Try_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bd5ffd9e",
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>H_c</th>\n",
       "      <th>G_c</th>\n",
       "      <th>S_c</th>\n",
       "      <th>β-O-4_c</th>\n",
       "      <th>Rf</th>\n",
       "      <th>P_O</th>\n",
       "      <th>Tem</th>\n",
       "      <th>Rt</th>\n",
       "      <th>C_c</th>\n",
       "      <th>BETs</th>\n",
       "      <th>AcCom</th>\n",
       "      <th>OxiStat</th>\n",
       "      <th>rpm</th>\n",
       "      <th>NaOH_c</th>\n",
       "      <th>Yld</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.50</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>120</td>\n",
       "      <td>60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Missing</td>\n",
       "      <td>Missing</td>\n",
       "      <td>Missing</td>\n",
       "      <td>400</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.019780</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.50</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Missing</td>\n",
       "      <td>Missing</td>\n",
       "      <td>Missing</td>\n",
       "      <td>400</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.034730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.50</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>160</td>\n",
       "      <td>60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Missing</td>\n",
       "      <td>Missing</td>\n",
       "      <td>Missing</td>\n",
       "      <td>400</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.048530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.50</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>170</td>\n",
       "      <td>60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Missing</td>\n",
       "      <td>Missing</td>\n",
       "      <td>Missing</td>\n",
       "      <td>400</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.036800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.50</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>200</td>\n",
       "      <td>60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Missing</td>\n",
       "      <td>Missing</td>\n",
       "      <td>Missing</td>\n",
       "      <td>400</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.034730</td>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>1.2</td>\n",
       "      <td>30.4</td>\n",
       "      <td>68.4</td>\n",
       "      <td>38.48</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>150</td>\n",
       "      <td>30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Missing</td>\n",
       "      <td>Missing</td>\n",
       "      <td>Missing</td>\n",
       "      <td>800</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.011905</td>\n",
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       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>1.2</td>\n",
       "      <td>30.4</td>\n",
       "      <td>68.4</td>\n",
       "      <td>38.48</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>150</td>\n",
       "      <td>30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.988</td>\n",
       "      <td>CuO</td>\n",
       "      <td>2</td>\n",
       "      <td>800</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.015186</td>\n",
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       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>1.2</td>\n",
       "      <td>30.4</td>\n",
       "      <td>68.4</td>\n",
       "      <td>38.48</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>150</td>\n",
       "      <td>30</td>\n",
       "      <td>1.5</td>\n",
       "      <td>21.988</td>\n",
       "      <td>CuO</td>\n",
       "      <td>2</td>\n",
       "      <td>800</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.015605</td>\n",
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       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>1.2</td>\n",
       "      <td>30.4</td>\n",
       "      <td>68.4</td>\n",
       "      <td>38.48</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>150</td>\n",
       "      <td>30</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.988</td>\n",
       "      <td>CuO</td>\n",
       "      <td>2</td>\n",
       "      <td>800</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.015954</td>\n",
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       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>1.2</td>\n",
       "      <td>30.4</td>\n",
       "      <td>68.4</td>\n",
       "      <td>38.48</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>150</td>\n",
       "      <td>30</td>\n",
       "      <td>2.5</td>\n",
       "      <td>21.988</td>\n",
       "      <td>CuO</td>\n",
       "      <td>2</td>\n",
       "      <td>800</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.016635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>239 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     H_c   G_c   S_c  β-O-4_c    Rf   P_O  Tem  Rt  C_c     BETs    AcCom  \\\n",
       "0    3.0  97.0   0.0    46.50  20.0  10.0  120  60  0.0  Missing  Missing   \n",
       "1    3.0  97.0   0.0    46.50  20.0  10.0  150  60  0.0  Missing  Missing   \n",
       "2    3.0  97.0   0.0    46.50  20.0  10.0  160  60  0.0  Missing  Missing   \n",
       "3    3.0  97.0   0.0    46.50  20.0  10.0  170  60  0.0  Missing  Missing   \n",
       "4    3.0  97.0   0.0    46.50  20.0  10.0  200  60  0.0  Missing  Missing   \n",
       "..   ...   ...   ...      ...   ...   ...  ...  ..  ...      ...      ...   \n",
       "234  1.2  30.4  68.4    38.48  50.0   4.0  150  30  0.0  Missing  Missing   \n",
       "235  1.2  30.4  68.4    38.48  50.0   4.0  150  30  1.0   21.988      CuO   \n",
       "236  1.2  30.4  68.4    38.48  50.0   4.0  150  30  1.5   21.988      CuO   \n",
       "237  1.2  30.4  68.4    38.48  50.0   4.0  150  30  2.0   21.988      CuO   \n",
       "238  1.2  30.4  68.4    38.48  50.0   4.0  150  30  2.5   21.988      CuO   \n",
       "\n",
       "     OxiStat  rpm  NaOH_c       Yld  \n",
       "0    Missing  400     2.0  0.019780  \n",
       "1    Missing  400     2.0  0.034730  \n",
       "2    Missing  400     2.0  0.048530  \n",
       "3    Missing  400     2.0  0.036800  \n",
       "4    Missing  400     2.0  0.034730  \n",
       "..       ...  ...     ...       ...  \n",
       "234  Missing  800     2.0  0.011905  \n",
       "235        2  800     2.0  0.015186  \n",
       "236        2  800     2.0  0.015605  \n",
       "237        2  800     2.0  0.015954  \n",
       "238        2  800     2.0  0.016635  \n",
       "\n",
       "[239 rows x 15 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "#2. 打开csv数据\n",
    "with open('Data_O2_Oxidation_3.csv', encoding = 'UTF-8') as f:\n",
    "    df = pd.read_csv(f)  #index_col=0 告诉第一列是索引\n",
    "df\n",
    "#first_column_header = df.columns[0]\n",
    "#print(first_column_header)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7fbcb8d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 分割数据\n",
    "df_O2_noC = df[df['C_c'] == 0]  # \"C_c\"列值为0的所有行   【行号不变的！】\n",
    "df_O2_C = df[df['C_c'] != 0]  # \"C_c\"列值不为0的所有行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "66c4697d",
   "metadata": {},
   "outputs": [
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       "      <th>H_c</th>\n",
       "      <th>G_c</th>\n",
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       "      <th>Rf</th>\n",
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       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
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       "      <td>3.0</td>\n",
       "      <td>97.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.50</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
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       "      <td>400</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>97.0</td>\n",
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       "      <th>200</th>\n",
       "      <td>33.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.30</td>\n",
       "      <td>60.0</td>\n",
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       "      <td>120</td>\n",
       "      <td>60</td>\n",
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       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>33.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.30</td>\n",
       "      <td>60.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>90</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>0.001290</td>\n",
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       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>33.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.30</td>\n",
       "      <td>60.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>1045</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.001183</td>\n",
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       "      <th>203</th>\n",
       "      <td>33.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.30</td>\n",
       "      <td>60.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120</td>\n",
       "      <td>150</td>\n",
       "      <td>1045</td>\n",
       "      <td>2.0</td>\n",
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       "      <th>234</th>\n",
       "      <td>1.2</td>\n",
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       "      <td>150</td>\n",
       "      <td>30</td>\n",
       "      <td>800</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.011905</td>\n",
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       "<p>184 rows × 11 columns</p>\n",
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      ],
      "text/plain": [
       "      H_c   G_c   S_c  β-O-4_c    Rf   P_O  Tem   Rt   rpm  NaOH_c       Yld\n",
       "0     3.0  97.0   0.0    46.50  20.0  10.0  120   60   400     2.0  0.019780\n",
       "1     3.0  97.0   0.0    46.50  20.0  10.0  150   60   400     2.0  0.034730\n",
       "2     3.0  97.0   0.0    46.50  20.0  10.0  160   60   400     2.0  0.048530\n",
       "3     3.0  97.0   0.0    46.50  20.0  10.0  170   60   400     2.0  0.036800\n",
       "4     3.0  97.0   0.0    46.50  20.0  10.0  200   60   400     2.0  0.034730\n",
       "..    ...   ...   ...      ...   ...   ...  ...  ...   ...     ...       ...\n",
       "200  33.0  30.0  37.0     1.30  60.0   5.0  120   60  1045     2.0  0.001613\n",
       "201  33.0  30.0  37.0     1.30  60.0   5.0  120   90  1045     2.0  0.001290\n",
       "202  33.0  30.0  37.0     1.30  60.0   5.0  120  120  1045     2.0  0.001183\n",
       "203  33.0  30.0  37.0     1.30  60.0   5.0  120  150  1045     2.0  0.001075\n",
       "234   1.2  30.4  68.4    38.48  50.0   4.0  150   30   800     2.0  0.011905\n",
       "\n",
       "[184 rows x 11 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 假设我们要删除的列是 'BETs', 'AcCom', 和 'OxiStat'\n",
    "df_O2_noC = df_O2_noC.drop(['C_c', 'BETs', 'AcCom', 'OxiStat'], axis=1)\n",
    "\n",
    "# 查看结果\n",
    "df_O2_noC  #【行号不变的！】\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "065a54fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.019780\n",
       "1      0.034730\n",
       "2      0.048530\n",
       "3      0.036800\n",
       "4      0.034730\n",
       "         ...   \n",
       "200    0.001613\n",
       "201    0.001290\n",
       "202    0.001183\n",
       "203    0.001075\n",
       "234    0.011905\n",
       "Name: Yld, Length: 184, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df_O2_noC.iloc[:, -1]\n",
    "X = df_O2_noC.drop('Yld', axis = 1)\n",
    "#X\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7b4da637",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .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>H_c</th>\n",
       "      <th>G_c</th>\n",
       "      <th>S_c</th>\n",
       "      <th>β-O-4_c</th>\n",
       "      <th>Rf</th>\n",
       "      <th>P_O</th>\n",
       "      <th>Tem</th>\n",
       "      <th>Rt</th>\n",
       "      <th>rpm</th>\n",
       "      <th>NaOH_c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>0.096774</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>0.096774</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>0.096774</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>0.096774</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>0.096774</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.096774</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.096774</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.288136</td>\n",
       "      <td>0.096774</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.593220</td>\n",
       "      <td>0.096774</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.242105</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.101695</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.488889</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.073684</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.220339</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.050847</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.118644</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.135593</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.294915</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.037288</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.138983</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.237288</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.488889</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.488889</td>\n",
       "      <td>0.057627</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.488889</td>\n",
       "      <td>0.091525</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.488889</td>\n",
       "      <td>0.166102</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.488889</td>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.073684</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.073684</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.073684</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.057627</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.166102</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.094915</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.073684</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.111864</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.152542</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.033898</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.050847</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.101695</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.118644</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.135593</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.152542</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.169492</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.203390</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.220339</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.237288</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.254237</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.271186</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.288136</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.305085</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.322034</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.338983</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.355932</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.372881</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.389831</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0.060606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>0.060606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.050847</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>0.060606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0.060606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.060606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.101695</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>0.060606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.135593</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0.060606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.152542</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>0.060606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>0.060606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.254237</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0.060606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.288136</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>0.060606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.322034</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0.060606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.118644</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.050847</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.118644</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.152542</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.144444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.177778</td>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.033898</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.050847</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.211111</td>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.211111</td>\n",
       "      <td>0.101695</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.118644</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.135593</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.188889</td>\n",
       "      <td>0.152542</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.177778</td>\n",
       "      <td>0.169492</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.203390</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.220339</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.237288</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.144444</td>\n",
       "      <td>0.254237</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.144444</td>\n",
       "      <td>0.271186</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.144444</td>\n",
       "      <td>0.322034</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.144444</td>\n",
       "      <td>0.338983</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.144444</td>\n",
       "      <td>0.355932</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.144444</td>\n",
       "      <td>0.372881</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.144444</td>\n",
       "      <td>0.389831</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>0.060606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.211111</td>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>0.060606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>0.050847</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>0.060606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.244444</td>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>0.060606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.244444</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>0.060606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.244444</td>\n",
       "      <td>0.101695</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>0.060606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.118644</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>0.060606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.244444</td>\n",
       "      <td>0.135593</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>0.060606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>0.152542</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>0.060606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>0.060606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.254237</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>0.060606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.188889</td>\n",
       "      <td>0.288136</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>0.060606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.177778</td>\n",
       "      <td>0.322034</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.050847</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.118644</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.188889</td>\n",
       "      <td>0.152542</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>0.151515</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014235</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.050847</td>\n",
       "      <td>0.512903</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>0.151515</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014235</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.355932</td>\n",
       "      <td>0.512903</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.188235</td>\n",
       "      <td>0.865854</td>\n",
       "      <td>0.661566</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.050847</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.288136</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.389831</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.525424</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.694915</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.796610</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.898305</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.050847</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.118644</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.152542</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.254237</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.322034</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.389831</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.050847</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.118644</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.152542</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.588889</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.588889</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.588889</td>\n",
       "      <td>0.288136</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.588889</td>\n",
       "      <td>0.389831</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.588889</td>\n",
       "      <td>0.491525</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.588889</td>\n",
       "      <td>0.593220</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.588889</td>\n",
       "      <td>0.694915</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.588889</td>\n",
       "      <td>0.796610</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.588889</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.588889</td>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.588889</td>\n",
       "      <td>0.050847</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.588889</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.588889</td>\n",
       "      <td>0.118644</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.588889</td>\n",
       "      <td>0.152542</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.588889</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.588889</td>\n",
       "      <td>0.220339</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.588889</td>\n",
       "      <td>0.254237</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.588889</td>\n",
       "      <td>0.288136</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.588889</td>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.588889</td>\n",
       "      <td>0.050847</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.588889</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.588889</td>\n",
       "      <td>0.118644</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.588889</td>\n",
       "      <td>0.152542</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.588889</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>0.151515</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.389474</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>0.151515</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014235</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.389474</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>0.151515</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014235</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.389474</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>0.575758</td>\n",
       "      <td>0.282353</td>\n",
       "      <td>0.536585</td>\n",
       "      <td>0.126335</td>\n",
       "      <td>0.246348</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.593220</td>\n",
       "      <td>0.354839</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>0.151515</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014235</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.161290</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.451220</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.512903</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.451220</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.512903</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.451220</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.033898</td>\n",
       "      <td>0.512903</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.451220</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.512903</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.451220</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.135593</td>\n",
       "      <td>0.512903</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.451220</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>0.512903</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.451220</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.288136</td>\n",
       "      <td>0.512903</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.451220</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.389831</td>\n",
       "      <td>0.512903</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.451220</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.491525</td>\n",
       "      <td>0.512903</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>0.036364</td>\n",
       "      <td>0.204706</td>\n",
       "      <td>0.834146</td>\n",
       "      <td>0.661566</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.354839</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          H_c       G_c       S_c   β-O-4_c        Rf       P_O       Tem  \\\n",
       "0    0.090909  0.988235  0.000000  0.804270  0.130435  1.000000  0.111111   \n",
       "1    0.090909  0.988235  0.000000  0.804270  0.130435  1.000000  0.444444   \n",
       "2    0.090909  0.988235  0.000000  0.804270  0.130435  1.000000  0.555556   \n",
       "3    0.090909  0.988235  0.000000  0.804270  0.130435  1.000000  0.666667   \n",
       "4    0.090909  0.988235  0.000000  0.804270  0.130435  1.000000  1.000000   \n",
       "5    0.090909  0.988235  0.000000  0.804270  0.130435  1.000000  0.555556   \n",
       "6    0.090909  0.988235  0.000000  0.804270  0.130435  1.000000  0.555556   \n",
       "7    0.090909  0.988235  0.000000  0.804270  0.130435  1.000000  0.555556   \n",
       "8    0.090909  0.988235  0.000000  0.804270  0.130435  1.000000  0.555556   \n",
       "9    0.090909  0.988235  0.000000  0.804270  0.478261  0.315789  0.000000   \n",
       "10   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.166667   \n",
       "11   0.090909  0.988235  0.000000  0.804270  0.478261  0.242105  0.255556   \n",
       "12   0.090909  0.988235  0.000000  0.804270  0.478261  0.368421  0.488889   \n",
       "13   0.090909  0.988235  0.000000  0.804270  0.478261  0.073684  0.222222   \n",
       "14   0.090909  0.988235  0.000000  0.804270  0.478261  0.473684  0.277778   \n",
       "15   0.090909  0.988235  0.000000  0.804270  0.217391  0.263158  0.200000   \n",
       "16   0.090909  0.988235  0.000000  0.804270  0.217391  0.368421  0.166667   \n",
       "17   0.090909  0.988235  0.000000  0.804270  0.478261  0.315789  0.000000   \n",
       "18   0.090909  0.988235  0.000000  0.804270  0.478261  0.315789  0.000000   \n",
       "19   0.090909  0.988235  0.000000  0.804270  0.478261  0.315789  0.000000   \n",
       "20   0.090909  0.988235  0.000000  0.804270  0.478261  0.315789  0.000000   \n",
       "21   0.090909  0.988235  0.000000  0.804270  0.478261  0.315789  0.000000   \n",
       "22   0.090909  0.988235  0.000000  0.804270  0.478261  0.368421  0.488889   \n",
       "23   0.090909  0.988235  0.000000  0.804270  0.478261  0.368421  0.488889   \n",
       "24   0.090909  0.988235  0.000000  0.804270  0.478261  0.368421  0.488889   \n",
       "25   0.090909  0.988235  0.000000  0.804270  0.478261  0.368421  0.488889   \n",
       "26   0.090909  0.988235  0.000000  0.804270  0.478261  0.368421  0.488889   \n",
       "27   0.090909  0.988235  0.000000  0.804270  0.478261  0.073684  0.222222   \n",
       "28   0.090909  0.988235  0.000000  0.804270  0.478261  0.073684  0.222222   \n",
       "29   0.090909  0.988235  0.000000  0.804270  0.478261  0.073684  0.222222   \n",
       "30   0.090909  0.988235  0.000000  0.804270  0.478261  0.473684  0.277778   \n",
       "31   0.090909  0.988235  0.000000  0.804270  0.478261  0.473684  0.277778   \n",
       "32   0.090909  0.988235  0.000000  0.804270  0.478261  0.473684  0.277778   \n",
       "33   0.090909  0.988235  0.000000  0.804270  0.478261  0.473684  0.277778   \n",
       "34   0.090909  0.988235  0.000000  0.804270  0.478261  0.073684  0.222222   \n",
       "35   0.090909  0.988235  0.000000  0.804270  1.000000  0.210526  0.111111   \n",
       "36   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "37   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "38   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "39   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "40   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "41   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "42   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "43   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "44   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "45   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "46   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "47   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "48   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "49   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "50   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "51   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "52   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "53   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "54   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "55   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "56   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "57   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "58   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "59   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "60   0.060606  1.000000  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "61   0.060606  1.000000  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "62   0.060606  1.000000  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "63   0.060606  1.000000  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "64   0.060606  1.000000  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "65   0.060606  1.000000  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "66   0.060606  1.000000  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "67   0.060606  1.000000  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "68   0.060606  1.000000  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "69   0.060606  1.000000  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "70   0.060606  1.000000  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "71   0.060606  1.000000  0.000000  0.804270  0.478261  0.263158  0.111111   \n",
       "72   0.000000  0.176471  0.878049  1.000000  0.478261  0.263158  0.111111   \n",
       "73   0.000000  0.176471  0.878049  1.000000  0.478261  0.263158  0.111111   \n",
       "74   0.000000  0.176471  0.878049  1.000000  0.478261  0.263158  0.111111   \n",
       "75   0.000000  0.176471  0.878049  1.000000  0.478261  0.263158  0.111111   \n",
       "76   0.000000  0.176471  0.878049  1.000000  0.478261  0.263158  0.111111   \n",
       "77   0.000000  0.176471  0.878049  1.000000  0.478261  0.263158  0.111111   \n",
       "78   0.000000  0.176471  0.878049  1.000000  0.478261  0.263158  0.111111   \n",
       "79   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.144444   \n",
       "80   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.177778   \n",
       "81   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.200000   \n",
       "82   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.200000   \n",
       "83   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.211111   \n",
       "84   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.200000   \n",
       "85   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.211111   \n",
       "86   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.200000   \n",
       "87   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.200000   \n",
       "88   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.188889   \n",
       "89   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.177778   \n",
       "90   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.166667   \n",
       "91   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.166667   \n",
       "92   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.166667   \n",
       "93   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.166667   \n",
       "94   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.144444   \n",
       "95   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.144444   \n",
       "96   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.144444   \n",
       "97   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.144444   \n",
       "98   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.144444   \n",
       "99   0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.144444   \n",
       "100  0.090909  0.988235  0.000000  0.804270  0.478261  0.263158  0.144444   \n",
       "101  0.060606  1.000000  0.000000  0.804270  0.478261  0.263158  0.211111   \n",
       "102  0.060606  1.000000  0.000000  0.804270  0.478261  0.263158  0.233333   \n",
       "103  0.060606  1.000000  0.000000  0.804270  0.478261  0.263158  0.244444   \n",
       "104  0.060606  1.000000  0.000000  0.804270  0.478261  0.263158  0.244444   \n",
       "105  0.060606  1.000000  0.000000  0.804270  0.478261  0.263158  0.244444   \n",
       "106  0.060606  1.000000  0.000000  0.804270  0.478261  0.263158  0.255556   \n",
       "107  0.060606  1.000000  0.000000  0.804270  0.478261  0.263158  0.244444   \n",
       "108  0.060606  1.000000  0.000000  0.804270  0.478261  0.263158  0.233333   \n",
       "109  0.060606  1.000000  0.000000  0.804270  0.478261  0.263158  0.233333   \n",
       "110  0.060606  1.000000  0.000000  0.804270  0.478261  0.263158  0.200000   \n",
       "111  0.060606  1.000000  0.000000  0.804270  0.478261  0.263158  0.188889   \n",
       "112  0.060606  1.000000  0.000000  0.804270  0.478261  0.263158  0.177778   \n",
       "113  0.000000  0.176471  0.878049  1.000000  0.478261  0.263158  0.255556   \n",
       "114  0.000000  0.176471  0.878049  1.000000  0.478261  0.263158  0.277778   \n",
       "115  0.000000  0.176471  0.878049  1.000000  0.478261  0.263158  0.266667   \n",
       "116  0.000000  0.176471  0.878049  1.000000  0.478261  0.263158  0.255556   \n",
       "117  0.000000  0.176471  0.878049  1.000000  0.478261  0.263158  0.222222   \n",
       "118  0.000000  0.176471  0.878049  1.000000  0.478261  0.263158  0.188889   \n",
       "119  0.151515  0.000000  1.000000  0.014235  0.478261  0.263158  0.111111   \n",
       "120  0.151515  0.000000  1.000000  0.014235  0.478261  0.263158  0.111111   \n",
       "121  0.000000  0.188235  0.865854  0.661566  0.000000  0.578947  0.444444   \n",
       "122  0.090909  0.988235  0.000000  0.804270  0.391304  0.000000  0.255556   \n",
       "123  0.090909  0.988235  0.000000  0.804270  0.391304  0.000000  0.255556   \n",
       "124  0.090909  0.988235  0.000000  0.804270  0.391304  0.000000  0.255556   \n",
       "125  0.090909  0.988235  0.000000  0.804270  0.391304  0.000000  0.255556   \n",
       "126  0.090909  0.988235  0.000000  0.804270  0.391304  0.000000  0.255556   \n",
       "127  0.090909  0.988235  0.000000  0.804270  0.391304  0.000000  0.255556   \n",
       "128  0.090909  0.988235  0.000000  0.804270  0.391304  0.000000  0.255556   \n",
       "129  0.090909  0.988235  0.000000  0.804270  0.391304  0.000000  0.255556   \n",
       "130  0.090909  0.988235  0.000000  0.804270  0.391304  0.263158  0.255556   \n",
       "131  0.090909  0.988235  0.000000  0.804270  0.391304  0.263158  0.255556   \n",
       "132  0.090909  0.988235  0.000000  0.804270  0.391304  0.263158  0.255556   \n",
       "133  0.090909  0.988235  0.000000  0.804270  0.391304  0.263158  0.255556   \n",
       "134  0.090909  0.988235  0.000000  0.804270  0.391304  0.263158  0.255556   \n",
       "135  0.090909  0.988235  0.000000  0.804270  0.391304  0.263158  0.255556   \n",
       "136  0.090909  0.988235  0.000000  0.804270  0.391304  0.263158  0.255556   \n",
       "137  0.090909  0.988235  0.000000  0.804270  0.391304  0.263158  0.255556   \n",
       "138  0.090909  0.988235  0.000000  0.804270  0.391304  0.263158  0.255556   \n",
       "139  0.090909  0.988235  0.000000  0.804270  0.391304  0.578947  0.255556   \n",
       "140  0.090909  0.988235  0.000000  0.804270  0.391304  0.578947  0.255556   \n",
       "141  0.090909  0.988235  0.000000  0.804270  0.391304  0.578947  0.255556   \n",
       "142  0.090909  0.988235  0.000000  0.804270  0.391304  0.578947  0.255556   \n",
       "143  0.090909  0.988235  0.000000  0.804270  0.391304  0.578947  0.255556   \n",
       "144  0.090909  0.988235  0.000000  0.804270  0.391304  0.578947  0.255556   \n",
       "145  0.090909  0.988235  0.000000  0.804270  0.391304  0.000000  0.588889   \n",
       "146  0.090909  0.988235  0.000000  0.804270  0.391304  0.000000  0.588889   \n",
       "147  0.090909  0.988235  0.000000  0.804270  0.391304  0.000000  0.588889   \n",
       "148  0.090909  0.988235  0.000000  0.804270  0.391304  0.000000  0.588889   \n",
       "149  0.090909  0.988235  0.000000  0.804270  0.391304  0.000000  0.588889   \n",
       "150  0.090909  0.988235  0.000000  0.804270  0.391304  0.000000  0.588889   \n",
       "151  0.090909  0.988235  0.000000  0.804270  0.391304  0.000000  0.588889   \n",
       "152  0.090909  0.988235  0.000000  0.804270  0.391304  0.000000  0.588889   \n",
       "153  0.090909  0.988235  0.000000  0.804270  0.391304  0.000000  0.588889   \n",
       "154  0.090909  0.988235  0.000000  0.804270  0.391304  0.263158  0.588889   \n",
       "155  0.090909  0.988235  0.000000  0.804270  0.391304  0.263158  0.588889   \n",
       "156  0.090909  0.988235  0.000000  0.804270  0.391304  0.263158  0.588889   \n",
       "157  0.090909  0.988235  0.000000  0.804270  0.391304  0.263158  0.588889   \n",
       "158  0.090909  0.988235  0.000000  0.804270  0.391304  0.263158  0.588889   \n",
       "159  0.090909  0.988235  0.000000  0.804270  0.391304  0.263158  0.588889   \n",
       "160  0.090909  0.988235  0.000000  0.804270  0.391304  0.263158  0.588889   \n",
       "161  0.090909  0.988235  0.000000  0.804270  0.391304  0.263158  0.588889   \n",
       "162  0.090909  0.988235  0.000000  0.804270  0.391304  0.263158  0.588889   \n",
       "163  0.090909  0.988235  0.000000  0.804270  0.391304  0.578947  0.588889   \n",
       "164  0.090909  0.988235  0.000000  0.804270  0.391304  0.578947  0.588889   \n",
       "165  0.090909  0.988235  0.000000  0.804270  0.391304  0.578947  0.588889   \n",
       "166  0.090909  0.988235  0.000000  0.804270  0.391304  0.578947  0.588889   \n",
       "167  0.090909  0.988235  0.000000  0.804270  0.391304  0.578947  0.588889   \n",
       "168  0.090909  0.988235  0.000000  0.804270  0.391304  0.578947  0.588889   \n",
       "169  0.151515  0.000000  1.000000  0.014235  0.000000  0.389474  0.444444   \n",
       "170  0.151515  0.000000  1.000000  0.014235  0.043478  0.389474  0.444444   \n",
       "171  0.151515  0.000000  1.000000  0.014235  0.130435  0.389474  0.444444   \n",
       "172  0.575758  0.282353  0.536585  0.126335  0.246348  0.473684  0.444444   \n",
       "173  0.151515  0.000000  1.000000  0.014235  0.043478  0.473684  0.444444   \n",
       "174  1.000000  0.200000  0.451220  0.000000  0.478261  0.473684  0.111111   \n",
       "175  1.000000  0.200000  0.451220  0.000000  0.478261  0.473684  0.111111   \n",
       "176  1.000000  0.200000  0.451220  0.000000  0.478261  0.473684  0.111111   \n",
       "177  1.000000  0.200000  0.451220  0.000000  0.478261  0.473684  0.111111   \n",
       "178  1.000000  0.200000  0.451220  0.000000  0.478261  0.473684  0.111111   \n",
       "179  1.000000  0.200000  0.451220  0.000000  0.478261  0.473684  0.111111   \n",
       "180  1.000000  0.200000  0.451220  0.000000  0.478261  0.473684  0.111111   \n",
       "181  1.000000  0.200000  0.451220  0.000000  0.478261  0.473684  0.111111   \n",
       "182  1.000000  0.200000  0.451220  0.000000  0.478261  0.473684  0.111111   \n",
       "183  0.036364  0.204706  0.834146  0.661566  0.391304  0.368421  0.444444   \n",
       "\n",
       "           Rt       rpm    NaOH_c  \n",
       "0    0.186441  0.096774  1.000000  \n",
       "1    0.186441  0.096774  1.000000  \n",
       "2    0.186441  0.096774  1.000000  \n",
       "3    0.186441  0.096774  1.000000  \n",
       "4    0.186441  0.096774  1.000000  \n",
       "5    0.016949  0.096774  1.000000  \n",
       "6    0.084746  0.096774  1.000000  \n",
       "7    0.288136  0.096774  1.000000  \n",
       "8    0.593220  0.096774  1.000000  \n",
       "9    0.186441  0.580645  1.000000  \n",
       "10   0.186441  0.580645  1.000000  \n",
       "11   0.101695  0.580645  1.000000  \n",
       "12   0.084746  0.580645  1.000000  \n",
       "13   0.220339  0.580645  1.000000  \n",
       "14   0.050847  0.580645  1.000000  \n",
       "15   0.118644  0.580645  1.000000  \n",
       "16   0.135593  0.580645  1.000000  \n",
       "17   0.294915  0.580645  1.000000  \n",
       "18   0.037288  0.580645  1.000000  \n",
       "19   0.084746  0.580645  1.000000  \n",
       "20   0.138983  0.580645  1.000000  \n",
       "21   0.237288  0.580645  1.000000  \n",
       "22   0.000000  0.580645  1.000000  \n",
       "23   0.057627  0.580645  1.000000  \n",
       "24   0.091525  0.580645  1.000000  \n",
       "25   0.166102  0.580645  1.000000  \n",
       "26   0.016949  0.580645  1.000000  \n",
       "27   0.000000  0.580645  1.000000  \n",
       "28   0.016949  0.580645  1.000000  \n",
       "29   0.057627  0.580645  1.000000  \n",
       "30   0.000000  0.580645  1.000000  \n",
       "31   0.016949  0.580645  1.000000  \n",
       "32   0.166102  0.580645  1.000000  \n",
       "33   0.094915  0.580645  1.000000  \n",
       "34   0.111864  0.580645  1.000000  \n",
       "35   0.152542  0.580645  1.000000  \n",
       "36   0.000000  0.548387  1.000000  \n",
       "37   0.016949  0.548387  1.000000  \n",
       "38   0.033898  0.548387  1.000000  \n",
       "39   0.050847  0.548387  1.000000  \n",
       "40   0.084746  0.548387  1.000000  \n",
       "41   0.101695  0.548387  1.000000  \n",
       "42   0.118644  0.548387  1.000000  \n",
       "43   0.135593  0.548387  1.000000  \n",
       "44   0.152542  0.548387  1.000000  \n",
       "45   0.169492  0.548387  1.000000  \n",
       "46   0.186441  0.548387  1.000000  \n",
       "47   0.203390  0.548387  1.000000  \n",
       "48   0.220339  0.548387  1.000000  \n",
       "49   0.237288  0.548387  1.000000  \n",
       "50   0.254237  0.548387  1.000000  \n",
       "51   0.271186  0.548387  1.000000  \n",
       "52   0.288136  0.548387  1.000000  \n",
       "53   0.305085  0.548387  1.000000  \n",
       "54   0.322034  0.548387  1.000000  \n",
       "55   0.338983  0.548387  1.000000  \n",
       "56   0.355932  0.548387  1.000000  \n",
       "57   0.372881  0.548387  1.000000  \n",
       "58   0.389831  0.548387  1.000000  \n",
       "59   0.067797  0.548387  1.000000  \n",
       "60   0.016949  0.548387  1.000000  \n",
       "61   0.050847  0.548387  1.000000  \n",
       "62   0.067797  0.548387  1.000000  \n",
       "63   0.084746  0.548387  1.000000  \n",
       "64   0.101695  0.548387  1.000000  \n",
       "65   0.135593  0.548387  1.000000  \n",
       "66   0.152542  0.548387  1.000000  \n",
       "67   0.186441  0.548387  1.000000  \n",
       "68   0.254237  0.548387  1.000000  \n",
       "69   0.288136  0.548387  1.000000  \n",
       "70   0.322034  0.548387  1.000000  \n",
       "71   0.118644  0.548387  1.000000  \n",
       "72   0.016949  0.548387  1.000000  \n",
       "73   0.050847  0.548387  1.000000  \n",
       "74   0.084746  0.548387  1.000000  \n",
       "75   0.118644  0.548387  1.000000  \n",
       "76   0.152542  0.548387  1.000000  \n",
       "77   0.186441  0.548387  1.000000  \n",
       "78   0.067797  0.548387  1.000000  \n",
       "79   0.000000  0.548387  1.000000  \n",
       "80   0.016949  0.548387  1.000000  \n",
       "81   0.033898  0.548387  1.000000  \n",
       "82   0.050847  0.548387  1.000000  \n",
       "83   0.067797  0.548387  1.000000  \n",
       "84   0.084746  0.548387  1.000000  \n",
       "85   0.101695  0.548387  1.000000  \n",
       "86   0.118644  0.548387  1.000000  \n",
       "87   0.135593  0.548387  1.000000  \n",
       "88   0.152542  0.548387  1.000000  \n",
       "89   0.169492  0.548387  1.000000  \n",
       "90   0.186441  0.548387  1.000000  \n",
       "91   0.203390  0.548387  1.000000  \n",
       "92   0.220339  0.548387  1.000000  \n",
       "93   0.237288  0.548387  1.000000  \n",
       "94   0.254237  0.548387  1.000000  \n",
       "95   0.271186  0.548387  1.000000  \n",
       "96   0.322034  0.548387  1.000000  \n",
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       "107  0.135593  0.548387  1.000000  \n",
       "108  0.152542  0.548387  1.000000  \n",
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       "110  0.254237  0.548387  1.000000  \n",
       "111  0.288136  0.548387  1.000000  \n",
       "112  0.322034  0.548387  1.000000  \n",
       "113  0.016949  0.548387  1.000000  \n",
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       "116  0.084746  0.548387  1.000000  \n",
       "117  0.118644  0.548387  1.000000  \n",
       "118  0.152542  0.548387  1.000000  \n",
       "119  0.050847  0.512903  1.000000  \n",
       "120  0.355932  0.512903  1.000000  \n",
       "121  0.050847  0.000000  0.285714  \n",
       "122  0.186441  0.548387  1.000000  \n",
       "123  0.288136  0.548387  1.000000  \n",
       "124  0.389831  0.548387  1.000000  \n",
       "125  0.525424  0.548387  1.000000  \n",
       "126  0.694915  0.548387  1.000000  \n",
       "127  0.796610  0.548387  1.000000  \n",
       "128  0.898305  0.548387  1.000000  \n",
       "129  1.000000  0.548387  1.000000  \n",
       "130  0.016949  0.548387  1.000000  \n",
       "131  0.050847  0.548387  1.000000  \n",
       "132  0.084746  0.548387  1.000000  \n",
       "133  0.118644  0.548387  1.000000  \n",
       "134  0.152542  0.548387  1.000000  \n",
       "135  0.186441  0.548387  1.000000  \n",
       "136  0.254237  0.548387  1.000000  \n",
       "137  0.322034  0.548387  1.000000  \n",
       "138  0.389831  0.548387  1.000000  \n",
       "139  0.016949  0.548387  1.000000  \n",
       "140  0.050847  0.548387  1.000000  \n",
       "141  0.084746  0.548387  1.000000  \n",
       "142  0.118644  0.548387  1.000000  \n",
       "143  0.152542  0.548387  1.000000  \n",
       "144  0.186441  0.548387  1.000000  \n",
       "145  0.084746  0.548387  1.000000  \n",
       "146  0.186441  0.548387  1.000000  \n",
       "147  0.288136  0.548387  1.000000  \n",
       "148  0.389831  0.548387  1.000000  \n",
       "149  0.491525  0.548387  1.000000  \n",
       "150  0.593220  0.548387  1.000000  \n",
       "151  0.694915  0.548387  1.000000  \n",
       "152  0.796610  0.548387  1.000000  \n",
       "153  1.000000  0.548387  1.000000  \n",
       "154  0.016949  0.548387  1.000000  \n",
       "155  0.050847  0.548387  1.000000  \n",
       "156  0.084746  0.548387  1.000000  \n",
       "157  0.118644  0.548387  1.000000  \n",
       "158  0.152542  0.548387  1.000000  \n",
       "159  0.186441  0.548387  1.000000  \n",
       "160  0.220339  0.548387  1.000000  \n",
       "161  0.254237  0.548387  1.000000  \n",
       "162  0.288136  0.548387  1.000000  \n",
       "163  0.016949  0.548387  1.000000  \n",
       "164  0.050847  0.548387  1.000000  \n",
       "165  0.084746  0.548387  1.000000  \n",
       "166  0.118644  0.548387  1.000000  \n",
       "167  0.152542  0.548387  1.000000  \n",
       "168  0.186441  0.548387  1.000000  \n",
       "169  0.186441  1.000000  0.000000  \n",
       "170  0.186441  1.000000  0.000000  \n",
       "171  0.186441  1.000000  0.000000  \n",
       "172  0.593220  0.354839  1.000000  \n",
       "173  0.016949  0.161290  1.000000  \n",
       "174  0.000000  0.512903  1.000000  \n",
       "175  0.016949  0.512903  1.000000  \n",
       "176  0.033898  0.512903  1.000000  \n",
       "177  0.084746  0.512903  1.000000  \n",
       "178  0.135593  0.512903  1.000000  \n",
       "179  0.186441  0.512903  1.000000  \n",
       "180  0.288136  0.512903  1.000000  \n",
       "181  0.389831  0.512903  1.000000  \n",
       "182  0.491525  0.512903  1.000000  \n",
       "183  0.084746  0.354839  1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 设置显示最大行数，None表示不限制显示的行数\n",
    "pd.set_option('display.max_rows', None)\n",
    "\n",
    "# 使用 MinMaxScaler 进行归一化\n",
    "scaler = MinMaxScaler()\n",
    "X_scaled = pd.DataFrame(scaler.fit_transform(X), columns=X.columns)\n",
    "\n",
    "X_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e7afef09",
   "metadata": {},
   "outputs": [],
   "source": [
    "#3.机器学习GBR\n",
    "df = pd.DataFrame(X_scaled)\n",
    "X = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2c78e1d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据划分\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2,\n",
    "                                                   random_state = 3)    # 把样品分为测试和验证集，用split进行分割，测试集的比例为0.2，random_state意思是随机切割、抽取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ce6da727",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'poly__degree': 4}\n",
      "Best R2 score: -1755336400499788.8\n",
      "training R2 = 0.986\n",
      "test R2 = -51647991469355064.000\n",
      "RMSE(training)0.003\n",
      "RMSE(test)5271295.246\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#1. 多项式回归\n",
    "\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import make_scorer, r2_score, mean_squared_error\n",
    "\n",
    "# 创建多项式回归的Pipeline\n",
    "# Pipeline将多项式特征和线性回归结合\n",
    "pipeline = Pipeline([\n",
    "    ('poly', PolynomialFeatures()),  # 多项式特征\n",
    "    ('linear', LinearRegression())   # 线性回归\n",
    "])\n",
    "\n",
    "# 定义参数网格\n",
    "param_grid_poly = {\n",
    "    'poly__degree': [2, 3, 4]  # 多项式的度数\n",
    "}\n",
    "\n",
    "# 定义评分方法\n",
    "scorer_poly = make_scorer(r2_score)  # 使用R2分数\n",
    "\n",
    "# 创建GridSearchCV对象\n",
    "grid_search_poly = GridSearchCV(estimator=pipeline, param_grid=param_grid_poly, scoring=scorer_poly, cv=10)\n",
    "\n",
    "# 拟合数据\n",
    "grid_search_poly.fit(X_train, y_train)\n",
    "\n",
    "# 输出最优参数和对应的分数\n",
    "print(\"Best parameters:\", grid_search_poly.best_params_)\n",
    "print(\"Best R2 score:\", grid_search_poly.best_score_)\n",
    "\n",
    "# 使用最优参数预测\n",
    "best_poly = grid_search_poly.best_estimator_\n",
    "\n",
    "# 使用最优模型进行预测\n",
    "y_pred_train_poly = best_poly.predict(X_train)\n",
    "y_pred_test_poly = best_poly.predict(X_test)\n",
    "\n",
    "# 作图\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.scatter(y_train, y_pred_train_poly, alpha=0.5, color='b', label='training')  # alpha设置透明度\n",
    "plt.scatter(y_test, y_pred_test_poly, alpha=0.5, color='r', label='test')\n",
    "\n",
    "# 添加 y=x 线\n",
    "plt.plot([min(-0.01, 0.14), max(-0.01, 0.14)],  # X 轴范围\n",
    "         [min(-0.01, 0.14), max(-0.01, 0.14)],  # Y 轴范围\n",
    "         'k--', label='Y=X')  # 'k--' 是黑色虚线\n",
    "\n",
    "# 设置图例字体大小\n",
    "plt.legend(fontsize=16, frameon=False)\n",
    "plt.xlabel('Actual', fontsize=20)\n",
    "plt.ylabel('Prediction', fontsize=20)\n",
    "\n",
    "# 设置轴刻度的字体大小\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "\n",
    "# 设置X轴和Y轴的范围\n",
    "plt.xlim([-0.01, 0.12])  # 替换min_value_x和max_value_x为您希望的范围的最小值和最大值\n",
    "plt.ylim([-0.01, 0.12])  # 替换min_value_y和max_value_y为您希望的范围的最小值和最大值\n",
    "\n",
    "# 在图的右上角添加文本\n",
    "plt.text(0.95, 0.01, 'Poly', fontsize=24, ha='right', va='bottom', transform=plt.gca().transAxes)\n",
    "\n",
    "plt.savefig('多项式回归_网格化搜索.tiff', dpi=720, bbox_inches='tight')\n",
    "\n",
    "# 计算R2和均方误差\n",
    "print('training R2 = %.3f' % best_poly.score(X_train, y_train))\n",
    "print('test R2 = %.3f' % best_poly.score(X_test, y_test))\n",
    "rmse_tr_poly = mean_squared_error(y_train, y_pred_train_poly, squared=False)  # 均方根误差\n",
    "rmse_te_poly = mean_squared_error(y_test, y_pred_test_poly, squared=False)\n",
    "print('RMSE(training)%.3f' % rmse_tr_poly)\n",
    "print('RMSE(test)%.3f' % rmse_te_poly)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "51e3fd88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'poly__degree': 3, 'ridge__alpha': 10}\n",
      "Best R2 score: 0.20628781203182442\n",
      "training R2 = 0.363\n",
      "test R2 = 0.317\n",
      "RMSE(training): 0.023\n",
      "RMSE(test): 0.019\n"
     ]
    }
   ],
   "source": [
    "#2. Ridge 回归\n",
    "\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# 创建多项式回归的Pipeline，使用Ridge回归代替普通线性回归\n",
    "pipeline_ridge = Pipeline([\n",
    "    ('poly', PolynomialFeatures()),  # 多项式特征\n",
    "    ('ridge', Ridge())  # Ridge 回归\n",
    "])\n",
    "\n",
    "# 定义参数网格，包括多项式度数和Ridge回归的正则化强度\n",
    "param_grid_ridge = {\n",
    "    'poly__degree': [2, 3],  # 降低多项式度数范围\n",
    "    'ridge__alpha': [0.1, 1, 10, 100]  # Ridge正则化强度\n",
    "}\n",
    "\n",
    "# 定义评分方法\n",
    "scorer_ridge = make_scorer(r2_score)\n",
    "\n",
    "# 创建GridSearchCV对象\n",
    "grid_search_ridge = GridSearchCV(estimator=pipeline_ridge, param_grid=param_grid_ridge, scoring=scorer_ridge, cv=10)\n",
    "\n",
    "# 拟合数据\n",
    "grid_search_ridge.fit(X_train, y_train)\n",
    "\n",
    "# 输出最优参数和对应的分数\n",
    "print(\"Best parameters:\", grid_search_ridge.best_params_)\n",
    "print(\"Best R2 score:\", grid_search_ridge.best_score_)\n",
    "\n",
    "# 使用最优参数预测\n",
    "best_ridge = grid_search_ridge.best_estimator_\n",
    "\n",
    "# 使用最优模型进行预测\n",
    "y_pred_train_ridge = best_ridge.predict(X_train)\n",
    "y_pred_test_ridge = best_ridge.predict(X_test)\n",
    "\n",
    "# 计算 R2 和 RMSE\n",
    "print('training R2 = %.3f' % best_ridge.score(X_train, y_train))\n",
    "print('test R2 = %.3f' % best_ridge.score(X_test, y_test))\n",
    "rmse_tr_ridge = mean_squared_error(y_train, y_pred_train_ridge, squared=False)\n",
    "rmse_te_ridge = mean_squared_error(y_test, y_pred_test_ridge, squared=False)\n",
    "print('RMSE(training): %.3f' % rmse_tr_ridge)\n",
    "print('RMSE(test): %.3f' % rmse_te_ridge)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "276b3810",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'C': 1, 'epsilon': 0.01, 'kernel': 'rbf'}\n",
      "Best R2 score: 0.2804695780226423\n",
      "training R2 = 0.630\n",
      "test R2 = 0.620\n",
      "RMSE(training) 0.017\n",
      "RMSE(test) 0.014\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3.支持向量机回归\n",
    "\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import make_scorer, mean_squared_error, r2_score\n",
    "\n",
    "# 定义模型\n",
    "svr = SVR()\n",
    "\n",
    "# 定义要搜索的参数网格\n",
    "param_grid_svr = {\n",
    "    'kernel': ['linear', 'rbf'],  # 核函数\n",
    "    'C': [0.1, 1, 10],  # 正则化参数\n",
    "    'epsilon': [0.01, 0.1, 0.2]  # epsilon损失\n",
    "}\n",
    "\n",
    "# 定义评分方法\n",
    "scorer_svr = make_scorer(r2_score)  # 使用R2分数\n",
    "\n",
    "# 创建GridSearchCV对象\n",
    "grid_search_svr = GridSearchCV(estimator=svr, param_grid=param_grid_svr, scoring=scorer_svr, cv=10)\n",
    "\n",
    "# 拟合数据\n",
    "grid_search_svr.fit(X_train, y_train)\n",
    "\n",
    "# 输出最优参数和对应的分数\n",
    "print(\"Best parameters:\", grid_search_svr.best_params_)\n",
    "print(\"Best R2 score:\", grid_search_svr.best_score_)\n",
    "\n",
    "# 使用最优参数预测\n",
    "best_svr = grid_search_svr.best_estimator_\n",
    "\n",
    "# 使用最优模型进行预测\n",
    "y_pred_train_svr = best_svr.predict(X_train)\n",
    "y_pred_test_svr = best_svr.predict(X_test)\n",
    "\n",
    "# 作图\n",
    "plt.figure(figsize = (6, 6))\n",
    "plt.scatter(y_train, y_pred_train_svr, alpha = 0.5, color = 'b', label = 'training')  # alpha设置透明度\n",
    "plt.scatter(y_test, y_pred_test_svr, alpha = 0.5, color = 'r', label = 'test')\n",
    "\n",
    "# 添加 y=x 线\n",
    "plt.plot([min(-0.01, 0.14), max(-0.01, 0.14)],  # X 轴范围\n",
    "         [min(-0.01, 0.14), max(-0.01, 0.14)],  # Y 轴范围\n",
    "         'k--', label='Y=X')  # 'k--' 是黑色虚线\n",
    "\n",
    "# 设置图例字体大小\n",
    "plt.legend(fontsize=16, frameon=False)\n",
    "plt.xlabel('Actual', fontsize=20)\n",
    "plt.ylabel('Prediction', fontsize=20)\n",
    "\n",
    "# 设置轴刻度的字体大小\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "\n",
    "# 设置X轴和Y轴的范围\n",
    "plt.xlim([-0.01, 0.12])  # 替换min_value_x和max_value_x为您希望的范围的最小值和最大值\n",
    "plt.ylim([-0.01, 0.12])  # 替换min_value_y和max_value_y为您希望的范围的最小值和最大值\n",
    "\n",
    "# 在图的右上角添加文本\n",
    "plt.text(0.95, 0.01, 'SVR', fontsize=24, ha='right', va='bottom', transform=plt.gca().transAxes)\n",
    "\n",
    "plt.savefig('支持向量回归_网格化搜索_不交叉验证.tiff', dpi = 720, bbox_inches='tight')\n",
    "\n",
    "# 计算R2和均方误差\n",
    "print('training R2 = %.3f' % best_svr.score(X_train, y_train))\n",
    "print('test R2 = %.3f' % best_svr.score(X_test, y_test))\n",
    "rmse_tr_svr = mean_squared_error(y_train, y_pred_train_svr, squared=False)  # 均方根误差\n",
    "rmse_te_svr = mean_squared_error(y_test, y_pred_test_svr, squared=False)\n",
    "print('RMSE(training) %.3f' % rmse_tr_svr)\n",
    "print('RMSE(test) %.3f' % rmse_te_svr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "741c5c8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'algorithm': 'auto', 'n_neighbors': 5, 'weights': 'distance'}\n",
      "Best R2 score: 0.6025451194030618\n",
      "training R2 = 1.000\n",
      "test R2 = 0.577\n",
      "RMSE(training) 0.000\n",
      "RMSE(test) 0.015\n"
     ]
    },
    {
     "data": {
      "image/png": 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sdwYvAGPMFuBH4JqSChtjCt24RzcgCig6aeI94AIRaex+dX2vX78TWx/4axuE6OhosrKy3D7/2LFjREZGnnTsnXfe8Xa1PNK2bVvS0tJYuXLl8WPGGD799FM/1kqp4hUUFHDDDTfwySefMHXqVA1eJfB3D6wVMLeY4+uA6714jxxgU5Hj6xyvLYGA6aMnJloJG65ZiMOHl++q0i1btuTgwYNMnz6dpKSkEpeo6d69O7NmzeKCCy6gWbNmpKSk8NNPP5VTbc9s6NChPP300/Tr14/JkydTq1Yt3nrrreMp/prNpQJJeHg4rVu3pnPnztxzzz3+rk7A83cAqwHYizl+ELAVc9zTexwyxphi7uF8P6D4exuE22+/neXLl/PYY49x6NAhGjZsyMyZM097/ssvv4wxhscffxyAnj17MmfOnJMSJ/wlKiqKBQsWcM8993DXXXdRpUoVbr75Zi699FIeeeQRqhV94KiUHxw7doxt27Zx3nnnMWHCBH9XJ2jIqZ/r5XhzkVzgOWPMo0WOTwb+ZYxxK8A6nmf9BQwzxsws8t6bQG9jTJ0ix88B/gSGGGNmF3PNEcAIgISEhDbbtm1zu10q8PXu3ZsNGzbw999/+7sqqoLLzMykT58+rF27lk2bNhEXF+fvKnmNiPxsjEny1fX93QOzU3wPyEbxPTNPHARsIiJFemE2l/dPYYx5A3gDICkpyX9RXpXZ888/T5UqVTjnnHM4cuQIn3zyCV999RXTp0/3d9VUBXf48GF69erFTz/9xKxZs0IqeJUHfwewdVjPqIpqCZyaOub5PaKBppz8HKyl49Vb91EBKjo6mhdeeIG0tDQKCgpo3rw5b731FsOHD/d31VQFdujQIbp3787q1auZM2cON9xwg7+rFHT8HcDmAVNFpIkxZjOAiDQC2gGPeOke3wK5wCBgosvxW4C1jqxHFcJGjx7N6NGj/V0NpU4yefJkfvnlF5KTk7n22mv9XZ2g5O8A9iZwNzBXRMYCBmvS8Xbgv86TRKQh8DfwhDHmCZfjHYBaQG3HoSQROQpgjEl2vO4TkReAR0XkCPALcCPQGTdS9ZVSyhcmTZrEtddeS7t27fxdlaDl1xxiY0wmViD5E5gNvI+V0t7ZGOM6E1aAcE6t70TgE+Blx/ejHd9/UuS8x4EngfuA+Vg9vBuMMV94rTFKKVWCvXv3MmjQIOx2OzExMRq8ysivWYjBIikpyaxevdrf1VBKBRnXnSWqV9/JZ59dxb592/nuu++47LLL/F09nwv1LESllApJrjtLVK2axowZncnM3Mubb35bIYJXedAAppRSPuDcWQK28O67ncnOtnPddQvZurVtSUWVm3QdHaWU8gHnzhJhYeFUqlSTIUMW0aJF24DZWSIUaA9MKaV8oFq17djtdalZM4E77liFiGC3B87OEqFAe2DqFJ9//jnPP1/STjae2bp1KxMmTGDz5s0+ub5S/pKaChMmwG23wahRa3nnnSQWL34Eux2MEb/sLBHqNICpU/g6gE2cOFEDmAopzoQNux0iItbwzjsdyc0NZ8SI4dhssGOH9TxszBj/LtQdanQIUSmlysiZsJGVtZrZs68mKqoK1167GLu9Gbq4vO9oD0ydZOjQocyaNYudO3ciIogIjRo1AiA9PZ2RI0dSr149oqOjadGiBW+88cZJ5ffs2cOtt95K3bp1iY6Opk6dOvTu3Zt9+/axdOlSOnXqBEDXrl2PX3/p0qXl3EqlvCstDWJjs5gzpw8xMdUYNuwHGjZspgkbPqY9sEDkOvsxIcEaNC+ncYdx48axf/9+Vq1axbx58wBrMdzDhw/Trl07srKymDBhAo0bN2b+/PmMHDmSnJyc45vvDR48mG3btvHss8/SoEED9u7dy6JFizh27BgXX3wxr776KqNHj+all17iH//4B2BtoKlUMEtIALu9Ev36fUCNGs2oVq2BJmyUAw1ggcZ19mP9+tag+tSp5TZ43rRpU2rVqkVUVBRt256YrzJp0iS2bdvG77//zjnnnANAly5dOHToEBMnTmTkyJFERESwbNkypkyZwqBBg46Xvf76E5trO4PVeeedd9L1lQpWixcvRmQrdvtt2GydqFqV4wkbuuGBb+kQYqBxDqbbbBAWduLPKSl+rda3337LpZdeSuPGjcnPzz/+1a1bNw4cOMD69dauNP/4xz949tlnmTZtGr///ju6VJkKZfPnz6dXr158+umL3HdfriZslDPtgQWatDSr5+WqWjX8PZi+b98+Nm3aRGRkZLHvHzhwAICPPvqIiRMn8swzz3D//fdTp04d7rrrLsaOHUtYmP6+pIKb6+h+dvaXJCf3p2XL81i4cCG1akXRpo2/a1ixaAALNNZgunMNGktGht8H02vWrEl8fDzTpk0r9v3mzZsDEB8fz6uvvsqrr77Kxo0bmTVrFv/+97+pVasWI0eOLM8qK+VVrqP7R49+xqef3ojN1pqXX55PrVrFbSyvfE1/JQ40/fqdGEAvLMQfsx+jo6PJyso66Vj37t35448/SEhIICkp6ZSvqlWrnnKd5s2bM2XKFGw2G2vXrj1+beCU6ysV6FxH9+32TdSt24YBA75j0SINXv6iPbBAk5hoDZ67ZiEOH16ug+ktW7bk4MGDTJ8+naSkJGJiYnjggQf46KOPuPLKK3nggQdo3rw5mZmZ/PHHH/zvf/9j7ty5ZGRk0KVLFwYNGkSLFi2IjIxk7ty52O12rr76agDOPfdcIiIimDFjBjVq1CA6OprmzZsXGwCVCgTOYcP334f4+ENccEF12rX7J23b3odIlL9H9ys0DWCBKDHRr09/b7/9dpYvX85jjz3GoUOHaNiwIVu3buWnn37iiSee4Omnn2bnzp1Ur16d5s2b079/fwBiYmK4+OKLefPNN9m2bRthYWE0b96c999/n2uusTa/rlmzJq+88gpPP/00HTp0oKCggCVLltCxY0e/tVep00lOhkmTIC8PDh58h82bHyI9/X906tSK2rWjNFXez3RDSzfohpZKVTzJyTB6tBW84L/Y7XcREXE1jRp9Tt26lbjwQmt0X7MNT8/XG1rqMzCllCoiNfVEz8uYl7Hb7yIqqhcNGswlN7cSu3Zpqnwg0CFEpZQqIiXFCl4REXPZv/9eKlW6jurVP6SgIIrKleGGG9A1DgOABjCllHJwTdjIy4MqVXqQlTWVqlXvJSIikiNHoHp13RIlUOgQolJKcWKe18GDBniV8PD92O1RxMc/RFRUJEePQmQkjBunw4aBQgOYUkph9byqVzf88stjbNp0N5mZrxMfDwUFEBsLDRrAq6/CgAH+rqly0iFEpZQCtm0zbNjwECtWvECbNneSlPQ4GzbArl3WM69y3BRCuUkDmFKqwissLGTduntZtepVLrnkHrp3n4aIEB0NnToVk7Dhxy2P1Ak6hKiUKp3UVOsT/bbbrNfUVH/XyCPJydCxI5xzDlx5ZQZbtiygZcsxXHrpNIyR06/i5nxYZrefvOVRkP49BDMNYEop94XAh3dqKlx/PQwbBuvXF1ClSj6ZmTZiY1cybNgz1KghZ94SJUC3PKqIdAhRKeU+1w9vOPGakhIUQ2jO+PvTT1CpUj5Hjw5hwwahVav3sNmq8+WXsHRpCRcJ0C2PKiLtgSml3JeWZn1YuwqSD+/UVLj3Xli9Gg4cyOPw4YFkZc0hKuoCDhwQ4uJg5043LpSQYG1x5CoAtjyqiDSAKaXcF6Qf3s6e1759UKNGDvn5A8jJSaZq1eex2R4hOxsOH4Z69dy4WABseaQsGsCUUu4L0g/vSZNg0SLYsgVWrBhCQcE8wsNfoaDgAcd6h1YAu/tuNy7m3PLIZuPMD8uUr+lq9G7Q1eiVchFEKeTORXk//xyioiAmBo4c+YHCwr+oVWs4GRkQEQHnnguPPKKTlL3N16vRaxKHUqp0/LxfnbtcEzYiI49SUDCf7Oz+VKvWnmPH2pORAZdcAi+9FBTNUcXQAKaUCjlDhlgL8hYWAhxGpCfGrKBSpT/IzW1KtWpw9KgGr2Cnz8CUUiFlyBCYPdsZvOxAV4xZQWTkHCIjm5Kba61veO65GryCnfbAlFIh5eOPrfnFcIDCwq7AWiCZvLxrOOss6zlY5crWMy8V3LQHppQKCampMHIk5ORYva/Cwm+ADYjMBa4BrGHDBg3gmWc0YSMUaA9MKRX0kpOtTPY9ewAMIMAtQHvAmqMWFgb/9386bBhKtAemlApqqanw4IOwfTvk5e0E/gEsc7ybgHOm0KBBGrxCjd8DmIg0EJFkEckQkcMikiIibk3rF5EYEXlWRHaLSJaILBOR9sWcV1NEponIZsd5W0TkFRGp5f0WKaXKS2oq3HSTFbwKC7dRWNge+BMoPH5OWBgMHgzvvuu3aiof8WsAE5FYYDHQArgVGAycAywRkcpuXOJt4A5gPNAb2A3MF5ELXe4hwDzgZuBZoIfjdSAwz/G+UirIpKbCY4/B5s0Am4EOwAFgIRER7YiIgLPOgnHjNHiFKn8/A7sDaAI0N8ZsAhCRVOAv4E7g+dMVFJHWWEHpNmPMO45j3wPrgCeAvo5TzwEuB+40xrzhOLZURAqB6cC5wEYvt0sp5WMpKbB/P4jswApex7B+H76Y/PwTO50E+CpXqgz8PYTYF1juDF4AxpgtwI8404bOXDYP+MilbD7wIdBNRKIdh6Mcr4eLlD/kePX334FSygNpaVbGYbVqtQkL64XIEkQuPv5+lSowZYo+9wpl/v7wboU1SaOodUBLN8puMcYcK6ZsFNDM5fsfgHEikiQiVUTkEqxhx2+MMRs8rr1Sym9iYtYhspuzzoqgevXXqVQpkbAwCA+HGjXg7bc1VT7U+XsIsQbWVPmiDgK2MpR1vo8xxohIT2A2sMrlvK+A6093cREZAYwASAjwrSKUqghc1xCOjPyVOXO6Eh19EWedtZDateHQIcjMhPh4q+elwSv0+bsHBtakjaLcSayQUpR9E2gL3IU1WH4XkAQki0ixfwfGmDeMMUnGmKRatTRZUSl/ci7Ma7dDWNgqZs3qTEFBLOPHT6dDB4iMtALXwIHWnDANXhWDv3tgdhw9pSJsFN+7cnUQ5wzFU8s630dEemFlHHYxxixyvPeDiGwGFgB9gLmlrLdSqpw4d1Letw+io39i/foexMbW5NprF3PgQCNef93fNVT+4u8e2DqsZ1lFtQTWu1G2sSMVv2jZXMCZGHKB43VVkfNWOl7Pc6+qSqny5rqTcs2ahr/+egg4m969v6dBg0akpfm7hsqf/B3A5gFtRaSJ84CINALaOd4rqWwkLs+xRCQCuBFYYIzJcRze43i9pEj5Sx2vOz2quVLK51JSrFT4+HjIzRUSEz+jZculbN/egIwMaz9NVXH5O4C9CWwF5orINSLSF2s4bzvwX+dJItJQRPJFZLzzmDFmDVYK/YsicruIXIWVQt8Y+LfLPVKAXcC7IjJSRDqJyEjgXcd9PvNlA5VSnktLg/T0+ezceQtZWXkUFNSmatW67NtnPQ/TOV4Vm1+fgRljMkWkM/ACVpagAIuA+40xR11OFSCcUwPuMGAy8CRQHfgN6G6M+cXlHodFpC0wAXgYqIO1YscXwIQi91FK+ZlrtuHKlV+wYcMA4uNbcsUVR9myxca+fVaPbMwYneNV0YkxxSXyKVdJSUlm9erV/q6GUiHvueesrU5yciAqKoX09BuJjLyQ3r3n06pVDTIyrJ7XGYOXawRMSLC6aRrp/EJEfjbGJPnq+v4eQlRKKcBKf3/ySWsvr4iIT9i//wbCwv7BxRd/x4EDNdixw3oeVmLwcubb169vvU6dah1XIcffafRKKQXAK6+ACMTGQm5uAypV6k7lynPYvbsqnTvDjBluXMSZ9WFzzKZxvqakaC8sBGkPTCkVEHbuhKio38nPh+jotsTHf0mlSlU5dKgU2YZpaVCt2snHqlVD8+1DkwYwpVRAEJnO3r2JHD2aQl4eGANZWRAdXYpsw4QEyMg4+Zjm24csDWBKKb+bNm0af/01itjYPsTH9yI8HI4cgfx8ePjhUoz+9etnPfey262Hac4/a759SNIAppTyq2effZb777+ffv368dZbydSrF010tBW03nwTHnqoFBdLTLSyPGw23Mv6UMFM0+jdoGn0SnlP0VXl33jjYm666SbeffddIiMj/V095UW+TqPXLESlVLlxZrnbbFaWe0bGRVx11Xz++c/OREbqx5EqHR1CVEqVm5QUqF7dsGbNeNLSvsdmg1atrmbePA1eqvT0p0YpVW62bTOsX/8AK1dOIzc3k0aNOmiWu/KYRwFMRGoAt2Gt8G7DWqewKGOMuaoMdVNKhZDCwkLWrr2b1aunc+ml93H11VMBzXJXnit1ABORFsBSoBZn3jlZs0OUClWlXG+wsLCQO++8k9Wr36JVq4e55JL/YIxw6JCV5T58ePlVXYUOT3pgU4F44D/AG8B2Y0yBV2ullApcRTMxnOsNFklXd41x9esbdu/OZty4cfTvP5HPPpPjsW/4cM1yV57xJIBdCXxljHnM25VRSgUBN9YbdMa4uLg8qlc/SEbG2dhssxgwIIzERGjd2k91VyHFkyxEAdZ7uyJKqSDhxnqDKSkQF5fL4sUDmTXrSipXzqRGjTBSUsq5riqkedID+xlo7u2KKKWCREKCNWzo7HkBZGSwJzqB1ydYcWz16hwyMq4nLe0LunV7gaioykREaLah8i5PAtgTwHwR6WiMWerl+iilXAXi5oz9+lnjg2D1vDIysG+282TGcFb9AllZWfz5Zz9ycr7liiteo23bkYBmGyrv8ySANQDmAgtEZA5Wj+xQcScaY971vGpKVXBuJkuUO+d6gy6B9ZWY4SzalEhcHBw48C9ycuYTHf0Whw4Np7CQ4zspa7ah8qZSr4UoIoVYKfKuKfRFLyJY88CKmx8WdHQtROUXEyacOlTn/H7CBH/V6iTODuJLL0FMDNStC9HR6WRk/A+4jvR06NkzcDqPqnwF4lqIw7xeC6XUqaz885OPBdCyFa4dxIiIDDIyniY/fwIJCWdx1lnXcewY1K7t5k7KSnmg1AHMGDPLFxVRShVxmmSJQHmQ5Mymj4mxk5nZjWPHfiUqqif7919xfD+vDh38XUsVynQtRKUCVTHJEoHyICk1FebOhby8dLZtu5rs7HXUqfMpIldw5IjV82raFEaN8ndNVSjzOICJSCzQD7gIqA5kAL8AnxljMr1SO6UqsmKSJQJh2Qrn0KHIPjZv7kJ29p80ajSXNm26k5YGubnQo4c+81K+5+livj2BWUANTk3meEFEhhljvvRC/ZSq2BITAy4KOIcOmzbdzdq1Bzn33K+Ii7uKtDRo3tz/SZKq4vBkMd+LgRSsFejfBxYDu4E6QGdgIJAsIu2MMT97sa5KqQCwadNhmjSJw2ZrTbVqm9i0KYZDh6z3NHip8uRJD+xxrJ7WlcaY5UXemykir2KtVv8Y0L9s1VNKBZJt27bx9dedadVqJFddNYb69WOOT1Gz2TR4qfLlyVqIVwKfFBO8ADDGrACSHecppULE33//Tfv27cnPP0jVqu2x26Gw0Apedrv1zEup8uRJD6wasL2Ec9KAOA+urZQKEK6rWMXGbuSTT64iPz+LpUsXERFxcaDllqgKyJMAtgtrJ+YzScJ6LqaUCkKuk5Tj44/yyiudyc/P4/33l3LxxRcAGrCU/3kyhPg10FlEHhGRk5aKEpEwEXkI6OI4TykVhFy3/IqJqcLVVz/NgAFL+f33C/xdNaWO86QHNgm4FpgM3Cki/8PqbdUGrgAaAXuAJ71TRaVUeUtLg/DwXzh4MJ2mTa8mMfEWCgsDZhUrpQDPlpLaIyLtgP8CXYGGRU5ZCNxljNEhRKWCVHj4SmbP7kbVqrUZOfJ3wsIiAmkVK6UADycyG2O2At1EpB7WShzVsFbi+NUYs9N71VNKlRdn0saqVT+ycGEPIiNr0bv3t0DE8UzDAFjFSqnjyrQWoiNYacBSKsg5kzYyM79n4cJeVK5cj4svXkRMTH127NBMQxWYdDFfpdTxpI0//kimevWGDB78Hfn5dcq29Vgg7iatQkqJAUxEZmCtvPGYMWav43t3GGOMDjgoFaBc48vPP+fRtm0kPXpMIzs7g0qVbGVL2gjU3aRVSHGnBzYUK4A9Dex1fO8OA2gAUyoAucaXzMx5/PnnP8nMXEiXLgnUrm3tP1ampA3XPHw48ZqSogFMeY0788AaA02AzS7fu/PVxJ0KiEgDEUkWkQwROSwiKSLi1n8bEYkRkWdFZLeIZInIMhFpf5pz64nIDBHZIyI5IrJFRJ5y5z5KhRpnfNm9+1OSk/sTF1edsLA4fv3VS8tDpaVZe5i5CqDdpFVoKLEHZozZdqbvy8Kxp9hiIAe4FavX9iSwREQS3dhX7G2gF/BPrAA7GpgvIpcZY9a43KcR8COwBbgXqyfZCGjmrbYoFUzS0uDQoTl8/vlg6te/lEGDvuHgwThWrsQ7SRsBvpu0Cg2ebKcyHlhqjPnhDOdcCXQyxjxRwuXuwOqpNTfGbHKUTQX+Au4Enj/DPVoDNwO3GWPecRz7HlgHPAH0dTn9daxsyU7GmDzHse9LqFto0wfsFVpW1hd8/vktJCRcyc03f0lUVBViYuCaa8qQtOEqgHeTVqHDk6WkJgAdSzinPfBvN67VF1juDF4AxpgtWL2la9womwd85FI2H/gQa45aNICINAW6AS+7BK+KzfkAxG4/+QF7aqq/a6bKyejRV9K8+d307Pk1ERFVvL+ivHM3aZvN6tLZbJrAobzOV2n0EUChG+e1AuYWc3wdcL0bZbcYY44VUzYKa3hwHdDOcTxLRBZiBddjwBfAA8aYA27UM7ToA/YKa+7cuXTr1o0rrqjORx9N8+2K8gG4m7QKLb4KYG2AdDfOqwHYizl+ELAVc9zdss73Aeo6XmcAs4GnsILbU0BLEbnEGHNKsBWREcAIgIRQG7dPS7N6Xq70AXvIe/HFF3nggQeYPHkyjz32mMYXFfTcCmAisrjIoaEi0rGYU8OBBljrI85xsw6muFu6Uy03yzqHSZcaY0Y7/rxYRDJwDDcC35xSKWPeAN4ASEpKKu4+wUsfsFcIro8509KeZtGiR+jfvz9jxozxd9WU8gp3e2AdXf5ssDL4GhVzXiFwAOu51ANuXNfOiZ6SKxvF965cHQSK+8S1ubyPoz5gLTLsaoHj9SKKCWAhTR+whzzXeV6bN0/i++/H06jRQB577F2ionQBHhUa3EriMMaEOb+wejgTXI+5fEUYY842xtxsjNnvxqXXYT3LKqolsN6Nso0dqfhFy+YCm1zOg+J7a+Des7rQog/YQ57zMWdk5F5WrpxG69ZD6N17NvPmafBSocOTn+ZhwBov3X8eMFVEmhhjNsPxOVvtgEfcKDsRK9ljlqNsBHAjsMAYk+M4bznW/mTdgVdcynd3vK4qezOCkD4ACUnOYcP33jPUrQstW57NiBGrqVYtAWPC9DGnCimlTqM3xswyxvzmpfu/CWwF5orINSLSFysrcTvWfmMAiEhDEcl3zEFz1mMN1lDliyJyu4hchfVMqzEuKfyO1PpHgF4i8rqIXC0io4DXgKVYE6mVCnrOYcODBw3Z2Q/w55//ZtkyyM5uhEiYPuZUIafUAUxE7hKRv0Wk7mner+d4v8QHKo6VNjoDf2JlCL6PtVpGZ2PMUdfLYiWIFK3vMOAdrNU7vsJKIOlujPmlyH1mAUOwdoz+AhgHvAf0McaEVoKGqrBSUqB69UJ++mkUO3dOo6DgCGDYsMELS0MpFYCktJ/fIvIDEGaMueIM53wPFBpjOpWxfgEhKSnJrF692t/VUOqMhg4t4LffRrBmzQzatfsX55//FBs2CLt2waBButiKKn8i8rMxJslX1/fkGVhzILmEc1KBAR5cWynlod9+u501a2bSvv14OnacgIgQHQ2dOnlpeSilAownAawacKiEcw5T8kRkpZQX3XBDF+AcLrzwMYyBQ4d0dkS50zVGy5UnayHuBkr6F0kE3EmjV0qVQW5uLsuXLwfg0UcHMWvWYzo7wl90jdFy50kPbAkwWESuMMb8X9E3HSvR98BKklBK+Uh2djbXX389Cxcu5M8//yQhIUFnR/iTrjFa7jzpgT2NNVH4OxF53pGW3srx+gLWihc5jvOUUj6QlZXFNZ078+WXX/LixReTMGOG/qbvb7qJZ7nzZB7YRuAGrCB1P9YyTKmO1/uAbOB6Y8wG71VTKeWUmZlJrw4dWLhsGW9fdRV3demiw1WBICHBWpbNlU6+8ymP1pUxxnwlIk2AocClQHWsxI7lwKwKuUVJsNOHz0FjxowZfL96Ne9efTW3XHaZdVCHq/xP1xgtd6WeB1YRhfw8MNeVX13/42kGgN8V93vFBRcYVvftyz8uugjCXAZRCgut7I0ZM/xX4YpOfxE8SSDOA1OhRh8+ByTX3ytq1jzI7Nm38dtvzzFxYlP+0aaNbokTiDSLplyVGMBEpL3jjyuNMdku35fIGPODxzVT5Uc3uAxIzt8roqPTmT27K/v3r+f88+8iJaUpiTpcpZRbPbClWFuRnIe1ZqHze3eEe1QrVb50g8uSlfPQUGoqzJ0Lubl72br1KnJy/uamm+bRpEk36/cK55Y4rnUaPlx/+1cVijsB7AmsgJVe5HsVKvS3+TNzHctznaDqo2eEztvBbjZv7kxubhqNGn1F1aqdT/69QoerVAVXYgAzxkw40/cqBOhv82dWzs8Inbe76KIqbNrUgMaN36BKlSv55Rdo3lx/r1DKSZM4lEV/mz+9cnhG6DpCuWJFGpdeWpOEhKrcfPN8Nm4UDh2yztPEUKVO0ACmVEl8/IzQdYQyNvZvNm3qxPbtlzBoUDJ16gh16py4vQYvpU5wJwvR0x2LjTHmKg/LKhU4fPyM0DlkWFCwkXff7UxYWA41a47ll1+gWzd9JKnU6bjTA+t4muMGa6fk0x3XRA8VGnz8jDAtDaKj1zN7dmfAMGzYUgoKzmfFCmtesj6SVKp47iRxnLReoohEAR8D5wOTsNLq9wC1gU7A48BarPUSlQoNPnxG2KBBIa+9NhCRMIYMWUStWudht8O11+pGlEqdiSfPwMYBScD5xphDLse3ATNFZB7wu+O88WWuoVIhpuiUsgsuCOOyy+Zgs0VSs+Y52O06ZKiUOzzZTmUQ8GmR4HWcMeYgkAzcUoZ6KRWSXPc8hOV888145s41DBnSksaNz9GNKJUqBU96YHWx9gM7kzygjgfXViqkORM2jhz5P95/vyeVK8dz4YUPsHatTYcLlSolT3pgO4BrHM/CTiEi0cA1wM6yVEypUJSWBnb7Ut57rztVq9Zl6NDvOftsmy47qZQHPAlgs4BmwGIRaS8i4QAiEi4iHYBFQBNgptdqqVSIyMtbyJw5PalevSFDhy4lLq6eLjuplIc8GUL8D9AG6AssAQpF5CBQAysgCjDPcZ6q6HR/pJP+CvbuPUzlyudxzTXfEhtbSxM2lCqDUvfAjDF5xphrsZI0FgMZWMErA6v3NcgYc60xJt+bFVVByDVjwXUR3Aq07b3zr2Dnzr3Urw/nndefdu1WEhFRSxM2lCojj5eSMsZ8AHzgxbqoUKMbZZKSAunpn7Bw4a0MHDiPJk260KxZODabzvFSqqw8eQamlHvS0qyll1xVsI0ylyz5gG+/vYk6dS6mXr1LgAr3V6CUz3gcwEQkUUT+IyJzReQ7l+ONROQGEbGdqbyqABISrIX8XFWgjIVZs2bxww+3ULdue2655Vuio+OACvVXoJRPeRTAROQJ4BfgYaAP1hJSrtecg05kVv36cTxLobDwxJ/79fN3zXxuxYoVDBs2jLZtu9C+/VdkZlapaH8FSvmcGFO6NXdF5CasZ1/zgX8BNwKPGGPCXc5ZARw2xnT1Yl39Jikpyaxevdrf1QhOFSQLsWgzr7vOsGrV29xyyy38+WdMRfgrUOoUIvKzMSbJV9f3JInjXmATcI0xJldErivmnA2cfhV7VZFUgI0yk5Nh0iTIy4O8vOk0b96JzZtbMGbM7cTEVIi/AqX8wpMhxAuA+caYMy0ntQs427MqKRU8kpNh9GjYvh12736KTZtGsXz5y+TnWz0ypZTveNIDE6CwhHPOBrI9uLZSvuelYc3nnoN//xsyMw3h4U9QUDCBmJibOfvsaezcCTExPqi7Uuo4T3pgfwGXn+5Nx9JSVwDrPK2UUj7jpcnVycnw5JOQm2sIC3ucgoIJiAwlJuZdMjMj2L9fMw2V8jVPAtjHwMUi8tBp3n8Ua61EneSsAo/r5OqwsBN/LuV43yuvgAjExOQCPyEygrCwtzl2LJzDhyEyUjMNlfI1T4YQXwSuB54RkRsAAyAiU4ErsTa7XA684aU6KuU9aWlWz8uVBzOLd+wwVK6cRU5OLAUF32BMDAUFQl6eFbzGjdPEDaV8rdQBzBiTJSKdgGlYm1s60+cfxHo29h5wt66FqAJSQoI1bGhzmWfvxsxi18dmDRoUkpExkqNH1xEX9x2VK1ciJwcKCiA2Fl59FQYM8HE7lFKeTWQ2xmQYY4ZiJWv0wJq03AeoY4y51RhzxN1riUgDEUkWkQwROSwiKSLi1tMDEYkRkWdFZLeIZInIMhFpX0KZgSJiRGSHu3VUIcSDydWpqfD44/DNN/DzzwW89tpw0tPfICKiPXFx0VSqBOHhVvCaOFGDl1LlpdQBTEQ2i8irAMaYg8aY+caYD4wxXxlj9pfyWrFYK9q3AG4FBgPnAEtEpLIbl3gbuAMYD/QGdgPzReTC09yvOvACsKc09VQhJDHRWv7dZsPd5eCnT4dNm8CYfHbvHkJ6+kzOOmsCbdtOpl49ISrKKv7mm/DQ6Z4MK6W8zpNnYLWwtk7xhjuwNr9sbozZBCAiqViZjncCz5+uoIi0Bm4GbjPGvOM49j1W9uMTWPuVFfUM8BtWoOvipTaoYOOcWewcF3zxxWLT6ZOTrWSNZcus51pRUQ9it39A48ZTOOusR0lPh19/9V8zlKroPBlCXAc09dL9+wLLncELwBizBfgRuMaNsnnARy5l84EPgW4iEu16soi0wxrqHO2dqqugVkI6fXIyPPwwHDoEERHW862jR++hdu3XSEh4FIBSrsKmlPIyTwLYS0AfEfFGjlUrYG0xx9cBLd0ou8UYc6yYslFYqfwAiEgkVlbks67BUlVgJaTTv/IKxMVBXFw2ERH/BQzh4edw7NhIsrLgyBFo29a/TVCqovNkCHEH8B3wo4j8F1iF9UzplN9HjTE/lHCtGoC9mOMHgZK2YzlTWef7Tv8CooGnSrjmcSIyAhgBkKAzUgODNxcGLiGdfudOiI8/xtq113L48EKqVr0AYy4n27G+TNOmMGpUGdqilCozTwLYUqxgJVip82caSAk/w3tOxZUXN8qJO2VFpBnwOHCdMcbt5a2MMW/gmMuWlJSkg0X+5hzys9lOHvIrIQHjtEpIp69dO5M1a/pw9OhSmjefQZUql7N9O1SuDD16OGInqTBBl5lXyl88CWBPcOagVRp2Tu4pOdkovnfl6iBQXNfI5vI+WEOei4HljixEsIYYxfF9jjEmqxR1Vv7gOuQHJ15TUtwLGkV7b+efD/PmWe9lZ5P50xqOph1gaURX5ry/jLXpD3P06E8kJMwmPn4Qhw9bweuZZxxp8t4OqEqpUvNkIvMEL95/HdazrKJaAuvdKHudiMQWeQ7WEsjF2vLF+X1Dig+IdqwJ2feXos7KH8qygkZxwWbePOjbF777jpyvF/H3gZqsyL6KbKK44NAEvihcTePGc6hR4wZ274Z69WDsWJc5XmUNqEqpMitVAHNMMP4HVg9slTFmexnvPw+YKiJNjDGbHfdoBLQDHnGj7ESsZa1mOcpGYG2wucAYk+M47yag6LrgjwBtHGV1QnMwKGkFjTM9HztdsFm7FmrX5rvoXvySY6NQComKCOPsghaMkUQ+DbuB3r1hwoRi6uOlJamUUp5zOwvRsdbhZqzFfD8BtojIs2W8/5vAVmCuiFwjIn2BucB24L8u924oIvkiMt55zBizBiuF/kURuV1ErsJKoW8M/NvlvOXGmKWuX1hJJzmO7zUrMRicaQWNklaYT0uzgosrR7BZmZzG6r+rcbgwk/8WvsUvuWvJDK9Gq7AD7N59hniUkGAFUFduLEmllPIetwKYiNyMlbAhwB/ARsefHxSRgZ7e3BiTCXQG/gRmA+8DW4DOxpijrlXASggpWt9hwDvAk8BXQAOguzHmF0/rpALUmVbQKGmF+dMEm5SfE/h6XQLCLmYxi/3sJ4ZKVMrNYEdYAvn5Z4hHHixJpZTyLjFuzMYUkUVYK813M8YscRzrAnwDfG+MCelVLZKSkszq1av9XQ11OrfdZvW8wlx+vykstALdjBknFjPctw9ycsgsiGbT4XgGp00ml31k0J+DHGMgA7kQGzbsTIsYw7a4RJYsOcMjLW+m9SsVgkTkZ2NMkq+u7+4zsETgc2fwAjDGfCcic4GOvqiYUm5zZ4V5xy9q9kOQtg925RnyOcJGRhJGPkMYzJXANmy8zXC2VUrk34+VEI+cS1IppfzC3QBmwxo2LOoP4Fqv1UYpT/TrZz3zAuvZVkaGFdCGD7eOpaRA06bsbZDExx9DThjEFNoZwAImMYRCujJL2jLTMRgRFgYfzdBV5ZUKdO4mcYRhrTtYVB7uTTpWyndKWmHekcSxahUcPQoHzEH+CsumsWwHxgFtMcYKXCJw//0avJQKBqVJo9fVKFTZ+PKZ0ZmG8xIS2P+XnT/+sLG3IJ0ZubOoIlGcFT6asELrcRlYAWzgQHjuOe9USSnlW6VZzHeCiBS4fmHtw0XR444v3ZFZnVBSqrsP/Xl+PzYus3M4dxNvF8wECrmXdiyIHUBMDERFwV13wc8/w7vv+rw6SikvKU0PrLRDhTq0qE4oOpk4Nxc2boQhQ+Daa32WwZeaCve+kgjZvVmVN5QoYHj0bXwWNoLf8xKJjYXOna1NK5VSwcWtHpgxJsyTL19XXgUR18nEe/fCTz+d2FDLB72x5GRISoIrroCVK+GXoynkRZ5Fw+iPiQ87i/vMi0xgAje1TGXsWK/dVilVjjTIqPLhOpl4wwaIibEyJqpXP3XicRk5N6O0Vo83iEB29gw6nP0mE2M+5qwIO4cq16eJzc6kalOtVeWVUkFHA5gqH64rVxw6ZPW+srOhRQvrfS+uI+jcjDIv73/Y7V2oWjWDsLBYOu1dRm5lG1nRNurUC+PKvjZsTbwXOJVS5cuT7VSUKj1nqntKitXzEoHLLoPata33vbCOoDPJ8ZdfoFKlxRw61IdKYfHcdewJmoud8/J/5Y+jl1DZBpdfDmefDRTqArxKBSvtganyk5hoLe0+axY0bw7R0V5bR9A1yTEmZj779vWiktThubAk6hXks1PqUxAexZXmB/pcstcKXqAL8CoVxDSAqfJX0sRjD0yfbiU1fvnlAtLT+xIW1pyHoq4jPKIuh7CRmx9Gev2LqFkTam77RRfgVSoE6BCi8g8vrSOYnAz/+Q/89puVFxIffy7Vq/eiUqW3OM8+hm359aldFy65BFq2rA2728OKFVbgTEiwlpvS9QyVCkoawFTQcmYbZmZCRMRK8vKS2LmzEU2apFC1KhwlgY717bTt4bLIb0yMNe+s2F0qlVLBRAOYOlkgbxFSpG5ffdmPuLhEDhx4j+zsW4mKmkpY2APs3Gk9YvssrB8D4qeCneIX+VVKBTV9BqZO8ONyT6Wp297I+iz/xk7X36aSu2Uihw8PISqqAzbbCKKirOx8EUjolYhtsneftSmlAof2wNQJRZd7cr6mpPj/Q99Rt/W7bXz/PRQW2vg/8zsbDs8mIuJqKlf+jPDwWGJioEoVqwc2ahS6Z5dSIUwDmDohLc3qebny4gTjMklL452F9dm2w/r2MIdJ5n+0pj5bYucSFRVDfj4cOwbdu2tHS6mKQAOYOsGdnY395PWvE8jea8faWxXiiONu+hEn5zJNYjh2DC6+GO6+W/fyUqqi0Gdg6gTX5Z4CaJ7UkCHw6t5+2LCzmgWs4VeqY+dCokkx11O3Ljz4ICxdqsFLqYpEA5g6wQcTjMvquefg/fdhLRfwT2L5imXsZz0Hqc5UxrCWRCIj/R5jlVJ+oEOI6mQBlPSQmgqTJkFhoQEeYz//5UIuZBit2e6y3dy4cQFTZaVUOdIemAo4qalw/fXQoQNkZBhgDPAfOnEug7mSnSRgw84YpjLkwlQdNlSqgtIApgJKaio89hgsWQJRURAWJoCNS7iEPnTjMDUwhHEIGxFn2Zh1jW6FolRFpQFMBZSUFNi/H6CQiIjNVKkCMJYRtOQw1QEQoHUiDBoZICn+Sim/0Gdgyr+KLA9VsKYf2dmtyM4ezsGD86hbdx1Qh+2HG2LDTla0jfbtrf28sAdGir9Syj+0B6b8p5jlodp8/wx7N/YlM3MWsbH3U1hYm8qVYamtH42r2xne387lbQMnxV8p5T/aA1P+41geam+ujZ+WQ0RUHC8Ufs3enE1ERz/F2Wc/Qna2tdr8rvhEYu4cQ60jLgsN61YoSlVoGsCU/6SlsTeyPt/Mt4LU0vxl/HB4E/fUuoTF8Y+Qng516kDbtjByJCQmJgIasJRSFg1gyi+eew7MxwlEZdo5JDbi4qBTbFvqF8TQOSGJ6j2tudQzZvi7pkqpQKXPwFS5e+45+Pe/4aO8flQN28935jP2ZByl0tEjdImqQQr9AmUJRqVUANMApsrdc89ZSy3+WtCEe81f/B+/c4xf2Z5p45OEMaw4lqj5GUqpEukQoipXqanWPK+oqMMUFvbkqPmV8PD3eDPyZnJzoXk2xMf7fQlGpVQQ0ACmyoVzutfnn4PIIbKyumPMz4SHf4jIAPLyoHJlSErS4KWUco8GMHWqIpOL6devTBElNRUefxz27bMuGRmZSV5eBhERyURGXkNurjWk2LatBi+llPs0gKmTOScX22zW7sx2u/W9h5ElNRVuugk2bwYRO2FhcURH16Ow8HcKCiIoKIBKleCyy2DBAh+0RykVsjSAqZM5Jhcf35XZ+ZqSUuoAlpoKo0fDxo0Auyks7EJY2JXk579O5coRREZawc1ut+KjUkqVht+zEEWkgYgki0iGiBwWkRQRcSuBWkRiRORZEdktIlkiskxE2hc551wRmSYiqSJy1HHuPBFp7ZsWBbm0NKhW7eRj1TxbNPe112DDBoCdFBZ2BLYhMhCAvDzrKwD2zFRKBSm/9sBEJBZYDOQAtwIGeBJYIiKJxpjMEi7xNtAL+CewGRgNzBeRy4wxaxznXA10AmYBvwDVgYeBFSLSzhjzs1cbFewSEqwukbPnBZR2UpbzEdrHH8OxY9swpjOwn/Dw+YSFtaOgAMLD4YYbYMIEr7dAKVVB+LsHdgfQBLjWGPO5MWYu0BdoCNx5poKOHtTNwAPGmDeNMYuAG4A04AmXUz8EWhtjnjPGLDHGfAZ0B7KA+7zeomDXr9+JhXILS79orsv6vERGFpCb2xNjDhARsZCwsHYUFlrnxcfDqFE+bIdSKuT5O4D1BZYbYzY5DxhjtgA/Ate4UTYP+MilbD5WwOomItGOY+nGGONa0BiTAfwJ1PNGI0JKYqI1pmezWWs5lWKMLzUV7r0XVq+G336DmjXDqVTpZSIjFxMZealjg0prRHLKFB02VEqVjb+TOFoBc4s5vg643o2yW4wxx4opGwU0c/z5FCJSAzgfeKdUta0oEhM9StiYOtVKlY+N3UBa2goiIoZSs2ZnsrLgyBFrnlfdulbwGjDAR3VXSlUY/g5gNQB7MccPArZijrtb1vn+6byMtbHviyXcQ7nJmbxYufJa1qy5irCwcC64oB9xcXGEh0NuLlxzTZmnlCml1HH+DmBgJW4UJW6UE0/KisijWM/OhrsOXRZz3ghgBECCripborQ0CA//lbVruwLRNG++mNjYONLTdXUNpZRv+DuA2Sm+p2Sj+N6Vq4NAcZHF5vL+SUTkLmAKMNYYc8aNOowxbwBvACQlJRUXKCs81wU71qxZxdq1VxMbG8d11y1m166m7Nun6xoqpXzH3wFsHdazrKJaAuvdKHudiMQWeQ7WEsgFTupdichg4DXgOWPMZM+rrODk5aFycmDv3mUUFtro2HEJzZo1pFatExOUNXgppXzB31mI84C2ItLEeUBEGgHtHO+VVDYSl2QPEYkAbgQWGGNyXI5fh5Ww8ZYxRtd88ILp02HTJigszKJaNUhIuJd69X5jx46GpU1edF9qqjVx7LbbrNfUVC9eXCkVbKRIhnn53lykMvAb1pyssVjPtCYBVYFEY8xRx3kNgb+BJ4wxT7iU/xDohjWReQswEugNXG6M+cVxTntgAVaP7m6g0KUKOcaYX0uqZ1JSklm9enXZGhtiLroIsrIWsWXLYM4//wuqVm3DsWPW6hq/lvg36gHXNRqrVbMmV2sXT6mAJiI/G2OSfHV9vw4hGmMyRaQz8AIwGysBYxFwvzN4OQgQzqk9xmHAZKzVO6pjBcPuzuDl0BmIBi7Cml/mahvQyBttqQhcn3lt3TqfjIxriY1tRnR0/ePnlPj7kKcr3XtxjUalVGjw9xAixpg0Y0x/Y0ycMaaqMeZaY8zWIudsNcaIMWZCkeNZxpgHjTG1jTExxphLjTFLi5wzwVG2uK9Gvm5fqHBdYePYsS/IyOiLSAsaNFhCZOTZx+d6tW3r5kVcV7p3ZyjQi2s0KqVCg7+TOFQQSE21Hjtt2wYFBcuw2/ths11ElSrzOXzYRnQ0REdD06YlLA9Vll6UF9ZoVEqFFr/3wFRgc2Yb/vUXREZCTEwSlSo9gs22kEsusREfDxdfDD16uLE8VFl6UWVco1EpFXq0B6bOKCXFSpUPD/+MwsJ2xMTEY7NNIjsbtm+3Vtdwe0X5svSinGs0uj4/Gz5cn38pVYFpAFNnlJYGu3bNwG6/nUqV7iI8/DXCwyErCw4cKGUHqF8/65kXnJxJOHy4e+U9WKNRKRW6NICpU7gmCv7f/01nx45RVKvWjSZNnuPgQcjMhKgo6Nq1lPFEe1FKKS/SAKZOkpoKjz0G+/fDjh3T2LXrfqKielOt2idERMQQH29lGzZtCiNHenAD7UUppbxEA5g6zplt+NdfULlyFgcPvo7N1o+4uDnExkaRl2fN8+rQwco21DiklPInDWAKgORkmDQJNm6EiIhC8vMrUbXqDyQkVCc8PJLcXB+tsKGUUh7SNHpFaqoVvMBQWDie7OybOXo0H5FaHDgQCYC4s8GNUkqVIw1gipQUyM012O2Pkpc3CagCCDk5VsJGiStsKKWUH+gQYgXWuzd88w0UFhrgQeBFatUaSX7+K+Tnh5GdDVWqQLNmHiZsKKWUD2kAqwiKWUC3Tx/okfYaT7CcaezmXfZRk0FkZ7/KOecIO3dCRAT07WsFL03YUEoFGg1goc51GxLHArq/9HycB3YeojbpHKYql1ODShTQnUOMO/I7WVmJnH02jBsHAwb4uwFKKVU8DWChyLXHtXkz1KsHNht798L8+TbO2bmPJmznM6pyEbE0J5bWxGHYTz9SkBsS3d7lRCml/EUDWKgp2uNavhwOHuRAXhyL1pzN9u3QgmweJJ3P2M3rVKE5Vckmhmpk0Ig0hk3wdyOUUqpkmoUYaly3LAkLg/h4CAsj/f82sHs3ZOcX8C+28xl5jKYBzakKQAzZZBNN1Dm6PYlSKjhoAAs1RbcsadGCrGOFhKXvIzcvl895n6XYGUVjRlGZGI4RwzHiOMyx2HgGJev2JEqp4KABLNQkJFirvAN798LSP2qzeN/57Cee9IKVrGML9Xic//E5S+hAFLlEk8euczpy1bLJ+uBLKRU09BlYqHFsWbI/HX76vRq2sAwKJYJn6r/Eov2JVGYtmRHns/co3J3/OjVqWIv3PvSQvyuulFKloz2wUOPYsmTtTht18neQWSWWUYVb+TPmMA0bQpUq5xMdDdWrQ5s2sGSJBi+lVHDSHliIsTLoE3l/ZyLx8Rn8vakH+46spEHmTs4+25qc3KGDtY/kmDE6YqiUCl4awEKIawZ9rVp2fv21G1lZv9K160c0bdqfX36xzrPZdB9JpVTw0wAWQqZPt7ZDOXbsEH//fRXZ2eto1OhTcnL6EhUFzZtrr0spFTo0gIWI556DmTOtDSejoqoSEdGaOnUmU6NGD3btgk6dtNellAotGsBCQHIyPPkkwG7CwgwFBXUpKHiHSpWsZ16DBsGECX6upFJKeZkGsBDwyitgzA7y8jpjTDUqVVpJeLiwd6/1fj+dm6yUCkEawELA1q3bOHq0M4WF6dSoMZP8fCEvD/LyoEsXHTZUSoUmDWBB7u+//2bPns4Yc5jq1RcSHX0JlSvDsWPWUoijRvm7hkop5RsawILcPffcQ2TkUWrWXESVKheTlQVHjljJHGPHau9LKRW6NIAFkdRUeO01a4cUEWjbFv75z5k888w+/vjjfF55BXbuhCZN4O67dTNKpVRo0wAWJFJTYfRo2LABCgpSyc19Cbt9Ojt2xDN5cjwDBmjAUkpVLLoWYpB47TUreBUW/sLhw53Izv6WjIzdbNlibQGmlFIVjfbAApy1tiF8/DEcO7aS3NxuhIXFcfbZi4EE0tOtLcCUUqqi0QAWwFJTra1O9u+HzMwfyc3tAdSiRo3FREY2JDcXCgqsLcCUUqqi0SHEAPbaa/D339afq1aNQqQFIt9z9GhD8vIgK8tamFcnKiulKiLtgQWg556Dl1+2hgbDw7eRkNCQRo3+QWHhCo4dE3JyIDcXatWCKVM0VV4pVTFpAAskqamkDE7BlprGrSQwi3i25T/I5s3TadJkGE2aCLt3Q3a2lZHYr18ABi/nQ7u0NGtsMyArqZQKBX4fQhSRBiKSLCIZInJYRFJExK2nOiISIyLPishuEckSkWUi0r6Y88JE5FER2Soi2SLym4j0935ryiA1lXW3TSUt1c526rOTn9nJPcTQGOjr6I1BXBzccIO1OG/AxQXnhmR2O9Svb71OnWodV0opL/NrABORWGAx0AK4FRgMnAMsEZHKblzibeAOYDzQG9gNzBeRC4ucNwmYALwC9ACWA5+ISM+yt8I71j2ZwtLfbBzCxjr+4B2+pi61uIe+QE3y863zmjWDkSP9WtXTS0mxHsrZbNY6Vs4/a56/UsoH/D2EeAfQBGhujNkEICKpwF/AncDzpysoIq2Bm4HbjDHvOI59D6wDngD6Oo7FA2OA/xhjpjqKLxGRZsB/gK990K5SSU2Fzd+lYS+ozyEO8SmfUo963MxAzmE/YG2L0qNHgI/IpaVZPS9X1appnr9Syif8PYTYF1juDF4AxpgtwI/ANW6UzQM+cimbD3wIdBORaMfhbkAU8F6R8u8BF4hI4zK1wAtSUmBHeAI1IjKwUZ3ruZ5buIWzyWYb1mhqp04BOmzoKiEBMjJOPpaRoXn+Simf8HcAawWsLeb4OqClG2W3GGOOFVM2Cmjmcl4OsKmY83DjPj6XlgZzYmFf+HpqiJ2WnEs8mdiwk0I/GjWyHiUFvH79rOdedjsUFp74s+b5K6V8wN8BrAZgL+b4QcBWhrLO952vh4wxpoTz/GbPnlf5KW0ib0ZAYTUbjSJ2kIGNF2QMtbskMndugPe8nBITYcwY67nXjh3W65gxQVJ5pbxj6NChiAgdO3Y87Tn79u2jdevWiAg1atRg1apVADRq1AgRQUR46KGHznif+vXrIyLMnDnzlPe8dZ1A5+9nYABFAwuAuFFO3Czr7nknvykyAhgBkODDIbAXXniBb755kPr1+3L55R+Tsiea/fshMhLGjQvCBXoTEzVgKXUGu3fv5qqrrmLDhg3UqlWLhQsX0rp161POe+2113jwwQepV69eme7nresEIn/3wOwU3wOyUXzvytXBM5R1vu98tYlI0YBV9LyTGGPeMMYkGWOSatWqVUJVPPP000/z4IMP0r9/fz7//BPOOy+apk2tNPnZs4MweCmlzmj79u20b9+eDRs2UKdOHZYuXVps8ALIzs7mySefLPM9vXWdQOTvALYO6xlVUS2B9W6UbexIxS9aNpcTz7zWAdFA02LOw437+IQxhvXr1zNw4EA+/PBD2rSJYsIEmDEjCJI1lFKltmXLFtq3b8+mTZto0KAB33//PS1bFv8IvkePHgC8/fbbbNmyxeN7eus6gcrfAWwe0FZEmjgPiEgjoJ3jvZLKRgLXu5SNAG4EFhhjchyHv8UKaIOKlL8FWOvIeiw3xhjsdjsiwowZM5g9ezYREYEwkquU8pU///yT9u3bs3XrVho3bswPP/zAOeecc9rz+/bty6WXXkpeXh4TJkzw+L7euk6g8ncAexPYCswVkWtEpC8wF9gO/Nd5kog0FJF8ERnvPGaMWYOVQv+iiNwuIldhpdA3Bv7tct4+4AXgURF5UEQ6ish0oDPwmK8b6MoYw8MPP0xSUhLp6emEh4cTHh5enlVQSpWz9evX06FDB3bs2MG5557LDz/8QKNGjUosN2nSJADef/99/vjjD4/v763rBCK/BjBjTCZWIPkTmA28D2wBOhtjjrqcKkA4p9Z3GPAO8CTwFdAA6G6M+aXIeY87zrkPmI/Vw7vBGPOFVxt0BsYY7r//fqZOnUqPHj2oUcPvyY9KKR/77bff6NixI3v27KFVq1Z8//331C862f80unbtSocOHSgoKGD8+PElF/DxdQKRv3tgGGPSjDH9jTFxxpiqxphrjTFbi5yz1RgjxpgJRY5nGWMeNMbUNsbEGGMuNcYsLeYeBcaYJ40xDY0x0caYRGNMsk8b5qKwsJDRo0fz0ksv8cADD/Dyyy8TFub3v3qllA9t3bqVTp06sX//fi688EKWLl1K7dq1S3WNyZMnA5CcnMyaNWs8rou3rhNo9FO0HDz99NNMnz6df/3rXzz33HOcmhCplAo127Ztw263kqlfffVVzjrrrFJfo127dnTv3h1jDGPHjvW4Lt66TqDRAFYO7rzzTl599VWeeuopDV5KVRBNmjQ5HrSuv/56Nm0quhiQe5wp8F999RXLli3zuD7euk4g0QDmI3l5eTz77LPk5ORQo0YNRo0apcFLqQqkQYMGfPfdd9hsNnbt2sVVV13Ftm3bSn2dNm3acN111wGUqffkresEEg1gPpCbm8uNN97Iww8/zFdffeXv6iil/KR169YsWLCAatWqkZaWRufOndm5c2eprzNp0iTCwsJYvHgxixcv9rg+3rpOoNAA5mXZ2dn079+fzz77jGnTptFPF7JVqkJLSkrim2++oUqVKmzevJnOnTuzZ8+eUl2jVatW3HTTTQA8/vjjHtfFW9cJFBrAvCgrK4trr72WL7/8kunTp3Pvvff6u0pKqQBw2WWX8eWXXxIbG8uff/5Jly5dSE9PL9U1Jk6cSEREBMuXL+fLL7/0uC7euk4g0ADmRdu2bePnn3/m7bff5q677vJ3dZRSAaRDhw58/vnnREdHs27dOrp27Xo8S9EdzZo149ZbbwVg3LhxnLrBRvleJxBoAPOCnBxr1aoWLVrw119/cdttt/m5RkqpQNS1a1c+/fRToqKiWLNmDd27d+fw4cNulx8/fvzxsrt27fK4Ht66jr9pACujjIwMOnXqdHyiYPXq1f1bIaVUQOvVqxcffvghERERrFy5kp49e5KZmelW2YSEBEaMGFHmOnjrOv6mAawMDh48SJcuXVi9ejXnnXeev6ujlAoS1113He+99x7h4eH8+OOP9OnTh6ysLLfKPv7448TGFt2Eo/S8dR1/kmAe/ywvSUlJZvXq1ScdS09Pp2vXrqxfv55PP/2U3r17+6l2SikVmETkZ2NMkq+ur/t4eCAvL48uXbqwceNG5s2bR7du3fxdJaWUqnA0gHkgMjKSBx98kPr169O5c2d/V0cppSokDWClsH37djZu3EiXLl0YMmSIv6ujlFIVmgYwN23dupXOnTuTmZnJ5s2bqVy5sr+rpJRSFZoGMDfk5OTQvn17jhw5woIFCzR4KaVUANAA5oaNGzcSFxfHkiVLuPDCC/1dHaWUUmgAc4sxhqVLl3L++ef7uypKKaUcdB6YG0RkP1D6jXxK5yygdKt7BrZQak8otQVCqz2h1BYIrfakA/WNMT77zV97YG4wxtTy9T1EZLUvJ/yVt1BqTyi1BUKrPaHUFgjN9vjy+rqUlFJKqaCkAUwppVRQ0gAWON7wdwW8LJTaE0ptgdBqTyi1BbQ9paJJHEoppYKS9sCUUkoFJQ1gXiQiDUQkWUQyROSwiKSISIKbZWNE5FkR2S0iWSKyTETaF3NemIg8KiJbRSRbRH4Tkf7B1hYROVdEpolIqogcdZw7T0Rae7st5dGeYsoMFBEjIju804KTrl0ubRGReiIyQ0T2iEiOiGwRkae825py+39T0/Hzttlx3hYReUVEvJphXMa2TBGRBSJywPGzM/Q055XLZ4DjXj5tT5k/B4wx+uWFLyAW+AtYC1wLXAP8DvwNVHaj/PvAIeAO4CogBcgCLixy3mQgBxgDdAL+CxQCPYOpLcDdQCrwkKMd1wHLgGygTTD+27icXx3YA+wGdgRjW4BGwE7g/4AbgA7ArcCkYGsPIMCPwH5gJNARGAUccPzMSYC05QjwP2AWYIChpznP558B5dUeyvg54LXGVvQv4D6gAGjmcqwxkA88WELZ1o5/4GEuxyKAjcA8l2Pxjh/ciUXKLwJSg6wtZxX94ACqAXbg3WD7tylS5g1gPjAT7wewcmkL8C2wEoj0Zv399LN2ruO8EUXK3+U43tzfbXGcG+Z4bcbpP/DL5TOgHNtTps8BHUL0nr7AcmPMJucBY8wWrN/8rnGjbB7wkUvZfOBDoJuIRDsOdwOigPeKlH8PuEBEGpepBSfXx6dtMcakG8dPq8t5GcCfQD1vNKJInXz9bwOAiLQDbgFGe6fqxdbHp20RkaZYP2svG2PyvFv9Yuvk63+bKMfr4SLlDzlevfU5WJa2YIwpdOMe5fUZAOXQnrJ+DmgA855WWF3totYBLd0ou8UYc6yYslFYv8E4z8sBNhVzHm7cx13l0ZZTiEgN4Hxgg/tVdUu5tEdEIrF6X8+6/qf3svJoSzvHa5aILHQ8/7KLyLsiUtPTip+hTr5uzzrgB2CciCSJSBURuQQYD3xjjPHWz1tZ2lKae5THZ4DzXr5uzylK8zmgAcx7amB1e4s6CNjKUNb5vvP1UNHfWIo5r6zKoy3FeRnrecWLJdyjtMqrPf8CogGvJzq4WR9vtaWu43UG1m/CPbDa1guYLyLe/NzweXsc/196Yg0trsJ6NrMC2Ax4M/mhLG0pzT3K4zPAeS1ft6c4bn8O6FqI3lX0hwqsf4iSiJtl3T3PG3zdlpPfFHkUuBkY7qPei0/bIyLNgMeB64wx2aWvXqn4+t/GGaCWGmOcQ6GLRSQDx/Ac8I07FXVTefysvQm0xXrutQE4D5gIJItIHzeH79zh6/+f5fkZQDnfq9SfAxrAvMdO8b/92Cj+txhXB4HiUlNtLu87X20iIkV+Ayt6XlmVR1uOE5G7gCnAWGPMjFLU013l0Z6XgMXAchGp7jgWBYjj+xxjTFYp6nw65dGWA47XhUXOW+B4vQjvBTCft0dEegEDgS7GmEWO934Qkc1YbeoDzC1lvYtTlra4q7w+A6B82nOcJ58DOoToPeuwxoyLagmsd6NsYxGJLaZsLifGu9dhDVE1LeY83LiPu8qjLQCIyGDgNeA5Y8xkz6pbovJoT0usYSq7y9dArOE4O94bViyvnzMo/rdvsFK2vaU82nOB43VVkfNWOl7Pc6+qJSpLW0pzj/L4DHDey9ftATz/HNAA5j3zgLYi0sR5QEQaYT0Qn+dG2UjgepeyEcCNwAJjTI7j8LdY/zEHFSl/C7DWkSHkDeXRFkTkOuAd4C1jzBgv1f10dfJ1e27Cmsfi+jUfa0+kTsAr3mgI5dOW5Vjz2LoXKe/8vmggKIvyaM8ex+slRcpf6njd6VHNi6+Pp21xV3l9BkD5tKdsnwPenDdQkb+Ayli/8f2OlWLaF/gN60FxFZfzGmLNoxhfpPyHWL+p3441ITMZazLfxUXO+4/j+INYEzKnY/1G3CeY2gK0dxz7Bbgc6/mE8+uiYPy3Kea+M/H+PLDy+jm7FasH9jpwNdbEXzuwBC9N/C3Hn7U4rCC1C2sicyfH6x4gzfU+fm5LB2AA1uReg/VLzwBgQJHzfP4ZUF7toYyfA15rrH4ZsMbjP8Wab3IE+BxoVOScRo5/zAlFjlcCnnf8p8rGypLqWMw9woGxWDtE52DNYh8QbG0BJjjKFve1Ndjac5p7zsTLAaw82wIMxkqjzsFaVeRlvPRh74f/Nw2At4EtjvO2YCV21Augtiw93f+JIueVy2dAebSHMn4O6Gr0SimlgpI+A1NKKRWUNIAppZQKShrAlFJKBSUNYEoppYKSBjCllFJBSQOYUkqpoKQBTCnlFscW9lv9XQ+lnDSAKeUDIvK4iBjHV3MvXE+Dh1JFaABTystERIDhnFgM9w4/VkepkKUBTCnvuxpoDMwC9gK3ikjUmYsopUpLA5hS3ufscb0JvA+cBVxX3IkiUl9EXhKRv0QkW0QOishKERnneL+jiBisBVMbugxLGhGZ6Tinkev3xdxjqeMarseiRORuEflaRLaJSI7j3t+JSA9v/CUo5Wu6FqJSXiQiZwPbgS3GmOYicj7Wat6LjTFXFTk3CWvLlRrAD1jbmMRi7bfU0RgT7ti+Yihwv6PYiy6XWGOM+dxxzhZgljFmaDF1Wgp0MMaIy7HaWCu0/wRsBPYDdbA2d6wB3GGMeavIdbYCGGMauf0XopQP6Y7MSnnXMKw9qmYCGGPWisgvQCcRaWYc26Q7hhQ/wQoWg4wxH7heREQaOMpvBSaIyFDH9xO8VE870NAYs6PIfasBPwLPiMj7xju7SCvlEzqEqJSXOJI3bsfam+ldl7dmAs73nPpgbUMxr2jwAjDGbPdZRa3r5xQNXo7jGcAMrG3j/+HLOihVVhrAlPKezlhbvS80xrju8vsB1i66Q0Uk0nGsreP1m3Ks30lEpJWIzBSRzSKS5Xy2BjznOKWev+qmlDt0CFEp7xnheJ3petAYc0BEvgD6Y+1smwxUd7ztre3sS0VE2gKLsT4DFmFtEX8Yq/d4IVY9o/1RN6XcpQFMKS8QkVrAtY5v54jInNOcOgIrgB1yfO+NXk6h4/V0/5+rF3NsLNZuxp2MMUtd3xCRR7ECmFIBTQOYUt5xKxAF/AysOc05fYEuItIYK+MQoAfwuhvXL3Bcvzh2x2uDom+ISBxwbjFlmgEHiwYvhw5u1Ecpv9MAppR3OBM0RhljVhZ3gohMwur53A5MBLYCfUVkoDFmTpFz6xV5jnYASBSRSkUzA40xR0TkD6CdiLQ0xqx3XCMceB6rp1XUVqC5iCQaY1Jd7jsc6OZuo5XyJ50HplQZiUhHYAnwuzEm8QznNQI2A3uABKxnTQuwMv6+x+qVxQDnAVcZYyJcyj4FPII1X+wHIAf4zRjzheP924C3sYYmPwGygU5YKf3ZQOsi88C6YyWQHAE+BjKAJOAKIAUYAAwzxsx0KbMVdB6YChyahahU2TlX3njrTCc55nR9h2PCsDFmNVYQm4610saDwGCsZ1b/LlL8SayhxqbAo8AkrKQQ57VnYPXsdmENZ96ANUm5HSeet7nW5VusVP71wI1YazfmYAW9r0pssVIBQHtgSimlgpL2wJRSSgUlDWBKKaWCkgYwpZRSQUkDmFJKqaCkAUwppVRQ0gCmlFIqKGkAU0opFZQ0gCmllApKGsCUUkoFJQ1gSimlgtL/A6ecannb7SP0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#4.K最近邻回归\n",
    "\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import make_scorer, mean_squared_error, r2_score\n",
    "\n",
    "# 定义模型\n",
    "knn = KNeighborsRegressor()\n",
    "\n",
    "# 定义要搜索的参数网格\n",
    "param_grid_knn = {\n",
    "    'n_neighbors': [3, 5, 7, 9],  # K值，最近邻居的个数\n",
    "    'weights': ['uniform', 'distance'],  # 权重方式，'uniform'表示等权，'distance'表示距离加权\n",
    "    'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute']  # 搜索算法\n",
    "}\n",
    "\n",
    "# 定义评分方法\n",
    "scorer_knn = make_scorer(r2_score)  # 使用R2分数\n",
    "\n",
    "# 创建GridSearchCV对象\n",
    "grid_search_knn = GridSearchCV(estimator=knn, param_grid=param_grid_knn, scoring=scorer_knn, cv=10)\n",
    "\n",
    "# 拟合数据\n",
    "grid_search_knn.fit(X_train, y_train)\n",
    "\n",
    "# 输出最优参数和对应的分数\n",
    "print(\"Best parameters:\", grid_search_knn.best_params_)\n",
    "print(\"Best R2 score:\", grid_search_knn.best_score_)\n",
    "\n",
    "# 使用最优参数预测\n",
    "best_knn = grid_search_knn.best_estimator_\n",
    "\n",
    "# 使用最优模型进行预测\n",
    "y_pred_train_knn = best_knn.predict(X_train)\n",
    "y_pred_test_knn = best_knn.predict(X_test)\n",
    "\n",
    "# 作图\n",
    "plt.figure(figsize = (6, 6))\n",
    "plt.scatter(y_train, y_pred_train_knn, alpha = 0.5, color = 'b', label = 'training')  # alpha设置透明度\n",
    "plt.scatter(y_test, y_pred_test_knn, alpha = 0.5, color = 'r', label = 'test')\n",
    "\n",
    "# 添加 y=x 线\n",
    "plt.plot([min(-0.01, 0.14), max(-0.01, 0.14)],  # X 轴范围\n",
    "         [min(-0.01, 0.14), max(-0.01, 0.14)],  # Y 轴范围\n",
    "         'k--', label='Y=X')  # 'k--' 是黑色虚线\n",
    "\n",
    "# 设置图例字体大小\n",
    "plt.legend(fontsize=16, frameon=False)\n",
    "plt.xlabel('Actual', fontsize=20)\n",
    "plt.ylabel('Prediction', fontsize=20)\n",
    "\n",
    "# 设置轴刻度的字体大小\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "\n",
    "# 设置X轴和Y轴的范围\n",
    "plt.xlim([-0.01, 0.12])  # 替换min_value_x和max_value_x为您希望的范围的最小值和最大值\n",
    "plt.ylim([-0.01, 0.12])  # 替换min_value_y和max_value_y为您希望的范围的最小值和最大值\n",
    "\n",
    "# 在图的右上角添加文本\n",
    "plt.text(0.95, 0.01, 'KNN', fontsize=24, ha='right', va='bottom', transform=plt.gca().transAxes)\n",
    "\n",
    "plt.savefig('K最近邻回归_网格化搜索_不交叉验证.tiff', dpi = 720, bbox_inches='tight')\n",
    "\n",
    "# 计算R2和均方误差\n",
    "print('training R2 = %.3f' % best_knn.score(X_train, y_train))\n",
    "print('test R2 = %.3f' % best_knn.score(X_test, y_test))\n",
    "rmse_tr_knn = mean_squared_error(y_train, y_pred_train_knn, squared=False)  # 均方根误差\n",
    "rmse_te_knn = mean_squared_error(y_test, y_pred_test_knn, squared=False)\n",
    "print('RMSE(training) %.3f' % rmse_tr_knn)\n",
    "print('RMSE(test) %.3f' % rmse_te_knn)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b821e164",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'alpha_1': 1e-06, 'alpha_2': 0.0001, 'lambda_1': 0.0001, 'lambda_2': 0.0001}\n",
      "Best R2 score: 0.11612809790292318\n",
      "training R2 = 0.353\n",
      "test R2 = 0.233\n",
      "RMSE(training) 0.023\n",
      "RMSE(test) 0.020\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#5. 贝叶斯回归（Bayesian Regression）\n",
    "\n",
    "from sklearn.linear_model import BayesianRidge\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import make_scorer, mean_squared_error, r2_score\n",
    "\n",
    "# 定义贝叶斯回归模型\n",
    "br = BayesianRidge()\n",
    "\n",
    "# 定义要搜索的参数网格\n",
    "param_grid_br = {\n",
    "    'alpha_1': [1e-6, 1e-5, 1e-4],  # Alpha的先验分布的shape参数\n",
    "    'alpha_2': [1e-6, 1e-5, 1e-4],  # Alpha的先验分布的scale参数\n",
    "    'lambda_1': [1e-6, 1e-5, 1e-4],  # Lambda的先验分布的shape参数\n",
    "    'lambda_2': [1e-6, 1e-5, 1e-4]   # Lambda的先验分布的scale参数\n",
    "}\n",
    "\n",
    "# 定义评分方法\n",
    "scorer_br = make_scorer(r2_score)  # 使用R2分数\n",
    "\n",
    "# 创建GridSearchCV对象\n",
    "grid_search_br = GridSearchCV(estimator=br, param_grid=param_grid_br, scoring=scorer_br, cv=10)\n",
    "\n",
    "# 拟合数据\n",
    "grid_search_br.fit(X_train, y_train)\n",
    "\n",
    "# 输出最优参数和对应的分数\n",
    "print(\"Best parameters:\", grid_search_br.best_params_)\n",
    "print(\"Best R2 score:\", grid_search_br.best_score_)\n",
    "\n",
    "# 使用最优参数预测\n",
    "best_br = grid_search_br.best_estimator_\n",
    "\n",
    "# 使用最优模型进行预测\n",
    "y_pred_train_br = best_br.predict(X_train)\n",
    "y_pred_test_br = best_br.predict(X_test)\n",
    "\n",
    "# 作图\n",
    "plt.figure(figsize = (6, 6))\n",
    "plt.scatter(y_train, y_pred_train_br, alpha = 0.5, color = 'b', label = 'training')  # alpha设置透明度\n",
    "plt.scatter(y_test, y_pred_test_br, alpha = 0.5, color = 'r', label = 'test')\n",
    "\n",
    "# 添加 y=x 线\n",
    "plt.plot([min(-0.01, 0.14), max(-0.01, 0.14)],  # X 轴范围\n",
    "         [min(-0.01, 0.14), max(-0.01, 0.14)],  # Y 轴范围\n",
    "         'k--', label='Y=X')  # 'k--' 是黑色虚线\n",
    "\n",
    "# 设置图例字体大小\n",
    "plt.legend(fontsize=16, frameon=False)\n",
    "plt.xlabel('Actual', fontsize=20)\n",
    "plt.ylabel('Prediction', fontsize=20)\n",
    "\n",
    "# 设置轴刻度的字体大小\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "\n",
    "# 设置X轴和Y轴的范围\n",
    "plt.xlim([-0.01, 0.12])  # 替换min_value_x和max_value_x为您希望的范围的最小值和最大值\n",
    "plt.ylim([-0.01, 0.12])  # 替换min_value_y和max_value_y为您希望的范围的最小值和最大值\n",
    "\n",
    "# 在图的右上角添加文本\n",
    "plt.text(0.95, 0.01, 'BayesianRidge', fontsize=24, ha='right', va='bottom', transform=plt.gca().transAxes)\n",
    "\n",
    "plt.savefig('贝叶斯回归_网格化搜索_不交叉验证.tiff', dpi = 720, bbox_inches='tight')\n",
    "\n",
    "# 计算R2和均方误差\n",
    "print('training R2 = %.3f' % best_br.score(X_train, y_train))\n",
    "print('test R2 = %.3f' % best_br.score(X_test, y_test))\n",
    "rmse_tr_br = mean_squared_error(y_train, y_pred_train_br, squared=False)  # 均方根误差\n",
    "rmse_te_br = mean_squared_error(y_test, y_pred_test_br, squared=False)\n",
    "print('RMSE(training) %.3f' % rmse_tr_br)\n",
    "print('RMSE(test) %.3f' % rmse_te_br)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "67aabe9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'activation': 'relu', 'alpha': 0.0001, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'solver': 'lbfgs'}\n",
      "Best R2 score: 0.2902451569473607\n",
      "training R2 = 0.700\n",
      "test R2 = 0.373\n",
      "RMSE(training) 0.016\n",
      "RMSE(test) 0.018\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#6. 神经网络回归（Neural Network Regression）\n",
    "\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import make_scorer, mean_squared_error, r2_score\n",
    "\n",
    "# 定义神经网络回归模型\n",
    "mlp = MLPRegressor(max_iter=2000)\n",
    "\n",
    "# 定义要搜索的参数网格\n",
    "param_grid_mlp = {\n",
    "    'hidden_layer_sizes': [(50,), (100,), (50, 50)],  # 隐藏层的结构\n",
    "    'activation': ['relu', 'tanh'],  # 激活函数\n",
    "    'solver': ['adam', 'lbfgs'],  # 优化算法\n",
    "    'alpha': [0.0001, 0.001],  # L2正则化参数\n",
    "    'learning_rate': ['constant', 'adaptive'],  # 学习率调整方式\n",
    "}\n",
    "\n",
    "# 定义评分方法\n",
    "scorer_mlp = make_scorer(r2_score)  # 使用R2分数\n",
    "\n",
    "# 创建GridSearchCV对象\n",
    "grid_search_mlp = GridSearchCV(estimator=mlp, param_grid=param_grid_mlp, scoring=scorer_mlp, cv=10)\n",
    "\n",
    "# 拟合数据\n",
    "grid_search_mlp.fit(X_train, y_train)\n",
    "\n",
    "# 输出最优参数和对应的分数\n",
    "print(\"Best parameters:\", grid_search_mlp.best_params_)\n",
    "print(\"Best R2 score:\", grid_search_mlp.best_score_)\n",
    "\n",
    "# 使用最优参数预测\n",
    "best_mlp = grid_search_mlp.best_estimator_\n",
    "\n",
    "# 使用最优模型进行预测\n",
    "y_pred_train_mlp = best_mlp.predict(X_train)\n",
    "y_pred_test_mlp = best_mlp.predict(X_test)\n",
    "\n",
    "# 作图\n",
    "plt.figure(figsize = (6, 6))\n",
    "plt.scatter(y_train, y_pred_train_mlp, alpha = 0.5, color = 'b', label = 'training')  # alpha设置透明度\n",
    "plt.scatter(y_test, y_pred_test_mlp, alpha = 0.5, color = 'r', label = 'test')\n",
    "\n",
    "# 添加 y=x 线\n",
    "plt.plot([min(-0.01, 0.14), max(-0.01, 0.14)],  # X 轴范围\n",
    "         [min(-0.01, 0.14), max(-0.01, 0.14)],  # Y 轴范围\n",
    "         'k--', label='Y=X')  # 'k--' 是黑色虚线\n",
    "\n",
    "# 设置图例字体大小\n",
    "plt.legend(fontsize=16, frameon=False)\n",
    "plt.xlabel('Actual', fontsize=20)\n",
    "plt.ylabel('Prediction', fontsize=20)\n",
    "\n",
    "# 设置轴刻度的字体大小\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "\n",
    "# 设置X轴和Y轴的范围\n",
    "plt.xlim([-0.01, 0.12])  # 替换min_value_x和max_value_x为您希望的范围的最小值和最大值\n",
    "plt.ylim([-0.01, 0.12])  # 替换min_value_y和max_value_y为您希望的范围的最小值和最大值\n",
    "\n",
    "# 在图的右上角添加文本\n",
    "plt.text(0.95, 0.01, 'MLP', fontsize=24, ha='right', va='bottom', transform=plt.gca().transAxes)\n",
    "\n",
    "plt.savefig('神经网络回归_网格化搜索_不交叉验证.tiff', dpi = 720, bbox_inches='tight')\n",
    "\n",
    "# 计算R2和均方误差\n",
    "print('training R2 = %.3f' % best_mlp.score(X_train, y_train))\n",
    "print('test R2 = %.3f' % best_mlp.score(X_test, y_test))\n",
    "rmse_tr_mlp = mean_squared_error(y_train, y_pred_train_mlp, squared=False)  # 均方根误差\n",
    "rmse_te_mlp = mean_squared_error(y_test, y_pred_test_mlp, squared=False)\n",
    "print('RMSE(training) %.3f' % rmse_tr_mlp)\n",
    "print('RMSE(test) %.3f' % rmse_te_mlp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "95838fe7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'alpha': 1, 'solver': 'sag'}\n",
      "Best R2 score: 0.20857625317238232\n",
      "training R2 = 0.349\n",
      "test R2 = 0.236\n",
      "RMSE(training) 0.023\n",
      "RMSE(test) 0.020\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#7.广义线性模型（Generalized Linear Models, GLM）\n",
    "\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import make_scorer, mean_squared_error, r2_score\n",
    "\n",
    "# 定义广义线性模型（岭回归）\n",
    "ridge = Ridge()\n",
    "\n",
    "# 定义要搜索的参数网格\n",
    "param_grid_ridge = {\n",
    "    'alpha': [0.01, 0.1, 1, 10, 100],  # 正则化强度，控制模型的复杂度\n",
    "    'solver': ['auto', 'svd', 'cholesky', 'sparse_cg', 'sag', 'saga']  # 优化算法\n",
    "}\n",
    "\n",
    "# 定义评分方法\n",
    "scorer_ridge = make_scorer(r2_score)  # 使用R2分数\n",
    "\n",
    "# 创建GridSearchCV对象\n",
    "grid_search_ridge = GridSearchCV(estimator=ridge, param_grid=param_grid_ridge, scoring=scorer_ridge, cv=10)\n",
    "\n",
    "# 拟合数据\n",
    "grid_search_ridge.fit(X_train, y_train)\n",
    "\n",
    "# 输出最优参数和对应的分数\n",
    "print(\"Best parameters:\", grid_search_ridge.best_params_)\n",
    "print(\"Best R2 score:\", grid_search_ridge.best_score_)\n",
    "\n",
    "# 使用最优参数预测\n",
    "best_ridge = grid_search_ridge.best_estimator_\n",
    "\n",
    "# 使用最优模型进行预测\n",
    "y_pred_train_ridge = best_ridge.predict(X_train)\n",
    "y_pred_test_ridge = best_ridge.predict(X_test)\n",
    "\n",
    "# 作图\n",
    "plt.figure(figsize = (6, 6))\n",
    "plt.scatter(y_train, y_pred_train_ridge, alpha = 0.5, color = 'b', label = 'training')  # alpha设置透明度\n",
    "plt.scatter(y_test, y_pred_test_ridge, alpha = 0.5, color = 'r', label = 'test')\n",
    "\n",
    "# 添加 y=x 线\n",
    "plt.plot([min(-0.01, 0.14), max(-0.01, 0.14)],  # X 轴范围\n",
    "         [min(-0.01, 0.14), max(-0.01, 0.14)],  # Y 轴范围\n",
    "         'k--', label='Y=X')  # 'k--' 是黑色虚线\n",
    "\n",
    "# 设置图例字体大小\n",
    "plt.legend(fontsize=16, frameon=False)\n",
    "plt.xlabel('Actual', fontsize=20)\n",
    "plt.ylabel('Prediction', fontsize=20)\n",
    "\n",
    "# 设置轴刻度的字体大小\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "\n",
    "# 设置X轴和Y轴的范围\n",
    "plt.xlim([-0.01, 0.12])  # 替换min_value_x和max_value_x为您希望的范围的最小值和最大值\n",
    "plt.ylim([-0.01, 0.12])  # 替换min_value_y和max_value_y为您希望的范围的最小值和最大值\n",
    "\n",
    "# 在图的右上角添加文本\n",
    "plt.text(0.95, 0.01, 'Ridge', fontsize=24, ha='right', va='bottom', transform=plt.gca().transAxes)\n",
    "\n",
    "plt.savefig('广义线性模型_岭回归_网格化搜索.tiff', dpi=720, bbox_inches='tight')\n",
    "\n",
    "# 计算R2和均方误差\n",
    "print('training R2 = %.3f' % best_ridge.score(X_train, y_train))\n",
    "print('test R2 = %.3f' % best_ridge.score(X_test, y_test))\n",
    "rmse_tr_ridge = mean_squared_error(y_train, y_pred_train_ridge, squared=False)  # 均方根误差\n",
    "rmse_te_ridge = mean_squared_error(y_test, y_pred_test_ridge, squared=False)\n",
    "print('RMSE(training) %.3f' % rmse_tr_ridge)\n",
    "print('RMSE(test) %.3f' % rmse_te_ridge)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c08092b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'alpha': 0.01, 'l1_ratio': 0.1}\n",
      "Best R2 score: 0.0720550944944576\n",
      "training R2 = 0.234\n",
      "test R2 = 0.234\n",
      "RMSE(training) 0.025\n",
      "RMSE(test) 0.020\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#8. 弹性网络回归（Elastic Net Regression）\n",
    "\n",
    "\n",
    "\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import make_scorer, mean_squared_error, r2_score\n",
    "\n",
    "# 定义弹性网络回归模型\n",
    "elastic_net = ElasticNet(max_iter=5000)\n",
    "\n",
    "# 定义要搜索的参数网格\n",
    "param_grid_en = {\n",
    "    'alpha': [0.01, 0.1, 1, 10, 100],  # 正则化强度，控制模型的复杂度\n",
    "    'l1_ratio': [0.1, 0.5, 0.7, 0.9, 1.0]  # L1 和 L2 的比例，1.0表示Lasso，0.0表示Ridge\n",
    "}\n",
    "\n",
    "# 定义评分方法\n",
    "scorer_en = make_scorer(r2_score)  # 使用R2分数\n",
    "\n",
    "# 创建GridSearchCV对象\n",
    "grid_search_en = GridSearchCV(estimator=elastic_net, param_grid=param_grid_en, scoring=scorer_en, cv=10)\n",
    "\n",
    "# 拟合数据\n",
    "grid_search_en.fit(X_train, y_train)\n",
    "\n",
    "# 输出最优参数和对应的分数\n",
    "print(\"Best parameters:\", grid_search_en.best_params_)\n",
    "print(\"Best R2 score:\", grid_search_en.best_score_)\n",
    "\n",
    "# 使用最优参数预测\n",
    "best_en = grid_search_en.best_estimator_\n",
    "\n",
    "# 使用最优模型进行预测\n",
    "y_pred_train_en = best_en.predict(X_train)\n",
    "y_pred_test_en = best_en.predict(X_test)\n",
    "\n",
    "# 作图\n",
    "plt.figure(figsize = (6, 6))\n",
    "plt.scatter(y_train, y_pred_train_en, alpha = 0.5, color = 'b', label = 'training')  # alpha设置透明度\n",
    "plt.scatter(y_test, y_pred_test_en, alpha = 0.5, color = 'r', label = 'test')\n",
    "\n",
    "# 添加 y=x 线\n",
    "plt.plot([min(-0.01, 0.14), max(-0.01, 0.14)],  # X 轴范围\n",
    "         [min(-0.01, 0.14), max(-0.01, 0.14)],  # Y 轴范围\n",
    "         'k--', label='Y=X')  # 'k--' 是黑色虚线\n",
    "\n",
    "# 设置图例字体大小\n",
    "plt.legend(fontsize=16, frameon=False)\n",
    "plt.xlabel('Actual', fontsize=20)\n",
    "plt.ylabel('Prediction', fontsize=20)\n",
    "\n",
    "# 设置轴刻度的字体大小\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "\n",
    "# 设置X轴和Y轴的范围\n",
    "plt.xlim([-0.01, 0.12])  # 替换min_value_x和max_value_x为您希望的范围的最小值和最大值\n",
    "plt.ylim([-0.01, 0.12])  # 替换min_value_y和max_value_y为您希望的范围的最小值和最大值\n",
    "\n",
    "# 在图的右上角添加文本\n",
    "plt.text(0.95, 0.01, 'ElasticNet', fontsize=24, ha='right', va='bottom', transform=plt.gca().transAxes)\n",
    "\n",
    "plt.savefig('弹性网络回归_网格化搜索.tiff', dpi=720, bbox_inches='tight')\n",
    "\n",
    "# 计算R2和均方误差\n",
    "print('training R2 = %.3f' % best_en.score(X_train, y_train))\n",
    "print('test R2 = %.3f' % best_en.score(X_test, y_test))\n",
    "rmse_tr_en = mean_squared_error(y_train, y_pred_train_en, squared=False)  # 均方根误差\n",
    "rmse_te_en = mean_squared_error(y_test, y_pred_test_en, squared=False)\n",
    "print('RMSE(training) %.3f' % rmse_tr_en)\n",
    "print('RMSE(test) %.3f' % rmse_te_en)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1fbc114f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'C': 1, 'epsilon': 0.01, 'gamma': 1, 'kernel': 'rbf'}\n",
      "Best R2 score: 0.3218579193917013\n",
      "training R2 = 0.674\n",
      "test R2 = 0.650\n",
      "RMSE(training) 0.016\n",
      "RMSE(test) 0.014\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#9. 核回归（Kernel Regression）\n",
    "\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import make_scorer, mean_squared_error, r2_score\n",
    "\n",
    "# 定义支持向量回归模型（模拟核回归）\n",
    "svr = SVR()\n",
    "\n",
    "# 定义要搜索的参数网格\n",
    "param_grid_svr = {\n",
    "    'kernel': ['rbf'],  # 核函数，使用径向基核RBF\n",
    "    'C': [0.1, 1, 10, 100],  # 正则化参数，控制模型的复杂度\n",
    "    'gamma': ['scale', 'auto', 0.001, 0.01, 0.1, 1],  # 核函数的系数\n",
    "    'epsilon': [0.01, 0.1, 0.2]  # epsilon损失\n",
    "}\n",
    "\n",
    "# 定义评分方法\n",
    "scorer_svr = make_scorer(r2_score)  # 使用R2分数\n",
    "\n",
    "# 创建GridSearchCV对象\n",
    "grid_search_svr = GridSearchCV(estimator=svr, param_grid=param_grid_svr, scoring=scorer_svr, cv=10)\n",
    "\n",
    "# 拟合数据\n",
    "grid_search_svr.fit(X_train, y_train)\n",
    "\n",
    "# 输出最优参数和对应的分数\n",
    "print(\"Best parameters:\", grid_search_svr.best_params_)\n",
    "print(\"Best R2 score:\", grid_search_svr.best_score_)\n",
    "\n",
    "# 使用最优参数预测\n",
    "best_svr = grid_search_svr.best_estimator_\n",
    "\n",
    "# 使用最优模型进行预测\n",
    "y_pred_train_svr = best_svr.predict(X_train)\n",
    "y_pred_test_svr = best_svr.predict(X_test)\n",
    "\n",
    "# 作图\n",
    "plt.figure(figsize = (6, 6))\n",
    "plt.scatter(y_train, y_pred_train_svr, alpha = 0.5, color = 'b', label = 'training')  # alpha设置透明度\n",
    "plt.scatter(y_test, y_pred_test_svr, alpha = 0.5, color = 'r', label = 'test')\n",
    "\n",
    "# 添加 y=x 线\n",
    "plt.plot([min(-0.01, 0.14), max(-0.01, 0.14)],  # X 轴范围\n",
    "         [min(-0.01, 0.14), max(-0.01, 0.14)],  # Y 轴范围\n",
    "         'k--', label='Y=X')  # 'k--' 是黑色虚线\n",
    "\n",
    "# 设置图例字体大小\n",
    "plt.legend(fontsize=16, frameon=False)\n",
    "plt.xlabel('Actual', fontsize=20)\n",
    "plt.ylabel('Prediction', fontsize=20)\n",
    "\n",
    "# 设置轴刻度的字体大小\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "\n",
    "# 设置X轴和Y轴的范围\n",
    "plt.xlim([-0.01, 0.12])  # 替换min_value_x和max_value_x为您希望的范围的最小值和最大值\n",
    "plt.ylim([-0.01, 0.12])  # 替换min_value_y和max_value_y为您希望的范围的最小值和最大值\n",
    "\n",
    "# 在图的右上角添加文本\n",
    "plt.text(0.95, 0.01, 'Kernel Regression (SVR)', fontsize=24, ha='right', va='bottom', transform=plt.gca().transAxes)\n",
    "\n",
    "plt.savefig('核回归_SVR_网格化搜索.tiff', dpi=720, bbox_inches='tight')\n",
    "\n",
    "# 计算R2和均方误差\n",
    "print('training R2 = %.3f' % best_svr.score(X_train, y_train))\n",
    "print('test R2 = %.3f' % best_svr.score(X_test, y_test))\n",
    "rmse_tr_svr = mean_squared_error(y_train, y_pred_train_svr, squared=False)  # 均方根误差\n",
    "rmse_te_svr = mean_squared_error(y_test, y_pred_test_svr, squared=False)\n",
    "print('RMSE(training) %.3f' % rmse_tr_svr)\n",
    "print('RMSE(test) %.3f' % rmse_te_svr)\n"
   ]
  }
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