04-knn-exercise.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K近邻分类练习题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import mglearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在这个练习中，我们使用电信企业的客户流失数据集，Orange_Telecom_Churn_Data.csv（存放在当前目录下）。我们先读入数据集，做一些数据预处理，然后使用K近邻模型根据用户的特点来预测其是否会流失。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 第一步：\n",
    "* 将数据集读入变量data中，并查看其前5行。\n",
    "* 去除其中的“state\"，\"area_code\"和\"phone_number\"三列。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>account_length</th>\n",
       "      <th>area_code</th>\n",
       "      <th>phone_number</th>\n",
       "      <th>intl_plan</th>\n",
       "      <th>voice_mail_plan</th>\n",
       "      <th>number_vmail_messages</th>\n",
       "      <th>total_day_minutes</th>\n",
       "      <th>total_day_calls</th>\n",
       "      <th>total_day_charge</th>\n",
       "      <th>...</th>\n",
       "      <th>total_eve_calls</th>\n",
       "      <th>total_eve_charge</th>\n",
       "      <th>total_night_minutes</th>\n",
       "      <th>total_night_calls</th>\n",
       "      <th>total_night_charge</th>\n",
       "      <th>total_intl_minutes</th>\n",
       "      <th>total_intl_calls</th>\n",
       "      <th>total_intl_charge</th>\n",
       "      <th>number_customer_service_calls</th>\n",
       "      <th>churned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KS</td>\n",
       "      <td>128</td>\n",
       "      <td>415</td>\n",
       "      <td>382-4657</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>25</td>\n",
       "      <td>265.1</td>\n",
       "      <td>110</td>\n",
       "      <td>45.07</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>16.78</td>\n",
       "      <td>244.7</td>\n",
       "      <td>91</td>\n",
       "      <td>11.01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OH</td>\n",
       "      <td>107</td>\n",
       "      <td>415</td>\n",
       "      <td>371-7191</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>123</td>\n",
       "      <td>27.47</td>\n",
       "      <td>...</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NJ</td>\n",
       "      <td>137</td>\n",
       "      <td>415</td>\n",
       "      <td>358-1921</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>114</td>\n",
       "      <td>41.38</td>\n",
       "      <td>...</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>84</td>\n",
       "      <td>408</td>\n",
       "      <td>375-9999</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>50.90</td>\n",
       "      <td>...</td>\n",
       "      <td>88</td>\n",
       "      <td>5.26</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OK</td>\n",
       "      <td>75</td>\n",
       "      <td>415</td>\n",
       "      <td>330-6626</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>12.61</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  state  account_length  area_code phone_number intl_plan voice_mail_plan  \\\n",
       "0    KS             128        415     382-4657        no             yes   \n",
       "1    OH             107        415     371-7191        no             yes   \n",
       "2    NJ             137        415     358-1921        no              no   \n",
       "3    OH              84        408     375-9999       yes              no   \n",
       "4    OK              75        415     330-6626       yes              no   \n",
       "\n",
       "   number_vmail_messages  total_day_minutes  total_day_calls  \\\n",
       "0                     25              265.1              110   \n",
       "1                     26              161.6              123   \n",
       "2                      0              243.4              114   \n",
       "3                      0              299.4               71   \n",
       "4                      0              166.7              113   \n",
       "\n",
       "   total_day_charge  ...  total_eve_calls  total_eve_charge  \\\n",
       "0             45.07  ...               99             16.78   \n",
       "1             27.47  ...              103             16.62   \n",
       "2             41.38  ...              110             10.30   \n",
       "3             50.90  ...               88              5.26   \n",
       "4             28.34  ...              122             12.61   \n",
       "\n",
       "   total_night_minutes  total_night_calls  total_night_charge  \\\n",
       "0                244.7                 91               11.01   \n",
       "1                254.4                103               11.45   \n",
       "2                162.6                104                7.32   \n",
       "3                196.9                 89                8.86   \n",
       "4                186.9                121                8.41   \n",
       "\n",
       "   total_intl_minutes  total_intl_calls  total_intl_charge  \\\n",
       "0                10.0                 3               2.70   \n",
       "1                13.7                 3               3.70   \n",
       "2                12.2                 5               3.29   \n",
       "3                 6.6                 7               1.78   \n",
       "4                10.1                 3               2.73   \n",
       "\n",
       "   number_customer_service_calls  churned  \n",
       "0                              1    False  \n",
       "1                              1    False  \n",
       "2                              0    False  \n",
       "3                              2    False  \n",
       "4                              3    False  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将数据集读入变量data中，并查看其前5行\n",
    "data = pd.read_csv(\"D:\\JupyterSpace\\AI&ML_assignment\\Orange_Telecom_Churn_Data.csv\")\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 18)\n"
     ]
    }
   ],
   "source": [
    "# 去除“state\"，\"area_code\"和\"phone_number\"三列\n",
    "data = data.drop(['state','area_code','phone_number'], axis=1)\n",
    "data.head(5)\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 第二步：\n",
    "* 有些列的值是分类数据，如'intl_plan', 'voice_mail_plan', 'churned'这三列，需要把它们转换成数值数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_length</th>\n",
       "      <th>intl_plan</th>\n",
       "      <th>voice_mail_plan</th>\n",
       "      <th>number_vmail_messages</th>\n",
       "      <th>total_day_minutes</th>\n",
       "      <th>total_day_calls</th>\n",
       "      <th>total_day_charge</th>\n",
       "      <th>total_eve_minutes</th>\n",
       "      <th>total_eve_calls</th>\n",
       "      <th>total_eve_charge</th>\n",
       "      <th>total_night_minutes</th>\n",
       "      <th>total_night_calls</th>\n",
       "      <th>total_night_charge</th>\n",
       "      <th>total_intl_minutes</th>\n",
       "      <th>total_intl_calls</th>\n",
       "      <th>total_intl_charge</th>\n",
       "      <th>number_customer_service_calls</th>\n",
       "      <th>churned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>265.1</td>\n",
       "      <td>110</td>\n",
       "      <td>45.07</td>\n",
       "      <td>197.4</td>\n",
       "      <td>99</td>\n",
       "      <td>16.78</td>\n",
       "      <td>244.7</td>\n",
       "      <td>91</td>\n",
       "      <td>11.01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>107</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>123</td>\n",
       "      <td>27.47</td>\n",
       "      <td>195.5</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>114</td>\n",
       "      <td>41.38</td>\n",
       "      <td>121.2</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>50.90</td>\n",
       "      <td>61.9</td>\n",
       "      <td>88</td>\n",
       "      <td>5.26</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
       "      <td>148.3</td>\n",
       "      <td>122</td>\n",
       "      <td>12.61</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   account_length  intl_plan  voice_mail_plan  number_vmail_messages  \\\n",
       "0             128          0                1                     25   \n",
       "1             107          0                1                     26   \n",
       "2             137          0                0                      0   \n",
       "3              84          1                0                      0   \n",
       "4              75          1                0                      0   \n",
       "\n",
       "   total_day_minutes  total_day_calls  total_day_charge  total_eve_minutes  \\\n",
       "0              265.1              110             45.07              197.4   \n",
       "1              161.6              123             27.47              195.5   \n",
       "2              243.4              114             41.38              121.2   \n",
       "3              299.4               71             50.90               61.9   \n",
       "4              166.7              113             28.34              148.3   \n",
       "\n",
       "   total_eve_calls  total_eve_charge  total_night_minutes  total_night_calls  \\\n",
       "0               99             16.78                244.7                 91   \n",
       "1              103             16.62                254.4                103   \n",
       "2              110             10.30                162.6                104   \n",
       "3               88              5.26                196.9                 89   \n",
       "4              122             12.61                186.9                121   \n",
       "\n",
       "   total_night_charge  total_intl_minutes  total_intl_calls  \\\n",
       "0               11.01                10.0                 3   \n",
       "1               11.45                13.7                 3   \n",
       "2                7.32                12.2                 5   \n",
       "3                8.86                 6.6                 7   \n",
       "4                8.41                10.1                 3   \n",
       "\n",
       "   total_intl_charge  number_customer_service_calls  churned  \n",
       "0               2.70                              1        0  \n",
       "1               3.70                              1        0  \n",
       "2               3.29                              0        0  \n",
       "3               1.78                              2        0  \n",
       "4               2.73                              3        0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "lb = LabelBinarizer()\n",
    "\n",
    "for col in ['intl_plan', 'voice_mail_plan', 'churned']:\n",
    "    data[col] = lb.fit_transform(data[col])\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 第三步：\n",
    "* 将“churned”列作为要预测的目标列数据，赋给y_data；除“churned”列之外的所有其他列的数据作为特征列数据，赋给X_data。\n",
    "* 使用课件中提到的某种数据缩放方法（scaling method）来缩放X_data。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 17)\n",
      "(5000,)\n"
     ]
    }
   ],
   "source": [
    "# 生成X_data和y_data\n",
    "y_data = data[\"churned\"]\n",
    "X_data = data.drop([\"churned\"], axis=1)\n",
    "print(X_data.shape)\n",
    "print(y_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 17)\n"
     ]
    }
   ],
   "source": [
    "# 缩放X_data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "StdSc = StandardScaler()\n",
    "X_scaled = StdSc.fit_transform(X_data)\n",
    "print(X_scaled.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 第四步：\n",
    "* 创建一个k=3的K近邻模型，并拟合X_data和y_data。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建一个3NN模型，并训练\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(X_scaled, y_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 第五步：\n",
    "* 用上一步训练好的K近邻模型预测相同的数据集，即X_data，并评测预测结果的精度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 93.96 %\n"
     ]
    }
   ],
   "source": [
    "# 预测并评价\n",
    "y_pred = knn.predict(X_scaled)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print('Accuracy:', float(accuracy_score(y_data, y_pred))*100, '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 第六步：\n",
    "* 构建一个同样是`n_neighbors=3`的模型，但是用距离作为聚集K个近邻预测结果的权重。同样计算此模型在X_data上的预测精度。 \n",
    "* 构建另一个K近邻模型：使用均匀分布的权重，但是将闵科夫斯基距离中的指数参数设为1(`p=1`)，即使用曼哈顿距离。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 100.0 %\n"
     ]
    }
   ],
   "source": [
    "# n_neighbors=3, weights='distance'\n",
    "knn_w = KNeighborsClassifier(n_neighbors=3, weights='distance')\n",
    "knn_w.fit(X_scaled, y_data)\n",
    "\n",
    "y_pred_w = knn_w.predict(X_scaled)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print('Accuracy:', float(accuracy_score(y_data, y_pred_w))*100, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 94.16 %\n"
     ]
    }
   ],
   "source": [
    "# n_neighbors=3, p=1\n",
    "knn_mht = KNeighborsClassifier(n_neighbors=3, p=1)\n",
    "knn_mht.fit(X_scaled, y_data)\n",
    "\n",
    "y_pred_mht = knn_mht.predict(X_scaled)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print('Accuracy:', float(accuracy_score(y_data, y_pred_mht))*100, '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 第七步：\n",
    "* 将K值从1变化到20，训练20个不同的K近邻模型。权重使用均匀分布的权重（缺省的）。闵科夫斯基距离的指数参数(`p`)可以设为1或者2（只要一致即可）。将每个模型得到的精度和其`k`值存到一个列表或字典中。\n",
    "* 将`accuracy`和`k`的关系绘成图表。当`k=1`时，你观察到了什么? 为什么?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "neighbors_settings = range(1, 21)\n",
    "accuracy = []\n",
    "\n",
    "for n_neighbors in neighbors_settings:\n",
    "    clf = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
    "    clf.fit(X_scaled, y_data)\n",
    "    y_pred = clf.predict(X_scaled)\n",
    "    accuracy.append(accuracy_score(y_data, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26859a4e910>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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55ZcxZswYNGvWDGFhYXjhhRcwb948/T5HjhxBjx49MG7cOPj6+iI0NBRjx45FdHS0uS7LYK76XkCWO0pFRERUl4iWABUVFSEmJgahoaGltoeGhiIyMrLcYwoLC6FUKktts7GxQVRUFFQqbQ+dnj17IiYmBlFRUQCAq1evYteuXXjssccqjKWwsBDZ2dmlXuakrwHiCBAREZFZiJYApaenQ61Ww93dvdR2d3d3pKamlnvMwIEDsXLlSsTExEAQBERHRyM8PBwqlQrp6ekAgLCwMHz66afo2bMnFAoFmjdvjocffhjvv/9+hbHMmTMHTk5O+pe3t7fxLrQKdLPAbrMImoiIyCxEL4KWSCSlfhYEocw2nZkzZ2LQoEHo1q0bFAoFhg8frq/vkclkAID9+/fj888/x9KlS3HixAls27YNv/32Gz799NMKY5g+fTqysrL0r8TERONcXBXpiqBzCotxr0ht1s8mIiKqj0RLgFxcXCCTycqM9qSlpZUZFdKxsbFBeHg48vPzcf36dSQkJMDX1xcODg5wcXEBoE2Sxo8fjxdffBHt27fHyJEjMXv2bMyZMwcajabc81pbW8PR0bHUy5zsreVQKrS3gs0QiYiITE+0BMjKygpBQUGIiIgotT0iIgLdu3ev9FiFQoEmTZpAJpNh48aNGDJkCKRS7aXk5+fr/6wjk8kgCILFLjYqkUj+UwfEQmgiIiJTE3Ua/LRp0zB+/Hh06dIFISEhWL58ORISEvDKK68A0D6aSkpK0vf6iY+PR1RUFIKDg3H37l0sXLgQZ8+exZo1a/TnHDp0KBYuXIjOnTsjODgYly9fxsyZMzFs2DD9YzJL5OZgjYQ7+SyEJiIiMgNRE6AxY8YgIyMDs2bNQkpKCgICArBr1y74+PgAAFJSUpCQkKDfX61WY8GCBYiLi4NCocDDDz+MyMhI+Pr66veZMWMGJBIJZsyYgaSkJLi6umLo0KH4/PPPzX15BtEvh5HNESAiIiJTkwiW+lxIRNnZ2XByckJWVpbZ6oE+3nkOqyOv47W+zfHuo23M8plERER1iSHf36LPAiOtf5sh8hEYERGRqTEBshBcDoOIiMh8mABZCC6ISkREZD5MgCyEbho81wMjIiIyPSZAFkI3CywjrwjF6vIbNhIREZFxMAGyEA1trSCTSiAI2iSIiIiITIcJkIWQSiVwsbcCAKRxUVQiIiKTYgJkQbgcBhERkXkwAbIgnAlGRERkHkyALIi+FxAfgREREZkUEyALohsBup3LR2BERESmxATIgrg6ltQAcQSIiIjIpJgAWRDWABEREZkHEyAL4sYFUYmIiMyCCZAF+e+K8IIgiBwNERFR3cUEyILoEqAitQZZ91QiR0NERFR3MQGyINZyGRrYKgCwDoiIiMiUmABZGDf2AiIiIjI5JkAWhsthEBERmR4TIAvjyplgREREJscEyMKwFxAREZHpMQGyMK5MgIiIiEyOCZCFcdMvh8EaICIiIlNhAmRhXO1ZA0RERGRqTIAsjJsjEyAiIiJTYwJkYXRF0DmFxbhXpBY5GiIiorqJCZCFsbeWw0YhA8BeQERERKbCBMjCSCQS/WMwzgQjIiIyDSZAFkhXCM3lMIiIiEyDCZAF+rcQmo/AiIiITIEJkAX6dz0wjgARERGZAhMgC8Ru0ERERKbFBMgCcT0wIiIi02ICZIH0I0BcDoOIiMgkmABZIF0NUHouR4CIiIhMgQmQBdLNAsvIK0KxWiNyNERERHUPEyAL1NDWCnKpBIIApOcWiR0OERFRncMEyAJJpRK46JohshcQERGR0TEBslC6QmiuCk9ERGR8TIAsFKfCExERmQ4TIAulXxCV64EREREZHRMgC+WqXw6DNUBERETGxgTIQnE5DCIiItNhAmSh3FgETUREZDJMgCwUEyAiIiLTYQJkodwctTVAt3MKIQiCyNEQERHVLUyALJSLvRUAoEitQWa+SuRoiIiI6hYmQBbKWi5DA1sFABZCExERGRsTIAvGOiAiIiLTYAJkwdzYC4iIiMgkmABZMC6HQUREZBpMgCyYK5fDICIiMgnRE6ClS5fCz88PSqUSQUFBOHjwYKX7L1myBG3btoWNjQ1at26NtWvXltknMzMTkyZNgqenJ5RKJdq2bYtdu3aZ6hJMxtVeNwLER2BERETGJBfzwzdt2oSpU6di6dKl6NGjB77//nsMGjQI58+fR9OmTcvsv2zZMkyfPh0rVqxA165dERUVhYkTJ8LZ2RlDhw4FABQVFWHAgAFwc3PDli1b0KRJEyQmJsLBwcHcl1dj/+0FRERERMYjEUTsshccHIzAwEAsW7ZMv61t27YYMWIE5syZU2b/7t27o0ePHpg/f75+29SpUxEdHY1Dhw4BAL777jvMnz8fFy9ehEKhqFZc2dnZcHJyQlZWFhwdHat1DmM4ejUDYcuPopmLHfa+3Ve0OIiIiGoDQ76/RXsEVlRUhJiYGISGhpbaHhoaisjIyHKPKSwshFKpLLXNxsYGUVFRUKm0zQJ37tyJkJAQTJo0Ce7u7ggICMDs2bOhVqsrjKWwsBDZ2dmlXpaARdBERESmIVoClJ6eDrVaDXd391Lb3d3dkZqaWu4xAwcOxMqVKxETEwNBEBAdHY3w8HCoVCqkp6cDAK5evYotW7ZArVZj165dmDFjBhYsWIDPP/+8wljmzJkDJycn/cvb29t4F1oDuhXhcwuLkV9ULHI0REREdYfoRdASiaTUz4IglNmmM3PmTAwaNAjdunWDQqHA8OHDMWHCBACATCYDAGg0Gri5uWH58uUICgpCWFgYPvzww1KP2e43ffp0ZGVl6V+JiYnGubgasreWw0ahvS7WARERERmPaAmQi4sLZDJZmdGetLS0MqNCOjY2NggPD0d+fj6uX7+OhIQE+Pr6wsHBAS4uLgAAT09PtGrVSp8QAdq6otTUVBQVFZV7Xmtrazg6OpZ6WQKJRAI3Rz4GIyIiMjbREiArKysEBQUhIiKi1PaIiAh079690mMVCgWaNGkCmUyGjRs3YsiQIZBKtZfSo0cPXL58GRqNRr9/fHw8PD09YWVlZfwLMTF9HRB7ARERERmNqI/Apk2bhpUrVyI8PBwXLlzAm2++iYSEBLzyyisAtI+mnnnmGf3+8fHxWLduHS5duoSoqCiEhYXh7NmzmD17tn6fV199FRkZGZgyZQri4+Px+++/Y/bs2Zg0aZLZr88YuBwGERGR8YnaB2jMmDHIyMjArFmzkJKSgoCAAOzatQs+Pj4AgJSUFCQkJOj3V6vVWLBgAeLi4qBQKPDwww8jMjISvr6++n28vb2xZ88evPnmm+jQoQMaN26MKVOm4L333jP35RmFK2eCERERGZ2ofYAslaX0AQKAJfsuY/6fcRgV1ARfPtlR1FiIiIgsWa3oA0RVw15ARERExscEyMLplsNIy2YNEBERkbEwAbJwuhEg9gEiIiIyHiZAFk5XBJ2RVwSVWvOAvYmIiKgqmABZuIa2VpBLtZ2xM3LLb+RIREREhmECZOGkUglc7HWF0KwDIiIiMgYmQLWAfjkMdoMmIiIyCoMTIF9fX8yaNatUg0IyLVd7ToUnIiIyJoMToLfeegu//PILmjVrhgEDBmDjxo0oLOQXsyn9uyAqH4EREREZg8EJ0BtvvIGYmBjExMTA398fkydPhqenJ15//XWcOHHCFDHWe64l64FxKjwREZFxVLsGqGPHjvj666+RlJSEjz76CCtXrkTXrl3RsWNHhIeHgytsGA+7QRMRERlXtRdDValU2L59O1atWoWIiAh069YNL7zwApKTk/Hhhx/ir7/+woYNG4wZa73FBIiIiMi4DE6ATpw4gVWrVuGnn36CTCbD+PHjsWjRIrRp00a/T2hoKHr37m3UQOszXTPE21wOg4iIyCgMToC6du2KAQMGYNmyZRgxYgQUCkWZffz9/REWFmaUAOnf9cBu5xZCEARIJBKRIyIiIqrdDE6Arl69Ch8fn0r3sbOzw6pVq6odFJWmmwavUgvIzFfB2c5K5IiIiIhqN4OLoNPS0nDs2LEy248dO4bo6GijBEWlWcmlcLbVjrSxDoiIiKjmDE6AJk2ahMTExDLbk5KSMGnSJKMERWW5lUyFZy8gIiKimjM4ATp//jwCAwPLbO/cuTPOnz9vlKCoLF0hNJfDICIiqjmDEyBra2vcunWrzPaUlBTI5dWeVU8PoJsKfzuXCRAREVFNGZwADRgwANOnT0dWVpZ+W2ZmJj744AMMGDDAqMHRv1y5ICoREZHRGDxks2DBAvTu3Rs+Pj7o3LkzAODkyZNwd3fHjz/+aPQASYs1QERERMZjcALUuHFjnD59GuvXr8epU6dgY2OD5557DmPHji23JxAZhyu7QRMRERlNtYp27Ozs8NJLLxk7FqqEvgaICRAREVGNVbtq+fz580hISEBRUVGp7cOGDatxUFQWEyAiIiLjqVYn6JEjR+LMmTOQSCT6Vd91yzOo1WrjRkgA/l0OI7ewGPlFxbC14ow7IiKi6jJ4FtiUKVPg5+eHW7duwdbWFufOncOBAwfQpUsX7N+/3wQhEgDYW8thayUDwJlgRERENWVwAnTkyBHMmjULrq6ukEqlkEql6NmzJ+bMmYPJkyebIkYqwUJoIiIi4zA4AVKr1bC3twcAuLi4IDk5GQDg4+ODuLg440ZHpbjpEyBOhSciIqoJgwtJAgICcPr0aTRr1gzBwcH44osvYGVlheXLl6NZs2amiJFK6HoBsRCaiIioZgxOgGbMmIG8vDwAwGeffYYhQ4agV69eaNSoETZt2mT0AOlffARGRERkHAYnQAMHDtT/uVmzZjh//jzu3LkDZ2dn/UwwMg03LodBRERkFAbVABUXF0Mul+Ps2bOltjds2JDJjxm42rMGiIiIyBgMSoDkcjl8fHzY60ckul5ArAEiIiKqGYNngc2YMQPTp0/HnTt3TBEPVYLdoImIiIzD4Bqgb775BpcvX4aXlxd8fHxgZ2dX6v0TJ04YLTgqTZcAZeQVQaXWQCEzOH8lIiIiVCMBGjFihAnCoKpwtrWCXCpBsUZAem4hPJ1sxA6JiIioVjI4Afroo49MEQdVgVQqgYu9NVKzC5CWzQSIiIiouvgMpZbRTYVnHRAREVH1GTwCJJVKK53yzhlipuXGZohEREQ1ZnACtH379lI/q1QqxMbGYs2aNfjkk0+MFhiVz7VkOQz2AiIiIqo+gxOg4cOHl9k2atQotGvXDps2bcILL7xglMCofFwOg4iIqOaMVgMUHByMv/76y1inowroH4FxOQwiIqJqM0oCdO/ePXz77bdo0qSJMU5HldA3Q8xlAkRERFRdBj8Cu3/RU0EQkJOTA1tbW6xbt86owVFZ+uUwslkDREREVF0GJ0CLFi0qlQBJpVK4uroiODgYzs7ORg2OyvrvCJAgCFyEloiIqBoMToAmTJhggjCoqlxKVoRXqQXczVehoZ2VyBERERHVPgbXAK1atQqbN28us33z5s1Ys2aNUYKiilnJpXC2VQDgVHgiIqLqMjgBmjt3LlxcXMpsd3Nzw+zZs40SFFXOraQXELtBExERVY/BCdCNGzfg5+dXZruPjw8SEhKMEhRVTrccBqfCExERVY/BCZCbmxtOnz5dZvupU6fQqFEjowRFlXO1ZzNEIiKimjA4AQoLC8PkyZOxb98+qNVqqNVq7N27F1OmTEFYWJgpYqT7uOpGgFgDREREVC0GJ0CfffYZgoOD0b9/f9jY2MDGxgahoaHo169ftWqAli5dCj8/PyiVSgQFBeHgwYOV7r9kyRK0bdsWNjY2aN26NdauXVvhvhs3boREIsGIESMMjsuSuenXA+MIEBERUXUYPA3eysoKmzZtwmeffYaTJ0/CxsYG7du3h4+Pj8EfvmnTJkydOhVLly5Fjx498P3332PQoEE4f/48mjZtWmb/ZcuWYfr06VixYgW6du2KqKgoTJw4Ec7Ozhg6dGipfW/cuIG3334bvXr1MjguS6fvBcQEiIiIqFokgiAIYn14cHAwAgMDsWzZMv22tm3bYsSIEZgzZ06Z/bt3744ePXpg/vz5+m1Tp05FdHQ0Dh06pN+mVqvRp08fPPfcczh48CAyMzOxY8eOKseVnZ0NJycnZGVlwdHRsXoXZ0LHrmZgzPKj8HOxw763+4odDhERkUUw5Pvb4Edgo0aNwty5c8tsnz9/Pp588skqn6eoqAgxMTEIDQ0ttT00NBSRkZHlHlNYWAilUllqm42NDaKioqBSqfTbZs2aBVdX1zq7Mr1+RXguh0FERFQtBidA//zzDx577LEy2x999FEcOHCgyudJT0+HWq2Gu7t7qe3u7u5ITU0t95iBAwdi5cqViImJgSAIiI6ORnh4OFQqFdLT0wEAhw8fxg8//IAVK1ZUOZbCwkJkZ2eXelky3XpgeUVq5BUWixwNERFR7WNwApSbmwsrq7LLLygUimolDvevZVXZ+lYzZ87EoEGD0K1bNygUCgwfPly/NIdMJkNOTg6efvpprFixotxmjRWZM2cOnJyc9C9vb2+Dr8Oc7K3lsLWSATBvHdDKg1fx6FcHkJx5z2yfSUREZAoGJ0ABAQHYtGlTme0bN26Ev79/lc/j4uICmUxWZrQnLS2tzKiQjo2NDcLDw5Gfn4/r168jISEBvr6+cHBwgIuLC65cuYLr169j6NChkMvlkMvlWLt2LXbu3Am5XI4rV66Ue97p06cjKytL/0pMTKzydYhFVwhtrplgWfkqLNgTj4upOdgem2SWzyQiIjIVg2eBzZw5E0888QSuXLmCfv36AQD+/vtvbNiwAVu2bKnyeaysrBAUFISIiAiMHDlSvz0iIgLDhw+v9FiFQoEmTZoA0CZeQ4YMgVQqRZs2bXDmzJlS+86YMQM5OTn4+uuvKxzZsba2hrW1dZVjtwRuDkpcz8g3Wy+gzTGJuKdSAwD+ib+NSQ+3MMvnEhERmYLBCdCwYcOwY8cOzJ49G1u2bIGNjQ06duyIvXv3Gjxjatq0aRg/fjy6dOmCkJAQLF++HAkJCXjllVcAaEdmkpKS9L1+4uPjERUVheDgYNy9excLFy7E2bNn9YuwKpVKBAQElPqMBg0aAECZ7bXdv4XQph8BUmsErD1yQ//ziRt3kVtYDHtrg//zISIisgjV+gZ77LHH9IXQmZmZWL9+PaZOnYpTp05BrVZX+TxjxoxBRkYGZs2ahZSUFAQEBGDXrl36nkIpKSml1hdTq9VYsGAB4uLioFAo8PDDDyMyMhK+vr7VuYxazdWMj8D2x6Uh4U4+HJVyOCgVSMq8h6NXMvCIf/mPKomIiCxdtf8Jv3fvXoSHh2Pbtm3w8fHBE088gR9++MHg87z22mt47bXXyn1v9erVpX5u27YtYmNjDTr//eeoK3QLopqjCHp15HUAwJiu3rinUmPd0QQcvHSbCRAREdVaBiVAN2/exOrVqxEeHo68vDyMHj0aKpUKW7duNagAmmru3+UwTFsDdOV2Lg5eSodEAozv5osLqdklCVC6ST+XiIjIlKo8C2zw4MHw9/fH+fPn8e233yI5ORnffvutKWOjSriaaTmMtSWjP/3buKFpI1uENG8EmVSCq+l5SLyTb9LPJiIiMpUqJ0B79uzBiy++iE8++QSPPfYYZDKZKeOiBzDHNPicAhW2xNwEADzb3RcA4KhUoLN3AwDgKBAREdVaVU6ADh48iJycHHTp0gXBwcFYvHgxbt++bcrYqBK6BOhOXhGKijUm+YxtJ5KQV6RGc1c79Gzxb2PJ3q1cAQAHL/H+ExFR7VTlBCgkJAQrVqxASkoKXn75ZWzcuBGNGzeGRqNBREQEcnJyTBkn3cfZ1gpyqbZjdkae8UeBNBoBa45cB6Ad/flvd+5eLbXJ0OHL6ShWmyb5IiIiMiWDO0Hb2tri+eefx6FDh3DmzBm89dZbmDt3Ltzc3DBs2DBTxEjlkEolJu0FdOhyOq7ezoO9tRyPBzYp9V6HJg3gqJQju6AYp25mGf2ziYiITM3gBOi/WrdujS+++AI3b97ETz/9ZKyYqIpM2QtoTUnx86igJmUaHsqkEvQsGQXiYzAiIqqNapQA6chkMowYMQI7d+40xumoiv4thDbuVPiEjHzsjUsDADwT4lPuPr1a6uqAWAhNRES1j1ESIBKHq64XkJEfga09ch2CoC12buZqX+4+ujqgk4mZyLqnMurnExERmRoToFpMNwJ0O9d4CVB+UTF+jk4EAEzoXv7oDwA0cbZFM1c7qDUCjlzhKBAREdUuTIBqMd1yGMYcAdoem4TsgmL4NLJF31Zule7bu+Qx2AE+BiMiolqGCVAt5mqv6wZtnBogQRD0xc/ju/lAKpVUur/uMdiB+NsQBMEoMRAREZkDE6BazM1Rtx6YcUaAjl69g/hbubBRyPBkF+8H7t+tWSMoZBLcvHsPNzK4LAYREdUeTIBqMV0NUHpuITSamo/A6EZ/Hg9sDCcbxQP3t7OWI8jHGQCnwxMRUe3CBKgWcyl5BKZSC8is4UyspMx72HM+FcC/635VhW46/D/xrAMiIqLagwlQLWYll8LZVjtSU9NeQOuO3oBGALo3b4RW7g5VPk5XCH3kSjpUXBaDiIhqCSZAtZybEXoBFajU2BiVAAB4JsTXoGPbeTmioZ0V8orUiE3IrHYMRERE5sQEqJbTT4WvQSH0zlPJuJuvQuMGNnikbeVT3+8nlUr0K8WzDoiIiGoLJkC1nG49sNvVTID+O/X96W4+kMsM/0/iv9PhiYiIagMmQLWc/hFYNWuAYm7cxbnkbFjLpQjr+uCp7+XRFUKfTsrC3byiap2DiIjInJgA1XI1XRF+dcnoz/BOXnC2s6rWOTyclGjt7gBBAA5zWQwiIqoFmADVcvr1wKpRBH0ruwC7zxo+9b08usdgBzkdnoiIagEmQLWcm34EyPBHYOuPJaBYI6CrrzPaeTnVKI5erXTrgnFZDCIisnxMgGo53XIYhhZBFxVrsOGYdup7TUd/AOAh34awkkuRklWAK7dza3w+IiIiU2ICVMvpRoDyitTIKyyu8nG7zqQgPbcQ7o7WGNjOo8Zx2FjJ8JBvQwDAAT4GIyIiC8cEqJazs5bD1koGwLBCaF3x81PBPlBUY+p7eXq3Yj8gIiKqHZgA1QH6OqDsqtUBnUrMxMnETFjJpBj7UFOjxaGbDn/06h0UFquNdl4iIiJjYwJUB/zbC6hqI0C6xoePdfDUT6M3hjYeDnCxt8Y9lRox1+8a7bxERETGxgSoDnB1rHo36PTcQvx2OgWAcYqf/0sikaC3riv0JdYBERGR5WICVAe42le9GeJPxxJQpNago3cDdPJuYPRYepdMh2cdEBERWTImQHXAvwuiVl4DpFJrsL5k6vuE7j4miaVHycKo55KzkZ5b/QVajU2l1mD5gSs4lZgpdihERGQBmADVAboaoAc9Attz7hZSswvgYm+Fwe09TRKLq4M1/D0dAQCHLOgx2LqjNzB710W8ui4GxWqN2OEQEZHImADVAf/OAqs8AdIVP497qCms5TKTxdOrla4OyDIegxUVa7DiwFUAQHJWAfbHWUZcREQkHiZAdYDuEdjtSh45nU/ORtT1O5BLJXiqm2kef+n0aamrA0q3iGUxfjmZhOSsfx8Prj92Q8RoiIjIEjABqgN0RdB38opQVFz+4x3d6M/AAA+4lyyfYSpBvs5QKqS4nVOIuFs5Jv2sB9FoBHz3zxUAwNiHvAEA++NvI/FOvphhERGRyJgA1QHOtlaQSyUAUG7h8d28Iuw4mQQAmGDkqe/lsZbL0K1ZIwDAgXhxHzftOZ+KK7fz4KiU44PBbdGrpQsEAdh4PEHUuIiISFxMgOoAqVSib2hY3lT4TdGJKCzWwN/TEV18nM0SU6//PAYTiyAIWLpfO/rzTIgvHJQKPBWs7Xy96XhihaNlRERU9zEBqiN0hdD3zwRTawT8eERb8zKhuy8kEolZ4tE1RDx27Q4KVOIsi3H4cgZO38yCUiHFcz18AQD927rDzcEa6blF2HM+VZS4iIhIfEyA6ghX/XIYpXsB/X3hFpIy78HZVoFhnbzMFk8LN3t4OilRVKxB1LU7Zvvc/1q6/zIAIKxrUzQqqZNSyKQI66qtBVp/lI/BiIjqKyZAdYRrBVPh1xy5DgAY07UplArTTX2/n0QiQa+W4q0OH5twF5FXMiCXSjCxd7NS74U91BRSCXDkagYup+WaPTYiIhIfE6A6wq2cGqBLt3Jw+HIGpBLg6W7GW/W9qnR1QAfizV8HpKv9GdG5MRo3sCn1nlcDG/Rr4w4A2HCMo0BERPURE6A6Qt8L6D+PwHSjPwP83dHE2dbsMfVs4QKJBIi7lYNb2ZUv02FM8bdyEHH+FiQS4JU+zcvd56mShHBLTKJoNUpERCQeJkB1xP3LYWQXqLDthHbqu7FXfa8qZzsrdGjsBMC8s8G+Kxn9GejvgRZu9uXu07ulK5o42yC7oBi/nU4xW2xERGQZmADVEfc/AtscfRP5RWq0crdHSElPHjH8Ox3ePHVAiXfy8cupZADAaw+XP/oDADKpBGMf0o4CsTM0EVH9wwSojnD9zzR47dT36wC0/W/MNfW9PP8WQqdDozH9shgrDl6FWiOgZwsXdGjSoNJ9R3fxhkImQWxCJs4lZ5k8NiIishxMgOoIl5Jp3sUaATtik3A9Ix8OSjlGdm4salydmzrDzkqGO3lFOJ+SbdLPup1TiE3HEwFUPvqj4+pgjYHtPAAA61kMTURUrzABqiOs5FI0tLMCACz6Kx6AdoTDzlouZliwkksR0tw8q8OHH76GwmINOnk3qPJjv6eCtQvD/hKbhNzCYlOGR0REFoQJUB2iqwO6efceJBLgmRDTrvpeVb1blTwGM+F0+OwCFdaVdLx+rW/zKj/269asIZq52iGvSI0dsUkmi4+IiCwLE6A6RFcHBAAPt3aDTyM7EaP5l64QOvrGHeSZaJTlxyM3kFNYjJZu9nikrXuVj5NIJPpRoPXHEiAIpq9TIiIi8TEBqkP+mwCJNfW9PL6NbNHE2QYqtYBj1zKMfv4ClRqrDl8DALzatzmkUsOKvkcFNoG1XIoLKdmITcw0enxERGR5mADVIbpeQM1c7NCrhYvI0fxLIpGgdyvTdYX+OToR6blFaNzABkM7Gr7emZOtQn/cuqOcEk9EVB8wAapDhnTwRBsPB8wc4m/wKIip9TbRumAqtQbf/3MVAPByn2ZQyKr3n/RTwdqeQL+dTkFmfpHR4iMiIsskegK0dOlS+Pn5QalUIigoCAcPHqx0/yVLlqBt27awsbFB69atsXbt2lLvr1ixAr169YKzszOcnZ3xyCOPICoqypSXYDECGjth99TeeLiNm9ihlBHS3AVSCXDldh6SMu8Z7by/nkpGUuY9uNhbYXQX72qfp5N3A/h7OqKoWIMtMTeNFh8REVkmUROgTZs2YerUqfjwww8RGxuLXr16YdCgQUhIKL8ny7JlyzB9+nR8/PHHOHfuHD755BNMmjQJv/76q36f/fv3Y+zYsdi3bx+OHDmCpk2bIjQ0FElJnOEjJicbBTp5NwAAHIw3ziiQRiNgWcmyF8/39KvRavcSiQRPd9MWQ29gMTQRUZ0nEUT8P31wcDACAwOxbNky/ba2bdtixIgRmDNnTpn9u3fvjh49emD+/Pn6bVOnTkV0dDQOHTpU7meo1Wo4Oztj8eLFeOaZZ6oUV3Z2NpycnJCVlQVHR0cDr4oqsigiHl//fQmPtffEkqcCa3y+P8+l4uUfY+BgLcfh6f3gqFTU6Hy5hcXoNvtv5BYWY8PEYHRvbjl1VERE9GCGfH+LNgJUVFSEmJgYhIaGltoeGhqKyMjIco8pLCyEUqkstc3GxgZRUVFQqVTlHpOfnw+VSoWGDRsaJ3CqNl0h9KHL6VDXcFkMQRCwtGT0Z3yIT42THwCwt5ZjRGdtMfT6o+wMTURUl4mWAKWnp0OtVsPdvXTPFnd3d6SmppZ7zMCBA7Fy5UrExMRAEARER0cjPDwcKpUK6enlzy56//330bhxYzzyyCMVxlJYWIjs7OxSLzK+jk2c4KCUI+ueCmeSarb21pErGTiVmAlruRTP9/QzUoTAuIe0j8H+PJeKtJwCo52XiIgsi+hF0Pd37BUEocIuvjNnzsSgQYPQrVs3KBQKDB8+HBMmTAAAyGRl6z+++OIL/PTTT9i2bVuZkaP/mjNnDpycnPQvb+/qF9NSxeQyKXrolsWoYR2QbvRnTFdv/TpoxuDv5YjApg1QrBGwOdpyiqH3x6VhYUQ8ioo1YodCRFQniJYAubi4QCaTlRntSUtLKzMqpGNjY4Pw8HDk5+fj+vXrSEhIgK+vLxwcHODiUrpe48svv8Ts2bOxZ88edOjQodJYpk+fjqysLP0rMTGxZhdHFerVqubT4U/fzMShy+mQSSWY2KuZsULT+28xdE0f1RnD2aQsvLQ2Bt/8fQlrj1wXOxwiojpBtATIysoKQUFBiIiIKLU9IiIC3bt3r/RYhUKBJk2aQCaTYePGjRgyZAik0n8vZf78+fj000+xe/dudOnS5YGxWFtbw9HRsdSLTKN3ybIYJxIykVNQft3Wgyzdpx39Gd7JC94NbY0Wm87g9p5oYKtAUuY9/BOfZvTzGyK7QIXX1p9AkVo78rNk32VkV/P3RkRE/xL1Edi0adOwcuVKhIeH48KFC3jzzTeRkJCAV155BYB2ZOa/M7fi4+Oxbt06XLp0CVFRUQgLC8PZs2cxe/Zs/T5ffPEFZsyYgfDwcPj6+iI1NRWpqanIzc01+/VRWd4NbeHnYge1RsCRK4Yvi3E5LQd/nteOGr7ap7mxwwMAKBUyjApsAkDcYmhBEPDu5tNIuJOPxg1s0MzFDnfzVVh54KpoMRER1RWiJkBjxozBV199hVmzZqFTp044cOAAdu3aBR8f7SOIlJSUUj2B1Go1FixYgI4dO2LAgAEoKChAZGQkfH199fssXboURUVFGDVqFDw9PfWvL7/80tyXRxXope8KbfiyGMv2X4UgAKH+7mjp7mDs0PTGlnSG3huXhpt38032OZVZHXkdu8+lQiGTYOlTgXj30dYAgJWHruF2TqEoMRER1RWi9gGyVOwDZFoR529h4tpo+DSyxT/vPFzl45Iy76HPF/tQrBGwY1IPfWNFUxm34igir2TgjX4t8FZoa5N+1v1iE+5i9PdHoFIL+GRYOzzb3ReCIGDE0kicSszEsyE++GR4gFljIiKydLWiDxDVXyHNG0EuleBGRj5uZORV+bgVB66iWCOge/NGJk9+gH+LoTceT4RKbb7ZV5n5RXh9QyxUagGPtffEMyHaOCQSCd4rGQXaEJWAhAxxRqaIiOoCJkBkdvbWcgT6OAOo+mOwjNxCbDyufRz6Wt8WJovtvwb4u8PVwRq3cwoRcf6WWT5ToxHw1s+nkJR5D76NbDH3ifal2kJ0b+6C3q1coVILWBgRZ5aYiIjqIiZAJApDV4dfdfg6ClQadGjihB4tGpkyND2FTIoxJQusrj92wyyfufzgVfx9MQ1WcimWPhUEh3I6XL87UDsK9MupZJxPZtNOIqLqYAJEouhVMh0+8nLGAx8v5RSosKak/81rfVtU2CjTFMIe8oZEAhy+nIGrt007kzDq2h3M/1M7qvPJsHbw9yr/+XVAYycM7egFQQDm/3nRpDEREdVVTIBIFAGNneBsq0BOYTFOJWZWuu/6YwnIKShGCzd7hPqX3yTTVJo426JfazcAwE9RppsSn55biDd+OgG1RsDIzo0R1rXybuRvDWgFuVSCfXG3ceyq4e0EiIjqOyZAJAqZVIIeLUqWxaikDqhApcbKg9cAAK/0aQ6p1HyjPzpPddNOid8ccxMFKrXRz6/WCHhz00ncyi5ECzd7fDYi4IGjXL4udgh7SJskzdt9EZzMSURkGCZAJBpdV+jK6oA2x9xEem4hGjewwfBOXuYKrZQ+rdzQuIENMvNV2HUmxejnX7LvMg5eSoeNQoZlTwXCzlpepeMm92sJG4UMJxIyzVakTURUVzABItH0LCmEPpWYiaz8sss7FKs1WH5Au+zFxF5+UMjE+c9VJpVg7EO6YmjjPgY7fDkdi/6KBwB8PjLAoOaObo5KPN/TFwAw/884i1i3jIiotmACRKLxamCDFm720AjA4StlH4P9djoFiXfuoZGdFcZ0bSpChP8a3dUbcqkEMTfu4kKKcWZepWUXYMrGWAgCENbVG4+XLL9hiJd6N4eTjQKX0nKx7YTlrF5PRGTpmACRqCp6DKbRCFi2Xzv681wPX9hYycwe23+5OSgxsJ0HAO0q8TVVrNbgjZ9ikZ5bhDYeDvh4WLtqncfJRoFJD2vXRPvqr0smqVEiIqqLmACRqHq1KimEjk8vVci792Ia4m7lwN5ajvEhviJFV9pTJeuDbY9NQl5hcY3OteiveBy7dgd2VjIsfSoQSkX1E7xnQnzh6aREUuY9rDtqnn5FhjiRcBe9vtiLpfsvix0KEZEeEyASVbBfQ1jJpEjKvIdr6dplMQRB0H9ZPt3NB042ZZsBiiGkeSM0c7FDbmExfjmZXO3z7ItLw5J92tGtuU90QDNX+xrFpVTIMPWRlgC0BdU5BWXrqcSSnHkPL62NQeKde/jyzzicTcoSOyQiIgBMgEhktlZydPHVLotxIF77GOzYtTs4kZAJK7lUX+RrCSQSCcaVjAKtP3ajWlPPkzPvYdqmkwCAZ0J8MLSjcWa2PRHYBM1d7XA3X4UVB64a5Zw1lV9UjIlro5GeWwipBNAIwPRtZ1BsxnXViIgqwgSIRNe7la4OSFsIvbSk9md0lyZwc1CKFld5nghsAiu5FOeSs3HqpmGjGSq1Bq9vOIG7+Sq0b+yEDx9ra7S45DIp3ilZImPloWu4nVNotHNXhyAIeGfzaZxLzkYjOytsfbU7HJRynEnKwpojlveYjojqHyZAJLpeJdPhj1zNwImEuzgQfxsyqQQv924ucmRlOdtZYUgHTwDAegPrbb7YfREnEjLhoJRj6VOBsJYbt7B7YDsPdPRugPwiNRbvvWTUcxvq272X8fuZFChkEnw3Pgidmzpj+iBtwrdgTxySMu+JGh8RERMgEl1bD0e42Fshv0iNN0seDw3t4AnvhrbiBlaBp4J9AAC/nk4ut39RefacS8WKko7WC57saJJrk0gkeO9R7SjQhqgEJGTkG/0zqmL32RQsjND2Nvp0eAC6+jYEoJ3q39XXGflFaszccZbdq4lIVEyASHRSqQQ9S5bFuFHypf1q3xZihlSpwKYN0MbDAQUqDbZWofdOQkY+3tp8CgDwYk8/hJZMpzeF7s1d0KulC1RqAQsj4kz2ORU5n5yNNzdpr3VCd1+EPfRv/yapVII5j7eHQibB3otp2HUm1ezxERHpMAEii6CrAwKAR9q6o7VH1Tsim5tEIsFT3bSjQA8qhi4sVmPShhPIKShGYNMGeG9QG5PH996j2s/45VQyzicbp2ljVaTnFmLi2mjcU6nRs4ULZpRT49TCzUGf3H786zlk3bOcGWtEVL8wASKL0LOlC3Trf772sOXV/txvZOfGsLOS4crtPBy7dqfC/T7//QLOJGXB2VaBxeMCzbKcR0BjJwzp4AlBAOb/edHknwcARcUavLbuBJIy78G3kS0Wj+sMeQXX+lrf5mjmYofbOYWYt9s88RER3Y8JEFkENwclvhzVEbNHtkdgU2exw3kge2s5hnduDKDi9cF+O52MtSUznhaO6QSvBjZmi+/t0NaQSyXYF3cbx65mmPSzBEHA/345i6jrd+BgLcfKZ7uiga1VhfsrFTLMfrw9AG1X7ePXK04giYhMhQkQWYwngpro++zUBuNK6lt2n00pM+386u1cvL/1DABg0sPN8XBrN7PG5utihzFdtQu4ztt90aQFx2sir2Pj8URIJcA34zqjhduDGzt2a9YIo7to1z77YNsZFBVbZm8gtUbAh9vPYOzyo8jIFbe1ABEZFxMgomoKaOyETt4NoFIL2ByTqN9eoFLjtfUnkFtYjGC/hnjzkVaixDelf0soFVKcSMjEXxfSTPIZhy6l49PfLwAApg9qa1Ci98HgtmhkZ4VLabn4/p8rJomvpj797TzWH0vAkasZ+HA7Z64R1SVMgIhqQLc+2IZjCdBotF+OH+88h4upOXCxt8K3YyuuhTE1N0clnu/hB0BbC6TWGPfL+1p6HiZtOAG1RsDjgY3xYi8/g45vYGuF/w31BwB8u+8yrt7ONWp8NbXq8DWsjrwOAJBLJdh9LhXbY5PEDYqIjIYJEFENDO3oBUelHDfv3sOBS7exNeYmNh5PhEQCfB3WGW6O4nayfrlPczjZKBB/K9eoX97ZBSq8uOY4su6p0LlpA8we2R4SXRW7AYZ19ELvVq4oKtbgg+1nLGaEJeL8Lcz67TwAYPqgNnhzgHYU76NfziGZTRyJ6gQmQEQ1oFTIMCpIW2uzKCIeM3acBQBM7d8KPUp6G4nJyUaB1/pqZ9UtiohHgUpd43OqNQIm/xSLK7fz4OmkxPfjg6q9mr1EIsHnIwKgVEhx9OodbI55cF8lUztzMwuTf4qFIABjH2qKl3o3w8u9m6Fz0wbIKSzGO1tO6Uf7iKj2YgJEVEO6wu1TN7NwT6VGr5YueL2f5TRyfLa7LzwclUjKvFfhjDVDzNt9EfvjbkOpkGL5+C41Xq/Nu6Gtvk5q9q4LSBex2Dg58x5eWHNcfx9nDW8HiUQCuUyKhaM7wUYhw+HLGVh75LpoMRKRcTABIqqhFm726NZMu9yDu6M1Fo3pBJnU8MdBpqJUyDD1kZYAgCX7LiOnoPrNB7fG3MTyktXm54/qiPZNnIwS4ws9/eDv6YjMfBU+K3n0ZG45BSo8v/o40nIK0drdAUueKt23yc/FDh8M1jaZnPPHRVxOs6yaJSIyDBMgIiP4YHBb9G3tiuXju8DF3lrscMoYFdQEzVztcCevSL8mmaFOJNzF9G3aqf1v9GuBoR29jBafXCbFnMfbQyoBdpxMxoH420Y7d1UUqzWYtCEWF1Nz4OpgjfDnusJRqSiz39PdfNCrpQsKizV46+eTUKktc/o+ET0YEyAiI+jQpAFWP/cQOno3EDuUcsllUrwTql0odeXBq2X6Fj1IStY9vLQ2BkVqDUL93U0ytb+jdwM8290XAPDhjjO4V1TzeqWqEAQB/9t5Dgfib8NGIUP4s13RuIKmlRKJBPNHdYSjUo5TN7OwdJ9lTt/XuXQrB5PWn8ChS+lih0JkcZgAEdUTjwZ4oGMTJ+QXqbFk3+UqH3evSI2X1sYgPbcQbTwcsGhMJ0hN9IjvrdDW8HRSIvHOPXz99yWTfMb9Vhy8ig3HEkpm7nV64GM9DyclPh0RAAD4du8lnL6ZaYYoDXfzbj6e/uEYfj+Tgpd/jMa19DyxQyKyKEyAiOoJiUSiXyh1/bEbSMjIf+AxgiDg3a2ncSYpCw3trLDimS6ws5abLEZ7azk+Ha5NLlYcvGryxVz/OJOC2bu065HNeMwfoe08qnTcsI5eeKyDJ4o1Aqb9fMoos+uM6W5eEZ4Jj8Kt7EJIJEBekRqvbziBwmLLipNITEyAiOqR7i1c0KulC1RqAQsj4h64/5J9l/HrqWTIpRIsfSoQ3g1tTR7jI/7uGBTgAbVGwPTtZ4zewFEnNuEupm46CQB4NsQHz/fwrfKxEokEnw0PgJuDNS6n5eKL3Q/+XZpLflExnl9zHFdv58HLSYltr3aHs60C55KzMWcXF58l0mECRFTP6EaBfjmVXOkIy55zqfhyTzwAYNbwAHRr1sgs8QHAx8PawcFajlOJmfjRBFPOE+/kY+LaaBQWa9CvjRtmDvE3uJGjs50V5o3qAAAIP3wNkVfEr7NRqTV4fUMsYhMy4WSjwNoXHkLnps5YMLojAGB15HXsPpsqcpREloEJEFE9E9DYCUM6eEIQtEtklOdianap0RFzL1Lr7qjEu4O0idr8P+OM2n05654Kz60+jvTcIvh7OtZouZKHW7vpfzfvbD6N7Bq0GKgpQRDwwbYz2HsxDdZyKcIndEELNwcAQL827nipdzMAwLtbTiHxzoMffxLVdUyAiOqht0JbQy6VYF/cbRy7mlHqvYzcQry4Jhr5RWr0aNEIM4f4ixLjUw81RZCPM/KK1Pho5zmjnLOoWINX18XgclouPByVCJ/QtcY1TR8OboumDW2RlHkPs34Vp4cRAHy5Jw6bY25CKgEWjwtEkE/DUu+/HdoanbwbILugGG/8FMsp/FTvMQEiqof8XOwwpqt2CY95uy/q1+AqKtbg1fUncPPuPfg0ssWScYGiLeYqlUow5/H2UMgkiDh/q8aPbgRBwIwdZxB5JQN2VjKET+gKD6ear9VmZy3HwtEdIZEAW2Ju4s9z5n/EtPrwNSwpmZI/e2R7DPB3L7OPlVyKb8d2hoNSjpOJmfjyT8upWyISAxMgonpqcv+WUCqkOJGQib8upEEQBHz86zlEXbsDe2s5Vj7TBQ1srUSNsZW7A17urV3L7KOdZ2v0iGnp/iv4OfrfERJ/L0djhYkuvg31cX6w7YxZl/P47XQyPinpnv3WgFYIe6jix5XeDW0xv6Ru6fsDV7EvLs0sMRJZIiZARPWUu6MSz/fwA6CtBVodeV3fD+ebsZ3Q0t1B5Ai1Xu/XAn4udriVXVjtUYudp5Ixv+TYT4a1w8Nt3IwZIgDgzQEt0cbDARl5RZi+zTwr20deSce0TacgCMAzIT5VWoPu0QBPPBviAwB46+dTSM0qMHWYRBaJCRBRPfZyn+ZwslEg/lYuPimpX3nv0Tbo16bsIxSxKBUyfF7SePDHozcQc+OuQcdHX7+DtzefAqBdc2x8iK+xQwQAWMtlWDSmk/6R3RYTr2x/LjlL3517UIAHPhrarsoz2aYPbot2Xo64k1eEyRtjUcx6IKqHmAAR1WNONgq81re5/ufHOzfGyyWzhSxJ9xYuGBXUBIKgfcRU1QLe6+l5mLg2GkXFGgzwd8cHg9uaNM62no6YNkC75Mgnv57HzbummW2VeCcfE1YdR25hMYL9Ghq8AK9SIcPicYGws5Ih6todfLO36p3BieoKJkBE9dyz3X0R7NcQ/dq4Yfbj7Q3uh2MuHw5ui4Z2Voi7laNfkb4yd/OK8Nzq47ibr0KHJk74OsywJKG6XurdDF18nJFbWIy3N5+CxsiNHDNyC/FMeBRu52iXJln+TBcoFTKDz+PnYofZj7cHoF3SI/Ky+H2MiMyJCRBRPadUyLDp5RCET+harS9Sc3G2s8LMIdoRnG/+voTrlaxtVVisxsvrYnAtPQ+NG9hg5bNdYGtluiU8/ksmlWDB6I6wtZLh6NU7CD98zWjnzissxvOrj+uva83zD8HJpuyq9VU1vFNjhHX1hiAAUzadNHiRXKLajAkQEdUaIzo1Rq+WLigs1uDDHeUXGguCgPe3nkHUtTtwsJYjfEJXuDnUfLq7IXwa2WHGY9r+SV/8GYdLt3JqfE6VWtui4NTNLDjbars8uzvW/Lo+GtoOrdztcTunENN+Pmn0ESsiS8UEiIhqDYlEgs9GBMBaLsXhyxnYHptUZp+v/rqE7bFJkEklWPJUIFp7iDObbexD3ujb2hVFxRq8+fPJGjUeFAQB7205jQPxt6FUSPHDhK5o7mpvlDhtrGRYMi4QSoUUBy+lY9k/V4xyXiJLxwSIiGoVn0Z2mPpIKwDAp7+dx528Iv17W2Nu4uu/LwEAPhsRgN6tXEWJEdAma1880QENbBU4m5SNb2tQaDx390VsK0nqlj4ViMCmzkaMFGjp7oBZw7Uz7RZGxOP49TtGPT+RJWICRES1zou9/NDGwwF381X4/PcLAICjVzPw/rbTAIBX+jTH2EoaApqLm6MSn5VM4V+y7zJOJmYafI4fDl3D9/9oi77nPt7eZC0KngxqgpGdG0OtETD5p1jc/U9iKYaUrHuYujEWr66LQW5hsaixUN3EBIiIah2FTIo5j7eHRAJsPXETPx69gZd/jIFKLWBwew+8O7C12CHqDengheGdvKDWCJi26STuFamrfOwvJ5PwaUmX53cfbY0nu3ibKkxIJBJ8OiIAzVzskJJVgLc3nzJLM8f7qTUC1kRex4CFB7DjZDL+OJuKNzacYK8iMjomQERUK3Vu6oxnumk7Gs/ccRZZ91To3LQBFo7uBKkZprsbYtawALg7WuNqeh7m7b5YpWMOXUrXN3Cc0N0Xr/Zp/oAjas7eWo5vx3WGlVyKvy+m4YdDxpvBVhUXU7Mx6rtIfLTzHHILi9GhiROUCin2xd3GJ7+eFyUho7qLCRAR1VpvD2wNj5KZUN4NbbCimj1xTM3JVoH5ozoCAFZHXsehS5X33DmblIWXf4yGSi3gsQ6e+N8Qf7P1Z2rn5YSZQ7Qz2Obtvlitx3aGKlCpMf/PixjyzSHEJmTC3lqOT4e3w47XeuCrMZ0gkWi7gIcfvm7yWKj+YAJERLWWg1KB5c8EIayrN9Y+HwwXe2uxQ6pQ71auGF8yYvXOllPIulf+wq43MvIwYVUU8orU6N68ERaO7mj2Ea2ng5ticHsPqNQC3vjpRIWxGkPk5XQ8+tUBLNl3BcUaAaH+7vhrWh+MD/GFVCrBowGe+GCQtv/TZ7+fx5/nUk0WC9UvEoFjimVkZ2fDyckJWVlZcHQ03orRRFS/5RcV47FvDuFaeh5Gdm6MRWM6lXr/dk4hRn0XiRsZ+fD3dMSml7vBQVn9Roc1kXVPhSHfHkTinXsY3N4DS8YFGnUU6m5eEWbvuoDNJWumuTta45NhAXg0wKPMvoIgYMaOs1h/LAFKhRQ/vxyCDk0aGC0WqjsM+f7mCBARkZnYWsmxYHRHSCXA9tgk7DqTon8vt7AYz62Owo2MfHg3tMHq57uKlvwA2nXiFo8NhEImwa4zqVh3LMEo5xUEAb+cTMIjC//B5pibkEiA8d18EDGtT7nJD6At0P5kWDv0aeWKApUGL6yJNtk6a1R/iJ4ALV26FH5+flAqlQgKCsLBgwcr3X/JkiVo27YtbGxs0Lp1a6xdu7bMPlu3boW/vz+sra3h7++P7du3myp8IiKDBDZ1xmt9WwAAPtx+Bmk5BSgq1uDVdTE4m5SNhnZWWPt8sNm7V5eno3cDvPdoGwDankvnkrNqdD7dIq5TNp5ERl4RWrnbY8srIfh0RAAcH5DsyWVSLB7XGW08HHA7pxAvrI5GdoHpHs1R3SdqArRp0yZMnToVH374IWJjY9GrVy8MGjQICQnl/0tj2bJlmD59Oj7++GOcO3cOn3zyCSZNmoRff/1Vv8+RI0cwZswYjB8/HqdOncL48eMxevRoHDt2zFyXRURUqcn9W8Lf0xF381V4f+sZvLPlFA5eSoetlQyrJnSFn4ud2CHqvdDTD4+0dUdRsQZvbIitVk+eYrUGKw5cReiiA/gn/jasZFK8NaAVfnujF4J8Glb5PA5KRcnSJtaIu5WDSetP1KjDNtVvotYABQcHIzAwEMuWLdNva9u2LUaMGIE5c+aU2b979+7o0aMH5s+fr982depUREdH49ChQwCAMWPGIDs7G3/88Yd+n0cffRTOzs746aefqhQXa4CIyNTiUnMw9NtDKCr5ApdLJfhhQlf0EbF7dUUy84sw+OuDSM4qwIhOXlg0plOV64HOJmXh/W2ncTYpGwAQ7NcQsx9vX6OlPM4mZeHJ747gnkqNsK7eJT2hLKv1AYmjVtQAFRUVISYmBqGhoaW2h4aGIjIystxjCgsLoVSWHha2sbFBVFQUVCrtUOiRI0fKnHPgwIEVnlN33uzs7FIvIiJTau3hgHf+07Bx/pMdLDL5AYAGtlb4ZmxnyKQS7DiZrC9crkx+UTE+//08hi0+hLNJ2XBUyjHvifb4aWK3Gq9jFtDYCd+O7QyJBNh4PBHfH7hao/OZgiAISMq8x95FFky0BCg9PR1qtRru7qXburu7uyM1tfxpjgMHDsTKlSsRExMDQRAQHR2N8PBwqFQqpKdr+2qkpqYadE4AmDNnDpycnPQvb2/TdVslItJ5vqcfZg7xx/LxQRjZuYnY4VSqi29DvBWqXYPtf7+crXSF+/1xaRiw8ABWHLwGjQAM6eCJv97qgzFdmxptSv8j/u74X0m/orl/XCxVUC629NxCvLAmGj3m7sXEtdG1bikPQRCwNeYmtp24WacTONGLoO8fthQEocKhzJkzZ2LQoEHo1q0bFAoFhg8fjgkTJgAAZLJ/m58Zck4AmD59OrKysvSvxMTEal4NEVHVyaQSvNDTD6Htyp/9ZGle6d0cvVq6oEClwaQNJ8os65GeW4gpG2MxYdVxJGXeQ+MGNgif0AWLxwWapKj7uR5+mNDdFwDw5qaTOJFw1+ifYaj9cWl49KuD2HsxDQDw14U0PPndESRn3hM5sqq5V6TG5I0n8dbmU5j28ynM/OUs1Jq6mQSJlgC5uLhAJpOVGZlJS0srM4KjY2Njg/DwcOTn5+P69etISEiAr68vHBwc4OLiAgDw8PAw6JwAYG1tDUdHx1IvIiIqTSqVYNGYTnB1sEb8rVx88us5ANp/ZP4cnYj+C/7BLyeTIZVoi6f3vNnbZIu36swc4o9H2rqhsFiDiWuikZAhzvT4ApUaH+88hwmrjiM9txCt3O3x1ZhOcLG3xoWUbAxfctgsXbVrIiXrHkZ/fwS/nkqGXCqBRAKsO5qASetPoEBV9TXsagvREiArKysEBQUhIiKi1PaIiAh079690mMVCgWaNGkCmUyGjRs3YsiQIZBKtZcSEhJS5px79ux54DmJiOjBXOyt8XVYJ339zXf/XMG4Fcfw7pbTyLqngr+nI3ZM6oGZQ/xhZy03eTwyqQRfh3VGOy9HZOQV4bnVUcjKN+/0+LjUHAxffBirI68D0K7dtvP1nhjRuTF2TOqun7o/5vsj+P205Tyq+6+YG3cw9NvDOJOUhYZ2Vlj/YjCWjAuElUyK3edSMf6HY2b/vZqaqLPANm3ahPHjx+O7775DSEgIli9fjhUrVuDcuXPw8fHB9OnTkZSUpO/1Ex8fj6ioKAQHB+Pu3btYuHAhIiIiEBMTA19fXwBAZGQkevfujc8//xzDhw/HL7/8ghkzZuDQoUMIDg6uUlycBUZEVLlFEfH4+u9L+p+VCinefKQVnu/pB4XM/P+2vpVdgBFLDiMlqwAhzRphzfMPwUpu2jgEQbty/ew/LqKoWAMXeyvMH9URD7dxK7VfbmExJv8Uq38s9nZoK0x6uIXFzFz7OToRM7afRZFagzYeDljxTBd4N7QFABy5koGX1kYjp7AYrdztsfq5h+DVwEbkiCtWK2aBAdop61999RVmzZqFTp064cCBA9i1axd8fLTr5aSkpJTqCaRWq7FgwQJ07NgRAwYMQEFBASIjI/XJD6CdKr9x40asWrUKHTp0wOrVq7Fp06YqJz9ERPRgk/u3RPfmjQAAvVq64M+pvfFyn+aiJD8A4O6oRPiErrCzkuHI1Qx8sP2MSQt4b+cU4rnVx/Hxr+dRVKzBw61d8ceU3mWSHwCwt5ZjxTNd8EJPPwDAl3viMe3nUygsFvexUrFag1m/nse7W06jSK3Bo+08sPXV7vrkBwBCmjfCz6+EwN1R+9jziWWRiK+kAL424Vpg5eAIEBHRgxUVa3A1PRet3R0sZjRjf1waXlgTDbVGwFsDWuGN/i2N/hl7L97CO5tPIyOvCFZyKT4c3BbPhPhU6Xew7ugNfLTzHNQaAV18nPH9+CA0EmER38z8IrzxUywOXtLOoJ76SEtM7teywll6N+/m49nwKFy5nQdHpRw/TOiKrr5Vb2JpLoZ8fzMBKgcTICKi2mvd0RuYseMsAODrsE4Y3qmxUc5boFJjzq4LWHPkBgCgjYcDvg7rjNYeDgad59CldLy6PgY5BcVo2tAW4RO6oIWbYeeoictpOXhxTTSuZ+TDRiHDwtEdMai95wOPu5tXhBfWHMeJhExYy6X4ZmxnDLSwGYy15hEYERGRsT3dzQcv9W4GAHhn82kcv36nxue8mJqNYYsP6ZOf53r4YsekHgYnPwDQs6ULtr/WA00b2iLhTj5GLo3EwUu3axxjVey9eAsjlkTiekY+GjewwdZXu1cp+QEAZzsrrH+xm37W3avrYrDu6A0TR2w6HAEqB0eAiIhqN41GwGvrT2D3uVQ42yqw7bUe1VpjTRAErDp8HXN36wqdrfHlkx3Qt3XZWh9D3ckrwis/xiDq+h3IpBJ8PKwdxnfzqfF5yyMIAr775yq++PMiBAF4yK8hlj0VWK3Hb8VqDWbsOIuNx7U98yb3b4k3H2lpEY9B+QishpgAERHVfveK1AhbfgSnbmbBz8UO217tDmc7qyofn5ZTgHc2n8Y/8drRmf5t3DBvVAe4GLFmp7BYjenbzmDbiSQA2pGlGY/5Q2akjtmA9tHde1tP45eTyQCAccFN8fHQdjWaJScIAhb9dQnflMwEDOvqjc9GBEAuUhG8DhOgGmICRERUN9zOKcSIJYeRlHkPXX2dse7FYFjLZQ887u8Lt/DuFm2hs7VcihmPtcXT3apW6GwoQRCwdP8VzP8zDgDwcGtXfDO2MxyUihqfOyXrHl7+MQanb2ZBLpXgIyOPMq0/dgMzd5yFRgAeaeuGb8cGwsbqwb9fU2ECVENMgIiI6o5Lt3Lw+LJI5BQUY3gnL3xVyWr2BSo1Zu+6gLX/KXT+ZmxntHI3fZHyrjMpmPbzSRSoNGjt7oAfJnRBE2fbBx9YgRMJd/HyjzG4nVMIZ1sFlj4VhJCS1gXG9Oe5VEz+KRaFxRoENm2AH57tatBImzGxCJqIiKhES3cHLHsqCHKpBL+cTMaivy6Vu9/55GwM/faQPvl5oacffnm9h1mSHwAY3N4Tm14KgauDNeJu5WDEksPVXt9sS8xNhH1/FLdzCtHa3QE7X+9pkuQHAAa288C6F4PhqJTjREImRn0XiZt3xVmSxBAcASoHR4CIiOqen48n4t2tpwEAC57siCeCmgDQFkyHH76GL3bHoUitgauDNRY82RG9W7mKEmdy5j28uCYa51OyYSWX4ssnO2JYR68qHVus1mDuHxex8tA1AECovzsWjukEezMsSxJ/KwfPhkchJasA7o7WWPP8Q2jjYd7vUD4CqyEmQEREddP8Py9iyb4rUMgkWPt8MJq72uGtzaf0DQEfaeuGeU90EKU54X/lFRZjysaT+OvCLQDAm4+0wuT+lS+fkZWvwus/ndBfy+T+LTG1f8XNDU0hJeseng2PQvytXDgotR2wuzUzzchTeZgA1RATICKiukmjETB5Yyx+O50CR6UccpkUd3SFzkP88XRwU4uYzg0Aao2AebsvYvmBqwCA4Z28MO+JDlAqyhYZX07LxcS10biWngcbhQxfPtkRj3WoWn8fY8vKV2Hi2mhEXb8DK5kUX4V1wuAq9hqqKSZANcQEiIio7ipQqfHUymOIuaGtr2nr6Yhvx3YyazdmQ2yMSsCMHWdRrBEQ2LQBlj/TpdRU/H0X0zD5p1jkFBajcQMbLH8mCO28nESMWPs7nrIxFn+euwWJBPh4aDs8293X5J/LBKiGmAAREdVtGbmF+N8v59DM1Q6v92tRpanxYoq8nI5X1sUgu6AYTZxt8MOzXdHK3R7fH7iKebu1zQ27+jpj2dNBRu1TVBNqjYCPdp7FuqPaRc0nPdwcb4e2NukIGxOgGmICRERElubq7Vw8v/o4rmfkw95ajof8GmLvxTQAwNiHvPHJsIAaNTc0BUEQsGTfZXy5Jx4AMCqoCeY83h4KEzVM5DR4IiKiOqaZqz22v9YDwX4NkVtYjL0X0yCTSjBreDvMHtne4pIfAJBIJHi9X0vMe6I9ZFIJtsTcxMS10cgvKhY7NCZAREREtYWznRV+fCEYz4b4oKWbPX58/iE8E+JrMYXbFRnTtSmWjw+CUiHF/rjbGLviGDJyC0WNiY/AysFHYERERMZ3IuEunl99HJn5KjRzscNvk3vC1sp4PYr4CIyIiIgsTmBTZ2x5pTsaN7DBiM6NjZr8GEq8TyYiIqJ6p4WbPXZN7gVHG3FTECZAREREZFZOtjVf6b6m+AiMiIiI6h0mQERERFTvMAEiIiKieocJEBEREdU7TICIiIio3mECRERERPUOEyAiIiKqd5gAERERUb3DBIiIiIjqHSZAREREVO8wASIiIqJ6hwkQERER1TtMgIiIiKje4Wrw5RAEAQCQnZ0tciRERERUVbrvbd33eGWYAJUjJycHAODt7S1yJERERGSonJwcODk5VbqPRKhKmlTPaDQaJCcnw8HBARKJROxwTCY7Oxve3t5ITEyEo6Oj2OGYXH26Xl5r3VWfrpfXWneZ6noFQUBOTg68vLwglVZe5cMRoHJIpVI0adJE7DDMxtHRsV78hdOpT9fLa6276tP18lrrLlNc74NGfnRYBE1ERET1DhMgIiIiqneYANVj1tbW+Oijj2BtbS12KGZRn66X11p31afr5bXWXZZwvSyCJiIionqHI0BERERU7zABIiIionqHCRARERHVO0yA6qg5c+aga9eucHBwgJubG0aMGIG4uLhKj9m/fz8kEkmZ18WLF80UdfV9/PHHZeL28PCo9Jh//vkHQUFBUCqVaNasGb777jszRVszvr6+5d6nSZMmlbt/bbqvBw4cwNChQ+Hl5QWJRIIdO3aUel8QBHz88cfw8vKCjY0N+vbti3Pnzj3wvFu3boW/vz+sra3h7++P7du3m+gKDFPZ9apUKrz33nto37497Ozs4OXlhWeeeQbJycmVnnP16tXl3u+CggITX03lHnRvJ0yYUCbmbt26PfC8lnhvH3St5d0fiUSC+fPnV3hOS72vVfmusdS/t0yA6qh//vkHkyZNwtGjRxEREYHi4mKEhoYiLy/vgcfGxcUhJSVF/2rZsqUZIq65du3alYr7zJkzFe577do1DB48GL169UJsbCw++OADTJ48GVu3bjVjxNVz/PjxUtcZEREBAHjyyScrPa423Ne8vDx07NgRixcvLvf9L774AgsXLsTixYtx/PhxeHh4YMCAAfrla8pz5MgRjBkzBuPHj8epU6cwfvx4jB49GseOHTPVZVRZZdebn5+PEydOYObMmThx4gS2bduG+Ph4DBs27IHndXR0LHWvU1JSoFQqTXEJVfagewsAjz76aKmYd+3aVek5LfXePuha77834eHhkEgkeOKJJyo9ryXe16p811js31uB6oW0tDQBgPDPP/9UuM++ffsEAMLdu3fNF5iRfPTRR0LHjh2rvP+7774rtGnTptS2l19+WejWrZuRIzO9KVOmCM2bNxc0Gk2579fW+wpA2L59u/5njUYjeHh4CHPnztVvKygoEJycnITvvvuuwvOMHj1aePTRR0ttGzhwoBAWFmb0mGvi/ustT1RUlABAuHHjRoX7rFq1SnBycjJucEZW3rU+++yzwvDhww06T224t1W5r8OHDxf69etX6T614b4KQtnvGkv+e8sRoHoiKysLANCwYcMH7tu5c2d4enqif//+2Ldvn6lDM5pLly7By8sLfn5+CAsLw9WrVyvc98iRIwgNDS21beDAgYiOjoZKpTJ1qEZTVFSEdevW4fnnn3/gunW19b7qXLt2DampqaXum7W1Nfr06YPIyMgKj6voXld2jKXKysqCRCJBgwYNKt0vNzcXPj4+aNKkCYYMGYLY2FjzBFhD+/fvh5ubG1q1aoWJEyciLS2t0v3rwr29desWfv/9d7zwwgsP3Lc23Nf7v2ss+e8tE6B6QBAETJs2DT179kRAQECF+3l6emL58uXYunUrtm3bhtatW6N///44cOCAGaOtnuDgYKxduxZ//vknVqxYgdTUVHTv3h0ZGRnl7p+amgp3d/dS29zd3VFcXIz09HRzhGwUO3bsQGZmJiZMmFDhPrX5vv5XamoqAJR733TvVXScocdYooKCArz//vsYN25cpWsntWnTBqtXr8bOnTvx008/QalUokePHrh06ZIZozXcoEGDsH79euzduxcLFizA8ePH0a9fPxQWFlZ4TF24t2vWrIGDgwMef/zxSverDfe1vO8aS/57y8VQ64HXX38dp0+fxqFDhyrdr3Xr1mjdurX+55CQECQmJuLLL79E7969TR1mjQwaNEj/5/bt2yMkJATNmzfHmjVrMG3atHKPuX/ERCjpCfqgkRRL8sMPP2DQoEHw8vKqcJ/afF/LU959e9A9q84xlkSlUiEsLAwajQZLly6tdN9u3bqVKh7u0aMHAgMD8e233+Kbb74xdajVNmbMGP2fAwIC0KVLF/j4+OD333+vNDmo7fc2PDwcTz311ANreWrDfa3su8YS/95yBKiOe+ONN7Bz507s27evWivcd+vWzaL+hVFVdnZ2aN++fYWxe3h4lPmXRFpaGuRyORo1amSOEGvsxo0b+Ouvv/Diiy8afGxtvK+6WX3l3bf7/6V4/3GGHmNJVCoVRo8ejWvXriEiIsLglbOlUim6du1a6+63p6cnfHx8Ko27tt/bgwcPIi4urlp/hy3tvlb0XWPJf2+ZANVRgiDg9ddfx7Zt27B37174+flV6zyxsbHw9PQ0cnSmV1hYiAsXLlQYe0hIiH72lM6ePXvQpUsXKBQKc4RYY6tWrYKbmxsee+wxg4+tjffVz88PHh4epe5bUVER/vnnH3Tv3r3C4yq615UdYyl0yc+lS5fw119/VSs5FwQBJ0+erHX3OyMjA4mJiZXGXZvvLaAdwQ0KCkLHjh0NPtZS7uuDvmss+u+t0cqpyaK8+uqrgpOTk7B//34hJSVF/8rPz9fv8/777wvjx4/X/7xo0SJh+/btQnx8vHD27Fnh/fffFwAIW7duFeMSDPLWW28J+/fvF65evSocPXpUGDJkiODg4CBcv35dEISy13r16lXB1tZWePPNN4Xz588LP/zwg6BQKIQtW7aIdQkGUavVQtOmTYX33nuvzHu1+b7m5OQIsbGxQmxsrABAWLhwoRAbG6uf9TR37lzByclJ2LZtm3DmzBlh7Nixgqenp5Cdna0/x/jx44X3339f//Phw4cFmUwmzJ07V7hw4YIwd+5cQS6XC0ePHjX79d2vsutVqVTCsGHDhCZNmggnT54s9fe4sLBQf477r/fjjz8Wdu/eLVy5ckWIjY0VnnvuOUEulwvHjh0T4xL1KrvWnJwc4a233hIiIyOFa9euCfv27RNCQkKExo0b18p7+6D/jgVBELKysgRbW1th2bJl5Z6jttzXqnzXWOrfWyZAdRSAcl+rVq3S7/Pss88Kffr00f88b948oXnz5oJSqRScnZ2Fnj17Cr///rv5g6+GMWPGCJ6enoJCoRC8vLyExx9/XDh37pz+/fuvVRAEYf/+/ULnzp0FKysrwdfXt8L/EVmiP//8UwAgxMXFlXmvNt9X3ZT9+1/PPvusIAjaKbUfffSR4OHhIVhbWwu9e/cWzpw5U+ocffr00e+vs3nzZqF169aCQqEQ2rRpYzHJX2XXe+3atQr/Hu/bt09/jvuvd+rUqULTpk0FKysrwdXVVQgNDRUiIyPNf3H3qexa8/PzhdDQUMHV1VVQKBRC06ZNhWeffVZISEgodY7acm8f9N+xIAjC999/L9jY2AiZmZnlnqO23NeqfNdY6t9brgZPRERE9Q5rgIiIiKjeYQJERERE9Q4TICIiIqp3mAARERFRvcMEiIiIiOodJkBERERU7zABIiIionqHCRARERHVO0yAiKhO6du3L6ZOnWrQMRKJBDt27Kjw/f3790MikSAzM7NGsRGR5ZCLHQARkTFt27at1ixoS0TiYQJERHVKw4YNxQ6hyoqKimBlZSV2GET1Eh+BEZFR9e3bF5MnT8a7776Lhg0bwsPDAx9//HGVjpVIJFi5ciVGjhwJW1tbtGzZEjt37iy1z/nz5zF48GDY29vD3d0d48ePR3p6eqnP/+8jsJSUFDz22GOwsbGBn58fNmzYAF9fX3z11Velzpuenl7p5wLA4cOH0bFjRyiVSgQHB+PMmTOl3t+6dSvatWsHa2tr+Pr6YsGCBaXe9/X1xWeffYYJEybAyckJEydORFFREV5//XV4enpCqVTC19cXc+bMqdLvi4iqjwkQERndmjVrYGdnh2PHjuGLL77ArFmzEBERUaVjP/nkE4wePRqnT5/G4MGD8dRTT+HOnTsAtMlMnz590KlTJ0RHR2P37t24desWRo8eXeH5nnnmGSQnJ2P//v3YunUrli9fjrS0NIM+V+edd97Bl19+iePHj8PNzQ3Dhg2DSqUCAMTExGD06NEICwvDmTNn8PHHH2PmzJlYvXp1qXPMnz8fAQEBiImJwcyZM/HNN99g586d+PnnnxEXF4d169bB19e3Sr8rIqoBo64tT0T1Xp8+fYSePXuW2ta1a1fhvffee+CxAIQZM2bof87NzRUkEonwxx9/CIIgCDNnzhRCQ0NLHZOYmCgAEOLi4vSfP2XKFEEQBOHChQsCAOH48eP6/S9duiQAEBYtWlTlz923b58AQNi4caN+n4yMDMHGxkbYtGmTIAiCMG7cOGHAgAGlYnvnnXcEf39//c8+Pj7CiBEjSu3zxhtvCP369RM0Gs0Dfz9EZDwcASIio+vQoUOpnz09PcsddXnQsXZ2dnBwcNAfGxMTg3379sHe3l7/atOmDQDgypUrZc4VFxcHuVyOwMBA/bYWLVrA2dnZoM/VCQkJ0f+5YcOGaN26NS5cuAAAuHDhAnr06FFq/x49euDSpUtQq9X6bV26dCm1z4QJE3Dy5Em0bt0akydPxp49eyr4zRCRMbEImoiM7v5ZWBKJBBqNpsbHajQaDB06FPPmzStznKenZ5ltgiCU+xnlba9uzBKJRH9O3Z8r+xw7O7tSPwcGBuLatWv4448/8Ndff2H06NF45JFHsGXLlgd+NhFVHxMgIqo1AgMDsXXrVvj6+kIuf/D/vtq0aYPi4mLExsYiKCgIAHD58uVq9/M5evQomjZtCgC4e/cu4uPj9SNQ/v7+OHToUKn9IyMj0apVK8hkskrP6+joiDFjxmDMmDEYNWoUHn30Udy5c6dWzWgjqm34CIyIao1Jkybhzp07GDt2LKKionD16lXs2bMHzz//fKnHTDpt2rTBI488gpdeeglRUVGIjY3FSy+9BBsbmzKjNVUxa9Ys/P333zh79iwmTJgAFxcXjBgxAgDw1ltv4e+//8ann36K+Ph4rFmzBosXL8bbb79d6TkXLVqEjRs34uLFi4iPj8fmzZvh4eGBBg0aGBwfEVUdEyAiqjW8vLxw+PBhqNVqDBw4EAEBAZgyZQqcnJwglZb/v7O1a9fC3d0dvXv3xsiRIzFx4kQ4ODhAqVQa/Plz587FlClTEBQUhJSUFOzcuVPfxycwMBA///wzNm7ciICAAPzvf//DrFmzMGHChErPaW9vj3nz5qFLly7o2rUrrl+/jl27dlV4PURkHBKhoofkRER10M2bN+Ht7Y2//voL/fv3FzscIhIJEyAiqtP27t2L3NxctG/fHikpKXj33XeRlJSE+Ph4LplBVI9xjJWIzGL9+vWlpq//99WuXTuTfa5KpcIHH3yAdu3aYeTIkXB1dcX+/fuZ/BDVcxwBIiKzyMnJwa1bt8p9T6FQwMfHx8wREVF9xgSIiIiI6h0+AiMiIqJ6hwkQERER1TtMgIiIiKjeYQJERERE9Q4TICIiIqp3mAARERFRvcMEiIiIiOodJkBERERU7/wfqKnVsdx9PHgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(neighbors_settings, accuracy, label='accuracy')\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.xlabel(\"n_neighbors\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}