theory/python/eyes/14.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第十四章 scikit-learn 库"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**scikit-learn 库是当今最流行的机器学习算法库之一**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**可用来解决分类与回归问题**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**本章以鸢尾花数据集为例，简单了解八大传统机器学习分类算法的sk-learn实现**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**欲深入了解传统机器算法的原理和公式推导，请继续学习《统计学习方法》或《西瓜书》**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.0 鸢尾花数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【1】下载数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = sns.load_dataset(\"iris\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【2】数据集的查看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      "sepal_length    150 non-null float64\n",
      "sepal_width     150 non-null float64\n",
      "petal_length    150 non-null float64\n",
      "petal_width     150 non-null float64\n",
      "species         150 non-null object\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 5.9+ KB\n"
     ]
    }
   ],
   "source": [
    "iris.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.057333      3.758000     1.199333\n",
       "std        0.828066     0.435866      1.765298     0.762238\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "virginica     50\n",
       "versicolor    50\n",
       "setosa        50\n",
       "Name: species, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.species.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x178f9d81160>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 804.75x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=iris, hue=\"species\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【3】数据清洗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   petal_length  petal_width species\n",
       "0           1.4          0.2  setosa\n",
       "1           1.4          0.2  setosa\n",
       "2           1.3          0.2  setosa\n",
       "3           1.5          0.2  setosa\n",
       "4           1.4          0.2  setosa"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_simple = iris.drop([\"sepal_length\", \"sepal_width\"], axis=1)\n",
    "iris_simple.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【4】标签编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "encoder = LabelEncoder()\n",
    "iris_simple[\"species\"] = encoder.fit_transform(iris_simple[\"species\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\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>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.4</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     petal_length  petal_width  species\n",
       "0             1.4          0.2        0\n",
       "1             1.4          0.2        0\n",
       "2             1.3          0.2        0\n",
       "3             1.5          0.2        0\n",
       "4             1.4          0.2        0\n",
       "5             1.7          0.4        0\n",
       "6             1.4          0.3        0\n",
       "7             1.5          0.2        0\n",
       "8             1.4          0.2        0\n",
       "9             1.5          0.1        0\n",
       "10            1.5          0.2        0\n",
       "11            1.6          0.2        0\n",
       "12            1.4          0.1        0\n",
       "13            1.1          0.1        0\n",
       "14            1.2          0.2        0\n",
       "15            1.5          0.4        0\n",
       "16            1.3          0.4        0\n",
       "17            1.4          0.3        0\n",
       "18            1.7          0.3        0\n",
       "19            1.5          0.3        0\n",
       "20            1.7          0.2        0\n",
       "21            1.5          0.4        0\n",
       "22            1.0          0.2        0\n",
       "23            1.7          0.5        0\n",
       "24            1.9          0.2        0\n",
       "25            1.6          0.2        0\n",
       "26            1.6          0.4        0\n",
       "27            1.5          0.2        0\n",
       "28            1.4          0.2        0\n",
       "29            1.6          0.2        0\n",
       "..            ...          ...      ...\n",
       "120           5.7          2.3        2\n",
       "121           4.9          2.0        2\n",
       "122           6.7          2.0        2\n",
       "123           4.9          1.8        2\n",
       "124           5.7          2.1        2\n",
       "125           6.0          1.8        2\n",
       "126           4.8          1.8        2\n",
       "127           4.9          1.8        2\n",
       "128           5.6          2.1        2\n",
       "129           5.8          1.6        2\n",
       "130           6.1          1.9        2\n",
       "131           6.4          2.0        2\n",
       "132           5.6          2.2        2\n",
       "133           5.1          1.5        2\n",
       "134           5.6          1.4        2\n",
       "135           6.1          2.3        2\n",
       "136           5.6          2.4        2\n",
       "137           5.5          1.8        2\n",
       "138           4.8          1.8        2\n",
       "139           5.4          2.1        2\n",
       "140           5.6          2.4        2\n",
       "141           5.1          2.3        2\n",
       "142           5.1          1.9        2\n",
       "143           5.9          2.3        2\n",
       "144           5.7          2.5        2\n",
       "145           5.2          2.3        2\n",
       "146           5.0          1.9        2\n",
       "147           5.2          2.0        2\n",
       "148           5.4          2.3        2\n",
       "149           5.1          1.8        2\n",
       "\n",
       "[150 rows x 3 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_simple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【5】数据集的标准化（本数据集特征比较接近，实际处理过程中未标准化）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.500000e+02</td>\n",
       "      <td>1.500000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-8.652338e-16</td>\n",
       "      <td>-4.662937e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.003350e+00</td>\n",
       "      <td>1.003350e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.567576e+00</td>\n",
       "      <td>-1.447076e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.226552e+00</td>\n",
       "      <td>-1.183812e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.364776e-01</td>\n",
       "      <td>1.325097e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.627583e-01</td>\n",
       "      <td>7.906707e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.785832e+00</td>\n",
       "      <td>1.712096e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       petal_length   petal_width\n",
       "count  1.500000e+02  1.500000e+02\n",
       "mean  -8.652338e-16 -4.662937e-16\n",
       "std    1.003350e+00  1.003350e+00\n",
       "min   -1.567576e+00 -1.447076e+00\n",
       "25%   -1.226552e+00 -1.183812e+00\n",
       "50%    3.364776e-01  1.325097e-01\n",
       "75%    7.627583e-01  7.906707e-01\n",
       "max    1.785832e+00  1.712096e+00"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans = StandardScaler()\n",
    "_iris_simple = trans.fit_transform(iris_simple[[\"petal_length\", \"petal_width\"]])\n",
    "_iris_simple = pd.DataFrame(_iris_simple, columns = [\"petal_length\", \"petal_width\"])\n",
    "_iris_simple.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【6】构建训练集和测试集（本课暂不考虑验证集）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>5.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     petal_length  petal_width  species\n",
       "3             1.5          0.2        0\n",
       "111           5.3          1.9        2\n",
       "24            1.9          0.2        0\n",
       "5             1.7          0.4        0\n",
       "92            4.0          1.2        1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_set, test_set = train_test_split(iris_simple, test_size=0.2)\n",
    "test_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     petal_length  petal_width\n",
       "63            4.7          1.4\n",
       "93            3.3          1.0\n",
       "34            1.5          0.2\n",
       "35            1.2          0.2\n",
       "126           4.8          1.8"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_x_train = train_set[[\"petal_length\", \"petal_width\"]]\n",
    "iris_x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63     1\n",
       "93     1\n",
       "34     0\n",
       "35     0\n",
       "126    2\n",
       "Name: species, dtype: int32"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_y_train = train_set[\"species\"].copy()\n",
    "iris_y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>5.3</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     petal_length  petal_width\n",
       "3             1.5          0.2\n",
       "111           5.3          1.9\n",
       "24            1.9          0.2\n",
       "5             1.7          0.4\n",
       "92            4.0          1.2"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_x_test = test_set[[\"petal_length\", \"petal_width\"]]\n",
    "iris_x_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3      0\n",
       "111    2\n",
       "24     0\n",
       "5      0\n",
       "92     1\n",
       "Name: species, dtype: int32"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_y_test = test_set[\"species\"].copy()\n",
    "iris_y_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.1 k近邻算法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【1】基本思想"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "与待预测点最近的训练数据集中的k个邻居  \n",
    "  \n",
    "把k个近邻中最常见的类别预测为带预测点的类别"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【2】sklearn实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 构建分类器对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = KNeighborsClassifier()\n",
    "clf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(iris_x_train, iris_y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 2 0 0 1 1 0 2 1 2 1 2 2 2 1 0 0 0 1 0 2 0 2 1 0 1 0 0 1 1]\n",
      "[0 2 0 0 1 1 0 2 2 2 1 2 2 2 1 0 0 0 1 0 2 0 2 1 0 1 0 0 1 1]\n"
     ]
    }
   ],
   "source": [
    "res = clf.predict(iris_x_test)\n",
    "print(res)\n",
    "print(iris_y_test.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 翻转"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'virginica', 'setosa', 'setosa', 'versicolor',\n",
       "       'versicolor', 'setosa', 'virginica', 'versicolor', 'virginica',\n",
       "       'versicolor', 'virginica', 'virginica', 'virginica', 'versicolor',\n",
       "       'setosa', 'setosa', 'setosa', 'versicolor', 'setosa', 'virginica',\n",
       "       'setosa', 'virginica', 'versicolor', 'setosa', 'versicolor',\n",
       "       'setosa', 'setosa', 'versicolor', 'versicolor'], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.inverse_transform(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测正确率:97%\n"
     ]
    }
   ],
   "source": [
    "accuracy = clf.score(iris_x_test, iris_y_test)\n",
    "print(\"预测正确率:{:.0%}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 存储数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>y</th>\n",
       "      <th>pre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>5.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>4.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>3.8</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>4.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>3.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>4.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     petal_length  petal_width  y  pre\n",
       "3             1.5          0.2  0    0\n",
       "111           5.3          1.9  2    2\n",
       "24            1.9          0.2  0    0\n",
       "5             1.7          0.4  0    0\n",
       "92            4.0          1.2  1    1\n",
       "57            3.3          1.0  1    1\n",
       "1             1.4          0.2  0    0\n",
       "112           5.5          2.1  2    2\n",
       "106           4.5          1.7  2    1\n",
       "136           5.6          2.4  2    2\n",
       "80            3.8          1.1  1    1\n",
       "131           6.4          2.0  2    2\n",
       "147           5.2          2.0  2    2\n",
       "113           5.0          2.0  2    2\n",
       "84            4.5          1.5  1    1\n",
       "39            1.5          0.2  0    0\n",
       "40            1.3          0.3  0    0\n",
       "17            1.4          0.3  0    0\n",
       "56            4.7          1.6  1    1\n",
       "2             1.3          0.2  0    0\n",
       "100           6.0          2.5  2    2\n",
       "42            1.3          0.2  0    0\n",
       "144           5.7          2.5  2    2\n",
       "79            3.5          1.0  1    1\n",
       "19            1.5          0.3  0    0\n",
       "75            4.4          1.4  1    1\n",
       "44            1.9          0.4  0    0\n",
       "37            1.4          0.1  0    0\n",
       "64            3.6          1.3  1    1\n",
       "90            4.4          1.2  1    1"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out = iris_x_test.copy()\n",
    "out[\"y\"] = iris_y_test\n",
    "out[\"pre\"] = res\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "out.to_csv(\"iris_predict.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【3】可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def draw(clf):\n",
    "\n",
    "    # 网格化\n",
    "    M, N = 500, 500\n",
    "    x1_min, x2_min = iris_simple[[\"petal_length\", \"petal_width\"]].min(axis=0)\n",
    "    x1_max, x2_max = iris_simple[[\"petal_length\", \"petal_width\"]].max(axis=0)\n",
    "    t1 = np.linspace(x1_min, x1_max, M)\n",
    "    t2 = np.linspace(x2_min, x2_max, N)\n",
    "    x1, x2 = np.meshgrid(t1, t2)\n",
    "    \n",
    "    # 预测\n",
    "    x_show = np.stack((x1.flat, x2.flat), axis=1)\n",
    "    y_predict = clf.predict(x_show)\n",
    "    \n",
    "    # 配色\n",
    "    cm_light = mpl.colors.ListedColormap([\"#A0FFA0\", \"#FFA0A0\", \"#A0A0FF\"])\n",
    "    cm_dark = mpl.colors.ListedColormap([\"g\", \"r\", \"b\"])\n",
    "    \n",
    "    # 绘制预测区域图\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.pcolormesh(t1, t2, y_predict.reshape(x1.shape), cmap=cm_light)\n",
    "    \n",
    "    # 绘制原始数据点\n",
    "    plt.scatter(iris_simple[\"petal_length\"], iris_simple[\"petal_width\"], label=None,\n",
    "                c=iris_simple[\"species\"], cmap=cm_dark, marker='o', edgecolors='k')\n",
    "    plt.xlabel(\"petal_length\")\n",
    "    plt.ylabel(\"petal_width\")\n",
    "    \n",
    "    # 绘制图例\n",
    "    color = [\"g\", \"r\", \"b\"]\n",
    "    species = [\"setosa\", \"virginica\", \"versicolor\"]\n",
    "    for i in range(3):\n",
    "        plt.scatter([], [], c=color[i], s=40, label=species[i])    # 利用空点绘制图例\n",
    "    plt.legend(loc=\"best\")\n",
    "    plt.title('iris_classfier')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw(clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.2 朴素贝叶斯算法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【1】基本思想"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当X=(x1, x2)发生的时候，哪一个yk发生的概率最大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【2】sklearn实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 构建分类器对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "clf = GaussianNB()\n",
    "clf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "clf.fit(iris_x_train, iris_y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 2 0 0 1 1 0 2 1 2 1 2 2 2 1 0 0 0 1 0 2 0 2 1 0 1 0 0 1 1]\n",
      "[0 2 0 0 1 1 0 2 2 2 1 2 2 2 1 0 0 0 1 0 2 0 2 1 0 1 0 0 1 1]\n"
     ]
    }
   ],
   "source": [
    "res = clf.predict(iris_x_test)\n",
    "print(res)\n",
    "print(iris_y_test.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测正确率:97%\n"
     ]
    }
   ],
   "source": [
    "accuracy = clf.score(iris_x_test, iris_y_test)\n",
    "print(\"预测正确率:{:.0%}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/LKBeo3pX3LN1w1YmTp/IT8t/4ud5P3PsyDFW717N+Mnj2bpha7pxEy8l0rBpQ1buWkmz1s349qtvr7rnsYGP8dATD7Fy10oWr19MydIlsdlsTP9pOsu3L+enFT8x6tlR1zz4XAghclJ8/FnGjetMdHQETmcMTmcMhw61ZezYjrhcGf9hOSt88EEPDh26COzDs3vTLvbs2cukSUNyNjGRJ+S/oiwhAaZMgcTEK9sTEz3tCQkZClunfh3OxpzlVNQp9uzaQ6HbClG2fNkr7glvH85tRTyHwG5au4kefXtgMBgoWaokLe9smW5ci8VCh24dPH2E1eH4keNXPk58AtEno+l6T1cAbDYbgYGBaK1586U3Ca8TTu92vTl18tTlUTYhhPAn69bNwOXqjGcHJSNgResXSEwsyu7dv+Vwdv9xu91ERq4CZuDd2QmoCkxl8+Zfci4xkWfkvzVlUVFgNKZ/zWj0XK9WLUOhu/fpzsI5C4k5FcM9/e+56npggf8OQfd11MpkNl3e6M9oNOJMM8V6rThzZs4h9kwsS7ctxWw206BiA1KSU3x9FCHETZCNZG9NTMxRHI7aV7W7XKHExh7LgYzSZ7cn4jm26I40V0JxuzP2A70QqeW/kbKQEHC50r/mcnmuZ9A9/e9h/vfzWTRnEd37dL/uvU1aNuHnuT/jdruJOR3DupXrMtRnweCChJQNYfF8z8HlKSkpJCYmEncxjmIlimE2m1m7Yi3Hjx6/QSQhhMgZ1ao1xmr9BUj9Q6Ydg2EJlSo1yqm0rmKzBWEw3Ab8kebKL1gs/v1igsgd8l9RFhQEQ4dCYOCV7YGBnvZbeAuzeq3qJMQnULpMaUqVLnXde7v37k5I2RBa1W7Fc/97jgZNGhBcKDhD/X767ad89clXhNcJp0vzLsSciqHPwD7s2rqLdg3bMWfmHKpWr5qh2FlJFvsLIQAaNuxNkSJxmExDgG3AWszmnlStWo/bb/efogygS5fHgH7AN3jWlX0ODOPee1/M0bxE3qBy4+Lvhg0b6q1br1wYHxkZSY0aNXwL4HR6FvVPmeKZsnS5PAXZ+PGQzlYVZzmbGWlfJSEhgaCgIM7FnqNj4478vO5nSpYqmSV9ZcThyMPsrbE3S/uQnf1FXiJTmBl36dIFFi16h82bF2I0Wmjduj+dOz+NyWTJ6dSu8ttvH7JgwcckJ8cRGHgb/fq9TOvWQ3M6LXGTBmfjf65KqW1a64Y3ui//rSkDT+E1YQK8/XaW7lN2IwO7DeTihYs47A6eefUZvyrIhBAiOxUoUJj+/d+mf/+3czqVG+rU6Rk6dXomp9MQeVD+LMr+FRSU4UX9mWHBygU51rcQQggh/Ev+W1Mm/IqsKxN5iWwkK4S4FVKUCSGEEEL4ASnKhBBCCCH8QP5eUyaEEJlMNpLNmNjYY3zzzUj27FmAwWChceP7GDBgHAUKFAY8G2WvWjWF+fPf48KFg5QoUZd+/V4jLKxnqhjHmTHjef788yeUMntjvE1QUJHL96xaNZX589/l/PkDlChRh3vvHUXDhldv9n0rzp+P4ttvR7Jr1zyUMtKwYT8GDhx3U4esX7gQzbffjmTnznkopWjQ4F7uv/8dgoOL30SMU8yY8Tw7dsxBKUX9+n144IF3CQ4ukZHHyvUSEmKZOfMFtmyZjdZO5s+/h4kT36VMmTI5ndplMlLmx8aNGseqpatu+nPrVq5jQLcBWZCREEJkvuTkBF5/PZzdu2vgckXhcESycaObceO6XD61ZOnSz/nuuw+5cGEakExMzFi+/PIJdu36d+PsS4wZ04adO6vicp3E6dzHpk0G3n678+XDwpct+5KZM9/l/Pkp3hjvMGnSk+zYsSjTnsVuT2bMmDvZubM8LtdxnM6DbNkSyFtvdcDtvsbG5Wk4HCmMGdOWHTvK4HIdxek8xLZtwbz5ZnufzwJ1Ou28+eZdbN9e0hvjMNu3F+WNN+7C6XTcyiPmSm63i7fe6sjmzVaczv24XCdYtOh2GjduQ1JSUk6nd1m+LsoSEmD//gwfd5kptNaX/4eR1gtjXiC8XXiW55D26KbsJov9RV4jC/5vzoYN35GSUg+3+zXgNiAEl+tLYmISiIxcgdvtZv78t7HbZwLNADPQCbt9AnPmeLbQ2Ljxe5KTa6H160ARoDQu12fExtrZu3cZWutUMVp4Y3TAbv/0cozMsHXrHBITK+F2jwWKAiVxuT7mwgUzf/7p2zme27bN49KlMrjd44BiQAlcrvHExQWya5dvZ2xu3z6fhIQSuN3vemMUx+V6n/j4wuzcmXlFaG6xZ88Szp8Hl2sCUAoogsv1BnFxVfnhhx9yOr3L8mVR5nRCRASUKAFhYZ6/R0R42jNqzPNjmPrZ1Mu/fnf0u3z2wWdMfG8i7Ru1J7xOOO+89g4Ax44co3mN5ox8fCRtG7Tl5PGTDB8ynFa1W9E6tDVfjP8CgOFDhrNwzkIAdmzZQZfmXWhTtw0dGncgIT6B5ORkIh6MoHVoa+6sfydrV6y9Kq/z584z6O5BhNcJp1PTTvz151+X83vm0Wfo26EvTwx6IuMPLoQQt+j48UhSUlqmaVW43S2IiookJeUSycnngPpp7mlBTEwkACdO/E1KSourYrhcLYiOjsRuT+LSpVNAWJp7WnLmTGSmPcvJk5Hp5uFwePLwheeZr/4+7PaWREX5FiM6+m+Sk6/OIyXF9xh5SXT03zgcLQB1RXtCQkv27PGf7yNfFmVPPw1Tp0JSkmeULCnJ8+unn854zLv738382fMv/3rBDwsoWrwohw4cYsnmJazYuYJd23axfvV6AA7uO8i9g+5lxY4VnDt7jlMnT7FmzxpW717NfQ/ed0Vsu93OI/0e4a2P32LlrpXMXToXW4CNqZ96isDVu1czadYkhg8eTnJy8hWffee1dwitH8qqP1fx8tiXryjAdm3bxTcLvuHL777M+IMLIcQtKleuBlZr2h8qNQbDOkJCamC1FsBmKwLsSHPPOkqU8JzkUrZsdazWtGcIa4zGdZQuXQOLJYACBUoBW9Pcs5bixX08DcYHZcrUSDcPs9mThy88z3z192GxrCUkxLcYpUtXx2a7Og+r1fcYeUnp0tUxm9dx5fmqEBS0ltq1/ef7yHdFWUKC53SlxMQr2xMTPe0ZncqsU78OZ2POcirqFHt27aHQbYXY++deVi5ZyZ3176Rtg7Yc+PsAhw4cAqBchXI0bOo5caHC7RU4eugoL0S8wLLfllEwuOAVsQ/uO0iJ0iWo38jzU2LB4IKYTCY2rd3EvQ/cC0DV6lUpW6Es/+z/54rPblq7ib4P9AWgVdtWnI89T9zFOAA69ehEQEBAxh5YCHFdMoXpu2bNBmC17sRgeB04D0RhNP6PEiWCqFHjTgwGA3ff/SIWy0BgA+AAfsViiaBPH8+Zk02b9sdm+wulXgPOAdEYjY9TtKiFmjXvQinljXE/sM4b43cslicux8gMDRv2ITDwMAbDS0AscAqj8SkKF3ZQp04nn2KEhfWiQIGTGAzPA2eB0xiNTxMcnEjdul19itGgwd0EBcVgMPwfcAaIwWh8loIFL1CvXveMPVwuVrt2B267DYzG4cAp4BxG4ysEBx/g3nvvzen0Lst3RVlUlOe4y/QYjZ7rGdW9T3cWzlnI/Nnzuaf/PWiteerFp1i5cyUrd65ky8Et3D/0fgACC/x3IHrh2wqzYtcKWrRpwdRPpzLi4RFXxNVao9SVQ67/tt9Ievf8Gyt1DkIIkVNstiBee20VdepEYjSGYDbXoGlTAy+8sPjy/6/atXuMAQOepXDhIYCNkiVfYdiwT6lbtwsAVmsBRo1aSb16BzAay2A2V6dJEzcvvfQbBoPnj7q77vofAwc+z223DQVslCjxAo8++gn162dekWKx2Bg1agX16x/HaCyHyVSVRo0SefnlJRgM1/jDJw2z2cqoUctp0CAao7ECJlNlwsLieOWVPzAafds0wWSy8MorywgLO4PRWBGT6XbCws7z6qvLMJnMt/KIuZLBYOTll3+ncWM7JlM1jMaydO9+mM2bV/rV4ES+2xIjJMRz/nh6XC7P9Yy6p/89PPPIM8SejWXBqgVE7o5k3Kvj6D2wN0FBQUSfjMZkvvorjz0bi8VioXvv7lSsXJGIIRFXXK9avSqno06zY8sO6jeqT0J8ArYAG81aN2POzDm0atuKf/b/w8ljJ6lyRxW2bvhveL5Z62bMnTmXZ199lnUr11GkWJGrRuL8wfTBcji5EPlZ0aLlGTHi+2teV0rRps1Q2rS59sHfRYuW46mnvrtuP+HhDxIe/mCG8/TFbbeFEBHxLfBthmMULlya4cO/Ab65hRileOKJacC0DMfIS4KCivK//33F//73FZC9B5L7Kt8VZUFBMHSoZw1Z6inMwEB46KFbO5e8eq3qJMQnULpMaUqVLkWp0qXYH7mfLs08P8kVCCrAZzM+w5hmqC76ZDRPPvjk5bcwX3n7lSuuWywWvpr9FS9GvEhyUjK2ABtzls7hwccf5Llhz9E6tDVGk5EJ0yZgtVqv+OzI0SN58sEnCa8TTkBgABOnT8z4AwohborsWSaEuBnKlykwf9OwYUO9deuVizUjIyOpUcO3xXpOp2dR/5QpnilLl8tTqI0fD6Z0ytSznM2MtHOdw5GH2Vtjb7b1JyNlIi+SokwI/5SdI2VKqW1a64Y3ui/fjZSBp/CaMAHeftuzhiwk5NZGyIQQQgghblW+W+ifWlAQVKsmBZm/kE1kRV4kb2EKIXyVL0fKhBBCiIyKjFzBTz+9x6lTByhbtja9e79A5cpNbjLGSm+M/ZQtW4tevZ6nSpVmWZRx7me3J/HLLx+wbt2PuN0umjW7h27dRhIQ4H8vrt2KPDVSlhvXx/kr+S6FEOJqO3YsYvz4gezf35+4uJ/Zu7cT77zTnX37VvscY+fOXxg//j727+/rjdGVd9/tSWTkyqxLPBfTWvP++/ewePEWzp79gnPnvub33w8ydmwnn88CzS3yTFFms9mIjY2VYiITaK2Jj40nyeY/h7QKkZvJFGbe8d13r2C3fw0MAu4A/ofdPp7vv3/9JmK8it0+BRjijfEIdvsnzJr1WlaknOvt27eao0eP4XDMxXP2aSOczpmcOePy+SzQ3CLPTF+WLVuWEydOcObMmUyPnUAOnlieQ5JsSRwreyyn0xBCCL/hdNo5e3Yv0D7Nla4cP/6YTzHcbhcxMTuBtLv7d+XkyWvvwZafHTq0GaezE1eWLAaSk7tw6NAWGjTomVOpZbo8U5SZzWYqVaqUJbGny0+52UY2kRV5lexZlvsZjWZstmIkJe0DUm/BtJtChcr7FEMpA4GBpUlM3AvUviJGcHC5TMw27yhWrAJm85KrNn63WHZTrFjaAjl3yzPTl0IIIURWUkrRqdNTWCwPA//OJOzHYnmCbt2e8jlG584jsFgeAY56Ww9isTxOt24jrvfRfKt+/Z5YLAdRajxgB5zAV5hM62jSpH8OZ5e5pCgTQohsImvLcr/u3UfSrl07LJb6WCxlsdla0qPHINq0edjnGF27PkuHDp2wWBpgsZTFam1Gt24DaNv2f1mYee5lNlt5+eU/qFhxESZTCUym4pQtO42XXlpCQEBwTqeXqfLMjv5ZSaYvs5dMX4q8TKYw8wa7PZn4+BgKFSqFyWS5pRjBwSUxm603/oAgLu4MWrspVKjkLcfyxx39ZaRM+B3ZRFbkZTJaljdYLDaKFi2f4YIsdQwpyHwXHFw8UwoyfyVFmRBCCCGEH5CiTAghspmMlgkh0iNFmRBC5AApzIQQaeWZfcqEEEKIG4mKiuTXXydy8uRBbr89lE6dIihWrMLl6y6Xk3XrvmHduvkYDEbCw/vRuPG9GAz/jWFER+/j118ncOLEASpVqk2nThEUL17xihjr13/L2rU/eWPcS+PG/a6IcSOeGDNYu3YeBoOBVq360rTpfTcVIzc5c+Ywv/46gSNH/qJs2Wp06RJBqVLVcjqtbCdFmfBLsomsECKz7du3hg8+6IXTGYHb3Y2jR1ewdm0TXn11OWXK1MTtdvPhh305cCAWu3044ODQoff4888VPProlwAcOLCe997ricPxBFp35ejRlaxb14SXX15KuXKhaK356KMNt0uRAAAgAElEQVR+7NsX443h5NChD9m5cxnDhk32KU+tNZ98MpC//z5BSkoE4ObQoY/YseMPnnhiWlZ9PTnm2LFdjB3bAbv9IdzuERw5spFNm1oycuSimz7oPbfLmyW3EELkAjKFmb2++WYkdvunuN2jgM64XO+SnPw8338/CoC9e5dy8OBB7PZlwL3AQFJSVrF16y8cP/4nANOnj8Ru/xitR3tjvENy8svMmvUqAJGRK9i/P9Ibo583xkq2b1/C0aM7fMpz377V/P33LlJSlgP9gQGkpKxk166VHD6cfdtBZZeZM18mOfk13O63gc643a+TkvI+33zzfE6nlu2kKBNCCJHn2e1JREdvB3qnuTKAffuWA7B370pSUvoC5lTXg3C7exAZuQKn087Jk5vwFGxXxjhwYIU3xgpvjNRbZRTA5epJZOQKn3KNjFxBSkofIPVWGYG4XPcQGbncpxi5yT//rAQGpGntz9Gja3C7Xel8Iu+SokwIIXKQjJZlD5PJgtEYAESnuXKMgIBiABQsWBSz+dhVnzUajxMUVBSDwYTZHAREpbnj+A1jmEyeGL4ICrpejGI+xchNbLaiwPE0rSexWAqhVP4qU/LX04pcRTaRFUJkFoPBSMuWQzCbnwISva0XsFj+jw4dHgWgWbMBKDUfWOm9roF5GAxbCAu7x7vgfqg3xiXvPRexWJ6lXbtHAGja9D6UWgQsSxVjAUptICysl0+5ehb0Lwb+SNW6CKVW06hRn4w8vl+7665HsFieAeK8LZcwm5+iTZtHUUrlZGrZTooyIYTIYTJalj3uu+9tate2YTaXJyCgNSZTJZo2DaVz56cBKFy4NE8+OYsCBe7HZquPzVabQoVG8n//txCrtQAA/fq9SZ06QZjNFbwxKtK4cXW6dn0OgEKFSjJixGwKFBiCzVYPmy2U4OCnee65BQQEFPQpz4IFizFixI8EBT2EzVYXm602wcFP8uyz8/PcWY8APXq8QKNGlTGbKxIQ0BqzuTx1695G376v53Rq2U7OvvSBnH2Zc+QNTJFfyJmY2Sc29hgxMYcICamR7pE9TqeDI0e2YjCYqFgxLN1tKGJjjxMT8w+lS1encOFSV113uZwcPrwFg8FIxYoNM7SVhSfGVpRSVKrUEIPBeNMxcpMLF6KJjt5HyZJVKFKkbJb3549nX8qWGEII4QcGM10Ks2xStGh5ihYtf83rJpOZKlWa3SBGOYoWLXfN60aj6YYxbsQTo+ktxchNChcuTeHCpXM6jRwl05dCCCGEEH5AijLh12Sxv8hPZG2ZEPmbFGVCCCGEEH4gS9eUKaXKAd8ApQA3MElr/XGaexTwMdAFz3vKQ7TW27MyLyGE8Fe5cW2Z1pq//lrKli0LMZuttGx5HxUrhl1xT1xcDGvWTCcq6h8qV65H8+b3Y7MFXRFj795lbN68ELPZQosW/alUqWGaGGe8MQ5y++11ad78fp/faBRZ59KlC6xd+w1Hj+6lfPnqtGo1mAIFbsv0fg4e3MCGDT/icrlo2rQXd9zROs9tmZGlb18qpUoDpbXW25VSBYFtwN1a672p7ukCROApypoAH2utr3vYlbx9mb/IG5giP8othZnWmkmTHmX79rWkpDyEUomYzZPo2fNZunZ9BoDjx/9k7NgOOJ1dcTjCsFr/IDDwL157bRWFC5dGa83kyY+xdetKb4wkzOZJ9OjxNN26ebaaOHFiD2+91R6nszMOR0Os1mXYbLsYPXo1t90WkpNfQb525sxhxoxpQ0pKc+z2Vlgs6zGbVzJq1EpKlqySaf3MnTuG33+fjMPxKFqbsFon06JFDwYN+jDDMf3x7cssnb7UWkf/O+qltY4HIoEyaW7rCXyjPTYChb3FnBBCCD8XGbmC7dtXk5KyFfg/tH4Nu30T8+e/yfnzJwGYPPlJkpLewOGYAjxOSspPXLzYnR9/9OxDtX//GrZuXeaNMdIbYwsLFrxNbKxnp/cpU54iKek1HI6p3hhziY/vzezZo3LkuYXHjBkvkJDwCHb7LOBx7PYZJCY+yfTp/5dpfZw6tZ/ffpuI3b4VrV8BXiAlZStr187Jc2eBZtuaMqVURaA+sCnNpTJceb7CCa4u3EQ+Jov9RX40mOm5YuH/9u2/kJIyGCiQqrUsSnVm9+7fSU5O4PjxzZBm5M/tfpwdO34GYNu2n0lJGQQEpbojBKW6sXv3r6SkJHL06DrgwTQxHmPXrl+y4KmEr/bs+RmtH7uiTevHiIz8hcyaidu1azFa9wJKpGotjMMx8PLvobwiW4oypVQQMBcYobWOS3s5nY9c9W9SKfWoUmqrUmrrmTNnsiJNIYTwO/5emJnNNgyG+KvaDYZ4LJYADAaTd91PUpo74jGbAwCwWgPSjaGU5x6j0YRSRv47Hum/GCZTQKY8h8gYz/ef9t9dAiaTLdP68Pw+uvbvsbwky4sypZQZT0E2U2s9L51bTgCpd+Ary9WnvaK1nqS1bqi1bli8ePGsSVYIIcRNadlyAEbjVOBQqtZ1aL2eunW7YbHYCA3tgdE4hv9+3nZgNr9Gq1YDAWjW7D6MxmnAwVQxNqD1KurX74HJZKFu3XswGkeniuG8IobIGc2b34/J9Crg8ra4MZleoUmTgZm2CD8srBda/4pnWfq/IjEYZtGkSb9M6cNfZGlR5n2zcgoQqbW+1mq8hcAg5dEUuKi1js7KvIQQIjfx59GyMmVq0b//GMzmMKzWPthsXbBaexIR8d3lNyMfeugTSpZcg81WB6t1MBZLFapVU/To8QIAISHVGTBgLGZzI6zW3thsXbBYujF8+EwCAwsB8OCDH1Gq1Cas1tDLMapUcXL33S/n2LMLz1mgFSqcwmq9A6t1CFZrdcqW/YcBA8ZlWh/BwcUZNmwqFkt7bLbu2Gz3YDY3Z8iQjylevGKm9eMPsvrty5bAGmA3ni0xAF4CygNorb/wFm4TgU54xqYf1Fpfd+WevH2ZP8lbmCK/8+c3MuPizrBnz++YTFbq1Ol8xXYX4HlLc9++1Zw5c4jy5etRoUL968SwUKdOl3Rj7N+/lpiYg5QrV5eKFRtk6TMJ32itOXRoM1FReyldujqVKzfNkq0qEhMv8uefv6K1m9DQTgQFFbmleP749qUcSO4DKcr8gxRlQvh3YSZEbuKPRZns6C+EELmIP09lCiFujRRlQgiRy0hhJkTeJEWZyDVkvzIh/iOFmRB5T5aefSmEEEIAOJ12duxYeHmhf82a7TAYrhwX+OuvZSxdOhGTyUy3bi9RoUK9m+4nJuYfduxYhNFopmHDXhQunL8PiImNPcb27fMBRYMGPSlatHxOpySuQ4oyIYTIpXLL4eWxscd48812JCWVweEIw2x+nhIlgnjxxcWXt814992u7N27GrgXSGTLlua0bfswgwZ94nM/P//8PgsWjEPr3iiVwuzZrzJkyARatMife5n98cfn/PDDK0AvQPPDD6/Rv/847rrr0ZxOTVyDTF8KIUQulhumMSdPjuDixQdITl6By/U+ycnbiIqqyE8/vQnAxo2z2bt3PbAfz9aWs4DNLF/+FdHR+3zq4/jx3Sxc+AEOx584nV/icEzD4VjLtGnDiYuLyapH81sxMYf44YdXcTi24XB8hcMxGYdjK99//yJnzx7N6fTENUhRJoQQuZw/F2YpKYns3/8HbvczqVoNOJ0vsGHDDwAsWTIBGAaknmqsDXRg8eJ3fOpn06YfcTgGAyGpWmtiMHRmx46Ft/QMudHWrXNxu/sBFVO13o7Wfdi2Lb3DdYQ/kKJM5Cqy2F+I9PlvYabxbIeZ9o8bI1p79hR3uzXpr6Yx4na702lPpxftBozptP/XT35yre/jZr5Tkf2kKBNCCJFlrNYCVK4cjlKfpWrVGI0f0LhxLwDvGqcvgNhU9xwAfqVTp+d86qdRo16YzdOBM6la/0HrX6hXr/stPUNuFBZ2DwbD91x5lPQJlPqRsLC7cyotcQNSlAkhRB7hr6NlDz88gaCgT7BaewCvY7O1pnjx7fTuPQqAVq0GU7FiTaAq8Cyeqcz6NGvWn3LlavvUR8WKDWjffigWS10MhucwGiMwm5vQv/+4fPkGZqlS1ejZcyRmc30MhqcxGkdgNjfg7rtfokSJyjmdnrgGOWbJB3LMkn+R45aEuDZ/fRszJeUSmzf/SEzMIcqXr0v9+j0wmcxX3LN58w8sX/4lZrOFLl2ep0aNNjfdz/Hju9m+fT5Go5nGjfvm+wIkKiqSrVvnoZQiLKwXISHVczolv+GPxyxJUeYDKcr8ixRlQlyfvxZmQvgTfyzKZPpS5Dqy2F8IIUReJEWZEELkMf66tkwIcX1SlAkhRB4khZkQuY8csySEEOK6XC4XP/88lkOHNtOoUV9athx01T3nzp1g2bLPsFgCad/+SQIDg2+6n6SkOCIjV2AyWahRoy1mszUz0r9pFy6cYtmyCRiNFtq3f4oCBQpfdc+xY7s4dWo/ZcuGZnjx/PHjfxIdvY+yZWsTElIjgzF2Ex39N2XK1KJMmZoZinEjKSmJREYuB6BGjbZYrYFZ0o+Qhf4+kYX+/kkW/AtxY7e66P/w4W2Mef0uNDagLrABizmAjz/5h4CAIAC+/voxVq2aBjQELgH7uP/+d2jXbrjP/axfP4tp057AaGyE1kkotZ+nnppN9erht5T/zfruu2dZsuQzoD7gAP6ib9/RdO06EoCkpHjGj7+XI0f+wmhshNO5nho1WhER8a3PRWRycgLjx/fj8OHdGAyNcLs3cMcdzYmImIHFYvMpRkrKJcaP78+hQzsxGBrjdm+kWrUmPPnkTCyWgIw9fDp27vyFzz8fglKhgELrXQwb9jX16+f+vd9kob8QQohsdavTmGPf6oymP55NSH8HTmN3VGX0a40A2L37d1atmgFsA9YA24GfmDFjpM9nTp4+fZCvv47Abl9NUtLvJCevJilpBuPH9yE5OeGW8r8Z+/evY8mSL4CNwHpgC/ArP/44mjNnPOdFzpgxkkOHSmK3HyIpaS4OxxEiI1Mun+Ppi+++e4F//imC3X6I5OS52O1H+PtvN/PmjfE5xqxZL3HwYEHs9sOXY+zbZ2TOnNE388jXFRcXw2efDSIlZRHJyctJTl5GSspiPv98CBcunMq0fsR/pCgTQgiRrtjYYzicF4Bx/PfHRQDwPqdjPDvFz58/FngESD111gFozKJFb/nUz7p1M3G7B+E57/Jf7VCqabaeW7lgwRvA/XhGBP8VDtzJwoWv43a72bRpBk7nOP5b/WPF4RjLqlW+Fb9aa9av/wan851UMSw4HGNZvXqazzHWrZueJoYZh+Ntn2P4YvPmH9G6K9A0VWtjtO7Bli0/ZFo/4j9SlAkhRB6X0dGyhIRzeP6YKJjmSjHABUByciJQPJ1PlyAx8YJP/SQlxeNyFb2q3e0uRlJS3E1kfGuSkhJI/1lKkph4EbfbhcuVDNyW5nox7Hbf8tTajcuVCBRJJ0a8z7k6nZeAtN9ZURwO32PcSHJy+v9enM6iJCVlXj/iP1KUCSFEPpCRwqxChXp4RsbSjopMwmb1rCdr2vRu4CsgJdX1GGAx4eGP+tRP/fqdsVpnAsmpWs+g9SJCQzvedN4Z1bx5f+BrIDFV6zlgLuHhD2MymalUqQ2k+S6VmkKtWp196sNgMFK5cjtvP1fGqFnTtxhKKapW7QBMSdM+lRo1fIvhi9DQTphMPwKpC854zOYfqFMn8/oR/5GiTORasomsEFmvW7fHgaHAcOBboC/wKcMemwxA587PExxsxDPl9znwPlCHqlWbUa1aC5/6qFGjLbVrN8BqbeaN8SEWSxM6dBhO8eKVMv2ZrqVt28cpWrQwUAf4DBgPhFKhQujlImTIkPex2V7FZPJ8H2bzQwQGfsaAAb6vKRs8+D0CAl7HZHrcG+NhAgI+YcAA36Z7PTHeJSDgLUymx7wxHiUg4AMGDhzr+wPfQIUK9WjevDdWaxPgE2ACVmsTmjbtQcWKDTKtH/EfefvSB/L2pf+SNzCFuDkZeRtz27af+PabZ7h0KYlixYoy7LGZ3lE0D6fTydy5L7Fx48+YTGbat3+IDh2euqk+3G43O3YsZNOmBZhMFlq1ui9DZ1/eKqfTyYIFr7Nu3U8YDAbuuusBOnZ8FoPhvzGM8+ejWL78K44f38/tt4fSps1QgoPTm/a8tgsXolm+/CuOHdvH7bfXpk2bhzMQ4xQrVkzm6NFIKlWqxZ13PkxwcImbinEjWmt27/6d9evnoLWmRYs+hIZ2QimVqf3kBH98+1KKMh9IUea/pCgT4ubJ2ZhC+GdRJtOXQgghhBB+QIoykavJujIhbp4cwSSEf5KiTAgh8iEpzITwP1KUCSFELuZyu/nz+HEOnLr5HdazuzCLizvDkSPbsnXvMSFyEzmQXAghcqnfd+/mgUkzSLIH4taJVCgWzIInh1K1VCmfYwxmepYv/Hc4Upg5+UG2bZtHObOV4047d7V/kp59x+WJt/iEyCwyUiaEELnQkTNn6DVhCmfivyMh5RCJ9pP8HR1B+LhPcLpcNxUrq0fM5n03ggLb53PCmcJfSXH87Ujm8NKJrFzxRZb2K0RuI0WZyPVksb/IjyavXofTdT/Q1ttiROsIEpJLsfSvv3IytSs4nQ7WrJ3Gl44kgr1tIcBH9kTW/Pp+TqYmhN+RokwIIXKh4+cSsLvuuKrdratw6uLFHMgofXZ7Ii63i5A07VWAC/FncyIlIfyWFGVCCJELdah1OwWss4HUG4DH43IvpUXVqjcdL6umMIcF/ETJwmX4PU37jyiqVfXtGCYh8gspyoQQIhfq27gxlUucwWbuCSwB5lDA0oqBzRre1EL/1LKiMFNK0WvwZwy0BPIRirXAKGXkDWsBuvV7N9P7EyI3k7cvhRAiF7KYTKx/5Rkm/LGMWRufJdBq5vG2jbm/efOcTu2yf4u8OnU6M/yFlcz75W0mRe+jzO1NeKH7S5QsWSWHMxTCv8jZlz6Qsy9zBzkHU4jMcatbZKQdcZOzNoU/8sezL2WkTAghxBUysneZnBAgxK2TokwIIcRVMrPIkpEzIXwjC/2FEEJkKxlVEyJ9UpSJPEM2kRX5VXxSEkl2e47HuBnXKsySkxNISUnMtjxyktvt4tKl87jd7pxORfgJmb4UQohcatexYzw5ZQpbjh9HKUX30FAmPPQQxYODb/xhr93HjxMxeTKbvTG61qrFhKFDKVmoUBZm7pF67drJk3uZPflB9h/dDgrq1riL+x7+msKFS2d5HtlNa83ixeP5+ef3cDgSsVoLcvfdL9O+/WM5nZrIYTJSJoQQuVBMXBwd336bQUePctHtJsrloszu3XQdNw5f36o/Gx9Ph7FjGeiNEe1yUXHPHrqOG5dtozeDmc6lSxf48K0WPHp4CxfdTmJcTu7cu4yPx7bC7b65czxzgyVLJrJgwbckJS3D6bzIpUu/8OOPn7B69bScTk3kMCnKhBAiF5q2ejXdnE6GAmagEPC+y0XSuXOs2b/fpxjT16yhk8vFI94YwcA7bjfO8+dZ+fffWZZ7Wu71w2nrtPMEGgtQEHjb7aRQXAx79vyRbXlkl0WL3sdunwrU9LbUxW7/ggUL5CzQ/E6KMpGnyLoykV8cioqigcNxRZsCGmjNoZgY32JER9MgzToyBTRwuzl85kwmZXpj/0RH08R+9Tqyhi4nZ84cyrY8soPb7SIh4ThQL82VMC5cOJwTKQk/IkWZEELkQvWqVGGp1XpFmwNYoTX1ypf3LUblyunHAOr6GCMz1K9cmT/S5OEClhkMlC+ftnjJ3QwGI0WL1gKWp7nyB6VK1c2JlIQfkaJMCCFyofubN2dPYCDPGgwcBLYDvc1mwqpWpV6FCj7FGNCsGfsKFOBpo5EDwA6gr9lMaOXKNKxUKQuzv9K9jRpxomBBhhuN7Ad2AX3MNgqVr0+VKs2yLY/s0r//aCyWIcBcIAqYhcXyOP37v5aziYkcJ0WZEELkQkE2G6tfe41LzZpxZ4EC9C9UiLBOnfh+xAifYxSwWln12mukNG9O2wIFuLdQIep17MgPTz+dhZlfzWaxsGLUKGjRgnYFCtA7OBh3uyd57LnfUUplay7ZoVGj3jz++BeUL/8JNlsDKlWazIgRMwgN7ZjTqYkcJmdf+kDOvsxd5AxMIfIG2flfZCV/PPtSRspEniOL/YXIG2Tnf5HfSFEmhBDCb0lhJvITKcqEEEL4NSnMRH4hRZkQQgi/N5jpUpyJPC9Lz75USk0FugExWuva6VxvAywA/t0xb57WekxW5iSEEFlNa8201av5aNEijl68SKNy5Rh93320qFo1U/v58LffGPP996QARsBgMLD+9depXa7c5Xu+XbeOD+fP5/CFC4SVLcuofv0Ir1798vWo8+d5ZdYsFu3ahc1k4v6WLXm1Vy8C0+wbdj07jx7lgU8+4Z/YWAxAnfLlmf/cc5RIdQbndxs2Mnr+Uk6cO0WNkIq8168LbWvWvHz91IULPDd7IQt3bMdkMPNA80a81acHQTbbrXxFQuQqWT1SNg3odIN71mit63n/koJMZApZ7C9y0keLF/PBzJl8fPYs/zgcPHDoEPe89x6b/vkn0/pYERnJq99/zzDgALAFaON20+LVVy/f8+mSJbw1fTrvnznDIYeDoYcP0/fDD1l34AAA8UlJtB49mpJbt7IjJYU/Ll3i0PLl9P7gA5/Pz4yJiyN89Gg6xMayD9gGlD52jPojR14+P/PLFat45OvfOHD6M5IcR9l+9BW6fzSFFZGRACSmpNDo9feYvaku8cm7OJ+4hi9XFqTdu59elYeMlom8zOeiTCnVSyl1QCl1USkVp5SKV0rFXe8zWuvVwLlbzlIIIXIJu9PJuEWLmGu30wYoCgwCxtjtvDNvXqb10/vjj2kLjAPKAjWAeYAN6DNhAk6Xi7fmz+dHu527gCLAAOAdu52xP/4IwMwNG6iTnMzbbjdlgerATIeD/UePsvmQb8cb/d+sWTTVmg+AcsAdwA+ASk7mq1WrcLvdvDJ3MYn2OUB7byb9SLR/zIs//gbA7M2bOZ9YC6f7Q6A8cAcpzu/462QSa9M5x1OmMkVedTMjZe8CPbTWhbTWwVrrglrr4Bt+6saaKaV2KaV+VUrVyoR4QgiRY05fvIjJ7eaONO1tgT+PH8+0ftzJyXRJ02YC2gBr9u8nNiEBh8NBaJp77gR2nzwJwO7Dh7kzJeWqGOFas/vECZ/y2HPkCJ3TtBmBu4DV+/YRl5REXFICEJbmrrbsjToGwNbDJ7mU0iHNdQMud5tM/c6E8Hc3U5Sd1lpHZnL/24EKWuu6wARg/rVuVEo9qpTaqpTaeiYbD8oVQoibUbxgQZKAY2naNwNVS5bMtH60xcKatG3ARqBW2bLcVqAAGAyknTDdAlQtXhyAKmXKsMViuSrGZqV8zrVSSEi6eWwAGlSoQMGAAGwWK/B3mrs2U7FYaQBqlilOoGX9VVFMxk1ULVXqmn3LaJnIa25YlHmnLXsBW5VSs5VS9/3b5m3PMK11nNY6wfvPiwGzUqrYNe6dpLVuqLVuWNz7PxQhrkfWlYmcYLNYGN6uHQMsFvbhKVCWAs9bLDx7992Z1s+koUNZAEwEkoFYYBhwHvj9mWewmEw81bEjAy0WIr15rACesVh4rpfnf92DW7ZkmcnEJ94Y54CnjEaCihWj9R1px/rS917//vwGjAeSvP1HAOdMJp5q3x6jwcDzXToQaOkP/OXNZA2Blsd5o1c7AO5v1owAyxqU+uByFJPxGUIKJ9Iu1csA6ZGpTJGX+DJS1t37VzCQCHRI1dbtVjpXSpVS3oPNlFKNvfnE3kpMIYTIaaP79KFz5860ttmwGQyMKFqUiY8+SrtambdCo1+TJgwMD+dVIAgoDcwGvomIwGw2A/DS3XdzT7dutA0IIMBg4IkiRfjw4YfpXKcOAEWCglj6yiv8WrUqhQwGyhmNXKxfn19efNHnMycrFS/OnGeeYbzNRkGgBLC0cGHWvfEGJpPnBf8Xu3VkVM+aFA5sg9FgI6TwACYN6UHPBg0AKBQYyIZXnqVV1VkYDYUwG0vTrc5W1rz0FAaD7Nwk8g+fz75USrXQWq+7UVua67PwLHEoBpwGXgPMAFrrL5RSw4HHACeeH4+e0VqnHcO+ipx9KXwl52CKnOR2u0l2OAiwWLL0YO1/Tp+mkM1GsUKFrplHitOJzWy+Zh4pDgdGgwGT0ZjhPOISEzEZDAReYxsLrTXJDscN8zAohdl0czs2yTmZ4mb549mXN/O7fgLQwIe2y7TW910voNZ6Ip7RdyGEyHMMBsNN7feVUZVvsP7LYDAQkGbtWFpW7+jarQgODLzudaVUtuQhRG51w6JMKdUMaA4UV0o9k+pSMJ6XbITwW9MHy2iZEEKI3MGXkTILniULJqBgqvY4oE9WJCWEEEIIkd/csCjTWq8CVimlpmmtj2ZDTkIIIYQQ+Y4v05eL8LzDnO7CTK11j8xPSwghssb5S5cYv3gxv23bRpDVyqB27RjcsmWWLsTPqI9+/51PFi4kISmJUkWK8N7gwXQMTbsd7PWt3rePjxcu5HBMDGFVqvBcjx7cUbr05etR588z+Msv2X3gAEaDgTvDwpj68MNY/r+9+w6Pqkz7OP59JplJIfSOIBCKgIBIE0Fcqoi6ou6uBQsqiogF1rZY1rLuK66uDSsICIgKiuKCCoKA0kF6lSpdQjC0BDKTZJ73jxkwJIFMYCYzSX6f68pFcs7kPvccJN45T7mzTbSfu3Ejb/pjXJyYyOO9etGoRo2gvc+iZuvWxUye/AZ7926idu1m9Or1KLVqNQ93WlIMBLLW+L/Aa/iahh8HPvR/pAJrQ5eaiEhwpbnd/OmFF9j5/fe8vm8fg3bs4N1x4xg0JvImHv593Dj+9dlnPJ2WxrdeLzccOMANr73GNytXBhxj4pIl3Pzaa1y5bh3Dk5OpvXgxHangl8QAACAASURBVJ9/nnX+Hf2PHDvGRY8/TulffuHzrCw+zMhgw6JFXPzEEydjfL1sGTe99ho9/DESlyzh8hdeKLE77a9dO4P//OdaVq3qSHLycJYta8aLL3Zl69bF4U5NioF8izJr7U/+IcyLrbU3WWun+D96A5eFPkWRc6NNZOWEj+fPp86hQ3yUmcllwLXAD2434+bPZ3sEdQrJzMxk1MyZTAH6Am2A54FngUc/+iigGF6vl8GffMJ4j4d7gdbAM14v/3C7edHf+3LwF19QLzOTL4HLgauAucDelBQmr1iBtZZ/fPwxn3g89PPHeNpanna7efHzz4P7pouIceOexOP5EHgAaI21j+Hx/IfPPnsu3KlJMVCQXfkqG2MST3xhjKkLaGt9ESkyFq5bx3UeD9kHKssCnaKiWPLrr+FKK5e1e/ZgraVDjuM3APuOHAkoxoHUVA6mpdExx/HrgQWbNwOwaP16boFT7kc80AP4aulSUtLSSEpNpVNeMbbmbOAUPoW1R1lmpoekpFXk3jf9erZvz3eLTZF8FaQo+zvwozHmR2PMj/g6dgwKSVYiIiFQvVIlNuXYHNUCG4Ea5cqFJae81KxQAQ+wP8fxjUB8gJu7lomNJcsYkvKIcZ5/k9lK5cuzLo/vXQ8kVqlCQkwMGMPePGLUKFMmoDyKk6goJy5XOcjVUXQjCQkld46dBE/ARZm1dhrQABjo/7jAWvt9qBITEQm2vp07MzIqipn4ijEP8H8OB9FlytChQYMwZ/eHSqVL06RqVe4GDvmPbcXXU/Kvf/pTQDFiXS76dOhAf6eTg/5j24DHY2J44Brfk56XbrqJT4Bv8N2PTHwTiHcYwxM9exLjdNK3Y0f6O52k+GNsx9c/84Grrz73N1rEGGPo3v0BXK7+wInh7t24XA9z1VUPhDM1KSYCaUjexf/nDcDVQD3/x9Xn2pBcRKQwNahWjXEPPUS/MmVIjImhhtPJ7Dp1mPKPf0Tc6stZzz7LzkqVqA7UApoBFzVrxtu33x5wjFduvZWqbdpQNzqaRrGxtI2Joc8113DrpZcC0LpuXV6+9VZuM4YqQAXgVaeTSU88Qax/5/0hvXtT65JLSIyOpnFsLK1cLm67+mr6XFYypxRff/0zdOhwEU5nQ2JjG+NyNadbt5507/5guFOTYiDf3pfGmBestc8ZY/KaXWqttXeHJrXTU+9LORva2V9O8Hq9bNq3j4TYWGpWqBDudM5oa1ISa/fsoWPDhlRISDirGCmpqew7fJg6lSrl2fbJ6/UyY906ysTFcWn9+nnGOJiWxm+HDp02RjiFo+/lsWOHOXhwNxUr1iY29uz+XiS8imTvS2vtc/4/7wpGYiIi4eZwOIrMPlv1qlbNt7dlfiokJJyxoHM4HPnuf1a+VCnKlyp1TnkUJ/HxZYmPz7sBvMjZCrghuTFmK7AI34rpOdba9SHLSkRERKSEKcjqyybAMKAi8F9jzDZjzKTQpCUiIiJSshSkKMsCMvx/eoEkcq/YFolY2kRWREQiWcDDl8ARYA3wOvChtfb30KQkIiXV+j17GDZjBnsPHOCSxo25p1MnyoVhHtOwWbP498SJZKSnU75SJcbdfz+t6tY9ef5YejoDP/2UOStW4HQ6uadHDwb16HFKjKmrV/PchAkcPHSI+rVr806fPqfMDTuWns6gzz7jp+XLcTqd3NW9O4/27HlKjE379jFsxgx27NtHqwsuoF+XLlTMNjcsMyuL8YsX882iRcTFxnJbp050bdLklBhbkpL4YMYMtv/2Gy0bNqRfly5UKl06mLdLRIIk39WXJ19oTC98bZXa4tveZwG+uWUzQ5de3rT6Us6WVmBGrm9XruSu995jQEYGjaxlstPJ0oQE5r/wApULcaPSu0eM4PN58xgINAe+BL4DJj32GN2bNuVYejoNBw2iRno6DwIpwMtAqyZN+NbfM/LVqVN5YcIEHgYuAr4GvjWGH597jpZ16pDu8dBg4ECqHj/OQ/j2InsZuOiCC5j25JMAzN6wgZvefJN+GRk083qZ5nQyOzaWeS+8QM0KFcjyern+1VdJ2baNe9xujgJvulz06dGDZ//yFwB++uUX/vr669ybmUkLf4yZsbHMe/55alWsWGj3NJTCsfJSiodIXH1ZkM1j/2etfRy4D9/PqDvx7TkoInJOvF4vD40axeceD89by83ApxkZdDtyhP9+U3g/ZrKyspg4bx5fAv8H3AR8DtwN3PX++wA8+eWXVE5PZyFwB762JiuA2evXs3rnTrxeL0O++ILPgZf8MT4D+lrLPR98AMDTX35JuePHWQT0wbcb9wpg7saNLN++HWstA0eOZITbzb+9Xm4CPsrI4Ka0NF6a5JvKO2XFCn7bto3Zbjd34ttYdqHHw5vTprE7JQVrLYNGjWK4x8NLXi83AqMyMrg1NZV/f/llIdxNESmogIsyY8yX/hWYbwGl8P08Kh+qxERCQfPKItO25GSy0tPJuVd9n6wspq9YUWh5TF65EgdwRY7jdwOpaWkAzFq+nH5A9mZH1fE19B45dy4rd+4kw+ulZx4xfk3yNT36Ydky7uPU+SPVgM7AiJ9+Yv+RI+w5dIg/54jRx+tl+qpVAExfsYJb3W6c2c5XAa5wOJi1YQO/p6bya0oKvXLEuNNavl+9Or9bISJhUJA5ZS8Dy621WXmdNMZ0t9bOCE5aIlKSlI6N5YjXixuIzXY8CSgbF1doeZxfoQJu4Bi+3zyz53HiN9j4uDh+y+N79wGXJSRQqXRpMoBUIPvMrSTA5XCcjJGznyTAb0DL0qWJd7nwWMtRIPvA7X7+uB9lExJIcjjA6z0lRpIxlI2LI87lIgvfZODsXT2TgHKFeE9FJHAFGb78+XQFmd9/gpCPiJRAVcuWpUO9ejzvcHCixEgBnne5uOuKnM+tQqdV3bokOBw8iW+ZOfjmez0OXOCf6P94r168BfyS7fsmAFuAR3r04PyKFalZujSDc8R4DOjaqhUAT1x3He/ga/x9whf4Gn0/cdVVlI6L45pmzXgqKupkjCPAMy4Xd3XrBsAdl1/OyOho1maL8RWw0eHgymbNKBUTQ6/mzXkyOppM//mjwNPZYohIZCnIlhj5iazGcSJSpIwcMIB5551Hg5gYesbFUd/ppNvll3NHhw6FmseUp57iY/4YkjwPOBQfz0/+Cfh/bdOGv3XowMXAJUBD4B5jGNm/P/Gxvud8U59+mkkuF9WBP/ljRFerxth+/QC4vlUrbunYkVb+GBcAdxvDsH79SPDHePeee/ildm0SXS6uioujrtNJi0suYUDXrgA0rlGDN/r04XKXi85xcbSKjeWR0qWZ/PjjxDh9g5pv9+3L1jp1Tsao43RyYZs2PNi9eyHcSREpqIBXX+YbyJjl1tqWQQmWD62+lHOhFZiRy1rLyp072XPwIK3q1KF6uXL5f1OIvPLddyzfvp07OnTgqosuynV+78GDjJgzhwqlStGvUydc0blng0z8+WdW7dzJtRdfTJvExFzn9x06xPCffqJ8fDz3de6cZ4w1u3ax/cABLq5dO88+nanp6czbtIk4l4sODRoQHRWV6zVrd+9m+4EDNK9Vi/OLyarLE7T6Us5WJK6+VFEWABVlxY8KM5HiQUWZnK1ILMqCOXy5PYixREREREqUfFdfGmNuONN5a+1X/j/P+DoREZFg0lMyKW4C2RIj51Y52Vl8C35ERERE5BzkW5RZa+8qjERERAqDtZa5mzYxdeVKEmJj6d2+PXUrVz7lNSmpqXyycCE7k5NpW78+17VsiTOPSfhncjAtjU8WLGBHcjJt6tfn+rOM8enChWzfv5/W9epxfatWeS4GOBfWWhZt3cqU5cuJdTq5+dJLaVitWlCvISKBKdBEf2PM1cCFZNvf0Vr7rxDkdUaa6C/BoMn+JY/X6+WeYcOYt3Ilt7rdpERF8YnDwdt33cUt7dsDsGLHDnq+/DLdsrJo6vHwTUwMmZUqMf2ZZygT4Karq3bu5MohQ+iSlUUzj4fvYmJIr1iRGc88Q9n4+IBirNm1ix5DhtApM5PmHg/TYmJIrVCBH555JmhN2q21DBw9mikLF3KHx0NqVBRjHQ6G9O7NPZ07B+UaoaThSzkXRXqivzHmA3xt3B7CtyfZ34DaZ52hiEgh+271apauXMlKt5vngLeyspidkcEDo0dz5PhxAO774ANeOX6ccR4Pg4G5bjf1kpIK1IOz/7BhvHT8OJ/4Y/zkdtNo/37+M3lywDHuHz6cfx07xqf+GLPdbpolJzPkf/8r0Hs+kzkbNzJ10SJWeTy8ALyWlcXCjAwe//RTko8cCdp1RCQwBVl92d5aewdw0Fr7AnApUCs0aYmIBN/XixZxr9tN9mdVzYC2Dgcz169nz8GDbDtwgFuznTfAwMxMvl60KKBrJB0+zC9JSdyRV4zFiwOKceDoUdb89ht35hVjyZKAYgTi6yVLuMvtPqWVU32gm8PB1DVrgnYdEQlMQYqy4/4/jxljagAZQN3gpyQiEhrOqCg8eRx3+89FOxxkWUvOfnJuyHNT1rxE+VtFZeY4fuIagcaw1uYdwxG8nYyc0dF4TO5mLG5jAs5VRIKnIP+6vzHGlANeBZbj25dsfCiSEikMYzQdpcS56bLLeM/l4kC2Yz8B64CuTZpQtWxZLq5Vi7ezFSoZwEtOJzd37BjQNSqVLk3b2rV5K68Yl18eUIzypUrRPjGRN7IVYJn+GDcFmEcgbmrfnhFO5ykN1n8G5nm9XNW8edCuIyKBKUhR9oq19pC19kt8c8kaAf8OTVoiIsHXqXFjbu3WjSZOJ/c6ndwQE8NfYmL49KGHiHO5ABhx//28X6YMnWJjeTA6msYxMcQ0bMignj0Dvs7w/v35sGxZ/uSP0SQmBlO/Po8UIMaw++5jdNmyXJ4tRmZiIo9ffXWB3/fptKpTh4evuYamTid3u1zcGBNDD5eLMfffH/CCBBEJnoBXX+bVRqkwWytlp9WXEixagVkybUlK4vs1a0iIjeW6li1zFSCezEy+XbWKXSkptK1bl0vq1cPkMcx3Jhn+GDt+/522iYm0O8sY361ezfYDB2hTty6X1q9f4BiB2J6czHerVxPnctHr4oupkJAQ9GuEglZfyrmIxNWXgezoXw04D4gzxlyMb74pQBlAv0qJSJFTv2pV6letetrzruhorm/V6pyu4YyO5rogxOjVMvS/99apXJkBXbuG/DrBpIJMiqNAdiHsAdwJ1ARez3b8CPBUCHISERERKXEC2dF/DDDGGPMX/3wykWJjTB8NYYqISGQoyET/+caYkcaYqQDGmCbGmL4hyktERESkRClIE7WP/B9P+7/eBEwARgY7KREJnCczk+9WrWLf4cN0aNCAZrVK9p7OGZmZTFuzhj0HD9I2MZGWderkes325GSmr1tHQkwMf27RgtIBtk8SEQmlghRllay1nxtjngSw1mYaY3LusSgihWjD3r30HDKEOhkZ1Pd6eRHo0aIFH/bvT1QQNxktKrbt38+VQ4ZQ5fhxmmRlMcQY2jVuzLiHHjrZDPzFr77izalTucYYUhwOBo4ezReDBtGpceMwZy8iJV1BfmqnGWMqAhbAGNMOOBySrEQKUVHdRNZay+1Dh/Lk0aP8mJ7OCI+HzR4Pv6xaxZh588KdXlj0fe89+h06xLz0dIZnZLDJ4+H3DRt4b+ZMAOZu3MioadPYkJHBGI+HKenpTHC7uWnoUNI9ee31LyJSeApSlD0CTAYSjTHzgbH4mpOLSBhsSUpiX0oK92Y7Fg/8w+3m09mzw5VW2Px26BCr9+xhYLa9F2OApzwePv3xRwA+mzuXAR4PVbJ9XxegsbXMXL++MNMVEcmlIMOX64FJwDHgKPA1vnllIhIGnqwsYo0h51aicYA7IyMcKYVVRlYWTiBnx8bs98OdkZHn5opxgDszZ6dJEZHCVZAnZWPxtVZ6CXgbaAB8HIqkRCR/jatXh9hYpmU75gXeczrpdeml4UorbGpVqEC18uWZmO2YBd51OunVrh0A115yCR/GxHA822s2AIuysujapEkhZisikltBirILrLX3WGtn+z/6AQ1DlZhIYSqK88ocDgcj+vfnjpgY7nM6eRW4LCaGg+edx4Bu3cKdXqEzxjDsvvt4MDaWvk4n/wU6x8SwqXJlHvX3i/xzixY0a9qUVjExvAQ8FhXF5U4nQ/v0Ua/HIkS7+UtxVZDhyxXGmHbW2kUAxphLgPmhSUtEAtGpcWNWvfwyH8+fz96UFB5t3JheLVsSHZVzEK9kuKRePda+8gofz5vH7gMH6N+wITe0bo3Lv/LS4XAw9sEHmbVhA98tX07puDjmd+hAw2rVwpy5iEjBirJLgDuMMTv9X58PbDDGrAGstbZ50LMTkXzVKF+ef1xzTbjTiBhVypTh0auuOu15YwxdmzTRcKWIRJyCFGVXhiwLERERkRIu4KLMWrsjlImIhJv6YIqISDiVvC2/RURERCJQQYYvRaQE23vwIOMWLKBqmTLc3qEDjrNo43QiRuUyZehzljFS09NZtHUrpWNjaVO37lnFCMT6PXvYlZJCi/PPp2rZsrnOp7ndLNq6lVIuF20TE0OWh4iUHCEtyowxo4BrgP3W2qZ5nDfAW8BV+DalvdNauzyUOYmciYYw83bH++/zxeLFXAgkAYM++ojxf/87PZo1CzjGXcOGMX7hQpoAB4BHPvqITwcNomfzwNcIjZ4zh0fHjePCqCgOWAulSvHlo4/SuEaNgr6l00pJTeWmN95gw65dNIqKYllGBv06d+bl3r3x/ciCT+bPZ+DYsTR2ODhoLRlxcUx85JES3wxeRM5NqH+1G82ZFwj0xLcJbQOgH/B+iPMRkQIaOWcO3yxezGpgKbAT+LfXy81vvEFmgLvgj5k7l68XLmQlsAzYDgzxern5zTcDjrFixw6eHDeOeR4Pc44fZ116Oo/+/jvXvvIKWV7vWb23vPQfPpxG27ez3ePhh+PH2ZKZycyffuKjOXMAWLt7N4+MGcOPbjdzjx9nTXo6zxw8yLWvvkpmVlbQ8hCRkiekRZm1dg6QcoaX9ALGWp9FQDljTPVQ5iQiBTN08mQG4/vNCcAAA4ByXi8j584NKMYb33zD48AF2WLcB1T2evnA35cyPx/NmsUDGRk0zhajL1AmPZ25GzcGFCM/KampfL9+PUOysk4OI1QE/u3xMHL6dABG//gj92VkcOLRvwFuB6p5POqfKSLnJNyTIM4DdmX7erf/WC7GmH7GmKXGmKXJycmFkpyIwLH09Fz/KA1QHV8T8EAcP3aMmnnEqAH8dvhwQDEOHj1KjWzNxk+oDqSkpQUUIz9H09Mp5XBQKq9rHDuWbx4Hg5SHiJRM4S7KcvZSBl+7utwHrR1urW1trW1duXLlEKclJVlRbLkUSm0vvJAPOfUf5k5gBdDnsssCinFp8+Z8iK835wm7gJ+BOzt0CChG14svZlxMzCkx9gDzs7Lo2DA4Hd9qVahAQnw8M3Mc/9jhoLt/7ls3fx7ZByqTgNmZmfypUaOg5CGnpxZLUpyFuyjbDWSfGVsT2BumXEQkD2/ffjvrnU66ARPwrcxpDfS86CLqBvgL0pu9e7PJ6aSLP8ZQf4wezZrRIMAWR7e0a0dW9er0dLkYD7wDXOZy8eSf/0zlMmUK/sby4HA4eOvuu+ntcvGiw8Ek4G6nky8TEhh83XUA/LV1a2Jr1qSHy8VnwHtAh5gYHr3qKqqXKxeUPESkZDI2j8fwQb2AMXWAb06z+vJq4EF8qy8vAYZaa9vmF7N169Z26dKlQc709Mag5XgljVZgniolNZUHxo5lydq1xMTEcF/Pngy84ooCx3jw449ZvGYNMTEx3NOjB49cWbBGIekeD2Pnz2fqkiWUKVWKPl260CUE7ZLW7t7NB9OnsyspibaNG3Nf165UKl365Hl3RgYfL1jAd4sXkxAXx+2dO9O9aa4fcRICelImwdKnEP9TMsYss9a2zvd1oSzKjDGfAZ2ASvie8D8HOAGstR/4t8R4B98KzWPAXdbafKstFWUSairKRCKTijIJlkgsykK6T5m19pZ8zlvggVDmIHI2tF+ZiIgUtnDPKRMRERERVJSJiIiIRAQVZSKnoa0xRESkMKkoExEREYkAKspEREREIoCKMhERKRK0HYYUdyrKRM5A88pERKSwqCgTERERiQAqykREREQigIoyERERkQigokwkH5pXJiIihUFFmYiIiEgEUFEmIiIiEgFUlIkEQEOYIiISairKRERERCKAijIREYl42s1fSgIVZSIiIiIRQEWZiIiISARQUSYSIE32FxGRUFJRJiIiIhIBVJSJiIiIRAAVZSIFoCFMEREJFRVlIiIiIhFARZmIiIhIBFBRJiIiIhIBVJSJFJDmlYmISCioKBMRERGJACrKREQk4vVhTLhTEAk5FWUiIiIiEUBFmYiIiEgEUFEmIiIiEgFUlImIiIhEABVlImdB22KIiEiwqSgTERERiQAqykREREQigIoyERERkQigokxEREQkAqgoExGRIkG7+ktxp6JMREREJAJEhzsBKRwrv13JjPdncPT3o7S8qiXdH+pOqXKlwp1WkTamD/TRL+4iIhIkelJWAkx+aTLv9X2PddHr2Fl/J99O+pbn2z3P8SPHw52aiEiBaAhTijMVZcVcakoqU4ZMwdPbAxcD9SGjVwaHYg/x44c/hjs9ERER8VNRVsz9+vOvRNeMhrKnHs9omMGqH1aFJ6liRDv7ixQ+PS2T4kpFWTFXpkoZvAe94D31uDlkKF+tfHiSEhERkVxUlBVz57c4n4rVK+KY54As/8F94FzmpPuA7mHNTURERP6goqyYM8bw2JTHqJVWC9c7LmJHxRI7IZY+b/YhsU1iuNMrFjSEKVL4NIQpxZG2xCgBKtSswAuLXmDf5n0cO3iMms1r4op1hTstERERyUZFWQlSrUG1cKcgIiIip6HhS5Eg0BCmSOHTEKYUNyrKRERERCKAijIRERGRCBDyoswYc6UxZqMxZosxZnAe5+80xiQbY1b6P+4JdU6Smyfdw5xRc3j3tncZ/8R49m3eF+6UihwNYYoUPg1hSnES0on+xpgo4F2gO7Ab+NkYM9lauz7HSydYax8MZS5yesePHufFji/yu/t33PXdRO2KYtbwWQz4ZAAtrm4R7vRERERKhFA/KWsLbLHWbrPWeoDxQK8QX1MKaMbQGSSTjPsmN7SGrC5ZeK73MOKeEWRlZuUfQERERM5ZqIuy84Bd2b7e7T+W01+MMauNMRONMbVCnJPksOR/S8hongEm28E6kOnIZM+6PeFKS0QkIBrClOIi1EWZyeOYzfH1FKCOtbY58APk/a/LGNPPGLPUGLM0OTk5yGmWbLEJsZCe46AXvMe9xJSKCUtORZXmlYmIyNkKdVG2G8j+5KsmsDf7C6y1v1tr3f4vPwRa5RXIWjvcWtvaWtu6cuXKIUm2pOp6T1diFsXAcf8BC2axoUpiFarWrxrW3EREREqKUBdlPwMNjDF1jTEu4GZgcvYXGGOqZ/vyWmBDiHOSHNrd0o4O13fA+Z6T2P/FEjs6lgpbK/Dw5w+HOzURkYBoCFOKg5CuvrTWZhpjHgS+B6KAUdbadcaYfwFLrbWTgYeNMdcCmUAKcGcoc5LcjDHc8dYd9Px7T7Yu3EqZqmVo1KkRDoe2sTsbY/pAH/3/QURECijkvS+ttd8B3+U49my2z58Engx1HpK/ynUqU7mOhoZFRETCQY9CRESkWNAQphR1KspEQkCrMEVEpKBUlImISLGhp2VSlIV8TpmcmbWWDbM2sHnBZspVL0fbG9sSVyauQDGysrIY99A41kxbQ9mqZek7qi81Gtc45TVHDxxl8YTFHDt4jCbdmlDvknoY88c2ctZafvnpFzbN3UTZamVpe2Nb4svGF/i9bJyzkY1zNlK2alna3NiGUuVKFSiGiIhISWWszbmXa+Rr3bq1Xbp0aaFdb0yIfvPKcGfw2p9f49f1v+JJ9OA64sKx28HgGYOpfXHtgGKkpqTy8PkP4432QlMgGdgG1z97Pb3+6etote6HdQz961BsfUtGXAbOTU5adG9B/7H9cTgcZGZk8sZ1b7Bl5RY89Ty4jrowOw1PTHuCxDaJAeWRmZHJmze8yeZlm/HU9+A86sSxw8HjUx+n3iX1zvIOFX1ahSlS+Mag+QOSvz6F+J+JMWaZtbZ1fq/T8GUYzXx3Jtt2bcN9txvb1eK+3s3xy4/z7q3vEmixPKTzELwVvfAwvrbvvYFeMOnfk8jKyiLTk8m7vd/FfZ0bz5892G4Wzz0eVs1bxdIvfYXt7GGz2bxlM+6+/jyuc5PeNZ13bn4n4DzmjJzDpo2bTsbwXOchvXs679zyDl6v9+xukIjIWdAQphRVKsrCaO6nc/G08fh2cDuhGRzaf4j9W/cHFGPPlj3QkVNjNAWiYe6ouWxesBlb2kLdbOdd4G7hZt4n8wCY99k8PK1y5NEE0lLT2Lv+lAYMZ34vrTynDog3hmPHjrFnrfpnioiI5EdFWTjl1RkUcncHPRv+GNnnjQV8/YK+JhjXERERKeFUlIVRx94dcf3sgqxsB9dAuWrlqFKvSkAxajaoCXPJFQMvdLy7I/Xb18ccNbAt23kPxKyIoeOtHQG4vPfluJa5fD0VTlgHpUqXyrVg4IzvZWmOGBsgPj6emk1rBhSjOBrTR9tjiISDhjClKNLqyzDq9kA3Vn2/im0jt/km2B924djr4IEZD5z5yVM2g2cN9k30H+qFC4H9wHa44bkbiIqKgih4cPyDvHXDW9h6lsy4TKI3RdPiyha0usHX+/1P/f7Eimkr2Dxy88mJ/o7dDh6a9lDAeVze93JWfLeCjSM24qnvwZXqWyzw0NTAY4iIiJRkWn0ZgFCtvoQ/tqLYPH8z5WuUp83f2hCbEFugGFlZWXw66FNWT11N2Wq+LTGqN6x+hZvcUwAAE05JREFUymtSf09lyRdLSEtJ48LuF+ZaVWmtZdPcTWycu9G3JcbfCr41h7WWTfM2ndwS42y29yiutApTpPBpFaacSSSuvlRRFoBQFmVScqgwEyl8KszkdCKxKNOcMhEREZEIoKJMREREJAKoKBMRkWJLqzClKFFRFgE2zNrAmAFjmPraVDIzM3OdTzucxge3fsAr3V9h9fer84yxf9t+FnyygPUz1+PNOrsd9JN/TT7nGHJ62hpDRETORFtihFFmZiZPt3iapM1Jvh33P4cv/vkFg2cMpmGHhgB8859vmPjPiVARSID116ynQu0KvL7ldcC34nH0A6NZ8MkCoupHQQrER8Xz5IwnqVy3ckB5WGsZ+/BY5o2dR1S9KDgI8Y54Bk8fHPB+aSIikaoPYzThX4oEPSkLozH9xpB0IAkeBW4BHgRvey///fN/AfAc9/gKsmuB/sBtwEBISUphdP/RACz4eAELv1tIxoAM0q9NJ/3OdA7WP8jQm4YGnMfCTxeyYPICX4xe/hgND/LWjW8F+R2LiIjI6agoC6PF/1sMnYEY/wEDtAfPMQ9bFm5h4pMTIQFolu2bSgOXwYIJCwD4YcQPeNp5INvWZratZd/mfST/mhxQHjNHzMR9iTtXjORfk0naknT2b1BEREQCpqIsjLIys04phADf30i0b7PXtENpuc8DxEGW19dXyZ3qzv2aKHDEOnCnuQPKw52WRwwHOOICjyEiEsk04V+KAhVlYZR4cSIs4dQG5NvAZBmaXtmUa/95ra9t0v5s573AEqh3UT0A2l7fFucqZ64YrihXwH0r217XFufqHDG2Q7Q3ukT3rQwFTfYXEZHTUVEWRv3H9idqZxSMBpYCU4Hx8Jfn/kJ0dDRV61WlYceGMAL4AV8BNwzMQcPDXz8MQI9BPahsKxMzIQZ+hqgfonD9z0W/kf1wRAX213vFw1dQxVHljxgzo3BNcnHvyHsDjiEiIiLnRm2WAhDKNkupKamMf3Q8G+ZvoFzlctw45EYuuPyCU14z/c3pTP7PZDweD40ubcT94+8nLuGPnpKedA+LP1vM2tlrqXheRTrd24kqiQVbNelJ97B4/GLWzlpLhRoV6HxvZ628DBG1WxIJH63ClBMisc2SirIAqPelBJOKMpHwUVEmJ0RiUaaxKREREZEIoKJMpJBpsr9I+GgVpkQyFWUiIiIiEUBF2RlYa1m/fj1bF28lw51x1nFWfrOSOSPn+PYdy8P25dsZdtswZrw947QxXur0Eo8lPsYv837J8/zSSUt5qslTDLtt2GljjBs4jv/2/C+71u7K83x6ajpbFm5h/7b9eZ4PhDvN7Yux9exjiIiIlESa6H8av/76K1dffzU79uzAW8oLR+HOd++k3c3tAo6xcd5GXr36VTIzMiEOOAqd7+1Mn3f/GL96sNqDpB5IhfK+8xh44tsnaNKlCQDDbh/Gwi8W+srnOOAIOBwORrlHnYxxZ+ydkAWU88cAev+3N1c8eAUA3776LV/88wtfjHjgMJSuXJq39759Msa0N6bx1XNfEVUpisyDmdRuUZuBXwykdKXSAb/f6W9PZ+LTE0/GOL/5+QycOJAylcsEHKOk0GR/kfDShH+JxIn+Ksry4PV6qd+kPjvq7MB7iddXzOwF1+cu/vnTP6nVvFZAMe4pcw/eS73QHl+M34DR0P+j/rS7pR3/uvRfbFu3De7GV5RlAtOAtTD6+GjS09PpX7Y/XIbvwwHsAz6C6onVGbJ+CA9UfoA0d9qpMb4HVsPo9NFkZGRwb8K90AHo6I+R5IvRolsLBk0ZxOqpq3nnznfw3OyBCkAWRM2Kon5cfZ6c8WRA92zt9LUMvW2oL0ZFf4zZUSQ6E3l61tMB3/uSQkWZSHipKJNILMo0fJmHhQsXciDtAN523j/uUA3IbJnJzGEzA4oxZ9QcvNFeXzF0IkZ1oD1M+r9JAGxbtQ2uwFdMAUQDPYAsWDxxMc9d9JyvL2bHbDGqAR3gt62/AZCWmgbdcsS4AvDCZ498xoe3fwiuHDGq+r5eOWMlAFOHTsXT3l+QAURBVucsti3Zxu87fw/o/U57ZxqeS/0F2YkYnbLYvnw7ydsD68FZkmiyv4iI5KSiLA/79+/HlDe+BuHZeMt4OZR0KKAYv+/4HcqSKwblIe2If26ZxTfkmJ0TiIWdy3dyJOnIaWMQle3rvGLEwa/LfmXfpn1Qhtx/0+X+OHZkv/862UVDVNkojh44eoZ3+YdD+w7lziMaospFcTQ5sBgiIoVFqzAlEqkoy0P79u3x/OqB1GwHLbg2uWjerXlAMdr1bgd7OTnH60QMVkLjdo0BiI6OhjU5vnEvkA49HunBlY9c6RuuPJIjxirfawDfXLLVOWLsA47BHe/fwQ0v3uAbrjycO4Yr2gVAs27NiP4l+tQYSUAq1GgSWP/Mi7pdlDtGMtjDVv0zRUREAqCiLA9Vq1blkb8/QqlPSsFKYBM4Jzmp5KxEhzs6BBTjvMbn+fpWfgis8MVgPETtj+L2d24H4J4P7/Gd+x+wFVgMjIVq9atRplIZej3by/eU7ESMzcAEYCfc//n9AFz3zHW+ouxrYIs/xhiIckVRs0lNWlzdguj4aF+M5f4YnwPb4dFvHwXgqkevotTeUkR/F+2L8bNv/tzNr9yMK9YV0Pvt8fceJOxLIPrbbDEmuLjp5ZtwxQUWQ0REpCTTRP/TsNYyZcoU3h7+NlsObaFNrzZ06d+FuNJx+X9zNl89+xUzR84kw51Bw7YN6TuiL+VrlD95fulXS/nw7g9xe9wYY2hzTRsGTBhwSoz7y93PcfdxX4Hmgb4j+tLxzo4nz095aQpfPvelb9jSC1VqVuGVLa+cEuOZi55h96bdYCDaEc2gSYNo2r3pyfNHko8wfeh01sxcQ/ka5en5cM9cPTjzc/TAUb4f+j1rflhD+erl6fFQDxp3alygGCWJJvuLhJ8m/JdckTjRX0VZANT7UkJFhZlIeKkoK7kisSjT8KWIiIhIBFBRJiIiJZZWYUokUVEmIiIiEgFUlJ0jr9fLoX2H8Bz3hPQ6h/YdYtfaXXi93jzPZ2ZmsnP1zoD3FRMREZHIEp3/S+R0Fk1YxKePfcrxI8fBC+1vb89tb9yGM8YZtGsk70jm33/6N4d3HwYHOFwObvzXjb49zPw+e/wzpr8zHeu1kAVVLqjCs3OfJaFCQtDykNAY00eT/UXCrQ9jNOFfIoKKsrO0YfYGRg0Yhec6D5wPHIUF3y8gY0AG/Ub2C9p1/tnqn6TXTodbgBjwbvQyfvB4zrvwPJr1aMas92fx/Tvfw81AbSAN9k/ez7Ntn+X1La8HLQ8REREJLQ1fnqXJr07Gc5m/IAMoDRlXZbBkwhLSDqUF5RrLJi0j/Vg6XA3E4tunrBFwCYwfPB6ASS9Ngq5AHf/5BOAGSNmRwp4Ne4KSh4iIiISeirKzlPxrsq85eHbxEFUmisP7Duf5PQW1c9VOqMypfS4BqsPB5IMAHDtyLHcesUAC7FmjokxEJBBahSmRQJvHnqXed/Rmwt4JeDtmm3j/O5QaW4rk35KJiyvYzv95+fnnn2nboS08iq/QOuEr6FSpE7NnzqZBkwZsqbTF97TshEPAO5C0J4kqVaqccx4iIiJy9rR5bIg9+9SzxC+Pxyww8DuwCeInxvPsM88GpSADaNOmDQ0vaAhjgF+BZGA6mF8MH7z3AQDD3hkGi4C5+PLYDIyBzp07qyATEREpQlSUnaVGjRqxaO4irkm4hkoTK9F0Y1NGvDaCJx57IqjXWbdiHTe0v4HoL6MxIw2JKYksnLOQCy7w9aXs0qUL3379LdW3VscMN7i+cXHfjffxw7QfgpqHiIiIhJaGL0VERERCSMOXIiIiIkWIijIRERGRCKCiTERERCQCqCgTERERiQAhL8qMMVcaYzYaY7YYYwbncT7GGDPBf36xMaZOqHMSERERiTQhLcqMMVHAu0BPoAlwizGmSY6X9QUOWmvrA28A/wllTiIiIiKRKNRPytoCW6y126y1HmA80CvHa3rByf4WE4GuxhgT4rxEREREIkqoi7LzgF3Zvt7tP5bna6y1mcBhoGKI8xIRERGJKKEuyvJ64pVzt9pAXoMxpp8xZqkxZmlycnJQkhMRERGJFKEuynYDtbJ9XRPYe7rXGGOigbJASs5A1trh1trW1trWlStXDlG6IiIiIuER6qLsZ6CBMaauMcYF3AxMzvGayUAf/+d/BWbZotj7SUREROQcRIcyuLU20xjzIPA9EAWMstauM8b8C1hqrZ0MjAQ+NsZswfeE7OZQ5iQiIiISiUJalAFYa78Dvstx7Nlsn6cDfwt1HiIiIiKRzBTFkUJjTDKwoxAvWQk4UIjXKwl0T4NP9zT4dE+DT/c0+HRPQyOY97W2tTbfCfFFsigrbMaYpdba1uHOozjRPQ0+3dPg0z0NPt3T4NM9DY1w3Ff1vhQRERGJACrKRERERCKAirLADA93AsWQ7mnw6Z4Gn+5p8OmeBp/uaWgU+n3VnDIRERGRCKAnZSIiIiIRQEXZGRhjRhlj9htj1oY7l+LCGFPLGDPbGLPBGLPOGDMw3DkVdcaYWGPMEmPMKv89fSHcORUXxpgoY8wKY8w34c6lODDGbDfGrDHGrDTGLA13PsWBMaacMWaiMeYX/8/VS8OdU1FmjLnA/9/niY8jxphBhXZ9DV+enjHmciAVGGutbRrufIoDY0x1oLq1drkxpjSwDLjOWrs+zKkVWcYYA5Sy1qYaY5zAPGCgtXZRmFMr8owxjwCtgTLW2mvCnU9RZ4zZDrS21mpPrSAxxowB5lprR/jbGcZbaw+FO6/iwBgTBewBLrHWFsreqHpSdgbW2jnk0Rxdzp619jdr7XL/50eBDcB54c2qaLM+qf4vnf4P/bZ1jowxNYGrgRHhzkUkL8aYMsDl+NoVYq31qCALqq7A1sIqyEBFmYSRMaYOcDGwOLyZFH3+YbaVwH5ghrVW9/TcvQk8AXjDnUgxYoHpxphlxph+4U6mGEgEkoGP/MPsI4wxpcKdVDFyM/BZYV5QRZmEhTEmAfgSGGStPRLufIo6a22WtbYFUBNoa4zRcPs5MMZcA+y31i4Ldy7FTAdrbUugJ/CAf4qInL1ooCXwvrX2YiANGBzelIoH/1DwtcAXhXldFWVS6Pzznr4EPrHWfhXufIoT/9DFj8CVYU6lqOsAXOufAzUe6GKMGRfelIo+a+1e/5/7gUlA2/BmVOTtBnZnezI+EV+RJueuJ7DcWptUmBdVUSaFyj8pfSSwwVr7erjzKQ6MMZWNMeX8n8cB3YBfwptV0WatfdJaW9NaWwffEMYsa+1tYU6rSDPGlPIv7sE/xHYFoJXt58Bauw/YZYy5wH+oK6BFU8FxC4U8dAm+R59yGsaYz4BOQCVjzG7gOWvtyPBmVeR1AG4H1vjnQAE8Za39Low5FXXVgTH+lUIO4HNrrbZwkEhTFZjk+72MaOBTa+208KZULDwEfOIfbtsG3BXmfIo8Y0w80B24r9CvrS0xRERERMJPw5ciIiIiEUBFmYiIiEgEUFEmIiIiEgFUlImIiIhEABVlIiIiIhFARZmIiIhIBFBRJiJFnjHmTmNMjQBeN9oY89cznP/RGNM6yLmVM8YMyPZ1J2OM9pETkVxUlIlIcXAnkG9RFiblgAH5vkpESjwVZSIScYwxdYwxvxhjxhhjVhtjJhpj4o0xrYwxPxljlhljvjfGVPc/+WqNb1fzlcaYOGPMs8aYn40xa40xw/3tvQqawxXGmIXGmOXGmC+MMQn+49uNMS/4j68xxjTyH69sjJnhPz7MGLPDGFMJeBmo58/tVX/4BP97+sUY88nZ5CcixY+KMhGJVBcAw621zYEjwAPA28BfrbWtgFHA/1lrJwJLgVuttS2stceBd6y1bay1TYE44JqCXNhfTD0DdLPWtvTHfyTbSw74j78PPOY/9hy+Hpkt8TXbPt9/fDCw1Z/b4/5jFwODgCZAIr72YyJSwqn3pYhEql3W2vn+z8cBTwFNgRn+B0tRwG+n+d7OxpgngHigArAOmFKAa7fDVzDN91/LBSzMdv4r/5/LgBv8n18GXA9grZ1mjDl4hvhLrLW7Afw9YOsA8wqQn4gUQyrKRCRS5WzMexRYZ6299EzfZIyJBd4DWltrdxljngdiC3htA8yw1t5ymvNu/59Z/PFztCBDkO5sn2ePISIlmIYvRSRSnW+MOVGA3QIsAiqfOGaMcRpjLvSfPwqU9n9+ogA74J8HdtrVlmewCOhgjKnvv1a8MaZhPt8zD7jR//orgPJ55CYicloqykQkUm0A+hhjVuMbgnwbX4H1H2PMKmAl0N7/2tHAB/6hQDfwIbAG+Br4uaAXttYm41vR+Zn/+ouARvl82wvAFcaY5UBPfEOrR621v+MbBl2bbaK/iEguxtqcIwQiIuFljKkDfOOfqF8kGGNigCxrbab/ad771toW4c5LRIoOzWMQEQmO84HPjTEOwAPcG+Z8RKSI0ZMyESlxjDGTgLo5Dv/DWvt9OPIREQEVZSIiIiIRQRP9RURERCKAijIRERGRCKCiTERERCQCqCgTERERiQAqykREREQiwP8DRE/IgP957h8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw(clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.3 决策树算法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【1】基本思想"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CART算法：每次通过一个特征，将数据尽可能的分为纯净的两类，递归的分下去"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【2】sklearn实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 构建分类器对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier()\n",
    "clf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(iris_x_train, iris_y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 2 0 0 1 1 0 2 1 2 1 2 2 2 1 0 0 0 1 0 2 0 2 1 0 1 0 0 1 1]\n",
      "[0 2 0 0 1 1 0 2 2 2 1 2 2 2 1 0 0 0 1 0 2 0 2 1 0 1 0 0 1 1]\n"
     ]
    }
   ],
   "source": [
    "res = clf.predict(iris_x_test)\n",
    "print(res)\n",
    "print(iris_y_test.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测正确率:97%\n"
     ]
    }
   ],
   "source": [
    "accuracy = clf.score(iris_x_test, iris_y_test)\n",
    "print(\"预测正确率:{:.0%}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw(clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.4 逻辑回归算法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【1】基本思想"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一种解释：\n",
    "  \n",
    "训练：通过一个映射方式，将特征X=（x1, x2） 映射成 P(y=ck), 求使得所有概率之积最大化的映射方式里的参数\n",
    "  \n",
    "预测：计算p(y=ck) 取概率最大的那个类别作为预测对象的分类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【2】sklearn实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 构建分类器对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='saga', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LogisticRegression(solver='saga', max_iter=1000)\n",
    "clf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ibm\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='saga', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(iris_x_train, iris_y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 2 0 0 1 1 0 2 1 2 1 2 2 2 1 0 0 0 1 0 2 0 2 1 0 1 0 0 1 1]\n",
      "[0 2 0 0 1 1 0 2 2 2 1 2 2 2 1 0 0 0 1 0 2 0 2 1 0 1 0 0 1 1]\n"
     ]
    }
   ],
   "source": [
    "res = clf.predict(iris_x_test)\n",
    "print(res)\n",
    "print(iris_y_test.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测正确率:97%\n"
     ]
    }
   ],
   "source": [
    "accuracy = clf.score(iris_x_test, iris_y_test)\n",
    "print(\"预测正确率:{:.0%}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw(clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.5 支持向量机算法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【1】基本思想"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以二分类为例，假设数据可用完全分开：\n",
    "  \n",
    "用一个超平面将两类数据完全分开，且最近点到平面的距离最大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【2】sklearn实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 构建分类器对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "    kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "    shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = SVC()\n",
    "clf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ibm\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: 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": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "    kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "    shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(iris_x_train, iris_y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 2 0 0 1 1 0 2 1 2 1 2 2 2 1 0 0 0 1 0 2 0 2 1 0 1 0 0 1 1]\n",
      "[0 2 0 0 1 1 0 2 2 2 1 2 2 2 1 0 0 0 1 0 2 0 2 1 0 1 0 0 1 1]\n"
     ]
    }
   ],
   "source": [
    "res = clf.predict(iris_x_test)\n",
    "print(res)\n",
    "print(iris_y_test.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测正确率:97%\n"
     ]
    }
   ],
   "source": [
    "accuracy = clf.score(iris_x_test, iris_y_test)\n",
    "print(\"预测正确率:{:.0%}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw(clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.7 集成方法——随机森林"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【1】基本思想"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练集m，有放回的随机抽取m个数据，构成一组，共抽取n组采样集\n",
    "  \n",
    "n组采样集训练得到n个弱分类器   弱分类器一般用决策树或神经网络\n",
    "  \n",
    "将n个弱分类器进行组合得到强分类器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【2】sklearn实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 构建分类器对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators='warn',\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = RandomForestClassifier()\n",
    "clf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ibm\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=10,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(iris_x_train, iris_y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 2 0 0 1 1 0 2 1 2 1 2 2 2 1 0 0 0 1 0 2 0 2 1 0 1 0 0 1 1]\n",
      "[0 2 0 0 1 1 0 2 2 2 1 2 2 2 1 0 0 0 1 0 2 0 2 1 0 1 0 0 1 1]\n"
     ]
    }
   ],
   "source": [
    "res = clf.predict(iris_x_test)\n",
    "print(res)\n",
    "print(iris_y_test.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测正确率:97%\n"
     ]
    }
   ],
   "source": [
    "accuracy = clf.score(iris_x_test, iris_y_test)\n",
    "print(\"预测正确率:{:.0%}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw(clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.7 集成方法——Adaboost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【1】基本思想"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练集m，用初始数据权重训练得到第一个弱分类器，根据误差率计算弱分类器系数，更新数据的权重\n",
    "  \n",
    "使用新的权重训练得到第二个弱分类器，以此类推\n",
    "  \n",
    "根据各自系数，将所有弱分类器加权求和获得强分类器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【2】sklearn实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 构建分类器对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1.0,\n",
       "                   n_estimators=50, random_state=None)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = AdaBoostClassifier()\n",
    "clf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1.0,\n",
       "                   n_estimators=50, random_state=None)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(iris_x_train, iris_y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 2 0 0 1 1 0 2 1 2 1 2 2 2 1 0 0 0 1 0 2 0 2 1 0 1 0 0 1 1]\n",
      "[0 2 0 0 1 1 0 2 2 2 1 2 2 2 1 0 0 0 1 0 2 0 2 1 0 1 0 0 1 1]\n"
     ]
    }
   ],
   "source": [
    "res = clf.predict(iris_x_test)\n",
    "print(res)\n",
    "print(iris_y_test.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测正确率:97%\n"
     ]
    }
   ],
   "source": [
    "accuracy = clf.score(iris_x_test, iris_y_test)\n",
    "print(\"预测正确率:{:.0%}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw(clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.8 集成方法——梯度提升树GBDT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【1】基本思想"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练集m，获得第一个弱分类器，获得残差，然后不断地拟合残差\n",
    "\n",
    "所有弱分类器相加得到强分类器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【2】sklearn实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 构建分类器对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "                           learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "                           max_features=None, max_leaf_nodes=None,\n",
       "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                           min_samples_leaf=1, min_samples_split=2,\n",
       "                           min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                           n_iter_no_change=None, presort='auto',\n",
       "                           random_state=None, subsample=1.0, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = GradientBoostingClassifier()\n",
    "clf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "                           learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "                           max_features=None, max_leaf_nodes=None,\n",
       "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                           min_samples_leaf=1, min_samples_split=2,\n",
       "                           min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                           n_iter_no_change=None, presort='auto',\n",
       "                           random_state=None, subsample=1.0, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(iris_x_train, iris_y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 2 0 0 1 1 0 2 1 2 1 2 2 2 1 0 0 0 1 0 2 0 2 1 0 1 0 0 1 1]\n",
      "[0 2 0 0 1 1 0 2 2 2 1 2 2 2 1 0 0 0 1 0 2 0 2 1 0 1 0 0 1 1]\n"
     ]
    }
   ],
   "source": [
    "res = clf.predict(iris_x_test)\n",
    "print(res)\n",
    "print(iris_y_test.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测正确率:97%\n"
     ]
    }
   ],
   "source": [
    "accuracy = clf.score(iris_x_test, iris_y_test)\n",
    "print(\"预测正确率:{:.0%}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw(clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.9 大杀器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【1】xgboost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GBDT的损失函数只对误差部分做负梯度（一阶泰勒）展开\n",
    "  \n",
    "XGBoost损失函数对误差部分做二阶泰勒展开，更加准确，更快收敛"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【2】lightgbm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "微软：快速的，分布式的，高性能的基于决策树算法的梯度提升框架\n",
    "  \n",
    "速度更快"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【3】stacking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "堆叠或者叫模型融合\n",
    "\n",
    "先建立几个简单的模型进行训练，第二级学习器会基于前级模型的预测结果进行再训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【4】神经网络"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}