Knn and ID3.ipynb

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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import operator\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def createDataSet():\n",
      "    group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])\n",
      "    labels = ['A','A','B','B']\n",
      "    return group, labels"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 433
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "group,labels = createDataSet()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "intx = [1.,2.0]\n",
      "a = ((group - intx)**2).sum(axis=1)\n",
      "b = (group - intx)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "intx."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def calssify(intX, dataset, labels, k):\n",
      "    A = (((dataset - intX)**2).sum(axis=1))**0.5\n",
      "    T = A.argsort()\n",
      "    mm = {}\n",
      "    for a in range(k):\n",
      "        vtl = labels[T[a]]\n",
      "        mm[vtl] = mm.get(vtl,0)+1\n",
      "        sortedClassCount = sorted(mm.iteritems(), key = lambda x:x[1],reverse=True)\n",
      "    return sortedClassCount"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "calssify([1.,2.], group, labels, 2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 84,
       "text": [
        "[('A', 2)]"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "help(sorted)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Help on built-in function sorted in module __builtin__:\n",
        "\n",
        "sorted(...)\n",
        "    sorted(iterable, cmp=None, key=None, reverse=False) --> new sorted list\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def file2Matrix(filename):\n",
      "    ff = open(filename)\n",
      "    lines = ff.readlines()\n",
      "    index = 0\n",
      "    mt = zeros((len(lines),3))\n",
      "    label = []\n",
      "    for rec in lines:\n",
      "        reclist = rec.split('\\t')\n",
      "        mt[index,:] = reclist[0:3]\n",
      "        label.append(reclist[-1].strip())\n",
      "        index = index+1\n",
      "    return mt, label"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 178
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = np.array([1,2,3])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 149
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "k=pd.Series(a).map({1:2,2:1,3:1})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 155
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "k.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 156,
       "text": [
        "array([2, 1, 1])"
       ]
      }
     ],
     "prompt_number": 156
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = array([2,3,4])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 137
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.tolist()+array([1]).tolist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 148,
       "text": [
        "[1, 2, 3, 1]"
       ]
      }
     ],
     "prompt_number": 148
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mt,label = file2Matrix('machinelearninginaction/Ch02/datingTestSet.txt')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 188
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "label = pd.Series(label).map({'didntLike':1,'smallDoses':2,'largeDoses':3}).values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 189
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "label.T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 191,
       "text": [
        "array([3, 2, 1, 1, 1, 1, 3, 3, 1, 3, 1, 1, 2, 1, 1, 1, 1, 1, 2, 3, 2, 1, 2,\n",
        "       3, 2, 3, 2, 3, 2, 1, 3, 1, 3, 1, 2, 1, 1, 2, 3, 3, 1, 2, 3, 3, 3, 1,\n",
        "       1, 1, 1, 2, 2, 1, 3, 2, 2, 2, 2, 3, 1, 2, 1, 2, 2, 2, 2, 2, 3, 2, 3,\n",
        "       1, 2, 3, 2, 2, 1, 3, 1, 1, 3, 3, 1, 2, 3, 1, 3, 1, 2, 2, 1, 1, 3, 3,\n",
        "       1, 2, 1, 3, 3, 2, 1, 1, 3, 1, 2, 3, 3, 2, 3, 3, 1, 2, 3, 2, 1, 3, 1,\n",
        "       2, 1, 1, 2, 3, 2, 3, 2, 3, 2, 1, 3, 3, 3, 1, 3, 2, 2, 3, 1, 3, 3, 3,\n",
        "       1, 3, 1, 1, 3, 3, 2, 3, 3, 1, 2, 3, 2, 2, 3, 3, 3, 1, 2, 2, 1, 1, 3,\n",
        "       2, 3, 3, 1, 2, 1, 3, 1, 2, 3, 2, 3, 1, 1, 1, 3, 2, 3, 1, 3, 2, 1, 3,\n",
        "       2, 2, 3, 2, 3, 2, 1, 1, 3, 1, 3, 2, 2, 2, 3, 2, 2, 1, 2, 2, 3, 1, 3,\n",
        "       3, 2, 1, 1, 1, 2, 1, 3, 3, 3, 3, 2, 1, 1, 1, 2, 3, 2, 1, 3, 1, 3, 2,\n",
        "       2, 3, 1, 3, 1, 1, 2, 1, 2, 2, 1, 3, 1, 3, 2, 3, 1, 2, 3, 1, 1, 1, 1,\n",
        "       2, 3, 2, 2, 3, 1, 2, 1, 1, 1, 3, 3, 2, 1, 1, 1, 2, 2, 3, 1, 1, 1, 2,\n",
        "       1, 1, 2, 1, 1, 1, 2, 2, 3, 2, 3, 3, 3, 3, 1, 2, 3, 1, 1, 1, 3, 1, 3,\n",
        "       2, 2, 1, 3, 1, 3, 2, 2, 1, 2, 2, 3, 1, 3, 2, 1, 1, 3, 3, 2, 3, 3, 2,\n",
        "       3, 1, 3, 1, 3, 3, 1, 3, 2, 1, 3, 1, 3, 2, 1, 2, 2, 1, 3, 1, 1, 3, 3,\n",
        "       2, 2, 3, 1, 2, 3, 3, 2, 2, 1, 1, 1, 1, 3, 2, 1, 1, 3, 2, 1, 1, 3, 3,\n",
        "       3, 2, 3, 2, 1, 1, 1, 1, 1, 3, 2, 2, 1, 2, 1, 3, 2, 1, 3, 2, 1, 3, 1,\n",
        "       1, 3, 3, 3, 3, 2, 1, 1, 2, 1, 3, 3, 2, 1, 2, 3, 2, 1, 2, 2, 2, 1, 1,\n",
        "       3, 1, 1, 2, 3, 1, 1, 2, 3, 1, 3, 1, 1, 2, 2, 1, 2, 2, 2, 3, 1, 1, 1,\n",
        "       3, 1, 3, 1, 3, 3, 1, 1, 1, 3, 2, 3, 3, 2, 2, 1, 1, 1, 2, 1, 2, 2, 3,\n",
        "       3, 3, 1, 1, 3, 3, 2, 3, 3, 2, 3, 3, 3, 2, 3, 3, 1, 2, 3, 2, 1, 1, 1,\n",
        "       1, 3, 3, 3, 3, 2, 1, 1, 1, 1, 3, 1, 1, 2, 1, 1, 2, 3, 2, 1, 2, 2, 2,\n",
        "       3, 2, 1, 3, 2, 3, 2, 3, 2, 1, 1, 2, 3, 1, 3, 3, 3, 1, 2, 1, 2, 2, 1,\n",
        "       2, 2, 2, 2, 2, 3, 2, 1, 3, 3, 2, 2, 2, 3, 1, 2, 1, 1, 3, 2, 3, 2, 3,\n",
        "       2, 3, 3, 2, 2, 1, 3, 1, 2, 1, 3, 1, 1, 1, 3, 1, 1, 3, 3, 2, 2, 1, 3,\n",
        "       1, 1, 3, 2, 3, 1, 1, 3, 1, 3, 3, 1, 2, 3, 1, 3, 1, 1, 2, 1, 3, 1, 1,\n",
        "       1, 1, 2, 1, 3, 1, 2, 1, 3, 1, 3, 1, 1, 2, 2, 2, 3, 2, 2, 1, 2, 3, 3,\n",
        "       2, 3, 3, 3, 2, 3, 3, 1, 3, 2, 3, 2, 1, 2, 1, 1, 1, 2, 3, 2, 2, 1, 2,\n",
        "       2, 1, 3, 1, 3, 3, 3, 2, 2, 3, 3, 1, 2, 2, 2, 3, 1, 2, 1, 3, 1, 2, 3,\n",
        "       1, 1, 1, 2, 2, 3, 1, 3, 1, 1, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 2, 2, 2,\n",
        "       3, 1, 3, 1, 2, 3, 2, 2, 3, 1, 2, 3, 2, 3, 1, 2, 2, 3, 1, 1, 1, 2, 2,\n",
        "       1, 1, 2, 1, 2, 1, 2, 3, 2, 1, 3, 3, 3, 1, 1, 3, 1, 2, 3, 3, 2, 2, 2,\n",
        "       1, 2, 3, 2, 2, 3, 2, 2, 2, 3, 3, 2, 1, 3, 2, 1, 3, 3, 1, 2, 3, 2, 1,\n",
        "       3, 3, 3, 1, 2, 2, 2, 3, 2, 3, 3, 1, 2, 1, 1, 2, 1, 3, 1, 2, 2, 1, 3,\n",
        "       2, 1, 3, 3, 2, 2, 2, 1, 2, 2, 1, 3, 1, 3, 1, 3, 3, 1, 1, 2, 3, 2, 2,\n",
        "       3, 1, 1, 1, 1, 3, 2, 2, 1, 3, 1, 2, 3, 1, 3, 1, 3, 1, 1, 3, 2, 3, 1,\n",
        "       1, 3, 3, 3, 3, 1, 3, 2, 2, 1, 1, 3, 3, 2, 2, 2, 1, 2, 1, 2, 1, 3, 2,\n",
        "       1, 2, 2, 3, 1, 2, 2, 2, 3, 2, 1, 2, 1, 2, 3, 3, 2, 3, 1, 1, 3, 3, 1,\n",
        "       2, 2, 2, 2, 2, 2, 1, 3, 3, 3, 3, 3, 1, 1, 3, 2, 1, 2, 1, 2, 2, 3, 2,\n",
        "       2, 2, 3, 1, 2, 1, 2, 2, 1, 1, 2, 3, 3, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3,\n",
        "       1, 3, 3, 2, 3, 2, 3, 3, 2, 2, 1, 1, 1, 3, 3, 1, 1, 1, 3, 3, 2, 1, 2,\n",
        "       1, 1, 2, 2, 1, 1, 1, 3, 1, 1, 2, 3, 2, 2, 1, 3, 1, 2, 3, 1, 2, 2, 2,\n",
        "       2, 3, 2, 3, 3, 1, 2, 1, 2, 3, 1, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
        "       2, 3, 2, 2, 2, 2, 2, 1, 3, 3, 3])"
       ]
      }
     ],
     "prompt_number": 191
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 160
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(111)\n",
      "ax.scatter(mt[:,0],mt[:,2],15.0*array(label), 15.0*array(label))\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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kZgDSXINTU8/QsXUnzp8/z+bNm6lUqRIzZ8/m24kTmfT996zX64lEUvgPgQ06\nHX6lS1OvXj1cXV1p9O67z5SeUKgUZKBAY2/PMaQsIGtmHxeRsnFGZ041j4yMJKRRI0pfu8ZAg4F+\naWk0S0pi/qxZHDlyBACNRsPQoUOx1+nIbS7sI3im3MFjpCRbd0CJjdLEoSAKMKLhtFXQvHnzrHNv\n3rzJiCFD6JyeTkOLhapAN6Cq1crcadNoVLdutvRcR0dHxo0bi739IlxcVuLicoYpUyY8I0PDRo2I\ndHDAmmP/LUDn4MDatesICWnDiBG/ULNmA1avXv1MH6/K999PJypKQWpqLzIy+vDjj/O4ePHiPz7O\nX6HT6XBzc+P8+fNYLP5ITsC8KBRFOHs2HJOpDNI8ggrcv3+HjIyMNy7jW8FLlXb7h/kvEeO1olYq\nRT69Rjj5OonirYKE3tNeVP+4mhiZMEyodWrh4OqQVabYaDT+aalhBzcHUbpTKTHa9qn4QnwmPjd/\nIgrWKiDU9moR1ChQOLo6imXLlz3Tzmq1io5dOwoHNwcRVC9QOLk7iV79eolGIY2ERwEPUbJpSeHo\n5ihGjxmd1WbNmjWipL+/UIBwcXAQI4YOFWlpaVnHt2/fLvIU9RRD7w4WX4jPxEfJI4R3+bxCrVSI\n/Pb2oiYITxBqEFqlUpQJDBT79u3Lat+9SxeRX6EQ9iCUIIqC6A7iPRB6lUqUK15cLF68WNhsNtG/\nd29RQasVXz5VwXIICAcQLiA656huORqEIwgFQQJ6CjWFRX6UQoWLgFChVgeJEiXKiaAiRYRapRJu\nTk6iskqVrY8xID4DoQVRyMFBrF279pn7GhERIf744w8RHx+f6+dls9lEw9q1RUmdTvTL7K8NCBeN\nRhQtWkyoVHYCBgsYI6CzKFq0+F9/of4m/fsPElA/c4wxwtm5iAgLC/vHx3lRwsLChF7vIaCHgC7C\n3t5VtGrVRuh0pQUMEkplA1G0aNC/Jt//Ci+rO2Uf/xvCzdUZn7retFz7AQqlgvSEdBZUWYyTjyPx\nF+Pp3K4LP/3wU65tU1JS+O2337h//z7169en1YetSLEm41LIhfzV83Nt+3UyEjNwKeRCgC6AMaPH\ncPLkSdzd3WnZsiWOjo6cPHmStp3akpSaREZqBkFBQSyau4iVv61kY8QGWqxqjlKtJO1BGsveWc6q\nhauz5bJbrVasVisHDhwgPT2d4OBg3N2lmcPjJoxj8pTJaN20pN18hFFIaZzVgBpIwdpkYLlWS60W\nLQgODqYJZL+fAAAgAElEQVRly5Z4eHjg5epKoMlETSTXzEVgB5JraX3mv8ccHGjbqxdfjhtHtYoV\neXD9OiWsVtKUSs4DCpWKDLMZLfAuUgZSAhBmb89tsxmFBdSo8MfCZdSk0RYpVG0BJtAaQTFgI1L2\n0ZMw/BO+AyoCPm3asGzVqr/9+WdkZPD1uHHMnzOHR0lJ+ObNy72HRozGxkhh5n5IbybX8fM7SVRU\nxN8e4884cuQIDRo0wWB4B40miXz5HnD58vnnTggD6XsXERGBj48PBQsWfGUZYmNjWbx4MTabja5d\nu7J161a++uobVCoVU6dOoFmzZvTs2Y+dO3fh5+fPihWLKVy48CuP+//My+pOWfG/IbwKeNFiQ3O8\nK+TL2nd48hEOjDvIpPGTGDp0aK6+zKtXr1KzXk3yVvHCqagTkWsicVA7cvvuLfyb+qPRa0h/aCD+\n0kNqj6tFxKRLxMfFE9iyGGl3DDw4+YBtm7bROKQxtWbUoETr4liNVnYP34vPQ18uRFyg9qIa+FR+\nMi/28KQjFLsTxI8zf8zat3XrVrp26ICzzYYOuGEyMXz4cMZ+/TUKhYJvvv6aGRMm0Dgjg8JIGUHb\nkVwwHYAtSH74EgA6HZeAd0NCOLl+PZ2t1mxumvPAYSAFKZjcHpir09GmXTu2rl5NQYOBVCBZpSLd\n0ZHDx4+j1Wrp3bsfZ44dxZBhwEmvJzElhQI6HRkZGdyzWLACCrTYqIVUWPoesAAvoC8WriDVH+pB\ndrfRPaRKoO8A3h06sHjZsr/56T9L3brvsm+fS+YdOQwcQqcrgVIZyeLFc3PNp39VDh48yNy5C3F3\nd+Ozzz7Gy8sLkArh7d27l0KFChEaGopCoSAyMpLq1WthMtljNj9k+vSp9O7d66XHfvjwISVKlCUh\nwRtQ4up6i4iI8OdWsZV5MWTF/19O6cqlKTepDEXqFc7at/ujPThHuLJty/OrLTZq1ghRx0a1kVUB\nyS//Y7E5eAS6Y0wx4pDHAe/K3lQdUpmdw3YTtTmKvld7Z2UGnZx9ipgFt7F6WGm980mZYWOKkZne\nsykSUIRK31egcJ0nOecHvgyjQnolvpvyHSA9fKqUL09Lg4HHZ6UAq/R6vpo+nQ4dO+Lj5UXH1FSe\nnh5mRrKU1Uj5/Z14MvP2FrAcaMSTAmqPkXL3oSHSdKRywA2djkibjQEmE09HPI4oldhq1mTHUzXK\nd+/eTbvQUNoZDFnyPACWqtV4CcENqxIrzkAa0AwNW+mNAXdgPlJFmnpIbyC3kSajVQHOOzoyd/Vq\nmjRpAkB4eDgDBgwjISGRgQN706/f08Uh/pxevfqxZMlpTKZ3gXvY2S1j8uRvqVWrFuXL57wjr489\ne/bQvPkHWK1BqNV3aN8+hHnzfiIk5H22bUtBiGAgATu7+SQnJ6DV5ixa8WIsXryYAQO+x2CQvoN6\n/QamT+9Hr14v/zCReXndKQd33xD9e/bnwEcHSYqRSjXHhMUQPv88X3z+xZ+227trL+V7Pim9pnPR\nUerDEjgXcMacakbnocO1kAubemzh6qZIioUWy1L6AOV6luXSuUuotE9mAKfFpXFs6jEU6SbsFXbs\n/ziM1NhUAO6dukf43PN07dQVgOjoaDq0bUvp9HSePBokS7y+wcCUCRMIDw/HTanMUrIxSEp9OpKb\nRwE48kTpJwGrMvel53LNxsw2VZFKRUQD6VYrBcxmciZVVrTZCDt8OFu54MnjxxP8lNIHyYVT22LB\narVSBiuQDxgEFMaGCW2mrJ2A6yoV04BvM+UsAlxxcKBS7do0btwYgISEBGrXbsChQw5cvFiakSPH\nsG7dulyuJncmTZpA8eI27OymYW+/gpUrf2XIkCFvVOkDfPXVNxgMdTEa3yUtrQO//LKYpKQkjh8/\ngRB7ke7CdYSQqtC+LDqdDqUyAynEL1Ao0rNlSMm8WWTF/4bo27svnZt3Zl6phUx2mcZvLdfiVdqL\nZu814+jRowDcv3+fCd9OoM+APqxevZrU1FSUGiUJ0YnZ+oq//JD7x+/TcfeHZDxKZ/9XB9C6aCne\nOpCEmwnZzk25k4KjiyM3D93k9tE7xF95yLzAOSR+c5jmNhueZ8J5FBHLT/5zmVNkHuubb2L29NmU\nKVOGbdu2Ub5UKW6ePUuBXKyK/MD127fR6/VSCQSk1bFWAUFIpR7aIFn71yCr1vwewIiaRBw4Qvby\nDCA5PkohKeIMJHdRtM1GvlxkSOXJAjiPuXr16jNzB0CqP5QE1MOGE1eBBaiYSTDSYjEgvcmY7ew4\nfuoUXXr2pHBAAPZVqvDFrFms2bAhaza2tMi7C1AJKIrBUI5t23bkMuoTTp8+zdy5czly5Aju7u6c\nOXOcu3dvkpj4ECEEpUpVoly5quzcufNP+/knUatVPPlkbAghWLVqFcnJSqSaP32BnTg5ufLJJ58T\nExPzt8ewWq00btyYwEB3nJyW4OS0BH9/p9fizpJ5MWRXzxskJiaGkuVK0m5Xa/KVy4dCqSBi1UUu\nT7zKqqWrqNOgNn7v+eEa5ELkqmsUcS3CgX0H8CzuQbMFIbgVdeXsgnAOjDtIpfKVOHvmLCaziUHR\n/XHwcsCcbmZ2sZ+pMrgSlQdUIvlOCjt67aRN7bZUq1yNdh3bgdlEDYOZak/JlQrM0+lYsW4dDRo0\nQKPRkJGRgW/evLyXnMwFJLdHnZzXA+zLn5+omBj8ChSg0p077EUKsPo/dZ4FaSLVB0BhYAIaLFRD\nUBc1q3HgEjV4Ety9i5RGaQf8CCi0Wtp07MiuVavolJqaZa1EAOvQIJRe2NunsWXLegICAmjdogX5\nTp4k5zSms8A+pZLBNhsWpHo/+5GqgxZC8uUfATr16MG8+fP/8rMMCipNenpHwBV7+7V8801fhg4d\nmuv5K1aspGfP/kih5+tMmjSGgQOlAnMnTpygTp3GGAxNAAt6/R+cOXOcYsWK/akM/wSHDh2iceOQ\nTLnu0Lt3B7RaDRMnHkH6xO8Bi4C6qFQG3N2juHr14nNXgsvJ2rVr6dSpKyaTkerVazFy5GC0Wi11\n69Z9bmlpmRdHdvX8D7B//34CGvnjXcEbhVIKH5ZoXZzoa9EM+3gYlT6rROOfG1J1aBU+DGvLtbhr\nFCxYAM8gD9Z8sI7vvWcSufUaaqHmt+W/sXzJcnxK+ODgJS3xp7HX0GlPB058f4rJztNYEbyKdnXa\nM+7LcWzbtAlHkw2TwUzlHHI5AqUtFg4dPIhGIzlkdu3albVqVUWkMgf3nmqTDuzV6xk8ciQ9unQh\nNi6O9Uhec7/s3aNG8uOfzdy2oUSQD1BgoRUpqIhECgDHA7WQHgA/KRR4+Pgwe8ECfpg9m7yBgWzU\naolDehNYjxorXbDZepGW1pjQ0NYULRrE8bMR7FGpeHr9s0TgkF5PwcBAFms0XEJyV3khlXE4nil7\nIGDOUWkzNwoWLMjPP/+ATvcLSuVkmjQpx4DMSqG58fHHozEYWmAwNMVgaMPnn3+VdezAgQNYLMWR\nik2UAPLzxRdfMHfuXIzGnO9DubN27VpCQt6nV69+xMbGvlAbkFb/Onv2JD/80J+NG5fx3XdTKFu2\nDHZ2Z5He0/YhRTiqYbXWJznZHh+fAnh6evPrr8v/tO+EhAQ6depGevqHWK2fcOJEPIcOHaVx48ay\n0v+XkWfuvkG8vLxIjM6+HGNqbBomk4kD+w/Qf/6T4KBSraTI+4UpfL0IW7Ztwbe8D7qaOq5ujmTK\n5Cl4e3tTrVo1HkZlX2PXbDCTkZjBrB9m0alTJxwdHTlz5gwrly2jXXo6v8AzC6ADaC0WDKmpWdtn\nz54lyWxkr1JBGZugCdIKWj6Z7W9pNPTq2pXYu3c5vHYtQ0wm4jPPyW0ylQU4B1xUKLAKIwo2I0hD\nTST5UNIOK4uBwAYNMNlsqDQafKOiuH3nDkP79OHjYcMYP3Eily9dYvGCBSSlpmK1IGXwA+BCcnIK\nUmhWgUF9jzkaEwE6HTYg2mxmzNixpCQn8+2337IVycovi1Tm4QZSBdHbQPSqVeTNk4cx48b96bqx\nnTt3pmPHjlgslr9UZJJR9tjOym6lBQYGotHcxGRKA+5hMFxl7VonNm/+noULl3Ho0F5UquyfmtVq\nJSkpCTc3NzZu3Ejnzn0xGGqgVp9j9+5aXL58/oWVq7+/P/7+0jtaTEwM/fsPQQgXFIr1aDRSLpTJ\nZANMGI2xQFPS013p1WsA5cuXe2a1ssfcvXsXtdqRxyspZGQU4dKlyBeSSeb1Ilv8b5AGDRqgTlWz\n77P9GOINxF2K49dGK/AI8kDtoCb2bHZLLfZ4LCqFiutXr/N5x9H0qt6biLMR9OsjFQnLkycPvXv3\nZnXDtUSsusjmPltZXH0JgS2K8eNWKWPn7NmzrF2zhpIZGeRByq+/kUMuGxDp6EilqlUZOWwYefN4\n8O30bwkYXpGM/hVYoNegBoYhFVy7qdFw6Ngxps2YwewffqCRwYAWSeXmQUrHfJp0pLLOJYBgIfAA\nNBhwZAfFiKIsZr5HUrr79+4lMjKSAwcOUPTaNYZkZDAwLY134+L4aNAgatSqReyjR2SYTAwYOAC9\nfg1wEI1mHVI+UFmgFCaLhYHDhvHxnDl8NncuN+/cISkhgSXffUdviwV1pqwVkDKHNiLNO/gM6JyR\nwfZZs2hcrx62XFYfexqlUvmnCjYxMZEvv/yKYsWKotOtQ6fbjl6/mjFjngT1Q0JC6NevA2r1DBSK\n1UBNrNZ3MRjacOFCJOfPZ7+j+/fvx80tD97e+QkMLM3cuYswGKoB5bBYGhIXl8LVq1f/VO7nMXfu\nPJKT/TGbuyHEMDQad/z9tTg4/Ixa/QNSxKYk4ItGU4iIiOfPN/Dz80OvV6FUHgSuodcfp0WLd19K\nLpl/Ftnif4OoVCr27dzHoBGDmO7zA2oHNY55Hag6oAoxB2+xvvNGms1rimeQB+G/nOfGoZtct0az\nct1KHB0d8fB059atW4wcOTLLxzp14lQqlKnAnHlziDhykd7hPXD3lyZWnfvlPF17d+Xduo1RCqmO\nfX2kaptNkby6icABrZa8/v4MHzyY/ElJJKmh3/X+OHlL2UHF25Xg90YraW0wE67X0697d8qXL8/W\nrVtJT0/PVnmzCfArEIvkuEgE9iKpisfFEWpmymDATBqwE0ld1wHsrVau37rF70gT9x/buQWABgYD\nX336aVaZhRkzvqNixXKcPh3OvXtu/PbbFZ6UabuKi4sL7du3B6R68DOmT6eHwYAL0tyAZUguJRPQ\nCrKKv+UB3svIYPGFC+zatYtGjZ6/xu2fYTQaqVKlBjdvajGZ3NBqzQwcGExo6CRq1aqVdZ5CoWDK\nlElMnPgNQ4YM5+efTyOVN7IihDnbg0UIQWhoS1JSmgFFiY7eg9V6GTs7J0wmASRiNqfg6emZU5wX\nQppLIp7ahh9/nIGnpycnT56kf/+PMBjuAhlYLDcpWzbnYp9P0Ol0HD68n379hnDv3iW6dfuI7t27\nv5Rcf8WVK1d4//22REdHUa1aDdauXYGbm9trGev/Adnif8P4+PiwdsVa7B3sEVZBj+PdqNi3Au8v\na0GNz4PZ2HUzS+svx/DAQPejXXHM50BQ+0Ba7mhB+UnlmL18Nt75vfluhpRjr1Ao6NChA3279yXo\n3aAspQ9QqmNJIq9EUqt2ba7o9ZiRrO5QpMyZCcBchYJGAwYQVLw4gYmJ+FssFKzknaX0AQoEF8Co\nVbHR3Z2B48YxbcYMABYsWIINJU+/p/gCvZCs6LUaDafz5qUw0gItj1ECdZFiBnZIfvamSGWTlUiB\n4ZZI3uWnw1b+wLlLl7K2FQoFXbp0YcaM7+jcuTP29nFI7xcG7O0fYjabqVmzISEh77N9+3bc1Oqs\nBVfyI+WslEJ6Tyic43NSAv6pqfyx7flzLP6K8PBw7t9PwmRqDtTEaKxGUlJaNqX/NCqVio8+GoGr\naxROTmtwcFhE8+ZNKF68eNY56enppKWlID2mFFgsfmg0dvj7m9HrZ6PVzmfKlG/Jly9frmP8FX36\n9MbVNRq9fj2OjsspW9af4OBgSpYsSZcuXfjmm8/Jm3cbBQocZvnyxX8ZgC5atCh//LGJc+dOMGzY\nkNdWcK1JkxZcvuxLRsYADh1KoFev/q9lnP8XZIv/XyA2NhZjhpHCDQqhdX4yIeadoVUIGxtG14Od\ncCvixqV1l3HwcqDpbOn12DPIk9brPuCX2ksZPW40kVcimTF9BnZ2dqhUKh5EZXcVZSRmgIDatWtT\nvV49VuzYQU2jkTxIdnGKXs+q33+nUaNGeDg709lqxQDERz5C2ERWADotLg2lVUnkreispSS3bt3K\nxo0bsOLIHNLpiTlrTdyHgMnBgchr1+jRsSP62NhnfP4OSJb2LaRgcBpSkPkxRZHKPKRD1gLqCYCb\nszO5ERISQpcuHzB//nQAmjYNZfLkGRgM9VEoUggL6wMmIxaefOmVSLNxjyIp/5w/BgPZHzx/F2dn\nZyyWNB4vRKnRJGOxmBgzZgwALVq0eCZvv2DBgly5coG9e/fi7u5O3bp1sylLvV5PmTIVuHBhB2Zz\nAHr9YVq16sDYsV8SHR2Nm5sbHh4evCz58+fn4sVwNm/ejKOjI++//362JR+HDBnMkCGDX7r/14HV\nauXGjUiEaAsoMZnKcvp02L8t1n81ssX/L7By5UqK1C3M/TOxWM1PajYmRCdiybBiNUn7kmOSyVvG\nK1tbr1J5MKWYaLW2JTsu/0H7LpIrY/2W9STfS+HQxMOY082k3k9lfceNhDQLQaPR8DAjmQRPe9a7\n6fhZreSQ4xOl/zTegGuqiQ3t1hN3KZ67J+6yMmQ1QiEIbRXKgwcPEELQrl1HLJYPgeHYqMdcNMxX\nqZij17PD3Z1N27aRN29eguvW5UaOAKkFmIcUbyiDFAiehZRe+ZjHU30eT/qyAgd0Onr27p3rPVUo\nFPz00w8kJMSTmPiQIkUKYzCUA0ohRDWUSi8KFS7Mscw8/ASkYPM5QK1QcDqHJZqKtN5w7Rxr7/4d\ngoKC6NmzK3r9PBwdF+GgvczGVSvYPX48u8ePp2GNGnTv3PmZOIKHhwetWrWiXr16uVrIO3duoXXr\nQCpUiGLkyE6MHfslKpUKf3//Z5S+EIK0tLS/lfLn5eVF9+7dadOmTVaW15skMjKSzp2706ZNR44d\nO/aX56tUKoKCSqFSHQQeodWeIDi42l+2e5uR8/jfMImJidSqX4uLEVJJXAcvPQ2m1EOlVbNz+G60\nLnYoFAqaL2xG6r0UNvXYwofb2+FaxBWtk5YLKyI48cNJuh3ugsVo4efCczm6/xjlK5aj9qSaHPzm\nMKn301BqlLgWcqVrs674+voya90PtNzwHvbu9ggh2N5rB7U96jB10lQAunXsSPTKldS1WjECezVK\nLtmpMCsVFKhfmPdXtCDsi0PoLzmwbcM27Oy02GyfIKnmE2jsd+BbxgdhFRhjTWzdsJXy5csTHx9P\nycBAKiYkUFEIVEiB1ESkGj6PbclHwAKkmbP5gF1KJaeAcioVdmYzUY6OFClRgmo1a3I0LAyvvHnp\nPXAgDRs2zFU5zp49m1GjfiA9vTWS62cRGzasomeXLqQ9eECq1UphpAldqTodarUaP6MRf7OZBOAg\nSrTOLtx7cO+lyxQ85uLFi6xcuZJfpk2jQ2YgHKSJa8v1esb++CNdunTJ1iY9PT3rTe5liYiIoGHD\npjx4cI+CBYuya9fW174AS0JCAtu2bUOv1xMSEvK3HxwPHjwgMLAUycmlsNm06PVHOHbsIKVK5Vxt\nITsxMTG0b9+Fq1evUqdObRYtmoujo+Oftvl/4F+r1bN9+3aGDh2K1WqlZ8+efPzxx9mOx8fH07Fj\nR+7fv4/FYmHkyJF07dr1HxH+v5WUlBSmz5zOtt3b8Mnnw4hBI7KWvGv6XlPi8sRSb1pdVHYqDk06\nwokfTqJz0+FgcaB0udKcPX2WhwkPsZgsqFQqNI4aLAYLHkHuJEYn0m5TG/JXk+amrm38O1OGTKXN\nh20QGhvvzmpM/uq+nJ5zluMzTqBz1JKenIFCpUDYBEUaFKHZ3CY8ikrg5JDTnD8hZYzcvHmTqhUr\n4p+URCmLBRNwWAEpJTzpfLwbGr0GS4aFmflmcyPqBl269GTPnmtkZPihtt9GqzXvEdBUSgk8vzyC\nc+POEXXpGgqFgitXrtCrSxfOnD2LVqUiJT2dXkKQszzXXiTXjw1QFijA0pUr2bt3L4a0NPwDAvho\n2DACMjIoajSSBJx0cKBj795M+e67Zz4Ds9lMgwZNOHgwDGGzUsyvKN/NnM6BfftYO2MGbUwmHodM\no4F19vb07d+fnVu3Ev8okZLlyrNo0fxXXgz9MdUrVqTg6dMUz7H/CnCtbFmOnZVmOezbt4/mzVuR\nmpqAWq1h4cL5dOrU8aXGLFasFJGRRYEKKJWHqVzZyNGj+1+o7YoVK1izZiMBAUUYPfqzF1KisbGx\nlC1bibQ0NyCNcuUKsXfvDpRKZdaM57/it99+o0eP8aSkSMuFqlQ7+Prr5nzyyScv1P5t419Zgctq\ntTJw4EB27dqFr68vlStXJjQ0NFswatasWZQvX55vv/2W+Ph4AgMD6dixYza/4f8TZrOZWvVrYitq\no9THJUmISqDp+01Zvmg55cuXJ+xAGIPu9ketk66/1pc1CF90DmfhxNEjx7hx4wah7UNpv7UNa1r9\nTvutbfGukI/U+6ksa7CcUh1KZSn9hOgEoo5co82xNlhsFmp9WoOSbUtgTDZyeu4Z3vs1lGLNA7Bk\nWNg24A+sZiuOeR1Z2/Z3ynUvi3e+J0qtUKFCnAoPZ8rEiWxavx6rxUKavZkeR7ui0UtWm1KtRKFU\nYLFYWLNmBfXqNeL48R04+zpmKX2AUu1LsGfUXpYvX86HH35IYGAgB44e5cGDB6SmpuLn50duXmgv\npLTPctWrs33nTvR6PdUzV+qqWaUKVZOSsk0+K5GWxryff6ZT167PLDd4+PBhwk8eIRgLhbGRfC2S\nHm3akGwy0cNs5ukEzCJASZsNJ2dnzvzF4iQGg4EzZ87g6en5t9aqjY2NpVwu+92AB3FxgGTtNmoU\ngtnsDfTDYnlEjx59qF+/Hj4+PqxZs4bRo8ejUCiYPHl8tkVkciMmJhoIARTYbEFcv/5itYQWLFjA\n4MGfYzBURavdxv79Bzl8eH+ub1bXr1/n119/xd7ensTEJB498sZsDgFsnDw5D1dXD9LT02jaNJTf\nflv+l/V5vLy8sNnikd6H1Gi18VlVRGX+OV7Jx3/8+HH8/f0pXLgwGo2Gdu3asWHDhmzneHt7k5ws\nrZuUnJyMh4fH/63SB9iwYQOp2lRCVzTDr3FRKg2oSON5Dfnkq09ISUlB56jNVjBNoVDg4evOjzN/\nIl++fOzfvx//9/24vO4qlfpXyCrj7JjPkcYzGnJ+6QUOfnOIY9OPs6DSIvwaFaHnhW7kLedFnpKS\nDX11UyS+7/gSGFoMhUKBxl5Dw2n1ubLuKnXG1SLuYjz7Rh1g1OBR2WT39fVl+g8/cO3WLSKiorCk\nWLl7UpqvK4Tg+IyTlChZgnz58mEymTh16iQ2WyvMhuxWh7AJzGnG/7B33mFRXV3b/02DKVRFQAFB\nBUFRQFHsokZD1FgTW2KsMRpNrDEmxqiJeUw1xthj7D32LvauoKKiYu8iRRGkTGHK/v44OIpgHhPN\n+zzf+3Jfl1cyZ/bss+cws2afte513wzu25cub72FtcB60dPTk4oVKxIcEFCklwDgKiDUahYvW4ZW\nq8VgMDBs2CfUqdOI4wkJRVQ8tUC1/HxWLJM6SI1GI2azGSEE7/foQSu9ntdsNiohdQ6/l5eHxWwu\n1qnLw2RizszZjBkzppDL1tNISUkhOLgarVq9R40adfnii7HFjisOderV41oxu95rMhlRdSTlVckR\nS43UP6xE+il04c6dO5w8eZIePfpx6VIYFy9Wo0uX94rw+59FdHRTHB13AbdQq/fRokXzF1rrnDmL\n0eubAjUxmdqSkHCShw8fFhl348YNatSI4quvtjFmzDKmTZuBzfb4sy3DaDSTl1cPm+0zdu26zL/+\n9e2/PXfjxo3p2rUdavV0NJrp1KtXkffee++F1l2CF8dLBf7k5GT8/Pzsj319fUlOTi40pl+/fnYz\nh/DwcKYUUAH/tyIpKQnvaO9CuyP/JuW5fP4ygYGBOGtduLj2kv25e8fvkXHpoZ0PXa5cOe4dSeHO\nkbv2nfZjqHQOyGyCR18f4t5neyHfSvul7XDxdaFC8wASF59FCIHNbEOlKfzjqnBQ2Jk6KrWKMSPH\n0KJFi+e+D51Ox5qVa9jSZRvL6q9kQfVF3Jx3i+ULlwPSnY1MJgf8MGYpOPLDMWwWG9Z8KwfG7KeM\nxcaHJhOnYmOZPXt2obnHTJjADq2W+wWPbUhNXxdVKvYcOIC/v6QD2q1bD2bN2kF8fCmE1VZsx7HK\nakWv19Ov34c4Obmg1ToxbNhwHmVk8CzRUIfUT3DimePngF1AZmoKP/9rIt7upTh4sCgrZMKEiaSm\nliM7uycGQz9+/vkX7ty5U2iMEIL4+HhmzpzJunXr7JILo8aMIV6t5hISU+ixoF2cRsOoMWPIzMzE\n19cXicd0FOln8CgqlYEqVapw9OhRhAhBIrUGIZNVtov7PQ+rVi2jS5cIqlQ5Rc+ejfn99+KNfp5F\n2bJeKBTpBY8ykMmkz8OzWLZsGXl5QVitMZhM7bBYXFGrL6JWb0atXolcrgdqAyqMxsqcOfPvzWVk\nMhm//z6TS5fOkpgYx86dW/8jBeb/7XiprfeLcHInTpxIREQE+/bt49q1a7Ro0YIzZ87g7FzYlPox\nxQ0kj9omL8Gm+E8iLCyMBd8tQHz9hA55fccNqoZXRSaTsWLRClq2bcmp6adBJeP2wdtghcDgQCpX\nrUxZz7Jk3svEr6EvcVOOE9azOroyOqxmK4e/PkLZPDNtLTaSgXRvJ5SO0p8wanBtFjVdysKGiynf\nxEXfDqcAACAASURBVI/Lm6+QkpBK2ZreCCE4/O0RKr5egRu7b6IRGoYNHVZk7Xfv3mXxksU8zHpI\n29Ztadq0Kfdu3+Pw4cNotVqioqLsuVoPDw/efPNNYmNXoteX5/A3hzjyzSFkNvCWwdt5ZlRAPb2e\n2b/+ysCBT3jV3bt3JzMjg/FffomzTEae1UoZb28OrlxJZGQkIBXrNm3ajM1WHqkdTHAUSQ/TG8mp\n1QpcdnJCeekSB3buASFDjhszZszBXWkrdmfvKJORJJPRyGZDA1wHtiEVmv0QCOBSbg5tW7YkLiGh\nEE89IyMLi+VxJ4AWhUJrv5sFqXDf5o03uHLuHAE2G1lKJf0VCjZu20bdunVZs2kTA99/n+2pqchk\nMjw8PVk8dSpDh37KsWNHkMtlfPBBX+bNW4jBsIayZb3YufMwLi4uhISEoFDcRCpHC+AmISEhf/pZ\ndHFxYeHCuX86pjh8//0EjhxphsFwCbM5m1mzZhSbotFoNCiVRqxWAdiQySzMnTubtLQ0lEolY8Z8\nRWZmAkKURas9SUzMqKInew5ehePX/0bs27ePfU95T/xt/C3DxgIcPXpUxMTE2B9PnDhRfPfdd4XG\ntGzZUhw6dMj+uFmzZuL48eOFxrzkMv4rcODAAdHmrTdFeFSY8AkoJyq/HiTeXt1RNP++mXAt4yp2\n794thBDCYrGI2vVrCd/avqLOiCjR7NsmQuelE53Xvy3qf1ZPuPq5iM/yRooxts9Foy8bCAdnB+Hf\n2F+U9i0tylfyE68/5Ser0yhFn7he4ksxWnwpRovex3oKjZNGtGvfTpRycBAaR4Xwi/ASzj7OQuuh\nEf6R3sJBrRRjx44tdv2uHq4isl9N0WhsQ+HkpRMVKwWIuLi4ImPNZrMYPHi48PMLFJUrVxXDhw8X\nFX18RDcQI57xq/0YhFouFyEVKojGDRqIQQMGiMOHDwubzSYMBoM4fvy4SEpKEjabzT5/bm6u8Pb2\nE9BQQDuhAOEMwg1EZRBaEJVAVADh4+EhfJVK0QvESBBvg9CAcHJ0FH2K8c51c3QUGrlcqAvm0oHo\n8My48SCaKJViQL9+hd73nj17hEbjJqCFcHSMENWrRwqz2Wx/vlO7dqKOg0MhT+B3QJRycRE5OTlC\nCMl/99q1a+LatWvCZrOJMWPGCrU6TMCXAgYJtdpZpKamFvsZGz/+a+HgoBGOjlrx7bc//PUP6Qvg\nl19+FSqVWiiVjqJp09dFcnLyc8dmZmaKSpVChLNzkHBy8hWvv95aWCwW+/NJSUmiXr0mokKFKuKr\nryYU+huX4NXg78bOl2L1WCwWgoOD2b17N+XKlSMqKorly5cXKu4OHz4cV1dXxo0bR1paGpGRkSQm\nJtr9WuH/f1ZPbGwsXXt2pcGEenhU8eD8ogvc3nqbauHVKO9TniEDhxAREYEQgg8HfciafWsYcK6f\n/Y7g6rZr7Bm9j6DWlZDJZTT5Oto+d0pCCkujVxB3JI7Lly8z5L33aFJginIN2KhRUrlDMCgVXN98\ngzkz5+Du5s5HnTrxdnY2d5BkD/KQ8nqJzs78a8EC2rZti16vt995hdYMpdrYqoR0kAqWhkwD0wOm\no8yXszk2tlC36Zgx45g8eQV6fVPk8uuUK3eV15s14ubSpTS2PulLAKk56iqSvuM+pKYtg8oRodTg\n4+vL7Nm/0rRp08KvOXaMmJiuZGf3Bi7gwEpa84TzbwZWA3dlMuRKJR8V+O3arxmSk5YKGTEIKiI1\nlR3SatH4+OB5/To1rFaSkailA5GUOp/GTeBctWocfyaPfuDAAZYtW4m3tycjRgy3X78HDx5QwdeX\nj0wmnt0br9HpGDZ1Kr179+ZZdOnyHn/8kYWk6w8uLnPZt2/Dcw1ZHn9P/okO2EuXLlGjRl0Mhp6A\nE1rtKn78cUihu7Vnodfr2b9/P2q1mujo6Bdm75Tg1eA/wupRKpVMmzaNmJgYrFYrffv2pUqVKvac\nbv/+/Rk9ejS9e/cmPDwcm83GDz/8UCjo/2/AmAljaDHzNXvQLN/QjzWt1/Fep/cKUVcXLlrIstXL\nqNazqj3oAwQ08+f+ufvU7BfBjT03C81tzjMjc5Dx0y8/cSz+GHllHNjt5szDyxmoZTIc5DKSVl7k\n/Q8+YOOpTZQvXx6TyUQGksTx07r4aUCyxcKOrVvp1b07+WYznh4ejPzsM65cuMJb7drbx2rcNVSO\nqYhi1UWGf/QRJxIT7c/t3LkXvb42UBabrSwPHsQRWacOS5csoRQFvrpIsg0HgO5I+owVkawYDWZH\nhLkzV65k06ZNR+LiDrFv3z5u37pFeEQEkZGR5OdnIqU1blEWScvnMVRAO+BnIahus/Esy74s4IyS\nLGLYxikE96gcGMgnw4ZxeN8+sq9cwQVwQfoxyqBo4M8AyvlK7KmUlBTOnz9P2bJlady4MY0aNcJk\nMhVKf9y7dw83R0fUxcgol8rLK2QU8zQ6dnyTzZuHoNc7I5eno1bb/pQt9E9JHoCUXnNw8MZgkDRu\n9Hpfrl69XmjM0aNHmTx5OjqdhrFjR1OhQgW7FWUJ/v/BS9NrWrZsWeQP379/f/v/e3h4sGnTppc9\nzX81rly8QuMGDQod86zvSdLFwtTASVMnETmkBpfWXS4kiXBj9008q5fBr5Efu0bu4dikeMJ6ViPj\nUgYbem3GK9KTDbs2ENazOp2/eguZTEZyXDJLY1bQbnEb4v4Vj9pJTfny5RFCkJuby4zffmNAnz5E\nmkyUs1pJlcuJc3DAq0wZji1ezPv5+bgAd1NT+W70aORCkHkji1KVpC+9EIL7Z+8TDay9cIHs7Gy7\nXENERDXOnInDZAoArqFQwOhPPqGuzcYRJF39x3uQDmCXclACpZFxh9oFR8shxAmiIiMJkctxMxjY\n6OREnpMTw4Z9zKRJk7FaLQQUvokAJEaPDjApFGRYrWxDRR5yojARgdQdDL7kE4RaPYdzVyQ5YEe1\nmklbtxKZlwdI6px7kH6cHlM8c4F4nY7fBw2i57vvsnbNGnzVah6YzZTy9OROWgYmk4EGDRqzdesG\nnJycCAgIICs/n1wKS08ApDo5PVe6uEuXLuTm5jJ79gK8vMowZcoBtFptsWP/Cej1ej755DOOHo0n\nIqI6QqQjuRO4oNWeoWXLJ/z506dP07x5S/T6+sjlejZurMfly+dfSiLiebBYLGzatImcnBxatmxZ\nYsr+ilHSufsK0CSmCS5dnYjoLe1LhU2wvPFKPu40mHLlyhEaGkrVqlUp61+W9rFt2TZgOw4ujtTo\nG07WjSz2jzuITw0f0hPTGThgIIlJiezcvhOVRonOS4d/U39Ozz1Du4VtuLD6omTT1y2UxCXnCO1S\nlYrNA/gteC5LFixhxOcjSElOQalU8k63bljyTCSdPYtn2bIcPX0cY3IaQ0RhOtcDYI5cjmuQO63m\ntUbnpSP+x2PcXnyO3nozk5VKHj56ZA9IOTk5dOjQmb17d1KmTFm83J0IvHjRrouZjRT4tyApgD7N\nvV8FJOGBoEfByIW8hZnqT405KpeTGRZG7N69LFiwgBmjRvFufn6ha64HpigUKORyhFlgJBqBJyo2\nUoc84pFhU9bH0fEGI0Z8wFdfScYner2esCpVKHfvHvUtFhyA5UAyECGXY1MqSZLJ+ODDD8l+9Iij\nK1bwpsGAGol9dBTYhxYzQ3F03Eb//k2YUiCYN7B/fw4sXkybp8afkMs56+nJ1Vu3/ivNR95+uxtb\ntiRhNEagVp+iYUMfhJDx6FE2w4cPsqubgkTA+PrrPQjxGgAuLn+wePG3tG3b9pWuyWq10rx5K06c\nuIoQzqjVaZw6FV+IQVgCCf+RVE8JJPww4Qdeb/062dezcQ9x59LSy+TeyWPcxHEENPTnzrG7NG/a\nnJgWMST+fpZu27pwctYpTkw/yf3EB/To0oMaNWrgNdiLyMhINm/dzM7YnVR7txotp8Vg1ps5u+gs\nh/51mHqf1kMml3FwwiEsJisKBzk6Tx0adw3de3enzdLWVHqjIo9uPWJj5y2M6DmC2XPnUta3LHki\nj/Bngv5jITJ3R0eybzxi8xsrMFkFQTZBD6OFUzIZ0Q0bFtqFOjs7s2vXNoQQCCFQKZU8/dV/LKNW\nDbjIk8BvAdKBSmRylSmoVI54yxVUN5kLXc8om41ply6RmpqKVqvljtVKIpIXgKxgnk1AleBgGjVp\nwrQZyxE0AsBMNHGKPfzw07fk5+cTGhpK69at7XNrtVoOxcUxZOBAft2yBYSgvI8PXw8aRFxcHOvW\nbsBqc+DnX6ajkNkYJqz2nL0caAAkYiGNa5hMgZw//4Sa+8vUqQwwGJj2xx/4qtVkWCyUK1+ePRs3\nvpKgL4QgKysLJycnO8Vx9+7djBgxmvx8M+PGjaJLly5/ac6dO3diNPYEXDAaSxMfv4hHjx4UO9bT\n0xO1+gEGgxUwY7Vm/CPNVcePH+fEiXPk5vYFFBiNO5g5cxYTJ/7rlZ/r/ypKAv8rQFRUFMePHGfa\nrGnc2niLWh612e+1n977etilDlbFrKFvq/c5OP8g605toFQ1d3Kv5tK+dXvCa4Qz6otReAZ7cv/q\nfZBJNoo1+0sFPrPBDDIZ3Xe/g5OXlEioFFOBX/2nU65WWVJOpWLIMlCte1UCW0rGh24BbjSZ1Iif\n+v7EpMmT0JNH4BsVyV2eBCYrVmCbWslZBDpXNTk5+USG1yA5KYmG+jycgYOOjlzTaDg4a1ax7/tx\nvtlRpcKYn18kxaFHytKnI5mYH0SiYYZg5Z5CwYjPRrD7+++LzKsA3FUqBvbrx61Tp2hstbIbqV7g\nhiTroASMSUl4eXujVJkwmx8A7igUVxkwsP9zvW8BvL29Wbl2LUajEaPRiKurK/Pnz2fTuk3YbLWA\nGAQPsYjpxfYOBJJPGpfR6TJo02aE/biDgwPzFi1i4g8/cPbsWby9valevXoxM/x1ZGdn06JFK06d\nSkChkLN48UIiIsJp2/Yt9PoYwIE+fQbi5+dn73Z+Efj6licp6TJQC5nsCuXL+z93bN++fVm+fA0J\nCTOx2fLp0aMndQqaz14lpIY/OY993ISQ2ZsAS/BqUBL4XxGCgoKYMklqTmvZviWRw2rYG7CUaiU1\nhkWwfeY2ks4ksXHjRm7fvk2DZQ1QqVS0aNOCHvHvUiqwFGaDmZVtVpFx+aGkzlndk4dXMvGo4mEP\n+gC6MjpKVy7Fwa8OcXPbbRrWb4jR01BoTdoyWlJTUlCqlXTb1hUXP2dmrrjAA+C8QkZGTS8+3twZ\njbuG5Ph7rGm9lm+++46t69eTmpFB05gY1g4Z8qd6NTKZjC6dOnF05UpaPNXxagIS1Gqy8vNZarPh\nhMTK8QRWyeVM/e03AgMDmTt5Mrb8/EJ3IbnAPYMB06lT9MrLQ4UknzwXiRXUA0n3Xw9s3r+fiOrV\nOZM0FyEEDRtG8/33/75DFCSjELVaTX5+PqOGD8fDKki2VyTcACU5WIuwdFIUCsp5PeTDD/szePDH\nReb19vb+23r4z8OYMeM4c8aA2TwSszmVHj168+uvP6NQVORxOT0/vwqHDh36S4H/jz8W89prb5CZ\nuZtSpTxYtmwLZrO52KYpR0dH9u/fyZUrV9BoNP8Y175OnTqEhlbg3LmVWK3OODreZMCAxf/Iuf6v\noiTw/wPQaXUYsvMKHTNl56PV6nB0dKRTp07245+M+oTq71ezG6ioNCpe+74pS1osJ3boTnSeWlwD\nXMm4lEHe/Tx0ZaQOSn2Gnqxrj2hfvwMLYheRlZVF5z6dqTWwpl2B88j3x/AILUPKiRTKRkrdxK/P\niGHeoFhkChndfn0djbskmewTVY5qPUNJf5DO1l27in1fd+7cYfPmzbi6utKpUyd7cPj+559pcOgQ\na+7fJ0ivxwCc1ulo17kzUfXrM2roUDL1eg7K5aidnVk4Zw5vv/02QgjKV65M7LlzNMnPR4Ok2rlF\nqyXA25vy16/bZZnvI1UEhvLElUsLdLBamXrxImfOnMLb2xs3NzeEEMyYMZOtW3cSFlaVL7/84k+9\ncxMTE9HYbESRz0a2YSYfBddRYuMYktrN4x+mi0C2kxO3riS9kiKsEIILFy5gMpkICwt7rhrnpUvX\nMJkqFKykHHK5mlKlSmG13ka6aiocHW/9W2OUZxEaGkpKym2ysrL47bffiYysjc1mo1+//syY8WsR\nFpFcLv9LGkV/B0qlkv37d7Jy5UpycnJo165dQVdzCV4VSgL/S+DevXvMnT+X5JRkWjRtQfv27VEo\nFAzoM4Bu73ejfCM/ylQtQ8blDOImxPP75N+LzCGXyx87cdshpEZIdK5alrf+A/0DPc6lnVne9A/q\nfhEFMjjxfQIfDfqIn777iUePHnHmzBnaxbRjTsg8KrWoyIPzD0i7kY5CrcDFz4Vr268T2LIS4b3D\n8a7lzYL6i1A+I+sgVyvIf6aI+hhXr14lMrIuZnMF5PJHzJw5l337dqBQKPD09OTUuXMsWrSI7Rs3\nUtrNjYV9+5KWlsbg/v2JMhjwEYIUm424/HyysrIA6W5h2+7d9O/Th2lbt+KsUqG32WgcHc3u2Fi7\nFSJAKpJL1rNhUYXED7pw4YK9k3XixO+YOHEmen0tdu/eSELCGb75Zhw/fPMNZ06fxtfPjyEjR9qL\nkg4ODuQLQTVAgZ6zbMcdK7WxsUAmY7pKRZBCQaZSSaZSyZbt29FqtWRmZjJt6lTWrViBQqmka48e\nDPjww2LlDYqDEIJ33unBxo3bkMlUVKsWxL59O4rtku3Y8U0OHfoavV6NQpFMqVLOtGnThq+++pwv\nvhhTEKwH065duxc699PIz89n27ZtjB8/EbN5IODA4sXLadassX2Tcu3aNcaNm0BOTh7Dhg36xzvr\nHR0d6dGjxz96jv/LKGH1/E2cPn2a12KaEfR2EC6BLlxedoVw/3BWL1+NyWRi8dLFjBn7BTKVDKvJ\nxtgvxzJscFGZhNOnT9P0jaZ029cZjxAP8vPy2fD2JjrU6khF/4qkpqbSpEkT6tevzx9//MH8ZfMx\nmUwElA0gMjKSzEeZ/DjpR7yqeJF+OZ3GjRrT4c0OVKxYkQ5dO/DmklbYrDY29NhErUGRuPg4c2LG\nSUy5+XhV9+StlR2QK+U8uv2IxXWXsWvzLmrWrFlkncOGjWDKlOMFjA4bTk6/s2fPemrXrl1kLEjs\nmXKennTLy+PppMcDYKFGQ3JaWiHZjszMTNLT01GpVISHhtLQaCQRkMp7UrPabuBZGxYB/AJ06tOH\nOXMleYLKlatz5UoUUB4wI5N9i6vakbpGI/5CcB84qtPx/pAhfP2vfyGEoFL58tS9e5en97KPgDmO\njiz94w+Sk5MpW7YsrVq1wsHBgQcPHlA3MhLXtDSqm0zYgNMaDcqKFTkYF/dCwX/v3r20bdud3Nze\ngAKtdhXffz+Qtm3b4uHhUeiOQgjBlClTWbJkJb6+PkydOsnOcrHZbAgh/pZ2f1paGnXqNCIt7RFG\nYxbQDKiLTLYDjeYc7u7udOrUgenT52A2A+hQq/Xs37+bqKioQnPl5uayY8cOHBwciImJKdHY+R/A\n346df6vf9xXjv2QZfwlNY5qIVrPesMslfG74VLj5uAonNyfhoHYQ1WqGir1794pbt24Jo9H43Hks\nFouYPmO6cCnlIvxr+AvnUs7i3V7vCpPJVOz4NWvXCJdSzqJW70gR0jpYaEqpxcc3BoovxWjxWd5I\nEfJGsPj6m6+FEEKoHFXis7yR4ksxWnyQ+L6IGlxLeNXwElU6hYiRWcOFV7incPJ0ElWahwidq078\nNPmn565z+PARQi5vKGC8gC+FTuddRHrjaWzZskUEu7gUkUIYDyJYqxUuLu5CqXQQ7dt3EgaDQQgh\nxPHjx0V0gwbCUy4X9QqkFyqA6ANiUIG8wtvPzNUGhAcInaOjePjwoRBCiEaNmguZ7I2CtfYRMiRJ\nh6df9wkIZ7Va3L59WwghxL59+4SrViuilUrRA0QrmUx4aLXih++/L/b9jRg6VEQ5OBSacxyI6hqN\nmPSTdB1TU1PFD99/L/r37SumTZsmsrKyCv8t16wRzs6hBescL2SyOsJRpRKltVrhpNGI/n36iLy8\nvOde41eBDz74UCiV9QvWMEyAg4COAtQCdALCBAQK0AoYIKCFAGfx+eejC82TmZkpAgKChJNTiHBy\nqiiiohqK/Pz8f3TtJfj7sbOkv/pvIu5IPFU7PZGmODkrAccyanqdfI9P80YQ/Fll2nRog9VqLdbB\nKTc3l369e+Ok1TLko48prXGmV5ueXDx7kSXzlxRL/zMajbzf/306bX+LlvNicK3sStTQKNwC3ABQ\naVXUG1eXxSukQlhYZJgk1nYyhZzkHBqPbwQCInqHo3ZVU39UPcJDw5k0/GduXr3JiKEj0Ov1PHr0\nqMi5P/74I1xdL6PTrcfJaSn16tUo9s7gMSwWS7GMGIB8vZ7s7FAsluFs355Er159qOjnR+PateHw\nYSrbbCQA7ZH8eLcAi5AKxutRMQ8HdiEVew8AXYDyjo4cOXIEgDlzpuHhcQZn59mo1X9QSqMqYqbu\nBITIZPbmwujoaI6fPk2199/nco0aOLVrx5pt2xj56afFvofVK1dS85m0mAwINxhYvnAhu3btIrhS\nJVaNG8etuXP5fdQoggICOHPmjH18kyZNcHTMQKHYBewHkUBns5mP9Xr6GwzELVtGp7+RuvkrSElJ\nx2LxKHjkgkzmAGwFvIDKSLb37wJlkAwr6wN6PDwKN20tXryY1FQtubldyc3tTlLSPWJjY//RtZfg\n76Mkx/834RvgS9qZNAKaBgBS4G+3qC3uFaXO16qdqnBly1XqNKjDxXMXC8lUpKamUq1KCNmGXLyC\nS5GdnIMpK50ZP/6Ih4cHFSpWRCaT0axZs0L53lOnTuHq50K52hLzRCajaH3AJuwFuS8//ZKuPbri\n4OaAq58LKQmpuAe6UymmImaDmdNTExn7wVhatmxJTk4OXd7rwsZ1GwGIjKrJwjmLqFRJoocGBASQ\nlHSG7du34+rqStu2bf9UlyU6Oprb+flkIfFjHiMbiY4J9QA1RmMVVv+xFq0w8RFSN+4aoAmSKn0w\n0LDgtUeAPZTnNuHc5gKlucLHWFAi/Sg8LuAGBwdz+/Z1bty4QUpKCr3at6c42KxW5v72G2uWLqVh\ns2Z8OGgQ02YWL12cm5tLVlYW5cqVQy6XY7Vai9U0VyBJVnfu2JEOeXlPfnDy8jgDdO7QgYvXJGey\nUqVKkZAQz1dffcPC+QvobjPbxzsDbYxGph85QmJiYhGjmVeFfv16snt3b/R6KypVKhaLGSngXwMe\nF8QF0hU2A6dQqdRF9HvMZjM22+PUjhyZTIXZXLg/owT/PSjZ8f9NjP18LNv77ODypivcv/CA3LQ8\ndF6F87ouvs4ovZV8+8MTeuGjR4+oFl6N7Hw9fRP60DuxHx+nD8WzQzBaaz7DR41g+I/DGPr9UPwq\n+BXSXC9TpgxZ9x7ZDdpDu4VyctYpMi5nAGDKMXFk3FF6vdsLgGWrlhLRK5zBNwbR61AP3j/Rm6xr\nWaxtu55ZFedQP7i+3eSiZ7+eXFNe4ePkgQx/OARtay3NWzYvxJ/29vamV69edOjQ4d/mk11dXRk7\nfjwrtFqSkAL+RWCFVotao0MmS0KSUzuIlzARhRT0KThaqZg5fQC4hiM7UHGRKCxcAtYD6RYLERFP\nPK7UajVVqlSRdHWUSu4+M5ceOJufj/OZM3gdOULsTz8RVqUKFy5cKHLehQsX4eHhTeXK1QgLq0Vm\nZiZt2rcnsRhDobNqNVXCwvCGIncZYUDO/fskJCTYj/n5+dGpU0eCnDVFxiuQtJb+ne7+y6BNmzas\nWrWILl3cadGiHDpdZaTdvTtSB8YCYDbOzhYcHLbh73+BEyeOFClAd+3aFSenOzg4bEajWUeZMvD6\n66//Y+suwcuhJPD/Tbzb7V1m/jSL6z/cZEubbfj5+BH3c7z9ecNDA2eXnKdG/3C2795uP75g4QJM\nwki1rlUpU1XSH5Er5DT6Jpp7ShlvrepAl32d6Lq/E01nRtPu7XbEx8cjhCAwMBAnnROb3t9Kblou\nLn7OuFV05bfw31kQsZgZ/rOpF1CfT4Z/ghCC9Ws20HBcfbsmUJmqZQjrUp3aHlEc3n2YJfOXoFAo\nyMzMJHZbLM2nvobaVY3CQUHdEVHgIti//8U8WovDyFGjmL54MbciIljo4sLV6tX5ed48jh2PIzIy\ni9KlN1HKIZtSFBZJc0ISSXsa6cAKoArQhlwaItiLZKDiAvjbbAQGBHDo0KFCr1OpVPw6cyZrtVpO\nIBWXLwBzgAigKdJdRUujkdqPHjHgGQXNzMxMBgwYhMnUC4NhKFeuqPjii7F8MXYsV93c2KVUko7E\nOtrq4MAjT09q16mDczENRzLAVaEgI6PwuytdujRZVivFleiyFQo8PDyKeebVoVWrVqxYsZiJEydg\ns90FbgF1gP5AbdRqE4cO7cZk0nPz5uVi7z68vLzYv38X337bjR9/7EdCwosVuEvwn0FJqucl8NZb\nb/HWW5IpdFpaGqE1QplzcC5e4V5c3XqV8D7hqHQOeHs94bWcOX8GjZcGa76VlJMpJC46i8VkReel\nQ+epI6j1Ez3NkPbBxA7eyZudW+Omc2fzus1k3s/EzeLKtEozkclkVHunKh6BZWjk0ojRo0fbG4eE\nEChVSizGwjaCIl9Qp0Ed4uPjGTF6BM5Ozrzd9m2UDkp7w5nNYuPOkbvYZKJIkPqr6NixIx07dixy\n/PjxQ2zdupXh3brhn2/gItg9aWsC+5G8cB/vKzcAr/FYvFhCODC7YLyb0chVo5H2b77Jzbt3mT1z\nJrOmTiU9I4Pw6tUZ88037Nq6lXWnT5OdnU3Z/Hye1ZSsKQQ/nzrFgwcP7MH2wYMHKBQawAOQkZ/v\ny40bt/Hx8eHE6dNMnDCBDWvXolQq6dStG5+NHk1ycjL/+vJLrBSmn+qBuyZTkdpIZGQkzmXKuUKd\nEQAAIABJREFUcCY3134NLiG5kt2xWF5a/TI9PZ2hQ0dy/fpNOnfuwLBhQ4pV+QwPD2fq1J8YOHAI\nJlM8EisKHBwU9pRfcTh79izNm7/Bo0fZuLm5smfPDlxdXZ87vgT/eZTs+F8RvLy82L9zP9nXcqTm\nqG1dqdAsgEOfH+bTIZ9iNBpZv349tnwbGmct5/+4wLJWK9GW0VK6cikSZidgzjMjbE/2fTarDYvJ\ngnsNd5wbOtGhSweUKiVNvolmVM4nfJo9gtazW6FUSl/Mp7tFZTIZPXr2YNfQPeTnSUXI6ztvcHX7\nNXbt38XXv32NposjuQ2yGTRyEE46J84uPkf6uXSmV55F7JAdGAx6Bg4eaC+abty4kUqVquLvH8T8\n+fOLvQ5Wq5V+/T5Eq3XB27v8cwt8NpsNm83GbYOBckg7+t1IxoOhSHWBycB2uZxNCgUPoIjnrhtS\nz+pjQ79AQJedg7eXL1O+/JLoO3for9fjGRfHN198QffevVHq3NDnl8EPijh0KQCVQoHRaLQfq1Ch\nAp6epVAqdwPn0WoP063b24DkUTx91izupqdz8949fpw0iezsbH6bORMBrJbLeezPlQGs02rp3bt3\nkR28TCZj1fr1HHF3Z61Ox3JkrMSZc0RjFW6MHz+h2Gv4IrDZbDRu3JxVq64SF+fL2LE/M2XK1OeO\n79OnD7m5mfTt+yYuLoupWPEsO3Zs+dPde6dO75KeXguT6RPS02vQpUsJ//6/HSU8/leMQ4cOMWL0\nCM6cOEOFyhWY8OUEKgdV5vVWLXALdsPBxZGLsRex2Wz0ieuFd7gXAHeP3WXp6yuoMyyKRl80QAjB\ngfEHSVp9kbrDozg+7SSGVANvtXuLhEcnab2wJQ5ODtw6cJsNHTeSlHiBcuXKFVrL8hXLeX/Q+1jM\nFtRuasx5+Xi4eJBryqP/tfdRaaQdftrZdFa9tgaFUoHerKfZd02p0VdSGr2y9Sp7+u1l57Zd1KvX\nGL2+DaBCq93I9u3raNSoUaFzTp48hTFjpqLXtwfuo9Vu4Natq6hUKuRyOZmZmWzYsIFJP/yAJTMT\nrV7PXSGoD9xGskK0AY5IpUSFTIa7uzv6zExGFPMZ2Y20e3ls5bIBOacIxYELDMZi1w+6A2x0cyMr\nz4TZHIQDZ6mNwB0pfaRDKmceLV+eyzdvFtoRp6am8umno7lz5x49enQp1lAFCno7GjemmsFAgMXC\nUeAG4KhQ4KDR8PGQIYz96qvn1kdycnJYunQpI0Z8jl7fGak1LQutdh55eUWZVi+ClJQUKlYMwWgc\nhvRTd5F69VI5cmTv35qvODg7lyroRXABsnBzW0pmZnqhMXfu3OHTT78gPf0B/fv3onPnzq/s/P+X\nUaLO+V+Chg0bEncgzv5YCEF47XDqTIgivLeUG01LrMO8Ogtw85duh4VNsKnPFuoMrc2dg3f42esX\nLCYr3jW86LGvOy4+zoR2rcoUv2lUqVyFc2vP8WvZ6Th7OiPLl7F88QrKlSuHyWTi4cOHeHl5IZfL\n+WrieDqsbIdXmCeGTCPuldz4vep8fGqUswd9AK/qnqCAZQuX0blXZyL6PMnhBrUK5FjZOL799lts\nttKAL6DGbA7h2LFjRQL/iROn0OtDkLL2zshkLtSuUYPku3elHbZMRqBMhspmIw0pdaNE2rXbkEqK\ncqAlUt78iBBEPnzIAaSi79OqQVakfP2bBY9twFUUQC1kPCCVFAILxiUCmVlZeAI5JGJDUp13A3Yi\npY2uaLXMmzKlSBrE29ubRYvm/du//Uf9+tEwJ4fHiZyKSM5n8x0cWL15M9HR0X/yakn1dMCAAfz0\n069cu/ZY8iMPR8einbzPw8OHD5k1axZ5eXp69HgPf39/ZDKBJDztg0p1k6Cg4r0B/i5iYmLYsmUn\nRmM4Gs1p3njjjULPGwwG6tZtRFpaAFarB8eODUStVr9yOecSvDhKUj3/MA4dOsSVS1e4d+IeV7df\nQwiBV5gn3jW8OP+HZNSSfPweZr0ZmVxGlU4hdFjRHq2nlt6He+LiI5U9Ne4avCO8mDR7EqpIJc6e\nzlSvXJ2bV27yxhtv8NU3X+FZzpMq4VUoX8mPdevXcfXCNco38kPnqcMjuDQKpYJyDcpy+8gdLKYn\nuf/7Fx4gsxUYXFtBWAunm1IuZrBu3X6MxnxgFnAPB4frVKtWjdzc3EJ1gOjoBmi1Z4ErKFiIJe8O\naXfv4gGUAoYJQWebje5IXbk7kQJ2MJL4Wm7Bf72R8vy9kWwbmwErkWwcbUipk5VIhWB/JLLhZsBI\nKUCDhQckAFMK/t0HRiB1/g5HqheokSQfooAzMhk/T59O++dQP/8dMjMzOZWYyLNlTx1Q3Whk3erV\nLzzX9OmT0Wo34+KyGo3mD2bMmPJCr8vLyyMysi5ffbWGb7/dT61a9bh16xYrVixBo1mJo+MkgoIM\nTJpUVBH1ZfDOO52wWK4BqylVSs+0aZMLPZ+UlEROjg2rtSlQHb2+DitXrn2layjBX0PJjv8fxNat\nW3mn1ztE9A/HqawTO4btwivMk8xrmaSfTSf1dBp3Dt8l+042FpOVfL2ZG8vOk3YmDWQyspNz7IHf\nYrJw/8IDeh3sQZmqHlhMFlY2W82qVasw5ZuYu3YuPU92xy3AjduH7tDnrT5UCq7E1W3XCGkvCRHY\nrDYenn5IWNUwVrdcS42hERgzjcRNiGfc2PEEBwcTEhzCoQlHaDCmHnKFnF2f7CFfr0SI95Gy4GtR\nKhfy6adjOH06kXbtOiKTyWnevAXr16+iX7/3OXYsniUL5lJbCKogUTn3Ie2uH+9d7yA1ZgHcRfK4\nNSIFcSfgHFJp8XH3Q00kh6xdSHcCTmo1lStX5kxiIpNxQI8VGWrMpAGzUGHDG6kHYCGSE9hjVrqs\nYL6LSG1KSUAdpZLzz/jr/hVYrVZkFL+TUgiBxWIp5pniERMTQ1LSGRITEwkJCSEoKOiFXnfo0CEy\nMgT5+dJOWq8XLF26jK+//oqsrAdkZWVRpkyZV2rfmJOTw3vv9cZi6Qr4kJGxiYkTv2PSpB/tY7y8\nvDCbs5AawFxxdLxHQMDzm/9K8M+jJPD/QxBCMGjYIN5c1oqKzSW5scptA/ktfC5t579JlbdDyL6T\nzeqOa3l4PZPeR3rwR/s1OPs4EdIxhAtrLrK46RJaTGqOSqtk/7iDKFRy5CoptCgdlVTrV5X1W9dx\n4+ZNGn3XwN7BW76hH2EfVKfMZU92fLCLjKSHuFZw4fy8C1QsU5Htm2L5fe7vrJy2EmcnZ+ZOmWc3\nK1m9bDWdu3dmWrmZKJRy3Jzd0WpdycuTziuXO/H556Po2LE9UVGNC0S9tOzd+wdz5sxh4MCBXD57\nmpZC8PRXOwiYiRTwdUiuV62R8usypMC/AnjMBREUDaLVCv4td3Zm0vLltG7dmvDqNUg8dwOojJKz\nVAWuY+M1JAOYDKSAXxzHxA8pFZMFlDKbuX39ejGjJBiNRjZu3EhycjIRERE0adKkUAD18PAgKDCQ\nC0lJhD71OitwQadjVAH760Xh7++Pv//ztfGLg1arRQg9klWNApUqD2dnqcrh4OCAp6cnhw8fZt26\n9fj6+jBgwIBCfHyLxcKuXbswGo00b94cJyfptffu3WP79u2UKlWKNm3aFKpRpKamIpdreMwAMhor\nce7cxULr8vX15YcfvmXkyE+Ry1WEhoby+eej/tJ7K8GrRUlx9yXx4MED7t+/T1BQEMqnGnoyMjLw\nr+TPsMzB9gAR90s8946n0GHpkzb8lFOpLItZQUBTfzyqlCZ6fGMA8u7nMTVgBjovLTovHVXeCkGm\nkJHw22k+PP8BMrmMw/86QuC9yuw9sJfG8xrYO3oBjv0cR/krAQz+cDBTZ00lJS2F1i1a06d3n2Il\nJJ7FnTt3sFgseHt7U7NmXW7dElgsWjSaq5w6Fc/ly5fp0mUY2dldC16xn08+qUX//h9QLSSEkVZr\nkV3FfqRdvRVp59/smecTgFigJ1Kg/hUYxBNHL5AC+QKNhnvp6aSmphJZrRpNTCZykO4WfIGJwOdI\naRwjEjtoME8axB5jNZK0823AV6Oh8/jxxUo0HD16lLatWuFpteJmMnHbwYEy/v7E7t1byAv2wIED\ntGvZknoGA5WFIBs4oFIRUK8eO/bu/dNO51cBm81Gx45d2LXrMHK5Bi8vR06cOGqnVu7cuZN27Tpj\nMNRArU6jVi1vDhzYhUwmGZ289lpLTp68jEymwd3dzKlT8WRkZFC7dn3M5vLI5Q9p0qQGGzeusX+m\n8/Pz8fcPJD09BJvNF612N999N5KPP/6oyPqys7PJzs7Gx8fnHzWN/7+EkuLu/zBMJhN9B/Rlw/oN\n6ErrEEbB3FlzqV27NmvWrMFgMEjNUdefGJgbs4w4+zgXmsfJS4dZb+bqtmu8Me1Jp6OujI4a/cLR\nlNbS+EtJtEAIQcLsUyTHJ2PKzifh19NMip1MKfdSbJ20hTZLvZEr5BizjJz77TzDf/mEsLAw5syY\n85ff39P+psePH2bhwoXk5eXRuXNnAgICcHFxQSZ7gEx2FCGc0WhOExbWm7q1aj1XzkCORNd8ALQo\n5vlAYJ9GwwohiMzPp4rNxnygOVJR9y5wUKtl4nff4eTkxPr166n6zJ2FFekO4vFXQY1ED40F2vGE\nV38NiUGkRuqwvengQJ++fYusSa/X06ZlS2IePeKx0r3Iz2fP5cv07NatkHdB48aN+eaHHxg5eDD7\nhMARcLbZSEhIID4+nrp16z7nar8ayOVy1q37g6NHj6LX62nQoEEhH4IpU2ZiMDQGamI0Wjl5chq3\nbt0iICCAffv2cfLkJXJzuwLrycm5RUhIGE2bNiI7uxpCNAEs7Nkzi/Pnz1OtWjVAupM4dGgvH3zw\nEcnJJ+nevT8ffTSo2PW5uLjg4uJS7HMl+J9FSeD/mxgzfgynH55iwI1+KFQK7h1PoWuHLsiEnOB2\nQcgdFZgtZta0W8drk5ui8dCQGp9O8vG71P+0LloPSXL3+C8ncVQ7YpPbyE15YrQC8OhmNqWCnmj8\nyGQyZMhY1HApFYIrsGTeEiIiIggMDORw+8PMrbIA7wgvru+9Qe+evYmJiXkl79XJyYlBgwp/mT08\nPDhyZD8jR35BdnYWI0cu4KeJE5E/eoQjEtvm2ZTHaaR8vyPwEGmH/jQeAmW9vVm1YQPTJk/mYlIS\ntXQ6rqamcigtjaCgIKYNG2bXnLfZbMie2e0okOQeTiKpAQHEIJm8Typ47iFS34CtYF3XtTq2bttG\n6dKFhccA1qxZQ1mrlaftTWRAI7OZqYcPk5ycjI+PJCZhNBr58vPP6WqzPXlvVisXc3Pp3KEDN5OT\n//Fdv0wme64Dl6urM3L5HWw2ACNWqwmtVovZbCY2NrZAdnkHUpVlOPfvn2fLllietNbJit1hVqpU\nid27tz13Tfn5+fzyyxSSki7Rpk1Le9NjCf5zKAn8fxPzF8ynwtsBTPWfgRACc54ZpUZJmVAP3pgX\ng1whp+aQCBZELSZx9Flyc/Po0KYDsuowp8rvBLTwJ/18OsYUIz9+8yO/zvqVDT030WlNR1zLu3J2\nyTmuxl7DbDCjv6+n2juhPLyaiTxH4sI7OTnZb5ednJzYv3M/cXFxXL9+naiJUQQGBj537enp6Xzy\n+Sds3rQZJxcnenXvRXnf8gghaNu2LV5eXi90DapWrcqWLesAKeUVd+IEPkArpECbg8TWeYTEt88G\nyvv6Uq9JE3asXk2w0chjxXkzcEir5aPBg6levTqz5xWmT65Yvpwxo0bRq3t3kMno2L49Az7+mO+U\nSpqYzTydvKoKbJXJ0CsUBFss5AFotdQMC6Pbe+/x4MEDvv32B8zGOlipDNZ4Fi1aRr169XgWd+/e\nxc1gKHLcASjt4EBKSoo98G/fvh0viv6gBQP7c3Lo1as3QUGBfPjhh/+4DENxmDBhHLGxDTCb72Gx\n3GfYsE/w8PDgtddaEh9/lfz8h0g/i12RkmCR6PXb0OlOY7HkolA8pF692oSGhv75iZ5Bx45d2LPn\nEgZDAKtWDeLBgwz693/WWaEE/6N4GS1oIYTYtm2bCA4OFoGBgeK7774rdszevXtFRESECA0NFdHR\n0UWefwXL+B+H1kUrykWVFTpvnXhj2uvic+On4qNrHwqfuuVE9NeN7Tr9laMri9jYWPvrEhIShKtO\nJyopFCIaxOsgPLRa8dW4ceLTzz8VWmetQI5wr+gqdN460WB0fRE1uJZwcHIQGie1OHz48Eut22Kx\niKoRVUW9oXXF4FuDRMuZbwiVViWqtAsRNd6JEM7uzmLN2jV/ed5bt24JrUwmOoEYDaI/iFAQLgV6\n+SoQubm5QgghbDabGDl8uHDVaER9pVI0kstFGa1WdGrfXpjN5iJzL12yRHhotaJngeb9CBB1lEpR\nNTBQ9O3RQwTodOJdEINBvAnCXasVs2fPFoMGDBBVKlQQdSIixOzZs+1z37t3T6jVrnYdfHhH1K0b\nXez72rhxo6jk7FzEU2AkCCe12u4BIIQQ8+fPF5E6XZGxg0HIUAqIEEplpPDxCRDZ2dnCZrOJzz77\nQnh4lBOBgaHi4MGD//Y6Jycni5iYNqJChRAxaNCQ5/o2PA+ZmZlix44dIjExUQghxKlTp4RO5yXg\nSwGfCXAXMlmIgI+FXP6aqFw5VNy8eVNMnz5dLF++3H4NbTabmD9/vujbt7+YNWu2sFqtxZ4vLy9P\nKBQqAWMKrnV3ERZW+y+tuQTPx9+NnS9V3LVarQQHB7Nr1y58fHyoXbs2y5cvp0qVJzr1WVlZNGjQ\ngNjYWHx9fQvpoDzG/4/FXZcyLtT8uAapp1LpvO5t+/GMyxksbLSY4WlDETbBb4FziV0ba1eOrBka\nin9SEhFPzZUDzHBQodA64l3Vi7tJydhsNt4/0YfSBameu8eSWd9uIym3U9iyZQsbtq6ntLsH/fr0\nK3S9/x127txJv8/78d7xdzA9MjG1wnTeXt2RCq9JzKOUkymsilnLvdv3CjlAHTlyhGXLVuDt7cWQ\nIYMLuWcBJCQk0CAyEjNSKsSfJ7n5h0iMnvtZWYU0XC5evMjq1auxWCy0bt26WDcvIQQVfHxolpKC\nP7AfOZJsnMBZ5cjMhfMwGo1MmzSJ1LQ0IiIiGD1+PEFBQRgMBvz8/IqkV6xWK/7+gaSkBGGzVUCr\n3cPIke8xfvzYIue3Wq1UDQzE784d6lutKJDqFJs1Ghq98w4zf39ip3nlyhVqh4XxkdHI095Ty4FL\nOCCVrM3odKVYuvRn0tPvM3ToN+j1bwIPcHLawc2bV4pNOT1G1arhXL7shtUajEZzkP79WzN58k/P\nHf/vkJiYSP36zcnL6w8okcl2UqlSJllZWfj7+/PRRx/QoEGDIpTSMWPGMXnyfPT6ULTaS/Tt245f\nf51cZH6r1YpO54LJ1AvwRCaLo0kTwZ49z08NleDF8Xdj50slHOPj4wkMDCQgIACVSkXXrl3ZsGFD\noTHLli3jrbfespsl/yducf8JCItA2GxFirXOPs7oMwzkpueyc/BuKvhWIDxckj+4desW12/cKNLk\nYwSEg4z3jr1Lt8NdGJ4+hIrNK5Aw64l8r29dH0zCRGDVQIZOGEJ6jTROOh6nfnQ9duzY8cLrTk5O\npnRwKWQyGZv6bsHBxdEe9AHKRpbFI6g08fFPlEYPHDhA8+atmD49iW++WUWDBk0L8dKvXLlC8+ho\nmgOfAaOQaJqLkVg4Z5BkC9LTC7fxh4SEMGbMGMaPH/9cC8e0tDQyMzMpj8S+OYQaG0OxMZJcs5qF\n8+bRu3dvTp47R/L9+/w4ZQqjhg2jUvny1KhShYp+fqx+pnlKCMGcOTOoXduAv/8++vdvz5gxo4s9\nv0KhYPfBgxhr1mSaRsNSV1emq9XU69KFKTNmFBobFBREqzffZIWjI/NRMQNHtiPjEnKkdrVBSIXV\nBzg5OXHw4BH0+lCkErMei0X+p2qoer2ey5cvFDRC+WAw1GPHjj3PHf8iqF69OtHR9XFyWoJOtwFX\n18vExm5izZrlXLx4gSFDfiQ8vDYLFy4s9LrffptbIN9RF72+PfPnLyh2foVCwW+/zUSjWYKLy0Lc\n3U8ybdqkl1pzCV4eLxX4k5OTC7E/fH19SU5OLjTmypUrPHz4kKZNm1KrVi0WL178Mqf8r0HLVm+Q\ney+PC6svkpeeZz9+YnoCjk6OzKzwG5VyA9mybos9F28wGHCUy4tc9PNAWO8wPIKlnZ5CpaDZt004\ntyLJPkafoceab0WUsRHYqRK1B9Ui+pvGvLEghsGfDH7hX/3GjRtzdcc1clJyuLr1GuY8c6EuXmET\nZNx5WGjXOWvWXAyG+kAjTKb23LiRXEi3/tsJE4gwGKiFRKFUIUkx1AbWAicAVCp7Lvwx0tPTGTb4\n/7H33mFRXev792caM0MHUVRQkWIBsYu9l6gxqIm9995rTMxJ1ZjYY+8ee9dYkahYsETEgliRoiBd\nOkyf/f6xEUXQmHjONyfvj/u6ckWGtdZuzLOf9ZT7nkTFsmVxLVOGcaNHEx8fX2iMtbU1BrMZPeLO\nQSzYtAUsMeNJTl4egYGB3L9/n6SkJFo1bYr9jRtM0emYpNHQKj6e0YMGERAQwOnTp6lfqxYWCis+\n7tyL0BuhDBzYlyVLFhYqxd2zZw/lylWidGkXli5djqurK8HXr3M9LIyNR48S9ewZG7ZsKVBJS0xM\nZPXq1SxatIjxU6aQJFfzlKYk05trOCDugV46POWwslLTunVrGjash1J+FTnLqMZJPLQvGNyvH9Mm\nFf881Wo19valEFvOTCgUD/Dxef/dXnGQSCQcPXqQHTuWsWrVZO7fv4O7uzvjx08lN7c9WVm90Gj6\nMWHClELzbG3tCp4IvMDa2qbI2i8xaNAgHj++R0DATqKjI/D2/s9SRpTgz+ODkrvvU4trMBi4efMm\nZ8+eJS8vj8aNG9OoUaMiW8dvvvmm4N+tWrWiVatWH3Jq/3Us/XkZLdu1RGWlYnXVdVT5xJOc2Dw0\nURru3rhbbHLVy8sLuVpNXG4urm/8zmQ0F/rZbDRj1BhJvpeCMc/A6cm/UbmtG/XH1+P4yJMYdSZK\n+zhRtWsVDj46jFarLVS6V7CO2Vwo1OHu7s6kCZNY3WAVZqMZl0blOTXuNG1/bo1ULiVo7gU0mXmk\npKQUzHF0tEcuf4Lo5GswGvMKleVdDAqifTH889URq2vKqFT0GDcOS0tLIiMjSUlJoWzZsrRu1oyy\nycl0MRiQArc3b6bugQP41K5PdFQUHm6udP7kE1o1b87V8+fxNZkQac/CAQlybnIrRMaUXr1INhhQ\n29hQWaOhwWtGszLQVqNh8pgxpCYnU1qjRYIPZj7DaMph/vyfGDp0EO7u7gCEh4czbNhYNJpPAQvm\nzp2Pr68P7dq1w9PTs8hzXbN6NbOmT6eqRIKF0ch8mQyNwQJRQwygNQ4OV9BofkWrdUOlusb8+V8j\nlUrx9PRERTbDMRX0Kmh0OjatXIlGr2fN2rWFjiWRSDh16ij+/p+RlHSQOnWasGbNL0XuO4ghlujo\naBwcHN4ZOgLRK+/6hsRjTk4ur1QSrMnJyeKLL77ihx++RSqVsmnTarp06Y5MFoLRmMqWLXveeQxX\nV9eCXX8J/jrOnz/P+fPnP3idDzL8Li4uxMbGFvwcGxtb5OFWqFABJycn1Go1arWaFi1acOfOnXca\n/n8CXFxceBD2gICAAG7duoXBYKDGxzXw9/cv1gCD+AVbuGwZE4YPp71OV1BaGCWRkLT9HnVG1qZc\nnbLoc/Wcm3Mep2ql2PvJfmRKGRY2FlRo7krg1DMobUVP88aqUC59fxm7UnZFFJEOHjrI51/N5sn9\nSKr6VuHneQvx/0Rs5e/Tow9bt21FY6fFwd0efbaB5RVWIpgF5BYyPsrR06NbN54nJaFWq5k7dw5H\njjQmO3sbBkM6EyaMK9RVamtrS04x15sDmCQS2vXty4jRo2lcrx6PHjzAXqEgPjeX6kDH114Y7Y1G\n9GlpXDwXSFVkKGKesCI4mERArlAQY2FBXY2GexxDhwEvmYSuBgPKzEzMwLq8PNyLOQ8v4ODTpwwC\nbiHHjAuiF26DYFYRGBjImDFjADHmLZdX5mUnql5fhdDQUNq1a1dk3Zs3bzJ35kyGabUF1BLNDAYW\nYUQkRSuLUhlF//69sLW14eHDJ3TrtpgBA/oDsGLxYloajYUa1NRAB0Fg2/r11K1bl5GjCle/NGjQ\ngISEZ5hMpreyfKanp9OsWRtiYuIwmTT8/POPTJo0sdixb0OlSq7ExOxH3LvdB3xYvnwHtrY2fP75\nLFq2bElU1CMePnyIl5dXIUrw4qDVasnLy8PBwaGkeesD8KZT/O233/6ldT4ouWs0GqlatSpnz56l\nfPny+Pn5FUnuPnz4kAkTJnD69Gl0Oh0NGzZk7969hbZ7/8Tk7l9Fbm4uZV3KYukoIy0uGys7JXae\npXAwlOLR40eoy6jJSczBtbELfY71Qq6So8/Vs7rqOqp28yLtSTr9TvZBIhXv2f7uB/EUvDj+6/GC\nY1y6dIlufbrReXtHKrWsSPTZGE4NPs2pI6do0KABHtU88P3cB7tKtuzvdhBrOwusSluSdD+VT/Rm\nagD7bGz4ZtMmevbsWXDeoaGhODk5Fdmqr1q1il9mzaJXXl6BJ2EEtkokJMmUSKRSbNUK6mVn42c2\nI0OkeutE0dLHRGALYjNXXv5/ZRHNdKxCgWO5crTp0IHt27YxSa/n9dfdEUTunTeLMlMQFbfmIO4X\ndqHESDukJKIgjAnTJrJosRh3vnv3Lo0atSQv7zNE+umDHD68vVgZwZFDhxK9bRvNzYV3a1eB08iR\nySQ0btyMU6eOFtAfvA7fKlVoFBFRZPeXAWwErBwdiUtKKhSGeh9MnTqD1asvoNd/DGSgUm0mKuoR\n5cqV+8O5L+HjU4/795XALcQnVRNxp3WCxo39CAg49t7NWOvXb2DixMkANGrUlICAo28iyxA6AAAg\nAElEQVR1jkrw5/C3JHflcjkrV67ko48+wtvbm969e1O9enXWrVvHunXrADGB17FjR2rWrEnDhg0Z\nOXLk/3MxPpPJxOq1q2nYwo8a9WqgLqNm1JNxzNF/zqSUqfQ725fHjx4TfiucmpVrIjFJeH7tORf+\ndZHrv4SwvtYmpGYJ93Y/oN7ougVSihKJhLpj6vA8+Tnr1q0jKCgIQRBYtnoZjb7yo3IbN6QyKR4d\n3Gkwuz4r1q4gNDQUvVwvUkRLJDhLofvzHJrfTmZavtEHsDEYCiVjraysKF26NDu2bWP08OGsWLGC\n9PR0AEaNGoVn06b828qKa8A1YJ1cTjK2GIwz0OuboMrMpHG+0Qex0apocEj8zIZXDVZZyHmAG/dx\nwNEgoE1NxbVSJcqp1bxJVlwbUZD99d2HGTivUGDO/7c70Bcd3gRSj9vUlJmxfa3SyNfXl3XrVlCm\nTACOjof49tvZb9WOPXM6EKc3jD6IbJ8SjGRlZXDp0rlijT5Azdq1eVqM9/sU8WVn1umIjo4u+Dwt\nLY1bt26RlZVVZM7riI2NR68vh/i6tEcutyY1NfWdc95E7dq+KBRaXu6MQI+oCebHzZs5fPHFV++1\nTlxcHJMnT0evH4FeP4Pr1xNZtKgkuft344MbuDp16lREGm706NGFfp4xYwYzZsz40EP9YzFu8jgC\nQk7R5OvGuAuVOTcniDU11jPyxjAUlooCQ25ra8uxQ8fQ6/XcuXOHEwEnSAtPY+lX/fH29mbpiiWk\nRBSujEl7nEZEZASbQjaSsDIBR2UpzIIZn/5vJP2k8ODBfVJSUhDyCQ1K+ziRpDXiCFi+NtQMRMlk\n+Pn5FXy28KefmP/tt/jq9ViaTBwDZk6ezPa9e+nZsyfH8pOn+3fvBqB82ANS7lQC5EjIoeob96Q6\nEIIYg3/d9IUgNmCFAzZIeUZ9BDoCRmJYQvO8PAKPHSNFp8MAhcom3QCVhQXrgFpmMxZGIxHW1rzI\nD8XcQWTl9AA8MJADbLBQ07t370LnNmBA/4JwDIh5qqNHj3L79m1cXFzo06cParWapwnxlEJG9Tde\nYdGAt6dnoXLY4jBjzhyaHzlCBYMhP7Ak7njOImoMHDUaC7zq3377je7deyKT2QG5nD0bQP369Ytd\nd+DA3pw6NYK8PBlyeTJOTtZUrfrmE3g3Vq1aRlxcT4KD9QjCXgRBj/hkmqPTPebBg4j3Wic+Ph4L\ni1JotWKeQautSETE28nwSvB/gxKStv8yEhIS8PL2YvzTMShtxf5STZqG5RVXUnNgDTqt6sj5uReJ\nPRhHamIqRoORGrV92LRmM1euXeHr774mKyOLMmXL0L93f1ZvWE371W1xb1+ZmPNPOTrkOL2P9qRS\ni4oIZoG9XfcTfy0BAYEhVwZhU86aA70OE389HsdKDiTdTwYJ+E1qwL3NdzBnaLE3mPkMkTY5FwhS\nKrGuX5+gfOHysLAwWjZqxFCNphB75nGk3JPLyNPrisRtt27dyvjxc8jLa4ZUGkJ14Rk9X3vGOsSQ\njjXQCNH43wKSgGHASsADCWHUAFwAexQcpwM5JNWogZOzMzkXL9ImPzEMYnXUabWa85cvc/jQIXJz\nc3EqXZoN8+fzUU4O2xFNlxciQfAl4NP+/fn3jh1vfX6xsbG0atoUWUYG5bOzybK0JEoiYfmqVQwZ\nMhw5UrphwCf/GpKBg5aWLNu0iT59+rx13ZfYuHEj40eOxB5xF5SFyGOklUrJ8/PjwtWrADg7u5Kc\n3AbxtXWHGjWecvdu6FvXPXjwIJs376BcOWfmzfv2vbuxi4PRaMTNzYvERHdMJlesrM4zf/6M98ob\n5OTk4O5ehRcvfDCbnbC0PMvOnev/su5BCQrjr9rOEsP/X0ZwcDB9J/dlaGhhHdINdTeR+vAFjmUd\nkZqk2FW3pdOWjliVtiRs210uzg5GYaOgy87O/L70OlGB0cgsZGAAg96A2WhGZaOixiAfOix5lXiM\nOf+Uc3OC8O5RnSsLrmLvZo+tmy2f7u6GVC7lRUQaW5r8G0Oqht6IoY/ziJ62DDDJ5fTq1YuVa9cW\nNGlNnTSJ26tX0+qNyp1cYAmQkZ1dbDhjy5Yt7Nx5gDJlnDh+ZD89NZoCz9YMnFIoSHByIicxERtB\noCpiKlGF+FJ4gcjrUwkJcUAmAtXlctpPnsyszz+nWuXKGHNy8ECM42cANioVgyZO5MeffwbE0syf\nR42ia3Y2WYilpfGIO5xctZoF27bRo0cP3oYWjRqhvHGDZq9dewxw2MoKjUFArzejwIQCA1ZyOSa1\nmu/mzWP8xPdPpi5fupS5c+bgaTDgajbz3NKSBEtLLl29WlBFpFJZodONR+QYTaJs2ZMkJMS89zE+\nFE+fPmXcuMk8e/acfv168vnnM987SRsZGcnUqbNITn7B2LHDGDy4RJP3P4USds7/UXh7e5P6OJXs\nhBxsyonGMTM2i/SoDKwsrRjw6QDWblmLc7nS6DK12JSzpvawWtzf8wCHqg6EbQ1DrpQzNX4ScrWc\nyIAojgw6ysjQYRz47BBqx8KR7pzEHFT2KvymNODagutkRmbRbbc/UrnoF5fycqTmgBrErriBV77S\nVlugBSJb5TlbW7bu2FHoS52alIR1MeWaloherl6vL/bahw4dWqBPe/p0P3p99hlugK1eT7RSiYuX\nF8GHD1O3Zk0aZ2UVcPNrET3npohCKi+5Nm8Av5lM/DppEk+fPkViMtEd0ehXJT8hrNWycsUKZn/x\nBfb29vj5+RFpMKBHrP5/SQWtA1YKQkE4y2w2c/LkSV68eEH79u0pX748MTEx3A0LY8Ib1+4GVJRI\n6DJ9EuvXbyY9/QV1GzRl0aIFNGjQ4L1or1/HoCFDyMzOJvjiRSzUaga3a8fgwYOxt7cvGNOrV28O\nHPgVjaYalpZ3GDJkwHuvHxgYyJEjx3B3r8SECROKVIC9DypVqsSJE0f+9DwQSdyOHj34l+aW4L+D\nEsP/X4ajoyOjRo1iU4MtNPuiCYJZ4NrS65Ryd8SUaiYw8jSd1n5EcngK/26+nT4neuPiVx6rslYg\ngbs77jExZhwKSzGa7dnJA7fWlYg+E0PntR3Z3mYXrk1ccWtdicTbSZz/8gLtl7ZDMAsgiOIcJkPh\nBKRRa8TeVNhLUCAazwOZmWg0mkLx6YbNm/PTr79i0ukoj9hCJUGMZSskEurXqsXpc+fIzc0lNTUV\nDw8PAgMD+T04mPIVKjB0+HA++ugj4hISOHjwICkpKfj5+dGiRQskEgkBZ87QtXNnArOykJvNZJjN\nWEkkNHnD4NYDbuf3AoSHh+NuNOKGaIhfwgYoq1Ry584dWrZsibu7O10++YQjx4/TQaPBHjHME6hW\n0717dypWFMnpPvusD2fO/I4glEIun0FIyBXS0tJwUCiQ55O0GRDDUfeBzJwcQq5f59Gje39YJ/8u\npKWl4etbl4wMOyQSIy4uUrbvHVkkP7B583p8fJZy48Yt2rSZXVB++kc4ePAgAweOQqOph0p1jpMn\nz3D27KmSksr/x1Fi+P8PsHzxcpQKJau+XInC0gJDjgGPMh5kuKXT7ZA/EokE757Vsatgy4WvL/Lx\n+s48ORaJS8PymI1m5MrCj0mukmPSmyhXrxwOZe05OyyI5PgklPYqWv3QEq+PPbn8/RW8vLwwGoz8\nNuUMPQ5+ioW1BfE3EgjbFk5xdVXJgKVSWajU7ty5c/xrzhxs9XqSECt27IEmiBq3HwsCyXFx1PH2\nxlqpxE4qJTo7Gwe5nHpGIxEKBSuWLWP56tUMGTKEIUOGFDnuvXv3yMnJwd5gIFkQkOYf482SMwlQ\nViIhNjZWbAQTeYQLwQyk5teLv8SWHTuYM2sWGzdsQCYImKVSRo0ezbwFCwB49OgRgYHnyMsbAyiQ\nSM6zcOESFi5cQIpeTzZiff1OxHBYY8QXZfilS9SrWZPfb94kLy+POTNmcOzECcyCQKcOHViweDFV\nqlR58xQLYc+ePaSnO6DRdAME4uP3cOLEiYIy2peQy+XMnj3znWsVh19+WYdG0waogVZr4sqVJaSk\npFCmTJlixwuCwJIly9i79xCVK1di+fJFf1ijX4J/HkrE1j8Qer2e9PT0P4yz/bzgZ1KepxJwMID7\nYfdp3LAxbl0qFfK8PDp5EHflOZtrbeWrOV9RWlcGSwc1F769VLB+UlgyDw48JOirCxwe+Ct5qRqe\n3H/CpQvBODs6c2NBKKsrrCP9ZCaREZE4fmKPRC5haflfWF5hBdta7sCYZ+ChhYytNhZclohhDx2i\nBq5XlSoF55SWlsan/v745+QwVBDwByYiJmSPIIaIqiDy7Lc2Ghmdm0u/7GxGAYLRSAwib/1AjYaJ\nY8eSkJBQ5L7ExcUxadw4PLRaZILAaGA0oljLmyq1ZuCp2UyNGjXYvnEj8YjiLK/jDqAxGAqFWyws\nLFi8bBnJaWmEP3lC0osX/Lx4MQqFuIsymV7Kt7z8OsgwGk3Y2dkxatQojlpaci1/RH/EnZE74K/X\n45KczOczZtCofn2SjxxhnE7HJL2e7BMnaNKgQaFyzOLwKkZ7C9iNVptCZmbmO+eAKGB+9OhR4uLe\nvAOF4eBgh1Sakf9TDkajgdzc3LeOX758BV9/vZSQkIocOvSUFi3aYS6mZLUE/2yUGP6/CJPJxMw5\nM3FyLoVLRRdq1PXhypUr75xjaWlJo0aNUKvVPH7wmIcHH5HxNJNLPwRzZvY5bm28jUwmY9/OfZw4\nfZyrF68iNymI3BPFGvf1bKi7ia3NttFomh9V/asQeTISvVHPpUuXaNy4MZEPIgk6HkTIpRDq1a1H\nnUm1af51M/qe6M34iDG4t65Mq+atkKvl1J/qR71NHxPT0YMVlgqWIhraxq+JeOzcuRMPs5nKr12D\nFLGdx4ho9MMRGTjrIxrGc8AmRJH0HETZw3jE8s29e/cWuSe7du3Cy2TiAdAzf14pxHDSCcTqcRDD\nLGcsLKhSowY6nY703Fy6I3rhxxGT0/sRZUSsgJ3FVOoolUrKly+PUqnk/v37+PrWx8GhDPPm/UTD\nhvWwstqNUnkSK6ubTJs2CYCflyyh+9ixXJFIaEDRL0w9o5ED+/fjmZ1NS7MZa8TcRzNBoEZuLj9+\n//07/yb69OmDShWPmGKvjdHoyY8/LnqnOPvateuoX78pAwd+SbVqvgTnV18Vh08//QSp9BoSyWJg\nJTJZaerXb0xMTEyx4w8c+JXc3KZAFYzGtsTGxpGYmPjOayjBPw8lhv8vYv5P8zly5TDD7g5heuYU\nqn9ZjY+7fkxSUtI75/3w4w+4ebrxKPch6VHprKu5gcxnWSjUcm6sCkWv0TNgyADs+9oxO2cGnwV2\nx8JBgaVMTeW2bkx5PpHWP7Tik00fU6FpBcxmEz169yAlJUUMGXl74+HhQfiDcFybvSJFs3a2pspn\nVQh7GEa7hW1ps6A13j2r0/tEL5xqO9MI0Fta0n/Qq4qLmOhoHIoRIbHM/y8XMbH6slLnDvAImAD0\nReSjHAb8Bhi1Wl68eFFkrRepqZj0espSWBO3O6LRXwxskMtZoVJh27w5v548SWBgIDLEIs8xiEnb\nxPzzqI+4I7l18yZBQUHFesRms5k2bT7i3r1yZGT05/Dhm9SoUZ0NG75j4cIB3Llzo0BaUCaT8dOi\nRdSqU6fYuKgMcdfnXUzYycdk4vSpd9MPOzg4ULt2XUQSa2+gE4mJqTx79qzY8YIgMHXqNDSagWRl\n9SA3ty2TJhUfArp16xZjx07GaGyLIDQF5Oj1vcnIqMKSJcuLnePpWRmFIgYxof4cicSMo6NjkXFG\no5EnT56QlpZW5Hcl+N9HieH/i1i7fg1tVrTG1tUWiVSCd4/qeHRxL9arfYkDBw4wf+E8Rt0fzqDg\nAUxNnIxHRw+UtkpaftOCsfdHIZVLcWlRnjojaqNQKyhXpyzt17QlOTmFBhPro7R5FcKo2LIi6lJq\nytZz5vDhw4WO5VfXj+iAmEKfRZ+MQZOrwaOzR8FnEomEKj2rEWqlpNfAgTRs2LDgd7Vq1yahmDLN\nDMTKG1vEZOpLPs3fgQ6IhvclyiDW6T+SSLC0tCwSNmjWvDkpajWZiKGcl1Ah7gCcgQyJhBXr1nHq\nzBlKlSqFhYUFToiNTraIFUmfIJrNm4hhojMBAYzp3p2q7u74VKnCypUrCzpe09LSyMjIQBDqA/Zo\ntXW4cuU6ffv2ZeLEiQWEba+jR79+hKvVvBnQuyOVYm9ri7bIDPEeqd+jgsbLyx2FIg7R2CZjMOQR\nEBBQ7IvSbDZjMBh41XJnRWZmZrHhodOnT6PX+wB1EJ9CJeAZgiDLX6MoFiz4gTJlkpDJfkKl2s3e\nvTuLVAGlpqbi41OHWrUaU758RTZs2FjsWiX430WJ4f+LyMnKwbJ04coLpZPynfHZGXNmUHdsXWxd\nxW5MqUxKy6+bEbruJv9uuZ3UB6l4tHXHYCr8pbSrZIdULiXy9KuOR0EQiDwVSW5yHpZOVkUM6uzp\ns4nY9YTACWe4v/8BASNPk/BbIr41fYkNji00NiYwhoGDh7NizZpCn/fs2ZMMtZoQiaTAKOcCh6VS\nJDIZh+zsuKpUEiGVEoVYLVMcG0x5QCkIrJ03D69KlQrFvTt37oxTpUqYEKPcINI23AG2IjZ0WRsM\n7N+5s2BOt27dyFMquQesQ+TGOYMo9iIAvsAMs5l+mZlMMRiQR0Tw9eTJuLm6EhwcjIODQ74xuwlk\nI5WG0KDB65LtRTFy1CjyypThqExGImKPwXmZjLu2toyZPJkblpaFXlxmIEStZmAxAu5v4scfv8fL\nS4tKtQyJZCMSiT2zZm3Ax6dWkR2kTCZj+PCRWFntBYKQSA4SG/sMZ2cXtm4tzJnv6uqKQvEcMVCW\nB8Qhk4VhZXWHiRPHFTkPQRAYPHgkaWkyzOaaSCQy7OzsSEtLK1SyO3fuN0RHW5GXNx6dbhgTJ04p\n8fz/YSgx/H8Rnbt0JmTpjYKfc5JyeLjrEV26dCl2vCAIPIt+hlFTOHZryDNiXdaaemPqsv/Tg6Tc\nTSU5JAV9zqsv2u0NYTRt2JTzsy4Q9NUFHhx8yP7uB4m/kUCdkbWJ/i26oBMyKiqKT/t8Ss26NbG1\nt8Uuyp6nS2LxNtXgxtUbLPh2AeenXyRkxQ1izj/l9NjfMESaWLBgQZESP7VaTVBwMM+rV2e1pSW7\n7OxYo1LhP3o0tx88YMmePTRs3gaphR27kCCjaLKV/M/cgaG5ubjHxVHPt2aBxymTyQi6fJnmHTpw\nCvg3IoHb74h+6ieITVxBQUFkZ2cDUL16dYaNGlVQ/XMZ0fjrEMvUPuJVuZpF/hpas5mW2dl069KF\nlJQU9HotIrPPGiAFR8dXNfPF4fDhwyQmJpIsCOxAJH0Lt7Qk4MwZvvjiC5xr1WK7SsUdIAzYrVaj\nqlqVKVOnvnNdgFKlSnH37k3Wr1+BlVV59PpR5OZ2Iz29HNu2bSsyfs2aFaxd+z21a+cglVbAYJiB\nTjeEMWPGFzgee/bsISDgDEZjCrAQWAbYU6WKmjt3bhTLl/Xs2TMuXryERtMHQeiMRtMcf/9elCvn\nip2dI3v2iLvZ2NjnGAzlEbM6jsjl6mJ3JyX430WJ4f+LWPrzMlJPvWCH3y6O9zvFJp8tTB43mTp1\n6hQ7XiKRYO9gR9i2u8RdE8VqNGkazn0eRO1htajR14cGE+uTl55H105d2ez7b85MPcfBToeJ3hVD\nm5atMepNhK4K5ddBx3gSEIldaVse74xg7py5nDx5kqCgIJq0bEKGbzqDbg2g3GfOnAs6R5I0iePB\nx6nVoBblypXjzMkz2Pxux/25D2lh15Lfg38vIqUIIs/K1s2bsbGyoo6fHyO+/JKYuDh+Wb0atVrN\niqVLOX8mAIM2B6hKLnacRBRXL1gD0Yg3QDQTTQBDbi7z5s0rGOPo6MiJ06dRKRRIEYMYI4BaQA1g\nKFDBaGTxwoUFc3xr18Ekt+YpMj4FZiFSHVSk6B+1AnHXYQmUNpvZvXs3crkdYjZiFmZzC8LDH771\nWT99+pQJY8YwWKdjpNnMDESVsapaLT/98ANKpZKFy5fz3CzhBE6cwIE45Bw6dgwrK6u3rvs6pFIp\nTk5OSCSvqJYFgWKrxaRSKQMGDMDJqSwm00uyCCdkMhVpaWmsXLmK4cOnsnNnCgaDE2Knw1TgY3Jy\ncosNZYGY/BYEE+IOAUBLdrYZvX4mWu1Ahg4dQXp6OoMG9cHSMhi4g0JxmvLlnalcuXKxa5bgfxMl\ndfx/EWXLluXe7XucOXOGxMREWsxr8Yd//NOmTWfF1hXs63YAJGDINVBrsC9NZjYCwKm6E4JUoJpn\nNUYPH82FCxdwbSAKWHzUoR1yiRnfbAGJIBAmAWWenAbN/Vi8ZhEVW1Qk6tsoBDk0ntkQbYaW35eG\nMOz6EJx9yyAIAhe/C6Z5u+bER8ezb8e+d55reHg4rZs1w0urpbJORwawJCSElMREps+ahV/duni+\neMEEBKQYCOURl5GiRPShyyHGuDMRCcdeVoJLgdJIefLkSZFjmiUS9EBzChtvCWKVzI6tW/nmu+8A\n2LXrAEZjR4zAHgIxk4UJST4vfmFjaUZMQtsClrm5CIKAQmFArAUqj1odSkKCK/XrN6Vx4wY0a9YY\nX19f0tPTmff11wRfuUJVjYbSb56TwcCKkyfJzs7m9OnTGI31MCPSZ9gqDhASEvKnxEfatGmDu3sp\nnjzZiyCosLZOZvDgwW8dP3Bgb65cmUleHigUz3F1LUfFihVZs2YTeXkdEPdZdYAfgTRUqmu0bt2i\nYP7z58/ZuHEjer0BHx9vvLy8GDFiOFu3bkUmK0Ne3hOMxqb5T6MscrkVycnJ9O7dG0EQ2LZtDxUq\n1GXevG//NHV0Cf5elDytD4BMJuOjjz567/FfzP4CiUTC0uVLSUtNw+tjLz76pYNYy20WuL0ljMrt\n3Phh0Q94enkyadIkRo4bye4du7EXBEYYzbxsrWoFbIpPIORBCCMeDkNmIcNkMLGj7S5ubriFzEJG\nxeYVcPYVG3UkEglNZjUi+IfLXL9+vRDzZnEYPWQIlTMzeYYYRlEBPrm5bFi9mtQXL6iUmUnb1/IK\nrREwYCIeGIJY238JmIL4RxaD+BIoBcRjolevXkWOWcvXl4jQ0CJ0y+QfX6vVsmHDRg4fPk58/HNk\nMj0mUxv09EMMvvQli0PcJI+XEXsBMRRki5hofmwWkMvlXLhwlmHDxpKU9BiDQcLt20YMhqqEhu5j\n/fp9QBZKDLTU6/HIP+83oQaUcjmZmZk4OzujUqWQl2cC9JhMKX+aGE2pVHLt2kUOHDiAVqulW7du\n79SoHjRoIAA7duyjUqXa/Pjj98hkMkqVKoVEkooguANpSKUyypU7S9u2rVizZgUgahnXqlWfjAw3\nTCYDEski1Gob+vbtQWDgIRITE3n69Clz5y4kL68MMlk89vaWBc5Nnz593ouErgT/myghafubkJmZ\niUvl8jh6O1K5nRtRgdFIZBL6n+5L7JU4QqbeoHPHj/ktIpDkW3G0fJZVwJX/EhFAYAUbxjx7RQj2\n8MgjQlbeQJOmRWltweCLAwt+l/cij2UuK/hi1hd8l+85F4fk5GQqli+PhcnEx4j1+llAEKLgudLB\ngc75AuiF5gGbgZmIZY5bEQ1uPKLPWAbxBaCwtORJbGyRMsFTp07R09+f6kYjb2ZKzkulaOvWI+x+\nPJo8J2TcxRozOsCMhI/8/QkKuog2OwMFAnaIu454RI//M+AqMh5KLdi6a0sBFbNWq8XKygaz+Yv8\ns3wKnEZBOv3R4IaYaL4DvEktlgAcyRdLEQSBzp27cvnyNcxmAyNGDGflymVvvcf/TYSHh9OiRVtM\nJisMhhds3LiWfv36FhqzceNGJk9eTV7eS8nF/UAl1OqrhIZepnr16giCwOrVa/j3v3fj4lKe5csX\nUrHim0+9BH8n/hYhlhL8ddjZ2bFy2SqSb6cQGRBFoxkNGRQ0AIWlgkotK/LkfiRbt26l9ZKWIEDR\nSmrxM0164UJCTaqGpFvJ6JJzib+RQPieewiCgD5HT+DUM8iVMs7/9ts7z02r1YLJxKdANShIonZF\nTJaajEaKo2XTAwIy1mLBzfy5D4GGwFigBzANqGYw0L8Yj79Tp04sWLKEMImEM0A2YhVRsETCHSsr\n7j+KJi/PDSvuMwozUxBfMl0QCA46x6VLQSiVFkxELPFMQ6y+MSEqWt1DhaW9TSEZRaVSiY2NHSLz\nkID4OlVjgbFAHcwbcbdynlcNZcnAcUtLvvzXv5DL5SgUCgIDT3DvXihRUY8KjP7Tp08ZMWQIZUuV\nwqVMGaZMnFhIz/i/gRo1ahATE0Fg4G6ePHlQxOhv2bKFWbO+RKN5KXdjRmy3UyOTqQs6eyUSCePH\nj+P69UscPry3xOj//wglHv/fjK+//ppVu1cx5sFIpDLxPfz4WATh39wnOiKa0REjCJp6hlJ7H9Dc\nXPge/Q6ct5DTL3gA5RuUJyE0gcPdj5Kdmo1jeUvKRmbwwNYCJBKMOiMWlgrccg2kW9mR8I4qjOfP\nn+Ph6srnFBZJATFskuDtjTkmhh55eYCoy5QMxEqlJErt0BuropKGY2OrQKXVMFxb+OVkAFaoVIQ9\nfFhIu/cloqKimDxuHEHnzyMAHdq2JfTWLXISX5ArSOmFljel7E8rldQYMICA/fsZ/ppClQ7RaN8C\nNLXrUKuGDzeuXsXO3p7h48YxePBggoOD6dzZH61Wg9ksBapgSTgzEQquPxOxkzgasLKwQK5W88W/\n/sWUqVPfSnj27NkzGtSpQ7XMTGqZTJiAmwoFSc7OhIaFFeIT+r/C5cuX6dChK3l5nwCnEe+QAEiw\nsChP1apKQkOvFtBZlOB/GyUe/z8UM2bMQPdCy452u7i39z6Xf7rK4f6/0rVTV6pWq8KFf13E7/Mm\nXFXJeYT4FRWASOC8hRS3Kl6c+CyAxXbLONbtJLMnzcbKzhIbFxvcgAp6M1KpBLfjndAAACAASURB\nVI82bsgkEvJkkj9kk7SyskIilxfhygHRA+/YqRP23t7ssbJiBzKOUprLtCJWsKT9R81p164U0+dM\n5fOv5lKhmD9KBWBnMhEYGFjs8d3d3TkWEECOVkuuVkuHLl2wz8pikKBHj5biUugVdDqiHz8mPZ9U\n7SWUiCGmJEtLnjx6SPSePTSOjKRiaCjzJ03i4w4dmDp+PJYY8bJWY6kwU7emBLtSjjx+bR07oBti\nQ9beY8eIT0lh6rRp72S5/OGbb6iWmUkbk4lS+efR0WDAITWVVStXvnXefxMhISGYTFUQK31GoFI5\nMXiwPzNnjuCHHwZx5cr59zL6sbGxfPfdd3z//ffFcjCV4H8bJR7/34wtW7awcP/PuHWpRNRv0ViV\nscS5tjNPN8SSFBmHhUmDzsYCpb2StIdpyMn3wq0VuH1SlVbOrVi+eDkZGRnY2dkhlUrx8vHC2b80\nYUtDsKvqyMDfh6BQKzDpTWxvtYNGrk3Yt+/dVT0dWrVCcvEiTV57LjnARrWa4JAQPD092b59O6NG\njkZgBmKxZAyeniFERIQDEBAQwLhevRicnV1o7ZdUDCqVigVLljBm7Nh3nku3zp2RnjpFrfx5AxA7\nel/HJakU98GDsbKy4sCGDWh14guyBTqyZTKuyWQ01esLCbEbgVUSCR5A53xWUC1wTK3Go00bLgYF\nUV+nw81kIg24ZmVFv1Gj+GnRIq5evUpSUhK1a9cuUh4ZFRXFl7NmceDgQWSIIa9WwEv/Pgq45+PD\njfDwd173fwNnz56la9f+5Ob2BEwolbtxcLDFZDIxadJ4vvzy8z+kbE5ISMDHpzZZWe75lA5x3L8f\n9kH01CX4ayjx+P8mhIWFERAQUCA8/j7Q6/UsXbaUxq0b8/2P31Paz4n64+rR63APPl7XGe+e1XkW\n9ZSs3FxG5Rr4LDGX1g/TmITIfVMdENRyIk9FUrmC6P86Ojoik8kwGo1MGjOJ68tCEOyUVB1Qg/2f\nHmSexQJWeq6hlI8Tz188/8NzXLNpE2GlSnFUreYksByRcM3CwoJLFy9iYWHB8OHDUalfsvYA5BTi\nkW/fvj1SBweu8IqOwYhIqlYWGKzVMnPatD/kN7JzcOAlY1A9xC7d13cj6cBNpZIxEyYwatw4Ek1S\nUunGC3pxGBWJnp7oTCbeVKiVAw0EASHf6INYPdRJo+Hc2bOcOnsWlz59uOrpSVaLFqzeuZNBQ4dS\ntXJl+nTsyHdDh1LHx4ee3bqhyec0io2NFZk6Dx9mMmKngANi0vtlf4MJ/rZQStu2bfn66xnY2OzE\nxmYvZrOOxMQWpKT4s2DBSg4e/GPBlCNHjqDVVsBk6ojR2Jnc3NKcOHHiT52HIAhkZ2f/P+vw/d0o\nMfx/EZmZmTRv25x2/m2ZunAKFd0rsm7Duvea26Xbx8xbNY9nuU/R2GgI33Ufk+GV6MjDQ4+o36gB\nTg4OJCCSkbkiGrtgKdxSyZGpFZhMJuYt/4GBw8TKnTt37lDRoyK/7PkF13oV0OXpuTz/Cp6dPJiZ\nPo0+x3sRfz2BtNQ/bq/38PDg3qNHlG3ZkgiplM7ADKBzZiYLZsxg6qRJSCQSVqxYhlq9Ezu7A1hZ\nnWHlysUFa8hkMr785hsuSiQsAf6NhIVANBJ6ISanq0qlHDp06J3nMnj4cG5bWaFFVORSACuAk8Ah\nmYxNKhU/LFxI3bp1uXnzJmp1dcTXowdyeT16DxiARCIpkq8AsfroTdNjDThaWKBSqdi6YwfhERH8\nduECHTp0oE3z5vg8e8bwnBy6Z2UxUasl4vRpJubvWn6aP59q2dm0MJuxQeQyaoXYiHYt/1i3LC3p\nm69M9ndg5swZZGWlsW/fTiwt3RC1y5zJza3M4sVLmTPnC6Ki3i6IbmlpiVSai3g1ZiSSnD8Uln8d\n0dHRuLtXw9GxNGXLunLnzp0PvKIS/FmUhHr+IkaMHcEdw20+WtceqUxK2pM0djTZzbWL16hWrVrB\nOKPRiF6vL/hinDt3jk5dO+E7sAZV/L2Iu/qc68tDsK9kR52RtcmMyOLh7kecPX2Wc2fOsOLrr1FL\nDESbBKQWMswS8N/mT7WuVch4msmujnvQpei4EHiBPoP64DO7Or4DayAIAis9VuPk7UTf470Lzifh\nZiJ7O+wjKzW7yDW9iRs3btC0YUPGmc28TmigAVYplTx48gRXV1cePXpEREQEderUwcXFpdAaPfz9\nSTx2jPuIptgRsaXoZX/qaYWCT+fPZ8aMGW89D0EQGD96NId37aJWbi6WQJhKRYZKxYzZsxkyZEiB\nWEhISAitWnUiL28AoMDKaje7dq1m4Q8/UCokhNf7qk2I9BDtEDn2X0IHrFAqiXz2rECwJDMzk2FD\nhxJ6+DBvmuxcYLVKRVxCAvV9fWkXF1eEsygesWDSHtA6OhKZzxoqkUj+khTifwIxMTH4+NQmL+9T\nxNfpv5FKawIWWFs/5O7dm8VW8mi1Who3bklkpFjG6utbmQsXfnvvXUyTJq35/Xc5ZnMz4DaVKz8g\nKurtndMleDtKQj3/xzh44CCNv2pUUInj6OmId79qHDp0iN17dlOjXg2sHayxcbLBzsGORi0acvfu\nXdauX0sVfy86r+6IZ0cPWn3bgiazG5H2JB376460d+zAndA71KlTh6nTp2NdpTLSLl5MSZ3K9Mzp\ndFrTiVNjTmHIM2BfyY7mc5uisFcwZtwYXmS+oMYAHwCenn+KLltP6eqFG4Ds3ezIzcojMjLyndeX\nnJxMu9atcX7D6IPYuOSi07F//34AqlatSpcuXYoYfYD0tDTKIxrTOoi0yS+NvhGIUCho27ZtseeQ\nmZnJjh07WLNmDeMmTWLXsWM49+1LVosW9Jk+nSfR0Xz++eeFFKIaNGjA99/PRancgFz+C2PHDuCT\nTz5h6erVXLCyIlgqJRWxn2AbYpgoDDgKBCOWkAZZWNC+ffsCo79jxw6cnctz+PDhYhPLVoCDhQVx\ncXGoVCp0xYzRIeY2PIHM7GxaNWmCnY0NttbWtG/ZkvC/Id7v5ubGzp1bKVPmN1SqnSgUFTGbP8Zs\nbo9W6/HWnZhKpeL33y9x5Mhmjh7dysWLZ/5U6Coi4hFmczSwHkgmLu7pf+JySvAn8MGGPyAggGrV\nquHl5cVPP/301nEhISHI5fI/3Nb/UyBXyDHpjGTHZ/Pg0EOeX4/HqDERfi+cKV9OofaCmgwPG0qj\nGX6oy6ix/8yOth+15UXWCzw6Fk4Gurd3R6FSsH3zdr79+lsqVKgAiLmAyCdRdF7bEQtrCyQSCb79\nfHCu5UzECZHyQK6Wo8/RkeWcSW5mDmajGE1/euEZXh97cn/fA7SZr8opQ9fexKGSPRcuXHjn9a1b\nu5ZyOh1FJdZFGIBjR/5YfLttp05EKJU4IxraSMR4fxKwA2jepk0RfiNBEPjuu+9wdnJizogRbJo+\nnRZ+fvzwzTf8fu0ad27cYOsvv1ClcmW2bt3Ks2fPWL16NatWrSImJoZp06ag0eSg02lYuFAkn6tf\nvz5nL15E26ABGyQS9gCxqAExBFQesST1FyC7cmU25ZOj5ebmMnLkGHS6epRDWkBB/TrygDStFhcX\nFwYOH84NtboIU2cwonh8U8DVYMBw+zazTSZmmkxYXLpEyyZN/lCt67+Bbt26ERn5gCpVqmEwRAOL\ngCvI5TlYF0PJ/RIWFha0adOG1q1b/ym6BrPZjFarQ7zjnYAYrK1tmDFjFrdu3fqD2SX4T+GDKBtM\nJhMTJkzgzJkzuLi40KBBA/z9/alevXqRcbNnz6Zjx47/uJDO2zBk8BB2fLadjLgMKjSvSEp4CtoX\nGtJc0+mwvi2V24q+YfO5zUgOT0FqIcW5gTOOSkcenXpIzYG+BWtFBUZRy7dWkS+QyWRCMAso1KI3\ndWdrGJd/ukpGVAY5SbnYuNpwef4VLGyVNJhSn7hrcZwcE0Cn1R9hXc6GhJuJVPusGut8N1L9s6q8\neJzGs4vPcPYs+4c6qpfPncPXYCAAsVv39Q1/CvAcSH+PL6p/165899VXuAE6pOxFgR4dcsDKrgyR\nz5LYsmULSYmJbFi1itT0dOQSCYbcXGoD2UYj9xHj5OcuXqQrYshIihg+mTxiBHqJBF+FAgSBOTNm\nMHrsWH5evBip9JVfo9PpGDx4BI8exWMQygBpyBHojYBX/pj6wBMgMCUFa2trTCZTvlKVDFBSFoEI\nRNWxl13URsT6/tatWuHg4MDESZM4tHcvux4+pFZ+/8JLDtcG+f/XA7V59eXzEwRyNBoW/fQTq9au\n/cN7+meg1+sZNWoc+/cfRKFQ8uWXM5g5s3BYberUGdy9+xiR0NoVOEOZMi7079+/YIxWq+X+/fs4\nOzsXu7N7X7x48QK9Xge0QaxPa01GxkkWL77OmjXruXjxLPXq1fvL65fg/fBBhv/69et4enri5uYG\niPwdv/76axHDv2LFCnr06EFISMiHHO5/Cl0/7srazWsZ+2A01mWtEcwCx4ac5PGRxzh5ly40trS3\nE1mx2ViWVVHXrS6/b/ydkyMDcKzuwMNDj0i5ncqRg0W9ZysrK5q0aML1ZTewqWhD8Pwr+G/5mDK+\nZbi78x472u7CwdMetYOKwMm/UbFlRR4ceMC9vfeRWkgRjALl65ej89qOPP71MQk3E6nQpAL6CAMX\nLl0g9FYoA/oNKJZcrky5ciRLJHwiCOwB/BBNQiIiB09LXhm0d2HB99/jJ5XS1mQiGzNnMJGGnFgg\nM9OPsDA7Ro6YTDm5jo56PTcQjemnvNqOJgBbgLqAz2trlwc6mkxcAT7OlypsDexav576DRsW0DIA\njB45kofht3HGAgOQihF7TAVG/yU8gSsGA6NHjODY0aPk5eVhNhqRS27xQJDQH4EDiITOjogNXSap\nlJr29kwcN47+gwZx4epVtm/fzrRx4yhlNFIb0aTK868lMf84r6Oq0UjQW/oaPgRz537Ntm1nEIT+\nwBNmzZpLqVKODBs2DBAN8a+/HkcQJIA/ooyNFw4OqoLcQ3x8PA0bNiMz04DBkMlPP81j0qSJbzvk\nO+Hg4IBSaYFe/wSRRC4cQfAAWpOXJ2Pjxi0lhv//AB8U6nn+/HlBWAJE4Yfnz58XGfPrr78yNr/q\n4Y9qhP8pOHriKHVH18a6rLgdlkglNPmiITILKfd23y8YZ9QZeXDgIU7eTjw6HEGPHj24cfUG1tE2\nXPnpGm6tK1F7RC16D+jN8ePHixxn89rNPN32jN+mnKHjyg5UaFoBpa2S+mPr4t3bG5WdijI1yjDq\nzgh67PuUSU8nYFlKTZe1nRh1ezgp91I53O9X7u15gCnLhNULa3L1uVyVX+Fk0gnq+NXhVDHygKPG\nj+emWk15RNK1XMSu3VuIHrdWJsM/XwOgOGRlZbF582b2HjxIeZMJAbHCpTtGWmFEjhOij+2FyexH\nOb0RZ+ABIp/+63+Y5RArbTzePAii6XhJPABiN0GT3Fx++OqrgjH79u3j5L59TEBgJDrGoaM1AraY\ni1kRcrVagvbsoVdGBrP0ekabzVSSZiC3UHBBoaA34gvGDrGuxWg2c2bfPi6tXYt/27bMmjaNESNG\ncOjkSTIsLUlTKHgMnJfL2SqR0AiR+uJ15AB29u/WBPgr+O238whCM6A00BiQsGGDGMaKj4+nXDk3\nUlLSEQNW4YivWLh7N5LJk6cD8Pnnc0lIcCU7ewRa7TBmzvz8T5Uvvw65XM7x40dwcPgNiWQeUulj\nRD5WUChysLOz/ZDLLcF74oM8/vcx4lOmTCkQ+RDya6aLwzfffFPw71atWtGqVasPObX/OqwsrTCk\nFe5t1WfrsbOx5/ai26TeSsW+uh13tt5FgoRzk4OY+8VcvLy8iI+PJ/RmKKPuDy94cVTp7knvbr2x\ntrbG2saa0cNGM33qdNzc3Hh49yHOFZxx8Cjc4u9UrRSPDj2k85qOBc9CZa+i7ug6RJ2JwbuXN5/u\n6ca56UG4pbmzcP5Cqtaoysj7wwqO69nNg9EjRxET8bRQaKRZs2aMnz6dpQsXUl2rxRqRvkwNqGQy\nHtnbs+01IfHU1FTWr1vH5aAgjIJA8JUreMpk1DWZOIfoIffLn28HmElDZNOxRspDyuQTrskQXxBF\n7jcvfdHCeEk8MQ8pXTBTB9HERUQ84ejRo/j7+7Pgu+9op9MVSlL7IsbddYhe+GksMCKhHjpeGAxM\n45W4YSmgt8nESoWCjiNGs2/XLpLT0lAj9hX4IYbDAgWBRnl57N+2jU+6d6d9+/bcvHuX1StWEH77\nNjWqV8fP2pqjK1di0mgKJbmvW1ry+biiqlgfivr1a3P7dhDifk1M6FeuLAbuJk6ciMFgh9gd8jti\n0KoTUAujUcOaNUv58cfvSUhIxmR6uYu1QyYTleb+KuVEixYtePEiEb1ez/jxU9i9ewdyuS329jB9\n+h8L1/ynIAgCWq0WtVr9x4P/R3D+/HnOnz//wet8kOF3cXEhNvaVjF9sbGwR/vHQ0NAC+tbU1FRO\nnTqFQqHA39+/0LjXDf8/AQP6DWBpo6V4dfekUouK5Cbncn7GJSaMncDI4SPZvmM70U+jadyzKRUr\nVqRDhw4FIZWrV69SublbgfEFqNSyEoJcoMPWtlhYW7B+5joSkxNZ8vMSZDIZHT/qSNjmuzSYUo/Q\ntbdIvptMfHACFgolmheFBdHzkvII23aXrLgs0iPSyUnIwae3N8HBwbi3qFzouG6tK3EiL6fI7g3g\n6+++o1ffvmzdsoVdO3eQnpCAXC4lVSqla6d2Bc/60aNHtGjcmEoaDW5aLemIBrw0oi8nIJqUU0Bn\n4BigxICWlfkUFFKsEQ2tDDHxWxyh8SXEUM9Ln9AAnEKCgVYIeHOC9VTGQAxgpjTLl6/F39+fyOho\n2r2xlj2iAu1WIAU5Rj4BVASwjwoYeLMq3QLwksupV78+U6dNo1b16ozT6Qposn3yr3cr0Dw3l41r\n1tC+fXvc3d1ZtHRpwTp6vZ6b16+zLTQU75wczMB9a2vqtmjBkCFDirnqD8OyZYu4fr0lYWELkUgs\ncHZ2YvFisQjj0aNHiHdahiiRc+21ma8ctDFjhnLlymjy8nQolQlUrer5wYRtEokEpVLJhg2rGTdu\nJJmZmfj5+b23cM2H4tatW3Ts+P+xd95hVZxb2//N7oWmYgEEBVTA3rsGY8OCGo29xxo1sUVjElua\n5dhi772iidHYe+9gw4JgQUBAkA6bzW7z/TGKIpjkTcw5530/7uvyQoYpz8yevZ71rHLf7XnxIoHy\n5X05fvzQ/0g74T+Ft53ib7/99i+d528Z/tq1axMREUFkZCSurq4EBQWxffv2PPu82QgycOBAAgMD\n8xn9/43w9vZmy/otDOs3DKPZSE5WDoMGDWLC+AnI5XK6ftyVhIQEKlWqhEqVd2Hv4eFB4r1ERJuI\nIJM89cznmdjMNtzquaGyU9FhV3tWVVhJ9y7dOXvuLCkvUrj2SzBXl17D9yMffDpWQKFQEHsqjhOf\nn+KjPZ1wKuvI46NPuL7mJq41S6JQy9GV0GHOMLFr+xY0Oh2J9/NeNyshC3O2OR9F8iv4+fnRoGFD\nthzYyue3xqBz1pGTkcPPAbtZvWY1w4cNZ2j//tRKTaXeG6u5akiCLH6AM9AciW4hC6mTtT8iOYhc\nBe5hYy+S91wb2AN0RzLONqRcQjyAILBMFKmIZIhvAyaKI9IYkCPHkYe84DhKLELpXDlFj9KliQsP\nzxdXd0di3LTijrQGAPAhm4JLKzMFAUdHR44ePYqvXM7bfmKJl/dmAmKePuXWrVtUqVIFmUyWu9JV\nqVQcPnGCgwcP8suOHcjkcsb27i11Ocvef3W1Xq/n1q1gkpKSSExMxNvbO7f0slGjRty9u+HlqCVd\nXpXqKHL5M2SyGD75ZCR6vZ4uXbqgUqnYunUn6ekCoaH3KF++MgsWzPrb32VBEKhZ8/f1jv8JBAZ2\nISGhPlCFhw/PMGDAUI4fP/hvH8d/Cn/L8CsUCpYsWULr1q2xWq0MGjQIPz8/Vq6UOliHDRv2Xgb5\n34r27dsT1SaKmJgYihUrhp2dHQaDge59u3PyxEnsSthhSbewZsUaPur0Ue5xtWvXxqu0NwcGHqL+\nV3XJSTdx+LMj1BhcHZWdCqvJyrnvL5CdbaRxs8b4dfbFqaYTwiWo1L0SbZcGAFCld2UODTmC/rEd\nm+tsxWKxoFQrkSsFBIUcU6aZav2r8vx2AjfX3WLT+vV4V/Fh/4CDaF20PLvyjIzIDDp27PS7ntaW\nnVuoObY6OmfJD1bbq6kzqTabf9pMl85duH7zJmPeCuHZIxn/O0gVOVokOoRIYDyScdyIFEYJQKrv\nuIZULeQgCCwRRVz1ejKsVoqXKsXFXbs4e+YMsyZPRjQaEYFs5IjkIKVYkzCRymEAhT122sckxWqo\nX706Hl5enIqOxiU7m1d3mYgUfvIHzhCHhVQkSrdYjMAD8jZ1PUXKJQQEBLBt2zYs7whzWoAoQSD6\ndhiNGrXGz8+LUkXtOXryJACBbdsye/58ihQpQtijGEwmEx2ys/8Ro/8mihUrlo9LZ8KECWzevJ3s\n7PuAleLFi3PgwF5CQkIoW7ZsHpGhkiVLUrp0SRYt2o3Z3BAoSc+e/bh27WKB+r3/zRBFkbi4KKTg\no4DV6kNExMn/9LD+rfjbClxt2rShTZs2eba9y+CvX7/+717uvw5yuTwPtfDAIQMJtzzg87iRKLVK\nnl15Rt+APtyvFZYbShEEgUN7DzFh0gTW1tmAvqSezPhMuv7aBYBTU86Q8jAFl5qlqD2yVm7p59NT\nT/H9yCfP9b0CPUlalUJiXCLp6em0admSqJwoBJkk6vLKs3cq68j5GRcJuxeGEC7gUr8U9cbUJfHO\nC3bN2sXVsxf44quvGDx4cD4jZMoxkfwomeyUbLRFJD/XarKiVCgwGAyo5HLk5IcWifQMpGi+Gcm0\n6pDCNsWQOPpfmdCywK+CgKZWLQ5t3Eh6ejpFixalQoUKAFSrVo3S7u7M+vZbgu/dexmMqAucAbIo\niY00hYIGDSsTHhyM48WLuAMR9+6RJggsU6kor1JhAsIzM2mJpBWgIIfjLMKGiAIZLYC9SIa/NNJk\n9ECt5pddu9BoNLRv357RI0eSCnnyBpFIDWBJIlgswzFZTNwKXkZRbIxDWr2E7N9PvTNnMJhEsrNb\nACp69/6EkyddqFevHiD1vKxZsx4nJ0cmTBj/uypcfwflypXjxo1rrF27Hp1Oy8iRIyhevDh16tTJ\ns9+8efOZMuUHsrONSKGhu8BTBMGLkJCQ/3WGXxAE6tdvzLVrxzGbK6HVXiYg4O1g4P9tFHbuvkeI\nosive36l9cKWubX3bvXc8O7ozcaNG/Psq1arSXrJiZ8Zl4muuJa1tddzeuoZgpeF0HpJK2KvxVG5\n5+sCRme/YkSfj8lznqiz0VSsUBGlUkmxYsWoVacOKZHpVOhYIdfoA/h29kFlr6L5T81wKu9Ij/3d\n8O3kQ5PJjWizIoDMF7HMHjeO/m/UbiclJREYEMCJfYcJm32FxS6LODRoP6lRqVz54RqD+gzG3d0d\nvYMDUW89CxuSefBEUu/6FcnI2pCStPd5LcD+JuqKIvdu3eL7qVOxWCxUqFCB1NRUGjXyR63WMHz4\nZyxcsYKLV66gUSmRcQYlBlSkY9ao6NWvH3dCQuhrMFAdqeqntdlME7OZ6tWq8cWKFXQeMwZPBwfq\nvbxmA0S+wcZkRCphxQKMQJqYooAwhYLfDh7M9YBLlCjB9z/+yBadjqtIq4HTwDaglIcHKp0P4ICc\ns9THRkOk1Y4OaGKzUSEzE9GiAqoCvphMlXITdiEhIfj7t2TVqnAWLDhJ7doNMLzUPXjfSE1NJTIy\nkl69ejB9+jSKFy+ebx9RFPn668lkZ/siSdIMAAYDKVgsD//XGf1X2LdvN507l8fXN4RPPmnJokUL\n/vig/0MoNPzvEbGxsVL8XJ73scpVcsLDw/Nsq1q7KiFJIXxydQDD7w7Bo7EHpkwzMTtiseXY0Dlr\n0ZfU8+LBa8GUeuPqcXneZS7Musizq7Gc/e4c11dcZ9zn43L3mThpEqLFRtS5vKY4NjiOot5FSHqQ\nTPnA8nkqsrxbeZJqsdIjK4v9e/bw9ddfc/z4cdo0b07yqVOMMZv5XBT5PMdK8vrbrC63ip5te9Kn\nTx9kMhn/mj+fPRoNYUj8N0nATiAVOAb8hGTwSyHF1XfL5VjJb/RBeiHlZjNpu3fzUevWzJ0zh1Gj\nxhAcnIUofkNKSkvatetIzZo1CQsPo3//nvj6ONOmbSt2HTyITqOhssGA+q3z1hRFQm7eJDAwkI4d\nO5JpsyG+HONFpBVILBKFgwqpfLQeoFWpqN+gAR9++GGe840ZN46fDx5EExjI7UqVcOvenXNXrnDx\n6lVksjhgFlZCeUZ+XYNKVitYkpGmRCNqdVQutfOmTVsxGGoCTTCb25KcbP1H+l8iIyMpX96Pbt1G\n06hRCyZO/KrA/URRxGZ71b/9ipZBBoj07t2N9es3MXjwcG7fvp3nuEuXLrFo0SLOnDnDvXv3WLt2\nLefOnXvv9/FXUbRoUXbs2Mz9+zdZsuQn1Oq335j/2ygkaXuP+HTkp6zZsIaKXf0IXNMOmUJGwt1E\n1tffyLoV63I7Ic+fP0+zVs0YFz8atYP0wt1Yd5Nj404g5tgoWrIYri1LUayiM7c33qbJlMaoHdU8\nOvyI21vuoFDKsVlFVBol3dv1YMHcBQQFBXEl5Ap+FfzQ6/QMGzWM2iNqUrl3ZRJCEzgx8SQdN3Ug\nMz6TW+tv0+dEr1zjfzfoHsFDDuJstvJAp6R8Wx9iL8VhevKC0TZbHgNt5CWB2dOnecTEP//8czYt\nWUKaKKJD4uVxQNLp9UJKfD4CEgWBNoGBHDp4kPIWC13eeob7kcxKeaT8wD1kmFEgBYUqACJq9Tyi\nox8X6KEOHzyYJ2vX0vCt7TZgjkpFTFwcRYoUoXzZsuijoohA8mPVwD0kfSlWdQAAIABJREFUAjqV\nSoWbVktsTg6NmzRhS1DQny5dTE1NxdW1DNnZnQEX5GynCVH4v9EzEAGcL1mSZ0kpiKLIoEFDWLFi\nCYIg8N133/Pjj79iMnUActDpVnPx4gmqVav2p67/Z9Gv3yds3foEm80fMKBWL+Px4we4urrm23fC\nhEksXbqB7Ow0oAJqtRFvby3x8fGkplbAZlOi11/n+vUrVKhQgQ0bNjJy5PiXgi93sdmsqFQVEcVI\nvvpqDJMnfw1IIa379+9Tp06dfE2fhfhz+Ku282/H+P9/hyiK3L9/nxcvXrB+y3qc/Zx5ejqKhR5L\ncCzjSOKdRBzsJKGLcRPGUbVSVUJDQ1E7qHONfui2u1yceYke+7tRokpx7m67x/EJJynmXoyslCx+\nG7AfpV6JJdsCIsjVcmwmG506fMTMH2bSyL8hGboMyrQvw4Uz50kOSeHU0VN8O+Nb9nbYR0ZWBq0W\ntsCrpSd3g+4Rdy2OX7r9So3B1XlxL5ELU89SN9PMXS8nRt0ejEqv4uriaySMOZ7PK9cApTWa3Pb9\nV6hatSoKvRq9xYxWIUeVZeK0KImmvCqS8wdOAFnp6YQ9fEjDOnXYk5hIPSTvPxjJ2NuQ0rXS5GHj\nGiZsBGGmCiIyihcv8U7Rj/adOjEqKIj6mZl5lrNhgE+5crnVS1O++45PBwxgOFJfAUgkArtUKgKG\nDSOwY0e8vb0pW7YsERERbN26FZVKRYcOHX6X7iI6OhqFwh4pYwFWqhNHLK/Uem1AsE7HF5MnM2zY\nMGw2Wx5vc/Toz9m2bRexsWuxWg3069frvRt9gIyMLGy2V6luNXK5+p0hpX/9ayZNmjTk6tWrZGZm\nEhMTw5UrIaSl2WGzSQR7OTnZ7Nmzh4kTJzJ58ncYDJ2Q1ndPgaaYzb5AOt9++x1ffz2JZcuW8+WX\n05DJymKzPWTHjs0EBga+9/ssRMEo9Pj/Bu7du8dH3T8iJTMFs8mMIT2Lfuf6cmPlDW5tDMVmtqEr\nrsNOaYdjeQfcWrgSezqejAfpJCQm0vtID9wbubO27nr8f/gA71avydsODz5K0oVkijUrSsufmiNT\nygjfF8HxISdZOG8hT548QalUkpGZwZ6bv9J5Xycy4zJ5EZbE/R1h1Nc1YMlPkrzfytUr+WLiF+iL\n68iISKAV8EIu8MRORabZSnGDBQeNghJzPqTOKEmu5P4vYVzr9xsDDXkDFVZgmU7H+ZCQXPppi8VC\npRqVcPjAngZf1CPreRa/dPoZt/gs3pZUNwGL1GoiIiNJTk6meqVKqJE87govf0YBvZEaq7Yjef/u\nQDTSxCCXy/H08GDKDz/Qs2fPPGErq9VK86ZNSbpxg4bZ2dgjVeicUavZe+gQzZo1A6Brp06k7d1L\no7fGFw/sKVaMuBcvsNlsjBw2jO1btuADWGQywm02pn//PePfQSOdlZWFu7sXKSl1ARdksr0Uk6fR\nyJyDiFQpn4KAf6u27Nu3O1+pL0i1/jdu3MDBweEf84RPnz5Nu3adMBiqo9EkUrNmCc6dO5EnsW+z\n2YiKisLJyQmnl13FS5YsZdKk2WRl+QIhwKeAHJ1uJwsXjmfw4MG4u5cjJqYJUtvdeqS3xhuJAHsJ\nJpMRJydnDIZ+SMW+EZQrdzNXua0Qfx6FHv+/GTabjfYftafSF35UH1wNRLg45xL7PznA4JBPCFjc\nGtEmsrTCClSuSiwqC/d2heHZvCxKRwWJ+16wtdV2KvepTMqjlDxduXEhcaQmpBL1JIqu1zojV0k1\nMz4dKnB1fjBDRg2hXHtvYq/Gkfk8k+azm3F07HFubwqlZNUSxN98TqT+KY4OjsTGxxLQPICI+xH4\nlStHP6RYO1YR0nIwI6lrmS1WNDGvOfrLty/HQUHIV9Z4QSajjJdXHs2Bo0ePYrGzELC4FYIg4FTW\nifIf+6JbEpLvuakAO5mMxMTE3PDJaF5Hj1fwurzzF6A9Ui8ASHX+NYCdVisVnjxhQO/eTP3qK375\n7bdcr1gul3P4xAlGfvopGzZsxIYcgWLIyMoNYyQkJHDkwAHaFfC5FgNepKYCsGrlSo5s28YIozE3\nZ5AGzJo2jdp16vDBBx/kO16v13PmzHEGDRrB8+cP6Nt3GD4+5dmydi1nz13AaGsO1OTcuT389NNC\nJk6ckP8ZqVS5FT7/FPz9/Tl58gi7d/+Kq6sLw4YNy2P009LSaNq0ORERj7Fac5g3bw6jRo1g795D\nZGXVQ2pZSwTmo9XaU62aH337SoJAc+b8wCeffIrRaEIU/ZGCaYeA1YCSPXv2vMwbvDI/kqhQIf59\nKDT8fxHBwcGYFCaqD64meZwCNJzQgMvzrhJ/8zmlqpXkbtA9DC8MWM1WWoxqThFPR26svcWzG8+o\nWK0it4Nv8+xKLAgCN9bcpNkPH7C3/z6iz0fjWtcVURQR5G8FW+QirRa1oFq/qoiiyO6ee7g89wqa\nIlo+ezwCjZOG7ORs1tbbwK7gnXi38+KrBZNwXe6KvUzG20EKJZJhjXMuye3VoZRtVgavVp6kPEpB\nV9yBAzYDoXI5RQwG7irUJJtMKB88olOnrvz883YUCoXUx+BXJI/n7dXCk1NLQmhG3iRuCpCSk4O3\ntzcPHz6kuF6PMisr9+9GpB6AGKRJwpe8KIPUIatDSr7GRUXR0t+fB48f504kGo0GHz8/bIqGWCwt\nAbBX/UZwcDA+Pj4sWbQIB5uNxwWc/zFQ6WX56KJ582j6VqLYEaibnc3i+fMLNPwAVapU4erVvIlM\nLy8v2rXrhzFNUv3Nzi7DgwcPCzz+34V69eq9c4L54YcZhIWByfQZkMb48RMoWtQJHx9vzp07T05O\nRaAWGk04x47tpn79+sjlkoPSo0cPPDw8aNy4Ka9rt+oidUKUoXv3vvj7N+XsWSmcBXI+/njMv+OW\nC/EShVU9fxFWqxWZQpaXr0iQll7r6m9gtt1cTo8+i06v46MtHajSqxKlG5Sm/eq26Jx16NV6qtWu\nRoXA8jSYUI/QLXdYWmEFCaGJjAgbzsc7O1P2wzKc//FC7lIu+mIMccFx+HSQDJMgCNQbXYfsZCMN\nJ9ZH4ySxKWqLamk6rQkyjYw6I2vT81x3HsY+IstsziczCIBGw8Svv2bX1l1cHRfMHLv57Gq+m6/H\nfkNsYiITly2j2ogRZAkqYAxm8ziOHr3BV199RWRkJE2bNiXi4COyU15TR1iMFjIESR/X9HJbCvAz\n4OTggE6nw9vbmwybjdQ3huKOFI/PRsonFFT5Y480QVR4+dM9J4fx48axYsWK3AqYihUrolI9RmoL\nO4nJ9DB3lXLot99oYLNxF7jFa4K3OCRBlukzZgAQGx9P/vQxFBdFov6H3PmVK1dGJstEEC4DEeh0\n1wkMbPOHx/0nkJyczLFjxzGZspE+iSKYTEqGDPmSo0dPUKOGHrl8JirVVlQqLa1atWXcuAl5Qg4N\nGjTA3d3z5f2mIAW5PIF2iKKK06dPIZeXRGrn68GyZStJTU0tYDSF+CdQaPj/IurWrYst3cbdIImJ\nUxRFbqy6ibOjM3Z2dpRvVg6PJu6kJ6XjVu81f7kgCLg3Ko2Ptw9pKWnc/zmM5zcTMKWbMGeZqTem\nDgqNtBBrv6YdYbsfsMh9Kdsa7GB76yC823jnGniAjLhMFBpFrgDLK4gWW646mFwpp1xXb+RqNW8L\n3KUhGdqPPvqIgIAAwu+EkxifSMKzBMZ+Pha9Xk/fvn1p1aoVanVpJLOrJDvblXWLFlPNz48xI0bQ\nq3svNtXawplp5zgy7CgHBh+iiiiVds5H0shdhdTU1eyl4pZer2fUZ5+xT6fLNf71gLNIzVDPkDgj\n34QZqTrIHakUUw+4Zmfz25YtbBg3jvbNmuHfqBFyuRzBmkxpDlCNs+hFI2NGjiQtLQ29Xo8MKY9w\n5Y3xbQU0Dg50esk6WtHXl4K0oaIVCqrXflu6/ffh6OjI+fOn+PBDgRo1HrFo0Yzc6/y3ICkpiW7d\nelGqlAehoTlI0+5yJHYlBQZDf2JiZPTt24O7d0ORy1Wkp3fEYBjEmjW78/SqCILAiROHqFEjHZVq\nLVL3RnOk8JAJUZRjMlVCWrt5IZPZExX1djdIIf4pFIZ6/iLkcjn7du+jQ5cOXPsxGKvFhtqmxpBl\noN3WNpQLkEiEn1d/Tvi+CCp1lxpdbBYbkQejKOJdDOdmRem1rDtXF13DmJaDUxkHsp6/Dns4uNnT\neEojYhbHMvuH2SQlJdFrQC8eHn6Ed2sv4m8+5+Dww4g2kSOfHyMhNJGm0xpjeJHN+RkXaDHntaRh\nyo0Uhn32GUsWLCDOZMLDbOaFIBCs1TJl2jRKly5NRkYGWq0We/v8/JhSSCAeyXOzQ8FVOplycAFO\nnz/PjYwMFs9awswfvify4SN0yLglCLQVRQKROHrSgf06HV9Onpx73h9mzkQQBJYsXoy9XE6ayUTF\nChUICQ3FFdgBBCKFd5KRIsXlkAz+eaRKoSdAFYsFf4sFG7AvOJjOHTrwscWSy89jM5k4fOMGwwcN\nYsCwYfwQGkqvrCyGIoWVbgDhgE6tZuHChQwdOpRvvv2Wgd264WwwUBKJtuwJcF2lYvHvaAS/CxUr\nVvyv4oNJTU1FLpfnft4tW7bl9u0krFZXoCsAgrADQQjDZhuOFIvXYjAYiImJQaVyITtbImszGCoQ\nHHwjD9FcuXLlCAm5SFJSEtWq1eHZswWAFaWyKBUrliYi4iYGgwsQh1yek9vLUIh/HoVVPX8TVqvU\nYCOXy1EoFLTv2Y5BYa/luKPORbG97U7qfVYHp3JO3NsQRjlHb0Lv3KHjofY4+zqzI3An1QZUxcnT\nie1tg+i8oxNlPvDg+a0E9n68j2G9hrE5aDOJCYlYzBasNisWowWFToGjuyOt5rdAqVNwZvo5ku+n\nYDFYkKvltF7RkqLlinJnw13iDz7n7s27xMXFsXjBAq5dvoxH2bKMGD0ahULB0FFDCL8XgVqjYuSo\nUXw75Vv+Ne9fLFuxlIy0TNq0a0P71u0Z2H8ILiI0ISc36WsDlmi1WAWBKgYDVZACBOeQKnF0SiV6\njQY0GlauX0+7dvnTqi9evGD69OncuX2bpMREYsPCqIVUD3IViT5ZBNyQ+PnvIqUMqwCbkTptX5Vl\nXkKK1fd+6xrZwGKViifR0XzSty/3Ll6kfGYmF5FqTmogTQLnBA2oVPz002xsVitfjBuPXBRQKwS0\nDg6s3bSJVq1a/a335m3s37+fwYNHYDBkMmzYEP71r1l/Sbvi1eT9e3KINpuN/v0HERQUBIh89tln\nODo6MG3aj5CbBeoPgE63C7U6gaysMlitSuzsHhIaeh2FQkH58hXJygoAnNDr97F27Ty6devG5s2b\nuXIlmAYN6tK7d28EQcBms/Hzzz9z5cpV/Px86d+/P9OmfcemTVtxdnZm/fqV+SQ4C/HH+Ku2s9Dw\nv0c8efKEGvVrMDJmOHKllOgSbSJLPVbQrkU7TFYTbVu0pVevXlSrW406P9WkzAdl2Df4AM6+xWjw\nRX3C9jzgxMRTpEenI8gFBvYZSNAvQbTf3hbP5mVJjUwjqP1OTAYzWfGZjHo8EnsXiWbZarKyxH05\nHzb5kOuh18nOzkYhKAhsH8i3U74tsP48ISEB38q++C9qSqVuFUmLTudA70N4aD2IMkbRYumH6Evo\nCF4YwsOtj3FOy6JLenq+8yyRyahps+VpnLIhFfMlK5UsXrmSfv365SYA34TBYMC9tBfJKSpkyBF5\nhiNmVMDwl/sYkeiaryI1QFVAmhSevvw5kdeVQSeQlrIFpV4XyGRcDA3Fx8eHPXv28OXYsRSPiSFA\nFEkDlqDATCdAj0q1l8qVfbh3Lx2j0QON5hqrVy+iR4/uhIeHU6RIEVxcXH7njfhziI6Oxte3MgZD\nZ8ARne5Xli//nn79+v3pc2RlZdG2bUcuXjyPQqFg48b1dOvWtcB9d+7cySeffElWVk8kD3wFcrkj\nRmMTpMBfGOCEXC6nXDknjh7dz7Zt2zCZTPTv3z+Xm+rUqVMMHfoZmZkZjBw5nG++mcSkSd+wZMlW\nDAYfdLowxo4dyA8//DXq4EL8Mf6q7SyM8b9HeHp6UqN6DU6MPUlORg6mLBMnvzxNGVcPNq3fRNDm\nIPr3749SqaR1s9YcGHqIpIhkag2vyYVZl3jwWzjerb1o9VML1A4qipUuRpYhi8oDK+HVwhNBECji\n6UTrxa1Q26vQldDnGn2QqCHsytpx+dEl3LuUpvqYqmQbs+ndvfc7m462b9+OVztPKveohCATcCrj\nSPMlzbh4+SLtt7ahZNUS2JWyw3/mB8icZMSaTPl0q2xAks3G2+S6MiS5REezmbMnT7Ji+XLKe3ig\n12io5uf30uOE2bNnk5IiBz7BxgCgLA2QaBOCkAJMNqR8wVMkha4ySB7/WKQk8KtcgBVpkogo4F7T\ngSybjYSEBORyOV26dCEtPZ26L784cYAMFyRFXU9MpkrcvHkDo7EL0ACjsSULFiyhSpVa1K37IZ6e\nFZg9e84734c/iwcPHqBUuiI1fRXBYKjA1av5S2F/D9999yNXr77AYpmI0diHAQMGkZycXOC+0dHR\nmM2uvKLMs1plGI3+SNNpe8BIyZICq1ZN58aNK3h4eDBp0iSmTp2ah5CwWbNmRETcIS7uKZMnf4Ug\nCKxcufpl81YjDIYOLF++6i88kUL80yiM8b9nzJ81nybNGxO85jqCAI7ujmjNOpKTk3O7TTMzM1m/\naT1enT3Z0GgTpkwTKr2SfQP3Y8o0U7ySM7VH1uLeijDsHezRFMvLI6Jz1mHJtmA1WngemkDJKiUA\nuL0llITQBCq0L0fqk1Sur7xBtYFVmDZjGqeanipwvKlpqWhKavJs05fUYzVbcSidVwavZLUSJCWJ\nXI6Pp6Httfm/ScHVNyCFZ5TA2VOnOLN7N/4v4+VRYWGM+eQTYqKjOXnsGDIEXlVyy5AhAD2ReHR+\nRsoRyF9ue1MuJhWpakiLZLh3IJmzdKRsRF2kCSgbieffWakkKiqKpKQkihYtis1my/V+igE2niMl\nIPUIQjgyGdhs6UBRBCGF5OQkYmMdMZmGAxlMn/49vXv3/FsiHhUrVsRiiUPKMjih092nceNB/6Nz\nhIVFYDR6Ij0lFxQKe+Li4grUWWjRogVTp/6AyVQSMCMIJhSKSCwWLyAanc6OmJjI3w0XvQt6vR1p\naalIisSp2NnZ/dEhhfgPoNDjf89Yvno5tUbUZELKOL5IGseI8OGU9C/BshXLcvc5duwYrrVcaL+q\nLWPjPmd8whg+jx6F2WDBrbIbdnp7Qpfe5ZegX+jSqQt3193HmCoRHIuiyIWZFzBlmanYvSKbmm7h\n/MyLXJp3mQPDDtHvdB+67OxM5+2d6P7bx9zefIeI8IL8Xwnt2rYjbOsDshJfJ5WDF4bgVMyJe7vu\n527LSsji4eFHbNq+nSfu7my0t+eIWs0We3uulyxJs6ZN84mvW4HrgE2hIP75c7obDJRB8tArAN0M\nBr6bNg2lIKAlASUbgS1oeUxlpAnjA+BzoAsgCgLpvNaGMgKHNBqcihRhtU7HBqAVUrx/CJJQyzxg\nBQLzXl43zWJh6KBBlHF1xcfTkyrVqnHrZeNScSAAMwpWAHNo2LAiP/30E1rtRhwcNmFnd43ixZ0x\nmYoiTXX2KBT2vHjx4s+8Gu+Eq6sre/b8TNmyITg772HixGF5hOI3bNhImTLl8fLyY8+ePQWeo1On\nduh0V4GHyGTn0OlkeHh4cOrUKU6fPo3F8roDu1q1avz228988EEWLVqI7Nv3M56eyajV89Hrd7N7\n986/ZPQB1qxZhk63F0fH7ej1+1mzZtkfH1SIfzsKPf73jGs3rlF9bpVcWmaAMm08uLrzSu7vMpkM\n0SqZL5lchspOhTnbjFKhZNHURajVavz9/dFoNFgsFrxcvFjoIdEvKLUKHMo40HpxSw4MPohjGUfi\nQuKIPBlJ0XJFcavriinLhCHRgFt9N7RFNJT3yKtUu2fPHhYsW0BU5FPsHR1wK+XGyvJr8O3oQ2pE\nKrI0ORtWbqDfJ/2IPPgUTQkNYdsfMPqz0TRt2pT7jx5x7NgxHjx4gKenJ23btuXx48fUrVGDTIOB\nqkihl0tISVmjSoWfKKKx5KV/KAqUVCgoV6kSydevU9YYyTkkL/1t6cM7gLefH3vvh3FMFHDESoJC\nQY+uXVmxZg1z585l0w8/UClb6iUoBrkVO+sQqYc0EdQWRRpZLKiBx0+fsv/5c6xaLVajkSpWKy6I\nVFXJMXp4cvjwfmw2G2lpKaxcsYr451ZCQ2N5qQeGUplO8eL274VWoXnz5ty9ex2tVpsnqXvmzBmG\nDRuFyVQDKEXv3gO5cqUclStXznP8gAH9yczMZN26Lbi4lGLevKP4+7ciIiIOUbRRpYoXZ84cy1Xf\nat68Oc2bv676CggIIDExEScnpwJpJP4s2rRpQ3j4XR48eICvr2+BpG+F+M+j0ON/z6jsV5no88/y\nbIs7H0dl3yq5v7ds2ZKE0EQe7JWomm0WG+enX6CJfxM6duxIQEAAGo0Ufpk0eRLPxef0O9OHYaFD\n8PvYF2zg95Ev9b+oj2MZR0rXd6NCYHkMiVmcmHSKhaWXsKHxZhZ7LiMtKp2vJ3yde+2Vq1cy7Ith\nPLePJ13IwH2QGyV7FEdtr8bX5seiyYu5d+se7du358HdBwyoPZAAhzYc33ec76Z+B0ilrAEBAYwe\nPZoOHTqgUCioUKECt+/fp0jLlmyVy/lFEIiSyajSuDFTv/0WSwFJXYAcUeSjjz4i0d6eTJkMJyRu\n+7tIK4YcpOqge8Cdew9AdCANG7Eokau0rFizBpVKhc1mo0ROTr7zl0YSFrwuk+EiCDTndWOYN9DW\naMSleHF8+/dnT/HiHHdzo/XYsRw9fZrg4GA83d3Z8f33eEc/pZTJiMWYiFzuja9vLIMH1+bKlXN/\nm9L3wYMHlClTDgcHJ8qUKZdL4W2xWBgwYCgmkz1S+Oko4EFISP74vyAIfPbZKC5cOEmdOjUYOHAo\nd+5EkZHRn8zMgYSGPmPv3r3vHIMgCJQoUSKf0U9OTiYsLAyTyfSOI/Pizp07DBgwlEmTpnH27Nk/\n+wgK8W9GYVXPe8a9e/do5N+IWmNrULqxG48OPuHBpnBuXruZx/u5dOkS3Xp3w6q0YkzPxs+3Irt3\n7M7DeGk0GinuUpzBdwdi7yrVWouiyKrqa2m9sCUZzzI4NuYEft18iL/xnOwUI0qdgh77u2PvYkfM\npRi2B+wk5JKkkiSKIi4eLny4xp9fe+5lZMRwdMUk3/rFgyS2NdpBXHQcWu3barL/M4iiSGxsLDqd\njiJFipCSkoKHqysDjEbe1JJ6Ahx0dKRxkybcvH6dbIOBF6mptEUKET1HMv5ySmOhPFpO0xERL6RE\n72EEvJs2oWe/fty9e5d9q1fTJzMzz1gMwEKZDBnQymbj7YJBGzBTLiftZRnkyZMn+eLzz7kbFoZo\ntdIVKSz1CsHAETR8Pn4Uc+e+O7F7+fJlVi5dSlx0NPWbNmX4iBHvTLBXrVqHO3eKI4p1EYSrVKny\nglu3rnLmzBnatOlJdvYQJB9tPwrFXS5dOkvtAhrIRFHE378VV68+x2gsg9QK1wyogV7/K0uXjqVr\n166cPn0ajUbDBx98UGCV1Sts3bqVwYOHo1DY4eSk5dKls7+by0hKSsLb25f09DqIoiM63TF+/XXb\ney99LcRrFFb1/JegYsWKXDxzEdeHboROukflzCpcu3gt35K3QYMGREZEcmjnIa6dD+bCqQuUKFGC\n5ORkzGYzIBFlIYjYvVG5IwgCxXyKkvY0jbtr7jG071BKxJUiLTwdU4aJVvNb5Fb6lG5QmjojarFh\n8wYAsrOzSU5MRrTYcK3jkmv0AZx9iqErpuPJH1ARWK1W9u3bx6D+/Rk+eDCnTp3K9+IJgoCbmxtF\nihQhLS2NZ8+eMXvOHLbpdFyUyXgInFYo+EWtxpCdTc6BA7SOjaVhaip2MhlPkDSePgFkaDEzEDmX\n6IuID1LsvxTQB5HLZ88yd9Qojq5Zw9PMTPbymoLBBBxQKlELAj42GxnkRxagVChQqVScP3+eLoGB\nlL97l05WKy7kNfogVSmpMVKvXt13PqN/zZxJ++bNebZ1K45nznB4zhyq+PoSGhpa4P6RkY8RRT9A\nQBR9iYx8DEj19grFmx64QMuWzQo0+iC9L5cuXcBo7IQkW98OOI9CcRCdLoEmTZpQtWptevQYQ8eO\nAwgICMwlRzt8+DBly/pQsqQ7o0ePflnyOQSjsS+ZmcOJi/Ng7NiJ77xngJs3bwLFEMV6gC8GQ3UO\nHDhc4L6iKLJ7924WLlzI3bt3f/e8hXj/KIzx/wPw8/Nj09rN3Lp1i+WrlzP+q3F0COhIr1698iTN\n5HJ5Lqvk4cOHGTFmBM/jnqNSqZjwxQQmTZiEvYMDDw8+onw7qQc183kmjw4/Jvp8DPWr12f27Nko\nlUpevHiBT9UKqBzyhh2UDkoMz6ViR61Wi7unO4YX2Ty/+VzKK7zMRWTEZZIWn/a7Hl1OTg4BzZvz\n5NYt/DIzsQoCvXfswL9tW7bs2JFPq/fkyZN07NgFQdAjl5tYsmoVRw8e5HF4OJWrV+fWzp10SU/P\n5esvBXjZbCxEavAvAeixkMZetFh4u2LehtTMZTEaqWs00gjYKAhEaTS4KJU8NZmQAR3NZjRIFT81\nkcpEQUoSX1Aq6d6tG3K5nCkTJ+JvMFARiXA4fz2M5Cm52dm9s1rl8ePH/Pjddww2GnlVE1XRaCTE\naGRQnz5cvXUr3zHNmn3IkSMnycmpgVp9I1ftq3Hjxjg4yMjIWA84IAgRTJ16psDrgvT5ymQCUo9z\ncWSyBLy9S9GnTzuGDBnCjh07iIlRkZPTDbBx+fIGTp8+jbe3N1269MBgCAQiWLRoNatXH8FkssDL\nWiur1YWYmNh3XhvA3d0dk+k5EjePHVptNOXLF0xL0b//IHbvPoHcfdYCAAAgAElEQVTF4opcPo1D\nh36jadOmv3v+Qrw/FHr8/xAOHTqEfyt/IlweYG5l4tuV3/Jxz48LXJaFh4fTo28PGi6uz7jU0fS+\n1IPl25ezbv06hg0cxu4eezgw7CAnJp1iZdU16Jy1ZCdms3nd5txknbOzM+1at+fcd+exWSWfNysx\niysLr+FXXko+CoLAormLODvxPPZu9mxpsY3Hx57w4LdwtrXejqDgd+Xx5s2dS8L16/TLzKQ+0EgU\n+SQri0sHD7Jjxw5AasZKTExEFEV69epPZmZ7MjKGkJrakDlzFrJx61bOXbtG34EDcRBF3p5m7AAf\nuZx1KhUn5XIqyKwohPuYsfBmBP8pEp20FSmRewQ4ANSUyWgVGMjMrVu5de8eaUYjXkgdv7WRaJ9P\nInH0rJPLyfT0ZO5PPwFw6dq1XLZOV6Tu37fJgo3AM7P5neIoO3fupKLVisNb26sDD8LDiY6OznfM\n1q3r6dWrDn5+N+jVqw6bN68DpC7cxMTnSBTIZRGEmixevLzA64Kk47x8+VI0mk04OKyjWLH7HDq0\nl6lTp+Li4oLRaMRq1SJlOOQIgpbs7Gzu3LmDUlka8EAKso0gO7sn0BJB+AW4hU53mgEDejFv3jyq\nV69Nx44fSSvSN1ChQgVmzfoepXIlCsVcWraswrBhw/KNMzk5maCgILKyepOTE4DB4M93380CpIbC\niIiIQprmfxiFMf5/ADabDb9qftSaUyOXs8dqsrK20gZ2b9xNw4Z5hQEnfTOJC9bzNJv1utf08fEn\n3PnmHhdPXcStjBuuzV2IOhuNYxkHKnarSOTRSKxPbFw5fwVnZ2dEUaRFmxbcjr6NMcVI8UrOPLsa\ni72rPZYkC99M/IbdB3Yjk8lo2bQlx48fJyItHLWDGrlKTtV+VVA7qXky9ynXzufVeL116xarli9n\n68aNfGw05qmjB6niJr5OHVxcXdl/6BByQcC5WDGinyditY5FqtGJx8XlMLGxUijpwoUL9GnblgEF\ndAHv12oJGD8ehULBzRs3uHL+PEnJyYiiiC+SUtZqpBJP75fHiMBh4A4Cjfw/4MgpqW+hZNGidElJ\n4VXmJB4IBZ4KApVatOC3/ftzE5p2ajUDTabcPMTWlyNv/fJnOnBYq6Vuly6s37y5wM9+ypQpnP3h\nBz4s4G/L9HrOhoTg4+NTwF/zQhRFNm/ezODBIzGbxyH5aLdo1crCkSO//e6x8fHxPHv2DD8/P3S6\n1+G8yMhIqlWrTVZWBRQKIy4umdy5c4P4+HiqVq2NwdAeqWXuVR/0HVxcQqhZszpdu3bi/v17zJ69\nEGgKRFGkSCovXjxD9lJfwWAw4OHhgclkIicnBweHt6c/CampqZQo4YrZ/DlSB0YwrVsLNG/+AVOm\nTEOh0OLp6c7586dwdHQs8ByFkFAY4/8vwPag7Xj5eiGXy4m4F4F369ekU3KVnLItyxRYkZGalorG\nOX+TVnpaGjqdjqMHjxJ/6jkutUox8GJ/6o+tS49D3SjSxIm6jeri6etJ2QpluXT1Im71XXH2K0bN\nYTUYfmcoI+4Po0xbD2YumUHZLzyw/0jP4vWLiE+Mp3KvSgw434++J3tTbUBVXGqWIvJxZJ5xLF2y\nhGYNGxK2Zg1mo5H89G0SX+etmzeJP3CAz00mxufk0Cw2Fj02lMrNwE10usP06dMj95g6deqQKQi8\nHTzIAsJsNoYOHYqHhweXjh2jbVISX4ki45DI2dYieeTebxwnIHE/moDH0XG520eNHs1JrTaXGroU\nUBXI0GhYsHBhniqWSlWqchIBGxJzfDOkcNJ8YKlWyyqtlhYDB7Jy7doCnoKEFi1a8FCvz7dSiAHk\nWi3lykkhO5PJxOrVq5kxYwZ37uRXnho/fiKffjoJi0WJFKS6iE53hpEjB7/z2q9QqlQpatWqlcfo\nA5QtW5Zbt4KZPr0tP/7Ym+vXr6DX6/H29mbjxtU4O59EoVCiVG5GEE6g1R5n1apF7N+/m/79+7F8\n+XqkTomGQDdSUtK4f/8+06Z9h5tbGby8KuLkVJKgoKB3Gn0AJycnhg4dil6/Cb1+H3r9eUaOHMLU\nqd+SkzOUrKyRhIfLmDFj1h/eayH+Gv624T98+DC+vr6UL1+e2bNn5/v71q1bqVatGlWrVqVRo0bc\nvn37717yvxLHjh3jsy8+o8mqRnxt/hJ9KT2x114bINEmEnc5vkBv76PAj7i79j7GtNdNWtfmB+Ng\n50iDZg3YvH0zVWtUpdqAqnlqvP16+JCpysSujo64qGcoDGaiTkdR69OaVPzYD0d36ctXtV8VdK56\nnhx/woUZl3D/0J00WRqX5lwh8/nrKpiwX8LxKOPB6dOnsdlsxMTE8NWECfQzGGhqteIJ+WidAcLk\nctRAc4uFV4GEMkAXqxUHTSaBgXJmzBjLrFk/5h6jUqlYuHQpP+t0hCDF9O8B2/R6Phs9Gjc3N6ZO\nmkSgwYD7y3NqkbxvR0GgoFoUFaBHQWbWa12Ar775hlqBgSzTajmo1bLbzo4tWi3LVq/OV3+/dcd2\nHsvlzETJSnSsQU24XEOfPn04ExJCXEICC5cu/d0696ZNm1Kxdm1+1WhIBCxI0o97dDpmzZuHXC7H\nZrPRsmU7xoyZz9SpB6lXr3GujgBIuZTFixdiMPRFFD8FVDg732XPnh106NDhndf+MyhbtiyTJ09m\n/PjxeQTkP/74Y54+jWDDhlUMGNCaL79sxPHjB2jfvn3uPhqNCumTAmmKNhMfH8+cOT9hNo/EZhtP\nerojgwePYN++fb87jsWLF7BnzyYWLx7J7dshuLm5oVQ6IlHtCZhMJYmJifvdcxTir+NvJXetViuj\nRo3i+PHjuLm5UadOHTp06JDnC+Xl5cXZs2dxdHTk8OHDDB06lMuXL//tgf+3Yc6iObi3Lk30hRjk\nKjnNfvyAXZ1/ofm/PsTBzY5r80JwwCFX8/VNtGrVis4BnVntsw7vll7E34gnKTqZ2p/WpGzLMlw/\nHMKta7epVbcGfl1ea0YlhCaiKaIm4reHNPy6IUqdkrPfnSf+xnMqfvz6M0i4nYBSpyDiwCM+vT8U\njaMGURTZ238fK/xWU7JmCWSCQNT5aMrL1PTv0AGFoyNdevbETxR5ZR4aIzFh6pCizlbghiBwSyaj\n7stKpDfhARhNJtauXU7x4vklTXr17k1pd3dmf/89++7cwc3NjQVffsnHH39MXFwcmRkZ+XIAAlBZ\nFLmqVMJb18wEMrHQvsXrQItCoWBrUBBhYWGcOnUKe3t7AgMDCwwhlCtXjn8tmM/48fOxmvsCVmSq\n9fTq1w8/Pz+MRiNpaWns2rWLjIwMOnfunIe7BqSl977Dh/lu+nTWrFxJcno6lX18WDNzJh07dgSk\nnE5w8E0MBkmv1mCwY/78xWzfvin3HNKdikjTmQ9eXgm0bNky35jfFwwGA7VrNyQ6OgdRVFCsWDZf\nfvllnn3Wrl1Ohw5dEMUIII22bdtKlBeyorxOmbtjNucwe/Yc7Ozs8Pf3B2D+/J9Ys2YjJUuWYPny\nhfj5+dGiRYvccxuNRooUUZOdfQyLxRmt9jJ9+mz5x+73/3f8LcN/9epVypUrR9myZQFJcm3v3r15\nDH+DBg1y/1+vXj1iYmL+ziX/K/H48WPOnT1H6cZuaJw0/NpzD+XaelOxhx/HvziOJduKaBVxLO6I\nl48X2zZuo0mTJrnHC4LAkgVLGDVsFJcvXyYoNYjsrgaaTmsMgGdzTzLjMrm6MBi9qx7vAC+enoni\n7PfnkSlk9DrSk9L1JbGX0o1Ls6X5Nhw9HPAO8ObpmShOTTmDd4AXZZuVReMoNYYlPUji0ZHHeLX2\npFTNUtxed4sygkBXgwEBuJWRwaqlS6n8RlOUC9ADif1yL1JVUtOGDenh7c3DLVvgrc5cI2ATRfR6\n/TufXdOmTWl67Fi+7fb29uRYrRiRGq7eRJZSiUkm4xTQBOklzgJ2Amq1ljp1arw0SK8XtL6+vnl0\ngt8Fo9GIFBCSA3IUiuJcuXKF7yZP5kpICFarFZVMj1XuzfTpP3L9+hW8vb3znEOj0TBj1ixmzCo4\nVKFWqxFFM9J6QI5MloNW+/ouVSoVY8eOY9myDYiiB/CAGTN++cOx/x3s37+f6GgjmZk9AAGL5WeC\ngoIYNmwYkZGRdOrUjdDQ6xQrVpzu3bvQrl072rRpQ2pqKipVBllZJ5Fa5S4BNq5dcyQwsBeDBvWi\nevUqTJkyh+zs1oSFxdGw4QfExDzJ815oNBquXDnPlCnfEh+fyNChG2jT5r9Toez/Av5Wcvfnn3/m\nyJEjrF69GoAtW7Zw5coVFi9eXOD+c+fOJTw8nFWr8jL2/W9P7nbs2pGMamk0ntwIgJyMHFbXWIvK\nXoXKTkXR8kVpuywAhUZBxMGHHO5/lMiHkTg6OpKQkIDNZsvT3FO7cW0qzfCjTFOP3G33d4cRsygW\nRwdHbt68iUKlwLGmA5GnnzI+Ia9e6eYGG8gJTSQJkClkiFawiTaq9q1C2+UBAOzq8gulG7jR4Iv6\nAFjNVtZVWkWziJRcnv0tOh1JNhujjMY8oZV4YKejI4+fPsXR0ZH79+/TsFYtBmZn86YffUKhoFT7\n9uz89de/9Fw7tWtHytGjfPjGhJICrNdoyLFaKWk2kwQ4IPW1ghwLH6BWP0SpzEKtVjJkyCC++WYS\nycnJlCxZ8g+7bB88eEDNmvUwGOogk5nR60PBks2H2dlUQVrlXANOo8EqVGHChObMnj3zT91PUlIS\nK5Yv5/BvvxEd84z4ZANKpRtKZSLBwZfyCZGcOHGCx48f07hx4/dCC/F7CAoKYsCAKRiNr1QMttO6\ntSfz5s2lRo36mM1lgE7AIxwdj/HiRXxuafKTJ0/o2rU316/fAEAUmyNp7WajVC6kWbPmHD0qA2q9\nPPdPHDkSVNjY9R7wH0nu/k+EIk6dOsW6desKzAMATJ8+Pfff6dOn/86w/u04feIU1Ye8Lu9T26vx\n6+qHKdNE/PXntJzXPFdOsXzbcrg3Ks3mzZtp1roZ3j5elK9YngYf1M9tnirvWZ4nRyLzXCPqaDTN\nP2jO4d8OEx8Vz7lj50g8n4gl20zKk9dapTaLjbQnaSitNir3rsyYuM+ZkDaOJpMbcXtTKGG/PkC0\niUSdjaJyr0q5x8mVcnz7V+XpG+LuLjk5FC1Vir0aDclIgYfHwK86HTNmzcoNl/j5+THl++9Zr9Vy\nQqHgCrDDzo740qVZvGLFX36uy9es4ZmrK0F2dlwBTiiVbNBq6dK9Oz4aDb2QIsLpSIIsZQA5p8nJ\n0ZCZmU5SUhfmzl1DsSJFqObjg6O9Ax/6NyMhIaHA64WGhvJJ796IZgNK2Vn02huUK+NKI6ORGkgr\nCzVSyKsqZmTEoFT+uUVzdHQ01SpWZM+PP+Jx7Ro142IpLhipXsmOsLDQAtWnmjdvzpAhQ/5xow/Q\nrl07RDEe2IK0dkrg+PETjB37JWazDqkgVQn4YjTm5KF89vT0JDj4IhkZL2jUqBGvAwkyBEGGNF3e\nR1rhxAKGAhPahfhjnD59Oo+t/Kv4Wx7/5cuXmT59OocPS915M2fORCaT5YsN3r59m86dO3P48OHc\nqoY8g/hf7vF7+Xrhv6FpbrgF4Oeuu8lJz+HpmSi+SBqLSv86Ibiv+wHSb2ZQ+mNXGk1tiCAT+H/s\nnXdUFNfbgJ9t7LJLFRFQQBQBO3Ys2Bv2WKPRWGKLxliSGKOJJTHG8ktsscZYEnuPGhV7L7FjwQ6i\nYEPpLLDtfn+Moghq1NQv85yz58DsnTvvzO6+c+etxyYf58bPN3FwcODSpUtYzBYq9ClPkfp+XNlw\nhTvb7xFxIoICBQpkz3P//n26dO/CqeunaDi5PjoXLfuG7yHt2B0ShGBo0sc5isX9WHIBOps9cTFx\nqHQqWq9olSPyaF3L1RTedJXHOanLHB0ZM3cuJ48d48d580g1GilWuDCjx43jnXfeyXUdLl68yKIF\nC7h7+zahderQtWvX566wz58/z7TvvuN8RAT+AQEM/PhjqlTJnQ2bmZnJ6tWr2b97N+4eHnTr0YOI\niAjG9uqFOjUVG9I69PEKJhZYhAILauAz4DiB7OAGCkxUBNJxcIjjzp2bOZKwbt++TdkSJaiWkkI5\nJENPFFI8zQDgWY9ANLBGpebqrZsvbcaSnJxMh9atydq3j7pPlbM2Az8ZDMxZuTLPrmR/NRUqVOP0\naQuSUc8PtXoO5ctX5fjxBCRjWmPgKoUKRXHz5vVcCXsgKaZmzd7CYimJRnOb1q3r0qhRPbp3/xCb\nLRWwQ6t14eefp9GhQ4e/9Pz+P/LaulO8AWazWRQtWlRER0eLrKwsERwcLCIjI3OMiYmJEf7+/uLI\nkSPPnecNxfjbmTFrhvAu6y16nXxPfJrysWg0raHQ6DWiypDKwqWIs6j6cYj4wjZcjBQjRM/jPYTW\nQSv0rnrxufkzMVKMECPFCPGFbbhw9nYSpTuWEg6eBvHese6i2icholhTf+FTzVu81a5Vnse22Wxi\n0U+LRLFSxYTORSd0LjqRr5irUNurRYcN7bLnHylGiOL1g8TGjRtFWlqa+Gz4Z8LgYRAdf+0gBlzv\nJ2qNChVO9moxDMRoEC0UCuHt4SGysrKyj2M2m597DZKTk8WIESNEYS8vYadSCa1aLUIrVxYnT57M\nNXbDhg3CRa8X9VUq0RVEY4VCONnZiRHDhwubzfbS652QkCAMWq2wA/EpiDHPvMqgFmAnoLXQ4C6C\nQCioJGDMo1dBsWTJkhxzDh82TFSzs8s1lxOI3nkcox2IWiEhL5TTYrGIjwYOFAatViifI2tTEJ3a\ntXvpOT9LamqqmD17tpgyZYqIi4t76XibzfbSaxseHi70eheh01UVBkNBMWTIJ2LlypXC3j6fAD+h\nUBhEvnxeIioq6oXznD17VkyePFmsXr1aWK1WYTKZRM2a9YXB4CUMBi9Rs2Z9YTKZXul8ZfLmdXXn\nGydwbd26lcGDB2O1WunZsyfDhw9n7ty5APTt25devXqxfv16fH0le7VGo+HYsWM55vi3r/iFEHw/\n83smTZ7E/bh72DlreXtzewpVLkja3TQW1fgZa5YNg7ue+EsPKBRSkMSoRAbGDMhhLpsZOAff2r7o\nnOxo+N2TiIe0u2nMK76A1KS8qs1IvN3lbWKco2kwrT5KtZJbh2NZFraCAdf7YXA3cOtwLOtbbiD2\nRiwODg7MmDGD6ZumkZGWScrNZBCCjLvpuFoFCnt7nD082LB16+9yiB44cIBmYWGYjEbCkMwuIJVB\nPuDgwNGTJwkMlKremEwmvD08aJmUlCMR7AFSVm0RPz82b99OQEDOUtLP8uXo0Uz86iuG5fHeYWAX\nCmwoCEGBE1Z2E4iFToANBTP4cOC7YLWSnJhIw6ZNmTN1KoVPnODZo+5EKhbXiSdPFWZgqcHAuB9+\nyPPJ5zGfDR3KulmzaGE0MgUYwZP2kI85A9C0Kes3b37h+T5NRkYG5cuHcPMm2Gw69PoYzp49mWe5\nDZvNxgcfDOLHH3/Azk7LjBnT6NGjRx6zSpw9e5YDBw5QtGhRwsLCUCgU7Ny5k82bt+LvX4S+fftm\nZ4u/ClarlRMnpI4NlSpVemFxOJnfj9xz9x/C6DGj2W3eRd1xT7Jw0+PTmVroe7ROWlrMb0ZgiwBm\nFZ9LnbG1KPV2SQCub4tidZs1VBtWjeToZFoufBI/HR8Zz/rGG7l7626ex7Raregd9Ay6MwCdy5Po\nkJUtVxN37DYFirmTcCmRFUtWEBYmOXePHj1Ky3da0vN8d7b0C+fWwVsUDStK7KFYEq8lcXDPQSpX\nrvzS883IyMDb0xO3lBR8gBpI5RDuoyYACw9UKvzeeYe+H3zA5AkTOHH8OA/v3uWtR3kBT7MMKXgx\n1sGBOjVrUiQggL79++eZ+2AymXAxGOhlseD2zHsLgVso8LPX0TUjg0xgHhpScQbM2BQZ2NspqGQ2\nY2+zcd3BgViTGUeTjSJYqY0tO5LoJrBCrcZLp6N4WhoW4LzBQPWGDVmxdm2e5g6QeuB6ubvT65HD\nezFSq8iKT40RwEqDgWEzZtC9e/eXXuvHbN68mU6dBpOa2hlQoFZv5auvWjF8+PBcYxcvXsz774/E\naHwbSMPefhmnTx/N85ru2LGDzp27k5SUQLt27fnpp/mvpeRl/jpeV3fKRdr+YAoVLETqjpwr88Rr\niejd9ZjTzAS2DEChUNBmeStWtlzN4YlHUKqVJFxLxKe6Nydnn8JmsXFp/WWC3gok/V46uz7cS9/e\nuWuenD17lm++/YZLVy5htVlzfQHUejVBbwVwcellTh47SYkSJTCZTNhsNkJCQnC1d2F28TloHLX0\nPd8bjb0GIQRb+myhR8/unD/78qqJGzduxFMI7gENgNWouYYXFkpxgUNUsaaxZfNmflm7lpCMDOoL\nwW1gPZKT9Gmrvhqp+WDJtDSUW7dyascOQubNY9a8ebzTuXOO49rZ2dG6bVtWrVxJZ6TInscRNwmA\nC4K7Vis3kDrZvo+ZmzwgCjghoG/Wk8hz+7Q0rqAnnUbEE00Ul+mLGQEcs7fnoyFDCC5fnl9WrUKj\n1TKkSxcaNWr0wuCGmJgYHNXqbN9APaQSEBakHIgM4KhWi52PDx07dnzeNHmi0WgehYMKQIFSaX5u\nUtmFC5EYjUWQsi/0aDSFuHr1ai7Fn5iYSOvW7UlPbwV4smrVGlauNODo6MzChT/QunXrV5JR5p+N\nvOL/g0lOTiaoVCBlBpambPcyJFxLZMO7mwhoWYxzi8/z3m/dyecvpUSZM83MDJhNpf6VCBlcGY29\nhjXt13Frbyw2m1Wqnqmxo0+fPvxv/P9yVPaMiIigdoPaVPmsMgVDvNj24XY8K3jSdE4YKo2Kmwdv\nsarVGvpf7sueQfvoFdqbIyeOsGblGiwWC3Ua1OHamUjMD+5SckJdQgY/UcH3zt7n55qLWbt8HSkp\nKTRo0ID8+fPnOleAadOmsXLYMG5lZVELWI4CwXCktXs0jizHpLTQ3WbLUV0zAZgLDEbKyDUCU5AK\nAjz9nHEf+NnenptxcTkyTUHqXezhlh+zKQt3pOgeVyRnbwawwdkZY1YW/kJgycrivkpFqhAE2Wy0\neTT+BnAYNXdpiVTMwYaSiYSQxS0HB4LKl2fzjh2v3GzlwYMH+Hl7MzAri8d73kGqkH8N0Ov19OrT\nh5FjxrxyPRqLxUKdOg2JiIgB7HFxySAi4kSe/XXDw8Np27YrRmMzIA2DYSeXL0vJck8TERFBrVrN\nSUl5XBLi9CNJq6LXr+bq1YtyN61/IPKK/x+Cs7MzB/YcZMiwIcwbv4ACXu5UKFaBdJFG1Y9DWNt+\nHQ2+rY8+v57DE4+gd9NTY1g1FEoFyTeTcSvhhltifsaNGkdgYCAuLi7Z3bieZuzEsYR8XoWQwZKa\n7LL7HRZUXsR37lNx9nXCGG/krSUtpZo/MSksvrWYTL8M+sf0RaPXcGTiUe4ceIC7xUbK9cQcc8f+\nFofNamPQNwOxz29P7369mTVjFu92fjeXHBUrVmSiWk25rCwOASoUWEgEPIAHZCoEBe3s8MrMzLFf\nPqTM3gtIynozUq7qs0GNBYBiSiXr16/nvffey/Geg4MDTRo3ImPTJryQ1rQCKYPXHXiYnIxaacdl\nmwk7oLDVSiCSStsFHEONoDAWHiAlHpUFkrBhIaNsWaaMHEnbtm1fKWz5Mfnz56dRgwbs276dhmYz\nCqRYmarAbYOB6Fu3ct3Ifi9qtZo9e7YTHh5OZmYmjRs3fm5tnLCwMKZM+ZpJk6ah1+uZOXNjLqUP\nUoa9UpmFdHU8kWqYZgDR2GwOREVFyYr//xHyiv8v4Pbt21SuXhmvep5YsBC9/QbmdDOFChbCu1Mh\nqg0NYdN7m4naHoWDlwPJ0Sm0b9OeC1cukJaWRpuWbfhi+Bc5wg9LVShJtR+qUrCStI6+vi2KHZ/s\nIvlGMlpHLWEzG1KkfhGOTT1B9OIbxN+P58O4/tg5SCYBIQSzS8zFmmHB+MBI84XNKdGmOPci7rGk\n4XJqjQrNfgqIj4xnSY3lXL98PUc46eN5qlWqBOfOkWg2cwdIR4MSJ6wkU6ZUEBkxMXR+pjMWSBHj\nUUgOT0ekkMk7QBGkVftj63K4VkvHiRMZNGhQrjmGDR3KkWnTqGA2s57HZh7JISuQFG0ppGDEQ0jP\nIY2BmaixUgup0mQW8C0K7FCSRVWsWO00xLq6cvzMmed2znoZDx8+pGGdOjy4cYPCmZmkaLXEAGt+\n+SVHuYKXIYTg4MGDpKSkULNmzRcWQHsTTp8+TY8efbl27Qrp6WqgB9JtdCFHjuylatWqf8pxZV4f\nuTrnP5iCBQsScSKCt4q1JsAUyIhBI7gdfZv1K9dzZnoEy5uuIiMhg4E3B9D3bG/K9Qlm69EtlPgy\niFoLQwmP2kpovVAOHTqUXae8UoXKXN8idWq6secGG7tvovaYmrwf2Yfqw6vyS5dNTM4/DYcIR5Yu\nWopWb4fG8MRRp1AocPZ1puG0hjh6O7Gz5xbGaSawuNZizOlmKn3wxA3pXtId//pF2b59e65zUygU\nbN25k0JhYcTZ2aHV69EoLPgVduCLkcMxpqQQnZbGw2f2S0UysyiBpsAHwLvAkEfvP+7bZAGuqlTZ\nNV+epXffvkSoVCwCij/avxfgg2Q2aoC00i4GdAGSkbJ8nVAg3QZAek5REkw6FbHgh6CRyYRPQgIT\nx43jdXFzc+NERAQL1q+n+ddfM2TGDG7dufPKSr9jxy40afI2nTp9TPHiZbh3795ry/Qiypcvz5kz\nxxg4sD9SbJYD4InBUIT4+Pg/5Zgyfw+yqecvIn/+/Hwx4osc21xcXDi87zAVqlXgnR0d0dhrsJqs\nnF10jp4neuBaxAWAFkubMd1vJm27t0Ft0bBp3SZGDR9F1dAQTIlZ3Dh8k3oT6mYXcKvyYWXSbqRR\n0742478ejxACt3z5ubjmEiXbS1mg987d5/aJO7StW5hKA2tcHpsAACAASURBVCpydfpV6gWW5K0O\nHeg3oB+mVBP2+Z703s1MyMzVderu3bu8+25Pzp+/QGhodc5fvkxaWhqFCxfm4sWLNKpTh6YZGdwB\nfkZSwj5Iq/rHtxAf4OmWJhqkG8F0pG5ZuwAvHx9KliyZ53UtVqwYXXv2ZP3MmTzucmBBatTS6Zmx\nKqA8UoXRYpg5yU7gPjZuo8CKGqns8y4kW3wjs5lFCxZQvnJl2rdv/1q9iJVKJQ0aNHglZf80Z86c\nYfPmHaSn9wE0ZGRsZdq06XzzTe4bkhACk8n0xs3fQ0ND0evnYzQWRXpWukO5cuXeaM68iI2NZdSo\nr0hISOKDD3r/qUXoZHIiK/6/kMzMTFatWsXZC2cpX7Y8DRo04KNhH2FKN/FTzcX41S2MoYAeq9lK\nalxqtuJXKBV4Vy9EQPMAlGoFrdq1IvpKNKePn+H7Wd8TeesH3EvlrH7pWtKVG/tvSPsrFCxduJQm\nLZpwavZptPm03NgdQ9PZYeicdSSeS6Rnz96M+GwEALv272TngF00nNUAOwc7ziyI4GHkQ7Zs2cbu\n3fv45JMh+Pr60rJlO06fVmCxNGfTpiPA56xevRSAsaNGEZqRQWGkGvktkcI8dyApdzVSRE9esSiG\nR9sXAyWA9JgY3m7ThnXPKfXr6OiYqzfuy7gDNMZCMqc4BrwP2Q1YagKbgKOAxWhk0gcfMGLoUPYc\nPPjS/II/mszMTJRKHY9/qhaLPenpxlzj9u3bx1tvtSMlJZHKlauxdevG1/IhnDt3jq++moi9vQY7\nu014ehZk1qz1+Pg8ybrYv38/e/bsISAggE6dOr2WDyQtLY3KlasTH++H1erMjh0d2Lp1g9x+8S9C\nNvX8RSQlJVGxakXGL/2GUy4n+WrBVxQvW5y7rnf46P4gPro3CJciLsQeiaP6sGqs67ieo5N/AyAz\nOZMbu2MoFFKQ0h1LIextnDx5Eo1GQ+eOnWn/Vnsil13MPpawCa6suEb9mvWzt1WpUoWY6zG8HdKR\nW7tiafC/ehSs7MWRSUeJ2nSDnj16Zo+dPX0OpbRlmOEzmylu04mZcwtjqoUff7zMrFknKF++CvHx\n8Zw+fRyLpSbgTlZWVQ4ePJQ9x4njxymGpOSVSOaWrkimGBPQDslxm1cX13Qkq/sgoBXQITOTg7t3\nZ9esN5lMXL16lQcPpNrwPr6+JD3VdESN5Cc488y8ViTXZcqjvyshOZRDeKL0eSRvPaTQ0tJA+7Q0\ngh88oFPbtnl9tH8qFStWpHBhd3S6dSiVOzAYztC7d88cY8xmMy1atCYpqQk22+ecPm1iyJChr3ys\ntLQ0atduwG+/OfHwYRgmk5aPPx6Qo5T4ihUraNKkNV9+uYs+fT6nb98PXuu8Tp06hdFoh9VaH6iE\n0ViJlSvXvNZcMq+OvOL/Azhx4gRbtmzB1dWVjh075ll7fur0qWjL2NHs5yaSQ+YzwcoWq3ENcsXe\nVTIhhH3fiO+LzKJ460DKdi3DnJJzMWdYOLfkPGW6lMYtIB9CCKxmK2O+HsP+A/tx9nTCGJ+BRqsm\n/WY67lXyc2NjDB5qD7p06ZJDBgcHB8aPH09wcDDDxwxn77D9FA8szu7tu/Hw8MgeZzAYWLJwCekz\n0snMzGTRokWMGLEGIWpjtYLJlMyuXbvw9w/iypXTCFEetfosZcqUJjo6muTkZFycnUlKSMAVKVZm\nF5IStyKFbnogRfPsQmqFWBpJCVuQVtuOSGGeIH1Jg7Ky2LZtGwf272f82LGorFbSzGZCq1dn8owZ\njFAqiYbspLB6wE9IvoSSSDeTvUCCQkGmUklfqxXVo+3PtpIEybpt40loaSWbjZnXr3Pp0qXflc38\nR2FnZ8eRI/uYP38+SUlJvP32j7mOn5SUhMlkQoqJUmAyBXDhQl4tc16M1OtWh3RLBKOxAtu27aZX\nryddv8aN+w6jsQkQQHp6JgsWfMfMmdNeOdGrQIECmM0JSM5jPVrtfQoVyqthpcyfgbzif0NGjBpB\nWOswtqeH89OJhQSVCsrRTekxO/btoHjnoOzHYoVCQfne5bh16EnzbaVKicHDQEZCJs4+TuQLzMfR\nqb/hE+pDg//VQ9gEJ2edwpRq5lL6RT64+T7td7bDr6kPaXcfYj6XhdMxF8Z+8DV7d+zLMww0KiqK\nQR8PJH+oGzXH1iBFn0zPfj0f1aHPicFgwM3NDVdXV9TqJCRVaAGScHV1ZdOmtQQGxqFWf0eJEkbi\n46KpUKoULWvX5lZcHFs0GkxItv0kYDawD+kp4A6SOecdYA9STP8yYBIQB7kasJhVKk4eP87kUaPo\nkJxMv7Q0BmVlYdq/nyYNG/LpiBGs1elYCGwB1iKFjKYgFVpbq1TywYQJ3EtIwNXLiwilEhtSC8dr\neXyuN5GijB5HxisBV40mh5MzISGB6OhoLM/0IfijcXBwYNCgQYwePTrPm46bmxuFCvmgUu0DbqLX\nH6V588Zs27aNKVOm/O7GRz4+PlgsScAtwIS9/VWCg0vnGKPTaZHCPAEyUSqVz81ezovHEUoxMTEM\nHNgPrXYOev1MSpc2MGjQwN89j8ybIYdzvgGXL18mpFYIvS70QJ9fMjWcXXKemzNvcerIqRxjO3Xr\nREK5B4QMeZIodWDcIW4fi+PtDVKVwtijcaxsuZpBNwdw7+w9ljRYjs5ZiynDjEIACgW+BX25efMm\n7x7oTFZyJqubrKS0yUagyUoScMJgoEufPvxv8uQ8Ze7cvTO3i8RRc7TUO0AIwU+hS+hcqzPjx+dd\nVz4rK4u6dRtx9uwVhLDQqFFd1q5dkf2DT0pKIsjfn8qJiVQQAhVS7Z3lKhVmpZJgsxkrkunFBWk1\nn46k9PVIt5MjSKt/PSoEVvyQmqkrkVbtP2i1aHU62iUn8zi40gJsRaoJ5KPXkywEVqUSq8lEBbOZ\nICBaqeSYTsfajRupX18yfUVFRdGmRQvuxMSQT6XiekoKtZFMPo9vSquRAj0fuzTTgDk6HTdv38Zo\nNNK7Wzf2HTiAXqNBpdUyeuxY+vXvn+f1+7OxWCx88cUoFixYghBWevbsTmpqCrNnL0AIqSHNvHnf\n58qDyItNmzbxzjtdSU9PRat1oHBhX374YUa27f3QoUM0btwclcoXi+UW48aNZvDg3GG2eSGEoHXr\nDuzceQiVygFXV8HWrRtRq9X4+/u/0g1ERkKu1fM3MGfOHOYf+5EmCxpnb7NZbUwyfEdSQlKOZtcn\nT56kfpP61JtWB7+6hYnaEc2WfuEobUqKNy+OyZzFte3XqTmqBjoXHTuH7qbG8OoUKOPOmQURxB6O\nQ++oJ6BQMY4dP0axsGJE/3KFhjbx6MFcwgjMUCpx9/ehRbMWjP58dI6sW78gP8LWNaLAU87gE7NP\nsu/zA0Rfis4Vp/8Yq9XKmTNn0Gg0lClTJodDb9rUqfz0+ee0MuZ0Ol4CftVoMFutCJuNIkAzJOft\nMqRVdSGFgnQhSEeqp38bCEQqr+wJeCoUHLe3p0f//syfNYuBTx1jK1JjltZIZiEbcEyp5KSTE8UD\nA7l//z6VQ0L49PPPKVOmDE8jhCAiIoK4uDicnJz4ZOBAzp4/j16lIsNiwQPoYLWif3SMLXo9zd57\nj28mTaJUYCB+d+5QzWrFDulGsVGvZ/R339H3/ffzvH5/Jn369Gfp0p0YjaXR68/RuXND5s2bj2Tw\ncgZ2YWdnJDExPlcD9rxYvXo13bsPwmhsDiTj4LCdq1cvZucz3Lx5k+PHj+Pv7/9K0T5HjhyhYcO2\npKf3BNTY2W1i1KjWfP75569z2jLIcfx/CwULFiT5WnKObckxyegd9LlC6uzs7FAKJdsGbmdOmXmc\n/fkcYTMao3PSob2ppZDRm+5dunN+ViR7Pt9Lk5mNqf5pVaxZVhy9HNDo1ZTrX5Z4VTwKlGSlZaFQ\nK3n2Zye1zLCRL9SZY1m/UaNODYxGY/aXo2jRotw9nTMO/O7pe7gWdWHVqlXPPVeVSkXFihXzLO51\n7PBhfJ5R+ilIJpdgs5n3bTbeR7Lpz1IomGlvj2PJkuw7fJhqb7+NSqUiAOlJoB3SzeEt4KJKhVPr\n1qzdupUvRo4k02LhsUEqE4hAihZ67AtQAlVtNtRJSRw/e57JM2eyeMWKXEofpB9MuXLlaNasGTVr\n1uS306e5ERvL0XPniL1/nxpt2zJTq2W2gwOLDAbaDRjAt1OnsmrVKtQJCcRbFSxFSwSS47q50cjY\nUaOwPVVv/004ffo03333HWvWrHnpD3vlylWPSjIEYzQ2Y+XKVUjPQ55IHpMymEyFqF27AQ8ePGDu\n3Ln88MMPJCcn5znfvn0HMRpLA4WAkqSl6ejVq292Domvry9t27Z95RBPo9GIUmkPj/q5mc32pKWl\nv9IcMn8MsuJ/A5o0aYL1vo29n+/nweWH/NpnC4tqLMbL24tDh55EuFgsFsJahqEpqKH5/GZ8Ej+E\nLjveoVz3slQcXJ44RRzmaibWbFrzaMWtwK9uYda0X8ehCUdwLZaPIvX92DtyP9qCWqxmK/W+roNK\npSCv4rZaG+hctDSa2QCjoxEHBwfcvdyZ8L8JjPx0JOEfhHN2yXniI+PZP/Yg1zZfw6uiJ6mpzy/7\nnJqaSu3aDdDrHTAYnGjRrBmfDh3KmTNn8PHzI/EZ595hJKdqQySFnw8IA8qqVLzVoQOnL1ygWrVq\nKG02qlittEaK33/saPUAFCoVc+bPp1atWjg5OdE0LIwDGg0CyWfgxJNCa09TDMjMDKJ9+06kpKT8\nno9SOqaHBwEBAeTLl48lK1cSd+8eh86c4U58PN9MnIhKpeLA7t3cMpq5QBViaMmvGLiApCJTU1Of\n290rLxISEmjbtiNFi5akW7eepKdLSnD79u2EhtZlxIh1dO/+CT175i7Q9zT587sjeUYAbuPu7oGz\ncz4k41kdoDbQicjIK5QqVY4hQ2bTr99k8uXzpFGjZjm6aQFUqBCMXn8VycgWCySza1cEX389jjZt\n3qZkyfJ88skwzM80u38Z1atXx8NDi1a74VGE0gW6dev6SnPI/DHIiv8N0Gg07N+1H/fr7iyovAhT\nqonWy1vh/0ER3nr7LTY/qrG+efNmTDoTGr0GrWPOyHWtsxa3oHzUGhNKxz0duHbpGjpnHb9NP0Hi\ntUR6HOpK1SFVaD6vGQ0m1uPa5usoVUpci7lin19P1DMyWYHzDhr8m0tVQH1qelNrTCjt97Rl9srZ\nRJyLoFqV6hwcd4hVrdeSeC2RtqvbcG1DFC1atHjuuY4YMZLffkvAZhtORkZ7tmzZxpHJk6lfowYJ\nCQmcVat5OrfzGuR6GgEoa7FwZP/+7P+LBQURn0fC0UNAa2eHo6Nj9rbZ8+eTXKQISxwcOI9kgsnK\ntSfEogEKYLEIRo4cyZ49e567ao6MjGT37t15KmxnZ2f8/f1zJG6p7OwwoUPQCCiFmfqcR4MRsAiR\nQ96X0br12/z6azTR0aGsWnWSnj0lM9HYsZMwGhtgMjUiPf0dlixZ/MKb8pIlC3By2oWj41ycnHaz\nZMl8jh49gKOj8dFVAsjEZEojNdWNjIzW2GydsNm82b37Mh075lS+3bt3p0yZQsBUYBHgSWZmWaZM\nmcmmTbFcvFie2bN/4bPPXs1EY29vz4kTRxg79h0+/7w+J04c+UsjpGSeICv+N6RQoUK0f6sDhSv5\n0npZK/zqFKZC7/I0mteAEV9KCVE/L/kZtb2KoLcCOfK/37CapEfmzKRMjs84SdBbUvpR/iA38vvl\nR6QKjk8/TmCrQFR2T9b0JTuWwJplRaFWsKLZSurNaMR6vZoIJNv+HWCFRolzRS/86hbGarZybcs1\nvKt7414iPw1m1WPqjKksX7wcdzt3HB0cUWYoWdV0DY3rNs5u/J4XFy5cJiurGNJjemFUaKhos9HL\naGTD8uWUCg5mLrAS2IbkvM1rPWiGHCWEe/XpQ6RKxc1nxuy0t+f9fv1yNOzInz8/py9coMdnn3FM\n4wC4EY4UnfOYK8AtLMAeXE1pnJo+nXdbtCCoaFFmzpyZ/QRw5coVfDw9qViqFI3r18fP05Owhg0f\nhUU+n/d69cJGBtKtyYaKa7hg5aBGQ8vmzTEYDC/c/2mOHj2EyVQXKERmZk327NkHgFqtenQVooB1\nWCyCq1evPneeatWqERcXw9GjO4mLu0HVqlUpXrw4kZFn8PC4hZPTEvT6H2nQoD4Wy+NbpQBsWK2B\nHDv2W475YmNjOXv2HFLxi08AKwrFAZKTH2CxNAAKYzSGsmVL7hIeL8PZ2ZmhQ4fy1Vdf5mk2lPlr\nkBX/H8DFSxfxCPXI4fD0renD1YvSj/XClfOkxKaSv4QbKp2K7/1nsazJCqZ6f499Ph2F60jdyUxp\nJpJvJ7NhzQawQtzRuBzHuRdxn3wBrrRf25b7Fx4QPnA7uiIubFEqmKJRstxVR4xGSb5yHpyae5qF\n1X6SzET1/ABw8nEi8WEinp6eXDh9gfmT5lNMGYBSqeSy7TI9Pu5ByeCSrF69mrt3czZ9ad68MVIk\nfAwK9qHDihOST6G80cjl48fpglT7PgPJTPObWs3T62wBHASib91i6pQpCCHw9vZmxdq1rHNwYIWj\nI78aDMzQ6Qhu2pSv8qiTo1arqVq1KlqdO2Z6ch5PJqNkk1rNckdHVisUWAgE2pKAKwYU9ExPR3sj\nho8HfUFwcCUePHhApTJlUN67R2fgI6CZEBzYuZPmTZq88LMOCQmhS5dOwCwUTMCeq9x10JNSpMgr\nN5b38/NHoTgP2FCpLhAUJC0Axo//Ep1uN9JtNAghQqlXr/EL6+U4ODhQsmTJHGU1vL29uXo1kvXr\n53L06F66d++KxRIDLEBqV2NEoUildOmcPpDo6Gjs7DyQvBc6oARC2JDUxSlAoFZfISjor81ilvnj\nkKN6/gA2bNjA4G8G0+VIJxRKSfmfX36B23PucnTfUeo1qYc1xMzpeWcweBhIvZOGNdNKyMeVuXfm\nPvHn42k2twkHxhwitGgojeo2Ztyycdy9dJcSbYMo1zOYh1cS2D5oB3XH16HU2yWZ6PQtNUeGsn/M\nAex19jT+sSFBrQK5Hh7Fpu6/4uddhJuxN+lzsWd2qOn+UQdwv1aA1cukDMlTp07RsEUDup18FwdP\nB4QQbB2wjegt0ZiSzPTv358JX09AoVBgs9lQq+zIhxpnbLTAzOOCABeR1MHTrVJigSVKJf5aLWUz\nMhBITVIeAErsMKmU9BvYLzvsNCMjg/DwcK5du8bBvXvZu3cvQgiaN2vGuEmTKFKkCNHR0WzcuBGT\nycS8eQuJi1NiNuuxs7vCp59+hI+PD7169cNm+wwpHewyhVhHb7JIBaaiRqsPoLCvIObSJYaQsxVi\nHJJh4+7Dh3nWtn+ayMhIfv7pJ0yZmYTWrk3Lli1z9EsAqfn8smXLsbfX4eDgwP379zl37hJms4Uh\nQz7Ax8eHsLCW3Lx5neLFy7Bt26bs0ggffjiQGTPOAFIIqpPTapYsGf9Cc9zLmDBhAl98sRGr1RWp\ngEYiRYoEcODArhylmh88eEDRokGkptZC8tBsBOqhVseg0VzAZDJRsmQZduzYkiPxT+av529ptv5H\n8Q8R47Uxm82iep3qonhYcdF6WStR98vawsXdRezfv18IIcSWLVuEm4+beGdbR1GwipeoMby6+MI6\nPLvJeuHavkLvbi9c8ruIPn37CBc3F5G/hJso3iZI6Nx0QuusFT6h3qLd2jZipBghPrjWT9jntxcf\n3ugvtAY7sWrVKmHvai8USoXQF9ALvzp+Ir9nflEppJLQu+mFb6iPKFjZS9gZ7ESHzh1EYmKiEEKI\n0WNGixqfVsvRkH3gzQFCn99efBw/WBQs4SU2bNiQfZ5VypUT7fNoGF4WRP08thdycBDdu3UTeoVC\nFAHhi0Ko8BbQVagoI5QKrUhKSsqePzY2Vni4uYk6SqX4CMTHIOoqlcLdxUUM6NdPOOl0IkSnE1W1\nWuGs04m6NWuK//3vfyImJkYIIcT169eFCqXQkl9oCBAKAkRpVNnyqFEJrbagUCsUIiQPecc8aq6+\nZs2aN/5OnDt3ThgMLkKhCBUKRXGhUDgIKC1AJ6CBsLd3EYcOHRJCSE3Zn2XOnDlCrw8U8IWAoUKv\nzy+OHz/+RjLt2bNH6PX5BbwnoLPQ613EpUuX8hx7/PhxERBQWigUBgFhAkYLe/tSYtasWSI9Pf2N\n5JD543hd3Smbev4A1Go1u7buYkCLAZjXWgmKL8GhvYeoWbMmIEX/zPpuFic+OUX8hQdU6FMu+8lA\noVAQ2CoQm0XgWsqFy4UuofXVkhKbSsEqXrRd/hZqnRonHydc/V2J3hXNmrZrqfpRCOcWn6deg/oc\nOHKACj3LMzzjUz66O4jm85uQlpmGpbiZOuNqgVLyJ/Q41p0o3XVatW8FgJOjE6aEnDbtjIQM7By1\n6PPrKT+4HEtWLcl+b+zEiezU67mOZLYxA0cUCq4gVdJ8GhuQZbPh7eNDANANSEKDleZAUay0xCZM\n2Q24Ab6dOJFiKSnUsdlwQgrvrG2z4Z6SwvJ58+ibmUmTzEzCsrLol5nJ9VOn8PDwwNfXl3PnzlGl\nfHkqKqE9D2jMVRy5igHJn3IbyVChUyZTQIg8ncK2R+c0dNAg6tWowerVq1/7SXTBgoWkpwcjRAOE\neBshHJAKQJQFrGRklGP9+g0AeTYef++99wgNDUCnm45G8z0lSvizdOlyoqKedef/furUqcP06RPw\n9T1AsWKnWbly8XPt7JUqVeLSpQjCwurj4BCBg8MCPD3NeHl5vVZRNpl/FrLifwNiY2Pp2rMrBf0K\nUr1udQq4F2DTmk3M/n52rjLCHdp34PLZy4TWDCVm7xNXps1q49qWazh6OxLYJgCP4AL0PNqdglUK\nonXSUrRhUfpf7svt47dZUncZ697egIOXI/d/u8+lH67wTrt3WLVuFbHHY7mwMhIEHPj6EFUGVaLF\nomZU7FuBrnu74FrUldhDt2g8tyGRly/w66+/olKpuLT2Cpc3XkEIQertVLYP2kH5XlKhZKvJit1T\nYZqNGjVi4bJlHC1cmCk6HVPs7DBWqYLSzi6XIr0AFChYkLS0NFIfKU8XQMHjGjKXUaBGr9dz+PBh\nJk2axNKlS3Ezm3lW1abbbNS2WHjabaoDaqSn8/233wLQt0cPqqWk0NRmoxhStZn3H8lxGVgDhLVo\nSoB/ESo92v5snMwlJNNPvbg48h0+zEc9ejDgNROyDAYDGs3j3AYLUuaBBqlEnQJ7+3v4+OTuhPUY\njUZDePgmDhzYhb29gVOn7Jg+/TgVKoRw+3Zepe1+Hz179iQm5gpXr16gefPmLxyrVCr59df1hIev\nxt/fk/j4DLp2/YTy5UNIy6Oxjsy/B1nxvybp6elUr12dGx5RtN7RiqCRAQz4bAA/L/n5hfvVqVaH\nbR/uYFPPzcwMnMM49QTijsaREW/kwaWHHJl0lB8rL6R4m6DsG4TOWUeVDyvj5enFhhUbKGtfFtek\nfFQoV4HBIwZTfmgw5XuX4/j3J1hcfylRO6Ip3vZJmJxCoSCodSBxR2+jVCmx97Sn47sd+fnYT2id\ntWx5L5xvnacwo9hs3Mu4U31YNRKjEjky4TfefSbUr1WrVlyOjuZydDRx9+6x/+hRvps6lcV6PftV\nKi4gdcza4+jIzytWYDGZiEVacbfGhDOHUDIWO35BiZm2LVvSPDSUGcOGkZSQwE7gR3Iq5TRyVs98\nTH7g9p07ku38/Pk8k9nKIin9QsDJ334jJDSUJI2GYGAOkqX7OlIk0kakBDIfpDYkXdLTWbFkCRER\nES/8TPNi4MAP8fB4gKPjMpTKGahUAo1mF3ABne4ElSsXom/fF8fnKxQKTp06hdnshxD1sdnqYzb7\nsnXr1lxjMzIyMBpzl2t+lszMTOLi4rKTsUDKYl6zZg2TJ0/mzJmcNU2VSiVJSUlcvx5PWloPUlO7\ncPMmLF68+PddCJl/JHJ1ztdk9erVOJdyos43tQFwC8iHnYMdX/cfy7ud3+XYsWOcP3+esmXLUrmy\nVONx1pxZzF42m4oDKnBy1ilaL2sFQrBz6G56nuiBncEOIQThH27n7E9n0bnomFNmHuZ0MxaThaIF\nijJ63GjiuU+R1n7sGrWL7oe7ZpdfCHorkGk+M9C5aLl78i4eZZ6UX7h76i6u/i48uPSAO+fv0v9S\nX5x9nRFCsKnrZu5svoWdOZVz884Qs+EqqfHpuKBi9fLlNG7cOMe5KxSKHO0I3+/Xj6rVqjFnxgxu\n3bhBo2rVWNe/P15eXoRv3YqfSsUSq5VgoDZmooGrSKaV0AcPKInkijUixbEokJR1DySTEkol0UJQ\n8BmzSzQQEBBAVlYWKqUy72Q2oCJS8tii9HTMNhu/IbVj9ERKcVIi5T+8D7g9ta8OKJmVxbq1awkO\nDuZVcHd35+LFsxw8eBB7e3uSk5PJzMykYsWKqNVqfH19SUlJwWg0vrBuvrOzMypV8iMJQalMztWc\nfejQz5g6dQoAvXv3ZebMaXmaY7Zt20bbtm9js0nJagcO7MLb25uePfuyatV2srK8gJF07/4O06dP\nz85fiIuLw2TKQCpqXQazWcuECd8yf/5Sxo79nCYviYSS+efxxlE94eHhDB48GKvVSq9evRg2bFiu\nMQMHDmTr1q3o9XoWLVpE+fLlcwrxL4zq+fLLL9lp3kHdr2tnb8tMyuR771nUbVCXU+dP4RPqzc39\nN6laoSqrl63B19+XlpuaE7EwAns3e2qNqkn4wO04+zpR7ZMn/UzjI+P5sfJCFAoFNUeG4lXBk3NL\nz3N9QxQFShSg88GOpMSmsLD6zwyO/TCHXKvbrsUjuADHZ56kwaR6eFX05OKaS/w29Til2pTk4rpL\nFGnsR9uVrbP3Ob3gDAd7bWWQEGQitSfMh6RqZmi1RN+6lWep6d9DTEwMZYoXp01mJlGP5nZHilDX\nKxS0f+Zzf4AUaMgjGe4DNoUCtVJJe6uVIkg3hpvAl+2t7QAAEhpJREFUUkCr1eKaLx8Wi4W68fEU\ne2ouK9KqvgmSv2A+4K5Q4CcEN4G7QIdHx/kRKWL9WXW5W6Gg5vDhfP0GLRifRQjBwIEf8cMPcxEC\nOnXqxKJFP+aprM1mMw0bNuPEiXMAhISUY9u2TdkRRLt376Zly86kp78LKDEYljJw4LtkZGRRrlxZ\nunbtmh2V5ezsRlpaa8AXlWofTZvmY+nSRbi5FcBsHoJ0mzyLQrGHKlVKc/jwXm7dukVwcCWSkwsi\nPYclIGVpNAQc0eu3s337r5QoUeKlkVAyfzx/S60eq9XKgAEDCA8PJzIykuXLl3Px4sUcY7Zs2cK1\na9e4evUqP/zwA/369XuTQ/5jqFGjBlHro7FkPSnJG7nyIj6+PlxPv07Pi91psqgxvS69R+TDi8z7\ncR7xd+LxKFsA44MMnLylhtn2bvakxOa0NqfEpqLSqGg0pQE1hlWjaMMitFrUAs+qnihdlCiUCgwe\nBqxZFh5efZJub8myEHc0juJti9N+TRsOfnOYNY3X4xaZn48/+Ji3i3fk3XfeJZ9fzhXm3ZN38UdS\nevZIK2G7R38X0umIjIx87etUuHBh/jdlCr/Y2yPUaryBuwYDCTodfnl8YfMjPQm4Iplq+gL9hcBf\nCFYqFMy2t2cy0pNBC2BIVha17twhKSWF1ZAjmW0lUokyP6Rqmw2AnkJQH+lpogVSJZvH+QjPlmc2\nA5fs7WnRsuVrn39e7N69m4ULV2IyDcRsHsy6dbtYv359nmM1Gg07d26hc+e3UCgyOXfuXI6xN27c\nQCpibQDsycry5NtvZzB1agT9+49m4ECpi3FGRgYZGY+7DyiwWgsTHR2DUql8pDgef4/NCOHOuXOR\nREVFsWDBQtLSAoA2QFfUah12dj5ItUxLkpUVQK1adfDy8qZt2445TEgy/1zeSPEfO3aMYsWK4efn\nh0ajoWPHjmzYsCHHmI0bN9KtWzdASn5JSkr605pF/5XUr1+fkNIhLK22nCPfHmXb+zs4MuooLm4u\nlBtQFpVGMjyo7FQEf1CGX7b+QongElzeeIWijYtyen4EVpOV4O5lOb/0PJGrL2Kz2rh39j7bBu0A\nAYVr++Y4ZkBzfxIuSYperVUT+kUNljVewdkl57my6SrLGq/At7YvBUq541vTFydvJ3q804N1q9Yz\nbtw4Pv30Uz4e/DHnF14gamc0QggSoxKJ3hJDch5lE6xAvMmEl5fXG12rvu+/z/GICGp98gmeXbvy\n8fffM2TYMO7Z5W68+LjAwEMkRZ0P6SbQzmajokqFs4cHgUolH/GkeYs/UDcrC73BwGHgO6Qevx5A\nR6SnAxOSyedpSiOpy2igMfALcOyRDFHASr2eOmFhVKlShT+SW7duIXUCsAe0mM1ej7blzaZNm1i6\ndANpaV2Ij29M9+69szN5Q0NDEeIq0i3vHFbrOczmRkBNjMZ2zJs3D5CczRUqVMbObgtwCb1+Lx06\ntMZgMDBkyBDU6nlInQt2AZWxWqUey2q1CqXy8U1BoNEoUSqTkDwvRqzWq9hsLTCZPiI8/AQ///xi\nH5fMP4M3UvxxcXE5enF6e3sTFxf30jGxsbFvcth/BAqFgjXL1zB99PcE3ipOK7+3OH/mAgW9CmKM\nz+lkM9434ursyozvZrC9904ennuINcvCDP9ZHB5/BCc3J7b0DWecZgJLGiyjQGl3hBBEbY/OMc/l\nX65gemhi/5iDxEfGYyhgwJxoJnZ2HBu6bSIhKpGQwVXISMzg0MQjxB2No3fv3jnmCAwMZNlPyzn0\nwRGmuE5ncZVl9O/enyStlivPnONRlYqgEiUIDHzVjra5CQgIYNz48cz/6Sd69OhBn759uapWc+Op\nMWYgHEnR50cyCT1NGYuFO7GxBNtsuez5xQCFUkmmXo8D0hr4OFIXrmU8jijKTT4kFVYM6SaxT6lk\nmbMzpwMDGTxpEstWrfrDwxdr166N5FI+AZxGpbr4wmbsp0+fJj09EOmq+KJW+3LhwgVA+jy3bt1I\njRrJVKv2EG/vQpB9dVLQ6Z6UYd6+fTNdu1aiRo37jBkzgM8/Hw7ApEnjWbp0Lo6Od3FwKIC9/RY+\n/vgjPD096dOnD/ny3cHBYQ0ODkuoXLkUgwf3R62ejlI5GY3GDsmFriEjoyAxMTF/6LWS+XN4I+fu\n7/1BPGuDymu/MWPGZP9dp04d6tSp8yai/SUolUpatWpFq1atsrcN6DOATj074VneA6+KXsQdu82x\n8SdYu2QttWvX5sSRE/y48EcCKwYR4BeATqejeKvi1KtXjx/n/8jaTWtwUjhTsHohdn22h/R4o2Tj\nX3KexLNJHD10lAnfTWBr6+0ULlyY9avW07BhQ76d8i1fjf+Kla1Wk5WUhb2zjnc6vpOn0m7SpAlR\nYVEkJCTg5OSERqOhSaMmNA8L46zNhqvRyB2DAeHqyu7nmCDeFC8vL9Zs2ECH1q1xMBpxsNmIfnRN\nNRoNTbJyR9qbkXImEiyWXO0SEwAvDw8WLltGp3bt0N68yQePtoO0ljWRs7m7FbgBhD76/75CgX9g\nIGciI//UWPUiRYqwa1c4I0eOxWrNYtSoDZQqVeq54ytXroxePw+jsSRgxGK5laPUdK1atTh4cBcg\nNV1v1uwt1OqzmEx3mDv3h+xxLi4uzJs3O89jdOjQgbCwME6cOIGHh0e2PB4eHly8eJbw8HAMBgNN\nmzZFrVbz5ZejuHPnDmXKVMBsPgbo0enOERY26c0vkMxz2fsoq/2NeZOssSNHjojGjRtn///NN9+I\nCRMm5BjTt29fsXz58uz/g4KCxN27d3OMeUMx/nEsWLRAeHh7CL2TXnj5eomfFv/0ynPYbDYxduxY\n4VXYU+QrlE+ENQ0T9+/ff+E+27ZtE+07txetOrQSq1atEjab7ZWOmZaWJn766Sfx9ddfi19++UWY\nzeZXlvtVycjIEOvXrxdjxowRM2bMEJGRkWL8N9+IMvb2YvRTGbWjQQTrdKJTx46ikMEghj313ucg\niun14vvvvxdCCJGYmCiKFy0qyul0oiuId0G4KZUiEMSnj/YZBqI0CFcQYSDKGAzC081NREZG/unn\n/DpMnDhJ5M9fUHh7F82RTZ0XcXFxIjw8XERFRf3pckVERIjGjVuImjUbiC1btvzpx5PJyevqzjfS\nuGazWRQtWlRER0eLrKwsERwcnOuHs3nzZtGkSRMhhHSjCAkJyS3E/zPFL4QQVqtVJCQkCKvV+neL\n8q8jLS1NVChdWpTU60VHEJ1AlNLrRXCJEiI5OVkMGThQuNrbixpqtaipVAp3vV50bNs2R+mDpKQk\nMf6bb0TFUqVEpTJlxPhvvhFvt2kj7BQK4QLCDoSTSiVcHB1Fx3btxJw5c0RqaurfeNYyMq/O6+rO\nNw7n3Lp1a3Y4Z8+ePRk+fDhz584FyE5QeRz5YzAYWLhwIRUq5Ezw/zeGc8r8uRiNRhYuXMiqn39G\nCEH7d9/lvffeyy57HBkZydq1a7FYLDRv3jw7V+JlJCYmsn37dh48eECJEiWoU6eO3OtV5l+L3HNX\nRkZG5j+G3HNXRkZGRuZ3ISt+GRkZmf8YsuKXkZGR+Y8hK34ZGRmZ/xiy4peRkZH5jyErfhkZGZn/\nGLLil5GRkfmPISt+GRkZmf8YsuKXkZGR+Y8hK34ZGRmZ/xiy4peRkZH5jyErfhkZGZn/GLLil5GR\nkfmPISt+GRkZmf8YsuKXkZGR+Y8hK34ZGRmZ/xiy4peRkZH5jyErfhkZGZn/GLLil5GRkfmPISt+\nGRkZmf8YsuKXkZGR+Y8hK34ZGRmZ/xivrfgTEhJo2LAhgYGBNGrUiKSkpFxjbt26Rd26dSlVqhSl\nS5dm+vTpbySsjIyMjMyb89qKf8KECTRs2JArV65Qv359JkyYkGuMRqNhypQpXLhwgaNHjzJz5kwu\nXrz4RgL/E9m7d+/fLcIbIcv/9yLL//fxb5b9TXhtxb9x40a6desGQLdu3fjll19yjfH09KRcuXIA\nODg4UKJECW7fvv26h/zH8m//8sjy/73I8v99/JtlfxNeW/Hfu3cPDw8PADw8PLh3794Lx9+4cYPT\np08TEhLyuoeUkZGRkfkDUL/ozYYNG3L37t1c28eNG5fjf4VCgUKheO48aWlptGvXjmnTpuHg4PCa\nosrIyMjI/CGI1yQoKEjcuXNHCCHE7du3RVBQUJ7jTCaTaNSokZgyZcpz5/L39xeA/JJf8kt+ya9X\nePn7+7+W/lYIIQSvwaeffoqbmxvDhg1jwoQJJCUl5XLwCiHo1q0bbm5uTJky5XUOIyMjIyPzB/Pa\nij8hIYEOHTpw8+ZN/Pz8WLVqFS4uLty+fZvevXuzefNmDh48SK1atShbtmy2KWj8+PGEhYX9oSch\nIyMjI/P7eW3FLyMjIyPz7+Rvydz9tyZ/hYeHU7x4cQICApg4cWKeYwYOHEhAQADBwcGcPn36L5bw\nxbxM/qVLlxIcHEzZsmWpUaMGZ8+e/RukfD6/5/oDHD9+HLVazbp16/5C6V7M75F97969lC9fntKl\nS1OnTp2/VsCX8DL5Hzx4QFhYGOXKlaN06dIsWrTorxfyObz33nt4eHhQpkyZ5475J/9uXyb/a/1u\nX8sz8IYMHTpUTJw4UQghxIQJE8SwYcNyjblz5444ffq0EEKI1NRUERgYKCIjI/9SOZ/GYrEIf39/\nER0dLUwmkwgODs4lz+bNm0WTJk2EEEIcPXpUhISE/B2i5snvkf/w4cMiKSlJCCHE1q1b/3XyPx5X\nt25d0axZM7Fmzf+1d/8gybVhHMd/wuPgEJH9T4XKbBBRh6IcCkJcJCKowaEgCIdosSEIGmqyoEla\nXGyJmt4GlwwiiqKEQMEgIiIiVBLCDKEgTnS9QzxST/659Xk7x17vzyae4duJ6yq5O/SPBKVfsbSn\nUinS6/UUjUaJiOj+/l6K1KxY+hcWFmhubo6I3tuVSiUJgiBF7heHh4cUDofJYDBkfb+c55aocH8p\ncyvJb/w/8eGv09NTdHR0oLW1FXK5HA6HA36//9M1H7+unp4ePD4+Fny+QSws/RaLBdXV1QDe+2Ox\nmBSpWbH0A8Dq6ipGR0dRX18vQWV2LO2bm5sYGRmBWq0GANTV1UmRmhVLf3NzM9LpNAAgnU6jtrYW\nv37l/Wtx0fT19aGmpibn++U8t0Dh/lLmVpLF/xMf/orH49BoNJnXarUa8Xi84DXlsjxZ+j/y+Xyw\n2+1ipDFhvf9+vx9TU1MAkPfZEjGxtF9dXeHh4QEDAwPo6urC+vq62Jk5sfQ7nU6cn5+jpaUFJpMJ\nHo9H7MySlfPcFot1br/tR/L/7eEv1iVCf5yVl8vyKaZjf38fa2trOD4+/sai4rD0u1wuLC8vQyaT\ngYi+fC+kwtIuCALC4TD29vbw/PwMi8WC3t5e6HQ6EQrzY+l3u90wm804ODjA9fU1bDYbIpEIqqqq\nRCj8e+U6t8UoZm6/bfHv7u7mfK+xsRGJRAJNTU24u7tDQ0ND1usEQcDIyAjGxsYwPDz8XalMVCoV\notFo5nU0Gs18LM91TSwWg0qlEq0xH5Z+ADg7O4PT6cTOzk7ej5diY+kPhUJwOBwA3g8bA4EA5HI5\nhoaGRG39E0u7RqNBXV0dFAoFFAoF+vv7EYlEymLxs/SfnJxgfn4eAKDVatHW1obLy0t0dXWJ2lqK\ncp5bVkXP7X92AlGE2dlZWl5eJiKipaWlrIe7b29vND4+Ti6XS+y8rARBoPb2drq5uaGXl5eCh7vB\nYLCsDolY+m9vb0mr1VIwGJSoMjeW/o8mJiZoa2tLxMLcWNovLi7IarXS6+srPT09kcFgoPPzc4mK\nP2Ppn5mZocXFRSIiSiQSpFKpKJlMSpGb1c3NDdPhbrnN7W/5+kuZW0kWfzKZJKvVSjqdjmw2G6VS\nKSIiisfjZLfbiYjo6OiIZDIZmUwmMpvNZDabKRAISJGbsb29TZ2dnaTVasntdhMRkdfrJa/Xm7lm\nenqatFotGY1GCoVCUqVmVah/cnKSlEpl5n53d3dLmfsFy/3/rZwWPxFb+8rKCun1ejIYDOTxeKRK\nzapQ//39PQ0ODpLRaCSDwUAbGxtS5n7icDioubmZ5HI5qdVq8vl8P2puC/WXMrf8AS6O47gKw//1\nIsdxXIXhi5/jOK7C8MXPcRxXYfji5ziOqzB88XMcx1UYvvg5juMqDF/8HMdxFYYvfo7juArzL98l\nk/FERcC4AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x46a7f10>"
       ]
      }
     ],
     "prompt_number": 202
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def autoNorm(dataSet):\n",
      "    minVals = dataSet.min(0)\n",
      "    maxVals = dataSet.max(0)\n",
      "    ranges = maxVals-minVals\n",
      "    m = shape(dataSet)[0]\n",
      "    normDataSet = zeros(shape(dataSet))\n",
      "    minVals = tile(minVals, (m,1))\n",
      "    maxVals = tile(maxVals, (m,1))\n",
      "    return (dataSet-minVals)/(maxVals-minVals)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 197
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mt = autoNorm(mt)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 199
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from os import listdir"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 203
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def createDataSet():\n",
      "    dataSet = [[1, 1, 'yes'],\n",
      "    [1, 1, 'yes'],\n",
      "    [1, 0, 'no'],\n",
      "    [0, 1, 'no'],\n",
      "    [0, 1, 'no']]\n",
      "    labels = ['no surfacing','flippers']\n",
      "    return dataSet, labels"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 455
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from math import log\n",
      "def calcShannonEnt(dataSet):\n",
      "    labelCnt = {}\n",
      "    entropy = 0.0\n",
      "    sumCnt = len(dataSet)\n",
      "    for la in dataSet:\n",
      "        labelCnt[la[-1]] = labelCnt.get(la[-1],0)+1\n",
      "    for key,cnt in labelCnt.iteritems():\n",
      "        entropy += -cnt/float(sumCnt)*log(cnt/float(sumCnt),2)\n",
      "    return entropy"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 491
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dataset,label = createDataSet()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 514
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "calcShannonEnt(dataset)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 494,
       "text": [
        "0.9709505944546686"
       ]
      }
     ],
     "prompt_number": 494
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def delFeature(dataSet, index, value):\n",
      "    retData = []\n",
      "    for data in dataSet:\n",
      "        if data[index]==value:\n",
      "            reducedData=data[:index]\n",
      "            reducedData.extend(data[index+1:])\n",
      "            retData.append(reducedData)\n",
      "    return retData"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 440
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def chooseBestFeatureToSplit(dataSet):\n",
      "    baseEntropy = calcShannonEnt(dataSet)\n",
      "    numFeature = len(dataSet[0])-1\n",
      "    maxGI = 0.0\n",
      "    for i in range(numFeature):\n",
      "        featureList = set([data[i] for data in dataSet])\n",
      "        sumEntropy = 0.0\n",
      "        for fec in featureList:\n",
      "            resData = delFeature(dataSet,i,fec)\n",
      "            prob=len(resData)/float(len(dataSet))\n",
      "            sumEntropy += prob*(calcShannonEnt(resData))\n",
      "        if(baseEntropy - sumEntropy > maxGI):\n",
      "            maxGI = baseEntropy - sumEntropy\n",
      "            bestFeature = i\n",
      "    return bestFeature"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 498
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def majorCnt(classList):\n",
      "    count_values={}\n",
      "    for cat in classList:\n",
      "        count_values[cat] = count_values.get(cat,0)+1\n",
      "    sorted_values = count_values.sorted(count_values.iteritems(), key=lambda x:x[1], reverse=True)\n",
      "    return sorted_Values[0][0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 499
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def set_trace():\n",
      "    from IPython.core.debugger import Pdb\n",
      "    Pdb(color_scheme='Linux').set_trace(sys._getframe().f_back)\n",
      "\n",
      "def debug(f, *args, **kwargs):\n",
      "    from IPython.core.debugger import Pdb\n",
      "    pdb = Pdb(color_scheme='Linux')\n",
      "    return pdb.runcall(f, *args, **kwargs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 478
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "chooseBestFeatureToSplit(dataset)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 501,
       "text": [
        "0"
       ]
      }
     ],
     "prompt_number": 501
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def createTree(dataSet, labels):\n",
      "    classList = [ex[-1] for ex in dataSet]\n",
      "    if len(set(classList))==1:\n",
      "        return classList[0]\n",
      "    if len(dataSet[0])==1:\n",
      "        return majorCnt(classList)\n",
      "    bestFeat = chooseBestFeatureToSplit(dataSet)\n",
      "    bestFeatLabel = labels[bestFeat]\n",
      "    tree = {bestFeatLabel:{}}\n",
      "    del(labels[bestFeat])\n",
      "    fecValue = [ex[bestFeat] for ex in dataSet]\n",
      "    uniqueVal = set(fecValue)\n",
      "    for val in uniqueVal:\n",
      "        subLabel = labels[:]\n",
      "        tree[bestFeatLabel][val] = createTree(delFeature(dataSet, bestFeat,val), subLabel)\n",
      "    return tree"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 500
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tree = createTree(dataset, label[:])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 524
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#####tree displayer###"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "decisionNode = dict(boxstyle=\"sawtooth\", fc=\"0.8\")\n",
      "leafNode = dict(boxstyle=\"round4\", fc=\"0.8\")\n",
      "arrow_args = dict(arrowstyle=\"<-\")\n",
      "\n",
      "def plotNode(nodeTxt, centerPt, parentPt, nodeType):\n",
      "    createPlot.ax1.annotate(nodeTxt, xy=parentPt,xycoords='axes fraction',\n",
      "                            xytext=centerPt, textcoords='axes fraction',\n",
      "                            va=\"center\", ha=\"center\", bbox=nodeType, arrowprops=arrow_args)\n",
      "def createPlot():\n",
      "    fig = plt.figure(1, facecolor='white')\n",
      "    fig.clf()\n",
      "    createPlot.ax1 = plt.subplot(111, frameon=False)\n",
      "    plotNode('a decision node', (0.5, 0.1), (0.1, 0.5), decisionNode)\n",
      "    plotNode('a leaf node', (0.8, 0.1), (0.3, 0.8), leafNode)\n",
      "    plt.show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 517
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "createPlot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Bwejbty/Wrl2LFi1aGDokMgGs3E0Qq3jzwo6S1Bys3E0cq3jzIZfLER4ejvLycuzZswdt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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x40ee610>"
       ]
      }
     ],
     "prompt_number": 518
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def retrieveTree(i):\n",
      "    listOfTrees =[{'no surfacing': {0: 'no', 1: {'flippers': \\\n",
      "    {0: 'no', 1: 'yes'}}}},\n",
      "    {'no surfacing': {0: 'no', 1: {'flippers': \\\n",
      "    {0: {'head': {0: 'no', 1: 'yes'}}, 1: 'no'}}}}\n",
      "    ]\n",
      "    return listOfTrees[i]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 519
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plotMidText(cntrPt, parentPt, txtString):\n",
      "    xMid = (parentPt[0]-cntrPt[0])/2.0 + cntrPt[0]\n",
      "    yMid = (parentPt[1]-cntrPt[1])/2.0 + cntrPt[1]\n",
      "    createPlot.ax1.text(xMid, yMid, txtString, va=\"center\", ha=\"center\", rotation=30)\n",
      "\n",
      "def plotTree(myTree, parentPt, nodeTxt):#if the first key tells you what feat was split on\n",
      "    numLeafs = getNumLeafs(myTree)  #this determines the x width of this tree\n",
      "    depth = getTreeDepth(myTree)\n",
      "    firstStr = myTree.keys()[0]     #the text label for this node should be this\n",
      "    cntrPt = (plotTree.xOff + (1.0 + float(numLeafs))/2.0/plotTree.totalW, plotTree.yOff)\n",
      "    plotMidText(cntrPt, parentPt, nodeTxt)\n",
      "    plotNode(firstStr, cntrPt, parentPt, decisionNode)\n",
      "    secondDict = myTree[firstStr]\n",
      "    plotTree.yOff = plotTree.yOff - 1.0/plotTree.totalD\n",
      "    for key in secondDict.keys():\n",
      "        if type(secondDict[key]).__name__=='dict':#test to see if the nodes are dictonaires, if not they are leaf nodes   \n",
      "            plotTree(secondDict[key],cntrPt,str(key))        #recursion\n",
      "        else:   #it's a leaf node print the leaf node\n",
      "            plotTree.xOff = plotTree.xOff + 1.0/plotTree.totalW\n",
      "            plotNode(secondDict[key], (plotTree.xOff, plotTree.yOff), cntrPt, leafNode)\n",
      "            plotMidText((plotTree.xOff, plotTree.yOff), cntrPt, str(key))\n",
      "    plotTree.yOff = plotTree.yOff + 1.0/plotTree.totalD"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 520
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def getNumLeafs(myTree):\n",
      "    numLeafs = 0\n",
      "    firstStr = myTree.keys()[0]\n",
      "    secondDict = myTree[firstStr]\n",
      "    for key in secondDict.keys():\n",
      "        if type(secondDict[key]).__name__=='dict':#test to see if the nodes are dictonaires, if not they are leaf nodes\n",
      "            numLeafs += getNumLeafs(secondDict[key])\n",
      "        else:   numLeafs +=1\n",
      "    return numLeafs\n",
      "\n",
      "def getTreeDepth(myTree):\n",
      "    maxDepth = 0\n",
      "    firstStr = myTree.keys()[0]\n",
      "    secondDict = myTree[firstStr]\n",
      "    for key in secondDict.keys():\n",
      "        if type(secondDict[key]).__name__=='dict':#test to see if the nodes are dictonaires, if not they are leaf nodes\n",
      "            thisDepth = 1 + getTreeDepth(secondDict[key])\n",
      "        else:   thisDepth = 1\n",
      "        if thisDepth > maxDepth: maxDepth = thisDepth\n",
      "    return maxDepth"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 521
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def createPlot(inTree):\n",
      "    fig = plt.figure(1, facecolor='white')\n",
      "    fig.clf()\n",
      "    axprops = dict(xticks=[], yticks=[])\n",
      "    createPlot.ax1 = plt.subplot(111, frameon=False, **axprops)    #no ticks\n",
      "    #createPlot.ax1 = plt.subplot(111, frameon=False) #ticks for demo puropses \n",
      "    plotTree.totalW = float(getNumLeafs(inTree))\n",
      "    plotTree.totalD = float(getTreeDepth(inTree))\n",
      "    plotTree.xOff = -0.5/plotTree.totalW; plotTree.yOff = 1.0;\n",
      "    plotTree(inTree, (0.5,1.0), '')\n",
      "    plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 522
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "createPlot(tree)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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bxUng/o+0XWEK0nSrMAncW0nbFcYmTbeKksC9PWm7wtik6VZBErh/T9quMCZpulWMBO7d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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x41303d0>"
       ]
      }
     ],
     "prompt_number": 527
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def classify(tree, label, testVec):\n",
      "    firstStr = tree.keys()[0]\n",
      "    secTree = tree[firstStr]\n",
      "    val = testVec[label.index(firstStr)]\n",
      "    for key in secTree.keys():\n",
      "        if val == key:\n",
      "            if type(secTree[key]).__name__ == 'dict':\n",
      "                classLabel = classify(secTree[key], label, testVec)\n",
      "            else:\n",
      "                classLabel = secTree[key]\n",
      "    return classLabel"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 535
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "classify(tree, label, [1,1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 537,
       "text": [
        "'yes'"
       ]
      }
     ],
     "prompt_number": 537
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#example of  lenses"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 538
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fr = open('machinelearninginaction/Ch03/lenses.txt')\n",
      "lenses = [rec.strip().split('\\t') for rec in fr.readlines()]\n",
      "lensesLabels=['age', 'prescript', 'astigmatic', 'tearRate']\n",
      "lenseTree = createTree(lenses, lensesLabels[:])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 545
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "createPlot(lenseTree)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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JYefOnQQFBbF79240Gg2mpqa8//77rFq1CgsLC2PvSr4g/q/yZm1tzbFjx7h9+zZz584l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       "text": [
        "<matplotlib.figure.Figure at 0x412f110>"
       ]
      }
     ],
     "prompt_number": 547
    }
   ],
   "metadata": {}
  }
 ]
}