MLR3f15.ipynb

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      "name": "MLR3f15.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNnVFtX/TTZ4Ku8PO8Hy/wO",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/VishnuBhaarath/Rainfallpred/blob/master/MLR3f15.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cou_fBaEkP6G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "from pandas import DataFrame\n",
        "import numpy as np\n",
        "from numpy import hstack\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from sklearn import preprocessing"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ACH71at4kV9t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df=pd.read_excel('trend_anna_imd_21.xlsx')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2hp4FJfQkk9o",
        "colab_type": "code",
        "outputId": "9cc90aba-ba74-4fa4-fe57-384f1b71b823",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 207,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1. Cuddalore</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1965-01-01 00:00:00</td>\n",
              "      <td>10.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1965-01-02 00:00:00</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>3</th>\n",
              "      <td>1965-01-03 00:00:00</td>\n",
              "      <td>13.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1965-01-04 00:00:00</td>\n",
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            "text/plain": [
              "            Unnamed: 0  Unnamed: 1\n",
              "0         1. Cuddalore         NaN\n",
              "1  1965-01-01 00:00:00        10.6\n",
              "2  1965-01-02 00:00:00         0.0\n",
              "3  1965-01-03 00:00:00        13.0\n",
              "4  1965-01-04 00:00:00         0.0"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 207
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RB9D0KOIkl-g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Cuddalore=df[3:19360]\n",
        "Panruti=df[23016:38721]\n",
        "Dharmapuri=df[42408:58082]\n",
        "Harur=df[61738:77443]\n",
        "Palacode=df[81158:96804]\n",
        "Pennagaram=df[100460:116165]\n",
        "Chengam=df[119821:135526]\n",
        "Sathanur=df[138817:154522]\n",
        "Tiruvanamalai=df[158178:173883]\n",
        "Sankarapuram=df[177539:193244]\n",
        "Tirukoilur=df[196899:212604]\n",
        "ulundurpet=df[216260:231965]\n",
        "Villupuram=df[235621:251326]\n",
        "Denkanikottai=df[274343:290048]\n",
        "Hosur=df[293703:309408]\n",
        "Krishnagiri=df[313063:328768]\n",
        "Marandahalli=df[332059:347764]\n",
        "Rayakottai=df[353250:368955]\n",
        "Uthangarai=df[372611:388316]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z7bqZet_0j0n",
        "colab_type": "code",
        "outputId": "06061951-7c94-4fdd-b663-6bf6b69cdb4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "Pennagaram.head()"
      ],
      "execution_count": 209,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Unnamed: 0</th>\n",
              "      <th>Unnamed: 1</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>100460</th>\n",
              "      <td>1975-01-03 00:00:00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100461</th>\n",
              "      <td>1975-01-04 00:00:00</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>100462</th>\n",
              "      <td>1975-01-05 00:00:00</td>\n",
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              "    <tr>\n",
              "      <th>100463</th>\n",
              "      <td>1975-01-06 00:00:00</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>100464</th>\n",
              "      <td>1975-01-07 00:00:00</td>\n",
              "      <td>0.0</td>\n",
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            "text/plain": [
              "                 Unnamed: 0  Unnamed: 1\n",
              "100460  1975-01-03 00:00:00         0.0\n",
              "100461  1975-01-04 00:00:00         0.0\n",
              "100462  1975-01-05 00:00:00         0.0\n",
              "100463  1975-01-06 00:00:00         0.0\n",
              "100464  1975-01-07 00:00:00         0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 209
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8lT9ZtR0mBO1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Cuddalore.columns = ['Date', 'Rainfall']\n",
        "Panruti.columns= ['Date', 'Rainfall']\n",
        "Dharmapuri.columns= ['Date', 'Rainfall']\n",
        "Harur.columns= ['Date', 'Rainfall']\n",
        "Palacode.columns= ['Date', 'Rainfall']\n",
        "Pennagaram.columns= ['Date', 'Rainfall']\n",
        "Chengam.columns= ['Date', 'Rainfall']\n",
        "Sathanur.columns= ['Date', 'Rainfall']\n",
        "Tiruvanamalai.columns= ['Date', 'Rainfall']\n",
        "Sankarapuram.columns= ['Date', 'Rainfall']\n",
        "Tirukoilur.columns= ['Date', 'Rainfall']\n",
        "ulundurpet.columns= ['Date', 'Rainfall']\n",
        "Villupuram.columns= ['Date', 'Rainfall']\n",
        "Denkanikottai.columns= ['Date', 'Rainfall']\n",
        "Hosur.columns= ['Date', 'Rainfall']\n",
        "Krishnagiri.columns= ['Date', 'Rainfall']\n",
        "Marandahalli.columns= ['Date', 'Rainfall']\n",
        "Rayakottai.columns= ['Date', 'Rainfall']\n",
        "Uthangarai.columns= ['Date', 'Rainfall']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ot2FqhbOm6dJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train=Cuddalore[0:15000]\n",
        "test=Cuddalore[15000:19004]\n",
        "train1=Panruti[0:12000]\n",
        "test1=Panruti[12000:15705]\n",
        "train2=Dharmapuri[0:12000]\n",
        "test2=Dharmapuri[12000:15674]\n",
        "train3=Harur[0:12000]\n",
        "test3=Harur[12000:15704]\n",
        "train4=Palacode[0:12000]\n",
        "test4=Palacode[12000:15646]\n",
        "train5=Pennagaram[0:12000]\n",
        "test5=Pennagaram[12000:15705]\n",
        "train6=Chengam[0:12000]\n",
        "test6=Chengam[12000:15705]\n",
        "train7=Sathanur[0:12000]\n",
        "test7=Sathanur[12000:15705]\n",
        "train8=Tiruvanamalai[0:12000]\n",
        "test8=Tiruvanamalai[12000:15165]\n",
        "train9=Sankarapuram[0:12000]\n",
        "test9=Sankarapuram[12000:15705]\n",
        "train10=Tirukoilur[0:12000]\n",
        "test10=Tirukoilur[12000:15705]\n",
        "train11=ulundurpet[0:12000]\n",
        "test11=ulundurpet[12000:15345]\n",
        "train12=Villupuram[0:12000]\n",
        "test12=Villupuram[12000:15705]\n",
        "train13=Denkanikottai[0:12000]\n",
        "test13=Denkanikottai[12000:15705]\n",
        "train14=Hosur[0:12000]\n",
        "test14=Hosur[12000:15678]\n",
        "train15=Krishnagiri[0:12000]\n",
        "test15=Krishnagiri[12000:15705]\n",
        "train16=Marandahalli[0:12000]\n",
        "test16=Marandahalli[12000:15705]\n",
        "train17=Rayakottai[0:12000]\n",
        "test17=Rayakottai[12000:15705]\n",
        "train18=Uthangarai[0:12000]\n",
        "test18=Uthangarai[12000:15705]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eWdgMaVPnV7A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train=train['Rainfall']\n",
        "X_test=test['Rainfall']\n",
        "X_train1=train1['Rainfall']\n",
        "X_test1=test1['Rainfall']\n",
        "X_train2=train2['Rainfall']\n",
        "X_test2=test2['Rainfall']\n",
        "X_train3=train3['Rainfall']\n",
        "X_test3=test3['Rainfall']\n",
        "X_train4=train4['Rainfall']\n",
        "X_test4=test4['Rainfall']\n",
        "X_train5=train5['Rainfall']\n",
        "X_test5=test5['Rainfall']\n",
        "X_train6=train6['Rainfall']\n",
        "X_test6=test6['Rainfall']\n",
        "X_train7=train7['Rainfall']\n",
        "X_test7=test7['Rainfall']\n",
        "X_train8=train8['Rainfall']\n",
        "X_test8=test8['Rainfall']\n",
        "X_train9=train9['Rainfall']\n",
        "X_test9=test9['Rainfall']\n",
        "X_train10=train10['Rainfall']\n",
        "X_test10=test10['Rainfall']\n",
        "X_train11=train11['Rainfall']\n",
        "X_test11=test11['Rainfall']\n",
        "X_train12=train12['Rainfall']\n",
        "X_test12=test12['Rainfall']\n",
        "X_train13=train13['Rainfall']\n",
        "X_test13=test13['Rainfall']\n",
        "X_train14=train14['Rainfall']\n",
        "X_test14=test14['Rainfall']\n",
        "X_train15=train15['Rainfall']\n",
        "X_test15=test15['Rainfall']\n",
        "X_train16=train16['Rainfall']\n",
        "X_test16=test16['Rainfall']\n",
        "X_train17=train17['Rainfall']\n",
        "X_test17=test17['Rainfall']\n",
        "X_train18=train18['Rainfall']\n",
        "X_test18=test18['Rainfall']\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QdTZBwX0mnEs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "min_max_scaler = preprocessing.MinMaxScaler()\n",
        "\n",
        "x_scaled = min_max_scaler.fit_transform(X_train.values.reshape(-1,1))\n",
        "X_train = pd.DataFrame(x_scaled)\n",
        "\n",
        "x_scaled1 = min_max_scaler.fit_transform(X_train1.values.reshape(-1,1))\n",
        "X_train1 = pd.DataFrame(x_scaled1)\n",
        "\n",
        "x_scaled2 = min_max_scaler.fit_transform(X_train2.values.reshape(-1,1))\n",
        "X_train2 = pd.DataFrame(x_scaled2)\n",
        "\n",
        "x_scaled3 = min_max_scaler.fit_transform(X_train3.values.reshape(-1,1))\n",
        "X_train3 = pd.DataFrame(x_scaled3)\n",
        "\n",
        "x_scaled4 = min_max_scaler.fit_transform(X_train4.values.reshape(-1,1))\n",
        "X_train4 = pd.DataFrame(x_scaled4)\n",
        "\n",
        "x_scaled5 = min_max_scaler.fit_transform(X_train5.values.reshape(-1,1))\n",
        "X_train5 = pd.DataFrame(x_scaled5)\n",
        "\n",
        "x_scaled6 = min_max_scaler.fit_transform(X_train6.values.reshape(-1,1))\n",
        "X_train6 = pd.DataFrame(x_scaled6)\n",
        "\n",
        "x_scaled7 = min_max_scaler.fit_transform(X_train7.values.reshape(-1,1))\n",
        "X_train7 = pd.DataFrame(x_scaled7)\n",
        "\n",
        "x_scaled8 = min_max_scaler.fit_transform(X_train8.values.reshape(-1,1))\n",
        "X_train8 = pd.DataFrame(x_scaled8)\n",
        "\n",
        "x_scaled9 = min_max_scaler.fit_transform(X_train9.values.reshape(-1,1))\n",
        "X_train9 = pd.DataFrame(x_scaled9)\n",
        "\n",
        "x_scaled10 = min_max_scaler.fit_transform(X_train10.values.reshape(-1,1))\n",
        "X_train10 = pd.DataFrame(x_scaled10)\n",
        "\n",
        "x_scaled11 = min_max_scaler.fit_transform(X_train11.values.reshape(-1,1))\n",
        "X_train11 = pd.DataFrame(x_scaled11)\n",
        "\n",
        "x_scaled1112 = min_max_scaler.fit_transform(X_train12.values.reshape(-1,1))\n",
        "X_train12 = pd.DataFrame(x_scaled1112)\n",
        "\n",
        "x_scaled13 = min_max_scaler.fit_transform(X_train13.values.reshape(-1,1))\n",
        "X_train13 = pd.DataFrame(x_scaled13)\n",
        "\n",
        "x_scaled14 = min_max_scaler.fit_transform(X_train14.values.reshape(-1,1))\n",
        "X_train14 = pd.DataFrame(x_scaled14)\n",
        "\n",
        "x_scaled15 = min_max_scaler.fit_transform(X_train15.values.reshape(-1,1))\n",
        "X_train15 = pd.DataFrame(x_scaled15)\n",
        "\n",
        "x_scaled16 = min_max_scaler.fit_transform(X_train16.values.reshape(-1,1))\n",
        "X_train16 = pd.DataFrame(x_scaled16)\n",
        "\n",
        "x_scaled17 = min_max_scaler.fit_transform(X_train17.values.reshape(-1,1))\n",
        "X_train17 = pd.DataFrame(x_scaled17)\n",
        "\n",
        "x_scaled18 = min_max_scaler.fit_transform(X_train18.values.reshape(-1,1))\n",
        "X_train18 = pd.DataFrame(x_scaled18)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CxbM6N2-oCPd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_scaled1 = min_max_scaler.fit_transform(X_test.values.reshape(-1,1))\n",
        "X_test = pd.DataFrame(x_scaled1)\n",
        "\n",
        "x_scaled11 = min_max_scaler.fit_transform(X_test1.values.reshape(-1,1))\n",
        "X_test1 = pd.DataFrame(x_scaled11)\n",
        "\n",
        "x_scaled12 = min_max_scaler.fit_transform(X_test2.values.reshape(-1,1))\n",
        "X_test2 = pd.DataFrame(x_scaled12)\n",
        "\n",
        "x_scaled13 = min_max_scaler.fit_transform(X_test3.values.reshape(-1,1))\n",
        "X_test3 = pd.DataFrame(x_scaled13)\n",
        "\n",
        "x_scaled14 = min_max_scaler.fit_transform(X_test4.values.reshape(-1,1))\n",
        "X_test4 = pd.DataFrame(x_scaled14)\n",
        "\n",
        "x_scaled15 = min_max_scaler.fit_transform(X_test5.values.reshape(-1,1))\n",
        "X_test5 = pd.DataFrame(x_scaled15)\n",
        "\n",
        "x_scaled16 = min_max_scaler.fit_transform(X_test6.values.reshape(-1,1))\n",
        "X_test6 = pd.DataFrame(x_scaled16)\n",
        "\n",
        "x_scaled17 = min_max_scaler.fit_transform(X_test7.values.reshape(-1,1))\n",
        "X_test7 = pd.DataFrame(x_scaled17)\n",
        "\n",
        "x_scaled18 = min_max_scaler.fit_transform(X_test8.values.reshape(-1,1))\n",
        "X_test8 = pd.DataFrame(x_scaled18)\n",
        "\n",
        "x_scaled19 = min_max_scaler.fit_transform(X_test9.values.reshape(-1,1))\n",
        "X_test9 = pd.DataFrame(x_scaled19)\n",
        "\n",
        "x_scaled110 = min_max_scaler.fit_transform(X_test10.values.reshape(-1,1))\n",
        "X_test10 = pd.DataFrame(x_scaled110)\n",
        "\n",
        "x_scaled111 = min_max_scaler.fit_transform(X_test11.values.reshape(-1,1))\n",
        "X_test11 = pd.DataFrame(x_scaled111)\n",
        "\n",
        "x_scaled112 = min_max_scaler.fit_transform(X_test12.values.reshape(-1,1))\n",
        "X_test12 = pd.DataFrame(x_scaled112)\n",
        "\n",
        "x_scaled113 = min_max_scaler.fit_transform(X_test13.values.reshape(-1,1))\n",
        "X_test13 = pd.DataFrame(x_scaled113)\n",
        "\n",
        "x_scaled114 = min_max_scaler.fit_transform(X_test14.values.reshape(-1,1))\n",
        "X_test14 = pd.DataFrame(x_scaled114)\n",
        "\n",
        "x_scaled115= min_max_scaler.fit_transform(X_test15.values.reshape(-1,1))\n",
        "X_test15 = pd.DataFrame(x_scaled115)\n",
        "\n",
        "x_scaled116 = min_max_scaler.fit_transform(X_test16.values.reshape(-1,1))\n",
        "X_test16 = pd.DataFrame(x_scaled116)\n",
        "\n",
        "x_scaled117 = min_max_scaler.fit_transform(X_test17.values.reshape(-1,1))\n",
        "X_test17 = pd.DataFrame(x_scaled117)\n",
        "\n",
        "x_scaled118 = min_max_scaler.fit_transform(X_test18.values.reshape(-1,1))\n",
        "X_test18 = pd.DataFrame(x_scaled118)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VZNCwdZhrTQD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "in_seq = np.array([X_train[0]])\n",
        "in_seqtest1 = np.array([X_test[0]])\n",
        "\n",
        "in_seq1 = np.array([X_train1[0]])\n",
        "in_seqt11 = np.array([X_test1[0]])\n",
        "\n",
        "in_seq12 = np.array([X_train2[0]])\n",
        "in_seqt112 = np.array([X_test2[0]])\n",
        "\n",
        "in_seq13 = np.array([X_train3[0]])\n",
        "in_seqt113 = np.array([X_test3[0]])\n",
        "\n",
        "in_seq14 = np.array([X_train4[0]])\n",
        "in_seqt114 = np.array([X_test4[0]])\n",
        "\n",
        "in_seq15 = np.array([X_train5[0]])\n",
        "in_seqt115 = np.array([X_test5[0]])\n",
        "\n",
        "in_seq16 = np.array([X_train6[0]])\n",
        "in_seqt116 = np.array([X_test6[0]])\n",
        "\n",
        "in_seq17 = np.array([X_train7[0]])\n",
        "in_seqt117 = np.array([X_test7[0]])\n",
        "\n",
        "in_seq18 = np.array([X_train8[0]])\n",
        "in_seqt118 = np.array([X_test8[0]])\n",
        "\n",
        "in_seq19 = np.array([X_train9[0]])\n",
        "in_seqt119 = np.array([X_test9[0]])\n",
        "\n",
        "in_seq110 = np.array([X_train10[0]])\n",
        "in_seqt1110 = np.array([X_test10[0]])\n",
        "\n",
        "in_seq111 = np.array([X_train11[0]])\n",
        "in_seqt1111 = np.array([X_test11[0]])\n",
        "\n",
        "in_seq112 = np.array([X_train12[0]])\n",
        "in_seqt1112 = np.array([X_test12[0]])\n",
        "\n",
        "in_seq113 = np.array([X_train13[0]])\n",
        "in_seqt1113 = np.array([X_test13[0]])\n",
        "\n",
        "in_seq114 = np.array([X_train14[0]])\n",
        "in_seqt1114 = np.array([X_test14[0]])\n",
        "\n",
        "in_seq115 = np.array([X_train15[0]])\n",
        "in_seqt1115 = np.array([X_test15[0]])\n",
        "\n",
        "in_seq116 = np.array([X_train16[0]])\n",
        "in_seqt1116 = np.array([X_test16[0]])\n",
        "\n",
        "in_seq117 = np.array([X_train17[0]])\n",
        "in_seqt1117 = np.array([X_test17[0]])\n",
        "\n",
        "in_seq118 = np.array([X_train18[0]])\n",
        "in_seqt1118 = np.array([X_test18[0]])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2vtw9-GtrbhY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "in_seq = in_seq.reshape((15000, 1))\n",
        "in_seqtest1 = in_seqtest1.reshape((4004, 1))\n",
        "\n",
        "in_seq1 = in_seq1.reshape((12000, 1))\n",
        "in_seqt11 = in_seqt11.reshape((3705, 1))\n",
        "\n",
        "in_seq12 = in_seq12.reshape((12000, 1))\n",
        "in_seqt112 = in_seqt112.reshape((3674, 1))\n",
        "\n",
        "in_seq13 = in_seq13.reshape((12000, 1))\n",
        "in_seqt113 = in_seqt113.reshape((3704, 1))\n",
        "\n",
        "in_seq14 = in_seq14.reshape((12000, 1))\n",
        "in_seqt114 = in_seqt114.reshape((3646, 1))\n",
        "\n",
        "in_seq15 = in_seq15.reshape((12000, 1))\n",
        "in_seqt115 = in_seqt115.reshape((3705, 1))\n",
        "\n",
        "in_seq16 = in_seq16.reshape((12000, 1))\n",
        "in_seqt116 = in_seqt116.reshape((3705, 1))\n",
        "\n",
        "in_seq17 = in_seq17.reshape((12000, 1))\n",
        "in_seqt117 = in_seqt117.reshape((3705, 1))\n",
        "\n",
        "in_seq18 = in_seq18.reshape((12000, 1))\n",
        "in_seqt118 = in_seqt118.reshape((3165, 1))\n",
        "\n",
        "in_seq19 = in_seq19.reshape((12000, 1))\n",
        "in_seqt119 = in_seqt119.reshape((3705, 1))\n",
        "\n",
        "in_seq110 = in_seq110.reshape((12000, 1))\n",
        "in_seqt1110 = in_seqt1110.reshape((3705, 1))\n",
        "\n",
        "in_seq111 = in_seq111.reshape((12000, 1))\n",
        "in_seqt1111 = in_seqt1111.reshape((3345, 1))\n",
        "\n",
        "in_seq112 = in_seq112.reshape((12000, 1))\n",
        "in_seqt1112 = in_seqt1112.reshape((3705, 1))\n",
        "\n",
        "in_seq113 = in_seq113.reshape((12000, 1))\n",
        "in_seqt1113 = in_seqt1113.reshape((3705, 1))\n",
        "\n",
        "in_seq114 = in_seq114.reshape((12000, 1))\n",
        "in_seqt1114 = in_seqt1114.reshape((3678, 1))\n",
        "\n",
        "in_seq115 = in_seq115.reshape((12000, 1))\n",
        "in_seqt1115 = in_seqt1115.reshape((3705, 1))\n",
        "\n",
        "in_seq116 = in_seq116.reshape((12000, 1))\n",
        "in_seqt1116 = in_seqt1116.reshape((3705, 1))\n",
        "\n",
        "in_seq117 = in_seq117.reshape((12000, 1))\n",
        "in_seqt1117 = in_seqt1117.reshape((3705, 1))\n",
        "\n",
        "in_seq118 = in_seq118.reshape((12000, 1))\n",
        "in_seqt1118 = in_seqt1118.reshape((3704, 1))\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dcthfPpSoN2u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def split_sequence(sequence, n_steps):\n",
        "\tX, y = list(), list()\n",
        "\tfor i in range(len(sequence)):\n",
        "\t\t# find the end of this pattern\n",
        "\t\tend_ix = i + n_steps\n",
        "\t\t# check if we are beyond the sequence\n",
        "\t\tif end_ix > len(sequence)-1:\n",
        "\t\t\tbreak\n",
        "\t\t# gather input and output parts of the pattern\n",
        "\t\tseq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
        "\t\tX.append(seq_x)\n",
        "\t\ty.append(seq_y)\n",
        "\treturn np.array(X), np.array(y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NLCLOXnyptMN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cuddataset1 = hstack((in_seqtest1))\n",
        "cuddataset = hstack((in_seq))\n",
        "\n",
        "pandataset1 = hstack((in_seqt11))\n",
        "pandataset = hstack((in_seq1))\n",
        "\n",
        "dhadataset1 = hstack((in_seqt112))\n",
        "dhadataset = hstack((in_seq12))\n",
        "\n",
        "hardataset1 = hstack((in_seqt113))\n",
        "hardataset = hstack((in_seq13))\n",
        "\n",
        "paldataset1 = hstack((in_seqt114))\n",
        "paldataset = hstack((in_seq14))\n",
        "\n",
        "pendataset1 = hstack((in_seqt115))\n",
        "pendataset = hstack((in_seq15))\n",
        "\n",
        "chedataset1 = hstack((in_seqt116))\n",
        "chedataset = hstack((in_seq16))\n",
        "\n",
        "satdataset1 = hstack((in_seqt117))\n",
        "satdataset = hstack((in_seq17))\n",
        "\n",
        "Tirdataset1 = hstack((in_seqt118))\n",
        "Tirdataset = hstack((in_seq18))\n",
        "\n",
        "Sandataset1 = hstack((in_seqt119))\n",
        "Sandataset = hstack((in_seq19))\n",
        "\n",
        "Tirudataset1 = hstack((in_seqt1110))\n",
        "Tirudataset = hstack((in_seq110))\n",
        "\n",
        "uludataset1 = hstack((in_seqt1111))\n",
        "uludataset = hstack((in_seq111))\n",
        "\n",
        "vildataset1 = hstack((in_seqt1112))\n",
        "vildataset = hstack((in_seq112))\n",
        "\n",
        "bardataset1 = hstack((in_seqt1113))\n",
        "bardataset = hstack((in_seq113))\n",
        "\n",
        "Dendataset1 = hstack((in_seqt1114))\n",
        "Dendataset = hstack((in_seq114))\n",
        "\n",
        "kridataset1 = hstack((in_seqt1115))\n",
        "kridataset = hstack((in_seq115))\n",
        "\n",
        "mardataset1 = hstack((in_seqt1116))\n",
        "mardataset = hstack((in_seq116))\n",
        "\n",
        "raydataset1 = hstack((in_seqt1117))\n",
        "raydataset = hstack((in_seq117))\n",
        "\n",
        "uthdataset1 = hstack((in_seqt1118))\n",
        "uthdataset = hstack((in_seq118))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VXevl6Wdrz2e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "n_steps = 3\n",
        "# convert into input/output\n",
        "X, y = split_sequence(cuddataset, n_steps)\n",
        "X1 , y1 = split_sequence(pandataset, n_steps)\n",
        "X2 , y2 = split_sequence(dhadataset, n_steps)\n",
        "X3 , y3 = split_sequence(hardataset, n_steps)\n",
        "X4 , y4 = split_sequence(paldataset, n_steps)\n",
        "X5 , y5 = split_sequence(pendataset, n_steps)\n",
        "X6, y6 = split_sequence(chedataset, n_steps)\n",
        "X7, y7 = split_sequence(satdataset, n_steps)\n",
        "X8, y8 = split_sequence(Tirdataset, n_steps)\n",
        "X9, y9 = split_sequence(Sandataset, n_steps)\n",
        "X10, y10 = split_sequence(Tirudataset, n_steps)\n",
        "X11, y11 = split_sequence(uludataset, n_steps)\n",
        "X12 , y12 = split_sequence(vildataset, n_steps)\n",
        "X13, y13 = split_sequence(bardataset, n_steps)\n",
        "X14, y14 = split_sequence(Dendataset, n_steps)\n",
        "X15, y15 = split_sequence(kridataset, n_steps)\n",
        "X16, y16 = split_sequence(mardataset, n_steps)\n",
        "X17, y17 = split_sequence(raydataset, n_steps)\n",
        "X18, y18 = split_sequence(uthdataset, n_steps)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wChOw-PbTin3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_test,y_test = split_sequence(cuddataset1, n_steps)\n",
        "X_test1,y_test1 = split_sequence(pandataset1, n_steps)\n",
        "X_test2,y_test2= split_sequence(dhadataset1, n_steps)\n",
        "X_test3,y_test3 = split_sequence(hardataset1, n_steps)\n",
        "X_test4,y_test4 = split_sequence(paldataset1, n_steps)\n",
        "X_test5,y_test5 = split_sequence(pendataset1, n_steps)\n",
        "X_test6,y_test6 = split_sequence(chedataset1, n_steps)\n",
        "X_test7,y_test7 = split_sequence(satdataset1, n_steps)\n",
        "X_test8,y_test8 = split_sequence(Tirdataset1, n_steps)\n",
        "X_test9,y_test9 = split_sequence(Sandataset1, n_steps)\n",
        "X_test10,y_test10 = split_sequence(Tirudataset1, n_steps)\n",
        "X_test11,y_test11= split_sequence(uludataset1, n_steps)\n",
        "X_test12,y_test12 = split_sequence(vildataset1, n_steps)\n",
        "X_test13,y_test13 = split_sequence(bardataset1, n_steps)\n",
        "X_test14,y_test14 = split_sequence(Dendataset1, n_steps)\n",
        "X_test15,y_test15 = split_sequence(kridataset1, n_steps)\n",
        "X_test16,y_test16 = split_sequence(mardataset1, n_steps)\n",
        "X_test17,y_test17= split_sequence(raydataset1, n_steps)\n",
        "X_test18,y_test18= split_sequence(uthdataset1, n_steps)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8zmd12H9K3iu",
        "colab_type": "code",
        "outputId": "dc817bdc-3abb-4d88-a3fb-3f0f7c1ea01f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X.shape"
      ],
      "execution_count": 221,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14997, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 221
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "feOCg2VVLLfe",
        "colab_type": "code",
        "outputId": "acc0c7ed-5daa-4ff2-9ef7-f5352b8e5195",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y.shape"
      ],
      "execution_count": 222,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14997,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 222
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sV15YyHcQ2vj",
        "colab_type": "code",
        "outputId": "92648572-2214-4a55-c62c-50f8b9ff0ab5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "from sklearn import linear_model\n",
        "reg = linear_model.Ridge(alpha=.5)\n",
        "reg.fit(X8,y8)"
      ],
      "execution_count": 223,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "      normalize=False, random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 223
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AvagXvUsREvQ",
        "colab_type": "code",
        "outputId": "dfde231b-c587-41c1-a091-1af3970c50e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "reg.coef_"
      ],
      "execution_count": 224,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.05534541, 0.12390131, 0.18994158])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 224
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jX4gtpjmSnDz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "fe865f36-648e-4a5c-b36c-ffa6bb40e7b7"
      },
      "source": [
        "Station8=reg.predict(X8)\n",
        "s=pd.DataFrame(Station8)\n",
        "s1=pd.DataFrame(y8)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 225,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.271074\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 225
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DF81OVdzRPEq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "5705d2ce-65ca-4d90-a329-37137a208359"
      },
      "source": [
        "Station8test=reg.predict(X_test8)\n",
        "s=pd.DataFrame(Station8test)\n",
        "s1=pd.DataFrame(y_test8)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 226,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.20198\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 226
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Uimz7o0-9S5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "363bcc37-7f6f-437d-e1d3-303ece86ca3c"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(y8)\n",
        "plt.plot(Station8)"
      ],
      "execution_count": 227,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f159785a2b0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 227
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BH7kGEZ4UzYF0xyyU14nM4hYz1P0rSDIlLILBq8fQlp9sc317ziZU7A7mYjQzzB7frI6M90WXT3v+xl045YgD5H9gEykNnYguJKLVRFRKRINNwv2ciAQRDXQvi/5l/LLUcvBXZ+b3vMU0tTmeGh4kIe41si+RQruQytjH3fKGuueTpRg+eZ0rcVnhh+0drNBWtRNF4+fPz3RtzkUPSw2diKIAhgM4D0A5gHlENFoIsUITrhuAOwDMyUdG/ciWmtThADsl3bL81mjfmbsJI79O+Qh7tdFYLuRDlpbt3IMtu/ciHjPXdZzUpdvZDV6NMUB+601GQ/8ugFIhxHohRBOAdwFcphPu3wAeAxDoI1CyhqcmYYO2wIc0n0d9HZwFH2pkhOknCyswefUO23Gf/cQUDBqZX50kFxOS35QCGYLVS+yTtCkH8ik2ZAR6bwCbVd/LlWttENHJAPoKIcaaRUREtxBRCRGVVFYGf9m2V/K8UOmu2FLryKXRD6yr3BPKE2mCRPBePfZI98PlW3Kb23KTnL1ciCgC4CkAf7YKK4QYIYQYKIQY2LNnz1yT9pygax5mmuKmqgZcPGw6Hh67snAZ8hFBFkZa8xmBeD8VFXoj6/S1QkzA5zMNGYFeAaCv6nsf5VqabgC+CWAKEZUBOA3A6I4wMaoeao2XPLWoEJz68EQ8N2mtZTiz4XvVntS8wEIfnJlplMuOJKJyfdbHLTblkom/qcXeaM1OnsPiySSD1yaXeQAGEFF/IkoAuBLA6PRNIUSNEKKHEKKfEKIfgNkALhVClOQlxwVGtp09NzlTgJbvasAbswvj/aKlsm4fnvhijWkYbZsK0nSAF53fq/Jxsn2u3ot6yup2E6fTRznmPmcLqIIuq7fs3qt73Y8vIUsvFyFECxHdDmACgCiAUUKI5UT0IIASIcRo8xjCi1kn//XIOdhY5W8fXj82SBmC9PJhgjGSEiLVH7RtSwiBW96YDwCo3tPkQc7sIbWwSAgxDsA4zbV/GIQ9O/dsBZ9deaz8groY+kB6GuUgoO+jDkMY6uftuZvaPpt5swghMHHlDpxz3MGIRrx7cl767xIyci9IGnFHP0SjEDQ2t+KZidZzHbnCVZmJHRVFdkuMsUu34jevl3juCswCPYC44YtMyBTa3uvhxvhhUjRrzsGF1F/+egOGTjSf63ALFurmyBxWY1bjO2pTTgQVBvb2zHjy13J5LxcL3Cx6r60XfhTajc2ttoeoYRFOjc2tOf3ej/WpR9AW4MmiVqz88oQs0DW41faCYLLQrhzVI98N9bj7x+OEw/bLOR63i9uL6pNpM+MdbLHrmYeO/7uAJeqXkczjeP3MbHJxiUJ2GqMh21crt+NvHy42/J1MW9tc3YBfj5yNPfty2wgszZz1VbjtrQWmYdxYaVfI8l+4aRdemVHmeryF0mT10gmB7M0Jo7L/dNGWPKTlepRtsIYeIm56zdz1Xxh8Vn8fOnENZpRW4aieXQGkjmKrbWzGfsVxR3m64dV5aGjKzbRghFeaZ/qYO1n2tbTiwc9W4IiDOucpR+Z4rTUWkubWJB78bAX+cO7ROLhbsW4Yp+1md0NzDjkrDKyhd2BMJ4BUjf6P7yzMf2ZsEDQB9b9FW/DWnE14bPxqAN7Ppejhwyw5YtKqHXhj9kbc/+ky1+NuMtnXyC/zBCzQ84QfhY7TLG3Yucdxmm60cxnbsvvl7V6E6c5u1entzrv4RIZYIuXS63JadsvGnqXcv7BAVxBC4JFxK1FaWZ9x3Wn16jUo14SOw46s/ZlefoK4PStgvwP3GzwWw77Kvw+4Gi/lrxeT9EFtS7Koi9RO+T7w2XIk87RZWmgE+ubqBikfUCO21jRixLT1mL/R2dmaMvhJo9I2v6wlzyrxs7GqAVWSh3ho8frgjAU6h1+nD8R+6ktnPuB2xZSsYMvnsN0Lk4Cbdf/QmBX47Rvy20PZTTnX4rFTvu/M3Zy3LXdDI9B/8PhkfP8/k7zORhtBMLmYCZoPSjIPf/7hE1McpZlPOSIjMH7235lYrxl1LXPQmdx4jlzjmLuh2tHvvG6KbvSFkV9vwITl211JK59KhtejklAI9OtGzc1b3IXSa655eQ4uefbrvKYhY3LZ19Kq/J85AZTreaZuotdpzDpyzV6PvRMk+7jVsP2XL85yITP+mgBtbG7F3z5cjF0ee5B4PZJ0i1C4LU5dk/vpR7lqEbk2B7uucLlilN+qenc3FQtHN8kk17bi5Pd62r2REHpucqlOmt5pjmYjk48XVOB9zWgw32ma4ceRtR1CoaEHBbe0AKexZJtc1HG6K3rLdzWg3mJx0u4GZy8Pvbza6cCF7rOy6eku+PGpgGlqSWLyKvMzW2XMD263OzedGPJJvkYELNDzhB/7YVYT0pEWbgmQMx+bjCtemGX69hn40ER3EisA6g5Y0FXBPh3iPPHFatzw6jzMWV/ldVZ8hdcvYBboCnYnM6avDf4h1/lm5dZaU02kxaHrll0buqM0fLaXi96tqavl2qDURKHNN0eZsjbBa9u3E/z6knSD0An09+dtxvbaxrynoz3izS8rxRhrCm0Dz5f9epGN814L8X7aXN2AusZsAW/WM/LnFdIx+2PgBXplXaZ/9N8+WpJXr5c0zTYPzPUbRi6MbsseP77n/JgnALbNF04fY96GakyW1O7t5OIHj0/Gz/47s+272VJ5v5Frm1ixNT9+5XYJtEDfsnsvTn042w5b5eD4N7uCrNXCXKCnlflNkOhNinrtR9uR+WKFtZ+1GqftadDIOc5+qMKov6zd0e7zP2TC6pzTcU5h2/HHCyo8SDWbQAv0rTX6phU3BadRBWkbtFaAB8EEUwg7cT5LIbOIjR9Gxv8+zPh5b/78Zc1ey3PidXL9K/NyTNV9QuGHnk+MKkjvdHAr/NSv9rUksXBTtg3WfZOLfrnktNinAOVoloRaQMqYFexktxsa0AnOtlkwzUOeyixdvbU6tnM7v3eLQr68djrcDiOfBFyge/0+tIfXSnu+m/q+llYUxaJSYe96b1Gec9OOnS0P7PL4eHfNClOK7sRBVAfgasuwdrRKt9uedo3Bt//1RcHSzgeunVTmTjSOCbTJxc/o2tA9yIds+m406GPvG4+Vmskho2jLdznfSM0TClR5KWEuiYcNakap/ARuc2vScFuLfI8cOhohFej2azPXdtWRFpsMmbDK8N7Sipqc49eaabSdvqQstUlVUPqsNv9e158e+TRVlO9qcKVdyJDrUxRKw85XGwi0QHdaKFtr9rpw4npm4lV72u1plXX7UO3A0ybfyB1yax1q+OR1xjcl6yQX+bG9Ntt26af5CVmacnR9tdP8g1g++UJ/X5xwEGiB7pTTH52E4+4f73iPbysuHjY9L/HmiheN1o/aqJol5bvz6pG0uboBd72vf3B3ofy0tTtn6uGVV1bY3jNevzg7pEBPk6HpGVSEkaad7eXS/lm72Kk9jM+lm09wUkxO+9Glz83A+yWbLcM5rbl3521y+EtrZJ85nyczyZSLJ4qE3fCSjW5peWFMR04JpUBnuamPlMkl77nwjoEPTcSwr9ZmaVFrt9fr/8BHFNpMEFTlw6mGvFtyT5qpa8x3mPSaUAr0aofbsuZCIZu/0603ZX7l9ZBRy9LyGstVubLsrN8nfeycW5OEZi6SMu6TZtmwI3T9Vq/5wul76KGxK9zNiEcE2g9ddtGP8e/VZ313kBafZ9zc53nt9jpc8lx+T3ECgjshltfj/STjXlJegzveXZi/jOQBvTaaVhqsXuRW5eL1qlwpDZ2ILiSi1URUSkSDde7fRUQriGgJEX1FREe4n1VGj6XlNeg3eCw2KNuZhoF0n6nM06R1zq5tNiLQC5u+Fgat+dWZG9Dcai7ltELwFFqNH0dm5yU/TsvUrZej16YqS4FORFEAwwFcBOB4AFcR0fGaYAsBDBRCfBvAhwAedzujdsnVLTEofLQgdXyX1ekxVqT31g6BjPEVfilPP41APyp6AMMTw7zORgayYtjvL2EZDf27AEqFEOuFEE0A3gVwmTqAEGKyEKJB+TobQB93s6mP2cvwxanr85t2Vl68fTPnmvpcZbFOoYaMMulYCaF8lrlv+q1I4vX4ozhDZJs18jopmse41eRvpajNzbkkw4fB5NIbgNqvq1y5ZsRNAD7Xu0FEtxBRCRGVVFbm98SfxpbwaujqRuWk/bwUfxIXRLJ3inMDN2SsE0Gdr47UFQ04CB66qjXvxVnRpXgKT2bdCqgjSl5xbHJxNxuepeeqlwsRXQ1gIIAheveFECOEEAOFEAN79uzpZtI5Y7chaIVOkPrWedH5eDExNOv6G7M3YlKOphs3Kavyfl5gatGdmF/8O6+zASctrD9tRVnxIJwWsfbg8NuIM5+YPZqVGAiDyaUCQF/V9z7KtQyI6EcA7gVwqRDC830lw9AehRB4a85GqbBJF1z77v90Wc5xyCLTL96eI78oJ1/9LL1ZlmMBZ3ZOqJ3fO0j+dEWQXxqZaRHSObn2M7/Y9tPPYfU4fpcrMgJ9HoABRNSfiBIArgQwWh2AiE4C8CJSwtw/Kl6O9MRuxNFiHRD5qegpaypx7yfZQlZPRny2ZIv7GfCIfPeZQmpZbiVFjjacc16SPpdbltheKeogjThaMLPodpwXKWm75vXryVKgCyFaANwOYAKAlQDeF0IsJ6IHiehSJdgQAF0BfEBEi4hotEF0BcP2Qb6a73G0YF7x7/F4/EWp35tpcCdSKZ6LPwORtGfXb9inH14vqbpGuRdPvgmit4Ddl3FhtUrjtHZ5sIDO7zitGycjsIOxC4dRNf4Zf932b/PVgqRs6EKIcUKIY4QQRwkhHlau/UMIMVr5/CMhRC8hxInK36XmMbqDWSXkqjHHFM38QpPJwzhacH/sDeyHetSaCNQXEkPxk+gcFDfuzLi+Zfde/OL5mdiVw86Mfhmy2sXtEc3fP1riboQu4daLSy+aTxZmWT4zEA7bxhuzyjDgXl2/BkYHJyOhQEyKBpmsM0ElOsNl0Rm4KfY5BsfedZTmiGnrUbJxV0bHbGhqfzHYWsAiHTK/g2k35Fda2Nt5/lXb5A+GcPISdPMF5IUd1k5ZviexWVmQ0Sv+0h3+389Hhg4t0FtUK9yMtH2jt68AEEVqW9L0/1bILIu/5+OlqrSN4lHlz+acWS52VRmCYHLZUmN9WpIb+RNCmO/lkucyyHddG7GpqiHjexDs8ekD551USYbyx9vn5p8NO/dg7fZsDe7decaaiJ3hqrrBrtFJxw6bJY5mGzk9e9GU7NL/IBlorPLqVNN9emJu28nKCuL+d4/D0Ilym4EZ0d4Oc5nglFnEpf6cWyvZvKvBOpBewh5wa3Q0yooH5RSHVy9OPTqEQP/hE1Nw3tBpWdffmdvuFme0MMX09Hedily4aZft/KmJSDTw6Wt3WgcywC+Nz0+Tov4meAXlB9c+2TwMjjszl/qV0At0N/yzdRHqj+ad7hBKCfnEXmtBbL7iUaAvbZfJnSGRPAj0BJoRhXsrc93csTEfuHZCvMz2B+mNvHIsk3y9FvRy5Wn9STyoHxZN5SsPgRboZkWSblRH3jNOKq51lUaTIvqplFXt0e1kZsPV7XM/sMyHWkPX9vcbouMxvehOnEAbLOMxIlfBoLWPAsCa4uvwv8T9AJwLu3/FXsWg6Fe2fuOWlu+GABoU/QrfJHv7B+VbsMSkXrICndHIh2UUGPZyySMbdu7BFS/MsvWbpECWdbPf4LGmNvRdqlNRXp9VhldnlmWFMXshDIysBgAcodLS7cq0XAX6WUMmt30+O7IIfSi1J883I2U285GZ8+tjX+CR+MuaQM72pj4YuZm9AGDBRuM49LL1SPxljCm6z1YacnLOeX09EH/NMsx10S+wovhGJOrL265pn28/7MFhMBhd2sjeIajCzdGxGdf8Y1AyVw//FPsQfah9zaTRnJXXbsShFeh2CnbFllqTeKTO+Wn7NG2t8aZj6g787KTSts9PqybOMjuTf5q7Hq8mHsfXRXe4Hq9dhW5vU7smemZkKeYW34bzc9x87F+f+fMEm2NoM2YW3Y4DYdxmtfw6ZjzySa+zSNSq5pM0YSYW/RUzi/9oK596jEg8hfvib2Vcy9o2B4IAAB12SURBVJemajfedD/Xe1kfTjvwp9jHeDn+RNu1met2OvbzzyeBFuhmHd/OMPqvH+qfym6FWxOM6kVJeg1qXtGt+HvsHf082F4R6/3wt7axGRW7JVwHNd9vjo7FH6IfZ4V7SeX1823F7HFSpP2FubWmETWSZ0YasXLdupx+DwDdUYf5Rb9F370rbf82XRa/jY3BYVSNH0YWGYbtp2zKJYNMaziYdltnTB2nQaRdYVznvVCNe2NvIiLpAvz0xDV49PPscpTtDs/Hh2a89GV+Vwz7CwBXbavN3zyeDoEW6G5hdhiGTEWr39TmLxlr9EYWPakWv4t9JvFrmfjdaVyvxB/TvS7zIr3o6emodrA69r74W/hz/MOs600tSRCSeDz2Ir4Ryd7M7PdvLcDZT0zOup6mxeLEHQD41c7hGek54bTIShxEdThr+xuOfq+GyDjPZqubc6GH3jbCEs0pPfnbmbL37Eu39iHxF/Gb2DhcFZ2ESyMzLON8euJax2ceCAAXRedhhGrH0bYXiRC4LjohY8tk0Ram/WGNLACTVrWbQxdu2oULn56eoXC0xZknGR9Ygf7DJ6bghan2tKYDUIuBtMq1PORD2/261LlLohXNLUnXBok/jDob1QCQ0s7VfIvW46V49n7gaQQE+lAlfhmbikui+keb7TLR0N+Ybb2jpVpz1B40/c+Yta06lc8UrUmjF4Jxe0q3NZlhvl6YxeXme7rLtOUSh9sIpydF095eesSVCdyH46MwLDEc803mMOykKUu6xIp2r8UD8dfwbPzZ9riUu2Yv0TRrtte3KYjpNSVLKvTKnr1cMtiwcw+mrrF3SMZ7iX/jw6IHs66ri7YITfhn7LW2CUiZhp6vAZWROUV9WU84XhudgO9R5nD004UV+PMHizXPI9Cftuae0Yy8uW9XfDo+HOdF52ddz4d73NFUjosic6TCpidfb4hNkIzdvGzOV+3al/1LYfrdKW3C3+GpKU2t2S8nJznTCsufPz8T+wp4SE26PFdsTilUZ0RXoKToVgCAEKoRuERcbVvxKh8IwF9j7+KsiHMlSJbACnQnHBMx38wIAK6OTsQNsQl4K/GoZdh826PVk30A8OPo3LbP6VPKxy3dlvW7B+Ov4b2if2dc+3xZSnCr83xzdBwmF/05JzdILUkDzej5+FA8EX/B8vezi25zJR+pLijQFTZWLQKYWPQ3PJ94xjDGNOdEFmBu8W0428SWbZw3/TI6gLJdZ8cvy3zhpot3SHxEm+//9tpG3P72Atwe/QS/iY4xbJXnRBbg08T9ICRRjH0YFP2qLS91jbnNMTjFzBd/5PQc3HNtTi6ly+ElVZo9KDXxnH7pRZDEnn0taGlNokp1gLmVHCAi3BYbjdcTj+EoSskgNrnkEXXVaxfImFVW2qZ2OBlvAX9N9AvH+frTe/rCQgB4fII901G6Aam1wJMiqSXw/XJcrJSRjsH1i6Lz8Ito9mpdLephuWyf1Osc50VK8FL8SSwrvjnD3cwtTlQmXb9tw/dc6HzSv59iU1UD/jna2NumCCkh/J/PV2HMkq34S/wD3Bt/Oyvc5NbvAACGxZ/DiZF16IJG/D32Lh6Jv4wzoqn4n1SZkayKXS3MdJ/DyRGCOmUyZMJq2/HkkgdA31yVNlQSgBenrce/x6zIKC8jPleUrZkqM+pXRX9FJzQ6ypsMLNA12HmvH0upvWDOii7FrVH9LeD/HX9VFbc7r2UC8NVKe0JKADiZ1uCpxAuqa6nql/UskEvI/Bl/PGx6rlHgW7QepUVXo3hvqgz0gh8V2YrzogsAmL9wzTgjoj5cJDOVNruqjTo1sn/3QA166vjOW52Lm067fJd2MyzShMu+3oNqdMOoKSsepDs/ILsPexGa8EjsJVTuyB5F5gsZzVyvfRnV4+DYO5hd/AcA7f3kixWZCpC2vNOmwEWbU95BVRoHgDhaeGGRDIQkPkn8Az+KZNtbM5EvToulLW2frpe2o+rTCY22zANONJADKHPRUzqGZxPP6W4pkNoT3l462tDfo5V4Pd5uvlpu4vNvHKd2EdIExCiJnjvavSHs7rljRV/ajrcTj5jE4XyuQDu3VlL8O8wrvs1QEKs/670UNuzMbDedkalB6z2/dvLYqIz05wfknv2K6FQMik3GoD3GXj1OSnHO+ioHvzKHILC3qTWrfG9VeZf1Utw3tXnuTVUoKx7U5gUks/0zm1wk6IQmnBQpxTPx50zDmXXwLmTsgbGvpRUtOpNAANCqFKVpJZo039lFt2NZ8c3GP86KK3fU+Wk3hwgcR5uQQDNKi6+1vde7tqE+m3gWZ0WX6gc2YK/iJZB2D8zWgNqHwE4oQhOGxZ9FL1Qbhulm4jOtRsbzIY1250QhRMZEWUJz3KFeW1KXhdFimDvjH2XGA4EiNKGb0ratys1KybW6/7Vm8zizEWDR3u0oKx6E70XkTIglZdX41Qh9T6ZcIAC798qNPIxGAccrq6Xb3BwNwl0QNZ78zpVQCfR0QVo2WJN7f4x9mvE9ouqwx943HoNemoNlFTU4hVZjUKzdtzkpUkWZFmi/s7kt5/5kb/LOrkTX0wj0hMOV0ckYXzQYFyi+zHb3V0lPiv419i7OiSwwfXn+JDILL8WfzPK0OfnfX6CkrNpQm9c+i11t58LIXFwanYV7dOzNaZKarqFtM7JtLc3pkeUYmch0vTxh10S8nmj35384Psrw9xGdFwdp/jeCIKRdK91g5NepicVjqH07gc4GduMDauytyH1tlrWLqRMIImet+b7Ym9gP9W194PADOwMAEtHMtjQkPgJdquwpObLE8hJrATkzshQLkgPQgmibhkMQpgJPK2TS3/aD9aklc8uqsbSiBjfGMo/oSmvo6biuiWVPhqrTrawzn1iyQu/xzowsxWWGizKyxwfqONL3vkGpDnNLbAwAYD9qwOvxR/Fl8hQAP7bOlxLpbbHUnEKl2N8w7HOJlK+vnktiicoP2bgq077Zds1C1vZvsxh7oAZ/in0sETLFN2k93kk83Pa9RfFD79ZkZdunrDyeoLNnjpEm2B6LwACVh5dejtXp5KIQqbk29iUAoAs1YkXxjTnF1RO70Yn2YdxSi2cVAjdHx6K89XLJmFNE2tqSc46JVODvsfcgxM8AAOcd3wuz1ldh0PcOBxZo0mvK7dwEIwKtofdGJd5MPIoxiXuwpvg6LC6+BYBqEYbBK1dvC1lCEkuU32uZuGJ71vJdbbNqM7kg1fgOI+PhvBWLN5sstVbQe7Y3E4/iipi+J8nq7XU6L7JsDT197VsqwXFWdGnG5K5pvjTfe5L5ghY9tNqS9lX0A8WEQ5rf2EkBAC6NGm/Ilm3Pbo//smj7S9PotCq1ae4gzdxFfdtWD8bCKXUUYeYzfT+yNGMTNLP9R9QQtHWdG0YmhzMiy3AEpSZA+9/dvgnXfgZzQ/0Gj8XaSrmDWeYV/x7Tiu5sc9c1Yr/K+bgv/hZu2jXUOJDBpKhMC0qg2bS8E2iWeysk83Ooe6AFehdKDeOOjGTOolPbP/q8n8heXGTWyG9+vQSjZmT6xGoFiHqI/mnR/brxyAkdgcuGWy99tsvm6mybsHrd6E3R1Iiji4RL1ZCYvj/5n2IfAq25n0QfgVC6l8DfYu9m7C4JtLs2ql9q9rxN2rk+Ol7XHGA2QakuNyOBrvZs0K7PlVn1efw/JmDol5knK2nXRqQ9VWRMLmp66uzNYkfIJ4XAGZFlKCselDGZ/nbiEUwtuguAZp2SSd3ILA6UUXDSRJIpV84uSf3R9trtdVhcrhefgBDCtE4A4JboGNUvssMSGa/FyAgnXPQsUxFogW5EFK2mb8mTIqUoKx7U5ipGsBYI5RJHw6XpTfqz8FEJoXOHzuZTejgZGmob4M+jX7d97kRNltrHdx+eCACGo4A/xT7GMVv03Te1HApjT4W0ht6ftuH3sdEoJv1FL211ZlEY2aam9iv/ir+Ou2PZtnQjIaz9/QlUppvmXz5on+w0EhJWdTh2qfkq3lujKQ8MK1c9rYY+qegvFimb8/yUdbgzltpT51RK+Yo/EnsppziNWLR5tz0FRzlQgHSE6vbaRpw3dBquHTU3697+lBopWAn0rtTYJgtmKe6MamQ1/W7b5VYj2yWUAj1GSXTbZ71YJm1XFMjdR3yAxCpUGTF8RWyqo/TT+5LrkbbXW+3kIkBoFcZNYoeE3T+alJsbGJr4r+G99sk+8/ISQuDx8auwTnLY/lL8SVwZnZTVabvrrNDUhokYCHQjD570XiQHohZvajRrt9YjpCdKrUwuEbKnDVq9IFZtq8WpkTUZeVA7CGTFl8Pzbq+1twgnvUy/tjF7pHiViXfM9KI7XdqfXki5FHfZmZ9J0UAKdLP9y9OcvvUNywpKr7QTwt7ik/WGpxuZV7fR8NwJ6Wc7nLajpOhWU9/7JcoQ00qgR5DMeZERCbn9N8yOrJPNQ+3eZvx3yjpMXLlNaofB86Lz8Z/4SEnDl/EUsh3x9Ito9gs6HfOOutzMU6SUk8yKWivN047QVU/CnhFZnvMhy2bImoLWbK9LLeRR5W31tsy5C5lN4axKwaqcIhBIagaOugfZuGCa1E8/YOxtasXFympD88K1bqDFaMLv3kwJwu9a+MHuVC13fmn6BnRysDfy8mQ/zRWBMyNL8bNIuwkj/UxT11SiC/ZifOLvWfF0o4Y2O90V0anoQbW4XGU+0RKJtOu8ZjwWH2FoTpFF1vXLTMCQouWcGpFb+v3TyAxbh/1q/b3TL/bM/GXnqf2evMW5xcCRTAhheOqNLH0o5e9t5eUSQTJjgyk9rol+2fY5LppwemS5YVi1Bn+BxIvU2SRsevRh/uv0JOn5Q6fhp8Nn4KGxqX4cQRIXPD0tw5mhPSr9RirTdrtZLP6LQOBUxTRpBrXm5uVmnH7AkN2LOl13vVCNiyOzcXkke8n5b2Jj8fmy1ITq9zOWeWfToNkoq6+ORv9Z4h70a92UdT1NIxJtnzuhEQ/HRuHNxKMZy/HT9vfrRs3FyZG1OC6yOSuen0Wnt9nx0lr3dyLGe4qkO/z+Fm6ZP43ONL0PtGuFhoik1JDTTChGFBv69yLmB0GkF/X0MtmWVY+nNeae83XcJvVYtS1zsyYrnow/jwuj2fbaCJKYslpup1AzpSW9GEfGzdCqRs6NLmz7fH3dSxlullrU59520dnjXDs/4sTk0ubBYxFup2ZfGfW+KwAwbln2PIRRfmSs32amJQBZayrOjcxHkY7y19pkc92JJIHzQ08X+tmRhSjW0azSbNndgBMBzCm+3TDMycoGS2dGlhruo21EN50Vpd+KlOHhhgcMf6M+tPfx+AjLNI3MKN/N0FyNm/zMdTtxxlE9ECHg8sh0DE08b5qeDBuKr7YIYf3C7U9bTVcGpieWmoV585S1tZ8ZWYadJv7weryfyNytkgBc8fwsLH3gAulVpD+P6u9b863IBsxsajXNdRGasE+lAJhhPSlqwwNICOxXvx6I6t9/6ovVliOCb0fWY2vyIKn0jV6OG4qvxpn7njE0J50dWYhu2IukOEc3vnSa9arTwNL51nNbBlIauuzL+nCDDe2+HdmALkr7OHj3YryceBKvtFyQFa462RUHSKVkj8Bp6OkR1KuJIXgh8bRhuBrJDYQA4PH4i5ZhCKnDcovQhAFUbngsl1mDOFalbX+DjDX5NNfFvjS8l/auMNvNcdBLqZn0CBHOMBlCu4oQqNlr/KK9JDITk4v+bBpFBAKlO6wXeckO5W+NfYbxRYMtw304v31lo7Z+42hBs7Ig6O8S5h2zvAlITGQq8xlmB3uk6S3Mj5wjiLZdFa1oSQrTuZZhk0p1PUjUnBedjyNpS9v3b0U26IYbQOWm8yVfF92RVU4/isxHd9Th1cQQPJt4ztBMkhbogz9eipHKiUEyCsCr8ccN76U5msoxNnGP4f30UXt9qlJm0Et01jvsie5nmY4TAifQhRCmLm9pZN2HgOxl3louicxEVLRgSfFvMC5xd9sui7r5M+kMf1BtK3B0ZIthODP7ZZp3FA3yQB0Pjaw8CeC4iPULxIouEpopiVbUNRovmng2Yb7PDgAsLb4Zhzauxa9iU8zTkhyWy7C5uiHD1VBLglqkbKxpj6IojMvgQKrPMFvo8f3IchxNFTgzat0WBu81VmwA+fJJ26Ot+sNBwtzE9YvotAzXSL193gHgy6K/YZjlvkuUoQ0/Gh+JRcW/bfuu9flO97/uqEdvVAIQeGhsynRHlFr019fAIyxSvwV9I9amsFHxIboj9DRxStV9bafDAQAbxCFZYcqLB1im44TgmVxEUtf/U0t60ZEV10YnICnItNU/m3gOY3anVn4eFdna5oOrhxuHvL2TeBg3NP3VNMx+Jg0qTV/ajl17mkCN1RkrP52yvPgmyzDRZDMeG78K1mLbnFPqpliG2VXfgGVFN2K7yH3w2myw6Vqa0yIr0bV1D0p31ONoiXgGk8XeKQK4KjrJ8PbIxJMoTR5mHoeC9UpROdXmlRkbcN0Z/Uw3U+uCvTi70XoLZFn0bPBqOtWtx7SiO9u+a1ceC+UldFN0LE6KrMOBSM1zHBnZhhnFd2BYy0/xVMsvAQC/w4e4rfgDw7SoUW4BUyeLPD8ffxrADW3f0y6eaiqjvaTSskvwNPRmudnhyyQm+IDU6T6HS7yV+za1TzoeFTFe8CHIokiTcm59rySGSIUzY3rRnZi7bhv6Jo1HA25T3FSN+J7c97/u3Grtmlq+eSO6UqNpfcgSFc1YW3SNaZhR0UewV8e/WQ3tTu2FcybMjxs7tOJzyxO0zEZxapoRN71/YkTu7N26nVssRyEzi/6APo3Whzu4Red685FltDq1mvb++Fv4SXR2lmnpj7FPMbfo9wCAX5C59wlJ9s0uMJdB34qUAS37EBXGbSWSp6X/JOORQEQXAngGqamSkUKI/2juFwF4HcApAKoA/EoIUWYW58CBA0VJif1tJOu/ehJdp2cv3ddDdDsMVFc4YSZNt8MAN/IlGU9L8YGINTrfW6Yj0NqpB6J7XTqg26369SAt0e1QUJ2758zmQpJiiAgL4SdTBhJhkrFiRFryd5qQmjd73oWrb/uno98S0XwhxEC9e5YaOhFFAQwHcBGA4wFcRUTHa4LdBGCXEOJoAEMBPIY80dzjG6gVnQDAdEhaKzpBHH2uVJw1orPlvZLOZ0nF9WnrGab3648fBBx9LirEQYZhWgVhXfJQ7BPGmtcO0R04+lzT3QwBYNPhl6O+3/nmmZZgSbI/ykUPqbAzW7XNI5M1yd6WcZTs9yPLMP9TytoqPSsWJY9C45GZZbQieURWuIXJo7H7uCuzru8V7d4odcdfBRx9Libp9zdbzGg9AXVKW9djQ7JXKq342W3X5iaP1Q37ceuZlukt634OWo807zPrk4fgk9bvm4aZnfyGZVrVoqtlmFrRGTv6/9Q0zNajrgCOPhcrk4cbhkn3lc+T3zONq27A5Vhr0TZbRARftp5sGgYAtogDsUUcaHh/a8y6DzjBUkMnotMB/EsIcYHy/W4AEEI8qgozQQkzi4hiALYB6ClMIneqof93SikeHy+34OTog7tKeUsUkt7dO6FzIoq1LuRrwMFdpeI5/MDO2FSdH7/XsHDEQZ2xscq6jA7ZrxjbTJajH7Z/MboUxVypXxlk24AsR/XsIr2VQiHonIhmrQHRIlMGMmEO3b8YW2sKo6Gfd3wvvHSts5d+Tho6gN4A1G4d5co13TBCiBYANQCyVFAiuoWISoiopLJSbmGFlqN7tr/ZuxWbz+ke08taC5DlBwPktNMfHtvT9P53+u6PAS7kKxYhqXguOKEXvtk7dxepvgd2QtTKNUPhqJ5dck7vnOMOtgzzf8ekyrrfQcYjLBkO7JLACYdllpHeo/bv0QUnH9HdNK4TD++OAb264sS+5uFk0OZJy/6d4hjQq2tG2zzSoOxl2sAPBvTAsYd0s0xTti+4QbqOzRjQqys6Jwwc51Vhenc3Hu0AwEmHd7eMB0iVgRXRCCERMxavv/6e8YgiFwrq5SKEGAFgBJDS0J3Ecf4Jh6DsP9YHLTAMw3Q0ZDT0CgB9Vd/7KNd0wygml/0BCWdxhmEYxjVkBPo8AAOIqD8RJQBcCUC76fVoANcpn38BYJKZ/ZxhGIZxH0uTixCihYhuBzABKbfFUUKI5UT0IIASIcRoAC8DeIOISgFUIyX0GYZhmAIiZUMXQowDME5z7R+qz40ArnA3awzDMIwdArdSlGEYhtGHBTrDMExIYIHOMAwTEligMwzDhASpzbnykjBRJYCNDn/eA4BLOyl5Dj+LPwnLs4TlOQB+ljRHCCF0l9B6JtBzgYhKjPYyCBr8LP4kLM8SlucA+FlkYJMLwzBMSGCBzjAMExKCKtBHeJ0BF+Fn8SdheZawPAfAz2JJIG3oDMMwTDZB1dAZhmEYDSzQGYZhQkLgBDoRXUhEq4molIgGe50fLUTUl4gmE9EKIlpORHco1w8koi+JaK3y/wHKdSKiYcrzLCGik1VxXaeEX0tE1xmlWYBnihLRQiIao3zvT0RzlDy/p2yrDCIqUr6XKvf7qeK4W7m+mogu8Og5uhPRh0S0iohWEtHpQawXIrpTaVvLiOgdIioOSp0Q0Sgi2kFEy1TXXKsDIjqFiJYqvxlGRHLHbLn3LEOU9rWEiD4hou6qe7rlbSTTjOrUFCFEYP6Q2r53HYAjASQALAZwvNf50uTxUAAnK5+7AViD1OHajwMYrFwfDOAx5fPFAD4HQABOAzBHuX4ggPXK/wconw/w6JnuAvA2gDHK9/cBXKl8fgHA75TPvwfwgvL5SgDvKZ+PV+qqCEB/pQ6jHjzHawBuVj4nAHQPWr0gddzjBgCdVHVxfVDqBMBZAE4GsEx1zbU6ADBXCUvKby8q8LOcDyCmfH5M9Sy65Q0TmWZUp6Z5KmSHcqEATwcwQfX9bgB3e50vizz/D8B5AFYDOFS5diiA1crnFwFcpQq/Wrl/FYAXVdczwhUw/30AfAXgHABjlI6yU9Vo2+oEqT3zT1c+x5RwpK0ndbgCPsf+SAlC0lwPVL2g/fzeA5UyHgPggiDVCYB+GiHoSh0o91aprmeEK8SzaO5dDuAt5bNuecNAppn1M7O/oJlcZA6s9g3K8PYkAHMA9BJCbFVubQPQS/ls9Ex+edanAfwNQFL5fhCA3SJ1GLg2X0aHhfvhWfoDqATwimI+GklEXRCwehFCVAB4AsAmAFuRKuP5CGadpHGrDnorn7XXveJGpEYJgP1nMetnhgRNoAcGIuoK4CMAfxJC1KrvidQr1/f+okT0EwA7hBDzvc6LC8SQGh4/L4Q4CcAepIb3bQShXhT78mVIvaAOA9AFwIWeZspFglAHMhDRvQBaALxVyHSDJtBlDqz2HCKKIyXM3xJCfKxc3k5Ehyr3DwWwQ7lu9Ex+eNbvA7iUiMoAvIuU2eUZAN0pdRi4Nl9Gh4X74VnKAZQLIeYo3z9ESsAHrV5+BGCDEKJSCNEM4GOk6imIdZLGrTqoUD5rrxcUIroewE8A/Fp5QQH2n6UKxnVqSNAEusyB1Z6izKq/DGClEOIp1S31QdrXIWVbT1+/VpnRPw1AjTL8nADgfCI6QNHKzleuFQwhxN1CiD5CiH5IlfUkIcSvAUxG6jBwvWfROyx8NIArFY+L/gAGIDV5VTCEENsAbCaiY5VL5wJYgeDVyyYApxFRZ6WtpZ8jcHWiwpU6UO7VEtFpStlcq4qrIBDRhUiZKC8VQjSobhmVt65MU+rIqE6NKcQkiMuTEBcj5TmyDsC9XudHJ39nIjVkXAJgkfJ3MVI2sa8ArAUwEcCBSngCMFx5nqUABqriuhFAqfJ3g8fPdTbavVyOVBpjKYAPABQp14uV76XK/SNVv79XecbVyKPngcUznAigRKmbT5HykAhcvQB4AMAqAMsAvIGU50Qg6gTAO0jZ/puRGjXd5GYdABiolMs6AM9BMwlegGcpRcomnu77L1iVNwxkmlGdmv3x0n+GYZiQEDSTC8MwDGMAC3SGYZiQwAKdYRgmJLBAZxiGCQks0BmGYUICC3SGYZiQwAKdYRgmJPw/cwXsLn6wOj0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vss4UCci_LuA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "357ca5bf-2463-4ef1-8492-54be82c71e1d"
      },
      "source": [
        "plt.plot(y_test8)\n",
        "plt.plot(Station8test)"
      ],
      "execution_count": 228,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f158cd1c2b0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 228
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ci7ExtrgSNlB",
        "colab_type": "code",
        "outputId": "82a005d7-b609-4e2d-aef0-a7d520964076",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "mean_squared_error(y_test8,Station8test )"
      ],
      "execution_count": 229,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.003935209872649475"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 229
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vQPSPw0msQpq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5f2fd187-13ea-4a3c-dabe-9b72fb519872"
      },
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "mean_absolute_error(y8,Station8)"
      ],
      "execution_count": 230,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.025571062205398433"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 230
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xhMlL2iLsbNW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "43e19343-5625-4ea5-96b9-6af8784697b7"
      },
      "source": [
        "mean_absolute_error(y_test8,Station8test)"
      ],
      "execution_count": 231,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.02660641155321751"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 231
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bR5Bg6PoSuRZ",
        "colab_type": "code",
        "outputId": "ce9241d3-4a66-4c92-8171-acc66502a20a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y8, Station8)"
      ],
      "execution_count": 232,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.003665847985761561"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 232
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WVso5i0YSciR",
        "colab_type": "code",
        "outputId": "ce9d5e8a-49ba-4402-f6ac-8bcb29608b2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        " from sklearn.metrics import r2_score\n",
        " r2_score(y_test8,Station8test)"
      ],
      "execution_count": 233,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.03707777633691356"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 233
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N31rAn-kS3LG",
        "colab_type": "code",
        "outputId": "05ff7f2f-9767-4ac7-f5b6-57a19c5eaff9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        " r2_score(y8,Station8)"
      ],
      "execution_count": 234,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.07347665944388737"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 234
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e0oWS2a2HSV2",
        "colab_type": "code",
        "outputId": "1c257d00-8b9c-4c6e-e2a5-766bdc88c7a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "from sklearn import linear_model\n",
        "reg = linear_model.Ridge(alpha=.5)\n",
        "reg.fit(X9,y9)"
      ],
      "execution_count": 235,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "      normalize=False, random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 235
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e12wvbpnHlt4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Station9=reg.predict(X9)\n",
        "Station9test=reg.predict(X_test9)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kiCBDEoN_QqH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "514b2c32-9f7c-43de-c8e0-9d3d5f9a4dde"
      },
      "source": [
        "plt.plot(y9)\n",
        "plt.plot(Station9)"
      ],
      "execution_count": 237,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1598165748>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 237
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pVm5/c1nObng/fW4eX35wStY0F/91Oj/8+5zcX5qnnHcYXWifJFq/M3f9aqzkl02kkwoRm1rRD9uvPaqem7k+6X1Hbl9RB/TahjCXPTqD656dy966RgYNezPjYItte+s6/BwOdMGJrka9vw4ozEEW6xa3J98LagfI1XPHzz17pGuYtMKbp6oTDrWiDuiN4WhUWlRVw4adBwB4bHLL29qNuw9w5h0TOry3Sbz6op3j+Zrqfaw6iD6w2WKb/+vQ/b590lk6s8xQ1AE9X6nDcdP94I1NEQYNe5PH01wQ2qq9Q8cX75vMBQ9kr9KAzEE63eJiDXf+vxD5R3e5WerIO3TfBPRM7Y9LN+3hskdnJC1Ld87Huib99b2KpOVtmV2tq7WJjp7VPAty1a7a7CdUF8nzwdpV28jq6tx1zdL5ivxQA0jboN+aAYyF4ruAXrljf9LQ6rvfyt5g156aG326xiG7aGNN/PXq6uxd6BJzvKWmLmd11Z66Rrbtyd4g1hRxrM1j3pu12/cXpOvoefdNbrHMDyNF8ynZdkYwaS2/dJGt2lWbtUFfjaJtUB+O8Id/5Rio0EE/cKCLDdJpzS1uOBLh1blVhJsinPWnCXwtS3e0LTV1nHffZM68M/vERA++u5L/undS1qC+bsd+/uveSdx3kCN182309PtI0c5siK+pbW4k37T7AFNXZX+YTVPE8dq8qqIN7HUZBh4lHooRbxvbm+8COsD0iuapUdOd3wc7UKC1mqtcusaBmk+oi+W0rjHCjS8viI+YrUwzXHnktOgI2Jlrd1K9N/0U+BOXN8+mPKcyOuvf5t0HktIkDoVe4905zFjdXtPbxsuF7fT5MnNt87678MEp/PeTs7Kmr9xRyy9fWsA/P2z7Ixa7qtfmbyzoM4EzKeqAnm6WQEidGral//vHovbMVguFjOdvLCz8QZ+tNFe9r2WgjrUrLN6YeSRiY1OESMTxg7/Pji8LBaN7o8GrEmuKOMJNEa57dm6LvMSqzZxzXf4pMV1RFylDsKcuc1VdamFrVx6jZItBupiTzwjgQijugJ5w0O5ICDz5VL/e9q+lrGljo1muknegFaMu83X98/OTtrUt8qln/TBlWPbq6n2cfMs4/vlh9ocrnHTzOH40KrleMfX3uOzRGZx487ikNo/UwSaj3l/HybeMy1k/XwhPTVvL5JXR6oENO2vT3nXMWL093jV2T10jFduydxd1zjFf85HnpatchAol29n1fjvdgRZ3QE94PXTkzLRpMtWpPjV9Ldc+M7fF8r314bxHdtY15ldyXL5lT1LVQ1sd7JDug+kmNnvdzqT3yzZHS+Xjl2R+ckvMeynbbCm1HR+meypPSrtD7MKRzwhKgI27DmRdn61R9LY3lvK9p6LVA5//80Q+fce7LdIMfWJmfGTstx97n/Pvz95d9PlZ67n0kRm8u7SQ0xp0/QbPdCIRR21DmFteX8TQJz6gWLejtZxzLeLQ+2vaJ6AX9eRcGftVk73KJaatw6NP/d1bWdfHSqTTK3YwvWIHK26/kLJQsE3fCRz0dKOrtyU3RjZXa2T+m9R1QW+bxi7KHdBTxfZF6u++KmEoe2rPh6DXspzvRfadZVv5TnAiU5s+ziaOyJKy7cXB5TnmpwFYtTW6bYkXpI6uWujogm9VhovqXyas4i8TVmX8O58V0JO6LafGofa6lPmmhJ60vIscGakl4skrsrf2p5q/flfakn2m7Rs07E1+keWxZKkXglhAmr9+V87nU8a0ZTBIa0bOxtLEqmASLwL14SYen7w67ZN/Shv3cnfJEzxXekeGTLRclO0RbeffP5mP3TqeB95ZmTHN1x+exiV/ncaFWear+VdC28f2AlWZZdOZ58BtbyxNuzxbMIeu03kgZnFCN9/WSLcVqedNew2qKu6AnmH/d5XjIpCy1+aua11d6qWPzEhqVIx5Z1nm2/d/5BmYE/3+X0vjz6fMdVK1ZS6UWD/4bN8wZeV2L403Y12awVkjp67lT+OW8+wH69J8QjTh4Zb/1LEvzd6QcV3Ftn3sqw9nDUaLNtawoKoma4k9cYrY7lHRkFtXHyn6tYenHdTfpTuFOmpTizagvzq3Kl6f25qW9CQ5Av8doSc505alXZc68CXdjI+p3/34lMI86GLW2uZ67VyNcolSLzAHoy2fEGtkzHbRiE0NG0sSb0hN+JtYP/Z00/Y2xUv2USOnrkn7ffUJ7R/FNmFXkWU3o2LfjLcWb85aSIs38NPyGGuvgV9FG9BvfHkBVz2Vvn9r8gl88D/cd0MTeKnsj2nX3f9O8sCXO8e2HJHaXoEi8WKSq1EuUSGyU4iLQj43ULE0qVMQT6/YzisZnsYEEPH2d2zg0O1vLks7kGl1woWwwwNkB3xhV5rhE2BmOzUCttaeusaCzQ563bPzWkwrEpV8dwnNgwzbW9EG9Gx2JYxUy3burMljGHomE5Yl122PnrW+RZr22omdWaUUKMARk18dejRR7AJSc6CRySurk3rOpH7OE1PWsG5HtEEukBDQYhfAV+ZW8c6ylu0YHR3PO+rk7kq+M+KDzs4CAJ/4/dt84vdv50z31uLsgw+XbMpcv556XLpoET1pWXtd04u6l0smB/MMwD0HUq/abY+ahSjNplOohxcYEY5kN1s5LP+/Kcg25c5/LEXAi34/e35e1vRrqvdxx9hl9KAOytMP7f/VywsYGu9k5DicGuoo9U0VRqKu0o7UGl0pz9c9m/14++pDmevX43eX8fctN0y9XNrZtx57v6Cfd6ChiU27s/eJBnh4wioGDXuTusYm7hq3nG17cw+gOdhh8YZhRBgWGk1/dnFD8B/MLL+eATSXWnOdUwWpcmlFnUu20mzix3wxZSKuxICe6evmlv+EiWU3ttuFNxMfXj8OSmrhoKtVEyUyInwzOJkQLdttUsdIpCtwqcqlQBJ/xz7s4+uBdHVesDl1zvQ2HlzXjJrDwylT8aZqCEe4z+sON2nFNh6bvJqbXk0/LcG3gxM52TL3xshl/JIt7Kpt4DOB5VwX+hf3ljzO54PR7zraEgYPOcctoWc41dL1ICnMgdmaOvRswTbbhSHfbB5pu30ZYDs6NNY2JAe6fLogpqb581srqNqV3wCy9rB4Yw2LqtJXpdwReop7Sx7np8ExLdbl8+jE1EZQdVvM4ESrit5mp4g/qDXhh3ug5FEeLv0rgyz35FzZfu8NO2vZ6/Ws6cP+tFftaRXbc37Hw+81d4V7fX60n3JdmkFDvwy9xJ9LnuDtst/m/Mx0du1v4MfPzGXVtn3xC1WZNcYbEBPrmxeu2ciPQuN4qfS2tJ9ViNb5fKqMUuvQ06ZJmfcFmveb0bzMuebPS70dho7t5XJOYAEL12c/Nk63tRzJroxB0YCPsJ1TbV3SzIapSgjz+cBC5lTuzJimUG77V3Lf88E3jc358PDUu6pwxPHT57JXdbSnrz08ja//tWVVShkNDA29B8BRlrlXSy8OMNCqmwsaWY6rVXk8F/ZgFHVADxDh3bLf8FjJgy3WpZu5bYBFT6Rysrdyl9HA4CxB//N/nshGrzplYfk1PFryl9ZkO277vuYRg295Q+kjaWYTuCH0eotl2XoNVJYP5c7QE/H34YReMREX3eWGiw+DD1rzlw57bTEAvajj6uCblNHAtLIbeKLk3ujfZThGfxcaRWX50Ix5AjgrsJTK8qH03LM2Y5pSGqksH8pljdGSUDDLERo7cUak6Q6amE2Hi98Wpxv6X4i51/Nxa+hpRpXeza9CL2dMc5pV8mbZzUwt+1/2ZgiIPdwBZpTfwLiym3jg3fQDnpxzrCq/imdK72LCO28WJP/ZbK6p4wxbyUdovljtbtEulVtXnIitJKHA9t1Q+imiP2PLGFc6jGllP2fqyuRxIs61vLM9cJDTd+RSlAE9dlsU+6HPCkRLB9cFx1BZPpSLAzPivSFiJUojklA6S3cCOz4su4ahwQncW/IYE8p+nXd+Lgi2nBMG4HSrpLJ8KJ8PLOScQMupM0Np6i9ylV6/FZwEOO4cl/3BHUNDE+OvE7+mr0VLBidbVVJwB1iwYXf8QRYBc/y/kuf4dehFBtp2LgjO89Imcvxf6DkGUM0PQ5mnQfhGYArnB+ZyaSBa+jlsR8tb1FNtHdcH/0EfooH3yoZXvXzkLj3vSLgwxralxJq83yq9xGMg3YjTI9nFoWQfnNSDOo6z3HO0nGLRHlA/CI0HyFpYGFv2f0D0DirTln9h2zPx1/nc7RzWikFWB8sMXiv7PTPKb4gvy1XtUk49leVDGRpsDpJdqWEU4DvBiUwu+0XWND2o48WyP3JcINoW9avQSwD037+SyvKh9N+3jGGvRas3R5fcTmX5UAbVtc+Dd4ouoG/YWRu/LQoRvcpFCDCAaoaVvADAXSVPMGZBtITeO1LD3aERrC2/klMD0Tro/tZyUqggEfrZfm4PPcXXg/l1sZpV9tOs6z8diO60Z0rvYlTp3Xw2kFw/HptONtHMtTvjpf907ikZwadtBQsSJra6r+RRni+5PePfxKoU+rKPEaUPAHCo7YtXudwdGsG3gpPYXx9ucbH7UWhcms9ygOPiwAyuDb3J9PKfx9c3n8TRNL2p5f7SxxhZeh8BrxrEpZSfA0QYV3YTvyp5mZO9fRSvDspSae+c473lW+ODkVLdUzLCS5f9whByySXJVVv3Mqv8Z8wvvw6AswNLGGjJ3VTrGpsYWXIfU7yT/Xjbwqet5Um6f+6LjC8bxpcDzWMmAnnWcGfKc2IVX8btyjGFdKEl5uMMWxnPQhkNHM0OLx8RTrBNDLRtnG6VHOFdaH4a+mf8wtjVGkbvLnmCwy158N6GlEbQkpQq1/8MRB+wc8LOaJwKLovebR5nWzk7GC18nrnvvXbJb9F1W9ydUGcY9AJ6D2tICiqBhPrTb25/lLNCk5I+Y1Tp3QDc0vgD4Kve38TqbPM7oL4RmMKRaS4MAC94fdIbKElafqwl94FOV0KHaAPpdz9zPGU0tDhYAHpafVKr12XB6NOEHuFBhocvaZE+NifFb0MvJC0fFIhW8xwXqOaewAg++fwFafOTlOdwLZXl3824PuIgaKRN861QbBBUdLtfLL2NzwSSg+DzpXcCzUF/YMNaXi69i+81DKOWciDauF1CE898sI6HEhqey6lv8RtDtIH6iauGcHZgCad4Dcs9rLlU/+UtjwMjuCb4BhvckVzwAFRGvyqlGukHnB1YwmcDi3ngnRO4KRg9cReVXc0hFrsI3xhPvbu2geObKiEE/xFobi/5RKBtDyJP7JWbOZ43nwOJ50N7SQzoHw1sYF7TyQCMKLmfLwQXcm79fVwWnMr/JFQfnl33MAADbTtTyn7BDxt+xQZ3TrvnNV+xO6tUqfXsqbugySsnO4v+X1NbzxBbzitlt2X+owIpuoAeO24eKBnOisixadOUW3PQD7jMdVW3l/wNiNa/l+aoV08UIsz3vdvnmA/LrgGidwXDXltEgEiLYHxXyUjgPgBuem0ho2dtoBcH+HhgLTMjH+W3oRd4ruk84GMAvFP66/htXKKnS+9mUN3zAFwQaK6+uCg4i4uCLUfPxqZI6GfJDTEDLLkefk9tPX1z/A6HVKevXopxkQgEcswo6ZUeU4N5olgQuqR6BKcFVvKd4EQG2nauTrhjuLruRibwHwD8PPgqvyh5tcXnvFp6K0N33cxX/jKVyvLmCbsuCTb3durTGK33vbkk+pvGftt0RnuTfg2a8h1u8oJ+czBPNmXV9viF6dpQcz32URkKAqkyVacs3ryPc2JnboY089bt4FzvdV87+AF0+Sqx5mP9zpInmRU5hYhzfCG4EIBJZTe2+JsTA8nzDp1ulaxzn2/XfJ4dWEIpYWIFuUxuDj3LNaGxadftrm3kaHYwuvR2nm26gDFNZyet70v093YWPQ8CRBgcSJ6dtGekExtFzexCM1thZhVmNizN+jIze9FbP9PMBhU6ozEBM86wlVwanB6vYknnskC0NBix7MHl0UmrueX1RSwpvzrvPFSUX8UnAsm3+f28k6Z6bz1HsZORJffyh5KnM37G6FkbON0qWVJ+NS+U3s7a8iu5LvQGU8t+QSgc/ax0wTwm1oXxidL7c+Y3aI7K8qFpg32iUhp5ry/MJC8AAAxjSURBVOxXWdO40l5Z10fCuWcStKbcfe1jF9iGYA8Abi15JimYA5wZWE5P6rgiOCFtMIdoyfgzgWVZL9gBki/6ZRRmetsbRs+nP+mDd21DmKv/Pjvp8Xupwvt3cfsbSxmX8sjExBD+6W0v8/r8jdwzPvniuD3hgSD3lIygYtterhk1J+vUy/XhJq4ZNYdVW7PPD3Tv+BUtZuf82q5nk96/W/YbXng3e9XlM6V3Jb2/seQVTt/xNoOGvcl/P5n8fIM9dY3xh48APD2jMuNozoVVu/nZ8/NoijjeXbo16WEso0vv4OnSu9lXH/39N+ysZcPOWu4Zv5w7QyN5pORBzvjjOxmDOUSrWKaX3cDgwFb+X8mzLc7zEwPRgl2shN7fdser/2KOr12c8fPbImcJ3cyCwHDgAqAKmG1mY5xzif2UrgZ2OedONLPLgbuB77RHhg/ZNI3Xyn6fM919pY/BvNP5t7rsP9ygCT/m64G12W+B5o3KL3PzRtG4cw8zy2/O+VnvlN7FSYH0MyN+6YOroMcNadfFvF32W5jXM2d+AD67MvMDnhMtL/9Bzs/rV5X94c027+9Q3jtrmqO2TYN5R2dN05tamDeKo+srM6b5cehNfhzK3YMjVsWWycdrJiXt4xXl30+fMCFNxh49c5+O30ZOKr2dQYH0jaav3zGUPzCX5Q8ey4CvDSUIEEm+o1vzyGV8sSHMkbN2s3Pz1RzWry9Ewvws1NwX+qKqB5m0biLfts2s3HUZJ5/wbxBp4hNLkhueP3z61/x072w2v30lg44+PG2eNm2r4WcVf2PR1iGcdN7n0m8f8O/T/sZu1xvct+LLztrbcjj9b5d9I+NnZPJQ6XAeYjhb1h/Kotd+wiGH9OH4Q3uwbMyj/Ltt4N3IyRxv2/he7NypfwjMaAw3MWn8q5Q17OYjtoPhgU2E/xDgfIvQ5IxI01+SqoXG/fFSngxNofovfehve/g1xKPhRU3fzJrHVeVXJb1PW1Ca9QQfqYnOYHppcHqL1esb+jIg718lf5bHdKlnA793zn3Ze38TgHPuTwlpxntp3jezELAF6O+yfPiQIUPcnDm5O+SnmjbyV3yu6oncCUVEuqhnw+dx5e2vHdTfmtlc59yQdOvyqUMfACQOUawCPpMpjXMubGY1wOFA0ggKM7sWuBbguOOOyyvzqeqHXMc5q08j7IIc3beczTX1lFkDEQIEiBAmiHOGGXzhlKOoOdDIh+t30pM6DlDKfteDPrafXe4QenOAYMAwF8ZhBIkQIEKdKwVgO33pxz7O/2jyk28mLt9G3x4h9tY1st+VUUYjQSKc+9GjAJi8YgshmggTBIx6F6KG3vRndzzNwqrd7N5/gACOAA7DeY2ojv886ShKAsak5VsxgyYXwHBEMBxGuTUQDJVx9r8dztrt+9m4owaHEfFq0OpdiCARzvtofwCanGP2yiqaCNDoQoQJEsBRYmEiBAi7AEEiDDy8F9t37mKP60WZNbDfldPHagm7IP379OD0j/QB4L3l1fS2AxygLOl3iTjjvz56ZDxNT6ujnhJCNFHryim1RiIuwLmnHIlZ9HcstwYaCFFGIzWuN+VWT8QF+PjAfhzRu5SGpggzVlVTbg3scb2IYPS1/QSIcMKAo6nec4CavXupc6U0EaCX1bHH9aKn1VFCmL2uJ3WU0MdqaXLBeJ6CRAi76B7/zxMPpzwUYPKKLQSJxHtP7XM94gOwjuzTk1OP6cPKrXvZvnsPTQTozQG2u76UWwOGo86VJh0r89fvwg7sZLvrS6k1UuvKCdFEkAglFj3mDMdZpxxHJBAkQpC3llZTSiM9qSdkTUS84/KUY4+mb68eNFmQzXsaWVK1kx7UU2aNNLoQPayenr37cerAw3AWIOyCvL2smj7Ucuyh5Rx+SDlLNmzncycfk6UhFaat3MwpHzmMIw8pS58ImLJiM316lPPJ4w6NL6trbGLG6p2ECTD4sDI27txPqVevHiBChADlNLCfchpdKN6bKoJRamHKaaCJAHtcLw6xWhoJ0uSC8d5OIYvQ5ALspQdH204iBKh3JUQI8KXTj2Lmmh0cqDvgHf8lBLzzJWTRfXl0v16cctQhzFyzg/rGBupdKQ6jlEaaCNDDGmhwIcwczkW/s9TCNLiQl88ADYToaXX0KCunrr4OB5TQRAMhSgmz3fWhkRCH216+cMpR1IcjvF9RTamFaSREvSvhUNtLBGPkzy/L+Pu2RYc2ijrnRgAjIFpCP5jPOO+TJ3LeJ08saL5Euor7OjsDUtTyaRTdCCR2JxnoLUubxqty6Qt0jQmQRUS6iXwC+mzgJDMbbGalwOVA6gw1Y4Dvea+/CbyXrf5cREQKL2eVi1cnfj0wHggCTznnlpjZbcAc59wY4EngGTOrAHYSDfoiItKB8qpDd86NBcamLPtdwus64FupfyciIh2n6OZyERGR9BTQRUR8QgFdRMQnFNBFRHwi59D/dvtis2og/YMrczuClFGoRUzb0jX5ZVv8sh2gbYk53jnXP92KTgvobWFmczLNZVBstC1dk1+2xS/bAdqWfKjKRUTEJxTQRUR8olgD+ojcSYqGtqVr8su2+GU7QNuSU1HWoYuISEvFWkIXEZEUCugiIj5RdAE91wOrO5uZHWtmE81sqZktMbOfe8sPM7N3zGyV9/+h3nIzs4e87VloZmckfNb3vPSrzOx7mb6zA7YpaGbzzewN7/1g72HgFd7DwUu95RkfFm5mN3nLV5jZlztpO/qZ2StmttzMlpnZ2cW4X8zsF96xtdjMRptZebHsEzN7ysy2mdnihGUF2wdm9h9mtsj7m4fMMj2fqd225R7v+FpoZv8ws34J69L+3pliWqZ9mpVzrmj+EZ2+dzVwAlAKLABO6+x8peTxGOAM7/UhwErgNODPwDBv+TDgbu/1RcA4oo+pPguY6S0/DFjj/X+o9/rQTtqmXwLPA294718CLvdePwb8xHv9U+Ax7/XlwIve69O8fVUGDPb2YbATtuNp4Efe61KgX7HtF6KPe1wL9EjYF98vln0CnAOcASxOWFawfQDM8tKa97df6eBt+RIQ8l7fnbAtaX9vssS0TPs0a5468oQqwA94NjA+4f1NwE2dna8cef4ncAGwAjjGW3YMsMJ7/ThwRUL6Fd76K4DHE5YnpevA/A8EJgBfBN7wTpTtCQdtfJ8QnTP/bO91yEtnqfspMV0HbkdfooHQUpYX1X6h+fm9h3m/8RvAl4tpnwCDUoJgQfaBt255wvKkdB2xLSnrLgWe816n/b3JENOynWfZ/hVblUu6B1YP6KS85OTd3n4KmAkc5Zzb7K3aAhzlvc60TV1lWx8EfgNEvPeHA7udc+E0+Up6WDgQe1h4V9iWwUA18Dev+mikmfWiyPaLc24jcC+wHthM9DeeS3Huk5hC7YMB3uvU5Z3lh0TvEqD125LtPMuo2AJ60TCz3sCrwP865/YkrnPRS26X7y9qZl8Dtjnn5nZ2XgogRPT2+FHn3KeA/URv7+OKYb949cuXEL1AfQToBVzYqZkqoGLYB/kws5uBMPBcR35vsQX0fB5Y3enMrIRoMH/OOfeat3irmR3jrT8G2OYtz7RNXWFbPwtcbGaVwAtEq13+AvSz6MPAU/OV6WHhXWFbqoAq59xM7/0rRAN8se2X84G1zrlq51wj8BrR/VSM+ySmUPtgo/c6dXmHMrPvA18DvutdoKD127KDzPs0o2IL6Pk8sLpTea3qTwLLnHP3J6xKfJD294jWrceWX+W16J8F1Hi3n+OBL5nZoV6p7Evesg7jnLvJOTfQOTeI6G/9nnPuu8BEog8DT7ct6R4WPga43OtxMRg4iWjjVYdxzm0BNpjZKd6i84ClFN9+WQ+cZWY9vWMtth1Ft08SFGQfeOv2mNlZ3m9zVcJndQgzu5BoFeXFzrnahFWZfu+0Mc3bR5n2aWYd0QhS4EaIi4j2HFkN3NzZ+UmTv88RvWVcCHzo/buIaJ3YBGAV8C5wmJfegOHe9iwChiR81g+BCu/fDzp5u86luZfLCd7BWAG8DJR5y8u99xXe+hMS/v5mbxtX0I49D3JswyeBOd6+eZ1oD4mi2y/AH4DlwGLgGaI9J4pinwCjidb9NxK9a7q6kPsAGOL9LquBv5LSCN4B21JBtE48du4/luv3JkNMy7RPs/3T0H8REZ8otioXERHJQAFdRMQnFNBFRHxCAV1ExCcU0EVEfEIBXUTEJxTQRUR84v8DxC5ZGX5h+zcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fGYgO_EZ_RXi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "18826daa-7590-45c6-a344-7b1967829ee1"
      },
      "source": [
        "plt.plot(y_test9)\n",
        "plt.plot(Station9test)"
      ],
      "execution_count": 238,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1598fa9860>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 238
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fgzed4Nr8dYC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "ea71a620-a505-4cb0-fd2b-1def74cdb694"
      },
      "source": [
        "s=pd.DataFrame(Station9)\n",
        "s1=pd.DataFrame(y9)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 239,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.254804\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 239
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iA3UFfig8ggu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "3f4be396-d98c-4b8d-b497-765663515742"
      },
      "source": [
        "s=pd.DataFrame(Station9test)\n",
        "s1=pd.DataFrame(y_test9)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 240,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0   -0.000444\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 240
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Ghtc5L5H8eX",
        "colab_type": "code",
        "outputId": "8ed7ac63-7e57-41d2-8117-46eca79d67c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "source": [
        "mean_squared_error(y_test9, Station9test)"
      ],
      "execution_count": 241,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-241-291b9396bf9b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mStation9test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mmean_squared_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput, squared)\u001b[0m\n\u001b[1;32m    250\u001b[0m     \"\"\"\n\u001b[1;32m    251\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 252\u001b[0;31m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[1;32m    253\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m     output_errors = np.average((y_true - y_pred) ** 2, axis=0,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     83\u001b[0m     \"\"\"\n\u001b[1;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 578\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m     58\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                     (type_err,\n\u001b[0;32m---> 60\u001b[0;31m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[1;32m     61\u001b[0m             )\n\u001b[1;32m     62\u001b[0m     \u001b[0;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "epygHk_tIthF",
        "colab_type": "code",
        "outputId": "d7124146-feef-474d-a8bf-cc5491117353",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "source": [
        " r2_score(y_test9,Station9test)"
      ],
      "execution_count": 242,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-242-b00ce5a4146f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mr2_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test9\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mStation9test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mr2_score\u001b[0;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[1;32m    575\u001b[0m     \"\"\"\n\u001b[1;32m    576\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 577\u001b[0;31m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[1;32m    578\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     83\u001b[0m     \"\"\"\n\u001b[1;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 578\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m     58\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                     (type_err,\n\u001b[0;32m---> 60\u001b[0;31m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[1;32m     61\u001b[0m             )\n\u001b[1;32m     62\u001b[0m     \u001b[0;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V5PLqMcYsj_c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ddec1c55-41a3-41ea-ce57-9ad0b734bc10"
      },
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "mean_absolute_error(y9,Station9)"
      ],
      "execution_count": 243,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.006602827317243643"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 243
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y5o8wzG6soTR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "d534fc25-a7df-420d-ebc4-1d777110e488"
      },
      "source": [
        "mean_absolute_error(y_test9,Station9test)"
      ],
      "execution_count": 244,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-244-effc1fbfad4c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmean_absolute_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test9\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mStation9test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mmean_absolute_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[1;32m    176\u001b[0m     \"\"\"\n\u001b[1;32m    177\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 178\u001b[0;31m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[1;32m    179\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m     output_errors = np.average(np.abs(y_pred - y_true),\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     83\u001b[0m     \"\"\"\n\u001b[1;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 578\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m     58\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                     (type_err,\n\u001b[0;32m---> 60\u001b[0;31m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[1;32m     61\u001b[0m             )\n\u001b[1;32m     62\u001b[0m     \u001b[0;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "61KmudDqH_8S",
        "colab_type": "code",
        "outputId": "57a557fe-4260-4a6e-f3ab-6634d5a5726d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y9, Station9)"
      ],
      "execution_count": 245,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00036777466198289533"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 245
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0mLzi688I2Gt",
        "colab_type": "code",
        "outputId": "4f8cac62-979d-48d3-e192-a82330531a45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y9,Station9)"
      ],
      "execution_count": 246,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.06454927759106444"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 246
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TOrXpPJsIGpW",
        "colab_type": "code",
        "outputId": "b9f01a39-c1f8-4164-99f1-d18ea340f56c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "from sklearn import linear_model\n",
        "reg = linear_model.Ridge(alpha=.5)\n",
        "reg.fit(X10,y10)"
      ],
      "execution_count": 247,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "      normalize=False, random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 247
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u7oUUVBgIY-U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Station10=reg.predict(X10)\n",
        "Station10test=reg.predict(X_test10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E9knCN7E_a2a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "8f8a7cca-4fec-4909-a262-dd75edfed11d"
      },
      "source": [
        "plt.plot(y10)\n",
        "plt.plot(Station10)"
      ],
      "execution_count": 249,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1598165fd0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 249
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yx_WCBU-_b3w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "afa6d307-4be0-42db-cf9e-61297cb68544"
      },
      "source": [
        "plt.plot(y_test10)\n",
        "plt.plot(Station10test)"
      ],
      "execution_count": 250,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1598509630>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 250
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eWpCo6yt8oyJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "498e363f-3af7-4109-a72a-5c94dc78949a"
      },
      "source": [
        "s=pd.DataFrame(Station10)\n",
        "s1=pd.DataFrame(y10)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 251,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.291227\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 251
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mOgPG7vQ8p07",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "44454558-db4d-4245-ebc8-4ef4fc779d5e"
      },
      "source": [
        "s=pd.DataFrame(Station10test)\n",
        "s1=pd.DataFrame(y_test10)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 252,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0   -0.000423\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 252
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6srjltieIcCZ",
        "colab_type": "code",
        "outputId": "3e1cf026-af8f-423c-aef4-56a7db0f84a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "source": [
        "mean_squared_error(y_test10, Station10test)"
      ],
      "execution_count": 253,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-253-06b8b79c368f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mStation10test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mmean_squared_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput, squared)\u001b[0m\n\u001b[1;32m    250\u001b[0m     \"\"\"\n\u001b[1;32m    251\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 252\u001b[0;31m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[1;32m    253\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m     output_errors = np.average((y_true - y_pred) ** 2, axis=0,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     83\u001b[0m     \"\"\"\n\u001b[1;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 578\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m     58\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                     (type_err,\n\u001b[0;32m---> 60\u001b[0;31m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[1;32m     61\u001b[0m             )\n\u001b[1;32m     62\u001b[0m     \u001b[0;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K05DZ9jisuJJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "62b14ace-4a77-484e-f3a6-20bf0f8de8b4"
      },
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "mean_absolute_error(y10,Station10)"
      ],
      "execution_count": 254,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.021889071408396066"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 254
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sR2IPZGCsw5R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "9de3756b-6781-4455-d914-928dc5e78389"
      },
      "source": [
        "mean_absolute_error(y_test10,Station10test)"
      ],
      "execution_count": 255,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-255-1339093f6270>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmean_absolute_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mStation10test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mmean_absolute_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[1;32m    176\u001b[0m     \"\"\"\n\u001b[1;32m    177\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 178\u001b[0;31m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[1;32m    179\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m     output_errors = np.average(np.abs(y_pred - y_true),\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     83\u001b[0m     \"\"\"\n\u001b[1;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 578\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m     58\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                     (type_err,\n\u001b[0;32m---> 60\u001b[0;31m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[1;32m     61\u001b[0m             )\n\u001b[1;32m     62\u001b[0m     \u001b[0;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "etTZW_1nIfyf",
        "colab_type": "code",
        "outputId": "dc7c7fe9-e111-4449-8c21-889b13736350",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y10, Station10)"
      ],
      "execution_count": 256,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0026824081479720346"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 256
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uoVxsrebIpT-",
        "colab_type": "code",
        "outputId": "f2299184-2479-46bc-c22f-dfa07a3da8fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y10,Station10)"
      ],
      "execution_count": 257,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.08480243651297859"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 257
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MfN_X25HI_-U",
        "colab_type": "code",
        "outputId": "92ab2d8f-601e-4d81-c389-4cef504fc01a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "source": [
        "r2_score(y_test10,Station10test)"
      ],
      "execution_count": 258,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-258-9fe60b04b017>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mr2_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mStation10test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mr2_score\u001b[0;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[1;32m    575\u001b[0m     \"\"\"\n\u001b[1;32m    576\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 577\u001b[0;31m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[1;32m    578\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     83\u001b[0m     \"\"\"\n\u001b[1;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 578\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m     58\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                     (type_err,\n\u001b[0;32m---> 60\u001b[0;31m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[1;32m     61\u001b[0m             )\n\u001b[1;32m     62\u001b[0m     \u001b[0;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CrgaGMyfJGWX",
        "colab_type": "code",
        "outputId": "ebc97bb4-e08c-4e8a-9ac1-45d5e9566ade",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "from sklearn import linear_model\n",
        "reg = linear_model.Ridge(alpha=.5)\n",
        "reg.fit(X11,y11)"
      ],
      "execution_count": 262,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "      normalize=False, random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 262
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9fPoegjHJh71",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Station11=reg.predict(X11)\n",
        "Station11test=reg.predict(X_test11)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eNcyrHUBAuIJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "31dd750f-3836-4304-bc4c-0df6c5a0cd3f"
      },
      "source": [
        "plt.plot(y11)\n",
        "plt.plot(Station11test)"
      ],
      "execution_count": 264,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f159888d9e8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 264
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qiAdigxhAvF5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "8a0a32dc-543b-457d-e55a-5aa9efafba52"
      },
      "source": [
        "plt.plot(y_test11)\n",
        "plt.plot(Station11test)"
      ],
      "execution_count": 265,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1596d1b860>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 265
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SDOIHEnh8xjA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "aac05924-115e-4178-e46a-993bc8902692"
      },
      "source": [
        "s=pd.DataFrame(Station11)\n",
        "s1=pd.DataFrame(y11)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 266,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.27497\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 266
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f3fJA1PZ8zex",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "85ee0229-c986-446f-93d6-f49c0984833b"
      },
      "source": [
        "s=pd.DataFrame(Station11test)\n",
        "s1=pd.DataFrame(y_test11)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 267,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0   -0.000466\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 267
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "277U-yKhs5yQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ed909d32-2385-49da-9255-d9bb1899ee26"
      },
      "source": [
        "mean_absolute_error(y11,Station11)"
      ],
      "execution_count": 268,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.021248591297090124"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 268
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ioS7kfFMs67e",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d702671e-93ad-4b26-863f-e4947b971827"
      },
      "source": [
        "mean_absolute_error(y_test11,Station11test)"
      ],
      "execution_count": 269,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.009101921005260449"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 269
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1g3RApY4Jnzd",
        "colab_type": "code",
        "outputId": "ce55bf6a-37f3-491d-cd80-4ec40b3c105c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y11,Station11)"
      ],
      "execution_count": 270,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.07559920511983453"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 270
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SHxiubcEJrCU",
        "colab_type": "code",
        "outputId": "b0b72f45-fef8-42ae-8164-3c87fc46e566",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y_test11,Station11test)"
      ],
      "execution_count": 271,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.30085520665991705"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 271
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b0PXVnPiJw78",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8VWpgScqJu29",
        "colab_type": "code",
        "outputId": "33515e8e-ddf0-4861-c8b1-b5abee6dbb8a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "from sklearn import linear_model\n",
        "reg = linear_model.Ridge(alpha=.5)\n",
        "reg.fit(X12,y12)"
      ],
      "execution_count": 272,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "      normalize=False, random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 272
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gl2k1F-cJzxk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Station12=reg.predict(X12)\n",
        "Station12test=reg.predict(X_test12)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ruN60G2gA2WS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "24f35b53-f615-4e1f-db99-c5866a386cf3"
      },
      "source": [
        "plt.plot(y12)\n",
        "plt.plot(Station12)"
      ],
      "execution_count": 274,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1594681eb8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 274
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LTy-orbhA3Bv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "512aa755-255d-4302-d9f3-8b43b9a273b8"
      },
      "source": [
        "plt.plot(y_test12)\n",
        "plt.plot(Station12test)"
      ],
      "execution_count": 275,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1595bfa6a0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 275
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xQbVZwtjJ3EQ",
        "colab_type": "code",
        "outputId": "c7a3a221-49b5-4096-8c44-c8f166a1e70d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y12,Station12)"
      ],
      "execution_count": 276,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.10731199677239234"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 276
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lTL8MpRqJ6El",
        "colab_type": "code",
        "outputId": "487010d6-e504-4a00-925c-d413c8bdda17",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "source": [
        "r2_score(y_test12,Station12test)"
      ],
      "execution_count": 277,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-277-1e9baed64d7b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mr2_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mStation12test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mr2_score\u001b[0;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[1;32m    575\u001b[0m     \"\"\"\n\u001b[1;32m    576\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 577\u001b[0;31m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[1;32m    578\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     83\u001b[0m     \"\"\"\n\u001b[1;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 578\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m     58\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                     (type_err,\n\u001b[0;32m---> 60\u001b[0;31m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[1;32m     61\u001b[0m             )\n\u001b[1;32m     62\u001b[0m     \u001b[0;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CdXW829UtBTi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "99ccedd9-90ab-4337-c022-e6d43245666c"
      },
      "source": [
        "mean_absolute_error(y_test12,Station12test)"
      ],
      "execution_count": 278,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-278-6ca282d87a42>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmean_absolute_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mStation12test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mmean_absolute_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[1;32m    176\u001b[0m     \"\"\"\n\u001b[1;32m    177\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 178\u001b[0;31m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[1;32m    179\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m     output_errors = np.average(np.abs(y_pred - y_true),\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     83\u001b[0m     \"\"\"\n\u001b[1;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 578\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m     58\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                     (type_err,\n\u001b[0;32m---> 60\u001b[0;31m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[1;32m     61\u001b[0m             )\n\u001b[1;32m     62\u001b[0m     \u001b[0;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UD0GYQBZtCAU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "82e39b6c-0cee-4a18-d586-84d279d25260"
      },
      "source": [
        "mean_absolute_error(y12,Station12)"
      ],
      "execution_count": 279,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.01709895847830041"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 279
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FqGiw9rU85IO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "b965c693-8af0-4ed4-eaa1-5d09bcbed2ee"
      },
      "source": [
        "s=pd.DataFrame(Station12)\n",
        "s1=pd.DataFrame(y12)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 280,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.327626\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 280
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P1BKmkO_87e4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "2f9b6229-cb86-4cfb-dbe7-33213a4e755e"
      },
      "source": [
        "s=pd.DataFrame(Station12test)\n",
        "s1=pd.DataFrame(y_test12)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 281,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0   -0.000428\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 281
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vy7__MV8J_LD",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RrowjLFxJ9Wj",
        "colab_type": "code",
        "outputId": "9ce048a4-cf10-4a33-def3-05c84ec4288d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "from sklearn import linear_model\n",
        "reg = linear_model.Ridge(alpha=.5)\n",
        "reg.fit(X13,y13)"
      ],
      "execution_count": 282,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "      normalize=False, random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 282
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZL-KUlaSKBd-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Station13=reg.predict(X13)\n",
        "Station13test=reg.predict(X_test13)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oc3vCPZtA9Yl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "7cbd63ea-2e99-45ea-b030-31a9d26c4685"
      },
      "source": [
        "plt.plot(y13)\n",
        "plt.plot(Station13)"
      ],
      "execution_count": 284,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f15967ed9e8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 284
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gIRB6ELQBANW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "24fbd7c2-a9a6-4f2b-9e95-bed1e391344a"
      },
      "source": [
        "plt.plot(y_test13)\n",
        "plt.plot(Station13test)"
      ],
      "execution_count": 285,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1597430dd8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 285
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAQ70lEQVR4nO3de6ykdX3H8ffHXS5VUNA9RcKCi+2auhqr9JTSaNRUxYUmbE1tXRojtcRNWmlstEaMLaU0TaqmtjWl2rVS1FYQ6W2TLqVeMCamIIcKyILIEbG7K7orIt7CTb/9Y54D49lz9szCzJlzfvN+JZPzXH7M881vZz488/vNM0+qCknS6veEcRcgSRoOA12SGmGgS1IjDHRJaoSBLkmNWDuuA69bt642bNgwrsNL0qp0ww03fKuqphbaN7ZA37BhAzMzM+M6vCStSkm+ttg+h1wkqREGuiQ1wkCXpEYY6JLUCANdkhqxZKAnuSTJviS3LLI/Sd6bZDbJzUlOGX6ZkqSlDHKGfimw+SD7zwA2do9twPsef1mSpEO1ZKBX1WeBbx+kyRbgw9VzLXBMkuOHVaAkDVtV8fGZ3Tzw8I/GXcpQDWMM/QRgd9/6nm7bAZJsSzKTZGb//v1DOLQkHbr/uuUbvPXKm/nrT94x7lKGalknRatqe1VNV9X01NSCV65K0sh99/6HALjn+w+MuZLhGkag7wVO7Ftf322TJC2jYQT6DuB13bddTgPuq6q7h/C8kqRDsOSPcyW5DHgpsC7JHuBPgMMAqur9wE7gTGAW+CHw+lEVK0nD1NotlZcM9Ko6e4n9BbxxaBVJ0oiFjLuEkfBKUUlqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJE6uxC0UNdEkTqM0LRQ10SWqFgS5JjTDQJU2ebvC8tV9bNNAlqREGuqTJ002KprHJUQNdkhphoEtSIwx0SRPLSVFJWuUaGzp/hIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLmliVWM3oTPQJU2ctPYzix0DXZIaYaBLUiMGCvQkm5PcnmQ2yfkL7D8pyTVJvpDk5iRnDr9USdLBLBnoSdYAFwNnAJuAs5Nsmtfsj4ArquoFwFbg74ZdqCTp4AY5Qz8VmK2qO6vqQeByYMu8NgU8uVt+CvD14ZUoSRrEIIF+ArC7b31Pt63fhcBrk+wBdgK/v9ATJdmWZCbJzP79+x9DuZKkxQxrUvRs4NKqWg+cCXwkyQHPXVXbq2q6qqanpqaGdGhJEgwW6HuBE/vW13fb+p0LXAFQVf8DHAmsG0aBkjQybV1XNFCgXw9sTHJyksPpTXrumNfm/4CXASR5Nr1Ad0xF0orU5mVFAwR6VT0MnAdcDdxG79ssu5JclOSsrtlbgDckuQm4DPjtqtZuvypJK9vaQRpV1U56k5392y7oW74VeOFwS5MkHQqvFJWkRhjoktQIA12SGmGgS1IjDHRJaoSBLmlitfbdagNdkhphoEuaOI3egc5Al6RWGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6pInV2n14DHRJE8cLiyRJK5qBLkmNMNAlTZy5ofO2RtANdElqhoEuaeLMTYq2NjdqoEtSIwx0SWqEgS5pYjkpKkmrXJobPe8x0CWpEQa6JDXCQJekRhjoktSIgQI9yeYktyeZTXL+Im1+M8mtSXYl+ehwy5QkLWXtUg2SrAEuBl4B7AGuT7Kjqm7ta7MReDvwwqq6N8lPj6pgSdLCBjlDPxWYrao7q+pB4HJgy7w2bwAurqp7Aapq33DLlCQtZZBAPwHY3be+p9vW71nAs5J8Lsm1STYv9ERJtiWZSTKzf//+x1axJA1JYzcsGtqk6FpgI/BS4GzgA0mOmd+oqrZX1XRVTU9NTQ3p0JIkGCzQ9wIn9q2v77b12wPsqKqHquqrwJfpBbwkrTiTfAu664GNSU5OcjiwFdgxr82/0zs7J8k6ekMwdw6xTknSEpYM9Kp6GDgPuBq4DbiiqnYluSjJWV2zq4F7ktwKXAO8taruGVXRkqQDLfm1RYCq2gnsnLftgr7lAt7cPSRJY+CVopLUCANdkhphoEtSIwx0SWqEgS5pYjV2oaiBLkmtMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5pYlVj96Az0CWpEQa6pImTRu9BZ6BLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA13SxGrrOlEDXdIEavOyIgNdkpphoEtSIwx0SROnDlhog4EuSY0w0CVNnByw0AYDXZIaMVCgJ9mc5PYks0nOP0i7X09SSaaHV6IkaRBLBnqSNcDFwBnAJuDsJJsWaHc08CbgumEXKUkjMYGToqcCs1V1Z1U9CFwObFmg3Z8B7wTuH2J9kjR0jd6waKBAPwHY3be+p9v2iCSnACdW1X8e7ImSbEsyk2Rm//79h1ysJGlxj3tSNMkTgPcAb1mqbVVtr6rpqpqempp6vIeWJPUZJND3Aif2ra/vts05Gngu8JkkdwGnATucGJWk5TVIoF8PbExycpLDga3AjrmdVXVfVa2rqg1VtQG4FjirqmZGUrEkaUFLBnpVPQycB1wN3AZcUVW7klyU5KxRFyhJGszaQRpV1U5g57xtFyzS9qWPvyxJ0qHySlFJaoSBLmliVWNXFhnoktQIA13SxElrP7PYMdAlqREGuiQ1wkCXNHHmJkOrrTlRA12SWmGgS5o4c5Oirf2MroEuSY0w0CWpEQa6pInlpKgkrXKtjZ3PMdAlqREGuiQ1wkCXNHHmxs4dQ5ckrUgGuqSJMzcp2trkqIEuSY0w0CWpEQa6pInlpKgkaUUy0CVNnMbmQh9hoEtSIwx0SWqEgS5JjTDQJakRBrqkibP2we9y15G/xc//4HPjLmWoDHRJE+eo730FgFfe+9ExVzJcBrqkCdTdJJq2riwaKNCTbE5ye5LZJOcvsP/NSW5NcnOSTyV5xvBLlaThqEd+lWvCAj3JGuBi4AxgE3B2kk3zmn0BmK6q5wFXAu8adqGSNCzJ3Bl6WwY5Qz8VmK2qO6vqQeByYEt/g6q6pqp+2K1eC6wfbpmSNDw1wUMuJwC7+9b3dNsWcy5w1UI7kmxLMpNkZv/+/YNXKUmj0Nivcw11UjTJa4Fp4N0L7a+q7VU1XVXTU1NTwzy0JA2s1TP0tQO02Quc2Le+vtv2E5K8HHgH8JKqemA45UnSKLQ2et4zyBn69cDGJCcnORzYCuzob5DkBcDfA2dV1b7hlylJQ5Q2z9CXDPSqehg4D7gauA24oqp2JbkoyVlds3cDRwEfT3Jjkh2LPJ0kjV+jX1scZMiFqtoJ7Jy37YK+5ZcPuS5JGqEJPUOXpNY8MinaVp4b6JImUKNDLga6pAnkkIskNcIzdElqQk3wb7lIUmMccpGkxhjokrS6pbXBlh4DXdLEcshFklrRVp4b6JIml2fokrTKPRrkBrokNaG1qVEDXdLEcshFkla5R28laqBL0irXC/J4k2hJWt0ava7IQJc0gbozc8fQJakRBrokrXJ+D12SGpF5f1thoEuaQJ6hS1JTHEOXpFWuyu+hS5JWMANd0sR5dFLUM3RJaoSBLkmr2tyZuWfoktQIv4cuSatd+T10SWpCup9bbG3IZe0gjZJsBv4GWAP8Q1X9xbz9RwAfBn4BuAd4TVXdNdxSO3d+Bm6/aiRPLWkybPj6bgCe+OMfwFVvW/4CnvMqOOm0oT/tkoGeZA1wMfAKYA9wfZIdVXVrX7NzgXur6meTbAXeCbxm6NUC7PsS3HTZSJ5a0mR42o/6zszHkSfHPXc8gQ6cCsxW1Z0ASS4HtgD9gb4FuLBbvhL42ySpGv5lWFes+VU+cPizh/20kibIvT98iG/d/wAAG5981LIf/4+P2sSLR/C8gwT6CcDuvvU9wC8t1qaqHk5yH/A04Fv9jZJsA7YBnHTSSY+p4GOeeBgbj1v+fwBJbdn5xW/wkmdN8aQj1iz7sY86cqDR7kM2mmddRFVtB7YDTE9PP6az99Of83ROf87Th1qXJLVgkG+57AVO7Ftf321bsE2StcBT6E2OSpKWySCBfj2wMcnJSQ4HtgI75rXZAZzTLb8a+PQoxs8lSYtbcsilGxM/D7ia3tcWL6mqXUkuAmaqagfwQeAjSWaBb9MLfUnSMhpoDL2qdgI75227oG/5fuA3hluaJOlQeKWoJDXCQJekRhjoktQIA12SGpFxfbswyX7ga4/xP1/HvKtQV6DVUCOsjjqtcTiscTjGXeMzqmpqoR1jC/THI8lMVU2Pu46DWQ01wuqo0xqHwxqHYyXX6JCLJDXCQJekRqzWQN8+7gIGsBpqhNVRpzUOhzUOx4qtcVWOoUuSDrRaz9AlSfMY6JLUiFUX6Ek2J7k9yWyS88dcy11JvpjkxiQz3banJvlEkju6v8d225PkvV3dNyc5ZUQ1XZJkX5Jb+rYdck1Jzuna35HknIWONeQaL0yyt+vLG5Oc2bfv7V2Ntyd5Zd/2kb0WkpyY5JoktybZleRN3fYV05cHqXHF9GWSI5N8PslNXY1/2m0/Ocl13fE+1v00N0mO6NZnu/0blqp9hDVemuSrff34/G77WN43A6mqVfOg9/O9XwGeCRwO3ARsGmM9dwHr5m17F3B+t3w+8M5u+UzgKiDAacB1I6rpxcApwC2PtSbgqcCd3d9ju+VjR1zjhcAfLtB2U/fvfARwcvfvv2bUrwXgeOCUbvlo4MtdLSumLw9S44rpy64/juqWDwOu6/rnCmBrt/39wO92y78HvL9b3gp87GC1j7jGS4FXL9B+LO+bQR6r7Qz9kRtWV9WDwNwNq1eSLcCHuuUPAb/Wt/3D1XMtcEyS44d98Kr6LL3fpH88Nb0S+ERVfbuq7gU+AWwecY2L2QJcXlUPVNVXgVl6r4ORvhaq6u6q+t9u+XvAbfTunbti+vIgNS5m2fuy64/vd6uHdY8CfoXeDeXhwH6c698rgZclyUFqH2WNixnL+2YQqy3QF7ph9cFewKNWwH8nuSG9G2ADHFdVd3fL3wCO65bHWfuh1jSuWs/rPsJeMjeUsRJq7D72v4DemduK7Mt5NcIK6sska5LcCOyjF3JfAb5TVQ8vcLyfuOE8MHfD+WWtsarm+vHPu378qyRHzK9xXi1jz6fVFugrzYuq6hTgDOCNSV7cv7N6n8NW1PdCV2JNnfcBPwM8H7gb+MvxltOT5CjgX4A/qKrv9u9bKX25QI0rqi+r6kdV9Xx69yM+Ffi5cdazkPk1Jnku8HZ6tf4ivWGUt42xxIGstkAf5IbVy6aq9nZ/9wH/Ru/F+s25oZTu776u+ThrP9Salr3Wqvpm96b6MfABHv04PbYakxxGLyj/uar+tdu8ovpyoRpXYl92dX0HuAb4ZXrDFHN3TOs/3mI3nF/uGjd3Q1pVVQ8A/8gK6ceDWW2BPsgNq5dFkiclOXpuGTgduIWfvGH2OcB/dMs7gNd1M+SnAff1fXQftUOt6Wrg9CTHdh/XT++2jcy8+YRX0evLuRq3dt9+OBnYCHyeEb8WunHbDwK3VdV7+natmL5crMaV1JdJppIc0y3/FPAKemP919C7oTwc2I8L3XB+sdpHVeOX+v7HHXpj/P39uCLeNwdYzhnYYTzozTB/md443DvGWMcz6c263wTsmquF3njfp4A7gE8CT61HZ9Iv7ur+IjA9orouo/cx+yF6Y3jnPpaagN+hN/E0C7x+GWr8SFfDzfTeMMf3tX9HV+PtwBnL8VoAXkRvOOVm4MbuceZK6suD1Lhi+hJ4HvCFrpZbgAv63j+f7/rk48AR3fYju/XZbv8zl6p9hDV+uuvHW4B/4tFvwozlfTPIw0v/JakRq23IRZK0CANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNeL/AawIU3g6T6aMAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c8-nr8p-tLLa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "5ace20b5-293d-4f96-cf9a-dc1cdb988b75"
      },
      "source": [
        "mean_absolute_error(y_test13,Station13test)"
      ],
      "execution_count": 286,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-286-8105063e991b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmean_absolute_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test13\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mStation13test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mmean_absolute_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[1;32m    176\u001b[0m     \"\"\"\n\u001b[1;32m    177\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 178\u001b[0;31m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[1;32m    179\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m     output_errors = np.average(np.abs(y_pred - y_true),\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     83\u001b[0m     \"\"\"\n\u001b[1;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 578\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m     58\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                     (type_err,\n\u001b[0;32m---> 60\u001b[0;31m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[1;32m     61\u001b[0m             )\n\u001b[1;32m     62\u001b[0m     \u001b[0;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nXgd7yyLtL-5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1e8465ff-7340-44f7-d1c5-4627cbead6ea"
      },
      "source": [
        "mean_absolute_error(y13,Station13)"
      ],
      "execution_count": 287,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.02136046978923799"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 287
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TjlqYbgU8_yf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "4b1f0ba3-51c4-44fc-fdc1-5a4bf744c09f"
      },
      "source": [
        "s=pd.DataFrame(Station13)\n",
        "s1=pd.DataFrame(y13)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 288,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.274063\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 288
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5vDfGV3k9BsA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "801881f4-04f1-4870-9dc2-f79f9ddafbb5"
      },
      "source": [
        "s=pd.DataFrame(Station13test)\n",
        "s1=pd.DataFrame(y_test13)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 289,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0   -0.000422\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 289
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ol96KuvdKEKL",
        "colab_type": "code",
        "outputId": "3f13e2f1-57ad-4506-f142-4e2d39f5dd04",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y13,Station13)"
      ],
      "execution_count": 290,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.002719669214864305"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 290
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iJJTc9s5KLLp",
        "colab_type": "code",
        "outputId": "dfe555aa-93e6-45ac-ee90-a683c95fd0d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "source": [
        "mean_squared_error(y_test13,Station13test)"
      ],
      "execution_count": 291,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-291-81f4a814bea1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test13\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mStation13test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mmean_squared_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput, squared)\u001b[0m\n\u001b[1;32m    250\u001b[0m     \"\"\"\n\u001b[1;32m    251\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 252\u001b[0;31m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[1;32m    253\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m     output_errors = np.average((y_true - y_pred) ** 2, axis=0,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     83\u001b[0m     \"\"\"\n\u001b[1;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 578\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m     58\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                     (type_err,\n\u001b[0;32m---> 60\u001b[0;31m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[1;32m     61\u001b[0m             )\n\u001b[1;32m     62\u001b[0m     \u001b[0;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "10PxmkIRKPpR",
        "colab_type": "code",
        "outputId": "d287024b-c3bf-4a45-ef2e-dfba09c01b97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y13,Station13)"
      ],
      "execution_count": 292,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0751014938967478"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 292
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kyYAI4YGKVBR",
        "colab_type": "code",
        "outputId": "e14b4627-1670-46f8-efe0-1bf27565e1c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "source": [
        "r2_score(y_test13,Station13test)"
      ],
      "execution_count": 293,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-293-88a47729c139>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mr2_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test13\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mStation13test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mr2_score\u001b[0;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[1;32m    575\u001b[0m     \"\"\"\n\u001b[1;32m    576\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 577\u001b[0;31m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[1;32m    578\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     83\u001b[0m     \"\"\"\n\u001b[1;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 578\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m     58\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                     (type_err,\n\u001b[0;32m---> 60\u001b[0;31m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[1;32m     61\u001b[0m             )\n\u001b[1;32m     62\u001b[0m     \u001b[0;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vhQFmtJDKfFO",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ZfdDR2RKY3g",
        "colab_type": "code",
        "outputId": "0694c279-f954-41fa-cbc4-790bdc12758d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "from sklearn import linear_model\n",
        "reg = linear_model.Ridge(alpha=.5)\n",
        "reg.fit(X14,y14)"
      ],
      "execution_count": 294,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "      normalize=False, random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 294
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X50GiIsoKg3z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Station14=reg.predict(X14)\n",
        "Station14test=reg.predict(X_test14)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LlqudEAzBEfL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "e6c6012d-fade-4d2b-fee5-8d9d1a5a193f"
      },
      "source": [
        "plt.plot(y14)\n",
        "plt.plot(Station14)"
      ],
      "execution_count": 296,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1597430b00>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 296
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vsTT2OfepqehzbEdX8vLSy8DWwiIhxDgA4zTH7jdIe27+YjHewUvNdf9DbRZ0U7Vb5WW31uf7OPBc+Yb8ZPfiq3a/XvofRHNAKb04vESh6/Zv7y/C50u25PbjHDWBq0XyojZi8ma/VOhBbctLaurQ55GvTG2Zbi9Q8RrFqtuPF27Cre8tKPh19EwuQWi/XitDUBwh9kuFboVfJ01XyZNTs9Y581dfuaUe78yqcl8gRp8cdIda3xRC9fi0yTMa9svgXFYPhF/f1soz6bQXfrHsm33VOT3cFYjJIF+d6UWd60WZioWdp6zYI2LuoQeI1MjCRhvyuvcEkwmRuz1zbf1r+zBbPLScHZBGnV7ai9UOMyt24JzHJxf1mvtlDz2oqizdQ7emWD0HHsq7g1rhunFLreol113qCzW67f+qFMeo6t+/LEj+uUhtVQ9T1xQ/vAn30C3wU0821UF38aHyqfUpbwpZbidZ6714vVwlsyu9uwHH/kDgFfpLU9dhhY0FD3tbElhUXZd13E9eIQ4sLr56URWTQo4o9PIeMqUCPQaaxrQrCrmWW+tA0Jxod0GaYFCKJyzwCv2JL1bhl/+1Dsh06/AF+PWQGdirWg7sNxQl7Wbv0j+vM3/y5ITVjn/jisnFhTwY7xF4hQ5kKzi93sjiGql33pZI+tZt0UkPPejkMrKaUbED67Z7a9MTr5u8ssx7NuX12iPm9ftsl0Ao9AnLt+LnT00ttRieQc+GPnnVNvzkP1MsJ7tWbqlHn0cmFkq0vPjb+4vw2LiV5onyUBRXvDYH9c3eHaG5OTfiNYVaTNyeuDXMrwT32PdeLmu3NeDW4QuQSDqvJL16EHCvwpUdTZqKZMZRRhZ60v/rk+XYVLcP2+rNQwO8Or0SO/amd7Px0nP/8UIpavM/LznJOJEPelqF8gSp2d2EaDiEow4ts0zr1hyKH0ezbt1+Lxbd9wr9/GenOf6NXkUUom5GyJvwNrYWZ6IoVQaTBluqRljsIa0XJ31zuffq22alPH/0xBQAubn2efF+FRM/OT+Y4XuFXmjyaeht7cXdszRtQzdunJaKNTeTqCFe7MXYIxgPuFPmb9iNYbOqcv69uu0JIQxfQqV8gbhVs1bPUinKGAgbupvkOhxua0/izRnrC67Em9sye/v7WtuxU46waOblUggfda+xZc8+tHu4fPmKVoy6u/bNufhk0eaCX6eUuH0fvWR2YoVugd2h2DuzNuDBz1bg7ZlVqWOFqOirXp+b8f2yl2fijEe+kq8nHcurvboscrH06/aGZpzz+GQ88cWq4lywSBRKibvVNL2jyrxHKfR8IBX6tnrncSh2N7UBAKaszm25bkOz9PtCe0nMrcpciafeJcZOcC7lTLEbW6Gvt6tRmshdtsm7u+bkew+81BP0M3pPh4cHdo4IpEK32i/TjKodjS5KUlzMeui2dUGRG3ZrIomkhYdSoj1Z9PkILVNXby/KdVoS7br3w03vKz2U9uHmSyORFIb1FqR3k5fMmIGcFDVqRG3tSewtop9x8dussdtirhS6sZ5w33j85rRupmkuHjwdax0u+HHba2Hc0i0498QjXc1TS6I9iRPv+wLX/KAHBvU9GYB+XbqhdIvRNi97aSYW1+zRPVdKHVgst0Ve+u8SRhX215GLcNrD6UUzQXFVUrBjQ7dszC63wk2791mmUfzLtbwzqwq1DS2OlbnXMaoCZS2F4u6qRl0tG3c24oPy6vyEKEIX2UiZlxo7z/2YxZuxdluDo3y31zdj2OwNuYrlCoHsoRvx+VJpD0gzdyK3404Xk3SpskuQ8oCxKp3Lhb/+nXIAQGOrs5FRxfa9uP/T5Rib676dLlOMHqXpixjpnvm/Pl0OAPhdn2NclyFfPe8h60Ne/GXEQgDmPv19X5iR8f2GYfOxuLoOPzuxM7p3PLCg8hmxXyn0oJNaKeqB4FxaU03SoQk8If9g7nrrcKzfGzTB1upIJxRCMVn5JSsvWyOl6iVbrZooErgyPBHvtF9g+zeltKG7dRubNAsGd8sT84l283osJIFU6JUuTmzarXx1usaWBL4uQXB7sw0uyOxkAeh173h8+8iDi3KthuYEGpqDZZZR8KIOTyYFxizejEtPORpEwI3hsbgrOgqtiAI4q9TilQQhBDZ6YEelQNrQaxvM45UoOI2pk0wKvDVjfWpxz/ilW7K8YgjAvR8vxS3DF2DFluK60NlZPFQs/ZBIitSm1bngtaXoXtCr09fucC2vfO7u6Pk1+Ov7i/DmjPUAgENJegYOwj5PvoAKjQAwYfm2UosBwOcKfcNO+z3xl6auS302GwqZNcjPlmzGoM9W4Lmv1gIAbh6+AL945uvM3wPYVCdNBDYWOba6Wbk27JR6DyuL/JLxIpvr9qHnPZ9j+WbjSbtCDpe9sGQ8n/LtaJQ6TLWpFcr+QjcoX54voj37WrOO8dJ/hziZMHNjBaGy+cWefW2pY+3J0tnLjDBrm+vlEUWxxfWS/XfSqu1ICuC9OdneJEa4Ib5VG9G9hqnHknfuKQAI36l2d/BSqX2t0HNFeQ7u+2QZgEy/9RemVGBdrT8XFyk9grXb/G9P9tILstgUo+xGvUenl9bm44d6K4S7sldMhPulQldYukkacve6d7yr+ZascuXLbqrbZ2pO8AP5B7KykcZBfkaKqqG5Db99cQYqa/N7iVr2tgvcGXfSYhVRlXZOGREWXRSqiARlTcp+qdBz7UXYDQuwpd56MY3bNLYkcPfoJWkZ6vTj2SjNtqE5ge2amDertzZg7FJv+H0XEjvVn7WzmsHz/v68aizYWIdn5XkVQDLDvfz1OuxzGAdf3+KSRyhkRhcn9y2fPYY5OJdDinHDlLrfuLMJr05fb+s31bskhV7X1GaR0j3enb0hw7vHqs02tCRw5mOTMo5d+Nw00y3q+g+dnVpw4RSnuqcodeuCQnzkc2lLvGqVy9qnizbh3+NX4ZmJzjaAVsIga/2bAbhuqDW6v07CCjiJbxNHK24Ij0UIScfXWeNwxaYVTqq973+/AQAchV2IQF+5C0C3fjy79J+ILiKi1URUQUQDdc7fQUQriGgJEU0iomPdFzWbYvZQtjeYR3DUkyUZsC7UrMqdGLN4MxZs3G2azirYlkJ5lfWioVyZs34XKgxCBhTiZZFQrZzaJ7u17m2x30MXAhg2y2TZuMF2ibnixi3QuqWKDONLJn+JfIR7o+/hf8POdxi7IIddydyickcjDsI+zCm7DQ9F3iyZHHaxVOhEFAYwBMDFAHoD6E9EvTXJFgLoI4T4PoDRAP7jtqB+Qf2gmPVC2pMir+FcKfntizNNzw+fk62YtO+2ZFLgspdnuSlWFte/PS+v36tNJk5srK6u1PVInyCRZ7TLQyCNWsuQ7d5XbPTmK8zu84GQOnPnhxfYy9/guNo7rlDY6aGfCaBCCFEphGgFMBJAP3UCIcQUIYQy5pwNoLu7YrqLF+ajB41Zju8+MCFl4vhwfo3lKMAM7bvDaJLNqatbLgqlps56DqEYMcKsrmG1TuCk+7+wfy0HIYuN5Fq5tfRrBIzu++vf2DM36uapytS4D+9lcmyNqoJvq2/BKQ9+6ZI8xthR6N0AqEO71cjHjLgOgK7bCBHdSETlRFReW5v/0vhiTjqMmJtndDsNHy2oAQC0tiexc28L7vxgMa55I78epR280uPzAmMWW2+11odW4bLw15bp1Ojd4rFLNmPhxjrT38yo2OnsOnlUprIoSE8O3fQmq6/9pqTzkbYlkW1Ga2lLYrBqUryUuBrLhYiuBNAHwE/1zgshhgIYCgB9+vTxVSv4UFbAgBTedKfDB8IMZXGS0UNmh0K5SuaiNOzI4oVRkh1Gxx8CANwhBpim0xtOq8t423vFmUwGgOe+WoPBk9Zi/ePGkQLfMbPXO5HDYUUaLT4y21DabXJ/Dwq8M3MDbvhJz4yjm2yMSIuFHYW+CYA6Tmd3+VgGRHQegHsB/FQIkbtmcpFCrqT7ckVm7Aa9tugXpbW/UYiXX4067nuRhkFGV3kuj96i0Z0xK9LpIel6vwgthN40tPq3Xu3Nm0klUv72+j10I7zq5TIPQC8iOo6IYgAuBzBGnYCITgPwCoC+Qoji7NVVJLy2vNoP+GG1oBVu1PqOPEZcTpnnksdQi4Hbqtmk8OmhCgDAOeEVGD0/N9NkUR8zh9fykwaw7KELIRJEdBuACQDCAN4QQiwnoocAlAshxgB4EsDBAD6Qh00bhRB9Cyi359BtkGZBwAomiXn+fnk/FfKlUPAXjnyBCcu3YdXW+qxtD/W9LMwnsa28bH7nkseQkeeVWjyzTo7dqIMBeOd7Elt+6EKIcUKIE4QQxwshHpWP3S8rcwghzhNCHCWEOFX+K4oyz1U5FVKnkcFnO+nVLNy4Gz0Gfo6a3e7GWNYqhh4DP885r7+MWIgBr87O6bpWdfCazUVcnkTVMCtrG11xz2xrd+Zq5zZGow239h5Xzq/e2pAKy1so1G3xONqCP4bH21oT4dRcxCtFA4a6+u/9eKnu5tVGTUTZV3JGRXYMbL3Gp208U1Ztt73AxwyzHMYs3oyZ6+x5ZjhVPiPnuetVZB+BqM6KQJ8MbHLGqnxLbOwPmhT5a7CLBk/Dg5+tyDsfM9RtcXRsEO6PDsNLk4yv6TSKpDKC4fC5AUNdncPnbMSUVdt1z+kxq1JSlHqK8LKXZ6U8Y4wYOa8aw3U2G3ZBx1vi5+H0leGvsLbsKkQbjWPa1DebLxBR32LbO17ZS1YyjFY9q4+GyHkpLgrNRU/anDYtFflGHAJpBFxpI8KqVyd01eyXCn3rHusFPAejCWE4C66UL7UNLalZdCUeTD5NaLOOO5XTcAR+Vs65cGlYMo+UNei79VXvasL3B9lfIKJn+3ZSA+beF+Y5TVi+1cGVLOQQ+p+d0oskBzllRPly7DlMjv89D8mcoye+YtFqak1kmSG16aevrUXfF74piGz54muFnqsHynaLLeoiSGBZ2fV4JvpSTvk7ZbUq+NALkyts/cZO2Z1umOAWWvNPBAlPzcbaekllRVuUDtjZN9IrRf3TsPk5/7Y71eLhyBupYFpG7Y0AbEgeaTvfP0S+0j1eqluW2mpXFsCOaenu0UtspWMbukPqmwsTC+WfkfcAAP3CMwvW0NT5vq+yF2sjNOajHPR6cB8tzFpCUFDiaEVF2VW4OfleUa+bK4q9tNjuqrlczk0Rtbrn2egQ/CHyFU6nNQCAzSajWr/tVKSuW8WMIlLnstMfTg1yWmd41Q/dswydVml6PtcGryyUyAX1JbUr3xx6NnqCXG6hejJICcrUNznJKLmnEAYTe8p9MKuvTxdlmhOk/LLT5bKZRKE4i1aiqmwADmrIfJZSPVcdaQshUjHfnxnPqPLNRIDB0SEAgI60V/5N/ljNgeWKrxW6loPRpOuh4JR8ehwtbWm7ey65eG3nlEK9cArXAxa4OjwBHVGgQFcmN+T2kYskCdT2Zp10XqrhfuEZAICOtXNzzqNCHA0A2CUOtpV+X2u7ZWA0txZKWZF+cRnTkZzFY7dTv4XaUSxQCn1Z2fUYHns073zUFWIWUEmPVyxGDWbXArI7CnYVfKHiYBgvUHI6uVocNdabNuDB6Nt4VjP/0dTq9EWfKW+hR1Jee5HroRe/iAgoT54IABjVfq6tfH78nyl46ss1Gce05f/b+4tyE1LF1j3Nugul9PcuSOrKoYfVs6bEtTdLNnlVYRbUB0qhA8CZIWe7xOih7qG/OdFeDGQ91hpssACYxMzQfjdoX7Yefzd0hI08tu5pxr7Wdjw+fiWa24rrGaQlJo/QOmh6VXZjz6dNK84WQmWkdXH04SVFf8YjRhOa2XuLmqG3SKkQA7azH5+EXz0/3TSN4mppZgHpRs6iYE5fK0WSNZtAX1TtrKNoF1ejLQYFtUJvbra3j6jzaxgcd7Fhu5FVg42l4H8esQA//HYnvPJ1JToeGDOOA24jol4yKRAK5d4ftmPrNv+9u31xr8cCOj4khRA2KvXPwwsxP3GCSQrrdnZfZBiujkzMSb58qdqZrVT1XpJpW7p5fk6qc1R5TdaxI7AHh1MDhOhsPyMHBK6H7jaVtbkvvSckMT72D1wUMrZPEoAtGR4EmS0mn8h5CgzmNagAAB1/SURBVEahfq04mdaDoB+saeqa9JBxXtXulJzanW2U0iQFcOJ9xhtGHE+bcBJtQM9/jivMTk4u6VU7q//Ul1pnMkrLFzfeFWeFVukeVxTcrZEx6B+ebHpd5SV4AmUrMAC4PqK7PULpEFJ7OxSNGccAYMBrc0x/+vTENabnVVmlOJ42IS7v1DQlficmxu8u2LiLFboObt3sMrTipFA1no2+aJpu/ob0Hp3ah8UohoY23cad2SMJpXe4uc75Tkjnhhbi8/i9uCGsH+tl4gp9GyARYdNu/fjQrSbbmE2K34Xx8XsAALsbC7tNmT1zlTD7apu3ZlbZEsAwf9Xx79BGTI/djsPkILWFmljTcrTG5KDeWYtAKYX+s/DiosjjBpPid+HD2CDVEfdUrPqVH0crJsXvwvPRFwAAh5LUQSzUyI0VusfItZ7fNtmwIJf50udlV627IqMc5TmvalfRfd3VGNlzjW6r1n3M7cfMjbUSl4ZmohtqcVvkExwTqsVPQksAIK+gXwdhHx6PvJr6TiYNL6QZpalXWrtl43fS7oUQGLN4c877nCqX6hVKt9NChcRQ5nR+EFpemAtoYIVeIprkDYg/XZS5DZrRAxJBAjG0WaZzi4Nk//EoOZvkXLNV38Urn0lGJxjZwM96LO0Hr47md/foJYY56aG8yA7F3tQw2i5xtOK/0efRsc1eiFmF/8ZewMfxB/KeH1BzfXgc+kemqI7YrxFt+IhizxJ8tmQL/jJioWOPMjOc+IU72aHIDTdqJwRaoRtVUQhJdCd7e5ranbnXj6Ro/ei9Mm1d5m8MLvdl7G6sKbvalizavHLpoVvJ7vG5PlPWbEvbtdVbCwLG5dYWd0nZjXjPoYvsz0KLcGl4Ni6rlUY/XyyzjrWiDM2PpDpXJ2y1gbTW7zC29V8RzlwUpjelqCaGNnSBtWfIMZR+sTnpoOySzZDb6o1Nie/ONh6x6rVd7SjEDnG0oqpsgOGes31oFRaU3eQ433wIhEI/HPXoG5phO/3tkQ/xTfz2jAaVD18s24pvdMLc2iF7gkmfniH3Ai2dE1qOQ1WbhZ1A1bguPE4jR+nWsLrxstB7EfcLfYOTKPtB13sZG+eb5gyDFcXWrqZJfLN2B256Nx1rZbGhG5t7S5PakwJXvTEXM9dlt9XVW40XYnUgY08vtQ1dYXD0Bcwu+7PhhDogrVCdHv9b6vvKLfX4P5smJMVTarzJC9FUoVu+kuzRCdIcxt8io3XP/08oPYGaNUItUIcoEAr9pdhzeD42xHb6H8r2rKOwW/e8U2V207vzTSvIrJeftfGDzYrWTydwXXhcylySHqITAIEQkjgAzRgRexSvx55K/Wps7J/4V/TdjJySFvdgzTYD00pWuANnPsoAsKgmdx9ds6sMjr2YmnhVoxtCwqAiKnfk48aavje7mzLNNb8farBRiEqOXO6lmrqmVkxbU4tbhy/IzsGBhlEn/V7zfNwfHZZx/oJQOQAgZCJnr1DmyOjBz1Zgrs3VoUoTqzUJsud0oV0u9zQpq0+j37YibPhbXvpvQhc4WyacTD0YaTpjN64I6y+ccIvahhY0tiSwcotxb0iv97DdZGip5iehJfhX9F0Mirydde6P4S9QWXYlOpPUqzhR5WIW09jJy9AC9d3Ra3yVtYVzxzPrLdrFyeO8S/aqeSIyFD8OLwMAPDZOf8MDs56fgtWj6kjViHQvN18buqLkhE4eDZoY7+adkDSX7v0g67yVopPycFaKc0ML0cvALVIPs9yFTnvORaEb1YdI/W+sXpsKtAAvEApd2zhOo/RQeEH8Rvwp/JlueoJI2ShfjT2NR6Nv4GjsyMncoKektY3kvk+W4ebhC3Dx4PTqtayOkU67OlM1oWcWo12xAypKOyUHAb8NS9dU5g6Mmu95oflYVXYt4pR+wB8em63c3Ao1cE34C5xCmSGDG1syy/jD0FL8LjwVZ9HKVPQ/I3LpxSpukr+PTLVMm0+xhc6nW8KfoKpsgKF5QjjooR+FXehsMOqUfmcNIYnOqMN3ab1hmgyZNDfkdFpj68WjfcasBghvxZ7ExPjdlvnawx2Ti1V9qI9mm1y4h65LVdkA9Ahl2sI/jj+Q+nw47cU90REAgAPQnPGWJwDH3SPZjjuRpJBj1JYxYWpXMRycrMf7sYdwlGq00Fu215aplOO89ZmjCbs2dIVvUab/98DICKyJ/wEA0IoogPTMujrvVnlRcBxmu+0I/DiU7fUxdslmnbT20JZHu33eoOg7+DR+f8YxxXc7jHbcEv4Uw2OP48noULwffxgfxQfpXoeQxNPRl9A7ZN2D1mLHzdKNbcX0Ogp3RqQerqF5Qkeh68kFAHPKbsO8slut5RDZ9aK08z+HP8G8sluyRm1qIqIVE2N34YehpdCqwq6Urt+1ZVfhR6GlqBcHZMtgKaUJNt6qZkmEyH555tZDly6SrjsBQjK1+1Ep5qF8rtDtV8JT0ZexsuyPmBi/O9WTHRx7IXW+O0kTRQPCk01jN5wTWp7qJf8otBTdZQV7WXgazgqtwg2Rz0FI4gSqxqmh7M0qrNqiECJj4YaWr2KZu7vcFPks9fC1CUlpx0gzfKa0si+TXe30FHsIwnYjtNtUlYUUCop5w04H5anoy7g7+r5pmrb2JMJoxzFUi/8NT8eT0aGpcxt0ln3bRVu+bzdJwaLs9NAPaapGVdkAnBtaaJC3ca/7gcjbOIPU8Yiktqrer7Mj7ZWVqfV9nKXa81WRXa93qOT+s7B1UKwf7J2IXqFNGB57POsJDCGZ0YbejT2um0c+ym6RTsC8Kau2Z4ySTRV6u54rYWZJTqc16GrhqXN2SBq5KnV3bfgLrC+7Eh3QIOeYFqJYqt3XCv3C0DzbaS8LT0t9Vt6oXUganl6nWg3ZEZmTfeqKOItWYkTsUfw58jEAqbFOi0kz9eph5l8jH+HL+D9S220Z5acmhjYQkkgKYMwi4x5xWHY303uQ2+XqjCAzchyB0CakCZqo/DIqozY8Gnk94/en0DpcE7G/vZoZL0WfxRORoXg5+hwAaaQkyWSPzqjDb8L6nkt7mtrw/KS1SCYFHhizHOvK/oBpKo8JADg5tMHkgRToG5qZ4devRdtj+2ndx/LxTA5Ec5biPrJOWjH5a438+rHF1fM5AtdGJuDD+IPp86kogMBvw9K2Z/dHh2F47HHE0YoVJvMxALCwOm2CMRtdUKqtWNdQVW36GdGWiXQt9NbqbOkm+6teta6mAHDtW/MyTJnmIym9HnrmXrEfxQdhRvwvOr9N35+0I4bAmbQSD8iTw4pe0d7JEym9x6/TrSDt4muFfhQZ2wud8K/o8NTn30WmGaQSeCz6GgDg25RWuIo/r1BNBClvbiUgvhrF9vwt2oYHI28CyXZEkcCasqtxb2S49DjkYKgNox03R6S5gtNCFeiMdC+GKD1RpbbBXxGZhKvDE1Lf1aYqPU6kjehFNYgigZ0Wy/MvDs/D7yNT0ZOMX04Rk0UXZsr2wc+W45mJazBl9XZMXmkchvTF2GDd4+eGFuP52Au402AVrC7KMF1TN89H/4u3Yk/id+Gp6sTyv+m0h6AJHVIbJKQfZmXhFkHobnlotoIzgnZU7WzE12sy11TE0YrTaC3OoNXYo9oBa8HGtKLRe7Vk/p+J4jmVTWZOIZse5VY99BpV+IgelN6wu98Q++7JhhhMimpjtIdI4InI0Ixjv9PxOe9M9RgVfzj1Xck9qVKvB1ILJsQHpkXIbZGrJb5W6EkT8d0O43pr+FMcH5Ia1q/Cs/GrUKbPbLqHLlLmjBbZzAEAr0afxgFIm1KGRAfj6shEHNtWkVJe10fGA8mEreGZ9rH5TfgbnB9O+zWfo1lqrHj2RDS20Qej2R4xWtrkHXQnxAdiYvxurC27CmZ97UyTgbYHJ7FmWwMqyq4yzMPsgVdW2bYmkqZDaz2fcwCpoEz9w1OMfwzJ5JOSJ2VDz+S8sNQ7V5t69BT60rLr8VT0FfXpDAgi1QPPREocJn0lBADvzcks56vRp/Fx/AF8GH8QuxpbU7Jf+1Z6RHuqZiKaNP9r+bvBy09bT2eGVtpaHaktjdnLfWr8ztRnY3/9TIzaxW9enIELn52anR5C1vMCt4Q/SR3XTpT3VL1cjDDTS6k03EN3xnf+9YXhME7PFGKEYm+/K5rZoF+I/Tfju9pVS3nQEio/1PPD83FhqDzV/MNymj3NSZwTSnuRnLd7JIikXtYN4bG6Xi1n0cqsYzHNA9GDtqXcswjAIbRPvq7zrsGefW2p7dUUCAKnUoVs9pKuczAkm/VZobR88Sx7vnQHrnvb3Fx2NBkv1ErZgmE+mDf2PpB+pbXva1OpzXQk99DtDJ6U6/42/A3+FP4MD+i4kdqW1cRfOa2EKePl85Pw0tTnD+bXZLtaCmClODbjUERuZ6eE9JfTd1FNdqqVuFa6AZEpWS8fvfusfREo7bc71eKW8CemCt4ORtW0cGOd7gT0IbQPQgh8h6pxd9R45Gavs6X8b5yaFxbpcIjhMFDC6G1u/iBn8ouwvQ0ulPoJQWoUAHB4lj1epFqEenPaq8Jpu3WXlg0gAKvLrsG90ffw+4yhvMT78YeB1kxzTkYoUAB3REfj1F2SBw8R0EdetXawxT0zQtleTSGMJD6J349XYs9icHQILgnNxrKy63FW+wJD/9vjVS9SIxun0rsfHX/IUBZlhaAQ5uapEATe+GZ91iRgLhNyyojoYNGIqrIBumn0tr27JzoC10YmaI468IPW8cjQ/uaL5VszXj5aPtZ48QhkLxyLmLjDApIJDQBOf3hifh4qkNwrj9eY4pSO0+exe3B3dBSeiL6q91PbbDdZdKS3zP/ByFsQwrrDczjq8YvQfCyOX2+Y5gDZ8cCsnR3VnrvnmBm+Vuj/iI40Pe/GsOYhGyaJY2lrqod+VWRiqld6riacqHqR9EkhaYJEgDJ6VITMDR6uCevHEBeJzJ7vQJ170a1R6fmn8/tTZKxledRsFx10j1+sivHeLzwTL8aeBwCcllxuaP+eFL8r9dlID38YfxAnU5Ut2azqN4QkHhq7Ass21WcE0lI/aFof+JR82gPytbq1G4/uFipxOyyandJDVJvFjFdVGmdmdw5pgY5XiPYFEiXrHvG14fHY1dia4ThgtaJYjzllt+EmTTtUcjlM7mydL682NfLXDiFp2ovfsqcZt4Y/0XWc0FPoSlz4TmQ+Oft/ka/xz8h7KTn1eDj6BgCgi4m33KCmx0yvkyu+VOhCCFtLZwsVElPL1/E7snrIeugtXNEGCTu2eVXGI3JCaBMu1Nsgw6TnpqAsVllTmR5GaxcdWXEk6Y9yjjWIg3OcqMbfoh+ayJT5vx5dbW75JU0gG59Xhv6t7e34Qzi9Y05/VbAprQ+8wtDoM5qLySMqsvPImDe8RHsSh+1ehhGq4F5G6wPMphgnxu8GhL25IsWD4wjsASXbsu5/FO34crl5vCDFi+MI1fZ+u5vciSZ4JO3GvZF0+ImISU+5DC2oLLvSdA7mQDTjrugovBJ7Nuuc3svzQGqBEMDbsSccSp7Nd+UOyV8inximiSed71FgB18q9Nemr8fx/xxnmc7Oaqy/hD9yQyTcFPnMMs1ZoVU4Dpvx90jat/rVWKbiOKqtJktLvRJ7LjszOwpdTjN3tf0l03r8IzIi65iRQj8haR7S9PDtUswSM1PJa7GnbcklbWlnJx1wn8qT6UfhzAnj63Q28cgOGyy1pePa1mWl1XLwPvPhdEuiHbHWzN61UVQ+sqjneIu9sBdb9zQjjHbML7sZD+KlrPY6IDxJd8u0YvFI5E3cEEk/02Yroi8NZwfxCqM9o+et9JL1MBoNuRWSOkLWz2bMYehlu/hSoY8qr7ZM87PQQlszD3dE9SOlOeVQsmebfgmP4rbIp6ZpjtijH0ckA1sKXZ4UDee3dezNkc+y7MNG7p0hC1tsmazsDhO5x2s5g1ZjXfwKHNC0Cb9uM97WTsL6MVW7rRqhKNYzWoy3E1TYW9bVQiLA6tFTOiP1TWYre4EumybiWyZRQ6vKBuD8UDkqaxtxpmxWuDSUrRDNIiqqOQj7MEA1wnFrEHxOOLPNx2UTkHaUfVdkpMajSGJd2R/wUSw92tJbHDh02jr8KLQU7xgsdqJme6PXI8hG27XQPWZuufngS4XeEfW4J2L+EL4ZexI9t1o97MXHLKQoANREj0OHvTb2EXVgcomL3PYUVTMp/nfrRIDlpg+bN1Rg2MxKPLBP/6Gyw4fxBxEmgS5bpuA3CfP9KqvKrsC6itWmaezQsbkGH5ZvRIKsX477YvrzDgoRkciKgaJFUWRXvarnypimU+3srEVVWl6NPYP73/0qZeIx2rTklKaZpvkAwPKy63AApev4e6H1lr/JFSFElhno1sgYw/Snhirxg4fHYcPORpwdyvYEe3bcIrwbexwnhfQ7hKEGe95vtl5+SfOOzWYcaetaTrHVdSOiiwAMBhAG8JoQ4t+a83EA7wA4A8BOAL8XQlS5K6pM9VyMqr/SluSnbnjdOpEdvn7SnXwAy+XE3dvWI7xZfx9PNdEZTwMdu5mmOW3XOGDakxi47hFHMupxuM4iKT0OgbkH0XlbXwO2vuZKV+LE1S8hLqwnBvt+82vrzKaZ13GP0Db0GPu9DFdUo3yO32buGXVGcik21Ji/iMTkR9AWimJOmfmLr+vmiabnFebaiPHy523/spVXxvXJWaRTJ4jP78R3t1j4fU99AgilG9PM9v7AfzVppjwGhKNYWWb+HBy62CV9AQBf/9v09AI6CSe5d7UUZGVnJqIwgDUAzgdQA2AegP5CiBWqNLcA+L4Q4iYiuhzAb4QQvzfLt0+fPqK8vNy5xDMGAxP1J7IYhmH8wON0He554BnrhDoQ0XwhRB+9c3Z66GcCqBBCVMqZjQTQD4Da6NUPwCD582gALxARiQLEiPwg2g/3NPeAgLRXSgLhlDug2hdc7ZaVRAghJNEun1HOqX1OkwghCcrIS4AQRhK9Oh+Eitq9kGKpSR7UBJH6rs6HIFLX0S6DVsuluDkK+biSrzKxI1ROjiEkkUAEUoABaaOKroeW4aB4BBW1ezNWpimLbZQFTt/ufHBKdiU/5X5oXdcIAkl5yigs3y91XoC0WCqUKgNl5KGUT5FHOS/k6yVBGf7Oikykyk8glHJHU2QhqGPYC4TlAFDqayuyqmPqaN3T1NdTSqSkU+6T3r1U7oc6H6XeFRmFXDb1/U6m2gDJ8hNCcksLkWRWaUcIIbnNRNCeur/Hd5IiFK7f0ZhRd9rFVEoZk8ic6lO3P63s6fKn89S2TSWNcl7rn63Irf6NOh91+ZW6BdITkur8KeMeIZWOIFL3SbmG9rddDoni0LIoKnTi8yv3MqQpfzvCIFUbUuquy6Fl2FrfLKcJISy3SLVsyn1Qrq+kExmyJ3F854OxTtWeSH7GQ/Iz3IYwsrdayR87A99uANRGpxr5mG4aIUQCwB4AR2gzIqIbiaiciMpra+3t6anlsIPKcMqxndGOsKzkSK4y5VaFUufOPeloJBBBEiEkEIG04IVS6RKIpP6SqnNKXpArv2eXDkgggnaEU+fbEU79ph3h1J/6OmqZFLm+0+3w1PWSquNKvj84oSt+3rtbKn9FdvkOpr5/79hOKbmUdEp+inzn9OqSIbuSBkAqjVb2pOreqPM68rCDU/c7nQ9llU8tj3JeuR5ASCCCUCSWIZNyfSVtAhGc1O2IjPKkX1Oh1G9/3rsbTj6mU4asQvUbpW7P6dUl63rqvA8/5CDTe6lcs+dRHfDjE7umZFXfL6VsPbt0wLknHZ1Ko24nyn34n55H4We9u6Xkbkc49Xvl/vbq2hG9unbMqjuhki+JEM7/bndEo/GstqZufwlE8K1Oh6Xy0suzHWH06Xlkqkza8+pnpceRHTLqW30f1Pkr35XP6u9KGm16bVv6ee/uGdfQynZqj86putP+Kffg7G9L9R+LxeV7jdQ9Utfd94/thLJ4Wao+9GRLIIKDDjggJbuSTl0nCURwfJcOOLn7ERkyK+VqQwSvX/0/Oek/K/Jzf3CIEGIogKGAZHLJJY8LTu6CC07u4qpcDMMwQcBOD30TgGNU37vLx3TTEFEEwGGAjW2/GYZhGNewo9DnAehFRMcRUQzA5QC0vkNjAFwtf74MwORC2M8ZhmEYYyxNLkKIBBHdBmACJLfFN4QQy4noIQDlQogxAF4HMIyIKgDsgqT0GYZhmCJiy4YuhBgHYJzm2P2qz80AfueuaAzDMIwTfLlSlGEYhsmGFTrDMExAYIXOMAwTEFihMwzDBATLWC4FuzBRLQD9XXyt6QTAeNNJf8Fl8SZBKUtQygFwWRSOFUJ01jtRMoWeD0RUbhScxm9wWbxJUMoSlHIAXBY7sMmFYRgmILBCZxiGCQh+VejZe1D5Fy6LNwlKWYJSDoDLYokvbegMwzBMNn7toTMMwzAaWKEzDMMEBN8pdCK6iIhWE1EFEQ0stTxaiOgYIppCRCuIaDkR3S4fP5yIJhLRWvn/jvJxIqLn5fIsIaLTVXldLadfS0RXG12zCGUKE9FCIhorfz+OiObIMr8vh1UGEcXl7xXy+R6qPO6Rj68mogtLVI4ORDSaiFYR0UoiOseP9UJEf5Pb1jIiGkFEZX6pEyJ6g4i2E9Ey1THX6oCIziCipfJvniciQoEwKMuTcvtaQkQfE1EH1Tnd+22k04zq1BQhhG/+IIXvXQegJ4AYgMUAepdaLo2MXQGcLn8+BNIG270B/AfAQPn4QABPyJ8vATAe0h5lZwOYIx8/HECl/H9H+XPHEpXpDgDvARgrfx8F4HL588sAbpY/3wLgZfnz5QDelz/3lusqDuA4uQ7DJSjH2wCulz/HAHTwW71A2u5xPYADVHVxjV/qBMBPAJwOYJnqmGt1AGCunJbk315c5LJcACAif35CVRbd+w0TnWZUp6YyFfOBcuEGngNggur7PQDuKbVcFjJ/CuB8AKsBdJWPdQWwWv78CoD+qvSr5fP9AbyiOp6RrojydwcwCcDPAYyVH5QdqkabqhNIMfPPkT9H5HSkrSd1uiKW4zBIipA0x31VL0jv33u4fI/HArjQT3UCoIdGCbpSB/K5VarjGemKURbNud8AGC5/1r3fMNBpZs+Z2Z/fTC52Nqz2DPLw9jQAcwAcJYTYIp/aCuAo+bNRmbxS1ucA3A2ktk4/AkCdkDYD18pltFm4F8pyHIBaAG/K5qPXiOgg+KxehBCbADwFYCOALZDu8Xz4s04U3KqDbvJn7fFS8UdIowTAeVnMnjND/KbQfQMRHQzgQwB/FULUq88J6ZXreX9RIvoVgO1CiPmllsUFIpCGxy8JIU4D0AhpeJ/CD/Ui25f7QXpBHQ3gIAAXlVQoF/FDHdiBiO4FkAAwvJjX9ZtCt7NhdckhoigkZT5cCPGRfHgbEXWVz3cFsF0+blQmL5T1hwD6ElEVgJGQzC6DAXQgaTNwrVxGm4V7oSw1AGqEEHPk76MhKXi/1ct5ANYLIWqFEG0APoJUT36sEwW36mCT/Fl7vKgQ0TUAfgXgCvkFBTgvy04Y16khflPodjasLinyrPrrAFYKIZ5RnVJvpH01JNu6cvwqeUb/bAB75OHnBAAXEFFHuVd2gXysaAgh7hFCdBdC9IB0rycLIa4AMAXSZuB6ZdHbLHwMgMtlj4vjAPSCNHlVNIQQWwFUE9GJ8qFfAFgB/9XLRgBnE9GBcltTyuG7OlHhSh3I5+qJ6Gz53lylyqsoENFFkEyUfYUQTapTRvdbV6fJdWRUp8YUYxLE5UmISyB5jqwDcG+p5dGR70eQhoxLACyS/y6BZBObBGAtgK8AHC6nJwBD5PIsBdBHldcfAVTIf9eWuFznIu3l0lNujBUAPgAQl4+Xyd8r5PM9Vb+/Vy7jahTQ88CiDKcCKJfr5hNIHhK+qxcADwJYBWAZgGGQPCd8UScARkCy/bdBGjVd52YdAOgj35d1AF6AZhK8CGWpgGQTV579l63uNwx0mlGdmv3x0n+GYZiA4DeTC8MwDGMAK3SGYZiAwAqdYRgmILBCZxiGCQis0BmGYQICK3SGYZiAwAqdYRgmIPw/51uX+eo1gYYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FK3PNBquCjMS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "dd3e5375-31d4-452c-a126-af1dad5f2e53"
      },
      "source": [
        "plt.plot(y_test14)\n",
        "plt.plot(Station14test)"
      ],
      "execution_count": 297,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1596f682e8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 297
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LSW1TODC9H0b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "39f5feb0-d3ff-43cf-ffe8-4e84da2ba911"
      },
      "source": [
        "s=pd.DataFrame(Station14)\n",
        "s1=pd.DataFrame(y14)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 298,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.269086\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 298
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OTIVwVgt9J-h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "87359955-8b12-4dad-ae96-3d84a692beeb"
      },
      "source": [
        "s=pd.DataFrame(Station14test)\n",
        "s1=pd.DataFrame(y_test14)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 299,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.200556\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 299
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0oJ51x8ZKljD",
        "colab_type": "code",
        "outputId": "8032c8a3-31ab-4122-94ed-580c1c3df6cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y_test14,Station14test)"
      ],
      "execution_count": 300,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.003973982974662373"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 300
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3urI3tUJKpEU",
        "colab_type": "code",
        "outputId": "a0a19b91-f2e4-417d-b17c-12f3c74e120c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y14,Station14)"
      ],
      "execution_count": 301,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0024870705076386946"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 301
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r299o65EKt1M",
        "colab_type": "code",
        "outputId": "94b2f976-6e21-462b-9cf2-28a8b14d49c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y_test14,Station14test)"
      ],
      "execution_count": 302,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.03556030769443286"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 302
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yLuUwwwxKxo6",
        "colab_type": "code",
        "outputId": "931092fe-30c6-4334-a3ae-0717e4b8b423",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y14,Station14)"
      ],
      "execution_count": 303,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.07239697768754261"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 303
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ykjrPMPOtYH2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3494a468-bcd4-4ac2-c832-7798180efd80"
      },
      "source": [
        "mean_absolute_error(y_test14,Station14test)"
      ],
      "execution_count": 304,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.02456287594700686"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 304
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZIhoQLY1tZdc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6dc67a50-7476-44e9-9a4e-b2eb1c34dc5c"
      },
      "source": [
        "mean_absolute_error(y14,Station14)"
      ],
      "execution_count": 305,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.020561705977435313"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 305
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9hoxLwahK2Bw",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FARhlbtwK1P-",
        "colab_type": "code",
        "outputId": "5af054ab-0b4c-4219-c4ae-1fb4bc4855a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "from sklearn import linear_model\n",
        "reg = linear_model.Ridge(alpha=.5)\n",
        "reg.fit(X15,y15)"
      ],
      "execution_count": 306,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "      normalize=False, random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 306
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AU24RhQvK5YG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Station15=reg.predict(X15)\n",
        "Station15test=reg.predict(X_test15)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EZky27mv9OnY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "34746c92-73da-4f54-e0f1-5626e62a6e86"
      },
      "source": [
        "s=pd.DataFrame(Station15)\n",
        "s1=pd.DataFrame(y15)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 308,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.220047\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 308
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PX40btob9Qj3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "b052e6c3-d141-4f77-e8c6-f413bbd5c7b3"
      },
      "source": [
        "s=pd.DataFrame(Station15test)\n",
        "s1=pd.DataFrame(y_test15)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 312,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.165867\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 312
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D8jexT6NCpva",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "501110f6-24a8-4c8b-818f-3c1ab2cbfbcb"
      },
      "source": [
        "plt.plot(y15)\n",
        "plt.plot(Station15)"
      ],
      "execution_count": 313,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1592fe0b70>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 313
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oOjRzrM9CrAM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "a3ad8ccf-2a3f-4d80-857b-999e2d15fb00"
      },
      "source": [
        "plt.plot(y_test15)\n",
        "plt.plot(Station15test)"
      ],
      "execution_count": 314,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1596125320>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 314
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mr9N0aIIK9y4",
        "colab_type": "code",
        "outputId": "4db91d78-2db4-4431-fcbf-5fafc9a706c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y15,Station15)"
      ],
      "execution_count": 315,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.003100934970136783"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 315
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8jOLt7v7LELm",
        "colab_type": "code",
        "outputId": "819d81a4-62f7-4cf6-cf4f-2f3f24623e9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "source": [
        "mean_squared_error(y_test15,Station15test)"
      ],
      "execution_count": 316,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-316-b564dab142bd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test15\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mStation15test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mmean_squared_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput, squared)\u001b[0m\n\u001b[1;32m    250\u001b[0m     \"\"\"\n\u001b[1;32m    251\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 252\u001b[0;31m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[1;32m    253\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m     output_errors = np.average((y_true - y_pred) ** 2, axis=0,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     83\u001b[0m     \"\"\"\n\u001b[1;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 578\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m     58\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                     (type_err,\n\u001b[0;32m---> 60\u001b[0;31m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[1;32m     61\u001b[0m             )\n\u001b[1;32m     62\u001b[0m     \u001b[0;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jYC0vqMRLJNo",
        "colab_type": "code",
        "outputId": "3a758aa7-2764-4bab-fdc2-5e1863e5a06f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "source": [
        "r2_score(y_test15,Station15test)"
      ],
      "execution_count": 317,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-317-bf2a7025d817>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mr2_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test15\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mStation15test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mr2_score\u001b[0;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[1;32m    575\u001b[0m     \"\"\"\n\u001b[1;32m    576\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 577\u001b[0;31m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[1;32m    578\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     83\u001b[0m     \"\"\"\n\u001b[1;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 578\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m     58\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                     (type_err,\n\u001b[0;32m---> 60\u001b[0;31m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[1;32m     61\u001b[0m             )\n\u001b[1;32m     62\u001b[0m     \u001b[0;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p4bRvqpMePAD",
        "colab_type": "code",
        "outputId": "7409f8b7-464f-4bce-d367-9368258c4235",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y15,Station15)"
      ],
      "execution_count": 318,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0484150979553275"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 318
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FZT55L6ptfL_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "bd65e870-a2ed-485c-cc6e-f95b62ee02e3"
      },
      "source": [
        "mean_absolute_error(y_test15,Station15test)"
      ],
      "execution_count": 319,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-319-7e49c7349b1c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmean_absolute_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test15\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mStation15test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mmean_absolute_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[1;32m    176\u001b[0m     \"\"\"\n\u001b[1;32m    177\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 178\u001b[0;31m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[1;32m    179\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m     output_errors = np.average(np.abs(y_pred - y_true),\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     83\u001b[0m     \"\"\"\n\u001b[1;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 578\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m     58\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                     (type_err,\n\u001b[0;32m---> 60\u001b[0;31m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[1;32m     61\u001b[0m             )\n\u001b[1;32m     62\u001b[0m     \u001b[0;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dis9yrd5tgl9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d9524049-df81-41ed-ed09-35ff394fc804"
      },
      "source": [
        "mean_absolute_error(y15,Station15)"
      ],
      "execution_count": 320,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.02295913448915798"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 320
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Epe0LAwneWVq",
        "colab_type": "code",
        "outputId": "67de73fb-2afd-4c5c-c642-ef97b5797bec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "from sklearn import linear_model\n",
        "reg = linear_model.Ridge(alpha=.5)\n",
        "reg.fit(X16,y16)"
      ],
      "execution_count": 321,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "      normalize=False, random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 321
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kEtCzh0Zea1Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Station16=reg.predict(X16)\n",
        "Station16test=reg.predict(X_test16)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ATvtkb0xCx03",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "63ab44e2-ef9d-4e2d-e7b1-1ab2b797e5cb"
      },
      "source": [
        "plt.plot(y16)\n",
        "plt.plot(Station16)"
      ],
      "execution_count": 323,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f159309cc88>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 323
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lLj8TlEeCyqu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "22c5f52e-4c74-463b-beb9-46160a7c48d8"
      },
      "source": [
        "plt.plot(y_test16)\n",
        "plt.plot(Station16test)"
      ],
      "execution_count": 324,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1592d1e278>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 324
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ml1Ff1a49V1i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "290b6665-1bd7-45cf-bea9-a07524bd08d6"
      },
      "source": [
        "s=pd.DataFrame(Station16)\n",
        "s1=pd.DataFrame(y16)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 325,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.223239\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 325
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eB5oNxJD9YFQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "6926795b-3c41-4e77-8ddf-cfda27ba4e00"
      },
      "source": [
        "s=pd.DataFrame(Station16test)\n",
        "s1=pd.DataFrame(y_test16)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 326,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.182919\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 326
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d__nNjHBee0c",
        "colab_type": "code",
        "outputId": "cb5180dc-02de-408d-f302-759f4771048b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y16,Station16)"
      ],
      "execution_count": 327,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0025212575783169536"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 327
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7gXIp4w6ehzY",
        "colab_type": "code",
        "outputId": "af5732b2-e5ad-458d-c514-cbb11885e096",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "source": [
        "mean_squared_error(y_test16,Station16test)"
      ],
      "execution_count": 328,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-328-098807eec6e0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test16\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mStation16test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mmean_squared_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput, squared)\u001b[0m\n\u001b[1;32m    250\u001b[0m     \"\"\"\n\u001b[1;32m    251\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 252\u001b[0;31m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[1;32m    253\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m     output_errors = np.average((y_true - y_pred) ** 2, axis=0,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     83\u001b[0m     \"\"\"\n\u001b[1;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 578\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m     58\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                     (type_err,\n\u001b[0;32m---> 60\u001b[0;31m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[1;32m     61\u001b[0m             )\n\u001b[1;32m     62\u001b[0m     \u001b[0;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eqF2CzEaely8",
        "colab_type": "code",
        "outputId": "6c7cb3e7-d540-4aee-8f59-dc5c9787f646",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y16,Station16)"
      ],
      "execution_count": 329,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.04982735299049468"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 329
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oQJNM6Hpeoo-",
        "colab_type": "code",
        "outputId": "bfd92ccf-6920-4a4f-864f-a9c1595427c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "source": [
        "r2_score(y_test16,Station16test)"
      ],
      "execution_count": 330,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-330-b5ee611a7f65>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mr2_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test16\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mStation16test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mr2_score\u001b[0;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[1;32m    575\u001b[0m     \"\"\"\n\u001b[1;32m    576\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 577\u001b[0;31m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[1;32m    578\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     83\u001b[0m     \"\"\"\n\u001b[1;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 578\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m     58\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                     (type_err,\n\u001b[0;32m---> 60\u001b[0;31m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[1;32m     61\u001b[0m             )\n\u001b[1;32m     62\u001b[0m     \u001b[0;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3zxtnFgWtmJ_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "aae1f184-9bf9-47de-84b0-d1411482acd6"
      },
      "source": [
        "mean_absolute_error(y_test16,Station16test)"
      ],
      "execution_count": 331,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-331-087b3bba3a8e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmean_absolute_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test16\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mStation16test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mmean_absolute_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[1;32m    176\u001b[0m     \"\"\"\n\u001b[1;32m    177\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 178\u001b[0;31m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[1;32m    179\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m     output_errors = np.average(np.abs(y_pred - y_true),\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     83\u001b[0m     \"\"\"\n\u001b[1;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 578\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m     58\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                     (type_err,\n\u001b[0;32m---> 60\u001b[0;31m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[1;32m     61\u001b[0m             )\n\u001b[1;32m     62\u001b[0m     \u001b[0;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NXsJCiIltm9d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2c6e91f5-083c-4d16-e687-2f209ede3ab2"
      },
      "source": [
        "mean_absolute_error(y16,Station16)"
      ],
      "execution_count": 332,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.020932512317628153"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 332
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OTsNsb0cewNj",
        "colab_type": "code",
        "outputId": "3b8290ad-1399-438b-e119-c9de37e5bb76",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "from sklearn import linear_model\n",
        "reg = linear_model.Ridge(alpha=.5)\n",
        "reg.fit(X17,y17)"
      ],
      "execution_count": 333,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "      normalize=False, random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 333
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o-z4yErUe1SJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Station17=reg.predict(X17)\n",
        "Station17test=reg.predict(X_test17)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "odHrDTLMC83n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "c6a58adf-b084-4b7e-8ce5-682cb3e302a6"
      },
      "source": [
        "plt.plot(y17)\n",
        "plt.plot(Station17)"
      ],
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1596a84198>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 187
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "teVrCTa-C9qJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "0dbf1069-31b3-4384-e005-fac9d77f78d8"
      },
      "source": [
        "plt.plot(y_test17)\n",
        "plt.plot(Station17test)"
      ],
      "execution_count": 335,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1592de8be0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 335
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ELQbDlDV2aqnc92uxI+opq7ppi/8+b33YIgSKvuINZrLICoGaRNzzuCq4HnuG+n2EnV/FWIvFihy0peoXUZe66ix0PUII/GzRdtQf7zLcK1f9tLmxzfLehXetxN/e/JLvMvAOlYXx1OVSIKwwyvKmxrayrUPFMpjJ4sfPbQkt/qpX6I2tvfj96/vwjUfWhi2KZ1x2/yrLewdbenyLl3W4Q6pEy/3rHyunTjmlsy+Nx9YcDC1+RwqdiC4iol1EVE9Es22e+zIRCSKa6Z2IwZC10UYPr9pneS/qFGMpr9h53AdJqoPOPvuFSlFq9EqRJVI9sABFKTXdoS8sIqI4gDkALgZwJoAriehMk+dGALgBQMWNOO482om9zUaXTKWy7kBxi4LC9gmXihd+3TsX7/BAkiIISKl5NeAZpfbAT6K49H8WgHohRIMQYgDAfACXmzx3O4BfAujzUL7IoBwhxjB2bGlqD1uEHP3pDBZuOmxpVZZSou0UlVrnbzwkjecs33lMuldCnNYR+hGoRVQRt1qcKPTJAA6pvjfK13IQ0TkApgohFtkFRETXEtE6IlrX3NzsWtigifi7izTlaIF5sYJza1OHB5K4x6x38b9LduG7T7yD1/acCLQsq9/9yt1SPd/a1IGTXf3BCeETbl0uZbdSlIhiAH4D4PuFnhVCzBVCzBRCzKyrqys16pIo9GKyOos8CAWVzmT9j8RHom69FCKYyidMPvnD4Xaps6xsD+wlxbzqwUwZtvIy5VK2nSj0JgBTVd+nyNcURgB4P4BXiWg/gPMBLCzHgVE1YQyE3rV0t+dhlqOlXOkErRqsikApA3xOXS5MsDhR6GsBzCCi6USUAnAFgIXKTSFEuxBivBBimhBiGoDVAC4TQqzzReKAMJuX7jfbD4fTXQek2RldETrA2C1eWNdBLf33Ixoz2Sl3L3qtevQkcob7Q6J9EcOSggpdCJEGcD2AJQB2AFgghNhGRLcR0WV+C1gKTW29AIA3693v/BfBOuArZ936Mt7/0yWehVdl2ccwkcDR0n8hxGIAi3XXbrF49lOli+Ut24+EZ/m64Wh7RU4QCoRy2pI2KpL61ehGJX1+EPW0VcVKUbMupxMLPGglsetYZ6DxMeHy2u5mfOmBVZg2exG+t2BjSWGZFWf1QF6QJbmSe2euXS7672EvLKpWynXzID3V5jqKOvrX8c5BaZ72sxuajA/7LUsRZaNaBzzLJdms0CPKQDoaUxjPv3N5Ub8LuiGplAY4yjz0xj40tvYWfM6J8gtzoPbGpzbhsvvfCCSuoNNZFdvnlmNVb+8dRN2ImpLDKVXRHe1w7tePghVTTr50LwlCcdz+wvaSw4jCfO6n1zcW/+OIK5OqsNDNynqh9xL2Sv+wLM57lu0JJV419ce7cPE9r6O9x/mCGEWRs6Wep6DqLGlzruJ/W83wiUUhkc5kK8Jf6Lbi3b3M+8VNbrnvlT3YcaQDr+w65vg35aLIhRChWKlRsIyrkaBLZVUo9Gpbpqwm0FSo8rlYBXvf8j3YdMj6gI7CIkRbcfn1Poo6U9QnaSqj5pQn1eFDL6KEhb67YjnWCg9kVm9/wN16b4hiPkZRJjvKpYNTFRa6GYUGkcqlC1+IsGYTlFuFLXdMl/77qITs6keZ6L5AiNzS/2qlUhRSOSfDzTvgQVH3+FXGK/kNlFq+eGGRB9i9hAF5y1rjqeTR6GY1d5a2h7RdARpIZ/G8zQEIpeBF3rmRKgxFfuBkN9btb3H1m7aewcB7TRXfyAWQvKLHZgLO+qrwodtx33Jpmp6ykZdC2Ba6Ev1XfveWb3Hcs3w35qzYiyHJuOdhh5V/QQ6KfvLXrwIA9v/iUle/W7T5iOeyFFLaZrnCe7k4p1waxaqw0M1QXs8Ryw2xvHmBGw62lmSR7TvR7YkcZihpb+0Z8C2OUgjckg0ovqDy20/FapdVdrlYjkfQbTzUFv4kCYdUrUIvhBd1e+XuZvz9A2/ij6v2lx5Y0dgNXkXbliqPKsRUMhsOtuKLc1Zhzor6on6vt+z9LtNVodB7B9zvi+JFxh9q6QEA7CnisAyvjMWwXUeMGT40pHYWs8W9UnokLRHt1ZVKz0AaHX35FcrH5F7s7mPBH3hTDFWh0J/ZUMLeDVWAVzpfWHz2JEDGNX6uDv37B970Leww+cjPX8HZt76c+67PwqgbSFWh0M0o9GIk6yXaLgkn2PozPU6e1z7ochmIKgfM3nWYuRtVxdju8YHaPA89Ing5BuJGcX4rvhBfjS8pS2WmtgjvXb4H02YvClGa6qJQaSlVsVz/+IbSAmACoeqnLfpJMXXoh8n5AIAj+KU3MjgRwjN/vccWetB7qpdfG+oIL9L1gouplpWVj6V1Y40nFvFui5Ys2nwE02YvQncJp9VbZW+llMnP//Y1y3thOJQGM1lMm70IC9YeCiF2Le/5yYu+hX3/K3tw7u1LfQvfCer3a+pyqZRCzuQoa4X+W3mrV/2iICcUcmmEeaJKueJkEK5D9lH+4qWdBZ/1+g3o32nfoH+nQv3vy7txstt8JogfY5VRKq5W6StHN2K5UdYKXaGUwqwuewvWaa1GrypeMcGYpWnOinrcuXhHyfIY4vKoorlpBKu5wQwn6cZS6Nv2uQ6C/fKDb0bmmEU7Sp3lEnQ5L2uF7vV+ITc9vTl/PYL65tdLdmHuaw2ehRfGXjVuptL5+Q4qrUEREBBCoHcgY3IvGgiRL3PNnf04cNKjVdBRSaADeGGRA/ywNDwJs4yVRgz+Wk+tDo6Xc/MOFm856ir+Yo4l9Aq/GtIn3j6E997yUm5Bm2KUB914RWFTu2qlrBV6KUvXlTJuFYKXdaCYAl5K9G7j06d1CjWjofZq/H3MekDVSThmhFnX1fKVb1NrzYtbpZkoDU73//Fr+1yrlak+Re+nb77U8hp0OStrhe4n+kJ5ULF63ISh+/6nN/fjeIfVZmDBY9UgziBpZe0X4qu9j9NFDSnjDk7glGNelaPMUaciFLpfBUOte67587qSwjp4sgc/XbgN//7o+tKEChAqwr4YhS54ZZf4Wd8rzYdeDEHngF95HuVN5nilqAv8PmLLy3cxmJV80u0OfMdAsAqn2K7wy9uOoku1BmB8bwM21V6Lf4q/WpwcIaa5kvjGI2sBaBVdFPzaxkNkfBj7EgILNx32bLtb/SA+n1gUAH5kkqdL/wO2IJzGVkolrz/eiWvnrccPVTOD6nr3AQA+EdtUfMBqPN8bxregQ0edHDNlVmnpteKZDU347hPv4OE39oUtSihUhEL3g+MdfRHuyAWDXfq7+6XpcWZjC3a/s2vczI4B9AszS6tYpVd/vNPV80GXK8vV0CErec+2iFal8GSXdGTj8U5vxqpKHxSN4Dx0IrqIiHYRUT0RzTa5/z0i2k5Em4loORG9y3tRg2X/SfeDoOWKsWIVLsbmu/eZLF7RB16BreRnfuNuNlBQqN9RkC4Xp64zP/b8j4JryYxa9OPd1OR7PAUVOhHFAcwBcDGAMwFcSURn6h57B8BMIcTZAJ4G8CuvBbXDr1aw1MJRSoENwnryZmGWvaBultcbNzIqQiCncZmETSTtNaNYeeXEA0WcqBO09dg7mMEpogXfjT8LL/tffu1TI4TA8RIPaVe4P3kvltf8ABj0t2w5sdBnAagXQjQIIQYAzAdwufoBIcQKIYRi0q4GMMVbMc0pZQN/Jy/c7Jk1DSeRjfj5gl4dbGA3y8XpuMCl971edPzBnykKzH5mC869YxkGM9Fflq7msTUH0dBsPf88SMPVqvxdeu/ruDPzG3wv+TTOpAO+NChejlc9tb4R//3cltICkZN4QWy79CHr7X7repwo9MkA1JucNMrXrPgmANNt7IjoWiJaR0TrmpubnUtZgKDq/fIdx/BPc1fjT2/tDybCgDCee1ja8nx1Q2CnZIxhlf4i7doydfhWUb2w+TAAIJ2JdqNthuXMjoCTYvUeBzMCtZAs1Biy/vbAPAhjdcNJY7hFBpzNL9stQaLCeDooSkRXA5gJ4Ndm94UQc4UQM4UQM+vq6kqPr+QQCoSvi0DZ1XFvs7vzBaPq1yslB027uS4T2jdo3HekVEqtL9F9V4XRN8yFkhLGoKhiLPiVzV6+Py+sfZH7G0zBcqLQmwBMVX2fIl/TQESfAfBjAJcJIcrGCemmTCuvxGlFKPfFK04WFrlOoup5/UZSfi0NNxfDPnRfl5OXcaPhBK3LReDK+HLUYED+pih0f+tGGFVvFu1AHdpM7wmTT37gRKGvBTCDiKYTUQrAFQAWqh8gog8B+B0kZX7cezG9p6gKW6E10avpgoX2xyk2vFKoP96JabMXYddR7dRCq7CDWDPgl7Kx3kPF/IZfqkVtyHw+thY/Tz6E7yee0sTppUL3baW4y6KwoOZ2LKy52So06Y/wd2ymoEIXQqQBXA9gCYAdABYIIbYR0W1EdJn82K8BDAfwFBFtJKKFFsF5ihf6tai9ykuPtnAcEZ/lovzWXSc/eJRdGJ/fdFi7sKjA77ybUlfdq1+HQ5oPPo465Ct5l4u/PvRwcmMStWjlMIgRvoUOIcRiIcQZQoh3CyF+Jl+7RQixUP78GSHEBCHEB+X/l9mH6C1OC8Y9y/YYf+sinmLVVdBqTh/fBLTgw1T4hKCS43WQULuKpj9KsJiir5fBejdNk4VFEJ53wqymRwZBrtEV0mdCFj9IzDd1CzR39uPSe18v6vQvO7I6F8ugPHBLHm+uQcgCEJ7N8JLC9I5cSstpUDRo8lais0y6Wz6yznH4Hh8QGxYv19yEp2pus7xv9F3r9q8wKYRK3jiZOeKUt/ZqZxV4ad26VR5ReXdusMsuIYDzYzvwncRC/DI5V76W/8GzGxqx7XAHHlnl7ZJ5pSwp++v75UPfV3s1nkzdno/XL7dW0b/LjcB5JYopZanQ56yox8ZD5oMPTlm7vxWAfSs8b/UBzXe1xeOG3sEMLrxrpeX9CWjBRBinSHnFKDJf9VqouXJb6RSlafc7u7xbss3dIRVqplAz1tZchyFdzo4RtBLDa+PZTQ6WPuvHPjblvdTCeNapX72GrMrFov4OlKZ03znYarh2Xmynp+/PSZ6kMIiRsJ6aq9QJRaELn9ewlKVC//WSXfjinFUlhfG1h98GEIwltmBdo+39NbXXY3Xtf2iuRelAXedug9Kq0182Hi76t1+KvY466sDUA89oJSKlJ6FNRyFl4lXvwE04T649VPihIiEyThk0PbXJ42JnZ6GXEtWXHnizVNEK4qSH/mTqdmyuvabgc3mXS8iDolEmvH2QnRVFr5b+Xxhbj0/FNjr+rWtrSyeoweXi/Kee4iTs595pxN7mroLzfN2OTXmVLDfhZEvMTOO2Ofr3KH1P0SDeSwcw97WG3PbHORdaSRIo8RjjpJylCs13LzALye9FagofitlvuaCIEZTLJeFr6AFRyrtzo/vCakAeSt0FAJjW97in4RYqsLZL/4vMCjczTZzwX09uAhHwHcU0KbEiqy16L6lDG36V/B1uGPwOgAkgZOVK7o0LohAEghBSXOfG9uDFmh/hgtX3Ip0V+PnfnxWAy0XrepBmubhP8DD0Yij60YzRuWtR3JzrG/EX5RR/GoBKobOFbo351Dk9pdy1+E0A3pAwHS76uO0sR3XHWdYXjq0vpULXYEA+6cg6bPtwjL0Ks2cKhS28bm1UYX4rsRB/F9+EbycWYkimG/tqr8Z18ed1MpUYV4Hr+vujqBudfdq9RdyU7ZsT8/BM6qeG6+o3kZVVTMyg0ItL7eLUj7C29tsFn/Pi9RkbB+E4g25JzsNPk/M8lccJZa3QC/Gl2OvYX/vPmAxv9o0pdlC0WA4VcY5pMRRKzvy3DxqumfdWCptHmlkx8t+nUv+DTbXXmjxbMDizGLQSkfqO9Yycs2kvINQdY3eRpzCICWgxXFfCOTvWAAC4LvECRmTbAQBXxFe4isMteoWUNXk/pRTlf0u8iHNjxqnAhGyuzinhx3Qxxag4H/q7YsZ1i5ptgosI0wlj0YH9tf+MT3X81fT+JItJDfk0Kj0/ttCL5vK4NHAyI2a9D7E7l4s7SqksQoiiZz04dQ0Vem56TJp14tUWonoUpXp2zHqqXN9gBtsPd1jez4VV8EFwKigAACAASURBVL5+nCDPBbGtWFjzE3zoyPz8gj6XL+/e5P1YU3s9AIGdR/PyKuEog4LquNXXpGfdl5izaS/mp25HCoV38cuaVXcHPRe3XI/5WFV7A07FCYNFnhWx3Pfo74yRrx+T6QQA4KOdS0yffEs3qUFPLh94Hro1BTcfMvnkBUHMQAmyrBvdEVLOTqaTpvcB+z2onTZ8TvLxxqc24ZJ7X0drt3GqnRn6rnyu0dKnUZWoKSRZk+O764seuroorpzjKXDRb41bBquVqYCi1EpDaYjOj+3ADGq0bRC6B9JIUdryvpdjB+dD2nL2FGrLpTun0HWzXrzGy4VFavJjAcXJHdQslwoZFDUvhU52ditUfv+GGlEvpO3dgx5w8Tw+XT45Dd/Wh665VVgdCssv5s+uPyDNN+4ZzGCM7dPGxPQNZtDVL1mug/J2uN+Iv4gGMQnAZ1TxGCur2xknWUGIkUAKafQjZbyvkU+Oj0rTno+n7sx9jiNr7UMXwJldb+FHqTuM93JrB7xDrcT1LhftvHRvzBa/pl9q3XXF+f6/PGcVMgCe50FRB5B9sVAK0RAU5zK4MLYey2puwmUx7ZzXQAZFS4zjo7Et+GvqZiSgssqKLEx+Jbegm0So50w7lEL12EW/fQ1zVuwFADy8ah+EkAarHkn9ynRqHSCKtlRjsnL+aeLPhjRo4zDO/FDoTxdf2fU+akC7kvqD/esN9/3a0lWt/PQWunpeuh/1yL/ZOvLgrss6NKRrP5o7+3PpZh96CXwqLp0+r+z0Zob+/X8stgXn0Q4AwAz5DMD/SDwnP+ulXeGEIksnAb9KzsUHYg04Rb1vh0Vh0itL49J/a8lKGyco/IxTBWumnOzOhVWHlxVG/2axbrULYtt0cml9xwCAmHbmh8Kvl+wqKk6gsE9akLGq++XHVhqsJDJIygZF3kI3T7vXeOEWVZcokbvmLtz8giolIFboJTOK7Jbmank09XM8WSPtCaEUzBmxJnwkti3QXbZKPfoqZwmpu/W6wuSlr1sbrjMXjT97kjv7jRL2V+Ir8JvUgwAklwuV2FIlSLfHe85CN17z0o9cSNEIi6qeG/fIDQaXrgiVsvdw6leYm7obQD6tah+6Vyp9SF9+5otVmT7W0YfBTBY/f3FHUXHkx0C8qwt+UNY+dKcKqZiKcy7t0gxkjUF+P23/DrjIP//2vpaSuo9ZQQDp0i6yGId2jKFOV7LZDYpqpyFKF2fFdiGGrPmsigLh6u87nSpabLX5VfL3pgIVG55VWdPsYeJgbMereHNxmhQmda/Gy3WMSrjDKO/q/ER8CyYNnlQpdO96B2qFnpNBFXZ3fxrn3bkcV3x4Kua72F5B60OXcNuz0M+/ZwvdAYUKhroox5DFGHSY3lMznPoMBd5vA/0DtNdw7W/Ifh8YKxTZ4zqF/krN97Gs5iadpazl8ph2nxy3h2KPpB58K57fEv+lrUcwbfYidPenNVa3k4FHp42a66XVpm6kvNrVi7bvRDemzV6EHUfsp1Am9FMR5b/axi0rx+ed9SYpSH14aneVhctFGRR1mNGj0Ynr48/Zzvaw8s2/N3Yg11PwsneibqweeNVYh3rl6b8vbz9Wwgrn4ma5GBZUsULXoi60+ZdjXzHUFefGxAK8U3tdTqkLAD9N/AnnK6dyqzBbiCH9xp9ulFr5DkMvCMDc5G9ch/MeHMgtwNBb6MrOiwLWlfiKxKua7+aptR9POCOWb4juXiotPjmoWyjlxkIr7J5xh9nzdj70l7ZKc/L/stF6TQMgTdXTxCN0FhpUbg4vFXqBhTpZk3dtZiUXeic/Sz6EG5NPGcYKNHEJ83KVQlozIOzZBmgUz30+0i4dqKEOWT2w7m7diXogWx7cdSDzXcn/U4WhG5/ieejWvHPQ2Ra66tf4udg6APkTVMZkTuDriSWYr5vSdU5st27usPs5rq09zuZOKySQ978qKwuL4b54vhHQW+gKaut42Y5jtuEVUwbVDUmxK2yFEIi5nHXidOGG+UBv1vO9XPIWurq3JzR/vcAsrHH9h7C35p8xsmtfbh8X7W/yfmynZwsMlWeM3aqbzaPGyl8vueFULhfbmJxTSE2TajZcsfPU83IXXuz35Xh+HULOQlfyny10LcVUNHVhz+hG2T/eu8L0NzcknjNY6G6LgjL32SnXJBbnPsdk5VJMpVfLrW4krBT6qvqTeDZ1C7bXfN0QVh3a7KuexhQyKi3pslKhhOZ547sUaKj5Z9yfvEcVjjEaczHcvR2zNJEHPnRDPCYBfbJDckeNo078OPEovhx7DbOouME6hRiyBqE/0LYMcRKYfmSR6XiG2h/sJPcui63C38kzx9Srr/WuKLPegBKfHz500/EBVdhqNxoBqEU/xqLw6mOzZLidtqi4aLwvWeaUn0K3udc3mMHNf9mC9l7tMmi1clEXKCk866Jsec/hO4n1tWF/7VW4MGacA2zGZ+P555SYh5L7OfTqyqt1uQizjwCAc2L1GEr9+K/E05rra2u/bbspv3b2Rv6bumdg5ZfWK9U4soiRwBfia/B48g4M6T9hGrY9pVQYlSfex65x7cm88r4msRh3pR7EgprbbX5RGLPBOmWqIomMaVlWlOqzGxqxZJvUS7NL9g2JZy3vvbjlSD5ei3oj+ewlmc6KNZSYxwJ/eL0Bb+09qXG5qO/n4lUN4BMBC1K3YUPtdbaht3YP6JaCSeH1p61X25rBg6JFoJSLJ94+iEdXH8R9y7UbBpGmfbQelLkyvlzzXa0Yi+mofXSUtKT8W4nnCzxpJCY3PRPI/clMGZXcVi4XIYCYSONf4i/n5goD5pWWhLtCDOisPwuXi/67+jcXxLfjg02PabrLesxm2Bxu68ELm80PytAMBAvJ4lSjHvByomum0jF8Pva2/UOKv1xVgPyYykYQmIJjeCX1PdRBWl2bK79CmI4HxeRzOL+3YBPeqD9huK/HbtaSus23Vuj5snld4gUMO+F8j389MQjcsWgHrvz9ase9MwHJL263d5DCz1/ckSt7cWRyMcRN9EYhOaW4FVceW+ga1BkyFh24JfFnIDso35Oup3UW5UjKH3yr30tCXbkui72l+Z26Eij7fQDObcB4LG6IwymlzAIQDhR6Vgh86OQLuD35CK6Jv2AbHgkzv6HiG1T3bfP5VYsBTKHjmsv6Y4H1uaJPczw7YFtVzerGziMduP7xd0yfz+pcKvem5mjuu904aUlqNn6X+q3tM0qKd2ZPy13zY1FNDAJX4CWcHjuKy+RN6TQWuumgqLGMWUlWgwFMIutjEjWzlywVelZzr6bLfoDZDk25Nlk0pW+8ZSELWmYfpp34l/jLOR0yEl3YW/svuVlbbuulfmERb86lQ50dP0nOwzcSL2H0wZcBGOdGL8rMMvxe70NXBxjXLQpRF74fJZ8AEXAO7QZlC+9sJ/0emrj2116FqwcWOPptQh6w6hK1uWun4gTeR4WtC7WF/pPko7nPIptPX1YIpNLSrJOxlJ9jb0rWmYWuLqufjG/GGzX/ieHoya+wle/vr70K+2uvAvVorUIzhQ4L694QtxP5NLKa2vyuBnDt3GEfj23G48k7ILJSmraI6bl7flnoRneiotCzpjNP3MxyuS95H4ZTn0Xs2nCsFbrunqmh4IyC8+7V8uRmGhXuaT9VcxtuTz6S+/04uW58JbFSjre42TnscnGA0kqTrHBiuu75pXFjd1joCr1d5dJ3MUd27cWzNbfii8cfcCYg5eeuKt35bww8hjcddG/jyEIIoelOvln7XSyq+XHB36p/ozkiS2OhAxnZ91ioGxkzUehCSINL6jDN8vKaxGLEcha6tqIlW7XHdxn2zBYZVUpMfMSqzz9JPibLYI3GQjd57QOD+XS6m4NhfPb+5L24IL4d6DcOvvmx02AsdwISUEfSfuuKb5lU9zS/cbEn+adj5r0eKW6hdbkIc7Wi9qEDwJFW6xXcZqgHXrVlTZuKoehDNmvcrljxoTtBUdoZnQ6IIws3yzL0Z6nyoKgOdUVMQy6wckuvvCy7BSvG7Tutn83oCmZiUDpV57ReZzMSsvJC3CQymgUn//O8cc67keKndekLYT5IVSHv70LWoUI3taSyaeys/Tq+O/AH9YOGx25IPJvfRK3AnjHK3jkKJDK20wjdWkqFHj/W3ou4yMhb0ToP9+bEo4ZrStlUejfqlCbIe4X+sdjWXNm+NrEIQD5/JZeL1V4u+oSaJzyXHhOkxqTwPI7x1I5/iL+W+/78RneL5i6+Jz8dcBTyjYF60H4qHcP22m9gVmt+YZt6+wXNGQB2ekJIxfYM3cK+GAQyLjS6wb3GFroWra9OFj8jK3TlGZv8zrtcjAp9Vky7OZK+6yjiNQCApHA2vzwrV6Ih6MfV8aW567uOFXBxAOjAsKKndVkPEqkC7O/EhkOSxZMoMLc2LkxcTLKi+n/pl3OXCu8nYn/lLzW3aL7HRNriSetrRhkELo2tRg0GCjYABIEbsvOwtOYmxDuMpzRZ8W+JFw3XMrICFBk5Daq4C+V3MXw9sQTnYmf+ghBo7ZPrhYXLxc0An71CF5qXYTV4+uOk9kzcWAnbBy+vuTH3Wf1ep5O0AOys9pX5+7nxHt1URNkF2djag+OdWneSADCjfRX+IJ/nm5MZWVdbK8dJa6HzbosmfCH2Fs6iBqQVCzpnoduvXrw/eQ8+HNsNAJgp/7VTQl+Ir9Z8V7qLyayzqYTKMt/TY0c15wsCwJbGdtvfSpVeYIRqQNcpVha6UHVDX9vdjO60MopfoJBlTBSQXKiTmu15zX+ubmi1S/8LLAjJZgo20sPQa3taz3vpIOak7sUdiYc1YXT2Gd1IBIGzIDXqsW77xVaFSCvvQB5vUZezGpgbBENg5aN2huYIvMwgdh6TV+aKbM64UDMWnbkyegpasb/2KpzZ9prhOcB+hot+o60Cr1Xzu2JR+/OPdeTryKDcK44LY7kUelNHNko+9ssVmPUz7Qw3kc1iXJ+xBxF3qdAN+oUHRbUIAdyfug/P19ycX46rdGt1g6J6vhBfk/s8Ozlf+o2NQv9sfIPmu7JKLOHQQiebwcTD7faKOo6s9fzvrL2FZ1n5VPlyuKUjp/j1OwTqGTN4xHDN3NIwl9fyvRQo3DFkEBMZnEUNpg4oIYBttd/Ec6m8Za9/n0NlJfmPidc0d3oHjI1ADFmklf3qTN7dTNrpaArnu6kpt6JSZAbRO6DN3yEWCv0niXmm152iSXumP+9eFOY7G96buh+XtUvuovfEpB7J+SefMw07bavQta4bKx+6HrdTAK1oV51mlRbKuEE+z/M+9Pw4G4BcY2vGwOAgMmTcuzCGrCuXi35VsN+7L5adQlej96Grl4kXcyBCIZTK7NTlYucvKyTfu6kJwqpBSNv3EKwVel6euBjMFf5CltIXD99tuEYmjYpVYVUPB2kaqQKzhUhk8dWB+Xi+5makmrca7itK/n2xA6a/vzq+FM/U/E8+OlWeZzLGNMcgcq4SZLSy1XVsx9M1t+ETh+baygwAy2t+gNHyls2UGcSn/neFxjKsJfPyc1ViRcGw7dAoyPRArsEmZLT7sauY1f0qALVla/5OMjYul7jusAqrWS56vHI9ZVVjPEqa46q6IwRwOh3GUPRghHor7Yx1+Vu58wiyMaNCjyOLrIt26PfJ32B/7VX5d8MuF2umyf6ynMsFUrcz6ybHXaAoMccWus20LBN9ouEnyceQTVsUuLR919x6hataoadziv9c2m0bXipr7E0I08portBnDqzF/tqrkGrfp30mY5+PMZHG6Zn9AIBEh/NtTxXuSP5RK51a6WSMjWWMRE6x6aemDhmUFutM7MmPswwKayWXizM7gGMd/VCne4K88MeUEsquZifAdF/BhUUAkBCD+ADV40MkLcaLWfRALAfaAVweXwXK5I0Mpwq9xsHB1naMRDeGok9jMV8jDwinsvk6IiDwSs2N+HPsDtyG/MZZdj3dGgwia2KhJyiLjAu3iTK1dVpMduH5pJsUyk6hq/PyE3HpEIiYrOBG9BzEmtrr8ckTTzh2VXXHhjmOW1HoKZc+dD0zqBHfeXwDnlxrP/CWtbIgCih0Sx+6Wp5sOteNPi3WbPq8gplCN8tgKwv9gj5pdsLwY2s1jcqJdmlLWisOnejEiX55poZJr8TsHX89sQTra/7dNLwP/2xZ7nPWZFzgFLTmB/90jakyI4hMrEE7KG30oSdtXFxioKtgmFaoxzP6+3tVJzGZz3KRfjOAv9bcgpuS0vqIuIVCtxsUvSP5R3ym8f7cd6crN99r0bOyYhK0C5s2116DN2q+i9d358vvxfJh3SMy+UZTKSdnxRpwiroxtekh3phYYKrQASDb4371dh52uWgw86XG5OmEtQPSC39/h8nAjoWG76MhjuLdm50EyN3RuNOuooWFvrTmJsygRjy+2r5AiyIVuhOXy5DBFjyQutc2HIUaM4VuZt1YlNX+mLQ4Kpbu1TQqh092aM881VFLA7kDl9UWYCHGUafp+1bHRT3GQxFOjx1V+dC1ea8oxJhLhY7sIN5NTbgwbj2PWxNPf/EKfZhqULWjqws/Sz4MQJbZwrhI6FwsVr1P/RRePXV9+3OfnSr0L8ffcPScwlu1/2G4Npa6NKu4FYZm1QfS5NPepz7AW3bL3JF4CP+X1LoVv5pYaupDBwDqMo4pOcZnl4ujE4uI6CIA9wCIA/iDEOIXuvs1AP4M4FwAJwH8kxBiv7eiynG157ve7WIoRlEPYpl+oGElRndKC1Um9u2D2LdS+8PdS4yBNazE1LQzK2EkdaOnVbUHdMNK64dlRrVa7xm9tOYmbGw/G2gwnsSuMKRplfmN/auANmsXxCSYL1yixvxCqzNPLDV9xoykGDCkd+gR1X4p8r0JfcbDBQBgbEaSZ9iJTYgdylfiqZ3voL72Cct4z4vtxHnyVLza5k0GGcjuUGWT911f+9Xc50m7zLd/nSDnXU3zVk18dZ3S2oHR/Ydz14c52DgtcXg9ltf8tOBzCmLvq8DoyY6fV5NBDEnZ2EjuXYYhsq9+RM8hvMsir0ZktVNoxw0cNi3bymIlK+p6G3K/mwz7Hp+GHS8ANSOcP2+CfmohIPvnZXlSHflGcrz6jN0DbwEt+3B1Qp7hokt3XZ+5bkgcfAMQxVnpieNbgIYUMH4GMPLUosKwgwrOzSWKA9gN4LMAGgGsBXClEGK76plvAzhbCHEdEV0B4EtCiH+yC3fmzJli3bp1rgUeWHk3Uitudf07hmGYyHDpb4APf7OonxLReiHETLN7Tiz0WQDqhRANcmDzAVwOQL3c8XIAt8qfnwZwPxGR8GFrsb+kz8eigR8igxgGRAJJSmPU8GGoTcTQ0j2AxGAnOsVQnDpmCI61deXcD6eOHoojbd2oxQCOi9EYS52oGz0Sja29aMEI1FE7ajCIGLLYJyZhFLoxkrqRQAZ7xGRMRCvGDkvgRHcGcWQxZUxhV033QBonu9MYR504KUYgDoEYZTESPcgghm7UYtLoYWhs7UWK0hiGPhwUp6AfSZxKJzFy+DA0dUpT6abScQwijjQSGDd6lG28ja29mECtSCCDIxiHJNKoxQCGjx6PptZe9CMppZUEBkUccWQxljrRhyR6RS2GUB+OizGYTkchAJwQowzpHcwIdHW2oQ8pTBo9LBdvP5LIIIah6Mco6kaHGAYB6aDu5LAxSMRjaO/oQJ9IYcSQJKivDV0Ygn6RBEFgj5iCM+gQhlMfkkijE0MxAj1IDR+LmoS2258VQGt7OzKIoV8k0YbhGEcdGIZejBo9DofbejAMvWgTwxGDwCiS3v8g4qCh49Dd04004jkXgeJzHkk9GDJiLJLxvOvgaEc/Utke9IjaXF40tfaijtqQBeGEGIU4ZTEo4hhOvRhAEkPRj2GjxuFYew+GoxctYoQcfje6xBA0iTqMom6MoU6MRDf6kcLoUaNzWyUUorG1FzU0iGYxGkmkMQT9GEr96BRDcOrIFFo7uzAWnTgRG4t0RmAsdeK4GA2CQC9qMIlOohtDcv79YehDpxiKySZlu6m1F8OpB02iDlOoOZeWJDIYQT2a3ynl7yRGol8k0YKR6BMpzIg1ol+kcARjkRZxTKYTmDy61nF6j7Z1oRYDyIJQi0F0oxZj0Ile1OTKGQBMpFYcFWNy7ymdFejoaMMAkkiLOMZQF9KIYdTocQCAY22dSCKNsaPH4HBbD07FCbRgJHpEDUZTF4agHy0YiTTiGI5eDBk5DglZ6MZW6WSxHtRgGPWhUwzBWOpEDAK16EcHhmEketCPBAaRwPhRIxAn4CvJ83C+s2S7wolCnwxA3b9vBHCe1TNCiDQRtQMYB2j7/kR0LYBrAeC0005DMYyeOB0rsy04Y8JwdPalcaS9D5e8ayL6AAwRwItbj+LT7zkF/ckY3mw5mvvdJZMnoiHdKs84AE4fNwwjJo3AmEkCb287hnoxxRiZqjkaNvFvUFc3DOu3HMXn3zcBfQ5KYRzAui1Htb5l1efzpo9F3/AU3m45avA/J045AyNPGY5NW6Q0bBPTAACpRAyfmXyKbbyZEf14/oBxJsUlkydKcQG4+P0T8aJ8rFptMoa+QfWujNKf7XKcn3nvBPQljOldseUoZk0bi74Rkl9y3KnIhXn2lFF4u7EdqXgM550+Fkv3nMAl0yYiDWClnKZL3j0Ri7ccxdhhKbSo5hLjtI/gVVn+s6eMwurG9tw71rOy9ShmnDIc3f1pHG7vQ/+YoWhq68XnJ09Ac7wTq5ulaWrvnTQSa1V7gVwyfSJe33JUE9bfnDIcY4emsGx/Cy45baJmpGSUnLaL3jcRfXK70pLowprjUnf+zEkjsf1IB04fPwzrTnRj3LAUTnYP4JIpE3Ek1oEDJ/PH740ZkkRrj+S7PiLG5fI7FY/hM1Ps362apmw7mtry4xuffs8peGWnNDYw4bSJWLvjOPrTWVx85kT0p7N4aad23GC7mIaPzxiP1/fkq+mF7zkFfUmjv7yrtgdrDkv5t1Po6q7QlZFRA3h+Xwv0pIediubOvJvqsBiPuikTHad390ALTnRZz4x6z8QRSMZjWNjUbkjHq1uO4gNTRyMZIyw50IoYARdNluLe0nMCyXgM500ei8PowOqWHowaksR508fi5e3GBWaXTJ2YG405Rqp3m5vwnn928ughuXc0oiaBj08Zj0EAqbHFudUK4ciH7hVCiLkA5gKSy6WYMD73vonY/4tLPZWLYRimEnAyy6UJwFTV9ynyNdNniCgBYBQA682TGYZhGM9xotDXAphBRNOJKAXgCgALdc8sBPA1+fM/AHjFD/85wzAMY01Bl4vsE78ewBJIbuGHhRDbiOg2AOuEEAsBPARgHhHVA2iBpPQZhmGYAHHkQxdCLAawWHftFtXnPgD/6K1oDMMwjBvKbqUowzAMYw4rdIZhmAqBFTrDMEyFwAqdYRimQii4l4tvERM1A3C3f2ae8YDFDlTRoRxkBMpDTpbRG1hGbwhbxncJIerMboSm0EuBiNZZbU4TFcpBRqA85GQZvYFl9IYoy8guF4ZhmAqBFTrDMEyFUK4KvfBJveFTDjIC5SEny+gNLKM3RFbGsvShMwzDMEbK1UJnGIZhdLBCZxiGqRDKTqET0UVEtIuI6olodsiy7CeiLUS0kYjWydfGEtFSItoj/x0jXyciuleWezMRneOTTA8T0XEi2qq65lomIvqa/PweIvqaWVwey3grETXJebmRiC5R3fuRLOMuIvq86rpvZYGIphLRCiLaTkTbiOgG+Xpk8tJGxsjkJRHVEtHbRLRJlvF/5OvTiWiNHN+T8tbcIKIa+Xu9fH9aIdl9lPERItqnyscPytdDqTeOEEKUzX9I2/fuBXA6gBSATQDODFGe/QDG6679CsBs+fNsAL+UP18C4EUABOB8AGt8kukTAM4BsLVYmQCMBdAg/x0jfx7js4y3ArjR5Nkz5fdcA2C6/P7jfpcFAJMAnCN/HgHpoPQzo5SXNjJGJi/l/Bguf04CWCPnzwIAV8jXHwTwLfnztwE8KH++AsCTdrL7LOMjAP7B5PlQ6o2T/+VmoecOrBZCDABQDqyOEpcD+JP8+U8Avqi6/mchsRrAaCKa5HXkQojXIO1JX4pMnwewVAjRIoRoBbAUwEU+y2jF5QDmCyH6hRD7ANRDKge+lgUhxBEhxAb5cyeAHZDOzo1MXtrIaEXgeSnnR5f8NSn/FwA+DelAecCYj0r+Pg3gQiIiG9n9lNGKUOqNE8pNoZsdWO3PaavOEABeJqL1JB2ADQAThBBH5M9HAUyQP4cpu1uZwpL1erkL+7DiyoiCjHK3/0OQLLdI5qVORiBCeUlEcSLaCOA4JCW3F0CbEEI5a1kdn+bAeQDKgfOByiiEUPLxZ3I+3k1ENXoZdbKErp/KTaFHjY8JIc4BcDGA7xDRJ9Q3hdQPi9S80CjKJPN/AN4N4IMAjgC4K1xxJIhoOIBnAPynEKJDfS8qeWkiY6TyUgiREUJ8ENJ5xLMAvCdMeczQy0hE7wfwI0iyfhiSG+WHIYroiHJT6E4OrA4MIUST/Pc4gOcgFdZjiitF/ntcfjxM2d3KFLisQohjcqXKAvg98t3p0GQkoiQkRfmYEOJZ+XKk8tJMxijmpSxXG4AVAD4CyU2hnJimjs/qwPmgZbxIdmkJIUQ/gD8iIvloR7kpdCcHVgcCEQ0johHKZwCfA7AV2gOzvwbgr/LnhQC+Ko+Qnw+gXdV19xu3Mi0B8DkiGiN31z8nX/MN3XjClyDlpSLjFfLsh+kAZgB4Gz6XBdlv+xCAHUKI36huRSYvrWSMUl4SUR0RjZY/DwHwWUi+/hWQDpQHjPloduC8lex+ybhT1XATJB+/Oh8jUW8MBDkC68V/SCPMuyH54X4cohynQxp13wRgmyILJH/fcgB7ACwDMFbkR9LnyHJvATDTJ7megNTNHoTkw/tmUw/3pQAAAKZJREFUMTIB+Aakgad6AF8PQMZ5sgybIVWYSarnfyzLuAvAxUGUBQAfg+RO2Qxgo/z/kijlpY2MkclLAGcDeEeWZSuAW1T15205T54CUCNfr5W/18v3Ty8ku48yviLn41YAjyI/EyaUeuPkPy/9ZxiGqRDKzeXCMAzDWMAKnWEYpkJghc4wDFMhsEJnGIapEFihMwzDVAis0BmGYSoEVugMwzAVwv8Hg19b/QesUykAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9kr4ej6Pe5Xq",
        "colab_type": "code",
        "outputId": "1ee664ae-c8c1-42fd-82cf-6169950edba7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y17,Station17)"
      ],
      "execution_count": 336,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.002510909383007862"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 336
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WXSNWquPe8tR",
        "colab_type": "code",
        "outputId": "4a5534e2-caa9-413a-c34f-ee4c5d2241bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "source": [
        "mean_squared_error(y_test17,Station17test)"
      ],
      "execution_count": 337,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-337-c2dfe7b3bd46>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test17\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mStation17test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mmean_squared_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput, squared)\u001b[0m\n\u001b[1;32m    250\u001b[0m     \"\"\"\n\u001b[1;32m    251\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 252\u001b[0;31m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[1;32m    253\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m     output_errors = np.average((y_true - y_pred) ** 2, axis=0,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     83\u001b[0m     \"\"\"\n\u001b[1;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 578\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m     58\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                     (type_err,\n\u001b[0;32m---> 60\u001b[0;31m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[1;32m     61\u001b[0m             )\n\u001b[1;32m     62\u001b[0m     \u001b[0;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pPlbPfb6fBWW",
        "colab_type": "code",
        "outputId": "74a961ba-b55e-475f-d8f0-79f87eaa7147",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y17,Station17)"
      ],
      "execution_count": 338,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.06426768724768528"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 338
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_X3m7cbS9e7O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "3568e67b-33ad-4319-e710-e0b5a8eb25ca"
      },
      "source": [
        "s=pd.DataFrame(Station17)\n",
        "s1=pd.DataFrame(y17)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 339,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.25353\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 339
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AZy83DBx9h4j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "f65be461-a67d-479e-86d7-f8add32afda0"
      },
      "source": [
        "s=pd.DataFrame(Station17test)\n",
        "s1=pd.DataFrame(y_test17)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 340,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.192015\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 340
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SsNIB251fFBX",
        "colab_type": "code",
        "outputId": "7ffdbc5e-a532-49e6-cae9-7ae00471998f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "source": [
        "r2_score(y_test17,Station17test)"
      ],
      "execution_count": 341,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-341-0ca0560a31aa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mr2_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test17\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mStation17test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mr2_score\u001b[0;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[1;32m    575\u001b[0m     \"\"\"\n\u001b[1;32m    576\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 577\u001b[0;31m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[1;32m    578\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     83\u001b[0m     \"\"\"\n\u001b[1;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 578\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m     58\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                     (type_err,\n\u001b[0;32m---> 60\u001b[0;31m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[1;32m     61\u001b[0m             )\n\u001b[1;32m     62\u001b[0m     \u001b[0;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cmxmMY65ttKL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "6fa089ab-63ef-4624-ae43-bf97bd79fbd1"
      },
      "source": [
        "mean_absolute_error(y_test17,Station17test)"
      ],
      "execution_count": 342,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-342-3d18208d58cc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmean_absolute_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test17\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mStation17test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mmean_absolute_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[1;32m    176\u001b[0m     \"\"\"\n\u001b[1;32m    177\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 178\u001b[0;31m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[1;32m    179\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m     output_errors = np.average(np.abs(y_pred - y_true),\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     83\u001b[0m     \"\"\"\n\u001b[1;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 578\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m     58\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                     (type_err,\n\u001b[0;32m---> 60\u001b[0;31m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[1;32m     61\u001b[0m             )\n\u001b[1;32m     62\u001b[0m     \u001b[0;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P4E5BCFytt9M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "328b79fa-8329-4624-b422-f78f66de25da"
      },
      "source": [
        "mean_absolute_error(y17,Station17)"
      ],
      "execution_count": 343,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.017821271158786425"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 343
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3L_-aVDIfIG4",
        "colab_type": "code",
        "outputId": "018f9eb3-028c-4852-f89b-e20eaedb7a96",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "from sklearn import linear_model\n",
        "reg = linear_model.Ridge(alpha=.5)\n",
        "reg.fit(X18,y18)"
      ],
      "execution_count": 344,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "      normalize=False, random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 344
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AeSFxHo9fMe6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Station18=reg.predict(X18)\n",
        "Station18test=reg.predict(X_test18)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xWutFlk0DGxp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "71400166-20ff-4e90-f69f-67335299e8f2"
      },
      "source": [
        "plt.plot(y18)\n",
        "plt.plot(Station18)"
      ],
      "execution_count": 346,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f159620a908>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 346
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VFpEBAHNDHg3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "0ce76c13-783f-42a3-9d0a-7e95adae4019"
      },
      "source": [
        "plt.plot(y_test18)\n",
        "plt.plot(Station18test)"
      ],
      "execution_count": 347,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1592ef2b00>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 347
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0irp1w8sfP-W",
        "colab_type": "code",
        "outputId": "acfff572-cbb2-464c-9fb6-20135c665f81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y18,Station18)"
      ],
      "execution_count": 348,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00015685437308258108"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 348
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W8NNvtbLfS4z",
        "colab_type": "code",
        "outputId": "616bfe5a-8963-4f7f-c664-8ed36b0dd8ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y_test18,Station18test)"
      ],
      "execution_count": 349,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0023664036512788572"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 349
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4lxslOv89neM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "4d0dad8e-6101-4f2f-cc74-44859639f276"
      },
      "source": [
        "s=pd.DataFrame(Station18)\n",
        "s1=pd.DataFrame(y18)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 350,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.106626\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 350
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gNh0Ydb89oTo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "56a24242-50dc-4fb6-a011-bf7c73c18c9b"
      },
      "source": [
        "s=pd.DataFrame(Station18test)\n",
        "s1=pd.DataFrame(y_test18)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 351,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.139846\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 351
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DHbhn-rFfWnw",
        "colab_type": "code",
        "outputId": "43272c7f-3dab-42c2-cbf6-802ea7876b4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y18,Station18)"
      ],
      "execution_count": 352,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.010951760479840478"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 352
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I68KysRefZ0F",
        "colab_type": "code",
        "outputId": "d1fa3536-ab31-4756-a010-43f76a1ebf2f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y_test18,Station18test)"
      ],
      "execution_count": 353,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.011633842738631994"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 353
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SFg_UX5Bt1nS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5f43cfa1-20a6-48af-e2f6-fb4fd184bdf6"
      },
      "source": [
        "mean_absolute_error(y_test18,Station18test)"
      ],
      "execution_count": 354,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.013575450603705231"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 354
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3MBnZalct4V8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "64346d4d-204e-4973-aa53-8d89ad0befa9"
      },
      "source": [
        "mean_absolute_error(y18,Station18)"
      ],
      "execution_count": 355,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0036996850200410694"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 355
        }
      ]
    }
  ]
}