v5KMPyfjEVvXy0YUAA0RsREdFrwB4bG4vy8nLeQsfzQUlJCfz8/MxfIoCeetTW1rrVbEtWVhYmTZqESZMmUU6TmZmJ77//HrW1tfwZJmIVrVaL48eP23RtJlmwYAHKy8tdchAy2DAYDEhPT0dAQAA++OADqzMlbBkzZgzee+89bNmyBXv27OE8fxH+KC8vh0ajQXJycr9jiYmJkMvlbruu19bHYctjTCEHakyD/VDhyJEjiIiIwOjRo60el0gkSEtLG/BLSYqKitDV1WVTPM6cORMSicTlXJy1Wi3OnDlj0+4hQ4YgPj6el2BWbNonG5dWPkUvGSeErfBkek0AmJeAOPOa0sFS9JIuzly4N/P5nuPjXarRaKBQKBAUFISOjg4YDAbKack92ENCQnhxR3e6e7PlV1+gx91Jp9Px4mLCF2RwDssBLFkvd5ntbWhowNdff015lpdk5cqV8PT0xI4dO3iyTMQWR48eRXd3t0PRO3fuXHh4eIizvS7AP/7xD+Tn5yM7O7vXlhlck5mZiRUrVuDhhx/GtWvXeCtHhFvINbtTp07td0yhUGDixIluu67XmugdNmwYfHx8WC8FspydYLLGjwpHjhxBWlqa3Q9Vc+fOxYULF9DY2Mh5+a5Cbm4uvL29bX4cDwgIQGJiosuJ3oKCAuj1epuiF+hxzeba7q6uLuh0OlazmmxnRIcNG9brby6wXKepVCoFqRe5w4grzvR2dnaisbGxlys9F6LXXWd6yTwBeu7olkGw+Ki302d6LTtAgLsvv85kINTj008/hUQiQXp6Oq10AQEBWLJkCbKyslxyDc9AJicnBxEREQ7XhPr5+eG2224TRa/AHD16FK+99hpeeuklsysdX0gkEmzevBlBQUFIT0936X0zRX6lsLAQo0ePNgft6EtSUpJbz/SGhoaat54AuAv6yGZQRYWmpiacO3fOpmszCXn8559/5rR8VyIvLw/Tp0+HXC63eQ65rteVyMvLg5+fH8aPH2/znNTUVFy6dAnNzc2clUsO0rkSvXTGWWRZ5HaGfIheIcU803o5QzySa3dF0dv//UwnX7btzBFOE70mkwllZWX9xGJkZCTkcrlbBYHqG5ES6IkqFxQU5Bb1IAgCWVlZuP/++82BAeiQmZmJK1eu4NSpUzxYJ2KLnJwcLFy4kJKL7MKFC3H48GEx0rZANDU1Yc2aNZg1axaeeeYZp5QZEBCA7OxsFBQU4OWXX3ZKmSLsKCwstOraTJKUlITi4mK3WjZDYs2zC+Am/gXfovfo0aMA4FD0RkREICYmZsC6OBMEYXddLElqaiquXLmChoYGJ1nmmNzcXMycORMymczmOampqSAIAidPnuSs3L6Ddrpt0zKwkNFohFarpZUW6BmP+vr6cvpckHkxrZflM8vkmigUCkau1d3d3eju7uZdPFru0UvCVvRatgW+AnDx4TXD5v3Mtp05wmmit7q6Gp2dnf06QQ8PD0RHR7vNDGlrayvq6+utduZxcXFuUY+zZ8/iwoULtF2bSdLS0hARESEGtHIiarUaV65ccejaTLJw4UK0t7dz2pmLUIMgCPz2t7+FVqvFzp077Q66uGbmzJl4+eWX8c9//nNAzz4NBIxGI4qKiqwGsSJJSkqCyWRyyy2prHlEAT2il0v3ZvJvLjl8+DCio6OtRv3ty0Der/fq1atoamqiJHoB4MSJE84wyyFGoxEnTpxwaHd0dDRCQ0M5naXmckbUMj+qaSUSCXx9fTkXeFzWi+46TzKtl5cX5HI5rbL7iii+RS85Gw30iN7GxkZaHy4ssbxm5FpXrunb3si121zmOShneq2t7yFxp8jHZWVlANy7HllZWRg2bBhlAdUXmUyGBx98ELt372b8MIvQIycnB1KpFPPmzaN0/uTJkzFkyBDRxVkANm/ejAMHDmDLli0YMWKE08t/+umnMWfOHKxevXpArzV0d65cuYKOjg67onfcuHHw8vJyOxdngiBsit64uDhUV1ez6jv4Fr3k/rxUSEtLQ3Fx8YAM7pibmwupVIoZM2bYPS8yMhIREREus673l19+gUajcSh6JRIJUlNTObVbaNHr5+cHqVTq0qIXoL/O09/fHxKJhHbZfIsoErVajaFDh0KhUJh/Y7ttEd/vOb7KEEUvekSvTCbDqFGj+h3j4suvsyDt5OsLNt90d3cjOzsba9euhYeHB+N81q1bh7a2Nuzbt49D60RskZOTg+nTpyMwMJDS+VKpFHfccQcOHjzIs2Uilly6dAlPPPEEfve732HRokWC2CCTybBz5050dXXhoYceEtfeuyiFhYWQSCSYMmWKzXM8PDwwefJktxO9jY2NaGlpseneDPz6AZkJltFYyb+5or6+HpcvX6Ysem+//XYAA3Ndb15eHiZMmNBrXbYtXGldb15eHjw8PDBt2jSH56ampuL06dOs944m4UocktecrmAgnwk+RK9MJoO3tzftvI1GI9rb2xk/s5b1ohtl2Jmit69nCBvRSxBEL/dmYHCJXr6ibTtN9JaUlGDUqFFWgyHExcWhsrLSLYKvlJaWIiQkxKr4iIuLQ1NTE6dBEbjmq6++wq1btxi7NpNER0dj9uzZoouzEzAYDPjpp59oz8wvXLgQRUVFLrXOaiDT2dmJlStXIiYmBm+99ZagtoSHhyMrKwtfffUV3n//fUFtEbFOYWEhxowZ41BQuGMwK0eeXQBYfSDmczBIuipTFb1Dhw5FQkLCgHRxprKelyQ1NRWFhYXo7Ozk2SrH5ObmYsqUKfD29nZ4bmpqKrq6ulBUVMRJ2eQMJpNBO0EQnMyIAvyIXqazraTLLBshZFkvJtGALdeImkwmyumpYk30hoeHQyKRMFrX29HRAaPRyKvo1ev10Gq1vIleJu7oGk3PHuyenp7uLXptuToBPZ2g0Wh0i602HNWDPMdVycrKwsyZMzFmzBjWea1fvx6HDx9GZWUle8NEbFJQUICWlhbaonfBggUgCAI//vgjT5aJWPLkk0+ivLwcu3fvhlKpFNoc3H///fj973+PJ598EhcvXhTaHJE+FBQU2HVtJklOTkZJSQlaWlr4N4ojyD4wJiam37GQkBAEBASw6ifJQZVSqYSHhwfnojc+Pt687QsV0tLScPjwYc5scAXq6upQVlZGS/Tq9XqX2GKLjlifNGkSvL29OZul1mg08PHxgYeHB+1Be1dXF/R6PWfikA/RyyTvvsLT8jdnlu3n5weCIHgJDGhN9Hp6emLo0KGMRC/ba0aFvh9ouCqD9MRh6o7OVxsGXET0ki5Qru4aDFiP3EzCxRdsPqmurkZOTg7rWV6SZcuWwdfXF9u2beMkPxHr5OTkIDAw0G6UV2sMHToUEydOFNf1OoH9+/dj06ZN+M9//oNx48YJbY6Zf//734iLi8PKlSvF9fcuhF6vx7lz5yiJXvIcrmainEFJSQlGjBhhdaaN7bZFBoMBHR0djGedHEFnPS/J3LlzUV5eznjtnitCisCUlBRK548fPx5+fn6Cr+utqqqCWq2mLHrlcjmmT5/Omd3WZiWpLjGxFDoqlarXb0zKFkWvc8QjQRCoqqqyGviOaQRnZ9jNVxls75fbi16DwYDy8nKr63uAng2nlUqlS8+QkpSWltqsh0qlwtChQ122Htu3b4dCocCKFSs4yc/HxwfLly/H1q1beXEXEekhJycH8+fPZxQFeOHChTh48KC4rpNHqqqq8NBDD+E3v/kNHn74YaHN6YVSqcTu3btx7do1PPHEE0KbI/L/88svv6C7u5uS6I2Li4Ovr69buTjb+8gNsIt/YRmNlfw/VwOjGzduoLS0FGlpabTSzZkzBwAGlItzXl4eoqKiKAfjk8lkmDlzpuDresny6eyNTgaz4qKf7DtoNxqN6OjooJyWTCeTyaBSqVxGMLiz6FUqlZDL5YxcxqnQ2NiIrq4u3kQvk/XdVLB8l3LlNUO2d65EL9fu6E4RvWq1Gnq93mYnKJVKERMT47JikaSpqQm3bt1y2Jm7Yj3IvXmXLl1KKSgFVTIzM1FZWWne11CEW5qbm3H69GnGkbYXLlyIuro6XLhwgWPLRICeF/yaNWvg4+ODjz76iNIeys5m7Nix+M9//oMPPvhADDznIhQWFkIqlWLy5MkOz5XJZJgyZcqAEr1stvezHAyS/+dqMEiKVjI4FVVCQkIwceLEAeXiTMdFmCQ1NRV5eXmCfgTPzc1FbGwswsLCKKdJTU1FU1MTrl69yrr8voN28jeqaS3TudIsWd+8Ozs7KcfhsawXuV7TmaKX6f2gCilqIyIi+h3jQvR6eHjAx8eH15lerrxm2H6U7Hu/uHZHd4rotRfUgiQuLs5l3YJJyHrYmuklj7liPU6cOIHS0lLOXJtJUlJSEBsbKwa04olDhw7BZDIxFr0pKSnw9vYWXZx54p///Cdyc3Px6aefIigoSGhzbLJhwwb85je/wUMPPcSoAxbhloKCAowbN45SoB2gZ12vK6yVpAK5XZG9fjI2NhY3b95kNMDiU/QePnwYiYmJCAkJoZ2W3K93IHjVdHR0oKioiJHo1Wg0+OWXX3iyzDFMxPqMGTMglUo5maUeLKLX0l4qaS3TObNezhS9tmZ6q6qqaL8X+HzP8VkG1/faMk8ucIroLSkpgaenp92N3l11htQSe8E5SMh6uFrHl5WVhZEjR9L+gu0IiUSCdevW4fPPP+fcZUSkx7V57NixjPd7VSgUmDt3rih6eSAvLw8vvfQSnn/+ecyePVtoc+wikUjw0UcfQaVSYfXq1TAYDEKbNKgpLCzE1KlTKZ+flJSEyspKt9h3uba2Fh0dHQ49ogBmQR/5numlu56XZO7cuVCr1aioqODEFiE5ffo0jEYj5fW8JNOmTYOHh4dg63pbW1tx8eJF2nb7+flhwoQJOH78OGsbXEkc6nQ6dHV1UU5PJ29Le6mk9fDwMAd4pFMvyzX8dNOytZsqarUaCoUCQ4YM6XcsMjISXV1dtHfR0Gg0kEgk8PX1BSCKXq5w2kxvdHS03TWJsbGxqKqqcolw97YoLS3FsGHDzI3QGrGxsWhtbXWpbWI6OjqwZ88ePPjgg5BKub/la9euRXd3N/bs2cN53oMZgiCQk5PDeJaXZOHChcjNzaW8rkjEMc3NzUhPT8dtt92Gv//970KbQ4mgoCB8+umnOHHiBF577TWhzRm0dHV14eLFi7QC05Frf8+cOcOXWZxBxbPLFUXvtWvXcP36ddrreUlmz54NqVQ6INb15ubmIiAggHZQPm9vb0yZMkWwdb0nT56EyWSiPdML/OqazRYuRC+5BI1O27bc7ohJ2VRsY1Mv0oWWTE81rTV32ba2Nsou9JZ2+/r6QiqV8iJ6IyMjrS5vIl2e6Qa5IyMgk2N2vkSvp6cnvLy8OCtDFL1wvL4H6HELJggC5eXlzjCJESUlJXZdtgDXjET9xRdfoL29HQ8++CAv+Y8YMQJ33HGH6OLMMcXFxbhx4wYnolen0+Hnn3/mxrBBDkEQeOSRR9Da2opPP/0UHh4eQptEmVmzZuGFF17AP/7xD05mNUToc/HiRej1elqiNzo6GgEBAW6xrrekpARSqRSjR4+2eU5AQACGDBniUqL38OHDkEql5qBUdAkICMCUKVMGjOi97bbbGH0kJ4NCCUFubi5CQkIcjtOskZqairKyMtTX17Oyga049PX1NU8Q0WnbWq3WvK8rk7Kp2MZW9JLQqZe1550gCPPev3TKlkgk8PPz4030WoP8ne6yIjbXTMgyRNELamLRHfa4pSLeo6Ojzee6CllZWZg7dy5GjRrFWxmZmZk4efIkrly5wlsZg42cnBx4eXmxdp2NjY3FyJEjRRdnjvjkk0+wd+9efPTRR3aXbLgqzz33HFJSUrB69Wo0NzcLbc6go6CgAHK5HBMmTKCcRiKRICkpyS3W9ZaWliIqKgoKhcLueUzjX/AxOwH0uDZPnjwZAQEBjPOYO3cuDh8+7HLLm+hgNBpx8uRJRrOlQI94vH79uiDbN5HreZkEFCRdotnO9vadWZRIJIKJQ8vf2WAymdDa2uoyotdZZVPFnugNCQmBl5eXKHoZ2OSWolen06GystKhWAwNDYVKpXIpsWgJGZzDUT2USiUiIiJcph4VFRX4+eefOQ9g1ZdFixYhICAAW7du5bWcwUROTg5mz55tXgfDFIlEgoULF4qilwOKi4vxhz/8AQ8//DCWLVsmtDmM8PDwwM6dO9HW1oYNGza49QDdHSksLERiYqJDUdiXpKQkt5jppdJPAszjePAxUCMIgtV6XpK0tDTU1dVxEgVYKC5evIi2tjbGopcr8UgXvV6P/Px8xnaPGDECUVFRrGepLdunVCqlNbPoqqK3vb0dBEGY86S7hc5gFr0SiYRRBGd3Fr1yubzXR8m2tjYYjUaHafV6PbRardkmHx8fzt3ReRe9FRUVMJlMDjtBiUTispGPAaC+vh5tbW2U3GZcqR7btm2DSqXC0qVLeS3Hy8sL6enp2L59uxgkhwM6Oztx9OhR1q7NJAsXLkRJSQkqKys5yW8w0tXVhZUrV2LkyJH4f//v/wltDisiIyPx8ccf44svvsBHH30ktDmDisLCQlquzSRJSUmorq5GbW0tD1ZxBxXPLuDXvXqZRDW1HKj5+fnRWuNnjZKSEtTU1DBez0uSmpoKDw8Pt3Zxzs3NhVwup7SHtDVCQ0MRGxvrdNF79uxZdHZ20g5iZQlb12wycJTltpB0RS+btGQay/9zIRj65q1QKKBQKBgLTzb1YiJ6La8p1+Kxu7sbdXV1dr2+mIpepm2BThlM74ujPElvC7IOVNzRyfXbZBo+3NF5F71UtvkhceUIzlSCc5C4Sj1MJhO2bduGlStXUt4agw3r169HbW0tDh48yHtZA53jx4+jq6uLM9GblpYGmUwmzvay4KmnnsLVq1exe/dupzxPfLN06VI88sgj+OMf/yjoFiODiY6ODvzyyy+MBAUplF15ttdoNKK8vJxSPxkXF4eWlhY0NTXRKsPa7ASdNX7WOHLkCGQyGeNZQhJfX19MmzbNrffrzc3NRVJSEisPIyHW9ebm5sLLywtTpkxhnEdqaiqKiooYB33sOytJ/pvtrCSVD0N9y+ZD9HJdLyZlu9pMb3V1NQDr2xWRDLaZ3r55kr9TSWuZhiubLHGK6PX29sbw4cMdnusqYtEapaWlkEgk5jW79oiNjUVZWZmgG7QDPR359evXeXdtJpkyZQoSExPFgFYckJOTg/DwcIwdO5aT/Pz9/TFz5kxR9DLk66+/xrvvvou33nqL1lpMV+edd97B6NGjsWrVKs62thCxzblz52AymRiJ3oiICAwZMsSlRW9VVRW6u7spfxwG6Me/sDWoYrNl3pEjR5CcnAyVSsU4D5K5c+fi559/Frz/ZwJBEIz2ue1LamoqLly4wPkg3R65ubmYPn06PD09GeeRkpICo9GI06dPM0pva9BOtW1arpsl0+r1enR3d1NKa1m2XC6Ht7c3J1tJ9s2b/DfVvK09s3SuSV93WUub7KHX69HZ2cmriCLFLBml2RpMRK+1tsD1tqDW7gtbrxk272e27YwKvIvekpISxMTEUAosEBcXh9raWlZfbPmipKQEkZGR5gfPHnFxcdBqtaipqXGCZbbZsmULxowZgxkzZjilPIlEgszMTHz11Ve0v96L9IbcqohJQA5bLFiwAD/99BP0ej1neQ4GampqkJmZifvuuw+///3vhTaHU7y9vbF7926UlJTgr3/9q9DmDHgKCwvh5eVFeysY4NdgVq4seul4dpH73TMRvX3dFcnfmUCu52Xr2kwyd+5cNDY2uqX3hFqtRnV1NSei12Qy4dSpUxxZZh+CIJCXl8fa7nHjxsHf35/xLDUfM6KW+VIp2/LDDVcCj4t69X1mqa7z7Osu6+3tDZlMRqlsWyJKCNFbV1dH6eMFibW20NXVBZ1Ox9xYCmUQBIH29nZO8yR/p5LWMg35b7eb6aUaPp788ltWVsanSYygGpwDcI1I1C0tLdi3bx8yMzM5FU6OWLNmDUwmE7Kzs51W5kDjxo0b+OWXX7BgwQJO8124cCFaW1uRn5/Pab4DGaPRiIyMDCgUCmzZssWpz5KzSExMxNtvv43//ve/+Oqrr4Q2Z0BTUFCASZMmQS6XM0pPRnB21eBjpaWl8PDwQFRUlMNzfXx8EB4eTjv+BZtBlTV++eUXNDQ0sA5iRXLbbbfB09PTLV2cSbF32223sconNjYWQ4YMcdq63rKyMty8eZPVel6gJ/BUSkqK24pelUpl3u6IbtmO8ra0h27etupFZYKrb1o66zydIaLUajVCQkLsLnkiBTHpCk0Frt9zzipDFL1uJhZtQUe8jxo1ClKpVNB67NmzBzqdDhkZGU4td8iQIbj33ntFF2cWHDx4EBKJBPPnz+c036lTpyIoKEh0cabBv//9bxw5cgQ7d+5ESEiI0Obwxu9+9zvcf//9yMzMpNUxi9CjsLCQcYAgoGddb0NDgyDbwVChpKQE0dHRlPeuZrKkieuB2pEjR+Dp6cla6JEolUrMnDnTLYNZ5ebmIj4+nvW7TiKRsBKPdMnNzYVEIsHMmTNZ55WamoqTJ09SmoXsi9Ci1zIt3bId5S2VSuHr60s7b5PJhLa2NkHqZe1+cB0YyV7kZhK6e/USBOE00cul1wzbPN1e9Gq1WlRVVVEWvUFBQQgODnaZyMckJpOJlnj39PTEqFGjBK1HVlYW7rzzTkprqbkmMzMTZ8+exblz55xe9kAgJycHycnJCA4O5jRfmUyGO+64QxS9FDl16hT+/ve/45lnnuFsFshVkUgk2LJlC5RKJdasWcNowCdin9bWVly9epWV6J06dSoA1w1mRaefBFxD9B4+fBgzZszgNDjd3LlzcfToUbd7jrhwESZJTU3FqVOnnLKcJi8vD4mJiaz2WCZJSUlBW1sbLl68SDvtQBa9fn5+vTydqObd1tbWa7sjMi2ZL5WyuRS95BpRrrxlqIhecqaXqujt7OyEwWDgVfQajUa0t7fzPtNLxx1do+m9Bztpk9uI3vLycgDU1veQuGIwq+rqanR1dfHemXNFcXEx8vPznRbAqi933XUXQkNDxdleBhiNRvz444+cRW3uy8KFC1FYWIjGxkZe8h8oaDQapKenIzk5GS+99JLQ5jiF4OBg7Ny5E0ePHsWbb74ptDkDjqKiIgBgtF0RyfDhwzF8+PABI3rJ7f3oDED7Dqp8fHwoD6r6YjKZcPToUc4/aqWlpaGlpcWtPvw2Nzfj0qVLnIrezs5OnD17lpP87MFF8C2S5ORkyOVyRrPUGo0GSqWy1/IFqoP27u5udHd3u6zo5Vp4Wh5zdtlGo5FxhO6+VFVVORS9SqUSQ4YMoSx62V4zKtha78y2DGvu6HTuF19tmIRX0Utnmx8SVxS9dIJzkAhZj6ysLAQFBeG+++4TpHy5XI6MjAx8+umnnC66HwwUFhaiubmZN9G7YMECEASBQ4cO8ZL/QIAgCGzcuBFNTU3Izs5mvP7SHbn99tvx7LPP4oUXXsDJkyeFNmdAUVBQAB8fH4wZM4ZVPuS6XldDr9ejoqKCdj/Z0dGBuro6Sudbm51gs5fj+fPn0dzczFkQK5Jp06ZBqVS6lYvzyZMnQRAE63WxJJMnT4aXlxfv63obGhpw9epVzuxWKpVISkpiLHptDdodfdix5YpreYxp2WwZaKIXYBftnYQgCEozvQC9CM7uLHr7Rp0m8xW6DZPwKnpLSkrg5+eHIUOGUE5Dfvl1JUpKSiCTyTBy5EjKaeLi4lBeXu509yaDwYAdO3Zg9erVUCgUTi3bkszMTDQ1NeHrr78WzAZ3JCcnB/7+/pg+fTov+YeHh2P8+PHiXsp22LZtG3bv3o3Nmzdj1KhRQpvjdF588UVMmzYNq1atQktLi9DmDBgKCwsxZcqUXoFmmJCcnIzCwkKXC2ZVWVkJo9FIe6YXoB7Hw9pAjfybycDo8OHD8PLy4vx9q1AokJqa6laiNzc3F2FhYZS2ZaSCp6cnpk+fzvu6XlJUczXTS+aVm5tL+xmzNWg3GAzo7Ox0mJY8n8TDwwM+Pj6CCwZ3Fr0KhaLXWJhL8djS0oL29na3FL3WymDjNQNYX79NlsHmXnPpjs77TG9cXBytiKexsbFoampCc3Mzj5bRo7S0FKNGjaI14xMbGwudTkd7by62/PDDD6irq8P69eudWm5fxo0bh2nTpokuzjQ5ePAg5s2bRzkQDBMWLlyInJwclxs0uwIlJSV47LHHkJmZiVWrVgltjiDI5XJkZ2ejubkZGzduFNsJR7ANYkWSlJSElpYWVFRUcGAVd5Afq+mI3tGjR0MqlVL+0M216D1y5AhSUlJ4+UA8d+5cHDt2zG22iCPX83IZoZ6peKRDXl4eIiIiKAkPqqSmpqK6upr13qoAdbFiTYSQf7uq6O3o6IDBYLCb1tozq1Qq4eHh4RTRy/R+UIFsH84QvXK5HEqlklfRy8ZrBrC+fpssg8394tIdnXfRS6cDBFwzgjOdyM0kQtUjKysLkyZNwqRJk5xarjUyMzPx/fffo7a2VmhT3AKNRoNTp07x5tpMsnDhQtTU1LjlPpJ80t3djZUrVyI8PBzvvvuu0OYIysiRI/Hhhx9iz5494ocrDmhqakJFRQWr9bwkZDArV3NxLi0thZeXF0aMGEE5jUKhQGRkJOV+kq0wsMRgMODYsWOcuzaTzJ07F+3t7Thz5gwv+XNJd3c3Tp8+zelsKdAjHm/evMnrNpRcruclISN5052lZiOy3FH0Ao7dhG2JK2fUy5VEb0REBKqqqih9AOLyPefMMvhqw5Z5s4V392amoteVXJyZ1CMqKgpyudyp9WhsbMTXX38tWACrvqxcuRKenp7YsWOH0Ka4BT/99BOMRiPvonfWrFlQKpViFOc+PPvss7h06RJ27drVa2uGwcqKFSuwfv16PP7447h69arQ5rg1pPDhYqZ3yJAhiIqKcrlgVqWlpYiJiYFUSm9YERcXJ4joPXPmDNra2niLzJ6UlASVSuUWLs5FRUXo6uribF0sycyZMyGRSHhb19vZ2YkzZ85wbndISAji4+MHhOjt6upiHVuFbb1kMhl8fHz6pXeU1mg08uIuS8VuKqjVasjlcoSFhTk8NzIyEu3t7ZSWDJG2qVSqXr+Lopc9vIne1tZW1NfX054hValUGDp0qMvM9BoMBlRUVNAWvTKZDNHR0U6tx6effgoASE9Pd1qZ9ggICMCSJUuQlZUlukhSICcnB2PGjEFUVBSv5Xh5eWHOnDmi6LXghx9+wDvvvIM333wTU6ZMEdocl+Hdd99FREQEVq5cie7ubqHNcVsKCwvh7+/P2XpJcl2vK8Hk4zDQ86Gb6sdhLgdqR44cgY+PDycfIqzh4eGBWbNm4fDhw7zkzyV5eXnw9vbm3EPM398fiYmJvK3rLSgogF6v53ymF+iZpaYr1rkQvZZ7nJLpHaW1tq8rnbIdwbZefbc7ItM7StvW1tarLMu0VNZ5WrNbpVJBIpFwJnpHjBhB6UMfnb16NRoNVCpVv/gPXIteDw8PKJVKzsoY1KKXdGdh2gm6iuhVq9XQ6/W0xTvg3HoQBIEtW7bg/vvvZ72xPJdkZmbiypUryM/PF9oUl4YgCOTk5PA+y0uycOFCHDt2DFqt1inluTJ1dXV48MEHcdddd+GPf/yj0Oa4FD4+Pti1axcuX76Mp59+Wmhz3BZyPS/dWVBbJCUl4cyZMzCZTJzkxwVMlgEBPf1keXk5pbrYGlQxWYd25MgRzJ49m9fo7HPnzkVeXp7LfzDKzc3FjBkzeLkW5LpePsjNzYWfnx/Gjx/Ped6pqam4dOkSrfgypMCzhGoEZo2m/3ZHZHpHabu6uqDX6xmX7Qi29er7vJLp2XwIMJlMaG9vp223VCqFSqXiTPRSXUtOV/T2tRtg9p6zV4a/v3+/jxFsymD7fmbTzqjCm+hlEtSCxJUiOLtLPc6ePYsLFy64jGszSVpaGiIiIsR1gQ4oKSnB9evXnSp6u7u7cezYMaeU56qYTCY8+OCDkEgk2Lp1K2eiZCAxefJk/Otf/8J//vMffPfdd0Kb45YUFBRwOqOYlJSE9vZ2l3E77+rqglqtZtxPdnV14caNGw7P5Wp2QqfTITc3lzfXZpK0tDR0dnbi9OnTvJbDBoIgeFkXS5KamoqrV6+ioaGB87xzc3Nx2223sY6Ibo2UlBQQBEFr6zZrAo+tOKTStu3NsFEp2x4EQbAO0CVUvdiUTQUqe/SShIWFQS6XUxa9fNrNVxmDeqa3tLQUISEhCAwMpJ2WnCF1BZfY0tJSKBQKRERE0E4bGxuLyspKp0RvzMrKwrBhw5wmmqgik8nw4IMPYvfu3eKsoh1ycnLg6emJOXPmOKW8+Ph4REREDHoX53feeQcHDx7E9u3bERoaKrQ5Lssf/vAH3HPPPVi3bp0YmI4mdXV1uHHjBqeilwxm5SouzuXl5SAIgvFML0At6KOt2Qm6A7XTp09Dq9XyFsSKZOLEiQgICHBpF+erV6+iqamJ83WxJGS+J06c4DRfk8mEEydO8GZ3dHQ0wsLCaM1SWxu0y2Qy+Pr6urQ4tEdHRweMRqMoeq1AZ6ZXKpUiIiJiwItemUwGb2/vfnm2trba9eaxtgc7wK07OsCz6GXy1Rfo6QRbW1t5+TJIl9LSUkRHRzP6khgbGwuj0Yhr167xYNmvdHd3Izs7GxkZGbxudcOUdevWobW1Ffv37xfaFJclJycHs2bN6hfsgS8kEol566LBSkFBAZ555hn89a9/xYIFC4Q2x6WRSCTIysqCTCbD2rVrXcqt1tXhMogVSUBAAGJjY11G9JKClUmfP3LkSHh4eFDyirI3UKOzl+ORI0cQEBDA+y4HMpkMt99+u0sHs8rLy4NUKsWMGTN4yT8yMhIRERGcuzj/8ssv0Gg0vM1QSyQSWut69Xo9tFqt3fZpD2uzqXTSkuf2TWt5nAm28vby8oJcLqcUvVmoetm7pmxFlMFgQHV1Na2tsqhuW8SmLVCFzX1xlKe1j5IEQdjddsjW+m3SHZ2revPq3szkqy/w64b1ruDizDQ4B+C8enz11Ve4deuWy7k2k0RHR2POnDnYsmWL0Ka4JN3d3fj555+dPku/cOFCFBcXo6qqyqnlugJtbW1YtWoVJk2ahFdffVVoc9yCIUOGYMeOHfjpp5/w1ltvCW2O21BQUICQkBDOA9QlJSW5zLZFJSUl8PX1pRTFtC8eHh4YPXo0rZnevpBr/Kju5Xj48GHMnj2bF7fYvsydOxcnT55EZ2cn72UxITc3FxMnTrS6hpAr+FjXm5ubCw8PD0ybNo3TfC1JTU3F6dOnKa3JtiXQyN9ceUaUSd5Utx0aqDO9NTU1MJlMtEUvlfGWO8/02sqTPG4vreW5XNnUF5ec6SUjXLpCMCumwTkAYPjw4VAqlbzXIysrCzNmzEB8fDyv5bAhMzMThw8fRmVlpdCmuBy5ubnQarVOF73z5s2DVCodlLO9jz32GOrr67Fr1y54enoKbY7bMH/+fPztb3/Dc88959LrFF0JMohV36/fbElKSsLZs2dhMBg4zZcJZD/JtI5Ugz6yGVSRdHV14eTJk7y7NpPMnTsXOp2Oc/deruBzPS9Jamoqzpw5w6nwz83NxdSpU/u5UnJJSkoKurq6UFRU5PBctoN2e23b0bZDtsr29PSEl5cXL6KX/I1v0WvLXdbSNmvYcpelWrYjTbyyhAABAABJREFU6OzRS+Iu7s10vGao5kket5fW8ty+6V1a9DY1NeHWrVuMRa9SqURERITgolen06GyspJxPaRSKWJiYnitR3V1NXJyclx2lpdk2bJl8PX1xbZt24Q2xeXIycnBsGHDkJiY6NRyAwMDMX369EEnenfu3Int27fj/fffR0xMjNDmuB2vvPIKpkyZglWrVnHmcjRQIQjCLHq5JikpCV1dXbh8+TLnedOFzUdugHrQRy5E78mTJ9Hd3c17ECuScePGYciQIS7p4lxfX4+ysjLe1sWSpKSkQK/Xc+qZkJuby7vdkyZNgre3NyUXZz7FoWX+9sq2NlvPVjDwWS9H6zxtucv6+vpCKpXaLZvtzLsjSPFKJ95PZGQkqqurHX6otHfNtFotJ3GC7JVhNBope81QzZM8bi+t5bl907u06CVnANh0gpGRkbSi5vHBlStXYDKZWNeDz+16tm7dCoVCgRUrVvBWBhf4+Phg+fLlyMrKgtFoFNocl+L777/HggULOJ8JosKCBQtw8OBBRi84d6S8vByPPvoo1qxZg4yMDKHNcUvkcjmys7PR0NCANWvWuKzbpitQWVmJ+vp6JCcnc543uZ+00BHYCYLA5cuXMXr0aMZ5jBw5EuXl5Q63IGlsbLQ6s0cOlG7duuWwrB9++AFBQUG8bHNjDalUitmzZ+O7775zubXw33zzDQDwLh7Hjx8PlUqFr776ipP8iouLoVareZ+hlsvlSE5OphS1nmx71gbt3t7eaGxstJu+qanJatsmhay9rZNu3rwJb29vqzFdfH19UV9fb7dse5B2W6uXj48Pbt68aTd9c3Oz1VglKpUKBEGY13Jao76+HiqVqt/vEokEvr6+dstuaWkBYP1DAPCrezJTCgoK4Ovra9U+W0RERMBkMuH69et2z7N3zQD2kYxNJhPq6uqsHiOvF3n96FBRUWH1d7LtNDU12UzrqJ1xFuOJ4IH58+cTAIiff/6ZcR4ACACE0Wjk0DJ6LF++nABA7Nu3j3EeKpWKAEB0dHRwaFkPLS0tBABi2LBhnOfNBx9//DEBgNiwYYPQprgMu3fvJgAQDz30kCDl/+tf/yIAEH/7298EKd+ZXLt2jYiNjSVGjx5NaDQaoc1xe1566SUCAJGeni60KS7LokWLCADEd999x3neJpOJAEBIpVLO86bDzz//TAAg5s2bxziP3/72twQA4sMPP7R5jsFgIAAQnp6e/Y5lZ2cTAIg1a9bYLUeoazZz5kwCAHHy5EmnlusIX19fAgDR1dXFe1nkmI4L7rrrLgIAkZOTw0l+9hg6dCgBgLh586bd85YuXUoAIL788st+xyQSCQGAMJlMVtM2NjbaHMu9+uqrBADi73//u82yhwwZQgAgmpub+x1je92TkpIIAMQvv/xCO+/Lly8TAIgpU6b0O5aZmUkAILZt22YzvVQqtZp/e3s7AYAIDAy0mfa1116zed3Cw8MJAERDQ4PN9I5QKBS0Ncq7775LACAef/xxm+eUlZURAIjExMR+xzZu3EgAIDZv3szIZpLa2loCABEVFdXv2F/+8hcCAPHWW2/RztdWeyD7iPnz59tMO3fuXAIAkZeXRzlfJvAieo8dO0bcf//9rATrzp07iczMTA6tos/BgweJCRMmEFqtlnEeX331FbFixQoOrfqVrq4uIiQkhHjjjTd4yZ9rmpqaiGHDhhH/7//9P6FNcRmKioqI4OBg4vTp04KU39TURIwbN444duyYIOU7k1mzZhEAiJ07dwptyoCgo6ODmDt3LvHxxx8LbYrLsmvXLiI+Pp5VH2KPZcuWEevWreMlb6q0tbURY8eOJU6cOME4j+rqaiI1NZVoamqye968efOIf/7zn/1+b25uJsaMGUMcP37cYVmJiYnEU089xdhWJpw6dYpITEwk2tranFquI5599lli7dq1TinrpZdeIlatWsVJXvv27SNSUlIInU7HSX72+Pbbb4mUlBSH49mffvqJiI+PJ9rb2/sde+mll4g777zTZlqTyURMnz6d+OCDD/odu379OhETE0MUFxfbTP/WW28Rc+bMsSqqly1bRvz5z3+2a7s9du3aRUyePJkwGAz9jj322GN276lOpyPGjx9PHDhwoN+xs2fPErGxsUR9fb3N9H/729+IxYsXWz1m611AUlZWRsTExBDl5eX9jh05coS1vnj//fdpv0eampqIxMRE4uzZszbPMRgMxIQJE4jPP/+837FLly4RMTExRHV1NV1z+/Hggw9afV+WlJQQMTExREVFBe08//73vxOffPJJv9+7urqIcePGEV9//bXNtF9++SUxfvx4oru7u9+xv/zlL8SSJUto22MNCUG4wGa4IiIiIjyzf/9+5Ofn4/XXXxfElVxEREREREREREQYRNErIiIiIiIiIiIiIiIiMmDpv+pdRERERETEBmq12mFQFjqEhITQ2vZBRERERERERIQutEUvVwMecqDDRX5MBk2uXA+hrgnXDJR6MMXV6s91mxeSgVQXd0KtViMhIQFarZazPL29vVFcXOx298GZz7eQ7xJn9221tbU2I4eSaUNCQqwe1+l0dvfdDggIwLBhwxzayQZXeO8L+X5kWzZ5/x3dK3vw1dbttU3Afvt01HbZtG1HaQH7bb+7uxsKhcLqMTZ1pnKcTb34uiZs26Cj9GyvKZX3mGU+9my0R99yLNNYs4HP58NWmZZQee5piV4uBzze3t44dOgQ5s+fzzo/uoMmV66HUNeEa7i6xkLXgymuVn+u2/xAaFuA8HVxNxobG6HVarFz504kJCSwzq+4uBhr1qxBY2OjW90DZz7fQr5LuCybat8mlUptbiVi7xiV41TsZPM+cIX3vpDvRy7KZnsPAf7aOpv2x7btssnbETKZzOZWknza7ei4UNeE7fXk+5o5wsvLCxKJxO52gkzKcOU2DFB77mmJXssBT2VlJQiCQFhYGCQSCTw9PaHRaCCRSBAVFQWpVIru7m60tLRAJpOhra0NkydPhlKpNA90KioqoNVq8eSTT0KhUGDkyJHw8/PDlStXMHz4cAQFBcHLywvV1dUgCAJRUVGoq6vD9evXcdttt6GmpgYeHh60B01cDdyY1mP06NFoaGiATCaDr68vGhsbzfUg87K07fLly6ioqICfnx/Cw8PR3d2NoKAglJeXQyKRQCKRYNq0aZDJZL3sEnIgaXmNCYLAjRs3EBISAolEArlcju7ublRWVkKhUGDGjBmoqamBQqGARqNBREQE2tvbYTAYBK8HU7hoY1zeR0t7GhsbcfPmTVrPW1JSEhoaGiCXywW/J1w/v+7YvoSmvLwckZGRMJlMuHDhApKSkuDl5QWpVIqrV68iIiICUqkU586dM++J2t7eDk9PT4SHh+PkyZMwmUycCGchcObzLeS7hMuyqfRtV69exfPPP2+1PDIfW7Y4Ok7VTjbvA6ZjpObmZigUCkyePBmVlZWs7LB1z2yNI9RqNbq6uiCXy1mPI6yVbavckpISeHh49Bq/WLuH9tKTg+Tbb7/dbAPbtm5rHNfS0oK//e1vjNof27bLJm9HfPfdd7w9c3zWi2paOuNPvV6Pmzdv4q9//SutsVJsbKx5z+D6+nr86U9/4u2aOYJMDwD/+Mc/IJfL+9X91KlT2LJlC60yLO2y9m67dOkS/v3vfwvShi3zcPTcM1rTq9VqERERgaSkJOj1elRWVsJgMGDChAnmTZNDQ0MRGhqKqqoqNDY2IjExES0tLeYGZImfnx+WLVtmzmv8+PGIjo425zVr1ixzXh4eHliyZAlqa2vh6ekJNnG4tFotLl261K8elmVbq8eVK1cAAHFxcb3yGzNmDBQKhTk/Ly+vXvmNGTOmX34RERFQqVTo6uqyaVtiYiJUKlU/2+bPn2/Oq6mpCePHj0ddXR2uXr3K+JpwjVarhUKhwP3339+vrZDXLzQ0FFOnTkVVVRU6OjowYsQIVFZWoqSkRGDr2VNeXo7JkydTal8dHR0ICQkxfwThQ4hptVoEBgbijjvuoPy83XXXXbhy5QpUKpV5QOQK0H1+m5qaEBMTg8rKSly5cgXjx48XuAbuy7333ou2tjZcv34d8+bNM1//hoYGJCUlma//7NmzERoaiqKiIkilUowYMQJXr16Fp6cnUlNTXepdxQQmfUhERATq6upgMBholcXkXVJXV4eysjLEx8ezqiedshsbGxEbG4sbN27AYDBg1KhRNq9Z376NdI2zVp5UKu2X3tpxurYmJiairKwM3t7erK5R3+tlOaZxZMM999yD+vp6XLx4sd/4iCl9r5NKpcK4ceP62TF9+nSzHX5+fjAYDGhsbGTsWty3bFvjl4ULF/ZrMx4ePUNSy3voyO6Ojg5ER0fjl19+gV6v79fe6GJrPFpQUNCvblTbH9W2y0fettpdcHAwKioq0NbWZjN9TU0NJ3Y7+5pZ2k1n/NnY2Ijg4GBzWjpjJZPJhMTERPzwww826xQdHW32JGB6zRy9d8lnCAAIgrBadwDYsmULrXdlYGCguV7W3m3d3d2c3C8mbbizs5OyWGYkeqdOnYopU6aY/544cSK6urqg0+ng5+fX61yyAQFAWFgYxowZg6Kiol7n3HvvvRg7diytvPz9/REfH98vLzb1aGlpgU6nw+XLlyGTyVBfX4+JEydCLpcjODgYVVVVCAsLQ1hYGAD0K5vpdbF2TdhcY6PRiFdeeYXu5eAFpvWIioqCSqVymp18Qbbto0ePIiAgAPX19Th79iza2trMbUur1aKjowNtbW1ISUnBmDFjAPRvX1zApl3xZRNTLOtC5fp2dnYiKioKUVFRmDNnjkvVxR2ZM2dOr7/ttaU77rjD/G+yfQ8E6LTBuro63HnnnQB6rgHd95tlP+mor+ro6EBpaSnuvfdeTto6nbIrKyvNzxlAr58kZ+6svTfJDyS2rjl5nOo71/J+hIWFcfo+YDKmCQsLw4QJEzizg+m7noTLsRXVspuamgAwu3633XYba7vtlU22TbJuR48eBdAjLLRaLQoLC3ulZ9J2mbRtR2lt9YeTJk1CREQEbty4YbNe5GQMW7udfc1IV20m7ZBsP0zbcGJiYr92ZPnOJNs402t28OBBTJ8+HeHh4VAoFLhy5QpSUlLM5ZP597XB3nu2r43W3uvkdaH6fNCpl6N25qgNW943R3ASvZk0vrm5Gd3d3eaLRd6UkpISzJs3j5O8zp07h/vuu48Ls/vRdxDXF/LiUsFRPX755Rdzh8s2Lz6vCZc4qodarQaAXg/wQIHLtsUVA6VdAa55fQcTVPoADw8Ph/fJnXFmGxSyvfPZT5KDYEvOnz+PMWPG9BtoOjruKu8EKv1ed3c30tLSBLWjpqYG06dPF6Ts0tJSm/XncizFxmZ7bVMikSAtLQ3vvvuu1eN02+7Ro0fNx0i3d6pp+x5XKBSYNWuW+TrLZDJ4eXnZrTuZ1sfHhze76V4zR3lbHrcW3IqtTmHThi3fRUVFRXjmmWdsXhdr99Pyms2cOdNcHulRSgVL+0lvVctjlvd83Lhx5ndTdXU1NBqN1bXu9vKkUy9H7cyyDavVaty6dQsmkwl5eXm09QIj0VtcXNzr75KSEpSUlCAqKgo6nQ7R0dHo6OjAN998g6FDhyIwMLCXCu+bnvz7zJkz/fIh3RFra2tx9epVKBQKhIeH28yLTT2slS+Xy6FWqyGTyVBTU9NLBDCpR2VlJTo7O6FUKildE8D29f38888RHByM+vp6VFdXY9q0aayvCdc4uibl5eUoKSlBZ2cnPD090dXVBaVS2SutO2Ov/nK5HA0NDVAoFGhoaIBOp8PcuXP7peXDHsD+c+vh4QGj0YjNmzeb2xVfNjHF0hZb17e4uBh+fn7QaDSQSqX91n6JMINOH0C6RRmNxn6zm+5+D6i0wQsXLpjf03fffbfVtFyVVVxcjJCQENy8eRNyuRyzZ89mVJatsu3VceTIkairqwMAc7nWyraXFylEyXNUKhVSU1MB/OptYpmfveP2+nODwQCFQoHu7m7zDCGXbdFRv1dcXGweDygUCixYsMA8HuDKDkftxdIOtVqNwMBAyOVyq+n5KPvs2bO4ePEi2tvbAfREZiXTObp+ZFqdTtdrFoqt3Zbp+77TSHdva23T8ncu2i6XeQNAa2srgJ5rTEJes2vXrgliN916sb0mtvqoY8eOwWg0orOzEwUFBf3eQYDjMX1zc7NZ5/S1q2/6iooKVtfU3r3sm6+t54j8KOCoDHIsrlKpzG7wVPOkUy86bVipVPbSgLTfmwQNrl+/Tnh7exMAWP/n7e1NnDhxgpP8vL29ievXrw+Iegh1TbiGq2ssdD2Y4mr157rND4S25Qp1cTe4vPbufA+c+XwL+S7hsmyqfZtUKmV0jMpxvtuiK7z3hXw/clE223vIZ1tn0/7Ytl02eTv6TyaTCWI3n9eMzTVhez35vmaO/vPy8iKUSiXnZbhyGwaoPfcSgqAXCcrRPmZ33HEHbt26hZ9//tnumiUq+/bde++96OzsxIIFC/DUU085zIsO9spta2szzwg9//zzWLx4scOy7eX39ttvIzs7Gy+++CLuv/9+Vnnl5ubij3/8I+bNm4d//etfDvMSElv16OjowJw5c/D000/jf//7H1auXIlHHnnEah6uUA+m2Ko/GS1xzZo1+P7775GTkwOJRGI1D2ft0/vNN9/gxRdfBAAcOnTIPDvHt01MsVWXDz74ADt37sTBgwdRVlaGdevW4f3337fptucKdXE3bF17sg19+umniI+Ph9FoxIoVKzB06FD897//tZmfu94De8/TsWPH8Oc//xkLFizA66+/bjMPLvbpzcnJwbPPPovf/OY3eO6551iXRafs//znP9ixYwcnfduJEyfw+OOPY+bMmfj9739v9Rym+3ZWVVXhmWeeQUxMDPbs2ePQTjbYq+Mnn3yC999/H0888QRWr17Nmx32bDh8+DD++te/YuLEiQgJCeF8HGGvbIIgkJycDLlcjpMnT1o9x94ep6+99hr27duHf/7zn1i4cCGndtuz/V//+hfy8vKQlpaGzz77DB9++KE5MI8l4j699I8LsU/vlStX8Oqrr2LSpEn45JNP+h13tM/uXXfdhZs3b9ocK9lLv2vXLrz11ltYtmyZTW3B5z69DQ0NuPPOOzFs2DDExMTYHHtbK8fePr3//e9/kZWVhfXr19t083a7fXoBIDIy0m6mZETKUaNGUXrp2MrPaDSioaEB8fHx6Ozs7LWgnAvs1ePChQsAYA7zTaVse/n5+PhAKpUiMDCQdV7kYnAvLy/OrwnX2KrHoUOHQBAE1qxZg0uXLqGiosLl68IEW/XfsWMHoqOjsXTpUuzcuRNDhw7t5a7hbHuAHiEeEBBgflG7+v2wVheCIPDTTz9hxYoVSElJwW233YbXXnsNp06dwqOPPiqQpQMPa9feYDBgxYoVWLx4MdLT082/v/7661i5ciW6u7sxc+ZMZ5vKK/aep8uXLwPoccXi4lmyVxYZVdbHx4eX59Ze2d7e3pBKpZTfGfbyKi0tBdAzsLEnCJmQm5sLAPD09OT93Wavjrt374ZMJoOfnx+vdtiz4euvv0ZISAhmzZqFH3/80aljq87OThAEAb1ej0mTJlkVjvYgI2wHBQU5ta2XlJRg/vz5WL9+PbZv347ExERMmjSJ8/JFnMM333wDAPD19WXUjshAWVFRUYiJiaGV9ttvv4VMJsPIkSM5f8/1xVpbPnbsGICeoFHNzc2c2UD2BfHx8bzXiw303jgOIAgCHR0dAGAOL80UcksH8uuwMyHL46psjUYDDw8P1teEzEsikZivszuSm5uLoKAgxMfHIzU1FSdPnqS9fYc7U1hYiOTkZCQlJZn/Fhq1Wm1+QTr7eeOKkydPory8HBkZGQAAiUSCjIwMfPHFF279vLgDO3fuRFlZmdlbgOSBBx7AuHHj+v0+0CHf0+R6Rb7L4qp/EbJsMg8+nlUyb2vBWJyJRqOBXC4X5F6RXLx4EePHj8eECRNQWlqKzs5Op5VN1psgCPMaQTq0trY6va23trbi/PnzSE1NRXJyMjw9Pc0fUUTcE7bvGjY6R8j3NdDzAUcqlWLy5MmcbgvqCu82KnAqeru6usxfQNhWnBx4x8TECCJ65XI5Ro8ejaqqKtb5aTQa88b0XOTlDg3LHrm5uUhJSYFUKkVKSgo6OjrMs+sDHYPBgKKiIiQlJSE8PBxDhw41z9QIiVqtxujRo+Hl5eW2onf79u2IiIjoFSlxzZo16OjowP79+wW0bGCj1+vxyiuvYMmSJf1mP6RSKV588UX8+OOPyMvLE8ZAAeDyne9KZVkrW6FQcNa38VUPMk8mQotrO4S6VyQXL15EYmIiEhMTYTKZnBpMzrLeTAUDV+2NKqdOnYLJZEJKSgq8vLwwdepUUfS6OeRHSSbvA4PBYN5ix13asCWlpaWIiopCQkICGhoaOLPDFd5tVOBU9LJ9oVlCis1x48ahubnZKV/MSdRqNUaMGIGoqCjOZnq9vLw4GxhwlZcQGAwGnDp1yhxmPCkpCQqFYtB0IleuXIFWq0VSUhIkEgmSkpJcZqY3KioKERERnHzocTbd3d347LPPsGbNml4uc6NGjcKsWbOwY8cOAa0b2OzYsQMVFRV46aWXrB5funQpEhMTbR4fiDjzPa3RaKBUKgUTve7Qt5GD3NbWVtAMY8K5HUL2352dnSgrK0NiYiLGjRsHALh06ZLTyudC9Dr7+uXl5SE4ONi8v3hqaipyc3MFbUci7GDTjsgowmQ+ziybC0pLSxEbG4vY2Fjz31wgdL2o4rKiV61WQ6VSIT4+HgCcOhCvqqpCRESE2b2Z7ctNo9HA29ubs4EBV3kJwfnz59HR0WEORa5QKJCcnDxoRG9hYWGvdeKk6BWyAyUIAlVVVeb1TO440/vtt9+iubnZ7NpsSUZGBg4dOoSamhoBLBvY6PV6vPrqq1i6dCkmTJhg9RxytvfQoUOD5jl35ntaaNHLVdl8XjMyb71eb56lEQKh++/i4mKYTCYkJiZCpVJh5MiRuHjxotPK50L0Orut5+bmIjU11RxsMjU1FdXV1W7ZT4r0wOY55KINC/kOKCkp6SV6uXJxFrpeVOFF9MpkMk5Eb2RkJKKiosx/Owuy7MjISHR3d6OhoYFVfhqNBr6+vpwNDMi83PFLY25uLhQKhXk9KwCkpKQMmi+nhYWFiI+PN0c2T05ORlNTEyorKwWzqaWlBe3t7b0+9Lgb27dvR1JSEhISEvode+CBByCXy5GdnS2AZQObbdu24dq1aw7X7C5ZsgQTJkwYNGt7uXznu1JZfJat0Wjg4+PDy2wsaSf5b6Eg6yiUDaTAJWd5ExMT3U70OrOt6/X6Xp5pAMz7Og+WD3gDEbId6XQ62h/ByLbH1EVZyHeAyWRCeXk54uLi4O/vj9DQUE5neoV8t1GFF9GrUqk4E73Dhw+HVCoVTPSSf7NBo9Fwck0s8zIYDE4NQMEVubm5SE5O7hUePzU1FbW1tYIKP2dRUFDQS/BPnToVgLDBrCwDt7mj6G1qasJ3331ndZYX6IkGe//994suzhyj0+nw2muv4YEHHkBiYqLdc8nZ3sOHD5ujRw5kyPd0d3c3uru7nVKWEB9C+ejbjEYj58GsyLzJfwsFl9eLCRcvXsSoUaPM18KdRC9BEE6/fufOnYNWqzV7pgE9u3rEx8eLoteNYfM+IM/38/NjLHqFegfcuHEDXV1d5lne2NhYTkUv02viTHgRvf7+/pyJXrlcjuHDhzttIG4wGFBdXc2Z6DUYDOjo6ODkmgA919jf39/8b3eCIAjk5eX1+moKDJ4vpzqdDufPn+8lesPCwhAREeFSore2tpb3gTqX7NmzByaTCStXrrR5TkZGBi5cuIDz58870bKBzbZt23D9+nW88MILlM5fvHgxJk6cOCjW9jrzPU2WZTQanRqd2Gg0oq2tzS36NlfpNzUaDQICAgQVvePHjzf/nZiYiJqaGty6dcsp5ZOzQUy8AbVaLYxGI2ftjQp5eXlWt4dMTU0dVIH5Bhps3gdsdQ5ZthDvANKVmRS9cXFxnLg3EwSB1tbWwSt6AwMDORO9AHdbB1GhtrYWRqMRkZGRCA4OhlKpZFU2ueidi2sC9FxjcjNsV29cfbl27Rpqa2t7fTUFevbcGzdu3IAXvb/88gu6u7t7iV4AggezIqOVh4WFmZ+56upqweyhy/bt23HXXXchNDTU5jl33nknQkJCxNlejtDpdHj11VfxwAMP9BpE20MqleKll17CkSNHcPToUZ4tFBZnvqcty7IMssI3ZORTrkQcn9fMFfpNo9GI9vZ2wUWvpVcG+W9nBbMiRb+fnx/ttsrl+JIqubm5mDZtWi/PNKBH9F66dAnNzc1OsUOEW9i8D8h2y/Q5Jsvu6Ohw+ladpaWl8PDwwMiRIwH8OtPL1kOI/CDlzGeTKZyLXl9fX9ZfMbRaLZqamswD8IiICKeJXstZL4lEwjqaLXkdgoKCOBsYBAUF9crbXSBFLTmzawm5rncgU1BQAJlM1m9bl+TkZBQWFsJkMglil1qtRkREBKRSqfmZc5cIziUlJcjPz7fp2kwil8uxatUqZGdnm7dVE2FOVlYWqqqqaK/RXbRoESZPnjzg1/Y68z2t0WgQHBzslLL6lgsAwcHBLt+3uUK/SQ6Wg4OD0dXVBZ1O59Tym5qaUFtb20v0xsXFQS6XO83FmZzlYjJG5Hos5QiCIMzbK/YlNTUVBEHg5MmTvNshwj1s3gcaTc/WPEwEnl6vh1arNZftzI+UQI/oHT16NDw8PAD0iN6WlhY0NjayyrdvX+DK8Xk4F71MX2iWkANuIWZ6yXIiIiI4KZu8DkOGDEFnZyf0ej0r+zQaDUJCQgA4/4FhS25uLsaNG2d+4C1JTU3F5cuXneZmJQSFhYUYN24cvL29e/2elJSE1tZWlJWVCWKXpVfFiBEjzL+5Azt27IC/vz/uu+8+h+dmZGSgtrYWP/30kxMsG7h0d3fjtddew4oVKzB27FhaaSUSCV566SUcPXoUR44c4clC4WltbcWQIUMA8CuySLcysk8QQvSGhIRwIuI0Go35mnHdt1n2m0KJXsvrJYQd5GyupeiVy+WIj493C9FLtomQkBCnbD1VXl6O+vr6fp5pADB69GiEhYUN+A/1AxXLdw0T0ctFG2ZSNlvIyM0kcXFxANhvW2T5bnP2Mhu6uKTotSY8q6qqnDITplarze43ZNlciV7Lv5lADnBIN053m+nNy8uz2oEAMP9+4sQJZ5rkVAoLC/u5NgPCB7OyFL3e3t4ICQlxC9FrMpmwc+dOPPDAA1AqlQ7PT0pKQnx8PLZv3+4E6wYuW7ZswY0bNyiv5e3Lfffdh6lTp+LFF1906S/CTDGZTGhra3PKe7qjowNGo1GQPoEsi4uyySBFfNWDdCkUMrool9eLCRcvXoRcLjcPdEmcGcyKi5ne0NBQXoKd9SUvLw8SiQQzZ87sd0wikYjret0YoUSv0O+A0tLSXs9/TEyM+Xc2CF0vOnAuev38/FgvZlar1ZBIJAgPDwfQIzz1ej3q6+u5MtVu2aQAIMvmQvSGhYX1+psJ7e3tMJlMnOTlbJqamnD58mWrrkIAMHLkSAwfPnzAfjnt6urCxYsXkZyc3O9YUFAQoqOjXUL0As71rGBDXl4eKisrHbo2k0gkEmRkZGD//v1ob2/n2bqBSXd3N/75z39i1apVVreHogI523v8+HEcPnyYYwuFp62tDQRBOOU9zWX/ImTZ5JqwIUOGQCKR8CJ6/fz8BAsgQ9oACHOvgB7Rm5CQALlc3uv3xMREXLp0ySkfoLgQDM66frm5uRg/frx57WdfUlNTcfr0abcK+ijy60fJwMBARvvKsnmXCPkOMBgMqKio6DXT6+3tjfDwcNbBrIR+t9HBZWd6hw0bBk9PTwBw6jrDqqoq8wwzWXZdXR3jFxt5HYYNGwaAnduWZSAHofZlZAo5g2trplcikSAlJWXAfjk9f/48DAaD1ZleoGcWsqCgwMlW9bwIa2pq3FL0bt++HSNHjrTZpqyxZs0aaLVa7Nu3j0fLBi4ff/wxampq8Pzzz7PK55577kFSUhJeeumlATfba7m+SalUOkX0Dh06tNffzoAsa/jw4azLtuzbuN7Ow2QyobW11Tw2EWpZEJfXiwmXLl2yGnRu/PjxaG1tdco7nwvR66y2bms9L0lqaiq6urpQVFTEqx0i3EJ+lGTTDtm2YSHeAZWVlTAYDL1EL9Dj4szVTC+pc1xZm7is6O07CCd/5xtbZd+4cYNRfhqNBnK5nJNpfzItF9fY2eTl5WH48OHmqHHWIL+c0t0s3B0oLCyEXC63uZ9pUlISioqKnB5kqaamBiaTqd+HHlcXvV1dXdi7dy/WrFkDqZT6aywyMhK333676OLMgK6uLrz++utYtWoV4uPjWeVFzvbm5uYOuDXWznxPWwb3cfbejxqNBjKZjJOv+3xes/b2dlaDXK4gyyU92JxpB0EQuHTpktX+x5kRnNkKBpVK5ZSAZA0NDbhy5YrdD6oTJ06Et7f3gP1QP1Bh+67hQvQK8Q4ghW1f0cvFXr0ajQYSiUSwD3p04FT09v2ayvQLfl/hSa7FEVL0Mi2bvCYBAQEA2DUG8gu10J03E3Jzc5GamgqJRGLznNTUVOh0Opw5c8aJljmHwsJCTJgwod/WByRJSUnQarW4cuWKU+2yjFZOQopeV56B+/rrr6HRaCi7NluSkZGBw4cPM/6QNVj5+OOPUVtby3gtb1/uvvtuTJs2bcCt7SXfy6QLHJ8zi33Lcmaf4C59m6v0mxqNBh4eHuaPBM6ccVar1WhtbbUqeiMjI+Hn5+eUdb1sBQOZFuD3+jnyTAN6goDNmDFjwC7JGqj0fR8w2TrL398ffn5+tIPTkmWFhYXB09PTqe+ikpISKBSKXhMcADfbFpEfpITYOo8uvM30EgTBeN1cX+FJbh3Et+hta2tDc3Nzr7LJaLZMXav7vqhd9Ws4n3R1daGgoMChG+qECRPg4+MzIDuRwsJCq+t5SaZMmQKJROL0db19g8aR/25vb3fp9rV9+3ZMnz69X1AWKixbtgwKhQKffvopD5YNTMhZ3tWrVzO65tYgZ3tPnDiBH3/8kZM8XQEhZnqF6BPcpW9zlX6TvF4KhQJeXl5OtYMUtNZEr0Qiwfjx491O9PJ5/fLy8hAeHt5rLGiN1NRU5ObmDqiPdgMdLmd6LfOjmtbT0xNeXl5OfxeVlpYiJiamn2dcXFwcOjo6UFtbyzhv8pqoVCrz364Kb6KX/JsuBEGgqqqq38vGGS6XfbdKAgClUokhQ4YwLtuyo1MoFC47MOCTwsJC6HQ6u+tjAMDDwwMzZ84ccO5CHR0duHz5ss31vEDPTM2YMWOcvq5XrVab19GROHM5ARMaGhrwww8/MJrlBXqu9eLFi7Fjxw5xsEKRDz/8EPX19azX8vblzjvvxPTp0wfUbK+zRa9EIoFKpRJM9MrlctZrlweT6CVtcbbo9ff37zfLQ+KMCM7ktlZMvQEtB9Z8BDuzhIpnGtAjepuamnD16lXebBHhFqFFr1DvgNLS0n6uzcCv7s5sXJzJeslkMqcvs6GLy4nehoYGdHd3CyJ6rbl6si2by0ZODnB8fX3dSvTm5eXB19cXEyZMcHguuQ2AM7anchZnz56FyWSyK3qBHhdnIWZ6rbV38pgrsnv3bkgkEqxYsYJxHhkZGfjll19w7tw57gwboHR2duL111/HmjVrrHaabJBIJHj55Zdx6tQp5OTkcJq3UJBrXb29vZ0ielUqFaRSqWCiF+CmbwPAi3gXRe+vQaxsibjx48fjypUrtFw16WJ5H/z8/GAymWh5A5LXTyqV8jqw7uzsRGFhIaUAiTNmzIBUKh1wH+oHMoNV9JaUlFj10ho9ejSkUimrCM4ajca8zauraxPORG93dze6u7tZi15r7paA80SvVCo1L8bmomyuBwZ+fn6CDHDYkJubi5kzZ8LDw8Phuampqbh165bT17bySWFhIby8vDB27Fi75yUlJeHcuXO8Djz6Yk30Dh06FHK53GVF7/bt23H33XebN3hnwoIFCxAaGioGtKLABx98gIaGBvz973/nJf8FCxZgxowZAyaSM/nOl0gkvL+nyXW1AJwembhv38Z2ZwKVSgWZTDZgRW/fe+XsmV5rkZtJEhMTodfrWW9dYo++688Bemv/nCUYCgoKoNfrKYlelUqFiRMnDsglWQMV8qOkj48P7XZEbnfEdG05l+9MOnR3d0OtVlv9aK1QKBAVFcVqplfIdxtdOBO9fTsWy9/oYG+2taGhAZ2dnSwttV92eHh4P3HmSqLXXRoWiclkQl5eHuVtZaZPnw6ZTDagOpHCwkJMnjy53/6IfUlOTkZ3d7dTomiSWBO9UqkUI0aMcEnRW1xcjMLCQsauzSQeHh5IT0/Hrl27YDAYOLJu4KHVavHGG28g4/9j77/D2zqu9HH8BUAQIECwi1WkCoskq1ESKVEWZcklduIWO2tns46V3jZ2EieOS7rtxNlPHDtOnPrNZlNckuwmTuLEm90kzkayKVs2qW41UoUCeycIAiDq/P7gb6BL4F7glrkFEt7n8WMK986cuXPPnTnvzDlndu2KH2TPGnS39/XXX8f//u//qiJDS2g5Tus5J3BlFxQUGHZu83g8MJvNuntI6fWuwuEwTp48KXhyAHAh1ldNF2elNqJW/bd37164XK6U/cUFjevNIjNAN4/kLEp6vV4AbHRY6ZgpBWfPnkUsFhP01FKawTmTuIkhSW9eXh5KS0sX/K7FWb18BIDKlpvN9lInvSdOnMDU1FTaeF6K/Px8NDc3X1TuQp2dnWldmwGgubkZZrNZUxdnvvh5wLjHFj3zzDMoKirCjTfeqLiuXbt2YWRk5KJKosQa/9//9/9hfHxctV1eire85S24/PLLL4rY3kuR9Bp5bks0cqlXmtbQ612dOnUK4XA4JYkrKSlBdXV1lvTigmeaxWIRdX97eztOnz6NkZERVdqTBVso0aNM0eFEUA8OoSSUjY2Nit2bM4WbqEJ68/PzYTabZZPeurq6pNgTLUhvKgLg8/kwNTUluU61DAMtV4mUYO/evbBYLNiyZYvoMhfTyqnH40F3d7co0utwOLB69WrNSO/MzAymp6d5k5vU1dWp+q3JQSwWw3PPPYd//ud/Fjz6SQo2bNiA1atXZ12cBeD3+/GNb3wD733ve1FfX6+qLLrb29nZiT//+c+qylIbWrp6XYykl85trBY/EttJf9Maer2rVJmbuVA7g3PiUTGAdMKgdtygVM80APEF/Ytpof5iRuJ3SBOsiS1Ly8lJTqvXGNDT04P8/HxUVlbyXm9qasKZM2dk59K55EmvyWSSTcqEdlvp0UFq7j653W5eAkB/k0MC1FwND4VCuqxYS0FHRwc2bNiA/Px80WXa29tx9uxZRSnUjYIDBw4AgCjSC8y7OGtFevmylVNocUSYVLz88stwu92KXZspTCYTdu3ahT/84Q+GPldOL/zwhz/ExMSE6ru8FFdffTXa29szPrY3cZyWepajElkXA+ktLCxEJBLB3NycKu2kv2kNvd7Vm2++iZqamvgZmkJQO4MzX0yvlD7QYjHp2LFj8Hg8kkhvTU0Nli5detEs1F/sUDIecHkO/X+mkN6GhgbBRHaNjY0IBoOKj2YFLlHSS//PkvTabDZUVFSoZojHYrGUO720bVIQjUbjQe8Ae8OA/mZk0NT/UnAxrZx2dXUhPz8fK1asEHV/S0sLjhw5wszgSwWh+Hn628DAgKHiXZ9++mksX74cl19+ObM63/3udyMYDOL5559nVufFAJ/Ph2984xt43/veh2XLlmkik+72dnV14cUXX9REphrQcpzmWwjVYuyIxWJMSYiafWaUeVPPnV4x8alr165Fb29vPG6RNTweDxwOB6xWK/Lz8yUdO8Q97ghQr/+oZ9rmzZsllbuYvNMudlyKpFcoczMFvSbHxZkQcmmTXqXuJ319fYJnydXV1eH8+fPyG5kCIyMjCIfDvLIrKiqQm5srWTY36J3+36iGgRoYHBzEuXPnRMfzUlRVVWH58uV45ZVXVGqZdujs7MSGDRtExwe1tLQgEong8OHDKrdsnvRaLBZUVVUlXaurq0M0GsXg4KDq7RADv9+P3/72t9i1a1fasxOlYPHixbjqqquyLs4J+MEPfoCpqSl84Qtf0FTulVdeiSuuuCKjd3v1JL1qyuJidnYWhJCMmNuMMG9ys77SdmhJelNlbqagxFitRIrc92A2myV5A7LaVEkH6pnmdDollWtvb8eBAwfg8/mYtykLtjAS6fV6vYhGo6LLy4XQGb0US5YsgdVqlZXMam5uDuFw+NIkvXQVD5D34MFgEMPDw7w7T4C6cYZ012vJkiVJ12g2W6myEz8Q1hkuuTKMCLpTK3Wnl5a5WHZ6W1tbRd+/bt06WK1W7N+/X8VWzUMoWzmgTQy9FPzxj3+E1+tl5trMxa5du7B7927DuXPrhdnZWTz22GN4//vfj6VLl2oq22Qy4aGHHsKBAwfwpz/9SVPZrHApkF7WJEQr0ksX5bWeN71eb9Iigd/vV/14Oq/Xi97eXlE7vatWrYLZbNaE9ALSdEZL0rt9+3bJ5bZt24ZoNIo33niDeZuyYAulpDcnJwd5eXnx8mLLRqNRzM7OJslWy7OCwufzYWBgICXpzcnJwbJly2SRXqFv06iL1kxJr9wBjaK/vx8Av7sl/V0twzSVq6dc2XzK4PP5ZLuMZhrp7ejoQH19vWDwfCq0t7fj0KFDkg6vNxomJiZw7tw5SaTXZrNh3bp16OzsVLFl8xAKJQAuxLEbhQg+/fTTuPzyy1VJqPSOd7wDDocDzz33HPO6MxE/+MEP4PF4NN/lpbjyyiuxY8eOjN3t1Wqc5nMrU0tWIvjmNiUk7mLf6eXrL0DaGZ9yQAmsGNKbl5eHhoYG1eJ61SC9LMeH/v5+nD9/XtYi/WWXXYaioqKsi3MGQCnpLSwsjHubSdFhbiI3ObLl4vTp0wCEMzdTNDU1yXJv5vs2w+GwJmE2cmAo0iuWeKphCLndbuTn56OoqCilbCkQmujkrOzoaeDIhZx4Xgq6cvr6668zbpV2oAmpxCaxomhpadEkmVUq0ltQUICioiJDkN7h4WH89a9/VWWXFwBcLhduvfVWPP300xlJslhidnYW3/zmN/GBD3yA1+tFKzz88MM4ePAgXnjhBd3aIAeEkKRYV0CdcToQCCASiRiG9ALySJzacxu3bqvViry8PM0T1wn1l9rv6s0334TFYsGqVatE3a9mMivWpDcSiSAQCDBrH/UskxqOBcx7A27bti1LejMALEgvhVIdliJbLujubaqdXnqd1U4v93ejwZCkl2ZqTkRdXR3m5uYwPj4uv6EpZPMdlcSVzYr0ylEGv9+PaDSqu5uWWHi9Xhw6dEjWBAIAK1euRElJSUZPIl1dXSgsLJS8O9nS0oLjx4+rHh8klK2cwihn9f7qV7+CxWLBO9/5TtVk7Nq1CydPntTErdzI+N73vgePx4PPf/7zurZjx44duPLKK/HQQw/JPkZBD8zOziIWi2kyTutpbLCUnRgTxrrPWNgmLNpAZXP/r3Y7jh49isbGRtjtdlH3U9KrxuKf0QlDR0cHGhoaUFFRIat8e3s7XnvtNU1iNLOQh8RFSboIdrGT3qKiIpSWlqa8r7GxEefOnZPsrXNJk146WQHy4lfdbjfKy8vj/vKJUDPOUChzM1f24OCgJIWgz89N7sX9XQoS67JarXA4HIZVrNdffx2xWEz2Tu/FsHLa1dWFlpYWyYmXWlpaEIvFcOjQIXUahvn4koGBgbQ6b4SY3meeeQY33ngjSkpKVJNx9dVXo6qq6pJOaOX1evHNb34TH/rQh1LqhVZ46KGHcPjwYfzhD3/QuymikWgAyDnLUa4sl8u14Hc1oebcZrFYkJ+fz+Q5qJHLtU2MQHq1WrQWm7mZYu3atZiYmMDIyAjztrAgDLTf1Og/JZ5pwPwOsdfrVfXYpyyUgS5KyuUqiTxHiQ5rRQ5p5uZ0dmhTUxMikQh6e3sl1a/X2CYXhtvpTWeE0/tYQ8yuVywWk5TN1uPxwGKxwOFwAGBjGOi9Yi0WHR0dKC0txcqVK2XXQVdOjXRsjhR0dnZKiuelWL16Nex2u6pxvTRbearvzQhn9b755ps4ePCgaq7NFDk5Objjjjvw61//WvXkMkbFd7/7XczOzuJzn/uc3k0BAFxxxRW46qqr8PDDD2fMbq+W43SirJycHDidTs1Ir9lsjp+/btS5zefzLfCQYlm3FOixG0IIEZ25mYISZDWIm1LS63Q640kXWfffzMwMjhw5ooj0tra2Ijc3N6MX6i92KB1rMnWnN51rM3DB/Vmqi7NeZF4umJFerssAMP/gMzMzktxk0pHeRYsWwWazqUZ60xEAQNouM+0TbtA7IE8ZEoPg6d9GVayOjg5s27ZN0fEy27Ztg8/n0+T4HtYYGhrCwMCA5HheYH4Xv7m5WdW43nTx8/Sa3qT3mWeeQUlJCa6//nrVZe3atQtjY2P4y1/+oroso2FmZgZPPPEEPvShD6Vc/NMaDz/8MI4cOYLf//73ejdFFBINAODCXKiWLD3mBLp7avS5zSjzptACuJqxxcPDw5iYmJC007t8+XLk5eVpRnrFPj9fWYBd/+3btw+xWEx2OBYA2O12tLS0ZEmvgSE0HijRQ7HJaRNl5+XlIScnRxPSmy6JFTAfVmq322WR3vz8/PixnFqMbUqg6k5vNBqF3+8XXUc6F2OTyaTK7lMgEMDY2Jgo0itFttBAbbTVcNaIRCLYt2+fogkEmHfztdlsGXl0EY0NlUN6aTkjkN6pqSnVU+oLIRqN4rnnnsO73vUu5Obmqi5v/fr1WLdu3SXp4my0XV6K9vZ2XHPNNRkT26vlOK0nocuUuc0o82biArjdbkdubq6q7ZCSuZnCYrHgsssu04T0SnUrZaVvfKCeaStWrFBUT3t7Ozo6Oi75hIhGhRo7vYA4gufxLDzuyGQyqT4WeTwejI6OitrpNZvNaGhokJzBmS+0lf5uRKhKeunvYkAISetiDKiz+0R3b1MRAJfLheLiYkWk1263w2q1Gs4wYI3Dhw/D5/MpchUC5uPhWltbM3LltKurC4sWLZIdG9nS0oJTp06p9n7dbjdcLtcCfUqE3mf17t69GwMDA3jPe96jmcxdu3bhj3/8I6anpzWTqTc8Hg+eeOIJfOQjHxFMIqgnHn74Ybz55pt4/vnn9W5KWujh3kxjedWUxSeb+4xKYpcvBdKb2F9atOPo0aNwOBxYvny5pHJr165lflZvKBTC3NwcbzykGILIR5jp7yxA43mVeKYB895pAwMDuntIZcEPtUivmPK0LFfH1B4DxGZuppCTwTmxT7QMs5EDw5Deqakp+Hy+tCRBDdIrZtdLjuzEPlGysqOngSMVHR0dsNls2LRpk+K6MnXltLOzU1YSKwoaC3zgwAGWzYojXbZyQN0YejF4+umn0djYiM2bN2sm84477kA4HMZvf/tbzWTqjaeeegp+v99wu7wUl19+Oa699tqMiO1NjHUF1CW9XLcyNWXxyU4kcXKSV9K6aHmKi5H0cp9Pi3YcPXoUq1evhtkszcxbu3Ytjh07xjQLsZBXgthjhxL1jWWys3A4jNdff13xIj0wP1YByMiF+ksBrEmvlMUXlmOmWOhBegHjchOAEekNh8MIBAKKSK9axFOsbJPJhJqaGqayWSqDx+OBy+XSxcCRio6ODrS2tsJmsymua9u2bRgaGsK5c+cYtEwbEELimZvlYsWKFXA6naq5OIvxqqiurobZbNaF9Pp8Pjz//PPYtWuX4tV3KaiursY111xzybg4T09P41vf+hY++tGPorq6Wu/mCOKhhx7CsWPHDL8YQcmNFqv5ehobrOc2bpIiJXXx1U3rY1231HbosdMrJYkVxdq1axEIBHD27FlmbRF6D9xr6cqr1X+HDh2C3+9XHI4FAGVlZVi1alWW9BoUHo8HJpNJ1qJkLBbjzV1E6xUjW+sxoLu7G+Xl5Sk9+rhoamrC+fPnMTc3J1rGJUl6lQ5ogDTSOzw8jFAoJKepvOjr60NlZWVakqY36c0ExSKEKE79zwVdOc2kuN7+/n6Mjo4qIr0WiwUbN25ULYNzusRtwLybSk1NjS6k9/e//z18Ph/uvPNOzWXv2rULr7zySkYttMjFU089hbm5OTz44IN6NyUltm7diuuuuw4PP/ywoc/B1HKcvphIr1rPQY1cvT2ktH5X0WgUx44dkxTPS6FGBmcjk96Ojg7Y7XZs3LhRcV3ABe+0LIwHuijJ9X4Qq0ezs7MghBhSh4UgNnMzRWNjIwghkha8MoWbUBiK9Obm5qK8vDzlfXV1dSCEYGBgQGZr+WWLyViaJb3pce7cOQwPDzMjvSUlJVi9enVGTSKUqCohvbS8mju9YuKN9crg/Mwzz2D79u1YtmyZ5rJvvfVWOJ1OPPvss5rL1hJ0l/djH/sYqqqq9G5OWjz88MM4fvw4fvOb3+jdFEEk7gQAlxbplZOxU23S63K5kozcQCCg6dFkWr+rM2fOYG5uThbpraioQGlp6SVFejdv3szEMw2Y9047duwYpqammNSXBTso0aNUOiw2kRWrMVMsxGZuppBzbFGmcBMK1Uivy+WCyWSSRHpra2vTxp+oEWcohQB4PB7Z/v+AsrgnreMB5ICSU7pDywKZtnLa1dWF6upqxe6ira2tOHfuHCYmJhi1bB5+vx8TExOidL62tlbzRFaDg4N46aWXVD+bVwhOpxP/9E//hGeeeSbjYsml4Nvf/jaCwSDuv/9+vZsiClu2bMHb3vY2Q+/2Znd6jTW3CdVNr2kFrd+VnMzNFCaTiXkyKxakV42YaEII9u7dy2yRHpi3VwgheO2115jVmQUbCH2Hfr8/7SIYnw5LSU6r9RhACEF3d7eknd7Kykrk5+dLyuCcJb20YrMZLpdLEukVa4TT+1lBCukFxGez1WI1fG5ujqmrt1J0dHRg9erVKC4uZlZne3s7jh8/jsnJSWZ1qgml8bwUtA56/BEriMlWTqHHTu8vf/lLWK1W3H777ZrK5WLXrl3o6enBG2+8oVsb1MTU1BSefPJJ/Ou//mtG7PJSPPTQQzh58iT+8z//U++m8EJonBZ7liMLWVqcj6iFFxPN+KtGOwFtz5HU2jA8evQoFi1ahIqKClnl165de0ns9J45cwYjIyNMSe/y5ctRWVmZUQv1lwqUjAd8ydikJKfVegyYmJjA9PS0JNJrMpkkJ7PS0ruJBVQjvfTfYh+8r69PlIuxw+FAWVkZM0OcHpXEmvTyBb0D7A0DwFiHQLOM56WgCSZeffVVpvWqAZrEimZfVoL6+noUFhYyj+sVGz9P7+nr69M0a+4zzzyDm2++GUVFRZrJTMSVV16Jmpqaizah1ZNPPolwOIwHHnhA76ZIwubNm3H99dfjkUceMeRur5bjtF4LoYSQjJnbUtV9Me/0yk1iRbF27Vr09PSIyqwsBh6PJ342MQWNs07XB8FgEMFgUJX+6+jogMlkwtatWxXVw4XJZMo477RLBUrGA6U8R+sxgBJXKe7NgPQMztmdXg6kPLhY4gnM7/ayIr3j4+OYm5sTJbuqqgoWi0WUbL6gd0Adw8AoyjUxMYETJ04wJ71Lly5FdXV1RkwiZ8+exdTUFJOdXrPZrEpcr9hs5cA86Q2HwxgZGWHaBiEcPnwYR44c0fRsXj5YLBa8+93vxq9//WtDeVKwwOTkJL7zne/g4x//uOydID3x0EMP4dSpU/j1r3+td1OSIOSGSa+xlqXHnODz+RCNRjNibjPCvMl6kUAMjh49Ksu1mWLt2rWIxWI4ceIEk/bwvQeLxSLKG5Bvh43+mwXpXbNmDfMF1m3btqGzsxPBYJBpvVkogxFJ78zMjCqbCtRFuaGhQVK5pqYm0e7Nai5IqQVmpDdxFQ8Q/+DhcBiDg4OiSS9Ll0spu14Wi0V0NlsWCwGJ9ek9eacD3Yllkfqfi0xaOaUElQXppfWoQXorKyuTvlc+aH1W7zPPPINFixbhuuuu00ReKuzatQuTk5P4n//5H72bwhRPPvkkIpFIxsTyJqK1tRU33ngjHnnkEeYuw0qh5TgtRKTUkMVFJs1tRpg3fT4fYrEYbztmZ2eZeywEAgGcPn1aEeldvXo1AHYZnPneAyBOZ1jrGxes43kp2tvbMTc3hwMHDjCvOwv5UEp6zWYznE5nUvl0ZWOxGLxeL69sQghmZ2fFPoJo9PT0YPHixXA4HJLKNTY2YnBwED6fL+29an6baoEZ6U1c3QbEJ6MYHBxELBYzPOmVIps+N9+qv9frlbyyo+UOglx0dHSguroaS5cuZV53e3s7Ojs7Fcd4qY2uri4sXboUZWVlTOpraWlBf38/hoeHmdQHSPOq0JL0RiIRPPfcc3jXu94Fq9Wqurx0WLNmDTZs2HBRuTjTXd677rorbaZ8I+Ohhx5Cd3c3fvWrX+ndlAXQMnGSXkmaUs1tckicmnObEeZNIcOQtou12/uJEycQi8UUkV6Xy4WlS5cyS2alBulVmuxsbGwMJ0+eVIX0Njc3w+l0ZsRC/aUEvvFA7JhJy3LPYAfE6bDX6wUhRNOxSOpxRRS0zOnTp9Pem+rbpLvARgMz0it3QAPkE08WmVX7+vpgt9tFkxSppFdoZcfr9YpuIyHEECvW6UDjeRMHBRbYtm0bQqEQ86ROrNHZ2clslxe4sGPMcrdXCuktKipCfn6+JqT373//O4aHh3V3beZi165dePHFFzMmiVo6PPHEE4hGo7jvvvv0booibNq0CTfffLOhdnu1HKdTuZWxlpWIVHMbIJ3Eab3Ta7PZYLPZdCe9ar0rujtLd2vlgmUyK6GNEaU7vUqSnanlmQbMn3Hf1taWJb0Gg9KdXjW8FcTIlgOpmZspaBkxLs56PJdSGIr0iklkBcwTz9nZWSYdSo9KEkvUWJBe7nUxmJubQzgcNrRizc3NoaurS5VVUwBYt24d8vPzDT2JxGIx7N+/nynpXbJkCcrKynQjvSaTSbMMzs888wxWrlyJTZs2qS5LLP7lX/4F0WgU//Vf/6V3UxRjfHwcTz31FO6++24sWrRI7+Yoxle+8hWcPn0azz33nN5NATB/FJhQrCvAdpzW09hgKVuIvLPasVZqm7CAHqR32bJlyM/PV1QPS9LL54oP6EsYOjo6sHjxYtFzoVRs27YNe/fuvaiPvcskCC1K0tDMi4n0EkIkn9FLUVpaiuLiYlHJrC5Z0ptqQBOz6ut2u1FSUiJ6kGbpcimFAFDZ/f39aV24UiVfAKQpg1Bdubm5sNvthlCsrq4uhEIhVVZNgfmV061btxqa9HZ3d2N2dpYp6TWZTGhpaWGWwZkQgr6+Psk6rzbp9Xq9+P3vf49du3ap4ikgF5WVlbj22mvxzDPP6N0UxfjWt74FQkjG7/JSbNy4EW9/+9vx1a9+1RC7vUIGAD3LkaUbq57GhhZzm9VqhcPhUPQcQgmkqDyjkF7W7s1Kk1hRrF27FoODg0y8XFIRhnTPr1b/0Xheteab9vZ2TExM4NSpU6rUn4U0BAIB3kVJQLweyh1LWI6ZYjA8PAyfzydrp1fKsUVaj20sYIidXjlGOKAf6Y1EImljLGnQeyKRl6PkQopFfzMC6e3o6EB+fj7WrVunmoz29nbs3btX0+NzpIDuxrLeqaTJrFisGI+NjSEYDErS+draWtFnU8vF7373O/j9ftx5552qypGDXbt24dVXX8WZM2f0bopsjI+P47vf/S4+8YlPMIs3NwIeeughnDlzBs8++6zeTREcp6Wc5ahUltVqRV5enuo7vSaTKX7kDIXR5jYhDykWdUuBHju9rEgvACZxvUp3yfLy8pLyPCjpv0AgoKpnGgC0tbXBbDYbeqH+UoLSsSaTdnqpa7Ic0kvLSXFv1jtvghRoQnrTGevUxVgsKisrYbVadSO9tFwqpAp6p9fFIlNI79atW5GTk6OajG3btmFqaorZMQqs0dnZicbGRubHH7S0tGB0dBT9/f2K65IaSgBos9P7zDPPYOfOnaq5minB29/+drhcroze7X388ccBAJ/97Gd1bglbNDc349Zbb8VXv/pVhMNhXdui5Tit55zg8XjgcrlgNi80H4w2txll3mS5AJ4OExMTGBoaYkJ6m5qaYLVambg4KyUMQmXpdano7OxEOBxWzTMNmE8G1tzcnCW9BoGapNfr9ab0/hSS7XQ6YbFYmI9FPT09MJvNWL58uazyTU1Nond6HQ4H0wUptaE66Q2Hw2kTDUglnmazGYsXL1ZsiAeDQQwNDalGelMN1NPT06JlGmXyFkIsFsOrr76q6gQCAFu2bIHFYsHevXtVlSMXXV1dTF2bKVpbWwGAiYuz1KRx9N6xsTEEAgHF8vnQ39+P//u//8OuXbtUqV8pHA4HbrvtNjz77LMZGZ81NjaG733ve/jkJz+J0tJSvZvDHF/5yldw9uxZ3RclLiXSy4qE6EV6CwoKMDU1Jbtuqe3gWwDPy8tDTk4O03dFd2VZkF6r1YqVK1delKR37969cLlcTPopFWhcbxb6Q03SCyBlclqPxwOLxZJ03JHJZFKchZwPPT09WLJkCWw2m6zyjY2NGBsbS8tT1Pg21QYT0js5OZn0MoELW96pOo4QgnPnzqGmpkaSzLKyMsWZfOmOYXV1tegyhYWFcDgcaRMLjY6O8sYoj42NAZiPrxOL8fHxuOxE5OXlYWhoSHRdauDIkSOYmppS1VUIQNx9+sUXX1RVjhx4vV7s379flSRM9GiZD3/4w4rr2r9/P3JzcyW5uNJv89ixY4rl8+HnP/85bDYbbrvtNlXqZ4Fdu3bhzJkz2L17t95NkYxvfvObMJvN+MxnPqN3U1TB+vXr8Y53vANf+9rXdN3tPXfuHADwjvtOpxOjo6PMZFFXez5Z+fn5TGUlQmhus9lssFgsoo66oEg1t9ntdkVzG41F5av7hRdewN/+9jdNFrF6enp4jU/qIs7yXf39739HTk6ObLfGRKxevRr79+9X1E+hUAg+n4/XRszPz09rWA8ODiIvLy/pd2pfUptKCv7nf/4nvoiuJtrb23H69GkmXlpZKMPExAQA/vHA6XSm1SMhnpObmwtgXk+FQMdMvvjx3NxcUbuqUvDaa68p8pqj40e6Ba+RkRHeucBqtcJut6s6D8kGUYje3l4CgKxYsSLp2j333EMAkG9/+9uC5f/2t78RAOStb32rJLkAiNLmf/KTnyQAyHe+8x3RZcLhMAFA8vLyZLUvFAqRJUuWkCeeeEK0zLa2NgKAHDp0SLQcLbFx40YCgBw7dkx1WQ6HgwAgfr9fdVlS8NhjjxEA5N5771Wl/lWrVkn+RviQn59PAJBAICC6zI9//GMCgHz4wx9WLD8RPp+PACDFxcXM62aJSCRCABCLxaJ3UyTh97//PQFA7rrrLr2boio6OzsJANLS0qJbG7Zs2UIAkCNHjiRdYz1OX3bZZQQAOXfunOqyxNY/PDxMAJC6ujrRdV1++eUEANm/f79oOWLx/ve/nwAgP//5z5OuffjDHyatra2y65YCu91OAJBIJLLgd7/fTwCQgoICZrKsVisBQKLRKJP6GhsbCQDS29sru44XX3yRACA33XRT0rWrrrqKACAdHR2C5YX04Pz58wQAaWpqktSegYEBAoDU1tZKKicHf/3rXwkAcsMNN6guK4vUuP766wkA8re//S3pWrqxZnBwkAAgS5YsSbp2//33EwDkscceEyxfVFREAJDZ2dkFv8diMQKA2Gw28Q+SBtFoVLGtsmfPHgKAXHXVVSnvE+q3yclJAoBUVFTIboNaUByAWVtbi+bmZjzwwANJ1z72sY/hxRdfxK233ipYvq2tDWvWrMHDDz8sSe5PfvITHD9+XHJ7ufj0pz+N7u5uSTtoOTk5uO+++1BfX5/yvo9//OO8K5hWqxW9vb2S2/n1r3+d99y9z3/+87JWOlnitttuQzQalZUeXSp++tOf4sc//jHvyq+euP322/H888/zfgcsoFTXKZ544gkcPXoUdrtddJldu3bht7/9Le69914mbeDCZrOhuroad999N/O6WcJiseCWW24xZDbCVKA7029/+9v1bYjKaG5uhtPphN/v160NTz31FJ566imsWbMm6drdd98d32lggR/84Af4xS9+gaVLlyZde//7369qBvS77747vkPLRUVFBT760Y/illtuEV3XZz7zGczOzvImQPziF7+oaKf3Ix/5CPbu3Yubbrop6dqPf/xj2fVKxb//+7/j2LFjSbuKeXl5eMtb3oIdO3Ywk/Xwww9jYmIiKd5aLr7xjW/gi1/8oiRvuERceeWV2LhxIx566KGka5///OcxODiIDRs2CJb/9Kc/zZudvba2Fps3b8aHPvQhSe2prKzE5Zdfjq985SuSyslBe3s7mpqaLvrxNxPwiU98Ar29vbweiZ///Odx5MgRwbKVlZXYtGkTPvGJTyRde/DBB3H8+HF89KMfFSx/33334R//+Aeve/PnPvc5rFy5UsKTpIbZbMb73vc+vPWtb5Vdx9atW7Fp0yZ8/OMfT3nfPffcg4GBgaTfi4qKsGPHDt6xV2+YCMnAILUsssgiiyzSIhqNYnJy8qI4lzcd6FRmpCOvssgiiyyyyCILYyBLerPIIossssgiiyyyyCKLLLK4aKHe+TKXKNxuN6/rl1yUlZUZ8hiXLNQBC/3J6kwWWWRxqSM7lmaRRRZZZMGFKNKbbvIYGhoSzMBHy6XKFltUVISqqirBeoWup4OS8mLKJj73+Pg4HnjgAQSDQcltFYLdbscPfvCDeIY4IaR7RrmTt16GA6vFAxZGi1Z94Ha7sWrVKsVxiXa7Hb/97W9V0fl0oM+Z6QankfQvEZnetyyhtC+k9IMYWanmQiD1OM1ti5ay+JBKvlS5fPfTe9LVxVdfYlsAJF0fGhrC7bffrviYNYfDgRMnTsjuK9oWJc+oxZiqpG45thIX4+Pj8Hq9cLlcvHaiFBsyla6JRabZSllcgFpcRYwOhkIhQTs9nY6L0VG1xgE1+Z1avEQJ0ro3izHEzWYzYrGY5GvpoKSsFrKF7nn22WexatUq0bLSTdBK+wEQN3knghUJkyqblVw5stVqi5h2HDhwAJs2bZKsPwAbI4+Vnr300ku45pprNNcbVjCS/iVCr2/SiGDRF2L7QawsJd8QbQsAzWQJEaBU8qXKVcs+EFNezlhKceLECdx5553Yv38/Nm7cyHuPUvtIDNQeU5V+R0psJTHlpfTfpWYrZXEBanIVtXVcDNQYB4D0c42az6WHvqfd6R0fH4ff7xecPOjEwHc91bV04JYlhKC/vx9lZWUwmUywWq0IBoPo7e2FzWZDW1sbBgcHYbPZMDQ0hKVLl+LMmTO4//77ce+998Jms2Hp0qUoKCjAyZMnUV1djZKSEtjtdgwMDIAQEj+MeWZmBuFwGHfddVdK2fv27cNPf/rTBc9G2zw5OYk333wTy5YtQywWw5EjR9DS0gK73Q6z2YxTp06htrYWZrMZhw4dwvT0NAKBAPM+TKxjfHxcknKle/dqyU6U+z//8z8ghKCiogImkwm5ubnweDwwmUxYsmQJzGYzgsEgpqenEQqFEIlEsGHDBvT29sp6bqG29Pb2im7H1NQUbDabrHasWrUKGzduxIsvvghCCOrq6tDb24uioiJUV1djdHQUwWAQS5cuRTgcRk9PD6qrq3l16Pjx4zh79iwKCgpQU1ODYDCIkpISnD17FjabDaFQCJs3b0Z3d3eSngmVPX36NPLy8uJlaVZS+q7Pnj2bpDdCdXV3d2Nubg65ubnYuXNnvN1ydZYFuO98fHwco6OjacePJUuWYHh4GHa7HS6XC2NjY7BarcyfQa9v0oigfSF2jF++fDl6e3thMplQVVWFrq4uPPTQQ6L6QYys/v5+3HPPPYrmO7pynm7M6e/vx2OPPaZYFt9zp9IxWvaRRx6B1WpNOycPDg7iC1/4gipzmxjbY9WqVRgcHMSyZcsAALFYDDMzM4jFYojFYigvL8fs7Cxyc3NRU1OD1157DbFYTHS23XR6MT09jfvvv1/xM3LHVCnzkJj5UMmYIuYdKrERpegIvVeKzdfX14dQKITq6mqMj48jJycno2ylLC5ALa7CvSZkD6T6zpXo5dKlSzEwMBC3Jeg4ILauJUuWYHBwENFolFfHAejC77jltdZ30TG9fr8fb775JlpaWhAOh9Hb24tIJBJPjc93nR7WLFS2vr4eHo8HAFBeXo7y8nL09fVhfHwc5eXl8bI2mw0333zzgrLr1q2LH5FTXl6OTZs2oa+vD4sWLUJjY2N8O76goAC33XZbvOyaNWsWyN2+fXtcbiwWQ1tbG/bt25dWNjB/fM6ZM2ewYcMGhMNhjIyMAAC2bdsGr9eLc+fOoaWlBVdccUW8bEFBASoqKhAKhVBeXo7bbrsNf/3rX9P2IVeOmP5bu3YtRkZGEAgEFB8lJEW2z+dDWVkZZmZm4Pf7UVNTI1su7Y9bb71V9HPX1tZienoac3Nzsl10hdpSW1ubpMPr1q1L2ZZwOCzZ3X3//v1xPQgGgzh06BDy8/ORl5eHkydPoqCgAFVVVSgsLERfXx/sdnv8GKxEHXK5XFi9enVSn23ZsiXezrq6Opw5cwbAwnctpmx5eTk8Hg8IIXHDkttntC1r166Fy+VKen/XXXddkt6eOnUKoVBI9rtiBb/fj+LiYrzlLW8RNX64XC4sXrw4vkjG6tgQPkj5JicmJrB48WIMDw/j5MmTvEfqZDKkjPEOhwMNDQ04efIktm7dKlnWihUrYLPZ4t+X3W6P9/3k5CQA6WN1Y2MjcnKSp+JUY05nZydTWXzgq5vqNCFE1JysxtzW2NgIj8cTP8YmVd379++HzWbD8uXL49fGxsbQ0tICj8eDEydOYNWqVfH68/Ly0NjYiD179kg6WlBIL/Lz8wXbmOo5S0tLcfbsWUxNTSW9E66ui7EDTp48iWg0ynu8FRdK7TShsnQXKZU+CekALSumbfQYvlTfqMfjwYoVK+LPUVJSEtep/v5+xUcDSrWVGhoaMDw8jOnpaU2OfLwUkI6rpNLDdDxGyB5INR7TuqVykcbGRvT29mLVqlVJ9pAUHefaoy6XC3Nzc0l9lu77k9pnLOch1hAtddOmTXEXnz179qCoqAgjIyM4deqU4PVoNJqy7MGDB+H1erF+/XpYrVb4/X4MDw/jrW99Kw4cOJBUFgDWr1+Pubk5hEIhFBQULGhjaWlp/O/m5mYAwI033ojLLrtMUll6nmIq2XRLn9a/Z88euFyu+L2JZ++lkr1ixYoF8vbs2QNg3rCgCsp9DrqCe/z4cVgsFoyMjMT7sLS0FJOTk6ioqEBFRQUAxPtSLqTI7uvrw7Zt2+JllciW++5ZyGbZFqkELlHWhg0b4rI2b97MK0vp90InXDnfC0Vif8ttS0VFRdxY1BNy279kyRIAbPUvEVK+SZ/Ph/HxcezYsQM7duxQtV16QI7OVlRUyOoHqXNCqrnO5/PhmmuuAQDeM3y1lMUHvrrpfC+2z1nPbRMTE/Hvi5LeVHUr+Ya583k6CMl54403kq6LeVfNzc1xI5ULuboOpB+PpLRxcnIS11xzjeC8w32PVN/E6hNfWTFto0a0kneel5eXso/SQYou9/b2Ytu2bczstCzmwTce+P1+dHV1AUith2J5DCB+PJY6ZoqxJeTq+IoVK3j1TGge6e7ultVnLOch1pBNtQ8fPowVK1YkdfCePXvi1/iCutMdxF5bW5vyOlXkqakpBIPB+EBSU1MDm82G7u5uXH311aqXpR8QF1RBpMoeHh5Oup/2YeJh1oDyPlQCPWWne3/79+/HLbfcopp8se0YHBzEli1bNJF17NixlIeQKymv5HuRWhfLPlML6Z7h1VdfxT//8z9r3i49v0kjId37cbvdmJ2dxXXXXaeKPL45gY7jNpsN27dvh81mw6FDh1BQUBA3etWWRT05Lr/8ctlnGAvN93xt03NuS1U3y/EsFbhyTp48mbKd3HfV3d2NnJyc+I6lFDly5oZUUPoeuOUPHDiAz33ucwuup9KndGW55bn998ILLyTdl66PDh06hJtuuinls8hFdlzWH1RPTCYTrrrqKjz11FO81/n0kF4XWoBP953LHTNZ21ZSdDxRZw8cOIDHH39c9HNxr3O/zcHBQczNzWF2dlZUO9SEaNJLg54BwOVyob29HcCFVUR6PdU1YN7dqLu7G0uWLEEoFEJ9fT2sVitOnDiBsrIyTExMYMeOHXElSle2oaEBvb296O7uxsTEBN7ylrfgwIED8XL0/0JlT5w4gaNHj8LhcGBmZgbFxcVJZYF5QsstT11nuc9Nd5e55dK1e2hoKG7AKO3DI0eOoLS0FH6/H4FAANdeey1ve6QiVR9arVYMDw9jZmYGdrsds7OzyM/Pj+9IKpEt5t2fOXMGR48ehclkgsViwbe//W1cccUVimWnakuiLtTX18Pn82Hv3r0AgGg0io6ODlx55ZWy2iFG1osvvoji4mIMDQ2hp6cHJSUlvLJSla+srMTs7Kyk7+XMmTM4ePBg3DA7evSoYH9LeY5QKASr1cpbVi/IHXv4yqvVNqFv0u12w2KxYHBwEBUVFQs8BIzQtyxBnyeVjjmdTkxPT8PlcsXfkZx+kDon0HEcAGZmZgAgHvKxdu3alG1hJSsajWLNmjU4ePCgpOcWMx8JfRevvfYaSktL8eabbyqqizu/EELipP2KK65IGrPSzZtC+vHSSy/Fv5Xi4mLe/hfbV4lyRkdHk64LvSuubCG9EDumVlZW4tSpU+jp6YkvrKR7HjE2xtmzZwEAk5OTuPHGG3ltpcTytIwc+yaxrJj+E9NHL7zwAgoKCjA9PY3u7m7Zc3UixNppVVVVmJqagslkwvbt25nIzmIeQmOi1PGCXufT8cR3Szf4lI5F9NSNaDSa0pYQa6eZzWbk5+fjRz/6UUqbXKi+VH2aqs8o6LdptVphtVoVz79MQNLg/PnzxOFwEACC/5nNZlnX0v2npKwWsvnukStTrT6k/zkcDnL+/Pl0r1vyu1dDNiu5cp9brz5QKkvP74X7nK+++qouesMKRtI/tdqmV9+yBIu+ENsPYmUp+YZoW7SUJedZpcpVc27Te95Uah+JbYOaY6oW846S9ySl/y41WymLC1CTq6it42J1g/U4oCe/00vf0x5ZBKh3jtP+/fvx5JNPYseOHfjWt74lWK/QWU9XXHEFfD4fXn/9dd6g6FTlf/SjH+Hf//3f8cADD+Cd73ynpLJjY2N461vfivLy8qR20/O4KPjO5Xr00Udx4sQJ/PSnP8X69esXyEtEUVERAAj27+OPP47e3l7U1tbi5z//Oe89gDpnz1133XUIh8O44YYbcO+99zKVnUruxMREfBf7q1/9Kq6//nqmsqW05Ve/+hWefPJJRKNRvPbaa4LntIltRypZx44dw3ve8x6sX78eP/3pT5Oup/te3v72t6O/vx///d//jcrKSknlf/e73+HRRx/FBz7wAdx1112C7RdzltxvfvMbfOMb3wAhBB0dHYJxVEY9p/ezn/0svF4vxsfHsXnzZjzwwAOC9Wh9Tu91112HRYsWIRwO4z//8z81bZce4OuLaDSKq666CnfeeScOHz4MAPje977HW57VOb29vb34p3/6JyxZsgSPPvqoYB1Kz+kNBoO44oorcM899+AnP/kJtm/fLuhSr8c5vR/4wAdgNpvxnve8Bx/96EcF7xdzTu9f/vIXPPPMM7jzzjvx6U9/WrAtQPI5vena+cYbb+Cpp57C1Vdfjccee4z3HkD8+ep8fTU9PY2rr74aH/vYx/CjH/0In/zkJ5NyMlAoPaf3r3/9Kz73uc/h1ltvxRe/+EXJz5Oq7kOHDuGDH/wgWltb8aMf/Sjperp55/rrr8fIyAi+//3vo7CwMOl6qjNM//rXv+Lpp5/GW9/6Vtx5552CzyXmnN7nnnsOTz31FCKRCPbt27fAs4gLNWylL3/5y9i3bx+Ki4sviXFZL+h1Tu+dd96JnJwcQRs8lY7/+7//O/bs2YN/+7d/i9u1fBAzDnz961/H3//+d+Tn5/O6+yfWBQj3mcfjwVVXXYXi4mJ897vfFXwuWh8fjHhOryj35rq6OlUaRgPES0tLBc/BS4VwOAwAqK+v502skwpOpxMWiwUFBQWSZVPXa5PJhHe/+92SygIXDLDKykpZz83FN77xDSxbtgwej0dxXXwQevehUAgTExNYuXIlgsEgc9mpdI5myissLITZbFblucW25Ze//CUqKysxMDCARYsWob6+XjVZNDtsTk6OrGemyRbq6uokZ/D9+9//DrPZDIfDIUp2quf4zW9+g4qKCgwPD6O0tBQrV66U1BYtkKr9p0+fxr/8y79gcHAQPT09quuf2LbNzs5ifHwc1157LX7/+9+jublZ1QzSRgBfXxw8eBCzs7O44447sHTpUnzta1/D2rVrBY1cJbIoaFIlq9Uqa04QK6ujowORSAR33HEHent7MTw8zESeWPmpMDk5iVAohIaGBkQiEcXfxdmzZ2GxWJjMk4mgGfXLysoU1y3UV3/+858BzC+S/fd//zdyc3MVvatU74SGSIkdn6XUTeOyrVaronnnmmuukZyh+Ny5c7BYLLjssssU6/mzzz6LyspK9Pf3o7y8POm0AaVI1Yc+nw+LFy9Gb2+v5vPFpQS1uEoq+Hw+APN85o477pCcN4GOE2J5UKpnDAQCWLx4Mbq7u0XP/0L1nTt3Lv63GnOMXtDVIvJ4PDCbzQt2RsUiHA7Hfc5pWmypsnNycmSXBSD7gGj6kciRnQi32426urp4mnOtQM8BoytPWoLK00M2X1u4K2ZqguoL1R+pUKJ3Ho8HVquVqc7SvzMJIyMj6OvrQ0tLC1pbW3Ho0KH44pve6OnpATCfTTEQCGBgYEDnFumD3bt3w263Y/PmzdixY0c8bl1NKJ0TxKKjowMulwtr165Fe3s73njjDd4jKPQA63FZyRwtpm6LxRKPOVMD+/btQ1lZGZYvX77gKEQ1QPtKjefR095hqQN6zjtUtsfjUUWfs9AP9H0SQmQlapqZmWGu46FQKJ5PQC60mtO0hu6klx6sLqcs399SyttsNkWyZ2dnZRFNr9cLk8mkWMnpANrQ0IBwOKxYyaWAThqNjY26TCB5eXlYvny57qTJ7XajoaEh/rea4OqdVHAHZLk6L/dbTQTtM5PJhL6+PsX1aQm6o9LS0hI/R5km69EblPTSxCj035ca9uzZg7a2NthsNmzatAlOpzOe4VIt0O9CzgKuFOzduxdtbW3IyclBe3s7QqEQ9u/fr6pMsaDfMqs5QckcLaZuVuOZEPbt24e2tjaYTCa0tbWhq6tLtQUyNZ/H4/HAZDLJ0u1oNBo3mrW20xLR19en2VzNRSwWQ19fHxobG+PtyOLiAQsuwtK2onqmVMdpe4LBoORjN40M3Umv3W7XjfQqla1kZYfFQE4HT+qqquVATmWtXr0aIyMjcXcxLdDX14e6ujrU1tbqTnr7+vqwbNkyLFq0SPXJTMngGAgE4uEEWn8viXC73ViyZAmqqqp0f39S0dXVhdLSUixdujTuPsR3fIwe6OnpQWlpKZqbm2GxWC5J0huLxfDyyy9j586dAOZdMrdt24bdu3erKlfpQqgYxGIx7N27N54dc/369XA6nfGs8XrD7XbDarVi5cqVcLvdEJEuJCVYjjla1g3Mv6vXX38dbW1tAIC2tjbMzc3hyJEjqshTu6/k2itcomyEeWf58uUoKyvTdN4ZGRlBOByOn9GaJb0XF/TkIon1zMzMxPmAUj1T+lxGhe6k1+Fw6EZ68/LyNJdNCFH03FzQgXvTpk0L/q0F+vr6UFZWFl9V6u/v10w2deGgbt16IRQKYWhoKN4WLXZ68/Ly4PV64wRWSlkAMJvNmn8vXESjUQwMDGjWZ6zR1dWFlpYWmEwmOBwOrF692jCkt7u7G01NTbBarVi2bFnKc8MvVhw9ehRTU1MLzhvcuXNnPBZWLVBiQAhRbbf3xIkTmJqaipPenJwctLW1oaOjQxV5UuF2u1FbW4slS5Zgbm4uZUIZMWA1T2pdNzCf92NmZgZbt24FAGzcuBFWq1U1F2et+krqQoaedhoXwWAQw8PD8cVyLe0GKmv9+vWwWCwZN+dlkRosdJzFt0v17LLLLoPD4WC205v4d6ZDd9LrdDp1I735+fmKlFSObL/fj2g0Kls2F319fbBYLFi5ciWcTqfmO721tbXxA9b1kF1XV4eZmRndPkhuXLNWpJcelC7VsKZ9pOR7Y6GzQ0NDiEajGUl6CSHo7OxES0tL/LfW1lbDkN6enp74IlRjY+MludO7e/du2Gy2+A4bAOzYsQNerzd+Tq0a4H6bao1HHR0dsFgs2LJlS/y39vZ27N27V9N8DkKgi5F0TmCx0yB3vBJTN4vxTAj79u2DyWRCa2srAMBut2PDhg2qkl61nofWHQ6HJceP0/Yo2WBg8Vx0UV6PeYfKWr58OWpqajJqzssiPfTkIlxwcyqw8IL0eDzxU3GypJcRPB4PCgoKEAgEJMe60JegJC7X5XIpKstth5SyAGTL5sLtdqOmpgZWq1WXgZxr4OghW+9kSNxBRivSq5feKfleuNC6z1hiYGAAIyMjC0hvS0sLjhw5YohkQlnSOx/Pu2XLFtjt9vhvLS0tcDgcqsb1Kvk2xWLv3r3YsGEDnE5n/Lf29nZMTk7GTxTQE6zHZVZjTqq65XjNiMG+ffuwZs2auE4AUDWZFX2emZkZxW7lQnXTv6WWBS7tecftdsPpdKK4uDjj5rws0kOp5+fMzAwzHbdYLKiqqmKiZ5Sf0b8vFhiC9NK/pZYFgIKCAtmDaWFhoaKy3HZIKQvIbzcX3GyEepHevLw8LFq0SDPZc3NzGBkZMRTppbvOLOLYUoGF3inVeTkublwkGh9aZx1XArqjS3dvgHlCFYlEVIvVE4upqSmMj4/HjwRpamrCmTNnVHXpNRpisRj27NkTj+elyM3NxeWXX65qXK+Sb1MsOjo64q7NFFu2bIHZbDZEXC+dExYtWgSbzcbE6JI7Xompm9oearij0yRWXLS1teH06dOK3b75QJ8nGo3Kzu6fqm4jzDtKQHVx8eLFqKurw/nz51WdqxNl19XVwWQyZUnvRQi6MCMndIzmgGDFBxYvXgyLxcKM9Ko9p+kBXUnvzMwMiouLAcgbTO12OwoLCyWn6Q8GgwgGgyguLpY9EBcVFcX/lloWAIqLixUfL6AX6SWE4Pz587rI5ropVVZWIicnR1fSW1ZWBofDgbq6Ovh8PkxNTakmz+PxxL8XqbrD1Tu5Ol9cXLwgG6ccuN1uFBYWoqCgAHV1dQgGgxgbG5Ndn5bo6upCZWUlqqur47+tW7cOVqtVdxdnuqvL3ekNh8OXlIF17NgxTE5OLojnpdi5cydeeeUVVXb1gIXfphoGwsDAAM6dO5dEel0uF5qbm3WP641EIhgcHGRq3NM+9fl8zBdvlIyl6eD1evHmm2/ykl4AeP3115nKA5TZUumgRLeV2jtUthxvQC7cbjfKy8uRl5eHuro6zM7OambI67k5kYX6oHxADnGl34Rcu4wL1nqmhOcYGbrv9JaUlMT/llq2sLBQ1iogvb+0tFR2UqDi4mJYLBbZsktKSpgquZbJGTweD2ZnZ3UZyOkz1tXVwWKxYPHixbqSXm4f0N/UgsfjQWlpafxvqWUBeXpHk6/J/Va5SNRZIHOyWdJ4Xu7h8zabDevWrUNnZ6eOLbtAeumRHJT8Xkouzrt370Zubm4S2QDm43pnZmZw6NAhVWQr+TbFgO7kbtu2Lelae3u77qR3cHAQsVgs/k0rnRPC4TD8fn98zGFNTNV8X52dnSCEJOnh0qVLUV5eroqLs5rPo9ROy8nJkWXURyIR+Hw+Js/V19e3QDcB7TzEEu2E/v5+1RbfstAerLgIa9I7OjqqKOyK8hw1s9zrAd1J76JFi+J/Sy2rVNHKysoASHdvmpmZkb2yQ+9ftGiRIkWKRqPo7+9fMJCPjo4iEAjIrlMsuC6q9P9aEReumxKVrRfppUcn0XbQ39SCx+OJ66wcvXO5XCgqKpJcliZfk/utcqH1QgErEELQ1dW1wLWZoqWlRfed3u7ublRVVcVj7+rq6pCbm3tJZXDes2cPNm/eHE8yyEVrayvy8vJUi+ulxEDOQqgY7N27F/X19aisrEy61t7ejrNnz2JoaIi5XLHgmxOUfNeU5Mod79JByViaDvv27UNBQQFWrly54Hd6Xi9r0ksXJY3YV0rsNJY6oOe8QxNvUtmRSAQjIyOayM5CfbDiIixCxxJ1XMmpKtznYr3oqCd0I73RaBSzs7MoLy8HoA/p1VO2UtI7PDyMSCTCVMnFQsjA0SJGhuumBEDXs3q5k1l5eTmsVqvqO70lJSXIycnJqO+FC26flZaWIi8vLyNIb29vLyYnJxcksaJoaWnB8ePHmcfSSQE3iRUAWCwW1NfXXzI7vYQQ3nheCpvNhq1bt6oW10tdwdSKQeWL56Wgu796xvVy8xsAykkvyzEnETS8SY26gXnSS2OtE9HW1obXX3+d6U5fIBBAJBJR7Xk8Hk9G2mlccAlBZWWl6nM1RSAQwNjYWJJ3UybMeVmIAysdj0Qisjet6CYYy4UdJc9lZOhGeunKQUVFBQB5g2lBQYGupFfuTi93x00uUeQjntzf1YTb7YbVao3vOmgRz8qVTZ+VytZjAkmMazabzaoTcCWxI3p+L1xw318mJfagO7l8pLe1tRWxWEw111kxSCS9wKWVwfn48eMYHx/njeelUDOul35fLBKSJMLr9eLQoUOCpLe6uhrLli3T1cXZ7XajuLh4gafB0NAQgsGgrPpoH8q1D/SqmxDCm8SKoq2tDV6vl2m2bdp+Oh+zfh4aLyzn+CilthKgfN4hhCyYd8xms2ZhUdxwLO7/M2HOy0IcWNhWSsci7lGQwAVPSKWkV+5zGRm6kV7utr4cn3EWqys0IY3WsmlZJUmBEkkvCyUXi76+PixevDi+kq014U4kvQMDA5pnqfV4FsY107ao1Qd0d4KF3sn9Xqqqqhb8Wyq8Xi+mpqYMsWghFZ2dnairq4sbYFxcdtllsNvtusX1EkLQ3d0dz9xM0djYeMm4N+/evRtWqxVbt24VvGfHjh2Ynp5WJdO2mqvi+/btQywWEyS9gP5xvXzjMjCfgEsOlM7RYuouLy9n7o5+7tw5jI2NCZLelpYWmM1mpi7OXMPZZDIxfR6aXVbPeUepDkxNTcHn8y3QT61yoCSS3sLCQrhcroyY87IQB6U6bjKZFJPeRD2z2+2oqKjI7vTyQHfSq+dgWlNTs+DfWsmmZeXIpujr64PL5YrXY7PZUFlZqRnxpG46gLYuO4my6+rqEI1GNY9nS1x0oH+r1QfUM4KF3kk9y5GV8ZE4MNO/M8EA6Orq4t3lBQCr1Yrm5mbd4nrHxsYwMzOTtNPb1NSE3t5ehEIhXdqlJfbs2YPW1tYFZ9gmYvPmzbDb7czjeufm5hAKhVQzEPbu3YvS0lKsWLFC8J729nYcOnQIs7OzTGWLRSLpVZqkTukcLaZuNdzRKZndsmUL73WXy4U1a9aoQnqLi4uZexpksp1GofVczSebbkpkkndTFuKgVMcLCgoUZ15PDC+hf2dJbzIylvTOzMwoKpuXlxdPkCAlSDvRwJEa4M1tN6BMyenxEBRaDuTcCaSiokKTGJlENyVAmwRSfNB6ImX1vRQUFCAWi0kyjqmOl5SUwOVyyU5qkKmkNxaLYf/+/YKkF9A3mRXdzeVzb47FYjh37pwezdIMNJ43lWszML/63dbWxjyuN3FBinXSj46ODmzbtm3BWJ+I9vZ2RKNRVY7DEQMh0iv32+aGP+Xm5jLtU6ULiKmwb98+NDY2xjMO84F1Mivu3KAW6aVujnKOyqNlqe0kFlRWeXk5bDabbB3Qm/RWVlbCZrNpLjsLbcC1rZRuginRcfqdUSjRM5pzKUt6GYLlCuLs7KykOC1a1uFwSHZvYtlubn1SkWhkANq57CTK1iKeFZh3U/L7/bykV+tJJDGumbZlcHBQ0XmCQtBT7+i91LNAic6azeYF59zW1tZiZGREduyfFujp6cHMzExK0tva2opTp07pkuWwp6cHJpMJ9fX1C36nJPhid3E+efIkRkdHBZNYcbFz5068/PLLiMVizOQr/TZTIRwOY9++fSldmwFg5cqVKCkp0c3FmXskDAA4HA6UlZXJHpc9Hg9yc3Nht9uZ96ma7ytVPC9FW1sbjh07xmysUPN59J53rFarYh3o6+uD1WpdEJqiVVhUomcalZ0lvRcHaOZ0I/IBJXpGT7TJkl6GYD2YSplAaFmTySRZtpFIrx6DaSQSwcDAAO8Hpjbh5luxpatbepBeblwzbVcsFsPg4CBzeXobHy6XCxaLRTHpra6uhtVqjf+mZdZxuUiVxIqCXtu/f78mbeKip6cHtbW18YzmFNXV1XA4HBd9Mqvdu3fDYrHg8ssvT3vvjh07MDU1haNHjzKTrybpOHz4MHw+X1rSazabcfnll+tCemdmZjA9Pc3U6KLjFQDDETkhBAIBHDx4UBTpJYQwywHAalEyVd16zTty7TQuqK2UOFdrERbFmoxkYSzMzc0hHA4rCh2ju8T033IgpGd9fX2ykuWqOafpDV1Jb05OjqxVPHpouVxlUTKhspoE1FJytY8OSswSlyhbTfCRXq1kJ4J7Ri+3HfQaaxjB+KDyWessvWZUdHV1ob6+Ph53w4cVK1bA6XTq4uLc3d2d5NoMzBOhhoaGi5700nje/Pz8tPe2tbXBZrMxjetV00DYu3cvbDYbNm7cmPbe9vZ27Nu3T/OkfnxhC/TfLEivGi67eXl5sFqtTOs+ePAgIpFIWtK7YsUKFBYWMnNx9ng8yM/PV7woyQelruBKwrkuhnlHSPbExITsJKZZGAeJY7/U5LTU/d9isSA/P5+5jvv9fkxOTkquL0t6VQAdDE0mk+SJJ3Eg5v4mBixJr9yVHXq0gxwXJ5/Ph4mJCV4lDwQCmJiYkFynWOhJPN1uN3Jzc5My6OpxVi/fTruaCb349E5qeT2+Fy74+kzLrONykSqJFYXFYsHGjRt1Ib09PT1JmZspLvYMzoQQ7N69O208L4XdbseWLVuYxvWqaSB0dHRg8+bNC2IChdDe3o7Z2VlVslOnghpzgto7vdy6WbkZ79u3D3l5eVi7dm3K+8xmM7Zs2cKM9FJbCmD7PMCF7LL5+fmS30MsFksivXrNO3yhYPSaWuDLQcKVrXUekizYI3Hs5/4mBonfrlEWdhKfy+/3qxK2pwd03ellRTy5v2klm7rVRqNR+Hw+ybItFgtcLpcsJU+1sg6oO5DzZYmjstWKZ+XKTnRTorL1IL2J/Z+fn4/i4mLVSC/dnZCqs4nJ12h9UmSrNTDn5eWhvLzcsKQ3EongwIEDaG1tTXtva2ur5qQ3Fovh9OnTvDu9wHwG54t5p7e7uxsjIyOi4nkpWMf1Js4JUhdChUAIQUdHR1rXZoqWlhbYbDbNXZzdbjcsFkv8SDMKJZ5HWpJeVnXv27cPLS0tC8I3hECTWbHQE7X7qqCgAGazWXLds7OzIIQYct4pKChAUVGRqvPO+Pg45ubmMtK7KQtx0JOLUPh8PkxOTqpKegH5SbaMhizpVUh6pcjmBr3LkU2RamWde10N9PX1oaioKO6ezZWtVjwrBd/kRWVrOYEIxTWr2Ra+3QmxBhP3e8nPz5d8liMLnY3FYrwu4YCxY5xOnjwJv9+fdqcXmCcdZ8+eVdXTIhGDg4Pw+/0pd3r7+voQCAQ0a5OWoPG827ZtE11mx44dmJiYwLFjx5i0wePxwOFwxBekpGZHF8LZs2cxPDwsmvTabDa0tLToQnpramqQk5Oz4Pe6ujrMzs5ienpacp2ZSnrTuTZTtLW1YXx8HGfPnlUs16h9ZQRCEA6HMTg4qMu8I7Q5UVNTA5PJZNg5LwvxMIKOC+nZokWLYLPZmJHei8XF2TCkV6rbCy2ntaLNzMwsMHCkyOYGvcuRTeF2u2EymeLn11EoUXIpshN3eQHtCLeQ7KmpqXjGObUhFNdM26IF6ZViWHO/F7PZLDmcgMXAPDIygnA4nHGkt6urCyaTCRs2bEh7rx7JrOgurtBOL/399OnTmrVJS+zZswebNm2Kh4uIQVtbG3Jzc5nF9SZ+H/Q3pdi7dy8AYOvWraLLtLe3Y+/evarmdUhEqsVIel0qjErkhDAwMIC+vj7RpHfz5s0AwMTFWcu+CgaDojPtc+cdq9WKvLw8zeedwcFBxGIxQf1U08VYaHMiNzcXVVVVhp3zshAPI5BeIe9LJaeqeDzzOZfy8vIU5x8yGgxDen0+n+gEHFxFy8vLQ05OjqY7vXINHG675cim6OvrQ2VlJXJzcxf8bjKZVD+2SMjA0SJGJp1xpVWMjNBkRn/TgvTS38SW5ZZTqvMsvRMA7Y7akoOuri6sWLEiybOBD/X19SgsLNTUxbmnpwcWiwXLli3jvU53gC9GF2ep8bwUDocDmzdvZhbXqxbp7ejowJo1a1ImUEtEe3s7BgcH0dvbq1i+WAgtRiqJXdQ6plfpIgE9H1ks6S0tLUVTU5NqpJfVokemzztU94T0U217xWazYdGiRUnXjLzQm4V4GMXzk28TDJCv47RdNHM6kHVvVgy+wVRsp3IHU7nHDmUy6RUif4D6g6mQbBrPqhZ5SeemRNumBYRW1mhbLgXSK9WoErNQoOXulFh0dnaKiucF5ldWW1paNCW93d3dWLZsmWAc4aJFi1BQUHBRkt7Tp09jaGhIUjwvxc6dO7Fnzx5VYirpb0ohJZ6Xgh7bpKWLs9CcUFlZCavVavidXhbu6Pv27UNtbe2CM8jTgcb1KkXi80QiEWbhDEabd6RCj7maK7u2thYmk0lz2VloA4/nQuZ0l8slKXQsEAggEokw0fHEoyAp5HozqDWnGQGGIr1SBlN6cD0tL7ZsOBxGIBDIeNLLN4gD80p+/vx5yXUaXTZ1U+KTXV1dDbPZrNkk0tfXh8LCQt7dv7q6Ong8HuarYh6PJy5Pb+MjHA5jbm5OdHlgvs8cDgfvrlVdXV08GYOREA6HcfjwYWzatEl0mZaWFmbnb4pBT0+PoGszMO/9cbFmcN69ezfMZrOkeF6KHTt2YHx8HMePH1fcDjUMhImJCZw4cULys5WUlGD16tWakd5YLIb+/n7ecdlsNmPx4sWy5oTEPvV6vYhGo4rby1c3/U0J9u3bhy1btkgq09bWhkOHDikmqGoaqEYivVK8ASncbjeKiop4wx/UDotKZytlSW/mg6ujZrNZUnJaI/OBLOlVAUoHU1qGlleiaDMzM6IzeXJlS00KxFLJlyxZwntNzTgVr9eLqakpXWTTCYJPttVqRVVVlabuzUI77WodR8DC+OCSZiUuOFJkU9A+E1r1Box3hMObb76JYDAoeqcXmCe9/f39GB4eVrFlF5CO9AIXbwbnPXv2YOPGjQvmArHYunUrrFYrk7heNQyEV199FQAk7/QCwLZt2+LxwGpjeHgY4XCY6ZwQjUYxOzub1KesyAnr9xUOh9HV1SUp9hqYJ700O7wSGJn0WiwWOJ3OeHmxZaPRKLxer2xvQIp0thKg3ryTSjYN6TGid1MW4sGai6jBB4aGhiSfqsJ9Lrvdjtzc3CzpVQojkV5CiKSkQNyVHSlJgVgoeaosuMAFJQ+FQpLqFQOhLHFc2WqtXqZyU1JbNl9bUvUBvYcllH4vTqcznl1Vit7xJV+TIptCjz5Tiq6uLlgsFjQ3N4suQwmyFsmsotEozpw5I5i5maKxsfGiI71y43kpnE4nWltbmcT1Ji6Ems1mxQZCR0cHampqBI2ZVGhvb8exY8c08ZxIFbZAf5f6XVNiY0Qix4ejR48iEAiIjuelWLt2LfLy8hS7OBuZ9BYUFMQXOqXMO3SBI5PnnXSyg8EgxsbGVJGdhTYwCulNpWeEEAwMDEiqU8lzGR26kN5YLMa7iqcX6dVatpwdN4qxsTEEg0HmSi4Gahg4UmSXlJQgPz9fc9l8bRHqg6qqKlgsFlVJr9PphMViyYjvhSJVn5WXlyM3N9dwpLezsxOrV6+Gw+EQXaaurg5lZWWauDi73W6EQqG0O72NjY0YHh6+aBJRAPPH+QwMDMgmvcC8izOLuF7u92UymSRnR+cDjefl84xIB7o7THeL1YQacwKrMYcPfOFNSuvet28frFarqAzvXOTk5KC1tVUR6eWewQ6oS3qlZnE1+ryjZlhUKBTC8PBwxi30ZiENrHU8FApJCh0TswkGSNezLOllDK/XGz+0HLi0SC8NeueWlWJ0iTEyuPexhNvthtlsFkzWQeNZ1fg4Uk1eVLYRSG9OTg5qampUJb1SDWujGx9KUuuria6uLlHn83JhMpk0S2aV7rgiCroTfDEdW7R7926YTCZs375ddh07d+7E6OgoTp48qagtrA2Eubk5dHV1yXJtBoClS5eiqqpKk7het9sNl8sl6GJeV1eHgYEBSbGYapJeNeret28fmpubkZeXJ7ms0mRWavZVYmiL1WqFw+G4aOYdteZqYP4IK0JIlvRe5NBbx0dHRxEKhdKG22VJ7wXoQnoTX7ZUn3HqNkOhZLdV6eqlVPKR2G6pSYHSuRireXRQX18fampq4m6yiVAzRibVahaV3dfXJzo2Wy5oXLOWBDxxdwJQrndafS/AfJbCsbExwyxaiMHc3ByOHj0qmfQCiJNeteO1uru7kZubm7JfgQuk+GJycd6zZw+am5tRVFQku47LL78cFotFcVxv4veldKe3q6sLoVBIVoIuYH7hhZ7XqzZSxeoD8/NRLBbD0NCQ6DpZjDli63Y6nZLycvBh3759kl2bKdra2tDf34/+/n5Z5ROfhyZsYtFXfr8f0WhU0byjpCwtw/2/lOeiC/Bi7AbWSLc5UVpairy8PEPNeVlIBwvbin6zcnQ8HR9wOp0oLS2VRXrlPpfRYQjSS//OhBVEpbITy0qRDcwPpnl5eSgtLeW97nA4UFZWptpOr1BMLaD+LnM62eFwGCMjI8xlc5FukKHXWPZBYowb/VupzoohZYnfixoDM6D+mYlScfjwYUQiEUlJrChaW1sxMjIi25AVi56eHtTX18c9R4RQXFyM0tLSiyaDM43nlXNUERf5+fmK43qpOxrLVfGOjg64XC6sXbtWdh3t7e144403JGdZlwo15gQtd3ql5uVIxMTEBHp6emSTXprxmZ7zKxWJz0OPTlGjr+jfmWCnAanP6KVQa95Jl4PEZDIZbqE3C+lg7flJfxeLdHoGyA8xye70MoRSI35mZkZRWZvNBpvNtqANYuLdhAwcsbFyfO0GpCu50NlvFDQzIGukczGm8ax6yNbKXSjdCi69xrIdSo0PPr2LRqPw+/2iynJl5+TkwOl0SooPFdtnRsre3NXVBavVKot40N1htV2cu7u707o2U1xMGZx7e3vR19enKJ6XQmlcr9BcpiR+uqOjA1u3bhX0qBGD9vZ2hEIh1ROqpfPAkeN5lNinDocDFouFSUw66/dFyapc0ltdXY26ujrZLs5KbalUSNxtpXWL7Su+3SK/3y8qk2zic9ntdlitVknvSY8Fagq3243S0tKU+SCypDfzkWhbSVlAE+IDUm0rh8OBkpISwXuk8oHEnEu0bVnSqwBCg6kStxmv1yvKtTWxrJRsm6xXPuXsmqUjf4C6A3kq2RaLRbV4VjFuSoD6x9709fWljGumbRkYGGB6riTAdsUdEDe4Kv1WgQvvZPHixYL31NXVYXBwUHJqfbXQ1dWFdevWxRfHpKC6uhpVVVWqk96enp60mZspLqYMznv27FEcz0uxc+dODA8Py94FV/ptJiIWi2Hv3r2y43kp1q1bB6fTqXpcb7o5oaCgAEVFRZJ3enNycuIxsiaTiTmRY/W+9u3bh0WLFmHZsmWy26Qkrpf187CsW+m8Y7FY4qRRjg643W5YLBZUVVUJ3qNWWJRYO81IC71ZSIcRPD9ThZcA0vnA7OzsgpxLtG1Z0qsAfIOp0sQ8hBBR5/gllpWSFEitSSATSG+6LHFqyhazYltcXAyn06nJTm91dXXKXZja2lqmrtZ66p3Hs/C4I6mygfk+q6ysTEkg6+rqEIvFMDg4KLpeNdHZ2SkrnpeitbVVVdIbCoXQ29sreqe3sbHxonFv3r17N9atW5dydVsstm3bpiiulzXpOHHiBKanp2XH81Lk5ORg69atqsb1+v1+jI+PM58T6HjFNeSMQuQSQeN55WTZpmhra0NXV5esBT8Wi5JCUMMjj9vmVGChA263O2UOEkC9sCgxdprRQnqykIZgMIhgMMjrNSI2dMyIm2BqLqQZAbqRXu6h5YB4t5nEQ8tpWVqvGNncsrS8FNLLaodaTdJ7/vx5pol0RkZGEA6HdSG9YtxjtYqREdv/9F4W0NPDQMn3QqFHnynB7OwsTpw4ISuel0LtZFbnzp1DNBqV5N48OTmpydmtamPPnj2K43kpXC4XNm3aJDuul7WB0NHRAYvFEo/1VAKazEqt5H5iFiPpdTmklwuWpJcb3qSk7lgshtdff122azNFW1sb5ubmcOTIEcllPR4PHA4HrFZr/DcWR2bRugH9Flszed4RK3t4eBjBYJCp7Cy0gdD3ITY5baKOS82ODojXs5mZGdnx9PTvLOlVABrrIWcVL/HQcu7fWpFePmXQYmUnGAxiZGRElJLPzs4yVVIxxJNeV2MCSeempJZsvrZkOuk1ovGhZtZxqTh06BBisZiind6WlhZMTk7i3LlzDFt2AdRVWYp7M7dcpuL8+fPo7e1lEs9LoSSuVw3Su3HjxgULwnKxbds2TE5OKj6SSQhqzQlqk15WdZ88eRIzMzOKSe+GDRtgtVpluTir3VfAheyyUuomhDDZ6eUiU0gvIUSSbLUTHmahDoTGfu61dOWNqONCzzU7O8ssZE9P6EZ6WRNP7jWtZUciEQQCAcmyaVIgsUpOB0c9BnIpBk5/fz/Tj8PtdmPx4sVps9Rq4S4kZpApLCyEy+ViSnrl7k4IJV+j9YqRzWJgTpVdEJiPrS8pKTEE6e3q6oLdbsdll10muw61k1n19PTA4XCkjC3noqGhAQAy3sWZuiFfccUVzOrcuXMnBgcHZZ1jrHQhNBEdHR2K43kptmzZAovFolpcr9vthslkQk1NTcr7pCZSyRTSu2/fPphMJkUeIcB8kqYNGzYYkvS6XK4F867Yun0+X9JxR0YkBEVFRcjPz2caW+vxeDA7O5tR3k1ZSIfepDcQCGB0dFQz0gtIS7JlVGRJrwTZQjEuWsgGpBFP7v0s4Ha7kZ+fn/ZczLq6OkQiEQwPDzOVne6ZqWw1JxCxcc2sXa2F9EZM7Ajf9yLlLEelOit21RswToxTZ2cnmpubF7gMSsWiRYuwZMkSdHZ2MmzZBXR3d6OhoUF0LKHL5UJVVVXG7/Tu3r0ba9euFTyyTQ7a29thNptlxfV6PJ74OfMUhYXis6NzMTAwgN7eXsXxvBQulwvNzc2qxfXSWH3us/Ohrq4OU1NTonJuAJlFetesWbNgJ1Qu5CazSjU3KEWm2mnAfBhcf39/2sVWk8nEfN4Ra6fRxI5GmPOykA69dVzsJlhlZSVycnKYkN6LwcU5S3olyuYzcMTI5gt6lyIbuDA4psqCC8wrudVqZTqY9vX1pT0qCVCHcFPZ6VBXV4exsTFRu+5yIDaumbZFbdIbjUbh8/nSlqX3U0g5y1HpwDw+Po65uTlDLFqIRVdXl+LdG+BCXK8akJK5meJiyODMMp6XoqCgABs3bpQV1yv0fdBrUkDJKSvSC8wTejV3esV+14D4zPqZRHqVujZTtLW14fTp0xgfH5dUTo++CgQCaZNu8c071FtJi3lneHgYkUhEl3lHzNmpAJCXl4fy8nJDzHlZSIfepFfMOdTAvL23ePFiSaQ3MeeSnCRbRoWhSK/X603rFsunaE6nU/Q5fkpJr1wDh6/d9N9iV2XdbjfKy8vjRzkIwWw2S1JysbKlGDh6ylbrGACxK7j0HrVJL5De3SSV3hnJO4Heo7cB4PF40N3drSiel6KlpQX79+9XJZGQlDN6KTI9g3NfXx/Onj3LNJ6XQm5cL0vS29HRgYaGBlRWVkoqlwrbtm3D2bNnVcmKrtacoBfplfLuvV4v3nzzTaakF7hw7q9Y6NFX9Fq6stz7ueXVttMAfecdt9uNnJwcUd+xEea8LOSB6mJiYlvutXTltdgEA6TpmcfDn3OJXst0GIr0AkjrAsU3mEo9dshopFeKkovZ8QTYnwEn1sApLJyPZ2Ulm7opGYH0il1Zo/ewaoeeesdqNVLs+9P73ML9+/cDABPS29raCq/Xy3x3NRAIoK+vTzLpbWpqQk9Pj2oZpdWGGvG8FDt37kR/fz/Onj0rqRxr0ssqnpeC7hqr4eIsJtQDmD+32mw2G5r0SnVH7+zsBCGEGeldunQpysvLJbs4Cz0PzeWgBJfSvMOa9IrJQQKwtROy0BYeD3/mdHotFebm5hAKhRTzgYqKCtjt9rT3StEzJZssmQDDkV4xgyn34Hpu+XRlI5EIfD5fRpNeMYM4wD4+UopslpOIFDcltWNk3G43nE4niouL095bV1eH8fFxyXF9fDCi8UHd9dPB7XbDZrNh0aJFae+tq6uDx+PRdTWxq6sLTqcTK1asUFzXxo0bAYB5XO+ZM2cAiM/cTNHY2Aiv14vR0VGm7dEKe/bswerVq0XpklS0t7fDZDJJjutlRXq9Xi8OHz7M1LUZmCecy5cvZ056pcTq5+TkoKamRjHpnZmZUew1wep97du3DwUFBVi5cqWi9lCYTCZZcb0sF11Y1q3WvCPGGxCYn3dcLldSHXxgHRYldjGIys7u9GYm+HTUYrEgPz/fcHxA6k6vWmOKEZCRpLewsDAptlSMsvAloqL/FpsUKLGs2KRARlbydAgEAhgbG9NFthQ3JZvNhsrKSlVJb11dnajkQSx3nVkYH1wXHFpeiZuZGNmAfn0mF11dXdi0aZOoVfp0KC4uRkNDA/O4XuqiLMe9mVs+07B7927m8bwURUVF2LBhg26kd9++fYjFYsx3eoH53V7Wcb1jY2MIBoPM54RYLAav18vbp4QQzM7OymovBcv3tWXLFpjN7EyotrY2vP7665JOPjAy6TWZTMjPz08qn65sLBZLOu6IK1tMQjQ95x05dlqmet9cyuD7PgBxOq4HHxB7qgrfc+Xl5SEnJydLeuVC6WCaaMDT8nIJQEFBgeikQEpXdvjIhxhFIoRIXkEcGBhAJBIRdX8qSHEVovfpQXpZy+Zri5R20DJKwad3Yt1oPJ7k5GuAOL2ju7l834sY2YB+fSYXXV1dTFybKVpbW5mT3p6eHhQUFEje8ayvr4fJZMrIZFYDAwM4ffq0KvG8FDt27JCczIpvPqKGvhQDoaOjA6WlpUw8DBLR3t6OgwcPis6eLAZSx2Wx7nVerxeEEEVjTirwvS+pdRNCmCaxomhra4PX65V0rjLf86hJeqXMOwUFBUmLAmLC0GZnZxXrgJ7zjlTZPp8PU1NTTGRnoR3U4iJqkd5oNIqhoaG09/I9l8lkYhZiojc0J718h5YD0gZTNVZX9JQtRpGmpqbg8/lUUfJ00JN4ut1uFBQUiHJTYi2bry1i+6CmpgYmk0k10utyuWAymQz9vQDS+qyqqgoWi0U30js+Po5z584xJb0tLS04cOAAk8UnCpq5WexxRRR5eXmora3NSNJLd2DVJL07d+6E2+1Gb2+v6DJCC6Fis6NT0Hheqe9UDNrb2xGLxSQnSUoFsRlqKcSOyyzGHCGkCm+SUve5c+cwNjbGnPS2tLTAbDaLdnHmO4MdMM5Or97zjljdpOdMs9jpjUQiGBgYyKiF3izkQS0dp/G+qSAlvASQpmdKnisToDnpnZ2dRSwWkx0oncmkNy8vL+nsT7GKJId4csspgdvthslkik8OYmRPTEyk3TkXK1vsM1PZRiC9ubm5qKqqUo30ms1mUYa1EYwPsX1msVgkxf6xBsskVhQtLS0IBAI4ceIEszrlZG6myNRji/bs2YNVq1ahvLxcNRnbt2+HyWSStNvLwkAIh8N4/fXXmcfzUqxcuRIlJSVM43rdbjfsdjvKyspE3U+T1KWLyVWT9KYKb5JSNyWlW7Zskd0WPrhcLqxZs0Y06VWzrwghvLot9tihTJp3WIZFDQ0NIRqNZknvJQA9dXxiYgKBQCBLemVAc9IrNPE4HA5YLBZRcblyX0i6SU8J4U5XNlW7xSQFkuNOxi2nBG63G5WVlbDZbKLuZxkjI5X00gRerGNkpMY1A2wIuNDuBCBe5/X6XkKhEIaHh0WvuAP6Jvbo6upCYWEhGhoamNW5YcMGmEwmpi7OPT09sklvU1NTRsb0qhnPS1FcXIz169dLiutN9X2JzXR5+PBh+Hw+VeJ5gfkFsssvv5xpXK+UmElg/rsOh8MYGRlJeZ/SMUdO3TQvh9i69+3bh8bGRpSWlspuixCkJLMSeh4WruBzc3MIh8OC7ptibCW5rp9KdcDn82FiYkKXxXKpdlp5eTlyc3OzpDcDITT2i3FRpjosFJqQTsel6hn1lhSjZ0psxkyA5qRXaIVDrM94Ju/0CpUVI9vtdiM3N1f0TkdBQQGKioqYDKZ9fX2SiQvAhnDLkT03N4eJiQnFsrno7++P1y+lLUqJv5ABQH8z8vcyMDAAQohhdurTobOzEy0tLUxdTF0uF1atWsWM9Hq9XgwPD0vO3EzR2NiI06dPq3J2sFoYGhpCd3e3qq7NFFLiepUuSFF0dHTAbrfHs32rgfb2drz22mvM3OzleODQcqmg5u6lUN1S3dFfe+015q7NFG1tbTh27Jiotgg9T05ODpxOpyp9RX8z8rwjNQcJvZcl6RVrs5jN5uyxRRkKpTqeeNwRLUuvp4KU4zMppISYZEkvQ+g9mFqt1qRzrcQoWjgcht/v15X01tbWSsoWyXIglzKBsIxnVcu4ktMOQNogw+LYKDW/l3RnOQrJtlqtyMvLE6WzgD7GhxywTmJF0dLSwuzYIuqarMS9ORAIYHBwkEl7tMDLL78MQN14XoqdO3eit7cX58+fT3uv0gUpio6ODmzevFm0J40ctLe3w+fz4fDhw0zq05r05ufnw2w260rkgHmPn0OHDqlKegkhosYLFs8jBKW6nWq3aHZ2NmUWWaHnEusNqOe809fXh8LCQt5dbiGwPl4yC22gxPOTBR8QexQkhRgdF8q5RNuWJb0yoKYRn+7YIVo2cSdHTFIgNXfc6PVU6O3tjZ9DKxZ6kV6r1YrKykpRhmMqyHVTAtQjvVLeAYvjCKheKMkSKFfvlH6rchYKpKTWZ4mhoSEMDAyoRnoPHz6cNjmFGCglvXSHOJNcnHfv3o0VK1agsrJSdVnbt28HAFEuzixIByEEe/fuVS2el6KlpQU2m41ZXK/UOaGoqAgOhyNtkjCPxwOLxQKn07ngd5PJJCmzqVDdgLL3RZPSqUV6V6xYgcLCQlEuzmqSXjXtNCC1+6bH44HZbE467kisN6DUHCQAu6ODpH4XXNlZZBb09vyUugm2ePHitHrm8/kQjUazpJclxsfHAfAPpnl5eWmzDU9NTSVNiMA8caVn/AlhdHQ0aSAFEB9gR0dHBcsODAwAQNLRL8D8CmQ6d9rx8XHedtN+mJycFCxLCMFvfvMbvPrqqyllJKK0tBTHjh1TNJAHg0GcO3cOFRUVksoNDQ3ha1/7mmy5AHDs2DEAkDR5LVq0CLm5uUwTBwHA8ePHUV5eLmk3pra2FsFgUNGu2i9+8QsAwNGjR5OuORyO+PckhImJCV69o89B9ZoPo6OjsNvtSS44wPzOS7r4vOPHj6OkpAQOhyPlfVxUVVUhGo1qnmyJkoHW1lbmdbe2tiIUCuGVV15RXFdHRweKiopQXFwsq/yyZctgNpvx2muvKW6LFpidncWPfvQjhMNhTeTROM33vve9ae+lYzbfgpSYOQGYj+cdHh5WLZ6XwmazYdOmTfjHP/6huK6pqSmMjIxInhP8fj++/OUvp7yHztF8IQYulyvtmJMKY2NjAPhtj3TzP8U//vEP2O12rF27VnY7UsFsNmPz5s2ixopUz2OxWHD69GnZ7aDGMZ+95HQ647L5EIvFMDQ0xGuQ029lenpasLxSHejq6kJpaSmvrSaEqqoqzM3NScrczodDhw5JPkquuroaZ8+ezZ7Vm0EIhUIIBALxfABc5OTkYHJyMuWcJcRF6Lec6vsCgIMHD0rWs1deeQWHDx9OaY/S75JvTrPb7RgfH898PSUaY+PGjQQAOXHiRNI1ACRVk44ePUoAkNbW1qRr733vewkA8swzzwiWN5vNvPXPzMwQAKSkpESw7KOPPkoAkM9//vNJ1xYvXkwAkOHhYd6ysVhM8Nn+/Oc/EwDk5ptvFpRNCCFr1qwhH/nIR1Lek4iamhoCgIyMjEgqx8Wf/vQnAoDcdNNNkspdffXV5K1vfatsuYQQ8sEPfpAAID/5yU9ElwkGgwQAcTqdimQnIp1u8iGVzojF//3f/5GysjLS29ubdM1isRAAJBaL8ZadnJwkAEhFRUXSta997WsEAPnSl74kKLuiooIAIBMTE0nXxPSHyWRK2T4+/Nu//RsBQB588EHRZVigvLycACAej4d53QcOHCAAyJYtWxTXJUcPuQgEAgQAcblcituiBUKhEHE6neSee+7RTOaWLVvIypUr09531113EQDkBz/4QdK1goICAoD4fL6UdezcuZMAIC+//LLs9ooFnQ/GxsYU1fOTn/yEACAf+MAHJJXbsmULeec735nynqKiIgKAzM7OJl0DQCwWiySZXKxZs4YAIKdPn+atW8x3pfT7E4Nly5YRAGRwcDDlfatWrSIAeOcGpe28+eabCQDy4osvSq57dHSUACDV1dVJ1770pS8RAORrX/uaYPni4mICgHi9Xl7ZZrM5ZdvlPDtt1yOPPCKpHBfUzjOZTJLKbdmyhQAg+/fvly07C23xwgsvEADkHe94R9K19vZ2AoC88cYbguWFdLS3t5cASDv/yNHx73znO6SiooKEQiHBex5//HECgHz2s59NurZ8+XICgPT19UmSazRoTnqfe+45snHjRhKJRJKu3X///eRjH/uYYNlgMEhWr15N/vjHPyZdO3jwIGlsbCSjo6OC5e+77z5y66238l676qqryP/7f/9PsOzU1BS58cYbyfT0dNK1//iP/yCtra0pjfsbbriBfPGLX0z63efzkcsuu4z85S9/ESwrFy+//HJaIyMd5ubmyNVXX03OnDnDqFXiceLECXLttdeSQCAgqdxtt91GHn74YaZt+fSnP03e//73SyozPDxMVq9ezbvAwwIPP/wwufbaawWvx2IxsmXLFvKjH/0o6drMzAy58cYbyfj4uGD5J554glxxxRW8en377benJSL3338/2bVrV8p7EuH1esl1112n+cB63333kauvvloSQReLWCxGbrjhBvLb3/5WcV2PPvoo7zgiBR/72MfIU089pbgtlzr2798vOOc8+eSTZPv27Wnr+PWvf02uuOIKEg6H1WjiAvzHf/wHWb58Oe/cKwUzMzPkLW95CxkaGmLUsgt49NFHyTXXXMN77e1vfzt54IEHZNf985//nLS0tJBoNJp07cMf/jB5z3vek7aOG264gXzwgx+U3QYxePbZZ0l9fX1K45SQC3YH3/M8/vjj5IknnpDdhqNHj5Ibb7yRVy+/8IUvkBtuuCFl+TvuuIP87W9/S/r97NmzpKGhgfT09AiW/frXv06uvvpq3mu33HILuf/++1PK/ta3vkW+/OUvp7wnEdPT0+Taa68V3LgQiw9/+MPk2WeflVTmH//4B1m/fj2Zm5tTJDsL7TA1NUWamprIq6++mnTtyJEj5MYbb+T9Line//73844j0WiUbN68mfzHf/xHSvl33323pM0gsTh37hxpaGgg3d3dSdd+97vfCdqDmQQTIZm+V51FFllkkUUWWWSRRRZZZJFFFvzQPKY3iyyyyCKLLLLIIossssgiiyy0Qo7eDchiPmlEuoREYlBWViY5c6ASZGq7jYRsH2ZxqYKF7ivVeyO04WJCdjzLIossstAP2TktNVQhvak6fWhoKGXmPlqurKxM1vVQKCSYtS9V2XT1pqtbbv3j4+N44IEHEAwGBesVC7vdjt/+9reoqqoSXYZmy5ZShpa7/fbbEQgEJJXjg9x2T09Po6ioSHLbuaDvQ8kgIbctLPswNzcXjz32WJJ+pdNZJd+L1+uFy+US/GZS9QeL9yd2YNZyElAiy0g6za1LjcnP7XZj1apV8Pv9iupxOBw4ceKErDayaoPQt5duThHznuX0v1J9N/J4lq5NwWAwZYb9VPaH0vcl9V2JeU9K7SWhNouZ99P1VarxX8m8k+q6GDtNrXlH6fis5ZyXhXIo4TFAettK6PtRouOsxmCh8TeVbAqj6yjzmN50hoTZbEYsFhMsr+Z1udfUlg0Azz77LFatWpVSPh+UKrmY506FTG03hd1uh8lkUjRIqNmHYvpJid6p+b2kAov3J4bwaEmulMoykk5TKCGVqXDgwAFs2rRJ9vgBACdOnMCdd96J/fv3Y+PGjczboPa3JwZS+5+Fvus5nimVbbFYUp7xreb7kvKuxL4npfaQknJqzStKrus572TKnJeFcijlMenuUXvekDunXQo6ynynd3x8HH6/n7fTqZEi9ELUvC722vj4OEZHR7F06VIUFBTg5MmTqK6uxvT0NO6//37VZK9atQobN27Eiy++CEII6urq0Nvbi6KiIlRXV2N0dBTBYBBLly5FOBxGT08PbrzxRhw4cACBQADPPvssent7QQhBRUUFTCYTcnNz4fF4YDKZsGTJEpjNZgSDwfgK1alTp/DYY48taNfx48dx9uxZFBQUoKamBsFgECUlJXC73bBYLAiFQti8eTO6u7sXtBuApLZXV1fH2y1G9tzcHGKxGHbu3JlWD8SC1gPIJ+9K2sJ994ODg1i2bBmA+XMOZ2ZmEIvFMDg4mNRPYuXTax//+MdRVVWVpBf9/f1J719KvSz6TKrOOhwOFBcXY3x8HHfeeSfGx8dTDq6pxiOp7RUr695774XNZksaQ0pKSmC32zEwMABCCBobGzE2Ngafzwev14t77rkn5RiUWH758uUYHh5GLBZDdXU1BgYGYLVaBXVa6PsaGxuLnym7c+dOyc+tBHLHPdZt4Pv+hoaGFH97Ysa37u5uFBcXY2JiQnH/K9V3KvORRx6B1WpFWVkZTCYTrFYrgsEgent7YbPZ0NbWhsHBQdhsNgQCAczMzCASieC+++6TPZ4pHVf+/Oc/40tf+hLzOVoMpL4rMe9JrL3z/ve/H1u3bk07VoyNjSEcDmN6ejo+1gjp5qlTpwT7Mp1uB4NBPPjgg8ztODF2WuJz19bWYmBgAKWlpdi7dy++9a1vSRqf+/r6EA6HEQqFcNddd4n+ngHAarVi8+bNsFgsTGwWLcbjLOYhlcco/X6k1M21i6enp+FyuZL0TGheHR0dVW385dZhZB1VLabX7/fjzTffREtLC8LhMHp7e+OHIp85cwYbNmyI/x6JRFBfXx9fVeErG4lE4oedp7vOV3+qsrRdfr8fxcXFeMtb3hK/tmbNGtTX16Ozs1NU2+XK3r9/f/xaMBjEoUOHkJ+fj7y8PJw8eRIFBQWoqqpCYWEh+vr6YLfbcf78eezfvz9ed21tbVKfrFu3Dh6PBwBQXl6O8vJy9PX1YXx8HBUVFXjssccWtGvt2rVwuVzx56Jld+zYsaBseXl5Urtra2tx9uxZdHV1YdOmTRgbG8PJkyfR0NCwoGxrayv++Mc/JvWXy+XC6tWrF8gtLy/Hli1b4mVramoWPDOfHiSW58pubGzEzMwM/H4/mpqaUuqrmLqoW7qQXgiVXbt2bfw59u/fD5vNhuXLl8fLj42NoaWlJd4+JXrX3NwMm8224NmcTmfcdUXJ96K0z2677TZJfdbb24vp6WksXbpUcOzhg5z3c/bsWcRiMcmuaAUFBQuei44hVNb27dvjsmw2GxYvXow33ngj3qdCYxBfeZPJhNbWVhw+fBj19fUL2pH4ftJ9Xz6fD6Wlpejt7QWABd+HWnjxxRdht9tFjx3bt2/Ha6+9hlAoFNdRFm1I1MOCggJQJygl843U8Y3b/zU1NbKfSeq36fP5sHjxYuTkzJsEhBDcfPPNSXMJ1Yny8nJs2rRpwXe9b98+APLHs3Tzu1DbS0tLcfbsWXi9XsXvS+6YJndnPZU8sfbQ0qVLsW3bNoTDYdjt9gXtXbFiRVJ76bifat6ni4xi+4pbPhQKCZZNN2elui7GTuMbI3Nzc9HY2Bhf2JMyPsdiMaxduxb/+7//m7bPaPnrrrsuST/odyVnHurv78fs7Gx8ESkL7SDW/lH6/UipW8gm5+qZ0Lw6MDAgSbaUMdDj8SASiWjzYhRANdK7adMmbNy4EXv27AEwP4nOzc0BAG688UZcdtllAIDp6WmEQiEcP348PijRsgCwZ88eFBUVYWRkBKdOnRKs2+/3o6ura0H9qcpy66buUNxrALB+/XrMzc0hFArFt/z56j548GB8dY+V7A0bNsRlb968eUHflpaWLujndG0vKCjgLX/gwAFVyzY3N6dtN1cXxMgVeubE9+H1erF+/XpYrVb4/X5MTk7immuuWVAnfQa+5+DqpcViwcjISLy+0tJS9PX1YcmSJfFnFNJpvrITExOoqKgQ9e5Y6x23fkq0WOlsuueenJwU7DOx776ioiLp3YmBlPfT19eHiooKJrLEPhff2Cel/BVXXJHUVrl1LVmyJKkutSDUV6nGjq1btzJtn1AbqMx081Gq70eOLrDof6nf5qlTp7Bt27a4Hqqlw6nGs1RjjtC47vP50NzcjNraWvT39y8oz8o+EJIdDAbjcwody1m+p8T+TPc8Yt+VmLlb6nvilk+cV4SeS+qcJsZOE2M3yNHttWvXKpLN912JmS+VzENZKIcafEEpF5GqZ3ztEis73Rg4MTGxwK6+pEkvF4cPH8aKFSvgdDqTru3YsSP+94EDB/C5z31OsHziy+ZeM5lMuOqqq/DUU0+JLkuv5+fn816jk8zU1BROnjwpKNtms6G9vR2PP/44E9lcucFgMD4Y1tTUwGazobu7G1dffTVvnUrLqy372LFjeOtb38pcNleP+FBbW5vyOsv6lJRN7ANq2HChRO/E6nRi2T179sSvCSVnYPncUvRGKljrSiqo+T1I7ZN09b366qv453/+Z4VPLB/p2ud2u+H1epnpQbo2JH4f3G/AZrNhw4YNSeXFfnt8z7d//37ccsst7B8K6XU+1XtXqocsxhybzYaNGzfG9WBgYAA5OTlxoisEJfaBluMEn0w+eyjV87Cc91PNO1LnFe57dDqdvK6TYuY0MXYaa3tHLXsF0Ee/slAGsTa71O9HSt1KbPJU3yafXXcx6qhqpPfEiRMAAJfLhfb2dgAXdmnoNWDeFaq7uxtLlizB2bNnk66nKs+9xv1drGx6PbFcYru4Wdi0kN3d3b1Adn19PXw+H377298iPz8fs7OzOHz4cDy+VWz5vXv3IhQKYXh4GMXFxZJll5aWYnBwEEePHsWiRYvS9ll9fT0aGhpw5swZdHd3IxqNYnZ2FiMjI3E3DFo+XdlQKIRIJMLbbr6yVqsVw8PD8Pv9cVdIu92+YNec2/Z0z2G1WjE2Ngav14toNIpAIIBrr71WlO7Q8idOnIjHe9x4443xAShV/1PXWhZ6J0WnU31rfPqc6rlpPPjg4CBuuukmSXr3wgsvoKysDNFodMFKd6LsdEinK0eOHEFpaSm8Xi8IIbjyyisVy0r3XDU1NRgZGUF5ebmkPnnppZdgsVgwNjaGkZGR+C5EYnkx3+aJEydw9OhRTE5OoqCgQFEfy4GY533xxReRk5ODUCiEnTt3xtvIqn1CbRgdHV1wXemYn0oXCgoKYLFYmPW/mPGou7sbOTk5iEajiEajkucTOicMDw8L6jCrMQcAZmZmAAB5eXnx3xobG+N9du7cuZTllYyV3LGsqKgIY2NjIITEy8t9V6n6KvGedM+T6vspLi7GwMCA6Hk/1byTTreVfDeprvO1O9Vz7927FwDic4cYu6GhoQFdXV3o7e3F8PAw7Ha75PF57969cTJSVFQEk8kk2mbp7u5GZWUlTp06hcLCwrgHD99zZ6E+5NrsUr8fobrT2cWBQACxWAxHjx7FFVdckba8lPFXSH4qPc0IHSWMcf78eeJwOAgA3v/MZrPgNbWvy71mBNlK2mXEsnrLpv/Z7XaSl5dn6OdQS++UlNXz3dP/HA4HOX/+vKLxyEiyjKTTUp5bDrR8L0raoOa3p8bzsehXPcczpX1msVh0ky3lXYl9T0rtIb3ek1rXM9nm0GM8yEIelPIYJd+P2npmtDlLazA/sgiQf77Viy++iF//+te46aabBN2t9Dqn933vex8ikQieffZZ3utK6k91bWRkBPfeey9qa2vxhz/8gVduqvPf7rzzTpw4cQK/+tWveBPTpDqv729/+xsefPBB3HzzzfjKV74iSS4AbNu2DcFgEJ2dnTCZTJLKf/e738XPf/5zfOELX8A73vEOSWWHhoZw4403puwzCjFnml599dWYnp7Gyy+/zOuin6otv/jFL/DUU0/hk5/8JN773vdKKuv1erFz504UFhbi+9//vmD7UulPqu/hve99L2KxGJ555hnJ9aY7p/Huu+/G9PQ0/u///g+FhYVJ11M997PPPosnn3wSd911Fz7wgQ/wto22S+nZuR/60IfQ19eHVatW4dvf/raqsr7zne/g6aefxpe+9CVeN9ZUfUJ3yZcsWYLf/e53KduQTqf7+vri8h9//PEFu9p8damVhVGor0ZHR/G2t70NH/nIR/DjH/8Yv/vd7+Jxrqzbl043Dh48iIceeggNDQ2898gd1z/96U8jGAzi2muv5Q3poVDjnN5bbrkFfX19+NOf/oTq6uqk66n08A9/+AO++tWv4j3veQ8+9alPSSpLCEFLSwtyc3Px05/+lLdt6cYVPc7p7evrw+c+9zmUlJTg+uuvx6c//Wne8qzP6d25cye8Xi9+/vOfxz2jxLb597//PZ5//nl85CMfwUc/+tGkcqnm/ampKVxzzTUoLS3Fd77zHUG5Qu/p3/7t33Ds2DF88YtfxMqVK3nLq3VO78c+9jHMzs7ilVdegcPhSLqeSj9/9rOf4Xvf+x7uuece7Nq1S1LZmZkZXHnllSgrK8Nf/vIX3ralKv/JT34Se/fuxQ9/+MOkHC5cGP0M1IsJcnnM6OgoPvOZz6CysjLJTZ9C7jm9kUgE73vf+5Cfnx93WxZqG5+ePfDAA3jppZdw77338obnpGrX6dOn8dBDD2H16tV4+umneWXTdhtZR1Vxb66rq5P10CdOnIDFYsHatWvx7ne/W4WWyUM0Go0fA/JP//RPsNvtmsmmGX5zcnJknUVpsVgAANXV1ZLLU7cxp9MpuSwhBMFgEIQQrFixQjAeRwgOhwNmsxlFRUWSZR85ciTeBrFlU+krDc5ftmwZFi9eLKktzz//PCwWC1wul+TncLvd8b9Zfw+RSCSu07fffntKA0QO7r77bgDzmUWlZp184YUXYLFYkJ+fL0vnE5FqPJqamkJtbS1mZmZUl6VEp+l3zEKnaQbG3NxcWK1WJs8tB0J99cILLwAAPvGJT+DHP/4xAoGAam1M9b5on2/duhXXXnstM5mxWAzvf//7sWLFClWeTez8W1tbi/Xr10uqe/fu3TCbzXA4HJLb7fP5AADhcBh33HEH70KoEfH73/8ewHwWfFbjBJD+PYXDYQDA2972tvhpCWJx8OBBWCwWLF68WHJ7T58+Hf9bzrzzwx/+EACwZcsW5seLpcPHPvYxAPNztdTs57/5zW9kz9U043okEpGlH3T+raio0G08zmIh5PKYgwcPAgBsNhtzu42GDsjVM8pdNm7ciDvuuENSWbqYI4cPGAlsznxgBI/HA6vVGjfKjAJ6FAIAzdtG5ck9EoEaGnLa7fF4kJOTE4+lkiqXOhHIlS1XF5T2GRfk/58pk1uv1LYofQ5uX7IC952qodN69ZkURKNR9Pf3o66ubsECg1qg35PeOk2fVavnlorOzk5UVlbGj/ehx8VpDSU6nAojIyMIh8O69T+LOUGJDhNCMDs7K7m8Xuju7kZBQQFWr14dzyysNqLRaPy0i0ybP9X6btLBCHO1HnZaFsYCy7laqO65ubl4tmUpmJ2dhclkUmyPZjIMR3pzc3MN9+Fz26MX6eUSbymgxoVcJZf7PpT2GQvZLAwrv98fT/Ou13NEIpG4AcQKaup0KBSKJ2LRus+kYHh4GJFIBI2NjZiamlLdEPd4PLDZbLrrtNvtRnl5OZYsWWJI0tvV1YWWlhaYTCa0tLTwZsLUAnTMZa2HtM8bGxt16X+lc4JSHZYrWy/09PSgqakJTU1NOHv2rCxjUyqULkqymHcCgYCsI0hmZmZgNps1f8c+n0/3uToUCsXPoZcCr9crezEpC2OB5VwtVDcAWZtRMzMzhrCr9YThSK/dbjfch28E0uv3+2VNQB6PBxaLRbaSy30fLEivUtmhUEgxWTTCc8iVnal1azUO9PX1AQBWr1694N9qgYUu0PP2lMDtdqOurg61tbWqP7NUEELipBcAWlpacODAAU2IRiJmZmZkE7xUoER39erV8Hg8sowXuQgGg4oXpIw4nqmJnp4eNDY2orGxEaFQSDOvEL6/pZRn8Z7kGtZ62HFG6TMjz3lZqA/uohENUWBdd+LfUsor0XE15kOtYTjS63A4DNepepNeGu8hdQKiBo7cPlXyPlh8nEpkm81m2bIT6+L7W0p5vfowU+vWahygxiuNT1HbmPV4PMjLy5PdJzTGVGm/9PX1oba21pDuzb29vZiYmEBrayuAedLr9/t5z3dVE7FYDDMzM6roYV9f34LzSrVceKDPosRFWYkO8/1tdHR3d6OxsTGeCFILF+dMnnf0suOyfZaFEeDxeOKJ5y4m24qWnZmZYR5upyUMR3qdTqfhPny9SS/NGCxVNr1fbp8qeR8sPk4lsmniLFakV667ltLnYEXe+erm+5tl3Urio7QYB9xuN1wuF1auXAmz2awJ6c3Pz1fUJ/RvJaA7vXV1dRgaGlK8c8wS1JV506ZNAOYXJEwmk+ZxvbOzsyCEqKKHtP9pRmotFx5YzAlKdJjvbyPD6/VieHgYTU1NqKurQ25uLnp6elSXq/f8STNgSy1PPVH0sOOoPCUxi0r6TC7RiUQi8Pl8hrR9s5AOlvYnX918f4sBIUSxjjudTsRisYx2cTYc6S0oKDDch6836XW5XLJk0/tdLpdsJXe5XJiZmYnHykiVrWQCkqsLSvqMry4Aigw9IzwHX918f7OsW4neaTEOUPKRm5uL6upqTUiv0j6hf8sFIWQB6SWEYGBgQHZ9rNHV1YXa2lpUVFQAAAoKCrBixQrN43ppH6uhh7T/q6qqNFls4YLVnHCpkF6aybixsREWiwXLly/XlPQq2VVX+o657ZBSFpCvX0qg57yjpM+oB58efZYFe6htt+Xl5cmqe25uDuFwWJGOs7BB9IbhSG9hYaHhOpSu4unlskPPOZU7AcntUypbTrZNj8cDk8kk6wMjhGBmZkbRxym3z/jqApT1oRGeg6/u3Nxc2UZVuroBffpMCij5ANTPZByLxeD1ehV/i/RvuZiamoLP51twHIORXJw7Ozvj8bwUeiSz0oL05uTkoKamRhfSq1QPvV6vrIVQl8ulS5IjuaCuzI2NjQCApqYmTd2blb4nrccape1WAqXfrNI+k0sI9OyzLNhDbbtN6beZJb0GASU6RUVFhvMZp4pWWFioadIRAPE+oe2QAno/7VOp8Hg8KC4uli27oKBA1kA+OzuLWCyG4uJizdudCCq/uLhYNnmnzyFVp1k+B1/dVKfVIr1y+gxAvM/kGNZSoCXp9Xq9IITI7hOPxyN7HOCCe1xRbW3tgt/0RiwWw/79++PxvBQtLS04dOiQpm7YXB1mPeZrqXeJUDKeARfGJLkLoUVFRYb05hJCT08PysrK4uNwY2OjZju9VqsVRUVFkvsqEonA7/crfseA9DwiSsd+JaAL7XLsHRrDT/tb6lxN5yzaDinQs8+yYA8t7DY5oWOsxn5uXZkIw5Bemh6/tLQU0WjUUGdBqUkQxMguLS2N/y21LACUlpbKVnIlsuWSXnp/SUmJrAnI4/GgpKRkQV1yQXcn5DyHz+dDNBpFSUkJIpEIAoGAZNlFRUWyXavT1a0m6bXb7bIMtmAwiGAwiJKSEhBCZB/VJQZako9EnZZTXu63yAWX9DocDpSVlRmG9J4+fRozMzNJO72tra0IBoM4duyYZm1R+r6EEAgEMDY2phvpZaGHcsdWPedRuaBJrCgaGxvR29ur+gKMkr6iBmlJSQn8fr/kDLJ03pFjWCu1OZRA6UI7IQQlJSUIh8OST32gfSbHG1CtsSYLfcBqrhaqu6ioSJFdrQcfMBIMQ3ppJ5aVlS34txGgN+ktKSmRdbYWt0/l7FJ6PB7Z72NmZkZ2n9H7Fy1aJHsCKi4uhtPpVLwipeTdc5+D+28tZOtZN6t3z/03a/j9fkxMTCwgH/39/artLFM9LCsrk3WUFtVppe7obrcbubm5KC8vBzD/3EY5tigxiRVFc3MzzGazpi7OSsbOVKB9zdU7rbM35+XlyVrtp0mK5M4JmUh66XFFFE1NTYhGozh37pyqclnMO/Q9ydmtVWpYs/5uxMpmNe/I6TMW7yuTd9CymAedq9U4goqVnsklvUbkZ1JhONJLjTEjdarepFeJkjscDtkGTjgclv0+WHycesgWqkuOS56RniNT6tZqHKBEg7r41tbWIhQKYXR0VBV5LHRB7i4GF263G7W1tfGs4LW1tYbZ6e3s7MTy5cvjO4kUDocDq1ev1jSDs8czf0QUa+Od9jXVO7rYotU5xBfLuKwVenp64kcVARdie9V2cc7U98SVnWm2EnDp9VkW7JEJtpXc5LRlZWWyk9MaBYYjvTRrp5E6lZXBKVe2EtKl1MVY6SSghCzK1QWW74vFIKP0OdSIgVO7br3evVhw3Xy5/1eLALLQBbl9ygXXpRvQ3r02Fbq6upLieSlaW1s13+ktKCiQ5aKfCrSvFy9eDGC+/8PhMEZGRpjJSIWLdTxTA5OTk5iYmFiw01tdXY28vDzNSK9e86fShfaioiLVczLwyVZq7+gxPns88/HbpaWlstzRszAWjGq3cXVcak4G6vmZaTkZ+GA40ltVVbXg30ZAJu/00rJSJyAqq7KyUla2TRbGlVxdUGOnV04SMyM9R6bUTe+vrq5e8G/WcLvdMJlMqKmpAaAd6ZX7XEpcxrno6+uL7zIC8899/vx53RMHRqNRHDhwICmel6KlpQVHjx6V7BYuF1wdluOOLgS3243Kysr4OahaZ9DW89vMtJ1eSmy5pNdsNqOxsVH1DM6Z+p64ZdXOyZBKdibZHLQsTVSYdXHObBjZtnI6nbJyMtCcS5k0fgvBcKSXGqFG6lS9JutwOAy/36/LBERlyV3ZUfpxmkwmWRMQ92gYo+z0ytVpow6eatat1TjgdrtRVVWF3NxcAIjHgKtJei0WCyorK+P/FguWOs230zs7O6v7eHvixAn4/f6UpDcSieDIkSOatIerw/TfLMDX//R3LZC4ECrFrZq7EGqxWAw1nqkBSnobGhoW/K5FBudMnXfU+m6kys72WRZ6IBKJwOfzGda2kqtn9N5MGr+FYDjSW11dDZPJZKjVLla7LHLkAhcUTWqfcNsNSFNypbKV7JDSM3rlpH2nWRhZuIJS2dx3L2VHbGZmBiaTSdaKOz3uSG4fimmbWnVz+2x2dlZTw1osEsmHyWRS1dXX47ngLgtIW82nxx0pfV/hcBiDg4O6ki4hdHV1wWQyYePGjbzX161bB6vVqllcL31frA3Rvr6+Bf1fWFgIl8ulC+kFIGkhlOqd3IVQNcccNdDd3Y2qqiq4XK4Fv2u100v1T87cm5ubG3fVlVI+HA4jEAjIdhNOtDm0fM+JdprUudpsNsuaqxMXJbW007IwFuh4akS7TQnpTeQDmayjhiK9LpcLVqsVLpfLUJ2q1wq10tUVPVd2lJBFWlbOQdisV6S4zxGNRuH3+yWVlavTXPJutBVDKXUD0gxrj2c+Jiw3N1fV2JFE0guoG99qBJ0eHBxELBYzLOldsWJFvH8SYbPZsG7dOs3ierXa6TWZTJomE2MxJ8ghRDQmLJOMpsTMzRRNTU3o6+uTfASdFHD7KhAISIrzpGXtdrvkUx/0tDmUgitb6hGBdJHBarXC6XRKanfiomQm9VkWbKHmjihdkMpEPmAkGIr00pdhpE6lh5bTl02PbdACRiC9LAwcORNQYWEhLBaL5DNq1SS93PqllKXtMcogE41GF6xMs06eoVefSYFepNdqtUo+y5GVLiQm7wLmd9StVqvupLezs1PQtZmipaUlo0kvIURzvUuE0m/T4XDAarVK1kN6GoDchVA9IER66W9nzpxRTXamzjtGIb1SZV+qfZYFW6hptxmBD2RJL0MYlfQmussC2g1KSognLa9kd8npdCInJ0ey7MSgdzmyaTmpu30sP05K3uW6ORrlORKR6IIDsHVDUzq4Un1VaxwghCS5mQLqnplqhAWQxONygPnEPIsXL9b1rN5QKITDhw+LIr3Hjx+Hz+dTvU10oZPlmD8+Po65uTlN9S4RRhnPpHrNaA1CCLq7uxccV0RBSa9aLs6Ji5KAdmMo141RaS4P+m8twF1ol2vvKB2fldppWdKb+dCS9MpJTltQUACn0yk5OS1XdjZ7MyNQIwMwFulNVDRAuzgVpX70iYOplHazNNK5v8mRLbXdXNlK9CgYDC7YneDWL7YtrJ4jGAwiGAxKaL34urm/KUVi8jWpdSeOA2p8a2NjYwgGgwvIHzBPBkdHR1VxWzQK6S0pKUF+fv6C3/U+q/fYsWMIBoOCxxVRtLS0IBaL4eDBg6q3KdGAZqGHfIsOgLY7vUrIPMvxTKpsrTE6Ogqv18u701teXo6CggLVklnRo0RYjaFKxho5hnVhYSGcTqeqORkSwbfQrpW9k2inyXVHt9lssNlshv4uskgNLUmv1OS0dFwwmUyyFy3l5hkwEgxDeo2606vnZM03AUlNCiR3AjIS6VUiW4k7upGeQ6psveqmgyGL3SS1xgE+N1/uv/v7+5nLZKkLct3R+VxrAf3P6u3q6oLZbEZzc3PK+1avXg273a6JizN9X3Lc0YWQSu/Gx8dV3/lUuiBl1PFMDfAdV0RhMplUzeBstHlHak4GuYa1ErDsM6VeDEpkG8n2zUI6EnXB5/MhEomoUjf3N7HllYwL+fn5sFgsGa+jWdKbBnqT3tzcXNjtdskTEDfoXe7KTqaSXovFAqfTqfh96f0cSmRnat1GIL1qEEAWOu1wOGTtYlAYlfR2dnZi9erVcDgcKe+zWq1obm5WPYMzN3M6wE4P3W43bDYbFi1atOB3+k7UdnHm7kg5HI6MWgjVGt3d3TCZTKivr+e93tTUpJp7cybPO3rZcdk+y8II8Hg8sFqtC2x2Vruieut4Ylmj52QQQpb0poHepJfbJ1Jkc9tN/6/1JKDHbp/HMx+3YDKZFA863D50uVwwmUyaPofZbEZ+fr7hiGkqcA3rvLw85OTkGM4AcLvdyMvLQ2lp6YLfFy9eHL/OGlQvAfnfIlen5fQLXxwzME+6BgYGmK1IS0VXV1faeF4KLZJZ+Xw+RKNRVUhvXV0dTCbTgt+1yqDN/e6pLmVKyIvW6OnpQW1tLfLy8nivX8w7vdTNVqrsUCiEubk53UmvHq77chfaufHbgPRd5iyMBVZztVDdgH5hg9yyUpPTGglZ0psGWdJ74QMRu7LDJT70jEO9VqSkyk6si9ZjNpslHzvEmrwbZfAUWzdtuxFJLx/5sNlsqKysVIV8KI2zY6HTbrc7KZ4UmCdd0WgUQ0NDkutUirm5ORw9ejRtPC9FS0sLuru7VR2DlY6dQhDaaa+pqYHJZNKU9NL/Z8pCqNYQytxM0djYiOHhYUmuv2LBfU/U0ysT5k+1vhupsuWe+qDHXM1NKkn/n8nxkpc6WNqffHXTY8i0XpBS87m0Rpb0pgF3FU/OBKRUtlFIbzQaFZ01lWvgWCwWSWSRm4WRRbu57ZEKvY1ENQfPnJwc5OXlMU+eoWefiYUQ+QDUcfVV6i7LQhc8Hg88Ho/gTi+gz1m9hw8fRiQSEb3TS8nxgQMHVGuT1qQ3NzcXVVVVGUd6pSyEejwXYsLkeM1oDaHMzRT0mhq7vZk67xiF9MqRfSn2WRbsobbdZhQ+IEW20WBo0msEn3HuKh6g/UCeiUrONXCkyvb7/YpcC9UgvXLcUo1E3oXqVkOn9TSsxaKvr493xxNQh/QqdZelYxAtS3+TAhovyvfc9Dc9SG9XVxdycnKwdu1aUfevWLECTqdT1bhetQxRrfUuEXovhNKycrxmtEQsFsPp06fT7vQC6pFeGsMPSHtPsVhsgbvspULgqBzqWSZ1rtZrUTJLei8uaEV6peZkyJLeCzAE6aUZdrmdahSfce7LBrKkV2pZqbKN9HF6PBfOKgakxdtwj1CgbaEuJmJl07I5OTnMMsgm1k3bxrJu6hEhtW5u8jVaNhaLxY/wYIVUO721tbXMEwoZQacpoVqyZEnSNepyqsdZvV1dXVi3bl1cX9LBYrFg48aNqsb1qmGIBoNBDA0Naap3icjUOUFrDA4OIhAIpCS9xcXFKC0tVY30yl2U5IYW0f/Pzs6KPvVBT8NaCTweD1wul6yFdr5FSSmnPnD7TKo3YJb0XlzQivRKDR0zgg1iFBiC9PK9EO7vesIopFdqUqBMNXD42i3ljFqubKvViry8PEWkl+VzcH9XU3am1q3FOBAMBjE8PJzWvZnl7jLfc8k1quS6o7vdblgsFlRVVfFe1yuDc1dXl+h4XorW1taMI70DAwMAkjOGU2i100v1B5D2XHwxYbROsbIzhfTSrMyp3JvpdTUyOKsxhoqNE9XTsFaCTJ2rs6T34gJXF2j8rVFtqyzp1RFZ0ptetpwJiH50gLR2U5JplElAK9ks6zLSc2RK3VqMA/QM3lTkIxAIYGJigplMI+iC2+3G4sWL4zshidCD9Pp8Phw/flx0PC9FS0sLzp07h/HxcVXa5fF4YDKZkJ+fD4DN9yF0TBaFGostibhYxzPW6OnpgcViwbJly1Lep1YG50x9T1nSy0a2Ub+LLNIjk2wrqTkZaFk5yWmNhCzpTQOjkF6psi+mSUAr2SzrMtJzZErdWowDYsgH9z4WMIIupHLpBvQhvQcPHkQsFpNFegFg//79ajQLHs98DLXZPD89siS9qWJ6g8EgxsbGFMlJhYt1PGONnp4eLF26FFarNeV9WdKbXDYvLy/eb1INayXI5D6jSSVpWSnu6FkYC5lkW8nNySA1Oa3RYCjSS5O1yDlnTS1QA4hCy3PUEpVciuzEdkuZgBLfR35+vqRsm0rbzZUtZwJKfG49Sa9cnVZT79SuW+m714L00jN5E6EF6ZWjC3L7lELojF4KPUhvV1cX7HY7Vq9eLalcfX09CgsLVXNx5utvKe7ofHC73SgrK4snJ0qEFhm0WY5nSsdlI59Hmi5zM0VTUxMmJiYwOTnJVD7LMVTLsYbvHauRk0GMbK11W2mfceO3AfHu6FkYC0a12/gWpOjv6UDnPlZ2td4wFOnN7vReQDQaxezsLNOdXrETUOL7kJptk4Vxxc3CyP09FRKzMEqVzdeWxLqkxEbRMtz/G2FnxKh1a0V6y8vL4yvriVi0aBFsNhtz0pvoLkt/TwdWOi10Ri9FXV0dpqamVDl3VAhdXV1obm5Ou6OWCLPZjJaWFs1ILws9FLPTTu9TC0LjWSwWE1WWlgGUL4Qa2WhKd0YvhVoZnDN515L1dyMW2T7LQm9Eo9EFmdMBY9lWcvUs8fuQKttoMBTpTdwVM8JqF5/BqUW7ErMwSpXN125uvWrKVkIWZ2ZmkrIw0jrTwefzIRaLKd4VoxAaZMTsltPnlaPTQkSHld6pWTdfn3m9XlGGdWKf5efnw2w2Mye9qciHyWRivutJV2i57rKAOF2YnZ0FIUTR+4pGo+jv7xdFurTM4NzZ2SnZtZmipaVFtWOL+HaNAGXzUTq9Ky0tRV5enuaklxAiaiE0cU4wm82Sxla95lGpiEQiOHPmjCjS29DQAEAd0svnpSW2bKK7LCBOdyORCHw+n2zZSmwOpVCiXzMzMzCZTEwX2pX2WaYSiksZdBw1ot2mhPQq5QNGg2FIL/doGIvFgvz8fEN8+HqtUCcuBEiVzedyw61XjGyWq0qZ0G6++hL7PxwOizp2yONZeFax1WoVfexQ4lnFVLYRVgzl1C3WsPZ4FmaXNZlMzN0g07n5AuxdffVeZR0eHkYkEjEU6fV4POju7lZEegcGBjA0NMS4ZersvqTTO7rYomb/C5F5uXoo1b0uE3YK3G43wuGwKPdml8uFqqoq5hmc+Yxbn8+HSCSStmyiu6yUUx+EjNtM2LXksxvEnvrg8cwfd0QXJaWc+kAX2lnZaVnSm7lQc0eULkgp+Tb1GvuNBsOQXm6HAsaYFIVW8cROQEqgBvHk1qu1bLFn1CaWlTIBqUF6lfShXJ1Wc/CMxWK8Os0qeYZefSYW6dx8gflkQ2qSXilnObLQhXTJuwCguroaZrNZs7jeAwcOAIDk44ooaDk1klmx3n0hhKTd6QXY610ilC4ock8DAMTrYeJpANyyWiQ5kgK6aytmp5fep4V7MyBuxzSxrJRTHy4m0pspc3WW9F48UNNuY/1t6rmZpDeypDcFEg8tB7Rz2dGb9HKD3qXIpkHvmTgBpWtLpj4HF0LusgAbnVa6opjYZyxXFMWSD7V3egFtdUEM6c3JyUF1dbVmpLerqwtOpxMrVqyQVb6urg5lZWWqxPWyNkSnp6cxOzurud4lgiWZouWV6LBYrxkt0d3djdzc3LTvioI16RVaaAeMP+9cTKRX7LyTyX2WBVuoabcJ1S0lOS23rMvlgslkEj3288nOVB3Nkt4UEHrZ3Gtay9aK9LKeOPWULeddCe1OcOWkawvr5wgEAgiHw6LaL7Vu7jW5EEq+JrZutceBqakp+Hw+UeRjeHhYUbZeLvTWabfbjYKCgqQ2JELLDM6dnZ3YuHGj4LnB6WAymVSL62VtiIpZdKDX9SC9Rp8TtERPTw/q6+tF62VTUxO6u7uZ7VjPzs4m5aXQ+z3JNaylJjuTCyMutEtxR+eWtdvtsFqthvsuskgPPUivlOS03LJSktPSe7LZmxkiS3rFy5aSbVPuBHSxkV6/3y+ZLBrxOcTK1qtuIRccsXWrPQ5IIR+EEAwMDDCRq4YuSHFHFxPHDGhLeru6umS7NlPQDM6sXWQT35cUd3Q+pDujl4IutoiJQ5QKoZgwILPHM9YQm7mZorGxEV6vF6Ojo0zkG3HeUWJYaxH7Z8Q+A9R3R8/CWBDSBa/Xqzh0TG8ddzgcsjw/jYgs6U0BvUkvNwsjlS0mKRCfgSNlAspk0ss9GoYrW+pRLHo/hxLZetWtFullFUoghfRy71cKpbpgNpt5dVpsv4iJYwa0I70TExM4d+6c7CRWFK2trRgdHUV/fz+jls27l7Kej9xuN6xWKyorK1PeR/WO5fNQ8H2bUrKjG3U8Yw2xZ/RSsD62KJPnHb3sOLXarcT1U6lso30XWaSHx+OBxWJZcBa7XPuTr25ufdy/tRgXLiYdzZLeFNCb9BYWFsazMEqRzWfg0H9rNXGyjOuUIpt7NIxU2Yl1ccsD0oP/lZJ3eoQCtx1GJr18dTudTlgsFkMMrm63G7m5uSgvL095HyWILEkv93sApOu0nHGAQkwcM4B49mAxniRKQJNPKSW9tDzLuN5AIIBIJMKc9C5evHjBuMQHNc/q5fs2pWRHvxRIbygUQm9vr6Sd3vr6ephMJmYZnPU2bq1WK+x2u2TZ4XAYfr/fMKRX67mab1EyXXm+pJK07Ub6LrIQByU2u5i6ufVJrVsN0is2Oa3RYAjSy/fhX+qkV6hPxMjmazf9t9IPJJ0roVoTkJJkK2Jlc8G3cCDlKC2lgwz3CAVuO/QePKXWrZVhLQZ0xzMd+XA4HCgrKzPMTq9SnZZCesPhMEZGRkTVKxednZ0oLCxEfX29onqqq6tRVVXFNK5X6djJB7H9v3jx4vj9rKHWQqiU3TC5C6Fa4ezZs4jFYpJIb15eHmpra1Xd6aVZs8X0lRJbSs+FdiXg6zMpRwTqtdDOl1RSiuwsjAWW9idf3TTMRmrd4XAYgUBAVxvESDAE6TXyTm/iKp6UCUipbKOR3kgkgkAgkLIsnfy4Bk5OTg6cTmda2UpdC1l+nGouHMgty22XXHg88y44Tqcz/hur5Bl69pkY9PX1iXLzBdgdH8OXjRXQThd8Ph8mJiZEkS7aN2qf1dvV1YWWlpa0iw9i0NraynSnVw3SKzamOi8vD4sWLVKl//VeCE2MCZOyEKoVKHGV4t4MsM3gnKnzjhrfjVhcjH2m9ukgWbCH2nab3LrVWvAUI9uIyJLeFOBbxQO0G8iNRnrFyk40cMTKnpubQzgcZtpuucZVpk6kYupOdJdllTxDT8NaDMTuuAHs4luVussq1QVKoMTu9ALq7DRyQUkvC7BOZqXnTi+gXly1nguhfGWleM1ohZ6eHjgcDlRXV0sqRzM4swBdaOcuSgLi3pOQu6zeBE4r0isnhETPhXY9+ywL9tCa9IrNyaAnHzAidCe91C+cr1P19hnne9lAlvRKLStWtpE+To8n+axisW3hO0KBlqXX0slOLKs0g2yqumnbWNSdmHxNbN18yddo2Wg0Cr/fr6htgD7kQ2+dFpu8CwCKi4vhdDpVJb3Dw8Po7+9nSnqnpqZw7tw5JvWxNkQjkQgGBgYuWtLLrVtKWSmytUJ3dzcaGhoWLAiKQWNjI06fPs0kFn5mZkb2Qnsqd1mv15u2fUKGtZhTH/QmvU6nEzk5OZJlp1qUFHPqA1+fifUGzJLeiwtak16xoWN62yBGg+6kV2iVzgjuT3RXLBFapeFPVDSn0ylpZUdp8hwuxL4PIQNHysfJ124xZ9TytVuuOzoL8q6kD9XSO7XrLiwsTDIaxdSdygWH1q0E4XAYg4ODksmH0h1EoQmnoKBAkVGVk5MjmvSaTCbU1NSkvddkMqmewZm6Iis9roiCkmdWcb2p3pccHRwcHEQsFjME6U1MUgSIG89okiK+cZnWnU62XvOoFPT09Eh2bQbmSW8gEMDg4KDiNrCYP/l0lxCSNoMsC8Oab87Twlbi0y8lczXth3RuxmrZO0b6LrIQBz49zMvLE53IU2rdgLJxQQkfyJJeBUj1QrjX9YDRdnqlTEBKDByhoHdat9R2i5WtVBdYvq+L5TkypW61xwE55GN2dhbT09OK5KZ7LjlGlRR39L6+PlRVVSV5LAhBC9JbVlYm+j2kw6JFi7BkyRJmcb20T7mZ0wH534eUnXZ6H4vFlkTouSCVKTu9Us/opaBEmYWLc6bOO3rv9Gb7LAu9oXSullo3oEzP6NgvJicDX1lu3ZmELOlNAaORXrGyhQycTJ4E1JbNsi4jPUem1K32OCCHfADKkzrprQtSXLoBbUhvS0uLZBfSVKBxvSzg8cxnTrdYLAt+V0p6xSZQq6urg8/nw9TUlGRZqZBKj8QsvNB7E8tyr8uRbRSjye/3o6+vT9ZO77Jly2CxWJgks8rUecfjSc4uyy3LehEnUXam9lniGey0rBh39CyMhUy0rSKRSNoQUj7ZYpPTGhGGJ716ZrHjSwoBiDMUlEKJkZKu3akmIKFVfbqyI9fdR2y7+WSLnYBSPbeWpDfdcyh9f0qgZt16vvt0kEM+uOXkQo2dXlpezPsyEuklhDBNYkXR0tKC/fv3MzESPR5hV0m/349IJCKpPrfbjaKioqSdYyGolUxMzfFMybhslCy1Z86cAQBZO71WqxVLly5VnfQqXZwQU15I95XMWdFoNG2yMyVQol966jaN3+bbnBDjjp6FsWBUu83j8cBms8FmsyWVpdeFIJRzSaxsI8LwpPdS3OmNxWLwer2Kd3r5yqbLtin0PsRm22SxIiUnfkAoC6NY2XxtycTV43Qwat1qjwN9fX0oKSlJWlUXQmVlJaxWKzPSy+cuy73OB6Hjjmh5tXZ6x8bGVDFS+/v7MTIywiyel6K1tRVer1d191JA+iKs2OOKKFh5GCQi3beZaiFU6NukOn0x7PRS3ZFDegF2GZwzdd5J992o+Z6zfZaF3mAxV6eCXnomtCgkVrYRYVjSawSfcb0ma6/Xy5uFUaxsJUou9D5YyBZTVigLY7p2C2VhFCubry0XA3nna5sedafzMEhlWIvJHpoObrdb9C4vAJjNZtTU1DAhvULusvS6EHw+H6LRqOz3FYvFJJMu2kf9/f2iy4gFdUFmvdO7cePGBfUrQSrDBZA+H0lddCgvL2ey2JKIVN9muuzoqRZCXS7XRUF6e3p6UFhYiEWLFskqz+qsXqXzTuIZ7LQsvS4ENRfa08lWikydq7Ok9+LB7OwsYrGYIe02PfmAEWEI0st3NIzVaoXD4bgkSa+axJNbv9ayjdxusW0pKChIe5SWx8N/VrGYY4eEzioG9B88ldQdjUbh8/lSluWLCTObzaIM63SQSj4ANq6+LHRabmbSsbExBINBWTuNarg4d3V1oaqqSvI5qOlQXFyMhoYGJqSXtSEqVe/MZjNqa2s1Jb30eqqy3HsTy18spLexsVF2rHljYyPOnDmDaDSqqB2p+srr9aas3+NJPoMdmD/1IV0GWepKm4kELpXsdKc+eDwe5OfnJy1KUndQNRfas6T34oGa5DAajeq2IJUlvSpgZGREMN7J5XJhZGRE4xbNgxCC6elpXndIl8sFr9crOb5LLGgSEz5j1+FwYGJiImX58fFxOByOpN+p4k5OTgqWHRsbW3AvF/n5+RgdHRUsSwjB1NRU0jmtwHyfzc3NIRgMCpYfGRnh7e/c3FzYbLaUslP1GTC/cyUlmcbk5GTSijkA/P3vfwcA/OUvfxEsq0SnabZgvudwOp0p31060JVpvj7Oz8/HzMyMbKMtFotheHiY9xp9llSZkEdHRwX7zGazKdpFIYTg9OnTqKqqklSuurpa8e7N4OAg77dot9thtVpT6vTzzz8PAHjttdeSrokZB44cOQIAWLx4sej20ntPnDghuoxYvPrqq8x3eSmam5vxj3/8Q3HCnPPnzyedkQpcGA/T9TkXhBCcO3dO1HFRXFRXV+PkyZOSyqTDxMQErx7Sb258fFyw7OjoKO9pAED6OSEUCiEQCPB+2/n5+Yqzo7PC0aNHsWzZMtnl6+vrEQ6HcfToUdl1RCIRjI2N8RJv2n+p3OuF5k+TyZT2PQ0NDQFAUtwfIG7eGRsb450v5Xw3UiE0V9M+kzPvEEKQl5eXcvxXOlcL9RmtTy/bNwvpUGqzpwL95vn01Ol0pk16ODo6mvLbFLLbgAtcgY8POByOOF/IKBCd4XA4CAASDAYX/B4OhwkAYrfbdWnXyy+/TACQq6++OunazTffTACQP//5z6rIvueeewgA8tRTTyVdKy4uJgCI1+vlLUv7zWazJV177rnnCADy3ve+V1D26tWrCQBy9uzZpGsASCqVeeONNwgAsm3btqRrt99+OwFAfve73wmWF6p/YmKCACCVlZWCZR988EECgHzjG99IulZWVkYAkJmZGcHyXJw7d44AICtXrky69te//pWUlZWR/v5+wfJ2u50AIOFweMHvoVCIACB5eXmCZb/zne8QAORTn/pU0rUVK1YQAKS3t1fUcyTiH//4BwFArr322qRrN9xwAwFA/vKXv8iqe2hoiAAgS5YsSbr22c9+lgAgjz/+uGB5l8tFAJBAILDg90gkongc6O/vJwDIsmXLJJUrKSmRpDd8ENLpsbExAoBUV1cLlj1y5AgpLS0l+/fvT7pGdXp6elqw/Dve8Q4CgPzhD38Q3d6pqSkCgJSXl4suIwYDAwMEAFm+fDnTeimampoIAHL69GnZdcRiMcH39cILLxAA5B3veIfo+v7+978Lfm+pkG6clQqPx0MAkLKysqRrjz32GAFAPvvZzwqWr66uJgDI2NiY5Lam6rcdO3YQAOS1114T+STqQMz8kg4/+clPCADygQ98QHYdp0+fJgDI2rVrk6598IMfJADIz372M8HyOTk5vO/C5/MRAKSwsFCw7De+8Q0CgNx3331J15YuXUoAkMHBQcHyQnrwt7/9jQAg119/vWBZJThz5gwBQC677LKka3fddRcBQH74wx8KlqdzdSQSWfB7IBAgAEhBQYFg2SeffJIAIJ/5zGeSrjU2NhIA5Pz584LlhfqM2p5XXXWVYNksjIXPfOYzBAD51re+lXSttLRUkR3xs5/9THBs2bBhAwFATpw4IVheSM8OHz5MAJDNmzcLlr311lsJAPLHP/4x6ZrJZCIASCwWE/kkxoDupPcrX/kKedvb3sZ77frrrydf+tKXNG7RPHw+H1m5ciX5+9//nnSto6ODNDU1KTKGU+HQoUOksbGRDA0NJV377ne/y0squbj66qvJ17/+9aTfp6amyIoVK8irr74qWPZnP/sZaWlpIdFoNOnahz/84ZSEORgMktbWVvLyyy8nXdu3bx9pamoik5OTguU/9alPkXe+851Jv8diMXLFFVeQJ598UrDsm2++SRobG0lfX1/Stb/85S/kIx/5iGDZRESjUbJ+/Xry61//WnQZLr785S8LTvJve9vbyFe+8hXBsm63mzQ0NPAOYr/61a9Ic3Mz77sRg9nZWbJy5Uryj3/8I+nayy+/rFin3/ve95JXXnkl6ffu7m7S0NDAu5BC8cgjjwiSgwcffJA8++yzstsVjUbJ5s2byfPPPy+p3M9+9jNyxRVXyJZLCCGf//znyYc+9KGk32OxGNm+fXtKnU6FH/zgB2Tr1q0pJ5yDBw+Sm2++OWnxJR2uv/568sQTT8hqlxBCoRBpamoiv/zlL5nWS/HKK6+Q7du3JxmvUvGpT32K/PznP0/63ev1kpUrV5KXXnpJdF1er5esXbuWvP7665La8LWvfY3ccsstksqkQiwWI1u3biXf//73k66NjY2R5ubmlIsF3/nOd0h7ezuvrr3rXe8in/jEJwTLTk1NkaamJt45529/+xtZtWoV8fv9Ip9EHcRiMdLa2kqee+452XXMzc2Ra6+9lhw9elRRO971rneRrq6upGuHDx8mDQ0NZHh4WLD8gw8+SN7+9rfzXrvmmmvIo48+Klh2amqKvOUtbyHj4+NJ137xi1+QTZs2pRxrbrrpJvL5z38+6fdAIEBWr16t2gZBJBIh69evJ//1X/+VdO3YsWOkoaEh5QL1F7/4RXLDDTfwXvvMZz5D/vM//1Ow7Pnz50lDQwM5depU0rVf/vKXaefqW265hXeRIRQKkba2NkljTRb64siRI6SxsZEMDAwkXfv+979PLr/8ctl1Dw8Pk8bGRnL48OGka3/4wx/ImjVrSCgUEiz/rne9i9x9991Jv0ejUdLe3k7+9Kc/CZZ96aWXyKpVq8js7GzStVTczcgwEaLiAWpZZJFFFllkkUUWWWSRRRZZZKEjdI/pzSKLLLLIIossssgiiyyyyCILtZCT/pYssrj04Ha7UyZ3SYeysjLJ2YKzuLSgVMcosrqWRRZZZJFFFllkkRqakN50xt3Q0JBghr2ioqKUWVeVGHxK2qW0bUplh0Ih5ObmSr6m9Dptc1lZmay6U5VXUne6ssDC98XXv/T60NAQbr/9dgQCAcG60iE3NxePPfYYb3uUvp9UekezcKa6Llenldat5N2n+9bStS1Vuenp6bT1yy0v1Cfj4+N44IEHUmYzFwshXUv3LpU8N3BhfNOSvCuVJfd9ccEdJ4TGEKXtEAtunwn1jZhn0mvcBtSd4wE2i0usdF3PMVRPu8CotpKa9oxS2Wp/F1lIg5p8IRgM8mZOF1u3njqeaXqqekyv2+3GqlWr4Pf7Be8xm82IxWKy6nc4HDhx4oTkTlW7XanaxkJ2qutKymZq3VLflZj7n332WaxatSrp93SkWM0+Sgcj183y/SXCZDLJOq5GqVylfSKkY4ByPUsFpc8NzI9vL730Eq655pqUY5mU+lKN5WLGzXRg+X3o3f/AhT4DINg3YmTpOSekg9w5HmCjM7QNLHTdqPOn2rLT4WK0lZTKTgcl30UW0qA2X7BYLCmPizSyjqeD0fRU9Z3e8fFx+P1+QePuxIkTuPPOO1Maf0KgZcfHxyV3qJrtStc2pbJTXVdSNlPrptfuvfde2Gw2LF26FAUFBTh58iSqq6tRUlICu92OgYEBkPmM5fjXf/3XtLLGxsbQ3NwMYP4c2pmZGcRiMQwODiIQCGjeR+mgVd2EEPT396OsrAwmkwlWqxXd3d149NFHNXl/S5YsweDgIM6cOYOdO3fi1VdfxUMPPST5uVh940qeedWqVdi4cSMA4MUXXwQhBHV1dejt7cXo6GhaPXvkkUdgtVoXvItgMIje3l7YbDa0tbVhcHAQNpsNgUAAoVAIs7OzuO+++2Q/N1f+2bNnU45lUutLNZZzx83x8XGMjo6m1ZXly5djYGAA4XAYfr8f99xzz4K2Hj9+HGfPnkVBQQGCwSAefPBBUc+i5ncqtR66Es/3HsTI0nNOkPKMcoymdHOtlDZwdV2s/i1ZsgQ+nw8ejwderzeuf729vSCEoKKiAiaTCbm5uXjzzTfxzW9+U5P3wNX7mpoanDp1Cl/60pdUkS22f/WylYTmnenpadx///262DvpoPS7yEIa1OQLf/7zn2V/e+mu62mPcssbSU81S2R15swZ2O12WCwW9PX14dy5czCbzfGVE6HrXq8XXq8XeXl5WLJkCcxmMyYnJ1FaWoqcHOWc3e/348033+SVLbVdixYtgtfrxejoqCgFkSs7XZ+JqVuubCXtUqvdg4ODAIAVK1ZgxYoV2LZtG1auXInm5maUlJSgvr4e+fn5WLFiBa644go0NTWJkuV0OrF//36YTCbEYjF0d3djdHQU+fn5qvd/qutC30RRUZHqdZ85cwYbN27E2972NixatAhFRUVYt24dlixZovjdFxQU4N3vfjc2b94Mu92ONWvWoLW1FWVlZcjPz8f27dtx2223obKyEnV1dfjIRz6CiooKVFdXS/5WxfZXqvGH7sCme+ZUOrt//348/fTTOH78OGpraxGJRNDV1YUlS5YgHA6n7TNCCG6++eZ4PzmdTrS2tuLaa6/Fjh07UFtbi5tuugmrVq1CXV0d/uVf/gWlpaWKnjsnJweRSET0WCZUV01NDQgh8Hq9WLZsmcAomQy/34/i4mJRurJo0SIsXrwYN998MxwOR1J/ulwurF69Gjt37kRJSYnoZ7Hb7ap+p0Jl6+vrEQgE4PV6UVNTk9Q3ibpC9SyVLL3Gba3m+FRtTyf7xIkTWLp0qWz9q6ysRGFhIa6++mpUVlbG23LrrbfipptuQnl5OYqLi7Fu3To0NjZq9h42btyIt7/97Vi2bBnMZjPq6+tVla30HatpzwjNO2LnU6P2WRbswZIvJL5LNfVMjo2iFQ/SGpp9OTfeeCMuu+wy7NmzB0VFRRgZGcHBgwfR3d2d8rrX68X69ethtVrh9/vh8/lwzTXXAAAmJiYUt2vTpk3xnRau7FOnTkluV3NzM2prawEABw4cUE12qj6jZdPVTa/v2bMHwLzx7Pf70dXVlbZueg0ApqenEQqFcPz48fj70KLd3OvULeT/1969R8lR3nfC/3b39PS9Z6S5SRqNxEVCkkEIiTsIYy6xsRdiwPFxDBgnu85x1glObCfY3rWzxLesbexkwRvHe7yxvQvZ7IL3LDHL5uCcOIptHDigCzddQAh6dJmRZkaqvs2lL/X+ofdparqrq+vadenv5xwOo65+qp7nqaeqnl/XU08plwHAtm3bsLCwgKWlJWSz2cbnIlgxsi3l+p577jlHyqFn/zz99NO48sorMT4+jlgshgMHDuDaa68F8Habc3Ldyn2vrF8x/MVM21BrV+32HYBG0CaIAEzvsTo7O4ubb75Ztb6a8x2JRDA9Pd1IPzQ0hKmpKaxfv76RplOZzbbZ5jq1q862bdum2U606qy5zgWjdTg5OYmxsTGMjY0B0He+bLctveW+9NJLNetM1LeeehEdFSN1ODU1hVtuucVSuxsYGGg5JpWa20q7dubWedvIOQew5xqvzLueep6cnGz8iHfxxRe31LPZ9tdcz81ptY53J66fyu1rXdOsbtuOfpzV66mZc2in84GT/TSt84feOiP72RkviPYvYgYn25nePoqReMCuOKjbXPm5aN++fdi0aRNisRh27tyJBx98sO3y6667DrFYDC+88AISiURLZ8upvDVfvNrl67XXXkMkEsGbb7657GJtxK5du5ate/v27Zrb1qoztXzrLVcoFMKNN96Ihx56SHfa66+/vvH37t278fnPf74r+VbWmdpD+OLgPX36NBYXFxsdm/HxcUxNTRnaVvP6Dhw4YCit3nJ02vehUAhXX301YrEYnnnmmZY7EO3o3ffKdW/evLnjsaZVJ8pypVIp1V/89NZ38/6LxWI4dOgQbrrpJtV8KdukGnFCbsdK+k7HA3C23GK0QDMr7axTnb3wwgu4/fbbO+ZbTac6c3p9WjqVe+/evbjttts6plWrb0D9vP+//tf/avmem+1OjVY7c+u8rVymPOfs3bsX2WwWxWIR9Xq9cYfBCXbWs5XzVHN6rePdif3QrW039+POPfdcvPXWW6b7S1rbNnI9NVonTvfTunnOJHsYjReOHz+OM2fOYGpqCtdccw1CoVBLOivtTE9avef+TvGAslx79+7F6OgorrrqKn0V56KuBb1iso1MJoOdO3e2fN5ueT6fB4Bl48HFrwcijR35at62uAPRKV9iCMzWrVuX/aqhJ29m60RreXO+jZbLyLpfeOEFHDp0COvXr8fS0pKlclnJd6d8nX/++diwYQMOHz6Ml156qTHkT++2mtcnguxu1z/wdrsTQ+Gi0WjL8WC1TW/cuBG1Wg2ZTEZz3YcOHWrUy8mTJ02XS+/+27NnD1566SUUi0XE43GsWLFCdd1q6aPRKE6dOoWlpSVEIhFccsklqmVql/bFF1/E0NAQZmdnsXr1amzdurVj+jfeeKPt/tCzbaPtTLk/RN5LpRL+4R/+AZFIBOVyGbt37zZU7v379yObzWJ6ehrpdBrvfOc7W9IaXVehUIAsy7jhhhtU03fSaVsbNmzA/v37G21lcHBQtdyd6ru5zoG3jxExVFVvuYeHhzE7O4toNNoYRq0nrWizU1NTWLduHbZu3apZZ81tRc++bpe2+XMnzneiPsVQ7Uwmg71792qW0Qw9dZ3L5VCtVlEsFgEA7373u1XzoPeYSyQSOHHiBAYHBxEKhQy3Pyf3Qze3DSzvx9VqNWzevNlQP86N/oyVOjFTZ1rXnnPOOQfT09O47rrrdNcZ2c/qeS4ajWJkZAQjIyPYs2cPAODIkSOaaY20sy1btmimtfPcryyXOH/v27evbX15huywt956S04mkzKAtv+Fw2HN5Vr/JZNJ+a233vJcvrTyZse2tZZbSevXdRvdV14th5my+GXddu4/u9I7vV2rZXaqzqyWGzh7fnvmmWc6nsuMrE/rXK7nvOnk/vJa/SvrTKturLazTsudPOfoaRdWr/N682BHW/fzdcfJ83cQ+0pu1RnZz+l4IRKJ+LaN+62dOv7KIoDv6XVi2158T+8f//Ef48SJE/jWt77V+OXLSHqn3nn30ksv4etf/zquuOIKfPe73wWg/Z7edssB4MUXX8Q3vvENAMB//I//EWvXrrW1HHqWa7U7t97T+7GPfQx79uzBAw88gA0bNqimN7vvv/e97+HnP/85vvGNb7QdKtgubwcPHsRdd92FCy+8EP/tv/031XRa70u988478dZbb+GJJ55Q3dda6Z944gl86Utfwvve9z7cddddqmUuFArIZDKq5b7nnnvQ19eHH/7wh6pl1kr/X//rf8XPfvYzfO1rX8N73vMeQ/l+/fXX8aEPfQibN2/Go48+qrptQN+7Sw8dOoQPf/jDAIC//Mu/xJVXXtlxfVo6nTfF+p999lnV5Vrl/sY3voH/+T//J373d3932a/ZzURarXPIP//zP+NTn/oU3v3ud+PP/uzPDOUDODsErVgs4tlnn9WctMbqe3rfeOMN/Mmf/AkA4Atf+AI2b96s+r2gvqf3/vvvx759+zA2NqZ6fmjOg9a6crkc7rjjDpx33nl47LHHVL+jdQ69++67ceDAAfzZn/1Z26GsTu2H3/7t30alUsEjjzyiurxT+iC+p/fhhx/Gs88+i09/+tPLnoPWm97Ktj/1qU/h1KlTeOqpp9r2pbz2/tOgC+J7eu+//34cP34cDz74YGP0kpH0gP/e09uV4c3r1q3zVKEFN/Pl1Tqx4gtf+AIA4F3velfbi4QbHn/8cQDAwMCA5XyVSqXGO2EvvvhivPe977Uji74nOuZXXXWVapBlxd/93d8BODspjNH9VygUAJx915yVfT8xMaH6fIyWXbt2IRwOY2JiAnfffbehtGKytVqthg9/+MOGn3H8+7//ewDAypUrDZdbbDsUCulKq3UuO3bsWGNdsVjM8vGnta1KpdJ4Dn3r1q2IRqOG1p1KpRAKhXDhhRca3l/NXn31VQBAIpEwVWYx9Oy8885r29loZuaa8uMf/7jx96ZNmyyX24u06qVQKGDt2rWYmpqy3NaV77I0s8/F8307d+7U/NHFbrIs4yMf+QgA4Pbbb0cqleratvVwq68kfrjYvn1714+LP/7jPwZwtuwXXnhhV7dN6oLYZ//iF78I4OyPrJdddpnLuemOrr2yiIJPTH8uSZLLOVlO5KdUKlleVy6Xw/j4OCKRCHK5nOX1BYWT+75QKCASiZhat0ij9VJ5LaLNmN12X1+fpXzL//8rfYzK5/OWt222zpRyuRyi0ShWrVrl+PGiLKt41shoerN1prauUCjUeD7UiEql0gh6nT6X5nI5JJNJDA0N9eT5LJfLYd26dZiamsLi4qKldVk9bty6fhaLRYgBf167drupUCggHA67UidWrjtEenm1z+4kBr1kG9HB89oBJEkSotGoqY5ws8nJSaxbtw7j4+M92UlsRwRmTux7SZLQ399vKYAzE3wo05ndttV8W9l2LBaztG0zwXazyclJrF27tjE01Elu1pnauszue+V5qhtBr7iD0Wvns8XFRUxNTTUmBRSjEsyyety4df20etwEVT6fN30MWyHLsmf7UhQsvdjO+IZrskW1WvXsr0aSJCEej9uSL9FJDIVCmJyctCF3wZDP520LGJpZ2X+SJJm+S1ypVLCwsNBYj5ltW8m32t/d2nYoFEI+n4csy6qvVdBLHC+jo6O+CHrtOk+4ue+NmJycxMTEBBKJRM+dz0SQe9FFFwE421bPO+880+tTjiiq1WqIRCK608qyDEmSXLmzyKBXnZ3nAyPK5XJjqDz3BzmlVqv15IgC3uklW4i7E278MtqJJElIJBK2Br29eGekHVmWkc/nkUwmHQt6za5bpF1YWFg2Rb/etMDZ1wxY2bZbQa/ZNi/SVqvVxvO9ZuVyOUxMTPjmTq9dbdjNfW9EL5/PRHnFs/pWyy/u7gPGh9cvLCygWq06dg7VwqBXnZ3nA6PbBeDa0GrqDeIc5dTNCq9i0Eu2EAdNOp323AEkSRLS6TQKhcKyyUaMqtfrjeHNvdhJbKdYLKJerzu278X+MxtAiIlZjKYX30+lUqa3bSXfan93a9vpdNr0tpVEUDUxMYHJyUk4+bIAO+rM7L5WW5db+94IBr3ABRdcgOHhYVuCXrPHjdVzjRUMetXZeT4wul0ArgTc1DvcPOe4iUEv2UIcNJlMxnMHkCRJyGQypicFEqanp1GpVBqdxKNHj6JWq9mYU39yct/XarXGa3nMBhDZbHZZPo2kBYBsNmt621byrfa3HmKopJVtZzIZU9tWqlarOHbsWON4WVhY0Hzlg1V2BL12tWGxrsXFRcMTJIntOz20cmFhAdPT0439k8/nPXfudlIul8Pw8DASiYQtQb+V48bN6yeD3lbiuHVzf5i97hDp0avtjEEv2cLLB5AkSRgYGGj8bZboFIlOYqVSwfT0tC159DNRpwMDA7bve/Ejhdl12xH0Wt12oVAw/OOIuMtg5nnkcrmMWq1mKd92HC/Hjx9HvV5f9qoHJ+8mStLZZ/DMDNdaWlrCwsKCbW3YSh06eTwpHT16FAC6tn+8RtzlBmBb0Ov1fd5u2+Fw2JM/WLvF7f0BeLMvRcHhZht3E4NesoU4aFasWOG5A0iSJKxYsaLxt1nNQa/ys14m6nRwcND2fS+eOzHbrpT73uhzdlbLlc/nG9s2OsJAkiQMDg4im82azrfZOsvn8xgcHFy2LjO6fbyIHxnMXMSV+9qOWd7F/lOuWy8xa6zTnZFeP585EfRaPde4cf1UHjd2tP0gcHt/iG1zf5BTnOy3eRmDXrKFOGhWrlzpuQNIkiQMDQ01/jZrcnISqVQKg4ODPdlJbEfU6dDQkO373mq7srLvrZbL6rYHBgYsBXBu1JmSmA14YmICw8PDiMfjjge9VuvMrjbs1r43QuyLtWvXYtWqVejr6+up81lz0Gv1mXNJkrBy5crG30bTAu5cP7vV3vzEyWuanm2HQiFP3kCg4HCzjbuJQS/ZQpLOzlzp1aB3eHi48bdZytcVDQwMIJPJ9NxrPtSIX6OHh4cdC3pHRkZMB3Bm9714DZOZzocYLmt221Y6osr9IV47ZHTbdgS9uVyucbc6FAo5PllSPp+3HPQODw+bGo6utj6x783c9etGEDI5OYmxsTHE43FEIhGsXbu2Z85nsiw3JiUEzga9xWIRZ86cMb1OEfT29fVZOmYZ9LpPeT5wY39kMpmeuwNH3SVJEqLRaM/9uMKgl2zh5QunJEkYHR1t/G2W8s5AKBTCxMRET90ZaUf8Mm02MO20bgAYHR01PSnQypUrTU0KZMedQ7Ptzq5tK9/FZyT9ihUrLD/jpzxeAHuGkGqxc39ZmfBOTL7mxr43QrxOSuil89mZM2dQLBaXBb2AtZE7Yki72faXSqVcG07r1Wu3W5TnAzt+BDO6be4PcppoZ7324wqDXrKFlefpnCTezzoyMmJqUiClbnfi/aJ539v5Who7gkcrz3kODAyYmlBEfH9sbGzZv42kt/p8qpVtmy23kltBrxv7S0kEzFbrvxtBb6+ez5TPMyv/bzXodeNcY5VXr91usvNHMDPbZtBLThPHfa9NmMagl2zh1RO1yIsYZumnTrxfKPd9tVrF/Py8reuORCKmgt5arYZisWjp7p/VO4dr1qxZ9u9ubnvVqlWGty3LsqVhwkrNdxL9cKfX7P5SW9fQ0BASiQSDXg9qDnrHxsYQjUYtB712nGvy+byl98kb5dVrt5skSUIikbDlMQ8z2+b+IKf1ajtj0Eu2UB5A5XIZlUrF7SwBePtiZfXgnp+fx6lTp3q2k6hFue/Fv+1et5mZcMVzcnZ0REulEqrVqqG0wNnOtJkRBlY70JlMxtSkOqVSqfG6IzuCXuXxMjExgampKcND1PWyWmfxeNyWZ/+tnnO60RmRZVk16O2Vd4/ncjlEo9HG3fhwOIy1a9eaPp8rX3lldZ/LsoxisWgqH2b0audXi5PXNCPbtnvkFJGgbGdiRGQvYNBLtlDeHQK6OxxIi7hYWX0lg5jgpbmTODMzg3K5bD2jPtbcQbDzNQtW1m1n0AsYa9PKEQZm2p3VSZnM1llzwGZ2XxYKBZw5c6bleAGAY8eOmVpnJ813y4xoPn9Z6eTa2e6c6mzPzc2hXC637J9arYYTJ044sk0vEaMQwuG3u0BWfsS0+1zTzVfV2DWyI0iazwfd3B/K4ea1Wq3n+xfkDDfbuJsY9JIt3PxlVItdd3qVr18RRIexV2Y8bacbd3rNrNvOO25ubttP+Rba/UgEOPeaL7fqTG1dgD373qlRM83De5V/98Loleah98Dbry0yw81zjVW8s9jKK/uj29um3tGr7YxBL9nCqweQXZ145TsthV7qJGph0KueNpFIIBqNGt62mKVaedfSSEdU5DuTySAUCrkS9KoFVSLIcOJ4qVarKJVKjXwXi0VDw3RFnSUSCVOvnGleF+Dtu34MepcP7Qas3en1a9Ary/KybVcqFSwsLHRl217GoJeCrlfbGYNesoU4gLLZbOPfXiA6jFZnp8zlcli1ahVisVjjs/HxcYRCId7p7ULQG41GDU8KpOyImp3R10rQK44Fo+2uOd/1et3QM34i3+Fw2PBrh5ofB7ByvITD4cbEUACQSCQwMjLiSFDVPLxU+Zkeos5CoZDlCe8k6ezka8lk0nAdisnXRP2L9dltcnISsVgMIyMjjc/ENnvhfKZ8R6+wbt06HDt2zNCz+4Kb5xoryuVy4xl+r1273ST2RzKZtPzWB6PEsFPuD3ISg14iC7x6AEmShGQyaeqOm5LanYH+/n6sWrWqJ+6MaHHyBw/RAQCsBY9mnvO00qbtzLdYn5H0Zrdt9dlEIZfLYXx8HH19fcs+d2ryN7U6M1rnyjqzcndVGUAbrUPx3LjTnd5cLoe1a9cue6YV6I139VarVRw7dkw16K3X6zh+/Ljhdbp5rrHC6rkmqKwcw1Y033kHuD/IGV7tszuNQS9ZVqvVUCgUPHkAWQkAlNSCXoAzOANv39WMRCJIp9OO3OkFzAWPfX19SCQShtM2D5cV6+tWvkU6N7YdCoWQyWR8dbw036FWfqY3vR3nieZ1Gb3rZ3Xf69XL57Pjx4+jXq+rBr2AueHdakGvkdcOiTaTTqcRDoddDXq9cu12k53nAyMWFhZQqVS4P8hR9Xrds312pzHoJcuUdyfi8Tj6+/s9cwAx6HWW8pdpwP4OgtUATvlrvZHXDinveMbjcUSj0Z4JejOZDMLhMAYGBhqvYjHKraDXjTqzc10Mep2n9jwzYO2Zc0mSGte+gQFjrx1SPsNvx/B6Ixj0qnMr6FXuj0wms+wzIrsUCgXIsoyBgQH09/cjHo/3TDtj0EuWKU/U4v9eOYDUhi0anZ1S7Z2WQi90ErXMz883ngkDvBn0irSA/qFiyjuHZoa4uR30WnmeuLnOzOzPTseL3TPEqj3T65Wg18orowDnJrLq1fOZKF/z7M2ZTAYrVqwwHfSaPW6U5xqRnkGvu7wQ9EYiEcNzMhDp4eU+u9MY9JJlXj6Ami9eRicFAoDZ2VksLCy0dJIA5zrxfuHkvpdl2fKzsWY7osogyo5tmwl6zQ7VdavOhHq9jqNHj6oeLxMTEyiVSjhz5oyhdXbi5Tu9Rl47pCyHU6NmKpUKjh8/3jbonZubM3yO9JNcLocVK1Y07qQpTUxMmJrIy81zjRXK9saJk86qVCqYn593fX90e9vUO3q5nTHoJcu8fADZ0YlvNxxOfLa4uIhTp05ZzKk/Obnvi8Ui6vW6KwGc1XKp3e3T+4yfJL09+Vo6nTb02qGFhQUsLS25GvROT0+jUqm0PV4A+1+LI0kSotEo4vG44eHolUoF5XLZsaAXMD7CwMlz6bFjxyDLsub+CfIMzu3ucgPm73S7ea6xQpLefoa/r68PqVTKM9dut7i9P9zaNvWOXm5nDHrJMi8fQHYGve3u9Cq/02uc3Pd2B57KdXZ720ae8VOmDYfDhp7xc7POBD3Hy1tvvWVonZ2IfIdCIcPD0e2+02a13YnJ1+zIixoR0Pbq+WxyclK17MDZ8ptpm34OesUz/N3etle5vT/c2jb1jl5uZwx6yTIvH0B2deJjsRhGR0dblonOU5DvjGhh0OvMtkUao9v2UtC7fv36lmVjY2OIRqO2Hy9u1plWXszu+1AoZEte1GiNXFmzZk3g3z3e6U5vt4c3ux1kmT1ugsrt/QG483w39Q4v99mdxqCXLOvG3Qmz7OrEr1u3rtERVRoeHkY8Hg/0nREtQQ56xXBZo9tWGy5rdNt2B3B6nzlXbtvsM365XA7pdBqDg4Mty8LhsCPvgrW7zoy+cqZdXrq57/XK5XJYuXIlUqlUy7JoNIo1a9YE+nzWKeg9c+aM6ffsAkAqlUIkEmHQ61Nu7490Oo1IJNL1bVPvkCQJkUgEyWQSQG+1Mwa9ZFk37k6YZWfQqyYUCvXEjKftiLoUk8I4HfTOz88bmhRIpDU6KZCVNq02XFZZHiP5NrpttTqrVquYn583vG2zz/hp/UgEODNDsN11ZmQ4ulK9Xm+ZSEy5DT156UbQ2+58BgR7Bud8Po8zZ85oBr2A8ZE7yv1m9LVDkiQhkUggGo0CYNDrNrt/BDO6be4PcpqX++xOY9BLlnn1RC3eMSryZnRSIKGXO4mdiGfCnPhlWq3zofxcT3orgZCdQVQ38+3WtgU3jhc360ypWCw23n9oZl0Mep0lgtlOQa/R8rt1rrHKq9duN6kNMTb7I5iZbXN/kNN6uZ0x6CXLJOnt94ICMPQrt5OaO7NGf4EXermT2EnzyTObzTZ+bLBj3eFwuDEM08hw23q9jkKhYLpdqpXLbBBldJhw8/FkJoAT6Y1sW5ZlS+UWJicnNY+XbgxvNrO/lJ1c5edG86FcVywWQywW81QA1MvnM63nmQFg9erViEQipu70Wjlmm9uumffJm2El30ElSVLjuAXMP+ZhdttWz79EnXi1z94NDHrJMrWOWrFYRK1WczFXrcGH+NvIwb20tISpqame7SR2orbvxed2rDubzS4bgqN33YVCYdkdN5HeSvBRKBR0tenmdpdOpxEOh7t2pzeVSqGvr29ZHvSkn5+fR7VateVOb7vZcYGzx8vx48dRrVYNrVeL1Trr7+9f9vy2+NxMPpTrMJMXLwS9k5OTXRnO2W25XA6RSASrV69WXd7X12f4mebmZ/gB6/u8VquhVCrpzoNZvXzHpx0nr2lmti1J+udkINJDrZ0ZeZ+8nzHoJcuUz7ABb18kCoWCW1kC0HrXBXj7+Ry9xDstO3Xip6amsLi4aD6zPtWug2B0Ihi71938XK3428j7UtW2rWeIW7sRBnq3rXY8Wc23nvTtAjYj+3J+fh6nTp3qGFTV63UcP35c93o7USu3lfoW6zSqXbvzStArSRLy+XzH/bO0tISTJ0/atl2vyOVyGB8fb/wopMboj5jiOmfnPgfsOYd2otb2ez3obXc+6Mb+ULvzXqlUerJvQc7xap+9Gxj0kmVu/jKqxY47vZ2GwymXHTt2zEw2fc3pO71m1+3EHTej27ZzuKMf8g10fmZSuczO0RF2PlNpZTijHe2uuf7tHDXj1v7xik5D7wHjQa/aPu/WMWsVh9O28tL+8EpfioKll9sZg16yzKsHkNW7LsDbHT+tO71iWRA7iZ0w6FVPqxwua2TblUoF8/Pzpoe4WQnguvUjkd3HS61WQ6FQUB2OrmeIbnOdGR2O3rwusX1lXqwG4HbdZer181mnofeAPUFvt841Vqg9wz8wMIDFxcWevrPoZn/GzlEnRO14tc/eDQx6yTKvHkB2deKHh4cb7zNTE+ROYidqd6bE53asW7nvjEwK5HbQq0xrZNvt8q33tUPN245EIkin010NekOhEMbHx9t+J5PJYMWKFbYdL2JIVnM7lGVZ13Ct5jozO+GdWFc4HEY6nW58pnddYvI1J4dWdnqmFQBWrlyJZDIZyPNZp+eZgbNB79GjR3XfXfdr0LuwsIBKpeLJa7eb7PwRzOq2uT/ICb3czhj0kmVePYAkSWq8n1Uw04nv1ElKJBIYGRkJZCexEzuHhnZaN2A9ePRr0Ovmto0eL6tWrWrMfNqOnZO/uVlnautSTr5mZF3tJl8T67VDLpfD2rVrG68YUyPePW50BmOvq9VqOHr0qK6gt1KpYHp6Wtd6/Rr0Wj1ugsrOH8Gsbpv7g5zQy+2MQS9ZUq/XPTskx47OrJ6gF+jdGZyb6zgajSKZTHoi6I1EIo3XHRlJ2264rFiv0/lWbs+NbVt9plfP8WLna4u8FvS6te/16OXz2fT0NCqViq6gF4DuoL/dftP72qHmNpPJZEy9T94oBr3q7DwfGCGGlXN/kJNkWfZsn70bGPSSJcViseXuRCKRQF9fn+sHEINe5zWfPAH7OghWAwi1O256XjukNhtrMplEJBIJfNCbTqeX3QUcGBjAwsIClpaWOqYH9D0zCdh7vLR7dh/wTtBrZfZs5TKrevl8pud5c8D44yqSpP4Mf71e7zjbu9oz/OFwGJlMhkGvS9wKetX2RzffEUy9oVgsol6vL2tnYkRkL7QzBr1kidqJOhQKdeUi0YnWxUvPL/CyLPd0J7ETEQx5NehVSwt0npZf7Y6nkTbtdtCrzLfRbberM737043jhXd69evl85neoHdwcBDpdNpQ0Gv2uFE714j0DHrd4aWgt6+vD6lUqqf3B9lLrZ2Jf/dCO2PQS5Z4+QBqd/Gq1Wool8sd0585cwbFYtFQJ7GXXiLv5L5XG4JjZN12dETNbttqvpu33a0Arl2+9W7b6I9EkiTZ9j5nwP46M5O3duvS89ohtTvWdo6a0ftMK3B2/5w8eVLX5Gl+kcvlkMlkWvZPM/FMsx1Bb6c25Ob1U+u46cY7ab2oWq2iVCp5Zn90a9vUO3q9nTHoJUu8fABZvXOl552WwsTEBEqlEs6cOWM8oz7l5L4vl8uo1WquBL1qwYcd29bzjJ8ktU6+lslkdOVb7ZkwO/KtZ9sAMDMzg4WFBd1BFaD/uUktknT2+W3lDOt6h6PXajUUi0XH7/QC5gIgO0fNTE1NoVar6Rp+Lr5z9OhRy9v1isnJSUxMTCx75KEdu4Jep881VqjdZY5Go0gkEq5fu93ihf3hxb4UBUevtzMGvWSJlw8gq514Pe+0FEQnPmhDArU4ue+trtvNO71aIwxKpZLhtJFIRNczfk4F62JZJ3qHjyq/Y8fxIvKtDGb0Bot2d3Kttrvmydes5KWZW/vHK/SOQgC6F/S6fae3+Rn+bm3bq9zeH25tm3pHr7czBr1kiZcPIDs68dFoFKtWrer43SB2Ejth0OvMttWGX+rZtpv5BowFVatXr0YkErE16G3WjTrTkxcj+7558jUreWlmZP+sXbt2WZogYNDbum2zx01Qub0/AHee76be4eU+ezcw6CVLJElCOBxuuTvRrffaaVG7qBuZDVG80zIc7nyYjI2NIRqNBqqT2Em7k6cd+75dB0DvukUA0ZxWuW6ttM3DZfVuu91wWSSIHQwAAEx6SURBVCPbbs43YC2AM1JnVo+XeDyO4eHhjt/t6+vDmjVrHA169ZRbq6Op95UzzetTW5dyW1ppnQxCcrkcstms6jaaxeNxjI2NBep8ZjTonZmZ0TX3g9o+T6fTul475OQ5tBMr55qgsnrdsbrtZDKJaDTa9W1T75AkCaFQyJN99m5g0EuWOH13wgo77lzp7SSFw2Fb3z3qB07+Mq31a2SpVEK1Wu2Yvjmt3uc81YbLim1bGS4r1m0033q3rVVnS0tLWFhYMLxt8SoWPfvTyDOTgH0zBDtVZ3qGoytpTb6m3JZWXswG73oYOZ8BwZrBuVwuY2ZmRnf5jTzTrLbfwuGw7h9dYrEYYrHYss95p9cdWucDMz+CGd029wc5TfTZm2/m9Eo7Y9BLlnj1RF2pVFAul1vypndSIKC3O4l6SJKEVCqFvr6+ZZ87HfQC+iYFak6r9zlPp4Io5fKgbRtw73hxs86USqVS28nX9KxLqxx2zKZrZv/YMdGYFxiZlFD5PT3t02r7c+v66dVrt5vs/BHMzLa5P8hpvd7OGPSSJWp3NgD7OmpmiW0334UUkwLpyRuDXm1aJ0/xDl8r6w6FQo0fKZTrBrSD3nZ33ER6MwGzkbTKfBrJt1ju1ratBl1eDHr11Lf4bnNasW692q0rHo8jGo26HgD18vnMyPPMADA+Po5QKKSr/FrHrNUfOpy8s2gl30GVz+cRjUYRj8eXfa73HGqF1nDzXn2FFNnPq332bmHQS5Z0umjX63UXctU+ABCfdbqoV6tVHDt2rGfvjOihte8Bax0ESZKQyWRUh+CI5e2USiXU63VXOqLt2l0mk9H9jJ+VfCcSiZZnwvTUmfiRott3eo8ePdrx/bWdWK2zvr4+JBKJlrRiuZF8KNMK3RhhoMfk5KSpoDcI7x6fnJxEKBTC+Pi4ru/HYjGsWrWqY9Bbq9VQKBQcOddUKpWOjyRY0et3fNRoPdoilju97Wa9vD/IflrtrFAoWL4eex2DXrJE6wCSZRnFYtGFXLW/6yI+63QROX78OOr1uqFO4sTEBI4dO9bxedOg6BT0WrlQW1m31R88tLZdr9c123S7bYfDYV2vHXJqqKQyb0byrXfbi4uLOHHihOGgqlqtYmpqSncaNXbUmR2dXCfbndVOb6lUwuzsrK7XrwkTExOYn5/H7OyspW17QS6Xw6pVq5a9/7oTPXe6C4UCAGf2uVjuFAZZrdzcH1p34KyOnCISOrVxcU4LKga9ZImbFwktVjugRofDie/W63UcP37cQE79qxeDXje37eV8Hzt2DICx40UEYFaH0LpVZ2rrUqa1My9WR80YfaZV+d0gDHE2OgoB0Bf0unmusYpBbyuv7g+nt029o9fbGYNesmR2drbl1S7A28/Snj59uttZAgC89dZbANDyTCgApFIpnDp1SjP97t27Abz9vko9RKfq0KFDutP42ZEjR1Q/FyfPubk50+s+efJky5T6ynVr7T+xXbUTezKZxMzMTMdtNz/TBehr06dOnUJfX59q+nQ6jZMnT7ZNW6lUUCqVVNtsOp3GmTNnNPN9/PjxlmG6ynw7WWd79+4FAEN3EsXrYL7yla/oTtNsYWEBknT2FVPN0uk0JEnSHJ578uRJpNNp1bSAdp01E3WkVofhcBivv/66ZvrTp0+rtvlUKtV4Tt0scU4yE/QePHjQ9Ha94tChQ1izZo2hNMPDw9i/f7/mcD9xLlB7FjOZTHa8Sz49Pa16rrDjHNrJmTNnVNt+JpPB/Px8T95Z7HTd6XQeNEuWZRw/flz19Yhu96UoWLzaZ+8amcikQqEgA5AHBwdblv2n//SfZADyH/7hH7qQM1m+4YYbZADyL37xi5ZlAOROTf+8886TAchHjx7Vvc1//ud/lgHIN9xwg+H8+lG7evzJT34iA5Bvv/1229f9+uuvywDkCy+8sG3ae+65RwYgP/rooy3L+vv7ZQBytVpVTavVpv/8z/9cBiB/+tOfbrvttWvXygDkkydP6i6T8OSTT8oA5Pe///0ty2688ca27bnT+t966y0ZgHzBBRe0Tftv/s2/kQHIf/3Xf92yLJlMygDkxcXFtulF/n7+85+3/U6zQqEgj42Nyf/5P/9n3Wma/dM//ZMMQL755ptblt1yyy0yAPmnP/1p2/Tt6uz48eMyAPncc8/VnZfLLrtMBiC/8sorurcjvPrqqzIAeceOHS3L7r33XhmA/MMf/lB3XppdccUVMgB53759utMcPnxYBiBv3rzZ9Ha9YGFhQQYgJ5NJQ+kuuOACGYB85MiRtt+57777ZADyww8/3LJscHBQBiAXi0XVtJVKRQYg9/f3tyz70Y9+JAOQf/u3f9tQnvX61a9+JQOQr7/++pZld955pwxA/j//5/84sm0va3ecvvLKKzIA+bLLLnNku3NzczIAeXR0tGXZV77yFRmA/IUvfMGRbVPvKJVKMgB5YGCgZdl3vvMdGYD8+7//+93PWBcx6CVLdu7cKT/00EMtn584cULeuHGjvHfvXhdyJcvPPfecfPvtt8v1er1l2Wc+8xn5Ax/4gGb6J554wnCHo1KpyJdffrn8xBNPGErnV5/97GflRx55pOXzYrEob9myRTPY6OTee++Vf+d3fqfl81qtJl922WWaAcAvfvELedOmTfLp06dbln3ta19TDZCUbrnlFtXgT7TpF198sW3ahx56SL722mtV291dd90l/97v/V7btGfOnJEvuOAC+Ze//GXLsn/4h3+QN2/eLJdKpbbpP/WpT8n33Xdfy+f1el2+4oor5P/yX/5L27TPPfecfMEFF8izs7Mty775zW/K73rXu9qmleWznegPfOADquV2UrVale+44w750KFDLct27dolb9q0SS4UCm3Tf/zjH5c/8pGPtHxer9flq6++Wv7Lv/xL3Xn5H//jf8jbt29X/UHlO9/5jvy1r32tbdqlpSX5oosuUg00Xn31VfmSSy5Rbc96/e3f/q189dVXy7VaTXeaer0uX3/99fIPfvAD09v1iptuukn+q7/6K0NpnnvuOfmOO+7QbNN79+6VN27cKE9NTbUse+ihh+SdO3dqbuP973+//J3vfKfl87m5OfmCCy6Qn3nmGUN51qtcLstbtmyRn3766ZZl//Iv/yJfcMEFltqbX911113yJz7xiZbPq9WqvH37dvlv/uZvHNv2vffeKz/11FMtn7/11lvyhg0b5P379zu2beodO3fulP/iL/6i5fOpqSl548aN8u7du13IVfeEZDkAUzMSERERERERqeAzvURERERERBRYfW5ngIiIgiuXy1meAGZ4eNjw7LtEREREAoNe6kir03rixAnNWWUHBwexevXqtsvNdmb1dKS18jYzM4NCoYBMJoPh4eGW5Vr5FuvtVDYtfujEd6rjTvse0K7HxcVFxGIxU+teWlrSfOem1nJRJrX9rmfdVtI7mdbqcivbBtT39YkTJ/DBD34Q8/PzbdPpEY/H8fjjj6u2pU5tRSvvncrVXKbmbSmXW82H1vlILS9KVs5Jfj+fWf1RpVP5rZyLrJ5rAO39rlXvVs/fTl273eZWf4b7g7rFi312r+AzvaQpl8thy5YtjdeLNAuHw5beH5lMJrF//35DB1GnPOnJm5V8Wy0zYK7c3aSnjq3WQyQSafs6kE7rtrLcyXU7uW0v57uTRx55BFu2bDGURm/A3M1yubVvreTLybSCW+czvdcCLV493vVoV+/dOH97/Rqmxq3+DPcHdYsX++xewju9pGlmZgblclm107p//37cc889pjq0yvQzMzOGDiC1PL366qt44403kM1mMT4+joMHD+KLX/xix3y/+eabkGUZY2NjCIVC6O/vhyRJCIVCWL9+PcLhMBYXF3HmzBmUy2VMTk7iG9/4hukyWyl3N4k6/sxnPoNYLIZzzjkH2WwWBw4cwJo1a3DmzBncf//9puvhqaee0rV/1NZtZbmT63Zy217Id7u2sHLlSsTjcRw7dgyyLGPjxo04fPgwwuEwisUi/vAP/xBbtmzB8ePHce655wIA6vU68vk86vU66vU6RkdHUSwW0d/fj/HxcfzqV7/Ca6+9hvn5+Uae/t//+38tx+rLL7+Mb37zm47Uqdl1qZ1TtPJp13nUTHqr21auw43zmdb1SY9ObbvTec7JY1Zv3tXqvVO9uHXtdptb/ZlOfZbFxUV87nOf67n9QfbzYp/dSziRFely+PBhxONxRCIRTE5O4siRI40XqZfLZbz88suqywuFAgqFAhKJRCOIHBkZQaFQsPwSbOV2t27dim3btmFiYgIbN27E2NhYx3wfPnwYd9xxB2677TaMjo5ixYoVuPjii3HxxRdj69at2LBhA6666iqcd955WLduHW677Tbs2LHDVJnn5uYwNDSEQqGAarVqqdzdlM1mcffdd+OKK65APB7HRRddhMsvvxyDg4MAzNdDX9/Z39vMtis9yzvte7Vl4tdRs+sW6e0u1/Hjx23Jt5U6a9cWhoeHkU6ncd111+E3fuM3sHLlSpx77rm4+eabkUwmAQAvvPAC5ubmEAqFUK1W8cYbb2D//v3IZDIAzl5M0+k01q5di6NHjyKRSGDTpk3L8qx2rG7cuNHR/aFsw2LIV6f9o5VPrbRmjqXx8XEkEgnNMmodh2JordnjOBKJWLrLaher56FNmzZh06ZNuPbaa7F582ZccsklWLlyJdauXdtx/YBz5xo9eTdbL05v28vc6s+067OIAKJX9wfZz842HqR25v8SUFfceuuteMc73oFdu3ZhcHAQ09PTOHjwIADg0ksvbQSDyuV79uxBoVDAtm3bEI1GUS6XUSqVcMkll2BiYqLR4TVLuV0A2LZtGxYWFrC0tNQYvqGVb7GsOW02m122naGhocbfs7Ozhss8OzuLm2++GQCwfv16y+XupnZ1JOrX6L4X9TAxMbFs/UbaVfPyXbt2AQBkWUa5XMbzzz/fcd3Kcp05cwZLS0t49dVXO+7f5vTNZT506JAj5VpYWLAl32bKZeV4ufTSS1u2qzf97t27O6bVOs6t7o/mNiz2gd79o/d4EUP8zZ5TzjnnHM22EYlEMD093Ug/NDSEubk5rF+/3tT5bGpqCrfccguAs+ezdo8odJPZ/KuVH3h73z333HOa6zd63Bg5ZjudQ0V6M/XS6Rz69NNP48orr8T4+DhisRgOHDiAa6+9trFePdv2Mrf6M2bbmR1tgXqLnW08SO2MQS/ptmvXLuzbtw+bNm1CLBbD9u3bNZfv2LEDsVgMuVwOhw8fRigUQiqVcixvAHD69GksLi42Ah8lkbfmTrZaetFJFBf9Q4cO4aabblLd9vXXX6+ZNxHg+Z2yjg4cOKD6HeX+v+666xCLxfDaa68hlUp1nGxGa/90Wi6WhUIh3HjjjXjooYd0p1Xuv927d+Pzn//8sjJrtfnmMu/cuRMPPvig7eVSO2608t1p3Z3Kdf3112PXrl2YmppSnYTHyvHSKe0rr7zSCEo6pVdrh3btD2UbPn78ON54442231XbP3qPl3Q63fK51XOKlfR+P59Zzb/WfrN63Bg5ZpXt75VXXkEikUA8HtfMeyft2r7Idy6XaywXx6M4f9dqNUQiEUvb9yIz54MXXngBK1assLQ/jLQz0Zd65plnMDg4iP379+O2224zvW3qLXr7AaKd7d27F/F4HCdPngxcO2PQS7rs378fW7Zswc6dO5d9pvx/JpNZtjyfzwMAEolEYwge8PYdHJHOSp6EQ4cO4dChQ1i/fj2WlpYaQxHV8iaGPmulP//881EqlfD444/jnHPOweuvv44VK1a0lBk4O3SzOW00GsWpU6cAnP117F3vepdqvr1O5LW5jGJYpLIs7fa/GAo9MTHR2PdHjhxZlr7T/tFa3rxdrXbZnLa5XM3rMLJuo9s2Ui61tMq8i4DMrjpTfqd5mVp737BhA/bs2YOXXnoJs7OzGBgYaHu8tDvWnnjiCYyPj2NqagoHDx5sBA1ax1pzO7RzfwBvt+FoNIoVK1YYWpeynCdPntRdv+3qOBqN4sUXX8TQ0BDK5TJkWcY73/lOXfsnGo0il8uhWq1ibm4Oo6OjuOKKKwxt+9ChQ0ilUpidncWv/dqvIRQKtaR1i57853I5DA4OolAoYHFxEddcc03H9tm83+w8btTy3q79jY6ONj4zcv3Uez7Q2rY4f+/bt091vX5k9XygfJ7RzP4weh4T2924cSNqtRrGx8cb29W7beotetp4u3Y2Pj7e+H/Q2hlnbyZNXpwJjrM3O4+zN3tv217Otxan03L25t49n3H2Zs4WbARnb6ag82Kf3UsY9FJHXnznV6c83XrrrVi9ejW++c1vqn5H672YDzzwAF5//fW2M9xpvdfxZz/7Gf7oj/4I733ve/GVr3ylbf798K4zrTr+5je/ib/927/Fxz/+cVx33XVt1+G39/T+xV/8BZ5//nncf//9uPjiiw2nt7JtK2m/9a1v4fDhwxgaGsKXv/zlrm774YcfxrPPPos/+ZM/wfvf//6W5VrHy0svvYTf+q3fwvbt2/H973/fUFoA+PVf/3UcO3YMDz/8cONOrJG8d+s9vU899RT+5m/+Brfeeit+8zd/UzUf7c5HCwsL+NjHPoZsNouf/exnquvXqqdPf/rT2LVrF77zne/g6quvNpR29+7d+J3f+R1cccUV+O53v6u6bcC77+k9deoUbrnlFqxevRpPPvmk6ne0yv+xj30Me/bswQMPPIANGzaopjd73Hzve9/Dz3/+c3zyk5/EFVdc0bZ8fE+vvbz4nt5PfvKT+OUvf4nPf/7zuPDCC23fNvUWL/bZvYLDm6mjdevWea6Ra+XpxRdfBHD2TuLdd99teN3iGcDVq1cvm3RCj5deegnA2V/DjKb1Gq06TqVSCIVCuPDCC03VsVc98sgjAIAdO3bgQx/6kMu50e/BBx/E+eefj7m5ua7vj8ceewzA2aF3Rtu8uDD39fWZOl7EL9Y33nhjY7IgLzp48CAikQguuugiw/vn2LFjAM6W1UwdiecOR0dHDacXM0v39/d79nymdZ4Sw/FkWTaVfzFb6VVXXYX3vOc95jOp4u/+7u8AnJ3MyIlj1ovXbS9wq160tit+NLn88stxxx13dDNbFEA89tvjK4socCRJAgDTQ95KpdKy9Rjdtpj2PcgkSUI0GjVVR15mZd+7KZfLYd26dTh69GjXZ9MtFAoIh8Omjxfg7Xo3yi/7y8rxItLMz8/DzMAsq+cz5Tr8xmr+xTXEifZVKBQQiUQ833bJeX45jxH5HYNeChxx4SgWi6bSi3RmO4n9/f2Bv3gFtZz5fN535SqVSpibm8OGDRtQq9Vw4sSJrm7fSp1ZOVZlWbZ0rHaTleNFpKnVaqaCt3w+byngDoVCvv0RTxn0mvnBQJTbifYV1HMoGVcoFNDX18e2QOQwBr0UOOLCsbS01Hh/phFWO4nxeDzwF6+gltOP5ZqcnASAxrNg4t/dYqXOJElCLBYzlbZcLjeGN3t9f1mtI7W/u7Vtvx0PSiLf9Xrd1A8r+XzedPvsxO91S/ZhWyDqDga9FDhiiLH424hqtYpSqYRkMmm6k2g2rZ8EtZx+LFculwNw9oXzyn93i5U6kyQJiUQC+Xze8IySYntmh1Z3k9U6Uvu7W9v22/GgZKXuZFlGPp93rPx+r1uyD9sCUXcw6KXAkSQJ6XS68bcR4j1lqVTKdCcxnU4H/uIlSZLpOvIq0cn1W7lyuRxCoRC2bNmCbDbrStBr9XhRDlU2khaALzqLVutI7W89RJs2e04S+8fsqBm3iWBC/G1EsVhEvV537HzeK9cK0iZ+aPfbdYfIjxj0UuBIkoRMJtP422haAMhms6Y7iZlMBuVyGZVKxXB6vxDlDNJFulQqoVarmd73bsnlclizZg2i0SjWrVvX1aB3cXERi4uLptuCm8dqN0mSZPmcIv42QgRubuwfLxD1Lv42mhaAI+e5Wq3WeEWVH+uV7CN+aGdbIHIeg14KHEmSMDAw0PjbaFrAWtArti0uZkFTqVQwPz+PgYGBQF2kRVn8Vi4xczOArge9VuvMjmPVD/tLlNNvdWRl217g1fYlJsjyQ9slZ/npPEbkdwx6KXAkScKKFSsafxshAtUVK1aY7iQODg4uW1fQWK0jrxJlGRwc9FW5vBD0mm0L+Xze9LHql/0ly3LjvJDP5w3PIizShkKhrteRlXOpFyjPx2brzonzXFDPoWSc8hgNap+ByCsY9FLgSJKElStXNv42mhYAhoaGTHcSh4aGTG3bL0S5Vq5cGagyWt33bpmcnMTExAQA94JeN44XSTr7Oh2vBw4LCwuoVCoYGhoy/ezy4OCgqdEnduwfs+dSLxD5t/KDgRPnuaCeQ8k4v153iPyIQS8Fjrg7YWZiCPH94eFh053E4eHhZesKGlGukZERlEolVKtVl3NkD6v73g31eh2Tk5PL7vTOzc2Zep+rGW4eL+J5U6/f6VXWkfLfRtIPDAyYGv5o5/7x410o5Q8GRvOvPM85FfQ6sW7yFz9ed4j8ikEvBY7VTmI0GjX1C3ylUkG5XMbo6GhjXUEkyiXK6cfOsBpluSRJMjwM1Q2nTp3C4uJiI+gVd3y79a5eZZ0VCgXUajXD6YeHhxGJRLoaDHZT8/HiRtBrtk1LkuTr85nVuguFQo4EI8r9IiaDo97UfIwSkXMY9FLgSNLZGTvNDgc020kSwd/Y2FhjXUEkyhW0copO7sjICGq1GsrlsttZ6kgMZVbe6VV+7rTmgE5M0GMk/cDAgOljNZvN+iboNXu8WD2fhcNhjI6OolKpGHrtUL1eR6FQwIoVK3zxWig1VtpIc1o7fwSz+kMIBYf4oX1oaCjwb30gchuDXgocq7/ui7Tz8/OGLkDKIWv9/f2B7ciIcq1evXrZv/1ODJf108Q9zUHv+Pg4QqFQV4PeRCJh6rnchYUFLC0t2XKsenlfWT1erJ7PstmsqcmcCoUCZFn2RR23Y9e1oFqtYn5+3tZ8RSIRBr3UaGdBnwCTyAsY9FKgiLsTdnR0xL+NpAXg606iHpIkIRaLYWRkpPHvILCy792Sy+WQTCYbkw1Fo1GsWbOmq0GvW8dLc0Dj1eHoolzj4+PL/m0kPc9nxolZs92oO73rNjuzNAWHH687RH7FoJcCpVgsLrs7YWbyEuUFyEh68V2/dhL1slJHXpbP531XLvG6olAo1PismzM4W6kzZVBlZuiuctteHo4u6mTNmjUIh8OGyinL8rJyGm2TzfvHyLb9fj5bWFhAtVq19LiKU+eDoJ5DyTgrxygRGcOglwLFzrtHyvV1Y9t+EdRfpv1YLuXrioRuBr1eudNrdNvdJEkSUqkUotGo4eB+fn7eUuDWy+czL7cvv7Rdch7bAlH3MOilQGHQ6zxRR/F4HNFoNDDl9GPnQ9zpVWLQ6y0inwAMl5PnM/O83L780nbJeWwLRN3DoJcCxY6OjpixU7k+vWn7+/sRj8dNDdf0C1FHoVDIl53hdkTnI5PJNP7tde2C3qNHj6Jerzu+fVFnyWTS8GuHvByU2MnuoNfIs8viWM1ms8vWZ3bbfuLl9iXWHY1GkUgkfFe3ZB/RFswco0RkDINeChSrHR0rz9c0d26D+pyWqCPAeCfey8T+i0QiyGQyni/XwsICpqenW4LeiYkJLC4u4tSpU47nQdSZmR9AxPEhfmQycrw0T1KkXJ/XWDkvNJ/PjM4irGzTqVTK8PlMpPPjcW7HDwZOBSNBPYeSccqRU/39/Z49jxEFAYNeCpTmjo54LYqR9AMDA4jFYojFYpaC3qB2ZIJaTr+V6+jRowCgeqcX6M67eq3exUylUujr6zOcdmFhAZVKxRd3SOy806v8rBvb9vOIjua6MzrZmfIHg3Q67cidXpE/v9Ut2Ydtgah7GPRSoDTfnRCf6aF83RFgrpPYCxevoJbTb+Vqfkev4Kegt/kOqN47ccqAxuvD0f0c9PrpeGgm8mvmcRXlSALA/vL7vW7JPmwLRN3DoJcCpfnuhPhMj0Kh0HjdEdB7nUS9glhOpzu5ThBB7dq1a5d9vnLlSiSTSd8FvbVaDaVSSXdakc7rw9HtCHozmYwtgZvRodUibTabNTxqxm2SJCGdTiMSiRgeDVAul1Gr1Rj0kqNqtZqlH9qJyBgGvRQozZ0J8ZnetMp0VjvxhUIBtVpNd3q/CGKHTflqGMAf5ZqcnMTo6Cji8fiyz0OhUFdmcK5UKpifn7fteBGf6U2rTOfl/WU16M1kMohEIobryGrgprZ//PS8oZX8O9m+lO9etnvd5C+FQgGAP85jREHAoJcCxUtBL/D2RS0oarUaisVi4C7SfgqiBLWZm4VuBL1OHC8MerXTis/0plWm6+a2vcCr7atYLKJer/ui7ZKz/HQeIwoCBr0UKOwkOkvcKQnaRdqPnY9cLoeJiQnVZQx6vUMt6DXy7LJIa/TZ5V4/n3m1ffmp7ZKz2BaIuotBLwUKg15nBfUi7cdy5XI5rF+/XnXZxMQEJicnHd2+F4Je8aymV/fX4uIiFhcXW55d1juLsLKOjD673OvnMzd/MHBr3eQvbAtE3cWglwJF2dGJRqNIJBLsJNpIrY6C8Oyy3zofsix3HN48PT2NhYUFx/LgdtArJikys+1uUasj5ed60os0Ij3PZ/q4+YOBW+smf2FbIOouBr0UKFY7iZFIBMlk0nBatWddxTqDpF0n3u/PLrfrfOgdhtptc3NzKJfLmkEv8Pa7fJ2gVmf5fB71el13epE2nU4jFAp1JRjsJga97rFad6FQqHGH2Omgd35+HpVKxZb1k38w6CXqLga9FChWOzoDAwMIhUKG06o96yrWGSRWO/FeJfKv7ORWKhVH75Ra0e4dvUI33tWrNsRYlmUUi0Xd6UX7CYfDyGazDHpV0lsNepVtWgy37qR5huH+/n7E43FP1nE7Vusuk8kgHA4bTqtn3WKdyv/7qW7JHpIkoa+vD4lEAsDZtlAsFn0/corIqxj0UqBIktTohAMw3JFuTlsqlVCtVnWlFWkAIJFIoK+vL3AdmeYOm9H3X3pV83BZr5erU9Ar3t3rdNAbi8UQi8UAGKuzpaUlLCwsLDvejAYlZtN2U/N5wWi7sno+aw7c9G5bzDDshzpux0obUat30WbtyFc4HEYqlWqsW3xOvUW0M+UP7YC/Xg1G5CcMeikwmu9OAObu9CrTAvouQM3BYCgU8l0nUQ9JkhCNRhvvhg3KXYp2+96r5crlcojFYhgZGVFdHo/HMTY25njQa7bOmo8X8bfVO6BeG47u9p1et/aPF7hVd3rWrRbo+KluyR5+u+4Q+R2DXgqMUqm07P2HwNvPGeqhFjCLz/WkVaYRfwft4iXqKGi/TFvZ926YnJzE2rVrG3fx1Dj92iIrdWY1qFLbtheHozefFzKZjKFnl9XKafV8pmfbfj+fLS4uYmlpydb2JT63ysqPqxQsVo5RIjKOQS8FhhN3j5TrNbptI0MR/UJtyKD43M/89ou71szNgtNBr9fu9OrddjdJkoREIoFoNArg7LPLemcRXlhYsBS49fKdXi+3L7+0XXJec1vgUHciZzHopcDwWtDrp06iXs11lEwmEYlEfF9Ov3VE9QS9ExMTDHpd1pxPQH85eT4zz8vtyy9tl5zHtkDUXQx6KTDc7iQqZ2E0um2/aK6joDy77Ldf3I3c6XXqOdfmOkun0wiHw10ZGeGX/eWloNdIHTHo7V7QKyaD80vdkn0Y9BJ1F4NeCgy3g17ls65Gt+0XVjrxXtZcrr6+PqRSKU+Wq1Kp4Pjx47qC3vn5eczNzTmSD7UfQPQGrl4OSuzkRNCrdxbh5m1Ho1Ekk0nd2w6FQkin04bz7QVa7UvPj0BO/qgS1HMoGdfcFoL61gcir2DQS4HRrqNTLpdRqVR0pVemjcfjiEajhoJepSB2ZIJaTj+V69ixY5BlWVfQCzj32iKrAZ3yWVcjacW7Zns16FUuc3Lb2Wx22URpXj0e1LSrO72TnVn5wcDoukXe/FK3ZJ+gjpwi8ioGvRQY7e5OAJ1nxlR73ZGRC1CvdGSCWk4/lavTO3oFrwe9amnz+XzHO3HthkYD3psB189Br1+OBzUin2Ym3ZNl2dHy+71uyT5sC0TdxaCXAqPd3QmxTEuxWGx53ZFI3wudRL2CWk4/lUsEsRMTE5rfGxkZQSwW81XQW61WMT8/3zGt+L7g1eHodgS9dgZuRoafq6XVO2rGbZIkIZlMLhtJoHeI8sLCAiqVCoNeclS9Xm/5oR1gWyByEoNeCox2nQmxrFNa5feV6a104guFAur1esf0fhHEDpvaq2EA75Yrl8thaGgIqVRK83vhcNixGZyr1SpKpZLpOmvX2QOcP1a7SeuOtp60qVQKfX19y9KKZVqsBm5a51Kv3U1X4+a1QIvaiCK71k3+UiwWIcuy6fMDERnHoJcCw4tBryzLKBaLHdP7Qb1eR6FQCFyHzU9BFABMTk52HNosOBX0ik6ZE0FVLwS9btaR3oA7qEFvp/w72b7K5TJqtZov2i45y0/nMaKgYNBLgeHFoFfPtv2iUCi0/WXaz2X0W+cjl8t1HNosiNcW2c3N48VP+0sr6NXz7LLaEGOxrFNasS21bZvNt55te4FX25ef2i45i22BqPsY9FJgMOh1VlAv0n4rl5539ApBDnqVz7oa2Xa3VCoVzM/Pq5ZTzyzCanWk99nlXj+fufmDgVvrJn9hWyDqPga9FBiSJLV0hGOxGPr7+013pI1M/KLWCVeu2+/aXaSz2Szy+bxvn122uu+7zWjQe+LECSwtLdmaBzePF0lqfd2RSO+l/aVVR8rlWumb04r0Tv8w4PfzmVr+jf5g4MT5wG/nGnKOX368IwoSBr0UGGq/7gP6LiKS1Pq6I71ptZ51FesOAq1fpv387LKffnGXJAn5fN5Q0CvLMo4dO2Z7PoD2z4yaGbqbyWSWrdtIWrFtL+0vrTpSLtdKb+V81m7bvXqnFzBWd04EI1r7pVQqoVqtWlo/+YefrjtEQcGglwLDaken+XVHetNqPesq1h0EVjvxXqXVyV1cXMTi4qIb2VKl9x29gvje5OSkrfnQagu1Wg2lUqlj+ua0kUgEmUyGQa8ivROB2/z8vOZrh9q97igej+saNeMFVuuuedZsvWn15Eusq3ndgD8mCSN7SJKEcDis+kN70N76QOQVDHopMNReBQHom7FUK22nC1C7mWxTqRQikYgvOol6aM3Yq1zuN/l8vuWdnoA3y2U06BUTXtn9XG8+n0c0GkU8Hl/2uZ46q1QqKJfLpoMSrWPVS8dap+PFSjn1nM/aBW6dtj0/P686w7BI76U6bsdKG9FKK15vZpYYUSRGNSjXLbZNvSGfzyObzSIUCi37XIycKhQKLuWMKLgY9FIgtLs7Aej/db9dWgCaF6B2v96HQqFAPaslSRIikQiSyeSyz4Nwp1dr33upXLlcDn19fVi1apWu76dSKQwNDdke9Io6U+uwieXttAsGxWe80/t2eqfOZ1rp2+Vb77a9wMm6sxKYSpKETCajOqJILKfe4KfrDlFQMOilQJifn0e1WmUn0UFWAh0v81PnI5fLYe3atYhEIrrTOPGuXjePF61te2k4ertyGpnIiucz49rNmg04X3ed+OlcQ85iWyDqPga9FAhOdqSV63di234R1Iu0n8plZOZmwYnXFnk16O207W6SJAmxWAyxWGzZ55FIBOl0WjOfS0tLWFhY4PnMBC+3L7+0XXIe2wJR9zHopUA4ffo0gNaJWwAgmUxidnZWM/3MzAxSqVTL5+ICpJV+ZmZm2XeVUqkUTp48qbltv5ienm6ZdAMA0uk0QqEQTp065UKurDt16pTmvhf71wtefPFFDA0NGUqzZs0avP766x1nVDbi5MmTputsbm5u2XebHTlyRHPbs7OzLUPsATSCS7tnqjbr5MmTLc9uCplMRvO8cObMmcb3mqVSqUYdttOpTU9PT7dNK851avsnHo9jampKc9tu07oWxOPxjuepmZkZ1fYl6qNT3WvpdNz49RxKxnU6Rjv1WYjIBJkoAD73uc/JAOSvf/3rLctGR0dlAPLc3Jxq2lqtJgOQ+/r6WpY99thjMgD5Qx/6UNttX3LJJTIA+eDBgy3LAMhBOczaleXEiRMyAHn9+vXdz5QN2pVr9+7dMgD5mmuucSFXrcrlsgxAzmQyhtKdf/75MgA5l8vZlpd2dfbKK6/IAOTLLrusbdoPfOADMgD5iSeeaLveer2umnZmZkYGIK9evbpl2QMPPCADkB944AEDJXFOKpWSAcgLCwvLPq9WqzIAORaLtU373e9+VwYgf+ITn2hZduGFF8oA5Ndff71t+nb757nnnpMByDt37myb9n3ve58MQP7pT3+qe71e8h/+w3+QAch/+qd/2rJs9erVMgD51KlTqmnr9XrbMv7kJz+RAci333676by1W/frr78uA5AvvPBC0+smf2nXFn7xi1/IAOQbb7zRhVwRBdvyqR2JfOqee+7Bj3/8Y9x1110ty770pS/hhz/8IQYHB1XThsNhvPvd78bOnTtblr3nPe/B5s2b8YlPfKLttv/oj/4I3/72t3H++ee3LPvd3/1d376/tlm7soyNjeGaa67B3Xff7UKurPvgBz+I8fHxls+3bt2Kbdu24VOf+pQLuWqVSCTw0Y9+FB/84AcNpfvWt76Fr371q6plNOuuu+5SPZ42bdqE7du349Of/nTbtJ/4xCfwyiuv4KabbmpZ9uijj2Lfvn0tz40LK1euxJVXXol//a//dcuyT3/603j++edx33336S+Ig+6//3786le/Uh3e/K/+1b/CJZdc0jbtbbfdhm9961uq550HHngAX/7yl3HOOee0Tf8bv/EbWLt2bcvnO3bswDXXXIMvfOELbdPed999ePPNN1XPh1/72tfwyiuvtE3rBXfddRceffRRfPSjH21Z9u1vfxvf//73246WCIVCeO9734vLL7+8ZdkNN9yALVu24Pd+7/dM5+3ee+9taQ8AcO655+Kyyy7D7//+75teN/nL7bffjo0bN7Z8ftlll+Giiy7CH/zBH7iQK6JgC8myjWPeiIiIiIiIiDyEz/QSERERERFRYDHoJSIiIiIiosDiM73kG7lcTnNW2BMnTjRmPVWztLSE/v5+1WVivcPDw4bT6lk+ODiI1atXqy4bHh42/Boap2jVcaf61Soj4G45/VCuTu27E1GOTvnVoiyLlTqzcqxpLZ+ZmUGhUEAmk2mbXk/5u7XPtOrJ6jml3XI9ddRp/1itY6ePdSttE7DWPjuVfXFxUfW5XT15c7teyV5+uO4Q9RIGveQLuVwOW7ZsQblcbvudcDiMer1uarmVtHqWa0kmk9i/f7/rF7BOdWyljIB75fRDufS0706slgN4uywALNWZU8eaHWUEurfP3Djn6Kkjp+vYyWPdjuPZyfJHIhHUajVH1u2VawV15ofrDlGvYdBLvjAzM4NyuYxHHnkEW7ZsaVm+f/9+3HPPPaaWW0mrZ7kWkXZmZsb1i5dWHVspozK9G+X0Q7mUeXzzzTchyzLGxsYQCoXQ398PSZIQCoWwfv16hMNhLC4u4syZM4hEIojFYpiZmcF9991nuhzNZQFgus70HmuyLOPo0aMYHh5GKBRCNBrFoUOH8NWvfrVjWiN1tLS0hGq1iu3btyORSDiyz7xyztHTnq3kSw+nj3Wrx7OT5X/qqafwxS9+0dPnGuoOP1x3iHoNn+klXymXy3j55ZcRj8cRiUQwOTmJI0eOIBw+25QPHz6sukz82tpuuZ51m912oVBAoVBAIpFodMjn5uYwNDSEvj7v/e5kto68Xk47yzUyMoJCoYDTp0/bmsdyuYyJiQnceeeduOKKKzAyMoJUKoXrrrsOW7duRTabxXnnnYfrr78eO3bswPj4OG644QZkMhnNMrbbN+Pj45BlGS+88ALGxsZsq7N2y48fP95Y744dO/De974XIyMjGBwcxMUXX4z169d3TGu0jjZs2IDbbrsNmUwGtVrN9k6ik+cco+tW1lG79Yq0nfaP0WNcLNN6lZKdOrWRTuV34nxQKBQcW7fd5xrqjqBeT4n8iEcO+cqll16KHTt2AAB27dqFwcFBTE9P4+DBgwCAW2+9Fe94xzuWLduzZw8OHTrUdrlI22ndYvmuXbsAALIso1wu4/nnn9fcdqFQwLZt2xCNRlEul1EqlXDzzTcDAGZnZ7tUc/qZqSM/lNPOcl1yySWYmJhoBJt2UeYDALZt24aFhQUsLS0hm80u+67yXaNbt25dVkYAjbucr776KiKRCKanpxvlGBoawuzsLMbGxjA2NoZLL70Uu3fvtqXOtI6XhYWFlnwqyyiG+6mtWwwZNVtHglo5rXDynGN03e3qSNkWxLGod//oaUfr169v/GBhd/22o7eN2LlfOp0Pjh496ti67T7XUHcE9XpK5EcMesmXdu3ahX379mHTpk2IxWLYvn17y3eUy3fu3IkHH3xQdXlzR1nPcrEsFArhxhtvxEMPPdR229dddx1isVjjLoToTPqB3jpSlvO1115DJBLBm2++iWuvvbbLOdbHTLmeeeYZrFq1ClNTU5BlGaFQyNE8imDk9OnTWFxcbAQb4+PjiMVieOWVV3DLLbe0TX/99ddrrn9iYsJQfuw4XlKpVMsyZTkPHDjQdt3pdFp1mdV6slM3zjla625XR8q2sHv3bnz+859ftmzXrl3I5XJYsWKFZlo1RtuRU9qVvzn/u3fvNr1flOeDvXv3Yt26dXjrrbdw9dVXN+7etUtr9ByaSqVw1VVX6Sk6+YTZ687mzZu7nFOiYGLQS74iJtnJZDLYuXNny+dmlothnWJZp+VGt53P5wEA0WgUAHDkyBEcOXKkZZteYaaOgLfLOTg4CODs3Udx18cL5bRaro0bNzaW79mzpyWdnXkEgEOHDuHQoUNYv349lpaWcP7556NUKuHv//7vkclk8Oabb+K1117Dtdde29IGX3jhhZa00WgUuVyucTdBlmW8853vVN1282d2HS9qaZXlPHnyZNt1N5exXTk3bNiA/fv346WXXsLs7CxWrlyJ0dFRzXJa4aVzjp46al6H0fSiLR06dAgrVqzA0tISLr30UtX1Osls/pvTmz0fjI+Po1arYfPmzdi7dy8AtJzXrZ5D9+3bp1pe8g87rju1Wg2JRMJT11MiPwrJsiy7nQmiTjh7s/OCOtukH8rF2ZvtSasXZ2/m7M2cvZmc5IfrDlGvYdBLvsH39DovqO8V9EO5tPJ4/Phx3HbbbVi/fj3+9//+36rf0XpP72/91m/hpZde6jhbqNvv6f3FL36Bv/qrv8J1112Hj3/846pptd4h+9GPfhShUAjPPvts27yJbffqe3o/85nPYHp6Gn/+53+OkZERQ+n/6Z/+Cd///vdx55134t//+3/fNm9efU/v/v378dWvfhUXXXQRPve5z6l+h+/pJbv44bpD1Es4vJl8Y926dTzJOyyodeyHcmnlMRKJADg72ZBygiK9xPOGa9as0Z3ejTo7ffo0QqEQNm7ciLvvvttQ2kqlgnvuuQfA2aH14nECJ/mhXTUTz/PecMMN2LZtm6G009PTCIfDGB0dNdUO7WK23p944gkAZyc4M9q+iIzy4/mBKMj4yiIiIo+TJAkATA9/LpVKy9bjVZIkoa+vz1Q+lWnE83DUykpbsLJ/vMDqcURERP7FoJeIyONEZ71YLJpKL9J5PViRJAmxWMxy0Ov1crrJSluwsn+8QJIkRCIR/ihCRNSDGPQSEXmcCDLE+2iNyufziEajng9WJElCPB5n0OuQxcXFRvsxW8dm948X+D3/RERkHoNeIiKPE3eoxN9GVCoVlMtlJBIJz3f2JUkynU8GvZ2JerEyhNwP7agdv+efiIjMY9BLRORxkiQhlUo1/jZCDOVMp9Oe7+xLkmQ6nwx6OxP1kkqlTNexH9pROyL/8/PzqFQqbmeHiIi6iEEvEZHHSZKEbDbb+NtoWgDIZDKeD1YkSTKdTwa9nVltC1b2jxeI/Iu/iYiodzDoJSLyOEmSMDAw0PjbaFoAGBgY8HxHX5TTzLPLYpIlP0+05DSrbUHsn0KhgHq9bnf2HGflOCIiIn9j0EtE5HGSJGFwcLDxt9G0ALBixQrPd/QlScKKFSsafxuRz+cxMDDgi+DeLWKou9m2IPaPLMumZxJ3k7J9cQZnIqLewqCXiMjjJEnC0NBQ42+jaQFg5cqVng8GrZaTQa82q21BkiSsXLly2br8xO/5JyIi8xj0EhF5nLhDZWbmWfH9kZERT3f0a7UaisUihoeHATDodYKYvdjMnV4x5Nzs/vECSZIwMjLS+JuIiHpHn9sZICIibWIiKzMBnXjW1et3esVw09HRUQDmg14zaXuFlR8GxPfN7h8vkCTJ1/knIiLzGPQSEXmcCFay2aypYCWbzZpK201WgyoGvZ1ZbUcAMDY2tuzfflGtVlEqlbBy5UpOdkZE1IM4vJmIyOOsTNKkvLtXLpc9+35SUa41a9Ys+7eR9BzerM2OO71m94/bxEgCthEiot7EoJeIyMPq9ToKhYItQS/g3VlrRbmGh4cRj8cZ9DpAWUeFQgG1Ws1QWgBYtWoVIpGI7+pY+bomthEiot7DoJeIyMMKhQJkWW501o0Grcq7xOLfXtR8J85oPhn0dtb8A4iR1w6J/TE4OOj5ofJqeKeXiKi3MeglIvIwq3eomgMdr3b27SynV8voNittQXzX7IRqbuOdXiKi3saJrIiIPMyOYHDVqlW+CHr7+vqQSCQMl1NMUiTKWCqVUK1W0dfHS5yS1aA3mUwiGo36Mmhk0EtE1Nt4p5eIyMN66U7vwMAAQqGQ4XKKoaviLqTyM3qb8tVX4t9G0op0fhzerDyO/Jh/IiKyhkEvEZGH9VrQC8B00CsCGrE+Wq75+W6zQa+ZZ67dJkkSotEo4vG4L/NPRETWMOglIvKw5qDX6GuHRLASj8fR39/v2WDQStDbXEfKz+isSqWCcrlsW9Drt/q1MpKAiIj8j0EvEZGHSZKESCSCZDJpeOhurVZDsVj0RbDCoNdZyrvhyWTS8GuHghL0Av7MPxERWcOgl4jIw5rvUInP9FAGOuL/Xu3s2x30cvjqcso6MnO30+9BY3P+i8WiofcUExGRvzHoJSLysObOuvhMb1plOi8HK7zT6yyrbSFoQS/AH0aIiHoJg14iIg/r1aDXyLPLykmK4vE4otGoZ8vpFruD3nw+D1mW7c+oQ6wcR0RE5H8MeomIPEy8ZgboraAX0H8nTjkEnBMVqbM76K3VaiiVSvZn1CEMeomIehuDXiIiD+vVO73iM6NpRXqvltMtdge9ynX6gd/zT0RE1jDoJSLyMGVnPRaLIRaLBS7ordfrKBQKDHodJElSo/0AxupoaWkJCwsLvg4aGfQSEfU2Br1ERB5mJaBTPutqNG03FQoFyLLMoNdBVtuRSKP8v5/qmEEvEVFvY9BLRORhVoMV8ayr0bTdZDWoYtDbWS8HvbVabdlIAk52RkTUexj0EhF5WHOwks1mDQUrYhIskdaL7ycV5RF5Ff83W04Gva2stiORBvBf0FsoFAC8nX9OdkZE1HsY9BIReZQsy8jn85bv9CrTAt57P2nzncR4PI7+/n7e6bWRWh3l83nU63VdaUUaAEin0wiFQr6p4+b8i7/9kn8iIrKOQS8RkUcVi8Vlz7oCbwcreqgFzOJzLxH5sbOcDGiWUwt6ZVnW9dqh5v0TDocN3Sl2W7v25Zf8ExGRdQx6iYg8yuodqnZ3er3W2XeinF4ro9vUhoCLz/WkVaYBjA2Pdpvf809ERNYx6CUi8qheCnojkQhSqVTjM73lbH7dEXA2oCkUCp57dtlNVtqCJEmNIefK9F5rR+1weDMRETHoJSLyqF4KerPZbGOWaUB/OZtfdyTSAmeHh9NZVoNeZVqR3mvtqB0GvURExKCXiMijeinoNRtUtasj5TJi0BuJRJBMJhuf+Sn/RERkHYNeIiKPahfQ6X3tUHOwkkgk0NfX57nOPoNeZ1WrVZRKpZ4OepXvqwb8lX8iIrKOQS8RkUdJkoRwOIx0Ot34TO8MzGrPunr1/aQMep2lNntxOp1GOBzuqaBXyU/5JyIi6xj0EhF5VLtnXcUyLWrPuor0XuvsM+h1llodhUIh3TMY+z1obJd/TnZGRNQ7GPQSEXlUu866WNYprfL7yvReC1YY9DpL7U6v+LeVoNdr73tuR+s44mRnRES9gUEvEZFH9XrQq+fZZbVJipLJJCKRiOfK6RarbUHrRwlZlu3LqEOsHEdERBQMDHqJiDyq14NeoPOzy2qTFHn12WW3OBX0VqtVzM/P25dRhzDoJSIiBr1ERB7FoFdfOZvTivReK6dbnAp6lev2Mr/nn4iIrGPQS0TkUWqd9Xg8ruu1Q2J5Nptd9rneyYu6RZZl5PN51XwC+oLe5rQAg14lSZIQjUYRj8eXfa6nLVQqFZTL5ZY69lPQqNZG/JR/IiKyjkEvEZFHqQW9eofuimddU6nUss+9FgwWi0XU63Xb7/R6Lbh3k9oQcEBfW9CaBEus2+t4p5eIiBj0EhF5lJWhu2qvO9Kbtpu0ht4ql2ulb1dHfpld2GlW25H4bnNa5XKvqtfryOfzLfnnZGdERL2FQS8RkUdZCejUOvoibaFQQL1ety2fVnS6k2ilnAxozrLajsR3m9OKdXtZqVRSfV81JzsjIuotDHqJiDxIPOtq5Q5du7SyLHvm/aTt7iQmEgndzy4z6NXWqY60XjvUbv9kMplly72qXf7FZ17PPxER2YNBLxGRB5VKJdRqNUeCXrHcC9oFJUaeXWZAo02rjmq1GsrlsmZa8V2lSCSCTCbj+Tpm0EtERACDXiIiT2o3+zLQG0Gv+IxBr3VW2oLfg0a/55+IiOzBoJeIyIN+/OMfAwB+9atftSxLJpOYnZ3VTD8zM9MyczPwdue/U/puOXXqFAAgnU63LEun0zh58mTbtLIsQ5Ik1bTJZBKSJHnm2WU3zc7OIplMtnwuflA5ffp027QnT55Ufd0R0Hn/eIFo52pBbyqVarQ/IiIKNga9REQedMMNN2BoaAh33nlny7LHH38cBw4caHuXql6v41/+5V/w+OOPtyx7+eWXAQDf+9737M2wSd/+9rcBAFNTUy3LXnzxRfzgBz9om/Yf//EfIcsyfvKTn7Qs+8lPfgJZlvGP//iP9mXWh/L5PA4ePIjHHnusZdlzzz0HAPjRj37UNv23v/1tVCoVzMzMtCw7cOCAZ9pROw8//DAA4PDhwy3LnnrqKfz0pz/tdpaIiMgFfW5ngIiIWm3dulU10ACAL33pS/jv//2/qw59BoBwOIxf+7Vfw/XXX9+y7JZbbsGmTZvwb//tv7U1v2bdf//9+NGPfoTVq1e3LPvIRz6CRCLRNu2VV16JTZs24XOf+1zLsi9/+cuYnZ3Fzp07bc2v32QyGVx99dW45557WpZ9+MMfxve+9z3ce++9bdN/9rOfxWOPPYahoaGWZb/5m7+JkZERW/Nrtz/4gz/AiRMncNVVV7Us++xnP4sDBw64kCsiIuq2kKw1bSMRERERERGRj3F4MxEREREREQUWhzcTEbkkl8u1HcKsx/DwMNatW2djjoiIiIiCh0EvEZELcrkctmzZovmO1E5isRi+/vWvY3h4uGWZCKbVlgHA4OCg6nO0gp0Bdafg/sSJEzhz5ozqsk7lWFpaQn9/f9t1d7OcbtKqY636BbTrsFP9A9p13I36tdK+APfzT0REzuMzvURELti9ezcuvfRSPPLII9iyZUvL8hMnTuCDH/wg5ufn264jHA63fSWP1jI9kskk9u/fb7nDrye4t1KOUCgEK5cxu8rppk513KkO/dCO2rHavjoJQvsgIiLe6SUictWWLVuwY8cOPPnkk5BlGevWrcObb76JkydPYn5+vm1QvH//ftxzzz2qy7WW6SHSz8zMWO7sz8zMoFwuO1IOL5XTTVp1bKUO/VC/VtpXJ0FpH0RExImsiIhc9eSTT+LVV1/FxMQEqtUqnn/+eaxfvx6VSgXA2feLxuNxRCIRTE5O4siRIwiHw407W+2WA0C5XMbLL7+surxQKKBQKCCRSGD9+vUIh8OYm5vD0NAQ+vrs/z3UqXJ4rZxuslKHdrajkZERFAoFnD59umtlN1u+XmofRES9jGd0IiIX3XrrrXjHO94BANi2bRsWFhawtLTUGI4plu/atQuDg4OYnp7Gnj17cOjQobbLDx48CAC49NJLsWPHDgBoSV8oFLBt2zZEo1GUy2WUSiXcfPPNAIDZ2VnHymlnOZqX79q1CwAgyzLK5TKefvppXHnllRgfH0csFsPevXtx0003NfLkRDndZKUO7WxHl1xyCSYmJpDJZLpW9nZt4Pnnn29bPjeOAyIicgeDXiIijxAd9tOnT+PAgQMty/ft24dNmzYhFoth586dePDBB1WXZ7NZ1fUr01933XWIxWJ47bXXkEqlunpXy65yNC+//vrrNbc7MTFhLeM+0q6Odu3ataz+t2/frjtt83JlO9q7dy9GR0fx+uuv45prrkEoFHKkXHqI/IVCIdx444146KGHVJc3HwdDQ0PI5/Oo1+uNO8RERBQMDHqJiFy0f/9+AMALL7yAQ4cOYf369ctm0xXLM5kMdu7c2ZJObfnY2NiyZWrp8/k8gLMz1wq7d+9uSWcXJ8rRvLy5Ds8//3xEo1HkcjlUq1UUi0W8+93vbtl2UOipQ6fa0fj4OABg69at2LNnT0s6p5kpH7D8OKjVahgbG8PevXuXpSEiIv/j7M1ERC6wY9ZZP8y66/TszV4pp5s4ezNnbyYiIm0MeomIXNLu/aKyLOPyyy9HX18ffvCDH7RNb+X9qkF/T+/c3Bw++clPYtWqVfi///f/tt12UN7Dyvf08j29RETUHoNeIiKPmZ+fRzKZRCgUQrVa5fOFJrzyyiu46KKLsGbNGhw7dszt7BAREZGL2JMiIvIYSZIAnL3jWygUXM6NP4k6LJVKLueEiIiI3Magl4jIY0TA1vw36SfqrVgsggOaiIiIehuDXiIij2HQa52ot1qtpjnJEREREQUfg14iIo9h0GudJEmNd8WyDomIiHobg14iIo9h0GudJElIpVKNv4mIiKh3MeglIvIYBr3WSZKETCbT+JuIiIh6F4NeIiKPEXcpI5EIAzaTJElCNptt/E1ERES9q8/tDBAR0XKSJGFwcBDlchn5fN7t7PhSPp/HihUrADDoJSIi6nUMeomIPEaSJAwMDCAajTJgM0mSJKxcuRKhUIh1SERE1OM4vJmIyGNE0DswMMCAzSRxtzyTybAOiYiIehyDXiIij2HQax3rkIiIiAQObyYi8hhJkjA8PMzhzRaIoDebzbIOiYiIehzv9BIReQzvUlrHOiQiIiKBd3qJiDxGBGz9/f0M2EyQZZlBLxERETXwTi8Rkcfk83kGbBaUy2XUarVGHfK1T0RERL2NQS8RkcfwLqU1os5Yh0RERARweDMRkacsLi5icXGxMbw5n89DlmWEQiG3s+YbDHqJiIhIiXd6iYg8RBmwZbNZ1Ot1FItFl3PlL6IOs9ksg14iIiJi0EtE5CXNdykB8JlUg0R9Ke/0yrLscq6IiIjILQx6iYg8RC3o5Z1KY5rrsFKpYGFhweVcERERkVv4TC8RkYcoA7ZoNLrsM9JH1Fcmk1n2w0EikXAzW0REROQS3uklIvIQ3um1TpIkZDIZRCIR1iERERHxTi8RkZcoJ2HinV5zxCufADDoJSIiIga9REReIkkSkskkotEoIpEIQqEQAzaDGPQSERGREoc3ExF5iDJgC4fDyGazDNgMYtBLRERESgx6iYg8RBmwAeB7Zk1Q1mE2m218RkRERL2JQS8RkYcw6LVOWYeRSATpdJp1SERE1MMY9BIReQiDXutYh0RERKTEoJeIyEMYsFnHOiQiIiIlBr1ERB4iSVLjOVSAAZsZzUEvJwMjIiLqbQx6iYg8hHcprZFlmXVIREREyzDoJSLykHw+3xKw5fN5F3PkLwsLC6hUKqxDIiIiamDQS0TkIRyaa42oKw4RJyIiIoFBLxGRR1QqFczPz6sOzZVl2cWc+YcIbjm8mYiIiAQGvUREHtEuYKtWq5ifn3crW77CoJeIiIiaMeglIvKIdgGbchlpY9BLREREzRj0EhF5BINe69rV4eLiIhYXF93KFhEREbmIQS8RkUcw6LWu3URWymVERETUWxj0EhF5BINe6yRJQiqVQl9fX+Mz1iEREVFvY9BLROQRDHqta37PMcA6JCIi6nUMeomIPEKSJMTjcfT39zc+y2QyjWXUWfN7jgEGvURERL2OQS8RkUeoBWyRSASZTIYBm04MeomIiKgZg14iIo9QC9gAvnLHCLU6FJNasQ6JiIh6E4NeIiKPkCRp2azDAoNe/dSC3mg0ikQiwTokIiLqUQx6iYg8gnd6rWMdEhERUTMGvUREHsGAzTrWIRERETVj0EtE5BFqr9sBzgZs+XzehRz5j1bQyzokIiLqTQx6iYg8gncprWMdEhERUTMGvUREHsGAzZrFxUUsLi6yDomIiGgZBr1ERB7BoNcaUUesQyIiIlJi0EtE5AGVSgWlUgmZTKZlWTqdxpkzZ7qfKZ85ffo0AKi+9imZTGJ2drbbWSIiIiIPYNBLROQBTz/9NADgySefbFn2xBNPoFKp4Je//GW3s+Urjz76KADgmWeeaVn2+OOP4/Dhw5iZmel2toiIiMhlDHqJiDxg586duOCCC3D//fe3LPt3/+7fYfPmzdi+fbsLOfOPu+66Cxs2bMBHPvKRlmV/+qd/iiuuuAJDQ0Mu5IyIiIjcFJJlWXY7E0RERERERERO4J1eIiIiIiIiCiwGvURERERERBRYfW5ngIioV+RyOc2JlE6cOKE5S/Pg4CBWr17ddvnw8DDWrVtnJYuexzokIiIioxj0EhF1QS6Xw5YtW1Aul9t+JxwOo16vm95GMpnE/v37Axu0sQ6JiIjIDAa9RERdMDMzg3K5jEceeQRbtmxpWb5//37cc889bZd3ItLPzMwENmBjHRIREZEZfKaXiKiLyuUyXn75ZcTjcUQiEUxOTuLIkSMIh8MdlxcKBRQKBSQSCaxfvx7hcBhzc3MYGhpCX1/v/IbZqQ4PHz6su/5GRkZQKBRw8uRJU4EyEREReV/v9JKIiDzg0ksvxY4dOwAAu3btwuDgIKanp3Hw4MFly3ft2gUAkGUZ5XIZTz/9NK688kqMj48jFovhwIEDuPbaaxvrnZ2d7X5hXNKpDm+99Va84x3vWLZsz549KBQK2LZtG6LRKMrlMkqlEi655BJMTEwAAHbv3u1amYiIiMg5vNNLROSiffv2YWBgANlsVnVZIpFANpvFnXfeifPOOw+vv/465ubmUKvVXMitt+zatQv79u3DwsICYrEYtm/f3vIdUb8rV67E+973Ppx33nnI5XJ49dVXcfjwYRdyTURERN3GO71ERF20f//+xt+ZTAY7d+4EAIyNjS1brlwGAPl8HgAwOjqKWq2GdDq97M6kcr1B166OxOed6nBwcBAAMDIy0rN1SERE1EtCsizLbmeCiCjoOPOwdaxDIiIiMoNBLxFRl/Ads9axDomIiMgoBr1EREREREQUWJzIioiIiIiIiAKLQS8REREREREFFoNeIiIiIiIiCiwGvURERERERBRYDHqJiIiIiIgosBj0EhERERERUWAx6CUiIiIiIqLAYtBLREREREREgcWgl4iIiIiIiAKLQS8REREREREFFoNeIiIiIiIiCiwGvURERERERBRYDHqJiIiIiIgosBj0EhERERERUWAx6CUiIiIiIqLAYtBLREREREREgcWgl4iIiIiIiAKLQS8REREREREFFoNeIiIiIiIiCiwGvURERERERBRYDHqJiIiIiIgosBj0EhERERERUWAx6CUiIiIiIqLAYtBLREREREREgcWgl4iIiIiIiAKLQS8REREREREFFoNeIiIiIiIiCiwGvURERERERBRYDHqJiIiIiIgosBj0EhERERERUWAx6CUiIiIiIqLAYtBLREREREREgcWgl4iIiIiIiAKLQS8REREREREFFoNeIiIiIiIiCiwGvURERERERBRYDHqJiIiIiIgosP4/Zlgg8F4X1J8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg = DTR()\n",
    "result=reg.fit(X_train, y_train)\n",
    "ax = subplots(figsize=(12,12))[1]\n",
    "plot_tree(reg,feature_names=feature_names,ax=ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7a678893",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.5790715"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypred=result.predict(X_test)\n",
    "MSE=np.mean((ypred-y_test)**2)\n",
    "MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6b0d4c81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.get_depth()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6558451",
   "metadata": {},
   "source": [
    "The test MSE is 5.56.<br>\n",
    "We have let The depth of the regression tree to be automatically obtained untill all the nodes are pure. The depth is finally 13."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d55b137",
   "metadata": {},
   "source": [
    "### (c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "810cab65",
   "metadata": {},
   "outputs": [],
   "source": [
    "ccp_path = reg.cost_complexity_pruning_path(X_train, y_train)\n",
    "kfold = skm.KFold(5,shuffle=True,random_state=35)\n",
    "grid = skm.GridSearchCV(reg,{'ccp_alpha': ccp_path.ccp_alphas},refit=True,cv=kfold,\n",
    "                        scoring='neg_mean_squared_error')\n",
    "G = grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b184430a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.09846073636363178"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_.ccp_alpha"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a94ae2f",
   "metadata": {},
   "source": [
    "The best tuning $\\alpha$ parameter obtained by corss-validation is 0.098"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "80219fe0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.997636769696412"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model=grid.best_estimator_\n",
    "pruning_MSE=np.mean((y_test-best_model.predict(X_test))**2)\n",
    "pruning_MSE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec128c28",
   "metadata": {},
   "source": [
    "The cost complexity pruning yiealds to a test MSE of approximately 5 which is better than the test MSE previously obtained on the full regression tree."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3828cbe9",
   "metadata": {},
   "source": [
    "### (d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e6236fe5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_features=11, random_state=36)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_features=11, random_state=36)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_features=11, random_state=36)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_seats = RF(max_features=X_train.shape[1], random_state=36)\n",
    "bag_seats.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "98b4fa64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.6145821358499997"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_MSE=np.mean((y_test - bag_seats.predict(X_test))**2)\n",
    "bag_MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2ca578f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>0.291835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShelveLoc[Good]</th>\n",
       "      <td>0.211079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>0.103316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.098761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CompPrice</th>\n",
       "      <td>0.095919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Advertising</th>\n",
       "      <td>0.056774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShelveLoc[Medium]</th>\n",
       "      <td>0.050990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Population</th>\n",
       "      <td>0.044953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>0.031706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urban[Yes]</th>\n",
       "      <td>0.009492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US[Yes]</th>\n",
       "      <td>0.005174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   importance\n",
       "Price                0.291835\n",
       "ShelveLoc[Good]      0.211079\n",
       "Income               0.103316\n",
       "Age                  0.098761\n",
       "CompPrice            0.095919\n",
       "Advertising          0.056774\n",
       "ShelveLoc[Medium]    0.050990\n",
       "Population           0.044953\n",
       "Education            0.031706\n",
       "Urban[Yes]           0.009492\n",
       "US[Yes]              0.005174"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame({'importance':bag_seats.feature_importances_},index=feature_names)\n",
    "df.sort_values(by='importance', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d265a28",
   "metadata": {},
   "source": [
    "The test MSE is nearly halved after use of bagging. Moreover, the top 3 most important predictors of Sales are: Price, ShelveLoc and Income."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98428eae",
   "metadata": {},
   "source": [
    "### (e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e568ad5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#As for the previous exercise, we will change the number of features among p, p/2 and sqrt(p)\n",
    "RF_MSE=np.zeros((1,3))\n",
    "RF_seats1=RF(max_features=1.0,random_state=0).fit(X_train,y_train)\n",
    "RF_seats2=RF(max_features=0.5,random_state=0).fit(X_train,y_train)\n",
    "RF_seats3=RF(max_features='sqrt',random_state=0).fit(X_train,y_train)\n",
    "ypred1=RF_seats1.predict(X_test)\n",
    "ypred2=RF_seats2.predict(X_test)\n",
    "ypred3=RF_seats3.predict(X_test)\n",
    "RF_MSE[0,0]=np.mean((y_test - ypred1)**2)\n",
    "RF_MSE[0,1]=np.mean((y_test - ypred2)**2)\n",
    "RF_MSE[0,2]=np.mean((y_test - ypred3)**2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b10305de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.57109663, 2.75792513, 3.04815656]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RF_MSE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afc0ba01",
   "metadata": {},
   "source": [
    "The best obaitned test MSE with random forests is around 2.60 which is practically the same for bagging.<br>\n",
    "Changing m has resulted in the change of test MSE form 2.60 to 3.05. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fdd28d6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>0.296556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShelveLoc[Good]</th>\n",
       "      <td>0.214089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>0.101554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CompPrice</th>\n",
       "      <td>0.100222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.089137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Advertising</th>\n",
       "      <td>0.056200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShelveLoc[Medium]</th>\n",
       "      <td>0.051287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Population</th>\n",
       "      <td>0.045874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>0.031734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urban[Yes]</th>\n",
       "      <td>0.008122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US[Yes]</th>\n",
       "      <td>0.005225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   importance\n",
       "Price                0.296556\n",
       "ShelveLoc[Good]      0.214089\n",
       "Income               0.101554\n",
       "CompPrice            0.100222\n",
       "Age                  0.089137\n",
       "Advertising          0.056200\n",
       "ShelveLoc[Medium]    0.051287\n",
       "Population           0.045874\n",
       "Education            0.031734\n",
       "Urban[Yes]           0.008122\n",
       "US[Yes]              0.005225"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame({'importance':RF_seats1.feature_importances_},index=feature_names)\n",
    "df.sort_values(by='importance', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc4652f8",
   "metadata": {},
   "source": [
    "As for the previous question, the most important features in Sales prediction are: Price, ShelveLoc and Income."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d4b7d28",
   "metadata": {},
   "source": [
    "### (f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "589956fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#The predefined BART class defined in ISLP contains an error in the fit function\n",
    "#Here we import the updated bart file, downloaded from Jonathan Taylor's github repo\n",
    "import bart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "fd648082",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BART(burnin=5, ndraw=15, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BART</label><div class=\"sk-toggleable__content\"><pre>BART(burnin=5, ndraw=15, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "BART(burnin=5, ndraw=15, random_state=0)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bart_seats = bart.BART(random_state=0, burnin=5, ndraw=15)\n",
    "bart_seats.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b11f1614",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3024315144273175"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat_test = bart_seats.predict(X_test.astype(np.float32))\n",
    "np.mean((y_test - yhat_test)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fb4e2dc",
   "metadata": {},
   "source": [
    "The test MSE obtained with BART is 1.30, which is half of both random forests and bagging approaches.We can see then that BART is truly competitive."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee543c1e",
   "metadata": {},
   "source": [
    "## Exercise 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "89da623a",
   "metadata": {},
   "outputs": [],
   "source": [
    "oj=load_data(\"OJ\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e1441131",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1070 entries, 0 to 1069\n",
      "Data columns (total 18 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Purchase        1070 non-null   object \n",
      " 1   WeekofPurchase  1070 non-null   int64  \n",
      " 2   StoreID         1070 non-null   int64  \n",
      " 3   PriceCH         1070 non-null   float64\n",
      " 4   PriceMM         1070 non-null   float64\n",
      " 5   DiscCH          1070 non-null   float64\n",
      " 6   DiscMM          1070 non-null   float64\n",
      " 7   SpecialCH       1070 non-null   int64  \n",
      " 8   SpecialMM       1070 non-null   int64  \n",
      " 9   LoyalCH         1070 non-null   float64\n",
      " 10  SalePriceMM     1070 non-null   float64\n",
      " 11  SalePriceCH     1070 non-null   float64\n",
      " 12  PriceDiff       1070 non-null   float64\n",
      " 13  Store7          1070 non-null   object \n",
      " 14  PctDiscMM       1070 non-null   float64\n",
      " 15  PctDiscCH       1070 non-null   float64\n",
      " 16  ListPriceDiff   1070 non-null   float64\n",
      " 17  STORE           1070 non-null   int64  \n",
      "dtypes: float64(11), int64(5), object(2)\n",
      "memory usage: 150.6+ KB\n"
     ]
    }
   ],
   "source": [
    "oj.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e049e1b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Purchase</th>\n",
       "      <th>WeekofPurchase</th>\n",
       "      <th>StoreID</th>\n",
       "      <th>PriceCH</th>\n",
       "      <th>PriceMM</th>\n",
       "      <th>DiscCH</th>\n",
       "      <th>DiscMM</th>\n",
       "      <th>SpecialCH</th>\n",
       "      <th>SpecialMM</th>\n",
       "      <th>LoyalCH</th>\n",
       "      <th>SalePriceMM</th>\n",
       "      <th>SalePriceCH</th>\n",
       "      <th>PriceDiff</th>\n",
       "      <th>Store7</th>\n",
       "      <th>PctDiscMM</th>\n",
       "      <th>PctDiscCH</th>\n",
       "      <th>ListPriceDiff</th>\n",
       "      <th>STORE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CH</td>\n",
       "      <td>237</td>\n",
       "      <td>1</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.99</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.24</td>\n",
       "      <td>No</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CH</td>\n",
       "      <td>239</td>\n",
       "      <td>1</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.75</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>No</td>\n",
       "      <td>0.150754</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CH</td>\n",
       "      <td>245</td>\n",
       "      <td>1</td>\n",
       "      <td>1.86</td>\n",
       "      <td>2.09</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>2.09</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.40</td>\n",
       "      <td>No</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.091398</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MM</td>\n",
       "      <td>227</td>\n",
       "      <td>1</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>No</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CH</td>\n",
       "      <td>228</td>\n",
       "      <td>7</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.956535</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Purchase  WeekofPurchase  StoreID  PriceCH  PriceMM  DiscCH  DiscMM  \\\n",
       "0       CH             237        1     1.75     1.99    0.00     0.0   \n",
       "1       CH             239        1     1.75     1.99    0.00     0.3   \n",
       "2       CH             245        1     1.86     2.09    0.17     0.0   \n",
       "3       MM             227        1     1.69     1.69    0.00     0.0   \n",
       "4       CH             228        7     1.69     1.69    0.00     0.0   \n",
       "\n",
       "   SpecialCH  SpecialMM   LoyalCH  SalePriceMM  SalePriceCH  PriceDiff Store7  \\\n",
       "0          0          0  0.500000         1.99         1.75       0.24     No   \n",
       "1          0          1  0.600000         1.69         1.75      -0.06     No   \n",
       "2          0          0  0.680000         2.09         1.69       0.40     No   \n",
       "3          0          0  0.400000         1.69         1.69       0.00     No   \n",
       "4          0          0  0.956535         1.69         1.69       0.00    Yes   \n",
       "\n",
       "   PctDiscMM  PctDiscCH  ListPriceDiff  STORE  \n",
       "0   0.000000   0.000000           0.24      1  \n",
       "1   0.150754   0.000000           0.24      1  \n",
       "2   0.000000   0.091398           0.23      1  \n",
       "3   0.000000   0.000000           0.00      1  \n",
       "4   0.000000   0.000000           0.00      0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oj.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9dc60505",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#By simple conditionning below we check that STORE contains dummy varaibles of the Store7 feature\n",
    "# By displaying the data set we suspect that whenever Store7=='Yes', STORE==0\n",
    "len(oj[((oj['Store7']=='Yes') & (oj['STORE']==1)) | ((oj['Store7']=='No') & (oj['STORE']==0))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0b8afa71",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We conclude from the previous line that STORE contains dummy variables of Store\n",
    "model = MS(oj.columns.drop(['Purchase','Store7']), intercept=False)\n",
    "D = model.fit_transform(oj)\n",
    "feature_names = list(D.columns)\n",
    "X = np.asarray(D)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac2383f3",
   "metadata": {},
   "source": [
    "### (a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ee661a03",
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, X_test, y_train, y_test) = skm.train_test_split(X,oj['Purchase'],train_size=800,\n",
    "                                                          random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75586d77",
   "metadata": {},
   "source": [
    "### (b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "808475e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = DTC(criterion='entropy', random_state=0)\n",
    "result=clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b6329416",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.008750000000000036"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_error=1-accuracy_score(y_train, clf.predict(X_train))\n",
    "training_error"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5da36dc",
   "metadata": {},
   "source": [
    "The training error rate is 0.87%, which is very low. We suspect that model overfitted the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ff983c3",
   "metadata": {},
   "source": [
    "### (c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "477bc457",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = subplots(figsize=(12,12))[1]\n",
    "plot_tree(clf,feature_names=feature_names,ax=ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9b783caa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "171"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.tree_.n_leaves"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b6d6080",
   "metadata": {},
   "source": [
    "Since we fitted the model using the maximal number of subtrees, the plot is hardly interpretable.<br>\n",
    "The number of terminal nodes is 171."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2db8decb",
   "metadata": {},
   "source": [
    "### (d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0fb5219b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- LoyalCH <= 0.51\n",
      "|   |--- LoyalCH <= 0.28\n",
      "|   |   |--- LoyalCH <= 0.04\n",
      "|   |   |   |--- StoreID <= 2.50\n",
      "|   |   |   |   |--- WeekofPurchase <= 269.50\n",
      "|   |   |   |   |   |--- WeekofPurchase <= 267.50\n",
      "|   |   |   |   |   |   |--- weights: [0.00, 2.00] class: MM\n",
      "|   |   |   |   |   |--- WeekofPurchase >  267.50\n",
      "|   |   |   |   |   |   |--- weights: [1.00, 0.00] class: CH\n",
      "|   |   |   |   |--- WeekofPurchase >  269.50\n",
      "|   |   |   |   |   |--- weights: [0.00, 3.00] class: MM\n",
      "|   |   |   |--- StoreID >  2.50\n",
      "|   |   |   |   |--- weights: [0.00, 43.00] class: MM\n",
      "|   |   |--- LoyalCH >  0.04\n",
      "|   |   |   |--- WeekofPurchase <= 273.50\n",
      "|   |   |   |   |--- SalePriceCH <= 1.94\n",
      "|   |   |   |   |   |--- WeekofPurchase <= 263.50\n",
      "|   |   |   |   |   |   |--- STORE <= 1.50\n",
      "|   |   |   |   |   |   |   |--- WeekofPurchase <= 228.00\n",
      "|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |   |--- WeekofPurchase >  228.00\n",
      "|   |   |   |   |   |   |   |   |--- LoyalCH <= 0.04\n",
      "|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |   |   |--- LoyalCH >  0.04\n",
      "|   |   |   |   |   |   |   |   |   |--- LoyalCH <= 0.08\n",
      "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.00] class: MM\n",
      "|   |   |   |   |   |   |   |   |   |--- LoyalCH >  0.08\n",
      "|   |   |   |   |   |   |   |   |   |   |--- LoyalCH <= 0.09\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |   |   |   |   |--- LoyalCH >  0.09\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |--- STORE >  1.50\n",
      "|   |   |   |   |   |   |   |--- PriceDiff <= 0.27\n",
      "|   |   |   |   |   |   |   |   |--- weights: [0.00, 23.00] class: MM\n",
      "|   |   |   |   |   |   |   |--- PriceDiff >  0.27\n",
      "|   |   |   |   |   |   |   |   |--- WeekofPurchase <= 236.50\n",
      "|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.00] class: MM\n",
      "|   |   |   |   |   |   |   |   |--- WeekofPurchase >  236.50\n",
      "|   |   |   |   |   |   |   |   |   |--- PriceMM <= 2.13\n",
      "|   |   |   |   |   |   |   |   |   |   |--- LoyalCH <= 0.05\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: MM\n",
      "|   |   |   |   |   |   |   |   |   |   |--- LoyalCH >  0.05\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- PriceMM >  2.13\n",
      "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: MM\n",
      "|   |   |   |   |   |--- WeekofPurchase >  263.50\n",
      "|   |   |   |   |   |   |--- SalePriceMM <= 2.12\n",
      "|   |   |   |   |   |   |   |--- WeekofPurchase <= 272.50\n",
      "|   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: MM\n",
      "|   |   |   |   |   |   |   |--- WeekofPurchase >  272.50\n",
      "|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |--- SalePriceMM >  2.12\n",
      "|   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: CH\n",
      "|   |   |   |   |--- SalePriceCH >  1.94\n",
      "|   |   |   |   |   |--- ListPriceDiff <= 0.17\n",
      "|   |   |   |   |   |   |--- weights: [2.00, 0.00] class: CH\n",
      "|   |   |   |   |   |--- ListPriceDiff >  0.17\n",
      "|   |   |   |   |   |   |--- LoyalCH <= 0.10\n",
      "|   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: MM\n",
      "|   |   |   |   |   |   |--- LoyalCH >  0.10\n",
      "|   |   |   |   |   |   |   |--- WeekofPurchase <= 252.50\n",
      "|   |   |   |   |   |   |   |   |--- WeekofPurchase <= 247.00\n",
      "|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |   |   |--- WeekofPurchase >  247.00\n",
      "|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: MM\n",
      "|   |   |   |   |   |   |   |--- WeekofPurchase >  252.50\n",
      "|   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: CH\n",
      "|   |   |   |--- WeekofPurchase >  273.50\n",
      "|   |   |   |   |--- weights: [0.00, 16.00] class: MM\n",
      "|   |--- LoyalCH >  0.28\n",
      "|   |   |--- PriceDiff <= 0.05\n",
      "|   |   |   |--- PriceDiff <= -0.24\n",
      "|   |   |   |   |--- weights: [0.00, 12.00] class: MM\n",
      "|   |   |   |--- PriceDiff >  -0.24\n",
      "|   |   |   |   |--- StoreID <= 1.50\n",
      "|   |   |   |   |   |--- SalePriceMM <= 1.84\n",
      "|   |   |   |   |   |   |--- weights: [0.00, 14.00] class: MM\n",
      "|   |   |   |   |   |--- SalePriceMM >  1.84\n",
      "|   |   |   |   |   |   |--- weights: [1.00, 0.00] class: CH\n",
      "|   |   |   |   |--- StoreID >  1.50\n",
      "|   |   |   |   |   |--- SalePriceMM <= 1.84\n",
      "|   |   |   |   |   |   |--- PctDiscCH <= 0.07\n",
      "|   |   |   |   |   |   |   |--- StoreID <= 3.50\n",
      "|   |   |   |   |   |   |   |   |--- LoyalCH <= 0.39\n",
      "|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: MM\n",
      "|   |   |   |   |   |   |   |   |--- LoyalCH >  0.39\n",
      "|   |   |   |   |   |   |   |   |   |--- WeekofPurchase <= 240.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- WeekofPurchase <= 230.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- WeekofPurchase >  230.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: MM\n",
      "|   |   |   |   |   |   |   |   |   |--- WeekofPurchase >  240.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- STORE <= 2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |   |   |   |   |--- STORE >  2.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |--- StoreID >  3.50\n",
      "|   |   |   |   |   |   |   |   |--- WeekofPurchase <= 236.50\n",
      "|   |   |   |   |   |   |   |   |   |--- WeekofPurchase <= 233.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- LoyalCH <= 0.37\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |   |   |   |   |--- LoyalCH >  0.37\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |--- WeekofPurchase >  233.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: MM\n",
      "|   |   |   |   |   |   |   |   |--- WeekofPurchase >  236.50\n",
      "|   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |--- PctDiscCH >  0.07\n",
      "|   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: MM\n",
      "|   |   |   |   |   |--- SalePriceMM >  1.84\n",
      "|   |   |   |   |   |   |--- weights: [0.00, 6.00] class: MM\n",
      "|   |   |--- PriceDiff >  0.05\n",
      "|   |   |   |--- SalePriceMM <= 2.21\n",
      "|   |   |   |   |--- LoyalCH <= 0.31\n",
      "|   |   |   |   |   |--- weights: [6.00, 0.00] class: CH\n",
      "|   |   |   |   |--- LoyalCH >  0.31\n",
      "|   |   |   |   |   |--- LoyalCH <= 0.48\n",
      "|   |   |   |   |   |   |--- WeekofPurchase <= 245.00\n",
      "|   |   |   |   |   |   |   |--- WeekofPurchase <= 234.00\n",
      "|   |   |   |   |   |   |   |   |--- LoyalCH <= 0.46\n",
      "|   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |   |   |--- LoyalCH >  0.46\n",
      "|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: MM\n",
      "|   |   |   |   |   |   |   |--- WeekofPurchase >  234.00\n",
      "|   |   |   |   |   |   |   |   |--- LoyalCH <= 0.45\n",
      "|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 10.00] class: MM\n",
      "|   |   |   |   |   |   |   |   |--- LoyalCH >  0.45\n",
      "|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |--- WeekofPurchase >  245.00\n",
      "|   |   |   |   |   |   |   |--- SpecialMM <= 0.50\n",
      "|   |   |   |   |   |   |   |   |--- LoyalCH <= 0.34\n",
      "|   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |   |   |--- LoyalCH >  0.34\n",
      "|   |   |   |   |   |   |   |   |   |--- WeekofPurchase <= 267.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- LoyalCH <= 0.34\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: MM\n",
      "|   |   |   |   |   |   |   |   |   |   |--- LoyalCH >  0.34\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |--- WeekofPurchase >  267.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- PctDiscMM <= 0.14\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [7.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |   |   |   |   |--- PctDiscMM >  0.14\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |--- SpecialMM >  0.50\n",
      "|   |   |   |   |   |   |   |   |--- LoyalCH <= 0.38\n",
      "|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: MM\n",
      "|   |   |   |   |   |   |   |   |--- LoyalCH >  0.38\n",
      "|   |   |   |   |   |   |   |   |   |--- WeekofPurchase <= 272.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- LoyalCH <= 0.42\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: CH\n",
      "|   |   |   |   |   |   |   |   |   |   |--- LoyalCH >  0.42\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: MM\n",
      "|   |   |   |   |   |   |   |   |   |--- WeekofPurchase >  272.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: CH\n",
      "|   |   |   |   |   |--- LoyalCH >  0.48\n",
      "|   |   |   |   |   |   |--- PriceCH <= 1.88\n",
      "|   |   |   |   |   |   |   |--- SalePriceCH <= 1.77\n",
      "|   |   |   |   |   |   |   |   |--- STORE <= 1.50\n",
      "|   |   |   |   |   |   |   |   |   |--- SalePriceMM <= 1.54\n",
      "|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |   |   |   |--- SalePriceMM >  1.54\n",
      "|   |   |   |   |   |   |   |   |   |   |--- WeekofPurchase <= 235.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: MM\n",
      "|   |   |   |   |   |   |   |   |   |   |--- WeekofPurchase >  235.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |--- STORE >  1.50\n",
      "|   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |   |--- SalePriceCH >  1.77\n",
      "|   |   |   |   |   |   |   |   |--- weights: [11.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |--- PriceCH >  1.88\n",
      "|   |   |   |   |   |   |   |--- WeekofPurchase <= 262.50\n",
      "|   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: MM\n",
      "|   |   |   |   |   |   |   |--- WeekofPurchase >  262.50\n",
      "|   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: CH\n",
      "|   |   |   |--- SalePriceMM >  2.21\n",
      "|   |   |   |   |--- LoyalCH <= 0.39\n",
      "|   |   |   |   |   |--- weights: [0.00, 6.00] class: MM\n",
      "|   |   |   |   |--- LoyalCH >  0.39\n",
      "|   |   |   |   |   |--- PriceDiff <= 0.37\n",
      "|   |   |   |   |   |   |--- ListPriceDiff <= 0.27\n",
      "|   |   |   |   |   |   |   |--- WeekofPurchase <= 249.00\n",
      "|   |   |   |   |   |   |   |   |--- LoyalCH <= 0.41\n",
      "|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: MM\n",
      "|   |   |   |   |   |   |   |   |--- LoyalCH >  0.41\n",
      "|   |   |   |   |   |   |   |   |   |--- LoyalCH <= 0.48\n",
      "|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |   |   |   |--- LoyalCH >  0.48\n",
      "|   |   |   |   |   |   |   |   |   |   |--- STORE <= 3.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- STORE >  3.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |   |--- WeekofPurchase >  249.00\n",
      "|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: MM\n",
      "|   |   |   |   |   |   |--- ListPriceDiff >  0.27\n",
      "|   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: CH\n",
      "|   |   |   |   |   |--- PriceDiff >  0.37\n",
      "|   |   |   |   |   |   |--- weights: [0.00, 3.00] class: MM\n",
      "|--- LoyalCH >  0.51\n",
      "|   |--- LoyalCH <= 0.71\n",
      "|   |   |--- PriceDiff <= 0.27\n",
      "|   |   |   |--- ListPriceDiff <= 0.16\n",
      "|   |   |   |   |--- DiscMM <= 0.47\n",
      "|   |   |   |   |   |--- LoyalCH <= 0.56\n",
      "|   |   |   |   |   |   |--- weights: [0.00, 3.00] class: MM\n",
      "|   |   |   |   |   |--- LoyalCH >  0.56\n",
      "|   |   |   |   |   |   |--- LoyalCH <= 0.57\n",
      "|   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |--- LoyalCH >  0.57\n",
      "|   |   |   |   |   |   |   |--- WeekofPurchase <= 272.50\n",
      "|   |   |   |   |   |   |   |   |--- PriceDiff <= -0.25\n",
      "|   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |   |   |--- PriceDiff >  -0.25\n",
      "|   |   |   |   |   |   |   |   |   |--- LoyalCH <= 0.60\n",
      "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: MM\n",
      "|   |   |   |   |   |   |   |   |   |--- LoyalCH >  0.60\n",
      "|   |   |   |   |   |   |   |   |   |   |--- LoyalCH <= 0.69\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12\n",
      "|   |   |   |   |   |   |   |   |   |   |--- LoyalCH >  0.69\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: MM\n",
      "|   |   |   |   |   |   |   |--- WeekofPurchase >  272.50\n",
      "|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: MM\n",
      "|   |   |   |   |--- DiscMM >  0.47\n",
      "|   |   |   |   |   |--- weights: [0.00, 6.00] class: MM\n",
      "|   |   |   |--- ListPriceDiff >  0.16\n",
      "|   |   |   |   |--- StoreID <= 2.50\n",
      "|   |   |   |   |   |--- WeekofPurchase <= 236.50\n",
      "|   |   |   |   |   |   |--- weights: [0.00, 3.00] class: MM\n",
      "|   |   |   |   |   |--- WeekofPurchase >  236.50\n",
      "|   |   |   |   |   |   |--- DiscMM <= 0.03\n",
      "|   |   |   |   |   |   |   |--- StoreID <= 1.50\n",
      "|   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |   |--- StoreID >  1.50\n",
      "|   |   |   |   |   |   |   |   |--- LoyalCH <= 0.58\n",
      "|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: MM\n",
      "|   |   |   |   |   |   |   |   |--- LoyalCH >  0.58\n",
      "|   |   |   |   |   |   |   |   |   |--- LoyalCH <= 0.64\n",
      "|   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |   |   |   |--- LoyalCH >  0.64\n",
      "|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: CH\n",
      "|   |   |   |   |   |   |--- DiscMM >  0.03\n",
      "|   |   |   |   |   |   |   |--- LoyalCH <= 0.55\n",
      "|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |   |--- LoyalCH >  0.55\n",
      "|   |   |   |   |   |   |   |   |--- ListPriceDiff <= 0.20\n",
      "|   |   |   |   |   |   |   |   |   |--- LoyalCH <= 0.62\n",
      "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: MM\n",
      "|   |   |   |   |   |   |   |   |   |--- LoyalCH >  0.62\n",
      "|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |   |   |--- ListPriceDiff >  0.20\n",
      "|   |   |   |   |   |   |   |   |   |--- PriceDiff <= -0.02\n",
      "|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: MM\n",
      "|   |   |   |   |   |   |   |   |   |--- PriceDiff >  -0.02\n",
      "|   |   |   |   |   |   |   |   |   |   |--- LoyalCH <= 0.58\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |   |   |   |   |--- LoyalCH >  0.58\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |--- StoreID >  2.50\n",
      "|   |   |   |   |   |--- LoyalCH <= 0.69\n",
      "|   |   |   |   |   |   |--- LoyalCH <= 0.52\n",
      "|   |   |   |   |   |   |   |--- PriceDiff <= 0.12\n",
      "|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: MM\n",
      "|   |   |   |   |   |   |   |--- PriceDiff >  0.12\n",
      "|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |--- LoyalCH >  0.52\n",
      "|   |   |   |   |   |   |   |--- weights: [18.00, 0.00] class: CH\n",
      "|   |   |   |   |   |--- LoyalCH >  0.69\n",
      "|   |   |   |   |   |   |--- weights: [0.00, 1.00] class: MM\n",
      "|   |   |--- PriceDiff >  0.27\n",
      "|   |   |   |--- LoyalCH <= 0.68\n",
      "|   |   |   |   |--- LoyalCH <= 0.61\n",
      "|   |   |   |   |   |--- WeekofPurchase <= 262.50\n",
      "|   |   |   |   |   |   |--- WeekofPurchase <= 260.00\n",
      "|   |   |   |   |   |   |   |--- ListPriceDiff <= 0.26\n",
      "|   |   |   |   |   |   |   |   |--- WeekofPurchase <= 251.50\n",
      "|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: MM\n",
      "|   |   |   |   |   |   |   |   |--- WeekofPurchase >  251.50\n",
      "|   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |   |--- ListPriceDiff >  0.26\n",
      "|   |   |   |   |   |   |   |   |--- weights: [16.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |--- WeekofPurchase >  260.00\n",
      "|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: MM\n",
      "|   |   |   |   |   |--- WeekofPurchase >  262.50\n",
      "|   |   |   |   |   |   |--- weights: [13.00, 0.00] class: CH\n",
      "|   |   |   |   |--- LoyalCH >  0.61\n",
      "|   |   |   |   |   |--- weights: [26.00, 0.00] class: CH\n",
      "|   |   |   |--- LoyalCH >  0.68\n",
      "|   |   |   |   |--- StoreID <= 3.50\n",
      "|   |   |   |   |   |--- weights: [1.00, 0.00] class: CH\n",
      "|   |   |   |   |--- StoreID >  3.50\n",
      "|   |   |   |   |   |--- weights: [0.00, 1.00] class: MM\n",
      "|   |--- LoyalCH >  0.71\n",
      "|   |   |--- PriceDiff <= -0.39\n",
      "|   |   |   |--- STORE <= 1.50\n",
      "|   |   |   |   |--- LoyalCH <= 0.98\n",
      "|   |   |   |   |   |--- weights: [0.00, 3.00] class: MM\n",
      "|   |   |   |   |--- LoyalCH >  0.98\n",
      "|   |   |   |   |   |--- WeekofPurchase <= 275.50\n",
      "|   |   |   |   |   |   |--- LoyalCH <= 1.00\n",
      "|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: MM\n",
      "|   |   |   |   |   |   |--- LoyalCH >  1.00\n",
      "|   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: CH\n",
      "|   |   |   |   |   |--- WeekofPurchase >  275.50\n",
      "|   |   |   |   |   |   |--- weights: [2.00, 0.00] class: CH\n",
      "|   |   |   |--- STORE >  1.50\n",
      "|   |   |   |   |--- weights: [6.00, 0.00] class: CH\n",
      "|   |   |--- PriceDiff >  -0.39\n",
      "|   |   |   |--- StoreID <= 5.50\n",
      "|   |   |   |   |--- SalePriceCH <= 1.77\n",
      "|   |   |   |   |   |--- weights: [32.00, 0.00] class: CH\n",
      "|   |   |   |   |--- SalePriceCH >  1.77\n",
      "|   |   |   |   |   |--- WeekofPurchase <= 271.50\n",
      "|   |   |   |   |   |   |--- LoyalCH <= 0.99\n",
      "|   |   |   |   |   |   |   |--- LoyalCH <= 0.98\n",
      "|   |   |   |   |   |   |   |   |--- ListPriceDiff <= 0.31\n",
      "|   |   |   |   |   |   |   |   |   |--- WeekofPurchase <= 236.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- weights: [11.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |   |   |   |--- WeekofPurchase >  236.50\n",
      "|   |   |   |   |   |   |   |   |   |   |--- WeekofPurchase <= 237.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: MM\n",
      "|   |   |   |   |   |   |   |   |   |   |--- WeekofPurchase >  237.50\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14\n",
      "|   |   |   |   |   |   |   |   |--- ListPriceDiff >  0.31\n",
      "|   |   |   |   |   |   |   |   |   |--- weights: [24.00, 0.00] class: CH\n",
      "|   |   |   |   |   |   |   |--- LoyalCH >  0.98\n",
      "|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: MM\n",
      "|   |   |   |   |   |   |--- LoyalCH >  0.99\n",
      "|   |   |   |   |   |   |   |--- weights: [13.00, 0.00] class: CH\n",
      "|   |   |   |   |   |--- WeekofPurchase >  271.50\n",
      "|   |   |   |   |   |   |--- weights: [27.00, 0.00] class: CH\n",
      "|   |   |   |--- StoreID >  5.50\n",
      "|   |   |   |   |--- WeekofPurchase <= 228.50\n",
      "|   |   |   |   |   |--- LoyalCH <= 0.85\n",
      "|   |   |   |   |   |   |--- weights: [0.00, 1.00] class: MM\n",
      "|   |   |   |   |   |--- LoyalCH >  0.85\n",
      "|   |   |   |   |   |   |--- weights: [1.00, 0.00] class: CH\n",
      "|   |   |   |   |--- WeekofPurchase >  228.50\n",
      "|   |   |   |   |   |--- weights: [118.00, 0.00] class: CH\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(export_text(clf,feature_names=feature_names,show_weights=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38edef9b",
   "metadata": {},
   "source": [
    "Let's take for instance the first terminal node shown in the previous output.<br>\n",
    "That node was classified as MM based on 2 votes over nil. It was obtained after splitting feature LoyalCH thrice, StoreID once and WeekofPurchase twice. Basically, it conveys that any Purchase for which $LoyalCH \\le 0,04, \\;  StoreID \\le 2,50\\;and\\;  WeekofPurchase \\le 267,50$ is classified as $MM$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8b3395c",
   "metadata": {},
   "source": [
    "### (e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "36a784c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Truth</th>\n",
       "      <th>CH</th>\n",
       "      <th>MM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Predicted</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <td>126</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MM</th>\n",
       "      <td>27</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Truth       CH  MM\n",
       "Predicted         \n",
       "CH         126  33\n",
       "MM          27  84"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion = confusion_table(clf.predict(X_test),y_test)\n",
    "confusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9a978b5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2222222222222222"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_error=(confusion.iloc[0,1]+confusion.iloc[1,0])/np.sum(confusion.values)\n",
    "test_error"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c02f477f",
   "metadata": {},
   "source": [
    "The test error is 22,22%, which is way higher than training error. The suspected overfitting is confirmed, we should look for the optimal tree size."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7424f724",
   "metadata": {},
   "source": [
    "### (f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a3ffc7f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The optimal tree size is 3\n"
     ]
    }
   ],
   "source": [
    "M=np.zeros(clf.get_depth())\n",
    "for i in range(1,clf.get_depth()+1):\n",
    "    clf1 = DTC(criterion='entropy', random_state=20,max_depth=i)\n",
    "    results1 = skm.cross_val_score(clf1,X_train,y_train,cv=10)#10-fold\n",
    "    M[i-1]=results1.mean()\n",
    "print('The optimal tree size is',M.argmax()+1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d1f5a4b",
   "metadata": {},
   "source": [
    "### (g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f872308f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=subplots(figsize=(8,8))\n",
    "ax.plot(range(1,clf.get_depth()+1),1-M)\n",
    "ax.set_xlabel(\"Tree size\")\n",
    "ax.set_ylabel(\"CV calssification error\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0378b5f9",
   "metadata": {},
   "source": [
    "### (h)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2fabdeb",
   "metadata": {},
   "source": [
    "The best tree size is 3."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b06ab44e",
   "metadata": {},
   "source": [
    "### (i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cd28b19c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=3, random_state=20)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=3, random_state=20)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=20)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_pruned=DTC(criterion='entropy', random_state=20,max_depth=M.argmax()+1)\n",
    "clf_pruned.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "121d7332",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = subplots(figsize=(12, 12))[1]\n",
    "plot_tree(clf_pruned,feature_names=feature_names,ax=ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e43e704",
   "metadata": {},
   "source": [
    "### (j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b2c4abfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1775"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_error_pruned=1-accuracy_score(y_train, clf_pruned.predict(X_train))\n",
    "training_error_pruned"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e61c9685",
   "metadata": {},
   "source": [
    "The training error is 17.75% way larger than the training error obtained with the full tree. This was expected as in question (b) there was overfit."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ce6b677",
   "metadata": {},
   "source": [
    "### (k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9b339ecc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18888888888888888"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_error_pruned=1-accuracy_score(y_test,clf_pruned.predict(X_test))\n",
    "test_error_pruned"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e63ea15",
   "metadata": {},
   "source": [
    "Now the test error is better (18,88%) than the test error obtained with the full tree (22,22%)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "836ccc01",
   "metadata": {},
   "source": [
    "## Exercise 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4cd7097d",
   "metadata": {},
   "outputs": [],
   "source": [
    "Hitters=load_data('Hitters')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4997fcbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 322 entries, 0 to 321\n",
      "Data columns (total 20 columns):\n",
      " #   Column     Non-Null Count  Dtype   \n",
      "---  ------     --------------  -----   \n",
      " 0   AtBat      322 non-null    int64   \n",
      " 1   Hits       322 non-null    int64   \n",
      " 2   HmRun      322 non-null    int64   \n",
      " 3   Runs       322 non-null    int64   \n",
      " 4   RBI        322 non-null    int64   \n",
      " 5   Walks      322 non-null    int64   \n",
      " 6   Years      322 non-null    int64   \n",
      " 7   CAtBat     322 non-null    int64   \n",
      " 8   CHits      322 non-null    int64   \n",
      " 9   CHmRun     322 non-null    int64   \n",
      " 10  CRuns      322 non-null    int64   \n",
      " 11  CRBI       322 non-null    int64   \n",
      " 12  CWalks     322 non-null    int64   \n",
      " 13  League     322 non-null    category\n",
      " 14  Division   322 non-null    category\n",
      " 15  PutOuts    322 non-null    int64   \n",
      " 16  Assists    322 non-null    int64   \n",
      " 17  Errors     322 non-null    int64   \n",
      " 18  Salary     263 non-null    float64 \n",
      " 19  NewLeague  322 non-null    category\n",
      "dtypes: category(3), float64(1), int64(16)\n",
      "memory usage: 44.2 KB\n"
     ]
    }
   ],
   "source": [
    "Hitters.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "266b7cc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "Hitters=Hitters.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "11d67553",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
       "      <th>Years</th>\n",
       "      <th>CAtBat</th>\n",
       "      <th>CHits</th>\n",
       "      <th>CHmRun</th>\n",
       "      <th>CRuns</th>\n",
       "      <th>CRBI</th>\n",
       "      <th>CWalks</th>\n",
       "      <th>League</th>\n",
       "      <th>Division</th>\n",
       "      <th>PutOuts</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Errors</th>\n",
       "      <th>Salary</th>\n",
       "      <th>NewLeague</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>315</td>\n",
       "      <td>81</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "      <td>14</td>\n",
       "      <td>3449</td>\n",
       "      <td>835</td>\n",
       "      <td>69</td>\n",
       "      <td>321</td>\n",
       "      <td>414</td>\n",
       "      <td>375</td>\n",
       "      <td>N</td>\n",
       "      <td>W</td>\n",
       "      <td>632</td>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
       "      <td>475.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>479</td>\n",
       "      <td>130</td>\n",
       "      <td>18</td>\n",
       "      <td>66</td>\n",
       "      <td>72</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "      <td>1624</td>\n",
       "      <td>457</td>\n",
       "      <td>63</td>\n",
       "      <td>224</td>\n",
       "      <td>266</td>\n",
       "      <td>263</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>880</td>\n",
       "      <td>82</td>\n",
       "      <td>14</td>\n",
       "      <td>480.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>496</td>\n",
       "      <td>141</td>\n",
       "      <td>20</td>\n",
       "      <td>65</td>\n",
       "      <td>78</td>\n",
       "      <td>37</td>\n",
       "      <td>11</td>\n",
       "      <td>5628</td>\n",
       "      <td>1575</td>\n",
       "      <td>225</td>\n",
       "      <td>828</td>\n",
       "      <td>838</td>\n",
       "      <td>354</td>\n",
       "      <td>N</td>\n",
       "      <td>E</td>\n",
       "      <td>200</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>500.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>321</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>39</td>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>396</td>\n",
       "      <td>101</td>\n",
       "      <td>12</td>\n",
       "      <td>48</td>\n",
       "      <td>46</td>\n",
       "      <td>33</td>\n",
       "      <td>N</td>\n",
       "      <td>E</td>\n",
       "      <td>805</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>91.5</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>594</td>\n",
       "      <td>169</td>\n",
       "      <td>4</td>\n",
       "      <td>74</td>\n",
       "      <td>51</td>\n",
       "      <td>35</td>\n",
       "      <td>11</td>\n",
       "      <td>4408</td>\n",
       "      <td>1133</td>\n",
       "      <td>19</td>\n",
       "      <td>501</td>\n",
       "      <td>336</td>\n",
       "      <td>194</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>282</td>\n",
       "      <td>421</td>\n",
       "      <td>25</td>\n",
       "      <td>750.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AtBat  Hits  HmRun  Runs  RBI  Walks  Years  CAtBat  CHits  CHmRun  CRuns  \\\n",
       "1    315    81      7    24   38     39     14    3449    835      69    321   \n",
       "2    479   130     18    66   72     76      3    1624    457      63    224   \n",
       "3    496   141     20    65   78     37     11    5628   1575     225    828   \n",
       "4    321    87     10    39   42     30      2     396    101      12     48   \n",
       "5    594   169      4    74   51     35     11    4408   1133      19    501   \n",
       "\n",
       "   CRBI  CWalks League Division  PutOuts  Assists  Errors  Salary NewLeague  \n",
       "1   414     375      N        W      632       43      10   475.0         N  \n",
       "2   266     263      A        W      880       82      14   480.0         A  \n",
       "3   838     354      N        E      200       11       3   500.0         N  \n",
       "4    46      33      N        E      805       40       4    91.5         N  \n",
       "5   336     194      A        W      282      421      25   750.0         A  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Hitters.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ef223ad2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['N', 'A']\n",
       "Categories (2, object): ['A', 'N']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Hitters['League'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "553ec1f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['W', 'E']\n",
       "Categories (2, object): ['E', 'W']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Hitters['Division'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "22972384",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['N', 'A']\n",
       "Categories (2, object): ['A', 'N']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Hitters['NewLeague'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7030783",
   "metadata": {},
   "source": [
    "### (a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "79f4c9d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "Hitters['League']=np.where(Hitters['League']=='A',1,0)\n",
    "Hitters['Division']=np.where(Hitters['Division']=='E',1,0)\n",
    "Hitters['NewLeague']=np.where(Hitters['NewLeague']=='A',1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dda87b2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "Hitters['LogSalary']=np.log(Hitters['Salary'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0af91692",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
       "      <th>Years</th>\n",
       "      <th>CAtBat</th>\n",
       "      <th>CHits</th>\n",
       "      <th>CHmRun</th>\n",
       "      <th>...</th>\n",
       "      <th>CRBI</th>\n",
       "      <th>CWalks</th>\n",
       "      <th>League</th>\n",
       "      <th>Division</th>\n",
       "      <th>PutOuts</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Errors</th>\n",
       "      <th>Salary</th>\n",
       "      <th>NewLeague</th>\n",
       "      <th>LogSalary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>315</td>\n",
       "      <td>81</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "      <td>14</td>\n",
       "      <td>3449</td>\n",
       "      <td>835</td>\n",
       "      <td>69</td>\n",
       "      <td>...</td>\n",
       "      <td>414</td>\n",
       "      <td>375</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>632</td>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
       "      <td>475.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.163315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>479</td>\n",
       "      <td>130</td>\n",
       "      <td>18</td>\n",
       "      <td>66</td>\n",
       "      <td>72</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "      <td>1624</td>\n",
       "      <td>457</td>\n",
       "      <td>63</td>\n",
       "      <td>...</td>\n",
       "      <td>266</td>\n",
       "      <td>263</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>880</td>\n",
       "      <td>82</td>\n",
       "      <td>14</td>\n",
       "      <td>480.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.173786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>496</td>\n",
       "      <td>141</td>\n",
       "      <td>20</td>\n",
       "      <td>65</td>\n",
       "      <td>78</td>\n",
       "      <td>37</td>\n",
       "      <td>11</td>\n",
       "      <td>5628</td>\n",
       "      <td>1575</td>\n",
       "      <td>225</td>\n",
       "      <td>...</td>\n",
       "      <td>838</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>200</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.214608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>321</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>39</td>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>396</td>\n",
       "      <td>101</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>46</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>805</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>91.5</td>\n",
       "      <td>0</td>\n",
       "      <td>4.516339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>594</td>\n",
       "      <td>169</td>\n",
       "      <td>4</td>\n",
       "      <td>74</td>\n",
       "      <td>51</td>\n",
       "      <td>35</td>\n",
       "      <td>11</td>\n",
       "      <td>4408</td>\n",
       "      <td>1133</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>336</td>\n",
       "      <td>194</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>282</td>\n",
       "      <td>421</td>\n",
       "      <td>25</td>\n",
       "      <td>750.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.620073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   AtBat  Hits  HmRun  Runs  RBI  Walks  Years  CAtBat  CHits  CHmRun  ...  \\\n",
       "1    315    81      7    24   38     39     14    3449    835      69  ...   \n",
       "2    479   130     18    66   72     76      3    1624    457      63  ...   \n",
       "3    496   141     20    65   78     37     11    5628   1575     225  ...   \n",
       "4    321    87     10    39   42     30      2     396    101      12  ...   \n",
       "5    594   169      4    74   51     35     11    4408   1133      19  ...   \n",
       "\n",
       "   CRBI  CWalks  League  Division  PutOuts  Assists  Errors  Salary  \\\n",
       "1   414     375       0         0      632       43      10   475.0   \n",
       "2   266     263       1         0      880       82      14   480.0   \n",
       "3   838     354       0         1      200       11       3   500.0   \n",
       "4    46      33       0         1      805       40       4    91.5   \n",
       "5   336     194       1         0      282      421      25   750.0   \n",
       "\n",
       "   NewLeague  LogSalary  \n",
       "1          0   6.163315  \n",
       "2          1   6.173786  \n",
       "3          0   6.214608  \n",
       "4          0   4.516339  \n",
       "5          1   6.620073  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Hitters.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69c289e4",
   "metadata": {},
   "source": [
    "### (b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3a0fa30f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=Hitters.drop(['Salary','LogSalary'],axis=1)\n",
    "y=Hitters['LogSalary']\n",
    "(X_train, X_test, y_train, y_test) = skm.train_test_split(X,y,train_size=200,\n",
    "                                                          random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb035345",
   "metadata": {},
   "source": [
    "### (c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2d527fdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "lambdagrid=np.linspace(0.001,0.04)#Typical values of lambda range between 0.001 and 0.01 according to\n",
    "# page 349 of the book. We chose 0.04 insetead of 0.01 to show the stabilization of training MSE\n",
    "training_mse=np.zeros(lambdagrid.shape[0])\n",
    "for i in range(lambdagrid.shape[0]):\n",
    "    boost_Hitters = GBR(n_estimators=1000,learning_rate=lambdagrid[i],random_state=3)\n",
    "    boost_Hitters.fit(X_train, y_train)\n",
    "    training_mse[i] = np.mean((y_train- boost_Hitters.predict(X_train))**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f498c85d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=subplots(figsize=(10,10))\n",
    "ax.plot(lambdagrid,training_mse)\n",
    "ax.set_xlabel('$\\lambda$')\n",
    "ax.set_ylabel('Training MSE');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76706f3f",
   "metadata": {},
   "source": [
    "### (d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "57f305c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_mse=np.zeros(lambdagrid.shape[0])\n",
    "for i in range(lambdagrid.shape[0]):\n",
    "    boost_Hitters = GBR(n_estimators=1000,learning_rate=lambdagrid[i],random_state=3)\n",
    "    boost_Hitters.fit(X_train, y_train)\n",
    "    test_mse[i] = np.mean((y_test- boost_Hitters.predict(X_test))**2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2edc6e94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=subplots(figsize=(10,10))\n",
    "ax.plot(lambdagrid,test_mse)\n",
    "ax.set_xlabel('$\\lambda$')\n",
    "ax.set_ylabel('Test MSE');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06be1d7d",
   "metadata": {},
   "source": [
    "### (e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c0921d99",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Multiple linear regression\n",
    "mlr_Hitters=LinearRegression().fit(X_train, y_train)\n",
    "mlr_testmse=np.mean((y_test- mlr_Hitters.predict(X_test))**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "afba0ee9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ridge regression\n",
    "ridge_Hitters=Ridge().fit(X_train,y_train)\n",
    "ridge_testmse=np.mean((y_test- ridge_Hitters.predict(X_test))**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3b724bb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Test Mse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Multiple linear regression</th>\n",
       "      <td>0.452639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridge</th>\n",
       "      <td>0.454074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boosting</th>\n",
       "      <td>0.220185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Test Mse\n",
       "Multiple linear regression  0.452639\n",
       "Ridge                       0.454074\n",
       "Boosting                    0.220185"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display a barplot which places different test MSE next to each other\n",
    "model=['Multiple linear regression','Ridge','Boosting']\n",
    "df=pd.DataFrame({'Test Mse':[mlr_testmse,ridge_testmse,min(test_mse)]},index=model)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "468609c3",
   "metadata": {},
   "source": [
    "The optimal test MSE obtained by boosting is half the test MSE obtained by both multiple linear regression and Ridge regression."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1326d5a6",
   "metadata": {},
   "source": [
    "### (f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2706568e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor(learning_rate=0.025673469387755103, n_estimators=1000,\n",
       "                          random_state=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor(learning_rate=0.025673469387755103, n_estimators=1000,\n",
       "                          random_state=10)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingRegressor(learning_rate=0.025673469387755103, n_estimators=1000,\n",
       "                          random_state=10)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Before looking for the most important features we should first refit the boosting model with \n",
    "# the optimal lambda value\n",
    "best_lmabda=lambdagrid[test_mse.argmin()]\n",
    "boost_Hitters_opt = GBR(n_estimators=1000,learning_rate=best_lmabda,random_state=10)\n",
    "boost_Hitters_opt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4ecb525e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['AtBat', 'Hits', 'HmRun', 'Runs', 'RBI', 'Walks', 'Years', 'CAtBat',\n",
       "       'CHits', 'CHmRun', 'CRuns', 'CRBI', 'CWalks', 'League', 'Division',\n",
       "       'PutOuts', 'Assists', 'Errors', 'NewLeague'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6da38601",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CAtBat</th>\n",
       "      <td>0.436033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHits</th>\n",
       "      <td>0.169179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AtBat</th>\n",
       "      <td>0.063362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHmRun</th>\n",
       "      <td>0.062273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RBI</th>\n",
       "      <td>0.041733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRBI</th>\n",
       "      <td>0.036453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRuns</th>\n",
       "      <td>0.033348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CWalks</th>\n",
       "      <td>0.029465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hits</th>\n",
       "      <td>0.024916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PutOuts</th>\n",
       "      <td>0.021996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Years</th>\n",
       "      <td>0.021348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walks</th>\n",
       "      <td>0.020696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs</th>\n",
       "      <td>0.010689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HmRun</th>\n",
       "      <td>0.009403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assists</th>\n",
       "      <td>0.009063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Errors</th>\n",
       "      <td>0.006732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>League</th>\n",
       "      <td>0.001562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Division</th>\n",
       "      <td>0.001097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NewLeague</th>\n",
       "      <td>0.000653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           importance\n",
       "CAtBat       0.436033\n",
       "CHits        0.169179\n",
       "AtBat        0.063362\n",
       "CHmRun       0.062273\n",
       "RBI          0.041733\n",
       "CRBI         0.036453\n",
       "CRuns        0.033348\n",
       "CWalks       0.029465\n",
       "Hits         0.024916\n",
       "PutOuts      0.021996\n",
       "Years        0.021348\n",
       "Walks        0.020696\n",
       "Runs         0.010689\n",
       "HmRun        0.009403\n",
       "Assists      0.009063\n",
       "Errors       0.006732\n",
       "League       0.001562\n",
       "Division     0.001097\n",
       "NewLeague    0.000653"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_names=X_train.columns\n",
    "feature_imp = pd.DataFrame({'importance':boost_Hitters_opt.feature_importances_},index=feature_names)\n",
    "feature_imp.sort_values(by='importance', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "440d06d9",
   "metadata": {},
   "source": [
    "The top 3 features by importance are: CAtBat, CHits and AtBat."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51886eb2",
   "metadata": {},
   "source": [
    "### (g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e636301d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_features=19, random_state=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_features=19, random_state=10)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_features=19, random_state=10)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_Hitters = RF(max_features=X_train.shape[1], random_state=10)\n",
    "bag_Hitters.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "5612d795",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2502380573947847"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_MSE=np.mean((y_test - bag_Hitters.predict(X_test))**2)\n",
    "bag_MSE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7aedfbf",
   "metadata": {},
   "source": [
    "The test MSE obtained by bagging is 0.25, which slightly larger than the one obtained with boosting."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eb422f1",
   "metadata": {},
   "source": [
    "## Exercise 11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d75f2ff7",
   "metadata": {},
   "outputs": [],
   "source": [
    "Caravan=load_data('Caravan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7464a0e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5822 entries, 0 to 5821\n",
      "Data columns (total 86 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   MOSTYPE   5822 non-null   int64 \n",
      " 1   MAANTHUI  5822 non-null   int64 \n",
      " 2   MGEMOMV   5822 non-null   int64 \n",
      " 3   MGEMLEEF  5822 non-null   int64 \n",
      " 4   MOSHOOFD  5822 non-null   int64 \n",
      " 5   MGODRK    5822 non-null   int64 \n",
      " 6   MGODPR    5822 non-null   int64 \n",
      " 7   MGODOV    5822 non-null   int64 \n",
      " 8   MGODGE    5822 non-null   int64 \n",
      " 9   MRELGE    5822 non-null   int64 \n",
      " 10  MRELSA    5822 non-null   int64 \n",
      " 11  MRELOV    5822 non-null   int64 \n",
      " 12  MFALLEEN  5822 non-null   int64 \n",
      " 13  MFGEKIND  5822 non-null   int64 \n",
      " 14  MFWEKIND  5822 non-null   int64 \n",
      " 15  MOPLHOOG  5822 non-null   int64 \n",
      " 16  MOPLMIDD  5822 non-null   int64 \n",
      " 17  MOPLLAAG  5822 non-null   int64 \n",
      " 18  MBERHOOG  5822 non-null   int64 \n",
      " 19  MBERZELF  5822 non-null   int64 \n",
      " 20  MBERBOER  5822 non-null   int64 \n",
      " 21  MBERMIDD  5822 non-null   int64 \n",
      " 22  MBERARBG  5822 non-null   int64 \n",
      " 23  MBERARBO  5822 non-null   int64 \n",
      " 24  MSKA      5822 non-null   int64 \n",
      " 25  MSKB1     5822 non-null   int64 \n",
      " 26  MSKB2     5822 non-null   int64 \n",
      " 27  MSKC      5822 non-null   int64 \n",
      " 28  MSKD      5822 non-null   int64 \n",
      " 29  MHHUUR    5822 non-null   int64 \n",
      " 30  MHKOOP    5822 non-null   int64 \n",
      " 31  MAUT1     5822 non-null   int64 \n",
      " 32  MAUT2     5822 non-null   int64 \n",
      " 33  MAUT0     5822 non-null   int64 \n",
      " 34  MZFONDS   5822 non-null   int64 \n",
      " 35  MZPART    5822 non-null   int64 \n",
      " 36  MINKM30   5822 non-null   int64 \n",
      " 37  MINK3045  5822 non-null   int64 \n",
      " 38  MINK4575  5822 non-null   int64 \n",
      " 39  MINK7512  5822 non-null   int64 \n",
      " 40  MINK123M  5822 non-null   int64 \n",
      " 41  MINKGEM   5822 non-null   int64 \n",
      " 42  MKOOPKLA  5822 non-null   int64 \n",
      " 43  PWAPART   5822 non-null   int64 \n",
      " 44  PWABEDR   5822 non-null   int64 \n",
      " 45  PWALAND   5822 non-null   int64 \n",
      " 46  PPERSAUT  5822 non-null   int64 \n",
      " 47  PBESAUT   5822 non-null   int64 \n",
      " 48  PMOTSCO   5822 non-null   int64 \n",
      " 49  PVRAAUT   5822 non-null   int64 \n",
      " 50  PAANHANG  5822 non-null   int64 \n",
      " 51  PTRACTOR  5822 non-null   int64 \n",
      " 52  PWERKT    5822 non-null   int64 \n",
      " 53  PBROM     5822 non-null   int64 \n",
      " 54  PLEVEN    5822 non-null   int64 \n",
      " 55  PPERSONG  5822 non-null   int64 \n",
      " 56  PGEZONG   5822 non-null   int64 \n",
      " 57  PWAOREG   5822 non-null   int64 \n",
      " 58  PBRAND    5822 non-null   int64 \n",
      " 59  PZEILPL   5822 non-null   int64 \n",
      " 60  PPLEZIER  5822 non-null   int64 \n",
      " 61  PFIETS    5822 non-null   int64 \n",
      " 62  PINBOED   5822 non-null   int64 \n",
      " 63  PBYSTAND  5822 non-null   int64 \n",
      " 64  AWAPART   5822 non-null   int64 \n",
      " 65  AWABEDR   5822 non-null   int64 \n",
      " 66  AWALAND   5822 non-null   int64 \n",
      " 67  APERSAUT  5822 non-null   int64 \n",
      " 68  ABESAUT   5822 non-null   int64 \n",
      " 69  AMOTSCO   5822 non-null   int64 \n",
      " 70  AVRAAUT   5822 non-null   int64 \n",
      " 71  AAANHANG  5822 non-null   int64 \n",
      " 72  ATRACTOR  5822 non-null   int64 \n",
      " 73  AWERKT    5822 non-null   int64 \n",
      " 74  ABROM     5822 non-null   int64 \n",
      " 75  ALEVEN    5822 non-null   int64 \n",
      " 76  APERSONG  5822 non-null   int64 \n",
      " 77  AGEZONG   5822 non-null   int64 \n",
      " 78  AWAOREG   5822 non-null   int64 \n",
      " 79  ABRAND    5822 non-null   int64 \n",
      " 80  AZEILPL   5822 non-null   int64 \n",
      " 81  APLEZIER  5822 non-null   int64 \n",
      " 82  AFIETS    5822 non-null   int64 \n",
      " 83  AINBOED   5822 non-null   int64 \n",
      " 84  ABYSTAND  5822 non-null   int64 \n",
      " 85  Purchase  5822 non-null   object\n",
      "dtypes: int64(85), object(1)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "Caravan.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de22bf06",
   "metadata": {},
   "source": [
    "### (a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5dc842ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MS(Caravan.columns.drop('Purchase'), intercept=False)\n",
    "D = model.fit_transform(Caravan)\n",
    "feature_names = list(D.columns)\n",
    "X = np.asarray(D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bbbc6e44",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=Caravan['Purchase']\n",
    "(X_train, X_test, y_train, y_test) = skm.train_test_split(X,y,train_size=1000,random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a122945",
   "metadata": {},
   "source": [
    "### (b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4a22f507",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(learning_rate=0.01, n_estimators=1000,\n",
       "                           random_state=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(learning_rate=0.01, n_estimators=1000,\n",
       "                           random_state=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier(learning_rate=0.01, n_estimators=1000,\n",
       "                           random_state=3)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boost_Caravan = GBC(n_estimators=1000,learning_rate=0.01,random_state=3)\n",
    "boost_Caravan.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1e47de07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MGODOV</th>\n",
       "      <td>0.061780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPERSAUT</th>\n",
       "      <td>0.059952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AFIETS</th>\n",
       "      <td>0.057126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APERSAUT</th>\n",
       "      <td>0.055229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MBERARBG</th>\n",
       "      <td>0.039077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVRAAUT</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPERSONG</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABESAUT</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PZEILPL</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PWERKT</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          importance\n",
       "MGODOV      0.061780\n",
       "PPERSAUT    0.059952\n",
       "AFIETS      0.057126\n",
       "APERSAUT    0.055229\n",
       "MBERARBG    0.039077\n",
       "...              ...\n",
       "AVRAAUT     0.000000\n",
       "PPERSONG    0.000000\n",
       "ABESAUT     0.000000\n",
       "PZEILPL     0.000000\n",
       "PWERKT      0.000000\n",
       "\n",
       "[85 rows x 1 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_imp = pd.DataFrame({'importance':boost_Caravan.feature_importances_},index=feature_names)\n",
    "feature_imp.sort_values(by='importance', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9210d9e",
   "metadata": {},
   "source": [
    "The top 3 features by importance are: MGODOV, PPERSAUT and AFIETS."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b41a5e52",
   "metadata": {},
   "source": [
    "### (c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9ced355b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Find post probabilites and define new classes according to the intructions\n",
    "ypred=boost_Caravan.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9d1452a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred=ypred[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "52ae46d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred=np.where(ypred>0.2,'Yes','No')\n",
    "confusion_boost = confusion_table(y_test, ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bd309d29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Truth</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Predicted</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>4292</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>240</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Truth        No  Yes\n",
       "Predicted           \n",
       "No         4292  241\n",
       "Yes         240   49"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_boost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "60c00bb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1695501730103806"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "49/(240+49)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1224bef0",
   "metadata": {},
   "source": [
    "Accordingly to the confusion table obtained by boosting there is 16.95% who truly purchased the product in comparison with those predicted to do so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3ee46fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fit logit\n",
    "(Caravan_train, Caravan_test) = skm.train_test_split(Caravan,train_size=1000,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "5e3018e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>std err</th>\n",
       "      <th>z</th>\n",
       "      <th>P&gt;|z|</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MOSTYPE</th>\n",
       "      <td>0.0051</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAANTHUI</th>\n",
       "      <td>-0.3717</td>\n",
       "      <td>0.605</td>\n",
       "      <td>-0.614</td>\n",
       "      <td>0.539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MGEMOMV</th>\n",
       "      <td>0.3373</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.843</td>\n",
       "      <td>0.399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MGEMLEEF</th>\n",
       "      <td>-0.4008</td>\n",
       "      <td>0.277</td>\n",
       "      <td>-1.446</td>\n",
       "      <td>0.148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOSHOOFD</th>\n",
       "      <td>0.0408</td>\n",
       "      <td>0.564</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZEILPL</th>\n",
       "      <td>-10.3345</td>\n",
       "      <td>65500.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APLEZIER</th>\n",
       "      <td>21.2280</td>\n",
       "      <td>131000.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AFIETS</th>\n",
       "      <td>2.2304</td>\n",
       "      <td>1.461</td>\n",
       "      <td>1.527</td>\n",
       "      <td>0.127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AINBOED</th>\n",
       "      <td>-24.1049</td>\n",
       "      <td>193000.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABYSTAND</th>\n",
       "      <td>3.8235</td>\n",
       "      <td>4.306</td>\n",
       "      <td>0.888</td>\n",
       "      <td>0.375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             coef     std err      z  P>|z|\n",
       "MOSTYPE    0.0051       0.126  0.041  0.967\n",
       "MAANTHUI  -0.3717       0.605 -0.614  0.539\n",
       "MGEMOMV    0.3373       0.400  0.843  0.399\n",
       "MGEMLEEF  -0.4008       0.277 -1.446  0.148\n",
       "MOSHOOFD   0.0408       0.564  0.072  0.942\n",
       "...           ...         ...    ...    ...\n",
       "AZEILPL  -10.3345   65500.000 -0.000  1.000\n",
       "APLEZIER  21.2280  131000.000  0.000  1.000\n",
       "AFIETS     2.2304       1.461  1.527  0.127\n",
       "AINBOED  -24.1049  193000.000 -0.000  1.000\n",
       "ABYSTAND   3.8235       4.306  0.888  0.375\n",
       "\n",
       "[85 rows x 4 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "design = MS(Caravan_train.columns.drop(['Purchase']),intercept=False)\n",
    "X_train_lk = design.fit_transform(Caravan_train)\n",
    "y_train_lk = Caravan_train.Purchase == 'Yes'\n",
    "glm = sm.GLM(y_train_lk,X_train_lk,family=sm.families.Binomial())\n",
    "results = glm.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "c3fc0b0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred_logit=np.where(results.predict(X_test)>0.2,'Yes','No')\n",
    "confusion_logit = confusion_table(y_test, ypred_logit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "bd2c6799",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Truth</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Predicted</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>4190</td>\n",
       "      <td>343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>235</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Truth        No  Yes\n",
       "Predicted           \n",
       "No         4190  343\n",
       "Yes         235   54"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_logit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "48dfa1cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18685121107266436"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "54/(54+235)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d127a809",
   "metadata": {},
   "source": [
    "For the logit: 18.68% predicted to purchase actually purchased."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "b083c38c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kabousselham\\AppData\\Local\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Truth</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Predicted</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>4384</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>149</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Truth        No  Yes\n",
       "Predicted           \n",
       "No         4384  253\n",
       "Yes         149   36"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# KNN\n",
    "knn = KNeighborsClassifier(n_neighbors=5).fit(X_train_lk, y_train_lk)\n",
    "knn_pred_prob = knn.predict_proba(X_test)[:,1]\n",
    "knn_pred=np.where(knn_pred_prob>0.2,'Yes','No')\n",
    "confusion_table(knn_pred, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "439e3103",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1945945945945946"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "36/(149+36)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "773b5c60",
   "metadata": {},
   "source": [
    "For KNN: 19.46% predicted to purchase actually purchased. KNN is the best model in this case."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32848d27",
   "metadata": {},
   "source": [
    "## Exercise 12"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf4f1e9c",
   "metadata": {},
   "source": [
    "We choose to load Weekly data set in this exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "5f1a5f89",
   "metadata": {},
   "outputs": [],
   "source": [
    "#instructions: fit to boosting, bagging, RF and BART\n",
    "# look for test set performance using deviance\n",
    "# Compare with logistic regression and give the best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bd49f45c",
   "metadata": {},
   "outputs": [],
   "source": [
    "Weekly=load_data('Weekly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "948534d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1089 entries, 0 to 1088\n",
      "Data columns (total 9 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Year       1089 non-null   int64  \n",
      " 1   Lag1       1089 non-null   float64\n",
      " 2   Lag2       1089 non-null   float64\n",
      " 3   Lag3       1089 non-null   float64\n",
      " 4   Lag4       1089 non-null   float64\n",
      " 5   Lag5       1089 non-null   float64\n",
      " 6   Volume     1089 non-null   float64\n",
      " 7   Today      1089 non-null   float64\n",
      " 8   Direction  1089 non-null   object \n",
      "dtypes: float64(7), int64(1), object(1)\n",
      "memory usage: 76.7+ KB\n"
     ]
    }
   ],
   "source": [
    "Weekly.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1c986989",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Lag1</th>\n",
       "      <th>Lag2</th>\n",
       "      <th>Lag3</th>\n",
       "      <th>Lag4</th>\n",
       "      <th>Lag5</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Today</th>\n",
       "      <th>Direction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990</td>\n",
       "      <td>0.816</td>\n",
       "      <td>1.572</td>\n",
       "      <td>-3.936</td>\n",
       "      <td>-0.229</td>\n",
       "      <td>-3.484</td>\n",
       "      <td>0.154976</td>\n",
       "      <td>-0.270</td>\n",
       "      <td>Down</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1990</td>\n",
       "      <td>-0.270</td>\n",
       "      <td>0.816</td>\n",
       "      <td>1.572</td>\n",
       "      <td>-3.936</td>\n",
       "      <td>-0.229</td>\n",
       "      <td>0.148574</td>\n",
       "      <td>-2.576</td>\n",
       "      <td>Down</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1990</td>\n",
       "      <td>-2.576</td>\n",
       "      <td>-0.270</td>\n",
       "      <td>0.816</td>\n",
       "      <td>1.572</td>\n",
       "      <td>-3.936</td>\n",
       "      <td>0.159837</td>\n",
       "      <td>3.514</td>\n",
       "      <td>Up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1990</td>\n",
       "      <td>3.514</td>\n",
       "      <td>-2.576</td>\n",
       "      <td>-0.270</td>\n",
       "      <td>0.816</td>\n",
       "      <td>1.572</td>\n",
       "      <td>0.161630</td>\n",
       "      <td>0.712</td>\n",
       "      <td>Up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1990</td>\n",
       "      <td>0.712</td>\n",
       "      <td>3.514</td>\n",
       "      <td>-2.576</td>\n",
       "      <td>-0.270</td>\n",
       "      <td>0.816</td>\n",
       "      <td>0.153728</td>\n",
       "      <td>1.178</td>\n",
       "      <td>Up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1084</th>\n",
       "      <td>2010</td>\n",
       "      <td>-0.861</td>\n",
       "      <td>0.043</td>\n",
       "      <td>-2.173</td>\n",
       "      <td>3.599</td>\n",
       "      <td>0.015</td>\n",
       "      <td>3.205160</td>\n",
       "      <td>2.969</td>\n",
       "      <td>Up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1085</th>\n",
       "      <td>2010</td>\n",
       "      <td>2.969</td>\n",
       "      <td>-0.861</td>\n",
       "      <td>0.043</td>\n",
       "      <td>-2.173</td>\n",
       "      <td>3.599</td>\n",
       "      <td>4.242568</td>\n",
       "      <td>1.281</td>\n",
       "      <td>Up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1086</th>\n",
       "      <td>2010</td>\n",
       "      <td>1.281</td>\n",
       "      <td>2.969</td>\n",
       "      <td>-0.861</td>\n",
       "      <td>0.043</td>\n",
       "      <td>-2.173</td>\n",
       "      <td>4.835082</td>\n",
       "      <td>0.283</td>\n",
       "      <td>Up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087</th>\n",
       "      <td>2010</td>\n",
       "      <td>0.283</td>\n",
       "      <td>1.281</td>\n",
       "      <td>2.969</td>\n",
       "      <td>-0.861</td>\n",
       "      <td>0.043</td>\n",
       "      <td>4.454044</td>\n",
       "      <td>1.034</td>\n",
       "      <td>Up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088</th>\n",
       "      <td>2010</td>\n",
       "      <td>1.034</td>\n",
       "      <td>0.283</td>\n",
       "      <td>1.281</td>\n",
       "      <td>2.969</td>\n",
       "      <td>-0.861</td>\n",
       "      <td>2.707105</td>\n",
       "      <td>0.069</td>\n",
       "      <td>Up</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1089 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year   Lag1   Lag2   Lag3   Lag4   Lag5    Volume  Today Direction\n",
       "0     1990  0.816  1.572 -3.936 -0.229 -3.484  0.154976 -0.270      Down\n",
       "1     1990 -0.270  0.816  1.572 -3.936 -0.229  0.148574 -2.576      Down\n",
       "2     1990 -2.576 -0.270  0.816  1.572 -3.936  0.159837  3.514        Up\n",
       "3     1990  3.514 -2.576 -0.270  0.816  1.572  0.161630  0.712        Up\n",
       "4     1990  0.712  3.514 -2.576 -0.270  0.816  0.153728  1.178        Up\n",
       "...    ...    ...    ...    ...    ...    ...       ...    ...       ...\n",
       "1084  2010 -0.861  0.043 -2.173  3.599  0.015  3.205160  2.969        Up\n",
       "1085  2010  2.969 -0.861  0.043 -2.173  3.599  4.242568  1.281        Up\n",
       "1086  2010  1.281  2.969 -0.861  0.043 -2.173  4.835082  0.283        Up\n",
       "1087  2010  0.283  1.281  2.969 -0.861  0.043  4.454044  1.034        Up\n",
       "1088  2010  1.034  0.283  1.281  2.969 -0.861  2.707105  0.069        Up\n",
       "\n",
       "[1089 rows x 9 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Weekly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "ad635783",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will cpnsider Direction as the response and all other features as dependant variables\n",
    "model = MS(Weekly.columns.drop(['Direction','Today']), intercept=False)#We drop 'Today' feature too \n",
    "# cause it is clearly correlated with Direction. Whenever Today is positive, Direction is up\n",
    "D = model.fit_transform(Weekly)\n",
    "feature_names = list(D.columns)\n",
    "X = np.asarray(D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "368997a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=Weekly['Direction']\n",
    "(X_train, X_test, y_train, y_test) = skm.train_test_split(X,y,train_size=0.7,random_state=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "4b7d4e21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-41 {color: black;background-color: white;}#sk-container-id-41 pre{padding: 0;}#sk-container-id-41 div.sk-toggleable {background-color: white;}#sk-container-id-41 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-41 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-41 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-41 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-41 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-41 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-41 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-41 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-41 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-41 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-41 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-41 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-41 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-41 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-41 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-41 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-41 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-41 div.sk-item {position: relative;z-index: 1;}#sk-container-id-41 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-41 div.sk-item::before, #sk-container-id-41 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-41 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-41 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-41 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-41 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-41 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-41 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-41 div.sk-label-container {text-align: center;}#sk-container-id-41 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-41 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-41\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(learning_rate=0.001, max_depth=1, n_estimators=1000,\n",
       "                           random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-41\" type=\"checkbox\" checked><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(learning_rate=0.001, max_depth=1, n_estimators=1000,\n",
       "                           random_state=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier(learning_rate=0.001, max_depth=1, n_estimators=1000,\n",
       "                           random_state=1)"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Boosting\n",
    "boost_Weekly = GBC(n_estimators=1000,learning_rate=0.001,random_state=1,max_depth=1)\n",
    "boost_Weekly.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "18184c03",
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred = boost_Weekly.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "3a28016e",
   "metadata": {},
   "outputs": [],
   "source": [
    "confusion_boost=confusion_table(y_test,ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "72d414db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Truth</th>\n",
       "      <th>Down</th>\n",
       "      <th>Up</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Predicted</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Down</th>\n",
       "      <td>1</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Up</th>\n",
       "      <td>0</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Truth      Down   Up\n",
       "Predicted           \n",
       "Down          1  142\n",
       "Up            0  184"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_boost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "4eb6e6be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.43425076452599387"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "142/np.sum(np.sum(confusion_boost))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d454cf76",
   "metadata": {},
   "source": [
    "We obtain a test error of 43.42%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "2b2cf3c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-47 {color: black;background-color: white;}#sk-container-id-47 pre{padding: 0;}#sk-container-id-47 div.sk-toggleable {background-color: white;}#sk-container-id-47 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-47 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-47 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-47 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-47 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-47 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-47 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-47 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-47 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-47 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-47 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-47 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-47 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-47 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-47 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-47 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-47 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-47 div.sk-item {position: relative;z-index: 1;}#sk-container-id-47 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-47 div.sk-item::before, #sk-container-id-47 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-47 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-47 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-47 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-47 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-47 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-47 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-47 div.sk-label-container {text-align: center;}#sk-container-id-47 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-47 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-47\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_features=7, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-47\" type=\"checkbox\" checked><label for=\"sk-estimator-id-47\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_features=7, random_state=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_features=7, random_state=1)"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Bagging\n",
    "bag_weekly = RF(max_features=X_train.shape[1], random_state=1)\n",
    "y_train=np.where(y_train=='Up',1,0)\n",
    "bag_weekly.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "3816f8f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred = bag_weekly.predict(X_test)\n",
    "y_pred=np.where(ypred>0.5,'Up','Down')\n",
    "confusion_bagging=confusion_table(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "b950cdb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Truth</th>\n",
       "      <th>Down</th>\n",
       "      <th>Up</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Predicted</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Down</th>\n",
       "      <td>59</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Up</th>\n",
       "      <td>60</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Truth      Down   Up\n",
       "Predicted           \n",
       "Down         59   84\n",
       "Up           60  124"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "783ec936",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.44036697247706424"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(60+84)/np.sum(np.sum(confusion_bagging))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bf6fd34",
   "metadata": {},
   "source": [
    "Bagging yileds to a classification error of 44%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "1baeb958",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Random forests with sqrt features supposed to be optimal\n",
    "RF_weekly=RF(max_features='sqrt',random_state=30).fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "264dedc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred = RF_weekly.predict(X_test)\n",
    "y_pred=np.where(ypred>0.5,'Up','Down')\n",
    "confusion_RF=confusion_table(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "c35f644f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Truth</th>\n",
       "      <th>Down</th>\n",
       "      <th>Up</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Predicted</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Down</th>\n",
       "      <td>55</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Up</th>\n",
       "      <td>59</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Truth      Down   Up\n",
       "Predicted           \n",
       "Down         55   88\n",
       "Up           59  125"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_RF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "2f2bec59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.44954128440366975"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(88+59)/np.sum(np.sum(confusion_RF))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a68f521",
   "metadata": {},
   "source": [
    "Random forests yiled to a classification error of 45%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "1ffbcc12",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Logistic regression\n",
    "glm = sm.GLM(y_train,X_train,family=sm.families.Binomial())\n",
    "results = glm.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "09731c78",
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred = results.predict(X_test)\n",
    "y_pred=np.where(ypred>0.5,'Up','Down')\n",
    "confusion_logit=confusion_table(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "91232c1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Truth</th>\n",
       "      <th>Down</th>\n",
       "      <th>Up</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Predicted</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Down</th>\n",
       "      <td>22</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Up</th>\n",
       "      <td>18</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Truth      Down   Up\n",
       "Predicted           \n",
       "Down         22  121\n",
       "Up           18  166"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_logit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "87b416f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42507645259938837"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(18+121)/np.sum(np.sum(confusion_logit))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "021cdb3d",
   "metadata": {},
   "source": [
    "Logistic regression yields to a classification error of 42.5%. Thus, it is the most performant model in this case. But overall, models have nearly the same performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4198cc59",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}