{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 逻辑回归\n", "\n", "逻辑回归(Logistic Regression, LR)模型其实仅在线性回归的基础上,套用了一个逻辑函数,但也就由于这个逻辑函数,使得逻辑回归模型能够输出类别的概率。逻辑回归的本质是:假设数据服从这个分布,然后使用极大似然估计做参数的估计。\n", "\n", "![theory](images/linear_logistic_regression.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. 什么是回归\n", "\n", "一说回归最先想到的是终结者那句:I'll be back\n", "\n", "regress,re表示back,gress等于go,数值go back to mean value,也就是I'll be back 的意思\n", "\n", "在数理统计中,回归是确定多种变量相互依赖的定量关系的方法\n", "\n", "> 通俗理解:越来越接近期望值的过程,***回归*** 于事物的本质\n", "\n", "最简单的回归是线性回归(Linear Regression),也就是通过最小二乘等方法得到模型的参数。线性回归假设输出变量是若干输出变量的线性组合,并根据这一关系求解线性组合中的最优系数。\n", "\n", "通俗理解:输出一个线性函数,例如$y=f(x; \\theta)$,通过寻找最优的参数$\\theta$使得观测数据与模型数据相吻合。\n", "\n", "![linear regression](images/linear_regression.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## 2. 逻辑回归模型\n", "回归是一种比较容易理解的模型,就相当于$y=f(x)$,表明自变量$x$与因变量$y$的关系。\n", "\n", "以常见的看医举例,医生治病时的望、闻、问、切,之后判定病人是否生病或生了什么病,其中的望闻问切就是获取自变量$x$,即特征数据,判断是否生病就相当于获取因变量$y$,即预测分类。$X$为数据点——肿瘤的大小,$Y$为观测值——是否是恶性肿瘤。通过构建线性回归模型,如$h_\\theta(x)$所示,构建线性回归模型后,即可以根据肿瘤大小,预测是否为恶性肿瘤$h_\\theta(x)) \\ge 0.5$为恶性,$h_\\theta(x) \\lt 0.5$为良性。\n", "\n", "![LinearRegression](images/fig1.gif)\n", "\n", "然而线性回归的鲁棒性很差,例如在上图的数据集上建立回归,因最右边噪点的存在,使回归模型在训练集上表现都很差。这主要是由于线性回归在整个实数域内敏感度一致,而分类范围,需要在$[0,1]$。\n", "\n", "逻辑回归就是一种减小预测范围,将预测值限定为$[0,1]$间的一种回归模型,其回归方程与回归曲线如下图所示。逻辑曲线在$z=0$时,十分敏感,在$z>>0$或$z<<0$处,都不敏感,将预测值限定为$(0,1)$。\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "plt.figure()\n", "plt.axis([-10,10,0,1])\n", "plt.grid(True)\n", "X=np.arange(-10,10,0.1)\n", "y=1/(1+np.e**(-X))\n", "plt.plot(X,y,'b-')\n", "plt.title(\"Logistic function\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.1 逻辑回归表达式\n", "\n", "这个函数称为Logistic函数(Logistic Function),也称为Sigmoid函数(Sigmoid Function)。函数公式如下:\n", "\n", "$$\n", "g(z) = \\frac{1}{1+e^{-z}}\n", "$$\n", "\n", "Logistic函数:\n", "* 当$z$趋近于无穷大时,$g(z)$趋近于1;\n", "* 当$z$趋近于无穷小时,$g(z)$趋近于0。\n", "\n", "Logistic函数的图形如上图所示。Logistic函数求导时有一个特性,这个特性将在下面的推导中用到,这个特性为:\n", "$$\n", "g'(z) = \\frac{d}{dz} \\frac{1}{1+e^{-z}} \\\\\n", " = \\frac{1}{(1+e^{-z})^2}(e^{-z}) \\\\\n", " = \\frac{1}{(1+e^{-z})} (1 - \\frac{1}{(1+e^{-z})}) \\\\\n", " = g(z)(1-g(z))\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "逻辑回归本质上是线性回归,只是在特征到结果的映射中加入了一层函数映射,即先把特征线性求和,然后使用函数$g(z)$将做为假设函数来预测。$g(z)$可以将连续值映射到0到1之间。线性回归模型的表达式带入$g(z)$,就得到逻辑回归的表达式:\n", "\n", "$$\n", "h_\\theta(x) = g(\\theta^T x) = \\frac{1}{1+e^{-\\theta^T x}}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.2 逻辑回归的软分类\n", "\n", "现在我们将y的取值$h_\\theta(x)$通过Logistic函数归一化到(0,1)间,$y$的取值有特殊的含义,它表示结果取1的概率,因此对于输入$x$分类结果为类别1和类别0的概率分别为:\n", "\n", "$$\n", "P(y=1|x,\\theta) = h_\\theta(x) \\\\\n", "P(y=0|x,\\theta) = 1 - h_\\theta(x)\n", "$$\n", "\n", "对上面的表达式合并一下就是:\n", "\n", "$$\n", "p(y|x,\\theta) = (h_\\theta(x))^y (1 - h_\\theta(x))^{1-y}\n", "$$\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3 梯度上升\n", "\n", "得到了逻辑回归的表达式,下一步跟线性回归类似,构建似然函数,然后最大似然估计,最终推导出$\\theta$的迭代更新表达式。只不过这里用的不是梯度下降,而是梯度上升,因为这里是最大化似然函数。\n", "\n", "假设训练样本相互独立,那么似然函数表达式为:\n", "![Loss](images/eq_loss.png)\n", "\n", "同样对似然函数取log,转换为:\n", "![LogLoss](images/eq_logloss.png)\n", "\n", "转换后的似然函数对$\\theta$求偏导,在这里我们以只有一个训练样本的情况为例:\n", "![LogLossDiff](images/eq_logloss_diff.png)\n", "\n", "这个求偏导过程中:\n", "* 第一步是对$\\theta$偏导的转化,依据偏导公式:$y=lnx$, $y'=1/x$。\n", "* 第二步是根据$g(z)$求导的特性$g'(z) = g(z)(1 - g(z))$ 。\n", "* 第三步就是普通的变换。\n", "\n", "这样我们就得到了梯度上升每次迭代的更新方向,那么$\\theta$的迭代表达式为:\n", "$$\n", "\\theta = \\theta + \\eta (y^i - h_\\theta(x^i)) x_j^i\n", "$$\n", "\n", "其中$\\eta$是学习速率。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.4 示例程序" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "#from __future__ import division\n", "import numpy as np\n", "import sklearn.datasets\n", "import matplotlib.pyplot as plt\n", "\n", "np.random.seed(0)\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Original Data')" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# load sample data\n", "data, label = sklearn.datasets.make_moons(200, noise=0.30)\n", "\n", "plt.scatter(data[:,0], data[:,1], c=label)\n", "plt.title(\"Original Data\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def plot_decision_boundary(predict_func, data, label):\n", " \"\"\"画出结果图\n", " Args:\n", " pred_func (callable): 预测函数\n", " data (numpy.ndarray): 训练数据集合\n", " label (numpy.ndarray): 训练数据标签\n", " \"\"\"\n", " x_min, x_max = data[:, 0].min() - .5, data[:, 0].max() + .5\n", " y_min, y_max = data[:, 1].min() - .5, data[:, 1].max() + .5\n", " h = 0.01\n", "\n", " xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n", "\n", " Z = predict_func(np.c_[xx.ravel(), yy.ravel()])\n", " Z = Z.reshape(xx.shape)\n", "\n", " plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral) #画出登高线并填充\n", " plt.scatter(data[:, 0], data[:, 1], c=label, cmap=plt.cm.Spectral)\n", " plt.show()\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "\n", "def sigmoid(x):\n", " return 1.0 / (1 + np.exp(-x))\n", "\n", "class Logistic(object):\n", " \"\"\"logistic回归模型\"\"\"\n", " def __init__(self, data, label):\n", " self.data = data\n", " self.label = label\n", "\n", " # parameters\n", " self.data_num, n = np.shape(data)\n", " self.weights = np.ones(n)\n", " self.b = 1\n", "\n", " def train(self, num_iteration=150):\n", " \"\"\"随机梯度上升算法\n", " Args:\n", " num_iteration (int): 迭代次数\n", " \"\"\"\n", " # 学习速率\n", " alpha = 0.01\n", " \n", " for j in range(num_iteration):\n", " data_index = list(range(self.data_num))\n", " for i in range(self.data_num):\n", " rand_index = int(np.random.uniform(0, len(data_index)))\n", " error = self.label[rand_index] - \\\n", " sigmoid(sum(self.data[rand_index] * self.weights + self.b))\n", " self.weights += alpha * error * self.data[rand_index]\n", " self.b += alpha * error\n", " del(data_index[rand_index])\n", "\n", " def predict(self, predict_data):\n", " \"\"\"预测函数\"\"\"\n", " result = list(map(lambda x: 1 if sum(self.weights * x + self.b) > 0 else 0,\n", " predict_data))\n", " return np.array(result)\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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9EXHmFm1F5GPAxwCm3fQpn83xUijaBL5uHN6ZTWZzFtOXt4QzChWbGwG1aoTtaGG/djPJ5nqA7yuyOYviqNMnlpNTLndrrYGBZGzSQfZRJJTOWLEhJN1o3SWdtvADhW3LgbJNRIRM9uTCF+EOnaOiqL8JSTJlkUjqxeowVN33JyJcupIknfVZXgg6LskkU9rtMm5WbtvC5auJrne+iFAu7dxBq9VSLMx5XL2xU4mY4UHgN37pJwD45qeKRzKL34mTEvx54GrP71fajw2glPo48HGAR9PFB7q3m4gwMeUyOu7ge3qxz3G3BCMMFXdeaRH4PSl95ZDpSy4zs8MXX1Npi6s3kqwsejRber/jE87QRiZRqFhfCyiXQiyBwqhNvmBTrQy6QIrogUAPBnKuumFlczaV0uB7ch0ZWCze3PBZWdyyZ8jlbWZm3e6dV3HUpVB0aLUUtgVuYvdQVO9gkE5bu/r1NOpR32BjeDB45zM/C7AVrvnUyR37pAT/U8DPiMivoxdrSxc9ft+LbQt2evBLvbke9Ik9tLN4Fn0dL99htp7OWFx7aPfm1ypSvPpKq68QankxYGVp0IvGsmBiyml3Z9q/CEWhYm01oFJuDyxjNsXRg+3rIExOO9SrIWGP5ovAzGx/umqtGurZe8/7r1ZCFuZUN9avXyukUgc7dzdhMVK0u8VYw4iio89cCsQGFM723FrDsfCmp/Td3Aet/14/cAyhmr1yVGmZvwa8B5gQkTngfwZcAKXUvwc+g07JfAmdlvnfHcVxH3Qq5XgxEHRz63TmcEIZhYNi3yHuuHoB82CLiVGkuHNbH6uz75XFgEZdHah5SLMZsbLo06hH2LZeDxndZSByHGGkaLOxtqX42ZylZ9s9bPfmB/3eq5WIpYUW+RGne4dzGKYv6SK7lUW/bxDqYDuCc4RTspKT5c+m3sZiagKAy41l3rP8ZXLh6QnQg8zP/8hPnfYpDHBUWTo/vsvzCvjpozjWRULfyseL8VHM+pYW/aEmY8Notk3D9kulHPaJPbRFtBzSakX76u7ktbT9QW+l7+qSvhvaqc6gvBmyud6vrLVqxPKiz/TlrdcFwfBrsrkeUdrwyOYsLl89nA2EiFAoOmRzNndebnatnDvkRyxuv9TqWmhMTu9s4bATvtj89uz7adkJlOh93E9N8dtX3s+P3/k97P34QRuGkvrcR/lPL6R0PP4McuYWbQ1bjI47NOrewGwzkRASh2x/p5SKjWfvyD6zfHqJM0zr0KzvT/DXVoLYSt/N9ZDxyeEx77XV+Jl7aTNkakZ1F7TTGQvfG35tlNIDRaUcMlI4/FfIcYQbD6cobQTUa9piQ9GfVVSv6UHu+kPJAw24r+SuElh2V+wBlGXh4XIne5mHarFLaoZdePITT/D1mw9vZdYc86LrYTGCf4bJ5W3GJpytLB70AuPs9aPpgbdXX/oOlsDCvEcU6qrQqRl3z1k2rjukuradVrp1TkrPxDe0OI8UbIpjTt96RXNI9aqItkWOWw8ButXC21G0Y+VtLRyfdKiWw1gLha3z1HcMRyH4vhdRr0ckkhaj4w5KwUvfbcYOTmsrPpev7j9zp+TkCazBtNlALMpu7qCnfiE5aJXrWcAI/hlnYspldMyh2dBpmcnU/srvQyxeyV1lOTlGwa/wSPUOycjXC45p6Zb39yIWzFxyWV0OuuEN22l3pmrTairm7nhcuZEgk9mDS+aorf1mtj1uW7qwqcPCnE+1sjWzXW3pRd5rN7dSHhPbrIs7KNU/eGwnlRlSn+DYfYVuiYTF9VtJ1pYDatsWeY8SpbSL5uZ6qBuroAet6cvuUOuJYV4/uzHhbeBGPv420XdUxHhr80D7PC80rQTPFB7hbuYymbDB46UXuNJY3vPr3/RUwL/8yN/fCtOcM5HvxQj+OcB2hOwByu2bVoL/cuX9NOwUgeXiRAFPjz3Oh+f/lFG/zPSlBPe2WSeIwI2HkiSSFvmCrc3XUNx+adDdUikdO792c/dzc12LK9cTLLTbDoK+S+iNgzebUZ/Yd47Raqk+F83xSZdadbDOID9iY9u6QrhSDrFtKIw5pNohkMlpl7v1FmG01eottG1eePydBIkNHqu83N1fIqH73yqleOm7zYHZvghD01z3gu/p99pdQFZb+r604A9N2TxoGuz12jyZoEnFsYjao5sVhYz4Va40lg60z/NA00rw/1z9YZpWgtDSf6/76WnetvZNHi+/NPR1T37iCf65/4YtkT/B1MnjxAj+OSCKFPVa1M2B36sXzpfHHqdmZ7pf8MByCAOfL7m3+IHSV0mldex4c92n1dQLg8UxpxunFxFcV8/sh4V/Wq29p/ZlsjYPvSaF7ynEYsAPvlEbYjQWQb225aKZSlvMXkuwtKAXnaVdOzA55XDvVa+v8YmOzzsUx1xSKYvGE49SXmqQL63RyOa588gThE6Cb4cZHuY2CfrPQUSYvZZg7q6nRVlt2Ujk8vuPpfu+Yv5uC681vIo5iiCT03cj2we18cmDfWVtFB+Z/xO+PPY4L+euISgertzhbevP8CBn+T9TeISGlex+B0B/D748/kYerdzGVVu3b296KthKnTzHs/idMIJ/ximXAhbnfXqjOJevJsjmdp9V385d7fugjy/c5XVf/3MA7kU+lgVXriWZnB5cE4gi1c4GGixK6mW/M04RITHEE8hx4uP8cZYQ2ZzNQ4/YRJEWfBGhtBkMdLlSStcV5AsOti3cnrzF0rVJAHKlNd7wpT/F8T0QuK0irlxxBq5tJmtz6zUpquWQMFRkcnb3rmE/KKXN6LxdMqMEbWftukJpQ9/xOA77WjOJIxV5vHv1q7x79asH3sd5427mct93oIOoiLVEkT/7P3/4zGbUHAdG8M8wvhd1bY57RWz+rset1+7ePEN6UllStQqPfe3PsNvxFAWEEdy70+LWa1Ldu4bAVyzMe13/92RKmJlNMDruDDQGF9FrDEdFNh/vognDm5n33u0MM3sT0VWrubxNOmyBUlhRyBv/8g9xfa9v2/m7HjcfSelF5h5sWw4VwgFt1Lab2yjo95/O2GRzDlMzqmv7YMzU9k8mbGzdlvXQchL8X+/+2wSfuljOpKbH2hlmJcbmuEO1vPtK4msrr2JHeruZey8iMV4yOp0xIAwVSinuvtrqij3oxdl7t1sUx2zGJp2u34zjCjOz7p7uNPaKZQnXbiRxXdGzdkvXG1y5nthxMbb7+h0GQKUUpY2AG/PfxVEh40tzfQNidzt0Y5LjIAzUdt0ZQEQ3Su8MZCLStW7uxfcjFuY8Xnq+we0Xm2yu+12vnotCy3Jp2MkdKwieKL1AItG/hQL8pE2QuFhiD2aGf2aJIkUlxpERtEiHoaJRD6lWQu3jXrRJbPNzecvGd1hMTbCaLJJs1rHiBC6C1eWA1eWAdMaKLTpSSs+eJyZdxid02uBe1xH2SzJlcfORJF67SCu5j6YgxVGb6pDq5Pv3dFhMFua4HnyNZjoXOwCidi68OgyptDV0AE8mhUzbCG+3GosgUNx5udVd/A5RLC0E1OsRl688+GZrdTvJZ6fewUJah+byfo33Ln+Z6dZa33adStfcRoPR5Xq3mYSftFm+MnLi530WMIJ/Ruks0g4TiEY9YnV5K8SyvhowfcntCzu4KuRD9z/LUmqCtbToRPohs/zOMeNQim4apIjsOks9LCIy1Pt/JzJZfReyvhJ00xyBboZN531efflZQsfBjhkARTjSu5ZeHFcojtl9BVWd9YlrN5N7tofeWIu3YqiUIiojQV97TK8VafsJR8jmDm8Hcdoo4Hcvv5eSm+8WkZUSI3z68g+Q/ukkVl4GYvLV0TS1Qgq3FRDZ1oWc2Xcwgn9W2WGSaTu60nPAVK3dGtHuWeAUYKa5yrStuJeCZmP/BVdisWNj9bPExKRLcdShXguxLMH3oz7Xyw52EJBMCp5Hn/im0hbZ3PG91449wsZaQBRtFdftpxdAozb8D7h03+92zFq671NuV1PrhW24ejO5r6rms8ZiaoKak+mrGAYd3ln5zSSlyUzs65QleGnTP9gI/hmltxipD9GZMY16vKtarRZf/Smiu0yVSiHlzXDH3qzb7yxsW6chnhccR7rXYGN9uDhmcsJY2unOuEeKFsU9NGE/DLqNonOoCl03KTSG+J2F7faY9XrY16qx8//8XY+bD8f79p8HZv7po7R+18Xa7uQKuDvYYRg0RvDPKJYtXJp1WZjX5a3d/O8RG7GgUY/xqgdkh6xqsYTiqENx1OHu7Vas6DsOjBQdypsBkdLHm5hyjy1mvxNhpPiadZ3nZl6Pl0gxXlvjXaVvMhmU9ryPXM5iJeZxnUuvm7gchT3CTgzrnnVQxsYdypvx4tZZ7B7W3SvwFV5LDXTmOsv0GpI5zwRcYvDvHwk000bOdsNcoTNMvuCQythUSvr2P5u3SactGvUw1ke90zFrL0zNuNpxclua5cyszvGfnD7d21+lFJ+3Xssr199A1O7mvlyY4XdyE3x0/o8ZCyp72o+bsBifdPosjzuFWtttkY+aKFLM3/Vo1LfWY9JZi9mriUMNoMmURWHUorQxOGBnsha2LURDunvttC50VnjyE08g3/tDWw1CegzJgqRDI+uSrvndWb4CItuiVuzp/xCpLa8KQxcj+Gcc1xXGJvrFN52xu3nxvVy+mthzLDiVtrj+UJK1lYBmUzs0jk+6ZyZWX24Ir7zucaJeQ3gRQsvmyyOP8YH1L+15X+OTLtm83U237PjZHxVhoChtBnrmnNaWx5YlLLf9+nvrKBq1iNUln6lLhzPAm76UIAo9KuWoq2mJpHCp3QltpGCz2hpcu0A4k7P7gSrXTw736F+dzZNfb5DfbCGRopFPsDmRQVmC2wwYX6ySaOo7oNpIgvXpLMo+G5/r08YI/imgIt3HdnMzRClFLm8zOeX2LbbuxuS0S2HUplaJsCwGFmv3QjKlPd2PAwU8n7/JN4qP0rBTzDRXePvatxjzy3t6/ZrKxObJY1mspsb3fT6plEVqB6/8g9JqRdx9pdUVdSnB+krA9YdSQ+/CSpshU5cOd1wR4fLVJL4X0WwqXFf6vPKLYw7lUthn4SACl2YP5+F/lPzGL/3EwapcRaiMZ6iM9y/Q2n7EzN0SEm1laGXKHq4XsnjjAMd5ADGCfwrM3/P6/OFLGyG1asTNh5P7utVPJCwS42dz5vL06Ov5VvFRgrZh1d3MJRbSU/ytuT+i4Fd3ff2IasaWxKMU+VZ8OCeKFIGve/juJ+vlMCzOe33GakrphixLi4N9DLbO8+iO7yYs3JhxzLKE6w8lqZT1Z8txhOKovafeu8dJ6nMf3fKOP2JDstxmE3rEHtqLua2QRDPASxm5M1dgB5TSpfDaT+ZoBKTZiGKbgYSBbkhy2PL9s4AvDt8sPtp1JwRALAIFXys+xntXvrzrPsYyITP3X2bp0s1uDB/ACkPeWn62b1ulFGsrAeurQdeLpzBqMzXjHutsNopUrL00QL0akc5YsQvjQzOwjpitjKDh2yilaDUVlsWhm+rE8eQnngDY8o8/xgYhiVYw1DrA8UIj+BjBH8rmut/XyDs/YjN9+fDZKsOadyili6kKo4fa/Zmg7OawlGJ7HomyLJb2GI6xLOGHKl/n81HA3JXXoERIthq8c+VrzPr9FZWbG1rslaJbv1Da0Hn4+1l87sTitXOoMFLUhmshFi/nrnEnc4ls2OB15ZcZ9XdeNBbRPWvv3m7pWje1lQs/dels5INXyiGL89pLSCmd7jt7LXEkdwHdfq4n6DrZSjukehZze/GS5yet+Dgxgh9DtRKyvK1Yp9L2rrl0gIbboGdS9VoUm04JWgjcA3qdnzUyQZ1IYkRDKQq7CGUv6SQ8FTyD9+Iz+JZDxg5ik07XV+Nj5RvrARNTe8ur91oRd9p9cpWCSlm3Upy9leHTN99Pyc0RWC6iIp4bucV7lr/Erdoc2ZxFbXtTFYGRoq1tIh5OsbEe0Gr3Ah4dc/bkC3TctJrai6f3urVainuvetx8ZP95+r/xSz8BcKrOk9ViipH1JipUW1XWAs2MS5B0QClym02yZT3IVQtJaoXkhcrkORLBF5EPAP8WsIH/oJT6hW3P/0Pgfwc6jTN/USn1H47i2MfB2sqgaZkWgZCpcHjP1GFEoTYl8zw10Iu1F8+LuP1SE8cRxiedQ1nhHgbf10Zq2sRs/1+GdORxs3aP29krfWEdR4W8eeO5fe8v4UKC4YZmQ1sXRrFGibEs3veJesZi7VcEt1cdNl+T774PJRaBWPzZ5Pdyo3afmdkE92638IOtDiaptHRdRB13f3cZO6GUIgh0Idxh7zQ312MyeIAgVDQa0Z66mB1nPH43knWfwmod14vwUjabExn8lMPC9QKjyzXSNR9lCdVCks2JDCjF5FyFVH3rDiDRDMhUPVZm8xdG9A8t+CJiA/8O+CFgDviKiHxKKfXstk1/Qyn1M4c93knQ28pvO2Gwf8FfXfGHN7wQnXoZBIryph4NvJaiUfeYuuRQHD2+2/8oUtSrEVGkyGRtIqW4f8/r+ubYjnD5ikt6D1/+7bxn+Su4EwEv5G+iBNJhi3etfJWZbQZXR0EyFd+q0XVlT8KolBpaeSyb1f61iB5WkqPMsMaNh5PUaxG+p0imLFLpgw2UO7G54fdZRIwUbaZn3G7j9f3iDxkkBV2pG8dAq79TatidLreYWKh2hduuRqRqJZauFfDSDqsxxmjJut8n9gCWglTNJ9kIaGXORpjtuDmKGf7bgJeUUq8AiMivAx8Gtgv+uSGdsbohnF6kLc77pTzEpx3g5q0k62sBpY3+4ykFK4sBhYIT+6VWkeraJFgWFEedtp/83s6vUY+Yu9PSk1IFSvmIRd8dSOAr7t3xeOjh1L7DEDYR7179Kt+3+nV8yyEZecfWWWlqRrdq3F5EdhSx8u2eLVuPC64K2seSYzNcg3aIcaF/Rl7a0M1YZg/Q0Bwgmx3sqAX6c9eb3tk3i4fTb/WnFGPL9T7hFkAUjC7XWLoev0KdqvtInBuJwgj+PpkF7vX8Pge8PWa7vyUi7wZeAP4/Sql7MducCSamHKrVsE/8RGBiOl58d2VHIzQZ7lIJeN5gGbxSWoh7uzvVax6FUZvpPRT0KKWYu9saSA+MDTcpKG0GjE8e7AthE2FH3u4b7oFqJWRl0cfzFI4rTEw6FEZ1EdW1m0lWl3WrxkRyfyExESE/Yg8M8iLgjmdxIp+gt/m3isgETca8vVs8HIa4ECNAtRzRbISk0vsfbAqjDhvrIYHfn6f/uh+bwf2379sS+VOaxQ9DFNhB/Pcl0dwh7GdbKGFA9JVAdEIpvGeBk1q0/V3g15RSLRH5fwP/CfjBuA1F5GPAxwCm3fQJnV4/iaTFjYeSrC4HNOohjiuMT7jkRg42i8sXbDY3wgHhT6V1uqfjCH5c2ztFbDFVtRLFtvIrbYSMjkcDvvjbiUsLHYZS7KlL03FTrYTcv7e1yBj4iqUF3fSjOKYdKK9cP7gX/PRlF8/Tnjedv1MyJVwZb7FaepnvFB7BIgIFrvJ5avHPD3zH4nsRjYbOjU9ndr8ri/1stFlbDZi9uv/PZSdPf3MtIBxx+W5rjOfe8mZ++cpr4N+cXQFUQqxwA4TO8M99fSTB6HJt8AmBWv54ig/PIkch+PPA1Z7fr7C1OAuAUqo3cPsfgH89bGdKqY8DHwd4NF08NaVJJI+uCnViytUxXl8v2orolnWdMvixCadPzAAQ7buyvZcrQK0yPERUr+0u+FEU2zo2FpHBvPEwVJRLIYEXkcroZt7HXb25shS/kL66HFAYPbzDpW1rAWzUdSw+kdyKxT+5/k0eL73AYnqCVOhxubGMtaert/189SBV3gy71UG2LVy7sXMqZCIZn88P0GocvIrr//uhnz7wa08NESqjKfIbzb6wTiRQGh8+QYxsi+WrI0zOV7qNbyJbWJnNXyjbhaMQ/K8Aj4jITbTQ/xjwE70biMglpdRC+9cPAftP1TjH2LZw41ayPTMPSSQs8gW7u6CYy9tMTDmsLgdbJlsZi8tDUkDtIX81Efa0oJzJDu+8tH1/bkLI57dmkM1mxL3bPVYC6yGJpG5NeJzVrcNmuWG490yc3RARHQbKDj6XCxs8XD1cFLJc6jG969ypRIr5ex43bqWGvm58ymHu1fiw2H6KpXpdJ88zm5MZJFLkSq3uY6XxtE6x3IFWxmXu4VESrbDb5vCiZOd0OLTgK6UCEfkZ4A/RaZmfUEp9R0T+V+BppdSngP9eRD4EBMA68A8Pe9zzRidOnB8SFhqbcCmOOXgthe3IjovDhVGHjbWYWb7szS3TtoWpGaev1kAsSKWEXN6mtBnquwBR2vHxnsfElA6bLMwNWgl4LcXaanCsDpuuK6yniqxPXsYJfCbv38H1W9h7/M42rCT3MjNYRFyrLZBQx9O3dieGpUJ6LYXnDb8zy2ZtMllrYK1HBMYnh3+F3/nMzwLEuk6ea0TYmMmxOZnFDiMCx9Ld3Pb42otccStnufHxo+mi+sTD7zrt0ziTVEoBi/e38kctC2avJfsyLOJoNSNWV3yaDYXjgOsIYgm5ka3QTKMexWa9zMy6LM7HLyA6rnDrNcNnqYdBAX9aeAu3izdRliBRBAiPP/1ZXmstMzq+80DzbP4h/nLiLQgRonSGzfuW/oob9fvHcr7DuP1Ss5vy2otYcP1mkmRq+N8uinQ4qNLuYGXZMD3jkt/m5d/nOmm4kPzZ//YjX1VKvTXuuYs71J0xgkCxvupTq0bYtjA24XRb1cWRLzhk8zbNRoSI7Cn3u9mI+jzwAx9aorh0xe2781iOMf7qxMuHEXfkKNRWBfVahJsQimPOrusLcdzNXOLO2A2iTvFT21TtO299D99z57fZaTVi083xlxNvJrRs9A2o5k+mn+S/vfO7pHbJIFJKUa1E1Kohji0UDmFAlsvbbHiDs3wBfF//3Yelv1qWtj6evqSIItp3NnrbJz/xBP/cf8PQUE2y0eCxLz/N1ZdfppnO8Oz3fg9zD9860HswnG+M4J8BwkDx6svNnoIXXQA1PunsmA5pWbKvatxhC5/LiwG5vN0VkGGGYHoxE7xW/+OdhiID7+mVFmGwlfa3uR5y5Xpi3xXEL+Rv9KdF9hx3MT3N1cbi0Ne+lLtOFDMQCopXs7M8Wrk99LVRpLj3aotWc+s9rK8FXL6a2HEwHsbYhEOl3J8KCbpXx8Kcj1I++YLNzOXhpm+WJViWDtd0QzU7+NUkmk3+xi//Csl6AycMgTUmFhd55u1v45l3vmPf7+E4cJsBI+sNXC+kmXapjKUJ3aNdSLWCiEzVAwWNnEvoXkxvHSP4MahIsbris7keEinIZCymLrnH1vx5fS3oK+sHLcRrKwGjY/trcL0TjSEZHYG/NWsE/X8YY/ljWXD5SoJ7r3pEim7GUSptMTre/1FaW/EJtqVzKgULcx4PvSa1r6watUMCpNplN4HYqBgPRYUQys5f+tJG0Cf2sPUeHn50f+8Bthbvy6WQWiXE8yL8dhP1ToOqSkkvgo9va3ozUOX6c/ENQtKVKg89+xzJRoP7N68zOTffI/Ya1/d54otf5LtveRN+6pBhOKVwvZDQtoh2SIscRrrqMTFfQZS+00k0Q3KlFos3CgSJIX+fSH8i1B7j9p3K3A6jy7A5nqYyEd/w/EHGCH4M9+c8atXeoibd5OLmASpO90K9Gp9mKaKzYo7KU8exBT+m9V0nTbTD2ITOGNoewy+OOSRTNg+9JkW1omeqqbTVzSWvVUM21gLCUMXGqkEPJIGv9mUU95rKq9zLzAzO8kW43IjrWLvFtdp9vlV4DSpG3K/tEsPfqUK62VCkM/v/LFjtvsKFos2LzzVj77g21wLGJ9z+WTzsWuU6+8pt3vPbnwKlsMOQR7/+DULL6hP7DpFtM760zOL1a/t+Dx2ym03GluuAQpQ2KVu5nNt7mqNSjC3WBqpmrUhRWK2zdjnft7kVRIwvVEnX9NpVK+2wNpMj2MEJ0wqjPhuGDsW1Bs1cAv+CLeBenATUPeJ5UZ/Yd4gUbKzvYLJzCIYNIkoRm4d/UMYm7IGMFi3kdt9sdXTcaT+mBwJpuz9OTOkvh2Vpn/WxCZdMVr92fdVn/q4eKJsNtWOTj/1WK1+v3+dGbR4nCkBF2FGAHQW8b+mvcFS8+2iHbxVf25/G084nfWLjefJBfcfXDpvBKw6fzdcxdoujbiX4+R/5qX6x3wUrCHj3p34PJwhwwhBBz+QTnkfcn0LCiGZ2+AzXCiKym02ypRZWOLiHVM1nbKmGFSmsqG1RUPOZnN+9uU0HO1Sx+xboinoXpZi5UyJd87WVAtoSYeZOCYnZR4d0Nf47Kwqy5Vbscw8yF2t42wNeS8U3elbDY9uHZXTcoVYdXChNpuRIm1IURh2CgL5GISOFwYblIsLUTILxyXYHKXfnBjBhqAbuCOLw3QSLr309z8xeJR20eLz0PFcbS7uetwA/uPwllpMvMpeZwY0CHq7eJRM2d3zdeqLAXGam3w9HBDsKyEa7i2lxzB6oaAYdmjlsX1jLFhIJ0ZW9PUTA4rWr8S+KobC6xvTcHMl6PXYEsZQaEPxIhPLYGJsTE7H7zG00GF3uHwxXL+VojGzluY+sNwZmzRaQbPjYfrinGHlkydBg3Xa7g1TNxw6ivu21h44iW2pRHRtSdLXDhzKuWvdBxwj+NhIJGfoZOa7mz5mszdQll5VFPRvRBla6Z+lRIqJte8cmHPx2K8A4IW81IzbWAjxPkc1ZFHfpwqUzhYZ/t0TATyT46rs/hJdM69aFSZjLTPNw5S4/sPJl7F0qVwWYbq0z3Vrf69tlNTGKxWAjltByWExN8Pryyzu+Pj9iU69GlNupkNKeWs5eO3xf2J//kZ9i6t4c7//Pn8QOQ90wxrYJHYevvufdu+9AKb7vM3/Ajedf0L8CThCfRdVKpXA9j8i2sVTE+tQUn/vIh2O3dbyQ0W3mZAATC1XmM243Tm/7Q+6sRM/cwz2UZChLqOcSpKveQNVsebRfwN0hx7MUuP7wGX4jl4ClQUsFJVC/QJYKHYzgbyORtLqt6fpi2BaMjh3f5SqOOowUbF14ZXOsvUctS0gm4wVru2dNsxGxsR5w46Hh6xe2M3yQzOSEQtHh2cuvx0+m+vrUKrF4MX+dspvlb9z/3IDot9ox+2R0sFBaPogPL9hRSNHbvRGLiDAzm2BsIqJe12mTuZx1IAO9uAYhy1ev8Hv/4O/x2Feepri2xsrlyzz31rdQz+vYtUQR6WoVL5UmSPQr6EPPPsf1F17oE/m4P4EC0s0mgeOAwGc/8mEWbt4cep7ZUmvozDdT9agW9SJvM+Pieq3BGboCf9hiawxrMzkm5yskGz5KBFGKSjFFtdg/2fGS8d+9SNixkCpyLDamMowu17vvSwnUCskL45DZixH8GGavJVhe9Ltl8Km0MH3paFq/7YRl6Xz600IpxeJ8f2hJKe2PvrriM3M5fkaUTApuQgYWakVgvB3nXyjOxvvKi7CaHOWV3FUeqd4FoOTk+Oz021lNjgEw2VznB5e/yEgQY361AzPNVbJBnZKb7wvrWEQ8Wnllz/tJJK1uaK3sZKk6Gca80q45/H2LrkMWXMvjY3zxA39t4PFb33qG7/38n2EH2jvilcdex5d+6H1Ejr6Gr/nGN3H9/hl9xx8pcBzsIOjGukHP/hXwli/8BZ/eQfBlp7hcz1Pl8QzZsocV9XeX2pzM7Dl7BkDZwvK1EWwvxAki/KRNFLPo20o7+EkbtxV27wYU2iNnN/Oz6miaZiZBttxClKKeT+ClL57YgxH8WCxLmLmsi1xg+OLdg4bvD19sHWjj14OIcOV6gvk7Hp63tQYyObNlUZwJmkNNb0LL4euFR/nK2OM0rQSh2Dp3vi3Sy6kxfnv2ffzE3d9jMTXJF8ffyKabJxs0eOvGt7sDxcB5AR+6/zk+N/l25jNTABS8Ku9Z+TLZXeL/2/HE4Y9mvo/F1AS2igjF5vWlF3nH+je7gvempwIy//p/3BL5fSy69jL7ym3e8Sef7Zu9P/TcdxEUf/nUBwCwh4RvAtflmbd9L49/+Su4fv+dkQCjK6u4rRZ+Mj5cWM8nyG80Y2f5jdyWSIauxcLNAiNrDdI1n9CxKI+ndQjlAIQJm3CnOwMRlq4VKKzUyZVbOp8+n2BjMrMnW4UgaVOavHhpmNsxgr8DF0XoO+zUHWq3TDvXtbjxcIpWMyIMdbpm7/6eKD3Pvcyl+Nx3pdhMFrZm4dsGBiUWgeXwtdHH+Fbhtd07hXIizxcmvxdfHB4bMmNPhy0+uPgFPHGIxNp1Vj6Mz0+9jcXUJKFld9cEni08zPf8tyP8Ly8/vrXhAUUedJHU+OISb/7Cnw/E450g4Oaz3+UrP/he/GSSVx57HcW19YHtQsfh20++nUe/8c0Bwe+gdvhce2mXaiFJrie0o0TnrW9fiA1dm42ZHBsHeK8HQVnC5nSWzeked7v2om1+vYEVKeq5BOXx9EBNQMchcz93Hw8iRvAPgFKK8mZIaVN/9YujNvmCfe4HiI4/e5xJ1+jE3j4qw/xgLjVXeXL1a/zF5FtjZ/nbM2m244vN87mbA2GhwHL4yvjjvK7yyo7+9AkV7M0POgZPHO5kLxNtG6wCy+GX/vgqPHSw/fbyxH/9Kx7/4peIbBvXix+UlGWRqjfwk0mef/ObuPnd5ymuruH6PkHbRe4vnvphlGXx0htez2NPf7UvBz8SYenqFYLEzrPwjZkc9ZEkmYqn490jyTObrz66XCO32eqGeUY2mmQrLe7fLKJsC9sPGb9fJdXQA2Mr7bB2KTe8qOsB52z+Fc8wSinm73p9TUSajYhqJToy//zT5NKVBPN3WrR60lMLozYjhUHrhLVVn0o5ardYtCmO7exL//rKK0x4m3xm5t34loWSvX/8XBXSsuOvr2cl8Cz3wIu7u/Ga9wvRXZu4Pur2Djnge+Xqiy/yhi9/WYtzW6AVg/5ESoTaiF7QjRyH3/9vfpyrL73Ma77+Dabm7yNK8d7f+V1eeexRvvKe9zA1f5/xpSVEKSLLopVO8xcffGpP59TKuGd+UdMOIvKb/YvMAlihIrfZpDKaZubVEna4tc7Qyd2fvzV6IWf7RvD3SaMeDXSMUkpntzQb0a5ulWcdxxGu39KhmcBXJNP9TViiSFEuBawsBn3x/pWlgEZDDfXw7zDdWufD9/+UT1754R1DC71hHTsKGfErhFhsJouD5xwFuNHR2x3//I/8VPdcZtnE2ZbRroBG9vCi+NjTXxu6ANu5Qr7j8LXv/z6uvfAiDz37HJFl8eITj9NKpZiev98X2rn57Hexg5A//PEfZfL+AmPLy1QLBe7fuI6yzvfns5dEM4jtfmUpXbgVunbfojK0c/cjRabi7eqf/yBiBH+f1IbYICgF9Vp47gW/QzJlkdxms+J52mIizmdHKaiWQ7xWtGux2PP5h3YWe7S5WSL0sFTEI5U7vHXjO8ylp/ns9DsIesI6ThTwps3nDtSBajtDG4SIsDaT1d2S2p4vEe2Y8h4XAiVSWKEidGQgZJWsx8f9IxH8VJLqSIFn3vE2bn3nWS7duduNzV9+9Q6tZHJgAdcJQ6698CLJZpOV2cuszF7e0zmeN0LHig3TKSBI2Dh+NLRxueMNqSOIFJmqhx1GtNLuA+ed/2C9mxPAcSxEBkV/r92mzjMLc16s2HcRHd7aTfBrTro/Zt9BKQRFLqjz/StPD1Th3qzP8+6Vr/DF8TfSsFO4UcCbN57ljaXnD/ButK2wfO8P7alBiJ8UxpdexAocWpk8EnncfuzG7hWlkWJsqdYt41eWsD6VwUu5pBo+oW0xd+sh8pubA543YcLlP//UPyGybWbu3O0Te9DWCY7vx1tT2zbpapVW+nT6Qp8EXtImSOhUzd5roAQqoylsPxrauDxOyJ1WyMzdkk5N7YRrsy4rs/kHpjOWEfx9ki/YrCwNxopFOHCT8/NAGCiazd1n0Xsxl7tWX+RO9vKAGZqtIj4y98eM+6WhC7CPVO/ycPUuoVjYKtpxoTaOvgYhnwQ+uYesGqV4/3/+JBMLi11RjkR43Tcy/NY//kc7LoKOL9bIVLYWFQkVEwu6nkAJIML6xMO00s9BQ7taRugY/Rd/6P1EbQvT2du3cWKybpQISg36gVpRRKVY3P29HRC3FeB4Om/+1BZARVhq96lNNnWdgbKEtUs5/KSDn1B6pu9t5e5HQODafSmmHSbny1hhfwgoVfPJbTSHWzecM4zg7xPH0Tnn9+9pi+D2d5bZa4kHeoa/l4BJJ8tnN25V7/Kt4mvYdPPdrBsn8nm0/AoTfmnX1wvgqP0tlv7GL/3EgXu5TiwuMr603DcDt5TCbbZ46DvP8cKb3xh/nmFEtjJYudr5lIgClCJyknz5vR9mZONVrtx+ldpInmff+j2sXZrpvqaVShHZNvb2uwDb1jPSMOyKvu+6fOsdbyd097G+oBSJZogVRrTSzlDHS4kUk3Nlko2t+Hkj67J6SrPgyLFYul7ACiKsSBG41tZ5iLB4bYTiaqN7h1XLJ3QYbtu5Ol6oQ0Db9m8pyO/k1XPOMIJ/ADJZm1uvTXXN1PbSbapDvRayvhoQBNqnZnTcPVJHzOPCcbQdQ2vILL/j/SMitCyXspMjH9Ri895tIj48/6c8O/IwL+eu4kYBry+/xM3a3JGd7zuf+Vn+2V8ubIn8LtbCOzG6vBJrFOQGAROLi7xAvODboYrNttmOAJHl8vxb3sq3hzQleeWx1/HGv/zi4BOW8Pt/9+/yxBe/xNTcPM1shmfe8XZuP/a6XY66heOFTN0rYwdR195gczJDJUbkxhZrJBuBnjG3L0m65lNYbZxqYVPkWLGuoMq22JjOsjEd05l+rzxAJmtG8A+IiOzbD31z3e9rHN5q6Vz+G7dS50L0L80muPtqa8vaV3SzFMcGz1PM32tx541v58WJh7FURCQ2D1fv8P0rTw/45Lgq5I2l5w8cf99Ob5WrFUSM/KNV0lWYdkqUR9M09mqUpRTZsrdVyJNPUC6Oxi4y+47D5vj40F0F+2kIEhNr7qU+MsIX/saP8P2/95nuuShL+Ozf/Ajrl2b4/N+MN0PbFaWYulfemt22P5zFlTpeyulPzVQq9o7FUpDfbJ7rStbAtYhsCyvoHzYigdrILp8dpUjXfDLtu4hqIUXrCLK3joMjEXwR+QDwb9FNQ/+DUuoXtj2fBH4F+B5gDfi7SqlXj+LY54UoUiwvbbMQ1nfirK/6TM2c/Rz+ZMri1iMpyuUQ39ONxDfWAlrtJJEXLz/Gq2O3iCynW436Uu4aybDFk+vfOvLzGWjY/XMNrDDi0qubWEE7ru1BolGhNJ6mvIcOR2NLNe0B3/475debhE6OcnGU0bVV7HYuaoReGH358dcP35klbE5kKK7W+/xfICbHHsFL7RwLv/fIw/zGz/xTpubnUWKxdGUWZR8ufp5ohQO2w6AHn/x6YzAXf5gjakxjHdCumoXVBqm6tl8ojadpHtB+4bhINHyyZY9m2tFtENGDWCTaCC7uTqeLUowvVMlUtOOnAjIVj0ox1V8RfEY4tOCLiA38O+CHgDngKyLyKaXUsz2b/SSwoZR6WER+DPjfgL972GOfJ7yW6uZW96F29qnZThgq1lcDKuWwW/BUGN254OkosWzp2iXffrG/Y9PcrdcTOf0CEVoOzxYe5h3r39r3AmscA12gtpHbaGKF/YuYloLCWoPKaGrHbky2H/aJPbQ7BIURf/nUh3jjFz/P1ZdeAaVYvTTDXz71w7tmwVTGdX/WwloDO4hopWycQHUXEhV68Xb1cm5PMfDQdVm4cWPX7faKFUbEfTAFHZLqf1DwkjbJVv86gkJns2zH9kMu3S51c+FdPyIxX2FjKkN19GzExItLNfKbW95BirZRW8qhmXG0N9AOf5dkI+iKPXQ8+vUdT3U0deYqeo9ihv824CWl1CsAIvLrwIeBXsH/MPC/tH/+f4BfFBFRareWGQ8Otj3cL36v4ZwoUtx5pdXXBHt5MaBRV1zapeDpONjevMN34wtZAnGIsLBjo6w786anAtJ/5y2895Pv0g/s4lWTrvoDXu4AiO6X2soOF/xkI754y1LgBMLnP/JhJAx15aqz969OfSRJvad5CEoX/qTapmPVYrIvvdMKArKVCo1sbsAW2QoicqUmdqD2JEi70Uq5sbP2aIhf/PqlHNN3St16hE5mzMbU4Gy2sNoYKHyyFIyu1KkWUnsyPTtO3GZAfrM50GIx2QxY36P9QrrqDQ3FpavezncHp8BRCP4scK/n9zng7cO2UUoFIlICxoHVIzj+ucBNWKTSQqM+aCE8tkefmkop7BN70INIpRwyvoeCp6PGdrR1coeRzRVK4zMD2xX98r7FvlvlCjp9co+EroVqxiyUKnTR0zCUIrsZ7xKpLYf1l1/Zu7Vq2QMig4NA+xwe/+KXePyLX9abKcULb3ycp9/7HpRlkaz7TN0rA1o4c5vgJ22WrhUObBOgbOnzixe02Aeu3fW+78X2wr4BQq/dqtjjp+rxNQIo3dDEH+Jxf1Jkhom12rtYRztc952eOy3O3KKtiHwM+BjAtHu2RsfDcvlqkvm7LVrNLZ+aiSmHbG5vt3212mC7PQAEGnsoeDpqxiccVnrWJR7+9lf4+vd9AGXZuoRfRTgq4l2rX9t1X3ENQg5CeSw1MOtSaGEMdhCYVN0nVQ9iBapTyNNBIkWqrnPimxn3wGIrYciVV14hv1lifWqK3MYGj3/xS302C4988xkCx+Xr734XE/cr/eEmBW4rJL/RoDx+8AXT6mgaP+no2W4QUc8lqBVTg+9LKcaXav3hMnR/3pnbm6zPZPvuOELHiu1GJUC410bnx8jQau/eRgK7UB9JUlhrxA4cZ7Gj1lEI/jzQ24TzSvuxuG3mRMQBCujF2wGUUh8HPg7waLr4QIV8HEe4/lAKz4sIA0UyZe1oSbydndKqTyPLpzjmEEW6R65SUKis8f5v/z6v3HqC1eQoo16Zt2w8y4S3Gfv6oQ1ClCJd9UhXfUJbqBX3Hgv10i7r01nGluuADpJ7KUdXS+5ApuzFiz1QKaYIkvr46YrHxP0KiOiUS6VYmc3veyEyUy7z1K/+OolWCzsMiSwLK4oG8uzdIOB1X/s63377k1jbY+po0c+WvEMJPuzNLM3xo9jFWQGcUDFxv0p5LN3N1imNp0nMVwbaFzaz7oB98XasICK32cQJIpoZV4vnEa9T1fIJCqv1IWK9N5+dIGGzNpNlfLHdnEd0oGtlNr/jetFpcRSC/xXgERG5iRb2HwN+Yts2nwL+AfBXwN8GPnuR4vfbSSQsOMDgXxx12FgbtHWwbcjsEJs+LkSE8UndIzcM9XmINLi5/KXY7d/0lJ65djNr4uLxSjF9t0yiGXQXNUc2mqxdyg2GQIZQK6aoFZK4rZDIFh0fV4qR1TojG00kUrTSDhtT2a7trxqiJQrw0nobO4i2Ztk9f4TJ+Qrzt0Z3FbFe3vWZPyBTrWK192OH4dBQkR0EWOFwc7hh575fHM9nan6e0HFYnr08YLS2U9Nx0IPPyLpeHI8ci2YuocNFK3Voh4uaWZfVyzsMvkqR32gyulxHoe8gsqUWhVWbxesF1BEWN4YJW08OtvW8XZvJap+ePVIvpGjkEvquT+RQd33HzaEFvx2T/xngD9FpmZ9QSn1HRP5X4Gml1KeA/wj8/0TkJWAdPSgY9ombsJi9lmBh3iNqTwQTSWH26uGbah8GEWGnNcy+ePwuZEutrtjDVtbD+EKVRi6x9y+SSJ+H+/Z0y1Rd2+Qu3CwSJGxq25p+bO2HbhenTp51HJmKR3V0MOYdh+P5TM/Nd8W+51Cx1PN5WukEodtAvP4UykgY6P96EG4++xxP/sEfdUU+dBz+5G9/lPWZ6a1jORbNtDs8Nt9+E8lm0L1m1dE01UIK1w8JbWvnQVEpJucrpKt+X1TFUuD4ISPrBy/usoJIt1BM2H2foVpRi3W6nY7ZyCdiWyzuhrItGnu8KzhNjiSGr5T6DPCZbY/9Tz0/N4G/cxTHuuhkcza3XpPC9xRi6U5TZ42hrpN7IFtuDc2ySTaC2PS/3dCZLYO+6bRzzTdmcnhpl/J4mpG1/ruOldl8VyCsSA11X7SG5KHHssPNbaeAtfNXDRyHL7/vvWBZLM/mmblb7jP3amQTsYur+2FkbY13/sEf9XfP8jz+2m/+Z37zp/7JVkaSUlRGk7itoJuyGbs4vl0wLdnTAm26qjOX4gYTS+nPxm6C7zYDMu3isPpIEj9hM36/Qrrmd9OKSuNpyuPpbogocixqh7yG54Uzt2hr2B0RIZE8W7eMffH4HVwnd2PoQpqC6IBjm+uFRCLYMTPq3lTM0kSG6kiSdM1HWVDPJfrisI1sgpGYBTol+/PFD5IJ1qanGF9Y7FsAjcRi7ubDBK7F9Pwc5bFRvvnOJ1m6drX9Ooe5W6Okax52oLr54vtlfGGR7/3s5xlfWqKVTlEaHcWK6ZErkWL29qvce+RhPfueq+jZvRrSoAVdsbpbAdkwsmUvfrDv7H+Xu9iR1XrfAmp+o0ngWjh+1GcFUVhrECTsPYcIHySM4BsOTOpzH+V/+DftNMxD9HLtpTKaIlUfzKWPLBnuTd4R8iGCELjWQPgEtrJ3egkTNtUhC8R+0qaeS5CpbglTJFAr7L8F4F988Cme+tVfwwkCnCAgsB28VIY7r/kevHSK//ojxfjwhyWHCh0UV1b44V//jW4mkFOpkq7WBtw2QS9IJ1o6jJUtt/r+Lp0rrfPw9Q9+wmblysFN1JTEDySgK5srO4SuHC+ksNboz6lX4HrxhmiFtYYRfINhJwYMyQ4xkx9GM+tSKabIbzb1A+1smOWrIwNCkmgEjC1VSTRD3Xu1kGRjKjsQ5w9dm3rOHSjKUoK+td8FtxUwvqCPA9BK2TpsYQvVkeSBwkzl8TH+y//rJ3nLF75OplqiUpxg9dI1lGUjoWJkvcFmTDHTYXnjf/0r7KA/E8hS8SZvEkUstO8utlcgd1AC61NZWhn30FWl1UKKTGUwN14Bjayz4xpJJwa/V7Z75lwUjOAbhtJrSAa0Z/HF4z2oCJvT2e5MP7ItHS7ZJuKOFzJ9t7Q141RalBw/0oPDNlYv5Rld0Q2vpT0bXZ/J7hpbtsKI6TvlvorRZDMkSCjuXykeKlXQUjb3b752IP5voX3Ye0nVPIorDRwvxE/YbE5mDmTQNba0HH+3I0Jg27hB0A7NuDz3PW+mPtK+ljvkrB+VJ34r61IZTZHfaPbunrXp7K4x9t3CPX3bousnLiJG8A19xBmSnQZhwsaLFIW1BsXVOs20Q3ksTdgWlvzGYFWspSBZ93G8cFCALGFjOqctABR7LuvPbrYQNdgX1fYjUvVti8hKkd8sETgOjVwWidrhjiFipFv0xYeagsRWkKWT+98Z3OxmwNRc+UD5/6XxMfKlwQYzkWXxlR98D9defIkgkeCFNz7Bwo3r3eerhSTJmFCbkh1CbQdgcypLtZgiVfOJLKGR31tmViOfGEiv7J5j+/9eK4jz7Ox5GIzgG4DDNQg5DlJVr6+HrNsKyZU9Fq4XsCJFptyKTw0UiRf8nuf34+Lm9nRL2o7jh4AW/Om79/j+T/8+yUYDUYpqfpTvvPU91PN5NqYy1AuDM9QgYdNKOyTrQV8MXQmUe8r6R5drA+dgKRhdrrOwT8H/1jvfwaW79/oycnzH4ZXXP8aLb3ojL77pjbGvq+cTpGrJbiMRAASWDxGz79K2F3Y8bbfQzOwcvokjdCwiAXvbdeqIfDPjaPO6jEupZ+Jw0TCCf0F55zM/y9dWb28tuh6iQciRo3Qf2O2mVkSKifsVXC8c7h2vFP4Rfpm9tEM0JFXUay/4Zstl3vfJ3+rrN5svrfHm//oHfOn9f4vxxRrKsmI9+Vdm80zcr5Ku+93Z5/p0Fi/tdt9PnD0B6MFov6xevsznPvIh3v4nnyVXKhE6Dt9985v4+rvftfMLRVi/lOsLtdX3OPveCSuImLlTwg6i7oqtn2j7A+2zyErZAkH8B2P1cn5fhXEPKkbwLwjxrpODRmdnAStSOEM8WBLbGlb3ErWLpI5y9lYbSery+2ArrBOJtmvohDIe+ea3sKL+87WUwvWaFFcX2Jy8TGG1Hiv4yrZYuTqCFUZY4bYWfQAihLYMWhXDYDVopEg2AyJLdPbRkJn3/Ydu8lsf+0msINA9c/cxQ/dTB0sFHcb4QrW/taDSA1lxpcbGTG5f+6rlkwPul51MLCP2GiP4DzgHdZ08TaJO2GU/tUzoMEhp4mgN95QlLNwoMrpcI13VxTvVQpLSxFZf1FypNOCB0yHZrAMMnaV3iGyLqD1O2V5IpuqhRMewS+Npiit1tnvSlMa3wh7pcouJjp+LUoS2xcrV/I6L0vuxeD4W2qGcuLTJbNljY5/zkdJEWoeG/LDbwESJ6F4DBsAI/gPHm54K+O6/+NGtUM15xNLpjturbqP2IBBX8NPIuse2EBc5Fms7+L8sXrvGtRdf7gvpgM5jL49OAlvhn93ItxepO4wu11ifylAaT1NYb7TDHsLmeLpbYet4IRML1f4QWBAxfbfM3MOjp9JcfE/sMKCLUth+2NcnYNfd2RYLNwtkKh6JZkDg2tRGEmfSxOy0MIL/APDkJ57YCtXAseTHnzQb01msMGqXxGsv6UoxqfO0g/6sGSVQGTu90vjbr3uUN3zpy+TKle5MP7RtVmeu0cgViAQ29zAYOa2wrx1ih7HlOvO3RimPp7FCRWRLn4jnYnz8tQeRIlXzz1xLwS6W6EXrRr8ttXYhhcuvbBIkbFZm83tP+xzWa8AAGME/t/RVuZ6TUM1+UJawemUE24+wA511E9kW1dGQqXtlvcgngiil+6Rmj0bUknWf4kod1wvxXZvSZGbXwqrQdfn03/tveMOXvsKN558nsmwWrr+WhauP4KccNqYytDIuEkZkyx52ENJKu3q/PcKdLceYt7XpmLNFMTbYcT1pAYg6mUQngFLYodJhlH3MqNcu5Zi5U0Ii1XVH7bwXUTo7a+ZO6WzfqZwjjOCfE06iyvUsEroWYY9BXJCwuf9QkURTG3i10s6B3A3jSNU8Jud68t3DgMRcmdXZfNf9cRh+KsXXf+D7+foPfH/s84lmwPTdMihtwKakiZe0We7rVjVE7XdZy2jkEn19VTsIUFitU88nhy9aKhV717AfknWf8YVqd+BpZlxWL+Vij+l42vXSbQZ4KYfyeJr5W6Nkyy0ypdbAbF/Qnj7pqncu3CjPOkbwzzB9hmQnUeV6TkjVfUbWmthBSDOb0AVZR+AaOro0GE6xFIwu1XYV/B1Rion5Sl9FrSidcZRfb1Ce0OGeej7JyFpz0OIAbe8wjHo+wci6TaIZDoilFRJv06AUo8vtymO0V9H6kHqBnXA8fcfVe91SNZ/pu6W2f/3W3yXR8Jm+W+7WViSbIblyi8XrBarFFG4rJD2kr7BzQa0Qjhoj+GeIPTUIueDkNhqMLm8Js+s1yZZbLNwoHlr0h+W1O36kK2IPOAN2/EiHoLbRsfztCL6f0k3JM1VvQLjzm3r2WxlLUxvZ1v1JhLWprLZO3n4MdO/W7YK/vT+AHSrGF2va834f4bFcTMWzoE3Lrry4QWki3X1/44txtRV6oF2+NqLv1jaJrXlopS+mFcJRYwT/DLCfBiEXmkj1iT10ZrGKkbUGGzOHMxsLbcGJyXc/TLgDdonIbLcqsOO7SgmQbIW4i1WSjSTr23LUI9camsq63Z9eIhVrhmYpKKw29iX4rhdfF9FpYNK1Is4ncFuDA6q2qNbZTfV8gsKajdNT3dxpiXiU9g0XGXMVT4HU5z4KcL5TJ08B1wu7GTu9CJCueWxwOMEvTaQHBhSd73643P4wYRO41oBVr0LPrBMNv1tZG9oy1CIYtCjnNluUx9J9mSuha+OlBjNeItGN3XuJu9vosN9F3mbGibWz7j3fkbWGrsoVYhelu2sYIixeLzCy3iC72cQKFRa6Z0FxqUZpMnNmWweeF4zgnxB9qZMXZMH1qInseLMx0J73O+F4IclGQGjLQHZMh2oxhYSKYiffHW2fXNmnr0scq7N5pl/tcfds/7MjxfS9su6Ja1tUC6lYY7jtpGoe1UT/QLQym2dqrqxn0u3Zfnk8PbDYGQy5G1Cw75l0tZhiZL2JhGroIGWHOqOq2m4juX1ALfc4YSpLKI+lybUrZgU9KOY3mySbAUvXBm2yDXvHCP4x0rfo+gCmTp40oWvRSrvatbHn8Wib2VgfbV+ebGnL9CuyhKVrBYLtxVAiVCYyVMbT2KEiPGQopxc/6bDRbpg9MBtWkCnrtMsgabM2k2V8sdYV/bgziAuPRI7F4o0iTivADhR+yo7PYBJhczzTl/Ov0PUMmxP7K15TtsXizSKFlRq5kjdwrr1WxBtTWZwg0tbP7QGnnk9Q3lYdPbJax95Wa2EpnemUaAZbPkOGfWME/wh501MB//Ijf38rddIsuh45K7M5JuerOu7bFo2NyczQ4qJMxRuIV0uomJorc/+hYrygixDG5Lv3oRSZike66umZeTG5q7e+HQ7vidvblKReSNHIJxm/XyFT9QdfQIyPTg9B0iHYJYOxMq4zmwqrDewgwmvXCxzEJyd0LNYv5Wlmmt2Bqs+KuDOIWMLKlRFsP8T1dEPx7QvtjhdSWB/MVOqQaIVG8A+BEfxD0jeLh7PlOvkAomyL5WtaNOxQO2PuFNfNbzRj89PtINLFVXtorj14Eorpu2USzaBbLJTbbLK+S6OOVtqJjWMrGcxCUZZQHk8PFXx/H5YDw9hzRapS5DabjKw3tcVw2mFjKjswONQLKULXZmStgeOHQ62IQ9ceapmwvYn8do7ifV9kDiX4IjIG/AZwA3gV+FGl1EbMdiHwTPvXu0qpDx3muKfJgFeNmcWfClo0dt9OoiHBcAE5YGp3ttTqin17V4jSqY71keTQAaiVdmhm3L5Fzo7zZlw1r7/DukS20qJROP5CJKcVMjlXxu1xtEzVA2bulFi8URgYMFsZl5VDdJNKNIPY2b1CZxu1MmaOehgOe/V+DvhTpdQviMjPtX//H2O2ayil3nTIY50qfQ1CzKLruaE2ksBdbQzM8hWClzrYbHG7qVsX0SmGQ9MaRVi5kie30STfXlOojiT1onBMaMkJVOwdQccm+rix/ZBLr252QzS9x6edwrk6m8fxQkaXa6RqPsoSKsWUdi09wPqHn3KGWmCvXD2CZisXnMMK/oeB97R//k/A54kX/HPHmW4QYtgz1dE02bLX7VzVWZxcu5w7sHhE1vDUyV17q4pQHUtTHbbI3MOwzCPF3t03D0MnW2hoXUDd1+6ea3Ukam/XbsDutgJWrwz2Ft6N0lhadzPrzeQB6iOJg4XfDH0c9gpOK6UW2j8vAtNDtkuJyNNAAPyCUuq3h+1QRD4GfAxg2j1ab/OdOE8NQgx7R1nC4o0C2bJHquYROhbVYupQTberoynt475t5h2Jdn88KpRtUSskBxadldCtXj0IiYZPpuyhLB3HHyak23P6+84NvQhdXKl3U0w7WAoytSG9hXchSNosXRthbLFGohVqJ9Riis2pi9mD9qjZ9dMpIn9CvPL9q95flFJKZGj28HWl1LyIPAR8VkSeUUq9HLehUurjwMcBHk0X99EC42CcxwYhhn0iQq2QpHZEMe+Of8/Ienv9pt1oY/nqSLcwrLhSJ7fZwlKKZtplfTo7mAa6B9ans4S2xchGE4n0IvX6dPbAlaejS1Xtn9P+Zo2sN9lsp6Jux0/aO4r+dqHvQ2n/n4MMrF7aZfFmcavm4qTCOEohkdJ3aQ9ogdeunxql1PuHPSciSyJySSm1ICKXgOUh+5hv//+KiHweeDMQK/gnQZ+1sMFwAEqTGarFFMm6T7StmGtyvkKqtrUwm6r7XLpT4v5DxR3TKWMRoTSZ0c1dDuHnA5BoBO1BqGf3CoqrdeojiYHMmfJYmmypP7zS+XEvZxFZhzS0O8F4fcfx0/F1bKqWT7A+k3vgKnsPe//5KeAfAL/Q/v93tm8gIqNAXSnVEpEJ4PuAf33I4+6LJz/xBF+/+fCWyJ+1RVelmJqf59LtO/ipFLdf91oaOdOW7awTuhb1bXcNjhf2iT20c9IjRW69QWm7a+V+OKQAZirD/fbTVZ/qaL/gBwmb5as6vNIxlqvlXDIx4aw4/BNYZzgKnNY2x0+l6zfsoMLytf2vQ5xlDiv4vwD8poj8JHAH+FEAEXkr8E+UUv8YeB3wSyISoc37fkEp9ewhj7srfVYGZzlUoxQ/8KnfY/aV2zi+T2jbvPnP/4LPf/hvMH/rodM+O8M+cdtx5+22BRaQbA63OO7Q8X63g4hmxj3ShuFqp/FiyHOtjMvCQ8V2qEM/lnp5E2sHPx4F+Anr3DQOH9loDAxgltIZVwdZhzjLHOrTpJRaA94X8/jTwD9u//yXwOOHOc5eGKhyPcsi38P1F15k9pXb3X6oTrtF3rt/99P8xs/809NvNG3YF37CjjcIQ+fb235Ifl37wngph/JoqluYlGgGTN0tI6jugFHPJ1i7dPCMol5qI0ntexNzfvVd/P57QxsrszndzAW6mU/d7drbrs4O7wF81nCHpIEqERzfCP6ZYaCX6zlMnXzo298ZaH7dYXpujoUbN072hAyHIkja2u+n4ff71FjaU+by7c1uCmOyEZDbbLJ4vYCftJmcq2BvKxTLVDyaWe9IFpyDpMPGZIbRlXrf42tDulMNw0u7zN8aJVdqYvu68lZZQqIZErgW9XwSZZ+f2Hcr7ZBoBGy/AqIUfuJcS+QA5+rdDDQIOSez+J3YKW9byfm4Jd4rEmo7g9CxhpbWPwisXMkzulzrLnh6KYe1mSyjS7WtfHV6KnQXa6zP6Kbt29F2yM0jyzCqjqVp5BOkaz4KaOQTB2oRGTkW5fH+VMnG+ZnU91EZS5PbbKGiLcO2SPQd0VF0UjtLnAvB76tyfcB4+fE3cPnO3YFZvhJh+crsKZ3VEaMUhbWG9klppy220i4rs7l9Nbw+LyhLWJ/JsT7dXqBtD+qpISmOyWagLSCGNDDZraftfgldm2rxwR1w90voWCzeKFBcrpGuB0SWUB5NURk7vC32WeNMC/58cVLnyZ/DUM1euffwLW4/+loeeu67iFLdVLbPf+RDRPaD8aXMVDxG1tr2Bu3c6mRDp8EdpBrz3LDt7i2yZCBkA3ox1UvZ7bu9/ucjgVrhEP10DXsiSNgP9mexzZkW/AuBCH/11A/z3e95C5devYOXSnLnNY/gpx6c2UVX7HvoVGNaYXSgkMJ5pFpMDrh3RqIfx7JYvZxncm5rMbRjrFbdwYHzOEhVPUaX67h+SOBYbE6k993c3HA2MYJ/RtiYmmRjavK0T+NYsGP6xIKey1qhInowbmR2ZXMyg+OFOn4ugihFM+uyMalDP82sXgzNlprYgX5uWHeu4yJV9Zicr2w1ifejrsf9TtbPhvOBEXzDsdPMunoBc9vjypJdWxM+UIiwemUE2wtx2/nd21P+IseiMn56vjHbe/qCvtsortT1wrFxqzzXXKBvm+G02JxIdx0mQc/sI9E+MRdRQMKETTOXOJP53e6QJuZ2qI588dhw8pgZvuHYCV2bhZsFRtabpOo+vmtRHk+bVnVnkMCxcP3B9NDIlr0Z6OwDCSMyVQ8r1OErY398/JgrbDgRQtdmY/oQPjLnFdWeGZ8TE67NyQzjC9WBheXN8YM1NBlGsu4zdU8vUHeaC9QKyQt713dSGME3GI6DSDG6XCPXLr7yEzbrM1lah2j/dxDsICK30STRDPCTNpXR1I5Fb/WRJBJpe2c7VES2sDmepjp6hAu2SjE5V+lfK1C6dWQjl6DRY/MgkcJtBYS2NdAb17B/jOAbDMfAxEKFdHXLXiHhaUfGuD6wx4XTCrl0pwSRwgJUzSe/2WTxWmFHU7ZaMaUXaDttvY54xp2qB0jMgoClILvZ7Ap+bqPB6HK9W5DmpRxWZvPnxpTtLGKunMFwxNh+RKbqD2S7iNI1CSfF2FINaYs9tLU70lYOu9JpAnIs4ZXhq78dY7dUze9mDFlRx70yYHK+cgznc3Ewgm8wHDGOHxLFCKWgnRlPilTDH1hnFdo2zer0Um5aaTdW86N2HB8gvz5YrCdoR1HHO7lr+KBhBN9gOGL8hI0VI6gKjrTn7W7EDTqwiy/+CaAsYfVynkh0g/JOmm4jm6Ce1+EcZ5jfvhBrMmfYG0bwDYYjJnIsqoUkUY+wdiySK2ODvWOPi2qx/xygZxZ9ypkwjXyCxWsjeCmbyBIC16Y2kuieVyPrEivrCpO+eQiM4BsMx8D6dJbSRJrAFiKBZtqmUkhSXKmTX6ufyCx1cyJDM+sSCYSWFvtW2mHjMG0WjwgriJieq5BshtiRIuGFTCxUya9pr/7yWJrIlj7RjwQ2pjIPXJ/Zk8QMlQbDcSBCeTxDeVz758y8WiLZ1A3E01UorDVZvFE43mpbS1i5MoLjhbitED9hEZyR2fHIegMJVd8ag6WguNqgWkwTORYLN4uMrDdI13xCx6I8lqKZNc6hh+Fs/PUNhgeYscUaVk9zDUuBUoqxxdqRNsmWMCK/0ewTSC/txnr2nDapmh8fXhDB9QK8tEvkWGxOZdk84XN7kDGCbzAcM6l6fLZMqu7rbJkjiKdbYcSl2yWsMOr2mU1XPdans2fS5TJ0LVRcL1mlLoxd9mlwqCsrIn9HRL4jIpGIvHWH7T4gIs+LyEsi8nOHOabBcN4YlhWjjrCoKb/e7Io96AHFUjC2XIOYpiunTXksPXBdOo3ez9rdyIPEYYfSbwMfBb4wbAMRsYF/BzwFPAb8uIg8dsjjGgznhlphSLbMyNH0qQW0CdkQXU+cYO7/XmllXNans0SWXtRWopu8r1w5p41xzwmHCukopZ4DkJ1nKW8DXlJKvdLe9teBDwPPHubYBsN5YWMqi9sKSTSD7mNe8mizZUJ72G1E2+nyDNKxcHC9kMi2CI1lwrFzEjH8WeBez+9zwNtP4LgGw5lAWcLS9QKJRoDrhfgJGy9lH2kufGUsTbLRb0imAD959hZs+xAxefUnyK5XWkT+BJiJeepfKaV+56hPSEQ+BnwMIDnyYLb8M1xMvLSDd0yVto1cgtJ4msJao2s25idsVi5AY27D3tn106eUev8hjzEPXO35/Ur7sWHH+zjwcYD8pUfO3mqTwXBGKU9kqIymSDZDQsfMnA2DnETQ7CvAIyJyU0QSwI8BnzqB4xoMFw5lW6Z7lGEoh03L/JsiMgc8CXxaRP6w/fhlEfkMgFIqAH4G+EPgOeA3lVLfOdxpGwwGg2G/HDZL57eA34p5/D7wwZ7fPwN85jDHMhgMBsPhMHlQBoPh8Ch1qh77hr1hAn0Gg+HAWEHE6FKNbNUDBY2cLqjaqW+u4fQwM3yDwXAwlGL6bolsxUOUzgZNV31mXi0hZ9DOwWAE32AwHJBUzcfxoz4DNAGsSJGptE7rtAw7YATfYDAcCNcLu03He7HUyfbuNewdI/gGg+FA+AkbFaMgkZg2hGcVI/gGg+FANLMugWPTO8lXaLO2TjNyw9nCCL7BYDgYIixdH6E2ktAWx0A957J4vWD6zp5RzH2XwWA4MJFtsXY5z9ppn4hhT5gZvsFgMFwQjOAbDAbDBcEIvsFgMFwQjOAbDAbDBcEIvsFgMFwQjOAbDAbDBcEIvsFgMFwQjOAbDAbDBcEIvsFgMFwQjOAbDAbDBcEIvsFgMFwQDiX4IvJ3ROQ7IhKJyFt32O5VEXlGRL4hIk8f5pgGg8FgOBiHNU/7NvBR4Jf2sO17lVKrhzyewWAwGA7IoQRfKfUcgIixQjUYDIazzknF8BXwRyLyVRH52Akd02AwGAw97DrDF5E/AWZinvpXSqnf2eNx3qWUmheRKeCPReS7SqkvDDnex4CPASRHJve4e4PBYDDsxq6Cr5R6/2EPopSab/+/LCK/BbwNiBV8pdTHgY8D5C89EtMi2WAwGAwH4dhDOiKSFZF852fgr6EXew0Gg8Fwghw2LfNvisgc8CTwaRH5w/bjl0XkM+3NpoG/EJFvAl8GPq2U+oPDHNdgMBgM++ewWTq/BfxWzOP3gQ+2f34FeONhjmMwGAyGw2MqbQ0Gg+GCYATfYDAYLghG8A0Gg+GCYATfYDAYLghG8A0Gg+GCIEqd3domEVkB7rR/nQCM+ZrGXIstzLXYwlyLLS7ytbiulIq1KTjTgt+LiDytlBpqwXyRMNdiC3MttjDXYgtzLeIxIR2DwWC4IBjBNxgMhgvCeRL8j5/2CZwhzLXYwlyLLcy12MJcixjOTQzfYDAYDIfjPM3wDQaDwXAIzo3gi8j/LiLfFZFvichviUjxtM/pNNlrA/kHGRH5gIg8LyIvicjPnfb5nBYi8gkRWRaRC287LiJXReRzIvJs+/vxz077nM4S50bwgT8G3qCUegJ4AfiXp3w+p02ngXxsI5kHHRGxgX8HPAU8Bvy4iDx2umd1avwy8IHTPokzQgD8rFLqMeAdwE9f4M/FAOdG8JVSf6SUCtq/fhG4cprnc9oopZ5TSj1/2udxirwNeEkp9YpSygN+HfjwKZ/TqdBuF7p+2udxFlBKLSilvtb+uQI8B8ye7lmdHc6N4G/jHwG/f9onYThVZoF7Pb/PYb7Yhh5E5AbwZuBLp3wqZ4ZDNUA5avbSMF1E/hX6tu1XT/LcToMjaiBvMFw4RCQHfBL450qp8mmfz1nhTAn+bg3TReQfAn8deJ+6APmkR9FA/gFmHrja8/uV9mOGC46IuGix/1Wl1H857fM5S5ybkI6IfAD4F8CHlFL10z4fw6nzFeAREbkpIgngx4BPnfI5GU4ZERHgPwLPKaX+j9M+n7PGuRF84BeBPPDHIvINEfn3p31Cp8mwBvIXhfYC/s8Af4hemPtNpdR3TvesTgcR+TXgr4DXisiciPzkaZ/TKfJ9wN8DfrCtE98QkQ+e9kmdFUylrcFgMFwQztMM32AwGAyHwAi+wWAwXBCM4BsMBsMFwQi+wWAwXBCM4BsMBsMFwQi+wWAwXBCM4BsMBsMFwQi+wWAwXBD+//9vPOqsFKBEAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "logistic = Logistic(data, label)\n", "logistic.train(200)\n", "plot_decision_boundary(lambda x: logistic.predict(x), data, label)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. 如何用sklearn解决逻辑回归问题?" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "accuracy train = 0.850000\n", "accuracy test = 0.825000\n" ] }, { "data": { "image/png": "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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from sklearn.linear_model import LogisticRegression\n", "from sklearn.metrics import confusion_matrix\n", "from sklearn.metrics import accuracy_score\n", "import matplotlib.pyplot as plt\n", "\n", "# calculate train/test data number\n", "N = len(data)\n", "N_train = int(N*0.6)\n", "N_test = N - N_train\n", "\n", "# split train/test data\n", "x_train = data[:N_train, :]\n", "y_train = label[:N_train]\n", "x_test = data[N_train:, :]\n", "y_test = label[N_train:]\n", "\n", "# do logistic regression\n", "lr=LogisticRegression()\n", "lr.fit(x_train,y_train)\n", "\n", "pred_train = lr.predict(x_train)\n", "pred_test = lr.predict(x_test)\n", "\n", "# calculate train/test accuracy\n", "acc_train = accuracy_score(y_train, pred_train)\n", "acc_test = accuracy_score(y_test, pred_test)\n", "print(\"accuracy train = %f\" % acc_train)\n", "print(\"accuracy test = %f\" % acc_test)\n", "\n", "# plot confusion matrix\n", "cm = confusion_matrix(y_test,pred_test)\n", "\n", "plt.matshow(cm)\n", "plt.title(u'Confusion Matrix')\n", "plt.colorbar()\n", "plt.ylabel(u'Groundtruth')\n", "plt.xlabel(u'Predict')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. 多类识别问题" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.1 加载显示数据" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt \n", "from sklearn.datasets import load_digits\n", "\n", "# load data\n", "digits = load_digits()\n", "\n", "# copied from notebook 02_sklearn_data.ipynb\n", "fig = plt.figure(figsize=(6, 6)) # figure size in inches\n", "fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)\n", "\n", "# plot the digits: each image is 8x8 pixels\n", "for i in range(64):\n", " ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])\n", " ax.imshow(digits.images[i], cmap=plt.cm.binary)\n", " \n", " # label the image with the target value\n", " ax.text(0, 7, str(digits.target[i]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.2 可视化特征\n", "\n", "针对机器学习的问题,一个比较好的方法是通过降维的方法将原始的高维特征降到2-3维并可视化处理,通过这样的方法可以对所要处理的数据有一个初步的认识。这里介绍最简单的降维方法主成分分析(Principal Component Analysis, PCA).\n", "\n", "PCA寻求具有最大方差的特征的正交线性组合,因此可以更好地了解数据的结构。在这里,我们将使用Randomized PCA,因为当数据个数$N$比较大时,计算的效率更好。\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from sklearn.decomposition import PCA\n", "pca = PCA(n_components=2, svd_solver=\"randomized\")\n", "proj = pca.fit_transform(digits.data)\n", "\n", "plt.scatter(proj[:, 0], proj[:, 1], c=digits.target)\n", "plt.colorbar()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "PCA的一个缺点是它可能会丢失数据中一些有趣的相互关系。如果想看到非线性的降维与映射\n", "我们可以使用几种流形模块中的方法。在这里,我们将使用[Isomap](https://blog.csdn.net/VictoriaW/article/details/78497316)(串联\n", "等距映射)是一种基于图论的流形降维方法。" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from sklearn.manifold import Isomap\n", "iso = Isomap(n_neighbors=5, n_components=2)\n", "proj = iso.fit_transform(digits.data)\n", "\n", "plt.scatter(proj[:, 0], proj[:, 1], c=digits.target)\n", "plt.colorbar()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.3 示例程序" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1797, 64)\n", "accuracy train = 0.995825, accuracy_test = 0.961111\n", "score_train = 0.995825, score_test = 0.961111\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/bushuhui/anaconda3/envs/dl/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" ] } ], "source": [ "from sklearn.datasets import load_digits\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.manifold import Isomap\n", "\n", "import matplotlib.pyplot as plt \n", "\n", "# load digital data\n", "digits, dig_label = load_digits(return_X_y=True)\n", "print(digits.shape)\n", "\n", "feature_trans = True\n", "if feature_trans:\n", " iso = Isomap(n_neighbors=5, n_components=8)\n", " digits = iso.fit_transform(digits)\n", "\n", "# calculate train/test data number\n", "N = len(digits)\n", "N_train = int(N*0.8)\n", "N_test = N - N_train\n", "\n", "# split train/test data\n", "x_train = digits[:N_train, :]\n", "y_train = dig_label[:N_train]\n", "x_test = digits[N_train:, :]\n", "y_test = dig_label[N_train:]\n", "\n", "# do logistic regression\n", "lr=LogisticRegression()\n", "lr.fit(x_train,y_train)\n", "\n", "pred_train = lr.predict(x_train)\n", "pred_test = lr.predict(x_test)\n", "\n", "# calculate train/test accuracy\n", "acc_train = accuracy_score(y_train, pred_train)\n", "acc_test = accuracy_score(y_test, pred_test)\n", "print(\"accuracy train = %f, accuracy_test = %f\" % (acc_train, acc_test))\n", "\n", "score_train = lr.score(x_train, y_train)\n", "score_test = lr.score(x_test, y_test)\n", "print(\"score_train = %f, score_test = %f\" % (score_train, score_test))\n", "\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from sklearn.metrics import confusion_matrix\n", "\n", "# plot confusion matrix\n", "cm = confusion_matrix(y_test,pred_test)\n", "\n", "plt.matshow(cm)\n", "plt.title(u'Confusion Matrix')\n", "plt.colorbar()\n", "plt.ylabel(u'Groundtruth')\n", "plt.xlabel(u'Predict')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. 深入思考\n", "\n", "1. 如何得到错误分类数据的下标?\n", "2. 如何根据下标,将这些错误的数据可视化出来?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "* [逻辑回归模型(Logistic Regression, LR)基础](https://www.cnblogs.com/sparkwen/p/3441197.html)\n", "* [逻辑回归(Logistic Regression)](http://www.cnblogs.com/BYRans/p/4713624.html)" ] } ], "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": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 2 }