交叉驗證(Cross Validation).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 交叉驗證(Cross-Validation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 先引入我們所要的packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "import math\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 資料前處理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>測站氣壓(hPa)</th>\n",
       "      <th>氣溫(℃)</th>\n",
       "      <th>露點溫度(℃)</th>\n",
       "      <th>相對溼度(%)</th>\n",
       "      <th>風速(m/s)</th>\n",
       "      <th>時間(days)</th>\n",
       "      <th>日照時數(hour)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1024.1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>85.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1021.2</td>\n",
       "      <td>19.9</td>\n",
       "      <td>13.9</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1018.6</td>\n",
       "      <td>21.0</td>\n",
       "      <td>13.8</td>\n",
       "      <td>65.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1017.1</td>\n",
       "      <td>21.1</td>\n",
       "      <td>14.5</td>\n",
       "      <td>68.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1017.4</td>\n",
       "      <td>22.1</td>\n",
       "      <td>16.2</td>\n",
       "      <td>71.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>361</td>\n",
       "      <td>1013.2</td>\n",
       "      <td>19.7</td>\n",
       "      <td>16.6</td>\n",
       "      <td>83.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>362.0</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>362</td>\n",
       "      <td>1012.4</td>\n",
       "      <td>20.2</td>\n",
       "      <td>16.9</td>\n",
       "      <td>82.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>363.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>363</td>\n",
       "      <td>1010.2</td>\n",
       "      <td>21.6</td>\n",
       "      <td>17.6</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>364.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>364</td>\n",
       "      <td>1022.3</td>\n",
       "      <td>12.7</td>\n",
       "      <td>7.4</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>365</td>\n",
       "      <td>1025.3</td>\n",
       "      <td>9.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>366.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>366 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     測站氣壓(hPa)  氣溫(℃)  露點溫度(℃)  相對溼度(%)  風速(m/s)  時間(days)  日照時數(hour)\n",
       "0       1024.1   16.0     13.5     85.0      3.9       1.0         0.1\n",
       "1       1021.2   19.9     13.9     70.0      3.8       2.0         9.8\n",
       "2       1018.6   21.0     13.8     65.0      3.1       3.0         9.2\n",
       "3       1017.1   21.1     14.5     68.0      2.6       4.0         9.4\n",
       "4       1017.4   22.1     16.2     71.0      3.7       5.0         6.1\n",
       "..         ...    ...      ...      ...      ...       ...         ...\n",
       "361     1013.2   19.7     16.6     83.0      1.5     362.0         2.1\n",
       "362     1012.4   20.2     16.9     82.0      4.1     363.0         0.1\n",
       "363     1010.2   21.6     17.6     79.0      1.2     364.0         2.0\n",
       "364     1022.3   12.7      7.4     70.0      3.0     365.0         0.0\n",
       "365     1025.3    9.9      3.1     62.0      2.5     366.0         0.0\n",
       "\n",
       "[366 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 設定放置檔案的資料夾路徑\n",
    "file_path=\"C:\\\\Users\\\\user\\\\Downloads\\\\2020天氣\"\n",
    "os.chdir(file_path)                                                \n",
    "file_list = os.listdir()\n",
    "# 先讀取第一個csv檔，並以DataFrame的形式存取\n",
    "df =  pd.read_csv(file_path+\"\\\\\"+file_list[0])\n",
    "# 把不要的行數過濾掉\n",
    "df = df.reindex(df.index.drop(0)).reset_index(drop=True)\n",
    "# 擷取我們想要的欄位，並以float形式讀取\n",
    "data=pd.concat([df['測站氣壓(hPa)'].astype(float),df['氣溫(℃)'].astype(float),\n",
    "                df['露點溫度(℃)'].astype(float),df['相對溼度(%)'].astype(float),\n",
    "                df['風速(m/s)'].astype(float),df['日照時數(hour)'].astype(float)], axis=1)\n",
    "# 以和上述相同方式讀取所有csv檔\n",
    "for i in range(1,12):\n",
    "    df =  pd.read_csv(file_path+\"\\\\\"+file_list[i])\n",
    "    df = df.reindex(df.index.drop(0)).reset_index(drop=True)\n",
    "    temp_data=pd.concat([df['測站氣壓(hPa)'].astype(float),df['氣溫(℃)'].astype(float),\n",
    "                         df['露點溫度(℃)'].astype(float),df['相對溼度(%)'].astype(float),\n",
    "                         df['風速(m/s)'].astype(float),df['日照時數(hour)'].astype(float)], axis=1)\n",
    "    # 每讀完一個資料夾就把這次讀取的資料和上一次作合併\n",
    "    data=pd.concat([data,temp_data],axis=0)\n",
    "# 重新排列我們的資料\n",
    "data=data.reset_index(drop=True)\n",
    "# 插入時間資料\n",
    "data.insert(5,\"時間(days)\",np.linspace(1,366,366)) \n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 繪圖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (18,18)\n",
    "fig, axs = plt.subplots(3, 2)\n",
    "# 時間和測站壓力關係圖\n",
    "axs[0, 0].plot(data.iloc[:,5], data.iloc[:,0], 'tab:blue')\n",
    "axs[0, 0].set_title(\"Pressure\")\n",
    "axs[0, 0].set_xlabel('Time(days)')\n",
    "axs[0, 0].set_ylabel('Pressure(hPa)')\n",
    "# 時間和氣溫關係圖\n",
    "axs[0, 1].plot(data.iloc[:,5], data.iloc[:,1], 'tab:orange')\n",
    "axs[0, 1].set_title('Temperature')\n",
    "axs[0, 1].set_xlabel('Time(days)')\n",
    "axs[0, 1].set_ylabel('Temperature(C)')\n",
    "#時間和露點溫度關係圖\n",
    "axs[1, 0].plot(data.iloc[:,5], data.iloc[:,2], 'tab:red')\n",
    "axs[1, 0].set_title('Dew point temperature')\n",
    "axs[1, 0].set_xlabel('Time(days)')\n",
    "axs[1, 0].set_ylabel('Dew point temperature(C)')\n",
    "# 時間和相對溼度關係圖\n",
    "axs[1, 1].plot(data.iloc[:,5], data.iloc[:,3], 'tab:green')\n",
    "axs[1, 1].set_title('Relative humidity')\n",
    "axs[1, 1].set_xlabel('Time(days)')\n",
    "axs[1, 1].set_ylabel('Relative humidity(%)')\n",
    "# 時間和風速關係圖\n",
    "axs[2, 0].plot(data.iloc[:,5], data.iloc[:,4], 'tab:olive')\n",
    "axs[2, 0].set_title('Wind speed')\n",
    "axs[2, 0].set_xlabel('Time(days)')\n",
    "axs[2, 0].set_ylabel('Wind speed(m/s)')\n",
    "# 時間和日照時間關係圖\n",
    "axs[2, 1].plot(data.iloc[:,5], data.iloc[:,6], 'tab:purple')\n",
    "axs[2, 1].set_title('Insolation duration')\n",
    "axs[2, 1].set_xlabel('Time(days)')\n",
    "axs[2, 1].set_ylabel('Insolation duration(hr)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 標準化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>測站氣壓(hPa)</th>\n",
       "      <th>氣溫(℃)</th>\n",
       "      <th>露點溫度(℃)</th>\n",
       "      <th>相對溼度(%)</th>\n",
       "      <th>風速(m/s)</th>\n",
       "      <th>時間(days)</th>\n",
       "      <th>日照時數(hour)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.224235</td>\n",
       "      <td>-1.543547</td>\n",
       "      <td>-1.174842</td>\n",
       "      <td>0.971111</td>\n",
       "      <td>1.502002</td>\n",
       "      <td>-1.727325</td>\n",
       "      <td>-1.039450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.747353</td>\n",
       "      <td>-0.811762</td>\n",
       "      <td>-1.087578</td>\n",
       "      <td>-0.398372</td>\n",
       "      <td>1.404670</td>\n",
       "      <td>-1.717860</td>\n",
       "      <td>1.764909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.319804</td>\n",
       "      <td>-0.605361</td>\n",
       "      <td>-1.109394</td>\n",
       "      <td>-0.854867</td>\n",
       "      <td>0.723344</td>\n",
       "      <td>-1.708395</td>\n",
       "      <td>1.591443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.073141</td>\n",
       "      <td>-0.586597</td>\n",
       "      <td>-0.956683</td>\n",
       "      <td>-0.580970</td>\n",
       "      <td>0.236682</td>\n",
       "      <td>-1.698930</td>\n",
       "      <td>1.649265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.122474</td>\n",
       "      <td>-0.398960</td>\n",
       "      <td>-0.585812</td>\n",
       "      <td>-0.307074</td>\n",
       "      <td>1.307337</td>\n",
       "      <td>-1.689466</td>\n",
       "      <td>0.695205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>361</td>\n",
       "      <td>0.431817</td>\n",
       "      <td>-0.849289</td>\n",
       "      <td>-0.498548</td>\n",
       "      <td>0.788513</td>\n",
       "      <td>-0.833973</td>\n",
       "      <td>1.689466</td>\n",
       "      <td>-0.461232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>362</td>\n",
       "      <td>0.300264</td>\n",
       "      <td>-0.755470</td>\n",
       "      <td>-0.433100</td>\n",
       "      <td>0.697214</td>\n",
       "      <td>1.696666</td>\n",
       "      <td>1.698930</td>\n",
       "      <td>-1.039450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>363</td>\n",
       "      <td>-0.061508</td>\n",
       "      <td>-0.492778</td>\n",
       "      <td>-0.280389</td>\n",
       "      <td>0.423317</td>\n",
       "      <td>-1.125970</td>\n",
       "      <td>1.708395</td>\n",
       "      <td>-0.490143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>364</td>\n",
       "      <td>1.928239</td>\n",
       "      <td>-2.162749</td>\n",
       "      <td>-2.505615</td>\n",
       "      <td>-0.398372</td>\n",
       "      <td>0.626011</td>\n",
       "      <td>1.717860</td>\n",
       "      <td>-1.068361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>365</td>\n",
       "      <td>2.421565</td>\n",
       "      <td>-2.688134</td>\n",
       "      <td>-3.443700</td>\n",
       "      <td>-1.128763</td>\n",
       "      <td>0.139350</td>\n",
       "      <td>1.727325</td>\n",
       "      <td>-1.068361</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>366 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     測站氣壓(hPa)     氣溫(℃)   露點溫度(℃)   相對溼度(%)   風速(m/s)  時間(days)  日照時數(hour)\n",
       "0     2.224235 -1.543547 -1.174842  0.971111  1.502002 -1.727325   -1.039450\n",
       "1     1.747353 -0.811762 -1.087578 -0.398372  1.404670 -1.717860    1.764909\n",
       "2     1.319804 -0.605361 -1.109394 -0.854867  0.723344 -1.708395    1.591443\n",
       "3     1.073141 -0.586597 -0.956683 -0.580970  0.236682 -1.698930    1.649265\n",
       "4     1.122474 -0.398960 -0.585812 -0.307074  1.307337 -1.689466    0.695205\n",
       "..         ...       ...       ...       ...       ...       ...         ...\n",
       "361   0.431817 -0.849289 -0.498548  0.788513 -0.833973  1.689466   -0.461232\n",
       "362   0.300264 -0.755470 -0.433100  0.697214  1.696666  1.698930   -1.039450\n",
       "363  -0.061508 -0.492778 -0.280389  0.423317 -1.125970  1.708395   -0.490143\n",
       "364   1.928239 -2.162749 -2.505615 -0.398372  0.626011  1.717860   -1.068361\n",
       "365   2.421565 -2.688134 -3.443700 -1.128763  0.139350  1.727325   -1.068361\n",
       "\n",
       "[366 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(0,7):\n",
    "    temp_array=data.iloc[:,i].values\n",
    "    data.iloc[:,i]=(temp_array-np.mean(temp_array))/np.std(temp_array)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 梯度下降"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def refresh(X,y,w,a):\n",
    "    # 這裡可以比照公式\n",
    "    dJ=(X.dot(w.T)-y).dot(X)/len(y)\n",
    "    # 更新權重\n",
    "    new_w=w-a*dJ\n",
    "    return new_w"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mean Square Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_square_error(x,y,w):\n",
    "    pred_y=x.dot(w.T)\n",
    "    error=sum((pred_y-y)**2)/len(y)\n",
    "    return error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mean Absolute Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_absolute_error(x,y,w):\n",
    "    pred_y=x.dot(w.T)\n",
    "    error=sum(abs(pred_y-y))/len(y)\n",
    "    return error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# R square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def r_square(x,y,w):\n",
    "    pred_y=x.dot(w.T)\n",
    "    r_2=1-((sum((pred_y-y)**2)/len(y))/(sum((np.mean(y)-y)**2)/len(y)))\n",
    "    return r_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 回歸分析(2次方特徵轉換)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Regression(a,train_x,train_y,test_x,test_y,p):\n",
    "    # a：學習速度參數\n",
    "    # X：資料矩陣\n",
    "    # w：權重\n",
    "    # feat_num：原本特徵的長度\n",
    "    feat_num=len(test_x[0])\n",
    "    # 創建訓練集和測試集之二次特徵轉換矩陣\n",
    "    # 數量為1+feat_num+(1+feat_num)*feat_num/2個\n",
    "    train_X=np.ones((len(train_x),int(1+feat_num+(1+feat_num)*feat_num/2)))\n",
    "    test_X=np.ones((len(test_x),int(1+feat_num+(1+feat_num)*feat_num/2)))\n",
    "    # 標號0的欄位特徵接為1\n",
    "    # 標號1~feat_num欄位的特徵\n",
    "    # 分別對應原本的資料\n",
    "    train_X[:,1:feat_num+1]=train_x\n",
    "    test_X[:,1:feat_num+1]=test_x\n",
    "    # 擴增特徵大小到二維\n",
    "    com=0\n",
    "    for i in range(0,feat_num):\n",
    "        for j in range(0,i+1):\n",
    "            train_X[:,feat_num+1+com]=train_x[:,i]*train_x[:,j]\n",
    "            test_X[:,feat_num+1+com]=test_x[:,i]*test_x[:,j]\n",
    "            com+=1\n",
    "    # 初始化我們的權重\n",
    "    w=np.zeros(int(1+feat_num+(1+feat_num)*feat_num/2))\n",
    "    # 跑迴圈執行演算法\n",
    "    dis=1\n",
    "    comp_MSE=1\n",
    "    while(dis>0.00001):\n",
    "        T=1000\n",
    "        for t in range(0,T):\n",
    "            w=refresh(train_X,train_y,w,a)\n",
    "        temp_MSE=mean_square_error(train_X,train_y,w)\n",
    "        dis=abs(comp_MSE-temp_MSE)\n",
    "        comp_MSE=temp_MSE\n",
    "    # 計算訓練集錯誤率\n",
    "    train_MSE=mean_square_error(train_X,train_y,w)\n",
    "    train_MAE=mean_absolute_error(train_X,train_y,w)\n",
    "    train_r2=r_square(train_X,train_y,w)\n",
    "    # 計算測試集錯誤率\n",
    "    test_MSE=mean_square_error(test_X,test_y,w)\n",
    "    test_MAE=mean_absolute_error(test_X,test_y,w)\n",
    "    test_r2=r_square(test_X,test_y,w)\n",
    "    # 把結果輸出\n",
    "    print(\"The training set:\")\n",
    "    print(\"MSE: \",train_MSE,\" / MAE: \",train_MAE,\" / R^2: \",train_r2)\n",
    "    print(\"The testing set:\")\n",
    "    print(\"MSE: \",test_MSE,\" / MAE: \",test_MAE,\" / R^2: \",test_r2)\n",
    "    # 如果p=1，就繪圖\n",
    "    if(p==1):\n",
    "        plt.rcParams[\"figure.figsize\"] = (18,18)\n",
    "        plt.plot([-2,4],[-2,4],\"g-\",linewidth=6,label=\"straight line\")\n",
    "        plt.plot(train_y,train_X.dot(w.T),\"bo\",markersize=9,label=\"trainging data\")\n",
    "        plt.plot(test_y,test_X.dot(w.T),\"ro\",markersize=9,label=\"testing data\")\n",
    "        plt.xticks(fontsize=30)\n",
    "        plt.yticks(fontsize=30)\n",
    "        plt.legend(loc=\"upper left\",fontsize=30)\n",
    "        plt.grid()\n",
    "        plt.xlabel(\"y\",fontsize=30)\n",
    "        plt.ylabel(\"h(x)\",fontsize=30)\n",
    "        plt.xlim([-2,4])\n",
    "        plt.ylim([-2,4])\n",
    "        plt.show()\n",
    "    # 回傳權重\n",
    "    return train_MSE,train_MAE,test_MSE,test_MAE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Holdout Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training set:\n",
      "MSE:  0.2040409361166814  / MAE:  0.35095795701583643  / R^2:  0.7938960699094876\n",
      "The testing set:\n",
      "MSE:  0.24977436956471863  / MAE:  0.4031362736507285  / R^2:  0.7397630506034342\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 這裡我們把資料分成訓練集和測試集\n",
    "sample = np.random.rand(len(data)) <= 0.8\n",
    "train = data[sample]\n",
    "test = data[~sample]\n",
    "# 我們把我們希望預測的欄位「總價元」分出來\n",
    "train_x,train_y=train.iloc[:,:6].values,train.iloc[:,6].values\n",
    "test_x,test_y=test.iloc[:,:6].values,test.iloc[:,6].values\n",
    "train_MSE,train_MAE,test_MSE,test_MAE=Regression(0.1,train_x,train_y,test_x,test_y,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Leave One Out Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0  iterations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training set:\n",
      "MSE:  0.20905851702792672  / MAE:  0.35673407718138317  / R^2:  0.7908936939287112\n",
      "The testing set:\n",
      "MSE:  0.36391094976407234  / MAE:  0.6032503209813256  / R^2:  -inf\n",
      "=======================================================\n",
      "1  iterations\n",
      "The training set:\n",
      "MSE:  0.20863795613975675  / MAE:  0.3563546735322994  / R^2:  0.7901411613818201\n",
      "The testing set:\n",
      "MSE:  0.5317946849269485  / MAE:  0.7292425419069766  / R^2:  -inf\n",
      "=======================================================\n",
      "2  iterations\n",
      "The training set:\n",
      "MSE:  0.20961489471935443  / MAE:  0.3574502852926764  / R^2:  0.7894971692040136\n",
      "The testing set:\n",
      "MSE:  0.10537505677663643  / MAE:  0.32461524421480337  / R^2:  -inf\n",
      "=======================================================\n",
      "3  iterations\n",
      "The training set:\n",
      "MSE:  0.20923333682360876  / MAE:  0.3568363896875161  / R^2:  0.7897716622277836\n",
      "The testing set:\n",
      "MSE:  0.2565136689406942  / MAE:  0.5064717849403797  / R^2:  -inf\n",
      "=======================================================\n",
      "4  iterations\n",
      "The training set:\n",
      "MSE:  0.2088860132170114  / MAE:  0.3569557693766642  / R^2:  0.7914085099683199\n",
      "The testing set:\n",
      "MSE:  0.4028833758475403  / MAE:  0.6347309476049993  / R^2:  -inf\n",
      "=======================================================\n",
      "5  iterations\n",
      "The training set:\n",
      "MSE:  0.2087193515569215  / MAE:  0.35734376313123456  / R^2:  0.7911946754523351\n",
      "The testing set:\n",
      "MSE:  0.4514998552452751  / MAE:  0.671937389378858  / R^2:  -inf\n",
      "=======================================================\n",
      "6  iterations\n",
      "The training set:\n",
      "MSE:  0.20848048694559665  / MAE:  0.356806079649371  / R^2:  0.7919951514440443\n",
      "The testing set:\n",
      "MSE:  0.533044835872216  / MAE:  0.7300991959125938  / R^2:  -inf\n",
      "=======================================================\n",
      "7  iterations\n",
      "The training set:\n",
      "MSE:  0.20988036280590236  / MAE:  0.3583259969716704  / R^2:  0.7906692793042818\n",
      "The testing set:\n",
      "MSE:  0.0003914836004337783  / MAE:  0.019785944517100473  / R^2:  -inf\n",
      "=======================================================\n",
      "8  iterations\n",
      "The training set:\n",
      "MSE:  0.2084294414293513  / MAE:  0.356071953100798  / R^2:  0.7912124452395554\n",
      "The testing set:\n",
      "MSE:  0.5825295739127428  / MAE:  0.7632362503921986  / R^2:  -inf\n",
      "=======================================================\n",
      "9  iterations\n",
      "The training set:\n",
      "MSE:  0.20969811555951742  / MAE:  0.3577662311084253  / R^2:  0.7908264838712588\n",
      "The testing set:\n",
      "MSE:  0.07810079002531414  / MAE:  0.27946518571248574  / R^2:  -inf\n",
      "=======================================================\n",
      "10  iterations\n",
      "The training set:\n",
      "MSE:  0.2098288040845098  / MAE:  0.3579087439380533  / R^2:  0.7907424918157266\n",
      "The testing set:\n",
      "MSE:  0.022181243072029167  / MAE:  0.148933686827491  / R^2:  -inf\n",
      "=======================================================\n",
      "11  iterations\n",
      "The training set:\n",
      "MSE:  0.20872877361461692  / MAE:  0.3570903032198748  / R^2:  0.7911885469912662\n",
      "The testing set:\n",
      "MSE:  0.45954820303939486  / MAE:  0.6778998473516533  / R^2:  -inf\n",
      "=======================================================\n",
      "12  iterations\n",
      "The training set:\n",
      "MSE:  0.20948762844072505  / MAE:  0.3571515208306852  / R^2:  0.7909629079741778\n",
      "The testing set:\n",
      "MSE:  0.14685835181180695  / MAE:  0.3832210221423232  / R^2:  -inf\n",
      "=======================================================\n",
      "13  iterations\n",
      "The training set:\n",
      "MSE:  0.20885378676996869  / MAE:  0.35689482297899866  / R^2:  0.7913950793824202\n",
      "The testing set:\n",
      "MSE:  0.3885039592243122  / MAE:  0.6233008577118374  / R^2:  -inf\n",
      "=======================================================\n",
      "14  iterations\n",
      "The training set:\n",
      "MSE:  0.2095410935898194  / MAE:  0.35753440861098895  / R^2:  0.7907789959466158\n",
      "The testing set:\n",
      "MSE:  0.13526185356929207  / MAE:  0.3677796263651537  / R^2:  -inf\n",
      "=======================================================\n",
      "15  iterations\n",
      "The training set:\n",
      "MSE:  0.2098018552269487  / MAE:  0.357988324021213  / R^2:  0.7907671941394562\n",
      "The testing set:\n",
      "MSE:  0.025766721765029147  / MAE:  0.16052015999565022  / R^2:  -inf\n",
      "=======================================================\n",
      "16  iterations\n",
      "The training set:\n",
      "MSE:  0.2093642255299942  / MAE:  0.3571984043043139  / R^2:  0.7905528433675191\n",
      "The testing set:\n",
      "MSE:  0.2004647691657203  / MAE:  0.4477329216907333  / R^2:  -inf\n",
      "=======================================================\n",
      "17  iterations\n",
      "The training set:\n",
      "MSE:  0.2095475281086969  / MAE:  0.3573314334633232  / R^2:  0.7903694681809728\n",
      "The testing set:\n",
      "MSE:  0.1384570157037444  / MAE:  0.3720981264448189  / R^2:  -inf\n",
      "=======================================================\n",
      "18  iterations\n",
      "The training set:\n",
      "MSE:  0.20985065901270297  / MAE:  0.3580230983554672  / R^2:  0.7900662172040172\n",
      "The testing set:\n",
      "MSE:  0.014953836403725636  / MAE:  0.12228587982152983  / R^2:  -inf\n",
      "=======================================================\n",
      "19  iterations\n",
      "The training set:\n",
      "MSE:  0.2098081690265713  / MAE:  0.35785490192900177  / R^2:  0.7907266592673377\n",
      "The testing set:\n",
      "MSE:  0.02979940333606109  / MAE:  0.17262503681697244  / R^2:  -inf\n",
      "=======================================================\n",
      "20  iterations\n",
      "The training set:\n",
      "MSE:  0.20716201195788175  / MAE:  0.35547673390900253  / R^2:  0.7918528371978848\n",
      "The testing set:\n",
      "MSE:  1.0615638266412981  / MAE:  1.0303221955491875  / R^2:  -inf\n",
      "=======================================================\n",
      "21  iterations\n",
      "The training set:\n",
      "MSE:  0.20980848522016185  / MAE:  0.3578211319020748  / R^2:  0.7905523083935939\n",
      "The testing set:\n",
      "MSE:  0.031006069640684416  / MAE:  0.17608540439424392  / R^2:  -inf\n",
      "=======================================================\n",
      "22  iterations\n",
      "The training set:\n",
      "MSE:  0.20633812916628957  / MAE:  0.35436694046928396  / R^2:  0.7938567656467144\n",
      "The testing set:\n",
      "MSE:  1.394723721000055  / MAE:  1.1809842170833846  / R^2:  -inf\n",
      "=======================================================\n",
      "23  iterations\n",
      "The training set:\n",
      "MSE:  0.2096606739085783  / MAE:  0.35724862402627267  / R^2:  0.7908317975464584\n",
      "The testing set:\n",
      "MSE:  0.09615956047212741  / MAE:  0.31009605039749766  / R^2:  -inf\n",
      "=======================================================\n",
      "24  iterations\n",
      "The training set:\n",
      "MSE:  0.2084503973584353  / MAE:  0.35634419896916475  / R^2:  0.7915019526093162\n",
      "The testing set:\n",
      "MSE:  0.5491238801071159  / MAE:  0.7410289333805502  / R^2:  -inf\n",
      "=======================================================\n",
      "25  iterations\n",
      "The training set:\n",
      "MSE:  0.2097226195751245  / MAE:  0.3579897654053743  / R^2:  0.7901943073591989\n",
      "The testing set:\n",
      "MSE:  0.07634945915402892  / MAE:  0.2763140589149038  / R^2:  -inf\n",
      "=======================================================\n",
      "26  iterations\n",
      "The training set:\n",
      "MSE:  0.20972403275141516  / MAE:  0.35795738766387075  / R^2:  0.790192893623136\n",
      "The testing set:\n",
      "MSE:  0.06937683161211997  / MAE:  0.26339482077694687  / R^2:  -inf\n",
      "=======================================================\n",
      "27  iterations\n",
      "The training set:\n",
      "MSE:  0.20943552390217252  / MAE:  0.3575538247496003  / R^2:  0.7904815167533973\n",
      "The testing set:\n",
      "MSE:  0.17563795694544046  / MAE:  0.41909182400213973  / R^2:  -inf\n",
      "=======================================================\n",
      "28  iterations\n",
      "The training set:\n",
      "MSE:  0.2097030355298932  / MAE:  0.35767472998564764  / R^2:  0.7902138991618514\n",
      "The testing set:\n",
      "MSE:  0.07654307000718126  / MAE:  0.2766641827327514  / R^2:  -inf\n",
      "=======================================================\n",
      "29  iterations\n",
      "The training set:\n",
      "MSE:  0.2097971712824835  / MAE:  0.3581825471356318  / R^2:  0.7901859070143457\n",
      "The testing set:\n",
      "MSE:  0.0385981143246474  / MAE:  0.19646402806785623  / R^2:  -inf\n",
      "=======================================================\n",
      "30  iterations\n",
      "The training set:\n",
      "MSE:  0.20873538050447552  / MAE:  0.35611792070704523  / R^2:  0.7907744625315578\n",
      "The testing set:\n",
      "MSE:  0.5224173328667377  / MAE:  0.7227844304263462  / R^2:  -inf\n",
      "=======================================================\n",
      "31  iterations\n",
      "The training set:\n",
      "MSE:  0.2044002151446611  / MAE:  0.35406112148473945  / R^2:  0.7943452313881312\n",
      "The testing set:\n",
      "MSE:  2.209517097911481  / MAE:  1.4864444483099533  / R^2:  -inf\n",
      "=======================================================\n",
      "32  iterations\n",
      "The training set:\n",
      "MSE:  0.20925989414631313  / MAE:  0.35700699456394636  / R^2:  0.7903375993436762\n",
      "The testing set:\n",
      "MSE:  0.2527908293821337  / MAE:  0.5027830838265481  / R^2:  -inf\n",
      "=======================================================\n",
      "33  iterations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training set:\n",
      "MSE:  0.20950971679724148  / MAE:  0.35758562187307075  / R^2:  0.791031731392345\n",
      "The testing set:\n",
      "MSE:  0.13825280659701308  / MAE:  0.37182362296795113  / R^2:  -inf\n",
      "=======================================================\n",
      "34  iterations\n",
      "The training set:\n",
      "MSE:  0.20974192937570663  / MAE:  0.35773434303660123  / R^2:  0.7901749899098265\n",
      "The testing set:\n",
      "MSE:  0.05449605835028279  / MAE:  0.23344390835976592  / R^2:  -inf\n",
      "=======================================================\n",
      "35  iterations\n",
      "The training set:\n",
      "MSE:  0.20972357890064197  / MAE:  0.35772050071436634  / R^2:  0.7901933476536838\n",
      "The testing set:\n",
      "MSE:  0.056131455557410094  / MAE:  0.2369207790747998  / R^2:  -inf\n",
      "=======================================================\n",
      "36  iterations\n",
      "The training set:\n",
      "MSE:  0.20893234275124592  / MAE:  0.3566291031737501  / R^2:  0.7914269442189287\n",
      "The testing set:\n",
      "MSE:  0.355499382681303  / MAE:  0.59623768975242  / R^2:  -inf\n",
      "=======================================================\n",
      "37  iterations\n",
      "The training set:\n",
      "MSE:  0.20986179331231553  / MAE:  0.358032145648691  / R^2:  0.7900550784940058\n",
      "The testing set:\n",
      "MSE:  0.009793591205331544  / MAE:  0.09896257477113024  / R^2:  -inf\n",
      "=======================================================\n",
      "38  iterations\n",
      "The training set:\n",
      "MSE:  0.20917976503337576  / MAE:  0.35613843026774317  / R^2:  0.790737376930643\n",
      "The testing set:\n",
      "MSE:  0.3237713884339589  / MAE:  0.5690091286033634  / R^2:  -inf\n",
      "=======================================================\n",
      "39  iterations\n",
      "The training set:\n",
      "MSE:  0.20987733230814729  / MAE:  0.35829946718255834  / R^2:  0.7900395333430343\n",
      "The testing set:\n",
      "MSE:  0.001845384690969422  / MAE:  0.04295794095355854  / R^2:  -inf\n",
      "=======================================================\n",
      "40  iterations\n",
      "The training set:\n",
      "MSE:  0.207895211314236  / MAE:  0.3559361902066338  / R^2:  0.7924223640151499\n",
      "The testing set:\n",
      "MSE:  0.7936623158083526  / MAE:  0.8908772731461683  / R^2:  -inf\n",
      "=======================================================\n",
      "41  iterations\n",
      "The training set:\n",
      "MSE:  0.20688829870948822  / MAE:  0.3541436398803645  / R^2:  0.7931308385265117\n",
      "The testing set:\n",
      "MSE:  1.266211843133279  / MAE:  1.1252607889432915  / R^2:  -inf\n",
      "=======================================================\n",
      "42  iterations\n",
      "The training set:\n",
      "MSE:  0.2098472967907715  / MAE:  0.3582495055027985  / R^2:  0.7893167833384536\n",
      "The testing set:\n",
      "MSE:  0.006068806895394828  / MAE:  0.07790254742558056  / R^2:  -inf\n",
      "=======================================================\n",
      "43  iterations\n",
      "The training set:\n",
      "MSE:  0.20956579081982535  / MAE:  0.35719147207508245  / R^2:  0.7905444691424262\n",
      "The testing set:\n",
      "MSE:  0.1310617938821491  / MAE:  0.3620245763510388  / R^2:  -inf\n",
      "=======================================================\n",
      "44  iterations\n",
      "The training set:\n",
      "MSE:  0.20784649064317073  / MAE:  0.35544327347528387  / R^2:  0.7921697208699738\n",
      "The testing set:\n",
      "MSE:  0.8059203270009321  / MAE:  0.8977306539274083  / R^2:  -inf\n",
      "=======================================================\n",
      "45  iterations\n",
      "The training set:\n",
      "MSE:  0.20977524241534473  / MAE:  0.35792725554447213  / R^2:  0.7907979129511442\n",
      "The testing set:\n",
      "MSE:  0.03586140075513778  / MAE:  0.18937106630934353  / R^2:  -inf\n",
      "=======================================================\n",
      "46  iterations\n",
      "The training set:\n",
      "MSE:  0.20988706548280495  / MAE:  0.35832587637900726  / R^2:  0.7900297963129763\n",
      "The testing set:\n",
      "MSE:  5.9382735814036845e-05  / MAE:  0.007706019453260993  / R^2:  -inf\n",
      "=======================================================\n",
      "47  iterations\n",
      "The training set:\n",
      "MSE:  0.20985524332049396  / MAE:  0.35820802826804987  / R^2:  0.7907063713080764\n",
      "The testing set:\n",
      "MSE:  0.013822043127298674  / MAE:  0.11756718558891624  / R^2:  -inf\n",
      "=======================================================\n",
      "48  iterations\n",
      "The training set:\n",
      "MSE:  0.20987536446672267  / MAE:  0.3582400866836467  / R^2:  0.7905052779904456\n",
      "The testing set:\n",
      "MSE:  0.004681033563187125  / MAE:  0.06841807921293264  / R^2:  -inf\n",
      "=======================================================\n",
      "49  iterations\n",
      "The training set:\n",
      "MSE:  0.20950790706721692  / MAE:  0.3575875729141722  / R^2:  0.7903696332470428\n",
      "The testing set:\n",
      "MSE:  0.15219804483939572  / MAE:  0.39012567826201305  / R^2:  -inf\n",
      "=======================================================\n",
      "50  iterations\n",
      "The training set:\n",
      "MSE:  0.20925896451213252  / MAE:  0.3572322837351526  / R^2:  0.7911896578485167\n",
      "The testing set:\n",
      "MSE:  0.25423014870763994  / MAE:  0.5042124043571716  / R^2:  -inf\n",
      "=======================================================\n",
      "51  iterations\n",
      "The training set:\n",
      "MSE:  0.20980471752082927  / MAE:  0.35799226778992704  / R^2:  0.7897476645099573\n",
      "The testing set:\n",
      "MSE:  0.039857460471174264  / MAE:  0.19964333314983063  / R^2:  -inf\n",
      "=======================================================\n",
      "52  iterations\n",
      "The training set:\n",
      "MSE:  0.20986151538888734  / MAE:  0.35816399371733393  / R^2:  0.7907100476643596\n",
      "The testing set:\n",
      "MSE:  0.011120130463816238  / MAE:  0.10545202920672621  / R^2:  -inf\n",
      "=======================================================\n",
      "53  iterations\n",
      "The training set:\n",
      "MSE:  0.20849082067943076  / MAE:  0.35583064255659974  / R^2:  0.7917323411123762\n",
      "The testing set:\n",
      "MSE:  0.6350692230478073  / MAE:  0.7969123057449968  / R^2:  -inf\n",
      "=======================================================\n",
      "54  iterations\n",
      "The training set:\n",
      "MSE:  0.20974177537082114  / MAE:  0.3578420285977217  / R^2:  0.7900984487978802\n",
      "The testing set:\n",
      "MSE:  0.05449653094295788  / MAE:  0.23344492057647748  / R^2:  -inf\n",
      "=======================================================\n",
      "55  iterations\n",
      "The training set:\n",
      "MSE:  0.2098312398725134  / MAE:  0.3576327757178482  / R^2:  0.7884323305336826\n",
      "The testing set:\n",
      "MSE:  0.03973296655244088  / MAE:  0.19933129847678432  / R^2:  -inf\n",
      "=======================================================\n",
      "56  iterations\n",
      "The training set:\n",
      "MSE:  0.20966431224911072  / MAE:  0.3574270761069974  / R^2:  0.7897503470117715\n",
      "The testing set:\n",
      "MSE:  0.0982227147864417  / MAE:  0.31340503312238255  / R^2:  -inf\n",
      "=======================================================\n",
      "57  iterations\n",
      "The training set:\n",
      "MSE:  0.20943905704320112  / MAE:  0.35721918799562524  / R^2:  0.7904779822128589\n",
      "The testing set:\n",
      "MSE:  0.17834220580903554  / MAE:  0.4223058202405403  / R^2:  -inf\n",
      "=======================================================\n",
      "58  iterations\n",
      "The training set:\n",
      "MSE:  0.20984168415977675  / MAE:  0.3581379808017426  / R^2:  0.790727473235363\n",
      "The testing set:\n",
      "MSE:  0.011961103043094267  / MAE:  0.10936682789170703  / R^2:  -inf\n",
      "=======================================================\n",
      "59  iterations\n",
      "The training set:\n",
      "MSE:  0.20973079881496665  / MAE:  0.35784017887698094  / R^2:  0.790811220729782\n",
      "The testing set:\n",
      "MSE:  0.05025976836370658  / MAE:  0.2241869049782047  / R^2:  -inf\n",
      "=======================================================\n",
      "60  iterations\n",
      "The training set:\n",
      "MSE:  0.20988602927535205  / MAE:  0.3583022590010701  / R^2:  0.7905801946135458\n",
      "The testing set:\n",
      "MSE:  0.0003073109467264798  / MAE:  0.017530286555743457  / R^2:  -inf\n",
      "=======================================================\n",
      "61  iterations\n",
      "The training set:\n",
      "MSE:  0.20871141885594016  / MAE:  0.3568170838376499  / R^2:  0.7913078870192757\n",
      "The testing set:\n",
      "MSE:  0.4666871217662371  / MAE:  0.6831450224997889  / R^2:  -inf\n",
      "=======================================================\n",
      "62  iterations\n",
      "The training set:\n",
      "MSE:  0.2097664342700461  / MAE:  0.3577607968657993  / R^2:  0.7907382257876511\n",
      "The testing set:\n",
      "MSE:  0.047680652796944174  / MAE:  0.21835899980752838  / R^2:  -inf\n",
      "=======================================================\n",
      "63  iterations\n",
      "The training set:\n",
      "MSE:  0.2098727622428482  / MAE:  0.35827725701098656  / R^2:  0.7900441052185785\n",
      "The testing set:\n",
      "MSE:  0.0014765689594245805  / MAE:  0.0384261494222955  / R^2:  -inf\n",
      "=======================================================\n",
      "64  iterations\n",
      "The training set:\n",
      "MSE:  0.20945085258271762  / MAE:  0.35719651084748  / R^2:  0.7904661820010204\n",
      "The testing set:\n",
      "MSE:  0.18683236104769047  / MAE:  0.4322410913456638  / R^2:  -inf\n",
      "=======================================================\n",
      "65  iterations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training set:\n",
      "MSE:  0.20984280602449606  / MAE:  0.357930179016494  / R^2:  0.790726087295119\n",
      "The testing set:\n",
      "MSE:  0.01823622629506057  / MAE:  0.13504157246959386  / R^2:  -inf\n",
      "=======================================================\n",
      "66  iterations\n",
      "The training set:\n",
      "MSE:  0.2097787250251413  / MAE:  0.3578515651732822  / R^2:  0.7907712741124827\n",
      "The testing set:\n",
      "MSE:  0.04057273302029601  / MAE:  0.20142674355779078  / R^2:  -inf\n",
      "=======================================================\n",
      "67  iterations\n",
      "The training set:\n",
      "MSE:  0.20988406150337796  / MAE:  0.35823568962248714  / R^2:  0.7906888673382684\n",
      "The testing set:\n",
      "MSE:  0.001074936472511764  / MAE:  0.03278622382208363  / R^2:  -inf\n",
      "=======================================================\n",
      "68  iterations\n",
      "The training set:\n",
      "MSE:  0.20831950255541223  / MAE:  0.3556733115807499  / R^2:  0.7920946234349224\n",
      "The testing set:\n",
      "MSE:  0.6463467382609448  / MAE:  0.8039569256253377  / R^2:  -inf\n",
      "=======================================================\n",
      "69  iterations\n",
      "The training set:\n",
      "MSE:  0.20977114338769093  / MAE:  0.35774125660290895  / R^2:  0.7901457643259024\n",
      "The testing set:\n",
      "MSE:  0.04245151147319703  / MAE:  0.20603764576697392  / R^2:  -inf\n",
      "=======================================================\n",
      "70  iterations\n",
      "The training set:\n",
      "MSE:  0.20817945422648618  / MAE:  0.3561241703665119  / R^2:  0.7918717753526823\n",
      "The testing set:\n",
      "MSE:  0.7915075600176646  / MAE:  0.8896671062918223  / R^2:  -inf\n",
      "=======================================================\n",
      "71  iterations\n",
      "The training set:\n",
      "MSE:  0.20982115207383575  / MAE:  0.3581225296588429  / R^2:  0.7905209333232557\n",
      "The testing set:\n",
      "MSE:  0.01954323134981973  / MAE:  0.13979710780205623  / R^2:  -inf\n",
      "=======================================================\n",
      "72  iterations\n",
      "The training set:\n",
      "MSE:  0.20963475673870768  / MAE:  0.35755873145446915  / R^2:  0.7903173224943109\n",
      "The testing set:\n",
      "MSE:  0.1027984920713177  / MAE:  0.32062203927883326  / R^2:  -inf\n",
      "=======================================================\n",
      "73  iterations\n",
      "The training set:\n",
      "MSE:  0.20983068722437057  / MAE:  0.3580272180754753  / R^2:  0.7900861969033594\n",
      "The testing set:\n",
      "MSE:  0.02069819326861736  / MAE:  0.14386866673677545  / R^2:  -inf\n",
      "=======================================================\n",
      "74  iterations\n",
      "The training set:\n",
      "MSE:  0.20985918439976048  / MAE:  0.35819521798959536  / R^2:  0.7883359915096175\n",
      "The testing set:\n",
      "MSE:  0.006194192110057559  / MAE:  0.07870318996112902  / R^2:  -inf\n",
      "=======================================================\n",
      "75  iterations\n",
      "The training set:\n",
      "MSE:  0.20381535551257274  / MAE:  0.3538093949780347  / R^2:  0.7944317983362335\n",
      "The testing set:\n",
      "MSE:  5.024817988374696  / MAE:  2.241610579109292  / R^2:  -inf\n",
      "=======================================================\n",
      "76  iterations\n",
      "The training set:\n",
      "MSE:  0.20842595905020733  / MAE:  0.35583090474645857  / R^2:  0.7921357168131249\n",
      "The testing set:\n",
      "MSE:  0.5822623379405469  / MAE:  0.763061162647233  / R^2:  -inf\n",
      "=======================================================\n",
      "77  iterations\n",
      "The training set:\n",
      "MSE:  0.2094817168173171  / MAE:  0.35705615056929707  / R^2:  0.7906010349820598\n",
      "The testing set:\n",
      "MSE:  0.15711951604807733  / MAE:  0.39638304207934694  / R^2:  -inf\n",
      "=======================================================\n",
      "78  iterations\n",
      "The training set:\n",
      "MSE:  0.20962208599209864  / MAE:  0.3577339863245874  / R^2:  0.7902948807645052\n",
      "The testing set:\n",
      "MSE:  0.09650382053932913  / MAE:  0.3106506406549472  / R^2:  -inf\n",
      "=======================================================\n",
      "79  iterations\n",
      "The training set:\n",
      "MSE:  0.20966301848188987  / MAE:  0.3577023628232362  / R^2:  0.7907553819829933\n",
      "The testing set:\n",
      "MSE:  0.07303405959879812  / MAE:  0.27024814448724366  / R^2:  -inf\n",
      "=======================================================\n",
      "80  iterations\n",
      "The training set:\n",
      "MSE:  0.20635989223324058  / MAE:  0.35602140960694456  / R^2:  0.7928692187144363\n",
      "The testing set:\n",
      "MSE:  1.3335245839758203  / MAE:  1.1547833493672397  / R^2:  -inf\n",
      "=======================================================\n",
      "81  iterations\n",
      "The training set:\n",
      "MSE:  0.20962553483136365  / MAE:  0.35763109173163155  / R^2:  0.7908080110784776\n",
      "The testing set:\n",
      "MSE:  0.08733229332803173  / MAE:  0.2955203771790225  / R^2:  -inf\n",
      "=======================================================\n",
      "82  iterations\n",
      "The training set:\n",
      "MSE:  0.2095659972924414  / MAE:  0.35744974823093784  / R^2:  0.790350991681414\n",
      "The testing set:\n",
      "MSE:  0.1312537134258372  / MAE:  0.3622895436330411  / R^2:  -inf\n",
      "=======================================================\n",
      "83  iterations\n",
      "The training set:\n",
      "MSE:  0.20973532778865367  / MAE:  0.3578391901074619  / R^2:  0.7907573190207032\n",
      "The testing set:\n",
      "MSE:  0.05937355250069013  / MAE:  0.2436668883962081  / R^2:  -inf\n",
      "=======================================================\n",
      "84  iterations\n",
      "The training set:\n",
      "MSE:  0.20940012581141043  / MAE:  0.3572908845957234  / R^2:  0.7909599655739934\n",
      "The testing set:\n",
      "MSE:  0.18706342732407288  / MAE:  0.4325082974048855  / R^2:  -inf\n",
      "=======================================================\n",
      "85  iterations\n",
      "The training set:\n",
      "MSE:  0.20988628645280236  / MAE:  0.3583066186492345  / R^2:  0.7901937620164989\n",
      "The testing set:\n",
      "MSE:  0.00018291656776724151  / MAE:  0.013524665162851224  / R^2:  -inf\n",
      "=======================================================\n",
      "86  iterations\n",
      "The training set:\n",
      "MSE:  0.20965838854777316  / MAE:  0.3576124398434813  / R^2:  0.7909126212844733\n",
      "The testing set:\n",
      "MSE:  0.07702903578754668  / MAE:  0.2775410524364759  / R^2:  -inf\n",
      "=======================================================\n",
      "87  iterations\n",
      "The training set:\n",
      "MSE:  0.20963388368941108  / MAE:  0.35746324546539165  / R^2:  0.7902830783940158\n",
      "The testing set:\n",
      "MSE:  0.09863576547999556  / MAE:  0.31406331444470803  / R^2:  -inf\n",
      "=======================================================\n",
      "88  iterations\n",
      "The training set:\n",
      "MSE:  0.20987330861560852  / MAE:  0.3581712744215681  / R^2:  0.7903249253191587\n",
      "The testing set:\n",
      "MSE:  0.0052926405556399485  / MAE:  0.07275053646290142  / R^2:  -inf\n",
      "=======================================================\n",
      "89  iterations\n",
      "The training set:\n",
      "MSE:  0.20970254838144167  / MAE:  0.3577108236959567  / R^2:  0.7902143865032669\n",
      "The testing set:\n",
      "MSE:  0.06269439902058009  / MAE:  0.2503884961825924  / R^2:  -inf\n",
      "=======================================================\n",
      "90  iterations\n",
      "The training set:\n",
      "MSE:  0.2097158020051291  / MAE:  0.3575775586650225  / R^2:  0.7907194452911182\n",
      "The testing set:\n",
      "MSE:  0.06736236609996904  / MAE:  0.25954260941118906  / R^2:  -inf\n",
      "=======================================================\n",
      "91  iterations\n",
      "The training set:\n",
      "MSE:  0.20984916008603727  / MAE:  0.35811820214943074  / R^2:  0.7900677167244217\n",
      "The testing set:\n",
      "MSE:  0.009371383070985109  / MAE:  0.096805904112224  / R^2:  -inf\n",
      "=======================================================\n",
      "92  iterations\n",
      "The training set:\n",
      "MSE:  0.2094967419745992  / MAE:  0.3573901886442064  / R^2:  0.7907011362775543\n",
      "The testing set:\n",
      "MSE:  0.14927319875050674  / MAE:  0.3863588988887233  / R^2:  -inf\n",
      "=======================================================\n",
      "93  iterations\n",
      "The training set:\n",
      "MSE:  0.2096432606913706  / MAE:  0.3575552298415011  / R^2:  0.7907918360515547\n",
      "The testing set:\n",
      "MSE:  0.09872562075842065  / MAE:  0.314206334688562  / R^2:  -inf\n",
      "=======================================================\n",
      "94  iterations\n",
      "The training set:\n",
      "MSE:  0.20986967337983412  / MAE:  0.3581776938553298  / R^2:  0.7900471953051197\n",
      "The testing set:\n",
      "MSE:  0.006327310222579084  / MAE:  0.07954439152183568  / R^2:  -inf\n",
      "=======================================================\n",
      "95  iterations\n",
      "The training set:\n",
      "MSE:  0.20986648463828952  / MAE:  0.3582566086609784  / R^2:  0.7900503853097542\n",
      "The testing set:\n",
      "MSE:  0.0036261308121728654  / MAE:  0.06021736304566039  / R^2:  -inf\n",
      "=======================================================\n",
      "96  iterations\n",
      "The training set:\n",
      "MSE:  0.20986816085899396  / MAE:  0.35811196013204394  / R^2:  0.7900487084250833\n",
      "The testing set:\n",
      "MSE:  0.007015381921662948  / MAE:  0.08375787677384705  / R^2:  -inf\n",
      "=======================================================\n",
      "97  iterations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training set:\n",
      "MSE:  0.2098381219967703  / MAE:  0.35797098880664224  / R^2:  0.7900787591859777\n",
      "The testing set:\n",
      "MSE:  0.02081191095498689  / MAE:  0.14426333891528675  / R^2:  -inf\n",
      "=======================================================\n",
      "98  iterations\n",
      "The training set:\n",
      "MSE:  0.2098384331369668  / MAE:  0.3581193968942472  / R^2:  0.7900784479225357\n",
      "The testing set:\n",
      "MSE:  0.013550270474533225  / MAE:  0.11640562905003016  / R^2:  -inf\n",
      "=======================================================\n",
      "99  iterations\n",
      "The training set:\n",
      "MSE:  0.2098092005124429  / MAE:  0.3579179738904982  / R^2:  0.7906262406244563\n",
      "The testing set:\n",
      "MSE:  0.023708926497866847  / MAE:  0.15397703237128207  / R^2:  -inf\n",
      "=======================================================\n",
      "100  iterations\n",
      "The training set:\n",
      "MSE:  0.20756407844512567  / MAE:  0.3562642442224896  / R^2:  0.7911015759202952\n",
      "The testing set:\n",
      "MSE:  0.9244720782646367  / MAE:  0.9614947104714808  / R^2:  -inf\n",
      "=======================================================\n",
      "101  iterations\n",
      "The training set:\n",
      "MSE:  0.2098781765269652  / MAE:  0.35834967658001216  / R^2:  0.7904198884480973\n",
      "The testing set:\n",
      "MSE:  5.2934523548244795e-05  / MAE:  0.00727561155836709  / R^2:  -inf\n",
      "=======================================================\n",
      "102  iterations\n",
      "The training set:\n",
      "MSE:  0.20987973008760058  / MAE:  0.35831992153706044  / R^2:  0.7902911325241988\n",
      "The testing set:\n",
      "MSE:  0.0002734000818448242  / MAE:  0.01653481423678005  / R^2:  -inf\n",
      "=======================================================\n",
      "103  iterations\n",
      "The training set:\n",
      "MSE:  0.20987927777291737  / MAE:  0.35837048643429464  / R^2:  0.7878097219744339\n",
      "The testing set:\n",
      "MSE:  1.1283320311281927e-05  / MAE:  0.003359065392528393  / R^2:  -inf\n",
      "=======================================================\n",
      "104  iterations\n",
      "The training set:\n",
      "MSE:  0.2098257633697067  / MAE:  0.35813990564464565  / R^2:  0.7900143968191207\n",
      "The testing set:\n",
      "MSE:  0.017568257762120544  / MAE:  0.13254530456459235  / R^2:  -inf\n",
      "=======================================================\n",
      "105  iterations\n",
      "The training set:\n",
      "MSE:  0.20896620344176625  / MAE:  0.3566305386969873  / R^2:  0.7915664785724277\n",
      "The testing set:\n",
      "MSE:  0.3674586719238926  / MAE:  0.6061836948680595  / R^2:  -inf\n",
      "=======================================================\n",
      "106  iterations\n",
      "The training set:\n",
      "MSE:  0.20875413390006106  / MAE:  0.3564493400831464  / R^2:  0.7890222385961978\n",
      "The testing set:\n",
      "MSE:  0.4803013345684834  / MAE:  0.6930377584002789  / R^2:  -inf\n",
      "=======================================================\n",
      "107  iterations\n",
      "The training set:\n",
      "MSE:  0.20890552107961885  / MAE:  0.35699838981019866  / R^2:  0.7912893079376804\n",
      "The testing set:\n",
      "MSE:  0.37176122597732764  / MAE:  0.6097222531426318  / R^2:  -inf\n",
      "=======================================================\n",
      "108  iterations\n",
      "The training set:\n",
      "MSE:  0.2098495172568304  / MAE:  0.35809161901028297  / R^2:  0.7906653472668685\n",
      "The testing set:\n",
      "MSE:  0.013228369858582886  / MAE:  0.11501465062583499  / R^2:  -inf\n",
      "=======================================================\n",
      "109  iterations\n",
      "The training set:\n",
      "MSE:  0.20950645138839682  / MAE:  0.35695844346969524  / R^2:  0.7905129280275911\n",
      "The testing set:\n",
      "MSE:  0.1559756185541742  / MAE:  0.39493748689403263  / R^2:  -inf\n",
      "=======================================================\n",
      "110  iterations\n",
      "The training set:\n",
      "MSE:  0.20939154860782971  / MAE:  0.35707525369435006  / R^2:  0.7911322838878315\n",
      "The testing set:\n",
      "MSE:  0.18849735646242752  / MAE:  0.43416282252448507  / R^2:  -inf\n",
      "=======================================================\n",
      "111  iterations\n",
      "The training set:\n",
      "MSE:  0.2098535351985717  / MAE:  0.3580058412092216  / R^2:  0.7900633398788648\n",
      "The testing set:\n",
      "MSE:  0.012233162569400389  / MAE:  0.11060362819275138  / R^2:  -inf\n",
      "=======================================================\n",
      "112  iterations\n",
      "The training set:\n",
      "MSE:  0.20988530320181178  / MAE:  0.358281892196871  / R^2:  0.7900315592920253\n",
      "The testing set:\n",
      "MSE:  0.0006023696399894211  / MAE:  0.024543219837450447  / R^2:  -inf\n",
      "=======================================================\n",
      "113  iterations\n",
      "The training set:\n",
      "MSE:  0.2098436448077618  / MAE:  0.3579300011895026  / R^2:  0.7901083866751764\n",
      "The testing set:\n",
      "MSE:  0.023535501457535017  / MAE:  0.15341284645535724  / R^2:  -inf\n",
      "=======================================================\n",
      "114  iterations\n",
      "The training set:\n",
      "MSE:  0.20935098232430388  / MAE:  0.3572047882979777  / R^2:  0.7905660918189654\n",
      "The testing set:\n",
      "MSE:  0.2253556418861641  / MAE:  0.4747163804696064  / R^2:  -inf\n",
      "=======================================================\n",
      "115  iterations\n",
      "The training set:\n",
      "MSE:  0.20958264416407374  / MAE:  0.3577532421069279  / R^2:  0.7903343382158021\n",
      "The testing set:\n",
      "MSE:  0.10354982174151948  / MAE:  0.32179158121604035  / R^2:  -inf\n",
      "=======================================================\n",
      "116  iterations\n",
      "The training set:\n",
      "MSE:  0.20961807788465187  / MAE:  0.3578424648349461  / R^2:  0.7902988904596002\n",
      "The testing set:\n",
      "MSE:  0.09159314582456392  / MAE:  0.3026435953800508  / R^2:  -inf\n",
      "=======================================================\n",
      "117  iterations\n",
      "The training set:\n",
      "MSE:  0.20964902772808375  / MAE:  0.3573545519450152  / R^2:  0.7902679283566496\n",
      "The testing set:\n",
      "MSE:  0.10050964190960994  / MAE:  0.31703255654523865  / R^2:  -inf\n",
      "=======================================================\n",
      "118  iterations\n",
      "The training set:\n",
      "MSE:  0.20951559569971823  / MAE:  0.35756704751442075  / R^2:  0.7901244247917368\n",
      "The testing set:\n",
      "MSE:  0.15702908563811233  / MAE:  0.39626895618772906  / R^2:  -inf\n",
      "=======================================================\n",
      "119  iterations\n",
      "The training set:\n",
      "MSE:  0.20924984724738632  / MAE:  0.3568775900831688  / R^2:  0.7902587877066432\n",
      "The testing set:\n",
      "MSE:  0.22454610572990547  / MAE:  0.4738629609179277  / R^2:  -inf\n",
      "=======================================================\n",
      "120  iterations\n",
      "The training set:\n",
      "MSE:  0.20944673052166407  / MAE:  0.35732779744384946  / R^2:  0.7888200218388315\n",
      "The testing set:\n",
      "MSE:  0.20114869819058956  / MAE:  0.4484960403287743  / R^2:  -inf\n",
      "=======================================================\n",
      "121  iterations\n",
      "The training set:\n",
      "MSE:  0.2076465117183506  / MAE:  0.35527544672965466  / R^2:  0.7928828062301055\n",
      "The testing set:\n",
      "MSE:  0.8861435869990177  / MAE:  0.9413519995193178  / R^2:  -inf\n",
      "=======================================================\n",
      "122  iterations\n",
      "The training set:\n",
      "MSE:  0.20834211082760706  / MAE:  0.3563091723774636  / R^2:  0.7921799806967819\n",
      "The testing set:\n",
      "MSE:  0.5838137095007147  / MAE:  0.764077031130183  / R^2:  -inf\n",
      "=======================================================\n",
      "123  iterations\n",
      "The training set:\n",
      "MSE:  0.20956877162882012  / MAE:  0.3574488832100563  / R^2:  0.7908646568838047\n",
      "The testing set:\n",
      "MSE:  0.1189138259753248  / MAE:  0.34483884058401076  / R^2:  -inf\n",
      "=======================================================\n",
      "124  iterations\n",
      "The training set:\n",
      "MSE:  0.20587641011008848  / MAE:  0.35612301012313125  / R^2:  0.7933545069592707\n",
      "The testing set:\n",
      "MSE:  1.484738970980621  / MAE:  1.2184986544845344  / R^2:  -inf\n",
      "=======================================================\n",
      "125  iterations\n",
      "The training set:\n",
      "MSE:  0.20987011883200496  / MAE:  0.3582475312445774  / R^2:  0.7906722607655955\n",
      "The testing set:\n",
      "MSE:  0.0023813946912065198  / MAE:  0.0487995357683505  / R^2:  -inf\n",
      "=======================================================\n",
      "126  iterations\n",
      "The training set:\n",
      "MSE:  0.20985058953759686  / MAE:  0.3580354608236863  / R^2:  0.7906843468917086\n",
      "The testing set:\n",
      "MSE:  0.01489268142179005  / MAE:  0.12203557441086615  / R^2:  -inf\n",
      "=======================================================\n",
      "127  iterations\n",
      "The training set:\n",
      "MSE:  0.2095172599723233  / MAE:  0.3572340198390436  / R^2:  0.7903997483068346\n",
      "The testing set:\n",
      "MSE:  0.14224830673619884  / MAE:  0.3771581985536028  / R^2:  -inf\n",
      "=======================================================\n",
      "128  iterations\n",
      "The training set:\n",
      "MSE:  0.20987899369820898  / MAE:  0.35834280519899747  / R^2:  0.7906902648922667\n",
      "The testing set:\n",
      "MSE:  7.149944543115458e-05  / MAE:  0.008455734470237022  / R^2:  -inf\n",
      "=======================================================\n",
      "129  iterations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training set:\n",
      "MSE:  0.20933410547303014  / MAE:  0.3571190907490582  / R^2:  0.7901743316239999\n",
      "The testing set:\n",
      "MSE:  0.21201475044926754  / MAE:  0.46045059501456564  / R^2:  -inf\n",
      "=======================================================\n",
      "130  iterations\n",
      "The training set:\n",
      "MSE:  0.20986595978087988  / MAE:  0.35824229762438337  / R^2:  0.7907054372822885\n",
      "The testing set:\n",
      "MSE:  0.0035780816112831013  / MAE:  0.059817067892726915  / R^2:  -inf\n",
      "=======================================================\n",
      "131  iterations\n",
      "The training set:\n",
      "MSE:  0.20986551785741525  / MAE:  0.35822897234757617  / R^2:  0.7907060560966541\n",
      "The testing set:\n",
      "MSE:  0.004155870058867913  / MAE:  0.06446603802676192  / R^2:  -inf\n",
      "=======================================================\n",
      "132  iterations\n",
      "The training set:\n",
      "MSE:  0.20973093733900577  / MAE:  0.35781970980679906  / R^2:  0.7901859863005746\n",
      "The testing set:\n",
      "MSE:  0.05093617495520396  / MAE:  0.225690440549005  / R^2:  -inf\n",
      "=======================================================\n",
      "133  iterations\n",
      "The training set:\n",
      "MSE:  0.20932097633130073  / MAE:  0.35698580690195514  / R^2:  0.7896277439515171\n",
      "The testing set:\n",
      "MSE:  0.24453784642405202  / MAE:  0.4945076808544555  / R^2:  -inf\n",
      "=======================================================\n",
      "134  iterations\n",
      "The training set:\n",
      "MSE:  0.20959267209781915  / MAE:  0.3576194237572439  / R^2:  0.7904872643899894\n",
      "The testing set:\n",
      "MSE:  0.10697736442007344  / MAE:  0.3270739433523763  / R^2:  -inf\n",
      "=======================================================\n",
      "135  iterations\n",
      "The training set:\n",
      "MSE:  0.20947964052130683  / MAE:  0.35736967783232143  / R^2:  0.7907157239300301\n",
      "The testing set:\n",
      "MSE:  0.151623044221283  / MAE:  0.3893880381075965  / R^2:  -inf\n",
      "=======================================================\n",
      "136  iterations\n",
      "The training set:\n",
      "MSE:  0.20980151055472737  / MAE:  0.3579498249188181  / R^2:  0.7907032338901594\n",
      "The testing set:\n",
      "MSE:  0.026353278891576272  / MAE:  0.16233693015323492  / R^2:  -inf\n",
      "=======================================================\n",
      "137  iterations\n",
      "The training set:\n",
      "MSE:  0.2069668400108183  / MAE:  0.35481713801982034  / R^2:  0.7923596361664739\n",
      "The testing set:\n",
      "MSE:  1.1595219093660425  / MAE:  1.0768109905485004  / R^2:  -inf\n",
      "=======================================================\n",
      "138  iterations\n",
      "The training set:\n",
      "MSE:  0.2098651966891986  / MAE:  0.35817942805816616  / R^2:  0.7901210102652942\n",
      "The testing set:\n",
      "MSE:  0.005513560309954112  / MAE:  0.07425335217991247  / R^2:  -inf\n",
      "=======================================================\n",
      "139  iterations\n",
      "The training set:\n",
      "MSE:  0.2098375099329152  / MAE:  0.35803835713368315  / R^2:  0.7900793714922769\n",
      "The testing set:\n",
      "MSE:  0.0189605869344868  / MAE:  0.13769744708776122  / R^2:  -inf\n",
      "=======================================================\n",
      "140  iterations\n",
      "The training set:\n",
      "MSE:  0.20983157353746065  / MAE:  0.35797989062755176  / R^2:  0.7901878359524335\n",
      "The testing set:\n",
      "MSE:  0.021240994837029216  / MAE:  0.14574290664395717  / R^2:  -inf\n",
      "=======================================================\n",
      "141  iterations\n",
      "The training set:\n",
      "MSE:  0.20983207579804386  / MAE:  0.3580410444836126  / R^2:  0.7901541333332651\n",
      "The testing set:\n",
      "MSE:  0.017769122400612163  / MAE:  0.13330087171737537  / R^2:  -inf\n",
      "=======================================================\n",
      "142  iterations\n",
      "The training set:\n",
      "MSE:  0.2098264076484652  / MAE:  0.3580240797631128  / R^2:  0.7900904781744442\n",
      "The testing set:\n",
      "MSE:  0.03225471150195351  / MAE:  0.17959596738778272  / R^2:  -inf\n",
      "=======================================================\n",
      "143  iterations\n",
      "The training set:\n",
      "MSE:  0.20974994250766338  / MAE:  0.3576540020637014  / R^2:  0.7901669736037942\n",
      "The testing set:\n",
      "MSE:  0.05346210232742933  / MAE:  0.23121873264817738  / R^2:  -inf\n",
      "=======================================================\n",
      "144  iterations\n",
      "The training set:\n",
      "MSE:  0.2087990176955563  / MAE:  0.3562786811891708  / R^2:  0.7914953828286273\n",
      "The testing set:\n",
      "MSE:  0.422480915622199  / MAE:  0.6499853195436025  / R^2:  -inf\n",
      "=======================================================\n",
      "145  iterations\n",
      "The training set:\n",
      "MSE:  0.20980723603158638  / MAE:  0.358033475341523  / R^2:  0.790593740899592\n",
      "The testing set:\n",
      "MSE:  0.02285754804926373  / MAE:  0.15118712924473343  / R^2:  -inf\n",
      "=======================================================\n",
      "146  iterations\n",
      "The training set:\n",
      "MSE:  0.20908493583454407  / MAE:  0.35725844424199454  / R^2:  0.7914694085346572\n",
      "The testing set:\n",
      "MSE:  0.2841651818985086  / MAE:  0.5330714604051774  / R^2:  -inf\n",
      "=======================================================\n",
      "147  iterations\n",
      "The training set:\n",
      "MSE:  0.20984332593636137  / MAE:  0.35803630688982446  / R^2:  0.7902395687098747\n",
      "The testing set:\n",
      "MSE:  0.016451819268411088  / MAE:  0.12826464543439509  / R^2:  -inf\n",
      "=======================================================\n",
      "148  iterations\n",
      "The training set:\n",
      "MSE:  0.20956620084961644  / MAE:  0.35734909503058193  / R^2:  0.7903507880436081\n",
      "The testing set:\n",
      "MSE:  0.13888807496784736  / MAE:  0.3726769042586988  / R^2:  -inf\n",
      "=======================================================\n",
      "149  iterations\n",
      "The training set:\n",
      "MSE:  0.2090163975773036  / MAE:  0.3567208140176488  / R^2:  0.791554162767733\n",
      "The testing set:\n",
      "MSE:  0.36749829289281877  / MAE:  0.6062163746491996  / R^2:  -inf\n",
      "=======================================================\n",
      "150  iterations\n",
      "The training set:\n",
      "MSE:  0.20984389383919513  / MAE:  0.35805172736195595  / R^2:  0.7906984179836257\n",
      "The testing set:\n",
      "MSE:  0.015933236490998676  / MAE:  0.12622692458821405  / R^2:  -inf\n",
      "=======================================================\n",
      "151  iterations\n",
      "The training set:\n",
      "MSE:  0.20953624226988202  / MAE:  0.35760610678669996  / R^2:  0.7907591977194462\n",
      "The testing set:\n",
      "MSE:  0.12389237854861532  / MAE:  0.3519834918694559  / R^2:  -inf\n",
      "=======================================================\n",
      "152  iterations\n",
      "The training set:\n",
      "MSE:  0.20926897388593638  / MAE:  0.3570401548043534  / R^2:  0.7913007977779177\n",
      "The testing set:\n",
      "MSE:  0.22139395443390986  / MAE:  0.47052519000996096  / R^2:  -inf\n",
      "=======================================================\n",
      "153  iterations\n",
      "The training set:\n",
      "MSE:  0.20979532664986372  / MAE:  0.35800235964474836  / R^2:  0.790777883591484\n",
      "The testing set:\n",
      "MSE:  0.02925450350331556  / MAE:  0.17103947937045283  / R^2:  -inf\n",
      "=======================================================\n",
      "154  iterations\n",
      "The training set:\n",
      "MSE:  0.20975548343693673  / MAE:  0.3577307200570517  / R^2:  0.7901219121662004\n",
      "The testing set:\n",
      "MSE:  0.05206596163597877  / MAE:  0.2281796696377194  / R^2:  -inf\n",
      "=======================================================\n",
      "155  iterations\n",
      "The training set:\n",
      "MSE:  0.20922900846256703  / MAE:  0.35706134777760634  / R^2:  0.7909925385038589\n",
      "The testing set:\n",
      "MSE:  0.23741793266369526  / MAE:  0.48725551065503125  / R^2:  -inf\n",
      "=======================================================\n",
      "156  iterations\n",
      "The training set:\n",
      "MSE:  0.20954451712748526  / MAE:  0.35720127724595857  / R^2:  0.791027573756054\n",
      "The testing set:\n",
      "MSE:  0.13440120910959313  / MAE:  0.3666077046511613  / R^2:  -inf\n",
      "=======================================================\n",
      "157  iterations\n",
      "The training set:\n",
      "MSE:  0.20973180581787032  / MAE:  0.35769691000562853  / R^2:  0.7906673333693669\n",
      "The testing set:\n",
      "MSE:  0.057889023436925514  / MAE:  0.24060137870952758  / R^2:  -inf\n",
      "=======================================================\n",
      "158  iterations\n",
      "The training set:\n",
      "MSE:  0.20986530022848424  / MAE:  0.3582211256511942  / R^2:  0.7901209067192037\n",
      "The testing set:\n",
      "MSE:  0.004010370109196156  / MAE:  0.06332748304801128  / R^2:  -inf\n",
      "=======================================================\n",
      "159  iterations\n",
      "The training set:\n",
      "MSE:  0.2096771171355938  / MAE:  0.3578184007941623  / R^2:  0.790239827822666\n",
      "The testing set:\n",
      "MSE:  0.0719972157765987  / MAE:  0.26832296915582665  / R^2:  -inf\n",
      "=======================================================\n",
      "160  iterations\n",
      "The training set:\n",
      "MSE:  0.20904683034543955  / MAE:  0.3566381450484276  / R^2:  0.7908703642752364\n",
      "The testing set:\n",
      "MSE:  0.3186284463021526  / MAE:  0.5644718295027242  / R^2:  -inf\n",
      "=======================================================\n",
      "161  iterations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training set:\n",
      "MSE:  0.20676665690722548  / MAE:  0.35497784367184293  / R^2:  0.7936101052433981\n",
      "The testing set:\n",
      "MSE:  1.1754874089975922  / MAE:  1.084198971129189  / R^2:  -inf\n",
      "=======================================================\n",
      "162  iterations\n",
      "The training set:\n",
      "MSE:  0.20899657502177185  / MAE:  0.3563780942032856  / R^2:  0.790955650094295\n",
      "The testing set:\n",
      "MSE:  0.36682553385072303  / MAE:  0.6056612368731575  / R^2:  -inf\n",
      "=======================================================\n",
      "163  iterations\n",
      "The training set:\n",
      "MSE:  0.20987878514316097  / MAE:  0.3583422504374736  / R^2:  0.7906926468393166\n",
      "The testing set:\n",
      "MSE:  0.00011834111117909334  / MAE:  0.010878470075295209  / R^2:  -inf\n",
      "=======================================================\n",
      "164  iterations\n",
      "The training set:\n",
      "MSE:  0.20979550095858146  / MAE:  0.35801382627552114  / R^2:  0.7907466857996934\n",
      "The testing set:\n",
      "MSE:  0.02759787170744752  / MAE:  0.16612607172700955  / R^2:  -inf\n",
      "=======================================================\n",
      "165  iterations\n",
      "The training set:\n",
      "MSE:  0.20914981666837718  / MAE:  0.3568275563870958  / R^2:  0.7913985335934905\n",
      "The testing set:\n",
      "MSE:  0.28049878097090203  / MAE:  0.5296213562262213  / R^2:  -inf\n",
      "=======================================================\n",
      "166  iterations\n",
      "The training set:\n",
      "MSE:  0.20941812831070034  / MAE:  0.35721868530495327  / R^2:  0.7906920539610913\n",
      "The testing set:\n",
      "MSE:  0.16275492043392434  / MAE:  0.40342895339071083  / R^2:  -inf\n",
      "=======================================================\n",
      "167  iterations\n",
      "The training set:\n",
      "MSE:  0.20982331087663683  / MAE:  0.35798196806222987  / R^2:  0.7890658868602938\n",
      "The testing set:\n",
      "MSE:  0.017310359592923288  / MAE:  0.13156883974909594  / R^2:  -inf\n",
      "=======================================================\n",
      "168  iterations\n",
      "The training set:\n",
      "MSE:  0.20980278121714038  / MAE:  0.35791611261982476  / R^2:  0.7896585179274106\n",
      "The testing set:\n",
      "MSE:  0.032869768678492145  / MAE:  0.18130021698412868  / R^2:  -inf\n",
      "=======================================================\n",
      "169  iterations\n",
      "The training set:\n",
      "MSE:  0.20976981246794046  / MAE:  0.3578703350268489  / R^2:  0.7893415952183883\n",
      "The testing set:\n",
      "MSE:  0.045934444859235625  / MAE:  0.2143232251979137  / R^2:  -inf\n",
      "=======================================================\n",
      "170  iterations\n",
      "The training set:\n",
      "MSE:  0.20985602688105648  / MAE:  0.3581188808698175  / R^2:  0.7896511846287627\n",
      "The testing set:\n",
      "MSE:  0.011630021941753135  / MAE:  0.10784257944686382  / R^2:  -inf\n",
      "=======================================================\n",
      "171  iterations\n",
      "The training set:\n",
      "MSE:  0.20926675429495004  / MAE:  0.3574653478529893  / R^2:  0.7904967121472075\n",
      "The testing set:\n",
      "MSE:  0.22107834482543126  / MAE:  0.47018969025855006  / R^2:  -inf\n",
      "=======================================================\n",
      "172  iterations\n",
      "The training set:\n",
      "MSE:  0.2095132875148971  / MAE:  0.3572814973280905  / R^2:  0.7907340460179836\n",
      "The testing set:\n",
      "MSE:  0.14670863304346105  / MAE:  0.38302562974748966  / R^2:  -inf\n",
      "=======================================================\n",
      "173  iterations\n",
      "The training set:\n",
      "MSE:  0.20977283100567903  / MAE:  0.3579098819811248  / R^2:  0.7899900634195552\n",
      "The testing set:\n",
      "MSE:  0.03725503177065028  / MAE:  0.19301562571628827  / R^2:  -inf\n",
      "=======================================================\n",
      "174  iterations\n",
      "The training set:\n",
      "MSE:  0.2097823924256423  / MAE:  0.35771261148977795  / R^2:  0.7902006870632116\n",
      "The testing set:\n",
      "MSE:  0.041428097576559283  / MAE:  0.20353893381011723  / R^2:  -inf\n",
      "=======================================================\n",
      "175  iterations\n",
      "The training set:\n",
      "MSE:  0.20987272850243183  / MAE:  0.35827207298439306  / R^2:  0.7905079091739946\n",
      "The testing set:\n",
      "MSE:  0.001570504767041975  / MAE:  0.03962959458588966  / R^2:  -inf\n",
      "=======================================================\n",
      "176  iterations\n",
      "The training set:\n",
      "MSE:  0.20972072959479124  / MAE:  0.3576097389651173  / R^2:  0.7907577383499083\n",
      "The testing set:\n",
      "MSE:  0.06290499844712953  / MAE:  0.25080868893866004  / R^2:  -inf\n",
      "=======================================================\n",
      "177  iterations\n",
      "The training set:\n",
      "MSE:  0.20917032902656987  / MAE:  0.35704858310588217  / R^2:  0.791343845909199\n",
      "The testing set:\n",
      "MSE:  0.25532027678646824  / MAE:  0.5052922686787007  / R^2:  -inf\n",
      "=======================================================\n",
      "178  iterations\n",
      "The training set:\n",
      "MSE:  0.20959431646499746  / MAE:  0.3574443973969639  / R^2:  0.7906776418617308\n",
      "The testing set:\n",
      "MSE:  0.10073528099020777  / MAE:  0.31738821810238604  / R^2:  -inf\n",
      "=======================================================\n",
      "179  iterations\n",
      "The training set:\n",
      "MSE:  0.20980140420713234  / MAE:  0.3579357762510758  / R^2:  0.790559377229879\n",
      "The testing set:\n",
      "MSE:  0.02566801794440619  / MAE:  0.16021241507575557  / R^2:  -inf\n",
      "=======================================================\n",
      "180  iterations\n",
      "The training set:\n",
      "MSE:  0.20975060989258382  / MAE:  0.3577210323137202  / R^2:  0.7906663610485885\n",
      "The testing set:\n",
      "MSE:  0.050461415600322  / MAE:  0.22463618497544424  / R^2:  -inf\n",
      "=======================================================\n",
      "181  iterations\n",
      "The training set:\n",
      "MSE:  0.20986687903503898  / MAE:  0.3582061865014056  / R^2:  0.7907020795475943\n",
      "The testing set:\n",
      "MSE:  0.003497547295783067  / MAE:  0.05914006506407536  / R^2:  -inf\n",
      "=======================================================\n",
      "182  iterations\n",
      "The training set:\n",
      "MSE:  0.20958154794128417  / MAE:  0.35757358452925336  / R^2:  0.790883999597348\n",
      "The testing set:\n",
      "MSE:  0.10708434895318146  / MAE:  0.3272374504135819  / R^2:  -inf\n",
      "=======================================================\n",
      "183  iterations\n",
      "The training set:\n",
      "MSE:  0.20959782322187304  / MAE:  0.35736480316427  / R^2:  0.7909631024989972\n",
      "The testing set:\n",
      "MSE:  0.11742051787585027  / MAE:  0.34266677381364286  / R^2:  -inf\n",
      "=======================================================\n",
      "184  iterations\n",
      "The training set:\n",
      "MSE:  0.20966980218334483  / MAE:  0.35768255165460444  / R^2:  0.7907104674538269\n",
      "The testing set:\n",
      "MSE:  0.06995858873880938  / MAE:  0.26449685960103453  / R^2:  -inf\n",
      "=======================================================\n",
      "185  iterations\n",
      "The training set:\n",
      "MSE:  0.20975120930082117  / MAE:  0.35776906914242385  / R^2:  0.7898453391559646\n",
      "The testing set:\n",
      "MSE:  0.05462984303982556  / MAE:  0.23373027839761273  / R^2:  -inf\n",
      "=======================================================\n",
      "186  iterations\n",
      "The training set:\n",
      "MSE:  0.2095152514394288  / MAE:  0.35732778739886806  / R^2:  0.7909987929651052\n",
      "The testing set:\n",
      "MSE:  0.13597612171658086  / MAE:  0.3687494023270829  / R^2:  -inf\n",
      "=======================================================\n",
      "187  iterations\n",
      "The training set:\n",
      "MSE:  0.20978344566633697  / MAE:  0.35777896491978006  / R^2:  0.7907587099367426\n",
      "The testing set:\n",
      "MSE:  0.03830439536603313  / MAE:  0.19571508722127973  / R^2:  -inf\n",
      "=======================================================\n",
      "188  iterations\n",
      "The training set:\n",
      "MSE:  0.2077273318431071  / MAE:  0.3556582379893144  / R^2:  0.7927037885525756\n",
      "The testing set:\n",
      "MSE:  0.8179271531606511  / MAE:  0.904393251390484  / R^2:  -inf\n",
      "=======================================================\n",
      "189  iterations\n",
      "The training set:\n",
      "MSE:  0.20976186049205467  / MAE:  0.35769278981192065  / R^2:  0.790793744502603\n",
      "The testing set:\n",
      "MSE:  0.050261716838188066  / MAE:  0.22419125058348746  / R^2:  -inf\n",
      "=======================================================\n",
      "190  iterations\n",
      "The training set:\n",
      "MSE:  0.2073157279233788  / MAE:  0.35589010797441833  / R^2:  0.7928530473400794\n",
      "The testing set:\n",
      "MSE:  1.0171204790164865  / MAE:  1.0085239109790538  / R^2:  -inf\n",
      "=======================================================\n",
      "191  iterations\n",
      "The training set:\n",
      "MSE:  0.20548528812606048  / MAE:  0.3546277309431272  / R^2:  0.7950528440072532\n",
      "The testing set:\n",
      "MSE:  1.6530866835203055  / MAE:  1.2857241864102524  / R^2:  -inf\n",
      "=======================================================\n",
      "192  iterations\n",
      "The training set:\n",
      "MSE:  0.20983931262048694  / MAE:  0.35796245694871737  / R^2:  0.790577834656096\n",
      "The testing set:\n",
      "MSE:  0.015970978782203403  / MAE:  0.12637633790470193  / R^2:  -inf\n",
      "=======================================================\n",
      "193  iterations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training set:\n",
      "MSE:  0.20892515807840617  / MAE:  0.35715932564219427  / R^2:  0.7897925155734112\n",
      "The testing set:\n",
      "MSE:  0.3654424845202862  / MAE:  0.6045183905558922  / R^2:  -inf\n",
      "=======================================================\n",
      "194  iterations\n",
      "The training set:\n",
      "MSE:  0.2093769084007346  / MAE:  0.35753346274793024  / R^2:  0.7898409320199564\n",
      "The testing set:\n",
      "MSE:  0.19180683988631006  / MAE:  0.4379575777244984  / R^2:  -inf\n",
      "=======================================================\n",
      "195  iterations\n",
      "The training set:\n",
      "MSE:  0.2098223503776  / MAE:  0.3580682243073215  / R^2:  0.7903733336910652\n",
      "The testing set:\n",
      "MSE:  0.01847297742921925  / MAE:  0.13591533184015425  / R^2:  -inf\n",
      "=======================================================\n",
      "196  iterations\n",
      "The training set:\n",
      "MSE:  0.2097448415031285  / MAE:  0.3576775175653122  / R^2:  0.7908164776680844\n",
      "The testing set:\n",
      "MSE:  0.05723949484582035  / MAE:  0.23924776873739148  / R^2:  -inf\n",
      "=======================================================\n",
      "197  iterations\n",
      "The training set:\n",
      "MSE:  0.20346865079195658  / MAE:  0.35409276101428866  / R^2:  0.7969021018603497\n",
      "The testing set:\n",
      "MSE:  2.3817586629595646  / MAE:  1.543294742736968  / R^2:  -inf\n",
      "=======================================================\n",
      "198  iterations\n",
      "The training set:\n",
      "MSE:  0.20958442114852374  / MAE:  0.3572303687887968  / R^2:  0.7907553175709965\n",
      "The testing set:\n",
      "MSE:  0.11551585989549074  / MAE:  0.33987624202861066  / R^2:  -inf\n",
      "=======================================================\n",
      "199  iterations\n",
      "The training set:\n",
      "MSE:  0.20946481016305724  / MAE:  0.35721928287442356  / R^2:  0.7906747463194246\n",
      "The testing set:\n",
      "MSE:  0.14965338305618656  / MAE:  0.3868505952640975  / R^2:  -inf\n",
      "=======================================================\n",
      "200  iterations\n",
      "The training set:\n",
      "MSE:  0.20940820954852227  / MAE:  0.35729606805341  / R^2:  0.791140196070028\n",
      "The testing set:\n",
      "MSE:  0.1676064937109133  / MAE:  0.4093977206957964  / R^2:  -inf\n",
      "=======================================================\n",
      "201  iterations\n",
      "The training set:\n",
      "MSE:  0.20987653149827593  / MAE:  0.35823327172271413  / R^2:  0.7904843791301113\n",
      "The testing set:\n",
      "MSE:  0.0023070703174701995  / MAE:  0.048031971825755804  / R^2:  -inf\n",
      "=======================================================\n",
      "202  iterations\n",
      "The training set:\n",
      "MSE:  0.20951064550340662  / MAE:  0.3573078048225844  / R^2:  0.7909226627932152\n",
      "The testing set:\n",
      "MSE:  0.13474415757272645  / MAE:  0.36707513886495563  / R^2:  -inf\n",
      "=======================================================\n",
      "203  iterations\n",
      "The training set:\n",
      "MSE:  0.20987818048166557  / MAE:  0.35833359706903  / R^2:  0.7903941461717631\n",
      "The testing set:\n",
      "MSE:  0.00012753068348593099  / MAE:  0.011292948396496416  / R^2:  -inf\n",
      "=======================================================\n",
      "204  iterations\n",
      "The training set:\n",
      "MSE:  0.2098871222274184  / MAE:  0.3583108516779187  / R^2:  0.7906167588233945\n",
      "The testing set:\n",
      "MSE:  0.0001740320552177822  / MAE:  0.013192120952211672  / R^2:  -inf\n",
      "=======================================================\n",
      "205  iterations\n",
      "The training set:\n",
      "MSE:  0.20980446753986798  / MAE:  0.3577881127344768  / R^2:  0.7901443490889417\n",
      "The testing set:\n",
      "MSE:  0.03039992804219519  / MAE:  0.17435575138834736  / R^2:  -inf\n",
      "=======================================================\n",
      "206  iterations\n",
      "The training set:\n",
      "MSE:  0.209779649402339  / MAE:  0.35776349592011547  / R^2:  0.7907632086705393\n",
      "The testing set:\n",
      "MSE:  0.04472869112236652  / MAE:  0.21149158641034996  / R^2:  -inf\n",
      "=======================================================\n",
      "207  iterations\n",
      "The training set:\n",
      "MSE:  0.20963071244775228  / MAE:  0.3573753970861856  / R^2:  0.7908836152396685\n",
      "The testing set:\n",
      "MSE:  0.10076044617763576  / MAE:  0.31742785980067306  / R^2:  -inf\n",
      "=======================================================\n",
      "208  iterations\n",
      "The training set:\n",
      "MSE:  0.20981842819866667  / MAE:  0.35775296606708185  / R^2:  0.7906313935749699\n",
      "The testing set:\n",
      "MSE:  0.03261316694003738  / MAE:  0.18059115963977135  / R^2:  -inf\n",
      "=======================================================\n",
      "209  iterations\n",
      "The training set:\n",
      "MSE:  0.20988727325337003  / MAE:  0.358339660542809  / R^2:  0.7906856643555176\n",
      "The testing set:\n",
      "MSE:  1.2195034274056677e-05  / MAE:  0.0034921389253660395  / R^2:  -inf\n",
      "=======================================================\n",
      "210  iterations\n",
      "The training set:\n",
      "MSE:  0.20917857309113078  / MAE:  0.35713889537548943  / R^2:  0.7885181421806582\n",
      "The testing set:\n",
      "MSE:  0.28321262407498793  / MAE:  0.5321772487386021  / R^2:  -inf\n",
      "=======================================================\n",
      "211  iterations\n",
      "The training set:\n",
      "MSE:  0.20311247179375708  / MAE:  0.3537147986030854  / R^2:  0.7970528816084281\n",
      "The testing set:\n",
      "MSE:  2.9621355288763356  / MAE:  1.7210855669827505  / R^2:  -inf\n",
      "=======================================================\n",
      "212  iterations\n",
      "The training set:\n",
      "MSE:  0.20957431727397352  / MAE:  0.35706489146866444  / R^2:  0.7897436734954446\n",
      "The testing set:\n",
      "MSE:  0.13978330011224557  / MAE:  0.37387604912891326  / R^2:  -inf\n",
      "=======================================================\n",
      "213  iterations\n",
      "The training set:\n",
      "MSE:  0.20910729349693258  / MAE:  0.3574982032036556  / R^2:  0.7890254084507676\n",
      "The testing set:\n",
      "MSE:  0.2947445438208933  / MAE:  0.5429038071526975  / R^2:  -inf\n",
      "=======================================================\n",
      "214  iterations\n",
      "The training set:\n",
      "MSE:  0.2095240804304305  / MAE:  0.3573773133507967  / R^2:  0.7904248046009145\n",
      "The testing set:\n",
      "MSE:  0.13513114519574387  / MAE:  0.367601884102549  / R^2:  -inf\n",
      "=======================================================\n",
      "215  iterations\n",
      "The training set:\n",
      "MSE:  0.20985110440851304  / MAE:  0.3580790009146796  / R^2:  0.7900657716317818\n",
      "The testing set:\n",
      "MSE:  0.013945291709784865  / MAE:  0.11809018464624765  / R^2:  -inf\n",
      "=======================================================\n",
      "216  iterations\n",
      "The training set:\n",
      "MSE:  0.2096033254060564  / MAE:  0.357619543067083  / R^2:  0.7906464338821444\n",
      "The testing set:\n",
      "MSE:  0.09949526165380394  / MAE:  0.315428695038679  / R^2:  -inf\n",
      "=======================================================\n",
      "217  iterations\n",
      "The training set:\n",
      "MSE:  0.20915541621910128  / MAE:  0.3568190919375743  / R^2:  0.7913358684373581\n",
      "The testing set:\n",
      "MSE:  0.2817903591337029  / MAE:  0.5308392969003923  / R^2:  -inf\n",
      "=======================================================\n",
      "218  iterations\n",
      "The training set:\n",
      "MSE:  0.20945566701450577  / MAE:  0.35710375365181335  / R^2:  0.7904580567011581\n",
      "The testing set:\n",
      "MSE:  0.15479436434102475  / MAE:  0.39343914947679615  / R^2:  -inf\n",
      "=======================================================\n",
      "219  iterations\n",
      "The training set:\n",
      "MSE:  0.20925797114159633  / MAE:  0.35722577552150886  / R^2:  0.790505505228132\n",
      "The testing set:\n",
      "MSE:  0.2356363840463617  / MAE:  0.4854239219963945  / R^2:  -inf\n",
      "=======================================================\n",
      "220  iterations\n",
      "The training set:\n",
      "MSE:  0.20983388094648248  / MAE:  0.3579960591113266  / R^2:  0.7907349881768432\n",
      "The testing set:\n",
      "MSE:  0.01801992087830251  / MAE:  0.13423829885059818  / R^2:  -inf\n",
      "=======================================================\n",
      "221  iterations\n",
      "The training set:\n",
      "MSE:  0.20907041665907142  / MAE:  0.3570601302654112  / R^2:  0.7910997870925031\n",
      "The testing set:\n",
      "MSE:  0.3398440770772992  / MAE:  0.5829614713489213  / R^2:  -inf\n",
      "=======================================================\n",
      "222  iterations\n",
      "The training set:\n",
      "MSE:  0.2097649859268923  / MAE:  0.3578912265115434  / R^2:  0.7907849771894804\n",
      "The testing set:\n",
      "MSE:  0.03979459110429831  / MAE:  0.19948581679983746  / R^2:  -inf\n",
      "=======================================================\n",
      "223  iterations\n",
      "The training set:\n",
      "MSE:  0.20982637795810832  / MAE:  0.3578747288684359  / R^2:  0.790708496752734\n",
      "The testing set:\n",
      "MSE:  0.02622769254982382  / MAE:  0.1619496605424779  / R^2:  -inf\n",
      "=======================================================\n",
      "224  iterations\n",
      "The training set:\n",
      "MSE:  0.2088697137951538  / MAE:  0.3566728423334829  / R^2:  0.7911134393080238\n",
      "The testing set:\n",
      "MSE:  0.4208582329695566  / MAE:  0.6487358730404513  / R^2:  -inf\n",
      "=======================================================\n",
      "225  iterations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training set:\n",
      "MSE:  0.2091755248243713  / MAE:  0.3568483433228206  / R^2:  0.7907022097709806\n",
      "The testing set:\n",
      "MSE:  0.28557071547976104  / MAE:  0.5343881692924732  / R^2:  -inf\n",
      "=======================================================\n",
      "226  iterations\n",
      "The training set:\n",
      "MSE:  0.20936283708495768  / MAE:  0.3568904063556598  / R^2:  0.790099591683705\n",
      "The testing set:\n",
      "MSE:  0.22044368817891272  / MAE:  0.46951431094154383  / R^2:  -inf\n",
      "=======================================================\n",
      "227  iterations\n",
      "The training set:\n",
      "MSE:  0.20966530464091498  / MAE:  0.3575862882841851  / R^2:  0.7906952427547788\n",
      "The testing set:\n",
      "MSE:  0.08367315031272862  / MAE:  0.28926311605997856  / R^2:  -inf\n",
      "=======================================================\n",
      "228  iterations\n",
      "The training set:\n",
      "MSE:  0.2095256093155025  / MAE:  0.3574893300624101  / R^2:  0.7907462598605193\n",
      "The testing set:\n",
      "MSE:  0.1275070488644087  / MAE:  0.3570812916751712  / R^2:  -inf\n",
      "=======================================================\n",
      "229  iterations\n",
      "The training set:\n",
      "MSE:  0.20857675200542014  / MAE:  0.35681478375899456  / R^2:  0.7904313694093913\n",
      "The testing set:\n",
      "MSE:  0.48913893780018247  / MAE:  0.6993846851341416  / R^2:  -inf\n",
      "=======================================================\n",
      "230  iterations\n",
      "The training set:\n",
      "MSE:  0.2054685070012604  / MAE:  0.3544175497417345  / R^2:  0.7950695811850507\n",
      "The testing set:\n",
      "MSE:  1.7219052320280772  / MAE:  1.3122138667260292  / R^2:  -inf\n",
      "=======================================================\n",
      "231  iterations\n",
      "The training set:\n",
      "MSE:  0.20968475082335689  / MAE:  0.35786526178481465  / R^2:  0.7881559903894637\n",
      "The testing set:\n",
      "MSE:  0.08362450948555146  / MAE:  0.2891790267041361  / R^2:  -inf\n",
      "=======================================================\n",
      "232  iterations\n",
      "The training set:\n",
      "MSE:  0.20944894653568225  / MAE:  0.3575929519444474  / R^2:  0.788467387802302\n",
      "The testing set:\n",
      "MSE:  0.1750602895641985  / MAE:  0.41840206687371717  / R^2:  -inf\n",
      "=======================================================\n",
      "233  iterations\n",
      "The training set:\n",
      "MSE:  0.2096081674403255  / MAE:  0.3576119870530124  / R^2:  0.7886572489711319\n",
      "The testing set:\n",
      "MSE:  0.11426882474497387  / MAE:  0.33803672100080173  / R^2:  -inf\n",
      "=======================================================\n",
      "234  iterations\n",
      "The training set:\n",
      "MSE:  0.20974785571353288  / MAE:  0.35756351973481354  / R^2:  0.7908252248260369\n",
      "The testing set:\n",
      "MSE:  0.057658483372377466  / MAE:  0.24012180944757489  / R^2:  -inf\n",
      "=======================================================\n",
      "235  iterations\n",
      "The training set:\n",
      "MSE:  0.20912240969565685  / MAE:  0.35717805142071707  / R^2:  0.7913100956546701\n",
      "The testing set:\n",
      "MSE:  0.2807799952788346  / MAE:  0.529886775904848  / R^2:  -inf\n",
      "=======================================================\n",
      "236  iterations\n",
      "The training set:\n",
      "MSE:  0.20836533470796112  / MAE:  0.3562374683485773  / R^2:  0.7908046487689444\n",
      "The testing set:\n",
      "MSE:  0.5871584130691655  / MAE:  0.7662626266947681  / R^2:  -inf\n",
      "=======================================================\n",
      "237  iterations\n",
      "The training set:\n",
      "MSE:  0.2087032402998876  / MAE:  0.3566224173795885  / R^2:  0.7892205125642013\n",
      "The testing set:\n",
      "MSE:  0.4841454792407584  / MAE:  0.6958056332344245  / R^2:  -inf\n",
      "=======================================================\n",
      "238  iterations\n",
      "The training set:\n",
      "MSE:  0.20903871489191445  / MAE:  0.35655038541126605  / R^2:  0.7903776909114076\n",
      "The testing set:\n",
      "MSE:  0.3580490701569821  / MAE:  0.5983720165223154  / R^2:  -inf\n",
      "=======================================================\n",
      "239  iterations\n",
      "The training set:\n",
      "MSE:  0.2097540924787064  / MAE:  0.35800732012768394  / R^2:  0.7907797966864867\n",
      "The testing set:\n",
      "MSE:  0.044199232453526624  / MAE:  0.21023613498522709  / R^2:  -inf\n",
      "=======================================================\n",
      "240  iterations\n",
      "The training set:\n",
      "MSE:  0.2087237107617555  / MAE:  0.3571721517084466  / R^2:  0.791789366313152\n",
      "The testing set:\n",
      "MSE:  0.42934267000271686  / MAE:  0.6552424513130364  / R^2:  -inf\n",
      "=======================================================\n",
      "241  iterations\n",
      "The training set:\n",
      "MSE:  0.20976480186485388  / MAE:  0.35774777800241725  / R^2:  0.7908064977867064\n",
      "The testing set:\n",
      "MSE:  0.0405327962820236  / MAE:  0.201327584503524  / R^2:  -inf\n",
      "=======================================================\n",
      "242  iterations\n",
      "The training set:\n",
      "MSE:  0.20861738717368353  / MAE:  0.35625794216172363  / R^2:  0.7902205232319947\n",
      "The testing set:\n",
      "MSE:  0.4923623619562601  / MAE:  0.7016853724827532  / R^2:  -inf\n",
      "=======================================================\n",
      "243  iterations\n",
      "The training set:\n",
      "MSE:  0.20468424778768932  / MAE:  0.35445162376481076  / R^2:  0.795647547695335\n",
      "The testing set:\n",
      "MSE:  2.024438856389744  / MAE:  1.422827767647843  / R^2:  -inf\n",
      "=======================================================\n",
      "244  iterations\n",
      "The training set:\n",
      "MSE:  0.20885535297699706  / MAE:  0.3572608652174797  / R^2:  0.7916671381240505\n",
      "The testing set:\n",
      "MSE:  0.3875554214162075  / MAE:  0.6225394938605964  / R^2:  -inf\n",
      "=======================================================\n",
      "245  iterations\n",
      "The training set:\n",
      "MSE:  0.20922410943008654  / MAE:  0.3570531114477833  / R^2:  0.7893690081753402\n",
      "The testing set:\n",
      "MSE:  0.25922251609679753  / MAE:  0.5091389948695715  / R^2:  -inf\n",
      "=======================================================\n",
      "246  iterations\n",
      "The training set:\n",
      "MSE:  0.20923269879393613  / MAE:  0.35684968309022924  / R^2:  0.7912806523693879\n",
      "The testing set:\n",
      "MSE:  0.24568924713239407  / MAE:  0.49567050258452344  / R^2:  -inf\n",
      "=======================================================\n",
      "247  iterations\n",
      "The training set:\n",
      "MSE:  0.2045573295761401  / MAE:  0.3545327837664536  / R^2:  0.7943031971164659\n",
      "The testing set:\n",
      "MSE:  1.9859593964474809  / MAE:  1.409240716289265  / R^2:  -inf\n",
      "=======================================================\n",
      "248  iterations\n",
      "The training set:\n",
      "MSE:  0.20988789030782198  / MAE:  0.35832374834058406  / R^2:  0.790408045494982\n",
      "The testing set:\n",
      "MSE:  9.069765720357432e-05  / MAE:  0.009523531761041926  / R^2:  -inf\n",
      "=======================================================\n",
      "249  iterations\n",
      "The training set:\n",
      "MSE:  0.2094122002956748  / MAE:  0.3577683372846587  / R^2:  0.7893024840221934\n",
      "The testing set:\n",
      "MSE:  0.20328201647453636  / MAE:  0.4508680699212757  / R^2:  -inf\n",
      "=======================================================\n",
      "250  iterations\n",
      "The training set:\n",
      "MSE:  0.20915264614606183  / MAE:  0.35597072428818566  / R^2:  0.7904450536329466\n",
      "The testing set:\n",
      "MSE:  0.4045024559623071  / MAE:  0.6360050754218138  / R^2:  -inf\n",
      "=======================================================\n",
      "251  iterations\n",
      "The training set:\n",
      "MSE:  0.20753014109286408  / MAE:  0.3555240966112714  / R^2:  0.7930294729229626\n",
      "The testing set:\n",
      "MSE:  0.9011286901078102  / MAE:  0.949277983578999  / R^2:  -inf\n",
      "=======================================================\n",
      "252  iterations\n",
      "The training set:\n",
      "MSE:  0.2095599723815308  / MAE:  0.3574529103141183  / R^2:  0.7909181288031752\n",
      "The testing set:\n",
      "MSE:  0.11420281063574492  / MAE:  0.3379390634948036  / R^2:  -inf\n",
      "=======================================================\n",
      "253  iterations\n",
      "The training set:\n",
      "MSE:  0.20942976944005176  / MAE:  0.3574591964035154  / R^2:  0.790988165556442\n",
      "The testing set:\n",
      "MSE:  0.16218993957773853  / MAE:  0.40272812116580403  / R^2:  -inf\n",
      "=======================================================\n",
      "254  iterations\n",
      "The training set:\n",
      "MSE:  0.20873533515942722  / MAE:  0.35681075229204684  / R^2:  0.7917877417590811\n",
      "The testing set:\n",
      "MSE:  0.4243742660068506  / MAE:  0.6514401476780891  / R^2:  -inf\n",
      "=======================================================\n",
      "255  iterations\n",
      "The training set:\n",
      "MSE:  0.20901031905958628  / MAE:  0.3568657909116114  / R^2:  0.7912387336136659\n",
      "The testing set:\n",
      "MSE:  0.34758380091420854  / MAE:  0.5895623808505835  / R^2:  -inf\n",
      "=======================================================\n",
      "256  iterations\n",
      "The training set:\n",
      "MSE:  0.20970828683541143  / MAE:  0.35769854836258747  / R^2:  0.7904624362048256\n",
      "The testing set:\n",
      "MSE:  0.07220418818359962  / MAE:  0.26870837014056637  / R^2:  -inf\n",
      "=======================================================\n",
      "257  iterations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training set:\n",
      "MSE:  0.20936646783329638  / MAE:  0.3570364086408413  / R^2:  0.7906821076631766\n",
      "The testing set:\n",
      "MSE:  0.197524183090659  / MAE:  0.44443692813565683  / R^2:  -inf\n",
      "=======================================================\n",
      "258  iterations\n",
      "The training set:\n",
      "MSE:  0.208382959643403  / MAE:  0.3564053014298722  / R^2:  0.7917841104711901\n",
      "The testing set:\n",
      "MSE:  0.5528880049117469  / MAE:  0.7435643919068119  / R^2:  -inf\n",
      "=======================================================\n",
      "259  iterations\n",
      "The training set:\n",
      "MSE:  0.20907208819735396  / MAE:  0.3568164084141809  / R^2:  0.7910379120105154\n",
      "The testing set:\n",
      "MSE:  0.29845077115551283  / MAE:  0.5463064809752057  / R^2:  -inf\n",
      "=======================================================\n",
      "260  iterations\n",
      "The training set:\n",
      "MSE:  0.2097505023973204  / MAE:  0.3577479620453989  / R^2:  0.7903598547482142\n",
      "The testing set:\n",
      "MSE:  0.05117654863864993  / MAE:  0.22622234336742675  / R^2:  -inf\n",
      "=======================================================\n",
      "261  iterations\n",
      "The training set:\n",
      "MSE:  0.20987068357633865  / MAE:  0.35809054308123894  / R^2:  0.790042869191041\n",
      "The testing set:\n",
      "MSE:  0.007501039873601304  / MAE:  0.08660854388339123  / R^2:  -inf\n",
      "=======================================================\n",
      "262  iterations\n",
      "The training set:\n",
      "MSE:  0.20984172859856895  / MAE:  0.35813577956003884  / R^2:  0.7907005776284942\n",
      "The testing set:\n",
      "MSE:  0.009500440544575611  / MAE:  0.0974702033678786  / R^2:  -inf\n",
      "=======================================================\n",
      "263  iterations\n",
      "The training set:\n",
      "MSE:  0.20981439896604784  / MAE:  0.35796881161232086  / R^2:  0.7904835754333416\n",
      "The testing set:\n",
      "MSE:  0.027306958663045566  / MAE:  0.16524817294919047  / R^2:  -inf\n",
      "=======================================================\n",
      "264  iterations\n",
      "The training set:\n",
      "MSE:  0.20979840653808993  / MAE:  0.35784676563808393  / R^2:  0.7907626108481536\n",
      "The testing set:\n",
      "MSE:  0.03300329326455947  / MAE:  0.18166808543208537  / R^2:  -inf\n",
      "=======================================================\n",
      "265  iterations\n",
      "The training set:\n",
      "MSE:  0.20896363571246912  / MAE:  0.3571257624307638  / R^2:  0.7900974146168176\n",
      "The testing set:\n",
      "MSE:  0.363814627556281  / MAE:  0.6031704796790713  / R^2:  -inf\n",
      "=======================================================\n",
      "266  iterations\n",
      "The training set:\n",
      "MSE:  0.20902436070658062  / MAE:  0.357084575991403  / R^2:  0.7895701000660519\n",
      "The testing set:\n",
      "MSE:  0.3460818572138505  / MAE:  0.5882872233984437  / R^2:  -inf\n",
      "=======================================================\n",
      "267  iterations\n",
      "The training set:\n",
      "MSE:  0.209793721626455  / MAE:  0.35790642763296093  / R^2:  0.7901231771436895\n",
      "The testing set:\n",
      "MSE:  0.03489825670916121  / MAE:  0.18681075105346912  / R^2:  -inf\n",
      "=======================================================\n",
      "268  iterations\n",
      "The training set:\n",
      "MSE:  0.20988645164522923  / MAE:  0.35832565949110023  / R^2:  0.7900304103936988\n",
      "The testing set:\n",
      "MSE:  8.731189068937275e-05  / MAE:  0.009344083191483943  / R^2:  -inf\n",
      "=======================================================\n",
      "269  iterations\n",
      "The training set:\n",
      "MSE:  0.20983199867845148  / MAE:  0.3579825372848208  / R^2:  0.7900848849297993\n",
      "The testing set:\n",
      "MSE:  0.020653832912495857  / MAE:  0.14371441442143462  / R^2:  -inf\n",
      "=======================================================\n",
      "270  iterations\n",
      "The training set:\n",
      "MSE:  0.2094965428448659  / MAE:  0.3573452801195431  / R^2:  0.7904204736405374\n",
      "The testing set:\n",
      "MSE:  0.14554057152360175  / MAE:  0.38149780015565193  / R^2:  -inf\n",
      "=======================================================\n",
      "271  iterations\n",
      "The training set:\n",
      "MSE:  0.20980345918176332  / MAE:  0.357883617697367  / R^2:  0.7901134357312456\n",
      "The testing set:\n",
      "MSE:  0.028171656652730966  / MAE:  0.16784414393338531  / R^2:  -inf\n",
      "=======================================================\n",
      "272  iterations\n",
      "The training set:\n",
      "MSE:  0.2076092164385115  / MAE:  0.35641105242189686  / R^2:  0.792502756890096\n",
      "The testing set:\n",
      "MSE:  0.8480723314280856  / MAE:  0.9209084272760705  / R^2:  -inf\n",
      "=======================================================\n",
      "273  iterations\n",
      "The training set:\n",
      "MSE:  0.20970502795791537  / MAE:  0.3576637670358087  / R^2:  0.7902470352116852\n",
      "The testing set:\n",
      "MSE:  0.06562569756797342  / MAE:  0.25617513065864417  / R^2:  -inf\n",
      "=======================================================\n",
      "274  iterations\n",
      "The training set:\n",
      "MSE:  0.20893929160479272  / MAE:  0.35653471914048396  / R^2:  0.7902801836006108\n",
      "The testing set:\n",
      "MSE:  0.40354640114648  / MAE:  0.635253021359584  / R^2:  -inf\n",
      "=======================================================\n",
      "275  iterations\n",
      "The training set:\n",
      "MSE:  0.20692739461206328  / MAE:  0.3555807828197297  / R^2:  0.7917423527365925\n",
      "The testing set:\n",
      "MSE:  1.1681422130977837  / MAE:  1.0808062791720743  / R^2:  -inf\n",
      "=======================================================\n",
      "276  iterations\n",
      "The training set:\n",
      "MSE:  0.209754415434757  / MAE:  0.35786293400020136  / R^2:  0.7893030816334214\n",
      "The testing set:\n",
      "MSE:  0.034799976134420804  / MAE:  0.18654751709529882  / R^2:  -inf\n",
      "=======================================================\n",
      "277  iterations\n",
      "The training set:\n",
      "MSE:  0.20977790956797063  / MAE:  0.3577336687720089  / R^2:  0.7888735129712278\n",
      "The testing set:\n",
      "MSE:  0.05270797257117881  / MAE:  0.22958216954105737  / R^2:  -inf\n",
      "=======================================================\n",
      "278  iterations\n",
      "The training set:\n",
      "MSE:  0.20968953965300496  / MAE:  0.3576791943913276  / R^2:  0.790227400384581\n",
      "The testing set:\n",
      "MSE:  0.06497037634876167  / MAE:  0.2548928722988575  / R^2:  -inf\n",
      "=======================================================\n",
      "279  iterations\n",
      "The training set:\n",
      "MSE:  0.20957580171052168  / MAE:  0.3572860390226932  / R^2:  0.7904757714870612\n",
      "The testing set:\n",
      "MSE:  0.13076609138820428  / MAE:  0.3616159445989685  / R^2:  -inf\n",
      "=======================================================\n",
      "280  iterations\n",
      "The training set:\n",
      "MSE:  0.2089263992428671  / MAE:  0.3565452316950963  / R^2:  0.7909908430816803\n",
      "The testing set:\n",
      "MSE:  0.3924981507889206  / MAE:  0.626496728474236  / R^2:  -inf\n",
      "=======================================================\n",
      "281  iterations\n",
      "The training set:\n",
      "MSE:  0.20904357863651  / MAE:  0.3568905344785999  / R^2:  0.7911801748094318\n",
      "The testing set:\n",
      "MSE:  0.31471513653473887  / MAE:  0.5609947740707919  / R^2:  -inf\n",
      "=======================================================\n",
      "282  iterations\n",
      "The training set:\n",
      "MSE:  0.20981763862039735  / MAE:  0.3579802482552885  / R^2:  0.7900992506760051\n",
      "The testing set:\n",
      "MSE:  0.02721423983925833  / MAE:  0.1649673902298825  / R^2:  -inf\n",
      "=======================================================\n",
      "283  iterations\n",
      "The training set:\n",
      "MSE:  0.2094530551318623  / MAE:  0.35733481811928725  / R^2:  0.7904639785794256\n",
      "The testing set:\n",
      "MSE:  0.14524870028856743  / MAE:  0.38111507486396734  / R^2:  -inf\n",
      "=======================================================\n",
      "284  iterations\n",
      "The training set:\n",
      "MSE:  0.20977247831171303  / MAE:  0.3578102392649508  / R^2:  0.7905254365527643\n",
      "The testing set:\n",
      "MSE:  0.036505583737286  / MAE:  0.1910643444949528  / R^2:  -inf\n",
      "=======================================================\n",
      "285  iterations\n",
      "The training set:\n",
      "MSE:  0.209849548757234  / MAE:  0.357998584248318  / R^2:  0.7906304607431499\n",
      "The testing set:\n",
      "MSE:  0.014321180199320051  / MAE:  0.11967113352567549  / R^2:  -inf\n",
      "=======================================================\n",
      "286  iterations\n",
      "The training set:\n",
      "MSE:  0.20971655992455496  / MAE:  0.35774824665751315  / R^2:  0.7903662846451601\n",
      "The testing set:\n",
      "MSE:  0.06114151608207766  / MAE:  0.24726810567090463  / R^2:  -inf\n",
      "=======================================================\n",
      "287  iterations\n",
      "The training set:\n",
      "MSE:  0.20966910108893683  / MAE:  0.3576458975971781  / R^2:  0.7907493115143085\n",
      "The testing set:\n",
      "MSE:  0.0760484942691271  / MAE:  0.27576891461716113  / R^2:  -inf\n",
      "=======================================================\n",
      "288  iterations\n",
      "The training set:\n",
      "MSE:  0.20988719395778452  / MAE:  0.3582855695582468  / R^2:  0.7905790325182449\n",
      "The testing set:\n",
      "MSE:  0.0005600260844335975  / MAE:  0.023664870260231674  / R^2:  -inf\n",
      "=======================================================\n",
      "289  iterations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training set:\n",
      "MSE:  0.20985560608673906  / MAE:  0.35812385807329256  / R^2:  0.7902868930460349\n",
      "The testing set:\n",
      "MSE:  0.007536777022776017  / MAE:  0.08681461295643733  / R^2:  -inf\n",
      "=======================================================\n",
      "290  iterations\n",
      "The training set:\n",
      "MSE:  0.20981904016018782  / MAE:  0.3579779728754022  / R^2:  0.7900978485810517\n",
      "The testing set:\n",
      "MSE:  0.019155438814698656  / MAE:  0.1384031748721779  / R^2:  -inf\n",
      "=======================================================\n",
      "291  iterations\n",
      "The training set:\n",
      "MSE:  0.209678008124715  / MAE:  0.3576440668197712  / R^2:  0.7902389364806157\n",
      "The testing set:\n",
      "MSE:  0.0697165058778315  / MAE:  0.26403883403361617  / R^2:  -inf\n",
      "=======================================================\n",
      "292  iterations\n",
      "The training set:\n",
      "MSE:  0.20962322353717325  / MAE:  0.3575566936413397  / R^2:  0.7902937427688383\n",
      "The testing set:\n",
      "MSE:  0.09614245706695576  / MAE:  0.310068471578385  / R^2:  -inf\n",
      "=======================================================\n",
      "293  iterations\n",
      "The training set:\n",
      "MSE:  0.20976993028227983  / MAE:  0.3578320977341371  / R^2:  0.7905044874162661\n",
      "The testing set:\n",
      "MSE:  0.038947824102796465  / MAE:  0.1973520309061867  / R^2:  -inf\n",
      "=======================================================\n",
      "294  iterations\n",
      "The training set:\n",
      "MSE:  0.20953722139885145  / MAE:  0.3573437953496568  / R^2:  0.7906050615398319\n",
      "The testing set:\n",
      "MSE:  0.14869629396442322  / MAE:  0.3856115843234267  / R^2:  -inf\n",
      "=======================================================\n",
      "295  iterations\n",
      "The training set:\n",
      "MSE:  0.20975643232751115  / MAE:  0.35779016081863646  / R^2:  0.7901604812132689\n",
      "The testing set:\n",
      "MSE:  0.05020655897142103  / MAE:  0.22406820160705765  / R^2:  -inf\n",
      "=======================================================\n",
      "296  iterations\n",
      "The training set:\n",
      "MSE:  0.20987647982880206  / MAE:  0.35817698341625614  / R^2:  0.7900403861600545\n",
      "The testing set:\n",
      "MSE:  0.0038461469831656755  / MAE:  0.06201731196339999  / R^2:  -inf\n",
      "=======================================================\n",
      "297  iterations\n",
      "The training set:\n",
      "MSE:  0.2060161928395847  / MAE:  0.3534778014462805  / R^2:  0.7942292920904934\n",
      "The testing set:\n",
      "MSE:  1.5928190387241126  / MAE:  1.2620693478268588  / R^2:  -inf\n",
      "=======================================================\n",
      "298  iterations\n",
      "The training set:\n",
      "MSE:  0.20882164845419315  / MAE:  0.3570868503517268  / R^2:  0.7913731023419988\n",
      "The testing set:\n",
      "MSE:  0.4144402665348174  / MAE:  0.6437703523266798  / R^2:  -inf\n",
      "=======================================================\n",
      "299  iterations\n",
      "The training set:\n",
      "MSE:  0.2087579162116013  / MAE:  0.3570200533467248  / R^2:  0.7904622365494235\n",
      "The testing set:\n",
      "MSE:  0.45143162359674116  / MAE:  0.671886615134385  / R^2:  -inf\n",
      "=======================================================\n",
      "300  iterations\n",
      "The training set:\n",
      "MSE:  0.20880779523830365  / MAE:  0.35633214030940646  / R^2:  0.7917608979819231\n",
      "The testing set:\n",
      "MSE:  0.41384035486932386  / MAE:  0.6433042475138213  / R^2:  -inf\n",
      "=======================================================\n",
      "301  iterations\n",
      "The training set:\n",
      "MSE:  0.20967040664004902  / MAE:  0.3575749099020827  / R^2:  0.7908793085077739\n",
      "The testing set:\n",
      "MSE:  0.08788263635651611  / MAE:  0.2964500571032431  / R^2:  -inf\n",
      "=======================================================\n",
      "302  iterations\n",
      "The training set:\n",
      "MSE:  0.20988482516997564  / MAE:  0.3581420456233848  / R^2:  0.7900320375132143\n",
      "The testing set:\n",
      "MSE:  0.009149438049193596  / MAE:  0.09565269493952377  / R^2:  -inf\n",
      "=======================================================\n",
      "303  iterations\n",
      "The training set:\n",
      "MSE:  0.2065467612611403  / MAE:  0.35481265513818916  / R^2:  0.7935934910528475\n",
      "The testing set:\n",
      "MSE:  1.3068783490206985  / MAE:  1.1431878012910646  / R^2:  -inf\n",
      "=======================================================\n",
      "304  iterations\n",
      "The training set:\n",
      "MSE:  0.20855944657008557  / MAE:  0.3570042935355345  / R^2:  0.7908105649739947\n",
      "The testing set:\n",
      "MSE:  0.5426587569486059  / MAE:  0.7366537564884916  / R^2:  -inf\n",
      "=======================================================\n",
      "305  iterations\n",
      "The training set:\n",
      "MSE:  0.20987450619679243  / MAE:  0.3582332806286247  / R^2:  0.7905249045625748\n",
      "The testing set:\n",
      "MSE:  0.002556417512745971  / MAE:  0.050561027607693765  / R^2:  -inf\n",
      "=======================================================\n",
      "306  iterations\n",
      "The training set:\n",
      "MSE:  0.209067359930229  / MAE:  0.35684297508096346  / R^2:  0.7913498518471274\n",
      "The testing set:\n",
      "MSE:  0.3087049278790314  / MAE:  0.5556122099801546  / R^2:  -inf\n",
      "=======================================================\n",
      "307  iterations\n",
      "The training set:\n",
      "MSE:  0.20768367050968434  / MAE:  0.3557881271678293  / R^2:  0.7924283429754746\n",
      "The testing set:\n",
      "MSE:  0.8079218936416674  / MAE:  0.8988447550281792  / R^2:  -inf\n",
      "=======================================================\n",
      "308  iterations\n",
      "The training set:\n",
      "MSE:  0.20931214349635888  / MAE:  0.3566785660586368  / R^2:  0.7912123788656208\n",
      "The testing set:\n",
      "MSE:  0.21833110905466224  / MAE:  0.4672591455013612  / R^2:  -inf\n",
      "=======================================================\n",
      "309  iterations\n",
      "The training set:\n",
      "MSE:  0.20956342719364376  / MAE:  0.3573602717919994  / R^2:  0.7906345140454301\n",
      "The testing set:\n",
      "MSE:  0.12565515550895862  / MAE:  0.3544787095284548  / R^2:  -inf\n",
      "=======================================================\n",
      "310  iterations\n",
      "The training set:\n",
      "MSE:  0.20987218328134863  / MAE:  0.35810115434485734  / R^2:  0.7906427368533047\n",
      "The testing set:\n",
      "MSE:  0.006199979833052921  / MAE:  0.07873995067977196  / R^2:  -inf\n",
      "=======================================================\n",
      "311  iterations\n",
      "The training set:\n",
      "MSE:  0.20863232245015403  / MAE:  0.35676737087655597  / R^2:  0.79193571581796\n",
      "The testing set:\n",
      "MSE:  0.47008313211101077  / MAE:  0.6856260876826455  / R^2:  -inf\n",
      "=======================================================\n",
      "312  iterations\n",
      "The training set:\n",
      "MSE:  0.20984792352511372  / MAE:  0.3580373993809293  / R^2:  0.7900689537751584\n",
      "The testing set:\n",
      "MSE:  0.01504292179875454  / MAE:  0.12264958947650229  / R^2:  -inf\n",
      "=======================================================\n",
      "313  iterations\n",
      "The training set:\n",
      "MSE:  0.20986133866471163  / MAE:  0.3580095701565851  / R^2:  0.7900555333216999\n",
      "The testing set:\n",
      "MSE:  0.013800307445650336  / MAE:  0.11747470981300756  / R^2:  -inf\n",
      "=======================================================\n",
      "314  iterations\n",
      "The training set:\n",
      "MSE:  0.20955747249434098  / MAE:  0.3573441023587203  / R^2:  0.7903595198562657\n",
      "The testing set:\n",
      "MSE:  0.1309963129126282  / MAE:  0.3619341278639363  / R^2:  -inf\n",
      "=======================================================\n",
      "315  iterations\n",
      "The training set:\n",
      "MSE:  0.2098377437824144  / MAE:  0.35807852876624896  / R^2:  0.7906121194011699\n",
      "The testing set:\n",
      "MSE:  0.019888475237774518  / MAE:  0.14102650544409912  / R^2:  -inf\n",
      "=======================================================\n",
      "316  iterations\n",
      "The training set:\n",
      "MSE:  0.20980419551842805  / MAE:  0.3579270730850046  / R^2:  0.7902152114389902\n",
      "The testing set:\n",
      "MSE:  0.025484559901687447  / MAE:  0.15963884208327073  / R^2:  -inf\n",
      "=======================================================\n",
      "317  iterations\n",
      "The training set:\n",
      "MSE:  0.20980660438852444  / MAE:  0.3580523443531047  / R^2:  0.7901102892786391\n",
      "The testing set:\n",
      "MSE:  0.024276588732753037  / MAE:  0.15580946291144526  / R^2:  -inf\n",
      "=======================================================\n",
      "318  iterations\n",
      "The training set:\n",
      "MSE:  0.20920920738129586  / MAE:  0.35714443366636633  / R^2:  0.7907079229203371\n",
      "The testing set:\n",
      "MSE:  0.24387532529432562  / MAE:  0.49383734700235626  / R^2:  -inf\n",
      "=======================================================\n",
      "319  iterations\n",
      "The training set:\n",
      "MSE:  0.2070419742840291  / MAE:  0.35532339905854815  / R^2:  0.7927240068200163\n",
      "The testing set:\n",
      "MSE:  1.0776410594579038  / MAE:  1.0380949183277528  / R^2:  -inf\n",
      "=======================================================\n",
      "320  iterations\n",
      "The training set:\n",
      "MSE:  0.20740902183111665  / MAE:  0.3560670441655368  / R^2:  0.7930214379392304\n",
      "The testing set:\n",
      "MSE:  0.9191232718739577  / MAE:  0.958709169599393  / R^2:  -inf\n",
      "=======================================================\n",
      "321  iterations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training set:\n",
      "MSE:  0.20959413945208602  / MAE:  0.3574568380193027  / R^2:  0.7906532895277016\n",
      "The testing set:\n",
      "MSE:  0.1099358751444639  / MAE:  0.3315657930855713  / R^2:  -inf\n",
      "=======================================================\n",
      "322  iterations\n",
      "The training set:\n",
      "MSE:  0.2070082721501666  / MAE:  0.3546996529830811  / R^2:  0.7923180691961891\n",
      "The testing set:\n",
      "MSE:  1.2157071938532162  / MAE:  1.1025911272331264  / R^2:  -inf\n",
      "=======================================================\n",
      "323  iterations\n",
      "The training set:\n",
      "MSE:  0.20928626899120326  / MAE:  0.35670109080833434  / R^2:  0.7904371451762956\n",
      "The testing set:\n",
      "MSE:  0.2539667926009394  / MAE:  0.5039511807714507  / R^2:  -inf\n",
      "=======================================================\n",
      "324  iterations\n",
      "The training set:\n",
      "MSE:  0.20987737328115988  / MAE:  0.35821590005852466  / R^2:  0.7906485655092068\n",
      "The testing set:\n",
      "MSE:  0.004492072068843391  / MAE:  0.06702292196587217  / R^2:  -inf\n",
      "=======================================================\n",
      "325  iterations\n",
      "The training set:\n",
      "MSE:  0.20984417940902558  / MAE:  0.3580949960151074  / R^2:  0.7900726993743254\n",
      "The testing set:\n",
      "MSE:  0.014981329918969742  / MAE:  0.12239824312043757  / R^2:  -inf\n",
      "=======================================================\n",
      "326  iterations\n",
      "The training set:\n",
      "MSE:  0.2090976454959529  / MAE:  0.35634615900919897  / R^2:  0.7912815864437839\n",
      "The testing set:\n",
      "MSE:  0.32168680002777944  / MAE:  0.5671744000109485  / R^2:  -inf\n",
      "=======================================================\n",
      "327  iterations\n",
      "The training set:\n",
      "MSE:  0.20979079764184755  / MAE:  0.3578701502975506  / R^2:  0.7901261022865143\n",
      "The testing set:\n",
      "MSE:  0.03592649183690166  / MAE:  0.18954284960636647  / R^2:  -inf\n",
      "=======================================================\n",
      "328  iterations\n",
      "The training set:\n",
      "MSE:  0.20948035990695874  / MAE:  0.3572827375497511  / R^2:  0.7910536327588955\n",
      "The testing set:\n",
      "MSE:  0.14627900187890042  / MAE:  0.38246437988249365  / R^2:  -inf\n",
      "=======================================================\n",
      "329  iterations\n",
      "The training set:\n",
      "MSE:  0.2067198133365094  / MAE:  0.3554045177787098  / R^2:  0.7928391503315374\n",
      "The testing set:\n",
      "MSE:  1.2467494310732676  / MAE:  1.1165793438324334  / R^2:  -inf\n",
      "=======================================================\n",
      "330  iterations\n",
      "The training set:\n",
      "MSE:  0.20981430770222928  / MAE:  0.35775999237287415  / R^2:  0.7891319349059991\n",
      "The testing set:\n",
      "MSE:  0.04425843666416511  / MAE:  0.21037689194435094  / R^2:  -inf\n",
      "=======================================================\n",
      "331  iterations\n",
      "The training set:\n",
      "MSE:  0.20936183406442352  / MAE:  0.35722440992325305  / R^2:  0.7905552357803713\n",
      "The testing set:\n",
      "MSE:  0.2117244195661648  / MAE:  0.46013521878483155  / R^2:  -inf\n",
      "=======================================================\n",
      "332  iterations\n",
      "The training set:\n",
      "MSE:  0.2090540680011357  / MAE:  0.3570263388841078  / R^2:  0.7908631237526379\n",
      "The testing set:\n",
      "MSE:  0.30367992340204963  / MAE:  0.55107161367834  / R^2:  -inf\n",
      "=======================================================\n",
      "333  iterations\n",
      "The training set:\n",
      "MSE:  0.20986991485403006  / MAE:  0.35810159239058215  / R^2:  0.7900821106237934\n",
      "The testing set:\n",
      "MSE:  0.008856361202601177  / MAE:  0.09410824194830747  / R^2:  -inf\n",
      "=======================================================\n",
      "334  iterations\n",
      "The training set:\n",
      "MSE:  0.20954528860858068  / MAE:  0.35743261428376466  / R^2:  0.7904068110761104\n",
      "The testing set:\n",
      "MSE:  0.13747419077515338  / MAE:  0.370775121569872  / R^2:  -inf\n",
      "=======================================================\n",
      "335  iterations\n",
      "The training set:\n",
      "MSE:  0.20987973494629028  / MAE:  0.3583199638449173  / R^2:  0.7900371297531842\n",
      "The testing set:\n",
      "MSE:  0.00029322673153184904  / MAE:  0.01712386438663449  / R^2:  -inf\n",
      "=======================================================\n",
      "336  iterations\n",
      "The training set:\n",
      "MSE:  0.2098849812159295  / MAE:  0.35828049353353464  / R^2:  0.7901666680657031\n",
      "The testing set:\n",
      "MSE:  0.0006947277417901529  / MAE:  0.026357688475853736  / R^2:  -inf\n",
      "=======================================================\n",
      "337  iterations\n",
      "The training set:\n",
      "MSE:  0.20981103052662406  / MAE:  0.35789820349278073  / R^2:  0.7901058613873053\n",
      "The testing set:\n",
      "MSE:  0.02891433462047977  / MAE:  0.17004215542176526  / R^2:  -inf\n",
      "=======================================================\n",
      "338  iterations\n",
      "The training set:\n",
      "MSE:  0.20982366562116528  / MAE:  0.35799884935308063  / R^2:  0.7900932212878865\n",
      "The testing set:\n",
      "MSE:  0.024885395921281164  / MAE:  0.15775105679925305  / R^2:  -inf\n",
      "=======================================================\n",
      "339  iterations\n",
      "The training set:\n",
      "MSE:  0.2096701938220299  / MAE:  0.35763884535919105  / R^2:  0.7902467538786183\n",
      "The testing set:\n",
      "MSE:  0.08215219133174369  / MAE:  0.28662203567022493  / R^2:  -inf\n",
      "=======================================================\n",
      "340  iterations\n",
      "The training set:\n",
      "MSE:  0.2098863895710552  / MAE:  0.35827870600974604  / R^2:  0.790030472492461\n",
      "The testing set:\n",
      "MSE:  0.0006379377876316715  / MAE:  0.025257430344983067  / R^2:  -inf\n",
      "=======================================================\n",
      "341  iterations\n",
      "The training set:\n",
      "MSE:  0.2091015562981851  / MAE:  0.35725895436674265  / R^2:  0.7908156166450356\n",
      "The testing set:\n",
      "MSE:  0.2824976561438629  / MAE:  0.5315050857177783  / R^2:  -inf\n",
      "=======================================================\n",
      "342  iterations\n",
      "The training set:\n",
      "MSE:  0.20988408827529947  / MAE:  0.3582642853748766  / R^2:  0.7900327746997812\n",
      "The testing set:\n",
      "MSE:  0.0009273689520388163  / MAE:  0.03045273307995222  / R^2:  -inf\n",
      "=======================================================\n",
      "343  iterations\n",
      "The training set:\n",
      "MSE:  0.20975246416662896  / MAE:  0.3577245619275794  / R^2:  0.7901644509459762\n",
      "The testing set:\n",
      "MSE:  0.05401943826359849  / MAE:  0.23242082149325283  / R^2:  -inf\n",
      "=======================================================\n",
      "344  iterations\n",
      "The training set:\n",
      "MSE:  0.20942847172868714  / MAE:  0.3574445830333227  / R^2:  0.7904885717203137\n",
      "The testing set:\n",
      "MSE:  0.17456869487302437  / MAE:  0.4178141870174161  / R^2:  -inf\n",
      "=======================================================\n",
      "345  iterations\n",
      "The training set:\n",
      "MSE:  0.20932638657397368  / MAE:  0.35728545044250937  / R^2:  0.7906598557929294\n",
      "The testing set:\n",
      "MSE:  0.22422735493932297  / MAE:  0.4735265092255374  / R^2:  -inf\n",
      "=======================================================\n",
      "346  iterations\n",
      "The training set:\n",
      "MSE:  0.20954534829577814  / MAE:  0.3574125674461615  / R^2:  0.7904408796802253\n",
      "The testing set:\n",
      "MSE:  0.131040641118062  / MAE:  0.3619953606305777  / R^2:  -inf\n",
      "=======================================================\n",
      "347  iterations\n",
      "The training set:\n",
      "MSE:  0.20953459104889746  / MAE:  0.35721520799903617  / R^2:  0.7910209454857516\n",
      "The testing set:\n",
      "MSE:  0.1457760610699457  / MAE:  0.3818063135543278  / R^2:  -inf\n",
      "=======================================================\n",
      "348  iterations\n",
      "The training set:\n",
      "MSE:  0.20953491934036936  / MAE:  0.3575575000455699  / R^2:  0.7903820819437498\n",
      "The testing set:\n",
      "MSE:  0.12487527402070617  / MAE:  0.35337695739918606  / R^2:  -inf\n",
      "=======================================================\n",
      "349  iterations\n",
      "The training set:\n",
      "MSE:  0.20973108449885758  / MAE:  0.3578416432110987  / R^2:  0.7901858390824315\n",
      "The testing set:\n",
      "MSE:  0.05980858312577055  / MAE:  0.2445579340887769  / R^2:  -inf\n",
      "=======================================================\n",
      "350  iterations\n",
      "The training set:\n",
      "MSE:  0.2095139147101379  / MAE:  0.3572285701890457  / R^2:  0.7904030948941092\n",
      "The testing set:\n",
      "MSE:  0.15872697921928194  / MAE:  0.39840554617033375  / R^2:  -inf\n",
      "=======================================================\n",
      "351  iterations\n",
      "The training set:\n",
      "MSE:  0.2096244262232796  / MAE:  0.3577626475447315  / R^2:  0.7904271589400054\n",
      "The testing set:\n",
      "MSE:  0.10394799576511268  / MAE:  0.32240967070656035  / R^2:  -inf\n",
      "=======================================================\n",
      "352  iterations\n",
      "The training set:\n",
      "MSE:  0.2093557629223706  / MAE:  0.356937150463952  / R^2:  0.7909640435077469\n",
      "The testing set:\n",
      "MSE:  0.21203523952547257  / MAE:  0.46047284341801586  / R^2:  -inf\n",
      "=======================================================\n",
      "353  iterations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training set:\n",
      "MSE:  0.2098868527682159  / MAE:  0.3583187245151827  / R^2:  0.7900300091118236\n",
      "The testing set:\n",
      "MSE:  0.00015892636295857535  / MAE:  0.012606599976146438  / R^2:  -inf\n",
      "=======================================================\n",
      "354  iterations\n",
      "The training set:\n",
      "MSE:  0.20963245741019892  / MAE:  0.357698572507843  / R^2:  0.7902845052381904\n",
      "The testing set:\n",
      "MSE:  0.10659856730898006  / MAE:  0.3264943603019508  / R^2:  -inf\n",
      "=======================================================\n",
      "355  iterations\n",
      "The training set:\n",
      "MSE:  0.2093789829384363  / MAE:  0.3575574850933914  / R^2:  0.7905380801135312\n",
      "The testing set:\n",
      "MSE:  0.1998863935974331  / MAE:  0.44708656163816096  / R^2:  -inf\n",
      "=======================================================\n",
      "356  iterations\n",
      "The training set:\n",
      "MSE:  0.2096604214504757  / MAE:  0.3576630765162771  / R^2:  0.7908191554404289\n",
      "The testing set:\n",
      "MSE:  0.0940152320084945  / MAE:  0.3066190339957624  / R^2:  -inf\n",
      "=======================================================\n",
      "357  iterations\n",
      "The training set:\n",
      "MSE:  0.20942226696177849  / MAE:  0.35735252383864047  / R^2:  0.7904947789449878\n",
      "The testing set:\n",
      "MSE:  0.21794336171263876  / MAE:  0.46684404431527105  / R^2:  -inf\n",
      "=======================================================\n",
      "358  iterations\n",
      "The training set:\n",
      "MSE:  0.20974068934239207  / MAE:  0.3577812245738905  / R^2:  0.7901762304343297\n",
      "The testing set:\n",
      "MSE:  0.06252145511012085  / MAE:  0.25004290653829964  / R^2:  -inf\n",
      "=======================================================\n",
      "359  iterations\n",
      "The training set:\n",
      "MSE:  0.20980942278137946  / MAE:  0.3578807955167244  / R^2:  0.7901074697693926\n",
      "The testing set:\n",
      "MSE:  0.028970771731291377  / MAE:  0.17020802487336306  / R^2:  -inf\n",
      "=======================================================\n",
      "360  iterations\n",
      "The training set:\n",
      "MSE:  0.20890088466909096  / MAE:  0.3563334032085127  / R^2:  0.7916217203380815\n",
      "The testing set:\n",
      "MSE:  0.3898559119576614  / MAE:  0.6243844264214646  / R^2:  -inf\n",
      "=======================================================\n",
      "361  iterations\n",
      "The training set:\n",
      "MSE:  0.20980129767553757  / MAE:  0.3577234691105718  / R^2:  0.7906499133348703\n",
      "The testing set:\n",
      "MSE:  0.03382459314227838  / MAE:  0.18391463547602288  / R^2:  -inf\n",
      "=======================================================\n",
      "362  iterations\n",
      "The training set:\n",
      "MSE:  0.2084877246633499  / MAE:  0.35602549574668857  / R^2:  0.7914646167716892\n",
      "The testing set:\n",
      "MSE:  0.6370271187308634  / MAE:  0.7981397864602813  / R^2:  -inf\n",
      "=======================================================\n",
      "363  iterations\n",
      "The training set:\n",
      "MSE:  0.20971410903582427  / MAE:  0.3579381949381176  / R^2:  0.7907211347468093\n",
      "The testing set:\n",
      "MSE:  0.06504054132477616  / MAE:  0.2550304713652393  / R^2:  -inf\n",
      "=======================================================\n",
      "364  iterations\n",
      "The training set:\n",
      "MSE:  0.2097774482858187  / MAE:  0.35812201539629857  / R^2:  0.7901394569303463\n",
      "The testing set:\n",
      "MSE:  0.0450747848248298  / MAE:  0.21230823070439309  / R^2:  -inf\n",
      "=======================================================\n",
      "365  iterations\n",
      "The training set:\n",
      "MSE:  0.2098716705199366  / MAE:  0.35830729038471765  / R^2:  0.7900451973739316\n",
      "The testing set:\n",
      "MSE:  0.012876341363106286  / MAE:  0.11347396777722318  / R^2:  -inf\n",
      "=======================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 創建布林運算矩陣，和MSE&MAE矩陣\n",
    "boolean=np.ones(len(data), dtype=bool)\n",
    "train_MSE=np.zeros(len(data))\n",
    "train_MAE=np.zeros(len(data))\n",
    "test_MSE=np.zeros(len(data))\n",
    "test_MAE=np.zeros(len(data))\n",
    "# 跑迴圈讓每筆資料輪流當驗證集\n",
    "for i in range(0,len(data)):\n",
    "    # 設定其中一筆資料為測試集\n",
    "    boolean[i]=False\n",
    "    # 分訓練集和測試集\n",
    "    train=data[boolean]\n",
    "    test=data[~boolean]\n",
    "    train_x,train_y=train.iloc[:,:6].values,train.iloc[:,6].values\n",
    "    test_x,test_y=test.iloc[:,:6].values,test.iloc[:,6].values\n",
    "    # 計算並輸出結果\n",
    "    print(i,\" iterations\")\n",
    "    train_MSE[i],train_MAE[i],test_MSE[i],test_MAE[i]=Regression(0.1,train_x,train_y,test_x,test_y,0)\n",
    "    print(\"=======================================================\")\n",
    "    # 把原本的資料調回True\n",
    "    boolean[i]=True\n",
    "# 繪圖\n",
    "x_axis=np.linspace(1,366,366)\n",
    "plt.rcParams[\"figure.figsize\"] = (18,18)\n",
    "plt.plot(x_axis,test_MSE,\"g-\",linewidth=3,label=\"testing MSE\")\n",
    "plt.plot(x_axis,test_MAE,\"y-\",linewidth=3,label=\"testing MAE\")\n",
    "plt.plot(x_axis,train_MSE,\"b-\",linewidth=3,label=\"training MSE\")\n",
    "plt.plot(x_axis,train_MAE,\"r-\",linewidth=3,label=\"training MAE\")\n",
    "plt.xticks(fontsize=30)\n",
    "plt.yticks(fontsize=30)\n",
    "plt.legend(loc=\"upper left\",fontsize=30)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-Fold Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0  fold\n",
      "The training set:\n",
      "MSE:  0.21073731712809363  / MAE:  0.3572060456787752  / R^2:  0.7930678577473054\n",
      "The testing set:\n",
      "MSE:  0.1769499224969389  / MAE:  0.35516120796441936  / R^2:  0.5601339675162496\n",
      "=======================================================\n",
      "1  fold\n",
      "The training set:\n",
      "MSE:  0.2125505710269998  / MAE:  0.36018563962859307  / R^2:  0.7884882936401596\n",
      "The testing set:\n",
      "MSE:  0.11510600691237192  / MAE:  0.26282632121366917  / R^2:  0.8142418976709065\n",
      "=======================================================\n",
      "2  fold\n",
      "The training set:\n",
      "MSE:  0.2092115460617797  / MAE:  0.3540985971057335  / R^2:  0.7902744916445598\n",
      "The testing set:\n",
      "MSE:  0.23392264742396032  / MAE:  0.4363872504620776  / R^2:  0.7793582335145288\n",
      "=======================================================\n",
      "3  fold\n",
      "The training set:\n",
      "MSE:  0.20631715673203774  / MAE:  0.3558514772483457  / R^2:  0.7916400569511016\n",
      "The testing set:\n",
      "MSE:  0.5829652466829797  / MAE:  0.48340010154999713  / R^2:  0.5485096425629414\n",
      "=======================================================\n",
      "4  fold\n",
      "The training set:\n",
      "MSE:  0.20966550664446176  / MAE:  0.3569024879671469  / R^2:  0.7928594499285706\n",
      "The testing set:\n",
      "MSE:  0.21408951136319462  / MAE:  0.38490651685258964  / R^2:  0.5020801002828115\n",
      "=======================================================\n",
      "5  fold\n",
      "The training set:\n",
      "MSE:  0.20528342592503887  / MAE:  0.3525927528112085  / R^2:  0.7948371592874374\n",
      "The testing set:\n",
      "MSE:  0.3791574328176282  / MAE:  0.5103113325688302  / R^2:  0.6108856196025905\n",
      "=======================================================\n",
      "6  fold\n",
      "The training set:\n",
      "MSE:  0.21026057751240426  / MAE:  0.35821704932453874  / R^2:  0.79011286020111\n",
      "The testing set:\n",
      "MSE:  0.18878432204842963  / MAE:  0.37595484732838896  / R^2:  0.79071592637832\n",
      "=======================================================\n",
      "7  fold\n",
      "The training set:\n",
      "MSE:  0.2123197183828797  / MAE:  0.3598018419909158  / R^2:  0.7900662283796429\n",
      "The testing set:\n",
      "MSE:  0.10530066099438484  / MAE:  0.28976667174986703  / R^2:  0.8196681177488782\n",
      "=======================================================\n",
      "8  fold\n",
      "The training set:\n",
      "MSE:  0.20287979617298527  / MAE:  0.3511699798809723  / R^2:  0.7968607612462122\n",
      "The testing set:\n",
      "MSE:  0.46104539750619067  / MAE:  0.5951504511405414  / R^2:  0.5396819845632133\n",
      "=======================================================\n",
      "9  fold\n",
      "The training set:\n",
      "MSE:  0.20944012752522456  / MAE:  0.35647889907456537  / R^2:  0.7931742851460508\n",
      "The testing set:\n",
      "MSE:  0.2269184811832258  / MAE:  0.3481551912390976  / R^2:  0.586837186191303\n",
      "=======================================================\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1296x1296 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 把資料分成10組\n",
    "k=10\n",
    "train_MSE=np.zeros(k)\n",
    "train_MAE=np.zeros(k)\n",
    "test_MSE=np.zeros(k)\n",
    "test_MAE=np.zeros(k)\n",
    "# 跑迴圈輪流計算每組\n",
    "for i in range(0,k):\n",
    "    # 初始化我們的布林值\n",
    "    boolean=np.ones(len(data), dtype=bool)\n",
    "    # 計算一組資料最少會有幾筆資料\n",
    "    k_num=int(len(data)/k)\n",
    "    # 跑迴圈標記所要測試集\n",
    "    for j in range(0,k_num+1):\n",
    "        if(i+k_num*j<len(data)):\n",
    "            boolean[i+k_num*j]=False\n",
    "    # 分訓練集和測試集\n",
    "    train=data[boolean]\n",
    "    test=data[~boolean]\n",
    "    train_x,train_y=train.iloc[:,:6].values,train.iloc[:,6].values\n",
    "    test_x,test_y=test.iloc[:,:6].values,test.iloc[:,6].values\n",
    "     # 計算並輸出結果\n",
    "    print(i,\" fold\")\n",
    "    train_MSE[i],train_MAE[i],test_MSE[i],test_MAE[i]=Regression(0.1,train_x,train_y,test_x,test_y,0)\n",
    "    print(\"=======================================================\")\n",
    "# 計算並輸出結果\n",
    "x_axis=np.linspace(1,10,10)\n",
    "plt.rcParams[\"figure.figsize\"] = (18,18)\n",
    "plt.plot(x_axis,test_MSE,\"g-\",linewidth=3,label=\"testing MSE\")\n",
    "plt.plot(x_axis,test_MAE,\"y-\",linewidth=3,label=\"testing MAE\")\n",
    "plt.plot(x_axis,train_MSE,\"b-\",linewidth=3,label=\"training MSE\")\n",
    "plt.plot(x_axis,train_MAE,\"r-\",linewidth=3,label=\"training MAE\")\n",
    "plt.xticks(fontsize=30)\n",
    "plt.yticks(fontsize=30)\n",
    "plt.legend(loc=\"upper left\",fontsize=30)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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