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/ 练习题-scikit-learn.ipynb

练习题-scikit-learn.ipynb @96fc089

96fc089
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E333D1B1D4604A1B9579241D78188A1A",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "scikit-learn 是基于 Python 语言的机器学习工具简单高效的数据挖掘和数据分析工具。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8706D955DC1B4FCF8375BC1211F3B00B",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## 一、获取数据\n",
    "\n",
    "1.导入sklearn的数据集模块  \n",
    "2.导入预置的手写数字数据集  \n",
    "3.生成数据用于聚类,100个样本,2个特征,5个类  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "id": "9889EC4A0B56451B8F68CDCA1AA94DFE",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "id": "5245D3AD5A5940918F5EB4CAC6020006",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "digits = datasets.load_digits()\n",
    "\n",
    "plt.matshow(digits.images[0])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "id": "8ABF44102D694501A3A158F72B923C4C",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data, label = datasets.make_blobs(n_samples=100, n_features=2, centers=5)\n",
    "\n",
    "plt.scatter(data[:, 0], data[:, 1], c=label)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "500852AD3E8441E9891E9702B65D7EA5",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## 二、数据预处理\n",
    "\n",
    "4.导入sklearn的预处理模块  \n",
    "5.计算一组数据的平均值和标准差(scaler)  \n",
    "6.使用上一题的scaler标准化现有数据  \n",
    "7.用最小最大规范化对数据进行线性变换,变换到[0,1]区间  \n",
    "8.用L2正则化对数据进行变换 \n",
    "9.对现有数据进行one-hot编码  \n",
    "10.给定阈值,将特征转换为0/1  \n",
    "11.对现有数据进行标签编码  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "id": "B240AFA95F954E3BBB56BC07B931C3C8",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "id": "1E0AF8E831F545DF8CC4A38FE210FB81",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.25 0.75]\n",
      "[0.6875 0.6875]\n"
     ]
    }
   ],
   "source": [
    "data = [[0, 0], [1, 0], [-1, 1], [1, 2]]\n",
    "\n",
    "scalerstd = preprocessing.StandardScaler().fit(data)\n",
    "\n",
    "print(scalerstd.mean_)\n",
    "print(scalerstd.var_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "id": "FFFE0C9C32394F6484807454DB82FCCC",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.30151134, -0.90453403],\n",
       "       [ 0.90453403, -0.90453403],\n",
       "       [-1.50755672,  0.30151134],\n",
       "       [ 0.90453403,  1.50755672]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scalerstd.transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "id": "861EE07038D148DE81E35C95FEABDF71",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.5, 0. ],\n",
       "       [1. , 0. ],\n",
       "       [0. , 0.5],\n",
       "       [1. , 1. ]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scalermm = preprocessing.MinMaxScaler(feature_range=(0, 1)).fit(data)\n",
    "scalermm.transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "id": "622480736EDA4C408994B73658561E2B",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.40824829, -0.40824829,  0.81649658],\n",
       "       [ 1.        ,  0.        ,  0.        ],\n",
       "       [ 0.        ,  0.70710678, -0.70710678]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = [[ 1., -1.,  2.],\r\n",
    "    [ 2.,  0.,  0.],\r\n",
    "    [ 0.,  1., -1.]]\r\n",
    "    \r\n",
    "scalernorm = preprocessing.Normalizer(norm='l2').fit(X)\r\n",
    "scalernorm.transform(X)\r\n",
    "\r\n",
    "# 方法二\r\n",
    "# preprocessing.normalize(X, norm='l2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "id": "CB791245CDC34331831B65766D6E92BC",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 1., 0., 0., 0., 0., 0., 1.],\n",
       "       [0., 1., 0., 1., 0., 1., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 1., 0., 1., 0., 0.],\n",
       "       [0., 1., 1., 0., 0., 0., 0., 1., 0.]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]]\r\n",
    "\r\n",
    "scaleronehot = preprocessing.OneHotEncoder().fit(data)\r\n",
    "scaleronehot.transform(data).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "id": "5D94FE6955324B80986A17DA71963F5F",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0],\n",
       "       [1, 0],\n",
       "       [0, 1],\n",
       "       [1, 1]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [[0, 0], [1, 0], [-1, 1], [1, 2]]\r\n",
    "\r\n",
    "scalerbin = preprocessing.Binarizer(threshold=0.5)\r\n",
    "scalerbin.transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "id": "11FA1765E2B14178866C9463154125D4",
    "jupyter": {},
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 1])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scalerlabel = preprocessing.LabelEncoder()\r\n",
    "scalerlabel.fit([\"paris\", \"paris\", \"tokyo\", \"amsterdam\"])\r\n",
    "scalerlabel.transform([\"tokyo\", \"tokyo\", \"paris\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F062FBAA818444A5B623219CD627A093",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## 三、数据及拆分\n",
    "12.将现有数据划分为训练集和测试集,测试集数量占比为30%  \n",
    "13.将现有数据划分为3折"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "id": "E29143D0ECCA47A99E8850A4D90C9C90",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506, 13)\n",
      "(354, 13) (152, 13) (354,) (152,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import model_selection\n",
    "\n",
    "dataset = datasets.load_boston()\n",
    "print(dataset.data.shape)\n",
    "\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(\n",
    "    dataset['data'], dataset['target'], test_size=0.3)\n",
    "\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "id": "5AC25FD3A46541D78235DFAA55F78764",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['c' 'd' 'e' 'f'] ['a' 'b']\n",
      "['a' 'b' 'e' 'f'] ['c' 'd']\n",
      "['a' 'b' 'c' 'd'] ['e' 'f']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "X = np.array(['a', 'b', 'c', 'd','e','f'])\n",
    "\n",
    "kfold = model_selection.KFold(n_splits=3)\n",
    "\n",
    "for train, test in kfold.split(X):\n",
    "    print(\"%s %s\" % (X[train], X[test]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "885CA26CAD8F48598D4E0C170780F47C",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## 四、使用模型\n",
    "14.定义一个线性回归模型   \n",
    "15.导入预置的波士顿房价数据集   \n",
    "16.设置房价为Y,剩余参数为X,30%为测试集 \n",
    "17.用线性回归模型拟合波士顿房价数据集  \n",
    "18.用训练完的模型进行预测  \n",
    "19.输出线性回归模型的斜率和截距  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "id": "077D9C2AD9C844C18DB5A26A54D75E2F",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\r\n",
    "model = LinearRegression(fit_intercept=True, normalize=False,\r\n",
    "    copy_X=True, n_jobs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "id": "C04B3C81C8CF477E92CBDA50BF7720E3",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X, y = datasets.load_boston(return_X_y=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "id": "99ABDB9E01704C5F8A65A03034CAC3F7",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "id": "4801CCD8008B4B1082FF6F3BEA74C7F1",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "id": "4A5A4DC52B4D441EB6B9BA502F42B750",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([26.25992442,  9.33883098, 21.75140576, 22.37604127, 20.35694029,\n",
       "       33.60313065, 32.74481597, 22.42294065, 19.77760437, -1.16132012,\n",
       "       17.42166101, 23.90283624, 22.91482041, 33.33912062, 24.90583361,\n",
       "       27.07570535, 22.50942758, 24.27950142, 23.14308679, 20.4519883 ,\n",
       "       21.18500858, 22.71856646, 28.96456539, 15.1816301 , 16.11395526,\n",
       "        4.45492848,  7.63407352, 23.21888697, 30.06713783, 15.66697326,\n",
       "        1.91385942, 19.5744595 , 18.36044805, 14.97085203, 33.07583387,\n",
       "       28.99710169, 17.08602777, 17.68206796, 24.38433921, 31.84395163,\n",
       "       27.17738463, 14.21843726, 19.80308011, 28.59901009, 24.97776751,\n",
       "       26.02398851, 16.91294041, 17.42587988,  0.55902561, 25.3388113 ,\n",
       "       36.41874456, 23.59401747, 17.72416277, 16.14813729, 28.00989384,\n",
       "       17.35799279, 36.41708025, 29.18561542, 23.79300249,  6.29263901,\n",
       "       17.60820019, 19.95362871, 31.42198875,  9.8463655 , 19.82505449,\n",
       "       17.07264779, 12.38176939, 12.07088755, 42.85010741, 13.27478279,\n",
       "       20.85344993,  9.39413037, 19.5308154 , 23.65314683, 32.08224811,\n",
       "       12.20150518, 19.13611999, 16.6004374 , 18.60276918, 28.50904817,\n",
       "       31.17134321, 14.49184249, 27.78355914, 21.24917749, 11.6699463 ,\n",
       "       24.73315594, 40.34475157, 33.52395684, 33.1660618 , 17.62539932,\n",
       "       10.1149138 ,  7.79037579, 33.96163483, 16.47708501, 15.76467167,\n",
       "       11.54067456, 16.43648691, 18.23904667, 16.19126795, 23.06735198,\n",
       "       19.08841561, 25.01201027, 24.98775488, 32.97720347, 41.44918068,\n",
       "       18.26663105, 22.87562075, 21.57163237,  4.03271866, 25.31185036,\n",
       "       12.13010727, 11.04373587, 24.544449  , 21.49494578, 28.42720974,\n",
       "       18.51370177, 24.11617214, 28.86911973, 21.84982351, 12.08741406,\n",
       "       29.43700453, 21.69498476, 18.06102892, 36.21763431, 14.22686119,\n",
       "       21.24394032, 17.04974228, 31.65802364, 26.97624501, 30.14965158,\n",
       "       39.8483785 , 14.42263502, 40.58493873, 18.32148322,  1.91991319,\n",
       "       29.94117969, 38.72774404, 19.90188405, 32.37801611, 32.66018266,\n",
       "       13.80732092, 31.3866927 , 34.15759749, 21.36491441, 17.51973851,\n",
       "       33.93425209, 11.09472335,  7.96496583, 21.28349987, 20.00715547,\n",
       "       30.40979304, 25.68242696])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "id": "A93B3F93D495430ABDF41FA407561204",
    "jupyter": {},
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-5.00288043e-02  3.56568764e-02  6.93465436e-02  2.16769447e+00\n",
      " -1.52079455e+01  4.15011712e+00 -2.37994210e-03 -1.13384381e+00\n",
      "  2.83383718e-01 -1.69703242e-02 -8.63184202e-01  7.58941210e-03\n",
      " -5.24753110e-01]\n",
      "31.871833311681854\n"
     ]
    }
   ],
   "source": [
    "print(model.coef_)\n",
    "print(model.intercept_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "37B27817E12741BDB1AA639CC07B8088",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## 五、评估\n",
    "20.使用随机森林对鸢尾花数据集进行预测,`n_estimators=100`  \n",
    "21.给产生的随机森林模型打分  \n",
    "22.输出模型的recision_score、recall_score、f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "id": "016B68FB4EF7412C8456F4D522CB6F1E",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 0, 1, 2, 2, 2, 1, 1, 0, 1, 0, 0, 0, 1, 2, 0, 0, 1, 0, 0, 0,\n",
       "       2, 2, 1, 0, 0, 2, 2, 0, 1, 2, 1, 0, 2, 0, 1, 2, 2, 0, 0, 0, 2, 0,\n",
       "       2])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "X,y = datasets.load_iris(return_X_y=True)\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.3)\n",
    "\n",
    "clf = RandomForestClassifier(n_estimators=100)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = clf.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "id": "2CB5AA05EC6348D88A363DDD34D2B176",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9866666666666667"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "id": "7CCF270A009F471581177C46E68E4FCD",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        20\n",
      "           1       0.90      0.90      0.90        10\n",
      "           2       0.93      0.93      0.93        15\n",
      "\n",
      "    accuracy                           0.96        45\n",
      "   macro avg       0.94      0.94      0.94        45\n",
      "weighted avg       0.96      0.96      0.96        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8023E1F89F43481980E3B333503C76B2",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "23.使用手写数字数据集,随机森林算法进行分类,画出学习曲线  \n",
    "24.使用手写数字数据集,随机森林算法进行分类,参数n_estimators选择范围为`[10,20,40,80,160,250]`,画出验证曲线  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "id": "046D87F540BE48778048ED45828E2D25",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import learning_curve\n",
    "\n",
    "X,y = datasets.load_digits(return_X_y=True)\n",
    "\n",
    "train_sizes,train_score,test_score = learning_curve(RandomForestClassifier(n_estimators = 10),X,y,train_sizes=[0.1,0.2,0.4,0.6,0.8,1],scoring='accuracy')\n",
    "\n",
    "train_error =  1- np.mean(train_score,axis=1)\n",
    "test_error = 1- np.mean(test_score,axis=1)\n",
    "\n",
    "plt.plot(train_sizes,train_error,'o-',color = 'r',label = 'training')\n",
    "plt.plot(train_sizes,test_error,'o-',color = 'g',label = 'testing')\n",
    "plt.legend(loc='best')\n",
    "plt.xlabel('traing examples')\n",
    "plt.ylabel('error')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "id": "A8E8F1E69E36407D8365E0A5CE732DE9",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import validation_curve\n",
    "\n",
    "X,y = datasets.load_digits(return_X_y=True)\n",
    "param_range = [10,20,40,80,160,250]\n",
    "\n",
    "train_score,test_score = validation_curve(RandomForestClassifier(),X,y,param_name='n_estimators',param_range=param_range,cv=10,scoring='accuracy')\n",
    "\n",
    "train_score =  np.mean(train_score,axis=1)\n",
    "test_score = np.mean(test_score,axis=1)\n",
    "\n",
    "plt.plot(param_range,train_score,'o-',color = 'r',label = 'training')\n",
    "plt.plot(param_range,test_score,'o-',color = 'g',label = 'testing')\n",
    "plt.legend(loc='best')\n",
    "plt.xlabel('number of tree')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EDA1626B8090472AB76AFE0CBB545DB7",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "25.使用网格搜索,得手写数字数据集+随机森林算法的最优参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "id": "DB020D40C8574D308949F89EE66663DC",
    "jupyter": {},
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                        criterion='gini', max_depth=None, max_features='auto',\n",
       "                        max_leaf_nodes=None, max_samples=None,\n",
       "                        min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                        min_samples_leaf=1, min_samples_split=3,\n",
       "                        min_weight_fraction_leaf=0.0, n_estimators=50,\n",
       "                        n_jobs=None, oob_score=False, random_state=None,\n",
       "                        verbose=0, warm_start=False),\n",
       " 0.9360290931600124,\n",
       " {'min_samples_split': 3, 'n_estimators': 50})"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "X,y = datasets.load_digits(return_X_y=True)\n",
    "# 随机森林的参数\n",
    "tree_param_grid={'min_samples_split':[3,6,9],'n_estimators':[10,50,100]}\n",
    "grid=GridSearchCV(RandomForestClassifier(),param_grid=tree_param_grid,cv=5)\n",
    "grid.fit(X,y)\n",
    "grid.best_estimator_,grid.best_score_,grid.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CB2F58C77FA44AFDA2F7D7E045B516F4",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "26.取鸢尾花数据集中,类型=0,1的数据,变成一个二分类问题,使用线性内核的SVM进行拟合   \n",
    "27.计算出对应的ROC曲线  \n",
    "28.计算出对应的AUC  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "id": "0A9E96C860CE40068C4938E14F53BF78",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',\n",
       "    max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n",
       "    verbose=False)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "X,y = datasets.load_iris(return_X_y=True)\n",
    "X, y = X[y != 2], y[y != 2]\n",
    "\n",
    "# 加一些噪音,不然模型太准了\n",
    "random_state = np.random.RandomState(0)\n",
    "n_samples, n_features = X.shape\n",
    "X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]\n",
    "\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=.3,random_state=0)\n",
    "\n",
    "svm = svm.SVC(kernel='linear', probability=True)\n",
    "\n",
    "svm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "id": "098C8F50FDA8465FA08E43D5526DDC6A",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe66c07ecc0>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "y_score = svm.decision_function(X_test)\n",
    "\n",
    "fpr,tpr,threshold = roc_curve(y_test, y_score)\n",
    "\n",
    "plt.plot(fpr, tpr, color='darkorange')\n",
    "plt.plot([0, 1], [0, 1], color='navy', linestyle='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "id": "5D0D8247535B4D3584C84CD003433EB1",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8133333333333334"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc = auc(fpr,tpr) \n",
    "roc_auc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F810D46A536D4579B0E014FFBDB860AB",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## 六、降维\n",
    "\n",
    "29.生成一组数据,10000个样本,3个特征,4个簇  \n",
    "30.对数据进行pca同纬度数量的投影,展示投影后的三个维度的分布  \n",
    "31.将3维数据降到2维  \n",
    "32.指定降维后主成分方差的比例(99%)进行降维  \n",
    "33.使用MLE算法,自动选择降维维度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "id": "B7661C237D0C4B468F61D7F0E3E6A7B8",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/matplotlib/collections.py:874: RuntimeWarning: invalid value encountered in sqrt\n",
      "  scale = np.sqrt(self._sizes) * dpi / 72.0 * self._factor\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7fe6368bc710>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\r\n",
    "from sklearn.decomposition import PCA\r\n",
    "\r\n",
    "X, y = datasets.make_blobs(n_samples=10000, n_features=3, centers=[[3,3, 3], [0,0,0], [1,1,1], [2,2,2]], cluster_std=[0.2, 0.1, 0.2, 0.2], \r\n",
    "                  random_state =9)\r\n",
    "\r\n",
    "fig = plt.figure()\r\n",
    "ax = Axes3D(fig, rect=[0, 0, 1, 1], elev=30, azim=20)\r\n",
    "plt.scatter(X[:, 0], X[:, 1], X[:, 2],marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "id": "3B348279CBB841EC8ECB0737A577446D",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.98318212 0.00850037 0.00831751]\n",
      "[3.78521638 0.03272613 0.03202212]\n"
     ]
    }
   ],
   "source": [
    "pca = PCA(n_components=3)\r\n",
    "pca.fit(X)\r\n",
    "print (pca.explained_variance_ratio_)\r\n",
    "print (pca.explained_variance_)\r\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "id": "EFE74CE1C3154A739946A32603F71D0A",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.98318212 0.00850037]\n",
      "[3.78521638 0.03272613]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca = PCA(n_components=2)\r\n",
    "pca.fit(X)\r\n",
    "print (pca.explained_variance_ratio_)\r\n",
    "print (pca.explained_variance_)\r\n",
    "\r\n",
    "X_new = pca.transform(X)\r\n",
    "plt.scatter(X_new[:, 0], X_new[:, 1],marker='o')\r\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "id": "605CED38E1F047CA84D407BBD2CB4CCF",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.98318212 0.00850037]\n",
      "[3.78521638 0.03272613]\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "pca = PCA(n_components=0.99)\r\n",
    "pca.fit(X)\r\n",
    "print (pca.explained_variance_ratio_)\r\n",
    "print (pca.explained_variance_)\r\n",
    "print (pca.n_components_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "id": "C800591C85FB419E9A0DCE6B5E4E6EE5",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.98318212]\n",
      "[3.78521638]\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "pca = PCA(n_components='mle')\r\n",
    "pca.fit(X)\r\n",
    "print (pca.explained_variance_ratio_)\r\n",
    "print (pca.explained_variance_)\r\n",
    "print (pca.n_components_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C321EB214D264FFB86F7BB344670E540",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "34.生成一组数据,10000个样本,3个特征,4个类别  \n",
    "35.对数据进行LDA降维"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "id": "BCBD89AAF2C4476387C7A821FEDA1A26",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7fe636396978>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X2, y2 = datasets.make_classification(n_samples=1000, n_features=3, n_redundant=0, n_classes=3, n_informative=2,\r\n",
    "                           n_clusters_per_class=1,class_sep =0.5, random_state =10)\r\n",
    "fig = plt.figure()\r\n",
    "ax = Axes3D(fig, rect=[0, 0, 1, 1], elev=30, azim=20)\r\n",
    "ax.scatter(X2[:, 0], X2[:, 1], X2[:, 2],marker='o',c=y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4OBXSBqQCum5SU5vmvgc/ZMr08L7RewMenjSNbMBklNF1Ply0nC2N0VpWjBh7A2JSiMDlvz6La247nw7d2qEmVfoP68WBx+xX0FWg5wzLeWyYFjk0QQotMtgo4Y4CEKa0ruEhBl03eeRfH1Gxdouvf98+nXjsP1dx6slD6bVPB2uvZwXf3LJZnWde+AwpJW+Pmcv51z3M8ef/gyt/8hTTZ69qyUz3WKzesjWkkAGkEiobaut2+XxixPi6QOxpGa4jRoyQM2fO3OXXffS213jjkXGFO6hq3kmcSOQT2aL6qeFwUwmQSljnBIS3r583XFXY/whoVVbM4CE9GTiwG+ecN4LOndu4522squOK6x8jG6HptCtrxWWXHcFjL0z0hbAWpRL87dbzOeTA3oXveQ/Gb98ew9vzFmEEvv/FiQSf/fxGWhftefkcMWI0BSHELCnliOb6xZpCCyClpFvvjs11yr82Cm+BKQOy3t0vRxGWltEcSfvCTIVTDYP6ugwzZ6zitVenc82Vj7By5Ub3lA4dSkl6wjm9GDigK0+9PNmf0wBkczqPPj+x6bnswfj+0YdRnEz4FLKSZIKrjjgkJoQYezXiMIsIVK6u4r9/f4d5k5bQuWcHStuVMmvCosKRQI6Qdu33TqmLwKYHinD9CZ5Wu+wF+fpHQglpCxIrVyEYsur2MEEKK49Byxk88M8x/POBKwBIqArXXTWKhx8f7xP+xUUJLr5gJJ/fNTryOZRXbG7qMe12zC2v5K/vfMLidRtpX1rCNceN4LKjh4eecRT6dmzPy1dfwt8+nsjstZV0aFXCdUeN4MKDh+6CmceI8fVFTAoBVK6u4ubj7iDdkME0JBvW2IJREYioUEVHADkrcSEsoQ7h0FIlnGvgH0rkucSUoOTrJElByOwUOl9KK6wVWDB/LVLmndrnnnkw7cpKeOaFyVRtrmPQgG5cf/UoBvTvSiKhktPC2k3Pbl/fxLBFX27k2kdfI2NHXm2oqee+9yexpSHNj047qkVjDOzSiccuPW9nTjNGjD0OMSkE8MJf33YJwQdTWkIWwqWuVQ8hBD3E3mEMM9+3KZimRSBOBKoiw0ltBeAQQzKZCK2Yjz92MMcfG94j4vLvHMZzr00N+RSu/+6xLbrmtmBrfZqXxs9hxuI19OhYxuUnH8Lg3qEaiM3iPx9NcQnBQUbTeebTWVx3wkhyhkFtOkOPdm1RW/jsYsSIEZNCCPMnLw0TQhDSI6S9Dubm4DgAAmahghvqePs2GeaKr4+QklGjwsI/m9Wo+LKa9u1L6dC+1G2/4oIjSCUTPP/6NGrq0nTvWsbNVx/P4Qfv27L7aiE21TRw6Z3PU9eYJacbzF1RycefL+POa0/nxIMHNj+ABwsqNkS2Z3Wdix58gfItNSRUQXEqya1nncgZwwbtiFuIEeMbj72OFHRN5/k7R/POwx+Srs8w5Kj9+OG9V7GvHWXTqUf7vMmoELzOXl9egR1HJApoA3bFVF/pbK9ZSAir/IUQVulr5z32+0IBrUEFRQh+ePNJvi6vvTmTJ57+FCEEum5w6CF9ufU3Z9GqVRFCCC45dySXnDsSwzBRmzFTbS8e/980ahoybsVTU0oyms6dz3/McQf1D63o3/98CY99OI2NNfUM7d2Nn5x1DPvvY2kVSgGSNCWs3FiNFFa+X0Yz+L/RH9KtrA0H9+mxU+5rd2J13RaeXz6L1XVbOLJLHy7qN5w2qeLdPa0YezD2Or36nmsf4vX73qVuSz16TmfuhIX89Lg/sKHc2rzn4p99K5y4lkogVCUvdyP2PLBe23+GEY4kcgS3KRGGaXVUhY8QvP8LZ2znEzIjxvJlv+UPH3/C/pS1K2XFyo3cftdbXHjZv3no4XGk0xqN6Rw5zWDGrFX8+e/vhZ7PziIEgEnzV0WWwK5tzDB5wWr3/dqqrfzjjQn88cUxrFi/mbp0lilLyrn0nhd4aeIcAEpTydA4eY3Jw5ESsprOExN3fRjzV8H6xjpeXT6f91YvJq1HVOAFJm9YzZljHue5ZTMZX7mcexd8wukfPMqmTFxOPMb2Y6/SFKoqNjPpjemhMtdaRuf1+9/j7B+cRs9+Xbju9gt48vbRdnKYwSEnDmHOZ8vIpXN5Qe8kqRVyHjt+gahidorIl7dINJ0BbUk4gQKYugFJlSiNQToJbxKmTV/B/96fywP/GevmJsjAkLpuMmX6CrbWNNKurFWzz25HoKy0mHWba0Ptpin5zePvce8Pzuafb09k1YZqspqeN63ZPGUCfxk9gb5d2jOwe2dWVuW3O82b4fLjOpFdUsKX1eECgWCZm1Zv3krH0hI6tS6N7LOr8eC8yfxr/mRUobga0ZMnXsBhXXu5faSU/HraO6SN/Hc5Y+hszjTw74WTuO2Q03b5vGN8M7BXkcLaJetIFiVDpKBrOu899jEfPDcJJHTs0Z6/vvULSloX065zG351zr3WOV7hLaWlETTlOHZMQ97zVMUKSrLbZJTfIKhhOP0CZOAIQqn4JWFjY45//PU9zKR17WAQFACKwDAlS5at5/AR/Qrfww7E5Scfwp3PjyUTUaU1q+n84pF3aDS1fC1BZ74moDiKluSB/33G784/gTHzPKXMPeQRhKoKRu67T6j9hRlzuGfcJASgGSaH99mHwd07887CJZhS8u399+PmY4+gdVFReNCdhFlVX/Lv+VPIGgaQjwi7dtzrzLzoZopU6ydb2VhLdS68i54mTT6qWBKTQoztxl5lPuo5oBtaNloV13IG2cYc2XSOdSs28Luz/krbDq0Z+/JUypdUhk9wqpk2uQ+zE5pq97GFt0sI3mNe4nBCUz0+CyGEJdhN8n4JBaTT7g7ogUe4ukM77fZ1Z8zadeUszjhsMN8+fP/IY1JCOqNFP06HGGyUV23lwN7dOXq/Pnnfgpvw4YcAWqWSXHvsSF/7J8tX8fePJ9KY02jIaWQNg4nl5Tw+dSaVtXVsqKvn+VlzufiZl9GaSEbc0Xhl2TwyRpg0JZLPKsvd9yWJZCgb20FpcteRWIxvHvYqUujapzMjThtOqiRsjw6u+DXN4KpDbuGJP77exGb30u9TCB0ObL7j1QSk45SONh0JkTeMS6xMaKmI/LkB5SKSGJqyStn9qjbVYZqSWXPLefP92cxbWNHE/W4/pJQsXrORo4fuS0IN2Hic+4zKC/SNYf3f295Y58GrzuGq4w6lTXFRuLMNVRHcc9G36VrW2tf+2GczSAdCWqX0cQ85w+DL2lrGLVvZ/A3uIKR1LbImE0DGYypqX9SKEZ16kRD+n3CJmuR7A5qtZBAjRkHsVeYjgFue/xGP3/Jf3n9iPLl0lvbd27NlY10opj+nmaBnrDdBZzAUzm5WAn103SIcZ69mr5bQBJzjUuTHFM57J9IpIBC8i+WWiPVkQmHIAT258kdPstEmB0UR7Nu7E/fecdEO28+5omorN//zDTbVNKAqAmlYwtq70hUCzKio3MD/xakEP/720fb8VX7+rWO5/sTDOPpPD2GY4btWFYXhEVt2bqwPOGMLEGhjTmPOukpOG7xtIbPbizP77s/YiuU0BpzLmmlydLe+vrZ/HnkO35vwX9Y2bEVBoJkG3+59AJcOOGSXzDXGNxN7HSmkilP88N6r+ME/rsQ0JXPGf8Edlz1ApiEb6OmREqZEqvlw0bw5KMKBXMhpHJXL4JCN47QOmpAUERrTywlNQQDSkKASyIuwPQzCcvA++dJkGgO7zC1btZFHn/uUn95wctMXaQGklNz8zzeoqKrxaSAqgkRCIaEoqKpCxtAxpOnLAne72+87t2vFjacczj4dy9xs7cacxq2vfYTh5JYEHv/BvXvkNQkbmmEwondPvtxag+4QSYGI35Jkgt7tyr7SM9gWnNxrAMd078ukytU06hqqECQVldtGnkxZkT/UtFNxa9477Xrmb6mkMl3L0Pbd6Vm66+Ya45uJvY4UHAghUFXBwScOYcBBfVg2e7W7BWeqOEXO9JpkJBimvTMaVvE6B46zubldzAR+LUH1E4AX0vm3QFKc5VuQSCUiES44kCn9RkKPuUY3TLQAIYDlX/lwwsIdQgqL12xkU01DyCRlmpIThvTnF5ccz8QvVvKPNz5Fy/nDVb2aj6pCVV0jd74+noQqKGvditsuPIl7P5jEqqot4Zu373Fk/7yDuT6b40//G8d785dgSBNpl5gyPecEXRNJVeXMA8KJgDsLihA8fPx5fLpuFWPWLKVNsogLBgxlv3adI/sLIRjWsQfD2LU5GFJKvqhZxerG9fRq1YVhZf1bVHMqxtcfey0pOFAUhbvf+TXvPPoxHz43EaEITr3iWB79w+v+jo4tP5kIr+oNI3IP5dD5DqJyAYLagAR0E1Lh6CaffJUuhdjzyo/nOJWlHb0DIKQdvgpWGe4C0PQd41ytbcigRhCmBNZtrqVtqyL6dfdUoA16xe35Gti3KkAzJZtqG/jRk2/bvhbPeZ5no6jw0CfTeHDcFDq1bkXb1iWs2LzZTfmQhnVep9YldG7bmpG99+HzdetYvKEKhKB/xw7cc/bpIU1jZ0MRguN79uP4nrsmKmxb0aBn+PWc/7AmvQEpJYoQ9CjuzD3Db6J1smR3Ty/GV8QOJQUhRBIYLaU8q8DxYuA1oBcwD/geUBRsk7t4k4dUcYrhxx1AaVkruvTqyPDjD2DiO7NZNCPgYAxmMXsRZQJy4CS1ObflNek0tVNbU09BEQgz30UAMpkfK+RvNvNk4D8QjWEHhEM4tweDenUmXSDia0XlJk765SNceuJwWpcUhWoZARaZ2UI/Qu7nI5Mc7vQ45w0Ti1gFVNU3srG+EamSJ0i7X1VjmkZDZ011DZpp8KNjj+Si4UPpULpr8jf2NDyy4i1WNVSiyfznVd64nn8vH81v9r98N84sxo7ADiMFIUQJMA3Yr4lu3wUqpJRnCiHeBU4Beke07bKNcrWczh2X3s/cTxZZYZ+KoF2XtvzqsRu57bIHaahN2ytUS+AWVJF124wU5YAOrZQLGLADEIDUTEgqnhbnP+EGL0nPofAgEaYpiS/ZLerc73/vuGbn1xL8543P8tnZznykRArI6iaoJk+NnZk3awXm4tMCbPh87Y42gf98l0AcLckhFcOT3yHyRNmQyxPXfyZO44g+vUKkUNXQwEvz5rF802aG9+jBBUOH0GYX5jA0BVNKPtuwkjX11exX1oURnXrtNHPO+A2f+wgBQJcGn2ycw68HXxabkfZw7DBSkFKmgWFCiIhd7F2cCDh2mXHACUCfiLZdRgqvP/A+cz9Z5PoTADKNWZ689WX67t+DxTNXWaGKElq1LSGT0TAjolwQwlqaKopfiDmRR14Y0rLjeJ3WUb4BR+LrElRhOYmDeyp4icGUrq8iLzgDJOX0VSMksH3Jww/dl4H9tr1yaRD1jVnenbwQw1vawr5fIUEmAs1RJNVS+bKtcqiJ0N2srvPirLm0LSni+c/n8mVtLf07duD5uXPQTZOsYfDxipU8PG06b15xOd3btGF3YnOmgUsnPMvGdB26aaIKwcCyzjxz3HcpTez4DYN0GW1a1KSOKU3UQrW/YuwR2NV5Ch0Bp95ALdChQJsPQogbhBAzhRAzq6qqduiE/vfEeB8hAEhTsmDyUhbNXIWuGRi6gTQMtHTWIoQo65Y3UU1R8n+RpiZpEYN7Qasst2NlAtzzXP+0R1sJQYLAMidFwRlXkOcioQSS3uy/Th1a88dfnR090DZiU00DCdt/IgJ/Lrwre2f+3sdrBt4H4DtUiBi20RgpgeVbqjn76Rd4YfZcPl6+ksemzaQho5G1fS1pXac6neavn3y6bYPvBPx+1nusra+mQc+RNXUaDY1FWzdw7/zxO+V6I9pHV5xVEHxSNXenXDPGrsOuJoVNgBMzV2a/j2rzQUr5qJRyhJRyROfO0VEY24L6rQ3868dPcUH3G9iwagNSNyITtvSslncwS0kuqyPNCMkb1AZk8M8/tmXGKCDBo/wWzj4OBUJerUtIt28hOGaTZFKlqCiZn4sHQ/brscPyE7p1aIMZMR+JnW8ReGTuC5sIfKG3nshRt8nxH6j+PqHrCc+w3m98gf7FSZUVW7eQ0XU3l8Lt6vnYDCkZv3JV9CC7CLppMqFyObo/EUROAAAgAElEQVT0f59ypsFb5fMLnmdKk4kbl3LHvDe5b9EHrKjbWLBvEGd0PyJ6TCQfVE5r8Tgxvp7Y1aTwMXCq/fpEYHyBtp0GQzf46ag/8sGT46ndXI90Vv5eYhACVNXKS/BWO02oeXupUwwvSAiOX8H5cxzQUXBMO86fo1kE+7fERqvYAs8OQw3IUt8nrQCKx8/hXcFPmrGc/46e3vz1WoDioiRXnj6S4pTfSinwzJU8WeUjpzwOdEdL8vy5pOL8LwiRh5tsLjzjObUEvdY0ExJCIOyxixMqfTq0jywhISJUkaKo3fh2IUwkZgF2CxKFA0Oa/HTmf/n15y8zeu0snl85mcsmPcwba2ZF9pdSkjZymPZ47VJtKFGjfSkmBRY7MfYY7DRSEELsK4S4J9D8AtBTCDEP2IJFCFFtOw1T3/ucqorNaBFF2bymHxFYqUcK6qgCdmE1wR+Z5HVYa2aePFqCpvraY+blqrQuJbH/cTtyzlmH8Jdbv0NxcTI0Y90weerFz1ixuuUrx0LYXNNA53atOfnQ/ejWsQ0lRUlGDOrF/115srXbWlDGethJOBPzNGN6bkXgOpB9ZicvcaiePoHoJDxkohtuSh8JofCHU0/AjNIII9A6laQuG0x83HVIKSqHduwVepQJoXBSj3DMx+KaSr4z4UE+3bCEtGGZTQ0kWVPj7gXvUKulff0/2biACz67m9PG38rpE27j0eUfMKBNTxIRfoNiJcVp3Q7bYfcWY/dghy9zpJQD7P9XAb8MHMsCZwZOiWrbaVgxt5x0faZwh4IRRh7zTKRdX3ryD0S43IWnoqq0r0PCsyQ2TesvIhHOraTqZigTnoPHyWy9J9/XE4GDgGRKpWf3dtx07fE88Ni4/P7M9umabvDhhEX84KrtdzaPnjCPf7w43s5REJim5PdXn0q/nh157L2p1NRlrHk5wr0Q7LlLTz/h1QK8/TyC3tUgPI+4UBST91E25DRufOlN9u/RmfmVG8IaQ2CMdXV1/Ojdd3n6/PObfiDbiAWb1/P8ktlszjZySq+BnLPvAW6F1CD+POJMLhj3FDlDJ21otFKTtE0V89uD/MmHFY3VXPXZE6TNbORXWJMGYyu/4Du9rdpJs7Ys544FL5I1rcisRiPLK2smkjU1fj/kSm6b/wQmJjlTp1hJcVC7AZzYxV9iw5AGn1ZN45ONUxBCcGKXozm600gUsauNFDFair0uea37vl0oKS0iHShrIRSBkkwUVn59ykDEat2rOThCPahtmCZSUSzyUCC/JPZGC5l4d26zzB6K3VXkI4o84wunydmtLTBdAtN45bUZvDp6Jocc0sfKzg4ICCkl+leoDFqxcSv3vjg+TzY27nhyDDIpyBmGW9PPjToqZCFTAv4GkRf43lOk5/hXRUNO49z996eippZNDfny1NJ1dNgQVk2i6WsrqKyro2vr1kypWEN5TQ37d+rM8K7dtis88+Vl87ht+kfkTMMKNa0s59nFn/Pa6ZdTnAgXc+zbpgPjv3UTb5XPZ3ntJoa27863ew2hJND32RWfkTXt/TUi1jYSeGn1NJcUnlz5kUsIDjKmxpsVU7m+/2k8e8TvmbBxNltz9RzSfj+GtfNnNUsp+fvih1hYu5SsaWklK+pXM7N6Hj/b7/ptfi4xdg32OlI49vzDeey3/yWTzln+BBuJpMqI04cz/aP5vnYXSsCi7Kz8RZAAIgjBge+XGBbGAsLXtgkhRACF3BROJ6/d3LNiFlKi5QxQBVOnrbSK0Cl4iAyKUkmOP2r79zQeO2NJZHE6TTcsF41Ho3HMQjJgjXB8AN6VvCv4m8hdkBK/hmRzdbCOkjteBEzgsakz2VxnE4JbD9E6WZoyb47CKoWxbPNmvvvmq2xoqLed64IDu3TlmXO+EynIC6FBy/HH6R/5ymc36horarbw+ooFXD7o4Mjz2iSL+e6AkZHHHCysWWfVl6Kwm2p53QbSeo6SRIqKxlDMh3UugupcPd1LOnDePqMKX692KQtrl7mEAJA1c8yuns/y+tUMaN23yfnG2D3Y63S44lZF3D/xdgaP7O9r1zST6WPm5h3PDtws5KDUsn0FkJc6TdmhvTWuW7h6zAt4f39LkIZFmgSPmUg0HYkppaUVBQSsAE45bn+GDt7+Wjq6YUbmchScj6MB4IkuiuBWEXoRAU+imuuXcAeNeB/4BThktK6uLn+pZsJiDdPkibmzWFNbQ4OmkdZ10rrG3A2V3D9tShOTDWPOpnWhvaoB0obGu6sXh9rnb1nH9RNf5vj3/sX1E19m/pZ1Bcce3LY7qohgVA9MJDlbmxjQJvo7IISgU1HbZu4EvqhZStYM+1t002BhzdKIM2J8HbDXkQJA935dGXL0YBLFKcuprKoIReRzEIIRQYoSFgrOMS8khYnBIZdCeQ54fA3bCFeIqp5543fQhiBEpH8ioSq0Li36Slmpo4YPIJmMTmAKltqQgRctuqpXqAcRoUEgbQe1iT/c1dFApIcrIibgI5cAShIJvn/YYUyuWIMe+OyzhsGrixa05I5clCZTBfezCFZJnbaxnEvHP8uEymVUNNQwoXIZl45/lmkby1lVt5nfzHyT0z/8N9+f/CJztlRwZf+jKVKaNg4IBHV2yfjr+59KkeLXcoqVJFf2PZFkM+MAtE22JiXCWlJCUWmT/HpsfRojjL2SFABmfjgPQzPyws8r4AORQgDouj9k1dvPC9MTURSVO+AIjsCK1e2iiiZXpc6pzkmWdiCQUUX2Cp0rPEIxMH9NN3nvo20TZEHs17szF544nFQi71h3BW6U1mO/EMG2iLm7oaxR1rmmfBM2MQhnDHvB7CUH8JBHM5+BAIZ06cI/vnUG14w4tKAgzxXKR/HAME0+LF/GbVPG8llFOW1SxaHbKEkkuSJgOvrT7DFkDN2nBGUMnd/Pepfzxz/GO2vns7p+MxPWL+Oqic+yrLaKJ466mgPbFa5rpQpBW7uo3YKta2z/j7C/zoKzex7OpX1GuWaopnBUp5GRiwuB4PAO8Z4PX1fsdT4FBx26t6d80Ze+NtFUcTrADVltbhWt6ZBK5s/xEI8QAmmYlq/AFW4iX6rCkO6nIiDf15pgPhLJEbDO66YcDXj4x9mjoQnkogrTbSN+ctEo9u3RgT89aVUskSLsN2gS9oR9PoXmWKMpXvT4VZz3DiE4w7luB4mvZlLUUEVC5cz9BnHaQGvznaFdujJvw3oflyQUhVP79Q8P4EFG17n0g5dYvKWKRl2jSFVBQLuiEnKGgRDWDnA3DT2Co7v39Z27pCY6bLi8YbP79XKvY+jcMed9xp/+E54/5nr++sV7vFQeTjQb2bEfbZMlfLx+Lv9Y/Lbnnq3v6KtrP+XNLz9FIumQasPV+57OmT2jk9naJlvzm8E3cd/SR9GkAVJSpKb45aAfUKwWMX7jeCZsnIAmNQ7rcBindTuNEjWusrq7sdeSwgU/PYOFU5eSbbSdYM72mDRBDNtqUlHywtz7v+VQtnMXJFZoqjdqyDAhqebbnHBLbELw1D/ymoessE1/WIlvle7RJlweQYbu6/CD9922+wygriHDlro0OU13zUXuvcnw/AoJXmmH1XoFua9v0FTkKGdBZ7LnPLdPxBAior8b3up2tk7OGgbr6mrdc/920mlc+NpL5EyDjK5TkkhSVlzEb44q7IgFeGHJHBZu3ug6lrN21FepmuKxE79DrZZlZJd96Fgcrthaliphay4dao+ydgJszjZQo2W4fe6bjFu/KE+CzrMBtuTqmb+1nD/Me8n3VCz/voEq8omRW3J1PLjsTRBwZo9oYhhSNohHRvyNlfVrEELQr7Q3ilB4eMXDzN46m5zthP5f5f+YWT2TPx7wRxItME3F2HnYa5/+iFOGcc0dF/PUH15BURW0nEaiOEUmrYUE1w5B0GxiC2QphL+QneMgNkxr5zVXExAe4VRgbm7wvvT5JxzikEre/CXtNhLhsb51ytDtusVsTufupz9i7PSl1m5qmuYXyE75bqd0hBPN48ludibs8zVIfNE+BeGYfpoiEu9lmviIIyOePGiVTHJ4r17u+/06duKTK6/ltUVfsKJ6Cwd16cbZg/anVbLpyKM3ln/hizRykDUM2qaKOap7n4LnXrPf4Ty06DPSRqA0eQGlUUHw8JJxfLx+Ed4H7m4oCKyqr+LuL163s6S9bCgxpYIQ/jDjrKnx9MoxBUkBQBUqA9vkFxpfpr/k8+rP0WR+3prU2JTdxIzqGRzZ8ciCY8XY+dhrSQHg3JtO5fSrj6N84Ze069yW9l3LGP3gGJ69+y1MI1CeomBSWwFourUhT1SVVM+YgM9GIoXwJGnZwtNRYLzEEEFczkLWzQHwClKJVW3VtqdLVfiT3WwUpRKkktv3tfjbsx/z8Yxl5HTDKhsSEMguOTlvImz6zlwdIeWs3p1n4LZ5SMS3krdPEE57hNnIuYbvvRdBDQQPOdjPvG/7dpw8wG8aaldcwnUHj4gYsDCSSgGHPJJUgWMOvr//0VTnGnlqqb8siWEKFEWGIrdO6jGQV8pneFo8V/NYJVc3RJmloqLZrJpcm3M1mNJscULaivoVVt/AgFkzy6LaRTEp7GbstY5mB8Wtihg0oh9d+3QiVZzk9KuOo12XtqgFomcAv0Rqqk8LuvmSfTyE4PgLfA5M1wlaQJMJ+P5cgaoIUEEoeeIoFAVlSskBg8Ib3TeHxkyOMVMXk/X6IyLu3+UJZ/qKv93LeYXgCmhPWSrnZO84QoLw7J/gJY2WlujxPWXPnF685GKSwTDl7cDlg4eHkswAOpW0YmC7jhFn5KGbJnM2f+mLaXC+FqYp/PENwPj1i5v9SnYpKiOlFNZuHJOjIkxUIVGEJKFI/rrovxhmyxIey5JlRNWRSogEHVNN33OMnY+9WlOIwqv3v0/tlgYMPSA17PLWrhCXEnTd+hUW0gZMg7xRvRktw1mmBfq65iRvuy4h6ScGdzVrTS7vTHYjbQJahbNi99oOBHz/ylGUFG97ldS6hqyvyJ53Ku4EvXNswn3jEkNERVPffYrAn/dkb1/p7+O2G0T/AjxjeKfvjFWUUCkt2v5Ksp99Wc6dU8ezbOtmOpeUMqisE4uqN4KwwjVTisJjJ32noGZaUb8VzTT5y9yP+HxTPi8hTwzWeaaJqzEIIGPqJB01KgKtEkn+cNA5jKmcxf/WfY70UYhEIDERJDDdOAf3njbN4/WKHpy3zyiW168hKRL0a71PpPYwtGwoxWoxWTPru4YqVEZ1btoHE2PnY68mBcMw+XL5ekpKi+i8j7VCmfbBXPSoYnkQvfJ3yCFoO3Z8Bd5+3l+Rtwy2CJwXeW3pdvUJOu/UbLuKb5oREVWu0LX3KEZIFFXhonNHcOHZ22b+cNCxXSnJhErG8+wElrPYt5mOd14F7iPS7OSMFax06nt20XNzTUnBtsD1g4QVaZsX8P0jms4cbgrTKtdy7ZjRrh+hsqGOrdk0Vww+hN5lZXQqKeWkXv0pjqi+urJ2Mz+cOJo19dVIJDmpB9QuGfqaed9L26cTuiUBbVNJNFPnB9OfQBUmQjjfN2lrYBLV9klFVXDPmhqvrh3P6IoPMLEWUKWJEm4dcgP9W/fy9VWFyu8G/44Hlz/I+sx6FKFQopZwY78baZ9qvx1PNcaOxF5LCjM+nMs91z1MpiGLoZv0G9abW1/8CW07toHlG8InOFE/UXB+wE2VtohKzfWs0t1Q06ac3N5jpr3iE/aObPag3igjKcibmyL8B075C4DjjhzINZceHX3dFiChKtxw3pH844UJ+el679XReDzC3CENr5D3+gF8KOA8jerjJ0X7f4PoEFMPAbjk4RlASNz9GACuO+xQbjqqsFO1OdwzY2LIsZzWdV5eMo/Z37u5oI8ha+hc/NFzbMk22s9Rus/QM1uCxOCaloRJQpEEoQgTRZE0Gk7msUSXggRYxCCkZSZqgaG5RqunSM3fWyaX5ffzH+SZw+8MmaS6FHfhjqF3sCm7Cc3U6FrcNS6S9zXBXvkpVCyr5E8X/5OtG2vJNGTRshrLPl/Fj0bdxsr5a6JPSqiFZVJT+Q2FjONS+jQJYXgM5FGb8jjtrsB0dmrzE4JU83syCMdyK8Nj5s0uVt+JU5fz0/97qWASVktw0ckH07lDazdgyJmTz27juyEbhnWCz/YfgHsfDpyLBBLNfKYlp+SF89g8fUOO5+B1AmO2LU5x3eGHcM4B+6N8hci0ZVs3R7Zrpkl1Jhxe6mDcl8t9iWqFTG952Ct9IRHCLLA/kyX0/e3WG91m5+Dur/mR/RCAKsJHDGkwY3PhZMhORZ3oXtI9JoSvEfaaT6J6Yw0zPpjNynnlvPvoWPRABU/TlGzd1ECmLh0tyJtLbCuEqCWWI3S8wznZRpLI6+Tty9LX5gp+Z46FYN+ulDLSB24YJqvXbGb2vAKk2AIIIbj7h2dSUpwkmbCSsIrUgL0iSgiD32nc0uvhEfYRTmfvn+86zmuv5iA8XOAlJgXUpCBrGDw1czYXPv8St48dt93k2bcs2jySUATtiwsnblWl6/1lNGRTz8ulZFRV+r66QQIwTSWiMou9yJAeTcNzTDdVX7uKQkpJoIqwo9mQJrV6Q8H7ivH1wzfefCSl5PHfPs+b/3qfZFESQzdQEip6TsMXZhpMNPMiuOlO+CIB265zXstJRJoSoSoewVb4XDf7eRvzKaRDOI6GYGAlu9nzzGkG8xZWMGxoL3dv5W3FsAE9eOWuq3h9/BzK11dz8H77MG3JGibOWxVxI/n/JLapptDcwc1VCHKMBEtjSBLuEL5cfrzgxQLmJWm3mVJi2Ellhq7z4pz59O/YgcuHH7RtYcrALw49hhs+fIO0x4RUkkhww7CRBU1HAAd37unXUBxTme8rYNGam+geQQT5/tI9Jon+KhkShFRQMfwmKSBnKiSEaT9/k6Gte7OsYUXkLnBDywY0+1xifH0gvoq5YHdgxIgRcubMmS3uP+6/E7nvxkfIOPsnOETgFeKKgkgmEU7xu6D9XwhIJAoLAEXJ+xW8SCascwuVxrB1emnPCzsM1spM9msm7qeUUPLvPbq946CVyfC1JJaD0fUrODkR7pj5/kpSIZlSufjsEVx9ydHREUXbCCklz304i3++PtG+PpGCWwJmIvq46alXVPBchxQ8ex0F+wBu/oZLNCJPAJFO71BZb4kqBEft25vHzjuXpKqysnoL7y1bQs4wOLX/QA7s0jU8SRsfrl7GnVPHs6auhnZFxfzgoMO5YVh0nSAvrv/kVSavX+1JVsubExUBvUvbsVmrs49L23SUJwlVMV1C8F/KejKqEm5P2E7nhGK6Y1mPynRfW74KSUoxUFwHtYViJcV1/c/nlK5HIYSgXm+gKruJLkWdKU2Es7Rj7DwIIWZJKZuNJPnGk8JNh/2WpTNXWG8cW3vwxycEIpHIC/4oU1FCtUgjatnlxKs7xxXFJ7ALkoJnNS4FFongEfgCN+vX6i/8Al3g380NMD1JaS7Z4CGFCMKwBKPIk4eA4qIEl553GNdcsv3O5yDWbKzmsrteoDGjFRbszhaaHpgCS6f1mHoiz/VqChHk4Qp5Jf/eJYUCdY4k0kr0E/42gOJUgp8fcxTJpMrdkz7FkCamaZJSVS44YCh3nHByeEAPDNOMLJNtmCabMg2UpYp9ezHopskLyz7n7tkfkzPzu+V5g9hO2qcfEzYscz1O3o87qRr2VzIo/PN3licGmyiEiWKTS0IxA34Dq10R0lY2TYpVk+A3LKmonN3jeDRZz6RNU0kIFV0anNDlWK7oc3HsT9hFaCkpfOPNR/XVHntmFCGA32hayCSjG1ZopedHLBwCcGCa+UJ4QfLwtgW323T/sbthmZNs7531f5SnUCh21JJzFgjTCgdEVfJmpkLnO9eStpbhEYyZrM7Lb83gyguPRN1OU1IQvTq34/QRgxg9aUE4dNKZj20Pclf1jobg9cEE/DGusA8O5n3r1QaCXZpbF3l9FeRfZ3Sdv3wyEVSJIaQ7h7Rh8NyCuSzevIlHvn0O7Uv8vgIpJStrqpFS0r9dB9938tVl87hr1njSug5ILhowjFtHnkRKVdmQrmNWVUWeEELTlHxSuQJU6XwbfEdNKRCy6UgiX/1GQEqBUKwchTwhBB6+/SUK7Afl9tWlwfvrP6JEFWhSc8tbTKiaRPtkGWf3/FbhCcXY5fjGU/QRZx1KItUC7lMUkNK/Mg/C2UfZu7kOeDxuSpgAvMddAZ5v9627pLO+Iy/IpSxoSpJ4x8ubEizrmCUYFG+UU4RWWFyUJFGs5uP/PcjlDNIZLXTO9uKzBat5f8aSiPuw/7eFv1vSwhs95OyF4DlZes5FcW8/P2gw2shbHZ08ATVNCvZnYCcvBrJAMKW0qqUb+e7OJGZVruN7b73mc0p/sWkDo156jDNHP8vZbzzH0S8+wuyNVgLa+IoV/GHaR2zNZsgaurUfw/L53D59LFuzac794Cn+t2ZRU5O1qpEWuA/DVKL2ZvJB2oUhfcJd4gln9TOjxMqcNsEtlmdKJ2FeOj0Q6OSk/7uUM3O8v35s0xOKscvxjSeFS393HmWd25JqIks3WZxX0Zssn+145LxC3tk/oVBpSgemJaEcrcAKHxWejGNnAljagVsMT4RKUgQWYvly2N4xQl5D056yf5Lt27diQP9o+3ebNsWUttqO7ObGDI+/NYUr/vg8P753NFMXrAbgrc++IJ3VfPeQJ0DPlH3k5rktf/CN29clNOk73RX4EYFbYfNSRPRTXnux193OqjjK1BTxSzKRrKiuZkGVVUuoQctx6bsvs7auhrSu0ahrrKuv44r3XqUmm+GBuZ/5HNBglbx+bcUCnls6iwY953fk+ubQMjNwc9Zi5+voVWZdF1whDz6QVExPH2s38YSSNy3lZALNDD+kBqMx1BZj92KHkIIQolgI8a4QYq4Q4jkRYaMRQhwvhJhk/60VQlwphDhdCFHhad/+jYELoF3nMh6b9w++e+v5DDioD4qajyRSFEFRqyIu+e25FLVKWatB0ywcbujclTcaSVX9W3U6pGEYfo3CdmILIWyTiEfoC1uom6ZHAnp+YhIrFCS4zIsqXwH5Utrkha4QItQugU3V9Zx76nCKAtpUUSrBD7533DZH19Q3Zrn8D8/z1HvTWVy+kSnzV/Orf73Nc+/PQDMCq1hJiBRdS1jQUua8MAONwdW+/dr9CBWLgF3B5mgh+IlF+E/P/ykejUwJHPROLGLOYG1aU1FbA8AHq5aGdmcDK2zz7eWLKK/bGh7AxrSNa/xJb56vjncihXMobBNQRAKbc1wRZuCrbGkMphnUj/wQSBK+fAeLEPzuOYEhFcxAavm+rfo0MXKM3YEdpSl8F6iQUh4EtAdOCXaQUk6QUh4jpTwGmAfMtg895LRLKZcEz9sRaNO+NZf+7js8NOtvPDTjLxx51qG07diGsi5ljDr/CE797ijOuPI4q7O98o8khigns/BIHE/imNvHyDsEXRSw77vX98Cyl3v6t2BXMHe63kHs95YZJt+g6ybzFqzl3tsv5MDBPWldWkT/vp257RdncsaJ215C+7Vxc9lS20DOkweSyek88sZkjhvWr8AEC008Ah6BHHoMQYGtEqqg6pCmiHqOIv/n+jKQvqin0McWZJMANNPkgM5dAKhqbCAXJEasjOa/zfyUrdlM5C1nDZ02iaIW/FiFm4cSRtNfGlWYJAJfX6fulmmHX4XHla5G4DUbNQXddBY7giIlxff6XtLMPcXY1dhRjuYTgdft1+OAE4APozoKIVoBA6SU84QQPYDzhRDnAGuBC+RODodKlaSYN3EJ2XQWLasz/uUpTHxjOtLrfTMMfy2jYFimaeaL4AWDwaOEfaGIpmA3sMo+2BLGMi9F5E/k7RqhMVxzTNS4an617GRCm6ZkXeVWhu2/D//5y2VNzq8lmDRvJVktLPhUVaF7+zaoqoJhmKE5+j70ZnY9c7UB7+Z10qMsOOfZ9xw1hnse5JP5ghpA0KwXON+dh7CupSQESOkqM8WJBCfv258+Ze0AGNGtJylVRQ8WWwRqtaydM2JfI3DvU9etbUFhVxMlsjajdAPdTKmAafp3n0UWTKkRtuNFuHOSvvPCmoegMAFZzNox1QGBJGemeXTlY5zW9VRO6LLtWmmMnYMdRQodgRr7dS3QlBnoFOBj+/UK4FYp5XtCiMnAccCEHTSnSDz8q+doqGl0NQFd09E1HaEqfjMQ5MkgIoTVheNPaA6OFGmOGOx55YVO+Bzpe+Gs+j3VVFuSW+CRir17dWi+f3PD2fPu1K7UL3FtGdGQ1XhnykISCAzhO+Q6ln1zI3/rXjOPTzgbIBMBESTAiXD0nVdo3k4/4e/a3MrEZ7WSlvA8unsfupaVMm7VSkqSSb43bDjXDD/UPefQrj05rPs+TKtca0cXQUpR0TExXIdFdLho2jRQhcBoqZrogSO4HTOOKRUUm2KEAGWbxhSowvCtcRTh/wCDJiIHUkK7VDs0s4G00QiYpI16nlvzDJM3f8Z1/a7h8+rp5Mwsw8oOYd/S/jFR7AbsKFLYBJTZr8vs94VwFjDafr0FcMIPVgNdok4QQtwA3ADQu3fvrzTROeMXRpqGpGH6fQVOSIUgL/ib0gb8M/a/NYy8ZuGMG+EPAECxf6Jex7HjpwhoG8KUloZjz1EgkInmNZL8TVtjfj53Dfc9MpbBA7px4jGDKCpqercwL6prG7nn2XFMmLkcCXTt2Dr8COzXH0xfnDe1OY/ZWc0HV+IGBXdQK6hVBEnDGT/Q7o4R7O/p29R6NzQHe6y5Gyq5uMuBTLrqekoidlwTQvDYaefx0qJ5vLR4HqaUDOzQkQ/XLiNtNK0H5AwdEp6ruppRXuCLiNpD3uPeyeqGgqrYwQeBNY5/zWP5GvLkJ4NfwwjOFeimcCOWnK+9IgQj2g9kavVU8JASwIqG5fx+/i0UKRIDk+kqtZ4AACAASURBVHEbxzCi/ZFc0efamBh2MXZI8poQ4hrgcCnljUKI94D7pJShWDPbAb0cGCKlzAgh7gKWAs8Bc4BLpJQLm7rWtiavBXFhjxup2VQXfTDpyVpOJNxvrLCP2feQ7+8IeidRTXiWqSHtAjfr2c1YDmoitkO6yYxmJ4PZIRVPP4m9cg78aqWw21R7Io7pRZCPdAJKipO0bVPCI/d8lw7tSws/RBuGaXLxb56msqoW3Ylucq4ZWG64GclgbzOav6+ocFh33oFx3PGVfB/LnxM9hgn5pY/I28WlkyTnFYDOC8UzL8enAIRIyb62BNdMlVAEB3XrxisXXtIiYbamdisnv/GEuzezW/3U+zUT1k4GUjUj+N7SV5SEdBPTfEQnZESmcv7chGplIauhwnjWcctEZOTzMzFJKn7zU9LWHIIQmCRV0yXYhw/5Ew+t/A+rG1bacwtfL+UZK6UU8cP+P2Nw2yFRk4+xjWhp8tqOcjS/APQUQszDWv2vEELcE9FvJPCFlNLxqD0IXA1MA95ojhB2BM688WSKSvxhlsmiJIMPH+CPKMKzkLTzBUI/cueH7NhAonIUHEisZasjD72rf7BW/G4GdHi1776zHdGFxI1b3ygIh0S8q+NApm46o7FpSz0PP/tJgdH9mDJ3NZu3NriE4MwruMwwnes7F2+pszyijzu+Z0UvPI8+dLrnG+7rE+GzcN/aclUY4NTNdj9ib+egNiRAl5I569cze31lC24Qerdtx8X7DaOVk7ksISGsIoetkylaJZIUJxKYSiFNwl64CN9bF00VzctXTxWYOOZH6f45PgNFEUhbnTBR0OyCeA4MKSKuI20FVmBKwcDSfela0pFuxV398408y0LOzDJjy5RCNxBjJ2GHmI+klFngzEDzLyP6TQfO9ryvBI7fEXNoKS6/5TzKF37J9Pdnk0glMDSDwYcN4KATh7ByYSVaNgcikPksAcO0ZHhUdq9j+JayaZr1mqAcMxKmr35RU2tLn8B1x/GfJIIb17vXjtL3ZV6zsWEYJhOnLIOfNDERG6vWbfZFGbkIOncDz8Q16xRwJtszs+D087ZHCEBh5rUHgYeIlMA52wpTuolxrpml0HxsGFIyqbycQ7r3aHLo8tpqZm1Yxym9BnB4t178d/FcGnWNc/rtz0m9+7N06ya6tWrDTRNfp7EhOjIJpM+H4tFtrWMIT2S0tHM0pa1k+vdecG/PNRFJ97W3h4m1D3RClfZ7a9Gkevwhwr6e8zn+fsgPATij2+lM3zIdUxbSLvzf3FqtJtwpxk7FN77MRRCJZII/vPxTVi5Yw+dj59N/WB8OPnEo9//4acvh7AvLCHxrTROpFCiV0RwCq39nDBnwL7i+4yYuIZ1yoraglzJQn8eUeZ+E8+fVStz7Eba5wn+xlpa16NezI8mE6tMUgmSUF5phzcd1MEcQRF4zcsxfgYED53iJJspf4B/UM9Go5+xtCxbh8zBzFEk4qMvl2JJupDSVokj1/8yklPx20hjeWL6QhLAWCm1TKV7+9iX0aZsvrd27jRW1NLJLbypW1WLaqWuhKYuoN941tzVpKa1oM0XJm5Ss9Y4VneRkJTcfNyHQpYLqSePWUUgqesAimp/Bhswm2iZL6VPamyv7fI+ny58KjClRIpzsrRLNmzFj7FjsdaQA8Op97/Hs7a+iJlT0nM7Qowdx7AVHUlxalK+mWgim9FcbjTIpRe3Z3JSg9dZTAqvuUUQEkYy4pimkWzk1L/Q9pGGbIizhapOH3VcRIA2Qap4YUkmV005omQ136IDu5PSmN2svEIjiIz9XoAv8AtY513Y649EEpH1PPq3E61SOIgUvETjneqSsjOjqhUtk3oYCZqhXFy/g2QWzEQJO7z+Qnxx2JH3L2iOEYPTyL3hrxWKyhkHW3ugirWlc/9GbfHj+1aHr/ujAoxlTsYQGLUehNLKg5dLlsMAq3yqLJV2hLfCSQL5Pc+ueMN9aZiKnSqp0nNL28QeWPcP9B/+eL9PrOaDtAdwy+BaeXv00lZlKFKEgpIES2I9BQWHf0v5NTyTGDsdeRwpT3pnFs7e/RrYx57bNn7gYJaHQuWcH1q+uQtOMJoyeHntG5AY6gV+Ua8cvIOQ98C1GA8SQJwSvUJRuuW2fFkLYxivsQaQnhNbpo5igFqskVIW+vTpy3XePib73AEZ/PBdVuPv3eOZZ+Jz8cUsaOxE/ToSKlIRX3rZCIz1C3X0GEtfZ7LWoRV/YM6RNiI5QD/oKXAKQwhet5guRLaDRSaAmm7E0OgFvLl/E2ysW07esHf869SyeXTiHtO6vA2QiKa/dSnlttU9bWFJdxfQNa/nZgaN4Z80XzNm8LmD+s1/Zvg9DgqKEI4R818pbLN1NeNxjUkERhfwX+es6kUvCMxfN3nvBOz3sMhfrMuv53rSfkFSsDO6eJd34+X4/o12qDEPq3DL/ZzQGSnyklBQjOxzZzFxi7GjsdaTwyj/eIdvo1wa0nM7cCQt5dPbfGPPcRMa+NJktVfXRA9iF83y+AQj/Ah0iEAGJ5/m1CqcAn/MrDYabSmyzkN3uMTO5QrFQaCtEC62Ivsmkyv6DerB09UYWrljPZTc9QSKpUteQZUCfznz/ilEMHRS2j0+cvZJcIBFLIV+JwkcQ7r1HzMP7CD2+AQnu3gcuMTg37iWAQHRQXqDjCnjfpbznR638Q1O098H2EoJzHVvjUIQgqagoiqDRyLmE4MCUkpVbq7nkzZfpUha9j4AqhJu/YErJLz57l/fLl9jHFLLkvHyK/41zgzKc9xFCftEQVa3W9LjGTHcN4d9LIWzqEaQUI7D+scux26astKlh2L6E8sa13LHwHu4/+M8klSS/GPR/PLzifmq0agBKE224od/NlCZaN3UjMXYC9jpS2LI+2nGVSCbQczrX3H4h19x+IQ/f8hIfPDuRjEej8Al5sIR5cHOdpgQ05KWVI2AUAaZVlz50rrSXfqmwOcq3WqWAvt+MCcB7mS+WfklOt2zWVVvq3fHmLKzgJ398hfv/eFGIGNq3KbBJikeZcskL8mW+C0VnBW9fafo44CcEr4ImmzABecZz+7nkFXEdr/YSOKZI2L9jZ+446STKiku45t3RlNd4vzP+/rppsG+bDqytryUbWBkXJRIMbNcRgLdXLeSDNUs99Y4kwruqt59l2L8lkLapEIKP2tl0J3+zuqEGQkoFhlQxpVX2QiIwTdN1Kiu2lhAVThod1iowJSSEQVIx3DlJKanWtvBF7WIOLDuAniW9uGPI39mY3YDEpGtR9+3z3cX4yvjGV0kN4pCTh6ImwrUPFFWh58Bu7vsb77qYP/73ZpJFCXsFqdh7FPhXZW6VVNP0E0YzcENHHT+AXYzPPW7/LxSbPTxLWveVqhCl6edNIX7zU7Q1GjTNIKcZHget/x6yWZ2Hn/s0dN4lpx9CcaCQnvr/7J13vB1F+fC/s7un3Jubm94rIRBCJwRCEQJI703pLwqKoGIFGxZ+dhSVpigKFqSjAgZCFSEQegklhJCEhPRe772n7O68f8zO7mw7uUkugh/z5HNyz5mdmZ2ZnX2eebolQuvTUL6dd+OMcUthjDUDOeu+TFFSFrHY6FMwD/IGVxH+jtXVrExGNxJmr1zF43PeZVSvXuw2YGCDoHQqztHoHr0Z3r2VpkABXbAsmhyHqw46Jky6c+vMtIgpNrlNIPh6gumwFKqTVLwiaeZjUB8LnaNJZMY32thwClbcC1qJ4STPrHgeUOandy+4lStmXM6P3/ouv59zHatqKzs3ya3QpfA/RxTO+uZJNLc24RQiwlBqLnLhL87BKTjUKnUWzV7K6mXrWLN8PfsdMw7hOIgkQcgDnQMhgcgzQV+2AmWwjtKqr2vRkS9DfUDYZRB8T0Bg9y/D+0YRE3QOgOB2+mnLOGa1itZGidk7c5enyvbaaTgXfmx/SkWHbk1FSkVHWS6ZNNc8sYt8bJYaY3CkF/rTaHAaqfvRp5N0KIIs7iB3tHGoeh5/n65cbL6w176UHSe3XbPj8MLSBcxdvYZa3aN/uRunj9mVh0/5JBOHbhP057K4fV1qMNIPEHgjCm9o06XhP2AJH8fJcWIL6oU6HbGxSClxnZS6a96AZMMwGnPb5wNw7TtX8uTyx2j32qj5VV5Z8yI/eeu7QTiMrfCfhP858VHfIb254eUruOuXk3j5X2/Qf1gfPv7V49jlgB24/ReTuP3KSbiuh+sG7Lf2Vt6YnV4YbgJCragUjcmuNiMNBLuRaEMShqxEVfG1UlmZj8SQuCVRVkTBDxnaGsrIUgpD1g6YWMX3ZGbgOBMG9O2eWX76EePYfnh/Xn9nEQ88M535y9You3h926TZqL61qROAWEIdqduZEBAGrVMQgQllrrK3AVIT5vVGp24ZiWHyQF+tBbqAbXv15u+nnskVU5/kiQVz8WVE5Eu2jWv5TFu+BFdFPmRVRzsPzXmHyyYcBEDd9zjj4dtY0r4hMbBgoj5IKxAB+TLyzQj1C0rRHLXRokm9zdLzySrzpMARqr1EESO95QopJTOAoOYLijHv6eAdakAUylaReW1zmNf+bpiRTbX0qfoVnlkxhUMGHJHbfit0PfzPEQWAPoN7ceGV58TKHvzLk9z2838qqyRD6St9Cb6HKCR431T00vB4TkwOkWffJ4j7LoQy7iito8bgEiKT1hziFIwWiVCsuQDsyKdCghI1WWo8pvjFsSxq+HGiZIy5XHI4//T9UvdcunI9F//0bpavVkr5jkpduT7oMBFZAzWWybTzN33opMhftoxJ51/Lua7FVDrXQliY1ackMl01r0ui8NoCVtcqfOyO2/nd8Scwpk9fbjruZKqeyx+nvcydM15HStht4EAeee8d2uoR8vOkpK1eY9Lst/nYmJ2ZPO9tZqxeTj0WQj2+R2TwHDVhMFkpKxHSwoqJi6LvERFJlqn7SWSgS1AlPspSzBIS3xfqPrGFUroI13cp2EafRCLB5NKWrCIf6bsvf1/wNzo8F4lQCuxgLDW/xrz2d9kK/1n4nxMf5cEdv5gUM1NNgqy7SB3WIoxzZGzzvEDynpcSI4VIPma6mrhG6lK8fkLHYCI31Ud8fOHBOIFrLEvQ0lJKDC7qv1tzkS+cdwgT99k+NbVLf3kPC5auoaNSD9N2BofLXJwsjDoIYv4H4e0TS5vZCTT0iG4E0rivaRCVpGLRugqEFFF4Dk3QjPt7UjJt6RI+O+mfYfuS7XDhuL3515nn8/hZ57NTv/6Z+RTa3Tqz1qgYko8tmE17ni4hOTiCsaGQdNK8FMxtKmJlioNQOgKJ8jFISjslVlBHKZHtwLxUolJ7en568T3s8LwTnXksqp4TE1EVhEPvQg9um/9H3lz/puZx8LFCo9aCKDC4aejG12IrdCn8T3IKWbB6WSDDbYSNfB9ZKGzE2i/jquvG8zM4Vr7QVh+TksWej9SERJ8YVehJA+NrypItU9GcgHnZ9yXr2ypYngpaJwxmp2/Pbtz5uwsoFNKypflLVjNv8Wol1krcwzQrjQ8jkDzrm/iisfgmAQYejApyxEcNr2VFKtH9h2sbFMbEbwFhEDIziJ/r+0xbspiF69YxpLU1dY/te/WlaNsJLgCanQJje6sAwX3KzSpEdhcEqkxCRCCSfgzqizJFzSAsJLelUV9Kg+tQkLXsEkHdt0PnNhvJ6vqicFzxu6m97dgF9u87cfMmuxU2G7ZyCgGM3n1E9oWYGaqIC1c3CQKxjZ1/vNUS2I0qqHVl820NPkITjQYQvsNCgAWeF5zTfGhuKtBULjCoXw+u/sFpmQQBoK2jFlrK5EG5YFPWJruCePRWfURPBMfLkGqE04W4rqChj1WjNegMIdrYMua0L9g2qysdmdcOGDqSoS09KBjr5giLnuUyR26jOLEzttuNgpVc84yRZDONDduFErAGczdbRHkYkgsdPaDIusk4m2T2qjasLwUWPjVZaziWfqWBfG3Md2lxsnVZW+H9g61EIYBP/+A0lac5hqEydqyfFgeFYEZKTV2DUNObsP6JgQhOo15GX56faE+QAzH+0oan9SyxlTDqECdwzU1FzjllH6754enc8btPM3xIfvKdbYf1zWR2HMdi2+F92W27wZx/wr7svv2Q6N76xrExEHeJzvgdEoSs+EhevE54rRFGz1V4xPvW65g5mpxIr1KqHAlZYAnBncefwYmjd1TRT22Ho0Ztzz0nnh0Sz+169uWn+x5Jk1NQcZEyKWZiawqB71sx89Os87r0s6KZ5oHMUBCnF86XwjibRG30WCx8bFSwvEDwhC1MS4QsEJwy9HQGNQ3p7GC3QhfCVvFRAGPGj+KXD1/GzT/+B2+9MJu1q7NPe3g+soDKkJZyNsvrXSiELoSKsipEFOtHNxJCBeMzFMOpbmXUHSKiMer9j8YjhEB6SrSUGpJp5RQoLGXQruZ69GxtZofRA5OtUlBwbL5x/mH88IaHqLkevi8pFR369erGDd8+jZZmpae45csvNewnJrYxy7QkTF/KY7CSBCaapgpj4UT9ZN1LQ1hkejQHF0xxW1gvcNCzhMAPMG2T43DZgQdRSjo0GtBSKHLp3gfywwMOSwXK0zCipSc2UI3IoTEoGZ+sZr6kwPfAdqKxprdnMFYjzEUSTGGZEjVK5b8sfIRUimDdVllAK85WBMZ3FlD3LWzhUYw50KnVl9jUfIui5YXPIuvsNaJ5ZPYAt8L7DluJArBozlL+ePndTHtiOt17tXDut09i6uTXeOmxN1OnbYSICEIs7rD5N0Q/RG+tumZKfUKEA1BIhLmAeF5lIaKEMsl6oDgLJ5LHxnwCQr4+g+AE1SRQ9zx+9uuHuP2eF/jJt09m2OBeNIJD9xnDiEG9ueuRV1i6cj377bYNxx20M83lKF/FwN7dWb2+Q58fIROzx5crVtX8JKqHnE+ybVDBlKSZSyCkYvi0GW6jaKfxe2oCrvopIjhu7I68vmwpQ7u38qk992TC0GG57W+dPo2fPT+FDreOJQT/b6c9+NreB8TEcMvbN3DKg7fgx0YeydniYv24d3Js7VB7y7fikVCktPCkD0IG5dFuEAl9gucL7DDbm4VEKiWw9EMlsnK6FxTxYyI3K9OjOnpYnhS4vq28nMP9GogxEVw36yq+s+P3O+cb9CGDjtp02qpTsa2etDYdhW39d0V67ZLMa/9J2NLMa0lYvnAVn9nrW3Ss7wiVpqXmIgefth/PPPg61Y4a1Q7DKqngROG1s6yQjIxtKSg66foEr4Jjp5TPqjwiFr4tcqOtSgBbhIpcCVAQKstZUBarixLHyGJ2f+VygZuvO48B/dIK002BR154m+/8YTJ1zzeUzxlS56RJaqDIlTaZPgdSt0mcmmOCbQG+kXUtq50022WoT0LiY8tYXd1HMbDZnDhyJL888ii6FYvpToDJc2bylccfCGMbgeIszt15HN+YcGBYduZDtzF16XuJ1r5BBDDOGREyFZZMmaPq0SurpGSSKFWmQ1bE8yao6wXLy8naJnFEFOrCwqfo+LHrZctVbTNXQ+VpKAiPouUqAmJUtFBZ176+w7cZ1rxl6Xf/kyClz4JVX2ZtxwMgPYQoABbb9LuF5tK4D3p4/Kczr/1XwowXZvO1I39M29r2mBVNtb3Gv257mqsf/RbnXnYih3x8H4rNRXDs6MXKQ/w67EUSgmOV9KUSJSXrZUmiEmal6kI+EZcxzgXFPQTe0FoXoU+7pbLDgQeMwckgMhKoVOvcds/zuffqDKxrq/Drvz+Fp9c2NOmUqbknDrmxU2eeDkD4CSYiUUcCuPFrKb1EwDkgA84sFwwlvnGIr3keNc/jiblzueShB3NbX/Xi1BhBABXy4i9vvELdMFN9YdmCxMT9+NxiW0KPRyBSSD24jg5LkbwogvwKIpMg2AHS96XAa6CLkDIZOkP1XZd5qEXpHRyh0oB6WHjSQsgwJxKgwmavCYLj/bfA2o5JrO2YjJQdSGr4sg1frmfeivORjTfXhwr+Z4nClH88z9eO+CGLZi/NvC4lrFy0ipM/exinfO5whGXHT1oZJ34gUPoGGEb6EWscWB3FEJgOQWEFN/TjeY6TILzsKxG+iAhCKHXxg3YBMnMKNiNH9uGyS49lwl6jsHJMY6WE199alDOSzsGfH3yBpavXhzJ3IZRNfZg1x8h/EOA25VCW0K/qOWX+lNm/Y+GKPCJRXXK6GYTE/JidS2Sk20gQqprn8fi777K6I1sXtbgtOy+46/tsqNeQUnL/uzOUJ70fjbkhmBLChlKWRhcDkaIW8OPjWH7AtEY3SPoxaLquuYwkeJrQmm2Cv0XLNYLnqVSgNRlP8elKlxHN2zSa1IcOVm24DSnTYTl82U5HbdoHMKLNg/9JnYLn+Vx78U2R93KG0rherfPd06+l/7A+rFiylpoWIQnZOHy2nexPph3VMJpZxLV+Ooy25hJM/YW+bsflHOGdfJmrcxBSUig6PHDvV7Fti7b2KvdNfjWWNS3Wp2CjOoWNwb9eeoe6Dq2dGBPBuJABjTD8GgToIKAbxYu5IKJP2I8RuVVDTOdg+lckBqtDZ5tqmmRKUceyWNXRQa+mptRwduzTn2cXz0+Vdy8W6VEqc+XLU7hp+kvBeASR2ZOxbuaJPik2S4GMbcE8iPIiRHdObCHiTyKuK4hHWFVg4VOwJDpdhbnINj6JJIRh/z4CG0lBFDi4/6G0FrZMdPmfBzenXCBTJnYfXvifJArL5q2Iey/nHLPa1nUwd/rCsI6wg+THgSVRiphkyO83Cll1pSQe90EGJ2nROAaToVlVp24RQxifPn8itm0xe+5yvvC1W3FdD7/uhw5aJu4olhzOPHnvzs8jA5pLhY1X0mOFuFgHDMsq428SQqRt/DaRoRWvmxaiZODMlChPkmv9ZGR/s4VgWI8e0SUpeXL+XB6eM4tBzd0pWjY1P0IOTY7DNyZMZE21gz+8+QJV09tZZI4sN/yH71lYdjqktT6NR7pDIy9CmHpThE5rjeIU6UdgB98kElfa2Ik0S4UwtLZlWPRKbOlj2Xn9Kx8GOyBO+/bpXKKnDxP0bD6V9tqrSBnnFgU2zcXdPqBRbTr8TxKFbj2b8TLCDTSEQB4vhIjnUEhaIOUi+c4TCqF1D8GRyjzlZg4tbGh8whzN6r6WJXjq6Xc46cTxfP+K+9gQpB0VgHSlipNkqfzMfXp145KLDmf7UQM6PeYsOGz89rz9no6umr0GWQQhBoZZadhGf0kShGRCHn1xE2h0FuRxLGZ5k+PwzQMnUgy4OF9KPjP5Xp5eMI92tx6EfbAY2tLK+nqVod178KXx+3HYyNE8ufBdHGGFqTnzQPoCkYNUowinUewgc7mltJCB97FSLpu6ABmuVaNtaqH8DSK1ldJXmNvbFnGFswqiYfySGZQ5uKrFUHVZ5/dzfs2uPXdiYHkIe/bal5JdbrAyHw4QoiWhKiuCsBnW9zeB0vm/A/4niUJr7xb2PHQXXnzkddyam8p6BijOIOsNSQXC6wR4XuotVYg+h4hAwI1YMYWzEllII+GLca4zLWo0IjT69n3Jm9MXcNEX/sL8hXEFngDwJAXL5q6bLqJna9MmmQLWXY9JT77BA0+/RcGxOGHiLixf18bv7nkmkexGGnMn9I8IC5OPQJcHIiZdJ0TGJgeRDKuh62mRUM50kgTGTBAUK28A4wcP4eJ99uGAESPCskfencXTC+eFcYx8Cb70WbB+HWP79OPmY06ld5NKUtS/qSXGQYQDS91bqOdvAzJhjhrWaCR0U3oBHSRPheGWocRTCqU7KAiZOMeo+tlJdEyioJ3U4t9NKasrhUI6Gf2bIbYXVRaxctl7FK0S/1x0J18d83/0LfXPmdcHD0vW/IKl664iFK8hsahQFD1YtupSat3OpnfrhQiRbZ32YYIuUTQLIcpCiElCiGlCiJtFBkYRQhwphFgghHgq+IzpTLv3Cy698SJ22m97iuWCSqQD2E6QM8Gy0hnV1CTSv82yPMsjiL3gIUJzGpiXQiRYT3Zj5FdQAxchYRNCKGuajAB9nieZOXMJXo4eoV73WLeuY5MIgu9Lvvjzv3PVrU/w2juLeOmtBfzwpke49u4pob7CQgVnjSHwYOkENA5XARlexUa7kACycQSuFc7BzxRB0F+l8YG01VFiHDeddFKMIABMmvU27UY0VHN8M1et4EuPPRBe2qF3PwY0J9NOCnMjRJ/YGKJBWbZvSBYbLYSMGX/ZxgEDlI+AH7BvUQA7gS3yuRgrMN+yiLIqiBhBCK4HiT4qnoPADgmYQGboJlT7ml+lzV3P7e/d2GBOHxx01GczZ8U3WLT2qmBN1SQKSGzAl2txvQWsXH8VC1d86oMcaqehqziFs4EFUspjhRCTgMOAhzPqXS+l/JH+IYT4VCfbdTm09OzGzx68jMVzlrFy8WqG7TCYerXOWy++y5UX3hjzTRCBZ7AwFb5ZoDOgJO3x7cDoPskZhPKNoG7SpDTEmvGXXEBkfppltopCmDJhdx+drrPlBIWCzYLFqxkxLDtMQxY8+/pcps9ZQqUWKdkqrps6bsS4gwQC10PK0xlkIe9Yx8nTfUYfoUgpcFrbaD4Fs0yC8NLrqet85YEHuOHEE2OXdKIdA0+E4EnJswvns7ZaoUdJiUVuPeI0Dvr7H/BN00UplOTeXKtgDFlcgjmutDgoOsHKkO2Kb4XIyU0TBpPHswA/bl+hkTl6GSNdQ7RtI/GRMBZk+5ZRzGybiY2nVHHCNEaT2DFthGTm+jfxpIctNpL4owvA86ssanuU9bU5dC+Oond5F5a2PUzdX0Pfpo/QuzwBIQRrO6Ywc/kF+LJCEUKDOifBIQFIWaG9+jSV2puUizu973PYEugqonAI8Lfg+7+Ag8lG7qcIIU4A5gOnbkK79w0GjerPoFERW3rA8b2ptFX4w3fuon1DFcsS9BrUkyXzV0WNGukIfF/FGtBchCmanAvzwgAAIABJREFUkoH0Vl8zT88yOAXaBO2iNorYJLBog8Ng+O5ljVMQRvpMBS8SsM3wvvkdZ8Dzb75HR3XjoZ5zzdb1rZOWPyLnexaYJqyN6gotKgNZSNQNljjyGjcvyUgclbyPgClz57GivZ2+zVHO6tPG7sKkWTNSvgnhUISgvV4PicLw1l787Ziz+OSjd1N13VAHsUv/fjwTOrOlkY1+2nr7mD4M0uAs1JbyjfwKqqIOf+3YfsbaRYRHSoGLpfoIxXjKMS7axiI4E0l8EflH2EgjuJ460Lyx/h2abY/ooajFVXmpZKhw1ve3gGdXPEy/8mBGt+yCJbpEyJGCirucJxeeTc1fjyfbsUQJKauUAESN+etup1d5L8b0+jKzll+CLysIfJVvIqDErlA8U0mmFf+V6nOUCmMQ4sMrue+qkfUB1gbf1wFjMurMBr4jpbxfCDEVmNjJdgghLgAuABg+/P33cDzsjP055OP7sm7lehbMWspfrriPZQvXROIabTaaBTodp20bxMDc4CL98oViFePUL0Qgc09juehUnY0BpXFL06FNBkfBELnJiDAUiw777LUtgwf2zJ5XDvRubaZYsKnVI/FCXI9gwMbEUkboCY3PGukDYhAS1+CnXvpEiOtwXOa9kn0kCYJllCceJUDBtliZIArjBw3hwj325uoXp2ZJv+jT1MTAbnGR0R79BvPSaZ/n1RWLqPs+4/oN4e7Zr/Hi8oXUfa/BOgikLyClhA7D04WWRqZNhIn0PV9g51oGydBk1ZcWUkgcPBxL4vkWvpBY0leiS2H6OCjwAHwz6qrAD04AsS0R/HCEiHE4JcvFFoL7F/8VW1i0Fnpz0bbfp6XQg66G11b8lIq3Am1C6ssqIKnhU0TiyTZWdPybdZWnKNKGjcSJ6VpEOOcqglKg3LeQOHSwZu13Wbvuh3Tvdg69enz7Q6mA7ipyuwLQT6hH8DsJq4BHg+9zgf6dbIeU8gYp5Xgp5fh+/fp10ZAbg21bvPLkDL592jW8MXUmvmGtVCwVGLF9wjJHmXU0Nhk1HNTiNxNRVpJMyC4XKvCMKfyNV/CVPDj0A0j4CrS2lCmWCrS2NnPaSXvx3UuOzR97Dhy1/9h0onqpQkKXA12NJQROIB4LJAox5BoTD8nEpxNK4vCyEbl0YwRFSKO+Xro8MZJBKGJqJBn9HdkzTUy/uNd+3HvK2XQvFsNw2Y4QNDkOPz/4yEzdjW1Z7Nl/KPsMHE6HV6dsF1SIkBydRjSQgPCn6gQ6BxEh9gyNn9onGe0t4VGwk1F4FUGI1kKooHmSsDzZv5dY3EZ0vsnuTskq4wiHsqU8riU+rqxR9SusrC7l7gW/bdDD5sPS9idJ+xQoGyodjUMArqxiIxvmsvawqKNEgA6aRHtI2cH6tptZueay92UOWwpdxSk8BhyOEgUdAvwqo85XgJlCiJuBnYEfAi2daPeBgFt3+fXXbjV0CxLpediOzcQTxtF3eF/mXW1IuhpZEkH4tglh2Iw30Ang+VBoEEdJs/C+9opWHEj4TmuuQBhy/AT069Odm64/L3/MnYB+vVr42ZdO4Nu/vh/X85FS0tJc4mdfPJ71HVUef/kdCrbNzAXLeWHWAjVtDE6mwfRkUixkfA/naSdwZeIkH5eBB2BFMvewrySiN8RDjTCYLQS7Dx7IovXr2aaXcvbbUKsxf91aBrW0sEv/gTx19gXcNv01nln4HiN79OLcXfZgVM/8sOQAd7zzGt97/pFgrQKET14sIZT9v56PhHigu2BqDaSeoILU2cJHhDRGZsY+ktj40iV+zlCEQTQwqzWtlKwc6wIbm48PO4cehRYWVebz2NJbqSWq+njMWPcKnnSxu0gMU3FX0eYuJuthC3zKBJkF1UwAC0+KTG/u+FgDzjnhOCllB21td9Kr9TJsu+s5ni2BriIKtwAnCyFeA6YBs4UQV0opLzHqXAfcBnwe+IeUcroQYnai3WNdNJ4thgWzl+FnWOl4rse0p2ey+v7AbT3PNyFPxGQrC6dQ5p+5CQ3xjr5H4jpJROdLpJ1A/toCyTYaBFBwbCaMH5Ue32bAhJ1HMPm6C5kxdym2ZTFmRP8wfMb8Zau59m9PUXO9AGkFqE0oghCbmgwMjfQJ33iRYojbKEsuYYjTBUhl1RsnGiLRPon0sziUjMekn4GL5NmFCznmrzfziyOO5PUVS7np1ZcpWBY13+OEMWP50UGHcuEee3PhHp1zBpy1ZgXfe/4RKp6pj1DsnvT9QNkcycsiT2dh7BmDroko9abWP2RZ+kgfLMewJmqA8FxphXkSOm2sFnpNq/41YY4Z8OEzqDyYYd2GM6Z1Z/619LaczmQgGu3kvXPAk3WeX/J93mt7GJA4oh6q9TQUA0/luIhIUqGAQyW3bwefcjBnTSotIqQrqbJk8ViaSvvR2utnOE7XvI9bCl1CFKSUVSApe7gkUWcxcFAn2n0ooHuPZlw3+9RTqdRxK/V8ggCkrIkS5TGOYZMgA0PFFLTqmjBqFiwLaVvUg/k4jkVL9zIfP2WvzDtsaFMK9uamzttUO7bFztsOipU99docrrrrydAySUBMkZu1dJZUWTpjczJP7wnCEOpSE8g+/K4tk/IssUyOwGyvPZX9dFvdXs/F9X1c3+fLD05GOJKq51ENts4/Z86gtVji2wcclO4kB+6a/brSIcRHm/gaPGchiSdqC86yvoqcqrZoep9F0VMCmbfOvxxur8iKKBvUFU8SIwx1KSgkCEXIuUjlOGcJP1duLZHcseA2LhnzdQB2bB3PtDVT8Q0ORCAY3m17HGvL5fHTVlwbEAR1AHSljSXccJ2FkJkpShX/AEjwRGByDZGFlVBWSGr76UREEj1ijR58JNXaVFYuO5Z+A6diWZum03s/4MOrAv+Aoc+gnuy417a8+dwsXFOJ6jisW90WvZw6FpEZbbSBg5t0vXhdGYgFMqyEFMeQQTgCvl1CwB2k3sDopyWoe5Lmkk2/vi10VOq4nk9zc5HJj7zBqSfsSbGotsHc+Sv50TUPMOtd5YW869ghfOuLRzGg7+bFoPnTA8/HTFUjsZGyMhEJlBMi5KwYRTlinDzxk0bmIviug+2lbpi8kSCmKM8i3mrd0+Ope57Kg2CUV1yXW9+Yxjf3PzA3fakvJb97/Tn+8OaLrKtVaC2VjBzNyeevkulYdsQdaMQeH2HE5sRDV0RkUx+0kyIitYUEUlrkOZFY+ArRCRncTd3HlxZ1/JAwaKsoKUVAEGQodhJkHwzeXj8jmJPg2MHnMKdtOh3eBmp+lYIo4VgFTh16Yea4NgWk9Jm99u/EHWEENelEGeMacEsiYHOkITQTUoUVL+GH5T4SIaFJNYor+yV4SARVOtrupFv3C7Z4XlsK/7NRUjsDl930GbbbfQSlpiJN3csI7dBm7pNQyYuJSfI5CIjyPFsW+DJE/roLhRgjZ7Qk6ADYUiuoE32H/RjipLb2GouXrGXNugrrN1RZuGgNf/7r01xy2Z1IKdnQVuWz37qVt2cvxfV8XM9n2vQFfPabt+UGzdsYLFu9IVVmSTKd0TDnnvceyoyfGcpkkfUj+ZiylNBJIqFPf9oA3dSDhPInozxn2DXPi8c1SsD3n/sX17w6lZWVduq+z8oOHWkzEgVFXI2I7zVEzM5AxoiJDJF2eqJqkskcDFYoZlL1dHTUKIpqkGLTkiF3oYmOtjrypZK3Kx/L4D74ASHYOHfsiEI4hu6FXly6w9WcMOR89ulzOEcPOotvjL2O/uUtT9Xp4+JTy7gi8JUHBTaeJqGJOpJuohbW1x+JwA71hyE/h0SFyzOlwrqpkqp2UK9O3eI5dQVsJQoNoLV3C7+a/A1+8+/vcMH3P065OUeckvRkzrIECkAk64SnBlMuErIJYRvzoCxtC+mkCUJ4BkwircBCSUXzjjBytebyzqylvDLtPR6Z8hb1uhcbtucrYvHsS3Oy570RGDdmaNoyyZxHRps8f4asA2teXQmg01IG9FnIoA9B4AuSbBD9laTraBt8CWHqyeThvGhnO1YN6d5KcyFb1LG2WuG2ma/S4SX8GRriTpUHISQA0gqitMv4KTQgGEJEMYpM4pHMtJYFUlrK7FQq1OYIFVrbPOWLwHTaM8JrR/tIE5HIU1q388l+TaT0WNSxMPxdtErs1ftgTh76afbvdxRNdtdkMrNFkW7OoJyrEhXUO0uE5lMWNaI8inGoY0UK9fAD9ZBzUIcjLdXU9hRu9d9sWPPNzRQrdx1sJQqdgCHbDmCbnYZEGdfyQHujZop8rLgjmy8VMdFvqwbzuu5LGmcVEf6Xvj1EmdqEiCGuMLlV4k2sVl2mz1jEgkWrqVTTjlZ112PJsnWN550Dnz5uX5rLBSwt6tIfEUeyMSYrNpkE+InKsQYGZMVB0qe2ZPv4wTopSUhVM8vNsoJl8fMjjqCpUAgJoUB5Nn//oI9mDFLB/PVrKFgZrtL6+ech7WAhNV8oLBm6xpgEQRMQ3xd4frSdfEmQkS13aPHbGRFMN8YII8ERfuiEZllqLPWAgwjdfaQVnpwjwwqJh8vN8/7YuYFtIezV/zvkLXJRuAZno05ZAh8Ht4GCPTBfDQhB7B0EkFBAmWkrXYMwfC9r1NtvpWP9T5AyLwz3+w9biUInYfQuw7FzYhXFbB6TSB7iOoZAJKQ2S3Cc10jEROSSuE9DeAQzkVvnThRh/bCv6GuhaNOrZzdeef09ssCxLUZvs3m+IUP69eCW757DhJ1GICyBFRJFzTMbwxFAcAJPWjWGr6VuI4zfWUuQ55+gkX6CEiU5MWGMTUOI5hOJ0DTUpU9rqcS9p53FMaPHMLJHTw4ZOYpbT/o4E0dskzEYBUO790golY075iJelXpTSXQUC5RGUEmHKqUj8KU++2ZlUxO4qSxr6kfB6lxUYS1mSp6B9HOv+Rae1MyrwJMWrtQcTfTo3t0wG9d//xHjgObxHDLk9zTbA/VAEQhKohYqmVWpInKWgEJwwsp++1SIjiQnaQUfD2KHhggP6DAfPrUNN9C26hN8UNnatiqac8BzPVYtXUtr7xZKTUWcgs03rj+PH5x3A27dxTOTx5gchDIpSFusZB0rpERhwvTRS0BkOaPLpIwii+r9YmsuQmw0lEQW+L5k4fI1zFuwMiIyIZGCbUf2Y7cdh256xwH0bm3m1dmLAnl+9BqZsYcEmnuIaIU0Qmbrqn6SAwg7yyjLAmOZzaVPVIkcxXL6lGZlAz59zz08c8FnuObIYzoxGAU9S02ctO1O3Dt7ekyEVHYcatSz1S9B7spQfCEFvicN5XOcYzBmhpTK6xjU+SW5ZSQCz5c44f4VWLgxL2U/iMmUZdaqt37Nsygm8jvo5DsSFZbVE2pPl8NMb8YUc/RpXQV1v4NXV17PnHWT8aXL0G4f4aA+F+L7FZ5YeFJAEEAKhcpj4rLgf2WdZW6UiN3UaxtLsRK8Xx1SUkRHtIkIRPSsPdzaC7jVJymUD3rf1iAPthKFDJj85ye58fK7qVddpJQcftb+XPiT09nz4B35/dPf49E7n2X5glU8ePtzhlzXQKibIhPchH0vAWlFmwgALzDhLOR3JGMN4t8PPWgsDz7+ZixMRTR+wU++eeIWvZxTXpvT6SnGpEIiIIqCXMujXEhkRAMiU8uk70OjwaTERzJOuRLgSsnd09/kM+OzTX3z4If7Hk6vUhN/mfEK7fUa2/Xsy/f3PZQvTLmHZZVkekcfy04i/GCMoU4hb2Zq8GFYLWnh+37KnUYkpFeWEbFFtROk1dcKPaqQDyJgmoVhvWMm34nGLrGo+ZKy4TVtC4c9eo5LBb+TUjKn7U1WVBcxoDyMEc07bNbelFLy2MIvsLL6Nr5UyuJ5G/7F0o5XaLF8PETELcq013LADIV+H+G48YNgeBZ1JA5+3MEv+OIhqaMUz6VA2JQeZDv16hNbicKHAZ554FV++83bY1FSH7n1aYQQfO7nZ9FvcC/O+NJRSCmZ+uibrF2RsLCREuHYIRvdCU1edjGkEI+0rZRyWUKE6ISI7qsKYqfasM9gTKWSw8nH78mTr8zOHINtiVwzys5CR7Ue5mg2xxu+TiaCzUD+MskdGGKf3AQ9PhkIPXEtGIAk5xFlPrbAQzyU4aVh5vLMSC0peH7xAq595RnmrlvN7v0G8cVx+/G1PQ/ElzJc82pWSJQEIjLHJn2BDEQbjXadOV/PV9FPQ8Wo6QgXoH7PF4lcCgJP2gipTExDcV5iZL4UYU6FRqG3vUCrX7IUOupX6s9ZI86N1Wl313PD7O+xqr4MKX2EsOhfGsKnR11OyU6nP20EKypvsLo6KyQIauYeFW81nlehYCmNgC9lKPF3wvH74Ra0jc2jQlnE1ygvWIG6qj51CcWgTlK44FaeQrZmmKu/z7BVp2CAW3f51cV/ihEEgGpHnYf++lSsvNpRo9KWZc4Ge310JwpFO+IacvIsSEtEOZ2TCmWIEQBf/86y5tG4Q0oVDykUfyiTRKdgKcWWDcVyAcexKBYdzjh1AtuNHsBH9hqNbae3wohhfejesmUZrybsOALPM+ZuDl8afzPwnIn0w8uRaiWKX2SCSViC7ynpnMZeRl9m91mcSUpSknMYnzB0KAvXr+Oyxx/ho3/9I//v3ruZuiCur3nw3Zmc++BdTFk4l/nr13L/u29z/D03M2PV8hgRLmwWQTatZZIzU1FSo60WnNaDaraQgQtNHL170spQlcnUwUWIpKWObrRxJ7gmqzenDz+bL2x3Cd/Z8Qd0c+LBAu9d+AeWVxdR8yvUZY2aX2FJ5T0eWHxzbs95sLo2O4PTUYTBj7GYSv/ihjHT1QmmjoNLpCQHEilJVV1DYpQ5a30XNxiLk0DH0ptBvTJ5U6bWJbCVKBhw0/fuZt2qtG09KKRiXps6+bUwlIMJTtFh93235bYX/o8f3Hi+cmzLyOwmBZGlEES7Rwu09ZORjd35M6UFUiqbtwAbekFMoqJlc/jBY9lmZD/69G1h9rzlzJy1hAvOOYBePZrDAHbFokNzU5FvXnxU7lp1Fgb27s6Zh+2RuVbxSeSUJ5G5iH8NCaJI1M8iGBA6pulgeVpZrfG8yXnlQs6L3uw47DN8GEff9hfunP46c9asYsr8eXxq0j+4+603VFMp+d7Ux2IhtX0paXfrXPH8k7H+jhu5Y6fvHV0Qof4m4hoVMdDbMJyrSauFxEdbKMnYcgoEdT/yPfBltLTaac0KLZNSJNbIxpY/7g1uG/v3PZDtum+fOhlLKXlj3XN4xBXPrqzz6pr4mnUGuheGBhxAeiwi45Rh4eNiUZc2rlRSABtUGRZ1aSXiQAVe3qReezSx0NfiDLD2QAIbgY1Fbd0vN3l+WwpbiUIAnudz/03/jp3YTSiUCvQeEAWuWrtqQ8zTWYNbc1m9bD3dupcZP3EHttvZUNIKomioWUjStBcMHMZC4pGDVLPETEAmQqzXPR58+HVmvbuMRYvXMGXqTD5/6a3MnbeSW359PhedO5HDDhzLOadM4NbfnM+YbbcsR7OakuT5t+ZHL0dMcZD4mjyBNziRZ4ImGCK7v5ALSF4LOSvjk7ivuc6WJMzipj9lx+G+s87mmheeYUO9hmvsoQ7X5QdP/Zu657G2WmFVSk+g4OVli2K/v7Dr/hQtOzHQiPGMnNUChBy+zUqUFPdZSId3VlOWqZDZWioScViSgq1ERREyU1b8eisLCDKvKf8JW/hYQvkuuFKE5qfxdyv63t1p4e11b7GqtjK1LhIZTz5kgCc3Mdc6MLBpHN0KA7Bi0nM1o0LCIcahTtHygxp6NFbgc6G0AdrBzZNqW3iQCI0RO3JQIE0sJFALrhexsbGwEFj+HOrrfrHJc9wS2EoUAqhX6tRrbuLEHsH5l5+KHZlksMs+ozPlheVuJXbbb7vw91kXH0qpqZDbbz4IpWi1Dc/mHIIlbZFoSfooiEIgruvjB1pEKZWfwq9+/TDNTUVOPnoc3/3KsXzitP3o0yuZHnLz4K15S3l30aq4CEmf4jUi1uMOyrUETL9HnVqxjYhdJYQOaTEGRGZ8N9xNYnTFYMtCwqCb+pKhPXrw7ML5MR2KBtf3eW35Uq584Snqrsz0uejb1Bxr06vUxE/3OcKcQSASNLaCCMosUxegPtrMU32SC6Qon+MECE8TAKHO/n5IbCSO5Sk/F91X0IMVrJHZVg9TEQsfR+h6AtcPkGlI0VQHJeFS8Vfym9lX853Xv871s66h7keiWUtYbNuyU0ohK7AY031caq03BkJYHD70eoa2HIDAQWDRt7QzzVYyJahHUUQEQZMBiaCOjZRKmexoQiKinVXBJgoEoPiSAlAicb4z1hOghBNaXikTdYnbdiN+7ZVNnufmwlaiEECpuUi/IUE4YxOBS8mIHQZx1LkHxurPm7lYEREDhBBsv+sw9jgwyhU04eCxfOprx9DUrYRlm+anDU7+EIicSNcz9A8SVAyeRqKZZP8ZdRcuXkO1E9nTNgau6zF30SpWr4tOwvOXrY2JjsyZC58UR2PmQNA4OE9HGeOScihHiHsiMXFsLFmgiVNMEpIQEGsTWv1xLIuXFi6kXyrXsoK67/GZh+/hjhmvRx0aRKHJKfDZ3Sak2p00ahcGdmtRXtSWVH+NU3wyeU5sHhvZFpHIJ33N9D72sXBlPNysIikivYhC4GGFXIW5SFo+r/2EJcrJzRYSH48Or4O6rPPG2te4e/4dsW5PHHIBTXYLBVECoChKdHNaOW7wJxtPMgfKdk8mDvoJZ47+N2eMfoJde5+GHfStQBErU9yTmCh1nNB0Na20Up7NGhpljNXELv9xVXE77u3ErLoGthKFAIQQfO4XZ1PSkUED875yU4FLro8n3K60V7n60ttTh3YpJaN2GqKctIwyCXTrXg6yahobwEsgeN1Itw+Py4Rt1MEwaBeeztIYUQCWbYV7tVwu4BTtzJ1XKNgUCltmiDbpqTc5/OLfcu7lt3Dcl3/Pl3/5Dza0V9luaN8wOmuuTkDTYEiPT5IdIE9/MXMumNezREKbAFrXkBkPzjwq669CYAnBRXvuRZMTD2lRsm1G9OrJ+lqVesyiSA2sbNt8bvcJnLL9zulbCcHndtovTqAMaMRFmdsivUVk1rYxxmXiOc09pBdRZpSFNzTonvoEpqq+CH8XrbTjXV3WeWrFk5jhHvqWBvG1Ha7jyEFnsXfvQzl60LlcOuZaehY3LX1sEizhYIsCi9uexpVRGGw7S/6aAD/Fu5ggVKwjg82skQ7tYSLgAhYePq70iIe6kMB/zsN5q0mqAXsfvis/ue8Sbvv5JBbMXMx2e4zkzK8fz8ix8eBbb74wB7ee/ZAevPUZPnP5KeHvG3/+AJNueYZqpZ5SOAtAegFy1/ZrBkEJD6cZ8sdMoWRQVCzafPXSYxg4uCcPTJ5GR0edgw/agUXL1vPHv06JhbMolRyOP3r3xorgjcDLM+bzsz8/FouI+vyb8/jmrydx7aWnsOeYoTwzfV5+B8HYk4lIgNASKO44FFzTymLSSE8E3TbK3GbePgsMaUjYq22hIpgm+rUti3GDB1Owbb48YS2/em4qthDUfY+PDBvJGq+D6to0y9OtUOCqQ47hsJGjM8fw9prl/OSVx3PHKGVelNT4902zaszKKKb0A3ai73ScHoX6PWnhSD9xY4mOKGSFJCIb6rKGJz0cI4lO2e7G/n2P3pSJdApWVd7i3Q33BQhaczgClwISl6LMyl+tdSh5oPUrZomgDjQJGYjjRGhEUkZgGyvi4mNJgS0soIxdPq6LZrtx2EoUErDj3qP5wV1falinUCrknrJMs9UN6zr4581TqWXEFEpBhlczAHWfYksRy7ao1z28UFAp4wl7giIsqLk+V1wxiQMn7sCXv3wkLYFZqe9LVq5azz33v0rBUf1N3H8Mn/nExI2PrwHc/MCLMYIAUHd9Xnl7AUtXreeKzxzDQV+6PlPWrsetr6RWYGNIPSAowlS36DYyg6gav2P31AQggwgjoVRw2H3wQK485kiufPopHnznHTzfpxAEwbv+uOPC75/eYzxn77wb765dTb/mbvRr7salj0/m5aWLUmvgS8nw1vzMW9e8NoWKW0+NPRqbQIauyaZHc1boCxMUtY1bICnO1hKRaGlj4EmBLaMIqJEyWmbsZ/VQQmQpIkKTrCqAP879Ledv81ks8f4KNJ5bqtJiaqY04uWVUtlFqgB2ocmtxMajgI8rRSopTwQRt2SU0AGUCTh+lK7BxkrpfVTIbYnTdARWsXMJmroCthKFzYCd9hqV67jc2juSKS+cuyL+oDXWSe4g24pOtjJ+vViwOef8ifzlxidCBXHYV/KNNuzifF/yxL/fYtnStVx73bkAWJbgc58+hP93xn4sWryGAf1b6dkjrtzcHFiycl36uC1UdreVa9sY0Ls7h4wbzaMvvROrklq+zWVWTOxu6iSE0keEDnAaqZo3zsiLkFKAAwO7dePGU0+kXCjwi6OO4rw99+TpefPoUS5z1HbbUbBtHp79Dq7vs/+wEfQol9mxb/+wj/N2Hc+kOW/HTFEdy2L7Xn0Z0zs/ttRrK5dsVNEupYX0fGwnWoxQApmFm4NJmk5quq7Ke5BUuKr6thUfSRSpS4YmmDpRjyctpC9xrPzRC6Dm2zTZ5oFChn9fX/sqL6x6hgl99t/ICmw+uH47G9z5wR1NM1KJoy0JhIUnlcmug0s5LFcRkTqwKPl1xewHhMAK1qROkHQonJUMVzxQXdGcQRAI69sUun/lP+rAtlWnsBlg2xYnnH9Qutyx+MQ3okRy0196V4mNNAS2hGbuhPgpVkZ/pYou+YVvH8ff73wuCGsdiAKC6sLMc2AlO1PdzJq1lFmzlsbKu7eUGbPdwC4hCABNxUIan0tl5rvN4D4AXP6JIyiXnJiM2aiaTxAMnUOsfvp2ndMdmDe34sUxSPSzoq2d5+dH4Zx36t+fC/bai9N22YVpy5aw9x8TTXCWAAAgAElEQVSu55JHHuTrjz7MPjf+jrunvxFrP7ZPP6756LH0KTfR5BQo2jZ7DxrKH486ueFwR3bvlT+o5IAlgUI6fTXrAGOF8vxI8SKEshIy/SmljEJAx+8oKdpKheqpCElhqhCIcjEEv3CES0F4OAEXIwMUWfOt8ASu+xUCan6Vp1c80XB9thR8WUcHpjSXzcGLMe8aKTtE1lp6ZgA1CngoB0AHiR1QF4kKmW3WdoL2+gzTCARF3LXfwmu/AymznWW7GrZyCpsJF3zvJFp6NnPXdY/g1j1KzUXO/upRHHH6vgD4vs/t1z8evY3C2PZxA/IQ1AElyGRlCf58/5eoVl062tObQUAYqTuiEulxep5k4cLVjB695T4HWfD0tDnMmr8idnuJOjF+8rgJYVlTqcDgPj2YvciwQw8uWgSxdiShlZA+TQlJLDCgvp4ZDylj/qG+IVFfE5FGetLwuoA2t85n7/knR43Zji8fsB+DW1U2unXVKhdNuo92N2699d1/P8aeg4ewTc8IqR82cjSHDB/Fe+vX0r1YpG/TxvMCXLzrR3hu2fwokmogass6OIbTFPq7Grzvy9CTWYMl0kHoon6M3AdBvyU7yT1ICrEy9cWVFgUi0ZUvlYWRNtuM96F6VzPLHo/shMJ3S6Bo96B7YSTr6yYXm6tlSoSyiMplIGJKS4EF2iUt5KKI0qYLCXV8bARZamtBO7L2NG79ZUT7XRT63IYQW56GtBFs5RQ2E4QQnPWlI/nbjJ9xyys/5M43fspJnzo4PFFsWNtBR5uR1Ft7Jmtls7lzPBlFIEO9KoWCzb8nv05TUzEuNjLAshNYLqOa7/uMGrV5oa87A3c8/ArVWlpnIi343b1TOeiz1/HJH9zKu4tWsnDlWvNQGgOhncF0+/BC8New62/IWSQlbBCF2JZReaa4O4vQGEHxK67LfdNncPyfbmFFWxsAj82ZnYmgXd/nnremp8pty2KbHr06RRAA9u4/jO/uqfMxRMqXCMGriQkrGdhOxL77vhXkVdAipY0JpdSktUGm68flbo10Dr5xbynBDjiDNEHQo89GQ0WrxL59Dsy81pWwV//LA5QsUlLZTYEoblQawvObSKjXhXJa84i8mcP+UIcSDx8p25HuDPzKQ5s3uE2ArURhC8F2bHr0bknFDmpuKWPnZOLKBM13B596zWPO20vo27+VbbcfECcAQLHkYBWC/lOymOCrhFKpgONswjg2EdZtqMR+SxRB8FGZ2zxf8uacJZz/o9sZ0DPfIS4U4+svOgC9MNCbZrpMAphE5klioz9agCsC8YrIIDyJhqlgfCjLo7ZajZteeJnJ78xkyry5uBmB6zzfp62ez+7PXrOKqYveY02lI7cOKEX0kvYNobxfBoplYSxCkiA0NjVtwFaq1hm/pfJMDglDY8uhuMVNY6GXJjoV30FKQmujklViu5Yx76s+QUPv8o6UrT6hvJ9c4iCpY2WWh4G0c5YlNEWVMnT60yBQSmUr4BXCrR9KFwJxs2zDrzy2BTPtHHQJURBClIUQk4QQ04QQN4sMrYhQ8GchxLNCiPuEEI4Q4kghxAIhxFPBZ0xW//+N4BRsTjh3f+XNbELeUSJRXCoXGL3jYAC+8+OPMXhIL5qaijQ1F3EcmwMP2ZHBw/tETcOIZYHOIngbKzWXT573B+5/YFpXTi+Eg8dvR2kjPg4SlcFtj1GDKSQD7wV4SmQg4GS1rL8x4pDTJtD9xQmB2TaroQx8AlMcmKTme9zw8ot87aGHmDzzncz8y02FAoeNSpuZrq50cPK9t3DM3/7MZx6+hwm3XM+VL0zJMO1UcMHjd3PdG88Qo37SCp+vdmZLzUlmTdD8rVN6JrkOUsnqBQQhK2zqvkJ/OcxrUD8KupeVjjXZN4CUFq5s4thBJ3PUwBO4cNsv8dH+h/Hy6qmsrC5v2EdXwNCWw6O9JXQGBcJ3Sq9NPUihI8P1VeUOkjBPW0YoD4ESG+nl8IJqmgAgZbjO+bkkbLB6d8V0G0JXcQpnAwuklLsBvYDDMursDzhSyn2AVuDwoPx6KeVHgs/bXTSeDxSqlToP3/U8a1duYOxuwymVCxSKDj16NbPTHsMpleNIVFgCu6iIhwSkENRdj0qlTqWjRt/+rdxwy0XssucIajWXQtHhiX9NZ8TQ3kpOqTvypTJP1MdroTZvreZy7bUPs2pVW5fP9dRDd2dAn+6Ui2pOec7alZpL0bY594jx0eUEhhcZSuVcMN5VnXeh4ZFUtzEgt7o0rqf6DU5vnqStXlcEwTevQLNT4KPbbMveQ9LJiS5+7J+8vnwJFc9lfa1K1fO48fWX+OfsGam6r61YzKMLs8OaS089+DQtEaGUKY64ImqhRUe+DALgATpns50SgQTpJUW0FLZQUtD4aVrlYtYchOYngCCbWx6BipDm7j1348hBx7Fvn/259b3f8Ke513Dn/Jv48Vtf5a75N+USzi2FZR0vsbo2Dy9kDZVbmp6NCBTt2sKqGri2BQG2sfBVThMEtZipmwxP/pr7MK8WgAIq0IYPeSmVDLCwm0/vqmnnQlcpmg8B/hZ8/xdwMPBwos5S4Orgu8lXnyKEOAGYD5wqP+is1VsIa1Zu4IsnXs261W1U2msUywUKBZvLbzyfsXuOxPd8fvvT+3nknpcBKDcVOfeLhzFvznIe+NuL1IOMbp4nueOmKTz58Jtc+9cLuP0vTzPtxbl4nqQj8IV4YeosBg3sycKlawwbfSskCCZYlsVzz83iqKN269L5dmsq8pcfnM19T7zB06/OwXYsXpgxn2rCua+pVGDHkQM59iM78cDzM1i0cl0M2epDuY5impQ7mCjEDIURXpf5TJjuyzz4awQXrltGWy9USMQvChJITgrwJINaW5kwbCjHbb8DE0eMTJ32VnS08fziBQmvZuhw6/zh9Rc5fvRYAGatWcGFT9zD7LUrc8ZmnGKlOkQIIY01ECiDBYE0FLgibKYVycpk1ArkdjmR2aO7ClOXoCKmCsARXiwtdjTvIB8DVmC1k+ZAdKIaIQSnDzsNKSU3zPk56+trYjL251c9yeiWsezRa9/8AW4GzFn3T15e/jM8WUHvkOgcIPCxAR9LKhJhCWWqWhCm5Zaaq0eUczlHIhkDF3CMBXelIjJOwnxMK6DtlkuxCtvxfkNXcQp9gLXB93VAiseRUr4jpXxeCHESUAQeAmYD35FS7g0MAiZmdS6EuEAI8aIQ4sXly99/VnJL4E9XPsDKZWupBBZDtUqd9g0VbvjRfdi2RaHocPF3T+Cuqd/mjw9+ldue/CbHfHxvzvj0RJIpsGpVlyULV/PopFe59+4XqCac4KpVlzUrNlBuKiJMb+lcEdXGjtKbB+vaKpQKNkfuN5b/u+BIdhjZn2Ih0mPYtkVrtzKHTxiDZQnOO3LvTA9qQYJbkBES1xViBEGLnpLtkiDjfQRFEfFJfox62d3JKLxG2EYwqmcvfnH4URw0cptM9n9dtYqTkyNhdaBbqLh1PvbQrcxau5J8f9moXOh0rCHXEAxIAEImxBHpMalwXFEilyx5ualUzlIWa/eYLP9LxVnI2G8lJgnqBqfwIU2D6Vfuy9LKQtbWV6XmXvOrTFn+SM56bB54ss6rK34ZEAQ1Ok8mZYxB7CdlpEoBLwqAF4Ng/RB4mboFGWRliyApWpNIXPxA/CuDx6hWzCofTaH7pzd3qpsEXcUprAC0W2aP4HcKhBDHA18EjpNSekKIVcCjweW5QP+sdlLKG4AbAMaPH/+h5iSmPvQGXj2+aaSEOW8tom19hW7dlXdxqVygVI70DdOnzadQsFNB9qqVOs9NmUl7WzXzfpWOGr+78UL+8qcpTJ++iNYeTcybv4p6Iqy370v22zc7lMKWwM2TXuCGv08Nkc8VUvK9C4/kjTlL+OfTb+K6PhPHbcvFHzuAcknNd1CfVppKBdoqcUVsgFtxLIFr5oNMKRJyvqcP9bEqElKpOsPqSc4kOD0j4hclEuyoRG/Gsm1z4tiM/AcGDG/tSdG2U+arjmVx8PBRADw0/x2qYa7m4A4Z8xKWHxIEXVd7JIdB8xqshAqNEaeAemki7inwTzCQuudb2Ha0t+xMBAkEfIoQ2vpI2/aYHIv6XhQO5448G4CarJKd6wBqfiWzfHNhVWU6bthnY+W5evLaGivfykgCVSxKeNiScI1t0jZWSeQrhOK+qsKnjB2EuFACKEv0Dp7Z+3OwM6GrOIXHiHQEhwCPJysIIQYClwLHSCnXB8VfAU4XQljAzsAbyXb/beAU8i19bCd/uVt7NqdlphZgCV58djY9e2WbMA4a0ouVy9fzrW+fwO13fp4bfn8+J54wjkLBxnEsSiWHYtHhkq8eRY8uclbT8PbcZfz+H89Qq3tUai4d1TqVmsv//fZBzjt+Ao9ccxGP/+ZzXP6pI+nTIxr/+DFDKeZYRFkW/PicI+nWXIgh7xjHkAECMnNIpCCDK0j227O5TCn1HIMGgSzdjMUkgJZiiePGNLaTcCyLHx9wOGXHQbtrlWybnqUyn99jHwCWtq83iELWuGUYLVWPIDqhB9TD0DU0wiERsjdEQhj+ghIcO54XQIjIkibiTLJBJL4ZU9CSLywkHh5XzbyWxR1LGNI0IjOsRUEUGddrv/zJbCK4foUpSy7HlzoSqnEICUer/lp4FPCwkdSC6E95Qm4tdtICtQKSEpJCgotS3FKCU9BBLiGwRAo9TfA77sJbczGy4/733Ymtq4jCLcAQIcRrwCpgthDiykSdc1EioocCS6PzgOuATwLPAf+QUqYNu//L4PBT96JYip8BbNti9/22o6wjsGbATrsPo3uP5ugkEPLkyjN4w+oN2LYVmqbqeitXtfH9y/7Gx4+/ipdffJdvfetO7rv3JRxbmS7uvttw/nrzZzj00J26fK6Tn56e4khAhdN4+tV3c9sVbJtvnH4IkJba+D7c+eRrfOqwCfncQAaEuoZEWawtoVSFGPaT8XGs7qjg1X0KCeSkOZksRFup18PYR43g6FFjuOu4Mzhu9A6M6z+YC3bbm4c/9kn6ByG39+g7hKIVT/4Sm7wgRhD0LOMHiogw5CEvS/ihsji+pup+Qa9ESXyi/M2eL/B8pYptlGhTop5n3ddmnLrv6EyuDCUk7V4Hf5z7Z2zhcPaIz1IQKtUMKH+F/qVBfKRflv3K5sHc9Y9Q9dbEELxy+jOfuYpxpGMzJV/NLNAzs5H4In6AUNcVOBCJifQ1o2Laka2GX5mMXPdt5PLDkN5S3i/oEvGRlLIKHJsoviRR5wrgiozmB3XFGD4scMbFhzH95bm88/oCfF9i2xY9+7bwlZ+f1rCdZVlc8btz+c4XbmHRglWpwGn1qodTgP0P25FZby9l0cLV+JJYxNNvffV2ZMnCdX10BpjXpr3HtGnv8dGPdj1RqLvJEL8KpFS5FRpBj5YyzeUCbWYYkOA9eHX2ImavXBX1l7geFpqEwo6/fEk8JyHmBS2M8piWNADPl+w4oD9vrVquoqLqHn3SGdahUwRBwy79BnL1IcnXRcH4/kPYte9Anls63yg1ZmOIcqLoqHGkpSeTjp6q+1LZ1lSUTkEYTTAD0Xm+FXouK/FUkMs5yOcshCINsRBHWs8jFOchfUspUC0tPlJ/zSxnEsnM9e/g+i479xjH18dewTMr/sWa+irGtu7GHj33wbG6zpN3acfLuLIDsIIAFXockWzLEn4GAYiivGaBhaRIpISuYoH0KaifIYn3gKqU2ELgJMRCFspvwU48EIkE2Qayglx3OaLX9Vu2CDmwNcxFF0OpXOCKWy/i7WnvMWf6IgYM680e+28Xy7GQB4OH9eYPf/88555wNUsXrUldLxYdPnbmvjzx+Azuvv058OLyEs/z8WsylnSnUqlzx+3Pvi9E4ZC9t2fSlOlUEgl6PN9nn11HNmw7dlh/5fSVhYikZPWGjrguwUDmoQlqABrhJ09kISo3dYeamIhEeWIcEnhj5TKjJIiQo+UeRv2ibXPKjo31CZ0FIQTHjtiBl5YtwM1IQZlQcWwELHzfT6XbBJXTwAoiq6q1MymsQvp6G3nSCpCkTrMJthURHCVgiSKIIsExxDCO5WEF8nJAiWIyEgNZwgpFR/1KAzl+yJmdmeRmQUthCCogRx0Xm6KRr6CKTXODKLM16WBTjx9CpKQgfEqWYj9N/s5DIdokBvAAW0o158QzreFRkiImYrLMHqv/ft90DFs9mt8HEEKww+4jOPrMfdnzgDGdIghm2x13GZppnePWPQYN6UXbhooRQjsBGQeY1au73j8BYNwOQzlswvaUSwWlULQEpaLDF844MKZDyILuzWUuOHofbCuOyUMpg8yXVmsRkAiyo+UpVc0XM3x3rPiFEMlmNM593YyQHI5lsdvAgXx5/67zvF1Vbc8kCBpkQAU3jg4ixW70kTi2wkBKvGQhwzhHSniizVQj7iOQbEsr5BZcP/BxMKiqJguKcCghkROk5BQG4XFxUmItW9iM67nH+x4mW8Po1uOxAu/ptMWQSPyNg4dFXSaCBiIo4UWHBhltM4nAzenLxky9qf4psiKpE+e2rf8Qut5KFD6EcMb5B1Isxpm4UrnA0SfvSUv3JvbZfzvKSU9pDYknalmCPcaNZO26Dp57YTZvv7Oky5yAhBBc9qnDufrSkzntiHGcc+xe/On7Z/Gxw/boVPvzjtibAb1a4kha/03EQop9N7F98lpWseYsMsREoY4h2YeAQiNiHuglCsLito9/nKZC14k2xvUbQrOT7E9mRnWNn1bTfVlWfBFtKyIUeiE9X3ECSuRjho82QaCjGmkrHE9ahr5AkYWi5Yf5opRS2lJmnuF4FfJ1pRU64RetIgPLA9ixdTR/fPe3/HPR31hVW5kcQJdCt0J/PjrkKro5g7BFCZ8yjigCfoxrgNwzA34gSvJRUVXVoUaVeQjcMEpsMgub0U/OSV9tL4mwBmBTwME26tpQOvh94RJgq/joQwkjRvXnZ7/7BL/95YPMnL6I7q1lTjpzXz52jrK+2Guf0ey623Bem/YelQ7FxpZKBcbvM4rnXnqXWs1Vsl/boqmpQI9ezXzszF9TLNh4vqR/v+78/Men0b9/6xaPVQjB7mOGsPuYIRuvnIC657HCyOcc9omhAE3ue1PkoyOnBv/pvMkbhU6IX/qUm1iXZ+VhtK3W3S5/OfcfNJKdeg/g9ZXK8xkkwlRZaPk+qBOpiPQHUfwedTFraFllnm9hO43Nt+LezpG4qe5bFG0/NE9NyuBdaWHLKKKqCgqnCJIlbA7tdwhvrHuOexbdQc2v4giHR5ZO5uLRl7Bd9x0ajmlLoH/Tbpw08u+0u0uxRJEH5x2BjcTDwpeCIh4e2korWtdCeO4PdyplkZBpop6FudWy1FEix+tS73HZdDJW9WHwV4DsANEEogei9fItnH0+iP82B+Lx48fLF1988YMexgcOnufz1BNv8/ijb1IqOxx97B7sNm4Eb765gNtve5bFS9aw667D2W7MIK759SMxub9lCbYZ2Y8/XP/JD2z802Yv4ou/uYe1HQn/i1BODb5NPGS21gNkcPdSgsw6rAfIUzpkEpiUElrALoMGcN2px/HY7Nn89N9TqHsenjSiWBqcimNZvP2Vxpn6NgdeWLaALz55H4vaA+ttK0C4sZO+TP0GqXQF+rROFKFUiMiCKAlC+Di2H/olpJkkSdF2c8JtKzFR2cm/XrQ845ryf3CCOdlYNDt1/MQJvXexDz/a+Vf/Edt8gHtm74tPB4QoX2KLOiUjFHioN0FSQiXWsfEo5+gglAhN1S+TTnPaTVi58aFsbMrdL6HUchFUHwd3FjjbQOmjbE74bCHES1LK8Rurt5VT+IBg/doOJv/teV5/cS5Dt+nL8afvy6BhnQ92ZdsWEw8Zy8RDxsbKd9ppKD/44anh70u/eUdKEez7kgULVzF/wSqGDe3aAFvr2ipYQtDy/9s77zi7yjLxf59zzm3TJ5NkMqmkdyAQAoEESEIgFBNBZMFlfyIKrottRVzZ1UVX18rquj+wUGQVUVdWRQQBSUACCIRQEkIgpBdSJ2UmmXLLOe/+ccqtM5mEO3NnJu/387nJuafd9z137vu871PLIh2e0xZPctP//x0t8WT+QO3N+JWv7pG8wy45+wWy6i74OxWg/OlZ7grBt1tI+tiAaJQHr78GQ4RrTzuVWSOG87s1a7nn5ZUFVc3dMana2LSfDy/9TUaQW+bAT9Zs3TW6e8c9SSCSNuArDPwiOUqBUdBJyncS9eIUlGA5Tl71tk6H5swVXOHDWX3JDHqzcWi3FeGcth1JHqYxsY9BkYIxrUUl5bSiSOa0VIJ8R9l9T9tGLEnmHc1H+SFH+UeUt5qQ/E8QDKzIRYhYEF1I4ZRyxUcLhRKwf28zn7zqTlqOtJOIp3jthQ089uDLfO2HH2b6zNFF/aym5sKpmU3T4PCR4kWIbt65n6/85HHWb3fTkEwf18BXbryYhoH5KqrlqzeR6MxlVWUM5AWOdWhVzohWzlUxBefk3Co4JO6p1WVRbntsGdfNOo2xAwcwYdBAvjjvXJ7fvpW1e/NTrJw6tKHjfhwnP1nzUuEANq/RimzBgPLUR6YTGIIDkwGusPB120q5zz0zIA0yM6y4EtJRabdL34W0s4A4wVVBpau5pe8fRD1keDDl3qOQQHFQhI2OY3uKSVtqL4ZY2Cr77zLUgQoO3AA1pcRLhlcIFRjvwzk9FNyEeI6kazH7Rmf/qzCA1KGPYtTeixGa8B56d2xoQ3MJ+PkdS2luaiXhxRikUg7tbUm+9+XfFX3mOfecCXlGawAUjBtTnBnYkbY4H/v6r3l7yx5StkPKdli1ficf+/qvC8YrHDjcStIu7I5qiGCG8qelgXakE4I/5kyX+04mccE9fSEiwuaDh/jf19dw+b0P8PCbb7H9UBNKKb6yYAExy8L0RghDhLJQiC/Pm9d5o46DNQf2eLERHZN9WBUYjPGWSWkruog72LsvfwWgsMzcayWrUE6m7jzzs/36AJb32Q5GYFzNfIGXKZW0ITuz/ZZYRHOWMAYGw2MjqQ7VdPocikXMGox9HJHC/hqt3Yt0zrAGBP9apMvv+kfDZEc0K8AmHZvg/muj7B3E91+FUtmr/e5EC4US8NLyddipfKNe494mDu0vrvvo5UtOZ2BdBZGIn9paiEQsPv3JhYWFxXHw5Ivr3BrSGfscR9HSFufZ1zflnR8OFygOlME1555KNGTljkXZnkIdXBsxDWJhd/BeNG0cr375Jr7yvvkF02ooCH4B7kzXvWlKKdpSKW7+w+Ms/Ml9LPjxfVSEwvz+2r9l8eTJTB40iCumTuHhv7uW6UOKX+Z06oD6QPgErQtyT0jQ/yDNhDjpfEfeEkGpoCQ4rsFZZRwTzxU1O96gM8ycbLGBYCDbNdZWhuuuqgTbUzuFDJuwaQf3sP0k28otpjO9egqn1c4gJCEiRoSIEaU2PIAbx37yOJ/gsWMZZRSaQaQKFtVxSauVXPWcrQi8kdyMqa49IRGo5dxXZ4tgR7lpu9NV4BSoJpLN33zPfewqWn1UAmJlYQ4V8LhTCsLR4n4lFeUR7v7hdTz62GpeXLGRgQMruGLJ6UycUDy1x469h2gvUJIzkbTZ1dict39ITSWmIcEgnIkhwqcXz+Hq82fwq+Wvcf/y19wfZY4tICBjf3kkRFVVjMYjLRiG8Nw7W3n/f/2C+2+8ikfXrGP1u3uIp1J5twjeS7agQFxBsb2piavu/xUvfOrvuf2SRUd9Hu+Vj087k4e3rMXOKN4TMUPMaRjFwLIyXtizle0th9K+/5IjPb3GZwXzFTAwpy0J+Q84t9ymkSFUcu+Se7UIQZyC4SXVy/1swWRsxUguabiQMwachiEGu9reZUvrJmpCtUysnNJjMQs+MbOeNntX1r64MgtkRXWfTViSntrRq7UgmSn1XC+mkOd15QoLd5Vkkm9DSJO933+2dusDqKpbEIm9ly52Cb1SKAGLrzkrK0MqgGWZnDZ7HOUV0aJ/XllZhA9+4Az+49tXc+stlxVVIABMGTOEWCTfGyJkmUwcla+imjlxBBErY7WQMfP/9PvnEA5ZDK+r5pbLz+eS0yYSzilFGszQcn5XrXaSvYePEE/ZJG2HlkSSXYea+cpDS7nn767ghjkzaaiuTF+Wa8jOtCjm/N+aTPHoWz1TA+rlPTvyxnhRwtdmXci3z7qUJaOnkJkpHVV4gPFv4ajMd9mkSzukvwjXxp95vspSBXWEa8tQhAwnSIMBbgRw7rUODg3RoZxZNzMY/Btiw5hdN5fJVdN6XCAATB7wcUyJeHYAT5gFCa/9pZo7vEdIYnrPxaSQ51FaQGQe8stsdvQoM8+1ModnCeEk3zj+zh0DWiiUgMUfms3chdMIhy3KKiJEY2FOmlDP5//9yqNf3As5b8ZY6gdUEspQ0YRDJuNGDOS0SfnVxyIhi9s/vphY2CIaMjENIWQaXHnOdK5dcFrWuSMH1bpqCNyXZQj/9P7zAnVY5j3FNFxbRQYpR7H8nS20J5Ncedo0PjBjKudPHE1lWZiySIiKSJiIZRIOGekP6WAS98au3cfxdI6NuJ3i6688RdzJtsXYONy99mUAqsLRjJQHaXIH3sxhSanc6meegTlHukrGMVcQeOuJgs9FBdf4tw5JriHb3Ug62UoTQTALu0KVjOHlFxEyolnR3CKQIOLN8N3APENAiRuglh+dkE3u4G8W2Jd5LCspnmSo5pSNyHuPK+oKWn1UAkzT4PPfuJJrb1rAhrd2Uj+0hvFTjj34q7dgWSb3fvlq7vr9Czz50jpMQ7h0zhSuX3xWh8vk2VNG8dg3buCp19bT0p5g9pRRjB06MOucFeu3c/eTL2UlB7Qdxe0PLecHH13Cf/xpOVv2HSRkGlx+xlT+sGotqUSGUPA+2lGK8759N7Y4KENIOQ5Ry6SuopyvXDqfM0aP4Gq0QvoAABwzSURBVOtPPM0f3niLBHaHGbgn1Xe/a+TGpgMF9ycdh+U7NwMwr2Ec//nGci+wDUBwbAPDdHKS4KUT5aWzlKaPC9lqnUzR4HgLB3e27xefVMF1PpZhB/bsdJK83NZLkG7bPxYyQpw7qLhV1N4r24/8kZRzOGevg+P3N+iYaxcJeQmybQQpmHwwf9Yd2Akk06AMFpKVAM8X+umsydWI1TMl7LVQKCFDhtUyZFhtqZtRFCrLo9x87Txuvrbr3jjV5VEunzO9w+P3P/NKQbuDoxQvrtvCw7d8mPZkipBpYBoGLakEj6xah52ZaM/7P2F7g5ehwID2lM2e5iO8tn0Xs8eO4t8uuYBPnTubB15dxY9fWJE3mwuZBn9zyrQu9+14qYvGSNmF3XX99Nrjqgfy4Qln8PN3VtJmJ/Hnno4tXhyCtwrwjejBakChlAT2AsmyG6QHff8OpmfVVwiO8n3t3X1+dLOftkJEdVCRLI0lVuBxc8mQC5hYWfyiT8eLUoq1B+5EUejZe9HMwWCuCHtRzSJuLiQT27X5Z7jJRQqqldKfh7iCIJQhAJRnujdzxIkROb/Hgvi0UND0WvYcOtLhsdVbXVVONJT+E/78xefy8uYdHGxpoy2VKuzW6hDUXUg5Dvf8dSU/+usKd3UzdSJfuuh8Glta+P2at1zhAoQtk1986IOYx5DY8HipL6tk5uDhrNi7PauWc8wMcePUWcH7L5wyj4XDJvCdVU/xyv7tOBnpml3DfOGUE6BwHCmYOTXrgamsSzANJ2slYSs8nbtbo9nyoqADh6iMW5kYnF47lbMHnkzciXNKzVTqo92/6joWGttXklItWasZn5AfaR3M7nPzQwkJTAwcTD8dtleHIVNZZOGro1xVkYXrhgoKSwwUysu2auQIgBhmqDhZeLuCFgqaXsvcKaN5+93CNbknDh+Yt6+uooxH//E6fvb8q9zx1Atuau6jkEjZKBMcW/GnN99h8/6D/M9HruajZ87kpW3bqY3FmDd2TJbw6W7uPG8Jn/jLQ7zauJOQYWArxedPncu8YWOzzjulbiijKmtYeWBrjr3cNRUXLkWXXhH4yfJyB0KVKRDINYr6nk6CgRAxXQOsH63rKMEUlZEkDwbHBvIP4/+W6lBl1p2UUuxu341C0RBt6LGZcCGaE5uCbLHZz8N1Ky2sZstEcDABBxOHFAY2DhWB4Z6sqHBXnaaCpIuZDt1OzhNHLMzY+99jD7uOFgqaXsvHF57Jz59+hXhOAJxlCH933ukFr4mELK6bczr3PPsyR+KFa0Bn7sh03EnYNu/s3c+aXXuYPnQIY+uKmwKkq9REYvzqomt490gzje0tTKgZSCwvayr8cuMrPLL9zSwDs4ii3IoQJ0G25k0VHPj9ATDfs8i1K7iupW5cg2Hg+d6bmIaNjU3CUW5WVNJqKt8xwJ8lL25YkCcQtrRs4c4Nd9Kccl2WK61K/mHsPzCmYsyxPq6iUBkahSEhUirh1an21GOu8ixLUDgdiAU/XsHFtcwrPAMy+cIkCYQK1ERw37kVGMQ6iXDN9xGjZ4zMoL2PNCVix75D3PHQc/zrfY/z2Iq3SRaIfA6HLB764nWMHFiNiFuvoTIW4QcfXcLowR0P2GHL5NZLzydWYHav/ImuKiAkcH/4W/bnFzgqBcMqqjhlYENBgQDwX29mGpvBjxloseOkHH8V4LmZ+sZlSZ/nlmzoxN3Koz5aw/ILvs1fFnyHR8/7OjeOuwgVrEIE28tJkl1bwQtsE+G/tz5E0km3s81u4zvrvkNjopGEkyDhJNif2M93132X1lR+1tyeYFBsFjGrAXHrpgVJwi1Jlw9Kk1t/AXwBaAZCQXmeRr69oPDnZkau+zWakzgkcU3cEp6DWN2XKbYQeqWg6XGeX7OZW+56JEiJsez1Dfz8zyv56Rf+hlg4ewAcVlfFI/9yPXsOHaE1nmDkoJou6fYvP20qI2qr+elzK9nddAQHxeYDB4l7RlzL8CJPc4K0HEcxfnBd0fraXWw9cpAD8QIDqPJSQIkXPZsV6ezip9p2xMAMZsHucsHMqb+glMORZAuXPfNVEipF1LRoTTXn1GkQ4o6JJbbnVpl2VfXv8+rBtZxZdzIAKw+sxClQRMjBYcWBFZw/+Pzjfi7Hi4jBecPu5fV93+LdlmWATV30HIaWncmGg990n2nGAJ7AJESKkNdXA0Uox7Ac8gSn753VmdFZ8AvtuOo/R7Vhikmq9QHEHES44u+7pd+F0EJB06OkbIcv3fd4VgR0WzzJ5t0H+P7vlgfzrEvOmMSMsWk33fqaimP+rJmjhzNztBsnoZTi6XWb+O1rb5JyHBZOHsd3ly2nOZ4IXF4jlsmM4Q1Mqh90/B3sIR7f/haQO9h4G0qlB/oOvOLTXkP+lU52QJynNjENRZtqp81LvdNmxwGTqCRz1B5CSpnYShGzsld9SimOZKwAmpJNJJ38XD4JJ0FTsqmrj6DohM1qZg35Js3xTbxz6G4OJdayo6UJIYQ7f/dRCIZXclR16mUEbqG+Qq6pglukieA7SN8k/VltJFvu1kJB039Z/+6+gqqidsfmwedXB7+WP65YywfnnMzNV5xXlM8VEeZPGsv8SWlj7ZmjR/CNP/+F5zdtJWJZfODUqXz2/OKV1exOOivXCYJycgrzFD4r2DZyVhOGV4s5e+B3XVpFFCnHIGRmryogNxLaxVYO06rT7qcTKicQMkLEnexaGhEjwvjK8Z03uptpir/Nszs/gq3igEOLN3ePgGc7URg4hHHTV3T2iFMIYc/JNIXCyglxVrju1WYH0dtBDWanZ9WZWihoepRIyMoKRoOMvENpdSztiRS/eXY1S2ZPZVxDvqdRMRhRW82P/mZJt9y7u7lg6ATuePNZEqqTFOSksxrlqy8UhuGvKHIHcpUnJPz7+VLbVoKpnCzXTMHNmJqZYjskFgvqZ1MfTX+H4yvGM65iHOuPrCfhuM4AYSPMmPIxTK7Mrg/S06zZ/z1slZlu3u1vgggx4pjYWNidrgwyr/RxEBKenSGzKmyhamyZx0QprPDU4+nKcVMUQ7OIREXkERFZJSL3SwHfskLndOU6Tf9i9JAB1Ndm5B+CDm2dKdvh2TWbe6hlfYuJNYO5bsKsDganwP0HxzECLyQ/bUU6FR7YjmDbx/KzU1iSImQoL9OqH/2svMI5EtzddfF0eGrvcyzb80JwBxHhs+M/y1XDr2Jk2UhGxkZy5fAr+dyEz5XULRXgYLxwfiGFUG6Nw5JhmEZFFzyR8LImpV+5hXbcaGg3NiE3Zb4f0awQwlW3HXd/jodieR9dC+xQSp0C1FK4RFChc7pynaYfISJ8/xOLqasqozwaIhaxMM3CPyzLFCI9GB/Q1/inUxbwqSlzsbLUD360sv9eOKl8MJ+YMNdVfYjjvgz3mD+IpxxyvGkKYxlOVm4g7x8vFXZ2ZlbTsHFIYSubH268n++tuxfbU3tZhsWC+gV8depX+eq0r7KwfiGWUfrvOmwUrt9gSoTZwx9ifN0nUcQzUodnkhYApieYbdyiOSYErrthIAKuTUIg6UUx+/EMVvDOxQidUuRedk6xhMJ84Elv+ylgXhfP6cp1mn7GSUMG8Kdv3sC3PnYpX7x6PrOnnFTwvJStWDijtDrm3s5npp3HjZNmY/rJ04SgtgJA1Axxy7QFPLd3PYaBZ0wu4GLpGNjKX024hXjyhYQTpLfIvd510zSwvcAPU9KqJf/8lw68zsPvLi1e57uBcTX/D1NyMxUb1Jedi4jJtqY7cFTaScLMEgt+hIHjeSW5zyVJup5C2N8rftUEQRHCMhqwMDAxsDCxMNz3EgaVm4+peymWUKgDfLeBZqCQE3mhc7pyHSJyo4isFJGV+/YVjnDV9C0s0+CcaaO5eNZkXnpnW8FzYmGLQdXH7nV0IrH6wE7uW/+SGwUr2fWE6yLl/NuMS1gwdCKNcTdliOogzbYbrOaqmkTAUe4AHxTyQWF2mCowTcpx7Q6FbBIpZfP47meOq589xeiqqxkUPct758/8bfa2PM2B9tdJpPaSXhn552Til+nMtpHZCCHvyykklFX4DJAolqc4EvFLczrEG69CdepYUFyKJRQagWpvu9p735VzunIdSqm7lFIzlVIzBw3q/e6Cmq7jxyoUPNaFNBUnOr/Y8DKJIIGepw5SEJUwPzzrKt4/yo0NOHlAfgrzXAzSNRDAFSBeCByWOEihyvPe8czP7/gcRWuqcM3w3kRz4k1/2MevreAQZ+3+71Ee9mslS0aQWzYK8aqtKQylsMS1LzjuwQIkwBxAOLoIPGGQxkE5u3ASzxe9nx1RLKGwDLjQ254PPN3Fc7pynaYfEw1bjGnIXyCKwKwJI0rQor7FvvaW/Fw5CKYYHEqmB+CbJs4nZrqBgb6x2cXfdnMYlZkRIoZFmRnGzLAf+Kmvs1cP6f99s6jheetkK1UcTE/PnlRt/HTTr0k5+ZX6egO2aiNuNwbiLXN4bk68zejaL2IE6iU3NsN/Npk2BV+cGOJmlDWko4gRECknEj4DwxhQWKSqFCq1sTgd7ALFEgoPAMNEZDVwANgoIrcf5ZxlHezTnGD86zUL3brK3kw0ZBqUR8PcfHlxYhT6MwuGjidqZkaBu4NSm52gKpTWjY+rGsz9cz7GnMHjMwrGe14xhoNlgmEIccdh9qBJfGbSIiJGuiSM8mfGyiCVIxgEMEUoM6PMHDCBCqsMx3G9kpRSiErbFhSKp/c9z12bHuj2Z3M8mBLBkEjBYxFzEDWxs5hW/9+Uh9wVQ3Z4oGS9QmLj+1D4T9xPhOejANMcSjR2MUZoClCW/8FiIdaE/P3dhOS6QvV2Zs6cqVauXFnqZmiKzLZ9h3jg6VfZsLOR6aMb+NB5Mxh8HFHMJxptqSSXL72Xd1sP0W67UcJ+Ns6IaXHT5HP5+KTsgLw9bc0s+ct/0mYnEXHy9P8CjCofSGNiL7ayczRCbj4g95p0CunxFcP58tTraIjVcf/mh/jdzifJHC4NVJaROiQWP575bSqs8m54Ku+Ntw/cwaamn2Or9mCfKVGm132JEVWLAdjV/FO2H/w2igQGTl4dahObmDiBx1EmJn6VNRCpYciQFRhGJUq10b53HjiNENR1CCHWOCIDH33P7roi8opSaubRztMJ8TS9gpGDarj1qvnc+9mr+OySuVogdJGYFeK3F1zPtWNnYkjaJVQB7XaKO9YuZ9uR7Gpu9bEqll/4z9w67VLKrVCeQVgBW1oaSTq5AsHFVka6dgOup9K21n3UhatoTbXxx12+Fjg9a3ZyVEqmYXIwUbqUFp0xsfYTnFR1NaZEMSWKJeVMqL2J4ZXvC86JWich4q7Qcu0KBoqInwqvwJzbxq2p4GAxYMCPMQw3g6xIjOjAhzCiC4EwSBlm7Aoidb/u0fiN0jsGazSa90S5FaY6EsUQycq6Ca66ZunOd7h+wllZ+8OmxdUnncUP1/+5cNmFjjXgHRwT9sQPsr11B5aYJFW+zcDJqPrmKIdBkd6ZeFDEZErd55hYexMJ5yARsw5DshM11sTOI2QOJJ6KAylsBNN79n6d6k4/A6it/i7RaLaKVMwhRGp/VKyuHBd6paDR9ANMMQr+mAXJCW7LZkbtSZ3E5BYiP5E0QMpJ8cSu5/jhhgc5kkpidxIMFzHCLB56EVGzsO6+t2AaEWLWkDyBAK7gmDrkQWpi5+P6Flk4hDAljGB49pP0K+/ewKGmWzl85Ofd2ofjQa8UNJp+wEXDJvNfbz7jF0nI4sJh2fn4m5NtrDqwnapQlE9NvJBXD2yh1faT0/mJ7bxkGFkF6d1jubWYw4ZFZUjxp13PZqwQXEOzGaSWNoiZBnWRapYMXcS8wWcXodelJWQOYuLgu1Eq5fkbQUt8Jc0tv+FI60Mo2vM8mAzSVdgU7Rxq+lfKYpdimr1n1aSFgkbTDxhZUcsXT76Ab61eGkQ2KwW3zVjEkLJ01a4HNr3Af771JCHDxFGK6nCM7874EHdtWMbqQ9tQpAct2zFQYns1GdzB3cQr5ul5FFliMr16JJtbNueojLxkfEoImSYX1p/D9aOvLHluo+5AxAoG/orobGLhybTFnyFlH8AmjqncFBeFsrmIWLTHn6G87IoebXNnaKGg0fRhdrU289VXH+eZ3RsxRZjXMIEZdUOJWWEWDJ3A4Fi6DOar+7fyg7eXEndSxL04gba2BLetepg/zf8M31r7Bx7b+Tphw3KNzDiYhpBUKcwM7xqF4bpVKhhfOZyR5XW8dXhdXtssMZk1YBrXjLqEUeVDe+Jx9ApMo4ZR9U9y4PCdHDp8DzZJr8Rn4cyz0oELbKnQQkGj6aO0pZJ8YOlPafQC2JLA0nfXselwI3+88MaMeASXX29ZQdzOLm6jgKZEK2uadvIv067ghnEL2HB4N0NiNdRHa/j99hdYuvt1trW+mxMk5957b/wQ9dFJhMTKMy6HDIsLhsw+oQSCj2XWURaeQYsRRqn8gkJpFNFI70r5poWCpt+hlGLp2g08+PIbJG2b9506hfedOomQeZSqM32MR7a9yZFkPGuwTiqHHS1NvLh3C2fXj846vynRWtDoaYhwONnOU7vXcNf6ZexuP8S4yiF8auIirh19PlePmstlz3yJ9pxqaQJMqhrBBfWz+c22J7OEgoFQbsaYUduz9YV7E8nUOyjVAqRjxv0qpiJhRCwGDrgPwygQsFZCtFDQ9Dtue2gZj656m7akO4it3r6bR1e9zd3XXYHRYf6evsdbTXtotfNnoSnHZn3TvjyhML9hMq8d3OYFuaXFQ8JO8eqBDfxi83O+xYBVB7dyw4s/IWqZNMSqabPdmIXMpxcyLK4fs4jacBVfm34Tt7/9Mw4mm1FKMbp8GF+Y/BHMo5V/68eErHGIlAeCwcGvnx2mqvw6aqpuwTB6XzyOFgqafsXGvfv54+tvEU9l1IBOpli1fRfPb9jK3Aknla5xRWZS9WDKrBCtqWzBYBkmY6vyq9UtGTGDB7e8zMYje7D9Ojzi1q24f/OzeecroC2VYltrIyCYRraxtCE2kHGVrmpoUtVo7j7jNvbFDxIyLGrDVXn3O9Eoj13M/qavYdvtpCOULQxjCLXVXwqC33obOk5B0694adN2CnmGtyaSPLd+S4+3pzu5bORUysxwlu0gZBg0lFXlrRLAra1w6fCTMcTwa+MA0GYnulBgR3Ac00v05r52tmVHSosIg6MDtEDwEAkzbPCjlMUW4c6/Q5THLmbo4Ed6rUAAvVLQ9DNqymKYhlvoMJOQaTCgPFaaRnUTZVaY311wPbe9+hjLd2/CEGHR8EncNmNRnpHZ5w87XiPVaV3njsmVG3URPfgfDcusZ0jdPUESvL7gkquFgqZfMW/SGE8oZGMaBotPnVKCFnUvQ8uruXvu1V0edJIFU1YLtkonaUujMIzCS4ioEeK6ky44rjafiPQFYeCj1UeafkUsHOKej1zBwIoyysMhyiNhKqNhvn/NZTTUVB79Bn0Uv1LX0Vg0dDphI9/4q1RunWeFeHUA/H2GOIQNiworyg1jL2bR0KMm3NT0QfRKQdPvmD58CE//0w2s2bGHpGNz8vAGwtaJ6wWTyfXjzuXp3W+zs+0QbXaCiGFhisGnJy3gt9tfZFtLY1AmJ9tRSzCwuO/Mm2mI1mIVECya/oEWCpp+iWkYnDKyodTN6HWUWxH+59xPsHTXWl7Zv4WhZTUsGXEadZEKrhl9NnE7yU/WP8Fvd7xIwsmOOzi5ZjQjyvK9mjT9Cy0UNJoTjJBhcfGwk7l42Ml5xyJmiI+NW8hrhzaztWUfSSdF2AhRbkX40rQPlqC1mp5GCwWNRpNFmRXh3jNvYuWBjaw/vIuGaC1zB08mZOjh4kRAf8sajSYPQwxm1Y1nVt34UjdF08No7yONRqPRBGihoNFoNJqAoggFEYmKyCMiskpE7pcOHKbF5Wci8qKIPCwilogsEpEdIvKc95pYjDZpNCc6cTvF2kO72NPWXOqmaPoQxbIpXAvsUEpdJiKPAAuBPxc47xzAUkqdJSJ/AS7ETRz4I6XUvxepLRrNCc8vN73M7W8sQwRSjsPpA0fy/VkfoDrcv1J9aIpPsdRH84Enve2ngI6qRuwBfuBtJzL2f0BEVojIbztaZWg0mq7x3J6NfOeNpbTaCVpSCeJOipcbt/DZl/631E3T9AGKJRTqgCZvuxkYUOgkpdR6pdQKEbkcCANPABuBLyulZgENwHm514nIjSKyUkRW7tu3r0hN1mj6J/e+81evZkKapOPwyv5tWpWkOSrFEgqNQLW3Xe29L4iILAY+A7xPKWUDB4Cl3uEtwODca5RSdymlZiqlZg4aNKhITdZo+id72w8X3B8yTPbHW3q4NZq+RrGEwjJc+wC4qqSnC50kIkOAW4BLlVL+X+7ngKtFxACmAWuK1CaN5oTk7MFjsCT/p62UYkylTlOh6ZxiCYUHgGEishp35r9MREaLyO05530YV0X0hOdpdD1wB/AR4CXg90qptUVqk0ZzQnLDhHOoDEWzBEPMDHHztAuImr23uIumdyDq6CWXehUzZ85UK1euLHUzNJpezd62w9zzzl95bu9G6qOVfHTC2cypH1vqZmlKiIi8opQ6ar5zneZCo+mHDI5V8s+nXFTqZmj6IDqiWaPRaDQBWihoNBqNJkALBY1Go9EEaKGg0Wg0mgAtFDQajUYToIWCRqPRaAK0UNBoNBpNQJ8LXhORfcDWLp4+kE7yMPUjdD/7F7qf/Yve0s9RSqmjJo/rc0LhWBCRlV2J4Ovr6H72L3Q/+xd9rZ9afaTRaDSaAC0UNBqNRhPQ34XCXaVuQA+h+9m/0P3sX/SpfvZrm4JGo9Fojo3+vlLQaDQazTHQ74WCiIRE5I8Z76Mi8oiIrBKR+0VEStm+YiIi5SLyBxF5XkS+U+r2dCci8gUReVZEHhORcKnb052IyD+KyNKjn9k3EZeficiLIvKwiPSLlP59dazp10JBRGLAK8DCjN3XAjuUUqcAtTnH+jp/C7yolDoHmCoik0vdoO5ARMYAU5VSc4HHgOElblK3ISKjgOtK3Y5u5hzAUkqdBVSRLu3b1+mTY02/FgpKqTal1MnAjozd84Enve2ngHk93rDuIw6UeTOSKJAocXu6iwVArYgsB+YCm0vcnu7kB8CtpW5EN7MHt5/Qv/5m++RY06+FQgfUAU3edjMwoIRtKTa/BC4G3gLeVkptLHF7uotBwD6l1Lm4q4Q5JW5PtyAiHwJWAf26brlSar1SaoWIXA6EgSdK3aYi0SfHmhNRKDQC1d52Nb0j/LxY3Ar8WCk1CRggImeXukHdRDOwztveBAwrYVu6k8twV0W/Bk4XkU+WuD3dhogsBj4DvE8pZZe6PUWiT441J6JQWEZaZzkfeLqEbSk2lUC7tx0HKkrYlu7kFeAMb3scrmDodyilPqSUmgNcDbyilLqj1G3qDkRkCHALcKlS6nCp21NE+uRYcyIKhQeAYSKyGjiA+8X1F+4EPiEiLwAx+lffApRSLwCNIvIysE4ptaLUbdK8Jz4MNABPiMhzInJ9qRtUJPrkWKOD1zQajUYTcCKuFDQajUbTAVooaDQajSZACwWNRqPRBGihoNFoNJoALRQ0Go1GE6CFgkaj0WgCtFDQaDQaTcD/AdyZlw4pZ0biAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "lda = LinearDiscriminantAnalysis(n_components=2)\n",
    "lda.fit(X2,y2)\n",
    "X2_new = lda.transform(X)\n",
    "plt.scatter(X2_new[:, 0], X2_new[:, 1],marker='o',c=X2_new[:, 0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}