汽车满意度预测_sklearn_分类-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本案例数据集Car Evaluation来自UCI，建模前对数据进行了适当的预处理。从汽车的价格、质量、及舒适度这三个方面的相关数据（详见数据说明）出发，对汽车的客户满意度（简称为汽车满意度）进行分类预测。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 目录\n",
    "[1. 准备](#1)<br>\n",
    "[2. 逻辑回归](#2)<br>\n",
    "[3. K近邻](#3)<br>\n",
    "[4. 决策树](#4)<br>\n",
    "[5. 朴素贝叶斯](#5)<br>\n",
    "[6. SVM](#6)<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据一共包含6个特征，1个类别变量（即汽车满意度），共1728个样本点。\n",
    "\n",
    "数据集链接：http://archive.ics.uci.edu/ml/datasets/Car+Evaluation\n",
    "\n",
    "|特征|说明|\n",
    "|-----:|-----:|\n",
    "|buying|购买价格（1：low；2：med；3：high；4：vhigh）|\n",
    "|maint|保养价格（1：low；2：med；3：high；4：vhigh）|\n",
    "|doors|门的个数（1：2；2：3；3：4；4：5more）|\n",
    "|persons|载客数（1：2；2：4；3：more）|\n",
    "|lug_boot|车身的大小（1：small；2：med；3：big）|\n",
    "|safety|安全等级（1：low；2：med；3：high）|\n",
    "|car_acceptability|汽车满意度（1：unacc；0：acc）|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"1\"></div>\n",
    "# 1. 准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>buying</th>\n",
       "      <th>maint</th>\n",
       "      <th>doors</th>\n",
       "      <th>persons</th>\n",
       "      <th>lug_boot</th>\n",
       "      <th>safety</th>\n",
       "      <th>car_acceptability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   buying   maint   doors   persons   lug_boot   safety  car_acceptability\n",
       "0       4       4       1         1          1        2                  1\n",
       "1       4       4       1         1          2        3                  1\n",
       "2       4       4       1         1          3        1                  1\n",
       "3       4       4       1         1          3        2                  1\n",
       "4       4       4       1         1          3        3                  1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "import pandas as pd\n",
    "data = pd.read_csv('./Car.csv')\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1210\n",
       "0     518\n",
       "Name: car_acceptability, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 观察样本类别分布\n",
    "data.car_acceptability.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x16ab232ef98>,\n",
       "  <matplotlib.axis.XTick at 0x16ab232e898>],\n",
       " <a list of 2 Text xticklabel objects>)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 类别分布条形图\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "data.car_acceptability.value_counts().plot(kind='bar', rot=360, title='类别数量分布条形图')\n",
    "plt.xticks([1, 0], ['满意', '不满意'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 数据集划分\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = data.drop(['car_acceptability'], axis=1)\n",
    "y = data['car_acceptability']\n",
    "\n",
    "train_x, test_x, train_y, test_y = train_test_split(X, y, test_size = 0.2, random_state = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"2\"></div>\n",
    "# 2. 逻辑回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=10, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# 模型构建与拟合\n",
    "clf = LogisticRegression(random_state=10)\n",
    "clf.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型预测\n",
    "y_pred = clf.predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分类正确率： 0.8555\n"
     ]
    }
   ],
   "source": [
    "# 分类正确率\n",
    "print(\"分类正确率：\",round(clf.score(test_x, test_y),4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.72      0.77       114\n",
      "           1       0.87      0.92      0.90       232\n",
      "\n",
      "   micro avg       0.86      0.86      0.86       346\n",
      "   macro avg       0.84      0.82      0.83       346\n",
      "weighted avg       0.85      0.86      0.85       346\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 构建显示主要分类指标的文本报告\n",
    "print(classification_report(test_y, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'混淆矩阵热力图')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "\n",
    "## 设置正常显示中文\n",
    "sns.set(font='SimHei')\n",
    "## 绘制热力图\n",
    "ax = sns.heatmap(confusion_matrix(test_y, y_pred), \n",
    "                 annot=True, fmt='d', \n",
    "                 xticklabels=[\"满意(0)\",\"不满意(1)\"], yticklabels=[\"满意(0)\",\"不满意(1)\"])\n",
    "ax.set_ylabel('真实')\n",
    "ax.set_xlabel('预测')\n",
    "ax.set_title('混淆矩阵热力图')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight='balanced', dual=False,\n",
       "          fit_intercept=True, intercept_scaling=1, max_iter=100,\n",
       "          multi_class='warn', n_jobs=None, penalty='l2', random_state=10,\n",
       "          solver='warn', tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 模型构建与拟合,平衡类别权重\n",
    "clf = LogisticRegression(random_state=10, class_weight ='balanced')\n",
    "clf.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分类正确率： 0.8468\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.86      0.79       114\n",
      "           1       0.92      0.84      0.88       232\n",
      "\n",
      "   micro avg       0.85      0.85      0.85       346\n",
      "   macro avg       0.83      0.85      0.83       346\n",
      "weighted avg       0.86      0.85      0.85       346\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 模型预测\n",
    "y_pred = clf.predict(test_x)\n",
    "# 分类正确率\n",
    "print(\"分类正确率：\",round(clf.score(test_x, test_y),4))\n",
    "# 构建显示主要分类指标的文本报告\n",
    "print(classification_report(test_y, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'混淆矩阵热力图')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 绘制热力图\n",
    "ax = sns.heatmap(confusion_matrix(test_y, y_pred), \n",
    "                 annot=True, fmt='d', \n",
    "                 xticklabels=[\"满意(0)\",\"不满意(1)\"], yticklabels=[\"满意(0)\",\"不满意(1)\"])\n",
    "ax.set_ylabel('真实')\n",
    "ax.set_xlabel('预测')\n",
    "ax.set_title('混淆矩阵热力图')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16ab4ce6fd0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 查看模型系数和截距项\n",
    "pd.Series(clf.coef_.reshape(-1), index=X.columns).plot(kind='barh', title='系数条形图')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"3\"></div>\n",
    "# 3. K近邻"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分类正确率： 0.9711\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.94      0.96       114\n",
      "           1       0.97      0.99      0.98       232\n",
      "\n",
      "   micro avg       0.97      0.97      0.97       346\n",
      "   macro avg       0.97      0.96      0.97       346\n",
      "weighted avg       0.97      0.97      0.97       346\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'混淆矩阵热力图')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "# 模型构建与拟合\n",
    "neigh = KNeighborsClassifier()\n",
    "neigh.fit(train_x, train_y)\n",
    "\n",
    "# 模型预测\n",
    "y_pred = neigh.predict(test_x)\n",
    "\n",
    "# 分类正确率\n",
    "print(\"分类正确率：\",round(neigh.score(test_x, test_y),4))\n",
    "\n",
    "# 分类报告\n",
    "print(classification_report(test_y, y_pred))\n",
    "\n",
    "# 绘制热力图\n",
    "ax = sns.heatmap(confusion_matrix(test_y, y_pred), \n",
    "                 annot=True, fmt='d', \n",
    "                 xticklabels=[\"满意(0)\",\"不满意(1)\"], yticklabels=[\"满意(0)\",\"不满意(1)\"])\n",
    "ax.set_ylabel('真实')\n",
    "ax.set_xlabel('预测')\n",
    "ax.set_title('混淆矩阵热力图')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'不同K值下模型预测正确率')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 不同K值对分类正确率的影响\n",
    "K_scores = [KNeighborsClassifier(n_neighbors=k).fit(train_x, train_y).score(test_x, test_y) for k in range(1,10)]\n",
    "\n",
    "# 绘制折线图\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(range(1, 10), K_scores, 'r-.', linewidth=3)\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('正确率')\n",
    "plt.title('不同K值下模型预测正确率')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'不同K值下模型预测正确率')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 距离加权下不同K值对分类正确率的影响\n",
    "K_scores_weights = [KNeighborsClassifier(n_neighbors=k,weights='distance').fit(train_x, train_y).score(test_x, test_y) for k in range(1,10)]\n",
    "\n",
    "# 绘制折线图\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(range(1, 10), K_scores_weights, 'g-.', linewidth=3)\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('正确率')\n",
    "plt.title('不同K值下模型预测正确率')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"4\"></div>\n",
    "# 4. 决策树"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分类正确率： 0.9855\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.96      0.98       114\n",
      "           1       0.98      1.00      0.99       232\n",
      "\n",
      "   micro avg       0.99      0.99      0.99       346\n",
      "   macro avg       0.99      0.98      0.98       346\n",
      "weighted avg       0.99      0.99      0.99       346\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'混淆矩阵热力图')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "# 模型构建与拟合\n",
    "DF_model = DecisionTreeClassifier(random_state=10)\n",
    "DF_model.fit(train_x, train_y)\n",
    "\n",
    "# 模型预测\n",
    "y_pred = DF_model.predict(test_x)\n",
    "\n",
    "# 分类正确率\n",
    "print(\"分类正确率：\",round(DF_model.score(test_x, test_y),4))\n",
    "\n",
    "# 分类报告\n",
    "print(classification_report(test_y, y_pred))\n",
    "\n",
    "# 绘制热力图\n",
    "ax = sns.heatmap(confusion_matrix(test_y, y_pred), \n",
    "                 annot=True, fmt='d', \n",
    "                 xticklabels=[\"满意(0)\",\"不满意(1)\"], yticklabels=[\"满意(0)\",\"不满意(1)\"])\n",
    "ax.set_ylabel('真实')\n",
    "ax.set_xlabel('预测')\n",
    "ax.set_title('混淆矩阵热力图')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16ab19210f0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEFCAYAAAAi1toCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAHRxJREFUeJzt3XtUVWXixvHncBsxQccgUyZd2uAlMM1RGO0ypist0VKm1BTU0GFwxkYbrJTKMifNJryE6Sy6mKJmpayssTVqmuY4DoyNVwTMyrxUIl4QBYQD+/dHy/MbExFfzk34fv4pNu+797O35/i4zz77HJtlWZYAADDg4+kAAIDrFyUCADBGiQAAjFEiAABjlAjgBN72/pTTp0/Lbrd7OgYaAEoEuIIdO3bo97///SXLMjIytGDBgsvGvvvuu5o2bVqdt1lUVKTTp087fq6qqlJ5eflV55WXl19SZGPHjtXhw4cvGfP6668rPT29zhmB/+Xn6QCAtwoICFCTJk0kSXa7XT4+PmrUqJFKSkpkWZYuXLigRo0aqaCgQAsXLtSiRYv05ptv6t1335W/v7/Ky8vl5+cnm82mc+fOKTMzUy1atNDx48e1fPlylZWV6fz58zpz5oxOnz6tkpISVVVVqV+/fpowYYIkKTc3Vy+++KICAgIkSXl5eWrTpo0k6ciRI2rfvr2kH0tkzpw5CgsLc2T39/eXJM2dO1ddu3ZVQECA/Px4ysO5eEQB1RgzZowmTpwom80mSdq0aZMWLVqkoqIi2e12rVu3TsHBwZo3b56SkpKUmJioyMhIde7cWePGjXOsY/z48YqOjnast6qqSs2aNVPPnj3VtGlTNW3aVMuXL1ePHj00ZswYx7iLpRUREaEnn3xSM2fOlCRVVlaqrKzMsa7z589LklJSUhQWFibLslRRUSGbzaYtW7bowQcf1L59+9S3b199/fXXstlsjjEXiwmoC0oE+IkjR44oLy9Py5YtU05OjubNm6dJkyapX79+yszMVGFhoRITEyVJ27ZtU9u2bdWrVy8NHTpUy5cvr/Ev5y+//FIpKSmXjPn+++/l6+urdevWOZZVVlZqypQp6tatm8rKytS+fXu9/PLLGjNmjJ577jlJUmpqqhYuXKgpU6Y4iuW7777Ts88+K0k6cOCAMjMzlZubqxdeeEEnT56UzWbTRx99pJtvvlkLFy50+rFDw0OJAD/x73//W+PGjVN0dLSWLFmiiRMnatKkSTp06JAqKyslSYsXL1ZoaKiGDBmiV199VWPHjtWvf/1r+fr6qqCgQDfddFO16+7QoYNWr16t7du366uvvpIkbdmyRYGBgYqKipIktW7dWvfcc49jjt1u1/79+zV37lwdPnxYS5culSR99dVXmjt3rvbv368HHnhAkvT111/r1ltvVU5Ojh566CGtWrVKkZGRSk9P11tvvSVfX99LzniAuqJEgJ/o2bOn/P39VVBQIMuytG/fPs2bN++SMffff78++ugjSdKSJUu0bds2vfnmmyooKNCwYcO0ZcsWSdK0adMUGBgoHx8fZWZmOub/4x//UJMmTdS5c2c1btxYjRs3VkhIiA4ePKidO3deUiLdu3fXq6++Kknq3LmzXnvtNb3yyisaOXKkJCkmJkatWrWSJJ08eVLdunVTTk6OWrRooT59+jiu6wCuQIkAPxEWFqacnBytWLFCn3/+uVq2bKkNGzY4ikGSjh07pkGDBikhIUErV650FEVAQMAlL1W9+OKLl1wTuchms2n9+vXavn27CgoK5Ofnp/z8fJ07d06dO3d2jJszZ462b98uPz8/WZalw4cPKyAgQNOnT5fdbpdlWfL391dJSYliYmIcL7NlZGRIku677z6lpqbq5Zdf1pkzZ2Sz2fThhx+qffv2euWVV1x1CNGAUCLAT+zYsUPz589XdHS0fvOb32jy5MkqLy/XgAED1LFjR0k/nomsWbNG+/bt05w5cxQfH39N22jXrp38/f0VHBys7OxsBQQEqGvXriouLtaNN97oGPfnP/9Z0o/3oUyZMkW33367UlJSVFpaqpEjR+qll15Sp06datxWcnKykpOTeTkLLkGJAD/Ro0cPLVu2THv37tWSJUskSX5+fkpOTlZaWpratWsnSfLx8dHtt99+zev/5ptv9MknnzjOXn744YdL3nq7d+9eRURE6K677nIsKygoUMuWLbV3714NHz5cFRUVioyMdLzFF/AUSgS4AsuyHDfw+fj46Nlnn5XNZnO8jPS/KisrdeHCBVVVVVW7rvLycvn4+MjPz09t27bVypUrHb9LS0tTUFBQtWcI5eXlOnXqlEpKShQREaELFy5ox44dGjJkiA4cOKCHHnpIkZGR6tOnj3r37u14Ka2yslJVVVWOt/v+9P6Qi/ew+PhwvzHqhhIBrqC8vFwVFRWOn3v27KlDhw6pZ8+e+u1vf3vJ2IqKCi1evFjr1q1TUFCQYmNjJUmzZ892/H7ChAnq2rWrEhMTdcMNN8jX11fSj2cZvr6+2rhxo6Qfy+v8+fP6y1/+ovDwcD311FPy9/d3lMWTTz7p+Mu/srJSmzdv1p49e9S3b19HHrvdroqKCq1evVqrV6++pETWrVun8vJyTZ8+XZGRkS44cmhIbHwpFXBtLMty3IQINHScywLXiAIB/h8lAgAwRokAAIw1qAvrdnulTp8u8XSMK/r5zxuTrw7IVzfenM+bs0n1P19oaNAVf9egzkT8/Hw9HaFG5Ksb8tWNN+fz5mxSw87XoEoEAOBclAgAwBglAgAwRokAAIxRIgAAY5QIAMAYJQIAMNagbjb83Sf/9XQEAHC7NwZ0c9m6ORMBABijRAAAxigRAIAxSgQAYIwSAQAYo0QAAMa8okSKi4s1atQoxcfHa8OGDVccl5ubq9zcXDcmAwDUxCvuE8nLy9Mdd9yhJ554osZxFwukU6dO7ogFALgKl5VIWVmZJk6cqHPnzqlZs2aaP3++Lly4oD/96U8qLS1VmzZtNGvWLC1ZskSZmZkqLi7Wf//7X82fP1+BgYF6+umndfLkSbVv317PP/+8UlNTHWcpa9as0ZIlS/Taa6/p1ltvVUxMjNLS0tSuXTvFxMS4apcA4LpV07cT1oXLSuTgwYOy2Wxavny5Nm7cqJKSEp06dUrx8fHq1auXxo4dq8LCQo0ePVodO3ZUdna2Hn/8cUnSO++8o/DwcL322muaMGGC8vLylJycrLZt20qSYmNjJUmDBw/WzJkzFRMTo3/+85/63e9+56rdAYDr2okTxcZzayogl5VIRESE2rdvr4SEBLVp00Z33323/Pz89MEHHygzM1NFRUUqKyurdu4333yjnTt3Kjs7W2fPntXx48fVsWPHy8a1bt1a58+fV1ZWlsLDw9WoUSNX7Q4AoBouu7Cel5enbt266e2339bZs2f1xRdfaNWqVerfv79SU1PVuHHjK85t27atRo8erYyMDE2aNEmtWrWSJDVq1EilpaWSJMuyJEkxMTFKSUnR4MGDXbUrAIArcFmJhIWFKSMjQ8OHD9eJEycUGRmpO++8U+np6Ro9erQk6fjx49XOHTp0qD7//HONHDlSK1euVMuWLSVJvXr10oYNGzR8+HDt2LFDktS/f3/ZbDb96le/ctWuAACuwGZd/Cf9dejLL7/U1KlTNWzYMD3yyCNXHc+n+AJoiN4Y0O36uybiDuHh4Vq1apWnYwBAg+UVNxsCAK5PlAgAwBglAgAwdl1fWDdRl4tLrhYaGkS+OiBf3XhzPm/OJtX/fDVdWOdMBABgjBIBABijRAAAxigRAIAxSgQAYIwSAQAYo0QAAMYoEQCAMUoEAGCMEgEAGKNEAADGKBEAgDFKBABgjBIBABijRAAAxigRAIAxSgQAYMzP0wHc6Yv1T3o6Qo0OezrAVZCvbtydr/Ud09y8RTREnIkAAIxRIgAAY5QIAMAYJQIAMEaJAACMUSIAAGOUCADAmFPuE0lLS9Pu3btVVlam5s2ba86cOfL19dVzzz2nQ4cOqXnz5po7d658fX0VHx+vvn37avXq1fr4449lWZaeeuopfffdd/Lz89OCBQsUFBSkGTNmKDc3V8HBwZo9e7Y2btyovLw85eXlqbCwUPPmzVN4eHi1cwEA7uG0mw27d++upKQkTZ8+XRs3bpSvr6/sdruWLVumWbNmafPmzerbt68KCgokSR9//LEk6cyZM8rPz9eaNWuUnZ2t4uJi7dixQxcuXNCKFSuUmZmpN954Q+3atdPevXu1dOlSrV27Vps2bVJoaOhlcykR4Eehodf+XDCZ4y7enE1quPmcViIRERGSpA4dOujYsWOy2+3auXOn4uPjdf78ed16662SpKCgII0aNcox7+c//7liY2M1btw4hYSEKCUlRQcPHlSXLl0kSV26dNGGDRvUrl07DRw4UP7+/mrZsqWOHDlS7VwAPzpxoviaxoeGBl3zHHfx5mxS/c9XUwE57ZrInj17JEn79+9X69at1a5dO8XExCgjI0MpKSmOEgkMDJSPz/9v9vvvv1ezZs301ltvqUWLFlq/fr3Cw8O1a9cuSdLu3bsVHh7umPu/qpsLAHAfp52J7N27V/Hx8QoJCdG9994rm82mzZs3Ky4uTjabTX/961+rnRcSEqJNmzZp5cqVqqqq0rBhwxQWFqatW7fq0UcfVdOmTR3XRGozFwDgPjbLsqy6riQtLU1RUVGKjo52RiaX8fYPYASc6Vo/gNGbX5Lx5mxS/c9X08tZTjkTefzxx52xGgDAdYb7RAAAxigRAIAxSgQAYKxBfbPhr/r9tV5f/HI18tWNt+cDTHAmAgAwRokAAIxRIgAAY5QIAMAYJQIAMEaJAACMUSIAAGOUCADAGCUCADBGiQAAjFEiAABjlAgAwBglAgAwRokAAIxRIgAAY5QIAMAYJQIAMNagvtnwxeSPPR0BBsZP6e3pCACugDMRAIAxSgQAYIwSAQAYo0QAAMYoEQCAMa8tkRMnTig9Pf2q47KysnT06FE3JAIA/JTXlkhoaKgSExOvOi47O1vHjh1zQyIAwE+59T6R2NhYNW/eXP7+/iosLFRsbKz69OmjSZMmyWazqUePHnriiSckSUePHtWCBQv08ssvS5KmTJmiX/ziF/rXv/6lqqoqvfPOO5o+fbqysrL06aef6pe//KVSU1PduTsA0OC5tURKS0s1f/58DRo0SO+8844WLVqkiIgIJScnq1OnTho+fLijRKpTUlKiFStWaOrUqdq/f79mzZqltLQ0RUVFKTo62o17AncKDQ1yyVhPIJ85b84mNdx8bi2RkJAQ3XDDDQoLC5OPj48sy5Kfn59ef/11NW7cWOfPn69x/pAhQyRJrVq1UkVFhTsiwwucOFFcq3GhoUG1HusJ5DPnzdmk+p+vpgLy+MeeLF68WImJierYsaMefPDBGscGBgZetuxnP/uZysrKJEmWZclms7kkJwDgch6/sH7vvffqhRde0Pjx4xUYGKjjx49f0/z+/fsrPT1dQ4cO1eHDh12UEgBQHZtlWZanQ7gLH8B4fartBzDW95cUXM2b83lzNqn+56vp5SyPn4kAAK5flAgAwBglAgAwRokAAIx5/C2+7jQtdVC9vvjlat6eD4D7cSYCADBGiQAAjFEiAABjlAgAwBglAgAwRokAAIxRIgAAY5QIAMAYJQIAMEaJAACMUSIAAGOUCADAGCUCADBGiQAAjFEiAABjlAgAwBglAgAw1qC+2XDbQ7/1dIQaHfB0gKtwZr72b77jxLUB8BTORAAAxigRAIAxSgQAYIwSAQAYo0QAAMYoEQCAMZeWSFpamrKysoznv/TSS05MAwBwNq8+E3nmmWc8HQEAUAObZVmWq1aelpamnJwcnTt3TqGhoerVq5d8fX0VGxurrKwsZWdnq1WrVjp58qQSExOVmZmpwsJCJSYmSpLi4+OVkZEhScrMzFReXp7y8vJUWFioefPmKTw8XJMnT9aRI0fUokULtWrVSlOnTr1iHm+/2bAhuXPNak9HAOAELr9jPTIyUhMmTNC0adN0/vx5BQcHX/L7/v37KyEhQYmJiVq3bp2mT59+xXXt3btXS5cu1dq1a7Vp0ybddNNNOnXqlN5//30NGTJEaWlprt4dOMmJE8VOX2doaJBL1uss5DPnzdmk+p8vNDToir9z+ctZXbp0kSTddtttqqqqciwvKyuTJDVp0kS33HKLsrOzVVVVpZtvvvmK6xo4cKD8/f3VsmVLVVRUKDAwUOXl5XrkkUc0aNAg1+4IAOAyLi+RnJwcSVJ+fr5CQkJ06tQpSdLWrVsdYwYPHqypU6cqJiamxnUFBgZe8vOePXt033336YMPPlBCQoKTkwMArsblL2ft2LFDcXFxat68uaKiopScnKxjx45dMqZXr16y2+3q16/fNa27Xbt2mjx5sjZu3KhmzZpp9OjR6t69uzPjAwBq4NISefzxxy9btnz58kt+PnPmjJKSkvTwww+rcePGl/zu4kV1SYqNjXX8f3R0tKKjo7V582bdcsst8vPzU0VFhY4fP+7kPQAA1MTjHwXfrFkzrVy50mhu79691bt3b+cGAgDUmlffJwIA8G6UCADAmMdfznKnO9esrtfv5XY1b88HwP04EwEAGKNEAADGKBEAgDFKBABgjBIBABijRAAAxigRAIAxSgQAYIwSAQAYo0QAAMYoEQCAMUoEAGCMEgEAGKNEAADGKBEAgDFKBABgjBIBABhrUN9sOPS98Z6O4DKv93nF0xEANECciQAAjFEiAABjlAgAwBglAgAwRokAAIxRIgAAY5QIAMCYcYnEx8c7JUBaWpqysrKM5x89erRO8wEA5q77mw2PHTum7OxsRUdHezqKR4WGBtWr7ZgiX914cz5vziY13HxOKZG0tDRFRUUpOjpamZmZkqQBAwZowoQJKioqUuvWrRUeHq6kpKRq5y9evFhpaWkKDQ3Vq6++Kl9fX82YMUO5ubkKDg7W7Nmz1bRp08uWffjhh8rMzFRxcbGys7M1f/58NW/e3Bm7dN05caLY5dsIDQ1yy3ZMka9uvDmfN2eT6n++mgrIZddEvv76a918881asWKFvv322ysWiCRFRkZq2bJlCgoK0meffabPPvtMFy5c0IoVK9SvXz+98cYb1S4bPXq0UlJSNGTIEGVkZDTYAgEAT3F6iZSVlUmSWrRooZycHMXFxWnUqFE1zunSpYsk6bbbbtPhw4d18OBBx7IuXbroq6++qnYZAMCznFIi/v7+OnXqlCRp69atjv/+4Q9/0HvvvacHH3ywxvk5OTmSpPz8fIWFhSk8PFy7du2SJO3evVvh4eHVLpOkRo0aqbS0VJJkWZYzdgcAUEtOKZE+ffpo2bJlmjZtmpo1ayZJ6tSpk2bMmKFRo0bpiSee0IEDB644f8eOHYqLi9PJkyfVt29f9e7dW40aNdKjjz6q9evXa+zYsdUuu7idb775RiNGjNAnn3zijN0BANSSzXLRP9/ff/99/f3vf5e/v7/8/PyUkJDg8XdQ8VHwdVPfLx66GvnMeXM2qf7nq+nCusve4jt06FANHTr0kmU/vbekSZMmWrRokasiAABczK33iWRkZLhzcwAAF+NjTwAAxq77O9avxfvDFtXr1y0BwN04EwEAGKNEAADGKBEAgDFKBABgjBIBABijRAAAxigRAIAxSgQAYIwSAQAYo0QAAMYoEQCAMUoEAGCMEgEAGKNEAADGKBEAgDFKBABgjBIBABhrUN9sOCh5jdu3+faUPm7fJgC4C2ciAABjlAgAwBglAgAwRokAAIxRIgAAYx4rkbS0NGVlZXlq8wAAJ+BMBABgzK33iRQVFWnixImqrKyUJEVFRam8vFxTpkxRQUGBWrRooVmzZknSZcsCAgIUHx+vyMhIHThwQG+99ZbKyso0ceJEnTt3Ts2aNdP8+fPl59egbn0BAI+yWZZluWtj6enpCggI0JgxY/TYY48pKSlJX375pYqLizV+/HilpaUpJCRElZWVly179NFH1blzZ6Wmpqpfv36SpH379mnBggX629/+po0bN6pHjx4KDg6+4vY9cbPhx6kPuX2bAOAubv1n+9GjRzVgwABJUmRkpCTp4MGDjlLo0qWLPv/8c9nt9suWSVJ4eLhjuSRFRESoffv2SkhIUJs2bXT33Xe7c3dq5cSJ4lqPDQ0Nuqbx7ka+uiGfOW/OJtX/fKGhQVf8nVuvibRq1UoHDx6UJOXm5kr6sRh27dolSdq9e7fCw8OrXSZJN9xwwyXry8vLU7du3fT222/r7Nmz+uKLL9y1KwAAublEhg4dqnXr1ik+Pl7nzp2TJD3yyCM6ePCgRo4cqUOHDmnIkCHVLqtOWFiYMjIyNHz4cJ04ccJxdgMAcA+3XhPxNG//AMb6fkrsauSrG2/O583ZpPqfz2tezgIA1C+UCADAGCUCADBGiQAAjDWo27s/Tn3Iqy9+AcD1hjMRAIAxSgQAYIwSAQAYo0QAAMYoEQCAMUoEAGCMEgEAGKNEAADGGtSn+AIAnIszEQCAMUoEAGCMEgEAGKNEAADGKBEAgDFKBABgjBIBcF04c+aMtm3bplOnTnk6ymW8OZur1asSSUlJ0bBhw7Rw4cJrGlObeZ7IZrfb1bt3b8XHxys+Pl75+fkezVdYWKgRI0Y4fq6oqFBSUpKGDx+uVatWuSybab7jx4/rnnvucRw/Vz7Br5avuLhY48aNU0JCgv74xz+qvLy8VvM8mc+bHn9FRUVKSkrSnj17NHr0aMefpTc8d6vL5k3H7qLCwkINHjz4muddTb0pkfXr16uqqkrvvfeejhw5okOHDtVqTG3meSpbfn6+YmJilJGRoYyMDHXo0MHp2Wqbr6ioSE8//bRKS0sdy5YtW6aIiAitXLlS69at07lz57wq3+7du5WUlOQ4fs2bN/dYvo8++kiPPfaY3n77bYWEhGjr1q1ueezVJZ83Pf7y8/M1ZcoUjR8/XnfddZdycnK85rlbXTZvOnYXzZ49W2VlZdc872rqTYlkZ2frgQcekCTddddd+uKLL2o1pjbzPJVt165d2rx5sx5++GGlpKTIbrc7PVtt8/n6+mrevHlq0qSJY1lWVpZjXo8ePbRv3z6vyrdr1y6tWrVKQ4YM0Zw5c1ySrbb5Ro4cqTvvvFOSdPr0ad14441ueezVJZ83Pf6ioqLUtWtX/ec//9GePXt0xx13eM1zt7ps3nTsJGn79u0KDAxUaGjoNc2rjXpTIiUlJWrRooUkqWnTpjp58mStxtRmnqeyde7cWYsXL9aqVatkt9u1ZcsWp2erbb4mTZooKCjokmWlpaUuP3Z1yXfPPfdo6dKlWrVqlXbu3Km8vDyP5bto586dKioqUteuXd3y2KtLPm96/EmSZVn65JNPFBwcLD8/P6957laXzZuOXXl5uRYuXKjJkydf07zaqjcl0rhxY8epWklJiaqqqmo1pjbzPJWtY8eOuummmyRJkZGR+vbbb52erbb5nDnPXfm6deumJk2ayNfXV7fddpvHj9+ZM2c0Y8YMzZw585rmeSqftz3+bDabnn/+eXXo0EGbNm3ymududdm86dilp6drxIgRCg4OvqZ5tVVvSiQyMtJxSpaXl6ewsLBajanNPE9le/LJJ5WXl6fKykp9+umn6tixo9Oz1TZfdSIiIlx+7OqSb+zYsSooKFBpaam2bdum8PBwj+UrLy/XxIkTlZyc7Pi9Ox57dcnnTY+/9PR0ffjhh5J+fBNAUFCQ1zx3q8vmTcdu+/btWrFiheLj45Wbm6tnnnnGucfOqieKi4utQYMGWTNnzrTuv/9+Kzc315ozZ06NY86ePVvtMm/Jlp+fbw0cONAaOHDgZePdne+iuLg4x/8fPXrUGjBggDVjxgwrNjbWstvtXpVv+/btVv/+/a2BAwdaGRkZLslW23zLly+3unfvbsXFxVlxcXHW2rVr3fLYq0s+b3r8nTlzxhozZow1YsQI6/nnn7eqqqq85rlbXTZvOnb/6+Lzw5nHrt6UiGX9+Ie5du1aq6Cg4JrG1Gaep7K5i+m2f/jhB2vt2rUu+wvwIk8em9owzeeu/eL4mePY1YzvEwEAGKs310QAAO5HiQAAjFEiAABjlAgAwBglAgAw9n9/NXLR1MH+EQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 特征重要性\n",
    "pd.Series(DF_model.feature_importances_, index=train_x.columns).sort_values().plot(kind='barh', title='特征重要性')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'不同树深度下模型分类正确率')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 限制树深度对分类正确率的影响\n",
    "depth_grid = [1, 3, 5, 7, 9, None]\n",
    "depth_scores = [DecisionTreeClassifier(random_state=10, max_depth=item).fit(train_x, train_y)\n",
    "                .score(test_x, test_y) for item in depth_grid]\n",
    "\n",
    "# 绘制折线图\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(depth_scores, 'b-*', linewidth=1)\n",
    "plt.xticks(range(6), [1, 3, 5, 7, 9, 'None'])\n",
    "plt.xlabel('深度')\n",
    "plt.ylabel('正确率')\n",
    "plt.title('不同树深度下模型分类正确率')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gEpY2juw9SuIeECl2jBlyEQ1oLG0chYZjRDHPX1q9AGjaNiGkUaKgREswgYMgJmfBc1sM+zKXVI7JQQDEfzsXLM+U+2H/OWSnHBfa5I55IUs6X/BlHe7vAUsbR6kvU0bC7rVC9TPXtnf3EZqmLO99q0p9tglIiwhiA0LRYA4Ahn3ujhxePDJPRwl9l3k6iv2+LkwCbV09OIQQok0oKNESzIv5p6M3YGhiBoCf6xDkOQ7ZydHsy5kpNz8kEV36DAUAlBUXYt2kvgj39xB7QT7MuYJVCQ/QRlcfBVfSELLQCVtmjYS9u4/YcUnnXzoeyraprLgQF2P2Iyk0EAVX0qQGDAVX0pAUGgh7dx+MnrYAbXT1UV1VgTMR25EUGijUCyLvfUvC1b5DnXpaYcL8Nbh77ZzEYI5ZvO7cwWChgI45Lk+gdTX+IK2+SwhpdCinREswXfrZydEouJKG6qoKdOkzFAG8MqHN85ikyfaduqKo4AZyePG4GLNfar02X3iyQwGCL0MmWBA9Lmr8gjVssGBoYobhTrPYdkpz5+pZsWsI5knkZ6QqfN+axHzwKIyaNh/uAZGYvGQrwv09UHAlTajM49vZYkM4Obx4PHt0r876mV4mB08/GsYhhDQq1FOiJRw8/ZDDixfKE5GWgyE6NCKLtOmk8r7sjDubC31mApT06L1Sgwambcsdukj8PjbID6OmzQeg2H2LUkZOSUP1t3NB1IZFOHcwmG1z5ukoxAb5iQ3tMLk5rd/RFeuRYjA/W9FpxoQQ0hhQUKIlTM37IoBXJpTEyqxr4eDpx76gmOETa+c5sLJzxrsG7aFnZIJVE3pwfAf1I+99ayomuBPsFZGWnzPg48kI9/dA5qnDYt9VlZaAdyQERfnX4RN5WSwYJISQxoCCEi1jat4XpuZ9YWXnjKeFdxGy0Ak5vHj2N35mOqlgL4WkVUKVpay4kO0dAd4mYNq7+0g9x9p5DtKj97I5K/Ko674lUWdOSaivG3J48WL3xKy6au08R+66RId1igpuICFkDUx79KPF0gghjRrllGiJo4He8LUxxIObGQD4wyQdzLpLLc8EB0wCqapcjNmPsuJCAPwAhVnM68NBtlLPsbJzBgCcidgutFR6wZU0+NoYIi0iiD2m6H1zZcAnrgCArORj7LHqqgr2eTD3DAAT5q8BADZHhsHMvmG+B/jPdMuskTDt0Q8Oc5dRQEIIadSop0RLDPlsGtKj9yLIU3y2xeQlW9m/T1+5B+H+Hgh0GyKxnpKHBUrv+l83SXgIxd7dR2bOh/ngUbB392Gn9gqytHHEIMep7Gd575trAz6ejMxThxG1YZHQ4meA+PMY5DgVd6+dQ8hCJ7F6RO//1sUkAJD4rBhczTIihBBlo6BES3TpM1Rs/RB7dx90thwstNjWgI8n4+XzKvbFaO/ug0GOU/H6ZTW2zBqJu9d4Sg1KHOYuQ1tdA8QG+SmUhOowdxlMulng7jUeuyja5CVb0cf2M6HeAHnvWxO4B0Qi83QUMk8dRg4vns3rEX0euu2M4bY8BHnpiWxZZoE70YXnRAMcQghpzJrV1tbWct2Ixig8PBwzZsxotL/FCq7oShoPXxtDHDhwANOnT+e6KYSQpmc+5ZQQQgghRCNQUEIIIYQQjUBBCSGEEEI0AiW6knqhXBJCCCHKRj0lhBBCCNEI1FPSSGnr7BjR/Wqktb+o4Aa2zBop8fvqqgpkJR9D7rmTMqfbKlqvIkSnBo9wmSO2JL6ie/NUV1XUOY1Y3udHCCGaiIISonWqSkuwZdZIqd+f2LmCXfsE4C/bzrzE3QMi612vvJgl5xnp0XuRHr1XbAO+ugiuw1JVWoIj6xcI1St4X7T8PCGkMaCghGgkWb/hn/p9vdTvigpuID16L+zdfTDcaRYMTcxQVlyI5P9uRnr0Xpkr2sqqV16Zp6OQw4vHhPlrMGziV2wPBrMDcNd+w9m9gurqBRq/4O1y8zfPnkAOL17qzsI3z57AcKdZbJ3y9MIQQoimoZwSolXSIoJQUfJY6vcPcq4A4C/lzrz8DU3MMMKFvyHeo1tZ9apXXpmnDgOAUEACABbW/GXymWXjpWF6ayYv2SoUPDEru0raWVjwe0II0WbUU6IhfG0MYe08R2h3X8bRQG+hXXWLCm4gPyMVsUF+AMDmFsgaGpCWYyLteMGVNGQnRyM9eq9Cy8crmiehiIIraYgN8sMP+8+J7aTLYDYH1GvXUei4npEJAKD4Xl696pUXc75o7grzWVpQxOAdCYGljSOGO80SOm5p4yizbZq25D4hhNQH9ZRoiAnz1yA9eq/QrrkA/zfn9Oi9mDB/Ddro6iOHF48ts0ayAQnAfxGG+3uwu8w2VMLutQhZ6MTmZeTw4hGy0AkJu9cqpf76KHlYgJCFTpi+co9YwqggZn8c0aCAybcQ3dRO3nrlxQQHgrv/Cn4WzHURVXAlDUmhgRg5dZ7Yd8M+dwcAsZ8x85n5nhBCtBn1lGiIHkPHAOC/mAR7PAqupAEAeo/kv+xCfd0AAPNDEtGlz1AA/N6BdZP6ItzfQ6FESkmYF6O9uw9GT1uANrr6qK6qwJmI7UgKDYTV2M9lvrxVMdujuqoCcdv9YO/u0+D7U3W9Az5xRQ4vHnnpiWydzPOry7mDwbC0cZTYI2Vp4wjPbTE4dzAY4f4eYsfl6cUihBBNRz0lGsLUvC8sbRzZnARG5qnDsHaew+YXBPDKEMArQ/tOXVFUcAM5vHhcjNmvtHbcuXoWANiABOD3OoyetgAAkJ+RqrRryetMxHbk8OJh84WnxtdrYT0OljaOCPf3gK+NIXxtDLHcoUud5z24mYEcXrzMHo/Ht7PFhnByePF49uheQ5tNCCEagXpKNMjIqfMQstCJnSFS8rAAObx4eG6LESqXsHut2DCEsjD1SnuRxgb5YdS0+VLPV3ZOSebpKCSFBmJ+SKJSp7yqqt42uvr4Yul23Dx7AlEbFgnl+8j6mV0+EQEA6N7/I6ntjQ3ykzr7pvU7ukrtRSKEEC5QUKJBzHoNAADcvcaDcWdzNimSOQ4AF2P2Iyk0ENbOc2Bl54x3DdpDz8gEqyb04KTNqsYMVQR5jpP4vWiirr27D5JCA1FdVSGUV8LkdNi7+9SrXkXotjPGcKdZQsmqTALuhPlrxMozeUP27j5SF3dj2itp9k24vwcyTx2moIQQovUoKNEgbXT1MXnJVkRtWIQ+tp8h3N8Dk5dsFXpRMVM/BWfpiCZVyks0qRYArJ3nCM30URTXK4iadLMAAFSWPhFqf+n/HgAAO01YVZiF00Sf39PCuwAAfWNTsXP+fvwXAKCz5eB6X7ehs4YIIUQTUE6Jhuk+0AYA2J6PXsPtJZYreVgAQP4kSmZWyIObGex5vCMhYuWs7JwB8PMtBIOWgitp8LUxRFpEkLy3ohRMDo3oH9HvGSZdewEArsYfZHsnyooLkZ1yHADQ5f9f/IrWK68Bn7gCALKSj7HHSh4WIDs5GgDQtd9wsXOK7uQAAIy7SF7UDXjbw1JwJU0oCGVm30jqgSGEEG1DPSUaxrizOdtbYe08R+w3++kr9yDc3wOBbkMkni9txVJmVojgcIWkF5n54FHsEIhoDoSljSMGOU6tz22pDZMwLKn91s7i+8/IS97hHCbRNWrDIrEFzaav3COxp4YZpmurayC13kGOU3H32jmELHQS+04bfi6EECIPCko0kJWdM9Kj92LIZ9PEvhvw8WS8fF7FvvDs3X0wyHEqXr+sxpZZI9l8FEnnAWA3c5u8ZCuGO80SWu+E4TB3GUy6WeDuNR67rsbkJVvRx/YzrdhfhUk0FdyQr/fIT9HfzkXl1xZNdAX4PyNZU6mZZyzr2eq2M4bb8pA6N+QjhBBt1qy2traW60Y0RuHh4ZgxYwbnORbaRpN3N/a1MdTIdklS3+foa2OIAwcOYPr06apoFiGEyDKfckoIkcODmxmYvGQr180ghJBGjYZviEbStB6Tv7IvylyfRVPQ7sCEEG1GPSWEyEEbAhJCCNF21FNCNIqm9IxoK3p+hBBtRj0lhBBCCNEIFJQ0YcyGcYQQQogmoKCEEEIIIRqBghJCCCGEaAQKSgghhBCiEWj2TSNVXVUhtCS5tfMc2LrNk7gEvaCighvIz0hll59nljJnlqlnFFxJQ3ZyNLtEurSl1OUtJ0qeXBdZM02krXMiepz5vDw2H1fjDyI2yE/qPcv7bOR99vX9GRFCSGNFQUkjFbnKU2g7+/TovUiP3osf9p+TGhDk8OIR6usmdoyph3n5SirHbIDnuS0G5oNHKVROExxZv4C9T3nvWVI5QP5nX5+fESGENGYUlDRCzMvS3t0Ho6ctQBtdfWSejkK4vwcuHNuLST6bJZ7HvHTnhySiS5+hAICy4kKsm9QX4f4e7IuXKffT0RvsrrcPbmYgyHMcspOj2WBD3nKSqHu9DdMe/eC2PARtdPVRcCUNIQudkHnqsNg91/Vs5H329f0ZEUJIY0ZBSSOUd/4UAMDmC09299gBH08WG2YQxQQCVaUlKCq4gdLiQjzMuSJWztLGETm8eGQnR6NTTyuY9RqALn2GigUS8pbTBILPSrCnhyHvs5H32df3Z0QIIY0ZBSUqoqurCwB4/aoaLVq1Ueu1mfwN3XbGCp+bsHstkkIDZZZx8PRDDi9eKLdi5NR5Yj0f8paTpKE5JYqS51nJ82zkffYN+RmpyutX1QDe/tslhBB1o6BERYyMjAAAzyvKoN/hPY5bI5+LMfuRFBoIa+c5sLJzxrsG7aFnZIJVE3oIlTM174sAXplQ4mcOLx6WNo5w8PRj8yHkLacN5H022uyf8lIAgLGx5gRKhJCmhYISFbG0tAQAFN/LVXtQYu08B+nRe1FVWqLQb+JRGxYBgFA+Q3VVhdTypuZ9YWreF1Z2znhaeBchC52Qw4sX68GQt5wgVQzxVJWW1PtceZ+NvM++vj8jVXryVx6At/92CSFE3WidEhVp164dLPv0xb2sC2q/dveBNgAA3pEQ9sWZeToKvjaGOBroXef5JQ8LAPBfumcitot9fzTQG742hnhwMwMAYGhihg5m3etdThUsbRwBgL12dVUFeEdCGlxvXc9G3mff0J+RKtzLugDLPn1hYGDAyfUJIYR6SlRokoszQiOP4ZOvf1LrdQd8PBmZpw6z028FjXCZI/W86Sv3INzfA4FuQyR+X/KwAMadzTHks2lIj96LIM9xYmUmL9nK/l3ecqow4BNX5PDiha49Yf6aetcn77OR99nX92ekSnnn4jDbbRIn1yaEEIB6SlTq66+/xuN7ebh/45Lar+22PEToxW/v7gOfyMsy8zgGfDxZ4jk/7D8HALh7jQcA6NJnKH7Yfw727j5CZd0DIjHcaRZ7TN5yqjDg48mYvnIP22MyeclWjJo2v0H1yfNsAPmffX1+Rqpy/8YlPL6bBw8PD7VfmxBCGM1qa2truW5EY+b5zbdIu5oLj60xXDeFEKn2LHLCqEG9EbLrN66bQghpuuZTT4mKrV2zGo/zs3E95TjXTSFEouspx/E4Pxtr16zmuimEkCaOghIVMzY2xuqVK3F802L8XXSf6+YQIuTvovs4vmkxVq9cSVOBCSGco+EbNXj9+jXGfeKA/IfF+GbHSXYFT0K4VF1VgV3ffYoenU2QeCoBLVpQ3jshhFM0fKMOLVq0QPSxo2jT7F+E/TQDL59Xcd0k0sS9fF6FsJ9moE2zfxF97CgFJIQQjUBBiZoYGBgg/kQcKh/fwU4vB5QVF3LdJNJElRUXYqeXAyof30H8iThal4QQojEoKFEjc3NzXM64BBP9NtjhaY+89ESum0SamLz0ROzwtIeJfhtczrgEc3NzrptECCEsCkrU7P3338fZtDP49GM77F3sin0+U/Cs8C7XzSKN3LPCu9jnMwV7F7vi04/tcDbtDN5//32um0UIIUIo0ZVDqamp+G7+Aty+dQt9R0/AoE+nwXzwKLXvKkwap9evqlFwJQ1XT0bgxplY9OzVCzuCtmPMmGcgxpkAACAASURBVDFcN40QQiSZT0EJx16/fo2DBw9iV8hu8Hjn0KyZDky79YKukQlavaPHdfMkelPzGjrNm3ZipCY/g1fPK1H1rBhF926htvYNbGxG4ttvPDFlyhRKaCWEaDIKSjTJ06dPkZKSgqysLBQVFaGyspLrJom5d+8ecnJy4OjoiObNm3PdHE7U1NQgPj4elpaW6NatG9fNEaOnpwdTU1P0798fY8eORYcOHbhuEiGEyIOCEiK/srIy9OzZE9OmTcPWrardUE/TLVq0CBEREbh9+zYMDQ25bg4hhDQGtE4JkZ+fnx90dHSwcuVKrpvCuZUrV0JHRwfLli3juimEENJoUFBC5JKZmYnffvsNGzZsoJ4BAIaGhtiwYQN27dqFzMxMrptDCCGNAg3fkDrV1tZi5MiRAIBz586hWbNmHLdIM9BzIYQQpaLhG1K3//73v7h48SJ27NhBL14BzZo1w44dO3Dx4kX897//5bo5hBCi9ainhMhUVlaGXr16wdXVFUFBQVw3RyPNnz8fhw8fxq1bt2hoixBC6o96Sohs/v7+qK2txZo1a7huisZas2YNamtr4e/vL1f5Zs2a1flHUfU9jxBCNAkFJUSqzMxMBAcH45dffqEeABmYpNfg4GBKeiWEkAag4RsiUW1tLUaPHo1///0X58+fp9/C61BbW4uPPvoILVu2xJkzZ2Q+L+Y7Zf6np4o6CSFEzWj4hkgWFhaG8+fPU3KrnJik1/PnzyMsLIzr5hBCiFaioISIqaiowJIlS+Dp6YlBgwZx3RytMWjQIHh6emLJkiWoqKhQWr1ZWVnYtGkTmzfi5OSEyMjIOs9LTk6Gl5cXe97PP/+MrKysOss6OTkhOTlZae0nhBC51RIiYtGiRbXGxsa1z54947opWufZs2e1xsbGtQsXLpRaBkCtvP/pxcTEsOVF/0REREitU9Z5SUlJQtfw8/OTWM7Pz0/BuyeEkAb5joISIuT69eu1LVq0qN29ezfXTdFau3fvrm3RokXt9evXJX4vLViQFKwwxy5cuMAeu3//vlhZaZ/v37/PHrtw4UItgNpvv/2WPZaUlMQGIGVlZbW1tbW1ZWVlbKCSmZnZsIdBCCHy+44SXQmr9v+TW1+9eoXz589DR4dG9+rjzZs3+Oijj9CqVSuJSa915ehI+k/yyZMnKCoqwoMHD3Dp0iV2ijZTVjTR1cnJCX/++Sc2btyIgQMHYvDgwTAwMBCr9+eff8aaNWtQVlYm9H15eTkMDQ2xceNGLF68WIG7J4SQeqNdgslb4eHhmDlzJi5evIghQ4Zw3RytdvnyZQwfPhxhYWGYPn260HeKzpRhAgdJpAUlWVlZGDBgAFtu4sSJ+P7772FnZyexLbLQ/0UQQtSEgpKGkndmiqY/5oqKCvTq1QvOzs7YuXMn181pFLy8vBAdHY1bt25BX1+fPa5IULJ79254enri22+/haurK4yMjGBqagoTExOhOqTVmZWVhdOnT+PHH38EwA9OVq9ejf79+wudJ4um/9slhDQaFJQ0VGMJSn788UeEhobi1q1bMDIy4ro5jcKzZ8/Qq1cvuLu7Y+PGjexxRYISSWWZoRXB43XV+eDBAxQUFMDe3l6onJeXF3777Tex4RtCCOEArVPSULW1tUJ/6jquiW7cuIGtW7di3bp1FJAokZGREdavX4+tW7fixo0bDarr9u3bAPgBiWCAIw0zvTc9PR0A0KVLF5ibm4uVc3V1BQBs3LgRT548YY8nJyejWbNm2LRpU4PaTQghiqCeEiXTtpU1a2trYW9vj8rKSly8eJGSW5XszZs3GD58OPT09JCUlCS0R408/0YiIyMxbdo0qd/funULPXv2FKszPT0dI0aMkHhOSEgI5s6dy36WlrMyceJE/P777+jYsWOd7SSEECWgnhJ1Y15KDx48gJOTE37++Weh49LKi1LWYlcRERE4c+YMgoODKSBRAR0dHQQHB+PMmTOIiIhQ+Hw3NzeEhISwn/38/HDr1i12j50zZ85IPM/a2hqZmZnw8/MTOjcmJkYoIAGA1atXIyIiAt9++y17LCQkhAISQojaUU+JktX1WzDzvZ+fH9asWYOIiAi4ublJPU/ScWm/2fr5+WH16tVyt7WqqgoWFhb49NNPsXv3brnPI4qbO3cuTp48idzcXOjp6XHdHEII0UTUU8KVPn36oLa2Fm5ubgqdl5ycjDVr1sDPzw9lZWWora1FWVkZG+RIW0ZckhUrVqC6uhq//PKLos0nCvrll19QXV2NlStXct0UQgjRWBSUcER0vQh5paSkAODPlmFmSxgYGLBTPk+fPi1XPbm5udi2bRtWr15Nya1qYGRkhNWrV2Pbtm3Izc3lujmEEKKRaPhGyeQdvpFnmEbScWWtK2FnZ4fKykqkp6ejefPmdZYnDSeY9Eob3hFCiBgavmmKDh48iNTUVGzfvp0CEjXS0dHB9u3bkZqaioMHD3LdHEII0TgUlGgwwXUjGMwMCSafRNIfWaqqqrB48WJ4eHjA2tpaJe0m0llbW8PDwwOLFy9GVVUV180hhBCNQkGJhpg4cSIAsItdlZeXY/v27WLlGrrY1Zo1a/DPP/9g3bp1ymo6UdC6devw/PlzhWZKEUJIU0BBiYZgNm0bMWIEmjVrBkNDQ3YpcUF2dnbsTBsTExN2HRN7e3tMnDgRM2fOlHqNvLw8bN68GevWrYOxsbHK7oXIZmxsjLVr12LLli3Iy8vjujmEEKIxKNFVyeqb6ArwV+8MDw/Hn3/+ya66Ka18ZGQkzpw5g99++w0Af7Grzz//XOZiV+PGjcPTp09x+fJlyiXhWE1NDYYMGYIOHTogMTGR6+YQQogmoA35morDhw9j6tSpOH/+POWSaIj09HR89NFHOHjwIDssRwghTRgFJU1BVVUV+vTpAzs7O+zbt4/r5hABs2fPRlJSEnJycqCrq8t1cwghhEs0JbgpWLt2LSoqKhAQEMB1U4iIgIAAVFZWYu3atVw3hRBCOEdBSSN369YtbNmyBatWraLkVg1kbGyM1atXY8uWLbh165bQd2lpaYiJieGoZYQQon40fNPIOTg44MmTJ5TcqsGYpNeOHTsiISEBRUVF8Pb2RmRkJAD5VuglhJBGYH4LrltAVCcqKgqJiYlIS0ujgESDNW/eHNu3b8eoUaPg7u6OQ4cO4fXr1+z39+7dQ7du3ThsISGEqAcN3zRSz58/h7e3N7766iuMHDmS6+aQOrx+/Rrt2rVDWFgYXrx4gX///RcAfwr55cuXOW4dIYSoBwUljdTatWtRXl6OX375heumEBkeP34MNzc3jB07FuXl5Xjz5o3Q9y1btkRGRgZHrSOEEPWi4ZtGKD8/H5s2bUJAQADee+89rptDpEhLS8Po0aPRogX/P8OamhqxMq9evcLFixfV3TRCCOEE9ZQ0QgsXLkSvXr0wb948rptCZBDtFZHm8uXLcpclhBBtRkFJIxMdHY2EhAQEBQWxv4ETzTRmzBjcvn0bH3zwAVq2bCm13PPnz5Gfn6/GlhFCCDcoKGlEnj9/jh9++AEzZsyAra0t180hcujRowcyMjJga2srdYaUjo4O5ZUQQpoECkoakV9++QV///03AgMDuW4KUUC7du2QkJCAb775RuL3LVq0oBk4hJAmgYISLbR582asWbMGL168YI/l5+cjMDAQK1asoORWLdSiRQvs2LEDwcHB0NHRgY7O2/80X716hQsXLnDYOkIIUQ9a0VULtWjRAjU1NTA1NcWOHTvg4uKCCRMm4P79+7h27Rrlkmi5pKQkuLi44MWLF+wiaq1bt0ZVVRX9bAkhjRntEqxtqqqqYGBggDdv3kBHRwdv3rzBkCFDcOXKFaSkpGD06NFcN5EoQX5+PhwcHFBYWMgupJaVlQUrKyuOW0YIISpDuwRrm2vXrrHTQ5n/zcrKgo6ODhISElBVVcVl84iS9OjRA1euXMHIkSPZoRzKKyGENHbUU6JltmzZgiVLlrC/PQtq0aIFjI2NsW3bNnzxxRcctE77vHr1CufPn0dGRgbu3r2L0tJSjVoTpLa2FteuXcOdO3dgZGQEOzs7rptEJGjdujXat2+Pvn37wtbWFhYWFlw3iRBtRMM32mbatGk4fPiwxNU/Af5eKbW1tVi/fj3+85//qLl12uPKlSvYtm0bjkVHo7KiAu93/gCdu3aHvoGhUJKppnj08D7atGkLI+OOXDeFSPDqZTXKy0qRn5eDivIydO3aDbNnu+Pbb79Fx470MyNEThSUaJsPPvgADx48kPp9y5YtUVNTg2vXrlH+gQRFRUVYvHgxIiMj0cdqIFy/8oCd40R06GjCddNII5F7PRPxMVGIOhCK1/++ws9+fliwYAFatWrFddMI0XQUlGiT0tJSGBkZQdqPrEWLFujbty/i4uLQqVMnNbdO84WEhMB78WK072AM3xUb8PH4z7luEmnEql88x64tGxD621Z07doVhw4eRL9+/bhuFiGajBJdtcmVK1ekBiQ6OjpwcXHB+fPnKSARUVNTA29vb3h5eWHWt4sQc/YaBSRE5dq0fQeLflqJWF4W2hm/h49sbHDy5Emum0WIRqOgRItcvnxZrAu4WbNmaNasGfz9/XHw4EG0bduWo9ZpppqaGkydOhW7doVg695ILFiyHG3a0DMi6vN+5w+w+2AsJkyehokTJ+L333/nukmEaCxaiUmLXLx4kV1MCwCaN2+O5s2b448//oCrqyuHLdNcCxcuROqZNPw35jT69B/EdXNIE9W8RQv4B26H2Qfd8N1336F79+40k4oQCainRIukp6ez01VbtmwJIyMjnD9/ngISKX777Tfs/v13bNp9oNEEJJUV5Tgctge9jVujt3FrnDh2SOE6igofqqBl6pWSEIfexq0VOod5ZpL+qIvHfG/M+HoeXCZNwu3bt9V2XUK0BQUlWuLJkyf43//+B4Cf0NqnTx9cu3YNgwcP5rhlmunevXv4/ocfsGpTMIaPbDyr3B75Yx+We89jPxcXPVbo/H3Bv8JuoLmym6VWeTezMe/LSQqdo0mB2OLl6zBkhC08PL6WmiNGSFNFs2+0RFxcHCZMmAAAcHV1xf79+yl/RAYXl0l4WauDzb+Hc90UpWJ+qz+XWwijDsb1Pj+35KVS26UuWZcvwu3TUexnee+jqPAh7Aaaw3flBsye972qmie3sr+f4bMR/RC0fRumT5/OdXMI0RTzlZJT8vTpU6SkpCArKwtFRUWorKxURrVEQFZWFgCgT58+AIBZs2Zx0g5tWLkyJSUFCacSEMfL5ropKlOfgESd8m5m48KZZKUGAPuCf0WA/xJsCgnDYs+ZCp17/94dAEDvfgOU1p6GMGxvhAX/8cd//rMUzs7OeOedd7huEiEaod49Ja9fv8bBgwcR/FsI0i/wgGY60DfrieZ6xkBrXWW3s8l78+9LvHn5D1rotue2Ia9fovZFOf55dAsvq8rR+YOu+HrObI1audJ6xAj0HjgMS1YFct0UpJ9NRUJMFCJDQwAAXt5L8YnTJFj0EV7YjnmJB/gvAQCMdRiPCZPd8JnLFLaMpNwHwZ4CwWuNdRiPr75ZCGvbMTLPH+swHiam78M/cLvQ8cqKcgz7sCPc3D3FvpMl6/JFRB/8g71fpn3y5G3U1evR27g1gv84irEO4xXu8Uk/m4rZkxyw72iC0DPhUk1NDT4Z0guLf/ge3t7eXDeHEE1Qv8XTUlNT4fXdfOTfvo32gz9FhxGu0LewgU5L9SWMEe798+AGnmXE4m9eBFrUvob/cu5Xrrx+/TqsrKxw4sJ1dDPvyVk7AH4yprTcB8GXo6xym0LC2MBEVlCybf0K7Ny8Xux7L++lWLh0hdTz9x1NwOxJDjiWmiEUKDHDJEwQIEtlRTkuXziHw2F7kJIQBzd3T4z62BFWg4exPTrKCEoEKRqUML0sx1IzcP1qBpuXs2pzMBw//wJ6+gZyX1uZgjeuRcKxg7h1K4+T6xOiYRQLSv755x94fD0XByMjYNTfHl3cVqFNx64qbB/RBm9evUBh3DYUJ4agW9duiDrM3cqVq1evxv6wA4g9z/3QDfPiTL5WAFOzzgDevuwFeyCYcpEn09B/yHAAb3MgAOEXr6SXMdML4OW9FLO/+wF6+gaorCjHvh1bsHPzeqGAQ9L5877k99wwwQvw9iV+Mv0Gun7YQ+L9FRU+xLWMC1jsOZPt2Rk4dAR7r6pU36BEkrEO47EheB8ngcnt3Bv4fNRg5ObmauRQKCFqJv+Kro8ePYKN7SjEJCSh9/dh6LnwvxSQEACATqu26OKyBFarUvGsRQdYf8TdypVpZ89iwLCPOLm2KKaHIT4mCulnU1FZUY7+Q4Yjt+Sl0JBIbslL5Ja8hFnX7si7mY2UhDgcDtsj93UunUsFADYgAQA9fQPM/u4HAMCFM8kyz//qm4XYuXm90AyVAP8lGOswXmpAAgB2A82x2HMmNoWEIfiPo/jMZYpaApL6YAKSyJNp7PPOLXmJTSFhSEmIw9mkBE7a1bN3X+jpGyAtLY2T6xOiaeTqKSkoKICN7ShUtzKE+YL9aN3+fXW0jWih2jev8dcBPzw5G45dv/2Gr7/+Wq3X7/T++3Cf542vvlmg1utKknczGy5jhrKfJeV5MKQNvwB195QoMjQirYdh3peTMPSjUZg973u23as2B8N1pofUOhXpKeF6+KauusY6jEfwH0cbXFd9TPt0FMaMHIEtW7Zwcn1CNEjdPSXl5eVw+PQzvG7fHRZLoikgITI102mBbjN/QedJS+E17zskJ8v+LV3ZSv/+G4btOE4G/n8WfayQW/ISx1Iz4LtyA1IS4jB7kgPmfTkJeTffDi8dDtuDnZvXw83dE/uOJuBYagbO5Raqta2uMz0Q4L8Ez56W4O5tfn5Dv0FDZZ5jatYZn7lMwaU7T+A60wOxUZGwG2iOlT4LkJIQh2dPS9TRdKVISYjj7NrtjTrg2bNnnF2fEE0ic0rw69ev4eTsgmevWqDX93vQvI32zKq54MEPnkbseaSW8xRV86ISTzNiUJqZiNKsRLTrPw4drF3Qrp8dmrfVU/n5qtbJ0QuvK0rwufMkXLl8CT17qifptLq6GjrNm6vlWvKy6GMFiz5WcHSajPv37mD2JAekJMSxv+UzSZeCQzqVFeVy1+/m7onI0BBcuvOk3nkRQ0aMBABcPJuC2KhItt3y0NM3wFiH8RjrMJ6dfcMk7jL3yPW6KPO+nISUhDixZ8Q8Zzd3T66ahnd09VBTU8PZ9QnRJDJ7SnYEByPjWjY+/G6vRrzoGpP7R9bi7n5flGYlAgBKsxKRv2se8nfLN+zQ0PPVofMXy9C2hzW+mu3RJFeuXOmzAL2NWyPr8kUA/J6FD7p9KLX8X3fyAYBNUpWXg9NkAMC+HVuEeifSz6ait3Fr7Av+Vewc0aBHT9+AXf8jJSEOXt5L5b6+oP5DhsM/cDvbO6QpJkx2AwCx3BHmM/MMCSHcktpTUlJSAr+fl6PzjEC07tBFnW1Sivr2dKi6hwQA/nmYg+LUMJhNWISOo2egdfv38fLvR3gUtx3FqWGoLr6LNibdVXa+ujTTaY6uszYh++dRiIiIaHIrVzpP/RKRoSFCK5AyVm0OZv/OBAOfWveVWM9fd/JlJpxa246Bl/dS7Ny8XiwvZazDeDhNmSH0OSUhTuIaJLb2DuzfR4/7tO4blIHpHeKKaM6Jrb0DxjqMx2LPmWILr3l5L9WYtUsIaeqk9pT8tOxntOncF0ZDZK9RQBRXde8aAMD4oy/YHJ3W7d+HyZiv+N/fv67S89WphW47mDr5YLHvf/D8+XOum6NW/YcMx7HUDKFeBy/vpQj+46hQAulnLlOEghQv76U4mX4Dx1IzAAAZ5+uembFw6QpsCgkTGoZYtTkYq3/dJbT668KlK9gyxUXCAbjgsIZZV+6DWmXS0zfAhuB92BQSxs6KYnJ4BKdCE0K4JbGn5P79+9i753dYLo1Wd3vk8vTScTxNP4bSrESYTVgE44++wLWfbAG87ekQzQ1hPg/ZkoWSC1G4f2gVm4fRYdjnbN3y5JQwZWSRdf6rZ/zvWuoLLxXeyoC/IuqLR7dk1t3Q89Wt4+gZuJGwA7/99luTW7mS6TGo68XnOtND4kwX0VwMWbkZn7lMwWcuU2SuwGrRxwr+gdsllmGGj9zcPTV+Gfu6clQkfa+nb8A+I0KIZpLYU/L7779Dz6wn9Lpr3nbvD48FIH/XPDaXojB2KxuQyONO6I+4f2gVgLd5GE8vHVdJW6UpjN0KAGJ5Oi31Owh9r6rz1a2ZTnO0+8gNO3bu4ropRIaYQwcAAFPd53LcEkJIUyWxp+TI0Wjo9XeQ9BWnynN5KIzdKjWXQh7vdrZEj7nb0bytHspzecjZOAVP048J9ZbURR15J41N+0GfIuv4RuTl5dHKlRpGcA0RL++lnOaCEEKaNrGektLSUuTl3IB+z+FctEemijweALABCcDPpej0ifzT+d6zn8P2MBj0tgEAtteFqM47ZhZo/a4+rVypgZgcC9+VGyi/ghDCKbGekpycHABA20691N6YujDDEqILuCky04QZ4miIhuaUNFXvdOqB3NxcrptBRHC1kikhhIgS6ylhVhZs8a6h2hvTVJhNWASAvwCaIOYz872qzueKzrvtaeVKQgghUon1lFRVVQEAdFrWvVeFuplNWITC2K14+fcjod6Sl3+rt1eiob0gbd/n90L9W1EilKxa/ZS/IVorI9k9MQ09nyvNWuvSypVyqO++LsrcD0aWyopyxB8/gpSEOKQkxLH73tjaO9RrRVlmrx1p7T5x7BBioyKRkhAHN3dPTHWfK1feS131EkI0j9y7BGsCfQt+DsiTMwfYQOTl34/w5MwBLpulsLam/IWwSs4fEbqPvy/HAgB0uw1U6fmENMTm1X5Y7j2P3S8mJSEOiz1nYsm82QrX9expidCmhaLmfTmJXWUWACJDQ+AyZihOHDvUoHoJIZpJ5t43msagtw3bW6Jp014V8W5nS7TrP07ifZiMmYl3O1sKHRNdO0XR84l2qe9v9uroEci7mY3I0BB4eS+F60wPmJp1RlHhQ4RsDUBkaEidq8+KCtqwSup3J44dQkpCHHxXbsAXX85me2FOHDuExZ4zpe5IXFe9hBDNpVU9JQDQ2cUXPb4JRrv+4wDwh3QGrjvLcasU96H7RnSfFcDeR7v+49B9VgA++GKZWs4npD6uX+WvMus0ZQYbEJiadWbXNsnJviZ3XfuCfxVbVVYQszGgYEACvF0O/1zKqXrVSwjRXFrVU8LoMOxzieuKmIx5u6eFaN6HtDwQecspW0v9DjAZNQMmo2bUWVZSmxQ5n2gOwfwIL++lcJoyg93zhunpEM0NYT6fyy1EzKEDCPBfwuZxCK5OKk9OieCaJNLIOr+okJ+3ZGTcUei4sYkpAKAgL6fO+gH+ZoEB/ktwLDWDHZoRxRwXzVNhPudkZ9arXkKI5tKqnpILHu/jgsf7qLx7lT1W86ISjxP4K4Xq9xrBVdMIqdO29SuE8iN2bl4vdRM+SX7+/hsE+C8B8DaPo67cCmVjNvwTDRSYZelFNwSU5K87+Zg9yQGbQsJkJqwy66eI7mjMfI4MDalXvYQQzaVVQYnFwlAAwI21E9kA5dJ8C3Yfm3b97LhtICFSpJ9Nxc7N6+HlvRTJ1wqQW/ISydcKhDbQq4tFHytcuvMEuSUvse9oAoC3Qxzyyi15WecfVaqsKEeA/xJ4eS+tcw+aCZPdAABnkxKEzt+3Y0uD6iWEaC6tCkra9R8Hyx8PCa3DYTJmJnp8E8wuHU+IJrp0LhUA2ORQgJ+LMevbhXLXMWPud2wPhbXtGADQuiGKfTu2ICUhDjPmfldnWVt7B4x1GI/FnjPR27g1ehu3xrAPO0osq0i9hBDNpXU5JQa9bWDQ2wadXXy5bgohcmOGNURniygyU0UZO/c2NKekIU4cO4Sdm9cj8mSaXPeip2+A1b/uQvLJGCz3nieURyM4TKRovYQQzaV1QQkhhDte3kuxc/N6VFaUC+WVMHkeXt5LpZ672JOfiO726SiJ30tK1DXqYAzXmR5wnenBHmOSbX1Xbqh3vYQQzURBCcTXAdFm/zzMQfaKcWL30tD9eqTVS+TDvMyLCh8K9ZYwL1h1aeiL2dyCvwbOs5InQkHJo4f3AYj3BDXEvC8nISUhDpfuCF/r/r07AAAT005KuxYhRDNoVU4Jke3fiqfIXjGuXucy650ou17CN2zkGADA4bA9bCBSVPgQh8P2cNgqxXXvaQEAiDl0QOg+TsXwN/XrN0j6Kqp1JdaKfmYSXeOPH2GP/XUnHwkxUQCAgUNH1KteQojmop6SRuTh8Y1Sv5PWw8H0gHSdurxe9RL5WNuOYXtL5Jk2q6ks+lhhrMN4iffh5u4pNhW3IUMnTKLrcu95WO49T+i7TSFhSu2VIYRoBuopaSQeJ+zCq9L/KXQO0wPSfVYA2ph0V1q9RLKFS1dgU0gYu/6Gl/dSnEy/wXGrFLf6111YtTmYvY+xDuOxanMwvH9eo9TrMImuqzYHs8e8vJfiWGoGTfslpJFSak9JeS4Pzy7/ieLUMAD8JeDbD5kgthfLPw9zUJ5zFvcP8fenaNd/HDpYuwit0iqY51GalYi8be5o138cTEbPYIcanl46jvxd/N+genwTLPV80XLt+tnJNX1Y8H7a9R8H03FzYdDbpt73LaqheR6C179/aBWsViSiNCuxzvKM/yXt5T9TKavC1rdeIt1nLlMkvlAF1ysR7VWQ1ssgbzllk5R8Ko08bZJVRpFr1efahBDNorSghAkcBDEbxln+eIh9mUsqV5r19qUnuny8YHmmnNWKRPx9OVZoMzom6JB0PvMdU65d/3HsQmzSPDwWIFQ/c22zCYuEpiPLe9+qUl18Fzkbp6DHN8EKbcRXnstj26jMeolkzDBG5Mk09B8yH2tu6wAAIABJREFUHAB/xsqRP/YBAIZ+ZMtZ2wghRFMoLShhXsyDAi+hdXt+D0Dl3au4sXYinl3+k305M+X6LvsTet0HAQBe/v0IV32GIX/XPLGgouruNQwLykPztnooz+UhZ+MUZK8YB7MJi8SOSzq/+MwBtk0v/36EJ2cOoDB2K8pzeVIDBuaFbTZhETo5eqF5Wz3+cvbxO1EYu1WoF0Te+5akoTNZal5U4q+Dq2A2YZHEvYBkKUrcjXb9x0lsX0PqJZIF/3EU876cJHHa6liH8ewmc4QQ0pQpLaeEGVJ5lhGL8lweal5UQq/7IIzY8wjdZ/7Clhux5xFG7HmENh264J+HOSjNSsSTMwek1vue/Rx2qEXwBcoEC6LHRXWdupwNFlq3fx8dR/OHKp5d/lPqORV5PLFrNG+rh06OXgCA8py3uxLLe9+q8Dh+J0qzEvGe/RyFzqu8exWlWYkwGS152Ka+9RLpxjqMx76jCULreLi5e2JTSBg2BO8T20uGEEKaIqX1lHR28UVpVqJQnoi0HAzRoRFZWup3kHhc3iXlRRM4mQClODVMatDAtO3SfAuJ398/tAqdHL4BoNh9i2pITsnTS8dRGLsVfZf9KfUZSVPC4w/Z6Pe0Vmq9RDZr2zGwth2DhUtXcN0UQgjRSEoLSt7tbIkRex4JJbGWZiWiXf9x6Oziyw53FKfxh09MxsyE0ZCJaKHbDq0MOuLyD/2V1RS1kve+lY3Jk7mxdqLE76UtCPdvxVMUp4bBbMIiiYFdfeslhBBCGkrp65S829kS73a2hNHQCagu/gs5G6egNCuRfYnd3c9PEhXspah5UansZrBe/v2I7R0B+AmcAIQ29RNlMmYmilPD2JwVedR135Jw8WKvfvoAAKDbfaDar024p+1Lrp84dgixUZFISYiDm7snprrPFVsbBeAnEccfP4KUhDikJMSx++bY2juIDZVVVpTjbFICW6+ssoQQ1VJaUHI37D8oTg1jE1gFAwFJqovvoo1JdzaBVFWenDmAjqNnsImuJef5q0PqW0gfXjEaMhHFqWF4HL8T79nPYYcxmITaD6YsZ4dvFL1vZZEW0NTVk/G8MBcA0Pa9D5VaLyGqxiw7z4gMDUFkaAg2hYSJTbPevNoPkaEh7GfB4CT4j6Ps8WdPS/Dz998I1StYdvWvu2iTP0LUSGlBibHNFBSnhkns9u8+K4D9e49vgpG/ax6u/SR5CiQTrCjTVZ9hQp/NJiySmfNh0NsGZhMWsVN7BbXrPw7GIyazn+W9b03xz/3rAIDmbfU5bgkh8jtx7BBSEuLgu3IDvvhyNtuDceLYISz2nImBQ0ewK7zm3cxGZGgIvLyXwnWmB0zNOqOo8CFCtgYgMjQEf93JZ3dnTj4Zg5SEOLHAhqk3+WRMvdZIIYTUj9Jm3+h1HwSrFYlCwyJmExbBYmGo0OJcHYZ9LvSyNpuwCAPXnYXVCv46JeW3LiirSQD4iagfTOEvod6u/zhY/nhIaJ0RWef1+CYYJmNmsse6zwrAh+4bhRJA5b1vTcEs8EZJrESbxEZFAoBQQAKAnUp9LuUUe+z61QwAgNOUGWygYmrWGVPd5wIAcrKvsWWZ5etFe1qYz6LL2xNCVEupOSVMXkVdL32TUTMkvrAFhwakDRMoehwAOjl8ww63KHJuh2Gf84OoOqb2ynvf6lDX8Ep9h19o2EZY+tlUJMREsUMEXt5L8YnTJLH8hryb2bhwJhkB/ksAgM1XEHwJCuZ5pCTEYd6XkzDWYTxcZ3qwS7kzv7kDEPutXvB80XLy5kUI3s9Yh/H46puFsLYdU+/7FsW0URZZeS7M8IrovTCfc7Iz2WPMRoFGxh2FyhqbmAIACvJy2GNjHcYLDd2IYp4/IUQ9aO8bQhSUkhCH2ZMchHIWdm5eD5cxQ5F+NlWonMuYoWxAwhxb7DkTJ46Jr6TLBCSCf8+7mY1t61ewgQYAmeeLllsyb3ad97Nt/Qqh+2Hub9v6FfW6b1VggoPKinKh48xn0TYB4gEMkxsiuJEgMzQj+jyZzzR0Q4h6UVBCiIKYwCH5WgFyS14it+QlIk+mAQASYqLEykWeTGPLJV8rAACh4IFx/WoGLt15gtySl9h3NAEA4DJmKACIHZd0/uGwPWybkq8VwMt7KVIS4mQGDOlnU7Fz83p4eS9lr3HpzhN2R+O8m9kK37ckTHlZf2SZMNkNAHA2KYE9VllRjn07tsg8ry7MonaxUZHobdya/RMbFYl9RxOop4QQNaOghBAFMS+q+JgopJ9NRWVFOfoPGY7ckpfwD9zOlmNetmZduyPvZjZSEuJwOGyP1HpnzP2O/e1ecOhk9nc/SDwuynflBqEcCua3fFkBw6VzqWLX0NM3wOzvfgAAXDiTrPB9q4KtvQPGOozHYs+ZbOAw7MOOdZ8oh9zrmWJDOCkJcXj41x2l1E8IkZ/S1ynRFJQDQVRl4dIVSEmIE8oTkZaDsW39CqHhAlmkTT2Vd60MZkYJgwlQIkNDpAYNTNukveAD/Jdg9rzvASh236IamlOip2+A1b/uQvLJGCz3nieUmyPv85XkxLFDCPBfInX2zbu6ehJ3dSaEqAb1lBCiIIs+VsgteYljqRnwXbmBzbVgckAYh8P2YOfm9XBz98S+owk4lpqBc7mFHLa8YeS9b1Ux6mAM15keyC15ieA/juIzlylsUqvvyg1sOWZ/IWn5J4L7DzHDYNJm3zCzfggh6qFxPSXaukiX6D42TPtrXlTiaUYMSjMT2eXnO1i7oF0/O7lXi63LPw9zkL1inNgza8jeOtLqlXafTZFFHytY9LGCo9Nk3L93B7MnOSAlIY79jZ+ZTirYSyH6olSmosKHbO8IAPx1Jx+A8EtYlJu7JyJDQ3DpzhO5e2Tqum9JGrqCLLNwmmg779/jD7GYmHZij5lb8Ld2eFYiXPbRw/sAIPSM6iJrZg4hRPmop0TF7h9Zi7v7+Zv2AUBpViLyd81D/u4FSqn/34qnyF4xrl7nMjscK7vexm6lzwL0Nm6NrMsXAfBfch90k7xCLvA2OFBGYqYsh8P2sD0HRYUPEXOIv/v2sJFjpJ7j4MRfCHDfji149rSEPZ5+NhW9jVtjX/Cv7DFF71uZmETX+ONH2GN/3cln82UGDh3BHu/ek7+RZsyhA0LP41QMfyXXfoOGsmWZHhYmR4bBzL4R7IEhhKiexvWUaDvBnoN/Huawm98JLnX/KG47ilPDlLJ67cPjG+VqiyCmB6Tr1OUK18vUKU8vTGPlPPVLRIaGwO3TUWLfrdoczP59U0gYFnvOxKfWfSXWI7iyqLLYDTQX+uzlvVRmzoe17Rh2po1obsZYh/FwmvJ2PSF571sVmETX5d7zxBY02xQSJtT7YdHHCmMdxku8Jzd3T6E1VZymzEDG+TTMnuQgdk3R+yeEqB71lKhQ1T3+ypHGH33B7onTuv37MBnzFf/7/1/yvb4eJ+zCq9L/KXQO0wPSfVaA1ICoPvU2Jf2HDMex1AyhYREv76UI/uOo0LoWn7lMEXpZe3kvxcn0GziWyl9xNON8mlLbtXDpCvY3e2aq68KlK+Q6b1NIGNzcPdljqzYHi+37Iu99qwKT6Cr6PI+lZkhMRGXKMjOGxjqMx6rNwfD+eY1QOaMOxtgQvA+bQsKEym4KCcOG4H207w0hatbgnpILHu/DZMxMiaueMpvVMbvt/vMwB+U5Z3H/0CoAYPMrOgz7XGb9gPhv/dKOl+fy8OzynyhODUO7/uNgOm6uzH1uROuTRdH8iVfP+OVb6gv/H1srA/5MhxePbilUn6DyXB7uH1oFqxWJ7NCQPP6XtBft+o+TugR+fettapi8irpe+q4zPSS+sAVzLKTlWyh6HABmz/uenS2jyLmfuUzBZy5T6pzaK+99qwKT6CpPAKRIWT19A/b+CSHcanBPyQdTlqM4NQz/VjwVOv5vxVMUp4bhgynL0bytHkqzEpG9YhwbkABv8yueXjre0GYAAB4eC0DOxins/i6lWYnI2TgFD49xszEes5mfaEIrs++M6GZ/8qouvoucjVPQ45tgvNvZUu7zynN5KIzdCtNxc5VaLyGEEKIMDe4pMbDk7/ZbnscT6vEoz+MBANoP4CdL5m1zBwD0XfYn9LoPAgC8/PsRrvoMQ/6ueTJ7S+TBvHDNJixCJ0cvNG+rh5oXlXgcvxOFsVvRfsgEmS9abZlFUvOiEn8dXAWzCYsUfmZFibvxf+zdd1gU1/oH8K8iIhdZBFEsWIKxoUI0NoKF6EVjQYM9ERSxo8GrRiwQWyAFRSMarCAJ+IudKCAqEbEFBdSAChoENUISBEEgiA39/bGZcWcbs8suswvv53l8rrt7ZuadWXPn7Jn3vMfc3lnuyFF19ksIIYRoQrVHSkza2MLc3hmFl6M47xdejoKVkzubt+AQmgeH0Dw0smyL8ocZKE6Lx6Nz+6p7eFbpv50gpkMCiEcoWn00HwBQknFBY8cS0p8nt6M4LR4thnqqtF1ZzjUUp8XDarD8xzbq7pcQQgjRFI3MvmnpPBsZGyexs0me5eegOC0etp9zF7l6GBWo9iOLqjD7TV7YRe7nDw6uV7pSsDZySjStMPkYcmO2oLtvNPsIiK+CS+LvQtSpv0b3S4RX3RoghBCiKzTSKWncXjzFruROEhpZ2bCzSpj3ASD//D7kxmyBlZM7mvZ2QYPG5mho1hypi+01EYJOsh69CLkxW1BZUcbJK6msKGM/V0XWTvFUyJsBLnI/V5T8y+T3WI9eJLdgm7r7JYQQQjRJI50SA2NT2EwPRM4PPrB4bziydnrBZnog5waY84MPAHBm6TA3Z1VJJ9UCgJWTO2emj6q0ccM1bt0ZAPCytIAT07NCcUGnhk1rptbHs8I/AACNbXrWyPHqKmZ9F30buZBel0ZR/LdvpcPVqY/cz8tKS3Dy2GGcPRWLs6di2bVpBg4dXmWlWGX7VfccJEnvV9Ox8r1+hJCqaax4mllncUVFZuSjSXcnue2YRzxMEmpVzO2dUZwWj7KcazC16YXKijL8fSZMpl3T3i7IT4zAnye3o8VQT/YxREnmJWRsnIR2k1YrfXyjDcYtxYWxCn49zCmeVpQaAwBo/I5qnQRFHaeqRjKe5maK42khv/qmuvsldcfjwgK4OvVR+PmmL/2wP3wX+1ryhh8SeVTt/fLBVG3lS8hYCSHKaaxT0sjKhh2tsHJyZ4uFMTrODUHWTi9cXzVQ7vaKqpta9ndFcVo859FCu0mylUjNujqyj0uk81bM7Z3RzGG8OqdVLUwSsLyYrJzcObOBtNkBKP/3cZqBsUjj+ya1h7Jf+Nu+Xa/ws9u30rE/fBfmL1mJie4z0dK6Df7KfYhdWwKxP3yX0sq1yvarKp913yqt0aKtWJnrxmclZEKIchqt6Nq0t7jj0MxRtgiRZd+xsJn+tl6I9ehF6PnVBditFRfoKrmTJHefln3HouPcEHadFpvpgQpHPNq4+qDj3BBYObmz79lMD0QHj42CJXB28NgIm+mBbPzm9uJqqu0m+NZYDEzdFkpiJerYG/Id8v9S3Fm+cU1coXbMpKlsufeW1m0w2UNcDycj/bpa++WLWZSva4/3qmwrdKyEEOU0uvaNWVdHpb/0rQZNlVtJVHIbedtb9h0rUztD0XGYtvIqzArBUGSp8LwlOYTmqb2eTFWjK+qOvtT2xzZdmxlhisccuVVM1y37jLN67u1b6Ug6l4DANcsBgM1DUFYFVFGOiaL3L19IxKnjR7A/fBc+HD4K0+Z6K123Rnp/yqib53D5QiIC1yxHVGKKwhVzmccnTZs157zfzKolAODu7Qy19qsN+hQrIXURrX2jI8pyrnFGkoj2+az7FvvDd3FWxwXEuQP7w3fBZ923MBWZ4eypWLg69WE7JIA4D2HpHHd2NdnqCv56LWaMG87mOpw9FYsZ44Yj+Ou1Gtm/Ou5nZ2HGuOEI2hXBWcROGrPonXSSKLNujPSieHz3y1fmjd8AAE0sLHAoIhRdmxmhazMjHIoI5az8qwuxEkKUo06JhiXNbK3WiEdZVkqVoym6QN3z00UOg4cAAK5cOMt5n3nNLNDm5TYOALA/7jwyC54js+A5Eq7fBQAsneOO6rp8IRHbN32N+UtWIjn7ETILniM5+xG7eu/tW+lKt2diUvZHVWWlJQhcsxzzl6zU6Jow2tovALg69eGsILx6iReWe82Q6Zjwpc1YCSHyUadER9T0zCDydon7mCP7Oe/HHNmPKR5z2IRH5sZu3d4Gt2+l4+ypWByKCNVYHMkXEwEAMxYsZn/Bm4rMMGPBYgBA0rkEjR2Lr73fb8bZU7GYOnuBzu+XGcGS7DRmFjxH0K4InD0ViwtnTulMrIQQ5TSaU1KX1fb8C0ZtO89pc70xY9xwdtbF/ewsnD0Vi71HuTey4K/Xygztawqz374dmsv9PHDNcqWzSjSdU3Ii6iC2b/oa++POs481NEFb+1W28vHSOe6IObJf5ZEObcVKCFGORkpIndbNXlwrJuXX8wDezr5g3geAQxGh2L7pa0zxmIO9R08hKjEFFzNzaz7YGsI8kpoyYhCbnyHZ8ZF+PX/JSgCQeUzCvGY+V3W/miKZnKrrsRJS1+nMSAkV6iJCMBWZYf2mEKxe4oUhI8Zg6Rx3rN8UwkmEZPIUJGfpqJunIJ1UCwBTPOZwZvqoSugKou92EdfbeVzAjT/v4QMAYKfeaouX2zicPRUrc/2Y72iKxxydiZUQohyNlJA6r88HgwAAA7pai//3w2Fy293PzgIgvtnt/X5zlftlEmXTUq+w2+3b/b1Mu+FjxIX99n6/mdNpuXwhEV2bGWFvyHd8T0UjqkqWlX5t00m8CObxg/vYKbd/5T7E6ePi6qg9evVRa798jR4/BQBkckeY18z11YVYCSHK6cxICSFCad+hIztaMcVjjsyv5aBdEVg6xx0j+neXu72iKqCjx0/B2VOxmDJiEPuez7pvZdr1H+jEzrSRzlv5cPgojJmk27OymIRhefFP8Zij9lRavmsJDRw6HB8OH4Wlc9xlZkPNX7KSU+tFW7ESQjSDRkoIwdtf0x9PdpP5bKTrJKzfFMK+nr9kJeIu30RUorg6KJOPIm+7oF0R7IjJ+k0hChNWvVeuRdCuCM6jhvWbQvDldzv1ItHyy+92Yv2mEPZcPxw+Cus3hWDJF/5aP7apyAzfhuzlXGsm/8d75VqdipUQoly9N2/evJF84//+7/8wdepUjeZ2VFaUofhGAgovR6E4LR5WTu5oNWwOZ60beTkl5Q8zUJJxAQ8OitecMLd3hmV/V5nqriWZl/A4NZotp249ehEseo/mrC2jSjtpfOpyKLteivJlpN9nXvfenIaCpCN4cHC9wnPme234XHtV2lVH1q6FGNbBGPv27dPYPqXVq1cPG3b8wA7pk6rp8urGXZsZ6WRc8qh7HZfNmw6RUX2t/ndBiJ5YWCOPb7J2f4bitHj2dX5iBPITI2C3Nl5hh6A4LR63gz1k3mP2w9x85bVjFsCz/fwgzLo6qtROF2SHf86eJ99zltcO4H/t1fmOCNGmtNQrnBEqQkjtp/VOCXOztB69CK0+mg8DY1MUJh9D1k4v5Cf+qHCNGuam2903GqY2vQAAz4vycG1ZX2Tt9GJvvEy7XhuS2ZWJy3Ku4WaACx6nRrOdDb7t5KnpGUEmbWzRcfZWGBiboiTzEjI2TkLh5SiZc67q2vC99up+R6R20bURk2vJSVWu+qsLaGowIZqj/U5J+hkAQIuhnjAwNgUgf4E9aUxH4GVpIcofZuBFUR7+yZFdwdPc3hnFafF4nBIDk7bd0bi9HUxtesl0JPi20wWS10pypIfB99rwvfbqfkeEaJM+dEgIIZol0ympX1+zua9M/oahyFLlbR9GBSI3ZovSNm1cfVCcFs/JrWjpPFtm5INvO3mqm1OiKj7Xis+14Xvtq/MdqeLNm0qt7p+oR1dGRvQVXT9CNEemU2JmJi4oVPnsHxg0alzjATHyz+9DbswWWDm5o2lvFzRobI6GZs2Rutie086kjS0cQvM4iZ/FafEwt3dGG1cfNh+Cbzt9wPfa6JyKUjRqpN2ODyGEEP0l0ylp1aoVAODFk3wYt6h+p8TKyR35iRF4WVqo0i/xnB98AICTz1BZUaawvUkbW5i0sUXTPqPxLP8+MjZOQnFavMwIBt92krTxiOdlaaHa2/K9NnyvvbrfkaoqS/LRsqXuJBQLQdfyNgghRJfIPKvp2rUrDBsa4enDDI0cQNTZAQDw95kw9sZZmHwMSTNbIydiRZXbP8vPASC+6f55crvM5zkRK5A0szXKcq4BAIwsWqORVXu122mDub0zALDHrqwow99nwqq936quDd9rX93viI/Xr16g9M+7sLfX8dEcQgghgpEZKWnYsCEGDhqEWxnn0bSPS7UPYNl3LAovR7HTbyVZOU1TuF3HuSHI2umF66sGyv38WX4OGlnZoJnjJOQnRuBmgGysNtMD2b/zbacNlv1dUZwWzzl2u0mr1d4f32vD99qr+x2povROEvDmNYYOHaqR/RFCCKl95Ga1fjplMkrTTuH1qxcaOUjH2Vs5N37r0YvQ86sLSvM4LPuOlbuN3VrxLJSSO0kAAFObXrBbK57OKtm2i3c4rAa9Lc/Nt502WPYdi45zQ9gRE5vpgWg1fG619sfn2gD8r70635EqilOO4cOh/4WFhYVG9kcIIaT2kanoCgBPnz5F67bt0HTMKjQfMFmIuEgt8rLsMdKW90XUkcMYNWqUVo8lZEXXstISXDhzCjFH9ovXvPGYg+nzvDnr4sjLKbl9Kx1J5xIQuGY5AHHZ89Hjp2Ck6yTO/i9fSMSp40ewP3wXAHG5+2Fjxsms18K3nTQ+9TaU5cIoypeRfp95fTEzF8cP7kPgmuUKz5nvteFz7VVpV5OooishrIVyOyUAsGPHDizzWw/bL8/BwMikpgMjtciDiGXogL9x/uwZrR9LyE6Jl9s4nD0VK/N+VGIK2yGQvkGfPRULL7dxcvcXtCuCvfkqa7f36Cl20Tm+7eSp6U7Jh8NHyVwvvucs2Q7gd+1VaVeTqFNCCGuhwqIks2fPRttWzfFnTM0um05ql3/u/YZHlw4jZKvymir67uypWJw9FYv5S1YiOfsRMgueI2iXuP7LgfDdCrdjbrr7484js+A5MgueI+H6XQDgrHjLtEu4fpdttz9OvBDgqeNHVG4nD9Ne2R9N6tLNjr1We4+eAgDEHNkvcy5VXRu+117d74gQUnMUVnQ1MDDAju+3YrCTE0ze6QmLXiNrMi5SC7wo/gs5O2ZjvpcXunfvLnQ4WnX+l5MAgKmzF8BUJK71M9J1ksxjBmnMjf5xYQFu30rHX7kPceNaikw7ZlTh5PEj6NrjPXSz7wn73v1kOgp82+kCyWslOdLD4Htt+F57db8jQkjNUVpmfuDAgQgICMCadd5o2KQlGtv0rKm4iJ57/aICOTvmoPM71gj85muhw9E6Jn+jqWUzlbcN/nottm9Sfo28V67F2VOxnNyKaXO9ZR7H8G0nT3Uf36iKz7Xic234XvvqfEfaVFlZCQVzDgipc6r8L2HlihWYMH487m6bjrKs5JqIiei5V+VP8HuwO0xePMbJ2Bg0atSoxo7d2NQUz58/q7HjVdehiFBs3/Q1pnjMwd6jpxCVmIKLmbky7bp0s0NmwXNEJabAZ923OHsqFjPGDYeX2zjcvpWucjt9wPfa6Luy0pIa/W+EEF3Gq3seHhaKEf/9EJlBU1CQpPy5NKnbnuXfQ+bXLjCt+BunT8ahWbOa/VXaqmUrPPr7rxo9JgBM8ZgDQPyoQRWrl3gBANZs2Ir+A53QpZsdGjZsqLB9l252mOH1PyRcv4u9R0/h7KlYuDr1UbudJG3klKh6PSTxvTZ8r72635G2Ffz9J1q2bCl0GIToBF6dkoYNG+LwoYNYtcIH2WH/Q87eJXhR8kjbsRE98ub1K/ydsBcZASPR/Z1WuJqSjK5du9Z4HO+9Z4/bN9Jq/Lh9PhAXstu3+3uUlZYAAE5EHUTXZkZYt+yzKre/n50FQPyree/3m2U+X7fsM3RtZoS01CsAgJbWbdDunQ5qt9OGD4eLp3szxy4rLcG+3d9Xe79VXRu+176635E2vHjxHNlZd6jSMSH/UjglWJGjR4/is0WLUVhUjJaj/wcrJ3eaMlyXvXmDJzfPIvfwl6jIv4+lSxZj3bp1MDKqOj9BG/bs2YNlPstxMTMXBgYGNXpsdaYEn4g6yJlJIi3u8k2079ARaalXMGXEILlt1m8KwUT3mQDAu502yDsXn3Xfsvkt0lOCq5o6zPfaAPo7JfjS2V8w95MxePToERUWJETZlGBFxo0bh7u/34bPkkXIjw7Cb5+/j+y9S1CYfAwVf2crXTSP6L/XL5/jRfHfKMk4jz+OfIObXwxC5nfuGNyzCzIzbuGbb74RrEMCAB9//DEqnpbj13Par4ki7duQvVi/KYR9PX/JSsRdvqn0ZjfSdZLcbaISxbNMUn4VT+e1790PUYkpmL9kJadtSORRTkeDbzttGOk6CUG7ItgRk/WbQjDD63/V2h+fawPwv/bqfEfadCLqIP77X2fqkBDyL5VHSiQ9efIE+/btw+GjUbh44QJevdRMWXqiHzp37YaxLqPg4eEhyKMaRWZ4euLew7+w46djQodCiEJFjwswtGdHHD50SOuVjgnRE4oruqrqxYsXyMzMxF9//YWyMhotqa2MjIzQtGlTdOvWDU2aNBE6HLny8vLQsVMnbA0/BMcP/yt0OITItXrJfDzOe4AzZ34ROhRCdIXmOiWE6JIvv/wSP0Tsw+GEK2jUyFjocAjhSL+WAneXIbh69WqtLyxIiApUzykhRB8sW7YMr1+9xOrF80H9bqJL/v4zD/+bMblOVDomRFU0UkJqrRs3bqB///6YsWAJFvp8IXQ4hOBZxVN4uA6HUYN6OJdXuj/wAAAgAElEQVSYSEXTCOFaqLTMPCH6rEePHvjpp58wfvx4GBgYYN6SlahXr57QYZE6qqS4CN4ek/Gk8BFSUpKpQ0KIHPT4htRqY8aMQWhoKHZs+horFnjixQvdW5iO1H4Pcu5iyoiBKPg7F3FxJ2q80jEh+oI6JaTWmzZtGk6fPo0LZ05iykcDcfXyRaFDInVE5atX2LdnOyY6f4DWLVsg+coVnZo+T4iuoU4JqRMGDx6MlORktGvdEu5j/ovP507DH/eyhQ6L1FJv3rzB+TOn8LFTbwSu8YHX/Hk488svsLS0FDo0QnQaJbqSOicqKgo+y5cjJzsb/QYMxpARY2DXqw/atreBqIk56tenvjpRzfPnz/CkqAjZdzJx5VIiTkcfxf3su3AZMwabgoLw7rvvCh0iIfqA6pSQuun169c4ceIEDh48iLiTJ1FYoFsrxxL91a1bd4waNVLnKh0TogeoU0IIANy/fx85OTkoLi7G69evhQ6H49atW1i/fj1WrlyJ9957T+hwBLN9+3bcuHEDmzdvFnR9JXn0odIxIXqAOiWE6LJXr16hZ8+eaNeuHWJiYoQOR1B///03OnfujEWLFmH9+vVCh0MI0Tyq6EqILgsODsbdu3exZcsWoUMRXIsWLbBu3ToEBgYiKytL6HAIIVpAIyWE6CgaGZBFI0eE1Go0UkKIrlq6dCmaNm2KFStWCB2KzmjQoAG+//57nDhxAj///LPQ4RBCNIxGSgjRQRcuXMDgwYNx9OhRfPzxx0KHo3OmTp2KpKQkZGRkULl2QmoPSnQlRNe8evUKvXr1Qps2bRAbGyt0ODrpr7/+QpcuXejRFiG1Cz2+IUTXhISE4Pfff8d3330ndCg6q2XLlli9ejU2bNiAnJwcocMhhGgIjZQQokOY5NbPPvsM/v7+Qoej016+fIlevXpR0ishtQeNlBCiS5YvX44mTZpg1apVQoei8wwNDREcHIzY2Fh6zEVILUEjJYToCCa59ciRI3B1dRU6HL1BSa+E1BqU6EqILqisrETv3r3RvHlznDp1Suhw9EpeXh66dOmCZcuWYfXq1UKHQwhRHz2+IUQXfP/998jMzMTWrVuFDkXvtG7dGmvWrMHXX3+Ne/fuCR0OIaQaaKSEEIE9evQInTt3xrx58/D1118LHY5eevnyJezs7NCpUyccO3ZM6HAIIeqhkRJChObj4wORSAQ/Pz+hQ9FbhoaGCAkJwfHjxynplRA9RiMlhAgoKSkJjo6OOHDgACZOnCh0OHpv0qRJ+O2335Cenk5Jr4ToH0p0JUQolNyqeUzSq4+PD7744guhwyGEqIYe3xAilJ07d+LWrVvYsmWL0KHUGq1bt4avry+++eYb3L9/X+hwCCEqopESQgRQUFCATp06UXKrFjBJr126dEFUVJTQ4RBC+KOREkKEsGLFCpiYmMDX11foUGodQ0NDbNmyBT///DNOnjwpdDiEEBXQSAkhNezy5cv44IMPKLlVy5ik1xs3bsDIyEjocAghVaNEV0JqUmVlJfr16weRSISEhAShw6nV/vjjD9ja2mLVqlW0lhAh+oEe3xBSk3bt2oX09HSEhIQIHUqt17ZtW/j5+SEgIAB//PGH0OEQQnigkRJCakhBQQG6du2KGTNmYMOGDUKHUyc8f/4cdnZ26NGjBw4fPix0OIQQ5WikhJCasmrVKjRq1Ahr1qwROpQ6w8jICFu3bsWRI0dw+vRpocMhhFSBRkoIqQHJyclwcHBAZGQkPvnkE6HDqXPGjRuHjIwMpKWlUdIrIbqLEl0J0bbXr1+jX79+MDU1peRWgfzxxx/o2rUrvvjiC6xYsULocAgh8tHjG0K0bffu3UhLS0NwcLDQodRZbdu2xapVqxAQEICHDx8KHQ4hRAEaKSFEix4/fozOnTvDw8MDGzduFDqcOu358+fo0aMH3nvvPRw8eFDocAghsmikhBBtWrVqFYyMjLB69WqhQ6nzjIyM8N133+HQoUP45ZdfhA6HECIHjZQQoiWpqano168fIiIi8OmnnwodDvmXq6srbt++jfT0dBgaGgodDiHkLUp0JUQbXr9+DQcHBzRq1AiJiYmoV6+e0CGRf927dw/du3fH6tWrsXz5cqHDIYS8RY9vCNGGsLAwXLt2Dd9//z11SHTMO++8gxUrVsDf3x95eXlCh0MIkUCdEkI07PHjx1i5ciUWLlyI7t27Cx0OkWPZsmWwsrLC0qVL2ffevHmDsLAwdOrUCYWFhQJGR0jdRZ0SQjTsiy++QIMGDbBu3TqhQyEKNGrUCFu2bMGBAwdw9uxZXL9+Hf369cPMmTORlZWF8+fPCx0iIXVSA6EDIKQ2uXr1Knbt2oXw8HCIRCKhwyFKjBo1Ch999BEmTpyI4uJi1K8v/o3WsGFDpKSkYNy4cQJHSEjdQ50SQjTkzZs3WLhwIT744ANMnTpV6HCIEm/evMHevXvx66+/4unTp3j9+jVev34NAHjx4gUuX74scISE1E3UKSFEQ8LCwpCamorU1FRKbtVh165dw9y5c3Ht2jW8efMG8iYgpqam4s2bN/Q9ElLDaEowIRrw5MkTdOrUCZ988gm2bNkidDhEgdLSUpiZmfFq+/vvv6Njx45ajogQIoGmBBOiCatWrYKBgQElt+o4kUgEPz8/1KtXT+koSP369ZGamlqDkRFCAJp9Q0i1/fbbb9i1axe++eYbNGnSROhwSBW+/PJLREVFoVGjRmjQQP4T7AYNGiAlJaWGIyOE0OMbQqrhzZs3cHR0RP369XHhwgXKQdAj6enp+Oijj1BYWIiXL1/KfN6/f38kJSUJEBkhdRY9viGkOn788UckJydj27Zt1CHRM3Z2dvjtt9/Qs2dPuSMmv/32GyorKwWIjJC6izolhPDw008/oV69eggKCmJ/VT958gTLli3D/Pnz8d577wkcIVFH8+bNcf78eUyePFmmU/ns2TNkZmYKFBkhdRN1Sgjh4cqVKwCA5cuXo1u3bjh79izWrFkDQJyjQPSXkZERIiMj8dVXX3ESYA0MDCjZlZAaRp0SQnhgcgsqKyuRk5ODIUOG4KeffsLKlSspubWWWLFiBScBtl69epTsSkgNo0RXQqpQWVkJExMTPH/+nPO+oaEhu8bNokWL0LBhQ4EiJJqUlpaGESNG4K+//oKpqSlKS0uFDomQumIhdUoIqUJ6ejrs7e0Vfm5gYIC2bdsiNDQUH374YQ1Gpp9SU1MRFxeHC+fP4dbNWygqLsYzqQ4fIVWpX78+zJuYweYdG/Tp1w8fffQRnJ2d0ahRI6FDI+qjTgkhVQkLC8OcOXOqnIlhaGiIFy9e1FBU+uXNmzf46aef8M3XX+HGzVuwsW6BQfbvokv7lrAQNYZRQ91b8eJV5Ws8LimDlQW/CrCkZlVWvkFJ+VPcyytA6p0HSL6ZDZHIFHPmzsPy5cvpsap+Wqh7/09AiI5JTU1F/fr1FXZKDA0N0bJlS8TFxdVwZPrh6tWrWLjAC8kpqfhkWH+EfOYL+45thA6L1DIFT8oQGfcrgneEIHTPbnzzbSA8PDzY1Z+JfqCREkKqYG9vj/T0dLmfNWjQAP369cPPP/8MS0vLGo5M923YsAErVqyAQ4938Y3XROqMEK0r+ecp/PdGI/T4eQwZ8iEOHjrMe70jIjh6fEOIMi9evICJiQlevXol81m9evUwY8YM7NixA4aGhgJEp7tevnwJr/nzER4ejm8WTMTssYOpuBypUel3H+KT1TshMm+G6NhYvPPOO0KHRKpGFV0JUSYtLU2mQ1K/fn3Ur18fmzZtQmhoKHVIpLx58wafTJmCgwf24/DXCzDnYyfqkJAaZ/duGyRs80EjPMeggQOQm5srdEiEB+qUEKJEamoqpwR5gwYNYGxsjJiYGPzvf/8TMDLdtWrVSsSfPoW4zYsxpLet0OGQOszKQoQTmxajnaUpRo0cgadPnwodEqkCJboSooRkRU8mofXEiRPo1q2bgFHprkOHDmHDhg044O+FHh2stX480ZB5AIDShB1aP5YuHLemlZZX4PSVWzh0JhlxSekY4WCHER/YYZSjPZo1Ma1ye+Y6yd13DV07YyNDRKyZhf96B2H6NHccOnykRo5L1EMjJYQocfHiRbx69QoNGjRA3759cfXqVeqQKFBaWgrvhQsQMG88hvXrLnQ4pJpKyysw+6u98PTfg7gkcaJ3XFI6vIMisXBDBAqelCndPvdRUU2EyYtlE1Mc9J+Hk3FxiI2NFTocogSNlBCiwNOnT/H7778DAKZNm4bt27dT1VYl/L/8Eq2ammLeuCFCh6J1tX2EBABOX7mFuKR0BC91wzin9yEyMUZpeQWCD8QjMPIE9p++jM8mOVe5n4B543m107ZObVvg86kfYeni/2HYsGGUC6ajaKRED1VUVCA6OhoLFixAz9590MSiKeobGLCLidEfzfwxMTFhr3lYWBiMjIwEiaO+gQGaWDRFz959sGDBAkRHR+PZs2cC/guUdffuXQQHB2PDgomoT0mtgriRnYutB+M1tr9DZ5IBAB6jBkBkYgwAEJkYw3uyuIPhu0P5Y5DsvAIAgF3HthqLqboWTBiKV8/KsXXrVqFDIQrQSIkeefLkCb799luE7NiFf8pK0eTd99GofW80tZ2A5v8RoV59A6FDJFrw5nUlKp+W4tGj+9h/5ip27NyFxqYieM2bozOVKzdv3oQ+tjboYyvctMvDCanw9N+DEQ52mDi0LyYM6c35XFEeiOT7k31D0MqyCTYv/pTTprS8AtYuizHTZRA2L/5UZl/M6+yjG7D/9GX47jiiMA4mViZPw8dtJKYM64de09bIja8qKRn38H+nkhAafR4A2FEJZfkc7HkpOdaBAC+57zMdFH1kZNgA810HI2hDILy9vTlJ7EQ30DeiB16/fo3w8HAs81mBpy8r0XzYPHQaMAWGpk2FDo0I4GVpIR5dOoDvQnZg1+5QbAj8RtDKlS9fvsS+yEh8M2+cIMcHgK0H49lf7nFJ6YhLSseltN9lOhdV8ZowFC5LN8NzzCBOou6dB38DAIb1V54rs3BDBCf/gvm7ZMfEP+w4AiNPsK8DI09wXvNRWl6BS+lZCI+5iLikdMx0GYQDAV7o3VX7ncK7ufkAgDC/WUrbpWf9AQCwEJkgPPYivIMiAYDzOEgIk//bFytDDuP06dMYOXKkIDEQxahTouNKSkowfuIknE1IgNWH09Bh7DI0+I9I6LCIgAxFlmg9YgGsBrsj99gGzJ4zF/+3/wCOHDooSOXKS5cuoaS0rMobtjZdTMtCxv6vYN3cArmPihAecxGBkSfwsdP7GNyzM+/9DO7ZGSMc7HDs3DVOp+TyzbsAgI5trJRu36ODNXavmgGRiTHOXb8Dl6WbcehMMtspOXf9DgIjT8DHbSQ8Rg9g4w3ad5Id6VAm91ERLt/M4YwIBS2aAuvmFjJttZX3sv/0FYxwsMOwfvwSvh1n+3NeewdFIu7XdPY61TSzxv9Bv+7vIjY2ljolOohySnTYvXv30M/hA1y+noHufifQ/pMvqUNCWA3+I0L7T75Ed78TuHw9A/0cPsC9e/dqPI6UlBS0aWHJa4qotgTMH8/emK2bW8Bj9AAAwM+JV1Xel9eEoQiMPMGZPcI8jnnXWnmnZO64D9kbLdMZYkZLAODC9TsAwHZImHgXTBzKKzbbKavg6b8HYX6zcCDACxOG9JbbIdEWZpTHz3NMlR0KZuTqzLblKE3Ywf4J85uFuKR0nL5yqyZCluu9jtZITb4i2PGJYjRSoqNyc3PxwYCBeGbcHF1XRcPQrLnQIREdZdK2G7quisbd7z3xwYCBSLlyGdbW2q8RwsjOzkaH1sL++5TuLDA36tDo8yo/wmFGS6ISr+KzSc64kS2uBDriA7sqt62qY8Y8ppHuSFTV2WFk7P+KHSk5dCYZE4f2Rf/uNnI7JtXNKZHGdEgu7fbjVYNG0b4nDOnNxi8v36YmvNOqGX46o3qHlWgfjZTooKdPn2L4iJF4IWqDzp8fpg4JqZKhWXN0/vwwXojaYPiIkTVaubKsrAyi/zSqsePVBI/RA+C74wgKnpSx+STvd2kvbFAQd2YmDOmN3OjN8Bg9AIfOJMN2yios3vx/iEtKr7J2iDoKnpTBP+w4bmTn4tqP6zRWFE9yBKmmNWlsjCclpYIdnyhGIyU6aKr7NDx8/A86L9+H+g315//sk2a2BgA4hObVyHaqqqwoQ2HKcRT/Fo/itHiY2zvDsr8rzHsMgYFx1Y8eqru9ttVv2Ag283bhzrdjMdV9GqKOHK6xYxs1FPb/SnIfFXFGC5hkTB835TkDim7ijnYdAQDnrt1hp8Zq4mbs4zaSfTQkGa+qhcZEJsbi6qoOduzsm8m+IQDejlBoIqfkRnYu/MOOo0cHa2xb5q7SI7rJviGIS0pHbvRmzqOe0vIKAMBMl0HVjq86KisrBT0+kY9GSnRMbGwsTsSdRIcF4TS7RsMeHA5Azg8+KE4T13IoTotH1k4vZO3+rEa2rwmGpk3RYUE4TsSdrFOVK8NjLrI39txHRfDdLs5nGCiR5DrCQfz4JSVDnHdTWl6BnUfPyt2fyMQYYX6z2GqmVXVu+GLikY43POai2vvsY/sONi/+FJd2+yFg3niNxMnE5TjbHz06WMPPc4zKOUMTh/YFAJncEeb1x07vayZQUqvQSIkOefnyJT773xK0HOUN45bvCh2OytQd6dD2CAkAlD/MQH5iBKxHL0LzwVNhZNEaz4vykBe7FfmJEXiWn4NGVjZa274mGbd8Fy1HeeOz/y2pM5Urra0sYDtlFec9H7eRnJk3E4f2RVxSOoYu/JZ9T9lNXHJ2yfD+PTQS5+CendnRElWnAVelRwdrja439EtKBgDlU5YlR2Oka7cM69cNIxzs4Om/B57+ezjbSX83hDBopESHbN26FY/LX6KF8xyhQ6l1/rl3HQDQ7IMJMLIQPy4ysmgNK6dp4s8f3NDq9jWthfMcPC5/WWcqV3qMGsDWzRjhYIcwv1nw8xzDaTNhSG+E+c1iR0yCl7opLX8u+cihfStLjcXq5zmGE4eP20hc+3GdxvavKUxdEXWJTIyxe9UMzrnOdBmE6KDFMt8NIQwaKdERr169wjcbNqLp0IWo30D31lcpTD6GwstRKE6Lh/XoRWj2wQRcXzUQwNuRDuncEOZ1781pKEg6ggcH17N5GJZ9x7L75pNTwrRRRtn2Lx6LPzMUNeO83/DfJOKKvDtK913d7Wta/QYN0XTILHyzYWOtrlwp+Ut9wpDeVc7mkNdGUe4Fk5cy02WQzKML6W0U7UPZDBR5sQqdZyFJ1ZwUee1FJsa8vhdCGDRSoiNOnz6NosJCNHPQ3DNhTXkYFYisnV5sLkVuzBa2Q8JHdvjneHBwPYC3eRiFyce0EqsiuTFbAEAmIdVQZMn5XFvbC8Gy/zgUFRbi9OnTQoeil/afFtex8ByjuY6CaMg8iIbMY/NaAHFuC7NmjaN9J40dixB9VDt/Pumh2NhYmHXsrROzOCSVZF5CbswWhbkUfJi0sUXH2VthYGyKksxLyNg4CYWXozijJVWpibyT2qbBf0Qw69ibKleqSLK+h4/bSI3maRwI8MJk3xBOXgtDlSqphNRWNFKiI5KupKBR26qLM9W00tuXAIDtkADiXIpWw/jnvbQY6sl2tsy6OgIAO+pCtMuoTQ8kXUkROgy9wuQ/BMwbr/HchxEOdogOWsyZzTPTZRDC/GYJVnadEF1CIyU6Ijs7G5a2uvfohnkswXRIGKrMNGEecVRHdXNK6qpGzdsjJ6Xm6pXUBopWx9WUwT07Y3DPzpTsSYgcNFKiI/4pK4GBcWOhw6i1rEcvAiAugCaJec18rq3thdLgPyKUlZYIHQYhhPBCIyU64rWOVhe0Hr0IuTFb8LwojzNa8ryoZkclqjsKYtxaXBPhZWkBJ2/nWeFDAEDDpspHYqq7vZB09d+WrpGus6Ht7VRVWl6Bo4lXEfdrOuKS0tlVgof166bWY58b2blwnO0vN27mWMy0YB+3kZgyrJ/CNXoOJ6Ti0JlkxCWlY6bLIHiOGaTRXBxSd9BICVFK1EWcA/Lo3D62I/K8KA+Pzu0TMiyVGbcUlw0v+PUw5zyKUmMAAI3f6anV7QmprjW7ouAdFMmuGROXlA5P/z2Y/dVelfdV8KQMjrP9FX4++6u9nDolgZEn0GvaGnZxQkmTfUPYyreAeBFEx9n+OJyQqnJchNBICVHKrKsjO1qii9Ne+TJpYwtze2e552Hl5A6TNrac96Rrp6i6PdE/6o50aHuEBBCPaoRGn4eP20h4jB4A6+YWyH1UhKB9JxEafR53c/N5rzQMAF/tjVb42eGEVMQlpSN4qRs8Rg0AAJy7fgcuSzcj7Dh31WWmbcC88Zg+agA7YnM4IRWe/nsUrmBMiCI0UkKq1MbVBx3nhsDcXlz90nr0IvT86oLAUamug8dG2EwPZM/D3N4ZNtMD0W6Cb41sT4i6rt6+DwCYMqwfe5O3bm7B1lD57feHvPe19WA8/ix8ovBzZgHCcRJr0zAl4UOjz8ttK9khAd6W6GdK1RPCF42UEF4s+46VW1fEysmd/bt03oeiPBC+7TTNUGQJq0FTYTVoapVt5cWkyvZEt0jmPDD5Eb2mrQHwdqRDOjeEeZ19dAP2n74M3x1H2DwOyQqlfHJKJGufKKJs+9x88eJ9zc1FnPdbNDUDANy+/2eV+wfEIx6+O47g0m4/9nGLNHmzj5i2TCl/6felc1qY12m//wGM4hUaIQBopIRUIWlmayTNbI2ynGvse5UVZfjz1E4AgKizg1ChEcKLf9hxTs4Dkx/B18INEfDdIV51mMnjqOl8CWZBPOmbP1P+ns/ifndz88WPYPxm8U5C3XowHqIh8zDZNwRhfrNkysUzNV1Kyys47zOvpUdWCKkKjZQQpbp4h+N2sAduBrjIfGZu7wzzHkMEiIoQfs5dv4PAyBMKczH46NHBmi1sxuRWHDqTrNJ6LjWRd6L0+OUV8N1+BD5uI1WK265jWwTMG4+LaVnsSr+S2zMrL5++cot9v7S8AsEHqDgiUQ91SohS5vbOsP38IEpvX2ITPK2c3CHq7ADzHkN0riw+IZIuXBcvlMh0SABxLsaCiUN5d0rmjvuQHaFgcisUPfrQVcEH4hGXlI5ty9yrbiyBKfT22SRnhMdehKf/HjQzN2Wvw7B+3TDCwQ6e/nvYTgsATsVaQlRBnRJSJbOujjDr6og2rj5Ch0KISpjHGtIzQFSZqSK9QrA6qptTUh2HE1IRGHkCZ7Ytr9a5jHN6H95BkQg5fIbtlIhMjLFtmTtiL6XBOyiSk3PD55ESIdKoU0IIITrOx20kAiNPoLS8gpNXwuRuKBuZYEYw5C0CCPAv/sYcV3qUqFkTU3iMGsBOHwaA3EfixNyAebq3dAbRbdQpIYKRrgWiTyorylCYchw5P4hHj6xHL0KzDybIXROIaVv8WzyK0+Jhbu8My/6uCh9/FSYfQ+HlKBSnxcPKyR1WTtOoDoqamJt57qMizmgJc9OsKdUdBenSvhUA4FFxKadT8uDvxwAAayvN1QKZ7BuCuKR05EZv5hyr4Il4SYWZLoOqbJudVwAAaGlprrG4SN1As28IUUPW7s/YDgkgXrjw+qqBKH8oW5fhweEA5Pzgw66MXJwWj6ydXsja/ZlM29vBHsja6cW2zU+MQPpaZxQmH9PSmdRuA/99zBAec5HtiOQ+KkJ4zEUhw1JZ53YtAAD7T1/hnMexc+JZce93aa9w29KEHXL/SH/OmDi0LwDgaOLVt23KK7D/9GUAwMcS9Uvktb2bm4+f/33dvzv/hTsJAWikhBCVFSYfQ3FaPGymB7I1S0oyLyFj4yTkJ/4IG/dv2LblDzOQnxgB69GL0HzwVBhZtMbzojzkxW5FfmIEnuXnsKMrzH7bTVoNq0GfsqMohcnHkLXTC6bv9pZZrZkoN7hnZ3a0RJ9zHHp0sMYIBzu55zHTRXadmeqsxzNhSG8cOpMM76BITql5QDzyxOSTAG8TXeW1DfObRdVcicpopIQQFRVejgIAWPZ5u/S8WVfxGkH5iRGctv/cuw4AaPbBBLZDYWTRGlZO08SfP7ghs1/JDgkAdtr1k5uJmjyNOsPPcwzC/GaxNTV83Ebi2o/rBI5KdduWuSN4qRt7HiMc7BC81A3r5rhq/FgHArw412ymyyBEBy2Gn+cYTjsm0TV4qRv7no/bSFza7afS1GNCGDRSUguUZF7C49Ro9oZoPXoRLHqPlslDKH+YgZKMC3hwcD0AsLkNkpVaJfM8itPicTvYA+b2zrAaPJUtr878cgeAjnNDFG4v3Y7vFGLJ8zG3d0ZL59nsTV+d85bGxKiMsjyXLt7hMu8xj1s6zg3hvP/isXg/hqJmnPcbmjUHAFTk3ZHZh/Q1Yl6XS3RgiGomDOkt9yYpmR8hPaqgaJSBbztNk5dQqgifmKpqo+iaVScuQqpCIyV6rjgt/t/HBm9/oefGbEH6WmeUZF7itEtf68x2SJj3snZ6yc1XYDokkn8vf5iBh1GBbEcDgNLtpdvJy6GQ9jAqkHM+zPk9jApU67y17c9TO5E0szVuB3vIdNCYmADZjoahyJLzOQC201dZUcZpy7yWHoUhVRMNmQfRkHlIybjHvldaXoGtB8UdQEf7TkKFRgiRg0ZK9BzTcei1IZl9PFCWcw03A1zwODWaHWFg2nX3jYapTS8AwPOiPFxb1hdZO71kbqb/5FxH3223YWBsyuZLpK91hvXoRTLvy9s+/9w+NqbnRXl4dG4fcmO2oCTzktxRD0A88pEbswXWoxeh1UfzYWBsKi5pf3I7cmO2cEZB+J63PJqc7WPStjvaTVqN0jtJbCdM3hpBfFj2d0VxWjyKbySw+2DOn6jnQIAXJvuGyJ0OO8LBjl04jhCiG6hToufM7Z1RnBaPxykxMGnbHY3b29qokn4AACAASURBVMHUppfCRe9elhai/GEGXhTl4Z+c6wr322KoJ/vrXvIGz3QWpN+X1n7yak4ORfPBU5Ebs0Vph6H09iWZYxgYm6LVR/PFHZqMC2ynhO95axtTWK7V8LnIP78PWTu9YGhqqfTaKGLeYwjM7Z3Fo0oSo0zWoxdpMuQ6ZYSDHaKDFuPCv+XmAfEjG0f7ThjWr5vMWjKEEGFRp0TPtXEVTzWVzBNRlIPxMCqQ87hAGebxgjS+ZeWl63UwHZT8xAjO7BRJTGzJC7vI/fzBwfVoNXwuANXOW1p1c0oUsewzBjk/+OCv+N1qdUoMjE3RwWMjin47hZwffDg5P3y/NyKLKZUunaRJCNE91CnRcyZtbOEQmsdJYmUKdLVx9WFHFvLPix+fWDm5o2lvFzRobI6GZs2Ruthe4DNQD9/zrklMh41JWAXEoxy5MVtQWVHG6dAxeSLSoyCGIktYDZrKTjUGxI/ZAKDdpNVai50QQnQBdUpqCZM2tjBpY4umfUbjWf59ZGychOK0ePYXP1PoS3KUQjqhUpOeF+Vxamo8y88BoPxRhJWTO/ITI9icFT6qOm95qvuI53awB4rT4mXifFlayJ4Hw7h1538/K+C0fVb4EADQsOnba6Rov8/y74vbmreoVtxEGNWpGSI0ZWv2yDufwwmpOHQmGXFJ6ZjpMgieY2RrqKjTltQdNPtGz+VErEDSzNYoyxFXdjSyaI1GVu0Vtmc6B9pOoHx0bh/7C/95UR4Kfj0MABB1UfxYo2lvFwDAnye3szd4QJwAmzSzNf48tZN9T9Xz1iTL/uK6EIUpx9n3KivKUJB0BMDb8wAA45YdAQAFvx7mXI+i1BgAQON3eird77P8HDxOjQYAmL5LdR9IzVG1FP9k3xB4+u9h18YJjT4Px9n+OJyQWq22pG6hkRI918xRPC32ZoCLzGc2099Oo+04NwRZO71wfdVAufuRrCyqKdeW9eW8th69SGmuhVlXR/Zxh3QOhbm9M5o5vF3ci+95a4Nl37EovByFnB98OKXmAdlzNGljC3N7Z7nnZOXkznnMxCS6yttvx7khVM2VCCJg3nh8NslZaZvDCamIS0pHwLzxmD5qAJtAfDghFZ7+e9C/uw1b3VWVtqTuoU6JnjO16QW7tfEoSo1hb3rWoxehsU1Ptu4FIL6RVj77R2YBucoXz8S1Pe4kabRT0sbVBwb/McODg+tVSkJt4+oD49adUXonia3LYTM9EBbvDeck3/I9b23p4h0us3Be094ucs+RSV6VXJDP/D1nTkVYQDbRlTknPgXhCNE0ZlE9u45tq2x76EwyAHA6GQDYKde/pGSwxdVUaUvqHuqU1AJMXkUbVx+l7aQTKBmSORaK8i1UfR8AWg2fy86WUWVby75jYdl3rMJZOgy+560tTJxVkZe8qom2dc2563fwc+JVhEafByAuZz52cC+ZPIQb2blIvJoJ3x3ix2kjHOwwcWhfTnVSyTyPuKR0TPYNwQgHO3iMHsCWVmd+uQPidVwUbS/dju9UY8nzGeFgB68JQznryqh63tKU5YMwNJXnwjyGkT5v5nXa738Ao1RvS+oeyikhhOi8uKR0uCzdzN6YASAw8gQcZ/vj3PU7nHaOs/3ZDgnznqf/Hrn5CkyHRPLvN7Jz4R92nO1oAFC6vXS72V/trfJ8/MOOc86HOT//sOOcdnzPWxvSs/4AAFiITBAee5GtjhseexGl5RWctkxHTvp95rVk/Kq0JXUPjZQQQnQe03HI2P8Vm2+QknEPQxd+i58Tr7IjDEy7M9uWo4/tOwDECZu2U1bB03+PzFouVzPvIzd6M0Qmxjh3/Q5clm6G42x/+LiNlHlf3vbhMRfZmHIfFSE85iICI0/g3PU7ckc9APHIR2DkCfi4jYT3ZGeITIxRWl6B4APxCIw8wRkF4Xve8mhqFMRxtj/ntXdQJOJ+TcfuVTPY0Y2JQ/siLikdp6/cYq8Rc07SVGlL6h4aKSGE6Dzm13VU4lWcu34HpeUV6GP7DkoTdmDz4k/ZdqUJO1CasAPtW1niRnYu4pLSER5zUeF+5477kL2xSt7gmc6C9PvSAuaPZzsL1s0t4DFanAvxc+JVhdtc+HeEQ/IYIhNjeE8W50IlXs1U+by1gRltOrNtOXtdSxN2IMxvFtupYAzr1w0jHOzg6b+HHVGxdlksd7+qtCV1D42UEI2q6TLvpG7w8xyDuKR0Tp6IohwM/7DjbEn5qjRrIr8eDt/y8+9aW3FeMx2U0OjzCjsNTGyKbsS+O46ws11UOW+Zc6hmTomizyYM6Q1P/z04dCaZHekQmRhj2zJ3xF5Kg3dQJCePR/q7UKUtqXuoU0II0Xk9OlijNGEHJ4k1LikdIxzs4Oc5hn3cER4rfnwy02UQPnZ6HxYiE7RoaoYO45YJfAbq4XveQmASVhnNmpjCY9QAzswZptZJwLzxarcldQt1Sgi7Foy+jXJIr2HDxF9ZUYbClOOcKbiW/V1h3mMI70qx0ph9Sk+pljeNWtXjS08ttnKaxpkCrOg866IeHazRo4M1XJ3eR3ZeAVyWbkZcUjr7q947KBIAuI90pBIqNSn3URGnpsbd3HwA4hkyisx0GYTQ6PNszgofVZ23PNXNKZnsG4K4pHSZOJnrOdNlUJVtmWnFLS3N1WpL6h7KKSG1zoPDAcj5wYddg6Y4LV688u7uz9TeZ9buzzgFzXJjtuD6qoEof5hRrePfDvZA1k4vtm1+YgTS1zqjMPmY2rHWRos3/x9EQ+YhJeMeAPFjkg6tmylsz3QOtJ1AGR5zkf2Fn/uoCPtPXwEADFTyeOVjp/cBAMEH4lHw5O1SD+eu34FoyDxsPfg2XlXPW5MmDhUXP5TMHZF8zZyHZNujErk0d3Pz2dya/t1t1GpL6h4aKSF6T3LkoPxhBvITI2A9ehGaD54KI4vWeF6Uh7zYrchPjFCrcm1h8jEUp8XDZnogWz+kJPMSMjZOQn7ij5x6Kqocn9lvu0mrYTXoU3YUpTD5GLJ2esH03d4wsmjNnh+f1Y1rq0+HOyA0+jyGLvxW5rPgpW7s38P8ZsHTfw96TVsjdz93c/Nl8kCqy3bKKs5rH7eRSnM+BvfsDB+3kQiMPCGTQzHCwQ5ThvVnX/M9b22QTEiVnPYMyJ4j09Y7KJIdrWKE+c3ijCap0pbUPTRSQmqVf+5dBwA0+2ACW5bdyKI1rJymiT9/cEPlfRZejgIATgVWpnIrU3VWneMz+5XskADicvMA8ORmosqx1lZ9bN/Bpd1+nMciPm4jcSDAi5OXMGFIb87N2sdtJK79uA6XdvsBAC6mZWk0Lj/PMWwOxAgHO0QHLYaf55gqthJvF+Y3i/MIJHipG7Ytc+ck3/I9b20QmRhj96oZCPObxc4CmukySO45Msmr0tf+0m4/mWnUqrQldQ+NlOihpJmtYeXkLrfiaU7ECs5Ku+UPM1CScQEPDq4HADa/QVklUkU5JoreL8m8hMep0chPjFCppDyfX/6q5k+8eCxubyjiDnE3NGsOAKjIU73gVBfvcJn3mMctHeeGqH18Zh/SeSbM63I1OlC1GZNXUdVNXzqBkiGZY6Eo30LV9wHgs0nOSteGUTaLZcKQ3lVO7eV73togMjFm46yKvORVTbQldQuNlOihdpNWIz8xgrOSLgC8LC1EfmIE2k1aDQNjUxSnxSN9rTPbIQHe5jdoKmfhYVTgv48xItj9Z2ychIdR2l0UTxFmHRzpGz2zbo70oniq+vPUTiTNbI3bwR7oODdEpnOnyvGZNXoqK8o4bZnX0qMwhBBS29FIiR4ysxWv9Fty+xLnplhy+xIAwOI98c3udrAHAKC7bzRMbXoBAJ4X5eHasr7I2unFa90WZUoyLyE3ZgusRy9Cq4/mw8DYFJUVZfjz5HbkxmypciE5fZxFYtK2O9pNWo3SO0nI2ukFAGpfR8v+rihOi0fxjQR2H8z1I4SQuog6JXrIpI0tzO2dUXg5inNDLLwcBSsndzaRkrnpvywtRPnDDLwoysM/Odc1Fkfpv50gpkMCiEcIWn00H7kxW1CScaHWrW5r1tURZl0d0Wr4XOSf34esnV4wNLXk9bhKmnmPITC3dxbPzPm3gwOIpxsTQkhdRJ0SPdXSeTYyNk5iZ3M8y89BcVo8bD8/yGn3MCqw2o8sFGH2m7ywi9zPHxxcr3SVYG3klNQkyz5jkPODD/6K361Wp8TA2BQdPDai6LdTyPnBh5Pvo63vjGiGptaVIYRwUadETzVuL86GL7mThEZWNuysDuZ9AMg/vw+5MVtg5eSOpr1d0KCxORqaNUfqYntBYq4J1qMXITdmCyoryjh5HUyehiZHIZj9Mwmr6hzfUGQJq0FT2anGgPgRGyDOHSKEkLqEOiV6ysDYFDbTA5Hzgw8s3huOrJ1esJkeyLkRMsW+JGfpSCdV8iWdVAsAVk7unJk+qtLGKIhxa3HthJelBZyYnhU+BAA0bKp6rY/bwR4oTouXOU/mmlg5uat1fEX7fZZ/X9zWvIXKsdZFzBov+jZ6Ib02jaL4b2TnwnG2v9zPS8srcDTxKuJ+TWfLz08c2hfD+nWrslqssv2qIy4pHZN9Q5Tu73BCKg6dSUZcUjpmugyC55hBckvlK1u3h9k/3+tH9AvNvtFjZp0dAIAd+WjS3Uluu2f5OQD4J1Eys0LKcq6x2/19JkymXdPeLgCAP09u53RaSjIvIWlma/x5aifPM9Ec45YdAQAFvx5mRxyeF+WhKDUGAND4nZ4q79OyvysAoDDlOPteZUUZCpLEi6Qx10HV48vb77P8HDxOjQYAmL5LNRvquoInZXCc7a/w8zW7ouAdFMmuQxOXlA5P/z2Y/dXeau1XVTeyczHZN0Rpm8m+IfD038PGGhp9Ho6z/XE4IZXTjqmQS+omGinRY42sbNjRCisnd7ZYF6Pj3BBk7fTC9VUD5W6vqLopMyvkZsDbm628RwlmXR3ZxxXSORDm9s5o5lDzC2sxScDyYrJycpe7pkxVIzaWfcei8HIUcn7w4ZSaB8SPYyTzSVQ5PpPoKm+/HeeGyHyfpHZS9gv/q73RCj+7kZ2L0Ojz8HEbCY/RA2Dd3AK5j4oQtO8kQqPPK61eq2y/qkrJuCe34qykwwmpiEtKR8C88Zg+agA7inM4IRWe/nvQv7uNTCXXgHnjedV/4bMaMtEfNFKi55hf6c0cJ8l8Ztl3LGymv60XYj16EXp+dQF2a8U5ECV3kuTu07LvWHScG8KOmNhMD1SYsNrG1Qcd54ZwHmHYTA9EB4+NbG2OmtbBYyNspgey8ZvbO8NmeiDaTfBVe59dvMM518TKyR22nx9EG1cfmbZ8j88kukp/R3Zr46s9XZvov60H4/Fn4ROFn1+9fR8AMGVYP/aGbt3cAp5jxFVif/v9oVr7VTXGoQu/RZjfLKXtDp1JBgBOhwQQl5wHgF9S3q4hxSzMZ9exrUZiJPqFRkr0nFlXR6W/9KWTKBmS28jb3rLvWJkbo6LjMG3lVZgVgrzkUXkcQvNUWk9G3jWpzvFVbVtbiIbMw0yXQXIrmS7e/H+cFXRvZOci8WomfHeIH5UxORPKKowqyjFR9P6563fwc+JVhEafxwgHO3hNGKp07Rrp/Smjbp7Duet34LvjCC7t9mMfd0jLzRc/5mhuLuK836KpGQDg9v0/1dqvKnx3HMGBAC92jRxFmGNJ57kwr9N+/wMYVe1wSC1AIyWkzirLucYZpSA1I2DeeIRGn+eskAuI8xxCo88jYN54iEyMEZeUDsfZ/myHBHibMyGdh6Au/7DjcFm6GaHR59n9uyzdDP+w41VsqT13c/PhsnQzwvxmyU0CZTCL+Unf6Jm1c6QX++O7X1WUJuxg18VRhmlTWl7B3f7f18z1B4D0rD8AABYiE4THXoRoyDyIhsxDeOxFme1J7UMjJUTv8c0NkVaWlaK0joquqG2rAzu93xUAcO7aHc6Ix7lr4nWBRnwgvoExiZNnti1HH9t3AIiTIG2nrIKn/55qL9527vodBEaegI/bSHhPdobIxBil5RUIPhCPwMgTGDu4l9KbtzZme5SWV8B3+xH4uI3U6OJ02tovXxOH9kVcUjpOX7nFHp+51opIJ+J6B0Ui7td07F41o8qZRUR/0UgJqbP0oUNSG/XoYI0RDnZsngHj0JlkzHQZxCZnlibsQGnCDrRvZYkb2bmIS0pHeMxFjcVx4bq4E8R0SADxqIP3ZHEuUOLVTI0di6/gA/GIS0rH3HEf6sV++RrWrxv7iIcZ+bB2WSy3LTMydmbbcvbfQGnCDoT5zWI7NqT2opESord0udqrJtXG8/SaMBQuSzezM0Tu5uYjLikd0UHcG5V/2HGZxxCawuxX2c1R2ewPTeeUHE5IRWDkCZzZtpx9BKMJ2tqvKkQmxti2zB2xl9LgHRTJyQ2S/n6Vrars6b8Hh84kCzLaQ2oGdUoIITWuZyfxzIqLaVl419qKnSnCvA8A4bEXERh5AjNdBuFjp/dhITJBi6Zm6DBumSAxaxuTKKpoeq10oq6P20gERp5AaXkF53EGk3fh4zZSrf1qS7MmpvAYNQAeowaw7zE1SQLm8S8foIkEXaK7qFNSh6mbi0FIdYlMjBG81A3eQZEY5WgPT/89CF7qxrm5egdFAgBnlo66iY7SSbUAMNNlEGemj6qEriDapX0rAMCj4lJO/A/+fgwAsLaykLudECb7hiAuKV3mWjPTf1tamlfZlvnuZ7oMqqGoiRAop4QQIogB9uLqt8zIx3/7yF9R+m5uPoCqEyMZzEyPlIx77HY7j56Vafex0/sAxPkWkp2Wc9fvQDRkHrYerPpYmiSZPyH5R/pzRud24mUI9p++wo445D4qwrFz4krM73dpr9Z+tWHi0L4AgKOJV9n37ubm4+d/X/fvbiPTVjp3hHnNfG+kdqKREkKIIN61tmJHK2a6DJKp6BnmNwue/nvQa9oaudsrqljKzPSQfFwh7/HA4J6d2Ucg0nkNIxzsMGVYf3VOq8YwCcPy4p/pIn9NGT608TiHSXT1DopkR8AYYX6zON+9ZFKsdO0TH7eRvGrIEP1FnRJCiGA+dnofodHn8elwB5nPJgzpjX8qnrE3MR+3kZgyrB8qnr+E42x/Nh9F3nYA2IXfgpe6wWPUAE69E4af5xh0ad8Kl9J+Z2tlBC91wyhHe8GSQlXBJI9KLsg34gM7jNOx0QTpRFdA/H3Km3YtMjHG7lUzcPrKLc7ifR87vU8dkjqg3ps3b94IHQQB6tWrh45ztsGyn6tG9ldZUYbiGwkovByF4rR4WDm5o9WwOZy1buTllJQ/zEBJxgU8OLgegLhEumV/V5lKpiWZl/A4NRr5iREAxOXRLXqP5qztoko7aXxqcyjLhVGULyP9PvO69+Y0FCQdwYOD6xWeM99rw+faq9KuOgqvRCFr10Jo8z/zqVOn4mX+XYT6emrtGLWRLq9uLBoyTyfjkked63joTDJmBoRp9b8LopaFNFJSS2Xt/gzFaW+fiecnRiA/MQJ2a+MVdgiK0+JxO9hD5j1mP8zNV147ZgE6288PsgvU8W2nC7LDP2fPk+85y2sH8L/26nxHhGhbSsY9BC91EzoMUkdRp6QWYm6W1qMXodVH82FgbIrC5GPI2umF/MQfFa5Rw9x0u/tGw9SmFwDgeVEeri3ri6ydXuyNl2nXa0Myu5JtWc413AxwwePUaLazwbedPDU9I8ikjS06zt4KA2NTlGReQsbGSSi8HCVzzlVdG77XXt3viNQ+ujZicvnmXaX1WXQFrQ5cO1GnpBYqTj8DAGgx1BMGxuLn4nwWk2M6Ai9LC1H+MAMvivLwT851mXbm9s4oTovH45QYmLTtjsbt7WBq00umI8G3nS6QvFaSIz0MvteG77VX9zsiRNv0oUNCai/qlOiI+gYGGtsXk79hKLJUeduHUYHIjdmitE0bVx8Up8VzcitaOs+WGfng206e6uaUqIrPteJzbfhe++p8R6rS5L8tojm6MjKir+j61U7UKdERjU3NUFnxj6Ax5J/fh9yYLbByckfT3i5o0NgcDc2aI3WxPaedSRtbOITmcRI/i9PiYW7vjDauPmw+BN92+oDvtdE1r56WwlRkJnQYhBDCC3VKdESHDh2Q/+ieRvZl5eSO/MQIvCwtVOmXeM4PPgDAyWeorJCthMkwaWMLkza2aNpnNJ7l30fGxkkoTouXGcHg206SNh7xvCwtVHtbvteG77VX9ztS1bNH92Fj00Fr+69tdC2/g5C6hiq66giHfn3w7A/NrOkg6iyu+fD3mTD2xlmYfAxJM1sjJ2JFlds/y88BIL7p/nlyu8znORErkDSzNcpyxJUjjSxao5FVe7XbaYO5vfi5OHPsyooy/H0mrNr7rera8L321f2O+Hr+8AYc+vXR2P4IIUSbaKRER4waNQo7d+3Gq6elaPAfUbX2Zdl3LAovR7HTbyVZOU1TuF3HuSHI2umF66sGyv38WX4OGlnZoJnjJOQnRuBmgItMG5vpgezf+bbTBsv+rihOi+ccu92k1Wrvj++14Xvt1f2OVPHqaQlKslIxaqOfRvZHCCHaRiMlOmLYsGGwsLRE4eWjGtlfx9lbOTd+69GL0POrC0rzOCz7jpW7jd1a8SyUkjtJAABTm16wWyuezirZtot3OKwGTWXf49tOGyz7jkXHuSHsiInN9EC0Gj63Wvvjc20A/tdene9IFYWXo2BhaYlhw4ZpZH+EEKJtVNFVh2zatAnrNm5D17UJqN+godDhED32+tULZK4dgjWfL8SSJUu0eix9qehaWl4hU7p8wcShnFL18nJKbmTnIvFqJlumfoSDHSYO7cuWs2ecu34HPydeZcvVKyqjzredND51OarKheF7LnyulSrtdA1VdNVZC6lTokNevnyJzrbd8fq98Wg1YqHQ4RA99mfcNtT/7QjuZNyEoaGhVo+lL52Syb4hiEuSzdu6tNuP7RBId0riktIx2TdE7v7C/GaxN3Nl7aKDFrNrtvBtJ091OyV8zwXgd61UaadrqFOis6jMvC4xNDTE1u82/X979x4WRdn+AfwLnk+AJzxbSpqCaIoaJqZocYp8RUMj4kWLtzWlTPh54LUUTQUtoAxQFEVeVFJCUHEVTEEBRQOUowqCiYKJJ1gPKKL8/thmYo8MC8ssy/25Lq6r3X1m5p7BmHufuZ/nwWzHeej+lg069XuD75BIC1R1+xpuH92Cg1H71Z6QtBTHzokXrFv+qR2+nvc+9Lp0wm+n0vHZ+lDsOnwGAUs/kbsdcxM/GbgCE4yHAABulT+A8cf/xWfrQ9kbOdMu/9eN7Iq3f+Rfxwz3TYhNymCTDa7t5GnsiCCu58L1Wql6TQlRhpISDfPBBx/AztYGJ4Pm480Vh9CuW0++QyItyItH91EUNB92tjb44IMP+A5HYySk5QIABLMtodelEwDxasLSjy2kMYnA3YpHyCm6hVvlD5Bx+U+ZdraTRuPYuWzEJGVg9LDBGDt8MCYYD5FJJLi2Uweu58L1Wql6TQlRhpISDbQ34n+Y8LY5ird9gWFL9kK3fUe+QyItwKvqZyje9gUG9eyKvRH/a9ZjP6+uadbjNRRTv9HboFuDt12/6zA27xEqbfPtZzNx7Fy2RK3Goo9myPR8cG0nT1PUlHA5F67XqjHXVBO0oZmONRIlJRqoc+fOiD8mxIS3zXH1x4/wxuJdaKdvyHdYRIO9qCzHtaDP0LGqHPHn09C5c+dmO3a3bt1Q9vRZsx2vOe0+moLNe4T4/MN3MWuaGXrodUHfnvowmr1Mop2p0UCITm2TKCQ9di4btpNG49vPZrL1FVzb8XkurUHF4yoY6Ddu6gWiHpSUaKiBAwfibEoybD+wx+WNH8Jo8S50GWzCd1hEAz0pyUNR0GcY2FsPx04kY+DA5i0wNDIygvBQdLMes6E+//Bd7DxyBncrHjXom/3XfnsAQKI+QvSkSmF7U6OBMDUaCIdpZigqvYsPPQNw7Fy2TA8G13Z1NfYRD9dz4XqtVL2mmuB62V0YDR3KdxhEDpqnRIMNGTIE58+dhflYY+Sut8Ofkd+h5qmI77CIhqh5KsKfkd8hd70dzMca4/y5sxgyZEizxzFhwgTc/Ose7lYoXpKAb5PHDAcAhBxMZG/Ev51Kh970hVgasK/e7a/dugNAfBPfsv+EzOdLA/ZBb/pC/JEvXipioGEPGA3orXI7darvXLheq8ZeUz5dKryF8RPf5jsMIgcNCW4BXr16hd27d2PZ8pV4+uIlDK0WwnDyvGZZYZZonheieyhP+RXlJ0LQuV0b/LDZF/Pnz4euLj/fMV68eIHevXrCd+FsONtM4iUGLlQZEsyMJlEk839r8cbAPuwIGnm2eH6K+R9YAADndurA9VwA7R4SXPHoKYzmLEfsoUOws7PjOxwiieYpaUkqKiqwadMmbN22HY9ElTAYNh4dX38LHXu/jjZd9KGjS4Vb2qj21Uu8fFKJZ3f/xLM/L6GiMB3d9A3wpeA/WLFiBQwMDPgOEYsXL0L22UQI/b/hOxSFRE+qcDApg32MsfxTO3xs9Xa9k6ftPpois03V8xeY/J/1EolETtEtHDqdyRaSLv/UDmYjX4ftpNEScXBtpw5cz4XLtWpIO02yPTYJ/gcScePmLbRtSxUMGoaSkpbo2bNn+P3333Hs2DGcO/8HiouLIKqsQO2rV3yHRtRAR1cXevoGGDrUCJPengBbW1u899576NhRc0ZlXbt2DaNMTCD0X8rOgUGIpnn+ogbmbhuw6Jv/U/tMx0QllJQQQprG8mXLcPLoQZwKXAZdHR2+wyFEhn9kPCKTcpCTl08TC2omdyp0JYQ0iW+/+w5l9x9h28FTfIdCiIyCkr/w497j8Av4iRISDUZJCSGkSejp6WFLYBBWbYtGwvlcvsMhhHWv4hHmfrsNNra2NNOxhqOkhBDSZBwdHbFs2TJ8tiEMOUW3grfpEQAAIABJREFU+A6HEFQ9fwGXtaHo2r0Xwv8XwXc4pB6UlBBCmtTGjT5438oatksDcCo9n+9wSCt254EIdh4BuHHvEY4KjzXrTMdENZSUEEKalI6ODiJ//RVz532Mj7yCsD02iZaIJ80u+9pNTHffjGfogDPJKc0+0zFRDSUlhJAm165dO+wIDcVGHx8sDzwAO48AZBXe5Dss0gpUPn6KZb/sx7QvfTHS9C2knjvHy0zHRDU0JJgQolYZGRlwX7wIF/5Ih5OVORY6WGLMsEF8h0W0zN2KR4g4lopfok5Bt207+G7azOtMx0QlNE8JIUT9amtrERkZCV+fjcjJzYPRoL6YMvoNjHi9H7p364KOHWiIJmmYly9rUfH4Ca6X3UPG1Rs4n3sNBvr6+M8XAo2Z6Zg0GCUlhJDmlZGRAaFQiOQzp5Gfl4/7Dx7g2fPnfIdFWhhdXV10N9CH0dChGD/xbY2c6Zg0GCUlhBACACEhIVi8eDHOnz8PMzMzvsPRONXV1Rg3bhz69u2LEydOQIdm7SVNj2Z0JYSQkpISLFu2DJ6enpSQKNC+fXvs2rULSUlJ2LFjB9/hEC1FPSWEkFattrYWNjY2KCkpwcWLF6n7vx7Lli3Djh07kJubS8NsSVOjxzeEkNZt9+7d+Pzzz5GSkoJJkybxHY7Gq6qqwpgxY/DGG29AKBTyHQ7RLvT4hhDSepWVlcHDwwNfffUVJSQcderUCbt27UJ8fDx2797NdzhEy1BPCSGk1Zo5cyby8/ORlZWFLl268B1Oi7JkyRJEREQgPz8fffv25Tscoh2op4QQ0jrt27cPcXFx2LlzJyUkKtiwYQMMDAywaNEivkMhWoSSEkJIq1NeXo4lS5ZAIBBg6tSpfIfTInXt2hU7duxAbGwsfv31V77DIVqCHt8QQlodR0dHXLhwAbm5uejWrRvf4bRoCxcuRHR0NPLz89G7d2++wyEtGz2+IYS0Lr/99huio6OxY8cOSkiawA8//ICOHTvi66+/5jsUogUoKSGEtBoPHjzAV199hQULFsDKyorvcLRCt27dsH37dvz666+IiYnhOxzSwtHjG0JIq+Hi4oJTp04hNzcX3bt35zscrbJgwQIcP34ceXl56NGjB9/hkJaJHt8QQlqHI0eOYM+ePdi2bRslJGrg7+8PXV1deHh48B0KacGop4QQovUqKythYmICS0tLRERE8B2O1oqJicHs2bMhFApha2vLdzik5aFp5gkh2s/NzQ1HjhxBXl4eevXqxXc4Ws3JyQkpKSnIzc2Fvr4+3+GQloUe3xBCtFtCQgJ27dqFoKAgSkiawS+//ILq6mr83//9H9+hkBaIekoIIVrr8ePHMDExwcSJExEVFcV3OK3GgQMH8PHHH+PEiROYMWMG3+GQloMe3xBCtJe7uzsiIyNx+fJlGBoa8h1OqzJ79mxcunQJ2dnZ6Nq1K9/hkJaBHt8QQrTT6dOnERwcjJ9//pkSEh4EBwejsrISK1eu5DsU0oJQTwkhROs8efIEY8aMgbGxMQ4fPsx3OK3Wnj178O9//xtnzpyBhYUF3+EQzUePbwgh2sfT0xO7du1CXl4e+vfvz3c4rZqdnR2uXbuGrKwsdOrUie9wiGajxzeEEO1y7tw5/Pzzz/jxxx8pIdEA27dvR3l5Ob799lu+QyEtAPWUEEK0xrNnzzB27FgMHjwYx48fh46ODt8hEQA7d+6EQCBAcnIyJk2axHc4RHPR4xtCiPbw8vJCUFAQcnNzMXjwYL7DIX+rra2FlZUVbt++jYyMDHTo0IHvkIhmosc3hBDtkJGRgR9//BG+vr6UkGgYHR0dbN++HTdu3MC6dev4DodoMOopIYS0eNXV1Rg/fjx69OiBxMREemyjoYKCgvDNN98gLS0NZmZmfIdDNA89viGEtHxr1qyBn58fsrKyYGRkxHc4RIHa2lpYWlqioqICf/zxB9q1a8d3SESz0OMbQkjLlpOTAx8fH6xfv54SEg2no6ODHTt2oKCgAD4+PnyHQzQQ9ZQQQlqsmpoamJubo3379khOTkabNm34Dolw4O/vDy8vL6Snp8PU1JTvcIjmoMc3hJCWa+PGjfj+++9x8eJFjBgxgu9wCEcvX77ElClTUF1djfPnz1MySRj0+IYQ0jJdvnwZ69atw9q1aykhaWHatGmDnTt3Ijc3F35+fnyHQzQI9ZQQQlqcut+009LS0LZtW75DIiqgni4ihXpKCCGa69GjRzh69ChevXol8f7PP/+MjIwMhIWFUULSgi1fvhwmJib47LPP8PLlS4nPLl26hNzcXJ4iI3yhpIQQorE2btwIe3t7TJ48GQUFBQCAwsJCfPvtt1i1ahUVSbZwbdu2RVhYGDIyMvDLL78AAKqqquDp6YmxY8di7NixPEdImhs9viGEaKz33nsPp06dQtu2baGjo4P169cjLi4OFRUVSE9Pp3kutAQzz0xoaChWrlyJ0tJS1NTUQEdHBw8ePICBgQHfIZLmQaNvCCGaqaamBt26dcOzZ8/Y93R1dWFoaIgtW7bA0dGRx+hIU7p//z6mTp2KvLw8tGnTRuJRzuHDh/Hhhx/yGB1pRlRTQgjRTOnp6RIJCQC8evUK9+7dwyeffIL169ejpqaGp+hIU0lISMCYMWNw9epVAJBISNq3b4+kpCSeIiN8oKSEEKKRkpKS5D6eqampQU1NDby9vTFixAhkZ2fzEB1prKdPn8LZ2Rk2Njb466+/5CaY1dXVSEhI4CE6whdKSgghGun3339X2hPy8uVLFBUV0TwXLVRiYiL27duH2tpamZE3deXn56OioqIZIyN8oqSEEKJxampqkJqaCmUlbzo6OnByckJQUFAzRkaaygcffIAdO3agXbt2Sod1v3r1CsnJyc0YGeETJSWEEI0jr56E0bZtW7Rr1w7BwcHYt28funbt2szRkabi5uaGCxcuYODAgQpHUlFdSetCSQkhROMoqidp164d+vbti7Nnz2LhwoU8REaa2ltvvYVLly7BxsYGurqytySqK2ldKCkhhGgcefUkurq6eP/995GTk4Px48fzFBlRB319fRw6dAg//vgj2rRpI7NAH9WVtB6UlBBCNIp0PUmbNm2gq6uLjRs3Ii4ujibS0lI6OjpYunQpTp8+jZ49e0r0lFFdSetBSQkhRKPUrSdp164dDAwMkJSUhBUrVkBHR4fn6Ii6TZ48Gbm5uZgyZQr7OIfqSloPSkoIIRqFufm0adMG5ubm7A2KtB69e/fGiRMnsGrVKujo6KC6uhqxsbF8h0WaAU0zT0gd1dXVyM/Px+3bt/H48WO+w2mV5s6dCwCYPXs25s6dK7f4sbl16NABPXr0wKhRo7Tm8VFFRQVyc3Px4MEDPH/+nO9wFMrKysKGDRsAAOHh4ejUqRPPERFA/LitR48eGDJkCIYMGdJUu6W1bwipqKjAnj17EHswGskpqah+8YLvkIgGMxk5Ah98OBPz58/HyJEj+Q6nQfLz8xEeHo6jQiHycnP5Dodoid69e8Pa2hrz5s2DnZ1dY75IUFJCWq+qqips3LgR/n4/oo1OLT58awCmjeiDUQMN0EevE7p2VDyhE2ldnr94iYdPq3H1tgipheU4knUbxX9VYKa9PfwCAvDGG2/wHaJS165dg6enJw4fPgyjYcPx4aw5mPzuVLw50hjdu/dAh44d+Q6RtDCvXr1CZcVD/Hn9OjIupCFeeATJSYkwMjLCpk2b4ODgoMpuKSkhrdPBgwexdMnXePjgHjysR8DVwghdOlASQriprQUSL/8F70M5uF7+GEs9PLB27Vp06NCB79AkPH/+HGvWrEFAQACGGL2BtT4/wPI9KyoYJmpxvegaNq33RkzUflhZWSEoKAhGRkYN2QUlJaR1qa2txZo1a7Bhw3rMe/t1eNmboo8+fUskqql5VYvw5CL4Hs3H6LfGIubQYfTq1YvvsAAA9+7dg4ODA7JzcuC1eh1c3QRKp3MnpKmkpaZg1f99g9JbJTh48CCmTp3KdVNKSkjrUV1dDedPnHDk8GH4f2KGjya8xndIREsU330M55CzQEd9xAmP8V5rcvnyZdjb2wO6bbAv+jCGvjGM13hI61P9/DmWLv4Ch6KjsH37dri6unLZjJIS0nq4fOqM40disetzc7xtpBnfZon2qHhajQWhaSh91hZ/pGeid+/evMRx9+5dTJg4EQMGDUZY5G/o3r0HL3EQUltbC/9NG+Dnsx7R0dGYOXNmfZtQUkJaB18fH6xb642DX03BuNd78h0O0VJV1S8xJygFbXsMRNKZFHRs5gLSZ8+eYdq0aah5VYuDwt/RqXPnZj0+IfL8sGEdgn76EWlpaTA1NVXW1J3/CQAIUbPk5GSs+nYVglzGa3RC0sf9APq4H2i27RpKVPUCe1KL4RKSgj7uB+ASkoLYjBKIqrgNoRZVvUBsRonE9ntSi3HvkewcGQ1pKy2vtKJZroc8ndq3QeiCibhZXAivFSua/fheXl64easUO/ceaHEJiWGXtjDs0vCaF1W3ayiRqBIRYaFwcZwFwy5t4eI4CzFR+yESVaq0v7ycbKVxx0TtZ4+1bMli5OVkN0lbPvzff7+D3Yez8K9ZsxSu/s2gnhKi1V6+fAmzsWMwpe8rfPcvpRk675gb6Z3Auc2yXUMt/zUD4SlFMu9bmfZHhMBC6baiqhdY/L/zSMgpk7t9wCcT0Ktbhwa3lXbv0XOYeB0CoP7roczFGw8w86ckZGRexKhRo5rlmLm5uTAzM8PhE6cxbvyEZjlmU2Ju0OVPaupp2TTbNdSyJYsRHhoi8761nT0ioho22+y9u+Uwfr0/APlxuzjOQrwwTub9kN174eA4T+W2fHpWVYUZkyfAxfkTfPfdd4qaUU8J0W47duxAedlNeNho/iRXdwLnqnQjVXW7hsgrrUB4ShGW2hgj83t73Amci8zv7eFqYYSEnDIUlT9Suv2p/NtIyCmDn9N4FP7ggDuBc1H4gwOW2hgjIacMURf+VKmttM1HNWNCsLGv9cDct4dgyVeLm+2YS775BnM/cWmRCQkgvjmrklioul1D5OVkIzw0BB4rViHzSjHKn9Qg80oxXN0EiBfGoaiwoEH727R+rcLPYqL2I14YB2+fzbh2+z57fiG790Iw3xm3bpao1JZvHTt1woYfAuDr64vS0lKF7SgpIVrr6dOn+G7Vf7HC9k2ag6SRLv75AADgOPE1DOgufiwwoHtnuE4Rz0GQc/Oh0u2j08V/HD+dPBR6ncSrv+p1aodFM94EAHjHZKnUtq6tJ6/idmVVw09OTf5rb4xz587h6NGjaj/W0aNHce7cOaxau17tx2qNMtMvAAAcnZwxcNBgAMDAQYMx300AAMi+dJHzvoK3+OOvMsU35YMHIgEAn87/HHp6+uz7M6xtAACJvyeo1FYTTJvxPt6ZMlVZTwnoLzXRWpGRkaitea4RQ39jM0oQnV6ChJwyLLUxhuPE1/DOumMA/nnMIP0Yhnmd5/MvRF34E94xWbAy7Y854wdjltlgdt9cHt9wqbFQtv2th08BAL27SRZu9tETr0Ny5bZI6b4VPd5hkg5V2zJSCsrhHZOFU15Wch/78KFn1w5wMBuMX7b8hA8++ECtx/olMBAOjvPQsxc/I37qExO1HwcPRCJeGAePFavg6OSMSW8ZA/jn8YX0Yxjmdf6fZTgQuQfeXsthbWeP2XOdJB5LcHl8w6XmRNn2pTdvAgB69+kj8X6fvn0BAFcv59e7fwBIPp0Ib6/lSEzLlPvIBQD7ft0ko+7ruglQQ9pqis8XLsb8j+dg8+bNcuf0oZ4SorUO/BoJW9N+aNeG33/mvnG5EISlsTfLgOP5bELCxdJ9f7C9Awk5ZRCEpSE2o3m7ZQOOi//oSicGTG0H83lDMY99QhaYq9y2qPwR5mxJQsgCc5gM0KzF8v41dgB+P3kKDx48UNsxHjx4gN9PnMCsOfzV0Cjju24NBPOd2Ruo/6YNbELCxdJFX8DbazkA8U1YMN8ZMVH71RKrIv6bxAsCSt/8e/U2lPhcmaLCAsyxex8hu/fCxHS0wnbWdvYAIFNAy7yuW9fSkLaaYtqM99G5SxeFqz5TUkK0UnV1NU6fOYN33zTkNY6UgnIEHM+XW4vBlckAA7a2IvrraQD+ecTBFVN3ouyHD1EXbsDKtD+mG/dTqa2o6gW8Y7Kw1MZYovdIU7wzzBC6Ojo4efKk2o5x8uRJ6Orq4h2Ld9V2DFUln06E/6YNcmsxuDIxHcPWS0QLTwD457EFV0ythbIfdRKJKuH93+XwWLGq3uLT2XOdAAAn449LbB/8k3+j2mqKNm3aYPK703DixAm5n1NSQrTS5cuX8bz6Be/fnFMKygEALpOHStRiCKYP57wPt6nD2B4Ki+HiJEtTHlE0hm9cLgKO52Ol/Silj2aUtQ0+eRUJOWVwm6qZM5a2b6uLYf26IytLfh1MU8jKysLwESPQXsPW3QGA1NNJAIBPF3wuUYux0H0J5324fbmY7aGYMtUSABQ++tBUwT/5I14YB7cv6y98nmFtA2s7ewjmO7PDnd/oJ38qg4a01STGo0yRlS1/2DLVlBCtVFYmvmn31e/EaxzMYw0mIWEYGXbjvA9Fw18borE1JU2NSTJOeVnVmzgqahubUYKA4/kQes5okmukLn302uP27dtq239ZWRn69O2vtv03BvNYg0lIGEbDuCflzCOSxmhsTUljxETth/+mDTiWmMrpXPT09BEQvB3H4g7D032hRB2N9GOihrTVJP36D0CZghE4lJQQrVRZKX6m2rUj/RNvCkttjBFwPB+iqhcSPRXMxGlLbbjVCNx79ByhpwuRV1qBs6ttlSZn9bUVhKUBAOz85D8aaa75W+qj16FNvRNGNcbz58+hp69ff0OiMo8Vq+C/aQNEokqJuhKmdsNjxSqF2wrmOwMAbC0ny/1cXqFur96GcFngBpcFbux7zPBeb5/NEts3pK2m6NipEx49kj+NAP3FJlrp1atXfIcA4J+beenDpxK9JaV/j2ZpLo29MY/opwcAuPvomURScvPBEwDAwO71zx6aV1oB37hcmAwwUDoBWkPbaro2ujrNcIw2aj+GKpib+a2bJRK9Jc09f0Zje0HeHClOuu/euSORlNy8cQMAMGDQoEbtvy5mMrRrt+9LHOt6sXjiwn79BqjUtqWgmhJC1IipAYlILWYTkdKHTxGRWsxnWA02rK84KYm6cEPiPI5cvAUAGPu68kXfSh8+xXSfBJgMMMBK+1FKkwyubesr2OWzgJeITZ46DQCwJ2wnm4jculmCPWE7eYyq4YaPEE++GBW5V+I8jsREAwDGjZ+ocNv6CmulXzPFq4eio9j3igoLcPjgbwCACeaTVGrbUlBPCSFqZDHckO0tUXXYrCYwGWAgnuJdznm4WhjJ1IVIPzpJzP8LAJReB1XaEs02Zaol21uiyTUO9TExHQ1rO3u55+HqJpAZ4tuYqe+Z4lVP94XwdF8o8VnI7r0SPU4NadtSUE8JIWq20n4UQhaYw8pUXIy41MYYZ1fb8hxVwwV8MgF+TuPZ87Ay7Q8/p/H49l+K51xgeEamcz5OQ9oSzbdy9VqE7N7LzqnhsWIVzl1qeQl6QPB2+AVuY8/D2s4efoHb8N33G5v0OEzxql/gNvY9jxWrkJiWKTOcuCFtWwpakI9opX379sHZ2Vmjv1H3cT8AVwsjbP7YjO9QiBp9uTsNHYdbYO/evWrZv7OzM57X1GJrWIRa9q8uhl3awtVNgB9+DuI7FNLMog9E4ssFLpCTftCCfISoUx/3A+jjfgAZ1++z74mqXmDryasAgHeGaea04IQ0BWbujIwL59n3RKJKBG8RT+6liRO+EX5RTQkhahQhsIBLSIrcYatcZzIlpKWKiIqFi+MsucNhre3s2YXjCGFQUkKIGlmZ9kf019PY6eYBcWHoO8N6Y7pxv3pnMiWkJbO2s0e08ARSTyexBaKubgK8Y/EuZljbyKwlQwglJYSomcVwQ1gMN8RK+1F8h0JIs5sy1RJTplpi5eq1fIdCWgCqKSGEEEKIRqCkhBAtxxTbtkSiqhfYk1rMnoNvXC6KyuVPT820dQlJQR/3A3AJSUFsRgk7Fb6q+yUtG1Ns2xKJRJWIidoPF8dZMOzSFi6OsxARFop7d8vltq/bdtmSxcjLkb/oHSCeZM133Rr2+ijbb3Nqmb8pQkirsPh/5yVWRGYmVJO3kN/6Q9kITyliXyfklCEhpwxWpv0RIbBQeb+E8EEkqsTiz10lVkSOF8YhXhiHBGEcAoK3Syzwx0w5zwgPDUF4aAhCdu+VmbMkLycblubjJN7zdF+IBGEcgnaG81rrQz0lhBCNFJtRgoScMvg5jWenjI/+ehoAIDy5SKJtXmkFwlOKsNTGGJnf2+NO4Fxkfm8PVwsjJOSUSfSCNGS/hPDlZPxxxAvj4Be4Dddu30f5kxpcu30fHitWIV4YhwORe9i2MVH7ES+Mg7fPZrZt+ZMahOzeC8F8Z4m1hkSiSliaj4O1nT0yrxSz+/X22Yx4YRxOxh/n43RZlJQQQjRSdLr4D+nMcf8sdsasJVS3RwQALv75AADgOPE1duHDAd07w3WKEQAg5+ZDlfZLCF8OHogEALgscGN7LvT09LHoGw8AgLfXcpm2n87/XKKXgxlynfh7Avte4ZUrAMTr5jDT0Ovp6ePT+Z9L7IsvlJQQwlFKQTmW/5ohUYeQV1oh0y6vtAJbT15l2zG1DXXVrfNIyClj29V9pBCbUcK2U7a9dDt5NRT1nY9LSApSCuQ/T+Z63tKY9sp+lIkQWOBO4FyJYdPM9QlZYC7R9tbfiwT27tZRMga9TgCAK7dFKu2X/CP5dCKWLVnM1iD4rlsjt2YhLycbwVv82XYujrMQE7Vfok3dOo94YRzbru7jh5io/Ww7ZdtLtxOJKht8Pi6Os5B8OrFR5y2Naa/sR5mIqFi5a+fIe7TCXDfpz5jX2Zcusu+dT0sFILtYn56ePsqf1CAiKrbec1MnSkoI4SAhpwxztiRJfJMOOJ6P6T4JEjfzhJwyTPdJgHdMlsR7grA0mcSC+cwlJEXiv/NKK+AblwtBWBrbTtn20u0W/++8TDtpvnG5EufDnJ9vXK5K561uTJLnEpKCkAXmmGUmudAYMweM9LwvzArDihb2q2+/RCxeGIc5du8jPDSEfc9/0wZYmo+TuJnHC+NgaT5O4lt8vDAOgvnOMokF85mL4yyJ/87LyYbvujUQzHdm2ynbXrrd4s9d6z0f33VrJM6HOT/fdWtUOu/mVFRYAEC84B6DWY9HOiFjXteN/1zyGQDAwEGDJQpjg7f4a0ShKyUlhHDAJA5MvcKdwLkQes4AABzOvCnTTug5g22X+b34D0bd5IGReeMBCn9wkKhrmO4j7mqVfl/e9hFniyVqKJbaGCMhp0xpwsBM5LbUxpg9RuEPDuxqxnV7QbietzxMe2U/XJkO6g5vhzGwMu2vMEFThbr2q22YxIGpQSh/UoNjieJv3IcP/ibT7lhiKtsu80oxAEgkD4yL6X+wNRDRwhMAwBZgSr8vb/s9YaFsTJlXitl6C2UJQ/LpRPhv2gCPFatkajX8N22Q6AXhet7yMO2V/agiKnKvzGy4s+c6AYBEPYhIVIngn/xltmd6VZjEj3nt7bUcSxd9wbmnSV0oKSGEA2Zl3MOZN5FSUA5R1QuYDemJO4FzJRbUY262r/XqirzSCiTklCEitVjhft2mDmO/3TN1DQCwaMabct+X5u0wRqKGwmXyUDZORZiEpe4x9Dq1w6IZbwIAzly50+DzVjeL4Yb4csabiBBYwM9pPARhaU3SU6Ou/Wob5pv44ZjfkHw6ESJRJcwmvo3yJzUSC+oxN9vXhgxBXk424oVx2BO2U+F+3b5czD5imDLVkn1/0Tcect+X5r1xM1sXMXDQYHy6QFwXoSxhSD2dJHOMurUapxN/b/B5NxffdWvgv2kDVq5eJ1M7Ym1nD8F8Z/bR0Bv9eta7v/w/yySKYjWh0JWGBBPCwUr7UUjIKWMfy1iZ9ofAcrjchME3Llfh4wJpzOMFaVynnzcy7CbxmklQwlOKFCYNTGzDlsXI/dw7Jgtf/p2gNOS8pXGZG0WVVZxnjhsEz8h0hCQWcIqD7/1qg5Wr14lHd/z9WMbazh5fuC+RmzAwN04u6g5prYvrkFSjYcMlXjMJSnhoiMKkgYlN0U3b22s5Fn0tTlAact7SuMyN0pDeEua6JqZlwsR0tMRnenr6CAjejmNxh+HpvhDWdvaYPdcJDo7zFP4u6iZlwD9FsQcPRMoMIW5O1FNCCAcmAwxwJ3AuTnlZwdthDFtrwdSAMPakFiPgeD5cLYwQ/fU0nPKyQp7Pv3iMvHG4nndzYhK2ukXBS22MAUCmyJd5zXze0P0SMRPT0Sh/UoPEtEx26Ogcu/fZGhBGRFgo/DdtgKubANHCE0hMy0T+ny33enI9b3W6d7f87+LaLJy7lC+TkDB69TaEywI3tljVwXEeOxTY22cz285jxSoAioti6xYb84F6SghpAJMBBjAZYICZ4wbh+t3HmLMlCQk5Zew3fs/IdACQ6KXgOhpGFaUPn7K9IwDY+TiU3YRdLYwQnlKEwh8cOPfI1Hfe8qjSC1IXMxpJOs57j56z58EY0U8PAHD30TOJtjcfPAEADKxzjRqyXyLJxHQ0TExHY6bDR7heXIQ5du8jXhjHfuP3dF8IABK9FOqsUbh1s4TtHQH+KQJlbrzyuLoJEB4agmu373PukanvvOVRtWakLnHR72qYmI6RmSytLmbkkvQ5XS8WF6j36zeAfe/NkeK/DdLXjvk9uboJGh13Y1BPCSEcMENiM67fByB+TDKkd1eF7ZnkQFT1AsEnr6otrojUYpT+PRy29OFTRF24AUB5HQozP0fwyavsjRgQ15r0cT+ArXXibeh5N6U548V/MOvWx4iqXiDfEuacAAAbGklEQVTqwp8AJOcZGdZXnJREXbghcT2OXLwFABj7eg+V9kvEmCGxGRfEI7sGDhqMIUMVJ29McqCo2LKp7AnbyfYG3LpZgqhI8YiUyVOnKdxm5uyPAADBP0mONkk+nciOQmE09Lyb0q2bJbA0HwcT0zFYuXqtwoQE+KfQ9VB0FPteUWEBW1tTd/gv8997wnZKJIxMLcl71rZNdxIqoJ4SQjiY9/brCE8pgp3fSZnP/JzGs/8dssAcgrA0vLPumNz9FJU/kqkDaaxx30l2ty61MVaalFgMN2RH2kjXvliZ9ofjxNfZ11zPWx1mmQ1GdHoJPCPT2R4ohvQ5mgwwgJVpf7nn5GphJDF1fEP2S8Q+dv43wkNDYGs5WeYzv8Bt7H8zM4hOekt+T11RYYFMHUhjjRsxVOK1x4pVSms+pky1ZEfaSNdbWNvZY67Tp+xrruetDsyEZ/LiZDC9MUyhq6f7Qra3ihGye69Ej8jAQYPZ35P0fl3dBGxxL18oKSGEA7MhPXHKywpHLt5ib3pLbYwx7rUe7AgVQHzDe/yshr3ZLbUxhuPE1/DsxUtM90nAucK7TZqUrLQfBf1O7eAdk9WgItSV9qMwop8ezhbeZecg8XMaD5vRAySKb7met7pECCwQm1GC6HTx1PCuFkaYOW6Q3HMM+GQCjmeXIj73nzVvrEf1l9vz0ZD9EsBs4ttITMvEkZho9kbmsWIVxo6fIHETc3Cch8ePH7E3Ro8Vq+Do5Ixnz57B0nwczqacadKkZOXqtdAz0Ie31/IGFaGuXL0Wb440xtmUM+wcHn6B22BrP1OiR4LreauDdHKhjHShKyCO80OHOXJrUBwc52Hwa6/j173/Q3hoiERhLN90amtra/kOgpCmtm/fPjg7Oze6rkFTMSNbtPX8tMmXu9PQcbgF9u7dW39jFTg7O+N5TS22hkWoZf+aiBnZ0hR1G6T5RR+IxJcLXCAn/XCnmhJCCCGEaARKSgghhBCiESgpIYQQQohGoEJXQlogqiUhrRnVkmgv6ikhhBBCiEagpIQQNejjfoDT2i+aholbOn5R1QvEZpTAJSQFfdwPwCUkBXtSiyUmX6urqPwRfONy2f0oa1tXXmlFo6+bqOoF9qQWs8f2jctlJ7OTp+55Lf81Q2b6fEXXhIgxC8C1NEzcyuKPF8Y16NzycrIVtheJKhERFgoXx1kw7NIWLo6zEBO1v8lmvK0vVunzre/ciwoL4LtuDdsmIixUYrI5LvtQRcv7l0QIaVaiqhdY/L/zEmvCJOSI5wKJzy1DwCcTJOY2ySutwHSfBIl9eEamIz63DEH/flvh1Pb3Hj2X2U4V0rEyE6qd8rKSmEQN+GfKeUZ4ShHCU4oQssAcs8wGg7ReeTnZcHGcxbn9vbvlsDQfp/Dz77/7LzsnCiBOIuKFcbC2s0dEVKxaY2VmvW3I/qTPxdN9IRKEcQjaGc55en5VUE8JIUTGncC5bN3KqfzbSMgpg5/TeBT+4IA7gXNR+IMDltoYIyGnjJ2eHRAnMNN9EmBl2h+Z39uzbZnF/E7l31Z4zM1Hcxsdd2xGCRsrcw7RX08DAIQnF8lt6+0whj2vO4Fz2Vl5menq614Lon3Kn9TI1KhkXDivNMGQZ9P6tQo/y8vJRnhoCDxWrELmlWKUP6lB5pViuLoJEC+MY6flV0VDYvX22cyeb92fukSiSliaj4O1nT0b67Xb99kFCZnp6OVt2xQoKSGEKBWdLv6W9enkoWwvh16ndlg0400AgHdMFtu28C8RAPH6MsxCgXqd2sH5naES+5K29eRV3K6sarJY687iyszSysxcK93W+Z2hEr030437AQAS8/9qdDyk5Qne4g9by8kI2c19srvgLf74q6xU4eeZ6RcAAI5OzuyU7wMHDcb8vxe/y750Ua2xMgvzmY4ZW+8+C69cASBeT4eJVU9PH5/O/xwAcPBApEqxckWPbwiBuG7A1cJIYnVfxvJfMyRW1c0rrcCZK3fYm7GVaX/MGT9YaXe/ohlYFb2fUlCOw5k3EZ5S1KDp47nUPDT0W3+EwELu+/Iew1wovgcAmDC0l0xbRcdNKSiHd0wWTnlZSTxKUYW8WJl9hiwwl/u+9Hkwr7NvPmxULJrKsEtbuLoJJFbyZSxbslhiBd28nGycTvwd3l7LAYDTdOSKZltV9H7y6UQcPvgbO90516niudQxqPJN3ttrOSKiYmFtZw/BfOd62yefToS313IkpmUiXhgnt03pTfHij7379JF4v0/fvgCAq5fzZbZRR6xcnE9LBSC5iB8gTkyaY9QT9ZQQAsDbYQzCU4pkijHvPXqO8JQieDuMgV6ndkjIKcN0nwSJ3oGEnDIIwtIQm9Gw57aK+MblYs6WJPabfUJOGeZsSYJvXOMfbzQlpni07s3+7LW7AMSrCdctIN0qtSJx3X3M2ZKEkAXmMvUejbX15FW2KFdejQizdo+o6oXE+8xr6Z4VbeHtsxnhoSESRYuAuCYiPDQE3j6boaenj3hhHCzNx7EJCSCugxDMd0ZM1P4micV33RrMsXufrbWIF8Zhjt378F23pkn2r4ryJzWc17UpKizAHLv3EbJ7r9w1ZhjMujnStRjMOjuKFtxrqlhzssQ9MT169EREWKhE8ap0oe255DMAxD05MVH72cLc4C3+Mv9m1IF6SggB8O4I8TeYlII7EjevlII7AP65gbmEpAAAhJ4zYDakJwCg9OFTjPsuDoKwtEYXR6YUlCPgeD6W2hhj0Yw3odepHURVLxB88ioCjufjw7EDld68m7P2IerCDViZ9mcfdwD/9D74xuVKrNbrHZOFs9fuShS6iqpewDsmC0ttjNVSVGo6qDu8Hcbg7LW7EISlAYDEceaMH8zWuTDvM9dam021fA8AkJyUKNHjkZyUCACwthXf5JjCyWOJqTCb+DYAccHkuBFDIZjv3OjF25JPJ8J/0wZ4rFiFRd94QE9PHyJRJYJ/8of/pg0KF5Nj8D1XiUhUCe//LofHilUasZAdF1yKV5neHt91aySSJW+v5TiXfIYKXQlpDiYDDGBl2l+m5iE6vQSuFkbsyr5M0eNrvboir7QCCTlliEgtbrI4UgrE30SYhASQrN84c+VOkx2rMZikY6X9KIWjafJ8/iVRPCpd6Bp88ioScsrgNnWYWmK0GG6IL2e8iQiBBfycxkMQlsZeX0BcO2Jl2h+CsDR2uO+wZTFqiUWTmJiOhrWdvUxtwMEDkXB1E7Cr+DKFjK8NGYK8nGzEC+OwJ2xnk8WRejoJANiEBBD3JCz6xgMAcDrx9yY7ljoE/+SPeGEc3L5czHco9WJ6u44lpkoUuIbs3itRvCot/88yzm2bCvWUEPI3geVwzNmShKLyRzAy7Iai8kdIyCljR28wpHsBmhKzX0U3R++YLHz5d4IijzpqSqQx5y9viC2jblIF/FM8Gp1egllmgxGbUYKA4/kQes6QGE6sLjPHDYJnZDpCEgvY2hy9Tu0Q8MkEHM8uhWdkukRtkLp+v5riC/clmGP3PooKC2A0bDiKCgsQL4xDtPCERDvpb8tNidnvG/16yv3c22s5Fn3toXB7ddWUcBETtR/+mzbgWGIq+whGkym6Dg6O8yCY74yDByJlenvqJosAMMPaBgDktm1KlJQQ8rfRg7oDAM4V3oWRYTfk/F3oyLwPAHtSixFwPB+uFkaYOW4Qundpjz56nWDidYiXmJvTvUfPEXq6EHmlFTi72pbtPaprqY0xAo7nKyweZR7vMI9T7PxOyj2WogJgVUkfn9GrWwd8OnkoPp08lH2PGQrs7TCmSY6ticaMFXfjn005A6Nhw9nRH8z7ABARFgr/TRvg6ibAzNkfoUePnujTty+MX+/PS8yahCkqtbWcLPdz6aJejxWr4L9pA0SiSokbPVPP4bFilTrDrVfdAl0mVulHNNKPd9SFkhJC/qbXqR38nMbDMzIdNqMHQBCWBj+n8RI3WM/IdACQGKUjXSjJlbzCT1cLI4mRPg2lrpqSvNIK+MblwmSAgcxkaXWN6KcHQHxjZ4YEA/9cI1cLI7XEx2AmQ5O+fsy1rnt8RW2v330MAOhn0EmtsfJJT08ffoHb4Om+ELb2MyGY7wy/wG0SNyJP94UAIDFKR9XZR+UVSLq6CSRG+jQU3zUlDfHmSGMAwN07dyTO9eaNGwCAAYMGyd2uqbg4zkK8ME7mWjO/T9e/hybXjfXWzRJ2SLCitupANSWE1DFpWG8AYHs+LI37ym3HjDzhWhjJFMpmXL/Pbhd6ulCmHTO/RrDUaJWUgnJ2FEtzK334FNN9EmAywAAr7UcpfdzCDAWOSC2WSNaYWpL3TMSPcZhaE+kfhqoTls0ZL/4jejjzJvueqOoFO8Fb3flL5LUtKn/EvpYe1qxt3rF4FwDYng/L96zktmMm9mKKUOvDjAbJuHCe3S50q+zw45mzPwIgrs2om7Qkn05kR3toKnkTkNVNkqRfDx8xEgAQFbmXnV311s0SHImJBgCMGz9RrfHOnusEADL1IMxr5ncB/DMUeE/YTokklGn7nrWtWmOlnhJC6jAy7Mb2VrhaGEl82wfAzvb5zrpjcrdn6lGkMSM96j6ukPd4wGK4IfsIRLquwcq0Pxwnvq7CWTUOM4mYvJgYTAIxoHtn9hpJt3W1MGKTs4bi+jhnltlgRKeXwDMyne3VYiy1MZaY64UpdJXXNmSBuczvXtsYDRvO9la4ugkkvhUDQMjuvRDMd8akt4zlbs/Uo0ibPdcJ8cI4iUcb3j6bZdpNmWrJPiqQrluxtrPHXKdPVTktjcQUF8s7V1c3gcQoI0XzuTTGDGsbdi4T6flMPFaskpgXZuCgwezvXl6sXIdLq4p6SgiRwnybnvf26zKfzTIbDD+n8ezrpTbGOLvaFqe8xN8yzxXelbvPWWaDEbLAnL0p+zmNV1iwutJ+FEIWmEs8avBzGq/0sYk6Sd+w6zPLbDCEnjPY+K1M+yNkgbncienUIUJgIXGtXS2MEP31NKy0HyXRjil0lf59nvKyajXr3jDfkD92/rfMZw6O8+AXuI197bFiFc5dykdiWiYAcT2KPA6O8xCyey978/IL3KawYHXl6rUI2b1X4pGAX+A2BARvbxEFpA0RELwdfoHb2OtibWcPv8Bt+O77jWo/tp6ePoJ2hkv8XlzdBIgWnsDK1bLT4zs4zsOxxFT292JtZ4+Q3XvlTrjX1HRqa2tr1X4UQprZvn374OzsTGuWNFBTF5g2pT7uB3iNS9Vr8+XuNHQcboG9e7lPW94Qzs7OeF5Ti61hEWrZv7ZSR49EUzHs0lYj45JHlesYfSASXy5wgZz0w516SgghGi/j+n2JHg1CtFXGhfMSPVStDdWUEEJkaFqPyYXie0rnZ1EnLnO/kJZL03pMzqelKp2fRVNwmSdGFdRTQgjReHwlJIQ0t5aQkKgT9ZQQQlia0jOiSeiaaCdN6RlpqdR1/ainhBBCCCEagZISQjQIszAcIdrEsEtbtdUgEO1CSQkhhBBCNAIlJYQQQgjRCJSUEEIIIUQjUFJCSDMRVb1AbEYJXEJS0Mf9AJb/msEu7KdMXmkFtp68ytabuISkIDajRKZdSkE5lv+awbbzjctFXmmFyu2kMe2V/TTVuXC9VqpeU9J0RKJKxETth4vjLBh2aYtlSxazi/gpk5eTjeAt/my9iYvjLMRE7Zdpl3w6EcuWLGbb+a5bg7ycbJXbSWPaK/vhsn197zOv790tZ89b0TlzvTZcr72qvyM+0DTzRCtp4jTzLiEpSMgpk3n/lJcVTAYYAJCdtCwhpwwuISly9xeywJxdo0VZu+ivp7EL0XFtJw+XpEPZ9eZ6LgC3a9WQdnzS9mnmXRxnIV4YJ/N+Ylomu9Cc9ARl8cI4uDjOkru/kN174eA4r9520cIT7EJyXNvJw6UAV9nwV0WTr0m/z7y2trOXuV5cz7luO4DbtW9Iu+ZC08wTwrOEnDIk5JRhqY0xCn9wwJ3AuQhZYA4ACE8uUrgdcxMXes7AncC5uBM4F5nfixfUEoSlybTL/N6ebSf0nAEAOJx5s8Ht5GHaK/tRhuu5cL1Wql5T0nTihXGIF8bBY8UqXLt9H+VPahCyW5x87Q4NUbgdc9M9lpiK8ic1KH9Sg8wrxQAgsYot0y7zSjHb7lhiKgDg8MHfGtxOHqa9sp+mZGI6hr1W0cITAICDByJlzqW+a8P12qv6O+ILjdEipBn8nncbAOA2dRj0OrUDIF5Nt77VaJkb/b1Hz5FXWoHSB0+ReeOBTDsr0/5IyCnD4cybMB3UHaMHdYfZkJ4yiQLXdurA9Vy4XitVrylpOr/HHwMAuH25GHp6+gDEK8zW/TYvD3Ojv3e3HHk52bh1swQX0/+Qacf0KhyO+Q2mY8ZizNhxMJv4tkyiwLWdJqh7rer29DC4Xhuu117V3xFfKCkhWqlr164AgOcvXqJDuzY8RwOEp4i/uffq1qHB2/rG5SLgeL7SNivtRyEhpwzeMVkAxMmHwHK4zOMYru3kaezjG4DbuXC9Vo25ps3pSfVLdGmjvn+Durq6ePy4/pogdQj/+5t2r971//uR5rtuDfw3bVDaZuXqdYgXxsHbazkAcfLxhfsSmccxXNvJ09jHNw3F5VpxuTZcr31jfkfq8rKmBh07dpT7GSUlRCv17NkTAPDwaTX66nfiORrV7UktRsDxfLhaGGHmuEHo3qU9+uh1gonXIYl2JgMMcCdwLvJKK3Dmyh14x2QhIacMVqb9sdJ+FFtfwbUdn+eibe4/qcHIv/89qkOvXr1wtbBlPa6KCAuF/6YNcHUTYObsj9CjR0/06dsXxq/3l2hnYjoa5U9qkJeTjdOJv8PbaznihXGwtrPHytXr2HoIru1aAq7XpiW7f+8eeij4f4KSEqKVjI2NAQBXb4s0IilxtTBCeEoR7j163qBv9p6R6QCAzR+bse+Jql4obG8ywAAmAwwwc9wgXL/7GHO2JCEhp0ymB4Nru7oa+4iH67lwvVaqXtPmVnC7Ev8ZOVJt+x85ciR2hYWpbf/KuLoJEB4agnt3yxv0TdzTfSEA4Iefg9j3RKJKhe1NTEfDxHQ0Zjp8hOvFRZhj9z7ihXEyPRhc29Wljkc89+6Wq7wt12vD9dqr+jtSp4Irl9m/0dKo0JVope7du2OU8QikFd3lOxQAwDvDegMAQk8Xsjfi2IwSdhhrfZhhrqKqFwg+eVXmc2aIb8b1+wCAAd07Y0jvriq3U6f6zoXrtWrsNW0Ol8sqIXr6DO+++67ajvHuu+9CVFmJy3m5ajuGIu9YiM8rdGsQe+OMidrPDjutDzMsVSSqRPBP/jKfM0N8My6cBwAMHDQYQ4YaqdxOHaztxMXazLFFokqEbg1Stgkn9V0brte+sb8jdbiQdhbvTpki9zMaEky01nfffYeD4duQuGI636EAUG1IcGxGicTIFGlnV9vCyLAbMq7fh53fSblt/JzG49PJQwGAczt14HougPYMCfY7lo9DBVW4UnBNrcd5c8QIOMx1gufKb9V6HHlUGRIcE7VfYiSJtHOX8mE0bDgyLpyHreVkuW38ArfBZYEbAHBupw7yzsXbZzNb3yI9JLi+ocNcrw3QMocEFxZcweSxo5CdnQ1TU1Ppj2lIMNFebm5uuFr6AOl/9wrwLejfb8PPaTz7eqmNMc6utlV685xlNljuNqe8rAAA5wrFPUFmQ3rilJcVltoYS7SNEFhIJBpc26kD13MBuF8rVa5pc3n5qhb7zt+E4Ev1fxsVfPEF9u7ehZcvX6r9WNKCdobDL3Ab+9pjxSqcu5Sv9Gbn4DhP7jaJaZkAgLMpZwAAZhPfRmJaJjxWrJJoGxEVK5FocG2nDg6O8xCyey/bY+IXuA2LvvZo1P64XBuA+7VX5XekLhG7QjFp0iR5CQkA6ikhWm7hF/9BXsoxRC+W/y2KEHXZnVyEwJQyXCm4hs6dO6v1WE+fPsWIkSPxlcdyzP/PQrUeixBV3bpZAotxoxAXFwdLS7kjo6inhGi37zdsRE5pJY5cvMV3KKQVefikGpuOXYHv5h/VnpAAQOfOneHr4wPfdWvw8IFm9AwSIm3tf5fDytpaUUICgApdiZbr3bs31q1bj5W/ZaHk/hO+wyGtwMtXtfhmXwaMR5nCycmp2Y7r5OQEExNjLFnoxstjHEKU2b/3f4gXxsHfz09pO0pKiNZbtHgxTMeawTU0TelwWkKawvrDOThX/BA7w8Kho6PTbMfV0dFBaGgozqWcwfffejXbcQmpT8qZJHi6L0RAQACGDBmitC0lJUTrtW3bFjGxh1HTQR8Ldp7D42eaN/U00Q5Bv19F6OlrOBh7CMOHD2/24w8fPhwHDx5E6LZABP30Y7MfnxBpWRcz8YWLE9zc3LBwYf31TpSUkFZBX18fR4/Fo7iiFh/+dBqlD5/yHRLRIjWvarF8fyY2HM5BUPBWTJ/O3zD06dOnIygoCOtXr8KyJYtRU0NJOOGH8HAs/mVtialT38WWLVs4bUNJCWk13njjDVxIz0Cn3oNg45eIk/m3+Q6JaIGb95/AaWsKDmbexpG4OLi5qXcIKhdubm44cuQIDu7fh49n2eHmjT/5Dom0Is+qqrB5/Vp85jwXgi++wP79+9GG4/pPlJSQVmXAgAE4nZyCGTb2+CQ4Gc7bUnH97mO+wyItUFX1S/gcyYHFhgTcfdUNqefOwdbWlu+wWLa2tkhNTcXdv25jspkpfNauRtVT6iEk6iU8HIt3J4zBtl8CsHXrVvj7+3NOSACap4S0YklJSXBfvAgFBQWwGzMQcycOxpThhhqxqjDRXDm3HuJI5i3sO1+CF7W6+Hb1Gnz11Vdo374936HJVV1djV9++QXr169Hu3bt8YnrZ5g5+yOYjnmL79CIlii/8xeOHTmEPbt3IftSJj7++GP4+fmhX79+Dd2VOyUlpFWrqanB/v37sSNkG1LOnoWujg6G9+8Ow27t0a09JSdE7PnLV3jw9CUKbleg8skzDH19MOZ/5gaBQABDQ81Y5Kw+5eXlCAkJQVhYGK5fvw59AwO8OcIY3Xv0QAcFy8gTosjLly8hqqzAn8XFuFlyA/r6+pg1axa++uormJmZ1b8D+SgpIYRx7949JCYmIisrC7dv38ajR4/4DoloiI4dO6JHjx4wMTHBlClTMGLECL5DapQrV64gOTkZeXl5ePDgAZ49e8Z3SKSF0dXVRffu3WFkZITx48fjnXfeaYreQkpKCCGEEKIRaJp5QgghhGgGSkoIIYQQohEoKSGEEEKIRvh/pmAfWb9kM8gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF_model_3 = DecisionTreeClassifier(random_state=10, max_depth=3)\n",
    "DF_model_3.fit(train_x, train_y)\n",
    "\n",
    "# 决策树可视化\n",
    "## 导入必要库\n",
    "from sklearn.tree import export_graphviz\n",
    "from sklearn.externals.six import StringIO\n",
    "from IPython.display import Image \n",
    "import pydotplus\n",
    "\n",
    "## 输出图片到dot文件\n",
    "export_graphviz(DF_model_3, out_file='tree.dot', \n",
    "                feature_names=train_x.columns,\n",
    "                rounded=True, filled=True,\n",
    "                class_names=['acc', 'unacc'])\n",
    "\n",
    "## 使用dot文件构造图\n",
    "graph= pydotplus.graph_from_dot_file('tree.dot')\n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    978\n",
       "0    404\n",
       "Name: car_acceptability, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "pd.Series(train_y).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"5\"></div>\n",
    "# 5. 朴素贝叶斯\n",
    "\n",
    "## 5.1 Gaussian朴素贝叶斯"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分类正确率： 0.8786\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.73      0.80       114\n",
      "           1       0.88      0.95      0.91       232\n",
      "\n",
      "   micro avg       0.88      0.88      0.88       346\n",
      "   macro avg       0.88      0.84      0.86       346\n",
      "weighted avg       0.88      0.88      0.88       346\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'混淆矩阵热力图')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 载入算法类\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "# 模型构建与拟合\n",
    "GNB= GaussianNB()\n",
    "GNB.fit(train_x, train_y)\n",
    "\n",
    "# 模型预测\n",
    "y_pred = GNB.predict(test_x)\n",
    "\n",
    "# 分类正确率\n",
    "print(\"分类正确率：\",round(GNB.score(test_x, test_y),4))\n",
    "\n",
    "# 分类报告\n",
    "print(classification_report(test_y, y_pred))\n",
    "\n",
    "# 绘制热力图\n",
    "ax = sns.heatmap(confusion_matrix(test_y, y_pred), \n",
    "                 annot=True, fmt='d', \n",
    "                 xticklabels=[\"满意(0)\",\"不满意(1)\"], yticklabels=[\"满意(0)\",\"不满意(1)\"])\n",
    "ax.set_ylabel('真实')\n",
    "ax.set_xlabel('预测')\n",
    "ax.set_title('混淆矩阵热力图')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.29232996 0.70767004]\n",
      "[[2.15346535 2.17574257 2.60148515 2.48762376 2.14851485 2.56683168]\n",
      " [2.63190184 2.64314928 2.4601227  1.77505112 1.91104294 1.74744376]]\n",
      "[[1.11011176 1.07555019 1.18524532 0.24984683 0.62645819 0.24553353]\n",
      " [1.24487184 1.29699295 1.26476972 0.69377428 0.67818071 0.64480431]]\n"
     ]
    }
   ],
   "source": [
    "## 查看先验概率和特征的均值方差\n",
    "### 先验概率\n",
    "print(GNB.class_prior_)\n",
    "\n",
    "### 均值\n",
    "print(GNB.theta_)\n",
    "\n",
    "### 方差\n",
    "print(GNB.sigma_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x16ab51f6278>,\n",
       "  <matplotlib.axis.XTick at 0x16ab51f69e8>,\n",
       "  <matplotlib.axis.XTick at 0x16ab51f1c50>,\n",
       "  <matplotlib.axis.XTick at 0x16ab6241278>,\n",
       "  <matplotlib.axis.XTick at 0x16ab62683c8>,\n",
       "  <matplotlib.axis.XTick at 0x16ab6268898>],\n",
       " <a list of 6 Text xticklabel objects>)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 不同类别下特征均值条形图\n",
    "pd.DataFrame(GNB.theta_.T, columns=['0类', '1类']).plot(kind='bar',rot=360, title='不同类别下特征均值条形图')\n",
    "plt.ylim(0, 3.5)\n",
    "plt.xticks(range(6), train_x.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x16ab629eba8>,\n",
       "  <matplotlib.axis.XTick at 0x16ab629e438>,\n",
       "  <matplotlib.axis.XTick at 0x16ab62a5240>,\n",
       "  <matplotlib.axis.XTick at 0x16ab62d4ef0>,\n",
       "  <matplotlib.axis.XTick at 0x16ab62dc6a0>,\n",
       "  <matplotlib.axis.XTick at 0x16ab62dcb70>],\n",
       " <a list of 6 Text xticklabel objects>)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AOsVybf43KSmJU6dO8fDDD9/wlEtV679RZd2tWV3KmtaqrPL6mp6eTpMmTcq9w1fqBoW5iIgBaM5cRMQAFOYiIgZQKydAU1LKP6FWHZo2dSY9PbtG91mT1L+6y8h9A/XP1kwml3Kf+1OMzB0cjH1iR/2ru4zcN1D/atKfIsxFRIxOYS4iYgAKcxERA1CYi4gYgMJcRMQAFOYiIgagMBcRMQCFuYiIAegrcEXEsMa8tq/iRjdg3fTeNtuWLb6x8/cU5gYwLHJildZb0ft1G1ciImVZtmwxvr6jOH78KL/++itPPz2EadMCeOONpTRo0NAm+9A0i4iIDYWEzOO5555lw4Y1QPFfaerd+wlWrVqBvb0D9vb1iIjYwKhRY3BwcLTZn+TTyFxExEa+/HIfZrOZVavW8+qrczlx4nveemsZTk5O5OXlERwcRNu27XFyasiZM6fZtOldgoJm4O7+Xze9b4W5iIiNfPttHL179wGgW7eHSEz8ieXLV3P06Hds2rSRZ58dT6NGjbhw4TxZWZmMH/98qT/4XVWaZhERsZGcnByaN28BQJMmrly6dInFixdx8uT3zJ0bQqdO95GXl0dAwEsMGeJNVNRWm+1bI3O55ekEr9QVTk7O5OXlAZCTk43ZXMTPP5/l11/P8+23saSlpZGZeYUTJ44DkJeXx+HDB+jRo9dN71thLiKGZctLCSvjnns6cOzYd3Tq9BfOnPmRO+5ow7JlY4HiP/wdGPgCbm4t8fEZwX33/dWm+1aYi4jYyP/7f39j0qTxpKWlcOTIIVat2kBeXh4xMYfZtm0rzz47no4dO/Gf/4Swb99eBg0aTPv2d9lk33+KMNfHdBGpCY0aNSYsbBVffx2Nr+8oCgsLefnlF+natTsLFizCxaX4z77NnDmXr78+wqpVK5g0aQpt2rS96X3/KcJcRKSmNGnShMcff8L6eOnSt8ps17XrQ3Tt+pDN9qurWUREDEBhLiJiAJUK89TUVHx9fct9/vz58/j5+TFq1ChmzZpls9tTRUSkciqcM8/IyGDatGnk5OSU2yYyMpI5c+bg4eHBuHHjOHXqFB06dLBpoSIiN+r5fS/ZdHu2vijClt+cWOHI3N7eniVLltC4ceNy2wQEBODh4QHA5cuXadq0qU2KExExArPZzOuvL+Tq1Sw2bQrniy8+IyXlIoGB/6q5L9q6Xoj/0aeffoqnpydubm7Xbde0qTMODvaV3m5tMZlcaruEaqX+3RrqSp1VZaT+ldWXspalpqbyr3/9i02bNmGxWDCbzfTt+zgffLCJ22+/naZNG/Pee+uYOXMGzZo54+Bw8xcW2uzSxKSkJNauXcuGDRsqbJuenm2r3VarlJTM2i6hWql/tc9kcqkTdVaV0fr3x76U1b8rV64wZ87LZGZmkZKSyU8/JRAa+jpOTk5cvnyZkye/p0OHe7Gzg9dee538/HxeffUNnJ2dK9z/9Q6MNrmaJSMjg3//+9+8+uqr1oviRUT+jOzt6zFvXgjOzo0AaN/eg2XL3mbQoCE0a9aMoUOH4+3ti7v7HbRs2Yq5c1+tVJBX5IZH5ocPHyYhIYGRI0dal61evZoLFy6wYMECAF544QW6det208WJsVT1T3g56a0kdUijRiWnpvPz81m0aD5//euDLFjwOp9++hFms5k5cxZy+PBBduz4ED+/f970fisd5uHh4QD06NGDHj16lHjuxRdf5MUXX7zpYkREjObYse9ITU3jq6++4KuvvuD8+WTs7Oz47LPdAOTm5tK9+0PcfffNXQGo2/lFxLBuhe9X6tKlG126FH+8zMi4zMsvv4STkxOTJ0/lzjvb2mw/CnMRkWqWnX2VAwf289FHHxIQ8BLNmzcnJGQenp738OSTT9nkz8bpdn4RkWqwfPlqAH7+OZEZM4LIysrijTeW4uFxF66utxESsph27TxYsuQ/XL58+ab3p5G5iEg1atOmbZnfnGhnZ0fv3n2sfzP0ZmlkLiJiAApzEREDUJiLiBiAwlxExAAU5iIiBqAwFxExgDp1aaK+20NEpGwamYuIGIDCXETEABTmIiIGoDAXETGAOnUC1Oh0gldEqkojcxERA1CYi4gYgMJcRMQAFOYiIgagMBcRMQCFuYiIASjMRUQMQGEuImIACnMREQOoVJinpqbi6+tb7vMFBQVMmDABHx8ftm3bZrPiRESkcioM84yMDKZNm0ZOTk65bSIiIujYsSNbtmxh165dZGVl2bRIERG5vgq/m8Xe3p4lS5YwadKkcttER0cTFBQEQNeuXfn+++956KGHym3ftKkzDg72VSi3ZplMLrVdQrVS/24NdaXOqlL/akaFYd64ceMKN5KTk4ObmxsArq6upKWlXbd9enp2JcurXSkpmbVdQrVS/2qfyeRSJ+qsKvXP9vsrj01OgDo7O5ObmwtAdnY2ZrPZFpsVEZFKskmYd+zYkbi4OADi4+Nxd3e3xWZFRKSSbvj7zA8fPkxCQgIjR460LvPy8sLf35/Y2FjOnDlD586dbVqkiIhcX6XDPDw8HIAePXrQo0ePEs+5u7uzbt064uLimDJlCvb2t/7JTRERI7HZXxpyc3NjwIABttqciIjcAN0BKiJiAApzEREDUJiLiBiAwlxExAAU5iIiBqAwFxExAIW5iIgBKMxFRAxAYS4iYgAKcxERA1CYi4gYgMJcRMQAFOYiIgagMBcRMQCFuYiIASjMRUQMQGEuImIACnMREQNQmIuIGIDCXETEABTmIiIGoDAXETEAhbmIiAFUKsyDg4Px9vZm5cqVZT6fkZHB+PHjGTJkCK+88opNCxQRkYpVGOa7d+/GbDYTGRlJUlISiYmJpdps376dgQMHEhUVxdWrVzl+/Hh11CoiIuVwqKhBTEwM/fv3B6BXr17ExcXRtm3bEm1uu+02fvzxR65cucKFCxdo1arVdbfZtKkzDg72Va+6hphMLrVdQrVS/24NdaXOqlL/akaFYZ6dnY2bmxsArq6uJCcnl2rz4IMP8uWXX/Luu+/i4eGBq6vrdbeZnp5dxXJrVkpKZm2XUK3Uv9pnMrnUiTqrSv2z/f7KU+E0i7OzM7m5uUBxsJvN5lJtli9fzty5c5k8eTLt27cnKirqJsoVEZEbVWGYd+rUibi4OADi4+Nxd3cv1ebKlSucOnWKoqIijh49ip2dne0rFRGRclUY5n369GH79u2EhISwc+dOPD09CQ0NLdHmueee45VXXqFLly5kZGTw1FNPVVvBIiJSWoVz5o0bNyY8PJyDBw8ybtw4TCYTHTp0KNHmvvvu45NPPqm2IkVE5PoqDHMoPvE5YMCA6q5FRESqSHeAiogYgMJcRMQAFOYiIgagMBcRMQCFuYiIASjMRUQMQGEuImIACnMREQNQmIuIGIDCXETEACp1O7+IVI9hkROrtN6K3q/buBKp6zQyFxExAIW5iIgBaJpFRKqNppFqjkbmIiIGoDAXETEAhbmIiAEozEVEDEAnQEVEquhWOsGrkbmIiAEozEVEDEDTLCJSoTGv7avSek7dbFyIlEsjcxERA6hUmAcHB+Pt7c3KlSuv227OnDns21e1I7iIiFRdhWG+e/duzGYzkZGRJCUlkZiYWGa72NhYUlNT6d27t61rFBGRClQ4Zx4TE0P//v0B6NWrF3FxcbRt27ZEm4KCAmbOnMmjjz7K3r176dOnz3W32bSpMw4O9lWvuoaYTC61XUK1Uv/qLiP3DdS/qqgwzLOzs3FzcwPA1dWV5OTkUm0+/PBD7rrrLsaNG0dERAQXLlzAz8+v3G2mp2ffRMk1JyUls7ZLqFbqX91l5L6B+lee6x0EKpxmcXZ2Jjc3FygOdrPZXKrNDz/8wLBhwzCZTAwaNIjo6OgqFSoiIlVTYZh36tSJuLg4AOLj43F3dy/V5s477yQpKQmA48eP07p1axuXKSIi11NhmPfp04ft27cTEhLCzp078fT0JDQ0tESbZ555hujoaEaMGMHmzZsZO3ZstRUsIiKlVThn3rhxY8LDwzl48CDjxo3DZDLRoUOHUm2WLVtWbUWKiMj1VeoOUFdXVwYMGFDdtYiISBXpDlAREQNQmIuIGIDCXETEABTmIiIGoDAXETEAhbmIiAEozEVEDEBhLiJiAApzEREDUJiLiBiAwlxExAAU5iIiBqAwFxExAIW5iIgBKMxFRAxAYS4iYgAKcxERA1CYi4gYgMJcRMQAFOYiIgagMBcRMQCFuYiIAVQqzIODg/H29mblypXXbZeamsrgwYNtUpiIiFRehWG+e/duzGYzkZGRJCUlkZiYWG7bRYsWkZuba8v6RESkEioM85iYGPr37w9Ar169iIuLK7Pd4cOHcXJywmQy2bZCERGpkENFDbKzs3FzcwPA1dWV5OTkUm3y8/NZuXIlK1as4Pnnn69wp02bOuPgYF+FcmuWyeRS2yVUK/Wv7jJy30D9q4oKw9zZ2dk6dZKdnY3ZbC7VZvXq1fj6+tKkSZNK7TQ9PfsGy6wdKSmZtV1CtVL/6i4j9w3Uv/Jc7yBQ4TRLp06drFMr8fHxuLu7l2pz+PBhNm3ahJ+fHz/88AMvv/xylQoVEZGqqXBk3qdPH3x9fbl48SL79+8nNDSU0NBQAgICrG3ee+896//9/PxYuHBh9VQrIiJlqjDMGzduTHh4OAcPHmTcuHGYTCY6dOhQbvvw8HCbFigiIhWrMMyh+MTngAEDqrsWERGpIt0BKiJiAApzEREDUJiLiBiAwlxExAAU5iIiBqAwFxExAIW5iIgBKMxFRAxAYS4iYgAKcxERA1CYi4gYgMJcRMQAFOYiIgagMBcRMQCFuYiIASjMRUQMQGEuImIACnMREQOo1J+NE5GKjXlt3w2v49StGgqRPyWFuYj86VXlQAy31sFY0ywiIgagMBcRMQCFuYiIAVRqzjw4OJiEhAQeffRRJk2aVOr5zMxMAgICMJvNODk5ERoaSv369W1erIiIlK3Ckfnu3bsxm81ERkaSlJREYmJiqTY7duzg2WefZd26dTRv3pyvvvqqOmoVEZFyVDgyj4mJoX///gD06tWLuLg42rZtW6LNiBEjrP9PT0/n9ttvt22VIiJyXRWGeXZ2Nm5ubgC4urqSnJxcbttvv/2WjIwM/vrXv153m02bOuPgYH+DpdY8k8mltkuoVupf3WXkvoH6VxUVhrmzszO5ublAcbCbzeYy212+fJn58+cTFhZW4U7T07NvsMzakZKSWdslVCv1r+4yct9A/SvP9Q4CFc6Zd+rUibi4OADi4+Nxd3cv1SY/P58pU6YQGBhY5vMiIlK9KgzzPn36sH37dkJCQti5cyeenp6EhoaWaLNt2zZOnjzJ22+/jZ+fH59++mm1FSwiIqVVOM3SuHFjwsPDOXjwIOPGjcNkMtGhQ4cSbXx9ffH19a22IkVE5PoqdZ25q6srAwYMqO5aRESkinQHqIiIASjMRUQMQGEuImIACnMREQNQmIuIGIDCXETEABTmIiIGoDAXETEAhbmIiAEozEVEDEBhLiJiAApzEREDUJiLiBiAwlxExAAU5iIiBqAwFxExAIW5iIgBKMxFRAxAYS4iYgAKcxERA1CYi4gYgMJcRMQAFOYiIgZQqTAPDg7G29ublStX3lQbERGpHhWG+e7duzGbzURGRpKUlERiYmKV2oiISPWpMMxjYmLo378/AL169SIuLq5KbUREpPo4VNQgOzsbNzc3AFxdXUlOTq5Sm98zmVyqUisfLX66SutBVderWepfeYzcPyP3DdS/mlPhyNzZ2Znc3FygOLTNZnOV2oiISPWpMMw7depknTaJj4/H3d29Sm1ERKT62FksFsv1GmRlZeHr60uPHj3Yv38/oaGh7Ny5k4CAgHLbbN26FReXqk2liIjIjaswzAEyMjI4ePAgXbt2xWQyVbmNiIhUj0qFuYiI3Np0B6iIiAHUqTAPCwsjOjq6yusvXLjQhtXcWlJSUli9enWF7aKjozl37lwNVHRjbva1FalpmZmZjBo1Cj8/P/bs2VNuux9++IEffvih2uupU2F+s15++eXaLqHamEwm/P39K2wXExNT4X0AIr/n5+dnk+3c7AH73Llzt9QBPz4+nvvvv5/w8HCeeOKJctvVVJhXeNPQrWb9+vXjwu8HAAAIPUlEQVSEhYVhMpno2bMn9vb2DBkyhOjoaGJiYmjdujVpaWn4+/sTFRVFamqqNeT8/PwIDw8HICoqivj4eOLj40lNTWXJkiV4enoSFBREUlISbm5utG7dmhkzZtRaX4cMGUKzZs1wdHQkNTWVIUOG0Lt3b6ZOnYqdnR1du3a1XlV07tw5li9fzmuvvQbA9OnT+a//+i8OHTqE2Wxmw4YNzJ07l+joaPbu3ctdd93F4sWLa61vUHzSfMqUKRQVFQHQrVs38vPzmT59OhcvXsTNzY2QkBCAUsvq16+Pn58fnTp14vTp06xdu5bc3FymTJlCVlYWt912G0uXLsXBofrf4mFhYRw9epTc3FyaNWvGm2++ib29PbNmzSIxMZFmzZoRGhqKvb09fn5+PP7443zwwQd89NFHWCwWXnrpJc6fP4+DgwPLly/HxcWF+fPn88MPP9CkSRMWLVrEZ599Vub7tax1jSo5OZmYmBi6d+9ebfso6z2Ul5fHv/71L3JycmjTpg0hISFs3LiRqKgoMjMz+eabb1i6dClOTk5MmzaNtLQ07r77bmbPns3ixYuto/bt27ezceNGli1bhoeHB0899RRhYWG0b9+ep5566qZrr3Mj806dOhEREYGLiwtXr14t9Xy/fv3Yu3cvALt27WLQoEHlbuv48eOsXbsWf39/9u3bR0ZGBpcuXWLr1q2cO3euVoMcICcnh6VLl3Lq1CkWL17MsWPH+O233wgMDOSdd95h3759110/OzubTZs20a5dO06ePElISAheXl4EBwfXepADREZG8re//Y3w8HBr6G7duhVPT08iIiJo27YtH3zwQZnLAL777jvuv/9+1q5dC8CZM2ews7PjvffeY8iQIWRnZ9dYX7p06UJERAS33347n332GZ999hmFhYVERETQqlUrvvjiCwAuXrwIwEcffQTA5cuXOXXqFBEREUyaNInMzEw+//xz8vLy2LRpE3379uWdd94BSr9fy1q3uv1+dB0VFUVUVBS5ubmMGzeOoUOHEhgYyNtvv13u+uvXr2fkyJEEBARYD+Lz58/H19eXCRMmkJGRUeayjRs38uqrr/I///M/+Pn5cenSpWrpX1nvoZSUFPz8/NiwYQPnzp0jNTWV0aNHExwcjJeXF+Hh4TRr1ozIyEg8PT157733SElJIT4+nsDAQPz9/fH392fjxo0ADB482Pr6HzhwgMcff9wmtde5MO/cuTMA9957b4k7Ta/dgdq4cWPuuOMOYmJiMJvNtGzZstxt/f3vf8fR0ZFWrVpRUFCAk5MT+fn5DB06lIEDB1ZvRyqhefPmNGrUCHd3d+rVq4fFYsHBwYF33nmHmTNnlnkw+z0vLy8AWrduTUFBQU2UfEPOnTtHhw4dgOKDNBT/Ml17jTt37kxCQkKZywA8PT3p27evdXsdO3bk7rvvZsyYMRw4cICGDRvWWF86duwIwD333ENycjI//fQT3377LX5+fnz99dekpaUB4OLiwqhRo6zrNW3alCFDhjBu3DiioqJo1KhRuf394/u1rHVrw08//UTLli3ZtGkTP//8MxMmTCi37e8HY59//nmZB66ylpUVntWhrPeQg4MD77//PkFBQWRkZFiz5o/Onj3Lnj178PPzIykpid9++63MdnfeeSdXr14lOjoaT09Pm71P61yYnzhxAoBTp07RvHlz6xH6q6++srYZPHgwM2bMqPCji5OTU4nHx44d44knnuD9999nzJgxNq7cNtavX4+/vz8LFizAzs7uum3/2D+ABg0aWN+MtX1VauvWrTlz5gyAdU7R09OT7777DoCjR4/i6elZ5jKgVHjFx8fzwAMPsG7dOq5cuVKjX/h27NgxAE6ePMmdd95p/egcHh5OcHAwHh4eQPFrUq/e//3aXbhwgdtuu421a9fi5ubG7t27y+3vH1/PstatSdfeR25ubpw4cYKRI0eWOFCV5feDsV9++eWGDt41oaz30LZt2+jXrx+LFy/G2dm53HXbtWvH6NGjCQ8PZ+rUqbRu3RqAhg0bkpOTA/zf79xTTz1FcHAwgwcPtlntdS7MY2NjGTlyJGlpaXTr1o3PP/+cuXPnWj+yAfTs2ZPCwsISo7bKaN++PevXr8fPz48XXniB2NhYW5d/0x577DHmzJnDxIkTcXJyKvfoX55+/fqxevVqhg0bxi+//FJNVVbOsGHD2LVrF35+fmRlZQEwdOhQzpw5w4gRI0hMTMTLy6vMZWVxd3cnPDwcHx8fUlJSrKP9mnD8+HH8/PzIzMzkscceo3fv3ly8eJGRI0eydOnScr/ionnz5uzbtw8fHx+OHDlCz549+dvf/kbDhg0ZPnw4u3fvZuzYsZVet7o5OjqWGkB99dVXTJo0icjIyOtOa0LJwZi7u/sNHbzLCkVbK+s99PDDD7N69WpGjx4NUO7v3LBhw9i/fz8jRoxgy5YttGrVCijOoz179uDj42PNlH79+mFnZ8eDDz5os9oNd9PQ5cuXmTBhAg8//DAvvPDCDa37xRdfsGbNGhwdHWnQoAEDBw60yYkJMbawsDC6detWrSfmatPvLxw4ffo0c+fOxcPDg4KCArp27UrHjh0ZP348bdu25fbbb2fixIncfffdpbZT1oliBwcH5s+fz8mTJ3F1dWXRokW4urqWuSw/P58pU6aQkZHBiBEj6uzv5o8//siMGTPw9vZm6NChNtuu4cJcRGrW1q1b+fjjj3F0dMTBwYExY8YY9sB2K1OYi4jN/fHa9MaNG/PWW2/VUjV/DgpzEREDqHMnQEVEpDSFuYiIASjMRUQMQGEuImIACnMREQP4//v18VPvFqqRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 不同类别下特征方差条形图\n",
    "pd.DataFrame(GNB.sigma_.T, columns=['0类', '1类']).plot(kind='bar',rot=360, title='不同类别下特征方差条形图')\n",
    "#plt.ylim(0, 2)\n",
    "plt.xticks(range(6), train_x.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 多项式朴素贝叶斯"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分类正确率： 0.7023\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.11      0.19       114\n",
      "           1       0.69      1.00      0.82       232\n",
      "\n",
      "   micro avg       0.70      0.70      0.70       346\n",
      "   macro avg       0.81      0.55      0.50       346\n",
      "weighted avg       0.77      0.70      0.61       346\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'混淆矩阵热力图')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "# 模型构建与拟合\n",
    "MNB = MultinomialNB()\n",
    "MNB.fit(train_x, train_y)\n",
    "\n",
    "# 模型预测\n",
    "y_pred = MNB.predict(test_x)\n",
    "\n",
    "# 分类正确率\n",
    "print(\"分类正确率：\",round(MNB.score(test_x, test_y),4))\n",
    "\n",
    "# 分类报告\n",
    "print(classification_report(test_y, y_pred))\n",
    "\n",
    "# 绘制热力图\n",
    "ax = sns.heatmap(confusion_matrix(test_y, y_pred), \n",
    "                 annot=True, fmt='d', \n",
    "                 xticklabels=[\"满意(0)\",\"不满意(1)\"], yticklabels=[\"满意(0)\",\"不满意(1)\"])\n",
    "ax.set_ylabel('真实')\n",
    "ax.set_xlabel('预测')\n",
    "ax.set_title('混淆矩阵热力图')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3 Bernoulli朴素贝叶斯"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分类正确率： 0.763\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.46      0.56       114\n",
      "           1       0.77      0.91      0.84       232\n",
      "\n",
      "   micro avg       0.76      0.76      0.76       346\n",
      "   macro avg       0.75      0.68      0.70       346\n",
      "weighted avg       0.76      0.76      0.75       346\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'混淆矩阵热力图')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import BernoulliNB\n",
    "\n",
    "# 模型构建与拟合\n",
    "BNB = BernoulliNB(binarize=2.0)\n",
    "BNB.fit(train_x, train_y)\n",
    "\n",
    "# 模型预测\n",
    "y_pred = BNB.predict(test_x)\n",
    "\n",
    "# 分类正确率\n",
    "print(\"分类正确率：\",round(BNB.score(test_x, test_y),4))\n",
    "\n",
    "# 分类报告\n",
    "print(classification_report(test_y, y_pred))\n",
    "\n",
    "# 绘制热力图\n",
    "ax = sns.heatmap(confusion_matrix(test_y, y_pred), \n",
    "                 annot=True, fmt='d', \n",
    "                 xticklabels=[\"满意(0)\",\"不满意(1)\"], yticklabels=[\"满意(0)\",\"不满意(1)\"])\n",
    "ax.set_ylabel('真实')\n",
    "ax.set_xlabel('预测')\n",
    "ax.set_title('混淆矩阵热力图')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 870.,  879., 1051., 1005.,  868., 1037.],\n",
       "       [2574., 2585., 2406., 1736., 1869., 1709.]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MNB.feature_count_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"6\"></div>\n",
    "# 6. 支持向量机\n",
    "\n",
    "## 6.1 LinearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分类正确率： 0.8584\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.74      0.77       114\n",
      "           1       0.88      0.92      0.90       232\n",
      "\n",
      "   micro avg       0.86      0.86      0.86       346\n",
      "   macro avg       0.85      0.83      0.84       346\n",
      "weighted avg       0.86      0.86      0.86       346\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'混淆矩阵热力图')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 载入算法类\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "# 模型构建与拟合\n",
    "lsvm = LinearSVC(random_state=10)\n",
    "lsvm.fit(train_x, train_y)\n",
    "\n",
    "# 模型预测\n",
    "y_pred = lsvm.predict(test_x)\n",
    "\n",
    "# 分类正确率\n",
    "print(\"分类正确率：\",round(lsvm.score(test_x, test_y),4))\n",
    "\n",
    "# 分类报告\n",
    "print(classification_report(test_y, y_pred))\n",
    "\n",
    "# 绘制热力图\n",
    "ax = sns.heatmap(confusion_matrix(test_y, y_pred), \n",
    "                 annot=True, fmt='d', \n",
    "                 xticklabels=[\"满意(0)\",\"不满意(1)\"], yticklabels=[\"满意(0)\",\"不满意(1)\"])\n",
    "ax.set_ylabel('真实')\n",
    "ax.set_xlabel('预测')\n",
    "ax.set_title('混淆矩阵热力图')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'不同惩罚系数下模型分类正确率')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GjNZ94wCEhITg448/9iwNS0pKgs1mw7Zt2wAAa9euRXZ2NpRKJQoKCnD//fejX79+UCgUmDt3LuLi4jBgwADcfffdUKvVEAQBVVVVsNvtDSbbvf3224iNjW1w7s2bN8PtdmP48OGer5WXlyMyMrJBu6+++srz5/pAHzduHLp164bZs2e3uq/NWb9+PXbt2gWj0Yh9+/bhpptuwvXXX4+qqipMmjTJE+zfffcd/ud//qfBh5Pi4mKo1Wp88803nq/VX9mPHTvWK/VRx8Zgpw7p3FBvaiOZevX3h2NiYprcWOTsjVksFgtWrlyJzMxM/PTTT5AkCQaDAdnZ2QgNDcWrr76K+++/H6+++iquu+46XHnllQDqPiAsW7YMISEhuOuuuzBz5kzPMR0Oh+fDQ21tLRYuXAiHw4ElS5ZAEATY7XbP6+np6Vi0aBEmTJiANWvWQK/Xo3fv3hg7dizeffddPPnkk8jLy8OmTZvwj3/8w7Pe/rXXXvPUcr7+16sPdaDuw87Zk+7uu+8+3HbbbXjnnXcwZcoUuN1ubNq0Cc888wwiIiIAAF26dGkQvtnZ2Th06FCzobt582YsXLjQs9a7fra7Vqttcs272+3Gli1b8NZbb2HevHm46aabsHjxYkyaNAkzZ87EFVdc4fm+n9u/c9VvKHT2OvexY8di6tSpWLJkCTZt2oS0tDTodDrP91un0wEARo8e3WgiZv0V+/3339/seYkuFIOdCHVXiefbDa66uvq87/vhhx8wZ84cz31Xt9uN559/Ht26dUP//v3xxhtvYNOmTRAEAaIowmq1Yvr06Vi9erXnl7/b7UZOTg7Gjx+PJ598ssGHjgcffBBHjhzB3Xff7fnaFVdcgXvuuQeCICA9PR3Lli3z3HNWqVR47rnn8PPPP0Ov16O2thaPPvooBg8ejPfeew8mkwnZ2dlYtWoV3n77bfTt2xeXXnopli5diqVLlzaafd/S96aey+WCy+VCamoqjhw5gjlz5njuq6tUKiQmJmLBggUwGo2YN28eQkNDPe+1WCxwOp3YunWr52t2ux1TpkzB+PHj8fLLL+PHH3/EP/7xD8TFxQEAVq9ejezsbPz9739vUIcoipg/fz527tyJESNGID09HX379gVQt1HMDz/8gJdffhlmsxmffvopDh06hGeffdbzwejLL7/0HOvs5YhOpxPr169HSEgIoqOjccMNNyA8PBxjx45FeHg4+vfvD7PZjNtuuw0bNmyA2Ww+77yF+u8Vka8IUksfV4novJpa934hysrKmlzHXV1d3WD52rksFguUSqXnQ4K/2e32Blf09RwOh2fova1qamqg1Wo9kwlbcuDAAURHRze5I129kpISREdHt7kWomDAYCciIpIRrmMnIiKSEQY7ERGRjMhi8lxFxfknN12IiAg9KiutXj2mv8ilL3LpB8C+BCq59EUu/QDYl+aYTOefe8Mr9iaoVE0/njMYyaUvcukHwL4EKrn0RS79ANiXC8VgJyIikhEGOxERkYww2ImIiGSEwU5ERCQjPg32lJQUJCYmNvn4QwCoqqrClClTkJCQgNTU1AavvfDCC/j22299WR4REZHs+CzYN23aBFEUsWbNGhQVFaGwsLBRm+zsbIwbNw5ZWVmwWCzYs2cPAGDXrl04fvw4Ro0a5avyiIiIZMln69h37NiBMWPGAABGjhyJnJwc9OnTp0Gb8PBw5Ofnw2w2e/bKdjqdePbZZ3HjjTdi8+bNjZ6M1JSICL3XlxI0t0Yw2MilL3LpB8C+BCq59EUu/QDYlwvhs2C3Wq2e5yQbjUaUlJQ0ajNixAhs2bIFK1euRExMDIxGI7744gv0798fkydPxscff4yysjIkJyc3ey5vb2BgMoV5fdMbf5FLX+TSD4B9CVRy6Ytc+gGwLy0d73x8NhSv1+ths9kA1IV8U8+6Tk9Px8KFCzFr1iz069cPWVlZ2LdvH+69916YTCbcfvvt2L59u69KJCIikh2fBXtcXBxycnIAALm5uU0+ItFsNiMvLw9utxu7d++GIAjo1asXioqKAAB79uxB9+7dfVUiERGR7PhsKH706NFISkpCeXk5vv/+e6SlpSEtLQ2zZ8/2tJk6dSrmz5+P0tJSDB8+HGPHjoUkSUhJScGGDRvgcrnw7rvv+qpEIiIi2fHp89irqqqwbds2XHXVVTCZTL46jdfvwfC+TuDp2tmAimPB3w8A6Goy4HhFjb/L8ArZ9EUAukV1ksX/K3L5fx5gX1o63vn49OluRqMR8fHxvjwFyZSzxAHr9hpYttfAur0Gf+yrBXz2EZQ6PBVgfb4PQqd28XclRBdNFo9tpeAmuSXYc2th3V4D644aWHdY4Cx2NGwkQDb/WgUIkGTyKUU2fXEBhc8VonesCoZRRn9XQ3RRZPKrkoKJWCui9hdLXYhvr4F1pwWi2d2gjcKohP6qUOivMUB/jQE9R0fiZI08nsvM4cXAU7GkDOWvlKLk8ULEfDcEqq5qf5dEdMEY7ORzruNOWHda6kJ8ew1sv1khORte5al7aqC/2uAJcu1AHQSF4HldGaIEZHArlwJT11mXwP6DBVVbqlA6+zB6royBIAgtv5EoADHYyaskSYKjwH7manx7DRwH7A0bCYAuLuRMkF9tgDpa45+CiQAISgGDMwdjx9CdqP6mCpUrjqPzw76b8EvkSwx2uiiSU4Ltdyss22tQezrMXRWuBm2EEAEhV9QNq4debUDIlQYoO3l3C2Cii6XrqUP3t3qjePIhHH2+CKHXG6AdEOLvsojajMFObeKucaN2l+XMRLccCyRrw10FlV1VDa7GdUNDoNDwCcEU+Iy3R6BmQhec+vQEiqcWoO/Xg6DQ8t8uBRcGOzXLedThGVK3bq+B7Y9a4JzdgTUx2gb3xzX9tLw/SUHrkld7wvpT3b/18ldLccnCHv4uiahNGOzkIYkS7PttZ67Gt9fAeeScZWcqIGS4/kyQX2WAqhtnEJN8KA1KRP+jLwr+losT/zgGw82dYLipk7/LImo1BnsHJtpE2HbX3R+37qi7R+4+dc6yM4MCIVfWhXjoNQaEXK6HIpT3x0ne9CNC0W1ud5QvLkXJY6eXwHXhr0sKDvyX2oG4Kl2o3XlmWL32VyskR8NlZ6pL1ND/6az740NCICg5rE4dT9cnLkHN/zPDur0GpbML0fMjLoGj4MBgP4doF2HZZ4H9ZK2/S7lokgs4esSCY5uPw7qjBvZcW6M22sG6hsvOemr4y4sIdUvgojP64ODN+1D9dRUqM4+j8wNcAtdeRIcIV42r5YbUCIP9HAXj8mD7VR47nJ1L0AoIGX5mN7eQK0OhiuA/AaLz0fTUovsbvVA8tQBHnytC6LVh0Mbq/F2W7LlPuVBwx37kH3Wi/844Lo9tI/5WP4fhxk5Q2ACXW2y5cRAIGxQK1XAd9NcYoLtMz6U7RG1kvLMzqjdXoWrtSRRPO4S+Gwdx+aYPiQ4RRZMOwb6vbtS09lcLDDdw8mJbMNjPEbkgGqZ3Bsli/2tAPnt5E/lT1OJedcs999SifHEpLknlEjhfkCQJZU8fgWXrmd9Z9n21DPY24sdOIqIWKMOU6PGPvoASOLH0GGq2mv1dkiwdf+8YTq06AUEnIHxC3SN0bXuDf75Te2OwExG1gv4qA0xzogAJKJlVCNdJTuzypqovK1H+cgkAoMfSvghPYrBfKAY7EVErmZ6Mgv7qULjKnCidcxiSJINn0QcAa44FJbMKAACRz0Wj07gI6AbX7dNvz6uF5Ob3uS0Y7ERErSSoBERn9IUiTIHq9adwatUJf5cU9BxH7DiSfACSTUL4/V3RZVYkAEBpVEHbSwvJVvfESGo9BjsRURtoemkR9VovAEDZgiLYDzbeH4Jax21248jEA3AfdyH0hjB0f61Xg300QoeGAuBwfFsx2ImI2ij87i4w3tUZklVE8bQCiA55LI9tT5JTQtGkg7Dn2aAdqEPPD/tBUDfcHMswzAAAsDPY24TBTkR0AaJe6wV1Lw1su62oeL3M3+UEFUmSUPbMEVi2VEPZVYVeH/eH0th49fWZK3Z5bhrmKwx2IqILoOykRI+lfQEFcPy9o7Bs434RrXUi4xgqM49D0AnotTIGmt7aJtvVX7Hb9vGKvS0Y7EREF0h/jQGm2XVL4IpnFsBVySVwLTGvr8SxF+uWtUWn94X+SsN524YMCIGgEeA87IC7xn3edtQQg52I6CKY5kQhZEQoXKVOlD3FJXDNqf3FguIZBYAEdFvQHcbbI5ptr1ArPHvz23nV3moMdiKiiyCoBPT4R18oDAqY/30Kpz7lErimOIoddcvaaiWEJ3VB18cvadX7tEPq1rNzOL71GOxERBdJ00eLqMV1S+COzi+C/RCXwJ3NXe3GkYn5cJW7EDoyDFGv92r146E9G9VwZnyrMdiJiLzAeE9ndLozAqJVRMn0AkhODskDgOSSUDzlEOz7bNDE6tBzWb82PR1PV3/FzmBvNQY7EZEXCIKA7q/3grqHBrW/WFH+Zqm/S/I7SZJQllKEmm/NUHZRofcn/aEMb9tDRbWX6gHUDcVz/kLrMNiJiLxEaVQhOqNP3RK4d47C8mPHXgJ34oNyVK6ogKAV0OujGGj6NL2srTmqbiooOyshVrnhKnX6oEr5YbATEXlR6J/C0PWJS+qeAjejAO6qjrkEzrzxFI49XwwAiH63D/RXn39ZW3MEQYBuyJmrdmoZg52IyMu6PdUdIVfo4SxxonTukQ43hFy724Li6aeXtT3THcY7O1/U8bS8z94mDHYiIi8T1KefAheqgPmLSlR9dtLfJbUbZ4kDR+4/CMkqIjyxC7rObt2ytuacmRnPrWVbw6fBnpKSgsTERGRkZDT5elVVFaZMmYKEhASkpqYCAFwuF2666SYkJycjOTkZeXl5viyRiMgntP10uGRRTwBA2TNHOsSjR901bhyeeACuY07orzMg6q3WL2trjo5r2dvEZ8G+adMmiKKINWvWoKioCIWFhY3aZGdnY9y4ccjKyoLFYsGePXuQl5eHsWPHIjMzE5mZmRg4cKCvSiQi8qnwxC7odHsERIuI4hnyXgInuSQUP3oI9r210MRo0XN5TJuWtTVHOzAEEAB7vo1P0muFtq07aIMdO3ZgzJgxAICRI0ciJycHffr0adAmPDwc+fn5MJvNKCsrQ1RUFL755ht899132L59OwYMGIAXX3wRKlXzZUZE6KFSKb1av8kU5tXj+ZNc+iKXfgDsS6DyRV8iVgzBrmG7UJtjgeX9E+j7Yl+vn+Nc/viZ5D+Wj5rNZqi6qDD868ug76/3ynHr+1LYPwS1+bUIPaH0PBwm2LTXz8VnwW61WhEZGQkAMBqNKCkpadRmxIgR2LJlC1auXImYmBgYjUYMHToUy5cvR7du3fD0009jy5Yt+Mtf/tLsuSorvXvfxWQKQ0WFPJapyKUvcukHwL4EKl/2JSq9Nwrv3I/DrxyGcJUOoX/yXTD542dy4n/KcTS9BIJGQI/lMbAY3bB4oYaz+6IeqEVtfi3KfjiO8KjgG/nw9s+luQ8JPhuK1+v1sNnqtlW0Wq0QxcbDJ+np6Vi4cCFmzZqFfv36ISsrC4MGDUK3bt0AAHFxcTh8+LCvSiQiaheh14XV7Y0uAiUz5bUErnrTKRx9rggA0P2d3j770KIdzJnxreWzYI+Li0NOTg4AIDc3F9HR0Y3amM1m5OXlwe12Y/fu3RAEAXPnzkVubi7cbjc2b96MQYMG+apEIqJ2Y5obBd1wPZxFDpTNK/J3OV5Ru8eK4kcLALGuf+F3d/HZueon0PEpby3zWbCPHj0a2dnZWLRoETZu3IjY2FikpaU1aDN16lSkpqbiyiuvRFVVFcaOHYuZM2di7ty5GD9+PC6//HJcd911viqRiKjdKDQK9Hi/LwS9AlVZJ3FqbXA/Bc5Z5sCRiQcgWkUY7+4M01NRPj2fZ5MaXrG3SJB8uHNCVVUVtm3bhquuugomk8lXp/H6/STeNww8cukHwL4EqvbqS+Unx1E6+zAUBgVi/t8QaHq3fZvV5rRHP9w1bhTekQfbnlro/2RA77WxUGi9f514dl8kUUJuv18hWkUMzL0Mqs4+myLmE7K4xw7UTZqLj4/3aagTEQWT8KQgdgvAAAAgAElEQVQu6PS3cIg1IoqnF0ByBddEMMktoWR6AWx7aqHpq0XPFTE+CfVzCQrBc5+dw/HN485zRETtSBAERL3VG6ooNWp3WVCRVubvktrk6PPFqP6mCsoIJXqt6t+uV86cQNc6DHYionamilChx9K+gABUvFUG644af5fUKic+LMfJf5ZDUAvouSIG2hhdu56fO9C1DoOdiMgPQkeGocvMSEAEimcUwF3t9ndJzareXIWjC04va3u7N0Kvbf9NcDwz47lnfLMY7EREftLtme7QDdPDecSBsnlH/F3Oedl+t6J4yqG6ZW1/j0J4ou+WtTXHMxSfa4MkBtfchPbEYCci8pMGS+A+P4lT6wLvKXDOow4cvv8ARIsIY0IETPN8u6ytOaoIFVRRakhWEY5C+T9U50Ix2ImI/EjbX4dLXuoBACh7+jAcRwInsESLG0fuPwhXqRP6q0PR/Z0+Xnla28U4MxzP++znw2AnIvKziPu7Iiw+HGK1iJIZgbEETnJLKJ5eANtvVqh7a+qWten8HxmcGd8y//+UiIg6OEEQ0P3t3lBdooZ1hwUVS476uyQcW1iM6q+roDAq0XtVLFRd1f4uCQBnxrcGg52IKACoOqsQ/V4fAEDFm6Ww7vLfEriTKypw4v1yQAX0Wh4DbWz7Lmtrjm4wh+JbwmAnIgoQhhs7ocuMSMANFE/3zxK4mm+rUDa/boZ+97d7I3Rk+y9ra44mVgeoAEehHaIlsJcI+guDnYgogHSb3x26uBA4DztwNKV9nwJn21uLosmHADfQ9clLEDGha7uevzUUGgW0sSGABNjybP4uJyAx2ImIAohCe3oJXIiAU2tOoOqL9lkC5zzmxJH7D0CsEdFpfAS6PdO9Xc57ITgc3zwGOxFRgNEOCMElC3sCAEqfOgJHscOn5xOtIooeOABnsQMhI0IRvaQPBIV/l7U1R8sJdM1isBMRBaCIB7si7K9GiGZ33RI4t2+WwEmihOKZBaj9xQp1Lw16rYyBIiSwo0F3af2SN24t25TA/ukREXVQniVw3VSw/lSD4+/5ZgncsZdKUL3+FBSdlOi9qj9UpsBY1tacs4fiJcn/a/4DDYOdiChAqbqqEf1eXwBA+eulsP5s8erxT2ZW4MTSY4AK6LmsH7QDQrx6fF9RRamhDFfCXemG65jT3+UEHAY7EVEAM9zcCV2mdgNcQMm0ArhrvLPEq+Y7M8qePr2s7Y3eMNzQySvHbQ+CIHAHumYw2ImIAly3BdHQDgmBo9DueXTqxbDl1qJo0sG6ZW2PRSJiYuAta2sJ94w/PwY7EVGAU+hOL4HTCTi1+gSqvqy84GO5yp04MvEAxGoRncaFo9uCaC9W2n48M+MZ7I0w2ImIgoBuUAgin697ClzpnMNwlrR9CZxYK+LIgwfhLHIg5Ao9otP7BvSytuboOBR/Xgx2IqIg0fkREwy3GCFWuVE8s21L4CRRQsmsAtTmWKDuqUGvlf0Dfllbc7SD6oLdkW+D5OTM+LMF70+ViKiDEQQB0e/0hsqkgvW/NTi+9Fir31v+ainM/z4FRZgCvT7pD1W3wF/W1hylQQl1bw0kpwT7AW4tezYGOxFREFGZ1Oh++ilw5YtLUPtry0vgKlcdx/F3jwJKoOeHMdANCo5lbS3RDdEDAOzcga4BBjsRUZAJG2VE5yl1S+CKW1gCV/O9GaVPHQYARL3WC4abgmdZW0t0nEDXJAY7EVEQinwuGtrBOjgO2XE0tbjJNvb9tSh65BDgArrMiETnB0ztXKVvnZkZz61lz8ZgJyIKQnVL4PpB0Ao49fFxmP/dcAmcq8KJw0kHIJrdCIsPR2RqcC5ra45nLTuH4htgsBMRBSnd4HOWwJXWLYETbaeXtR1xQDdcjx4ZwbusrTmaPloIIQKcJU64T7n8XU7AYLATEQWxzpNMMPylE9yn3Ch5rBCSW0LJ44Wo3WWBOlqNXpn9odDL81e9oBSgHchHuJ5Lnj9tIqIOQhAERC/pA2VXFSxbq/HztT/D/EUlFAYFen0SC3VkcC9rawkn0DXGYCciCnKqbmpEL+kDAKjeWQ0ogR7/6ucJPTk7+xGuVIfBTkQkA2G3GNFlViQEtYCoxb0QNsro75LahWdmPIfiPVT+LoCIiLzjktQeGLJ4AE5aO87yL88V+75aSKIky0mCbeXTK/aUlBQkJiYiIyOjyderqqowZcoUJCQkIDU1tcFrx48fx/jx431ZHhGR7ChDlf4uoV2puqqh6qaCaBHhPNL2B+PIkc+CfdOmTRBFEWvWrEFRUREKCwsbtcnOzsa4ceOQlZUFi8WCPXv2eF577bXXYLNx/18iImqe9vTWshyOr+OzofgdO3ZgzJgxAICRI0ciJycHffr0adAmPDwc+fn5MJvNKCsrQ1RUFADgxx9/REhICEym1u2SFBGhh0rl3U+pJlOYV4/nT3Lpi1z6AbAvgUoufZFLP4DW9cV8pRGW78xQHnYHdN/bqzafBbvVakVkZCQAwGg0oqSkpFGbESNGYMuWLVi5ciViYmJgNBrhcDiQkZGBpUuXYubMma06V2Wld+8nmUxhqKio9uox/UUufZFLPwD2JVDJpS9y6QfQ+r6Ivesu7E7uPIXQAO27t38uzX1I8Fmw6/V6z1C61WqFKIqN2qSnp2PhwoUwGAxYvnw5srKyUFFRgaSkJHTqJJ8HFRARke9wZnxDPrvHHhcXh5ycHABAbm4uoqMb71NsNpuRl5cHt9uN3bt3QxAE/Pjjj1i1ahWSk5Oxb98+LFiwwFclEhGRDGgH6AAl4Dhkh1jb+CKyo/HZFfvo0aORlJSE8vJyfP/990hLS0NaWhpmz57taTN16lTMnz8fpaWlGD58OMaOHYt7773X83pycjJeeeUVX5VIREQyoNAqoO2vgz3PBvv+WoRcFurvkvzKZ8FuMBiQmZmJbdu2YfLkyTCZTBg0aFCDNsOGDcP69evPe4zMzExflUdERDKiHRICe54Ntr0Mdp+uYzcajYiPj2/17HYiIqILUb9RDfeM55ayREQkA55nszPYGexERBT8PJvU7K2FJEl+rsa/GOxERBT01NFqKDop4T7hgqvc5e9y/IrBTkREQU8QhAYPhOnIGOxERCQLWk6gA8BgJyIimfBMoOMVOxERUfCrD3bb3o7zPPqmMNiJiEgW6ofi7fttkFwdd2Y8g52IiGRBGaaEupcGkl2C45DN3+X4DYOdiIhkgzvQMdiJiEhG+AhXBjsREckIt5ZlsBMRkYxwKJ7BTkREMqLpp4OgFeAscsBtdvu7HL9gsBMRkWwIKgHagR17oxoGOxERyUpHH45nsBMRkax09JnxDHYiIpIVz1PeOujWsgx2IiKSFd1ZV+yS1PG2lmWwExGRrKi6qaHsqoJYLcJZ7PB3Oe2OwU5ERLJzZji+491nZ7ATEZHs6DrwBDoGOxERyY720o675I3BTkREssOheCIiIhnRDggBFID9oA2iTfR3Oe2KwU5ERLKjCFFA008LuAF7vs3f5bQrBjsREcmSbogeQMcbjmewExGRLHlmxjPYiYiIgp/W8zCYjrW1LIOdiIhkqf6KvaM9vpXBTkREsqTuqYHCoICr3AVXhdPf5bSbgAz2U6dOYdu2bTh58qS/SyEioiAlKARoB3W8Heh8GuwpKSlITExERkZGk69XVVVhypQpSEhIQGpqqudr06ZNw2+//YYHH3yQ4U5ERBesIw7H+yzYN23aBFEUsWbNGhQVFaGwsLBRm+zsbIwbNw5ZWVmwWCzYs2cP8vLy8Mwzz2D69OkYOXIk/vjjD1+VSEREMndmAl3HCXaVrw68Y8cOjBkzBgAwcuRI5OTkoE+fPg3ahIeHIz8/H2azGWVlZYiKikLXrl0BADt37sRvv/2GmTNntniuiAg9VCqlV+s3mcK8ejx/kktf5NIPgH0JVHLpi1z6AVx8X9TXu3EURXDnO/z+fWmv8/ss2K1WKyIjIwEARqMRJSUljdqMGDECW7ZswcqVKxETEwOj0QgAkCQJGzZsQKdOnaBStVxiZaV3lzKYTGGoqKj26jH9RS59kUs/APYlUMmlL3LpB+CdvrijJABAze81KD9qhqAUvFFam3n759LchwSfDcXr9XrYbHXb+FmtVohi471609PTsXDhQsyaNQv9+vVDVlYWAEAQBDz//PMYOHAgvv32W1+VSEREMqc0qqCOVkOySXAU2P1dTrvwWbDHxcUhJycHAJCbm4vo6OhGbcxmM/Ly8uB2u7F7924IgoB//vOf+OKLLwAA1dXVCAuTz5ASERG1P+3prWU7ysx4nwX76NGjkZ2djUWLFmHjxo2IjY1FWlpagzZTp05FamoqrrzySlRVVWHs2LFITExEdnY2Jk6cCLfbjZEjR/qqRCIi6gA8M+P/6BjB7rN77AaDAZmZmdi2bRsmT54Mk8mEQYMGNWgzbNgwrF+/vtF7ly9f7quyiIiog6l/NntHuWL3WbADdZPm4uPjfXkKIiKiZmmHdKw94wNy5zkiIiJv0cboIKgFOA874K5x+7scn2OwExGRrAlqAdoBOgCAPVf+w/EMdiIikj1tB3o2O4OdiIhkr34CnZ3BTkREFPzql7x1hJnxDHYiIpK9s4fiJUnyczW+xWAnIiLZU0WqoeyshFjlhqvM6e9yfIrBTkREsicIAnT1W8vK/D47g52IiDqEjvJsdgY7ERF1CJ494xnsREREwe/MzHh5by17wcH++eefe7MOIiIin9IODAEEwJ5vg+gQ/V2Oz7QY7LfffjvWr1+PgwcPAgCmTZuGiooKfPnllz4vjoiIyFsUegU0fbWAC3Dk2/xdjs+0GOyhoaGIjY3FggULsH79elx66aXo2rUrlEple9RHRETkNR1hAl2Lwa5WqxEbG4vFixfjz3/+Mw4dOoT169dDEIT2qI+IiMhrOsIOdC0GuyiK+PXXX/HSSy9hx44dKCsrwy233NIetREREXlVR5gZr2ruxYceegjFxcUwm824/PLLsXbtWtx4440YO3YsTpw4gVtvvRVWqxU//PBDe9VLRER0wTr8UHxGRgaioqKQnZ2NHTt2YOjQoVi3bh3eeustDB8+HJs2bWKoExFR0ND00ULQK+A66oTrpMvf5fhEs8Gu1+uhVqvx9ttv4/LLL8fVV1+NBx54AIIg8B47EREFHUEhnHmEq0zvs7d4j93pdKKkpATFxcXo0qULfvnlFwwbNqw9aiMiIvI6z3B8Rw12rVaLAwcO4IEHHkBMTAx69OiBzZs3w263t0d9REREXqUbIu/77M1OngOAFStWNPj7448/DrVaDYfD4auaiIiIfMYzFL9XnlvLthjs9Q4ePNjg3vrgwYNRUFCA8PBwRERE+KxAIiIib/IMxefaIIkSBIW85oy1GOxOpxNqtRqPPvoorr76amzduhWDBw+GJEkwmUzYuXMnvvjiCxgMhvaol4iI6KKoOqugilLDVeaEo9AObT+dv0vyqhaDffz48bj33nvRvXt3LFq0CMnJyXj00Ufhcrlw7bXX4rfffoNGo2mPWomIiLxCNzgENWVO2PfWdqxgt9ls6Ny5MyIiIrB//3488sgj2L9/PxYvXgxJkhAeHo4BAwZwljwREQUV7ZAQ1Hxrhm1fLTr9TV63k88b7DU1Nbjrrrug1+tx++23Y+3atVi2bBmSk5Px+OOPe67YiYiIgo2cZ8afd7mbwWDARx99hJ49e2LevHnQarWYOnUqNBoN3nnnHSxatAizZs3Cb7/91p71EhERXTQ5b1LT7FB8586dcc0118Bms+GGG25AaWkpFIq6zwKiKOLIkSNYuXIl3nzzzXYploiIyBs0sTpABTgK7BAtbihC5fMo8mY3qBFFEWvWrMGkSZMQGxuL5cuXo6CgAIcOHcLixYvRtWtXhjoREQUdhUYBbawOkABbns3f5XhVs1fsCoUCR44cwd13342rrroKWq0W0dHRkCQJGo0GGo0GeXl5GDhwYHvVS0RE5BW6wXrY99lg31cL/RWh/i7Ha5q9Ync6nejVqxc+//xz3HLLLRAEAQsWLEBBQQHuvfdelJaW4uTJk+1VKxERkddoZTqBrtkrdr1ej+XLlwMArrjiCrz//vv44osvcOutt0Kv1wNAs8GekpKCgwcP4sYbb8SMGTMavV5VVYWnnnoKJ06cQFxcHF588UVUV1dj9uzZEEURISEhSEtL4zp5IiLyujMz4+W1tWyzV+yCIGDJkiWev69duxbjxo3zhLrL5cKkSZOafO+mTZs89+iLiopQWFjYqE12djbGjRuHrKwsWCwW7NmzB19++SUefvhhLFu2DF27dsXWrVsvontERERNqw92+75aSJLk52q8p8Wd537//XfPn9etW4d77rnnzJtVqvNeTe/YsQNjxowBAIwcORI5OTno06dPgzbh4eHIz8+H2WxGWVkZoqKiMHToUM/rlZWV6NKlS4udiIjQQ6Xy7oxGkynMq8fzJ7n0RS79ANiXQCWXvsilH4Bv+yJ1lXAoXAXXSReMLi203bU+OxfQfj+XFoO9/qEvAJr8RHP262ezWq2IjIwEABiNRpSUlDRqM2LECGzZsgUrV65ETEwMjEaj57VffvkFVVVVGD58eIudqKz07jCKyRSGiopqrx7TX+TSF7n0A2BfApVc+iKXfgDt0xfNYB1cP9agdGsFDKOMLb/hAnm7L819SGjxeeyiKKK8vBzHjh2D0+n0/Ln+v/PR6/Ww2eqWEFitVoii2KhNeno6Fi5ciFmzZqFfv37IysoCAJw6dQovvfQSXn311RY7R0REdKHkuANdi1fspaWleOqppyBJEkJDQzFnzpxWHTguLg45OTkYPnw4cnNz0bdv30ZtzGYz8vLyMHz4cOzevRvXXXcdHA4HnnjiCcyZMwfR0dFt7xEREVEreR7h2pGCvUePHli5cuV5X58wYUKTXx89ejSSkpJQXl6O77//HmlpaUhLS8Ps2bM9baZOnYr58+ejtLQUw4cPx9ixY/H5559j7969eP/99/H+++/jvvvuQ3x8/AV0jYiIqHlnT6CTi1bfY3e73SgsLPRsKdsSg8GAzMxMbNu2DZMnT4bJZMKgQYMatBk2bBjWr1/f4GtJSUlISkpqbf1EREQXTDvodLDvt0FyShDUTc8bCyYtBrvL5QJQt+Z8wYIFUKvVrT640Wjk1TYREQUspUEJdW8NnIcdsB+weR4OE8xaDPZrrrkGQN0DYT799NMGr4miiMTERN9URkRE1A50Q/R1wb6vVhbB3uK4+vz585t9ffr06V4rhoiIqL3pZDaBrnU3zM/3ZoUCo0aN8lYtRERE7U576elgl8kEuosKdiIiomDnmRkvkz3jGexERNShafpoIYQIcJY44T7l8nc5F43BTkREHZqgFKAdeHo4Pjf4h+MZ7ERE1OF5tpb9g8FOREQU9OpnxsthBzoGOxERdXhaGT0MhsFOREQd3tlX7JLY+BHlwYTBTkREHZ6qqxqqbiqIFhHOIoe/y7koDHYiIiIA2iF6AME/HM9gJyIigny2lmWwExERQT7PZmewExER4eyZ8cG9tSyDnYiICIB2gA5QAo5Ddoi1or/LuWAMdiIiIgAKrQLa/jpABOz7g3c4nsFORER0mlYGE+gY7ERERKedeYQrg52IiCjo6WSwtSyDnYiI6DTPUHwQL3ljsBMREZ2m7qGBIkwB93EXXOVOf5dzQRjsREREpwmCAF2Qby3LYCciIjpLsA/HM9iJiIjOEuwz4xnsREREZznzMJjg3FqWwU5ERHSW+qF4+34bJJfk52rajsFORER0FmUnJdS9NJDsEhyHbP4up80Y7EREROcI5mezM9iJiIjO4XmEaxDOjGewExERnaP+ij0YZ8Yz2ImIiM6h4xV701JSUpCYmIiMjIwmX6+qqsKUKVOQkJCA1NRUz9ePHz+OpKQkX5ZGRER0Xpp+OghaAc4jDrjNbn+X0yY+C/ZNmzZBFEWsWbMGRUVFKCwsbNQmOzsb48aNQ1ZWFiwWC/bs2YOqqirMmzcPtbXB9ymJiIjkQVAJ0A7QAQDsQXbV7rNg37FjB8aMGQMAGDlyJHJychq1CQ8PR35+PsxmM8rKyhAVFQWlUol33nkHBoPBV6URERG1yLNnfJAFu8pXB7ZarYiMjAQAGI1GlJSUNGozYsQIbNmyBStXrkRMTAyMRiPUanWbzxURoYdKpbzoms9mMoV59Xj+JJe+yKUfAPsSqOTSF7n0A/BvX2xXh+PUmhMQClxeqaO9+uKzYNfr9bDZ6hb2W61WiKLYqE16ejoWLlwIg8GA5cuXIysrC4mJiW0+V2Wld7f9M5nCUFFR7dVj+otc+iKXfgDsS6CSS1/k0g/A/31x9awb1D71s/mi6/B2X5r7kOCzofi4uDjP8Htubi6io6MbtTGbzcjLy4Pb7cbu3bshCIKvyiEiImoTz8z4vVZIUvBsLeuzYB89ejSys7OxaNEibNy4EbGxsUhLS2vQZurUqUhNTcWVV16JqqoqjB071lflEBERtYmqmxrKriqI1SKcxQ5/l9NqPhuKNxgMyMzMxLZt2zB58mSYTCYMGjSoQZthw4Zh/fr1Tb4/MzPTV6URERG1im5wCCxbq2HfVwtNT62/y2kVn65jNxqNiI+Ph8lk8uVpiIiIfOLMcHzwzIznznNERETnoWWwExERyUf9FXswbVLDYCciIjoP7YAQQAHYD9gg2hsv2w5EDHYiIqLzUIQooOmnBdyAfb/N3+W0CoOdiIioGfVbywbLI1wZ7ERERM3QDg6uCXQMdiIiomYE27PZGexERETN8MyM3+vd55L4CoOdiIioGeqeGihCFXCVu+A67vR3OS1isBMRETVDUAhn7rMHwXA8g52IiKgFZ4bjGexERERBj1fsREREMqK7NHiWvDHYiYiIWqAbdHooPrcWklvyczXNY7ATERG1QBmugjpaDckmwVFo93c5zWKwExERtYL29NaygT4cz2AnIiJqBd3pCXT2PxjsREREQS9YtpZlsBMREbXCmYfBBPbWsgx2IiKiVtD210FQC3AedsBd4/Z3OefFYCciImoFQS1AO0AHoG7ZW6BisBMREbVSMDybncFORETUSp494wN4Ah2DnYiIqJU8M+N5xU5ERBT8tGddsUtSYG4ty2AnIiJqJVWkGsrOSrhPueEqc/q7nCYx2ImIiFpJEISAn0DHYCciImoDXYDvGc9gJyIiagPPzHgGOxERUfDzDMXvC8ytZRnsREREbaAbqAMEwJ5vg+gQ/V1OIwx2IiKiNlCEKqHpqwVcgCPf5u9yGvFpsKekpCAxMREZGRlNvl5VVYUpU6YgISEBqamprX4fERGRP50Zjg+8++w+C/ZNmzZBFEWsWbMGRUVFKCwsbNQmOzsb48aNQ1ZWFiwWC/bs2dOq9xEREflTIO9A57Ng37FjB8aMGQMAGDlyJHJychq1CQ8PR35+PsxmM8rKyhAVFdWq9xEREfmTbnDgzoxX+erAVqsVkZGRAACj0YiSkpJGbUaMGIEtW7Zg5cqViImJgdFobNX7zhURoYdKpfRq/SZTmFeP509y6Ytc+gGwL4FKLn2RSz+AwO1L6J+VKMIhOPJsra6xvfris2DX6/Ww2eomFVitVohi45mD6enpWLhwIQwGA5YvX46srKxWve9clZXeXXJgMoWhoqLaq8f0F7n0RS79ANiXQCWXvsilH0Bg90UKkyDoFXCUOFCWVwlV5+bj1Nt9ae5Dgs+G4uPi4jzD6Lm5uYiOjm7Uxmw2Iy8vD263G7t374YgCK16HxERkT8JCgG6QToAgfcIV59dsY8ePRpJSUkoLy/H999/j7S0NKSlpWH27NmeNlOnTsX8+fNRWlqK4cOHY+zYsZAkqcH7PvvsM1+VSEREdMG0Q/So/dkK275ahF4fOLcMfBbsBoMBmZmZ2LZtGyZPngyTyYRBgwY1aDNs2DCsX7++0XvPfl9YWOB8s4iIiOoF6sx4nwU7UDf5LT4+vt3eR0RE1F48M+MDbCieO88RERFdgLM3qZFEyc/VnMFgJyIiugCqziqoLlFDsopwFNr9XY4Hg52IiOgCeR7hGkDD8Qx2IiKiC6QNwAl0DHYiIqILVD+BjsFOREQkAxyKJyIikhFNrA5QAY4CO0SL29/lAGCwExERXTCFRgFtrA6QAPt+m7/LAcBgJyIiuii6wXoAgXOfncFORER0EQJtZjyDnYiI6CIE2gQ6BjsREdFFOPMwGCskyf9byzLYiYiILoIqSg2FUQn3STdc5S5/l8NgJyIiuhiCIJwZjv/D6udqGOxEREQXLZB2oGOwExERXSTPzPgAmEDHYCciIrpInqF4XrETEREFP+2g08G+3wbJ6d+Z8Qx2IiKii6Q0KKHurYHklGA/6N+tZRnsREREXqAbUre1rL+H4xnsREREXuCZGe/nCXQMdiIiIi/QXhoYS94Y7ERERF5Qf8Vu3+vfTWoY7ERERF6g6auFoBPgLHHCXeW/rWUZ7ERERF4gKAXPsjd/3mdnsBMREXmJZwLdHwx2IiKioBcIz2ZnsBMREXmJZ894P86MZ7ATERF5iWdmfG4tJNE/W8sy2ImIiLxE1VUNVTcVxBoRziKHX2pgsBMREXmR1s/PZmewExEReZFnz3g/TaBT+fLgKSkpOHjwIG688UbMmDGj0eurVq3Cxo0bAQBmsxmXXXYZpkyZgpdeegk1NTUYNmwYnnnmGV+WSERE5FU6P0+g89kV+6ZNmyCKItasWYOioiIUFhY2apOUlITMzExkZmbiyiuvxD333IM333wTM2bMwKpVq3D06FFs377dVyUSERF53ZmZ8f7ZWtZnwb5jxw6MGTMGADBy5Ejk5OSct+2xY8dw/PhxDB06FIWFhRgyZAgAoEuXLqiurvZViURERF6njdUBSsBxyA6xVmz38/tsKN5qtSIyMhIAYDQaUVJSct62n3zyCe677z4AwG233WYmTFgAAAyRSURBVIalS5fisssuw9atW/H3v/+9xXNFROihUim9U/hpJlOYV4/nT3Lpi1z6AbAvgUoufZFLP4Dg7cvhgXpY91qhr1AgbERdH9qrLz4Ldr1eD5vNBqAu5EWx6U8toihi+/btmD17NgBgxowZ2LVrFz788EOMHz8eoaGhLZ6rstK7wx0mUxgqKuQxUiCXvsilHwD7Eqjk0he59AMI7r6oBmiBvVaU/fc4bL2835fmPiT4bCg+Li7OM/yem5uL6OjoJtvt2rULw4YNgyAInq8NHjwYZWVlePjhh31VHhERkc94tpb1w57xPgv20aNHIzs7G4sWLcLGjRsRGxuLtLS0Ru1++OEHXHXVVQ2+9uGHH+Khhx5CSEiIr8ojIiLyGZ0f17L7bCjeYDAgMzMT27Ztw+TJk2EymTBo0KBG7Zq6h/7444/7qiwiIiKf88yM98Nadp+uYzcajYiPj/flKYiIiAKOuocGijAF3MddcJU7AVP7nZs7zxEREXmZIAhnhuPb+aqdwU5EROQD2tNby7b3fXYGOxERkQ94HuHKYCciIgp+Oj9NoGOwExER+UD941vtebUQXe23tSyDnYiIyAeUnZRQ99RAskuozW+/q3YGOxERkY/UD8db9lja7ZwMdiIiIh+p36im5readjsng52IiMhH6mfG84qdiIhIBnTD6tayO4452u2cPt1SloiIqCPT9tOh50cx6DY0HLZ2Oiev2ImIiHyo05hwhF1+/uenexuDnYiISEYY7ERERDLCYCciIpIRBjsREZGMMNiJiIhkhMFOREQkIwx2IiIiGWGwExERyQiDnYiISEYY7ERERDLCYCciIpIRBjsREZGMCJIkSf4ugoiIiLyDV+xEREQywmAnIiKSEQY7ERGRjDDYiYiIZITBTkREJCMMdiIiIhlhsHcgp06dwrZt23Dy5El/l0JERD7SoYM9JSUFiYmJyMjIOG+b48ePIykpqR2rujAt9aWqqgrTpk3Db7/9hgcffDBgw72lfrhcLtx0001ITk5GcnIy8vLy2rnC1mupL6tWrfL044477kBqamo7V3hxguX/jZZUV1dj8uTJ+P/t3X9MVfUfx/EnXmQIiJSQiBtzKYNCAhREFP6KtkLdcuQuKjXUMh1jbDWDiU7vRHMC6cJNlvPnpSmNmLS6DMEKE3/B7YepA3LrB8sGQRem3uvuz/5w3a+p6QH9nuu99/34C8aB+3p/7vmc9+45h/NZtWoVRUVFWK1WT0caE2+aGw9y535ls9lYu3Yt+fn5NDQ0eDiZMg/Lr0ZNftvYT5w4gdPppL6+nr6+Pn755Zd7thkZGaG0tBSLxaJ+wFFQUktPTw9lZWWsW7eOrKwsLl++rH7Qh1Bax8KFC9Hr9ej1euLj49UPqoCSWpYvX+6uIy0tjaVLl6ofdIy8ZW4o8dlnn7Fy5UoOHDhAZGQk33zzjacjjYm3zI0HuXu/qqurIzExkWPHjtHS0sKNGzc8nPDBlORXoya/bewXLlzglVdeASArKwuj0XjPNhqNht27dxMWFqZ2vFFRUsvcuXNJSUmhs7OTixcvkpqaqnbMh1JSx/fff8/XX3/Na6+9xoYNG7Db7WrHVERJLf/o7+9ncHCQpKQkteI9Mm+ZG0qsWLGCBQsWAGAymZg8ebKHE42Nt8yNB7l7vzp//rx7HqWnp3Pp0iVPxnsoJfnVqMlvG7vZbGbKlCkATJo0iaGhoXu2CQsLY+LEiWpHGzUltQC4XC4MBgPh4eEEBgaqGVERJXUkJSVx8OBBGhoasNvttLe3qx1TEaXvCcDHH3/MsmXL1Ir2WHjL3BiN7777jpGREVJSUjwdZUy8ZW48yN37lcViUTyPngRK8qtRk9829pCQEG7dugXcPgg7nU4PJxo7pbUEBASwefNm4uPj+fLLL9WMqIiSOhISEnjmmWcAmDVrFr/++quqGZVS+p44nU7Onz9PRkaGmvHEXYaHh9m6dSvbt2/3dJQx85a5MRrefpy+X341avLbxj5r1iz36dHu7m6mTZvm4URjp6SWjz76iOPHjwO3bxZ6Ej9tKalj/fr1dHd343A4aGtrIyEhQe2Yiijdv7q6unjhhRcICAhQM564g9VqpaSkhHfffderjwPeMjdGIzEx0auP0/fLr0ZNftvYc3JyaGpq4v3336e5uZm4uDh27drl6VhjoqQWrVZLU1MTK1aswOFwkJWV5aG0/01JHUVFRaxfv55XX32V1NRU5s+f76G0D6Z0/zp9+jTp6ekeSCj+0dDQwJUrV6itreX111/HYDB4OtKYeMvcGI0lS5ZQU1NDRUUFV69eJTk52dORRuV++dWoya+XbR0ZGaGjo4P09HSioqI8HeeR+EotvlIH+FYtQnhKf38/RqOR7OzsJ/JM48PcL///uya/buxCCCGEr/HbU/FCCCGEL5LGLoQQQvgQaexCCCGED5HGLoQfOXnyJCdPnsRms93zs7E8I93lcmGz2TCbzQwMDPDTTz9x6tQp9Ho9paWlnDhxwr1tTU0Nt27dIjc3l59//pn6+nrOnTtHZWXlI9UkhPi3J+/xY0KIx87lcqHX62ltbaWqqoqKigp6enq4efMm169fZ+rUqcTExFBdXc3q1asZGhoiKCiIq1evMnPmTPr6+oiMjESj0RAdHU1tbS0A+/fvx2AwEBoayvDwMGazGa1WS0xMDHl5eURGRrozzJkzh2+//ZYJEyZw7tw5EhMT6erqYubMmZ4aFiF8ktwVL4QfuHbtGp988gkZGRnu54n39fURHByM0WikqKgIh8PB+PHjWbt2LRs3biQ6OpqVK1ei1+spLy8nPz+f8PBwKisr2bNnz7/+fmdnJ/v27eOtt95ixowZfPjhh2RnZ/Piiy8C8Mcff1BcXEx/fz83btzgueeeY9KkSe5sISEhOJ1OKisriY2NVX18hPAl0tiF8BNtbW3odDr0ej2///47RqORtLQ0NmzYQHR0NC+//DKFhYXs2LEDo9H4n+sJpKam8t5777m/f+eddzAYDKSlpREYGIjL5eLNN98kOzv7X7/X09ODTqfDarWSmppKSUkJeXl5tLS00NjYiMViYfny5fIUPiEekVxjF8IP/PDDDzQ1NREREcH06dMJCAhwN9C8vDyOHTtGYWEhAGVlZWRmZhIeHs7OnTs5evQoBQUFHD16lLfffvueFd02btxIbW0tsbGxLFmyhEOHDuF0OqmoqHBft7948SKlpaVUVVVhtVqZPn06Op2Oa9eu4XA4GBwcZMqUKdLUhXgM5BO7EH5k8eLFlJeXo9PpsNvthISEYDabiYyMxGazUVZWhsViQaPRMG7cOGbPnk1QUBALFy7kiy++oK2tjcuXL1NSUgJAR0cHRUVF2O12MjIysFqtmM1mHA4HJpOJ2NhYampqCA0N5fr16xw/fpzo6Ghyc3OpqKjAZDKxevVq6uvrKSgoIC4uzsMjJIT3k5vnhPAjOTk5zJs3j+bmZg4cOIBWqyU0NBSr1UpgYCC9vb10dnZSV1dHc3MzQUFBAIwb97+Te3d+PXv2bA4fPkxUVBTh4eF88MEHvPTSS+6lT7/66itsNhtXrlyhqqqKS5cukZycTHV1NatWrWLBggW0t7fT29vLs88+q+5gCOGj5FS8EH7i0KFDpKSkUF5ezm+//YZGo6Gurg6AxsZGdDodCQkJrFu3jqeeegqn08nhw4eB28tL3o9Go2H37t2cOnWKsLAw9yf9jo4OtFotcXFxREVFkZycTHFxMUlJSWzbto2YmBhyc3PJysrCYDAQExODRqNRbSyE8GXS2IXwA2azmU8//ZQ5c+bgcDhwuVxotVoKCgo4e/YswcHBDAwMuFc2s9vtFBcXM378eAD3/6ObzWb3dXCTyURhYSGLFi0iPz+fwcFBhoaG0Gg05OTksGnTJtasWcPAwAAAzz//PG+88QbLli3jzz//5MyZM/z1118EBARgsViw2+0eGBkhfI9cYxfCD5hMJrq7u8nMzOTIkSPU1dUREREB3F5pasuWLWRmZhIUFMTw8DBLly6lurrafUodQK/X09jYyJYtW0hOTsblctHb20t8fDwAa9asITg4mO3bt7tvsBseHiYiIoKbN2/y+eef09raSm5uLosWLWLXrl10dXWxbds2WlpaOH36NHv37uXpp59Wf4CE8CHS2IUQ97Bare7r64+Dy+WitbWV7OxsJkyYAEB7ezsZGRkEBwcD8OOPP5KUlPTYXlMIfyWNXQghhPAhco1dCCGE8CHS2IUQQggfIo1dCCGE8CHS2IUQQggfIo1dCCGE8CF/A3wT3IHQkPoBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 调整惩罚系数的取值\n",
    "C_grid = [0.1, 0.3, 0.5, 0.7, 1, 2, 5, 10, 100]\n",
    "C_scores = [LinearSVC(C=c, random_state=10).fit(train_x, train_y).score(test_x, test_y) \n",
    "            for c in C_grid]\n",
    "\n",
    "# 绘制折线图\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(C_scores, 'm-x', linewidth=2)\n",
    "plt.xticks(range(9), C_grid)\n",
    "plt.xlabel('惩罚系数')\n",
    "plt.ylabel('正确率')\n",
    "plt.title('不同惩罚系数下模型分类正确率')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16ab51d2160>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看模型系数\n",
    "pd.Series(lsvm.coef_.reshape(-1), index=train_x.columns).plot(kind='barh', title='系数条形图')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2 SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分类正确率： 0.9682\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.96      0.95       114\n",
      "           1       0.98      0.97      0.98       232\n",
      "\n",
      "   micro avg       0.97      0.97      0.97       346\n",
      "   macro avg       0.96      0.97      0.96       346\n",
      "weighted avg       0.97      0.97      0.97       346\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'混淆矩阵热力图')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 载入算法类\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# 模型构建与拟合\n",
    "ksvm = SVC(random_state=10)\n",
    "ksvm.fit(train_x, train_y)\n",
    "\n",
    "# 模型预测\n",
    "y_pred = ksvm.predict(test_x)\n",
    "\n",
    "# 分类正确率\n",
    "print(\"分类正确率：\",round(ksvm.score(test_x, test_y),4))\n",
    "\n",
    "# 分类报告\n",
    "print(classification_report(test_y, y_pred))\n",
    "\n",
    "# 绘制热力图\n",
    "ax = sns.heatmap(confusion_matrix(test_y, y_pred), \n",
    "                 annot=True, fmt='d', \n",
    "                 xticklabels=[\"满意(0)\",\"不满意(1)\"], yticklabels=[\"满意(0)\",\"不满意(1)\"])\n",
    "ax.set_ylabel('真实')\n",
    "ax.set_xlabel('预测')\n",
    "ax.set_title('混淆矩阵热力图')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "poly       0.939306\n",
      "sigmoid    0.598266\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# 调整核函数\n",
    "kernel_grid = ['poly','sigmoid']\n",
    "kernel_scores = [SVC(kernel=k, random_state=10).fit(train_x, train_y).score(test_x, test_y) \n",
    "                for k in kernel_grid]\n",
    "print(pd.Series(kernel_scores, index=['poly', 'sigmoid']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'不同惩罚系数下模型分类正确率')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 调整惩罚系数的取值\n",
    "C_grid = [0.1, 0.3, 0.5, 0.7, 1, 2, 5, 10, 100]\n",
    "C_scores = [SVC(C=c, random_state=10).fit(train_x, train_y).score(test_x, test_y) \n",
    "            for c in C_grid]\n",
    "\n",
    "# 绘制折线图\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(C_scores, 'k-o', linewidth=2)\n",
    "plt.xticks(range(9), C_grid)\n",
    "plt.xlabel('惩罚系数')\n",
    "plt.ylabel('正确率')\n",
    "plt.title('不同惩罚系数下模型分类正确率')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2. 1. 1. 3. 2. 2.]\n",
      " [2. 4. 1. 2. 3. 2.]\n",
      " [2. 2. 4. 3. 1. 2.]\n",
      " [2. 4. 2. 2. 1. 3.]\n",
      " [1. 4. 1. 2. 2. 3.]\n",
      " [4. 1. 3. 2. 2. 2.]\n",
      " [2. 1. 4. 3. 1. 2.]\n",
      " [1. 3. 1. 3. 2. 2.]\n",
      " [1. 1. 4. 2. 3. 2.]\n",
      " [1. 3. 2. 2. 2. 2.]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# 查看支持向量\n",
    "print(SVC(C=100, random_state=10).fit(train_x, train_y).support_vectors_[:10])# 为了表示方便，只输出了列表前10个"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}